From 6052a51074d487b60a08a78f8fd96574acbcf659 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Thu, 14 Sep 2023 05:27:46 +0800
Subject: [PATCH 01/51] adding moon

---
 baselines/moon/EXTENDED_README.md          | 123 ++++++
 baselines/moon/LICENSE                     | 202 ++++++++++
 baselines/moon/README.md                   |  87 ++++
 baselines/moon/moon/__init__.py            |   1 +
 baselines/moon/moon/client.py              | 183 +++++++++
 baselines/moon/moon/conf/base.yaml         |  24 ++
 baselines/moon/moon/dataset.py             | 197 ++++++++++
 baselines/moon/moon/dataset_preparation.py | 106 +++++
 baselines/moon/moon/main.py                |  77 ++++
 baselines/moon/moon/models.py              | 436 +++++++++++++++++++++
 baselines/moon/moon/server.py              |   5 +
 baselines/moon/moon/strategy.py            |   5 +
 baselines/moon/moon/utils.py               |  77 ++++
 baselines/moon/pyproject.toml              | 135 +++++++
 14 files changed, 1658 insertions(+)
 create mode 100644 baselines/moon/EXTENDED_README.md
 create mode 100644 baselines/moon/LICENSE
 create mode 100644 baselines/moon/README.md
 create mode 100644 baselines/moon/moon/__init__.py
 create mode 100644 baselines/moon/moon/client.py
 create mode 100644 baselines/moon/moon/conf/base.yaml
 create mode 100644 baselines/moon/moon/dataset.py
 create mode 100644 baselines/moon/moon/dataset_preparation.py
 create mode 100644 baselines/moon/moon/main.py
 create mode 100644 baselines/moon/moon/models.py
 create mode 100644 baselines/moon/moon/server.py
 create mode 100644 baselines/moon/moon/strategy.py
 create mode 100644 baselines/moon/moon/utils.py
 create mode 100644 baselines/moon/pyproject.toml

diff --git a/baselines/moon/EXTENDED_README.md b/baselines/moon/EXTENDED_README.md
new file mode 100644
index 000000000000..9c8f5bc72fa9
--- /dev/null
+++ b/baselines/moon/EXTENDED_README.md
@@ -0,0 +1,123 @@
+
+# Extended Readme
+
+> The baselines are expected to run in a machine running Ubuntu 22.04
+
+While `README.md` should include information about the baseline you implement and how to run it, this _extended_ readme provides info on what's the expected directory structure for a new baseline and more generally the instructions to follow before your baseline can be merged into the Flower repository. Please follow closely these instructions. It is likely that you have already completed steps 1-2.
+
+1. Fork the Flower repository and clone it.
+2. Navigate to the `baselines/` directory and from there run:
+    ```bash
+    # This will create a new directory with the same structure as this `baseline_template` directory.
+    ./dev/create-baseline.sh <baseline-name>
+    ``` 
+3. All your code and configs should go into a sub-directory with the same name as the name of your baseline.
+    *    The sub-directory contains a series of Python scripts that you can edit. Please stick to these files and consult with us if you need additional ones.
+    *    There is also a basic config structure in `<baseline-name>/conf` ready be parsed by [Hydra](https://hydra.cc/) when executing your `main.py`.
+4. Therefore, the directory structure in your baseline should look like:
+    ```bash
+    baselines/<baseline-name>
+                    ├── README.md # describes your baseline and everything needed to use it
+                    ├── EXTENDED_README.md # to remove before creating your PR
+                    ├── pyproject.toml # details your Python environment
+                    └── <baseline-name>
+                                ├── *.py # several .py files including main.py and __init__.py
+                                └── conf
+                                     └── *.yaml # one or more Hydra config files
+
+    ```
+> :warning: Make sure the variable `name` in `pyproject.toml` is set to the name of the sub-directory containing all your code.
+
+5. Add your dependencies to the `pyproject.toml` (see below a few examples on how to do it). Read more about Poetry below in this `EXTENDED_README.md`.
+6. Regularly check that your coding style and the documentation you add follow good coding practices. To test whether your code meets the requirements, please run the following:
+    ```bash
+    # After activating your environment and from your baseline's directory
+    cd .. # to go to the top-level directory of all baselines
+    ./dev/test-baseline.sh <baseline-name>
+    ./dev/test-baseline-structure.sh <baseline-name>
+    ```
+    Both `test-baseline.sh` and `test-baseline-structure.sh` will also be automatically run when you create a PR, and both tests need to pass for the baseline to be merged.
+    To automatically solve some formatting issues and apply easy fixes, please run the formatting script:
+    ```bash
+    # After activating your environment and from your baseline's directory
+    cd .. # to go to the top-level directory of all baselines
+    ./dev/format-baseline.sh <baseline-name>
+    ```
+7. Ensure that the Python environment for your baseline can be created without errors by simply running `poetry install` and that this is properly described later when you complete the `Environment Setup` section in `README.md`. This is specially important if your environment requires additional steps after doing `poetry install`.
+8. Ensure that your baseline runs with default arguments by running `poetry run python -m <baseline-name>.main`. Then, describe this and other forms of running your code in the `Running the Experiments` section in `README.md`.
+9. Once your code is ready and you have checked:
+    *    that following the instructions in your `README.md` the Python environment can be created correctly
+
+    *    that running the code following your instructions can reproduce the experiments in the paper
+   
+   , then you just need to create a Pull Request (PR) to kickstart the process of merging your baseline into the Flower repository.
+
+> Once you are happy to merge your baseline contribution, please delete this `EXTENDED_README.md` file.
+
+
+## About Poetry
+
+We use Poetry to manage the Python environment for each individual baseline. You can follow the instructions [here](https://python-poetry.org/docs/) to install Poetry in your machine. 
+
+
+### Specifying a Python Version (optional)
+By default, Poetry will use the Python version in your system. In some settings, you might want to specify a particular version of Python to use inside your Poetry environment. You can do so with [`pyenv`](https://github.com/pyenv/pyenv). Check the documentation for the different ways of installing `pyenv`, but one easy way is using the [automatic installer](https://github.com/pyenv/pyenv-installer):
+```bash
+curl https://pyenv.run | bash # then, don't forget links to your .bashrc/.zshrc
+```
+
+You can then install any Python version with `pyenv install <python-version>` (e.g. `pyenv install 3.9.17`). Then, in order to use that version for your baseline, you'd do the following:
+
+```bash
+# cd to your baseline directory (i.e. where the `pyproject.toml` is)
+pyenv local <python-version>
+
+# set that version for poetry
+poetry env use <python-version>
+
+# then you can install your Poetry environment (see the next setp)
+```
+
+### Installing Your Environment
+With the Poetry tool already installed, you can create an environment for this baseline with commands:
+```bash
+# run this from the same directory as the `pyproject.toml` file is
+poetry install
+```
+
+This will create a basic Python environment with just Flower and additional packages, including those needed for simulation. Next, you should add the dependencies for your code. It is **critical** that you fix the version of the packages you use using a `=` not a `=^`. You can do so via [`poetry add`](https://python-poetry.org/docs/cli/#add). Below are some examples:
+
+```bash
+# For instance, if you want to install tqdm
+poetry add tqdm==4.65.0
+
+# If you already have a requirements.txt, you can add all those packages (but ensure you have fixed the version) in one go as follows:
+poetry add $( cat requirements.txt )
+```
+With each `poetry add` command, the `pyproject.toml` gets automatically updated so you don't need to keep that `requirements.txt` as part of this baseline.
+
+
+More critically however, is adding your ML framework of choice to the list of dependencies. For some frameworks you might be able to do so with the `poetry add` command. Check [the Poetry documentation](https://python-poetry.org/docs/cli/#add) for how to add packages in various ways. For instance, let's say you want to use PyTorch:
+
+```bash
+# with plain `pip`  you'd run a command such as:
+pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
+
+# to add the same 3 dependencies to your Poetry environment you'd need to add the URL to the wheel that the above pip command auto-resolves for you.
+# You can find those wheels in `https://download.pytorch.org/whl/cu117`. Copy the link and paste it after the `poetry add` command.
+# For instance to add `torch==1.13.1+cu117` and a x86 Linux system with Python3.8 you'd:
+poetry add https://download.pytorch.org/whl/cu117/torch-1.13.1%2Bcu117-cp38-cp38-linux_x86_64.whl
+# you'll need to repeat this for both `torchvision` and `torchaudio`
+```
+The above is just an example of how you can add these dependencies. Please refer to the Poetry documentation to extra reference.
+
+If all attempts fail, you can still install packages via standard `pip`. You'd first need to source/activate your Poetry environment.
+```bash
+# first ensure you have created your environment
+# and installed the base packages provided in the template
+poetry install 
+
+# then activate it
+poetry shell
+```
+Now you are inside your environment (pretty much as when you use `virtualenv` or `conda`) so you can install further packages with `pip`. Please note that, unlike with `poetry add`, these extra requirements won't be captured by `pyproject.toml`. Therefore, please ensure that you provide all instructions needed to: (1) create the base environment with Poetry and (2) install any additional dependencies via `pip` when you complete your `README.md`.
\ No newline at end of file
diff --git a/baselines/moon/LICENSE b/baselines/moon/LICENSE
new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/moon/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/baselines/moon/README.md b/baselines/moon/README.md
new file mode 100644
index 000000000000..682952717426
--- /dev/null
+++ b/baselines/moon/README.md
@@ -0,0 +1,87 @@
+---
+title: title of the paper
+url: URL to the paper page (not the pdf)
+labels: [label1, label2] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. "system heterogeneity", "image classification", "asynchronous", "weight sharing", "cross-silo")
+dataset: [dataset1, dataset2] # list of datasets you include in your baseline
+---
+
+# :warning:*_Title of your baseline_*
+
+> Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
+
+> :warning: This is the template to follow when creating a new Flower Baseline. Please follow the instructions in `EXTENDED_README.md`
+
+> :warning: Please follow the instructions carefully. You can see the [FedProx-MNIST baseline](https://github.com/adap/flower/tree/main/baselines/fedprox) as an example of a baseline that followed this guide.
+
+> :warning: Please complete the metadata section at the very top of this README. This generates a table at the top of the file that will facilitate indexing baselines.
+
+****Paper:**** :warning: *_add the URL of the paper page (not to the .pdf). For instance if you link a paper on ArXiv, add here the URL to the abstract page (e.g. https://arxiv.org/abs/1512.03385). If your paper is in from a journal or conference proceedings, please follow the same logic._*
+
+****Authors:**** :warning: *_list authors of the paper_*
+
+****Abstract:**** :warning: *_add here the abstract of the paper you are implementing_*
+
+
+## About this baseline
+
+****What’s implemented:**** :warning: *_Concisely describe what experiment(s) in the publication can be replicated by running the code. Please only use a few sentences. Start with: “The code in this directory …”_*
+
+****Datasets:**** :warning: *_List the datasets you used (if you used a medium to large dataset, >10GB please also include the sizes of the dataset)._*
+
+****Hardware Setup:**** :warning: *_Give some details about the hardware (e.g. a server with 8x V100 32GB and 256GB of RAM) you used to run the experiments for this baseline. Someone out there might not have access to the same resources you have so, could list the absolute minimum hardware needed to run the experiment in a reasonable amount of time ? (e.g. minimum is 1x 16GB GPU otherwise a client model can’t be trained with a sufficiently large batch size). Could you test this works too?_*
+
+****Contributors:**** :warning: *_let the world know who contributed to this baseline. This could be either your name, your name and affiliation at the time, or your GitHub profile name if you prefer. If multiple contributors signed up for this baseline, please list yourself and your colleagues_*
+
+
+## Experimental Setup
+
+****Task:**** :warning: *_what’s the primary task that is being federated? (e.g. image classification, next-word prediction). If you have experiments for several, please list them_*
+
+****Model:**** :warning: *_provide details about the model you used in your experiments (if more than use a list). If your model is small, describing it as a table would be :100:. Some FL methods do not use an off-the-shelve model (e.g. ResNet18) instead they create your own. If this is your case, please provide a summary here and give pointers to where in the paper (e.g. Appendix B.4) is detailed._*
+
+****Dataset:**** :warning: *_Earlier you listed already the datasets that your baseline uses. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if  your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table._*
+
+****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._*
+
+
+## Environment Setup
+
+:warning: _The Python environment for all baselines should follow these guidelines in the `EXTENDED_README`. Specify the steps to create and activate your environment. If there are any external system-wide requirements, please include instructions for them too. These instructions should be comprehensive enough so anyone can run them (if non standard, describe them step-by-step)._
+
+
+## Running the Experiments
+
+:warning: _Provide instructions on the steps to follow to run all the experiments._
+```bash  
+# The main experiment implemented in your baseline using default hyperparameters (that should be setup in the Hydra configs) should run (including dataset download and necessary partitioning) by executing the command:
+
+poetry run python -m <baseline-name>.main <no additional arguments> # where <baseline-name> is the name of this directory and that of the only sub-directory in this directory (i.e. where all your source code is)
+
+# If you are using a dataset that requires a complicated download (i.e. not using one natively supported by TF/PyTorch) + preprocessing logic, you might want to tell people to run one script first that will do all that. Please ensure the download + preprocessing can be configured to suit (at least!) a different download directory (and use as default the current directory). The expected command to run to do this is:
+
+poetry run python -m <baseline-name>.dataset_preparation <optional arguments, but default should always run>
+
+# It is expected that you baseline supports more than one dataset and different FL settings (e.g. different number of clients, dataset partitioning methods, etc). Please provide a list of commands showing how these experiments are run. Include also a short explanation of what each one does. Here it is expected you'll be using the Hydra syntax to override the default config.
+
+poetry run python -m <baseline-name>.main  <override_some_hyperparameters>
+.
+.
+.
+poetry run python -m <baseline-name>.main  <override_some_hyperparameters>
+```
+
+
+## Expected Results
+
+:warning: _Your baseline implementation should replicate several of the experiments in the original paper. Please include here the exact command(s) needed to run each of those experiments followed by a figure (e.g. a line plot) or table showing the results you obtained when you ran the code. Below is an example of how you can present this. Please add command followed by results for all your experiments._
+
+```bash
+# it is likely that for one experiment you need to sweep over different hyperparameters. You are encouraged to use Hydra's multirun functionality for this. This is an example of how you could achieve this for some typical FL hyperparameteres
+
+poetry run python -m <baseline-name>.main --multirun num_client_per_round=5,10,50 dataset=femnist,cifar10
+# the above command will run a total of 6 individual experiments (because 3client_configs x 2datasets = 6 -- you can think of it as a grid).
+
+[Now show a figure/table displaying the results of the above command]
+
+# add more commands + plots for additional experiments.
+```
diff --git a/baselines/moon/moon/__init__.py b/baselines/moon/moon/__init__.py
new file mode 100644
index 000000000000..a5e567b59135
--- /dev/null
+++ b/baselines/moon/moon/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
new file mode 100644
index 000000000000..c7dc362c056d
--- /dev/null
+++ b/baselines/moon/moon/client.py
@@ -0,0 +1,183 @@
+"""Define your client class and a function to construct such clients.
+
+Please overwrite `flwr.client.NumPyClient` or `flwr.client.Client` and create a function
+to instantiate your client.
+"""
+"""Defines the MNIST Flower Client and a function to instantiate it."""
+
+
+from collections import OrderedDict
+from typing import Callable, Dict, List, Tuple
+
+import flwr as fl
+import numpy as np
+import torch
+from flwr.common.typing import NDArrays, Scalar
+from hydra.utils import instantiate
+from omegaconf import DictConfig
+from torch.utils.data import DataLoader
+import os
+
+
+from moon.models import train_moon, train_fedprox, init_net
+
+
+class FlowerClient(
+    fl.client.NumPyClient
+):  
+    """Standard Flower client for CNN training."""
+    def __init__(
+        self,
+        net: torch.nn.Module,
+        net_id: int,
+        dataset: str,
+        model: str,
+        output_dim: int,
+        trainloader: DataLoader,
+        valloader: DataLoader,
+        device: torch.device,
+        num_epochs: int,
+        learning_rate: float,
+        mu: float,
+        temperature: float,
+        model_dir: str,
+        alg: str,
+    ):  # pylint: disable=too-many-arguments
+        self.net = net
+        self.net_id = net_id
+        self.dataset = dataset
+        self.model = model
+        self.output_dim = output_dim
+        self.trainloader = trainloader
+        self.valloader = valloader
+        self.device = device
+        self.num_epochs = num_epochs
+        self.learning_rate = learning_rate
+        self.mu = mu
+        self.temperature = temperature
+        self.model_dir = model_dir
+        self.alg = alg
+        
+
+    def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
+        """Returns the parameters of the current net."""
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def set_parameters(self, parameters: NDArrays) -> None:
+        """Changes the parameters of the model using the given ones."""
+        params_dict = zip(self.net.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        self.net.load_state_dict(state_dict, strict=True)
+
+    def fit(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[NDArrays, int, Dict]:
+        """Implements distributed fit function for a given client."""
+        self.set_parameters(parameters)
+        
+        #load previous model from model_dir
+        self.prev_net = init_net(self.dataset, self.model, self.output_dim)
+        self.prev_net.load_state_dict(torch.load(os.path.join(self.model_dir, "prev_net.pt")))
+        global_net = init_net(self.dataset, self.model, self.output_dim)
+        global_net.load_state_dict(self.net.state_dict())
+        if self.alg == "moon":
+            train_moon(
+                self.net,
+                global_net,
+                self.prev_net,
+                self.trainloader,
+                self.num_epochs,
+                self.learning_rate,
+                self.mu,
+                self.temperature,
+                self.device
+            )
+        elif self.alg == "fedprox":
+            train_fedprox(
+                self.net,
+                global_net,
+                self.trainloader,
+                self.num_epochs,
+                self.learning_rate,
+                self.mu,
+                self.device
+            )
+        torch.save(self.net.state_dict(), os.path.join(self.model_dir, "prev_net.pt"))
+        return self.get_parameters({}), len(self.trainloader), {"is_straggler": False}
+
+    def evaluate(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[float, int, Dict]:
+        """Implements distributed evaluation for a given client."""
+        self.set_parameters(parameters)
+        loss, accuracy = test(self.net, self.valloader, self.device)
+        return float(loss), len(self.valloader), {"accuracy": float(accuracy)}
+
+
+def gen_client_fn(
+    num_clients: int,
+    num_rounds: int,
+    num_epochs: int,
+    trainloaders: List[DataLoader],
+    valloaders: List[DataLoader],
+    learning_rate: float,
+    stragglers: float,
+    model: DictConfig,
+) -> Tuple[
+    Callable[[str], FlowerClient], DataLoader
+]:  # pylint: disable=too-many-arguments
+    """Generates the client function that creates the Flower Clients.
+
+    Parameters
+    ----------
+    num_clients : int
+        The number of clients present in the setup
+    num_rounds: int
+        The number of rounds in the experiment. This is used to construct
+        the scheduling for stragglers
+    num_epochs : int
+        The number of local epochs each client should run the training for before
+        sending it to the server.
+    trainloaders: List[DataLoader]
+        A list of DataLoaders, each pointing to the dataset training partition
+        belonging to a particular client.
+    valloaders: List[DataLoader]
+        A list of DataLoaders, each pointing to the dataset validation partition
+        belonging to a particular client.
+    learning_rate : float
+        The learning rate for the SGD  optimizer of clients.
+    stragglers : float
+        Proportion of stragglers in the clients, between 0 and 1.
+
+    Returns
+    -------
+    Tuple[Callable[[str], FlowerClient], DataLoader]
+        A tuple containing the client function that creates Flower Clients and
+        the DataLoader that will be used for testing
+    """
+
+    def client_fn(cid: str) -> FlowerClient:
+        """Create a Flower client representing a single organization."""
+
+        # Load model
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        net = init_net(model.dataset, model.model, model.output_dim)
+        net = instantiate(model).to(device)
+
+        # Note: each client gets a different trainloader/valloader, so each client
+        # will train and evaluate on their own unique data
+        trainloader = trainloaders[int(cid)]
+        valloader = valloaders[int(cid)]
+
+        return FlowerClient(
+            net,
+            net_id, 
+            trainloader,
+            valloader,
+            device,
+            num_epochs,
+            learning_rate,
+            stragglers_mat[int(cid)],
+        )
+
+    return client_fn
diff --git a/baselines/moon/moon/conf/base.yaml b/baselines/moon/moon/conf/base.yaml
new file mode 100644
index 000000000000..e996b897ef7b
--- /dev/null
+++ b/baselines/moon/moon/conf/base.yaml
@@ -0,0 +1,24 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+num_clients: 10
+batch_size: 32
+
+dataset:
+  # dataset config
+  name: cifar10
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+
+model:
+  # model config
+
+strategy:
+  _target_: # points to your strategy (either custom or exiting in Flower)
+  # rest of strategy config
+
+client:
+  # client config
diff --git a/baselines/moon/moon/dataset.py b/baselines/moon/moon/dataset.py
new file mode 100644
index 000000000000..701eeda8ecb8
--- /dev/null
+++ b/baselines/moon/moon/dataset.py
@@ -0,0 +1,197 @@
+"""Handle basic dataset creation.
+
+In case of PyTorch it should return dataloaders for your dataset (for both the clients
+and the server). If you are using a custom dataset class, this module is the place to
+define it. If your dataset requires to be downloaded (and this is not done
+automatically -- e.g. as it is the case for many dataset in TorchVision) and
+partitioned, please include all those functions and logic in the
+`dataset_preparation.py` module. You can use all those functions from functions/methods
+defined here of course.
+"""
+
+# https://github.com/QinbinLi/MOON/blob/main/datasets.py
+
+import torch.utils.data as data
+from PIL import Image
+import numpy as np
+import torchvision
+from torchvision.datasets import CIFAR10, CIFAR100
+import torchvision.transforms as transforms
+from torch.autograd import Variable
+import torch.nn.functional as F
+import torch.utils.data as data
+
+import os
+import os.path
+import logging
+
+logging.basicConfig()
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+
+IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')
+
+
+
+class CIFAR10_sub(data.Dataset):
+
+    def __init__(self, root, dataidxs=None, train=True, transform=None, target_transform=None, download=False):
+
+        self.root = root
+        self.dataidxs = dataidxs
+        self.train = train
+        self.transform = transform
+        self.target_transform = target_transform
+        self.download = download
+
+        self.data, self.target = self.__build_sub_dataset__()
+
+    def __build_sub_dataset__(self):
+
+        cifar_dataobj = CIFAR10(self.root, self.train, self.transform, self.target_transform, self.download)
+
+        if torchvision.__version__ == '0.2.1':
+            if self.train:
+                data, target = cifar_dataobj.train_data, np.array(cifar_dataobj.train_labels)
+            else:
+                data, target = cifar_dataobj.test_data, np.array(cifar_dataobj.test_labels)
+        else:
+            data = cifar_dataobj.data
+            target = np.array(cifar_dataobj.targets)
+
+        if self.dataidxs is not None:
+            data = data[self.dataidxs]
+            target = target[self.dataidxs]
+
+        return data, target
+
+    def truncate_channel(self, index):
+        for i in range(index.shape[0]):
+            gs_index = index[i]
+            self.data[gs_index, :, :, 1] = 0.0
+            self.data[gs_index, :, :, 2] = 0.0
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.target[index]
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.data)
+
+
+class CIFAR100_sub(data.Dataset):
+
+    def __init__(self, root, dataidxs=None, train=True, transform=None, target_transform=None, download=False):
+
+        self.root = root
+        self.dataidxs = dataidxs
+        self.train = train
+        self.transform = transform
+        self.target_transform = target_transform
+        self.download = download
+
+        self.data, self.target = self.__build_sub_dataset__()
+
+    def __build_sub_dataset__(self):
+
+        cifar_dataobj = CIFAR100(self.root, self.train, self.transform, self.target_transform, self.download)
+
+        if torchvision.__version__ == '0.2.1':
+            if self.train:
+                data, target = cifar_dataobj.train_data, np.array(cifar_dataobj.train_labels)
+            else:
+                data, target = cifar_dataobj.test_data, np.array(cifar_dataobj.test_labels)
+        else:
+            data = cifar_dataobj.data
+            target = np.array(cifar_dataobj.targets)
+
+        if self.dataidxs is not None:
+            data = data[self.dataidxs]
+            target = target[self.dataidxs]
+
+        return data, target
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.target[index]
+        img = Image.fromarray(img)
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.data)
+
+
+def get_dataloader(dataset, datadir, train_bs, test_bs, dataidxs=None, noise_level=0):
+    if dataset == 'cifar10':
+        dl_obj = CIFAR10_sub
+        normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
+                                            std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
+        transform_train = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Lambda(lambda x: F.pad(
+                Variable(x.unsqueeze(0), requires_grad=False),
+                (4, 4, 4, 4), mode='reflect').data.squeeze()),
+            transforms.ToPILImage(),
+            transforms.ColorJitter(brightness=noise_level),
+            transforms.RandomCrop(32),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            normalize
+        ])
+        # data prep for test set
+        transform_test = transforms.Compose([
+            transforms.ToTensor(),
+            normalize])
+
+    elif dataset == 'cifar100':
+        dl_obj = CIFAR100_sub
+
+        normalize = transforms.Normalize(mean=[0.5070751592371323, 0.48654887331495095, 0.4409178433670343],
+                                            std=[0.2673342858792401, 0.2564384629170883, 0.27615047132568404])
+
+        transform_train = transforms.Compose([
+            transforms.RandomCrop(32, padding=4),
+            transforms.RandomHorizontalFlip(),
+            transforms.RandomRotation(15),
+            transforms.ToTensor(),
+            normalize
+        ])
+        # data prep for test set
+        transform_test = transforms.Compose([
+            transforms.ToTensor(),
+            normalize])
+
+        train_ds = dl_obj(datadir, dataidxs=dataidxs, train=True, transform=transform_train, download=True)
+        test_ds = dl_obj(datadir, train=False, transform=transform_test, download=True)
+
+        train_dl = data.DataLoader(dataset=train_ds, batch_size=train_bs, drop_last=True, shuffle=True)
+        test_dl = data.DataLoader(dataset=test_ds, batch_size=test_bs, shuffle=False)
+
+    return train_dl, test_dl, train_ds, test_ds
diff --git a/baselines/moon/moon/dataset_preparation.py b/baselines/moon/moon/dataset_preparation.py
new file mode 100644
index 000000000000..ec45cfb4d6d8
--- /dev/null
+++ b/baselines/moon/moon/dataset_preparation.py
@@ -0,0 +1,106 @@
+"""Handle the dataset partitioning and (optionally) complex downloads.
+
+Please add here all the necessary logic to either download, uncompress, pre/post-process
+your dataset (or all of the above). If the desired way of running your baseline is to
+first download the dataset and partition it and then run the experiments, please
+uncomment the lines below and tell us in the README.md (see the "Running the Experiment"
+block) that this file should be executed first.
+"""
+# import hydra
+# from hydra.core.hydra_config import HydraConfig
+# from hydra.utils import call, instantiate
+# from omegaconf import DictConfig, OmegaConf
+
+
+# @hydra.main(config_path="conf", config_name="base", version_base=None)
+# def download_and_preprocess(cfg: DictConfig) -> None:
+#     """Does everything needed to get the dataset.
+
+#     Parameters
+#     ----------
+#     cfg : DictConfig
+#         An omegaconf object that stores the hydra config.
+#     """
+
+#     ## 1. print parsed config
+#     print(OmegaConf.to_yaml(cfg))
+
+#     # Please include here all the logic
+#     # Please use the Hydra config style as much as possible specially
+#     # for parts that can be customised (e.g. how data is partitioned)
+
+# if __name__ == "__main__":
+
+#     download_and_preprocess()
+
+import numpy as np
+import torchvision.transforms as transforms
+from dataset import CIFAR10_truncated, CIFAR100_truncated
+
+
+def load_cifar10_data(datadir):
+    transform = transforms.Compose([transforms.ToTensor()])
+
+    cifar10_train_ds = CIFAR10_truncated(datadir, train=True, download=True, transform=transform)
+    cifar10_test_ds = CIFAR10_truncated(datadir, train=False, download=True, transform=transform)
+
+    X_train, y_train = cifar10_train_ds.data, cifar10_train_ds.target
+    X_test, y_test = cifar10_test_ds.data, cifar10_test_ds.target
+
+    return (X_train, y_train, X_test, y_test)
+
+
+def load_cifar100_data(datadir):
+    transform = transforms.Compose([transforms.ToTensor()])
+
+    cifar100_train_ds = CIFAR100_truncated(datadir, train=True, download=True, transform=transform)
+    cifar100_test_ds = CIFAR100_truncated(datadir, train=False, download=True, transform=transform)
+
+    X_train, y_train = cifar100_train_ds.data, cifar100_train_ds.target
+    X_test, y_test = cifar100_test_ds.data, cifar100_test_ds.target
+
+    return (X_train, y_train, X_test, y_test)
+
+
+def partition_data(dataset, datadir, partition, num_clients, beta):
+    if dataset == 'cifar10':
+        X_train, y_train, X_test, y_test = load_cifar10_data(datadir)
+    elif dataset == 'cifar100':
+        X_train, y_train, X_test, y_test = load_cifar100_data(datadir)
+
+    n_train = y_train.shape[0]
+
+    if partition == "homo" or partition == "iid":
+        idxs = np.random.permutation(n_train)
+        batch_idxs = np.array_split(idxs, num_clients)
+        net_dataidx_map = {i: batch_idxs[i] for i in range(num_clients)}
+
+
+    elif partition == "noniid-labeldir" or partition == "noniid":
+        min_size = 0
+        min_require_size = 10
+        K = 10
+        if dataset == 'cifar100':
+            K = 100
+        elif dataset == 'tinyimagenet':
+            K = 200
+
+        N = y_train.shape[0]
+        net_dataidx_map = {}
+
+        while min_size < min_require_size:
+            idx_batch = [[] for _ in range(num_clients)]
+            for k in range(K):
+                idx_k = np.where(y_train == k)[0]
+                np.random.shuffle(idx_k)
+                proportions = np.random.dirichlet(np.repeat(beta, num_clients))
+                proportions = np.array([p * (len(idx_j) < N / num_clients) for p, idx_j in zip(proportions, idx_batch)])
+                proportions = proportions / proportions.sum()
+                proportions = (np.cumsum(proportions) * len(idx_k)).astype(int)[:-1]
+                idx_batch = [idx_j + idx.tolist() for idx_j, idx in zip(idx_batch, np.split(idx_k, proportions))]
+                min_size = min([len(idx_j) for idx_j in idx_batch])
+        for j in range(num_clients):
+            np.random.shuffle(idx_batch[j])
+            net_dataidx_map[j] = idx_batch[j]
+
+    return (X_train, y_train, X_test, y_test, net_dataidx_map)
\ No newline at end of file
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
new file mode 100644
index 000000000000..6b875a92ac9f
--- /dev/null
+++ b/baselines/moon/moon/main.py
@@ -0,0 +1,77 @@
+"""Create and connect the building blocks for your experiments; start the simulation.
+
+It includes processioning the dataset, instantiate strategy, specify how the global
+model is going to be evaluated, etc. At the end, this script saves the results.
+"""
+# these are the basic packages you'll need here
+# feel free to remove some if aren't needed
+import hydra
+from omegaconf import DictConfig, OmegaConf
+from moon import client, server
+from moon.dataset_preparation import partition_data, get_dataloader
+
+
+@hydra.main(config_path="conf", config_name="base", version_base=None)
+def main(cfg: DictConfig) -> None:
+    """Run the baseline.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    # 1. Print parsed config
+    print(OmegaConf.to_yaml(cfg))
+
+    # 2. Prepare your dataset
+    # here you should call a function in datasets.py that returns whatever is needed to:
+    # (1) ensure the server can access the dataset used to evaluate your model after
+    # aggregation
+    # (2) tell each client what dataset partitions they should use (e.g. a this could
+    # be a location in the file system, a list of dataloader, a list of ids to extract
+    # from a dataset, it's up to you)
+    X_train, y_train, X_test, y_test, net_dataidx_map, traindata_cls_counts = partition_data(
+        dataset=cfg.dataset.name, 
+        datadir=cfg.dataset.dir, 
+        parittion=cfg.dataset.partition, 
+        num_clients=cfg.num_clients, 
+        beta=cfg.dataset.beta,
+    )
+    
+    train_dl_global, test_dl, train_ds_global, test_ds_global = get_dataloader(dataset=cfg.dataset.name,
+                                                                            datadir=cfg.dataset.dir,
+                                                                            train_bs=cfg.batch_size,
+                                                                            test_bs=32)
+    
+
+    # 3. Define your clients
+    # Define a function that returns another function that will be used during
+    # simulation to instantiate each individual client
+    # client_fn = client.<my_function_that_returns_a_function>()
+    client_fn = client.gen_client_fn(
+        num_clients=cfg.num_clients,
+        num_epochs=cfg.num_epochs,
+        trainloaders=trainloaders,
+        valloaders=valloaders,
+        num_rounds=cfg.num_rounds,
+        learning_rate=cfg.learning_rate,
+        model=cfg.model,
+    )
+
+    # 4. Define your strategy
+    # pass all relevant argument (including the global dataset used after aggregation,
+    # if needed by your method.)
+    # strategy = instantiate(cfg.strategy, <additional arguments if desired>)
+
+    # 5. Start Simulation
+    # history = fl.simulation.start_simulation(<arguments for simulation>)
+
+    # 6. Save your results
+    # Here you can save the `history` returned by the simulation and include
+    # also other buffers, statistics, info needed to be saved in order to later
+    # on generate the plots you provide in the README.md. You can for instance
+    # access elements that belong to the strategy for example:
+    # data = strategy.get_my_custom_data() -- assuming you have such method defined.
+    # Hydra will generate for you a directory each time you run the code. You
+    # can retrieve the path to that directory with this:
+    # save_path = HydraConfig.get().runtime.output_dir
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
new file mode 100644
index 000000000000..55682d3d88c7
--- /dev/null
+++ b/baselines/moon/moon/models.py
@@ -0,0 +1,436 @@
+"""Define our models, and training and eval functions.
+
+If your model is 100% off-the-shelf (e.g. directly from torchvision without requiring
+modifications) you might be better off instantiating your  model directly from the Hydra
+config. In this way, swapping your model for  another one can be done without changing
+the python code at all
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+import torchvision.models as models
+import torch.optim as optim
+from moon.utils import compute_accuracy
+
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=dilation, groups=groups, bias=False, dilation=dilation)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None):
+        super(BasicBlock, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        if groups != 1 or base_width != 64:
+            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+        if dilation > 1:
+            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = norm_layer(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = norm_layer(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
+    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
+    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
+    # This variant is also known as ResNet V1.5 and improves accuracy according to
+    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
+
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+                 base_width=64, dilation=1, norm_layer=None):
+        super(Bottleneck, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        width = int(planes * (base_width / 64.)) * groups
+        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+        self.conv1 = conv1x1(inplanes, width)
+        self.bn1 = norm_layer(width)
+        self.conv2 = conv3x3(width, width, stride, groups, dilation)
+        self.bn2 = norm_layer(width)
+        self.conv3 = conv1x1(width, planes * self.expansion)
+        self.bn3 = norm_layer(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class ResNetCifar10(nn.Module):
+
+    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
+                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
+                 norm_layer=None):
+        super(ResNetCifar10, self).__init__()
+        if norm_layer is None:
+            norm_layer = nn.BatchNorm2d
+        self._norm_layer = norm_layer
+
+        self.inplanes = 64
+        self.dilation = 1
+        if replace_stride_with_dilation is None:
+            # each element in the tuple indicates if we should replace
+            # the 2x2 stride with a dilated convolution instead
+            replace_stride_with_dilation = [False, False, False]
+        if len(replace_stride_with_dilation) != 3:
+            raise ValueError("replace_stride_with_dilation should be None "
+                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+        self.groups = groups
+        self.base_width = width_per_group
+        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1,
+                               bias=False)
+        self.bn1 = norm_layer(self.inplanes)
+        self.relu = nn.ReLU(inplace=True)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
+                                       dilate=replace_stride_with_dilation[0])
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
+                                       dilate=replace_stride_with_dilation[1])
+        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
+                                       dilate=replace_stride_with_dilation[2])
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        # Zero-initialize the last BN in each residual branch,
+        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+        if zero_init_residual:
+            for m in self.modules():
+                if isinstance(m, Bottleneck):
+                    nn.init.constant_(m.bn3.weight, 0)
+                elif isinstance(m, BasicBlock):
+                    nn.init.constant_(m.bn2.weight, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+        norm_layer = self._norm_layer
+        downsample = None
+        previous_dilation = self.dilation
+        if dilate:
+            self.dilation *= stride
+            stride = 1
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes * block.expansion, stride),
+                norm_layer(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
+                            self.base_width, previous_dilation, norm_layer))
+        self.inplanes = planes * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes, groups=self.groups,
+                                base_width=self.base_width, dilation=self.dilation,
+                                norm_layer=norm_layer))
+
+        return nn.Sequential(*layers)
+
+    def _forward_impl(self, x):
+        # See note [TorchScript super()]
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.fc(x)
+
+        return x
+
+    def forward(self, x):
+        return self._forward_impl(x)
+
+def ResNet50_cifar10(**kwargs):
+    r"""ResNet-50 model from
+    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+        progress (bool): If True, displays a progress bar of the download to stderr
+    """
+    return ResNetCifar10(Bottleneck, [3, 4, 6, 3], **kwargs)
+
+
+class SimpleCNN_header(nn.Module):
+    def __init__(self, input_dim, hidden_dims, output_dim=10):
+        super(SimpleCNN_header, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.relu = nn.ReLU()
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+
+        # for now, we hard coded this network
+        # i.e. we fix the number of hidden layers i.e. 2 layers
+        self.fc1 = nn.Linear(input_dim, hidden_dims[0])
+        self.fc2 = nn.Linear(hidden_dims[0], hidden_dims[1])
+        #self.fc3 = nn.Linear(hidden_dims[1], output_dim)
+
+    def forward(self, x):
+
+        x = self.pool(self.relu(self.conv1(x)))
+        x = self.pool(self.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        # x = self.fc3(x)
+        return x
+
+class ModelFedCon(nn.Module):
+
+    def __init__(self, base_model, out_dim, n_classes):
+        super(ModelFedCon, self).__init__()
+
+        if base_model == "resnet50-cifar10" or base_model == "resnet50-cifar100" or base_model == "resnet50-smallkernel" or base_model == "resnet50":
+            basemodel = ResNet50_cifar10()
+            self.features = nn.Sequential(*list(basemodel.children())[:-1])
+            num_ftrs = basemodel.fc.in_features
+        elif base_model == 'simple-cnn':
+            self.features = SimpleCNN_header(input_dim=(16 * 5 * 5), hidden_dims=[120, 84], output_dim=n_classes)
+            num_ftrs = 84
+
+        #summary(self.features.to('cuda:0'), (3,32,32))
+        #print("features:", self.features)
+        # projection MLP
+        self.l1 = nn.Linear(num_ftrs, num_ftrs)
+        self.l2 = nn.Linear(num_ftrs, out_dim)
+
+        # last layer
+        self.l3 = nn.Linear(out_dim, n_classes)
+
+    def _get_basemodel(self, model_name):
+        try:
+            model = self.model_dict[model_name]
+            #print("Feature extractor:", model_name)
+            return model
+        except:
+            raise ("Invalid model name. Check the config file and pass one of: resnet18 or resnet50")
+
+    def forward(self, x):
+        h = self.features(x)
+        #print("h before:", h)
+        #print("h size:", h.size())
+        h = h.squeeze()
+        #print("h after:", h)
+        x = self.l1(h)
+        x = F.relu(x)
+        x = self.l2(x)
+
+        y = self.l3(x)
+        return h, x, y
+
+def init_net(dataset, model, output_dim, device='cpu'):
+    if dataset == 'cifar10':
+        n_classes = 10
+    elif dataset == 'cifar100':
+        n_classes = 100
+
+    net = ModelFedCon(model, output_dim, n_classes)
+    if device == 'cpu':
+        net.to(device)
+    else:
+        net = net.cuda()
+
+    return net
+
+
+def train_moon(net, global_net, previous_net, train_dataloader, epochs, lr, mu, temperature, device="cpu"):
+    net = nn.DataParallel(net)
+    net.cuda()
+
+    print('n_training: %d' % len(train_dataloader))
+
+    train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
+
+    print('>> Pre-Training Training accuracy: {}'.format(train_acc))
+
+    optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=lr, momentum=0.9,
+                              weight_decay=1e-5)
+
+    criterion = nn.CrossEntropyLoss().cuda()
+    # global_net.to(device)
+
+    for previous_net in previous_nets:
+        previous_net.cuda()
+
+    cnt = 0
+    cos=torch.nn.CosineSimilarity(dim=-1)
+    # mu = 0.001
+
+    for epoch in range(epochs):
+        epoch_loss_collector = []
+        epoch_loss1_collector = []
+        epoch_loss2_collector = []
+        for _, (x, target) in enumerate(train_dataloader):
+            x, target = x.cuda(), target.cuda()
+
+            optimizer.zero_grad()
+            x.requires_grad = False
+            target.requires_grad = False
+            target = target.long()
+
+            _, pro1, out = net(x)
+            _, pro2, _ = global_net(x)
+
+            posi = cos(pro1, pro2)
+            logits = posi.reshape(-1,1)
+
+            
+            previous_net.cuda()
+            _, pro3, _ = previous_net(x)
+            nega = cos(pro1, pro3)
+            logits = torch.cat((logits, nega.reshape(-1,1)), dim=1)
+
+            previous_net.to('cpu')
+
+            logits /= temperature
+            labels = torch.zeros(x.size(0)).cuda().long()
+
+            loss2 = mu * criterion(logits, labels)
+
+
+            loss1 = criterion(out, target)
+            loss = loss1 + loss2
+
+            loss.backward()
+            optimizer.step()
+
+            cnt += 1
+            epoch_loss_collector.append(loss.item())
+            epoch_loss1_collector.append(loss1.item())
+            epoch_loss2_collector.append(loss2.item())
+
+        epoch_loss = sum(epoch_loss_collector) / len(epoch_loss_collector)
+        epoch_loss1 = sum(epoch_loss1_collector) / len(epoch_loss1_collector)
+        epoch_loss2 = sum(epoch_loss2_collector) / len(epoch_loss2_collector)
+        print('Epoch: %d Loss: %f Loss1: %f Loss2: %f' % (epoch, epoch_loss, epoch_loss1, epoch_loss2))
+
+    previous_net.to('cpu')
+    train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
+
+    print('>> Training accuracy: %f' % train_acc)
+    net.to('cpu')
+    print(' ** Training complete **')
+    return net
+
+def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu"):
+    net = nn.DataParallel(net)
+    net.cuda()
+
+    print('n_training: %d' % len(train_dataloader))
+
+    train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
+    
+    print('>> Pre-Training Training accuracy: {}'.format(train_acc))
+
+    optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=lr, momentum=0.9, weight_decay=1e-5)
+
+    criterion = nn.CrossEntropyLoss().cuda()
+
+    cnt = 0
+    global_weight_collector = list(global_net.cuda().parameters())
+
+    for epoch in range(epochs):
+        epoch_loss_collector = []
+        for _, (x, target) in enumerate(train_dataloader):
+            x, target = x.cuda(), target.cuda()
+
+            optimizer.zero_grad()
+            x.requires_grad = False
+            target.requires_grad = False
+            target = target.long()
+
+            _,_,out = net(x)
+            loss = criterion(out, target)
+
+            # for fedprox
+            fed_prox_reg = 0.0
+            # fed_prox_reg += np.linalg.norm([i - j for i, j in zip(global_weight_collector, get_trainable_parameters(net).tolist())], ord=2)
+            for param_index, param in enumerate(net.parameters()):
+                fed_prox_reg += ((mu / 2) * torch.norm((param - global_weight_collector[param_index])) ** 2)
+            loss += fed_prox_reg
+
+            loss.backward()
+            optimizer.step()
+
+            cnt += 1
+            epoch_loss_collector.append(loss.item())
+
+        epoch_loss = sum(epoch_loss_collector) / len(epoch_loss_collector)
+
+    train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
+
+    print('>> Training accuracy: %f' % train_acc)
+    net.to('cpu')
+    print(' ** Training complete **')
+    return net
\ No newline at end of file
diff --git a/baselines/moon/moon/server.py b/baselines/moon/moon/server.py
new file mode 100644
index 000000000000..2fd7d42cde5a
--- /dev/null
+++ b/baselines/moon/moon/server.py
@@ -0,0 +1,5 @@
+"""Create global evaluation function.
+
+Optionally, also define a new Server class (please note this is not needed in most
+settings).
+"""
diff --git a/baselines/moon/moon/strategy.py b/baselines/moon/moon/strategy.py
new file mode 100644
index 000000000000..17436c401c30
--- /dev/null
+++ b/baselines/moon/moon/strategy.py
@@ -0,0 +1,5 @@
+"""Optionally define a custom strategy.
+
+Needed only when the strategy is not yet implemented in Flower or because you want to
+extend or modify the functionality of an existing strategy.
+"""
diff --git a/baselines/moon/moon/utils.py b/baselines/moon/moon/utils.py
new file mode 100644
index 000000000000..4051c129c9e6
--- /dev/null
+++ b/baselines/moon/moon/utils.py
@@ -0,0 +1,77 @@
+"""Define any utility function.
+
+They are not directly relevant to  the other (more FL specific) python modules. For
+example, you may define here things like: loading a model from a checkpoint, saving
+results, plotting.
+"""
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+
+def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
+    was_training = False
+    if model.training:
+        model.eval()
+        was_training = True
+
+    true_labels_list, pred_labels_list = np.array([]), np.array([])
+
+    correct, total = 0, 0
+    if device == 'cpu':
+        criterion = nn.CrossEntropyLoss()
+    elif "cuda" in device.type:
+        criterion = nn.CrossEntropyLoss().cuda()
+    loss_collector = []
+    if multiloader:
+        for loader in dataloader:
+            with torch.no_grad():
+                for batch_idx, (x, target) in enumerate(loader):
+                    #print("x:",x)
+                    #print("target:",target)
+                    if device != 'cpu':
+                        x, target = x.cuda(), target.to(dtype=torch.int64).cuda()
+                    _, _, out = model(x)
+                    if len(target)==1:
+                        loss = criterion(out, target)
+                    else:
+                        loss = criterion(out, target)
+                    _, pred_label = torch.max(out.data, 1)
+                    loss_collector.append(loss.item())
+                    total += x.data.size()[0]
+                    correct += (pred_label == target.data).sum().item()
+
+                    if device == "cpu":
+                        pred_labels_list = np.append(pred_labels_list, pred_label.numpy())
+                        true_labels_list = np.append(true_labels_list, target.data.numpy())
+                    else:
+                        pred_labels_list = np.append(pred_labels_list, pred_label.cpu().numpy())
+                        true_labels_list = np.append(true_labels_list, target.data.cpu().numpy())
+        avg_loss = sum(loss_collector) / len(loss_collector)
+    else:
+        with torch.no_grad():
+            for batch_idx, (x, target) in enumerate(dataloader):
+                #print("x:",x)
+                if device != 'cpu':
+                    x, target = x.cuda(), target.to(dtype=torch.int64).cuda()
+                _,_,out = model(x)
+                loss = criterion(out, target)
+                _, pred_label = torch.max(out.data, 1)
+                loss_collector.append(loss.item())
+                total += x.data.size()[0]
+                correct += (pred_label == target.data).sum().item()
+
+                if device == "cpu":
+                    pred_labels_list = np.append(pred_labels_list, pred_label.numpy())
+                    true_labels_list = np.append(true_labels_list, target.data.numpy())
+                else:
+                    pred_labels_list = np.append(pred_labels_list, pred_label.cpu().numpy())
+                    true_labels_list = np.append(true_labels_list, target.data.cpu().numpy())
+            avg_loss = sum(loss_collector) / len(loss_collector)
+
+
+    if was_training:
+        model.train()
+
+
+    return correct / float(total), avg_loss
diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
new file mode 100644
index 000000000000..514068a5c96c
--- /dev/null
+++ b/baselines/moon/pyproject.toml
@@ -0,0 +1,135 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "moon" # <----- Ensure it matches the name of your baseline directory containing all the source code
+version = "1.0.0"
+description = "Flower Baselines"
+license = "Apache-2.0"
+authors = ["The Flower Authors <hello@flower.dev>"]
+readme = "README.md"
+homepage = "https://flower.dev"
+repository = "https://github.com/adap/flower"
+documentation = "https://flower.dev"
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Mathematics",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Typing :: Typed",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.8.15, <3.12.0"  # don't change this
+flwr = { extras = ["simulation"], version = "1.5.0" }
+hydra-core = "1.3.2" # don't change this
+scikit-learn = "1.3.0"
+
+[tool.poetry.dev-dependencies]
+isort = "==5.11.5"
+black = "==23.1.0"
+docformatter = "==1.5.1"
+mypy = "==1.4.1"
+pylint = "==2.8.2"
+flake8 = "==3.9.2"
+pytest = "==6.2.4"
+pytest-watch = "==4.2.0"
+ruff = "==0.0.272"
+types-requests = "==2.27.7"
+
+[tool.isort]
+line_length = 88
+indent = "    "
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+
+[tool.black]
+line-length = 88
+target-version = ["py38", "py39", "py310", "py311"]
+
+[tool.pytest.ini_options]
+minversion = "6.2"
+addopts = "-qq"
+testpaths = [
+    "flwr_baselines",
+]
+
+[tool.mypy]
+ignore_missing_imports = true
+strict = false
+plugins = "numpy.typing.mypy_plugin"
+
+[tool.pylint."MESSAGES CONTROL"]
+disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
+good-names = "i,j,k,_,x,y,X,Y"
+signature-mutators="hydra.main.main"
+
+[[tool.mypy.overrides]]
+module = [
+    "importlib.metadata.*",
+    "importlib_metadata.*",
+]
+follow_imports = "skip"
+follow_imports_for_stubs = true
+disallow_untyped_calls = false
+
+[[tool.mypy.overrides]]
+module = "torch.*"
+follow_imports = "skip"
+follow_imports_for_stubs = true
+
+[tool.docformatter]
+wrap-summaries = 88
+wrap-descriptions = 88
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+select = ["D", "E", "F", "W", "B", "ISC", "C4"]
+fixable = ["D", "E", "F", "W", "B", "ISC", "C4"]
+ignore = ["B024", "B027"]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "proto",
+]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"

From 6c1141459de3bbc424f13d64bfdff2aee520ec87 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Thu, 14 Sep 2023 07:10:23 +0800
Subject: [PATCH 02/51] adding moon

---
 baselines/moon/moon/client.py      | 31 ++++++------
 baselines/moon/moon/conf/base.yaml | 11 ++++
 baselines/moon/moon/main.py        | 80 +++++++++++++++++++++++++++---
 baselines/moon/moon/models.py      |  6 ++-
 baselines/moon/moon/server.py      | 52 +++++++++++++++++++
 5 files changed, 156 insertions(+), 24 deletions(-)

diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index c7dc362c056d..ea795cf68104 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -115,14 +115,9 @@ def evaluate(
 
 
 def gen_client_fn(
-    num_clients: int,
-    num_rounds: int,
-    num_epochs: int,
     trainloaders: List[DataLoader],
-    valloaders: List[DataLoader],
-    learning_rate: float,
-    stragglers: float,
-    model: DictConfig,
+    testloaders: List[DataLoader],
+    cfg: DictConfig,
 ) -> Tuple[
     Callable[[str], FlowerClient], DataLoader
 ]:  # pylint: disable=too-many-arguments
@@ -161,23 +156,27 @@ def client_fn(cid: str) -> FlowerClient:
 
         # Load model
         device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        net = init_net(model.dataset, model.model, model.output_dim)
-        net = instantiate(model).to(device)
+        net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
 
         # Note: each client gets a different trainloader/valloader, so each client
         # will train and evaluate on their own unique data
         trainloader = trainloaders[int(cid)]
-        valloader = valloaders[int(cid)]
+        testloader = testloaders[int(cid)]
 
         return FlowerClient(
             net,
-            net_id, 
+            int(cid),
+            cfg.dataset.name,
+            cfg.model.name,
+            cfg.model.output_dim,
             trainloader,
-            valloader,
+            testloader,
             device,
-            num_epochs,
-            learning_rate,
-            stragglers_mat[int(cid)],
+            cfg.num_epochs,
+            cfg.learning_rate,
+            cfg.mu,
+            cfg.temperature,
+            cfg.model.dir,
+            cfg.alg,
         )
-
     return client_fn
diff --git a/baselines/moon/moon/conf/base.yaml b/baselines/moon/moon/conf/base.yaml
index e996b897ef7b..907e0fe3d02b 100644
--- a/baselines/moon/moon/conf/base.yaml
+++ b/baselines/moon/moon/conf/base.yaml
@@ -4,7 +4,15 @@
 # a similar configuration structure and hence be easy to customise)
 
 num_clients: 10
+num_epochs: 10
+fraction_fit: 1.0
 batch_size: 32
+learning_rate: 0.01
+mu: 1
+temperature: 0.5
+alg: moon
+
+
 
 dataset:
   # dataset config
@@ -15,6 +23,9 @@ dataset:
 
 model:
   # model config
+  name: simple-cnn
+  output_dim: 256
+  dir: ./models/moon/
 
 strategy:
   _target_: # points to your strategy (either custom or exiting in Flower)
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 6b875a92ac9f..8921b152bb66 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -5,8 +5,11 @@
 """
 # these are the basic packages you'll need here
 # feel free to remove some if aren't needed
+import flwr as fl
 import hydra
 from omegaconf import DictConfig, OmegaConf
+# from hydra.utils import instantiate
+
 from moon import client, server
 from moon.dataset_preparation import partition_data, get_dataloader
 
@@ -43,28 +46,63 @@ def main(cfg: DictConfig) -> None:
                                                                             train_bs=cfg.batch_size,
                                                                             test_bs=32)
     
+    trainloaders = []
+    testloaders = []
+    for idx in range(cfg.num_clients):
+        train_dl, test_dl, _, _ = get_dataloader(cfg.dataset.name,
+                                                cfg.dataset.dir,
+                                                cfg.batch_size,
+                                                32,
+                                                net_dataidx_map[idx])
 
+        trainloaders.append(train_dl)
+        testloaders.append(test_dl)
     # 3. Define your clients
     # Define a function that returns another function that will be used during
     # simulation to instantiate each individual client
     # client_fn = client.<my_function_that_returns_a_function>()
     client_fn = client.gen_client_fn(
-        num_clients=cfg.num_clients,
-        num_epochs=cfg.num_epochs,
         trainloaders=trainloaders,
-        valloaders=valloaders,
-        num_rounds=cfg.num_rounds,
-        learning_rate=cfg.learning_rate,
-        model=cfg.model,
+        testloaders=testloaders,
+        config=cfg,
     )
 
+    # get function that will executed by the strategy's evaluate() method
+    # Set server's device
+    device = cfg.server_device
+    evaluate_fn = server.gen_evaluate_fn(test_dl, device=device, model=cfg.model)
+
+    # get a function that will be used to construct the config that the client's
+    # fit() method will received
+    def get_on_fit_config():
+        def fit_config_fn(server_round: int):
+            # resolve and convert to python dict
+            fit_config = OmegaConf.to_container(cfg.fit_config, resolve=True)
+            fit_config["curr_round"] = server_round  # add round info
+            return fit_config
+
+        return fit_config_fn
+    
     # 4. Define your strategy
     # pass all relevant argument (including the global dataset used after aggregation,
     # if needed by your method.)
     # strategy = instantiate(cfg.strategy, <additional arguments if desired>)
-
+    strategy = fl.server.strategy.FedAvg(
+        fraction_fit = cfg.fraction_fit,
+        on_fit_config_fn = get_on_fit_config()
+    )
     # 5. Start Simulation
     # history = fl.simulation.start_simulation(<arguments for simulation>)
+    history = fl.simulation.start_simulation(
+        client_fn=client_fn,
+        num_clients=cfg.num_clients,
+        config=fl.server.ServerConfig(num_rounds=cfg.num_rounds),
+        client_resources={
+            "num_cpus": cfg.client_resources.num_cpus,
+            "num_gpus": cfg.client_resources.num_gpus,
+        },
+        strategy=strategy,
+    )
 
     # 6. Save your results
     # Here you can save the `history` returned by the simulation and include
@@ -75,3 +113,31 @@ def main(cfg: DictConfig) -> None:
     # Hydra will generate for you a directory each time you run the code. You
     # can retrieve the path to that directory with this:
     # save_path = HydraConfig.get().runtime.output_dir
+
+    # Experiment completed. Now we save the results and
+    # generate plots using the `history`
+    print("................")
+    print(history)
+
+    # Hydra automatically creates an output directory
+    # Let's retrieve it and save some results there
+    # save_path = HydraConfig.get().runtime.output_dir
+
+    # # save results as a Python pickle using a file_path
+    # # the directory created by Hydra for each run
+    # save_results_as_pickle(history, file_path=save_path, extra_results={})
+
+    # # plot results and include them in the readme
+    # strategy_name = strategy.__class__.__name__
+    # file_suffix: str = (
+    #     f"_{strategy_name}"
+    #     f"{'_iid' if cfg.dataset_config.iid else ''}"
+    #     f"{'_balanced' if cfg.dataset_config.balance else ''}"
+    #     f"{'_powerlaw' if cfg.dataset_config.power_law else ''}"
+    #     f"_C={cfg.num_clients}"
+    #     f"_B={cfg.batch_size}"
+    #     f"_E={cfg.num_epochs}"
+    #     f"_R={cfg.num_rounds}"
+    #     f"_mu={cfg.mu}"
+    #     f"_strag={cfg.stragglers_fraction}"
+    # )
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
index 55682d3d88c7..04d7560c3240 100644
--- a/baselines/moon/moon/models.py
+++ b/baselines/moon/moon/models.py
@@ -433,4 +433,8 @@ def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu
     print('>> Training accuracy: %f' % train_acc)
     net.to('cpu')
     print(' ** Training complete **')
-    return net
\ No newline at end of file
+    return net
+
+def test(net, test_dataloader, device='cpu'):
+    test_acc, loss = compute_accuracy(net, test_dataloader, device=device)
+    return test_acc, loss
\ No newline at end of file
diff --git a/baselines/moon/moon/server.py b/baselines/moon/moon/server.py
index 2fd7d42cde5a..21e9e057b00e 100644
--- a/baselines/moon/moon/server.py
+++ b/baselines/moon/moon/server.py
@@ -3,3 +3,55 @@
 Optionally, also define a new Server class (please note this is not needed in most
 settings).
 """
+
+from collections import OrderedDict
+from typing import Callable, Dict, Optional, Tuple
+
+import torch
+from flwr.common.typing import NDArrays, Scalar
+from hydra.utils import instantiate
+from omegaconf import DictConfig
+from torch.utils.data import DataLoader
+
+from moon.models import test, init_net
+
+
+def gen_evaluate_fn(
+    testloader: DataLoader,
+    device: torch.device,
+    cfg: DictConfig,
+) -> Callable[
+    [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]
+]:
+    """Generates the function for centralized evaluation.
+
+    Parameters
+    ----------
+    testloader : DataLoader
+        The dataloader to test the model with.
+    device : torch.device
+        The device to test the model on.
+
+    Returns
+    -------
+    Callable[ [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]] ]
+        The centralized evaluation function.
+    """
+
+    def evaluate(
+        server_round: int, parameters_ndarrays: NDArrays, config: Dict[str, Scalar]
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        # pylint: disable=unused-argument
+        """Use the entire CIFAR-10 test set for evaluation."""
+
+        net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
+        params_dict = zip(net.state_dict().keys(), parameters_ndarrays)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        net.load_state_dict(state_dict, strict=True)
+        net.to(device)
+
+        accuracy, loss = test(net, testloader, device=device)
+        # return statistics
+        return loss, {"accuracy": accuracy}
+
+    return evaluate

From 9bbe58f134fa061fd89cb013b4d584a2a86e9518 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Thu, 14 Sep 2023 08:14:09 +0800
Subject: [PATCH 03/51] formatting

---
 baselines/moon/moon/client.py              |  39 ++--
 baselines/moon/moon/dataset.py             | 196 ++++++++++------
 baselines/moon/moon/dataset_preparation.py |  39 +++-
 baselines/moon/moon/main.py                |  51 +++--
 baselines/moon/moon/models.py              | 252 ++++++++++++++-------
 baselines/moon/moon/server.py              |   4 +-
 baselines/moon/moon/utils.py               |  48 ++--
 7 files changed, 398 insertions(+), 231 deletions(-)

diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index ea795cf68104..a0a2b6846eb8 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -3,29 +3,23 @@
 Please overwrite `flwr.client.NumPyClient` or `flwr.client.Client` and create a function
 to instantiate your client.
 """
-"""Defines the MNIST Flower Client and a function to instantiate it."""
-
 
+import os
 from collections import OrderedDict
 from typing import Callable, Dict, List, Tuple
 
 import flwr as fl
-import numpy as np
 import torch
 from flwr.common.typing import NDArrays, Scalar
-from hydra.utils import instantiate
 from omegaconf import DictConfig
 from torch.utils.data import DataLoader
-import os
 
+from moon.models import init_net, test, train_fedprox, train_moon
 
-from moon.models import train_moon, train_fedprox, init_net
 
-
-class FlowerClient(
-    fl.client.NumPyClient
-):  
+class FlowerClient(fl.client.NumPyClient):
     """Standard Flower client for CNN training."""
+
     def __init__(
         self,
         net: torch.nn.Module,
@@ -57,14 +51,13 @@ def __init__(
         self.temperature = temperature
         self.model_dir = model_dir
         self.alg = alg
-        
 
     def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
-        """Returns the parameters of the current net."""
+        """Return the parameters of the current net."""
         return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
 
     def set_parameters(self, parameters: NDArrays) -> None:
-        """Changes the parameters of the model using the given ones."""
+        """Change the parameters of the model using the given ones."""
         params_dict = zip(self.net.state_dict().keys(), parameters)
         state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
         self.net.load_state_dict(state_dict, strict=True)
@@ -72,12 +65,14 @@ def set_parameters(self, parameters: NDArrays) -> None:
     def fit(
         self, parameters: NDArrays, config: Dict[str, Scalar]
     ) -> Tuple[NDArrays, int, Dict]:
-        """Implements distributed fit function for a given client."""
+        """Implement distributed fit function for a given client."""
         self.set_parameters(parameters)
-        
-        #load previous model from model_dir
+
+        # load previous model from model_dir
         self.prev_net = init_net(self.dataset, self.model, self.output_dim)
-        self.prev_net.load_state_dict(torch.load(os.path.join(self.model_dir, "prev_net.pt")))
+        self.prev_net.load_state_dict(
+            torch.load(os.path.join(self.model_dir, "prev_net.pt"))
+        )
         global_net = init_net(self.dataset, self.model, self.output_dim)
         global_net.load_state_dict(self.net.state_dict())
         if self.alg == "moon":
@@ -90,7 +85,7 @@ def fit(
                 self.learning_rate,
                 self.mu,
                 self.temperature,
-                self.device
+                self.device,
             )
         elif self.alg == "fedprox":
             train_fedprox(
@@ -100,7 +95,7 @@ def fit(
                 self.num_epochs,
                 self.learning_rate,
                 self.mu,
-                self.device
+                self.device,
             )
         torch.save(self.net.state_dict(), os.path.join(self.model_dir, "prev_net.pt"))
         return self.get_parameters({}), len(self.trainloader), {"is_straggler": False}
@@ -108,7 +103,7 @@ def fit(
     def evaluate(
         self, parameters: NDArrays, config: Dict[str, Scalar]
     ) -> Tuple[float, int, Dict]:
-        """Implements distributed evaluation for a given client."""
+        """Implement distributed evaluation for a given client."""
         self.set_parameters(parameters)
         loss, accuracy = test(self.net, self.valloader, self.device)
         return float(loss), len(self.valloader), {"accuracy": float(accuracy)}
@@ -121,7 +116,7 @@ def gen_client_fn(
 ) -> Tuple[
     Callable[[str], FlowerClient], DataLoader
 ]:  # pylint: disable=too-many-arguments
-    """Generates the client function that creates the Flower Clients.
+    """Generate the client function that creates the Flower Clients.
 
     Parameters
     ----------
@@ -153,7 +148,6 @@ def gen_client_fn(
 
     def client_fn(cid: str) -> FlowerClient:
         """Create a Flower client representing a single organization."""
-
         # Load model
         device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
@@ -179,4 +173,5 @@ def client_fn(cid: str) -> FlowerClient:
             cfg.model.dir,
             cfg.alg,
         )
+
     return client_fn
diff --git a/baselines/moon/moon/dataset.py b/baselines/moon/moon/dataset.py
index 701eeda8ecb8..62e105a3aecd 100644
--- a/baselines/moon/moon/dataset.py
+++ b/baselines/moon/moon/dataset.py
@@ -11,32 +11,46 @@
 
 # https://github.com/QinbinLi/MOON/blob/main/datasets.py
 
-import torch.utils.data as data
-from PIL import Image
+import logging
+
 import numpy as np
+import torch.nn.functional as F
+import torch.utils.data as data
 import torchvision
-from torchvision.datasets import CIFAR10, CIFAR100
 import torchvision.transforms as transforms
+from PIL import Image
 from torch.autograd import Variable
-import torch.nn.functional as F
-import torch.utils.data as data
-
-import os
-import os.path
-import logging
+from torchvision.datasets import CIFAR10, CIFAR100
 
 logging.basicConfig()
 logger = logging.getLogger()
 logger.setLevel(logging.INFO)
 
-IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp')
-
+IMG_EXTENSIONS = (
+    ".jpg",
+    ".jpeg",
+    ".png",
+    ".ppm",
+    ".bmp",
+    ".pgm",
+    ".tif",
+    ".tiff",
+    ".webp",
+)
 
 
 class CIFAR10_sub(data.Dataset):
-
-    def __init__(self, root, dataidxs=None, train=True, transform=None, target_transform=None, download=False):
-
+    """CIFAR-10 dataset with idxs."""
+
+    def __init__(
+        self,
+        root,
+        dataidxs=None,
+        train=True,
+        transform=None,
+        target_transform=None,
+        download=False,
+    ):
         self.root = root
         self.dataidxs = dataidxs
         self.train = train
@@ -47,14 +61,20 @@ def __init__(self, root, dataidxs=None, train=True, transform=None, target_trans
         self.data, self.target = self.__build_sub_dataset__()
 
     def __build_sub_dataset__(self):
+        """Build sub dataset given idxs."""
+        cifar_dataobj = CIFAR10(
+            self.root, self.train, self.transform, self.target_transform, self.download
+        )
 
-        cifar_dataobj = CIFAR10(self.root, self.train, self.transform, self.target_transform, self.download)
-
-        if torchvision.__version__ == '0.2.1':
+        if torchvision.__version__ == "0.2.1":
             if self.train:
-                data, target = cifar_dataobj.train_data, np.array(cifar_dataobj.train_labels)
+                data, target = cifar_dataobj.train_data, np.array(
+                    cifar_dataobj.train_labels
+                )
             else:
-                data, target = cifar_dataobj.test_data, np.array(cifar_dataobj.test_labels)
+                data, target = cifar_dataobj.test_data, np.array(
+                    cifar_dataobj.test_labels
+                )
         else:
             data = cifar_dataobj.data
             target = np.array(cifar_dataobj.targets)
@@ -65,18 +85,14 @@ def __build_sub_dataset__(self):
 
         return data, target
 
-    def truncate_channel(self, index):
-        for i in range(index.shape[0]):
-            gs_index = index[i]
-            self.data[gs_index, :, :, 1] = 0.0
-            self.data[gs_index, :, :, 2] = 0.0
-
     def __getitem__(self, index):
-        """
+        """Get item by index.
+
         Args:
-            index (int): Index
+            index (int): Index.
 
-        Returns:
+        Returns
+        -------
             tuple: (image, target) where target is index of the target class.
         """
         img, target = self.data[index], self.target[index]
@@ -90,13 +106,25 @@ def __getitem__(self, index):
         return img, target
 
     def __len__(self):
+        """Length.
+
+        Returns
+        -------
+            int: length of data
+        """
         return len(self.data)
 
 
 class CIFAR100_sub(data.Dataset):
-
-    def __init__(self, root, dataidxs=None, train=True, transform=None, target_transform=None, download=False):
-
+    def __init__(
+        self,
+        root,
+        dataidxs=None,
+        train=True,
+        transform=None,
+        target_transform=None,
+        download=False,
+    ):
         self.root = root
         self.dataidxs = dataidxs
         self.train = train
@@ -107,14 +135,19 @@ def __init__(self, root, dataidxs=None, train=True, transform=None, target_trans
         self.data, self.target = self.__build_sub_dataset__()
 
     def __build_sub_dataset__(self):
+        cifar_dataobj = CIFAR100(
+            self.root, self.train, self.transform, self.target_transform, self.download
+        )
 
-        cifar_dataobj = CIFAR100(self.root, self.train, self.transform, self.target_transform, self.download)
-
-        if torchvision.__version__ == '0.2.1':
+        if torchvision.__version__ == "0.2.1":
             if self.train:
-                data, target = cifar_dataobj.train_data, np.array(cifar_dataobj.train_labels)
+                data, target = cifar_dataobj.train_data, np.array(
+                    cifar_dataobj.train_labels
+                )
             else:
-                data, target = cifar_dataobj.test_data, np.array(cifar_dataobj.test_labels)
+                data, target = cifar_dataobj.test_data, np.array(
+                    cifar_dataobj.test_labels
+                )
         else:
             data = cifar_dataobj.data
             target = np.array(cifar_dataobj.targets)
@@ -128,9 +161,10 @@ def __build_sub_dataset__(self):
     def __getitem__(self, index):
         """
         Args:
-            index (int): Index
+            index (int): Index.
 
-        Returns:
+        Returns
+        -------
             tuple: (image, target) where target is index of the target class.
         """
         img, target = self.data[index], self.target[index]
@@ -149,49 +183,65 @@ def __len__(self):
 
 
 def get_dataloader(dataset, datadir, train_bs, test_bs, dataidxs=None, noise_level=0):
-    if dataset == 'cifar10':
+    if dataset == "cifar10":
         dl_obj = CIFAR10_sub
-        normalize = transforms.Normalize(mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
-                                            std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
-        transform_train = transforms.Compose([
-            transforms.ToTensor(),
-            transforms.Lambda(lambda x: F.pad(
-                Variable(x.unsqueeze(0), requires_grad=False),
-                (4, 4, 4, 4), mode='reflect').data.squeeze()),
-            transforms.ToPILImage(),
-            transforms.ColorJitter(brightness=noise_level),
-            transforms.RandomCrop(32),
-            transforms.RandomHorizontalFlip(),
-            transforms.ToTensor(),
-            normalize
-        ])
+        normalize = transforms.Normalize(
+            mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
+            std=[x / 255.0 for x in [63.0, 62.1, 66.7]],
+        )
+        transform_train = transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Lambda(
+                    lambda x: F.pad(
+                        Variable(x.unsqueeze(0), requires_grad=False),
+                        (4, 4, 4, 4),
+                        mode="reflect",
+                    ).data.squeeze()
+                ),
+                transforms.ToPILImage(),
+                transforms.ColorJitter(brightness=noise_level),
+                transforms.RandomCrop(32),
+                transforms.RandomHorizontalFlip(),
+                transforms.ToTensor(),
+                normalize,
+            ]
+        )
         # data prep for test set
-        transform_test = transforms.Compose([
-            transforms.ToTensor(),
-            normalize])
+        transform_test = transforms.Compose([transforms.ToTensor(), normalize])
 
-    elif dataset == 'cifar100':
+    elif dataset == "cifar100":
         dl_obj = CIFAR100_sub
 
-        normalize = transforms.Normalize(mean=[0.5070751592371323, 0.48654887331495095, 0.4409178433670343],
-                                            std=[0.2673342858792401, 0.2564384629170883, 0.27615047132568404])
-
-        transform_train = transforms.Compose([
-            transforms.RandomCrop(32, padding=4),
-            transforms.RandomHorizontalFlip(),
-            transforms.RandomRotation(15),
-            transforms.ToTensor(),
-            normalize
-        ])
+        normalize = transforms.Normalize(
+            mean=[0.5070751592371323, 0.48654887331495095, 0.4409178433670343],
+            std=[0.2673342858792401, 0.2564384629170883, 0.27615047132568404],
+        )
+
+        transform_train = transforms.Compose(
+            [
+                transforms.RandomCrop(32, padding=4),
+                transforms.RandomHorizontalFlip(),
+                transforms.RandomRotation(15),
+                transforms.ToTensor(),
+                normalize,
+            ]
+        )
         # data prep for test set
-        transform_test = transforms.Compose([
-            transforms.ToTensor(),
-            normalize])
-
-        train_ds = dl_obj(datadir, dataidxs=dataidxs, train=True, transform=transform_train, download=True)
+        transform_test = transforms.Compose([transforms.ToTensor(), normalize])
+
+        train_ds = dl_obj(
+            datadir,
+            dataidxs=dataidxs,
+            train=True,
+            transform=transform_train,
+            download=True,
+        )
         test_ds = dl_obj(datadir, train=False, transform=transform_test, download=True)
 
-        train_dl = data.DataLoader(dataset=train_ds, batch_size=train_bs, drop_last=True, shuffle=True)
+        train_dl = data.DataLoader(
+            dataset=train_ds, batch_size=train_bs, drop_last=True, shuffle=True
+        )
         test_dl = data.DataLoader(dataset=test_ds, batch_size=test_bs, shuffle=False)
 
     return train_dl, test_dl, train_ds, test_ds
diff --git a/baselines/moon/moon/dataset_preparation.py b/baselines/moon/moon/dataset_preparation.py
index ec45cfb4d6d8..3a4ea9c4e8a8 100644
--- a/baselines/moon/moon/dataset_preparation.py
+++ b/baselines/moon/moon/dataset_preparation.py
@@ -41,8 +41,12 @@
 def load_cifar10_data(datadir):
     transform = transforms.Compose([transforms.ToTensor()])
 
-    cifar10_train_ds = CIFAR10_truncated(datadir, train=True, download=True, transform=transform)
-    cifar10_test_ds = CIFAR10_truncated(datadir, train=False, download=True, transform=transform)
+    cifar10_train_ds = CIFAR10_truncated(
+        datadir, train=True, download=True, transform=transform
+    )
+    cifar10_test_ds = CIFAR10_truncated(
+        datadir, train=False, download=True, transform=transform
+    )
 
     X_train, y_train = cifar10_train_ds.data, cifar10_train_ds.target
     X_test, y_test = cifar10_test_ds.data, cifar10_test_ds.target
@@ -53,8 +57,12 @@ def load_cifar10_data(datadir):
 def load_cifar100_data(datadir):
     transform = transforms.Compose([transforms.ToTensor()])
 
-    cifar100_train_ds = CIFAR100_truncated(datadir, train=True, download=True, transform=transform)
-    cifar100_test_ds = CIFAR100_truncated(datadir, train=False, download=True, transform=transform)
+    cifar100_train_ds = CIFAR100_truncated(
+        datadir, train=True, download=True, transform=transform
+    )
+    cifar100_test_ds = CIFAR100_truncated(
+        datadir, train=False, download=True, transform=transform
+    )
 
     X_train, y_train = cifar100_train_ds.data, cifar100_train_ds.target
     X_test, y_test = cifar100_test_ds.data, cifar100_test_ds.target
@@ -63,9 +71,9 @@ def load_cifar100_data(datadir):
 
 
 def partition_data(dataset, datadir, partition, num_clients, beta):
-    if dataset == 'cifar10':
+    if dataset == "cifar10":
         X_train, y_train, X_test, y_test = load_cifar10_data(datadir)
-    elif dataset == 'cifar100':
+    elif dataset == "cifar100":
         X_train, y_train, X_test, y_test = load_cifar100_data(datadir)
 
     n_train = y_train.shape[0]
@@ -75,14 +83,13 @@ def partition_data(dataset, datadir, partition, num_clients, beta):
         batch_idxs = np.array_split(idxs, num_clients)
         net_dataidx_map = {i: batch_idxs[i] for i in range(num_clients)}
 
-
     elif partition == "noniid-labeldir" or partition == "noniid":
         min_size = 0
         min_require_size = 10
         K = 10
-        if dataset == 'cifar100':
+        if dataset == "cifar100":
             K = 100
-        elif dataset == 'tinyimagenet':
+        elif dataset == "tinyimagenet":
             K = 200
 
         N = y_train.shape[0]
@@ -94,13 +101,21 @@ def partition_data(dataset, datadir, partition, num_clients, beta):
                 idx_k = np.where(y_train == k)[0]
                 np.random.shuffle(idx_k)
                 proportions = np.random.dirichlet(np.repeat(beta, num_clients))
-                proportions = np.array([p * (len(idx_j) < N / num_clients) for p, idx_j in zip(proportions, idx_batch)])
+                proportions = np.array(
+                    [
+                        p * (len(idx_j) < N / num_clients)
+                        for p, idx_j in zip(proportions, idx_batch)
+                    ]
+                )
                 proportions = proportions / proportions.sum()
                 proportions = (np.cumsum(proportions) * len(idx_k)).astype(int)[:-1]
-                idx_batch = [idx_j + idx.tolist() for idx_j, idx in zip(idx_batch, np.split(idx_k, proportions))]
+                idx_batch = [
+                    idx_j + idx.tolist()
+                    for idx_j, idx in zip(idx_batch, np.split(idx_k, proportions))
+                ]
                 min_size = min([len(idx_j) for idx_j in idx_batch])
         for j in range(num_clients):
             np.random.shuffle(idx_batch[j])
             net_dataidx_map[j] = idx_batch[j]
 
-    return (X_train, y_train, X_test, y_test, net_dataidx_map)
\ No newline at end of file
+    return (X_train, y_train, X_test, y_test, net_dataidx_map)
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 8921b152bb66..4a5279db0007 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -8,10 +8,11 @@
 import flwr as fl
 import hydra
 from omegaconf import DictConfig, OmegaConf
-# from hydra.utils import instantiate
 
 from moon import client, server
-from moon.dataset_preparation import partition_data, get_dataloader
+from moon.dataset_preparation import get_dataloader, partition_data
+
+# from hydra.utils import instantiate
 
 
 @hydra.main(config_path="conf", config_name="base", version_base=None)
@@ -33,27 +34,34 @@ def main(cfg: DictConfig) -> None:
     # (2) tell each client what dataset partitions they should use (e.g. a this could
     # be a location in the file system, a list of dataloader, a list of ids to extract
     # from a dataset, it's up to you)
-    X_train, y_train, X_test, y_test, net_dataidx_map, traindata_cls_counts = partition_data(
-        dataset=cfg.dataset.name, 
-        datadir=cfg.dataset.dir, 
-        parittion=cfg.dataset.partition, 
-        num_clients=cfg.num_clients, 
+    (
+        X_train,
+        y_train,
+        X_test,
+        y_test,
+        net_dataidx_map,
+        traindata_cls_counts,
+    ) = partition_data(
+        dataset=cfg.dataset.name,
+        datadir=cfg.dataset.dir,
+        parittion=cfg.dataset.partition,
+        num_clients=cfg.num_clients,
         beta=cfg.dataset.beta,
     )
-    
-    train_dl_global, test_dl, train_ds_global, test_ds_global = get_dataloader(dataset=cfg.dataset.name,
-                                                                            datadir=cfg.dataset.dir,
-                                                                            train_bs=cfg.batch_size,
-                                                                            test_bs=32)
-    
+
+    train_dl_global, test_dl, train_ds_global, test_ds_global = get_dataloader(
+        dataset=cfg.dataset.name,
+        datadir=cfg.dataset.dir,
+        train_bs=cfg.batch_size,
+        test_bs=32,
+    )
+
     trainloaders = []
     testloaders = []
     for idx in range(cfg.num_clients):
-        train_dl, test_dl, _, _ = get_dataloader(cfg.dataset.name,
-                                                cfg.dataset.dir,
-                                                cfg.batch_size,
-                                                32,
-                                                net_dataidx_map[idx])
+        train_dl, test_dl, _, _ = get_dataloader(
+            cfg.dataset.name, cfg.dataset.dir, cfg.batch_size, 32, net_dataidx_map[idx]
+        )
 
         trainloaders.append(train_dl)
         testloaders.append(test_dl)
@@ -70,7 +78,7 @@ def main(cfg: DictConfig) -> None:
     # get function that will executed by the strategy's evaluate() method
     # Set server's device
     device = cfg.server_device
-    evaluate_fn = server.gen_evaluate_fn(test_dl, device=device, model=cfg.model)
+    server.gen_evaluate_fn(test_dl, device=device, model=cfg.model)
 
     # get a function that will be used to construct the config that the client's
     # fit() method will received
@@ -82,14 +90,13 @@ def fit_config_fn(server_round: int):
             return fit_config
 
         return fit_config_fn
-    
+
     # 4. Define your strategy
     # pass all relevant argument (including the global dataset used after aggregation,
     # if needed by your method.)
     # strategy = instantiate(cfg.strategy, <additional arguments if desired>)
     strategy = fl.server.strategy.FedAvg(
-        fraction_fit = cfg.fraction_fit,
-        on_fit_config_fn = get_on_fit_config()
+        fraction_fit=cfg.fraction_fit, on_fit_config_fn=get_on_fit_config()
     )
     # 5. Start Simulation
     # history = fl.simulation.start_simulation(<arguments for simulation>)
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
index 04d7560c3240..052988277dd5 100644
--- a/baselines/moon/moon/models.py
+++ b/baselines/moon/moon/models.py
@@ -6,35 +6,53 @@
 the python code at all
 """
 
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import math
-import torchvision.models as models
 import torch.optim as optim
+
 from moon.utils import compute_accuracy
 
+
 def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
-    """3x3 convolution with padding"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
-                     padding=dilation, groups=groups, bias=False, dilation=dilation)
+    """3x3 convolution with padding."""
+    return nn.Conv2d(
+        in_planes,
+        out_planes,
+        kernel_size=3,
+        stride=stride,
+        padding=dilation,
+        groups=groups,
+        bias=False,
+        dilation=dilation,
+    )
 
 
 def conv1x1(in_planes, out_planes, stride=1):
-    """1x1 convolution"""
+    """1x1 convolution."""
     return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
 
 
 class BasicBlock(nn.Module):
     expansion = 1
 
-    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
-                 base_width=64, dilation=1, norm_layer=None):
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None,
+    ):
         super(BasicBlock, self).__init__()
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         if groups != 1 or base_width != 64:
-            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
         if dilation > 1:
             raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
         # Both self.conv1 and self.downsample layers downsample the input when stride != 1
@@ -74,12 +92,21 @@ class Bottleneck(nn.Module):
 
     expansion = 4
 
-    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
-                 base_width=64, dilation=1, norm_layer=None):
+    def __init__(
+        self,
+        inplanes,
+        planes,
+        stride=1,
+        downsample=None,
+        groups=1,
+        base_width=64,
+        dilation=1,
+        norm_layer=None,
+    ):
         super(Bottleneck, self).__init__()
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
-        width = int(planes * (base_width / 64.)) * groups
+        width = int(planes * (base_width / 64.0)) * groups
         # Both self.conv2 and self.downsample layers downsample the input when stride != 1
         self.conv1 = conv1x1(inplanes, width)
         self.bn1 = norm_layer(width)
@@ -115,10 +142,17 @@ def forward(self, x):
 
 
 class ResNetCifar10(nn.Module):
-
-    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
-                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
-                 norm_layer=None):
+    def __init__(
+        self,
+        block,
+        layers,
+        num_classes=1000,
+        zero_init_residual=False,
+        groups=1,
+        width_per_group=64,
+        replace_stride_with_dilation=None,
+        norm_layer=None,
+    ):
         super(ResNetCifar10, self).__init__()
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
@@ -131,27 +165,33 @@ def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
             # the 2x2 stride with a dilated convolution instead
             replace_stride_with_dilation = [False, False, False]
         if len(replace_stride_with_dilation) != 3:
-            raise ValueError("replace_stride_with_dilation should be None "
-                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+            raise ValueError(
+                "replace_stride_with_dilation should be None "
+                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
+            )
         self.groups = groups
         self.base_width = width_per_group
-        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1,
-                               bias=False)
+        self.conv1 = nn.Conv2d(
+            3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False
+        )
         self.bn1 = norm_layer(self.inplanes)
         self.relu = nn.ReLU(inplace=True)
         self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
-                                       dilate=replace_stride_with_dilation[0])
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
-                                       dilate=replace_stride_with_dilation[1])
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
-                                       dilate=replace_stride_with_dilation[2])
+        self.layer2 = self._make_layer(
+            block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
+        )
+        self.layer3 = self._make_layer(
+            block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
+        )
+        self.layer4 = self._make_layer(
+            block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]
+        )
         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
         self.fc = nn.Linear(512 * block.expansion, num_classes)
 
         for m in self.modules():
             if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
             elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                 nn.init.constant_(m.weight, 1)
                 nn.init.constant_(m.bias, 0)
@@ -180,13 +220,30 @@ def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
             )
 
         layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
-                            self.base_width, previous_dilation, norm_layer))
+        layers.append(
+            block(
+                self.inplanes,
+                planes,
+                stride,
+                downsample,
+                self.groups,
+                self.base_width,
+                previous_dilation,
+                norm_layer,
+            )
+        )
         self.inplanes = planes * block.expansion
         for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes, groups=self.groups,
-                                base_width=self.base_width, dilation=self.dilation,
-                                norm_layer=norm_layer))
+            layers.append(
+                block(
+                    self.inplanes,
+                    planes,
+                    groups=self.groups,
+                    base_width=self.base_width,
+                    dilation=self.dilation,
+                    norm_layer=norm_layer,
+                )
+            )
 
         return nn.Sequential(*layers)
 
@@ -210,9 +267,11 @@ def _forward_impl(self, x):
     def forward(self, x):
         return self._forward_impl(x)
 
+
 def ResNet50_cifar10(**kwargs):
-    r"""ResNet-50 model from
-    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
+    r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition".
+
+    <https://arxiv.org/pdf/1512.03385.pdf>`_
 
     Args:
         pretrained (bool): If True, returns a model pre-trained on ImageNet
@@ -233,10 +292,9 @@ def __init__(self, input_dim, hidden_dims, output_dim=10):
         # i.e. we fix the number of hidden layers i.e. 2 layers
         self.fc1 = nn.Linear(input_dim, hidden_dims[0])
         self.fc2 = nn.Linear(hidden_dims[0], hidden_dims[1])
-        #self.fc3 = nn.Linear(hidden_dims[1], output_dim)
+        # self.fc3 = nn.Linear(hidden_dims[1], output_dim)
 
     def forward(self, x):
-
         x = self.pool(self.relu(self.conv1(x)))
         x = self.pool(self.relu(self.conv2(x)))
         x = x.view(-1, 16 * 5 * 5)
@@ -246,21 +304,28 @@ def forward(self, x):
         # x = self.fc3(x)
         return x
 
-class ModelFedCon(nn.Module):
 
+class ModelFedCon(nn.Module):
     def __init__(self, base_model, out_dim, n_classes):
         super(ModelFedCon, self).__init__()
 
-        if base_model == "resnet50-cifar10" or base_model == "resnet50-cifar100" or base_model == "resnet50-smallkernel" or base_model == "resnet50":
+        if (
+            base_model == "resnet50-cifar10"
+            or base_model == "resnet50-cifar100"
+            or base_model == "resnet50-smallkernel"
+            or base_model == "resnet50"
+        ):
             basemodel = ResNet50_cifar10()
             self.features = nn.Sequential(*list(basemodel.children())[:-1])
             num_ftrs = basemodel.fc.in_features
-        elif base_model == 'simple-cnn':
-            self.features = SimpleCNN_header(input_dim=(16 * 5 * 5), hidden_dims=[120, 84], output_dim=n_classes)
+        elif base_model == "simple-cnn":
+            self.features = SimpleCNN_header(
+                input_dim=(16 * 5 * 5), hidden_dims=[120, 84], output_dim=n_classes
+            )
             num_ftrs = 84
 
-        #summary(self.features.to('cuda:0'), (3,32,32))
-        #print("features:", self.features)
+        # summary(self.features.to('cuda:0'), (3,32,32))
+        # print("features:", self.features)
         # projection MLP
         self.l1 = nn.Linear(num_ftrs, num_ftrs)
         self.l2 = nn.Linear(num_ftrs, out_dim)
@@ -271,17 +336,19 @@ def __init__(self, base_model, out_dim, n_classes):
     def _get_basemodel(self, model_name):
         try:
             model = self.model_dict[model_name]
-            #print("Feature extractor:", model_name)
+            # print("Feature extractor:", model_name)
             return model
         except:
-            raise ("Invalid model name. Check the config file and pass one of: resnet18 or resnet50")
+            raise (
+                "Invalid model name. Check the config file and pass one of: resnet18 or resnet50"
+            )
 
     def forward(self, x):
         h = self.features(x)
-        #print("h before:", h)
-        #print("h size:", h.size())
+        # print("h before:", h)
+        # print("h size:", h.size())
         h = h.squeeze()
-        #print("h after:", h)
+        # print("h after:", h)
         x = self.l1(h)
         x = F.relu(x)
         x = self.l2(x)
@@ -289,14 +356,15 @@ def forward(self, x):
         y = self.l3(x)
         return h, x, y
 
-def init_net(dataset, model, output_dim, device='cpu'):
-    if dataset == 'cifar10':
+
+def init_net(dataset, model, output_dim, device="cpu"):
+    if dataset == "cifar10":
         n_classes = 10
-    elif dataset == 'cifar100':
+    elif dataset == "cifar100":
         n_classes = 100
 
     net = ModelFedCon(model, output_dim, n_classes)
-    if device == 'cpu':
+    if device == "cpu":
         net.to(device)
     else:
         net = net.cuda()
@@ -304,18 +372,32 @@ def init_net(dataset, model, output_dim, device='cpu'):
     return net
 
 
-def train_moon(net, global_net, previous_net, train_dataloader, epochs, lr, mu, temperature, device="cpu"):
+def train_moon(
+    net,
+    global_net,
+    previous_net,
+    train_dataloader,
+    epochs,
+    lr,
+    mu,
+    temperature,
+    device="cpu",
+):
     net = nn.DataParallel(net)
     net.cuda()
 
-    print('n_training: %d' % len(train_dataloader))
+    print("n_training: %d" % len(train_dataloader))
 
     train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
 
-    print('>> Pre-Training Training accuracy: {}'.format(train_acc))
+    print(">> Pre-Training Training accuracy: {}".format(train_acc))
 
-    optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=lr, momentum=0.9,
-                              weight_decay=1e-5)
+    optimizer = optim.SGD(
+        filter(lambda p: p.requires_grad, net.parameters()),
+        lr=lr,
+        momentum=0.9,
+        weight_decay=1e-5,
+    )
 
     criterion = nn.CrossEntropyLoss().cuda()
     # global_net.to(device)
@@ -324,7 +406,7 @@ def train_moon(net, global_net, previous_net, train_dataloader, epochs, lr, mu,
         previous_net.cuda()
 
     cnt = 0
-    cos=torch.nn.CosineSimilarity(dim=-1)
+    cos = torch.nn.CosineSimilarity(dim=-1)
     # mu = 0.001
 
     for epoch in range(epochs):
@@ -343,22 +425,20 @@ def train_moon(net, global_net, previous_net, train_dataloader, epochs, lr, mu,
             _, pro2, _ = global_net(x)
 
             posi = cos(pro1, pro2)
-            logits = posi.reshape(-1,1)
+            logits = posi.reshape(-1, 1)
 
-            
             previous_net.cuda()
             _, pro3, _ = previous_net(x)
             nega = cos(pro1, pro3)
-            logits = torch.cat((logits, nega.reshape(-1,1)), dim=1)
+            logits = torch.cat((logits, nega.reshape(-1, 1)), dim=1)
 
-            previous_net.to('cpu')
+            previous_net.to("cpu")
 
             logits /= temperature
             labels = torch.zeros(x.size(0)).cuda().long()
 
             loss2 = mu * criterion(logits, labels)
 
-
             loss1 = criterion(out, target)
             loss = loss1 + loss2
 
@@ -373,34 +453,43 @@ def train_moon(net, global_net, previous_net, train_dataloader, epochs, lr, mu,
         epoch_loss = sum(epoch_loss_collector) / len(epoch_loss_collector)
         epoch_loss1 = sum(epoch_loss1_collector) / len(epoch_loss1_collector)
         epoch_loss2 = sum(epoch_loss2_collector) / len(epoch_loss2_collector)
-        print('Epoch: %d Loss: %f Loss1: %f Loss2: %f' % (epoch, epoch_loss, epoch_loss1, epoch_loss2))
+        print(
+            "Epoch: %d Loss: %f Loss1: %f Loss2: %f"
+            % (epoch, epoch_loss, epoch_loss1, epoch_loss2)
+        )
 
-    previous_net.to('cpu')
+    previous_net.to("cpu")
     train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
 
-    print('>> Training accuracy: %f' % train_acc)
-    net.to('cpu')
-    print(' ** Training complete **')
+    print(">> Training accuracy: %f" % train_acc)
+    net.to("cpu")
+    print(" ** Training complete **")
     return net
 
+
 def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu"):
     net = nn.DataParallel(net)
     net.cuda()
 
-    print('n_training: %d' % len(train_dataloader))
+    print("n_training: %d" % len(train_dataloader))
 
     train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
-    
-    print('>> Pre-Training Training accuracy: {}'.format(train_acc))
 
-    optimizer = optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=lr, momentum=0.9, weight_decay=1e-5)
+    print(">> Pre-Training Training accuracy: {}".format(train_acc))
+
+    optimizer = optim.SGD(
+        filter(lambda p: p.requires_grad, net.parameters()),
+        lr=lr,
+        momentum=0.9,
+        weight_decay=1e-5,
+    )
 
     criterion = nn.CrossEntropyLoss().cuda()
 
     cnt = 0
     global_weight_collector = list(global_net.cuda().parameters())
 
-    for epoch in range(epochs):
+    for _epoch in range(epochs):
         epoch_loss_collector = []
         for _, (x, target) in enumerate(train_dataloader):
             x, target = x.cuda(), target.cuda()
@@ -410,14 +499,16 @@ def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu
             target.requires_grad = False
             target = target.long()
 
-            _,_,out = net(x)
+            _, _, out = net(x)
             loss = criterion(out, target)
 
             # for fedprox
             fed_prox_reg = 0.0
             # fed_prox_reg += np.linalg.norm([i - j for i, j in zip(global_weight_collector, get_trainable_parameters(net).tolist())], ord=2)
             for param_index, param in enumerate(net.parameters()):
-                fed_prox_reg += ((mu / 2) * torch.norm((param - global_weight_collector[param_index])) ** 2)
+                fed_prox_reg += (mu / 2) * torch.norm(
+                    (param - global_weight_collector[param_index])
+                ) ** 2
             loss += fed_prox_reg
 
             loss.backward()
@@ -426,15 +517,16 @@ def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu
             cnt += 1
             epoch_loss_collector.append(loss.item())
 
-        epoch_loss = sum(epoch_loss_collector) / len(epoch_loss_collector)
+        sum(epoch_loss_collector) / len(epoch_loss_collector)
 
     train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
 
-    print('>> Training accuracy: %f' % train_acc)
-    net.to('cpu')
-    print(' ** Training complete **')
+    print(">> Training accuracy: %f" % train_acc)
+    net.to("cpu")
+    print(" ** Training complete **")
     return net
 
-def test(net, test_dataloader, device='cpu'):
+
+def test(net, test_dataloader, device="cpu"):
     test_acc, loss = compute_accuracy(net, test_dataloader, device=device)
-    return test_acc, loss
\ No newline at end of file
+    return test_acc, loss
diff --git a/baselines/moon/moon/server.py b/baselines/moon/moon/server.py
index 21e9e057b00e..31cea41393de 100644
--- a/baselines/moon/moon/server.py
+++ b/baselines/moon/moon/server.py
@@ -9,11 +9,10 @@
 
 import torch
 from flwr.common.typing import NDArrays, Scalar
-from hydra.utils import instantiate
 from omegaconf import DictConfig
 from torch.utils.data import DataLoader
 
-from moon.models import test, init_net
+from moon.models import init_net, test
 
 
 def gen_evaluate_fn(
@@ -43,7 +42,6 @@ def evaluate(
     ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
         # pylint: disable=unused-argument
         """Use the entire CIFAR-10 test set for evaluation."""
-
         net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
         params_dict = zip(net.state_dict().keys(), parameters_ndarrays)
         state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
diff --git a/baselines/moon/moon/utils.py b/baselines/moon/moon/utils.py
index 4051c129c9e6..bbdbde33c085 100644
--- a/baselines/moon/moon/utils.py
+++ b/baselines/moon/moon/utils.py
@@ -6,9 +6,9 @@
 """
 import numpy as np
 import torch
-import torch.nn.functional as F
 import torch.nn as nn
 
+
 def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
     was_training = False
     if model.training:
@@ -18,7 +18,7 @@ def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
     true_labels_list, pred_labels_list = np.array([]), np.array([])
 
     correct, total = 0, 0
-    if device == 'cpu':
+    if device == "cpu":
         criterion = nn.CrossEntropyLoss()
     elif "cuda" in device.type:
         criterion = nn.CrossEntropyLoss().cuda()
@@ -26,13 +26,13 @@ def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
     if multiloader:
         for loader in dataloader:
             with torch.no_grad():
-                for batch_idx, (x, target) in enumerate(loader):
-                    #print("x:",x)
-                    #print("target:",target)
-                    if device != 'cpu':
+                for _batch_idx, (x, target) in enumerate(loader):
+                    # print("x:",x)
+                    # print("target:",target)
+                    if device != "cpu":
                         x, target = x.cuda(), target.to(dtype=torch.int64).cuda()
                     _, _, out = model(x)
-                    if len(target)==1:
+                    if len(target) == 1:
                         loss = criterion(out, target)
                     else:
                         loss = criterion(out, target)
@@ -42,19 +42,27 @@ def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
                     correct += (pred_label == target.data).sum().item()
 
                     if device == "cpu":
-                        pred_labels_list = np.append(pred_labels_list, pred_label.numpy())
-                        true_labels_list = np.append(true_labels_list, target.data.numpy())
+                        pred_labels_list = np.append(
+                            pred_labels_list, pred_label.numpy()
+                        )
+                        true_labels_list = np.append(
+                            true_labels_list, target.data.numpy()
+                        )
                     else:
-                        pred_labels_list = np.append(pred_labels_list, pred_label.cpu().numpy())
-                        true_labels_list = np.append(true_labels_list, target.data.cpu().numpy())
+                        pred_labels_list = np.append(
+                            pred_labels_list, pred_label.cpu().numpy()
+                        )
+                        true_labels_list = np.append(
+                            true_labels_list, target.data.cpu().numpy()
+                        )
         avg_loss = sum(loss_collector) / len(loss_collector)
     else:
         with torch.no_grad():
-            for batch_idx, (x, target) in enumerate(dataloader):
-                #print("x:",x)
-                if device != 'cpu':
+            for _batch_idx, (x, target) in enumerate(dataloader):
+                # print("x:",x)
+                if device != "cpu":
                     x, target = x.cuda(), target.to(dtype=torch.int64).cuda()
-                _,_,out = model(x)
+                _, _, out = model(x)
                 loss = criterion(out, target)
                 _, pred_label = torch.max(out.data, 1)
                 loss_collector.append(loss.item())
@@ -65,13 +73,15 @@ def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
                     pred_labels_list = np.append(pred_labels_list, pred_label.numpy())
                     true_labels_list = np.append(true_labels_list, target.data.numpy())
                 else:
-                    pred_labels_list = np.append(pred_labels_list, pred_label.cpu().numpy())
-                    true_labels_list = np.append(true_labels_list, target.data.cpu().numpy())
+                    pred_labels_list = np.append(
+                        pred_labels_list, pred_label.cpu().numpy()
+                    )
+                    true_labels_list = np.append(
+                        true_labels_list, target.data.cpu().numpy()
+                    )
             avg_loss = sum(loss_collector) / len(loss_collector)
 
-
     if was_training:
         model.train()
 
-
     return correct / float(total), avg_loss

From bf18cfbc1334012221e407aa361fc69107baf0b4 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Sat, 16 Sep 2023 05:22:54 +0800
Subject: [PATCH 04/51] formatting

---
 baselines/moon/moon/client.py              |  41 ++------
 baselines/moon/moon/conf/base.yaml         |   1 +
 baselines/moon/moon/dataset.py             |  26 +++--
 baselines/moon/moon/dataset_preparation.py |  19 ++--
 baselines/moon/moon/main.py                |  53 ++++++-----
 baselines/moon/moon/models.py              | 106 ++++++++++-----------
 baselines/moon/moon/server.py              |  19 +---
 baselines/moon/moon/utils.py               |   7 +-
 baselines/moon/pyproject.toml              |   9 +-
 9 files changed, 134 insertions(+), 147 deletions(-)

diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index a0a2b6846eb8..6b8d2de51989 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -16,7 +16,10 @@
 
 from moon.models import init_net, test, train_fedprox, train_moon
 
+# pylint: disable=E1101
 
+
+# pylint: disable=too-many-instance-attributes
 class FlowerClient(fl.client.NumPyClient):
     """Standard Flower client for CNN training."""
 
@@ -47,10 +50,11 @@ def __init__(
         self.device = device
         self.num_epochs = num_epochs
         self.learning_rate = learning_rate
-        self.mu = mu
+        self.mu = mu  # pylint: disable=invalid-name
         self.temperature = temperature
         self.model_dir = model_dir
         self.alg = alg
+        self.prev_net = init_net(self.dataset, self.model, self.output_dim)
 
     def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
         """Return the parameters of the current net."""
@@ -69,7 +73,6 @@ def fit(
         self.set_parameters(parameters)
 
         # load previous model from model_dir
-        self.prev_net = init_net(self.dataset, self.model, self.output_dim)
         self.prev_net.load_state_dict(
             torch.load(os.path.join(self.model_dir, "prev_net.pt"))
         )
@@ -113,38 +116,8 @@ def gen_client_fn(
     trainloaders: List[DataLoader],
     testloaders: List[DataLoader],
     cfg: DictConfig,
-) -> Tuple[
-    Callable[[str], FlowerClient], DataLoader
-]:  # pylint: disable=too-many-arguments
-    """Generate the client function that creates the Flower Clients.
-
-    Parameters
-    ----------
-    num_clients : int
-        The number of clients present in the setup
-    num_rounds: int
-        The number of rounds in the experiment. This is used to construct
-        the scheduling for stragglers
-    num_epochs : int
-        The number of local epochs each client should run the training for before
-        sending it to the server.
-    trainloaders: List[DataLoader]
-        A list of DataLoaders, each pointing to the dataset training partition
-        belonging to a particular client.
-    valloaders: List[DataLoader]
-        A list of DataLoaders, each pointing to the dataset validation partition
-        belonging to a particular client.
-    learning_rate : float
-        The learning rate for the SGD  optimizer of clients.
-    stragglers : float
-        Proportion of stragglers in the clients, between 0 and 1.
-
-    Returns
-    -------
-    Tuple[Callable[[str], FlowerClient], DataLoader]
-        A tuple containing the client function that creates Flower Clients and
-        the DataLoader that will be used for testing
-    """
+) -> Callable[[str], FlowerClient]:
+    """Generate the client function that creates the Flower Clients."""
 
     def client_fn(cid: str) -> FlowerClient:
         """Create a Flower client representing a single organization."""
diff --git a/baselines/moon/moon/conf/base.yaml b/baselines/moon/moon/conf/base.yaml
index 907e0fe3d02b..7b5f8f207f5c 100644
--- a/baselines/moon/moon/conf/base.yaml
+++ b/baselines/moon/moon/conf/base.yaml
@@ -11,6 +11,7 @@ learning_rate: 0.01
 mu: 1
 temperature: 0.5
 alg: moon
+seed: 0
 
 
 
diff --git a/baselines/moon/moon/dataset.py b/baselines/moon/moon/dataset.py
index 62e105a3aecd..bf10ce02e922 100644
--- a/baselines/moon/moon/dataset.py
+++ b/baselines/moon/moon/dataset.py
@@ -39,7 +39,7 @@
 )
 
 
-class CIFAR10_sub(data.Dataset):
+class CIFAR10Sub(data.Dataset):
     """CIFAR-10 dataset with idxs."""
 
     def __init__(
@@ -68,10 +68,12 @@ def __build_sub_dataset__(self):
 
         if torchvision.__version__ == "0.2.1":
             if self.train:
+                # pylint: disable=redefined-outer-name
                 data, target = cifar_dataobj.train_data, np.array(
                     cifar_dataobj.train_labels
                 )
             else:
+                # pylint: disable=redefined-outer-name
                 data, target = cifar_dataobj.test_data, np.array(
                     cifar_dataobj.test_labels
                 )
@@ -115,7 +117,9 @@ def __len__(self):
         return len(self.data)
 
 
-class CIFAR100_sub(data.Dataset):
+class CIFAR100Sub(data.Dataset):
+    """CIFAR-100 dataset with idxs."""
+
     def __init__(
         self,
         root,
@@ -135,19 +139,21 @@ def __init__(
         self.data, self.target = self.__build_sub_dataset__()
 
     def __build_sub_dataset__(self):
+        """Build sub dataset given idxs."""
         cifar_dataobj = CIFAR100(
             self.root, self.train, self.transform, self.target_transform, self.download
         )
 
         if torchvision.__version__ == "0.2.1":
             if self.train:
+                # pylint: disable=redefined-outer-name
                 data, target = cifar_dataobj.train_data, np.array(
                     cifar_dataobj.train_labels
                 )
             else:
                 data, target = cifar_dataobj.test_data, np.array(
                     cifar_dataobj.test_labels
-                )
+                )  # pylint: disable=redefined-outer-name
         else:
             data = cifar_dataobj.data
             target = np.array(cifar_dataobj.targets)
@@ -159,7 +165,8 @@ def __build_sub_dataset__(self):
         return data, target
 
     def __getitem__(self, index):
-        """
+        """Get item by index.
+
         Args:
             index (int): Index.
 
@@ -179,12 +186,19 @@ def __getitem__(self, index):
         return img, target
 
     def __len__(self):
+        """Length.
+
+        Returns
+        -------
+            int: length of data
+        """
         return len(self.data)
 
 
 def get_dataloader(dataset, datadir, train_bs, test_bs, dataidxs=None, noise_level=0):
+    """Get dataloader for a given dataset."""
     if dataset == "cifar10":
-        dl_obj = CIFAR10_sub
+        dl_obj = CIFAR10Sub
         normalize = transforms.Normalize(
             mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
             std=[x / 255.0 for x in [63.0, 62.1, 66.7]],
@@ -211,7 +225,7 @@ def get_dataloader(dataset, datadir, train_bs, test_bs, dataidxs=None, noise_lev
         transform_test = transforms.Compose([transforms.ToTensor(), normalize])
 
     elif dataset == "cifar100":
-        dl_obj = CIFAR100_sub
+        dl_obj = CIFAR100Sub
 
         normalize = transforms.Normalize(
             mean=[0.5070751592371323, 0.48654887331495095, 0.4409178433670343],
diff --git a/baselines/moon/moon/dataset_preparation.py b/baselines/moon/moon/dataset_preparation.py
index 3a4ea9c4e8a8..8abe0d50ca2e 100644
--- a/baselines/moon/moon/dataset_preparation.py
+++ b/baselines/moon/moon/dataset_preparation.py
@@ -35,16 +35,18 @@
 
 import numpy as np
 import torchvision.transforms as transforms
-from dataset import CIFAR10_truncated, CIFAR100_truncated
+
+from moon.dataset import CIFAR10Sub, CIFAR100Sub
 
 
 def load_cifar10_data(datadir):
+    """Load CIFAR10 dataset."""
     transform = transforms.Compose([transforms.ToTensor()])
 
-    cifar10_train_ds = CIFAR10_truncated(
+    cifar10_train_ds = CIFAR10Sub(
         datadir, train=True, download=True, transform=transform
     )
-    cifar10_test_ds = CIFAR10_truncated(
+    cifar10_test_ds = CIFAR10Sub(
         datadir, train=False, download=True, transform=transform
     )
 
@@ -55,12 +57,13 @@ def load_cifar10_data(datadir):
 
 
 def load_cifar100_data(datadir):
+    """Load CIFAR100 dataset."""
     transform = transforms.Compose([transforms.ToTensor()])
 
-    cifar100_train_ds = CIFAR100_truncated(
+    cifar100_train_ds = CIFAR100Sub(
         datadir, train=True, download=True, transform=transform
     )
-    cifar100_test_ds = CIFAR100_truncated(
+    cifar100_test_ds = CIFAR100Sub(
         datadir, train=False, download=True, transform=transform
     )
 
@@ -70,7 +73,9 @@ def load_cifar100_data(datadir):
     return (X_train, y_train, X_test, y_test)
 
 
+# pylint: disable=too-many-locals
 def partition_data(dataset, datadir, partition, num_clients, beta):
+    """Partition data into train and test sets for IID and non-IID experiments."""
     if dataset == "cifar10":
         X_train, y_train, X_test, y_test = load_cifar10_data(datadir)
     elif dataset == "cifar100":
@@ -78,12 +83,12 @@ def partition_data(dataset, datadir, partition, num_clients, beta):
 
     n_train = y_train.shape[0]
 
-    if partition == "homo" or partition == "iid":
+    if partition in ("homo", "iid"):
         idxs = np.random.permutation(n_train)
         batch_idxs = np.array_split(idxs, num_clients)
         net_dataidx_map = {i: batch_idxs[i] for i in range(num_clients)}
 
-    elif partition == "noniid-labeldir" or partition == "noniid":
+    elif partition in ("noniid-labeldir", "noniid"):
         min_size = 0
         min_require_size = 10
         K = 10
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 4a5279db0007..41609e77d03c 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -3,16 +3,19 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
+import random
+
 # these are the basic packages you'll need here
 # feel free to remove some if aren't needed
 import flwr as fl
 import hydra
+import numpy as np
+import torch
 from omegaconf import DictConfig, OmegaConf
 
 from moon import client, server
-from moon.dataset_preparation import get_dataloader, partition_data
-
-# from hydra.utils import instantiate
+from moon.dataset import get_dataloader
+from moon.dataset_preparation import partition_data
 
 
 @hydra.main(config_path="conf", config_name="base", version_base=None)
@@ -34,22 +37,26 @@ def main(cfg: DictConfig) -> None:
     # (2) tell each client what dataset partitions they should use (e.g. a this could
     # be a location in the file system, a list of dataloader, a list of ids to extract
     # from a dataset, it's up to you)
+    np.random.seed(cfg.seed)
+    torch.manual_seed(cfg.seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(cfg.seed)
+    random.seed(cfg.seed)
     (
-        X_train,
-        y_train,
-        X_test,
-        y_test,
+        _,
+        _,
+        _,
+        _,
         net_dataidx_map,
-        traindata_cls_counts,
     ) = partition_data(
         dataset=cfg.dataset.name,
         datadir=cfg.dataset.dir,
-        parittion=cfg.dataset.partition,
+        partition=cfg.dataset.partition,
         num_clients=cfg.num_clients,
         beta=cfg.dataset.beta,
     )
 
-    train_dl_global, test_dl, train_ds_global, test_ds_global = get_dataloader(
+    _, test_dl, _, _ = get_dataloader(
         dataset=cfg.dataset.name,
         datadir=cfg.dataset.dir,
         train_bs=cfg.batch_size,
@@ -72,32 +79,30 @@ def main(cfg: DictConfig) -> None:
     client_fn = client.gen_client_fn(
         trainloaders=trainloaders,
         testloaders=testloaders,
-        config=cfg,
+        cfg=cfg,
     )
 
     # get function that will executed by the strategy's evaluate() method
     # Set server's device
     device = cfg.server_device
-    server.gen_evaluate_fn(test_dl, device=device, model=cfg.model)
+    server.gen_evaluate_fn(test_dl, device=device, cfg=cfg)
 
-    # get a function that will be used to construct the config that the client's
-    # fit() method will received
-    def get_on_fit_config():
-        def fit_config_fn(server_round: int):
-            # resolve and convert to python dict
-            fit_config = OmegaConf.to_container(cfg.fit_config, resolve=True)
-            fit_config["curr_round"] = server_round  # add round info
-            return fit_config
+    # # get a function that will be used to construct the config that the client's
+    # # fit() method will received
+    # def get_on_fit_config():
+    #     def fit_config_fn(server_round: int):
+    #         # resolve and convert to python dict
+    #         fit_config = OmegaConf.to_container(cfg.fit_config, resolve=True)
+    #         fit_config["curr_round"] = server_round  # add round info
+    #         return fit_config
 
-        return fit_config_fn
+    #     return fit_config_fn
 
     # 4. Define your strategy
     # pass all relevant argument (including the global dataset used after aggregation,
     # if needed by your method.)
     # strategy = instantiate(cfg.strategy, <additional arguments if desired>)
-    strategy = fl.server.strategy.FedAvg(
-        fraction_fit=cfg.fraction_fit, on_fit_config_fn=get_on_fit_config()
-    )
+    strategy = fl.server.strategy.FedAvg(fraction_fit=cfg.fraction_fit)
     # 5. Start Simulation
     # history = fl.simulation.start_simulation(<arguments for simulation>)
     history = fl.simulation.start_simulation(
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
index 052988277dd5..830e9cbb0e95 100644
--- a/baselines/moon/moon/models.py
+++ b/baselines/moon/moon/models.py
@@ -35,6 +35,8 @@ def conv1x1(in_planes, out_planes, stride=1):
 
 
 class BasicBlock(nn.Module):
+    """Basic Block for resnet."""
+
     expansion = 1
 
     def __init__(
@@ -48,14 +50,13 @@ def __init__(
         dilation=1,
         norm_layer=None,
     ):
-        super(BasicBlock, self).__init__()
+        super().__init__()
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         if groups != 1 or base_width != 64:
             raise ValueError("BasicBlock only supports groups=1 and base_width=64")
         if dilation > 1:
             raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
-        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
         self.conv1 = conv3x3(inplanes, planes, stride)
         self.bn1 = norm_layer(planes)
         self.relu = nn.ReLU(inplace=True)
@@ -65,6 +66,7 @@ def __init__(
         self.stride = stride
 
     def forward(self, x):
+        """Forward."""
         identity = x
 
         out = self.conv1(x)
@@ -84,11 +86,7 @@ def forward(self, x):
 
 
 class Bottleneck(nn.Module):
-    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
-    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
-    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
-    # This variant is also known as ResNet V1.5 and improves accuracy according to
-    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
+    """Bottleneck in torchvision places the stride."""
 
     expansion = 4
 
@@ -103,11 +101,10 @@ def __init__(
         dilation=1,
         norm_layer=None,
     ):
-        super(Bottleneck, self).__init__()
+        super().__init__()
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         width = int(planes * (base_width / 64.0)) * groups
-        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
         self.conv1 = conv1x1(inplanes, width)
         self.bn1 = norm_layer(width)
         self.conv2 = conv3x3(width, width, stride, groups, dilation)
@@ -119,6 +116,7 @@ def __init__(
         self.stride = stride
 
     def forward(self, x):
+        """Forward."""
         identity = x
 
         out = self.conv1(x)
@@ -142,6 +140,8 @@ def forward(self, x):
 
 
 class ResNetCifar10(nn.Module):
+    """ResNet model."""
+
     def __init__(
         self,
         block,
@@ -153,7 +153,7 @@ def __init__(
         replace_stride_with_dilation=None,
         norm_layer=None,
     ):
-        super(ResNetCifar10, self).__init__()
+        super().__init__()
         if norm_layer is None:
             norm_layer = nn.BatchNorm2d
         self._norm_layer = norm_layer
@@ -189,22 +189,21 @@ def __init__(
         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
         self.fc = nn.Linear(512 * block.expansion, num_classes)
 
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
-            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
+        for module in self.modules():
+            if isinstance(module, nn.Conv2d):
+                nn.init.kaiming_normal_(
+                    module.weight, mode="fan_out", nonlinearity="relu"
+                )
+            elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
+                nn.init.constant_(module.weight, 1)
+                nn.init.constant_(module.bias, 0)
 
-        # Zero-initialize the last BN in each residual branch,
-        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
-        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
         if zero_init_residual:
-            for m in self.modules():
-                if isinstance(m, Bottleneck):
-                    nn.init.constant_(m.bn3.weight, 0)
-                elif isinstance(m, BasicBlock):
-                    nn.init.constant_(m.bn2.weight, 0)
+            for module in self.modules():
+                if isinstance(module, Bottleneck):
+                    nn.init.constant_(module.bn3.weight, 0)
+                elif isinstance(module, BasicBlock):
+                    nn.init.constant_(module.bn2.weight, 0)
 
     def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
         norm_layer = self._norm_layer
@@ -259,16 +258,17 @@ def _forward_impl(self, x):
         x = self.layer4(x)
 
         x = self.avgpool(x)
-        x = torch.flatten(x, 1)
+        x = torch.flatten(x, 1)  # pylint: disable=E1101
         x = self.fc(x)
 
         return x
 
     def forward(self, x):
+        """Forward."""
         return self._forward_impl(x)
 
 
-def ResNet50_cifar10(**kwargs):
+def resnet50_cifar10(**kwargs):
     r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition".
 
     <https://arxiv.org/pdf/1512.03385.pdf>`_
@@ -280,21 +280,21 @@ def ResNet50_cifar10(**kwargs):
     return ResNetCifar10(Bottleneck, [3, 4, 6, 3], **kwargs)
 
 
-class SimpleCNN_header(nn.Module):
-    def __init__(self, input_dim, hidden_dims, output_dim=10):
-        super(SimpleCNN_header, self).__init__()
+class SimpleCNNHeader(nn.Module):
+    """Simple CNN model."""
+
+    def __init__(self, input_dim, hidden_dims):
+        super().__init__()
         self.conv1 = nn.Conv2d(3, 6, 5)
         self.relu = nn.ReLU()
         self.pool = nn.MaxPool2d(2, 2)
         self.conv2 = nn.Conv2d(6, 16, 5)
 
-        # for now, we hard coded this network
-        # i.e. we fix the number of hidden layers i.e. 2 layers
         self.fc1 = nn.Linear(input_dim, hidden_dims[0])
         self.fc2 = nn.Linear(hidden_dims[0], hidden_dims[1])
-        # self.fc3 = nn.Linear(hidden_dims[1], output_dim)
 
     def forward(self, x):
+        """Forward."""
         x = self.pool(self.relu(self.conv1(x)))
         x = self.pool(self.relu(self.conv2(x)))
         x = x.view(-1, 16 * 5 * 5)
@@ -305,22 +305,24 @@ def forward(self, x):
         return x
 
 
-class ModelFedCon(nn.Module):
+class ModelMOON(nn.Module):
+    """Model for MOON."""
+
     def __init__(self, base_model, out_dim, n_classes):
-        super(ModelFedCon, self).__init__()
+        super().__init__()
 
-        if (
-            base_model == "resnet50-cifar10"
-            or base_model == "resnet50-cifar100"
-            or base_model == "resnet50-smallkernel"
-            or base_model == "resnet50"
+        if base_model in (
+            "resnet50-cifar10",
+            "resnet50-cifar100",
+            "resnet50-smallkernel",
+            "resnet50",
         ):
-            basemodel = ResNet50_cifar10()
+            basemodel = resnet50_cifar10()
             self.features = nn.Sequential(*list(basemodel.children())[:-1])
             num_ftrs = basemodel.fc.in_features
         elif base_model == "simple-cnn":
-            self.features = SimpleCNN_header(
-                input_dim=(16 * 5 * 5), hidden_dims=[120, 84], output_dim=n_classes
+            self.features = SimpleCNNHeader(
+                input_dim=(16 * 5 * 5), hidden_dims=[120, 84]
             )
             num_ftrs = 84
 
@@ -338,12 +340,11 @@ def _get_basemodel(self, model_name):
             model = self.model_dict[model_name]
             # print("Feature extractor:", model_name)
             return model
-        except:
-            raise (
-                "Invalid model name. Check the config file and pass one of: resnet18 or resnet50"
-            )
+        except KeyError as err:
+            raise ValueError("Invalid model name.") from err
 
     def forward(self, x):
+        """Forward."""
         h = self.features(x)
         # print("h before:", h)
         # print("h size:", h.size())
@@ -358,12 +359,13 @@ def forward(self, x):
 
 
 def init_net(dataset, model, output_dim, device="cpu"):
+    """Initialize model."""
     if dataset == "cifar10":
         n_classes = 10
     elif dataset == "cifar100":
         n_classes = 100
 
-    net = ModelFedCon(model, output_dim, n_classes)
+    net = ModelMOON(model, output_dim, n_classes)
     if device == "cpu":
         net.to(device)
     else:
@@ -383,6 +385,7 @@ def train_moon(
     temperature,
     device="cpu",
 ):
+    """Training function for MOON."""
     net = nn.DataParallel(net)
     net.cuda()
 
@@ -402,8 +405,7 @@ def train_moon(
     criterion = nn.CrossEntropyLoss().cuda()
     # global_net.to(device)
 
-    for previous_net in previous_nets:
-        previous_net.cuda()
+    previous_net.cuda()
 
     cnt = 0
     cos = torch.nn.CosineSimilarity(dim=-1)
@@ -468,6 +470,7 @@ def train_moon(
 
 
 def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu"):
+    """Training function for FedProx."""
     net = nn.DataParallel(net)
     net.cuda()
 
@@ -502,9 +505,7 @@ def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu
             _, _, out = net(x)
             loss = criterion(out, target)
 
-            # for fedprox
             fed_prox_reg = 0.0
-            # fed_prox_reg += np.linalg.norm([i - j for i, j in zip(global_weight_collector, get_trainable_parameters(net).tolist())], ord=2)
             for param_index, param in enumerate(net.parameters()):
                 fed_prox_reg += (mu / 2) * torch.norm(
                     (param - global_weight_collector[param_index])
@@ -517,8 +518,6 @@ def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu
             cnt += 1
             epoch_loss_collector.append(loss.item())
 
-        sum(epoch_loss_collector) / len(epoch_loss_collector)
-
     train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
 
     print(">> Training accuracy: %f" % train_acc)
@@ -528,5 +527,6 @@ def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu
 
 
 def test(net, test_dataloader, device="cpu"):
+    """Test function."""
     test_acc, loss = compute_accuracy(net, test_dataloader, device=device)
     return test_acc, loss
diff --git a/baselines/moon/moon/server.py b/baselines/moon/moon/server.py
index 31cea41393de..f6354fb6413e 100644
--- a/baselines/moon/moon/server.py
+++ b/baselines/moon/moon/server.py
@@ -17,31 +17,17 @@
 
 def gen_evaluate_fn(
     testloader: DataLoader,
-    device: torch.device,
+    device: torch.device,  # pylint: disable=E1101
     cfg: DictConfig,
 ) -> Callable[
     [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]
 ]:
-    """Generates the function for centralized evaluation.
-
-    Parameters
-    ----------
-    testloader : DataLoader
-        The dataloader to test the model with.
-    device : torch.device
-        The device to test the model on.
-
-    Returns
-    -------
-    Callable[ [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]] ]
-        The centralized evaluation function.
-    """
+    """Generate the function for centralized evaluation."""
 
     def evaluate(
         server_round: int, parameters_ndarrays: NDArrays, config: Dict[str, Scalar]
     ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
         # pylint: disable=unused-argument
-        """Use the entire CIFAR-10 test set for evaluation."""
         net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
         params_dict = zip(net.state_dict().keys(), parameters_ndarrays)
         state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
@@ -49,7 +35,6 @@ def evaluate(
         net.to(device)
 
         accuracy, loss = test(net, testloader, device=device)
-        # return statistics
         return loss, {"accuracy": accuracy}
 
     return evaluate
diff --git a/baselines/moon/moon/utils.py b/baselines/moon/moon/utils.py
index bbdbde33c085..5109e7dcdeab 100644
--- a/baselines/moon/moon/utils.py
+++ b/baselines/moon/moon/utils.py
@@ -10,6 +10,7 @@
 
 
 def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
+    """Compute accuracy."""
     was_training = False
     if model.training:
         model.eval()
@@ -26,9 +27,7 @@ def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
     if multiloader:
         for loader in dataloader:
             with torch.no_grad():
-                for _batch_idx, (x, target) in enumerate(loader):
-                    # print("x:",x)
-                    # print("target:",target)
+                for _, (x, target) in enumerate(loader):
                     if device != "cpu":
                         x, target = x.cuda(), target.to(dtype=torch.int64).cuda()
                     _, _, out = model(x)
@@ -58,7 +57,7 @@ def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
         avg_loss = sum(loss_collector) / len(loss_collector)
     else:
         with torch.no_grad():
-            for _batch_idx, (x, target) in enumerate(dataloader):
+            for _, (x, target) in enumerate(dataloader):
                 # print("x:",x)
                 if device != "cpu":
                     x, target = x.cuda(), target.to(dtype=torch.int64).cuda()
diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index 514068a5c96c..70be7077666f 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -79,8 +79,13 @@ strict = false
 plugins = "numpy.typing.mypy_plugin"
 
 [tool.pylint."MESSAGES CONTROL"]
-disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
-good-names = "i,j,k,_,x,y,X,Y"
+disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias,E1101"
+good-names = "i,j,k,_,x,y,X,Y,K,N,X_train,X_test,fc,l1,l2,l3,h,lr,mu"
+max-args = 10
+max-attributes = 15
+max-locals = 35
+max-branches = 20
+max-statements = 55
 signature-mutators="hydra.main.main"
 
 [[tool.mypy.overrides]]

From 098ab0556416bc47af0bcf7de4950abba604fc6b Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Thu, 21 Sep 2023 03:17:05 +0800
Subject: [PATCH 05/51] enable resnet

---
 baselines/moon/moon/client.py              | 53 ++++++++++++++++------
 baselines/moon/moon/conf/base.yaml         | 17 +++----
 baselines/moon/moon/dataset.py             | 34 ++++++++------
 baselines/moon/moon/dataset_preparation.py |  2 +
 baselines/moon/moon/main.py                | 16 +++++--
 baselines/moon/moon/models.py              | 24 ++++++----
 baselines/moon/moon/server.py              |  2 +-
 7 files changed, 97 insertions(+), 51 deletions(-)

diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index 6b8d2de51989..0f34f68ced36 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -6,6 +6,7 @@
 
 import os
 from collections import OrderedDict
+import copy
 from typing import Callable, Dict, List, Tuple
 
 import flwr as fl
@@ -25,7 +26,7 @@ class FlowerClient(fl.client.NumPyClient):
 
     def __init__(
         self,
-        net: torch.nn.Module,
+        # net: torch.nn.Module,
         net_id: int,
         dataset: str,
         model: str,
@@ -40,7 +41,7 @@ def __init__(
         model_dir: str,
         alg: str,
     ):  # pylint: disable=too-many-arguments
-        self.net = net
+        self.net = init_net(dataset, model, output_dim)
         self.net_id = net_id
         self.dataset = dataset
         self.model = model
@@ -54,28 +55,49 @@ def __init__(
         self.temperature = temperature
         self.model_dir = model_dir
         self.alg = alg
-        self.prev_net = init_net(self.dataset, self.model, self.output_dim)
+        # self.prev_net = init_net(self.dataset, self.model, self.output_dim)
+        self.prev_net = None
 
     def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
         """Return the parameters of the current net."""
+        # print("self.net:", self.net.state_dict())
+        self.net.eval()
+        for param in self.net.parameters():
+            param.requires_grad = False
         return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
 
     def set_parameters(self, parameters: NDArrays) -> None:
         """Change the parameters of the model using the given ones."""
         params_dict = zip(self.net.state_dict().keys(), parameters)
-        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        # print("params_dict:", params_dict)
+        # state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        state_dict = OrderedDict({k: torch.from_numpy(v) for k, v in params_dict})
+        # print("state_dict:", state_dict)
+        # try:
         self.net.load_state_dict(state_dict, strict=True)
+        # except:
+        #     print("error in loading")
+        #     print("params_dict:", params_dict)
+        #     print("state_dict:", state_dict)
+        #     exit(0)
+        # self.net.load_state_dict(state_dict)
 
     def fit(
         self, parameters: NDArrays, config: Dict[str, Scalar]
     ) -> Tuple[NDArrays, int, Dict]:
         """Implement distributed fit function for a given client."""
         self.set_parameters(parameters)
-
-        # load previous model from model_dir
-        self.prev_net.load_state_dict(
-            torch.load(os.path.join(self.model_dir, "prev_net.pt"))
-        )
+        if self.prev_net is None:
+            self.prev_net = init_net(self.dataset, self.model, self.output_dim)
+            self.prev_net = copy.deepcopy(self.net)
+        else:
+        # if os.path.exists(os.path.join(self.model_dir, str(self.net_id), "prev_net.pt")):
+            # load previous model from model_dir
+            self.prev_net.load_state_dict(
+                torch.load(os.path.join(self.model_dir, str(self.net_id), "prev_net.pt"))
+            )
+        # else:
+            # self.prev_net = copy.deepcopy(self.net)
         global_net = init_net(self.dataset, self.model, self.output_dim)
         global_net.load_state_dict(self.net.state_dict())
         if self.alg == "moon":
@@ -100,7 +122,9 @@ def fit(
                 self.mu,
                 self.device,
             )
-        torch.save(self.net.state_dict(), os.path.join(self.model_dir, "prev_net.pt"))
+        if not os.path.exists(os.path.join(self.model_dir, str(self.net_id))):
+            os.makedirs(os.path.join(self.model_dir, str(self.net_id)))
+        torch.save(self.net.state_dict(), os.path.join(self.model_dir, str(self.net_id), "prev_net.pt"))
         return self.get_parameters({}), len(self.trainloader), {"is_straggler": False}
 
     def evaluate(
@@ -108,7 +132,10 @@ def evaluate(
     ) -> Tuple[float, int, Dict]:
         """Implement distributed evaluation for a given client."""
         self.set_parameters(parameters)
-        loss, accuracy = test(self.net, self.valloader, self.device)
+        # loss, accuracy = test(self.net, self.valloader, self.device)
+        # skip evaluation in the client-side
+        loss = 0.0
+        accuracy = 0.0
         return float(loss), len(self.valloader), {"accuracy": float(accuracy)}
 
 
@@ -123,7 +150,8 @@ def client_fn(cid: str) -> FlowerClient:
         """Create a Flower client representing a single organization."""
         # Load model
         device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
+        print("device:", device)
+        # net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
 
         # Note: each client gets a different trainloader/valloader, so each client
         # will train and evaluate on their own unique data
@@ -131,7 +159,6 @@ def client_fn(cid: str) -> FlowerClient:
         testloader = testloaders[int(cid)]
 
         return FlowerClient(
-            net,
             int(cid),
             cfg.dataset.name,
             cfg.model.name,
diff --git a/baselines/moon/moon/conf/base.yaml b/baselines/moon/moon/conf/base.yaml
index 7b5f8f207f5c..7b49dd8ca4a2 100644
--- a/baselines/moon/moon/conf/base.yaml
+++ b/baselines/moon/moon/conf/base.yaml
@@ -8,12 +8,16 @@ num_epochs: 10
 fraction_fit: 1.0
 batch_size: 32
 learning_rate: 0.01
-mu: 1
+mu: 5
 temperature: 0.5
 alg: moon
 seed: 0
+server_device: cpu
+num_rounds: 100
 
-
+client_resources:
+  num_cpus: 2
+  num_gpus: 1
 
 dataset:
   # dataset config
@@ -26,11 +30,4 @@ model:
   # model config
   name: simple-cnn
   output_dim: 256
-  dir: ./models/moon/
-
-strategy:
-  _target_: # points to your strategy (either custom or exiting in Flower)
-  # rest of strategy config
-
-client:
-  # client config
+  dir: ./models/moon/
\ No newline at end of file
diff --git a/baselines/moon/moon/dataset.py b/baselines/moon/moon/dataset.py
index bf10ce02e922..461c4b47b237 100644
--- a/baselines/moon/moon/dataset.py
+++ b/baselines/moon/moon/dataset.py
@@ -21,6 +21,7 @@
 from PIL import Image
 from torch.autograd import Variable
 from torchvision.datasets import CIFAR10, CIFAR100
+import os
 
 logging.basicConfig()
 logger = logging.getLogger()
@@ -243,19 +244,24 @@ def get_dataloader(dataset, datadir, train_bs, test_bs, dataidxs=None, noise_lev
         )
         # data prep for test set
         transform_test = transforms.Compose([transforms.ToTensor(), normalize])
-
-        train_ds = dl_obj(
-            datadir,
-            dataidxs=dataidxs,
-            train=True,
-            transform=transform_train,
-            download=True,
-        )
-        test_ds = dl_obj(datadir, train=False, transform=transform_test, download=True)
-
-        train_dl = data.DataLoader(
-            dataset=train_ds, batch_size=train_bs, drop_last=True, shuffle=True
-        )
-        test_dl = data.DataLoader(dataset=test_ds, batch_size=test_bs, shuffle=False)
+    if dataset == "cifar10" and os.path.isdir(os.path.join(datadir, "cifar-10-batches-py")):
+        download=False
+    elif dataset == "cifar100" and os.path.isdir(os.path.join(datadir, "cifar-100-python")):
+        download=False
+    else:
+        download=True
+    train_ds = dl_obj(
+        datadir,
+        dataidxs=dataidxs,
+        train=True,
+        transform=transform_train,
+        download=download,
+    )
+    test_ds = dl_obj(datadir, train=False, transform=transform_test, download=download)
+
+    train_dl = data.DataLoader(
+        dataset=train_ds, batch_size=train_bs, drop_last=True, shuffle=True
+    )
+    test_dl = data.DataLoader(dataset=test_ds, batch_size=test_bs, shuffle=False)
 
     return train_dl, test_dl, train_ds, test_ds
diff --git a/baselines/moon/moon/dataset_preparation.py b/baselines/moon/moon/dataset_preparation.py
index 8abe0d50ca2e..ec216a408870 100644
--- a/baselines/moon/moon/dataset_preparation.py
+++ b/baselines/moon/moon/dataset_preparation.py
@@ -122,5 +122,7 @@ def partition_data(dataset, datadir, partition, num_clients, beta):
         for j in range(num_clients):
             np.random.shuffle(idx_batch[j])
             net_dataidx_map[j] = idx_batch[j]
+            
+    # print("net_dataidx_map", net_dataidx_map)
 
     return (X_train, y_train, X_test, y_test, net_dataidx_map)
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 41609e77d03c..d4a53afb8db3 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -12,6 +12,7 @@
 import numpy as np
 import torch
 from omegaconf import DictConfig, OmegaConf
+import os
 
 from moon import client, server
 from moon.dataset import get_dataloader
@@ -29,7 +30,6 @@ def main(cfg: DictConfig) -> None:
     """
     # 1. Print parsed config
     print(OmegaConf.to_yaml(cfg))
-
     # 2. Prepare your dataset
     # here you should call a function in datasets.py that returns whatever is needed to:
     # (1) ensure the server can access the dataset used to evaluate your model after
@@ -56,7 +56,7 @@ def main(cfg: DictConfig) -> None:
         beta=cfg.dataset.beta,
     )
 
-    _, test_dl, _, _ = get_dataloader(
+    _, test_global_dl, _, _ = get_dataloader(
         dataset=cfg.dataset.name,
         datadir=cfg.dataset.dir,
         train_bs=cfg.batch_size,
@@ -85,7 +85,7 @@ def main(cfg: DictConfig) -> None:
     # get function that will executed by the strategy's evaluate() method
     # Set server's device
     device = cfg.server_device
-    server.gen_evaluate_fn(test_dl, device=device, cfg=cfg)
+    evaluate_fn = server.gen_evaluate_fn(test_global_dl, device=device, cfg=cfg)
 
     # # get a function that will be used to construct the config that the client's
     # # fit() method will received
@@ -102,7 +102,7 @@ def main(cfg: DictConfig) -> None:
     # pass all relevant argument (including the global dataset used after aggregation,
     # if needed by your method.)
     # strategy = instantiate(cfg.strategy, <additional arguments if desired>)
-    strategy = fl.server.strategy.FedAvg(fraction_fit=cfg.fraction_fit)
+    strategy = fl.server.strategy.FedAvg(fraction_fit=cfg.fraction_fit, evaluate_fn = evaluate_fn)
     # 5. Start Simulation
     # history = fl.simulation.start_simulation(<arguments for simulation>)
     history = fl.simulation.start_simulation(
@@ -115,7 +115,9 @@ def main(cfg: DictConfig) -> None:
         },
         strategy=strategy,
     )
-
+    # remove saved models
+    if cfg.alg == "moon":
+        os.rmdir(cfg.model.dir)
     # 6. Save your results
     # Here you can save the `history` returned by the simulation and include
     # also other buffers, statistics, info needed to be saved in order to later
@@ -153,3 +155,7 @@ def main(cfg: DictConfig) -> None:
     #     f"_mu={cfg.mu}"
     #     f"_strag={cfg.stragglers_fraction}"
     # )
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
index 830e9cbb0e95..e694f242b2a3 100644
--- a/baselines/moon/moon/models.py
+++ b/baselines/moon/moon/models.py
@@ -326,8 +326,6 @@ def __init__(self, base_model, out_dim, n_classes):
             )
             num_ftrs = 84
 
-        # summary(self.features.to('cuda:0'), (3,32,32))
-        # print("features:", self.features)
         # projection MLP
         self.l1 = nn.Linear(num_ftrs, num_ftrs)
         self.l2 = nn.Linear(num_ftrs, out_dim)
@@ -338,7 +336,6 @@ def __init__(self, base_model, out_dim, n_classes):
     def _get_basemodel(self, model_name):
         try:
             model = self.model_dict[model_name]
-            # print("Feature extractor:", model_name)
             return model
         except KeyError as err:
             raise ValueError("Invalid model name.") from err
@@ -386,8 +383,12 @@ def train_moon(
     device="cpu",
 ):
     """Training function for MOON."""
-    net = nn.DataParallel(net)
-    net.cuda()
+    # net = nn.DataParallel(net)
+    # net.cuda()
+    print("device:", device)
+    net.to(device)
+    global_net.to(device)
+    previous_net.to(device)
 
     print("n_training: %d" % len(train_dataloader))
 
@@ -405,10 +406,13 @@ def train_moon(
     criterion = nn.CrossEntropyLoss().cuda()
     # global_net.to(device)
 
+    previous_net.eval()
+    for param in previous_net.parameters():
+        param.requires_grad = False
     previous_net.cuda()
 
     cnt = 0
-    cos = torch.nn.CosineSimilarity(dim=-1)
+    cos = torch.nn.CosineSimilarity(dim=-1).to(device)
     # mu = 0.001
 
     for epoch in range(epochs):
@@ -416,7 +420,7 @@ def train_moon(
         epoch_loss1_collector = []
         epoch_loss2_collector = []
         for _, (x, target) in enumerate(train_dataloader):
-            x, target = x.cuda(), target.cuda()
+            x, target = x.to(device), target.to(device)
 
             optimizer.zero_grad()
             x.requires_grad = False
@@ -429,7 +433,7 @@ def train_moon(
             posi = cos(pro1, pro2)
             logits = posi.reshape(-1, 1)
 
-            previous_net.cuda()
+            previous_net.to(device)
             _, pro3, _ = previous_net(x)
             nega = cos(pro1, pro3)
             logits = torch.cat((logits, nega.reshape(-1, 1)), dim=1)
@@ -465,6 +469,7 @@ def train_moon(
 
     print(">> Training accuracy: %f" % train_acc)
     net.to("cpu")
+    global_net.to("cpu")
     print(" ** Training complete **")
     return net
 
@@ -528,5 +533,8 @@ def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu
 
 def test(net, test_dataloader, device="cpu"):
     """Test function."""
+    net.to(device)
     test_acc, loss = compute_accuracy(net, test_dataloader, device=device)
+    print("test acc:", test_acc)
+    net.to("cpu")
     return test_acc, loss
diff --git a/baselines/moon/moon/server.py b/baselines/moon/moon/server.py
index f6354fb6413e..cf33c2664a37 100644
--- a/baselines/moon/moon/server.py
+++ b/baselines/moon/moon/server.py
@@ -30,7 +30,7 @@ def evaluate(
         # pylint: disable=unused-argument
         net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
         params_dict = zip(net.state_dict().keys(), parameters_ndarrays)
-        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        state_dict = OrderedDict({k: torch.from_numpy(v) for k, v in params_dict})
         net.load_state_dict(state_dict, strict=True)
         net.to(device)
 

From 92844615cea1ef9cecb000337cc21721b1db22ed Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Sat, 23 Sep 2023 15:18:11 +0800
Subject: [PATCH 06/51] update config and plot

---
 baselines/moon/moon/client.py                 | 32 ++++-------
 baselines/moon/moon/conf/cifar10.yaml         | 33 ++++++++++++
 baselines/moon/moon/conf/cifar100.yaml        | 33 ++++++++++++
 .../moon/moon/conf/cifar100_100clients.yaml   | 33 ++++++++++++
 .../moon/moon/conf/cifar100_50clients.yaml    | 33 ++++++++++++
 .../moon/moon/conf/cifar100_fedprox.yaml      | 33 ++++++++++++
 baselines/moon/moon/conf/cifar10_fedprox.yaml | 33 ++++++++++++
 baselines/moon/moon/dataset.py                | 16 +++---
 baselines/moon/moon/dataset_preparation.py    | 28 ----------
 baselines/moon/moon/main.py                   | 54 +++++++++++--------
 baselines/moon/moon/models.py                 | 18 +------
 baselines/moon/moon/utils.py                  | 47 ++++++++++++++++
 baselines/moon/pyproject.toml                 |  3 +-
 13 files changed, 300 insertions(+), 96 deletions(-)
 create mode 100644 baselines/moon/moon/conf/cifar10.yaml
 create mode 100644 baselines/moon/moon/conf/cifar100.yaml
 create mode 100644 baselines/moon/moon/conf/cifar100_100clients.yaml
 create mode 100644 baselines/moon/moon/conf/cifar100_50clients.yaml
 create mode 100644 baselines/moon/moon/conf/cifar100_fedprox.yaml
 create mode 100644 baselines/moon/moon/conf/cifar10_fedprox.yaml

diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index 0f34f68ced36..9be9abbbef6b 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -4,9 +4,9 @@
 to instantiate your client.
 """
 
+import copy
 import os
 from collections import OrderedDict
-import copy
 from typing import Callable, Dict, List, Tuple
 
 import flwr as fl
@@ -15,7 +15,7 @@
 from omegaconf import DictConfig
 from torch.utils.data import DataLoader
 
-from moon.models import init_net, test, train_fedprox, train_moon
+from moon.models import init_net, train_fedprox, train_moon
 
 # pylint: disable=E1101
 
@@ -60,27 +60,13 @@ def __init__(
 
     def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
         """Return the parameters of the current net."""
-        # print("self.net:", self.net.state_dict())
-        self.net.eval()
-        for param in self.net.parameters():
-            param.requires_grad = False
         return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
 
     def set_parameters(self, parameters: NDArrays) -> None:
         """Change the parameters of the model using the given ones."""
         params_dict = zip(self.net.state_dict().keys(), parameters)
-        # print("params_dict:", params_dict)
-        # state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
         state_dict = OrderedDict({k: torch.from_numpy(v) for k, v in params_dict})
-        # print("state_dict:", state_dict)
-        # try:
         self.net.load_state_dict(state_dict, strict=True)
-        # except:
-        #     print("error in loading")
-        #     print("params_dict:", params_dict)
-        #     print("state_dict:", state_dict)
-        #     exit(0)
-        # self.net.load_state_dict(state_dict)
 
     def fit(
         self, parameters: NDArrays, config: Dict[str, Scalar]
@@ -91,13 +77,14 @@ def fit(
             self.prev_net = init_net(self.dataset, self.model, self.output_dim)
             self.prev_net = copy.deepcopy(self.net)
         else:
-        # if os.path.exists(os.path.join(self.model_dir, str(self.net_id), "prev_net.pt")):
             # load previous model from model_dir
             self.prev_net.load_state_dict(
-                torch.load(os.path.join(self.model_dir, str(self.net_id), "prev_net.pt"))
+                torch.load(
+                    os.path.join(self.model_dir, str(self.net_id), "prev_net.pt")
+                )
             )
         # else:
-            # self.prev_net = copy.deepcopy(self.net)
+        # self.prev_net = copy.deepcopy(self.net)
         global_net = init_net(self.dataset, self.model, self.output_dim)
         global_net.load_state_dict(self.net.state_dict())
         if self.alg == "moon":
@@ -124,7 +111,10 @@ def fit(
             )
         if not os.path.exists(os.path.join(self.model_dir, str(self.net_id))):
             os.makedirs(os.path.join(self.model_dir, str(self.net_id)))
-        torch.save(self.net.state_dict(), os.path.join(self.model_dir, str(self.net_id), "prev_net.pt"))
+        torch.save(
+            self.net.state_dict(),
+            os.path.join(self.model_dir, str(self.net_id), "prev_net.pt"),
+        )
         return self.get_parameters({}), len(self.trainloader), {"is_straggler": False}
 
     def evaluate(
@@ -132,7 +122,6 @@ def evaluate(
     ) -> Tuple[float, int, Dict]:
         """Implement distributed evaluation for a given client."""
         self.set_parameters(parameters)
-        # loss, accuracy = test(self.net, self.valloader, self.device)
         # skip evaluation in the client-side
         loss = 0.0
         accuracy = 0.0
@@ -150,7 +139,6 @@ def client_fn(cid: str) -> FlowerClient:
         """Create a Flower client representing a single organization."""
         # Load model
         device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        print("device:", device)
         # net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
 
         # Note: each client gets a different trainloader/valloader, so each client
diff --git a/baselines/moon/moon/conf/cifar10.yaml b/baselines/moon/moon/conf/cifar10.yaml
new file mode 100644
index 000000000000..af1511f5b356
--- /dev/null
+++ b/baselines/moon/moon/conf/cifar10.yaml
@@ -0,0 +1,33 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+num_clients: 10
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 5
+temperature: 0.5
+alg: moon
+seed: 0
+server_device: cpu
+num_rounds: 100
+
+client_resources:
+  num_cpus: 4
+  num_gpus: 1
+
+dataset:
+  # dataset config
+  name: cifar10
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+
+model:
+  # model config
+  name: simple-cnn
+  output_dim: 256
+  dir: ./models/moon/cifar10/
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar100.yaml b/baselines/moon/moon/conf/cifar100.yaml
new file mode 100644
index 000000000000..d4c3da9d6229
--- /dev/null
+++ b/baselines/moon/moon/conf/cifar100.yaml
@@ -0,0 +1,33 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+num_clients: 10
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 1
+temperature: 0.5
+alg: moon
+seed: 0
+server_device: cpu
+num_rounds: 100
+
+client_resources:
+  num_cpus: 4
+  num_gpus: 1
+
+dataset:
+  # dataset config
+  name: cifar100
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+
+model:
+  # model config
+  name: resnet50
+  output_dim: 256
+  dir: ./models/moon/cifar100/
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar100_100clients.yaml b/baselines/moon/moon/conf/cifar100_100clients.yaml
new file mode 100644
index 000000000000..4d23b2c85faa
--- /dev/null
+++ b/baselines/moon/moon/conf/cifar100_100clients.yaml
@@ -0,0 +1,33 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+num_clients: 100
+num_epochs: 10
+fraction_fit: 0.2
+batch_size: 64
+learning_rate: 0.01
+mu: 10
+temperature: 0.5
+alg: moon
+seed: 0
+server_device: cpu
+num_rounds: 500
+
+client_resources:
+  num_cpus: 8
+  num_gpus: 1
+
+dataset:
+  # dataset config
+  name: cifar100
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+
+model:
+  # model config
+  name: resnet50
+  output_dim: 256
+  dir: ./models/moon/cifar100_100c/
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar100_50clients.yaml b/baselines/moon/moon/conf/cifar100_50clients.yaml
new file mode 100644
index 000000000000..737f1df9298a
--- /dev/null
+++ b/baselines/moon/moon/conf/cifar100_50clients.yaml
@@ -0,0 +1,33 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+num_clients: 50
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 10
+temperature: 0.5
+alg: moon
+seed: 0
+server_device: cpu
+num_rounds: 200
+
+client_resources:
+  num_cpus: 4
+  num_gpus: 1
+
+dataset:
+  # dataset config
+  name: cifar100
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+
+model:
+  # model config
+  name: simple-cnn
+  output_dim: 256
+  dir: ./models/moon/cifar100_50clients/
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar100_fedprox.yaml b/baselines/moon/moon/conf/cifar100_fedprox.yaml
new file mode 100644
index 000000000000..48e98c2b3a42
--- /dev/null
+++ b/baselines/moon/moon/conf/cifar100_fedprox.yaml
@@ -0,0 +1,33 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+num_clients: 10
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 0.001
+temperature: 0.5
+alg: moon
+seed: 0
+server_device: cpu
+num_rounds: 100
+
+client_resources:
+  num_cpus: 4
+  num_gpus: 1
+
+dataset:
+  # dataset config
+  name: cifar100
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+
+model:
+  # model config
+  name: resnet50
+  output_dim: 256
+  dir: ./models/moon/cifar100_fedprox/
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar10_fedprox.yaml b/baselines/moon/moon/conf/cifar10_fedprox.yaml
new file mode 100644
index 000000000000..599d5fd7e251
--- /dev/null
+++ b/baselines/moon/moon/conf/cifar10_fedprox.yaml
@@ -0,0 +1,33 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+num_clients: 10
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 0.001
+temperature: 0.5
+alg: fedprox
+seed: 0
+server_device: cpu
+num_rounds: 100
+
+client_resources:
+  num_cpus: 4
+  num_gpus: 1
+
+dataset:
+  # dataset config
+  name: cifar10
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+
+model:
+  # model config
+  name: simple-cnn
+  output_dim: 256
+  dir: ./models/moon/cifar10_fedprox/
\ No newline at end of file
diff --git a/baselines/moon/moon/dataset.py b/baselines/moon/moon/dataset.py
index 461c4b47b237..0ec5c6ae9e27 100644
--- a/baselines/moon/moon/dataset.py
+++ b/baselines/moon/moon/dataset.py
@@ -12,6 +12,7 @@
 # https://github.com/QinbinLi/MOON/blob/main/datasets.py
 
 import logging
+import os
 
 import numpy as np
 import torch.nn.functional as F
@@ -21,7 +22,6 @@
 from PIL import Image
 from torch.autograd import Variable
 from torchvision.datasets import CIFAR10, CIFAR100
-import os
 
 logging.basicConfig()
 logger = logging.getLogger()
@@ -244,12 +244,16 @@ def get_dataloader(dataset, datadir, train_bs, test_bs, dataidxs=None, noise_lev
         )
         # data prep for test set
         transform_test = transforms.Compose([transforms.ToTensor(), normalize])
-    if dataset == "cifar10" and os.path.isdir(os.path.join(datadir, "cifar-10-batches-py")):
-        download=False
-    elif dataset == "cifar100" and os.path.isdir(os.path.join(datadir, "cifar-100-python")):
-        download=False
+    if dataset == "cifar10" and os.path.isdir(
+        os.path.join(datadir, "cifar-10-batches-py")
+    ):
+        download = False
+    elif dataset == "cifar100" and os.path.isdir(
+        os.path.join(datadir, "cifar-100-python")
+    ):
+        download = False
     else:
-        download=True
+        download = True
     train_ds = dl_obj(
         datadir,
         dataidxs=dataidxs,
diff --git a/baselines/moon/moon/dataset_preparation.py b/baselines/moon/moon/dataset_preparation.py
index ec216a408870..11103d37763b 100644
--- a/baselines/moon/moon/dataset_preparation.py
+++ b/baselines/moon/moon/dataset_preparation.py
@@ -6,32 +6,6 @@
 uncomment the lines below and tell us in the README.md (see the "Running the Experiment"
 block) that this file should be executed first.
 """
-# import hydra
-# from hydra.core.hydra_config import HydraConfig
-# from hydra.utils import call, instantiate
-# from omegaconf import DictConfig, OmegaConf
-
-
-# @hydra.main(config_path="conf", config_name="base", version_base=None)
-# def download_and_preprocess(cfg: DictConfig) -> None:
-#     """Does everything needed to get the dataset.
-
-#     Parameters
-#     ----------
-#     cfg : DictConfig
-#         An omegaconf object that stores the hydra config.
-#     """
-
-#     ## 1. print parsed config
-#     print(OmegaConf.to_yaml(cfg))
-
-#     # Please include here all the logic
-#     # Please use the Hydra config style as much as possible specially
-#     # for parts that can be customised (e.g. how data is partitioned)
-
-# if __name__ == "__main__":
-
-#     download_and_preprocess()
 
 import numpy as np
 import torchvision.transforms as transforms
@@ -122,7 +96,5 @@ def partition_data(dataset, datadir, partition, num_clients, beta):
         for j in range(num_clients):
             np.random.shuffle(idx_batch[j])
             net_dataidx_map[j] = idx_batch[j]
-            
-    # print("net_dataidx_map", net_dataidx_map)
 
     return (X_train, y_train, X_test, y_test, net_dataidx_map)
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index d4a53afb8db3..73b723a0f8cc 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -4,6 +4,8 @@
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
 import random
+import shutil
+from pathlib import Path
 
 # these are the basic packages you'll need here
 # feel free to remove some if aren't needed
@@ -11,12 +13,13 @@
 import hydra
 import numpy as np
 import torch
+from hydra.core.hydra_config import HydraConfig
 from omegaconf import DictConfig, OmegaConf
-import os
 
 from moon import client, server
 from moon.dataset import get_dataloader
 from moon.dataset_preparation import partition_data
+from moon.utils import plot_metric_from_history
 
 
 @hydra.main(config_path="conf", config_name="base", version_base=None)
@@ -102,7 +105,9 @@ def main(cfg: DictConfig) -> None:
     # pass all relevant argument (including the global dataset used after aggregation,
     # if needed by your method.)
     # strategy = instantiate(cfg.strategy, <additional arguments if desired>)
-    strategy = fl.server.strategy.FedAvg(fraction_fit=cfg.fraction_fit, evaluate_fn = evaluate_fn)
+    strategy = fl.server.strategy.FedAvg(
+        fraction_fit=cfg.fraction_fit, evaluate_fn=evaluate_fn
+    )
     # 5. Start Simulation
     # history = fl.simulation.start_simulation(<arguments for simulation>)
     history = fl.simulation.start_simulation(
@@ -117,7 +122,8 @@ def main(cfg: DictConfig) -> None:
     )
     # remove saved models
     if cfg.alg == "moon":
-        os.rmdir(cfg.model.dir)
+        # os.rmdir(cfg.model.dir)
+        shutil.rmtree(cfg.model.dir)
     # 6. Save your results
     # Here you can save the `history` returned by the simulation and include
     # also other buffers, statistics, info needed to be saved in order to later
@@ -135,27 +141,29 @@ def main(cfg: DictConfig) -> None:
 
     # Hydra automatically creates an output directory
     # Let's retrieve it and save some results there
-    # save_path = HydraConfig.get().runtime.output_dir
+    save_path = HydraConfig.get().runtime.output_dir
+
+    # plot results and include them in the readme
+    strategy_name = strategy.__class__.__name__
+    file_suffix: str = (
+        f"_{strategy_name}"
+        f"{'_iid' if cfg.dataset_config.iid else ''}"
+        f"{'_balanced' if cfg.dataset_config.balance else ''}"
+        f"{'_powerlaw' if cfg.dataset_config.power_law else ''}"
+        f"_C={cfg.num_clients}"
+        f"_B={cfg.batch_size}"
+        f"_E={cfg.num_epochs}"
+        f"_R={cfg.num_rounds}"
+        f"_mu={cfg.mu}"
+        f"_strag={cfg.stragglers_fraction}"
+    )
 
-    # # save results as a Python pickle using a file_path
-    # # the directory created by Hydra for each run
-    # save_results_as_pickle(history, file_path=save_path, extra_results={})
-
-    # # plot results and include them in the readme
-    # strategy_name = strategy.__class__.__name__
-    # file_suffix: str = (
-    #     f"_{strategy_name}"
-    #     f"{'_iid' if cfg.dataset_config.iid else ''}"
-    #     f"{'_balanced' if cfg.dataset_config.balance else ''}"
-    #     f"{'_powerlaw' if cfg.dataset_config.power_law else ''}"
-    #     f"_C={cfg.num_clients}"
-    #     f"_B={cfg.batch_size}"
-    #     f"_E={cfg.num_epochs}"
-    #     f"_R={cfg.num_rounds}"
-    #     f"_mu={cfg.mu}"
-    #     f"_strag={cfg.stragglers_fraction}"
-    # )
+    plot_metric_from_history(
+        history,
+        Path(save_path),
+        (file_suffix),
+    )
 
 
 if __name__ == "__main__":
-    main()
\ No newline at end of file
+    main()
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
index e694f242b2a3..6b34a0b5cb27 100644
--- a/baselines/moon/moon/models.py
+++ b/baselines/moon/moon/models.py
@@ -343,10 +343,7 @@ def _get_basemodel(self, model_name):
     def forward(self, x):
         """Forward."""
         h = self.features(x)
-        # print("h before:", h)
-        # print("h size:", h.size())
         h = h.squeeze()
-        # print("h after:", h)
         x = self.l1(h)
         x = F.relu(x)
         x = self.l2(x)
@@ -385,17 +382,10 @@ def train_moon(
     """Training function for MOON."""
     # net = nn.DataParallel(net)
     # net.cuda()
-    print("device:", device)
     net.to(device)
     global_net.to(device)
     previous_net.to(device)
-
-    print("n_training: %d" % len(train_dataloader))
-
     train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
-
-    print(">> Pre-Training Training accuracy: {}".format(train_acc))
-
     optimizer = optim.SGD(
         filter(lambda p: p.requires_grad, net.parameters()),
         lr=lr,
@@ -404,7 +394,6 @@ def train_moon(
     )
 
     criterion = nn.CrossEntropyLoss().cuda()
-    # global_net.to(device)
 
     previous_net.eval()
     for param in previous_net.parameters():
@@ -412,8 +401,7 @@ def train_moon(
     previous_net.cuda()
 
     cnt = 0
-    cos = torch.nn.CosineSimilarity(dim=-1).to(device)
-    # mu = 0.001
+    cos = torch.nn.CosineSimilarity(dim=-1)
 
     for epoch in range(epochs):
         epoch_loss_collector = []
@@ -479,8 +467,6 @@ def train_fedprox(net, global_net, train_dataloader, epochs, lr, mu, device="cpu
     net = nn.DataParallel(net)
     net.cuda()
 
-    print("n_training: %d" % len(train_dataloader))
-
     train_acc, _ = compute_accuracy(net, train_dataloader, device=device)
 
     print(">> Pre-Training Training accuracy: {}".format(train_acc))
@@ -535,6 +521,6 @@ def test(net, test_dataloader, device="cpu"):
     """Test function."""
     net.to(device)
     test_acc, loss = compute_accuracy(net, test_dataloader, device=device)
-    print("test acc:", test_acc)
+    print(">> Test accuracy: %f" % test_acc)
     net.to("cpu")
     return test_acc, loss
diff --git a/baselines/moon/moon/utils.py b/baselines/moon/moon/utils.py
index 5109e7dcdeab..84c4b83c85b1 100644
--- a/baselines/moon/moon/utils.py
+++ b/baselines/moon/moon/utils.py
@@ -4,9 +4,14 @@
 example, you may define here things like: loading a model from a checkpoint, saving
 results, plotting.
 """
+from pathlib import Path
+from typing import Optional
+
+import matplotlib.pyplot as plt
 import numpy as np
 import torch
 import torch.nn as nn
+from flwr.server.history import History
 
 
 def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
@@ -84,3 +89,45 @@ def compute_accuracy(model, dataloader, device="cpu", multiloader=False):
         model.train()
 
     return correct / float(total), avg_loss
+
+
+def plot_metric_from_history(
+    hist: History,
+    save_plot_path: Path,
+    suffix: Optional[str] = "",
+) -> None:
+    """Plot data from Flower server History.
+
+    Parameters
+    ----------
+    hist : History
+        Object containing evaluation for all rounds.
+    save_plot_path : Path
+        Folder to save the plot to.
+    suffix: Optional[str]
+        Optional string to add at the end of the filename for the plot.
+    """
+    metric_type = "centralized"
+    metric_dict = (
+        hist.metrics_centralized
+        if metric_type == "centralized"
+        else hist.metrics_distributed
+    )
+    _rounds, values = zip(*metric_dict["accuracy"])
+
+    # let's extract centralised loss (main metric reported in FedProx paper)
+    rounds_loss, values_loss = zip(*hist.losses_centralized)
+
+    _fig, axs = plt.subplots(nrows=2, ncols=1, sharex="row")
+    axs[0].plot(np.asarray(rounds_loss), np.asarray(values_loss))
+    axs[1].plot(np.asarray(rounds_loss), np.asarray(values))
+
+    axs[0].set_ylabel("Loss")
+    axs[1].set_ylabel("Accuracy")
+
+    # plt.title(f"{metric_type.capitalize()} Validation - MNIST")
+    plt.xlabel("Rounds")
+    # plt.legend(loc="lower right")
+
+    plt.savefig(Path(save_plot_path) / Path(f"{metric_type}_metrics{suffix}.png"))
+    plt.close()
diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index 70be7077666f..efa449fbe33b 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -41,6 +41,7 @@ python = ">=3.8.15, <3.12.0"  # don't change this
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 scikit-learn = "1.3.0"
+matplotlib = "3.8.0"
 
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
@@ -83,7 +84,7 @@ disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import
 good-names = "i,j,k,_,x,y,X,Y,K,N,X_train,X_test,fc,l1,l2,l3,h,lr,mu"
 max-args = 10
 max-attributes = 15
-max-locals = 35
+max-locals = 36
 max-branches = 20
 max-statements = 55
 signature-mutators="hydra.main.main"

From 24fd24bd72142b5223881834d561d1fb2f6d78e9 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Sun, 24 Sep 2023 14:01:03 +0800
Subject: [PATCH 07/51] update README

---
 baselines/moon/README.md | 99 ++++++++++++++++++++++++++--------------
 1 file changed, 64 insertions(+), 35 deletions(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 682952717426..def22e5f606a 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -1,75 +1,104 @@
 ---
-title: title of the paper
-url: URL to the paper page (not the pdf)
-labels: [label1, label2] # please add between 4 and 10 single-word (maybe two-words) labels (e.g. "system heterogeneity", "image classification", "asynchronous", "weight sharing", "cross-silo")
-dataset: [dataset1, dataset2] # list of datasets you include in your baseline
+title: Model-Contrastive Federated Learning
+url: https://arxiv.org/abs/2103.16257
+labels: [data heterogeneity, image classification]
+dataset: [CIFAR-10, CIFAR-100] # list of datasets you include in your baseline
 ---
 
 # :warning:*_Title of your baseline_*
 
 > Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
 
-> :warning: This is the template to follow when creating a new Flower Baseline. Please follow the instructions in `EXTENDED_README.md`
 
-> :warning: Please follow the instructions carefully. You can see the [FedProx-MNIST baseline](https://github.com/adap/flower/tree/main/baselines/fedprox) as an example of a baseline that followed this guide.
+****Paper:**** :https://arxiv.org/abs/2103.16257
+****Authors:**** :Qinbin Li, Bingsheng He, Dawn Song
 
-> :warning: Please complete the metadata section at the very top of this README. This generates a table at the top of the file that will facilitate indexing baselines.
+****Abstract:**** :Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: modelcontrastive federated learning. MOON is a simple and effective federated learning framework. The key idea of MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. Our extensive experiments show that MOON significantly outperforms the other state-of-the-art federated learning algorithms on various image classification tasks.
 
-****Paper:**** :warning: *_add the URL of the paper page (not to the .pdf). For instance if you link a paper on ArXiv, add here the URL to the abstract page (e.g. https://arxiv.org/abs/1512.03385). If your paper is in from a journal or conference proceedings, please follow the same logic._*
-
-****Authors:**** :warning: *_list authors of the paper_*
-
-****Abstract:**** :warning: *_add here the abstract of the paper you are implementing_*
 
 
 ## About this baseline
 
-****What’s implemented:**** :warning: *_Concisely describe what experiment(s) in the publication can be replicated by running the code. Please only use a few sentences. Start with: “The code in this directory …”_*
-
-****Datasets:**** :warning: *_List the datasets you used (if you used a medium to large dataset, >10GB please also include the sizes of the dataset)._*
+****What’s implemented:**** : The code in this directory replicates the experiments in *Model-Contrastive Federated Learning* (Li et al., 2021), which proposed the MOON algorithm. Concretely ,it replicates the results of MOON for CIFAR-10 and CIFAR-100 in Table 1 and Figure 8.
 
-****Hardware Setup:**** :warning: *_Give some details about the hardware (e.g. a server with 8x V100 32GB and 256GB of RAM) you used to run the experiments for this baseline. Someone out there might not have access to the same resources you have so, could list the absolute minimum hardware needed to run the experiment in a reasonable amount of time ? (e.g. minimum is 1x 16GB GPU otherwise a client model can’t be trained with a sufficiently large batch size). Could you test this works too?_*
+****Datasets:**** : CIFAR-10 and CIFAR-100
 
-****Contributors:**** :warning: *_let the world know who contributed to this baseline. This could be either your name, your name and affiliation at the time, or your GitHub profile name if you prefer. If multiple contributors signed up for this baseline, please list yourself and your colleagues_*
+****Hardware Setup:**** :The experiments are run on a server with 4x Intel Xeon Gold 6226R and 8x Nvidia GeForce RTX 3090. A machine with at least 1x 16GB GPU should be able to run the experiments in a reasonable time.
 
+****Contributors:**** : Qinbin Li
 
 ## Experimental Setup
 
-****Task:**** :warning: *_what’s the primary task that is being federated? (e.g. image classification, next-word prediction). If you have experiments for several, please list them_*
+****Task:**** : Image classification.
+
+****Model:**** : This directory implements two models as same as the paper:
+* A simple-CNN with a projection head for CIFAR-10
+* A ResNet-50 with a projection head for CIFAR-100.
+  
+****Dataset:**** : This directory includes CIFAR-10 and CIFAR-100. They are partitioned in the same way as the paper. The settings are as follow:
+
+| Dataset | partitioning method |
+| :------ | :---: |
+| CIFAR-10  | Dirichlet with beta 0.5 |
+| CIFAR-100 | Dirichlet with beta 0.5 |
 
-****Model:**** :warning: *_provide details about the model you used in your experiments (if more than use a list). If your model is small, describing it as a table would be :100:. Some FL methods do not use an off-the-shelve model (e.g. ResNet18) instead they create your own. If this is your case, please provide a summary here and give pointers to where in the paper (e.g. Appendix B.4) is detailed._*
 
-****Dataset:**** :warning: *_Earlier you listed already the datasets that your baseline uses. Now you should include a breakdown of the details about each of them. Please include information about: how the dataset is partitioned (e.g. LDA with alpha 0.1 as default and all clients have the same number of training examples; or each client gets assigned a different number of samples following a power-law distribution with each client only instances of 2 classes)? if  your dataset is naturally partitioned just state “naturally partitioned”; how many partitions there are (i.e. how many clients)? Please include this an all information relevant about the dataset and its partitioning into a table._*
+****Training Hyperparameters:**** :
 
-****Training Hyperparameters:**** :warning: *_Include a table with all the main hyperparameters in your baseline. Please show them with their default value._*
+warning: The following tables show the default hyperparameters.
 
+| Description | Default Value |
+| ----------- | ----- |
+| number of clients | 10 |
+| number of local epochs | 10 |
+| fraction fit | 1.0 |
+| batch size | 64 |
+| learning rate | 0.01 |
+| mu | 1 |
+| temperature | 0.5 |
+| alg | moon |
+| seed | 0 |
+| service_device | cpu |
+| number of rounds | 100 |
+| client resources | {'num_cpus': 2.0, 'num_gpus': 0.0 }|
 
 ## Environment Setup
 
-:warning: _The Python environment for all baselines should follow these guidelines in the `EXTENDED_README`. Specify the steps to create and activate your environment. If there are any external system-wide requirements, please include instructions for them too. These instructions should be comprehensive enough so anyone can run them (if non standard, describe them step-by-step)._
+To construct the Python environment follow these steps:
 
+```bash
+# install the base Poetry environment
+poetry install
 
-## Running the Experiments
+# activate the environment
+poetry shell
 
-:warning: _Provide instructions on the steps to follow to run all the experiments._
-```bash  
-# The main experiment implemented in your baseline using default hyperparameters (that should be setup in the Hydra configs) should run (including dataset download and necessary partitioning) by executing the command:
+# install PyTorch with GPU support.
+pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
+```
 
-poetry run python -m <baseline-name>.main <no additional arguments> # where <baseline-name> is the name of this directory and that of the only sub-directory in this directory (i.e. where all your source code is)
 
-# If you are using a dataset that requires a complicated download (i.e. not using one natively supported by TF/PyTorch) + preprocessing logic, you might want to tell people to run one script first that will do all that. Please ensure the download + preprocessing can be configured to suit (at least!) a different download directory (and use as default the current directory). The expected command to run to do this is:
+## Running the Experiments
 
-poetry run python -m <baseline-name>.dataset_preparation <optional arguments, but default should always run>
+First ensure you have activated your Poetry environment (execute `poetry shell` from this directory). To run MOON on CIFAR-10 (Table 1 of the paper), you should run:
+```bash  
+poetry run python -m moon.main cifar10 
+```
 
-# It is expected that you baseline supports more than one dataset and different FL settings (e.g. different number of clients, dataset partitioning methods, etc). Please provide a list of commands showing how these experiments are run. Include also a short explanation of what each one does. Here it is expected you'll be using the Hydra syntax to override the default config.
+To run MOON on CIFAR-100 (Table 1 of the paper), you should run:
+```bash
+poetry run python -m moon.main cifar100
+```
 
-poetry run python -m <baseline-name>.main  <override_some_hyperparameters>
-.
-.
-.
-poetry run python -m <baseline-name>.main  <override_some_hyperparameters>
+To run MOON on CIFAR-100 with 50 clients (Figure 8(a) of the paper), you should run:
+```bash
+poetry run python -m moon.main cifar100_50clients
 ```
 
+To run MOON on CIFAR-100 with 100 clients (Figure 8(b) of the paper), you should run:
+```bash
+poetry run python -m moon.main cifar100_100clients
+```
 
 ## Expected Results
 

From 409e95bc8770b4e7b3e531804442e8626ded853a Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Sun, 24 Sep 2023 14:01:27 +0800
Subject: [PATCH 08/51] update config

---
 baselines/moon/moon/conf/base.yaml               | 4 ++--
 baselines/moon/moon/conf/cifar100.yaml           | 2 +-
 baselines/moon/moon/conf/cifar100_50clients.yaml | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/baselines/moon/moon/conf/base.yaml b/baselines/moon/moon/conf/base.yaml
index 7b49dd8ca4a2..a2d3ddfb7bde 100644
--- a/baselines/moon/moon/conf/base.yaml
+++ b/baselines/moon/moon/conf/base.yaml
@@ -6,9 +6,9 @@
 num_clients: 10
 num_epochs: 10
 fraction_fit: 1.0
-batch_size: 32
+batch_size: 64
 learning_rate: 0.01
-mu: 5
+mu: 1
 temperature: 0.5
 alg: moon
 seed: 0
diff --git a/baselines/moon/moon/conf/cifar100.yaml b/baselines/moon/moon/conf/cifar100.yaml
index d4c3da9d6229..10864c088697 100644
--- a/baselines/moon/moon/conf/cifar100.yaml
+++ b/baselines/moon/moon/conf/cifar100.yaml
@@ -13,7 +13,7 @@ temperature: 0.5
 alg: moon
 seed: 0
 server_device: cpu
-num_rounds: 100
+num_rounds: 110
 
 client_resources:
   num_cpus: 4
diff --git a/baselines/moon/moon/conf/cifar100_50clients.yaml b/baselines/moon/moon/conf/cifar100_50clients.yaml
index 737f1df9298a..eed1a412b2f2 100644
--- a/baselines/moon/moon/conf/cifar100_50clients.yaml
+++ b/baselines/moon/moon/conf/cifar100_50clients.yaml
@@ -28,6 +28,6 @@ dataset:
 
 model:
   # model config
-  name: simple-cnn
+  name: resnet50
   output_dim: 256
   dir: ./models/moon/cifar100_50clients/
\ No newline at end of file

From 3e34e794a2662d1b8b2d80a299faa043c39c6c53 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Mon, 25 Sep 2023 14:34:58 +0800
Subject: [PATCH 09/51] update

---
 baselines/moon/moon/client.py |  4 ----
 baselines/moon/moon/main.py   | 17 +----------------
 baselines/moon/moon/utils.py  | 24 +++++++++---------------
 3 files changed, 10 insertions(+), 35 deletions(-)

diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index 9be9abbbef6b..d6c8491aa7c3 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -137,12 +137,8 @@ def gen_client_fn(
 
     def client_fn(cid: str) -> FlowerClient:
         """Create a Flower client representing a single organization."""
-        # Load model
         device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-        # net = init_net(cfg.dataset.name, cfg.model.name, cfg.model.output_dim)
 
-        # Note: each client gets a different trainloader/valloader, so each client
-        # will train and evaluate on their own unique data
         trainloader = trainloaders[int(cid)]
         testloader = testloaders[int(cid)]
 
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 73b723a0f8cc..21f5278f54ae 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -78,7 +78,6 @@ def main(cfg: DictConfig) -> None:
     # 3. Define your clients
     # Define a function that returns another function that will be used during
     # simulation to instantiate each individual client
-    # client_fn = client.<my_function_that_returns_a_function>()
     client_fn = client.gen_client_fn(
         trainloaders=trainloaders,
         testloaders=testloaders,
@@ -90,17 +89,6 @@ def main(cfg: DictConfig) -> None:
     device = cfg.server_device
     evaluate_fn = server.gen_evaluate_fn(test_global_dl, device=device, cfg=cfg)
 
-    # # get a function that will be used to construct the config that the client's
-    # # fit() method will received
-    # def get_on_fit_config():
-    #     def fit_config_fn(server_round: int):
-    #         # resolve and convert to python dict
-    #         fit_config = OmegaConf.to_container(cfg.fit_config, resolve=True)
-    #         fit_config["curr_round"] = server_round  # add round info
-    #         return fit_config
-
-    #     return fit_config_fn
-
     # 4. Define your strategy
     # pass all relevant argument (including the global dataset used after aggregation,
     # if needed by your method.)
@@ -147,15 +135,12 @@ def main(cfg: DictConfig) -> None:
     strategy_name = strategy.__class__.__name__
     file_suffix: str = (
         f"_{strategy_name}"
-        f"{'_iid' if cfg.dataset_config.iid else ''}"
-        f"{'_balanced' if cfg.dataset_config.balance else ''}"
-        f"{'_powerlaw' if cfg.dataset_config.power_law else ''}"
+        f"{'_dataset' if cfg.dataset.name else ''}"
         f"_C={cfg.num_clients}"
         f"_B={cfg.batch_size}"
         f"_E={cfg.num_epochs}"
         f"_R={cfg.num_rounds}"
         f"_mu={cfg.mu}"
-        f"_strag={cfg.stragglers_fraction}"
     )
 
     plot_metric_from_history(
diff --git a/baselines/moon/moon/utils.py b/baselines/moon/moon/utils.py
index 84c4b83c85b1..4b99a480f77b 100644
--- a/baselines/moon/moon/utils.py
+++ b/baselines/moon/moon/utils.py
@@ -113,21 +113,15 @@ def plot_metric_from_history(
         if metric_type == "centralized"
         else hist.metrics_distributed
     )
-    _rounds, values = zip(*metric_dict["accuracy"])
-
-    # let's extract centralised loss (main metric reported in FedProx paper)
-    rounds_loss, values_loss = zip(*hist.losses_centralized)
-
-    _fig, axs = plt.subplots(nrows=2, ncols=1, sharex="row")
-    axs[0].plot(np.asarray(rounds_loss), np.asarray(values_loss))
-    axs[1].plot(np.asarray(rounds_loss), np.asarray(values))
-
-    axs[0].set_ylabel("Loss")
-    axs[1].set_ylabel("Accuracy")
-
-    # plt.title(f"{metric_type.capitalize()} Validation - MNIST")
-    plt.xlabel("Rounds")
-    # plt.legend(loc="lower right")
+    rounds, values = zip(*metric_dict["accuracy"])
+
+    # Plot the curve
+    plt.figure(figsize=(10, 6))
+    plt.plot(rounds, values)
+    plt.xlabel("#round")
+    plt.ylabel("Test accuracy")
+    plt.legend()
+    plt.show()
 
     plt.savefig(Path(save_plot_path) / Path(f"{metric_type}_metrics{suffix}.png"))
     plt.close()

From de1172ea934fd2ef2c2e45aeb5f0ec5786db4533 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Mon, 25 Sep 2023 14:35:11 +0800
Subject: [PATCH 10/51] add results

---
 baselines/moon/results/cifar10_fedprox.png | Bin 0 -> 27130 bytes
 baselines/moon/results/cifar10_moon.png    | Bin 0 -> 26412 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 baselines/moon/results/cifar10_fedprox.png
 create mode 100644 baselines/moon/results/cifar10_moon.png

diff --git a/baselines/moon/results/cifar10_fedprox.png b/baselines/moon/results/cifar10_fedprox.png
new file mode 100644
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From f9dc2c0ab1067cbdc745f3a6e0cff30fb983a82f Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Mon, 25 Sep 2023 14:35:34 +0800
Subject: [PATCH 11/51] update READDME

---
 baselines/moon/README.md | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index def22e5f606a..5a47084bc578 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -82,35 +82,37 @@ pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --e
 
 First ensure you have activated your Poetry environment (execute `poetry shell` from this directory). To run MOON on CIFAR-10 (Table 1 of the paper), you should run:
 ```bash  
-poetry run python -m moon.main cifar10 
+python -m moon.main cifar10 
 ```
 
 To run MOON on CIFAR-100 (Table 1 of the paper), you should run:
 ```bash
-poetry run python -m moon.main cifar100
+python -m moon.main cifar100
 ```
 
 To run MOON on CIFAR-100 with 50 clients (Figure 8(a) of the paper), you should run:
 ```bash
-poetry run python -m moon.main cifar100_50clients
+python -m moon.main cifar100_50clients
 ```
 
 To run MOON on CIFAR-100 with 100 clients (Figure 8(b) of the paper), you should run:
 ```bash
-poetry run python -m moon.main cifar100_100clients
+python -m moon.main cifar100_100clients
 ```
 
-## Expected Results
+You can also run FedProx on CIFAR-10:
+```base
+python -m moon.main cifar10_fedprox.yaml
+```
 
-:warning: _Your baseline implementation should replicate several of the experiments in the original paper. Please include here the exact command(s) needed to run each of those experiments followed by a figure (e.g. a line plot) or table showing the results you obtained when you ran the code. Below is an example of how you can present this. Please add command followed by results for all your experiments._
+To run FedProx on CIFAR-100:
+```base
+python -m moon.main cifar100_fedprox.yaml.
+```
 
-```bash
-# it is likely that for one experiment you need to sweep over different hyperparameters. You are encouraged to use Hydra's multirun functionality for this. This is an example of how you could achieve this for some typical FL hyperparameteres
+## Expected Results
 
-poetry run python -m <baseline-name>.main --multirun num_client_per_round=5,10,50 dataset=femnist,cifar10
-# the above command will run a total of 6 individual experiments (because 3client_configs x 2datasets = 6 -- you can think of it as a grid).
+You can find the output log in `results` directory. After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR10 with MOON, the accuracy after running 100 rounds is 0.7107 (see `results/cifar10.log`). You can find the curve below.
 
-[Now show a figure/table displaying the results of the above command]
+![](results/cifar10_moon.png)
 
-# add more commands + plots for additional experiments.
-```

From 7178d126b9e7a0cc50db6635229d0ffed77eb576 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Mon, 25 Sep 2023 14:36:31 +0800
Subject: [PATCH 12/51] remove extended_readme

---
 baselines/moon/EXTENDED_README.md | 123 ------------------------------
 1 file changed, 123 deletions(-)
 delete mode 100644 baselines/moon/EXTENDED_README.md

diff --git a/baselines/moon/EXTENDED_README.md b/baselines/moon/EXTENDED_README.md
deleted file mode 100644
index 9c8f5bc72fa9..000000000000
--- a/baselines/moon/EXTENDED_README.md
+++ /dev/null
@@ -1,123 +0,0 @@
-
-# Extended Readme
-
-> The baselines are expected to run in a machine running Ubuntu 22.04
-
-While `README.md` should include information about the baseline you implement and how to run it, this _extended_ readme provides info on what's the expected directory structure for a new baseline and more generally the instructions to follow before your baseline can be merged into the Flower repository. Please follow closely these instructions. It is likely that you have already completed steps 1-2.
-
-1. Fork the Flower repository and clone it.
-2. Navigate to the `baselines/` directory and from there run:
-    ```bash
-    # This will create a new directory with the same structure as this `baseline_template` directory.
-    ./dev/create-baseline.sh <baseline-name>
-    ``` 
-3. All your code and configs should go into a sub-directory with the same name as the name of your baseline.
-    *    The sub-directory contains a series of Python scripts that you can edit. Please stick to these files and consult with us if you need additional ones.
-    *    There is also a basic config structure in `<baseline-name>/conf` ready be parsed by [Hydra](https://hydra.cc/) when executing your `main.py`.
-4. Therefore, the directory structure in your baseline should look like:
-    ```bash
-    baselines/<baseline-name>
-                    ├── README.md # describes your baseline and everything needed to use it
-                    ├── EXTENDED_README.md # to remove before creating your PR
-                    ├── pyproject.toml # details your Python environment
-                    └── <baseline-name>
-                                ├── *.py # several .py files including main.py and __init__.py
-                                └── conf
-                                     └── *.yaml # one or more Hydra config files
-
-    ```
-> :warning: Make sure the variable `name` in `pyproject.toml` is set to the name of the sub-directory containing all your code.
-
-5. Add your dependencies to the `pyproject.toml` (see below a few examples on how to do it). Read more about Poetry below in this `EXTENDED_README.md`.
-6. Regularly check that your coding style and the documentation you add follow good coding practices. To test whether your code meets the requirements, please run the following:
-    ```bash
-    # After activating your environment and from your baseline's directory
-    cd .. # to go to the top-level directory of all baselines
-    ./dev/test-baseline.sh <baseline-name>
-    ./dev/test-baseline-structure.sh <baseline-name>
-    ```
-    Both `test-baseline.sh` and `test-baseline-structure.sh` will also be automatically run when you create a PR, and both tests need to pass for the baseline to be merged.
-    To automatically solve some formatting issues and apply easy fixes, please run the formatting script:
-    ```bash
-    # After activating your environment and from your baseline's directory
-    cd .. # to go to the top-level directory of all baselines
-    ./dev/format-baseline.sh <baseline-name>
-    ```
-7. Ensure that the Python environment for your baseline can be created without errors by simply running `poetry install` and that this is properly described later when you complete the `Environment Setup` section in `README.md`. This is specially important if your environment requires additional steps after doing `poetry install`.
-8. Ensure that your baseline runs with default arguments by running `poetry run python -m <baseline-name>.main`. Then, describe this and other forms of running your code in the `Running the Experiments` section in `README.md`.
-9. Once your code is ready and you have checked:
-    *    that following the instructions in your `README.md` the Python environment can be created correctly
-
-    *    that running the code following your instructions can reproduce the experiments in the paper
-   
-   , then you just need to create a Pull Request (PR) to kickstart the process of merging your baseline into the Flower repository.
-
-> Once you are happy to merge your baseline contribution, please delete this `EXTENDED_README.md` file.
-
-
-## About Poetry
-
-We use Poetry to manage the Python environment for each individual baseline. You can follow the instructions [here](https://python-poetry.org/docs/) to install Poetry in your machine. 
-
-
-### Specifying a Python Version (optional)
-By default, Poetry will use the Python version in your system. In some settings, you might want to specify a particular version of Python to use inside your Poetry environment. You can do so with [`pyenv`](https://github.com/pyenv/pyenv). Check the documentation for the different ways of installing `pyenv`, but one easy way is using the [automatic installer](https://github.com/pyenv/pyenv-installer):
-```bash
-curl https://pyenv.run | bash # then, don't forget links to your .bashrc/.zshrc
-```
-
-You can then install any Python version with `pyenv install <python-version>` (e.g. `pyenv install 3.9.17`). Then, in order to use that version for your baseline, you'd do the following:
-
-```bash
-# cd to your baseline directory (i.e. where the `pyproject.toml` is)
-pyenv local <python-version>
-
-# set that version for poetry
-poetry env use <python-version>
-
-# then you can install your Poetry environment (see the next setp)
-```
-
-### Installing Your Environment
-With the Poetry tool already installed, you can create an environment for this baseline with commands:
-```bash
-# run this from the same directory as the `pyproject.toml` file is
-poetry install
-```
-
-This will create a basic Python environment with just Flower and additional packages, including those needed for simulation. Next, you should add the dependencies for your code. It is **critical** that you fix the version of the packages you use using a `=` not a `=^`. You can do so via [`poetry add`](https://python-poetry.org/docs/cli/#add). Below are some examples:
-
-```bash
-# For instance, if you want to install tqdm
-poetry add tqdm==4.65.0
-
-# If you already have a requirements.txt, you can add all those packages (but ensure you have fixed the version) in one go as follows:
-poetry add $( cat requirements.txt )
-```
-With each `poetry add` command, the `pyproject.toml` gets automatically updated so you don't need to keep that `requirements.txt` as part of this baseline.
-
-
-More critically however, is adding your ML framework of choice to the list of dependencies. For some frameworks you might be able to do so with the `poetry add` command. Check [the Poetry documentation](https://python-poetry.org/docs/cli/#add) for how to add packages in various ways. For instance, let's say you want to use PyTorch:
-
-```bash
-# with plain `pip`  you'd run a command such as:
-pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117
-
-# to add the same 3 dependencies to your Poetry environment you'd need to add the URL to the wheel that the above pip command auto-resolves for you.
-# You can find those wheels in `https://download.pytorch.org/whl/cu117`. Copy the link and paste it after the `poetry add` command.
-# For instance to add `torch==1.13.1+cu117` and a x86 Linux system with Python3.8 you'd:
-poetry add https://download.pytorch.org/whl/cu117/torch-1.13.1%2Bcu117-cp38-cp38-linux_x86_64.whl
-# you'll need to repeat this for both `torchvision` and `torchaudio`
-```
-The above is just an example of how you can add these dependencies. Please refer to the Poetry documentation to extra reference.
-
-If all attempts fail, you can still install packages via standard `pip`. You'd first need to source/activate your Poetry environment.
-```bash
-# first ensure you have created your environment
-# and installed the base packages provided in the template
-poetry install 
-
-# then activate it
-poetry shell
-```
-Now you are inside your environment (pretty much as when you use `virtualenv` or `conda`) so you can install further packages with `pip`. Please note that, unlike with `poetry add`, these extra requirements won't be captured by `pyproject.toml`. Therefore, please ensure that you provide all instructions needed to: (1) create the base environment with Poetry and (2) install any additional dependencies via `pip` when you complete your `README.md`.
\ No newline at end of file

From bb41b93af7172097e69b2fdfed900b243c49134c Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Tue, 26 Sep 2023 10:59:30 +0800
Subject: [PATCH 13/51] update README

---
 baselines/moon/README.md               | 33 +++++++++++++++++---------
 baselines/moon/moon/conf/cifar100.yaml |  4 ++--
 2 files changed, 24 insertions(+), 13 deletions(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 5a47084bc578..4d7a115ec34c 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -19,7 +19,7 @@ dataset: [CIFAR-10, CIFAR-100] # list of datasets you include in your baseline
 
 ## About this baseline
 
-****What’s implemented:**** : The code in this directory replicates the experiments in *Model-Contrastive Federated Learning* (Li et al., 2021), which proposed the MOON algorithm. Concretely ,it replicates the results of MOON for CIFAR-10 and CIFAR-100 in Table 1 and Figure 8.
+****What’s implemented:**** : The code in this directory replicates the experiments in *Model-Contrastive Federated Learning* (Li et al., 2021), which proposed the MOON algorithm. Concretely ,it replicates the results of MOON for CIFAR-10 and CIFAR-100 in Table 1.
 
 ****Datasets:**** : CIFAR-10 and CIFAR-100
 
@@ -90,15 +90,6 @@ To run MOON on CIFAR-100 (Table 1 of the paper), you should run:
 python -m moon.main cifar100
 ```
 
-To run MOON on CIFAR-100 with 50 clients (Figure 8(a) of the paper), you should run:
-```bash
-python -m moon.main cifar100_50clients
-```
-
-To run MOON on CIFAR-100 with 100 clients (Figure 8(b) of the paper), you should run:
-```bash
-python -m moon.main cifar100_100clients
-```
 
 You can also run FedProx on CIFAR-10:
 ```base
@@ -112,7 +103,27 @@ python -m moon.main cifar100_fedprox.yaml.
 
 ## Expected Results
 
-You can find the output log in `results` directory. After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR10 with MOON, the accuracy after running 100 rounds is 0.7107 (see `results/cifar10.log`). You can find the curve below.
+You can find the output log in `results` directory. After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7107 (see `results/cifar10_moon.log`). You can find the curve below.
 
 ![](results/cifar10_moon.png)
 
+For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see `results/cifar10_fedprox.log`). For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6799 (see`results/cifar100_moon.log`). For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
+
+
+|  | CIFAR-10 | CIFAR-100 |
+| ----------- | ----- | ----- |
+| MOON | 0.7107 | 0.6799 |
+| FedProx| 0.6852 | 0.6494 |
+
+
+You can tune the hyperparameter `mu` for both MOON and FedProx by changing the configuration file in `conf`.
+
+You can also run the experiments in Figure 8 of the paper. To run MOON on CIFAR-100 with 50 clients (Figure 8(a) of the paper):
+```bash
+python -m moon.main cifar100_50clients
+```
+
+To run MOON on CIFAR-100 with 100 clients (Figure 8(b) of the paper):
+```bash
+python -m moon.main cifar100_100clients
+```
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar100.yaml b/baselines/moon/moon/conf/cifar100.yaml
index 10864c088697..453add60e1c2 100644
--- a/baselines/moon/moon/conf/cifar100.yaml
+++ b/baselines/moon/moon/conf/cifar100.yaml
@@ -8,12 +8,12 @@ num_epochs: 10
 fraction_fit: 1.0
 batch_size: 64
 learning_rate: 0.01
-mu: 1
+mu: 5
 temperature: 0.5
 alg: moon
 seed: 0
 server_device: cpu
-num_rounds: 110
+num_rounds: 100
 
 client_resources:
   num_cpus: 4

From d65fa3034950e63a2a6960ea17a5d9a9f45c7c2e Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Tue, 26 Sep 2023 11:00:00 +0800
Subject: [PATCH 14/51] add results

---
 baselines/moon/results/cifar100_fedprox.png | Bin 0 -> 24465 bytes
 baselines/moon/results/cifar100_moon.png    | Bin 0 -> 30799 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 baselines/moon/results/cifar100_fedprox.png
 create mode 100644 baselines/moon/results/cifar100_moon.png

diff --git a/baselines/moon/results/cifar100_fedprox.png b/baselines/moon/results/cifar100_fedprox.png
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HcmV?d00001


From baffe5cd3de3ce9f5ae32ed230258b7ffdd485e0 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Mon, 25 Sep 2023 20:09:25 -0700
Subject: [PATCH 15/51] remove comments

---
 baselines/moon/moon/client.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index d6c8491aa7c3..c5609b7e4d1f 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -83,8 +83,6 @@ def fit(
                     os.path.join(self.model_dir, str(self.net_id), "prev_net.pt")
                 )
             )
-        # else:
-        # self.prev_net = copy.deepcopy(self.net)
         global_net = init_net(self.dataset, self.model, self.output_dim)
         global_net.load_state_dict(self.net.state_dict())
         if self.alg == "moon":

From 6500d303bcc9625331bc407d87f934d16ae64748 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Tue, 26 Sep 2023 19:04:20 -0700
Subject: [PATCH 16/51] Update baselines/moon/README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 4d7a115ec34c..fdf05a9052b5 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -1,7 +1,7 @@
 ---
 title: Model-Contrastive Federated Learning
 url: https://arxiv.org/abs/2103.16257
-labels: [data heterogeneity, image classification]
+labels: [data heterogeneity, image classification, cross-silo, constrastive-learning]
 dataset: [CIFAR-10, CIFAR-100] # list of datasets you include in your baseline
 ---
 

From 5a63da5a0bb4209dbe426acbd3316e24a04bf9b0 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Tue, 26 Sep 2023 19:04:34 -0700
Subject: [PATCH 17/51] Update baselines/moon/README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index fdf05a9052b5..6303bf11ddbb 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -10,7 +10,8 @@ dataset: [CIFAR-10, CIFAR-100] # list of datasets you include in your baseline
 > Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
 
 
-****Paper:**** :https://arxiv.org/abs/2103.16257
+****Paper:**** : [arxiv.org/abs/2103.16257](https://arxiv.org/abs/2103.16257)
+
 ****Authors:**** :Qinbin Li, Bingsheng He, Dawn Song
 
 ****Abstract:**** :Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: modelcontrastive federated learning. MOON is a simple and effective federated learning framework. The key idea of MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. Our extensive experiments show that MOON significantly outperforms the other state-of-the-art federated learning algorithms on various image classification tasks.

From 7d9cc47453fb1f574d3260d658734c838209a67d Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 27 Sep 2023 10:13:21 +0800
Subject: [PATCH 18/51] fix loading previous model

---
 baselines/moon/moon/client.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index d6c8491aa7c3..e1edd123d539 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -55,8 +55,6 @@ def __init__(
         self.temperature = temperature
         self.model_dir = model_dir
         self.alg = alg
-        # self.prev_net = init_net(self.dataset, self.model, self.output_dim)
-        self.prev_net = None
 
     def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
         """Return the parameters of the current net."""
@@ -73,25 +71,24 @@ def fit(
     ) -> Tuple[NDArrays, int, Dict]:
         """Implement distributed fit function for a given client."""
         self.set_parameters(parameters)
-        if self.prev_net is None:
-            self.prev_net = init_net(self.dataset, self.model, self.output_dim)
-            self.prev_net = copy.deepcopy(self.net)
+        # if self.prev_net is None:
+        prev_net = init_net(self.dataset, self.model, self.output_dim)
+        if not os.path.exists(os.path.join(self.model_dir, str(self.net_id))):
+            prev_net = copy.deepcopy(self.net)
         else:
             # load previous model from model_dir
-            self.prev_net.load_state_dict(
+            prev_net.load_state_dict(
                 torch.load(
                     os.path.join(self.model_dir, str(self.net_id), "prev_net.pt")
                 )
             )
-        # else:
-        # self.prev_net = copy.deepcopy(self.net)
         global_net = init_net(self.dataset, self.model, self.output_dim)
         global_net.load_state_dict(self.net.state_dict())
         if self.alg == "moon":
             train_moon(
                 self.net,
                 global_net,
-                self.prev_net,
+                prev_net,
                 self.trainloader,
                 self.num_epochs,
                 self.learning_rate,

From a197d7196c332e6883b29e17dc5b109cbcb28bb5 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Tue, 26 Sep 2023 19:30:46 -0700
Subject: [PATCH 19/51] Update baselines/moon/moon/conf/cifar10.yaml

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/moon/conf/cifar10.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/moon/conf/cifar10.yaml b/baselines/moon/moon/conf/cifar10.yaml
index af1511f5b356..3a6f7877e992 100644
--- a/baselines/moon/moon/conf/cifar10.yaml
+++ b/baselines/moon/moon/conf/cifar10.yaml
@@ -30,4 +30,4 @@ model:
   # model config
   name: simple-cnn
   output_dim: 256
-  dir: ./models/moon/cifar10/
\ No newline at end of file
+  dir: ./client_states/moon/cifar10/
\ No newline at end of file

From df7b6cabeed6a577335930e21806c6daf246b7d4 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 27 Sep 2023 10:31:16 +0800
Subject: [PATCH 20/51] update README

---
 baselines/moon/README.md | 16 +++++++++++-----
 1 file changed, 11 insertions(+), 5 deletions(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 4d7a115ec34c..35b990041f39 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -27,6 +27,8 @@ dataset: [CIFAR-10, CIFAR-100] # list of datasets you include in your baseline
 
 ****Contributors:**** : Qinbin Li
 
+****Description:****: MOON requires to compute the model-contrastive loss in local training, which requires access to the local model of the previous round (Lines 14-17 of Algorithm 1 of the paper). Since `FlowerClient` does not preserve the states when starting a new round, we store the local models into the specified `model_dir` in local training indexed by the client ID, which will be loaded to the corresponding client in the next round.
+
 ## Experimental Setup
 
 ****Task:**** : Image classification.
@@ -67,7 +69,11 @@ warning: The following tables show the default hyperparameters.
 To construct the Python environment follow these steps:
 
 ```bash
-# install the base Poetry environment
+# set local python version via pyenv
+pyenv local 3.10.6
+# then fix that for poetry
+poetry env use 3.10.6
+# then install poetry env
 poetry install
 
 # activate the environment
@@ -82,23 +88,23 @@ pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --e
 
 First ensure you have activated your Poetry environment (execute `poetry shell` from this directory). To run MOON on CIFAR-10 (Table 1 of the paper), you should run:
 ```bash  
-python -m moon.main cifar10 
+python -m moon.main --config-name cifar10 
 ```
 
 To run MOON on CIFAR-100 (Table 1 of the paper), you should run:
 ```bash
-python -m moon.main cifar100
+python -m moon.main --config-name cifar100
 ```
 
 
 You can also run FedProx on CIFAR-10:
 ```base
-python -m moon.main cifar10_fedprox.yaml
+python -m moon.main --config-name cifar10_fedprox
 ```
 
 To run FedProx on CIFAR-100:
 ```base
-python -m moon.main cifar100_fedprox.yaml.
+python -m moon.main --config-name cifar100_fedprox
 ```
 
 ## Expected Results

From 96346f8c857a9bd0c75cda7c5e6f8fb5beef76c3 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 27 Sep 2023 10:32:34 +0800
Subject: [PATCH 21/51] update configuration

---
 baselines/moon/moon/conf/cifar100.yaml            | 4 ++--
 baselines/moon/moon/conf/cifar100_100clients.yaml | 2 +-
 baselines/moon/moon/conf/cifar100_50clients.yaml  | 2 +-
 baselines/moon/moon/conf/cifar100_fedprox.yaml    | 2 +-
 baselines/moon/moon/conf/cifar10_fedprox.yaml     | 2 +-
 5 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/baselines/moon/moon/conf/cifar100.yaml b/baselines/moon/moon/conf/cifar100.yaml
index 453add60e1c2..f5737715e781 100644
--- a/baselines/moon/moon/conf/cifar100.yaml
+++ b/baselines/moon/moon/conf/cifar100.yaml
@@ -8,7 +8,7 @@ num_epochs: 10
 fraction_fit: 1.0
 batch_size: 64
 learning_rate: 0.01
-mu: 5
+mu: 1
 temperature: 0.5
 alg: moon
 seed: 0
@@ -30,4 +30,4 @@ model:
   # model config
   name: resnet50
   output_dim: 256
-  dir: ./models/moon/cifar100/
\ No newline at end of file
+  dir: ./client_states/moon/cifar100/
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar100_100clients.yaml b/baselines/moon/moon/conf/cifar100_100clients.yaml
index 4d23b2c85faa..e40ee6cbe1d9 100644
--- a/baselines/moon/moon/conf/cifar100_100clients.yaml
+++ b/baselines/moon/moon/conf/cifar100_100clients.yaml
@@ -30,4 +30,4 @@ model:
   # model config
   name: resnet50
   output_dim: 256
-  dir: ./models/moon/cifar100_100c/
\ No newline at end of file
+  dir: ./client_states/moon/cifar100_100c/
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar100_50clients.yaml b/baselines/moon/moon/conf/cifar100_50clients.yaml
index eed1a412b2f2..08932b95b4de 100644
--- a/baselines/moon/moon/conf/cifar100_50clients.yaml
+++ b/baselines/moon/moon/conf/cifar100_50clients.yaml
@@ -30,4 +30,4 @@ model:
   # model config
   name: resnet50
   output_dim: 256
-  dir: ./models/moon/cifar100_50clients/
\ No newline at end of file
+  dir: ./client_states/moon/cifar100_50clients/
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar100_fedprox.yaml b/baselines/moon/moon/conf/cifar100_fedprox.yaml
index 48e98c2b3a42..971f0a92d930 100644
--- a/baselines/moon/moon/conf/cifar100_fedprox.yaml
+++ b/baselines/moon/moon/conf/cifar100_fedprox.yaml
@@ -30,4 +30,4 @@ model:
   # model config
   name: resnet50
   output_dim: 256
-  dir: ./models/moon/cifar100_fedprox/
\ No newline at end of file
+  dir: ./client_states/moon/cifar100_fedprox/
\ No newline at end of file
diff --git a/baselines/moon/moon/conf/cifar10_fedprox.yaml b/baselines/moon/moon/conf/cifar10_fedprox.yaml
index 599d5fd7e251..20532793067b 100644
--- a/baselines/moon/moon/conf/cifar10_fedprox.yaml
+++ b/baselines/moon/moon/conf/cifar10_fedprox.yaml
@@ -30,4 +30,4 @@ model:
   # model config
   name: simple-cnn
   output_dim: 256
-  dir: ./models/moon/cifar10_fedprox/
\ No newline at end of file
+  dir: ./client_states/moon/cifar10_fedprox/
\ No newline at end of file

From 90c66b8eac1269faf81d7d46a2c6a756726153fb Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Tue, 26 Sep 2023 19:33:36 -0700
Subject: [PATCH 22/51] Update baselines/moon/pyproject.toml

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/pyproject.toml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index efa449fbe33b..29f71dbfa7f6 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -42,7 +42,8 @@ flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 scikit-learn = "1.3.0"
 matplotlib = "3.8.0"
-
+torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.12.0%2Bcu116-cp310-cp310-linux_x86_64.whl"}
+torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.13.0%2Bcu116-cp310-cp310-linux_x86_64.whl"}
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
 black = "==23.1.0"

From 50b23a918d5c1458108fc274e56c8e3cbaa0e9e6 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Tue, 26 Sep 2023 19:33:52 -0700
Subject: [PATCH 23/51] Update baselines/moon/pyproject.toml

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index 29f71dbfa7f6..071ca86695b0 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -37,7 +37,7 @@ classifiers = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.8.15, <3.12.0"  # don't change this
+python = ">=3.10.0, <3.12.0"  # don't change this```
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 scikit-learn = "1.3.0"

From 6ba32f4b322e276a3cd9793730c2ba0324ba5398 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 29 Sep 2023 06:17:39 +0800
Subject: [PATCH 24/51] update README

---
 baselines/moon/README.md | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 08ef0de84dc6..b9855c41b348 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -80,8 +80,6 @@ poetry install
 # activate the environment
 poetry shell
 
-# install PyTorch with GPU support.
-pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
 ```
 
 
@@ -99,22 +97,20 @@ python -m moon.main --config-name cifar100
 
 
 You can also run FedProx on CIFAR-10:
-```base
+```bash
 python -m moon.main --config-name cifar10_fedprox
 ```
 
 To run FedProx on CIFAR-100:
-```base
+```bash
 python -m moon.main --config-name cifar100_fedprox
 ```
 
 ## Expected Results
 
-You can find the output log in `results` directory. After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7107 (see `results/cifar10_moon.log`). You can find the curve below.
-
-![](results/cifar10_moon.png)
+You can find the output log in `_static` directory. After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071 (see `_static/cifar10_moon.log`). 
 
-For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see `results/cifar10_fedprox.log`). For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6799 (see`results/cifar100_moon.log`). For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
+For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see `_static/cifar10_fedprox.log`). For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6799 (see`_static/cifar100_moon.log`). For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
 
 
 |  | CIFAR-10 | CIFAR-100 |
@@ -122,6 +118,10 @@ For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see
 | MOON | 0.7107 | 0.6799 |
 | FedProx| 0.6852 | 0.6494 |
 
+You can find the curve comparing MOON and FedProx on CIFAR-10 below.
+
+![](_static/cifar10_moon_fedprox.png)
+
 
 You can tune the hyperparameter `mu` for both MOON and FedProx by changing the configuration file in `conf`.
 

From b398846fdcb88ec1ed0203910014d26e159a663f Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 29 Sep 2023 06:18:25 +0800
Subject: [PATCH 25/51] update configuration files

---
 baselines/moon/moon/conf/cifar10.yaml             | 2 +-
 baselines/moon/moon/conf/cifar100.yaml            | 2 +-
 baselines/moon/moon/conf/cifar100_100clients.yaml | 2 +-
 baselines/moon/moon/conf/cifar100_50clients.yaml  | 2 +-
 baselines/moon/moon/conf/cifar100_fedprox.yaml    | 2 +-
 baselines/moon/moon/conf/cifar10_fedprox.yaml     | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/baselines/moon/moon/conf/cifar10.yaml b/baselines/moon/moon/conf/cifar10.yaml
index 3a6f7877e992..672427495dfe 100644
--- a/baselines/moon/moon/conf/cifar10.yaml
+++ b/baselines/moon/moon/conf/cifar10.yaml
@@ -17,7 +17,7 @@ num_rounds: 100
 
 client_resources:
   num_cpus: 4
-  num_gpus: 1
+  num_gpus: 0.2
 
 dataset:
   # dataset config
diff --git a/baselines/moon/moon/conf/cifar100.yaml b/baselines/moon/moon/conf/cifar100.yaml
index f5737715e781..33dc6d289456 100644
--- a/baselines/moon/moon/conf/cifar100.yaml
+++ b/baselines/moon/moon/conf/cifar100.yaml
@@ -17,7 +17,7 @@ num_rounds: 100
 
 client_resources:
   num_cpus: 4
-  num_gpus: 1
+  num_gpus: 0.5
 
 dataset:
   # dataset config
diff --git a/baselines/moon/moon/conf/cifar100_100clients.yaml b/baselines/moon/moon/conf/cifar100_100clients.yaml
index e40ee6cbe1d9..b314497b9411 100644
--- a/baselines/moon/moon/conf/cifar100_100clients.yaml
+++ b/baselines/moon/moon/conf/cifar100_100clients.yaml
@@ -17,7 +17,7 @@ num_rounds: 500
 
 client_resources:
   num_cpus: 8
-  num_gpus: 1
+  num_gpus: 0.5
 
 dataset:
   # dataset config
diff --git a/baselines/moon/moon/conf/cifar100_50clients.yaml b/baselines/moon/moon/conf/cifar100_50clients.yaml
index 08932b95b4de..d8c5877a1dcc 100644
--- a/baselines/moon/moon/conf/cifar100_50clients.yaml
+++ b/baselines/moon/moon/conf/cifar100_50clients.yaml
@@ -17,7 +17,7 @@ num_rounds: 200
 
 client_resources:
   num_cpus: 4
-  num_gpus: 1
+  num_gpus: 0.5
 
 dataset:
   # dataset config
diff --git a/baselines/moon/moon/conf/cifar100_fedprox.yaml b/baselines/moon/moon/conf/cifar100_fedprox.yaml
index 971f0a92d930..1544f8e3a348 100644
--- a/baselines/moon/moon/conf/cifar100_fedprox.yaml
+++ b/baselines/moon/moon/conf/cifar100_fedprox.yaml
@@ -17,7 +17,7 @@ num_rounds: 100
 
 client_resources:
   num_cpus: 4
-  num_gpus: 1
+  num_gpus: 0.5
 
 dataset:
   # dataset config
diff --git a/baselines/moon/moon/conf/cifar10_fedprox.yaml b/baselines/moon/moon/conf/cifar10_fedprox.yaml
index 20532793067b..d0f9c5e8e163 100644
--- a/baselines/moon/moon/conf/cifar10_fedprox.yaml
+++ b/baselines/moon/moon/conf/cifar10_fedprox.yaml
@@ -17,7 +17,7 @@ num_rounds: 100
 
 client_resources:
   num_cpus: 4
-  num_gpus: 1
+  num_gpus: 0.2
 
 dataset:
   # dataset config

From fdc2aeb2fd9f9021e7723afbf6e79b8a60d97ac4 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 29 Sep 2023 06:18:42 +0800
Subject: [PATCH 26/51] remove comment

---
 baselines/moon/moon/main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 21f5278f54ae..959ae729b824 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -110,8 +110,8 @@ def main(cfg: DictConfig) -> None:
     )
     # remove saved models
     if cfg.alg == "moon":
-        # os.rmdir(cfg.model.dir)
         shutil.rmtree(cfg.model.dir)
+        
     # 6. Save your results
     # Here you can save the `history` returned by the simulation and include
     # also other buffers, statistics, info needed to be saved in order to later

From 1e6b0eb2fe8633e0088e2f7c5582650ac9152e59 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 29 Sep 2023 06:19:26 +0800
Subject: [PATCH 27/51] rename results to _static

---
 .../{results => _static}/cifar100_fedprox.png   | Bin
 .../moon/{results => _static}/cifar100_moon.png | Bin
 .../{results => _static}/cifar10_fedprox.png    | Bin
 .../moon/{results => _static}/cifar10_moon.png  | Bin
 baselines/moon/_static/cifar10_moon_fedprox.png | Bin 0 -> 33727 bytes
 5 files changed, 0 insertions(+), 0 deletions(-)
 rename baselines/moon/{results => _static}/cifar100_fedprox.png (100%)
 rename baselines/moon/{results => _static}/cifar100_moon.png (100%)
 rename baselines/moon/{results => _static}/cifar10_fedprox.png (100%)
 rename baselines/moon/{results => _static}/cifar10_moon.png (100%)
 create mode 100644 baselines/moon/_static/cifar10_moon_fedprox.png

diff --git a/baselines/moon/results/cifar100_fedprox.png b/baselines/moon/_static/cifar100_fedprox.png
similarity index 100%
rename from baselines/moon/results/cifar100_fedprox.png
rename to baselines/moon/_static/cifar100_fedprox.png
diff --git a/baselines/moon/results/cifar100_moon.png b/baselines/moon/_static/cifar100_moon.png
similarity index 100%
rename from baselines/moon/results/cifar100_moon.png
rename to baselines/moon/_static/cifar100_moon.png
diff --git a/baselines/moon/results/cifar10_fedprox.png b/baselines/moon/_static/cifar10_fedprox.png
similarity index 100%
rename from baselines/moon/results/cifar10_fedprox.png
rename to baselines/moon/_static/cifar10_fedprox.png
diff --git a/baselines/moon/results/cifar10_moon.png b/baselines/moon/_static/cifar10_moon.png
similarity index 100%
rename from baselines/moon/results/cifar10_moon.png
rename to baselines/moon/_static/cifar10_moon.png
diff --git a/baselines/moon/_static/cifar10_moon_fedprox.png b/baselines/moon/_static/cifar10_moon_fedprox.png
new file mode 100644
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From de1d4d095fc7c07c8f865e1624d257ded93c9b5a Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 29 Sep 2023 09:45:18 -0700
Subject: [PATCH 28/51] Update baselines/moon/README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index b9855c41b348..973788bf5836 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -5,7 +5,7 @@ labels: [data heterogeneity, image classification, cross-silo, constrastive-lear
 dataset: [CIFAR-10, CIFAR-100] # list of datasets you include in your baseline
 ---
 
-# :warning:*_Title of your baseline_*
+# Model-Contrastive Federated Learning
 
 > Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
 

From 3e6dcb982ffbff05ec10f0cf2aa842f614eb1257 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 29 Sep 2023 09:45:23 -0700
Subject: [PATCH 29/51] Update baselines/moon/README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 973788bf5836..cb47ab6a2f58 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -2,7 +2,7 @@
 title: Model-Contrastive Federated Learning
 url: https://arxiv.org/abs/2103.16257
 labels: [data heterogeneity, image classification, cross-silo, constrastive-learning]
-dataset: [CIFAR-10, CIFAR-100] # list of datasets you include in your baseline
+dataset: [CIFAR-10, CIFAR-100]
 ---
 
 # Model-Contrastive Federated Learning

From ab8493c0501bae21ce311d2b132fb1b051c969d3 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 29 Sep 2023 09:45:30 -0700
Subject: [PATCH 30/51] Update baselines/moon/README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index cb47ab6a2f58..cfb6f23b2b19 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -28,7 +28,7 @@ dataset: [CIFAR-10, CIFAR-100]
 
 ****Contributors:**** : Qinbin Li
 
-****Description:****: MOON requires to compute the model-contrastive loss in local training, which requires access to the local model of the previous round (Lines 14-17 of Algorithm 1 of the paper). Since `FlowerClient` does not preserve the states when starting a new round, we store the local models into the specified `model_dir` in local training indexed by the client ID, which will be loaded to the corresponding client in the next round.
+****Description:****: MOON requires to compute the model-contrastive loss in local training, which requires access to the local model of the previous round (Lines 14-17 of Algorithm 1 of the paper). Since currently `FlowerClient` does not preserve the states when starting a new round, we store the local models into the specified `model.dir` in local training indexed by the client ID, which will be loaded to the corresponding client in the next round.
 
 ## Experimental Setup
 

From ce50584287964ebdf3541c35636e1d20892f27db Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 29 Sep 2023 09:46:09 -0700
Subject: [PATCH 31/51] Update baselines/moon/README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index cfb6f23b2b19..8423d631b525 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -20,7 +20,7 @@ dataset: [CIFAR-10, CIFAR-100]
 
 ## About this baseline
 
-****What’s implemented:**** : The code in this directory replicates the experiments in *Model-Contrastive Federated Learning* (Li et al., 2021), which proposed the MOON algorithm. Concretely ,it replicates the results of MOON for CIFAR-10 and CIFAR-100 in Table 1.
+****What’s implemented:**** The code in this directory replicates the experiments in *Model-Contrastive Federated Learning* (Li et al., 2021), which proposed the MOON algorithm. Concretely ,it replicates the results of MOON for CIFAR-10 and CIFAR-100 in Table 1.
 
 ****Datasets:**** : CIFAR-10 and CIFAR-100
 

From f0d2169f6cf2259f647e83fd833d10fab5185682 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 29 Sep 2023 09:46:38 -0700
Subject: [PATCH 32/51] Update baselines/moon/pyproject.toml

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index 071ca86695b0..61be3d48a8d2 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -44,6 +44,7 @@ scikit-learn = "1.3.0"
 matplotlib = "3.8.0"
 torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.12.0%2Bcu116-cp310-cp310-linux_x86_64.whl"}
 torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.13.0%2Bcu116-cp310-cp310-linux_x86_64.whl"}
+
 [tool.poetry.dev-dependencies]
 isort = "==5.11.5"
 black = "==23.1.0"

From 1f990e48e44aa2c87047c4c3d969eafab1d98f66 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Sat, 30 Sep 2023 01:49:01 +0800
Subject: [PATCH 33/51] update figure

---
 baselines/moon/_static/cifar100_fedprox.png     | Bin 24465 -> 0 bytes
 baselines/moon/_static/cifar100_moon.png        | Bin 30799 -> 0 bytes
 .../moon/_static/cifar100_moon_fedprox.png      | Bin 0 -> 30253 bytes
 baselines/moon/_static/cifar10_fedprox.png      | Bin 27130 -> 0 bytes
 baselines/moon/_static/cifar10_moon.png         | Bin 26412 -> 0 bytes
 5 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 baselines/moon/_static/cifar100_fedprox.png
 delete mode 100644 baselines/moon/_static/cifar100_moon.png
 create mode 100644 baselines/moon/_static/cifar100_moon_fedprox.png
 delete mode 100644 baselines/moon/_static/cifar10_fedprox.png
 delete mode 100644 baselines/moon/_static/cifar10_moon.png

diff --git a/baselines/moon/_static/cifar100_fedprox.png b/baselines/moon/_static/cifar100_fedprox.png
deleted file mode 100644
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From 604e30f758ce0e25686cf25baf4cac25dd9d975a Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Sat, 30 Sep 2023 01:52:29 +0800
Subject: [PATCH 34/51] update figure

---
 .../moon/_static/cifar100_moon_fedprox.png    | Bin 30253 -> 34021 bytes
 .../moon/_static/cifar10_moon_fedprox.png     | Bin 33727 -> 37724 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)

diff --git a/baselines/moon/_static/cifar100_moon_fedprox.png b/baselines/moon/_static/cifar100_moon_fedprox.png
index 30dc3302dcc1bb03e78550d2eb801cf780e60533..ceb1f963de7eac609ee6e41b721dce8b0a8afc54 100644
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From de7731eb6c747cab4baa3f3024dd9b62e7ce5be8 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Sat, 30 Sep 2023 01:54:08 +0800
Subject: [PATCH 35/51] update README

---
 baselines/moon/README.md | 28 ++++++++++++++--------------
 1 file changed, 14 insertions(+), 14 deletions(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 8423d631b525..81ae9d4fd7a6 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -22,23 +22,23 @@ dataset: [CIFAR-10, CIFAR-100]
 
 ****What’s implemented:**** The code in this directory replicates the experiments in *Model-Contrastive Federated Learning* (Li et al., 2021), which proposed the MOON algorithm. Concretely ,it replicates the results of MOON for CIFAR-10 and CIFAR-100 in Table 1.
 
-****Datasets:**** : CIFAR-10 and CIFAR-100
+****Datasets:**** CIFAR-10 and CIFAR-100
 
-****Hardware Setup:**** :The experiments are run on a server with 4x Intel Xeon Gold 6226R and 8x Nvidia GeForce RTX 3090. A machine with at least 1x 16GB GPU should be able to run the experiments in a reasonable time.
+****Hardware Setup:**** The experiments are run on a server with 4x Intel Xeon Gold 6226R and 8x Nvidia GeForce RTX 3090. A machine with at least 1x 16GB GPU should be able to run the experiments in a reasonable time.
 
-****Contributors:**** : Qinbin Li
+****Contributors:**** Qinbin Li
 
-****Description:****: MOON requires to compute the model-contrastive loss in local training, which requires access to the local model of the previous round (Lines 14-17 of Algorithm 1 of the paper). Since currently `FlowerClient` does not preserve the states when starting a new round, we store the local models into the specified `model.dir` in local training indexed by the client ID, which will be loaded to the corresponding client in the next round.
+****Description:**** MOON requires to compute the model-contrastive loss in local training, which requires access to the local model of the previous round (Lines 14-17 of Algorithm 1 of the paper). Since currently `FlowerClient` does not preserve the states when starting a new round, we store the local models into the specified `model.dir` in local training indexed by the client ID, which will be loaded to the corresponding client in the next round.
 
 ## Experimental Setup
 
-****Task:**** : Image classification.
+****Task:**** Image classification.
 
-****Model:**** : This directory implements two models as same as the paper:
+****Model:**** This directory implements two models as same as the paper:
 * A simple-CNN with a projection head for CIFAR-10
 * A ResNet-50 with a projection head for CIFAR-100.
   
-****Dataset:**** : This directory includes CIFAR-10 and CIFAR-100. They are partitioned in the same way as the paper. The settings are as follow:
+****Dataset:**** This directory includes CIFAR-10 and CIFAR-100. They are partitioned in the same way as the paper. The settings are as follow:
 
 | Dataset | partitioning method |
 | :------ | :---: |
@@ -46,9 +46,7 @@ dataset: [CIFAR-10, CIFAR-100]
 | CIFAR-100 | Dirichlet with beta 0.5 |
 
 
-****Training Hyperparameters:**** :
-
-warning: The following tables show the default hyperparameters.
+****Training Hyperparameters:****
 
 | Description | Default Value |
 | ----------- | ----- |
@@ -110,17 +108,19 @@ python -m moon.main --config-name cifar100_fedprox
 
 You can find the output log in `_static` directory. After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071 (see `_static/cifar10_moon.log`). 
 
-For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see `_static/cifar10_fedprox.log`). For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6799 (see`_static/cifar100_moon.log`). For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
+For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see `_static/cifar10_fedprox.log`). For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6636 (see`_static/cifar100_moon.log`). For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
 
 
 |  | CIFAR-10 | CIFAR-100 |
 | ----------- | ----- | ----- |
-| MOON | 0.7107 | 0.6799 |
+| MOON | 0.7071 | 0.6636 |
 | FedProx| 0.6852 | 0.6494 |
 
-You can find the curve comparing MOON and FedProx on CIFAR-10 below.
+### Figure 6
+You can find the curve comparing MOON and FedProx on CIFAR-10 and CIFAR-100 below.
+
 
-![](_static/cifar10_moon_fedprox.png)
+<img alt="CIFAR-10" src="_static/cifar10_moon_fedprox.png" width="400"/> <img alt="CIFAR-100" src="_static/cifar100_moon_fedprox.png" width="400"/>
 
 
 You can tune the hyperparameter `mu` for both MOON and FedProx by changing the configuration file in `conf`.

From 1106a441512f009e9a0d1d9db38bc17cfa783025 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 18 Oct 2023 09:12:47 +0800
Subject: [PATCH 36/51] Update baselines/moon/README.md

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 81ae9d4fd7a6..3c8e3e736f6c 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -77,7 +77,6 @@ poetry install
 
 # activate the environment
 poetry shell
-
 ```
 
 

From 9b691f0b64654ff01c43cf992afcfeaf776996ed Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 18 Oct 2023 09:17:12 +0800
Subject: [PATCH 37/51] update README

---
 baselines/moon/README.md | 19 ++++++++++++-------
 1 file changed, 12 insertions(+), 7 deletions(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 81ae9d4fd7a6..ef5cfce41694 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -10,11 +10,11 @@ dataset: [CIFAR-10, CIFAR-100]
 > Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
 
 
-****Paper:**** : [arxiv.org/abs/2103.16257](https://arxiv.org/abs/2103.16257)
+****Paper:**** [arxiv.org/abs/2103.16257](https://arxiv.org/abs/2103.16257)
 
-****Authors:**** :Qinbin Li, Bingsheng He, Dawn Song
+****Authors:**** Qinbin Li, Bingsheng He, Dawn Song
 
-****Abstract:**** :Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: modelcontrastive federated learning. MOON is a simple and effective federated learning framework. The key idea of MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. Our extensive experiments show that MOON significantly outperforms the other state-of-the-art federated learning algorithms on various image classification tasks.
+****Abstract:**** Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: modelcontrastive federated learning. MOON is a simple and effective federated learning framework. The key idea of MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. Our extensive experiments show that MOON significantly outperforms the other state-of-the-art federated learning algorithms on various image classification tasks.
 
 
 
@@ -120,17 +120,22 @@ For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see
 You can find the curve comparing MOON and FedProx on CIFAR-10 and CIFAR-100 below.
 
 
-<img alt="CIFAR-10" src="_static/cifar10_moon_fedprox.png" width="400"/> <img alt="CIFAR-100" src="_static/cifar100_moon_fedprox.png" width="400"/>
+<img alt="CIFAR-10" src="_static/cifar10_moon_fedprox.png" width="500"/> <img alt="CIFAR-100" src="_static/cifar100_moon_fedprox.png" width="500"/>
 
 
 You can tune the hyperparameter `mu` for both MOON and FedProx by changing the configuration file in `conf`.
 
-You can also run the experiments in Figure 8 of the paper. To run MOON on CIFAR-100 with 50 clients (Figure 8(a) of the paper):
+### Figure 8(a)
+You can run the experiments in Figure 8 of the paper. To run MOON on CIFAR-100 with 50 clients (Figure 8(a) of the paper):
 ```bash
-python -m moon.main cifar100_50clients
+python -m moon.main --config-name cifar100_50clients
 ```
 
+You can find the curve presenting MOON (\mu=10) below.
+
+<img alt="CIFAR-100" src="_static/cifar100_50clients_moon.png" width="500"/>
+
 To run MOON on CIFAR-100 with 100 clients (Figure 8(b) of the paper):
 ```bash
-python -m moon.main cifar100_100clients
+python -m moon.main --config-name cifar100_100clients
 ```
\ No newline at end of file

From 6c38764a6393c86a34e44e7adf263ad751e84777 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 18 Oct 2023 09:17:28 +0800
Subject: [PATCH 38/51] formatting

---
 baselines/moon/moon/main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 959ae729b824..221ab2bf0e7a 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -111,7 +111,7 @@ def main(cfg: DictConfig) -> None:
     # remove saved models
     if cfg.alg == "moon":
         shutil.rmtree(cfg.model.dir)
-        
+
     # 6. Save your results
     # Here you can save the `history` returned by the simulation and include
     # also other buffers, statistics, info needed to be saved in order to later

From ff417c0ecee4ab06f9646f4c42644a3f0d737f36 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 18 Oct 2023 09:17:43 +0800
Subject: [PATCH 39/51] add info

---
 baselines/moon/pyproject.toml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index 61be3d48a8d2..01155eca250e 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -5,9 +5,9 @@ build-backend = "poetry.masonry.api"
 [tool.poetry]
 name = "moon" # <----- Ensure it matches the name of your baseline directory containing all the source code
 version = "1.0.0"
-description = "Flower Baselines"
+description = "Flower Baselines - Model-Contrastive Federated Learning"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.dev>", "Qinbin Li <liqinbin1998@gmail.com>"]
 readme = "README.md"
 homepage = "https://flower.dev"
 repository = "https://github.com/adap/flower"

From 1b2cb4362c6e0a74b9ef69897c521c09646bcd46 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 18 Oct 2023 09:26:59 +0800
Subject: [PATCH 40/51] add log

---
 .../moon/_static/cifar100_50clients_moon.png  |   Bin 0 -> 29532 bytes
 .../_static/cifar100_50clients_moon_log.txt   | 93907 ++++++++++++++++
 .../moon/_static/cifar100_fedprox_log.txt     | 17647 +++
 baselines/moon/_static/cifar100_moon_log.txt  | 12852 +++
 .../moon/_static/cifar10_fedprox_log.txt      |  6852 ++
 baselines/moon/_static/cifar10_moon_log.txt   | 12852 +++
 6 files changed, 144110 insertions(+)
 create mode 100644 baselines/moon/_static/cifar100_50clients_moon.png
 create mode 100644 baselines/moon/_static/cifar100_50clients_moon_log.txt
 create mode 100644 baselines/moon/_static/cifar100_fedprox_log.txt
 create mode 100644 baselines/moon/_static/cifar100_moon_log.txt
 create mode 100644 baselines/moon/_static/cifar10_fedprox_log.txt
 create mode 100644 baselines/moon/_static/cifar10_moon_log.txt

diff --git a/baselines/moon/_static/cifar100_50clients_moon.png b/baselines/moon/_static/cifar100_50clients_moon.png
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diff --git a/baselines/moon/_static/cifar100_50clients_moon_log.txt b/baselines/moon/_static/cifar100_50clients_moon_log.txt
new file mode 100644
index 000000000000..bb5684a67359
--- /dev/null
+++ b/baselines/moon/_static/cifar100_50clients_moon_log.txt
@@ -0,0 +1,93907 @@
+INFO flwr 2023-10-08 11:50:04,801 | app.py:175 | Starting Flower simulation, config: ServerConfig(num_rounds=200, round_timeout=None)
+num_clients: 50
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 10
+temperature: 0.5
+alg: moon
+seed: 0
+server_device: cpu
+num_rounds: 200
+client_resources:
+  num_cpus: 4
+  num_gpus: 0.5
+dataset:
+  name: cifar100
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+model:
+  name: resnet50
+  output_dim: 256
+  dir: ./client_states/moon/cifar100_50clients/
+
+Files already downloaded and verified
+Files already downloaded and verified
+[2023-10-08 11:50:04,801][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=200, round_timeout=None)
+2023-10-08 11:50:18,357	INFO worker.py:1621 -- Started a local Ray instance.
+INFO flwr 2023-10-08 11:50:19,193 | app.py:210 | Flower VCE: Ray initialized with resources: {'memory': 81556637492.0, 'node:__internal_head__': 1.0, 'node:172.31.26.157': 1.0, 'object_store_memory': 39238558924.0, 'accelerator_type:A10G': 1.0, 'CPU': 32.0, 'GPU': 1.0}
+[2023-10-08 11:50:19,193][flwr][INFO] - Flower VCE: Ray initialized with resources: {'memory': 81556637492.0, 'node:__internal_head__': 1.0, 'node:172.31.26.157': 1.0, 'object_store_memory': 39238558924.0, 'accelerator_type:A10G': 1.0, 'CPU': 32.0, 'GPU': 1.0}
+INFO flwr 2023-10-08 11:50:19,193 | app.py:224 | Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 0.5}
+[2023-10-08 11:50:19,193][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 0.5}
+INFO flwr 2023-10-08 11:50:19,204 | app.py:270 | Flower VCE: Creating VirtualClientEngineActorPool with 2 actors
+[2023-10-08 11:50:19,204][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 2 actors
+INFO flwr 2023-10-08 11:50:19,205 | server.py:89 | Initializing global parameters
+[2023-10-08 11:50:19,205][flwr][INFO] - Initializing global parameters
+INFO flwr 2023-10-08 11:50:19,205 | server.py:276 | Requesting initial parameters from one random client
+[2023-10-08 11:50:19,205][flwr][INFO] - Requesting initial parameters from one random client
+INFO flwr 2023-10-08 11:50:35,071 | server.py:280 | Received initial parameters from one random client
+[2023-10-08 11:50:35,071][flwr][INFO] - Received initial parameters from one random client
+INFO flwr 2023-10-08 11:50:35,071 | server.py:91 | Evaluating initial parameters
+[2023-10-08 11:50:35,071][flwr][INFO] - Evaluating initial parameters
+INFO flwr 2023-10-08 11:51:32,221 | server.py:94 | initial parameters (loss, other metrics): 8.480555293659052, {'accuracy': 0.01}
+>> Test accuracy: 0.010000
+[2023-10-08 11:51:32,221][flwr][INFO] - initial parameters (loss, other metrics): 8.480555293659052, {'accuracy': 0.01}
+INFO flwr 2023-10-08 11:51:32,221 | server.py:104 | FL starting
+[2023-10-08 11:51:32,221][flwr][INFO] - FL starting
+DEBUG flwr 2023-10-08 11:51:32,222 | server.py:222 | fit_round 1: strategy sampled 50 clients (out of 50)
+[2023-10-08 11:51:32,222][flwr][DEBUG] - fit_round 1: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.141429 Loss1: 4.623704 Loss2: 2.517726
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.815246 Loss1: 4.377664 Loss2: 2.437582 [repeated 2x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.374610 Loss1: 4.066654 Loss2: 2.307956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.241068 Loss1: 3.962804 Loss2: 2.278263 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.166380 Loss1: 3.903815 Loss2: 2.262565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.178163 Loss1: 3.900820 Loss2: 2.277343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.104259 Loss1: 3.844198 Loss2: 2.260060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.088216 Loss1: 3.827865 Loss2: 2.260351 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 6.078566 Loss1: 3.824166 Loss2: 2.254400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 6.061410 Loss1: 3.801478 Loss2: 2.259932 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.066667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.998799 Loss1: 4.461400 Loss2: 2.537398 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.123958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.011871 Loss1: 3.718402 Loss2: 2.293469 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.956115 Loss1: 3.669647 Loss2: 2.286468
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.181386 Loss1: 4.618802 Loss2: 2.562584
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.918989 Loss1: 3.647250 Loss2: 2.271739
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.842120 Loss1: 4.361955 Loss2: 2.480164
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.840175 Loss1: 3.571401 Loss2: 2.268774
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.376729 Loss1: 4.042464 Loss2: 2.334265
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.816871 Loss1: 3.543732 Loss2: 2.273139
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.195627 Loss1: 3.891672 Loss2: 2.303955
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.087691 Loss1: 3.799478 Loss2: 2.288213
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.803647 Loss1: 3.532195 Loss2: 2.271452
+(DefaultActor pid=3764) Epoch: 5 Loss: 6.080831 Loss1: 3.788156 Loss2: 2.292675
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.779242 Loss1: 3.500759 Loss2: 2.278483
+(DefaultActor pid=3764) Epoch: 6 Loss: 6.061819 Loss1: 3.781776 Loss2: 2.280043
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.756836 Loss1: 3.483867 Loss2: 2.272970
+(DefaultActor pid=3765) >> Training accuracy: 0.127930
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 6.033707 Loss1: 3.753887 Loss2: 2.279820 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.112500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.068958 Loss1: 4.511810 Loss2: 2.557148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.362267 Loss1: 3.947855 Loss2: 2.414412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.128006 Loss1: 3.790947 Loss2: 2.337059
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.435804 Loss1: 4.878289 Loss2: 2.557515
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.608788 Loss1: 4.109474 Loss2: 2.499314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.094079 Loss1: 3.772397 Loss2: 2.321682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.994196 Loss1: 3.713923 Loss2: 2.280273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.007387 Loss1: 3.731402 Loss2: 2.275984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.920667 Loss1: 3.655903 Loss2: 2.264764 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.142708
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.904706 Loss1: 3.569312 Loss2: 2.335394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.931455 Loss1: 3.660315 Loss2: 2.271140
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.929248 Loss1: 3.664463 Loss2: 2.264785
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.866408 Loss1: 3.607654 Loss2: 2.258754
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.924855 Loss1: 3.665475 Loss2: 2.259380
+(DefaultActor pid=3764) >> Training accuracy: 0.151042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.026988 Loss1: 4.495027 Loss2: 2.531961
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.383704 Loss1: 3.998182 Loss2: 2.385522
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.127318 Loss1: 3.837085 Loss2: 2.290233
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.027926 Loss1: 3.748938 Loss2: 2.278988
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.322096 Loss1: 4.777526 Loss2: 2.544570
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.986878 Loss1: 3.704436 Loss2: 2.282442
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.791483 Loss1: 4.334203 Loss2: 2.457280
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.984870 Loss1: 3.723202 Loss2: 2.261669
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.353883 Loss1: 4.023972 Loss2: 2.329911
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.280449 Loss1: 3.991762 Loss2: 2.288688
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.931523 Loss1: 3.659399 Loss2: 2.272124
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.231285 Loss1: 3.950874 Loss2: 2.280410
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.896951 Loss1: 3.632989 Loss2: 2.263962
+(DefaultActor pid=3764) Epoch: 5 Loss: 6.169997 Loss1: 3.900239 Loss2: 2.269759
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.910534 Loss1: 3.634108 Loss2: 2.276426
+(DefaultActor pid=3764) Epoch: 6 Loss: 6.139892 Loss1: 3.865301 Loss2: 2.274591
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.853818 Loss1: 3.583443 Loss2: 2.270374
+(DefaultActor pid=3765) >> Training accuracy: 0.123047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 6.097143 Loss1: 3.818687 Loss2: 2.278456 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.097917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.064795 Loss1: 4.524777 Loss2: 2.540019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.211571 Loss1: 3.885131 Loss2: 2.326441 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.083697 Loss1: 3.802457 Loss2: 2.281240
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.191528 Loss1: 4.638426 Loss2: 2.553101
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.033659 Loss1: 3.769624 Loss2: 2.264035
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.570753 Loss1: 4.101366 Loss2: 2.469386
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.993022 Loss1: 3.724712 Loss2: 2.268310
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.215262 Loss1: 3.883995 Loss2: 2.331266
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.982977 Loss1: 3.722326 Loss2: 2.260651
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.122611 Loss1: 3.804615 Loss2: 2.317996
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.957207 Loss1: 3.691987 Loss2: 2.265220
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.093045 Loss1: 3.801351 Loss2: 2.291694
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.972124 Loss1: 3.710627 Loss2: 2.261497
+(DefaultActor pid=3764) Epoch: 5 Loss: 6.037806 Loss1: 3.764050 Loss2: 2.273756
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.986772 Loss1: 3.719188 Loss2: 2.267583
+(DefaultActor pid=3764) Epoch: 6 Loss: 6.057003 Loss1: 3.766012 Loss2: 2.290990
+(DefaultActor pid=3765) >> Training accuracy: 0.103125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 6.033271 Loss1: 3.750150 Loss2: 2.283120
+(DefaultActor pid=3764) Epoch: 8 Loss: 6.016183 Loss1: 3.735457 Loss2: 2.280726
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.967865 Loss1: 3.693606 Loss2: 2.274260
+(DefaultActor pid=3764) >> Training accuracy: 0.093750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.128295 Loss1: 4.605429 Loss2: 2.522866
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.619682 Loss1: 4.190636 Loss2: 2.429046
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.287781 Loss1: 3.987398 Loss2: 2.300383
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.127619 Loss1: 4.577561 Loss2: 2.550058
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.212747 Loss1: 3.932031 Loss2: 2.280716
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.510792 Loss1: 4.070234 Loss2: 2.440558
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.190337 Loss1: 3.911305 Loss2: 2.279032
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.141527 Loss1: 3.816872 Loss2: 2.324654
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.185869 Loss1: 3.899968 Loss2: 2.285900
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.180899 Loss1: 3.896198 Loss2: 2.284701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.140523 Loss1: 3.868726 Loss2: 2.271797 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 6.131425 Loss1: 3.858042 Loss2: 2.273383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 6.111321 Loss1: 3.834449 Loss2: 2.276872 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.086914
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.860326 Loss1: 3.542153 Loss2: 2.318173 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.155208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.038699 Loss1: 4.460744 Loss2: 2.577954 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.233620 Loss1: 3.851945 Loss2: 2.381675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.117404 Loss1: 4.564858 Loss2: 2.552547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.737299 Loss1: 4.298208 Loss2: 2.439091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.295244 Loss1: 3.939232 Loss2: 2.356012 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.913371 Loss1: 3.635229 Loss2: 2.278142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.869057 Loss1: 3.595674 Loss2: 2.273383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.862464 Loss1: 3.576904 Loss2: 2.285560 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.151442
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 6.053141 Loss1: 3.776185 Loss2: 2.276957 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 6.024238 Loss1: 3.745498 Loss2: 2.278740 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.094866
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.506909 Loss1: 4.091000 Loss2: 2.415909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.151743 Loss1: 3.832490 Loss2: 2.319253 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.199954 Loss1: 4.610970 Loss2: 2.588984
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.077528 Loss1: 3.763050 Loss2: 2.314478
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.350672 Loss1: 3.863934 Loss2: 2.486738
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.056630 Loss1: 3.753362 Loss2: 2.303268
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.903112 Loss1: 3.575800 Loss2: 2.327312
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.013280 Loss1: 3.710236 Loss2: 2.303044
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.891206 Loss1: 3.571019 Loss2: 2.320186
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.006853 Loss1: 3.701072 Loss2: 2.305780
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.816187 Loss1: 3.504008 Loss2: 2.312180
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.991969 Loss1: 3.695174 Loss2: 2.296794
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.741192 Loss1: 3.443818 Loss2: 2.297374
+(DefaultActor pid=3765) Epoch: 9 Loss: 6.020029 Loss1: 3.715516 Loss2: 2.304514
+(DefaultActor pid=3765) >> Training accuracy: 0.115625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.752878 Loss1: 3.460613 Loss2: 2.292265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.686469 Loss1: 3.386332 Loss2: 2.300137 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.261458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.627912 Loss1: 4.215508 Loss2: 2.412404 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.195109 Loss1: 3.896981 Loss2: 2.298128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.155177 Loss1: 3.880020 Loss2: 2.275157
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.072679 Loss1: 4.504504 Loss2: 2.568176
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.125374 Loss1: 3.845791 Loss2: 2.279583
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.174388 Loss1: 3.692269 Loss2: 2.482119
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.063761 Loss1: 3.786196 Loss2: 2.277565
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.857945 Loss1: 3.521350 Loss2: 2.336595
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.072601 Loss1: 3.798733 Loss2: 2.273868
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.700555 Loss1: 3.384597 Loss2: 2.315958
+(DefaultActor pid=3765) Epoch: 8 Loss: 6.053261 Loss1: 3.783617 Loss2: 2.269644
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.632288 Loss1: 3.341463 Loss2: 2.290825
+(DefaultActor pid=3765) Epoch: 9 Loss: 6.021415 Loss1: 3.752326 Loss2: 2.269089
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.641399 Loss1: 3.352493 Loss2: 2.288906
+(DefaultActor pid=3765) >> Training accuracy: 0.120833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.627193 Loss1: 3.333685 Loss2: 2.293508
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.639312 Loss1: 3.350642 Loss2: 2.288670
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.612912 Loss1: 3.319420 Loss2: 2.293492
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.578630 Loss1: 3.292686 Loss2: 2.285944
+(DefaultActor pid=3764) >> Training accuracy: 0.162500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.084108 Loss1: 4.538016 Loss2: 2.546092
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.432670 Loss1: 4.026887 Loss2: 2.405783
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.157578 Loss1: 3.833854 Loss2: 2.323724
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.060689 Loss1: 3.779562 Loss2: 2.281127
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.011738 Loss1: 3.744229 Loss2: 2.267509
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.993636 Loss1: 3.719531 Loss2: 2.274105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.988533 Loss1: 3.717606 Loss2: 2.270927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.951626 Loss1: 3.673982 Loss2: 2.277644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.916644 Loss1: 3.648319 Loss2: 2.268324 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.872494 Loss1: 3.588714 Loss2: 2.283780 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.146875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.935379 Loss1: 3.649701 Loss2: 2.285678 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.871895 Loss1: 3.580260 Loss2: 2.291635 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.131250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.663894 Loss1: 4.227140 Loss2: 2.436755 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.155752 Loss1: 3.870981 Loss2: 2.284770 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.089448 Loss1: 3.814068 Loss2: 2.275380
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.240052 Loss1: 4.693091 Loss2: 2.546962
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.030305 Loss1: 3.769179 Loss2: 2.261127
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.772390 Loss1: 4.322044 Loss2: 2.450345
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.033571 Loss1: 3.753089 Loss2: 2.280482
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.354126 Loss1: 4.043020 Loss2: 2.311106
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.011510 Loss1: 3.742050 Loss2: 2.269460
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.243465 Loss1: 3.954798 Loss2: 2.288666
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.991652 Loss1: 3.722581 Loss2: 2.269072
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.190981 Loss1: 3.918658 Loss2: 2.272324
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.964920 Loss1: 3.691332 Loss2: 2.273588
+(DefaultActor pid=3764) Epoch: 5 Loss: 6.152439 Loss1: 3.876621 Loss2: 2.275818
+(DefaultActor pid=3765) >> Training accuracy: 0.143750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 6.128333 Loss1: 3.856822 Loss2: 2.271511
+(DefaultActor pid=3764) Epoch: 7 Loss: 6.077478 Loss1: 3.805771 Loss2: 2.271708
+(DefaultActor pid=3764) Epoch: 8 Loss: 6.014235 Loss1: 3.733060 Loss2: 2.281175
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.996519 Loss1: 3.702227 Loss2: 2.294292
+(DefaultActor pid=3764) >> Training accuracy: 0.107292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.171661 Loss1: 4.635104 Loss2: 2.536557
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.552720 Loss1: 4.164377 Loss2: 2.388342
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.309671 Loss1: 4.002199 Loss2: 2.307472
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.206646 Loss1: 3.933991 Loss2: 2.272655
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.161701 Loss1: 3.902683 Loss2: 2.259019
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.055893 Loss1: 4.510388 Loss2: 2.545505
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.125587 Loss1: 3.868824 Loss2: 2.256763
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.411029 Loss1: 4.004062 Loss2: 2.406967
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.109979 Loss1: 3.846207 Loss2: 2.263772
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.086884 Loss1: 3.762529 Loss2: 2.324356
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.073465 Loss1: 3.812326 Loss2: 2.261139
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.997496 Loss1: 3.722405 Loss2: 2.275091
+(DefaultActor pid=3765) Epoch: 8 Loss: 6.084823 Loss1: 3.806532 Loss2: 2.278291
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.945211 Loss1: 3.683070 Loss2: 2.262142
+(DefaultActor pid=3765) Epoch: 9 Loss: 6.057984 Loss1: 3.784317 Loss2: 2.273667
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.908533 Loss1: 3.641167 Loss2: 2.267366
+(DefaultActor pid=3765) >> Training accuracy: 0.094792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.854538 Loss1: 3.595176 Loss2: 2.259362
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.794705 Loss1: 3.520395 Loss2: 2.274310
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.794794 Loss1: 3.498505 Loss2: 2.296289
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.717706 Loss1: 3.440580 Loss2: 2.277126
+(DefaultActor pid=3764) >> Training accuracy: 0.178125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.213911 Loss1: 4.680363 Loss2: 2.533548
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.774469 Loss1: 4.291164 Loss2: 2.483305
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.270601 Loss1: 3.988687 Loss2: 2.281915
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.180628 Loss1: 3.902276 Loss2: 2.278352
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.967719 Loss1: 4.443473 Loss2: 2.524246
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.514890 Loss1: 4.103145 Loss2: 2.411746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.163852 Loss1: 3.838869 Loss2: 2.324983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.065022 Loss1: 3.777764 Loss2: 2.287258 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.995721 Loss1: 3.725123 Loss2: 2.270598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 6.014970 Loss1: 3.730334 Loss2: 2.284636 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.100586
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 6.007014 Loss1: 3.730481 Loss2: 2.276533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.962610 Loss1: 3.683919 Loss2: 2.278691 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.092773
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.989625 Loss1: 4.492349 Loss2: 2.497276 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.120593 Loss1: 3.843326 Loss2: 2.277266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.079006 Loss1: 3.804085 Loss2: 2.274921
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.181620 Loss1: 4.648129 Loss2: 2.533491
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.006323 Loss1: 3.747408 Loss2: 2.258915
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.685569 Loss1: 4.231674 Loss2: 2.453894
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.977309 Loss1: 3.708946 Loss2: 2.268362
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.207360 Loss1: 3.903498 Loss2: 2.303863
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.137814 Loss1: 3.855406 Loss2: 2.282408
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.964624 Loss1: 3.705809 Loss2: 2.258814
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.083649 Loss1: 3.826849 Loss2: 2.256800
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.972669 Loss1: 3.711862 Loss2: 2.260807
+(DefaultActor pid=3764) Epoch: 5 Loss: 6.083215 Loss1: 3.815381 Loss2: 2.267834
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.952184 Loss1: 3.689802 Loss2: 2.262382
+(DefaultActor pid=3764) Epoch: 6 Loss: 6.070061 Loss1: 3.808608 Loss2: 2.261453
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.942261 Loss1: 3.671258 Loss2: 2.271003
+(DefaultActor pid=3765) >> Training accuracy: 0.159007
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 6.012077 Loss1: 3.738924 Loss2: 2.273153 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.118164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.145576 Loss1: 4.591568 Loss2: 2.554008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.059524 Loss1: 3.756231 Loss2: 2.303292 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.983273 Loss1: 3.697950 Loss2: 2.285323
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.180025 Loss1: 4.658500 Loss2: 2.521525
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.950432 Loss1: 3.681686 Loss2: 2.268747
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.592387 Loss1: 4.125542 Loss2: 2.466845
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.914484 Loss1: 3.655438 Loss2: 2.259046
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.148547 Loss1: 3.842844 Loss2: 2.305702
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.892381 Loss1: 3.625403 Loss2: 2.266978
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.052101 Loss1: 3.771008 Loss2: 2.281093
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.878439 Loss1: 3.616365 Loss2: 2.262073
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.005809 Loss1: 3.733280 Loss2: 2.272530
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.865431 Loss1: 3.599052 Loss2: 2.266380
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.968922 Loss1: 3.712544 Loss2: 2.256379
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.832757 Loss1: 3.564582 Loss2: 2.268176
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.958870 Loss1: 3.703643 Loss2: 2.255226
+(DefaultActor pid=3765) >> Training accuracy: 0.120833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.944243 Loss1: 3.675237 Loss2: 2.269006
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.935714 Loss1: 3.680455 Loss2: 2.255259
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.932854 Loss1: 3.666784 Loss2: 2.266070
+(DefaultActor pid=3764) >> Training accuracy: 0.135417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.152187 Loss1: 4.625960 Loss2: 2.526227
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.581698 Loss1: 4.154398 Loss2: 2.427300
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.156885 Loss1: 3.866930 Loss2: 2.289955
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.041232 Loss1: 3.783649 Loss2: 2.257583
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.382555 Loss1: 4.821339 Loss2: 2.561216
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.938643 Loss1: 4.405163 Loss2: 2.533480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.493779 Loss1: 4.165896 Loss2: 2.327884 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.279132 Loss1: 3.983918 Loss2: 2.295214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.253925 Loss1: 3.971184 Loss2: 2.282741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 6.212219 Loss1: 3.928256 Loss2: 2.283963 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.136458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.936466 Loss1: 3.690321 Loss2: 2.246145
+(DefaultActor pid=3764) Epoch: 6 Loss: 6.139661 Loss1: 3.864607 Loss2: 2.275054
+(DefaultActor pid=3764) Epoch: 7 Loss: 6.145165 Loss1: 3.859347 Loss2: 2.285818
+(DefaultActor pid=3764) Epoch: 8 Loss: 6.092483 Loss1: 3.809030 Loss2: 2.283453
+(DefaultActor pid=3764) Epoch: 9 Loss: 6.064030 Loss1: 3.766958 Loss2: 2.297072
+(DefaultActor pid=3764) >> Training accuracy: 0.104167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.214621 Loss1: 4.676474 Loss2: 2.538147
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.549240 Loss1: 4.119652 Loss2: 2.429588
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.212656 Loss1: 3.892913 Loss2: 2.319743
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.113772 Loss1: 3.831893 Loss2: 2.281880
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.896751 Loss1: 4.340740 Loss2: 2.556011
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.268480 Loss1: 3.866587 Loss2: 2.401893 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.948745 Loss1: 3.593974 Loss2: 2.354771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.846087 Loss1: 3.509974 Loss2: 2.336114 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.812407 Loss1: 3.495653 Loss2: 2.316755 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.780797 Loss1: 3.456718 Loss2: 2.324079 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.089583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.723133 Loss1: 3.406896 Loss2: 2.316236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.764972 Loss1: 3.441460 Loss2: 2.323512 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.156250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.225350 Loss1: 4.689721 Loss2: 2.535629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.378551 Loss1: 4.061458 Loss2: 2.317093 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.188882 Loss1: 3.917229 Loss2: 2.271653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.141548 Loss1: 3.866832 Loss2: 2.274715 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.138974 Loss1: 3.877311 Loss2: 2.261663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.115868 Loss1: 3.851701 Loss2: 2.264167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 6.117192 Loss1: 3.849115 Loss2: 2.268077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 6.105393 Loss1: 3.838330 Loss2: 2.267063 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.101562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.950829 Loss1: 3.684106 Loss2: 2.266723 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.979712 Loss1: 3.706565 Loss2: 2.273147 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.104911
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.190710 Loss1: 4.656746 Loss2: 2.533965
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.489152 Loss1: 4.074285 Loss2: 2.414866
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.201815 Loss1: 3.896238 Loss2: 2.305577
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.163559 Loss1: 3.874082 Loss2: 2.289477
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.114285 Loss1: 4.554744 Loss2: 2.559541
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.506265 Loss1: 4.028638 Loss2: 2.477627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.085327 Loss1: 3.743407 Loss2: 2.341920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.079062 Loss1: 3.804695 Loss2: 2.274367
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.942504 Loss1: 3.630262 Loss2: 2.312242
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.062537 Loss1: 3.786662 Loss2: 2.275874
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.909682 Loss1: 3.591185 Loss2: 2.318497
+(DefaultActor pid=3765) Epoch: 8 Loss: 6.039089 Loss1: 3.769985 Loss2: 2.269104
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.849954 Loss1: 3.525251 Loss2: 2.324703
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.843701 Loss1: 3.522789 Loss2: 2.320912
+(DefaultActor pid=3765) Epoch: 9 Loss: 6.014301 Loss1: 3.747548 Loss2: 2.266754
+(DefaultActor pid=3765) >> Training accuracy: 0.125000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.796873 Loss1: 3.477319 Loss2: 2.319553 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.119792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.275325 Loss1: 4.714535 Loss2: 2.560790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.339616 Loss1: 4.032198 Loss2: 2.307418 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.127757 Loss1: 3.839748 Loss2: 2.288010
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.145596 Loss1: 4.594524 Loss2: 2.551072
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.080274 Loss1: 3.804215 Loss2: 2.276059
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.642611 Loss1: 4.182493 Loss2: 2.460118
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.015004 Loss1: 3.748530 Loss2: 2.266474
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.326457 Loss1: 3.990239 Loss2: 2.336218
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.011532 Loss1: 3.727954 Loss2: 2.283578
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.194300 Loss1: 3.893483 Loss2: 2.300817
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.969237 Loss1: 3.680405 Loss2: 2.288832
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.116862 Loss1: 3.822944 Loss2: 2.293918
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.923430 Loss1: 3.625415 Loss2: 2.298015
+(DefaultActor pid=3764) Epoch: 5 Loss: 6.106173 Loss1: 3.826514 Loss2: 2.279659
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.932329 Loss1: 3.645882 Loss2: 2.286447
+(DefaultActor pid=3764) Epoch: 6 Loss: 6.052308 Loss1: 3.766482 Loss2: 2.285826
+(DefaultActor pid=3765) >> Training accuracy: 0.133333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 6.019671 Loss1: 3.730621 Loss2: 2.289050
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.984038 Loss1: 3.691334 Loss2: 2.292704
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.937605 Loss1: 3.639196 Loss2: 2.298409
+(DefaultActor pid=3764) >> Training accuracy: 0.144792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.136243 Loss1: 4.614826 Loss2: 2.521418
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.635895 Loss1: 4.218840 Loss2: 2.417055
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.353589 Loss1: 4.042289 Loss2: 2.311300
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.019168 Loss1: 4.493465 Loss2: 2.525703
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.241870 Loss1: 3.970135 Loss2: 2.271735
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.383961 Loss1: 4.000970 Loss2: 2.382990
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.208028 Loss1: 3.936542 Loss2: 2.271486
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.139002 Loss1: 3.811059 Loss2: 2.327944
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.226305 Loss1: 3.948728 Loss2: 2.277577
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.022970 Loss1: 3.747508 Loss2: 2.275462
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.192274 Loss1: 3.917312 Loss2: 2.274962
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.171634 Loss1: 3.897760 Loss2: 2.273874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 6.159508 Loss1: 3.883811 Loss2: 2.275696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 6.142049 Loss1: 3.856993 Loss2: 2.285056 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.133789
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.937371 Loss1: 3.672004 Loss2: 2.265367 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.154167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.196278 Loss1: 4.628571 Loss2: 2.567707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.392679 Loss1: 4.002028 Loss2: 2.390651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.202050 Loss1: 3.921360 Loss2: 2.280690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.169333 Loss1: 3.887614 Loss2: 2.281719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 6.167543 Loss1: 3.891992 Loss2: 2.275551 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 6.117102 Loss1: 3.843067 Loss2: 2.274035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 6.094176 Loss1: 3.826054 Loss2: 2.268122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 6.089311 Loss1: 3.830210 Loss2: 2.259101
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.924723 Loss1: 3.660154 Loss2: 2.264568
+(DefaultActor pid=3765) >> Training accuracy: 0.102865
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.931353 Loss1: 3.646852 Loss2: 2.284501
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.859534 Loss1: 3.579844 Loss2: 2.279690
+DEBUG flwr 2023-10-08 12:23:44,163 | server.py:236 | fit_round 1 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.852454 Loss1: 3.583233 Loss2: 2.269220
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.819475 Loss1: 3.538484 Loss2: 2.280991
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.015870 Loss1: 4.487405 Loss2: 2.528464
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.791314 Loss1: 3.507455 Loss2: 2.283859
+(DefaultActor pid=3764) >> Training accuracy: 0.167708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.064575 Loss1: 3.746441 Loss2: 2.318134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.917632 Loss1: 3.626283 Loss2: 2.291349 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.178667 Loss1: 4.624337 Loss2: 2.554330
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.924207 Loss1: 3.635186 Loss2: 2.289021
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.591751 Loss1: 4.134132 Loss2: 2.457620
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.910531 Loss1: 3.616131 Loss2: 2.294400
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.148717 Loss1: 3.795228 Loss2: 2.353489
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.856889 Loss1: 3.559833 Loss2: 2.297056
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.877022 Loss1: 3.581382 Loss2: 2.295640 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.851040 Loss1: 3.560739 Loss2: 2.290301 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.125977
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.974001 Loss1: 3.655436 Loss2: 2.318564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.940438 Loss1: 3.629637 Loss2: 2.310801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.930029 Loss1: 3.618528 Loss2: 2.311501
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.184542 Loss1: 4.603412 Loss2: 2.581130
+(DefaultActor pid=3764) >> Training accuracy: 0.089583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 7.117036 Loss1: 4.613428 Loss2: 2.503608
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.479479 Loss1: 4.107953 Loss2: 2.371526
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.294121 Loss1: 3.966963 Loss2: 2.327159
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.209696 Loss1: 3.903325 Loss2: 2.306371
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.161352 Loss1: 3.864282 Loss2: 2.297070
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.033862 Loss1: 4.487883 Loss2: 2.545979
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.527542 Loss1: 4.055054 Loss2: 2.472488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.992077 Loss1: 3.678016 Loss2: 2.314061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.889023 Loss1: 3.604848 Loss2: 2.284174 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.097356
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.807133 Loss1: 3.541518 Loss2: 2.265615 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.773297 Loss1: 3.505598 Loss2: 2.267699 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.166290 Loss1: 4.609559 Loss2: 2.556731
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.751809 Loss1: 3.471298 Loss2: 2.280510
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.482598 Loss1: 4.050295 Loss2: 2.432302
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.685450 Loss1: 3.407820 Loss2: 2.277630
+(DefaultActor pid=3764) >> Training accuracy: 0.188542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.046612 Loss1: 3.766048 Loss2: 2.280564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.000233 Loss1: 3.738040 Loss2: 2.262193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.998445 Loss1: 3.733374 Loss2: 2.265071
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.181176 Loss1: 4.629001 Loss2: 2.552175
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.987026 Loss1: 3.724103 Loss2: 2.262924
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.556262 Loss1: 4.127262 Loss2: 2.429000
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.952155 Loss1: 3.683132 Loss2: 2.269023
+(DefaultActor pid=3764) Epoch: 2 Loss: 6.192904 Loss1: 3.877822 Loss2: 2.315081
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.959764 Loss1: 3.689728 Loss2: 2.270036
+(DefaultActor pid=3764) Epoch: 3 Loss: 6.124613 Loss1: 3.813027 Loss2: 2.311585
+(DefaultActor pid=3765) >> Training accuracy: 0.109375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 6.024185 Loss1: 3.722135 Loss2: 2.302050
+(DefaultActor pid=3764) Epoch: 5 Loss: 6.011222 Loss1: 3.717666 Loss2: 2.293556
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.999209 Loss1: 3.688202 Loss2: 2.311007
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.972166 Loss1: 3.674102 Loss2: 2.298063
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.981482 Loss1: 3.669964 Loss2: 2.311518
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.946748 Loss1: 3.647218 Loss2: 2.299530
+(DefaultActor pid=3764) >> Training accuracy: 0.162500
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 12:23:44,163][flwr][DEBUG] - fit_round 1 received 50 results and 0 failures
+WARNING flwr 2023-10-08 12:23:48,551 | fedavg.py:242 | No fit_metrics_aggregation_fn provided
+[2023-10-08 12:23:48,551][flwr][WARNING] - No fit_metrics_aggregation_fn provided
+INFO flwr 2023-10-08 12:24:27,083 | server.py:125 | fit progress: (1, 4.678643322600343, {'accuracy': 0.01}, 1974.861092187)
+>> Test accuracy: 0.010000
+[2023-10-08 12:24:27,083][flwr][INFO] - fit progress: (1, 4.678643322600343, {'accuracy': 0.01}, 1974.861092187)
+DEBUG flwr 2023-10-08 12:24:27,083 | server.py:173 | evaluate_round 1: strategy sampled 50 clients (out of 50)
+[2023-10-08 12:24:27,083][flwr][DEBUG] - evaluate_round 1: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 12:33:31,389 | server.py:187 | evaluate_round 1 received 50 results and 0 failures
+[2023-10-08 12:33:31,389][flwr][DEBUG] - evaluate_round 1 received 50 results and 0 failures
+WARNING flwr 2023-10-08 12:33:31,389 | fedavg.py:273 | No evaluate_metrics_aggregation_fn provided
+[2023-10-08 12:33:31,389][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
+DEBUG flwr 2023-10-08 12:33:31,389 | server.py:222 | fit_round 2: strategy sampled 50 clients (out of 50)
+[2023-10-08 12:33:31,389][flwr][DEBUG] - fit_round 2: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.637836 Loss1: 4.190580 Loss2: 6.447256
+(DefaultActor pid=3765) Epoch: 1 Loss: 9.976554 Loss1: 3.921571 Loss2: 6.054984
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.692835 Loss1: 3.727470 Loss2: 5.965365
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.640974 Loss1: 3.700444 Loss2: 5.940530
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.594960 Loss1: 4.241553 Loss2: 6.353407
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.594271 Loss1: 3.694578 Loss2: 5.899693
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.841622 Loss1: 3.874930 Loss2: 5.966692
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.507963 Loss1: 3.643234 Loss2: 5.864729
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.613104 Loss1: 3.770304 Loss2: 5.842801
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.459440 Loss1: 3.612508 Loss2: 5.846931
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.488919 Loss1: 3.701728 Loss2: 5.787192
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.453676 Loss1: 3.607583 Loss2: 5.846092
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.427125 Loss1: 3.663602 Loss2: 5.763523
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.425889 Loss1: 3.600830 Loss2: 5.825059
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.340038 Loss1: 3.626946 Loss2: 5.713092
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.439987 Loss1: 3.615669 Loss2: 5.824318
+(DefaultActor pid=3765) >> Training accuracy: 0.115625
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.319818 Loss1: 3.606650 Loss2: 5.713168
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.324199 Loss1: 3.620062 Loss2: 5.704137
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.314916 Loss1: 3.616407 Loss2: 5.698508
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.207206 Loss1: 3.564857 Loss2: 5.642349
+(DefaultActor pid=3764) >> Training accuracy: 0.175000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.771675 Loss1: 4.280236 Loss2: 6.491439
+(DefaultActor pid=3765) Epoch: 1 Loss: 10.018878 Loss1: 3.975184 Loss2: 6.043695
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.807585 Loss1: 3.922243 Loss2: 5.885342
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.824159 Loss1: 4.297789 Loss2: 6.526370
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.717546 Loss1: 3.855670 Loss2: 5.861875
+(DefaultActor pid=3764) Epoch: 1 Loss: 10.015044 Loss1: 3.921217 Loss2: 6.093827
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.673834 Loss1: 3.831498 Loss2: 5.842335
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.816785 Loss1: 3.864733 Loss2: 5.952052
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.649830 Loss1: 3.823077 Loss2: 5.826753
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.621138 Loss1: 3.810802 Loss2: 5.810336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.638162 Loss1: 3.806122 Loss2: 5.832040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.578147 Loss1: 3.773955 Loss2: 5.804191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.536077 Loss1: 3.748276 Loss2: 5.787801 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.119792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.474311 Loss1: 3.605538 Loss2: 5.868773 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.137277
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.652685 Loss1: 4.252413 Loss2: 6.400272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.763214 Loss1: 3.864845 Loss2: 5.898369 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.654279 Loss1: 3.793074 Loss2: 5.861205
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.641576 Loss1: 4.212208 Loss2: 6.429368
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.967749 Loss1: 3.908536 Loss2: 6.059214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.691604 Loss1: 3.758919 Loss2: 5.932685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.647544 Loss1: 3.754553 Loss2: 5.892991 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.558958 Loss1: 3.697666 Loss2: 5.861293 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.538385 Loss1: 3.676304 Loss2: 5.862081 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.130208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.487256 Loss1: 3.657618 Loss2: 5.829637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.407890 Loss1: 3.589601 Loss2: 5.818289 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.152344
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.601140 Loss1: 4.224583 Loss2: 6.376557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.736439 Loss1: 3.946441 Loss2: 5.789997 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.719372 Loss1: 4.297521 Loss2: 6.421851 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 10.000176 Loss1: 3.953815 Loss2: 6.046362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.770465 Loss1: 3.821641 Loss2: 5.948824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.700196 Loss1: 3.795949 Loss2: 5.904247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.619483 Loss1: 3.741422 Loss2: 5.878061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.580794 Loss1: 3.726423 Loss2: 5.854370 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.101042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.525874 Loss1: 3.706135 Loss2: 5.819739 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.477863 Loss1: 3.662309 Loss2: 5.815554 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.125000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 10.107184 Loss1: 4.061199 Loss2: 6.045985 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.882278 Loss1: 3.970554 Loss2: 5.911724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.713988 Loss1: 4.202564 Loss2: 6.511424
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.826803 Loss1: 3.959741 Loss2: 5.867061
+(DefaultActor pid=3764) Epoch: 1 Loss: 10.117976 Loss1: 3.992430 Loss2: 6.125546
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.792426 Loss1: 3.941595 Loss2: 5.850831
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.934952 Loss1: 3.892701 Loss2: 6.042251
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.797875 Loss1: 3.931617 Loss2: 5.866258
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.850462 Loss1: 3.853579 Loss2: 5.996883
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.775853 Loss1: 3.933323 Loss2: 5.842530
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.750509 Loss1: 3.818110 Loss2: 5.932399
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.762758 Loss1: 3.920850 Loss2: 5.841909
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.746376 Loss1: 3.799555 Loss2: 5.946821
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.734156 Loss1: 3.904936 Loss2: 5.829220
+(DefaultActor pid=3765) >> Training accuracy: 0.115234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.672107 Loss1: 3.743145 Loss2: 5.928962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.644898 Loss1: 3.728153 Loss2: 5.916744 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.099609
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 9.795480 Loss1: 3.750212 Loss2: 6.045268 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.488928 Loss1: 3.632664 Loss2: 5.856264 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.425572 Loss1: 3.598649 Loss2: 5.826923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.398169 Loss1: 3.590263 Loss2: 5.807906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.340383 Loss1: 3.536039 Loss2: 5.804344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.326616 Loss1: 3.537983 Loss2: 5.788633 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.550909 Loss1: 3.859723 Loss2: 5.691185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.492956 Loss1: 3.809476 Loss2: 5.683480 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.113281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.416769 Loss1: 3.764166 Loss2: 5.652603 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.090402
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.833566 Loss1: 4.327127 Loss2: 6.506439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.946128 Loss1: 3.860144 Loss2: 6.085984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.839081 Loss1: 4.220955 Loss2: 6.618126
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.859521 Loss1: 3.821896 Loss2: 6.037624
+(DefaultActor pid=3764) Epoch: 1 Loss: 10.117047 Loss1: 3.900791 Loss2: 6.216255
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.740665 Loss1: 3.722306 Loss2: 6.018360
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.893418 Loss1: 3.846798 Loss2: 6.046620
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.759525 Loss1: 3.730355 Loss2: 6.029170
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.815371 Loss1: 3.830314 Loss2: 5.985056
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.698248 Loss1: 3.690235 Loss2: 6.008013
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.653507 Loss1: 3.651367 Loss2: 6.002140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.629687 Loss1: 3.615256 Loss2: 6.014431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.587763 Loss1: 3.589767 Loss2: 5.997996 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.147461
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.598274 Loss1: 3.710448 Loss2: 5.887827 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.098958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.714870 Loss1: 4.268578 Loss2: 6.446293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.824622 Loss1: 3.931455 Loss2: 5.893168 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.720104 Loss1: 3.864763 Loss2: 5.855341
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.858602 Loss1: 4.382486 Loss2: 6.476115
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.669850 Loss1: 3.865755 Loss2: 5.804095
+(DefaultActor pid=3764) Epoch: 1 Loss: 10.101550 Loss1: 3.990484 Loss2: 6.111066
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.631948 Loss1: 3.838572 Loss2: 5.793375
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.893639 Loss1: 3.913948 Loss2: 5.979691
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.613233 Loss1: 3.832063 Loss2: 5.781170
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.810846 Loss1: 3.846251 Loss2: 5.964595
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.719163 Loss1: 3.800570 Loss2: 5.918593
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.554400 Loss1: 3.810565 Loss2: 5.743835
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.701065 Loss1: 3.802588 Loss2: 5.898477
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.552926 Loss1: 3.784079 Loss2: 5.768847
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.674050 Loss1: 3.796071 Loss2: 5.877979
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.477926 Loss1: 3.733268 Loss2: 5.744657
+(DefaultActor pid=3765) >> Training accuracy: 0.104167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.645398 Loss1: 3.796198 Loss2: 5.849200 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.103795
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.554327 Loss1: 4.122124 Loss2: 6.432204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.413669 Loss1: 3.507592 Loss2: 5.906077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.383995 Loss1: 3.541977 Loss2: 5.842017
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.459539 Loss1: 4.115703 Loss2: 6.343837
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.712369 Loss1: 3.787863 Loss2: 5.924506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.459639 Loss1: 3.653027 Loss2: 5.806611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.377694 Loss1: 3.605024 Loss2: 5.772671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.325220 Loss1: 3.616160 Loss2: 5.709060 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.294610 Loss1: 3.593041 Loss2: 5.701569 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.248958
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.120221 Loss1: 3.361153 Loss2: 5.759068
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.260141 Loss1: 3.565868 Loss2: 5.694273
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.225864 Loss1: 3.549637 Loss2: 5.676227
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.156384 Loss1: 3.497253 Loss2: 5.659131
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.152884 Loss1: 3.491014 Loss2: 5.661870
+(DefaultActor pid=3764) >> Training accuracy: 0.111458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.541196 Loss1: 4.225392 Loss2: 6.315804
+(DefaultActor pid=3765) Epoch: 1 Loss: 9.777527 Loss1: 3.935185 Loss2: 5.842342
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.563549 Loss1: 3.828953 Loss2: 5.734595
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.442931 Loss1: 3.752719 Loss2: 5.690212
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.793967 Loss1: 4.285539 Loss2: 6.508428
+(DefaultActor pid=3764) Epoch: 1 Loss: 10.103335 Loss1: 4.007536 Loss2: 6.095799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.353892 Loss1: 3.719582 Loss2: 5.634310
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.866446 Loss1: 3.931696 Loss2: 5.934750
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.741218 Loss1: 3.877523 Loss2: 5.863695
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.313694 Loss1: 3.713614 Loss2: 5.600080
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.302611 Loss1: 3.709807 Loss2: 5.592804 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.318756 Loss1: 3.692372 Loss2: 5.626384 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.261945 Loss1: 3.675049 Loss2: 5.586896 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.106250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.530101 Loss1: 3.750192 Loss2: 5.779909 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.106971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.744404 Loss1: 4.296521 Loss2: 6.447883
+(DefaultActor pid=3765) Epoch: 1 Loss: 9.937258 Loss1: 3.930883 Loss2: 6.006375
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.737718 Loss1: 3.785188 Loss2: 5.952531
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.627638 Loss1: 3.731983 Loss2: 5.895654
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.682749 Loss1: 4.142052 Loss2: 6.540697
+(DefaultActor pid=3764) Epoch: 1 Loss: 10.009344 Loss1: 3.843287 Loss2: 6.166057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.788435 Loss1: 3.742180 Loss2: 6.046254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.734535 Loss1: 3.736097 Loss2: 5.998438 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.659610 Loss1: 3.694191 Loss2: 5.965419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.588574 Loss1: 3.666707 Loss2: 5.921867 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.138542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.445799 Loss1: 3.632870 Loss2: 5.812929
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.569317 Loss1: 3.660271 Loss2: 5.909046
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.525640 Loss1: 3.615687 Loss2: 5.909954
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.493718 Loss1: 3.603991 Loss2: 5.889727
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.453791 Loss1: 3.570200 Loss2: 5.883591
+(DefaultActor pid=3764) >> Training accuracy: 0.119792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.657896 Loss1: 4.177572 Loss2: 6.480324
+(DefaultActor pid=3765) Epoch: 1 Loss: 10.003504 Loss1: 3.919831 Loss2: 6.083673
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.748192 Loss1: 3.780401 Loss2: 5.967791
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.664308 Loss1: 3.757226 Loss2: 5.907081
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.356075 Loss1: 4.164318 Loss2: 6.191757
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.614167 Loss1: 3.718640 Loss2: 5.895527
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.538110 Loss1: 3.789763 Loss2: 5.748347
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.536380 Loss1: 3.690014 Loss2: 5.846366
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.367953 Loss1: 3.703727 Loss2: 5.664226
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.541355 Loss1: 3.670259 Loss2: 5.871095
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.272350 Loss1: 3.676055 Loss2: 5.596296
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.465765 Loss1: 3.641314 Loss2: 5.824452
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.213708 Loss1: 3.641877 Loss2: 5.571830
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.441137 Loss1: 3.585224 Loss2: 5.855913
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.129308 Loss1: 3.578903 Loss2: 5.550405
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.391586 Loss1: 3.548695 Loss2: 5.842891
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.094977 Loss1: 3.549146 Loss2: 5.545831
+(DefaultActor pid=3765) >> Training accuracy: 0.137500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.064834 Loss1: 3.511552 Loss2: 5.553283
+(DefaultActor pid=3764) Epoch: 8 Loss: 8.999669 Loss1: 3.472173 Loss2: 5.527496
+(DefaultActor pid=3764) Epoch: 9 Loss: 8.946095 Loss1: 3.431198 Loss2: 5.514897
+(DefaultActor pid=3764) >> Training accuracy: 0.153125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.575111 Loss1: 4.096109 Loss2: 6.479002
+(DefaultActor pid=3765) Epoch: 1 Loss: 9.911172 Loss1: 3.847541 Loss2: 6.063631
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.718339 Loss1: 3.729885 Loss2: 5.988455
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.618442 Loss1: 3.691965 Loss2: 5.926476
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.836223 Loss1: 4.347470 Loss2: 6.488752
+(DefaultActor pid=3764) Epoch: 1 Loss: 10.052698 Loss1: 3.993670 Loss2: 6.059028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.872723 Loss1: 3.929094 Loss2: 5.943629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.727139 Loss1: 3.845502 Loss2: 5.881636 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.715427 Loss1: 3.885239 Loss2: 5.830188 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.647742 Loss1: 3.799690 Loss2: 5.848052 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.166667
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.107588 Loss1: 3.480518 Loss2: 5.627070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.649327 Loss1: 3.801738 Loss2: 5.847589
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.597159 Loss1: 3.786555 Loss2: 5.810604
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.516748 Loss1: 3.708655 Loss2: 5.808094
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.530191 Loss1: 3.734705 Loss2: 5.795485
+(DefaultActor pid=3764) >> Training accuracy: 0.115625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.686062 Loss1: 4.196250 Loss2: 6.489812
+(DefaultActor pid=3765) Epoch: 1 Loss: 9.989050 Loss1: 3.928703 Loss2: 6.060347
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.728920 Loss1: 3.821730 Loss2: 5.907190
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.687483 Loss1: 3.805944 Loss2: 5.881539
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.503141 Loss1: 4.138347 Loss2: 6.364794
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.831063 Loss1: 3.874199 Loss2: 5.956864 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.561377 Loss1: 3.722831 Loss2: 5.838545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.495142 Loss1: 3.716147 Loss2: 5.778995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.468325 Loss1: 3.679229 Loss2: 5.789096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.441754 Loss1: 3.656899 Loss2: 5.784855 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.115625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.319609 Loss1: 3.605931 Loss2: 5.713678 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.285955 Loss1: 3.578991 Loss2: 5.706963 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.136719
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 9.785292 Loss1: 4.004951 Loss2: 5.780341 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.454764 Loss1: 3.838589 Loss2: 5.616174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 11.029210 Loss1: 4.420167 Loss2: 6.609043
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.382492 Loss1: 3.785829 Loss2: 5.596663
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.287707 Loss1: 3.721356 Loss2: 5.566351 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.282151 Loss1: 3.721080 Loss2: 5.561071 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.270940 Loss1: 3.730857 Loss2: 5.540083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.815740 Loss1: 3.849322 Loss2: 5.966418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.775648 Loss1: 3.834996 Loss2: 5.940652 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.145833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.727230 Loss1: 3.774140 Loss2: 5.953091 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.102865
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.681232 Loss1: 4.270233 Loss2: 6.410999 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.726148 Loss1: 3.857509 Loss2: 5.868639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.614128 Loss1: 3.807013 Loss2: 5.807115
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.534492 Loss1: 4.114267 Loss2: 6.420225
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.855433 Loss1: 3.796730 Loss2: 6.058703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.624794 Loss1: 3.689479 Loss2: 5.935316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.548176 Loss1: 3.650949 Loss2: 5.897227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.446170 Loss1: 3.572997 Loss2: 5.873172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.421490 Loss1: 3.566178 Loss2: 5.855312 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.109375
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.345133 Loss1: 3.654656 Loss2: 5.690477
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.382988 Loss1: 3.518498 Loss2: 5.864490
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.372723 Loss1: 3.482304 Loss2: 5.890419
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.347202 Loss1: 3.459721 Loss2: 5.887481
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.322032 Loss1: 3.435241 Loss2: 5.886791
+(DefaultActor pid=3764) >> Training accuracy: 0.179167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.331028 Loss1: 4.164000 Loss2: 6.167028
+(DefaultActor pid=3765) Epoch: 1 Loss: 9.695290 Loss1: 3.895446 Loss2: 5.799843
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.430650 Loss1: 3.751587 Loss2: 5.679062
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.310185 Loss1: 3.703583 Loss2: 5.606602
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.669753 Loss1: 4.189651 Loss2: 6.480102
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.959448 Loss1: 3.896816 Loss2: 6.062632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.726233 Loss1: 3.762142 Loss2: 5.964091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.605897 Loss1: 3.729162 Loss2: 5.876735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.563627 Loss1: 3.670131 Loss2: 5.893496 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.534119 Loss1: 3.684701 Loss2: 5.849417 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.153125
+(DefaultActor pid=3765) Epoch: 9 Loss: 8.732340 Loss1: 3.445579 Loss2: 5.286761
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.491700 Loss1: 3.639489 Loss2: 5.852211
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.518808 Loss1: 3.650798 Loss2: 5.868010
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.435658 Loss1: 3.589059 Loss2: 5.846600
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.434950 Loss1: 3.592908 Loss2: 5.842043
+(DefaultActor pid=3764) >> Training accuracy: 0.157292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.848761 Loss1: 4.324665 Loss2: 6.524096
+(DefaultActor pid=3765) Epoch: 1 Loss: 10.072184 Loss1: 3.884131 Loss2: 6.188053
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.821466 Loss1: 3.761192 Loss2: 6.060273
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.776668 Loss1: 3.784546 Loss2: 5.992122
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.688833 Loss1: 3.726467 Loss2: 5.962366
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.621974 Loss1: 3.684707 Loss2: 5.937267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.558368 Loss1: 3.645302 Loss2: 5.913067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.552690 Loss1: 3.635356 Loss2: 5.917335 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.476609 Loss1: 3.561661 Loss2: 5.914947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.263857 Loss1: 3.766380 Loss2: 5.497477
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.475863 Loss1: 3.564449 Loss2: 5.911414
+(DefaultActor pid=3765) >> Training accuracy: 0.138221
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.178989 Loss1: 3.744039 Loss2: 5.434949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.184733 Loss1: 3.749408 Loss2: 5.435325 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.772070 Loss1: 4.302489 Loss2: 6.469581
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.173345 Loss1: 3.721502 Loss2: 5.451843
+(DefaultActor pid=3764) >> Training accuracy: 0.108398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.875539 Loss1: 3.954466 Loss2: 5.921073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.686421 Loss1: 3.848566 Loss2: 5.837855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.620740 Loss1: 3.796890 Loss2: 5.823850
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.718717 Loss1: 4.255756 Loss2: 6.462961
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.904809 Loss1: 3.954151 Loss2: 5.950658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.654817 Loss1: 3.810745 Loss2: 5.844072 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.590017 Loss1: 3.780618 Loss2: 5.809399 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.108333
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.497835 Loss1: 3.663922 Loss2: 5.833913
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.528583 Loss1: 3.754888 Loss2: 5.773695
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.488371 Loss1: 3.740890 Loss2: 5.747481
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.448910 Loss1: 3.706955 Loss2: 5.741955
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.457279 Loss1: 3.712904 Loss2: 5.744375
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.461905 Loss1: 3.704660 Loss2: 5.757245
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.589808 Loss1: 4.269219 Loss2: 6.320589
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.476294 Loss1: 3.696930 Loss2: 5.779364
+(DefaultActor pid=3764) >> Training accuracy: 0.129167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.686617 Loss1: 3.882598 Loss2: 5.804019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.531678 Loss1: 3.817210 Loss2: 5.714468 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.413258 Loss1: 3.731065 Loss2: 5.682193
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.573467 Loss1: 4.215427 Loss2: 6.358040
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.830434 Loss1: 3.878489 Loss2: 5.951945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.598990 Loss1: 3.808078 Loss2: 5.790912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.525582 Loss1: 3.753918 Loss2: 5.771664 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.080208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.465058 Loss1: 3.719705 Loss2: 5.745353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.331377 Loss1: 3.630269 Loss2: 5.701109 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.222510 Loss1: 3.568976 Loss2: 5.653534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.235193 Loss1: 3.603144 Loss2: 5.632049 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.138542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.740749 Loss1: 3.842407 Loss2: 5.898342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.588993 Loss1: 3.755523 Loss2: 5.833471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.557939 Loss1: 3.744185 Loss2: 5.813753
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.604908 Loss1: 4.151583 Loss2: 6.453326
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.824718 Loss1: 3.828602 Loss2: 5.996116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.611880 Loss1: 3.724438 Loss2: 5.887442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.508505 Loss1: 3.682727 Loss2: 5.825778 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.136458
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.436936 Loss1: 3.608846 Loss2: 5.828090
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.527967 Loss1: 3.699391 Loss2: 5.828576
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.450804 Loss1: 3.642496 Loss2: 5.808309
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.395119 Loss1: 3.611091 Loss2: 5.784029
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.405193 Loss1: 3.615323 Loss2: 5.789870
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.347845 Loss1: 3.574632 Loss2: 5.773213
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.577079 Loss1: 4.189072 Loss2: 6.388008
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.326152 Loss1: 3.575595 Loss2: 5.750557
+(DefaultActor pid=3764) >> Training accuracy: 0.145833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.711740 Loss1: 3.780396 Loss2: 5.931343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.587536 Loss1: 3.743034 Loss2: 5.844502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.406955 Loss1: 4.075046 Loss2: 6.331909
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.517713 Loss1: 3.700468 Loss2: 5.817244
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.595434 Loss1: 3.707498 Loss2: 5.887936
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.492055 Loss1: 3.716743 Loss2: 5.775312
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.408548 Loss1: 3.618060 Loss2: 5.790488
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.466571 Loss1: 3.680417 Loss2: 5.786154
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.290920 Loss1: 3.579918 Loss2: 5.711002
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.436718 Loss1: 3.660235 Loss2: 5.776483
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.420137 Loss1: 3.644818 Loss2: 5.775319 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.119141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.174120 Loss1: 3.510401 Loss2: 5.663719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.138565 Loss1: 3.454635 Loss2: 5.683930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.054536 Loss1: 3.417136 Loss2: 5.637400
+(DefaultActor pid=3764) >> Training accuracy: 0.193750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.782680 Loss1: 4.165881 Loss2: 6.616799
+(DefaultActor pid=3765) Epoch: 1 Loss: 10.090299 Loss1: 3.825660 Loss2: 6.264639
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.937680 Loss1: 3.821735 Loss2: 6.115944
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.843707 Loss1: 3.785454 Loss2: 6.058253
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.830243 Loss1: 3.790780 Loss2: 6.039462
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.319664 Loss1: 4.140807 Loss2: 6.178857
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.746831 Loss1: 3.719699 Loss2: 6.027133
+(DefaultActor pid=3765) Epoch: 6 Loss: 9.702945 Loss1: 3.691170 Loss2: 6.011775 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 9.679168 Loss1: 3.676987 Loss2: 6.002181 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 9.649411 Loss1: 3.666260 Loss2: 5.983150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.634332 Loss1: 3.659953 Loss2: 5.974379 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.132292
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-08 13:02:33,601 | server.py:236 | fit_round 2 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.015283 Loss1: 3.514796 Loss2: 5.500487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 8.939481 Loss1: 3.451104 Loss2: 5.488377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 8.937252 Loss1: 3.410933 Loss2: 5.526319
+(DefaultActor pid=3764) >> Training accuracy: 0.188542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.551498 Loss1: 4.195489 Loss2: 6.356010
+(DefaultActor pid=3765) Epoch: 1 Loss: 9.807654 Loss1: 3.888830 Loss2: 5.918824
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.591351 Loss1: 3.790048 Loss2: 5.801303
+(DefaultActor pid=3765) Epoch: 3 Loss: 9.427212 Loss1: 3.693550 Loss2: 5.733662
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.367227 Loss1: 3.681224 Loss2: 5.686003
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.645859 Loss1: 4.267877 Loss2: 6.377981
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.831931 Loss1: 3.871746 Loss2: 5.960185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.629921 Loss1: 3.760460 Loss2: 5.869461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.565801 Loss1: 3.736602 Loss2: 5.829199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.564130 Loss1: 3.739203 Loss2: 5.824927 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.126953
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.516901 Loss1: 3.702281 Loss2: 5.814620 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.474562 Loss1: 3.674293 Loss2: 5.800269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 10.639294 Loss1: 4.150609 Loss2: 6.488685 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.106618
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 9.703857 Loss1: 3.746675 Loss2: 5.957182 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 9.583931 Loss1: 3.689402 Loss2: 5.894529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 9.565596 Loss1: 3.658388 Loss2: 5.907209
+(DefaultActor pid=3764) Epoch: 0 Loss: 10.382244 Loss1: 3.952499 Loss2: 6.429745
+(DefaultActor pid=3764) Epoch: 1 Loss: 9.695833 Loss1: 3.584588 Loss2: 6.111245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 9.446024 Loss1: 3.507743 Loss2: 5.938281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 9.310786 Loss1: 3.422200 Loss2: 5.888586 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.114583
+(DefaultActor pid=3765) Epoch: 9 Loss: 9.477358 Loss1: 3.599002 Loss2: 5.878356
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 9.240992 Loss1: 3.403801 Loss2: 5.837191
+(DefaultActor pid=3764) Epoch: 5 Loss: 9.207359 Loss1: 3.411104 Loss2: 5.796255
+(DefaultActor pid=3764) Epoch: 6 Loss: 9.168722 Loss1: 3.373243 Loss2: 5.795479
+(DefaultActor pid=3764) Epoch: 7 Loss: 9.120609 Loss1: 3.347399 Loss2: 5.773210
+(DefaultActor pid=3764) Epoch: 8 Loss: 9.085291 Loss1: 3.335649 Loss2: 5.749642
+(DefaultActor pid=3764) Epoch: 9 Loss: 9.104629 Loss1: 3.329757 Loss2: 5.774872
+(DefaultActor pid=3764) >> Training accuracy: 0.159375
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 13:02:33,601][flwr][DEBUG] - fit_round 2 received 50 results and 0 failures
+INFO flwr 2023-10-08 13:03:15,667 | server.py:125 | fit progress: (2, 4.821835554445895, {'accuracy': 0.01}, 4303.445578031)
+>> Test accuracy: 0.010000
+[2023-10-08 13:03:15,667][flwr][INFO] - fit progress: (2, 4.821835554445895, {'accuracy': 0.01}, 4303.445578031)
+DEBUG flwr 2023-10-08 13:03:15,667 | server.py:173 | evaluate_round 2: strategy sampled 50 clients (out of 50)
+[2023-10-08 13:03:15,667][flwr][DEBUG] - evaluate_round 2: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 13:12:22,946 | server.py:187 | evaluate_round 2 received 50 results and 0 failures
+[2023-10-08 13:12:22,946][flwr][DEBUG] - evaluate_round 2 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 13:12:22,946 | server.py:222 | fit_round 3: strategy sampled 50 clients (out of 50)
+[2023-10-08 13:12:22,946][flwr][DEBUG] - fit_round 3: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.509499 Loss1: 4.291107 Loss2: 2.218391
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.782715 Loss1: 3.970325 Loss2: 1.812390
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.608737 Loss1: 3.888419 Loss2: 1.720317
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.553848 Loss1: 3.857506 Loss2: 1.696343
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.473258 Loss1: 3.770481 Loss2: 1.702777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.440834 Loss1: 3.777198 Loss2: 1.663636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.442849 Loss1: 3.783813 Loss2: 1.659036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.369352 Loss1: 3.702770 Loss2: 1.666583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.406806 Loss1: 3.736604 Loss2: 1.670203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.388356 Loss1: 3.725838 Loss2: 1.662518 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.115625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.558604 Loss1: 3.449669 Loss2: 2.108935 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.192708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 7.128228 Loss1: 4.188956 Loss2: 2.939271 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 6.130828 Loss1: 3.639144 Loss2: 2.491685 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.037510 Loss1: 3.609220 Loss2: 2.428290
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.839636 Loss1: 4.293334 Loss2: 2.546302
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.900643 Loss1: 3.756669 Loss2: 2.143975 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.728970 Loss1: 3.624116 Loss2: 2.104854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.672135 Loss1: 3.578416 Loss2: 2.093719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.575873 Loss1: 3.520588 Loss2: 2.055285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.519092 Loss1: 3.465894 Loss2: 2.053199 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.154167
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.886233 Loss1: 3.518525 Loss2: 2.367708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.454450 Loss1: 3.429420 Loss2: 2.025031
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.447165 Loss1: 3.422253 Loss2: 2.024912
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.494466 Loss1: 3.436932 Loss2: 2.057534
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.409122 Loss1: 3.400326 Loss2: 2.008796
+(DefaultActor pid=3764) >> Training accuracy: 0.187500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.118442 Loss1: 4.318853 Loss2: 1.799589
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.366549 Loss1: 3.922341 Loss2: 1.444207
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.184735 Loss1: 3.776878 Loss2: 1.407857
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.104487 Loss1: 3.703748 Loss2: 1.400739
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.720279 Loss1: 4.255459 Loss2: 2.464820
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.828644 Loss1: 3.751709 Loss2: 2.076936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.658790 Loss1: 3.640234 Loss2: 2.018556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.560093 Loss1: 3.580053 Loss2: 1.980041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.568995 Loss1: 3.561187 Loss2: 2.007808 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.553685 Loss1: 3.562483 Loss2: 1.991202 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.119792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.495091 Loss1: 3.530910 Loss2: 1.964181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.383349 Loss1: 3.447998 Loss2: 1.935351 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.164583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.870425 Loss1: 4.356579 Loss2: 2.513846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.867490 Loss1: 3.805292 Loss2: 2.062198 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.694060 Loss1: 3.707523 Loss2: 1.986537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.685203 Loss1: 3.706526 Loss2: 1.978677 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.659501 Loss1: 3.684159 Loss2: 1.975342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.620306 Loss1: 3.663343 Loss2: 1.956963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.607583 Loss1: 3.659464 Loss2: 1.948119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.559990 Loss1: 3.632932 Loss2: 1.927058 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.132812
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.724758 Loss1: 3.668533 Loss2: 2.056225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.675342 Loss1: 3.606726 Loss2: 2.068616 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.105208
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.676518 Loss1: 3.608096 Loss2: 2.068421
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.637448 Loss1: 4.292729 Loss2: 2.344719
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.673161 Loss1: 3.737708 Loss2: 1.935453
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.458543 Loss1: 3.607386 Loss2: 1.851157
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.345862 Loss1: 3.566317 Loss2: 1.779545
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.316035 Loss1: 3.530180 Loss2: 1.785855
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.540706 Loss1: 4.354466 Loss2: 2.186240
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.279355 Loss1: 3.538543 Loss2: 1.740812
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.690161 Loss1: 3.958630 Loss2: 1.731531
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.468895 Loss1: 3.821352 Loss2: 1.647543
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.272803 Loss1: 3.527242 Loss2: 1.745561
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.362704 Loss1: 3.732241 Loss2: 1.630462
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.274422 Loss1: 3.526969 Loss2: 1.747453
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.263406 Loss1: 3.488579 Loss2: 1.774826 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.201451 Loss1: 3.433850 Loss2: 1.767601 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.140625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.253331 Loss1: 3.684080 Loss2: 1.569252 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.099760
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.636875 Loss1: 4.244285 Loss2: 2.392590 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.338592 Loss1: 3.511187 Loss2: 1.827405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.151580 Loss1: 3.419614 Loss2: 1.731966
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.378741 Loss1: 4.315368 Loss2: 2.063373
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.596110 Loss1: 3.872143 Loss2: 1.723966 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.385690 Loss1: 3.755969 Loss2: 1.629721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.301792 Loss1: 3.681638 Loss2: 1.620154 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.264299 Loss1: 3.686232 Loss2: 1.578067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.254095 Loss1: 3.660621 Loss2: 1.593474 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.170833
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.939447 Loss1: 3.284833 Loss2: 1.654614
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.218619 Loss1: 3.628859 Loss2: 1.589759
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.213140 Loss1: 3.619688 Loss2: 1.593452
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.186658 Loss1: 3.600146 Loss2: 1.586511
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.178976 Loss1: 3.605775 Loss2: 1.573201
+(DefaultActor pid=3764) >> Training accuracy: 0.140625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.441238 Loss1: 4.167719 Loss2: 2.273519
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.543196 Loss1: 3.629273 Loss2: 1.913922
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.377174 Loss1: 3.521849 Loss2: 1.855324
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.272371 Loss1: 3.448905 Loss2: 1.823467
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.179101 Loss1: 4.339722 Loss2: 1.839380
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.547049 Loss1: 3.958807 Loss2: 1.588243 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.366866 Loss1: 3.882616 Loss2: 1.484250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.283809 Loss1: 3.807469 Loss2: 1.476340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.251715 Loss1: 3.784272 Loss2: 1.467443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.232827 Loss1: 3.784192 Loss2: 1.448636 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.192708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.830456 Loss1: 3.207497 Loss2: 1.622959
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.214114 Loss1: 3.766779 Loss2: 1.447335
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.152651 Loss1: 3.716606 Loss2: 1.436045
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.125604 Loss1: 3.698681 Loss2: 1.426923
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.152112 Loss1: 3.703309 Loss2: 1.448803
+(DefaultActor pid=3764) >> Training accuracy: 0.091667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.227456 Loss1: 4.140491 Loss2: 2.086964
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.477879 Loss1: 3.724529 Loss2: 1.753350
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.205857 Loss1: 3.547070 Loss2: 1.658787
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.157387 Loss1: 3.517769 Loss2: 1.639618
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.200932 Loss1: 4.292203 Loss2: 1.908728
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.115675 Loss1: 3.488301 Loss2: 1.627374
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.483138 Loss1: 3.928472 Loss2: 1.554666
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.077896 Loss1: 3.455822 Loss2: 1.622074
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.321397 Loss1: 3.828254 Loss2: 1.493143
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.268517 Loss1: 3.802015 Loss2: 1.466501
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.037838 Loss1: 3.425193 Loss2: 1.612644
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.217051 Loss1: 3.748135 Loss2: 1.468916
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.043260 Loss1: 3.404107 Loss2: 1.639153
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.254624 Loss1: 3.790932 Loss2: 1.463692
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.015454 Loss1: 3.390243 Loss2: 1.625211
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.256500 Loss1: 3.789494 Loss2: 1.467006
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.040310 Loss1: 3.375351 Loss2: 1.664958
+(DefaultActor pid=3765) >> Training accuracy: 0.146484
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.207428 Loss1: 3.760213 Loss2: 1.447216 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.107292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.648278 Loss1: 4.277422 Loss2: 2.370855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.611748 Loss1: 3.678749 Loss2: 1.932998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.709167 Loss1: 4.240226 Loss2: 2.468941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.868630 Loss1: 3.754162 Loss2: 2.114468 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.348988 Loss1: 3.523137 Loss2: 1.825852 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.262994 Loss1: 3.455916 Loss2: 1.807078 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.218221 Loss1: 3.404802 Loss2: 1.813420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.150533 Loss1: 3.375025 Loss2: 1.775508 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.177885
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.445896 Loss1: 3.513781 Loss2: 1.932115 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.338440 Loss1: 3.438521 Loss2: 1.899919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.329170 Loss1: 3.444045 Loss2: 1.885125
+(DefaultActor pid=3764) >> Training accuracy: 0.159375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.738125 Loss1: 4.224071 Loss2: 2.514054
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.748603 Loss1: 3.652374 Loss2: 2.096229
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.589993 Loss1: 3.559067 Loss2: 2.030926
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.434966 Loss1: 3.474177 Loss2: 1.960788
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.410427 Loss1: 3.460586 Loss2: 1.949841
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.700931 Loss1: 4.313076 Loss2: 2.387855
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.375773 Loss1: 3.417338 Loss2: 1.958435
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.307643 Loss1: 3.375863 Loss2: 1.931781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.298883 Loss1: 3.350369 Loss2: 1.948514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.229314 Loss1: 3.332626 Loss2: 1.896688 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.264847 Loss1: 3.332724 Loss2: 1.932123 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.200000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.475168 Loss1: 3.577481 Loss2: 1.897688 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.432457 Loss1: 3.558734 Loss2: 1.873723 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.133929
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.119756 Loss1: 3.861742 Loss2: 2.258015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.845227 Loss1: 3.691676 Loss2: 2.153551 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.847890 Loss1: 3.697695 Loss2: 2.150195
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.310255 Loss1: 4.284118 Loss2: 2.026137
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.820598 Loss1: 3.686020 Loss2: 2.134578
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.412409 Loss1: 3.711430 Loss2: 1.700979
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.754983 Loss1: 3.626014 Loss2: 2.128969
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.274426 Loss1: 3.623027 Loss2: 1.651398
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.826242 Loss1: 3.688282 Loss2: 2.137960
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.176818 Loss1: 3.553246 Loss2: 1.623572
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.751460 Loss1: 3.629709 Loss2: 2.121751
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.097551 Loss1: 3.492310 Loss2: 1.605241
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.773168 Loss1: 3.630421 Loss2: 2.142747
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.103396 Loss1: 3.525095 Loss2: 1.578300
+(DefaultActor pid=3765) >> Training accuracy: 0.128125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.030870 Loss1: 3.468246 Loss2: 1.562624
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.024772 Loss1: 3.458450 Loss2: 1.566323
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.928369 Loss1: 3.367201 Loss2: 1.561168
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.823274 Loss1: 3.290642 Loss2: 1.532632
+(DefaultActor pid=3764) >> Training accuracy: 0.208333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.668985 Loss1: 4.426165 Loss2: 2.242820
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.911075 Loss1: 4.025209 Loss2: 1.885866
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.741209 Loss1: 3.889287 Loss2: 1.851922
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.638609 Loss1: 3.859427 Loss2: 1.779182
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.647425 Loss1: 3.840076 Loss2: 1.807350
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.572669 Loss1: 3.792617 Loss2: 1.780052 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.568381 Loss1: 3.802917 Loss2: 1.765463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.463024 Loss1: 3.877448 Loss2: 1.585576
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.541440 Loss1: 3.774713 Loss2: 1.766727
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.514571 Loss1: 3.752424 Loss2: 1.762146
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.433111 Loss1: 3.863991 Loss2: 1.569120
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.545861 Loss1: 3.776311 Loss2: 1.769549
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.415099 Loss1: 3.854392 Loss2: 1.560707
+(DefaultActor pid=3765) >> Training accuracy: 0.084821
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.406793 Loss1: 3.853584 Loss2: 1.553208
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.352568 Loss1: 3.802358 Loss2: 1.550211
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.340669 Loss1: 3.793463 Loss2: 1.547207
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.351124 Loss1: 3.806072 Loss2: 1.545053
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.938561 Loss1: 4.274014 Loss2: 2.664547
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.326867 Loss1: 3.787764 Loss2: 1.539103
+(DefaultActor pid=3764) >> Training accuracy: 0.123047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.933189 Loss1: 3.781034 Loss2: 2.152155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.824275 Loss1: 3.716818 Loss2: 2.107457 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.796566 Loss1: 3.692860 Loss2: 2.103707
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.348866 Loss1: 4.269794 Loss2: 2.079072
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.437487 Loss1: 3.736722 Loss2: 1.700765 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.402978 Loss1: 3.732933 Loss2: 1.670045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.307423 Loss1: 3.661779 Loss2: 1.645644 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.094792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.237968 Loss1: 3.613423 Loss2: 1.624545 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.206759 Loss1: 3.597171 Loss2: 1.609588 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.216167 Loss1: 3.602919 Loss2: 1.613248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.198789 Loss1: 3.576024 Loss2: 1.622764 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.142463
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.527649 Loss1: 3.637852 Loss2: 1.889797 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.434810 Loss1: 3.600605 Loss2: 1.834205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.300026 Loss1: 3.529970 Loss2: 1.770056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.251029 Loss1: 3.499068 Loss2: 1.751961 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.167585 Loss1: 3.439355 Loss2: 1.728230 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.160417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.299968 Loss1: 3.721409 Loss2: 1.578559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.257002 Loss1: 3.694298 Loss2: 1.562704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.215166 Loss1: 3.654680 Loss2: 1.560486
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.573303 Loss1: 4.257578 Loss2: 2.315725
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.750493 Loss1: 3.841529 Loss2: 1.908964 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.120833
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.188747 Loss1: 3.625065 Loss2: 1.563682
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.523194 Loss1: 3.708078 Loss2: 1.815116
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.445943 Loss1: 3.651641 Loss2: 1.794302
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.443331 Loss1: 3.658676 Loss2: 1.784655
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.430508 Loss1: 3.644905 Loss2: 1.785602
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.392459 Loss1: 3.615992 Loss2: 1.776467
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.863149 Loss1: 4.383745 Loss2: 2.479404
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.394233 Loss1: 3.620371 Loss2: 1.773861
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.396060 Loss1: 3.614506 Loss2: 1.781554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.766683 Loss1: 3.822181 Loss2: 1.944502 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.132292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.667250 Loss1: 3.761311 Loss2: 1.905939 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.596326 Loss1: 3.707634 Loss2: 1.888691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.592643 Loss1: 3.727317 Loss2: 1.865326 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.105469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.890499 Loss1: 3.776319 Loss2: 2.114180 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.690188 Loss1: 3.654875 Loss2: 2.035313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.649350 Loss1: 3.623629 Loss2: 2.025721
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.583308 Loss1: 4.268462 Loss2: 2.314847
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.698276 Loss1: 3.757323 Loss2: 1.940954 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.534160 Loss1: 3.651824 Loss2: 1.882336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.488287 Loss1: 3.624619 Loss2: 1.863668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.402980 Loss1: 3.550019 Loss2: 1.852962 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.135417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.337267 Loss1: 3.503699 Loss2: 1.833568 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.229219 Loss1: 3.436992 Loss2: 1.792227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.553301 Loss1: 4.260844 Loss2: 2.292457
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.159898 Loss1: 3.384278 Loss2: 1.775619
+(DefaultActor pid=3764) >> Training accuracy: 0.178711
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.490527 Loss1: 3.681302 Loss2: 1.809225 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.467865 Loss1: 3.664389 Loss2: 1.803476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.459998 Loss1: 3.658836 Loss2: 1.801162
+(DefaultActor pid=3764) Epoch: 0 Loss: 7.202234 Loss1: 4.365919 Loss2: 2.836316
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.109084 Loss1: 3.783174 Loss2: 2.325909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.992916 Loss1: 3.722439 Loss2: 2.270477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.910675 Loss1: 3.644982 Loss2: 2.265693 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.156250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.879080 Loss1: 3.646835 Loss2: 2.232245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.809757 Loss1: 3.597236 Loss2: 2.212521 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.776847 Loss1: 3.572643 Loss2: 2.204204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.497892 Loss1: 4.359822 Loss2: 2.138070
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.744608 Loss1: 3.525905 Loss2: 2.218703
+(DefaultActor pid=3764) >> Training accuracy: 0.109375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.425192 Loss1: 3.721792 Loss2: 1.703400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.398973 Loss1: 3.679215 Loss2: 1.719758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.781611 Loss1: 4.325584 Loss2: 2.456027
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.376683 Loss1: 3.665006 Loss2: 1.711677
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.888939 Loss1: 3.826974 Loss2: 2.061965
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.331333 Loss1: 3.649171 Loss2: 1.682162
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.652126 Loss1: 3.665258 Loss2: 1.986868
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.336973 Loss1: 3.687385 Loss2: 1.649588
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.231027 Loss1: 3.599498 Loss2: 1.631529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.267945 Loss1: 3.595534 Loss2: 1.672411 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.111328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.318373 Loss1: 3.466116 Loss2: 1.852257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.200200 Loss1: 3.399547 Loss2: 1.800653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.153771 Loss1: 3.359763 Loss2: 1.794008
+(DefaultActor pid=3764) >> Training accuracy: 0.170833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.902587 Loss1: 4.236542 Loss2: 2.666045
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.045047 Loss1: 3.775249 Loss2: 2.269798
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.815690 Loss1: 3.614953 Loss2: 2.200737
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.727063 Loss1: 3.563436 Loss2: 2.163627
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.694475 Loss1: 3.544533 Loss2: 2.149942
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.450687 Loss1: 4.214126 Loss2: 2.236560
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.742648 Loss1: 3.852119 Loss2: 1.890529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.505725 Loss1: 3.662183 Loss2: 1.843542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.434321 Loss1: 3.625126 Loss2: 1.809195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.371062 Loss1: 3.598039 Loss2: 1.773023 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.131836
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.598423 Loss1: 3.477808 Loss2: 2.120615
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.329597 Loss1: 3.568354 Loss2: 1.761243
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.256723 Loss1: 3.535310 Loss2: 1.721414
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.217144 Loss1: 3.542045 Loss2: 1.675099
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.174930 Loss1: 3.488323 Loss2: 1.686608
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.142006 Loss1: 3.484467 Loss2: 1.657539
+(DefaultActor pid=3764) >> Training accuracy: 0.106445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.285078 Loss1: 4.015767 Loss2: 2.269310
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.417541 Loss1: 3.586205 Loss2: 1.831336
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.210712 Loss1: 3.448782 Loss2: 1.761930
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.135158 Loss1: 3.403987 Loss2: 1.731171
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.067918 Loss1: 3.351372 Loss2: 1.716546
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.979857 Loss1: 4.396101 Loss2: 2.583756
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.049196 Loss1: 3.334401 Loss2: 1.714795
+(DefaultActor pid=3764) Epoch: 1 Loss: 6.060441 Loss1: 3.864132 Loss2: 2.196309
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.018751 Loss1: 3.323872 Loss2: 1.694878
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.661571 Loss1: 3.700789 Loss2: 1.960782
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.995537 Loss1: 3.298363 Loss2: 1.697174
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.044254 Loss1: 3.341787 Loss2: 1.702468
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.423207 Loss1: 3.582910 Loss2: 1.840297
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.970595 Loss1: 3.291227 Loss2: 1.679367
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.307689 Loss1: 3.546158 Loss2: 1.761530
+(DefaultActor pid=3765) >> Training accuracy: 0.269792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.250746 Loss1: 3.516769 Loss2: 1.733977
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.219923 Loss1: 3.518770 Loss2: 1.701153
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.165997 Loss1: 3.472164 Loss2: 1.693833
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.102147 Loss1: 3.442149 Loss2: 1.659998
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.755708 Loss1: 4.241524 Loss2: 2.514184
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.117368 Loss1: 3.448877 Loss2: 1.668491
+(DefaultActor pid=3764) >> Training accuracy: 0.138672
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.721055 Loss1: 3.666276 Loss2: 2.054779 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.641993 Loss1: 3.586458 Loss2: 2.055535 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.632628 Loss1: 3.594145 Loss2: 2.038483
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.666313 Loss1: 4.313137 Loss2: 2.353176
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.912234 Loss1: 3.886761 Loss2: 2.025472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.738351 Loss1: 3.768423 Loss2: 1.969928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.703192 Loss1: 3.745427 Loss2: 1.957765 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.150000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.485243 Loss1: 3.504643 Loss2: 1.980600
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.593531 Loss1: 3.685970 Loss2: 1.907561
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.614480 Loss1: 3.676200 Loss2: 1.938280
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.555312 Loss1: 3.620263 Loss2: 1.935050
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.542142 Loss1: 3.617121 Loss2: 1.925021
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.461168 Loss1: 3.576626 Loss2: 1.884542
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.490227 Loss1: 4.273312 Loss2: 2.216915
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.465781 Loss1: 3.562895 Loss2: 1.902886
+(DefaultActor pid=3764) >> Training accuracy: 0.135417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.623223 Loss1: 3.803966 Loss2: 1.819257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.496081 Loss1: 3.738313 Loss2: 1.757768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.509066 Loss1: 4.273288 Loss2: 2.235778
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.461298 Loss1: 3.691830 Loss2: 1.769467
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.393234 Loss1: 3.654195 Loss2: 1.739039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.379604 Loss1: 3.630687 Loss2: 1.748917 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.327989 Loss1: 3.600242 Loss2: 1.727747 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.286108 Loss1: 3.566857 Loss2: 1.719252 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.142578
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.280581 Loss1: 3.589303 Loss2: 1.691278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.252442 Loss1: 3.561602 Loss2: 1.690840 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.144792
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.213292 Loss1: 3.511872 Loss2: 1.701420
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.746030 Loss1: 4.423893 Loss2: 2.322137
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.946753 Loss1: 3.953257 Loss2: 1.993497
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.767600 Loss1: 3.851511 Loss2: 1.916090
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.725563 Loss1: 3.813681 Loss2: 1.911883
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.657981 Loss1: 3.781198 Loss2: 1.876784
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.730249 Loss1: 4.065112 Loss2: 2.665137
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.597556 Loss1: 3.739025 Loss2: 1.858531
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.769182 Loss1: 3.449020 Loss2: 2.320162
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.584744 Loss1: 3.698117 Loss2: 1.886626
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.599129 Loss1: 3.380095 Loss2: 2.219034
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.507457 Loss1: 3.306899 Loss2: 2.200558
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.497208 Loss1: 3.654501 Loss2: 1.842706
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.397430 Loss1: 3.246061 Loss2: 2.151368
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.423074 Loss1: 3.608405 Loss2: 1.814670
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.345630 Loss1: 3.223569 Loss2: 2.122061
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.471888 Loss1: 3.618592 Loss2: 1.853296
+(DefaultActor pid=3765) >> Training accuracy: 0.111328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.271960 Loss1: 3.195892 Loss2: 2.076069 [repeated 2x across cluster]
+DEBUG flwr 2023-10-08 13:41:01,600 | server.py:236 | fit_round 3 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.206812 Loss1: 3.150896 Loss2: 2.055917 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.142708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 6.276676 Loss1: 3.574323 Loss2: 2.702353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 6.132503 Loss1: 3.469489 Loss2: 2.663014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.398414 Loss1: 4.250052 Loss2: 2.148362
+(DefaultActor pid=3765) Epoch: 4 Loss: 6.003278 Loss1: 3.393034 Loss2: 2.610243
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.628065 Loss1: 3.827264 Loss2: 1.800801
+(DefaultActor pid=3765) Epoch: 5 Loss: 6.000932 Loss1: 3.405060 Loss2: 2.595872
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.393520 Loss1: 3.683033 Loss2: 1.710487
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.881842 Loss1: 3.320844 Loss2: 2.560999
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.318447 Loss1: 3.633140 Loss2: 1.685307
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.856018 Loss1: 3.298348 Loss2: 2.557669
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.334231 Loss1: 3.644618 Loss2: 1.689613
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.795250 Loss1: 3.252646 Loss2: 2.542605
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.253840 Loss1: 3.587477 Loss2: 1.666363
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.808585 Loss1: 3.263955 Loss2: 2.544630
+(DefaultActor pid=3765) >> Training accuracy: 0.195833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.244376 Loss1: 3.582774 Loss2: 1.661602 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.127149 Loss1: 3.495455 Loss2: 1.631694 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.141667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.564099 Loss1: 3.832230 Loss2: 1.731869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.008653 Loss1: 3.477129 Loss2: 1.531524 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.811212 Loss1: 4.307578 Loss2: 2.503634
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.871952 Loss1: 3.411036 Loss2: 1.460917
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.932590 Loss1: 3.882903 Loss2: 2.049687
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.767583 Loss1: 3.360200 Loss2: 1.407383
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.668679 Loss1: 3.698201 Loss2: 1.970478
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.689080 Loss1: 3.307840 Loss2: 1.381240
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.594124 Loss1: 3.649628 Loss2: 1.944496
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.631038 Loss1: 3.261961 Loss2: 1.369077
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.551496 Loss1: 3.609912 Loss2: 1.941584
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.636485 Loss1: 3.256297 Loss2: 1.380188
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.542715 Loss1: 3.604177 Loss2: 1.938538
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.573450 Loss1: 3.211592 Loss2: 1.361858
+(DefaultActor pid=3765) >> Training accuracy: 0.192708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.521203 Loss1: 3.588348 Loss2: 1.932855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.459185 Loss1: 3.531509 Loss2: 1.927675 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.121875
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 13:41:01,600][flwr][DEBUG] - fit_round 3 received 50 results and 0 failures
+INFO flwr 2023-10-08 13:41:43,485 | server.py:125 | fit progress: (3, 4.760899214698864, {'accuracy': 0.01}, 6611.263448652)
+>> Test accuracy: 0.010000
+[2023-10-08 13:41:43,485][flwr][INFO] - fit progress: (3, 4.760899214698864, {'accuracy': 0.01}, 6611.263448652)
+DEBUG flwr 2023-10-08 13:41:43,485 | server.py:173 | evaluate_round 3: strategy sampled 50 clients (out of 50)
+[2023-10-08 13:41:43,485][flwr][DEBUG] - evaluate_round 3: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 13:50:48,071 | server.py:187 | evaluate_round 3 received 50 results and 0 failures
+[2023-10-08 13:50:48,071][flwr][DEBUG] - evaluate_round 3 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 13:50:48,072 | server.py:222 | fit_round 4: strategy sampled 50 clients (out of 50)
+[2023-10-08 13:50:48,072][flwr][DEBUG] - fit_round 4: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.844691 Loss1: 4.278797 Loss2: 1.565894
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.080191 Loss1: 3.768652 Loss2: 1.311539
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.920249 Loss1: 3.649276 Loss2: 1.270973
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.134528 Loss1: 4.409609 Loss2: 1.724919
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.368229 Loss1: 3.913881 Loss2: 1.454348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.112838 Loss1: 3.722394 Loss2: 1.390444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.047701 Loss1: 3.667627 Loss2: 1.380075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.769319 Loss1: 3.539148 Loss2: 1.230171
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.947771 Loss1: 3.580849 Loss2: 1.366922
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.703801 Loss1: 3.477871 Loss2: 1.225930
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.915109 Loss1: 3.546890 Loss2: 1.368219
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.907364 Loss1: 3.538434 Loss2: 1.368930
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.707812 Loss1: 3.476274 Loss2: 1.231538
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.848440 Loss1: 3.484444 Loss2: 1.363995
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.695703 Loss1: 3.466603 Loss2: 1.229100
+(DefaultActor pid=3765) >> Training accuracy: 0.159926
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.845906 Loss1: 3.482581 Loss2: 1.363325 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.127083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.321091 Loss1: 4.139068 Loss2: 2.182023
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.326005 Loss1: 3.472348 Loss2: 1.853657
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.083334 Loss1: 3.332510 Loss2: 1.750824
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.019605 Loss1: 3.287279 Loss2: 1.732327
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.104270 Loss1: 4.429928 Loss2: 1.674342
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.021011 Loss1: 3.309015 Loss2: 1.711995
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.398769 Loss1: 4.006677 Loss2: 1.392092
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.939359 Loss1: 3.247996 Loss2: 1.691363
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.097217 Loss1: 3.759848 Loss2: 1.337369
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.964931 Loss1: 3.237538 Loss2: 1.727393
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.008381 Loss1: 3.699625 Loss2: 1.308756
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.925406 Loss1: 3.214171 Loss2: 1.711234
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.963797 Loss1: 3.664055 Loss2: 1.299741
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.910108 Loss1: 3.218019 Loss2: 1.692089
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.902445 Loss1: 3.618707 Loss2: 1.283738
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.807803 Loss1: 3.157842 Loss2: 1.649962
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.923825 Loss1: 3.622673 Loss2: 1.301152
+(DefaultActor pid=3765) >> Training accuracy: 0.172917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.913186 Loss1: 3.614046 Loss2: 1.299140
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.824933 Loss1: 3.538606 Loss2: 1.286327
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.797131 Loss1: 3.503533 Loss2: 1.293598
+(DefaultActor pid=3764) >> Training accuracy: 0.141667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.145307 Loss1: 4.337297 Loss2: 1.808010
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.357828 Loss1: 3.852297 Loss2: 1.505531
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.167208 Loss1: 3.668811 Loss2: 1.498397
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.035082 Loss1: 3.620491 Loss2: 1.414590
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.803567 Loss1: 4.295406 Loss2: 1.508160
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.978763 Loss1: 3.580445 Loss2: 1.398318
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.061271 Loss1: 3.774611 Loss2: 1.286660
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.965755 Loss1: 3.567565 Loss2: 1.398190
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.834399 Loss1: 3.630298 Loss2: 1.204101
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.936459 Loss1: 3.541227 Loss2: 1.395231
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.791394 Loss1: 3.597514 Loss2: 1.193880
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.945916 Loss1: 3.539010 Loss2: 1.406906
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.755868 Loss1: 3.585717 Loss2: 1.170151
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.886392 Loss1: 3.494043 Loss2: 1.392349
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.736972 Loss1: 3.553199 Loss2: 1.183772
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.854318 Loss1: 3.444978 Loss2: 1.409340
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.727726 Loss1: 3.535695 Loss2: 1.192031
+(DefaultActor pid=3765) >> Training accuracy: 0.137500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.691622 Loss1: 3.524446 Loss2: 1.167176
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.630140 Loss1: 3.460924 Loss2: 1.169216
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.601058 Loss1: 3.403299 Loss2: 1.197759
+(DefaultActor pid=3764) >> Training accuracy: 0.165625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.932342 Loss1: 4.384148 Loss2: 1.548195
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.127146 Loss1: 3.821315 Loss2: 1.305830
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.882576 Loss1: 3.646340 Loss2: 1.236236
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.837084 Loss1: 3.613046 Loss2: 1.224038
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.459057 Loss1: 4.263563 Loss2: 2.195494
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.739089 Loss1: 3.529132 Loss2: 1.209957
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.587187 Loss1: 3.675679 Loss2: 1.911507
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.350633 Loss1: 3.578013 Loss2: 1.772620 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.227666 Loss1: 3.490536 Loss2: 1.737131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.185766 Loss1: 3.461212 Loss2: 1.724553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.096364 Loss1: 3.417117 Loss2: 1.679247 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.142708
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.655743 Loss1: 3.432484 Loss2: 1.223259
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.009110 Loss1: 3.354543 Loss2: 1.654567
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.932518 Loss1: 3.342567 Loss2: 1.589951
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.813094 Loss1: 3.262709 Loss2: 1.550385
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.713664 Loss1: 3.205247 Loss2: 1.508417
+(DefaultActor pid=3764) >> Training accuracy: 0.193750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.425271 Loss1: 4.374965 Loss2: 2.050306
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.565929 Loss1: 3.848974 Loss2: 1.716955
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.344169 Loss1: 3.684735 Loss2: 1.659433
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.239172 Loss1: 3.617215 Loss2: 1.621957
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.025611 Loss1: 4.310849 Loss2: 1.714762
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.314745 Loss1: 3.824660 Loss2: 1.490085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.070875 Loss1: 3.652709 Loss2: 1.418166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.004500 Loss1: 3.597905 Loss2: 1.406595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.977521 Loss1: 3.555078 Loss2: 1.422443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.933316 Loss1: 3.525930 Loss2: 1.407385 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.146875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.905517 Loss1: 3.508758 Loss2: 1.396759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.845104 Loss1: 3.455062 Loss2: 1.390042 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.152083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.982456 Loss1: 4.291523 Loss2: 1.690933 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.952970 Loss1: 3.634888 Loss2: 1.318082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.862345 Loss1: 3.561360 Loss2: 1.300985
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.467949 Loss1: 4.412515 Loss2: 2.055434
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.715646 Loss1: 3.936963 Loss2: 1.778683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.467784 Loss1: 3.793313 Loss2: 1.674472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.330522 Loss1: 3.688815 Loss2: 1.641707 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.306795 Loss1: 3.695163 Loss2: 1.611632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.290725 Loss1: 3.695542 Loss2: 1.595183 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.128125
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.778292 Loss1: 3.504912 Loss2: 1.273380
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.265249 Loss1: 3.668298 Loss2: 1.596951
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.223015 Loss1: 3.622178 Loss2: 1.600837
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.201135 Loss1: 3.579084 Loss2: 1.622051
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.101452 Loss1: 3.544143 Loss2: 1.557309
+(DefaultActor pid=3764) >> Training accuracy: 0.136458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.016205 Loss1: 4.386092 Loss2: 1.630114
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.293610 Loss1: 3.860582 Loss2: 1.433028
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.075467 Loss1: 3.681402 Loss2: 1.394065
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.893750 Loss1: 3.566531 Loss2: 1.327219
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.459579 Loss1: 4.444006 Loss2: 2.015573
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.690144 Loss1: 3.963400 Loss2: 1.726744 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.523372 Loss1: 3.834162 Loss2: 1.689210 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.403177 Loss1: 3.757471 Loss2: 1.645706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.385986 Loss1: 3.754303 Loss2: 1.631683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.342846 Loss1: 3.713406 Loss2: 1.629440 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.164583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.285377 Loss1: 3.679809 Loss2: 1.605568 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.259119 Loss1: 3.679570 Loss2: 1.579549 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.122070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.434370 Loss1: 3.776455 Loss2: 1.657915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.190957 Loss1: 3.616205 Loss2: 1.574752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.159294 Loss1: 3.582117 Loss2: 1.577177
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.087707 Loss1: 4.292841 Loss2: 1.794866
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.146968 Loss1: 3.588334 Loss2: 1.558634
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.201386 Loss1: 3.723196 Loss2: 1.478190
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.118019 Loss1: 3.584533 Loss2: 1.533486
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.018721 Loss1: 3.602373 Loss2: 1.416348
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.944611 Loss1: 3.550432 Loss2: 1.394179 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.912232 Loss1: 3.509691 Loss2: 1.402541 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.135417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.889521 Loss1: 3.465564 Loss2: 1.423957 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.859373 Loss1: 3.444588 Loss2: 1.414785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.783815 Loss1: 3.373955 Loss2: 1.409861 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.155273
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.085178 Loss1: 3.597289 Loss2: 1.487888 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.886924 Loss1: 3.432477 Loss2: 1.454447 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.861033 Loss1: 3.421842 Loss2: 1.439191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.835253 Loss1: 3.408244 Loss2: 1.427009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.801702 Loss1: 3.393648 Loss2: 1.408055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.762097 Loss1: 3.345761 Loss2: 1.416335 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.733652 Loss1: 3.316520 Loss2: 1.417132 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.197917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.099166 Loss1: 3.633428 Loss2: 1.465738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.080691 Loss1: 3.604497 Loss2: 1.476193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.117188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.022845 Loss1: 4.301452 Loss2: 1.721393 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.990974 Loss1: 3.648794 Loss2: 1.342180 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.836868 Loss1: 3.540798 Loss2: 1.296070 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.820153 Loss1: 3.529836 Loss2: 1.290316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.812571 Loss1: 3.526847 Loss2: 1.285724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.763952 Loss1: 3.470612 Loss2: 1.293341 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.766718 Loss1: 3.458367 Loss2: 1.308351 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.724314 Loss1: 3.420329 Loss2: 1.303984 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.212500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.888086 Loss1: 3.515453 Loss2: 1.372634 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.792005 Loss1: 3.437495 Loss2: 1.354510 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.126042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.331278 Loss1: 3.697644 Loss2: 1.633634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.070746 Loss1: 3.528177 Loss2: 1.542569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.029602 Loss1: 3.523749 Loss2: 1.505853
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.937587 Loss1: 4.399371 Loss2: 1.538216
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.219694 Loss1: 3.847863 Loss2: 1.371831 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.012477 Loss1: 3.721732 Loss2: 1.290745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.882817 Loss1: 3.642822 Loss2: 1.239995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.839223 Loss1: 3.574858 Loss2: 1.264365 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.188542
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.828552 Loss1: 3.306262 Loss2: 1.522290
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.811923 Loss1: 3.528266 Loss2: 1.283657
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.753648 Loss1: 3.496584 Loss2: 1.257064
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.721086 Loss1: 3.474196 Loss2: 1.246890
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.604016 Loss1: 3.405654 Loss2: 1.198362
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.571364 Loss1: 3.394488 Loss2: 1.176876
+(DefaultActor pid=3764) >> Training accuracy: 0.170833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.000725 Loss1: 4.299792 Loss2: 1.700933
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.218339 Loss1: 3.738250 Loss2: 1.480089
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.980838 Loss1: 3.607363 Loss2: 1.373475
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.869777 Loss1: 3.523686 Loss2: 1.346091
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.813766 Loss1: 3.492331 Loss2: 1.321435
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.119655 Loss1: 4.399344 Loss2: 1.720311
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.449465 Loss1: 4.005602 Loss2: 1.443863 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.286088 Loss1: 3.890777 Loss2: 1.395311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.214712 Loss1: 3.840348 Loss2: 1.374364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.170902 Loss1: 3.810980 Loss2: 1.359922 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.152083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.174476 Loss1: 3.810741 Loss2: 1.363736 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.111647 Loss1: 3.779678 Loss2: 1.331970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.072481 Loss1: 3.746862 Loss2: 1.325619 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.126953
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.222474 Loss1: 3.716274 Loss2: 1.506201 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.129758 Loss1: 3.679290 Loss2: 1.450467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.021300 Loss1: 4.373652 Loss2: 1.647648 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.298789 Loss1: 3.909124 Loss2: 1.389666 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.030596 Loss1: 3.541169 Loss2: 1.489427 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.138021
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.905743 Loss1: 3.626357 Loss2: 1.279385 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.899095 Loss1: 3.602519 Loss2: 1.296576 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.156646 Loss1: 4.420819 Loss2: 1.735827
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.907970 Loss1: 3.607944 Loss2: 1.300026
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.379586 Loss1: 3.951435 Loss2: 1.428152
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.867426 Loss1: 3.572975 Loss2: 1.294451
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.161027 Loss1: 3.789738 Loss2: 1.371289
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.835458 Loss1: 3.551211 Loss2: 1.284247
+(DefaultActor pid=3764) >> Training accuracy: 0.131250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.098986 Loss1: 3.739820 Loss2: 1.359166 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.039136 Loss1: 3.695258 Loss2: 1.343878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.038328 Loss1: 3.690714 Loss2: 1.347613
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.177197 Loss1: 4.282611 Loss2: 1.894585
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.985398 Loss1: 3.630321 Loss2: 1.355077
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.315193 Loss1: 3.690918 Loss2: 1.624276
+(DefaultActor pid=3765) >> Training accuracy: 0.121875
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.975105 Loss1: 3.621978 Loss2: 1.353126
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.074265 Loss1: 3.532563 Loss2: 1.541702
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.037125 Loss1: 3.501069 Loss2: 1.536056
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.996598 Loss1: 3.457142 Loss2: 1.539456
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.983211 Loss1: 3.470782 Loss2: 1.512430
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.908168 Loss1: 3.411795 Loss2: 1.496373
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.136380 Loss1: 4.141817 Loss2: 1.994563
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.162734 Loss1: 3.517898 Loss2: 1.644836 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.034732 Loss1: 3.436933 Loss2: 1.597799 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.152344
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.908198 Loss1: 3.404501 Loss2: 1.503697
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.935772 Loss1: 3.350354 Loss2: 1.585419
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.949319 Loss1: 3.345329 Loss2: 1.603990
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.850578 Loss1: 3.271782 Loss2: 1.578795
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.867491 Loss1: 3.303113 Loss2: 1.564378
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.820437 Loss1: 3.210602 Loss2: 1.609835
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.014271 Loss1: 4.251495 Loss2: 1.762777
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.796405 Loss1: 3.201211 Loss2: 1.595194
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.757965 Loss1: 3.197197 Loss2: 1.560768 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.191667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.900474 Loss1: 3.518912 Loss2: 1.381561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.789549 Loss1: 3.403132 Loss2: 1.386416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.816477 Loss1: 3.423682 Loss2: 1.392795
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.033089 Loss1: 4.297840 Loss2: 1.735249
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.250787 Loss1: 3.791057 Loss2: 1.459730 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.947172 Loss1: 3.575974 Loss2: 1.371198 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.898812 Loss1: 3.516844 Loss2: 1.381968
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.760956 Loss1: 3.374892 Loss2: 1.386064
+(DefaultActor pid=3764) >> Training accuracy: 0.191667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.767052 Loss1: 3.404382 Loss2: 1.362671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.668153 Loss1: 3.330751 Loss2: 1.337402 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.659927 Loss1: 3.347230 Loss2: 1.312698 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.185096
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.148606 Loss1: 3.704379 Loss2: 1.444227 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.000164 Loss1: 3.581232 Loss2: 1.418932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.931105 Loss1: 3.551057 Loss2: 1.380048 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.864508 Loss1: 3.489953 Loss2: 1.374555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.864999 Loss1: 3.520592 Loss2: 1.344407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.836242 Loss1: 3.460938 Loss2: 1.375304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.793462 Loss1: 3.424979 Loss2: 1.368483 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.150391
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.980441 Loss1: 3.634676 Loss2: 1.345765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.874070 Loss1: 3.566117 Loss2: 1.307953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.851202 Loss1: 3.536299 Loss2: 1.314903
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.790894 Loss1: 4.369197 Loss2: 1.421698
+(DefaultActor pid=3765) >> Training accuracy: 0.128906
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.002728 Loss1: 3.758812 Loss2: 1.243916
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.776688 Loss1: 3.636894 Loss2: 1.139794
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.678358 Loss1: 3.542966 Loss2: 1.135392
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.658652 Loss1: 3.534558 Loss2: 1.124094
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.002225 Loss1: 4.413736 Loss2: 1.588489
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.657446 Loss1: 3.527312 Loss2: 1.130134
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.543684 Loss1: 3.443924 Loss2: 1.099759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.530206 Loss1: 3.420809 Loss2: 1.109396 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.927819 Loss1: 3.688461 Loss2: 1.239358 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.899357 Loss1: 3.695326 Loss2: 1.204031 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.164062
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.876089 Loss1: 3.651915 Loss2: 1.224174 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.758633 Loss1: 3.565444 Loss2: 1.193189 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.108173
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.146806 Loss1: 4.467016 Loss2: 1.679790
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.307363 Loss1: 3.911381 Loss2: 1.395982
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.053593 Loss1: 3.770443 Loss2: 1.283150
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.970016 Loss1: 3.696887 Loss2: 1.273129
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.522144 Loss1: 4.450119 Loss2: 2.072025
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.696340 Loss1: 3.998142 Loss2: 1.698199 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.420270 Loss1: 3.821018 Loss2: 1.599252 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.329094 Loss1: 3.765953 Loss2: 1.563141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.332515 Loss1: 3.732023 Loss2: 1.600492 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.296210 Loss1: 3.684343 Loss2: 1.611867 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.103125
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.837653 Loss1: 3.592419 Loss2: 1.245234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.280915 Loss1: 3.671797 Loss2: 1.609118
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.208472 Loss1: 3.622351 Loss2: 1.586121
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.194609 Loss1: 3.611390 Loss2: 1.583220
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.172710 Loss1: 3.578960 Loss2: 1.593751
+(DefaultActor pid=3765) >> Training accuracy: 0.118750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.402382 Loss1: 4.323937 Loss2: 2.078445
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.612452 Loss1: 3.809243 Loss2: 1.803210
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.412907 Loss1: 3.710613 Loss2: 1.702293
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.319114 Loss1: 3.659403 Loss2: 1.659711
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.114987 Loss1: 4.321159 Loss2: 1.793828
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.411817 Loss1: 3.882987 Loss2: 1.528830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.204815 Loss1: 3.612872 Loss2: 1.591942
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.119776 Loss1: 3.670232 Loss2: 1.449545
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.046038 Loss1: 3.535337 Loss2: 1.510701
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.006657 Loss1: 3.604206 Loss2: 1.402451
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.985320 Loss1: 3.506605 Loss2: 1.478715
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.997809 Loss1: 3.589743 Loss2: 1.408067
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.946395 Loss1: 3.527970 Loss2: 1.418425
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.973900 Loss1: 3.505980 Loss2: 1.467919
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.913771 Loss1: 3.525242 Loss2: 1.388529
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.939408 Loss1: 3.459829 Loss2: 1.479579
+(DefaultActor pid=3764) >> Training accuracy: 0.128906
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.925597 Loss1: 3.507691 Loss2: 1.417906 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.155208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.141739 Loss1: 4.377557 Loss2: 1.764181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.157374 Loss1: 3.717684 Loss2: 1.439690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.082452 Loss1: 3.682276 Loss2: 1.400176
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.189450 Loss1: 4.387096 Loss2: 1.802355
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.334341 Loss1: 3.823895 Loss2: 1.510446 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.111303 Loss1: 3.684281 Loss2: 1.427023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.042259 Loss1: 3.628937 Loss2: 1.413323 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.927879 Loss1: 3.540995 Loss2: 1.386884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.876912 Loss1: 3.506911 Loss2: 1.370001 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.127083
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.942963 Loss1: 3.584045 Loss2: 1.358918
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.816944 Loss1: 3.482169 Loss2: 1.334775
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.811980 Loss1: 3.443299 Loss2: 1.368681
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.780915 Loss1: 3.422989 Loss2: 1.357926
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.730913 Loss1: 3.381368 Loss2: 1.349545
+(DefaultActor pid=3765) >> Training accuracy: 0.189583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.657172 Loss1: 4.422149 Loss2: 2.235023
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.715478 Loss1: 3.828318 Loss2: 1.887160
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.527238 Loss1: 3.714598 Loss2: 1.812640
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.446415 Loss1: 3.657413 Loss2: 1.789002
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.977610 Loss1: 4.295782 Loss2: 1.681828
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.104732 Loss1: 3.691032 Loss2: 1.413700
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.388377 Loss1: 3.591562 Loss2: 1.796815
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.765672 Loss1: 3.450720 Loss2: 1.314952
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.376724 Loss1: 3.590170 Loss2: 1.786554
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.687902 Loss1: 3.392504 Loss2: 1.295397
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.324730 Loss1: 3.538793 Loss2: 1.785936
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.660190 Loss1: 3.365150 Loss2: 1.295041
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.329393 Loss1: 3.529941 Loss2: 1.799451
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.553788 Loss1: 3.276850 Loss2: 1.276938
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.302363 Loss1: 3.502657 Loss2: 1.799706
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.284703 Loss1: 3.488412 Loss2: 1.796291 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.153320
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.531618 Loss1: 3.261703 Loss2: 1.269915 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.275000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.980387 Loss1: 4.404908 Loss2: 2.575479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.830568 Loss1: 3.760052 Loss2: 2.070516 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.651596 Loss1: 3.631489 Loss2: 2.020107
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.249212 Loss1: 4.451821 Loss2: 1.797390
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.576951 Loss1: 4.017952 Loss2: 1.558999
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.589877 Loss1: 3.633106 Loss2: 1.956771
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.311196 Loss1: 3.833775 Loss2: 1.477421
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.607117 Loss1: 3.640604 Loss2: 1.966512
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.185576 Loss1: 3.746640 Loss2: 1.438935
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.526922 Loss1: 3.599381 Loss2: 1.927541
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.119826 Loss1: 3.699128 Loss2: 1.420698
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.470339 Loss1: 3.549065 Loss2: 1.921274
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.483116 Loss1: 3.560017 Loss2: 1.923099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.499488 Loss1: 3.566647 Loss2: 1.932841 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.132292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.052352 Loss1: 3.614526 Loss2: 1.437826 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.117188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.037470 Loss1: 4.283843 Loss2: 1.753627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.000373 Loss1: 3.534168 Loss2: 1.466205 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.881819 Loss1: 3.464629 Loss2: 1.417190
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.059982 Loss1: 4.471628 Loss2: 1.588354
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.841020 Loss1: 3.431747 Loss2: 1.409273
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.319042 Loss1: 3.993307 Loss2: 1.325735
+DEBUG flwr 2023-10-08 14:19:48,676 | server.py:236 | fit_round 4 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.761805 Loss1: 3.377317 Loss2: 1.384488
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.100400 Loss1: 3.819093 Loss2: 1.281307
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.790762 Loss1: 3.381699 Loss2: 1.409063
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.044012 Loss1: 3.791536 Loss2: 1.252476
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.701863 Loss1: 3.326918 Loss2: 1.374945
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.992100 Loss1: 3.744666 Loss2: 1.247434
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.702039 Loss1: 3.326954 Loss2: 1.375084
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.979109 Loss1: 3.733936 Loss2: 1.245173
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.659274 Loss1: 3.319977 Loss2: 1.339298
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.955080 Loss1: 3.704076 Loss2: 1.251004
+(DefaultActor pid=3764) >> Training accuracy: 0.186458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.935631 Loss1: 3.686009 Loss2: 1.249621
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.934930 Loss1: 3.683681 Loss2: 1.251249
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.955066 Loss1: 3.680863 Loss2: 1.274203
+(DefaultActor pid=3765) >> Training accuracy: 0.117708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.936339 Loss1: 4.431189 Loss2: 1.505149
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.279033 Loss1: 3.994380 Loss2: 1.284653
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.107112 Loss1: 3.860553 Loss2: 1.246558
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.042828 Loss1: 3.815475 Loss2: 1.227353
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.198831 Loss1: 4.376030 Loss2: 1.822801
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.352185 Loss1: 3.846339 Loss2: 1.505846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.108207 Loss1: 3.668402 Loss2: 1.439805 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.056090 Loss1: 3.639738 Loss2: 1.416352 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.992052 Loss1: 3.561158 Loss2: 1.430894 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.968873 Loss1: 3.565364 Loss2: 1.403510 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.125000
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.867059 Loss1: 3.656841 Loss2: 1.210217
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.942275 Loss1: 3.540120 Loss2: 1.402154
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.871355 Loss1: 3.478864 Loss2: 1.392491
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.890644 Loss1: 3.494892 Loss2: 1.395753
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.892013 Loss1: 3.496340 Loss2: 1.395673
+(DefaultActor pid=3765) >> Training accuracy: 0.152083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.253345 Loss1: 4.419525 Loss2: 1.833820
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.289994 Loss1: 3.779649 Loss2: 1.510345
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.034729 Loss1: 3.642034 Loss2: 1.392695
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.000304 Loss1: 3.599775 Loss2: 1.400530
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.959750 Loss1: 3.557150 Loss2: 1.402601
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.930915 Loss1: 3.542151 Loss2: 1.388764
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.864145 Loss1: 3.493682 Loss2: 1.370463
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.885165 Loss1: 3.510075 Loss2: 1.375090
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.896219 Loss1: 3.490527 Loss2: 1.405692
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.860034 Loss1: 3.459637 Loss2: 1.400396
+(DefaultActor pid=3764) >> Training accuracy: 0.143973
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 14:19:48,676][flwr][DEBUG] - fit_round 4 received 50 results and 0 failures
+INFO flwr 2023-10-08 14:20:31,003 | server.py:125 | fit progress: (4, 4.635519420757842, {'accuracy': 0.0117}, 8938.781652659)
+>> Test accuracy: 0.011700
+[2023-10-08 14:20:31,003][flwr][INFO] - fit progress: (4, 4.635519420757842, {'accuracy': 0.0117}, 8938.781652659)
+DEBUG flwr 2023-10-08 14:20:31,003 | server.py:173 | evaluate_round 4: strategy sampled 50 clients (out of 50)
+[2023-10-08 14:20:31,003][flwr][DEBUG] - evaluate_round 4: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 14:29:34,905 | server.py:187 | evaluate_round 4 received 50 results and 0 failures
+[2023-10-08 14:29:34,905][flwr][DEBUG] - evaluate_round 4 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 14:29:34,905 | server.py:222 | fit_round 5: strategy sampled 50 clients (out of 50)
+[2023-10-08 14:29:34,905][flwr][DEBUG] - fit_round 5: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.248227 Loss1: 4.396324 Loss2: 1.851903
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.437683 Loss1: 3.940437 Loss2: 1.497247
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.135633 Loss1: 3.704142 Loss2: 1.431491
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.080403 Loss1: 3.685175 Loss2: 1.395228
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.029415 Loss1: 3.626819 Loss2: 1.402596
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.275757 Loss1: 4.395536 Loss2: 1.880221
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.938820 Loss1: 3.546676 Loss2: 1.392145
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.163097 Loss1: 3.631471 Loss2: 1.531626 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.890016 Loss1: 3.502588 Loss2: 1.387428 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.152344
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.876185 Loss1: 3.489396 Loss2: 1.386788 [repeated 2x across cluster]
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.999109 Loss1: 3.496803 Loss2: 1.502307 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.936079 Loss1: 3.425255 Loss2: 1.510824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.937275 Loss1: 3.420856 Loss2: 1.516420 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.169792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.209014 Loss1: 3.549661 Loss2: 1.659354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.094582 Loss1: 3.442069 Loss2: 1.652512 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.082614 Loss1: 3.431744 Loss2: 1.650870
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.170768 Loss1: 4.320873 Loss2: 1.849896
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.068074 Loss1: 3.423430 Loss2: 1.644644
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.340757 Loss1: 3.783302 Loss2: 1.557454
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.022859 Loss1: 3.393030 Loss2: 1.629829
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.148643 Loss1: 3.672436 Loss2: 1.476207
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.004888 Loss1: 3.371407 Loss2: 1.633481
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.071111 Loss1: 3.619124 Loss2: 1.451988
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.002692 Loss1: 3.554666 Loss2: 1.448025
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.009919 Loss1: 3.366019 Loss2: 1.643900
+(DefaultActor pid=3765) >> Training accuracy: 0.152344
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.963398 Loss1: 3.528541 Loss2: 1.434857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.936816 Loss1: 3.511676 Loss2: 1.425140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.928544 Loss1: 3.496450 Loss2: 1.432093
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.897887 Loss1: 4.313379 Loss2: 1.584508
+(DefaultActor pid=3764) >> Training accuracy: 0.145833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.101164 Loss1: 3.805344 Loss2: 1.295820
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.884700 Loss1: 3.652114 Loss2: 1.232586
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.806347 Loss1: 3.587775 Loss2: 1.218572
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.774664 Loss1: 3.558422 Loss2: 1.216243
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.051109 Loss1: 4.201374 Loss2: 1.849734
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.213894 Loss1: 3.633573 Loss2: 1.580321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.924420 Loss1: 3.436781 Loss2: 1.487639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.874754 Loss1: 3.408929 Loss2: 1.465825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.784650 Loss1: 3.322793 Loss2: 1.461857 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.664679 Loss1: 3.454271 Loss2: 1.210408
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.734873 Loss1: 3.291652 Loss2: 1.443222
+(DefaultActor pid=3765) >> Training accuracy: 0.140625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.746703 Loss1: 3.293628 Loss2: 1.453074
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.678842 Loss1: 3.226787 Loss2: 1.452055
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.714074 Loss1: 3.274148 Loss2: 1.439926
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.684555 Loss1: 3.248963 Loss2: 1.435592
+(DefaultActor pid=3764) >> Training accuracy: 0.205208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.176817 Loss1: 4.347385 Loss2: 1.829433
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.347177 Loss1: 3.848861 Loss2: 1.498316
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.118245 Loss1: 3.685415 Loss2: 1.432830
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.047235 Loss1: 3.621655 Loss2: 1.425580
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.233440 Loss1: 4.248360 Loss2: 1.985080
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.022778 Loss1: 3.610098 Loss2: 1.412680
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.287085 Loss1: 3.653982 Loss2: 1.633104
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.036688 Loss1: 3.626031 Loss2: 1.410657
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.022395 Loss1: 3.605789 Loss2: 1.416606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.001181 Loss1: 3.587896 Loss2: 1.413286 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.946688 Loss1: 3.528826 Loss2: 1.417862 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.931571 Loss1: 3.519768 Loss2: 1.411803 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.145833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.707641 Loss1: 3.256998 Loss2: 1.450643 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.188702
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.936291 Loss1: 4.176821 Loss2: 1.759469 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.867047 Loss1: 3.467207 Loss2: 1.399839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.763824 Loss1: 3.355619 Loss2: 1.408206
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.996476 Loss1: 4.262129 Loss2: 1.734347
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.106373 Loss1: 3.667289 Loss2: 1.439084
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.743772 Loss1: 3.349176 Loss2: 1.394596
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.877481 Loss1: 3.515296 Loss2: 1.362185
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.704488 Loss1: 3.311117 Loss2: 1.393371
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.873707 Loss1: 3.507744 Loss2: 1.365963
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.689974 Loss1: 3.298988 Loss2: 1.390987
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.829263 Loss1: 3.461350 Loss2: 1.367914
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.677427 Loss1: 3.256553 Loss2: 1.420874
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.696993 Loss1: 3.289210 Loss2: 1.407783 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.646649 Loss1: 3.239275 Loss2: 1.407374 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.161133
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.767833 Loss1: 3.409063 Loss2: 1.358770 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.160417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.033716 Loss1: 4.229845 Loss2: 1.803871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.992559 Loss1: 3.567420 Loss2: 1.425139 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.892283 Loss1: 3.493009 Loss2: 1.399274
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.485543 Loss1: 4.458547 Loss2: 2.026996
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.724026 Loss1: 4.027135 Loss2: 1.696890
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.946388 Loss1: 3.522031 Loss2: 1.424356
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.427771 Loss1: 3.793197 Loss2: 1.634573
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.875647 Loss1: 3.476208 Loss2: 1.399439
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.382699 Loss1: 3.758434 Loss2: 1.624265
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.855267 Loss1: 3.438740 Loss2: 1.416527
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.837242 Loss1: 3.435909 Loss2: 1.401333 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.844639 Loss1: 3.435986 Loss2: 1.408652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.801041 Loss1: 3.406739 Loss2: 1.394302 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.169792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.252579 Loss1: 3.656683 Loss2: 1.595896 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.117188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.014563 Loss1: 4.089986 Loss2: 1.924577 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.877215 Loss1: 3.369084 Loss2: 1.508131 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.843180 Loss1: 3.358614 Loss2: 1.484567
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.095216 Loss1: 4.340099 Loss2: 1.755118
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.290531 Loss1: 3.840277 Loss2: 1.450254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.108811 Loss1: 3.700719 Loss2: 1.408091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.042723 Loss1: 3.653357 Loss2: 1.389365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.989940 Loss1: 3.603667 Loss2: 1.386273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.044553 Loss1: 3.636714 Loss2: 1.407839 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.267708
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.573172 Loss1: 3.115901 Loss2: 1.457271
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.974860 Loss1: 3.586772 Loss2: 1.388088
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.974766 Loss1: 3.581476 Loss2: 1.393290
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.004089 Loss1: 3.609915 Loss2: 1.394174
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.929292 Loss1: 3.544703 Loss2: 1.384588
+(DefaultActor pid=3764) >> Training accuracy: 0.133333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.275703 Loss1: 4.361426 Loss2: 1.914277
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.433223 Loss1: 3.852173 Loss2: 1.581050
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.263215 Loss1: 3.727729 Loss2: 1.535486
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.182521 Loss1: 3.654693 Loss2: 1.527828
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.966209 Loss1: 4.249379 Loss2: 1.716830
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.162322 Loss1: 3.626975 Loss2: 1.535347
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.090662 Loss1: 3.674542 Loss2: 1.416120
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.137459 Loss1: 3.623739 Loss2: 1.513721
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.877579 Loss1: 3.489159 Loss2: 1.388420
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.882158 Loss1: 3.495637 Loss2: 1.386522
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.112182 Loss1: 3.596541 Loss2: 1.515641
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.784684 Loss1: 3.416334 Loss2: 1.368350
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.103623 Loss1: 3.577765 Loss2: 1.525858
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.766490 Loss1: 3.412103 Loss2: 1.354387
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.117812 Loss1: 3.598554 Loss2: 1.519258
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.708643 Loss1: 3.349897 Loss2: 1.358745
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.084374 Loss1: 3.564294 Loss2: 1.520080
+(DefaultActor pid=3765) >> Training accuracy: 0.102539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.689035 Loss1: 3.321248 Loss2: 1.367787 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.189583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.037046 Loss1: 4.395273 Loss2: 1.641774 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.124501 Loss1: 3.823172 Loss2: 1.301329 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.067388 Loss1: 3.791907 Loss2: 1.275481
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.123525 Loss1: 4.280477 Loss2: 1.843048
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.006750 Loss1: 3.740342 Loss2: 1.266408
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.226769 Loss1: 3.716358 Loss2: 1.510411
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.027659 Loss1: 3.734084 Loss2: 1.293575
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.066754 Loss1: 3.604297 Loss2: 1.462458
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.986648 Loss1: 3.697460 Loss2: 1.289189
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.978138 Loss1: 3.520193 Loss2: 1.457945
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.004017 Loss1: 3.711715 Loss2: 1.292302
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.931963 Loss1: 3.489566 Loss2: 1.442397
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.942760 Loss1: 3.668104 Loss2: 1.274656
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.905329 Loss1: 3.454457 Loss2: 1.450872
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.940113 Loss1: 3.644934 Loss2: 1.295179
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.939594 Loss1: 3.471929 Loss2: 1.467666
+(DefaultActor pid=3765) >> Training accuracy: 0.108333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.903750 Loss1: 3.443640 Loss2: 1.460110
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.887494 Loss1: 3.414445 Loss2: 1.473049
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.871818 Loss1: 3.425493 Loss2: 1.446325
+(DefaultActor pid=3764) >> Training accuracy: 0.170833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.844048 Loss1: 4.073249 Loss2: 1.770799
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.880139 Loss1: 3.384256 Loss2: 1.495884
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.693494 Loss1: 3.273870 Loss2: 1.419624
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.628786 Loss1: 3.228191 Loss2: 1.400595
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.991105 Loss1: 4.207611 Loss2: 1.783494
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.561520 Loss1: 3.186256 Loss2: 1.375263
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.189485 Loss1: 3.724101 Loss2: 1.465384
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.464151 Loss1: 3.105676 Loss2: 1.358475
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.034788 Loss1: 3.596951 Loss2: 1.437837
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.495109 Loss1: 3.143578 Loss2: 1.351531
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.958946 Loss1: 3.561812 Loss2: 1.397134
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.411705 Loss1: 3.059224 Loss2: 1.352481
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.900460 Loss1: 3.477978 Loss2: 1.422482
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.401866 Loss1: 3.050941 Loss2: 1.350924
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.824949 Loss1: 3.422143 Loss2: 1.402806
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.411535 Loss1: 3.064841 Loss2: 1.346695
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.827405 Loss1: 3.416798 Loss2: 1.410606
+(DefaultActor pid=3765) >> Training accuracy: 0.208333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.861161 Loss1: 3.409067 Loss2: 1.452095
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.813267 Loss1: 3.408139 Loss2: 1.405128
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.746246 Loss1: 3.354181 Loss2: 1.392065
+(DefaultActor pid=3764) >> Training accuracy: 0.170833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.874426 Loss1: 4.191581 Loss2: 1.682845
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.062355 Loss1: 3.686215 Loss2: 1.376140
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.902113 Loss1: 3.565889 Loss2: 1.336224
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.861385 Loss1: 3.542317 Loss2: 1.319069
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.988082 Loss1: 4.258927 Loss2: 1.729155
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.129481 Loss1: 3.728837 Loss2: 1.400644 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.944524 Loss1: 3.590279 Loss2: 1.354245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.908145 Loss1: 3.547454 Loss2: 1.360691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.851616 Loss1: 3.519531 Loss2: 1.332085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.786244 Loss1: 3.451364 Loss2: 1.334880 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.185417
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.695717 Loss1: 3.363672 Loss2: 1.332044
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.773929 Loss1: 3.440882 Loss2: 1.333047
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.780695 Loss1: 3.449910 Loss2: 1.330784
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.759300 Loss1: 3.437248 Loss2: 1.322052
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.817690 Loss1: 3.481751 Loss2: 1.335938
+(DefaultActor pid=3764) >> Training accuracy: 0.120833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.310742 Loss1: 4.329351 Loss2: 1.981391
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.466906 Loss1: 3.787037 Loss2: 1.679869
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.265440 Loss1: 3.655424 Loss2: 1.610016
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.184478 Loss1: 3.597900 Loss2: 1.586578
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.114390 Loss1: 4.330266 Loss2: 1.784124
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.128939 Loss1: 3.533588 Loss2: 1.595350
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.241712 Loss1: 3.803063 Loss2: 1.438649
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.034592 Loss1: 3.653464 Loss2: 1.381128
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.055486 Loss1: 3.484360 Loss2: 1.571126
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.977578 Loss1: 3.624401 Loss2: 1.353177
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.031647 Loss1: 3.467525 Loss2: 1.564122
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.936401 Loss1: 3.588098 Loss2: 1.348304
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.035346 Loss1: 3.448346 Loss2: 1.587000
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.071075 Loss1: 3.483138 Loss2: 1.587937 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.010749 Loss1: 3.422961 Loss2: 1.587788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.156250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.866613 Loss1: 3.514177 Loss2: 1.352436 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.139509
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.193827 Loss1: 4.212172 Loss2: 1.981654 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.116516 Loss1: 3.565942 Loss2: 1.550574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.066870 Loss1: 3.517170 Loss2: 1.549700
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.966674 Loss1: 4.243962 Loss2: 1.722712
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.096607 Loss1: 3.536973 Loss2: 1.559633
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.120398 Loss1: 3.714333 Loss2: 1.406064
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.002222 Loss1: 3.458877 Loss2: 1.543345
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.930463 Loss1: 3.569493 Loss2: 1.360969
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.980090 Loss1: 3.425353 Loss2: 1.554737
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.919087 Loss1: 3.547888 Loss2: 1.371199
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.973177 Loss1: 3.426381 Loss2: 1.546796
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.896906 Loss1: 3.527421 Loss2: 1.369485
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.910350 Loss1: 3.380716 Loss2: 1.529634
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.833732 Loss1: 3.479708 Loss2: 1.354024
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.970606 Loss1: 3.436000 Loss2: 1.534606
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.798624 Loss1: 3.433832 Loss2: 1.364792
+(DefaultActor pid=3765) >> Training accuracy: 0.160417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.787188 Loss1: 3.428345 Loss2: 1.358844
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.747484 Loss1: 3.390720 Loss2: 1.356764
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.758271 Loss1: 3.407769 Loss2: 1.350503
+(DefaultActor pid=3764) >> Training accuracy: 0.144792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.223810 Loss1: 4.351334 Loss2: 1.872476
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.274381 Loss1: 3.703205 Loss2: 1.571175
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.012777 Loss1: 3.530215 Loss2: 1.482562
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.947204 Loss1: 3.474327 Loss2: 1.472878
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.848904 Loss1: 4.121425 Loss2: 1.727479
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.994584 Loss1: 3.541449 Loss2: 1.453135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.756469 Loss1: 3.391157 Loss2: 1.365312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.606575 Loss1: 3.228916 Loss2: 1.377659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.647834 Loss1: 3.282440 Loss2: 1.365393 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.583512 Loss1: 3.235576 Loss2: 1.347936 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.144792
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.853919 Loss1: 3.402141 Loss2: 1.451778
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.538981 Loss1: 3.176256 Loss2: 1.362725
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.519399 Loss1: 3.169229 Loss2: 1.350170
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.535126 Loss1: 3.167554 Loss2: 1.367571
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.484691 Loss1: 3.122071 Loss2: 1.362620
+(DefaultActor pid=3764) >> Training accuracy: 0.210417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.458938 Loss1: 4.357315 Loss2: 2.101622
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.527286 Loss1: 3.728953 Loss2: 1.798333
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.231243 Loss1: 3.526716 Loss2: 1.704528
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.107083 Loss1: 3.453794 Loss2: 1.653288
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.266578 Loss1: 4.258020 Loss2: 2.008558
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.436154 Loss1: 3.738599 Loss2: 1.697555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.248469 Loss1: 3.600257 Loss2: 1.648212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.157104 Loss1: 3.554015 Loss2: 1.603088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.098155 Loss1: 3.547212 Loss2: 1.550943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.030888 Loss1: 3.505714 Loss2: 1.525174 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.179167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.989312 Loss1: 3.473508 Loss2: 1.515804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.909078 Loss1: 3.407057 Loss2: 1.502021 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.156250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.202071 Loss1: 4.415309 Loss2: 1.786762 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.126114 Loss1: 3.745287 Loss2: 1.380827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.056205 Loss1: 3.695010 Loss2: 1.361195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.032873 Loss1: 3.670146 Loss2: 1.362727 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.952590 Loss1: 3.607761 Loss2: 1.344829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.940806 Loss1: 3.602065 Loss2: 1.338741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.918009 Loss1: 3.568245 Loss2: 1.349764
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.176924 Loss1: 3.744330 Loss2: 1.432594
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.911287 Loss1: 3.584703 Loss2: 1.326584
+(DefaultActor pid=3765) >> Training accuracy: 0.123798
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.125448 Loss1: 3.691320 Loss2: 1.434128
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.110269 Loss1: 3.673906 Loss2: 1.436362
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.111414 Loss1: 3.663177 Loss2: 1.448237
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.091220 Loss1: 3.626910 Loss2: 1.464310
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.087340 Loss1: 3.645285 Loss2: 1.442055
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.136401 Loss1: 4.282081 Loss2: 1.854320
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.023902 Loss1: 3.571834 Loss2: 1.452068
+(DefaultActor pid=3764) >> Training accuracy: 0.117708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.059288 Loss1: 3.589754 Loss2: 1.469534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.942432 Loss1: 3.499689 Loss2: 1.442743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.970481 Loss1: 4.244631 Loss2: 1.725851 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.066617 Loss1: 3.662277 Loss2: 1.404340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.903230 Loss1: 3.562155 Loss2: 1.341075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.802703 Loss1: 3.378252 Loss2: 1.424451 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.152902
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.792861 Loss1: 3.448662 Loss2: 1.344199 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.703588 Loss1: 3.372432 Loss2: 1.331155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.656076 Loss1: 3.313738 Loss2: 1.342337
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.062823 Loss1: 4.332961 Loss2: 1.729863
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.697212 Loss1: 3.342597 Loss2: 1.354615
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.167326 Loss1: 3.749741 Loss2: 1.417584
+(DefaultActor pid=3764) >> Training accuracy: 0.182292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.994416 Loss1: 3.616642 Loss2: 1.377774
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.935058 Loss1: 3.573152 Loss2: 1.361905
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.863803 Loss1: 3.508332 Loss2: 1.355472
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.883169 Loss1: 3.506356 Loss2: 1.376813
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.812017 Loss1: 3.452371 Loss2: 1.359646
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.514833 Loss1: 4.405469 Loss2: 2.109364
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.808978 Loss1: 3.448397 Loss2: 1.360581
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.558289 Loss1: 3.819908 Loss2: 1.738381
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.829557 Loss1: 3.445816 Loss2: 1.383741
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.320909 Loss1: 3.652682 Loss2: 1.668227
+(DefaultActor pid=3765) >> Training accuracy: 0.144792
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.832551 Loss1: 3.457470 Loss2: 1.375081
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.200123 Loss1: 3.597568 Loss2: 1.602555
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.120202 Loss1: 3.514878 Loss2: 1.605324
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.124148 Loss1: 3.526049 Loss2: 1.598099
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.070772 Loss1: 3.502750 Loss2: 1.568021
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.057203 Loss1: 3.487625 Loss2: 1.569578
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.915707 Loss1: 4.308788 Loss2: 1.606919
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.182079 Loss1: 3.858659 Loss2: 1.323420 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.125977
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.071507 Loss1: 3.472212 Loss2: 1.599294
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.028622 Loss1: 3.764951 Loss2: 1.263671
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.973244 Loss1: 3.716870 Loss2: 1.256374
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.915452 Loss1: 3.669137 Loss2: 1.246315
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.912814 Loss1: 3.666364 Loss2: 1.246451
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.849370 Loss1: 3.610924 Loss2: 1.238445
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.984219 Loss1: 4.273950 Loss2: 1.710269
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.859123 Loss1: 3.606709 Loss2: 1.252414
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.825590 Loss1: 3.578293 Loss2: 1.247297 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.851396 Loss1: 3.598878 Loss2: 1.252518 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.137500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.717858 Loss1: 3.344199 Loss2: 1.373659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.613629 Loss1: 3.251746 Loss2: 1.361883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.633906 Loss1: 3.270237 Loss2: 1.363669
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.303412 Loss1: 4.440942 Loss2: 1.862469
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.537331 Loss1: 3.982646 Loss2: 1.554686 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.219792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.331561 Loss1: 3.813058 Loss2: 1.518503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.250189 Loss1: 3.756704 Loss2: 1.493485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.205583 Loss1: 3.710532 Loss2: 1.495051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.179644 Loss1: 3.690856 Loss2: 1.488788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.209982 Loss1: 3.698870 Loss2: 1.511112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.129807 Loss1: 3.647090 Loss2: 1.482717 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.171875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.736772 Loss1: 3.270849 Loss2: 1.465923 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.716012 Loss1: 3.267961 Loss2: 1.448051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.656000 Loss1: 3.212285 Loss2: 1.443715
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.127029 Loss1: 4.279263 Loss2: 1.847765
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.620254 Loss1: 3.154267 Loss2: 1.465987
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.268786 Loss1: 3.734849 Loss2: 1.533937
+(DefaultActor pid=3764) >> Training accuracy: 0.206250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.010444 Loss1: 3.531623 Loss2: 1.478821
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.981697 Loss1: 3.525613 Loss2: 1.456084
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.964194 Loss1: 3.513674 Loss2: 1.450520
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.957538 Loss1: 3.492545 Loss2: 1.464993
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.149681 Loss1: 4.295852 Loss2: 1.853829
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.878979 Loss1: 3.431632 Loss2: 1.447347
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.381590 Loss1: 3.856818 Loss2: 1.524772
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.839280 Loss1: 3.380714 Loss2: 1.458565
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.182480 Loss1: 3.695604 Loss2: 1.486877
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.829252 Loss1: 3.365752 Loss2: 1.463500
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.115884 Loss1: 3.650051 Loss2: 1.465833
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.810015 Loss1: 3.345449 Loss2: 1.464566
+(DefaultActor pid=3765) >> Training accuracy: 0.148958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.133920 Loss1: 3.641389 Loss2: 1.492531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.031794 Loss1: 3.553700 Loss2: 1.478094 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.031757 Loss1: 3.557031 Loss2: 1.474726
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.577476 Loss1: 4.293759 Loss2: 2.283718
+(DefaultActor pid=3764) >> Training accuracy: 0.115625
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.985081 Loss1: 3.529562 Loss2: 1.455519
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.389064 Loss1: 3.591048 Loss2: 1.798016
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.109888 Loss1: 3.431988 Loss2: 1.677900
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.001347 Loss1: 3.347924 Loss2: 1.653422
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.954816 Loss1: 3.328564 Loss2: 1.626251
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.949210 Loss1: 3.321456 Loss2: 1.627753
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.016297 Loss1: 4.290928 Loss2: 1.725369
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.884270 Loss1: 3.259957 Loss2: 1.624313
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.836367 Loss1: 3.243576 Loss2: 1.592791 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.846810 Loss1: 3.247223 Loss2: 1.599587 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.800766 Loss1: 3.225079 Loss2: 1.575688 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.215625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.958115 Loss1: 3.594476 Loss2: 1.363639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.895042 Loss1: 3.541516 Loss2: 1.353527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.843781 Loss1: 3.497213 Loss2: 1.346568
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.298930 Loss1: 4.462518 Loss2: 1.836412
+(DefaultActor pid=3764) >> Training accuracy: 0.128125
+DEBUG flwr 2023-10-08 14:58:18,604 | server.py:236 | fit_round 5 received 50 results and 0 failures
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.348853 Loss1: 3.795747 Loss2: 1.553107 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.013264 Loss1: 3.536259 Loss2: 1.477006 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.914841 Loss1: 3.463826 Loss2: 1.451015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.933671 Loss1: 3.480856 Loss2: 1.452815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.915397 Loss1: 3.451782 Loss2: 1.463615 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.284101 Loss1: 3.726254 Loss2: 1.557847
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.894823 Loss1: 3.440153 Loss2: 1.454670
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.237134 Loss1: 3.682134 Loss2: 1.555001
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.867351 Loss1: 3.389054 Loss2: 1.478297
+(DefaultActor pid=3765) >> Training accuracy: 0.146875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.193575 Loss1: 3.639431 Loss2: 1.554144 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.074186 Loss1: 3.545534 Loss2: 1.528652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.036974 Loss1: 4.201509 Loss2: 1.835466
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.060800 Loss1: 3.539042 Loss2: 1.521758
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.230979 Loss1: 3.725925 Loss2: 1.505054
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.035204 Loss1: 3.507255 Loss2: 1.527949
+(DefaultActor pid=3764) >> Training accuracy: 0.140625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.016281 Loss1: 3.582462 Loss2: 1.433818 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.946687 Loss1: 3.506771 Loss2: 1.439916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.916209 Loss1: 3.481599 Loss2: 1.434610
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.361835 Loss1: 4.425004 Loss2: 1.936831
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.506723 Loss1: 3.880847 Loss2: 1.625876 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.262552 Loss1: 3.705358 Loss2: 1.557194 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.119792
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.943290 Loss1: 3.494575 Loss2: 1.448715
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.198644 Loss1: 3.657667 Loss2: 1.540977
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.121425 Loss1: 3.585423 Loss2: 1.536003
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.157029 Loss1: 3.621476 Loss2: 1.535552
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.076208 Loss1: 3.546539 Loss2: 1.529669
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.049770 Loss1: 3.516577 Loss2: 1.533192
+(DefaultActor pid=3764) Epoch: 8 Loss: 5.025371 Loss1: 3.515226 Loss2: 1.510145
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.109057 Loss1: 4.273945 Loss2: 1.835111
+(DefaultActor pid=3764) Epoch: 9 Loss: 5.036649 Loss1: 3.509686 Loss2: 1.526963
+(DefaultActor pid=3764) >> Training accuracy: 0.132292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.202959 Loss1: 3.691898 Loss2: 1.511062
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.059697 Loss1: 3.580099 Loss2: 1.479598
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.018920 Loss1: 3.532723 Loss2: 1.486196
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.981694 Loss1: 3.503720 Loss2: 1.477975
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.961469 Loss1: 3.505772 Loss2: 1.455697
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.773865 Loss1: 4.178549 Loss2: 1.595316
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.945578 Loss1: 3.483688 Loss2: 1.461890
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.933481 Loss1: 3.621221 Loss2: 1.312260
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.873480 Loss1: 3.432373 Loss2: 1.441107
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.748705 Loss1: 3.501107 Loss2: 1.247599
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.856634 Loss1: 3.419680 Loss2: 1.436954
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.736013 Loss1: 3.492485 Loss2: 1.243528
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.662867 Loss1: 3.408507 Loss2: 1.254359
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.854236 Loss1: 3.395561 Loss2: 1.458675
+(DefaultActor pid=3765) >> Training accuracy: 0.165441
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.625659 Loss1: 3.370930 Loss2: 1.254728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.569144 Loss1: 3.323865 Loss2: 1.245280 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.189453
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 14:58:18,604][flwr][DEBUG] - fit_round 5 received 50 results and 0 failures
+INFO flwr 2023-10-08 14:59:00,061 | server.py:125 | fit progress: (5, 4.483498603772051, {'accuracy': 0.0252}, 11247.839639639002)
+>> Test accuracy: 0.025200
+[2023-10-08 14:59:00,061][flwr][INFO] - fit progress: (5, 4.483498603772051, {'accuracy': 0.0252}, 11247.839639639002)
+DEBUG flwr 2023-10-08 14:59:00,061 | server.py:173 | evaluate_round 5: strategy sampled 50 clients (out of 50)
+[2023-10-08 14:59:00,061][flwr][DEBUG] - evaluate_round 5: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 15:08:04,825 | server.py:187 | evaluate_round 5 received 50 results and 0 failures
+[2023-10-08 15:08:04,825][flwr][DEBUG] - evaluate_round 5 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 15:08:04,825 | server.py:222 | fit_round 6: strategy sampled 50 clients (out of 50)
+[2023-10-08 15:08:04,825][flwr][DEBUG] - fit_round 6: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.900495 Loss1: 4.190622 Loss2: 1.709874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.865359 Loss1: 3.540431 Loss2: 1.324928 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.779275 Loss1: 3.445119 Loss2: 1.334156
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.001278 Loss1: 4.130771 Loss2: 1.870508
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.768931 Loss1: 3.462789 Loss2: 1.306142
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.994466 Loss1: 3.474292 Loss2: 1.520175
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.784043 Loss1: 3.462651 Loss2: 1.321392
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.802204 Loss1: 3.352487 Loss2: 1.449717
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.716503 Loss1: 3.400823 Loss2: 1.315680
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.695584 Loss1: 3.273607 Loss2: 1.421977
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.708305 Loss1: 3.386377 Loss2: 1.321928
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.693200 Loss1: 3.279491 Loss2: 1.413709
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.723875 Loss1: 3.400935 Loss2: 1.322939
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.642843 Loss1: 3.227508 Loss2: 1.415335
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.639668 Loss1: 3.318440 Loss2: 1.321228
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.641895 Loss1: 3.225845 Loss2: 1.416051
+(DefaultActor pid=3765) >> Training accuracy: 0.157292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.620603 Loss1: 3.203999 Loss2: 1.416604
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.535747 Loss1: 3.118311 Loss2: 1.417436
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.574317 Loss1: 3.147155 Loss2: 1.427162
+(DefaultActor pid=3764) >> Training accuracy: 0.226042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.017918 Loss1: 4.162757 Loss2: 1.855161
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.160018 Loss1: 3.684721 Loss2: 1.475298
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.017787 Loss1: 3.590470 Loss2: 1.427317
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.887454 Loss1: 4.133293 Loss2: 1.754162
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.929651 Loss1: 3.506397 Loss2: 1.423254
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.113038 Loss1: 3.704368 Loss2: 1.408670
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.871113 Loss1: 3.457027 Loss2: 1.414086
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.912863 Loss1: 3.579697 Loss2: 1.333166
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.883475 Loss1: 3.468271 Loss2: 1.415205
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.866570 Loss1: 3.435923 Loss2: 1.430647 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.825548 Loss1: 3.394127 Loss2: 1.431421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.850552 Loss1: 3.427612 Loss2: 1.422939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.785982 Loss1: 3.360210 Loss2: 1.425772 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.164062
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.714159 Loss1: 3.382138 Loss2: 1.332021 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.167708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.116914 Loss1: 4.322986 Loss2: 1.793928 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.100621 Loss1: 3.699335 Loss2: 1.401286 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.093172 Loss1: 4.288610 Loss2: 1.804562
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.024551 Loss1: 3.627579 Loss2: 1.396973
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.199244 Loss1: 3.743526 Loss2: 1.455719
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.968036 Loss1: 3.575035 Loss2: 1.393001
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.002848 Loss1: 3.596365 Loss2: 1.406483
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.948707 Loss1: 3.557294 Loss2: 1.391413
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.954697 Loss1: 3.547844 Loss2: 1.406853
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.957041 Loss1: 3.558582 Loss2: 1.398459
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.913095 Loss1: 3.510335 Loss2: 1.402760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.893018 Loss1: 3.477491 Loss2: 1.415527 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.903140 Loss1: 3.492807 Loss2: 1.410332 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.161133
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.881218 Loss1: 3.478768 Loss2: 1.402450 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.138542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.042389 Loss1: 4.253516 Loss2: 1.788873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.996743 Loss1: 3.606897 Loss2: 1.389846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.986165 Loss1: 3.603759 Loss2: 1.382406
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.764183 Loss1: 3.953877 Loss2: 1.810307
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.933302 Loss1: 3.561912 Loss2: 1.371390
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.817218 Loss1: 3.326884 Loss2: 1.490334
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.867694 Loss1: 3.500260 Loss2: 1.367434
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.574329 Loss1: 3.168850 Loss2: 1.405478
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.853813 Loss1: 3.487550 Loss2: 1.366263
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.501027 Loss1: 3.113116 Loss2: 1.387911
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.882283 Loss1: 3.501856 Loss2: 1.380427
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.469000 Loss1: 3.101293 Loss2: 1.367707
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.818039 Loss1: 3.441287 Loss2: 1.376752
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.484371 Loss1: 3.112909 Loss2: 1.371462
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.847353 Loss1: 3.460899 Loss2: 1.386454
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.430749 Loss1: 3.056573 Loss2: 1.374176
+(DefaultActor pid=3765) >> Training accuracy: 0.172917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.419612 Loss1: 3.037464 Loss2: 1.382148
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.380751 Loss1: 2.996945 Loss2: 1.383807
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.418750 Loss1: 3.033888 Loss2: 1.384862
+(DefaultActor pid=3764) >> Training accuracy: 0.197917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.912304 Loss1: 4.215448 Loss2: 1.696855
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.988488 Loss1: 3.623635 Loss2: 1.364853
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.818206 Loss1: 3.508465 Loss2: 1.309742
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.780328 Loss1: 3.495998 Loss2: 1.284330
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.083412 Loss1: 4.157688 Loss2: 1.925724
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.177584 Loss1: 3.642300 Loss2: 1.535284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.997444 Loss1: 3.525554 Loss2: 1.471890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.925260 Loss1: 3.464175 Loss2: 1.461086 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.846005 Loss1: 3.386912 Loss2: 1.459092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.884293 Loss1: 3.414852 Loss2: 1.469441 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.190848
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.797090 Loss1: 3.327232 Loss2: 1.469859 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.838164 Loss1: 3.360513 Loss2: 1.477650 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.200000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.504930 Loss1: 3.887771 Loss2: 1.617159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.253801 Loss1: 3.693406 Loss2: 1.560394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.035985 Loss1: 4.229272 Loss2: 1.806713
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.230895 Loss1: 3.672288 Loss2: 1.558608
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.128233 Loss1: 3.669396 Loss2: 1.458837
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.183580 Loss1: 3.634563 Loss2: 1.549017
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.914528 Loss1: 3.508706 Loss2: 1.405822
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.153188 Loss1: 3.595882 Loss2: 1.557306
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.908757 Loss1: 3.514628 Loss2: 1.394129
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.185896 Loss1: 3.628005 Loss2: 1.557892
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.844971 Loss1: 3.437727 Loss2: 1.407243
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.157240 Loss1: 3.588160 Loss2: 1.569080
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.799697 Loss1: 3.406186 Loss2: 1.393512
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.147233 Loss1: 3.580865 Loss2: 1.566368
+(DefaultActor pid=3765) >> Training accuracy: 0.115625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.799571 Loss1: 3.417449 Loss2: 1.382122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.755260 Loss1: 3.366211 Loss2: 1.389049 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.194792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.170287 Loss1: 3.624128 Loss2: 1.546159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.821129 Loss1: 3.368615 Loss2: 1.452514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.815090 Loss1: 3.368572 Loss2: 1.446518
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.306738 Loss1: 4.260717 Loss2: 2.046020
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.491572 Loss1: 3.892842 Loss2: 1.598731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.245380 Loss1: 3.689671 Loss2: 1.555709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.145076 Loss1: 3.599199 Loss2: 1.545877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.085076 Loss1: 3.561955 Loss2: 1.523121 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.187500
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.629272 Loss1: 3.191520 Loss2: 1.437752
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.054762 Loss1: 3.523465 Loss2: 1.531297
+(DefaultActor pid=3764) Epoch: 6 Loss: 5.061757 Loss1: 3.537703 Loss2: 1.524054
+(DefaultActor pid=3764) Epoch: 7 Loss: 5.043039 Loss1: 3.508419 Loss2: 1.534620
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.968924 Loss1: 3.450937 Loss2: 1.517988
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.969599 Loss1: 3.434942 Loss2: 1.534657
+(DefaultActor pid=3764) >> Training accuracy: 0.132212
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.949450 Loss1: 4.034739 Loss2: 1.914711
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.989085 Loss1: 3.461128 Loss2: 1.527957
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.842441 Loss1: 3.401521 Loss2: 1.440920
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.705468 Loss1: 3.301997 Loss2: 1.403471
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.755518 Loss1: 3.969894 Loss2: 1.785624
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.847053 Loss1: 3.376386 Loss2: 1.470668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.697880 Loss1: 3.272468 Loss2: 1.425411 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.628244 Loss1: 3.229339 Loss2: 1.398905 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.564061 Loss1: 3.164602 Loss2: 1.399459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.562999 Loss1: 3.163247 Loss2: 1.399752 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.239583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.517033 Loss1: 3.113955 Loss2: 1.403077 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.457467 Loss1: 3.061669 Loss2: 1.395798 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.211458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.090448 Loss1: 3.620023 Loss2: 1.470425 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.864920 Loss1: 3.452369 Loss2: 1.412551 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.173282 Loss1: 4.142182 Loss2: 2.031101
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.819239 Loss1: 3.413786 Loss2: 1.405452
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.174537 Loss1: 3.558094 Loss2: 1.616443
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.769577 Loss1: 3.371006 Loss2: 1.398571
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.013973 Loss1: 3.448444 Loss2: 1.565529
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.837401 Loss1: 3.426828 Loss2: 1.410573
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.951961 Loss1: 3.402062 Loss2: 1.549899
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.749455 Loss1: 3.356050 Loss2: 1.393405
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.872127 Loss1: 3.337023 Loss2: 1.535104
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.754837 Loss1: 3.337635 Loss2: 1.417202
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.895206 Loss1: 3.341175 Loss2: 1.554031
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.716931 Loss1: 3.304541 Loss2: 1.412391
+(DefaultActor pid=3765) >> Training accuracy: 0.191667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.874123 Loss1: 3.331623 Loss2: 1.542499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.812605 Loss1: 3.265287 Loss2: 1.547317 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.182292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.288959 Loss1: 3.789329 Loss2: 1.499629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.084346 Loss1: 3.616719 Loss2: 1.467627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.907100 Loss1: 4.146392 Loss2: 1.760709
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.033474 Loss1: 3.577135 Loss2: 1.456339
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.074194 Loss1: 3.639661 Loss2: 1.434533
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.978576 Loss1: 3.531641 Loss2: 1.446936
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.912390 Loss1: 3.539961 Loss2: 1.372428
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.979457 Loss1: 3.526134 Loss2: 1.453323
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.801068 Loss1: 3.423091 Loss2: 1.377978
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.973787 Loss1: 3.514077 Loss2: 1.459710
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.816428 Loss1: 3.448115 Loss2: 1.368313
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.937902 Loss1: 3.476426 Loss2: 1.461476
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.794434 Loss1: 3.422371 Loss2: 1.372063
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.924125 Loss1: 3.449554 Loss2: 1.474572
+(DefaultActor pid=3765) >> Training accuracy: 0.139583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.711958 Loss1: 3.332775 Loss2: 1.379183 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.653985 Loss1: 3.287806 Loss2: 1.366179 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.180208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.106849 Loss1: 3.647388 Loss2: 1.459460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.761543 Loss1: 3.371061 Loss2: 1.390482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.809561 Loss1: 3.421881 Loss2: 1.387681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.749323 Loss1: 3.355412 Loss2: 1.393911 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.720212 Loss1: 3.355411 Loss2: 1.364801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.700033 Loss1: 3.308286 Loss2: 1.391747 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.677193 Loss1: 3.295032 Loss2: 1.382161 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.716810 Loss1: 3.307753 Loss2: 1.409057 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.157292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.759330 Loss1: 3.392793 Loss2: 1.366537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.722877 Loss1: 3.359832 Loss2: 1.363045 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.161458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.088366 Loss1: 3.653181 Loss2: 1.435185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.875359 Loss1: 3.484424 Loss2: 1.390936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.154654 Loss1: 4.308972 Loss2: 1.845682
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.820303 Loss1: 3.442054 Loss2: 1.378249
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.794756 Loss1: 3.413323 Loss2: 1.381433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.821599 Loss1: 3.435956 Loss2: 1.385643 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.772473 Loss1: 3.383898 Loss2: 1.388575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.715563 Loss1: 3.322806 Loss2: 1.392757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.713150 Loss1: 3.321113 Loss2: 1.392036 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.182292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.970504 Loss1: 3.572242 Loss2: 1.398262 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.910600 Loss1: 3.504137 Loss2: 1.406463 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.137277
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.887755 Loss1: 4.116374 Loss2: 1.771381
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.911408 Loss1: 3.503385 Loss2: 1.408024
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.692416 Loss1: 3.340216 Loss2: 1.352200
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.645603 Loss1: 3.307734 Loss2: 1.337869
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.112074 Loss1: 4.234205 Loss2: 1.877869
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.549956 Loss1: 3.204361 Loss2: 1.345595
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.198334 Loss1: 3.699264 Loss2: 1.499069
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.010109 Loss1: 3.567791 Loss2: 1.442318 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.958405 Loss1: 3.521415 Loss2: 1.436991 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.930761 Loss1: 3.511956 Loss2: 1.418804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.935011 Loss1: 3.519267 Loss2: 1.415745 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.286458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.871100 Loss1: 3.443579 Loss2: 1.427520 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.870384 Loss1: 3.431652 Loss2: 1.438732 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.154297
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.145118 Loss1: 4.307555 Loss2: 1.837562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.039475 Loss1: 3.623639 Loss2: 1.415836 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.003579 Loss1: 4.140804 Loss2: 1.862775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.129599 Loss1: 3.653289 Loss2: 1.476310 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.954580 Loss1: 3.529028 Loss2: 1.425552 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.902599 Loss1: 3.486025 Loss2: 1.416575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.874943 Loss1: 3.455481 Loss2: 1.419462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.795775 Loss1: 3.392416 Loss2: 1.403359 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.180208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.766301 Loss1: 3.365693 Loss2: 1.400608 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.723397 Loss1: 3.309710 Loss2: 1.413687 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.158333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.959039 Loss1: 3.594554 Loss2: 1.364485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.781290 Loss1: 3.491584 Loss2: 1.289706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.171215 Loss1: 4.245531 Loss2: 1.925684
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.743430 Loss1: 3.449541 Loss2: 1.293889
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.287514 Loss1: 3.759159 Loss2: 1.528355
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.722643 Loss1: 3.427930 Loss2: 1.294712
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.063794 Loss1: 3.593779 Loss2: 1.470015
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.664392 Loss1: 3.366356 Loss2: 1.298035
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.010719 Loss1: 3.542091 Loss2: 1.468628
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.685854 Loss1: 3.382105 Loss2: 1.303749
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.974262 Loss1: 3.516078 Loss2: 1.458185
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.717534 Loss1: 3.420514 Loss2: 1.297019
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.956004 Loss1: 3.492770 Loss2: 1.463233
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.673639 Loss1: 3.365646 Loss2: 1.307994
+(DefaultActor pid=3765) >> Training accuracy: 0.171875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.905698 Loss1: 3.426512 Loss2: 1.479186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.902738 Loss1: 3.439476 Loss2: 1.463263 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.138542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.139108 Loss1: 3.634856 Loss2: 1.504251 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.928181 Loss1: 3.511480 Loss2: 1.416701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.848770 Loss1: 3.457666 Loss2: 1.391105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.873812 Loss1: 3.478449 Loss2: 1.395363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.830602 Loss1: 3.437365 Loss2: 1.393237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.767233 Loss1: 3.384145 Loss2: 1.383087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.743262 Loss1: 3.349278 Loss2: 1.393984 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.711056 Loss1: 3.322242 Loss2: 1.388815 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.164062
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.713176 Loss1: 3.246326 Loss2: 1.466850 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.202083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.102814 Loss1: 4.257854 Loss2: 1.844961 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.033513 Loss1: 3.648315 Loss2: 1.385198 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.061708 Loss1: 3.956660 Loss2: 2.105047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.187903 Loss1: 3.551522 Loss2: 1.636381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.939535 Loss1: 3.360026 Loss2: 1.579509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.883019 Loss1: 3.332928 Loss2: 1.550090 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.864056 Loss1: 3.302192 Loss2: 1.561864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.839549 Loss1: 3.467564 Loss2: 1.371985 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.137277
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.799998 Loss1: 3.240765 Loss2: 1.559233 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.831819 Loss1: 3.246471 Loss2: 1.585348 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.217708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.093101 Loss1: 3.644579 Loss2: 1.448522 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.828982 Loss1: 3.447756 Loss2: 1.381226 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.790535 Loss1: 3.412505 Loss2: 1.378031
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.955093 Loss1: 4.140942 Loss2: 1.814151
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.795711 Loss1: 3.407630 Loss2: 1.388082
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.061069 Loss1: 3.615036 Loss2: 1.446033
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.797533 Loss1: 3.410275 Loss2: 1.387258
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.897374 Loss1: 3.490711 Loss2: 1.406662
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.737346 Loss1: 3.358606 Loss2: 1.378740
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.867033 Loss1: 3.481235 Loss2: 1.385799
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.792440 Loss1: 3.387177 Loss2: 1.405263
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.813183 Loss1: 3.417042 Loss2: 1.396141
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.783186 Loss1: 3.380184 Loss2: 1.403003
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.762241 Loss1: 3.373296 Loss2: 1.388945
+(DefaultActor pid=3765) >> Training accuracy: 0.170833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.733892 Loss1: 3.351853 Loss2: 1.382039
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.708665 Loss1: 3.317689 Loss2: 1.390975
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.682162 Loss1: 3.291540 Loss2: 1.390622
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.720846 Loss1: 3.314270 Loss2: 1.406575
+(DefaultActor pid=3764) >> Training accuracy: 0.147917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.004221 Loss1: 4.070209 Loss2: 1.934012
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.139836 Loss1: 3.582363 Loss2: 1.557473
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.954651 Loss1: 3.477057 Loss2: 1.477594
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.897405 Loss1: 3.410963 Loss2: 1.486442
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.070187 Loss1: 4.137527 Loss2: 1.932660
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.834641 Loss1: 3.364026 Loss2: 1.470615
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.122775 Loss1: 3.551768 Loss2: 1.571007
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.812565 Loss1: 3.326925 Loss2: 1.485640
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.796167 Loss1: 3.319472 Loss2: 1.476695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.736452 Loss1: 3.244031 Loss2: 1.492421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.749728 Loss1: 3.250148 Loss2: 1.499580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.721893 Loss1: 3.228841 Loss2: 1.493051 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.182617
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.748466 Loss1: 3.224877 Loss2: 1.523589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.660783 Loss1: 3.133765 Loss2: 1.527018 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.206250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.861306 Loss1: 4.118803 Loss2: 1.742503
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.914680 Loss1: 3.495785 Loss2: 1.418895
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.764870 Loss1: 3.419926 Loss2: 1.344944
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.707296 Loss1: 3.360585 Loss2: 1.346711
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.194680 Loss1: 4.263971 Loss2: 1.930709
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.671610 Loss1: 3.329105 Loss2: 1.342506
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.362858 Loss1: 3.833443 Loss2: 1.529415
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.677735 Loss1: 3.321791 Loss2: 1.355944
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.177871 Loss1: 3.681223 Loss2: 1.496648
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.684950 Loss1: 3.316000 Loss2: 1.368950
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.127153 Loss1: 3.639321 Loss2: 1.487832
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.633258 Loss1: 3.271786 Loss2: 1.361472
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.056634 Loss1: 3.579429 Loss2: 1.477205
+(DefaultActor pid=3764) Epoch: 5 Loss: 5.016617 Loss1: 3.530343 Loss2: 1.486274
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.654396 Loss1: 3.278349 Loss2: 1.376047
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.981990 Loss1: 3.495665 Loss2: 1.486325
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.560900 Loss1: 3.188841 Loss2: 1.372060
+(DefaultActor pid=3765) >> Training accuracy: 0.177734
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.937392 Loss1: 3.441221 Loss2: 1.496171 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.181250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.040676 Loss1: 4.199370 Loss2: 1.841306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.081421 Loss1: 3.641873 Loss2: 1.439548 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.026292 Loss1: 3.595884 Loss2: 1.430408
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.018125 Loss1: 4.269140 Loss2: 1.748985
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.247547 Loss1: 3.853748 Loss2: 1.393799
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.981043 Loss1: 3.552052 Loss2: 1.428991
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.980262 Loss1: 3.642676 Loss2: 1.337586
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.961691 Loss1: 3.534210 Loss2: 1.427481
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.905241 Loss1: 3.582345 Loss2: 1.322896
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.926727 Loss1: 3.495520 Loss2: 1.431207
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.865127 Loss1: 3.558102 Loss2: 1.307025
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.912124 Loss1: 3.476661 Loss2: 1.435463
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.931462 Loss1: 3.490275 Loss2: 1.441187 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.944127 Loss1: 3.492136 Loss2: 1.451991 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.143555
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.791220 Loss1: 3.463364 Loss2: 1.327856 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.126042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.113930 Loss1: 4.235195 Loss2: 1.878735 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.036779 Loss1: 3.656704 Loss2: 1.380075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.882803 Loss1: 3.527293 Loss2: 1.355510 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.093565 Loss1: 3.590762 Loss2: 1.502803 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.873465 Loss1: 3.432956 Loss2: 1.440509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.755460 Loss1: 3.396954 Loss2: 1.358507 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.194010
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.769506 Loss1: 3.399049 Loss2: 1.370457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.698421 Loss1: 3.284605 Loss2: 1.413816 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.675345 Loss1: 3.256050 Loss2: 1.419294 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.602403 Loss1: 3.194816 Loss2: 1.407587 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.176042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.915057 Loss1: 3.470351 Loss2: 1.444706 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.798841 Loss1: 3.349311 Loss2: 1.449530 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.780125 Loss1: 3.332587 Loss2: 1.447537
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.976750 Loss1: 4.283407 Loss2: 1.693343
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.779923 Loss1: 3.319507 Loss2: 1.460416
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.248631 Loss1: 3.883528 Loss2: 1.365103
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.743389 Loss1: 3.271524 Loss2: 1.471865
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.114146 Loss1: 3.783651 Loss2: 1.330495
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.708039 Loss1: 3.248012 Loss2: 1.460027
+(DefaultActor pid=3764) Epoch: 3 Loss: 5.044365 Loss1: 3.719691 Loss2: 1.324674
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.713047 Loss1: 3.237591 Loss2: 1.475456
+(DefaultActor pid=3764) Epoch: 4 Loss: 5.009922 Loss1: 3.684106 Loss2: 1.325817
+(DefaultActor pid=3765) >> Training accuracy: 0.209961
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.996174 Loss1: 3.666032 Loss2: 1.330142
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.996227 Loss1: 3.662542 Loss2: 1.333685
+DEBUG flwr 2023-10-08 15:36:46,038 | server.py:236 | fit_round 6 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.971051 Loss1: 3.632509 Loss2: 1.338541
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.951487 Loss1: 3.615491 Loss2: 1.335997
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.922954 Loss1: 4.156120 Loss2: 1.766834
+(DefaultActor pid=3764) >> Training accuracy: 0.149414
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.099912 Loss1: 3.656556 Loss2: 1.443355 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.862869 Loss1: 3.502635 Loss2: 1.360233 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.766301 Loss1: 3.410951 Loss2: 1.355349 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.728923 Loss1: 3.368720 Loss2: 1.360203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.741903 Loss1: 3.378148 Loss2: 1.363756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.824529 Loss1: 3.481812 Loss2: 1.342717
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.742281 Loss1: 3.375051 Loss2: 1.367230
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.703484 Loss1: 3.329820 Loss2: 1.373664
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.802292 Loss1: 3.475934 Loss2: 1.326358
+(DefaultActor pid=3765) >> Training accuracy: 0.159375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.753922 Loss1: 3.417251 Loss2: 1.336672
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.721835 Loss1: 3.407196 Loss2: 1.314639
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.724094 Loss1: 3.407470 Loss2: 1.316624
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.687509 Loss1: 3.352246 Loss2: 1.335264
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.282365 Loss1: 4.244117 Loss2: 2.038249
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.418847 Loss1: 3.797866 Loss2: 1.620980 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.176471
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.686946 Loss1: 3.352942 Loss2: 1.334004
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.278955 Loss1: 3.695839 Loss2: 1.583116
+(DefaultActor pid=3765) Epoch: 3 Loss: 5.264377 Loss1: 3.663128 Loss2: 1.601250
+(DefaultActor pid=3765) Epoch: 4 Loss: 5.210522 Loss1: 3.615818 Loss2: 1.594703
+(DefaultActor pid=3765) Epoch: 5 Loss: 5.149733 Loss1: 3.574951 Loss2: 1.574783
+(DefaultActor pid=3765) Epoch: 6 Loss: 5.134275 Loss1: 3.553866 Loss2: 1.580410
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.807259 Loss1: 4.192339 Loss2: 1.614920
+(DefaultActor pid=3765) Epoch: 7 Loss: 5.118162 Loss1: 3.533702 Loss2: 1.584459
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.997415 Loss1: 3.661630 Loss2: 1.335785
+(DefaultActor pid=3765) Epoch: 8 Loss: 5.072049 Loss1: 3.473844 Loss2: 1.598204
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.773219 Loss1: 3.499507 Loss2: 1.273712
+(DefaultActor pid=3765) Epoch: 9 Loss: 5.022581 Loss1: 3.424430 Loss2: 1.598150
+(DefaultActor pid=3765) >> Training accuracy: 0.136458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.651687 Loss1: 3.404164 Loss2: 1.247523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.606433 Loss1: 3.359651 Loss2: 1.246782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.572852 Loss1: 3.327156 Loss2: 1.245696 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.194792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 15:36:46,038][flwr][DEBUG] - fit_round 6 received 50 results and 0 failures
+INFO flwr 2023-10-08 15:37:27,327 | server.py:125 | fit progress: (6, 4.310671744636073, {'accuracy': 0.0415}, 13555.105305370998)
+>> Test accuracy: 0.041500
+[2023-10-08 15:37:27,327][flwr][INFO] - fit progress: (6, 4.310671744636073, {'accuracy': 0.0415}, 13555.105305370998)
+DEBUG flwr 2023-10-08 15:37:27,327 | server.py:173 | evaluate_round 6: strategy sampled 50 clients (out of 50)
+[2023-10-08 15:37:27,327][flwr][DEBUG] - evaluate_round 6: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 15:46:33,816 | server.py:187 | evaluate_round 6 received 50 results and 0 failures
+[2023-10-08 15:46:33,816][flwr][DEBUG] - evaluate_round 6 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 15:46:33,816 | server.py:222 | fit_round 7: strategy sampled 50 clients (out of 50)
+[2023-10-08 15:46:33,816][flwr][DEBUG] - fit_round 7: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.765522 Loss1: 3.878056 Loss2: 1.887467 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.710307 Loss1: 3.271797 Loss2: 1.438509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.693316 Loss1: 3.268632 Loss2: 1.424684
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.078043 Loss1: 4.216658 Loss2: 1.861384
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.605845 Loss1: 3.185500 Loss2: 1.420344
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.054804 Loss1: 3.586362 Loss2: 1.468443
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.493393 Loss1: 3.082758 Loss2: 1.410635
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.872199 Loss1: 3.456689 Loss2: 1.415510
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.495708 Loss1: 3.063748 Loss2: 1.431961
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.844780 Loss1: 3.434999 Loss2: 1.409781
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.502387 Loss1: 3.084897 Loss2: 1.417490
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.802559 Loss1: 3.373953 Loss2: 1.428606
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.443765 Loss1: 3.027313 Loss2: 1.416451
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.755748 Loss1: 3.346167 Loss2: 1.409581
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.451998 Loss1: 3.020045 Loss2: 1.431953
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.694337 Loss1: 3.297671 Loss2: 1.396666
+(DefaultActor pid=3765) >> Training accuracy: 0.290625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.688641 Loss1: 3.283071 Loss2: 1.405571
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.669550 Loss1: 3.254969 Loss2: 1.414581
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.626493 Loss1: 3.206832 Loss2: 1.419661
+(DefaultActor pid=3764) >> Training accuracy: 0.171875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.031734 Loss1: 4.130672 Loss2: 1.901061
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.160477 Loss1: 3.697156 Loss2: 1.463320
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.974419 Loss1: 3.565532 Loss2: 1.408887
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.901216 Loss1: 3.502155 Loss2: 1.399061
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.940227 Loss1: 4.207410 Loss2: 1.732817
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.183246 Loss1: 3.807387 Loss2: 1.375859 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.011132 Loss1: 3.676096 Loss2: 1.335036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.933195 Loss1: 3.612709 Loss2: 1.320486 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.896582 Loss1: 3.570027 Loss2: 1.326556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.902611 Loss1: 3.578310 Loss2: 1.324301 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.166295
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.895185 Loss1: 3.558117 Loss2: 1.337068 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.808270 Loss1: 3.469482 Loss2: 1.338788 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.156250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.976738 Loss1: 3.396877 Loss2: 1.579861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.580909 Loss1: 3.090620 Loss2: 1.490289 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.498582 Loss1: 3.014327 Loss2: 1.484255
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.953140 Loss1: 4.226318 Loss2: 1.726823
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.487269 Loss1: 2.992405 Loss2: 1.494863
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.148478 Loss1: 3.757320 Loss2: 1.391158
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.485171 Loss1: 2.991943 Loss2: 1.493228
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.049447 Loss1: 3.690109 Loss2: 1.359338
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.989268 Loss1: 3.621503 Loss2: 1.367766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.948962 Loss1: 3.586596 Loss2: 1.362366 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.227083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.890918 Loss1: 3.529130 Loss2: 1.361788 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.844452 Loss1: 3.461747 Loss2: 1.382705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.835670 Loss1: 3.447820 Loss2: 1.387850 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.197266
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.985327 Loss1: 3.541985 Loss2: 1.443341 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.915458 Loss1: 3.471690 Loss2: 1.443768 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.875964 Loss1: 3.435466 Loss2: 1.440497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.817450 Loss1: 3.367993 Loss2: 1.449458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.802672 Loss1: 3.352373 Loss2: 1.450298 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.789484 Loss1: 3.328934 Loss2: 1.460550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.778589 Loss1: 3.320841 Loss2: 1.457748 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.141667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.768111 Loss1: 3.387505 Loss2: 1.380606 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.767327 Loss1: 3.384816 Loss2: 1.382511 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.173077
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.966586 Loss1: 4.151413 Loss2: 1.815173
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.005542 Loss1: 3.603673 Loss2: 1.401869
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.846401 Loss1: 3.463010 Loss2: 1.383391
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.779155 Loss1: 3.413657 Loss2: 1.365498
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.951396 Loss1: 4.131328 Loss2: 1.820068
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.041213 Loss1: 3.637880 Loss2: 1.403333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.908441 Loss1: 3.546745 Loss2: 1.361696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.846603 Loss1: 3.488497 Loss2: 1.358106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.806935 Loss1: 3.441564 Loss2: 1.365371 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.793242 Loss1: 3.429326 Loss2: 1.363916 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.173958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.742105 Loss1: 3.382335 Loss2: 1.359771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.723696 Loss1: 3.355044 Loss2: 1.368652 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.178125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.918618 Loss1: 4.058139 Loss2: 1.860479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.906554 Loss1: 3.465118 Loss2: 1.441436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.860744 Loss1: 4.061261 Loss2: 1.799483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.024759 Loss1: 3.601649 Loss2: 1.423110 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.841288 Loss1: 3.474323 Loss2: 1.366965 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.744061 Loss1: 3.380781 Loss2: 1.363280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.736666 Loss1: 3.374570 Loss2: 1.362095 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.671337 Loss1: 3.293089 Loss2: 1.378248 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.189583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.680529 Loss1: 3.305703 Loss2: 1.374827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.676805 Loss1: 3.289222 Loss2: 1.387583 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.204167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.980671 Loss1: 4.072246 Loss2: 1.908425 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.854593 Loss1: 3.400391 Loss2: 1.454202 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.866885 Loss1: 4.124938 Loss2: 1.741947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.743117 Loss1: 3.309021 Loss2: 1.434096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.685871 Loss1: 3.242415 Loss2: 1.443456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.708609 Loss1: 3.259450 Loss2: 1.449159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.715694 Loss1: 3.261265 Loss2: 1.454429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.650982 Loss1: 3.189805 Loss2: 1.461177 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.191964
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.759896 Loss1: 3.383975 Loss2: 1.375920 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.742977 Loss1: 3.361769 Loss2: 1.381208 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.184375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.099248 Loss1: 3.540193 Loss2: 1.559055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.852996 Loss1: 3.368176 Loss2: 1.484820 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.869326 Loss1: 3.388120 Loss2: 1.481206
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.691860 Loss1: 3.915558 Loss2: 1.776302
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.893999 Loss1: 3.491739 Loss2: 1.402260 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.685797 Loss1: 3.317494 Loss2: 1.368303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.688504 Loss1: 3.325453 Loss2: 1.363051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.578559 Loss1: 3.209682 Loss2: 1.368877 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.177083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.571236 Loss1: 3.189254 Loss2: 1.381982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.553369 Loss1: 3.166951 Loss2: 1.386418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.530425 Loss1: 3.133869 Loss2: 1.396556 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.209961
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.636871 Loss1: 3.299933 Loss2: 1.336938 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.494812 Loss1: 3.180056 Loss2: 1.314756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.921184 Loss1: 4.067470 Loss2: 1.853714
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.533286 Loss1: 3.211014 Loss2: 1.322272
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.075478 Loss1: 3.622444 Loss2: 1.453034
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.465007 Loss1: 3.142697 Loss2: 1.322309
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.813908 Loss1: 3.424736 Loss2: 1.389171
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.476875 Loss1: 3.150891 Loss2: 1.325984
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.807403 Loss1: 3.427884 Loss2: 1.379519
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.499944 Loss1: 3.166853 Loss2: 1.333091
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.754844 Loss1: 3.364780 Loss2: 1.390063
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.419280 Loss1: 3.089635 Loss2: 1.329646
+(DefaultActor pid=3765) >> Training accuracy: 0.213542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.683333 Loss1: 3.287867 Loss2: 1.395466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.668056 Loss1: 3.280246 Loss2: 1.387810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.610353 Loss1: 3.216328 Loss2: 1.394025
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.051537 Loss1: 4.190912 Loss2: 1.860626
+(DefaultActor pid=3764) >> Training accuracy: 0.193750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.116455 Loss1: 3.646526 Loss2: 1.469929
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.994476 Loss1: 3.551939 Loss2: 1.442538
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.875236 Loss1: 3.459339 Loss2: 1.415897
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.857166 Loss1: 3.435189 Loss2: 1.421978
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.311000 Loss1: 4.361141 Loss2: 1.949859
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.813073 Loss1: 3.404545 Loss2: 1.408528
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.812696 Loss1: 3.394264 Loss2: 1.418432 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.758459 Loss1: 3.328876 Loss2: 1.429583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.801055 Loss1: 3.362680 Loss2: 1.438376 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.717031 Loss1: 3.286759 Loss2: 1.430271 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.196875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.896202 Loss1: 3.446753 Loss2: 1.449449 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.942290 Loss1: 3.476609 Loss2: 1.465681 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.148438
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.825514 Loss1: 3.368598 Loss2: 1.456916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.567259 Loss1: 3.155689 Loss2: 1.411570 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.573493 Loss1: 3.162981 Loss2: 1.410512
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.942528 Loss1: 4.008909 Loss2: 1.933620
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.513639 Loss1: 3.118245 Loss2: 1.395394
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.063349 Loss1: 3.544235 Loss2: 1.519114
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.458058 Loss1: 3.058927 Loss2: 1.399131
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.937773 Loss1: 3.453664 Loss2: 1.484109
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.393611 Loss1: 2.984714 Loss2: 1.408898
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.814662 Loss1: 3.324101 Loss2: 1.490561
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.426897 Loss1: 3.021286 Loss2: 1.405611
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.791511 Loss1: 3.300390 Loss2: 1.491121
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.426860 Loss1: 3.022721 Loss2: 1.404139
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.734654 Loss1: 3.247197 Loss2: 1.487457
+(DefaultActor pid=3765) >> Training accuracy: 0.253125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.772195 Loss1: 3.276688 Loss2: 1.495507
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.716148 Loss1: 3.213471 Loss2: 1.502676
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.657377 Loss1: 3.154089 Loss2: 1.503288
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.700728 Loss1: 3.188406 Loss2: 1.512323
+(DefaultActor pid=3764) >> Training accuracy: 0.196875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.842840 Loss1: 4.067979 Loss2: 1.774861
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.967930 Loss1: 3.576777 Loss2: 1.391153
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.727372 Loss1: 3.387901 Loss2: 1.339471
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.692167 Loss1: 3.363693 Loss2: 1.328473
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.627109 Loss1: 3.286565 Loss2: 1.340544
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.664244 Loss1: 3.323247 Loss2: 1.340997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.621992 Loss1: 3.273979 Loss2: 1.348013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.577510 Loss1: 3.221077 Loss2: 1.356434 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.577569 Loss1: 3.217171 Loss2: 1.360398 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.556039 Loss1: 3.192538 Loss2: 1.363501 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.200000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.610083 Loss1: 3.187652 Loss2: 1.422431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.550461 Loss1: 3.140686 Loss2: 1.409775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.520248 Loss1: 3.101343 Loss2: 1.418905
+(DefaultActor pid=3764) >> Training accuracy: 0.247070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.800726 Loss1: 4.040547 Loss2: 1.760179
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.039594 Loss1: 3.660698 Loss2: 1.378896
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.864738 Loss1: 3.518649 Loss2: 1.346090
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.803732 Loss1: 3.463542 Loss2: 1.340189
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.783877 Loss1: 3.444086 Loss2: 1.339791
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.935534 Loss1: 4.148066 Loss2: 1.787468
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.784640 Loss1: 3.426171 Loss2: 1.358469
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.734799 Loss1: 3.387772 Loss2: 1.347027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.731115 Loss1: 3.371363 Loss2: 1.359752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.691772 Loss1: 3.336319 Loss2: 1.355453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.690272 Loss1: 3.326758 Loss2: 1.363514 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.180208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.735011 Loss1: 3.381898 Loss2: 1.353113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.744087 Loss1: 3.388455 Loss2: 1.355632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.695206 Loss1: 3.332778 Loss2: 1.362428
+(DefaultActor pid=3764) >> Training accuracy: 0.175000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.785653 Loss1: 3.985734 Loss2: 1.799919
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.957932 Loss1: 3.545876 Loss2: 1.412056
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.834531 Loss1: 3.449989 Loss2: 1.384543
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.760249 Loss1: 3.378187 Loss2: 1.382063
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.713488 Loss1: 3.331451 Loss2: 1.382037
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.817401 Loss1: 3.969547 Loss2: 1.847854
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.705491 Loss1: 3.313308 Loss2: 1.392183
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.959122 Loss1: 3.521701 Loss2: 1.437421
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.673869 Loss1: 3.288367 Loss2: 1.385503
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.781402 Loss1: 3.392738 Loss2: 1.388664
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.625015 Loss1: 3.231371 Loss2: 1.393644
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.714223 Loss1: 3.330091 Loss2: 1.384132
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.616474 Loss1: 3.201511 Loss2: 1.414963
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.629167 Loss1: 3.258338 Loss2: 1.370829
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.630540 Loss1: 3.216617 Loss2: 1.413923
+(DefaultActor pid=3765) >> Training accuracy: 0.173958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.574790 Loss1: 3.185625 Loss2: 1.389164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.523142 Loss1: 3.133329 Loss2: 1.389813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.567172 Loss1: 3.164210 Loss2: 1.402962
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.714686 Loss1: 3.865424 Loss2: 1.849262
+(DefaultActor pid=3764) >> Training accuracy: 0.208333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.874650 Loss1: 3.421805 Loss2: 1.452845
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.658646 Loss1: 3.272831 Loss2: 1.385815
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.553121 Loss1: 3.178746 Loss2: 1.374375
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.509130 Loss1: 3.129435 Loss2: 1.379695
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.908279 Loss1: 4.170110 Loss2: 1.738169
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.540933 Loss1: 3.144252 Loss2: 1.396681
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.058474 Loss1: 3.690743 Loss2: 1.367731
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.500387 Loss1: 3.111550 Loss2: 1.388837
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.440182 Loss1: 3.049378 Loss2: 1.390804
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.890563 Loss1: 3.560225 Loss2: 1.330338
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.395765 Loss1: 3.003295 Loss2: 1.392470
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.851247 Loss1: 3.519491 Loss2: 1.331755
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.371797 Loss1: 2.976317 Loss2: 1.395481
+(DefaultActor pid=3765) >> Training accuracy: 0.240625
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.860014 Loss1: 3.521869 Loss2: 1.338145
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.868676 Loss1: 3.535723 Loss2: 1.332953
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.800294 Loss1: 3.454740 Loss2: 1.345554
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.796871 Loss1: 3.456011 Loss2: 1.340860
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.732751 Loss1: 3.391190 Loss2: 1.341561
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.004228 Loss1: 4.082413 Loss2: 1.921815
+(DefaultActor pid=3764) >> Training accuracy: 0.142578
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.079705 Loss1: 3.568352 Loss2: 1.511354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.886727 Loss1: 3.417140 Loss2: 1.469587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.737961 Loss1: 4.017025 Loss2: 1.720936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.775930 Loss1: 3.409368 Loss2: 1.366562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.647571 Loss1: 3.310725 Loss2: 1.336846 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.580684 Loss1: 3.258442 Loss2: 1.322242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.564810 Loss1: 3.234272 Loss2: 1.330537 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.190257
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.506893 Loss1: 3.176679 Loss2: 1.330214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.463221 Loss1: 3.127281 Loss2: 1.335941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.421931 Loss1: 3.072639 Loss2: 1.349292 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.208984
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.913330 Loss1: 3.425939 Loss2: 1.487392 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.787828 Loss1: 3.307841 Loss2: 1.479987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.835910 Loss1: 4.007331 Loss2: 1.828579
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.804729 Loss1: 3.320914 Loss2: 1.483814
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.970318 Loss1: 3.560412 Loss2: 1.409907
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.786392 Loss1: 3.307968 Loss2: 1.478423
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.812779 Loss1: 3.436155 Loss2: 1.376623
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.749194 Loss1: 3.266529 Loss2: 1.482665
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.753797 Loss1: 3.379664 Loss2: 1.374134
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.752034 Loss1: 3.258514 Loss2: 1.493520
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.689928 Loss1: 3.317634 Loss2: 1.372294
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.721657 Loss1: 3.224806 Loss2: 1.496851
+(DefaultActor pid=3765) >> Training accuracy: 0.183333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.659178 Loss1: 3.276134 Loss2: 1.383044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.687347 Loss1: 3.294362 Loss2: 1.392985 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.645054 Loss1: 3.259079 Loss2: 1.385975
+(DefaultActor pid=3764) >> Training accuracy: 0.208333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.984502 Loss1: 4.135516 Loss2: 1.848986
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.167237 Loss1: 3.689921 Loss2: 1.477316
+(DefaultActor pid=3765) Epoch: 2 Loss: 5.019366 Loss1: 3.606566 Loss2: 1.412800
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.963334 Loss1: 3.541706 Loss2: 1.421628
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.903580 Loss1: 3.483024 Loss2: 1.420556
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.900696 Loss1: 4.048868 Loss2: 1.851829
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.015458 Loss1: 3.556897 Loss2: 1.458560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.795166 Loss1: 3.370083 Loss2: 1.425083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.730918 Loss1: 3.336500 Loss2: 1.394418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.727822 Loss1: 3.329095 Loss2: 1.398727 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.811575 Loss1: 3.372752 Loss2: 1.438824
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.716740 Loss1: 3.311779 Loss2: 1.404961
+(DefaultActor pid=3765) >> Training accuracy: 0.164062
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.650094 Loss1: 3.258073 Loss2: 1.392022
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.660122 Loss1: 3.265852 Loss2: 1.394270
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.647533 Loss1: 3.248833 Loss2: 1.398700
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.588265 Loss1: 3.194235 Loss2: 1.394030
+(DefaultActor pid=3764) >> Training accuracy: 0.178125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.009679 Loss1: 4.052616 Loss2: 1.957062
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.010831 Loss1: 3.530729 Loss2: 1.480101
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.761121 Loss1: 3.339173 Loss2: 1.421949
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.663968 Loss1: 3.254828 Loss2: 1.409140
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.659124 Loss1: 3.249025 Loss2: 1.410099
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.661671 Loss1: 3.241739 Loss2: 1.419932
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.013005 Loss1: 4.107547 Loss2: 1.905458
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.166743 Loss1: 3.676427 Loss2: 1.490316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.963398 Loss1: 3.511800 Loss2: 1.451598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.935404 Loss1: 3.493812 Loss2: 1.441592 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.215144
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.876692 Loss1: 3.434095 Loss2: 1.442597 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.873781 Loss1: 3.416758 Loss2: 1.457023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.865072 Loss1: 4.106703 Loss2: 1.758369
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.805690 Loss1: 3.355089 Loss2: 1.450602
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.147706 Loss1: 3.735916 Loss2: 1.411791
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.803078 Loss1: 3.335742 Loss2: 1.467335
+(DefaultActor pid=3764) >> Training accuracy: 0.185547
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.966798 Loss1: 3.608805 Loss2: 1.357993 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.817144 Loss1: 3.461057 Loss2: 1.356087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.807279 Loss1: 3.434224 Loss2: 1.373055
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.082740 Loss1: 4.153858 Loss2: 1.928881
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.801611 Loss1: 3.423310 Loss2: 1.378301
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.104655 Loss1: 3.570529 Loss2: 1.534126
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.786272 Loss1: 3.396399 Loss2: 1.389873
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.903832 Loss1: 3.432683 Loss2: 1.471149
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.705628 Loss1: 3.324259 Loss2: 1.381369
+(DefaultActor pid=3765) >> Training accuracy: 0.161458
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.836885 Loss1: 3.375518 Loss2: 1.461367
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.783799 Loss1: 3.315096 Loss2: 1.468703
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.803865 Loss1: 3.317832 Loss2: 1.486033
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.713900 Loss1: 3.232833 Loss2: 1.481067
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.696299 Loss1: 3.215379 Loss2: 1.480919
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.627573 Loss1: 3.146482 Loss2: 1.481091
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.912611 Loss1: 4.104510 Loss2: 1.808101
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.607869 Loss1: 3.125394 Loss2: 1.482475
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.028396 Loss1: 3.617588 Loss2: 1.410808
+(DefaultActor pid=3764) >> Training accuracy: 0.203125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.877849 Loss1: 3.484606 Loss2: 1.393243
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.847284 Loss1: 3.471752 Loss2: 1.375532
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.818860 Loss1: 3.450862 Loss2: 1.367999
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.767224 Loss1: 3.384400 Loss2: 1.382825
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.731488 Loss1: 3.997179 Loss2: 1.734309
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.746577 Loss1: 3.356829 Loss2: 1.389749
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.700153 Loss1: 3.321650 Loss2: 1.378503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.650083 Loss1: 3.258488 Loss2: 1.391595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.660645 Loss1: 3.268833 Loss2: 1.391812 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.194336
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.569484 Loss1: 3.254441 Loss2: 1.315042 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.441464 Loss1: 3.136019 Loss2: 1.305445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.494505 Loss1: 3.184552 Loss2: 1.309953
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.647471 Loss1: 3.908748 Loss2: 1.738724
+(DefaultActor pid=3764) >> Training accuracy: 0.254167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.853522 Loss1: 3.482275 Loss2: 1.371247 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.554965 Loss1: 3.247881 Loss2: 1.307084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.425602 Loss1: 3.128048 Loss2: 1.297554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.401871 Loss1: 3.094285 Loss2: 1.307586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.399461 Loss1: 3.081362 Loss2: 1.318100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.912490 Loss1: 3.478165 Loss2: 1.434325 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.852118 Loss1: 3.428794 Loss2: 1.423323 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.259375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.806991 Loss1: 3.366976 Loss2: 1.440016 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.773474 Loss1: 3.326088 Loss2: 1.447386 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.192708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.858489 Loss1: 3.970440 Loss2: 1.888049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.849661 Loss1: 3.426260 Loss2: 1.423401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.973313 Loss1: 4.086156 Loss2: 1.887157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.141702 Loss1: 3.662750 Loss2: 1.478952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 5.014499 Loss1: 3.568582 Loss2: 1.445917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.946375 Loss1: 3.509799 Loss2: 1.436576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.882738 Loss1: 3.435288 Loss2: 1.447451 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.864486 Loss1: 3.406801 Loss2: 1.457685 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.161458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.838354 Loss1: 3.375960 Loss2: 1.462393 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.767691 Loss1: 3.290695 Loss2: 1.476996 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.190625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.079730 Loss1: 3.570363 Loss2: 1.509368 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.824476 Loss1: 3.343894 Loss2: 1.480582 [repeated 2x across cluster]
+DEBUG flwr 2023-10-08 16:15:13,625 | server.py:236 | fit_round 7 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.783453 Loss1: 3.302113 Loss2: 1.481340 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.768858 Loss1: 3.284133 Loss2: 1.484724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.797041 Loss1: 3.307468 Loss2: 1.489573 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.703858 Loss1: 3.208962 Loss2: 1.494897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.720214 Loss1: 3.217509 Loss2: 1.502705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.691067 Loss1: 3.179479 Loss2: 1.511588 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.172917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.490961 Loss1: 3.100768 Loss2: 1.390194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.440509 Loss1: 3.044649 Loss2: 1.395861 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.234375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.048418 Loss1: 3.587933 Loss2: 1.460485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.816245 Loss1: 3.411020 Loss2: 1.405225 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.827086 Loss1: 3.410607 Loss2: 1.416479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.784714 Loss1: 3.364431 Loss2: 1.420283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.754191 Loss1: 3.349100 Loss2: 1.405091 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.745663 Loss1: 3.324458 Loss2: 1.421205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.704642 Loss1: 3.279416 Loss2: 1.425226 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.699322 Loss1: 3.263530 Loss2: 1.435793 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.174805
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.773272 Loss1: 3.277173 Loss2: 1.496100 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.196875
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 16:15:13,625][flwr][DEBUG] - fit_round 7 received 50 results and 0 failures
+INFO flwr 2023-10-08 16:15:54,592 | server.py:125 | fit progress: (7, 4.30479558816733, {'accuracy': 0.0497}, 15862.370257114002)
+>> Test accuracy: 0.049700
+[2023-10-08 16:15:54,592][flwr][INFO] - fit progress: (7, 4.30479558816733, {'accuracy': 0.0497}, 15862.370257114002)
+DEBUG flwr 2023-10-08 16:15:54,592 | server.py:173 | evaluate_round 7: strategy sampled 50 clients (out of 50)
+[2023-10-08 16:15:54,592][flwr][DEBUG] - evaluate_round 7: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 16:25:00,961 | server.py:187 | evaluate_round 7 received 50 results and 0 failures
+[2023-10-08 16:25:00,961][flwr][DEBUG] - evaluate_round 7 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 16:25:00,961 | server.py:222 | fit_round 8: strategy sampled 50 clients (out of 50)
+[2023-10-08 16:25:00,961][flwr][DEBUG] - fit_round 8: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.598194 Loss1: 3.801508 Loss2: 1.796686 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.670448 Loss1: 3.300732 Loss2: 1.369716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.545829 Loss1: 3.186581 Loss2: 1.359248
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.898422 Loss1: 3.910995 Loss2: 1.987428
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.026556 Loss1: 3.453524 Loss2: 1.573032
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.559240 Loss1: 3.201887 Loss2: 1.357353
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.764776 Loss1: 3.264322 Loss2: 1.500454
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.435083 Loss1: 3.091858 Loss2: 1.343225
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.671630 Loss1: 3.194996 Loss2: 1.476634
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.470167 Loss1: 3.103637 Loss2: 1.366530
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.614289 Loss1: 3.138440 Loss2: 1.475849
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.414784 Loss1: 3.046238 Loss2: 1.368545
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.331379 Loss1: 2.960608 Loss2: 1.370771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.324274 Loss1: 2.947200 Loss2: 1.377074 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.247070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.451031 Loss1: 2.964839 Loss2: 1.486192 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.259375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.844169 Loss1: 3.904169 Loss2: 1.940001 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.637368 Loss1: 3.183440 Loss2: 1.453927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.636300 Loss1: 3.190630 Loss2: 1.445670
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.712364 Loss1: 4.013050 Loss2: 1.699314
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.905418 Loss1: 3.560286 Loss2: 1.345132 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.753472 Loss1: 3.446803 Loss2: 1.306670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.703250 Loss1: 3.404604 Loss2: 1.298646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.709824 Loss1: 3.406147 Loss2: 1.303677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.697756 Loss1: 3.382929 Loss2: 1.314827 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.235417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.615040 Loss1: 3.306340 Loss2: 1.308700 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.564416 Loss1: 3.245714 Loss2: 1.318701 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.175781
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.556870 Loss1: 3.705114 Loss2: 1.851756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.537643 Loss1: 3.164716 Loss2: 1.372926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.590648 Loss1: 3.773791 Loss2: 1.816857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.568447 Loss1: 3.160906 Loss2: 1.407541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.394303 Loss1: 3.033607 Loss2: 1.360695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.315118 Loss1: 2.965438 Loss2: 1.349680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.278117 Loss1: 2.929740 Loss2: 1.348377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.229322 Loss1: 2.892652 Loss2: 1.336671 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.267708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.204345 Loss1: 2.859031 Loss2: 1.345314 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.185927 Loss1: 2.839051 Loss2: 1.346876 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.226042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.637509 Loss1: 3.269088 Loss2: 1.368421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.355572 Loss1: 3.028518 Loss2: 1.327053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.715957 Loss1: 3.887944 Loss2: 1.828012
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.320428 Loss1: 2.974642 Loss2: 1.345786
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.888116 Loss1: 3.448276 Loss2: 1.439839
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.288116 Loss1: 2.956323 Loss2: 1.331794
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.685319 Loss1: 3.296019 Loss2: 1.389300
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.295373 Loss1: 2.947413 Loss2: 1.347960
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.616561 Loss1: 3.242202 Loss2: 1.374359
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.267360 Loss1: 2.922687 Loss2: 1.344673
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.571036 Loss1: 3.192571 Loss2: 1.378465
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.250874 Loss1: 2.914286 Loss2: 1.336588
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.557252 Loss1: 3.170169 Loss2: 1.387084
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.180932 Loss1: 2.845117 Loss2: 1.335815
+(DefaultActor pid=3765) >> Training accuracy: 0.284375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.465531 Loss1: 3.080740 Loss2: 1.384791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.430866 Loss1: 3.039130 Loss2: 1.391736 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.234375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.717838 Loss1: 3.869272 Loss2: 1.848565
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.876863 Loss1: 3.423305 Loss2: 1.453558
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.696934 Loss1: 3.291459 Loss2: 1.405475
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.598680 Loss1: 3.187835 Loss2: 1.410845
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.824100 Loss1: 4.034732 Loss2: 1.789368
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.882943 Loss1: 3.524159 Loss2: 1.358784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.691977 Loss1: 3.365675 Loss2: 1.326302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.499435 Loss1: 3.086275 Loss2: 1.413160
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.632939 Loss1: 3.309445 Loss2: 1.323494
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.496953 Loss1: 3.087917 Loss2: 1.409035
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.549170 Loss1: 3.224474 Loss2: 1.324696
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.451445 Loss1: 3.029659 Loss2: 1.421786
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.532880 Loss1: 3.205948 Loss2: 1.326933
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.484867 Loss1: 3.061125 Loss2: 1.423742
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.508654 Loss1: 3.187140 Loss2: 1.321514
+(DefaultActor pid=3765) >> Training accuracy: 0.247070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.460658 Loss1: 3.124124 Loss2: 1.336535
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.492179 Loss1: 3.145539 Loss2: 1.346640
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.465521 Loss1: 3.124606 Loss2: 1.340915
+(DefaultActor pid=3764) >> Training accuracy: 0.204167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.738029 Loss1: 3.957879 Loss2: 1.780150
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.854367 Loss1: 3.467434 Loss2: 1.386933
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.670518 Loss1: 3.336448 Loss2: 1.334069
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.568709 Loss1: 3.241754 Loss2: 1.326955
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.771067 Loss1: 4.002056 Loss2: 1.769010
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.981811 Loss1: 3.619525 Loss2: 1.362286 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.750862 Loss1: 3.424998 Loss2: 1.325864 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.698463 Loss1: 3.374110 Loss2: 1.324353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.693446 Loss1: 3.369965 Loss2: 1.323482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.583561 Loss1: 3.268067 Loss2: 1.315495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.222917
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.402239 Loss1: 3.029520 Loss2: 1.372719
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.562243 Loss1: 3.246403 Loss2: 1.315840
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.581497 Loss1: 3.232962 Loss2: 1.348535
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.627604 Loss1: 3.282703 Loss2: 1.344900
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.536925 Loss1: 3.199084 Loss2: 1.337841
+(DefaultActor pid=3764) >> Training accuracy: 0.205208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.795355 Loss1: 4.025287 Loss2: 1.770068
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.083944 Loss1: 3.677656 Loss2: 1.406288
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.945394 Loss1: 3.572615 Loss2: 1.372779
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.777453 Loss1: 3.977005 Loss2: 1.800449
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.863818 Loss1: 3.486066 Loss2: 1.377752
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.888963 Loss1: 3.489646 Loss2: 1.399318
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.883246 Loss1: 3.488770 Loss2: 1.394477
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.748099 Loss1: 3.382284 Loss2: 1.365815
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.823335 Loss1: 3.436482 Loss2: 1.386853
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.840783 Loss1: 3.438934 Loss2: 1.401849 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.785643 Loss1: 3.396743 Loss2: 1.388899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.759696 Loss1: 3.359549 Loss2: 1.400147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.714686 Loss1: 3.307322 Loss2: 1.407364 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.200195
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.415353 Loss1: 3.038086 Loss2: 1.377266 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.222917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.931275 Loss1: 4.054254 Loss2: 1.877021 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.865052 Loss1: 3.442877 Loss2: 1.422175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.797275 Loss1: 3.377430 Loss2: 1.419845
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.688281 Loss1: 3.923647 Loss2: 1.764634
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.756711 Loss1: 3.347485 Loss2: 1.409226
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.870664 Loss1: 3.472995 Loss2: 1.397668
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.716368 Loss1: 3.299845 Loss2: 1.416523
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.705154 Loss1: 3.355846 Loss2: 1.349307
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.700035 Loss1: 3.278191 Loss2: 1.421844
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.627779 Loss1: 3.282549 Loss2: 1.345230
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.679291 Loss1: 3.237443 Loss2: 1.441848
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.629156 Loss1: 3.280321 Loss2: 1.348835
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.675219 Loss1: 3.244498 Loss2: 1.430721
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.571930 Loss1: 3.221172 Loss2: 1.350758
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.620481 Loss1: 3.184551 Loss2: 1.435931
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.568786 Loss1: 3.218923 Loss2: 1.349863
+(DefaultActor pid=3765) >> Training accuracy: 0.191667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.544887 Loss1: 3.190011 Loss2: 1.354876
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.532632 Loss1: 3.164685 Loss2: 1.367947
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.495631 Loss1: 3.138049 Loss2: 1.357583
+(DefaultActor pid=3764) >> Training accuracy: 0.208333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.728536 Loss1: 3.874015 Loss2: 1.854522
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.873837 Loss1: 3.425233 Loss2: 1.448604
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.625989 Loss1: 3.229133 Loss2: 1.396857
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.597479 Loss1: 3.195718 Loss2: 1.401761
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.626297 Loss1: 3.760236 Loss2: 1.866061
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.540309 Loss1: 3.128881 Loss2: 1.411428
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.742966 Loss1: 3.313111 Loss2: 1.429855
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.566670 Loss1: 3.158867 Loss2: 1.407803
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.517950 Loss1: 3.142241 Loss2: 1.375709
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.532787 Loss1: 3.115059 Loss2: 1.417728
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.418088 Loss1: 3.031094 Loss2: 1.386994
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.530453 Loss1: 3.124266 Loss2: 1.406187
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.435474 Loss1: 3.066141 Loss2: 1.369333
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.486250 Loss1: 3.067461 Loss2: 1.418789
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.415612 Loss1: 3.026511 Loss2: 1.389101
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.448624 Loss1: 3.029834 Loss2: 1.418790
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.399685 Loss1: 3.009553 Loss2: 1.390132
+(DefaultActor pid=3765) >> Training accuracy: 0.221875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.385974 Loss1: 2.994547 Loss2: 1.391427
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.373578 Loss1: 2.978311 Loss2: 1.395267
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.357461 Loss1: 2.940462 Loss2: 1.416998
+(DefaultActor pid=3764) >> Training accuracy: 0.239583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.815493 Loss1: 4.007737 Loss2: 1.807757
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.953453 Loss1: 3.559306 Loss2: 1.394147
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.776447 Loss1: 3.409439 Loss2: 1.367007
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.669699 Loss1: 3.313966 Loss2: 1.355733
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.953243 Loss1: 4.055139 Loss2: 1.898104
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.705499 Loss1: 3.353097 Loss2: 1.352402
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.091744 Loss1: 3.648985 Loss2: 1.442759
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.902275 Loss1: 3.490105 Loss2: 1.412170
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.638346 Loss1: 3.281446 Loss2: 1.356901
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.670014 Loss1: 3.298972 Loss2: 1.371042 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.613822 Loss1: 3.245502 Loss2: 1.368321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.611585 Loss1: 3.238724 Loss2: 1.372861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.594342 Loss1: 3.210868 Loss2: 1.383474 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.216667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.641101 Loss1: 3.230744 Loss2: 1.410357 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.206731
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.940000 Loss1: 4.069937 Loss2: 1.870064
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.080808 Loss1: 3.606177 Loss2: 1.474630
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.928162 Loss1: 3.500295 Loss2: 1.427867
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.742032 Loss1: 3.963257 Loss2: 1.778775
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.871794 Loss1: 3.436762 Loss2: 1.435032
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.882069 Loss1: 3.479100 Loss2: 1.402968
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.822637 Loss1: 3.392264 Loss2: 1.430373
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.696905 Loss1: 3.349302 Loss2: 1.347603
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.766848 Loss1: 3.340228 Loss2: 1.426620
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.693239 Loss1: 3.341671 Loss2: 1.351569
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.781102 Loss1: 3.350940 Loss2: 1.430162
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.604780 Loss1: 3.251241 Loss2: 1.353538
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.721566 Loss1: 3.280234 Loss2: 1.441332
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.579120 Loss1: 3.216796 Loss2: 1.362324
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.700296 Loss1: 3.251748 Loss2: 1.448549
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.548684 Loss1: 3.190628 Loss2: 1.358056
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.675613 Loss1: 3.228994 Loss2: 1.446620
+(DefaultActor pid=3765) >> Training accuracy: 0.202148
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.494034 Loss1: 3.104354 Loss2: 1.389680 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.232422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.757329 Loss1: 3.883418 Loss2: 1.873910 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.642391 Loss1: 3.273687 Loss2: 1.368704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.998033 Loss1: 4.133665 Loss2: 1.864368 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.497302 Loss1: 3.129629 Loss2: 1.367673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.413982 Loss1: 3.050773 Loss2: 1.363210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.399322 Loss1: 3.025042 Loss2: 1.374280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.330265 Loss1: 2.956101 Loss2: 1.374164 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.324398 Loss1: 2.955068 Loss2: 1.369331 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.242788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.903145 Loss1: 3.473466 Loss2: 1.429678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.787520 Loss1: 3.354841 Loss2: 1.432679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.780087 Loss1: 3.328323 Loss2: 1.451764
+(DefaultActor pid=3764) >> Training accuracy: 0.181250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.712178 Loss1: 4.043608 Loss2: 1.668571
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.802044 Loss1: 3.477012 Loss2: 1.325032
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.650677 Loss1: 3.362605 Loss2: 1.288072
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.631770 Loss1: 3.342905 Loss2: 1.288865
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.648744 Loss1: 3.929681 Loss2: 1.719063
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.616113 Loss1: 3.321576 Loss2: 1.294537
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.837964 Loss1: 3.485593 Loss2: 1.352371
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.571580 Loss1: 3.282262 Loss2: 1.289319
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.520131 Loss1: 3.221566 Loss2: 1.298565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.509240 Loss1: 3.202945 Loss2: 1.306295 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.442452 Loss1: 3.138292 Loss2: 1.304160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.437244 Loss1: 3.124806 Loss2: 1.312438 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.201287
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.453144 Loss1: 3.131272 Loss2: 1.321872 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.182292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.096672 Loss1: 4.175593 Loss2: 1.921078 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.917638 Loss1: 3.497304 Loss2: 1.420334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.930730 Loss1: 4.076249 Loss2: 1.854481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.084402 Loss1: 3.617459 Loss2: 1.466943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.895930 Loss1: 3.471989 Loss2: 1.423941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.820768 Loss1: 3.407650 Loss2: 1.413118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.781056 Loss1: 3.360896 Loss2: 1.420160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.738415 Loss1: 3.321111 Loss2: 1.417304 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.184152
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.648116 Loss1: 3.231165 Loss2: 1.416952 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.698072 Loss1: 3.253363 Loss2: 1.444709 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.161458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.964474 Loss1: 3.502647 Loss2: 1.461827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.689903 Loss1: 3.292774 Loss2: 1.397129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.772421 Loss1: 3.945021 Loss2: 1.827401
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.627491 Loss1: 3.237185 Loss2: 1.390305
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.912718 Loss1: 3.484239 Loss2: 1.428479
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.586114 Loss1: 3.197574 Loss2: 1.388541
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.647029 Loss1: 3.268448 Loss2: 1.378581
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.523071 Loss1: 3.125031 Loss2: 1.398040
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.593690 Loss1: 3.217047 Loss2: 1.376643
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.528973 Loss1: 3.128397 Loss2: 1.400576
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.573309 Loss1: 3.189414 Loss2: 1.383896
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.525541 Loss1: 3.130072 Loss2: 1.395469
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.527809 Loss1: 3.152135 Loss2: 1.375674
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.505181 Loss1: 3.089392 Loss2: 1.415788
+(DefaultActor pid=3765) >> Training accuracy: 0.217708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.542681 Loss1: 3.153947 Loss2: 1.388734 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.505937 Loss1: 3.087829 Loss2: 1.418108 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.237500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.728120 Loss1: 3.336003 Loss2: 1.392117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.511459 Loss1: 3.176721 Loss2: 1.334738 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.506765 Loss1: 3.154854 Loss2: 1.351911 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.468131 Loss1: 3.109537 Loss2: 1.358595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.470986 Loss1: 3.108611 Loss2: 1.362374 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.408607 Loss1: 3.065102 Loss2: 1.343505 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.387886 Loss1: 3.025269 Loss2: 1.362617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.414251 Loss1: 3.050827 Loss2: 1.363424 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.201172
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.486096 Loss1: 3.110884 Loss2: 1.375211 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.207292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.661441 Loss1: 3.874842 Loss2: 1.786598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.667636 Loss1: 3.303108 Loss2: 1.364528 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.559824 Loss1: 3.200682 Loss2: 1.359142
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.767191 Loss1: 3.928330 Loss2: 1.838861
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.497264 Loss1: 3.142888 Loss2: 1.354375
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.908450 Loss1: 3.480121 Loss2: 1.428329
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.555179 Loss1: 3.187154 Loss2: 1.368024
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.706757 Loss1: 3.318073 Loss2: 1.388684
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.478233 Loss1: 3.109450 Loss2: 1.368783
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.655970 Loss1: 3.276158 Loss2: 1.379811
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.459065 Loss1: 3.090015 Loss2: 1.369050
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.639046 Loss1: 3.268214 Loss2: 1.370832
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.395022 Loss1: 3.028121 Loss2: 1.366900
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.531715 Loss1: 3.140001 Loss2: 1.391714
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.383020 Loss1: 3.006827 Loss2: 1.376193
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.551010 Loss1: 3.154070 Loss2: 1.396940
+(DefaultActor pid=3765) >> Training accuracy: 0.243750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.537999 Loss1: 3.138270 Loss2: 1.399729
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.469170 Loss1: 3.062163 Loss2: 1.407007
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.401499 Loss1: 2.997893 Loss2: 1.403607
+(DefaultActor pid=3764) >> Training accuracy: 0.240625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.820160 Loss1: 3.988503 Loss2: 1.831657
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.849737 Loss1: 3.425838 Loss2: 1.423899
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.747079 Loss1: 3.346763 Loss2: 1.400317
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.668457 Loss1: 3.268183 Loss2: 1.400274
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.643474 Loss1: 3.840915 Loss2: 1.802559
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.623233 Loss1: 3.216955 Loss2: 1.406279
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.890307 Loss1: 3.475550 Loss2: 1.414756
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.555877 Loss1: 3.160733 Loss2: 1.395144
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.722506 Loss1: 3.342072 Loss2: 1.380434
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.603838 Loss1: 3.193210 Loss2: 1.410628
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.591882 Loss1: 3.224026 Loss2: 1.367855
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.486573 Loss1: 3.081842 Loss2: 1.404731
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.589592 Loss1: 3.228012 Loss2: 1.361580
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.451554 Loss1: 3.038099 Loss2: 1.413455
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.560570 Loss1: 3.199733 Loss2: 1.360836
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.418881 Loss1: 2.998473 Loss2: 1.420408
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.504264 Loss1: 3.133137 Loss2: 1.371126
+(DefaultActor pid=3765) >> Training accuracy: 0.256250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.522445 Loss1: 3.145015 Loss2: 1.377429
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.468861 Loss1: 3.078847 Loss2: 1.390015
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.422754 Loss1: 3.049249 Loss2: 1.373504
+(DefaultActor pid=3764) >> Training accuracy: 0.223958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.852764 Loss1: 3.950390 Loss2: 1.902374
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.993531 Loss1: 3.489059 Loss2: 1.504472
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.831100 Loss1: 3.361360 Loss2: 1.469741
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.786409 Loss1: 3.934690 Loss2: 1.851719
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.749935 Loss1: 3.289705 Loss2: 1.460230
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.694951 Loss1: 3.236326 Loss2: 1.458625 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.700332 Loss1: 3.231293 Loss2: 1.469040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.683817 Loss1: 3.210110 Loss2: 1.473707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.685944 Loss1: 3.208844 Loss2: 1.477100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.597820 Loss1: 3.120193 Loss2: 1.477627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.484482 Loss1: 3.117309 Loss2: 1.367174 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.203125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.449090 Loss1: 3.065872 Loss2: 1.383218 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.232143
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.733132 Loss1: 3.961504 Loss2: 1.771628
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.857214 Loss1: 3.478915 Loss2: 1.378299
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.714834 Loss1: 3.378789 Loss2: 1.336046
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.630817 Loss1: 3.297441 Loss2: 1.333376
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.774149 Loss1: 3.868448 Loss2: 1.905701
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.811634 Loss1: 3.342485 Loss2: 1.469149 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.595548 Loss1: 3.176057 Loss2: 1.419490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.451477 Loss1: 3.026118 Loss2: 1.425359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.451061 Loss1: 3.021089 Loss2: 1.429972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.386132 Loss1: 2.972618 Loss2: 1.413514 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.215625
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.497651 Loss1: 3.147874 Loss2: 1.349777
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.366043 Loss1: 2.939404 Loss2: 1.426638
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.273754 Loss1: 2.836582 Loss2: 1.437173
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.268298 Loss1: 2.815137 Loss2: 1.453161
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.247085 Loss1: 2.797419 Loss2: 1.449666
+(DefaultActor pid=3764) >> Training accuracy: 0.348958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.958805 Loss1: 4.084408 Loss2: 1.874398
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.045172 Loss1: 3.608491 Loss2: 1.436682
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.842490 Loss1: 3.464717 Loss2: 1.377773
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.776972 Loss1: 3.396654 Loss2: 1.380319
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.620730 Loss1: 3.788964 Loss2: 1.831766
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.747126 Loss1: 3.305649 Loss2: 1.441477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.561835 Loss1: 3.170548 Loss2: 1.391287 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.513357 Loss1: 3.113513 Loss2: 1.399844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.436297 Loss1: 3.054222 Loss2: 1.382075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.403909 Loss1: 3.015162 Loss2: 1.388746 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.190625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.368425 Loss1: 2.980249 Loss2: 1.388176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.363174 Loss1: 2.974053 Loss2: 1.389121 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.263542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.872035 Loss1: 4.010292 Loss2: 1.861743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.847047 Loss1: 3.425304 Loss2: 1.421743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.797937 Loss1: 3.371046 Loss2: 1.426890
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.722508 Loss1: 3.943267 Loss2: 1.779241
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.799628 Loss1: 3.408039 Loss2: 1.391590 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.717964 Loss1: 3.370175 Loss2: 1.347789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.586656 Loss1: 3.246556 Loss2: 1.340100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.669102 Loss1: 3.223063 Loss2: 1.446039
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.550552 Loss1: 3.195314 Loss2: 1.355238
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.472076 Loss1: 3.135949 Loss2: 1.336127 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.226042
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.641129 Loss1: 3.183702 Loss2: 1.457426
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.513927 Loss1: 3.161731 Loss2: 1.352196
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.520655 Loss1: 3.155241 Loss2: 1.365414
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.414212 Loss1: 3.047562 Loss2: 1.366650
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.432325 Loss1: 3.064755 Loss2: 1.367571
+(DefaultActor pid=3764) >> Training accuracy: 0.221875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.921916 Loss1: 4.022102 Loss2: 1.899814
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.010016 Loss1: 3.589897 Loss2: 1.420120
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.759962 Loss1: 3.401536 Loss2: 1.358426
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.665759 Loss1: 3.322816 Loss2: 1.342944
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.621219 Loss1: 3.273683 Loss2: 1.347536
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.566390 Loss1: 3.213488 Loss2: 1.352902 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.613171 Loss1: 3.250661 Loss2: 1.362509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.561683 Loss1: 3.185939 Loss2: 1.375743 [repeated 2x across cluster]
+DEBUG flwr 2023-10-08 16:54:08,040 | server.py:236 | fit_round 8 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.549646 Loss1: 3.168704 Loss2: 1.380942
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.619256 Loss1: 3.278140 Loss2: 1.341116
+(DefaultActor pid=3765) >> Training accuracy: 0.229167
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.516659 Loss1: 3.139677 Loss2: 1.376982
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.585836 Loss1: 3.241877 Loss2: 1.343959
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.529704 Loss1: 3.180816 Loss2: 1.348888
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.543818 Loss1: 3.191245 Loss2: 1.352573
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.523758 Loss1: 3.165668 Loss2: 1.358089
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.499898 Loss1: 3.138786 Loss2: 1.361112
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.838026 Loss1: 4.043191 Loss2: 1.794835
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.418179 Loss1: 3.066265 Loss2: 1.351914
+(DefaultActor pid=3764) >> Training accuracy: 0.216667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.823950 Loss1: 3.436042 Loss2: 1.387909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.735656 Loss1: 3.355443 Loss2: 1.380213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.701402 Loss1: 3.325447 Loss2: 1.375955
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.864302 Loss1: 4.010571 Loss2: 1.853732
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.098195 Loss1: 3.653788 Loss2: 1.444407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.871621 Loss1: 3.447442 Loss2: 1.424179 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.860027 Loss1: 3.441261 Loss2: 1.418766 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.179167
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.572575 Loss1: 3.182581 Loss2: 1.389994
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.760167 Loss1: 3.343243 Loss2: 1.416924
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.759782 Loss1: 3.336250 Loss2: 1.423532
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.689790 Loss1: 3.265807 Loss2: 1.423983
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.735505 Loss1: 3.296395 Loss2: 1.439109
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.658213 Loss1: 3.218133 Loss2: 1.440080
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.915537 Loss1: 4.118868 Loss2: 1.796669
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.643863 Loss1: 3.204822 Loss2: 1.439041
+(DefaultActor pid=3764) >> Training accuracy: 0.196875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.943322 Loss1: 3.574087 Loss2: 1.369235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.764574 Loss1: 3.413220 Loss2: 1.351354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.821279 Loss1: 4.113729 Loss2: 1.707550 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.917879 Loss1: 3.586689 Loss2: 1.331190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.621247 Loss1: 3.257353 Loss2: 1.363893 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.650853 Loss1: 3.273169 Loss2: 1.377684 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.176339
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.572204 Loss1: 3.263601 Loss2: 1.308603 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.547916 Loss1: 3.233820 Loss2: 1.314096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.518986 Loss1: 3.185468 Loss2: 1.333519 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.214844
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 16:54:08,040][flwr][DEBUG] - fit_round 8 received 50 results and 0 failures
+INFO flwr 2023-10-08 16:54:49,722 | server.py:125 | fit progress: (8, 4.350954430552717, {'accuracy': 0.0537}, 18197.50099603)
+>> Test accuracy: 0.053700
+[2023-10-08 16:54:49,722][flwr][INFO] - fit progress: (8, 4.350954430552717, {'accuracy': 0.0537}, 18197.50099603)
+DEBUG flwr 2023-10-08 16:54:49,723 | server.py:173 | evaluate_round 8: strategy sampled 50 clients (out of 50)
+[2023-10-08 16:54:49,723][flwr][DEBUG] - evaluate_round 8: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 17:03:52,702 | server.py:187 | evaluate_round 8 received 50 results and 0 failures
+[2023-10-08 17:03:52,702][flwr][DEBUG] - evaluate_round 8 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 17:03:52,702 | server.py:222 | fit_round 9: strategy sampled 50 clients (out of 50)
+[2023-10-08 17:03:52,702][flwr][DEBUG] - fit_round 9: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.565408 Loss1: 3.772120 Loss2: 1.793288
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.744628 Loss1: 3.346770 Loss2: 1.397858
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.536483 Loss1: 3.169311 Loss2: 1.367173
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.827645 Loss1: 3.895229 Loss2: 1.932416
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.463214 Loss1: 3.094219 Loss2: 1.368995
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.864692 Loss1: 3.390317 Loss2: 1.474376
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.424830 Loss1: 3.060264 Loss2: 1.364566
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.750479 Loss1: 3.292933 Loss2: 1.457546
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.379152 Loss1: 3.015094 Loss2: 1.364058
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.720466 Loss1: 3.267483 Loss2: 1.452983
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.373637 Loss1: 3.001676 Loss2: 1.371961
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.338513 Loss1: 2.970817 Loss2: 1.367696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.346071 Loss1: 2.960761 Loss2: 1.385310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.299399 Loss1: 2.906787 Loss2: 1.392612 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.230469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.562975 Loss1: 3.104404 Loss2: 1.458571 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.255208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.682514 Loss1: 3.843689 Loss2: 1.838825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.528577 Loss1: 3.148107 Loss2: 1.380470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.491326 Loss1: 3.112698 Loss2: 1.378628
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.580949 Loss1: 3.681861 Loss2: 1.899088
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.651312 Loss1: 3.210970 Loss2: 1.440342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.464848 Loss1: 3.073569 Loss2: 1.391279 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.367950 Loss1: 2.975450 Loss2: 1.392501 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.294402 Loss1: 2.907828 Loss2: 1.386574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.329495 Loss1: 2.931109 Loss2: 1.398386 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.271875
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.260864 Loss1: 2.870536 Loss2: 1.390328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.245025 Loss1: 2.850402 Loss2: 1.394623
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.278446 Loss1: 2.871684 Loss2: 1.406763
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.202046 Loss1: 2.800409 Loss2: 1.401638
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.223056 Loss1: 2.807615 Loss2: 1.415441
+(DefaultActor pid=3764) >> Training accuracy: 0.301042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.822111 Loss1: 3.922429 Loss2: 1.899682
+(DefaultActor pid=3765) Epoch: 1 Loss: 5.080897 Loss1: 3.599439 Loss2: 1.481458
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.849555 Loss1: 3.383324 Loss2: 1.466231
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.845445 Loss1: 3.379847 Loss2: 1.465597
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.696833 Loss1: 3.777013 Loss2: 1.919820
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.796788 Loss1: 3.312633 Loss2: 1.484155
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.785634 Loss1: 3.311194 Loss2: 1.474440
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.690747 Loss1: 3.245247 Loss2: 1.445500
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.742181 Loss1: 3.268869 Loss2: 1.473312
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.693772 Loss1: 3.257587 Loss2: 1.436186
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.689795 Loss1: 3.208774 Loss2: 1.481022
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.613422 Loss1: 3.175682 Loss2: 1.437740
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.684020 Loss1: 3.198210 Loss2: 1.485811
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.648094 Loss1: 3.159010 Loss2: 1.489084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.614618 Loss1: 3.123970 Loss2: 1.490648 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.215820
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.501397 Loss1: 3.044013 Loss2: 1.457383 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.260417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.704258 Loss1: 3.838409 Loss2: 1.865850 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.709926 Loss1: 3.320893 Loss2: 1.389033 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.677468 Loss1: 3.280015 Loss2: 1.397453
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.655943 Loss1: 3.808042 Loss2: 1.847902
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.851392 Loss1: 3.428963 Loss2: 1.422429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.650994 Loss1: 3.276569 Loss2: 1.374425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.622542 Loss1: 3.246588 Loss2: 1.375954 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.573405 Loss1: 3.193818 Loss2: 1.379587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.514322 Loss1: 3.134994 Loss2: 1.379328 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.231250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.485651 Loss1: 3.089896 Loss2: 1.395755 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.436415 Loss1: 3.035150 Loss2: 1.401265 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.223633
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.755438 Loss1: 3.800353 Loss2: 1.955085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.547193 Loss1: 3.115706 Loss2: 1.431487 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.442252 Loss1: 3.022076 Loss2: 1.420176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.296507 Loss1: 2.883066 Loss2: 1.413441 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.870200 Loss1: 3.483518 Loss2: 1.386682
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.256021 Loss1: 2.830125 Loss2: 1.425896
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.251213 Loss1: 2.810130 Loss2: 1.441083
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.708331 Loss1: 3.353780 Loss2: 1.354551
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.648129 Loss1: 3.298087 Loss2: 1.350042 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.292067
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.645556 Loss1: 3.273206 Loss2: 1.372350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.549932 Loss1: 3.188782 Loss2: 1.361151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.482692 Loss1: 3.103483 Loss2: 1.379209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.473654 Loss1: 3.095126 Loss2: 1.378527 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.218750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.819749 Loss1: 3.405120 Loss2: 1.414629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.665773 Loss1: 3.245815 Loss2: 1.419958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.648575 Loss1: 3.223807 Loss2: 1.424768
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.889294 Loss1: 3.909280 Loss2: 1.980015
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.884836 Loss1: 3.367283 Loss2: 1.517553
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.629163 Loss1: 3.200437 Loss2: 1.428726
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.694903 Loss1: 3.218996 Loss2: 1.475907
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.609920 Loss1: 3.180591 Loss2: 1.429329
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.695157 Loss1: 3.218954 Loss2: 1.476203
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.625117 Loss1: 3.176975 Loss2: 1.448142
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.605247 Loss1: 3.128893 Loss2: 1.476354
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.568764 Loss1: 3.127855 Loss2: 1.440909
+(DefaultActor pid=3765) >> Training accuracy: 0.201172
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.607153 Loss1: 3.122176 Loss2: 1.484977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.543550 Loss1: 3.052861 Loss2: 1.490689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.490153 Loss1: 2.996462 Loss2: 1.493691
+(DefaultActor pid=3764) >> Training accuracy: 0.246875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.746030 Loss1: 3.883533 Loss2: 1.862497
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.821553 Loss1: 3.369870 Loss2: 1.451683
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.671197 Loss1: 3.261579 Loss2: 1.409618
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.581196 Loss1: 3.168857 Loss2: 1.412339
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.528360 Loss1: 3.129345 Loss2: 1.399015
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.513094 Loss1: 3.672528 Loss2: 1.840565
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.504506 Loss1: 3.082582 Loss2: 1.421924
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.642558 Loss1: 3.224698 Loss2: 1.417860
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.454210 Loss1: 3.040046 Loss2: 1.414164
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.433886 Loss1: 3.067873 Loss2: 1.366014
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.435995 Loss1: 3.027325 Loss2: 1.408670
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.255988 Loss1: 2.902157 Loss2: 1.353831
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.439377 Loss1: 3.011835 Loss2: 1.427543
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.194458 Loss1: 2.846763 Loss2: 1.347695
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.431396 Loss1: 3.008183 Loss2: 1.423213
+(DefaultActor pid=3765) >> Training accuracy: 0.207292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.118274 Loss1: 2.773391 Loss2: 1.344884 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.101319 Loss1: 2.743741 Loss2: 1.357578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.032909 Loss1: 2.674891 Loss2: 1.358018
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.613171 Loss1: 3.775716 Loss2: 1.837456
+(DefaultActor pid=3764) >> Training accuracy: 0.258333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.871121 Loss1: 3.454734 Loss2: 1.416387
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.694574 Loss1: 3.293444 Loss2: 1.401130
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.620914 Loss1: 3.226656 Loss2: 1.394258
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.587400 Loss1: 3.202438 Loss2: 1.384962
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.664068 Loss1: 3.728651 Loss2: 1.935417
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.524859 Loss1: 3.121891 Loss2: 1.402968
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.878846 Loss1: 3.384440 Loss2: 1.494407
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.514233 Loss1: 3.115149 Loss2: 1.399084
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.807009 Loss1: 3.353110 Loss2: 1.453899
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.497203 Loss1: 3.088622 Loss2: 1.408580
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.597705 Loss1: 3.138903 Loss2: 1.458802
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.478720 Loss1: 3.056077 Loss2: 1.422642
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.605596 Loss1: 3.158857 Loss2: 1.446739
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.469746 Loss1: 3.067379 Loss2: 1.402367
+(DefaultActor pid=3765) >> Training accuracy: 0.241667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.520463 Loss1: 3.054620 Loss2: 1.465843 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.490321 Loss1: 3.021599 Loss2: 1.468723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.451478 Loss1: 2.996386 Loss2: 1.455091
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.706498 Loss1: 3.770821 Loss2: 1.935677
+(DefaultActor pid=3764) >> Training accuracy: 0.239583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.865225 Loss1: 3.362527 Loss2: 1.502698
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.711587 Loss1: 3.256866 Loss2: 1.454721
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.589644 Loss1: 3.138454 Loss2: 1.451190
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.543666 Loss1: 3.092626 Loss2: 1.451040
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.762352 Loss1: 3.826716 Loss2: 1.935636
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.529295 Loss1: 3.082845 Loss2: 1.446450
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.458466 Loss1: 3.006085 Loss2: 1.452381 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.484174 Loss1: 3.024471 Loss2: 1.459703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.473680 Loss1: 3.006969 Loss2: 1.466710 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.434759 Loss1: 2.959225 Loss2: 1.475534 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.257292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.528949 Loss1: 3.086239 Loss2: 1.442710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.435204 Loss1: 2.980026 Loss2: 1.455177 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.271205
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.538268 Loss1: 3.676051 Loss2: 1.862217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.421621 Loss1: 3.021235 Loss2: 1.400386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.262751 Loss1: 2.874913 Loss2: 1.387838 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.225079 Loss1: 2.823409 Loss2: 1.401670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.273425 Loss1: 2.875447 Loss2: 1.397978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.180629 Loss1: 2.783557 Loss2: 1.397072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.140666 Loss1: 2.733493 Loss2: 1.407172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.155685 Loss1: 2.747199 Loss2: 1.408486 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.305208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.271387 Loss1: 2.867633 Loss2: 1.403753 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.219377 Loss1: 2.802560 Loss2: 1.416817 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.295833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.869369 Loss1: 3.412948 Loss2: 1.456420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.543988 Loss1: 3.118399 Loss2: 1.425589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.796354 Loss1: 3.992439 Loss2: 1.803914
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.498637 Loss1: 3.090957 Loss2: 1.407681
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.976073 Loss1: 3.585143 Loss2: 1.390929
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.454618 Loss1: 3.026123 Loss2: 1.428495
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.829482 Loss1: 3.473277 Loss2: 1.356205
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.452806 Loss1: 3.014118 Loss2: 1.438688
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.725812 Loss1: 3.360828 Loss2: 1.364983
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.377618 Loss1: 2.940493 Loss2: 1.437125
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.629195 Loss1: 3.261908 Loss2: 1.367287
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.356891 Loss1: 2.924464 Loss2: 1.432427
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.628560 Loss1: 3.252511 Loss2: 1.376048
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.375839 Loss1: 2.927493 Loss2: 1.448346
+(DefaultActor pid=3765) >> Training accuracy: 0.277083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.574491 Loss1: 3.195089 Loss2: 1.379401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.509960 Loss1: 3.116123 Loss2: 1.393837 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.202083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.832544 Loss1: 3.477054 Loss2: 1.355490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.612687 Loss1: 3.287701 Loss2: 1.324986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.619294 Loss1: 3.805962 Loss2: 1.813332
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.565662 Loss1: 3.232767 Loss2: 1.332895
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.818676 Loss1: 3.416664 Loss2: 1.402012
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.513460 Loss1: 3.190492 Loss2: 1.322968
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.724651 Loss1: 3.349842 Loss2: 1.374808
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.419761 Loss1: 3.093626 Loss2: 1.326136
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.626659 Loss1: 3.254459 Loss2: 1.372200
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.392299 Loss1: 3.064166 Loss2: 1.328133
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.633211 Loss1: 3.244921 Loss2: 1.388290
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.466729 Loss1: 3.119852 Loss2: 1.346877
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.568694 Loss1: 3.174908 Loss2: 1.393786
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.404157 Loss1: 3.063694 Loss2: 1.340463
+(DefaultActor pid=3765) >> Training accuracy: 0.207292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.437785 Loss1: 3.041134 Loss2: 1.396651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.431079 Loss1: 3.029474 Loss2: 1.401606 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.247917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.803914 Loss1: 3.343524 Loss2: 1.460390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.540571 Loss1: 3.123722 Loss2: 1.416849 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.799002 Loss1: 3.898858 Loss2: 1.900144
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.532910 Loss1: 3.112693 Loss2: 1.420217
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.965723 Loss1: 3.482540 Loss2: 1.483183
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.520101 Loss1: 3.089551 Loss2: 1.430550
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.851643 Loss1: 3.406748 Loss2: 1.444895
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.441466 Loss1: 3.007486 Loss2: 1.433979
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.392720 Loss1: 2.967383 Loss2: 1.425337
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.823015 Loss1: 3.382821 Loss2: 1.440194
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.380924 Loss1: 2.943353 Loss2: 1.437571
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.739335 Loss1: 3.296254 Loss2: 1.443081
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.365826 Loss1: 2.923711 Loss2: 1.442115
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.721882 Loss1: 3.267616 Loss2: 1.454266
+(DefaultActor pid=3765) >> Training accuracy: 0.279167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.641705 Loss1: 3.189193 Loss2: 1.452512
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.647877 Loss1: 3.194853 Loss2: 1.453023
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.627187 Loss1: 3.157535 Loss2: 1.469652
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.554425 Loss1: 3.087156 Loss2: 1.467269
+(DefaultActor pid=3764) >> Training accuracy: 0.191406
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.792248 Loss1: 3.834586 Loss2: 1.957662
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.833264 Loss1: 3.316145 Loss2: 1.517119
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.629601 Loss1: 3.175944 Loss2: 1.453657
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.545500 Loss1: 3.090963 Loss2: 1.454538
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.518751 Loss1: 3.059809 Loss2: 1.458942
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.715845 Loss1: 3.846219 Loss2: 1.869626
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.858112 Loss1: 3.398590 Loss2: 1.459523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.682075 Loss1: 3.247294 Loss2: 1.434781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.571170 Loss1: 3.144140 Loss2: 1.427030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.521305 Loss1: 3.087362 Loss2: 1.433942 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.277083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.462917 Loss1: 3.033903 Loss2: 1.429014 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.454722 Loss1: 3.001407 Loss2: 1.453315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.343489 Loss1: 2.913375 Loss2: 1.430114
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.466782 Loss1: 3.594367 Loss2: 1.872415
+(DefaultActor pid=3764) >> Training accuracy: 0.237305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.626089 Loss1: 3.185152 Loss2: 1.440937
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.474759 Loss1: 3.068206 Loss2: 1.406552
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.373352 Loss1: 2.975274 Loss2: 1.398078
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.353856 Loss1: 2.951772 Loss2: 1.402084
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.344277 Loss1: 2.927115 Loss2: 1.417162
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.669243 Loss1: 3.831637 Loss2: 1.837606
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.293172 Loss1: 2.883530 Loss2: 1.409642
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.696298 Loss1: 3.294257 Loss2: 1.402040
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.219552 Loss1: 2.809139 Loss2: 1.410413
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.492148 Loss1: 3.125997 Loss2: 1.366152
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.141250 Loss1: 2.724643 Loss2: 1.416607
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.426830 Loss1: 3.063352 Loss2: 1.363478
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.161165 Loss1: 2.736645 Loss2: 1.424520
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.418015 Loss1: 3.054781 Loss2: 1.363235
+(DefaultActor pid=3765) >> Training accuracy: 0.304167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.344968 Loss1: 2.984460 Loss2: 1.360508
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.334191 Loss1: 2.961829 Loss2: 1.372362
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.325692 Loss1: 2.940788 Loss2: 1.384904
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.244368 Loss1: 2.869933 Loss2: 1.374435
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.275624 Loss1: 2.891040 Loss2: 1.384584
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.818058 Loss1: 3.954138 Loss2: 1.863919
+(DefaultActor pid=3764) >> Training accuracy: 0.244792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.958701 Loss1: 3.523218 Loss2: 1.435483
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.705214 Loss1: 3.316462 Loss2: 1.388752
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.647645 Loss1: 3.243600 Loss2: 1.404046
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.630533 Loss1: 3.221552 Loss2: 1.408981
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.515475 Loss1: 3.654797 Loss2: 1.860678
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.614178 Loss1: 3.199718 Loss2: 1.414460
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.674562 Loss1: 3.246019 Loss2: 1.428543
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.604287 Loss1: 3.186978 Loss2: 1.417310
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.511426 Loss1: 3.114689 Loss2: 1.396737
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.549386 Loss1: 3.122474 Loss2: 1.426912
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.440160 Loss1: 3.039818 Loss2: 1.400342
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.536157 Loss1: 3.113671 Loss2: 1.422487
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.356895 Loss1: 2.947274 Loss2: 1.409620
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.497007 Loss1: 3.074065 Loss2: 1.422942
+(DefaultActor pid=3765) >> Training accuracy: 0.247070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.329553 Loss1: 2.915053 Loss2: 1.414499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.230072 Loss1: 2.812364 Loss2: 1.417708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.585869 Loss1: 3.786868 Loss2: 1.799001
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.314266 Loss1: 2.877860 Loss2: 1.436405
+(DefaultActor pid=3764) >> Training accuracy: 0.274414
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.579854 Loss1: 3.228451 Loss2: 1.351403 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.432701 Loss1: 3.080350 Loss2: 1.352351 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.372616 Loss1: 3.023912 Loss2: 1.348704
+(DefaultActor pid=3764) Epoch: 0 Loss: 6.038271 Loss1: 4.087950 Loss2: 1.950321
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.340961 Loss1: 2.976039 Loss2: 1.364923
+(DefaultActor pid=3764) Epoch: 1 Loss: 5.071873 Loss1: 3.567594 Loss2: 1.504279
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.881033 Loss1: 3.434323 Loss2: 1.446710
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.313389 Loss1: 2.938634 Loss2: 1.374755
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.812080 Loss1: 3.371885 Loss2: 1.440195
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.279340 Loss1: 2.914024 Loss2: 1.365316
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.763638 Loss1: 3.329016 Loss2: 1.434622
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.303798 Loss1: 2.929505 Loss2: 1.374294
+(DefaultActor pid=3765) >> Training accuracy: 0.276042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.686506 Loss1: 3.240618 Loss2: 1.445888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.662005 Loss1: 3.217695 Loss2: 1.444310 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.171875
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.646308 Loss1: 3.191260 Loss2: 1.455048
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.702707 Loss1: 3.845522 Loss2: 1.857186
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.864442 Loss1: 3.420413 Loss2: 1.444029
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.723404 Loss1: 3.320896 Loss2: 1.402508
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.622400 Loss1: 3.221593 Loss2: 1.400807
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.584124 Loss1: 3.173120 Loss2: 1.411004
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.938826 Loss1: 4.035775 Loss2: 1.903051
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.992138 Loss1: 3.546472 Loss2: 1.445666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.795912 Loss1: 3.389181 Loss2: 1.406731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.743668 Loss1: 3.333938 Loss2: 1.409731 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.492217 Loss1: 3.070274 Loss2: 1.421943
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.694131 Loss1: 3.298926 Loss2: 1.395205
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.479435 Loss1: 3.042729 Loss2: 1.436706
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.687590 Loss1: 3.267153 Loss2: 1.420437
+(DefaultActor pid=3765) >> Training accuracy: 0.232292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.631794 Loss1: 3.209426 Loss2: 1.422368
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.602979 Loss1: 3.181215 Loss2: 1.421764
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.591458 Loss1: 3.182894 Loss2: 1.408564
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.532870 Loss1: 3.112174 Loss2: 1.420696
+(DefaultActor pid=3764) >> Training accuracy: 0.209821
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.605850 Loss1: 3.636264 Loss2: 1.969586
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.749958 Loss1: 3.213896 Loss2: 1.536062
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.606008 Loss1: 3.115720 Loss2: 1.490288
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.492655 Loss1: 3.009213 Loss2: 1.483442
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.562552 Loss1: 3.676678 Loss2: 1.885874
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.786354 Loss1: 3.328827 Loss2: 1.457527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.623459 Loss1: 3.205244 Loss2: 1.418215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.561851 Loss1: 3.149291 Loss2: 1.412560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.498579 Loss1: 3.073057 Loss2: 1.425522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.467007 Loss1: 3.047755 Loss2: 1.419253 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.292708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.363515 Loss1: 2.946533 Loss2: 1.416982 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.393588 Loss1: 2.954937 Loss2: 1.438651 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.251042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.825044 Loss1: 3.364662 Loss2: 1.460383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.594516 Loss1: 3.155055 Loss2: 1.439461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.508840 Loss1: 3.759040 Loss2: 1.749800
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.514773 Loss1: 3.085820 Loss2: 1.428953
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.569184 Loss1: 3.222283 Loss2: 1.346901
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.482753 Loss1: 3.059217 Loss2: 1.423535
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.360701 Loss1: 3.043787 Loss2: 1.316914
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.428678 Loss1: 2.996480 Loss2: 1.432198
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.278668 Loss1: 2.986773 Loss2: 1.291894
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.444057 Loss1: 3.005900 Loss2: 1.438157
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.163987 Loss1: 2.850745 Loss2: 1.313242
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.404150 Loss1: 2.955079 Loss2: 1.449070
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.135027 Loss1: 2.822040 Loss2: 1.312987
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.413553 Loss1: 2.959940 Loss2: 1.453613
+(DefaultActor pid=3765) >> Training accuracy: 0.238542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.126244 Loss1: 2.815073 Loss2: 1.311171 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.095190 Loss1: 2.780331 Loss2: 1.314859 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.325000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.686657 Loss1: 3.256096 Loss2: 1.430561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.453576 Loss1: 3.051737 Loss2: 1.401839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.417503 Loss1: 3.009651 Loss2: 1.407852 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.350440 Loss1: 2.937176 Loss2: 1.413264 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.356138 Loss1: 2.939497 Loss2: 1.416641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.298514 Loss1: 2.874891 Loss2: 1.423624 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.284297 Loss1: 2.860956 Loss2: 1.423341 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.249014 Loss1: 2.813871 Loss2: 1.435143 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.270508
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.436481 Loss1: 3.008926 Loss2: 1.427554 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.253125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.891856 Loss1: 3.902026 Loss2: 1.989830 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.727788 Loss1: 3.249999 Loss2: 1.477788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.650740 Loss1: 3.184869 Loss2: 1.465871
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.858932 Loss1: 3.927098 Loss2: 1.931834
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.898477 Loss1: 3.365251 Loss2: 1.533226 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.704418 Loss1: 3.209817 Loss2: 1.494602 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.682114 Loss1: 3.180231 Loss2: 1.501883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.650393 Loss1: 3.152574 Loss2: 1.497819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.428584 Loss1: 2.948081 Loss2: 1.480503 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.262500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.544496 Loss1: 3.034512 Loss2: 1.509984 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.470134 Loss1: 2.959471 Loss2: 1.510663 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.258272
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.806262 Loss1: 3.397705 Loss2: 1.408557 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.595042 Loss1: 3.195580 Loss2: 1.399461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.590252 Loss1: 3.173923 Loss2: 1.416329
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.964526 Loss1: 3.958540 Loss2: 2.005986
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.996893 Loss1: 3.534340 Loss2: 1.462553 [repeated 2x across cluster]
+DEBUG flwr 2023-10-08 17:32:29,490 | server.py:236 | fit_round 9 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.725282 Loss1: 3.288695 Loss2: 1.436587 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.546178 Loss1: 3.132751 Loss2: 1.413427
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.613428 Loss1: 3.199292 Loss2: 1.414136
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.579973 Loss1: 3.159427 Loss2: 1.420546
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.416337 Loss1: 3.008864 Loss2: 1.407474
+(DefaultActor pid=3765) >> Training accuracy: 0.233173
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.482042 Loss1: 3.047623 Loss2: 1.434419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.421677 Loss1: 2.981141 Loss2: 1.440536 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.266927
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.632924 Loss1: 3.791371 Loss2: 1.841553 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.564889 Loss1: 3.179630 Loss2: 1.385259 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.473056 Loss1: 3.086487 Loss2: 1.386569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.418707 Loss1: 3.032709 Loss2: 1.385999 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.402035 Loss1: 3.012971 Loss2: 1.389065 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.402192 Loss1: 3.001658 Loss2: 1.400534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.367096 Loss1: 2.962856 Loss2: 1.404240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.355400 Loss1: 2.965861 Loss2: 1.389540 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.241667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.330692 Loss1: 2.966116 Loss2: 1.364576 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.355749 Loss1: 2.963886 Loss2: 1.391863 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.264583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.885193 Loss1: 3.459017 Loss2: 1.426176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.607108 Loss1: 3.217585 Loss2: 1.389523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.586711 Loss1: 3.193312 Loss2: 1.393399
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.657619 Loss1: 3.798494 Loss2: 1.859124
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.525522 Loss1: 3.129424 Loss2: 1.396099
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.770299 Loss1: 3.347837 Loss2: 1.422462
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.551641 Loss1: 3.155655 Loss2: 1.395986
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.604227 Loss1: 3.203908 Loss2: 1.400319
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.501517 Loss1: 3.104254 Loss2: 1.397263
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.541602 Loss1: 3.140371 Loss2: 1.401230
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.444893 Loss1: 3.047870 Loss2: 1.397023
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.497455 Loss1: 3.098284 Loss2: 1.399171
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.473847 Loss1: 3.064856 Loss2: 1.408991
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.511160 Loss1: 3.102533 Loss2: 1.408626
+(DefaultActor pid=3765) >> Training accuracy: 0.226042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.487462 Loss1: 3.073603 Loss2: 1.413859
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.435634 Loss1: 3.030099 Loss2: 1.405535
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.314151 Loss1: 2.907370 Loss2: 1.406780
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.402080 Loss1: 2.979253 Loss2: 1.422827
+(DefaultActor pid=3764) >> Training accuracy: 0.268750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 17:32:29,490][flwr][DEBUG] - fit_round 9 received 50 results and 0 failures
+INFO flwr 2023-10-08 17:33:10,854 | server.py:125 | fit progress: (9, 4.2343795116717065, {'accuracy': 0.0634}, 20498.632143409002)
+>> Test accuracy: 0.063400
+[2023-10-08 17:33:10,854][flwr][INFO] - fit progress: (9, 4.2343795116717065, {'accuracy': 0.0634}, 20498.632143409002)
+DEBUG flwr 2023-10-08 17:33:10,854 | server.py:173 | evaluate_round 9: strategy sampled 50 clients (out of 50)
+[2023-10-08 17:33:10,854][flwr][DEBUG] - evaluate_round 9: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 17:42:12,578 | server.py:187 | evaluate_round 9 received 50 results and 0 failures
+[2023-10-08 17:42:12,578][flwr][DEBUG] - evaluate_round 9 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 17:42:12,578 | server.py:222 | fit_round 10: strategy sampled 50 clients (out of 50)
+[2023-10-08 17:42:12,578][flwr][DEBUG] - fit_round 10: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.490547 Loss1: 3.657879 Loss2: 1.832668
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.608509 Loss1: 3.174912 Loss2: 1.433597
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.468246 Loss1: 3.073031 Loss2: 1.395215
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.719433 Loss1: 3.736987 Loss2: 1.982446
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.412262 Loss1: 3.004707 Loss2: 1.407555
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.372880 Loss1: 2.969746 Loss2: 1.403134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.265336 Loss1: 2.862289 Loss2: 1.403046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.554800 Loss1: 3.095774 Loss2: 1.459026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.509914 Loss1: 3.050159 Loss2: 1.459754 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.511482 Loss1: 3.048255 Loss2: 1.463227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.479134 Loss1: 2.997247 Loss2: 1.481887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.395708 Loss1: 2.920767 Loss2: 1.474940 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.290039
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.376722 Loss1: 3.461251 Loss2: 1.915471 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.262019
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.373269 Loss1: 2.970722 Loss2: 1.402547 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.319090 Loss1: 2.942010 Loss2: 1.377080
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.445089 Loss1: 3.663626 Loss2: 1.781462
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.559748 Loss1: 3.173599 Loss2: 1.386149 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.395149 Loss1: 3.049083 Loss2: 1.346066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.323018 Loss1: 2.990325 Loss2: 1.332693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.330251 Loss1: 2.990885 Loss2: 1.339366 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.282664 Loss1: 2.930126 Loss2: 1.352538 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.289583
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.126491 Loss1: 2.725589 Loss2: 1.400902
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.194973 Loss1: 2.840512 Loss2: 1.354462
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.211344 Loss1: 2.851307 Loss2: 1.360036
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.137764 Loss1: 2.775106 Loss2: 1.362658
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.095132 Loss1: 2.714591 Loss2: 1.380541
+(DefaultActor pid=3764) >> Training accuracy: 0.297917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.720532 Loss1: 3.904563 Loss2: 1.815969
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.696883 Loss1: 3.320260 Loss2: 1.376623
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.512905 Loss1: 3.170187 Loss2: 1.342718
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.433279 Loss1: 3.104389 Loss2: 1.328890
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.450482 Loss1: 3.491134 Loss2: 1.959348
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.375839 Loss1: 3.041575 Loss2: 1.334263
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.605928 Loss1: 3.114339 Loss2: 1.491589
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.357902 Loss1: 3.022562 Loss2: 1.335341
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.373571 Loss1: 2.913128 Loss2: 1.460442
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.311475 Loss1: 2.972261 Loss2: 1.339214
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.321956 Loss1: 2.886254 Loss2: 1.435702
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.287191 Loss1: 2.938836 Loss2: 1.348356
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.256100 Loss1: 2.804515 Loss2: 1.451585
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.241738 Loss1: 2.887090 Loss2: 1.354648
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.208129 Loss1: 2.754756 Loss2: 1.453373
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.222899 Loss1: 2.881664 Loss2: 1.341234
+(DefaultActor pid=3765) >> Training accuracy: 0.270833
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.201161 Loss1: 2.752310 Loss2: 1.448851
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.187039 Loss1: 2.721831 Loss2: 1.465208
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.116930 Loss1: 2.653271 Loss2: 1.463659
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.111304 Loss1: 2.644543 Loss2: 1.466761
+(DefaultActor pid=3764) >> Training accuracy: 0.343750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.679110 Loss1: 3.823044 Loss2: 1.856066
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.800754 Loss1: 3.378435 Loss2: 1.422319
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.603693 Loss1: 3.210681 Loss2: 1.393013
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.544942 Loss1: 3.157418 Loss2: 1.387524
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.662154 Loss1: 3.795543 Loss2: 1.866611
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.495993 Loss1: 3.115059 Loss2: 1.380934
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.777170 Loss1: 3.320551 Loss2: 1.456619
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.635183 Loss1: 3.213589 Loss2: 1.421594
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.428538 Loss1: 3.040631 Loss2: 1.387907
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.617737 Loss1: 3.196991 Loss2: 1.420746
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.427921 Loss1: 3.015843 Loss2: 1.412078
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.531672 Loss1: 3.116436 Loss2: 1.415236
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.481294 Loss1: 3.065999 Loss2: 1.415296
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.534800 Loss1: 3.111497 Loss2: 1.423303
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.352168 Loss1: 2.940303 Loss2: 1.411865
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.360133 Loss1: 2.942154 Loss2: 1.417978 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.281250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.354233 Loss1: 2.921004 Loss2: 1.433229 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.259375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.682875 Loss1: 3.778213 Loss2: 1.904662 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.444787 Loss1: 3.009043 Loss2: 1.435744 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.378877 Loss1: 2.951990 Loss2: 1.426887
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.676869 Loss1: 3.805412 Loss2: 1.871457
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.285868 Loss1: 2.873739 Loss2: 1.412128
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.830969 Loss1: 3.384892 Loss2: 1.446077
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.354435 Loss1: 2.927096 Loss2: 1.427339
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.595805 Loss1: 3.173652 Loss2: 1.422154
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.357990 Loss1: 2.932614 Loss2: 1.425376
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.512498 Loss1: 3.102306 Loss2: 1.410193
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.263162 Loss1: 2.831720 Loss2: 1.431442
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.546719 Loss1: 3.113997 Loss2: 1.432722
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.244560 Loss1: 2.810900 Loss2: 1.433660
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.481684 Loss1: 3.065448 Loss2: 1.416236
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.231805 Loss1: 2.791980 Loss2: 1.439824
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.435882 Loss1: 3.015230 Loss2: 1.420653
+(DefaultActor pid=3765) >> Training accuracy: 0.266667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.364706 Loss1: 2.943616 Loss2: 1.421090
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.352230 Loss1: 2.910357 Loss2: 1.441873
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.360139 Loss1: 2.916837 Loss2: 1.443302
+(DefaultActor pid=3764) >> Training accuracy: 0.244792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.576628 Loss1: 3.706997 Loss2: 1.869631
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.825664 Loss1: 3.393864 Loss2: 1.431799
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.634858 Loss1: 3.224633 Loss2: 1.410225
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.533348 Loss1: 3.126704 Loss2: 1.406645
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.682081 Loss1: 3.770246 Loss2: 1.911836
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.790707 Loss1: 3.328547 Loss2: 1.462160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.632509 Loss1: 3.203358 Loss2: 1.429151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.575401 Loss1: 3.155569 Loss2: 1.419832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.504187 Loss1: 3.077000 Loss2: 1.427186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.470175 Loss1: 3.048020 Loss2: 1.422155 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.267708
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.340447 Loss1: 2.893636 Loss2: 1.446811
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.470180 Loss1: 3.043820 Loss2: 1.426359
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.390114 Loss1: 2.950068 Loss2: 1.440046
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.336272 Loss1: 2.901190 Loss2: 1.435082
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.361699 Loss1: 2.914046 Loss2: 1.447653
+(DefaultActor pid=3764) >> Training accuracy: 0.252083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.610469 Loss1: 3.833945 Loss2: 1.776524
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.829599 Loss1: 3.439581 Loss2: 1.390018
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.665699 Loss1: 3.303617 Loss2: 1.362082
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.570547 Loss1: 3.209324 Loss2: 1.361223
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.622267 Loss1: 3.865443 Loss2: 1.756824
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.569313 Loss1: 3.192800 Loss2: 1.376512
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.764374 Loss1: 3.397590 Loss2: 1.366784
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.537144 Loss1: 3.153360 Loss2: 1.383784
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.610563 Loss1: 3.273047 Loss2: 1.337516
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.484051 Loss1: 3.105212 Loss2: 1.378839
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.535223 Loss1: 3.195959 Loss2: 1.339264
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.518482 Loss1: 3.126181 Loss2: 1.392301
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.519395 Loss1: 3.176951 Loss2: 1.342444
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.436014 Loss1: 3.035196 Loss2: 1.400817
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.474287 Loss1: 3.124804 Loss2: 1.349483
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.417834 Loss1: 3.026392 Loss2: 1.391442
+(DefaultActor pid=3765) >> Training accuracy: 0.246094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.446855 Loss1: 3.101883 Loss2: 1.344972
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.423910 Loss1: 3.060786 Loss2: 1.363124
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.443498 Loss1: 3.083905 Loss2: 1.359593
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.400028 Loss1: 3.029763 Loss2: 1.370265
+(DefaultActor pid=3764) >> Training accuracy: 0.232422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.601104 Loss1: 3.753640 Loss2: 1.847463
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.694757 Loss1: 3.268076 Loss2: 1.426681
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.509627 Loss1: 3.116014 Loss2: 1.393613
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.600015 Loss1: 3.694911 Loss2: 1.905104
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.506221 Loss1: 3.095271 Loss2: 1.410950
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.672834 Loss1: 3.225341 Loss2: 1.447492
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.462820 Loss1: 3.052183 Loss2: 1.410637
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.507884 Loss1: 3.095220 Loss2: 1.412664
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.398135 Loss1: 3.001474 Loss2: 1.396661
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.361195 Loss1: 2.962287 Loss2: 1.398908 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.413483 Loss1: 3.003010 Loss2: 1.410474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.268127 Loss1: 2.869436 Loss2: 1.398691 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.222946 Loss1: 2.807436 Loss2: 1.415511 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.273897
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.277135 Loss1: 2.848659 Loss2: 1.428476 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.289583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.794312 Loss1: 3.866240 Loss2: 1.928072
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.972043 Loss1: 3.495656 Loss2: 1.476387
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.809533 Loss1: 3.376406 Loss2: 1.433127
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.688100 Loss1: 3.254974 Loss2: 1.433126
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.714361 Loss1: 3.877039 Loss2: 1.837322
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.875133 Loss1: 3.439106 Loss2: 1.436027 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.656325 Loss1: 3.266835 Loss2: 1.389490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.549607 Loss1: 3.107633 Loss2: 1.441974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.494377 Loss1: 3.053722 Loss2: 1.440655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.437605 Loss1: 2.993233 Loss2: 1.444371 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.237723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.471484 Loss1: 3.096049 Loss2: 1.375435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.386112 Loss1: 2.991649 Loss2: 1.394462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.323507 Loss1: 2.928168 Loss2: 1.395339
+(DefaultActor pid=3764) >> Training accuracy: 0.266602
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.527264 Loss1: 3.583177 Loss2: 1.944087
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.737954 Loss1: 3.232187 Loss2: 1.505767
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.555113 Loss1: 3.096192 Loss2: 1.458921
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.421675 Loss1: 2.979838 Loss2: 1.441836
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.431565 Loss1: 2.978530 Loss2: 1.453036
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.472136 Loss1: 3.520656 Loss2: 1.951480
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.350358 Loss1: 2.901430 Loss2: 1.448928
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.367445 Loss1: 2.902338 Loss2: 1.465107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.473551 Loss1: 3.032581 Loss2: 1.440970
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.371972 Loss1: 2.913053 Loss2: 1.458918
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.340190 Loss1: 2.891306 Loss2: 1.448885
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.286316 Loss1: 2.824334 Loss2: 1.461982
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.270115 Loss1: 2.826071 Loss2: 1.444044
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.230959 Loss1: 2.776473 Loss2: 1.454486
+(DefaultActor pid=3765) >> Training accuracy: 0.251042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.286012 Loss1: 2.830060 Loss2: 1.455951 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.180282 Loss1: 2.727398 Loss2: 1.452884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.779049 Loss1: 3.751105 Loss2: 2.027944
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.124553 Loss1: 2.669446 Loss2: 1.455107
+(DefaultActor pid=3764) >> Training accuracy: 0.299805
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.745658 Loss1: 3.258182 Loss2: 1.487476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.600650 Loss1: 3.110266 Loss2: 1.490384 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.595360 Loss1: 3.096040 Loss2: 1.499321
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.488850 Loss1: 3.583361 Loss2: 1.905489
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.526355 Loss1: 3.036418 Loss2: 1.489937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.349161 Loss1: 2.908895 Loss2: 1.440266 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.257906 Loss1: 2.838853 Loss2: 1.419053 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.246875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.474013 Loss1: 2.956296 Loss2: 1.517717
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.175074 Loss1: 2.756038 Loss2: 1.419036
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.187963 Loss1: 2.769778 Loss2: 1.418184
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.130349 Loss1: 2.712624 Loss2: 1.417725
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.140697 Loss1: 2.718200 Loss2: 1.422497
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.119688 Loss1: 2.697581 Loss2: 1.422107
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.595066 Loss1: 3.644091 Loss2: 1.950975
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.046250 Loss1: 2.616421 Loss2: 1.429829
+(DefaultActor pid=3764) >> Training accuracy: 0.302083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.408505 Loss1: 2.983119 Loss2: 1.425386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.272555 Loss1: 2.866654 Loss2: 1.405900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.218716 Loss1: 2.807117 Loss2: 1.411599 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.142196 Loss1: 2.713848 Loss2: 1.428349 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.171341 Loss1: 2.751390 Loss2: 1.419951 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.072093 Loss1: 2.651140 Loss2: 1.420953 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.324519
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.327206 Loss1: 2.975527 Loss2: 1.351680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.361723 Loss1: 3.001241 Loss2: 1.360482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.284547 Loss1: 2.912543 Loss2: 1.372004
+(DefaultActor pid=3765) Epoch: 0 Loss: 6.043284 Loss1: 3.994438 Loss2: 2.048846
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.978825 Loss1: 3.429846 Loss2: 1.548980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.233025 Loss1: 2.848030 Loss2: 1.384995
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.786470 Loss1: 3.291998 Loss2: 1.494472
+(DefaultActor pid=3764) >> Training accuracy: 0.280208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.684866 Loss1: 3.195074 Loss2: 1.489792
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.667822 Loss1: 3.170108 Loss2: 1.497715
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.636948 Loss1: 3.147558 Loss2: 1.489390
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.612948 Loss1: 3.116791 Loss2: 1.496157
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.570458 Loss1: 3.074732 Loss2: 1.495726
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.483323 Loss1: 3.664341 Loss2: 1.818982
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.595671 Loss1: 3.170762 Loss2: 1.424909 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.229911
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.531421 Loss1: 3.025454 Loss2: 1.505967
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.421492 Loss1: 3.038975 Loss2: 1.382517
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.351489 Loss1: 2.978796 Loss2: 1.372693
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.299247 Loss1: 2.938647 Loss2: 1.360600
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.247848 Loss1: 2.878182 Loss2: 1.369665
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.217575 Loss1: 2.833275 Loss2: 1.384300
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.474878 Loss1: 3.700997 Loss2: 1.773880
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.187149 Loss1: 2.803456 Loss2: 1.383693
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.574611 Loss1: 3.164531 Loss2: 1.410080
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.179975 Loss1: 2.784556 Loss2: 1.395418
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.126130 Loss1: 2.747820 Loss2: 1.378310
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.398833 Loss1: 3.014412 Loss2: 1.384421
+(DefaultActor pid=3764) >> Training accuracy: 0.281250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.350179 Loss1: 2.963128 Loss2: 1.387050
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.303776 Loss1: 2.910647 Loss2: 1.393130
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.307796 Loss1: 2.926048 Loss2: 1.381748
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.219083 Loss1: 2.823602 Loss2: 1.395481
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.724944 Loss1: 3.751462 Loss2: 1.973483
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.190671 Loss1: 2.785446 Loss2: 1.405225
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.184901 Loss1: 2.776329 Loss2: 1.408572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.474774 Loss1: 3.085414 Loss2: 1.389360 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.267578
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.379953 Loss1: 2.988473 Loss2: 1.391480 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.237030 Loss1: 2.836337 Loss2: 1.400693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.278543 Loss1: 2.860532 Loss2: 1.418011 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.278646
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.808355 Loss1: 3.286367 Loss2: 1.521988 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.554811 Loss1: 3.077945 Loss2: 1.476866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.581118 Loss1: 3.721138 Loss2: 1.859980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.722557 Loss1: 3.310184 Loss2: 1.412373 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.584901 Loss1: 3.200723 Loss2: 1.384178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.514988 Loss1: 3.139173 Loss2: 1.375815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.427795 Loss1: 3.047863 Loss2: 1.379932 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.265625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.400813 Loss1: 3.019390 Loss2: 1.381423 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.361741 Loss1: 2.966433 Loss2: 1.395308 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.291184 Loss1: 2.896704 Loss2: 1.394480 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.243750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.594219 Loss1: 3.160668 Loss2: 1.433551 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.384807 Loss1: 2.985157 Loss2: 1.399650 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.328914 Loss1: 2.922232 Loss2: 1.406682
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.510330 Loss1: 3.625880 Loss2: 1.884450
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.684885 Loss1: 3.248463 Loss2: 1.436422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.487235 Loss1: 3.104277 Loss2: 1.382958 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.374179 Loss1: 3.002234 Loss2: 1.371945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.382237 Loss1: 2.997138 Loss2: 1.385099 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.288542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.327339 Loss1: 2.938163 Loss2: 1.389177 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.312689 Loss1: 2.910183 Loss2: 1.402506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.166412 Loss1: 2.777234 Loss2: 1.389178 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.300000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.367435 Loss1: 2.921909 Loss2: 1.445526 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.289506 Loss1: 2.842557 Loss2: 1.446949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.728558 Loss1: 3.706765 Loss2: 2.021793
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.187613 Loss1: 2.736950 Loss2: 1.450663
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.640068 Loss1: 3.059842 Loss2: 1.580226
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.145102 Loss1: 2.696437 Loss2: 1.448665
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.431615 Loss1: 2.906043 Loss2: 1.525571
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.117932 Loss1: 2.655756 Loss2: 1.462176
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.316672 Loss1: 2.793249 Loss2: 1.523423
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.175292 Loss1: 2.698750 Loss2: 1.476542
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.323122 Loss1: 2.789977 Loss2: 1.533145
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.079264 Loss1: 2.620170 Loss2: 1.459094
+(DefaultActor pid=3765) >> Training accuracy: 0.283333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.231300 Loss1: 2.710566 Loss2: 1.520734 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.191656 Loss1: 2.656539 Loss2: 1.535117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.128143 Loss1: 2.612235 Loss2: 1.515908
+(DefaultActor pid=3764) >> Training accuracy: 0.378125
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.632347 Loss1: 3.749747 Loss2: 1.882600
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.777262 Loss1: 3.327409 Loss2: 1.449853
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.624656 Loss1: 3.212395 Loss2: 1.412261
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.527811 Loss1: 3.132504 Loss2: 1.395306
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.463888 Loss1: 3.059957 Loss2: 1.403931
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.630234 Loss1: 3.725014 Loss2: 1.905220
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.465559 Loss1: 3.051511 Loss2: 1.414048
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.705034 Loss1: 3.238883 Loss2: 1.466151
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.463427 Loss1: 3.045448 Loss2: 1.417979
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.525963 Loss1: 3.104564 Loss2: 1.421399
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.371816 Loss1: 2.954611 Loss2: 1.417205
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.467722 Loss1: 3.039654 Loss2: 1.428069
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.396507 Loss1: 2.971884 Loss2: 1.424623
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.401042 Loss1: 2.981337 Loss2: 1.419705
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.391933 Loss1: 2.959854 Loss2: 1.432079
+(DefaultActor pid=3765) >> Training accuracy: 0.222917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.308957 Loss1: 2.864427 Loss2: 1.444530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.264896 Loss1: 2.825066 Loss2: 1.439830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.233537 Loss1: 2.806410 Loss2: 1.427126
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.588558 Loss1: 3.781320 Loss2: 1.807238
+(DefaultActor pid=3764) >> Training accuracy: 0.272917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.717557 Loss1: 3.310348 Loss2: 1.407209
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.566462 Loss1: 3.206496 Loss2: 1.359966
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.469648 Loss1: 3.114319 Loss2: 1.355329
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.409802 Loss1: 3.047271 Loss2: 1.362531
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.810967 Loss1: 3.834629 Loss2: 1.976339
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.368056 Loss1: 3.000248 Loss2: 1.367808
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.311918 Loss1: 2.945523 Loss2: 1.366395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.326640 Loss1: 2.948348 Loss2: 1.378293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.320698 Loss1: 2.949448 Loss2: 1.371250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.293254 Loss1: 2.915414 Loss2: 1.377839 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.263542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.416780 Loss1: 2.936227 Loss2: 1.480553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.336502 Loss1: 2.856586 Loss2: 1.479916 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.291295
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.499451 Loss1: 3.701365 Loss2: 1.798086 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.449455 Loss1: 3.108238 Loss2: 1.341217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.359309 Loss1: 3.014971 Loss2: 1.344338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.322606 Loss1: 2.972239 Loss2: 1.350367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.384581 Loss1: 3.027657 Loss2: 1.356925 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.284936 Loss1: 2.940483 Loss2: 1.344454 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.205835 Loss1: 2.845522 Loss2: 1.360313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.203110 Loss1: 2.847220 Loss2: 1.355890 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.262500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.513354 Loss1: 3.090446 Loss2: 1.422908 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.381876 Loss1: 2.939240 Loss2: 1.442636 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.250000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.796063 Loss1: 3.261778 Loss2: 1.534284 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.537174 Loss1: 3.069003 Loss2: 1.468171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.454329 Loss1: 2.982304 Loss2: 1.472025
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.727298 Loss1: 3.907898 Loss2: 1.819401
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.818438 Loss1: 3.422047 Loss2: 1.396391 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.699435 Loss1: 3.322513 Loss2: 1.376922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.612242 Loss1: 3.242848 Loss2: 1.369394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.595289 Loss1: 3.202352 Loss2: 1.392937 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.258333
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.362882 Loss1: 2.875558 Loss2: 1.487324
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.563731 Loss1: 3.175652 Loss2: 1.388080
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.538595 Loss1: 3.149453 Loss2: 1.389142
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.526759 Loss1: 3.121500 Loss2: 1.405260
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.475269 Loss1: 3.070336 Loss2: 1.404934
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.411118 Loss1: 3.008847 Loss2: 1.402271
+(DefaultActor pid=3764) >> Training accuracy: 0.201042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.594228 Loss1: 3.682998 Loss2: 1.911230
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.707294 Loss1: 3.247546 Loss2: 1.459747
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.520578 Loss1: 3.103023 Loss2: 1.417554
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.448639 Loss1: 3.041606 Loss2: 1.407033
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.394179 Loss1: 2.982253 Loss2: 1.411925
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.478075 Loss1: 3.655541 Loss2: 1.822534
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.708983 Loss1: 3.275788 Loss2: 1.433195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.534465 Loss1: 3.143586 Loss2: 1.390879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.462973 Loss1: 3.079770 Loss2: 1.383203 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.339290 Loss1: 2.962001 Loss2: 1.377288 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.279167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.411428 Loss1: 3.028583 Loss2: 1.382845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.242863 Loss1: 2.851386 Loss2: 1.391477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.250783 Loss1: 2.842087 Loss2: 1.408696 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.278320
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.429972 Loss1: 2.966957 Loss2: 1.463016 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.292302 Loss1: 2.832961 Loss2: 1.459341 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.295521 Loss1: 2.842206 Loss2: 1.453315
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.394688 Loss1: 3.562679 Loss2: 1.832010
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.179949 Loss1: 2.728115 Loss2: 1.451833
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.560098 Loss1: 3.160656 Loss2: 1.399442
+DEBUG flwr 2023-10-08 18:10:48,233 | server.py:236 | fit_round 10 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.188297 Loss1: 2.735032 Loss2: 1.453265
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.316348 Loss1: 2.942408 Loss2: 1.373940
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.175935 Loss1: 2.719123 Loss2: 1.456812
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.274779 Loss1: 2.906863 Loss2: 1.367916
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.156534 Loss1: 2.697511 Loss2: 1.459023
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.233258 Loss1: 2.862655 Loss2: 1.370603
+(DefaultActor pid=3765) >> Training accuracy: 0.315625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.127497 Loss1: 2.761128 Loss2: 1.366369
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.122948 Loss1: 2.759441 Loss2: 1.363506
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.077047 Loss1: 2.700835 Loss2: 1.376212
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.088078 Loss1: 2.708499 Loss2: 1.379579
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.039217 Loss1: 2.672773 Loss2: 1.366444
+(DefaultActor pid=3764) >> Training accuracy: 0.330208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.394720 Loss1: 3.543457 Loss2: 1.851262
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.680211 Loss1: 3.267463 Loss2: 1.412748
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.455865 Loss1: 3.068214 Loss2: 1.387651
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.385407 Loss1: 2.979100 Loss2: 1.406307
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.378750 Loss1: 2.996437 Loss2: 1.382313
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.698202 Loss1: 3.781303 Loss2: 1.916899
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.303235 Loss1: 2.909454 Loss2: 1.393781
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.813492 Loss1: 3.299170 Loss2: 1.514322
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.203189 Loss1: 2.821061 Loss2: 1.382127
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.643188 Loss1: 3.168713 Loss2: 1.474475
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.295624 Loss1: 2.884248 Loss2: 1.411376
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.578566 Loss1: 3.111164 Loss2: 1.467402
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.116480 Loss1: 2.713074 Loss2: 1.403407
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.173383 Loss1: 2.771285 Loss2: 1.402097
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.533325 Loss1: 3.059615 Loss2: 1.473710
+(DefaultActor pid=3765) >> Training accuracy: 0.298958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.547041 Loss1: 3.061703 Loss2: 1.485338
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.505237 Loss1: 3.021145 Loss2: 1.484091
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.444359 Loss1: 2.964666 Loss2: 1.479693
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.408370 Loss1: 2.927613 Loss2: 1.480757
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.622188 Loss1: 3.684749 Loss2: 1.937439
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.365241 Loss1: 2.881613 Loss2: 1.483629
+(DefaultActor pid=3764) >> Training accuracy: 0.250977
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.679543 Loss1: 3.208461 Loss2: 1.471082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.525289 Loss1: 3.060094 Loss2: 1.465195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.460247 Loss1: 2.988795 Loss2: 1.471452
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.584363 Loss1: 3.665743 Loss2: 1.918619
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.678379 Loss1: 3.222017 Loss2: 1.456362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.513811 Loss1: 3.082691 Loss2: 1.431120 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.454077 Loss1: 3.022949 Loss2: 1.431129 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.281250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.335101 Loss1: 2.844803 Loss2: 1.490299
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.480319 Loss1: 3.036630 Loss2: 1.443689
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.363343 Loss1: 2.926205 Loss2: 1.437137
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.287945 Loss1: 2.847384 Loss2: 1.440561
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.283769 Loss1: 2.833984 Loss2: 1.449785
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.291862 Loss1: 2.846277 Loss2: 1.445585
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.263851 Loss1: 2.810779 Loss2: 1.453073
+(DefaultActor pid=3764) >> Training accuracy: 0.281250
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 18:10:48,233][flwr][DEBUG] - fit_round 10 received 50 results and 0 failures
+INFO flwr 2023-10-08 18:11:29,549 | server.py:125 | fit progress: (10, 4.114291173581498, {'accuracy': 0.0769}, 22797.328031038)
+>> Test accuracy: 0.076900
+[2023-10-08 18:11:29,549][flwr][INFO] - fit progress: (10, 4.114291173581498, {'accuracy': 0.0769}, 22797.328031038)
+DEBUG flwr 2023-10-08 18:11:29,550 | server.py:173 | evaluate_round 10: strategy sampled 50 clients (out of 50)
+[2023-10-08 18:11:29,550][flwr][DEBUG] - evaluate_round 10: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 18:20:34,870 | server.py:187 | evaluate_round 10 received 50 results and 0 failures
+[2023-10-08 18:20:34,870][flwr][DEBUG] - evaluate_round 10 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 18:20:34,871 | server.py:222 | fit_round 11: strategy sampled 50 clients (out of 50)
+[2023-10-08 18:20:34,871][flwr][DEBUG] - fit_round 11: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.447740 Loss1: 3.525053 Loss2: 1.922687
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.434265 Loss1: 2.966626 Loss2: 1.467639
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.244946 Loss1: 2.825804 Loss2: 1.419143
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.128324 Loss1: 2.710900 Loss2: 1.417424
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.598630 Loss1: 3.072165 Loss2: 1.526465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.303037 Loss1: 2.850390 Loss2: 1.452647 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.187674 Loss1: 2.730020 Loss2: 1.457653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.144572 Loss1: 2.690031 Loss2: 1.454540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.123314 Loss1: 2.648075 Loss2: 1.475239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.077006 Loss1: 2.616945 Loss2: 1.460061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.921319 Loss1: 2.471353 Loss2: 1.449965
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.046273 Loss1: 2.558448 Loss2: 1.487825
+(DefaultActor pid=3765) >> Training accuracy: 0.308333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.022440 Loss1: 2.539124 Loss2: 1.483317
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.057734 Loss1: 2.572590 Loss2: 1.485143
+(DefaultActor pid=3764) >> Training accuracy: 0.370192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.712925 Loss1: 3.776170 Loss2: 1.936756
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.836736 Loss1: 3.351638 Loss2: 1.485098
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.624766 Loss1: 3.159064 Loss2: 1.465702
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.439217 Loss1: 3.560886 Loss2: 1.878331
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.675273 Loss1: 3.248524 Loss2: 1.426749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.533511 Loss1: 3.126423 Loss2: 1.407088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.455148 Loss1: 3.067598 Loss2: 1.387550 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.419909 Loss1: 3.026692 Loss2: 1.393217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.318385 Loss1: 2.922499 Loss2: 1.395887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.332007 Loss1: 2.917216 Loss2: 1.414791 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.274414
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.318371 Loss1: 2.893369 Loss2: 1.425002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.269468 Loss1: 2.850907 Loss2: 1.418561 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.269792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.315198 Loss1: 3.380105 Loss2: 1.935093
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.377056 Loss1: 2.903469 Loss2: 1.473587
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.203190 Loss1: 2.759918 Loss2: 1.443272
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.147798 Loss1: 2.706427 Loss2: 1.441370
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.421306 Loss1: 3.511200 Loss2: 1.910106
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.625484 Loss1: 3.151107 Loss2: 1.474377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.392339 Loss1: 2.949259 Loss2: 1.443079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.361902 Loss1: 2.924406 Loss2: 1.437496 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.347016 Loss1: 2.890498 Loss2: 1.456518 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.295376 Loss1: 2.843763 Loss2: 1.451613 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.309375
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.951254 Loss1: 2.496803 Loss2: 1.454451
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.260694 Loss1: 2.794671 Loss2: 1.466023
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.219997 Loss1: 2.762726 Loss2: 1.457271
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.207523 Loss1: 2.744474 Loss2: 1.463049
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.236142 Loss1: 2.759720 Loss2: 1.476422
+(DefaultActor pid=3764) >> Training accuracy: 0.292708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.499980 Loss1: 3.583586 Loss2: 1.916395
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.699924 Loss1: 3.236773 Loss2: 1.463152
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.479326 Loss1: 3.054654 Loss2: 1.424672
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.910708 Loss1: 3.904617 Loss2: 2.006091
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.400429 Loss1: 2.978248 Loss2: 1.422180
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.922684 Loss1: 3.400343 Loss2: 1.522341
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.349337 Loss1: 2.929187 Loss2: 1.420149
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.774353 Loss1: 3.303428 Loss2: 1.470925
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.274876 Loss1: 2.850771 Loss2: 1.424105
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.279895 Loss1: 2.844226 Loss2: 1.435669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.233184 Loss1: 2.798407 Loss2: 1.434777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.236229 Loss1: 2.801671 Loss2: 1.434558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.187722 Loss1: 2.747082 Loss2: 1.440641 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.288542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.390624 Loss1: 2.912571 Loss2: 1.478053 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.267857
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.480075 Loss1: 3.560720 Loss2: 1.919354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.433094 Loss1: 2.993674 Loss2: 1.439420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.376754 Loss1: 2.946039 Loss2: 1.430715
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.749433 Loss1: 3.677922 Loss2: 2.071512
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.787152 Loss1: 3.261177 Loss2: 1.525975
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.286099 Loss1: 2.847660 Loss2: 1.438440
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.607651 Loss1: 3.105618 Loss2: 1.502033
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.181427 Loss1: 2.734134 Loss2: 1.447293
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.192835 Loss1: 2.749676 Loss2: 1.443159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.153747 Loss1: 2.702310 Loss2: 1.451437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.126781 Loss1: 2.675929 Loss2: 1.450852 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.151889 Loss1: 2.674390 Loss2: 1.477499 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.320833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.289273 Loss1: 2.782240 Loss2: 1.507033 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.312500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.690743 Loss1: 3.706613 Loss2: 1.984130
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.822636 Loss1: 3.314879 Loss2: 1.507757
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.497700 Loss1: 3.028357 Loss2: 1.469343
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.501848 Loss1: 3.041780 Loss2: 1.460067
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.565705 Loss1: 3.644022 Loss2: 1.921683
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.668073 Loss1: 3.199153 Loss2: 1.468920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.523692 Loss1: 3.079619 Loss2: 1.444073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.449715 Loss1: 3.010943 Loss2: 1.438772 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.415860 Loss1: 2.981932 Loss2: 1.433928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.331532 Loss1: 2.903386 Loss2: 1.428146 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.275000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.378170 Loss1: 2.933189 Loss2: 1.444982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.307249 Loss1: 2.865122 Loss2: 1.442126 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.277344
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.497467 Loss1: 3.712789 Loss2: 1.784678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.266638 Loss1: 2.941812 Loss2: 1.324826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.464146 Loss1: 3.558785 Loss2: 1.905361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.610208 Loss1: 3.134616 Loss2: 1.475592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.416720 Loss1: 2.980009 Loss2: 1.436711 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.328027 Loss1: 2.882695 Loss2: 1.445332 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.272340 Loss1: 2.826777 Loss2: 1.445562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.241576 Loss1: 2.778983 Loss2: 1.462594 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.318750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.178871 Loss1: 2.720068 Loss2: 1.458803 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.116510 Loss1: 2.643711 Loss2: 1.472799 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.330208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.705118 Loss1: 3.234506 Loss2: 1.470612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.469481 Loss1: 3.038787 Loss2: 1.430694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.412238 Loss1: 2.970078 Loss2: 1.442160
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.600072 Loss1: 3.708742 Loss2: 1.891330
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.470330 Loss1: 3.012039 Loss2: 1.458292
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.701201 Loss1: 3.219822 Loss2: 1.481379
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.437895 Loss1: 3.007038 Loss2: 1.430857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.398152 Loss1: 2.964418 Loss2: 1.433734 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.368983 Loss1: 2.938825 Loss2: 1.430159 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.252083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.335777 Loss1: 2.884974 Loss2: 1.450803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.241644 Loss1: 2.795011 Loss2: 1.446633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.389226 Loss1: 3.548069 Loss2: 1.841157 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.295956
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.142749 Loss1: 2.780308 Loss2: 1.362441 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.044974 Loss1: 2.670566 Loss2: 1.374408 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.023060 Loss1: 2.668429 Loss2: 1.354631
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.547384 Loss1: 3.654024 Loss2: 1.893360
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.573609 Loss1: 3.118768 Loss2: 1.454841 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.946688 Loss1: 2.577955 Loss2: 1.368734
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.376770 Loss1: 2.988253 Loss2: 1.388517
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.933004 Loss1: 2.559775 Loss2: 1.373229
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.307257 Loss1: 2.896102 Loss2: 1.411155
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.943135 Loss1: 2.560015 Loss2: 1.383120
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.261130 Loss1: 2.856315 Loss2: 1.404815
+(DefaultActor pid=3765) >> Training accuracy: 0.404167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.249936 Loss1: 2.834865 Loss2: 1.415071
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.247072 Loss1: 2.824527 Loss2: 1.422545
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.220437 Loss1: 2.791651 Loss2: 1.428786
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.124281 Loss1: 2.702401 Loss2: 1.421880
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.127057 Loss1: 2.695649 Loss2: 1.431407
+(DefaultActor pid=3764) >> Training accuracy: 0.301339
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.538120 Loss1: 3.592758 Loss2: 1.945362
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.670517 Loss1: 3.207242 Loss2: 1.463275
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.504119 Loss1: 3.068643 Loss2: 1.435476
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.368859 Loss1: 2.955412 Loss2: 1.413447
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.336106 Loss1: 2.911320 Loss2: 1.424787
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.607795 Loss1: 3.682204 Loss2: 1.925591
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.623582 Loss1: 3.147238 Loss2: 1.476344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.458855 Loss1: 3.024876 Loss2: 1.433979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.437721 Loss1: 3.005537 Loss2: 1.432184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.322668 Loss1: 2.904574 Loss2: 1.418093 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.308333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.348041 Loss1: 2.910064 Loss2: 1.437977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.183505 Loss1: 2.733140 Loss2: 1.450365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.237853 Loss1: 2.776458 Loss2: 1.461395 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.258333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.585150 Loss1: 3.065580 Loss2: 1.519571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.360175 Loss1: 2.875547 Loss2: 1.484628 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.296064 Loss1: 2.820456 Loss2: 1.475608
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.394305 Loss1: 3.549390 Loss2: 1.844914
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.599826 Loss1: 3.185667 Loss2: 1.414159 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.482656 Loss1: 3.098620 Loss2: 1.384036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.364378 Loss1: 2.992096 Loss2: 1.372283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.318561 Loss1: 2.935804 Loss2: 1.382757 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.337500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.254412 Loss1: 2.877628 Loss2: 1.376784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.216809 Loss1: 2.824366 Loss2: 1.392443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.139720 Loss1: 2.725467 Loss2: 1.414253 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.297917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.771036 Loss1: 3.313158 Loss2: 1.457877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.550525 Loss1: 3.132045 Loss2: 1.418480 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.489597 Loss1: 3.058683 Loss2: 1.430914
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.451880 Loss1: 3.582980 Loss2: 1.868900
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.487782 Loss1: 3.053631 Loss2: 1.434151
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.589962 Loss1: 3.167700 Loss2: 1.422261
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.441319 Loss1: 3.007667 Loss2: 1.433652
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.409169 Loss1: 3.027596 Loss2: 1.381573
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.314608 Loss1: 2.930440 Loss2: 1.384168
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.410990 Loss1: 2.966509 Loss2: 1.444480
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.227019 Loss1: 2.845935 Loss2: 1.381084
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.334062 Loss1: 2.885670 Loss2: 1.448391
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.238332 Loss1: 2.851465 Loss2: 1.386867
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.318629 Loss1: 2.869012 Loss2: 1.449618
+(DefaultActor pid=3765) >> Training accuracy: 0.286133
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.185716 Loss1: 2.791581 Loss2: 1.394135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.114031 Loss1: 2.733216 Loss2: 1.380815 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.280208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.747399 Loss1: 3.229156 Loss2: 1.518242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.466734 Loss1: 2.997120 Loss2: 1.469613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.346489 Loss1: 2.870234 Loss2: 1.476254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.335008 Loss1: 2.855188 Loss2: 1.479820 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.646674 Loss1: 3.189913 Loss2: 1.456761
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.340925 Loss1: 2.862438 Loss2: 1.478487
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.566999 Loss1: 3.118343 Loss2: 1.448656
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.339747 Loss1: 2.838953 Loss2: 1.500793
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.497096 Loss1: 3.043301 Loss2: 1.453795
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.320940 Loss1: 2.817194 Loss2: 1.503747
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.270761 Loss1: 2.767235 Loss2: 1.503526
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.514418 Loss1: 3.049945 Loss2: 1.464473
+(DefaultActor pid=3765) >> Training accuracy: 0.314583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.478579 Loss1: 2.999823 Loss2: 1.478755
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.454969 Loss1: 2.981027 Loss2: 1.473942
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.432456 Loss1: 2.957320 Loss2: 1.475136
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.387717 Loss1: 2.903810 Loss2: 1.483907
+(DefaultActor pid=3764) >> Training accuracy: 0.259766
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.600529 Loss1: 3.671233 Loss2: 1.929296
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.717769 Loss1: 3.254019 Loss2: 1.463750
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.522911 Loss1: 3.086267 Loss2: 1.436644
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.488928 Loss1: 3.051120 Loss2: 1.437808
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.385575 Loss1: 2.949055 Loss2: 1.436520
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.378149 Loss1: 3.621019 Loss2: 1.757129
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.474078 Loss1: 3.093303 Loss2: 1.380775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.278658 Loss1: 2.924556 Loss2: 1.354102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.193679 Loss1: 2.859576 Loss2: 1.334103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.104124 Loss1: 2.766469 Loss2: 1.337655 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.271875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.094557 Loss1: 2.745415 Loss2: 1.349143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.047386 Loss1: 2.689409 Loss2: 1.357978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.408479 Loss1: 3.544830 Loss2: 1.863648 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.308594
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.291177 Loss1: 2.930734 Loss2: 1.360443 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.116712 Loss1: 2.768926 Loss2: 1.347786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.034391 Loss1: 2.684859 Loss2: 1.349532
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.488457 Loss1: 3.604444 Loss2: 1.884013
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.963010 Loss1: 2.615897 Loss2: 1.347113
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.696435 Loss1: 3.269225 Loss2: 1.427210
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.923125 Loss1: 2.561319 Loss2: 1.361806
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.566436 Loss1: 3.169331 Loss2: 1.397105
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.889968 Loss1: 2.516329 Loss2: 1.373640
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.437175 Loss1: 3.037204 Loss2: 1.399972
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.938844 Loss1: 2.566229 Loss2: 1.372614
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.378418 Loss1: 2.980460 Loss2: 1.397958
+(DefaultActor pid=3765) >> Training accuracy: 0.275000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.423880 Loss1: 3.011930 Loss2: 1.411949
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.370438 Loss1: 2.974677 Loss2: 1.395761
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.340102 Loss1: 2.922061 Loss2: 1.418042
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.299956 Loss1: 2.874890 Loss2: 1.425066
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.776750 Loss1: 3.631357 Loss2: 2.145392
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.287297 Loss1: 2.857329 Loss2: 1.429968
+(DefaultActor pid=3764) >> Training accuracy: 0.266667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.612610 Loss1: 3.098737 Loss2: 1.513873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.409652 Loss1: 2.911294 Loss2: 1.498358 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.331855 Loss1: 2.830794 Loss2: 1.501060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.275008 Loss1: 2.774596 Loss2: 1.500412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.287242 Loss1: 2.783525 Loss2: 1.503717
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.748852 Loss1: 3.146223 Loss2: 1.602629
+(DefaultActor pid=3765) >> Training accuracy: 0.291667
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.307215 Loss1: 2.793444 Loss2: 1.513771
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.615801 Loss1: 3.046759 Loss2: 1.569042
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.557665 Loss1: 3.000821 Loss2: 1.556844
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.446246 Loss1: 2.885985 Loss2: 1.560262
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.464840 Loss1: 2.894205 Loss2: 1.570635
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.358507 Loss1: 2.804145 Loss2: 1.554362
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.678832 Loss1: 3.648826 Loss2: 2.030006
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.320439 Loss1: 2.754913 Loss2: 1.565526
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.336639 Loss1: 2.763800 Loss2: 1.572839
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.776859 Loss1: 3.208845 Loss2: 1.568013
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.313207 Loss1: 2.733143 Loss2: 1.580064
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.601306 Loss1: 3.067913 Loss2: 1.533393
+(DefaultActor pid=3764) >> Training accuracy: 0.319792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.498689 Loss1: 2.978600 Loss2: 1.520089
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.470575 Loss1: 2.946593 Loss2: 1.523982
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.338550 Loss1: 2.813806 Loss2: 1.524744
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.357847 Loss1: 2.825214 Loss2: 1.532633
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.766458 Loss1: 3.786601 Loss2: 1.979857
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.333951 Loss1: 2.804213 Loss2: 1.529738
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.326661 Loss1: 2.799973 Loss2: 1.526688 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.293561 Loss1: 2.757167 Loss2: 1.536394 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.263672
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.607025 Loss1: 3.116479 Loss2: 1.490546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.519524 Loss1: 3.023940 Loss2: 1.495584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.448101 Loss1: 2.951709 Loss2: 1.496392
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.505255 Loss1: 3.656946 Loss2: 1.848309
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.514765 Loss1: 3.109305 Loss2: 1.405460 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.214583
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.446017 Loss1: 2.934694 Loss2: 1.511323
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.348570 Loss1: 2.985642 Loss2: 1.362927
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.230299 Loss1: 2.876194 Loss2: 1.354105
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.235148 Loss1: 2.867854 Loss2: 1.367294
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.143851 Loss1: 2.777645 Loss2: 1.366207
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.131769 Loss1: 2.763724 Loss2: 1.368045
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.786756 Loss1: 3.868781 Loss2: 1.917975
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.113097 Loss1: 2.737116 Loss2: 1.375981
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.081647 Loss1: 2.702571 Loss2: 1.379076 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.069822 Loss1: 2.696967 Loss2: 1.372855 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.279167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.403716 Loss1: 2.990845 Loss2: 1.412870 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.325185 Loss1: 2.899424 Loss2: 1.425761 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.324080 Loss1: 2.887738 Loss2: 1.436342
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.324846 Loss1: 3.468452 Loss2: 1.856394
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.523241 Loss1: 3.100859 Loss2: 1.422382 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.263542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.372643 Loss1: 2.986030 Loss2: 1.386613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.295006 Loss1: 2.902654 Loss2: 1.392352 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.199771 Loss1: 2.801035 Loss2: 1.398736 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.164216 Loss1: 2.744917 Loss2: 1.419299 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.128600 Loss1: 2.715332 Loss2: 1.413268 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.079355 Loss1: 2.669022 Loss2: 1.410333 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.294792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.384972 Loss1: 3.024871 Loss2: 1.360101 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.331326 Loss1: 2.969714 Loss2: 1.361612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.259243 Loss1: 2.882739 Loss2: 1.376504
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.391775 Loss1: 3.561786 Loss2: 1.829988
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.278817 Loss1: 2.900602 Loss2: 1.378215
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.470418 Loss1: 3.076054 Loss2: 1.394364
+(DefaultActor pid=3764) >> Training accuracy: 0.226562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.330041 Loss1: 2.971685 Loss2: 1.358356
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.227728 Loss1: 2.875849 Loss2: 1.351878
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.208067 Loss1: 2.842820 Loss2: 1.365247
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.187324 Loss1: 2.815015 Loss2: 1.372309
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.624397 Loss1: 3.748587 Loss2: 1.875810
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.139239 Loss1: 2.764715 Loss2: 1.374524
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.764210 Loss1: 3.311664 Loss2: 1.452546
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.056442 Loss1: 2.694554 Loss2: 1.361889
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.617339 Loss1: 3.187673 Loss2: 1.429665
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.074921 Loss1: 2.703826 Loss2: 1.371095
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.554812 Loss1: 3.143212 Loss2: 1.411600
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.094911 Loss1: 2.718262 Loss2: 1.376649
+(DefaultActor pid=3765) >> Training accuracy: 0.298828
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.525144 Loss1: 3.089542 Loss2: 1.435602 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.445652 Loss1: 3.000346 Loss2: 1.445306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.471860 Loss1: 3.465953 Loss2: 2.005907
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.431895 Loss1: 2.977435 Loss2: 1.454460
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.470252 Loss1: 2.967762 Loss2: 1.502491
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.420106 Loss1: 2.957121 Loss2: 1.462985
+(DefaultActor pid=3764) >> Training accuracy: 0.263672
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.256058 Loss1: 2.792511 Loss2: 1.463546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.152548 Loss1: 2.693562 Loss2: 1.458986 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.138468 Loss1: 2.664735 Loss2: 1.473732
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.366966 Loss1: 3.466219 Loss2: 1.900747
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.497577 Loss1: 3.058420 Loss2: 1.439157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.287217 Loss1: 2.893294 Loss2: 1.393923 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.328125
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.046538 Loss1: 2.573761 Loss2: 1.472777
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.223399 Loss1: 2.823559 Loss2: 1.399840
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.119445 Loss1: 2.716911 Loss2: 1.402534
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.114447 Loss1: 2.709519 Loss2: 1.404928
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.122442 Loss1: 2.720882 Loss2: 1.401560
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.064574 Loss1: 2.653710 Loss2: 1.410864
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.730715 Loss1: 3.722483 Loss2: 2.008231
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.983389 Loss1: 2.574455 Loss2: 1.408934
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.769823 Loss1: 3.259524 Loss2: 1.510298
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.032246 Loss1: 2.602746 Loss2: 1.429501
+(DefaultActor pid=3764) >> Training accuracy: 0.358333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.402914 Loss1: 2.938078 Loss2: 1.464835 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.347436 Loss1: 2.880983 Loss2: 1.466453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.332430 Loss1: 2.849407 Loss2: 1.483024
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.332414 Loss1: 3.437447 Loss2: 1.894967
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.489232 Loss1: 3.046409 Loss2: 1.442823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.290252 Loss1: 2.887415 Loss2: 1.402837 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.301042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.161372 Loss1: 2.759987 Loss2: 1.401385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.136898 Loss1: 2.717886 Loss2: 1.419012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.071945 Loss1: 2.662566 Loss2: 1.409379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.010282 Loss1: 2.585937 Loss2: 1.424345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.984842 Loss1: 2.568272 Loss2: 1.416571 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.330078
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.338242 Loss1: 2.991140 Loss2: 1.347102 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.250203 Loss1: 2.890710 Loss2: 1.359493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.430447 Loss1: 3.433301 Loss2: 1.997146
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.329785 Loss1: 2.957580 Loss2: 1.372205
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.555141 Loss1: 3.044270 Loss2: 1.510871
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.257464 Loss1: 2.880050 Loss2: 1.377414
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.315167 Loss1: 2.845200 Loss2: 1.469967
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.264860 Loss1: 2.886094 Loss2: 1.378766
+(DefaultActor pid=3765) >> Training accuracy: 0.304167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.233346 Loss1: 2.766161 Loss2: 1.467185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.236512 Loss1: 2.764887 Loss2: 1.471625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.216882 Loss1: 2.728621 Loss2: 1.488261
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.518362 Loss1: 3.590420 Loss2: 1.927941
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.137514 Loss1: 2.653724 Loss2: 1.483791
+DEBUG flwr 2023-10-08 18:49:19,704 | server.py:236 | fit_round 11 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.644777 Loss1: 3.172139 Loss2: 1.472638
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.041456 Loss1: 2.562732 Loss2: 1.478723
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.407437 Loss1: 2.962528 Loss2: 1.444909
+(DefaultActor pid=3764) >> Training accuracy: 0.357292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.377740 Loss1: 2.939399 Loss2: 1.438340
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.353962 Loss1: 2.910822 Loss2: 1.443140
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.262464 Loss1: 2.821808 Loss2: 1.440656
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.240542 Loss1: 2.780519 Loss2: 1.460023
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.479660 Loss1: 3.559673 Loss2: 1.919987
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.216669 Loss1: 2.765665 Loss2: 1.451004
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.563823 Loss1: 3.117070 Loss2: 1.446753
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.208601 Loss1: 2.753693 Loss2: 1.454908
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.369440 Loss1: 2.954241 Loss2: 1.415199
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.161868 Loss1: 2.700563 Loss2: 1.461306
+(DefaultActor pid=3765) >> Training accuracy: 0.284375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.268790 Loss1: 2.837998 Loss2: 1.430792 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.309809 Loss1: 2.878739 Loss2: 1.431070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.205480 Loss1: 2.769081 Loss2: 1.436400
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.594874 Loss1: 3.625276 Loss2: 1.969597
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.131494 Loss1: 2.708950 Loss2: 1.422544
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.716796 Loss1: 3.216508 Loss2: 1.500288
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.123229 Loss1: 2.691331 Loss2: 1.431899
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.615500 Loss1: 3.136149 Loss2: 1.479352
+(DefaultActor pid=3764) >> Training accuracy: 0.341667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.508441 Loss1: 3.025273 Loss2: 1.483169
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.534764 Loss1: 3.048294 Loss2: 1.486470
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.432513 Loss1: 2.955774 Loss2: 1.476739
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.418451 Loss1: 2.935604 Loss2: 1.482847
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.597166 Loss1: 3.573975 Loss2: 2.023191
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.384843 Loss1: 2.894402 Loss2: 1.490441
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.648384 Loss1: 3.108873 Loss2: 1.539510
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.404547 Loss1: 2.896290 Loss2: 1.508257
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.536076 Loss1: 3.011270 Loss2: 1.524806
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.333698 Loss1: 2.826319 Loss2: 1.507379
+(DefaultActor pid=3765) >> Training accuracy: 0.295833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.399715 Loss1: 2.884842 Loss2: 1.514873 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.346972 Loss1: 2.816295 Loss2: 1.530677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.269078 Loss1: 2.730646 Loss2: 1.538431 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.329167
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 18:49:19,704][flwr][DEBUG] - fit_round 11 received 50 results and 0 failures
+INFO flwr 2023-10-08 18:50:00,263 | server.py:125 | fit progress: (11, 4.006379742972767, {'accuracy': 0.0903}, 25108.041343163)
+>> Test accuracy: 0.090300
+[2023-10-08 18:50:00,263][flwr][INFO] - fit progress: (11, 4.006379742972767, {'accuracy': 0.0903}, 25108.041343163)
+DEBUG flwr 2023-10-08 18:50:00,263 | server.py:173 | evaluate_round 11: strategy sampled 50 clients (out of 50)
+[2023-10-08 18:50:00,263][flwr][DEBUG] - evaluate_round 11: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 18:59:05,242 | server.py:187 | evaluate_round 11 received 50 results and 0 failures
+[2023-10-08 18:59:05,242][flwr][DEBUG] - evaluate_round 11 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 18:59:05,242 | server.py:222 | fit_round 12: strategy sampled 50 clients (out of 50)
+[2023-10-08 18:59:05,242][flwr][DEBUG] - fit_round 12: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.410837 Loss1: 3.520975 Loss2: 1.889862 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.341465 Loss1: 2.928652 Loss2: 1.412813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.210141 Loss1: 2.787406 Loss2: 1.422735
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.642732 Loss1: 3.667675 Loss2: 1.975057
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.729386 Loss1: 3.220852 Loss2: 1.508534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.545397 Loss1: 3.084955 Loss2: 1.460443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.494853 Loss1: 3.037123 Loss2: 1.457730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.398857 Loss1: 2.945858 Loss2: 1.452999 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.292060 Loss1: 2.834785 Loss2: 1.457275 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.335417
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.026035 Loss1: 2.582041 Loss2: 1.443995
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.262347 Loss1: 2.803703 Loss2: 1.458644
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.394903 Loss1: 2.905238 Loss2: 1.489665
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.347983 Loss1: 2.865840 Loss2: 1.482142
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.324770 Loss1: 2.850961 Loss2: 1.473809
+(DefaultActor pid=3764) >> Training accuracy: 0.261458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.643559 Loss1: 3.757672 Loss2: 1.885886
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.742463 Loss1: 3.310681 Loss2: 1.431782
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.577351 Loss1: 3.185801 Loss2: 1.391550
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.497440 Loss1: 3.113677 Loss2: 1.383763
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.420718 Loss1: 3.454349 Loss2: 1.966369
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.501680 Loss1: 3.016828 Loss2: 1.484852 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.211140 Loss1: 2.789963 Loss2: 1.421176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.093454 Loss1: 2.691234 Loss2: 1.402219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.031222 Loss1: 2.627209 Loss2: 1.404013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.215007 Loss1: 2.811994 Loss2: 1.403013
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.048223 Loss1: 2.632400 Loss2: 1.415823
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.218360 Loss1: 2.808903 Loss2: 1.409457
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.913922 Loss1: 2.488234 Loss2: 1.425687
+(DefaultActor pid=3765) >> Training accuracy: 0.292411
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.890278 Loss1: 2.474112 Loss2: 1.416166
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.867090 Loss1: 2.448288 Loss2: 1.418802
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.838153 Loss1: 2.422113 Loss2: 1.416040
+(DefaultActor pid=3764) >> Training accuracy: 0.375000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.362514 Loss1: 3.467669 Loss2: 1.894845
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.555531 Loss1: 3.134711 Loss2: 1.420819
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.340444 Loss1: 2.958520 Loss2: 1.381924
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.220510 Loss1: 2.835006 Loss2: 1.385504
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.571934 Loss1: 3.526945 Loss2: 2.044990
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.210292 Loss1: 2.818879 Loss2: 1.391413
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.675492 Loss1: 3.103125 Loss2: 1.572366
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.161378 Loss1: 2.768565 Loss2: 1.392813
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.489033 Loss1: 2.960743 Loss2: 1.528289
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.121290 Loss1: 2.734961 Loss2: 1.386329
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.389477 Loss1: 2.874645 Loss2: 1.514832
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.060719 Loss1: 2.671643 Loss2: 1.389075
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.387763 Loss1: 2.864214 Loss2: 1.523549
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.029307 Loss1: 2.642653 Loss2: 1.386653
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.277090 Loss1: 2.736190 Loss2: 1.540901
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.072286 Loss1: 2.665919 Loss2: 1.406367
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.208234 Loss1: 2.692792 Loss2: 1.515442
+(DefaultActor pid=3765) >> Training accuracy: 0.317708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.194952 Loss1: 2.667451 Loss2: 1.527501
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.141703 Loss1: 2.606052 Loss2: 1.535651
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.117590 Loss1: 2.578458 Loss2: 1.539132
+(DefaultActor pid=3764) >> Training accuracy: 0.320833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.329261 Loss1: 3.486057 Loss2: 1.843204
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.615774 Loss1: 3.203652 Loss2: 1.412121
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.482720 Loss1: 3.091613 Loss2: 1.391107
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.362221 Loss1: 2.986006 Loss2: 1.376215
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.338705 Loss1: 3.477142 Loss2: 1.861562
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.461372 Loss1: 3.060861 Loss2: 1.400512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.248952 Loss1: 2.884319 Loss2: 1.364633 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.197379 Loss1: 2.835866 Loss2: 1.361513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.146433 Loss1: 2.771248 Loss2: 1.375185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.112739 Loss1: 2.735409 Loss2: 1.377330 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.270833
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.162401 Loss1: 2.755908 Loss2: 1.406493
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.061343 Loss1: 2.691322 Loss2: 1.370021
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.011930 Loss1: 2.632380 Loss2: 1.379550
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.025625 Loss1: 2.640756 Loss2: 1.384870
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.949446 Loss1: 2.563334 Loss2: 1.386112
+(DefaultActor pid=3764) >> Training accuracy: 0.323958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.497854 Loss1: 3.712194 Loss2: 1.785660
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.619612 Loss1: 3.251968 Loss2: 1.367644
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.452520 Loss1: 3.112198 Loss2: 1.340323
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.350740 Loss1: 3.026026 Loss2: 1.324714
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.265574 Loss1: 3.336510 Loss2: 1.929064
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.457354 Loss1: 2.996538 Loss2: 1.460817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.251703 Loss1: 2.810952 Loss2: 1.440751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.178116 Loss1: 2.747555 Loss2: 1.430561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.175482 Loss1: 2.722490 Loss2: 1.452993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.126932 Loss1: 2.686000 Loss2: 1.440932 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.251042
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.233795 Loss1: 2.864016 Loss2: 1.369780
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.058000 Loss1: 2.613227 Loss2: 1.444773
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.029077 Loss1: 2.571124 Loss2: 1.457953
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.992992 Loss1: 2.534029 Loss2: 1.458963
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.980200 Loss1: 2.523273 Loss2: 1.456927
+(DefaultActor pid=3764) >> Training accuracy: 0.352083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.370407 Loss1: 3.435310 Loss2: 1.935097
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.527445 Loss1: 3.051867 Loss2: 1.475578
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.405456 Loss1: 2.939042 Loss2: 1.466414
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.267028 Loss1: 2.816129 Loss2: 1.450899
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.329820 Loss1: 3.496996 Loss2: 1.832824
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.524917 Loss1: 3.059202 Loss2: 1.465715 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.272860 Loss1: 2.855545 Loss2: 1.417315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.167322 Loss1: 2.757104 Loss2: 1.410218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.119619 Loss1: 2.708756 Loss2: 1.410862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.068970 Loss1: 2.648902 Loss2: 1.420068 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.346875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.078682 Loss1: 2.652122 Loss2: 1.426560 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.939208 Loss1: 2.504718 Loss2: 1.434490 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.327148
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.542552 Loss1: 3.062974 Loss2: 1.479578 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.204451 Loss1: 2.773963 Loss2: 1.430488 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.201475 Loss1: 2.773344 Loss2: 1.428131
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.455081 Loss1: 3.515901 Loss2: 1.939180
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.118519 Loss1: 2.684253 Loss2: 1.434266
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.524278 Loss1: 3.035230 Loss2: 1.489048
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.090103 Loss1: 2.655395 Loss2: 1.434708
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.365481 Loss1: 2.920732 Loss2: 1.444749
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.077060 Loss1: 2.630175 Loss2: 1.446885
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.323043 Loss1: 2.876514 Loss2: 1.446530
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.250605 Loss1: 2.799634 Loss2: 1.450971 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.328125
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.006833 Loss1: 2.555307 Loss2: 1.451526
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.239973 Loss1: 2.769965 Loss2: 1.470008
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.207703 Loss1: 2.751419 Loss2: 1.456284
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.104747 Loss1: 2.642906 Loss2: 1.461841
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.139827 Loss1: 2.672280 Loss2: 1.467547
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.113257 Loss1: 2.645315 Loss2: 1.467942
+(DefaultActor pid=3764) >> Training accuracy: 0.322266
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.679287 Loss1: 3.736273 Loss2: 1.943014
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.768916 Loss1: 3.259148 Loss2: 1.509768
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.587425 Loss1: 3.134381 Loss2: 1.453045
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.480604 Loss1: 3.036254 Loss2: 1.444350
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.453092 Loss1: 2.996907 Loss2: 1.456185
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.423947 Loss1: 3.639868 Loss2: 1.784079
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.634392 Loss1: 3.253568 Loss2: 1.380824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.526386 Loss1: 3.160090 Loss2: 1.366296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.405283 Loss1: 3.040996 Loss2: 1.364287 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.328468 Loss1: 2.964655 Loss2: 1.363813 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.303711
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.377547 Loss1: 3.011008 Loss2: 1.366540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.267249 Loss1: 2.873079 Loss2: 1.394170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.257259 Loss1: 2.867370 Loss2: 1.389889 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.278320
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.361234 Loss1: 2.949309 Loss2: 1.411925 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.190181 Loss1: 2.783075 Loss2: 1.407106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.606540 Loss1: 3.713196 Loss2: 1.893345
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.127050 Loss1: 2.708467 Loss2: 1.418583
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.563739 Loss1: 3.143424 Loss2: 1.420315
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.167712 Loss1: 2.749677 Loss2: 1.418034
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.353152 Loss1: 2.985076 Loss2: 1.368077
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.035504 Loss1: 2.623437 Loss2: 1.412068
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.287486 Loss1: 2.922224 Loss2: 1.365262
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.970405 Loss1: 2.536847 Loss2: 1.433558
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.260267 Loss1: 2.887310 Loss2: 1.372957
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.982213 Loss1: 2.548828 Loss2: 1.433385
+(DefaultActor pid=3765) >> Training accuracy: 0.339583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.189006 Loss1: 2.821097 Loss2: 1.367908 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.099150 Loss1: 2.697000 Loss2: 1.402151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.062228 Loss1: 2.667327 Loss2: 1.394901
+(DefaultActor pid=3764) >> Training accuracy: 0.266667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.372348 Loss1: 3.386925 Loss2: 1.985423
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.487434 Loss1: 2.963566 Loss2: 1.523867
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.286967 Loss1: 2.809498 Loss2: 1.477469
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.134552 Loss1: 2.679592 Loss2: 1.454960
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.123046 Loss1: 2.660317 Loss2: 1.462728
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.495721 Loss1: 3.419380 Loss2: 2.076341
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.086629 Loss1: 2.615423 Loss2: 1.471206
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.108097 Loss1: 2.636983 Loss2: 1.471115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.997443 Loss1: 2.528387 Loss2: 1.469057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.378748 Loss1: 2.840065 Loss2: 1.538684
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.014195 Loss1: 2.531766 Loss2: 1.482429
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.228880 Loss1: 2.701609 Loss2: 1.527271
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.892206 Loss1: 2.410165 Loss2: 1.482041
+(DefaultActor pid=3765) >> Training accuracy: 0.353125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.220625 Loss1: 2.664940 Loss2: 1.555685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.205327 Loss1: 2.642409 Loss2: 1.562918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.117879 Loss1: 2.560434 Loss2: 1.557445
+(DefaultActor pid=3764) >> Training accuracy: 0.339583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.660444 Loss1: 3.708933 Loss2: 1.951511
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.736850 Loss1: 3.283369 Loss2: 1.453482
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.504853 Loss1: 3.065876 Loss2: 1.438977
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.440932 Loss1: 3.001924 Loss2: 1.439008
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.374605 Loss1: 2.932612 Loss2: 1.441994
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.479106 Loss1: 3.483874 Loss2: 1.995232
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.442705 Loss1: 2.905110 Loss2: 1.537595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.309296 Loss1: 2.814231 Loss2: 1.495065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.157626 Loss1: 2.668325 Loss2: 1.489301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.165696 Loss1: 2.686039 Loss2: 1.479657
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.089434 Loss1: 2.605893 Loss2: 1.483541
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.201131 Loss1: 2.728319 Loss2: 1.472812
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.055542 Loss1: 2.565075 Loss2: 1.490467
+(DefaultActor pid=3765) >> Training accuracy: 0.300781
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.023578 Loss1: 2.542342 Loss2: 1.481237
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.958904 Loss1: 2.477250 Loss2: 1.481654
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.962906 Loss1: 2.461901 Loss2: 1.501005
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.911880 Loss1: 2.415071 Loss2: 1.496809
+(DefaultActor pid=3764) >> Training accuracy: 0.428125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.517319 Loss1: 3.663372 Loss2: 1.853947
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.707114 Loss1: 3.274071 Loss2: 1.433044
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.521364 Loss1: 3.102667 Loss2: 1.418697
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.467989 Loss1: 3.047038 Loss2: 1.420951
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.337223 Loss1: 3.493683 Loss2: 1.843540
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.516273 Loss1: 3.107242 Loss2: 1.409030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.348135 Loss1: 2.989226 Loss2: 1.358909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.240659 Loss1: 2.876049 Loss2: 1.364611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.205829 Loss1: 2.831399 Loss2: 1.374430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.152682 Loss1: 2.789832 Loss2: 1.362850 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.272461
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.237582 Loss1: 2.801606 Loss2: 1.435976
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.122596 Loss1: 2.732461 Loss2: 1.390135
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.079440 Loss1: 2.700733 Loss2: 1.378707
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.002787 Loss1: 2.611364 Loss2: 1.391423
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.010634 Loss1: 2.617335 Loss2: 1.393298
+(DefaultActor pid=3764) >> Training accuracy: 0.332292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.687851 Loss1: 3.676700 Loss2: 2.011151
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.621574 Loss1: 3.085425 Loss2: 1.536149
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.396427 Loss1: 2.908260 Loss2: 1.488168
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.325783 Loss1: 2.838064 Loss2: 1.487719
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.356433 Loss1: 3.407482 Loss2: 1.948951
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.600087 Loss1: 3.099263 Loss2: 1.500824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.439803 Loss1: 2.960818 Loss2: 1.478985 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.379293 Loss1: 2.904388 Loss2: 1.474905 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.260988 Loss1: 2.777505 Loss2: 1.483483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.156036 Loss1: 2.682799 Loss2: 1.473236 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.316667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.169092 Loss1: 2.692781 Loss2: 1.476311 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.087158 Loss1: 2.603229 Loss2: 1.483930 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.330078
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.593559 Loss1: 3.120977 Loss2: 1.472582 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.329996 Loss1: 2.903830 Loss2: 1.426167 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.472355 Loss1: 3.528671 Loss2: 1.943684
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.248709 Loss1: 2.822585 Loss2: 1.426124
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.583600 Loss1: 3.081578 Loss2: 1.502022
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.322925 Loss1: 2.880073 Loss2: 1.442851
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.387326 Loss1: 2.913226 Loss2: 1.474100
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.210354 Loss1: 2.771186 Loss2: 1.439168
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.287180 Loss1: 2.834161 Loss2: 1.453019
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.150819 Loss1: 2.711143 Loss2: 1.439675
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.270284 Loss1: 2.794181 Loss2: 1.476103
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.138452 Loss1: 2.694151 Loss2: 1.444301
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.141866 Loss1: 2.660884 Loss2: 1.480982
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.066304 Loss1: 2.607947 Loss2: 1.458357
+(DefaultActor pid=3765) >> Training accuracy: 0.273958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.124578 Loss1: 2.648596 Loss2: 1.475982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.050949 Loss1: 2.538436 Loss2: 1.512513 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.337500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.440255 Loss1: 2.928165 Loss2: 1.512089 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.121469 Loss1: 2.649906 Loss2: 1.471563 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.118146 Loss1: 2.636881 Loss2: 1.481264 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.064080 Loss1: 2.566358 Loss2: 1.497723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.305832 Loss1: 2.924539 Loss2: 1.381293 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.246175 Loss1: 2.856002 Loss2: 1.390172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.275799 Loss1: 2.891297 Loss2: 1.384502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.187684 Loss1: 2.801570 Loss2: 1.386114 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.120544 Loss1: 2.735306 Loss2: 1.385238 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.377930
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.131382 Loss1: 2.714359 Loss2: 1.417023 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.338942
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.404452 Loss1: 3.496361 Loss2: 1.908091
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.381919 Loss1: 2.955043 Loss2: 1.426876
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.122422 Loss1: 2.716712 Loss2: 1.405710
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.049246 Loss1: 2.660975 Loss2: 1.388271
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.605341 Loss1: 3.565104 Loss2: 2.040237
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.009307 Loss1: 2.612148 Loss2: 1.397159
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.705904 Loss1: 3.166594 Loss2: 1.539311
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.955970 Loss1: 2.567385 Loss2: 1.388585
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.571672 Loss1: 3.070293 Loss2: 1.501379
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.937452 Loss1: 2.537036 Loss2: 1.400416
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.469611 Loss1: 2.981792 Loss2: 1.487819
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.874222 Loss1: 2.466507 Loss2: 1.407716
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.417548 Loss1: 2.929845 Loss2: 1.487703
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.796439 Loss1: 2.398396 Loss2: 1.398042
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.441462 Loss1: 2.942767 Loss2: 1.498695
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.742630 Loss1: 2.336245 Loss2: 1.406385
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.302061 Loss1: 2.802976 Loss2: 1.499085
+(DefaultActor pid=3765) >> Training accuracy: 0.326042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.302414 Loss1: 2.808061 Loss2: 1.494353
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.354063 Loss1: 2.831085 Loss2: 1.522978
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.294660 Loss1: 2.774173 Loss2: 1.520487
+(DefaultActor pid=3764) >> Training accuracy: 0.291667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.607250 Loss1: 3.552418 Loss2: 2.054832
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.589143 Loss1: 3.105586 Loss2: 1.483557
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.449895 Loss1: 3.012004 Loss2: 1.437891
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.258312 Loss1: 2.824082 Loss2: 1.434229
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.198569 Loss1: 2.755503 Loss2: 1.443065
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.223537 Loss1: 2.789020 Loss2: 1.434517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.144494 Loss1: 2.709268 Loss2: 1.435226 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.036390 Loss1: 2.593377 Loss2: 1.443013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.120729 Loss1: 2.668094 Loss2: 1.452635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.142224 Loss1: 2.682209 Loss2: 1.460014 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.325521
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.011212 Loss1: 2.621383 Loss2: 1.389828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.031168 Loss1: 2.624892 Loss2: 1.406276 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.951451 Loss1: 2.538508 Loss2: 1.412943
+(DefaultActor pid=3764) >> Training accuracy: 0.334821
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.833239 Loss1: 3.837714 Loss2: 1.995525
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.802821 Loss1: 3.287133 Loss2: 1.515688
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.568191 Loss1: 3.096776 Loss2: 1.471416
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.533400 Loss1: 3.068107 Loss2: 1.465293
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.493452 Loss1: 3.032330 Loss2: 1.461121
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.344088 Loss1: 2.877919 Loss2: 1.466169
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.476538 Loss1: 3.581547 Loss2: 1.894991
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.324783 Loss1: 2.850721 Loss2: 1.474062
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.573095 Loss1: 3.110186 Loss2: 1.462909
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.437585 Loss1: 3.013266 Loss2: 1.424319 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.332707 Loss1: 2.913574 Loss2: 1.419133 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.242188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.283984 Loss1: 2.866145 Loss2: 1.417839 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.183047 Loss1: 2.754533 Loss2: 1.428514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.598793 Loss1: 3.630121 Loss2: 1.968672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.670407 Loss1: 3.185123 Loss2: 1.485285 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.302734
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.403551 Loss1: 2.957885 Loss2: 1.445666 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.334514 Loss1: 2.883406 Loss2: 1.451108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.301904 Loss1: 2.847503 Loss2: 1.454401
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.293300 Loss1: 3.353897 Loss2: 1.939404
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.273411 Loss1: 2.801660 Loss2: 1.471751
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.394025 Loss1: 2.944177 Loss2: 1.449848
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.306031 Loss1: 2.840687 Loss2: 1.465344
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.134473 Loss1: 2.717214 Loss2: 1.417259
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.159808 Loss1: 2.692446 Loss2: 1.467362
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.128903 Loss1: 2.705575 Loss2: 1.423328
+(DefaultActor pid=3765) >> Training accuracy: 0.307292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.101704 Loss1: 2.680675 Loss2: 1.421028
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.036140 Loss1: 2.613332 Loss2: 1.422808
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.001814 Loss1: 2.587712 Loss2: 1.414102
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.988085 Loss1: 2.556348 Loss2: 1.431738
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.412854 Loss1: 3.473701 Loss2: 1.939153
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.875783 Loss1: 2.441724 Loss2: 1.434059
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.532794 Loss1: 3.080089 Loss2: 1.452705
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.853956 Loss1: 2.426223 Loss2: 1.427733
+(DefaultActor pid=3764) >> Training accuracy: 0.356250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.205843 Loss1: 2.792888 Loss2: 1.412955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.132221 Loss1: 2.719197 Loss2: 1.413024 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.117817 Loss1: 2.686121 Loss2: 1.431696
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.321253 Loss1: 3.446312 Loss2: 1.874941
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.004359 Loss1: 2.591983 Loss2: 1.412375
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.589490 Loss1: 3.136125 Loss2: 1.453365
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.066663 Loss1: 2.643411 Loss2: 1.423252
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.348678 Loss1: 2.934678 Loss2: 1.414000
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.084662 Loss1: 2.642983 Loss2: 1.441679
+(DefaultActor pid=3765) >> Training accuracy: 0.297917
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.339478 Loss1: 2.928294 Loss2: 1.411184
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.297265 Loss1: 2.883829 Loss2: 1.413436
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.238028 Loss1: 2.811119 Loss2: 1.426909
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.196919 Loss1: 2.778094 Loss2: 1.418826
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.143430 Loss1: 2.720087 Loss2: 1.423344
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.113477 Loss1: 2.705089 Loss2: 1.408388
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.543605 Loss1: 3.619670 Loss2: 1.923935
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.096836 Loss1: 2.658582 Loss2: 1.438254
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.558009 Loss1: 3.099600 Loss2: 1.458409
+(DefaultActor pid=3764) >> Training accuracy: 0.325000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.401659 Loss1: 2.966686 Loss2: 1.434973
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.321776 Loss1: 2.880106 Loss2: 1.441670
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.245406 Loss1: 2.790797 Loss2: 1.454609
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.231766 Loss1: 2.789628 Loss2: 1.442137
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.212980 Loss1: 3.307665 Loss2: 1.905315
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.203355 Loss1: 2.751874 Loss2: 1.451481
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.300713 Loss1: 2.846314 Loss2: 1.454398
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.151378 Loss1: 2.704021 Loss2: 1.447357
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.047971 Loss1: 2.651897 Loss2: 1.396074
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.098638 Loss1: 2.696679 Loss2: 1.401959
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.132693 Loss1: 2.674570 Loss2: 1.458123
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.019869 Loss1: 2.613763 Loss2: 1.406106
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.107590 Loss1: 2.651389 Loss2: 1.456201
+(DefaultActor pid=3765) >> Training accuracy: 0.341912
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.913162 Loss1: 2.511888 Loss2: 1.401273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.919109 Loss1: 2.492599 Loss2: 1.426511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.867710 Loss1: 2.450785 Loss2: 1.416925
+(DefaultActor pid=3764) >> Training accuracy: 0.394792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.508099 Loss1: 3.600644 Loss2: 1.907454
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.593299 Loss1: 3.140874 Loss2: 1.452426
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.371104 Loss1: 2.957690 Loss2: 1.413414
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.300791 Loss1: 2.899806 Loss2: 1.400985
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.262831 Loss1: 2.855900 Loss2: 1.406931
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.499739 Loss1: 3.557675 Loss2: 1.942063
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.160435 Loss1: 2.760655 Loss2: 1.399780
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.137087 Loss1: 2.720692 Loss2: 1.416395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.135879 Loss1: 2.722033 Loss2: 1.413846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.125718 Loss1: 2.710676 Loss2: 1.415042 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.095683 Loss1: 2.680649 Loss2: 1.415034 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.311458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.208582 Loss1: 2.754108 Loss2: 1.454474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.182624 Loss1: 2.712981 Loss2: 1.469643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.104896 Loss1: 2.637301 Loss2: 1.467595
+DEBUG flwr 2023-10-08 19:27:52,814 | server.py:236 | fit_round 12 received 50 results and 0 failures
+(DefaultActor pid=3764) >> Training accuracy: 0.326042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.786204 Loss1: 3.775515 Loss2: 2.010689
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.804978 Loss1: 3.289705 Loss2: 1.515272
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.544593 Loss1: 3.086038 Loss2: 1.458556
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.460979 Loss1: 3.004627 Loss2: 1.456352
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.434062 Loss1: 2.966292 Loss2: 1.467770
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.337413 Loss1: 3.404670 Loss2: 1.932742
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.392584 Loss1: 2.922329 Loss2: 1.470254
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.284934 Loss1: 2.810638 Loss2: 1.474296 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.381503 Loss1: 2.883656 Loss2: 1.497847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.254799 Loss1: 2.781703 Loss2: 1.473095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.229605 Loss1: 2.739876 Loss2: 1.489729 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.296875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.042441 Loss1: 2.638978 Loss2: 1.403463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.947152 Loss1: 2.548366 Loss2: 1.398786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.933794 Loss1: 2.522447 Loss2: 1.411347
+(DefaultActor pid=3764) >> Training accuracy: 0.365625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.414107 Loss1: 3.510519 Loss2: 1.903587
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.597112 Loss1: 3.175640 Loss2: 1.421472
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.378472 Loss1: 2.988921 Loss2: 1.389551
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.328606 Loss1: 2.932787 Loss2: 1.395819
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.279336 Loss1: 2.886777 Loss2: 1.392559
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.206782 Loss1: 3.350803 Loss2: 1.855979
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.319135 Loss1: 2.880670 Loss2: 1.438465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.097673 Loss1: 2.688581 Loss2: 1.409092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.989168 Loss1: 2.577796 Loss2: 1.411372 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.939142 Loss1: 2.536453 Loss2: 1.402689 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.268750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.974656 Loss1: 2.553832 Loss2: 1.420824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.875100 Loss1: 2.441895 Loss2: 1.433205 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.808619 Loss1: 2.377071 Loss2: 1.431547 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.381250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.466470 Loss1: 3.095840 Loss2: 1.370630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.265795 Loss1: 2.934401 Loss2: 1.331395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.166424 Loss1: 2.835333 Loss2: 1.331091
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.571924 Loss1: 3.661048 Loss2: 1.910877
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.729516 Loss1: 3.268596 Loss2: 1.460920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.449066 Loss1: 3.015299 Loss2: 1.433767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.417996 Loss1: 3.000176 Loss2: 1.417819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.259048 Loss1: 2.837808 Loss2: 1.421240 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.334375
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.050870 Loss1: 2.692380 Loss2: 1.358489
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.253757 Loss1: 2.818313 Loss2: 1.435444
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.317760 Loss1: 2.863746 Loss2: 1.454013
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.189300 Loss1: 2.743850 Loss2: 1.445450
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.149534 Loss1: 2.700391 Loss2: 1.449143
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.106765 Loss1: 2.659438 Loss2: 1.447327
+(DefaultActor pid=3764) >> Training accuracy: 0.279167
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 19:27:52,814][flwr][DEBUG] - fit_round 12 received 50 results and 0 failures
+INFO flwr 2023-10-08 19:28:33,828 | server.py:125 | fit progress: (12, 3.9032422269876013, {'accuracy': 0.1017}, 27421.606692538)
+>> Test accuracy: 0.101700
+[2023-10-08 19:28:33,828][flwr][INFO] - fit progress: (12, 3.9032422269876013, {'accuracy': 0.1017}, 27421.606692538)
+DEBUG flwr 2023-10-08 19:28:33,828 | server.py:173 | evaluate_round 12: strategy sampled 50 clients (out of 50)
+[2023-10-08 19:28:33,828][flwr][DEBUG] - evaluate_round 12: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 19:37:36,836 | server.py:187 | evaluate_round 12 received 50 results and 0 failures
+[2023-10-08 19:37:36,836][flwr][DEBUG] - evaluate_round 12 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 19:37:36,837 | server.py:222 | fit_round 13: strategy sampled 50 clients (out of 50)
+[2023-10-08 19:37:36,837][flwr][DEBUG] - fit_round 13: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.377749 Loss1: 3.460418 Loss2: 1.917331
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.640888 Loss1: 3.160430 Loss2: 1.480457
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.402727 Loss1: 2.961238 Loss2: 1.441489
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.307202 Loss1: 2.866631 Loss2: 1.440572
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.530783 Loss1: 3.545818 Loss2: 1.984965
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.715094 Loss1: 3.205001 Loss2: 1.510093 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.520742 Loss1: 3.042618 Loss2: 1.478124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.450010 Loss1: 2.969685 Loss2: 1.480325 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.381247 Loss1: 2.889376 Loss2: 1.491871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.390372 Loss1: 2.891341 Loss2: 1.499031 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.336458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.348545 Loss1: 2.840679 Loss2: 1.507866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.247070 Loss1: 2.733971 Loss2: 1.513099 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.298828
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.308724 Loss1: 3.414457 Loss2: 1.894267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.172117 Loss1: 2.788537 Loss2: 1.383580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.086061 Loss1: 2.700296 Loss2: 1.385764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.012200 Loss1: 2.623565 Loss2: 1.388635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.953364 Loss1: 2.549131 Loss2: 1.404233 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.954901 Loss1: 2.536852 Loss2: 1.418049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.829685 Loss1: 2.413240 Loss2: 1.416445 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.873692 Loss1: 2.455838 Loss2: 1.417854 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.332292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.971672 Loss1: 2.532976 Loss2: 1.438696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.878587 Loss1: 2.426669 Loss2: 1.451918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.879225 Loss1: 2.418277 Loss2: 1.460947
+(DefaultActor pid=3764) >> Training accuracy: 0.333008
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.357183 Loss1: 3.433857 Loss2: 1.923325
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.484369 Loss1: 3.010061 Loss2: 1.474309
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.313030 Loss1: 2.887616 Loss2: 1.425414
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.194395 Loss1: 2.770958 Loss2: 1.423437
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.150758 Loss1: 2.730824 Loss2: 1.419934
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.450850 Loss1: 3.478205 Loss2: 1.972646
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.092135 Loss1: 2.672806 Loss2: 1.419329
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.518682 Loss1: 2.990854 Loss2: 1.527828
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.282842 Loss1: 2.805254 Loss2: 1.477587
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.017849 Loss1: 2.596706 Loss2: 1.421142
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.193944 Loss1: 2.737309 Loss2: 1.456635
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.039743 Loss1: 2.603191 Loss2: 1.436552
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.003142 Loss1: 2.577899 Loss2: 1.425243 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.984187 Loss1: 2.550405 Loss2: 1.433782 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.349609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.016209 Loss1: 2.532289 Loss2: 1.483919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.953864 Loss1: 2.472306 Loss2: 1.481558 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.377232
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.330394 Loss1: 3.428100 Loss2: 1.902294
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.498446 Loss1: 3.044238 Loss2: 1.454208
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.347444 Loss1: 2.902265 Loss2: 1.445179
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.308582 Loss1: 3.439815 Loss2: 1.868767
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.123041 Loss1: 2.695206 Loss2: 1.427835
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.437297 Loss1: 3.007174 Loss2: 1.430123
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.094069 Loss1: 2.674249 Loss2: 1.419820
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.260903 Loss1: 2.864821 Loss2: 1.396081
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.014443 Loss1: 2.592577 Loss2: 1.421866
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.035457 Loss1: 2.655476 Loss2: 1.379981
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.133711 Loss1: 2.683397 Loss2: 1.450314
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.002590 Loss1: 2.564128 Loss2: 1.438462 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.974484 Loss1: 2.532082 Loss2: 1.442402 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.939959 Loss1: 2.493151 Loss2: 1.446808 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.384766
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.909223 Loss1: 2.492772 Loss2: 1.416451 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.348958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.487700 Loss1: 3.551976 Loss2: 1.935724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.341685 Loss1: 2.907199 Loss2: 1.434486 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.272760 Loss1: 2.831445 Loss2: 1.441315
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.353933 Loss1: 3.425492 Loss2: 1.928441
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.283466 Loss1: 2.847756 Loss2: 1.435710
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.419527 Loss1: 2.976986 Loss2: 1.442541
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.249102 Loss1: 2.808308 Loss2: 1.440794
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.262590 Loss1: 2.851235 Loss2: 1.411355
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.145491 Loss1: 2.710712 Loss2: 1.434779
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.208849 Loss1: 2.800637 Loss2: 1.408212
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.084259 Loss1: 2.630482 Loss2: 1.453777
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.131393 Loss1: 2.728601 Loss2: 1.402791
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.055208 Loss1: 2.605455 Loss2: 1.449753
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.103610 Loss1: 2.680531 Loss2: 1.423079
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.024758 Loss1: 2.560670 Loss2: 1.464088
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.003536 Loss1: 2.583908 Loss2: 1.419628
+(DefaultActor pid=3765) >> Training accuracy: 0.358333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.931376 Loss1: 2.519904 Loss2: 1.411472
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.905755 Loss1: 2.490408 Loss2: 1.415347
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.998757 Loss1: 2.562786 Loss2: 1.435970
+(DefaultActor pid=3764) >> Training accuracy: 0.318750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.394792 Loss1: 3.405028 Loss2: 1.989764
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.408124 Loss1: 2.874176 Loss2: 1.533948
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.190838 Loss1: 2.704757 Loss2: 1.486081
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.055940 Loss1: 2.589550 Loss2: 1.466390
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.326077 Loss1: 3.361973 Loss2: 1.964103
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.465139 Loss1: 2.965138 Loss2: 1.500001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.266892 Loss1: 2.793595 Loss2: 1.473297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.185897 Loss1: 2.708180 Loss2: 1.477717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.133347 Loss1: 2.649067 Loss2: 1.484280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.176373 Loss1: 2.685234 Loss2: 1.491139 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.397917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.725856 Loss1: 2.229556 Loss2: 1.496300
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.067019 Loss1: 2.575506 Loss2: 1.491512
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.047810 Loss1: 2.558904 Loss2: 1.488907
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.006863 Loss1: 2.514888 Loss2: 1.491975
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.971564 Loss1: 2.467679 Loss2: 1.503885
+(DefaultActor pid=3764) >> Training accuracy: 0.355208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.385894 Loss1: 3.403877 Loss2: 1.982016
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.342336 Loss1: 2.864191 Loss2: 1.478145
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.090756 Loss1: 2.649180 Loss2: 1.441576
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.018921 Loss1: 2.565154 Loss2: 1.453767
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.554394 Loss1: 3.611453 Loss2: 1.942940
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.480940 Loss1: 3.005086 Loss2: 1.475854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.279741 Loss1: 2.847933 Loss2: 1.431808 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.160908 Loss1: 2.729555 Loss2: 1.431353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.145442 Loss1: 2.692127 Loss2: 1.453314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.094216 Loss1: 2.635812 Loss2: 1.458404 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.332292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.036960 Loss1: 2.580966 Loss2: 1.455994 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.983935 Loss1: 2.521237 Loss2: 1.462698 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.357292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.060460 Loss1: 3.206066 Loss2: 1.854394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.132077 Loss1: 2.754113 Loss2: 1.377963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.007655 Loss1: 2.637797 Loss2: 1.369857
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.374660 Loss1: 3.492541 Loss2: 1.882118
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.468128 Loss1: 3.035245 Loss2: 1.432883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.340358 Loss1: 2.942959 Loss2: 1.397399 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.240538 Loss1: 2.853949 Loss2: 1.386589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.210496 Loss1: 2.807014 Loss2: 1.403482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.152119 Loss1: 2.746038 Loss2: 1.406081 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.408333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.072540 Loss1: 2.665528 Loss2: 1.407012 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.035077 Loss1: 2.616781 Loss2: 1.418296 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.341797
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.520935 Loss1: 3.023013 Loss2: 1.497921 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.269912 Loss1: 2.810058 Loss2: 1.459854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.228462 Loss1: 2.758935 Loss2: 1.469527
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.185041 Loss1: 3.341919 Loss2: 1.843122
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.341363 Loss1: 2.932520 Loss2: 1.408843 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.130898 Loss1: 2.751787 Loss2: 1.379110 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.075795 Loss1: 2.705608 Loss2: 1.370187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.089553 Loss1: 2.707069 Loss2: 1.382484 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.341667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.983940 Loss1: 2.486195 Loss2: 1.497745
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.947274 Loss1: 2.571936 Loss2: 1.375338
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.978920 Loss1: 2.599632 Loss2: 1.379288
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.876301 Loss1: 2.494017 Loss2: 1.382284
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.831082 Loss1: 2.458784 Loss2: 1.372297
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.873743 Loss1: 2.485201 Loss2: 1.388543
+(DefaultActor pid=3764) >> Training accuracy: 0.338542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.336586 Loss1: 3.310404 Loss2: 2.026182
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.406324 Loss1: 2.889903 Loss2: 1.516421
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.271390 Loss1: 2.787496 Loss2: 1.483894
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.065813 Loss1: 2.588330 Loss2: 1.477482
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.050759 Loss1: 2.561899 Loss2: 1.488860
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.679357 Loss1: 3.673675 Loss2: 2.005682
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.023449 Loss1: 2.531734 Loss2: 1.491714
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.769876 Loss1: 3.260178 Loss2: 1.509698
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.999649 Loss1: 2.507014 Loss2: 1.492635
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.527558 Loss1: 3.063951 Loss2: 1.463607
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.969912 Loss1: 2.465820 Loss2: 1.504093
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.406911 Loss1: 2.944295 Loss2: 1.462616
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.366090 Loss1: 2.908044 Loss2: 1.458046
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.854932 Loss1: 2.357690 Loss2: 1.497242
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.319858 Loss1: 2.837327 Loss2: 1.482531
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.883799 Loss1: 2.371897 Loss2: 1.511902
+(DefaultActor pid=3765) >> Training accuracy: 0.360417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.186865 Loss1: 2.716469 Loss2: 1.470396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.099518 Loss1: 2.620283 Loss2: 1.479235 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.313616
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.500455 Loss1: 2.994595 Loss2: 1.505860 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.289300 Loss1: 2.820691 Loss2: 1.468609 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.120524 Loss1: 2.641561 Loss2: 1.478963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.130433 Loss1: 2.644813 Loss2: 1.485620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.072049 Loss1: 2.585219 Loss2: 1.486829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.008436 Loss1: 2.513193 Loss2: 1.495244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.026439 Loss1: 2.512984 Loss2: 1.513455 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.999870 Loss1: 2.474663 Loss2: 1.525207 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.353125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.115422 Loss1: 2.678398 Loss2: 1.437024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.029958 Loss1: 2.590012 Loss2: 1.439946 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.335417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.232629 Loss1: 3.244761 Loss2: 1.987867
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.302683 Loss1: 2.788731 Loss2: 1.513952
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.110314 Loss1: 2.632266 Loss2: 1.478048
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.033219 Loss1: 2.551919 Loss2: 1.481300
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.313668 Loss1: 3.373578 Loss2: 1.940090
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.488075 Loss1: 3.028389 Loss2: 1.459686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.281215 Loss1: 2.836151 Loss2: 1.445064 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.106119 Loss1: 2.677894 Loss2: 1.428225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.078378 Loss1: 2.639597 Loss2: 1.438781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.020708 Loss1: 2.605812 Loss2: 1.414895 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.428125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.962818 Loss1: 2.525256 Loss2: 1.437562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.920412 Loss1: 2.469965 Loss2: 1.450446 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.356250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.348260 Loss1: 3.466729 Loss2: 1.881531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.378787 Loss1: 2.972612 Loss2: 1.406175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.307848 Loss1: 2.921484 Loss2: 1.386364
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.452768 Loss1: 3.460755 Loss2: 1.992013
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.664228 Loss1: 3.176655 Loss2: 1.487574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.463387 Loss1: 3.008874 Loss2: 1.454513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.337515 Loss1: 2.885514 Loss2: 1.452001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.233257 Loss1: 2.772061 Loss2: 1.461196 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.244619 Loss1: 2.773895 Loss2: 1.470725 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.308333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.214236 Loss1: 2.735281 Loss2: 1.478955 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.133211 Loss1: 2.643612 Loss2: 1.489599 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.355208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.338750 Loss1: 3.365329 Loss2: 1.973422 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.350121 Loss1: 2.896077 Loss2: 1.454044 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.235016 Loss1: 2.779692 Loss2: 1.455324
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.466162 Loss1: 3.459998 Loss2: 2.006164
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.536776 Loss1: 3.020064 Loss2: 1.516712 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.308742 Loss1: 2.813046 Loss2: 1.495696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.247259 Loss1: 2.766233 Loss2: 1.481026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.179943 Loss1: 2.679812 Loss2: 1.500131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.174157 Loss1: 2.677054 Loss2: 1.497103 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.347917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.109830 Loss1: 2.615557 Loss2: 1.494273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.062707 Loss1: 2.551858 Loss2: 1.510849 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.347917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.429872 Loss1: 3.534455 Loss2: 1.895418 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.310728 Loss1: 2.875224 Loss2: 1.435504 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.557493 Loss1: 3.624861 Loss2: 1.932632
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.224798 Loss1: 2.777687 Loss2: 1.447112
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.580085 Loss1: 3.101035 Loss2: 1.479049
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.163237 Loss1: 2.693382 Loss2: 1.469855
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.405577 Loss1: 2.961102 Loss2: 1.444475
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.132015 Loss1: 2.671024 Loss2: 1.460990
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.305242 Loss1: 2.866103 Loss2: 1.439139
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.093393 Loss1: 2.626305 Loss2: 1.467088
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.272793 Loss1: 2.815601 Loss2: 1.457192
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.028222 Loss1: 2.564345 Loss2: 1.463876
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.000909 Loss1: 2.530859 Loss2: 1.470050 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.988550 Loss1: 2.516731 Loss2: 1.471819 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.326287
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.099772 Loss1: 2.645495 Loss2: 1.454277 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.291016
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.536008 Loss1: 3.465272 Loss2: 2.070736 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.383391 Loss1: 2.882577 Loss2: 1.500814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.194798 Loss1: 2.713444 Loss2: 1.481354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.162427 Loss1: 2.676354 Loss2: 1.486073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.124245 Loss1: 2.631894 Loss2: 1.492351 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.084108 Loss1: 2.576861 Loss2: 1.507247 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.043783 Loss1: 2.558629 Loss2: 1.485153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.043007 Loss1: 2.542352 Loss2: 1.500655 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.356971
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.062673 Loss1: 2.554284 Loss2: 1.508388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.078904 Loss1: 2.568887 Loss2: 1.510016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.980648 Loss1: 2.465563 Loss2: 1.515085
+(DefaultActor pid=3764) >> Training accuracy: 0.378125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.618766 Loss1: 3.609449 Loss2: 2.009318
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.744254 Loss1: 3.209699 Loss2: 1.534555
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.610008 Loss1: 3.111946 Loss2: 1.498063
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.446907 Loss1: 2.935193 Loss2: 1.511714
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.405658 Loss1: 2.895597 Loss2: 1.510061
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.581841 Loss1: 3.450255 Loss2: 2.131586
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.382612 Loss1: 2.876285 Loss2: 1.506327
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.308007 Loss1: 2.782085 Loss2: 1.525922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.220423 Loss1: 2.733482 Loss2: 1.486941 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.217879 Loss1: 2.683518 Loss2: 1.534360 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.211023 Loss1: 2.686098 Loss2: 1.524925 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.306250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.010153 Loss1: 2.512094 Loss2: 1.498059 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.365885
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.530095 Loss1: 3.554373 Loss2: 1.975722 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.374021 Loss1: 2.894739 Loss2: 1.479283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.276054 Loss1: 2.797323 Loss2: 1.478732
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.515643 Loss1: 3.531603 Loss2: 1.984040
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.653147 Loss1: 3.171056 Loss2: 1.482092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.335653 Loss1: 2.882554 Loss2: 1.453099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.232774 Loss1: 2.785417 Loss2: 1.447358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.193705 Loss1: 2.737677 Loss2: 1.456028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.090204 Loss1: 2.631475 Loss2: 1.458729 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.295833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.022799 Loss1: 2.519044 Loss2: 1.503755
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.064651 Loss1: 2.600160 Loss2: 1.464492
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.121655 Loss1: 2.646447 Loss2: 1.475208
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.039837 Loss1: 2.576993 Loss2: 1.462845
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.010309 Loss1: 2.530411 Loss2: 1.479898
+(DefaultActor pid=3764) >> Training accuracy: 0.332292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.397949 Loss1: 3.506947 Loss2: 1.891002
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.587787 Loss1: 3.137587 Loss2: 1.450200
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.340806 Loss1: 2.915548 Loss2: 1.425257
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.222793 Loss1: 2.803526 Loss2: 1.419267
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.205407 Loss1: 3.280435 Loss2: 1.924972
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.331743 Loss1: 2.877317 Loss2: 1.454426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.093716 Loss1: 2.673341 Loss2: 1.420375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.028219 Loss1: 2.600349 Loss2: 1.427869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.001794 Loss1: 2.564554 Loss2: 1.437240 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.960878 Loss1: 2.521405 Loss2: 1.439473 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.277083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.925290 Loss1: 2.480042 Loss2: 1.445249 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.813549 Loss1: 2.377187 Loss2: 1.436362 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.361328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.430409 Loss1: 3.417700 Loss2: 2.012709 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.195803 Loss1: 2.710005 Loss2: 1.485797 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.013648 Loss1: 2.568245 Loss2: 1.445403 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.920448 Loss1: 2.463523 Loss2: 1.456925 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.932687 Loss1: 2.471220 Loss2: 1.461467
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.665801 Loss1: 3.155667 Loss2: 1.510135
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.833823 Loss1: 2.377640 Loss2: 1.456184
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.566857 Loss1: 3.069022 Loss2: 1.497836
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.415395 Loss1: 2.920745 Loss2: 1.494651 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.393029
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.473900 Loss1: 2.977021 Loss2: 1.496879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.327897 Loss1: 2.818148 Loss2: 1.509748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.299201 Loss1: 2.776917 Loss2: 1.522285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.219856 Loss1: 2.690240 Loss2: 1.529616 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.291016
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.418712 Loss1: 2.965216 Loss2: 1.453496 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.251738 Loss1: 2.783850 Loss2: 1.467889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.188719 Loss1: 3.307284 Loss2: 1.881435
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.200255 Loss1: 2.729216 Loss2: 1.471039
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.227772 Loss1: 2.800733 Loss2: 1.427039
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.191761 Loss1: 2.707810 Loss2: 1.483952
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.033985 Loss1: 2.639537 Loss2: 1.394448
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.160923 Loss1: 2.665931 Loss2: 1.494992
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.973424 Loss1: 2.587490 Loss2: 1.385934
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.090595 Loss1: 2.603132 Loss2: 1.487463
+(DefaultActor pid=3765) >> Training accuracy: 0.320833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.821244 Loss1: 2.429784 Loss2: 1.391460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.750794 Loss1: 2.350084 Loss2: 1.400709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.785160 Loss1: 2.374968 Loss2: 1.410192
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.554639 Loss1: 3.551373 Loss2: 2.003266
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.799973 Loss1: 2.387950 Loss2: 1.412024
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.650762 Loss1: 3.138716 Loss2: 1.512046
+(DefaultActor pid=3764) >> Training accuracy: 0.440625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.410634 Loss1: 2.918386 Loss2: 1.492248
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.318744 Loss1: 2.843931 Loss2: 1.474813
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.240572 Loss1: 2.755338 Loss2: 1.485234
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.196392 Loss1: 2.716710 Loss2: 1.479682
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.541352 Loss1: 3.544138 Loss2: 1.997214
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.225955 Loss1: 2.732038 Loss2: 1.493917
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.500367 Loss1: 3.005598 Loss2: 1.494769
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.166934 Loss1: 2.668726 Loss2: 1.498208
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.360869 Loss1: 2.889708 Loss2: 1.471161
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.149695 Loss1: 2.642745 Loss2: 1.506950
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.265518 Loss1: 2.813309 Loss2: 1.452209
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.059216 Loss1: 2.530608 Loss2: 1.528609
+(DefaultActor pid=3765) >> Training accuracy: 0.351042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.122992 Loss1: 2.662633 Loss2: 1.460359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.034804 Loss1: 2.567519 Loss2: 1.467285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.059986 Loss1: 2.592823 Loss2: 1.467163
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.362145 Loss1: 3.457913 Loss2: 1.904232
+(DefaultActor pid=3764) >> Training accuracy: 0.318750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.457457 Loss1: 3.013922 Loss2: 1.443535 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.102756 Loss1: 2.689095 Loss2: 1.413661 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.012405 Loss1: 2.582249 Loss2: 1.430156 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.494716 Loss1: 3.559329 Loss2: 1.935387
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.029242 Loss1: 2.597959 Loss2: 1.431283
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.604507 Loss1: 3.128906 Loss2: 1.475601
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.032268 Loss1: 2.584965 Loss2: 1.447303
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.489289 Loss1: 3.052249 Loss2: 1.437040
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.980623 Loss1: 2.537752 Loss2: 1.442871
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.356641 Loss1: 2.923419 Loss2: 1.433222
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.930247 Loss1: 2.482582 Loss2: 1.447664
+(DefaultActor pid=3765) >> Training accuracy: 0.359375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.200963 Loss1: 2.752018 Loss2: 1.448945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.161469 Loss1: 2.703783 Loss2: 1.457686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.170486 Loss1: 2.718561 Loss2: 1.451925
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.275775 Loss1: 3.242250 Loss2: 2.033525
+(DefaultActor pid=3764) >> Training accuracy: 0.317708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.450766 Loss1: 2.944450 Loss2: 1.506316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.167090 Loss1: 2.683068 Loss2: 1.484022 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.050898 Loss1: 2.574363 Loss2: 1.476535 [repeated 2x across cluster]
+DEBUG flwr 2023-10-08 20:06:07,198 | server.py:236 | fit_round 13 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.009029 Loss1: 2.510114 Loss2: 1.498915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.655114 Loss1: 3.195857 Loss2: 1.459257
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.005406 Loss1: 2.512340 Loss2: 1.493066
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.482451 Loss1: 3.055059 Loss2: 1.427392
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.891993 Loss1: 2.383492 Loss2: 1.508501
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.364092 Loss1: 2.940424 Loss2: 1.423668
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.875340 Loss1: 2.383562 Loss2: 1.491778
+(DefaultActor pid=3765) >> Training accuracy: 0.367708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.302227 Loss1: 2.875097 Loss2: 1.427130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.182934 Loss1: 2.730038 Loss2: 1.452896 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.217387 Loss1: 3.291394 Loss2: 1.925993
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.171139 Loss1: 2.723210 Loss2: 1.447928
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.086349 Loss1: 2.651123 Loss2: 1.435226 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.320312
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.048400 Loss1: 2.645037 Loss2: 1.403363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.915023 Loss1: 2.527172 Loss2: 1.387851 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.830818 Loss1: 2.429598 Loss2: 1.401220
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.837804 Loss1: 3.709448 Loss2: 2.128356
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.826199 Loss1: 2.420684 Loss2: 1.405516
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.824022 Loss1: 3.241432 Loss2: 1.582590
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.766714 Loss1: 2.358665 Loss2: 1.408048
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.588733 Loss1: 3.043895 Loss2: 1.544838
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.732179 Loss1: 2.319283 Loss2: 1.412896
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.457303 Loss1: 2.917654 Loss2: 1.539649
+(DefaultActor pid=3765) >> Training accuracy: 0.362500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.452275 Loss1: 2.896098 Loss2: 1.556177
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.389587 Loss1: 2.832711 Loss2: 1.556876
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.339756 Loss1: 2.773407 Loss2: 1.566349
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.299803 Loss1: 2.741410 Loss2: 1.558394
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.294272 Loss1: 2.717617 Loss2: 1.576656
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.249973 Loss1: 2.676419 Loss2: 1.573554
+(DefaultActor pid=3764) >> Training accuracy: 0.292411
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 20:06:07,198][flwr][DEBUG] - fit_round 13 received 50 results and 0 failures
+INFO flwr 2023-10-08 20:06:48,260 | server.py:125 | fit progress: (13, 3.7946639914101303, {'accuracy': 0.119}, 29716.038441409)
+>> Test accuracy: 0.119000
+[2023-10-08 20:06:48,260][flwr][INFO] - fit progress: (13, 3.7946639914101303, {'accuracy': 0.119}, 29716.038441409)
+DEBUG flwr 2023-10-08 20:06:48,260 | server.py:173 | evaluate_round 13: strategy sampled 50 clients (out of 50)
+[2023-10-08 20:06:48,260][flwr][DEBUG] - evaluate_round 13: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 20:15:52,509 | server.py:187 | evaluate_round 13 received 50 results and 0 failures
+[2023-10-08 20:15:52,509][flwr][DEBUG] - evaluate_round 13 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 20:15:52,509 | server.py:222 | fit_round 14: strategy sampled 50 clients (out of 50)
+[2023-10-08 20:15:52,509][flwr][DEBUG] - fit_round 14: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.218975 Loss1: 3.297913 Loss2: 1.921062
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.374268 Loss1: 2.911961 Loss2: 1.462307
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.123111 Loss1: 2.701898 Loss2: 1.421212
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.984862 Loss1: 2.551388 Loss2: 1.433473
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.321269 Loss1: 3.352669 Loss2: 1.968599
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.009016 Loss1: 2.561161 Loss2: 1.447855
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.466407 Loss1: 2.963215 Loss2: 1.503192
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.209139 Loss1: 2.736703 Loss2: 1.472436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.143210 Loss1: 2.659766 Loss2: 1.483444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.093480 Loss1: 2.607238 Loss2: 1.486243 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.974586 Loss1: 2.495788 Loss2: 1.478798 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.400000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.022538 Loss1: 2.531050 Loss2: 1.491487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.962940 Loss1: 2.458943 Loss2: 1.503996 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.355469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.235302 Loss1: 3.227597 Loss2: 2.007705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.219193 Loss1: 2.708461 Loss2: 1.510732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.400670 Loss1: 3.460910 Loss2: 1.939761 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.449889 Loss1: 2.997765 Loss2: 1.452124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.304068 Loss1: 2.882316 Loss2: 1.421751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.204740 Loss1: 2.773837 Loss2: 1.430903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.236517 Loss1: 2.803295 Loss2: 1.433222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.145690 Loss1: 2.704207 Loss2: 1.441483 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.389583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.078971 Loss1: 2.647015 Loss2: 1.431956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.978110 Loss1: 2.519007 Loss2: 1.459104 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.327083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.452575 Loss1: 3.460377 Loss2: 1.992198 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.299641 Loss1: 2.837219 Loss2: 1.462422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.351422 Loss1: 3.450519 Loss2: 1.900903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.505441 Loss1: 3.015042 Loss2: 1.490399 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.367363 Loss1: 2.915929 Loss2: 1.451434 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.216438 Loss1: 2.771789 Loss2: 1.444649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.069648 Loss1: 2.629352 Loss2: 1.440296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.040850 Loss1: 2.591764 Loss2: 1.449086 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.377083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.981550 Loss1: 2.505448 Loss2: 1.476101 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.929024 Loss1: 2.452785 Loss2: 1.476240 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.322266
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.472484 Loss1: 2.934371 Loss2: 1.538113 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.181036 Loss1: 2.696212 Loss2: 1.484824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.066330 Loss1: 2.573561 Loss2: 1.492768
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.565942 Loss1: 3.572950 Loss2: 1.992993
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.011676 Loss1: 2.525108 Loss2: 1.486568
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.604392 Loss1: 3.114427 Loss2: 1.489965
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.434537 Loss1: 2.968766 Loss2: 1.465771
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.915862 Loss1: 2.422976 Loss2: 1.492885
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.321534 Loss1: 2.872527 Loss2: 1.449007
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.958911 Loss1: 2.459289 Loss2: 1.499622
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.232525 Loss1: 2.758637 Loss2: 1.473888
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.908220 Loss1: 2.410841 Loss2: 1.497379
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.866100 Loss1: 2.360068 Loss2: 1.506033 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.407292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.091252 Loss1: 2.601780 Loss2: 1.489472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.104823 Loss1: 2.623619 Loss2: 1.481203 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.290179
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.372816 Loss1: 3.499970 Loss2: 1.872845
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.544901 Loss1: 3.135776 Loss2: 1.409125
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.338983 Loss1: 2.961499 Loss2: 1.377484
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.201671 Loss1: 2.815801 Loss2: 1.385870
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.379556 Loss1: 3.398511 Loss2: 1.981045
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.438835 Loss1: 2.918711 Loss2: 1.520124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.298307 Loss1: 2.833558 Loss2: 1.464749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.092435 Loss1: 2.628295 Loss2: 1.464139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.086576 Loss1: 2.620979 Loss2: 1.465597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.015577 Loss1: 2.584849 Loss2: 1.430729 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.339583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.865714 Loss1: 2.383447 Loss2: 1.482268 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.906354 Loss1: 2.420161 Loss2: 1.486194 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.384766
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.596184 Loss1: 3.113297 Loss2: 1.482887 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.237592 Loss1: 2.794550 Loss2: 1.443042 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.225461 Loss1: 2.769453 Loss2: 1.456008
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.382177 Loss1: 3.406845 Loss2: 1.975332
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.093485 Loss1: 2.648359 Loss2: 1.445126
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.442503 Loss1: 2.949943 Loss2: 1.492560
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.038040 Loss1: 2.581045 Loss2: 1.456994
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.305713 Loss1: 2.826259 Loss2: 1.479453
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.959245 Loss1: 2.495095 Loss2: 1.464150
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.083204 Loss1: 2.611620 Loss2: 1.471584
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.208834 Loss1: 2.736774 Loss2: 1.472060 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.355469
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.982547 Loss1: 2.497186 Loss2: 1.485361
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.122305 Loss1: 2.629706 Loss2: 1.492599
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.063993 Loss1: 2.585226 Loss2: 1.478767
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.976348 Loss1: 2.489013 Loss2: 1.487334
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.979615 Loss1: 2.486517 Loss2: 1.493098
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.956934 Loss1: 2.451471 Loss2: 1.505463
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.387165 Loss1: 3.471146 Loss2: 1.916019
+(DefaultActor pid=3764) >> Training accuracy: 0.388787
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.517656 Loss1: 3.053902 Loss2: 1.463754
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.239991 Loss1: 2.805808 Loss2: 1.434183
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.124605 Loss1: 2.702780 Loss2: 1.421825
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.087422 Loss1: 2.667086 Loss2: 1.420336
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.348487 Loss1: 3.330512 Loss2: 2.017975
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.099995 Loss1: 2.657385 Loss2: 1.442610
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.403844 Loss1: 2.895133 Loss2: 1.508710
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.994144 Loss1: 2.556538 Loss2: 1.437606
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.209992 Loss1: 2.714276 Loss2: 1.495717
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.957466 Loss1: 2.516053 Loss2: 1.441412
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.128024 Loss1: 2.639948 Loss2: 1.488076
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.942828 Loss1: 2.492639 Loss2: 1.450189
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.137882 Loss1: 2.646791 Loss2: 1.491091
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.852827 Loss1: 2.388962 Loss2: 1.463865
+(DefaultActor pid=3765) >> Training accuracy: 0.337500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.999206 Loss1: 2.505238 Loss2: 1.493968 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.960989 Loss1: 2.447605 Loss2: 1.513383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.949914 Loss1: 2.430693 Loss2: 1.519221
+(DefaultActor pid=3764) >> Training accuracy: 0.363542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.449357 Loss1: 3.388207 Loss2: 2.061151
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.565997 Loss1: 3.003477 Loss2: 1.562520
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.378792 Loss1: 2.869616 Loss2: 1.509177
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.241791 Loss1: 2.735238 Loss2: 1.506553
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.248911 Loss1: 2.743658 Loss2: 1.505253
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.350720 Loss1: 3.433465 Loss2: 1.917255
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.194589 Loss1: 2.667302 Loss2: 1.527287
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.535585 Loss1: 3.040908 Loss2: 1.494677
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.137950 Loss1: 2.616544 Loss2: 1.521406
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.233684 Loss1: 2.788396 Loss2: 1.445287
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.098503 Loss1: 2.571438 Loss2: 1.527066
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.066278 Loss1: 2.631825 Loss2: 1.434453
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.062904 Loss1: 2.530803 Loss2: 1.532101
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.032795 Loss1: 2.600080 Loss2: 1.432715
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.048057 Loss1: 2.527625 Loss2: 1.520432
+(DefaultActor pid=3765) >> Training accuracy: 0.330208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.916823 Loss1: 2.467285 Loss2: 1.449538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.893296 Loss1: 2.429371 Loss2: 1.463925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.883152 Loss1: 2.408143 Loss2: 1.475010
+(DefaultActor pid=3764) >> Training accuracy: 0.362500
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.659058 Loss1: 3.599910 Loss2: 2.059148
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.573735 Loss1: 3.023690 Loss2: 1.550046
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.281686 Loss1: 2.788349 Loss2: 1.493337
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.271400 Loss1: 2.776119 Loss2: 1.495281
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.215690 Loss1: 2.722203 Loss2: 1.493487
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.282655 Loss1: 3.324413 Loss2: 1.958242
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.115176 Loss1: 2.617041 Loss2: 1.498135
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.521885 Loss1: 3.035385 Loss2: 1.486500
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.083637 Loss1: 2.580803 Loss2: 1.502834
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.335987 Loss1: 2.885778 Loss2: 1.450209
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.014241 Loss1: 2.505202 Loss2: 1.509039
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.282211 Loss1: 2.831324 Loss2: 1.450886
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.972887 Loss1: 2.470848 Loss2: 1.502039
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.189787 Loss1: 2.730322 Loss2: 1.459465
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.893926 Loss1: 2.385053 Loss2: 1.508873
+(DefaultActor pid=3765) >> Training accuracy: 0.359375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.140971 Loss1: 2.659696 Loss2: 1.481275 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.013394 Loss1: 2.545565 Loss2: 1.467829 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.991369 Loss1: 2.510806 Loss2: 1.480562
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.332425 Loss1: 3.306840 Loss2: 2.025585
+(DefaultActor pid=3764) >> Training accuracy: 0.368750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.298776 Loss1: 2.760516 Loss2: 1.538260
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.064912 Loss1: 2.581909 Loss2: 1.483004
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.966149 Loss1: 2.502143 Loss2: 1.464006
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.970895 Loss1: 2.505341 Loss2: 1.465553
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.808604 Loss1: 2.344387 Loss2: 1.464217
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.215499 Loss1: 3.303982 Loss2: 1.911516
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.348720 Loss1: 2.888047 Loss2: 1.460674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.814820 Loss1: 2.328048 Loss2: 1.486772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.706241 Loss1: 2.229493 Loss2: 1.476749 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.411058
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.938906 Loss1: 2.508726 Loss2: 1.430180 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.868387 Loss1: 2.440947 Loss2: 1.427440 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.339286 Loss1: 3.441003 Loss2: 1.898283
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.904323 Loss1: 2.443423 Loss2: 1.460900
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.498779 Loss1: 3.060857 Loss2: 1.437922
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.810389 Loss1: 2.373632 Loss2: 1.436757
+(DefaultActor pid=3764) >> Training accuracy: 0.386458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.232474 Loss1: 2.821883 Loss2: 1.410591 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.086851 Loss1: 2.665213 Loss2: 1.421639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.009915 Loss1: 2.584428 Loss2: 1.425487
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.114815 Loss1: 3.196586 Loss2: 1.918229
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.956652 Loss1: 2.536264 Loss2: 1.420388
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.244769 Loss1: 2.759386 Loss2: 1.485384
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.957290 Loss1: 2.516259 Loss2: 1.441032
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.992529 Loss1: 2.530588 Loss2: 1.461940
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.887635 Loss1: 2.439631 Loss2: 1.448004
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.937165 Loss1: 2.490588 Loss2: 1.446577
+(DefaultActor pid=3765) >> Training accuracy: 0.353125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.876458 Loss1: 2.417468 Loss2: 1.458991
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.775829 Loss1: 2.327631 Loss2: 1.448198
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.724680 Loss1: 2.277306 Loss2: 1.447374
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.658073 Loss1: 2.194346 Loss2: 1.463727
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.138178 Loss1: 3.215599 Loss2: 1.922579
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.649293 Loss1: 2.176882 Loss2: 1.472411
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.257311 Loss1: 2.818944 Loss2: 1.438367
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.636301 Loss1: 2.157670 Loss2: 1.478631
+(DefaultActor pid=3764) >> Training accuracy: 0.434375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.955450 Loss1: 2.534022 Loss2: 1.421428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.844306 Loss1: 2.416223 Loss2: 1.428083 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.829337 Loss1: 2.401447 Loss2: 1.427890
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.447963 Loss1: 3.509114 Loss2: 1.938850
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.859165 Loss1: 2.416748 Loss2: 1.442417
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.599299 Loss1: 3.116782 Loss2: 1.482517
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.724018 Loss1: 2.282409 Loss2: 1.441609
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.467291 Loss1: 2.995379 Loss2: 1.471912
+(DefaultActor pid=3765) >> Training accuracy: 0.386458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.638388 Loss1: 2.193938 Loss2: 1.444449
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.365743 Loss1: 2.893089 Loss2: 1.472654
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.267442 Loss1: 2.794825 Loss2: 1.472617
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.269825 Loss1: 2.796674 Loss2: 1.473151
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.284169 Loss1: 2.801586 Loss2: 1.482583
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.150092 Loss1: 2.659343 Loss2: 1.490749
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.253760 Loss1: 3.300546 Loss2: 1.953213
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.395568 Loss1: 2.893367 Loss2: 1.502200 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.369141
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.136590 Loss1: 2.633801 Loss2: 1.502789
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.275453 Loss1: 2.805722 Loss2: 1.469731
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.122793 Loss1: 2.661767 Loss2: 1.461026
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.113898 Loss1: 2.644936 Loss2: 1.468961
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.016589 Loss1: 2.554130 Loss2: 1.462459
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.973734 Loss1: 2.498421 Loss2: 1.475313
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.733555 Loss1: 3.654997 Loss2: 2.078558
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.005385 Loss1: 2.529879 Loss2: 1.475505
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.053710 Loss1: 2.569278 Loss2: 1.484433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.892659 Loss1: 2.426602 Loss2: 1.466057 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.385417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.230579 Loss1: 2.738238 Loss2: 1.492341 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.160560 Loss1: 2.642661 Loss2: 1.517899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.206887 Loss1: 2.683386 Loss2: 1.523501 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.127014 Loss1: 2.595021 Loss2: 1.531993 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.324777
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.988641 Loss1: 2.502991 Loss2: 1.485650 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.878530 Loss1: 2.388752 Loss2: 1.489778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.405884 Loss1: 3.360203 Loss2: 2.045682
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.765870 Loss1: 2.268899 Loss2: 1.496971
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.741398 Loss1: 2.237685 Loss2: 1.503713 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.675389 Loss1: 2.180635 Loss2: 1.494754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.748068 Loss1: 2.219749 Loss2: 1.528318 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.378125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.032568 Loss1: 2.546574 Loss2: 1.485995 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.879729 Loss1: 2.396762 Loss2: 1.482966 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.350962
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.842378 Loss1: 2.336366 Loss2: 1.506012
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.590078 Loss1: 3.603738 Loss2: 1.986339
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.585914 Loss1: 3.107786 Loss2: 1.478127
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.350337 Loss1: 2.908367 Loss2: 1.441970
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.268581 Loss1: 2.825742 Loss2: 1.442839
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.143643 Loss1: 2.694027 Loss2: 1.449616
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.479384 Loss1: 3.345597 Loss2: 2.133787
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.520217 Loss1: 3.011678 Loss2: 1.508539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.305598 Loss1: 2.843566 Loss2: 1.462032
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.060340 Loss1: 2.601566 Loss2: 1.458774
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.062948 Loss1: 2.595509 Loss2: 1.467438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.054283 Loss1: 2.581007 Loss2: 1.473276 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.012954 Loss1: 2.529966 Loss2: 1.482988 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.340625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.945671 Loss1: 2.464418 Loss2: 1.481253 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.385417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.253174 Loss1: 3.271968 Loss2: 1.981206 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.245318 Loss1: 2.733677 Loss2: 1.511642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.071774 Loss1: 2.607782 Loss2: 1.463992
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.151221 Loss1: 3.144712 Loss2: 2.006510
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.004787 Loss1: 2.537556 Loss2: 1.467230
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.273336 Loss1: 2.758160 Loss2: 1.515176
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.107905 Loss1: 2.615102 Loss2: 1.492803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.010825 Loss1: 2.533137 Loss2: 1.477688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.990039 Loss1: 2.497323 Loss2: 1.492716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.859064 Loss1: 2.374815 Loss2: 1.484250 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.357292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.805914 Loss1: 2.319692 Loss2: 1.486222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.750417 Loss1: 2.237600 Loss2: 1.512818 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.415039
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.329609 Loss1: 3.357710 Loss2: 1.971900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.066872 Loss1: 2.595255 Loss2: 1.471617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.348462 Loss1: 3.323076 Loss2: 2.025386 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.449730 Loss1: 2.933371 Loss2: 1.516359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.252880 Loss1: 2.776652 Loss2: 1.476227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.063017 Loss1: 2.588943 Loss2: 1.474074 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.039961 Loss1: 2.579358 Loss2: 1.460602 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.992462 Loss1: 2.523516 Loss2: 1.468946 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.439583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.029950 Loss1: 2.548530 Loss2: 1.481420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.935673 Loss1: 2.432219 Loss2: 1.503454 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.401786
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.333657 Loss1: 3.321056 Loss2: 2.012601 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.080097 Loss1: 2.610839 Loss2: 1.469258 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.958793 Loss1: 2.499296 Loss2: 1.459497
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.556978 Loss1: 3.530268 Loss2: 2.026711
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.552781 Loss1: 3.025953 Loss2: 1.526828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.341405 Loss1: 2.842271 Loss2: 1.499134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.286149 Loss1: 2.793658 Loss2: 1.492491 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.214698 Loss1: 2.712277 Loss2: 1.502421 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.175346 Loss1: 2.672671 Loss2: 1.502675 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.388542
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.820050 Loss1: 2.333867 Loss2: 1.486183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.176627 Loss1: 2.653653 Loss2: 1.522974
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.104985 Loss1: 2.591613 Loss2: 1.513372
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.038298 Loss1: 2.519300 Loss2: 1.518998
+(DefaultActor pid=3764) Epoch: 9 Loss: 4.024096 Loss1: 2.498817 Loss2: 1.525280
+(DefaultActor pid=3764) >> Training accuracy: 0.328125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.268582 Loss1: 3.304436 Loss2: 1.964145
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.243090 Loss1: 2.782795 Loss2: 1.460295
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.973233 Loss1: 2.534434 Loss2: 1.438800
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.877586 Loss1: 2.451846 Loss2: 1.425740
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.166445 Loss1: 3.311749 Loss2: 1.854695
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.868369 Loss1: 2.440206 Loss2: 1.428163
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.323649 Loss1: 2.852569 Loss2: 1.471080
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.739393 Loss1: 2.305882 Loss2: 1.433511
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.118314 Loss1: 2.685429 Loss2: 1.432886
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.026241 Loss1: 2.595436 Loss2: 1.430805 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.007221 Loss1: 2.571761 Loss2: 1.435460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.915031 Loss1: 2.464969 Loss2: 1.450062 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.381250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.897303 Loss1: 2.443785 Loss2: 1.453517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.759079 Loss1: 2.297668 Loss2: 1.461411 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.380859
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.503627 Loss1: 3.548924 Loss2: 1.954703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.415424 Loss1: 2.966398 Loss2: 1.449026 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.379973 Loss1: 2.921169 Loss2: 1.458804
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.243489 Loss1: 3.336919 Loss2: 1.906570
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.437825 Loss1: 2.982066 Loss2: 1.455759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.218652 Loss1: 2.796031 Loss2: 1.422621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.143333 Loss1: 2.716776 Loss2: 1.426557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.121191 Loss1: 2.681742 Loss2: 1.439450 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.075377 Loss1: 2.602860 Loss2: 1.472517
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.078381 Loss1: 2.636771 Loss2: 1.441609
+(DefaultActor pid=3765) Epoch: 9 Loss: 4.004289 Loss1: 2.532668 Loss2: 1.471621
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.026907 Loss1: 2.576910 Loss2: 1.449997
+(DefaultActor pid=3765) >> Training accuracy: 0.343750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.981832 Loss1: 2.539768 Loss2: 1.442064
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.008627 Loss1: 2.558053 Loss2: 1.450573
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.856273 Loss1: 2.409462 Loss2: 1.446811
+(DefaultActor pid=3764) >> Training accuracy: 0.386458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.367664 Loss1: 3.377251 Loss2: 1.990413
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.500898 Loss1: 2.954961 Loss2: 1.545937
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.191266 Loss1: 2.701897 Loss2: 1.489369
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.127386 Loss1: 2.631882 Loss2: 1.495504
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.380781 Loss1: 3.331965 Loss2: 2.048817
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.087211 Loss1: 2.590858 Loss2: 1.496353
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.490001 Loss1: 2.937248 Loss2: 1.552753
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.056870 Loss1: 2.560254 Loss2: 1.496617
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.272333 Loss1: 2.751424 Loss2: 1.520909
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.975160 Loss1: 2.484798 Loss2: 1.490363
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.141141 Loss1: 2.613493 Loss2: 1.527648
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.981451 Loss1: 2.470401 Loss2: 1.511050
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.154469 Loss1: 2.623050 Loss2: 1.531419
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.914869 Loss1: 2.415170 Loss2: 1.499699
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.123149 Loss1: 2.577325 Loss2: 1.545824
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.804851 Loss1: 2.304000 Loss2: 1.500851
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.049681 Loss1: 2.507618 Loss2: 1.542063
+(DefaultActor pid=3765) >> Training accuracy: 0.378125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.999964 Loss1: 2.451998 Loss2: 1.547966
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.941061 Loss1: 2.395144 Loss2: 1.545917
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.940777 Loss1: 2.383409 Loss2: 1.557368
+(DefaultActor pid=3764) >> Training accuracy: 0.378125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.298840 Loss1: 3.360110 Loss2: 1.938730
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.390366 Loss1: 2.919792 Loss2: 1.470574
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.197941 Loss1: 2.745894 Loss2: 1.452047
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.110495 Loss1: 2.671149 Loss2: 1.439346
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.312244 Loss1: 3.241455 Loss2: 2.070789
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.301094 Loss1: 2.714547 Loss2: 1.586547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.172316 Loss1: 2.635907 Loss2: 1.536409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.029860 Loss1: 2.496012 Loss2: 1.533848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.997352 Loss1: 2.458586 Loss2: 1.538766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.953669 Loss1: 2.423385 Loss2: 1.530284 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.398958
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.785350 Loss1: 2.332011 Loss2: 1.453339
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.879664 Loss1: 2.330390 Loss2: 1.549275
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.823515 Loss1: 2.286671 Loss2: 1.536844
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.761688 Loss1: 2.210268 Loss2: 1.551420
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.710774 Loss1: 2.156834 Loss2: 1.553940
+(DefaultActor pid=3764) >> Training accuracy: 0.417708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.392356 Loss1: 3.474351 Loss2: 1.918005
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.390922 Loss1: 2.960472 Loss2: 1.430450
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.250108 Loss1: 2.851048 Loss2: 1.399060
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.155328 Loss1: 2.773300 Loss2: 1.382028
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.236581 Loss1: 3.313253 Loss2: 1.923327
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.505037 Loss1: 3.041815 Loss2: 1.463222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.206353 Loss1: 2.792531 Loss2: 1.413823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.076578 Loss1: 2.667360 Loss2: 1.409218 [repeated 2x across cluster]
+DEBUG flwr 2023-10-08 20:44:42,081 | server.py:236 | fit_round 14 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.974785 Loss1: 2.555707 Loss2: 1.419077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.946234 Loss1: 2.520259 Loss2: 1.425975 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.384375
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.835282 Loss1: 2.410015 Loss2: 1.425267
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.853742 Loss1: 2.424056 Loss2: 1.429686
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.896136 Loss1: 2.456617 Loss2: 1.439519
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.857308 Loss1: 2.413817 Loss2: 1.443491
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.823028 Loss1: 2.382625 Loss2: 1.440403
+(DefaultActor pid=3764) >> Training accuracy: 0.416667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.437683 Loss1: 3.458297 Loss2: 1.979386
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.616248 Loss1: 3.104179 Loss2: 1.512068
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.397432 Loss1: 2.913504 Loss2: 1.483928
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.246935 Loss1: 2.788239 Loss2: 1.458696
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.291682 Loss1: 3.235639 Loss2: 2.056043
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.347411 Loss1: 2.829718 Loss2: 1.517693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.114264 Loss1: 2.632089 Loss2: 1.482175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.017986 Loss1: 2.556583 Loss2: 1.461403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.962692 Loss1: 2.492597 Loss2: 1.470095 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.915401 Loss1: 2.449887 Loss2: 1.465515 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.342708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.846669 Loss1: 2.369976 Loss2: 1.476693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.870327 Loss1: 2.374729 Loss2: 1.495598 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.365625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.373791 Loss1: 3.495273 Loss2: 1.878518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.359940 Loss1: 2.947126 Loss2: 1.412814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.294998 Loss1: 2.880432 Loss2: 1.414566
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.389983 Loss1: 3.474328 Loss2: 1.915655
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.391852 Loss1: 2.941911 Loss2: 1.449941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.178444 Loss1: 2.770258 Loss2: 1.408187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.007076 Loss1: 2.617177 Loss2: 1.389899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.107600 Loss1: 2.672131 Loss2: 1.435468
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.018130 Loss1: 2.622340 Loss2: 1.395790
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.990381 Loss1: 2.545748 Loss2: 1.444632
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.930825 Loss1: 2.537107 Loss2: 1.393718
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.994259 Loss1: 2.543509 Loss2: 1.450749
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.897653 Loss1: 2.496723 Loss2: 1.400931
+(DefaultActor pid=3765) >> Training accuracy: 0.360352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.837082 Loss1: 2.432379 Loss2: 1.404703
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.793531 Loss1: 2.375483 Loss2: 1.418048
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.776108 Loss1: 2.367715 Loss2: 1.408393
+(DefaultActor pid=3764) >> Training accuracy: 0.369792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 20:44:42,081][flwr][DEBUG] - fit_round 14 received 50 results and 0 failures
+INFO flwr 2023-10-08 20:45:23,822 | server.py:125 | fit progress: (14, 3.694523496749683, {'accuracy': 0.1316}, 32031.600813974997)
+>> Test accuracy: 0.131600
+[2023-10-08 20:45:23,822][flwr][INFO] - fit progress: (14, 3.694523496749683, {'accuracy': 0.1316}, 32031.600813974997)
+DEBUG flwr 2023-10-08 20:45:23,823 | server.py:173 | evaluate_round 14: strategy sampled 50 clients (out of 50)
+[2023-10-08 20:45:23,823][flwr][DEBUG] - evaluate_round 14: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 20:54:30,870 | server.py:187 | evaluate_round 14 received 50 results and 0 failures
+[2023-10-08 20:54:30,870][flwr][DEBUG] - evaluate_round 14 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 20:54:30,871 | server.py:222 | fit_round 15: strategy sampled 50 clients (out of 50)
+[2023-10-08 20:54:30,871][flwr][DEBUG] - fit_round 15: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.332531 Loss1: 3.395482 Loss2: 1.937049
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.515927 Loss1: 3.040229 Loss2: 1.475698
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.277959 Loss1: 2.849290 Loss2: 1.428669
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.142993 Loss1: 2.705938 Loss2: 1.437056
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.141977 Loss1: 3.131861 Loss2: 2.010115
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.068556 Loss1: 2.639447 Loss2: 1.429109
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.296860 Loss1: 2.797893 Loss2: 1.498967
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.906008 Loss1: 2.472763 Loss2: 1.433246
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.035630 Loss1: 2.572822 Loss2: 1.462808
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.946380 Loss1: 2.492709 Loss2: 1.453670
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.900146 Loss1: 2.455720 Loss2: 1.444426
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.941273 Loss1: 2.486460 Loss2: 1.454812
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.829113 Loss1: 2.374357 Loss2: 1.454756
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.966295 Loss1: 2.502582 Loss2: 1.463713
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.803541 Loss1: 2.345890 Loss2: 1.457650
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.932843 Loss1: 2.458672 Loss2: 1.474171
+(DefaultActor pid=3765) >> Training accuracy: 0.344792
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.792812 Loss1: 2.334256 Loss2: 1.458556
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.706124 Loss1: 2.248535 Loss2: 1.457589
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.720101 Loss1: 2.254707 Loss2: 1.465394
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.636786 Loss1: 2.167554 Loss2: 1.469232
+(DefaultActor pid=3764) >> Training accuracy: 0.435417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.195003 Loss1: 3.260360 Loss2: 1.934643
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.346742 Loss1: 2.892650 Loss2: 1.454093
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.114602 Loss1: 2.665339 Loss2: 1.449263
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.973213 Loss1: 2.545911 Loss2: 1.427301
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.148642 Loss1: 3.238893 Loss2: 1.909749
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.294889 Loss1: 2.855832 Loss2: 1.439057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.076593 Loss1: 2.668394 Loss2: 1.408199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.961216 Loss1: 2.551396 Loss2: 1.409820 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.890235 Loss1: 2.482024 Loss2: 1.408211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.872102 Loss1: 2.456667 Loss2: 1.415434 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.409375
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.713751 Loss1: 2.239053 Loss2: 1.474698
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.835968 Loss1: 2.401429 Loss2: 1.434539
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.867407 Loss1: 2.431847 Loss2: 1.435559
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.782709 Loss1: 2.349064 Loss2: 1.433645
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.748081 Loss1: 2.298644 Loss2: 1.449438
+(DefaultActor pid=3764) >> Training accuracy: 0.396875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.226228 Loss1: 3.298784 Loss2: 1.927445
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.290773 Loss1: 2.848188 Loss2: 1.442585
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.104517 Loss1: 2.693189 Loss2: 1.411328
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.988373 Loss1: 2.584697 Loss2: 1.403675
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.472599 Loss1: 3.320378 Loss2: 2.152221
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.465917 Loss1: 2.934386 Loss2: 1.531531
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.941837 Loss1: 2.527786 Loss2: 1.414050
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.868513 Loss1: 2.462555 Loss2: 1.405958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.830277 Loss1: 2.419653 Loss2: 1.410624 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.790199 Loss1: 2.362918 Loss2: 1.427282 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.792789 Loss1: 2.303465 Loss2: 1.489324 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.864810 Loss1: 2.373493 Loss2: 1.491317 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.401042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.716819 Loss1: 2.204181 Loss2: 1.512638 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.440104
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.267665 Loss1: 3.230600 Loss2: 2.037066
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.190132 Loss1: 2.647112 Loss2: 1.543019
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.966296 Loss1: 2.467290 Loss2: 1.499007
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.817482 Loss1: 2.328301 Loss2: 1.489181
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.440281 Loss1: 3.374136 Loss2: 2.066146
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.494524 Loss1: 2.950957 Loss2: 1.543567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.248921 Loss1: 2.726095 Loss2: 1.522826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.121283 Loss1: 2.618003 Loss2: 1.503280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.086268 Loss1: 2.574411 Loss2: 1.511857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.026721 Loss1: 2.520826 Loss2: 1.505895 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.428125
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.584403 Loss1: 2.074238 Loss2: 1.510164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.977728 Loss1: 2.449868 Loss2: 1.527860
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.975525 Loss1: 2.452410 Loss2: 1.523114
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.860634 Loss1: 2.338265 Loss2: 1.522369
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.841161 Loss1: 2.299805 Loss2: 1.541356
+(DefaultActor pid=3764) >> Training accuracy: 0.386458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.353877 Loss1: 3.317719 Loss2: 2.036158
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.477921 Loss1: 2.969465 Loss2: 1.508456
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.272947 Loss1: 2.783802 Loss2: 1.489145
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.160458 Loss1: 2.675678 Loss2: 1.484780
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.569891 Loss1: 3.549888 Loss2: 2.020003
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.553440 Loss1: 3.060539 Loss2: 1.492901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.347787 Loss1: 2.890981 Loss2: 1.456806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.023646 Loss1: 2.520427 Loss2: 1.503218
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.187505 Loss1: 2.735739 Loss2: 1.451766
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.969634 Loss1: 2.468743 Loss2: 1.500892
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.118494 Loss1: 2.662383 Loss2: 1.456111
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.897016 Loss1: 2.386809 Loss2: 1.510208
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.140608 Loss1: 2.647115 Loss2: 1.493493
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.035020 Loss1: 2.552067 Loss2: 1.482954
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.849114 Loss1: 2.330599 Loss2: 1.518515
+(DefaultActor pid=3765) >> Training accuracy: 0.365625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.914701 Loss1: 2.420262 Loss2: 1.494439 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.372768
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.081463 Loss1: 3.171251 Loss2: 1.910211 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.030826 Loss1: 2.626968 Loss2: 1.403858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.923758 Loss1: 2.519737 Loss2: 1.404021
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.389831 Loss1: 3.343828 Loss2: 2.046004
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.862823 Loss1: 2.437782 Loss2: 1.425042
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.456632 Loss1: 2.935989 Loss2: 1.520643
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.914828 Loss1: 2.487850 Loss2: 1.426977
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.234389 Loss1: 2.735639 Loss2: 1.498750
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.869144 Loss1: 2.447161 Loss2: 1.421982
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.128266 Loss1: 2.631999 Loss2: 1.496267
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.681658 Loss1: 2.254923 Loss2: 1.426735
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.060857 Loss1: 2.556213 Loss2: 1.504644
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.703804 Loss1: 2.277837 Loss2: 1.425967
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.024848 Loss1: 2.518689 Loss2: 1.506160
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.588415 Loss1: 2.155497 Loss2: 1.432918
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.974153 Loss1: 2.462086 Loss2: 1.512066
+(DefaultActor pid=3765) >> Training accuracy: 0.419792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.955772 Loss1: 2.433468 Loss2: 1.522304
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.899696 Loss1: 2.366990 Loss2: 1.532707
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.887822 Loss1: 2.350835 Loss2: 1.536988
+(DefaultActor pid=3764) >> Training accuracy: 0.368750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.612262 Loss1: 3.541078 Loss2: 2.071184
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.599471 Loss1: 3.061353 Loss2: 1.538118
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.457392 Loss1: 2.942410 Loss2: 1.514983
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.316879 Loss1: 2.808819 Loss2: 1.508060
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.181022 Loss1: 3.232354 Loss2: 1.948668
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.324613 Loss1: 2.861462 Loss2: 1.463151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.182315 Loss1: 2.745857 Loss2: 1.436457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.098275 Loss1: 2.667363 Loss2: 1.430912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.022476 Loss1: 2.589195 Loss2: 1.433281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.934842 Loss1: 2.492183 Loss2: 1.442659 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.340402
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.811367 Loss1: 2.365817 Loss2: 1.445550 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.775967 Loss1: 2.312801 Loss2: 1.463166 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.382292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.263904 Loss1: 2.748940 Loss2: 1.514965 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.967542 Loss1: 2.489538 Loss2: 1.478004 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.862933 Loss1: 2.374101 Loss2: 1.488832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.055012 Loss1: 2.508645 Loss2: 1.546367 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.985681 Loss1: 2.456528 Loss2: 1.529153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.821795 Loss1: 2.294376 Loss2: 1.527420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.817223 Loss1: 2.281537 Loss2: 1.535685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.801956 Loss1: 2.263998 Loss2: 1.537958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.686382 Loss1: 2.175883 Loss2: 1.510499
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.695013 Loss1: 2.157133 Loss2: 1.537880
+(DefaultActor pid=3765) >> Training accuracy: 0.386719
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.647796 Loss1: 2.108128 Loss2: 1.539668
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.574187 Loss1: 2.017205 Loss2: 1.556982
+(DefaultActor pid=3764) >> Training accuracy: 0.436298
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.347582 Loss1: 3.282245 Loss2: 2.065337
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.371563 Loss1: 2.809689 Loss2: 1.561874
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.112410 Loss1: 2.585268 Loss2: 1.527142
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.033364 Loss1: 2.521352 Loss2: 1.512012
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.053573 Loss1: 2.541438 Loss2: 1.512136 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.958301 Loss1: 2.431235 Loss2: 1.527066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.896830 Loss1: 2.362520 Loss2: 1.534310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.869404 Loss1: 2.342840 Loss2: 1.526564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.790061 Loss1: 2.258330 Loss2: 1.531730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.981583 Loss1: 2.562610 Loss2: 1.418973
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.785275 Loss1: 2.236497 Loss2: 1.548778
+(DefaultActor pid=3765) >> Training accuracy: 0.435268
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.963668 Loss1: 2.529757 Loss2: 1.433910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.863985 Loss1: 2.425809 Loss2: 1.438175 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.366667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.340187 Loss1: 2.776756 Loss2: 1.563430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.057123 Loss1: 2.506010 Loss2: 1.551114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.876148 Loss1: 2.340704 Loss2: 1.535444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.866076 Loss1: 2.317676 Loss2: 1.548399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.822647 Loss1: 2.272859 Loss2: 1.549788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.784142 Loss1: 2.228466 Loss2: 1.555675 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.761812 Loss1: 2.189462 Loss2: 1.572351 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.697308 Loss1: 2.137147 Loss2: 1.560161 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.419792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.908963 Loss1: 2.433484 Loss2: 1.475479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.816237 Loss1: 2.331105 Loss2: 1.485132 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.391667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.119493 Loss1: 3.282285 Loss2: 1.837209
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.230348 Loss1: 2.834874 Loss2: 1.395473
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.025008 Loss1: 2.649370 Loss2: 1.375638
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.916683 Loss1: 2.555121 Loss2: 1.361561
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.347308 Loss1: 3.349542 Loss2: 1.997766
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.426601 Loss1: 2.907656 Loss2: 1.518945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.226147 Loss1: 2.732440 Loss2: 1.493707 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.093730 Loss1: 2.618151 Loss2: 1.475579 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.001958 Loss1: 2.522540 Loss2: 1.479418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.948476 Loss1: 2.450547 Loss2: 1.497929 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.396484
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.939633 Loss1: 2.440457 Loss2: 1.499175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.903048 Loss1: 2.404303 Loss2: 1.498745 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.379883
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.310540 Loss1: 3.330612 Loss2: 1.979928 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.243604 Loss1: 2.762186 Loss2: 1.481419 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.188517 Loss1: 2.716744 Loss2: 1.471773
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.237429 Loss1: 3.270517 Loss2: 1.966912
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.298796 Loss1: 2.824382 Loss2: 1.474415 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.100180 Loss1: 2.653163 Loss2: 1.447018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.010634 Loss1: 2.566618 Loss2: 1.444016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.944154 Loss1: 2.491093 Loss2: 1.453061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.825010 Loss1: 2.316951 Loss2: 1.508059
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.867440 Loss1: 2.416678 Loss2: 1.450762
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.829739 Loss1: 2.305829 Loss2: 1.523909
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.872145 Loss1: 2.405686 Loss2: 1.466458
+(DefaultActor pid=3765) >> Training accuracy: 0.387695
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.831876 Loss1: 2.363875 Loss2: 1.468001
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.714740 Loss1: 2.245619 Loss2: 1.469121
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.655439 Loss1: 2.181768 Loss2: 1.473671
+(DefaultActor pid=3764) >> Training accuracy: 0.391667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.404466 Loss1: 3.399908 Loss2: 2.004558
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.606980 Loss1: 3.086835 Loss2: 1.520145
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.427197 Loss1: 2.913328 Loss2: 1.513869
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.386419 Loss1: 3.397363 Loss2: 1.989056
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.309566 Loss1: 2.795488 Loss2: 1.514079
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.504309 Loss1: 3.007384 Loss2: 1.496925
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.229585 Loss1: 2.713350 Loss2: 1.516236
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.343116 Loss1: 2.868680 Loss2: 1.474436
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.222063 Loss1: 2.698181 Loss2: 1.523882
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.204592 Loss1: 2.732768 Loss2: 1.471825
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.214345 Loss1: 2.663073 Loss2: 1.551272
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.215860 Loss1: 2.729811 Loss2: 1.486048
+(DefaultActor pid=3765) Epoch: 7 Loss: 4.167460 Loss1: 2.630650 Loss2: 1.536811
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.067883 Loss1: 2.584708 Loss2: 1.483175
+(DefaultActor pid=3765) Epoch: 8 Loss: 4.089576 Loss1: 2.551676 Loss2: 1.537900
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.012532 Loss1: 2.518755 Loss2: 1.493777
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.996384 Loss1: 2.461837 Loss2: 1.534546
+(DefaultActor pid=3765) >> Training accuracy: 0.325195
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 4.052939 Loss1: 2.548917 Loss2: 1.504023 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.342773
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.412603 Loss1: 3.449291 Loss2: 1.963312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.138442 Loss1: 2.692382 Loss2: 1.446060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.016430 Loss1: 2.573467 Loss2: 1.442963
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.325592 Loss1: 3.363364 Loss2: 1.962228
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.930655 Loss1: 2.480394 Loss2: 1.450262
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.456190 Loss1: 2.957311 Loss2: 1.498879
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.842055 Loss1: 2.381939 Loss2: 1.460116
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.240613 Loss1: 2.782668 Loss2: 1.457946
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.845629 Loss1: 2.389417 Loss2: 1.456212
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.077876 Loss1: 2.621414 Loss2: 1.456462
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.794621 Loss1: 2.325574 Loss2: 1.469047
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.020142 Loss1: 2.557670 Loss2: 1.462472
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.687461 Loss1: 2.216619 Loss2: 1.470842
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.962225 Loss1: 2.494086 Loss2: 1.468138
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.691753 Loss1: 2.195465 Loss2: 1.496287
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.900475 Loss1: 2.433499 Loss2: 1.466976
+(DefaultActor pid=3765) >> Training accuracy: 0.417708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.921774 Loss1: 2.451244 Loss2: 1.470531
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.822681 Loss1: 2.342246 Loss2: 1.480435
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.861020 Loss1: 2.374909 Loss2: 1.486111
+(DefaultActor pid=3764) >> Training accuracy: 0.360417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.160991 Loss1: 3.274547 Loss2: 1.886445
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.173961 Loss1: 2.749190 Loss2: 1.424771
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.024072 Loss1: 2.629495 Loss2: 1.394577
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.906185 Loss1: 2.515885 Loss2: 1.390300
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.265044 Loss1: 3.298498 Loss2: 1.966546
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.816827 Loss1: 2.421137 Loss2: 1.395690
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.411728 Loss1: 2.919543 Loss2: 1.492185
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.765383 Loss1: 2.365315 Loss2: 1.400068
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.168062 Loss1: 2.715275 Loss2: 1.452787
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.775915 Loss1: 2.353223 Loss2: 1.422692
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.055048 Loss1: 2.597526 Loss2: 1.457522
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.743001 Loss1: 2.317699 Loss2: 1.425302
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.961018 Loss1: 2.500332 Loss2: 1.460686
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.617504 Loss1: 2.191248 Loss2: 1.426257
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.957287 Loss1: 2.496413 Loss2: 1.460874
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.626953 Loss1: 2.202021 Loss2: 1.424932
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.919896 Loss1: 2.451959 Loss2: 1.467937
+(DefaultActor pid=3765) >> Training accuracy: 0.454167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.888145 Loss1: 2.401593 Loss2: 1.486552
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.886034 Loss1: 2.394123 Loss2: 1.491911
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.782775 Loss1: 2.306241 Loss2: 1.476534
+(DefaultActor pid=3764) >> Training accuracy: 0.394792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.136901 Loss1: 3.242877 Loss2: 1.894024
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.054006 Loss1: 2.643141 Loss2: 1.410865
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.828380 Loss1: 2.447662 Loss2: 1.380719
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.792489 Loss1: 2.402688 Loss2: 1.389800
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.263560 Loss1: 3.318403 Loss2: 1.945157
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.717434 Loss1: 2.319881 Loss2: 1.397553
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.414610 Loss1: 2.941188 Loss2: 1.473422
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.666126 Loss1: 2.272832 Loss2: 1.393294
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.244223 Loss1: 2.793383 Loss2: 1.450840
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.670075 Loss1: 2.262609 Loss2: 1.407465
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.125079 Loss1: 2.675464 Loss2: 1.449615
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.577906 Loss1: 2.174429 Loss2: 1.403477
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.035091 Loss1: 2.587134 Loss2: 1.447957
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.550676 Loss1: 2.127837 Loss2: 1.422839
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.062766 Loss1: 2.599258 Loss2: 1.463508
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.533740 Loss1: 2.106869 Loss2: 1.426871
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.972912 Loss1: 2.507485 Loss2: 1.465427
+(DefaultActor pid=3765) >> Training accuracy: 0.418750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.895595 Loss1: 2.436905 Loss2: 1.458690
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.949902 Loss1: 2.475400 Loss2: 1.474502
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.850547 Loss1: 2.382443 Loss2: 1.468104
+(DefaultActor pid=3764) >> Training accuracy: 0.395833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.188350 Loss1: 3.243060 Loss2: 1.945290
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.346755 Loss1: 2.871256 Loss2: 1.475498
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.100989 Loss1: 2.651947 Loss2: 1.449042
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.942062 Loss1: 2.504592 Loss2: 1.437470
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.228406 Loss1: 3.244127 Loss2: 1.984279
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.940038 Loss1: 2.500145 Loss2: 1.439893
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.407892 Loss1: 2.934433 Loss2: 1.473459
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.852401 Loss1: 2.393978 Loss2: 1.458422
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.179729 Loss1: 2.722571 Loss2: 1.457159
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.914542 Loss1: 2.443712 Loss2: 1.470830
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.042653 Loss1: 2.593422 Loss2: 1.449231
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.850377 Loss1: 2.378775 Loss2: 1.471603
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.045085 Loss1: 2.585391 Loss2: 1.459694
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.781234 Loss1: 2.302460 Loss2: 1.478774
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.868267 Loss1: 2.424339 Loss2: 1.443928
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.753415 Loss1: 2.266629 Loss2: 1.486787
+(DefaultActor pid=3765) >> Training accuracy: 0.451042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.816344 Loss1: 2.340202 Loss2: 1.476142
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.782069 Loss1: 2.306309 Loss2: 1.475761
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.780718 Loss1: 2.296894 Loss2: 1.483823
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.784842 Loss1: 2.301446 Loss2: 1.483396
+(DefaultActor pid=3764) >> Training accuracy: 0.417708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.304189 Loss1: 3.322428 Loss2: 1.981761
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.341544 Loss1: 2.903729 Loss2: 1.437815
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.166713 Loss1: 2.744473 Loss2: 1.422241
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.049673 Loss1: 2.642768 Loss2: 1.406905
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.189463 Loss1: 3.205032 Loss2: 1.984431
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.135117 Loss1: 2.652705 Loss2: 1.482412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.859252 Loss1: 2.417510 Loss2: 1.441742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.821688 Loss1: 2.392948 Loss2: 1.428740 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.751074 Loss1: 2.306960 Loss2: 1.444114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.796896 Loss1: 2.354842 Loss2: 1.442054 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.387019
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.688476 Loss1: 2.211814 Loss2: 1.476662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.639010 Loss1: 2.162113 Loss2: 1.476897 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.447917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.710062 Loss1: 2.205883 Loss2: 1.504180
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.012079 Loss1: 3.050673 Loss2: 1.961405
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.108827 Loss1: 2.634779 Loss2: 1.474048
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.897591 Loss1: 2.461005 Loss2: 1.436586
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.873953 Loss1: 2.451445 Loss2: 1.422508
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.745778 Loss1: 2.312394 Loss2: 1.433384
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.379466 Loss1: 3.415865 Loss2: 1.963601
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.491808 Loss1: 3.017900 Loss2: 1.473908 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.351915 Loss1: 2.905568 Loss2: 1.446348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.197195 Loss1: 2.750452 Loss2: 1.446743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.129378 Loss1: 2.677176 Loss2: 1.452202 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.430208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.130805 Loss1: 2.669569 Loss2: 1.461236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.061822 Loss1: 2.587078 Loss2: 1.474744 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.976563 Loss1: 2.483316 Loss2: 1.493247 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.342708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.190376 Loss1: 2.703097 Loss2: 1.487279 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.900286 Loss1: 2.448029 Loss2: 1.452257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.846434 Loss1: 2.385233 Loss2: 1.461201
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.166511 Loss1: 3.155253 Loss2: 2.011258
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.360726 Loss1: 2.829933 Loss2: 1.530793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.750745 Loss1: 2.288811 Loss2: 1.461934
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.144107 Loss1: 2.643304 Loss2: 1.500803
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.626729 Loss1: 2.154486 Loss2: 1.472243
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.026279 Loss1: 2.520379 Loss2: 1.505901
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.614289 Loss1: 2.135383 Loss2: 1.478906
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.026959 Loss1: 2.516283 Loss2: 1.510676
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.937010 Loss1: 2.436243 Loss2: 1.500766
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.598007 Loss1: 2.116972 Loss2: 1.481035
+(DefaultActor pid=3765) >> Training accuracy: 0.443359
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.892123 Loss1: 2.355630 Loss2: 1.536492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.780261 Loss1: 2.256330 Loss2: 1.523931 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.377083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.467248 Loss1: 2.987914 Loss2: 1.479334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.033659 Loss1: 2.596914 Loss2: 1.436745 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.981110 Loss1: 2.531017 Loss2: 1.450094
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.248870 Loss1: 3.172794 Loss2: 2.076076
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.971665 Loss1: 2.509023 Loss2: 1.462641
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.243293 Loss1: 2.703819 Loss2: 1.539474
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.915754 Loss1: 2.447311 Loss2: 1.468443
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.033893 Loss1: 2.536558 Loss2: 1.497334
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.827353 Loss1: 2.357672 Loss2: 1.469682
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.964334 Loss1: 2.471512 Loss2: 1.492822
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.765828 Loss1: 2.304401 Loss2: 1.461428
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.867692 Loss1: 2.369927 Loss2: 1.497765
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.780868 Loss1: 2.294242 Loss2: 1.486626
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.812120 Loss1: 2.305681 Loss2: 1.506439
+(DefaultActor pid=3765) >> Training accuracy: 0.400000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.800287 Loss1: 2.287480 Loss2: 1.512807
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.819807 Loss1: 2.302504 Loss2: 1.517304
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.799092 Loss1: 2.281380 Loss2: 1.517711
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.717519 Loss1: 2.199971 Loss2: 1.517548
+(DefaultActor pid=3764) >> Training accuracy: 0.435417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.553380 Loss1: 3.528462 Loss2: 2.024918
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.538916 Loss1: 3.003147 Loss2: 1.535769
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.371720 Loss1: 2.868600 Loss2: 1.503120
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.194902 Loss1: 2.702367 Loss2: 1.492535
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.228300 Loss1: 2.699581 Loss2: 1.528719
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.261996 Loss1: 3.378879 Loss2: 1.883117
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.396598 Loss1: 2.968346 Loss2: 1.428252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.236097 Loss1: 2.812708 Loss2: 1.423389 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.124264 Loss1: 2.703086 Loss2: 1.421178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.002423 Loss1: 2.572747 Loss2: 1.429676 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.405208
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.963067 Loss1: 2.428518 Loss2: 1.534548
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-08 21:23:35,803 | server.py:236 | fit_round 15 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.986424 Loss1: 2.548728 Loss2: 1.437697
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.967346 Loss1: 2.519278 Loss2: 1.448068
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.977714 Loss1: 2.524790 Loss2: 1.452924
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.946559 Loss1: 2.487100 Loss2: 1.459459
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.878918 Loss1: 2.418292 Loss2: 1.460626
+(DefaultActor pid=3764) >> Training accuracy: 0.383333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.078732 Loss1: 3.116748 Loss2: 1.961985
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.158943 Loss1: 2.681449 Loss2: 1.477494
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.958710 Loss1: 2.504565 Loss2: 1.454146
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.804463 Loss1: 2.364228 Loss2: 1.440236
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.783919 Loss1: 2.327440 Loss2: 1.456480
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.384942 Loss1: 3.433440 Loss2: 1.951502
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.517136 Loss1: 3.031913 Loss2: 1.485222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.313382 Loss1: 2.867591 Loss2: 1.445791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.235708 Loss1: 2.777601 Loss2: 1.458108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.153192 Loss1: 2.693195 Loss2: 1.459997 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.405208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.064535 Loss1: 2.614061 Loss2: 1.450474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 4.015063 Loss1: 2.541820 Loss2: 1.473243 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.877820 Loss1: 2.391629 Loss2: 1.486191 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.332031
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.444838 Loss1: 2.919762 Loss2: 1.525077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.081691 Loss1: 2.577804 Loss2: 1.503887 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.009714 Loss1: 2.499116 Loss2: 1.510598
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.223780 Loss1: 3.166488 Loss2: 2.057292
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.014257 Loss1: 2.499218 Loss2: 1.515039
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.369595 Loss1: 2.828178 Loss2: 1.541417
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.153545 Loss1: 2.627231 Loss2: 1.526314
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.936418 Loss1: 2.406434 Loss2: 1.529984
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.057430 Loss1: 2.532420 Loss2: 1.525010
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.893539 Loss1: 2.367926 Loss2: 1.525613
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.873328 Loss1: 2.348530 Loss2: 1.524798 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.825495 Loss1: 2.300309 Loss2: 1.525186 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.403493
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.903878 Loss1: 2.342502 Loss2: 1.561376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.755262 Loss1: 2.201878 Loss2: 1.553383 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.410417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.449481 Loss1: 3.526274 Loss2: 1.923207
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.437615 Loss1: 2.961255 Loss2: 1.476360
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.206920 Loss1: 2.767907 Loss2: 1.439013
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.157767 Loss1: 2.725804 Loss2: 1.431963
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.375661 Loss1: 3.370970 Loss2: 2.004691
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.400005 Loss1: 2.880017 Loss2: 1.519988 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.022685 Loss1: 2.571504 Loss2: 1.451181
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.230140 Loss1: 2.750374 Loss2: 1.479766
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.965191 Loss1: 2.510388 Loss2: 1.454802
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.088025 Loss1: 2.617854 Loss2: 1.470170
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.881807 Loss1: 2.418497 Loss2: 1.463311
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.102647 Loss1: 2.613063 Loss2: 1.489584
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.017916 Loss1: 2.522647 Loss2: 1.495269
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.837146 Loss1: 2.366204 Loss2: 1.470941
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.988104 Loss1: 2.489461 Loss2: 1.498643
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.900182 Loss1: 2.424519 Loss2: 1.475662
+(DefaultActor pid=3765) >> Training accuracy: 0.410156
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.937659 Loss1: 2.409587 Loss2: 1.528073 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.342708
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 21:23:35,803][flwr][DEBUG] - fit_round 15 received 50 results and 0 failures
+INFO flwr 2023-10-08 21:24:16,658 | server.py:125 | fit progress: (15, 3.606255607483105, {'accuracy': 0.1469}, 34364.437004246)
+>> Test accuracy: 0.146900
+[2023-10-08 21:24:16,658][flwr][INFO] - fit progress: (15, 3.606255607483105, {'accuracy': 0.1469}, 34364.437004246)
+DEBUG flwr 2023-10-08 21:24:16,659 | server.py:173 | evaluate_round 15: strategy sampled 50 clients (out of 50)
+[2023-10-08 21:24:16,659][flwr][DEBUG] - evaluate_round 15: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 21:33:17,637 | server.py:187 | evaluate_round 15 received 50 results and 0 failures
+[2023-10-08 21:33:17,637][flwr][DEBUG] - evaluate_round 15 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 21:33:17,637 | server.py:222 | fit_round 16: strategy sampled 50 clients (out of 50)
+[2023-10-08 21:33:17,637][flwr][DEBUG] - fit_round 16: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.262776 Loss1: 3.236910 Loss2: 2.025866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.097632 Loss1: 2.587082 Loss2: 1.510550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.994579 Loss1: 2.495561 Loss2: 1.499018
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.022405 Loss1: 3.130563 Loss2: 1.891842
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.048157 Loss1: 2.624573 Loss2: 1.423584
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.912171 Loss1: 2.409911 Loss2: 1.502260
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.826442 Loss1: 2.449197 Loss2: 1.377245
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.830994 Loss1: 2.309676 Loss2: 1.521318
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.820008 Loss1: 2.283608 Loss2: 1.536400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.707270 Loss1: 2.186594 Loss2: 1.520676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.694082 Loss1: 2.157879 Loss2: 1.536203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.778284 Loss1: 2.224630 Loss2: 1.553653 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.455208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.314564 Loss1: 1.908254 Loss2: 1.406309 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.484375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.188935 Loss1: 3.179953 Loss2: 2.008982
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.222028 Loss1: 2.750287 Loss2: 1.471741
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.022886 Loss1: 2.575868 Loss2: 1.447018
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.906828 Loss1: 2.474105 Loss2: 1.432724
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.097709 Loss1: 3.038766 Loss2: 2.058943
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.170564 Loss1: 2.608036 Loss2: 1.562528 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.985346 Loss1: 2.455650 Loss2: 1.529696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.806549 Loss1: 2.332043 Loss2: 1.474506 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.639505 Loss1: 2.180375 Loss2: 1.459130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.604936 Loss1: 2.139882 Loss2: 1.465054 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.401786
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.768441 Loss1: 2.221958 Loss2: 1.546483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.588420 Loss1: 2.040724 Loss2: 1.547696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.459761 Loss1: 1.918750 Loss2: 1.541012
+(DefaultActor pid=3764) >> Training accuracy: 0.487305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.115712 Loss1: 3.168729 Loss2: 1.946983
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.338239 Loss1: 2.838995 Loss2: 1.499244
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.110972 Loss1: 2.633709 Loss2: 1.477263
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.008589 Loss1: 2.539311 Loss2: 1.469278
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.877150 Loss1: 2.407138 Loss2: 1.470012
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.175311 Loss1: 3.202042 Loss2: 1.973269
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.299817 Loss1: 2.798070 Loss2: 1.501747 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.127190 Loss1: 2.652360 Loss2: 1.474830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.951832 Loss1: 2.483782 Loss2: 1.468050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.874043 Loss1: 2.388470 Loss2: 1.485573 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.428125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.782671 Loss1: 2.310432 Loss2: 1.472239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.706764 Loss1: 2.205771 Loss2: 1.500993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.659862 Loss1: 2.150835 Loss2: 1.509027 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.416667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.474672 Loss1: 2.977870 Loss2: 1.496802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.155078 Loss1: 2.696398 Loss2: 1.458680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.114682 Loss1: 2.646191 Loss2: 1.468491
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.341729 Loss1: 3.353589 Loss2: 1.988140
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.451334 Loss1: 2.949067 Loss2: 1.502266 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.227510 Loss1: 2.752046 Loss2: 1.475464 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.862201 Loss1: 2.383553 Loss2: 1.478648
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.175793 Loss1: 2.690802 Loss2: 1.484990
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.810868 Loss1: 2.318049 Loss2: 1.492819
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.068574 Loss1: 2.577178 Loss2: 1.491396
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.834997 Loss1: 2.333659 Loss2: 1.501338
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.057460 Loss1: 2.557947 Loss2: 1.499513
+(DefaultActor pid=3765) >> Training accuracy: 0.400391
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.042672 Loss1: 2.528841 Loss2: 1.513831
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.958121 Loss1: 2.452793 Loss2: 1.505328
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.957580 Loss1: 2.445128 Loss2: 1.512452
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.909606 Loss1: 2.383246 Loss2: 1.526360
+(DefaultActor pid=3764) >> Training accuracy: 0.354167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.137123 Loss1: 3.099210 Loss2: 2.037912
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.237758 Loss1: 2.689985 Loss2: 1.547773
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.098115 Loss1: 2.569541 Loss2: 1.528574
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.961480 Loss1: 2.449070 Loss2: 1.512410
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.159061 Loss1: 3.161979 Loss2: 1.997083
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.269545 Loss1: 2.753893 Loss2: 1.515652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.073976 Loss1: 2.588341 Loss2: 1.485635 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.882678 Loss1: 2.408835 Loss2: 1.473842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.862937 Loss1: 2.381673 Loss2: 1.481264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.820318 Loss1: 2.325236 Loss2: 1.495082 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.431250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.654521 Loss1: 2.159007 Loss2: 1.495514 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.679269 Loss1: 2.149868 Loss2: 1.529400 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.430208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.200308 Loss1: 2.728466 Loss2: 1.471842 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.788585 Loss1: 2.350093 Loss2: 1.438492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.361894 Loss1: 3.391775 Loss2: 1.970119
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.778594 Loss1: 2.340884 Loss2: 1.437709
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.458238 Loss1: 2.952916 Loss2: 1.505321
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.632379 Loss1: 2.200291 Loss2: 1.432089
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.618773 Loss1: 2.176407 Loss2: 1.442365
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.264581 Loss1: 2.783425 Loss2: 1.481156
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.518427 Loss1: 2.068054 Loss2: 1.450372
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.149427 Loss1: 2.666592 Loss2: 1.482835
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.601704 Loss1: 2.141921 Loss2: 1.459783
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.056528 Loss1: 2.567693 Loss2: 1.488836
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.557782 Loss1: 2.105422 Loss2: 1.452360
+(DefaultActor pid=3765) >> Training accuracy: 0.457292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.031480 Loss1: 2.540746 Loss2: 1.490734
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.988334 Loss1: 2.498076 Loss2: 1.490258
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.885222 Loss1: 2.384303 Loss2: 1.500919
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.921762 Loss1: 2.405605 Loss2: 1.516157
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.838848 Loss1: 2.310106 Loss2: 1.528742
+(DefaultActor pid=3764) >> Training accuracy: 0.388672
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.287879 Loss1: 3.301108 Loss2: 1.986771
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.355565 Loss1: 2.845472 Loss2: 1.510093
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.147597 Loss1: 2.670681 Loss2: 1.476916
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.026292 Loss1: 2.545286 Loss2: 1.481006
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.984409 Loss1: 2.494396 Loss2: 1.490014
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.108943 Loss1: 3.166898 Loss2: 1.942045
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.289868 Loss1: 2.788109 Loss2: 1.501759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.062031 Loss1: 2.619608 Loss2: 1.442423 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.936742 Loss1: 2.491684 Loss2: 1.445057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.841780 Loss1: 2.382003 Loss2: 1.459778 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.450000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.776838 Loss1: 2.327232 Loss2: 1.449606 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.683286 Loss1: 2.210498 Loss2: 1.472788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.157245 Loss1: 3.177703 Loss2: 1.979542
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.615173 Loss1: 2.143691 Loss2: 1.471481
+(DefaultActor pid=3764) >> Training accuracy: 0.437500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.955879 Loss1: 2.498332 Loss2: 1.457548 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.857034 Loss1: 2.379264 Loss2: 1.477770 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.835179 Loss1: 2.352263 Loss2: 1.482917
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.170973 Loss1: 3.134119 Loss2: 2.036854
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.306007 Loss1: 2.761659 Loss2: 1.544348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.082874 Loss1: 2.571775 Loss2: 1.511099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.886027 Loss1: 2.377239 Loss2: 1.508788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.416667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.844815 Loss1: 2.339164 Loss2: 1.505651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.736126 Loss1: 2.202087 Loss2: 1.534039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.678254 Loss1: 2.141263 Loss2: 1.536991 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.646282 Loss1: 2.099490 Loss2: 1.546792 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.426042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.873498 Loss1: 2.447085 Loss2: 1.426414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.601567 Loss1: 2.188023 Loss2: 1.413544 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.528105 Loss1: 2.103971 Loss2: 1.424134
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.535009 Loss1: 3.473225 Loss2: 2.061783
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.581278 Loss1: 3.049237 Loss2: 1.532041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.308637 Loss1: 2.821896 Loss2: 1.486741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.106037 Loss1: 2.627678 Loss2: 1.478360 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.493750
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.453070 Loss1: 2.015550 Loss2: 1.437520
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.031878 Loss1: 2.539673 Loss2: 1.492205
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.063159 Loss1: 2.560951 Loss2: 1.502208
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.037526 Loss1: 2.533607 Loss2: 1.503919
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.934791 Loss1: 2.414526 Loss2: 1.520266
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.827114 Loss1: 2.305301 Loss2: 1.521813
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.815607 Loss1: 2.286938 Loss2: 1.528669
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.331084 Loss1: 3.304396 Loss2: 2.026688
+(DefaultActor pid=3764) >> Training accuracy: 0.385045
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.415464 Loss1: 2.861206 Loss2: 1.554257
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.098713 Loss1: 2.570483 Loss2: 1.528231
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.968938 Loss1: 2.466912 Loss2: 1.502026
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.001955 Loss1: 2.484624 Loss2: 1.517331
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.315318 Loss1: 3.186873 Loss2: 2.128444
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.836799 Loss1: 2.319309 Loss2: 1.517490
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.788210 Loss1: 2.274912 Loss2: 1.513298 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.739939 Loss1: 2.207595 Loss2: 1.532343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.707580 Loss1: 2.171650 Loss2: 1.535931 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.675055 Loss1: 2.142107 Loss2: 1.532948 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.417708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.799452 Loss1: 2.214640 Loss2: 1.584812 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.757572 Loss1: 2.181742 Loss2: 1.575830 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.411058
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.184347 Loss1: 2.622625 Loss2: 1.561722 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.863670 Loss1: 2.362574 Loss2: 1.501096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.815326 Loss1: 2.315444 Loss2: 1.499882 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.109266 Loss1: 2.696926 Loss2: 1.412340
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.787838 Loss1: 2.264051 Loss2: 1.523788
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.892140 Loss1: 2.509390 Loss2: 1.382750
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.739279 Loss1: 2.219741 Loss2: 1.519538
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.773005 Loss1: 2.401516 Loss2: 1.371489
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.649670 Loss1: 2.130872 Loss2: 1.518798
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.604372 Loss1: 2.087236 Loss2: 1.517136
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.731608 Loss1: 2.348731 Loss2: 1.382878
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.567451 Loss1: 2.040346 Loss2: 1.527106
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.615143 Loss1: 2.229107 Loss2: 1.386035
+(DefaultActor pid=3765) >> Training accuracy: 0.436458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.654705 Loss1: 2.265391 Loss2: 1.389313
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.626026 Loss1: 2.225561 Loss2: 1.400465
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.595687 Loss1: 2.186532 Loss2: 1.409155
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.491667 Loss1: 2.083055 Loss2: 1.408613
+(DefaultActor pid=3764) >> Training accuracy: 0.432617
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.405519 Loss1: 3.313698 Loss2: 2.091820
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.508036 Loss1: 2.930015 Loss2: 1.578021
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.305339 Loss1: 2.756626 Loss2: 1.548714
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.226906 Loss1: 2.671154 Loss2: 1.555752
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.192765 Loss1: 2.632696 Loss2: 1.560069
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.397818 Loss1: 3.382960 Loss2: 2.014858
+(DefaultActor pid=3765) Epoch: 5 Loss: 4.127967 Loss1: 2.556497 Loss2: 1.571471
+(DefaultActor pid=3765) Epoch: 6 Loss: 4.034217 Loss1: 2.469820 Loss2: 1.564397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.975956 Loss1: 2.395431 Loss2: 1.580525 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.195649 Loss1: 2.674749 Loss2: 1.520901
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.912817 Loss1: 2.335511 Loss2: 1.577307
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.079079 Loss1: 2.555857 Loss2: 1.523222
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.950976 Loss1: 2.379902 Loss2: 1.571074
+(DefaultActor pid=3765) >> Training accuracy: 0.370833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.936080 Loss1: 2.403655 Loss2: 1.532425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.873561 Loss1: 2.333340 Loss2: 1.540221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.854069 Loss1: 2.310801 Loss2: 1.543267 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.417969
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.026047 Loss1: 2.539228 Loss2: 1.486818 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.911963 Loss1: 2.402745 Loss2: 1.509218 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.770976 Loss1: 2.234786 Loss2: 1.536190 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.297798 Loss1: 2.738138 Loss2: 1.559660 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.453125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.125257 Loss1: 2.612854 Loss2: 1.512402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.886140 Loss1: 2.361379 Loss2: 1.524761 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.821386 Loss1: 2.284293 Loss2: 1.537093 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.771922 Loss1: 2.220707 Loss2: 1.551215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.717703 Loss1: 2.170550 Loss2: 1.547153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.636295 Loss1: 2.076055 Loss2: 1.560239 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.469792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.130600 Loss1: 2.611844 Loss2: 1.518756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.941011 Loss1: 2.411975 Loss2: 1.529036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.880479 Loss1: 2.345629 Loss2: 1.534850
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.149729 Loss1: 3.150496 Loss2: 1.999233
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.115665 Loss1: 2.630304 Loss2: 1.485362 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.354167
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.927301 Loss1: 2.391881 Loss2: 1.535421
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.906248 Loss1: 2.451960 Loss2: 1.454288
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.712462 Loss1: 2.260037 Loss2: 1.452425
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.622721 Loss1: 2.162283 Loss2: 1.460438
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.552445 Loss1: 2.095595 Loss2: 1.456850
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.544545 Loss1: 2.079956 Loss2: 1.464590
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.092514 Loss1: 3.086513 Loss2: 2.006001
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.533917 Loss1: 2.068956 Loss2: 1.464961
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.222050 Loss1: 2.725457 Loss2: 1.496593
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.503661 Loss1: 2.022658 Loss2: 1.481003
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.990829 Loss1: 2.520660 Loss2: 1.470169
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.407444 Loss1: 1.926580 Loss2: 1.480864
+(DefaultActor pid=3764) >> Training accuracy: 0.450000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.825296 Loss1: 2.342201 Loss2: 1.483094 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.684514 Loss1: 2.207261 Loss2: 1.477253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.639741 Loss1: 2.144398 Loss2: 1.495343
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.390399 Loss1: 3.341868 Loss2: 2.048531
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.387017 Loss1: 2.831992 Loss2: 1.555025 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.435417
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.574622 Loss1: 2.061924 Loss2: 1.512698
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.086478 Loss1: 2.574470 Loss2: 1.512008
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.960081 Loss1: 2.459373 Loss2: 1.500708
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.897514 Loss1: 2.391506 Loss2: 1.506008
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.779213 Loss1: 2.276821 Loss2: 1.502392
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.848618 Loss1: 2.314005 Loss2: 1.534613
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.170814 Loss1: 3.302174 Loss2: 1.868640
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.743265 Loss1: 2.210233 Loss2: 1.533032
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.405486 Loss1: 2.982141 Loss2: 1.423345
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.707624 Loss1: 2.184526 Loss2: 1.523098
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.656392 Loss1: 2.120711 Loss2: 1.535682
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.228943 Loss1: 2.817048 Loss2: 1.411895
+(DefaultActor pid=3764) >> Training accuracy: 0.414583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.124542 Loss1: 2.710516 Loss2: 1.414026
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.037502 Loss1: 2.615092 Loss2: 1.422410
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.963757 Loss1: 2.533497 Loss2: 1.430259
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.986562 Loss1: 2.556541 Loss2: 1.430021
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.243751 Loss1: 3.258208 Loss2: 1.985543
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.879083 Loss1: 2.444779 Loss2: 1.434304
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.284928 Loss1: 2.764873 Loss2: 1.520055
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.880668 Loss1: 2.429920 Loss2: 1.450749
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.784699 Loss1: 2.341583 Loss2: 1.443116 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.370117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.839215 Loss1: 2.360153 Loss2: 1.479061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.795555 Loss1: 2.308301 Loss2: 1.487254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.779364 Loss1: 2.284008 Loss2: 1.495356
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.148727 Loss1: 3.251061 Loss2: 1.897665
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.323116 Loss1: 2.896810 Loss2: 1.426305 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.391667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.087024 Loss1: 2.689409 Loss2: 1.397615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.876659 Loss1: 2.465922 Loss2: 1.410737 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.818145 Loss1: 2.392155 Loss2: 1.425990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.804367 Loss1: 2.378841 Loss2: 1.425526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.778065 Loss1: 2.348630 Loss2: 1.429435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.657639 Loss1: 2.233172 Loss2: 1.424467 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.418750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.938304 Loss1: 2.392490 Loss2: 1.545814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.865511 Loss1: 2.299231 Loss2: 1.566280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.788000 Loss1: 2.218659 Loss2: 1.569341
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.880004 Loss1: 2.947474 Loss2: 1.932530
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.035265 Loss1: 2.591843 Loss2: 1.443422 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.425000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.910592 Loss1: 2.469695 Loss2: 1.440898 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.693579 Loss1: 2.256329 Loss2: 1.437250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.515824 Loss1: 2.067382 Loss2: 1.448442 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.459973 Loss1: 2.012573 Loss2: 1.447400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.515620 Loss1: 2.051575 Loss2: 1.464046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.435534 Loss1: 1.980534 Loss2: 1.455000 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.451042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.708852 Loss1: 2.187744 Loss2: 1.521108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.664547 Loss1: 2.131573 Loss2: 1.532975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.245310 Loss1: 3.299682 Loss2: 1.945628 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.383651 Loss1: 2.940023 Loss2: 1.443628 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.498958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.152978 Loss1: 2.730295 Loss2: 1.422684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.933593 Loss1: 2.483874 Loss2: 1.449719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.904689 Loss1: 2.432650 Loss2: 1.472039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.876110 Loss1: 2.412388 Loss2: 1.463722 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.803833 Loss1: 2.337775 Loss2: 1.466058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.798792 Loss1: 2.318581 Loss2: 1.480211 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.388542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.148791 Loss1: 2.571353 Loss2: 1.577438 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 4.076174 Loss1: 2.481013 Loss2: 1.595161 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.167849 Loss1: 3.234952 Loss2: 1.932897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.279565 Loss1: 2.828213 Loss2: 1.451352 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.396875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.035226 Loss1: 2.620491 Loss2: 1.414735 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.812149 Loss1: 2.387304 Loss2: 1.424845 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.881604 Loss1: 2.438148 Loss2: 1.443457
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.412463 Loss1: 3.443588 Loss2: 1.968875
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.334447 Loss1: 2.866684 Loss2: 1.467763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.076934 Loss1: 2.646324 Loss2: 1.430610 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.417708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.004311 Loss1: 2.564806 Loss2: 1.439505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.821414 Loss1: 2.378567 Loss2: 1.442847 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.743303 Loss1: 2.287034 Loss2: 1.456269 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.697369 Loss1: 2.245715 Loss2: 1.451654 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.702727 Loss1: 2.238328 Loss2: 1.464399 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.402083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.074758 Loss1: 2.629067 Loss2: 1.445692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.935617 Loss1: 2.485611 Loss2: 1.450007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.822852 Loss1: 2.350311 Loss2: 1.472541 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.795839 Loss1: 2.300880 Loss2: 1.494959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.781270 Loss1: 2.299028 Loss2: 1.482243 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.407366
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.899880 Loss1: 2.470083 Loss2: 1.429797 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.790242 Loss1: 2.359838 Loss2: 1.430404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.742709 Loss1: 2.296541 Loss2: 1.446167
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.318235 Loss1: 3.321047 Loss2: 1.997188
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.371653 Loss1: 2.861737 Loss2: 1.509916
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.672536 Loss1: 2.215305 Loss2: 1.457231
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.177058 Loss1: 2.702409 Loss2: 1.474649
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.726167 Loss1: 2.263621 Loss2: 1.462547
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.041593 Loss1: 2.556297 Loss2: 1.485296
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.615455 Loss1: 2.146046 Loss2: 1.469410
+(DefaultActor pid=3764) >> Training accuracy: 0.430147
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.962192 Loss1: 2.453008 Loss2: 1.509184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.848146 Loss1: 2.335650 Loss2: 1.512496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.792164 Loss1: 2.277516 Loss2: 1.514648
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.956624 Loss1: 3.003016 Loss2: 1.953608
+(DefaultActor pid=3765) >> Training accuracy: 0.438542
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.717777 Loss1: 2.195021 Loss2: 1.522756
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.126200 Loss1: 2.614995 Loss2: 1.511206
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.946527 Loss1: 2.475127 Loss2: 1.471401
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.787554 Loss1: 2.330287 Loss2: 1.457267
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.715700 Loss1: 2.264648 Loss2: 1.451052
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.571846 Loss1: 2.119572 Loss2: 1.452274
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.130162 Loss1: 3.115109 Loss2: 2.015053
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.492935 Loss1: 2.035118 Loss2: 1.457817
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.468346 Loss1: 1.999049 Loss2: 1.469297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.571594 Loss1: 2.100592 Loss2: 1.471001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.465481 Loss1: 2.005557 Loss2: 1.459924 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.456250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.748560 Loss1: 2.276476 Loss2: 1.472084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.605749 Loss1: 2.117398 Loss2: 1.488351 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.785584 Loss1: 2.278097 Loss2: 1.507488
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.151516 Loss1: 3.206426 Loss2: 1.945089
+(DefaultActor pid=3765) >> Training accuracy: 0.423958
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.660386 Loss1: 2.165655 Loss2: 1.494731
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.355132 Loss1: 2.891760 Loss2: 1.463371
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.096868 Loss1: 2.659269 Loss2: 1.437599
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.041850 Loss1: 2.598062 Loss2: 1.443788
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.009840 Loss1: 2.568650 Loss2: 1.441190
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.976042 Loss1: 2.534355 Loss2: 1.441687
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.259264 Loss1: 3.309028 Loss2: 1.950236
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.808731 Loss1: 2.359266 Loss2: 1.449465
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.394809 Loss1: 2.905155 Loss2: 1.489654
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.877662 Loss1: 2.417861 Loss2: 1.459801
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.169752 Loss1: 2.708689 Loss2: 1.461063
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.756657 Loss1: 2.288320 Loss2: 1.468337
+DEBUG flwr 2023-10-08 22:01:52,537 | server.py:236 | fit_round 16 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.067492 Loss1: 2.606226 Loss2: 1.461266
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.771047 Loss1: 2.301015 Loss2: 1.470032
+(DefaultActor pid=3764) >> Training accuracy: 0.392708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.913687 Loss1: 2.428476 Loss2: 1.485211 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.768974 Loss1: 2.286827 Loss2: 1.482147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.680693 Loss1: 2.185413 Loss2: 1.495280
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.361032 Loss1: 3.298405 Loss2: 2.062627
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.648225 Loss1: 2.152644 Loss2: 1.495581
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.410745 Loss1: 2.867782 Loss2: 1.542962
+(DefaultActor pid=3765) >> Training accuracy: 0.434375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.118900 Loss1: 2.610147 Loss2: 1.508752
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.980439 Loss1: 2.473917 Loss2: 1.506522
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.960027 Loss1: 2.445183 Loss2: 1.514844
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.977794 Loss1: 2.442073 Loss2: 1.535721
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.893446 Loss1: 2.371281 Loss2: 1.522165
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.137937 Loss1: 3.128884 Loss2: 2.009053
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.240623 Loss1: 2.704971 Loss2: 1.535651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.084462 Loss1: 2.571443 Loss2: 1.513018 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.419792
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.752456 Loss1: 2.205439 Loss2: 1.547017
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.111526 Loss1: 2.592284 Loss2: 1.519243
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.998894 Loss1: 2.477976 Loss2: 1.520918
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.908859 Loss1: 2.388368 Loss2: 1.520491
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.849810 Loss1: 2.316541 Loss2: 1.533269
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.839083 Loss1: 2.302348 Loss2: 1.536735
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.152687 Loss1: 3.256494 Loss2: 1.896194
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.707102 Loss1: 2.176209 Loss2: 1.530893
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.333844 Loss1: 2.880105 Loss2: 1.453739
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.714130 Loss1: 2.175465 Loss2: 1.538664
+(DefaultActor pid=3765) >> Training accuracy: 0.431641
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.076243 Loss1: 2.622746 Loss2: 1.453496
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.971513 Loss1: 2.536620 Loss2: 1.434893
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.963279 Loss1: 2.516525 Loss2: 1.446754
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.812515 Loss1: 2.359082 Loss2: 1.453433
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.819361 Loss1: 2.360691 Loss2: 1.458671
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.799412 Loss1: 2.337152 Loss2: 1.462261
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.751435 Loss1: 2.279406 Loss2: 1.472029
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.602928 Loss1: 2.126620 Loss2: 1.476308
+(DefaultActor pid=3764) >> Training accuracy: 0.430664
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 22:01:52,537][flwr][DEBUG] - fit_round 16 received 50 results and 0 failures
+INFO flwr 2023-10-08 22:02:34,375 | server.py:125 | fit progress: (16, 3.5286484503517515, {'accuracy': 0.1625}, 36662.153458993)
+>> Test accuracy: 0.162500
+[2023-10-08 22:02:34,375][flwr][INFO] - fit progress: (16, 3.5286484503517515, {'accuracy': 0.1625}, 36662.153458993)
+DEBUG flwr 2023-10-08 22:02:34,375 | server.py:173 | evaluate_round 16: strategy sampled 50 clients (out of 50)
+[2023-10-08 22:02:34,375][flwr][DEBUG] - evaluate_round 16: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 22:11:38,186 | server.py:187 | evaluate_round 16 received 50 results and 0 failures
+[2023-10-08 22:11:38,186][flwr][DEBUG] - evaluate_round 16 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 22:11:38,186 | server.py:222 | fit_round 17: strategy sampled 50 clients (out of 50)
+[2023-10-08 22:11:38,186][flwr][DEBUG] - fit_round 17: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.134847 Loss1: 3.144107 Loss2: 1.990740
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.293025 Loss1: 2.759237 Loss2: 1.533788
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.015880 Loss1: 2.537912 Loss2: 1.477968
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.449250 Loss1: 3.175766 Loss2: 2.273484
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.986560 Loss1: 2.504592 Loss2: 1.481968
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.861494 Loss1: 2.391727 Loss2: 1.469767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.739726 Loss1: 2.275082 Loss2: 1.464645 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.862827 Loss1: 2.309249 Loss2: 1.553578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.807669 Loss1: 2.222128 Loss2: 1.585541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.725876 Loss1: 2.144568 Loss2: 1.581308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.599156 Loss1: 2.103922 Loss2: 1.495233
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.593449 Loss1: 2.014369 Loss2: 1.579080
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.567572 Loss1: 1.989213 Loss2: 1.578359
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.534545 Loss1: 2.034743 Loss2: 1.499802
+(DefaultActor pid=3765) >> Training accuracy: 0.429688
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.157190 Loss1: 3.160573 Loss2: 1.996618 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.490885
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.181703 Loss1: 2.687134 Loss2: 1.494570 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.968767 Loss1: 2.476224 Loss2: 1.492543
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.065940 Loss1: 3.043198 Loss2: 2.022742
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.197074 Loss1: 2.675419 Loss2: 1.521655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.946938 Loss1: 2.467037 Loss2: 1.479901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.841186 Loss1: 2.355936 Loss2: 1.485250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.721791 Loss1: 2.235923 Loss2: 1.485869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.629734 Loss1: 2.138978 Loss2: 1.490756 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.433333
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.678090 Loss1: 2.140526 Loss2: 1.537564
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.654763 Loss1: 2.156154 Loss2: 1.498609
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.642774 Loss1: 2.138721 Loss2: 1.504052
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.539225 Loss1: 2.038519 Loss2: 1.500706
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.575316 Loss1: 2.065105 Loss2: 1.510212
+(DefaultActor pid=3764) >> Training accuracy: 0.418750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.278222 Loss1: 3.268329 Loss2: 2.009892
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.333789 Loss1: 2.816755 Loss2: 1.517035
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.149120 Loss1: 2.663277 Loss2: 1.485844
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.983890 Loss1: 2.505595 Loss2: 1.478295
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.147700 Loss1: 3.102962 Loss2: 2.044739
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.135692 Loss1: 2.638062 Loss2: 1.497630 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.921202 Loss1: 2.458171 Loss2: 1.463031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.808595 Loss1: 2.344420 Loss2: 1.464175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.788460 Loss1: 2.314227 Loss2: 1.474233 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.701107 Loss1: 2.223473 Loss2: 1.477634 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.412500
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.675928 Loss1: 2.172022 Loss2: 1.503906
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.587067 Loss1: 2.110651 Loss2: 1.476416
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.554797 Loss1: 2.063549 Loss2: 1.491248
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.583891 Loss1: 2.082561 Loss2: 1.501330
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.521947 Loss1: 2.009571 Loss2: 1.512376
+(DefaultActor pid=3764) >> Training accuracy: 0.464583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.429219 Loss1: 3.472476 Loss2: 1.956743
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.286585 Loss1: 2.800064 Loss2: 1.486521
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.102362 Loss1: 2.653130 Loss2: 1.449232
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.017416 Loss1: 2.576359 Loss2: 1.441057
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.008440 Loss1: 3.023049 Loss2: 1.985392
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.270022 Loss1: 2.767352 Loss2: 1.502671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.070245 Loss1: 2.596741 Loss2: 1.473504 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.976782 Loss1: 2.496569 Loss2: 1.480213 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.857212 Loss1: 2.386892 Loss2: 1.470320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.845049 Loss1: 2.376527 Loss2: 1.468522 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.439583
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.552261 Loss1: 2.069645 Loss2: 1.482617
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.735950 Loss1: 2.250739 Loss2: 1.485211
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.637658 Loss1: 2.146095 Loss2: 1.491563
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.633337 Loss1: 2.133046 Loss2: 1.500291
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.605336 Loss1: 2.099100 Loss2: 1.506236
+(DefaultActor pid=3764) >> Training accuracy: 0.466667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.889649 Loss1: 2.899184 Loss2: 1.990465
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.998966 Loss1: 2.508923 Loss2: 1.490043
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.815653 Loss1: 2.360807 Loss2: 1.454846
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.740528 Loss1: 2.270378 Loss2: 1.470150
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.993010 Loss1: 3.043376 Loss2: 1.949634
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.908268 Loss1: 2.458297 Loss2: 1.449971
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.723553 Loss1: 2.243061 Loss2: 1.480492
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.717715 Loss1: 2.283548 Loss2: 1.434168
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.574743 Loss1: 2.099819 Loss2: 1.474924
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.666199 Loss1: 2.232085 Loss2: 1.434113
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.484999 Loss1: 2.017155 Loss2: 1.467845
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.557918 Loss1: 2.108955 Loss2: 1.448962
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.460807 Loss1: 1.981695 Loss2: 1.479112
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.442571 Loss1: 1.974638 Loss2: 1.467932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.416241 Loss1: 1.927928 Loss2: 1.488313 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.488281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.396188 Loss1: 1.924882 Loss2: 1.471306 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.475000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.065637 Loss1: 3.034733 Loss2: 2.030904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.961333 Loss1: 2.464561 Loss2: 1.496772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.825951 Loss1: 2.338276 Loss2: 1.487675
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.093987 Loss1: 3.153622 Loss2: 1.940364
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.149728 Loss1: 2.701600 Loss2: 1.448128 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.004337 Loss1: 2.581040 Loss2: 1.423297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.870297 Loss1: 2.445148 Loss2: 1.425149 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.711436 Loss1: 2.298285 Loss2: 1.413150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.709451 Loss1: 2.276668 Loss2: 1.432783 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.456473
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.655654 Loss1: 2.196448 Loss2: 1.459207 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.506692 Loss1: 2.052315 Loss2: 1.454377 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.441667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.247743 Loss1: 2.652987 Loss2: 1.594756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.764069 Loss1: 2.213692 Loss2: 1.550376 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.659132 Loss1: 2.113809 Loss2: 1.545324
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.104496 Loss1: 3.082697 Loss2: 2.021800
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.267547 Loss1: 2.741761 Loss2: 1.525786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.088472 Loss1: 2.559015 Loss2: 1.529457 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.438387 Loss1: 1.875043 Loss2: 1.563344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.478591 Loss1: 1.895119 Loss2: 1.583472 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.479567
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.899759 Loss1: 2.357475 Loss2: 1.542284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.760058 Loss1: 2.221752 Loss2: 1.538305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.721180 Loss1: 2.161231 Loss2: 1.559949
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.190115 Loss1: 3.060088 Loss2: 2.130026
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.334249 Loss1: 2.803860 Loss2: 1.530390 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.472656
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.960590 Loss1: 2.445324 Loss2: 1.515266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.746310 Loss1: 2.237919 Loss2: 1.508391 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.100988 Loss1: 3.074570 Loss2: 2.026418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.193452 Loss1: 2.684127 Loss2: 1.509325 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.559472 Loss1: 2.038321 Loss2: 1.521151 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.481971
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.716727 Loss1: 2.219625 Loss2: 1.497103 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.683302 Loss1: 2.187028 Loss2: 1.496274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.629258 Loss1: 2.126446 Loss2: 1.502813
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.382518 Loss1: 3.262355 Loss2: 2.120164
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.629362 Loss1: 2.111135 Loss2: 1.518226
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.510498 Loss1: 2.900056 Loss2: 1.610442
+(DefaultActor pid=3764) >> Training accuracy: 0.436458
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.485980 Loss1: 1.961067 Loss2: 1.524912
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.296692 Loss1: 2.717007 Loss2: 1.579685
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.247090 Loss1: 2.671256 Loss2: 1.575834
+(DefaultActor pid=3765) Epoch: 4 Loss: 4.126950 Loss1: 2.546923 Loss2: 1.580027
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.994305 Loss1: 2.419700 Loss2: 1.574604
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.906839 Loss1: 2.320877 Loss2: 1.585963
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.092221 Loss1: 3.202494 Loss2: 1.889728
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.155588 Loss1: 2.752035 Loss2: 1.403553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.944880 Loss1: 2.569028 Loss2: 1.375852 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.435547
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.705022 Loss1: 2.100035 Loss2: 1.604987
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.852158 Loss1: 2.482180 Loss2: 1.369978
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.749536 Loss1: 2.361536 Loss2: 1.388000
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.614129 Loss1: 2.219195 Loss2: 1.394934
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.567137 Loss1: 2.158860 Loss2: 1.408278
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.555780 Loss1: 2.157757 Loss2: 1.398022
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.045798 Loss1: 3.116668 Loss2: 1.929129
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.517036 Loss1: 2.116088 Loss2: 1.400947
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.190876 Loss1: 2.737818 Loss2: 1.453058
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.528490 Loss1: 2.104076 Loss2: 1.424414
+(DefaultActor pid=3764) >> Training accuracy: 0.413542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.779750 Loss1: 2.353437 Loss2: 1.426313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.683769 Loss1: 2.257491 Loss2: 1.426279 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.626309 Loss1: 2.181371 Loss2: 1.444938 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.649073 Loss1: 2.189357 Loss2: 1.459716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.539395 Loss1: 2.077033 Loss2: 1.462362 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.534016 Loss1: 2.059544 Loss2: 1.474472 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.458008
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.641130 Loss1: 2.154051 Loss2: 1.487079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.643464 Loss1: 2.152536 Loss2: 1.490927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.395889 Loss1: 3.368073 Loss2: 2.027815 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.472917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.131501 Loss1: 2.650083 Loss2: 1.481418 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.945644 Loss1: 2.455007 Loss2: 1.490637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.311236 Loss1: 3.258989 Loss2: 2.052247 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.825034 Loss1: 2.315910 Loss2: 1.509125 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.750084 Loss1: 2.223242 Loss2: 1.526842 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.629301 Loss1: 2.112619 Loss2: 1.516682 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.416295
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.047032 Loss1: 2.485801 Loss2: 1.561231 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.869549 Loss1: 2.303105 Loss2: 1.566444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.862620 Loss1: 2.303301 Loss2: 1.559319
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.043623 Loss1: 2.981539 Loss2: 2.062084
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.097664 Loss1: 2.534774 Loss2: 1.562891 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.427734
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.693646 Loss1: 2.101539 Loss2: 1.592107
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.853149 Loss1: 2.313671 Loss2: 1.539479
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.806353 Loss1: 2.277948 Loss2: 1.528405
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.738745 Loss1: 2.202490 Loss2: 1.536254
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.636397 Loss1: 2.104102 Loss2: 1.532295
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.563400 Loss1: 2.018400 Loss2: 1.544999
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.006398 Loss1: 2.999631 Loss2: 2.006767
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.448757 Loss1: 1.910264 Loss2: 1.538493
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.447711 Loss1: 1.903712 Loss2: 1.543999 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.413604 Loss1: 1.853416 Loss2: 1.560188 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.489583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.714464 Loss1: 2.225706 Loss2: 1.488758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.563633 Loss1: 2.081626 Loss2: 1.482007 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.523549 Loss1: 2.023304 Loss2: 1.500245
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.284673 Loss1: 3.281603 Loss2: 2.003071
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.304087 Loss1: 2.790214 Loss2: 1.513874 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.461458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.086836 Loss1: 2.601153 Loss2: 1.485683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.863981 Loss1: 2.378273 Loss2: 1.485708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.851072 Loss1: 2.336694 Loss2: 1.514379 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.752068 Loss1: 2.246976 Loss2: 1.505092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.693036 Loss1: 2.177090 Loss2: 1.515946 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.667984 Loss1: 2.152573 Loss2: 1.515411 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.439453
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.519543 Loss1: 2.103131 Loss2: 1.416412 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.498181 Loss1: 2.050334 Loss2: 1.447847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.186843 Loss1: 3.249977 Loss2: 1.936866
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.378720 Loss1: 1.938748 Loss2: 1.439972
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.229714 Loss1: 1.792146 Loss2: 1.437568
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.405493 Loss1: 2.926493 Loss2: 1.479000
+(DefaultActor pid=3764) >> Training accuracy: 0.520833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.212973 Loss1: 2.749155 Loss2: 1.463818
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.108797 Loss1: 2.649101 Loss2: 1.459696
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.961469 Loss1: 2.497200 Loss2: 1.464268
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.940425 Loss1: 2.478874 Loss2: 1.461551
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.256394 Loss1: 3.190360 Loss2: 2.066034
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.957684 Loss1: 2.466996 Loss2: 1.490688
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.306236 Loss1: 2.764843 Loss2: 1.541393
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.886215 Loss1: 2.401020 Loss2: 1.485195
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.788185 Loss1: 2.301274 Loss2: 1.486912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.706045 Loss1: 2.218858 Loss2: 1.487186 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.437500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.815001 Loss1: 2.291813 Loss2: 1.523187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.767127 Loss1: 2.244934 Loss2: 1.522193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.726402 Loss1: 2.196048 Loss2: 1.530354
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.992670 Loss1: 3.034397 Loss2: 1.958274
+(DefaultActor pid=3764) >> Training accuracy: 0.434375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.099743 Loss1: 2.641431 Loss2: 1.458312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.756055 Loss1: 2.322549 Loss2: 1.433506 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.534100 Loss1: 2.088019 Loss2: 1.446081 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.579655 Loss1: 2.133761 Loss2: 1.445894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.147289 Loss1: 2.600808 Loss2: 1.546481
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.546301 Loss1: 2.077290 Loss2: 1.469011
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.890249 Loss1: 2.375402 Loss2: 1.514847
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.437054 Loss1: 1.965198 Loss2: 1.471856
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.745866 Loss1: 2.232820 Loss2: 1.513046
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.507837 Loss1: 2.033649 Loss2: 1.474188
+(DefaultActor pid=3765) >> Training accuracy: 0.435417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.692104 Loss1: 2.165575 Loss2: 1.526529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.642536 Loss1: 2.091826 Loss2: 1.550710 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.110546 Loss1: 3.116981 Loss2: 1.993565
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.558067 Loss1: 2.017138 Loss2: 1.540929
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.241899 Loss1: 2.717181 Loss2: 1.524718
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.432913 Loss1: 1.889566 Loss2: 1.543346
+(DefaultActor pid=3764) >> Training accuracy: 0.470703
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.862273 Loss1: 2.387152 Loss2: 1.475121 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.710932 Loss1: 2.214911 Loss2: 1.496021 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.652868 Loss1: 2.153676 Loss2: 1.499192
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.227131 Loss1: 3.249134 Loss2: 1.977997
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.156346 Loss1: 2.676564 Loss2: 1.479782 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.579417 Loss1: 2.067974 Loss2: 1.511443
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.947643 Loss1: 2.518136 Loss2: 1.429506
+(DefaultActor pid=3765) >> Training accuracy: 0.487500
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.499839 Loss1: 1.992685 Loss2: 1.507154
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.829861 Loss1: 2.401219 Loss2: 1.428641
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.729938 Loss1: 2.301729 Loss2: 1.428209
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.648954 Loss1: 2.210172 Loss2: 1.438782
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.615178 Loss1: 2.155412 Loss2: 1.459766
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.708569 Loss1: 2.252350 Loss2: 1.456219
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.292249 Loss1: 3.256464 Loss2: 2.035785
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.547436 Loss1: 2.086448 Loss2: 1.460988
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.223573 Loss1: 2.699160 Loss2: 1.524413
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.548055 Loss1: 2.070089 Loss2: 1.477966
+(DefaultActor pid=3764) >> Training accuracy: 0.413542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.923497 Loss1: 2.424381 Loss2: 1.499116 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.848862 Loss1: 2.319302 Loss2: 1.529560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.826247 Loss1: 2.314228 Loss2: 1.512020
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.161209 Loss1: 3.164372 Loss2: 1.996837
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.730176 Loss1: 2.204929 Loss2: 1.525247
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.346789 Loss1: 2.827877 Loss2: 1.518912
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.718614 Loss1: 2.189760 Loss2: 1.528854
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.151354 Loss1: 2.652103 Loss2: 1.499251
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.688151 Loss1: 2.146807 Loss2: 1.541344
+(DefaultActor pid=3765) >> Training accuracy: 0.441667
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.988611 Loss1: 2.495913 Loss2: 1.492698
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.952402 Loss1: 2.446721 Loss2: 1.505681
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.881634 Loss1: 2.375140 Loss2: 1.506495
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.829683 Loss1: 2.314530 Loss2: 1.515154
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.747265 Loss1: 2.217828 Loss2: 1.529438
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.205224 Loss1: 3.217572 Loss2: 1.987652
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.706491 Loss1: 2.183365 Loss2: 1.523127
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.313116 Loss1: 2.810887 Loss2: 1.502229
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.700258 Loss1: 2.161544 Loss2: 1.538714
+(DefaultActor pid=3764) >> Training accuracy: 0.440625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.988237 Loss1: 2.495145 Loss2: 1.493092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.848884 Loss1: 2.338485 Loss2: 1.510399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.876299 Loss1: 2.359757 Loss2: 1.516541
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.183969 Loss1: 3.093826 Loss2: 2.090143
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.820942 Loss1: 2.302922 Loss2: 1.518020
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.275564 Loss1: 2.720138 Loss2: 1.555426
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.758875 Loss1: 2.223614 Loss2: 1.535261
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.976252 Loss1: 2.436030 Loss2: 1.540221
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.740985 Loss1: 2.219838 Loss2: 1.521147
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.884951 Loss1: 2.339418 Loss2: 1.545533
+(DefaultActor pid=3765) >> Training accuracy: 0.384375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.957051 Loss1: 2.398721 Loss2: 1.558330
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.911887 Loss1: 2.340368 Loss2: 1.571519
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.779970 Loss1: 2.209824 Loss2: 1.570146
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.717788 Loss1: 2.148098 Loss2: 1.569690
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.909962 Loss1: 2.917664 Loss2: 1.992298
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.589626 Loss1: 1.999660 Loss2: 1.589967
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.198479 Loss1: 2.701873 Loss2: 1.496605
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.573989 Loss1: 2.001055 Loss2: 1.572934
+(DefaultActor pid=3764) >> Training accuracy: 0.471875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.690284 Loss1: 2.205835 Loss2: 1.484449 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.570991 Loss1: 2.097236 Loss2: 1.473755 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.536012 Loss1: 2.052773 Loss2: 1.483240
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.242693 Loss1: 3.214388 Loss2: 2.028305
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.504381 Loss1: 2.014124 Loss2: 1.490257
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.441236 Loss1: 2.903425 Loss2: 1.537811
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.366165 Loss1: 1.884224 Loss2: 1.481941
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.203738 Loss1: 2.686626 Loss2: 1.517112
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.463073 Loss1: 1.973336 Loss2: 1.489736
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.147760 Loss1: 2.631704 Loss2: 1.516056
+(DefaultActor pid=3765) >> Training accuracy: 0.440625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.968800 Loss1: 2.460584 Loss2: 1.508217
+(DefaultActor pid=3764) Epoch: 5 Loss: 4.012980 Loss1: 2.481291 Loss2: 1.531689
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.910814 Loss1: 2.376252 Loss2: 1.534562
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.814518 Loss1: 2.280883 Loss2: 1.533635
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.444588 Loss1: 3.346004 Loss2: 2.098584
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.831128 Loss1: 2.284028 Loss2: 1.547100
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.808854 Loss1: 2.256606 Loss2: 1.552248 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.409375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.091913 Loss1: 2.558601 Loss2: 1.533312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.975964 Loss1: 2.429849 Loss2: 1.546115 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.174518 Loss1: 3.035845 Loss2: 2.138672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.211014 Loss1: 2.586392 Loss2: 1.624623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.670358 Loss1: 2.094906 Loss2: 1.575453 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.446429
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.671639 Loss1: 2.117931 Loss2: 1.553708 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.569510 Loss1: 1.992035 Loss2: 1.577474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.395099 Loss1: 1.820692 Loss2: 1.574407
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.358487 Loss1: 3.254277 Loss2: 2.104209
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.426627 Loss1: 1.855382 Loss2: 1.571245
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.373459 Loss1: 2.793284 Loss2: 1.580176
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.437589 Loss1: 1.848394 Loss2: 1.589195
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.156422 Loss1: 2.614844 Loss2: 1.541578
+(DefaultActor pid=3764) >> Training accuracy: 0.514583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 4.007383 Loss1: 2.451469 Loss2: 1.555914
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.935263 Loss1: 2.392507 Loss2: 1.542756
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.913031 Loss1: 2.351186 Loss2: 1.561845
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.849076 Loss1: 2.281509 Loss2: 1.567567
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.794091 Loss1: 2.221286 Loss2: 1.572805
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.036055 Loss1: 3.102839 Loss2: 1.933216
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.725054 Loss1: 2.152536 Loss2: 1.572518
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.145929 Loss1: 2.704882 Loss2: 1.441047
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.721020 Loss1: 2.125638 Loss2: 1.595382
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.998387 Loss1: 2.562134 Loss2: 1.436253
+(DefaultActor pid=3765) >> Training accuracy: 0.394792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.847084 Loss1: 2.425449 Loss2: 1.421635
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.759110 Loss1: 2.337424 Loss2: 1.421686
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.664710 Loss1: 2.230675 Loss2: 1.434036
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.611984 Loss1: 2.173984 Loss2: 1.438000
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.636967 Loss1: 2.196614 Loss2: 1.440352
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.098783 Loss1: 3.045274 Loss2: 2.053508
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.612993 Loss1: 2.170920 Loss2: 1.442072
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.120877 Loss1: 2.600023 Loss2: 1.520854
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.576208 Loss1: 2.110555 Loss2: 1.465654
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.917171 Loss1: 2.433736 Loss2: 1.483435
+(DefaultActor pid=3764) >> Training accuracy: 0.431250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.796545 Loss1: 2.312751 Loss2: 1.483794
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.725068 Loss1: 2.243977 Loss2: 1.481090
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.615338 Loss1: 2.133221 Loss2: 1.482117
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.506893 Loss1: 2.021990 Loss2: 1.484902
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.497716 Loss1: 2.004321 Loss2: 1.493395
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.072381 Loss1: 3.057726 Loss2: 2.014655
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.481233 Loss1: 1.978205 Loss2: 1.503027
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.119953 Loss1: 2.622601 Loss2: 1.497352
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.522705 Loss1: 2.004288 Loss2: 1.518417
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.886175 Loss1: 2.419734 Loss2: 1.466442
+(DefaultActor pid=3765) >> Training accuracy: 0.476042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.762833 Loss1: 2.300734 Loss2: 1.462099
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.685907 Loss1: 2.214863 Loss2: 1.471043
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.558585 Loss1: 2.097441 Loss2: 1.461144
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.551032 Loss1: 2.076079 Loss2: 1.474953
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.042608 Loss1: 3.088410 Loss2: 1.954198
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.579728 Loss1: 2.097597 Loss2: 1.482131
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.181392 Loss1: 2.704890 Loss2: 1.476502
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.535667 Loss1: 2.036658 Loss2: 1.499009
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.978478 Loss1: 2.526640 Loss2: 1.451839
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.473704 Loss1: 1.982901 Loss2: 1.490803
+(DefaultActor pid=3764) >> Training accuracy: 0.470833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.761816 Loss1: 2.311466 Loss2: 1.450350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.569887 Loss1: 2.114711 Loss2: 1.455176 [repeated 2x across cluster]
+DEBUG flwr 2023-10-08 22:40:24,829 | server.py:236 | fit_round 17 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.639442 Loss1: 2.169914 Loss2: 1.469528
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.154678 Loss1: 3.211535 Loss2: 1.943142
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.594998 Loss1: 2.136366 Loss2: 1.458632
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.194095 Loss1: 2.729342 Loss2: 1.464753
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.476258 Loss1: 1.999840 Loss2: 1.476418
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.127178 Loss1: 2.674701 Loss2: 1.452477
+(DefaultActor pid=3765) >> Training accuracy: 0.500000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.888936 Loss1: 2.449047 Loss2: 1.439889
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.815183 Loss1: 2.371818 Loss2: 1.443365
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.778069 Loss1: 2.325728 Loss2: 1.452341
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.682486 Loss1: 2.227258 Loss2: 1.455228
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.014866 Loss1: 3.027522 Loss2: 1.987344
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.718556 Loss1: 2.258237 Loss2: 1.460319
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.067814 Loss1: 2.581766 Loss2: 1.486048
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.669812 Loss1: 2.189227 Loss2: 1.480585
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.815400 Loss1: 2.352085 Loss2: 1.463316
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.579868 Loss1: 2.106626 Loss2: 1.473241
+(DefaultActor pid=3764) >> Training accuracy: 0.407292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.654822 Loss1: 2.183617 Loss2: 1.471204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.523941 Loss1: 2.033886 Loss2: 1.490055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.594250 Loss1: 2.087038 Loss2: 1.507212
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.244058 Loss1: 3.192288 Loss2: 2.051770
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.506502 Loss1: 2.015703 Loss2: 1.490799
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.304546 Loss1: 2.767828 Loss2: 1.536718
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.547488 Loss1: 2.036185 Loss2: 1.511303
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.111373 Loss1: 2.602236 Loss2: 1.509137
+(DefaultActor pid=3765) >> Training accuracy: 0.477083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.970151 Loss1: 2.462044 Loss2: 1.508107
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.924894 Loss1: 2.413082 Loss2: 1.511812
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.943880 Loss1: 2.420718 Loss2: 1.523163
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.808321 Loss1: 2.257115 Loss2: 1.551206
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.294998 Loss1: 3.324915 Loss2: 1.970084
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.707533 Loss1: 2.163469 Loss2: 1.544064
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.299321 Loss1: 2.850791 Loss2: 1.448530
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.673149 Loss1: 2.129042 Loss2: 1.544107
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.101271 Loss1: 2.668946 Loss2: 1.432325
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.588566 Loss1: 2.037909 Loss2: 1.550657
+(DefaultActor pid=3764) >> Training accuracy: 0.443750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.899267 Loss1: 2.465458 Loss2: 1.433809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.729068 Loss1: 2.280067 Loss2: 1.449001 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.737645 Loss1: 2.286394 Loss2: 1.451251
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.969348 Loss1: 3.106342 Loss2: 1.863007
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.123208 Loss1: 2.699982 Loss2: 1.423226 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.439583
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.620855 Loss1: 2.148351 Loss2: 1.472504
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.879321 Loss1: 2.467776 Loss2: 1.411545
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.840819 Loss1: 2.426964 Loss2: 1.413856
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.732987 Loss1: 2.307626 Loss2: 1.425361
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.672554 Loss1: 2.253782 Loss2: 1.418773
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.773309 Loss1: 2.326047 Loss2: 1.447263
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.673546 Loss1: 2.239659 Loss2: 1.433887
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.582616 Loss1: 2.137355 Loss2: 1.445261
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.581756 Loss1: 2.135336 Loss2: 1.446421
+(DefaultActor pid=3764) >> Training accuracy: 0.454044
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 22:40:24,829][flwr][DEBUG] - fit_round 17 received 50 results and 0 failures
+INFO flwr 2023-10-08 22:41:06,375 | server.py:125 | fit progress: (17, 3.453257521120504, {'accuracy': 0.177}, 38974.153807173)
+>> Test accuracy: 0.177000
+[2023-10-08 22:41:06,375][flwr][INFO] - fit progress: (17, 3.453257521120504, {'accuracy': 0.177}, 38974.153807173)
+DEBUG flwr 2023-10-08 22:41:06,376 | server.py:173 | evaluate_round 17: strategy sampled 50 clients (out of 50)
+[2023-10-08 22:41:06,376][flwr][DEBUG] - evaluate_round 17: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 22:50:13,891 | server.py:187 | evaluate_round 17 received 50 results and 0 failures
+[2023-10-08 22:50:13,891][flwr][DEBUG] - evaluate_round 17 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 22:50:13,892 | server.py:222 | fit_round 18: strategy sampled 50 clients (out of 50)
+[2023-10-08 22:50:13,892][flwr][DEBUG] - fit_round 18: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.136857 Loss1: 3.140472 Loss2: 1.996385
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.228466 Loss1: 2.718414 Loss2: 1.510052
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.023183 Loss1: 2.544073 Loss2: 1.479110
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.922538 Loss1: 2.438141 Loss2: 1.484397
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.873132 Loss1: 2.855610 Loss2: 2.017522
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.857537 Loss1: 2.373934 Loss2: 1.483603
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.026071 Loss1: 2.494871 Loss2: 1.531199
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.762636 Loss1: 2.262105 Loss2: 1.500531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.609611 Loss1: 2.118757 Loss2: 1.490854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.584407 Loss1: 2.071478 Loss2: 1.512929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.445993 Loss1: 1.954765 Loss2: 1.491228 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.409375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.406152 Loss1: 1.898818 Loss2: 1.507335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.341649 Loss1: 1.810192 Loss2: 1.531457 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.525391
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.892033 Loss1: 3.026592 Loss2: 1.865441 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.765162 Loss1: 2.352000 Loss2: 1.413162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.620161 Loss1: 2.217396 Loss2: 1.402765
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.167570 Loss1: 3.196818 Loss2: 1.970751
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.364502 Loss1: 2.876802 Loss2: 1.487699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.111766 Loss1: 2.635910 Loss2: 1.475856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.401349 Loss1: 1.979432 Loss2: 1.421916
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.922685 Loss1: 2.453939 Loss2: 1.468746
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.378768 Loss1: 1.947744 Loss2: 1.431024
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.845754 Loss1: 2.381169 Loss2: 1.464585
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.367800 Loss1: 1.932775 Loss2: 1.435025
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.775102 Loss1: 2.306053 Loss2: 1.469049
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.677647 Loss1: 2.188639 Loss2: 1.489009
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.292364 Loss1: 1.834490 Loss2: 1.457874
+(DefaultActor pid=3765) >> Training accuracy: 0.498047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.664521 Loss1: 2.160348 Loss2: 1.504173 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.439583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.989953 Loss1: 3.019784 Loss2: 1.970169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.684422 Loss1: 2.268359 Loss2: 1.416063 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.992588 Loss1: 2.978023 Loss2: 2.014565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.473776 Loss1: 2.034412 Loss2: 1.439363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.462472 Loss1: 2.035281 Loss2: 1.427191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.386785 Loss1: 1.942451 Loss2: 1.444334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.249171 Loss1: 1.819041 Loss2: 1.430129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.204916 Loss1: 1.763739 Loss2: 1.441177 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.516827
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.594002 Loss1: 2.057863 Loss2: 1.536139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.436020 Loss1: 1.903785 Loss2: 1.532235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.282197 Loss1: 1.741382 Loss2: 1.540815
+(DefaultActor pid=3764) >> Training accuracy: 0.526042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.103454 Loss1: 3.089332 Loss2: 2.014122
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.164985 Loss1: 2.603914 Loss2: 1.561071
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.949426 Loss1: 2.448265 Loss2: 1.501160
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.826870 Loss1: 2.319902 Loss2: 1.506967
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.797896 Loss1: 2.298459 Loss2: 1.499437
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.064793 Loss1: 3.062428 Loss2: 2.002366
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.153704 Loss1: 2.622728 Loss2: 1.530976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.976632 Loss1: 2.460171 Loss2: 1.516461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.866215 Loss1: 2.352014 Loss2: 1.514201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.761251 Loss1: 2.238314 Loss2: 1.522936 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.430664
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.552870 Loss1: 2.008083 Loss2: 1.544787
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.664376 Loss1: 2.142818 Loss2: 1.521558
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.601964 Loss1: 2.063864 Loss2: 1.538100
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.546453 Loss1: 2.005873 Loss2: 1.540580
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.429206 Loss1: 1.882954 Loss2: 1.546252
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.424694 Loss1: 1.869653 Loss2: 1.555041
+(DefaultActor pid=3764) >> Training accuracy: 0.468750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.069230 Loss1: 3.047811 Loss2: 2.021418
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.141062 Loss1: 2.605958 Loss2: 1.535104
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.879763 Loss1: 2.381155 Loss2: 1.498608
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.741405 Loss1: 2.239363 Loss2: 1.502042
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.684994 Loss1: 2.182724 Loss2: 1.502270
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.043820 Loss1: 2.958250 Loss2: 2.085570
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.688361 Loss1: 2.169502 Loss2: 1.518859
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.582165 Loss1: 2.043994 Loss2: 1.538170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.575648 Loss1: 2.043309 Loss2: 1.532339 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.742944 Loss1: 2.230734 Loss2: 1.512210 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.707794 Loss1: 2.206238 Loss2: 1.501556 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.501042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.477023 Loss1: 1.956385 Loss2: 1.520638 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.395898 Loss1: 1.867736 Loss2: 1.528162 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.456731
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.903450 Loss1: 2.858301 Loss2: 2.045149
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.971832 Loss1: 2.444302 Loss2: 1.527530
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.727639 Loss1: 2.245395 Loss2: 1.482244
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.625122 Loss1: 2.135027 Loss2: 1.490095
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.169075 Loss1: 3.187548 Loss2: 1.981527
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.211943 Loss1: 2.714667 Loss2: 1.497276 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.026342 Loss1: 2.558240 Loss2: 1.468102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.920508 Loss1: 2.441635 Loss2: 1.478873 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.816808 Loss1: 2.345090 Loss2: 1.471718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.737671 Loss1: 2.257639 Loss2: 1.480032 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.537500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.726513 Loss1: 2.237203 Loss2: 1.489311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.590603 Loss1: 2.085853 Loss2: 1.504749 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.480469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.342172 Loss1: 3.212704 Loss2: 2.129468 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.035687 Loss1: 2.498410 Loss2: 1.537277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.015082 Loss1: 2.979133 Loss2: 2.035949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.040547 Loss1: 2.545013 Loss2: 1.495534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.811555 Loss1: 2.350347 Loss2: 1.461209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.685713 Loss1: 2.222568 Loss2: 1.463146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.672194 Loss1: 2.202422 Loss2: 1.469772 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.580227 Loss1: 2.101003 Loss2: 1.479224 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.465625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.347346 Loss1: 1.882237 Loss2: 1.465109 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.350867 Loss1: 1.848596 Loss2: 1.502271 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.497917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.064620 Loss1: 3.035146 Loss2: 2.029475
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.203400 Loss1: 2.669160 Loss2: 1.534240
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.975891 Loss1: 2.460292 Loss2: 1.515599
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.725892 Loss1: 2.214667 Loss2: 1.511225
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.348292 Loss1: 3.311788 Loss2: 2.036504
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.754263 Loss1: 2.230060 Loss2: 1.524202
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.327909 Loss1: 2.792004 Loss2: 1.535905
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.758425 Loss1: 2.223623 Loss2: 1.534801
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.045135 Loss1: 2.560593 Loss2: 1.484542
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.989900 Loss1: 2.499978 Loss2: 1.489921
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.602157 Loss1: 2.060404 Loss2: 1.541753
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.892243 Loss1: 2.391415 Loss2: 1.500828
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.640272 Loss1: 2.094384 Loss2: 1.545888
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.794003 Loss1: 2.298710 Loss2: 1.495293
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.662736 Loss1: 2.085624 Loss2: 1.577112
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.536419 Loss1: 1.975467 Loss2: 1.560952 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.470588
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.578106 Loss1: 2.071716 Loss2: 1.506390 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.465625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.145815 Loss1: 3.198774 Loss2: 1.947040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.127567 Loss1: 2.645168 Loss2: 1.482399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.972456 Loss1: 2.475946 Loss2: 1.496510
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.723014 Loss1: 2.834152 Loss2: 1.888862
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.943391 Loss1: 2.447306 Loss2: 1.496085
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.964330 Loss1: 2.510117 Loss2: 1.454213
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.642351 Loss1: 2.211695 Loss2: 1.430656
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.933039 Loss1: 2.432049 Loss2: 1.500990
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.487414 Loss1: 2.066214 Loss2: 1.421200
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.792663 Loss1: 2.280344 Loss2: 1.512318
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.410813 Loss1: 1.984408 Loss2: 1.426405
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.781826 Loss1: 2.257652 Loss2: 1.524175
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.445152 Loss1: 2.010223 Loss2: 1.434929
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.717398 Loss1: 2.190719 Loss2: 1.526679
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.704640 Loss1: 2.167500 Loss2: 1.537139 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.408203
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.262903 Loss1: 1.823208 Loss2: 1.439696 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.520833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.025727 Loss1: 3.036086 Loss2: 1.989641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.843790 Loss1: 2.387549 Loss2: 1.456242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.784551 Loss1: 2.334372 Loss2: 1.450179
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.424323 Loss1: 3.334541 Loss2: 2.089782
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.674097 Loss1: 2.213049 Loss2: 1.461047
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.368145 Loss1: 2.834369 Loss2: 1.533776
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.578540 Loss1: 2.112984 Loss2: 1.465556
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.121531 Loss1: 2.627670 Loss2: 1.493861
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.035580 Loss1: 2.541624 Loss2: 1.493955
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.581543 Loss1: 2.112220 Loss2: 1.469323
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.893403 Loss1: 2.386820 Loss2: 1.506583
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.487015 Loss1: 2.003838 Loss2: 1.483176
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.877181 Loss1: 2.366824 Loss2: 1.510357
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.413895 Loss1: 1.928843 Loss2: 1.485051
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.461693 Loss1: 1.967186 Loss2: 1.494507 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.414583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.605720 Loss1: 2.086282 Loss2: 1.519438 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.439732
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.091544 Loss1: 3.203201 Loss2: 1.888343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.986058 Loss1: 2.589545 Loss2: 1.396513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.107671 Loss1: 3.163756 Loss2: 1.943915
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.916972 Loss1: 2.494431 Loss2: 1.422541
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.141039 Loss1: 2.674817 Loss2: 1.466222
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.839056 Loss1: 2.417291 Loss2: 1.421764
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.990064 Loss1: 2.543089 Loss2: 1.446975
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.713749 Loss1: 2.284326 Loss2: 1.429424
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.826848 Loss1: 2.386842 Loss2: 1.440007
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.621271 Loss1: 2.187033 Loss2: 1.434239
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.682741 Loss1: 2.235324 Loss2: 1.447417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.605288 Loss1: 2.148528 Loss2: 1.456760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.527465 Loss1: 2.077744 Loss2: 1.449721 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.478516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.504496 Loss1: 2.051189 Loss2: 1.453307 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.446875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.034540 Loss1: 2.954860 Loss2: 2.079680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.992308 Loss1: 2.473217 Loss2: 1.519091 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.856452 Loss1: 2.351427 Loss2: 1.505026
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.123329 Loss1: 3.102525 Loss2: 2.020804
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.112024 Loss1: 2.577816 Loss2: 1.534209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.002348 Loss1: 2.483305 Loss2: 1.519043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.866624 Loss1: 2.360702 Loss2: 1.505923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.719173 Loss1: 2.212149 Loss2: 1.507024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.714405 Loss1: 2.199677 Loss2: 1.514728 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.469792
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.459076 Loss1: 1.908583 Loss2: 1.550493
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.709587 Loss1: 2.190146 Loss2: 1.519441
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.541225 Loss1: 2.024201 Loss2: 1.517024
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.476162 Loss1: 1.948619 Loss2: 1.527543
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.400798 Loss1: 1.876410 Loss2: 1.524388
+(DefaultActor pid=3764) >> Training accuracy: 0.507292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.097794 Loss1: 3.113074 Loss2: 1.984720
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.252139 Loss1: 2.768504 Loss2: 1.483635
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.973524 Loss1: 2.512721 Loss2: 1.460803
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.849772 Loss1: 2.384767 Loss2: 1.465004
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.012365 Loss1: 2.950857 Loss2: 2.061508
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.070839 Loss1: 2.533646 Loss2: 1.537193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.843883 Loss1: 2.342898 Loss2: 1.500986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.726759 Loss1: 2.233618 Loss2: 1.493141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.673106 Loss1: 2.196229 Loss2: 1.476876 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.537881 Loss1: 2.047233 Loss2: 1.490648 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.472917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.450776 Loss1: 1.957958 Loss2: 1.492818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.442553 Loss1: 1.925186 Loss2: 1.517368 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.539583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.075341 Loss1: 3.045691 Loss2: 2.029650 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.901708 Loss1: 2.384401 Loss2: 1.517307 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.003312 Loss1: 2.969862 Loss2: 2.033450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.175782 Loss1: 2.617694 Loss2: 1.558088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.916917 Loss1: 2.389819 Loss2: 1.527098 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.799294 Loss1: 2.277692 Loss2: 1.521601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.636779 Loss1: 2.107200 Loss2: 1.529579 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.565044 Loss1: 2.041422 Loss2: 1.523622 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.472917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.626963 Loss1: 2.081231 Loss2: 1.545732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.515536 Loss1: 1.963458 Loss2: 1.552078 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.488542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.075425 Loss1: 3.046418 Loss2: 2.029007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.732601 Loss1: 2.269164 Loss2: 1.463437 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.025945 Loss1: 3.036373 Loss2: 1.989573 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.105590 Loss1: 2.600028 Loss2: 1.505563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.871448 Loss1: 2.395607 Loss2: 1.475841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.726250 Loss1: 2.240676 Loss2: 1.485574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.587235 Loss1: 2.114081 Loss2: 1.473153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.602007 Loss1: 2.115349 Loss2: 1.486658 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.553125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.550414 Loss1: 2.046696 Loss2: 1.503719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.371633 Loss1: 1.880415 Loss2: 1.491219 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.485417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.998755 Loss1: 3.034883 Loss2: 1.963872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.898037 Loss1: 2.447613 Loss2: 1.450424 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.714400 Loss1: 2.254080 Loss2: 1.460320
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.241281 Loss1: 3.242595 Loss2: 1.998686
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.315682 Loss1: 2.852326 Loss2: 1.463356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.085498 Loss1: 2.624589 Loss2: 1.460909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.972056 Loss1: 2.512713 Loss2: 1.459343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.502855 Loss1: 2.031488 Loss2: 1.471367
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.866344 Loss1: 2.405977 Loss2: 1.460367
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.482371 Loss1: 1.981391 Loss2: 1.500980
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.916344 Loss1: 2.444389 Loss2: 1.471955
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.468255 Loss1: 1.982601 Loss2: 1.485654
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.750614 Loss1: 2.263537 Loss2: 1.487076
+(DefaultActor pid=3765) >> Training accuracy: 0.520833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.691835 Loss1: 2.196427 Loss2: 1.495408
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.572349 Loss1: 2.086155 Loss2: 1.486194
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.542712 Loss1: 2.042966 Loss2: 1.499745
+(DefaultActor pid=3764) >> Training accuracy: 0.448661
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.127391 Loss1: 3.130291 Loss2: 1.997099
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.192497 Loss1: 2.661050 Loss2: 1.531447
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.995484 Loss1: 2.487517 Loss2: 1.507967
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.867549 Loss1: 2.385513 Loss2: 1.482036
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.258976 Loss1: 3.189566 Loss2: 2.069411
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.396623 Loss1: 2.844891 Loss2: 1.551732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.228615 Loss1: 2.665580 Loss2: 1.563034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.044827 Loss1: 2.507626 Loss2: 1.537201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 4.035102 Loss1: 2.489722 Loss2: 1.545380 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.877158 Loss1: 2.325798 Loss2: 1.551360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.485655 Loss1: 1.959385 Loss2: 1.526269
+(DefaultActor pid=3765) >> Training accuracy: 0.485417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.775944 Loss1: 2.224883 Loss2: 1.551061
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.673990 Loss1: 2.111534 Loss2: 1.562456
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.655873 Loss1: 2.086996 Loss2: 1.568877
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.550161 Loss1: 1.970050 Loss2: 1.580111
+(DefaultActor pid=3764) >> Training accuracy: 0.463542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.198133 Loss1: 3.092431 Loss2: 2.105701
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.123361 Loss1: 2.650983 Loss2: 1.472379
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.832096 Loss1: 2.409306 Loss2: 1.422790
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.701225 Loss1: 2.273242 Loss2: 1.427982
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.630001 Loss1: 2.200158 Loss2: 1.429843
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.573044 Loss1: 2.106498 Loss2: 1.466546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.503626 Loss1: 2.054071 Loss2: 1.449555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.440542 Loss1: 1.985562 Loss2: 1.454980
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.000702 Loss1: 2.525415 Loss2: 1.475287
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.880687 Loss1: 2.398068 Loss2: 1.482619 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.447917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.759302 Loss1: 2.269827 Loss2: 1.489474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.730388 Loss1: 2.230071 Loss2: 1.500317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.505397 Loss1: 2.008840 Loss2: 1.496557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.530874 Loss1: 2.024314 Loss2: 1.506560 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.444792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.875475 Loss1: 2.406811 Loss2: 1.468664 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.575464 Loss1: 2.115630 Loss2: 1.459834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.136703 Loss1: 3.172098 Loss2: 1.964605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.281000 Loss1: 2.786262 Loss2: 1.494738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.058994 Loss1: 2.595074 Loss2: 1.463920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.971870 Loss1: 2.503355 Loss2: 1.468515 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.485417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.778384 Loss1: 2.302738 Loss2: 1.475646 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.643145 Loss1: 2.145874 Loss2: 1.497271 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.596417 Loss1: 2.090685 Loss2: 1.505732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.554506 Loss1: 2.035088 Loss2: 1.519418 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.384766
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.901377 Loss1: 2.421582 Loss2: 1.479794 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.767505 Loss1: 2.291974 Loss2: 1.475531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.643018 Loss1: 2.156585 Loss2: 1.486433
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.858801 Loss1: 2.880962 Loss2: 1.977839
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.971848 Loss1: 2.480022 Loss2: 1.491826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.670298 Loss1: 2.209589 Loss2: 1.460708 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.430208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.605696 Loss1: 2.139946 Loss2: 1.465749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.487289 Loss1: 2.001588 Loss2: 1.485701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.376135 Loss1: 1.888128 Loss2: 1.488008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.422233 Loss1: 1.908688 Loss2: 1.513545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.296432 Loss1: 1.794720 Loss2: 1.501712 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.456250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.808500 Loss1: 2.398917 Loss2: 1.409583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.679736 Loss1: 2.264393 Loss2: 1.415343 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.001831 Loss1: 3.057885 Loss2: 1.943946 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.164283 Loss1: 2.673362 Loss2: 1.490921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.903135 Loss1: 2.435096 Loss2: 1.468039 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.491667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.802453 Loss1: 2.318238 Loss2: 1.484215 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.626839 Loss1: 2.131655 Loss2: 1.495184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.194524 Loss1: 3.168151 Loss2: 2.026373
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.613106 Loss1: 2.105421 Loss2: 1.507685
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.528531 Loss1: 2.025502 Loss2: 1.503029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.427780 Loss1: 1.920379 Loss2: 1.507401 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.523438
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.808811 Loss1: 2.298587 Loss2: 1.510223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.631047 Loss1: 2.125147 Loss2: 1.505900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.551929 Loss1: 2.032010 Loss2: 1.519919
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.970894 Loss1: 2.979744 Loss2: 1.991150
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.958130 Loss1: 2.461423 Loss2: 1.496707 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.427083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.700985 Loss1: 2.238834 Loss2: 1.462151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.482674 Loss1: 2.026143 Loss2: 1.456531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.339024 Loss1: 1.861952 Loss2: 1.477072 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.297998 Loss1: 1.820956 Loss2: 1.477042 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.268558 Loss1: 1.781059 Loss2: 1.487499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.194554 Loss1: 1.700070 Loss2: 1.494484 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.511458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.763030 Loss1: 2.238118 Loss2: 1.524911 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.680691 Loss1: 2.132960 Loss2: 1.547731 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.570827 Loss1: 2.031043 Loss2: 1.539785
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.047855 Loss1: 3.129664 Loss2: 1.918191
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.180180 Loss1: 2.734321 Loss2: 1.445860 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.487500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.950427 Loss1: 2.521507 Loss2: 1.428920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.855448 Loss1: 2.411966 Loss2: 1.443482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.687226 Loss1: 2.231550 Loss2: 1.455676 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.640407 Loss1: 2.197043 Loss2: 1.443364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.526356 Loss1: 2.054329 Loss2: 1.472027 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.547500 Loss1: 2.070129 Loss2: 1.477371 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.433333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.706876 Loss1: 2.158816 Loss2: 1.548059 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.510163 Loss1: 1.962809 Loss2: 1.547354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.402958 Loss1: 1.833950 Loss2: 1.569008
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.242576 Loss1: 3.199058 Loss2: 2.043518
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.320773 Loss1: 2.789723 Loss2: 1.531051 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.506250
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.345365 Loss1: 1.766079 Loss2: 1.579286
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.123133 Loss1: 2.610609 Loss2: 1.512524
+DEBUG flwr 2023-10-08 23:18:56,950 | server.py:236 | fit_round 18 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 3 Loss: 4.003972 Loss1: 2.500763 Loss2: 1.503209
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.965033 Loss1: 2.431108 Loss2: 1.533925
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.891870 Loss1: 2.350512 Loss2: 1.541357
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.898049 Loss1: 2.350176 Loss2: 1.547874
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.010351 Loss1: 2.946829 Loss2: 2.063522
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.788175 Loss1: 2.238156 Loss2: 1.550020
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.768595 Loss1: 2.205190 Loss2: 1.563404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.726301 Loss1: 2.170041 Loss2: 1.556260 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.468750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.653268 Loss1: 2.119437 Loss2: 1.533831 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.594810 Loss1: 2.054821 Loss2: 1.539990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.499322 Loss1: 1.933876 Loss2: 1.565446
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.116009 Loss1: 3.025553 Loss2: 2.090456
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.122729 Loss1: 2.584518 Loss2: 1.538211 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.453125
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.384165 Loss1: 1.826298 Loss2: 1.557867
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.898850 Loss1: 2.389616 Loss2: 1.509234
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.883273 Loss1: 2.378252 Loss2: 1.505021
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.682668 Loss1: 2.170777 Loss2: 1.511890
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.561650 Loss1: 2.053251 Loss2: 1.508399
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.501749 Loss1: 1.977951 Loss2: 1.523798
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.501401 Loss1: 1.975127 Loss2: 1.526274
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.499304 Loss1: 1.959909 Loss2: 1.539395
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.565146 Loss1: 2.023971 Loss2: 1.541175
+(DefaultActor pid=3764) >> Training accuracy: 0.506696
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 23:18:56,950][flwr][DEBUG] - fit_round 18 received 50 results and 0 failures
+INFO flwr 2023-10-08 23:19:38,794 | server.py:125 | fit progress: (18, 3.4021275843294285, {'accuracy': 0.1934}, 41286.572079595004)
+>> Test accuracy: 0.193400
+[2023-10-08 23:19:38,794][flwr][INFO] - fit progress: (18, 3.4021275843294285, {'accuracy': 0.1934}, 41286.572079595004)
+DEBUG flwr 2023-10-08 23:19:38,794 | server.py:173 | evaluate_round 18: strategy sampled 50 clients (out of 50)
+[2023-10-08 23:19:38,794][flwr][DEBUG] - evaluate_round 18: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-08 23:28:44,755 | server.py:187 | evaluate_round 18 received 50 results and 0 failures
+[2023-10-08 23:28:44,755][flwr][DEBUG] - evaluate_round 18 received 50 results and 0 failures
+DEBUG flwr 2023-10-08 23:28:44,755 | server.py:222 | fit_round 19: strategy sampled 50 clients (out of 50)
+[2023-10-08 23:28:44,755][flwr][DEBUG] - fit_round 19: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.089913 Loss1: 3.076141 Loss2: 2.013773
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.176945 Loss1: 2.706927 Loss2: 1.470018
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.951002 Loss1: 2.498280 Loss2: 1.452723
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.797290 Loss1: 2.345836 Loss2: 1.451455
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.209688 Loss1: 3.127394 Loss2: 2.082294
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.291119 Loss1: 2.730826 Loss2: 1.560292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.072488 Loss1: 2.513165 Loss2: 1.559323 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.945890 Loss1: 2.399739 Loss2: 1.546151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.764027 Loss1: 2.216241 Loss2: 1.547786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.753012 Loss1: 2.188893 Loss2: 1.564118 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.412500
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.395006 Loss1: 1.927366 Loss2: 1.467639
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.686006 Loss1: 2.123265 Loss2: 1.562741
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.574511 Loss1: 2.005052 Loss2: 1.569458
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.576838 Loss1: 1.997628 Loss2: 1.579210
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.548590 Loss1: 1.965880 Loss2: 1.582711
+(DefaultActor pid=3764) >> Training accuracy: 0.489583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.096405 Loss1: 3.008433 Loss2: 2.087972
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.117826 Loss1: 2.606875 Loss2: 1.510951
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.768026 Loss1: 2.273596 Loss2: 1.494430
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.738310 Loss1: 2.251553 Loss2: 1.486758
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.999451 Loss1: 3.027936 Loss2: 1.971515
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.579213 Loss1: 2.062156 Loss2: 1.517057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.483785 Loss1: 1.961061 Loss2: 1.522724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.381782 Loss1: 1.856715 Loss2: 1.525067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.475067 Loss1: 1.935663 Loss2: 1.539404 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.317636 Loss1: 1.777365 Loss2: 1.540271 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.520433
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.615137 Loss1: 2.125198 Loss2: 1.489939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.412083 Loss1: 1.916840 Loss2: 1.495243 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.501042
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.321498 Loss1: 1.831151 Loss2: 1.490348
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.922782 Loss1: 2.962954 Loss2: 1.959828
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.934246 Loss1: 2.486180 Loss2: 1.448066
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.692704 Loss1: 2.273170 Loss2: 1.419534
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.615671 Loss1: 2.195205 Loss2: 1.420465
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.546161 Loss1: 2.115677 Loss2: 1.430484
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.219511 Loss1: 3.064341 Loss2: 2.155170
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.229197 Loss1: 2.621347 Loss2: 1.607850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 4.002032 Loss1: 2.416285 Loss2: 1.585748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.826224 Loss1: 2.240317 Loss2: 1.585907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.765190 Loss1: 2.175487 Loss2: 1.589702 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.555208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.685772 Loss1: 2.084004 Loss2: 1.601769 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.584340 Loss1: 1.969944 Loss2: 1.614396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.456462 Loss1: 1.837057 Loss2: 1.619404 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.547917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.033860 Loss1: 2.502881 Loss2: 1.530979 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.613492 Loss1: 2.105588 Loss2: 1.507904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.513431 Loss1: 2.003961 Loss2: 1.509470
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.844160 Loss1: 2.899974 Loss2: 1.944186
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.999211 Loss1: 2.526557 Loss2: 1.472654 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.780445 Loss1: 2.327614 Loss2: 1.452831 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.618886 Loss1: 2.162168 Loss2: 1.456718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.575032 Loss1: 2.123905 Loss2: 1.451127 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.591667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.501502 Loss1: 2.027064 Loss2: 1.474438 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.324530 Loss1: 1.862713 Loss2: 1.461817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.273708 Loss1: 1.780889 Loss2: 1.492820 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.486458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.816901 Loss1: 2.285856 Loss2: 1.531045 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.448285 Loss1: 1.969048 Loss2: 1.479237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.400039 Loss1: 1.917298 Loss2: 1.482741
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.139302 Loss1: 3.122867 Loss2: 2.016435
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.118099 Loss1: 2.627565 Loss2: 1.490535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.930096 Loss1: 2.463692 Loss2: 1.466404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.793535 Loss1: 2.305554 Loss2: 1.487980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.671428 Loss1: 2.187213 Loss2: 1.484216 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.529167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.565879 Loss1: 2.089625 Loss2: 1.476254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.507821 Loss1: 2.000415 Loss2: 1.507407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.531028 Loss1: 2.008578 Loss2: 1.522451 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.469792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.323590 Loss1: 2.839481 Loss2: 1.484110 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.853518 Loss1: 2.392834 Loss2: 1.460684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.958836 Loss1: 2.999041 Loss2: 1.959795 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.647281 Loss1: 2.148312 Loss2: 1.498969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.567484 Loss1: 2.070825 Loss2: 1.496659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.544781 Loss1: 2.024120 Loss2: 1.520661 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.440080 Loss1: 1.931585 Loss2: 1.508496 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.426339
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.560383 Loss1: 2.098014 Loss2: 1.462368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.447260 Loss1: 1.959282 Loss2: 1.487978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.361900 Loss1: 1.880909 Loss2: 1.480991
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.050568 Loss1: 2.951685 Loss2: 2.098884
+(DefaultActor pid=3764) >> Training accuracy: 0.485352
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.363150 Loss1: 1.854086 Loss2: 1.509064
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.073209 Loss1: 2.505143 Loss2: 1.568067
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.964173 Loss1: 2.411022 Loss2: 1.553151
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.783686 Loss1: 2.233718 Loss2: 1.549969
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.630552 Loss1: 2.087797 Loss2: 1.542755
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.532711 Loss1: 1.980396 Loss2: 1.552315
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.016294 Loss1: 2.947727 Loss2: 2.068567
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.058034 Loss1: 2.521256 Loss2: 1.536778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.781580 Loss1: 2.280471 Loss2: 1.501109 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.626379 Loss1: 2.113918 Loss2: 1.512460 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.523958
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.391578 Loss1: 1.809623 Loss2: 1.581955
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.601928 Loss1: 2.085730 Loss2: 1.516198
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.527153 Loss1: 2.011109 Loss2: 1.516044
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.445193 Loss1: 1.923521 Loss2: 1.521672
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.556871 Loss1: 2.005438 Loss2: 1.551434
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.308936 Loss1: 1.767031 Loss2: 1.541905
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.409373 Loss1: 1.857183 Loss2: 1.552190
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.919472 Loss1: 2.969889 Loss2: 1.949582
+(DefaultActor pid=3764) >> Training accuracy: 0.441964
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.086334 Loss1: 2.614330 Loss2: 1.472004
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.815677 Loss1: 2.371289 Loss2: 1.444388
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.667137 Loss1: 2.241101 Loss2: 1.426036
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.559048 Loss1: 2.110953 Loss2: 1.448095
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.941390 Loss1: 2.917656 Loss2: 2.023734
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.467936 Loss1: 2.018594 Loss2: 1.449341
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.441897 Loss1: 1.977705 Loss2: 1.464192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.429968 Loss1: 1.955616 Loss2: 1.474352 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.343428 Loss1: 1.861191 Loss2: 1.482237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.318019 Loss1: 1.819444 Loss2: 1.498575 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.469792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.303172 Loss1: 1.829954 Loss2: 1.473218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.268389 Loss1: 1.764499 Loss2: 1.503890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.193060 Loss1: 1.690116 Loss2: 1.502944
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.977144 Loss1: 2.918306 Loss2: 2.058838
+(DefaultActor pid=3764) >> Training accuracy: 0.535417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.953075 Loss1: 2.421759 Loss2: 1.531316
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.715598 Loss1: 2.211410 Loss2: 1.504188
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.670228 Loss1: 2.179644 Loss2: 1.490584
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.586490 Loss1: 2.087386 Loss2: 1.499104
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.057611 Loss1: 3.129134 Loss2: 1.928478
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.497210 Loss1: 2.000436 Loss2: 1.496774
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.182592 Loss1: 2.749189 Loss2: 1.433403
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.445421 Loss1: 1.943024 Loss2: 1.502396
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.993801 Loss1: 2.568874 Loss2: 1.424927
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.435087 Loss1: 1.905949 Loss2: 1.529138
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.304090 Loss1: 1.782660 Loss2: 1.521430
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.856704 Loss1: 2.438021 Loss2: 1.418683
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.271402 Loss1: 1.750026 Loss2: 1.521376
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.745292 Loss1: 2.304547 Loss2: 1.440744
+(DefaultActor pid=3765) >> Training accuracy: 0.530208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.724574 Loss1: 2.275543 Loss2: 1.449032
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.795014 Loss1: 2.342680 Loss2: 1.452334
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.653133 Loss1: 2.194904 Loss2: 1.458228
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.622327 Loss1: 2.152324 Loss2: 1.470003
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.132833 Loss1: 3.067871 Loss2: 2.064962
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.454674 Loss1: 1.982266 Loss2: 1.472408
+(DefaultActor pid=3764) >> Training accuracy: 0.453125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.855770 Loss1: 2.342111 Loss2: 1.513659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.730199 Loss1: 2.187277 Loss2: 1.542922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.763025 Loss1: 2.196699 Loss2: 1.566325
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.056782 Loss1: 2.822443 Loss2: 2.234339
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.126935 Loss1: 2.459749 Loss2: 1.667186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.807699 Loss1: 2.185434 Loss2: 1.622265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.589832 Loss1: 1.981744 Loss2: 1.608088 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.520833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.493808 Loss1: 1.883939 Loss2: 1.609870 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.436588 Loss1: 1.807838 Loss2: 1.628751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.374860 Loss1: 1.731141 Loss2: 1.643718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.317084 Loss1: 1.658438 Loss2: 1.658646 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.559375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.937007 Loss1: 2.452897 Loss2: 1.484110 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.693736 Loss1: 2.218124 Loss2: 1.475611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.921881 Loss1: 2.934937 Loss2: 1.986944
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.666012 Loss1: 2.168257 Loss2: 1.497755
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.033067 Loss1: 2.550783 Loss2: 1.482284
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.553601 Loss1: 2.058888 Loss2: 1.494713
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.775623 Loss1: 2.303619 Loss2: 1.472004
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.532857 Loss1: 2.030999 Loss2: 1.501858
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.704175 Loss1: 2.237674 Loss2: 1.466501
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.487656 Loss1: 1.963505 Loss2: 1.524150
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.586191 Loss1: 2.072595 Loss2: 1.513596 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.491211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.461766 Loss1: 1.975268 Loss2: 1.486498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.405667 Loss1: 1.917930 Loss2: 1.487737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.252554 Loss1: 1.753155 Loss2: 1.499399
+(DefaultActor pid=3764) >> Training accuracy: 0.550000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.914835 Loss1: 2.967542 Loss2: 1.947293
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.975795 Loss1: 2.528458 Loss2: 1.447336
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.808115 Loss1: 2.374488 Loss2: 1.433627
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.684810 Loss1: 2.250420 Loss2: 1.434390
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.518664 Loss1: 2.075319 Loss2: 1.443345
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.079766 Loss1: 3.058990 Loss2: 2.020776
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.550821 Loss1: 2.109310 Loss2: 1.441511
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.492068 Loss1: 2.022960 Loss2: 1.469108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.379027 Loss1: 1.908036 Loss2: 1.470991 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.358095 Loss1: 1.879946 Loss2: 1.478149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.247407 Loss1: 1.774037 Loss2: 1.473371 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.529167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.576019 Loss1: 2.058808 Loss2: 1.517212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.448316 Loss1: 1.918651 Loss2: 1.529665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.519099 Loss1: 1.981065 Loss2: 1.538035
+(DefaultActor pid=3764) >> Training accuracy: 0.507292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.913638 Loss1: 2.890271 Loss2: 2.023367
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.988202 Loss1: 2.489276 Loss2: 1.498926
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.737506 Loss1: 2.251480 Loss2: 1.486026
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.609152 Loss1: 2.121536 Loss2: 1.487616
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.553862 Loss1: 2.054349 Loss2: 1.499513
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.028243 Loss1: 3.058635 Loss2: 1.969607
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.087957 Loss1: 2.608833 Loss2: 1.479124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.868774 Loss1: 2.421043 Loss2: 1.447732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.712837 Loss1: 2.262879 Loss2: 1.449959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.644205 Loss1: 2.198738 Loss2: 1.445468 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.489583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.561960 Loss1: 2.106620 Loss2: 1.455340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.449828 Loss1: 1.987983 Loss2: 1.461845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.349421 Loss1: 1.869906 Loss2: 1.479516 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.448958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.960479 Loss1: 2.497082 Loss2: 1.463397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.603888 Loss1: 2.164807 Loss2: 1.439081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.939550 Loss1: 2.961392 Loss2: 1.978157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.102744 Loss1: 2.635816 Loss2: 1.466928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.870405 Loss1: 2.425212 Loss2: 1.445193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.673594 Loss1: 2.226823 Loss2: 1.446770 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.583479 Loss1: 2.127477 Loss2: 1.456003 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.510417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.453250 Loss1: 1.982673 Loss2: 1.470578 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.409958 Loss1: 1.915099 Loss2: 1.494859 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.201320 Loss1: 1.719579 Loss2: 1.481741
+(DefaultActor pid=3764) >> Training accuracy: 0.492708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.140947 Loss1: 3.172425 Loss2: 1.968522
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.169630 Loss1: 2.679426 Loss2: 1.490204
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.961882 Loss1: 2.491438 Loss2: 1.470444
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.870900 Loss1: 2.415680 Loss2: 1.455220
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.736898 Loss1: 2.267933 Loss2: 1.468965
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.874324 Loss1: 2.843837 Loss2: 2.030487
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.738673 Loss1: 2.260790 Loss2: 1.477882
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.905379 Loss1: 2.368523 Loss2: 1.536856
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.607628 Loss1: 2.135383 Loss2: 1.472244
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.724343 Loss1: 2.226791 Loss2: 1.497552
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.581919 Loss1: 2.108522 Loss2: 1.473397
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.579969 Loss1: 2.066058 Loss2: 1.513911
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.488036 Loss1: 1.993973 Loss2: 1.494063
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.481074 Loss1: 1.990750 Loss2: 1.490324
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.505494 Loss1: 1.987544 Loss2: 1.517950
+(DefaultActor pid=3765) >> Training accuracy: 0.447917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.350760 Loss1: 1.826496 Loss2: 1.524265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.221038 Loss1: 1.691380 Loss2: 1.529658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.122945 Loss1: 1.597716 Loss2: 1.525228
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.304473 Loss1: 3.035558 Loss2: 2.268915
+(DefaultActor pid=3764) >> Training accuracy: 0.617708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.194320 Loss1: 2.604977 Loss2: 1.589344
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.935812 Loss1: 2.383433 Loss2: 1.552379
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.724684 Loss1: 2.165851 Loss2: 1.558833
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.618225 Loss1: 2.067650 Loss2: 1.550575
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.556053 Loss1: 1.999824 Loss2: 1.556228
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.483925 Loss1: 1.923417 Loss2: 1.560508
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.539653 Loss1: 1.962136 Loss2: 1.577516 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.468937 Loss1: 1.884088 Loss2: 1.584849 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.368440 Loss1: 1.789579 Loss2: 1.578861 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.490885
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.772098 Loss1: 2.238371 Loss2: 1.533727 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.684207 Loss1: 2.130789 Loss2: 1.553418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.565151 Loss1: 2.013225 Loss2: 1.551925
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.015288 Loss1: 2.911719 Loss2: 2.103570
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.527899 Loss1: 1.966305 Loss2: 1.561594
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.100549 Loss1: 2.531909 Loss2: 1.568639
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.435094 Loss1: 1.872114 Loss2: 1.562980
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.877933 Loss1: 2.335977 Loss2: 1.541957
+(DefaultActor pid=3764) >> Training accuracy: 0.492708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.731203 Loss1: 2.182469 Loss2: 1.548734
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.688275 Loss1: 2.127323 Loss2: 1.560951
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.525563 Loss1: 1.960562 Loss2: 1.565001
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.514877 Loss1: 1.937535 Loss2: 1.577342
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.166128 Loss1: 2.942385 Loss2: 2.223742
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.455309 Loss1: 1.879134 Loss2: 1.576175
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.460086 Loss1: 1.879651 Loss2: 1.580435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.454809 Loss1: 1.851491 Loss2: 1.603318 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.516667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.494758 Loss1: 1.877884 Loss2: 1.616874 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.487662 Loss1: 1.865316 Loss2: 1.622346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.854349 Loss1: 3.010912 Loss2: 1.843437 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.522837
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.876128 Loss1: 2.476713 Loss2: 1.399416 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.528221 Loss1: 2.150749 Loss2: 1.377472 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.385198 Loss1: 1.995978 Loss2: 1.389220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.400030 Loss1: 2.010475 Loss2: 1.389555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.331451 Loss1: 1.925257 Loss2: 1.406193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.284245 Loss1: 1.868372 Loss2: 1.415873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.204495 Loss1: 1.798417 Loss2: 1.406078 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.526367
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.710342 Loss1: 2.157372 Loss2: 1.552970 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.570139 Loss1: 2.012194 Loss2: 1.557945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.546324 Loss1: 1.971897 Loss2: 1.574427 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.472656
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.972527 Loss1: 2.506260 Loss2: 1.466267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.724342 Loss1: 2.266625 Loss2: 1.457717 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.769084 Loss1: 2.282966 Loss2: 1.486118
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.116138 Loss1: 3.113293 Loss2: 2.002845
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.615203 Loss1: 2.127599 Loss2: 1.487604
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.118346 Loss1: 2.637427 Loss2: 1.480919
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.524224 Loss1: 2.028773 Loss2: 1.495451
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.839969 Loss1: 2.357648 Loss2: 1.482320
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.497755 Loss1: 2.002440 Loss2: 1.495315
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.675157 Loss1: 2.210203 Loss2: 1.464954
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.640969 Loss1: 2.162500 Loss2: 1.478469
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.398847 Loss1: 1.900249 Loss2: 1.498598
+(DefaultActor pid=3765) >> Training accuracy: 0.496094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.525723 Loss1: 2.032051 Loss2: 1.493672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.402450 Loss1: 1.888865 Loss2: 1.513585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.311307 Loss1: 3.176785 Loss2: 2.134522
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.439530 Loss1: 1.927147 Loss2: 1.512383
+(DefaultActor pid=3764) >> Training accuracy: 0.502083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 4.132410 Loss1: 2.576794 Loss2: 1.555615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.862407 Loss1: 2.295183 Loss2: 1.567224 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.804395 Loss1: 2.227232 Loss2: 1.577163
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.878956 Loss1: 2.932483 Loss2: 1.946473
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.007979 Loss1: 2.511170 Loss2: 1.496809 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.807728 Loss1: 2.332913 Loss2: 1.474815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.658728 Loss1: 2.193966 Loss2: 1.464762 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.497768
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.438619 Loss1: 1.956458 Loss2: 1.482161 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.339382 Loss1: 1.852507 Loss2: 1.486875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.344948 Loss1: 1.839551 Loss2: 1.505397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.201285 Loss1: 1.700915 Loss2: 1.500370 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.527344
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.500111 Loss1: 2.031632 Loss2: 1.468479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.357345 Loss1: 1.873400 Loss2: 1.483945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.180358 Loss1: 1.698996 Loss2: 1.481362
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.111529 Loss1: 3.050317 Loss2: 2.061212
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.104945 Loss1: 2.567307 Loss2: 1.537638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.915143 Loss1: 2.399395 Loss2: 1.515748 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.600000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.082053 Loss1: 1.569360 Loss2: 1.512693
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.851045 Loss1: 2.334248 Loss2: 1.516797
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.702307 Loss1: 2.170160 Loss2: 1.532147
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.731394 Loss1: 2.195406 Loss2: 1.535987
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.571294 Loss1: 2.039360 Loss2: 1.531934
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.472252 Loss1: 1.930624 Loss2: 1.541629
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.832310 Loss1: 2.962175 Loss2: 1.870136
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.448401 Loss1: 1.891856 Loss2: 1.556545
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.070898 Loss1: 2.646673 Loss2: 1.424225
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.435476 Loss1: 1.874556 Loss2: 1.560920
+(DefaultActor pid=3764) >> Training accuracy: 0.504167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.629636 Loss1: 2.247706 Loss2: 1.381929 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.446900 Loss1: 2.045553 Loss2: 1.401347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.135490 Loss1: 3.123315 Loss2: 2.012175
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.477880 Loss1: 2.049094 Loss2: 1.428786
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.340214 Loss1: 1.926719 Loss2: 1.413495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.262639 Loss1: 1.854252 Loss2: 1.408388 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.189290 Loss1: 1.778749 Loss2: 1.410541 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.505859
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.668515 Loss1: 2.149114 Loss2: 1.519401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.519199 Loss1: 1.979485 Loss2: 1.539714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.466669 Loss1: 1.941681 Loss2: 1.524988
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.656154 Loss1: 2.699399 Loss2: 1.956755
+(DefaultActor pid=3764) >> Training accuracy: 0.418750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.907081 Loss1: 2.455993 Loss2: 1.451087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.556960 Loss1: 2.106141 Loss2: 1.450819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.399911 Loss1: 1.948302 Loss2: 1.451609 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.417896 Loss1: 1.961641 Loss2: 1.456255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.236740 Loss1: 1.772059 Loss2: 1.464680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.133340 Loss1: 1.689164 Loss2: 1.444176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.088934 Loss1: 1.621312 Loss2: 1.467622 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.512500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.405998 Loss1: 1.958817 Loss2: 1.447181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.351403 Loss1: 1.891347 Loss2: 1.460057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.297691 Loss1: 1.828730 Loss2: 1.468961
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.081005 Loss1: 3.129293 Loss2: 1.951711
+(DefaultActor pid=3764) >> Training accuracy: 0.504167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.345571 Loss1: 1.865181 Loss2: 1.480389
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.154626 Loss1: 2.721826 Loss2: 1.432799
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.896181 Loss1: 2.471694 Loss2: 1.424487
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.850838 Loss1: 2.423028 Loss2: 1.427810
+DEBUG flwr 2023-10-08 23:57:00,444 | server.py:236 | fit_round 19 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.698078 Loss1: 2.261565 Loss2: 1.436513
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.645545 Loss1: 2.207908 Loss2: 1.437638
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.158892 Loss1: 3.050023 Loss2: 2.108869
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.558062 Loss1: 2.103245 Loss2: 1.454817
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.568903 Loss1: 2.103930 Loss2: 1.464973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.427979 Loss1: 1.954778 Loss2: 1.473200 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.379789 Loss1: 1.904794 Loss2: 1.474995 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.525000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.766914 Loss1: 2.198827 Loss2: 1.568087 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.686368 Loss1: 2.103141 Loss2: 1.583228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.643393 Loss1: 2.055399 Loss2: 1.587995
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.857039 Loss1: 2.944999 Loss2: 1.912040
+(DefaultActor pid=3764) >> Training accuracy: 0.444792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.030730 Loss1: 2.564030 Loss2: 1.466701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.665960 Loss1: 2.222721 Loss2: 1.443239 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.474075 Loss1: 2.023171 Loss2: 1.450904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.972554 Loss1: 2.437543 Loss2: 1.535011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.702438 Loss1: 2.196032 Loss2: 1.506407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.549171 Loss1: 2.044448 Loss2: 1.504723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.518444 Loss1: 2.008851 Loss2: 1.509594 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.522978
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.386611 Loss1: 1.861772 Loss2: 1.524839 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.217485 Loss1: 1.681367 Loss2: 1.536118 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.549805
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-08 23:57:00,444][flwr][DEBUG] - fit_round 19 received 50 results and 0 failures
+INFO flwr 2023-10-08 23:57:42,206 | server.py:125 | fit progress: (19, 3.324841410969012, {'accuracy': 0.208}, 43569.985048101)
+>> Test accuracy: 0.208000
+[2023-10-08 23:57:42,206][flwr][INFO] - fit progress: (19, 3.324841410969012, {'accuracy': 0.208}, 43569.985048101)
+DEBUG flwr 2023-10-08 23:57:42,207 | server.py:173 | evaluate_round 19: strategy sampled 50 clients (out of 50)
+[2023-10-08 23:57:42,207][flwr][DEBUG] - evaluate_round 19: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 00:06:44,806 | server.py:187 | evaluate_round 19 received 50 results and 0 failures
+[2023-10-09 00:06:44,806][flwr][DEBUG] - evaluate_round 19 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 00:06:44,806 | server.py:222 | fit_round 20: strategy sampled 50 clients (out of 50)
+[2023-10-09 00:06:44,806][flwr][DEBUG] - fit_round 20: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.977883 Loss1: 2.903936 Loss2: 2.073947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.701241 Loss1: 2.208853 Loss2: 1.492387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.528964 Loss1: 2.050114 Loss2: 1.478850
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.896231 Loss1: 2.965932 Loss2: 1.930299
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.941887 Loss1: 2.490072 Loss2: 1.451816 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.709979 Loss1: 2.281983 Loss2: 1.427995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.577378 Loss1: 2.142024 Loss2: 1.435354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.555845 Loss1: 2.108341 Loss2: 1.447504 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.473184 Loss1: 2.019735 Loss2: 1.453449 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.583333
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.120472 Loss1: 1.598982 Loss2: 1.521489
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.338299 Loss1: 1.871062 Loss2: 1.467237
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.394014 Loss1: 1.926036 Loss2: 1.467978
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.343309 Loss1: 1.866243 Loss2: 1.477066
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.214856 Loss1: 1.740474 Loss2: 1.474381
+(DefaultActor pid=3764) >> Training accuracy: 0.568750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.914293 Loss1: 2.950825 Loss2: 1.963468
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.982598 Loss1: 2.512120 Loss2: 1.470478
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.744240 Loss1: 2.306291 Loss2: 1.437949
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.624397 Loss1: 2.182130 Loss2: 1.442266
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.906375 Loss1: 2.890385 Loss2: 2.015991
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.947323 Loss1: 2.425548 Loss2: 1.521775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.701953 Loss1: 2.193568 Loss2: 1.508384 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.412068 Loss1: 1.937852 Loss2: 1.474216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.315069 Loss1: 1.842195 Loss2: 1.472874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.274044 Loss1: 1.793684 Loss2: 1.480360 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.522917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.402466 Loss1: 1.868143 Loss2: 1.534323 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.367403 Loss1: 1.818072 Loss2: 1.549332 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.369251 Loss1: 1.815966 Loss2: 1.553285
+(DefaultActor pid=3764) >> Training accuracy: 0.539522
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.842456 Loss1: 2.863302 Loss2: 1.979154
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.004423 Loss1: 2.501596 Loss2: 1.502827
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.836335 Loss1: 2.359949 Loss2: 1.476385
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.666124 Loss1: 2.184937 Loss2: 1.481186
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.661493 Loss1: 2.183693 Loss2: 1.477800
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.069759 Loss1: 2.966932 Loss2: 2.102827
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.569461 Loss1: 2.054536 Loss2: 1.514925
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.177565 Loss1: 2.604991 Loss2: 1.572574
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.447916 Loss1: 1.959114 Loss2: 1.488802
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.986336 Loss1: 2.422569 Loss2: 1.563767
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.326948 Loss1: 1.836515 Loss2: 1.490432
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.798910 Loss1: 2.236805 Loss2: 1.562105
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.282666 Loss1: 1.779265 Loss2: 1.503402
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.676579 Loss1: 2.103261 Loss2: 1.573319
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.404330 Loss1: 1.892031 Loss2: 1.512299
+(DefaultActor pid=3765) >> Training accuracy: 0.507292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.550487 Loss1: 1.964778 Loss2: 1.585708 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.475745 Loss1: 1.881988 Loss2: 1.593756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.371070 Loss1: 1.771587 Loss2: 1.599484
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.954745 Loss1: 2.855772 Loss2: 2.098973
+(DefaultActor pid=3764) >> Training accuracy: 0.498958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.013773 Loss1: 2.431053 Loss2: 1.582719
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.787108 Loss1: 2.232302 Loss2: 1.554806
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.649454 Loss1: 2.099881 Loss2: 1.549573
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.627890 Loss1: 2.065061 Loss2: 1.562829
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.644526 Loss1: 2.723969 Loss2: 1.920558
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.630831 Loss1: 2.047175 Loss2: 1.583655
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.686778 Loss1: 2.248843 Loss2: 1.437935
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.528557 Loss1: 1.962381 Loss2: 1.566176
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.435751 Loss1: 2.022217 Loss2: 1.413534
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.426056 Loss1: 1.836940 Loss2: 1.589116
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.272405 Loss1: 1.849121 Loss2: 1.423284
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.345811 Loss1: 1.756175 Loss2: 1.589636
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.287741 Loss1: 1.860771 Loss2: 1.426970
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.305289 Loss1: 1.703368 Loss2: 1.601921
+(DefaultActor pid=3765) >> Training accuracy: 0.562500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.152109 Loss1: 1.713032 Loss2: 1.439077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.010516 Loss1: 1.569957 Loss2: 1.440560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.980643 Loss1: 1.535103 Loss2: 1.445540
+(DefaultActor pid=3764) >> Training accuracy: 0.589583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.845133 Loss1: 2.889283 Loss2: 1.955850
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.017186 Loss1: 2.533860 Loss2: 1.483326
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.750551 Loss1: 2.270295 Loss2: 1.480256
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.612670 Loss1: 2.128593 Loss2: 1.484077
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.580313 Loss1: 2.099525 Loss2: 1.480788
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.076365 Loss1: 2.991660 Loss2: 2.084704
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.203816 Loss1: 2.625816 Loss2: 1.578001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.937338 Loss1: 2.380709 Loss2: 1.556630 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.811938 Loss1: 2.237813 Loss2: 1.574124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.671336 Loss1: 2.099233 Loss2: 1.572103 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.485352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.595959 Loss1: 2.022441 Loss2: 1.573518 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.560859 Loss1: 1.951158 Loss2: 1.609701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.378862 Loss1: 1.779286 Loss2: 1.599576 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.486328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.713196 Loss1: 2.253233 Loss2: 1.459962 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.495164 Loss1: 2.034551 Loss2: 1.460612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.165381 Loss1: 3.237120 Loss2: 1.928261
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.537758 Loss1: 2.065329 Loss2: 1.472429
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.161623 Loss1: 2.715940 Loss2: 1.445683
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.345578 Loss1: 1.854342 Loss2: 1.491236
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.850806 Loss1: 2.442252 Loss2: 1.408554
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.321644 Loss1: 1.847523 Loss2: 1.474120
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.222934 Loss1: 1.732452 Loss2: 1.490482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.144960 Loss1: 1.653037 Loss2: 1.491922 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.483333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.419583 Loss1: 1.992619 Loss2: 1.426964 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.367548 Loss1: 1.934767 Loss2: 1.432781 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.502232
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.321294 Loss1: 1.883165 Loss2: 1.438129
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.732676 Loss1: 2.803532 Loss2: 1.929144
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.804281 Loss1: 2.356787 Loss2: 1.447495
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.564970 Loss1: 2.132295 Loss2: 1.432675
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.489564 Loss1: 2.058293 Loss2: 1.431271
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.458632 Loss1: 2.010806 Loss2: 1.447826
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.096803 Loss1: 2.880175 Loss2: 2.216628
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.839017 Loss1: 2.331975 Loss2: 1.507042 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.267238 Loss1: 1.800184 Loss2: 1.467054 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.236932 Loss1: 1.781949 Loss2: 1.454983 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.144677 Loss1: 1.674573 Loss2: 1.470104 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.516667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.203641 Loss1: 1.671251 Loss2: 1.532390 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.524740
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.094457 Loss1: 3.096110 Loss2: 1.998347 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.884122 Loss1: 2.381271 Loss2: 1.502851 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.767147 Loss1: 2.253792 Loss2: 1.513356
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.820773 Loss1: 2.867251 Loss2: 1.953521
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.684498 Loss1: 2.179631 Loss2: 1.504867
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.936750 Loss1: 2.477679 Loss2: 1.459070
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.713531 Loss1: 2.266527 Loss2: 1.447004
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.613955 Loss1: 2.098364 Loss2: 1.515591
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.544144 Loss1: 2.094727 Loss2: 1.449417
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.523136 Loss1: 1.997353 Loss2: 1.525784
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.383590 Loss1: 1.929205 Loss2: 1.454386
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.454878 Loss1: 1.925962 Loss2: 1.528916
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.380637 Loss1: 1.930215 Loss2: 1.450422
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.446646 Loss1: 1.906557 Loss2: 1.540088
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.457671 Loss1: 1.914087 Loss2: 1.543584 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.487305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.306457 Loss1: 1.820173 Loss2: 1.486284 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.584375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.010294 Loss1: 3.051531 Loss2: 1.958763 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.823953 Loss1: 2.373686 Loss2: 1.450267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.710586 Loss1: 2.278439 Loss2: 1.432146
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.124790 Loss1: 2.926192 Loss2: 2.198598
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.156829 Loss1: 2.460269 Loss2: 1.696560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.909907 Loss1: 2.267655 Loss2: 1.642253 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.746921 Loss1: 2.091538 Loss2: 1.655383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.716147 Loss1: 2.058457 Loss2: 1.657690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.669602 Loss1: 2.000996 Loss2: 1.668605 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.504167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.489644 Loss1: 1.819877 Loss2: 1.669767 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.412624 Loss1: 1.727952 Loss2: 1.684672 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.466797
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.749315 Loss1: 2.306442 Loss2: 1.442873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.246929 Loss1: 1.825842 Loss2: 1.421087 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.035904 Loss1: 2.985119 Loss2: 2.050785
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.243200 Loss1: 1.822986 Loss2: 1.420214
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.104644 Loss1: 2.575364 Loss2: 1.529281
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.182665 Loss1: 1.745817 Loss2: 1.436848
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.811682 Loss1: 2.308276 Loss2: 1.503406
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.179121 Loss1: 1.743562 Loss2: 1.435559
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.687497 Loss1: 2.192315 Loss2: 1.495183
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.143305 Loss1: 1.698272 Loss2: 1.445033
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.583127 Loss1: 2.067471 Loss2: 1.515656
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.113783 Loss1: 1.664994 Loss2: 1.448789
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.507925 Loss1: 1.985987 Loss2: 1.521938
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.010671 Loss1: 1.552589 Loss2: 1.458082
+(DefaultActor pid=3765) >> Training accuracy: 0.530208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.444659 Loss1: 1.901866 Loss2: 1.542793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.316589 Loss1: 1.763295 Loss2: 1.553294 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.493750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.224615 Loss1: 2.656655 Loss2: 1.567960 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.864132 Loss1: 2.338506 Loss2: 1.525626 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.856169 Loss1: 3.028429 Loss2: 1.827740
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.758803 Loss1: 2.236038 Loss2: 1.522765
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.882443 Loss1: 2.512329 Loss2: 1.370114
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.637112 Loss1: 2.102302 Loss2: 1.534810
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.771128 Loss1: 2.403091 Loss2: 1.368036
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.580207 Loss1: 2.037345 Loss2: 1.542862
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.603519 Loss1: 2.233673 Loss2: 1.369846
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.446686 Loss1: 1.908201 Loss2: 1.538484
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.495227 Loss1: 2.131396 Loss2: 1.363831
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.473874 Loss1: 1.914107 Loss2: 1.559767
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.445961 Loss1: 2.064630 Loss2: 1.381331
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.342167 Loss1: 1.781123 Loss2: 1.561045
+(DefaultActor pid=3765) >> Training accuracy: 0.538542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.334505 Loss1: 1.944401 Loss2: 1.390105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.175972 Loss1: 1.775482 Loss2: 1.400490 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.483333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.058250 Loss1: 2.487054 Loss2: 1.571197 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.706658 Loss1: 2.185451 Loss2: 1.521207 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.517399 Loss1: 1.985136 Loss2: 1.532262 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.401521 Loss1: 1.871689 Loss2: 1.529832 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.350386 Loss1: 1.817068 Loss2: 1.533318 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.259804 Loss1: 1.703808 Loss2: 1.555996 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.225366 Loss1: 1.681747 Loss2: 1.543619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.054799 Loss1: 1.507951 Loss2: 1.546849 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.615625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.473952 Loss1: 1.957306 Loss2: 1.516646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.283992 Loss1: 1.764808 Loss2: 1.519184 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.547917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.887992 Loss1: 2.324445 Loss2: 1.563546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.479539 Loss1: 1.951621 Loss2: 1.527918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.930998 Loss1: 2.932883 Loss2: 1.998115
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.398721 Loss1: 1.870040 Loss2: 1.528681
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.072435 Loss1: 2.520210 Loss2: 1.552225
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.411569 Loss1: 1.860522 Loss2: 1.551047
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.827100 Loss1: 2.327024 Loss2: 1.500076
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.326344 Loss1: 1.775578 Loss2: 1.550767
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.200294 Loss1: 1.645185 Loss2: 1.555109
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.747923 Loss1: 2.230971 Loss2: 1.516952
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.138064 Loss1: 1.592707 Loss2: 1.545356
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.585974 Loss1: 2.058910 Loss2: 1.527063
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.124816 Loss1: 1.561008 Loss2: 1.563808
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.498967 Loss1: 1.982620 Loss2: 1.516347
+(DefaultActor pid=3765) >> Training accuracy: 0.607292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.403253 Loss1: 1.886470 Loss2: 1.516783
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.365104 Loss1: 1.821449 Loss2: 1.543655
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.323442 Loss1: 1.783476 Loss2: 1.539967
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.356339 Loss1: 1.811027 Loss2: 1.545312
+(DefaultActor pid=3764) >> Training accuracy: 0.512695
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.736074 Loss1: 2.722464 Loss2: 2.013611
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.797442 Loss1: 2.281052 Loss2: 1.516390
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.587648 Loss1: 2.089843 Loss2: 1.497805
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.438101 Loss1: 1.950783 Loss2: 1.487317
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.007884 Loss1: 2.994990 Loss2: 2.012894
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.336516 Loss1: 1.853404 Loss2: 1.483113
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.004447 Loss1: 2.477135 Loss2: 1.527312
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.353403 Loss1: 1.851734 Loss2: 1.501669
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.709061 Loss1: 2.222025 Loss2: 1.487036
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.311592 Loss1: 1.807006 Loss2: 1.504586
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.563609 Loss1: 2.084051 Loss2: 1.479558
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.194232 Loss1: 1.696649 Loss2: 1.497583
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.211545 Loss1: 1.694423 Loss2: 1.517122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.121033 Loss1: 1.610776 Loss2: 1.510257 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.573242
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.316702 Loss1: 1.803982 Loss2: 1.512720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.163946 Loss1: 1.647361 Loss2: 1.516585 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.565625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.941213 Loss1: 2.905854 Loss2: 2.035359
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.019379 Loss1: 2.497752 Loss2: 1.521627
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.771418 Loss1: 2.255704 Loss2: 1.515714
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.613491 Loss1: 2.097720 Loss2: 1.515771
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.077635 Loss1: 3.079509 Loss2: 1.998126
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.127621 Loss1: 2.633481 Loss2: 1.494140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.865539 Loss1: 2.405155 Loss2: 1.460384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.704824 Loss1: 2.229724 Loss2: 1.475100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.635956 Loss1: 2.153456 Loss2: 1.482500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.628564 Loss1: 2.141164 Loss2: 1.487400 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.603125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.552978 Loss1: 2.058176 Loss2: 1.494802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.345828 Loss1: 1.840463 Loss2: 1.505365 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.501042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.083073 Loss1: 3.096941 Loss2: 1.986132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.833638 Loss1: 2.389657 Loss2: 1.443981 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.056935 Loss1: 3.114067 Loss2: 1.942868 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.109673 Loss1: 2.624350 Loss2: 1.485323 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.876284 Loss1: 2.407937 Loss2: 1.468347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.841135 Loss1: 2.362534 Loss2: 1.478601 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.396904 Loss1: 1.903474 Loss2: 1.493430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.338221 Loss1: 1.828142 Loss2: 1.510079 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.435417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.501454 Loss1: 2.001207 Loss2: 1.500247 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.404872 Loss1: 1.889921 Loss2: 1.514951 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.486328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.858987 Loss1: 2.329334 Loss2: 1.529653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.498256 Loss1: 2.007144 Loss2: 1.491111 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.432149 Loss1: 1.945075 Loss2: 1.487074
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.876186 Loss1: 2.725207 Loss2: 2.150979
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.999996 Loss1: 2.385992 Loss2: 1.614005 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.673571 Loss1: 2.099594 Loss2: 1.573977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.486388 Loss1: 1.922861 Loss2: 1.563527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.409241 Loss1: 1.833594 Loss2: 1.575647 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.110036 Loss1: 1.579238 Loss2: 1.530798
+(DefaultActor pid=3765) >> Training accuracy: 0.594792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.404730 Loss1: 1.811052 Loss2: 1.593679
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.327731 Loss1: 1.738351 Loss2: 1.589380
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.241177 Loss1: 1.659286 Loss2: 1.581891
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.191989 Loss1: 1.595000 Loss2: 1.596989
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.145124 Loss1: 1.550017 Loss2: 1.595107
+(DefaultActor pid=3764) >> Training accuracy: 0.557292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.831788 Loss1: 2.771568 Loss2: 2.060220
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.911953 Loss1: 2.354341 Loss2: 1.557613
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.725032 Loss1: 2.204089 Loss2: 1.520943
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.540422 Loss1: 2.020847 Loss2: 1.519575
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.443200 Loss1: 1.912935 Loss2: 1.530264
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.899117 Loss1: 2.811801 Loss2: 2.087317
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.343274 Loss1: 1.821766 Loss2: 1.521508
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.871451 Loss1: 2.317428 Loss2: 1.554023
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.337654 Loss1: 1.803515 Loss2: 1.534139
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.666893 Loss1: 2.123045 Loss2: 1.543848
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.172436 Loss1: 1.650886 Loss2: 1.521550
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.531532 Loss1: 1.982332 Loss2: 1.549199
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.158804 Loss1: 1.622851 Loss2: 1.535954
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.572593 Loss1: 2.016080 Loss2: 1.556513
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.080918 Loss1: 1.549585 Loss2: 1.531334
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.468680 Loss1: 1.895047 Loss2: 1.573633
+(DefaultActor pid=3765) >> Training accuracy: 0.575000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.382409 Loss1: 1.808457 Loss2: 1.573952
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.259586 Loss1: 1.675781 Loss2: 1.583805
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.255114 Loss1: 1.671978 Loss2: 1.583135
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.194260 Loss1: 1.599067 Loss2: 1.595193
+(DefaultActor pid=3764) >> Training accuracy: 0.503125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.046278 Loss1: 2.877017 Loss2: 2.169261
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.087590 Loss1: 2.454794 Loss2: 1.632796
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.782018 Loss1: 2.164803 Loss2: 1.617215
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.666676 Loss1: 2.062929 Loss2: 1.603746
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.582205 Loss1: 1.964131 Loss2: 1.618073
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.531312 Loss1: 1.902893 Loss2: 1.628419 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.539774 Loss1: 1.890498 Loss2: 1.649275 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.454652 Loss1: 1.806615 Loss2: 1.648037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.416704 Loss1: 1.782093 Loss2: 1.634610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.418871 Loss1: 1.761774 Loss2: 1.657098 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.522917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.435059 Loss1: 1.940249 Loss2: 1.494810 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.376015 Loss1: 1.871263 Loss2: 1.504752 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.510417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.984752 Loss1: 2.508353 Loss2: 1.476399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.575538 Loss1: 2.136900 Loss2: 1.438638 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.431408 Loss1: 1.987766 Loss2: 1.443642
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.035122 Loss1: 3.056020 Loss2: 1.979102
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.433548 Loss1: 1.980662 Loss2: 1.452885
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.253407 Loss1: 2.754517 Loss2: 1.498891
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.345129 Loss1: 1.892508 Loss2: 1.452621
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.973829 Loss1: 2.460718 Loss2: 1.513111
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.296314 Loss1: 1.833244 Loss2: 1.463070
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.821230 Loss1: 2.312588 Loss2: 1.508642
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.273059 Loss1: 1.798949 Loss2: 1.474110
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.735767 Loss1: 2.218781 Loss2: 1.516987
+(DefaultActor pid=3765) >> Training accuracy: 0.500000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.217066 Loss1: 1.750936 Loss2: 1.466130
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.697555 Loss1: 2.168681 Loss2: 1.528874
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.628340 Loss1: 2.091381 Loss2: 1.536958
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.559097 Loss1: 2.012090 Loss2: 1.547008
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.517915 Loss1: 1.970051 Loss2: 1.547864
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.398090 Loss1: 1.848807 Loss2: 1.549283
+(DefaultActor pid=3764) >> Training accuracy: 0.518555
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.779616 Loss1: 2.881438 Loss2: 1.898178
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.896373 Loss1: 2.420820 Loss2: 1.475552
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.623706 Loss1: 2.188959 Loss2: 1.434748
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.453552 Loss1: 2.016997 Loss2: 1.436555
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.462897 Loss1: 2.010746 Loss2: 1.452151
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.904825 Loss1: 2.942296 Loss2: 1.962529
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.369334 Loss1: 1.926696 Loss2: 1.442637
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.992953 Loss1: 2.538555 Loss2: 1.454397
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.276100 Loss1: 1.821703 Loss2: 1.454397
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.797005 Loss1: 2.360065 Loss2: 1.436939
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.240310 Loss1: 1.772604 Loss2: 1.467706
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.650341 Loss1: 2.213905 Loss2: 1.436436
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.606681 Loss1: 2.167638 Loss2: 1.439044
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.165499 Loss1: 1.690960 Loss2: 1.474539
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.426985 Loss1: 1.990406 Loss2: 1.436579
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.191314 Loss1: 1.715365 Loss2: 1.475949
+(DefaultActor pid=3765) >> Training accuracy: 0.541016
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.318625 Loss1: 1.861050 Loss2: 1.457575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.219278 Loss1: 1.742078 Loss2: 1.477200 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.557292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.752754 Loss1: 2.310569 Loss2: 1.442186 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.369673 Loss1: 1.968703 Loss2: 1.400969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.917774 Loss1: 2.882296 Loss2: 2.035477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.920828 Loss1: 2.406686 Loss2: 1.514142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.042829 Loss1: 1.613742 Loss2: 1.429088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.998230 Loss1: 1.572478 Loss2: 1.425752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.065344 Loss1: 1.621730 Loss2: 1.443614 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.556490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.497321 Loss1: 1.987204 Loss2: 1.510117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.289013 Loss1: 1.762185 Loss2: 1.526828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.310012 Loss1: 1.795714 Loss2: 1.514298
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.089954 Loss1: 3.118711 Loss2: 1.971243
+(DefaultActor pid=3764) >> Training accuracy: 0.529167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.144606 Loss1: 2.681674 Loss2: 1.462933 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.825460 Loss1: 2.374542 Loss2: 1.450918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.540867 Loss1: 2.079349 Loss2: 1.461518 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.956552 Loss1: 2.921935 Loss2: 2.034616
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.548845 Loss1: 2.074387 Loss2: 1.474458
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.450367 Loss1: 1.969428 Loss2: 1.480939
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.012646 Loss1: 2.489372 Loss2: 1.523274
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.384649 Loss1: 1.911576 Loss2: 1.473072
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.776149 Loss1: 2.285545 Loss2: 1.490604
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.421138 Loss1: 1.924896 Loss2: 1.496243
+DEBUG flwr 2023-10-09 00:35:20,188 | server.py:236 | fit_round 20 received 50 results and 0 failures
+(DefaultActor pid=3765) >> Training accuracy: 0.445312
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.631434 Loss1: 2.129455 Loss2: 1.501979
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.594404 Loss1: 2.093780 Loss2: 1.500625
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.458145 Loss1: 1.943853 Loss2: 1.514292
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.453825 Loss1: 1.945532 Loss2: 1.508293
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.322349 Loss1: 1.800724 Loss2: 1.521626
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.024960 Loss1: 2.917579 Loss2: 2.107380
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.237480 Loss1: 1.711055 Loss2: 1.526425
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.255825 Loss1: 1.719113 Loss2: 1.536712 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.506250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.607342 Loss1: 2.088195 Loss2: 1.519148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.406214 Loss1: 1.870852 Loss2: 1.535362 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.391318 Loss1: 1.834289 Loss2: 1.557029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.402757 Loss1: 1.826453 Loss2: 1.576304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.383473 Loss1: 1.811386 Loss2: 1.572087 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.472356
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.548676 Loss1: 2.069668 Loss2: 1.479008 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.395852 Loss1: 1.902370 Loss2: 1.493482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.226075 Loss1: 1.735714 Loss2: 1.490361
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.079024 Loss1: 3.056403 Loss2: 2.022620
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.125598 Loss1: 2.614865 Loss2: 1.510733 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.550223
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.883597 Loss1: 2.381676 Loss2: 1.501921 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.700838 Loss1: 2.189863 Loss2: 1.510975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.511088 Loss1: 1.976098 Loss2: 1.534990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.509660 Loss1: 1.975985 Loss2: 1.533676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.462186 Loss1: 1.906285 Loss2: 1.555901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.396478 Loss1: 1.851142 Loss2: 1.545336 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.523958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.527536 Loss1: 2.061856 Loss2: 1.465679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.241312 Loss1: 1.789072 Loss2: 1.452240 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.201318 Loss1: 1.717982 Loss2: 1.483336 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.536458
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 00:35:20,188][flwr][DEBUG] - fit_round 20 received 50 results and 0 failures
+INFO flwr 2023-10-09 00:36:02,123 | server.py:125 | fit progress: (20, 3.2592665303629427, {'accuracy': 0.2236}, 45869.901820325)
+>> Test accuracy: 0.223600
+[2023-10-09 00:36:02,123][flwr][INFO] - fit progress: (20, 3.2592665303629427, {'accuracy': 0.2236}, 45869.901820325)
+DEBUG flwr 2023-10-09 00:36:02,124 | server.py:173 | evaluate_round 20: strategy sampled 50 clients (out of 50)
+[2023-10-09 00:36:02,124][flwr][DEBUG] - evaluate_round 20: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 00:45:07,574 | server.py:187 | evaluate_round 20 received 50 results and 0 failures
+[2023-10-09 00:45:07,574][flwr][DEBUG] - evaluate_round 20 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 00:45:07,575 | server.py:222 | fit_round 21: strategy sampled 50 clients (out of 50)
+[2023-10-09 00:45:07,575][flwr][DEBUG] - fit_round 21: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.980237 Loss1: 2.887763 Loss2: 2.092474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.809417 Loss1: 2.278761 Loss2: 1.530656 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.604696 Loss1: 2.077398 Loss2: 1.527298
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.977505 Loss1: 2.983080 Loss2: 1.994425
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.167466 Loss1: 2.678797 Loss2: 1.488670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.837671 Loss1: 2.358943 Loss2: 1.478728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.756292 Loss1: 2.278421 Loss2: 1.477871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.562187 Loss1: 2.078906 Loss2: 1.483281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.517710 Loss1: 2.030138 Loss2: 1.487573 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.572917
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.195448 Loss1: 1.624119 Loss2: 1.571330
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.478674 Loss1: 1.979040 Loss2: 1.499633
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.317724 Loss1: 1.819485 Loss2: 1.498240
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.358057 Loss1: 1.827947 Loss2: 1.530111
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.259865 Loss1: 1.733500 Loss2: 1.526365
+(DefaultActor pid=3764) >> Training accuracy: 0.511458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.924045 Loss1: 2.900949 Loss2: 2.023096
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.982428 Loss1: 2.475801 Loss2: 1.506627
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.634698 Loss1: 2.161568 Loss2: 1.473130
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.584817 Loss1: 2.102953 Loss2: 1.481864
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.878973 Loss1: 2.802859 Loss2: 2.076115
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.891057 Loss1: 2.355768 Loss2: 1.535289 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.607511 Loss1: 2.096546 Loss2: 1.510965 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.499107 Loss1: 1.986520 Loss2: 1.512586 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.439626 Loss1: 1.917449 Loss2: 1.522177 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.331139 Loss1: 1.796723 Loss2: 1.534416 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.531250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.263435 Loss1: 1.716348 Loss2: 1.547087 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.190727 Loss1: 1.631538 Loss2: 1.559189 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.605208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.811267 Loss1: 2.841441 Loss2: 1.969826 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.677334 Loss1: 2.200193 Loss2: 1.477140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.051830 Loss1: 2.954013 Loss2: 2.097817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.944655 Loss1: 2.399208 Loss2: 1.545448 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.707184 Loss1: 2.181715 Loss2: 1.525469 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.514335 Loss1: 1.984648 Loss2: 1.529687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.449912 Loss1: 1.923102 Loss2: 1.526810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.371843 Loss1: 1.822067 Loss2: 1.549776 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.544792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.283136 Loss1: 1.738120 Loss2: 1.545016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.254666 Loss1: 1.692508 Loss2: 1.562157 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.581250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.830647 Loss1: 2.828667 Loss2: 2.001981 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.680549 Loss1: 2.181493 Loss2: 1.499056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.490783 Loss1: 1.984382 Loss2: 1.506401
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.875508 Loss1: 2.874183 Loss2: 2.001326
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.963751 Loss1: 2.478695 Loss2: 1.485056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.757259 Loss1: 2.288726 Loss2: 1.468533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.217433 Loss1: 1.694273 Loss2: 1.523160
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.654341 Loss1: 2.190443 Loss2: 1.463898
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.166474 Loss1: 1.642046 Loss2: 1.524428
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.568148 Loss1: 2.086714 Loss2: 1.481434
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.162883 Loss1: 1.620014 Loss2: 1.542869
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.418109 Loss1: 1.931779 Loss2: 1.486329
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.385767 Loss1: 1.905542 Loss2: 1.480225
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.140322 Loss1: 1.573273 Loss2: 1.567049
+(DefaultActor pid=3765) >> Training accuracy: 0.599609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.184481 Loss1: 1.682966 Loss2: 1.501515 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.519792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.855185 Loss1: 2.755798 Loss2: 2.099387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.605318 Loss1: 2.078239 Loss2: 1.527079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.476403 Loss1: 1.975413 Loss2: 1.500990
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.947983 Loss1: 2.954666 Loss2: 1.993317
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.014855 Loss1: 2.496758 Loss2: 1.518097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.701314 Loss1: 2.220900 Loss2: 1.480414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.503950 Loss1: 2.041527 Loss2: 1.462422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.471175 Loss1: 1.993334 Loss2: 1.477840 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.181955 Loss1: 1.628073 Loss2: 1.553881
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.405469 Loss1: 1.923185 Loss2: 1.482284
+(DefaultActor pid=3765) >> Training accuracy: 0.573958
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.123595 Loss1: 1.559791 Loss2: 1.563804
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.302106 Loss1: 1.802597 Loss2: 1.499509
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.388548 Loss1: 1.890405 Loss2: 1.498143
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.263858 Loss1: 1.745428 Loss2: 1.518430
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.099776 Loss1: 1.601699 Loss2: 1.498077
+(DefaultActor pid=3764) >> Training accuracy: 0.564583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.856276 Loss1: 2.786319 Loss2: 2.069957
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.836739 Loss1: 2.310278 Loss2: 1.526461
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.560922 Loss1: 2.061467 Loss2: 1.499455
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.401848 Loss1: 1.900256 Loss2: 1.501592
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.359053 Loss1: 3.114650 Loss2: 2.244403
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.325497 Loss1: 1.813540 Loss2: 1.511957
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.259976 Loss1: 2.585018 Loss2: 1.674957
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.386467 Loss1: 1.855972 Loss2: 1.530495
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.985347 Loss1: 2.343111 Loss2: 1.642236
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.220893 Loss1: 1.691282 Loss2: 1.529611
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.853641 Loss1: 2.187808 Loss2: 1.665833
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.755559 Loss1: 2.105348 Loss2: 1.650211
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.163846 Loss1: 1.643177 Loss2: 1.520669
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.664339 Loss1: 1.997547 Loss2: 1.666792
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.119099 Loss1: 1.592385 Loss2: 1.526713
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.634697 Loss1: 1.950820 Loss2: 1.683877
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.123107 Loss1: 1.572952 Loss2: 1.550155
+(DefaultActor pid=3765) >> Training accuracy: 0.497917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.481880 Loss1: 1.786354 Loss2: 1.695526 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.476562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.253926 Loss1: 3.156827 Loss2: 2.097099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.895085 Loss1: 2.374807 Loss2: 1.520278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.767843 Loss1: 2.671945 Loss2: 2.095898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.896499 Loss1: 2.305422 Loss2: 1.591077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.468084 Loss1: 1.915269 Loss2: 1.552815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.487736 Loss1: 1.918883 Loss2: 1.568853 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.387051 Loss1: 1.831000 Loss2: 1.556051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.353748 Loss1: 1.773967 Loss2: 1.579781 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.496652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.222833 Loss1: 1.647581 Loss2: 1.575252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.196079 Loss1: 1.605123 Loss2: 1.590956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.052865 Loss1: 1.484932 Loss2: 1.567933
+(DefaultActor pid=3764) >> Training accuracy: 0.560547
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.779875 Loss1: 2.752650 Loss2: 2.027225
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.757135 Loss1: 2.260645 Loss2: 1.496490
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.529979 Loss1: 2.038521 Loss2: 1.491458
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.461443 Loss1: 1.972886 Loss2: 1.488557
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.391480 Loss1: 1.894279 Loss2: 1.497201
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.638018 Loss1: 2.597697 Loss2: 2.040321
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.243701 Loss1: 1.725104 Loss2: 1.518598
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.717646 Loss1: 2.172116 Loss2: 1.545530
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.156792 Loss1: 1.634100 Loss2: 1.522692
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.577290 Loss1: 2.080655 Loss2: 1.496635
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.226473 Loss1: 1.693731 Loss2: 1.532743
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.422516 Loss1: 1.921959 Loss2: 1.500557
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.099324 Loss1: 1.558272 Loss2: 1.541052
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.265530 Loss1: 1.777141 Loss2: 1.488389
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.042735 Loss1: 1.504094 Loss2: 1.538641
+(DefaultActor pid=3765) >> Training accuracy: 0.625000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.066832 Loss1: 1.589277 Loss2: 1.477554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.980235 Loss1: 1.489571 Loss2: 1.490665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.960723 Loss1: 1.468755 Loss2: 1.491969
+(DefaultActor pid=3764) >> Training accuracy: 0.611458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.874925 Loss1: 2.884772 Loss2: 1.990153
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.956578 Loss1: 2.431067 Loss2: 1.525511
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.659118 Loss1: 2.171091 Loss2: 1.488027
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.530328 Loss1: 2.021888 Loss2: 1.508440
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.525797 Loss1: 2.013608 Loss2: 1.512189
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.716410 Loss1: 2.808067 Loss2: 1.908343
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.922994 Loss1: 2.480800 Loss2: 1.442195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.658967 Loss1: 2.230429 Loss2: 1.428538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.502621 Loss1: 2.070124 Loss2: 1.432497 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.396167 Loss1: 1.958563 Loss2: 1.437604 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.537109
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.300185 Loss1: 1.853277 Loss2: 1.446909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.283610 Loss1: 1.821944 Loss2: 1.461666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.011762 Loss1: 1.566631 Loss2: 1.445131 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.549805
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.851710 Loss1: 2.368308 Loss2: 1.483402 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.627553 Loss1: 2.094569 Loss2: 1.532984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.732983 Loss1: 2.670678 Loss2: 2.062305
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.561146 Loss1: 2.027987 Loss2: 1.533159
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.846108 Loss1: 2.299819 Loss2: 1.546288
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.405524 Loss1: 1.887238 Loss2: 1.518286
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.642175 Loss1: 2.135008 Loss2: 1.507166
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.288397 Loss1: 1.751493 Loss2: 1.536904
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.386209 Loss1: 1.873540 Loss2: 1.512668
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.287745 Loss1: 1.744455 Loss2: 1.543290
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.318407 Loss1: 1.808466 Loss2: 1.509940
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.261129 Loss1: 1.689325 Loss2: 1.571804
+(DefaultActor pid=3765) >> Training accuracy: 0.543750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.126121 Loss1: 1.605248 Loss2: 1.520873 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.081578 Loss1: 1.552380 Loss2: 1.529199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.071356 Loss1: 1.520227 Loss2: 1.551128
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.777130 Loss1: 2.823147 Loss2: 1.953983
+(DefaultActor pid=3764) >> Training accuracy: 0.575000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.726967 Loss1: 2.276355 Loss2: 1.450613
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.591187 Loss1: 2.168209 Loss2: 1.422978
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.401142 Loss1: 1.983393 Loss2: 1.417749
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.280343 Loss1: 1.864608 Loss2: 1.415736
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.208455 Loss1: 1.779828 Loss2: 1.428627
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.985447 Loss1: 3.030869 Loss2: 1.954577
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.106218 Loss1: 1.664170 Loss2: 1.442048
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.085713 Loss1: 2.622359 Loss2: 1.463353
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.150780 Loss1: 1.692046 Loss2: 1.458734
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.869311 Loss1: 2.410527 Loss2: 1.458784
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.692532 Loss1: 2.230120 Loss2: 1.462412 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.559375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.042521 Loss1: 1.559098 Loss2: 1.483422
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.631846 Loss1: 2.153601 Loss2: 1.478245
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.596927 Loss1: 2.107402 Loss2: 1.489525
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.446075 Loss1: 1.952402 Loss2: 1.493673
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.384221 Loss1: 1.885843 Loss2: 1.498377
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.357960 Loss1: 1.844052 Loss2: 1.513907
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.056495 Loss1: 2.957832 Loss2: 2.098663
+(DefaultActor pid=3764) >> Training accuracy: 0.491211
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.409060 Loss1: 1.894829 Loss2: 1.514230
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.068947 Loss1: 2.488964 Loss2: 1.579983
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.815249 Loss1: 2.239833 Loss2: 1.575416
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.781742 Loss1: 2.215160 Loss2: 1.566582
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.633410 Loss1: 2.059547 Loss2: 1.573863
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.478250 Loss1: 1.893701 Loss2: 1.584550
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.119567 Loss1: 3.004083 Loss2: 2.115484
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.460753 Loss1: 1.863837 Loss2: 1.596917
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.124384 Loss1: 2.532785 Loss2: 1.591598
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.419466 Loss1: 1.822461 Loss2: 1.597006
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.805053 Loss1: 2.251590 Loss2: 1.553463
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.356577 Loss1: 1.757703 Loss2: 1.598874
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.663000 Loss1: 2.108267 Loss2: 1.554733
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.267861 Loss1: 1.656514 Loss2: 1.611347
+(DefaultActor pid=3765) >> Training accuracy: 0.502083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.541461 Loss1: 1.957983 Loss2: 1.583478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.449937 Loss1: 1.858967 Loss2: 1.590969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.431081 Loss1: 1.829062 Loss2: 1.602019
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.760608 Loss1: 2.838097 Loss2: 1.922511
+(DefaultActor pid=3764) >> Training accuracy: 0.535417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.341053 Loss1: 1.726684 Loss2: 1.614369
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.817957 Loss1: 2.382645 Loss2: 1.435312
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.627321 Loss1: 2.198369 Loss2: 1.428952
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.489912 Loss1: 2.040022 Loss2: 1.449890
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.397478 Loss1: 1.963493 Loss2: 1.433985
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.354842 Loss1: 1.904297 Loss2: 1.450545
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.807905 Loss1: 2.779845 Loss2: 2.028060
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.353618 Loss1: 1.906963 Loss2: 1.446654
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.846465 Loss1: 2.348891 Loss2: 1.497574
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.654349 Loss1: 2.194036 Loss2: 1.460313
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.207379 Loss1: 1.749398 Loss2: 1.457981
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.447164 Loss1: 1.977949 Loss2: 1.469215
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.207712 Loss1: 1.742170 Loss2: 1.465541
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.137753 Loss1: 1.651177 Loss2: 1.486576 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.578125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.202839 Loss1: 1.715159 Loss2: 1.487679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.162965 Loss1: 1.652877 Loss2: 1.510088 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.587054
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.983579 Loss1: 1.490869 Loss2: 1.492710
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.005561 Loss1: 2.885524 Loss2: 2.120037
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.003518 Loss1: 2.423353 Loss2: 1.580165
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.867655 Loss1: 2.292577 Loss2: 1.575079
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.689968 Loss1: 2.136263 Loss2: 1.553705
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.536687 Loss1: 1.976419 Loss2: 1.560268
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.866973 Loss1: 2.855077 Loss2: 2.011896
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.915577 Loss1: 2.437571 Loss2: 1.478006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.642798 Loss1: 2.173784 Loss2: 1.469014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.506377 Loss1: 2.036081 Loss2: 1.470296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.436001 Loss1: 1.945443 Loss2: 1.490558 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.495833
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.260685 Loss1: 1.658282 Loss2: 1.602404
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.328940 Loss1: 1.838348 Loss2: 1.490592
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.246805 Loss1: 1.755655 Loss2: 1.491150
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.267134 Loss1: 1.752166 Loss2: 1.514969
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.189942 Loss1: 1.681716 Loss2: 1.508225
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.210786 Loss1: 1.696756 Loss2: 1.514030
+(DefaultActor pid=3764) >> Training accuracy: 0.525000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.925626 Loss1: 2.868827 Loss2: 2.056800
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.938668 Loss1: 2.454099 Loss2: 1.484570
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.655367 Loss1: 2.196926 Loss2: 1.458441
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.510582 Loss1: 2.038416 Loss2: 1.472166
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.380050 Loss1: 1.907106 Loss2: 1.472943
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.332907 Loss1: 1.850452 Loss2: 1.482455
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.629289 Loss1: 2.696884 Loss2: 1.932404
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.785130 Loss1: 2.341711 Loss2: 1.443418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.497320 Loss1: 2.086087 Loss2: 1.411233 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.354710 Loss1: 1.929333 Loss2: 1.425377 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.546875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.194525 Loss1: 1.751725 Loss2: 1.442800 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.133895 Loss1: 1.670873 Loss2: 1.463022 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.125638 Loss1: 2.919677 Loss2: 2.205961
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.051262 Loss1: 1.585820 Loss2: 1.465442
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.991456 Loss1: 1.537156 Loss2: 1.454299 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.594792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.406682 Loss1: 1.904005 Loss2: 1.502677 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.189481 Loss1: 1.677730 Loss2: 1.511751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.972617 Loss1: 2.896739 Loss2: 2.075878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.150589 Loss1: 1.608693 Loss2: 1.541896 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.546875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.617769 Loss1: 2.106612 Loss2: 1.511157 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.486440 Loss1: 1.986649 Loss2: 1.499791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.387302 Loss1: 1.866904 Loss2: 1.520398
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.828127 Loss1: 2.787272 Loss2: 2.040856
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.295489 Loss1: 1.767912 Loss2: 1.527577
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.861556 Loss1: 2.349889 Loss2: 1.511667
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.349566 Loss1: 1.815235 Loss2: 1.534331
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.730703 Loss1: 2.228987 Loss2: 1.501716
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.283641 Loss1: 1.741039 Loss2: 1.542601
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.517821 Loss1: 2.006459 Loss2: 1.511362
+(DefaultActor pid=3764) >> Training accuracy: 0.539583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.451466 Loss1: 1.936021 Loss2: 1.515446
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.351019 Loss1: 1.829856 Loss2: 1.521163
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.265863 Loss1: 1.733102 Loss2: 1.532762
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.122515 Loss1: 1.581897 Loss2: 1.540618
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.238225 Loss1: 1.701388 Loss2: 1.536838
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.060532 Loss1: 3.047835 Loss2: 2.012697
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.130882 Loss1: 1.585474 Loss2: 1.545408
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.122513 Loss1: 2.621286 Loss2: 1.501227
+(DefaultActor pid=3765) >> Training accuracy: 0.539583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.796026 Loss1: 2.317235 Loss2: 1.478791
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.706479 Loss1: 2.215721 Loss2: 1.490758
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.514733 Loss1: 2.017596 Loss2: 1.497137
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.481317 Loss1: 1.983344 Loss2: 1.497973
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.350317 Loss1: 1.839444 Loss2: 1.510873
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.938753 Loss1: 2.814127 Loss2: 2.124626
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.375968 Loss1: 1.853921 Loss2: 1.522047
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.942977 Loss1: 2.384060 Loss2: 1.558917
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.323567 Loss1: 1.800986 Loss2: 1.522581
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.762286 Loss1: 2.226522 Loss2: 1.535764
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.265019 Loss1: 1.728589 Loss2: 1.536430
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.625168 Loss1: 2.087094 Loss2: 1.538074
+(DefaultActor pid=3764) >> Training accuracy: 0.495833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.367686 Loss1: 1.841933 Loss2: 1.525754
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.368056 Loss1: 1.851240 Loss2: 1.516816
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.356761 Loss1: 1.814210 Loss2: 1.542551
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.245644 Loss1: 1.694688 Loss2: 1.550956
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.867792 Loss1: 2.776085 Loss2: 2.091708
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.150961 Loss1: 1.608137 Loss2: 1.542824
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.685702 Loss1: 2.169151 Loss2: 1.516551
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.169487 Loss1: 1.615046 Loss2: 1.554440
+(DefaultActor pid=3765) >> Training accuracy: 0.568750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.341485 Loss1: 1.862375 Loss2: 1.479110 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.211501 Loss1: 1.693036 Loss2: 1.518465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.192560 Loss1: 1.676278 Loss2: 1.516282
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.912572 Loss1: 2.739337 Loss2: 2.173235
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.786937 Loss1: 2.183246 Loss2: 1.603691 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.608803 Loss1: 2.043440 Loss2: 1.565364 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.584375
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.036809 Loss1: 1.507389 Loss2: 1.529420
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.336534 Loss1: 1.777612 Loss2: 1.558921
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.190888 Loss1: 1.638160 Loss2: 1.552728
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.224021 Loss1: 1.669923 Loss2: 1.554098
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.093720 Loss1: 1.525106 Loss2: 1.568614
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.101275 Loss1: 1.523584 Loss2: 1.577691
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.061312 Loss1: 1.478643 Loss2: 1.582669
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.913014 Loss1: 2.846032 Loss2: 2.066982
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.928983 Loss1: 2.394079 Loss2: 1.534904
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.014327 Loss1: 1.411984 Loss2: 1.602343
+(DefaultActor pid=3765) >> Training accuracy: 0.625000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.647651 Loss1: 2.135893 Loss2: 1.511757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.418868 Loss1: 1.899378 Loss2: 1.519490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.344430 Loss1: 1.811784 Loss2: 1.532646
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.573093 Loss1: 2.581144 Loss2: 1.991949
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.263653 Loss1: 1.721415 Loss2: 1.542238
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.731615 Loss1: 2.262627 Loss2: 1.468989
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.277648 Loss1: 1.730020 Loss2: 1.547627
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.487106 Loss1: 2.024763 Loss2: 1.462343
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.228467 Loss1: 1.662364 Loss2: 1.566104
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.355256 Loss1: 1.897743 Loss2: 1.457513
+(DefaultActor pid=3764) >> Training accuracy: 0.498958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.334638 Loss1: 1.852089 Loss2: 1.482550
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.214410 Loss1: 1.748821 Loss2: 1.465589
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.087823 Loss1: 1.626532 Loss2: 1.461291
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.103564 Loss1: 1.605921 Loss2: 1.497643
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.021929 Loss1: 1.521063 Loss2: 1.500865
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.156756 Loss1: 3.031122 Loss2: 2.125634
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.025729 Loss1: 1.529176 Loss2: 1.496553
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.114829 Loss1: 2.542909 Loss2: 1.571920
+(DefaultActor pid=3765) >> Training accuracy: 0.527083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.872104 Loss1: 2.329562 Loss2: 1.542542
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.708442 Loss1: 2.150911 Loss2: 1.557532
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.586649 Loss1: 2.022606 Loss2: 1.564042
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.477926 Loss1: 1.921717 Loss2: 1.556210
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.356376 Loss1: 1.789043 Loss2: 1.567333
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.711123 Loss1: 2.769356 Loss2: 1.941767
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.843115 Loss1: 2.390597 Loss2: 1.452517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.600195 Loss1: 2.142378 Loss2: 1.457817 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.541667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.498477 Loss1: 2.038710 Loss2: 1.459767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.330428 Loss1: 1.861877 Loss2: 1.468551 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.051287 Loss1: 2.834386 Loss2: 2.216901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.982455 Loss1: 2.357110 Loss2: 1.625345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.681034 Loss1: 2.077821 Loss2: 1.603213 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.500351 Loss1: 1.905074 Loss2: 1.595277
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.055267 Loss1: 1.560130 Loss2: 1.495137
+(DefaultActor pid=3765) >> Training accuracy: 0.591912
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.307577 Loss1: 1.699670 Loss2: 1.607907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.110929 Loss1: 1.498256 Loss2: 1.612673 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.139636 Loss1: 1.517464 Loss2: 1.622172 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.578125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.736281 Loss1: 2.259473 Loss2: 1.476808 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.554067 Loss1: 2.079295 Loss2: 1.474773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.947766 Loss1: 2.841744 Loss2: 2.106022
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.484835 Loss1: 1.993093 Loss2: 1.491742
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.003450 Loss1: 2.432469 Loss2: 1.570980
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.408238 Loss1: 1.901568 Loss2: 1.506669
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.317237 Loss1: 1.816703 Loss2: 1.500534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.311379 Loss1: 1.804323 Loss2: 1.507056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.206451 Loss1: 1.688259 Loss2: 1.518191 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.511719
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.359444 Loss1: 1.794618 Loss2: 1.564826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.205518 Loss1: 1.629493 Loss2: 1.576025 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.601042
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.140761 Loss1: 1.556354 Loss2: 1.584407
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.953671 Loss1: 2.853422 Loss2: 2.100249
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.093243 Loss1: 2.510371 Loss2: 1.582872
+DEBUG flwr 2023-10-09 01:13:31,145 | server.py:236 | fit_round 21 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.927434 Loss1: 2.361162 Loss2: 1.566272
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.776367 Loss1: 2.202698 Loss2: 1.573669
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.635349 Loss1: 2.058756 Loss2: 1.576593
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.903560 Loss1: 2.870389 Loss2: 2.033171
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.600699 Loss1: 2.025699 Loss2: 1.575000
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.948336 Loss1: 2.451231 Loss2: 1.497105
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.478212 Loss1: 1.901897 Loss2: 1.576315
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.734981 Loss1: 2.245190 Loss2: 1.489792
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.430218 Loss1: 1.838869 Loss2: 1.591349
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.596434 Loss1: 2.108307 Loss2: 1.488127
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.500122 Loss1: 1.885708 Loss2: 1.614415
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.461488 Loss1: 1.964915 Loss2: 1.496574
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.321548 Loss1: 1.720245 Loss2: 1.601303
+(DefaultActor pid=3765) >> Training accuracy: 0.547917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.303675 Loss1: 1.797246 Loss2: 1.506429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.253475 Loss1: 1.736103 Loss2: 1.517372 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.234663 Loss1: 1.695396 Loss2: 1.539267
+(DefaultActor pid=3764) >> Training accuracy: 0.553125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.125733 Loss1: 3.080737 Loss2: 2.044995
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.079906 Loss1: 2.514880 Loss2: 1.565027
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.868138 Loss1: 2.347094 Loss2: 1.521045
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.703254 Loss1: 2.186810 Loss2: 1.516444
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.616628 Loss1: 2.095131 Loss2: 1.521497
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.193142 Loss1: 3.061593 Loss2: 2.131549
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.216269 Loss1: 2.616512 Loss2: 1.599757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.990658 Loss1: 2.409791 Loss2: 1.580867 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.746510 Loss1: 2.163371 Loss2: 1.583138 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.654098 Loss1: 2.063589 Loss2: 1.590510 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.557617
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.587909 Loss1: 1.982286 Loss2: 1.605624 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.542109 Loss1: 1.920471 Loss2: 1.621638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.347221 Loss1: 1.724894 Loss2: 1.622327 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.511719
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.769669 Loss1: 2.262466 Loss2: 1.507203 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.512110 Loss1: 2.000530 Loss2: 1.511580 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.811193 Loss1: 2.798653 Loss2: 2.012540
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.371513 Loss1: 1.852804 Loss2: 1.518709
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.904902 Loss1: 2.423140 Loss2: 1.481762
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.361454 Loss1: 1.833394 Loss2: 1.528060
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.642385 Loss1: 2.171506 Loss2: 1.470879
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.293674 Loss1: 1.750317 Loss2: 1.543356
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.448758 Loss1: 1.972427 Loss2: 1.476331
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.179495 Loss1: 1.626768 Loss2: 1.552727
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.324598 Loss1: 1.852163 Loss2: 1.472435
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.243640 Loss1: 1.695260 Loss2: 1.548380
+(DefaultActor pid=3765) >> Training accuracy: 0.572917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.270543 Loss1: 1.785576 Loss2: 1.484967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.144273 Loss1: 1.629335 Loss2: 1.514938 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.501042
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 01:13:31,145][flwr][DEBUG] - fit_round 21 received 50 results and 0 failures
+INFO flwr 2023-10-09 01:14:12,992 | server.py:125 | fit progress: (21, 3.1982289168019644, {'accuracy': 0.2382}, 48160.770377396)
+>> Test accuracy: 0.238200
+[2023-10-09 01:14:12,992][flwr][INFO] - fit progress: (21, 3.1982289168019644, {'accuracy': 0.2382}, 48160.770377396)
+DEBUG flwr 2023-10-09 01:14:12,992 | server.py:173 | evaluate_round 21: strategy sampled 50 clients (out of 50)
+[2023-10-09 01:14:12,992][flwr][DEBUG] - evaluate_round 21: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 01:23:16,033 | server.py:187 | evaluate_round 21 received 50 results and 0 failures
+[2023-10-09 01:23:16,033][flwr][DEBUG] - evaluate_round 21 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 01:23:16,040 | server.py:222 | fit_round 22: strategy sampled 50 clients (out of 50)
+[2023-10-09 01:23:16,040][flwr][DEBUG] - fit_round 22: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.762805 Loss1: 2.726738 Loss2: 2.036067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.515542 Loss1: 2.010606 Loss2: 1.504936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.337188 Loss1: 1.829396 Loss2: 1.507791
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.812987 Loss1: 2.840249 Loss2: 1.972738
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.300224 Loss1: 1.787791 Loss2: 1.512434
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.841792 Loss1: 2.354946 Loss2: 1.486846
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.248475 Loss1: 1.729435 Loss2: 1.519041
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.613533 Loss1: 2.153459 Loss2: 1.460074
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.106811 Loss1: 1.586610 Loss2: 1.520201
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.492187 Loss1: 2.027235 Loss2: 1.464952
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.021254 Loss1: 1.490228 Loss2: 1.531026
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.350501 Loss1: 1.884397 Loss2: 1.466103
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.217690 Loss1: 1.664681 Loss2: 1.553009
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.210080 Loss1: 1.745395 Loss2: 1.464685
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.146457 Loss1: 1.588690 Loss2: 1.557767
+(DefaultActor pid=3765) >> Training accuracy: 0.590625
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.230136 Loss1: 1.735298 Loss2: 1.494838
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.163630 Loss1: 1.667618 Loss2: 1.496012
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.070602 Loss1: 1.567540 Loss2: 1.503063
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.087365 Loss1: 1.564326 Loss2: 1.523040
+(DefaultActor pid=3764) >> Training accuracy: 0.526042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.662243 Loss1: 2.658929 Loss2: 2.003314
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.776364 Loss1: 2.284774 Loss2: 1.491589
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.487143 Loss1: 2.014223 Loss2: 1.472920
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.391424 Loss1: 1.924136 Loss2: 1.467289
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.826313 Loss1: 2.795519 Loss2: 2.030794
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.922054 Loss1: 2.357933 Loss2: 1.564122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.704208 Loss1: 2.167972 Loss2: 1.536236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.533962 Loss1: 1.985359 Loss2: 1.548603 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.460681 Loss1: 1.907144 Loss2: 1.553538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.316969 Loss1: 1.767491 Loss2: 1.549478 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.623958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.165469 Loss1: 1.598583 Loss2: 1.566887 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.146647 Loss1: 1.559969 Loss2: 1.586678 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.589844
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.150800 Loss1: 2.530357 Loss2: 1.620443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.684347 Loss1: 2.115255 Loss2: 1.569092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.540963 Loss1: 1.956011 Loss2: 1.584952
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.846458 Loss1: 2.842253 Loss2: 2.004205
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.496576 Loss1: 1.916773 Loss2: 1.579803
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.867176 Loss1: 2.384434 Loss2: 1.482742
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.406571 Loss1: 1.812600 Loss2: 1.593970
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.651498 Loss1: 2.194099 Loss2: 1.457400
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.372796 Loss1: 1.784318 Loss2: 1.588478
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.518151 Loss1: 2.048439 Loss2: 1.469712
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.325375 Loss1: 1.705903 Loss2: 1.619472
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.354796 Loss1: 1.885261 Loss2: 1.469535
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.188069 Loss1: 1.576269 Loss2: 1.611800
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.281320 Loss1: 1.796109 Loss2: 1.485211
+(DefaultActor pid=3765) >> Training accuracy: 0.544792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.207505 Loss1: 1.718532 Loss2: 1.488973
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.042282 Loss1: 1.534357 Loss2: 1.507925
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.080499 Loss1: 1.567672 Loss2: 1.512828
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.079698 Loss1: 1.555540 Loss2: 1.524157
+(DefaultActor pid=3764) >> Training accuracy: 0.564583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.879180 Loss1: 2.985972 Loss2: 1.893208
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.974848 Loss1: 2.560990 Loss2: 1.413858
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.627830 Loss1: 2.227417 Loss2: 1.400413
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.515689 Loss1: 2.105032 Loss2: 1.410657
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.595586 Loss1: 2.639868 Loss2: 1.955718
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.593941 Loss1: 2.161554 Loss2: 1.432387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.289237 Loss1: 1.876535 Loss2: 1.412702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.228546 Loss1: 1.812257 Loss2: 1.416288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.113037 Loss1: 1.685060 Loss2: 1.427978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.957219 Loss1: 1.539860 Loss2: 1.417359 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.541992
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.931551 Loss1: 1.499966 Loss2: 1.431585 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.808458 Loss1: 1.348476 Loss2: 1.459982 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.596875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.807135 Loss1: 2.879880 Loss2: 1.927255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.682805 Loss1: 2.235759 Loss2: 1.447046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.519919 Loss1: 2.076406 Loss2: 1.443513
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.961016 Loss1: 2.856359 Loss2: 2.104658
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.967513 Loss1: 2.380353 Loss2: 1.587160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.635841 Loss1: 2.101340 Loss2: 1.534501 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.565221 Loss1: 2.003598 Loss2: 1.561623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.150623 Loss1: 1.672377 Loss2: 1.478246
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.429027 Loss1: 1.868435 Loss2: 1.560593
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.042759 Loss1: 1.560279 Loss2: 1.482480
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.301926 Loss1: 1.742716 Loss2: 1.559211
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.115949 Loss1: 1.631003 Loss2: 1.484946
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.244581 Loss1: 1.671788 Loss2: 1.572793
+(DefaultActor pid=3765) >> Training accuracy: 0.562500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.254719 Loss1: 1.663011 Loss2: 1.591708
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.107517 Loss1: 1.513166 Loss2: 1.594351
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.971714 Loss1: 1.381770 Loss2: 1.589944
+(DefaultActor pid=3764) >> Training accuracy: 0.555208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.017978 Loss1: 2.948685 Loss2: 2.069293
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.939285 Loss1: 2.449207 Loss2: 1.490078
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.657692 Loss1: 2.175023 Loss2: 1.482669
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.433623 Loss1: 1.961484 Loss2: 1.472139
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.801161 Loss1: 2.681894 Loss2: 2.119266
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.858540 Loss1: 2.309734 Loss2: 1.548806 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.618487 Loss1: 2.076547 Loss2: 1.541940 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.565387 Loss1: 2.010670 Loss2: 1.554718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.371803 Loss1: 1.819508 Loss2: 1.552295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.369974 Loss1: 1.799787 Loss2: 1.570187 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.538542
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.161290 Loss1: 1.633723 Loss2: 1.527567
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.301337 Loss1: 1.718245 Loss2: 1.583092
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.254940 Loss1: 1.672058 Loss2: 1.582882
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.179699 Loss1: 1.593124 Loss2: 1.586575
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.147550 Loss1: 1.547940 Loss2: 1.599609
+(DefaultActor pid=3764) >> Training accuracy: 0.548958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.870552 Loss1: 2.841334 Loss2: 2.029218
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.804941 Loss1: 2.292275 Loss2: 1.512666
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.522026 Loss1: 2.037826 Loss2: 1.484200
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.431883 Loss1: 1.944588 Loss2: 1.487295
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.853797 Loss1: 2.763050 Loss2: 2.090747
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.884640 Loss1: 2.289878 Loss2: 1.594763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.617577 Loss1: 2.059491 Loss2: 1.558086 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.500700 Loss1: 1.935443 Loss2: 1.565257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.417752 Loss1: 1.846377 Loss2: 1.571376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.400792 Loss1: 1.826255 Loss2: 1.574536 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.577083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.230136 Loss1: 1.645560 Loss2: 1.584576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.108097 Loss1: 1.511561 Loss2: 1.596537 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.579102
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.822185 Loss1: 2.708262 Loss2: 2.113923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.803465 Loss1: 2.230791 Loss2: 1.572673 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.771339 Loss1: 2.767898 Loss2: 2.003440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.726080 Loss1: 2.244521 Loss2: 1.481559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.508841 Loss1: 2.060944 Loss2: 1.447897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.305479 Loss1: 1.856053 Loss2: 1.449426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.229641 Loss1: 1.774186 Loss2: 1.455455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.136644 Loss1: 1.689745 Loss2: 1.446899 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.555208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.994542 Loss1: 1.530381 Loss2: 1.464161 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.818420 Loss1: 1.356790 Loss2: 1.461630 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.621875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.881985 Loss1: 2.378195 Loss2: 1.503790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.496107 Loss1: 1.998912 Loss2: 1.497195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.625890 Loss1: 2.554332 Loss2: 2.071558
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.434621 Loss1: 1.919976 Loss2: 1.514645
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.713187 Loss1: 2.158537 Loss2: 1.554649
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.306508 Loss1: 1.794061 Loss2: 1.512448
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.485089 Loss1: 1.964934 Loss2: 1.520155
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.310182 Loss1: 1.776465 Loss2: 1.533717
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.270510 Loss1: 1.749749 Loss2: 1.520761
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.234971 Loss1: 1.691991 Loss2: 1.542980
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.100794 Loss1: 1.589441 Loss2: 1.511353
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.104226 Loss1: 1.586351 Loss2: 1.517876
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.104598 Loss1: 1.577453 Loss2: 1.527146
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.149891 Loss1: 1.607219 Loss2: 1.542673
+(DefaultActor pid=3765) >> Training accuracy: 0.571875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.024708 Loss1: 1.494014 Loss2: 1.530694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.926528 Loss1: 1.379508 Loss2: 1.547019 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.629167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.951551 Loss1: 2.505205 Loss2: 1.446346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.602955 Loss1: 2.170814 Loss2: 1.432141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.461669 Loss1: 2.010867 Loss2: 1.450802
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.805842 Loss1: 2.783095 Loss2: 2.022748
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.367940 Loss1: 1.907497 Loss2: 1.460444
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.851176 Loss1: 2.303537 Loss2: 1.547639
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.305548 Loss1: 1.834903 Loss2: 1.470645
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.611202 Loss1: 2.087475 Loss2: 1.523728
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.263198 Loss1: 1.778508 Loss2: 1.484690
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.468245 Loss1: 1.951050 Loss2: 1.517195
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.378315 Loss1: 1.837800 Loss2: 1.540516 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.523958
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.182715 Loss1: 1.675723 Loss2: 1.506991
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.339175 Loss1: 1.788409 Loss2: 1.550765
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.247258 Loss1: 1.692599 Loss2: 1.554658
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.262491 Loss1: 1.692466 Loss2: 1.570025
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.224975 Loss1: 1.652133 Loss2: 1.572842
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.139327 Loss1: 1.564284 Loss2: 1.575043
+(DefaultActor pid=3764) >> Training accuracy: 0.546875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.812736 Loss1: 2.731002 Loss2: 2.081733
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.885562 Loss1: 2.358535 Loss2: 1.527027
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.678275 Loss1: 2.191810 Loss2: 1.486465
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.450887 Loss1: 1.973949 Loss2: 1.476938
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.353218 Loss1: 1.871524 Loss2: 1.481695
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.195667 Loss1: 1.699134 Loss2: 1.496532
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.673361 Loss1: 2.637891 Loss2: 2.035469
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.148108 Loss1: 1.656373 Loss2: 1.491735
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.665959 Loss1: 2.157653 Loss2: 1.508307
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.474739 Loss1: 1.971798 Loss2: 1.502941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.393909 Loss1: 1.889290 Loss2: 1.504618 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.583705
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.284287 Loss1: 1.773791 Loss2: 1.510495 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.216297 Loss1: 1.683410 Loss2: 1.532888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.895368 Loss1: 1.391110 Loss2: 1.504257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.853177 Loss1: 1.329138 Loss2: 1.524039 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.605208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.719254 Loss1: 2.223243 Loss2: 1.496011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.398120 Loss1: 1.894636 Loss2: 1.503483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.325909 Loss1: 1.818397 Loss2: 1.507512
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.008120 Loss1: 2.812854 Loss2: 2.195265
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.558343 Loss1: 2.043912 Loss2: 1.514431 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.147468 Loss1: 1.613342 Loss2: 1.534126 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.125841 Loss1: 1.585085 Loss2: 1.540756 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.568750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.124936 Loss1: 1.580157 Loss2: 1.544779 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.064548 Loss1: 1.493461 Loss2: 1.571087 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.589844
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.679044 Loss1: 2.169214 Loss2: 1.509829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.416382 Loss1: 1.924517 Loss2: 1.491865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.275355 Loss1: 1.765678 Loss2: 1.509677 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.191347 Loss1: 1.683787 Loss2: 1.507559 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.210815 Loss1: 1.699312 Loss2: 1.511503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.138549 Loss1: 1.611164 Loss2: 1.527385 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.137085 Loss1: 1.609621 Loss2: 1.527465 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.086612 Loss1: 1.540520 Loss2: 1.546092 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.505208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.138106 Loss1: 1.661001 Loss2: 1.477106 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.564732
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.878619 Loss1: 2.803029 Loss2: 2.075591 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.736839 Loss1: 2.170626 Loss2: 1.566213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.582125 Loss1: 2.020950 Loss2: 1.561175
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.851623 Loss1: 2.850593 Loss2: 2.001030
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.408303 Loss1: 1.836935 Loss2: 1.571368
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.959807 Loss1: 2.496506 Loss2: 1.463300
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.704569 Loss1: 2.265718 Loss2: 1.438850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.518839 Loss1: 2.061066 Loss2: 1.457773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.136674 Loss1: 1.563117 Loss2: 1.573557
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.482056 Loss1: 2.030758 Loss2: 1.451298
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.342971 Loss1: 1.885234 Loss2: 1.457736 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.616667
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.169139 Loss1: 1.566774 Loss2: 1.602365
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.286403 Loss1: 1.810787 Loss2: 1.475616
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.173504 Loss1: 1.691898 Loss2: 1.481606
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.132612 Loss1: 1.654202 Loss2: 1.478410
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.067506 Loss1: 1.576472 Loss2: 1.491033
+(DefaultActor pid=3764) >> Training accuracy: 0.529167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.839624 Loss1: 2.928381 Loss2: 1.911243
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.835877 Loss1: 2.407960 Loss2: 1.427917
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.554877 Loss1: 2.150443 Loss2: 1.404434
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.410867 Loss1: 2.005514 Loss2: 1.405353
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.046794 Loss1: 3.050846 Loss2: 1.995948
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.301304 Loss1: 1.884490 Loss2: 1.416814
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.052759 Loss1: 2.546267 Loss2: 1.506492
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.792415 Loss1: 2.299651 Loss2: 1.492763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.610624 Loss1: 2.124058 Loss2: 1.486566 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.531310 Loss1: 2.030080 Loss2: 1.501230 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.363659 Loss1: 1.853230 Loss2: 1.510429 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.591667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.314803 Loss1: 1.793396 Loss2: 1.521407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.267353 Loss1: 1.736507 Loss2: 1.530846 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.568359
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.905557 Loss1: 2.807394 Loss2: 2.098163 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.704193 Loss1: 2.141757 Loss2: 1.562436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.532029 Loss1: 2.649618 Loss2: 1.882411 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.624309 Loss1: 2.208652 Loss2: 1.415656 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.337683 Loss1: 1.924748 Loss2: 1.412935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.141618 Loss1: 1.754756 Loss2: 1.386862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.007205 Loss1: 1.614265 Loss2: 1.392940 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.927040 Loss1: 1.525063 Loss2: 1.401977 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.551042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.789631 Loss1: 1.378383 Loss2: 1.411248 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.720273 Loss1: 1.285773 Loss2: 1.434501 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.606250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.837915 Loss1: 2.732153 Loss2: 2.105761
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.862470 Loss1: 2.274185 Loss2: 1.588286
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.550899 Loss1: 1.980086 Loss2: 1.570813
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.496281 Loss1: 1.912453 Loss2: 1.583828
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.979119 Loss1: 2.952454 Loss2: 2.026665
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.084944 Loss1: 2.549554 Loss2: 1.535391 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.898154 Loss1: 2.351554 Loss2: 1.546600 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.719722 Loss1: 2.169951 Loss2: 1.549771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.186198 Loss1: 1.578742 Loss2: 1.607456
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.708067 Loss1: 2.146490 Loss2: 1.561576
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.201469 Loss1: 1.580590 Loss2: 1.620880
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.536973 Loss1: 1.972838 Loss2: 1.564135
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.054093 Loss1: 1.432095 Loss2: 1.621998
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.455871 Loss1: 1.873273 Loss2: 1.582598
+(DefaultActor pid=3765) >> Training accuracy: 0.623162
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.408282 Loss1: 1.826746 Loss2: 1.581535
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.332061 Loss1: 1.743402 Loss2: 1.588658
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.371577 Loss1: 1.773970 Loss2: 1.597608
+(DefaultActor pid=3764) >> Training accuracy: 0.490234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.070738 Loss1: 3.036139 Loss2: 2.034599
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.068473 Loss1: 2.569682 Loss2: 1.498791
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.798393 Loss1: 2.340294 Loss2: 1.458100
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.699282 Loss1: 2.227193 Loss2: 1.472088
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.777790 Loss1: 2.757742 Loss2: 2.020048
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.652089 Loss1: 2.168982 Loss2: 1.483107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.433117 Loss1: 1.975788 Loss2: 1.457328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.259733 Loss1: 1.793194 Loss2: 1.466539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.146041 Loss1: 1.682034 Loss2: 1.464007 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.077986 Loss1: 1.605039 Loss2: 1.472947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.133815 Loss1: 1.627711 Loss2: 1.506104
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.011828 Loss1: 1.530106 Loss2: 1.481721
+(DefaultActor pid=3765) >> Training accuracy: 0.530134
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.932054 Loss1: 1.447757 Loss2: 1.484297
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.906634 Loss1: 1.411872 Loss2: 1.494762
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.885194 Loss1: 1.384474 Loss2: 1.500720
+(DefaultActor pid=3764) >> Training accuracy: 0.592548
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.609170 Loss1: 2.679954 Loss2: 1.929216
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.611753 Loss1: 2.147261 Loss2: 1.464492
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.432072 Loss1: 1.975968 Loss2: 1.456105
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.905778 Loss1: 2.798043 Loss2: 2.107735
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.227759 Loss1: 1.780530 Loss2: 1.447228
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.979380 Loss1: 2.383749 Loss2: 1.595630
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.113841 Loss1: 1.667489 Loss2: 1.446352
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.032864 Loss1: 1.572906 Loss2: 1.459958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.051790 Loss1: 1.585062 Loss2: 1.466729 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.940043 Loss1: 1.469266 Loss2: 1.470777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.032636 Loss1: 1.542265 Loss2: 1.490371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.933223 Loss1: 1.442549 Loss2: 1.490674 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.567383
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.166952 Loss1: 1.575883 Loss2: 1.591069 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.586458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.937336 Loss1: 2.765341 Loss2: 2.171996 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.713993 Loss1: 2.157685 Loss2: 1.556309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.876129 Loss1: 2.785745 Loss2: 2.090384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.736376 Loss1: 2.214668 Loss2: 1.521707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.231025 Loss1: 1.640355 Loss2: 1.590670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.268254 Loss1: 1.660279 Loss2: 1.607975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.287041 Loss1: 1.696418 Loss2: 1.590623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.172524 Loss1: 1.551009 Loss2: 1.621515 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.570913
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.111646 Loss1: 1.602068 Loss2: 1.509578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.991435 Loss1: 1.464143 Loss2: 1.527292 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.583333
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.050106 Loss1: 1.507155 Loss2: 1.542951
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.990061 Loss1: 2.923356 Loss2: 2.066706
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.980067 Loss1: 2.458112 Loss2: 1.521955
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.766596 Loss1: 2.252587 Loss2: 1.514009
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.580586 Loss1: 2.056008 Loss2: 1.524578
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.432656 Loss1: 1.879924 Loss2: 1.552732
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.683766 Loss1: 2.706121 Loss2: 1.977645
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.806552 Loss1: 2.317349 Loss2: 1.489203 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.553633 Loss1: 2.099847 Loss2: 1.453787 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.420919 Loss1: 1.955111 Loss2: 1.465808 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.269465 Loss1: 1.791787 Loss2: 1.477678 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.560417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.199874 Loss1: 1.733618 Loss2: 1.466256 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.176918 Loss1: 1.658025 Loss2: 1.518894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.134864 Loss1: 1.620347 Loss2: 1.514517 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.562500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.895931 Loss1: 2.304711 Loss2: 1.591219 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.416657 Loss1: 1.857012 Loss2: 1.559645 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.322929 Loss1: 1.770755 Loss2: 1.552174
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.953490 Loss1: 2.894116 Loss2: 2.059374
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.954181 Loss1: 2.413726 Loss2: 1.540455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.705773 Loss1: 2.179732 Loss2: 1.526041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.643801 Loss1: 2.113879 Loss2: 1.529923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.458261 Loss1: 1.919528 Loss2: 1.538732 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.641667
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.065793 Loss1: 1.461954 Loss2: 1.603839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.394762 Loss1: 1.847673 Loss2: 1.547089
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.289203 Loss1: 1.740967 Loss2: 1.548236
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.243986 Loss1: 1.686131 Loss2: 1.557855
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.213157 Loss1: 1.645407 Loss2: 1.567750
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.225121 Loss1: 1.642914 Loss2: 1.582207
+(DefaultActor pid=3764) >> Training accuracy: 0.521875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.944157 Loss1: 2.902392 Loss2: 2.041765
+(DefaultActor pid=3765) Epoch: 1 Loss: 4.051422 Loss1: 2.529155 Loss2: 1.522267
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.771054 Loss1: 2.274821 Loss2: 1.496233
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.598490 Loss1: 2.099126 Loss2: 1.499364
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.477433 Loss1: 1.984784 Loss2: 1.492649
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.795529 Loss1: 2.741372 Loss2: 2.054158
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.893727 Loss1: 2.361533 Loss2: 1.532194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.694812 Loss1: 2.163372 Loss2: 1.531440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.459044 Loss1: 1.936875 Loss2: 1.522169 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.395745 Loss1: 1.875194 Loss2: 1.520551 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.532292
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.266998 Loss1: 1.705461 Loss2: 1.561537
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.258996 Loss1: 1.732639 Loss2: 1.526357
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.182657 Loss1: 1.638951 Loss2: 1.543706
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.145744 Loss1: 1.609143 Loss2: 1.536601
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.121147 Loss1: 1.569165 Loss2: 1.551983
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.101967 Loss1: 1.533115 Loss2: 1.568852
+(DefaultActor pid=3764) >> Training accuracy: 0.548958
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-09 01:51:51,667 | server.py:236 | fit_round 22 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.771830 Loss1: 2.815022 Loss2: 1.956808
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.776371 Loss1: 2.261215 Loss2: 1.515156
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.491980 Loss1: 2.005062 Loss2: 1.486918
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.407287 Loss1: 1.930649 Loss2: 1.476639
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.264186 Loss1: 1.773598 Loss2: 1.490587
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.608723 Loss1: 2.538752 Loss2: 2.069971
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.164188 Loss1: 1.665734 Loss2: 1.498454
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.684004 Loss1: 2.135801 Loss2: 1.548204
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.191023 Loss1: 1.675026 Loss2: 1.515997
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.449012 Loss1: 1.931825 Loss2: 1.517187
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.377651 Loss1: 1.847091 Loss2: 1.530560
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.203820 Loss1: 1.680156 Loss2: 1.523665
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.245813 Loss1: 1.709068 Loss2: 1.536745
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.046177 Loss1: 1.533299 Loss2: 1.512879
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.102719 Loss1: 1.572553 Loss2: 1.530166
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.926731 Loss1: 1.411947 Loss2: 1.514784
+(DefaultActor pid=3765) >> Training accuracy: 0.587891
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.092139 Loss1: 1.536494 Loss2: 1.555645 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.848278 Loss1: 1.289109 Loss2: 1.559169 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.630208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.957743 Loss1: 2.449150 Loss2: 1.508592 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.610742 Loss1: 2.115035 Loss2: 1.495707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.421621 Loss1: 1.924156 Loss2: 1.497466
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.726980 Loss1: 2.793489 Loss2: 1.933491
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.451915 Loss1: 1.942345 Loss2: 1.509571
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.825049 Loss1: 2.372133 Loss2: 1.452916
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.309696 Loss1: 1.797278 Loss2: 1.512418
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.519351 Loss1: 2.096909 Loss2: 1.422442
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.281753 Loss1: 1.744856 Loss2: 1.536898
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.375976 Loss1: 1.958213 Loss2: 1.417763
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.222848 Loss1: 1.690369 Loss2: 1.532478
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.296833 Loss1: 1.863982 Loss2: 1.432851
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.093775 Loss1: 1.546014 Loss2: 1.547761
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.205364 Loss1: 1.756860 Loss2: 1.448503
+(DefaultActor pid=3765) >> Training accuracy: 0.580208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.139442 Loss1: 1.693367 Loss2: 1.446075
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.046191 Loss1: 1.587248 Loss2: 1.458944
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.030569 Loss1: 1.558102 Loss2: 1.472467
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.919998 Loss1: 1.451400 Loss2: 1.468598
+(DefaultActor pid=3764) >> Training accuracy: 0.562500
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 01:51:51,667][flwr][DEBUG] - fit_round 22 received 50 results and 0 failures
+INFO flwr 2023-10-09 01:52:32,678 | server.py:125 | fit progress: (22, 3.1335429638719408, {'accuracy': 0.2531}, 50460.456895762)
+>> Test accuracy: 0.253100
+[2023-10-09 01:52:32,678][flwr][INFO] - fit progress: (22, 3.1335429638719408, {'accuracy': 0.2531}, 50460.456895762)
+DEBUG flwr 2023-10-09 01:52:32,679 | server.py:173 | evaluate_round 22: strategy sampled 50 clients (out of 50)
+[2023-10-09 01:52:32,679][flwr][DEBUG] - evaluate_round 22: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 02:01:35,282 | server.py:187 | evaluate_round 22 received 50 results and 0 failures
+[2023-10-09 02:01:35,282][flwr][DEBUG] - evaluate_round 22 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 02:01:35,283 | server.py:222 | fit_round 23: strategy sampled 50 clients (out of 50)
+[2023-10-09 02:01:35,283][flwr][DEBUG] - fit_round 23: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.678517 Loss1: 2.698942 Loss2: 1.979575
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.716214 Loss1: 2.241053 Loss2: 1.475162
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.545252 Loss1: 2.086019 Loss2: 1.459233
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.364681 Loss1: 1.897292 Loss2: 1.467389
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.999355 Loss1: 2.846088 Loss2: 2.153267
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.225873 Loss1: 1.756555 Loss2: 1.469318
+(DefaultActor pid=3764) Epoch: 1 Loss: 4.094074 Loss1: 2.469896 Loss2: 1.624178
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.154124 Loss1: 1.692189 Loss2: 1.461935
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.835541 Loss1: 2.224403 Loss2: 1.611138
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.211791 Loss1: 1.712290 Loss2: 1.499501
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.659259 Loss1: 2.057448 Loss2: 1.601811
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.052070 Loss1: 1.574756 Loss2: 1.477314
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.554770 Loss1: 1.956399 Loss2: 1.598371
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.898516 Loss1: 1.422208 Loss2: 1.476308
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.440761 Loss1: 1.820555 Loss2: 1.620205
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.915078 Loss1: 1.428353 Loss2: 1.486724
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.433771 Loss1: 1.805938 Loss2: 1.627834
+(DefaultActor pid=3765) >> Training accuracy: 0.578125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.375306 Loss1: 1.744153 Loss2: 1.631153
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.214404 Loss1: 1.568983 Loss2: 1.645421
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.277167 Loss1: 1.622823 Loss2: 1.654343
+(DefaultActor pid=3764) >> Training accuracy: 0.535417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.660432 Loss1: 2.734943 Loss2: 1.925488
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.698181 Loss1: 2.242982 Loss2: 1.455199
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.433901 Loss1: 2.014204 Loss2: 1.419698
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.272030 Loss1: 1.847788 Loss2: 1.424241
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.827595 Loss1: 2.760081 Loss2: 2.067514
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.862262 Loss1: 2.323318 Loss2: 1.538944
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.169128 Loss1: 1.729309 Loss2: 1.439819
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.674340 Loss1: 2.170014 Loss2: 1.504326
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.059236 Loss1: 1.630333 Loss2: 1.428903
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.407317 Loss1: 1.898676 Loss2: 1.508641
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.981616 Loss1: 1.529488 Loss2: 1.452128
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.395285 Loss1: 1.881639 Loss2: 1.513646
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.922163 Loss1: 1.467832 Loss2: 1.454330
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.836585 Loss1: 1.372109 Loss2: 1.464476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.802055 Loss1: 1.348504 Loss2: 1.453551 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.644531
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.048362 Loss1: 1.513444 Loss2: 1.534918 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.606250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.870869 Loss1: 2.794209 Loss2: 2.076660 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.632702 Loss1: 2.106235 Loss2: 1.526467 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.481779 Loss1: 1.957640 Loss2: 1.524140
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.856189 Loss1: 2.946751 Loss2: 1.909438
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.428518 Loss1: 1.899122 Loss2: 1.529396
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.879270 Loss1: 2.453852 Loss2: 1.425419
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.291310 Loss1: 1.749376 Loss2: 1.541933
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.659937 Loss1: 2.240802 Loss2: 1.419135
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.555102 Loss1: 2.139366 Loss2: 1.415736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.376973 Loss1: 1.932588 Loss2: 1.444385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.346226 Loss1: 1.901027 Loss2: 1.445200 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.618750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.282583 Loss1: 1.814802 Loss2: 1.467782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.114343 Loss1: 1.636304 Loss2: 1.478040 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.552734
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.791074 Loss1: 2.697179 Loss2: 2.093895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.487375 Loss1: 1.958790 Loss2: 1.528585 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.889607 Loss1: 2.862356 Loss2: 2.027251 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.931426 Loss1: 2.418747 Loss2: 1.512679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.636396 Loss1: 2.141715 Loss2: 1.494681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.465233 Loss1: 1.984409 Loss2: 1.480824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.281403 Loss1: 1.790618 Loss2: 1.490785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.196122 Loss1: 1.700249 Loss2: 1.495873 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.671875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.179332 Loss1: 1.667076 Loss2: 1.512256 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.100273 Loss1: 1.578023 Loss2: 1.522249 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.536458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.777029 Loss1: 2.333942 Loss2: 1.443087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.393981 Loss1: 1.967807 Loss2: 1.426174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.838719 Loss1: 2.851385 Loss2: 1.987334
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.338226 Loss1: 1.903820 Loss2: 1.434406
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.880028 Loss1: 2.401094 Loss2: 1.478934
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.237374 Loss1: 1.785280 Loss2: 1.452094
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.573910 Loss1: 2.137310 Loss2: 1.436600
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.186351 Loss1: 1.722999 Loss2: 1.463352
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.384416 Loss1: 1.944110 Loss2: 1.440306
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.117195 Loss1: 1.650466 Loss2: 1.466729
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.249246 Loss1: 1.813373 Loss2: 1.435874
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.995196 Loss1: 1.527375 Loss2: 1.467821
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.192291 Loss1: 1.733355 Loss2: 1.458935
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.906781 Loss1: 1.433772 Loss2: 1.473009
+(DefaultActor pid=3765) >> Training accuracy: 0.542708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.183262 Loss1: 1.711500 Loss2: 1.471762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.974275 Loss1: 1.491207 Loss2: 1.483068 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.585417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.703044 Loss1: 2.251614 Loss2: 1.451430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.358043 Loss1: 1.920401 Loss2: 1.437642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.315500 Loss1: 1.866686 Loss2: 1.448814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.271333 Loss1: 1.790580 Loss2: 1.480753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.195694 Loss1: 1.730922 Loss2: 1.464772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.012600 Loss1: 1.544744 Loss2: 1.467856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.880335 Loss1: 1.420609 Loss2: 1.459726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.971764 Loss1: 1.477612 Loss2: 1.494152 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.582292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.045801 Loss1: 1.515828 Loss2: 1.529974 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.650670
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.454333 Loss1: 2.541705 Loss2: 1.912628 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.337007 Loss1: 1.918772 Loss2: 1.418235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.155951 Loss1: 1.742744 Loss2: 1.413207
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.825458 Loss1: 2.807048 Loss2: 2.018409
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.971935 Loss1: 1.550792 Loss2: 1.421144
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.814711 Loss1: 2.305494 Loss2: 1.509217
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.961495 Loss1: 1.537901 Loss2: 1.423594
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.533057 Loss1: 2.026511 Loss2: 1.506547
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.898215 Loss1: 1.464093 Loss2: 1.434122
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.433837 Loss1: 1.940577 Loss2: 1.493260
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.775600 Loss1: 1.351850 Loss2: 1.423750
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.286497 Loss1: 1.779536 Loss2: 1.506961
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.700851 Loss1: 1.261922 Loss2: 1.438929
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.196006 Loss1: 1.688569 Loss2: 1.507437
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.708094 Loss1: 1.252758 Loss2: 1.455336
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.128113 Loss1: 1.604711 Loss2: 1.523402
+(DefaultActor pid=3765) >> Training accuracy: 0.636458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.082994 Loss1: 1.569501 Loss2: 1.513493
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.076445 Loss1: 1.548023 Loss2: 1.528423
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.031317 Loss1: 1.482848 Loss2: 1.548469
+(DefaultActor pid=3764) >> Training accuracy: 0.605208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.605990 Loss1: 2.687290 Loss2: 1.918699
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.765481 Loss1: 2.294835 Loss2: 1.470646
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.565319 Loss1: 2.111886 Loss2: 1.453432
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.898873 Loss1: 2.872833 Loss2: 2.026040
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.371047 Loss1: 1.907424 Loss2: 1.463623
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.848276 Loss1: 2.345192 Loss2: 1.503084
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.256908 Loss1: 1.788317 Loss2: 1.468591
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.131526 Loss1: 1.652369 Loss2: 1.479157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.051332 Loss1: 1.558665 Loss2: 1.492667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.018138 Loss1: 1.527348 Loss2: 1.490791 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.980638 Loss1: 1.485716 Loss2: 1.494922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.945117 Loss1: 1.432878 Loss2: 1.512239 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.633789
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.037460 Loss1: 1.517065 Loss2: 1.520396 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.616667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.740144 Loss1: 2.632844 Loss2: 2.107300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.482335 Loss1: 1.961728 Loss2: 1.520606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.290100 Loss1: 1.773352 Loss2: 1.516748
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.767973 Loss1: 2.723382 Loss2: 2.044592
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.086489 Loss1: 1.596196 Loss2: 1.490293
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.668338 Loss1: 2.176530 Loss2: 1.491808
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.980113 Loss1: 1.476286 Loss2: 1.503827
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.470167 Loss1: 1.996257 Loss2: 1.473910
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.979744 Loss1: 1.467627 Loss2: 1.512116
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.315698 Loss1: 1.837561 Loss2: 1.478138
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.930317 Loss1: 1.407329 Loss2: 1.522988
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.227626 Loss1: 1.737424 Loss2: 1.490202
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.898587 Loss1: 1.362085 Loss2: 1.536502
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.198023 Loss1: 1.695130 Loss2: 1.502893
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.886515 Loss1: 1.358214 Loss2: 1.528301
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.153912 Loss1: 1.638406 Loss2: 1.515506
+(DefaultActor pid=3765) >> Training accuracy: 0.677083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.991105 Loss1: 1.468752 Loss2: 1.522353
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.924090 Loss1: 1.392210 Loss2: 1.531880
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.834982 Loss1: 1.311342 Loss2: 1.523641
+(DefaultActor pid=3764) >> Training accuracy: 0.667708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.859676 Loss1: 2.760308 Loss2: 2.099368
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.905759 Loss1: 2.357017 Loss2: 1.548742
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.713465 Loss1: 2.179401 Loss2: 1.534065
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.534166 Loss1: 1.984209 Loss2: 1.549957
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.942992 Loss1: 2.926102 Loss2: 2.016890
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.930827 Loss1: 2.411663 Loss2: 1.519164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.657094 Loss1: 2.165489 Loss2: 1.491605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.476736 Loss1: 1.974088 Loss2: 1.502648 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.401383 Loss1: 1.893337 Loss2: 1.508046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.313441 Loss1: 1.790929 Loss2: 1.522512 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.577083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.212320 Loss1: 1.691700 Loss2: 1.520621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.096985 Loss1: 1.560210 Loss2: 1.536774 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.612305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.571622 Loss1: 2.562492 Loss2: 2.009130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.464017 Loss1: 1.988811 Loss2: 1.475206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.699529 Loss1: 2.656045 Loss2: 2.043484 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.738246 Loss1: 2.231747 Loss2: 1.506499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.460566 Loss1: 1.946937 Loss2: 1.513629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.272845 Loss1: 1.771420 Loss2: 1.501425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.202275 Loss1: 1.699682 Loss2: 1.502594 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.092255 Loss1: 1.589509 Loss2: 1.502746 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.667708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.059644 Loss1: 1.536658 Loss2: 1.522987 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.867150 Loss1: 1.339173 Loss2: 1.527978 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.619792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.820961 Loss1: 2.278356 Loss2: 1.542605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.380678 Loss1: 1.855773 Loss2: 1.524905 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.951424 Loss1: 2.925895 Loss2: 2.025529
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.258489 Loss1: 1.713776 Loss2: 1.544713
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.881788 Loss1: 2.356036 Loss2: 1.525752
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.175495 Loss1: 1.634489 Loss2: 1.541006
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.645124 Loss1: 2.166107 Loss2: 1.479018
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.144585 Loss1: 1.597226 Loss2: 1.547359
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.490255 Loss1: 2.015784 Loss2: 1.474471
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.103779 Loss1: 1.540127 Loss2: 1.563652
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.397274 Loss1: 1.911268 Loss2: 1.486006
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.078158 Loss1: 1.493770 Loss2: 1.584388
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.295701 Loss1: 1.816443 Loss2: 1.479257
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.089501 Loss1: 1.507456 Loss2: 1.582045
+(DefaultActor pid=3765) >> Training accuracy: 0.563542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.188564 Loss1: 1.672367 Loss2: 1.516197 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.110832 Loss1: 1.574950 Loss2: 1.535883 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.601042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.796642 Loss1: 2.298773 Loss2: 1.497870 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.409746 Loss1: 1.914303 Loss2: 1.495444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.283613 Loss1: 1.773537 Loss2: 1.510075
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.773932 Loss1: 2.662630 Loss2: 2.111302
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.286288 Loss1: 1.771083 Loss2: 1.515204
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.741612 Loss1: 2.166905 Loss2: 1.574707
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.161147 Loss1: 1.642593 Loss2: 1.518554
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.545092 Loss1: 1.988045 Loss2: 1.557047
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.028729 Loss1: 1.505692 Loss2: 1.523036
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.394931 Loss1: 1.828856 Loss2: 1.566076
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.066596 Loss1: 1.547996 Loss2: 1.518600
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.292835 Loss1: 1.719749 Loss2: 1.573086
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.000159 Loss1: 1.441234 Loss2: 1.558925
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.191306 Loss1: 1.626859 Loss2: 1.564447
+(DefaultActor pid=3765) >> Training accuracy: 0.594792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.081603 Loss1: 1.501348 Loss2: 1.580255
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.091327 Loss1: 1.497773 Loss2: 1.593554
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.974510 Loss1: 1.375499 Loss2: 1.599012
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.959018 Loss1: 1.360820 Loss2: 1.598197
+(DefaultActor pid=3764) >> Training accuracy: 0.619792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.600028 Loss1: 2.714191 Loss2: 1.885837
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.667125 Loss1: 2.246555 Loss2: 1.420570
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.524342 Loss1: 2.094009 Loss2: 1.430333
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.355835 Loss1: 1.943838 Loss2: 1.411997
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.895191 Loss1: 2.747372 Loss2: 2.147819
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.786272 Loss1: 2.209898 Loss2: 1.576374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.499070 Loss1: 1.961483 Loss2: 1.537587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.391554 Loss1: 1.862124 Loss2: 1.529430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.296453 Loss1: 1.743712 Loss2: 1.552741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.181816 Loss1: 1.628851 Loss2: 1.552965 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.622070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.117192 Loss1: 1.548081 Loss2: 1.569112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.120758 Loss1: 1.547783 Loss2: 1.572975 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.593750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.723542 Loss1: 2.690619 Loss2: 2.032923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.559307 Loss1: 2.049407 Loss2: 1.509900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.381385 Loss1: 1.883190 Loss2: 1.498195
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.756902 Loss1: 2.660720 Loss2: 2.096182
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.774032 Loss1: 2.181067 Loss2: 1.592966 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.456889 Loss1: 1.923391 Loss2: 1.533498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.288813 Loss1: 1.753068 Loss2: 1.535745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.214566 Loss1: 1.675285 Loss2: 1.539281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.133134 Loss1: 1.581677 Loss2: 1.551457 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.613542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.983856 Loss1: 1.435383 Loss2: 1.548472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.902263 Loss1: 1.335644 Loss2: 1.566620 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.606250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.539230 Loss1: 2.540653 Loss2: 1.998577 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.276880 Loss1: 1.836692 Loss2: 1.440188 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.086895 Loss1: 1.662237 Loss2: 1.424657
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.074204 Loss1: 2.959089 Loss2: 2.115115
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.960055 Loss1: 2.360240 Loss2: 1.599815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.726660 Loss1: 2.174601 Loss2: 1.552058 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.573711 Loss1: 2.007437 Loss2: 1.566274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.501176 Loss1: 1.946003 Loss2: 1.555173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.425342 Loss1: 1.860282 Loss2: 1.565060 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.594792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.235530 Loss1: 1.655880 Loss2: 1.579650 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.180660 Loss1: 1.586750 Loss2: 1.593911 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.522461
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.706221 Loss1: 2.247052 Loss2: 1.459169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.295982 Loss1: 1.858827 Loss2: 1.437155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.104308 Loss1: 2.780006 Loss2: 2.324302
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.249825 Loss1: 1.793937 Loss2: 1.455888
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.182474 Loss1: 1.722558 Loss2: 1.459916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.042487 Loss1: 1.585687 Loss2: 1.456800 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.018429 Loss1: 1.552796 Loss2: 1.465633 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.230773 Loss1: 1.609428 Loss2: 1.621345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.289888 Loss1: 1.656080 Loss2: 1.633808 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.638542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.051670 Loss1: 1.422599 Loss2: 1.629071 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.563802
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.151806 Loss1: 3.044017 Loss2: 2.107790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.775054 Loss1: 2.231596 Loss2: 1.543457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.767051 Loss1: 2.704457 Loss2: 2.062594 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.707530 Loss1: 2.202868 Loss2: 1.504662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.486301 Loss1: 1.977025 Loss2: 1.509276 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.321753 Loss1: 1.813719 Loss2: 1.508034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.265944 Loss1: 1.751735 Loss2: 1.514209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.069694 Loss1: 1.458458 Loss2: 1.611236 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.587054
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.079440 Loss1: 1.549485 Loss2: 1.529955 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.925375 Loss1: 1.384615 Loss2: 1.540760 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.609375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.825807 Loss1: 2.356201 Loss2: 1.469605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.407874 Loss1: 1.954721 Loss2: 1.453154 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.295136 Loss1: 1.847771 Loss2: 1.447365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.348433 Loss1: 1.868160 Loss2: 1.480274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.213291 Loss1: 1.740688 Loss2: 1.472604 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.093810 Loss1: 1.613338 Loss2: 1.480471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.037645 Loss1: 1.553928 Loss2: 1.483717 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.096108 Loss1: 1.588668 Loss2: 1.507440 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.569336
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.899664 Loss1: 1.323672 Loss2: 1.575992 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.638542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.136145 Loss1: 3.031801 Loss2: 2.104344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.740990 Loss1: 2.191423 Loss2: 1.549566 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.575620 Loss1: 2.032757 Loss2: 1.542862
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.858954 Loss1: 2.661546 Loss2: 2.197407
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.690055 Loss1: 2.115358 Loss2: 1.574697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.556019 Loss1: 1.996165 Loss2: 1.559854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.392185 Loss1: 1.817349 Loss2: 1.574836 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.330602 Loss1: 1.737040 Loss2: 1.593562
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.269135 Loss1: 1.704637 Loss2: 1.564498
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.131695 Loss1: 1.550062 Loss2: 1.581633
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.244330 Loss1: 1.650706 Loss2: 1.593624
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.039152 Loss1: 1.449715 Loss2: 1.589437
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.137162 Loss1: 1.537211 Loss2: 1.599951
+(DefaultActor pid=3765) >> Training accuracy: 0.562500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.880636 Loss1: 1.313563 Loss2: 1.567073 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.581731
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.766543 Loss1: 2.548254 Loss2: 2.218289 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.448771 Loss1: 1.848510 Loss2: 1.600261 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.315084 Loss1: 1.729435 Loss2: 1.585649
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.794966 Loss1: 2.706330 Loss2: 2.088636
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.778489 Loss1: 2.249957 Loss2: 1.528532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.478585 Loss1: 1.958254 Loss2: 1.520330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.308807 Loss1: 1.793648 Loss2: 1.515159 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.334212 Loss1: 1.812434 Loss2: 1.521778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.181914 Loss1: 1.645741 Loss2: 1.536173 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.661458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.225937 Loss1: 1.688683 Loss2: 1.537254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.056753 Loss1: 1.509467 Loss2: 1.547286 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.583984
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.834894 Loss1: 2.726798 Loss2: 2.108096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.481646 Loss1: 1.988752 Loss2: 1.492895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.311981 Loss1: 1.817669 Loss2: 1.494312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.215373 Loss1: 1.705768 Loss2: 1.509605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.163329 Loss1: 1.645387 Loss2: 1.517942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.025509 Loss1: 1.499773 Loss2: 1.525735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.432630 Loss1: 1.948936 Loss2: 1.483694
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.916678 Loss1: 1.391695 Loss2: 1.524983
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.945383 Loss1: 1.407797 Loss2: 1.537587
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.322262 Loss1: 1.845333 Loss2: 1.476929
+(DefaultActor pid=3765) >> Training accuracy: 0.614183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.208467 Loss1: 1.706298 Loss2: 1.502169
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.126528 Loss1: 1.624435 Loss2: 1.502093
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.130511 Loss1: 1.618581 Loss2: 1.511931
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.090654 Loss1: 1.567704 Loss2: 1.522950
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.090636 Loss1: 1.556538 Loss2: 1.534098
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.684923 Loss1: 2.720704 Loss2: 1.964219
+(DefaultActor pid=3764) >> Training accuracy: 0.577083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.682239 Loss1: 2.208932 Loss2: 1.473306
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.365956 Loss1: 1.908952 Loss2: 1.457003
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.226600 Loss1: 1.759396 Loss2: 1.467204
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.154826 Loss1: 1.689350 Loss2: 1.465475
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.940195 Loss1: 2.840877 Loss2: 2.099318
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.949894 Loss1: 2.384259 Loss2: 1.565636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.073330 Loss1: 1.578076 Loss2: 1.495253
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.629219 Loss1: 2.066949 Loss2: 1.562270
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.088031 Loss1: 1.578487 Loss2: 1.509544
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.540771 Loss1: 1.979397 Loss2: 1.561374
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.541129 Loss1: 1.951403 Loss2: 1.589726
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.032377 Loss1: 1.508703 Loss2: 1.523674
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.480529 Loss1: 1.869278 Loss2: 1.611251
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.887202 Loss1: 1.380491 Loss2: 1.506711
+(DefaultActor pid=3765) >> Training accuracy: 0.581801
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.315475 Loss1: 1.713821 Loss2: 1.601654 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.127817 Loss1: 1.528516 Loss2: 1.599301 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.567708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.849079 Loss1: 2.300787 Loss2: 1.548292 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.382128 Loss1: 1.862470 Loss2: 1.519658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.911733 Loss1: 2.854893 Loss2: 2.056840
+DEBUG flwr 2023-10-09 02:30:35,118 | server.py:236 | fit_round 23 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.278056 Loss1: 1.742551 Loss2: 1.535505
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.959064 Loss1: 2.418882 Loss2: 1.540182
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.206755 Loss1: 1.653654 Loss2: 1.553100
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.661598 Loss1: 2.151645 Loss2: 1.509953
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.248317 Loss1: 1.705484 Loss2: 1.542833
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.486208 Loss1: 1.979367 Loss2: 1.506840
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.149174 Loss1: 1.586815 Loss2: 1.562360
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.483974 Loss1: 1.959511 Loss2: 1.524463
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.083745 Loss1: 1.526772 Loss2: 1.556974
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.365837 Loss1: 1.829101 Loss2: 1.536737
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.968821 Loss1: 1.403127 Loss2: 1.565694
+(DefaultActor pid=3765) >> Training accuracy: 0.598958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.133113 Loss1: 1.591019 Loss2: 1.542094 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.086823 Loss1: 1.514961 Loss2: 1.571862 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.607292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.606427 Loss1: 2.109801 Loss2: 1.496626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.229972 Loss1: 1.751908 Loss2: 1.478065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.822460 Loss1: 2.801486 Loss2: 2.020974
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.130042 Loss1: 1.644625 Loss2: 1.485417
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.018286 Loss1: 1.531564 Loss2: 1.486721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.949215 Loss1: 1.455454 Loss2: 1.493761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.065643 Loss1: 1.556841 Loss2: 1.508802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.842073 Loss1: 1.345942 Loss2: 1.496131 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.911275 Loss1: 1.399034 Loss2: 1.512241 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.606445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.146149 Loss1: 1.638715 Loss2: 1.507433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.031474 Loss1: 1.511608 Loss2: 1.519866 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.588542
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 02:30:35,118][flwr][DEBUG] - fit_round 23 received 50 results and 0 failures
+INFO flwr 2023-10-09 02:31:15,011 | server.py:125 | fit progress: (23, 3.1089451796711445, {'accuracy': 0.2637}, 52782.789583785)
+>> Test accuracy: 0.263700
+[2023-10-09 02:31:15,011][flwr][INFO] - fit progress: (23, 3.1089451796711445, {'accuracy': 0.2637}, 52782.789583785)
+DEBUG flwr 2023-10-09 02:31:15,011 | server.py:173 | evaluate_round 23: strategy sampled 50 clients (out of 50)
+[2023-10-09 02:31:15,011][flwr][DEBUG] - evaluate_round 23: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 02:40:14,636 | server.py:187 | evaluate_round 23 received 50 results and 0 failures
+[2023-10-09 02:40:14,636][flwr][DEBUG] - evaluate_round 23 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 02:40:14,636 | server.py:222 | fit_round 24: strategy sampled 50 clients (out of 50)
+[2023-10-09 02:40:14,636][flwr][DEBUG] - fit_round 24: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.760952 Loss1: 2.653937 Loss2: 2.107015
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.756374 Loss1: 2.227787 Loss2: 1.528587
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.480698 Loss1: 1.975641 Loss2: 1.505057
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.328113 Loss1: 1.818558 Loss2: 1.509556
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.654900 Loss1: 2.590500 Loss2: 2.064400
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.100901 Loss1: 1.581601 Loss2: 1.519300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.964285 Loss1: 1.430750 Loss2: 1.533535 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.942502 Loss1: 1.416837 Loss2: 1.525665 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.897719 Loss1: 1.351867 Loss2: 1.545852 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.845396 Loss1: 1.304670 Loss2: 1.540726 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.618990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.966727 Loss1: 1.439391 Loss2: 1.527336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.889628 Loss1: 1.337384 Loss2: 1.552243 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.830977 Loss1: 1.292676 Loss2: 1.538301
+(DefaultActor pid=3764) >> Training accuracy: 0.656250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.709526 Loss1: 2.649017 Loss2: 2.060509
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.630948 Loss1: 2.129925 Loss2: 1.501024
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.355767 Loss1: 1.879169 Loss2: 1.476598
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.185230 Loss1: 1.713181 Loss2: 1.472049
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.048971 Loss1: 1.575955 Loss2: 1.473017
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.577066 Loss1: 2.537719 Loss2: 2.039348
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.918509 Loss1: 1.433055 Loss2: 1.485454
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.558899 Loss1: 2.043792 Loss2: 1.515107
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.957169 Loss1: 1.469877 Loss2: 1.487292
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.311287 Loss1: 1.802419 Loss2: 1.508868
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.843390 Loss1: 1.339533 Loss2: 1.503857
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.115310 Loss1: 1.619141 Loss2: 1.496168
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.769856 Loss1: 1.267646 Loss2: 1.502209
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.136625 Loss1: 1.623984 Loss2: 1.512640
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.734688 Loss1: 1.220000 Loss2: 1.514688
+(DefaultActor pid=3765) >> Training accuracy: 0.568750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.925198 Loss1: 1.402280 Loss2: 1.522918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.727222 Loss1: 1.199436 Loss2: 1.527786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.669192 Loss1: 1.150681 Loss2: 1.518511
+(DefaultActor pid=3764) >> Training accuracy: 0.723958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.632093 Loss1: 2.634803 Loss2: 1.997291
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.674421 Loss1: 2.161750 Loss2: 1.512671
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.382078 Loss1: 1.899684 Loss2: 1.482394
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.180078 Loss1: 1.702205 Loss2: 1.477874
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.066419 Loss1: 1.571532 Loss2: 1.494887
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.854767 Loss1: 2.828173 Loss2: 2.026594
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.988271 Loss1: 1.492007 Loss2: 1.496264
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.968479 Loss1: 1.462993 Loss2: 1.505486 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.845280 Loss1: 1.334903 Loss2: 1.510377 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.907830 Loss1: 1.389465 Loss2: 1.518365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.781183 Loss1: 1.241437 Loss2: 1.539747 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.616667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.234159 Loss1: 1.710235 Loss2: 1.523924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.975756 Loss1: 1.440681 Loss2: 1.535076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.056444 Loss1: 1.498985 Loss2: 1.557460
+(DefaultActor pid=3764) >> Training accuracy: 0.613542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.795912 Loss1: 2.710013 Loss2: 2.085899
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.696117 Loss1: 2.176776 Loss2: 1.519341
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.517716 Loss1: 2.009801 Loss2: 1.507915
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.291235 Loss1: 1.777868 Loss2: 1.513367
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.260477 Loss1: 1.734221 Loss2: 1.526256
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.440163 Loss1: 2.456429 Loss2: 1.983734
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.158412 Loss1: 1.610965 Loss2: 1.547447
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.538296 Loss1: 2.070217 Loss2: 1.468079
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.061166 Loss1: 1.512173 Loss2: 1.548993
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.087702 Loss1: 1.539106 Loss2: 1.548596 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.000485 Loss1: 1.424729 Loss2: 1.575756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.030856 Loss1: 1.449768 Loss2: 1.581088 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.579167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.842084 Loss1: 1.382659 Loss2: 1.459425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.753559 Loss1: 1.282841 Loss2: 1.470717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.735687 Loss1: 1.242542 Loss2: 1.493145
+(DefaultActor pid=3764) >> Training accuracy: 0.659375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.749488 Loss1: 2.634281 Loss2: 2.115208
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.804382 Loss1: 2.212358 Loss2: 1.592025
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.471003 Loss1: 1.906405 Loss2: 1.564598
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.428909 Loss1: 1.850133 Loss2: 1.578775
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.253322 Loss1: 1.674867 Loss2: 1.578456
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.506197 Loss1: 2.586615 Loss2: 1.919582
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.155174 Loss1: 1.563234 Loss2: 1.591940
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.632467 Loss1: 2.180873 Loss2: 1.451593
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.003011 Loss1: 1.403991 Loss2: 1.599020
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.090934 Loss1: 1.480800 Loss2: 1.610134
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.377103 Loss1: 1.947066 Loss2: 1.430037
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.043409 Loss1: 1.423541 Loss2: 1.619868
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.193023 Loss1: 1.748760 Loss2: 1.444263
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.879218 Loss1: 1.250492 Loss2: 1.628725
+(DefaultActor pid=3765) >> Training accuracy: 0.629167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.118245 Loss1: 1.673353 Loss2: 1.444892
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.962046 Loss1: 1.520837 Loss2: 1.441209
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.847292 Loss1: 1.397420 Loss2: 1.449872
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.842414 Loss1: 1.364974 Loss2: 1.477439
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.824685 Loss1: 1.350689 Loss2: 1.473996
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.910082 Loss1: 2.735390 Loss2: 2.174692
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.720992 Loss1: 1.249761 Loss2: 1.471231
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.823516 Loss1: 2.182997 Loss2: 1.640519
+(DefaultActor pid=3764) >> Training accuracy: 0.666360
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.542945 Loss1: 1.937664 Loss2: 1.605281
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.418702 Loss1: 1.803189 Loss2: 1.615512
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.292815 Loss1: 1.664213 Loss2: 1.628602
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.370539 Loss1: 1.732505 Loss2: 1.638034
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.158329 Loss1: 1.510512 Loss2: 1.647817
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.526834 Loss1: 2.460882 Loss2: 2.065951
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.217549 Loss1: 1.559270 Loss2: 1.658278
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.585669 Loss1: 2.041080 Loss2: 1.544589
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.094736 Loss1: 1.412840 Loss2: 1.681896
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.256716 Loss1: 1.746866 Loss2: 1.509851
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.988274 Loss1: 1.322840 Loss2: 1.665434
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.151195 Loss1: 1.645346 Loss2: 1.505849
+(DefaultActor pid=3765) >> Training accuracy: 0.623958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.063346 Loss1: 1.553976 Loss2: 1.509370
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.937916 Loss1: 1.416352 Loss2: 1.521564
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.024156 Loss1: 1.488117 Loss2: 1.536040
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.867481 Loss1: 1.336562 Loss2: 1.530919
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.587762 Loss1: 2.631049 Loss2: 1.956713
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.772717 Loss1: 1.237519 Loss2: 1.535198
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.577666 Loss1: 2.107014 Loss2: 1.470652
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.665087 Loss1: 1.142657 Loss2: 1.522430
+(DefaultActor pid=3764) >> Training accuracy: 0.739583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.247489 Loss1: 1.788878 Loss2: 1.458611 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.037451 Loss1: 1.567690 Loss2: 1.469760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.943248 Loss1: 1.461520 Loss2: 1.481728
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.923459 Loss1: 2.871076 Loss2: 2.052383
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.990794 Loss1: 2.430012 Loss2: 1.560781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.737077 Loss1: 2.171581 Loss2: 1.565495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.641667
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.847358 Loss1: 1.345099 Loss2: 1.502259
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.572315 Loss1: 2.013063 Loss2: 1.559252
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.634321 Loss1: 2.039103 Loss2: 1.595218
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.392876 Loss1: 1.816925 Loss2: 1.575951
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.252625 Loss1: 1.676907 Loss2: 1.575718
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.281191 Loss1: 1.669744 Loss2: 1.611447
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.722728 Loss1: 2.708818 Loss2: 2.013909
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.693928 Loss1: 2.212924 Loss2: 1.481004 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.587891
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.052710 Loss1: 1.438447 Loss2: 1.614263
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.480208 Loss1: 2.006821 Loss2: 1.473387
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.225162 Loss1: 1.743500 Loss2: 1.481661
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.210582 Loss1: 1.725079 Loss2: 1.485503
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.059752 Loss1: 1.565135 Loss2: 1.494617
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.925968 Loss1: 1.440451 Loss2: 1.485516
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.570323 Loss1: 2.611753 Loss2: 1.958569
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.881489 Loss1: 1.377452 Loss2: 1.504036
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.827559 Loss1: 1.325296 Loss2: 1.502263 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.803960 Loss1: 1.289866 Loss2: 1.514094 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.647917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.931683 Loss1: 1.505825 Loss2: 1.425858 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.778793 Loss1: 1.346058 Loss2: 1.432735 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.869031 Loss1: 2.725016 Loss2: 2.144015 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.736779
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.624475 Loss1: 2.051280 Loss2: 1.573194 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.219320 Loss1: 1.636171 Loss2: 1.583148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.177747 Loss1: 1.590736 Loss2: 1.587011
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.610595 Loss1: 2.642846 Loss2: 1.967749
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.736034 Loss1: 2.264317 Loss2: 1.471717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.409836 Loss1: 1.943594 Loss2: 1.466243 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.265493 Loss1: 1.794507 Loss2: 1.470986 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.610417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.136945 Loss1: 1.657047 Loss2: 1.479898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.957012 Loss1: 1.464210 Loss2: 1.492802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.922725 Loss1: 1.410518 Loss2: 1.512207 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.803699 Loss1: 2.289045 Loss2: 1.514654 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.645508
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.387040 Loss1: 1.884456 Loss2: 1.502585 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.163467 Loss1: 1.634552 Loss2: 1.528915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.121244 Loss1: 1.577027 Loss2: 1.544217
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.765023 Loss1: 2.670578 Loss2: 2.094445
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.044348 Loss1: 1.516947 Loss2: 1.527401
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.645140 Loss1: 2.113741 Loss2: 1.531399
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.038986 Loss1: 1.497339 Loss2: 1.541648
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.502873 Loss1: 1.966904 Loss2: 1.535969
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.047872 Loss1: 1.499593 Loss2: 1.548278
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.313233 Loss1: 1.777249 Loss2: 1.535984
+(DefaultActor pid=3765) >> Training accuracy: 0.540625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.213592 Loss1: 1.660540 Loss2: 1.553052
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.099666 Loss1: 1.547603 Loss2: 1.552063
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.972815 Loss1: 1.429947 Loss2: 1.542868
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.946101 Loss1: 1.379191 Loss2: 1.566910
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.843875 Loss1: 2.732601 Loss2: 2.111274
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.921628 Loss1: 1.352020 Loss2: 1.569608
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.999952 Loss1: 1.405714 Loss2: 1.594238
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.923596 Loss1: 2.326596 Loss2: 1.597000
+(DefaultActor pid=3764) >> Training accuracy: 0.606250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.641786 Loss1: 2.055655 Loss2: 1.586131
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.502565 Loss1: 1.911191 Loss2: 1.591374
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.415841 Loss1: 1.816188 Loss2: 1.599653
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.329178 Loss1: 1.720874 Loss2: 1.608305
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.803199 Loss1: 2.705055 Loss2: 2.098144
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.200786 Loss1: 1.579219 Loss2: 1.621568
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.125045 Loss1: 1.501463 Loss2: 1.623582 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.076364 Loss1: 1.444138 Loss2: 1.632226 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.064082 Loss1: 1.429194 Loss2: 1.634887 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.665039
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.973117 Loss1: 1.441653 Loss2: 1.531464 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.971971 Loss1: 1.428031 Loss2: 1.543940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.878644 Loss1: 2.814380 Loss2: 2.064264 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.687500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.772375 Loss1: 2.311804 Loss2: 1.460570 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.312039 Loss1: 1.862036 Loss2: 1.450003 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.177022 Loss1: 1.706829 Loss2: 1.470193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.072530 Loss1: 1.593129 Loss2: 1.479400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.995741 Loss1: 1.513246 Loss2: 1.482494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.946145 Loss1: 1.457183 Loss2: 1.488962 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.914244 Loss1: 1.417617 Loss2: 1.496627 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.637500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.887041 Loss1: 1.433489 Loss2: 1.453551 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.826751 Loss1: 1.337944 Loss2: 1.488807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.768451 Loss1: 1.278889 Loss2: 1.489562
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.777734 Loss1: 2.824436 Loss2: 1.953298
+(DefaultActor pid=3764) >> Training accuracy: 0.684375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.775557 Loss1: 2.245792 Loss2: 1.529765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.346744 Loss1: 1.858995 Loss2: 1.487749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.227268 Loss1: 1.711796 Loss2: 1.515472 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.161343 Loss1: 1.632296 Loss2: 1.529047 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.091826 Loss1: 1.555509 Loss2: 1.536317 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.883953 Loss1: 1.371239 Loss2: 1.512714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.504496 Loss1: 1.972817 Loss2: 1.531679
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.847846 Loss1: 1.312584 Loss2: 1.535261
+(DefaultActor pid=3765) >> Training accuracy: 0.635417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.259352 Loss1: 1.706147 Loss2: 1.553205 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.089986 Loss1: 1.508007 Loss2: 1.581980 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.741727 Loss1: 2.625441 Loss2: 2.116285
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.158520 Loss1: 1.574052 Loss2: 1.584468
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.126974 Loss1: 1.552461 Loss2: 1.574514 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.605469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.321328 Loss1: 1.794803 Loss2: 1.526525 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.052886 Loss1: 1.526480 Loss2: 1.526405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.786686 Loss1: 2.746680 Loss2: 2.040006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.859076 Loss1: 2.332905 Loss2: 1.526171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.844753 Loss1: 1.280379 Loss2: 1.564374 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.660714
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.271164 Loss1: 1.738414 Loss2: 1.532750 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.061200 Loss1: 1.501490 Loss2: 1.559710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.975934 Loss1: 1.416173 Loss2: 1.559761
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.903939 Loss1: 2.752022 Loss2: 2.151916
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.978754 Loss1: 1.409116 Loss2: 1.569638
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.885186 Loss1: 2.289344 Loss2: 1.595842
+(DefaultActor pid=3764) Epoch: 9 Loss: 3.021926 Loss1: 1.429039 Loss2: 1.592887
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.633518 Loss1: 2.035699 Loss2: 1.597819
+(DefaultActor pid=3764) >> Training accuracy: 0.604167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.495176 Loss1: 1.898106 Loss2: 1.597070
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.404701 Loss1: 1.793032 Loss2: 1.611669
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.316834 Loss1: 1.698018 Loss2: 1.618816
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.211492 Loss1: 1.572335 Loss2: 1.639156
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.151273 Loss1: 1.524934 Loss2: 1.626339
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.767759 Loss1: 2.686964 Loss2: 2.080795
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.115655 Loss1: 1.467385 Loss2: 1.648269
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.868019 Loss1: 2.323327 Loss2: 1.544693
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.030904 Loss1: 1.380888 Loss2: 1.650016
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.539444 Loss1: 2.002099 Loss2: 1.537345
+(DefaultActor pid=3765) >> Training accuracy: 0.567708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.403422 Loss1: 1.870697 Loss2: 1.532725
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.226189 Loss1: 1.683392 Loss2: 1.542798
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.186074 Loss1: 1.637904 Loss2: 1.548169
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.031495 Loss1: 1.481394 Loss2: 1.550101
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.048232 Loss1: 2.905198 Loss2: 2.143035
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.979531 Loss1: 1.415202 Loss2: 1.564329
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.953345 Loss1: 2.350194 Loss2: 1.603152
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.027137 Loss1: 1.443449 Loss2: 1.583688
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.619880 Loss1: 2.054167 Loss2: 1.565713
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.960865 Loss1: 1.377579 Loss2: 1.583286
+(DefaultActor pid=3764) >> Training accuracy: 0.543750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.347208 Loss1: 1.775227 Loss2: 1.571981 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.271257 Loss1: 1.679457 Loss2: 1.591800 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.161064 Loss1: 1.571512 Loss2: 1.589552
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.782841 Loss1: 2.875561 Loss2: 1.907281
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.129743 Loss1: 1.536848 Loss2: 1.592895
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.814550 Loss1: 2.378528 Loss2: 1.436023
+(DefaultActor pid=3765) >> Training accuracy: 0.652083
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.034093 Loss1: 1.447186 Loss2: 1.586906
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.529075 Loss1: 2.109676 Loss2: 1.419398
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.397598 Loss1: 1.962473 Loss2: 1.435124
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.336623 Loss1: 1.893569 Loss2: 1.443054
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.224268 Loss1: 1.777418 Loss2: 1.446849
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.197284 Loss1: 1.734333 Loss2: 1.462951
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.789549 Loss1: 2.724653 Loss2: 2.064896
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.781053 Loss1: 2.222330 Loss2: 1.558723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.573802 Loss1: 2.038736 Loss2: 1.535066 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.585938
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.930220 Loss1: 1.454443 Loss2: 1.475777
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.378567 Loss1: 1.838938 Loss2: 1.539628
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.207411 Loss1: 1.661772 Loss2: 1.545639
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.116123 Loss1: 1.570014 Loss2: 1.546109
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.018874 Loss1: 1.477750 Loss2: 1.541124
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.043614 Loss1: 1.479008 Loss2: 1.564606
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.751998 Loss1: 2.678760 Loss2: 2.073238
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.979604 Loss1: 1.410472 Loss2: 1.569131
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.751680 Loss1: 2.181803 Loss2: 1.569877
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.836971 Loss1: 1.258948 Loss2: 1.578023
+(DefaultActor pid=3765) >> Training accuracy: 0.654167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.202005 Loss1: 1.674648 Loss2: 1.527357 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.976904 Loss1: 1.425181 Loss2: 1.551723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.926328 Loss1: 1.373984 Loss2: 1.552344
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.798393 Loss1: 2.820883 Loss2: 1.977510
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.698879 Loss1: 2.244361 Loss2: 1.454518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.508242 Loss1: 2.065763 Loss2: 1.442480 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.667708
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.852364 Loss1: 1.286752 Loss2: 1.565611
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.320393 Loss1: 1.880255 Loss2: 1.440137
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.196310 Loss1: 1.740216 Loss2: 1.456094
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.073944 Loss1: 1.606920 Loss2: 1.467024
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.068125 Loss1: 1.595493 Loss2: 1.472632
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.962084 Loss1: 1.486506 Loss2: 1.475578
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.759611 Loss1: 2.711174 Loss2: 2.048437
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.956063 Loss1: 1.460454 Loss2: 1.495609
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.790632 Loss1: 2.298770 Loss2: 1.491862
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.025805 Loss1: 1.531510 Loss2: 1.494294
+(DefaultActor pid=3765) >> Training accuracy: 0.554167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.399642 Loss1: 1.901909 Loss2: 1.497734 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.156256 Loss1: 1.660869 Loss2: 1.495387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.049297 Loss1: 1.547845 Loss2: 1.501452
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.656156 Loss1: 2.622634 Loss2: 2.033522
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.022599 Loss1: 1.511630 Loss2: 1.510969
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.714068 Loss1: 2.212515 Loss2: 1.501553
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.990895 Loss1: 1.470103 Loss2: 1.520792
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.434066 Loss1: 1.932977 Loss2: 1.501090
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.903190 Loss1: 1.380811 Loss2: 1.522379
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.271795 Loss1: 1.768740 Loss2: 1.503055
+(DefaultActor pid=3764) >> Training accuracy: 0.620833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.057443 Loss1: 1.542299 Loss2: 1.515143
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.137899 Loss1: 1.613563 Loss2: 1.524336
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.015193 Loss1: 1.487211 Loss2: 1.527983
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.033293 Loss1: 1.507556 Loss2: 1.525737
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.870623 Loss1: 1.328491 Loss2: 1.542132
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.711986 Loss1: 2.648519 Loss2: 2.063468
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.871097 Loss1: 1.337112 Loss2: 1.533985
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.705684 Loss1: 2.161516 Loss2: 1.544168
+(DefaultActor pid=3765) >> Training accuracy: 0.660417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.567465 Loss1: 2.036169 Loss2: 1.531296
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.341816 Loss1: 1.799262 Loss2: 1.542554
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.185615 Loss1: 1.645687 Loss2: 1.539928
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.219562 Loss1: 1.662765 Loss2: 1.556797
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.874813 Loss1: 2.765985 Loss2: 2.108828
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.064480 Loss1: 1.494899 Loss2: 1.569582
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.028125 Loss1: 1.450323 Loss2: 1.577802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.959826 Loss1: 1.388828 Loss2: 1.570998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.952746 Loss1: 1.369863 Loss2: 1.582883 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.549805
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.181208 Loss1: 1.611835 Loss2: 1.569373 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.008472 Loss1: 1.438950 Loss2: 1.569522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.677942 Loss1: 2.742845 Loss2: 1.935097 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.543750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.464831 Loss1: 2.048328 Loss2: 1.416502 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.233877 Loss1: 1.802125 Loss2: 1.431751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.129751 Loss1: 1.699756 Loss2: 1.429994
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.513634 Loss1: 2.442469 Loss2: 2.071165
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.595893 Loss1: 2.044565 Loss2: 1.551328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.356823 Loss1: 1.814477 Loss2: 1.542346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.175158 Loss1: 1.645360 Loss2: 1.529798 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.582292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.120082 Loss1: 1.593034 Loss2: 1.527049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.935763 Loss1: 1.391202 Loss2: 1.544560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.780108 Loss1: 1.226352 Loss2: 1.553756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.737743 Loss1: 1.172119 Loss2: 1.565625 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.690625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.307804 Loss1: 1.867072 Loss2: 1.440732 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.959003 Loss1: 1.529993 Loss2: 1.429010 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.887926 Loss1: 1.426358 Loss2: 1.461568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.838713 Loss1: 1.371443 Loss2: 1.467270 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.709573 Loss1: 1.237189 Loss2: 1.472384 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.651042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.438383 Loss1: 1.871226 Loss2: 1.567158 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.196488 Loss1: 1.604235 Loss2: 1.592253 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.960876 Loss1: 2.898780 Loss2: 2.062096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.883437 Loss1: 2.356366 Loss2: 1.527071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.614380 Loss1: 2.093005 Loss2: 1.521375 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.582292
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.039478 Loss1: 1.426237 Loss2: 1.613242
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.426828 Loss1: 1.909762 Loss2: 1.517066
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.306353 Loss1: 1.782718 Loss2: 1.523635
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.149187 Loss1: 1.617412 Loss2: 1.531774
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.193702 Loss1: 1.648899 Loss2: 1.544803
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.274512 Loss1: 1.716117 Loss2: 1.558395
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.077722 Loss1: 1.507711 Loss2: 1.570011
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.646117 Loss1: 2.554205 Loss2: 2.091912
+(DefaultActor pid=3764) >> Training accuracy: 0.659598
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.992811 Loss1: 1.440634 Loss2: 1.552177
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.588864 Loss1: 2.001762 Loss2: 1.587102
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.388031 Loss1: 1.827495 Loss2: 1.560536
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.182998 Loss1: 1.626794 Loss2: 1.556204
+DEBUG flwr 2023-10-09 03:08:37,261 | server.py:236 | fit_round 24 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.110209 Loss1: 1.552025 Loss2: 1.558184
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.098277 Loss1: 1.532764 Loss2: 1.565512
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.017718 Loss1: 2.937537 Loss2: 2.080181
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.887947 Loss1: 2.374136 Loss2: 1.513811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.923068 Loss1: 1.346148 Loss2: 1.576920
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.622228 Loss1: 2.123494 Loss2: 1.498734
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.939910 Loss1: 1.346455 Loss2: 1.593455
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.516554 Loss1: 2.012503 Loss2: 1.504051
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.337034 Loss1: 1.830711 Loss2: 1.506323
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.748050 Loss1: 1.152349 Loss2: 1.595701
+(DefaultActor pid=3765) >> Training accuracy: 0.649414
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.126133 Loss1: 1.587418 Loss2: 1.538715 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.962413 Loss1: 1.413783 Loss2: 1.548630 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.579241
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.914360 Loss1: 1.364904 Loss2: 1.549456
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.627992 Loss1: 2.573908 Loss2: 2.054084
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.599432 Loss1: 2.066642 Loss2: 1.532789
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.405006 Loss1: 1.888168 Loss2: 1.516839
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.246356 Loss1: 1.725769 Loss2: 1.520587
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.149040 Loss1: 1.613334 Loss2: 1.535705
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.799669 Loss1: 2.630136 Loss2: 2.169533
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.048813 Loss1: 1.513845 Loss2: 1.534968
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.921118 Loss1: 1.379372 Loss2: 1.541746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.554183 Loss1: 1.921409 Loss2: 1.632774
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.794110 Loss1: 1.253735 Loss2: 1.540375
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.444706 Loss1: 1.795159 Loss2: 1.649547
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.780732 Loss1: 1.234329 Loss2: 1.546404
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.310333 Loss1: 1.657177 Loss2: 1.653156
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.750650 Loss1: 1.200558 Loss2: 1.550092
+(DefaultActor pid=3765) >> Training accuracy: 0.612500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.186302 Loss1: 1.528739 Loss2: 1.657563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.066412 Loss1: 1.395211 Loss2: 1.671201 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.850856 Loss1: 2.821658 Loss2: 2.029198
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.999234 Loss1: 1.316556 Loss2: 1.682679
+(DefaultActor pid=3764) >> Training accuracy: 0.625000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.559956 Loss1: 2.069715 Loss2: 1.490242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.293821 Loss1: 1.799928 Loss2: 1.493893 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.239318 Loss1: 1.733956 Loss2: 1.505362
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.686047 Loss1: 2.648707 Loss2: 2.037339
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.650478 Loss1: 2.105333 Loss2: 1.545145 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.504658 Loss1: 1.976226 Loss2: 1.528432 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.342972 Loss1: 1.792127 Loss2: 1.550845 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.610417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.161871 Loss1: 1.621385 Loss2: 1.540486 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.969333 Loss1: 1.411786 Loss2: 1.557547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.905695 Loss1: 1.338103 Loss2: 1.567593 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.648438
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 03:08:37,261][flwr][DEBUG] - fit_round 24 received 50 results and 0 failures
+INFO flwr 2023-10-09 03:09:18,444 | server.py:125 | fit progress: (24, 3.037447242691113, {'accuracy': 0.2814}, 55066.222770296)
+>> Test accuracy: 0.281400
+[2023-10-09 03:09:18,444][flwr][INFO] - fit progress: (24, 3.037447242691113, {'accuracy': 0.2814}, 55066.222770296)
+DEBUG flwr 2023-10-09 03:09:18,445 | server.py:173 | evaluate_round 24: strategy sampled 50 clients (out of 50)
+[2023-10-09 03:09:18,445][flwr][DEBUG] - evaluate_round 24: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 03:18:23,968 | server.py:187 | evaluate_round 24 received 50 results and 0 failures
+[2023-10-09 03:18:23,968][flwr][DEBUG] - evaluate_round 24 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 03:18:23,968 | server.py:222 | fit_round 25: strategy sampled 50 clients (out of 50)
+[2023-10-09 03:18:23,968][flwr][DEBUG] - fit_round 25: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.758622 Loss1: 2.624612 Loss2: 2.134010 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.447285 Loss1: 1.890091 Loss2: 1.557194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.373739 Loss1: 1.822851 Loss2: 1.550888
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.700071 Loss1: 2.711642 Loss2: 1.988429
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.639695 Loss1: 2.166549 Loss2: 1.473146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.400738 Loss1: 1.938982 Loss2: 1.461756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.249397 Loss1: 1.765611 Loss2: 1.483786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.091157 Loss1: 1.609342 Loss2: 1.481815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.064099 Loss1: 1.576738 Loss2: 1.487361 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.641667
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.920360 Loss1: 1.310621 Loss2: 1.609739
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.935856 Loss1: 1.444374 Loss2: 1.491483
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.838853 Loss1: 1.335447 Loss2: 1.503407
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.779670 Loss1: 1.281564 Loss2: 1.498106
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.793441 Loss1: 1.276380 Loss2: 1.517061
+(DefaultActor pid=3764) >> Training accuracy: 0.623958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.805565 Loss1: 2.709743 Loss2: 2.095822
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.773263 Loss1: 2.250748 Loss2: 1.522515
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.534471 Loss1: 2.035545 Loss2: 1.498926
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.343902 Loss1: 1.834430 Loss2: 1.509472
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.700185 Loss1: 2.572584 Loss2: 2.127600
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.626774 Loss1: 2.074673 Loss2: 1.552102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.334753 Loss1: 1.832283 Loss2: 1.502470 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.116758 Loss1: 1.603466 Loss2: 1.513292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.030690 Loss1: 1.510270 Loss2: 1.520420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.913069 Loss1: 1.382056 Loss2: 1.531012 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.626042
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.879188 Loss1: 1.345668 Loss2: 1.533520
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.816927 Loss1: 1.287957 Loss2: 1.528970
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.779680 Loss1: 1.243672 Loss2: 1.536007
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.671570 Loss1: 1.134897 Loss2: 1.536673
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.789793 Loss1: 1.238224 Loss2: 1.551568
+(DefaultActor pid=3764) >> Training accuracy: 0.659375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.601666 Loss1: 2.564824 Loss2: 2.036842
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.636274 Loss1: 2.131801 Loss2: 1.504472
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.359240 Loss1: 1.865449 Loss2: 1.493791
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.134934 Loss1: 1.652797 Loss2: 1.482137
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.796732 Loss1: 2.731544 Loss2: 2.065189
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.051088 Loss1: 1.558660 Loss2: 1.492428
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.675278 Loss1: 2.177357 Loss2: 1.497921
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.992663 Loss1: 1.495474 Loss2: 1.497189
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.387780 Loss1: 1.907212 Loss2: 1.480567
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.814305 Loss1: 1.303729 Loss2: 1.510576
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.163201 Loss1: 1.676952 Loss2: 1.486250
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.742852 Loss1: 1.234258 Loss2: 1.508594
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.067976 Loss1: 1.578773 Loss2: 1.489203
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.672472 Loss1: 1.168641 Loss2: 1.503831
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.007327 Loss1: 1.504033 Loss2: 1.503294
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.603545 Loss1: 1.088876 Loss2: 1.514669
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.906742 Loss1: 1.404192 Loss2: 1.502550
+(DefaultActor pid=3765) >> Training accuracy: 0.650000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.879492 Loss1: 1.351562 Loss2: 1.527930
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.843535 Loss1: 1.320223 Loss2: 1.523312
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.798624 Loss1: 1.256512 Loss2: 1.542113
+(DefaultActor pid=3764) >> Training accuracy: 0.648958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.634760 Loss1: 2.699455 Loss2: 1.935305
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.563979 Loss1: 2.100035 Loss2: 1.463945
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.311240 Loss1: 1.874402 Loss2: 1.436838
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.452418 Loss1: 2.388442 Loss2: 2.063976
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.119477 Loss1: 1.672481 Loss2: 1.446997
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.487680 Loss1: 1.957788 Loss2: 1.529892
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.953657 Loss1: 1.495316 Loss2: 1.458341
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.214063 Loss1: 1.703168 Loss2: 1.510895
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.850950 Loss1: 1.402398 Loss2: 1.448552
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.078838 Loss1: 1.569553 Loss2: 1.509285
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.914297 Loss1: 1.441726 Loss2: 1.472571
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.811684 Loss1: 1.328703 Loss2: 1.482981 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.758066 Loss1: 1.269684 Loss2: 1.488382 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.661119 Loss1: 1.189735 Loss2: 1.471384 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.697266
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.805550 Loss1: 1.275353 Loss2: 1.530198 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.668750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.710633 Loss1: 2.659512 Loss2: 2.051122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.547262 Loss1: 2.061624 Loss2: 1.485639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.330540 Loss1: 1.839041 Loss2: 1.491499
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.505037 Loss1: 2.486282 Loss2: 2.018755
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.096238 Loss1: 1.631797 Loss2: 1.464441
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.494637 Loss1: 2.012455 Loss2: 1.482182
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.004830 Loss1: 1.512705 Loss2: 1.492125
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.250306 Loss1: 1.783433 Loss2: 1.466873
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.961162 Loss1: 1.467723 Loss2: 1.493438
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.205304 Loss1: 1.730286 Loss2: 1.475017
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.919269 Loss1: 1.431753 Loss2: 1.487515
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.004495 Loss1: 1.528780 Loss2: 1.475714
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.921963 Loss1: 1.420227 Loss2: 1.501737
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.843918 Loss1: 1.374824 Loss2: 1.469094
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.812537 Loss1: 1.308005 Loss2: 1.504532
+(DefaultActor pid=3765) >> Training accuracy: 0.630208
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.748509 Loss1: 1.268277 Loss2: 1.480231
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.845916 Loss1: 1.355832 Loss2: 1.490084
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.679001 Loss1: 1.180692 Loss2: 1.498309
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.658726 Loss1: 1.157916 Loss2: 1.500810
+(DefaultActor pid=3764) >> Training accuracy: 0.706250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.624840 Loss1: 2.578649 Loss2: 2.046191
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.597754 Loss1: 2.076392 Loss2: 1.521361
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.426688 Loss1: 1.904290 Loss2: 1.522398
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.621562 Loss1: 2.543661 Loss2: 2.077900
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.182736 Loss1: 1.648051 Loss2: 1.534685
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.067148 Loss1: 1.539484 Loss2: 1.527664 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.047584 Loss1: 1.519775 Loss2: 1.527809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.989873 Loss1: 1.432633 Loss2: 1.557240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.927116 Loss1: 1.380229 Loss2: 1.546887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.779491 Loss1: 1.264849 Loss2: 1.514642 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.677129 Loss1: 1.144465 Loss2: 1.532664 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.655331
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.680624 Loss1: 1.147660 Loss2: 1.532964 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.692708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.863483 Loss1: 2.841819 Loss2: 2.021663
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.760017 Loss1: 2.230489 Loss2: 1.529527
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.490939 Loss1: 2.004668 Loss2: 1.486271
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.899677 Loss1: 2.819067 Loss2: 2.080609
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.343421 Loss1: 1.848073 Loss2: 1.495348
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.833296 Loss1: 2.301821 Loss2: 1.531475
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.195880 Loss1: 1.698812 Loss2: 1.497068
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.494720 Loss1: 1.977995 Loss2: 1.516724
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.198267 Loss1: 1.688211 Loss2: 1.510056
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.074567 Loss1: 1.558776 Loss2: 1.515790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.015939 Loss1: 1.481437 Loss2: 1.534502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.960690 Loss1: 1.433078 Loss2: 1.527612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.945503 Loss1: 1.402550 Loss2: 1.542954 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.601562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.990705 Loss1: 1.430802 Loss2: 1.559904 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.639583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.925094 Loss1: 2.833470 Loss2: 2.091624 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.599072 Loss1: 2.025782 Loss2: 1.573289 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.586207 Loss1: 2.566081 Loss2: 2.020126
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.408130 Loss1: 1.816144 Loss2: 1.591986
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.659901 Loss1: 2.155415 Loss2: 1.504486
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.239921 Loss1: 1.653578 Loss2: 1.586343
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.386662 Loss1: 1.913625 Loss2: 1.473037
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.226671 Loss1: 1.629760 Loss2: 1.596911
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.181008 Loss1: 1.691180 Loss2: 1.489828
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.130844 Loss1: 1.522920 Loss2: 1.607924
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.036626 Loss1: 1.430134 Loss2: 1.606491 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.988179 Loss1: 1.362761 Loss2: 1.625418 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.901273 Loss1: 1.270650 Loss2: 1.630623 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.608398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.869861 Loss1: 1.353080 Loss2: 1.516781 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.639583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.929000 Loss1: 2.825696 Loss2: 2.103304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.564244 Loss1: 2.023338 Loss2: 1.540906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.404220 Loss1: 1.860373 Loss2: 1.543846
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.696196 Loss1: 2.519664 Loss2: 2.176532
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.179262 Loss1: 1.638966 Loss2: 1.540296
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.606072 Loss1: 2.011786 Loss2: 1.594286
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.134679 Loss1: 1.573369 Loss2: 1.561310
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.293278 Loss1: 1.732822 Loss2: 1.560455
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.118953 Loss1: 1.564045 Loss2: 1.554908
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.059838 Loss1: 1.518473 Loss2: 1.541365
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.967852 Loss1: 1.405263 Loss2: 1.562589
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.928965 Loss1: 1.391095 Loss2: 1.537870
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.941842 Loss1: 1.376349 Loss2: 1.565494
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.843185 Loss1: 1.295909 Loss2: 1.547276
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.999112 Loss1: 1.412028 Loss2: 1.587083
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.783645 Loss1: 1.224310 Loss2: 1.559335
+(DefaultActor pid=3765) >> Training accuracy: 0.602083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.691438 Loss1: 1.131125 Loss2: 1.560313
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.707772 Loss1: 1.146443 Loss2: 1.561329
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.582646 Loss1: 1.011193 Loss2: 1.571452
+(DefaultActor pid=3764) >> Training accuracy: 0.721875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.720674 Loss1: 2.637022 Loss2: 2.083652
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.587784 Loss1: 2.042808 Loss2: 1.544976
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.299823 Loss1: 1.794596 Loss2: 1.505227
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.115198 Loss1: 1.602639 Loss2: 1.512559
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.722489 Loss1: 2.611937 Loss2: 2.110552
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.046196 Loss1: 1.533120 Loss2: 1.513076
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.732376 Loss1: 2.226165 Loss2: 1.506211
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.440650 Loss1: 1.954812 Loss2: 1.485838
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.982909 Loss1: 1.459961 Loss2: 1.522948
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.829137 Loss1: 1.302508 Loss2: 1.526629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.737662 Loss1: 1.216052 Loss2: 1.521610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.847143 Loss1: 1.300232 Loss2: 1.546911 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.680512 Loss1: 1.132230 Loss2: 1.548282 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.682292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.694341 Loss1: 1.173825 Loss2: 1.520516 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.682692
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 5.047488 Loss1: 2.963862 Loss2: 2.083626
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.864638 Loss1: 2.345052 Loss2: 1.519586
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.610396 Loss1: 2.097882 Loss2: 1.512514
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.435427 Loss1: 1.910028 Loss2: 1.525399
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.847634 Loss1: 2.593246 Loss2: 2.254388
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.691803 Loss1: 2.112386 Loss2: 1.579417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.459346 Loss1: 1.908962 Loss2: 1.550384
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.195232 Loss1: 1.656770 Loss2: 1.538462
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.143506 Loss1: 1.582357 Loss2: 1.561148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.100083 Loss1: 1.537608 Loss2: 1.562475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 3.023564 Loss1: 1.466612 Loss2: 1.556952 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.892801 Loss1: 1.307586 Loss2: 1.585215 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.650670
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.729294 Loss1: 1.127558 Loss2: 1.601736 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.682292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.919187 Loss1: 2.806054 Loss2: 2.113134
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.933713 Loss1: 2.343413 Loss2: 1.590301
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.655206 Loss1: 2.075726 Loss2: 1.579480
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.384679 Loss1: 1.806409 Loss2: 1.578270
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.572667 Loss1: 2.486039 Loss2: 2.086628
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.583249 Loss1: 2.078702 Loss2: 1.504547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.190496 Loss1: 1.698426 Loss2: 1.492069 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.097812 Loss1: 1.617711 Loss2: 1.480101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.991910 Loss1: 1.506402 Loss2: 1.485508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.916311 Loss1: 1.409984 Loss2: 1.506327 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.604167
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.955842 Loss1: 1.343985 Loss2: 1.611857
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.787759 Loss1: 1.278714 Loss2: 1.509045
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.754906 Loss1: 1.239428 Loss2: 1.515478
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.641671 Loss1: 1.138362 Loss2: 1.503309
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.606056 Loss1: 1.096208 Loss2: 1.509848
+(DefaultActor pid=3764) >> Training accuracy: 0.744792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.937547 Loss1: 2.766107 Loss2: 2.171440
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.956941 Loss1: 2.335864 Loss2: 1.621077
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.651764 Loss1: 2.063499 Loss2: 1.588265
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.473945 Loss1: 1.877195 Loss2: 1.596750
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.702874 Loss1: 2.641433 Loss2: 2.061442
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.569916 Loss1: 2.072077 Loss2: 1.497839 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.333718 Loss1: 1.851761 Loss2: 1.481958 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.144838 Loss1: 1.659555 Loss2: 1.485284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.031351 Loss1: 1.535833 Loss2: 1.495519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.947111 Loss1: 1.443147 Loss2: 1.503964 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.636458
+(DefaultActor pid=3765) Epoch: 9 Loss: 3.030277 Loss1: 1.390605 Loss2: 1.639672
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.885417 Loss1: 1.385808 Loss2: 1.499609
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.827968 Loss1: 1.306837 Loss2: 1.521131
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.903989 Loss1: 1.380455 Loss2: 1.523534
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.786422 Loss1: 1.250227 Loss2: 1.536196
+(DefaultActor pid=3764) >> Training accuracy: 0.696875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.535926 Loss1: 2.560176 Loss2: 1.975750
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.586563 Loss1: 2.138969 Loss2: 1.447594
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.427794 Loss1: 1.985087 Loss2: 1.442707
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.230373 Loss1: 1.781694 Loss2: 1.448679
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.790292 Loss1: 2.717210 Loss2: 2.073082
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.114069 Loss1: 1.653256 Loss2: 1.460813
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.889366 Loss1: 2.349758 Loss2: 1.539608
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.907942 Loss1: 1.444886 Loss2: 1.463056
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.496041 Loss1: 1.997561 Loss2: 1.498480
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.933971 Loss1: 1.463755 Loss2: 1.470216
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.336055 Loss1: 1.833509 Loss2: 1.502545
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.954228 Loss1: 1.460663 Loss2: 1.493565
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.196054 Loss1: 1.699979 Loss2: 1.496075
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.798494 Loss1: 1.313585 Loss2: 1.484910
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.154202 Loss1: 1.651717 Loss2: 1.502485
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.734286 Loss1: 1.231764 Loss2: 1.502522
+(DefaultActor pid=3765) >> Training accuracy: 0.664583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.968865 Loss1: 1.467581 Loss2: 1.501284
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.874011 Loss1: 1.362645 Loss2: 1.511366
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.952754 Loss1: 1.430844 Loss2: 1.521910
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.856056 Loss1: 1.328839 Loss2: 1.527217
+(DefaultActor pid=3764) >> Training accuracy: 0.690625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.621748 Loss1: 2.587666 Loss2: 2.034081
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.634195 Loss1: 2.142757 Loss2: 1.491438
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.401257 Loss1: 1.917392 Loss2: 1.483864
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.151891 Loss1: 1.676148 Loss2: 1.475742
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.508019 Loss1: 2.623510 Loss2: 1.884509
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.628063 Loss1: 2.202696 Loss2: 1.425367 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.309590 Loss1: 1.889473 Loss2: 1.420117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.165517 Loss1: 1.740242 Loss2: 1.425275 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.088732 Loss1: 1.654301 Loss2: 1.434431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.968879 Loss1: 1.530753 Loss2: 1.438125 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.645833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.855750 Loss1: 1.403378 Loss2: 1.452372 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.746463 Loss1: 1.287498 Loss2: 1.458965 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.632812
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.668257 Loss1: 1.210252 Loss2: 1.458005
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.668800 Loss1: 2.639780 Loss2: 2.029020
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.694257 Loss1: 2.202345 Loss2: 1.491911
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.450609 Loss1: 1.974492 Loss2: 1.476117
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.239637 Loss1: 1.759843 Loss2: 1.479795
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.103347 Loss1: 1.617234 Loss2: 1.486113
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.715748 Loss1: 2.623295 Loss2: 2.092453
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.005249 Loss1: 1.519115 Loss2: 1.486133
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.596095 Loss1: 2.090628 Loss2: 1.505467
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.937705 Loss1: 1.432332 Loss2: 1.505373
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.353374 Loss1: 1.862017 Loss2: 1.491357
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.887550 Loss1: 1.388920 Loss2: 1.498630
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.251739 Loss1: 1.751113 Loss2: 1.500626
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.938554 Loss1: 1.419356 Loss2: 1.519198
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.112364 Loss1: 1.597681 Loss2: 1.514684
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.838039 Loss1: 1.320565 Loss2: 1.517475
+(DefaultActor pid=3765) >> Training accuracy: 0.670833
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.972315 Loss1: 1.466085 Loss2: 1.506230
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.881029 Loss1: 1.370756 Loss2: 1.510273
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.830039 Loss1: 1.306946 Loss2: 1.523094
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.747656 Loss1: 1.210760 Loss2: 1.536896
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.796925 Loss1: 1.262265 Loss2: 1.534660
+(DefaultActor pid=3764) >> Training accuracy: 0.628125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.739313 Loss1: 2.725661 Loss2: 2.013652
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.843252 Loss1: 2.355030 Loss2: 1.488222
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.481519 Loss1: 2.029264 Loss2: 1.452255
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.252355 Loss1: 1.782676 Loss2: 1.469680
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.128264 Loss1: 1.655263 Loss2: 1.473001
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.077348 Loss1: 1.595296 Loss2: 1.482052 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.059210 Loss1: 1.566886 Loss2: 1.492324 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.973033 Loss1: 1.473115 Loss2: 1.499918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.829166 Loss1: 1.339560 Loss2: 1.489606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.800454 Loss1: 1.305372 Loss2: 1.495081 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.626042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.074115 Loss1: 1.593474 Loss2: 1.480640 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 3.025514 Loss1: 1.526755 Loss2: 1.498759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.925345 Loss1: 1.409999 Loss2: 1.515346
+(DefaultActor pid=3764) >> Training accuracy: 0.598633
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.797218 Loss1: 2.632484 Loss2: 2.164734
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.750441 Loss1: 2.170611 Loss2: 1.579830
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.471419 Loss1: 1.899821 Loss2: 1.571598
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.244188 Loss1: 1.676851 Loss2: 1.567337
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.153002 Loss1: 1.582025 Loss2: 1.570977
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.437227 Loss1: 2.395507 Loss2: 2.041720
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.485342 Loss1: 1.993840 Loss2: 1.491502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.231645 Loss1: 1.749305 Loss2: 1.482339 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.968004 Loss1: 1.499247 Loss2: 1.468757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.921808 Loss1: 1.316831 Loss2: 1.604977
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.953210 Loss1: 1.476980 Loss2: 1.476229
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.904951 Loss1: 1.289335 Loss2: 1.615616
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.867349 Loss1: 1.393250 Loss2: 1.474099
+(DefaultActor pid=3765) >> Training accuracy: 0.622070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.773598 Loss1: 1.282709 Loss2: 1.490889
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.745622 Loss1: 1.254745 Loss2: 1.490877
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.720538 Loss1: 1.220002 Loss2: 1.500535
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.701650 Loss1: 1.191731 Loss2: 1.509919
+(DefaultActor pid=3764) >> Training accuracy: 0.679167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.836627 Loss1: 2.727757 Loss2: 2.108870
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.667911 Loss1: 2.121408 Loss2: 1.546504
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.378036 Loss1: 1.858628 Loss2: 1.519408
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.257736 Loss1: 1.721974 Loss2: 1.535761
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.202916 Loss1: 1.650185 Loss2: 1.552732
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.700792 Loss1: 2.679752 Loss2: 2.021040
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.034771 Loss1: 1.487253 Loss2: 1.547518
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.738746 Loss1: 2.247312 Loss2: 1.491433
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.389586 Loss1: 1.908440 Loss2: 1.481146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.303594 Loss1: 1.819243 Loss2: 1.484351 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.201986 Loss1: 1.698953 Loss2: 1.503033 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.655208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.122220 Loss1: 1.624192 Loss2: 1.498028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.949058 Loss1: 1.440583 Loss2: 1.508475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.743353 Loss1: 1.220317 Loss2: 1.523037 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.619141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.419665 Loss1: 1.834264 Loss2: 1.585401 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.179585 Loss1: 1.583235 Loss2: 1.596350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.742974 Loss1: 2.605635 Loss2: 2.137338
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.061367 Loss1: 1.454257 Loss2: 1.607110
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.637231 Loss1: 2.065468 Loss2: 1.571763
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.962526 Loss1: 1.353456 Loss2: 1.609070
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.453906 Loss1: 1.911084 Loss2: 1.542822
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.973464 Loss1: 1.367825 Loss2: 1.605639
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.262867 Loss1: 1.720921 Loss2: 1.541946
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.955017 Loss1: 1.328650 Loss2: 1.626367
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.209587 Loss1: 1.657097 Loss2: 1.552490
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.853344 Loss1: 1.225449 Loss2: 1.627895
+(DefaultActor pid=3765) >> Training accuracy: 0.636458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.048318 Loss1: 1.476088 Loss2: 1.572230 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.910057 Loss1: 1.332077 Loss2: 1.577980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.937038 Loss1: 1.347613 Loss2: 1.589425
+(DefaultActor pid=3764) >> Training accuracy: 0.635417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.521407 Loss1: 2.472238 Loss2: 2.049168
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.571314 Loss1: 2.046489 Loss2: 1.524825
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.266404 Loss1: 1.765477 Loss2: 1.500927
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.126989 Loss1: 1.625712 Loss2: 1.501277
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.947273 Loss1: 1.438841 Loss2: 1.508432
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.639433 Loss1: 2.649187 Loss2: 1.990247
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.556501 Loss1: 2.069743 Loss2: 1.486758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.282305 Loss1: 1.804780 Loss2: 1.477525 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.196995 Loss1: 1.721265 Loss2: 1.475729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.993958 Loss1: 1.530596 Loss2: 1.463361 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.678711
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.645231 Loss1: 1.102600 Loss2: 1.542631
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.857301 Loss1: 1.390803 Loss2: 1.466499
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.781587 Loss1: 1.310923 Loss2: 1.470665
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.651253 Loss1: 1.178190 Loss2: 1.473063
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.699885 Loss1: 1.200555 Loss2: 1.499330
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.739856 Loss1: 1.229548 Loss2: 1.510308
+(DefaultActor pid=3764) >> Training accuracy: 0.725000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.529736 Loss1: 2.577196 Loss2: 1.952540
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.626764 Loss1: 2.168267 Loss2: 1.458497
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.312363 Loss1: 1.868361 Loss2: 1.444002
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.134530 Loss1: 1.697718 Loss2: 1.436812
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.985590 Loss1: 1.544018 Loss2: 1.441572
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.704123 Loss1: 2.655407 Loss2: 2.048716
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.771843 Loss1: 2.244491 Loss2: 1.527351
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.980800 Loss1: 1.530139 Loss2: 1.450660
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.484269 Loss1: 1.958466 Loss2: 1.525803
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.986243 Loss1: 1.532379 Loss2: 1.453864
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.321310 Loss1: 1.789174 Loss2: 1.532136
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.871113 Loss1: 1.400269 Loss2: 1.470844
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.183537 Loss1: 1.651610 Loss2: 1.531927
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.797357 Loss1: 1.339212 Loss2: 1.458145
+DEBUG flwr 2023-10-09 03:47:28,943 | server.py:236 | fit_round 25 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.743318 Loss1: 1.262502 Loss2: 1.480815 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.670898
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.964291 Loss1: 1.407835 Loss2: 1.556456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.920217 Loss1: 1.345513 Loss2: 1.574704 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.602083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.600794 Loss1: 2.107359 Loss2: 1.493435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.263416 Loss1: 1.777136 Loss2: 1.486281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.762475 Loss1: 2.674292 Loss2: 2.088183
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.115043 Loss1: 1.626951 Loss2: 1.488092
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.717936 Loss1: 2.186090 Loss2: 1.531846
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.002014 Loss1: 1.498179 Loss2: 1.503835
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.457362 Loss1: 1.955069 Loss2: 1.502292
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.909575 Loss1: 1.401319 Loss2: 1.508255
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.293979 Loss1: 1.781527 Loss2: 1.512452
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.821326 Loss1: 1.303888 Loss2: 1.517438
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.070169 Loss1: 1.558452 Loss2: 1.511717
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.835520 Loss1: 1.305215 Loss2: 1.530305
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.085111 Loss1: 1.555556 Loss2: 1.529555
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.822913 Loss1: 1.301303 Loss2: 1.521611
+(DefaultActor pid=3765) >> Training accuracy: 0.671875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.897209 Loss1: 1.360096 Loss2: 1.537113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.857031 Loss1: 1.304056 Loss2: 1.552975 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.641667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.816317 Loss1: 2.330808 Loss2: 1.485509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.352427 Loss1: 1.875051 Loss2: 1.477376 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.195760 Loss1: 1.711872 Loss2: 1.483888 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.135302 Loss1: 1.649749 Loss2: 1.485553 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.154154 Loss1: 1.658199 Loss2: 1.495955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 3.069602 Loss1: 1.558976 Loss2: 1.510626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.944295 Loss1: 1.439881 Loss2: 1.504414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.946820 Loss1: 1.314751 Loss2: 1.632069 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.655208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.933457 Loss1: 1.272985 Loss2: 1.660472 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.695913
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.952699 Loss1: 2.824236 Loss2: 2.128463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.632518 Loss1: 2.110016 Loss2: 1.522502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.708544 Loss1: 2.613972 Loss2: 2.094572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.630904 Loss1: 2.114382 Loss2: 1.516523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.352510 Loss1: 1.856077 Loss2: 1.496433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.180838 Loss1: 1.674747 Loss2: 1.506092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.121997 Loss1: 1.605119 Loss2: 1.516878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.885643 Loss1: 1.373782 Loss2: 1.511861 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.627232
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.936573 Loss1: 1.401835 Loss2: 1.534739 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.787918 Loss1: 1.245072 Loss2: 1.542847 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.694196
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 03:47:28,943][flwr][DEBUG] - fit_round 25 received 50 results and 0 failures
+INFO flwr 2023-10-09 03:48:10,075 | server.py:125 | fit progress: (25, 2.9935917195420676, {'accuracy': 0.295}, 57397.853067455)
+>> Test accuracy: 0.295000
+[2023-10-09 03:48:10,075][flwr][INFO] - fit progress: (25, 2.9935917195420676, {'accuracy': 0.295}, 57397.853067455)
+DEBUG flwr 2023-10-09 03:48:10,075 | server.py:173 | evaluate_round 25: strategy sampled 50 clients (out of 50)
+[2023-10-09 03:48:10,075][flwr][DEBUG] - evaluate_round 25: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 03:57:16,539 | server.py:187 | evaluate_round 25 received 50 results and 0 failures
+[2023-10-09 03:57:16,539][flwr][DEBUG] - evaluate_round 25 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 03:57:16,539 | server.py:222 | fit_round 26: strategy sampled 50 clients (out of 50)
+[2023-10-09 03:57:16,539][flwr][DEBUG] - fit_round 26: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.660271 Loss1: 2.646804 Loss2: 2.013467
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.632214 Loss1: 2.142981 Loss2: 1.489233
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.307941 Loss1: 1.855366 Loss2: 1.452575
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.100412 Loss1: 1.649711 Loss2: 1.450700
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.540738 Loss1: 2.562757 Loss2: 1.977980
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.535762 Loss1: 2.044041 Loss2: 1.491721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.317622 Loss1: 1.835988 Loss2: 1.481634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.127749 Loss1: 1.640698 Loss2: 1.487051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.018424 Loss1: 1.532230 Loss2: 1.486194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.932044 Loss1: 1.420818 Loss2: 1.511226 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.625000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.908481 Loss1: 1.397230 Loss2: 1.511251 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.729799 Loss1: 1.221604 Loss2: 1.508195 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.718750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.772119 Loss1: 2.651176 Loss2: 2.120943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.425149 Loss1: 1.898173 Loss2: 1.526976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.712814 Loss1: 2.555112 Loss2: 2.157702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.661181 Loss1: 2.107865 Loss2: 1.553315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.484666 Loss1: 1.951884 Loss2: 1.532782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.183115 Loss1: 1.640499 Loss2: 1.542617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.048678 Loss1: 1.495582 Loss2: 1.553096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.813494 Loss1: 1.251934 Loss2: 1.561559
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.002201 Loss1: 1.470187 Loss2: 1.532014
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.810448 Loss1: 1.260351 Loss2: 1.550097
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.840315 Loss1: 1.283825 Loss2: 1.556490
+(DefaultActor pid=3765) >> Training accuracy: 0.606250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.735866 Loss1: 1.159394 Loss2: 1.576472 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.670673
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.802859 Loss1: 2.661870 Loss2: 2.140988 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.578109 Loss1: 2.025375 Loss2: 1.552734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.328271 Loss1: 1.760919 Loss2: 1.567352
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.603985 Loss1: 2.594769 Loss2: 2.009216
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.544526 Loss1: 2.074781 Loss2: 1.469745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.235526 Loss1: 1.775754 Loss2: 1.459772 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.130137 Loss1: 1.668453 Loss2: 1.461684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.021911 Loss1: 1.558397 Loss2: 1.463513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.958258 Loss1: 1.467153 Loss2: 1.491105 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.629167
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.853497 Loss1: 1.241131 Loss2: 1.612366
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.939707 Loss1: 1.446403 Loss2: 1.493304
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.815334 Loss1: 1.323210 Loss2: 1.492124
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.868393 Loss1: 1.358077 Loss2: 1.510315
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.685112 Loss1: 1.179058 Loss2: 1.506053
+(DefaultActor pid=3764) >> Training accuracy: 0.656250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.735424 Loss1: 2.608798 Loss2: 2.126626
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.628557 Loss1: 2.076748 Loss2: 1.551808
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.419693 Loss1: 1.883848 Loss2: 1.535845
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.110947 Loss1: 1.582528 Loss2: 1.528419
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.707984 Loss1: 2.696107 Loss2: 2.011877
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.759380 Loss1: 2.254839 Loss2: 1.504541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.392100 Loss1: 1.891412 Loss2: 1.500688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.304162 Loss1: 1.800873 Loss2: 1.503288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.089821 Loss1: 1.588548 Loss2: 1.501273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.001091 Loss1: 1.471591 Loss2: 1.529500 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.604911
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.834413 Loss1: 1.295865 Loss2: 1.538548 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.814718 Loss1: 1.270998 Loss2: 1.543720 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.647917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.549853 Loss1: 2.029049 Loss2: 1.520804 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.097311 Loss1: 1.562272 Loss2: 1.535039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.628418 Loss1: 2.703066 Loss2: 1.925352
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.990473 Loss1: 1.477399 Loss2: 1.513074
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.657296 Loss1: 2.211974 Loss2: 1.445322
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.890533 Loss1: 1.349109 Loss2: 1.541424
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.396223 Loss1: 1.985033 Loss2: 1.411190
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.779235 Loss1: 1.219705 Loss2: 1.559530
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.199692 Loss1: 1.768003 Loss2: 1.431689
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.822535 Loss1: 1.278952 Loss2: 1.543582
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.047410 Loss1: 1.605498 Loss2: 1.441912
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.768687 Loss1: 1.203254 Loss2: 1.565432
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.997634 Loss1: 1.541384 Loss2: 1.456249
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.744575 Loss1: 1.190265 Loss2: 1.554310
+(DefaultActor pid=3765) >> Training accuracy: 0.676042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.879008 Loss1: 1.403707 Loss2: 1.475301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.738246 Loss1: 1.252036 Loss2: 1.486210 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.680208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.471446 Loss1: 1.970334 Loss2: 1.501112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.077525 Loss1: 1.594229 Loss2: 1.483297 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.886298 Loss1: 1.411919 Loss2: 1.474380
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.564854 Loss1: 2.596241 Loss2: 1.968613
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.837196 Loss1: 1.359220 Loss2: 1.477976
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.637204 Loss1: 2.159106 Loss2: 1.478098
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.740911 Loss1: 1.247088 Loss2: 1.493822
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.318048 Loss1: 1.859309 Loss2: 1.458739
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.163157 Loss1: 1.685550 Loss2: 1.477607 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.058009 Loss1: 1.569763 Loss2: 1.488247 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.654167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.981857 Loss1: 1.474131 Loss2: 1.507726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.806136 Loss1: 1.296429 Loss2: 1.509707 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.796706 Loss1: 1.280539 Loss2: 1.516166 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.656250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.627914 Loss1: 2.114444 Loss2: 1.513470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.138203 Loss1: 1.653718 Loss2: 1.484485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.627128 Loss1: 2.552920 Loss2: 2.074208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.602257 Loss1: 2.068956 Loss2: 1.533301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.401690 Loss1: 1.874027 Loss2: 1.527664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.219711 Loss1: 1.687158 Loss2: 1.532554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.087480 Loss1: 1.557073 Loss2: 1.530407 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.694792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.961168 Loss1: 1.396821 Loss2: 1.564347 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.746319 Loss1: 1.180375 Loss2: 1.565944 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.820464 Loss1: 1.248449 Loss2: 1.572015
+(DefaultActor pid=3764) >> Training accuracy: 0.679167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.632346 Loss1: 2.631100 Loss2: 2.001246
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.601669 Loss1: 2.125964 Loss2: 1.475705
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.316318 Loss1: 1.858902 Loss2: 1.457416
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.070891 Loss1: 1.603754 Loss2: 1.467137
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.932953 Loss1: 1.469117 Loss2: 1.463836
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.523646 Loss1: 2.529831 Loss2: 1.993815
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.027806 Loss1: 1.544929 Loss2: 1.482877
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.479008 Loss1: 1.968548 Loss2: 1.510460
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.846751 Loss1: 1.339772 Loss2: 1.506979
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.161233 Loss1: 1.670090 Loss2: 1.491143
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.811194 Loss1: 1.318348 Loss2: 1.492847
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.095298 Loss1: 1.589949 Loss2: 1.505349
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.707050 Loss1: 1.209787 Loss2: 1.497264
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.660442 Loss1: 1.150807 Loss2: 1.509636
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.946711 Loss1: 1.435245 Loss2: 1.511466
+(DefaultActor pid=3765) >> Training accuracy: 0.654167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.925337 Loss1: 1.415232 Loss2: 1.510104
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.767276 Loss1: 1.240905 Loss2: 1.526371
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.749655 Loss1: 1.219576 Loss2: 1.530079
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.612396 Loss1: 1.081826 Loss2: 1.530570
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.791992 Loss1: 2.703441 Loss2: 2.088551
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.619294 Loss1: 1.078403 Loss2: 1.540891
+(DefaultActor pid=3764) >> Training accuracy: 0.728516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.434793 Loss1: 1.915603 Loss2: 1.519190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.134113 Loss1: 1.610431 Loss2: 1.523683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.047092 Loss1: 1.500389 Loss2: 1.546703
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.552274 Loss1: 2.406583 Loss2: 2.145691
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.519830 Loss1: 1.955490 Loss2: 1.564341 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.266055 Loss1: 1.721066 Loss2: 1.544989 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.064252 Loss1: 1.512637 Loss2: 1.551614 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.663542
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.854908 Loss1: 1.300481 Loss2: 1.554427
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.966883 Loss1: 1.409755 Loss2: 1.557129
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.905599 Loss1: 1.339219 Loss2: 1.566380
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.849049 Loss1: 1.264515 Loss2: 1.584534
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.738936 Loss1: 1.160936 Loss2: 1.578000
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.709725 Loss1: 1.130024 Loss2: 1.579701
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.503669 Loss1: 2.537355 Loss2: 1.966314
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.633629 Loss1: 1.035314 Loss2: 1.598315
+(DefaultActor pid=3764) >> Training accuracy: 0.685417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.307595 Loss1: 1.842499 Loss2: 1.465096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.971827 Loss1: 1.508221 Loss2: 1.463606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.468697 Loss1: 2.453622 Loss2: 2.015075
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.848135 Loss1: 1.368632 Loss2: 1.479503
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.516648 Loss1: 1.991889 Loss2: 1.524758
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.818886 Loss1: 1.331747 Loss2: 1.487139
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.189817 Loss1: 1.672926 Loss2: 1.516891
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.742434 Loss1: 1.241699 Loss2: 1.500735
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.022741 Loss1: 1.514402 Loss2: 1.508339
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.696422 Loss1: 1.202662 Loss2: 1.493760
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.933363 Loss1: 1.424882 Loss2: 1.508481
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.779156 Loss1: 1.270524 Loss2: 1.508631
+(DefaultActor pid=3765) >> Training accuracy: 0.610352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.692832 Loss1: 1.171650 Loss2: 1.521182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.675913 Loss1: 1.144036 Loss2: 1.531877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.864168 Loss1: 2.756434 Loss2: 2.107734
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.652922 Loss1: 1.110224 Loss2: 1.542698
+(DefaultActor pid=3764) >> Training accuracy: 0.688477
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.447238 Loss1: 1.926343 Loss2: 1.520895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.209478 Loss1: 1.657178 Loss2: 1.552300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.579379 Loss1: 2.534659 Loss2: 2.044720
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.065849 Loss1: 1.502675 Loss2: 1.563173
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.390320 Loss1: 1.888521 Loss2: 1.501799
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.994750 Loss1: 1.433206 Loss2: 1.561543
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.954327 Loss1: 1.368398 Loss2: 1.585929 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.870506 Loss1: 1.296101 Loss2: 1.574405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.840550 Loss1: 1.253110 Loss2: 1.587440 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.656250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.689037 Loss1: 1.214465 Loss2: 1.474573 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.559913 Loss1: 1.073315 Loss2: 1.486598 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.701923
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.623045 Loss1: 2.085893 Loss2: 1.537152 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.191542 Loss1: 1.698122 Loss2: 1.493420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.585650 Loss1: 2.530614 Loss2: 2.055036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.575259 Loss1: 2.068141 Loss2: 1.507119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.805150 Loss1: 1.275785 Loss2: 1.529365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.101543 Loss1: 1.595918 Loss2: 1.505625 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.717448
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.982532 Loss1: 1.479052 Loss2: 1.503480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.817075 Loss1: 1.289823 Loss2: 1.527252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.699017 Loss1: 1.152825 Loss2: 1.546192 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.642453 Loss1: 1.103855 Loss2: 1.538598 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.728125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.283997 Loss1: 1.776185 Loss2: 1.507811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.995031 Loss1: 1.484660 Loss2: 1.510371 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.599215 Loss1: 2.575007 Loss2: 2.024209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.648765 Loss1: 2.147256 Loss2: 1.501509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.370713 Loss1: 1.873132 Loss2: 1.497581 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.166874 Loss1: 1.684170 Loss2: 1.482704 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.716667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.068622 Loss1: 1.563001 Loss2: 1.505620 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.890690 Loss1: 1.371002 Loss2: 1.519687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.733212 Loss1: 1.206857 Loss2: 1.526355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.631598 Loss1: 1.097639 Loss2: 1.533960 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.731250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.440699 Loss1: 1.881407 Loss2: 1.559292 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.166934 Loss1: 1.603163 Loss2: 1.563771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.323950 Loss1: 2.340011 Loss2: 1.983939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.292531 Loss1: 1.832482 Loss2: 1.460048 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.082715 Loss1: 1.641846 Loss2: 1.440869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.913581 Loss1: 1.476802 Loss2: 1.436779 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.672917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.764559 Loss1: 1.302757 Loss2: 1.461803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.667689 Loss1: 1.194624 Loss2: 1.473065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.598924 Loss1: 1.130703 Loss2: 1.468221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.518126 Loss1: 1.032333 Loss2: 1.485793 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.718750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.183477 Loss1: 1.671189 Loss2: 1.512288 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.978662 Loss1: 1.463655 Loss2: 1.515007 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.487080 Loss1: 2.518489 Loss2: 1.968591
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.963208 Loss1: 1.441702 Loss2: 1.521507
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.311355 Loss1: 1.900056 Loss2: 1.411299
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.836522 Loss1: 1.312629 Loss2: 1.523893
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.070211 Loss1: 1.673507 Loss2: 1.396704
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.691932 Loss1: 1.146508 Loss2: 1.545425
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.935083 Loss1: 1.521812 Loss2: 1.413271
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.624120 Loss1: 1.086699 Loss2: 1.537421
+(DefaultActor pid=3765) >> Training accuracy: 0.694792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.741136 Loss1: 1.319543 Loss2: 1.421592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.554657 Loss1: 1.132740 Loss2: 1.421917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.557387 Loss1: 1.121659 Loss2: 1.435727
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.653096 Loss1: 2.570085 Loss2: 2.083010
+(DefaultActor pid=3764) >> Training accuracy: 0.730208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.612543 Loss1: 2.049518 Loss2: 1.563025 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.240593 Loss1: 1.680657 Loss2: 1.559936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.919774 Loss1: 1.354668 Loss2: 1.565106 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.947828 Loss1: 1.379873 Loss2: 1.567955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.845472 Loss1: 1.259648 Loss2: 1.585824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.839163 Loss1: 1.251908 Loss2: 1.587255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.773444 Loss1: 1.173649 Loss2: 1.599795 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.678711
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.998556 Loss1: 1.463345 Loss2: 1.535211 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.819586 Loss1: 1.263133 Loss2: 1.556454 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.655333 Loss1: 2.655230 Loss2: 2.000104
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.790155 Loss1: 1.220699 Loss2: 1.569457
+(DefaultActor pid=3764) >> Training accuracy: 0.664583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.399312 Loss1: 1.931236 Loss2: 1.468076 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.123024 Loss1: 1.649561 Loss2: 1.473463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.113090 Loss1: 1.626875 Loss2: 1.486215
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.718956 Loss1: 2.726100 Loss2: 1.992856
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.747952 Loss1: 2.242910 Loss2: 1.505042 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.516652 Loss1: 2.013982 Loss2: 1.502670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.361280 Loss1: 1.845898 Loss2: 1.515382 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.664583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.209163 Loss1: 1.680199 Loss2: 1.528963 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.059566 Loss1: 1.521024 Loss2: 1.538542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.887061 Loss1: 1.324332 Loss2: 1.562729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.821787 Loss1: 1.259023 Loss2: 1.562764 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.565430
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.333804 Loss1: 1.754738 Loss2: 1.579066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.120805 Loss1: 1.508779 Loss2: 1.612026 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.963377 Loss1: 1.358088 Loss2: 1.605289
+(DefaultActor pid=3764) Epoch: 0 Loss: 5.001184 Loss1: 2.884897 Loss2: 2.116287
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.779694 Loss1: 2.238826 Loss2: 1.540867
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.822915 Loss1: 1.231324 Loss2: 1.591591
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.460212 Loss1: 1.929710 Loss2: 1.530502
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.870894 Loss1: 1.252424 Loss2: 1.618470
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.754084 Loss1: 1.145674 Loss2: 1.608410 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.696381 Loss1: 1.087032 Loss2: 1.609349 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.706801
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.040226 Loss1: 1.460401 Loss2: 1.579825 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.854422 Loss1: 1.277068 Loss2: 1.577355 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.647321
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.703431 Loss1: 2.230574 Loss2: 1.472858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.250231 Loss1: 1.792231 Loss2: 1.458000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.129741 Loss1: 1.647430 Loss2: 1.482311
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.672448 Loss1: 2.575571 Loss2: 2.096876
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.555867 Loss1: 2.025400 Loss2: 1.530466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.215857 Loss1: 1.694300 Loss2: 1.521556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.145762 Loss1: 1.617802 Loss2: 1.527959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.104596 Loss1: 1.569210 Loss2: 1.535386 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.648438
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.809608 Loss1: 1.266487 Loss2: 1.543120 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.694858 Loss1: 1.136037 Loss2: 1.558821 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.607823 Loss1: 1.046575 Loss2: 1.561248
+(DefaultActor pid=3764) >> Training accuracy: 0.718750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.686455 Loss1: 2.636714 Loss2: 2.049741
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.631676 Loss1: 2.095729 Loss2: 1.535947
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.358520 Loss1: 1.852183 Loss2: 1.506337
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.261299 Loss1: 1.742331 Loss2: 1.518968
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.113935 Loss1: 1.596681 Loss2: 1.517255
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.674066 Loss1: 2.655293 Loss2: 2.018773
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.625826 Loss1: 2.138033 Loss2: 1.487793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.389964 Loss1: 1.902903 Loss2: 1.487061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.311651 Loss1: 1.825249 Loss2: 1.486401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.119779 Loss1: 1.635639 Loss2: 1.484140 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.646875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.022153 Loss1: 1.528701 Loss2: 1.493452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.841986 Loss1: 1.338470 Loss2: 1.503516 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.728489 Loss1: 1.200378 Loss2: 1.528111 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.654167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.460737 Loss1: 1.926472 Loss2: 1.534265 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.968966 Loss1: 1.466757 Loss2: 1.502210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.875160 Loss1: 1.371984 Loss2: 1.503176
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.559153 Loss1: 2.476352 Loss2: 2.082801
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.605700 Loss1: 2.044924 Loss2: 1.560776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.249147 Loss1: 1.717138 Loss2: 1.532008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.082806 Loss1: 1.557988 Loss2: 1.524818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.906307 Loss1: 1.379511 Loss2: 1.526795 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.715625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.911285 Loss1: 1.376032 Loss2: 1.535253 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.714872 Loss1: 1.173571 Loss2: 1.541301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.639731 Loss1: 1.085324 Loss2: 1.554407 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.737500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.564905 Loss1: 2.087757 Loss2: 1.477148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.077169 Loss1: 1.609997 Loss2: 1.467172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.935898 Loss1: 1.472163 Loss2: 1.463736
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.691481 Loss1: 2.519438 Loss2: 2.172043
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.690428 Loss1: 2.087220 Loss2: 1.603208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.412985 Loss1: 1.811135 Loss2: 1.601851 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.304204 Loss1: 1.691385 Loss2: 1.612819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.206862 Loss1: 1.580354 Loss2: 1.626508 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.663542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.094891 Loss1: 1.469137 Loss2: 1.625755 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.034779 Loss1: 1.385705 Loss2: 1.649074 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.967578 Loss1: 1.321795 Loss2: 1.645784 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.610417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.434402 Loss1: 1.914660 Loss2: 1.519742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.948843 Loss1: 1.466920 Loss2: 1.481923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.822807 Loss1: 1.350287 Loss2: 1.472520
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.742306 Loss1: 2.738199 Loss2: 2.004107
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.816121 Loss1: 2.311640 Loss2: 1.504481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.428846 Loss1: 1.940460 Loss2: 1.488386 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.272581 Loss1: 1.783229 Loss2: 1.489351 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.150093 Loss1: 1.643146 Loss2: 1.506947 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.753125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.956249 Loss1: 1.432853 Loss2: 1.523396 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.848632 Loss1: 1.311061 Loss2: 1.537572 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.736680 Loss1: 2.580710 Loss2: 2.155970
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.718568 Loss1: 1.180585 Loss2: 1.537983
+(DefaultActor pid=3764) >> Training accuracy: 0.589844
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.379572 Loss1: 1.797578 Loss2: 1.581994 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.231874 Loss1: 1.623445 Loss2: 1.608430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.099298 Loss1: 1.483988 Loss2: 1.615310
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.992461 Loss1: 2.825600 Loss2: 2.166861
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.001624 Loss1: 1.385597 Loss2: 1.616026
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.873290 Loss1: 2.263111 Loss2: 1.610179
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.833068 Loss1: 1.215237 Loss2: 1.617831
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.549872 Loss1: 1.967445 Loss2: 1.582426
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.864361 Loss1: 1.244943 Loss2: 1.619418
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.386485 Loss1: 1.805514 Loss2: 1.580971
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.811198 Loss1: 1.180910 Loss2: 1.630288
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.336967 Loss1: 1.748697 Loss2: 1.588270
+(DefaultActor pid=3765) >> Training accuracy: 0.712500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.185742 Loss1: 1.600525 Loss2: 1.585216
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.130328 Loss1: 1.547891 Loss2: 1.582438
+(DefaultActor pid=3764) Epoch: 7 Loss: 3.004195 Loss1: 1.403764 Loss2: 1.600431
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.960940 Loss1: 1.370289 Loss2: 1.590651
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.865305 Loss1: 1.265103 Loss2: 1.600202
+DEBUG flwr 2023-10-09 04:25:49,168 | server.py:236 | fit_round 26 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.701109 Loss1: 2.614935 Loss2: 2.086174
+(DefaultActor pid=3764) >> Training accuracy: 0.637500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.728596 Loss1: 2.185852 Loss2: 1.542744
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.513810 Loss1: 1.975608 Loss2: 1.538202
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.330056 Loss1: 1.778107 Loss2: 1.551949
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.204759 Loss1: 1.657633 Loss2: 1.547127
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.074056 Loss1: 1.518925 Loss2: 1.555131
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.761382 Loss1: 2.568094 Loss2: 2.193288
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.084621 Loss1: 1.517981 Loss2: 1.566640
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.517912 Loss1: 1.937957 Loss2: 1.579955
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.943634 Loss1: 1.392518 Loss2: 1.551116
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.268473 Loss1: 1.724374 Loss2: 1.544098
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.891120 Loss1: 1.319124 Loss2: 1.571996
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.130178 Loss1: 1.580147 Loss2: 1.550031
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.862657 Loss1: 1.290723 Loss2: 1.571934
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.982664 Loss1: 1.432318 Loss2: 1.550346
+(DefaultActor pid=3765) >> Training accuracy: 0.596875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.920072 Loss1: 1.361052 Loss2: 1.559021
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.842452 Loss1: 1.264793 Loss2: 1.577660
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.796578 Loss1: 1.222308 Loss2: 1.574269
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.675688 Loss1: 1.095692 Loss2: 1.579996
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.632979 Loss1: 1.050895 Loss2: 1.582083
+(DefaultActor pid=3764) >> Training accuracy: 0.742708
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 04:25:49,168][flwr][DEBUG] - fit_round 26 received 50 results and 0 failures
+INFO flwr 2023-10-09 04:26:30,752 | server.py:125 | fit progress: (26, 2.974912224486232, {'accuracy': 0.3054}, 59698.530597688004)
+>> Test accuracy: 0.305400
+[2023-10-09 04:26:30,752][flwr][INFO] - fit progress: (26, 2.974912224486232, {'accuracy': 0.3054}, 59698.530597688004)
+DEBUG flwr 2023-10-09 04:26:30,752 | server.py:173 | evaluate_round 26: strategy sampled 50 clients (out of 50)
+[2023-10-09 04:26:30,752][flwr][DEBUG] - evaluate_round 26: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 04:35:32,608 | server.py:187 | evaluate_round 26 received 50 results and 0 failures
+[2023-10-09 04:35:32,608][flwr][DEBUG] - evaluate_round 26 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 04:35:32,608 | server.py:222 | fit_round 27: strategy sampled 50 clients (out of 50)
+[2023-10-09 04:35:32,608][flwr][DEBUG] - fit_round 27: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.713092 Loss1: 2.677320 Loss2: 2.035771
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.644535 Loss1: 2.137809 Loss2: 1.506727
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.363990 Loss1: 1.870256 Loss2: 1.493734
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.172298 Loss1: 1.677563 Loss2: 1.494735
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.027373 Loss1: 1.532261 Loss2: 1.495112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.969047 Loss1: 1.464056 Loss2: 1.504992 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.952123 Loss1: 1.436918 Loss2: 1.515204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.788788 Loss1: 1.246154 Loss2: 1.542634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.709060 Loss1: 1.180747 Loss2: 1.528313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.597734 Loss1: 1.054955 Loss2: 1.542779 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.610417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.847406 Loss1: 1.269414 Loss2: 1.577993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.642200 Loss1: 1.052043 Loss2: 1.590157 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.738281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.363046 Loss1: 1.870741 Loss2: 1.492304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.925175 Loss1: 1.436214 Loss2: 1.488961 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.883371 Loss1: 1.393043 Loss2: 1.490328
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.509911 Loss1: 2.508742 Loss2: 2.001169
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.734878 Loss1: 1.238118 Loss2: 1.496761
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.634009 Loss1: 2.139250 Loss2: 1.494759
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.644038 Loss1: 1.144572 Loss2: 1.499467
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.261101 Loss1: 1.792908 Loss2: 1.468192
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.628747 Loss1: 1.109715 Loss2: 1.519033
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.117880 Loss1: 1.652057 Loss2: 1.465822
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.535052 Loss1: 1.025614 Loss2: 1.509439
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.980586 Loss1: 1.503596 Loss2: 1.476990
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.530913 Loss1: 1.001959 Loss2: 1.528954
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.923313 Loss1: 1.449563 Loss2: 1.473750
+(DefaultActor pid=3765) >> Training accuracy: 0.672917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.841173 Loss1: 1.356436 Loss2: 1.484736
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.794262 Loss1: 1.305033 Loss2: 1.489229
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.660779 Loss1: 1.171083 Loss2: 1.489696
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.589359 Loss1: 1.091580 Loss2: 1.497780
+(DefaultActor pid=3764) >> Training accuracy: 0.647917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.691495 Loss1: 2.689687 Loss2: 2.001808
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.732218 Loss1: 2.206433 Loss2: 1.525786
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.413630 Loss1: 1.903379 Loss2: 1.510251
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.232676 Loss1: 1.720980 Loss2: 1.511696
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.060973 Loss1: 1.533233 Loss2: 1.527739
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.684861 Loss1: 2.657636 Loss2: 2.027225
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.979451 Loss1: 1.456010 Loss2: 1.523441
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.637811 Loss1: 2.141032 Loss2: 1.496779
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.879115 Loss1: 1.339063 Loss2: 1.540052
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.347019 Loss1: 1.861857 Loss2: 1.485162
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.823424 Loss1: 1.285420 Loss2: 1.538004
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.179570 Loss1: 1.696197 Loss2: 1.483374
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.758059 Loss1: 1.197484 Loss2: 1.560575
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.112278 Loss1: 1.616918 Loss2: 1.495359
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.670972 Loss1: 1.122859 Loss2: 1.548113
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.977939 Loss1: 1.461317 Loss2: 1.516621
+(DefaultActor pid=3765) >> Training accuracy: 0.704102
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 3.034238 Loss1: 1.535064 Loss2: 1.499174
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.918629 Loss1: 1.382205 Loss2: 1.536425
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.728844 Loss1: 1.198684 Loss2: 1.530160
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.751865 Loss1: 1.240178 Loss2: 1.511688
+(DefaultActor pid=3764) >> Training accuracy: 0.706055
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.797431 Loss1: 2.674811 Loss2: 2.122620
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.901377 Loss1: 2.290733 Loss2: 1.610644
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.544046 Loss1: 1.960752 Loss2: 1.583294
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.269130 Loss1: 1.694014 Loss2: 1.575116
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.153436 Loss1: 1.577890 Loss2: 1.575545
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.501971 Loss1: 2.477091 Loss2: 2.024880
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.472518 Loss1: 2.010403 Loss2: 1.462115 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.171178 Loss1: 1.714023 Loss2: 1.457154 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.941982 Loss1: 1.491825 Loss2: 1.450158 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.850740 Loss1: 1.386066 Loss2: 1.464674 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.651042
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.808895 Loss1: 1.201913 Loss2: 1.606983
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.827893 Loss1: 1.357651 Loss2: 1.470242
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.728195 Loss1: 1.237106 Loss2: 1.491089
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.583870 Loss1: 1.107806 Loss2: 1.476064
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.580900 Loss1: 1.091308 Loss2: 1.489592
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.533969 Loss1: 1.043658 Loss2: 1.490311
+(DefaultActor pid=3764) >> Training accuracy: 0.700000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.698020 Loss1: 2.602638 Loss2: 2.095382
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.575467 Loss1: 2.061007 Loss2: 1.514460
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.299524 Loss1: 1.799256 Loss2: 1.500268
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.132939 Loss1: 1.614427 Loss2: 1.518512
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.013202 Loss1: 1.506168 Loss2: 1.507034
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.659546 Loss1: 2.523617 Loss2: 2.135929
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.644626 Loss1: 2.071145 Loss2: 1.573481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.301824 Loss1: 1.762973 Loss2: 1.538851 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.091607 Loss1: 1.524360 Loss2: 1.567247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.997439 Loss1: 1.438639 Loss2: 1.558800 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.678125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.685029 Loss1: 1.155865 Loss2: 1.529164
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.998813 Loss1: 1.428366 Loss2: 1.570447
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.930138 Loss1: 1.350473 Loss2: 1.579665
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.823888 Loss1: 1.247205 Loss2: 1.576682
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.843556 Loss1: 1.248538 Loss2: 1.595019
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.625412 Loss1: 1.038053 Loss2: 1.587360
+(DefaultActor pid=3764) >> Training accuracy: 0.730208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.425824 Loss1: 2.494988 Loss2: 1.930836
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.474947 Loss1: 2.031402 Loss2: 1.443545
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.270972 Loss1: 1.828319 Loss2: 1.442653
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.147188 Loss1: 1.678151 Loss2: 1.469037
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.579512 Loss1: 2.548540 Loss2: 2.030972
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.996864 Loss1: 1.536342 Loss2: 1.460521
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.514308 Loss1: 2.030202 Loss2: 1.484106
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.953467 Loss1: 1.495437 Loss2: 1.458031
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.202807 Loss1: 1.722090 Loss2: 1.480717
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.749909 Loss1: 1.282262 Loss2: 1.467647
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.045968 Loss1: 1.575982 Loss2: 1.469986
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.686926 Loss1: 1.220560 Loss2: 1.466367
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.629618 Loss1: 1.149312 Loss2: 1.480306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.520124 Loss1: 1.048829 Loss2: 1.471295 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.663086
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.708271 Loss1: 1.200949 Loss2: 1.507321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.543391 Loss1: 1.028973 Loss2: 1.514418 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.675000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.549840 Loss1: 1.973829 Loss2: 1.576011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.103551 Loss1: 1.533130 Loss2: 1.570421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.940905 Loss1: 1.375776 Loss2: 1.565129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.783437 Loss1: 1.206659 Loss2: 1.576778 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.832887 Loss1: 1.260627 Loss2: 1.572260 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.718142 Loss1: 1.120871 Loss2: 1.597271 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.675159 Loss1: 1.086578 Loss2: 1.588581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.565540 Loss1: 0.966051 Loss2: 1.599489 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.693750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.805780 Loss1: 1.231106 Loss2: 1.574674 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.661365 Loss1: 1.077989 Loss2: 1.583375 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.660417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.730340 Loss1: 2.639307 Loss2: 2.091033
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.699555 Loss1: 2.139927 Loss2: 1.559629
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.397769 Loss1: 1.862292 Loss2: 1.535477
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.213782 Loss1: 1.664476 Loss2: 1.549307
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.611801 Loss1: 2.509006 Loss2: 2.102795
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.495231 Loss1: 1.953900 Loss2: 1.541331 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.180125 Loss1: 1.652376 Loss2: 1.527750 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.073896 Loss1: 1.523830 Loss2: 1.550067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.900928 Loss1: 1.368069 Loss2: 1.532859 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.906664 Loss1: 1.322818 Loss2: 1.583846
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.832819 Loss1: 1.295191 Loss2: 1.537628
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.776442 Loss1: 1.165744 Loss2: 1.610698
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.728074 Loss1: 1.163699 Loss2: 1.564375
+(DefaultActor pid=3765) >> Training accuracy: 0.616211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.679603 Loss1: 1.121209 Loss2: 1.558394
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.730268 Loss1: 1.167428 Loss2: 1.562840
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.703971 Loss1: 1.118803 Loss2: 1.585168
+(DefaultActor pid=3764) >> Training accuracy: 0.605208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.604800 Loss1: 2.510443 Loss2: 2.094357
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.497321 Loss1: 1.971347 Loss2: 1.525974
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.271354 Loss1: 1.765610 Loss2: 1.505745
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.047399 Loss1: 1.532902 Loss2: 1.514496
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.452656 Loss1: 2.314573 Loss2: 2.138083
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.444763 Loss1: 1.870365 Loss2: 1.574398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.149176 Loss1: 1.615195 Loss2: 1.533981 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.929024 Loss1: 1.394787 Loss2: 1.534237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.853516 Loss1: 1.312613 Loss2: 1.540904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.715676 Loss1: 1.177970 Loss2: 1.537706 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.691667
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.703737 Loss1: 1.150867 Loss2: 1.552869
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.734494 Loss1: 1.181969 Loss2: 1.552525
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.675290 Loss1: 1.114862 Loss2: 1.560428
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.716312 Loss1: 1.145516 Loss2: 1.570795
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.661104 Loss1: 1.076915 Loss2: 1.584189
+(DefaultActor pid=3764) >> Training accuracy: 0.741667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.785231 Loss1: 2.648845 Loss2: 2.136386
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.680537 Loss1: 2.084652 Loss2: 1.595885
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.390518 Loss1: 1.801361 Loss2: 1.589157
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.181920 Loss1: 1.598440 Loss2: 1.583480
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.511681 Loss1: 2.470109 Loss2: 2.041573
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.520838 Loss1: 2.029444 Loss2: 1.491394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.320536 Loss1: 1.829430 Loss2: 1.491106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.108097 Loss1: 1.628723 Loss2: 1.479374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.024951 Loss1: 1.530111 Loss2: 1.494840 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.955025 Loss1: 1.459213 Loss2: 1.495812 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.705208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.840915 Loss1: 1.336733 Loss2: 1.504182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.791580 Loss1: 1.268990 Loss2: 1.522591 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.646875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.696771 Loss1: 2.603821 Loss2: 2.092949 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.342847 Loss1: 1.813131 Loss2: 1.529716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.108571 Loss1: 1.592234 Loss2: 1.516337
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.789954 Loss1: 2.652066 Loss2: 2.137888
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.628442 Loss1: 2.088028 Loss2: 1.540414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.318630 Loss1: 1.808887 Loss2: 1.509743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.108183 Loss1: 1.580571 Loss2: 1.527611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.000616 Loss1: 1.474723 Loss2: 1.525893 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.916697 Loss1: 1.374333 Loss2: 1.542364 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.680208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.747546 Loss1: 1.173887 Loss2: 1.573659
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.917239 Loss1: 1.357567 Loss2: 1.559671
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.838947 Loss1: 1.269490 Loss2: 1.569457
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.870821 Loss1: 1.293606 Loss2: 1.577215
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.795924 Loss1: 1.210336 Loss2: 1.585587
+(DefaultActor pid=3764) >> Training accuracy: 0.669792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.694525 Loss1: 2.534014 Loss2: 2.160511
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.598757 Loss1: 2.041612 Loss2: 1.557144
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.347334 Loss1: 1.807389 Loss2: 1.539945
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.202864 Loss1: 1.653915 Loss2: 1.548949
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.309908 Loss1: 2.317321 Loss2: 1.992587
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.304228 Loss1: 1.832610 Loss2: 1.471618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.057261 Loss1: 1.598187 Loss2: 1.459075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.891434 Loss1: 1.433308 Loss2: 1.458126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.816889 Loss1: 1.354886 Loss2: 1.462003 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.759206 Loss1: 1.283817 Loss2: 1.475390 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.693750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.626118 Loss1: 1.135912 Loss2: 1.490206 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.530716 Loss1: 1.051365 Loss2: 1.479352 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.717708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.541902 Loss1: 2.486062 Loss2: 2.055840
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.622368 Loss1: 2.122521 Loss2: 1.499846
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.355624 Loss1: 1.865180 Loss2: 1.490444
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.156076 Loss1: 1.659166 Loss2: 1.496910
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.669960 Loss1: 2.525027 Loss2: 2.144934
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.485907 Loss1: 2.005133 Loss2: 1.480774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.179093 Loss1: 1.734849 Loss2: 1.444244
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.988413 Loss1: 1.464646 Loss2: 1.523768
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.777009 Loss1: 1.249379 Loss2: 1.527630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.756304 Loss1: 1.235752 Loss2: 1.520553 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.706839 Loss1: 1.162297 Loss2: 1.544542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.741610 Loss1: 1.250180 Loss2: 1.491430 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.713542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.586051 Loss1: 1.087378 Loss2: 1.498673 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.766927
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.856191 Loss1: 2.789671 Loss2: 2.066520
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.700891 Loss1: 2.165005 Loss2: 1.535886
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.361284 Loss1: 1.840620 Loss2: 1.520664
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.208564 Loss1: 1.690312 Loss2: 1.518252
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.757350 Loss1: 2.559147 Loss2: 2.198202
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.103995 Loss1: 1.559518 Loss2: 1.544477
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.630087 Loss1: 2.066025 Loss2: 1.564062
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.345988 Loss1: 1.795132 Loss2: 1.550856
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.981049 Loss1: 1.441731 Loss2: 1.539318
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.950660 Loss1: 1.383355 Loss2: 1.567305 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.789840 Loss1: 1.226834 Loss2: 1.563006 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.805923 Loss1: 1.244942 Loss2: 1.560980 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.800018 Loss1: 1.234706 Loss2: 1.565312 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.680208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.630934 Loss1: 1.029641 Loss2: 1.601293 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.658654
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.726774 Loss1: 2.486494 Loss2: 2.240280
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.534642 Loss1: 1.907394 Loss2: 1.627248
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.272460 Loss1: 1.694591 Loss2: 1.577869
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.988601 Loss1: 1.398732 Loss2: 1.589869
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.679251 Loss1: 2.550539 Loss2: 2.128712
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.889506 Loss1: 1.286919 Loss2: 1.602587 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.752149 Loss1: 1.142902 Loss2: 1.609248 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.683884 Loss1: 1.093453 Loss2: 1.590431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.560048 Loss1: 0.945650 Loss2: 1.614399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.655325 Loss1: 1.040038 Loss2: 1.615288 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.625000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.866034 Loss1: 1.285731 Loss2: 1.580304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.697942 Loss1: 1.117261 Loss2: 1.580681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.555250 Loss1: 0.969712 Loss2: 1.585538
+(DefaultActor pid=3764) >> Training accuracy: 0.729167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.632187 Loss1: 2.540202 Loss2: 2.091985
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.518907 Loss1: 2.034426 Loss2: 1.484482
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.156987 Loss1: 1.690738 Loss2: 1.466249
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.062839 Loss1: 1.590971 Loss2: 1.471868
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.001955 Loss1: 1.511733 Loss2: 1.490222
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.736515 Loss1: 2.675362 Loss2: 2.061152
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.871751 Loss1: 1.393870 Loss2: 1.477880
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.832312 Loss1: 1.329990 Loss2: 1.502322 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.670838 Loss1: 1.168475 Loss2: 1.502363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.597197 Loss1: 1.113962 Loss2: 1.483236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.592782 Loss1: 1.095937 Loss2: 1.496845 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.660417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.960014 Loss1: 1.425678 Loss2: 1.534336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.863969 Loss1: 1.312975 Loss2: 1.550993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.847470 Loss1: 1.285100 Loss2: 1.562369
+(DefaultActor pid=3764) >> Training accuracy: 0.637500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.575305 Loss1: 2.453059 Loss2: 2.122245
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.541549 Loss1: 1.982926 Loss2: 1.558623
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.348894 Loss1: 1.816068 Loss2: 1.532826
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.037259 Loss1: 1.507195 Loss2: 1.530064
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.917481 Loss1: 1.378661 Loss2: 1.538821
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.565985 Loss1: 2.601684 Loss2: 1.964301
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.895927 Loss1: 1.358929 Loss2: 1.536998
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.828835 Loss1: 1.288270 Loss2: 1.540566 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.754159 Loss1: 1.208252 Loss2: 1.545908 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.616681 Loss1: 1.058109 Loss2: 1.558572 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.523251 Loss1: 0.972798 Loss2: 1.550453 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.690625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.763450 Loss1: 1.310088 Loss2: 1.453361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.696332 Loss1: 1.230271 Loss2: 1.466061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.628545 Loss1: 1.153147 Loss2: 1.475398
+(DefaultActor pid=3764) >> Training accuracy: 0.734375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.626081 Loss1: 2.445026 Loss2: 2.181055
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.469577 Loss1: 1.869333 Loss2: 1.600244
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.235806 Loss1: 1.672602 Loss2: 1.563205
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.929839 Loss1: 1.379504 Loss2: 1.550335
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.832717 Loss1: 1.271083 Loss2: 1.561634
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.358295 Loss1: 2.272322 Loss2: 2.085973
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.762649 Loss1: 1.204940 Loss2: 1.557709
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.574385 Loss1: 2.013216 Loss2: 1.561169
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.738732 Loss1: 1.171191 Loss2: 1.567541
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.217822 Loss1: 1.687305 Loss2: 1.530517
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.614264 Loss1: 1.041249 Loss2: 1.573015
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.017472 Loss1: 1.503498 Loss2: 1.513973
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.548006 Loss1: 0.979512 Loss2: 1.568494
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.872169 Loss1: 1.352558 Loss2: 1.519610
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.535300 Loss1: 0.949547 Loss2: 1.585753
+(DefaultActor pid=3765) >> Training accuracy: 0.665625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.647226 Loss1: 1.127593 Loss2: 1.519634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.677247 Loss1: 1.118135 Loss2: 1.559111 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.516505 Loss1: 0.977833 Loss2: 1.538672
+(DefaultActor pid=3764) >> Training accuracy: 0.736458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.540265 Loss1: 2.373479 Loss2: 2.166787
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.438168 Loss1: 1.824462 Loss2: 1.613706
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.162815 Loss1: 1.584036 Loss2: 1.578780
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.964385 Loss1: 1.382735 Loss2: 1.581649
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.925895 Loss1: 1.343053 Loss2: 1.582842
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.740650 Loss1: 2.672411 Loss2: 2.068239
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.787325 Loss1: 1.195838 Loss2: 1.591487
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.731163 Loss1: 1.126707 Loss2: 1.604456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.735211 Loss1: 1.140343 Loss2: 1.594868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.716280 Loss1: 1.096511 Loss2: 1.619769 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.620235 Loss1: 1.000122 Loss2: 1.620113 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.601562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.856485 Loss1: 1.301796 Loss2: 1.554690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.788331 Loss1: 1.213392 Loss2: 1.574938 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.703125
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.778893 Loss1: 1.213691 Loss2: 1.565202
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.631003 Loss1: 2.463728 Loss2: 2.167275
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.515008 Loss1: 1.941440 Loss2: 1.573569
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.151888 Loss1: 1.600389 Loss2: 1.551500
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.894008 Loss1: 1.352825 Loss2: 1.541183
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.967781 Loss1: 1.391986 Loss2: 1.575796
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.773856 Loss1: 2.669182 Loss2: 2.104674
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.715967 Loss1: 2.167453 Loss2: 1.548514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.485484 Loss1: 1.928369 Loss2: 1.557115 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.141898 Loss1: 1.604676 Loss2: 1.537221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.006491 Loss1: 1.474362 Loss2: 1.532129 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.681250
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.517457 Loss1: 0.950497 Loss2: 1.566960
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.916121 Loss1: 1.381165 Loss2: 1.534956
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.909041 Loss1: 1.365512 Loss2: 1.543528
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.743230 Loss1: 1.179153 Loss2: 1.564077
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.730592 Loss1: 1.177263 Loss2: 1.553328
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.600335 Loss1: 1.040285 Loss2: 1.560050
+(DefaultActor pid=3764) >> Training accuracy: 0.714583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.844787 Loss1: 2.747975 Loss2: 2.096812
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.722859 Loss1: 2.196089 Loss2: 1.526770
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.362793 Loss1: 1.857402 Loss2: 1.505390
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.129472 Loss1: 1.611325 Loss2: 1.518147
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.110578 Loss1: 1.581482 Loss2: 1.529096
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.544687 Loss1: 2.547072 Loss2: 1.997614
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.006552 Loss1: 1.460803 Loss2: 1.545749
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.977236 Loss1: 1.434165 Loss2: 1.543070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.165490 Loss1: 1.728837 Loss2: 1.436653
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.813685 Loss1: 1.265958 Loss2: 1.547727
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.937317 Loss1: 1.506444 Loss2: 1.430873
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.791388 Loss1: 1.249446 Loss2: 1.541942
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.712497 Loss1: 1.150145 Loss2: 1.562351
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.880181 Loss1: 1.443732 Loss2: 1.436449
+(DefaultActor pid=3765) >> Training accuracy: 0.705357
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.812754 Loss1: 1.348984 Loss2: 1.463770
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.581795 Loss1: 1.136107 Loss2: 1.445688
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.640321 Loss1: 1.190498 Loss2: 1.449823
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.580767 Loss1: 1.122680 Loss2: 1.458087
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.552700 Loss1: 1.074258 Loss2: 1.478442
+(DefaultActor pid=3764) >> Training accuracy: 0.693750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.683076 Loss1: 2.706327 Loss2: 1.976749
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.670808 Loss1: 2.169538 Loss2: 1.501270
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.447708 Loss1: 1.954462 Loss2: 1.493246
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.239885 Loss1: 1.728858 Loss2: 1.511027
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.080100 Loss1: 1.562820 Loss2: 1.517280
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.601798 Loss1: 2.536348 Loss2: 2.065449
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.538862 Loss1: 2.034984 Loss2: 1.503878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.012930 Loss1: 1.489092 Loss2: 1.523837
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.321914 Loss1: 1.834141 Loss2: 1.487772
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.853184 Loss1: 1.299162 Loss2: 1.554022
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.062014 Loss1: 1.564542 Loss2: 1.497472
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.780656 Loss1: 1.235791 Loss2: 1.544865
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.993831 Loss1: 1.501993 Loss2: 1.491838
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.912213 Loss1: 1.405329 Loss2: 1.506884
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.774188 Loss1: 1.225329 Loss2: 1.548859
+(DefaultActor pid=3765) >> Training accuracy: 0.710938
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.759149 Loss1: 1.238171 Loss2: 1.520978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.587662 Loss1: 1.056056 Loss2: 1.531606 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.643750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.572142 Loss1: 2.058277 Loss2: 1.513865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.054879 Loss1: 1.573109 Loss2: 1.481770 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.923857 Loss1: 1.428376 Loss2: 1.495481
+DEBUG flwr 2023-10-09 05:04:07,558 | server.py:236 | fit_round 27 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.532323 Loss1: 2.494618 Loss2: 2.037705
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.485085 Loss1: 1.952865 Loss2: 1.532221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.191747 Loss1: 1.677732 Loss2: 1.514015 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.962887 Loss1: 1.458698 Loss2: 1.504189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.657990 Loss1: 1.138439 Loss2: 1.519551 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.741071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.732485 Loss1: 1.208324 Loss2: 1.524161 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.594366 Loss1: 1.053738 Loss2: 1.540628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.563723 Loss1: 1.014704 Loss2: 1.549019 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.697266
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.253333 Loss1: 1.703472 Loss2: 1.549862 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 3.005219 Loss1: 1.435949 Loss2: 1.569269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 3.048890 Loss1: 1.459328 Loss2: 1.589562
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.398605 Loss1: 2.392662 Loss2: 2.005943
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.423427 Loss1: 1.940080 Loss2: 1.483348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.278956 Loss1: 1.796270 Loss2: 1.482686 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.603795
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.036236 Loss1: 1.552729 Loss2: 1.483506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.872589 Loss1: 1.382703 Loss2: 1.489886 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.378789 Loss1: 2.364115 Loss2: 2.014674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.446838 Loss1: 1.943316 Loss2: 1.503522 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.132827 Loss1: 1.657203 Loss2: 1.475624 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.727941
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.930736 Loss1: 1.465862 Loss2: 1.464874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.723597 Loss1: 1.235122 Loss2: 1.488476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.580628 Loss1: 1.094980 Loss2: 1.485648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.480303 Loss1: 0.972050 Loss2: 1.508253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.576101 Loss1: 1.065386 Loss2: 1.510715 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.662500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.153606 Loss1: 1.636580 Loss2: 1.517026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.922794 Loss1: 1.395245 Loss2: 1.527549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.709935 Loss1: 1.186891 Loss2: 1.523043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.719120 Loss1: 1.170088 Loss2: 1.549032 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.682617
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 05:04:07,558][flwr][DEBUG] - fit_round 27 received 50 results and 0 failures
+INFO flwr 2023-10-09 05:04:49,181 | server.py:125 | fit progress: (27, 2.8897676616431043, {'accuracy': 0.3206}, 61996.959205162)
+>> Test accuracy: 0.320600
+[2023-10-09 05:04:49,181][flwr][INFO] - fit progress: (27, 2.8897676616431043, {'accuracy': 0.3206}, 61996.959205162)
+DEBUG flwr 2023-10-09 05:04:49,181 | server.py:173 | evaluate_round 27: strategy sampled 50 clients (out of 50)
+[2023-10-09 05:04:49,181][flwr][DEBUG] - evaluate_round 27: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 05:13:58,269 | server.py:187 | evaluate_round 27 received 50 results and 0 failures
+[2023-10-09 05:13:58,269][flwr][DEBUG] - evaluate_round 27 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 05:13:58,270 | server.py:222 | fit_round 28: strategy sampled 50 clients (out of 50)
+[2023-10-09 05:13:58,270][flwr][DEBUG] - fit_round 28: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.365372 Loss1: 2.301188 Loss2: 2.064185
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.316292 Loss1: 1.792139 Loss2: 1.524153
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.106327 Loss1: 1.622868 Loss2: 1.483459
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.985845 Loss1: 1.479769 Loss2: 1.506076
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.507830 Loss1: 2.451972 Loss2: 2.055858
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.520976 Loss1: 2.014362 Loss2: 1.506614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.211341 Loss1: 1.719061 Loss2: 1.492280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.056736 Loss1: 1.557756 Loss2: 1.498980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.919814 Loss1: 1.408379 Loss2: 1.511435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.878898 Loss1: 1.378716 Loss2: 1.500182 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.727083
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.435000 Loss1: 0.900683 Loss2: 1.534318
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.742664 Loss1: 1.229367 Loss2: 1.513297
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.648614 Loss1: 1.120947 Loss2: 1.527667
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.583202 Loss1: 1.066516 Loss2: 1.516686
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.565707 Loss1: 1.026016 Loss2: 1.539691
+(DefaultActor pid=3764) >> Training accuracy: 0.730208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.622533 Loss1: 2.571311 Loss2: 2.051222
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.552055 Loss1: 2.055052 Loss2: 1.497003
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.234959 Loss1: 1.770820 Loss2: 1.464139
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.991399 Loss1: 1.529012 Loss2: 1.462387
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.673960 Loss1: 2.588760 Loss2: 2.085200
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.702693 Loss1: 2.124326 Loss2: 1.578367 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.436245 Loss1: 1.864993 Loss2: 1.571253 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.269657 Loss1: 1.705877 Loss2: 1.563780 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.184757 Loss1: 1.596726 Loss2: 1.588031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 3.056522 Loss1: 1.464921 Loss2: 1.591602 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.679167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.919105 Loss1: 1.318959 Loss2: 1.600146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.817801 Loss1: 1.204920 Loss2: 1.612882 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.654297
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.562553 Loss1: 2.581524 Loss2: 1.981029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.230004 Loss1: 1.768081 Loss2: 1.461923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.965280 Loss1: 1.497367 Loss2: 1.467913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.887307 Loss1: 1.413094 Loss2: 1.474213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.895838 Loss1: 1.402903 Loss2: 1.492935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.758846 Loss1: 1.261339 Loss2: 1.497507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.672361 Loss1: 1.179067 Loss2: 1.493294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.571605 Loss1: 1.075243 Loss2: 1.496362 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.706250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.678120 Loss1: 1.167627 Loss2: 1.510493 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.561964 Loss1: 1.043328 Loss2: 1.518636 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.714583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.569738 Loss1: 2.129122 Loss2: 1.440616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.991370 Loss1: 1.548310 Loss2: 1.443060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.840284 Loss1: 1.414675 Loss2: 1.425608 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.863519 Loss1: 1.418076 Loss2: 1.445443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.740652 Loss1: 1.290633 Loss2: 1.450020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.035758 Loss1: 1.454135 Loss2: 1.581623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.624860 Loss1: 1.167055 Loss2: 1.457805 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.528225 Loss1: 1.074031 Loss2: 1.454194 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.740625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.738947 Loss1: 1.108622 Loss2: 1.630325 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.744792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.609554 Loss1: 2.624721 Loss2: 1.984833
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.547672 Loss1: 2.080868 Loss2: 1.466804
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.245447 Loss1: 1.790679 Loss2: 1.454768
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.048893 Loss1: 1.588722 Loss2: 1.460171
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.421519 Loss1: 2.411058 Loss2: 2.010462
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.313360 Loss1: 1.822456 Loss2: 1.490903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.040914 Loss1: 1.568420 Loss2: 1.472494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.831730 Loss1: 1.367012 Loss2: 1.464718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.745260 Loss1: 1.272446 Loss2: 1.472814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.634908 Loss1: 1.164439 Loss2: 1.470470 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.688542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.591462 Loss1: 1.109729 Loss2: 1.481733
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.565702 Loss1: 1.072801 Loss2: 1.492900
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.479111 Loss1: 0.996087 Loss2: 1.483024
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.442547 Loss1: 0.949396 Loss2: 1.493151
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.494684 Loss1: 0.988824 Loss2: 1.505860
+(DefaultActor pid=3764) >> Training accuracy: 0.663542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.438875 Loss1: 2.388548 Loss2: 2.050326
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.366840 Loss1: 1.838190 Loss2: 1.528649
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.227035 Loss1: 1.697102 Loss2: 1.529933
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.640431 Loss1: 2.557899 Loss2: 2.082531
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.931311 Loss1: 1.401032 Loss2: 1.530279
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.897209 Loss1: 1.372121 Loss2: 1.525088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.897114 Loss1: 1.344047 Loss2: 1.553067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.730179 Loss1: 1.192057 Loss2: 1.538122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.645919 Loss1: 1.087136 Loss2: 1.558783 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.615941 Loss1: 1.071938 Loss2: 1.544003 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.822257 Loss1: 1.282946 Loss2: 1.539311 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.650735
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.566419 Loss1: 1.019838 Loss2: 1.546581 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.662500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.454402 Loss1: 2.435796 Loss2: 2.018607
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.377093 Loss1: 1.851219 Loss2: 1.525874
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.064191 Loss1: 1.559104 Loss2: 1.505087
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.972554 Loss1: 1.446686 Loss2: 1.525867
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.297640 Loss1: 2.276067 Loss2: 2.021572
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.209336 Loss1: 1.739808 Loss2: 1.469529
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.788666 Loss1: 1.277204 Loss2: 1.511462
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.024875 Loss1: 1.567569 Loss2: 1.457306
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.788807 Loss1: 1.258414 Loss2: 1.530393
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.835179 Loss1: 1.374179 Loss2: 1.461001
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.657914 Loss1: 1.129473 Loss2: 1.528441
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.816415 Loss1: 1.342959 Loss2: 1.473456
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.560312 Loss1: 1.051249 Loss2: 1.509062
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.436921 Loss1: 0.896613 Loss2: 1.540308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.398151 Loss1: 0.864343 Loss2: 1.533808 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.766602
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.474928 Loss1: 0.968469 Loss2: 1.506459 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.652083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.566149 Loss1: 2.483672 Loss2: 2.082477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.283814 Loss1: 1.759928 Loss2: 1.523886 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.097469 Loss1: 1.556368 Loss2: 1.541101
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.713631 Loss1: 2.684408 Loss2: 2.029223
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.616481 Loss1: 2.104275 Loss2: 1.512207 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.370517 Loss1: 1.865707 Loss2: 1.504810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.182848 Loss1: 1.668297 Loss2: 1.514551 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.005225 Loss1: 1.484605 Loss2: 1.520620 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.888996 Loss1: 1.368921 Loss2: 1.520075 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.676042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.872123 Loss1: 1.340725 Loss2: 1.531398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.687316 Loss1: 1.130772 Loss2: 1.556544 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.701172
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.391982 Loss1: 2.424568 Loss2: 1.967414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.062104 Loss1: 1.627246 Loss2: 1.434858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.799234 Loss1: 1.346300 Loss2: 1.452935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.723291 Loss1: 2.186660 Loss2: 1.536631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.396305 Loss1: 1.875232 Loss2: 1.521073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.182203 Loss1: 1.662425 Loss2: 1.519777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.997508 Loss1: 1.472324 Loss2: 1.525183 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.877758 Loss1: 1.344774 Loss2: 1.532983 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.733333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.839116 Loss1: 1.266184 Loss2: 1.572932 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.728872 Loss1: 1.162105 Loss2: 1.566767 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.698661
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.576861 Loss1: 2.486254 Loss2: 2.090607
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.615868 Loss1: 2.076239 Loss2: 1.539629
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.262671 Loss1: 1.739944 Loss2: 1.522727
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.086008 Loss1: 1.565754 Loss2: 1.520254
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.385970 Loss1: 2.396061 Loss2: 1.989909
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.222797 Loss1: 1.769913 Loss2: 1.452884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.949722 Loss1: 1.396849 Loss2: 1.552873
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.085523 Loss1: 1.670809 Loss2: 1.414714
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.929566 Loss1: 1.485651 Loss2: 1.443915
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.821845 Loss1: 1.259168 Loss2: 1.562677
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.748277 Loss1: 1.324317 Loss2: 1.423960
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.725353 Loss1: 1.167652 Loss2: 1.557701
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.638993 Loss1: 1.088575 Loss2: 1.550418 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.659455 Loss1: 1.093905 Loss2: 1.565549 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.697917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.261763 Loss1: 0.819787 Loss2: 1.441976 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.762019
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.279238 Loss1: 2.331619 Loss2: 1.947619
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.254299 Loss1: 1.856817 Loss2: 1.397482
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.932914 Loss1: 1.556414 Loss2: 1.376500
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.730014 Loss1: 1.354768 Loss2: 1.375247
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.479751 Loss1: 2.485187 Loss2: 1.994565
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.490179 Loss1: 1.993636 Loss2: 1.496543 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.170582 Loss1: 1.687700 Loss2: 1.482882 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.021783 Loss1: 1.530772 Loss2: 1.491011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.820298 Loss1: 1.321765 Loss2: 1.498533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.811939 Loss1: 1.296358 Loss2: 1.515580 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.690625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.724017 Loss1: 1.197767 Loss2: 1.526250 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.487549 Loss1: 0.968142 Loss2: 1.519406 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.720703
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.452326 Loss1: 1.940682 Loss2: 1.511644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.006540 Loss1: 1.510480 Loss2: 1.496060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.911475 Loss1: 1.419383 Loss2: 1.492092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.749712 Loss1: 1.235172 Loss2: 1.514540 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.697503 Loss1: 1.181657 Loss2: 1.515846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.636659 Loss1: 1.118619 Loss2: 1.518040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.550612 Loss1: 1.035206 Loss2: 1.515406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.453743 Loss1: 0.939168 Loss2: 1.514574 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.771484
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.738369 Loss1: 1.120782 Loss2: 1.617587 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.577083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.717927 Loss1: 2.635449 Loss2: 2.082477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.300664 Loss1: 1.783809 Loss2: 1.516854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.176140 Loss1: 1.629882 Loss2: 1.546258
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.626212 Loss1: 2.626277 Loss2: 1.999935
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.524716 Loss1: 2.053384 Loss2: 1.471332
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.035733 Loss1: 1.487374 Loss2: 1.548359
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.354971 Loss1: 1.897159 Loss2: 1.457812
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.903380 Loss1: 1.349200 Loss2: 1.554180
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.194929 Loss1: 1.700071 Loss2: 1.494858
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.883527 Loss1: 1.324161 Loss2: 1.559366
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.062545 Loss1: 1.574237 Loss2: 1.488307
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.763848 Loss1: 1.191184 Loss2: 1.572665
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.699975 Loss1: 1.114422 Loss2: 1.585553 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.585044 Loss1: 1.016226 Loss2: 1.568818 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.729492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.788850 Loss1: 1.274997 Loss2: 1.513853 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.600000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.564151 Loss1: 2.455422 Loss2: 2.108729 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.086076 Loss1: 1.570281 Loss2: 1.515795 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.908997 Loss1: 1.396564 Loss2: 1.512433
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.787080 Loss1: 2.683522 Loss2: 2.103558
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.910073 Loss1: 1.390570 Loss2: 1.519503
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.705989 Loss1: 2.147616 Loss2: 1.558373
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.723050 Loss1: 1.194412 Loss2: 1.528639
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.387541 Loss1: 1.846339 Loss2: 1.541203
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.202036 Loss1: 1.647815 Loss2: 1.554221
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.559135 Loss1: 1.035777 Loss2: 1.523357
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.095631 Loss1: 1.536951 Loss2: 1.558680
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.559870 Loss1: 1.039080 Loss2: 1.520791
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.947391 Loss1: 1.399335 Loss2: 1.548056
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.541479 Loss1: 1.007461 Loss2: 1.534018
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.460407 Loss1: 0.931781 Loss2: 1.528626 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.661458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.839566 Loss1: 1.260282 Loss2: 1.579284 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.642857
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.590837 Loss1: 2.479707 Loss2: 2.111130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.318369 Loss1: 1.819344 Loss2: 1.499024 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.868502 Loss1: 1.378984 Loss2: 1.489517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.750572 Loss1: 1.253075 Loss2: 1.497497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.640257 Loss1: 1.137896 Loss2: 1.502360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.587655 Loss1: 1.086546 Loss2: 1.501109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.565922 Loss1: 1.050620 Loss2: 1.515302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.554788 Loss1: 1.032844 Loss2: 1.521944 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.703125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.853866 Loss1: 1.285077 Loss2: 1.568789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.587170 Loss1: 1.026975 Loss2: 1.560194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.481270 Loss1: 0.923355 Loss2: 1.557915
+(DefaultActor pid=3764) >> Training accuracy: 0.732292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.518194 Loss1: 2.524928 Loss2: 1.993266
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.525307 Loss1: 2.015941 Loss2: 1.509366
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.222757 Loss1: 1.730054 Loss2: 1.492703
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.008571 Loss1: 1.497137 Loss2: 1.511434
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.891473 Loss1: 1.388081 Loss2: 1.503393
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.556353 Loss1: 2.534512 Loss2: 2.021841
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.495881 Loss1: 2.017696 Loss2: 1.478185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.770696 Loss1: 1.252094 Loss2: 1.518602
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.256313 Loss1: 1.792801 Loss2: 1.463512
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.677517 Loss1: 1.146314 Loss2: 1.531203
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.081192 Loss1: 1.610340 Loss2: 1.470852
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.583622 Loss1: 1.046877 Loss2: 1.536745
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.931901 Loss1: 1.454744 Loss2: 1.477157
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.641237 Loss1: 1.102053 Loss2: 1.539185
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.783669 Loss1: 1.302709 Loss2: 1.480959
+(DefaultActor pid=3765) >> Training accuracy: 0.674805
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.817446 Loss1: 1.329869 Loss2: 1.487577
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.679318 Loss1: 1.167519 Loss2: 1.511800
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.687865 Loss1: 1.174153 Loss2: 1.513712
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.536632 Loss1: 1.022428 Loss2: 1.514204
+(DefaultActor pid=3764) >> Training accuracy: 0.680208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.579403 Loss1: 2.569015 Loss2: 2.010388
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.465915 Loss1: 1.987426 Loss2: 1.478489
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.155315 Loss1: 1.692198 Loss2: 1.463117
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.051572 Loss1: 1.590201 Loss2: 1.461371
+(DefaultActor pid=3765) Epoch: 4 Loss: 3.001347 Loss1: 1.520722 Loss2: 1.480625
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.907962 Loss1: 1.401161 Loss2: 1.506801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.850717 Loss1: 1.360966 Loss2: 1.489751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.677614 Loss1: 1.183751 Loss2: 1.493863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.710708 Loss1: 1.204741 Loss2: 1.505966 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.558325 Loss1: 1.048584 Loss2: 1.509741 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.706250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.603252 Loss1: 1.036841 Loss2: 1.566411 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.560296 Loss1: 0.991162 Loss2: 1.569134 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.758333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.425616 Loss1: 1.995101 Loss2: 1.430514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.849585 Loss1: 1.416022 Loss2: 1.433563 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.727230 Loss1: 1.293096 Loss2: 1.434134
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.760350 Loss1: 2.655733 Loss2: 2.104616
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.654146 Loss1: 1.203710 Loss2: 1.450437
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.662425 Loss1: 2.107860 Loss2: 1.554565
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.652056 Loss1: 1.203440 Loss2: 1.448617
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.315159 Loss1: 1.780860 Loss2: 1.534299
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.573924 Loss1: 1.119147 Loss2: 1.454777
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.111448 Loss1: 1.587756 Loss2: 1.523692
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.458265 Loss1: 1.002342 Loss2: 1.455923
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.008421 Loss1: 1.486422 Loss2: 1.521999
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.499710 Loss1: 1.020871 Loss2: 1.478839
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.939216 Loss1: 1.399892 Loss2: 1.539325
+(DefaultActor pid=3765) >> Training accuracy: 0.712500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.892224 Loss1: 1.348803 Loss2: 1.543421
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.850796 Loss1: 1.310898 Loss2: 1.539898
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.717423 Loss1: 1.162581 Loss2: 1.554841
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.692427 Loss1: 1.130629 Loss2: 1.561798
+(DefaultActor pid=3764) >> Training accuracy: 0.623958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.195312 Loss1: 2.276155 Loss2: 1.919157
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.125697 Loss1: 1.710954 Loss2: 1.414743
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.898023 Loss1: 1.516593 Loss2: 1.381430
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.649178 Loss1: 1.261672 Loss2: 1.387506
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.714608 Loss1: 1.321537 Loss2: 1.393072
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.401142 Loss1: 2.373812 Loss2: 2.027330
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.594182 Loss1: 1.181168 Loss2: 1.413014
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.378993 Loss1: 1.886089 Loss2: 1.492904
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.187318 Loss1: 1.695614 Loss2: 1.491703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.954901 Loss1: 1.442843 Loss2: 1.512059 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.893362 Loss1: 1.392043 Loss2: 1.501319 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.782292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.771197 Loss1: 1.249924 Loss2: 1.521274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.602310 Loss1: 1.066996 Loss2: 1.535314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.506383 Loss1: 0.964052 Loss2: 1.542331 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.714844
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.499273 Loss1: 2.031954 Loss2: 1.467319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.027935 Loss1: 1.577173 Loss2: 1.450762 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.884373 Loss1: 1.416187 Loss2: 1.468187
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.659949 Loss1: 2.502799 Loss2: 2.157150
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.366954 Loss1: 1.810114 Loss2: 1.556840 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.116915 Loss1: 1.599469 Loss2: 1.517446 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.944949 Loss1: 1.427074 Loss2: 1.517875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.827691 Loss1: 1.296745 Loss2: 1.530946 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.764583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.715276 Loss1: 1.176730 Loss2: 1.538546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.703804 Loss1: 1.162182 Loss2: 1.541622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.463129 Loss1: 0.911969 Loss2: 1.551160 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.763542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.251606 Loss1: 1.789406 Loss2: 1.462199 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.837413 Loss1: 1.393888 Loss2: 1.443525 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.759228 Loss1: 1.299722 Loss2: 1.459506
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.592394 Loss1: 2.511380 Loss2: 2.081014
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.584222 Loss1: 1.130937 Loss2: 1.453285
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.544168 Loss1: 2.003420 Loss2: 1.540748
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.426201 Loss1: 0.986895 Loss2: 1.439307
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.175763 Loss1: 1.667866 Loss2: 1.507897
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.524035 Loss1: 1.073197 Loss2: 1.450838
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.987349 Loss1: 1.468792 Loss2: 1.518557
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.841325 Loss1: 1.314604 Loss2: 1.526721
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.457327 Loss1: 1.003229 Loss2: 1.454097
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.674605 Loss1: 1.158684 Loss2: 1.515921
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.390083 Loss1: 0.925528 Loss2: 1.464555
+(DefaultActor pid=3765) >> Training accuracy: 0.750977
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.665930 Loss1: 1.113263 Loss2: 1.552667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.448246 Loss1: 0.907606 Loss2: 1.540640 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.736458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.599705 Loss1: 2.028312 Loss2: 1.571393 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.063092 Loss1: 1.503560 Loss2: 1.559532 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.944771 Loss1: 1.388736 Loss2: 1.556035
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.404190 Loss1: 2.396922 Loss2: 2.007268
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.371662 Loss1: 1.900755 Loss2: 1.470907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.094635 Loss1: 1.639337 Loss2: 1.455299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.901555 Loss1: 1.449173 Loss2: 1.452382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.792367 Loss1: 1.327309 Loss2: 1.465058 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.751116
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.733482 Loss1: 1.256124 Loss2: 1.477358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.692101 Loss1: 1.193637 Loss2: 1.498464 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.465727 Loss1: 0.977275 Loss2: 1.488452 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.718750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.399203 Loss1: 1.858577 Loss2: 1.540626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.920616 Loss1: 1.425231 Loss2: 1.495385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.375022 Loss1: 2.392186 Loss2: 1.982836 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.422953 Loss1: 1.947564 Loss2: 1.475388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.139899 Loss1: 1.683124 Loss2: 1.456775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.993625 Loss1: 1.522647 Loss2: 1.470978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.849943 Loss1: 1.387535 Loss2: 1.462409 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.670833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.746935 Loss1: 1.264392 Loss2: 1.482542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.747282 Loss1: 1.248531 Loss2: 1.498752 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.478205 Loss1: 0.982979 Loss2: 1.495226 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.706250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.384118 Loss1: 1.865442 Loss2: 1.518675 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.972059 Loss1: 1.463778 Loss2: 1.508281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.596941 Loss1: 2.525695 Loss2: 2.071246 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.502290 Loss1: 1.976885 Loss2: 1.525405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.216600 Loss1: 1.707213 Loss2: 1.509387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.033001 Loss1: 1.500063 Loss2: 1.532938 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.985173 Loss1: 1.453846 Loss2: 1.531327 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.698958
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-09 05:43:08,831 | server.py:236 | fit_round 28 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.736782 Loss1: 1.186086 Loss2: 1.550696 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.576852 Loss1: 1.014568 Loss2: 1.562283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.541062 Loss1: 0.986104 Loss2: 1.554957
+(DefaultActor pid=3764) >> Training accuracy: 0.736458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.584299 Loss1: 2.523987 Loss2: 2.060311
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.588875 Loss1: 2.020927 Loss2: 1.567948
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.293983 Loss1: 1.745828 Loss2: 1.548156
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.062799 Loss1: 1.501029 Loss2: 1.561769
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.884624 Loss1: 1.339680 Loss2: 1.544944
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.656877 Loss1: 2.616404 Loss2: 2.040474
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.875027 Loss1: 1.320625 Loss2: 1.554401
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.646779 Loss1: 2.131006 Loss2: 1.515774
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.830808 Loss1: 1.262638 Loss2: 1.568170
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.331243 Loss1: 1.831745 Loss2: 1.499498
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.759588 Loss1: 1.174909 Loss2: 1.584679
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.054494 Loss1: 1.565387 Loss2: 1.489107
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.723413 Loss1: 1.135692 Loss2: 1.587721
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.980435 Loss1: 1.482623 Loss2: 1.497813
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.608485 Loss1: 1.029025 Loss2: 1.579460
+(DefaultActor pid=3765) >> Training accuracy: 0.676042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.762551 Loss1: 1.262537 Loss2: 1.500013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.662378 Loss1: 1.125932 Loss2: 1.536446 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.709375
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 05:43:08,831][flwr][DEBUG] - fit_round 28 received 50 results and 0 failures
+INFO flwr 2023-10-09 05:43:50,143 | server.py:125 | fit progress: (28, 2.9036672896089644, {'accuracy': 0.3281}, 64337.92139886)
+>> Test accuracy: 0.328100
+[2023-10-09 05:43:50,143][flwr][INFO] - fit progress: (28, 2.9036672896089644, {'accuracy': 0.3281}, 64337.92139886)
+DEBUG flwr 2023-10-09 05:43:50,143 | server.py:173 | evaluate_round 28: strategy sampled 50 clients (out of 50)
+[2023-10-09 05:43:50,143][flwr][DEBUG] - evaluate_round 28: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 05:52:55,855 | server.py:187 | evaluate_round 28 received 50 results and 0 failures
+[2023-10-09 05:52:55,855][flwr][DEBUG] - evaluate_round 28 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 05:52:55,855 | server.py:222 | fit_round 29: strategy sampled 50 clients (out of 50)
+[2023-10-09 05:52:55,855][flwr][DEBUG] - fit_round 29: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.759630 Loss1: 2.690108 Loss2: 2.069523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.311894 Loss1: 1.828917 Loss2: 1.482977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.384087 Loss1: 2.353710 Loss2: 2.030377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.294308 Loss1: 1.849680 Loss2: 1.444627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.110643 Loss1: 1.670384 Loss2: 1.440259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.901891 Loss1: 1.457521 Loss2: 1.444370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.756974 Loss1: 1.310167 Loss2: 1.446807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.633116 Loss1: 1.177630 Loss2: 1.455486 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.741071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.519179 Loss1: 1.040843 Loss2: 1.478336 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.340242 Loss1: 0.852567 Loss2: 1.487675 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.726562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.583391 Loss1: 2.409678 Loss2: 2.173713
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.483710 Loss1: 1.895971 Loss2: 1.587739
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.038539 Loss1: 1.485777 Loss2: 1.552763
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.937016 Loss1: 1.380634 Loss2: 1.556382
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.353486 Loss1: 2.413577 Loss2: 1.939910
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.329034 Loss1: 1.869215 Loss2: 1.459818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.056855 Loss1: 1.620318 Loss2: 1.436537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.870255 Loss1: 1.425707 Loss2: 1.444548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.739051 Loss1: 1.282153 Loss2: 1.456898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.633647 Loss1: 1.179662 Loss2: 1.453985 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.716667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.586637 Loss1: 1.084919 Loss2: 1.501719 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.428590 Loss1: 0.942107 Loss2: 1.486483 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.744141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.445902 Loss1: 1.959825 Loss2: 1.486077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.964016 Loss1: 1.478493 Loss2: 1.485523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.822177 Loss1: 1.341217 Loss2: 1.480960
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.422986 Loss1: 2.370660 Loss2: 2.052326
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.663499 Loss1: 1.171944 Loss2: 1.491555
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.331639 Loss1: 1.832112 Loss2: 1.499527
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.605430 Loss1: 1.100460 Loss2: 1.504970
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.054354 Loss1: 1.569681 Loss2: 1.484673
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.679867 Loss1: 1.154003 Loss2: 1.525864
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.930779 Loss1: 1.436579 Loss2: 1.494199
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.589266 Loss1: 1.064234 Loss2: 1.525033
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.833424 Loss1: 1.330383 Loss2: 1.503042
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.446355 Loss1: 0.933149 Loss2: 1.513206
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.702538 Loss1: 1.191235 Loss2: 1.511303
+(DefaultActor pid=3765) >> Training accuracy: 0.698958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.590656 Loss1: 1.078525 Loss2: 1.512130
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.421488 Loss1: 0.913306 Loss2: 1.508182
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.513621 Loss1: 0.988930 Loss2: 1.524691
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.376835 Loss1: 0.858030 Loss2: 1.518805
+(DefaultActor pid=3764) >> Training accuracy: 0.753125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.652812 Loss1: 2.446524 Loss2: 2.206288
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.516755 Loss1: 1.964553 Loss2: 1.552202
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.176509 Loss1: 1.684786 Loss2: 1.491723
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.021993 Loss1: 1.502757 Loss2: 1.519236
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.866460 Loss1: 1.342518 Loss2: 1.523943
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.673867 Loss1: 1.145445 Loss2: 1.528422
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.503532 Loss1: 2.277246 Loss2: 2.226285
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.395150 Loss1: 1.751034 Loss2: 1.644117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.130709 Loss1: 1.515993 Loss2: 1.614716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.617540 Loss1: 1.054088 Loss2: 1.563452 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.756510
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.760820 Loss1: 1.146957 Loss2: 1.613863 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.659095 Loss1: 1.031641 Loss2: 1.627454 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.430988 Loss1: 0.810176 Loss2: 1.620812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.491396 Loss1: 0.864849 Loss2: 1.626547 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.782292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.785951 Loss1: 1.333918 Loss2: 1.452032 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.615888 Loss1: 1.129809 Loss2: 1.486080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.523252 Loss1: 2.479130 Loss2: 2.044122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.492509 Loss1: 2.005032 Loss2: 1.487477 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.693510
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.996411 Loss1: 1.545391 Loss2: 1.451020 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.772642 Loss1: 1.294025 Loss2: 1.478617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.683613 Loss1: 1.197730 Loss2: 1.485883
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.476473 Loss1: 2.301393 Loss2: 2.175080
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.428084 Loss1: 1.822856 Loss2: 1.605228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.169091 Loss1: 1.578897 Loss2: 1.590195 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.767708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.837314 Loss1: 1.249555 Loss2: 1.587759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.707388 Loss1: 1.108422 Loss2: 1.598966 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.561439 Loss1: 0.957363 Loss2: 1.604076 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.503203 Loss1: 0.907981 Loss2: 1.595221 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.434268 Loss1: 0.817385 Loss2: 1.616883 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.781250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.875965 Loss1: 1.357532 Loss2: 1.518432 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.714237 Loss1: 1.178161 Loss2: 1.536076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.698575 Loss1: 1.156120 Loss2: 1.542455 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.252720 Loss1: 1.781874 Loss2: 1.470846
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.664946 Loss1: 1.103731 Loss2: 1.561215
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.022716 Loss1: 1.568589 Loss2: 1.454127
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.584035 Loss1: 1.025055 Loss2: 1.558980
+(DefaultActor pid=3764) >> Training accuracy: 0.678711
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.765487 Loss1: 1.280400 Loss2: 1.485087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.523383 Loss1: 1.048508 Loss2: 1.474875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.478749 Loss1: 2.482954 Loss2: 1.995795
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.469105 Loss1: 0.994282 Loss2: 1.474823
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.480398 Loss1: 0.989124 Loss2: 1.491274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.485876 Loss1: 0.972769 Loss2: 1.513108 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.724265
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.757454 Loss1: 1.322471 Loss2: 1.434983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.485721 Loss1: 1.041907 Loss2: 1.443814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.693945 Loss1: 2.605325 Loss2: 2.088620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.639204 Loss1: 2.089316 Loss2: 1.549888 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.769792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.244549 Loss1: 1.721155 Loss2: 1.523394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.922462 Loss1: 1.394806 Loss2: 1.527655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.837696 Loss1: 1.278959 Loss2: 1.558737 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.741507 Loss1: 1.172081 Loss2: 1.569425 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.595295 Loss1: 1.026033 Loss2: 1.569261 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.624917 Loss1: 1.052774 Loss2: 1.572143 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.653125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.713302 Loss1: 1.258779 Loss2: 1.454523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.522070 Loss1: 1.100208 Loss2: 1.421862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.473897 Loss1: 1.027924 Loss2: 1.445974
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.613074 Loss1: 2.519239 Loss2: 2.093835
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.642394 Loss1: 2.078869 Loss2: 1.563525 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.725000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.294944 Loss1: 1.753146 Loss2: 1.541798 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.901231 Loss1: 1.372783 Loss2: 1.528448 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.660569 Loss1: 1.117514 Loss2: 1.543055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.629184 Loss1: 1.085028 Loss2: 1.544156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.541422 Loss1: 0.984596 Loss2: 1.556825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.491233 Loss1: 0.930566 Loss2: 1.560667 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.731250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.637599 Loss1: 1.196448 Loss2: 1.441151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.528962 Loss1: 1.076696 Loss2: 1.452266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.443164 Loss1: 2.298388 Loss2: 2.144776 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.270821 Loss1: 1.692067 Loss2: 1.578754 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.807292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.895110 Loss1: 1.358274 Loss2: 1.536836 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.727991 Loss1: 1.179797 Loss2: 1.548194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.451385 Loss1: 2.447626 Loss2: 2.003759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.366906 Loss1: 1.909192 Loss2: 1.457714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.005798 Loss1: 1.570934 Loss2: 1.434864 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.767708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.768245 Loss1: 1.322762 Loss2: 1.445483 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.607788 Loss1: 1.145264 Loss2: 1.462524 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.651098 Loss1: 2.596390 Loss2: 2.054708
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.563313 Loss1: 1.088069 Loss2: 1.475244
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.515234 Loss1: 1.031248 Loss2: 1.483986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.423718 Loss1: 0.950348 Loss2: 1.473370 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.753906
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.955685 Loss1: 1.426404 Loss2: 1.529281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.777709 Loss1: 1.244595 Loss2: 1.533114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.742631 Loss1: 1.204479 Loss2: 1.538153
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.509922 Loss1: 2.456898 Loss2: 2.053024
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.432167 Loss1: 1.943550 Loss2: 1.488616 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.704167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.158860 Loss1: 1.684337 Loss2: 1.474523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.904440 Loss1: 1.425408 Loss2: 1.479031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.583354 Loss1: 1.091969 Loss2: 1.491385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.536552 Loss1: 1.052240 Loss2: 1.484313 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.488618 Loss1: 0.984964 Loss2: 1.503654 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.474385 Loss1: 0.961483 Loss2: 1.512903 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.707292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.812549 Loss1: 1.344836 Loss2: 1.467713 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.725630 Loss1: 1.223889 Loss2: 1.501741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.629562 Loss1: 2.575563 Loss2: 2.054000 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.675767 Loss1: 2.148297 Loss2: 1.527471 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.718750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.144072 Loss1: 1.627399 Loss2: 1.516673 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.760854 Loss1: 1.249769 Loss2: 1.511085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.763867 Loss1: 1.235186 Loss2: 1.528681
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.432555 Loss1: 2.333996 Loss2: 2.098559
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.430712 Loss1: 1.883343 Loss2: 1.547369 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.991532 Loss1: 1.470819 Loss2: 1.520713 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.712500
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.618655 Loss1: 1.057565 Loss2: 1.561090
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.830595 Loss1: 1.320688 Loss2: 1.509907
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.711518 Loss1: 1.190226 Loss2: 1.521292
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.576023 Loss1: 1.055218 Loss2: 1.520805
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.643076 Loss1: 1.122606 Loss2: 1.520470
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.603619 Loss1: 1.051848 Loss2: 1.551771
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.586349 Loss1: 2.454384 Loss2: 2.131965
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.502160 Loss1: 0.961577 Loss2: 1.540583
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.418733 Loss1: 1.862399 Loss2: 1.556334
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.469415 Loss1: 0.938952 Loss2: 1.530464
+(DefaultActor pid=3765) >> Training accuracy: 0.693750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.882410 Loss1: 1.354513 Loss2: 1.527897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.631884 Loss1: 1.090355 Loss2: 1.541529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.531990 Loss1: 0.997350 Loss2: 1.534640
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.528616 Loss1: 2.467546 Loss2: 2.061070
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.512650 Loss1: 0.963127 Loss2: 1.549523
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.477278 Loss1: 1.968794 Loss2: 1.508483
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.470481 Loss1: 0.909066 Loss2: 1.561416
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.120721 Loss1: 1.629361 Loss2: 1.491360
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.507934 Loss1: 0.938956 Loss2: 1.568978
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.016640 Loss1: 1.523740 Loss2: 1.492899
+(DefaultActor pid=3764) >> Training accuracy: 0.773958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.823416 Loss1: 1.325498 Loss2: 1.497917
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.656083 Loss1: 1.152423 Loss2: 1.503660
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.584249 Loss1: 1.076878 Loss2: 1.507371
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.617382 Loss1: 1.095107 Loss2: 1.522274
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.522637 Loss1: 2.545828 Loss2: 1.976809
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.510342 Loss1: 0.984621 Loss2: 1.525721
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.381116 Loss1: 1.913285 Loss2: 1.467831
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.558620 Loss1: 1.043954 Loss2: 1.514666
+(DefaultActor pid=3765) >> Training accuracy: 0.701042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.894865 Loss1: 1.434295 Loss2: 1.460570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.651657 Loss1: 1.182985 Loss2: 1.468672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.658534 Loss1: 1.166704 Loss2: 1.491831
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.320977 Loss1: 2.401852 Loss2: 1.919125
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.230923 Loss1: 1.797890 Loss2: 1.433034 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.928055 Loss1: 1.513101 Loss2: 1.414955 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.753125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.498182 Loss1: 1.002047 Loss2: 1.496136
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.772037 Loss1: 1.360679 Loss2: 1.411358
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.732889 Loss1: 1.304053 Loss2: 1.428836
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.684026 Loss1: 1.251782 Loss2: 1.432244
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.525984 Loss1: 1.098368 Loss2: 1.427616
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.460491 Loss1: 1.019317 Loss2: 1.441174
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.467200 Loss1: 2.487523 Loss2: 1.979677
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.492453 Loss1: 2.052170 Loss2: 1.440283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.353667 Loss1: 0.899074 Loss2: 1.454593
+(DefaultActor pid=3765) >> Training accuracy: 0.682617
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.154154 Loss1: 1.728623 Loss2: 1.425531
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.011466 Loss1: 1.574982 Loss2: 1.436484
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.788062 Loss1: 1.357249 Loss2: 1.430814
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.732061 Loss1: 1.288720 Loss2: 1.443341
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.675015 Loss1: 1.218466 Loss2: 1.456550
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.665684 Loss1: 1.214485 Loss2: 1.451199
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.496357 Loss1: 2.451484 Loss2: 2.044873
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.566830 Loss1: 1.093931 Loss2: 1.472899
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.432099 Loss1: 1.911113 Loss2: 1.520986
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.551837 Loss1: 1.067881 Loss2: 1.483956
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.202868 Loss1: 1.704596 Loss2: 1.498272
+(DefaultActor pid=3764) >> Training accuracy: 0.713542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.022777 Loss1: 1.510558 Loss2: 1.512219
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.771050 Loss1: 1.257042 Loss2: 1.514008
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.762052 Loss1: 1.250284 Loss2: 1.511768
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.699087 Loss1: 1.183459 Loss2: 1.515628
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.543028 Loss1: 2.392749 Loss2: 2.150280
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.572803 Loss1: 1.050598 Loss2: 1.522205
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.475444 Loss1: 0.949601 Loss2: 1.525844 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.482564 Loss1: 0.949902 Loss2: 1.532661 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.707031
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.851091 Loss1: 1.267397 Loss2: 1.583694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.723240 Loss1: 1.135363 Loss2: 1.587878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.549496 Loss1: 0.970097 Loss2: 1.579399
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.542804 Loss1: 2.413568 Loss2: 2.129236
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.350955 Loss1: 1.773295 Loss2: 1.577660 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.739583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.472625 Loss1: 0.878924 Loss2: 1.593701
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.987700 Loss1: 1.463974 Loss2: 1.523726
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.722690 Loss1: 1.193086 Loss2: 1.529604
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.738641 Loss1: 1.217154 Loss2: 1.521487
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.736883 Loss1: 1.189920 Loss2: 1.546963
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.573783 Loss1: 1.017397 Loss2: 1.556387
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.545816 Loss1: 2.436977 Loss2: 2.108839
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.416176 Loss1: 0.872372 Loss2: 1.543804
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.352074 Loss1: 0.806762 Loss2: 1.545312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.298048 Loss1: 0.750233 Loss2: 1.547816 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.779167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.904609 Loss1: 1.367734 Loss2: 1.536875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.696321 Loss1: 1.131386 Loss2: 1.564935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.730233 Loss1: 1.161570 Loss2: 1.568663
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.557751 Loss1: 2.489242 Loss2: 2.068508
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.574509 Loss1: 2.021371 Loss2: 1.553138 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.706250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.339691 Loss1: 1.803838 Loss2: 1.535853 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.922349 Loss1: 1.409764 Loss2: 1.512586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.689683 Loss1: 1.154403 Loss2: 1.535280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.729800 Loss1: 1.186964 Loss2: 1.542836 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.647951 Loss1: 1.101984 Loss2: 1.545967 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.595890 Loss1: 1.047821 Loss2: 1.548069 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.709375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.766860 Loss1: 1.301288 Loss2: 1.465572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.535305 Loss1: 1.049011 Loss2: 1.486294 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.447825 Loss1: 0.959304 Loss2: 1.488521
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.433686 Loss1: 2.377015 Loss2: 2.056671
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.363252 Loss1: 1.889627 Loss2: 1.473624 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.806250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.052502 Loss1: 1.600946 Loss2: 1.451556 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.779614 Loss1: 1.312004 Loss2: 1.467610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.603345 Loss1: 1.124834 Loss2: 1.478511 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.590704 Loss1: 1.101682 Loss2: 1.489022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.522381 Loss1: 2.012979 Loss2: 1.509403
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.516826 Loss1: 1.028070 Loss2: 1.488755
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.239776 Loss1: 1.756310 Loss2: 1.483465
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.398394 Loss1: 0.904556 Loss2: 1.493838
+(DefaultActor pid=3765) >> Training accuracy: 0.747917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.875381 Loss1: 1.380426 Loss2: 1.494955 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.755898 Loss1: 1.242585 Loss2: 1.513313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.533662 Loss1: 2.416834 Loss2: 2.116827
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.665780 Loss1: 1.137194 Loss2: 1.528587
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.433080 Loss1: 1.917451 Loss2: 1.515629
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.578953 Loss1: 1.062684 Loss2: 1.516269
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.109794 Loss1: 1.604429 Loss2: 1.505365
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.569473 Loss1: 1.051109 Loss2: 1.518364
+(DefaultActor pid=3764) >> Training accuracy: 0.717773
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.826657 Loss1: 1.306498 Loss2: 1.520159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.756139 Loss1: 1.216597 Loss2: 1.539542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.699801 Loss1: 1.162235 Loss2: 1.537566
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.655441 Loss1: 2.523661 Loss2: 2.131779
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.573178 Loss1: 1.995961 Loss2: 1.577217 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.696875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.584037 Loss1: 1.035240 Loss2: 1.548796
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.310207 Loss1: 1.726029 Loss2: 1.584179
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.119070 Loss1: 1.543022 Loss2: 1.576048
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.064608 Loss1: 1.486300 Loss2: 1.578308
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.901521 Loss1: 1.317191 Loss2: 1.584331
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.846205 Loss1: 1.243732 Loss2: 1.602473
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.496851 Loss1: 2.447075 Loss2: 2.049776
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.723391 Loss1: 1.124699 Loss2: 1.598692
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.725995 Loss1: 1.129032 Loss2: 1.596963 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.838139 Loss1: 1.209774 Loss2: 1.628365 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.654167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.902374 Loss1: 1.404719 Loss2: 1.497655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.682184 Loss1: 1.179597 Loss2: 1.502587 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.549324 Loss1: 1.037133 Loss2: 1.512191
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.565549 Loss1: 2.488839 Loss2: 2.076711
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.593025 Loss1: 2.085712 Loss2: 1.507313 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.729167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.198292 Loss1: 1.696582 Loss2: 1.501710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.860125 Loss1: 1.365659 Loss2: 1.494466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.700840 Loss1: 1.180884 Loss2: 1.519957 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.668928 Loss1: 1.155346 Loss2: 1.513583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.574202 Loss1: 1.043950 Loss2: 1.530252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.498781 Loss1: 0.966518 Loss2: 1.532262 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.748958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.676389 Loss1: 1.151295 Loss2: 1.525094 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.647722 Loss1: 1.104516 Loss2: 1.543206 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.460134 Loss1: 0.919028 Loss2: 1.541106
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.600466 Loss1: 2.470683 Loss2: 2.129783
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.607139 Loss1: 2.030719 Loss2: 1.576420 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.764583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.216293 Loss1: 1.656855 Loss2: 1.559439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.844393 Loss1: 1.297269 Loss2: 1.547124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.639024 Loss1: 1.071387 Loss2: 1.567637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.615724 Loss1: 1.050907 Loss2: 1.564817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.646443 Loss1: 1.057318 Loss2: 1.589125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.556990 Loss1: 0.954426 Loss2: 1.602564 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.636458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.935788 Loss1: 1.357344 Loss2: 1.578444 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.692882 Loss1: 1.095641 Loss2: 1.597241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.673501 Loss1: 2.453003 Loss2: 2.220498
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.627540 Loss1: 1.023652 Loss2: 1.603888
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.409654 Loss1: 1.809122 Loss2: 1.600532
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.604175 Loss1: 1.000280 Loss2: 1.603896
+(DefaultActor pid=3765) >> Training accuracy: 0.716518
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.816107 Loss1: 1.268359 Loss2: 1.547748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.677954 Loss1: 1.120616 Loss2: 1.557339 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.543769 Loss1: 0.949512 Loss2: 1.594258 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.397034 Loss1: 0.799493 Loss2: 1.597541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.457351 Loss1: 0.876713 Loss2: 1.580638 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.675481
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.031842 Loss1: 1.529230 Loss2: 1.502612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.891146 Loss1: 1.369275 Loss2: 1.521872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.782447 Loss1: 1.238158 Loss2: 1.544289
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.643344 Loss1: 2.483301 Loss2: 2.160043
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.666939 Loss1: 1.116991 Loss2: 1.549948
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.597986 Loss1: 2.000884 Loss2: 1.597102
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.701791 Loss1: 1.157243 Loss2: 1.544548
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.217528 Loss1: 1.657236 Loss2: 1.560292
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.627049 Loss1: 1.084838 Loss2: 1.542211
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.093503 Loss1: 1.522730 Loss2: 1.570773
+(DefaultActor pid=3765) >> Training accuracy: 0.666016
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.900754 Loss1: 1.323711 Loss2: 1.577043
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.914316 Loss1: 1.332007 Loss2: 1.582309
+DEBUG flwr 2023-10-09 06:21:11,143 | server.py:236 | fit_round 29 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.706029 Loss1: 1.102386 Loss2: 1.603642
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.613379 Loss1: 1.014911 Loss2: 1.598468
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.626757 Loss1: 1.031847 Loss2: 1.594910
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.628185 Loss1: 2.631646 Loss2: 1.996539
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.650684 Loss1: 1.023573 Loss2: 1.627112
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.589175 Loss1: 2.098646 Loss2: 1.490530
+(DefaultActor pid=3764) >> Training accuracy: 0.687500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.304669 Loss1: 1.832549 Loss2: 1.472120
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.089237 Loss1: 1.615837 Loss2: 1.473401
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.950359 Loss1: 1.463457 Loss2: 1.486902
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.896219 Loss1: 1.393061 Loss2: 1.503159
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.582149 Loss1: 2.487113 Loss2: 2.095036
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.831525 Loss1: 1.315308 Loss2: 1.516217
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.731339 Loss1: 1.231175 Loss2: 1.500164 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.728010 Loss1: 1.210621 Loss2: 1.517390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.722719 Loss1: 1.193717 Loss2: 1.529003 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.646484
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.693684 Loss1: 1.165425 Loss2: 1.528259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.546591 Loss1: 0.996950 Loss2: 1.549641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.418791 Loss1: 0.865002 Loss2: 1.553790 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.782292
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 06:21:11,143][flwr][DEBUG] - fit_round 29 received 50 results and 0 failures
+INFO flwr 2023-10-09 06:21:52,206 | server.py:125 | fit progress: (29, 2.8099579156016388, {'accuracy': 0.3431}, 66619.98491549399)
+>> Test accuracy: 0.343100
+[2023-10-09 06:21:52,206][flwr][INFO] - fit progress: (29, 2.8099579156016388, {'accuracy': 0.3431}, 66619.98491549399)
+DEBUG flwr 2023-10-09 06:21:52,207 | server.py:173 | evaluate_round 29: strategy sampled 50 clients (out of 50)
+[2023-10-09 06:21:52,207][flwr][DEBUG] - evaluate_round 29: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 06:30:53,875 | server.py:187 | evaluate_round 29 received 50 results and 0 failures
+[2023-10-09 06:30:53,875][flwr][DEBUG] - evaluate_round 29 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 06:30:53,875 | server.py:222 | fit_round 30: strategy sampled 50 clients (out of 50)
+[2023-10-09 06:30:53,875][flwr][DEBUG] - fit_round 30: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.353549 Loss1: 2.276282 Loss2: 2.077266
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.436343 Loss1: 1.913829 Loss2: 1.522514
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.086424 Loss1: 1.572775 Loss2: 1.513649
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.782151 Loss1: 1.269480 Loss2: 1.512671
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.278353 Loss1: 2.324226 Loss2: 1.954128
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.757900 Loss1: 1.252558 Loss2: 1.505343
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.249419 Loss1: 1.772385 Loss2: 1.477035
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.577968 Loss1: 1.055818 Loss2: 1.522150
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.890840 Loss1: 1.452773 Loss2: 1.438067
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.436822 Loss1: 0.913250 Loss2: 1.523572
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.670553 Loss1: 1.233303 Loss2: 1.437249
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.430415 Loss1: 0.893972 Loss2: 1.536443
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.615157 Loss1: 1.181831 Loss2: 1.433326
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.477422 Loss1: 0.931202 Loss2: 1.546220
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.551840 Loss1: 1.087663 Loss2: 1.464177
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.431249 Loss1: 0.872769 Loss2: 1.558480
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.576670 Loss1: 1.113277 Loss2: 1.463393
+(DefaultActor pid=3765) >> Training accuracy: 0.792708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.391546 Loss1: 0.919459 Loss2: 1.472086
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.333502 Loss1: 0.867109 Loss2: 1.466392
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.414811 Loss1: 0.939314 Loss2: 1.475497
+(DefaultActor pid=3764) >> Training accuracy: 0.709375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.575865 Loss1: 2.474985 Loss2: 2.100879
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.579255 Loss1: 2.013487 Loss2: 1.565768
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.231861 Loss1: 1.674932 Loss2: 1.556929
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.977413 Loss1: 1.423975 Loss2: 1.553437
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.545009 Loss1: 2.495855 Loss2: 2.049154
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.561978 Loss1: 2.050080 Loss2: 1.511898 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.768667 Loss1: 1.199426 Loss2: 1.569241
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.262016 Loss1: 1.757057 Loss2: 1.504959
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.064510 Loss1: 1.554831 Loss2: 1.509680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.917238 Loss1: 1.407196 Loss2: 1.510042 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.811238 Loss1: 1.284105 Loss2: 1.527133 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.728125
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.581292 Loss1: 0.987967 Loss2: 1.593324
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.666543 Loss1: 1.129369 Loss2: 1.537174
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.637737 Loss1: 1.103639 Loss2: 1.534098
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.603425 Loss1: 1.042974 Loss2: 1.560451
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.604917 Loss1: 1.056157 Loss2: 1.548760
+(DefaultActor pid=3764) >> Training accuracy: 0.741667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.562856 Loss1: 2.406680 Loss2: 2.156176
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.471359 Loss1: 1.893596 Loss2: 1.577763
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.246560 Loss1: 1.676774 Loss2: 1.569786
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.046948 Loss1: 1.463899 Loss2: 1.583049
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.197156 Loss1: 2.158743 Loss2: 2.038414
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.238700 Loss1: 1.738882 Loss2: 1.499819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.929234 Loss1: 1.451969 Loss2: 1.477265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.757287 Loss1: 1.262657 Loss2: 1.494630 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.694909 Loss1: 1.201844 Loss2: 1.493065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.550722 Loss1: 1.048800 Loss2: 1.501922 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.703125
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.618859 Loss1: 1.012300 Loss2: 1.606559
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.431611 Loss1: 0.940487 Loss2: 1.491124
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.369801 Loss1: 0.870977 Loss2: 1.498824
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.346802 Loss1: 0.847003 Loss2: 1.499799
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.363726 Loss1: 0.856292 Loss2: 1.507434
+(DefaultActor pid=3764) >> Training accuracy: 0.761458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.419694 Loss1: 2.347664 Loss2: 2.072031
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.271574 Loss1: 1.779568 Loss2: 1.492007
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.966791 Loss1: 1.479002 Loss2: 1.487789
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.812898 Loss1: 1.327191 Loss2: 1.485707
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.355485 Loss1: 2.319688 Loss2: 2.035797
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.295212 Loss1: 1.775874 Loss2: 1.519338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.041651 Loss1: 1.543283 Loss2: 1.498368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.919297 Loss1: 1.410036 Loss2: 1.509261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.802706 Loss1: 1.293903 Loss2: 1.508803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.659380 Loss1: 1.133322 Loss2: 1.526057 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.690625
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.396387 Loss1: 0.873753 Loss2: 1.522634
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.576265 Loss1: 1.063660 Loss2: 1.512605
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.576287 Loss1: 1.046584 Loss2: 1.529703
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.422566 Loss1: 0.878806 Loss2: 1.543760
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.406973 Loss1: 0.875332 Loss2: 1.531642
+(DefaultActor pid=3764) >> Training accuracy: 0.776042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.656557 Loss1: 2.528642 Loss2: 2.127915
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.482549 Loss1: 1.932037 Loss2: 1.550512
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.177234 Loss1: 1.661156 Loss2: 1.516078
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.013918 Loss1: 1.477618 Loss2: 1.536300
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.426713 Loss1: 2.372234 Loss2: 2.054478
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.357910 Loss1: 1.830431 Loss2: 1.527479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.048630 Loss1: 1.521719 Loss2: 1.526911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.839103 Loss1: 1.312041 Loss2: 1.527062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.827893 Loss1: 1.301038 Loss2: 1.526855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.730148 Loss1: 1.178325 Loss2: 1.551823 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.730208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.586132 Loss1: 1.021011 Loss2: 1.565121 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.614016 Loss1: 1.024451 Loss2: 1.589565 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.678711
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.379975 Loss1: 1.765503 Loss2: 1.614472 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.873663 Loss1: 1.276085 Loss2: 1.597578 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.722346 Loss1: 1.116224 Loss2: 1.606122
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.476732 Loss1: 2.432877 Loss2: 2.043855
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.617446 Loss1: 0.998030 Loss2: 1.619416
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.389846 Loss1: 1.882473 Loss2: 1.507373
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.678969 Loss1: 1.068937 Loss2: 1.610032
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.157837 Loss1: 1.658419 Loss2: 1.499417
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.580985 Loss1: 0.964141 Loss2: 1.616844
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.897660 Loss1: 1.389245 Loss2: 1.508415
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.512350 Loss1: 0.887868 Loss2: 1.624482
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.753082 Loss1: 1.243316 Loss2: 1.509766
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.430090 Loss1: 0.787328 Loss2: 1.642762
+(DefaultActor pid=3765) >> Training accuracy: 0.795833
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.760516 Loss1: 1.231817 Loss2: 1.528699
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.673300 Loss1: 1.132588 Loss2: 1.540713
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.610597 Loss1: 1.054322 Loss2: 1.556275
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.452314 Loss1: 0.898499 Loss2: 1.553815
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.452824 Loss1: 0.904991 Loss2: 1.547833
+(DefaultActor pid=3764) >> Training accuracy: 0.707292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.361617 Loss1: 2.327404 Loss2: 2.034213
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.267905 Loss1: 1.767224 Loss2: 1.500681
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.017878 Loss1: 1.531078 Loss2: 1.486800
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.821968 Loss1: 1.345877 Loss2: 1.476090
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.369882 Loss1: 2.358950 Loss2: 2.010932
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.640236 Loss1: 1.172944 Loss2: 1.467292
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.197337 Loss1: 1.724096 Loss2: 1.473242
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.579032 Loss1: 1.108992 Loss2: 1.470040
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.514448 Loss1: 1.044011 Loss2: 1.470437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.464494 Loss1: 0.977433 Loss2: 1.487061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.346173 Loss1: 0.868732 Loss2: 1.477441 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.324676 Loss1: 0.847308 Loss2: 1.477368 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.685417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.294016 Loss1: 0.860225 Loss2: 1.433791 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.753606
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.303447 Loss1: 2.356814 Loss2: 1.946632 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.031398 Loss1: 1.588593 Loss2: 1.442806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.854888 Loss1: 1.391927 Loss2: 1.462961
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.589781 Loss1: 2.608483 Loss2: 1.981297
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.706959 Loss1: 1.243293 Loss2: 1.463666
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.573362 Loss1: 2.063825 Loss2: 1.509536
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.597029 Loss1: 1.128627 Loss2: 1.468402
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.207437 Loss1: 1.718928 Loss2: 1.488509
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.552703 Loss1: 1.076137 Loss2: 1.476566
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.961054 Loss1: 1.475024 Loss2: 1.486030
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.407834 Loss1: 0.937852 Loss2: 1.469982
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.896370 Loss1: 1.396140 Loss2: 1.500230
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.415458 Loss1: 0.931209 Loss2: 1.484249
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.892793 Loss1: 1.363051 Loss2: 1.529742
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.311075 Loss1: 0.834756 Loss2: 1.476319
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.685662 Loss1: 1.187552 Loss2: 1.498110
+(DefaultActor pid=3765) >> Training accuracy: 0.800781
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.559893 Loss1: 1.043368 Loss2: 1.516525
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.572898 Loss1: 1.052721 Loss2: 1.520177
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.480973 Loss1: 0.954504 Loss2: 1.526470
+(DefaultActor pid=3764) >> Training accuracy: 0.789062
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.439296 Loss1: 2.444893 Loss2: 1.994404
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.400500 Loss1: 1.925608 Loss2: 1.474891
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.059828 Loss1: 1.622194 Loss2: 1.437634
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.835756 Loss1: 1.395655 Loss2: 1.440100
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.703036 Loss1: 2.565544 Loss2: 2.137491
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.673592 Loss1: 1.234604 Loss2: 1.438988
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.541219 Loss1: 1.992457 Loss2: 1.548762
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.592346 Loss1: 1.145832 Loss2: 1.446514
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.287195 Loss1: 1.737199 Loss2: 1.549996
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.101660 Loss1: 1.554472 Loss2: 1.547188
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.436453 Loss1: 0.973794 Loss2: 1.462659
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.944738 Loss1: 1.382704 Loss2: 1.562035
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.436294 Loss1: 0.979529 Loss2: 1.456765
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.782136 Loss1: 1.233811 Loss2: 1.548325
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.389912 Loss1: 0.926515 Loss2: 1.463397
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.364363 Loss1: 0.881534 Loss2: 1.482829 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.794792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.623445 Loss1: 1.050392 Loss2: 1.573053 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.757812
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.425136 Loss1: 2.389197 Loss2: 2.035939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.180142 Loss1: 1.683591 Loss2: 1.496552 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.390603 Loss1: 2.356593 Loss2: 2.034010
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.934710 Loss1: 1.433309 Loss2: 1.501401
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.318226 Loss1: 1.823338 Loss2: 1.494887
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.831380 Loss1: 1.319261 Loss2: 1.512119
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.123181 Loss1: 1.652348 Loss2: 1.470833
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.749589 Loss1: 1.224454 Loss2: 1.525135
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.657469 Loss1: 1.120538 Loss2: 1.536931 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.572884 Loss1: 1.032226 Loss2: 1.540658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.522239 Loss1: 0.988143 Loss2: 1.534097 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.424560 Loss1: 0.890467 Loss2: 1.534093 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.744141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.384794 Loss1: 0.871756 Loss2: 1.513038 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.779167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.629985 Loss1: 2.431992 Loss2: 2.197992 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.212071 Loss1: 1.661805 Loss2: 1.550266 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.384837 Loss1: 2.348803 Loss2: 2.036035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.743277 Loss1: 1.155363 Loss2: 1.587913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.677600 Loss1: 1.088100 Loss2: 1.589500 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.643401 Loss1: 1.044256 Loss2: 1.599145 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.590833 Loss1: 0.978955 Loss2: 1.611878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.442350 Loss1: 0.840415 Loss2: 1.601935 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.774038
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.576771 Loss1: 1.082217 Loss2: 1.494554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.454316 Loss1: 0.951790 Loss2: 1.502526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.487208 Loss1: 0.966412 Loss2: 1.520796
+(DefaultActor pid=3764) >> Training accuracy: 0.727083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.447153 Loss1: 2.413970 Loss2: 2.033183
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.363238 Loss1: 1.866917 Loss2: 1.496322
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.109196 Loss1: 1.629151 Loss2: 1.480044
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.935533 Loss1: 1.450865 Loss2: 1.484668
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.750771 Loss1: 1.270092 Loss2: 1.480679
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.540968 Loss1: 2.412053 Loss2: 2.128915
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.725652 Loss1: 1.240457 Loss2: 1.485195
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.609894 Loss1: 1.108452 Loss2: 1.501442 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.654629 Loss1: 1.160382 Loss2: 1.494247 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.633253 Loss1: 1.133482 Loss2: 1.499771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.511408 Loss1: 0.991905 Loss2: 1.519503 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.717708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.705367 Loss1: 1.107839 Loss2: 1.597528 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.639421 Loss1: 1.039840 Loss2: 1.599582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.574704 Loss1: 0.970094 Loss2: 1.604610
+(DefaultActor pid=3764) >> Training accuracy: 0.743750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.528603 Loss1: 2.351422 Loss2: 2.177180
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.486575 Loss1: 1.842704 Loss2: 1.643870
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.111446 Loss1: 1.485322 Loss2: 1.626124
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.959508 Loss1: 1.336633 Loss2: 1.622875
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.872133 Loss1: 1.241848 Loss2: 1.630285
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.558950 Loss1: 2.437690 Loss2: 2.121261
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.258970 Loss1: 1.732766 Loss2: 1.526204
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.832588 Loss1: 1.200853 Loss2: 1.631735
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.908133 Loss1: 1.428487 Loss2: 1.479645
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.749254 Loss1: 1.101318 Loss2: 1.647936
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.833069 Loss1: 1.347240 Loss2: 1.485829
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.653289 Loss1: 0.999328 Loss2: 1.653961
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.586384 Loss1: 0.942631 Loss2: 1.643753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.552853 Loss1: 0.887318 Loss2: 1.665535 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.807904
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.476467 Loss1: 0.951292 Loss2: 1.525175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.378075 Loss1: 0.856825 Loss2: 1.521250 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.678125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.359638 Loss1: 2.332969 Loss2: 2.026669
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.245359 Loss1: 1.781622 Loss2: 1.463737
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.946366 Loss1: 1.507879 Loss2: 1.438487
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.736662 Loss1: 1.296564 Loss2: 1.440098
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.346007 Loss1: 2.334139 Loss2: 2.011869
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.226376 Loss1: 1.729705 Loss2: 1.496671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.825049 Loss1: 1.377520 Loss2: 1.447529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.708096 Loss1: 1.259269 Loss2: 1.448826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.561221 Loss1: 1.090369 Loss2: 1.470852 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.507038 Loss1: 1.040097 Loss2: 1.466941 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.780208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.479918 Loss1: 1.000105 Loss2: 1.479813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.364041 Loss1: 0.872284 Loss2: 1.491757 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.737500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.647880 Loss1: 2.609561 Loss2: 2.038319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.358250 Loss1: 1.818299 Loss2: 1.539951 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.118844 Loss1: 1.562569 Loss2: 1.556274
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.275499 Loss1: 2.189460 Loss2: 2.086040
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.216487 Loss1: 1.675641 Loss2: 1.540847 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.922107 Loss1: 1.413150 Loss2: 1.508956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.836659 Loss1: 1.273320 Loss2: 1.563340
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.699014 Loss1: 1.194485 Loss2: 1.504529
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.668041 Loss1: 1.095796 Loss2: 1.572244
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.704758 Loss1: 1.206813 Loss2: 1.497945
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.664698 Loss1: 1.082712 Loss2: 1.581986
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.541972 Loss1: 1.034512 Loss2: 1.507460
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.551437 Loss1: 0.968300 Loss2: 1.583136
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.427854 Loss1: 0.930540 Loss2: 1.497314
+(DefaultActor pid=3765) >> Training accuracy: 0.700195
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.402859 Loss1: 0.895212 Loss2: 1.507648
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.355787 Loss1: 0.845708 Loss2: 1.510078
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.319605 Loss1: 0.792746 Loss2: 1.526859
+(DefaultActor pid=3764) >> Training accuracy: 0.798958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.282206 Loss1: 2.284597 Loss2: 1.997609
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.215355 Loss1: 1.708721 Loss2: 1.506634
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.930934 Loss1: 1.457489 Loss2: 1.473445
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.359455 Loss1: 2.298170 Loss2: 2.061285
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.703414 Loss1: 1.239958 Loss2: 1.463457
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.317270 Loss1: 1.780427 Loss2: 1.536843
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.581000 Loss1: 1.108116 Loss2: 1.472884
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.003185 Loss1: 1.504096 Loss2: 1.499089
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.474117 Loss1: 0.989997 Loss2: 1.484120
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.475215 Loss1: 0.989804 Loss2: 1.485411 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.412051 Loss1: 0.921311 Loss2: 1.490740 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.378566 Loss1: 0.889951 Loss2: 1.488616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.308758 Loss1: 0.813249 Loss2: 1.495509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.790039
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.534781 Loss1: 0.992273 Loss2: 1.542508 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.741667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.520796 Loss1: 2.398175 Loss2: 2.122621 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.221549 Loss1: 1.682019 Loss2: 1.539531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 3.007557 Loss1: 1.467809 Loss2: 1.539748
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.280670 Loss1: 2.227209 Loss2: 2.053461
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.900950 Loss1: 1.337485 Loss2: 1.563466
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.281909 Loss1: 1.763119 Loss2: 1.518790
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.804072 Loss1: 1.244511 Loss2: 1.559562
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.950713 Loss1: 1.478849 Loss2: 1.471864
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.645122 Loss1: 1.069647 Loss2: 1.575476
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.735662 Loss1: 1.258335 Loss2: 1.477327
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.632617 Loss1: 1.060634 Loss2: 1.571983
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.608337 Loss1: 1.132351 Loss2: 1.475985
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.553033 Loss1: 0.967861 Loss2: 1.585173
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.510750 Loss1: 1.022409 Loss2: 1.488341
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.491117 Loss1: 0.906262 Loss2: 1.584855
+(DefaultActor pid=3765) >> Training accuracy: 0.713542
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.360202 Loss1: 0.894013 Loss2: 1.466189
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.384090 Loss1: 0.893193 Loss2: 1.490897
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.261360 Loss1: 0.760982 Loss2: 1.500378
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.271794 Loss1: 0.781009 Loss2: 1.490785
+(DefaultActor pid=3764) >> Training accuracy: 0.800000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.547841 Loss1: 2.517596 Loss2: 2.030245
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.393381 Loss1: 1.944802 Loss2: 1.448580
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.108843 Loss1: 1.664938 Loss2: 1.443904
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.865451 Loss1: 1.418186 Loss2: 1.447265
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.551572 Loss1: 2.477258 Loss2: 2.074314
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.394788 Loss1: 1.870096 Loss2: 1.524692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.134441 Loss1: 1.621150 Loss2: 1.513291 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.956117 Loss1: 1.432908 Loss2: 1.523209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.760028 Loss1: 1.242521 Loss2: 1.517507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.679714 Loss1: 1.149369 Loss2: 1.530345 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.711458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.346882 Loss1: 0.875583 Loss2: 1.471299
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.615292 Loss1: 1.073131 Loss2: 1.542161
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.607262 Loss1: 1.073884 Loss2: 1.533378
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.530612 Loss1: 0.984102 Loss2: 1.546510
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.477923 Loss1: 0.929762 Loss2: 1.548161
+(DefaultActor pid=3764) >> Training accuracy: 0.713542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.461617 Loss1: 2.440098 Loss2: 2.021519
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.335257 Loss1: 1.844401 Loss2: 1.490856
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.083886 Loss1: 1.612993 Loss2: 1.470894
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.813485 Loss1: 1.335747 Loss2: 1.477738
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.384487 Loss1: 2.309636 Loss2: 2.074851
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.345846 Loss1: 1.835786 Loss2: 1.510060 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.072428 Loss1: 1.548761 Loss2: 1.523667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.980537 Loss1: 1.455817 Loss2: 1.524720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.741255 Loss1: 1.208728 Loss2: 1.532527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.731959 Loss1: 1.194069 Loss2: 1.537890 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.769792
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.375691 Loss1: 0.858014 Loss2: 1.517677
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.676315 Loss1: 1.133753 Loss2: 1.542562
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.560924 Loss1: 1.008539 Loss2: 1.552385
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.484518 Loss1: 0.937382 Loss2: 1.547137
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.480923 Loss1: 0.925561 Loss2: 1.555362
+(DefaultActor pid=3764) >> Training accuracy: 0.705208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.535026 Loss1: 2.476644 Loss2: 2.058382
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.514520 Loss1: 1.984880 Loss2: 1.529640
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.209016 Loss1: 1.684656 Loss2: 1.524360
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.976332 Loss1: 1.463952 Loss2: 1.512380
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.721156 Loss1: 2.527065 Loss2: 2.194091
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.608670 Loss1: 1.991035 Loss2: 1.617635 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.248296 Loss1: 1.662502 Loss2: 1.585793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.047537 Loss1: 1.446584 Loss2: 1.600953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 3.008988 Loss1: 1.405235 Loss2: 1.603752 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.804316 Loss1: 1.201117 Loss2: 1.603199 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.748958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.496294 Loss1: 0.948926 Loss2: 1.547367
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.748802 Loss1: 1.149712 Loss2: 1.599089
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.748618 Loss1: 1.138745 Loss2: 1.609873
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.780007 Loss1: 1.133842 Loss2: 1.646165
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.526693 Loss1: 0.902394 Loss2: 1.624299
+(DefaultActor pid=3764) >> Training accuracy: 0.767708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.587109 Loss1: 2.545176 Loss2: 2.041933
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.493889 Loss1: 1.961382 Loss2: 1.532506
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.178274 Loss1: 1.672536 Loss2: 1.505738
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.908106 Loss1: 1.389702 Loss2: 1.518405
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.389937 Loss1: 2.330104 Loss2: 2.059832
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.335375 Loss1: 1.841391 Loss2: 1.493984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.940568 Loss1: 1.479298 Loss2: 1.461269 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.891598 Loss1: 1.430105 Loss2: 1.461493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.593763 Loss1: 1.039350 Loss2: 1.554414
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.759696 Loss1: 1.275374 Loss2: 1.484322
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.421780 Loss1: 0.879759 Loss2: 1.542021
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.574803 Loss1: 1.095967 Loss2: 1.478836
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.466478 Loss1: 0.984833 Loss2: 1.481645
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.426430 Loss1: 0.864404 Loss2: 1.562026
+(DefaultActor pid=3765) >> Training accuracy: 0.732292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.498234 Loss1: 0.981785 Loss2: 1.516448 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.784598
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.314563 Loss1: 2.269185 Loss2: 2.045378 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.029006 Loss1: 1.531173 Loss2: 1.497834 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.845491 Loss1: 1.348334 Loss2: 1.497157
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.376818 Loss1: 2.345307 Loss2: 2.031511
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.706491 Loss1: 1.207405 Loss2: 1.499086
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.299691 Loss1: 1.758142 Loss2: 1.541549
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.713804 Loss1: 1.195326 Loss2: 1.518478
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.029959 Loss1: 1.507665 Loss2: 1.522293
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.622444 Loss1: 1.104146 Loss2: 1.518298
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.811841 Loss1: 1.285750 Loss2: 1.526091
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.548968 Loss1: 1.016581 Loss2: 1.532388
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.649760 Loss1: 1.136429 Loss2: 1.513330
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.435814 Loss1: 0.900053 Loss2: 1.535762
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.554744 Loss1: 1.023084 Loss2: 1.531659
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.426772 Loss1: 0.896611 Loss2: 1.530161
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.530535 Loss1: 1.000071 Loss2: 1.530463
+(DefaultActor pid=3765) >> Training accuracy: 0.749023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.458964 Loss1: 0.913510 Loss2: 1.545454
+DEBUG flwr 2023-10-09 06:59:49,582 | server.py:236 | fit_round 30 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.406099 Loss1: 0.868087 Loss2: 1.538011
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.510045 Loss1: 0.959909 Loss2: 1.550135
+(DefaultActor pid=3764) >> Training accuracy: 0.691406
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.566852 Loss1: 2.510802 Loss2: 2.056051
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.493663 Loss1: 1.947088 Loss2: 1.546574
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.208920 Loss1: 1.698930 Loss2: 1.509990
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.974135 Loss1: 1.474203 Loss2: 1.499932
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.534829 Loss1: 2.486819 Loss2: 2.048011
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.545076 Loss1: 2.026859 Loss2: 1.518216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.190362 Loss1: 1.681775 Loss2: 1.508587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.890501 Loss1: 1.391106 Loss2: 1.499395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.889683 Loss1: 1.374322 Loss2: 1.515360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.773353 Loss1: 1.234570 Loss2: 1.538783 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.631250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.652236 Loss1: 1.122174 Loss2: 1.530063 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.443871 Loss1: 0.906497 Loss2: 1.537375 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.761719
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.465362 Loss1: 1.916234 Loss2: 1.549128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.907763 Loss1: 1.386503 Loss2: 1.521260 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.664894 Loss1: 1.145962 Loss2: 1.518932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.525234 Loss1: 0.986176 Loss2: 1.539058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.527250 Loss1: 0.977240 Loss2: 1.550010 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.426585 Loss1: 0.861847 Loss2: 1.564738 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.400255 Loss1: 0.851621 Loss2: 1.548635 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.718750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.808156 Loss1: 1.301531 Loss2: 1.506625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.619453 Loss1: 1.088636 Loss2: 1.530817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.350708 Loss1: 2.331487 Loss2: 2.019221 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.733259
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.088518 Loss1: 1.648494 Loss2: 1.440024 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.690572 Loss1: 1.235903 Loss2: 1.454669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.588711 Loss1: 1.126245 Loss2: 1.462466
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.480731 Loss1: 2.452847 Loss2: 2.027884
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.475193 Loss1: 1.940489 Loss2: 1.534704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.132364 Loss1: 1.605177 Loss2: 1.527187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.980536 Loss1: 1.431693 Loss2: 1.548844 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.763542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.838466 Loss1: 1.299327 Loss2: 1.539139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.762664 Loss1: 1.196383 Loss2: 1.566282 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.541884 Loss1: 0.984278 Loss2: 1.557606 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.777083
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 06:59:49,582][flwr][DEBUG] - fit_round 30 received 50 results and 0 failures
+INFO flwr 2023-10-09 07:00:31,787 | server.py:125 | fit progress: (30, 2.813382360881891, {'accuracy': 0.3485}, 68939.56565338999)
+>> Test accuracy: 0.348500
+[2023-10-09 07:00:31,787][flwr][INFO] - fit progress: (30, 2.813382360881891, {'accuracy': 0.3485}, 68939.56565338999)
+DEBUG flwr 2023-10-09 07:00:31,787 | server.py:173 | evaluate_round 30: strategy sampled 50 clients (out of 50)
+[2023-10-09 07:00:31,787][flwr][DEBUG] - evaluate_round 30: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 07:09:35,722 | server.py:187 | evaluate_round 30 received 50 results and 0 failures
+[2023-10-09 07:09:35,722][flwr][DEBUG] - evaluate_round 30 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 07:09:35,722 | server.py:222 | fit_round 31: strategy sampled 50 clients (out of 50)
+[2023-10-09 07:09:35,722][flwr][DEBUG] - fit_round 31: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.386367 Loss1: 2.350478 Loss2: 2.035889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.053638 Loss1: 1.572814 Loss2: 1.480824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.829509 Loss1: 1.368429 Loss2: 1.461080
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.593356 Loss1: 2.536169 Loss2: 2.057187
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.701281 Loss1: 1.226000 Loss2: 1.475281
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.468189 Loss1: 1.978558 Loss2: 1.489631
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.151484 Loss1: 1.684710 Loss2: 1.466773
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.655647 Loss1: 1.179597 Loss2: 1.476050
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.938170 Loss1: 1.468556 Loss2: 1.469614
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.458786 Loss1: 0.976605 Loss2: 1.482181
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.754696 Loss1: 1.268391 Loss2: 1.486306
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.504259 Loss1: 1.010599 Loss2: 1.493660
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.705956 Loss1: 1.212679 Loss2: 1.493277
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.393418 Loss1: 0.881734 Loss2: 1.511684
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.343655 Loss1: 0.844867 Loss2: 1.498788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.736458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.505238 Loss1: 0.991468 Loss2: 1.513770 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.726562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.311514 Loss1: 2.276111 Loss2: 2.035403 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.955564 Loss1: 1.501227 Loss2: 1.454338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.768968 Loss1: 1.290093 Loss2: 1.478876
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.312017 Loss1: 2.385259 Loss2: 1.926758
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.262147 Loss1: 1.814410 Loss2: 1.447737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.884532 Loss1: 1.470396 Loss2: 1.414136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.802576 Loss1: 1.370515 Loss2: 1.432061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.630642 Loss1: 1.184771 Loss2: 1.445872 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.570289 Loss1: 1.114493 Loss2: 1.455796 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.787500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.374012 Loss1: 0.924373 Loss2: 1.449639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.204837 Loss1: 0.749201 Loss2: 1.455636 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.748047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.618468 Loss1: 2.300313 Loss2: 2.318155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.052315 Loss1: 1.434781 Loss2: 1.617534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.645387 Loss1: 1.025739 Loss2: 1.619648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.544901 Loss1: 0.914092 Loss2: 1.630810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.101532 Loss1: 1.622607 Loss2: 1.478925
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.484739 Loss1: 0.854816 Loss2: 1.629922
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.507032 Loss1: 0.865727 Loss2: 1.641305
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.840174 Loss1: 1.401294 Loss2: 1.438880
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.707501 Loss1: 1.266240 Loss2: 1.441261 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.806490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.575145 Loss1: 1.111728 Loss2: 1.463417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.381179 Loss1: 0.921368 Loss2: 1.459811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.303107 Loss1: 0.838933 Loss2: 1.464174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.289030 Loss1: 0.798305 Loss2: 1.490724 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.772917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.923206 Loss1: 1.485688 Loss2: 1.437518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.613505 Loss1: 1.155203 Loss2: 1.458302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.518898 Loss1: 1.057214 Loss2: 1.461684
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.362617 Loss1: 2.258495 Loss2: 2.104122
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.276070 Loss1: 1.732604 Loss2: 1.543466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.936118 Loss1: 1.416292 Loss2: 1.519826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.698338 Loss1: 1.170628 Loss2: 1.527710 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.700000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.571899 Loss1: 1.042749 Loss2: 1.529150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.520304 Loss1: 0.984968 Loss2: 1.535335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.361352 Loss1: 0.813504 Loss2: 1.547848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.319435 Loss1: 1.789034 Loss2: 1.530401 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.708008
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.853247 Loss1: 1.343290 Loss2: 1.509957 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.560247 Loss1: 1.040731 Loss2: 1.519515 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.528357 Loss1: 1.009722 Loss2: 1.518635
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.432114 Loss1: 2.304821 Loss2: 2.127293
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.453443 Loss1: 0.913846 Loss2: 1.539597
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.359615 Loss1: 1.829359 Loss2: 1.530256
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.415967 Loss1: 0.879351 Loss2: 1.536616
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.036711 Loss1: 1.524455 Loss2: 1.512256
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.409825 Loss1: 0.860737 Loss2: 1.549089
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.826297 Loss1: 1.309893 Loss2: 1.516403
+(DefaultActor pid=3765) >> Training accuracy: 0.694792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.730638 Loss1: 1.203545 Loss2: 1.527092
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.569486 Loss1: 1.034054 Loss2: 1.535433
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.475656 Loss1: 0.932911 Loss2: 1.542744
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.484156 Loss1: 0.936995 Loss2: 1.547160
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.467052 Loss1: 0.901276 Loss2: 1.565776
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.475201 Loss1: 2.390354 Loss2: 2.084846
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.453029 Loss1: 0.890702 Loss2: 1.562326
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.255929 Loss1: 1.761451 Loss2: 1.494478
+(DefaultActor pid=3764) >> Training accuracy: 0.764583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.056362 Loss1: 1.558835 Loss2: 1.497527
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.937592 Loss1: 1.438248 Loss2: 1.499344
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.705762 Loss1: 1.212764 Loss2: 1.492998
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.606246 Loss1: 1.099078 Loss2: 1.507169
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.493565 Loss1: 2.359102 Loss2: 2.134463
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.587435 Loss1: 1.062171 Loss2: 1.525264
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.535359 Loss1: 1.004115 Loss2: 1.531244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.429332 Loss1: 0.896878 Loss2: 1.532454 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.450144 Loss1: 0.927455 Loss2: 1.522689 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.780208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.441222 Loss1: 0.931877 Loss2: 1.509344 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.384844 Loss1: 0.858369 Loss2: 1.526474 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.792067
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.337555 Loss1: 0.802315 Loss2: 1.535240
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.318293 Loss1: 2.287372 Loss2: 2.030921
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.168922 Loss1: 1.688489 Loss2: 1.480433
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.941778 Loss1: 1.453255 Loss2: 1.488523
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.768722 Loss1: 1.290011 Loss2: 1.478711
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.434128 Loss1: 2.311652 Loss2: 2.122477
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.659948 Loss1: 1.173150 Loss2: 1.486798
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.231629 Loss1: 1.679300 Loss2: 1.552328
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.487670 Loss1: 0.997637 Loss2: 1.490033
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.532570 Loss1: 1.018342 Loss2: 1.514228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.457662 Loss1: 0.953470 Loss2: 1.504192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.438714 Loss1: 0.924667 Loss2: 1.514047 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.333323 Loss1: 0.814728 Loss2: 1.518595 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.687500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.378621 Loss1: 0.816984 Loss2: 1.561637 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.796875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.423593 Loss1: 2.439198 Loss2: 1.984395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.052994 Loss1: 1.615792 Loss2: 1.437202 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.812050 Loss1: 1.371082 Loss2: 1.440968
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.386801 Loss1: 2.258507 Loss2: 2.128294
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.394497 Loss1: 1.819071 Loss2: 1.575426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.988268 Loss1: 1.451649 Loss2: 1.536619 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.783766 Loss1: 1.268260 Loss2: 1.515506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.541605 Loss1: 1.026250 Loss2: 1.515355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.542438 Loss1: 1.028616 Loss2: 1.513821 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.773958
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.246129 Loss1: 0.794215 Loss2: 1.451914
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.442054 Loss1: 0.929888 Loss2: 1.512166
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.247581 Loss1: 0.724585 Loss2: 1.522995
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.158239 Loss1: 0.650281 Loss2: 1.507958
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.257661 Loss1: 0.741372 Loss2: 1.516290
+(DefaultActor pid=3764) >> Training accuracy: 0.791667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.384295 Loss1: 2.355806 Loss2: 2.028490
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.367956 Loss1: 1.902942 Loss2: 1.465014
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.091639 Loss1: 1.633378 Loss2: 1.458261
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.842056 Loss1: 1.378488 Loss2: 1.463569
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.516516 Loss1: 2.421833 Loss2: 2.094683
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.424747 Loss1: 1.863203 Loss2: 1.561544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.136173 Loss1: 1.600377 Loss2: 1.535796 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.938499 Loss1: 1.399669 Loss2: 1.538829 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.846291 Loss1: 1.294757 Loss2: 1.551534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.773212 Loss1: 1.210449 Loss2: 1.562762 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.781250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.623918 Loss1: 1.067468 Loss2: 1.556450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.464158 Loss1: 0.895271 Loss2: 1.568887 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.717708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.482283 Loss1: 2.473114 Loss2: 2.009169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.088252 Loss1: 1.626725 Loss2: 1.461527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.240282 Loss1: 2.358041 Loss2: 1.882242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.234814 Loss1: 1.815913 Loss2: 1.418901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.852052 Loss1: 1.449232 Loss2: 1.402819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.559481 Loss1: 1.168739 Loss2: 1.390741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.503437 Loss1: 1.100539 Loss2: 1.402898 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.433009 Loss1: 0.921671 Loss2: 1.511338 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.750000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.321063 Loss1: 0.888891 Loss2: 1.432172 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.140027 Loss1: 0.725595 Loss2: 1.414432 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.809570
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.225157 Loss1: 1.723094 Loss2: 1.502063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.665671 Loss1: 1.178971 Loss2: 1.486700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.616306 Loss1: 1.122403 Loss2: 1.493903
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.416090 Loss1: 2.290999 Loss2: 2.125090
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.222423 Loss1: 1.688968 Loss2: 1.533455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.935995 Loss1: 1.422941 Loss2: 1.513054 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.663587 Loss1: 1.163729 Loss2: 1.499858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.554200 Loss1: 1.056305 Loss2: 1.497895 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.790625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.207021 Loss1: 0.691094 Loss2: 1.515927
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.541951 Loss1: 1.027804 Loss2: 1.514147
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.439947 Loss1: 0.921462 Loss2: 1.518484
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.343050 Loss1: 0.811289 Loss2: 1.531761
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.289924 Loss1: 0.773877 Loss2: 1.516048
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.246636 Loss1: 0.709825 Loss2: 1.536811
+(DefaultActor pid=3764) >> Training accuracy: 0.813542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.271414 Loss1: 2.165826 Loss2: 2.105588
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.234581 Loss1: 1.700189 Loss2: 1.534393
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.840710 Loss1: 1.331128 Loss2: 1.509583
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.751359 Loss1: 1.254529 Loss2: 1.496830
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.660717 Loss1: 1.147585 Loss2: 1.513133
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.528846 Loss1: 2.446644 Loss2: 2.082202
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.386500 Loss1: 1.851196 Loss2: 1.535305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.085275 Loss1: 1.576707 Loss2: 1.508568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.939441 Loss1: 1.425222 Loss2: 1.514219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.735837 Loss1: 1.225929 Loss2: 1.509908 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.787500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.751927 Loss1: 1.235965 Loss2: 1.515962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.411345 Loss1: 0.882657 Loss2: 1.528688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.490330 Loss1: 0.947550 Loss2: 1.542780 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.755208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.394928 Loss1: 1.889938 Loss2: 1.504991 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.928941 Loss1: 1.448161 Loss2: 1.480780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.785947 Loss1: 1.290295 Loss2: 1.495652
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.566613 Loss1: 2.389061 Loss2: 2.177552
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.286302 Loss1: 1.766775 Loss2: 1.519527
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.661641 Loss1: 1.155174 Loss2: 1.506467
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.956345 Loss1: 1.467815 Loss2: 1.488530
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.412040 Loss1: 0.900246 Loss2: 1.511793 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.473520 Loss1: 0.960511 Loss2: 1.513009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.548369 Loss1: 1.011158 Loss2: 1.537211 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.766667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.431047 Loss1: 0.887802 Loss2: 1.543244 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.796875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.458712 Loss1: 2.296886 Loss2: 2.161826 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.995972 Loss1: 1.463812 Loss2: 1.532160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.963869 Loss1: 1.423757 Loss2: 1.540112
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.448322 Loss1: 2.386884 Loss2: 2.061438
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.313464 Loss1: 1.807560 Loss2: 1.505904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.979196 Loss1: 1.495216 Loss2: 1.483980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.713180 Loss1: 1.233180 Loss2: 1.480000 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.677671 Loss1: 1.184869 Loss2: 1.492803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.736380 Loss1: 1.230667 Loss2: 1.505713 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.809375
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.376482 Loss1: 0.814491 Loss2: 1.561991
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.564104 Loss1: 1.042602 Loss2: 1.521502
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.477284 Loss1: 0.960989 Loss2: 1.516295
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.468429 Loss1: 0.934056 Loss2: 1.534373
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.412050 Loss1: 0.872200 Loss2: 1.539851
+(DefaultActor pid=3764) >> Training accuracy: 0.784375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.422984 Loss1: 2.289618 Loss2: 2.133365
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.379575 Loss1: 1.826406 Loss2: 1.553169
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.172639 Loss1: 1.604686 Loss2: 1.567953
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.850094 Loss1: 1.285854 Loss2: 1.564240
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.595733 Loss1: 2.530717 Loss2: 2.065016
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.433125 Loss1: 1.873692 Loss2: 1.559432 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.122996 Loss1: 1.593503 Loss2: 1.529492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.001870 Loss1: 1.461774 Loss2: 1.540096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.802621 Loss1: 1.243074 Loss2: 1.559546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.679935 Loss1: 1.131457 Loss2: 1.548478 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.750000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.416805 Loss1: 0.824504 Loss2: 1.592300
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.719740 Loss1: 1.137916 Loss2: 1.581824
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.637119 Loss1: 1.064167 Loss2: 1.572952
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.537611 Loss1: 0.959794 Loss2: 1.577817
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.438568 Loss1: 0.861347 Loss2: 1.577221
+(DefaultActor pid=3764) >> Training accuracy: 0.730469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.470866 Loss1: 1.915856 Loss2: 1.555010 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.870138 Loss1: 1.322216 Loss2: 1.547923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.308120 Loss1: 2.299385 Loss2: 2.008735
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.744762 Loss1: 1.199324 Loss2: 1.545438
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.326239 Loss1: 1.860215 Loss2: 1.466024
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.698362 Loss1: 1.138087 Loss2: 1.560275
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.961262 Loss1: 1.492018 Loss2: 1.469243
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.615577 Loss1: 1.062364 Loss2: 1.553213
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.544573 Loss1: 0.978647 Loss2: 1.565926
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.736020 Loss1: 1.285901 Loss2: 1.450119
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.461249 Loss1: 0.894154 Loss2: 1.567095
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.610129 Loss1: 1.148750 Loss2: 1.461379
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.428992 Loss1: 0.863319 Loss2: 1.565673
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.668349 Loss1: 1.195254 Loss2: 1.473095
+(DefaultActor pid=3765) >> Training accuracy: 0.780208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.450060 Loss1: 0.973634 Loss2: 1.476426
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.388942 Loss1: 0.917838 Loss2: 1.471104
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.452953 Loss1: 0.969514 Loss2: 1.483439
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.349036 Loss1: 0.862989 Loss2: 1.486047
+(DefaultActor pid=3764) >> Training accuracy: 0.751953
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.420306 Loss1: 2.370042 Loss2: 2.050263
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.391161 Loss1: 1.916680 Loss2: 1.474481
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.088575 Loss1: 1.612723 Loss2: 1.475852
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.952218 Loss1: 1.465682 Loss2: 1.486536
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.850809 Loss1: 1.348075 Loss2: 1.502734
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.826838 Loss1: 2.658516 Loss2: 2.168321
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.642920 Loss1: 2.029301 Loss2: 1.613619 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.275646 Loss1: 1.674992 Loss2: 1.600655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.033248 Loss1: 1.426021 Loss2: 1.607227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.457060 Loss1: 0.934444 Loss2: 1.522616
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.957510 Loss1: 1.360304 Loss2: 1.597206
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.384305 Loss1: 0.864815 Loss2: 1.519490
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.810605 Loss1: 1.182508 Loss2: 1.628096
+(DefaultActor pid=3765) >> Training accuracy: 0.778125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.679262 Loss1: 1.056751 Loss2: 1.622510
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.580855 Loss1: 0.961983 Loss2: 1.618872
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.441082 Loss1: 0.802457 Loss2: 1.638625
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.462063 Loss1: 0.833054 Loss2: 1.629009
+(DefaultActor pid=3764) >> Training accuracy: 0.800223
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.573121 Loss1: 2.517812 Loss2: 2.055309
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.490495 Loss1: 1.979142 Loss2: 1.511352
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.178845 Loss1: 1.698200 Loss2: 1.480645
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.880541 Loss1: 1.372167 Loss2: 1.508374
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.482581 Loss1: 2.412514 Loss2: 2.070067
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.423938 Loss1: 1.937927 Loss2: 1.486011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.183242 Loss1: 1.715304 Loss2: 1.467938 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.892319 Loss1: 1.417533 Loss2: 1.474785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.782609 Loss1: 1.297124 Loss2: 1.485484 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.722420 Loss1: 1.240675 Loss2: 1.481745 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.797917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.479634 Loss1: 0.973317 Loss2: 1.506316 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.402031 Loss1: 0.903391 Loss2: 1.498640 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.710417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.522647 Loss1: 2.524908 Loss2: 1.997738
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.433729 Loss1: 1.958079 Loss2: 1.475649
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.077219 Loss1: 1.634534 Loss2: 1.442686
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.946594 Loss1: 1.494265 Loss2: 1.452329
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.314721 Loss1: 2.188104 Loss2: 2.126617
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.334552 Loss1: 1.762473 Loss2: 1.572079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.010372 Loss1: 1.465919 Loss2: 1.544453 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.894159 Loss1: 1.340320 Loss2: 1.553839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.620959 Loss1: 1.140444 Loss2: 1.480515
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.613194 Loss1: 1.067908 Loss2: 1.545286
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.524489 Loss1: 1.034391 Loss2: 1.490098
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.469864 Loss1: 0.938897 Loss2: 1.530967
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.454294 Loss1: 0.968302 Loss2: 1.485992
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.452180 Loss1: 0.906910 Loss2: 1.545270
+(DefaultActor pid=3765) >> Training accuracy: 0.686523
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.414847 Loss1: 0.866022 Loss2: 1.548826
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.425553 Loss1: 0.875266 Loss2: 1.550287
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.372651 Loss1: 0.803491 Loss2: 1.569159
+(DefaultActor pid=3764) >> Training accuracy: 0.804167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.569056 Loss1: 2.513035 Loss2: 2.056021
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.449259 Loss1: 1.934865 Loss2: 1.514394
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.224074 Loss1: 1.735322 Loss2: 1.488752
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.936897 Loss1: 1.450375 Loss2: 1.486522
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.553742 Loss1: 2.397101 Loss2: 2.156641
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.813524 Loss1: 1.325495 Loss2: 1.488029
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.467561 Loss1: 1.868091 Loss2: 1.599470
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.635165 Loss1: 1.148838 Loss2: 1.486327
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.089522 Loss1: 1.514519 Loss2: 1.575003
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.561071 Loss1: 1.057813 Loss2: 1.503257
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.927499 Loss1: 1.338624 Loss2: 1.588875
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.553373 Loss1: 1.052967 Loss2: 1.500406
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.791183 Loss1: 1.220456 Loss2: 1.570726
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.407221 Loss1: 0.909552 Loss2: 1.497669
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.634957 Loss1: 1.054746 Loss2: 1.580212
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.428580 Loss1: 0.925750 Loss2: 1.502830
+(DefaultActor pid=3765) >> Training accuracy: 0.760417
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.627720 Loss1: 1.035196 Loss2: 1.592524
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.480570 Loss1: 0.888913 Loss2: 1.591657
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.575276 Loss1: 0.977163 Loss2: 1.598113
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.492750 Loss1: 0.871932 Loss2: 1.620818
+(DefaultActor pid=3764) >> Training accuracy: 0.752083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.513213 Loss1: 2.405217 Loss2: 2.107995
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.398300 Loss1: 1.844468 Loss2: 1.553831
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.140265 Loss1: 1.609181 Loss2: 1.531083
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.964918 Loss1: 1.424543 Loss2: 1.540375
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.157508 Loss1: 2.119817 Loss2: 2.037691
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.807273 Loss1: 1.261648 Loss2: 1.545625
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.166947 Loss1: 1.665495 Loss2: 1.501452
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.717630 Loss1: 1.159186 Loss2: 1.558444
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.887562 Loss1: 1.399968 Loss2: 1.487594
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.536543 Loss1: 0.985905 Loss2: 1.550638
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.614643 Loss1: 1.122178 Loss2: 1.492464
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.537043 Loss1: 0.981590 Loss2: 1.555453
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.569715 Loss1: 1.080141 Loss2: 1.489574
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.355824 Loss1: 0.784336 Loss2: 1.571488
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.581140 Loss1: 1.074191 Loss2: 1.506948
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.364518 Loss1: 0.810500 Loss2: 1.554018
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.526693 Loss1: 1.008709 Loss2: 1.517984
+(DefaultActor pid=3765) >> Training accuracy: 0.788542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.476187 Loss1: 0.962682 Loss2: 1.513505
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.285824 Loss1: 0.768801 Loss2: 1.517023
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.251097 Loss1: 0.738851 Loss2: 1.512246
+(DefaultActor pid=3764) >> Training accuracy: 0.794792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.573516 Loss1: 2.357962 Loss2: 2.215554
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.409488 Loss1: 1.789509 Loss2: 1.619979
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.108154 Loss1: 1.530064 Loss2: 1.578090
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.862254 Loss1: 1.275903 Loss2: 1.586351
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.577045 Loss1: 2.477430 Loss2: 2.099615
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.427993 Loss1: 1.872670 Loss2: 1.555324 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.118364 Loss1: 1.590757 Loss2: 1.527608 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.507622 Loss1: 0.893549 Loss2: 1.614074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.547200 Loss1: 0.921282 Loss2: 1.625918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.479766 Loss1: 0.855602 Loss2: 1.624164 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.786830
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.558123 Loss1: 1.008450 Loss2: 1.549673 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.458095 Loss1: 0.886663 Loss2: 1.571432 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.770833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.315721 Loss1: 1.751394 Loss2: 1.564327 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.867238 Loss1: 1.321337 Loss2: 1.545900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.674495 Loss1: 1.118453 Loss2: 1.556042
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.582265 Loss1: 2.534895 Loss2: 2.047370
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.423505 Loss1: 1.896230 Loss2: 1.527274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.166550 Loss1: 1.664851 Loss2: 1.501699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.938625 Loss1: 1.433134 Loss2: 1.505491 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.741326 Loss1: 1.232483 Loss2: 1.508843 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.760417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.740235 Loss1: 1.215204 Loss2: 1.525031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.586812 Loss1: 1.049262 Loss2: 1.537550 [repeated 2x across cluster]
+DEBUG flwr 2023-10-09 07:38:25,396 | server.py:236 | fit_round 31 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.542377 Loss1: 0.977009 Loss2: 1.565367 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.739258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.963178 Loss1: 1.468204 Loss2: 1.494974 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.709087 Loss1: 1.216542 Loss2: 1.492545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.600682 Loss1: 1.090592 Loss2: 1.510090
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.408054 Loss1: 2.359103 Loss2: 2.048951
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.457989 Loss1: 0.949744 Loss2: 1.508245
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.366735 Loss1: 1.852646 Loss2: 1.514089
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.388427 Loss1: 0.880337 Loss2: 1.508091
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.105779 Loss1: 1.617533 Loss2: 1.488246
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.870747 Loss1: 1.386255 Loss2: 1.484491 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.760417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.314588 Loss1: 0.799370 Loss2: 1.515218
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.714327 Loss1: 1.205093 Loss2: 1.509234
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.668032 Loss1: 1.150480 Loss2: 1.517552
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.629249 Loss1: 1.109305 Loss2: 1.519944
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.510959 Loss1: 0.991061 Loss2: 1.519898
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.454163 Loss1: 0.928426 Loss2: 1.525737
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.477938 Loss1: 2.347618 Loss2: 2.130320
+(DefaultActor pid=3764) >> Training accuracy: 0.687500
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.569233 Loss1: 1.034036 Loss2: 1.535197
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.393047 Loss1: 1.818279 Loss2: 1.574768
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.062754 Loss1: 1.501067 Loss2: 1.561687
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.945989 Loss1: 1.370207 Loss2: 1.575782
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.868089 Loss1: 1.302624 Loss2: 1.565465
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.780574 Loss1: 1.200741 Loss2: 1.579833
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.484938 Loss1: 2.406784 Loss2: 2.078154
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.602634 Loss1: 1.011595 Loss2: 1.591038
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.443094 Loss1: 1.937131 Loss2: 1.505962
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.566378 Loss1: 0.986025 Loss2: 1.580353
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.996046 Loss1: 1.508415 Loss2: 1.487631
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.561619 Loss1: 0.971941 Loss2: 1.589677
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.822860 Loss1: 1.326850 Loss2: 1.496010
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.539951 Loss1: 0.949960 Loss2: 1.589991
+(DefaultActor pid=3765) >> Training accuracy: 0.751042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.606119 Loss1: 1.092192 Loss2: 1.513927 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.481346 Loss1: 0.956893 Loss2: 1.524453 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.326616 Loss1: 0.791823 Loss2: 1.534792 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.658333
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 07:38:25,396][flwr][DEBUG] - fit_round 31 received 50 results and 0 failures
+INFO flwr 2023-10-09 07:39:08,541 | server.py:125 | fit progress: (31, 2.7770677180335928, {'accuracy': 0.3589}, 71256.31940825199)
+>> Test accuracy: 0.358900
+[2023-10-09 07:39:08,541][flwr][INFO] - fit progress: (31, 2.7770677180335928, {'accuracy': 0.3589}, 71256.31940825199)
+DEBUG flwr 2023-10-09 07:39:08,541 | server.py:173 | evaluate_round 31: strategy sampled 50 clients (out of 50)
+[2023-10-09 07:39:08,541][flwr][DEBUG] - evaluate_round 31: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 07:48:15,164 | server.py:187 | evaluate_round 31 received 50 results and 0 failures
+[2023-10-09 07:48:15,164][flwr][DEBUG] - evaluate_round 31 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 07:48:15,164 | server.py:222 | fit_round 32: strategy sampled 50 clients (out of 50)
+[2023-10-09 07:48:15,164][flwr][DEBUG] - fit_round 32: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.404019 Loss1: 2.383968 Loss2: 2.020051
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.352896 Loss1: 1.885405 Loss2: 1.467491
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.060808 Loss1: 1.599234 Loss2: 1.461574
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.881011 Loss1: 1.411356 Loss2: 1.469655
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.263204 Loss1: 2.184284 Loss2: 2.078920
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.119162 Loss1: 1.586200 Loss2: 1.532962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.891673 Loss1: 1.381549 Loss2: 1.510125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.645576 Loss1: 1.139120 Loss2: 1.506456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.602054 Loss1: 1.082727 Loss2: 1.519327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.545817 Loss1: 1.021292 Loss2: 1.524525 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.742708
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.414475 Loss1: 0.897701 Loss2: 1.516774
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.477138 Loss1: 0.948834 Loss2: 1.528305
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.353092 Loss1: 0.821022 Loss2: 1.532070
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.272598 Loss1: 0.740819 Loss2: 1.531778
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.280416 Loss1: 0.756314 Loss2: 1.524101
+(DefaultActor pid=3764) >> Training accuracy: 0.741667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.428863 Loss1: 2.422046 Loss2: 2.006816
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.252062 Loss1: 1.828564 Loss2: 1.423498
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.915699 Loss1: 1.502876 Loss2: 1.412824
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.801738 Loss1: 1.384102 Loss2: 1.417636
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.448062 Loss1: 2.380597 Loss2: 2.067466
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.287583 Loss1: 1.793374 Loss2: 1.494208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.117207 Loss1: 1.634275 Loss2: 1.482932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.947015 Loss1: 1.433121 Loss2: 1.513894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.733488 Loss1: 1.234039 Loss2: 1.499449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.609055 Loss1: 1.106444 Loss2: 1.502611 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.753125
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.252049 Loss1: 0.798910 Loss2: 1.453139
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.499295 Loss1: 0.993542 Loss2: 1.505753
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.415445 Loss1: 0.908827 Loss2: 1.506618
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.405508 Loss1: 0.879029 Loss2: 1.526479
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.373371 Loss1: 0.833496 Loss2: 1.539875
+(DefaultActor pid=3764) >> Training accuracy: 0.738542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.495524 Loss1: 2.428205 Loss2: 2.067319
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.311596 Loss1: 1.764476 Loss2: 1.547120
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.035924 Loss1: 1.534730 Loss2: 1.501193
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.864653 Loss1: 1.333756 Loss2: 1.530897
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.532396 Loss1: 2.411087 Loss2: 2.121309
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.386587 Loss1: 1.825838 Loss2: 1.560749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.112635 Loss1: 1.552855 Loss2: 1.559779 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 3.026463 Loss1: 1.453253 Loss2: 1.573209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.971089 Loss1: 1.354728 Loss2: 1.616361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.794516 Loss1: 1.196443 Loss2: 1.598073 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.721875
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.368061 Loss1: 0.823126 Loss2: 1.544935
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.559432 Loss1: 0.986491 Loss2: 1.572941
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.478598 Loss1: 0.901521 Loss2: 1.577078
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.323189 Loss1: 0.730665 Loss2: 1.592523
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.324183 Loss1: 0.726880 Loss2: 1.597303
+(DefaultActor pid=3764) >> Training accuracy: 0.750000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.273262 Loss1: 2.177638 Loss2: 2.095624
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.350050 Loss1: 1.809853 Loss2: 1.540197
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.017798 Loss1: 1.480600 Loss2: 1.537198
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.865371 Loss1: 1.318434 Loss2: 1.546937
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.346630 Loss1: 2.298508 Loss2: 2.048122
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.701992 Loss1: 1.158452 Loss2: 1.543540
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.268912 Loss1: 1.780886 Loss2: 1.488026
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.598852 Loss1: 1.043183 Loss2: 1.555669
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.915126 Loss1: 1.443572 Loss2: 1.471554
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.733513 Loss1: 1.255108 Loss2: 1.478405
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.462783 Loss1: 0.899687 Loss2: 1.563096
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.664055 Loss1: 1.175450 Loss2: 1.488605
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.528191 Loss1: 0.962262 Loss2: 1.565929
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.573132 Loss1: 1.072452 Loss2: 1.500680
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.496297 Loss1: 0.909558 Loss2: 1.586740
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.489252 Loss1: 0.977495 Loss2: 1.511756
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.403203 Loss1: 0.835192 Loss2: 1.568012
+(DefaultActor pid=3765) >> Training accuracy: 0.769531
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.382630 Loss1: 0.848915 Loss2: 1.533715 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.820833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.424715 Loss1: 2.315948 Loss2: 2.108767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.061613 Loss1: 1.540379 Loss2: 1.521235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.883276 Loss1: 1.358999 Loss2: 1.524276
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.437647 Loss1: 2.268525 Loss2: 2.169122
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.664168 Loss1: 1.135822 Loss2: 1.528346
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.274423 Loss1: 1.723871 Loss2: 1.550552
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.016782 Loss1: 1.481581 Loss2: 1.535202
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.611786 Loss1: 1.077079 Loss2: 1.534707
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.904964 Loss1: 1.357318 Loss2: 1.547646
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.535853 Loss1: 0.976412 Loss2: 1.559440
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.669095 Loss1: 1.112425 Loss2: 1.556670
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.528381 Loss1: 0.964617 Loss2: 1.563764
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.501628 Loss1: 0.937142 Loss2: 1.564486 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.357475 Loss1: 0.787897 Loss2: 1.569578 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.775000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.361838 Loss1: 0.788511 Loss2: 1.573327 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.776786
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.080883 Loss1: 2.111343 Loss2: 1.969540 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.758636 Loss1: 1.368978 Loss2: 1.389657 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.576900 Loss1: 1.163478 Loss2: 1.413421
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.357083 Loss1: 2.262950 Loss2: 2.094133
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.428876 Loss1: 1.033352 Loss2: 1.395525
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.298737 Loss1: 1.730461 Loss2: 1.568277
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.316966 Loss1: 0.913632 Loss2: 1.403334
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.102321 Loss1: 1.552463 Loss2: 1.549858
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.163014 Loss1: 0.751964 Loss2: 1.411050
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.926869 Loss1: 1.366738 Loss2: 1.560131
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.077214 Loss1: 0.678250 Loss2: 1.398964
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.802465 Loss1: 1.242221 Loss2: 1.560243
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.124242 Loss1: 0.713444 Loss2: 1.410798
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.637835 Loss1: 1.059620 Loss2: 1.578216
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.167169 Loss1: 0.742832 Loss2: 1.424337
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.619504 Loss1: 1.052440 Loss2: 1.567064
+(DefaultActor pid=3765) >> Training accuracy: 0.768750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.512875 Loss1: 0.921337 Loss2: 1.591538
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.501084 Loss1: 0.912134 Loss2: 1.588950
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.458398 Loss1: 0.866935 Loss2: 1.591463
+(DefaultActor pid=3764) >> Training accuracy: 0.713542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.445464 Loss1: 2.423129 Loss2: 2.022335
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.360349 Loss1: 1.827467 Loss2: 1.532882
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.126544 Loss1: 1.608793 Loss2: 1.517751
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.389700 Loss1: 2.250704 Loss2: 2.138996
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.868233 Loss1: 1.327839 Loss2: 1.540394
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.195749 Loss1: 1.661313 Loss2: 1.534436
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.847041 Loss1: 1.307963 Loss2: 1.539078
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.737989 Loss1: 1.177935 Loss2: 1.560054 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.689702 Loss1: 1.127772 Loss2: 1.561930 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.680468 Loss1: 1.118978 Loss2: 1.561489 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.543154 Loss1: 0.970325 Loss2: 1.572829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.495918 Loss1: 0.926879 Loss2: 1.569039 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.746094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.250005 Loss1: 0.691763 Loss2: 1.558242 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.810417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.250209 Loss1: 2.199197 Loss2: 2.051012 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.888567 Loss1: 1.391072 Loss2: 1.497495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.793557 Loss1: 1.272950 Loss2: 1.520607
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.369852 Loss1: 2.317239 Loss2: 2.052614
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.291249 Loss1: 1.802557 Loss2: 1.488692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.038688 Loss1: 1.550045 Loss2: 1.488643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.801259 Loss1: 1.298825 Loss2: 1.502434 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.731215 Loss1: 1.222621 Loss2: 1.508594 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.644790 Loss1: 1.130623 Loss2: 1.514167 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.736458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.438021 Loss1: 0.937896 Loss2: 1.500125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.311984 Loss1: 0.793211 Loss2: 1.518773 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.814583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.378421 Loss1: 2.363567 Loss2: 2.014854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.026422 Loss1: 1.566956 Loss2: 1.459466 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.826295 Loss1: 1.351277 Loss2: 1.475018
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.362628 Loss1: 2.291315 Loss2: 2.071313
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.222740 Loss1: 1.690694 Loss2: 1.532045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.938031 Loss1: 1.430614 Loss2: 1.507417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.849076 Loss1: 1.328244 Loss2: 1.520832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.534323 Loss1: 1.022833 Loss2: 1.511490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.586043 Loss1: 1.059532 Loss2: 1.526511 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.785417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.280751 Loss1: 0.781504 Loss2: 1.499247
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.491501 Loss1: 0.944936 Loss2: 1.546566
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.391277 Loss1: 0.850005 Loss2: 1.541273
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.298949 Loss1: 0.745444 Loss2: 1.553505
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.352802 Loss1: 0.810237 Loss2: 1.542565
+(DefaultActor pid=3764) >> Training accuracy: 0.805208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.330342 Loss1: 2.290225 Loss2: 2.040117
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.308504 Loss1: 1.810982 Loss2: 1.497522
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.992914 Loss1: 1.504479 Loss2: 1.488435
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.794985 Loss1: 1.302892 Loss2: 1.492093
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.683792 Loss1: 2.552687 Loss2: 2.131105
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.537392 Loss1: 2.011158 Loss2: 1.526234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.171204 Loss1: 1.674122 Loss2: 1.497081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.843355 Loss1: 1.326483 Loss2: 1.516871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.569055 Loss1: 1.057610 Loss2: 1.511445
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.728791 Loss1: 1.213991 Loss2: 1.514800
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.432092 Loss1: 0.915753 Loss2: 1.516339
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.611851 Loss1: 1.091206 Loss2: 1.520645
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.338063 Loss1: 0.820244 Loss2: 1.517819
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.644564 Loss1: 1.100369 Loss2: 1.544195
+(DefaultActor pid=3765) >> Training accuracy: 0.692708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.522389 Loss1: 0.985168 Loss2: 1.537222
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.460228 Loss1: 0.922968 Loss2: 1.537260
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.344734 Loss1: 0.799987 Loss2: 1.544746
+(DefaultActor pid=3764) >> Training accuracy: 0.758929
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.436614 Loss1: 2.406762 Loss2: 2.029852
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.203637 Loss1: 1.709338 Loss2: 1.494299
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.931011 Loss1: 1.454201 Loss2: 1.476810
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.885584 Loss1: 1.381629 Loss2: 1.503955
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.406447 Loss1: 2.283303 Loss2: 2.123144
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.649005 Loss1: 1.161884 Loss2: 1.487121
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.212759 Loss1: 1.688219 Loss2: 1.524540
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.539884 Loss1: 1.060100 Loss2: 1.479784
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.991596 Loss1: 1.498778 Loss2: 1.492818
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.510451 Loss1: 1.012658 Loss2: 1.497794
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.720216 Loss1: 1.227132 Loss2: 1.493083
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.451824 Loss1: 0.950946 Loss2: 1.500878
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.564860 Loss1: 1.077966 Loss2: 1.486894
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.372934 Loss1: 0.859299 Loss2: 1.513635
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.451912 Loss1: 0.958241 Loss2: 1.493671
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.362108 Loss1: 0.854092 Loss2: 1.508016
+(DefaultActor pid=3765) >> Training accuracy: 0.795833
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.344401 Loss1: 0.849003 Loss2: 1.495398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.457893 Loss1: 0.943032 Loss2: 1.514862
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.357492 Loss1: 0.826491 Loss2: 1.531001
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.254158 Loss1: 0.729953 Loss2: 1.524205
+(DefaultActor pid=3764) >> Training accuracy: 0.794792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.183288 Loss1: 2.104032 Loss2: 2.079256
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.178721 Loss1: 1.656307 Loss2: 1.522414
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.913000 Loss1: 1.403613 Loss2: 1.509387
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.682801 Loss1: 1.187258 Loss2: 1.495544
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.641235 Loss1: 2.341574 Loss2: 2.299661
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.442479 Loss1: 1.848101 Loss2: 1.594378 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.119093 Loss1: 1.568338 Loss2: 1.550755
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.400786 Loss1: 0.905873 Loss2: 1.494913
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.309194 Loss1: 0.817677 Loss2: 1.491517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.279280 Loss1: 0.760503 Loss2: 1.518777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.216318 Loss1: 0.705893 Loss2: 1.510426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.487379 Loss1: 0.912054 Loss2: 1.575325 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.791667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.287052 Loss1: 0.716523 Loss2: 1.570529 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.712240
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.375375 Loss1: 2.360126 Loss2: 2.015250
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.320110 Loss1: 1.864945 Loss2: 1.455165
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.900689 Loss1: 1.463281 Loss2: 1.437407
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.791414 Loss1: 1.358933 Loss2: 1.432481
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.345179 Loss1: 2.266658 Loss2: 2.078521
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.268144 Loss1: 1.736978 Loss2: 1.531166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.073751 Loss1: 1.544926 Loss2: 1.528825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.818927 Loss1: 1.283614 Loss2: 1.535313 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.750070 Loss1: 1.215266 Loss2: 1.534804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.568372 Loss1: 1.036937 Loss2: 1.531436 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.798958
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.280881 Loss1: 0.816681 Loss2: 1.464200
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.450873 Loss1: 0.916380 Loss2: 1.534492
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.440331 Loss1: 0.897599 Loss2: 1.542732
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.486567 Loss1: 0.932040 Loss2: 1.554526
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.343171 Loss1: 0.797854 Loss2: 1.545316
+(DefaultActor pid=3764) >> Training accuracy: 0.844792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.269153 Loss1: 2.214228 Loss2: 2.054924
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.089719 Loss1: 1.610717 Loss2: 1.479001
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.771973 Loss1: 1.367397 Loss2: 1.404575
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.541561 Loss1: 1.137962 Loss2: 1.403599
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.431422 Loss1: 1.008010 Loss2: 1.423413
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.373642 Loss1: 0.946387 Loss2: 1.427255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.325312 Loss1: 0.895060 Loss2: 1.430252 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.306179 Loss1: 0.862840 Loss2: 1.443340 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.262467 Loss1: 0.819578 Loss2: 1.442889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.109924 Loss1: 0.674486 Loss2: 1.435438 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.781250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.259775 Loss1: 0.825827 Loss2: 1.433948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.154898 Loss1: 0.712231 Loss2: 1.442667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.236064 Loss1: 0.782323 Loss2: 1.453741
+(DefaultActor pid=3764) >> Training accuracy: 0.773958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.256138 Loss1: 2.098140 Loss2: 2.157998
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.170372 Loss1: 1.603258 Loss2: 1.567114
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.912746 Loss1: 1.378053 Loss2: 1.534693
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.718626 Loss1: 1.164166 Loss2: 1.554459
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.552992 Loss1: 1.002450 Loss2: 1.550541
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.404766 Loss1: 2.314014 Loss2: 2.090752
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.452093 Loss1: 0.900098 Loss2: 1.551995
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.397172 Loss1: 1.875279 Loss2: 1.521893
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.444940 Loss1: 0.896044 Loss2: 1.548896
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.052540 Loss1: 1.531666 Loss2: 1.520874
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.390372 Loss1: 0.828083 Loss2: 1.562289
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.912916 Loss1: 1.383820 Loss2: 1.529096
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.380529 Loss1: 0.810452 Loss2: 1.570076
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.742658 Loss1: 1.236161 Loss2: 1.506497
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.295832 Loss1: 0.716794 Loss2: 1.579038
+(DefaultActor pid=3765) >> Training accuracy: 0.789583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.674533 Loss1: 1.134234 Loss2: 1.540298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.475343 Loss1: 0.931301 Loss2: 1.544042 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.381076 Loss1: 0.835703 Loss2: 1.545373
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.399511 Loss1: 2.314161 Loss2: 2.085350
+(DefaultActor pid=3764) >> Training accuracy: 0.812500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.186301 Loss1: 1.680695 Loss2: 1.505606
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.971348 Loss1: 1.481483 Loss2: 1.489865
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.797723 Loss1: 1.279431 Loss2: 1.518292
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.634923 Loss1: 1.128970 Loss2: 1.505952
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.440358 Loss1: 2.345879 Loss2: 2.094479
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.477843 Loss1: 0.973162 Loss2: 1.504681
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.254402 Loss1: 1.706382 Loss2: 1.548019
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.409955 Loss1: 0.898587 Loss2: 1.511368
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.904117 Loss1: 1.396178 Loss2: 1.507939
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.376271 Loss1: 0.846060 Loss2: 1.530211
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.679932 Loss1: 1.183109 Loss2: 1.496823
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.385697 Loss1: 0.842800 Loss2: 1.542898
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.493111 Loss1: 0.984835 Loss2: 1.508276
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.319837 Loss1: 0.773757 Loss2: 1.546079
+(DefaultActor pid=3765) >> Training accuracy: 0.740625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.305438 Loss1: 0.804141 Loss2: 1.501296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.234384 Loss1: 0.722902 Loss2: 1.511482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.230780 Loss1: 0.709785 Loss2: 1.520996
+(DefaultActor pid=3764) >> Training accuracy: 0.847917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.483039 Loss1: 2.497024 Loss2: 1.986015
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.366304 Loss1: 1.883849 Loss2: 1.482455
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.102711 Loss1: 1.646262 Loss2: 1.456449
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.788237 Loss1: 1.312642 Loss2: 1.475595
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.693533 Loss1: 1.220919 Loss2: 1.472614
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.521751 Loss1: 2.467010 Loss2: 2.054741
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.366647 Loss1: 1.847819 Loss2: 1.518828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.079784 Loss1: 1.562862 Loss2: 1.516922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.888553 Loss1: 1.370772 Loss2: 1.517781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.737389 Loss1: 1.216957 Loss2: 1.520432 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.749023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.298911 Loss1: 0.811534 Loss2: 1.487378
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.609516 Loss1: 1.093828 Loss2: 1.515688
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.561323 Loss1: 1.040589 Loss2: 1.520734
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.625868 Loss1: 1.083175 Loss2: 1.542693
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.484832 Loss1: 0.933039 Loss2: 1.551793
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.337733 Loss1: 0.799158 Loss2: 1.538574
+(DefaultActor pid=3764) >> Training accuracy: 0.744141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.238238 Loss1: 2.211643 Loss2: 2.026595
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.284062 Loss1: 1.769372 Loss2: 1.514690
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.868026 Loss1: 1.370547 Loss2: 1.497479
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.611396 Loss1: 1.125196 Loss2: 1.486200
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.585225 Loss1: 1.094908 Loss2: 1.490317
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.535969 Loss1: 2.450299 Loss2: 2.085669
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.313959 Loss1: 1.768196 Loss2: 1.545763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.081176 Loss1: 1.556632 Loss2: 1.524544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.858151 Loss1: 1.327155 Loss2: 1.530996 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.299312 Loss1: 0.776604 Loss2: 1.522709
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.656358 Loss1: 1.127257 Loss2: 1.529100
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.208861 Loss1: 0.690683 Loss2: 1.518178
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.611393 Loss1: 1.068272 Loss2: 1.543121
+(DefaultActor pid=3765) >> Training accuracy: 0.849609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.510294 Loss1: 0.957321 Loss2: 1.552973
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.403658 Loss1: 0.856706 Loss2: 1.546952
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.344301 Loss1: 0.803405 Loss2: 1.540896
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.333012 Loss1: 0.775067 Loss2: 1.557945
+(DefaultActor pid=3764) >> Training accuracy: 0.752083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.592925 Loss1: 2.505093 Loss2: 2.087832
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.490909 Loss1: 1.969861 Loss2: 1.521048
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.111992 Loss1: 1.607174 Loss2: 1.504818
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.830892 Loss1: 1.319320 Loss2: 1.511572
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.686054 Loss1: 1.181483 Loss2: 1.504570
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.614393 Loss1: 1.103224 Loss2: 1.511169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.510964 Loss1: 0.998453 Loss2: 1.512510 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.417889 Loss1: 0.900438 Loss2: 1.517451 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.415998 Loss1: 0.887530 Loss2: 1.528468 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.363536 Loss1: 0.810327 Loss2: 1.553209 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.774554
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.456400 Loss1: 0.943700 Loss2: 1.512700 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.430305 Loss1: 0.906005 Loss2: 1.524300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.359290 Loss1: 0.829110 Loss2: 1.530179
+(DefaultActor pid=3764) >> Training accuracy: 0.752083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.267057 Loss1: 2.285246 Loss2: 1.981811
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.236547 Loss1: 1.766648 Loss2: 1.469899
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.942776 Loss1: 1.487508 Loss2: 1.455267
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.690549 Loss1: 1.228275 Loss2: 1.462274
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.544447 Loss1: 1.073673 Loss2: 1.470774
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.451059 Loss1: 0.988697 Loss2: 1.462362
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.153228 Loss1: 2.171105 Loss2: 1.982124
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.441910 Loss1: 0.951775 Loss2: 1.490135
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.168532 Loss1: 1.686429 Loss2: 1.482104
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.333988 Loss1: 0.853325 Loss2: 1.480663
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.906409 Loss1: 1.452221 Loss2: 1.454189
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.776757 Loss1: 1.316596 Loss2: 1.460161 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.716667
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.248590 Loss1: 0.761122 Loss2: 1.487468
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.623727 Loss1: 1.165817 Loss2: 1.457910
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.487617 Loss1: 1.023464 Loss2: 1.464153
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.394322 Loss1: 0.917835 Loss2: 1.476486
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.457569 Loss1: 0.972057 Loss2: 1.485512
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.240252 Loss1: 2.170214 Loss2: 2.070037
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.357065 Loss1: 0.866164 Loss2: 1.490900
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.169741 Loss1: 1.687193 Loss2: 1.482549
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.218386 Loss1: 0.746492 Loss2: 1.471894
+(DefaultActor pid=3764) >> Training accuracy: 0.788603
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.598901 Loss1: 1.125275 Loss2: 1.473627 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.397386 Loss1: 0.919688 Loss2: 1.477698 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.378123 Loss1: 0.883795 Loss2: 1.494328
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.519500 Loss1: 2.440633 Loss2: 2.078867
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.420965 Loss1: 1.883034 Loss2: 1.537931 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.077657 Loss1: 1.556581 Loss2: 1.521075 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.739583
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.226918 Loss1: 0.725398 Loss2: 1.501520
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.909290 Loss1: 1.401483 Loss2: 1.507807
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.700358 Loss1: 1.184450 Loss2: 1.515908
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.613118 Loss1: 1.088112 Loss2: 1.525006
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.541166 Loss1: 1.002353 Loss2: 1.538813
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.425416 Loss1: 0.890234 Loss2: 1.535181
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.419838 Loss1: 2.370552 Loss2: 2.049285
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.441760 Loss1: 0.896618 Loss2: 1.545141
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.392155 Loss1: 1.823354 Loss2: 1.568802
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.421481 Loss1: 0.873885 Loss2: 1.547596
+(DefaultActor pid=3764) >> Training accuracy: 0.732292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.861079 Loss1: 1.313163 Loss2: 1.547916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.647540 Loss1: 1.096676 Loss2: 1.550865 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.357236 Loss1: 2.309960 Loss2: 2.047276
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.612374 Loss1: 1.043076 Loss2: 1.569297
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.277896 Loss1: 1.741029 Loss2: 1.536867
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.539389 Loss1: 0.969034 Loss2: 1.570355
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.986589 Loss1: 1.464984 Loss2: 1.521605
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.444678 Loss1: 0.859067 Loss2: 1.585611
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.718497 Loss1: 1.201203 Loss2: 1.517294
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.332325 Loss1: 0.766919 Loss2: 1.565405
+DEBUG flwr 2023-10-09 08:17:02,905 | server.py:236 | fit_round 32 received 50 results and 0 failures
+(DefaultActor pid=3765) >> Training accuracy: 0.744141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.732451 Loss1: 1.187469 Loss2: 1.544982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.436505 Loss1: 0.884313 Loss2: 1.552192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.419097 Loss1: 2.332030 Loss2: 2.087067
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.333701 Loss1: 0.805011 Loss2: 1.528690
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.311978 Loss1: 1.787518 Loss2: 1.524460
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.420676 Loss1: 0.883178 Loss2: 1.537499
+(DefaultActor pid=3764) >> Training accuracy: 0.782227
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.885242 Loss1: 1.353438 Loss2: 1.531804 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.607054 Loss1: 1.066057 Loss2: 1.540997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.570677 Loss1: 1.022187 Loss2: 1.548491
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.238298 Loss1: 2.192422 Loss2: 2.045876
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.457795 Loss1: 0.899871 Loss2: 1.557925
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.231914 Loss1: 1.731151 Loss2: 1.500762
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.457156 Loss1: 0.893316 Loss2: 1.563841
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.880719 Loss1: 1.402939 Loss2: 1.477780
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.420837 Loss1: 0.860287 Loss2: 1.560550
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.639412 Loss1: 1.158833 Loss2: 1.480579
+(DefaultActor pid=3765) >> Training accuracy: 0.731250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.559329 Loss1: 1.075617 Loss2: 1.483712
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.487440 Loss1: 0.979826 Loss2: 1.507614
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.421228 Loss1: 0.911201 Loss2: 1.510026
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.369062 Loss1: 0.851956 Loss2: 1.517106
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.240199 Loss1: 2.240012 Loss2: 2.000187
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.325741 Loss1: 0.813012 Loss2: 1.512729
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.170410 Loss1: 0.652884 Loss2: 1.517526
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.289452 Loss1: 1.783580 Loss2: 1.505872
+(DefaultActor pid=3764) >> Training accuracy: 0.748958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.949715 Loss1: 1.455901 Loss2: 1.493814
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.721215 Loss1: 1.209509 Loss2: 1.511706
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.664765 Loss1: 1.153596 Loss2: 1.511169
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.523647 Loss1: 1.010422 Loss2: 1.513225
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.374050 Loss1: 2.329184 Loss2: 2.044866
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.484985 Loss1: 0.963206 Loss2: 1.521779
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.388690 Loss1: 0.877079 Loss2: 1.511611 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.338010 Loss1: 0.811218 Loss2: 1.526792 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.330302 Loss1: 0.793644 Loss2: 1.536658 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.777344
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.493731 Loss1: 0.986430 Loss2: 1.507301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.481116 Loss1: 0.949359 Loss2: 1.531756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.271895 Loss1: 2.200834 Loss2: 2.071061 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.779167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.889140 Loss1: 1.348751 Loss2: 1.540389 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.566334 Loss1: 1.026431 Loss2: 1.539904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.502852 Loss1: 2.340203 Loss2: 2.162649
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.470126 Loss1: 0.922843 Loss2: 1.547283
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.406005 Loss1: 0.841396 Loss2: 1.564609 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.389176 Loss1: 0.826735 Loss2: 1.562441 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.641535 Loss1: 1.102149 Loss2: 1.539386 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.568528 Loss1: 1.029035 Loss2: 1.539493 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.819336
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.419965 Loss1: 0.864233 Loss2: 1.555732 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.306281 Loss1: 0.751209 Loss2: 1.555072 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.753606
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 08:17:02,905][flwr][DEBUG] - fit_round 32 received 50 results and 0 failures
+INFO flwr 2023-10-09 08:17:45,012 | server.py:125 | fit progress: (32, 2.7599665619694767, {'accuracy': 0.3679}, 73572.790189721)
+>> Test accuracy: 0.367900
+[2023-10-09 08:17:45,012][flwr][INFO] - fit progress: (32, 2.7599665619694767, {'accuracy': 0.3679}, 73572.790189721)
+DEBUG flwr 2023-10-09 08:17:45,012 | server.py:173 | evaluate_round 32: strategy sampled 50 clients (out of 50)
+[2023-10-09 08:17:45,012][flwr][DEBUG] - evaluate_round 32: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 08:26:49,669 | server.py:187 | evaluate_round 32 received 50 results and 0 failures
+[2023-10-09 08:26:49,669][flwr][DEBUG] - evaluate_round 32 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 08:26:49,669 | server.py:222 | fit_round 33: strategy sampled 50 clients (out of 50)
+[2023-10-09 08:26:49,669][flwr][DEBUG] - fit_round 33: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.445686 Loss1: 2.404748 Loss2: 2.040938
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.335787 Loss1: 1.813025 Loss2: 1.522762
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.990020 Loss1: 1.513607 Loss2: 1.476413
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.812124 Loss1: 1.329160 Loss2: 1.482964
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.362882 Loss1: 2.297994 Loss2: 2.064888
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.190812 Loss1: 1.699054 Loss2: 1.491758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.920291 Loss1: 1.444139 Loss2: 1.476152 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.773655 Loss1: 1.287735 Loss2: 1.485921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.618606 Loss1: 1.129645 Loss2: 1.488961 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.489422 Loss1: 0.985625 Loss2: 1.503796 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.775000
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.215653 Loss1: 0.719413 Loss2: 1.496240
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.412814 Loss1: 0.905924 Loss2: 1.506890
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.383736 Loss1: 0.875781 Loss2: 1.507955
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.341984 Loss1: 0.823929 Loss2: 1.518056
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.357428 Loss1: 0.832399 Loss2: 1.525029
+(DefaultActor pid=3764) >> Training accuracy: 0.784375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.979161 Loss1: 2.024905 Loss2: 1.954256
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.992138 Loss1: 1.553108 Loss2: 1.439030
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.640812 Loss1: 1.232748 Loss2: 1.408064
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.513551 Loss1: 1.099854 Loss2: 1.413696
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.314280 Loss1: 2.208738 Loss2: 2.105542
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.137429 Loss1: 1.625736 Loss2: 1.511693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.005513 Loss1: 1.485371 Loss2: 1.520141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.661831 Loss1: 1.145261 Loss2: 1.516571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.598356 Loss1: 1.086808 Loss2: 1.511548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.459843 Loss1: 0.947065 Loss2: 1.512779 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.801042
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.135004 Loss1: 0.690338 Loss2: 1.444666
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.311616 Loss1: 0.800724 Loss2: 1.510892
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.328778 Loss1: 0.809205 Loss2: 1.519573
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.439938 Loss1: 0.898772 Loss2: 1.541166
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.286033 Loss1: 0.749094 Loss2: 1.536939
+(DefaultActor pid=3764) >> Training accuracy: 0.792708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.149742 Loss1: 2.121241 Loss2: 2.028501
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.087445 Loss1: 1.648972 Loss2: 1.438472
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.741939 Loss1: 1.298404 Loss2: 1.443535
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.516156 Loss1: 1.076038 Loss2: 1.440118
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.392297 Loss1: 2.238098 Loss2: 2.154199
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.246365 Loss1: 1.700054 Loss2: 1.546311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.369671 Loss1: 0.905051 Loss2: 1.464620
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.945150 Loss1: 1.431947 Loss2: 1.513202
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.393916 Loss1: 0.916928 Loss2: 1.476988
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.695782 Loss1: 1.177273 Loss2: 1.518510
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.532846 Loss1: 1.018177 Loss2: 1.514670
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.238404 Loss1: 0.765463 Loss2: 1.472941
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.439359 Loss1: 0.909382 Loss2: 1.529976
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.172662 Loss1: 0.712922 Loss2: 1.459740
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.388873 Loss1: 0.854136 Loss2: 1.534737
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.199209 Loss1: 0.717238 Loss2: 1.481971
+(DefaultActor pid=3765) >> Training accuracy: 0.835417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.307990 Loss1: 0.761434 Loss2: 1.546556 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.765625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.461326 Loss1: 2.423059 Loss2: 2.038268 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.960653 Loss1: 1.496579 Loss2: 1.464074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.654164 Loss1: 1.195683 Loss2: 1.458482
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.457612 Loss1: 2.371401 Loss2: 2.086211
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.436378 Loss1: 1.891916 Loss2: 1.544461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.997456 Loss1: 1.471139 Loss2: 1.526318 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.829187 Loss1: 1.299001 Loss2: 1.530185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.771431 Loss1: 1.240041 Loss2: 1.531390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.625107 Loss1: 1.075360 Loss2: 1.549747 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.776042
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.406949 Loss1: 0.893208 Loss2: 1.513741
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.530593 Loss1: 0.984532 Loss2: 1.546061
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.345367 Loss1: 0.788108 Loss2: 1.557258
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.373745 Loss1: 0.818285 Loss2: 1.555460
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.327404 Loss1: 0.754259 Loss2: 1.573145
+(DefaultActor pid=3764) >> Training accuracy: 0.828125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.320425 Loss1: 2.272690 Loss2: 2.047735
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.240170 Loss1: 1.735925 Loss2: 1.504245
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.102791 Loss1: 1.596731 Loss2: 1.506060
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.700030 Loss1: 1.202681 Loss2: 1.497349
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.317028 Loss1: 2.267838 Loss2: 2.049190
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.255865 Loss1: 1.756020 Loss2: 1.499845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.846183 Loss1: 1.375017 Loss2: 1.471166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.582624 Loss1: 1.112404 Loss2: 1.470220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.504094 Loss1: 1.035578 Loss2: 1.468517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.386151 Loss1: 0.911199 Loss2: 1.474951 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.773958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.309022 Loss1: 0.782200 Loss2: 1.526823
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.287362 Loss1: 0.812254 Loss2: 1.475108
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.270489 Loss1: 0.806341 Loss2: 1.464148
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.270022 Loss1: 0.777938 Loss2: 1.492084
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.190643 Loss1: 0.695711 Loss2: 1.494933
+(DefaultActor pid=3764) >> Training accuracy: 0.802083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.388349 Loss1: 2.312456 Loss2: 2.075893
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.386992 Loss1: 1.865944 Loss2: 1.521048
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.048336 Loss1: 1.534839 Loss2: 1.513497
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.851937 Loss1: 1.345048 Loss2: 1.506889
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.411611 Loss1: 2.317748 Loss2: 2.093863
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.262507 Loss1: 1.749402 Loss2: 1.513105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.822581 Loss1: 1.319998 Loss2: 1.502583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.768456 Loss1: 1.268667 Loss2: 1.499789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.598361 Loss1: 1.093961 Loss2: 1.504400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.480524 Loss1: 0.981629 Loss2: 1.498896 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.767708
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.381917 Loss1: 0.832152 Loss2: 1.549765
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.307088 Loss1: 0.798077 Loss2: 1.509011
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.344249 Loss1: 0.833331 Loss2: 1.510918
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.221197 Loss1: 0.706186 Loss2: 1.515011
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.164039 Loss1: 0.646197 Loss2: 1.517842
+(DefaultActor pid=3764) >> Training accuracy: 0.792708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.456300 Loss1: 2.363165 Loss2: 2.093135
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.265344 Loss1: 1.717935 Loss2: 1.547409
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.964598 Loss1: 1.454516 Loss2: 1.510081
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.839400 Loss1: 1.330670 Loss2: 1.508731
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.058310 Loss1: 1.999619 Loss2: 2.058692
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.100753 Loss1: 1.624338 Loss2: 1.476415 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.832935 Loss1: 1.354789 Loss2: 1.478147 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.643158 Loss1: 1.178501 Loss2: 1.464657 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.473365 Loss1: 1.000635 Loss2: 1.472730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.414332 Loss1: 0.944723 Loss2: 1.469608 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.782292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.299658 Loss1: 0.816139 Loss2: 1.483519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.251427 Loss1: 0.758805 Loss2: 1.492622 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.787500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.405630 Loss1: 2.274243 Loss2: 2.131387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.002514 Loss1: 1.430749 Loss2: 1.571765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.762651 Loss1: 1.215640 Loss2: 1.547011
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.459364 Loss1: 2.343313 Loss2: 2.116051
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.639291 Loss1: 1.082718 Loss2: 1.556574
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.321219 Loss1: 1.770657 Loss2: 1.550562
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.568683 Loss1: 1.002213 Loss2: 1.566470
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.086840 Loss1: 1.552524 Loss2: 1.534316
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.519910 Loss1: 0.953246 Loss2: 1.566664
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.841736 Loss1: 1.284179 Loss2: 1.557557
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.590914 Loss1: 1.039337 Loss2: 1.551577
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.349656 Loss1: 0.775621 Loss2: 1.574035
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.613595 Loss1: 1.075165 Loss2: 1.538430
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.439261 Loss1: 0.862531 Loss2: 1.576730
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.624276 Loss1: 1.053021 Loss2: 1.571255
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.430554 Loss1: 0.836386 Loss2: 1.594168
+(DefaultActor pid=3765) >> Training accuracy: 0.829102
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.500872 Loss1: 0.925969 Loss2: 1.574904 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.712500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.318654 Loss1: 2.314337 Loss2: 2.004316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.797864 Loss1: 1.386362 Loss2: 1.411502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.603728 Loss1: 1.195781 Loss2: 1.407947
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.445963 Loss1: 2.316633 Loss2: 2.129330
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.458686 Loss1: 1.044807 Loss2: 1.413879
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.312682 Loss1: 1.783906 Loss2: 1.528776
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.385426 Loss1: 0.969754 Loss2: 1.415672
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.982203 Loss1: 1.471906 Loss2: 1.510297
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.288926 Loss1: 0.867725 Loss2: 1.421201
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.772944 Loss1: 1.250713 Loss2: 1.522231
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.209056 Loss1: 0.788860 Loss2: 1.420196
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.613119 Loss1: 1.082102 Loss2: 1.531017
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.246370 Loss1: 0.814257 Loss2: 1.432113
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.498195 Loss1: 0.974233 Loss2: 1.523962
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.149771 Loss1: 0.706210 Loss2: 1.443562
+(DefaultActor pid=3765) >> Training accuracy: 0.787500
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.434143 Loss1: 0.899321 Loss2: 1.534821
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.359697 Loss1: 0.822552 Loss2: 1.537145
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.371700 Loss1: 0.830164 Loss2: 1.541536
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.351389 Loss1: 0.790688 Loss2: 1.560701
+(DefaultActor pid=3764) >> Training accuracy: 0.740625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.720040 Loss1: 2.560873 Loss2: 2.159167
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.529304 Loss1: 1.976651 Loss2: 1.552653
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.117187 Loss1: 1.583220 Loss2: 1.533967
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.872318 Loss1: 1.331324 Loss2: 1.540994
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.564008 Loss1: 2.462521 Loss2: 2.101487
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.277828 Loss1: 1.750093 Loss2: 1.527735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.999607 Loss1: 1.510668 Loss2: 1.488939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.794495 Loss1: 1.287658 Loss2: 1.506837 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.416043 Loss1: 0.839913 Loss2: 1.576130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.462254 Loss1: 0.881498 Loss2: 1.580755 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.802455
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.423260 Loss1: 0.882716 Loss2: 1.540545 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.310486 Loss1: 0.766213 Loss2: 1.544273 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.803125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.083481 Loss1: 1.606120 Loss2: 1.477362 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.577138 Loss1: 1.137346 Loss2: 1.439792 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.426866 Loss1: 0.970196 Loss2: 1.456669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.330223 Loss1: 0.868482 Loss2: 1.461741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.258958 Loss1: 0.793852 Loss2: 1.465106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.526659 Loss1: 0.990529 Loss2: 1.536130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.339129 Loss1: 0.802558 Loss2: 1.536571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.269260 Loss1: 0.710859 Loss2: 1.558401 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.798828
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.177038 Loss1: 0.613491 Loss2: 1.563547 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.822115
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.424885 Loss1: 2.354199 Loss2: 2.070686 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.956765 Loss1: 1.464483 Loss2: 1.492282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.795460 Loss1: 1.295535 Loss2: 1.499925
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.315682 Loss1: 2.336170 Loss2: 1.979512
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.171207 Loss1: 1.687650 Loss2: 1.483557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.848110 Loss1: 1.369135 Loss2: 1.478975 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.614274 Loss1: 1.148212 Loss2: 1.466062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.555635 Loss1: 1.079526 Loss2: 1.476109 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.482030 Loss1: 0.995944 Loss2: 1.486086 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.789583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.369859 Loss1: 0.876391 Loss2: 1.493468 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.385346 Loss1: 0.866404 Loss2: 1.518942 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.758789
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.319066 Loss1: 1.731311 Loss2: 1.587755 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.874240 Loss1: 1.297051 Loss2: 1.577189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.717600 Loss1: 1.138897 Loss2: 1.578703
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.229250 Loss1: 2.170209 Loss2: 2.059041
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.581005 Loss1: 0.996355 Loss2: 1.584651
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.077174 Loss1: 1.582791 Loss2: 1.494384
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.526846 Loss1: 0.940740 Loss2: 1.586106
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.727840 Loss1: 1.262075 Loss2: 1.465765
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.449131 Loss1: 0.864251 Loss2: 1.584879
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.625525 Loss1: 1.146462 Loss2: 1.479064
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.414236 Loss1: 0.816484 Loss2: 1.597752
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.492518 Loss1: 1.010513 Loss2: 1.482005
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.371368 Loss1: 0.781050 Loss2: 1.590318
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.432948 Loss1: 0.940864 Loss2: 1.492084
+(DefaultActor pid=3765) >> Training accuracy: 0.768750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.321117 Loss1: 0.823303 Loss2: 1.497814
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.396396 Loss1: 0.887569 Loss2: 1.508826
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.296616 Loss1: 0.782880 Loss2: 1.513735
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.254549 Loss1: 0.750895 Loss2: 1.503654
+(DefaultActor pid=3764) >> Training accuracy: 0.757292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.610090 Loss1: 2.508000 Loss2: 2.102090
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.389581 Loss1: 1.854513 Loss2: 1.535067
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.085076 Loss1: 1.567102 Loss2: 1.517974
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.957837 Loss1: 1.418028 Loss2: 1.539808
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.881220 Loss1: 1.330368 Loss2: 1.550852
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.620351 Loss1: 1.082745 Loss2: 1.537605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.455206 Loss1: 0.903378 Loss2: 1.551827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.406358 Loss1: 0.858251 Loss2: 1.548108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.298111 Loss1: 0.760869 Loss2: 1.537242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.368918 Loss1: 0.811546 Loss2: 1.557373 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.761161
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.349089 Loss1: 0.798976 Loss2: 1.550113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.200262 Loss1: 0.633853 Loss2: 1.566409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.201299 Loss1: 0.632496 Loss2: 1.568804
+(DefaultActor pid=3764) >> Training accuracy: 0.820833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.493597 Loss1: 2.359496 Loss2: 2.134101
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.377238 Loss1: 1.822819 Loss2: 1.554419
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.927918 Loss1: 1.395260 Loss2: 1.532658
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.730142 Loss1: 1.176513 Loss2: 1.553628
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.631959 Loss1: 1.094146 Loss2: 1.537814
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.412662 Loss1: 2.335797 Loss2: 2.076865
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.427569 Loss1: 1.923990 Loss2: 1.503579 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.963499 Loss1: 1.490337 Loss2: 1.473162 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.801795 Loss1: 1.322458 Loss2: 1.479337 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.629820 Loss1: 1.142469 Loss2: 1.487351 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.795833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.456636 Loss1: 0.967336 Loss2: 1.489300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.515773 Loss1: 0.993259 Loss2: 1.522514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.335466 Loss1: 0.823995 Loss2: 1.511471 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.818750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.278161 Loss1: 1.758167 Loss2: 1.519993 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.884640 Loss1: 1.384974 Loss2: 1.499666 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.748799 Loss1: 1.238437 Loss2: 1.510361
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.188014 Loss1: 2.183543 Loss2: 2.004471
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.631751 Loss1: 1.127901 Loss2: 1.503851
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.037576 Loss1: 1.557440 Loss2: 1.480135
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.510592 Loss1: 1.005282 Loss2: 1.505310
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.842809 Loss1: 1.374111 Loss2: 1.468698
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.434073 Loss1: 0.902366 Loss2: 1.531707
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.606447 Loss1: 1.120287 Loss2: 1.486160
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.392931 Loss1: 0.877484 Loss2: 1.515447
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.431682 Loss1: 0.969113 Loss2: 1.462569
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.376892 Loss1: 0.908662 Loss2: 1.468230
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.337612 Loss1: 0.814742 Loss2: 1.522870
+(DefaultActor pid=3765) >> Training accuracy: 0.724609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.212510 Loss1: 0.730128 Loss2: 1.482382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.175081 Loss1: 0.669239 Loss2: 1.505843 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.782292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.330341 Loss1: 1.821568 Loss2: 1.508773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.746034 Loss1: 1.225968 Loss2: 1.520066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.725040 Loss1: 1.213867 Loss2: 1.511173 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.544027 Loss1: 1.001129 Loss2: 1.542897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.454921 Loss1: 0.933984 Loss2: 1.520936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.419677 Loss1: 0.882020 Loss2: 1.537657 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.380559 Loss1: 0.825969 Loss2: 1.554591 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.419721 Loss1: 0.863108 Loss2: 1.556613 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.756250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.474316 Loss1: 0.902190 Loss2: 1.572127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.422257 Loss1: 0.832457 Loss2: 1.589801 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.759375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.460670 Loss1: 1.907044 Loss2: 1.553626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.877269 Loss1: 1.337907 Loss2: 1.539363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.685269 Loss1: 1.157334 Loss2: 1.527935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.583097 Loss1: 1.044446 Loss2: 1.538651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.513838 Loss1: 0.973729 Loss2: 1.540109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.467356 Loss1: 0.899913 Loss2: 1.567444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.368982 Loss1: 0.808361 Loss2: 1.560620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.373878 Loss1: 0.818180 Loss2: 1.555698 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.708008
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.325729 Loss1: 0.848694 Loss2: 1.477035 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.340141 Loss1: 0.849036 Loss2: 1.491105 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.734375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.262585 Loss1: 1.766709 Loss2: 1.495876 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.692108 Loss1: 1.203694 Loss2: 1.488414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.285492 Loss1: 2.215802 Loss2: 2.069690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.217072 Loss1: 1.717714 Loss2: 1.499358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.876637 Loss1: 1.384704 Loss2: 1.491932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.703977 Loss1: 1.223265 Loss2: 1.480711 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.275690 Loss1: 0.771267 Loss2: 1.504423 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.832933
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.255368 Loss1: 0.775326 Loss2: 1.480042 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.287248 Loss1: 0.779504 Loss2: 1.507744 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.363339 Loss1: 2.217705 Loss2: 2.145635
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.290531 Loss1: 0.768401 Loss2: 1.522130
+(DefaultActor pid=3764) >> Training accuracy: 0.805208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.842932 Loss1: 1.306229 Loss2: 1.536703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.500458 Loss1: 0.976597 Loss2: 1.523861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.477246 Loss1: 2.360861 Loss2: 2.116385
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.395195 Loss1: 0.866597 Loss2: 1.528598
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.327205 Loss1: 0.791147 Loss2: 1.536058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.263940 Loss1: 0.728717 Loss2: 1.535223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.196345 Loss1: 0.649104 Loss2: 1.547241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.457276 Loss1: 0.994378 Loss2: 1.462898 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.829167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.323580 Loss1: 0.856664 Loss2: 1.466916 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.148571 Loss1: 0.675716 Loss2: 1.472855 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.769531
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.185838 Loss1: 2.098918 Loss2: 2.086920
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.146533 Loss1: 1.609787 Loss2: 1.536746
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.750069 Loss1: 1.235305 Loss2: 1.514764
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.363953 Loss1: 2.267022 Loss2: 2.096931
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.676986 Loss1: 1.174068 Loss2: 1.502917
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.211517 Loss1: 1.680180 Loss2: 1.531337
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.457573 Loss1: 0.945659 Loss2: 1.511914
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.892704 Loss1: 1.385481 Loss2: 1.507223
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.335444 Loss1: 0.825759 Loss2: 1.509686
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.300935 Loss1: 0.777678 Loss2: 1.523257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.341205 Loss1: 0.819767 Loss2: 1.521439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.335934 Loss1: 0.815231 Loss2: 1.520703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.178864 Loss1: 0.646160 Loss2: 1.532704 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.816406
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.198077 Loss1: 0.688649 Loss2: 1.509428 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.785417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.252320 Loss1: 2.255686 Loss2: 1.996634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.911247 Loss1: 1.493515 Loss2: 1.417732 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.717238 Loss1: 1.284853 Loss2: 1.432385
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.175238 Loss1: 2.168623 Loss2: 2.006614
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.109844 Loss1: 1.606898 Loss2: 1.502947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.892926 Loss1: 1.436251 Loss2: 1.456675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.590018 Loss1: 1.114991 Loss2: 1.475027 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.459758 Loss1: 1.005170 Loss2: 1.454588 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.399602 Loss1: 0.938151 Loss2: 1.461451 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.729167
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.427925 Loss1: 0.946565 Loss2: 1.481360
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.396307 Loss1: 0.934596 Loss2: 1.461711
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.341359 Loss1: 0.863196 Loss2: 1.478164
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.158163 Loss1: 0.690880 Loss2: 1.467282
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.127539 Loss1: 0.651090 Loss2: 1.476449
+(DefaultActor pid=3764) >> Training accuracy: 0.790625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.174194 Loss1: 2.213169 Loss2: 1.961026
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.215269 Loss1: 1.756729 Loss2: 1.458539
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.838723 Loss1: 1.385708 Loss2: 1.453015
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.353975 Loss1: 2.355895 Loss2: 1.998080
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.608775 Loss1: 1.176084 Loss2: 1.432691
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.538847 Loss1: 1.090594 Loss2: 1.448253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.437865 Loss1: 0.981698 Loss2: 1.456168 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.355355 Loss1: 0.891119 Loss2: 1.464236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.219756 Loss1: 0.759728 Loss2: 1.460027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.334600 Loss1: 0.868711 Loss2: 1.465889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.392608 Loss1: 0.896032 Loss2: 1.496575 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.775735
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.222252 Loss1: 0.724354 Loss2: 1.497897 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.786458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.465827 Loss1: 2.425931 Loss2: 2.039897
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.367582 Loss1: 1.865840 Loss2: 1.501742
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.045182 Loss1: 1.551092 Loss2: 1.494090
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.334894 Loss1: 2.305036 Loss2: 2.029858
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.845480 Loss1: 1.342762 Loss2: 1.502717
+DEBUG flwr 2023-10-09 08:55:41,996 | server.py:236 | fit_round 33 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.219250 Loss1: 1.739828 Loss2: 1.479422
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.718253 Loss1: 1.201132 Loss2: 1.517121
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.805650 Loss1: 1.336537 Loss2: 1.469113
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.543593 Loss1: 1.023736 Loss2: 1.519858
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.648098 Loss1: 1.182489 Loss2: 1.465608
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.500526 Loss1: 0.986655 Loss2: 1.513871
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.567845 Loss1: 1.091245 Loss2: 1.476600
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.367952 Loss1: 0.844432 Loss2: 1.523520
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.426479 Loss1: 0.936909 Loss2: 1.489570
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.320003 Loss1: 0.780846 Loss2: 1.539157
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.305337 Loss1: 0.823873 Loss2: 1.481464
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.449621 Loss1: 0.909320 Loss2: 1.540301
+(DefaultActor pid=3765) >> Training accuracy: 0.697266
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.246913 Loss1: 0.758878 Loss2: 1.488035 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.772461
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.475298 Loss1: 2.295559 Loss2: 2.179739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.056462 Loss1: 1.476064 Loss2: 1.580397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.823719 Loss1: 1.246223 Loss2: 1.577496
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.421435 Loss1: 2.344734 Loss2: 2.076701
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.396794 Loss1: 1.865267 Loss2: 1.531526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.103901 Loss1: 1.569868 Loss2: 1.534033 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.916528 Loss1: 1.388366 Loss2: 1.528162 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.696877 Loss1: 1.164316 Loss2: 1.532560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.540074 Loss1: 1.011825 Loss2: 1.528249 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.825000
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.436869 Loss1: 0.818016 Loss2: 1.618852
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.532021 Loss1: 0.983724 Loss2: 1.548298
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.503122 Loss1: 0.950826 Loss2: 1.552296
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.461263 Loss1: 0.893916 Loss2: 1.567347
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.537008 Loss1: 0.966383 Loss2: 1.570625
+(DefaultActor pid=3764) >> Training accuracy: 0.721875
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 08:55:41,996][flwr][DEBUG] - fit_round 33 received 50 results and 0 failures
+INFO flwr 2023-10-09 08:56:23,658 | server.py:125 | fit progress: (33, 2.7204779908299064, {'accuracy': 0.3764}, 75891.436895839)
+>> Test accuracy: 0.376400
+[2023-10-09 08:56:23,658][flwr][INFO] - fit progress: (33, 2.7204779908299064, {'accuracy': 0.3764}, 75891.436895839)
+DEBUG flwr 2023-10-09 08:56:23,659 | server.py:173 | evaluate_round 33: strategy sampled 50 clients (out of 50)
+[2023-10-09 08:56:23,659][flwr][DEBUG] - evaluate_round 33: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 09:05:27,014 | server.py:187 | evaluate_round 33 received 50 results and 0 failures
+[2023-10-09 09:05:27,014][flwr][DEBUG] - evaluate_round 33 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 09:05:27,015 | server.py:222 | fit_round 34: strategy sampled 50 clients (out of 50)
+[2023-10-09 09:05:27,015][flwr][DEBUG] - fit_round 34: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.222358 Loss1: 2.238348 Loss2: 1.984010
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.144200 Loss1: 1.674757 Loss2: 1.469443
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.906062 Loss1: 1.452050 Loss2: 1.454012
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.344052 Loss1: 2.125761 Loss2: 2.218291
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.626324 Loss1: 1.159369 Loss2: 1.466956
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.145256 Loss1: 1.536794 Loss2: 1.608462
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.510956 Loss1: 1.054332 Loss2: 1.456624
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.836997 Loss1: 1.254651 Loss2: 1.582346
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.389443 Loss1: 0.923503 Loss2: 1.465940
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.655923 Loss1: 1.078744 Loss2: 1.577178
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.287629 Loss1: 0.825406 Loss2: 1.462223
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.190309 Loss1: 0.729464 Loss2: 1.460845 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.117926 Loss1: 0.650903 Loss2: 1.467023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.259262 Loss1: 0.784540 Loss2: 1.474721 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.777344
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.348158 Loss1: 0.730868 Loss2: 1.617289 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.835417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.319429 Loss1: 2.169097 Loss2: 2.150332 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.974026 Loss1: 1.374623 Loss2: 1.599403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.366228 Loss1: 2.340827 Loss2: 2.025400
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.742180 Loss1: 1.149588 Loss2: 1.592592
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.229144 Loss1: 1.728124 Loss2: 1.501021
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.588378 Loss1: 0.981066 Loss2: 1.607312
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.867571 Loss1: 1.371827 Loss2: 1.495744
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.502888 Loss1: 0.890164 Loss2: 1.612723
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.627438 Loss1: 1.129618 Loss2: 1.497821
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.486702 Loss1: 0.875580 Loss2: 1.611122
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.426959 Loss1: 0.797586 Loss2: 1.629373 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.293016 Loss1: 0.676230 Loss2: 1.616786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.319538 Loss1: 0.695067 Loss2: 1.624471 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.819336
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.240991 Loss1: 0.728816 Loss2: 1.512175 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.830208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.421464 Loss1: 2.339249 Loss2: 2.082215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.007886 Loss1: 1.488344 Loss2: 1.519542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.822655 Loss1: 1.285468 Loss2: 1.537187
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.231240 Loss1: 2.207974 Loss2: 2.023266
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.671308 Loss1: 1.138092 Loss2: 1.533217
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.998110 Loss1: 1.511049 Loss2: 1.487061
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.707997 Loss1: 1.252955 Loss2: 1.455042
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.727205 Loss1: 1.173592 Loss2: 1.553613
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.500362 Loss1: 1.041905 Loss2: 1.458457
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.460099 Loss1: 0.909334 Loss2: 1.550766
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.394837 Loss1: 0.924029 Loss2: 1.470808
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.410310 Loss1: 0.853645 Loss2: 1.556665
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.312406 Loss1: 0.852668 Loss2: 1.459738
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.320319 Loss1: 0.757613 Loss2: 1.562706
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.272185 Loss1: 0.722169 Loss2: 1.550017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.784180
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.178972 Loss1: 0.700398 Loss2: 1.478574 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.778125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.200657 Loss1: 2.176517 Loss2: 2.024140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.789660 Loss1: 1.289939 Loss2: 1.499721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.610475 Loss1: 1.101563 Loss2: 1.508912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.478182 Loss1: 0.960641 Loss2: 1.517540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.653075 Loss1: 1.159636 Loss2: 1.493439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.533807 Loss1: 1.032193 Loss2: 1.501614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.463238 Loss1: 0.967620 Loss2: 1.495618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.301276 Loss1: 0.803020 Loss2: 1.498256 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.284904 Loss1: 0.774516 Loss2: 1.510388
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.187397 Loss1: 0.654231 Loss2: 1.533166
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.138257 Loss1: 0.619471 Loss2: 1.518786 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.797852
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.286033 Loss1: 2.206441 Loss2: 2.079592 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.833333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.868187 Loss1: 1.377159 Loss2: 1.491028 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.625435 Loss1: 1.129833 Loss2: 1.495602
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.186680 Loss1: 2.162583 Loss2: 2.024097
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.385129 Loss1: 0.909940 Loss2: 1.475190
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.124547 Loss1: 1.647821 Loss2: 1.476726
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.386791 Loss1: 0.911040 Loss2: 1.475751
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.780739 Loss1: 1.300624 Loss2: 1.480115
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.301201 Loss1: 0.801236 Loss2: 1.499964
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.522708 Loss1: 1.036040 Loss2: 1.486668
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.224468 Loss1: 0.736353 Loss2: 1.488115
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.556706 Loss1: 1.082702 Loss2: 1.474004
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.307460 Loss1: 0.806921 Loss2: 1.500539
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.404887 Loss1: 0.892498 Loss2: 1.512390
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.143795 Loss1: 0.629302 Loss2: 1.514493
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.344381 Loss1: 0.845749 Loss2: 1.498632
+(DefaultActor pid=3765) >> Training accuracy: 0.832292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.242935 Loss1: 0.751357 Loss2: 1.491578
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.215540 Loss1: 0.728021 Loss2: 1.487519
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.115423 Loss1: 0.612821 Loss2: 1.502602
+(DefaultActor pid=3764) >> Training accuracy: 0.846875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.364782 Loss1: 2.305702 Loss2: 2.059080
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.430463 Loss1: 1.922528 Loss2: 1.507935
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.006028 Loss1: 1.504450 Loss2: 1.501579
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.656440 Loss1: 1.172787 Loss2: 1.483653
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.289074 Loss1: 2.247091 Loss2: 2.041982
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.197469 Loss1: 1.719732 Loss2: 1.477737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.889318 Loss1: 1.434640 Loss2: 1.454678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.689864 Loss1: 1.218884 Loss2: 1.470980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.571060 Loss1: 1.097504 Loss2: 1.473556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.506066 Loss1: 1.009739 Loss2: 1.496328 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.751042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.418520 Loss1: 0.934789 Loss2: 1.483731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.227647 Loss1: 0.734080 Loss2: 1.493567 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.806250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.218866 Loss1: 2.193234 Loss2: 2.025632 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.770606 Loss1: 1.274383 Loss2: 1.496223 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.402710 Loss1: 2.340840 Loss2: 2.061870
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.703358 Loss1: 1.197197 Loss2: 1.506161
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.256725 Loss1: 1.753945 Loss2: 1.502781
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.496990 Loss1: 0.989430 Loss2: 1.507559
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.978718 Loss1: 1.500865 Loss2: 1.477853
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.455827 Loss1: 0.950537 Loss2: 1.505290
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.293013 Loss1: 0.790063 Loss2: 1.502950 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.311473 Loss1: 0.798687 Loss2: 1.512786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.190240 Loss1: 0.667464 Loss2: 1.522776 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.182120 Loss1: 0.671178 Loss2: 1.510943 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.846507
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.155380 Loss1: 0.654899 Loss2: 1.500482 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.803125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.269794 Loss1: 2.183401 Loss2: 2.086392
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.232942 Loss1: 1.678974 Loss2: 1.553968
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.898872 Loss1: 1.367211 Loss2: 1.531660
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.673956 Loss1: 1.131140 Loss2: 1.542816
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.406831 Loss1: 2.292334 Loss2: 2.114497
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.628022 Loss1: 1.076365 Loss2: 1.551657
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.409568 Loss1: 1.872035 Loss2: 1.537533
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.936545 Loss1: 1.432040 Loss2: 1.504506
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.558328 Loss1: 1.006894 Loss2: 1.551434
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.722165 Loss1: 1.208275 Loss2: 1.513890
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.324586 Loss1: 0.764223 Loss2: 1.560362
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.666114 Loss1: 1.140573 Loss2: 1.525542
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.279265 Loss1: 0.732851 Loss2: 1.546414
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.531962 Loss1: 1.011260 Loss2: 1.520702
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.313646 Loss1: 0.745772 Loss2: 1.567874
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.321221 Loss1: 0.746676 Loss2: 1.574545 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.764648
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.362305 Loss1: 0.823335 Loss2: 1.538969 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.750000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.307487 Loss1: 2.283973 Loss2: 2.023514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.922420 Loss1: 1.448583 Loss2: 1.473837 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.626814 Loss1: 1.142308 Loss2: 1.484507
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.327203 Loss1: 2.230212 Loss2: 2.096990
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.198937 Loss1: 1.677007 Loss2: 1.521930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.934719 Loss1: 1.409314 Loss2: 1.525404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.695234 Loss1: 1.165978 Loss2: 1.529256 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.540244 Loss1: 1.008939 Loss2: 1.531305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.419466 Loss1: 0.883816 Loss2: 1.535650 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.795833
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.119002 Loss1: 0.633267 Loss2: 1.485735
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.368558 Loss1: 0.843552 Loss2: 1.525006
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.327097 Loss1: 0.784582 Loss2: 1.542515
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.329441 Loss1: 0.777481 Loss2: 1.551960
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.247739 Loss1: 0.686051 Loss2: 1.561688
+(DefaultActor pid=3764) >> Training accuracy: 0.813542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.418702 Loss1: 2.384183 Loss2: 2.034519
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.222075 Loss1: 1.739603 Loss2: 1.482471
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.925990 Loss1: 1.452936 Loss2: 1.473054
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.673603 Loss1: 1.194474 Loss2: 1.479129
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.296359 Loss1: 2.204315 Loss2: 2.092043
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.229916 Loss1: 1.693056 Loss2: 1.536860 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.926533 Loss1: 1.415810 Loss2: 1.510723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.608104 Loss1: 1.089777 Loss2: 1.518326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.527514 Loss1: 1.014081 Loss2: 1.513433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.551385 Loss1: 1.013748 Loss2: 1.537636 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.812500
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.217906 Loss1: 0.717003 Loss2: 1.500902
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.512777 Loss1: 0.960607 Loss2: 1.552170
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.391159 Loss1: 0.847299 Loss2: 1.543860
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.223132 Loss1: 0.688805 Loss2: 1.534327
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.337669 Loss1: 0.789859 Loss2: 1.547810
+(DefaultActor pid=3764) >> Training accuracy: 0.755208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.511739 Loss1: 2.435189 Loss2: 2.076550
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.348617 Loss1: 1.816781 Loss2: 1.531836
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.015640 Loss1: 1.503062 Loss2: 1.512578
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.825475 Loss1: 1.317079 Loss2: 1.508397
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.474779 Loss1: 2.385989 Loss2: 2.088790
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.247836 Loss1: 1.749459 Loss2: 1.498377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.914876 Loss1: 1.427001 Loss2: 1.487875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.705044 Loss1: 1.217255 Loss2: 1.487789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.585290 Loss1: 1.087342 Loss2: 1.497948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.536491 Loss1: 1.038824 Loss2: 1.497667 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.812500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.285721 Loss1: 0.782224 Loss2: 1.503497 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.283343 Loss1: 0.766589 Loss2: 1.516754 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.822917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.351481 Loss1: 1.795869 Loss2: 1.555612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.933319 Loss1: 1.391087 Loss2: 1.542232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.706695 Loss1: 1.151375 Loss2: 1.555320
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.301691 Loss1: 2.239493 Loss2: 2.062197
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.485612 Loss1: 0.948740 Loss2: 1.536872
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.259527 Loss1: 1.746709 Loss2: 1.512818
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.902645 Loss1: 1.417872 Loss2: 1.484773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.749134 Loss1: 1.257224 Loss2: 1.491910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.552184 Loss1: 1.056986 Loss2: 1.495199 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.766741
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.488874 Loss1: 0.981603 Loss2: 1.507271 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.404052 Loss1: 0.886433 Loss2: 1.517619 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.332489 Loss1: 0.808616 Loss2: 1.523873 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.785417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.058387 Loss1: 1.519772 Loss2: 1.538615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.535332 Loss1: 0.999305 Loss2: 1.536027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.438446 Loss1: 0.909446 Loss2: 1.529001
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.288409 Loss1: 2.187247 Loss2: 2.101162
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.223479 Loss1: 1.676567 Loss2: 1.546911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.871527 Loss1: 1.346461 Loss2: 1.525067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.616773 Loss1: 1.082419 Loss2: 1.534354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.527501 Loss1: 0.993747 Loss2: 1.533754 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.744792
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.283644 Loss1: 0.729946 Loss2: 1.553698
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.517512 Loss1: 0.975271 Loss2: 1.542241
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.332457 Loss1: 0.786890 Loss2: 1.545566
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.253303 Loss1: 0.706196 Loss2: 1.547106
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.379985 Loss1: 0.817167 Loss2: 1.562817
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.342369 Loss1: 0.768742 Loss2: 1.573628
+(DefaultActor pid=3764) >> Training accuracy: 0.798958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.286006 Loss1: 2.272825 Loss2: 2.013181
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.161737 Loss1: 1.649379 Loss2: 1.512358
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.872651 Loss1: 1.390144 Loss2: 1.482508
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.649083 Loss1: 1.153184 Loss2: 1.495899
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.589626 Loss1: 1.093239 Loss2: 1.496387
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.235774 Loss1: 2.120121 Loss2: 2.115653
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.467996 Loss1: 0.969979 Loss2: 1.498017
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.415212 Loss1: 0.922102 Loss2: 1.493110 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.309586 Loss1: 0.797222 Loss2: 1.512364 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.317570 Loss1: 0.813692 Loss2: 1.503878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.217075 Loss1: 0.696777 Loss2: 1.520298 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.841667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.221060 Loss1: 0.707043 Loss2: 1.514017 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.160871 Loss1: 0.621813 Loss2: 1.539058 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.105183 Loss1: 0.578691 Loss2: 1.526492
+(DefaultActor pid=3764) >> Training accuracy: 0.860417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.472986 Loss1: 2.340649 Loss2: 2.132337
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.295750 Loss1: 1.765191 Loss2: 1.530559
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.048677 Loss1: 1.533447 Loss2: 1.515230
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.824646 Loss1: 1.287014 Loss2: 1.537632
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.701979 Loss1: 1.164218 Loss2: 1.537762
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.319053 Loss1: 2.306644 Loss2: 2.012409
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.603822 Loss1: 1.055965 Loss2: 1.547857
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.554852 Loss1: 1.003704 Loss2: 1.551149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.497853 Loss1: 0.948024 Loss2: 1.549828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.455079 Loss1: 0.898366 Loss2: 1.556713 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.353433 Loss1: 0.782846 Loss2: 1.570587 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.767708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.215629 Loss1: 0.735440 Loss2: 1.480189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.162829 Loss1: 0.681794 Loss2: 1.481035 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.171499 Loss1: 0.679628 Loss2: 1.491871
+(DefaultActor pid=3764) >> Training accuracy: 0.790625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.493800 Loss1: 2.353522 Loss2: 2.140277
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.384979 Loss1: 1.812480 Loss2: 1.572499
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.184834 Loss1: 1.602024 Loss2: 1.582810
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.829682 Loss1: 1.277451 Loss2: 1.552231
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.610324 Loss1: 1.063933 Loss2: 1.546391
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.091633 Loss1: 2.044318 Loss2: 2.047316
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.516559 Loss1: 0.958551 Loss2: 1.558008
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.468406 Loss1: 0.901808 Loss2: 1.566598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.370116 Loss1: 0.807880 Loss2: 1.562236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.385842 Loss1: 0.807144 Loss2: 1.578698 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.325080 Loss1: 0.742524 Loss2: 1.582556 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.752083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.219154 Loss1: 0.745672 Loss2: 1.473482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.185754 Loss1: 0.717677 Loss2: 1.468077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.258638 Loss1: 0.784644 Loss2: 1.473993
+(DefaultActor pid=3764) >> Training accuracy: 0.851042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.410369 Loss1: 2.403733 Loss2: 2.006636
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.370879 Loss1: 1.864414 Loss2: 1.506466
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.915472 Loss1: 1.440237 Loss2: 1.475235
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.693203 Loss1: 1.209694 Loss2: 1.483509
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.699238 Loss1: 1.202992 Loss2: 1.496246
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.258831 Loss1: 2.215816 Loss2: 2.043014
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.574536 Loss1: 1.069588 Loss2: 1.504948
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.258545 Loss1: 1.743828 Loss2: 1.514717
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.485564 Loss1: 0.978314 Loss2: 1.507250
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.904127 Loss1: 1.390541 Loss2: 1.513586
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.438650 Loss1: 0.913370 Loss2: 1.525280
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.709996 Loss1: 1.181239 Loss2: 1.528757
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.375067 Loss1: 0.855402 Loss2: 1.519665
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.603806 Loss1: 1.087971 Loss2: 1.515835
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.230924 Loss1: 0.700933 Loss2: 1.529991
+(DefaultActor pid=3765) >> Training accuracy: 0.788086
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.488457 Loss1: 0.970352 Loss2: 1.518105
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.558613 Loss1: 1.011494 Loss2: 1.547119
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.493433 Loss1: 0.933587 Loss2: 1.559846
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.401512 Loss1: 0.866454 Loss2: 1.535057
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.300583 Loss1: 0.749075 Loss2: 1.551508
+(DefaultActor pid=3764) >> Training accuracy: 0.821289
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.399257 Loss1: 2.382696 Loss2: 2.016561
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.317018 Loss1: 1.798329 Loss2: 1.518689
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.964030 Loss1: 1.456569 Loss2: 1.507461
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.812513 Loss1: 1.291326 Loss2: 1.521187
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.675937 Loss1: 1.154655 Loss2: 1.521282
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.172393 Loss1: 2.135632 Loss2: 2.036761
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.172771 Loss1: 1.705226 Loss2: 1.467545
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.572086 Loss1: 1.035893 Loss2: 1.536192
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.861162 Loss1: 1.405808 Loss2: 1.455354
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.557268 Loss1: 1.008922 Loss2: 1.548346
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.652105 Loss1: 1.172141 Loss2: 1.479964
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.515100 Loss1: 0.973901 Loss2: 1.541200
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.530563 Loss1: 1.045046 Loss2: 1.485517
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.553378 Loss1: 0.994929 Loss2: 1.558449
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.429778 Loss1: 0.855890 Loss2: 1.573888 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.713867
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.219885 Loss1: 0.728270 Loss2: 1.491615 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.186527 Loss1: 0.691317 Loss2: 1.495210 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.814583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.406486 Loss1: 2.287651 Loss2: 2.118835
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.298539 Loss1: 1.752418 Loss2: 1.546122
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.043228 Loss1: 1.505384 Loss2: 1.537844
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.898705 Loss1: 1.342920 Loss2: 1.555786
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.995836 Loss1: 2.042151 Loss2: 1.953685
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.998626 Loss1: 1.589878 Loss2: 1.408748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.686902 Loss1: 1.307699 Loss2: 1.379203 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.381107 Loss1: 1.018253 Loss2: 1.362853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.313751 Loss1: 0.928737 Loss2: 1.385014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.223667 Loss1: 0.840020 Loss2: 1.383647 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.785417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.122179 Loss1: 0.720566 Loss2: 1.401613 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.051391 Loss1: 0.651925 Loss2: 1.399466 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.853125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.985919 Loss1: 2.016486 Loss2: 1.969434
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.987619 Loss1: 1.516292 Loss2: 1.471327
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.664639 Loss1: 1.224291 Loss2: 1.440348
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.450215 Loss1: 1.017134 Loss2: 1.433081
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.258509 Loss1: 2.231464 Loss2: 2.027046
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.243565 Loss1: 1.759958 Loss2: 1.483607 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.289433 Loss1: 0.829250 Loss2: 1.460183
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.876513 Loss1: 1.405791 Loss2: 1.470722
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.194048 Loss1: 0.748804 Loss2: 1.445244
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.698405 Loss1: 1.234141 Loss2: 1.464264
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.178338 Loss1: 0.723196 Loss2: 1.455142
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.531405 Loss1: 1.046373 Loss2: 1.485032
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.206391 Loss1: 0.748196 Loss2: 1.458195
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.468332 Loss1: 0.986938 Loss2: 1.481394
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.183706 Loss1: 0.710259 Loss2: 1.473447
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.402760 Loss1: 0.903856 Loss2: 1.498904
+(DefaultActor pid=3765) >> Training accuracy: 0.847656
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.321890 Loss1: 0.835423 Loss2: 1.486467
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.302250 Loss1: 0.809924 Loss2: 1.492327
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.217847 Loss1: 0.728043 Loss2: 1.489804
+(DefaultActor pid=3764) >> Training accuracy: 0.789583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.109878 Loss1: 2.081811 Loss2: 2.028067
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.103558 Loss1: 1.588121 Loss2: 1.515437
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.798829 Loss1: 1.310797 Loss2: 1.488032
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.560976 Loss1: 1.091302 Loss2: 1.469673
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.215958 Loss1: 2.149858 Loss2: 2.066101
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.039607 Loss1: 1.571389 Loss2: 1.468218
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.441685 Loss1: 0.969589 Loss2: 1.472095
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.673932 Loss1: 1.257471 Loss2: 1.416461
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.327046 Loss1: 0.850191 Loss2: 1.476855
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.353148 Loss1: 0.857940 Loss2: 1.495208 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.260385 Loss1: 0.762259 Loss2: 1.498126 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.129168 Loss1: 0.631080 Loss2: 1.498088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.116655 Loss1: 0.622301 Loss2: 1.494354 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.810417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.062701 Loss1: 0.619972 Loss2: 1.442729 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.843750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.262967 Loss1: 2.127169 Loss2: 2.135799
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.093401 Loss1: 1.563938 Loss2: 1.529463
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.900824 Loss1: 1.379869 Loss2: 1.520954
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.606468 Loss1: 1.087403 Loss2: 1.519065
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.395145 Loss1: 2.270745 Loss2: 2.124400
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.436921 Loss1: 0.912178 Loss2: 1.524743
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.264599 Loss1: 1.728549 Loss2: 1.536049
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.348441 Loss1: 0.819324 Loss2: 1.529117
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.911001 Loss1: 1.393526 Loss2: 1.517475
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.322357 Loss1: 0.787619 Loss2: 1.534737
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.734583 Loss1: 1.202990 Loss2: 1.531593
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.369384 Loss1: 0.826384 Loss2: 1.543000
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.601772 Loss1: 1.075404 Loss2: 1.526368
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.335638 Loss1: 0.774189 Loss2: 1.561449
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.499080 Loss1: 0.957957 Loss2: 1.541123
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.293703 Loss1: 0.733857 Loss2: 1.559846
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.417189 Loss1: 0.873243 Loss2: 1.543946
+(DefaultActor pid=3765) >> Training accuracy: 0.795833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.436149 Loss1: 0.894169 Loss2: 1.541980
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.412517 Loss1: 0.840693 Loss2: 1.571825
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.289160 Loss1: 0.729182 Loss2: 1.559978
+(DefaultActor pid=3764) >> Training accuracy: 0.818750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.378419 Loss1: 2.243186 Loss2: 2.135232
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.204915 Loss1: 1.677742 Loss2: 1.527173
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.820675 Loss1: 1.333336 Loss2: 1.487339
+DEBUG flwr 2023-10-09 09:34:51,916 | server.py:236 | fit_round 34 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.657319 Loss1: 1.155376 Loss2: 1.501943
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.370656 Loss1: 2.210430 Loss2: 2.160226
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.322199 Loss1: 0.825355 Loss2: 1.496844 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.303796 Loss1: 0.791274 Loss2: 1.512522 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.360361 Loss1: 0.816328 Loss2: 1.544033 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.206547 Loss1: 0.674579 Loss2: 1.531968 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.141791 Loss1: 0.610253 Loss2: 1.531539 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.830529
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.278084 Loss1: 0.730133 Loss2: 1.547951 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.257700 Loss1: 0.692215 Loss2: 1.565485 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.723214
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.232559 Loss1: 1.722297 Loss2: 1.510262 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.675025 Loss1: 1.201496 Loss2: 1.473529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.323050 Loss1: 2.153659 Loss2: 2.169391
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.579024 Loss1: 1.088393 Loss2: 1.490630
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.148022 Loss1: 1.568029 Loss2: 1.579993
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.447677 Loss1: 0.965953 Loss2: 1.481724
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.847577 Loss1: 1.295848 Loss2: 1.551730
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.397595 Loss1: 0.886064 Loss2: 1.511530
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.673678 Loss1: 1.104606 Loss2: 1.569072
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.229017 Loss1: 0.732877 Loss2: 1.496140
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.511255 Loss1: 0.965547 Loss2: 1.545708
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.196350 Loss1: 0.701606 Loss2: 1.494744
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.436249 Loss1: 0.879165 Loss2: 1.557084
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.272236 Loss1: 0.766318 Loss2: 1.505919
+(DefaultActor pid=3765) >> Training accuracy: 0.732292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.286298 Loss1: 0.724329 Loss2: 1.561969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.122625 Loss1: 0.553773 Loss2: 1.568852 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.840625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.162796 Loss1: 1.606786 Loss2: 1.556010 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.618035 Loss1: 1.111787 Loss2: 1.506247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.432616 Loss1: 2.281058 Loss2: 2.151558
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.473197 Loss1: 0.947918 Loss2: 1.525280
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.271885 Loss1: 1.703095 Loss2: 1.568789
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.364959 Loss1: 0.851019 Loss2: 1.513940
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.018994 Loss1: 1.472422 Loss2: 1.546572
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.248724 Loss1: 0.726264 Loss2: 1.522460
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.771743 Loss1: 1.206356 Loss2: 1.565387
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.282307 Loss1: 0.751441 Loss2: 1.530865
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.660703 Loss1: 1.087325 Loss2: 1.573377
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.234515 Loss1: 0.682081 Loss2: 1.552434
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.548351 Loss1: 0.961314 Loss2: 1.587037
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.243926 Loss1: 0.702872 Loss2: 1.541054
+(DefaultActor pid=3765) >> Training accuracy: 0.820833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.393748 Loss1: 0.810047 Loss2: 1.583700 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.383805 Loss1: 0.793798 Loss2: 1.590006 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.818750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 09:34:51,916][flwr][DEBUG] - fit_round 34 received 50 results and 0 failures
+INFO flwr 2023-10-09 09:35:34,074 | server.py:125 | fit progress: (34, 2.68708546359699, {'accuracy': 0.3834}, 78241.852389781)
+>> Test accuracy: 0.383400
+[2023-10-09 09:35:34,074][flwr][INFO] - fit progress: (34, 2.68708546359699, {'accuracy': 0.3834}, 78241.852389781)
+DEBUG flwr 2023-10-09 09:35:34,074 | server.py:173 | evaluate_round 34: strategy sampled 50 clients (out of 50)
+[2023-10-09 09:35:34,074][flwr][DEBUG] - evaluate_round 34: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 09:44:37,258 | server.py:187 | evaluate_round 34 received 50 results and 0 failures
+[2023-10-09 09:44:37,258][flwr][DEBUG] - evaluate_round 34 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 09:44:37,258 | server.py:222 | fit_round 35: strategy sampled 50 clients (out of 50)
+[2023-10-09 09:44:37,258][flwr][DEBUG] - fit_round 35: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.122047 Loss1: 1.999771 Loss2: 2.122276
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.985779 Loss1: 1.450234 Loss2: 1.535545
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.641013 Loss1: 1.127759 Loss2: 1.513254
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.545282 Loss1: 1.023113 Loss2: 1.522168
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.271664 Loss1: 2.143282 Loss2: 2.128382
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.236754 Loss1: 1.673676 Loss2: 1.563078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.948477 Loss1: 1.406340 Loss2: 1.542136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.762158 Loss1: 1.208015 Loss2: 1.554143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.550349 Loss1: 0.997863 Loss2: 1.552485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.651764 Loss1: 1.088936 Loss2: 1.562828 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.843750
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.186872 Loss1: 0.644523 Loss2: 1.542349
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.518187 Loss1: 0.949885 Loss2: 1.568302
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.397036 Loss1: 0.827388 Loss2: 1.569648
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.260921 Loss1: 0.695794 Loss2: 1.565127
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.230037 Loss1: 0.664809 Loss2: 1.565228
+(DefaultActor pid=3764) >> Training accuracy: 0.838542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.312375 Loss1: 2.294363 Loss2: 2.018012
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.191612 Loss1: 1.707794 Loss2: 1.483818
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.868278 Loss1: 1.420575 Loss2: 1.447703
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.671845 Loss1: 1.205113 Loss2: 1.466731
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.359090 Loss1: 2.287070 Loss2: 2.072020
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.160934 Loss1: 1.626783 Loss2: 1.534151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.865779 Loss1: 1.346299 Loss2: 1.519480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.632999 Loss1: 1.117669 Loss2: 1.515331 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.466646 Loss1: 0.947347 Loss2: 1.519299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.433695 Loss1: 0.904779 Loss2: 1.528915 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.815625
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.242755 Loss1: 0.768282 Loss2: 1.474473
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.358392 Loss1: 0.825223 Loss2: 1.533169
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.372097 Loss1: 0.815656 Loss2: 1.556441
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.310083 Loss1: 0.764535 Loss2: 1.545549
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.233097 Loss1: 0.684827 Loss2: 1.548270
+(DefaultActor pid=3764) >> Training accuracy: 0.818750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.404034 Loss1: 2.249441 Loss2: 2.154594
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.075589 Loss1: 1.539980 Loss2: 1.535609
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.815763 Loss1: 1.307200 Loss2: 1.508563
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.711745 Loss1: 1.194060 Loss2: 1.517685
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.274992 Loss1: 2.203559 Loss2: 2.071433
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.208507 Loss1: 1.679897 Loss2: 1.528610 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.862807 Loss1: 1.345701 Loss2: 1.517105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.629209 Loss1: 1.103880 Loss2: 1.525329 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.572725 Loss1: 1.043403 Loss2: 1.529322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.533950 Loss1: 0.997959 Loss2: 1.535991 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.821875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.280192 Loss1: 0.749939 Loss2: 1.530253 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.288957 Loss1: 0.745585 Loss2: 1.543372 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.782227
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.280292 Loss1: 1.718199 Loss2: 1.562093 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.798306 Loss1: 1.246194 Loss2: 1.552112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.424332 Loss1: 2.283360 Loss2: 2.140972
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.680740 Loss1: 1.132403 Loss2: 1.548337
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.223005 Loss1: 1.658922 Loss2: 1.564082
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.519167 Loss1: 0.961782 Loss2: 1.557384
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.530851 Loss1: 0.958299 Loss2: 1.572552 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.471090 Loss1: 0.880551 Loss2: 1.590539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.355812 Loss1: 0.784340 Loss2: 1.571472 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.307454 Loss1: 0.724250 Loss2: 1.583204 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.750000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.338252 Loss1: 0.764065 Loss2: 1.574187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.273289 Loss1: 0.702246 Loss2: 1.571043 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.769792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.452469 Loss1: 2.324601 Loss2: 2.127868
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.290467 Loss1: 1.771029 Loss2: 1.519438
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.991717 Loss1: 1.482294 Loss2: 1.509423
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.688357 Loss1: 1.174953 Loss2: 1.513403
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.250730 Loss1: 2.190326 Loss2: 2.060404
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.161685 Loss1: 1.664518 Loss2: 1.497167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.439406 Loss1: 0.909099 Loss2: 1.530307 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.387731 Loss1: 0.858744 Loss2: 1.528987 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.292597 Loss1: 0.733693 Loss2: 1.558904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.291356 Loss1: 0.745581 Loss2: 1.545775 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.834821
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.229295 Loss1: 0.722274 Loss2: 1.507021 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.073586 Loss1: 0.570402 Loss2: 1.503184 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.828125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.189279 Loss1: 1.698250 Loss2: 1.491029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.509824 Loss1: 1.031930 Loss2: 1.477894 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.459791 Loss1: 0.974957 Loss2: 1.484834
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.375721 Loss1: 2.291706 Loss2: 2.084015
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.059449 Loss1: 1.538879 Loss2: 1.520570
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.404194 Loss1: 0.897256 Loss2: 1.506937
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.778715 Loss1: 1.282174 Loss2: 1.496541
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.245409 Loss1: 0.746684 Loss2: 1.498726
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.583627 Loss1: 1.071147 Loss2: 1.512480
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.179823 Loss1: 0.678571 Loss2: 1.501252
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.488442 Loss1: 0.972813 Loss2: 1.515629
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.219100 Loss1: 0.706078 Loss2: 1.513022
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.235323 Loss1: 0.724180 Loss2: 1.511142 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.845703
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.285081 Loss1: 0.754720 Loss2: 1.530360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.157096 Loss1: 0.622743 Loss2: 1.534353 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.783333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.147702 Loss1: 1.659108 Loss2: 1.488594 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.633181 Loss1: 1.170727 Loss2: 1.462454 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.503654 Loss1: 1.030003 Loss2: 1.473651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.368233 Loss1: 0.886902 Loss2: 1.481331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.304287 Loss1: 0.822238 Loss2: 1.482049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.247679 Loss1: 0.763684 Loss2: 1.483995 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.192025 Loss1: 0.704470 Loss2: 1.487555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.116452 Loss1: 0.624277 Loss2: 1.492175 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.831250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.326867 Loss1: 0.789360 Loss2: 1.537507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.103654 Loss1: 0.568240 Loss2: 1.535414 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.853125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.299694 Loss1: 1.764888 Loss2: 1.534806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.715707 Loss1: 1.186011 Loss2: 1.529696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.044301 Loss1: 2.072455 Loss2: 1.971845
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.596476 Loss1: 1.039103 Loss2: 1.557374
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.886905 Loss1: 1.439768 Loss2: 1.447137
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.489127 Loss1: 0.937275 Loss2: 1.551853
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.675887 Loss1: 1.262569 Loss2: 1.413318
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.397844 Loss1: 0.839193 Loss2: 1.558651
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.477050 Loss1: 1.060345 Loss2: 1.416705
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.365613 Loss1: 0.812966 Loss2: 1.552647
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.338578 Loss1: 0.916560 Loss2: 1.422018
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.386748 Loss1: 0.797323 Loss2: 1.589425
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.315131 Loss1: 0.884035 Loss2: 1.431096
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.298638 Loss1: 0.710179 Loss2: 1.588459
+(DefaultActor pid=3765) >> Training accuracy: 0.761458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.136320 Loss1: 0.696213 Loss2: 1.440107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.010535 Loss1: 0.585326 Loss2: 1.425209 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.839583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.321292 Loss1: 1.779076 Loss2: 1.542216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.684919 Loss1: 1.159169 Loss2: 1.525750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.635306 Loss1: 1.094342 Loss2: 1.540963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.535410 Loss1: 0.979278 Loss2: 1.556132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.523051 Loss1: 0.974753 Loss2: 1.548298 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.442662 Loss1: 0.873222 Loss2: 1.569440 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.240698 Loss1: 0.680021 Loss2: 1.560677 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.278864 Loss1: 0.711412 Loss2: 1.567452 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.812500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.075659 Loss1: 0.595363 Loss2: 1.480296 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.848958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.198367 Loss1: 2.103298 Loss2: 2.095069 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.877417 Loss1: 1.353268 Loss2: 1.524149 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.316129 Loss1: 2.169981 Loss2: 2.146149
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.593809 Loss1: 1.059076 Loss2: 1.534733
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.406317 Loss1: 0.887939 Loss2: 1.518378 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.448449 Loss1: 0.928742 Loss2: 1.519707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.323738 Loss1: 0.781880 Loss2: 1.541858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.398488 Loss1: 0.880261 Loss2: 1.518228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.341143 Loss1: 0.816546 Loss2: 1.524597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.274813 Loss1: 0.755481 Loss2: 1.519332 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.778320
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.225021 Loss1: 0.700637 Loss2: 1.524384 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.835337
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.377612 Loss1: 2.252582 Loss2: 2.125029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.870479 Loss1: 1.333853 Loss2: 1.536626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.654708 Loss1: 1.123435 Loss2: 1.531273
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.147441 Loss1: 2.123389 Loss2: 2.024052
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.489898 Loss1: 0.941580 Loss2: 1.548317
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.993312 Loss1: 1.503606 Loss2: 1.489706
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.532691 Loss1: 0.975421 Loss2: 1.557270
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.642051 Loss1: 1.196012 Loss2: 1.446039
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.428673 Loss1: 0.864179 Loss2: 1.564493
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.445520 Loss1: 0.988100 Loss2: 1.457421
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.381710 Loss1: 0.808705 Loss2: 1.573005
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.401575 Loss1: 0.936292 Loss2: 1.465283
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.354030 Loss1: 0.774891 Loss2: 1.579139
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.345182 Loss1: 0.862620 Loss2: 1.482562
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.202284 Loss1: 0.628158 Loss2: 1.574127
+(DefaultActor pid=3765) >> Training accuracy: 0.853125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.208414 Loss1: 0.735452 Loss2: 1.472963
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.188615 Loss1: 0.706143 Loss2: 1.482472
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.187060 Loss1: 0.709014 Loss2: 1.478046
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.108677 Loss1: 0.625530 Loss2: 1.483148
+(DefaultActor pid=3764) >> Training accuracy: 0.871875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.303352 Loss1: 2.197572 Loss2: 2.105779
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.053806 Loss1: 1.534733 Loss2: 1.519073
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.803887 Loss1: 1.293267 Loss2: 1.510620
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.638822 Loss1: 1.126141 Loss2: 1.512682
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.357479 Loss1: 2.364691 Loss2: 1.992789
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.172921 Loss1: 1.710742 Loss2: 1.462178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.934582 Loss1: 1.470867 Loss2: 1.463714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.699431 Loss1: 1.223289 Loss2: 1.476142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.493105 Loss1: 1.041211 Loss2: 1.451894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.380333 Loss1: 0.904895 Loss2: 1.475438 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.838542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.408242 Loss1: 0.912713 Loss2: 1.495529 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.151289 Loss1: 0.660014 Loss2: 1.491275 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.838867
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.167643 Loss1: 1.664592 Loss2: 1.503051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.649216 Loss1: 1.147031 Loss2: 1.502185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.540517 Loss1: 1.047612 Loss2: 1.492905
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.366496 Loss1: 2.253113 Loss2: 2.113383
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.182930 Loss1: 1.626274 Loss2: 1.556655
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.435641 Loss1: 0.930152 Loss2: 1.505489
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.359836 Loss1: 0.847465 Loss2: 1.512371
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.815627 Loss1: 1.293664 Loss2: 1.521963
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.344306 Loss1: 0.824508 Loss2: 1.519798
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.658711 Loss1: 1.125245 Loss2: 1.533465
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.259959 Loss1: 0.755864 Loss2: 1.504094
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.468302 Loss1: 0.938342 Loss2: 1.529960
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.216364 Loss1: 0.692268 Loss2: 1.524097
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.479231 Loss1: 0.947709 Loss2: 1.531522
+(DefaultActor pid=3765) >> Training accuracy: 0.828125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.490671 Loss1: 0.931694 Loss2: 1.558977
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.351400 Loss1: 0.777524 Loss2: 1.573876
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.233799 Loss1: 0.679523 Loss2: 1.554276
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.345298 Loss1: 0.784950 Loss2: 1.560348
+(DefaultActor pid=3764) >> Training accuracy: 0.794792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.185753 Loss1: 2.006236 Loss2: 2.179517
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.082043 Loss1: 1.498182 Loss2: 1.583861
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.672560 Loss1: 1.125472 Loss2: 1.547089
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.484103 Loss1: 0.948955 Loss2: 1.535148
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.177395 Loss1: 2.076625 Loss2: 2.100769
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.963913 Loss1: 1.473533 Loss2: 1.490381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.736171 Loss1: 1.252204 Loss2: 1.483967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.503119 Loss1: 1.017109 Loss2: 1.486010 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.463474 Loss1: 0.964357 Loss2: 1.499117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.379802 Loss1: 0.863374 Loss2: 1.516428 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.814583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.234998 Loss1: 0.726245 Loss2: 1.508753 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.077930 Loss1: 0.579877 Loss2: 1.498053 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.838542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.131113 Loss1: 1.672068 Loss2: 1.459046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.581169 Loss1: 1.141854 Loss2: 1.439315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.139959 Loss1: 1.997741 Loss2: 2.142219
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.472035 Loss1: 1.004599 Loss2: 1.467437
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.014114 Loss1: 1.447584 Loss2: 1.566530
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.463627 Loss1: 0.989351 Loss2: 1.474275
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.756882 Loss1: 1.208459 Loss2: 1.548422
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.403739 Loss1: 0.925113 Loss2: 1.478627
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.552776 Loss1: 1.009414 Loss2: 1.543362
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.339134 Loss1: 0.860777 Loss2: 1.478357
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.451257 Loss1: 0.897795 Loss2: 1.553462
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.312413 Loss1: 0.827218 Loss2: 1.485195
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.454494 Loss1: 0.907258 Loss2: 1.547236
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.172642 Loss1: 0.686221 Loss2: 1.486420
+(DefaultActor pid=3765) >> Training accuracy: 0.834375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.176610 Loss1: 0.621040 Loss2: 1.555570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.139933 Loss1: 0.575258 Loss2: 1.564675 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.863542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.124725 Loss1: 1.616645 Loss2: 1.508080 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.527728 Loss1: 1.062486 Loss2: 1.465242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.977523 Loss1: 2.022622 Loss2: 1.954902
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.375739 Loss1: 0.897418 Loss2: 1.478322
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.045170 Loss1: 1.601484 Loss2: 1.443686
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.384573 Loss1: 0.904755 Loss2: 1.479818
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.655756 Loss1: 1.221073 Loss2: 1.434683
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.308083 Loss1: 0.825777 Loss2: 1.482306
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.467033 Loss1: 1.042910 Loss2: 1.424123
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.156172 Loss1: 0.668004 Loss2: 1.488168
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.469012 Loss1: 1.027394 Loss2: 1.441618
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.126695 Loss1: 0.642631 Loss2: 1.484064
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.109738 Loss1: 0.616704 Loss2: 1.493034
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.301937 Loss1: 0.854446 Loss2: 1.447491
+(DefaultActor pid=3765) >> Training accuracy: 0.786133
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.208592 Loss1: 0.770098 Loss2: 1.438493
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.086043 Loss1: 0.648606 Loss2: 1.437436
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.116473 Loss1: 0.675518 Loss2: 1.440955
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.182600 Loss1: 0.727530 Loss2: 1.455070
+(DefaultActor pid=3764) >> Training accuracy: 0.765625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.293410 Loss1: 2.157686 Loss2: 2.135724
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.121035 Loss1: 1.575872 Loss2: 1.545163
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.748664 Loss1: 1.242884 Loss2: 1.505780
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.645490 Loss1: 1.131468 Loss2: 1.514022
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.532207 Loss1: 0.989869 Loss2: 1.542338
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.332314 Loss1: 2.267782 Loss2: 2.064532
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.402297 Loss1: 1.889008 Loss2: 1.513289 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 3.063341 Loss1: 1.549136 Loss2: 1.514205 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.836157 Loss1: 1.332114 Loss2: 1.504044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.636455 Loss1: 1.126471 Loss2: 1.509984 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.797917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.480911 Loss1: 0.971252 Loss2: 1.509659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.368847 Loss1: 0.841961 Loss2: 1.526886 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.278079 Loss1: 0.735557 Loss2: 1.542522 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.778125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.988347 Loss1: 1.523947 Loss2: 1.464400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.513603 Loss1: 1.054129 Loss2: 1.459473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.231757 Loss1: 2.159284 Loss2: 2.072473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.994125 Loss1: 1.516711 Loss2: 1.477414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.660109 Loss1: 1.208024 Loss2: 1.452085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.579806 Loss1: 1.107372 Loss2: 1.472434 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.411192 Loss1: 0.934369 Loss2: 1.476823 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.821875
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.127366 Loss1: 0.646347 Loss2: 1.481020
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.310872 Loss1: 0.830328 Loss2: 1.480543
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.227609 Loss1: 0.751318 Loss2: 1.476290
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.200022 Loss1: 0.708153 Loss2: 1.491869
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.225623 Loss1: 0.728453 Loss2: 1.497170
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.160588 Loss1: 0.663102 Loss2: 1.497486
+(DefaultActor pid=3764) >> Training accuracy: 0.785714
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.600061 Loss1: 2.441676 Loss2: 2.158385
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.324298 Loss1: 1.769194 Loss2: 1.555104
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.956588 Loss1: 1.417360 Loss2: 1.539229
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.780436 Loss1: 1.218954 Loss2: 1.561482
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.658708 Loss1: 1.085936 Loss2: 1.572772
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.594252 Loss1: 1.012520 Loss2: 1.581732 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.401679 Loss1: 0.828361 Loss2: 1.573319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.342134 Loss1: 0.766210 Loss2: 1.575924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.291919 Loss1: 0.710321 Loss2: 1.581598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.221862 Loss1: 0.637914 Loss2: 1.583948 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.799107
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.410493 Loss1: 0.848163 Loss2: 1.562330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.182445 Loss1: 0.661684 Loss2: 1.520762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.201513 Loss1: 0.676731 Loss2: 1.524783
+(DefaultActor pid=3764) >> Training accuracy: 0.785417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.470110 Loss1: 2.321181 Loss2: 2.148929
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.248047 Loss1: 1.678823 Loss2: 1.569223
+(DefaultActor pid=3765) Epoch: 2 Loss: 3.035786 Loss1: 1.485546 Loss2: 1.550240
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.681336 Loss1: 1.117013 Loss2: 1.564323
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.598053 Loss1: 1.044818 Loss2: 1.553235
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.455368 Loss1: 2.172505 Loss2: 2.282864
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.624077 Loss1: 1.066089 Loss2: 1.557988
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.431925 Loss1: 0.861306 Loss2: 1.570619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.341586 Loss1: 0.774661 Loss2: 1.566925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.525637 Loss1: 0.971970 Loss2: 1.553666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.399948 Loss1: 0.833749 Loss2: 1.566199 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.804167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.361662 Loss1: 0.778334 Loss2: 1.583328 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.230710 Loss1: 0.653204 Loss2: 1.577507 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.802083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.335184 Loss1: 2.257864 Loss2: 2.077320
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.166925 Loss1: 1.645705 Loss2: 1.521221
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.872852 Loss1: 1.377960 Loss2: 1.494892
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.672706 Loss1: 1.174138 Loss2: 1.498568
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.414193 Loss1: 2.363901 Loss2: 2.050291
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.289822 Loss1: 1.757299 Loss2: 1.532524 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.924220 Loss1: 1.433702 Loss2: 1.490517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.727615 Loss1: 1.241241 Loss2: 1.486373 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.610313 Loss1: 1.120584 Loss2: 1.489729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.573167 Loss1: 1.062983 Loss2: 1.510183 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.803125
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.216143 Loss1: 0.700456 Loss2: 1.515687
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.452669 Loss1: 0.942987 Loss2: 1.509682
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.385519 Loss1: 0.887592 Loss2: 1.497926
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.229793 Loss1: 0.740962 Loss2: 1.488831
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.148747 Loss1: 0.654453 Loss2: 1.494294
+(DefaultActor pid=3764) >> Training accuracy: 0.815625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.207533 Loss1: 2.111807 Loss2: 2.095726
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.098901 Loss1: 1.537432 Loss2: 1.561469
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.815829 Loss1: 1.279267 Loss2: 1.536562
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.371268 Loss1: 2.117803 Loss2: 2.253465
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.586720 Loss1: 1.048766 Loss2: 1.537954
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.190644 Loss1: 1.609717 Loss2: 1.580927
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.414717 Loss1: 0.873410 Loss2: 1.541307
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.365496 Loss1: 0.840191 Loss2: 1.525305 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.296005 Loss1: 0.747758 Loss2: 1.548248 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.227734 Loss1: 0.686001 Loss2: 1.541732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.252474 Loss1: 0.701079 Loss2: 1.551394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.156616 Loss1: 0.596881 Loss2: 1.559735 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.802734
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.102095 Loss1: 0.550177 Loss2: 1.551918 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.818510
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.307417 Loss1: 2.182948 Loss2: 2.124469
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.092518 Loss1: 1.544703 Loss2: 1.547815
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.704718 Loss1: 1.204150 Loss2: 1.500567
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.531480 Loss1: 1.036164 Loss2: 1.495316
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.193075 Loss1: 2.143734 Loss2: 2.049341
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.172370 Loss1: 1.666883 Loss2: 1.505487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.839195 Loss1: 1.357156 Loss2: 1.482038 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.721830 Loss1: 1.238002 Loss2: 1.483828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.490873 Loss1: 1.014446 Loss2: 1.476427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.358412 Loss1: 0.876766 Loss2: 1.481647 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.786458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.367265 Loss1: 0.887040 Loss2: 1.480224 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.273667 Loss1: 0.778998 Loss2: 1.494669 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.806641
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.131151 Loss1: 2.106961 Loss2: 2.024190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.660235 Loss1: 1.218865 Loss2: 1.441371 [repeated 2x across cluster]
+DEBUG flwr 2023-10-09 10:13:50,019 | server.py:236 | fit_round 35 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.296898 Loss1: 2.225329 Loss2: 2.071570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.313269 Loss1: 1.816099 Loss2: 1.497170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.918511 Loss1: 1.432801 Loss2: 1.485709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.728394 Loss1: 1.237870 Loss2: 1.490524 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.587323 Loss1: 1.084830 Loss2: 1.502493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.440848 Loss1: 0.943311 Loss2: 1.497536 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.836458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.314972 Loss1: 0.803849 Loss2: 1.511123 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.182198 Loss1: 0.677027 Loss2: 1.505171 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.781250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.136185 Loss1: 1.602074 Loss2: 1.534112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.630380 Loss1: 1.106867 Loss2: 1.523513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.348381 Loss1: 2.263473 Loss2: 2.084908
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.544028 Loss1: 1.012486 Loss2: 1.531542
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.145397 Loss1: 1.652451 Loss2: 1.492947
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.439417 Loss1: 0.910463 Loss2: 1.528954
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.869996 Loss1: 1.385947 Loss2: 1.484049
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.329536 Loss1: 0.775886 Loss2: 1.553651
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.663426 Loss1: 1.186547 Loss2: 1.476879
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.347699 Loss1: 0.804972 Loss2: 1.542728
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.643998 Loss1: 1.158278 Loss2: 1.485720
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.265842 Loss1: 0.706818 Loss2: 1.559025
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.485308 Loss1: 0.989668 Loss2: 1.495640
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.297935 Loss1: 0.739901 Loss2: 1.558035
+(DefaultActor pid=3765) >> Training accuracy: 0.781250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.233117 Loss1: 0.750954 Loss2: 1.482162 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.268684 Loss1: 0.768871 Loss2: 1.499812 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.815625
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 10:13:50,019][flwr][DEBUG] - fit_round 35 received 50 results and 0 failures
+INFO flwr 2023-10-09 10:14:31,970 | server.py:125 | fit progress: (35, 2.6667960539412574, {'accuracy': 0.392}, 80579.74808721)
+>> Test accuracy: 0.392000
+[2023-10-09 10:14:31,970][flwr][INFO] - fit progress: (35, 2.6667960539412574, {'accuracy': 0.392}, 80579.74808721)
+DEBUG flwr 2023-10-09 10:14:31,970 | server.py:173 | evaluate_round 35: strategy sampled 50 clients (out of 50)
+[2023-10-09 10:14:31,970][flwr][DEBUG] - evaluate_round 35: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 10:23:34,698 | server.py:187 | evaluate_round 35 received 50 results and 0 failures
+[2023-10-09 10:23:34,698][flwr][DEBUG] - evaluate_round 35 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 10:23:34,698 | server.py:222 | fit_round 36: strategy sampled 50 clients (out of 50)
+[2023-10-09 10:23:34,698][flwr][DEBUG] - fit_round 36: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.407950 Loss1: 2.231254 Loss2: 2.176696
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.215303 Loss1: 1.695213 Loss2: 1.520090
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.796550 Loss1: 1.334688 Loss2: 1.461862
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.528894 Loss1: 1.048436 Loss2: 1.480458
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.424044 Loss1: 0.941915 Loss2: 1.482128
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.257532 Loss1: 1.760725 Loss2: 1.496807 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.108526 Loss1: 0.632340 Loss2: 1.476186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.646433 Loss1: 1.141301 Loss2: 1.505132 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.824219
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.510615 Loss1: 1.000626 Loss2: 1.509989 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.404677 Loss1: 0.877792 Loss2: 1.526885 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.380037 Loss1: 0.843311 Loss2: 1.536725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.245327 Loss1: 1.750767 Loss2: 1.494561 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.766602
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.569611 Loss1: 1.087272 Loss2: 1.482339 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.380863 Loss1: 0.902324 Loss2: 1.478538 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.264802 Loss1: 0.789328 Loss2: 1.475474
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.189996 Loss1: 2.164681 Loss2: 2.025315
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.083938 Loss1: 1.616545 Loss2: 1.467393 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.822440 Loss1: 1.364950 Loss2: 1.457491 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.743304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.398619 Loss1: 0.935963 Loss2: 1.462656 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.255698 Loss1: 0.759717 Loss2: 1.495981 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.211598 Loss1: 0.720516 Loss2: 1.491082
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.390541 Loss1: 2.257216 Loss2: 2.133324
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.188689 Loss1: 1.622127 Loss2: 1.566562 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.825000
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.108410 Loss1: 0.624020 Loss2: 1.484391
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.870816 Loss1: 1.324194 Loss2: 1.546622
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.610556 Loss1: 1.065156 Loss2: 1.545400
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.487233 Loss1: 0.938379 Loss2: 1.548854
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.383242 Loss1: 0.834270 Loss2: 1.548972
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.337040 Loss1: 0.777064 Loss2: 1.559976
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.145879 Loss1: 2.186440 Loss2: 1.959439
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.142311 Loss1: 0.589287 Loss2: 1.553024
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.003706 Loss1: 1.563524 Loss2: 1.440183
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.150446 Loss1: 0.604053 Loss2: 1.546393
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.621891 Loss1: 1.211726 Loss2: 1.410165
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.217777 Loss1: 0.655454 Loss2: 1.562323
+(DefaultActor pid=3765) >> Training accuracy: 0.864583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.373303 Loss1: 0.961727 Loss2: 1.411576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.189521 Loss1: 0.758639 Loss2: 1.430882 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.056354 Loss1: 0.638416 Loss2: 1.417938
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.187401 Loss1: 2.228261 Loss2: 1.959140
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.054730 Loss1: 0.633163 Loss2: 1.421567
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.180452 Loss1: 1.728596 Loss2: 1.451855
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.996009 Loss1: 0.559648 Loss2: 1.436361
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.804847 Loss1: 1.368876 Loss2: 1.435971
+(DefaultActor pid=3764) >> Training accuracy: 0.831250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.463711 Loss1: 1.033666 Loss2: 1.430045
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.408815 Loss1: 0.970157 Loss2: 1.438659
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.334708 Loss1: 0.895743 Loss2: 1.438965
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.211077 Loss1: 0.764971 Loss2: 1.446105
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.244886 Loss1: 2.133740 Loss2: 2.111145
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.100467 Loss1: 0.666435 Loss2: 1.434032
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.121802 Loss1: 1.599042 Loss2: 1.522760
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.093255 Loss1: 0.654414 Loss2: 1.438840
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.753679 Loss1: 1.256554 Loss2: 1.497125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.970515 Loss1: 0.521557 Loss2: 1.448958
+(DefaultActor pid=3765) >> Training accuracy: 0.819792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.443453 Loss1: 0.954281 Loss2: 1.489172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.314322 Loss1: 0.803017 Loss2: 1.511304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.197495 Loss1: 0.670629 Loss2: 1.526865
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.030407 Loss1: 1.955859 Loss2: 2.074549
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.890910 Loss1: 1.406092 Loss2: 1.484818 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.840625
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.070234 Loss1: 0.560057 Loss2: 1.510177
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.615481 Loss1: 1.155059 Loss2: 1.460421
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.433747 Loss1: 0.980168 Loss2: 1.453578
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.296943 Loss1: 0.839402 Loss2: 1.457541
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.193150 Loss1: 0.739899 Loss2: 1.453251
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.101764 Loss1: 0.637103 Loss2: 1.464661
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.972413 Loss1: 1.901227 Loss2: 2.071185
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.141597 Loss1: 0.668070 Loss2: 1.473526
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.121134 Loss1: 0.628305 Loss2: 1.492829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.054988 Loss1: 0.575654 Loss2: 1.479334 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.798958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.275868 Loss1: 0.779611 Loss2: 1.496257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.164215 Loss1: 0.672640 Loss2: 1.491575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.165747 Loss1: 0.657691 Loss2: 1.508056
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.317211 Loss1: 2.283360 Loss2: 2.033851
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.112426 Loss1: 1.599135 Loss2: 1.513291 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.811458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.028914 Loss1: 0.523945 Loss2: 1.504969
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.862800 Loss1: 1.368149 Loss2: 1.494651
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.685802 Loss1: 1.169815 Loss2: 1.515987
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.468570 Loss1: 0.972137 Loss2: 1.496433
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.356045 Loss1: 0.853912 Loss2: 1.502133
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.246945 Loss1: 0.728915 Loss2: 1.518030
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.164651 Loss1: 2.131465 Loss2: 2.033186
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.247595 Loss1: 0.728594 Loss2: 1.519002
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.124655 Loss1: 1.574479 Loss2: 1.550175
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.250940 Loss1: 0.732142 Loss2: 1.518798
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.175646 Loss1: 0.658174 Loss2: 1.517471
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.789398 Loss1: 1.266528 Loss2: 1.522870
+(DefaultActor pid=3765) >> Training accuracy: 0.836458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.515835 Loss1: 1.005871 Loss2: 1.509964
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.463070 Loss1: 0.948193 Loss2: 1.514877
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.353694 Loss1: 0.832078 Loss2: 1.521616
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.286369 Loss1: 0.761908 Loss2: 1.524461
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.281759 Loss1: 2.192204 Loss2: 2.089555
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.125726 Loss1: 1.607928 Loss2: 1.517797 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.229585 Loss1: 0.686625 Loss2: 1.542960
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.783009 Loss1: 1.281167 Loss2: 1.501842
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.574773 Loss1: 1.066445 Loss2: 1.508328
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.214746 Loss1: 0.671059 Loss2: 1.543687
+(DefaultActor pid=3764) >> Training accuracy: 0.809743
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.401468 Loss1: 0.881869 Loss2: 1.519600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.132471 Loss1: 0.603765 Loss2: 1.528706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.414611 Loss1: 2.349174 Loss2: 2.065437
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.116273 Loss1: 0.593107 Loss2: 1.523166
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.228148 Loss1: 1.704103 Loss2: 1.524045
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.165317 Loss1: 0.631331 Loss2: 1.533986
+(DefaultActor pid=3765) >> Training accuracy: 0.850000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.626205 Loss1: 1.136223 Loss2: 1.489982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.371141 Loss1: 0.866143 Loss2: 1.504998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.407295 Loss1: 0.900494 Loss2: 1.506801
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.407875 Loss1: 2.345793 Loss2: 2.062082
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.212143 Loss1: 1.682665 Loss2: 1.529478 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.819329 Loss1: 1.318785 Loss2: 1.500544 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.796875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.628379 Loss1: 1.123511 Loss2: 1.504868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.461474 Loss1: 0.943794 Loss2: 1.517680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.343207 Loss1: 0.794038 Loss2: 1.549169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.216798 Loss1: 0.685920 Loss2: 1.530878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.387411 Loss1: 0.842968 Loss2: 1.544443 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.781250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.438181 Loss1: 0.944053 Loss2: 1.494129 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.126158 Loss1: 0.631702 Loss2: 1.494456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.089465 Loss1: 0.591457 Loss2: 1.498008
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.229278 Loss1: 2.182295 Loss2: 2.046983
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.198924 Loss1: 1.713522 Loss2: 1.485402 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.850446
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.194696 Loss1: 0.680926 Loss2: 1.513770
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.843062 Loss1: 1.367479 Loss2: 1.475583
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.535371 Loss1: 1.062743 Loss2: 1.472627
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.523916 Loss1: 1.037964 Loss2: 1.485952
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.431535 Loss1: 0.931634 Loss2: 1.499901
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.335012 Loss1: 0.822293 Loss2: 1.512719
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.152803 Loss1: 2.202968 Loss2: 1.949835
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.197573 Loss1: 0.697790 Loss2: 1.499783
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.110722 Loss1: 1.640262 Loss2: 1.470460
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.119265 Loss1: 0.631639 Loss2: 1.487626
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.087221 Loss1: 0.595601 Loss2: 1.491620
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.748959 Loss1: 1.317152 Loss2: 1.431807
+(DefaultActor pid=3765) >> Training accuracy: 0.818750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.520881 Loss1: 1.093086 Loss2: 1.427795
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.363619 Loss1: 0.923586 Loss2: 1.440032
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.268157 Loss1: 0.825263 Loss2: 1.442893
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.205307 Loss1: 0.771606 Loss2: 1.433701
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.157789 Loss1: 2.138147 Loss2: 2.019642
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.270244 Loss1: 0.810211 Loss2: 1.460033
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.048834 Loss1: 1.590390 Loss2: 1.458444
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.227009 Loss1: 0.759629 Loss2: 1.467380
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.164096 Loss1: 0.697695 Loss2: 1.466401 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.817383
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.462299 Loss1: 0.993411 Loss2: 1.468888 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.304415 Loss1: 0.823993 Loss2: 1.480422 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.177751 Loss1: 0.706074 Loss2: 1.471678
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.385568 Loss1: 2.311433 Loss2: 2.074135
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.258848 Loss1: 1.742814 Loss2: 1.516034 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.861458
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.108634 Loss1: 0.633160 Loss2: 1.475475
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.856509 Loss1: 1.377833 Loss2: 1.478675
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.637250 Loss1: 1.148715 Loss2: 1.488535
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.576320 Loss1: 1.082557 Loss2: 1.493763
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.397152 Loss1: 0.891449 Loss2: 1.505704
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.276121 Loss1: 0.776367 Loss2: 1.499754
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.176013 Loss1: 2.216321 Loss2: 1.959692
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.241686 Loss1: 0.725760 Loss2: 1.515926
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.060275 Loss1: 1.623843 Loss2: 1.436432
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.266120 Loss1: 0.746148 Loss2: 1.519972
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.740817 Loss1: 1.329795 Loss2: 1.411022
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.194819 Loss1: 0.671222 Loss2: 1.523597
+(DefaultActor pid=3764) >> Training accuracy: 0.754167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.385860 Loss1: 0.964236 Loss2: 1.421624 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.305545 Loss1: 0.870010 Loss2: 1.435535 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.217586 Loss1: 0.785654 Loss2: 1.431932
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.216173 Loss1: 2.162735 Loss2: 2.053438
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.948600 Loss1: 1.469355 Loss2: 1.479245 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.827083
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.023127 Loss1: 0.603544 Loss2: 1.419584
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.622863 Loss1: 1.162662 Loss2: 1.460200
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.397734 Loss1: 0.951990 Loss2: 1.445744
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.351205 Loss1: 0.895214 Loss2: 1.455991
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.265316 Loss1: 0.779960 Loss2: 1.485357
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.203663 Loss1: 0.743158 Loss2: 1.460505
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.147568 Loss1: 2.057566 Loss2: 2.090002
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.095989 Loss1: 0.622470 Loss2: 1.473520
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.031470 Loss1: 0.577872 Loss2: 1.453598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.023306 Loss1: 0.560764 Loss2: 1.462542 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.834375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.280240 Loss1: 0.801439 Loss2: 1.478801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.187598 Loss1: 0.694059 Loss2: 1.493539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.248908 Loss1: 0.743552 Loss2: 1.505355
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.025552 Loss1: 2.028630 Loss2: 1.996922
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.006192 Loss1: 1.506152 Loss2: 1.500040 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.851042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.647517 Loss1: 1.160308 Loss2: 1.487209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.369775 Loss1: 0.880974 Loss2: 1.488802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.362260 Loss1: 0.860329 Loss2: 1.501930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.237061 Loss1: 0.725155 Loss2: 1.511906 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.219744 Loss1: 0.708153 Loss2: 1.511591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.251480 Loss1: 0.742592 Loss2: 1.508889 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.772461
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.415324 Loss1: 0.982291 Loss2: 1.433032 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.182817 Loss1: 0.746502 Loss2: 1.436315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.167413 Loss1: 2.153286 Loss2: 2.014127
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.159363 Loss1: 0.719066 Loss2: 1.440297
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.069740 Loss1: 1.601353 Loss2: 1.468387
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.074340 Loss1: 0.620186 Loss2: 1.454155
+(DefaultActor pid=3765) >> Training accuracy: 0.844792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.652669 Loss1: 1.191254 Loss2: 1.461414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.471176 Loss1: 0.998683 Loss2: 1.472493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.303606 Loss1: 0.821253 Loss2: 1.482353
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.449665 Loss1: 2.263764 Loss2: 2.185901
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.083271 Loss1: 1.493408 Loss2: 1.589863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.763530 Loss1: 1.227968 Loss2: 1.535562 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.830208
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.130007 Loss1: 0.637267 Loss2: 1.492740
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.538388 Loss1: 0.988465 Loss2: 1.549923
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.445686 Loss1: 0.900071 Loss2: 1.545614
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.284808 Loss1: 0.743604 Loss2: 1.541204
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.305053 Loss1: 0.758537 Loss2: 1.546516
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.221529 Loss1: 0.682036 Loss2: 1.539493
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.935775 Loss1: 1.918544 Loss2: 2.017231
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.161736 Loss1: 0.600501 Loss2: 1.561235
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.848321 Loss1: 1.398747 Loss2: 1.449574
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.095954 Loss1: 0.543474 Loss2: 1.552480
+(DefaultActor pid=3765) >> Training accuracy: 0.817708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.380903 Loss1: 0.928012 Loss2: 1.452891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.126571 Loss1: 0.695578 Loss2: 1.430993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.071942 Loss1: 0.647910 Loss2: 1.424032
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.335669 Loss1: 2.255692 Loss2: 2.079977
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.094432 Loss1: 0.652522 Loss2: 1.441910
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.211648 Loss1: 1.717071 Loss2: 1.494578
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.080920 Loss1: 0.622633 Loss2: 1.458286
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.860250 Loss1: 1.381414 Loss2: 1.478836
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.043304 Loss1: 0.588254 Loss2: 1.455050
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.613975 Loss1: 1.138403 Loss2: 1.475572
+(DefaultActor pid=3764) >> Training accuracy: 0.848958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.512490 Loss1: 1.033186 Loss2: 1.479304
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.376657 Loss1: 0.889982 Loss2: 1.486674
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.333911 Loss1: 0.853399 Loss2: 1.480512
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.317593 Loss1: 0.823533 Loss2: 1.494060
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.302930 Loss1: 2.243879 Loss2: 2.059051
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.314004 Loss1: 0.803958 Loss2: 1.510046
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.153954 Loss1: 1.656676 Loss2: 1.497278
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.164757 Loss1: 0.662285 Loss2: 1.502472
+(DefaultActor pid=3765) >> Training accuracy: 0.814583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.527686 Loss1: 1.053211 Loss2: 1.474474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.324111 Loss1: 0.833094 Loss2: 1.491018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.283188 Loss1: 0.782699 Loss2: 1.500489
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.190643 Loss1: 2.051493 Loss2: 2.139151
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.261030 Loss1: 0.752979 Loss2: 1.508051
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.075573 Loss1: 1.529150 Loss2: 1.546424
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.234237 Loss1: 0.722513 Loss2: 1.511723
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.871214 Loss1: 1.329593 Loss2: 1.541621
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.242430 Loss1: 0.731941 Loss2: 1.510490
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.594169 Loss1: 1.055499 Loss2: 1.538669
+(DefaultActor pid=3764) >> Training accuracy: 0.834375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.500284 Loss1: 0.964991 Loss2: 1.535293
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.371590 Loss1: 0.818345 Loss2: 1.553245
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.180985 Loss1: 0.637800 Loss2: 1.543186
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.140387 Loss1: 0.605233 Loss2: 1.535154
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.490788 Loss1: 2.381577 Loss2: 2.109210
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.071261 Loss1: 0.532882 Loss2: 1.538379
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.353435 Loss1: 1.812643 Loss2: 1.540792
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.220795 Loss1: 0.668317 Loss2: 1.552479
+(DefaultActor pid=3765) >> Training accuracy: 0.845833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.647117 Loss1: 1.126925 Loss2: 1.520193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.391224 Loss1: 0.873095 Loss2: 1.518129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.167051 Loss1: 2.172444 Loss2: 1.994607 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.096529 Loss1: 1.630311 Loss2: 1.466217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.770589 Loss1: 1.325971 Loss2: 1.444619 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.809152
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.425502 Loss1: 0.973336 Loss2: 1.452166 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.132501 Loss1: 0.695310 Loss2: 1.437191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.109342 Loss1: 0.658730 Loss2: 1.450612
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.065263 Loss1: 2.017521 Loss2: 2.047742
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.023164 Loss1: 1.520571 Loss2: 1.502593 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.851042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.807804 Loss1: 1.303795 Loss2: 1.504009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.269405 Loss1: 0.783818 Loss2: 1.485587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.143928 Loss1: 0.652559 Loss2: 1.491368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.159382 Loss1: 0.667565 Loss2: 1.491817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.136576 Loss1: 0.632478 Loss2: 1.504099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.460532 Loss1: 1.000466 Loss2: 1.460067 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.790039
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.269801 Loss1: 0.799325 Loss2: 1.470477 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.189338 Loss1: 0.692497 Loss2: 1.496842 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.164123 Loss1: 0.687348 Loss2: 1.476775
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.258091 Loss1: 2.206825 Loss2: 2.051266
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.131490 Loss1: 0.653744 Loss2: 1.477745
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.196237 Loss1: 1.688107 Loss2: 1.508130
+(DefaultActor pid=3765) >> Training accuracy: 0.833333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.801089 Loss1: 1.327536 Loss2: 1.473553
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.580285 Loss1: 1.092155 Loss2: 1.488131
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.482719 Loss1: 0.998854 Loss2: 1.483866
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.320351 Loss1: 0.829473 Loss2: 1.490877
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.317620 Loss1: 2.182069 Loss2: 2.135551
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.320177 Loss1: 0.826413 Loss2: 1.493763
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.255033 Loss1: 0.753040 Loss2: 1.501993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.189365 Loss1: 0.683340 Loss2: 1.506026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.175637 Loss1: 0.660170 Loss2: 1.515467 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.816667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.203368 Loss1: 0.697132 Loss2: 1.506236 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.105716 Loss1: 0.589547 Loss2: 1.516168 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.876202
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.004117 Loss1: 0.487700 Loss2: 1.516417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.036440 Loss1: 2.065175 Loss2: 1.971265
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.052789 Loss1: 1.584447 Loss2: 1.468343
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.666095 Loss1: 1.219823 Loss2: 1.446272
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.444110 Loss1: 0.992576 Loss2: 1.451534
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.361588 Loss1: 2.248307 Loss2: 2.113280
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.432072 Loss1: 0.987022 Loss2: 1.445051
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.221797 Loss1: 1.676717 Loss2: 1.545080
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.295098 Loss1: 0.844138 Loss2: 1.450960
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.775406 Loss1: 1.263227 Loss2: 1.512179
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.197492 Loss1: 0.739867 Loss2: 1.457625
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.641117 Loss1: 1.119237 Loss2: 1.521880
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.090173 Loss1: 0.640658 Loss2: 1.449515
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.524440 Loss1: 0.981287 Loss2: 1.543153
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.094261 Loss1: 0.632214 Loss2: 1.462048
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.062504 Loss1: 0.596118 Loss2: 1.466386 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.811523
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.288725 Loss1: 0.747619 Loss2: 1.541106 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.199094 Loss1: 0.639438 Loss2: 1.559656 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.821875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.231379 Loss1: 2.142490 Loss2: 2.088890
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.209883 Loss1: 1.621261 Loss2: 1.588622
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.788483 Loss1: 1.221418 Loss2: 1.567065
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.641717 Loss1: 1.076493 Loss2: 1.565224
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.303130 Loss1: 2.292986 Loss2: 2.010145
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.114209 Loss1: 1.600034 Loss2: 1.514175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.428291 Loss1: 0.852582 Loss2: 1.575709
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.815535 Loss1: 1.318660 Loss2: 1.496875
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.624852 Loss1: 1.118701 Loss2: 1.506150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.507052 Loss1: 0.985899 Loss2: 1.521153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.468977 Loss1: 0.944812 Loss2: 1.524165 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.781250
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.180054 Loss1: 0.595831 Loss2: 1.584223
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.406061 Loss1: 0.885622 Loss2: 1.520439
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.286551 Loss1: 0.753425 Loss2: 1.533127
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.179741 Loss1: 0.662182 Loss2: 1.517559
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.116226 Loss1: 0.595857 Loss2: 1.520369
+(DefaultActor pid=3765) >> Training accuracy: 0.803711
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.036890 Loss1: 1.563540 Loss2: 1.473350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.517168 Loss1: 1.049856 Loss2: 1.467312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.338514 Loss1: 0.864159 Loss2: 1.474354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.216432 Loss1: 0.735590 Loss2: 1.480842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.237294 Loss1: 0.761695 Loss2: 1.475598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.197677 Loss1: 0.696310 Loss2: 1.501367 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.092075 Loss1: 0.605569 Loss2: 1.486506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.099269 Loss1: 0.602974 Loss2: 1.496295 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.793750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.182628 Loss1: 0.687400 Loss2: 1.495227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.105519 Loss1: 0.615301 Loss2: 1.490218 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.819792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.866259 Loss1: 1.394099 Loss2: 1.472160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.331986 Loss1: 0.905755 Loss2: 1.426231 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.259037 Loss1: 0.829022 Loss2: 1.430015
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.109828 Loss1: 2.093022 Loss2: 2.016806
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.062053 Loss1: 1.607202 Loss2: 1.454851 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.655224 Loss1: 1.216364 Loss2: 1.438859 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.452406 Loss1: 1.008085 Loss2: 1.444321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.336953 Loss1: 0.891755 Loss2: 1.445198 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.893029
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.228477 Loss1: 0.767399 Loss2: 1.461079 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.188215 Loss1: 0.700298 Loss2: 1.487916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.145388 Loss1: 0.667660 Loss2: 1.477728
+(DefaultActor pid=3765) >> Training accuracy: 0.767708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.097458 Loss1: 2.096232 Loss2: 2.001226
+DEBUG flwr 2023-10-09 10:51:53,629 | server.py:236 | fit_round 36 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.998584 Loss1: 1.467189 Loss2: 1.531395
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.666754 Loss1: 1.170010 Loss2: 1.496744
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.430889 Loss1: 0.925705 Loss2: 1.505184
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.350942 Loss1: 0.860648 Loss2: 1.490293
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.084629 Loss1: 1.950439 Loss2: 2.134190
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.054312 Loss1: 1.490291 Loss2: 1.564021 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.787730 Loss1: 1.251083 Loss2: 1.536647 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.487495 Loss1: 0.954553 Loss2: 1.532942 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.105350 Loss1: 0.598653 Loss2: 1.506697
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.333258 Loss1: 0.808303 Loss2: 1.524955
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.162237 Loss1: 0.658511 Loss2: 1.503726
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.245465 Loss1: 0.739271 Loss2: 1.506194
+(DefaultActor pid=3764) >> Training accuracy: 0.784180
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.188010 Loss1: 0.669773 Loss2: 1.518237
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.239061 Loss1: 0.704742 Loss2: 1.534318
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.145037 Loss1: 0.612119 Loss2: 1.532918
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.120670 Loss1: 0.582156 Loss2: 1.538514
+(DefaultActor pid=3765) >> Training accuracy: 0.832292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.488898 Loss1: 2.369114 Loss2: 2.119784
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.276779 Loss1: 1.747753 Loss2: 1.529026
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.914177 Loss1: 1.419383 Loss2: 1.494793
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.665974 Loss1: 1.165723 Loss2: 1.500250
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.526314 Loss1: 1.018686 Loss2: 1.507628
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.373405 Loss1: 0.859919 Loss2: 1.513486 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.398250 Loss1: 0.871116 Loss2: 1.527134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.278996 Loss1: 0.740846 Loss2: 1.538150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.259709 Loss1: 0.724662 Loss2: 1.535047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.195324 Loss1: 0.655300 Loss2: 1.540025 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.759375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.227599 Loss1: 0.688993 Loss2: 1.538606 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.083609 Loss1: 0.545721 Loss2: 1.537888 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.890625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.247196 Loss1: 1.685445 Loss2: 1.561752 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.756149 Loss1: 1.201147 Loss2: 1.555003 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.460016 Loss1: 0.902341 Loss2: 1.557675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.265969 Loss1: 0.681873 Loss2: 1.584096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.297336 Loss1: 0.725539 Loss2: 1.571797 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.770833
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 10:51:53,629][flwr][DEBUG] - fit_round 36 received 50 results and 0 failures
+INFO flwr 2023-10-09 10:52:35,576 | server.py:125 | fit progress: (36, 2.6492519972804254, {'accuracy': 0.3976}, 82863.354786335)
+>> Test accuracy: 0.397600
+[2023-10-09 10:52:35,576][flwr][INFO] - fit progress: (36, 2.6492519972804254, {'accuracy': 0.3976}, 82863.354786335)
+DEBUG flwr 2023-10-09 10:52:35,577 | server.py:173 | evaluate_round 36: strategy sampled 50 clients (out of 50)
+[2023-10-09 10:52:35,577][flwr][DEBUG] - evaluate_round 36: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 11:01:38,899 | server.py:187 | evaluate_round 36 received 50 results and 0 failures
+[2023-10-09 11:01:38,899][flwr][DEBUG] - evaluate_round 36 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 11:01:38,900 | server.py:222 | fit_round 37: strategy sampled 50 clients (out of 50)
+[2023-10-09 11:01:38,900][flwr][DEBUG] - fit_round 37: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.388906 Loss1: 2.263984 Loss2: 2.124922
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.201628 Loss1: 1.673343 Loss2: 1.528285
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.816801 Loss1: 1.312576 Loss2: 1.504225
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.595514 Loss1: 1.091711 Loss2: 1.503803
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.164509 Loss1: 2.071459 Loss2: 2.093049
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.005406 Loss1: 1.500850 Loss2: 1.504556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.594514 Loss1: 1.098447 Loss2: 1.496067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.452124 Loss1: 0.956534 Loss2: 1.495590 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.302117 Loss1: 0.806659 Loss2: 1.495459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.237276 Loss1: 0.739682 Loss2: 1.497594 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.835938
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.186153 Loss1: 0.673033 Loss2: 1.513119 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.994035 Loss1: 0.488725 Loss2: 1.505309 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.890625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.110629 Loss1: 1.556018 Loss2: 1.554610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.700869 Loss1: 1.149695 Loss2: 1.551174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.300030 Loss1: 2.259035 Loss2: 2.040995
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.457407 Loss1: 0.931027 Loss2: 1.526380
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.179020 Loss1: 1.707168 Loss2: 1.471852
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.354528 Loss1: 0.822836 Loss2: 1.531692
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.826224 Loss1: 1.367742 Loss2: 1.458481
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.311558 Loss1: 0.768086 Loss2: 1.543471
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.531505 Loss1: 1.061432 Loss2: 1.470074
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.302551 Loss1: 0.754810 Loss2: 1.547741
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.315526 Loss1: 0.848903 Loss2: 1.466623
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.289486 Loss1: 0.722262 Loss2: 1.567224
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.195634 Loss1: 0.732023 Loss2: 1.463611
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.145870 Loss1: 0.592948 Loss2: 1.552922
+(DefaultActor pid=3765) >> Training accuracy: 0.862500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.108629 Loss1: 0.638958 Loss2: 1.469671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.034763 Loss1: 0.542167 Loss2: 1.492596 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.815625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.964752 Loss1: 1.507258 Loss2: 1.457494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.531188 Loss1: 1.096548 Loss2: 1.434640 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.273384 Loss1: 2.227166 Loss2: 2.046218
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.368667 Loss1: 0.948102 Loss2: 1.420565
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.062439 Loss1: 1.562437 Loss2: 1.500001
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.277794 Loss1: 0.851950 Loss2: 1.425844
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.839784 Loss1: 1.360399 Loss2: 1.479385
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.096766 Loss1: 0.675994 Loss2: 1.420771
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.680216 Loss1: 1.171872 Loss2: 1.508344
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.069540 Loss1: 0.653715 Loss2: 1.415826
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.561570 Loss1: 1.070070 Loss2: 1.491500
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.067420 Loss1: 0.634301 Loss2: 1.433119
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.406358 Loss1: 0.905534 Loss2: 1.500824
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.986988 Loss1: 0.549404 Loss2: 1.437584
+(DefaultActor pid=3765) >> Training accuracy: 0.883333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.139324 Loss1: 0.659026 Loss2: 1.480298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.072059 Loss1: 0.571777 Loss2: 1.500282 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.852083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.094579 Loss1: 1.571053 Loss2: 1.523526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.479380 Loss1: 0.953341 Loss2: 1.526039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.387611 Loss1: 0.884756 Loss2: 1.502855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.315585 Loss1: 0.800893 Loss2: 1.514692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.290170 Loss1: 0.767475 Loss2: 1.522695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.124748 Loss1: 0.595375 Loss2: 1.529373 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.167299 Loss1: 0.639809 Loss2: 1.527490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.197407 Loss1: 0.728952 Loss2: 1.468455
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.056436 Loss1: 0.522993 Loss2: 1.533443
+(DefaultActor pid=3765) >> Training accuracy: 0.841667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.156070 Loss1: 0.662761 Loss2: 1.493309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.103708 Loss1: 0.620946 Loss2: 1.482762 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.846875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.044948 Loss1: 1.500306 Loss2: 1.544642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.488848 Loss1: 0.981085 Loss2: 1.507764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.369825 Loss1: 0.852369 Loss2: 1.517456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.314888 Loss1: 0.796304 Loss2: 1.518585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.279444 Loss1: 0.758678 Loss2: 1.520766 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.170573 Loss1: 0.634645 Loss2: 1.535928 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.022003 Loss1: 0.510283 Loss2: 1.511720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.036872 Loss1: 0.520336 Loss2: 1.516536 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.858398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.129636 Loss1: 0.617021 Loss2: 1.512614 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.858333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.274657 Loss1: 2.171143 Loss2: 2.103514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.797949 Loss1: 1.294313 Loss2: 1.503636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.574048 Loss1: 1.057765 Loss2: 1.516282
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.212708 Loss1: 2.179620 Loss2: 2.033088
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.434738 Loss1: 0.928222 Loss2: 1.506515
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.008323 Loss1: 1.523443 Loss2: 1.484879
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.320141 Loss1: 0.813985 Loss2: 1.506156
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.739553 Loss1: 1.274341 Loss2: 1.465212
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.148940 Loss1: 0.641409 Loss2: 1.507531
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.601103 Loss1: 1.140474 Loss2: 1.460629
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.152990 Loss1: 0.650948 Loss2: 1.502041
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.506261 Loss1: 1.026800 Loss2: 1.479460
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.182569 Loss1: 0.656125 Loss2: 1.526444
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.300592 Loss1: 0.830914 Loss2: 1.469678
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.108323 Loss1: 0.589238 Loss2: 1.519085
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.204982 Loss1: 0.738864 Loss2: 1.466118
+(DefaultActor pid=3765) >> Training accuracy: 0.784375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.218876 Loss1: 0.740369 Loss2: 1.478507
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.185784 Loss1: 0.700015 Loss2: 1.485769
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.175794 Loss1: 0.682881 Loss2: 1.492913
+(DefaultActor pid=3764) >> Training accuracy: 0.818750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.070295 Loss1: 1.964275 Loss2: 2.106020
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.934883 Loss1: 1.404924 Loss2: 1.529959
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.679167 Loss1: 1.174103 Loss2: 1.505064
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.439769 Loss1: 0.935360 Loss2: 1.504409
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.225505 Loss1: 2.208803 Loss2: 2.016702
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.147798 Loss1: 1.658401 Loss2: 1.489397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.764100 Loss1: 1.292075 Loss2: 1.472025 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.594201 Loss1: 1.107723 Loss2: 1.486478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.341522 Loss1: 0.862423 Loss2: 1.479100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.337779 Loss1: 0.866140 Loss2: 1.471639 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.811458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.297227 Loss1: 0.807228 Loss2: 1.489999 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.158215 Loss1: 0.664175 Loss2: 1.494040 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.846875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.141875 Loss1: 2.058066 Loss2: 2.083809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.691916 Loss1: 1.168953 Loss2: 1.522963 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.276320 Loss1: 2.201149 Loss2: 2.075171
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.605443 Loss1: 1.075850 Loss2: 1.529593
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.159951 Loss1: 1.612628 Loss2: 1.547323
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.474850 Loss1: 0.941974 Loss2: 1.532877
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.682449 Loss1: 1.156347 Loss2: 1.526102
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.349347 Loss1: 0.809779 Loss2: 1.539568
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.529623 Loss1: 0.985408 Loss2: 1.544215
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.221103 Loss1: 0.694142 Loss2: 1.526961
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.427110 Loss1: 0.881681 Loss2: 1.545429
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.213966 Loss1: 0.679284 Loss2: 1.534682
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.094832 Loss1: 0.566405 Loss2: 1.528427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.061136 Loss1: 0.535147 Loss2: 1.525989 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.835478
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.301434 Loss1: 0.730311 Loss2: 1.571124 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.832031
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.235885 Loss1: 2.136555 Loss2: 2.099330 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.712869 Loss1: 1.202709 Loss2: 1.510160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.489084 Loss1: 0.979261 Loss2: 1.509823
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.435911 Loss1: 2.376945 Loss2: 2.058966
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.328255 Loss1: 0.830016 Loss2: 1.498239
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.248087 Loss1: 1.755998 Loss2: 1.492090
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.310681 Loss1: 0.791979 Loss2: 1.518702
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.846547 Loss1: 1.367189 Loss2: 1.479358
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.241727 Loss1: 0.724517 Loss2: 1.517210
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.483612 Loss1: 0.998010 Loss2: 1.485602
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.410193 Loss1: 0.942784 Loss2: 1.467409
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.147550 Loss1: 0.621532 Loss2: 1.526018
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.330202 Loss1: 0.839439 Loss2: 1.490763
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.127409 Loss1: 0.597635 Loss2: 1.529774
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.251460 Loss1: 0.763834 Loss2: 1.487626
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.104494 Loss1: 0.567364 Loss2: 1.537130
+(DefaultActor pid=3765) >> Training accuracy: 0.860417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.245742 Loss1: 0.736821 Loss2: 1.508920 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.799107
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.388254 Loss1: 2.323591 Loss2: 2.064663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.809979 Loss1: 1.292042 Loss2: 1.517937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.096273 Loss1: 2.076009 Loss2: 2.020264
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.589577 Loss1: 1.078742 Loss2: 1.510835
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.062506 Loss1: 1.578194 Loss2: 1.484312
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.402680 Loss1: 0.891191 Loss2: 1.511489
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.744287 Loss1: 1.280036 Loss2: 1.464251
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.354000 Loss1: 0.833161 Loss2: 1.520839
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.501180 Loss1: 1.032882 Loss2: 1.468298
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.284327 Loss1: 0.751815 Loss2: 1.532511
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.412732 Loss1: 0.954660 Loss2: 1.458072
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.330913 Loss1: 0.796238 Loss2: 1.534676
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.332778 Loss1: 0.855377 Loss2: 1.477401
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.213966 Loss1: 0.673310 Loss2: 1.540656
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.274924 Loss1: 0.792544 Loss2: 1.482381
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.241248 Loss1: 0.710600 Loss2: 1.530648
+(DefaultActor pid=3765) >> Training accuracy: 0.817383
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.153583 Loss1: 0.677228 Loss2: 1.476355 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.847656
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.226126 Loss1: 2.006352 Loss2: 2.219774 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.691962 Loss1: 1.193327 Loss2: 1.498635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.296496 Loss1: 0.804271 Loss2: 1.492225 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.239356 Loss1: 0.731540 Loss2: 1.507816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.259372 Loss1: 0.747967 Loss2: 1.511405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.147973 Loss1: 0.642211 Loss2: 1.505761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.019632 Loss1: 0.505747 Loss2: 1.513885 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.990822 Loss1: 0.483567 Loss2: 1.507255 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.843750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.076508 Loss1: 0.593739 Loss2: 1.482769 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.120056 Loss1: 0.609842 Loss2: 1.510215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.076519 Loss1: 0.557055 Loss2: 1.519463
+(DefaultActor pid=3764) >> Training accuracy: 0.864583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.169370 Loss1: 2.089664 Loss2: 2.079706
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.959767 Loss1: 1.450943 Loss2: 1.508824
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.698226 Loss1: 1.219651 Loss2: 1.478576
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.558726 Loss1: 1.057454 Loss2: 1.501273
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.390053 Loss1: 0.885783 Loss2: 1.504271
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.218251 Loss1: 2.071823 Loss2: 2.146428
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.295924 Loss1: 0.808948 Loss2: 1.486976
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.148087 Loss1: 0.658073 Loss2: 1.490014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.155649 Loss1: 0.665884 Loss2: 1.489765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.021117 Loss1: 0.526301 Loss2: 1.494816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.913784 Loss1: 0.441725 Loss2: 1.472059 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.878125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.235898 Loss1: 0.713316 Loss2: 1.522583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.143886 Loss1: 0.618337 Loss2: 1.525549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.063327 Loss1: 0.537404 Loss2: 1.525923
+(DefaultActor pid=3764) >> Training accuracy: 0.835417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.435198 Loss1: 2.285380 Loss2: 2.149819
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.213288 Loss1: 1.622255 Loss2: 1.591032
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.912903 Loss1: 1.356015 Loss2: 1.556887
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.633715 Loss1: 1.075097 Loss2: 1.558618
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.628866 Loss1: 1.073283 Loss2: 1.555583
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.096043 Loss1: 2.026558 Loss2: 2.069485
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.505433 Loss1: 0.930137 Loss2: 1.575296
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.348226 Loss1: 0.782092 Loss2: 1.566134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.361738 Loss1: 0.791770 Loss2: 1.569969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.437711 Loss1: 0.852325 Loss2: 1.585386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.283555 Loss1: 0.691239 Loss2: 1.592316 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.753125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.101459 Loss1: 0.624865 Loss2: 1.476594 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.108089 Loss1: 0.615173 Loss2: 1.492915 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.868750
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.119954 Loss1: 0.609869 Loss2: 1.510084
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.160640 Loss1: 2.138975 Loss2: 2.021665
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.980234 Loss1: 1.519198 Loss2: 1.461036
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.755032 Loss1: 1.310633 Loss2: 1.444399
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.450114 Loss1: 1.002177 Loss2: 1.447937
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.254675 Loss1: 0.818805 Loss2: 1.435869
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.345662 Loss1: 2.273643 Loss2: 2.072019
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.176112 Loss1: 1.641557 Loss2: 1.534554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.852446 Loss1: 1.346697 Loss2: 1.505749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.622607 Loss1: 1.114139 Loss2: 1.508468 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.404995 Loss1: 0.893387 Loss2: 1.511609 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.796875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.256690 Loss1: 0.744957 Loss2: 1.511733 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.224645 Loss1: 0.682322 Loss2: 1.542322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.209527 Loss1: 0.663185 Loss2: 1.546342
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.011833 Loss1: 1.947158 Loss2: 2.064675
+(DefaultActor pid=3764) >> Training accuracy: 0.853516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.987039 Loss1: 1.477874 Loss2: 1.509165
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.588280 Loss1: 1.093416 Loss2: 1.494864
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.412280 Loss1: 0.936333 Loss2: 1.475947
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.382275 Loss1: 0.899650 Loss2: 1.482625
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.148631 Loss1: 2.073746 Loss2: 2.074885
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.202859 Loss1: 0.721173 Loss2: 1.481687
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.084594 Loss1: 0.602833 Loss2: 1.481761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.106620 Loss1: 0.627304 Loss2: 1.479316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.143358 Loss1: 0.630990 Loss2: 1.512368 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.008309 Loss1: 0.509890 Loss2: 1.498419 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.837500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.110767 Loss1: 0.649510 Loss2: 1.461257 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.108636 Loss1: 0.632986 Loss2: 1.475650 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.844952
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.993513 Loss1: 1.479450 Loss2: 1.514062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.449779 Loss1: 0.955371 Loss2: 1.494408 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.439807 Loss1: 0.945057 Loss2: 1.494750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.248543 Loss1: 0.754087 Loss2: 1.494456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.226962 Loss1: 0.712592 Loss2: 1.514370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.422899 Loss1: 0.902668 Loss2: 1.520231 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.291502 Loss1: 0.772777 Loss2: 1.518725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.219565 Loss1: 0.704200 Loss2: 1.515365 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.854167
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.115465 Loss1: 0.595530 Loss2: 1.519935
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.132438 Loss1: 0.610293 Loss2: 1.522145
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.158630 Loss1: 0.638319 Loss2: 1.520311
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.086112 Loss1: 0.562788 Loss2: 1.523324
+(DefaultActor pid=3764) >> Training accuracy: 0.792969
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.239290 Loss1: 2.225748 Loss2: 2.013542
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.133087 Loss1: 1.646877 Loss2: 1.486210
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.795473 Loss1: 1.331064 Loss2: 1.464409
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.236665 Loss1: 2.022413 Loss2: 2.214252
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.122686 Loss1: 1.539341 Loss2: 1.583345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.804187 Loss1: 1.230936 Loss2: 1.573251 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.599342 Loss1: 1.027622 Loss2: 1.571720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.491932 Loss1: 0.929018 Loss2: 1.562914 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.260384 Loss1: 0.765938 Loss2: 1.494445
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.392160 Loss1: 0.817970 Loss2: 1.574189
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.179222 Loss1: 0.675170 Loss2: 1.504052
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.321186 Loss1: 0.739555 Loss2: 1.581631
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.191367 Loss1: 0.607021 Loss2: 1.584346
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.115197 Loss1: 0.617769 Loss2: 1.497428
+(DefaultActor pid=3765) >> Training accuracy: 0.806641
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.043381 Loss1: 0.474201 Loss2: 1.569180 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.864955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.226776 Loss1: 2.185888 Loss2: 2.040888
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.203574 Loss1: 1.718076 Loss2: 1.485498
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.871352 Loss1: 1.387884 Loss2: 1.483468
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.528727 Loss1: 1.057328 Loss2: 1.471398
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.226052 Loss1: 2.191200 Loss2: 2.034852
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.242119 Loss1: 1.756805 Loss2: 1.485314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.832227 Loss1: 1.362454 Loss2: 1.469773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.634426 Loss1: 1.164640 Loss2: 1.469786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.565661 Loss1: 1.070780 Loss2: 1.494881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.380056 Loss1: 0.902325 Loss2: 1.477731 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.795833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.245270 Loss1: 0.724641 Loss2: 1.520629
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.257329 Loss1: 0.777596 Loss2: 1.479733
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.354843 Loss1: 0.874145 Loss2: 1.480698
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.190921 Loss1: 0.681792 Loss2: 1.509129
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.084135 Loss1: 0.586603 Loss2: 1.497532
+(DefaultActor pid=3764) >> Training accuracy: 0.883333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.853673 Loss1: 1.841618 Loss2: 2.012055
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.895871 Loss1: 1.436150 Loss2: 1.459721
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.556698 Loss1: 1.088376 Loss2: 1.468322
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.408171 Loss1: 0.944002 Loss2: 1.464169
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.183605 Loss1: 2.166463 Loss2: 2.017142
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.105881 Loss1: 1.607570 Loss2: 1.498311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.720476 Loss1: 1.254155 Loss2: 1.466321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.474094 Loss1: 1.012263 Loss2: 1.461831 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.378475 Loss1: 0.912577 Loss2: 1.465898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.303037 Loss1: 0.830473 Loss2: 1.472564 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.831250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.251182 Loss1: 0.775914 Loss2: 1.475269 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.118104 Loss1: 0.638208 Loss2: 1.479896 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.839583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.237294 Loss1: 2.134486 Loss2: 2.102807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.854976 Loss1: 1.310125 Loss2: 1.544851 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.581798 Loss1: 1.048447 Loss2: 1.533350
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.986318 Loss1: 2.068350 Loss2: 1.917967
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.054300 Loss1: 1.629997 Loss2: 1.424303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.642919 Loss1: 1.233415 Loss2: 1.409504 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.462244 Loss1: 1.060560 Loss2: 1.401684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.304989 Loss1: 0.878588 Loss2: 1.426401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.139843 Loss1: 0.734960 Loss2: 1.404883 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.787500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.127076 Loss1: 0.706108 Loss2: 1.420968 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.923892 Loss1: 0.511844 Loss2: 1.412048 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.799805
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.052016 Loss1: 1.551763 Loss2: 1.500253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.570605 Loss1: 1.083315 Loss2: 1.487290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.376309 Loss1: 0.895683 Loss2: 1.480626
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.071248 Loss1: 2.010987 Loss2: 2.060261
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.280318 Loss1: 0.799185 Loss2: 1.481133
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.877523 Loss1: 1.390807 Loss2: 1.486716
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.698589 Loss1: 1.210865 Loss2: 1.487724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.432798 Loss1: 0.929968 Loss2: 1.502831 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.323772 Loss1: 0.846615 Loss2: 1.477157 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.839583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.183315 Loss1: 0.709245 Loss2: 1.474070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.073166 Loss1: 0.591496 Loss2: 1.481670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.990094 Loss1: 0.514162 Loss2: 1.475933 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.868164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.725499 Loss1: 1.218101 Loss2: 1.507398 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.495377 Loss1: 0.964480 Loss2: 1.530897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.314395 Loss1: 2.140981 Loss2: 2.173414
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.271662 Loss1: 0.724360 Loss2: 1.547302
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.273544 Loss1: 1.671920 Loss2: 1.601623
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.287292 Loss1: 0.767500 Loss2: 1.519792
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.857312 Loss1: 1.264740 Loss2: 1.592571
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.168141 Loss1: 0.629264 Loss2: 1.538877
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.716398 Loss1: 1.124965 Loss2: 1.591433
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.173327 Loss1: 0.636450 Loss2: 1.536877
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.547929 Loss1: 0.950938 Loss2: 1.596991
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.211157 Loss1: 0.663991 Loss2: 1.547166
+(DefaultActor pid=3765) >> Training accuracy: 0.818750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.338518 Loss1: 0.741812 Loss2: 1.596706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.261794 Loss1: 0.650513 Loss2: 1.611281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.207495 Loss1: 0.587991 Loss2: 1.619504
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.304576 Loss1: 2.235024 Loss2: 2.069553
+(DefaultActor pid=3764) >> Training accuracy: 0.809375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.112810 Loss1: 1.592054 Loss2: 1.520757
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.764895 Loss1: 1.257217 Loss2: 1.507678
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.653300 Loss1: 1.141872 Loss2: 1.511428
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.530140 Loss1: 1.007252 Loss2: 1.522887
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.296666 Loss1: 2.161130 Loss2: 2.135535
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.383580 Loss1: 0.854927 Loss2: 1.528654
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.111231 Loss1: 1.579317 Loss2: 1.531915
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.306359 Loss1: 0.786519 Loss2: 1.519839
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.819182 Loss1: 1.322275 Loss2: 1.496907
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.282091 Loss1: 0.733239 Loss2: 1.548852
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.709333 Loss1: 1.179580 Loss2: 1.529754
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.182393 Loss1: 0.644813 Loss2: 1.537580
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.596169 Loss1: 1.056794 Loss2: 1.539376
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.121201 Loss1: 0.572989 Loss2: 1.548212
+(DefaultActor pid=3765) >> Training accuracy: 0.861458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.299140 Loss1: 0.759143 Loss2: 1.539997 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.245664 Loss1: 0.701747 Loss2: 1.543917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.201497 Loss1: 0.657366 Loss2: 1.544131
+(DefaultActor pid=3764) >> Training accuracy: 0.808333
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-09 11:30:32,491 | server.py:236 | fit_round 37 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.218055 Loss1: 2.189287 Loss2: 2.028768
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.117063 Loss1: 1.645473 Loss2: 1.471590
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.787638 Loss1: 1.332415 Loss2: 1.455224
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.575069 Loss1: 1.127080 Loss2: 1.447989
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.420939 Loss1: 0.952502 Loss2: 1.468437
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.276022 Loss1: 2.171014 Loss2: 2.105008
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.219341 Loss1: 0.770968 Loss2: 1.448373
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.138276 Loss1: 0.688515 Loss2: 1.449760
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.065737 Loss1: 1.508603 Loss2: 1.557133
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.220843 Loss1: 0.747207 Loss2: 1.473636
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.764026 Loss1: 1.231896 Loss2: 1.532130
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.145830 Loss1: 0.663483 Loss2: 1.482347
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.557567 Loss1: 1.015756 Loss2: 1.541811
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.191817 Loss1: 0.711035 Loss2: 1.480782
+(DefaultActor pid=3765) >> Training accuracy: 0.825000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.402571 Loss1: 0.877079 Loss2: 1.525492
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.296868 Loss1: 0.755927 Loss2: 1.540941
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.188626 Loss1: 0.655351 Loss2: 1.533275
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.172454 Loss1: 0.628053 Loss2: 1.544401
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.137147 Loss1: 0.584635 Loss2: 1.552511
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.355804 Loss1: 2.212598 Loss2: 2.143207
+(DefaultActor pid=3764) >> Training accuracy: 0.857422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.171246 Loss1: 1.619932 Loss2: 1.551314 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.554289 Loss1: 1.013525 Loss2: 1.540763 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.366656 Loss1: 0.808593 Loss2: 1.558063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.335046 Loss1: 0.762697 Loss2: 1.572350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.288045 Loss1: 0.713335 Loss2: 1.574710 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.202340 Loss1: 0.628406 Loss2: 1.573935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.111384 Loss1: 0.539779 Loss2: 1.571605 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.868750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.256520 Loss1: 0.803597 Loss2: 1.452922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.058040 Loss1: 0.595775 Loss2: 1.462265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.027042 Loss1: 0.554930 Loss2: 1.472111 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.844792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 11:30:32,491][flwr][DEBUG] - fit_round 37 received 50 results and 0 failures
+INFO flwr 2023-10-09 11:31:12,846 | server.py:125 | fit progress: (37, 2.6360169702444596, {'accuracy': 0.4045}, 85180.624192099)
+>> Test accuracy: 0.404500
+[2023-10-09 11:31:12,846][flwr][INFO] - fit progress: (37, 2.6360169702444596, {'accuracy': 0.4045}, 85180.624192099)
+DEBUG flwr 2023-10-09 11:31:12,846 | server.py:173 | evaluate_round 37: strategy sampled 50 clients (out of 50)
+[2023-10-09 11:31:12,846][flwr][DEBUG] - evaluate_round 37: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 11:40:20,253 | server.py:187 | evaluate_round 37 received 50 results and 0 failures
+[2023-10-09 11:40:20,253][flwr][DEBUG] - evaluate_round 37 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 11:40:20,253 | server.py:222 | fit_round 38: strategy sampled 50 clients (out of 50)
+[2023-10-09 11:40:20,253][flwr][DEBUG] - fit_round 38: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.091216 Loss1: 2.038684 Loss2: 2.052531
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.021559 Loss1: 1.531124 Loss2: 1.490436
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.556654 Loss1: 1.085217 Loss2: 1.471438
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.329188 Loss1: 0.874353 Loss2: 1.454836
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.398541 Loss1: 2.303997 Loss2: 2.094545
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.226944 Loss1: 0.781597 Loss2: 1.445347
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.196580 Loss1: 1.674918 Loss2: 1.521662
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.124879 Loss1: 0.657175 Loss2: 1.467703
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.774110 Loss1: 1.289378 Loss2: 1.484732
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.034765 Loss1: 0.570678 Loss2: 1.464087
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.594901 Loss1: 1.096628 Loss2: 1.498273
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.103234 Loss1: 0.638211 Loss2: 1.465023
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.418315 Loss1: 0.926200 Loss2: 1.492115
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.101696 Loss1: 0.634778 Loss2: 1.466918
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.397483 Loss1: 0.891742 Loss2: 1.505741
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.043020 Loss1: 0.570195 Loss2: 1.472825
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.352292 Loss1: 0.829838 Loss2: 1.522453
+(DefaultActor pid=3765) >> Training accuracy: 0.862500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.262136 Loss1: 0.722961 Loss2: 1.539174
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.197562 Loss1: 0.677785 Loss2: 1.519777
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.190770 Loss1: 0.669445 Loss2: 1.521325
+(DefaultActor pid=3764) >> Training accuracy: 0.805208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.044852 Loss1: 1.949577 Loss2: 2.095274
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.015638 Loss1: 1.489337 Loss2: 1.526302
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.664716 Loss1: 1.147245 Loss2: 1.517471
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.373567 Loss1: 0.855403 Loss2: 1.518165
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.145852 Loss1: 2.107962 Loss2: 2.037890
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.240936 Loss1: 0.738135 Loss2: 1.502801
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.110070 Loss1: 1.603951 Loss2: 1.506119
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.204734 Loss1: 0.703485 Loss2: 1.501249
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.789013 Loss1: 1.310375 Loss2: 1.478638
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.104461 Loss1: 0.588788 Loss2: 1.515673
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.528751 Loss1: 1.050351 Loss2: 1.478401
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.165134 Loss1: 0.651706 Loss2: 1.513429
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.322357 Loss1: 0.838834 Loss2: 1.483523
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.145987 Loss1: 0.611325 Loss2: 1.534662
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.256827 Loss1: 0.774405 Loss2: 1.482422
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.051526 Loss1: 0.523700 Loss2: 1.527825
+(DefaultActor pid=3765) >> Training accuracy: 0.889583
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.299451 Loss1: 0.804497 Loss2: 1.494954
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.194051 Loss1: 0.700521 Loss2: 1.493530
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.093907 Loss1: 0.591764 Loss2: 1.502143
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.160095 Loss1: 0.658012 Loss2: 1.502083
+(DefaultActor pid=3764) >> Training accuracy: 0.790625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.085805 Loss1: 2.028490 Loss2: 2.057314
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.980861 Loss1: 1.479513 Loss2: 1.501348
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.729870 Loss1: 1.248788 Loss2: 1.481082
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.508543 Loss1: 1.040510 Loss2: 1.468033
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.177719 Loss1: 2.040281 Loss2: 2.137437
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.090837 Loss1: 1.533595 Loss2: 1.557242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.705313 Loss1: 1.183344 Loss2: 1.521969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.475474 Loss1: 0.955003 Loss2: 1.520471 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.244362 Loss1: 0.738078 Loss2: 1.506285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.151907 Loss1: 0.644260 Loss2: 1.507648 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.860417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.916903 Loss1: 0.441916 Loss2: 1.474986
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.088206 Loss1: 0.570326 Loss2: 1.517880
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.073653 Loss1: 0.564902 Loss2: 1.508751
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.077117 Loss1: 0.563698 Loss2: 1.513419
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.039589 Loss1: 0.520734 Loss2: 1.518855
+(DefaultActor pid=3764) >> Training accuracy: 0.858333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.060901 Loss1: 2.046216 Loss2: 2.014685
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.044577 Loss1: 1.514425 Loss2: 1.530152
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.705082 Loss1: 1.204567 Loss2: 1.500515
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.489512 Loss1: 0.989316 Loss2: 1.500196
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.080268 Loss1: 2.048688 Loss2: 2.031580
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.324198 Loss1: 0.818385 Loss2: 1.505813
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.058151 Loss1: 1.524028 Loss2: 1.534123
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.305173 Loss1: 0.799472 Loss2: 1.505700
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.622387 Loss1: 1.131115 Loss2: 1.491272
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.149035 Loss1: 0.643853 Loss2: 1.505182
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.421682 Loss1: 0.926486 Loss2: 1.495196
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.111841 Loss1: 0.598583 Loss2: 1.513258
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.330185 Loss1: 0.827873 Loss2: 1.502312
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.102147 Loss1: 0.590983 Loss2: 1.511164
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.234005 Loss1: 0.721527 Loss2: 1.512478
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.051364 Loss1: 0.534091 Loss2: 1.517273
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.251317 Loss1: 0.746332 Loss2: 1.504985
+(DefaultActor pid=3765) >> Training accuracy: 0.862305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.177915 Loss1: 0.643633 Loss2: 1.534283
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.024082 Loss1: 0.508742 Loss2: 1.515340
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.022386 Loss1: 0.510946 Loss2: 1.511440
+(DefaultActor pid=3764) >> Training accuracy: 0.891602
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.126957 Loss1: 2.052028 Loss2: 2.074929
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.965753 Loss1: 1.489673 Loss2: 1.476080
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.615226 Loss1: 1.158786 Loss2: 1.456440
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.381743 Loss1: 0.931757 Loss2: 1.449985
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.124948 Loss1: 2.091911 Loss2: 2.033037
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.047962 Loss1: 1.570123 Loss2: 1.477838 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.733742 Loss1: 1.258380 Loss2: 1.475362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.543355 Loss1: 1.068921 Loss2: 1.474435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.402689 Loss1: 0.928669 Loss2: 1.474020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.234872 Loss1: 0.766904 Loss2: 1.467968 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.809152
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.189226 Loss1: 0.700089 Loss2: 1.489137 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.110269 Loss1: 0.621920 Loss2: 1.488348 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.837500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.880607 Loss1: 1.428743 Loss2: 1.451864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.445384 Loss1: 0.999429 Loss2: 1.445955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.188800 Loss1: 0.746207 Loss2: 1.442593 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.218374 Loss1: 0.777712 Loss2: 1.440661 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.119454 Loss1: 0.663924 Loss2: 1.455530 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.156628 Loss1: 0.689391 Loss2: 1.467237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.199219 Loss1: 0.724586 Loss2: 1.474633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.166695 Loss1: 0.693427 Loss2: 1.473268 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.811523
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.195181 Loss1: 0.584490 Loss2: 1.610690 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.840625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.239241 Loss1: 2.124876 Loss2: 2.114365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.804058 Loss1: 1.299868 Loss2: 1.504190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.648761 Loss1: 1.127704 Loss2: 1.521057
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.218946 Loss1: 2.226974 Loss2: 1.991972
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.159820 Loss1: 1.663779 Loss2: 1.496041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.810959 Loss1: 1.317989 Loss2: 1.492970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.646058 Loss1: 1.147861 Loss2: 1.498197 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.461316 Loss1: 0.970233 Loss2: 1.491083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.304605 Loss1: 0.801653 Loss2: 1.502952 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.777083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.235540 Loss1: 0.738338 Loss2: 1.497202 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.296874 Loss1: 0.767311 Loss2: 1.529563 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.834961
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.153187 Loss1: 0.629297 Loss2: 1.523890
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.299028 Loss1: 2.165354 Loss2: 2.133674
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.047958 Loss1: 1.500629 Loss2: 1.547329
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.727368 Loss1: 1.213472 Loss2: 1.513896
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.497589 Loss1: 0.960014 Loss2: 1.537575
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.391279 Loss1: 0.862553 Loss2: 1.528726
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.142823 Loss1: 2.055998 Loss2: 2.086824
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.313511 Loss1: 0.778018 Loss2: 1.535494
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.000482 Loss1: 1.473378 Loss2: 1.527104
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.233289 Loss1: 0.684951 Loss2: 1.548339
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.583748 Loss1: 1.060917 Loss2: 1.522831
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.386039 Loss1: 0.889859 Loss2: 1.496180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.239708 Loss1: 0.733003 Loss2: 1.506705 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.887500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.149218 Loss1: 0.594120 Loss2: 1.555097
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.178519 Loss1: 0.685807 Loss2: 1.492712
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.268999 Loss1: 0.746397 Loss2: 1.522602
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.212598 Loss1: 0.684316 Loss2: 1.528282
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.126143 Loss1: 0.602529 Loss2: 1.523614
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.020242 Loss1: 0.501709 Loss2: 1.518533
+(DefaultActor pid=3764) >> Training accuracy: 0.862305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.194900 Loss1: 2.111661 Loss2: 2.083239
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.037304 Loss1: 1.529604 Loss2: 1.507700
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.718373 Loss1: 1.226899 Loss2: 1.491474
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.501459 Loss1: 1.000775 Loss2: 1.500684
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.330070 Loss1: 0.836437 Loss2: 1.493633
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.229801 Loss1: 2.144513 Loss2: 2.085288
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.248731 Loss1: 0.748880 Loss2: 1.499851
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.130196 Loss1: 0.631676 Loss2: 1.498520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.110778 Loss1: 0.613853 Loss2: 1.496925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.489195 Loss1: 0.992682 Loss2: 1.496513
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.098572 Loss1: 0.591604 Loss2: 1.506968
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.049967 Loss1: 0.536565 Loss2: 1.513402 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.854167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.172737 Loss1: 0.657059 Loss2: 1.515677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.130019 Loss1: 0.602186 Loss2: 1.527834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.173244 Loss1: 0.641077 Loss2: 1.532167
+(DefaultActor pid=3764) >> Training accuracy: 0.831250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.223695 Loss1: 2.193725 Loss2: 2.029970
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.027499 Loss1: 1.551601 Loss2: 1.475898
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.673213 Loss1: 1.219861 Loss2: 1.453353
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.367031 Loss1: 0.907098 Loss2: 1.459933
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.306116 Loss1: 0.827969 Loss2: 1.478147
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.229433 Loss1: 2.190405 Loss2: 2.039028
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.220935 Loss1: 0.732519 Loss2: 1.488416
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.099413 Loss1: 1.586082 Loss2: 1.513331
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.183961 Loss1: 0.701149 Loss2: 1.482811
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.676774 Loss1: 1.188510 Loss2: 1.488264
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.169407 Loss1: 0.675495 Loss2: 1.493912
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.502953 Loss1: 1.019097 Loss2: 1.483857
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.113202 Loss1: 0.626700 Loss2: 1.486502
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.052830 Loss1: 0.558884 Loss2: 1.493946
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.335460 Loss1: 0.855703 Loss2: 1.479757
+(DefaultActor pid=3765) >> Training accuracy: 0.794792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.287882 Loss1: 0.792173 Loss2: 1.495710
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.181751 Loss1: 0.690313 Loss2: 1.491438
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.194853 Loss1: 0.687849 Loss2: 1.507003
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.033079 Loss1: 0.523618 Loss2: 1.509462
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.533491 Loss1: 2.359257 Loss2: 2.174234
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.092062 Loss1: 0.600641 Loss2: 1.491421
+(DefaultActor pid=3764) >> Training accuracy: 0.756836
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.812640 Loss1: 1.272232 Loss2: 1.540408 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.476686 Loss1: 0.915127 Loss2: 1.561559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.027082 Loss1: 1.906129 Loss2: 2.120952 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.226066 Loss1: 0.642275 Loss2: 1.583791 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.119128 Loss1: 0.558653 Loss2: 1.560475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.153247 Loss1: 0.596046 Loss2: 1.557201 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.800223
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.151873 Loss1: 0.653910 Loss2: 1.497963 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.163442 Loss1: 0.661059 Loss2: 1.502383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.284562 Loss1: 2.167929 Loss2: 2.116633
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.094983 Loss1: 0.578198 Loss2: 1.516785
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.129492 Loss1: 1.586427 Loss2: 1.543064
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.981461 Loss1: 0.479146 Loss2: 1.502316
+(DefaultActor pid=3764) >> Training accuracy: 0.879167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.496846 Loss1: 0.967595 Loss2: 1.529251 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.337250 Loss1: 0.792835 Loss2: 1.544416 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.375578 Loss1: 0.817759 Loss2: 1.557819
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.137417 Loss1: 2.031941 Loss2: 2.105477
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.865221 Loss1: 1.360364 Loss2: 1.504857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.521676 Loss1: 1.040638 Loss2: 1.481038 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.844792
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.117330 Loss1: 0.567514 Loss2: 1.549816
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.396710 Loss1: 0.910792 Loss2: 1.485918
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.328131 Loss1: 0.839122 Loss2: 1.489009
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.175142 Loss1: 0.668885 Loss2: 1.506257
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.185901 Loss1: 0.684392 Loss2: 1.501508
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.059848 Loss1: 0.555132 Loss2: 1.504716
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.168961 Loss1: 2.118547 Loss2: 2.050414
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.009807 Loss1: 0.507216 Loss2: 1.502591
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.017643 Loss1: 1.525433 Loss2: 1.492209
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.015276 Loss1: 0.520516 Loss2: 1.494759
+(DefaultActor pid=3764) >> Training accuracy: 0.842708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.363880 Loss1: 0.884798 Loss2: 1.479082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.252644 Loss1: 0.775735 Loss2: 1.476909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.210038 Loss1: 0.716450 Loss2: 1.493588
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.199652 Loss1: 2.189120 Loss2: 2.010532
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.114427 Loss1: 0.620563 Loss2: 1.493864
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.010632 Loss1: 1.592532 Loss2: 1.418100
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.060455 Loss1: 0.561103 Loss2: 1.499352
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.705720 Loss1: 1.293554 Loss2: 1.412166
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.982552 Loss1: 0.487490 Loss2: 1.495062
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.481870 Loss1: 1.070724 Loss2: 1.411145
+(DefaultActor pid=3765) >> Training accuracy: 0.851042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.331744 Loss1: 0.916912 Loss2: 1.414832
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.188770 Loss1: 0.772222 Loss2: 1.416548
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.147913 Loss1: 0.720796 Loss2: 1.427117
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.095478 Loss1: 0.657284 Loss2: 1.438194
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.257692 Loss1: 2.160512 Loss2: 2.097179
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.093317 Loss1: 0.667913 Loss2: 1.425403
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.090356 Loss1: 1.532650 Loss2: 1.557707
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.008659 Loss1: 0.571098 Loss2: 1.437561
+(DefaultActor pid=3764) >> Training accuracy: 0.844792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.511496 Loss1: 0.983306 Loss2: 1.528190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.275802 Loss1: 0.735887 Loss2: 1.539915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.312409 Loss1: 0.771534 Loss2: 1.540875
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.237347 Loss1: 2.226681 Loss2: 2.010667
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.221847 Loss1: 0.666214 Loss2: 1.555634
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.136092 Loss1: 1.663311 Loss2: 1.472781
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.144604 Loss1: 0.589007 Loss2: 1.555597
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.755186 Loss1: 1.292547 Loss2: 1.462640
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.166229 Loss1: 0.616672 Loss2: 1.549556
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.475692 Loss1: 1.017291 Loss2: 1.458400
+(DefaultActor pid=3765) >> Training accuracy: 0.812500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.423606 Loss1: 0.963607 Loss2: 1.459998
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.341286 Loss1: 0.867579 Loss2: 1.473708
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.258553 Loss1: 0.789917 Loss2: 1.468636
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.205143 Loss1: 0.728520 Loss2: 1.476623
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.446143 Loss1: 2.296440 Loss2: 2.149703
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.117796 Loss1: 0.649690 Loss2: 1.468107
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.029199 Loss1: 0.561341 Loss2: 1.467858 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.870833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.629104 Loss1: 1.079625 Loss2: 1.549479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.379363 Loss1: 0.838463 Loss2: 1.540901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.237317 Loss1: 2.193645 Loss2: 2.043673 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.057007 Loss1: 1.562031 Loss2: 1.494975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.209447 Loss1: 0.641294 Loss2: 1.568153 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.832589
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.414485 Loss1: 0.928445 Loss2: 1.486040 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.312836 Loss1: 0.817113 Loss2: 1.495723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.376969 Loss1: 2.207627 Loss2: 2.169342
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.180497 Loss1: 0.691077 Loss2: 1.489420
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.157263 Loss1: 1.587759 Loss2: 1.569503
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.200315 Loss1: 0.703551 Loss2: 1.496764
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.948068 Loss1: 1.391460 Loss2: 1.556608
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.081320 Loss1: 0.583403 Loss2: 1.497918
+(DefaultActor pid=3764) >> Training accuracy: 0.897917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.543133 Loss1: 0.988751 Loss2: 1.554382 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.232908 Loss1: 0.678075 Loss2: 1.554832 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.188129 Loss1: 0.632540 Loss2: 1.555588
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.239624 Loss1: 2.248674 Loss2: 1.990950
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.192261 Loss1: 0.632969 Loss2: 1.559292
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.193686 Loss1: 1.713664 Loss2: 1.480022
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.172965 Loss1: 0.600610 Loss2: 1.572355
+(DefaultActor pid=3765) >> Training accuracy: 0.810417
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.758851 Loss1: 1.302856 Loss2: 1.455995
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.471443 Loss1: 1.010634 Loss2: 1.460809
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.392204 Loss1: 0.934686 Loss2: 1.457518
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.371020 Loss1: 0.897871 Loss2: 1.473149
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.228167 Loss1: 0.739820 Loss2: 1.488347
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.107622 Loss1: 2.104524 Loss2: 2.003098
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.151634 Loss1: 0.686575 Loss2: 1.465059
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.148879 Loss1: 0.674771 Loss2: 1.474109 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.078982 Loss1: 0.588661 Loss2: 1.490321 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.873047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.273224 Loss1: 0.848426 Loss2: 1.424799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.134015 Loss1: 0.689958 Loss2: 1.444057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.143916 Loss1: 0.702075 Loss2: 1.441841
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.936687 Loss1: 1.860324 Loss2: 2.076364
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.903761 Loss1: 1.381954 Loss2: 1.521807 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.865625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.592770 Loss1: 1.089378 Loss2: 1.503392 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.274807 Loss1: 0.788454 Loss2: 1.486353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.149585 Loss1: 0.629058 Loss2: 1.520527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.999550 Loss1: 0.490810 Loss2: 1.508739 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.937900 Loss1: 0.432473 Loss2: 1.505427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.935134 Loss1: 0.444306 Loss2: 1.490828 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.863542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.346433 Loss1: 0.843130 Loss2: 1.503302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.198496 Loss1: 0.698573 Loss2: 1.499923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.243409 Loss1: 0.721643 Loss2: 1.521766
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.072300 Loss1: 2.026937 Loss2: 2.045363
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.983468 Loss1: 1.530661 Loss2: 1.452807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.057208 Loss1: 0.542638 Loss2: 1.514570
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.562250 Loss1: 1.144474 Loss2: 1.417776
+(DefaultActor pid=3765) >> Training accuracy: 0.856250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.314531 Loss1: 0.915813 Loss2: 1.398718
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.123602 Loss1: 0.725181 Loss2: 1.398422
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.995204 Loss1: 0.597648 Loss2: 1.397556
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.007618 Loss1: 0.601522 Loss2: 1.406096
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.959098 Loss1: 0.546214 Loss2: 1.412883
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.973557 Loss1: 1.957244 Loss2: 2.016314
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.950278 Loss1: 0.526297 Loss2: 1.423980 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.837740
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.647699 Loss1: 1.160041 Loss2: 1.487658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.259500 Loss1: 0.772405 Loss2: 1.487095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.247187 Loss1: 0.759504 Loss2: 1.487683
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.255701 Loss1: 2.168101 Loss2: 2.087600
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.093940 Loss1: 0.606570 Loss2: 1.487369
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.969833 Loss1: 1.450754 Loss2: 1.519079
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.718192 Loss1: 1.198828 Loss2: 1.519363
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.058755 Loss1: 0.565157 Loss2: 1.493598
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.513873 Loss1: 0.979646 Loss2: 1.534227
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.120764 Loss1: 0.630973 Loss2: 1.489791
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.270087 Loss1: 0.756226 Loss2: 1.513861
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.099634 Loss1: 0.589982 Loss2: 1.509651
+(DefaultActor pid=3765) >> Training accuracy: 0.813419
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.258348 Loss1: 0.726837 Loss2: 1.531512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.176392 Loss1: 0.635854 Loss2: 1.540538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.060198 Loss1: 0.520748 Loss2: 1.539450
+(DefaultActor pid=3764) >> Training accuracy: 0.865625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.226471 Loss1: 2.086883 Loss2: 2.139589
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.155288 Loss1: 1.595505 Loss2: 1.559783
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.740827 Loss1: 1.210076 Loss2: 1.530751
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.544074 Loss1: 0.998791 Loss2: 1.545282
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.486135 Loss1: 0.934492 Loss2: 1.551643
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.192944 Loss1: 2.044161 Loss2: 2.148783
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.384756 Loss1: 0.826013 Loss2: 1.558743
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.266102 Loss1: 0.711377 Loss2: 1.554725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.228249 Loss1: 0.673915 Loss2: 1.554334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.184266 Loss1: 0.625323 Loss2: 1.558942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.182213 Loss1: 0.604426 Loss2: 1.577787 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.860417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.157169 Loss1: 0.640350 Loss2: 1.516819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.055557 Loss1: 0.545328 Loss2: 1.510229 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.861458
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.003224 Loss1: 0.503284 Loss2: 1.499939
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.193733 Loss1: 2.103678 Loss2: 2.090055
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.084035 Loss1: 1.520647 Loss2: 1.563388
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.726528 Loss1: 1.194889 Loss2: 1.531640
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.530408 Loss1: 0.989392 Loss2: 1.541017
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.435006 Loss1: 0.889793 Loss2: 1.545213
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.352792 Loss1: 2.163071 Loss2: 2.189721
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.069740 Loss1: 1.508287 Loss2: 1.561453
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.274525 Loss1: 0.718383 Loss2: 1.556142
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.235232 Loss1: 0.679530 Loss2: 1.555702 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.179844 Loss1: 0.608318 Loss2: 1.571526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.147546 Loss1: 0.581382 Loss2: 1.566164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.180339 Loss1: 0.634672 Loss2: 1.545667 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.847656
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.185831 Loss1: 0.619921 Loss2: 1.565911 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.820913
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.190397 Loss1: 2.028029 Loss2: 2.162368 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.633819 Loss1: 1.062369 Loss2: 1.571450 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.616173 Loss1: 1.033122 Loss2: 1.583050
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.129860 Loss1: 2.170782 Loss2: 1.959078
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.445822 Loss1: 0.847096 Loss2: 1.598726
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.078978 Loss1: 1.640128 Loss2: 1.438850
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.325636 Loss1: 0.738199 Loss2: 1.587437
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.676979 Loss1: 1.255561 Loss2: 1.421418
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.297854 Loss1: 0.699347 Loss2: 1.598507
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.529179 Loss1: 1.113630 Loss2: 1.415549
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.185104 Loss1: 0.591439 Loss2: 1.593666
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.344090 Loss1: 0.918018 Loss2: 1.426072
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.123500 Loss1: 0.533526 Loss2: 1.589973
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.206119 Loss1: 0.788011 Loss2: 1.418108
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.132350 Loss1: 0.527186 Loss2: 1.605164
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.106109 Loss1: 0.685103 Loss2: 1.421006
+(DefaultActor pid=3765) >> Training accuracy: 0.846875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.012728 Loss1: 0.592120 Loss2: 1.420608
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.964604 Loss1: 0.547648 Loss2: 1.416956
+DEBUG flwr 2023-10-09 12:09:08,973 | server.py:236 | fit_round 38 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.040684 Loss1: 0.608553 Loss2: 1.432131
+(DefaultActor pid=3764) >> Training accuracy: 0.829167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.370579 Loss1: 2.242888 Loss2: 2.127691
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.167422 Loss1: 1.616494 Loss2: 1.550928
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.849157 Loss1: 1.324508 Loss2: 1.524648
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.598005 Loss1: 1.052390 Loss2: 1.545614
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.247605 Loss1: 2.115454 Loss2: 2.132152
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.458334 Loss1: 0.917249 Loss2: 1.541085
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.168607 Loss1: 1.579016 Loss2: 1.589591
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.414308 Loss1: 0.875113 Loss2: 1.539195
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.779821 Loss1: 1.207280 Loss2: 1.572541
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.610209 Loss1: 1.046557 Loss2: 1.563652
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.309092 Loss1: 0.763641 Loss2: 1.545450
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.468276 Loss1: 0.886727 Loss2: 1.581549
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.296983 Loss1: 0.741308 Loss2: 1.555675
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.309677 Loss1: 0.733394 Loss2: 1.576283
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.153898 Loss1: 0.602957 Loss2: 1.550941
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.381649 Loss1: 0.799554 Loss2: 1.582095
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.197422 Loss1: 0.652553 Loss2: 1.544869
+(DefaultActor pid=3765) >> Training accuracy: 0.775391
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.215604 Loss1: 0.627306 Loss2: 1.588298 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.865625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.114188 Loss1: 2.065345 Loss2: 2.048843 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.626671 Loss1: 1.175432 Loss2: 1.451239 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.523987 Loss1: 1.068955 Loss2: 1.455032
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.176568 Loss1: 2.090218 Loss2: 2.086350
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.248131 Loss1: 0.806181 Loss2: 1.441950
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.050092 Loss1: 1.541408 Loss2: 1.508684
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.186879 Loss1: 0.735798 Loss2: 1.451082
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.749522 Loss1: 1.232047 Loss2: 1.517475
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.140684 Loss1: 0.670050 Loss2: 1.470634
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.469317 Loss1: 0.951794 Loss2: 1.517523
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.045483 Loss1: 0.590590 Loss2: 1.454893
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.372576 Loss1: 0.849645 Loss2: 1.522930
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.018949 Loss1: 0.559276 Loss2: 1.459673
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.312277 Loss1: 0.800160 Loss2: 1.512116
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.065234 Loss1: 0.599084 Loss2: 1.466150
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.158233 Loss1: 0.641145 Loss2: 1.517089
+(DefaultActor pid=3765) >> Training accuracy: 0.823958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.179426 Loss1: 0.646680 Loss2: 1.532746
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.239062 Loss1: 0.710934 Loss2: 1.528127
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.132365 Loss1: 0.597316 Loss2: 1.535049
+(DefaultActor pid=3764) >> Training accuracy: 0.850000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.305501 Loss1: 2.117324 Loss2: 2.188178
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.178762 Loss1: 1.574096 Loss2: 1.604666
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.790747 Loss1: 1.181325 Loss2: 1.609421
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.558047 Loss1: 0.969168 Loss2: 1.588879
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.401532 Loss1: 2.167755 Loss2: 2.233777
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.095954 Loss1: 1.547097 Loss2: 1.548857
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.473887 Loss1: 0.881845 Loss2: 1.592042
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.331385 Loss1: 0.736267 Loss2: 1.595118 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.194894 Loss1: 0.597606 Loss2: 1.597288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.270173 Loss1: 0.768893 Loss2: 1.501280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.123737 Loss1: 0.618335 Loss2: 1.505402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.137457 Loss1: 0.626797 Loss2: 1.510660 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.842708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.081488 Loss1: 0.570915 Loss2: 1.510572 [repeated 3x across cluster]
+[2023-10-09 12:09:08,973][flwr][DEBUG] - fit_round 38 received 50 results and 0 failures
+INFO flwr 2023-10-09 12:09:51,103 | server.py:125 | fit progress: (38, 2.5861118205439166, {'accuracy': 0.412}, 87498.881200555)
+>> Test accuracy: 0.412000
+[2023-10-09 12:09:51,103][flwr][INFO] - fit progress: (38, 2.5861118205439166, {'accuracy': 0.412}, 87498.881200555)
+DEBUG flwr 2023-10-09 12:09:51,103 | server.py:173 | evaluate_round 38: strategy sampled 50 clients (out of 50)
+[2023-10-09 12:09:51,103][flwr][DEBUG] - evaluate_round 38: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 12:18:56,444 | server.py:187 | evaluate_round 38 received 50 results and 0 failures
+[2023-10-09 12:18:56,444][flwr][DEBUG] - evaluate_round 38 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 12:18:56,444 | server.py:222 | fit_round 39: strategy sampled 50 clients (out of 50)
+[2023-10-09 12:18:56,444][flwr][DEBUG] - fit_round 39: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3764) >> Training accuracy: 0.812500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.293504 Loss1: 2.199001 Loss2: 2.094503
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.100369 Loss1: 1.609471 Loss2: 1.490897
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.729316 Loss1: 1.253802 Loss2: 1.475515
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.430743 Loss1: 0.961166 Loss2: 1.469576
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.194396 Loss1: 2.131031 Loss2: 2.063365
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.034906 Loss1: 1.526162 Loss2: 1.508743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.695080 Loss1: 1.205789 Loss2: 1.489290 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.469000 Loss1: 0.981852 Loss2: 1.487148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.284272 Loss1: 0.816562 Loss2: 1.467710 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.000323 Loss1: 0.519280 Loss2: 1.481042 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.849330
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.021468 Loss1: 0.537272 Loss2: 1.484196 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.959907 Loss1: 0.471162 Loss2: 1.488745 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.872917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.992690 Loss1: 1.522998 Loss2: 1.469693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.477742 Loss1: 0.996979 Loss2: 1.480764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.354956 Loss1: 0.883635 Loss2: 1.471321
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.209120 Loss1: 2.119059 Loss2: 2.090061
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.146465 Loss1: 0.681568 Loss2: 1.464896
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.076553 Loss1: 1.507172 Loss2: 1.569381
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.092289 Loss1: 0.631283 Loss2: 1.461006
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.767779 Loss1: 1.238445 Loss2: 1.529334
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.069255 Loss1: 0.596760 Loss2: 1.472495
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.570838 Loss1: 1.040842 Loss2: 1.529995
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.094291 Loss1: 0.609563 Loss2: 1.484727
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.384689 Loss1: 0.857000 Loss2: 1.527690
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.058801 Loss1: 0.564647 Loss2: 1.494154
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.290169 Loss1: 0.750350 Loss2: 1.539820
+(DefaultActor pid=3765) >> Training accuracy: 0.825000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.178867 Loss1: 0.631619 Loss2: 1.547249
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.183184 Loss1: 0.639106 Loss2: 1.544078
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.131200 Loss1: 0.591792 Loss2: 1.539408
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.143860 Loss1: 0.584350 Loss2: 1.559510
+(DefaultActor pid=3764) >> Training accuracy: 0.866667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.210236 Loss1: 2.156873 Loss2: 2.053363
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.017878 Loss1: 1.514773 Loss2: 1.503106
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.679779 Loss1: 1.199109 Loss2: 1.480671
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.404756 Loss1: 0.930526 Loss2: 1.474230
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.284519 Loss1: 0.813735 Loss2: 1.470784
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.278160 Loss1: 0.797536 Loss2: 1.480623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.206741 Loss1: 0.712800 Loss2: 1.493941 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.167690 Loss1: 0.688230 Loss2: 1.479460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.002756 Loss1: 0.518735 Loss2: 1.484021 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.997236 Loss1: 0.525922 Loss2: 1.471315 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.862500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.172504 Loss1: 0.716828 Loss2: 1.455675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.107686 Loss1: 0.629002 Loss2: 1.478685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.006039 Loss1: 0.555883 Loss2: 1.450156
+(DefaultActor pid=3764) >> Training accuracy: 0.824219
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.072454 Loss1: 1.902043 Loss2: 2.170411
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.902866 Loss1: 1.353377 Loss2: 1.549490
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.501792 Loss1: 0.988026 Loss2: 1.513766
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.400212 Loss1: 0.893891 Loss2: 1.506320
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.279118 Loss1: 0.764286 Loss2: 1.514832
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.131813 Loss1: 2.079542 Loss2: 2.052272
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.189855 Loss1: 0.681010 Loss2: 1.508845
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.170067 Loss1: 0.662554 Loss2: 1.507513 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.107761 Loss1: 0.572993 Loss2: 1.534768 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.016404 Loss1: 0.505462 Loss2: 1.510942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.951717 Loss1: 0.441638 Loss2: 1.510079 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.912500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.180293 Loss1: 0.703821 Loss2: 1.476471 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.120375 Loss1: 0.639614 Loss2: 1.480761 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.995594 Loss1: 0.519952 Loss2: 1.475641
+(DefaultActor pid=3764) >> Training accuracy: 0.837500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.133684 Loss1: 2.077564 Loss2: 2.056120
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.025416 Loss1: 1.543623 Loss2: 1.481794
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.601434 Loss1: 1.158249 Loss2: 1.443185
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.421210 Loss1: 0.970457 Loss2: 1.450753
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.338507 Loss1: 0.882265 Loss2: 1.456242
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.179836 Loss1: 2.042721 Loss2: 2.137115
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.002846 Loss1: 1.464897 Loss2: 1.537949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.658426 Loss1: 1.159263 Loss2: 1.499162 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.497015 Loss1: 0.992743 Loss2: 1.504272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.039523 Loss1: 0.561293 Loss2: 1.478230
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.258393 Loss1: 0.741246 Loss2: 1.517148
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.048680 Loss1: 0.581373 Loss2: 1.467307
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.254328 Loss1: 0.757913 Loss2: 1.496416
+(DefaultActor pid=3765) >> Training accuracy: 0.820833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.217640 Loss1: 0.689869 Loss2: 1.527771
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.140967 Loss1: 0.618704 Loss2: 1.522262
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.085238 Loss1: 0.564193 Loss2: 1.521045
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.044757 Loss1: 0.533619 Loss2: 1.511138
+(DefaultActor pid=3764) >> Training accuracy: 0.821429
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.021888 Loss1: 1.997570 Loss2: 2.024318
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.884271 Loss1: 1.432427 Loss2: 1.451844
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.492192 Loss1: 1.058914 Loss2: 1.433278
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.295408 Loss1: 0.873767 Loss2: 1.421641
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.300677 Loss1: 2.155037 Loss2: 2.145641
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.144345 Loss1: 1.585514 Loss2: 1.558831 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.745633 Loss1: 1.200661 Loss2: 1.544972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.595088 Loss1: 1.041373 Loss2: 1.553714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.422992 Loss1: 0.870838 Loss2: 1.552154 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.314803 Loss1: 0.751336 Loss2: 1.563468 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.886458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.167653 Loss1: 0.600987 Loss2: 1.566666 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.131704 Loss1: 0.566667 Loss2: 1.565037 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.848958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.822868 Loss1: 1.339963 Loss2: 1.482904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.347706 Loss1: 0.868424 Loss2: 1.479282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.141598 Loss1: 0.665258 Loss2: 1.476340 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.072764 Loss1: 0.594153 Loss2: 1.478611 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.100719 Loss1: 0.611891 Loss2: 1.488828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.044835 Loss1: 0.561342 Loss2: 1.483493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.038734 Loss1: 0.554064 Loss2: 1.484670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.914640 Loss1: 0.432136 Loss2: 1.482504 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.894531
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.168336 Loss1: 0.645981 Loss2: 1.522354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.074023 Loss1: 0.557921 Loss2: 1.516102 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.836458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.137292 Loss1: 2.081294 Loss2: 2.055998
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.915729 Loss1: 1.422355 Loss2: 1.493374
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.606246 Loss1: 1.136028 Loss2: 1.470218
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.306049 Loss1: 0.842443 Loss2: 1.463606
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.027046 Loss1: 2.059913 Loss2: 1.967133
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.948994 Loss1: 1.506679 Loss2: 1.442315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.509651 Loss1: 1.089005 Loss2: 1.420646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.329802 Loss1: 0.910540 Loss2: 1.419263 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.288432 Loss1: 0.862500 Loss2: 1.425933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.163488 Loss1: 0.738782 Loss2: 1.424706 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.885417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.043427 Loss1: 0.617568 Loss2: 1.425860 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.008494 Loss1: 0.559055 Loss2: 1.449440 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.844727
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.136334 Loss1: 1.581042 Loss2: 1.555292 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.530457 Loss1: 0.977283 Loss2: 1.553175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.105676 Loss1: 2.070015 Loss2: 2.035661
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.414223 Loss1: 0.869298 Loss2: 1.544924
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.835820 Loss1: 1.383142 Loss2: 1.452678
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.291337 Loss1: 0.737082 Loss2: 1.554255
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.469639 Loss1: 1.034390 Loss2: 1.435248
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.262503 Loss1: 0.708244 Loss2: 1.554259
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.338547 Loss1: 0.894182 Loss2: 1.444365
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.223323 Loss1: 0.665701 Loss2: 1.557622
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.179368 Loss1: 0.728796 Loss2: 1.450572
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.101837 Loss1: 0.534267 Loss2: 1.567570
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.139121 Loss1: 0.693043 Loss2: 1.446078
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.981912 Loss1: 0.446285 Loss2: 1.535627
+(DefaultActor pid=3765) >> Training accuracy: 0.873958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.088607 Loss1: 0.643199 Loss2: 1.445408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.955829 Loss1: 0.502377 Loss2: 1.453452 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.863542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.979722 Loss1: 1.497400 Loss2: 1.482322 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.515853 Loss1: 1.035904 Loss2: 1.479949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.207012 Loss1: 2.137728 Loss2: 2.069284
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.321732 Loss1: 0.852520 Loss2: 1.469212
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.194042 Loss1: 0.719457 Loss2: 1.474585
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.104012 Loss1: 1.584161 Loss2: 1.519851
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.071901 Loss1: 0.600787 Loss2: 1.471114
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.831662 Loss1: 1.336979 Loss2: 1.494683
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.035025 Loss1: 0.562015 Loss2: 1.473010
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.545995 Loss1: 1.034404 Loss2: 1.511592
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.999599 Loss1: 0.518712 Loss2: 1.480887
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.400006 Loss1: 0.891194 Loss2: 1.508812
+(DefaultActor pid=3765) >> Training accuracy: 0.868750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.003814 Loss1: 0.514897 Loss2: 1.488917
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.311894 Loss1: 0.807737 Loss2: 1.504157
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.302348 Loss1: 0.778555 Loss2: 1.523793
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.245888 Loss1: 0.723480 Loss2: 1.522408
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.151881 Loss1: 0.628927 Loss2: 1.522954
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.076853 Loss1: 0.558968 Loss2: 1.517885
+(DefaultActor pid=3764) >> Training accuracy: 0.842773
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.166123 Loss1: 2.067107 Loss2: 2.099016
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.048828 Loss1: 1.522463 Loss2: 1.526365
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.841293 Loss1: 1.339328 Loss2: 1.501965
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.522158 Loss1: 1.000028 Loss2: 1.522130
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.450588 Loss1: 0.933207 Loss2: 1.517380
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.056857 Loss1: 2.038198 Loss2: 2.018658
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.349842 Loss1: 0.821638 Loss2: 1.528205
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.008617 Loss1: 1.510942 Loss2: 1.497676
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.216728 Loss1: 0.692432 Loss2: 1.524296
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.602856 Loss1: 1.121066 Loss2: 1.481790
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.207221 Loss1: 0.681991 Loss2: 1.525230
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.165480 Loss1: 0.631383 Loss2: 1.534097
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.436016 Loss1: 0.962469 Loss2: 1.473547
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.100565 Loss1: 0.551852 Loss2: 1.548713
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.262627 Loss1: 0.775283 Loss2: 1.487344
+(DefaultActor pid=3765) >> Training accuracy: 0.898958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.164804 Loss1: 0.677558 Loss2: 1.487245
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.114691 Loss1: 0.638402 Loss2: 1.476289
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.048699 Loss1: 0.552047 Loss2: 1.496652
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.075207 Loss1: 0.576079 Loss2: 1.499129
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.242032 Loss1: 2.193658 Loss2: 2.048374
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.051629 Loss1: 0.545674 Loss2: 1.505956
+(DefaultActor pid=3764) >> Training accuracy: 0.875000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.703774 Loss1: 1.224957 Loss2: 1.478817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.352591 Loss1: 0.885317 Loss2: 1.467274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.205438 Loss1: 0.735955 Loss2: 1.469483
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.393138 Loss1: 2.279459 Loss2: 2.113679
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.133840 Loss1: 1.605012 Loss2: 1.528828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.749947 Loss1: 1.247621 Loss2: 1.502326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.471593 Loss1: 0.968109 Loss2: 1.503483 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.820833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.137589 Loss1: 0.643639 Loss2: 1.493950
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.412711 Loss1: 0.911494 Loss2: 1.501217
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.240169 Loss1: 0.712464 Loss2: 1.527705
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.131174 Loss1: 0.619042 Loss2: 1.512131
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.143719 Loss1: 0.633990 Loss2: 1.509729
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.107564 Loss1: 0.565833 Loss2: 1.541731
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.050343 Loss1: 0.520870 Loss2: 1.529474
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.102151 Loss1: 2.030658 Loss2: 2.071493
+(DefaultActor pid=3764) >> Training accuracy: 0.824777
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.991169 Loss1: 1.519237 Loss2: 1.471933
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.691260 Loss1: 1.245379 Loss2: 1.445881
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.473331 Loss1: 1.027132 Loss2: 1.446199
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.317112 Loss1: 0.876482 Loss2: 1.440629
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.201330 Loss1: 0.751686 Loss2: 1.449644
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.314021 Loss1: 2.184245 Loss2: 2.129775
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.040527 Loss1: 1.527425 Loss2: 1.513102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.591185 Loss1: 1.148032 Loss2: 1.443154 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.953038 Loss1: 0.490928 Loss2: 1.462111
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.376912 Loss1: 0.935812 Loss2: 1.441099
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.229516 Loss1: 0.784184 Loss2: 1.445332
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.889082 Loss1: 0.431189 Loss2: 1.457893
+(DefaultActor pid=3765) >> Training accuracy: 0.888221
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.117646 Loss1: 0.672035 Loss2: 1.445611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.014938 Loss1: 0.558790 Loss2: 1.456148 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.884115
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.147538 Loss1: 2.025480 Loss2: 2.122058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.692769 Loss1: 1.206841 Loss2: 1.485929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.874288 Loss1: 1.824703 Loss2: 2.049585 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.829089 Loss1: 1.352980 Loss2: 1.476109 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.540780 Loss1: 1.077449 Loss2: 1.463331 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.361823 Loss1: 0.896075 Loss2: 1.465748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.162593 Loss1: 0.706608 Loss2: 1.455986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.100634 Loss1: 0.642349 Loss2: 1.458285 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.814583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.052346 Loss1: 0.578130 Loss2: 1.474216 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.989689 Loss1: 0.502663 Loss2: 1.487026 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.858333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.057596 Loss1: 1.422233 Loss2: 1.635363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.453737 Loss1: 0.877549 Loss2: 1.576188 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.173445 Loss1: 2.089422 Loss2: 2.084023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.085424 Loss1: 1.570893 Loss2: 1.514531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.119949 Loss1: 0.541361 Loss2: 1.578588 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.091694 Loss1: 0.505293 Loss2: 1.586401 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.152630 Loss1: 0.555349 Loss2: 1.597281 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.786058
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.349281 Loss1: 0.842094 Loss2: 1.507187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.177339 Loss1: 0.652095 Loss2: 1.525243 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.202192 Loss1: 2.130923 Loss2: 2.071269 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.872917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.539029 Loss1: 1.072564 Loss2: 1.466465 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.308462 Loss1: 0.820674 Loss2: 1.487788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.248809 Loss1: 0.767967 Loss2: 1.480842
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.047080 Loss1: 1.955839 Loss2: 2.091241
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.212206 Loss1: 0.717695 Loss2: 1.494510
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.960717 Loss1: 1.458972 Loss2: 1.501745
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.150548 Loss1: 0.638674 Loss2: 1.511874
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.635271 Loss1: 1.123440 Loss2: 1.511831
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.007877 Loss1: 0.506622 Loss2: 1.501255
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.432124 Loss1: 0.927377 Loss2: 1.504747
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.968348 Loss1: 0.484519 Loss2: 1.483829
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.316614 Loss1: 0.814316 Loss2: 1.502298
+(DefaultActor pid=3765) >> Training accuracy: 0.879167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.186865 Loss1: 0.666792 Loss2: 1.520074
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.164518 Loss1: 0.663219 Loss2: 1.501299
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.124468 Loss1: 0.606596 Loss2: 1.517872
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.019237 Loss1: 0.500259 Loss2: 1.518977
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.994126 Loss1: 0.481730 Loss2: 1.512396
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.148103 Loss1: 2.141963 Loss2: 2.006140
+(DefaultActor pid=3764) >> Training accuracy: 0.904167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.982991 Loss1: 1.518194 Loss2: 1.464797
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.719321 Loss1: 1.258615 Loss2: 1.460705
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.485892 Loss1: 1.020848 Loss2: 1.465044
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.350494 Loss1: 0.888720 Loss2: 1.461774
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.015519 Loss1: 2.026001 Loss2: 1.989518
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.200187 Loss1: 0.736259 Loss2: 1.463928
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.160987 Loss1: 0.699514 Loss2: 1.461473 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.149552 Loss1: 0.676730 Loss2: 1.472823 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.147196 Loss1: 0.666552 Loss2: 1.480644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.016435 Loss1: 0.535865 Loss2: 1.480570 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.818359
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.042032 Loss1: 0.644233 Loss2: 1.397798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.095068 Loss1: 0.659795 Loss2: 1.435273 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.846875
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.001844 Loss1: 0.568813 Loss2: 1.433031
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.106715 Loss1: 1.988871 Loss2: 2.117844
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.986384 Loss1: 1.466841 Loss2: 1.519543
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.511517 Loss1: 1.020297 Loss2: 1.491219
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.334790 Loss1: 0.858405 Loss2: 1.476385
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.350158 Loss1: 0.870237 Loss2: 1.479921
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.300158 Loss1: 2.239717 Loss2: 2.060440
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.266194 Loss1: 0.771907 Loss2: 1.494286
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.031310 Loss1: 0.542492 Loss2: 1.488818 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.022630 Loss1: 0.535471 Loss2: 1.487159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.973958 Loss1: 0.492417 Loss2: 1.481541 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.933140 Loss1: 0.443743 Loss2: 1.489397 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.873958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.111672 Loss1: 0.643854 Loss2: 1.467818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.055517 Loss1: 0.570616 Loss2: 1.484901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.027105 Loss1: 0.552322 Loss2: 1.474783
+(DefaultActor pid=3764) >> Training accuracy: 0.878125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.963324 Loss1: 1.965402 Loss2: 1.997921
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.861200 Loss1: 1.366717 Loss2: 1.494483
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.535329 Loss1: 1.071630 Loss2: 1.463699
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.406869 Loss1: 0.950486 Loss2: 1.456383
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.285639 Loss1: 0.809299 Loss2: 1.476339
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.037062 Loss1: 1.922343 Loss2: 2.114719
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.895403 Loss1: 1.379359 Loss2: 1.516044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.575128 Loss1: 1.078046 Loss2: 1.497082 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.398700 Loss1: 0.891588 Loss2: 1.507112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.251175 Loss1: 0.749618 Loss2: 1.501557 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.839844
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.001504 Loss1: 0.516722 Loss2: 1.484782
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.173763 Loss1: 0.661020 Loss2: 1.512743
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.168243 Loss1: 0.642789 Loss2: 1.525453
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.104786 Loss1: 0.587429 Loss2: 1.517357
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.048011 Loss1: 0.522637 Loss2: 1.525374
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.030908 Loss1: 0.499605 Loss2: 1.531303
+(DefaultActor pid=3764) >> Training accuracy: 0.853125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.153963 Loss1: 2.003892 Loss2: 2.150071
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.027258 Loss1: 1.459944 Loss2: 1.567314
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.694005 Loss1: 1.128654 Loss2: 1.565351
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.443218 Loss1: 0.872158 Loss2: 1.571060
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.249245 Loss1: 2.195710 Loss2: 2.053535
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.420325 Loss1: 0.836768 Loss2: 1.583557
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.264353 Loss1: 1.758188 Loss2: 1.506165
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.379492 Loss1: 0.800921 Loss2: 1.578571
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.233299 Loss1: 0.670641 Loss2: 1.562658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.184688 Loss1: 0.611893 Loss2: 1.572794 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.081568 Loss1: 0.511599 Loss2: 1.569969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.080452 Loss1: 0.513516 Loss2: 1.566936 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.861328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.211990 Loss1: 0.698525 Loss2: 1.513465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.162791 Loss1: 0.647754 Loss2: 1.515036 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.790625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.052938 Loss1: 1.940534 Loss2: 2.112404
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.910680 Loss1: 1.400974 Loss2: 1.509707
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.672831 Loss1: 1.181595 Loss2: 1.491237
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.454326 Loss1: 0.941391 Loss2: 1.512935
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.258744 Loss1: 2.148240 Loss2: 2.110504
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.184709 Loss1: 1.635941 Loss2: 1.548768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.749670 Loss1: 1.222546 Loss2: 1.527124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.574423 Loss1: 1.045774 Loss2: 1.528649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.473377 Loss1: 0.936574 Loss2: 1.536803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.293119 Loss1: 0.758074 Loss2: 1.535044 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.800000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.147223 Loss1: 0.612677 Loss2: 1.534546 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.080010 Loss1: 0.542321 Loss2: 1.537689 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.853125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.840591 Loss1: 1.321208 Loss2: 1.519383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.330923 Loss1: 0.827945 Loss2: 1.502978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.236072 Loss1: 0.733799 Loss2: 1.502272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.178158 Loss1: 0.672359 Loss2: 1.505799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.094756 Loss1: 0.584110 Loss2: 1.510645 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.084330 Loss1: 0.577035 Loss2: 1.507295 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.035577 Loss1: 0.523433 Loss2: 1.512144 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.981890 Loss1: 0.465376 Loss2: 1.516514 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.854167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.174419 Loss1: 0.602310 Loss2: 1.572109 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.170563 Loss1: 0.587438 Loss2: 1.583125 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.773958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.744670 Loss1: 1.296018 Loss2: 1.448652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.363916 Loss1: 0.908947 Loss2: 1.454970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.363093 Loss1: 2.250942 Loss2: 2.112152
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.161207 Loss1: 0.725559 Loss2: 1.435648
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.138230 Loss1: 1.565839 Loss2: 1.572391
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.087227 Loss1: 0.650022 Loss2: 1.437205
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.826611 Loss1: 1.270653 Loss2: 1.555958
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.056784 Loss1: 0.604695 Loss2: 1.452089
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.567588 Loss1: 1.008870 Loss2: 1.558719
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.008958 Loss1: 0.561494 Loss2: 1.447464
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.950169 Loss1: 0.494027 Loss2: 1.456142
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.361934 Loss1: 0.811071 Loss2: 1.550863
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.878019 Loss1: 0.426470 Loss2: 1.451550
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.279014 Loss1: 0.723129 Loss2: 1.555885
+(DefaultActor pid=3765) >> Training accuracy: 0.883333
+DEBUG flwr 2023-10-09 12:47:05,215 | server.py:236 | fit_round 39 received 50 results and 0 failures
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.188780 Loss1: 0.634206 Loss2: 1.554574
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.248420 Loss1: 0.681049 Loss2: 1.567371
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.239370 Loss1: 0.676661 Loss2: 1.562709
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.121510 Loss1: 0.544623 Loss2: 1.576887
+(DefaultActor pid=3764) >> Training accuracy: 0.819336
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.342266 Loss1: 2.210231 Loss2: 2.132036
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.169453 Loss1: 1.616303 Loss2: 1.553150
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.677606 Loss1: 1.166435 Loss2: 1.511172
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.497532 Loss1: 0.981017 Loss2: 1.516514
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.477943 Loss1: 0.949354 Loss2: 1.528589
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.932141 Loss1: 1.900091 Loss2: 2.032049
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.924642 Loss1: 1.417873 Loss2: 1.506768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.599734 Loss1: 1.103180 Loss2: 1.496554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.435813 Loss1: 0.933870 Loss2: 1.501943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.024343 Loss1: 0.483402 Loss2: 1.540941 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.864583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.110423 Loss1: 0.610137 Loss2: 1.500286 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.026803 Loss1: 0.526488 Loss2: 1.500315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.043885 Loss1: 0.538504 Loss2: 1.505380 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.865809
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.570098 Loss1: 1.072951 Loss2: 1.497147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.262558 Loss1: 0.763164 Loss2: 1.499394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.139310 Loss1: 2.127994 Loss2: 2.011315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.083441 Loss1: 1.623277 Loss2: 1.460164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.690022 Loss1: 1.251069 Loss2: 1.438953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.500313 Loss1: 1.054305 Loss2: 1.446008 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.876042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.223557 Loss1: 0.768669 Loss2: 1.454888 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.085759 Loss1: 0.630765 Loss2: 1.454995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.932792 Loss1: 0.485872 Loss2: 1.446920 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.871875
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 12:47:05,215][flwr][DEBUG] - fit_round 39 received 50 results and 0 failures
+INFO flwr 2023-10-09 12:47:45,935 | server.py:125 | fit progress: (39, 2.5775355591941564, {'accuracy': 0.4201}, 89773.71312695)
+>> Test accuracy: 0.420100
+[2023-10-09 12:47:45,935][flwr][INFO] - fit progress: (39, 2.5775355591941564, {'accuracy': 0.4201}, 89773.71312695)
+DEBUG flwr 2023-10-09 12:47:45,935 | server.py:173 | evaluate_round 39: strategy sampled 50 clients (out of 50)
+[2023-10-09 12:47:45,935][flwr][DEBUG] - evaluate_round 39: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 12:56:49,087 | server.py:187 | evaluate_round 39 received 50 results and 0 failures
+[2023-10-09 12:56:49,087][flwr][DEBUG] - evaluate_round 39 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 12:56:49,087 | server.py:222 | fit_round 40: strategy sampled 50 clients (out of 50)
+[2023-10-09 12:56:49,087][flwr][DEBUG] - fit_round 40: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.164358 Loss1: 2.135789 Loss2: 2.028570
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.059771 Loss1: 1.588662 Loss2: 1.471108
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.662705 Loss1: 1.222569 Loss2: 1.440136
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.466969 Loss1: 1.009117 Loss2: 1.457852
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.290765 Loss1: 2.205651 Loss2: 2.085113
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.036830 Loss1: 1.517504 Loss2: 1.519326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.678863 Loss1: 1.188168 Loss2: 1.490695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.455354 Loss1: 0.965226 Loss2: 1.490127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.347224 Loss1: 0.850665 Loss2: 1.496559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.149303 Loss1: 0.648960 Loss2: 1.500343 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.845833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.959335 Loss1: 0.489671 Loss2: 1.469665
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.173797 Loss1: 0.678637 Loss2: 1.495159
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.108809 Loss1: 0.597697 Loss2: 1.511112
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.003167 Loss1: 0.494173 Loss2: 1.508994
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.000929 Loss1: 0.495277 Loss2: 1.505652
+(DefaultActor pid=3764) >> Training accuracy: 0.825000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.306053 Loss1: 2.198566 Loss2: 2.107487
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.109818 Loss1: 1.581032 Loss2: 1.528786
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.734002 Loss1: 1.221340 Loss2: 1.512662
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.575522 Loss1: 1.049565 Loss2: 1.525957
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.963161 Loss1: 1.949554 Loss2: 2.013607
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.818687 Loss1: 1.367394 Loss2: 1.451293 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.597907 Loss1: 1.175554 Loss2: 1.422353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.280842 Loss1: 0.837325 Loss2: 1.443517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.144908 Loss1: 0.718268 Loss2: 1.426640 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.087492 Loss1: 0.651441 Loss2: 1.436051 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.780208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.131758 Loss1: 0.590436 Loss2: 1.541322
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.010343 Loss1: 0.571348 Loss2: 1.438996
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.969523 Loss1: 0.524823 Loss2: 1.444699
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.006640 Loss1: 0.556817 Loss2: 1.449823
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.986311 Loss1: 0.529987 Loss2: 1.456324
+(DefaultActor pid=3764) >> Training accuracy: 0.879167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.934450 Loss1: 1.958042 Loss2: 1.976408
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.932482 Loss1: 1.484697 Loss2: 1.447785
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.508626 Loss1: 1.063147 Loss2: 1.445478
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.405845 Loss1: 0.960239 Loss2: 1.445607
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.100098 Loss1: 2.066173 Loss2: 2.033924
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.182909 Loss1: 0.735556 Loss2: 1.447353
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.928231 Loss1: 1.435948 Loss2: 1.492283
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.666721 Loss1: 1.206509 Loss2: 1.460212
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.134526 Loss1: 0.698580 Loss2: 1.435946
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.397410 Loss1: 0.922916 Loss2: 1.474494
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.030470 Loss1: 0.578317 Loss2: 1.452153
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.226576 Loss1: 0.765688 Loss2: 1.460888
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.997849 Loss1: 0.533762 Loss2: 1.464086
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.122379 Loss1: 0.664850 Loss2: 1.457529
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.946782 Loss1: 0.498090 Loss2: 1.448693
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.985357 Loss1: 0.529466 Loss2: 1.455891 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.876953
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.960367 Loss1: 0.479513 Loss2: 1.480853 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.880208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.120790 Loss1: 2.053109 Loss2: 2.067680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.618580 Loss1: 1.128819 Loss2: 1.489761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.425334 Loss1: 0.921784 Loss2: 1.503550
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.077439 Loss1: 1.932998 Loss2: 2.144442
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.893078 Loss1: 1.368735 Loss2: 1.524343
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.326886 Loss1: 0.810457 Loss2: 1.516429
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.294655 Loss1: 0.774943 Loss2: 1.519712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.177395 Loss1: 0.657766 Loss2: 1.519630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.107069 Loss1: 0.579449 Loss2: 1.527620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.097066 Loss1: 0.583451 Loss2: 1.513615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.072790 Loss1: 0.535497 Loss2: 1.537292 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.798958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.930126 Loss1: 0.461256 Loss2: 1.468870 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.897837
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.238692 Loss1: 2.174303 Loss2: 2.064389
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.979382 Loss1: 1.469375 Loss2: 1.510007
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.635913 Loss1: 1.148229 Loss2: 1.487684
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.439858 Loss1: 0.953490 Loss2: 1.486368
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.958082 Loss1: 1.908758 Loss2: 2.049324
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.364799 Loss1: 0.861680 Loss2: 1.503119
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.875171 Loss1: 1.390142 Loss2: 1.485029
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.244602 Loss1: 0.730115 Loss2: 1.514486
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.515360 Loss1: 1.037821 Loss2: 1.477539
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.315867 Loss1: 0.800072 Loss2: 1.515795
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.298410 Loss1: 0.833643 Loss2: 1.464768
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.217034 Loss1: 0.685693 Loss2: 1.531341
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.221237 Loss1: 0.755252 Loss2: 1.465985
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.045345 Loss1: 0.526833 Loss2: 1.518512
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.132518 Loss1: 0.658465 Loss2: 1.474052
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.005061 Loss1: 0.504431 Loss2: 1.500630
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.033507 Loss1: 0.570606 Loss2: 1.462901
+(DefaultActor pid=3765) >> Training accuracy: 0.890625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.010865 Loss1: 0.535217 Loss2: 1.475648
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.069988 Loss1: 0.588760 Loss2: 1.481228
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.995675 Loss1: 0.513658 Loss2: 1.482017
+(DefaultActor pid=3764) >> Training accuracy: 0.877083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.184162 Loss1: 2.156576 Loss2: 2.027587
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.009009 Loss1: 1.520407 Loss2: 1.488602
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.637504 Loss1: 1.146580 Loss2: 1.490924
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.473335 Loss1: 0.986359 Loss2: 1.486976
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.170683 Loss1: 2.104838 Loss2: 2.065845
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.401848 Loss1: 0.911937 Loss2: 1.489910
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.147370 Loss1: 1.641388 Loss2: 1.505982
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.294272 Loss1: 0.810577 Loss2: 1.483695
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.716773 Loss1: 1.242973 Loss2: 1.473800
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.249492 Loss1: 0.750228 Loss2: 1.499264
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.568420 Loss1: 1.086721 Loss2: 1.481699
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.164187 Loss1: 0.670026 Loss2: 1.494161
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.297637 Loss1: 0.819204 Loss2: 1.478433
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.130715 Loss1: 0.633991 Loss2: 1.496724
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.144564 Loss1: 0.675114 Loss2: 1.469450
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.138260 Loss1: 0.638742 Loss2: 1.499518
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.111739 Loss1: 0.640418 Loss2: 1.471321
+(DefaultActor pid=3765) >> Training accuracy: 0.870833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.023729 Loss1: 0.555946 Loss2: 1.467783
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.033411 Loss1: 0.555714 Loss2: 1.477697
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.057746 Loss1: 0.573785 Loss2: 1.483961
+(DefaultActor pid=3764) >> Training accuracy: 0.726042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.081274 Loss1: 2.079579 Loss2: 2.001695
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.958683 Loss1: 1.475473 Loss2: 1.483210
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.606802 Loss1: 1.147055 Loss2: 1.459747
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.406003 Loss1: 0.944456 Loss2: 1.461547
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.228404 Loss1: 2.128110 Loss2: 2.100294
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.115672 Loss1: 1.535696 Loss2: 1.579977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.755023 Loss1: 1.203114 Loss2: 1.551909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.485273 Loss1: 0.929443 Loss2: 1.555829 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.343027 Loss1: 0.790356 Loss2: 1.552672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.281371 Loss1: 0.723283 Loss2: 1.558088 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.857292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.227871 Loss1: 0.674951 Loss2: 1.552920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.156614 Loss1: 0.597828 Loss2: 1.558785 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.827148
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.175230 Loss1: 2.071625 Loss2: 2.103605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.759064 Loss1: 1.203706 Loss2: 1.555358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.118976 Loss1: 2.016311 Loss2: 2.102666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.855700 Loss1: 1.357966 Loss2: 1.497734 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.511062 Loss1: 1.059081 Loss2: 1.451981 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.314206 Loss1: 0.861189 Loss2: 1.453017 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.167725 Loss1: 0.722006 Loss2: 1.445718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.044164 Loss1: 0.589076 Loss2: 1.455088 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.863542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.960156 Loss1: 0.503029 Loss2: 1.457127 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.876072 Loss1: 0.421290 Loss2: 1.454782 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.896875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.948691 Loss1: 1.461075 Loss2: 1.487616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.363107 Loss1: 0.885190 Loss2: 1.477918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.175670 Loss1: 2.110591 Loss2: 2.065080
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.124812 Loss1: 0.654864 Loss2: 1.469948
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.071762 Loss1: 1.570123 Loss2: 1.501639
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.027009 Loss1: 0.556580 Loss2: 1.470430
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.630609 Loss1: 1.146258 Loss2: 1.484351
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.056572 Loss1: 0.581474 Loss2: 1.475098
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.480146 Loss1: 0.986689 Loss2: 1.493457
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.164022 Loss1: 0.671608 Loss2: 1.492414
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.439876 Loss1: 0.939432 Loss2: 1.500444
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.131716 Loss1: 0.621878 Loss2: 1.509839
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.360687 Loss1: 0.857076 Loss2: 1.503611
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.043378 Loss1: 0.521163 Loss2: 1.522215
+(DefaultActor pid=3765) >> Training accuracy: 0.810417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.110823 Loss1: 0.608663 Loss2: 1.502160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.030150 Loss1: 0.510387 Loss2: 1.519762 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.846875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.919457 Loss1: 1.444232 Loss2: 1.475224 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.314080 Loss1: 0.856069 Loss2: 1.458011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.206806 Loss1: 0.743886 Loss2: 1.462920
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.077993 Loss1: 2.022737 Loss2: 2.055256
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.039639 Loss1: 0.570415 Loss2: 1.469224
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.870720 Loss1: 1.361614 Loss2: 1.509106
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.025954 Loss1: 0.558413 Loss2: 1.467541
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.458638 Loss1: 0.967374 Loss2: 1.491264
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.047445 Loss1: 0.581604 Loss2: 1.465841
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.334623 Loss1: 0.850901 Loss2: 1.483722
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.937715 Loss1: 0.470664 Loss2: 1.467052
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.328153 Loss1: 0.831210 Loss2: 1.496943
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.001487 Loss1: 0.546944 Loss2: 1.454543
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.291736 Loss1: 0.777423 Loss2: 1.514313
+(DefaultActor pid=3765) >> Training accuracy: 0.858333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.071596 Loss1: 0.565898 Loss2: 1.505698
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.004480 Loss1: 0.506243 Loss2: 1.498237
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.058223 Loss1: 0.546735 Loss2: 1.511488
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.997977 Loss1: 0.477246 Loss2: 1.520731
+(DefaultActor pid=3764) >> Training accuracy: 0.870833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.053590 Loss1: 1.988261 Loss2: 2.065330
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.824026 Loss1: 1.311242 Loss2: 1.512785
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.594443 Loss1: 1.109549 Loss2: 1.484894
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.469152 Loss1: 0.959341 Loss2: 1.509811
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.284704 Loss1: 0.781548 Loss2: 1.503156
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.135221 Loss1: 0.641291 Loss2: 1.493930 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.098179 Loss1: 0.611854 Loss2: 1.486325 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.074228 Loss1: 0.581034 Loss2: 1.493193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.984076 Loss1: 0.487226 Loss2: 1.496850 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.999802 Loss1: 0.498821 Loss2: 1.500982 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.901042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.080913 Loss1: 0.596812 Loss2: 1.484100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.002065 Loss1: 0.519360 Loss2: 1.482705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.068903 Loss1: 2.020551 Loss2: 2.048352
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.102491 Loss1: 0.603017 Loss2: 1.499474
+(DefaultActor pid=3764) >> Training accuracy: 0.857537
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.527938 Loss1: 1.036313 Loss2: 1.491626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.305491 Loss1: 0.814644 Loss2: 1.490847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.258744 Loss1: 0.755849 Loss2: 1.502894
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.984201 Loss1: 2.041573 Loss2: 1.942628
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.006868 Loss1: 1.578761 Loss2: 1.428107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.548279 Loss1: 1.151712 Loss2: 1.396567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.331767 Loss1: 0.930537 Loss2: 1.401231 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.870833
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.071874 Loss1: 0.579500 Loss2: 1.492374
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.202178 Loss1: 0.806224 Loss2: 1.395953
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.217899 Loss1: 0.807547 Loss2: 1.410352
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.080289 Loss1: 0.668154 Loss2: 1.412135
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.975971 Loss1: 0.572610 Loss2: 1.403361
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.982903 Loss1: 0.580380 Loss2: 1.402522
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.893019 Loss1: 0.470457 Loss2: 1.422563
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.175707 Loss1: 2.144391 Loss2: 2.031316
+(DefaultActor pid=3764) >> Training accuracy: 0.878125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.014761 Loss1: 1.489903 Loss2: 1.524858
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.709614 Loss1: 1.206861 Loss2: 1.502753
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.483617 Loss1: 0.971916 Loss2: 1.511702
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.366784 Loss1: 0.861486 Loss2: 1.505298
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.995693 Loss1: 1.935992 Loss2: 2.059701
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.249260 Loss1: 0.737542 Loss2: 1.511718
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.923578 Loss1: 1.423575 Loss2: 1.500003
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.123645 Loss1: 0.618174 Loss2: 1.505471
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.700269 Loss1: 1.205375 Loss2: 1.494894
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.085131 Loss1: 0.578079 Loss2: 1.507052
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.003707 Loss1: 0.491178 Loss2: 1.512529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.007838 Loss1: 0.485646 Loss2: 1.522193 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.833984
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.121381 Loss1: 0.609903 Loss2: 1.511478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.052433 Loss1: 0.543169 Loss2: 1.509264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.019123 Loss1: 0.513622 Loss2: 1.505501
+(DefaultActor pid=3764) >> Training accuracy: 0.869792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.365027 Loss1: 2.248913 Loss2: 2.116114
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.125208 Loss1: 1.603293 Loss2: 1.521915
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.728415 Loss1: 1.253820 Loss2: 1.474595
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.472441 Loss1: 0.994257 Loss2: 1.478184
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.337278 Loss1: 0.850966 Loss2: 1.486313
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.264308 Loss1: 0.780215 Loss2: 1.484093
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.142450 Loss1: 2.031303 Loss2: 2.111148
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.002454 Loss1: 1.468656 Loss2: 1.533798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.668152 Loss1: 1.162850 Loss2: 1.505302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.489617 Loss1: 0.978130 Loss2: 1.511487 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.828125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.351943 Loss1: 0.826165 Loss2: 1.525778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.105975 Loss1: 0.590526 Loss2: 1.515449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.114795 Loss1: 0.594423 Loss2: 1.520371 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.130396 Loss1: 0.599343 Loss2: 1.531053 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.835417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.638785 Loss1: 1.120300 Loss2: 1.518484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.304732 Loss1: 0.795969 Loss2: 1.508763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.371254 Loss1: 2.098956 Loss2: 2.272298
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.257207 Loss1: 0.742574 Loss2: 1.514633
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.210260 Loss1: 0.677804 Loss2: 1.532456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.147220 Loss1: 0.607718 Loss2: 1.539502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.381248 Loss1: 0.838648 Loss2: 1.542600 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.312994 Loss1: 0.767255 Loss2: 1.545740 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.884766
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.083449 Loss1: 0.540075 Loss2: 1.543374 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.018986 Loss1: 0.474029 Loss2: 1.544957 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.903646
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.937169 Loss1: 1.909383 Loss2: 2.027786
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.886138 Loss1: 1.425781 Loss2: 1.460357
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.375053 Loss1: 0.959591 Loss2: 1.415462
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.242670 Loss1: 0.837487 Loss2: 1.405184
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.096083 Loss1: 2.063308 Loss2: 2.032775
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.897432 Loss1: 1.427929 Loss2: 1.469503 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.568160 Loss1: 1.124990 Loss2: 1.443170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.391830 Loss1: 0.927163 Loss2: 1.464666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.263044 Loss1: 0.785407 Loss2: 1.477637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.126390 Loss1: 0.662387 Loss2: 1.464003 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.892708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.880133 Loss1: 0.446520 Loss2: 1.433613
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.146834 Loss1: 0.680448 Loss2: 1.466387
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.982811 Loss1: 0.509782 Loss2: 1.473028
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.942868 Loss1: 0.481044 Loss2: 1.461824
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.939301 Loss1: 0.474591 Loss2: 1.464710
+(DefaultActor pid=3764) >> Training accuracy: 0.873958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.161165 Loss1: 2.077180 Loss2: 2.083986
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.965622 Loss1: 1.449153 Loss2: 1.516469
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.754357 Loss1: 1.262540 Loss2: 1.491817
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.523651 Loss1: 1.015114 Loss2: 1.508538
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.010924 Loss1: 1.955870 Loss2: 2.055054
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.406295 Loss1: 0.893849 Loss2: 1.512445
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.776860 Loss1: 1.313891 Loss2: 1.462969
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.225733 Loss1: 0.709610 Loss2: 1.516123
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.539044 Loss1: 1.101787 Loss2: 1.437257
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.189125 Loss1: 0.678977 Loss2: 1.510148
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.285383 Loss1: 0.831216 Loss2: 1.454166
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.212175 Loss1: 0.764586 Loss2: 1.447589
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.177134 Loss1: 0.666171 Loss2: 1.510964
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.089734 Loss1: 0.643460 Loss2: 1.446274
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.141684 Loss1: 0.630398 Loss2: 1.511286
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.093333 Loss1: 0.634465 Loss2: 1.458868
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.049132 Loss1: 0.522704 Loss2: 1.526429
+(DefaultActor pid=3765) >> Training accuracy: 0.876042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.957996 Loss1: 0.504370 Loss2: 1.453626 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.885045
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.961243 Loss1: 2.020228 Loss2: 1.941014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.557624 Loss1: 1.166049 Loss2: 1.391575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.298450 Loss1: 0.916795 Loss2: 1.381655
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.057950 Loss1: 2.057932 Loss2: 2.000018
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.877852 Loss1: 1.405483 Loss2: 1.472369 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.617857 Loss1: 1.171974 Loss2: 1.445883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.371205 Loss1: 0.911428 Loss2: 1.459778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.255547 Loss1: 0.795481 Loss2: 1.460066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.101065 Loss1: 0.663915 Loss2: 1.437151 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.776042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.915627 Loss1: 0.519107 Loss2: 1.396520
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.107940 Loss1: 0.654414 Loss2: 1.453526
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.054404 Loss1: 0.586967 Loss2: 1.467437
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.959965 Loss1: 0.500324 Loss2: 1.459641
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.050323 Loss1: 0.587909 Loss2: 1.462414
+(DefaultActor pid=3764) >> Training accuracy: 0.854167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.343098 Loss1: 2.202766 Loss2: 2.140332
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.097913 Loss1: 1.571918 Loss2: 1.525995
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.731414 Loss1: 1.205687 Loss2: 1.525727
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.474912 Loss1: 0.965722 Loss2: 1.509190
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.940942 Loss1: 1.880894 Loss2: 2.060048
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.769735 Loss1: 1.304450 Loss2: 1.465285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.489407 Loss1: 1.023292 Loss2: 1.466114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.166050 Loss1: 0.617952 Loss2: 1.548099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.120940 Loss1: 0.585547 Loss2: 1.535392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.986578 Loss1: 0.454287 Loss2: 1.532291 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.892857
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.903788 Loss1: 0.449207 Loss2: 1.454581 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.923980 Loss1: 0.473967 Loss2: 1.450013 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.901042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.997204 Loss1: 1.417791 Loss2: 1.579413 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.433021 Loss1: 0.919463 Loss2: 1.513558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.206907 Loss1: 2.049013 Loss2: 2.157894
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.349233 Loss1: 0.819226 Loss2: 1.530006
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.168593 Loss1: 0.638193 Loss2: 1.530400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.144649 Loss1: 0.614275 Loss2: 1.530374 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.084839 Loss1: 0.549218 Loss2: 1.535621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.179425 Loss1: 0.674055 Loss2: 1.505370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.117372 Loss1: 0.606473 Loss2: 1.510900 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.862305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.066235 Loss1: 0.542591 Loss2: 1.523644 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.870192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.947540 Loss1: 1.869754 Loss2: 2.077786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.512366 Loss1: 1.036050 Loss2: 1.476316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.315022 Loss1: 0.834937 Loss2: 1.480085
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.943945 Loss1: 1.925038 Loss2: 2.018907
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.970598 Loss1: 1.425909 Loss2: 1.544689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.561280 Loss1: 1.046832 Loss2: 1.514448 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.442930 Loss1: 0.936684 Loss2: 1.506246 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.266685 Loss1: 0.747013 Loss2: 1.519672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.159514 Loss1: 0.646815 Loss2: 1.512699 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.887500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.048302 Loss1: 0.546766 Loss2: 1.501536 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.064284 Loss1: 0.556563 Loss2: 1.507721 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.846680
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.037643 Loss1: 2.096909 Loss2: 1.940733 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.678434 Loss1: 1.222366 Loss2: 1.456068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.431812 Loss1: 0.985951 Loss2: 1.445860
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.229542 Loss1: 2.175597 Loss2: 2.053946
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.026621 Loss1: 1.507543 Loss2: 1.519078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.656679 Loss1: 1.166593 Loss2: 1.490087 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.482224 Loss1: 0.975980 Loss2: 1.506244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.430724 Loss1: 0.932490 Loss2: 1.498234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.295347 Loss1: 0.794978 Loss2: 1.500369 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.850586
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.228730 Loss1: 0.726306 Loss2: 1.502424 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.091723 Loss1: 0.596289 Loss2: 1.495435 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.834961
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.179531 Loss1: 2.180525 Loss2: 1.999006 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.541211 Loss1: 1.115735 Loss2: 1.425476 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.146913 Loss1: 2.120504 Loss2: 2.026409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.100844 Loss1: 1.659043 Loss2: 1.441801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.713818 Loss1: 1.283459 Loss2: 1.430358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.356376 Loss1: 0.927772 Loss2: 1.428603 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.154102 Loss1: 0.740004 Loss2: 1.414097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.165150 Loss1: 0.741305 Loss2: 1.423845 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.895833
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-09 13:25:48,586 | server.py:236 | fit_round 40 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.006104 Loss1: 0.569090 Loss2: 1.437014 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.905583 Loss1: 0.469120 Loss2: 1.436463 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.847917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.836660 Loss1: 1.348283 Loss2: 1.488377 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.463990 Loss1: 0.997765 Loss2: 1.466225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.936075 Loss1: 1.901204 Loss2: 2.034871
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.254805 Loss1: 0.783403 Loss2: 1.471402
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.957884 Loss1: 1.468966 Loss2: 1.488918
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.128414 Loss1: 0.663577 Loss2: 1.464837
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.631195 Loss1: 1.143055 Loss2: 1.488140
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.973984 Loss1: 0.525957 Loss2: 1.448027
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.003115 Loss1: 0.541894 Loss2: 1.461221
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.317084 Loss1: 0.829178 Loss2: 1.487905
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.956859 Loss1: 0.500156 Loss2: 1.456703
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.198218 Loss1: 0.738136 Loss2: 1.460082
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.875810 Loss1: 0.416003 Loss2: 1.459807
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.146041 Loss1: 0.668601 Loss2: 1.477440
+(DefaultActor pid=3765) >> Training accuracy: 0.892708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.048797 Loss1: 0.580909 Loss2: 1.467889
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.934163 Loss1: 0.459944 Loss2: 1.474219
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.947154 Loss1: 0.480788 Loss2: 1.466366
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.021385 Loss1: 0.547078 Loss2: 1.474308
+(DefaultActor pid=3764) >> Training accuracy: 0.859375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.156804 Loss1: 2.042761 Loss2: 2.114043
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.953303 Loss1: 1.381935 Loss2: 1.571368
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.655663 Loss1: 1.108608 Loss2: 1.547055
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.487629 Loss1: 0.935997 Loss2: 1.551632
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.356087 Loss1: 0.816296 Loss2: 1.539791
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.844857 Loss1: 1.770001 Loss2: 2.074856
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.807275 Loss1: 1.304154 Loss2: 1.503121 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.531803 Loss1: 1.043666 Loss2: 1.488138 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.325079 Loss1: 0.841437 Loss2: 1.483642 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.236051 Loss1: 0.756663 Loss2: 1.479388 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.881250
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.028913 Loss1: 0.473423 Loss2: 1.555489
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.134617 Loss1: 0.644860 Loss2: 1.489757
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.023457 Loss1: 0.531663 Loss2: 1.491794
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.961181 Loss1: 0.482091 Loss2: 1.479090
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.961144 Loss1: 0.463914 Loss2: 1.497231
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.938062 Loss1: 0.459767 Loss2: 1.478295
+(DefaultActor pid=3764) >> Training accuracy: 0.832292
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 13:25:48,586][flwr][DEBUG] - fit_round 40 received 50 results and 0 failures
+INFO flwr 2023-10-09 13:26:29,768 | server.py:125 | fit progress: (40, 2.5703621779006127, {'accuracy': 0.4248}, 92097.546352868)
+>> Test accuracy: 0.424800
+[2023-10-09 13:26:29,768][flwr][INFO] - fit progress: (40, 2.5703621779006127, {'accuracy': 0.4248}, 92097.546352868)
+DEBUG flwr 2023-10-09 13:26:29,768 | server.py:173 | evaluate_round 40: strategy sampled 50 clients (out of 50)
+[2023-10-09 13:26:29,768][flwr][DEBUG] - evaluate_round 40: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 13:35:37,089 | server.py:187 | evaluate_round 40 received 50 results and 0 failures
+[2023-10-09 13:35:37,089][flwr][DEBUG] - evaluate_round 40 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 13:35:37,089 | server.py:222 | fit_round 41: strategy sampled 50 clients (out of 50)
+[2023-10-09 13:35:37,089][flwr][DEBUG] - fit_round 41: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.029467 Loss1: 1.959127 Loss2: 2.070340
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.916623 Loss1: 1.425392 Loss2: 1.491231
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.580515 Loss1: 1.099210 Loss2: 1.481305
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.325493 Loss1: 0.845462 Loss2: 1.480031
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.992454 Loss1: 1.869414 Loss2: 2.123040
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.218203 Loss1: 0.732269 Loss2: 1.485934
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.820516 Loss1: 1.314699 Loss2: 1.505818
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.152797 Loss1: 0.658545 Loss2: 1.494252
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.480245 Loss1: 0.987987 Loss2: 1.492258
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.074360 Loss1: 0.583066 Loss2: 1.491294
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.233596 Loss1: 0.749959 Loss2: 1.483637
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.984058 Loss1: 0.493319 Loss2: 1.490740
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.092962 Loss1: 0.622981 Loss2: 1.469981
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.933200 Loss1: 0.437658 Loss2: 1.495542
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.040110 Loss1: 0.569344 Loss2: 1.470766
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.889660 Loss1: 0.394102 Loss2: 1.495558
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.004992 Loss1: 0.523851 Loss2: 1.481141
+(DefaultActor pid=3765) >> Training accuracy: 0.877083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.994775 Loss1: 0.518699 Loss2: 1.476076
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.118707 Loss1: 0.610463 Loss2: 1.508244
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.102364 Loss1: 0.593492 Loss2: 1.508871
+(DefaultActor pid=3764) >> Training accuracy: 0.796875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.153081 Loss1: 2.084759 Loss2: 2.068322
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.990231 Loss1: 1.492551 Loss2: 1.497681
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.701201 Loss1: 1.231590 Loss2: 1.469611
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.413377 Loss1: 0.932836 Loss2: 1.480542
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.244167 Loss1: 2.167663 Loss2: 2.076504
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.284576 Loss1: 0.803570 Loss2: 1.481006
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.054871 Loss1: 1.565165 Loss2: 1.489707
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.157972 Loss1: 0.680335 Loss2: 1.477637
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.843639 Loss1: 1.398227 Loss2: 1.445412
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.497371 Loss1: 1.037691 Loss2: 1.459680
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.189265 Loss1: 0.698488 Loss2: 1.490777
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.330326 Loss1: 0.879971 Loss2: 1.450355
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.152570 Loss1: 0.654668 Loss2: 1.497902
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.156262 Loss1: 0.695422 Loss2: 1.460841
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.010586 Loss1: 0.505809 Loss2: 1.504777
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.947861 Loss1: 0.466859 Loss2: 1.481002 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.888542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.954773 Loss1: 0.501890 Loss2: 1.452883 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.831473
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.101859 Loss1: 2.032850 Loss2: 2.069009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.681377 Loss1: 1.161307 Loss2: 1.520070 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.440901 Loss1: 0.921030 Loss2: 1.519871
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.178180 Loss1: 2.118144 Loss2: 2.060036
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.372680 Loss1: 0.845497 Loss2: 1.527182
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.096759 Loss1: 1.596059 Loss2: 1.500700
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.368960 Loss1: 0.837584 Loss2: 1.531376
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.656675 Loss1: 1.172064 Loss2: 1.484611
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.212917 Loss1: 0.663777 Loss2: 1.549140
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.529287 Loss1: 1.035215 Loss2: 1.494072
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.114962 Loss1: 0.586110 Loss2: 1.528852
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.309051 Loss1: 0.809211 Loss2: 1.499840
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.053733 Loss1: 0.527256 Loss2: 1.526477
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.274815 Loss1: 0.775370 Loss2: 1.499445
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.923705 Loss1: 0.398354 Loss2: 1.525351
+(DefaultActor pid=3765) >> Training accuracy: 0.896875
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.145996 Loss1: 0.633984 Loss2: 1.512012
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.131891 Loss1: 0.622099 Loss2: 1.509792
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.106364 Loss1: 0.583536 Loss2: 1.522828
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.093075 Loss1: 0.563870 Loss2: 1.529205
+(DefaultActor pid=3764) >> Training accuracy: 0.869792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.264735 Loss1: 2.194481 Loss2: 2.070254
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.049217 Loss1: 1.535987 Loss2: 1.513231
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.732540 Loss1: 1.255320 Loss2: 1.477220
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.463571 Loss1: 0.969449 Loss2: 1.494122
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.174825 Loss1: 1.966709 Loss2: 2.208116
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.851034 Loss1: 1.321495 Loss2: 1.529539
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.367023 Loss1: 0.871830 Loss2: 1.495193
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.221904 Loss1: 0.729805 Loss2: 1.492099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.116424 Loss1: 0.609132 Loss2: 1.507292 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.980187 Loss1: 0.485412 Loss2: 1.494775 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.921915 Loss1: 0.429992 Loss2: 1.491923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.950394 Loss1: 0.455395 Loss2: 1.494998 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.848958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.939075 Loss1: 0.432757 Loss2: 1.506318 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.887019
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.966996 Loss1: 1.905123 Loss2: 2.061874
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.816495 Loss1: 1.300088 Loss2: 1.516407
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.597309 Loss1: 1.106181 Loss2: 1.491128
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.082755 Loss1: 2.056699 Loss2: 2.026056
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.292370 Loss1: 0.800415 Loss2: 1.491955
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.839938 Loss1: 1.381774 Loss2: 1.458163
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.136396 Loss1: 0.661406 Loss2: 1.474990
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.494735 Loss1: 1.063117 Loss2: 1.431619
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.086651 Loss1: 0.603825 Loss2: 1.482825
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.056835 Loss1: 0.573089 Loss2: 1.483746 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.001563 Loss1: 0.510472 Loss2: 1.491091 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.938455 Loss1: 0.438726 Loss2: 1.499729 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.920746 Loss1: 0.431511 Loss2: 1.489235 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.898438
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.942512 Loss1: 0.504818 Loss2: 1.437694 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.829167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.122839 Loss1: 2.017077 Loss2: 2.105762 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.616017 Loss1: 1.091686 Loss2: 1.524331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.384890 Loss1: 0.866455 Loss2: 1.518435
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.158353 Loss1: 2.109308 Loss2: 2.049044
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.961916 Loss1: 1.459100 Loss2: 1.502816 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.715620 Loss1: 1.231391 Loss2: 1.484229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.442902 Loss1: 0.935863 Loss2: 1.507039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.995613 Loss1: 0.473899 Loss2: 1.521714
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.302026 Loss1: 0.823207 Loss2: 1.478819
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.166844 Loss1: 0.678074 Loss2: 1.488770 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.974145 Loss1: 0.462214 Loss2: 1.511931
+(DefaultActor pid=3765) >> Training accuracy: 0.853125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.119576 Loss1: 0.644999 Loss2: 1.474577
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.044123 Loss1: 0.563084 Loss2: 1.481039
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.032097 Loss1: 0.541800 Loss2: 1.490298
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.068481 Loss1: 0.580753 Loss2: 1.487728
+(DefaultActor pid=3764) >> Training accuracy: 0.864583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.873752 Loss1: 1.818110 Loss2: 2.055642
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.867339 Loss1: 1.350556 Loss2: 1.516783
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.524703 Loss1: 1.019015 Loss2: 1.505688
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.049610 Loss1: 1.906341 Loss2: 2.143269
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.330434 Loss1: 0.836040 Loss2: 1.494395
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.188340 Loss1: 0.689583 Loss2: 1.498758 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.095588 Loss1: 0.595352 Loss2: 1.500236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.123393 Loss1: 0.618707 Loss2: 1.504686 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.210483 Loss1: 0.687117 Loss2: 1.523366 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.085431 Loss1: 0.560340 Loss2: 1.525092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.922195 Loss1: 0.446486 Loss2: 1.475708 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.817096
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.889025 Loss1: 0.409340 Loss2: 1.479685 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.848958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.092665 Loss1: 1.987369 Loss2: 2.105296
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.981309 Loss1: 1.468239 Loss2: 1.513070
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.673752 Loss1: 1.167221 Loss2: 1.506531
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.347289 Loss1: 0.850424 Loss2: 1.496865
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.977328 Loss1: 1.810373 Loss2: 2.166955
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.306354 Loss1: 0.811712 Loss2: 1.494641
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.834670 Loss1: 1.280886 Loss2: 1.553784
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.201717 Loss1: 0.698496 Loss2: 1.503221
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.532062 Loss1: 0.987436 Loss2: 1.544626
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.058684 Loss1: 0.557102 Loss2: 1.501582
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.328865 Loss1: 0.788902 Loss2: 1.539963
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.030008 Loss1: 0.537393 Loss2: 1.492616
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.105677 Loss1: 0.564462 Loss2: 1.541214
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.037732 Loss1: 0.530647 Loss2: 1.507084
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.159435 Loss1: 0.622215 Loss2: 1.537221
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.930764 Loss1: 0.422810 Loss2: 1.507954
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.077659 Loss1: 0.528873 Loss2: 1.548786
+(DefaultActor pid=3765) >> Training accuracy: 0.932292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.011030 Loss1: 0.465352 Loss2: 1.545678
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.949396 Loss1: 0.400513 Loss2: 1.548883
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.936052 Loss1: 0.391070 Loss2: 1.544982
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.074624 Loss1: 1.992505 Loss2: 2.082120
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.937790 Loss1: 1.423662 Loss2: 1.514128
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.537973 Loss1: 1.045674 Loss2: 1.492299
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.133322 Loss1: 2.060068 Loss2: 2.073254
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.399189 Loss1: 0.896853 Loss2: 1.502336
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.106343 Loss1: 1.571461 Loss2: 1.534882
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.253380 Loss1: 0.751509 Loss2: 1.501871
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.619916 Loss1: 1.107808 Loss2: 1.512107
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.151085 Loss1: 0.659293 Loss2: 1.491792
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.387144 Loss1: 0.895871 Loss2: 1.491273
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.185060 Loss1: 0.682971 Loss2: 1.502089
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.127550 Loss1: 0.616776 Loss2: 1.510773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.068224 Loss1: 0.570357 Loss2: 1.497866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.997614 Loss1: 0.499106 Loss2: 1.498509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.854492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.127734 Loss1: 0.630256 Loss2: 1.497478 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.862500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.945913 Loss1: 1.942987 Loss2: 2.002926 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.494911 Loss1: 1.042328 Loss2: 1.452583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.293992 Loss1: 0.830082 Loss2: 1.463910
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.065247 Loss1: 2.011778 Loss2: 2.053468
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.014746 Loss1: 1.507780 Loss2: 1.506967
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.243023 Loss1: 0.776471 Loss2: 1.466552
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.635814 Loss1: 1.147100 Loss2: 1.488714
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.167511 Loss1: 0.695605 Loss2: 1.471906
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.388545 Loss1: 0.896566 Loss2: 1.491979
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.039144 Loss1: 0.562904 Loss2: 1.476240
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.264213 Loss1: 0.772602 Loss2: 1.491611
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.959909 Loss1: 0.501714 Loss2: 1.458196
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.024916 Loss1: 0.553890 Loss2: 1.471026 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.974417 Loss1: 0.493807 Loss2: 1.480610 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.875977
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.921455 Loss1: 0.432349 Loss2: 1.489107 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.864583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.136476 Loss1: 2.022006 Loss2: 2.114469 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.586062 Loss1: 1.127246 Loss2: 1.458816 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.068759 Loss1: 2.044280 Loss2: 2.024479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.122666 Loss1: 0.657391 Loss2: 1.465275 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.093744 Loss1: 0.626071 Loss2: 1.467673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.089294 Loss1: 0.615781 Loss2: 1.473513 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.017667 Loss1: 0.523559 Loss2: 1.494108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.901539 Loss1: 0.426790 Loss2: 1.474749 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.877404
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.252039 Loss1: 0.757034 Loss2: 1.495006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.050703 Loss1: 0.569861 Loss2: 1.480842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.029387 Loss1: 0.543315 Loss2: 1.486072
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.079140 Loss1: 2.064505 Loss2: 2.014635
+(DefaultActor pid=3764) >> Training accuracy: 0.881836
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.872030 Loss1: 1.418769 Loss2: 1.453262
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.479438 Loss1: 1.069820 Loss2: 1.409619
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.268853 Loss1: 0.853446 Loss2: 1.415407
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.114249 Loss1: 0.706585 Loss2: 1.407664
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.024947 Loss1: 0.613012 Loss2: 1.411935
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.071137 Loss1: 2.021340 Loss2: 2.049797
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.031609 Loss1: 0.602872 Loss2: 1.428737
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.079291 Loss1: 1.533222 Loss2: 1.546069
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.923362 Loss1: 0.497768 Loss2: 1.425594
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.626606 Loss1: 1.102659 Loss2: 1.523946
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.960699 Loss1: 0.525480 Loss2: 1.435218
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.367300 Loss1: 0.842762 Loss2: 1.524538
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.940127 Loss1: 0.507743 Loss2: 1.432384
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.309412 Loss1: 0.774810 Loss2: 1.534601
+(DefaultActor pid=3765) >> Training accuracy: 0.883333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.260726 Loss1: 0.727074 Loss2: 1.533652
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.211768 Loss1: 0.671646 Loss2: 1.540122
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.111077 Loss1: 0.581109 Loss2: 1.529969
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.107006 Loss1: 0.570453 Loss2: 1.536553
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.040301 Loss1: 0.499716 Loss2: 1.540585
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.116042 Loss1: 1.998229 Loss2: 2.117813
+(DefaultActor pid=3764) >> Training accuracy: 0.831250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.926883 Loss1: 1.424686 Loss2: 1.502196
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.638729 Loss1: 1.144168 Loss2: 1.494561
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.330961 Loss1: 0.830734 Loss2: 1.500227
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.218937 Loss1: 0.727372 Loss2: 1.491565
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.186661 Loss1: 0.696982 Loss2: 1.489679
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.105192 Loss1: 2.068433 Loss2: 2.036759
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.129315 Loss1: 0.626950 Loss2: 1.502365
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.100058 Loss1: 1.598324 Loss2: 1.501734
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.692029 Loss1: 1.185636 Loss2: 1.506394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.417300 Loss1: 0.934857 Loss2: 1.482443 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.891667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.927075 Loss1: 0.418864 Loss2: 1.508211
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.283532 Loss1: 0.795662 Loss2: 1.487870
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.169808 Loss1: 0.680254 Loss2: 1.489554
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.074063 Loss1: 0.578086 Loss2: 1.495978
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.027887 Loss1: 0.535751 Loss2: 1.492136
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.985368 Loss1: 0.493016 Loss2: 1.492352
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.176904 Loss1: 2.109502 Loss2: 2.067402
+(DefaultActor pid=3764) >> Training accuracy: 0.839844
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.922402 Loss1: 0.434069 Loss2: 1.488333
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.970568 Loss1: 1.486428 Loss2: 1.484140
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.628500 Loss1: 1.137991 Loss2: 1.490509
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.480311 Loss1: 0.986735 Loss2: 1.493575
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.351671 Loss1: 0.845450 Loss2: 1.506221
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.289778 Loss1: 0.769430 Loss2: 1.520348
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.296687 Loss1: 2.180554 Loss2: 2.116133
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.173078 Loss1: 0.668829 Loss2: 1.504248
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.131381 Loss1: 0.621412 Loss2: 1.509969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.007357 Loss1: 0.500573 Loss2: 1.506784 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.014868 Loss1: 0.503673 Loss2: 1.511195 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.868750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.148434 Loss1: 0.638623 Loss2: 1.509811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.054363 Loss1: 0.539183 Loss2: 1.515180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.006133 Loss1: 0.481569 Loss2: 1.524564
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.205087 Loss1: 2.119962 Loss2: 2.085125
+(DefaultActor pid=3764) >> Training accuracy: 0.881250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.926946 Loss1: 0.405406 Loss2: 1.521541
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.959267 Loss1: 1.464420 Loss2: 1.494847
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.559800 Loss1: 1.088110 Loss2: 1.471691
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.406228 Loss1: 0.944943 Loss2: 1.461285
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.242485 Loss1: 0.770196 Loss2: 1.472289
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.164294 Loss1: 0.683806 Loss2: 1.480488
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.082311 Loss1: 2.016862 Loss2: 2.065448
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.078120 Loss1: 0.602280 Loss2: 1.475840
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.059310 Loss1: 0.570090 Loss2: 1.489220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.080124 Loss1: 0.593892 Loss2: 1.486232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.329755 Loss1: 0.846770 Loss2: 1.482984
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.046726 Loss1: 0.549333 Loss2: 1.497394
+(DefaultActor pid=3765) >> Training accuracy: 0.887500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.151314 Loss1: 0.670868 Loss2: 1.480446 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.995426 Loss1: 0.517354 Loss2: 1.478072 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.972843 Loss1: 0.490863 Loss2: 1.481980
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.284574 Loss1: 2.131005 Loss2: 2.153569
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.196059 Loss1: 1.634435 Loss2: 1.561624
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.007091 Loss1: 0.506688 Loss2: 1.500404
+(DefaultActor pid=3764) >> Training accuracy: 0.832031
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.493429 Loss1: 0.963403 Loss2: 1.530026 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.363222 Loss1: 0.811469 Loss2: 1.551753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.246334 Loss1: 0.702548 Loss2: 1.543785
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.101135 Loss1: 1.995936 Loss2: 2.105198
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.116971 Loss1: 0.571865 Loss2: 1.545106
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.891979 Loss1: 1.376059 Loss2: 1.515919
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.095762 Loss1: 0.540140 Loss2: 1.555622
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.556212 Loss1: 1.062492 Loss2: 1.493720
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.095650 Loss1: 0.544341 Loss2: 1.551309
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.351091 Loss1: 0.848353 Loss2: 1.502738
+(DefaultActor pid=3765) >> Training accuracy: 0.873958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.176114 Loss1: 0.666827 Loss2: 1.509287
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.189817 Loss1: 0.679289 Loss2: 1.510528
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.175903 Loss1: 0.647869 Loss2: 1.528034
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.101056 Loss1: 0.580237 Loss2: 1.520819
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.120529 Loss1: 0.599873 Loss2: 1.520655
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.274420 Loss1: 2.049031 Loss2: 2.225389
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.945695 Loss1: 0.429111 Loss2: 1.516584
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.165182 Loss1: 1.534828 Loss2: 1.630354
+(DefaultActor pid=3764) >> Training accuracy: 0.904167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.829010 Loss1: 1.218665 Loss2: 1.610344
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.607074 Loss1: 1.003247 Loss2: 1.603826
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.378913 Loss1: 0.781238 Loss2: 1.597675
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.324114 Loss1: 0.722061 Loss2: 1.602054
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.719673 Loss1: 1.789765 Loss2: 1.929908
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.182416 Loss1: 0.583132 Loss2: 1.599284
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.690089 Loss1: 1.283860 Loss2: 1.406228
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.134094 Loss1: 0.536992 Loss2: 1.597102
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.299432 Loss1: 0.916233 Loss2: 1.383200
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.059491 Loss1: 0.450409 Loss2: 1.609082
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.139106 Loss1: 0.744154 Loss2: 1.394952
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.016389 Loss1: 0.419984 Loss2: 1.596404
+(DefaultActor pid=3765) >> Training accuracy: 0.870833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.024051 Loss1: 0.620360 Loss2: 1.403691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.834865 Loss1: 0.428253 Loss2: 1.406612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.854826 Loss1: 0.457217 Loss2: 1.397609
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.913343 Loss1: 1.871560 Loss2: 2.041783
+(DefaultActor pid=3764) >> Training accuracy: 0.863542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.849246 Loss1: 1.319622 Loss2: 1.529624 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.319459 Loss1: 0.820567 Loss2: 1.498892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.011015 Loss1: 0.530395 Loss2: 1.480620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.031983 Loss1: 0.539245 Loss2: 1.492738 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.988591 Loss1: 0.481020 Loss2: 1.507572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.296923 Loss1: 0.804040 Loss2: 1.492883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.187975 Loss1: 0.699173 Loss2: 1.488803 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.922852
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.086434 Loss1: 0.581281 Loss2: 1.505153 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.059762 Loss1: 0.555747 Loss2: 1.504015 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.912946
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.978491 Loss1: 0.468979 Loss2: 1.509513
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.059226 Loss1: 2.012782 Loss2: 2.046444
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.863436 Loss1: 1.417883 Loss2: 1.445553
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.529350 Loss1: 1.097296 Loss2: 1.432054
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.299266 Loss1: 0.845664 Loss2: 1.453601
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.128926 Loss1: 0.675017 Loss2: 1.453909
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.142999 Loss1: 2.102201 Loss2: 2.040798
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.091564 Loss1: 0.653180 Loss2: 1.438384
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.028515 Loss1: 0.578783 Loss2: 1.449732 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.063975 Loss1: 0.583706 Loss2: 1.480269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.009223 Loss1: 0.540929 Loss2: 1.468293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.051054 Loss1: 0.555713 Loss2: 1.495341 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.787500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.092315 Loss1: 0.623274 Loss2: 1.469041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.994813 Loss1: 0.536278 Loss2: 1.458535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.935933 Loss1: 0.473769 Loss2: 1.462164
+(DefaultActor pid=3764) >> Training accuracy: 0.845833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.346930 Loss1: 2.226744 Loss2: 2.120186
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.018012 Loss1: 1.478838 Loss2: 1.539174
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.707925 Loss1: 1.194396 Loss2: 1.513528
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.488370 Loss1: 0.974771 Loss2: 1.513599
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.311104 Loss1: 0.792007 Loss2: 1.519097
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.179140 Loss1: 0.650736 Loss2: 1.528404
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.876021 Loss1: 1.883146 Loss2: 1.992875
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.849543 Loss1: 1.401031 Loss2: 1.448511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.524574 Loss1: 1.084936 Loss2: 1.439638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.241859 Loss1: 0.822647 Loss2: 1.419212 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.862723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.146875 Loss1: 0.727196 Loss2: 1.419679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.015472 Loss1: 0.584679 Loss2: 1.430793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.852791 Loss1: 0.424860 Loss2: 1.427931 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.905359 Loss1: 0.484438 Loss2: 1.420921 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.883333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.531277 Loss1: 1.042418 Loss2: 1.488859 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.252442 Loss1: 0.779234 Loss2: 1.473208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.862857 Loss1: 1.797935 Loss2: 2.064922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.849732 Loss1: 1.349042 Loss2: 1.500690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.498276 Loss1: 1.021133 Loss2: 1.477143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.238895 Loss1: 0.769746 Loss2: 1.469148 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.846875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.143568 Loss1: 0.674922 Loss2: 1.468646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.994638 Loss1: 0.520979 Loss2: 1.473659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.927386 Loss1: 0.465415 Loss2: 1.461971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.854338 Loss1: 0.379683 Loss2: 1.474656 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.902083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.522811 Loss1: 1.009744 Loss2: 1.513067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.321530 Loss1: 0.791019 Loss2: 1.530511 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.266373 Loss1: 0.729798 Loss2: 1.536575
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.182066 Loss1: 2.146178 Loss2: 2.035888
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.057930 Loss1: 1.537885 Loss2: 1.520045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.745377 Loss1: 1.250624 Loss2: 1.494753 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.491587 Loss1: 0.987469 Loss2: 1.504119 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.880208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.205894 Loss1: 0.721006 Loss2: 1.484888 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.018983 Loss1: 0.510880 Loss2: 1.508103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.081182 Loss1: 2.002722 Loss2: 2.078460
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.997448 Loss1: 0.503128 Loss2: 1.494320
+DEBUG flwr 2023-10-09 14:04:49,974 | server.py:236 | fit_round 41 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.848415 Loss1: 1.343279 Loss2: 1.505137
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.993700 Loss1: 0.499797 Loss2: 1.493903
+(DefaultActor pid=3764) >> Training accuracy: 0.890625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.341955 Loss1: 0.867647 Loss2: 1.474308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.063234 Loss1: 0.594124 Loss2: 1.469110 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.046378 Loss1: 0.570282 Loss2: 1.476096
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.011938 Loss1: 1.914347 Loss2: 2.097591
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.071436 Loss1: 0.597647 Loss2: 1.473789
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.860419 Loss1: 1.325046 Loss2: 1.535373
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.957391 Loss1: 0.473055 Loss2: 1.484336
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.552972 Loss1: 1.051968 Loss2: 1.501005
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.904202 Loss1: 0.419521 Loss2: 1.484681
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.296782 Loss1: 0.805223 Loss2: 1.491559
+(DefaultActor pid=3765) >> Training accuracy: 0.873958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.181577 Loss1: 0.678111 Loss2: 1.503465
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.099556 Loss1: 0.592819 Loss2: 1.506737
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.995521 Loss1: 0.493752 Loss2: 1.501769
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.963560 Loss1: 0.461848 Loss2: 1.501712
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.999986 Loss1: 2.080735 Loss2: 1.919251
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.965536 Loss1: 0.463367 Loss2: 1.502168
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.974548 Loss1: 1.551909 Loss2: 1.422638
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.920403 Loss1: 0.410612 Loss2: 1.509790
+(DefaultActor pid=3764) >> Training accuracy: 0.856250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.366497 Loss1: 0.980472 Loss2: 1.386025 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.073943 Loss1: 0.687575 Loss2: 1.386368 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.985563 Loss1: 0.594744 Loss2: 1.390819
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.015316 Loss1: 1.920117 Loss2: 2.095199
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.865193 Loss1: 0.475792 Loss2: 1.389400
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.898450 Loss1: 1.403773 Loss2: 1.494677
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.863195 Loss1: 0.476051 Loss2: 1.387144
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.514862 Loss1: 1.015344 Loss2: 1.499517
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.940864 Loss1: 0.552373 Loss2: 1.388491
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.351681 Loss1: 0.852001 Loss2: 1.499681
+(DefaultActor pid=3765) >> Training accuracy: 0.868750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.199866 Loss1: 0.698500 Loss2: 1.501367
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.111056 Loss1: 0.613706 Loss2: 1.497349
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.084489 Loss1: 0.573679 Loss2: 1.510811
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.069675 Loss1: 0.574526 Loss2: 1.495149
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.166929 Loss1: 2.031840 Loss2: 2.135088
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.076074 Loss1: 0.570035 Loss2: 1.506038
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.039239 Loss1: 0.514602 Loss2: 1.524637 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.887500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.265053 Loss1: 0.823075 Loss2: 1.441978 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.091349 Loss1: 0.633814 Loss2: 1.457536 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.906712 Loss1: 0.452502 Loss2: 1.454209 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.834635
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.870611 Loss1: 0.408683 Loss2: 1.461928 [repeated 2x across cluster]
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.441183 Loss1: 0.936529 Loss2: 1.504653 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.207400 Loss1: 0.697754 Loss2: 1.509646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.149089 Loss1: 0.634516 Loss2: 1.514573 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.124177 Loss1: 0.599037 Loss2: 1.525139 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.779297
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 14:04:49,974][flwr][DEBUG] - fit_round 41 received 50 results and 0 failures
+INFO flwr 2023-10-09 14:05:31,774 | server.py:125 | fit progress: (41, 2.547065445409415, {'accuracy': 0.4319}, 94439.55233840099)
+>> Test accuracy: 0.431900
+[2023-10-09 14:05:31,774][flwr][INFO] - fit progress: (41, 2.547065445409415, {'accuracy': 0.4319}, 94439.55233840099)
+DEBUG flwr 2023-10-09 14:05:31,774 | server.py:173 | evaluate_round 41: strategy sampled 50 clients (out of 50)
+[2023-10-09 14:05:31,774][flwr][DEBUG] - evaluate_round 41: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 14:14:39,084 | server.py:187 | evaluate_round 41 received 50 results and 0 failures
+[2023-10-09 14:14:39,084][flwr][DEBUG] - evaluate_round 41 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 14:14:39,084 | server.py:222 | fit_round 42: strategy sampled 50 clients (out of 50)
+[2023-10-09 14:14:39,084][flwr][DEBUG] - fit_round 42: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.217871 Loss1: 2.123617 Loss2: 2.094254
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.065605 Loss1: 1.521396 Loss2: 1.544209
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.670205 Loss1: 1.161960 Loss2: 1.508245
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.395288 Loss1: 0.890797 Loss2: 1.504491
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.140532 Loss1: 2.087916 Loss2: 2.052616
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.326606 Loss1: 0.811288 Loss2: 1.515318
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.966481 Loss1: 1.475607 Loss2: 1.490874
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.633236 Loss1: 1.159671 Loss2: 1.473565
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.294745 Loss1: 0.772500 Loss2: 1.522246
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.490363 Loss1: 1.015978 Loss2: 1.474384
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.160577 Loss1: 0.620394 Loss2: 1.540182
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.341042 Loss1: 0.849901 Loss2: 1.491142
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.152276 Loss1: 0.629702 Loss2: 1.522573
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.132864 Loss1: 0.654878 Loss2: 1.477987
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.138906 Loss1: 0.607442 Loss2: 1.531464
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.101853 Loss1: 0.565955 Loss2: 1.535898 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.879883
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.979736 Loss1: 0.502566 Loss2: 1.477170 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.871875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.091055 Loss1: 1.998006 Loss2: 2.093049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.669378 Loss1: 1.148597 Loss2: 1.520781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.424112 Loss1: 0.917615 Loss2: 1.506497
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.088048 Loss1: 1.936764 Loss2: 2.151284
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.410887 Loss1: 0.886266 Loss2: 1.524622
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.819238 Loss1: 1.276974 Loss2: 1.542264
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.228095 Loss1: 0.708229 Loss2: 1.519866
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.574857 Loss1: 1.078875 Loss2: 1.495982
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.064918 Loss1: 0.543578 Loss2: 1.521340
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.329715 Loss1: 0.830679 Loss2: 1.499037
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.994464 Loss1: 0.476455 Loss2: 1.518009
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.261157 Loss1: 0.764426 Loss2: 1.496731
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.956708 Loss1: 0.447044 Loss2: 1.509664
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.079826 Loss1: 0.576778 Loss2: 1.503048
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.890910 Loss1: 0.372091 Loss2: 1.518820
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.990633 Loss1: 0.494770 Loss2: 1.495863
+(DefaultActor pid=3765) >> Training accuracy: 0.870833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.014479 Loss1: 0.515244 Loss2: 1.499236
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.061795 Loss1: 0.562413 Loss2: 1.499382
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.929177 Loss1: 0.425018 Loss2: 1.504159
+(DefaultActor pid=3764) >> Training accuracy: 0.879167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.048688 Loss1: 1.972483 Loss2: 2.076205
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.935667 Loss1: 1.428631 Loss2: 1.507037
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.646987 Loss1: 1.158699 Loss2: 1.488289
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.405512 Loss1: 0.909350 Loss2: 1.496162
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.162036 Loss1: 2.002674 Loss2: 2.159361
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.849446 Loss1: 1.318680 Loss2: 1.530767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.537567 Loss1: 1.041213 Loss2: 1.496354
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.125263 Loss1: 0.625793 Loss2: 1.499469
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.299291 Loss1: 0.805285 Loss2: 1.494007
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.984367 Loss1: 0.493409 Loss2: 1.490958
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.014001 Loss1: 0.530463 Loss2: 1.483537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.051719 Loss1: 0.560554 Loss2: 1.491166 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.900902 Loss1: 0.396209 Loss2: 1.504693 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.909375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.775961 Loss1: 0.302810 Loss2: 1.473151 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.918269
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.056170 Loss1: 2.002462 Loss2: 2.053708
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.915564 Loss1: 1.412763 Loss2: 1.502801
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.649253 Loss1: 1.174215 Loss2: 1.475038
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.481236 Loss1: 0.981983 Loss2: 1.499252
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.931365 Loss1: 1.910714 Loss2: 2.020651
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.904540 Loss1: 1.420105 Loss2: 1.484435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.532185 Loss1: 1.048791 Loss2: 1.483393 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.243046 Loss1: 0.775027 Loss2: 1.468018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.227690 Loss1: 0.754470 Loss2: 1.473220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.079271 Loss1: 0.608220 Loss2: 1.471051 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.859375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.953030 Loss1: 0.451816 Loss2: 1.501214
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.004914 Loss1: 0.529216 Loss2: 1.475699
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.000620 Loss1: 0.521709 Loss2: 1.478911
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.929591 Loss1: 0.453807 Loss2: 1.475784
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.811459 Loss1: 0.333735 Loss2: 1.477724
+(DefaultActor pid=3764) >> Training accuracy: 0.893750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.760078 Loss1: 1.711210 Loss2: 2.048867
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.805540 Loss1: 1.306285 Loss2: 1.499255
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.571969 Loss1: 1.087175 Loss2: 1.484793
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.197789 Loss1: 0.735471 Loss2: 1.462318
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.084879 Loss1: 2.037854 Loss2: 2.047025
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.853473 Loss1: 1.339307 Loss2: 1.514166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.595855 Loss1: 1.106160 Loss2: 1.489695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.455107 Loss1: 0.935533 Loss2: 1.519574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.227698 Loss1: 0.724217 Loss2: 1.503481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.166267 Loss1: 0.672832 Loss2: 1.493435 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.909375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.814410 Loss1: 0.357430 Loss2: 1.456980
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.131411 Loss1: 0.627735 Loss2: 1.503676
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.972820 Loss1: 0.473336 Loss2: 1.499483
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.951689 Loss1: 0.457491 Loss2: 1.494198
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.955827 Loss1: 0.449731 Loss2: 1.506096
+(DefaultActor pid=3764) >> Training accuracy: 0.912500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.809678 Loss1: 1.770281 Loss2: 2.039397
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.668408 Loss1: 1.224115 Loss2: 1.444292
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.279017 Loss1: 0.861402 Loss2: 1.417615
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.222571 Loss1: 0.795884 Loss2: 1.426686
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.143493 Loss1: 2.002002 Loss2: 2.141491
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.056371 Loss1: 0.629586 Loss2: 1.426784
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.907160 Loss1: 1.382403 Loss2: 1.524756
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.568989 Loss1: 1.094404 Loss2: 1.474586
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.046366 Loss1: 0.617932 Loss2: 1.428434
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.972325 Loss1: 0.539393 Loss2: 1.432932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.908684 Loss1: 0.480639 Loss2: 1.428045 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.780358 Loss1: 0.352836 Loss2: 1.427522 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.780608 Loss1: 0.362849 Loss2: 1.417759 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.860417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.884889 Loss1: 0.396210 Loss2: 1.488679 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.913462
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.955796 Loss1: 1.934589 Loss2: 2.021207
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.814653 Loss1: 1.352666 Loss2: 1.461987
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.515288 Loss1: 1.076018 Loss2: 1.439269
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.263648 Loss1: 0.807381 Loss2: 1.456266
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.080851 Loss1: 2.046593 Loss2: 2.034258
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.922744 Loss1: 1.407590 Loss2: 1.515155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.541106 Loss1: 1.050682 Loss2: 1.490424 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.335741 Loss1: 0.832263 Loss2: 1.503478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.309308 Loss1: 0.812429 Loss2: 1.496880 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.149025 Loss1: 0.636937 Loss2: 1.512087 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.858333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.143723 Loss1: 0.650477 Loss2: 1.493245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.056730 Loss1: 0.548760 Loss2: 1.507970 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.819336
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.062699 Loss1: 2.031252 Loss2: 2.031447 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.495814 Loss1: 1.052526 Loss2: 1.443288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.002626 Loss1: 1.973708 Loss2: 2.028918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.840037 Loss1: 1.313853 Loss2: 1.526184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.625288 Loss1: 1.109808 Loss2: 1.515480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.316799 Loss1: 0.801974 Loss2: 1.514825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.994190 Loss1: 0.511250 Loss2: 1.482940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.913733 Loss1: 0.442845 Loss2: 1.470888 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.914583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.092387 Loss1: 0.568442 Loss2: 1.523945 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.980569 Loss1: 0.450380 Loss2: 1.530189 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.888672
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.840641 Loss1: 1.360281 Loss2: 1.480360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.276487 Loss1: 0.818915 Loss2: 1.457573 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.133341 Loss1: 0.675119 Loss2: 1.458222 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.105420 Loss1: 0.641313 Loss2: 1.464108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.990001 Loss1: 0.525519 Loss2: 1.464482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.975623 Loss1: 0.506431 Loss2: 1.469192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.890793 Loss1: 0.433306 Loss2: 1.457487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.049020 Loss1: 0.602602 Loss2: 1.446419 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.921875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.928575 Loss1: 0.477767 Loss2: 1.450808 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.905208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.045082 Loss1: 1.953011 Loss2: 2.092071 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.515688 Loss1: 1.032159 Loss2: 1.483530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.883175 Loss1: 1.936152 Loss2: 1.947023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.746599 Loss1: 1.342059 Loss2: 1.404540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.526357 Loss1: 1.151880 Loss2: 1.374478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.300338 Loss1: 0.895448 Loss2: 1.404890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.091891 Loss1: 0.705217 Loss2: 1.386674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.963199 Loss1: 0.460368 Loss2: 1.502831 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.868304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.927640 Loss1: 0.531369 Loss2: 1.396271 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.910516 Loss1: 0.510205 Loss2: 1.400311 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.876042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.934936 Loss1: 1.404050 Loss2: 1.530886 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.389962 Loss1: 0.903094 Loss2: 1.486868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.096564 Loss1: 0.604182 Loss2: 1.492382 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.958353 Loss1: 0.480563 Loss2: 1.477790 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.773446 Loss1: 1.302474 Loss2: 1.470972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.528913 Loss1: 1.059294 Loss2: 1.469619 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.872396
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.382194 Loss1: 0.912360 Loss2: 1.469835 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.124227 Loss1: 0.651093 Loss2: 1.473134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.942776 Loss1: 0.464617 Loss2: 1.478159 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.921080 Loss1: 0.450481 Loss2: 1.470599 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.945213 Loss1: 0.460555 Loss2: 1.484658 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.864258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.300590 Loss1: 0.757954 Loss2: 1.542636 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.095610 Loss1: 0.556574 Loss2: 1.539036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.153303 Loss1: 2.123670 Loss2: 2.029633
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.096615 Loss1: 0.560790 Loss2: 1.535825
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.991935 Loss1: 1.523555 Loss2: 1.468380
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.076778 Loss1: 0.530095 Loss2: 1.546683
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.661369 Loss1: 1.204310 Loss2: 1.457059
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.083612 Loss1: 0.533536 Loss2: 1.550077
+(DefaultActor pid=3765) >> Training accuracy: 0.895833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.157404 Loss1: 0.706858 Loss2: 1.450546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.068946 Loss1: 0.607283 Loss2: 1.461664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.061258 Loss1: 0.597311 Loss2: 1.463947
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.948874 Loss1: 1.957664 Loss2: 1.991209
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.836914 Loss1: 1.389779 Loss2: 1.447135 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.834375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.015085 Loss1: 0.553460 Loss2: 1.461625
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.559802 Loss1: 1.140566 Loss2: 1.419236
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.319596 Loss1: 0.890710 Loss2: 1.428886
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.076531 Loss1: 0.655402 Loss2: 1.421129
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.026005 Loss1: 0.606913 Loss2: 1.419092
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.009789 Loss1: 0.576854 Loss2: 1.432934
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.213882 Loss1: 2.015657 Loss2: 2.198225
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.953205 Loss1: 0.518870 Loss2: 1.434335
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.928242 Loss1: 0.493373 Loss2: 1.434870 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.907771 Loss1: 0.471881 Loss2: 1.435890 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.876042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.388323 Loss1: 0.813800 Loss2: 1.574523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.280349 Loss1: 0.682916 Loss2: 1.597433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.188392 Loss1: 0.594447 Loss2: 1.593945
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.003441 Loss1: 1.924883 Loss2: 2.078557
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.959315 Loss1: 1.426133 Loss2: 1.533182 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.879167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.549044 Loss1: 1.035305 Loss2: 1.513739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.130359 Loss1: 0.624694 Loss2: 1.505665 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.004902 Loss1: 0.502262 Loss2: 1.502640 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.918052 Loss1: 0.409075 Loss2: 1.508977 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.935110 Loss1: 0.430002 Loss2: 1.505108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.986818 Loss1: 0.479493 Loss2: 1.507325 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.809375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.109229 Loss1: 0.688918 Loss2: 1.420311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.903404 Loss1: 0.473765 Loss2: 1.429639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.860225 Loss1: 0.427800 Loss2: 1.432425
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.144867 Loss1: 2.107444 Loss2: 2.037423
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.950499 Loss1: 1.457718 Loss2: 1.492781 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.922917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.672744 Loss1: 1.186072 Loss2: 1.486671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.292945 Loss1: 0.807292 Loss2: 1.485653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.151124 Loss1: 0.645796 Loss2: 1.505328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.112999 Loss1: 0.612217 Loss2: 1.500782 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.037883 Loss1: 0.525324 Loss2: 1.512559 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.937942 Loss1: 0.444045 Loss2: 1.493897 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.912109
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.057508 Loss1: 0.605502 Loss2: 1.452006 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.885058 Loss1: 0.411808 Loss2: 1.473250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.959116 Loss1: 0.499776 Loss2: 1.459340
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.913511 Loss1: 1.904624 Loss2: 2.008887
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.941052 Loss1: 0.444833 Loss2: 1.496219
+(DefaultActor pid=3764) >> Training accuracy: 0.851042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.787550 Loss1: 1.277085 Loss2: 1.510465
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.484465 Loss1: 0.987024 Loss2: 1.497441
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.255883 Loss1: 0.770300 Loss2: 1.485583
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.136254 Loss1: 0.653758 Loss2: 1.482496
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.078395 Loss1: 0.599901 Loss2: 1.478494
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.077333 Loss1: 2.030331 Loss2: 2.047003
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.035607 Loss1: 1.526092 Loss2: 1.509515 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.575496 Loss1: 1.107964 Loss2: 1.467533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.355868 Loss1: 0.893864 Loss2: 1.462004 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.880859
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.857869 Loss1: 0.375135 Loss2: 1.482734
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.213384 Loss1: 0.744108 Loss2: 1.469276
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.083864 Loss1: 0.610392 Loss2: 1.473471
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.153742 Loss1: 0.668902 Loss2: 1.484840
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.061176 Loss1: 0.562912 Loss2: 1.498264
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.974109 Loss1: 0.485930 Loss2: 1.488180
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.818395 Loss1: 1.834832 Loss2: 1.983562
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.000029 Loss1: 0.516021 Loss2: 1.484007
+(DefaultActor pid=3764) >> Training accuracy: 0.885417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.335619 Loss1: 0.947587 Loss2: 1.388031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.107047 Loss1: 0.712375 Loss2: 1.394673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.983129 Loss1: 0.585556 Loss2: 1.397573
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.083029 Loss1: 2.003742 Loss2: 2.079287
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.874410 Loss1: 1.376041 Loss2: 1.498369 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.524409 Loss1: 1.051042 Loss2: 1.473366 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.328670 Loss1: 0.843748 Loss2: 1.484922 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.935417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.773406 Loss1: 0.378775 Loss2: 1.394631
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.236802 Loss1: 0.759721 Loss2: 1.477081
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.241670 Loss1: 0.755697 Loss2: 1.485973
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.211760 Loss1: 0.707460 Loss2: 1.504300
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.137535 Loss1: 0.629863 Loss2: 1.507672
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.015295 Loss1: 0.517496 Loss2: 1.497799
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.030407 Loss1: 2.016168 Loss2: 2.014238
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.960766 Loss1: 0.473950 Loss2: 1.486815
+(DefaultActor pid=3764) >> Training accuracy: 0.902083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.537567 Loss1: 1.091862 Loss2: 1.445705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.210782 Loss1: 0.774580 Loss2: 1.436202 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.155930 Loss1: 0.697937 Loss2: 1.457993
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.012783 Loss1: 1.889347 Loss2: 2.123436
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.944592 Loss1: 1.391219 Loss2: 1.553374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.556904 Loss1: 1.002521 Loss2: 1.554382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.374147 Loss1: 0.829130 Loss2: 1.545016 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.886458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.946324 Loss1: 0.484224 Loss2: 1.462100
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.197162 Loss1: 0.659616 Loss2: 1.537546
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.099917 Loss1: 0.568607 Loss2: 1.531310
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.059272 Loss1: 0.522060 Loss2: 1.537213
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.021084 Loss1: 0.483991 Loss2: 1.537093
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.996982 Loss1: 0.457443 Loss2: 1.539539
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.161250 Loss1: 2.101755 Loss2: 2.059496
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.985636 Loss1: 0.441252 Loss2: 1.544384
+(DefaultActor pid=3764) >> Training accuracy: 0.831250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.560437 Loss1: 1.092565 Loss2: 1.467871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.265264 Loss1: 0.788417 Loss2: 1.476848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.178934 Loss1: 0.699751 Loss2: 1.479183
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.971939 Loss1: 2.006008 Loss2: 1.965931
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.979599 Loss1: 1.490202 Loss2: 1.489397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.538934 Loss1: 1.060071 Loss2: 1.478863 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.377818 Loss1: 0.912598 Loss2: 1.465220 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.881250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.272789 Loss1: 0.801934 Loss2: 1.470856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.109906 Loss1: 0.622389 Loss2: 1.487517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.980425 Loss1: 0.497420 Loss2: 1.483005 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.959094 Loss1: 0.477209 Loss2: 1.481885 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.868164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.477711 Loss1: 0.978290 Loss2: 1.499421 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.160219 Loss1: 0.652812 Loss2: 1.507407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.840389 Loss1: 1.829588 Loss2: 2.010801
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.144280 Loss1: 0.628774 Loss2: 1.515507
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.703846 Loss1: 1.230956 Loss2: 1.472889
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.160563 Loss1: 0.632437 Loss2: 1.528126
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.031951 Loss1: 0.517019 Loss2: 1.514931
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.324318 Loss1: 0.881349 Loss2: 1.442969
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.008695 Loss1: 0.485609 Loss2: 1.523085
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.220231 Loss1: 0.771650 Loss2: 1.448580
+(DefaultActor pid=3765) >> Training accuracy: 0.815625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.117593 Loss1: 0.668355 Loss2: 1.449238
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.022115 Loss1: 0.575864 Loss2: 1.446251
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.949666 Loss1: 0.500598 Loss2: 1.449068
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.882264 Loss1: 0.435937 Loss2: 1.446327
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.949199 Loss1: 1.951741 Loss2: 1.997458
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.902502 Loss1: 0.451002 Loss2: 1.451500
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.778633 Loss1: 0.330067 Loss2: 1.448566 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.903320
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.212805 Loss1: 0.769345 Loss2: 1.443461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.132541 Loss1: 0.678236 Loss2: 1.454305 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.034308 Loss1: 0.576177 Loss2: 1.458131
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.896424 Loss1: 1.838864 Loss2: 2.057560
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.738274 Loss1: 1.241773 Loss2: 1.496500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.452197 Loss1: 0.975583 Loss2: 1.476614 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.901042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.822760 Loss1: 0.375484 Loss2: 1.447276
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.329399 Loss1: 0.848839 Loss2: 1.480559
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.082189 Loss1: 0.599134 Loss2: 1.483055
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.998309 Loss1: 0.529350 Loss2: 1.468960
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.978865 Loss1: 0.503927 Loss2: 1.474939
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.917586 Loss1: 0.439562 Loss2: 1.478024
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.187780 Loss1: 2.051003 Loss2: 2.136778
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.842309 Loss1: 0.363746 Loss2: 1.478564
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.837457 Loss1: 0.370375 Loss2: 1.467082 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.932292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.504307 Loss1: 0.982026 Loss2: 1.522282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.198178 Loss1: 0.683205 Loss2: 1.514974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.097640 Loss1: 0.570949 Loss2: 1.526691
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.941054 Loss1: 1.843494 Loss2: 2.097560
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.782542 Loss1: 1.285231 Loss2: 1.497311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.511660 Loss1: 1.025440 Loss2: 1.486219 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.875000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.275254 Loss1: 0.781568 Loss2: 1.493685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.184254 Loss1: 0.687743 Loss2: 1.496511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.003482 Loss1: 0.505029 Loss2: 1.498453 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.980198 Loss1: 0.477425 Loss2: 1.502773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.902872 Loss1: 0.399354 Loss2: 1.503518 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.829167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.225524 Loss1: 0.742834 Loss2: 1.482690 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.172783 Loss1: 0.693482 Loss2: 1.479301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.081607 Loss1: 0.597580 Loss2: 1.484026
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.068926 Loss1: 1.968920 Loss2: 2.100006
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.906095 Loss1: 1.396136 Loss2: 1.509959 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.873884
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.941271 Loss1: 0.470696 Loss2: 1.470575
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.616228 Loss1: 1.115354 Loss2: 1.500874
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.370372 Loss1: 0.847754 Loss2: 1.522618
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.131993 Loss1: 0.627703 Loss2: 1.504290
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.203807 Loss1: 0.702103 Loss2: 1.501704
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.163245 Loss1: 0.647547 Loss2: 1.515697
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.936534 Loss1: 1.982714 Loss2: 1.953820
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.070462 Loss1: 0.553320 Loss2: 1.517142
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.887073 Loss1: 1.418625 Loss2: 1.468447
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.002149 Loss1: 0.484152 Loss2: 1.517997
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.479441 Loss1: 1.036962 Loss2: 1.442479
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.903300 Loss1: 0.390437 Loss2: 1.512863
+(DefaultActor pid=3764) >> Training accuracy: 0.930208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.227271 Loss1: 0.796546 Loss2: 1.430725 [repeated 2x across cluster]
+DEBUG flwr 2023-10-09 14:43:16,469 | server.py:236 | fit_round 42 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.018711 Loss1: 0.567759 Loss2: 1.450953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.874161 Loss1: 1.842393 Loss2: 2.031769
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.982091 Loss1: 0.535288 Loss2: 1.446804
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.808616 Loss1: 1.332244 Loss2: 1.476372
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.022723 Loss1: 0.569495 Loss2: 1.453228
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.985981 Loss1: 0.531017 Loss2: 1.454964 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.857422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.154793 Loss1: 0.689863 Loss2: 1.464930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.944828 Loss1: 0.488612 Loss2: 1.456217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.956807 Loss1: 0.499488 Loss2: 1.457319
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.287256 Loss1: 2.173367 Loss2: 2.113890
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.065525 Loss1: 1.537320 Loss2: 1.528204 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.886458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.913887 Loss1: 0.445322 Loss2: 1.468565
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.654650 Loss1: 1.139676 Loss2: 1.514974
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.462007 Loss1: 0.934112 Loss2: 1.527895
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.259792 Loss1: 0.747463 Loss2: 1.512328
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.198420 Loss1: 0.702522 Loss2: 1.495898
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.138105 Loss1: 0.617797 Loss2: 1.520308
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.081015 Loss1: 0.559482 Loss2: 1.521532
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.901198 Loss1: 1.897205 Loss2: 2.003992
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.051025 Loss1: 0.526446 Loss2: 1.524578
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.830504 Loss1: 1.363128 Loss2: 1.467376
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.011719 Loss1: 0.494178 Loss2: 1.517541
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.506630 Loss1: 1.053340 Loss2: 1.453290
+(DefaultActor pid=3765) >> Training accuracy: 0.880580
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.272280 Loss1: 0.834320 Loss2: 1.437960
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.171525 Loss1: 0.731509 Loss2: 1.440016
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.124863 Loss1: 0.686060 Loss2: 1.438802
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.113501 Loss1: 0.654761 Loss2: 1.458740
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.084731 Loss1: 0.624018 Loss2: 1.460713
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.983005 Loss1: 0.513882 Loss2: 1.469123
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.932427 Loss1: 0.473589 Loss2: 1.458839
+(DefaultActor pid=3764) >> Training accuracy: 0.836458
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 14:43:16,469][flwr][DEBUG] - fit_round 42 received 50 results and 0 failures
+INFO flwr 2023-10-09 14:43:58,568 | server.py:125 | fit progress: (42, 2.541129877772956, {'accuracy': 0.4394}, 96746.3462451)
+>> Test accuracy: 0.439400
+[2023-10-09 14:43:58,568][flwr][INFO] - fit progress: (42, 2.541129877772956, {'accuracy': 0.4394}, 96746.3462451)
+DEBUG flwr 2023-10-09 14:43:58,568 | server.py:173 | evaluate_round 42: strategy sampled 50 clients (out of 50)
+[2023-10-09 14:43:58,568][flwr][DEBUG] - evaluate_round 42: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 14:53:05,181 | server.py:187 | evaluate_round 42 received 50 results and 0 failures
+[2023-10-09 14:53:05,181][flwr][DEBUG] - evaluate_round 42 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 14:53:05,182 | server.py:222 | fit_round 43: strategy sampled 50 clients (out of 50)
+[2023-10-09 14:53:05,182][flwr][DEBUG] - fit_round 43: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.009983 Loss1: 2.006484 Loss2: 2.003499
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.803456 Loss1: 1.323936 Loss2: 1.479520
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.464917 Loss1: 1.005729 Loss2: 1.459188
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.908839 Loss1: 1.879812 Loss2: 2.029027
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.308293 Loss1: 0.846108 Loss2: 1.462185
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.747280 Loss1: 1.263266 Loss2: 1.484014
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.195895 Loss1: 0.730255 Loss2: 1.465641
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.137946 Loss1: 0.674299 Loss2: 1.463647 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.153248 Loss1: 0.678062 Loss2: 1.475186 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.961335 Loss1: 0.482257 Loss2: 1.479078 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.914777 Loss1: 0.443069 Loss2: 1.471707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.854464 Loss1: 0.395116 Loss2: 1.459348 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.917969
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.917110 Loss1: 0.465494 Loss2: 1.451616 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.901042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.019286 Loss1: 1.966015 Loss2: 2.053270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.454909 Loss1: 1.007489 Loss2: 1.447420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.244881 Loss1: 0.800284 Loss2: 1.444597
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.009382 Loss1: 1.985120 Loss2: 2.024262
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.844768 Loss1: 1.376244 Loss2: 1.468525 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.383280 Loss1: 0.943884 Loss2: 1.439396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.204192 Loss1: 0.765525 Loss2: 1.438667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.086782 Loss1: 0.646747 Loss2: 1.440034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.062991 Loss1: 0.609367 Loss2: 1.453624 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.918750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.781367 Loss1: 0.345634 Loss2: 1.435733
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.096816 Loss1: 0.628873 Loss2: 1.467943
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.971271 Loss1: 0.507123 Loss2: 1.464148
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.964193 Loss1: 0.496004 Loss2: 1.468189
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.961401 Loss1: 0.492533 Loss2: 1.468868
+(DefaultActor pid=3764) >> Training accuracy: 0.875000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.992411 Loss1: 1.936600 Loss2: 2.055811
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.966921 Loss1: 1.468235 Loss2: 1.498685
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.651303 Loss1: 1.166702 Loss2: 1.484602
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.436439 Loss1: 0.951447 Loss2: 1.484992
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.900651 Loss1: 1.867061 Loss2: 2.033590
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.694602 Loss1: 1.211127 Loss2: 1.483474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.080391 Loss1: 0.595367 Loss2: 1.485025 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.048275 Loss1: 0.550462 Loss2: 1.497813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.031781 Loss1: 0.540321 Loss2: 1.491461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.980391 Loss1: 0.470651 Loss2: 1.509740 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.843750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.960358 Loss1: 0.488923 Loss2: 1.471435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.927523 Loss1: 0.451632 Loss2: 1.475891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.927201 Loss1: 0.455860 Loss2: 1.471341
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.148326 Loss1: 2.032903 Loss2: 2.115423
+(DefaultActor pid=3764) >> Training accuracy: 0.904412
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.882079 Loss1: 1.349919 Loss2: 1.532161
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.557446 Loss1: 1.041795 Loss2: 1.515651
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.380537 Loss1: 0.864952 Loss2: 1.515585
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.234857 Loss1: 0.716816 Loss2: 1.518042
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.109318 Loss1: 1.945346 Loss2: 2.163971
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.230258 Loss1: 0.698245 Loss2: 1.532013
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.968920 Loss1: 1.394776 Loss2: 1.574143
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.082592 Loss1: 0.552547 Loss2: 1.530046
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.596885 Loss1: 1.046603 Loss2: 1.550281
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.063983 Loss1: 0.544193 Loss2: 1.519790
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.379959 Loss1: 0.822673 Loss2: 1.557286
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.111794 Loss1: 0.572207 Loss2: 1.539586
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.300049 Loss1: 0.761594 Loss2: 1.538456
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.027664 Loss1: 0.486227 Loss2: 1.541437
+(DefaultActor pid=3765) >> Training accuracy: 0.902083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.136036 Loss1: 0.578106 Loss2: 1.557930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.026613 Loss1: 0.461413 Loss2: 1.565200 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.983044 Loss1: 0.426293 Loss2: 1.556751
+(DefaultActor pid=3764) >> Training accuracy: 0.904167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.101761 Loss1: 2.039463 Loss2: 2.062299
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.864638 Loss1: 1.370264 Loss2: 1.494374
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.612633 Loss1: 1.135540 Loss2: 1.477092
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.382182 Loss1: 0.914043 Loss2: 1.468139
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.185486 Loss1: 0.701197 Loss2: 1.484290
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.228711 Loss1: 2.168610 Loss2: 2.060101
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.169837 Loss1: 0.687686 Loss2: 1.482150
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.111016 Loss1: 0.619211 Loss2: 1.491805 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.043740 Loss1: 0.539701 Loss2: 1.504039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.001309 Loss1: 0.507692 Loss2: 1.493617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.921729 Loss1: 0.427532 Loss2: 1.494197 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.891667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.943305 Loss1: 0.490433 Loss2: 1.452872 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.897904 Loss1: 0.434465 Loss2: 1.463439 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.899554
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.740877 Loss1: 1.257019 Loss2: 1.483858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.424597 Loss1: 0.932066 Loss2: 1.492531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.246362 Loss1: 0.762692 Loss2: 1.483669
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.831068 Loss1: 1.829031 Loss2: 2.002038
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.082521 Loss1: 0.612292 Loss2: 1.470229
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.754052 Loss1: 1.308587 Loss2: 1.445464
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.016611 Loss1: 0.538323 Loss2: 1.478289
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.311336 Loss1: 0.888184 Loss2: 1.423152
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.989371 Loss1: 0.520528 Loss2: 1.468843
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.085873 Loss1: 0.681107 Loss2: 1.404766
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.990665 Loss1: 0.506727 Loss2: 1.483937
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.084912 Loss1: 0.674905 Loss2: 1.410007
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.998085 Loss1: 0.508617 Loss2: 1.489468
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.972401 Loss1: 0.549749 Loss2: 1.422652
+(DefaultActor pid=3765) >> Training accuracy: 0.853125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.001197 Loss1: 0.578451 Loss2: 1.422746
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.851955 Loss1: 0.424711 Loss2: 1.427243
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.831951 Loss1: 0.420662 Loss2: 1.411289
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.823438 Loss1: 0.408501 Loss2: 1.414937
+(DefaultActor pid=3764) >> Training accuracy: 0.882292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.094040 Loss1: 2.079248 Loss2: 2.014792
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.850448 Loss1: 1.361277 Loss2: 1.489171
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.470091 Loss1: 1.012962 Loss2: 1.457129
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.290485 Loss1: 0.845747 Loss2: 1.444738
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.122382 Loss1: 2.030155 Loss2: 2.092227
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.160190 Loss1: 0.699532 Loss2: 1.460658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.074394 Loss1: 0.620558 Loss2: 1.453836 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.076153 Loss1: 0.616926 Loss2: 1.459227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.065744 Loss1: 0.597829 Loss2: 1.467914 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.928175 Loss1: 0.458335 Loss2: 1.469841 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.883333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.085504 Loss1: 0.558206 Loss2: 1.527298 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.964209 Loss1: 0.429196 Loss2: 1.535013 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.847917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.848269 Loss1: 1.367987 Loss2: 1.480283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.315674 Loss1: 0.882934 Loss2: 1.432739 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.117169 Loss1: 2.002343 Loss2: 2.114826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.923377 Loss1: 1.362662 Loss2: 1.560715 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.925128 Loss1: 0.488170 Loss2: 1.436958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.891826 Loss1: 0.442469 Loss2: 1.449357 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.803089 Loss1: 0.351242 Loss2: 1.451846 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.882212
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.202137 Loss1: 0.648288 Loss2: 1.553849 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.067100 Loss1: 0.514880 Loss2: 1.552220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.035963 Loss1: 1.993210 Loss2: 2.042753 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.904167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 3.048540 Loss1: 1.526157 Loss2: 1.522384 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.373528 Loss1: 0.877986 Loss2: 1.495542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.194342 Loss1: 0.695943 Loss2: 1.498400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.800154 Loss1: 1.301319 Loss2: 1.498835 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.411174 Loss1: 0.931540 Loss2: 1.479634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.269372 Loss1: 0.796422 Loss2: 1.472950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.126132 Loss1: 0.657127 Loss2: 1.469006 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.968633 Loss1: 0.489431 Loss2: 1.479202 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.897681 Loss1: 0.411000 Loss2: 1.486680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.821453 Loss1: 0.341220 Loss2: 1.480234
+(DefaultActor pid=3764) >> Training accuracy: 0.887500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.162173 Loss1: 2.062037 Loss2: 2.100136
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.873160 Loss1: 1.340543 Loss2: 1.532617
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.413391 Loss1: 0.916494 Loss2: 1.496897
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.186601 Loss1: 0.683476 Loss2: 1.503126
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.194600 Loss1: 0.697030 Loss2: 1.497570
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.819457 Loss1: 1.833563 Loss2: 1.985894
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.174580 Loss1: 0.665081 Loss2: 1.509499
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.091537 Loss1: 0.553719 Loss2: 1.537817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.954146 Loss1: 0.435848 Loss2: 1.518298 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.916985 Loss1: 0.409546 Loss2: 1.507439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.956986 Loss1: 0.433351 Loss2: 1.523635 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.861458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.927057 Loss1: 0.520259 Loss2: 1.406797 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.857047 Loss1: 0.446460 Loss2: 1.410587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.925153 Loss1: 0.516952 Loss2: 1.408201
+(DefaultActor pid=3764) >> Training accuracy: 0.868750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.233738 Loss1: 2.153852 Loss2: 2.079886
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.957481 Loss1: 1.416781 Loss2: 1.540700
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.695013 Loss1: 1.176836 Loss2: 1.518177
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.497341 Loss1: 0.978232 Loss2: 1.519110
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.294655 Loss1: 0.784582 Loss2: 1.510073
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.021988 Loss1: 1.973775 Loss2: 2.048213
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.915657 Loss1: 1.443362 Loss2: 1.472295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.486681 Loss1: 1.041503 Loss2: 1.445177 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.258624 Loss1: 0.805218 Loss2: 1.453406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.159617 Loss1: 0.729082 Loss2: 1.430535 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.859375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.066245 Loss1: 0.627013 Loss2: 1.439233 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.047857 Loss1: 0.590507 Loss2: 1.457351 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.853093 Loss1: 0.396518 Loss2: 1.456574 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.897917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.921817 Loss1: 1.455148 Loss2: 1.466669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.319423 Loss1: 0.866264 Loss2: 1.453159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.154695 Loss1: 0.707300 Loss2: 1.447395
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.955030 Loss1: 1.912776 Loss2: 2.042253
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.821197 Loss1: 1.367226 Loss2: 1.453971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.484974 Loss1: 1.052298 Loss2: 1.432676 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.261615 Loss1: 0.826451 Loss2: 1.435164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.110150 Loss1: 0.690394 Loss2: 1.419756 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.896875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.000614 Loss1: 0.588434 Loss2: 1.412180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.862284 Loss1: 0.441931 Loss2: 1.420352 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.883302 Loss1: 0.448317 Loss2: 1.434985 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.891667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.999605 Loss1: 1.486527 Loss2: 1.513078 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.357421 Loss1: 0.864936 Loss2: 1.492485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.174427 Loss1: 0.667504 Loss2: 1.506923
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.314428 Loss1: 2.168743 Loss2: 2.145684
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.048041 Loss1: 1.488428 Loss2: 1.559613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.605666 Loss1: 1.078926 Loss2: 1.526741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.983734 Loss1: 0.493505 Loss2: 1.490229
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.430090 Loss1: 0.878712 Loss2: 1.551379
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.941146 Loss1: 0.445953 Loss2: 1.495194
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.257424 Loss1: 0.720389 Loss2: 1.537035
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.221353 Loss1: 0.681874 Loss2: 1.539479
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.876714 Loss1: 0.379621 Loss2: 1.497094
+(DefaultActor pid=3765) >> Training accuracy: 0.869792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.085915 Loss1: 0.540244 Loss2: 1.545671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.989013 Loss1: 0.438504 Loss2: 1.550509 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.856027
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.810039 Loss1: 1.321020 Loss2: 1.489019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.283869 Loss1: 0.809867 Loss2: 1.474001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.743448 Loss1: 1.737070 Loss2: 2.006378
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.164495 Loss1: 0.671235 Loss2: 1.493260
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.641806 Loss1: 1.191572 Loss2: 1.450234
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.087267 Loss1: 0.604734 Loss2: 1.482532
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.300282 Loss1: 0.854337 Loss2: 1.445945
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.049210 Loss1: 0.549196 Loss2: 1.500015
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.126012 Loss1: 0.689311 Loss2: 1.436701
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.040053 Loss1: 0.535849 Loss2: 1.504204
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.113440 Loss1: 0.665537 Loss2: 1.447902
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.009476 Loss1: 0.519051 Loss2: 1.490425
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.040572 Loss1: 0.580701 Loss2: 1.459871
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.984963 Loss1: 0.485290 Loss2: 1.499673
+(DefaultActor pid=3765) >> Training accuracy: 0.896875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.862941 Loss1: 0.402726 Loss2: 1.460215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.823353 Loss1: 0.370322 Loss2: 1.453031 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.897393 Loss1: 1.413300 Loss2: 1.484092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.350247 Loss1: 0.861810 Loss2: 1.488438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.255457 Loss1: 0.788821 Loss2: 1.466636 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.880528 Loss1: 1.424571 Loss2: 1.455957
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.155381 Loss1: 0.678315 Loss2: 1.477066
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.556107 Loss1: 1.114321 Loss2: 1.441786
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.044420 Loss1: 0.570779 Loss2: 1.473641
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.297573 Loss1: 0.856109 Loss2: 1.441465
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.081636 Loss1: 0.598697 Loss2: 1.482938
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.007917 Loss1: 0.527354 Loss2: 1.480564
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.266317 Loss1: 0.830987 Loss2: 1.435329
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.932094 Loss1: 0.448471 Loss2: 1.483623
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.131227 Loss1: 0.682713 Loss2: 1.448514
+(DefaultActor pid=3765) >> Training accuracy: 0.868750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.062201 Loss1: 0.622973 Loss2: 1.439228
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.975617 Loss1: 0.527231 Loss2: 1.448386
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.921373 Loss1: 0.482938 Loss2: 1.438435
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.955727 Loss1: 0.509387 Loss2: 1.446340
+(DefaultActor pid=3764) >> Training accuracy: 0.877930
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.759709 Loss1: 1.802821 Loss2: 1.956888
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.737767 Loss1: 1.255582 Loss2: 1.482185
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.348448 Loss1: 0.903585 Loss2: 1.444863
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.221732 Loss1: 0.769765 Loss2: 1.451966
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.129762 Loss1: 0.679167 Loss2: 1.450595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.069967 Loss1: 0.619200 Loss2: 1.450768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.293986 Loss1: 0.822608 Loss2: 1.471378 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.893822 Loss1: 0.446627 Loss2: 1.447196 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.879407 Loss1: 0.444542 Loss2: 1.434865 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.981545 Loss1: 0.490192 Loss2: 1.491353 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.903320
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.922542 Loss1: 0.446269 Loss2: 1.476272 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.895833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.944605 Loss1: 1.941261 Loss2: 2.003344
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.814308 Loss1: 1.349010 Loss2: 1.465298
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.457094 Loss1: 0.998956 Loss2: 1.458138
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.325301 Loss1: 0.859667 Loss2: 1.465634
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.144105 Loss1: 2.051720 Loss2: 2.092386
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.889381 Loss1: 1.392892 Loss2: 1.496489 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.450445 Loss1: 0.987041 Loss2: 1.463403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.246979 Loss1: 0.780001 Loss2: 1.466978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.134536 Loss1: 0.670240 Loss2: 1.464296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.070110 Loss1: 0.595737 Loss2: 1.474373 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.908333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.800654 Loss1: 0.346360 Loss2: 1.454294
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.052441 Loss1: 0.580193 Loss2: 1.472248
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.985878 Loss1: 0.504534 Loss2: 1.481344
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.933585 Loss1: 0.450249 Loss2: 1.483336
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.954894 Loss1: 0.476827 Loss2: 1.478066
+(DefaultActor pid=3764) >> Training accuracy: 0.904167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.999378 Loss1: 1.849277 Loss2: 2.150101
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.723394 Loss1: 1.256410 Loss2: 1.466983
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.361833 Loss1: 0.938496 Loss2: 1.423338
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.179497 Loss1: 0.741912 Loss2: 1.437585
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.041062 Loss1: 0.591379 Loss2: 1.449684
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.970059 Loss1: 0.530276 Loss2: 1.439783 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.883385 Loss1: 0.441091 Loss2: 1.442294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.881940 Loss1: 0.447895 Loss2: 1.434046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.861970 Loss1: 0.425734 Loss2: 1.436236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.816575 Loss1: 0.375015 Loss2: 1.441559 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.915865
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.120383 Loss1: 0.547248 Loss2: 1.573135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.979465 Loss1: 0.413948 Loss2: 1.565517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.974142 Loss1: 0.408494 Loss2: 1.565647
+(DefaultActor pid=3764) >> Training accuracy: 0.902083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.925377 Loss1: 1.887854 Loss2: 2.037523
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.833029 Loss1: 1.330308 Loss2: 1.502721
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.546448 Loss1: 1.052557 Loss2: 1.493891
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.327289 Loss1: 0.832991 Loss2: 1.494298
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.226048 Loss1: 0.727016 Loss2: 1.499032
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.785818 Loss1: 1.796509 Loss2: 1.989308
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.684839 Loss1: 1.238992 Loss2: 1.445848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.333521 Loss1: 0.908517 Loss2: 1.425004 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.989732 Loss1: 0.487663 Loss2: 1.502069
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.100498 Loss1: 0.679909 Loss2: 1.420589
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.893183 Loss1: 0.393986 Loss2: 1.499197
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.040235 Loss1: 0.623218 Loss2: 1.417018
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.864327 Loss1: 0.371080 Loss2: 1.493247
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.037971 Loss1: 0.614838 Loss2: 1.423133
+(DefaultActor pid=3765) >> Training accuracy: 0.942383
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.879078 Loss1: 0.453626 Loss2: 1.425453
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.866236 Loss1: 0.441773 Loss2: 1.424463
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.892772 Loss1: 0.462497 Loss2: 1.430275
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.821591 Loss1: 0.382304 Loss2: 1.439287
+(DefaultActor pid=3764) >> Training accuracy: 0.907292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.024801 Loss1: 1.990303 Loss2: 2.034497
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.973943 Loss1: 1.487428 Loss2: 1.486515
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.526497 Loss1: 1.052158 Loss2: 1.474339
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.386896 Loss1: 0.916001 Loss2: 1.470895
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.178366 Loss1: 0.710716 Loss2: 1.467650
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.128840 Loss1: 0.669670 Loss2: 1.459170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.055115 Loss1: 0.589277 Loss2: 1.465839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.009765 Loss1: 0.539129 Loss2: 1.470636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.951838 Loss1: 0.473367 Loss2: 1.478471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.949230 Loss1: 0.481317 Loss2: 1.467913 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.864583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.942176 Loss1: 0.488181 Loss2: 1.453994 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.794852 Loss1: 0.340704 Loss2: 1.454148 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.885094 Loss1: 1.362443 Loss2: 1.522651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.421508 Loss1: 0.928315 Loss2: 1.493193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.212258 Loss1: 0.714427 Loss2: 1.497831
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.800984 Loss1: 1.758471 Loss2: 2.042513
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.652801 Loss1: 1.165886 Loss2: 1.486915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.370494 Loss1: 0.890617 Loss2: 1.479877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.231057 Loss1: 0.741218 Loss2: 1.489839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.897801 Loss1: 0.400407 Loss2: 1.497395 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.911830
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.980773 Loss1: 0.503068 Loss2: 1.477705 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.863194 Loss1: 0.393866 Loss2: 1.469328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.996785 Loss1: 1.959989 Loss2: 2.036796
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.849305 Loss1: 0.381818 Loss2: 1.467487
+(DefaultActor pid=3764) >> Training accuracy: 0.895508
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.591039 Loss1: 1.073684 Loss2: 1.517355 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.292421 Loss1: 0.776117 Loss2: 1.516304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.196674 Loss1: 0.673225 Loss2: 1.523449
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.095959 Loss1: 2.053910 Loss2: 2.042049
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.938997 Loss1: 1.464850 Loss2: 1.474147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.167522 Loss1: 0.626494 Loss2: 1.541028
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.526657 Loss1: 1.060390 Loss2: 1.466267
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.084167 Loss1: 0.557092 Loss2: 1.527075
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.395412 Loss1: 0.924657 Loss2: 1.470755
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.965783 Loss1: 0.442248 Loss2: 1.523535
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.237451 Loss1: 0.756039 Loss2: 1.481412
+(DefaultActor pid=3765) >> Training accuracy: 0.887695
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.104187 Loss1: 0.635207 Loss2: 1.468980
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.023667 Loss1: 0.556973 Loss2: 1.466694
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.087357 Loss1: 0.606061 Loss2: 1.481296
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.978388 Loss1: 0.484905 Loss2: 1.493483
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.943966 Loss1: 0.463323 Loss2: 1.480643
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.987571 Loss1: 1.949402 Loss2: 2.038169
+(DefaultActor pid=3764) >> Training accuracy: 0.894792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.848727 Loss1: 1.360423 Loss2: 1.488303
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.424611 Loss1: 0.942094 Loss2: 1.482517
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.236432 Loss1: 0.774312 Loss2: 1.462120
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.136426 Loss1: 0.673685 Loss2: 1.462741
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.983096 Loss1: 1.929805 Loss2: 2.053291
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.086518 Loss1: 0.615814 Loss2: 1.470704
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.003829 Loss1: 0.531849 Loss2: 1.471980
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.886482 Loss1: 1.387101 Loss2: 1.499381
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.998249 Loss1: 0.519672 Loss2: 1.478577
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.566763 Loss1: 1.080646 Loss2: 1.486117
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.926597 Loss1: 0.447506 Loss2: 1.479091
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.358706 Loss1: 0.874489 Loss2: 1.484217
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.879398 Loss1: 0.401425 Loss2: 1.477973
+(DefaultActor pid=3765) >> Training accuracy: 0.910417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.133218 Loss1: 0.661267 Loss2: 1.471951
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.052226 Loss1: 0.582912 Loss2: 1.469313
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.007194 Loss1: 0.536030 Loss2: 1.471165
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.042762 Loss1: 0.554128 Loss2: 1.488635
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.063117 Loss1: 0.567557 Loss2: 1.495560
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.034621 Loss1: 1.978511 Loss2: 2.056110
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.040041 Loss1: 0.539625 Loss2: 1.500416
+DEBUG flwr 2023-10-09 15:21:39,998 | server.py:236 | fit_round 43 received 50 results and 0 failures
+(DefaultActor pid=3764) >> Training accuracy: 0.858398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.497194 Loss1: 1.011626 Loss2: 1.485568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.243666 Loss1: 0.754385 Loss2: 1.489281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.064711 Loss1: 0.576089 Loss2: 1.488622
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.116911 Loss1: 1.988948 Loss2: 2.127963
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.043540 Loss1: 0.559837 Loss2: 1.483703
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.952641 Loss1: 1.417338 Loss2: 1.535303
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.037507 Loss1: 0.539887 Loss2: 1.497620
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.624146 Loss1: 1.113835 Loss2: 1.510311
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.335558 Loss1: 0.805433 Loss2: 1.530125
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.992970 Loss1: 0.494641 Loss2: 1.498329
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.175793 Loss1: 0.663646 Loss2: 1.512147
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.896043 Loss1: 0.402662 Loss2: 1.493381
+(DefaultActor pid=3765) >> Training accuracy: 0.845703
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.020174 Loss1: 0.493236 Loss2: 1.526937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.893538 Loss1: 0.379059 Loss2: 1.514479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.914834 Loss1: 0.407545 Loss2: 1.507289
+(DefaultActor pid=3764) >> Training accuracy: 0.854167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.989079 Loss1: 1.927598 Loss2: 2.061481
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.784980 Loss1: 1.275356 Loss2: 1.509624
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.493274 Loss1: 1.012687 Loss2: 1.480587
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.261443 Loss1: 0.783289 Loss2: 1.478153
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.105523 Loss1: 0.627922 Loss2: 1.477600
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.886387 Loss1: 1.728244 Loss2: 2.158143
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.981454 Loss1: 0.506104 Loss2: 1.475349
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.746665 Loss1: 1.210215 Loss2: 1.536451
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.924131 Loss1: 0.443290 Loss2: 1.480842
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.485088 Loss1: 0.973299 Loss2: 1.511789
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.947194 Loss1: 0.467743 Loss2: 1.479451
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.235337 Loss1: 0.726389 Loss2: 1.508947
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.012148 Loss1: 0.518656 Loss2: 1.493492
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.105201 Loss1: 0.607072 Loss2: 1.498130
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.892964 Loss1: 0.399932 Loss2: 1.493032
+(DefaultActor pid=3765) >> Training accuracy: 0.881250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.973737 Loss1: 0.476040 Loss2: 1.497697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.964344 Loss1: 0.459490 Loss2: 1.504854 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.895833
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 15:21:39,998][flwr][DEBUG] - fit_round 43 received 50 results and 0 failures
+INFO flwr 2023-10-09 15:22:22,122 | server.py:125 | fit progress: (43, 2.521814847144837, {'accuracy': 0.4441}, 99049.900299082)
+>> Test accuracy: 0.444100
+[2023-10-09 15:22:22,122][flwr][INFO] - fit progress: (43, 2.521814847144837, {'accuracy': 0.4441}, 99049.900299082)
+DEBUG flwr 2023-10-09 15:22:22,122 | server.py:173 | evaluate_round 43: strategy sampled 50 clients (out of 50)
+[2023-10-09 15:22:22,122][flwr][DEBUG] - evaluate_round 43: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 15:31:27,128 | server.py:187 | evaluate_round 43 received 50 results and 0 failures
+[2023-10-09 15:31:27,128][flwr][DEBUG] - evaluate_round 43 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 15:31:27,128 | server.py:222 | fit_round 44: strategy sampled 50 clients (out of 50)
+[2023-10-09 15:31:27,128][flwr][DEBUG] - fit_round 44: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.160709 Loss1: 2.036126 Loss2: 2.124583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.646797 Loss1: 1.084617 Loss2: 1.562180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.928122 Loss1: 1.894784 Loss2: 2.033338
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.436261 Loss1: 0.872854 Loss2: 1.563407
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.783617 Loss1: 1.306993 Loss2: 1.476624
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.326332 Loss1: 0.774760 Loss2: 1.551573
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.506259 Loss1: 1.056874 Loss2: 1.449384
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.233699 Loss1: 0.673866 Loss2: 1.559833
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.275895 Loss1: 0.827841 Loss2: 1.448054
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.097962 Loss1: 0.531222 Loss2: 1.566739
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.028166 Loss1: 0.476975 Loss2: 1.551191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.984806 Loss1: 0.432076 Loss2: 1.552730 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.927112 Loss1: 0.376847 Loss2: 1.550265 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.882812
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.935844 Loss1: 0.476237 Loss2: 1.459607 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.935417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.947380 Loss1: 1.904224 Loss2: 2.043156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.401931 Loss1: 0.955442 Loss2: 1.446489 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.234977 Loss1: 0.768282 Loss2: 1.466696
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.940473 Loss1: 1.934001 Loss2: 2.006471
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.855220 Loss1: 1.363174 Loss2: 1.492047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.477958 Loss1: 0.998327 Loss2: 1.479632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.219108 Loss1: 0.744661 Loss2: 1.474447 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.121127 Loss1: 0.647922 Loss2: 1.473205 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.015394 Loss1: 0.546527 Loss2: 1.468866 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.884375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.036357 Loss1: 0.545752 Loss2: 1.490605 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.959918 Loss1: 0.463124 Loss2: 1.496794 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.863281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.839457 Loss1: 1.372765 Loss2: 1.466693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.230162 Loss1: 0.817751 Loss2: 1.412411 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.055570 Loss1: 0.639742 Loss2: 1.415828
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.056066 Loss1: 2.055350 Loss2: 2.000717
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.881138 Loss1: 1.423868 Loss2: 1.457270 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.450769 Loss1: 1.006385 Loss2: 1.444383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.184924 Loss1: 0.761427 Loss2: 1.423498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.043415 Loss1: 0.615705 Loss2: 1.427710 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.873884
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.979944 Loss1: 0.540872 Loss2: 1.439071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.986974 Loss1: 0.536063 Loss2: 1.450911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.827599 Loss1: 0.388187 Loss2: 1.439413 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.893750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.972297 Loss1: 1.498783 Loss2: 1.473514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.412095 Loss1: 0.968070 Loss2: 1.444025 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.100725 Loss1: 1.996281 Loss2: 2.104444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.939524 Loss1: 1.387185 Loss2: 1.552339 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.528145 Loss1: 1.019422 Loss2: 1.508723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.355655 Loss1: 0.843665 Loss2: 1.511990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.169746 Loss1: 0.646549 Loss2: 1.523197 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.882292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.013960 Loss1: 0.496032 Loss2: 1.517928 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.917592 Loss1: 0.406975 Loss2: 1.510618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.001059 Loss1: 0.471956 Loss2: 1.529103
+(DefaultActor pid=3764) >> Training accuracy: 0.840625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.950299 Loss1: 1.927716 Loss2: 2.022583
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.799688 Loss1: 1.347959 Loss2: 1.451729
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.589478 Loss1: 1.117709 Loss2: 1.471769
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.289998 Loss1: 0.840671 Loss2: 1.449328
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.180095 Loss1: 0.742385 Loss2: 1.437710
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.802151 Loss1: 1.689604 Loss2: 2.112547
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.961403 Loss1: 0.514544 Loss2: 1.446859
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.948301 Loss1: 0.518206 Loss2: 1.430095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.958499 Loss1: 0.518410 Loss2: 1.440089 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.003862 Loss1: 0.543822 Loss2: 1.460040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.883395 Loss1: 0.435562 Loss2: 1.447833 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.876042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.998684 Loss1: 0.485855 Loss2: 1.512829 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.976989 Loss1: 0.454836 Loss2: 1.522153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.899691 Loss1: 0.383067 Loss2: 1.516624
+(DefaultActor pid=3764) >> Training accuracy: 0.939583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.828069 Loss1: 1.832765 Loss2: 1.995304
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.849076 Loss1: 1.349532 Loss2: 1.499544
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.543714 Loss1: 1.083142 Loss2: 1.460573
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.227221 Loss1: 0.743893 Loss2: 1.483329
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.206057 Loss1: 0.740038 Loss2: 1.466019
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.079446 Loss1: 2.026740 Loss2: 2.052707
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.008373 Loss1: 1.480239 Loss2: 1.528134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.576958 Loss1: 1.063441 Loss2: 1.513517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.354339 Loss1: 0.846533 Loss2: 1.507807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.313861 Loss1: 0.802008 Loss2: 1.511852 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.877930
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.928411 Loss1: 0.442433 Loss2: 1.485978
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.166471 Loss1: 0.652713 Loss2: 1.513758
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.088674 Loss1: 0.590549 Loss2: 1.498124
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.988144 Loss1: 0.476217 Loss2: 1.511927
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.048251 Loss1: 0.533329 Loss2: 1.514922
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.992774 Loss1: 0.474012 Loss2: 1.518762
+(DefaultActor pid=3764) >> Training accuracy: 0.871094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.855652 Loss1: 1.774897 Loss2: 2.080755
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.645884 Loss1: 1.167531 Loss2: 1.478353
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.375321 Loss1: 0.908616 Loss2: 1.466705
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.173065 Loss1: 0.711776 Loss2: 1.461288
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.055257 Loss1: 0.591719 Loss2: 1.463538
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.813352 Loss1: 1.782761 Loss2: 2.030592
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.675138 Loss1: 1.162014 Loss2: 1.513124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.417974 Loss1: 0.927468 Loss2: 1.490506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.259657 Loss1: 0.770612 Loss2: 1.489045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.078708 Loss1: 0.583278 Loss2: 1.495430 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.905208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.937494 Loss1: 0.456144 Loss2: 1.481350 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.988503 Loss1: 0.496579 Loss2: 1.491924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.127258 Loss1: 2.051378 Loss2: 2.075880
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.859710 Loss1: 0.369092 Loss2: 1.490617
+(DefaultActor pid=3764) >> Training accuracy: 0.918945
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.629913 Loss1: 1.142275 Loss2: 1.487638 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.179157 Loss1: 0.689173 Loss2: 1.489984 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.077565 Loss1: 0.586447 Loss2: 1.491117
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.032743 Loss1: 1.919801 Loss2: 2.112942
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.839086 Loss1: 1.297264 Loss2: 1.541822 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.478949 Loss1: 0.953420 Loss2: 1.525529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.336545 Loss1: 0.805389 Loss2: 1.531156 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.827083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.206284 Loss1: 0.675565 Loss2: 1.530719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.062539 Loss1: 0.529747 Loss2: 1.532792 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.995390 Loss1: 0.468167 Loss2: 1.527223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.741811 Loss1: 1.727050 Loss2: 2.014761
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.093661 Loss1: 0.553725 Loss2: 1.539937
+(DefaultActor pid=3764) >> Training accuracy: 0.869792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.445008 Loss1: 0.964587 Loss2: 1.480421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.132445 Loss1: 0.645643 Loss2: 1.486803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.943588 Loss1: 1.935917 Loss2: 2.007671
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.031438 Loss1: 0.554513 Loss2: 1.476925
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.990493 Loss1: 0.505960 Loss2: 1.484533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.925309 Loss1: 0.438980 Loss2: 1.486330 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.910080 Loss1: 0.422833 Loss2: 1.487248 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.944758 Loss1: 0.455281 Loss2: 1.489477 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.874081
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.868317 Loss1: 0.447395 Loss2: 1.420922 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.765942 Loss1: 0.343893 Loss2: 1.422049 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.908333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.948479 Loss1: 1.444621 Loss2: 1.503858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.278763 Loss1: 0.809725 Loss2: 1.469037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.165725 Loss1: 0.711417 Loss2: 1.454307
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.823139 Loss1: 1.767891 Loss2: 2.055248
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.081111 Loss1: 0.616145 Loss2: 1.464966
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.801419 Loss1: 1.292808 Loss2: 1.508611
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.058733 Loss1: 0.586189 Loss2: 1.472544
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.526223 Loss1: 1.018034 Loss2: 1.508190
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.928609 Loss1: 0.454103 Loss2: 1.474507
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.276818 Loss1: 0.775220 Loss2: 1.501597
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.913947 Loss1: 0.452721 Loss2: 1.461226
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.175869 Loss1: 0.698271 Loss2: 1.477599
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.832499 Loss1: 0.372289 Loss2: 1.460210
+(DefaultActor pid=3765) >> Training accuracy: 0.872917
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.076101 Loss1: 0.596649 Loss2: 1.479452
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.950208 Loss1: 0.468236 Loss2: 1.481972
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.883995 Loss1: 0.413076 Loss2: 1.470919
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.928011 Loss1: 0.444427 Loss2: 1.483585
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.815886 Loss1: 0.326664 Loss2: 1.489222
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.125634 Loss1: 1.986237 Loss2: 2.139397
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.916392 Loss1: 1.384153 Loss2: 1.532239
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.573794 Loss1: 1.093426 Loss2: 1.480368
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.297447 Loss1: 0.803918 Loss2: 1.493529
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.173950 Loss1: 0.678242 Loss2: 1.495708
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.037851 Loss1: 0.542950 Loss2: 1.494900
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.132418 Loss1: 2.006709 Loss2: 2.125709
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.848014 Loss1: 1.313756 Loss2: 1.534258 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.508152 Loss1: 1.017463 Loss2: 1.490689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.328211 Loss1: 0.824249 Loss2: 1.503961 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.919471
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.144517 Loss1: 0.629528 Loss2: 1.514989 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.998164 Loss1: 0.477433 Loss2: 1.520731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.040505 Loss1: 0.529049 Loss2: 1.511456
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.144371 Loss1: 1.925766 Loss2: 2.218605
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.954067 Loss1: 0.442363 Loss2: 1.511704
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.955119 Loss1: 1.385959 Loss2: 1.569160
+(DefaultActor pid=3764) >> Training accuracy: 0.833333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.551512 Loss1: 1.018245 Loss2: 1.533267
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.246538 Loss1: 0.717048 Loss2: 1.529490
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.181220 Loss1: 0.664412 Loss2: 1.516808
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.024991 Loss1: 0.494221 Loss2: 1.530770
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.991053 Loss1: 0.463256 Loss2: 1.527797
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.100100 Loss1: 1.997768 Loss2: 2.102332
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.962996 Loss1: 1.439943 Loss2: 1.523053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.522544 Loss1: 1.010943 Loss2: 1.511600 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.913462
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.217518 Loss1: 0.703696 Loss2: 1.513822 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.073778 Loss1: 0.552534 Loss2: 1.521244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.071283 Loss1: 0.563554 Loss2: 1.507729
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.890333 Loss1: 1.850490 Loss2: 2.039843
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.854576 Loss1: 1.374574 Loss2: 1.480002 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.872768
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.516256 Loss1: 1.035307 Loss2: 1.480948 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.197088 Loss1: 0.733362 Loss2: 1.463726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.035160 Loss1: 0.567765 Loss2: 1.467395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.003179 Loss1: 0.526738 Loss2: 1.476441 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.994746 Loss1: 0.517356 Loss2: 1.477390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.969014 Loss1: 0.499977 Loss2: 1.469037 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.879167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.140869 Loss1: 0.685879 Loss2: 1.454990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.035989 Loss1: 0.556296 Loss2: 1.479694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.087412 Loss1: 1.939436 Loss2: 2.147976 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.940403 Loss1: 1.351867 Loss2: 1.588536 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.859375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.594086 Loss1: 1.034776 Loss2: 1.559310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.259242 Loss1: 0.710678 Loss2: 1.548564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.023442 Loss1: 0.474610 Loss2: 1.548833 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.911739 Loss1: 0.377553 Loss2: 1.534186 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.972944 Loss1: 0.427244 Loss2: 1.545699 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.947139 Loss1: 0.392483 Loss2: 1.554656 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.875000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.038825 Loss1: 0.584703 Loss2: 1.454122 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.924432 Loss1: 0.449966 Loss2: 1.474466 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.923404 Loss1: 1.761878 Loss2: 2.161526
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.871266 Loss1: 0.404972 Loss2: 1.466293
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.800624 Loss1: 1.277639 Loss2: 1.522984
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.874634 Loss1: 0.395527 Loss2: 1.479107
+(DefaultActor pid=3764) >> Training accuracy: 0.914583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.251838 Loss1: 0.756774 Loss2: 1.495064 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.957650 Loss1: 0.469896 Loss2: 1.487754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.886347 Loss1: 0.406528 Loss2: 1.479819
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.065768 Loss1: 1.941436 Loss2: 2.124332
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.835913 Loss1: 1.321871 Loss2: 1.514042 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.401430 Loss1: 0.912341 Loss2: 1.489089 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.895833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.887527 Loss1: 0.390751 Loss2: 1.496776
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.200670 Loss1: 0.739076 Loss2: 1.461593
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.093835 Loss1: 0.621359 Loss2: 1.472475
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.976769 Loss1: 0.501582 Loss2: 1.475187
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.968432 Loss1: 0.502295 Loss2: 1.466137
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.951768 Loss1: 0.482410 Loss2: 1.469357
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.767129 Loss1: 1.708214 Loss2: 2.058916
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.882264 Loss1: 0.406812 Loss2: 1.475452
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.758431 Loss1: 1.292245 Loss2: 1.466186
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.866997 Loss1: 0.387701 Loss2: 1.479296
+(DefaultActor pid=3764) >> Training accuracy: 0.892708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.178566 Loss1: 0.729860 Loss2: 1.448706 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.035536 Loss1: 0.579440 Loss2: 1.456096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.965746 Loss1: 0.495396 Loss2: 1.470350
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.978150 Loss1: 1.918797 Loss2: 2.059354
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.844952 Loss1: 1.318692 Loss2: 1.526260 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.530610 Loss1: 1.026771 Loss2: 1.503839 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.927083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.849361 Loss1: 0.385971 Loss2: 1.463390
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.216410 Loss1: 0.715312 Loss2: 1.501098
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.082254 Loss1: 0.601970 Loss2: 1.480284
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.061239 Loss1: 0.573489 Loss2: 1.487750
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.989698 Loss1: 0.495513 Loss2: 1.494185
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.992328 Loss1: 0.505006 Loss2: 1.487322
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.139517 Loss1: 2.027653 Loss2: 2.111864
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.952010 Loss1: 0.452575 Loss2: 1.499435
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.917296 Loss1: 1.365940 Loss2: 1.551356
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.819178 Loss1: 0.323795 Loss2: 1.495383
+(DefaultActor pid=3764) >> Training accuracy: 0.920833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.370405 Loss1: 0.853853 Loss2: 1.516552 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.057918 Loss1: 0.549460 Loss2: 1.508458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.023646 Loss1: 0.512747 Loss2: 1.510899
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.979888 Loss1: 1.930668 Loss2: 2.049219
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.881944 Loss1: 1.396195 Loss2: 1.485749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.521922 Loss1: 1.053147 Loss2: 1.468775 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.911143 Loss1: 0.392364 Loss2: 1.518778
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.414637 Loss1: 0.938187 Loss2: 1.476449
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.225209 Loss1: 0.745934 Loss2: 1.479275
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.088789 Loss1: 0.603336 Loss2: 1.485454
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.045046 Loss1: 0.568107 Loss2: 1.476939
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.960983 Loss1: 0.474851 Loss2: 1.486132
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.888877 Loss1: 1.844595 Loss2: 2.044282
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.973791 Loss1: 0.493655 Loss2: 1.480137
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.593079 Loss1: 1.125152 Loss2: 1.467927
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.946767 Loss1: 0.449618 Loss2: 1.497149
+(DefaultActor pid=3764) >> Training accuracy: 0.875000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.147300 Loss1: 0.704510 Loss2: 1.442789 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.957088 Loss1: 0.533101 Loss2: 1.423987 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.948078 Loss1: 0.518171 Loss2: 1.429908
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.091565 Loss1: 2.025552 Loss2: 2.066013
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.868199 Loss1: 0.427728 Loss2: 1.440471
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.866439 Loss1: 1.353229 Loss2: 1.513210
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.787575 Loss1: 0.347298 Loss2: 1.440277
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.539619 Loss1: 1.048101 Loss2: 1.491518
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.765711 Loss1: 0.339856 Loss2: 1.425855
+(DefaultActor pid=3765) >> Training accuracy: 0.915625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.261930 Loss1: 0.769293 Loss2: 1.492638
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.114928 Loss1: 0.620867 Loss2: 1.494061
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.053722 Loss1: 0.573327 Loss2: 1.480395
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.034779 Loss1: 0.540077 Loss2: 1.494703
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.943128 Loss1: 0.455289 Loss2: 1.487839
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.845676 Loss1: 0.364135 Loss2: 1.481541
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.053209 Loss1: 2.015544 Loss2: 2.037665
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.864938 Loss1: 0.389615 Loss2: 1.475323
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.860602 Loss1: 1.389188 Loss2: 1.471414
+(DefaultActor pid=3764) >> Training accuracy: 0.910417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.512891 Loss1: 1.059799 Loss2: 1.453092
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.306653 Loss1: 0.844674 Loss2: 1.461979
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.101009 Loss1: 0.646377 Loss2: 1.454632
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.013891 Loss1: 0.562818 Loss2: 1.451073
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.135130 Loss1: 2.035735 Loss2: 2.099395
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.989060 Loss1: 0.528866 Loss2: 1.460194
+(DefaultActor pid=3764) Epoch: 1 Loss: 3.026684 Loss1: 1.525202 Loss2: 1.501482
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.040590 Loss1: 0.577982 Loss2: 1.462608
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.562076 Loss1: 1.080272 Loss2: 1.481804
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.941623 Loss1: 0.476424 Loss2: 1.465198
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.371637 Loss1: 0.881373 Loss2: 1.490264
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.831174 Loss1: 0.370852 Loss2: 1.460323
+(DefaultActor pid=3765) >> Training accuracy: 0.880208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.181398 Loss1: 0.678826 Loss2: 1.502572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.149187 Loss1: 0.635800 Loss2: 1.513387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.012920 Loss1: 0.503310 Loss2: 1.509610
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.976375 Loss1: 1.873575 Loss2: 2.102800
+(DefaultActor pid=3764) >> Training accuracy: 0.830208
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.987822 Loss1: 0.481189 Loss2: 1.506633
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.949656 Loss1: 1.419231 Loss2: 1.530425
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.410774 Loss1: 0.898815 Loss2: 1.511959
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.262421 Loss1: 0.759622 Loss2: 1.502799
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.196708 Loss1: 0.675394 Loss2: 1.521314
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.157442 Loss1: 0.636810 Loss2: 1.520632
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.989528 Loss1: 1.862334 Loss2: 2.127194
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.055324 Loss1: 0.536655 Loss2: 1.518669
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.945759 Loss1: 0.421468 Loss2: 1.524291 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.950735 Loss1: 0.443155 Loss2: 1.507579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.871625 Loss1: 0.357844 Loss2: 1.513781 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.904167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.102360 Loss1: 0.602898 Loss2: 1.499462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.955437 Loss1: 0.451563 Loss2: 1.503874 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.906973 Loss1: 0.398574 Loss2: 1.508399 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.920759
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.595209 Loss1: 1.116323 Loss2: 1.478886 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.139413 Loss1: 0.681449 Loss2: 1.457964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.047207 Loss1: 0.580576 Loss2: 1.466631
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.839722 Loss1: 1.863140 Loss2: 1.976582
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.079889 Loss1: 0.603401 Loss2: 1.476488
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.841259 Loss1: 1.350097 Loss2: 1.491162
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.022435 Loss1: 0.534845 Loss2: 1.487590
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.346002 Loss1: 0.882865 Loss2: 1.463137
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.191074 Loss1: 0.739813 Loss2: 1.451261 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.925000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.954514 Loss1: 0.470723 Loss2: 1.483791
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.119249 Loss1: 0.657669 Loss2: 1.461580
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.024218 Loss1: 0.556272 Loss2: 1.467946
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.001942 Loss1: 0.527815 Loss2: 1.474127
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.974348 Loss1: 0.500099 Loss2: 1.474248
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.016985 Loss1: 0.535678 Loss2: 1.481307
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.029419 Loss1: 1.907838 Loss2: 2.121582
+(DefaultActor pid=3764) >> Training accuracy: 0.857422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.889791 Loss1: 1.355904 Loss2: 1.533887 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.385661 Loss1: 0.864773 Loss2: 1.520888 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.020632 Loss1: 0.522459 Loss2: 1.498174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.921375 Loss1: 1.892839 Loss2: 2.028536
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.019671 Loss1: 0.522085 Loss2: 1.497586
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.761120 Loss1: 1.268533 Loss2: 1.492586
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.976430 Loss1: 0.468518 Loss2: 1.507912
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.479794 Loss1: 1.010933 Loss2: 1.468862
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.941410 Loss1: 0.429646 Loss2: 1.511764
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.884014 Loss1: 0.384204 Loss2: 1.499810
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.334855 Loss1: 0.844641 Loss2: 1.490214
+(DefaultActor pid=3765) >> Training accuracy: 0.883333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.111627 Loss1: 0.636324 Loss2: 1.475303
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.027708 Loss1: 0.566706 Loss2: 1.461002
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.992885 Loss1: 0.527539 Loss2: 1.465346
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.996073 Loss1: 0.520404 Loss2: 1.475668
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.082307 Loss1: 1.984239 Loss2: 2.098069
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.887398 Loss1: 0.414012 Loss2: 1.473386
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.883493 Loss1: 1.333952 Loss2: 1.549541
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.945627 Loss1: 0.481034 Loss2: 1.464593
+(DefaultActor pid=3764) >> Training accuracy: 0.882812
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.374096 Loss1: 0.850985 Loss2: 1.523110 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.184213 Loss1: 0.665993 Loss2: 1.518221 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.073143 Loss1: 0.549737 Loss2: 1.523406
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.299875 Loss1: 2.058188 Loss2: 2.241687
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.907601 Loss1: 1.336705 Loss2: 1.570896 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.627961 Loss1: 1.126015 Loss2: 1.501946
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.959666 Loss1: 0.448207 Loss2: 1.511458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.969585 Loss1: 0.452196 Loss2: 1.517390 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.837500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.054873 Loss1: 0.548383 Loss2: 1.506489 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.827675 Loss1: 0.333276 Loss2: 1.494400 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.910156
+DEBUG flwr 2023-10-09 15:59:43,670 | server.py:236 | fit_round 44 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.895454 Loss1: 0.393353 Loss2: 1.502101
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.199282 Loss1: 2.108859 Loss2: 2.090424
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.864873 Loss1: 1.334497 Loss2: 1.530376
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.601654 Loss1: 1.089573 Loss2: 1.512081
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.465691 Loss1: 0.940930 Loss2: 1.524762
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.222964 Loss1: 0.715142 Loss2: 1.507822
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.799320 Loss1: 1.773116 Loss2: 2.026203
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.156325 Loss1: 0.638504 Loss2: 1.517821
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.105403 Loss1: 0.578944 Loss2: 1.526459 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.141395 Loss1: 0.630800 Loss2: 1.510595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.202767 Loss1: 0.741992 Loss2: 1.460775
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.045065 Loss1: 0.523948 Loss2: 1.521118
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.066742 Loss1: 0.597769 Loss2: 1.468974
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.016002 Loss1: 0.491574 Loss2: 1.524428
+(DefaultActor pid=3765) >> Training accuracy: 0.835417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.976733 Loss1: 0.502899 Loss2: 1.473834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.934740 Loss1: 0.463555 Loss2: 1.471185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.975328 Loss1: 1.928097 Loss2: 2.047232
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.871899 Loss1: 0.401816 Loss2: 1.470082
+(DefaultActor pid=3764) >> Training accuracy: 0.885742
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.514614 Loss1: 1.046040 Loss2: 1.468574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.165383 Loss1: 0.686366 Loss2: 1.479017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.997690 Loss1: 0.527423 Loss2: 1.470268
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.992210 Loss1: 1.985492 Loss2: 2.006718
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.930324 Loss1: 1.424221 Loss2: 1.506103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.565404 Loss1: 1.070052 Loss2: 1.495353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.457352 Loss1: 0.943477 Loss2: 1.513875 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.873958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.281792 Loss1: 0.779611 Loss2: 1.502181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.067863 Loss1: 0.559322 Loss2: 1.508541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.964662 Loss1: 0.465597 Loss2: 1.499065 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.904297
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 15:59:43,670][flwr][DEBUG] - fit_round 44 received 50 results and 0 failures
+INFO flwr 2023-10-09 16:00:24,719 | server.py:125 | fit progress: (44, 2.493049050672367, {'accuracy': 0.4467}, 101332.49732903899)
+>> Test accuracy: 0.446700
+[2023-10-09 16:00:24,719][flwr][INFO] - fit progress: (44, 2.493049050672367, {'accuracy': 0.4467}, 101332.49732903899)
+DEBUG flwr 2023-10-09 16:00:24,719 | server.py:173 | evaluate_round 44: strategy sampled 50 clients (out of 50)
+[2023-10-09 16:00:24,719][flwr][DEBUG] - evaluate_round 44: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 16:09:27,129 | server.py:187 | evaluate_round 44 received 50 results and 0 failures
+[2023-10-09 16:09:27,129][flwr][DEBUG] - evaluate_round 44 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 16:09:27,130 | server.py:222 | fit_round 45: strategy sampled 50 clients (out of 50)
+[2023-10-09 16:09:27,130][flwr][DEBUG] - fit_round 45: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.848211 Loss1: 1.847998 Loss2: 2.000213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.372534 Loss1: 0.906929 Loss2: 1.465604 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.922325 Loss1: 1.814255 Loss2: 2.108070
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.239572 Loss1: 0.781661 Loss2: 1.457912
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.798010 Loss1: 1.289453 Loss2: 1.508557
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.045095 Loss1: 0.586264 Loss2: 1.458831
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.957793 Loss1: 0.501519 Loss2: 1.456274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.900906 Loss1: 0.438232 Loss2: 1.462674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.854426 Loss1: 0.403221 Loss2: 1.451205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.811161 Loss1: 0.356365 Loss2: 1.454796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.741084 Loss1: 0.292007 Loss2: 1.449077 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.929688
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.899934 Loss1: 0.407600 Loss2: 1.492334 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.868750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.778713 Loss1: 1.776410 Loss2: 2.002303 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.401260 Loss1: 0.930385 Loss2: 1.470875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.859391 Loss1: 1.784575 Loss2: 2.074817
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.266052 Loss1: 0.797684 Loss2: 1.468368
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.085114 Loss1: 0.614866 Loss2: 1.470249 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.110976 Loss1: 0.645371 Loss2: 1.465605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.018285 Loss1: 0.549185 Loss2: 1.469101 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.893634 Loss1: 0.424661 Loss2: 1.468973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.854958 Loss1: 0.395870 Loss2: 1.459088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.953599 Loss1: 0.468141 Loss2: 1.485458 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.915441
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.899267 Loss1: 0.421691 Loss2: 1.477577 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.896875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.890507 Loss1: 1.869701 Loss2: 2.020806
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.771235 Loss1: 1.331757 Loss2: 1.439478
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.422687 Loss1: 0.995247 Loss2: 1.427440
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.263843 Loss1: 0.832183 Loss2: 1.431661
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.931568 Loss1: 1.777972 Loss2: 2.153596
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.091739 Loss1: 0.671331 Loss2: 1.420408
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.756894 Loss1: 1.225573 Loss2: 1.531321
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.032414 Loss1: 0.608405 Loss2: 1.424009
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.391125 Loss1: 0.890065 Loss2: 1.501060
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.937847 Loss1: 0.499935 Loss2: 1.437912
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.258965 Loss1: 0.753097 Loss2: 1.505867
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.852455 Loss1: 0.432001 Loss2: 1.420454
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.089114 Loss1: 0.587704 Loss2: 1.501410
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.759776 Loss1: 0.340116 Loss2: 1.419659
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.001159 Loss1: 0.490342 Loss2: 1.510817
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.795794 Loss1: 0.380797 Loss2: 1.414997
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.889914 Loss1: 0.401420 Loss2: 1.488494
+(DefaultActor pid=3765) >> Training accuracy: 0.909375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.938128 Loss1: 0.440118 Loss2: 1.498010
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.871262 Loss1: 0.375232 Loss2: 1.496030
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.794432 Loss1: 0.305069 Loss2: 1.489363
+(DefaultActor pid=3764) >> Training accuracy: 0.917708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.904620 Loss1: 1.890294 Loss2: 2.014326
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.708509 Loss1: 1.230314 Loss2: 1.478195
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.455675 Loss1: 0.993438 Loss2: 1.462237
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.936395 Loss1: 1.859688 Loss2: 2.076707
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.189027 Loss1: 0.741863 Loss2: 1.447164
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.687961 Loss1: 1.191684 Loss2: 1.496278
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.127127 Loss1: 0.682166 Loss2: 1.444961
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.303001 Loss1: 0.852069 Loss2: 1.450932
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.085413 Loss1: 0.638283 Loss2: 1.447130
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.095057 Loss1: 0.651949 Loss2: 1.443108
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.053679 Loss1: 0.593572 Loss2: 1.460107
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.898620 Loss1: 0.444255 Loss2: 1.454364 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.988931 Loss1: 0.532764 Loss2: 1.456167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.985335 Loss1: 0.520610 Loss2: 1.464725 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.863281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.794468 Loss1: 0.361791 Loss2: 1.432677 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.882292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.038474 Loss1: 1.949292 Loss2: 2.089182 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.485378 Loss1: 0.978041 Loss2: 1.507337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.321973 Loss1: 0.811657 Loss2: 1.510316
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.990812 Loss1: 1.945452 Loss2: 2.045360
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.809094 Loss1: 1.346193 Loss2: 1.462901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.407574 Loss1: 0.961542 Loss2: 1.446032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.293818 Loss1: 0.838711 Loss2: 1.455107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.167022 Loss1: 0.701600 Loss2: 1.465422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.046671 Loss1: 0.583671 Loss2: 1.463000 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.884375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.891099 Loss1: 0.385559 Loss2: 1.505540
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.022514 Loss1: 0.551269 Loss2: 1.471244
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.980012 Loss1: 0.510545 Loss2: 1.469468
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.996452 Loss1: 0.524260 Loss2: 1.472192
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.916711 Loss1: 0.449023 Loss2: 1.467688
+(DefaultActor pid=3764) >> Training accuracy: 0.886458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.851625 Loss1: 1.814541 Loss2: 2.037084
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.773554 Loss1: 1.298980 Loss2: 1.474574
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.395101 Loss1: 0.927003 Loss2: 1.468098
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.216687 Loss1: 0.763965 Loss2: 1.452722
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.090127 Loss1: 2.032334 Loss2: 2.057793
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.919811 Loss1: 1.405289 Loss2: 1.514523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.569639 Loss1: 1.061601 Loss2: 1.508039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.303282 Loss1: 0.818482 Loss2: 1.484799 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.131794 Loss1: 0.655379 Loss2: 1.476414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.090423 Loss1: 0.612253 Loss2: 1.478170 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.897917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.902590 Loss1: 0.436433 Loss2: 1.466157
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.030651 Loss1: 0.541995 Loss2: 1.488656
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.979330 Loss1: 0.476679 Loss2: 1.502652
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.923807 Loss1: 0.434850 Loss2: 1.488956
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.803530 Loss1: 0.329569 Loss2: 1.473961
+(DefaultActor pid=3764) >> Training accuracy: 0.919792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.852853 Loss1: 1.731159 Loss2: 2.121694
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.717680 Loss1: 1.181582 Loss2: 1.536098
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.399909 Loss1: 0.879523 Loss2: 1.520385
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.228389 Loss1: 0.712440 Loss2: 1.515949
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.942789 Loss1: 1.834204 Loss2: 2.108585
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.731055 Loss1: 1.220179 Loss2: 1.510877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.036738 Loss1: 0.526601 Loss2: 1.510137
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.463555 Loss1: 0.984224 Loss2: 1.479331
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.915576 Loss1: 0.391989 Loss2: 1.523586
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.306411 Loss1: 0.817402 Loss2: 1.489009
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.920239 Loss1: 0.405378 Loss2: 1.514861
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.184266 Loss1: 0.694645 Loss2: 1.489621
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.103811 Loss1: 0.615001 Loss2: 1.488810
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.925936 Loss1: 0.409151 Loss2: 1.516786
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.048208 Loss1: 0.554121 Loss2: 1.494087
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.905888 Loss1: 0.391578 Loss2: 1.514310
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.889945 Loss1: 0.405986 Loss2: 1.483958 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.922991
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.206758 Loss1: 2.052028 Loss2: 2.154730 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.551940 Loss1: 1.022282 Loss2: 1.529658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.414386 Loss1: 0.878398 Loss2: 1.535988
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.887100 Loss1: 1.834870 Loss2: 2.052230
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.754866 Loss1: 1.252359 Loss2: 1.502507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.445611 Loss1: 0.951348 Loss2: 1.494263 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.183249 Loss1: 0.700680 Loss2: 1.482569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.000950 Loss1: 0.449748 Loss2: 1.551202 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.932099 Loss1: 0.388359 Loss2: 1.543741 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.912946
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.877723 Loss1: 0.411409 Loss2: 1.466314 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.871488 Loss1: 0.399425 Loss2: 1.472063 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.895508
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.797365 Loss1: 1.357600 Loss2: 1.439765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.322550 Loss1: 0.896964 Loss2: 1.425586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.172005 Loss1: 0.757910 Loss2: 1.414095
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.944285 Loss1: 1.866548 Loss2: 2.077737
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.735807 Loss1: 1.254555 Loss2: 1.481252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.312506 Loss1: 0.849916 Loss2: 1.462591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.248140 Loss1: 0.787951 Loss2: 1.460189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.087658 Loss1: 0.609011 Loss2: 1.478647 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.908333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.985434 Loss1: 0.526200 Loss2: 1.459234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.867155 Loss1: 0.392987 Loss2: 1.474169 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.750936 Loss1: 0.297189 Loss2: 1.453748 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.881250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.796363 Loss1: 1.305145 Loss2: 1.491218 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.188498 Loss1: 0.721714 Loss2: 1.466784 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.108707 Loss1: 0.645362 Loss2: 1.463345
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.828668 Loss1: 1.766951 Loss2: 2.061717
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.746987 Loss1: 1.265283 Loss2: 1.481704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.479418 Loss1: 0.991559 Loss2: 1.487859 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.205802 Loss1: 0.704572 Loss2: 1.501230 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.138306 Loss1: 0.657168 Loss2: 1.481138 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.920833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.809379 Loss1: 0.333167 Loss2: 1.476213
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.009913 Loss1: 0.523837 Loss2: 1.486076
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.913773 Loss1: 0.443693 Loss2: 1.470081
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.912709 Loss1: 0.431438 Loss2: 1.481271
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.887302 Loss1: 0.398749 Loss2: 1.488553
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.848395 Loss1: 0.361391 Loss2: 1.487004
+(DefaultActor pid=3764) >> Training accuracy: 0.914583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.936416 Loss1: 1.943658 Loss2: 1.992758
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.805021 Loss1: 1.345338 Loss2: 1.459683
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.443638 Loss1: 0.997164 Loss2: 1.446474
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.202473 Loss1: 0.747420 Loss2: 1.455053
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.026622 Loss1: 0.587771 Loss2: 1.438850
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.865574 Loss1: 1.766488 Loss2: 2.099086
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.959205 Loss1: 0.521305 Loss2: 1.437900
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.688418 Loss1: 1.159106 Loss2: 1.529312
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.948927 Loss1: 0.500945 Loss2: 1.447982
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.412265 Loss1: 0.893939 Loss2: 1.518326
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.916132 Loss1: 0.456887 Loss2: 1.459244
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.231641 Loss1: 0.715948 Loss2: 1.515693
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.121541 Loss1: 0.612254 Loss2: 1.509287
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.915803 Loss1: 0.461363 Loss2: 1.454440
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.070454 Loss1: 0.561855 Loss2: 1.508599
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.024607 Loss1: 0.569489 Loss2: 1.455118
+(DefaultActor pid=3765) >> Training accuracy: 0.853516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.944601 Loss1: 0.423005 Loss2: 1.521596 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.903762 Loss1: 0.388456 Loss2: 1.515305 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.906250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.772297 Loss1: 1.292544 Loss2: 1.479753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.237821 Loss1: 0.766603 Loss2: 1.471218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.893233 Loss1: 1.823174 Loss2: 2.070059
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.082023 Loss1: 0.619027 Loss2: 1.462996
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.751732 Loss1: 1.291794 Loss2: 1.459938
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.013627 Loss1: 0.545547 Loss2: 1.468080
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.403413 Loss1: 0.960677 Loss2: 1.442736
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.959814 Loss1: 0.485079 Loss2: 1.474735
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.159439 Loss1: 0.716025 Loss2: 1.443414
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.034567 Loss1: 0.559528 Loss2: 1.475040
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.052501 Loss1: 0.628013 Loss2: 1.424488
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.989604 Loss1: 0.491287 Loss2: 1.498316
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.970500 Loss1: 0.538243 Loss2: 1.432258
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.946135 Loss1: 0.468912 Loss2: 1.477224
+(DefaultActor pid=3765) >> Training accuracy: 0.907292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.904178 Loss1: 0.462483 Loss2: 1.441695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.838289 Loss1: 0.398072 Loss2: 1.440217 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.848958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.862309 Loss1: 1.355606 Loss2: 1.506703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.289012 Loss1: 0.795122 Loss2: 1.493890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.106531 Loss1: 2.018430 Loss2: 2.088101
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.168633 Loss1: 0.665333 Loss2: 1.503300
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.931941 Loss1: 1.411158 Loss2: 1.520784
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.049062 Loss1: 0.544078 Loss2: 1.504984
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.589324 Loss1: 1.098904 Loss2: 1.490421
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.970404 Loss1: 0.488870 Loss2: 1.481535
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.320543 Loss1: 0.816408 Loss2: 1.504135
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.980560 Loss1: 0.493061 Loss2: 1.487499
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.184025 Loss1: 0.688219 Loss2: 1.495806
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.897012 Loss1: 0.403649 Loss2: 1.493363
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.153228 Loss1: 0.665984 Loss2: 1.487244
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.877720 Loss1: 0.378676 Loss2: 1.499044
+(DefaultActor pid=3765) >> Training accuracy: 0.891667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.046084 Loss1: 0.538305 Loss2: 1.507779 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.882763 Loss1: 0.389035 Loss2: 1.493728 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.865625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.960329 Loss1: 1.444653 Loss2: 1.515676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.406814 Loss1: 0.904192 Loss2: 1.502622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.831819 Loss1: 1.833684 Loss2: 1.998135
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.236554 Loss1: 0.730967 Loss2: 1.505587
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.590990 Loss1: 1.165779 Loss2: 1.425211
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.164750 Loss1: 0.673332 Loss2: 1.491419
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.302363 Loss1: 0.889878 Loss2: 1.412485
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.092821 Loss1: 0.589179 Loss2: 1.503642
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.127862 Loss1: 0.713889 Loss2: 1.413973
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.087755 Loss1: 0.578792 Loss2: 1.508963
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.096845 Loss1: 0.669749 Loss2: 1.427096
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.998210 Loss1: 0.488325 Loss2: 1.509885
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.942891 Loss1: 0.513649 Loss2: 1.429242
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.948764 Loss1: 0.446159 Loss2: 1.502605
+(DefaultActor pid=3765) >> Training accuracy: 0.875000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.819621 Loss1: 0.391017 Loss2: 1.428604 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.843961 Loss1: 0.412607 Loss2: 1.431353 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.931250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.902655 Loss1: 1.318928 Loss2: 1.583727 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.327834 Loss1: 0.766374 Loss2: 1.561461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.222686 Loss1: 0.673942 Loss2: 1.548744 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.093349 Loss1: 0.523438 Loss2: 1.569912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.001627 Loss1: 0.441207 Loss2: 1.560420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.050367 Loss1: 0.494850 Loss2: 1.555517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.998674 Loss1: 0.431803 Loss2: 1.566872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.907885 Loss1: 0.349781 Loss2: 1.558104 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.905273
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.844467 Loss1: 0.426472 Loss2: 1.417994 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.888542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.153979 Loss1: 2.051108 Loss2: 2.102872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.539010 Loss1: 0.996018 Loss2: 1.542992 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.306730 Loss1: 0.759453 Loss2: 1.547277
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.912710 Loss1: 1.844906 Loss2: 2.067804
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.193704 Loss1: 0.642458 Loss2: 1.551246
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.707506 Loss1: 1.234325 Loss2: 1.473181
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.126828 Loss1: 0.577962 Loss2: 1.548867
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.327874 Loss1: 0.868448 Loss2: 1.459426
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.135943 Loss1: 0.687382 Loss2: 1.448561
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.089994 Loss1: 0.531274 Loss2: 1.558720
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.052353 Loss1: 0.591592 Loss2: 1.460761
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.074168 Loss1: 0.508574 Loss2: 1.565593
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.113290 Loss1: 0.634383 Loss2: 1.478907
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.064757 Loss1: 0.494600 Loss2: 1.570157
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.907165 Loss1: 0.419475 Loss2: 1.487691
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.994183 Loss1: 0.414691 Loss2: 1.579492
+(DefaultActor pid=3765) >> Training accuracy: 0.866211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.972093 Loss1: 0.493632 Loss2: 1.478460 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.897917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.971797 Loss1: 1.955432 Loss2: 2.016365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.480180 Loss1: 1.029027 Loss2: 1.451153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.304057 Loss1: 0.841860 Loss2: 1.462197
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.027255 Loss1: 1.896186 Loss2: 2.131068
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.859304 Loss1: 1.383036 Loss2: 1.476268
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.135618 Loss1: 0.678568 Loss2: 1.457050
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.010775 Loss1: 0.559159 Loss2: 1.451616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.971311 Loss1: 0.516446 Loss2: 1.454865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.993558 Loss1: 0.548115 Loss2: 1.445443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.899815 Loss1: 0.473321 Loss2: 1.426494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.889810 Loss1: 0.462199 Loss2: 1.427611 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.888542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.776422 Loss1: 0.344369 Loss2: 1.432053 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.010773 Loss1: 1.888938 Loss2: 2.121835
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.802127 Loss1: 1.325594 Loss2: 1.476533
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.536117 Loss1: 1.085819 Loss2: 1.450298
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.171757 Loss1: 0.727085 Loss2: 1.444672
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.074840 Loss1: 1.937773 Loss2: 2.137067
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.931669 Loss1: 0.497293 Loss2: 1.434376 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.848239 Loss1: 0.395213 Loss2: 1.453026 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.835310 Loss1: 0.394169 Loss2: 1.441141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.845587 Loss1: 0.403990 Loss2: 1.441597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.830535 Loss1: 0.383626 Loss2: 1.446909 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.918269
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.216776 Loss1: 0.663333 Loss2: 1.553443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.032966 Loss1: 0.468801 Loss2: 1.564165 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.949915 Loss1: 0.400417 Loss2: 1.549498
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.772841 Loss1: 1.716901 Loss2: 2.055941
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.538397 Loss1: 1.065256 Loss2: 1.473141
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.290609 Loss1: 0.831191 Loss2: 1.459418
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.174725 Loss1: 0.713862 Loss2: 1.460862
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.989841 Loss1: 0.528301 Loss2: 1.461540
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.013779 Loss1: 1.810872 Loss2: 2.202907
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.762087 Loss1: 1.244082 Loss2: 1.518005 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.939462 Loss1: 0.490278 Loss2: 1.449184
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.437826 Loss1: 0.961135 Loss2: 1.476692
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.190217 Loss1: 0.709700 Loss2: 1.480516
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.845329 Loss1: 0.389848 Loss2: 1.455481
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.068133 Loss1: 0.592042 Loss2: 1.476091
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.778755 Loss1: 0.316893 Loss2: 1.461862
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.798508 Loss1: 0.338880 Loss2: 1.459628 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.944792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.918278 Loss1: 0.439798 Loss2: 1.478480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.864308 Loss1: 0.375295 Loss2: 1.489013 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.915865
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.905192 Loss1: 1.820909 Loss2: 2.084283
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.854543 Loss1: 1.369263 Loss2: 1.485280
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.538855 Loss1: 1.069827 Loss2: 1.469027
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.266214 Loss1: 0.800011 Loss2: 1.466203
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.923939 Loss1: 1.892797 Loss2: 2.031142
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.781311 Loss1: 1.286169 Loss2: 1.495142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.421640 Loss1: 0.937313 Loss2: 1.484327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.244620 Loss1: 0.751385 Loss2: 1.493235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.161098 Loss1: 0.681123 Loss2: 1.479975 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.142550 Loss1: 0.641808 Loss2: 1.500742 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.875000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.897071 Loss1: 0.420241 Loss2: 1.476830
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.068011 Loss1: 0.569959 Loss2: 1.498051
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.045984 Loss1: 0.550212 Loss2: 1.495772
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.951274 Loss1: 0.449781 Loss2: 1.501493
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.883310 Loss1: 0.393529 Loss2: 1.489782
+(DefaultActor pid=3764) >> Training accuracy: 0.888542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.959383 Loss1: 1.975316 Loss2: 1.984067
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.908611 Loss1: 1.462817 Loss2: 1.445795
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.541277 Loss1: 1.110373 Loss2: 1.430904
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.236079 Loss1: 0.820564 Loss2: 1.415515
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.950553 Loss1: 1.883037 Loss2: 2.067516
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.835178 Loss1: 1.309335 Loss2: 1.525843 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.485517 Loss1: 0.976535 Loss2: 1.508982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.229267 Loss1: 0.718080 Loss2: 1.511188 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.132063 Loss1: 0.633905 Loss2: 1.498158 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.047911 Loss1: 0.548160 Loss2: 1.499751 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.884766
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.810536 Loss1: 0.398792 Loss2: 1.411744
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.914998 Loss1: 0.418947 Loss2: 1.496051
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.942464 Loss1: 0.448097 Loss2: 1.494367
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.925819 Loss1: 0.415732 Loss2: 1.510087
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.934483 Loss1: 0.421996 Loss2: 1.512488
+(DefaultActor pid=3764) >> Training accuracy: 0.902344
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.020127 Loss1: 1.931961 Loss2: 2.088166
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.833735 Loss1: 1.327401 Loss2: 1.506334
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.515796 Loss1: 1.022817 Loss2: 1.492979
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.356723 Loss1: 0.864099 Loss2: 1.492624
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.903852 Loss1: 1.863741 Loss2: 2.040110
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.702813 Loss1: 1.246022 Loss2: 1.456791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.396306 Loss1: 0.953959 Loss2: 1.442347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.168417 Loss1: 0.736150 Loss2: 1.432267 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.052970 Loss1: 0.627665 Loss2: 1.425305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.901978 Loss1: 0.472345 Loss2: 1.429633 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.881250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.842098 Loss1: 0.413758 Loss2: 1.428340 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.890792 Loss1: 0.443402 Loss2: 1.447391 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.878125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.982428 Loss1: 1.927948 Loss2: 2.054480
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.793779 Loss1: 1.315952 Loss2: 1.477828
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.453583 Loss1: 0.975238 Loss2: 1.478345
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.221968 Loss1: 0.753001 Loss2: 1.468967
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.129344 Loss1: 2.013746 Loss2: 2.115598
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.958277 Loss1: 1.433515 Loss2: 1.524763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.572446 Loss1: 1.053882 Loss2: 1.518564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.317445 Loss1: 0.800850 Loss2: 1.516595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.230680 Loss1: 0.714213 Loss2: 1.516467 [repeated 2x across cluster]
+DEBUG flwr 2023-10-09 16:38:42,534 | server.py:236 | fit_round 45 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.114012 Loss1: 0.600070 Loss2: 1.513942 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.863542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.099179 Loss1: 0.572028 Loss2: 1.527152 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 2.002026 Loss1: 0.476846 Loss2: 1.525180 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.851562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.930941 Loss1: 1.408220 Loss2: 1.522721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.397557 Loss1: 0.905192 Loss2: 1.492365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.959239 Loss1: 1.923944 Loss2: 2.035295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.815218 Loss1: 1.333165 Loss2: 1.482053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.414990 Loss1: 0.971162 Loss2: 1.443828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.913162 Loss1: 0.418089 Loss2: 1.495072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.933082 Loss1: 0.444073 Loss2: 1.489009 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.940848
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.923064 Loss1: 0.466331 Loss2: 1.456733 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.901534 Loss1: 0.450962 Loss2: 1.450571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.883933 Loss1: 0.425304 Loss2: 1.458629
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.733806 Loss1: 1.694151 Loss2: 2.039655
+(DefaultActor pid=3764) >> Training accuracy: 0.908333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.717681 Loss1: 1.239741 Loss2: 1.477940
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.369369 Loss1: 0.910503 Loss2: 1.458866
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.122126 Loss1: 0.663088 Loss2: 1.459038
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.028328 Loss1: 0.585995 Loss2: 1.442333
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.047798 Loss1: 0.589971 Loss2: 1.457827
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.016135 Loss1: 1.976942 Loss2: 2.039193
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.940121 Loss1: 0.479833 Loss2: 1.460288
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.839946 Loss1: 1.345745 Loss2: 1.494201
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.868665 Loss1: 0.406350 Loss2: 1.462315
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.441653 Loss1: 0.965310 Loss2: 1.476342
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.783778 Loss1: 0.332840 Loss2: 1.450938
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.292794 Loss1: 0.809876 Loss2: 1.482918
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.837136 Loss1: 0.390187 Loss2: 1.446949
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.171737 Loss1: 0.679073 Loss2: 1.492663
+(DefaultActor pid=3765) >> Training accuracy: 0.870833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.164195 Loss1: 0.670835 Loss2: 1.493360
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.040769 Loss1: 0.542416 Loss2: 1.498353
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.899012 Loss1: 0.424283 Loss2: 1.474730
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.929188 Loss1: 0.442238 Loss2: 1.486950
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.862679 Loss1: 0.373677 Loss2: 1.489002
+(DefaultActor pid=3764) >> Training accuracy: 0.872917
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 16:38:42,534][flwr][DEBUG] - fit_round 45 received 50 results and 0 failures
+INFO flwr 2023-10-09 16:39:24,842 | server.py:125 | fit progress: (45, 2.4865793888561263, {'accuracy': 0.4568}, 103672.620332282)
+>> Test accuracy: 0.456800
+[2023-10-09 16:39:24,842][flwr][INFO] - fit progress: (45, 2.4865793888561263, {'accuracy': 0.4568}, 103672.620332282)
+DEBUG flwr 2023-10-09 16:39:24,842 | server.py:173 | evaluate_round 45: strategy sampled 50 clients (out of 50)
+[2023-10-09 16:39:24,842][flwr][DEBUG] - evaluate_round 45: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 16:48:28,679 | server.py:187 | evaluate_round 45 received 50 results and 0 failures
+[2023-10-09 16:48:28,679][flwr][DEBUG] - evaluate_round 45 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 16:48:28,680 | server.py:222 | fit_round 46: strategy sampled 50 clients (out of 50)
+[2023-10-09 16:48:28,680][flwr][DEBUG] - fit_round 46: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.898112 Loss1: 1.853889 Loss2: 2.044223
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.729719 Loss1: 1.186862 Loss2: 1.542857
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.466418 Loss1: 0.952810 Loss2: 1.513608
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.222674 Loss1: 0.715911 Loss2: 1.506763
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.943204 Loss1: 1.428497 Loss2: 1.514708 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.574735 Loss1: 1.096975 Loss2: 1.477760 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.295067 Loss1: 0.813656 Loss2: 1.481411 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.142012 Loss1: 0.675186 Loss2: 1.466826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.017083 Loss1: 0.539378 Loss2: 1.477705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.997761 Loss1: 0.515450 Loss2: 1.482311 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.880859
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.001556 Loss1: 0.516017 Loss2: 1.485539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.932500 Loss1: 0.441700 Loss2: 1.490800 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.910417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.761184 Loss1: 1.734121 Loss2: 2.027063
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.687831 Loss1: 1.190025 Loss2: 1.497806
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.469809 Loss1: 0.965423 Loss2: 1.504385
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.760040 Loss1: 1.765523 Loss2: 1.994516
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.153301 Loss1: 0.658214 Loss2: 1.495087
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.739263 Loss1: 1.241146 Loss2: 1.498117
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.962698 Loss1: 0.481518 Loss2: 1.481180
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.394155 Loss1: 0.904240 Loss2: 1.489915
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.920449 Loss1: 0.454009 Loss2: 1.466440
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.127734 Loss1: 0.672360 Loss2: 1.455375
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.946062 Loss1: 0.462725 Loss2: 1.483337
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.022594 Loss1: 0.558440 Loss2: 1.464154
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.864460 Loss1: 0.390952 Loss2: 1.473508
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.899104 Loss1: 0.444021 Loss2: 1.455082
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.862593 Loss1: 0.388539 Loss2: 1.474054
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.870282 Loss1: 0.422048 Loss2: 1.448235
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.794030 Loss1: 0.314279 Loss2: 1.479751
+(DefaultActor pid=3765) >> Training accuracy: 0.902344
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.838687 Loss1: 0.376897 Loss2: 1.461791 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.904297
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.952092 Loss1: 1.801029 Loss2: 2.151063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.382622 Loss1: 0.869013 Loss2: 1.513608 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.152899 Loss1: 0.655167 Loss2: 1.497732
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.021287 Loss1: 1.923733 Loss2: 2.097554
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.080146 Loss1: 0.592120 Loss2: 1.488026
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.754514 Loss1: 1.270256 Loss2: 1.484258
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.028690 Loss1: 0.537629 Loss2: 1.491061
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.456511 Loss1: 0.991750 Loss2: 1.464761
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.026053 Loss1: 0.521466 Loss2: 1.504587
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.218508 Loss1: 0.748778 Loss2: 1.469730
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.879998 Loss1: 0.375404 Loss2: 1.504594
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.117776 Loss1: 0.643708 Loss2: 1.474069
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.876797 Loss1: 0.370074 Loss2: 1.506723
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.088037 Loss1: 0.604149 Loss2: 1.483887
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.822104 Loss1: 0.315657 Loss2: 1.506448
+(DefaultActor pid=3765) >> Training accuracy: 0.927083
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.010313 Loss1: 0.530039 Loss2: 1.480274
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.923296 Loss1: 0.448125 Loss2: 1.475171
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.909444 Loss1: 0.439379 Loss2: 1.470066
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.838018 Loss1: 0.369944 Loss2: 1.468073
+(DefaultActor pid=3764) >> Training accuracy: 0.903125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.798731 Loss1: 1.688574 Loss2: 2.110157
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.736223 Loss1: 1.247539 Loss2: 1.488683
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.396585 Loss1: 0.934815 Loss2: 1.461769
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.274651 Loss1: 0.800336 Loss2: 1.474315
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.965348 Loss1: 1.948297 Loss2: 2.017051
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.855283 Loss1: 1.371183 Loss2: 1.484100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.452585 Loss1: 1.021079 Loss2: 1.431506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.194080 Loss1: 0.752641 Loss2: 1.441439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.116601 Loss1: 0.681828 Loss2: 1.434773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.984943 Loss1: 0.559300 Loss2: 1.425642 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.920833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.793470 Loss1: 0.335128 Loss2: 1.458342
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.946595 Loss1: 0.510644 Loss2: 1.435951
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.872771 Loss1: 0.439430 Loss2: 1.433341
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.846747 Loss1: 0.412711 Loss2: 1.434037
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.763374 Loss1: 0.323919 Loss2: 1.439455
+(DefaultActor pid=3764) >> Training accuracy: 0.905208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.822930 Loss1: 1.781192 Loss2: 2.041738
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.730099 Loss1: 1.285597 Loss2: 1.444502
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.442970 Loss1: 0.982172 Loss2: 1.460798
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.259761 Loss1: 0.820008 Loss2: 1.439753
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.901593 Loss1: 1.814455 Loss2: 2.087138
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.714659 Loss1: 1.208829 Loss2: 1.505830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.362696 Loss1: 0.878741 Loss2: 1.483955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.800000 Loss1: 0.367221 Loss2: 1.432779 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.786968 Loss1: 0.355605 Loss2: 1.431363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.784056 Loss1: 0.346139 Loss2: 1.437917 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.896994 Loss1: 0.418001 Loss2: 1.478993 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.870639 Loss1: 0.375567 Loss2: 1.495073 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.894792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.703884 Loss1: 1.193327 Loss2: 1.510557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.132811 Loss1: 0.632931 Loss2: 1.499880 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.069207 Loss1: 0.583921 Loss2: 1.485286
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.056048 Loss1: 1.957619 Loss2: 2.098429
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.005622 Loss1: 0.510077 Loss2: 1.495546
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.966457 Loss1: 1.414370 Loss2: 1.552087
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.991923 Loss1: 0.485648 Loss2: 1.506274
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.503494 Loss1: 0.975238 Loss2: 1.528255
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.001945 Loss1: 0.491729 Loss2: 1.510216
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.293108 Loss1: 0.777190 Loss2: 1.515918
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.972821 Loss1: 0.452855 Loss2: 1.519966
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.183175 Loss1: 0.672694 Loss2: 1.510482
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.829841 Loss1: 0.334412 Loss2: 1.495429
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.059354 Loss1: 0.542534 Loss2: 1.516820
+(DefaultActor pid=3765) >> Training accuracy: 0.891667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.981674 Loss1: 0.471103 Loss2: 1.510571
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.986509 Loss1: 0.484972 Loss2: 1.501537
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.970793 Loss1: 0.451921 Loss2: 1.518871
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.881460 Loss1: 0.365808 Loss2: 1.515652
+(DefaultActor pid=3764) >> Training accuracy: 0.905208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.759562 Loss1: 1.748079 Loss2: 2.011483
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.713004 Loss1: 1.236733 Loss2: 1.476271
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.366985 Loss1: 0.930972 Loss2: 1.436014
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.141586 Loss1: 0.707234 Loss2: 1.434352
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.999723 Loss1: 0.569518 Loss2: 1.430206
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.936485 Loss1: 0.503339 Loss2: 1.433146 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.836137 Loss1: 0.408508 Loss2: 1.427629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.855326 Loss1: 0.427198 Loss2: 1.428128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.830481 Loss1: 0.385930 Loss2: 1.444551 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.764466 Loss1: 0.329375 Loss2: 1.435091 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.909375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.003573 Loss1: 0.551560 Loss2: 1.452013 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.876245 Loss1: 0.418450 Loss2: 1.457795 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.876042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.745130 Loss1: 1.271311 Loss2: 1.473819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.204307 Loss1: 0.732171 Loss2: 1.472136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.104231 Loss1: 1.940797 Loss2: 2.163434
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.122934 Loss1: 0.645607 Loss2: 1.477327
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.786621 Loss1: 1.257559 Loss2: 1.529062
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.048146 Loss1: 0.579238 Loss2: 1.468908
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.002113 Loss1: 0.530750 Loss2: 1.471363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.006913 Loss1: 0.524743 Loss2: 1.482170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.002109 Loss1: 0.507704 Loss2: 1.494405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.951222 Loss1: 0.462837 Loss2: 1.488385 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.875000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.896553 Loss1: 0.391563 Loss2: 1.504990 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.907452
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.102697 Loss1: 1.896855 Loss2: 2.205841 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.343864 Loss1: 0.844264 Loss2: 1.499600 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.781297 Loss1: 1.774658 Loss2: 2.006639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.954221 Loss1: 0.448383 Loss2: 1.505837 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.827008 Loss1: 0.324298 Loss2: 1.502709 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.832753 Loss1: 0.343118 Loss2: 1.489635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.829280 Loss1: 0.341120 Loss2: 1.488160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.782683 Loss1: 0.290830 Loss2: 1.491853 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.933894
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.840127 Loss1: 0.434384 Loss2: 1.405743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.898800 Loss1: 0.459086 Loss2: 1.439715 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.877083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.874962 Loss1: 0.437599 Loss2: 1.437362
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.774345 Loss1: 1.780132 Loss2: 1.994213
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.720804 Loss1: 1.258075 Loss2: 1.462730
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.378753 Loss1: 0.935597 Loss2: 1.443155
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.096433 Loss1: 0.661850 Loss2: 1.434583
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.996576 Loss1: 0.565094 Loss2: 1.431481
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.934219 Loss1: 1.887049 Loss2: 2.047170
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.728556 Loss1: 1.244622 Loss2: 1.483934 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.413945 Loss1: 0.941542 Loss2: 1.472403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.152652 Loss1: 0.693937 Loss2: 1.458715 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.983963 Loss1: 0.533468 Loss2: 1.450495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.881250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.846735 Loss1: 0.394339 Loss2: 1.452396
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.898618 Loss1: 0.452873 Loss2: 1.445744
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.844945 Loss1: 0.399012 Loss2: 1.445933
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.855870 Loss1: 0.398822 Loss2: 1.457048
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.871061 Loss1: 0.398461 Loss2: 1.472600
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.765573 Loss1: 0.299948 Loss2: 1.465624
+(DefaultActor pid=3764) >> Training accuracy: 0.898958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.967009 Loss1: 1.939254 Loss2: 2.027755
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.814081 Loss1: 1.348866 Loss2: 1.465215
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.498866 Loss1: 1.059515 Loss2: 1.439351
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.361052 Loss1: 0.913634 Loss2: 1.447419
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.117704 Loss1: 0.667853 Loss2: 1.449851
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.906858 Loss1: 1.864788 Loss2: 2.042070
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.807700 Loss1: 1.315105 Loss2: 1.492595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.367294 Loss1: 0.897303 Loss2: 1.469991 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.181674 Loss1: 0.716871 Loss2: 1.464803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.130904 Loss1: 0.668174 Loss2: 1.462730 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.862500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.046350 Loss1: 0.566820 Loss2: 1.479530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.999098 Loss1: 0.516321 Loss2: 1.482777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.828949 Loss1: 0.358870 Loss2: 1.470079 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.878125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.669049 Loss1: 1.206748 Loss2: 1.462301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.169721 Loss1: 0.710688 Loss2: 1.459033 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.060433 Loss1: 0.614206 Loss2: 1.446227
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.798177 Loss1: 1.832458 Loss2: 1.965719
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.793725 Loss1: 1.347364 Loss2: 1.446361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.439410 Loss1: 1.014108 Loss2: 1.425301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.153268 Loss1: 0.735346 Loss2: 1.417922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.042981 Loss1: 0.647630 Loss2: 1.395351 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.926042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.812118 Loss1: 0.371569 Loss2: 1.440549
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.047189 Loss1: 0.641235 Loss2: 1.405953
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.935068 Loss1: 0.515401 Loss2: 1.419667
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.834791 Loss1: 0.423289 Loss2: 1.411502
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.832428 Loss1: 0.419232 Loss2: 1.413196
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.775148 Loss1: 0.365675 Loss2: 1.409473
+(DefaultActor pid=3764) >> Training accuracy: 0.918750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.903865 Loss1: 1.866701 Loss2: 2.037165
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.740885 Loss1: 1.247951 Loss2: 1.492934
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.515479 Loss1: 1.027189 Loss2: 1.488290
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.298930 Loss1: 0.807473 Loss2: 1.491457
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.137060 Loss1: 0.657316 Loss2: 1.479744
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.872790 Loss1: 1.832693 Loss2: 2.040097
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.783940 Loss1: 1.252525 Loss2: 1.531416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.436412 Loss1: 0.928568 Loss2: 1.507844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.318637 Loss1: 0.803290 Loss2: 1.515347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.170900 Loss1: 0.644370 Loss2: 1.526529 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.862500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.115410 Loss1: 0.589792 Loss2: 1.525618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.985932 Loss1: 0.479515 Loss2: 1.506418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.938073 Loss1: 0.415289 Loss2: 1.522785 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.903320
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.573339 Loss1: 1.061679 Loss2: 1.511660 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.086903 Loss1: 0.587257 Loss2: 1.499646 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.133257 Loss1: 0.636083 Loss2: 1.497174
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.937540 Loss1: 1.969403 Loss2: 1.968137
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.826829 Loss1: 1.358869 Loss2: 1.467960 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.507961 Loss1: 1.043702 Loss2: 1.464259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.313412 Loss1: 0.849382 Loss2: 1.464029 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.889509
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.008430 Loss1: 0.550594 Loss2: 1.457836 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.972873 Loss1: 0.502554 Loss2: 1.470320 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.682660 Loss1: 1.650550 Loss2: 2.032110
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.934203 Loss1: 0.461414 Loss2: 1.472790
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.646712 Loss1: 1.160518 Loss2: 1.486194
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.855400 Loss1: 0.392320 Loss2: 1.463080
+(DefaultActor pid=3764) >> Training accuracy: 0.881836
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.037761 Loss1: 0.587337 Loss2: 1.450424 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.864653 Loss1: 0.438908 Loss2: 1.425745 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.794499 Loss1: 0.370766 Loss2: 1.423733
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.883989 Loss1: 1.882288 Loss2: 2.001701
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.731990 Loss1: 0.294884 Loss2: 1.437105
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.652939 Loss1: 1.200398 Loss2: 1.452542
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.845136 Loss1: 0.409563 Loss2: 1.435574
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.399782 Loss1: 0.958991 Loss2: 1.440791
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.857284 Loss1: 0.401290 Loss2: 1.455994
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.263404 Loss1: 0.808039 Loss2: 1.455365
+(DefaultActor pid=3765) >> Training accuracy: 0.901042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.162523 Loss1: 0.703802 Loss2: 1.458722
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.941673 Loss1: 0.490507 Loss2: 1.451165
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.929045 Loss1: 0.480720 Loss2: 1.448325
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.882411 Loss1: 0.434099 Loss2: 1.448312
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.864601 Loss1: 0.411611 Loss2: 1.452990
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.939220 Loss1: 1.914346 Loss2: 2.024874
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.858746 Loss1: 0.398716 Loss2: 1.460031
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.895163 Loss1: 1.397313 Loss2: 1.497850
+(DefaultActor pid=3764) >> Training accuracy: 0.879167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.523257 Loss1: 1.056213 Loss2: 1.467044
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.233707 Loss1: 0.766382 Loss2: 1.467325
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.123882 Loss1: 0.671353 Loss2: 1.452529
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.948390 Loss1: 0.489786 Loss2: 1.458604
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.912617 Loss1: 0.454104 Loss2: 1.458512
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.755719 Loss1: 1.799876 Loss2: 1.955843
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.855370 Loss1: 0.411618 Loss2: 1.443752
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.720306 Loss1: 1.280361 Loss2: 1.439945
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.813842 Loss1: 0.355482 Loss2: 1.458360
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.370550 Loss1: 0.959834 Loss2: 1.410716
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.852050 Loss1: 0.400694 Loss2: 1.451356
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.180609 Loss1: 0.774557 Loss2: 1.406052
+(DefaultActor pid=3765) >> Training accuracy: 0.917708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.093911 Loss1: 0.700953 Loss2: 1.392957
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.946921 Loss1: 0.542085 Loss2: 1.404836
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.827727 Loss1: 0.429661 Loss2: 1.398066
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.782659 Loss1: 0.381370 Loss2: 1.401288
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.189286 Loss1: 1.939614 Loss2: 2.249672
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.715488 Loss1: 0.324046 Loss2: 1.391441
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.671909 Loss1: 0.286893 Loss2: 1.385015 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.123789 Loss1: 0.642905 Loss2: 1.480884 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.990531 Loss1: 0.502238 Loss2: 1.488294 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.829190 Loss1: 1.840104 Loss2: 1.989087 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.624927 Loss1: 1.191596 Loss2: 1.433332 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.888021
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.196831 Loss1: 0.763094 Loss2: 1.433736 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.892877 Loss1: 0.472777 Loss2: 1.420100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.898632 Loss1: 0.485611 Loss2: 1.413021
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.905751 Loss1: 1.864723 Loss2: 2.041029
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.881599 Loss1: 0.452196 Loss2: 1.429402
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.704667 Loss1: 1.202579 Loss2: 1.502088
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.838989 Loss1: 0.413171 Loss2: 1.425817
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.433337 Loss1: 0.955411 Loss2: 1.477926
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.809755 Loss1: 0.387329 Loss2: 1.422427
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.334342 Loss1: 0.843748 Loss2: 1.490594
+(DefaultActor pid=3764) >> Training accuracy: 0.905208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.147926 Loss1: 0.652310 Loss2: 1.495615
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.065688 Loss1: 0.585499 Loss2: 1.480189
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.996472 Loss1: 0.517512 Loss2: 1.478960
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.931385 Loss1: 0.443220 Loss2: 1.488165
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.855582 Loss1: 0.374653 Loss2: 1.480930
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.790922 Loss1: 1.730082 Loss2: 2.060839
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.884810 Loss1: 0.402757 Loss2: 1.482053
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.632691 Loss1: 1.151099 Loss2: 1.481591
+(DefaultActor pid=3765) >> Training accuracy: 0.879167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.356589 Loss1: 0.904006 Loss2: 1.452583
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.133600 Loss1: 0.694945 Loss2: 1.438655
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.918369 Loss1: 0.483643 Loss2: 1.434725
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.979985 Loss1: 0.551092 Loss2: 1.428893
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.858816 Loss1: 0.409998 Loss2: 1.448818
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.924503 Loss1: 1.948090 Loss2: 1.976412
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.772133 Loss1: 0.335919 Loss2: 1.436213
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.783810 Loss1: 1.320372 Loss2: 1.463438
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.787311 Loss1: 0.351634 Loss2: 1.435677
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.444229 Loss1: 1.000709 Loss2: 1.443520
+(DefaultActor pid=3764) >> Training accuracy: 0.859375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.841943 Loss1: 0.393677 Loss2: 1.448266
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.260168 Loss1: 0.824624 Loss2: 1.435544
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.109192 Loss1: 0.671561 Loss2: 1.437631
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.977887 Loss1: 0.549645 Loss2: 1.428242
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.969335 Loss1: 0.537126 Loss2: 1.432209
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.886079 Loss1: 0.443798 Loss2: 1.442281
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.654952 Loss1: 1.669385 Loss2: 1.985566
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.654212 Loss1: 1.223553 Loss2: 1.430659 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.882812
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.836055 Loss1: 0.398690 Loss2: 1.437365
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.258174 Loss1: 0.826425 Loss2: 1.431749
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.026243 Loss1: 0.628954 Loss2: 1.397289
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.939182 Loss1: 0.541887 Loss2: 1.397295
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.902359 Loss1: 0.503813 Loss2: 1.398546
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.849378 Loss1: 0.447421 Loss2: 1.401957
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.852172 Loss1: 1.836610 Loss2: 2.015562
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.765211 Loss1: 0.362941 Loss2: 1.402270
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.730244 Loss1: 1.272923 Loss2: 1.457321
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.733883 Loss1: 0.346472 Loss2: 1.387411
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.375218 Loss1: 0.938159 Loss2: 1.437059
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.733567 Loss1: 0.336894 Loss2: 1.396673
+(DefaultActor pid=3764) >> Training accuracy: 0.862500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.026577 Loss1: 0.597640 Loss2: 1.428936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.864445 Loss1: 0.431002 Loss2: 1.433442 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.848087 Loss1: 0.416514 Loss2: 1.431573
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.952802 Loss1: 1.898438 Loss2: 2.054364
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.794348 Loss1: 1.292123 Loss2: 1.502225 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.877083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.871252 Loss1: 0.441092 Loss2: 1.430160
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.336301 Loss1: 0.856517 Loss2: 1.479784
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.176808 Loss1: 0.710003 Loss2: 1.466805
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.031765 Loss1: 0.564174 Loss2: 1.467591
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.009280 Loss1: 0.544523 Loss2: 1.464757
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.913466 Loss1: 0.443247 Loss2: 1.470219
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.757613 Loss1: 1.802718 Loss2: 1.954894
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.902840 Loss1: 0.430703 Loss2: 1.472136
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.752102 Loss1: 1.309503 Loss2: 1.442599
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.820767 Loss1: 0.341967 Loss2: 1.478800
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.845701 Loss1: 0.386193 Loss2: 1.459509
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.343934 Loss1: 0.907953 Loss2: 1.435981
+(DefaultActor pid=3764) >> Training accuracy: 0.933333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.177248 Loss1: 0.754685 Loss2: 1.422562
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.954215 Loss1: 0.537100 Loss2: 1.417115
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.850309 Loss1: 0.433859 Loss2: 1.416450
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.861303 Loss1: 0.442704 Loss2: 1.418599
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.224396 Loss1: 2.098600 Loss2: 2.125795
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.921940 Loss1: 1.415423 Loss2: 1.506517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.543493 Loss1: 1.078570 Loss2: 1.464923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.826751 Loss1: 0.389182 Loss2: 1.437568
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.327575 Loss1: 0.862584 Loss2: 1.464991
+(DefaultActor pid=3765) >> Training accuracy: 0.891602
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.078690 Loss1: 0.624841 Loss2: 1.453849
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.014383 Loss1: 0.554980 Loss2: 1.459403
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.972388 Loss1: 0.505004 Loss2: 1.467384
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.885919 Loss1: 0.424816 Loss2: 1.461103
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.830684 Loss1: 0.366135 Loss2: 1.464549
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.132836 Loss1: 2.005347 Loss2: 2.127490
+(DefaultActor pid=3764) >> Training accuracy: 0.920759
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.827715 Loss1: 0.372511 Loss2: 1.455204
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.934103 Loss1: 1.380920 Loss2: 1.553184
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.558032 Loss1: 1.012246 Loss2: 1.545786
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.298647 Loss1: 0.766598 Loss2: 1.532049
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.200774 Loss1: 0.665140 Loss2: 1.535634
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.109251 Loss1: 0.574462 Loss2: 1.534790
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.842736 Loss1: 1.822837 Loss2: 2.019899
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.030510 Loss1: 0.495014 Loss2: 1.535496
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.020685 Loss1: 0.488614 Loss2: 1.532071
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.724726 Loss1: 1.246626 Loss2: 1.478100
+(DefaultActor pid=3765) Epoch: 8 Loss: 2.048561 Loss1: 0.508131 Loss2: 1.540430
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.380960 Loss1: 0.916185 Loss2: 1.464775
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.020846 Loss1: 0.473109 Loss2: 1.547737
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.260916 Loss1: 0.801564 Loss2: 1.459352
+(DefaultActor pid=3765) >> Training accuracy: 0.873958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.141287 Loss1: 0.679433 Loss2: 1.461854
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.964344 Loss1: 0.502976 Loss2: 1.461369
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.876099 Loss1: 0.427637 Loss2: 1.448462
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.930780 Loss1: 0.476618 Loss2: 1.454162
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.136747 Loss1: 2.055446 Loss2: 2.081301
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.896153 Loss1: 0.435922 Loss2: 1.460231
+DEBUG flwr 2023-10-09 17:17:08,335 | server.py:236 | fit_round 46 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.869756 Loss1: 0.402940 Loss2: 1.466815 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.903320
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.333425 Loss1: 0.834991 Loss2: 1.498434 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.095223 Loss1: 0.606360 Loss2: 1.488863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.052557 Loss1: 0.559616 Loss2: 1.492940
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.935006 Loss1: 1.888418 Loss2: 2.046588
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.778430 Loss1: 1.288593 Loss2: 1.489837 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.381809 Loss1: 0.912798 Loss2: 1.469011 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.919792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.209491 Loss1: 0.741788 Loss2: 1.467703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.060124 Loss1: 0.589431 Loss2: 1.470694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.918670 Loss1: 0.444394 Loss2: 1.474276 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.899051 Loss1: 0.432890 Loss2: 1.466161 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.867857 Loss1: 0.396696 Loss2: 1.471161 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.888542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.308039 Loss1: 0.762677 Loss2: 1.545362 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.109694 Loss1: 0.567282 Loss2: 1.542412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.146847 Loss1: 0.605626 Loss2: 1.541221
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.017790 Loss1: 1.992411 Loss2: 2.025379
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.845920 Loss1: 1.343522 Loss2: 1.502398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.458677 Loss1: 0.988512 Loss2: 1.470164 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.246405 Loss1: 0.784713 Loss2: 1.461693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.098636 Loss1: 0.638459 Loss2: 1.460178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.942630 Loss1: 0.461804 Loss2: 1.480827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.889634 Loss1: 0.421086 Loss2: 1.468548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.786976 Loss1: 0.318114 Loss2: 1.468862 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.125373 Loss1: 0.662103 Loss2: 1.463270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.915239 Loss1: 0.454671 Loss2: 1.460568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.901996 Loss1: 0.443794 Loss2: 1.458203
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.918361 Loss1: 1.886153 Loss2: 2.032208
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.706065 Loss1: 1.243240 Loss2: 1.462826
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.847821 Loss1: 0.395745 Loss2: 1.452076
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.467386 Loss1: 1.025058 Loss2: 1.442328
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.882789 Loss1: 0.432147 Loss2: 1.450642
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.260493 Loss1: 0.805369 Loss2: 1.455124
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.836658 Loss1: 0.376684 Loss2: 1.459974
+(DefaultActor pid=3765) >> Training accuracy: 0.905331
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.947265 Loss1: 0.504642 Loss2: 1.442623 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.828933 Loss1: 0.379247 Loss2: 1.449686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.794212 Loss1: 0.352019 Loss2: 1.442193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.864583
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 17:17:08,335][flwr][DEBUG] - fit_round 46 received 50 results and 0 failures
+INFO flwr 2023-10-09 17:17:48,699 | server.py:125 | fit progress: (46, 2.4787738273699826, {'accuracy': 0.4587}, 105976.477566338)
+>> Test accuracy: 0.458700
+[2023-10-09 17:17:48,699][flwr][INFO] - fit progress: (46, 2.4787738273699826, {'accuracy': 0.4587}, 105976.477566338)
+DEBUG flwr 2023-10-09 17:17:48,699 | server.py:173 | evaluate_round 46: strategy sampled 50 clients (out of 50)
+[2023-10-09 17:17:48,699][flwr][DEBUG] - evaluate_round 46: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 17:26:51,884 | server.py:187 | evaluate_round 46 received 50 results and 0 failures
+[2023-10-09 17:26:51,884][flwr][DEBUG] - evaluate_round 46 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 17:26:51,885 | server.py:222 | fit_round 47: strategy sampled 50 clients (out of 50)
+[2023-10-09 17:26:51,885][flwr][DEBUG] - fit_round 47: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.940602 Loss1: 1.887008 Loss2: 2.053594
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.837054 Loss1: 1.336933 Loss2: 1.500121
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.459444 Loss1: 0.963679 Loss2: 1.495765
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.337841 Loss1: 0.842763 Loss2: 1.495078
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.687968 Loss1: 1.666684 Loss2: 2.021284
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.698159 Loss1: 1.233550 Loss2: 1.464609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.263088 Loss1: 0.792946 Loss2: 1.470142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.140859 Loss1: 0.684973 Loss2: 1.455887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.004093 Loss1: 0.548117 Loss2: 1.455976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.008871 Loss1: 0.538412 Loss2: 1.470459 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.900000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.795492 Loss1: 0.344491 Loss2: 1.451001 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.788151 Loss1: 0.327333 Loss2: 1.460819 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.889706
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.740587 Loss1: 1.211647 Loss2: 1.528940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.158102 Loss1: 0.653613 Loss2: 1.504489 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.016937 Loss1: 0.525128 Loss2: 1.491809
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.845049 Loss1: 1.878989 Loss2: 1.966060
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.756342 Loss1: 1.299618 Loss2: 1.456724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.351043 Loss1: 0.915211 Loss2: 1.435832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.130769 Loss1: 0.702046 Loss2: 1.428724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.052073 Loss1: 0.621766 Loss2: 1.430308 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.927083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.921444 Loss1: 0.488922 Loss2: 1.432521 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.893942 Loss1: 0.447069 Loss2: 1.446873 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.799186 Loss1: 0.359909 Loss2: 1.439277 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.924805
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.344887 Loss1: 0.865185 Loss2: 1.479702 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.019203 Loss1: 0.564644 Loss2: 1.454559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.909982 Loss1: 1.864032 Loss2: 2.045950
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.980283 Loss1: 0.519681 Loss2: 1.460601
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.809648 Loss1: 1.333059 Loss2: 1.476589
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.925172 Loss1: 0.455465 Loss2: 1.469707
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.503099 Loss1: 1.045550 Loss2: 1.457549
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.857541 Loss1: 0.385778 Loss2: 1.471763
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.228512 Loss1: 0.754301 Loss2: 1.474210
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.850075 Loss1: 0.395041 Loss2: 1.455034
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.059881 Loss1: 0.607964 Loss2: 1.451917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.891035 Loss1: 0.424485 Loss2: 1.466550
+(DefaultActor pid=3765) >> Training accuracy: 0.830208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.935460 Loss1: 0.470091 Loss2: 1.465368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.903931 Loss1: 0.447585 Loss2: 1.456346 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.829344 Loss1: 0.360887 Loss2: 1.468457
+(DefaultActor pid=3764) >> Training accuracy: 0.908333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.823755 Loss1: 1.800680 Loss2: 2.023075
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.612568 Loss1: 1.162382 Loss2: 1.450186
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.313926 Loss1: 0.870621 Loss2: 1.443305
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.233166 Loss1: 0.781937 Loss2: 1.451229
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.982638 Loss1: 0.535789 Loss2: 1.446849
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.983803 Loss1: 1.907742 Loss2: 2.076061
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.002308 Loss1: 0.556938 Loss2: 1.445370
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.945254 Loss1: 0.498775 Loss2: 1.446479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.928985 Loss1: 0.478385 Loss2: 1.450600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.785487 Loss1: 0.334287 Loss2: 1.451200 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.770026 Loss1: 0.324558 Loss2: 1.445469 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.916667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.980877 Loss1: 0.490370 Loss2: 1.490507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.844382 Loss1: 0.361528 Loss2: 1.482855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.899873 Loss1: 0.416036 Loss2: 1.483837
+(DefaultActor pid=3764) >> Training accuracy: 0.910417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.971724 Loss1: 1.873796 Loss2: 2.097928
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.812271 Loss1: 1.302410 Loss2: 1.509862
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.470845 Loss1: 0.986806 Loss2: 1.484039
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.213899 Loss1: 0.744553 Loss2: 1.469346
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.112562 Loss1: 0.638601 Loss2: 1.473961
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.796954 Loss1: 1.723377 Loss2: 2.073577
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.974223 Loss1: 0.499927 Loss2: 1.474296
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.951150 Loss1: 0.480008 Loss2: 1.471142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.904979 Loss1: 0.429107 Loss2: 1.475872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.847188 Loss1: 0.358889 Loss2: 1.488299 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.830587 Loss1: 0.357763 Loss2: 1.472823 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.894792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.921000 Loss1: 0.441259 Loss2: 1.479741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.804127 Loss1: 0.332086 Loss2: 1.472042 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.762208 Loss1: 0.279354 Loss2: 1.482854
+(DefaultActor pid=3764) >> Training accuracy: 0.917708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.967509 Loss1: 1.940739 Loss2: 2.026770
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.905734 Loss1: 1.394025 Loss2: 1.511709
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.553204 Loss1: 1.057423 Loss2: 1.495781
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.279926 Loss1: 0.788709 Loss2: 1.491217
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.110810 Loss1: 0.629529 Loss2: 1.481281
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.836198 Loss1: 1.836505 Loss2: 1.999692
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.733823 Loss1: 1.260954 Loss2: 1.472869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.462037 Loss1: 1.015111 Loss2: 1.446926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.265104 Loss1: 0.806942 Loss2: 1.458162 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.084045 Loss1: 0.628225 Loss2: 1.455820 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.840657 Loss1: 0.349240 Loss2: 1.491416
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.002476 Loss1: 0.563829 Loss2: 1.438647
+(DefaultActor pid=3765) >> Training accuracy: 0.903320
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.977219 Loss1: 0.527743 Loss2: 1.449475
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.891906 Loss1: 0.443351 Loss2: 1.448555
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.893904 Loss1: 0.439459 Loss2: 1.454445
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.743070 Loss1: 0.303228 Loss2: 1.439842
+(DefaultActor pid=3764) >> Training accuracy: 0.940625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.906139 Loss1: 1.842239 Loss2: 2.063899
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.676515 Loss1: 1.223178 Loss2: 1.453336
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.361950 Loss1: 0.947814 Loss2: 1.414136
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.193904 Loss1: 0.764226 Loss2: 1.429678
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.996118 Loss1: 0.573853 Loss2: 1.422265
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.808421 Loss1: 0.396631 Loss2: 1.411790
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.790741 Loss1: 1.651152 Loss2: 2.139589
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.632510 Loss1: 1.120010 Loss2: 1.512500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.260984 Loss1: 0.776958 Loss2: 1.484027 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.021968 Loss1: 0.548639 Loss2: 1.473329 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962740
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.909619 Loss1: 0.437328 Loss2: 1.472291 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.789104 Loss1: 0.314030 Loss2: 1.475074 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.805312 Loss1: 0.340822 Loss2: 1.464489
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.818011 Loss1: 1.814994 Loss2: 2.003017
+(DefaultActor pid=3764) >> Training accuracy: 0.936458
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.765079 Loss1: 0.291770 Loss2: 1.473308
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.702426 Loss1: 1.233856 Loss2: 1.468570
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.397983 Loss1: 0.953032 Loss2: 1.444951
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.128689 Loss1: 0.673653 Loss2: 1.455036
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.059828 Loss1: 0.616384 Loss2: 1.443443
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.983439 Loss1: 0.534403 Loss2: 1.449036
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.954789 Loss1: 1.857525 Loss2: 2.097264
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.738251 Loss1: 1.233314 Loss2: 1.504937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.337653 Loss1: 0.870297 Loss2: 1.467356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.145056 Loss1: 0.664538 Loss2: 1.480518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.836053 Loss1: 0.392306 Loss2: 1.443747
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.075548 Loss1: 0.606506 Loss2: 1.469042
+(DefaultActor pid=3765) >> Training accuracy: 0.872070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.983420 Loss1: 0.519791 Loss2: 1.463628
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.909775 Loss1: 0.448389 Loss2: 1.461386
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.927574 Loss1: 0.460270 Loss2: 1.467305
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.855345 Loss1: 0.380258 Loss2: 1.475087
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.809071 Loss1: 1.735142 Loss2: 2.073928
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.763933 Loss1: 0.296451 Loss2: 1.467483
+(DefaultActor pid=3764) >> Training accuracy: 0.922917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.382158 Loss1: 0.866668 Loss2: 1.515490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.074706 Loss1: 0.575988 Loss2: 1.498718 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.965333 Loss1: 0.469450 Loss2: 1.495883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.899082 Loss1: 0.406233 Loss2: 1.492848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.873270 Loss1: 0.380025 Loss2: 1.493245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.775908 Loss1: 0.284813 Loss2: 1.491095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.803355 Loss1: 0.312457 Loss2: 1.490899 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.916016
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.874764 Loss1: 0.443899 Loss2: 1.430865 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.719887 Loss1: 0.294140 Loss2: 1.425747 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.644923 Loss1: 1.611742 Loss2: 2.033181 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.264006 Loss1: 0.799778 Loss2: 1.464228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.846452 Loss1: 1.760898 Loss2: 2.085554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.710073 Loss1: 1.208334 Loss2: 1.501738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.337613 Loss1: 0.856031 Loss2: 1.481582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.159153 Loss1: 0.687158 Loss2: 1.471996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.015275 Loss1: 0.554451 Loss2: 1.460824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.937618 Loss1: 0.465459 Loss2: 1.472159 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.913542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.854595 Loss1: 0.385634 Loss2: 1.468961 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.757144 Loss1: 0.295384 Loss2: 1.461760 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.923958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.836479 Loss1: 1.321751 Loss2: 1.514729 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.343057 Loss1: 0.842021 Loss2: 1.501036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.966444 Loss1: 1.859178 Loss2: 2.107266
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.199490 Loss1: 0.699341 Loss2: 1.500150
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.798121 Loss1: 1.262044 Loss2: 1.536077
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.118173 Loss1: 0.603941 Loss2: 1.514232
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.467926 Loss1: 0.969880 Loss2: 1.498046
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.086438 Loss1: 0.573694 Loss2: 1.512743
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.250807 Loss1: 0.753561 Loss2: 1.497246
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.022914 Loss1: 0.504646 Loss2: 1.518267
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.019049 Loss1: 0.534645 Loss2: 1.484404
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.942518 Loss1: 0.428584 Loss2: 1.513934
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.932794 Loss1: 0.438209 Loss2: 1.494585
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.965468 Loss1: 0.452707 Loss2: 1.512762
+(DefaultActor pid=3765) >> Training accuracy: 0.910417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.924243 Loss1: 0.435758 Loss2: 1.488485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.791561 Loss1: 0.316005 Loss2: 1.475557 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.928125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.646106 Loss1: 1.196060 Loss2: 1.450046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.134581 Loss1: 0.716969 Loss2: 1.417612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.942738 Loss1: 0.543198 Loss2: 1.399540 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.903111 Loss1: 0.509169 Loss2: 1.393942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.933040 Loss1: 0.536256 Loss2: 1.396784 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.842662 Loss1: 0.429216 Loss2: 1.413445 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.871334 Loss1: 0.473008 Loss2: 1.398326 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.762612 Loss1: 0.368174 Loss2: 1.394438 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.896875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.826252 Loss1: 0.394718 Loss2: 1.431534 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.919643
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.860965 Loss1: 1.805368 Loss2: 2.055597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.444749 Loss1: 0.974018 Loss2: 1.470732 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.185516 Loss1: 0.732569 Loss2: 1.452947
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.050073 Loss1: 2.036828 Loss2: 2.013245
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.901120 Loss1: 1.403850 Loss2: 1.497270 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.496592 Loss1: 1.039720 Loss2: 1.456872 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.283244 Loss1: 0.835523 Loss2: 1.447721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.058603 Loss1: 0.600709 Loss2: 1.457894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.976052 Loss1: 0.539936 Loss2: 1.436116 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.928125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.825402 Loss1: 0.379708 Loss2: 1.445693
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.904188 Loss1: 0.463273 Loss2: 1.440915
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.923208 Loss1: 0.480475 Loss2: 1.442733
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.903676 Loss1: 0.450258 Loss2: 1.453418
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.830194 Loss1: 0.377662 Loss2: 1.452532
+(DefaultActor pid=3764) >> Training accuracy: 0.890625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.964086 Loss1: 1.920647 Loss2: 2.043439
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.753348 Loss1: 1.274954 Loss2: 1.478394
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.447427 Loss1: 0.997281 Loss2: 1.450146
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.246491 Loss1: 0.800213 Loss2: 1.446278
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.898418 Loss1: 1.923086 Loss2: 1.975332
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.755110 Loss1: 1.311578 Loss2: 1.443531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.342258 Loss1: 0.922627 Loss2: 1.419631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.149249 Loss1: 0.730426 Loss2: 1.418823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.950663 Loss1: 0.526982 Loss2: 1.423681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.999985 Loss1: 0.579050 Loss2: 1.420935 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.889583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.892710 Loss1: 0.466152 Loss2: 1.426559 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.875597 Loss1: 0.441702 Loss2: 1.433895 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.889648
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.738838 Loss1: 1.298990 Loss2: 1.439849 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.230894 Loss1: 0.822166 Loss2: 1.408728 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.997198 Loss1: 0.582576 Loss2: 1.414623
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.844893 Loss1: 1.770095 Loss2: 2.074798
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.986798 Loss1: 0.566982 Loss2: 1.419816
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.680268 Loss1: 1.153618 Loss2: 1.526650
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.924880 Loss1: 0.499800 Loss2: 1.425080
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.495537 Loss1: 0.995127 Loss2: 1.500410
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.923861 Loss1: 0.499105 Loss2: 1.424756
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.176617 Loss1: 0.671437 Loss2: 1.505180
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.000458 Loss1: 0.519313 Loss2: 1.481145 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.729690 Loss1: 0.326353 Loss2: 1.403337
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.929157 Loss1: 0.448172 Loss2: 1.480984
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.954161 Loss1: 0.472473 Loss2: 1.481688
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.849491 Loss1: 0.366702 Loss2: 1.482789
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.856834 Loss1: 0.384498 Loss2: 1.472336
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.798109 Loss1: 0.315204 Loss2: 1.482904
+(DefaultActor pid=3764) >> Training accuracy: 0.942383
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.992043 Loss1: 1.925936 Loss2: 2.066107
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.757522 Loss1: 1.276263 Loss2: 1.481259
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.324008 Loss1: 0.860376 Loss2: 1.463632
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.108746 Loss1: 0.657064 Loss2: 1.451682
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.029675 Loss1: 0.577349 Loss2: 1.452326
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.903972 Loss1: 1.874248 Loss2: 2.029724
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.819546 Loss1: 1.308708 Loss2: 1.510838 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.416236 Loss1: 0.904967 Loss2: 1.511269 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.197295 Loss1: 0.701627 Loss2: 1.495668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.033742 Loss1: 0.551237 Loss2: 1.482505 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.915625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.947797 Loss1: 0.462633 Loss2: 1.485164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 2.025430 Loss1: 0.527857 Loss2: 1.497573 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.857556 Loss1: 0.359674 Loss2: 1.497882 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.917708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.760447 Loss1: 1.220452 Loss2: 1.539995 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.223870 Loss1: 0.721913 Loss2: 1.501957 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.148225 Loss1: 0.645359 Loss2: 1.502866
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.992678 Loss1: 1.849320 Loss2: 2.143358
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.017089 Loss1: 0.494099 Loss2: 1.522990
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.720168 Loss1: 1.184933 Loss2: 1.535235
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.488187 Loss1: 0.988080 Loss2: 1.500107
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.907735 Loss1: 0.406543 Loss2: 1.501192
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.228942 Loss1: 0.725107 Loss2: 1.503835
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.939794 Loss1: 0.447087 Loss2: 1.492708
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.864235 Loss1: 0.350815 Loss2: 1.513420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.886505 Loss1: 0.384140 Loss2: 1.502365 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.894531
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.778344 Loss1: 0.297429 Loss2: 1.480915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.820713 Loss1: 0.333471 Loss2: 1.487241 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.919643
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.926424 Loss1: 1.921604 Loss2: 2.004820
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.835801 Loss1: 1.364711 Loss2: 1.471090
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.483144 Loss1: 1.019159 Loss2: 1.463985
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.045386 Loss1: 1.968747 Loss2: 2.076639
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.198963 Loss1: 0.737527 Loss2: 1.461437
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.881532 Loss1: 1.352891 Loss2: 1.528641
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.081744 Loss1: 0.627247 Loss2: 1.454497
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.614134 Loss1: 1.107293 Loss2: 1.506841
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.015996 Loss1: 0.555956 Loss2: 1.460040
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.332360 Loss1: 0.811520 Loss2: 1.520839
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.977938 Loss1: 0.507862 Loss2: 1.470077
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.912056 Loss1: 0.456853 Loss2: 1.455203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.879739 Loss1: 0.418357 Loss2: 1.461382 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.814070 Loss1: 0.355216 Loss2: 1.458853 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.894531
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.887663 Loss1: 0.385719 Loss2: 1.501944 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.110715 Loss1: 2.016639 Loss2: 2.094075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.507711 Loss1: 1.001774 Loss2: 1.505937 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.285612 Loss1: 0.776719 Loss2: 1.508894
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.199659 Loss1: 2.087698 Loss2: 2.111961
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.931320 Loss1: 1.404776 Loss2: 1.526543
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.165092 Loss1: 0.657607 Loss2: 1.507485
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.509332 Loss1: 1.001745 Loss2: 1.507587
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.091048 Loss1: 0.572408 Loss2: 1.518640
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.226154 Loss1: 0.736765 Loss2: 1.489389
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.018818 Loss1: 0.507407 Loss2: 1.511411
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.000180 Loss1: 0.491662 Loss2: 1.508518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.973193 Loss1: 0.451043 Loss2: 1.522150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 2.016222 Loss1: 0.484923 Loss2: 1.531299 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.826042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.903888 Loss1: 0.409231 Loss2: 1.494656 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.915762 Loss1: 1.822445 Loss2: 2.093317 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.426304 Loss1: 1.016887 Loss2: 1.409417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.731978 Loss1: 1.656042 Loss2: 2.075936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.845069 Loss1: 0.437090 Loss2: 1.407978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.806218 Loss1: 0.408051 Loss2: 1.398167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.771355 Loss1: 0.368160 Loss2: 1.403195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.722957 Loss1: 0.326379 Loss2: 1.396579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.697108 Loss1: 0.300619 Loss2: 1.396489 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.923077
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.873746 Loss1: 0.405364 Loss2: 1.468382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.819897 Loss1: 0.354049 Loss2: 1.465848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.758442 Loss1: 0.295460 Loss2: 1.462981
+(DefaultActor pid=3764) >> Training accuracy: 0.926042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.094367 Loss1: 1.989475 Loss2: 2.104892
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.803102 Loss1: 1.295634 Loss2: 1.507468
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.350186 Loss1: 0.865580 Loss2: 1.484606
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.172823 Loss1: 0.698010 Loss2: 1.474813
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.121349 Loss1: 0.639031 Loss2: 1.482318
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.977203 Loss1: 1.891413 Loss2: 2.085790
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.033976 Loss1: 0.548375 Loss2: 1.485601
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.072983 Loss1: 0.578424 Loss2: 1.494558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.979521 Loss1: 0.485525 Loss2: 1.493996 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.879722 Loss1: 0.396065 Loss2: 1.483657 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.877519 Loss1: 0.401941 Loss2: 1.475578 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.939583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.025475 Loss1: 0.521892 Loss2: 1.503582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.838366 Loss1: 0.335185 Loss2: 1.503182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.817405 Loss1: 0.325105 Loss2: 1.492299
+(DefaultActor pid=3764) >> Training accuracy: 0.884375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.898334 Loss1: 1.821504 Loss2: 2.076831
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.755651 Loss1: 1.267366 Loss2: 1.488285
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.515237 Loss1: 1.035826 Loss2: 1.479411
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.245156 Loss1: 0.755547 Loss2: 1.489609
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.041519 Loss1: 0.567991 Loss2: 1.473528
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.776636 Loss1: 1.695424 Loss2: 2.081212
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.655034 Loss1: 1.161658 Loss2: 1.493376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.292687 Loss1: 0.818968 Loss2: 1.473719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.143827 Loss1: 0.672620 Loss2: 1.471206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.036158 Loss1: 0.576305 Loss2: 1.459853 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.976093 Loss1: 0.508729 Loss2: 1.467364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.834847 Loss1: 0.369631 Loss2: 1.465216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.782826 Loss1: 0.320835 Loss2: 1.461991 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.633091 Loss1: 1.108243 Loss2: 1.524848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.091996 Loss1: 0.616420 Loss2: 1.475576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.801312 Loss1: 1.758904 Loss2: 2.042408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.783272 Loss1: 1.290410 Loss2: 1.492862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.433251 Loss1: 0.952495 Loss2: 1.480756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.212689 Loss1: 0.729393 Loss2: 1.483296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.992884 Loss1: 0.520678 Loss2: 1.472206 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.918750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.967556 Loss1: 0.484479 Loss2: 1.483077 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.834104 Loss1: 0.342297 Loss2: 1.491807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.806657 Loss1: 0.328299 Loss2: 1.478359 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.905273
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.303035 Loss1: 0.841160 Loss2: 1.461875 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.997283 Loss1: 0.541461 Loss2: 1.455822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.943586 Loss1: 0.503467 Loss2: 1.440119
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.907618 Loss1: 1.878975 Loss2: 2.028643
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.807772 Loss1: 1.344458 Loss2: 1.463314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.466313 Loss1: 1.011295 Loss2: 1.455018 [repeated 2x across cluster]
+DEBUG flwr 2023-10-09 17:55:35,424 | server.py:236 | fit_round 47 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.280451 Loss1: 0.827987 Loss2: 1.452464 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.735691 Loss1: 0.301283 Loss2: 1.434408
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.125632 Loss1: 0.669212 Loss2: 1.456420
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.979516 Loss1: 0.531763 Loss2: 1.447753
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.943680 Loss1: 0.494714 Loss2: 1.448966
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.886436 Loss1: 0.430448 Loss2: 1.455989
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.856533 Loss1: 0.409217 Loss2: 1.447316
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.159525 Loss1: 1.946974 Loss2: 2.212552
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.866664 Loss1: 0.409651 Loss2: 1.457013
+(DefaultActor pid=3764) >> Training accuracy: 0.888542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.248781 Loss1: 0.765847 Loss2: 1.482934 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.936031 Loss1: 0.457029 Loss2: 1.479002 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.829076 Loss1: 0.356371 Loss2: 1.472705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.791014 Loss1: 0.311622 Loss2: 1.479391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.818821 Loss1: 0.337305 Loss2: 1.481516 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.898438
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.018500 Loss1: 0.607498 Loss2: 1.411001 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.940462 Loss1: 0.511134 Loss2: 1.429327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.765381 Loss1: 0.351932 Loss2: 1.413449 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 17:55:35,424][flwr][DEBUG] - fit_round 47 received 50 results and 0 failures
+INFO flwr 2023-10-09 17:56:17,586 | server.py:125 | fit progress: (47, 2.4710606367062455, {'accuracy': 0.4661}, 108285.36502193799)
+>> Test accuracy: 0.466100
+[2023-10-09 17:56:17,586][flwr][INFO] - fit progress: (47, 2.4710606367062455, {'accuracy': 0.4661}, 108285.36502193799)
+DEBUG flwr 2023-10-09 17:56:17,587 | server.py:173 | evaluate_round 47: strategy sampled 50 clients (out of 50)
+[2023-10-09 17:56:17,587][flwr][DEBUG] - evaluate_round 47: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 18:05:26,013 | server.py:187 | evaluate_round 47 received 50 results and 0 failures
+[2023-10-09 18:05:26,013][flwr][DEBUG] - evaluate_round 47 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 18:05:26,013 | server.py:222 | fit_round 48: strategy sampled 50 clients (out of 50)
+[2023-10-09 18:05:26,013][flwr][DEBUG] - fit_round 48: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.830594 Loss1: 1.817152 Loss2: 2.013442 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.329906 Loss1: 0.869905 Loss2: 1.460001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.781638 Loss1: 1.728705 Loss2: 2.052933
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.242293 Loss1: 0.783992 Loss2: 1.458301
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.697314 Loss1: 1.205224 Loss2: 1.492091
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.085824 Loss1: 0.627570 Loss2: 1.458254
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.442687 Loss1: 0.973330 Loss2: 1.469357
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.015390 Loss1: 0.568895 Loss2: 1.446495
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.901157 Loss1: 0.449947 Loss2: 1.451210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.809175 Loss1: 0.353305 Loss2: 1.455870 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.775326 Loss1: 0.333259 Loss2: 1.442067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.771737 Loss1: 0.330756 Loss2: 1.440981 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.918945
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.860997 Loss1: 0.394040 Loss2: 1.466957 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.915625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.804182 Loss1: 1.760464 Loss2: 2.043719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.215219 Loss1: 0.782946 Loss2: 1.432274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.081206 Loss1: 0.649234 Loss2: 1.431972
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.095448 Loss1: 1.997119 Loss2: 2.098329
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.957283 Loss1: 0.525900 Loss2: 1.431382
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.834967 Loss1: 1.311913 Loss2: 1.523054
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.943010 Loss1: 0.503540 Loss2: 1.439470
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.492886 Loss1: 0.980595 Loss2: 1.512291
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.242039 Loss1: 0.732959 Loss2: 1.509080
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.886181 Loss1: 0.446927 Loss2: 1.439253
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.131418 Loss1: 0.634533 Loss2: 1.496885
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.852768 Loss1: 0.411978 Loss2: 1.440790
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.036459 Loss1: 0.545222 Loss2: 1.491237
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.848594 Loss1: 0.409525 Loss2: 1.439069
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.744959 Loss1: 0.303157 Loss2: 1.441802 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.931250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.903681 Loss1: 0.400077 Loss2: 1.503604 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.920759
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.819904 Loss1: 1.759644 Loss2: 2.060260 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.379137 Loss1: 0.892742 Loss2: 1.486395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.279823 Loss1: 0.808984 Loss2: 1.470839
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.107259 Loss1: 1.932818 Loss2: 2.174440
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.419771 Loss1: 0.974971 Loss2: 1.444800 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.909517 Loss1: 0.451398 Loss2: 1.458119 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.806704 Loss1: 0.353983 Loss2: 1.452721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.779409 Loss1: 0.322742 Loss2: 1.456667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.848549 Loss1: 0.390032 Loss2: 1.458517 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.915179
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.824701 Loss1: 0.374251 Loss2: 1.450450 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.928385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.844671 Loss1: 1.865004 Loss2: 1.979667
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.745175 Loss1: 1.294257 Loss2: 1.450917
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.322381 Loss1: 0.869636 Loss2: 1.452745
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.757709 Loss1: 1.692599 Loss2: 2.065111
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.204604 Loss1: 0.763146 Loss2: 1.441457
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.729045 Loss1: 1.233542 Loss2: 1.495503
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.029098 Loss1: 0.576527 Loss2: 1.452571
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.382740 Loss1: 0.881347 Loss2: 1.501393
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.984913 Loss1: 0.543324 Loss2: 1.441588
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.135986 Loss1: 0.656508 Loss2: 1.479477
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.963757 Loss1: 0.507972 Loss2: 1.455785
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.880847 Loss1: 0.431656 Loss2: 1.449191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.870943 Loss1: 0.419768 Loss2: 1.451175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.802913 Loss1: 0.346855 Loss2: 1.456058 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.933594
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.823220 Loss1: 0.358468 Loss2: 1.464752 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.909375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.798561 Loss1: 1.762435 Loss2: 2.036126 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.234406 Loss1: 0.792683 Loss2: 1.441723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.056675 Loss1: 0.623891 Loss2: 1.432785
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.867017 Loss1: 1.771205 Loss2: 2.095812
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.797256 Loss1: 1.248067 Loss2: 1.549189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.434174 Loss1: 0.904618 Loss2: 1.529556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.224191 Loss1: 0.701359 Loss2: 1.522832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.014938 Loss1: 0.505666 Loss2: 1.509272 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.998186 Loss1: 0.491769 Loss2: 1.506418 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.894792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.768811 Loss1: 0.334307 Loss2: 1.434504
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.943477 Loss1: 0.435889 Loss2: 1.507588
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.964578 Loss1: 0.454694 Loss2: 1.509884
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.994979 Loss1: 0.463755 Loss2: 1.531224
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.870643 Loss1: 0.339296 Loss2: 1.531347
+(DefaultActor pid=3764) >> Training accuracy: 0.909375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.903061 Loss1: 1.837661 Loss2: 2.065400
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.734923 Loss1: 1.239346 Loss2: 1.495578
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.455851 Loss1: 0.972977 Loss2: 1.482873
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.273701 Loss1: 0.791973 Loss2: 1.481728
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.021214 Loss1: 1.888217 Loss2: 2.132997
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.690994 Loss1: 1.150792 Loss2: 1.540201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.351597 Loss1: 0.859651 Loss2: 1.491947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.105534 Loss1: 0.623497 Loss2: 1.482036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.982591 Loss1: 0.517871 Loss2: 1.464720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.882549 Loss1: 0.414101 Loss2: 1.468448 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.904167
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.864567 Loss1: 0.391836 Loss2: 1.472731
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.810444 Loss1: 0.344850 Loss2: 1.465594
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.838253 Loss1: 0.374350 Loss2: 1.463903
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.830125 Loss1: 0.363925 Loss2: 1.466200
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.815570 Loss1: 0.337082 Loss2: 1.478488
+(DefaultActor pid=3764) >> Training accuracy: 0.920833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.748714 Loss1: 1.648471 Loss2: 2.100243
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.716092 Loss1: 1.154894 Loss2: 1.561198
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.353847 Loss1: 0.834596 Loss2: 1.519251
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.097506 Loss1: 0.587961 Loss2: 1.509545
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.000710 Loss1: 1.936558 Loss2: 2.064153
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.791416 Loss1: 1.298435 Loss2: 1.492981 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.533342 Loss1: 1.065582 Loss2: 1.467760 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.281580 Loss1: 0.802949 Loss2: 1.478631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.101388 Loss1: 0.633096 Loss2: 1.468293 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.944658 Loss1: 0.490707 Loss2: 1.453951 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.905208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.912798 Loss1: 0.456265 Loss2: 1.456533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.927696 Loss1: 0.455324 Loss2: 1.472372 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.913542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.933245 Loss1: 1.874601 Loss2: 2.058645 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.460671 Loss1: 0.976080 Loss2: 1.484591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.896571 Loss1: 1.839015 Loss2: 2.057556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.840147 Loss1: 1.319253 Loss2: 1.520894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.416213 Loss1: 0.924193 Loss2: 1.492020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.187452 Loss1: 0.698336 Loss2: 1.489116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.040202 Loss1: 0.567693 Loss2: 1.472508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.941366 Loss1: 0.462973 Loss2: 1.478392 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.892483 Loss1: 0.405499 Loss2: 1.486983 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.824767 Loss1: 0.334900 Loss2: 1.489867 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.907292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.836444 Loss1: 1.761508 Loss2: 2.074937
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.689491 Loss1: 1.188237 Loss2: 1.501253
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.251695 Loss1: 0.787745 Loss2: 1.463950
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.077010 Loss1: 0.627591 Loss2: 1.449419
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.682130 Loss1: 1.660789 Loss2: 2.021341
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.690105 Loss1: 1.234403 Loss2: 1.455702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.364245 Loss1: 0.930675 Loss2: 1.433569 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.091737 Loss1: 0.656809 Loss2: 1.434927 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.926994 Loss1: 0.504128 Loss2: 1.422867 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.861527 Loss1: 0.437648 Loss2: 1.423880 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.933333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.823024 Loss1: 0.396914 Loss2: 1.426110 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.772762 Loss1: 0.348375 Loss2: 1.424387 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.939583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.754562 Loss1: 1.719280 Loss2: 2.035281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.273858 Loss1: 0.828802 Loss2: 1.445056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.104134 Loss1: 0.655985 Loss2: 1.448149
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.836563 Loss1: 1.800260 Loss2: 2.036303
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.687599 Loss1: 1.223406 Loss2: 1.464193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.350506 Loss1: 0.893215 Loss2: 1.457291 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.200996 Loss1: 0.734692 Loss2: 1.466305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.976822 Loss1: 0.527858 Loss2: 1.448965 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.962868 Loss1: 0.508875 Loss2: 1.453993 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.909375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.885390 Loss1: 0.425559 Loss2: 1.459831 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.758997 Loss1: 0.309593 Loss2: 1.449404 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.922917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.969950 Loss1: 1.945011 Loss2: 2.024939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.521349 Loss1: 1.058302 Loss2: 1.463048 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.193559 Loss1: 0.739314 Loss2: 1.454245
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.764270 Loss1: 1.768237 Loss2: 1.996033
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.582512 Loss1: 1.074383 Loss2: 1.508130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.256749 Loss1: 0.778361 Loss2: 1.478388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.151393 Loss1: 0.669977 Loss2: 1.481417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.025004 Loss1: 0.556675 Loss2: 1.468328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.932490 Loss1: 0.459702 Loss2: 1.472788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.925000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.758424 Loss1: 0.297520 Loss2: 1.460904 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.716805 Loss1: 0.271913 Loss2: 1.444892 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.917969
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.861107 Loss1: 1.385275 Loss2: 1.475832 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.262984 Loss1: 0.795014 Loss2: 1.467970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.754451 Loss1: 1.838063 Loss2: 1.916389
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.135083 Loss1: 0.656402 Loss2: 1.478681
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.606195 Loss1: 1.167883 Loss2: 1.438311
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.016719 Loss1: 0.538205 Loss2: 1.478514
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.376609 Loss1: 0.959611 Loss2: 1.416998
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.903203 Loss1: 0.433288 Loss2: 1.469915
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.116365 Loss1: 0.698339 Loss2: 1.418026
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.946106 Loss1: 0.474241 Loss2: 1.471865
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.928109 Loss1: 0.530306 Loss2: 1.397803
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.913917 Loss1: 0.433531 Loss2: 1.480385
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.843927 Loss1: 0.451157 Loss2: 1.392770
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.814586 Loss1: 0.342263 Loss2: 1.472322
+(DefaultActor pid=3765) >> Training accuracy: 0.904297
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.821262 Loss1: 0.410534 Loss2: 1.410728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.726240 Loss1: 0.317651 Loss2: 1.408589 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.923828
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.844157 Loss1: 1.302836 Loss2: 1.541321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.259655 Loss1: 0.762301 Loss2: 1.497355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.757906 Loss1: 1.792762 Loss2: 1.965144 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.704165 Loss1: 1.268531 Loss2: 1.435634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.289374 Loss1: 0.874250 Loss2: 1.415124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.084639 Loss1: 0.684186 Loss2: 1.400453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.869026 Loss1: 0.371862 Loss2: 1.497164 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.917766 Loss1: 0.490671 Loss2: 1.427095 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.809239 Loss1: 0.396602 Loss2: 1.412637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.787618 Loss1: 0.382127 Loss2: 1.405491
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.985168 Loss1: 1.927201 Loss2: 2.057967
+(DefaultActor pid=3764) >> Training accuracy: 0.895833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.726137 Loss1: 1.252708 Loss2: 1.473429
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.450316 Loss1: 0.997125 Loss2: 1.453191
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.182769 Loss1: 0.732110 Loss2: 1.450658
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.017383 Loss1: 0.574446 Loss2: 1.442936
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.990082 Loss1: 1.920504 Loss2: 2.069578
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.982594 Loss1: 0.541929 Loss2: 1.440664
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.731120 Loss1: 1.208384 Loss2: 1.522736
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.924092 Loss1: 0.470888 Loss2: 1.453204
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.414717 Loss1: 0.919157 Loss2: 1.495560
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.906707 Loss1: 0.447544 Loss2: 1.459163
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.211063 Loss1: 0.717037 Loss2: 1.494026
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.799792 Loss1: 0.344332 Loss2: 1.455460
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.981757 Loss1: 0.510599 Loss2: 1.471158
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.826107 Loss1: 0.377487 Loss2: 1.448619
+(DefaultActor pid=3765) >> Training accuracy: 0.936458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.922898 Loss1: 0.440260 Loss2: 1.482638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.865627 Loss1: 0.393320 Loss2: 1.472307 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.867782 Loss1: 0.383942 Loss2: 1.483840
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.982774 Loss1: 1.810626 Loss2: 2.172148
+(DefaultActor pid=3764) >> Training accuracy: 0.887500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.721830 Loss1: 1.185350 Loss2: 1.536480
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.487449 Loss1: 0.985122 Loss2: 1.502327
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.157832 Loss1: 0.653273 Loss2: 1.504559
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.099693 Loss1: 0.590808 Loss2: 1.508884
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.995865 Loss1: 0.484131 Loss2: 1.511735
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.735149 Loss1: 1.715854 Loss2: 2.019295
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.674288 Loss1: 1.196735 Loss2: 1.477553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.433431 Loss1: 0.979195 Loss2: 1.454236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.789551 Loss1: 0.281594 Loss2: 1.507957 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.899038
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.948676 Loss1: 0.486101 Loss2: 1.462575 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.803740 Loss1: 0.347211 Loss2: 1.456529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.815418 Loss1: 1.782877 Loss2: 2.032540
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.785266 Loss1: 0.330755 Loss2: 1.454511
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.754052 Loss1: 1.267311 Loss2: 1.486741
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.743101 Loss1: 0.284645 Loss2: 1.458456
+(DefaultActor pid=3764) >> Training accuracy: 0.892708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.239766 Loss1: 0.772626 Loss2: 1.467141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.930660 Loss1: 0.485468 Loss2: 1.445193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.968069 Loss1: 0.519830 Loss2: 1.448238
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.819484 Loss1: 1.801029 Loss2: 2.018455
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.888933 Loss1: 0.426403 Loss2: 1.462530
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.859665 Loss1: 1.351579 Loss2: 1.508085
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.542196 Loss1: 1.019617 Loss2: 1.522580 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.901042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.812196 Loss1: 0.366317 Loss2: 1.445879
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.260051 Loss1: 0.769700 Loss2: 1.490351
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.059583 Loss1: 0.576157 Loss2: 1.483426
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.055609 Loss1: 0.564134 Loss2: 1.491475
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.952746 Loss1: 0.459830 Loss2: 1.492916
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.920803 Loss1: 0.431690 Loss2: 1.489113
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.897504 Loss1: 1.908033 Loss2: 1.989471
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.727095 Loss1: 1.250851 Loss2: 1.476245 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.878906
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.397256 Loss1: 0.950843 Loss2: 1.446413 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.996700 Loss1: 0.549852 Loss2: 1.446849 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.850452 Loss1: 0.428635 Loss2: 1.421817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.812258 Loss1: 0.383993 Loss2: 1.428266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.800199 Loss1: 0.375407 Loss2: 1.424792 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.722896 Loss1: 0.303993 Loss2: 1.418904 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.948958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.085280 Loss1: 0.574903 Loss2: 1.510377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.025838 Loss1: 0.527919 Loss2: 1.497919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.885829 Loss1: 0.386192 Loss2: 1.499638
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.713502 Loss1: 1.751519 Loss2: 1.961983
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.685918 Loss1: 1.233871 Loss2: 1.452046 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.908333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.287842 Loss1: 0.840942 Loss2: 1.446900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.009823 Loss1: 0.578432 Loss2: 1.431391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.829469 Loss1: 0.397369 Loss2: 1.432101 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.782093 Loss1: 0.345568 Loss2: 1.436526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.859593 Loss1: 0.432468 Loss2: 1.427125 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.778208 Loss1: 0.339794 Loss2: 1.438414 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.896484
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.064723 Loss1: 0.546452 Loss2: 1.518271 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.921803 Loss1: 0.399013 Loss2: 1.522790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.819710 Loss1: 1.836104 Loss2: 1.983606
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.965000 Loss1: 0.446564 Loss2: 1.518436
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.781903 Loss1: 1.349605 Loss2: 1.432298
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.907247 Loss1: 0.377081 Loss2: 1.530167
+(DefaultActor pid=3764) >> Training accuracy: 0.909375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.128203 Loss1: 0.702503 Loss2: 1.425700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.811209 Loss1: 0.409827 Loss2: 1.401382 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.779655 Loss1: 0.380167 Loss2: 1.399488
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.773786 Loss1: 1.718201 Loss2: 2.055585
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.625759 Loss1: 1.159605 Loss2: 1.466155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.438343 Loss1: 0.977461 Loss2: 1.460883 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.909375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.733872 Loss1: 0.322888 Loss2: 1.410984
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.110461 Loss1: 0.645869 Loss2: 1.464592
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.031464 Loss1: 0.576166 Loss2: 1.455298
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.084646 Loss1: 0.621229 Loss2: 1.463417
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.986846 Loss1: 0.494959 Loss2: 1.491887
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.869384 Loss1: 0.400535 Loss2: 1.468848
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.688607 Loss1: 1.649808 Loss2: 2.038799
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.829013 Loss1: 0.375392 Loss2: 1.453621
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.777978 Loss1: 0.321914 Loss2: 1.456064
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.657946 Loss1: 1.173812 Loss2: 1.484134
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.411220 Loss1: 0.912536 Loss2: 1.498684
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.137975 Loss1: 0.653644 Loss2: 1.484331
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.041690 Loss1: 0.566388 Loss2: 1.475302
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.957898 Loss1: 0.483497 Loss2: 1.474401
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.878344 Loss1: 1.867726 Loss2: 2.010617
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.871725 Loss1: 0.405565 Loss2: 1.466159
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.824371 Loss1: 0.359462 Loss2: 1.464909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.814450 Loss1: 0.348038 Loss2: 1.466412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.762887 Loss1: 0.295932 Loss2: 1.466955 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.927734
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.973774 Loss1: 0.545535 Loss2: 1.428239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.813831 Loss1: 0.385813 Loss2: 1.428018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.839088 Loss1: 0.410331 Loss2: 1.428757
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.728093 Loss1: 1.717195 Loss2: 2.010897
+(DefaultActor pid=3764) >> Training accuracy: 0.920833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.649995 Loss1: 1.175223 Loss2: 1.474773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.128168 Loss1: 0.681192 Loss2: 1.446976 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.990850 Loss1: 0.548816 Loss2: 1.442034 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.867143 Loss1: 0.422855 Loss2: 1.444288 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.785481 Loss1: 0.351373 Loss2: 1.434108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.763596 Loss1: 0.334808 Loss2: 1.428788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.721014 Loss1: 0.298215 Loss2: 1.422800 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.947064 Loss1: 0.484302 Loss2: 1.462763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.834595 Loss1: 0.374806 Loss2: 1.459788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.898422 Loss1: 1.917922 Loss2: 1.980501 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.858333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.302137 Loss1: 0.889129 Loss2: 1.413008 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.955195 Loss1: 0.531169 Loss2: 1.424027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.798579 Loss1: 0.387661 Loss2: 1.410918
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.821791 Loss1: 1.759262 Loss2: 2.062530
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.789217 Loss1: 1.305216 Loss2: 1.484001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.372163 Loss1: 0.889478 Loss2: 1.482685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.050547 Loss1: 0.587501 Loss2: 1.463046 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.923958
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.799424 Loss1: 0.365783 Loss2: 1.433641
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.957686 Loss1: 0.514580 Loss2: 1.443106
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.902692 Loss1: 0.453989 Loss2: 1.448702
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.818991 Loss1: 0.375073 Loss2: 1.443918
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.839224 Loss1: 0.398107 Loss2: 1.441116
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.823992 Loss1: 0.369880 Loss2: 1.454113
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.633348 Loss1: 1.591559 Loss2: 2.041789
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.695894 Loss1: 0.253920 Loss2: 1.441975
+(DefaultActor pid=3764) >> Training accuracy: 0.940625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.277638 Loss1: 0.853576 Loss2: 1.424062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.949925 Loss1: 0.537436 Loss2: 1.412489 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.814146 Loss1: 0.408511 Loss2: 1.405635
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.720557 Loss1: 1.742177 Loss2: 1.978380
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.548950 Loss1: 1.082676 Loss2: 1.466274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.247996 Loss1: 0.790594 Loss2: 1.457401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.145912 Loss1: 0.688550 Loss2: 1.457361 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.940625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.906578 Loss1: 0.465496 Loss2: 1.441082 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.843574 Loss1: 0.400684 Loss2: 1.442890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.803314 Loss1: 0.354272 Loss2: 1.449042 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.771487 Loss1: 0.328979 Loss2: 1.442507 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.909007
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.126207 Loss1: 0.655138 Loss2: 1.471069 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.945059 Loss1: 0.479985 Loss2: 1.465074 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.015929 Loss1: 1.886195 Loss2: 2.129734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.737340 Loss1: 0.282302 Loss2: 1.455038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.819769 Loss1: 0.372307 Loss2: 1.447462 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.882292
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-09 18:34:03,017 | server.py:236 | fit_round 48 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.159705 Loss1: 0.612960 Loss2: 1.546745 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.010095 Loss1: 0.480707 Loss2: 1.529389 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.927513 Loss1: 0.392145 Loss2: 1.535368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.954847 Loss1: 0.415742 Loss2: 1.539105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.904946 Loss1: 0.346743 Loss2: 1.558203 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.916016
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.843273 Loss1: 0.388470 Loss2: 1.454804 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.732851 Loss1: 0.281142 Loss2: 1.451709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.967174 Loss1: 1.953680 Loss2: 2.013495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.909856
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.471255 Loss1: 1.035341 Loss2: 1.435914 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.146531 Loss1: 0.699619 Loss2: 1.446912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.947476 Loss1: 0.516752 Loss2: 1.430724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.894487 Loss1: 0.451715 Loss2: 1.442772 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.932292
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 18:34:03,017][flwr][DEBUG] - fit_round 48 received 50 results and 0 failures
+INFO flwr 2023-10-09 18:34:44,478 | server.py:125 | fit progress: (48, 2.4517499086575008, {'accuracy': 0.47}, 110592.256799552)
+>> Test accuracy: 0.470000
+[2023-10-09 18:34:44,478][flwr][INFO] - fit progress: (48, 2.4517499086575008, {'accuracy': 0.47}, 110592.256799552)
+DEBUG flwr 2023-10-09 18:34:44,479 | server.py:173 | evaluate_round 48: strategy sampled 50 clients (out of 50)
+[2023-10-09 18:34:44,479][flwr][DEBUG] - evaluate_round 48: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 18:43:47,396 | server.py:187 | evaluate_round 48 received 50 results and 0 failures
+[2023-10-09 18:43:47,396][flwr][DEBUG] - evaluate_round 48 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 18:43:47,396 | server.py:222 | fit_round 49: strategy sampled 50 clients (out of 50)
+[2023-10-09 18:43:47,396][flwr][DEBUG] - fit_round 49: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.819391 Loss1: 1.772309 Loss2: 2.047082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.364190 Loss1: 0.894387 Loss2: 1.469803 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.177684 Loss1: 0.695625 Loss2: 1.482059
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.892109 Loss1: 1.834559 Loss2: 2.057550
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.069691 Loss1: 0.598580 Loss2: 1.471111
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.736827 Loss1: 1.266804 Loss2: 1.470023
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.931409 Loss1: 0.456415 Loss2: 1.474994
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.396915 Loss1: 0.933263 Loss2: 1.463651
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.896259 Loss1: 0.432316 Loss2: 1.463943
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.069763 Loss1: 0.619684 Loss2: 1.450080
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.926131 Loss1: 0.455847 Loss2: 1.470284
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.990005 Loss1: 0.543428 Loss2: 1.446577
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.849525 Loss1: 0.375996 Loss2: 1.473529
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.997618 Loss1: 0.541633 Loss2: 1.455986
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.758623 Loss1: 0.297912 Loss2: 1.460711
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.983864 Loss1: 0.520960 Loss2: 1.462903
+(DefaultActor pid=3765) >> Training accuracy: 0.916667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.835935 Loss1: 0.367475 Loss2: 1.468460
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.825995 Loss1: 0.372642 Loss2: 1.453352
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.738976 Loss1: 0.286640 Loss2: 1.452336
+(DefaultActor pid=3764) >> Training accuracy: 0.926042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.052822 Loss1: 1.896919 Loss2: 2.155904
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.901300 Loss1: 1.327927 Loss2: 1.573373
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.432939 Loss1: 0.904318 Loss2: 1.528622
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.225456 Loss1: 0.710030 Loss2: 1.515426
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.568474 Loss1: 1.567856 Loss2: 2.000619
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.075930 Loss1: 0.567060 Loss2: 1.508870
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.504737 Loss1: 1.048666 Loss2: 1.456071
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.999587 Loss1: 0.500837 Loss2: 1.498750
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.204251 Loss1: 0.765842 Loss2: 1.438409
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.968039 Loss1: 0.453767 Loss2: 1.514272
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.992979 Loss1: 0.566141 Loss2: 1.426837
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.916545 Loss1: 0.409117 Loss2: 1.507427
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.921979 Loss1: 0.485537 Loss2: 1.436443
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.910556 Loss1: 0.409857 Loss2: 1.500699
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.863332 Loss1: 0.429595 Loss2: 1.433737
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.815043 Loss1: 0.306427 Loss2: 1.508616
+(DefaultActor pid=3765) >> Training accuracy: 0.931250
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.849906 Loss1: 0.422495 Loss2: 1.427411
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.816494 Loss1: 0.383944 Loss2: 1.432550
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.710030 Loss1: 0.285400 Loss2: 1.424629
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.693803 Loss1: 0.273328 Loss2: 1.420474
+(DefaultActor pid=3764) >> Training accuracy: 0.923958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.107934 Loss1: 1.869652 Loss2: 2.238282
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.770318 Loss1: 1.187308 Loss2: 1.583011
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.377339 Loss1: 0.859113 Loss2: 1.518226
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.181520 Loss1: 0.675178 Loss2: 1.506343
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.175128 Loss1: 0.663729 Loss2: 1.511399
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.053352 Loss1: 0.523698 Loss2: 1.529653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.917381 Loss1: 0.398768 Loss2: 1.518614 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.934585 Loss1: 0.423153 Loss2: 1.511432 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.859511 Loss1: 0.347403 Loss2: 1.512109
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.165918 Loss1: 0.724013 Loss2: 1.441904
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.829275 Loss1: 0.316364 Loss2: 1.512911
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.978777 Loss1: 0.545218 Loss2: 1.433559
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.945822 Loss1: 0.519112 Loss2: 1.426710
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.962144 Loss1: 0.524211 Loss2: 1.437933
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.924294 Loss1: 0.468956 Loss2: 1.455338
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.837254 Loss1: 0.392062 Loss2: 1.445192
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.781606 Loss1: 1.802721 Loss2: 1.978885
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.723604 Loss1: 0.297892 Loss2: 1.425712
+(DefaultActor pid=3764) >> Training accuracy: 0.910417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.337684 Loss1: 0.887783 Loss2: 1.449901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.078445 Loss1: 0.662994 Loss2: 1.415450 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.917680 Loss1: 0.489976 Loss2: 1.427704
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.939001 Loss1: 1.834331 Loss2: 2.104670
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.711564 Loss1: 1.186600 Loss2: 1.524964 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.314018 Loss1: 0.792178 Loss2: 1.521841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.231441 Loss1: 0.709565 Loss2: 1.521876 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.116935 Loss1: 0.605610 Loss2: 1.511324 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.955186 Loss1: 0.439143 Loss2: 1.516043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.853223 Loss1: 0.333017 Loss2: 1.520206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.808495 Loss1: 0.308895 Loss2: 1.499600 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.907292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.423108 Loss1: 0.959724 Loss2: 1.463384 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.014388 Loss1: 0.566586 Loss2: 1.447802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.972791 Loss1: 1.722331 Loss2: 2.250460
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.975181 Loss1: 0.526173 Loss2: 1.449008
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.982159 Loss1: 0.517839 Loss2: 1.464320 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.948865 Loss1: 0.485130 Loss2: 1.463735 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.933739 Loss1: 0.457255 Loss2: 1.476484 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.996646 Loss1: 0.507459 Loss2: 1.489188 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.926758
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.849663 Loss1: 0.353190 Loss2: 1.496473 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.896887 Loss1: 0.406484 Loss2: 1.490403 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.908654
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.794487 Loss1: 1.672733 Loss2: 2.121753
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.748384 Loss1: 1.205485 Loss2: 1.542899
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.454752 Loss1: 0.930137 Loss2: 1.524615
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.194549 Loss1: 0.678129 Loss2: 1.516420
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.886794 Loss1: 1.758551 Loss2: 2.128243
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.008239 Loss1: 0.499961 Loss2: 1.508279
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.693637 Loss1: 1.164962 Loss2: 1.528675
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.970792 Loss1: 0.466982 Loss2: 1.503810
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.346350 Loss1: 0.858522 Loss2: 1.487828
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.927583 Loss1: 0.425087 Loss2: 1.502497
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.127902 Loss1: 0.638569 Loss2: 1.489332
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.069187 Loss1: 0.578212 Loss2: 1.490975
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.903037 Loss1: 0.393869 Loss2: 1.509167
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.918097 Loss1: 0.441632 Loss2: 1.476465
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.775383 Loss1: 0.267074 Loss2: 1.508309
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.809107 Loss1: 0.339439 Loss2: 1.469669
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.753189 Loss1: 0.264714 Loss2: 1.488475
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.848365 Loss1: 0.380211 Loss2: 1.468154 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.914062
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.145518 Loss1: 2.040906 Loss2: 2.104611 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.436837 Loss1: 0.955314 Loss2: 1.481523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.839056 Loss1: 1.719571 Loss2: 2.119486 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.648902 Loss1: 1.119675 Loss2: 1.529227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.315580 Loss1: 0.804288 Loss2: 1.511292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.125560 Loss1: 0.623613 Loss2: 1.501947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.047830 Loss1: 0.552524 Loss2: 1.495306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.017305 Loss1: 0.523101 Loss2: 1.494204 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.905134
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.872149 Loss1: 0.372738 Loss2: 1.499411 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.834448 Loss1: 0.325970 Loss2: 1.508478 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.913542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.635769 Loss1: 1.148082 Loss2: 1.487687 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.139620 Loss1: 0.679336 Loss2: 1.460284 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.983766 Loss1: 0.522961 Loss2: 1.460805 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.935619 Loss1: 0.474868 Loss2: 1.460751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.963840 Loss1: 0.512340 Loss2: 1.451500 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.932863 Loss1: 0.464701 Loss2: 1.468162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.812961 Loss1: 0.351601 Loss2: 1.461360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.002708 Loss1: 0.499815 Loss2: 1.502893 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.914522
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.846696 Loss1: 0.351777 Loss2: 1.494919 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.910156
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.889499 Loss1: 1.857135 Loss2: 2.032365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.447207 Loss1: 0.968821 Loss2: 1.478386 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.925315 Loss1: 1.829580 Loss2: 2.095734
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.182529 Loss1: 0.707313 Loss2: 1.475217
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.888977 Loss1: 1.351875 Loss2: 1.537103
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.011596 Loss1: 0.540458 Loss2: 1.471138
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.612109 Loss1: 1.072605 Loss2: 1.539504
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.029526 Loss1: 0.562652 Loss2: 1.466875
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.304412 Loss1: 0.776535 Loss2: 1.527877
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.888848 Loss1: 0.421503 Loss2: 1.467345
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.831743 Loss1: 0.365248 Loss2: 1.466495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.865280 Loss1: 0.390034 Loss2: 1.475246 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.837671 Loss1: 0.367810 Loss2: 1.469862 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.905273
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.949340 Loss1: 0.420843 Loss2: 1.528497 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.910417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.981290 Loss1: 1.937746 Loss2: 2.043545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.338007 Loss1: 0.894076 Loss2: 1.443931 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.130657 Loss1: 0.682263 Loss2: 1.448393
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.862665 Loss1: 1.820834 Loss2: 2.041831
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.757667 Loss1: 1.245566 Loss2: 1.512101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.415062 Loss1: 0.910553 Loss2: 1.504508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.167100 Loss1: 0.666454 Loss2: 1.500646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.997200 Loss1: 0.513795 Loss2: 1.483404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.891297 Loss1: 0.417112 Loss2: 1.474185 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.857292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.868442 Loss1: 0.417234 Loss2: 1.451207
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.928418 Loss1: 0.452983 Loss2: 1.475435
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.892102 Loss1: 0.411587 Loss2: 1.480515
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.877242 Loss1: 0.399100 Loss2: 1.478142
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.831554 Loss1: 0.351400 Loss2: 1.480154
+(DefaultActor pid=3764) >> Training accuracy: 0.892708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.777422 Loss1: 1.734444 Loss2: 2.042978
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.659290 Loss1: 1.213569 Loss2: 1.445721
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.399409 Loss1: 0.995967 Loss2: 1.403442
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.180801 Loss1: 0.751046 Loss2: 1.429754
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.967651 Loss1: 0.557932 Loss2: 1.409720
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.890835 Loss1: 0.487630 Loss2: 1.403205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.845165 Loss1: 0.433896 Loss2: 1.411270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.784277 Loss1: 0.378899 Loss2: 1.405379 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.683405 Loss1: 0.281229 Loss2: 1.402176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.648860 Loss1: 0.251162 Loss2: 1.397698 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.950721
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.885909 Loss1: 0.412608 Loss2: 1.473301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.838538 Loss1: 0.355969 Loss2: 1.482568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.838631 Loss1: 0.360405 Loss2: 1.478226
+(DefaultActor pid=3764) >> Training accuracy: 0.915625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.060241 Loss1: 1.920024 Loss2: 2.140217
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.830524 Loss1: 1.292095 Loss2: 1.538430
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.400109 Loss1: 0.873446 Loss2: 1.526663
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.163237 Loss1: 0.647554 Loss2: 1.515683
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.025434 Loss1: 0.513998 Loss2: 1.511436
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.784777 Loss1: 1.778577 Loss2: 2.006201
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.937610 Loss1: 0.422301 Loss2: 1.515310
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.609939 Loss1: 1.161074 Loss2: 1.448865
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.888949 Loss1: 0.376211 Loss2: 1.512738
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.356051 Loss1: 0.926373 Loss2: 1.429678
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.861261 Loss1: 0.353568 Loss2: 1.507693
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.148863 Loss1: 0.711255 Loss2: 1.437608
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.819793 Loss1: 0.305745 Loss2: 1.514049
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.049762 Loss1: 0.617560 Loss2: 1.432202
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.797234 Loss1: 0.290749 Loss2: 1.506484
+(DefaultActor pid=3765) >> Training accuracy: 0.939583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.880651 Loss1: 0.452865 Loss2: 1.427786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.760280 Loss1: 0.332854 Loss2: 1.427426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.742169 Loss1: 0.315813 Loss2: 1.426356
+(DefaultActor pid=3764) >> Training accuracy: 0.942708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.809150 Loss1: 1.809652 Loss2: 1.999497
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.639333 Loss1: 1.182770 Loss2: 1.456563
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.307248 Loss1: 0.855242 Loss2: 1.452006
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.097864 Loss1: 0.671795 Loss2: 1.426070
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.956063 Loss1: 0.541361 Loss2: 1.414702
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.892142 Loss1: 0.479413 Loss2: 1.412728
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.879903 Loss1: 1.837348 Loss2: 2.042555
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.842456 Loss1: 0.427129 Loss2: 1.415326
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.693735 Loss1: 1.181090 Loss2: 1.512646
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.802287 Loss1: 0.381308 Loss2: 1.420979
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.305022 Loss1: 0.805416 Loss2: 1.499606
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.835903 Loss1: 0.415479 Loss2: 1.420424
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.171347 Loss1: 0.686343 Loss2: 1.485004
+(DefaultActor pid=3765) >> Training accuracy: 0.893750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.883577 Loss1: 0.446279 Loss2: 1.437297
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.114100 Loss1: 0.617002 Loss2: 1.497099
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.985255 Loss1: 0.477197 Loss2: 1.508058
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.932910 Loss1: 0.444298 Loss2: 1.488612
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.889735 Loss1: 0.393930 Loss2: 1.495804
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.815776 Loss1: 0.323208 Loss2: 1.492568
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.053208 Loss1: 1.930494 Loss2: 2.122714
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.746357 Loss1: 0.260137 Loss2: 1.486221
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.836065 Loss1: 1.284664 Loss2: 1.551402
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.564422 Loss1: 1.032102 Loss2: 1.532321
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.343305 Loss1: 0.781225 Loss2: 1.562080
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.183831 Loss1: 0.651765 Loss2: 1.532065
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.118734 Loss1: 0.586611 Loss2: 1.532123
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.728281 Loss1: 1.717550 Loss2: 2.010731
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.009973 Loss1: 0.472712 Loss2: 1.537261
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.592749 Loss1: 1.148210 Loss2: 1.444539
+(DefaultActor pid=3765) Epoch: 7 Loss: 2.016122 Loss1: 0.489547 Loss2: 1.526576
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.353014 Loss1: 0.895459 Loss2: 1.457555
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.969054 Loss1: 0.421531 Loss2: 1.547523
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.070380 Loss1: 0.624642 Loss2: 1.445738
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.893564 Loss1: 0.356273 Loss2: 1.537292
+(DefaultActor pid=3765) >> Training accuracy: 0.926042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.854711 Loss1: 0.421253 Loss2: 1.433458 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.768847 Loss1: 0.334415 Loss2: 1.434432 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.772625 Loss1: 0.332961 Loss2: 1.439664
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.754133 Loss1: 1.715079 Loss2: 2.039054
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.682263 Loss1: 0.249596 Loss2: 1.432667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.613936 Loss1: 1.116017 Loss2: 1.497919
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.250479 Loss1: 0.776936 Loss2: 1.473543
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.032738 Loss1: 0.577159 Loss2: 1.455578
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.921414 Loss1: 0.467442 Loss2: 1.453972
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.901035 Loss1: 0.449993 Loss2: 1.451042
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.797469 Loss1: 1.736533 Loss2: 2.060936
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.687813 Loss1: 1.224018 Loss2: 1.463795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.328820 Loss1: 0.883716 Loss2: 1.445103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.135840 Loss1: 0.690740 Loss2: 1.445099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.816417 Loss1: 0.356920 Loss2: 1.459497
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.019743 Loss1: 0.578408 Loss2: 1.441334
+(DefaultActor pid=3765) >> Training accuracy: 0.885742
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.941925 Loss1: 0.504050 Loss2: 1.437875
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.812783 Loss1: 0.380387 Loss2: 1.432395
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.777089 Loss1: 0.353009 Loss2: 1.424080
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.784633 Loss1: 0.344741 Loss2: 1.439892
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.833789 Loss1: 1.807568 Loss2: 2.026221
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.753252 Loss1: 0.320530 Loss2: 1.432722
+(DefaultActor pid=3764) >> Training accuracy: 0.928125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.318264 Loss1: 0.894911 Loss2: 1.423353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.073906 Loss1: 0.642579 Loss2: 1.431327 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.012488 Loss1: 0.564832 Loss2: 1.447656
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.879287 Loss1: 1.796202 Loss2: 2.083085
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.841462 Loss1: 0.392905 Loss2: 1.448558
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.810514 Loss1: 1.284314 Loss2: 1.526200
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.416555 Loss1: 0.911191 Loss2: 1.505364
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.760922 Loss1: 0.334557 Loss2: 1.426365
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.727930 Loss1: 0.299459 Loss2: 1.428471
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.167243 Loss1: 0.664977 Loss2: 1.502267
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.781315 Loss1: 0.356137 Loss2: 1.425178
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.067067 Loss1: 0.566883 Loss2: 1.500184
+(DefaultActor pid=3765) >> Training accuracy: 0.920833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.979922 Loss1: 0.488803 Loss2: 1.491119
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.923391 Loss1: 0.435876 Loss2: 1.487515
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.965804 Loss1: 0.467193 Loss2: 1.498610
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.877246 Loss1: 0.376554 Loss2: 1.500691
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.793838 Loss1: 0.300231 Loss2: 1.493607
+(DefaultActor pid=3764) >> Training accuracy: 0.954167
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.816495 Loss1: 1.839122 Loss2: 1.977373
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.633886 Loss1: 1.143891 Loss2: 1.489994
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.201831 Loss1: 0.756322 Loss2: 1.445509
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.987176 Loss1: 0.545198 Loss2: 1.441978
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.887103 Loss1: 0.463593 Loss2: 1.423509
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.989428 Loss1: 1.869516 Loss2: 2.119912
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.782758 Loss1: 1.254419 Loss2: 1.528339 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.497876 Loss1: 0.964497 Loss2: 1.533379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.287658 Loss1: 0.772770 Loss2: 1.514888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.048663 Loss1: 0.534773 Loss2: 1.513890 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.855469
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.759847 Loss1: 0.325377 Loss2: 1.434470
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.942638 Loss1: 0.436075 Loss2: 1.506564
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.926714 Loss1: 0.406452 Loss2: 1.520262
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.932887 Loss1: 0.412576 Loss2: 1.520311
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.847502 Loss1: 0.329580 Loss2: 1.517922
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.812213 Loss1: 0.307543 Loss2: 1.504671
+(DefaultActor pid=3764) >> Training accuracy: 0.918750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.897015 Loss1: 1.720615 Loss2: 2.176400
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.530861 Loss1: 0.966086 Loss2: 1.564775
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.339680 Loss1: 0.812930 Loss2: 1.526750
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.154120 Loss1: 0.621139 Loss2: 1.532981
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.019459 Loss1: 0.506752 Loss2: 1.512707
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.730448 Loss1: 1.674124 Loss2: 2.056324
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.934858 Loss1: 0.424437 Loss2: 1.510420
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.652745 Loss1: 1.176063 Loss2: 1.476683
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.919575 Loss1: 0.410510 Loss2: 1.509065
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.263836 Loss1: 0.788379 Loss2: 1.475457
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.871640 Loss1: 0.345381 Loss2: 1.526259
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.003111 Loss1: 0.547684 Loss2: 1.455427
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.869820 Loss1: 0.365829 Loss2: 1.503991
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.925142 Loss1: 0.481587 Loss2: 1.443554
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.844803 Loss1: 0.326549 Loss2: 1.518254
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.899832 Loss1: 0.451246 Loss2: 1.448586
+(DefaultActor pid=3765) >> Training accuracy: 0.885417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.754661 Loss1: 0.303770 Loss2: 1.450891
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.737451 Loss1: 0.294835 Loss2: 1.442615
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.734596 Loss1: 0.287280 Loss2: 1.447316
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.794315 Loss1: 0.348733 Loss2: 1.445582
+(DefaultActor pid=3764) >> Training accuracy: 0.920833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.782316 Loss1: 1.700201 Loss2: 2.082115
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.684542 Loss1: 1.192204 Loss2: 1.492338
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.320112 Loss1: 0.830671 Loss2: 1.489441
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.124356 Loss1: 0.649020 Loss2: 1.475336
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.036367 Loss1: 0.563560 Loss2: 1.472806
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.928078 Loss1: 0.456772 Loss2: 1.471306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.867641 Loss1: 0.393250 Loss2: 1.474391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.816783 Loss1: 0.358658 Loss2: 1.458125 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.755382 Loss1: 0.295851 Loss2: 1.459530 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.789123 Loss1: 0.324632 Loss2: 1.464490 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.919792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.877789 Loss1: 0.395163 Loss2: 1.482626 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.807729 Loss1: 0.334651 Loss2: 1.473077 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.905208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.796115 Loss1: 1.307415 Loss2: 1.488700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.228495 Loss1: 0.753155 Loss2: 1.475339 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.772436 Loss1: 1.701946 Loss2: 2.070490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.773055 Loss1: 1.227992 Loss2: 1.545063 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.400475 Loss1: 0.888238 Loss2: 1.512236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.864064 Loss1: 0.392047 Loss2: 1.472017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.806361 Loss1: 0.333557 Loss2: 1.472804 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.868304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.955477 Loss1: 0.446740 Loss2: 1.508738 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.784470 Loss1: 0.289967 Loss2: 1.494502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.745394 Loss1: 0.251721 Loss2: 1.493673 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.944336
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.526859 Loss1: 1.061856 Loss2: 1.465003 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.026534 Loss1: 0.569717 Loss2: 1.456817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.926945 Loss1: 0.477346 Loss2: 1.449599
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.806616 Loss1: 1.773735 Loss2: 2.032882
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.756265 Loss1: 1.280577 Loss2: 1.475688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.338124 Loss1: 0.895832 Loss2: 1.442292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.127827 Loss1: 0.684602 Loss2: 1.443225 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.896875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.008977 Loss1: 0.567367 Loss2: 1.441611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.827253 Loss1: 0.405143 Loss2: 1.422110 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.775491 Loss1: 0.347441 Loss2: 1.428050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.764343 Loss1: 0.334347 Loss2: 1.429996 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.907292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.224385 Loss1: 0.747210 Loss2: 1.477175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.907421 Loss1: 0.465474 Loss2: 1.441946 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.840938 Loss1: 0.399323 Loss2: 1.441615
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.915974 Loss1: 1.822292 Loss2: 2.093682
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.810973 Loss1: 1.276362 Loss2: 1.534611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.566109 Loss1: 1.022259 Loss2: 1.543850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.217311 Loss1: 0.690912 Loss2: 1.526399 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.936458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.064800 Loss1: 0.546355 Loss2: 1.518445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.996771 Loss1: 0.470457 Loss2: 1.526314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 2.003144 Loss1: 0.453497 Loss2: 1.549647 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.958091 Loss1: 0.417481 Loss2: 1.540610 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.906250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.330448 Loss1: 0.922997 Loss2: 1.407451 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.025599 Loss1: 0.613122 Loss2: 1.412477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.995316 Loss1: 0.564567 Loss2: 1.430749
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.732989 Loss1: 1.723115 Loss2: 2.009874DEBUG flwr 2023-10-09 19:12:18,080 | server.py:236 | fit_round 49 received 50 results and 0 failures
+
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.648320 Loss1: 1.202978 Loss2: 1.445342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.288589 Loss1: 0.848481 Loss2: 1.440108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.159115 Loss1: 0.725796 Loss2: 1.433319 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.854167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.990878 Loss1: 0.560458 Loss2: 1.430420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.800266 Loss1: 0.363564 Loss2: 1.436702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.716448 Loss1: 0.283757 Loss2: 1.432692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.751739 Loss1: 0.324520 Loss2: 1.427219 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.930208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.300789 Loss1: 0.831728 Loss2: 1.469060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.018034 Loss1: 0.561861 Loss2: 1.456173 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.841213 Loss1: 0.391016 Loss2: 1.450197
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.973468 Loss1: 1.933581 Loss2: 2.039886
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.786596 Loss1: 0.345421 Loss2: 1.441174
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.834192 Loss1: 1.342908 Loss2: 1.491284
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.416159 Loss1: 0.942442 Loss2: 1.473717
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.742227 Loss1: 0.298551 Loss2: 1.443676
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.180132 Loss1: 0.710785 Loss2: 1.469347
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.811941 Loss1: 0.361836 Loss2: 1.450105
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.035878 Loss1: 0.568525 Loss2: 1.467353
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.788232 Loss1: 0.334789 Loss2: 1.453443
+(DefaultActor pid=3765) >> Training accuracy: 0.858398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.898227 Loss1: 0.435397 Loss2: 1.462830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.839178 Loss1: 0.379793 Loss2: 1.459385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.794836 Loss1: 0.326969 Loss2: 1.467867
+(DefaultActor pid=3764) >> Training accuracy: 0.918750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.793537 Loss1: 1.623137 Loss2: 2.170400
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.604894 Loss1: 1.059550 Loss2: 1.545344
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.370088 Loss1: 0.835394 Loss2: 1.534694
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.143037 Loss1: 0.622770 Loss2: 1.520268
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.991351 Loss1: 0.478668 Loss2: 1.512683
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.891053 Loss1: 0.388648 Loss2: 1.502405
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.866888 Loss1: 1.850517 Loss2: 2.016372
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.816331 Loss1: 0.321625 Loss2: 1.494706
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.765823 Loss1: 1.260176 Loss2: 1.505647
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.761601 Loss1: 0.274681 Loss2: 1.486919
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.396814 Loss1: 0.911688 Loss2: 1.485126
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.806966 Loss1: 0.321543 Loss2: 1.485423
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.142470 Loss1: 0.667416 Loss2: 1.475053
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.800055 Loss1: 0.298722 Loss2: 1.501332
+(DefaultActor pid=3765) >> Training accuracy: 0.893750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.998485 Loss1: 0.523409 Loss2: 1.475076
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.955679 Loss1: 0.478728 Loss2: 1.476951
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.896873 Loss1: 0.416611 Loss2: 1.480263
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.880168 Loss1: 0.395913 Loss2: 1.484256
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.838587 Loss1: 0.349528 Loss2: 1.489060
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.771574 Loss1: 0.294239 Loss2: 1.477335
+(DefaultActor pid=3764) >> Training accuracy: 0.934570
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 19:12:18,080][flwr][DEBUG] - fit_round 49 received 50 results and 0 failures
+INFO flwr 2023-10-09 19:13:00,199 | server.py:125 | fit progress: (49, 2.436963511732059, {'accuracy': 0.4738}, 112887.97707739999)
+>> Test accuracy: 0.473800
+[2023-10-09 19:13:00,199][flwr][INFO] - fit progress: (49, 2.436963511732059, {'accuracy': 0.4738}, 112887.97707739999)
+DEBUG flwr 2023-10-09 19:13:00,199 | server.py:173 | evaluate_round 49: strategy sampled 50 clients (out of 50)
+[2023-10-09 19:13:00,199][flwr][DEBUG] - evaluate_round 49: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 19:22:08,911 | server.py:187 | evaluate_round 49 received 50 results and 0 failures
+[2023-10-09 19:22:08,911][flwr][DEBUG] - evaluate_round 49 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 19:22:08,912 | server.py:222 | fit_round 50: strategy sampled 50 clients (out of 50)
+[2023-10-09 19:22:08,912][flwr][DEBUG] - fit_round 50: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.773367 Loss1: 1.785448 Loss2: 1.987919
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.768236 Loss1: 1.268820 Loss2: 1.499416
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.374965 Loss1: 0.885865 Loss2: 1.489101
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.625046 Loss1: 1.589398 Loss2: 2.035648
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.663706 Loss1: 1.190584 Loss2: 1.473122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.333692 Loss1: 0.865836 Loss2: 1.467856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.119584 Loss1: 0.660200 Loss2: 1.459384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.018646 Loss1: 0.562784 Loss2: 1.455862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.930371 Loss1: 0.476992 Loss2: 1.453379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.907962 Loss1: 0.448683 Loss2: 1.459279 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.915039
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.817003 Loss1: 0.361979 Loss2: 1.455024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.749171 Loss1: 0.296716 Loss2: 1.452455 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.915625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.691789 Loss1: 1.721158 Loss2: 1.970631
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.621496 Loss1: 1.185643 Loss2: 1.435853
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.285068 Loss1: 0.873101 Loss2: 1.411967
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.103896 Loss1: 0.683593 Loss2: 1.420303
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.900323 Loss1: 1.825604 Loss2: 2.074719
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.711352 Loss1: 1.222993 Loss2: 1.488359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.497280 Loss1: 1.017596 Loss2: 1.479684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.194305 Loss1: 0.709457 Loss2: 1.484848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.086832 Loss1: 0.615177 Loss2: 1.471655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.971095 Loss1: 0.502216 Loss2: 1.468879 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.890625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.748809 Loss1: 0.351368 Loss2: 1.397441
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.975698 Loss1: 0.505935 Loss2: 1.469763
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.910771 Loss1: 0.436953 Loss2: 1.473818
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.840680 Loss1: 0.362945 Loss2: 1.477734
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.780937 Loss1: 0.322407 Loss2: 1.458530
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.778757 Loss1: 1.729168 Loss2: 2.049588
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.628938 Loss1: 1.153657 Loss2: 1.475281
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.372976 Loss1: 0.906487 Loss2: 1.466490
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.184553 Loss1: 0.706015 Loss2: 1.478538
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.837552 Loss1: 1.761109 Loss2: 2.076443
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.728427 Loss1: 1.236374 Loss2: 1.492053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.435778 Loss1: 0.947795 Loss2: 1.487983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.207589 Loss1: 0.718219 Loss2: 1.489370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.028322 Loss1: 0.558018 Loss2: 1.470304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.019399 Loss1: 0.544163 Loss2: 1.475236 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.934375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.740983 Loss1: 0.281691 Loss2: 1.459292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.978362 Loss1: 0.492498 Loss2: 1.485863
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.878947 Loss1: 0.393671 Loss2: 1.485276
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.856223 Loss1: 0.382464 Loss2: 1.473759
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.861428 Loss1: 0.381474 Loss2: 1.479955
+(DefaultActor pid=3764) >> Training accuracy: 0.901042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.870721 Loss1: 1.812654 Loss2: 2.058067
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.625794 Loss1: 1.136548 Loss2: 1.489246
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.332171 Loss1: 0.859914 Loss2: 1.472256
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.095438 Loss1: 0.633077 Loss2: 1.462361
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.669222 Loss1: 1.706862 Loss2: 1.962360
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.515801 Loss1: 1.043604 Loss2: 1.472197 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.128512 Loss1: 0.691228 Loss2: 1.437284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.992281 Loss1: 0.559399 Loss2: 1.432882 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.829604 Loss1: 0.405881 Loss2: 1.423723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.759821 Loss1: 0.346863 Loss2: 1.412958 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.929167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.760093 Loss1: 0.343333 Loss2: 1.416760 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.720853 Loss1: 0.300912 Loss2: 1.419941 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.906250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.746843 Loss1: 1.697481 Loss2: 2.049363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.201204 Loss1: 0.771252 Loss2: 1.429952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.803451 Loss1: 1.724250 Loss2: 2.079201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.638572 Loss1: 1.119493 Loss2: 1.519079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.292106 Loss1: 0.787822 Loss2: 1.504284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.063158 Loss1: 0.567325 Loss2: 1.495833 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.984679 Loss1: 0.492346 Loss2: 1.492333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.942866 Loss1: 0.452448 Loss2: 1.490418 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.946875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.887560 Loss1: 0.385766 Loss2: 1.501794 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.781393 Loss1: 0.289044 Loss2: 1.492349 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.887500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.795364 Loss1: 1.250950 Loss2: 1.544414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.208945 Loss1: 0.723722 Loss2: 1.485222 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.953493 Loss1: 0.468783 Loss2: 1.484711 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.844149 Loss1: 0.372656 Loss2: 1.471494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.794727 Loss1: 0.319843 Loss2: 1.474884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.837408 Loss1: 0.365991 Loss2: 1.471417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.725561 Loss1: 0.247051 Loss2: 1.478510 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.012112 Loss1: 0.576152 Loss2: 1.435959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.803039 Loss1: 0.376444 Loss2: 1.426595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.818899 Loss1: 1.717144 Loss2: 2.101755
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.699804 Loss1: 0.288811 Loss2: 1.410992
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.681493 Loss1: 1.199886 Loss2: 1.481607
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.680740 Loss1: 0.274387 Loss2: 1.406353
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.190070 Loss1: 0.711765 Loss2: 1.478305 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.923950 Loss1: 0.445904 Loss2: 1.478047 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.797621 Loss1: 0.325153 Loss2: 1.472468
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.981976 Loss1: 1.909951 Loss2: 2.072025
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.851983 Loss1: 1.327493 Loss2: 1.524490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.541476 Loss1: 0.994433 Loss2: 1.547044 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.926339
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.056582 Loss1: 0.548435 Loss2: 1.508148 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.981275 Loss1: 0.469105 Loss2: 1.512170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.922324 Loss1: 0.412029 Loss2: 1.510295
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.758849 Loss1: 1.733384 Loss2: 2.025465
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.672486 Loss1: 1.212322 Loss2: 1.460164 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.905208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.268486 Loss1: 0.822217 Loss2: 1.446269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.959852 Loss1: 0.517034 Loss2: 1.442818 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.905540 Loss1: 0.453268 Loss2: 1.452272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.888163 Loss1: 0.436103 Loss2: 1.452060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.853964 Loss1: 0.395675 Loss2: 1.458289 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.817708 Loss1: 0.361169 Loss2: 1.456539 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.894792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.060424 Loss1: 0.577902 Loss2: 1.482522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.845403 Loss1: 0.378418 Loss2: 1.466985 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.899926 Loss1: 1.807316 Loss2: 2.092610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.780100 Loss1: 1.263096 Loss2: 1.517004 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.928125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.167250 Loss1: 0.676037 Loss2: 1.491213 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.060630 Loss1: 0.550713 Loss2: 1.509917 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.984798 Loss1: 0.478022 Loss2: 1.506776
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.019315 Loss1: 1.861052 Loss2: 2.158263
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.709886 Loss1: 1.133958 Loss2: 1.575928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.471681 Loss1: 0.907025 Loss2: 1.564656 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.944792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.799815 Loss1: 0.306531 Loss2: 1.493284
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.288971 Loss1: 0.719029 Loss2: 1.569942
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.178214 Loss1: 0.621005 Loss2: 1.557209
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.116607 Loss1: 0.564219 Loss2: 1.552387
+(DefaultActor pid=3764) Epoch: 6 Loss: 2.019212 Loss1: 0.455250 Loss2: 1.563963
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.921201 Loss1: 0.372136 Loss2: 1.549065
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.763554 Loss1: 1.726703 Loss2: 2.036850
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.902352 Loss1: 0.357055 Loss2: 1.545297
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.681783 Loss1: 1.206488 Loss2: 1.475295
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.912906 Loss1: 0.367827 Loss2: 1.545079
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.024231 Loss1: 0.578400 Loss2: 1.445832 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.895472 Loss1: 0.431483 Loss2: 1.463989 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.822171 Loss1: 0.372113 Loss2: 1.450058
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.840351 Loss1: 1.815216 Loss2: 2.025135
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.758129 Loss1: 1.277470 Loss2: 1.480659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.395501 Loss1: 0.938446 Loss2: 1.457056 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.897917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.795661 Loss1: 0.340237 Loss2: 1.455425
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.204861 Loss1: 0.742020 Loss2: 1.462841
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.039926 Loss1: 0.583016 Loss2: 1.456911
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.973651 Loss1: 0.518889 Loss2: 1.454762
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.863227 Loss1: 0.404038 Loss2: 1.459189
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.861122 Loss1: 0.409066 Loss2: 1.452055
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.810874 Loss1: 1.721486 Loss2: 2.089388
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.722262 Loss1: 0.262698 Loss2: 1.459564
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.757507 Loss1: 1.215536 Loss2: 1.541972
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.725085 Loss1: 0.283712 Loss2: 1.441373
+(DefaultActor pid=3764) >> Training accuracy: 0.915625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.161078 Loss1: 0.645546 Loss2: 1.515532 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.000855 Loss1: 0.487932 Loss2: 1.512922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.875165 Loss1: 0.370448 Loss2: 1.504717
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.813504 Loss1: 1.754418 Loss2: 2.059086
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.909738 Loss1: 0.408873 Loss2: 1.500865
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.610562 Loss1: 1.146092 Loss2: 1.464470
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.778105 Loss1: 0.280310 Loss2: 1.497794
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.388760 Loss1: 0.937610 Loss2: 1.451151
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.763870 Loss1: 0.274769 Loss2: 1.489101
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.108329 Loss1: 0.638385 Loss2: 1.469943
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.922942 Loss1: 0.472613 Loss2: 1.450329
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.873554 Loss1: 0.439789 Loss2: 1.433765
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.931823 Loss1: 0.483699 Loss2: 1.448124
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.935603 Loss1: 0.477327 Loss2: 1.458275
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.759187 Loss1: 0.300633 Loss2: 1.458554
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.954116 Loss1: 1.837499 Loss2: 2.116617
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.710559 Loss1: 0.268793 Loss2: 1.441766
+(DefaultActor pid=3764) >> Training accuracy: 0.906250
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.822771 Loss1: 1.272857 Loss2: 1.549914
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.425691 Loss1: 0.892159 Loss2: 1.533532
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.147098 Loss1: 0.616274 Loss2: 1.530824
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.068143 Loss1: 0.544948 Loss2: 1.523196
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.965152 Loss1: 0.441383 Loss2: 1.523769
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.922853 Loss1: 1.854068 Loss2: 2.068785
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.915121 Loss1: 0.387467 Loss2: 1.527654
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.946584 Loss1: 0.420454 Loss2: 1.526130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.924320 Loss1: 0.394719 Loss2: 1.529601 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.920072 Loss1: 0.402389 Loss2: 1.517684 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.926758
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.024267 Loss1: 0.545039 Loss2: 1.479228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.931257 Loss1: 0.431625 Loss2: 1.499633 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.820380 Loss1: 0.333888 Loss2: 1.486492
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.740181 Loss1: 1.679363 Loss2: 2.060817
+(DefaultActor pid=3764) >> Training accuracy: 0.931250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.644770 Loss1: 1.147027 Loss2: 1.497743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.010680 Loss1: 0.547664 Loss2: 1.463016 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.845598 Loss1: 0.402490 Loss2: 1.443107 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.834711 Loss1: 0.379611 Loss2: 1.455100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.768046 Loss1: 0.312582 Loss2: 1.455465 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.712469 Loss1: 0.267092 Loss2: 1.445377 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.718613 Loss1: 0.277308 Loss2: 1.441305 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.917708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.006961 Loss1: 0.506241 Loss2: 1.500721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.982651 Loss1: 0.474029 Loss2: 1.508622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.750121 Loss1: 1.828470 Loss2: 1.921652 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.907292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.139487 Loss1: 0.757935 Loss2: 1.381552 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.838418 Loss1: 0.469526 Loss2: 1.368892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.702033 Loss1: 1.695189 Loss2: 2.006844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.585672 Loss1: 1.112227 Loss2: 1.473445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.248428 Loss1: 0.783429 Loss2: 1.464999 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.128243 Loss1: 0.667796 Loss2: 1.460446 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.915625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.912052 Loss1: 0.445091 Loss2: 1.466960 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.807676 Loss1: 0.351165 Loss2: 1.456511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.788869 Loss1: 0.326983 Loss2: 1.461886
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.785843 Loss1: 1.811111 Loss2: 1.974731
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.779077 Loss1: 0.323735 Loss2: 1.455342
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.741477 Loss1: 1.327074 Loss2: 1.414403
+(DefaultActor pid=3764) >> Training accuracy: 0.924805
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.440320 Loss1: 1.026893 Loss2: 1.413427
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.161658 Loss1: 0.742513 Loss2: 1.419145
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.974348 Loss1: 0.575070 Loss2: 1.399278
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.898227 Loss1: 0.499609 Loss2: 1.398617
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.828104 Loss1: 0.433448 Loss2: 1.394656
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.892550 Loss1: 1.740883 Loss2: 2.151667
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.779375 Loss1: 0.385619 Loss2: 1.393756
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.628133 Loss1: 1.110908 Loss2: 1.517225
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.686732 Loss1: 0.292377 Loss2: 1.394355
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.374880 Loss1: 0.884325 Loss2: 1.490555
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.642453 Loss1: 0.251227 Loss2: 1.391226
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.145898 Loss1: 0.646084 Loss2: 1.499814
+(DefaultActor pid=3765) >> Training accuracy: 0.933333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.057223 Loss1: 0.574902 Loss2: 1.482321
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.932620 Loss1: 0.458490 Loss2: 1.474130
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.916003 Loss1: 0.441415 Loss2: 1.474588
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.861579 Loss1: 0.375377 Loss2: 1.486202
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.481570 Loss1: 1.489513 Loss2: 1.992057
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.759836 Loss1: 0.276907 Loss2: 1.482928
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.571556 Loss1: 1.114104 Loss2: 1.457452
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.761942 Loss1: 0.296305 Loss2: 1.465638
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.013459 Loss1: 0.581259 Loss2: 1.432200 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.854190 Loss1: 0.436640 Loss2: 1.417550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.810767 Loss1: 0.385791 Loss2: 1.424977
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.706111 Loss1: 1.612310 Loss2: 2.093801
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.691815 Loss1: 0.276270 Loss2: 1.415545
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.627540 Loss1: 1.124687 Loss2: 1.502853
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.698090 Loss1: 0.278255 Loss2: 1.419835
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.312905 Loss1: 0.803080 Loss2: 1.509825
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.665793 Loss1: 0.248172 Loss2: 1.417621
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.105707 Loss1: 0.603072 Loss2: 1.502636
+(DefaultActor pid=3765) >> Training accuracy: 0.940625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.992121 Loss1: 0.502092 Loss2: 1.490029
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.880702 Loss1: 0.390105 Loss2: 1.490597
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.859261 Loss1: 0.376989 Loss2: 1.482272
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.920804 Loss1: 0.425521 Loss2: 1.495283
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.886552 Loss1: 1.819585 Loss2: 2.066967
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.856017 Loss1: 0.354767 Loss2: 1.501250
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.763550 Loss1: 1.249017 Loss2: 1.514533
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.771792 Loss1: 0.286292 Loss2: 1.485500
+(DefaultActor pid=3764) >> Training accuracy: 0.932292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.202115 Loss1: 0.720482 Loss2: 1.481633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.974564 Loss1: 0.501754 Loss2: 1.472810 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.969057 Loss1: 0.495164 Loss2: 1.473893
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.920029 Loss1: 1.859406 Loss2: 2.060623
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.885746 Loss1: 0.408060 Loss2: 1.477686
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.765421 Loss1: 1.259978 Loss2: 1.505443
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.917812 Loss1: 0.439863 Loss2: 1.477949
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.424971 Loss1: 0.956581 Loss2: 1.468390
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.884185 Loss1: 0.398856 Loss2: 1.485330
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.243112 Loss1: 0.770688 Loss2: 1.472424
+(DefaultActor pid=3765) >> Training accuracy: 0.903125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.154236 Loss1: 0.680989 Loss2: 1.473246
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.051039 Loss1: 0.567934 Loss2: 1.483106
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.956470 Loss1: 0.485605 Loss2: 1.470865
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.886848 Loss1: 0.412906 Loss2: 1.473942
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.691634 Loss1: 1.705973 Loss2: 1.985661
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.800591 Loss1: 0.326399 Loss2: 1.474191
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.495898 Loss1: 1.079196 Loss2: 1.416703
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.787759 Loss1: 0.327110 Loss2: 1.460649
+(DefaultActor pid=3764) >> Training accuracy: 0.925000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.976585 Loss1: 0.588648 Loss2: 1.387936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.875826 Loss1: 0.488708 Loss2: 1.387119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.720647 Loss1: 0.332975 Loss2: 1.387672
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.747306 Loss1: 1.776276 Loss2: 1.971030
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.684852 Loss1: 0.303097 Loss2: 1.381754
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.590410 Loss1: 1.130762 Loss2: 1.459648
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.214673 Loss1: 0.768702 Loss2: 1.445971 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.646048 Loss1: 0.261266 Loss2: 1.384782
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.992060 Loss1: 0.552254 Loss2: 1.439807
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.930348 Loss1: 0.498381 Loss2: 1.431967
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.878961 Loss1: 0.438550 Loss2: 1.440411
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.783230 Loss1: 0.346743 Loss2: 1.436487
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.754474 Loss1: 0.324102 Loss2: 1.430372
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.860553 Loss1: 1.786114 Loss2: 2.074439
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.763752 Loss1: 1.249842 Loss2: 1.513910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.808324 Loss1: 0.379116 Loss2: 1.429208
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.363059 Loss1: 0.867970 Loss2: 1.495089
+(DefaultActor pid=3764) >> Training accuracy: 0.908203
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.108525 Loss1: 0.618081 Loss2: 1.490444
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.011535 Loss1: 0.526369 Loss2: 1.485166
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.993246 Loss1: 0.506671 Loss2: 1.486574
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.970948 Loss1: 0.472254 Loss2: 1.498694
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.979701 Loss1: 0.482051 Loss2: 1.497650
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.582422 Loss1: 1.632898 Loss2: 1.949524
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.900694 Loss1: 0.408961 Loss2: 1.491733
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.622499 Loss1: 1.172084 Loss2: 1.450415
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.838923 Loss1: 0.345419 Loss2: 1.493504
+(DefaultActor pid=3765) >> Training accuracy: 0.900000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.241595 Loss1: 0.795341 Loss2: 1.446254
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.065179 Loss1: 0.636611 Loss2: 1.428568
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.034415 Loss1: 0.606278 Loss2: 1.428137
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.877300 Loss1: 0.452495 Loss2: 1.424805
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.719276 Loss1: 0.309172 Loss2: 1.410105
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.879926 Loss1: 1.721035 Loss2: 2.158890
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.673410 Loss1: 1.155101 Loss2: 1.518310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.278801 Loss1: 0.803385 Loss2: 1.475416
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.824078 Loss1: 0.404589 Loss2: 1.419490
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.762350 Loss1: 0.334454 Loss2: 1.427896 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.906250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.840479 Loss1: 0.382649 Loss2: 1.457830 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.672003 Loss1: 0.211439 Loss2: 1.460564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.723273 Loss1: 0.264507 Loss2: 1.458766 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.936298
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.320326 Loss1: 0.851964 Loss2: 1.468361 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.928792 Loss1: 0.481941 Loss2: 1.446850 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.784704 Loss1: 1.788578 Loss2: 1.996126
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.826312 Loss1: 0.380213 Loss2: 1.446100
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.636012 Loss1: 1.193465 Loss2: 1.442547
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.810887 Loss1: 0.364726 Loss2: 1.446160
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.327442 Loss1: 0.898775 Loss2: 1.428667
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.794400 Loss1: 0.353289 Loss2: 1.441111
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.019563 Loss1: 0.584426 Loss2: 1.435137
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.793637 Loss1: 0.345874 Loss2: 1.447763
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.911692 Loss1: 0.490074 Loss2: 1.421618
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.916134 Loss1: 0.452847 Loss2: 1.463287
+(DefaultActor pid=3764) >> Training accuracy: 0.881250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.781477 Loss1: 0.361573 Loss2: 1.419904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.760548 Loss1: 0.339064 Loss2: 1.421484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.744874 Loss1: 0.317780 Loss2: 1.427094
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.088958 Loss1: 2.013983 Loss2: 2.074974
+(DefaultActor pid=3765) >> Training accuracy: 0.929167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.778436 Loss1: 1.285337 Loss2: 1.493100
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.425565 Loss1: 0.957068 Loss2: 1.468497
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.160927 Loss1: 0.693919 Loss2: 1.467008
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.039389 Loss1: 0.577557 Loss2: 1.461832
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.945468 Loss1: 0.491675 Loss2: 1.453793
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.072748 Loss1: 2.012233 Loss2: 2.060515
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.876984 Loss1: 0.417201 Loss2: 1.459783
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.802925 Loss1: 1.339698 Loss2: 1.463227
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.810265 Loss1: 0.357225 Loss2: 1.453040
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.393132 Loss1: 0.959415 Loss2: 1.433717
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.813432 Loss1: 0.373219 Loss2: 1.440213
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.149742 Loss1: 0.732610 Loss2: 1.417132
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.763208 Loss1: 0.312782 Loss2: 1.450426
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.977390 Loss1: 0.563895 Loss2: 1.413495
+(DefaultActor pid=3764) >> Training accuracy: 0.896205
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.854165 Loss1: 0.437244 Loss2: 1.416921
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.807769 Loss1: 0.394047 Loss2: 1.413722
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.767491 Loss1: 0.357188 Loss2: 1.410303
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.726570 Loss1: 0.310502 Loss2: 1.416067
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.726381 Loss1: 0.316500 Loss2: 1.409881
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.826026 Loss1: 1.735342 Loss2: 2.090683
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.651327 Loss1: 1.159331 Loss2: 1.491996
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.337051 Loss1: 0.884780 Loss2: 1.452271
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.121302 Loss1: 0.673353 Loss2: 1.447949
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.985291 Loss1: 0.524834 Loss2: 1.460457
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.830459 Loss1: 0.385262 Loss2: 1.445197
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.796945 Loss1: 0.345297 Loss2: 1.451647
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.778483 Loss1: 0.327149 Loss2: 1.451334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.704666 Loss1: 0.267498 Loss2: 1.437168 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.698378 Loss1: 0.262542 Loss2: 1.435836 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.927885
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.902407 Loss1: 0.484090 Loss2: 1.418318 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.932628 Loss1: 0.490543 Loss2: 1.442085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.790836 Loss1: 1.805038 Loss2: 1.985798
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.843858 Loss1: 0.411056 Loss2: 1.432802
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.886144 Loss1: 1.408055 Loss2: 1.478089
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.759979 Loss1: 0.340064 Loss2: 1.419915
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.436203 Loss1: 0.954352 Loss2: 1.481851
+DEBUG flwr 2023-10-09 19:50:49,254 | server.py:236 | fit_round 50 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.749195 Loss1: 0.322067 Loss2: 1.427128
+(DefaultActor pid=3765) >> Training accuracy: 0.936581
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.988890 Loss1: 0.532048 Loss2: 1.456842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.875753 Loss1: 0.417372 Loss2: 1.458381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.767324 Loss1: 0.322412 Loss2: 1.444912
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.658296 Loss1: 1.614547 Loss2: 2.043749
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.855745 Loss1: 0.409572 Loss2: 1.446173
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.511278 Loss1: 1.035402 Loss2: 1.475876
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.862348 Loss1: 0.398599 Loss2: 1.463750
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.210540 Loss1: 0.767942 Loss2: 1.442597
+(DefaultActor pid=3764) >> Training accuracy: 0.927734
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.003343 Loss1: 0.558739 Loss2: 1.444603
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.907713 Loss1: 0.466571 Loss2: 1.441141
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.795724 Loss1: 0.363569 Loss2: 1.432155
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.778274 Loss1: 0.338814 Loss2: 1.439460
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.799829 Loss1: 0.366434 Loss2: 1.433396
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.671197 Loss1: 1.705365 Loss2: 1.965832
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.667696 Loss1: 1.188167 Loss2: 1.479528 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.944792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.696798 Loss1: 0.267114 Loss2: 1.429684
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.247625 Loss1: 0.798880 Loss2: 1.448745
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.102547 Loss1: 0.658144 Loss2: 1.444402
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.014232 Loss1: 0.569705 Loss2: 1.444527
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.898521 Loss1: 0.445192 Loss2: 1.453329
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.887190 Loss1: 0.446210 Loss2: 1.440981
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.723901 Loss1: 1.661300 Loss2: 2.062601
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.572815 Loss1: 1.077382 Loss2: 1.495433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.228793 Loss1: 0.764483 Loss2: 1.464311 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.875000
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.785596 Loss1: 0.347738 Loss2: 1.437858
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.094503 Loss1: 0.622759 Loss2: 1.471745
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.041866 Loss1: 0.568820 Loss2: 1.473046
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.896494 Loss1: 0.425941 Loss2: 1.470552
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.896045 Loss1: 0.428233 Loss2: 1.467813
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.835081 Loss1: 0.361435 Loss2: 1.473646
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.875267 Loss1: 1.843606 Loss2: 2.031661
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.774333 Loss1: 0.314673 Loss2: 1.459660
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.761859 Loss1: 0.295051 Loss2: 1.466809
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.740459 Loss1: 1.243592 Loss2: 1.496867
+(DefaultActor pid=3765) >> Training accuracy: 0.934375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.387483 Loss1: 0.897236 Loss2: 1.490247
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.239011 Loss1: 0.746527 Loss2: 1.492484
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.104793 Loss1: 0.623769 Loss2: 1.481024
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.949469 Loss1: 0.461284 Loss2: 1.488185
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.892782 Loss1: 0.418012 Loss2: 1.474770
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.835641 Loss1: 0.357884 Loss2: 1.477757
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.801920 Loss1: 0.325763 Loss2: 1.476157
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.787427 Loss1: 0.316555 Loss2: 1.470873
+(DefaultActor pid=3764) >> Training accuracy: 0.925781
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 19:50:49,254][flwr][DEBUG] - fit_round 50 received 50 results and 0 failures
+INFO flwr 2023-10-09 19:51:29,754 | server.py:125 | fit progress: (50, 2.4152099930059414, {'accuracy': 0.4764}, 115197.53232752299)
+>> Test accuracy: 0.476400
+[2023-10-09 19:51:29,754][flwr][INFO] - fit progress: (50, 2.4152099930059414, {'accuracy': 0.4764}, 115197.53232752299)
+DEBUG flwr 2023-10-09 19:51:29,754 | server.py:173 | evaluate_round 50: strategy sampled 50 clients (out of 50)
+[2023-10-09 19:51:29,754][flwr][DEBUG] - evaluate_round 50: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 20:00:34,307 | server.py:187 | evaluate_round 50 received 50 results and 0 failures
+[2023-10-09 20:00:34,307][flwr][DEBUG] - evaluate_round 50 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 20:00:34,307 | server.py:222 | fit_round 51: strategy sampled 50 clients (out of 50)
+[2023-10-09 20:00:34,307][flwr][DEBUG] - fit_round 51: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 4.016628 Loss1: 1.932497 Loss2: 2.084130
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.851809 Loss1: 1.311681 Loss2: 1.540129
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.418402 Loss1: 0.910998 Loss2: 1.507404
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.251455 Loss1: 0.758436 Loss2: 1.493019
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.994693 Loss1: 0.496564 Loss2: 1.498129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.918124 Loss1: 0.427569 Loss2: 1.490554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.839432 Loss1: 0.354486 Loss2: 1.484946 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.824559 Loss1: 0.338971 Loss2: 1.485588 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.830594 Loss1: 0.350372 Loss2: 1.480221 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.869247 Loss1: 0.372670 Loss2: 1.496577 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.835417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.780733 Loss1: 0.331848 Loss2: 1.448885 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.770852 Loss1: 0.314541 Loss2: 1.456311 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.906250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.630595 Loss1: 1.174986 Loss2: 1.455609 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.100720 Loss1: 0.647592 Loss2: 1.453128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.961859 Loss1: 0.514370 Loss2: 1.447489
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.933970 Loss1: 1.884497 Loss2: 2.049474
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.905188 Loss1: 0.471885 Loss2: 1.433303
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.846202 Loss1: 1.349260 Loss2: 1.496942
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.820511 Loss1: 0.385503 Loss2: 1.435009
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.446812 Loss1: 0.965212 Loss2: 1.481601
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.770042 Loss1: 0.331729 Loss2: 1.438314
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.192467 Loss1: 0.710106 Loss2: 1.482362
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.742299 Loss1: 0.300870 Loss2: 1.441429
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.076971 Loss1: 0.602468 Loss2: 1.474503
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.727005 Loss1: 0.299373 Loss2: 1.427631
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.985382 Loss1: 0.506872 Loss2: 1.478510
+(DefaultActor pid=3765) >> Training accuracy: 0.920833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.910520 Loss1: 0.441799 Loss2: 1.468721
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.836105 Loss1: 0.366447 Loss2: 1.469658
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.812591 Loss1: 0.339071 Loss2: 1.473520
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.883029 Loss1: 0.411812 Loss2: 1.471217
+(DefaultActor pid=3764) >> Training accuracy: 0.853125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.781321 Loss1: 1.704854 Loss2: 2.076466
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.684799 Loss1: 1.163193 Loss2: 1.521607
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.369990 Loss1: 0.837226 Loss2: 1.532763
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.165859 Loss1: 0.650076 Loss2: 1.515783
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.862972 Loss1: 1.708635 Loss2: 2.154336
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.586080 Loss1: 1.116464 Loss2: 1.469617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.239238 Loss1: 0.802492 Loss2: 1.436747 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.028743 Loss1: 0.583045 Loss2: 1.445698 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.890777 Loss1: 0.452501 Loss2: 1.438275 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.779150 Loss1: 0.356223 Loss2: 1.422928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.734774 Loss1: 0.308341 Loss2: 1.426433
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.768685 Loss1: 0.272762 Loss2: 1.495923
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.648350 Loss1: 0.239168 Loss2: 1.409182 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.956731
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.778002 Loss1: 1.676808 Loss2: 2.101195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.274935 Loss1: 0.818685 Loss2: 1.456250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.085444 Loss1: 0.633444 Loss2: 1.452000
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.712415 Loss1: 1.661584 Loss2: 2.050831
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.597062 Loss1: 1.084103 Loss2: 1.512959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.316696 Loss1: 0.802550 Loss2: 1.514146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.126366 Loss1: 0.618529 Loss2: 1.507836 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.031542 Loss1: 0.529115 Loss2: 1.502427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.967766 Loss1: 0.464047 Loss2: 1.503719 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.935268
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.829779 Loss1: 0.343150 Loss2: 1.486629 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.775672 Loss1: 0.289607 Loss2: 1.486065 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.928125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.664804 Loss1: 1.157248 Loss2: 1.507556 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.053054 Loss1: 0.571086 Loss2: 1.481968 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.949862 Loss1: 0.480012 Loss2: 1.469850 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.953311 Loss1: 0.479180 Loss2: 1.474132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.928290 Loss1: 0.441512 Loss2: 1.486777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.843682 Loss1: 0.357708 Loss2: 1.485974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.785470 Loss1: 0.312587 Loss2: 1.472883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.763577 Loss1: 0.290568 Loss2: 1.473010 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.919792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.780370 Loss1: 0.328141 Loss2: 1.452229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.826034 Loss1: 0.363262 Loss2: 1.462772 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.884375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.813215 Loss1: 1.762428 Loss2: 2.050786
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.696641 Loss1: 1.191512 Loss2: 1.505130
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.348719 Loss1: 0.854972 Loss2: 1.493747
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.163989 Loss1: 0.674001 Loss2: 1.489988
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.759521 Loss1: 1.661393 Loss2: 2.098128
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.731326 Loss1: 1.162943 Loss2: 1.568384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.372247 Loss1: 0.831034 Loss2: 1.541212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.061705 Loss1: 0.540471 Loss2: 1.521233 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.972830 Loss1: 0.450797 Loss2: 1.522033 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.860344 Loss1: 0.362603 Loss2: 1.497741 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.932617
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.873790 Loss1: 0.374663 Loss2: 1.499127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.924363 Loss1: 0.419824 Loss2: 1.504540 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.898438
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.762774 Loss1: 1.717610 Loss2: 2.045164 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.309398 Loss1: 0.854049 Loss2: 1.455348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.826930 Loss1: 1.812055 Loss2: 2.014874 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.766419 Loss1: 1.300515 Loss2: 1.465904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.335076 Loss1: 0.894372 Loss2: 1.440703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.089468 Loss1: 0.642053 Loss2: 1.447415 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.018068 Loss1: 0.591920 Loss2: 1.426148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.919574 Loss1: 0.475077 Loss2: 1.444496 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.938542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.836875 Loss1: 0.397565 Loss2: 1.439310 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.783891 Loss1: 0.349296 Loss2: 1.434595 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.919792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.590654 Loss1: 1.134912 Loss2: 1.455743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.976023 Loss1: 0.565833 Loss2: 1.410191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.831832 Loss1: 0.431770 Loss2: 1.400062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.844867 Loss1: 0.443295 Loss2: 1.401572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.401177 Loss1: 0.927261 Loss2: 1.473916
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.778640 Loss1: 0.363652 Loss2: 1.414988
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.104550 Loss1: 0.629626 Loss2: 1.474924
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.717585 Loss1: 0.314764 Loss2: 1.402820
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.920561 Loss1: 0.466865 Loss2: 1.453696
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.684155 Loss1: 0.283925 Loss2: 1.400231
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.716993 Loss1: 0.310283 Loss2: 1.406710
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.826106 Loss1: 0.376582 Loss2: 1.449524
+(DefaultActor pid=3765) >> Training accuracy: 0.927083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.824325 Loss1: 0.368357 Loss2: 1.455968
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.800986 Loss1: 0.351037 Loss2: 1.449949
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.841916 Loss1: 0.387155 Loss2: 1.454761
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.778178 Loss1: 0.315509 Loss2: 1.462668
+(DefaultActor pid=3764) >> Training accuracy: 0.894531
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.716531 Loss1: 1.697013 Loss2: 2.019518
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.548641 Loss1: 1.081056 Loss2: 1.467585
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.329209 Loss1: 0.866894 Loss2: 1.462315
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.185640 Loss1: 0.710866 Loss2: 1.474774
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.986610 Loss1: 1.841948 Loss2: 2.144662
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.909605 Loss1: 0.448111 Loss2: 1.461494
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.886879 Loss1: 0.441872 Loss2: 1.445007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.816847 Loss1: 0.369499 Loss2: 1.447348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.981737 Loss1: 0.550807 Loss2: 1.430930 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.750937 Loss1: 0.305305 Loss2: 1.445632 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.713276 Loss1: 0.270910 Loss2: 1.442366 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.732572 Loss1: 0.315839 Loss2: 1.416734 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.924479
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.803648 Loss1: 1.818619 Loss2: 1.985029
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.626385 Loss1: 1.177973 Loss2: 1.448412
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.336004 Loss1: 0.891800 Loss2: 1.444205
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.118275 Loss1: 0.680338 Loss2: 1.437937
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.939751 Loss1: 1.899307 Loss2: 2.040444
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.891424 Loss1: 0.459153 Loss2: 1.432272
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.749127 Loss1: 1.255422 Loss2: 1.493706
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.867652 Loss1: 0.450961 Loss2: 1.416691
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.435322 Loss1: 0.956900 Loss2: 1.478422
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.855104 Loss1: 0.437980 Loss2: 1.417124
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.155471 Loss1: 0.686912 Loss2: 1.468559
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.830811 Loss1: 0.392195 Loss2: 1.438616
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.059057 Loss1: 0.603534 Loss2: 1.455524
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.834597 Loss1: 0.405388 Loss2: 1.429209
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.027377 Loss1: 0.556611 Loss2: 1.470766
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.810305 Loss1: 0.378007 Loss2: 1.432298
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.922445 Loss1: 0.456727 Loss2: 1.465718
+(DefaultActor pid=3765) >> Training accuracy: 0.925000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.822343 Loss1: 0.364151 Loss2: 1.458193
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.776137 Loss1: 0.320751 Loss2: 1.455386
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.790531 Loss1: 0.326462 Loss2: 1.464069
+(DefaultActor pid=3764) >> Training accuracy: 0.935417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.875036 Loss1: 1.832135 Loss2: 2.042901
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.683008 Loss1: 1.179707 Loss2: 1.503301
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.286478 Loss1: 0.784685 Loss2: 1.501792
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.070355 Loss1: 0.588690 Loss2: 1.481664
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.714657 Loss1: 1.689628 Loss2: 2.025028
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.693071 Loss1: 1.229910 Loss2: 1.463161 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.290475 Loss1: 0.824857 Loss2: 1.465618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.115116 Loss1: 0.656403 Loss2: 1.458713 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.938268 Loss1: 0.492937 Loss2: 1.445330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.857847 Loss1: 0.412968 Loss2: 1.444879 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.939583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.724088 Loss1: 0.262976 Loss2: 1.461112
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.854578 Loss1: 0.405210 Loss2: 1.449368
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.821855 Loss1: 0.372241 Loss2: 1.449614
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.761562 Loss1: 0.313212 Loss2: 1.448350
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.735561 Loss1: 0.294111 Loss2: 1.441450
+(DefaultActor pid=3764) >> Training accuracy: 0.937500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.843202 Loss1: 1.758165 Loss2: 2.085038
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.833115 Loss1: 1.303914 Loss2: 1.529201
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.396592 Loss1: 0.884721 Loss2: 1.511872
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.233199 Loss1: 0.746824 Loss2: 1.486375
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.722273 Loss1: 1.721671 Loss2: 2.000602
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.097062 Loss1: 0.603871 Loss2: 1.493191
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.660889 Loss1: 1.211475 Loss2: 1.449415
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.953952 Loss1: 0.468270 Loss2: 1.485682
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.300790 Loss1: 0.892834 Loss2: 1.407956
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.863781 Loss1: 0.389767 Loss2: 1.474014
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.017073 Loss1: 0.611438 Loss2: 1.405635
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.912946 Loss1: 0.431838 Loss2: 1.481108
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.852853 Loss1: 0.458979 Loss2: 1.393874
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.826710 Loss1: 0.341965 Loss2: 1.484745
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.781995 Loss1: 0.388257 Loss2: 1.393738
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.805875 Loss1: 0.325583 Loss2: 1.480292
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.840319 Loss1: 0.437624 Loss2: 1.402695
+(DefaultActor pid=3765) >> Training accuracy: 0.948958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.823027 Loss1: 0.416560 Loss2: 1.406466
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.792289 Loss1: 0.382350 Loss2: 1.409939
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.677290 Loss1: 0.273711 Loss2: 1.403579
+(DefaultActor pid=3764) >> Training accuracy: 0.915625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.754885 Loss1: 1.767457 Loss2: 1.987427
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.698759 Loss1: 1.235034 Loss2: 1.463726
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.263245 Loss1: 0.801946 Loss2: 1.461299
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.885164 Loss1: 1.848980 Loss2: 2.036184
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.134923 Loss1: 0.687486 Loss2: 1.447437
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.520120 Loss1: 1.055653 Loss2: 1.464467
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.975972 Loss1: 0.526031 Loss2: 1.449941
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.938682 Loss1: 0.492040 Loss2: 1.446641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.843048 Loss1: 0.391575 Loss2: 1.451473 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.840664 Loss1: 0.401245 Loss2: 1.439419 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.756228 Loss1: 0.306505 Loss2: 1.449723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.696645 Loss1: 0.258925 Loss2: 1.437719 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.905273
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.810430 Loss1: 0.355407 Loss2: 1.455023 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.907292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.714062 Loss1: 1.652062 Loss2: 2.062001 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.294662 Loss1: 0.818362 Loss2: 1.476300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.081812 Loss1: 0.592787 Loss2: 1.489026
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.829965 Loss1: 1.720190 Loss2: 2.109775
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.984507 Loss1: 0.511682 Loss2: 1.472825
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.561675 Loss1: 1.071761 Loss2: 1.489914
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.882565 Loss1: 0.412844 Loss2: 1.469722
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.119143 Loss1: 0.664954 Loss2: 1.454190
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.847741 Loss1: 0.379119 Loss2: 1.468622
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.027403 Loss1: 0.602311 Loss2: 1.425091
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.775875 Loss1: 0.315095 Loss2: 1.460781
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.857518 Loss1: 0.422962 Loss2: 1.434556
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.732203 Loss1: 0.267487 Loss2: 1.464716
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.838326 Loss1: 0.412126 Loss2: 1.426200
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.725891 Loss1: 0.264376 Loss2: 1.461516
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.809749 Loss1: 0.374329 Loss2: 1.435420
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.790241 Loss1: 0.342779 Loss2: 1.447462
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.703214 Loss1: 0.273146 Loss2: 1.430068
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.696231 Loss1: 0.272947 Loss2: 1.423284
+(DefaultActor pid=3764) >> Training accuracy: 0.925000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.707663 Loss1: 1.688117 Loss2: 2.019546
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.612307 Loss1: 1.167268 Loss2: 1.445039
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.237704 Loss1: 0.815404 Loss2: 1.422301
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.113726 Loss1: 0.684338 Loss2: 1.429388
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.895790 Loss1: 1.857146 Loss2: 2.038644
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.993343 Loss1: 0.554778 Loss2: 1.438565
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.644714 Loss1: 1.142445 Loss2: 1.502268
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.897325 Loss1: 0.473429 Loss2: 1.423895
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.458877 Loss1: 0.974007 Loss2: 1.484870
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.909789 Loss1: 0.482537 Loss2: 1.427252
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.191693 Loss1: 0.698599 Loss2: 1.493094
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.857207 Loss1: 0.427210 Loss2: 1.429997
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.023762 Loss1: 0.534773 Loss2: 1.488989
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.745157 Loss1: 0.314888 Loss2: 1.430269
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.943587 Loss1: 0.451796 Loss2: 1.491791
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.673488 Loss1: 0.250775 Loss2: 1.422713
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.922550 Loss1: 0.443686 Loss2: 1.478864
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.825325 Loss1: 0.352925 Loss2: 1.472400
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.861352 Loss1: 0.378569 Loss2: 1.482784
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.807770 Loss1: 0.319394 Loss2: 1.488376
+(DefaultActor pid=3764) >> Training accuracy: 0.913542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.654495 Loss1: 1.639405 Loss2: 2.015089
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.608377 Loss1: 1.141096 Loss2: 1.467280
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.237503 Loss1: 0.764871 Loss2: 1.472632
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.001517 Loss1: 0.572653 Loss2: 1.428864
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.808870 Loss1: 1.781108 Loss2: 2.027761
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.876489 Loss1: 0.447931 Loss2: 1.428558
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.695309 Loss1: 1.229564 Loss2: 1.465746
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.833331 Loss1: 0.405133 Loss2: 1.428198
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.374057 Loss1: 0.918752 Loss2: 1.455305
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.757794 Loss1: 0.336050 Loss2: 1.421744
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.141845 Loss1: 0.699282 Loss2: 1.442563
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.680211 Loss1: 0.258757 Loss2: 1.421454
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.989252 Loss1: 0.548035 Loss2: 1.441216
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.687417 Loss1: 0.260677 Loss2: 1.426740
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.839224 Loss1: 0.416938 Loss2: 1.422286
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.667313 Loss1: 0.236851 Loss2: 1.430462
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.778625 Loss1: 0.351131 Loss2: 1.427493
+(DefaultActor pid=3765) >> Training accuracy: 0.928125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.796342 Loss1: 0.367272 Loss2: 1.429070
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.793618 Loss1: 0.360736 Loss2: 1.432882
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.768630 Loss1: 0.324190 Loss2: 1.444440
+(DefaultActor pid=3764) >> Training accuracy: 0.917708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.707964 Loss1: 1.639119 Loss2: 2.068845
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.527196 Loss1: 1.092930 Loss2: 1.434266
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.263494 Loss1: 0.853318 Loss2: 1.410177
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.082449 Loss1: 0.667968 Loss2: 1.414482
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.780826 Loss1: 1.748005 Loss2: 2.032821
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.837822 Loss1: 0.410553 Loss2: 1.427269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.817061 Loss1: 0.395956 Loss2: 1.421105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.748029 Loss1: 0.336836 Loss2: 1.411194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.704051 Loss1: 0.293343 Loss2: 1.410708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.651410 Loss1: 0.237622 Loss2: 1.413788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.930288
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.828710 Loss1: 0.369014 Loss2: 1.459695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.797842 Loss1: 0.338900 Loss2: 1.458943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.743202 Loss1: 0.288120 Loss2: 1.455081
+(DefaultActor pid=3764) >> Training accuracy: 0.923958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.563821 Loss1: 1.553800 Loss2: 2.010021
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.569344 Loss1: 1.082426 Loss2: 1.486919
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.230387 Loss1: 0.749580 Loss2: 1.480807
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.066623 Loss1: 0.606243 Loss2: 1.460380
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.958990 Loss1: 1.911906 Loss2: 2.047085
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.942884 Loss1: 0.484673 Loss2: 1.458210
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.806950 Loss1: 1.324359 Loss2: 1.482591
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.880796 Loss1: 0.414810 Loss2: 1.465986
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.851110 Loss1: 0.397179 Loss2: 1.453932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.790631 Loss1: 0.329559 Loss2: 1.461072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.800000 Loss1: 0.340939 Loss2: 1.459061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.916784 Loss1: 0.456554 Loss2: 1.460229 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.948529
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.777058 Loss1: 0.332202 Loss2: 1.444857 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.938616
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.722222 Loss1: 1.730725 Loss2: 1.991497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.334559 Loss1: 0.883148 Loss2: 1.451411 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.103641 Loss1: 0.643314 Loss2: 1.460327
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.647783 Loss1: 1.611785 Loss2: 2.035998
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.645794 Loss1: 1.129822 Loss2: 1.515972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.231266 Loss1: 0.725014 Loss2: 1.506252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.059671 Loss1: 0.566915 Loss2: 1.492756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.910886 Loss1: 0.432110 Loss2: 1.478776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.863697 Loss1: 0.393282 Loss2: 1.470415 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.804906 Loss1: 0.335168 Loss2: 1.469737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.795562 Loss1: 0.320712 Loss2: 1.474850 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.922852
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.805764 Loss1: 0.332079 Loss2: 1.473685
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.873014 Loss1: 1.761331 Loss2: 2.111683
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.607093 Loss1: 1.088992 Loss2: 1.518102
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.329264 Loss1: 0.847366 Loss2: 1.481899
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.136090 Loss1: 0.642027 Loss2: 1.494063
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.969657 Loss1: 0.485819 Loss2: 1.483839
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.695968 Loss1: 1.735626 Loss2: 1.960342
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.503017 Loss1: 1.080841 Loss2: 1.422175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.184865 Loss1: 0.778147 Loss2: 1.406718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.082224 Loss1: 0.676903 Loss2: 1.405321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.867733 Loss1: 0.466095 Loss2: 1.401638 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.940625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.786268 Loss1: 0.325547 Loss2: 1.460721
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.820789 Loss1: 0.425804 Loss2: 1.394986
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.772628 Loss1: 0.374771 Loss2: 1.397857
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.649816 Loss1: 0.256255 Loss2: 1.393561
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.632003 Loss1: 0.249530 Loss2: 1.382472
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.623006 Loss1: 0.233652 Loss2: 1.389355
+(DefaultActor pid=3764) >> Training accuracy: 0.947917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.809575 Loss1: 1.787642 Loss2: 2.021933
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.606542 Loss1: 1.136252 Loss2: 1.470290
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.398897 Loss1: 0.949294 Loss2: 1.449604
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.130301 Loss1: 0.678675 Loss2: 1.451625
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.946524 Loss1: 0.507014 Loss2: 1.439509
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.769223 Loss1: 1.734633 Loss2: 2.034590
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.578784 Loss1: 1.097330 Loss2: 1.481454 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.237825 Loss1: 0.773263 Loss2: 1.464563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.069769 Loss1: 0.631370 Loss2: 1.438399 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.938443 Loss1: 0.503567 Loss2: 1.434876 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.913542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.754924 Loss1: 0.317954 Loss2: 1.436970
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.859146 Loss1: 0.423245 Loss2: 1.435901
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.851093 Loss1: 0.410042 Loss2: 1.441052
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.775570 Loss1: 0.345131 Loss2: 1.430439
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.778102 Loss1: 0.344287 Loss2: 1.433815
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.716823 Loss1: 0.285873 Loss2: 1.430949
+(DefaultActor pid=3764) >> Training accuracy: 0.938542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.724214 Loss1: 1.741112 Loss2: 1.983101
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.564584 Loss1: 1.126269 Loss2: 1.438315
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.326015 Loss1: 0.895324 Loss2: 1.430692
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.138346 Loss1: 0.701703 Loss2: 1.436643
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.998113 Loss1: 0.565205 Loss2: 1.432909
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.783501 Loss1: 1.732537 Loss2: 2.050965
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.706819 Loss1: 1.207418 Loss2: 1.499402
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.904284 Loss1: 0.483048 Loss2: 1.421236
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.313194 Loss1: 0.801520 Loss2: 1.511673
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.809262 Loss1: 0.381936 Loss2: 1.427327
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.148643 Loss1: 0.657973 Loss2: 1.490670
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.849388 Loss1: 0.427030 Loss2: 1.422358
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.061343 Loss1: 0.579141 Loss2: 1.482201
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.863238 Loss1: 0.436800 Loss2: 1.426438
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.751241 Loss1: 0.314786 Loss2: 1.436455 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.933594
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.866420 Loss1: 0.391985 Loss2: 1.474435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.779597 Loss1: 0.307846 Loss2: 1.471751 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.813111 Loss1: 1.760952 Loss2: 2.052159
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.654809 Loss1: 1.149351 Loss2: 1.505458
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.421875 Loss1: 0.943666 Loss2: 1.478209
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.159949 Loss1: 0.675355 Loss2: 1.484594
+DEBUG flwr 2023-10-09 20:29:20,865 | server.py:236 | fit_round 51 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.730119 Loss1: 1.629655 Loss2: 2.100464
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.518743 Loss1: 1.002091 Loss2: 1.516653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.210043 Loss1: 0.721201 Loss2: 1.488842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.078781 Loss1: 0.579790 Loss2: 1.498992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.035011 Loss1: 0.533488 Loss2: 1.501522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.943225 Loss1: 0.441243 Loss2: 1.501981 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.857571 Loss1: 0.365972 Loss2: 1.491600 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.743751 Loss1: 0.264631 Loss2: 1.479120 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.951042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.651863 Loss1: 1.550921 Loss2: 2.100942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.302494 Loss1: 0.819773 Loss2: 1.482721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.040992 Loss1: 0.550499 Loss2: 1.490492
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.160250 Loss1: 1.997390 Loss2: 2.162860
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.869236 Loss1: 1.310004 Loss2: 1.559232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.466254 Loss1: 0.940087 Loss2: 1.526167 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.204245 Loss1: 0.673784 Loss2: 1.530461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.695862 Loss1: 0.238135 Loss2: 1.457727
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.062740 Loss1: 0.534777 Loss2: 1.527962
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.737460 Loss1: 0.292966 Loss2: 1.444494
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.998824 Loss1: 0.467711 Loss2: 1.531113
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.698815 Loss1: 0.240006 Loss2: 1.458810
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.959369 Loss1: 0.427695 Loss2: 1.531675
+(DefaultActor pid=3765) >> Training accuracy: 0.923958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.903520 Loss1: 0.368397 Loss2: 1.535123
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.843883 Loss1: 0.318832 Loss2: 1.525051
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.831418 Loss1: 0.317476 Loss2: 1.513942
+(DefaultActor pid=3764) >> Training accuracy: 0.909598
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.692131 Loss1: 1.665932 Loss2: 2.026199
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.630442 Loss1: 1.138299 Loss2: 1.492144
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.284484 Loss1: 0.799954 Loss2: 1.484531
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.956006 Loss1: 1.847772 Loss2: 2.108233
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.084975 Loss1: 0.612280 Loss2: 1.472695
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.743526 Loss1: 1.210085 Loss2: 1.533440
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.995015 Loss1: 0.523677 Loss2: 1.471338
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.410017 Loss1: 0.911336 Loss2: 1.498681
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.905988 Loss1: 0.439963 Loss2: 1.466025
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.142975 Loss1: 0.628182 Loss2: 1.514793
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.888342 Loss1: 0.407108 Loss2: 1.481234
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.897582 Loss1: 0.420696 Loss2: 1.476886 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.816527 Loss1: 0.338256 Loss2: 1.478271 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.855701 Loss1: 0.380614 Loss2: 1.475087 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.862305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.819305 Loss1: 0.321869 Loss2: 1.497435 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.906250
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 20:29:20,865][flwr][DEBUG] - fit_round 51 received 50 results and 0 failures
+INFO flwr 2023-10-09 20:30:02,969 | server.py:125 | fit progress: (51, 2.3997931175719436, {'accuracy': 0.4815}, 117510.747286994)
+>> Test accuracy: 0.481500
+[2023-10-09 20:30:02,969][flwr][INFO] - fit progress: (51, 2.3997931175719436, {'accuracy': 0.4815}, 117510.747286994)
+DEBUG flwr 2023-10-09 20:30:02,969 | server.py:173 | evaluate_round 51: strategy sampled 50 clients (out of 50)
+[2023-10-09 20:30:02,969][flwr][DEBUG] - evaluate_round 51: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 20:39:09,425 | server.py:187 | evaluate_round 51 received 50 results and 0 failures
+[2023-10-09 20:39:09,425][flwr][DEBUG] - evaluate_round 51 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 20:39:09,425 | server.py:222 | fit_round 52: strategy sampled 50 clients (out of 50)
+[2023-10-09 20:39:09,425][flwr][DEBUG] - fit_round 52: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.797124 Loss1: 1.771976 Loss2: 2.025149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.318046 Loss1: 0.867626 Loss2: 1.450421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.036815 Loss1: 0.594989 Loss2: 1.441826
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.689877 Loss1: 1.650800 Loss2: 2.039077
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.587639 Loss1: 1.105255 Loss2: 1.482384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.257818 Loss1: 0.816967 Loss2: 1.440852 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.010696 Loss1: 0.575918 Loss2: 1.434778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.889472 Loss1: 0.464850 Loss2: 1.424622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.786444 Loss1: 0.361298 Loss2: 1.425145 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.914583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.749943 Loss1: 0.303451 Loss2: 1.446492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.740738 Loss1: 0.320395 Loss2: 1.420343
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.743944 Loss1: 0.330644 Loss2: 1.413300
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.712671 Loss1: 0.293150 Loss2: 1.419521
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.658292 Loss1: 0.237961 Loss2: 1.420331
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.624404 Loss1: 1.584656 Loss2: 2.039748
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.478083 Loss1: 1.040290 Loss2: 1.437792
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.377075 Loss1: 0.931061 Loss2: 1.446014
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.015666 Loss1: 1.781704 Loss2: 2.233962
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.977309 Loss1: 0.531017 Loss2: 1.446292
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.775223 Loss1: 0.373160 Loss2: 1.402063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.759794 Loss1: 0.362648 Loss2: 1.397146 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.707067 Loss1: 0.307661 Loss2: 1.399406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.922190 Loss1: 0.439547 Loss2: 1.482644 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.870793 Loss1: 0.393542 Loss2: 1.477250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.828397 Loss1: 0.344766 Loss2: 1.483630 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.709162 Loss1: 0.233114 Loss2: 1.476048 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.708662 Loss1: 1.648217 Loss2: 2.060444
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.612708 Loss1: 1.157449 Loss2: 1.455259
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.276732 Loss1: 0.814149 Loss2: 1.462583
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.070317 Loss1: 0.624308 Loss2: 1.446009
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.557959 Loss1: 1.507685 Loss2: 2.050274
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.486832 Loss1: 1.006217 Loss2: 1.480615 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.268646 Loss1: 0.791717 Loss2: 1.476928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.083801 Loss1: 0.607459 Loss2: 1.476342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.944451 Loss1: 0.493452 Loss2: 1.450999 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.804588 Loss1: 0.359395 Loss2: 1.445194 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.895089
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.715115 Loss1: 0.283212 Loss2: 1.431903 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.637262 Loss1: 0.207296 Loss2: 1.429967 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.942708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.664408 Loss1: 1.148158 Loss2: 1.516250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.148246 Loss1: 0.667831 Loss2: 1.480415 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.711088 Loss1: 1.723349 Loss2: 1.987739
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.002522 Loss1: 0.531164 Loss2: 1.471358
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.714734 Loss1: 1.253617 Loss2: 1.461117
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.971283 Loss1: 0.502003 Loss2: 1.469280
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.383897 Loss1: 0.935386 Loss2: 1.448511
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.875795 Loss1: 0.397709 Loss2: 1.478086
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.156782 Loss1: 0.710215 Loss2: 1.446568
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.862844 Loss1: 0.397820 Loss2: 1.465025
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.007616 Loss1: 0.578151 Loss2: 1.429465
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.835235 Loss1: 0.362215 Loss2: 1.473020
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.927894 Loss1: 0.498818 Loss2: 1.429077
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.765840 Loss1: 0.295175 Loss2: 1.470665
+(DefaultActor pid=3765) >> Training accuracy: 0.919792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.796233 Loss1: 0.384028 Loss2: 1.412205 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.714764 Loss1: 0.302806 Loss2: 1.411958 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.723119 Loss1: 1.248803 Loss2: 1.474316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.130140 Loss1: 0.655721 Loss2: 1.474419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.787801 Loss1: 1.775319 Loss2: 2.012482
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.002965 Loss1: 0.551282 Loss2: 1.451683
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.686913 Loss1: 1.237416 Loss2: 1.449497
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.909831 Loss1: 0.460395 Loss2: 1.449436
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.271603 Loss1: 0.846067 Loss2: 1.425537
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.863708 Loss1: 0.414771 Loss2: 1.448936
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.017105 Loss1: 0.587490 Loss2: 1.429615
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.775196 Loss1: 0.327076 Loss2: 1.448120
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.898456 Loss1: 0.491429 Loss2: 1.407027
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.759413 Loss1: 0.305908 Loss2: 1.453504
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.837489 Loss1: 0.417990 Loss2: 1.419499
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.802811 Loss1: 0.357107 Loss2: 1.445704
+(DefaultActor pid=3765) >> Training accuracy: 0.933333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.805196 Loss1: 0.383422 Loss2: 1.421774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.767088 Loss1: 0.338710 Loss2: 1.428378 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.842708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.648282 Loss1: 1.201204 Loss2: 1.447078 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.027057 Loss1: 0.607206 Loss2: 1.419851 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.914282 Loss1: 0.498327 Loss2: 1.415955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.813521 Loss1: 0.393503 Loss2: 1.420018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.774342 Loss1: 0.364886 Loss2: 1.409456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.708518 Loss1: 0.295120 Loss2: 1.413398 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.727076 Loss1: 0.315079 Loss2: 1.411997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.703266 Loss1: 0.291155 Loss2: 1.412112 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.923828
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.819789 Loss1: 0.379030 Loss2: 1.440758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.714509 Loss1: 0.275476 Loss2: 1.439033 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.554459 Loss1: 1.606086 Loss2: 1.948373
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.477374 Loss1: 1.007157 Loss2: 1.470217
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.234579 Loss1: 0.775913 Loss2: 1.458666
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.982953 Loss1: 0.536880 Loss2: 1.446073
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.666615 Loss1: 1.695332 Loss2: 1.971283
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.824006 Loss1: 0.404822 Loss2: 1.419184
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.594215 Loss1: 1.151691 Loss2: 1.442524
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.778582 Loss1: 0.359503 Loss2: 1.419079
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.245327 Loss1: 0.795787 Loss2: 1.449539
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.825501 Loss1: 0.402945 Loss2: 1.422556
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.993039 Loss1: 0.559823 Loss2: 1.433216
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.821867 Loss1: 0.382080 Loss2: 1.439787
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.944911 Loss1: 0.526127 Loss2: 1.418784
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.823830 Loss1: 0.373776 Loss2: 1.450053
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.896724 Loss1: 0.459456 Loss2: 1.437269
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.714360 Loss1: 0.283252 Loss2: 1.431108
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.837779 Loss1: 0.405637 Loss2: 1.432142
+(DefaultActor pid=3765) >> Training accuracy: 0.958008
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.747050 Loss1: 0.321783 Loss2: 1.425267
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.709785 Loss1: 0.293968 Loss2: 1.415818
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.663248 Loss1: 0.241806 Loss2: 1.421442
+(DefaultActor pid=3764) >> Training accuracy: 0.944336
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.861995 Loss1: 1.845722 Loss2: 2.016273
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.803420 Loss1: 1.334230 Loss2: 1.469190
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.352444 Loss1: 0.853676 Loss2: 1.498768
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.131946 Loss1: 0.673501 Loss2: 1.458445
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.950547 Loss1: 1.874306 Loss2: 2.076241
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.749373 Loss1: 1.276296 Loss2: 1.473077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.293804 Loss1: 0.855492 Loss2: 1.438312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.814318 Loss1: 0.370498 Loss2: 1.443820
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.065678 Loss1: 0.635758 Loss2: 1.429919
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.837475 Loss1: 0.388322 Loss2: 1.449154
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.018890 Loss1: 0.592385 Loss2: 1.426506
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.828255 Loss1: 0.368684 Loss2: 1.459571
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.867136 Loss1: 0.430659 Loss2: 1.436477
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.801361 Loss1: 0.376697 Loss2: 1.424664
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.841199 Loss1: 0.390445 Loss2: 1.450754
+(DefaultActor pid=3765) >> Training accuracy: 0.887500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.794953 Loss1: 0.360556 Loss2: 1.434397 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.928571
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.696971 Loss1: 1.660571 Loss2: 2.036399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.295740 Loss1: 0.822889 Loss2: 1.472850 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.063646 Loss1: 0.603432 Loss2: 1.460214
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.662905 Loss1: 1.606858 Loss2: 2.056047
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.508936 Loss1: 1.035824 Loss2: 1.473111 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.312258 Loss1: 0.843725 Loss2: 1.468533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.100296 Loss1: 0.628855 Loss2: 1.471441 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.937970 Loss1: 0.477746 Loss2: 1.460224 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.901873 Loss1: 0.447246 Loss2: 1.454627 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.745059 Loss1: 0.310070 Loss2: 1.434989
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.852084 Loss1: 0.400861 Loss2: 1.451224
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.740229 Loss1: 0.279879 Loss2: 1.460350
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.714615 Loss1: 0.265736 Loss2: 1.448880
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.685912 Loss1: 0.250318 Loss2: 1.435594
+(DefaultActor pid=3764) >> Training accuracy: 0.943750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.604438 Loss1: 1.568512 Loss2: 2.035926
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.514557 Loss1: 1.081996 Loss2: 1.432561
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.159514 Loss1: 0.753061 Loss2: 1.406452
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.954034 Loss1: 0.536525 Loss2: 1.417509
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.735413 Loss1: 1.729006 Loss2: 2.006407
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.574554 Loss1: 1.113845 Loss2: 1.460708 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.264878 Loss1: 0.825179 Loss2: 1.439700 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.055928 Loss1: 0.609323 Loss2: 1.446605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.947995 Loss1: 0.494661 Loss2: 1.453334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.927614 Loss1: 0.479830 Loss2: 1.447784 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.934375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.686236 Loss1: 0.282563 Loss2: 1.403673
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.909550 Loss1: 0.448573 Loss2: 1.460977
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.821759 Loss1: 0.366962 Loss2: 1.454797
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.805036 Loss1: 0.352406 Loss2: 1.452630
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.695777 Loss1: 0.252149 Loss2: 1.443627
+(DefaultActor pid=3764) >> Training accuracy: 0.938542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.723661 Loss1: 1.699679 Loss2: 2.023982
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.546635 Loss1: 1.080027 Loss2: 1.466609
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.216361 Loss1: 0.775790 Loss2: 1.440572
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.048338 Loss1: 0.593927 Loss2: 1.454411
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.854527 Loss1: 1.742366 Loss2: 2.112160
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.720245 Loss1: 1.189262 Loss2: 1.530983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.336677 Loss1: 0.823542 Loss2: 1.513135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.136869 Loss1: 0.617651 Loss2: 1.519218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.024933 Loss1: 0.513270 Loss2: 1.511663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.947604 Loss1: 0.440056 Loss2: 1.507547 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.677944 Loss1: 0.234397 Loss2: 1.443546
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.838040 Loss1: 0.338841 Loss2: 1.499200
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.768215 Loss1: 0.270094 Loss2: 1.498121
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.741433 Loss1: 0.254654 Loss2: 1.486779
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.785055 Loss1: 0.293470 Loss2: 1.491585
+(DefaultActor pid=3764) >> Training accuracy: 0.919792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.615849 Loss1: 1.551866 Loss2: 2.063982
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.611508 Loss1: 1.113404 Loss2: 1.498104
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.298609 Loss1: 0.782820 Loss2: 1.515789
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.029746 Loss1: 0.546301 Loss2: 1.483445
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.967721 Loss1: 1.857504 Loss2: 2.110217
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.739785 Loss1: 1.213160 Loss2: 1.526625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.340055 Loss1: 0.853012 Loss2: 1.487043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.105843 Loss1: 0.610567 Loss2: 1.495276 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.015582 Loss1: 0.536995 Loss2: 1.478587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.008217 Loss1: 0.509283 Loss2: 1.498935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.674228 Loss1: 0.212695 Loss2: 1.461533
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.892974 Loss1: 0.394597 Loss2: 1.498377
+(DefaultActor pid=3765) >> Training accuracy: 0.929167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.870664 Loss1: 0.393789 Loss2: 1.476874
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.807109 Loss1: 0.321262 Loss2: 1.485848
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.718262 Loss1: 0.242830 Loss2: 1.475432
+(DefaultActor pid=3764) >> Training accuracy: 0.920759
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.985749 Loss1: 1.902277 Loss2: 2.083472
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.828770 Loss1: 1.312566 Loss2: 1.516204
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.341986 Loss1: 0.855842 Loss2: 1.486143
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.123100 Loss1: 0.626970 Loss2: 1.496130
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.743050 Loss1: 1.711977 Loss2: 2.031074
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.962539 Loss1: 0.496597 Loss2: 1.465942
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.558035 Loss1: 1.102878 Loss2: 1.455157
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.882871 Loss1: 0.422921 Loss2: 1.459950
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.273844 Loss1: 0.833793 Loss2: 1.440051
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.927190 Loss1: 0.453881 Loss2: 1.473308
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.113029 Loss1: 0.661997 Loss2: 1.451031
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.874358 Loss1: 0.391660 Loss2: 1.482697
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.005362 Loss1: 0.556822 Loss2: 1.448540
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.829891 Loss1: 0.353306 Loss2: 1.476585
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.865829 Loss1: 0.422655 Loss2: 1.443173
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.777273 Loss1: 0.312557 Loss2: 1.464716
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.861713 Loss1: 0.425067 Loss2: 1.436645
+(DefaultActor pid=3765) >> Training accuracy: 0.918750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.799161 Loss1: 0.354811 Loss2: 1.444350
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.766803 Loss1: 0.324926 Loss2: 1.441877
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.780199 Loss1: 0.331422 Loss2: 1.448777
+(DefaultActor pid=3764) >> Training accuracy: 0.942708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.955627 Loss1: 1.907195 Loss2: 2.048432
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.794084 Loss1: 1.284463 Loss2: 1.509622
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.364081 Loss1: 0.900486 Loss2: 1.463595
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.196065 Loss1: 0.728684 Loss2: 1.467381
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.689771 Loss1: 1.616102 Loss2: 2.073670
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.601136 Loss1: 1.109956 Loss2: 1.491180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.473267 Loss1: 0.963311 Loss2: 1.509956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.237868 Loss1: 0.726142 Loss2: 1.511726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.037889 Loss1: 0.543489 Loss2: 1.494400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.978039 Loss1: 0.477815 Loss2: 1.500224 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.886458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.821515 Loss1: 0.348857 Loss2: 1.472658
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.950913 Loss1: 0.460212 Loss2: 1.490701
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.910913 Loss1: 0.428024 Loss2: 1.482889
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.802694 Loss1: 0.307899 Loss2: 1.494795
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.698513 Loss1: 0.222275 Loss2: 1.476239
+(DefaultActor pid=3764) >> Training accuracy: 0.912500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.769230 Loss1: 1.731044 Loss2: 2.038186
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.584079 Loss1: 1.066928 Loss2: 1.517151
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.311376 Loss1: 0.825025 Loss2: 1.486351
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.036405 Loss1: 0.566800 Loss2: 1.469605
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.728479 Loss1: 1.746554 Loss2: 1.981925
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.546858 Loss1: 1.059286 Loss2: 1.487572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.214004 Loss1: 0.754505 Loss2: 1.459498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.073937 Loss1: 0.625167 Loss2: 1.448771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.990953 Loss1: 0.530405 Loss2: 1.460548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.853163 Loss1: 0.392652 Loss2: 1.460511 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.950000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.781202 Loss1: 0.338345 Loss2: 1.442857 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.746247 Loss1: 0.302180 Loss2: 1.444067 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.917969
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.734804 Loss1: 1.252267 Loss2: 1.482537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.059060 Loss1: 0.589985 Loss2: 1.469075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.890604 Loss1: 1.797299 Loss2: 2.093305
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.017348 Loss1: 0.554753 Loss2: 1.462594
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.801515 Loss1: 1.293400 Loss2: 1.508115
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.935335 Loss1: 0.466975 Loss2: 1.468360
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.881867 Loss1: 0.407966 Loss2: 1.473900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.840198 Loss1: 0.368492 Loss2: 1.471706 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.780326 Loss1: 0.312576 Loss2: 1.467750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.791332 Loss1: 0.319929 Loss2: 1.471404 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.904297
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.906736 Loss1: 0.416713 Loss2: 1.490023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.811455 Loss1: 0.327291 Loss2: 1.484164 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.926042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.563360 Loss1: 1.579409 Loss2: 1.983950
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.585812 Loss1: 1.105477 Loss2: 1.480335
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.261401 Loss1: 0.794308 Loss2: 1.467092
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.648192 Loss1: 1.622267 Loss2: 2.025924
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.084287 Loss1: 0.624307 Loss2: 1.459980
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.536680 Loss1: 1.076975 Loss2: 1.459705
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.956326 Loss1: 0.507750 Loss2: 1.448576
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.239017 Loss1: 0.799680 Loss2: 1.439337
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.873207 Loss1: 0.422241 Loss2: 1.450966
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.816311 Loss1: 0.366421 Loss2: 1.449890 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.722863 Loss1: 0.282985 Loss2: 1.439878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.735730 Loss1: 0.295995 Loss2: 1.439735 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.774756 Loss1: 0.326128 Loss2: 1.448628 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.944853
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.725552 Loss1: 0.293249 Loss2: 1.432303 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.894792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.696648 Loss1: 1.659999 Loss2: 2.036649
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.566471 Loss1: 1.100418 Loss2: 1.466054
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.223931 Loss1: 0.767806 Loss2: 1.456125
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.796801 Loss1: 1.686128 Loss2: 2.110672
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.014185 Loss1: 0.558251 Loss2: 1.455934
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.602406 Loss1: 1.100749 Loss2: 1.501657
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.822933 Loss1: 0.383067 Loss2: 1.439867
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.821966 Loss1: 0.385358 Loss2: 1.436608 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.821512 Loss1: 0.380234 Loss2: 1.441278 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.771061 Loss1: 0.315611 Loss2: 1.455451 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.796172 Loss1: 0.341983 Loss2: 1.454189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.822917 Loss1: 0.369478 Loss2: 1.453439 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.908333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.701297 Loss1: 0.242735 Loss2: 1.458562 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.949519
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.788075 Loss1: 1.757616 Loss2: 2.030459
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.668403 Loss1: 1.176663 Loss2: 1.491739
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.274305 Loss1: 0.801864 Loss2: 1.472441
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.132612 Loss1: 0.664934 Loss2: 1.467678
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.768573 Loss1: 1.720139 Loss2: 2.048434
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.963958 Loss1: 0.502556 Loss2: 1.461402
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.556166 Loss1: 1.071620 Loss2: 1.484545
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.958085 Loss1: 0.492764 Loss2: 1.465321
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.289984 Loss1: 0.819629 Loss2: 1.470355
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.959264 Loss1: 0.481441 Loss2: 1.477823
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.098603 Loss1: 0.610210 Loss2: 1.488393
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.903326 Loss1: 0.435409 Loss2: 1.467917
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.004078 Loss1: 0.526217 Loss2: 1.477862
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.826997 Loss1: 0.367704 Loss2: 1.459293
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.949695 Loss1: 0.471992 Loss2: 1.477703
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.788439 Loss1: 0.323017 Loss2: 1.465422
+(DefaultActor pid=3765) >> Training accuracy: 0.904167
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.946160 Loss1: 0.467172 Loss2: 1.478989
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.861485 Loss1: 0.377566 Loss2: 1.483920
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.803082 Loss1: 0.326288 Loss2: 1.476794
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.753859 Loss1: 0.281448 Loss2: 1.472410
+(DefaultActor pid=3764) >> Training accuracy: 0.927083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.795949 Loss1: 1.785532 Loss2: 2.010417
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.703091 Loss1: 1.211911 Loss2: 1.491180
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.354104 Loss1: 0.865396 Loss2: 1.488708
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.132188 Loss1: 0.661296 Loss2: 1.470892
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.686997 Loss1: 1.697079 Loss2: 1.989918
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.097589 Loss1: 0.600158 Loss2: 1.497431
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.677361 Loss1: 1.186139 Loss2: 1.491222
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.035407 Loss1: 0.547645 Loss2: 1.487762
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.347398 Loss1: 0.857914 Loss2: 1.489484
+(DefaultActor pid=3765) Epoch: 6 Loss: 2.002440 Loss1: 0.505417 Loss2: 1.497023
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.064885 Loss1: 0.605781 Loss2: 1.459104
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.962164 Loss1: 0.458922 Loss2: 1.503242
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.928197 Loss1: 0.475033 Loss2: 1.453164
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.913034 Loss1: 0.416528 Loss2: 1.496506
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.818324 Loss1: 0.375630 Loss2: 1.442694
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.856317 Loss1: 0.364053 Loss2: 1.492264
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.872245 Loss1: 0.413864 Loss2: 1.458381
+(DefaultActor pid=3765) >> Training accuracy: 0.910156
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.782709 Loss1: 0.333241 Loss2: 1.449468
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.751291 Loss1: 0.309900 Loss2: 1.441391
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.764940 Loss1: 0.320007 Loss2: 1.444932
+(DefaultActor pid=3764) >> Training accuracy: 0.927734
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.698257 Loss1: 1.626771 Loss2: 2.071486
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.638036 Loss1: 1.149440 Loss2: 1.488596
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.305754 Loss1: 0.815594 Loss2: 1.490160
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.059764 Loss1: 0.577570 Loss2: 1.482194
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.869590 Loss1: 1.786900 Loss2: 2.082690
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.758932 Loss1: 1.229705 Loss2: 1.529227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.349168 Loss1: 0.838202 Loss2: 1.510966 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.175543 Loss1: 0.665363 Loss2: 1.510180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.074180 Loss1: 0.566978 Loss2: 1.507202 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.992186 Loss1: 0.492711 Loss2: 1.499476 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.921390 Loss1: 0.422039 Loss2: 1.499351 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.809443 Loss1: 0.292266 Loss2: 1.517178 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.932617
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.689471 Loss1: 1.197719 Loss2: 1.491752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.131474 Loss1: 0.632609 Loss2: 1.498865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.139860 Loss1: 0.659970 Loss2: 1.479890
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.720290 Loss1: 1.669318 Loss2: 2.050972
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.418490 Loss1: 0.950274 Loss2: 1.468216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.118856 Loss1: 0.692349 Loss2: 1.426508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.945789 Loss1: 0.512418 Loss2: 1.433371 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.872735 Loss1: 0.441500 Loss2: 1.431235 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.930208
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.767500 Loss1: 0.294774 Loss2: 1.472727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.845719 Loss1: 0.409551 Loss2: 1.436169
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.809936 Loss1: 0.365118 Loss2: 1.444818
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.757628 Loss1: 0.325900 Loss2: 1.431729
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.741044 Loss1: 0.315698 Loss2: 1.425345
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.720896 Loss1: 0.284760 Loss2: 1.436137
+(DefaultActor pid=3764) >> Training accuracy: 0.954167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.734793 Loss1: 1.744596 Loss2: 1.990197
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.657896 Loss1: 1.176450 Loss2: 1.481446
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.278306 Loss1: 0.816539 Loss2: 1.461766
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.035593 Loss1: 0.583819 Loss2: 1.451773
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.886417 Loss1: 0.451495 Loss2: 1.434922
+DEBUG flwr 2023-10-09 21:08:10,917 | server.py:236 | fit_round 52 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.724197 Loss1: 1.659378 Loss2: 2.064819
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.856152 Loss1: 0.421480 Loss2: 1.434671
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.604509 Loss1: 1.108108 Loss2: 1.496401
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.796777 Loss1: 0.366876 Loss2: 1.429901
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.244078 Loss1: 0.743585 Loss2: 1.500493
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.722686 Loss1: 0.297630 Loss2: 1.425056
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.117847 Loss1: 0.633334 Loss2: 1.484513
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.721798 Loss1: 0.299041 Loss2: 1.422757
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.997314 Loss1: 0.519398 Loss2: 1.477916
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.699162 Loss1: 0.275843 Loss2: 1.423319
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.928263 Loss1: 0.451797 Loss2: 1.476466
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.879529 Loss1: 0.397596 Loss2: 1.481933
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.787672 Loss1: 0.316253 Loss2: 1.471419
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.759675 Loss1: 0.293461 Loss2: 1.466215
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.713737 Loss1: 0.244642 Loss2: 1.469094
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.660937 Loss1: 1.661143 Loss2: 1.999794
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.517873 Loss1: 1.076034 Loss2: 1.441839
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.189825 Loss1: 0.750349 Loss2: 1.439476
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.988919 Loss1: 0.550971 Loss2: 1.437948
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.884516 Loss1: 1.763098 Loss2: 2.121418
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.930125 Loss1: 0.499999 Loss2: 1.430127
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.677446 Loss1: 1.182672 Loss2: 1.494774
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.876977 Loss1: 0.441551 Loss2: 1.435426
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.847031 Loss1: 0.411695 Loss2: 1.435336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.809326 Loss1: 0.372196 Loss2: 1.437130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.755682 Loss1: 0.318530 Loss2: 1.437152 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.734242 Loss1: 0.300370 Loss2: 1.433871 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.672508 Loss1: 0.254003 Loss2: 1.418506 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.956731
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.795394 Loss1: 1.681396 Loss2: 2.113998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.171847 Loss1: 0.713069 Loss2: 1.458777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.004992 Loss1: 0.546943 Loss2: 1.458049
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.608047 Loss1: 1.599643 Loss2: 2.008404
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.632341 Loss1: 1.149074 Loss2: 1.483268 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.254748 Loss1: 0.772238 Loss2: 1.482509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.043726 Loss1: 0.584325 Loss2: 1.459400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.983998 Loss1: 0.526387 Loss2: 1.457611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.878594 Loss1: 0.430216 Loss2: 1.448378 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.735307 Loss1: 0.298262 Loss2: 1.437045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.643091 Loss1: 0.209092 Loss2: 1.433998 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962891
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 21:08:10,917][flwr][DEBUG] - fit_round 52 received 50 results and 0 failures
+INFO flwr 2023-10-09 21:08:52,520 | server.py:125 | fit progress: (52, 2.3937867083869424, {'accuracy': 0.4836}, 119840.298075107)
+>> Test accuracy: 0.483600
+[2023-10-09 21:08:52,520][flwr][INFO] - fit progress: (52, 2.3937867083869424, {'accuracy': 0.4836}, 119840.298075107)
+DEBUG flwr 2023-10-09 21:08:52,520 | server.py:173 | evaluate_round 52: strategy sampled 50 clients (out of 50)
+[2023-10-09 21:08:52,520][flwr][DEBUG] - evaluate_round 52: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 21:17:58,156 | server.py:187 | evaluate_round 52 received 50 results and 0 failures
+[2023-10-09 21:17:58,156][flwr][DEBUG] - evaluate_round 52 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 21:17:58,157 | server.py:222 | fit_round 53: strategy sampled 50 clients (out of 50)
+[2023-10-09 21:17:58,157][flwr][DEBUG] - fit_round 53: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.925190 Loss1: 1.771658 Loss2: 2.153532 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.269745 Loss1: 0.823687 Loss2: 1.446057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.910876 Loss1: 0.478924 Loss2: 1.431951 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.718502 Loss1: 1.173357 Loss2: 1.545145 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.699405 Loss1: 0.286705 Loss2: 1.412700 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.073564 Loss1: 0.564193 Loss2: 1.509371 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.936198
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.016798 Loss1: 0.509981 Loss2: 1.506818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.845833 Loss1: 0.345713 Loss2: 1.500121 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.866200 Loss1: 0.372403 Loss2: 1.493797 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.857754 Loss1: 0.345127 Loss2: 1.512627 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.866211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.944833 Loss1: 0.515309 Loss2: 1.429524 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.797884 Loss1: 0.383320 Loss2: 1.414564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.766637 Loss1: 0.350697 Loss2: 1.415940
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.762880 Loss1: 1.707143 Loss2: 2.055737
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.766083 Loss1: 0.341044 Loss2: 1.425039
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.641690 Loss1: 1.164346 Loss2: 1.477344
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.739077 Loss1: 0.317779 Loss2: 1.421298
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.269240 Loss1: 0.785368 Loss2: 1.483873
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.699603 Loss1: 0.282393 Loss2: 1.417211
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.063675 Loss1: 0.586229 Loss2: 1.477446
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.011485 Loss1: 0.548368 Loss2: 1.463117
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.932424 Loss1: 0.456719 Loss2: 1.475705
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.895595 Loss1: 0.422209 Loss2: 1.473386
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.797318 Loss1: 0.334240 Loss2: 1.463078
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.838314 Loss1: 1.763506 Loss2: 2.074808
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.759393 Loss1: 0.298752 Loss2: 1.460640
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.556727 Loss1: 1.067458 Loss2: 1.489269
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.691667 Loss1: 0.225474 Loss2: 1.466193
+(DefaultActor pid=3764) >> Training accuracy: 0.926042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.076285 Loss1: 0.601044 Loss2: 1.475241 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.910152 Loss1: 0.445677 Loss2: 1.464475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.891467 Loss1: 0.423613 Loss2: 1.467854
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.838280 Loss1: 1.781528 Loss2: 2.056752
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.890167 Loss1: 0.408151 Loss2: 1.482016
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.604599 Loss1: 1.121488 Loss2: 1.483111
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.826246 Loss1: 0.351628 Loss2: 1.474618
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.333401 Loss1: 0.865439 Loss2: 1.467963
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.785717 Loss1: 0.322535 Loss2: 1.463182
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.138049 Loss1: 0.656174 Loss2: 1.481875
+(DefaultActor pid=3765) >> Training accuracy: 0.947917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.022120 Loss1: 0.552085 Loss2: 1.470035
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.947994 Loss1: 0.482703 Loss2: 1.465291
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.849637 Loss1: 0.385140 Loss2: 1.464498
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.852917 Loss1: 0.392624 Loss2: 1.460293
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.822517 Loss1: 1.699173 Loss2: 2.123344
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.745838 Loss1: 0.281146 Loss2: 1.464691
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.664890 Loss1: 1.161858 Loss2: 1.503032
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.693426 Loss1: 0.247095 Loss2: 1.446331
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.103913 Loss1: 0.620193 Loss2: 1.483720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.879918 Loss1: 0.399620 Loss2: 1.480298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.631281 Loss1: 1.639198 Loss2: 1.992082 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.489435 Loss1: 1.027484 Loss2: 1.461951 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.318198 Loss1: 0.868402 Loss2: 1.449797 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.916295
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.849237 Loss1: 0.419821 Loss2: 1.429416 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.773661 Loss1: 0.344913 Loss2: 1.428748 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.853567 Loss1: 1.664193 Loss2: 2.189374
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.721593 Loss1: 0.298910 Loss2: 1.422683
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.526508 Loss1: 1.022403 Loss2: 1.504104
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.702372 Loss1: 0.279240 Loss2: 1.423132
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.649888 Loss1: 0.235793 Loss2: 1.414095 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.887830 Loss1: 0.428542 Loss2: 1.459288 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.740229 Loss1: 0.294877 Loss2: 1.445352 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.765597 Loss1: 0.295795 Loss2: 1.469802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.775404 Loss1: 0.311208 Loss2: 1.464197 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.078266 Loss1: 0.601033 Loss2: 1.477233 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.934261 Loss1: 0.463702 Loss2: 1.470559 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.659508 Loss1: 1.541665 Loss2: 2.117842
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.912130 Loss1: 0.418845 Loss2: 1.493285
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.579604 Loss1: 1.046928 Loss2: 1.532677
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.807400 Loss1: 0.323224 Loss2: 1.484176
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.118167 Loss1: 0.609988 Loss2: 1.508179
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.729841 Loss1: 0.274703 Loss2: 1.455138
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.004176 Loss1: 0.509699 Loss2: 1.494477
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.705780 Loss1: 0.240833 Loss2: 1.464947
+(DefaultActor pid=3764) >> Training accuracy: 0.946875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.955150 Loss1: 0.438173 Loss2: 1.516977 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.784877 Loss1: 0.288744 Loss2: 1.496133 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.825254 Loss1: 0.326481 Loss2: 1.498773
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.732425 Loss1: 1.666932 Loss2: 2.065493
+(DefaultActor pid=3765) >> Training accuracy: 0.919792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.765589 Loss1: 0.264854 Loss2: 1.500735
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.589699 Loss1: 1.061244 Loss2: 1.528455
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.299573 Loss1: 0.798369 Loss2: 1.501204
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.031537 Loss1: 0.534933 Loss2: 1.496604
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.940507 Loss1: 0.449954 Loss2: 1.490553
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.910941 Loss1: 0.435885 Loss2: 1.475056
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.765914 Loss1: 1.717484 Loss2: 2.048430
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.853212 Loss1: 0.355314 Loss2: 1.497898
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.531621 Loss1: 1.073155 Loss2: 1.458466
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.773171 Loss1: 0.292316 Loss2: 1.480855
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.211572 Loss1: 0.762514 Loss2: 1.449058
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.788148 Loss1: 0.312384 Loss2: 1.475764
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.045749 Loss1: 0.607871 Loss2: 1.437878
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.856287 Loss1: 0.426657 Loss2: 1.429629
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.776121 Loss1: 0.297375 Loss2: 1.478746
+(DefaultActor pid=3764) >> Training accuracy: 0.930664
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.729910 Loss1: 0.303615 Loss2: 1.426295 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.677947 Loss1: 0.249406 Loss2: 1.428541 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.702897 Loss1: 0.278475 Loss2: 1.424422
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.722387 Loss1: 1.704950 Loss2: 2.017438
+(DefaultActor pid=3765) >> Training accuracy: 0.946875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.698814 Loss1: 1.228561 Loss2: 1.470253
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.237328 Loss1: 0.787468 Loss2: 1.449860
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.032145 Loss1: 0.590671 Loss2: 1.441474
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.916548 Loss1: 0.484229 Loss2: 1.432319
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.886305 Loss1: 1.834374 Loss2: 2.051930
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.847312 Loss1: 0.413828 Loss2: 1.433484
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.664102 Loss1: 1.180117 Loss2: 1.483985
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.836496 Loss1: 0.407933 Loss2: 1.428564
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.323517 Loss1: 0.853587 Loss2: 1.469930
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.766682 Loss1: 0.327449 Loss2: 1.439233
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.180117 Loss1: 0.695777 Loss2: 1.484340
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.717263 Loss1: 0.288884 Loss2: 1.428378
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.004672 Loss1: 0.533166 Loss2: 1.471507
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.712744 Loss1: 0.283215 Loss2: 1.429529
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.870943 Loss1: 0.403938 Loss2: 1.467005 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.784865 Loss1: 0.320275 Loss2: 1.464589 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.734837 Loss1: 0.277072 Loss2: 1.457766
+(DefaultActor pid=3765) >> Training accuracy: 0.931250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.600443 Loss1: 1.575186 Loss2: 2.025257
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.555019 Loss1: 1.072481 Loss2: 1.482537
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.195124 Loss1: 0.727030 Loss2: 1.468093
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.071449 Loss1: 0.621870 Loss2: 1.449579
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.001948 Loss1: 0.539137 Loss2: 1.462811
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.585680 Loss1: 1.541823 Loss2: 2.043857
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.969375 Loss1: 0.502491 Loss2: 1.466884
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.532394 Loss1: 1.023664 Loss2: 1.508729
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.847274 Loss1: 0.372193 Loss2: 1.475081
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.213914 Loss1: 0.709719 Loss2: 1.504194
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.759328 Loss1: 0.295308 Loss2: 1.464019
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.732891 Loss1: 0.272266 Loss2: 1.460625
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.031725 Loss1: 0.524700 Loss2: 1.507025
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.714831 Loss1: 0.255681 Loss2: 1.459151
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.852240 Loss1: 0.358952 Loss2: 1.493288
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.810076 Loss1: 0.341229 Loss2: 1.468847
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.763072 Loss1: 0.288020 Loss2: 1.475052
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.776194 Loss1: 0.288839 Loss2: 1.487356
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.887733 Loss1: 0.402763 Loss2: 1.484970
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.580285 Loss1: 1.549769 Loss2: 2.030515
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.762629 Loss1: 0.268442 Loss2: 1.494187
+(DefaultActor pid=3765) >> Training accuracy: 0.934570
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.251334 Loss1: 0.765741 Loss2: 1.485593 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.925231 Loss1: 0.452102 Loss2: 1.473129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.734090 Loss1: 1.696545 Loss2: 2.037545
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.829129 Loss1: 0.374041 Loss2: 1.455088
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.688604 Loss1: 1.177089 Loss2: 1.511515
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.831073 Loss1: 0.366837 Loss2: 1.464236
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.332123 Loss1: 0.836777 Loss2: 1.495346
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.831487 Loss1: 0.356701 Loss2: 1.474786
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.784469 Loss1: 0.312635 Loss2: 1.471834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.764121 Loss1: 0.298519 Loss2: 1.465602 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.941176
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.886052 Loss1: 0.398896 Loss2: 1.487156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.898996 Loss1: 0.403765 Loss2: 1.495231 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.814286 Loss1: 0.326767 Loss2: 1.487519
+(DefaultActor pid=3765) >> Training accuracy: 0.914062
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.588029 Loss1: 1.536904 Loss2: 2.051125
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.609817 Loss1: 1.124148 Loss2: 1.485669
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.296242 Loss1: 0.816073 Loss2: 1.480169
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.083985 Loss1: 0.604039 Loss2: 1.479946
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.865089 Loss1: 0.401704 Loss2: 1.463385
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.751036 Loss1: 1.729800 Loss2: 2.021236
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.640339 Loss1: 1.147504 Loss2: 1.492835 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.300179 Loss1: 0.794287 Loss2: 1.505893 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.051751 Loss1: 0.564509 Loss2: 1.487242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.715287 Loss1: 0.275239 Loss2: 1.440048
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.910493 Loss1: 0.439483 Loss2: 1.471010
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.701618 Loss1: 0.254165 Loss2: 1.447453
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.849976 Loss1: 0.379083 Loss2: 1.470892
+(DefaultActor pid=3764) >> Training accuracy: 0.935547
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.860529 Loss1: 0.396141 Loss2: 1.464388
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.919666 Loss1: 0.447073 Loss2: 1.472592
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.777354 Loss1: 0.298670 Loss2: 1.478684
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.730992 Loss1: 0.266213 Loss2: 1.464779
+(DefaultActor pid=3765) >> Training accuracy: 0.911458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.765621 Loss1: 1.786782 Loss2: 1.978839
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.703496 Loss1: 1.259203 Loss2: 1.444293
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.370252 Loss1: 0.925500 Loss2: 1.444752
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.125249 Loss1: 0.684995 Loss2: 1.440255
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.710202 Loss1: 1.585217 Loss2: 2.124986
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.436177 Loss1: 0.936253 Loss2: 1.499924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.282141 Loss1: 0.795768 Loss2: 1.486373 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.195372 Loss1: 0.691448 Loss2: 1.503924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.976868 Loss1: 0.488104 Loss2: 1.488765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.807834 Loss1: 0.350069 Loss2: 1.457765 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.934570
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.758506 Loss1: 0.343700 Loss2: 1.414807
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.772300 Loss1: 0.322209 Loss2: 1.450091
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.704327 Loss1: 0.255949 Loss2: 1.448378
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.704487 Loss1: 0.252450 Loss2: 1.452037
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.722417 Loss1: 0.269053 Loss2: 1.453364
+(DefaultActor pid=3765) >> Training accuracy: 0.934375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.800234 Loss1: 1.740543 Loss2: 2.059691
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.632414 Loss1: 1.133607 Loss2: 1.498807
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.347818 Loss1: 0.853141 Loss2: 1.494677
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.156253 Loss1: 0.645499 Loss2: 1.510754
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.920739 Loss1: 1.898259 Loss2: 2.022480
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.580063 Loss1: 1.157436 Loss2: 1.422627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.958044 Loss1: 0.472955 Loss2: 1.485089
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.169632 Loss1: 0.756422 Loss2: 1.413211
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.924610 Loss1: 0.422479 Loss2: 1.502131
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.048888 Loss1: 0.634810 Loss2: 1.414079
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.916282 Loss1: 0.510170 Loss2: 1.406112
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.791561 Loss1: 0.295166 Loss2: 1.496395
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.887153 Loss1: 0.473371 Loss2: 1.413782
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.816103 Loss1: 0.324986 Loss2: 1.491117
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.802047 Loss1: 0.388350 Loss2: 1.413697
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.847024 Loss1: 0.351160 Loss2: 1.495864
+(DefaultActor pid=3764) >> Training accuracy: 0.931250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.756541 Loss1: 0.355055 Loss2: 1.401486 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.911830
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.936814 Loss1: 1.786783 Loss2: 2.150032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.453714 Loss1: 0.912293 Loss2: 1.541421 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.220650 Loss1: 0.686177 Loss2: 1.534473
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.724058 Loss1: 1.731243 Loss2: 1.992815
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.076367 Loss1: 0.547743 Loss2: 1.528624
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.552457 Loss1: 1.125342 Loss2: 1.427115
+(DefaultActor pid=3764) Epoch: 5 Loss: 2.053701 Loss1: 0.519654 Loss2: 1.534047
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.155957 Loss1: 0.759132 Loss2: 1.396824
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.910241 Loss1: 0.373355 Loss2: 1.536887
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.958118 Loss1: 0.554559 Loss2: 1.403558
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.848344 Loss1: 0.329120 Loss2: 1.519224
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.854258 Loss1: 0.456071 Loss2: 1.398187
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.868952 Loss1: 0.354250 Loss2: 1.514702
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.758960 Loss1: 0.367294 Loss2: 1.391666
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.909615 Loss1: 0.382723 Loss2: 1.526891
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.710162 Loss1: 0.326648 Loss2: 1.383514
+(DefaultActor pid=3764) >> Training accuracy: 0.908333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.667119 Loss1: 0.275712 Loss2: 1.391407
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.733999 Loss1: 0.340551 Loss2: 1.393449
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.721769 Loss1: 0.321601 Loss2: 1.400168
+(DefaultActor pid=3765) >> Training accuracy: 0.929167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.797432 Loss1: 1.768636 Loss2: 2.028796
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.534438 Loss1: 1.052733 Loss2: 1.481704
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.244200 Loss1: 0.778620 Loss2: 1.465580
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.048932 Loss1: 0.564732 Loss2: 1.484199
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.682659 Loss1: 1.650351 Loss2: 2.032308
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.924519 Loss1: 0.455423 Loss2: 1.469096
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.625319 Loss1: 1.140920 Loss2: 1.484399
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.951338 Loss1: 0.489708 Loss2: 1.461630
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.258226 Loss1: 0.777352 Loss2: 1.480874
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.899139 Loss1: 0.414495 Loss2: 1.484644
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.121554 Loss1: 0.644177 Loss2: 1.477378
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.826471 Loss1: 0.353765 Loss2: 1.472706
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.046895 Loss1: 0.587682 Loss2: 1.459213
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.726365 Loss1: 0.262393 Loss2: 1.463973
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.940369 Loss1: 0.460765 Loss2: 1.479605
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.708386 Loss1: 0.247583 Loss2: 1.460803
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.880593 Loss1: 0.412229 Loss2: 1.468364
+(DefaultActor pid=3764) >> Training accuracy: 0.914583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.855718 Loss1: 0.389468 Loss2: 1.466250
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.779499 Loss1: 0.309114 Loss2: 1.470386
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.698965 Loss1: 0.237027 Loss2: 1.461937
+(DefaultActor pid=3765) >> Training accuracy: 0.933333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.659448 Loss1: 1.580020 Loss2: 2.079428
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.532695 Loss1: 1.026791 Loss2: 1.505904
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.250451 Loss1: 0.779496 Loss2: 1.470955
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.100094 Loss1: 0.625558 Loss2: 1.474536
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.694312 Loss1: 1.666038 Loss2: 2.028274
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.930377 Loss1: 0.454907 Loss2: 1.475470
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.646617 Loss1: 1.175499 Loss2: 1.471118
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.828804 Loss1: 0.366076 Loss2: 1.462728
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.334124 Loss1: 0.883703 Loss2: 1.450422
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.703746 Loss1: 0.248025 Loss2: 1.455721
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.085670 Loss1: 0.637927 Loss2: 1.447742
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.651102 Loss1: 0.206837 Loss2: 1.444264
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.894384 Loss1: 0.460723 Loss2: 1.433660
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.724642 Loss1: 0.288006 Loss2: 1.436636
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.830077 Loss1: 0.420545 Loss2: 1.409532
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.757733 Loss1: 0.311483 Loss2: 1.446250
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.759312 Loss1: 0.326557 Loss2: 1.432755
+(DefaultActor pid=3764) >> Training accuracy: 0.942708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.703436 Loss1: 0.291724 Loss2: 1.411713
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.656683 Loss1: 0.247666 Loss2: 1.409017
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.658461 Loss1: 0.254765 Loss2: 1.403696
+(DefaultActor pid=3765) >> Training accuracy: 0.929167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.980123 Loss1: 1.849443 Loss2: 2.130680
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.755450 Loss1: 1.205432 Loss2: 1.550019
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.381533 Loss1: 0.863904 Loss2: 1.517629
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.193436 Loss1: 0.684282 Loss2: 1.509154
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.647065 Loss1: 1.625050 Loss2: 2.022015
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.563204 Loss1: 1.117024 Loss2: 1.446180 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.239533 Loss1: 0.800892 Loss2: 1.438641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.011636 Loss1: 0.584443 Loss2: 1.427193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.815547 Loss1: 0.328060 Loss2: 1.487487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.811443 Loss1: 0.315564 Loss2: 1.495880 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.943080
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.792732 Loss1: 0.383314 Loss2: 1.409418 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.642816 Loss1: 0.234624 Loss2: 1.408192 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.923958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.682253 Loss1: 1.203835 Loss2: 1.478418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.146986 Loss1: 0.702254 Loss2: 1.444731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.938020 Loss1: 0.499116 Loss2: 1.438904
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.657808 Loss1: 1.609924 Loss2: 2.047884
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.480296 Loss1: 0.990948 Loss2: 1.489348 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.203715 Loss1: 0.731745 Loss2: 1.471969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.030089 Loss1: 0.568156 Loss2: 1.461933 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.916056 Loss1: 0.454501 Loss2: 1.461555 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.884375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.761324 Loss1: 0.321884 Loss2: 1.439441
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.869957 Loss1: 0.421170 Loss2: 1.448787
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.820815 Loss1: 0.366778 Loss2: 1.454037
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.757971 Loss1: 0.297008 Loss2: 1.460963
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.752017 Loss1: 0.291927 Loss2: 1.460090
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.717252 Loss1: 0.261141 Loss2: 1.456111
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.655683 Loss1: 1.659900 Loss2: 1.995784
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.582080 Loss1: 1.125095 Loss2: 1.456985
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.276189 Loss1: 0.826609 Loss2: 1.449579
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.017053 Loss1: 0.577645 Loss2: 1.439408
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.845795 Loss1: 0.414054 Loss2: 1.431741
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.767328 Loss1: 1.683045 Loss2: 2.084283
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.641618 Loss1: 1.170933 Loss2: 1.470685
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.826506 Loss1: 0.396137 Loss2: 1.430370
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.282181 Loss1: 0.852263 Loss2: 1.429918
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.820697 Loss1: 0.385962 Loss2: 1.434735
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.794434 Loss1: 0.358303 Loss2: 1.436132 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.719168 Loss1: 0.285740 Loss2: 1.433429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.735627 Loss1: 0.300167 Loss2: 1.435460 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.946875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.701473 Loss1: 0.265811 Loss2: 1.435662 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.842152 Loss1: 1.756658 Loss2: 2.085494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.372639 Loss1: 0.879780 Loss2: 1.492859 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.071093 Loss1: 0.593140 Loss2: 1.477953
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.788929 Loss1: 1.730080 Loss2: 2.058848
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.699160 Loss1: 1.198865 Loss2: 1.500295 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.300768 Loss1: 0.812073 Loss2: 1.488696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.159570 Loss1: 0.682503 Loss2: 1.477067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.028763 Loss1: 0.551770 Loss2: 1.476993 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.917830 Loss1: 0.447350 Loss2: 1.470480 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.933333
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.834463 Loss1: 0.362558 Loss2: 1.471905
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.875857 Loss1: 0.403945 Loss2: 1.471912
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.793798 Loss1: 0.329167 Loss2: 1.464630
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.768282 Loss1: 0.301941 Loss2: 1.466341
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.812050 Loss1: 0.342376 Loss2: 1.469674
+(DefaultActor pid=3765) >> Training accuracy: 0.928125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.726553 Loss1: 1.707714 Loss2: 2.018839
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.642221 Loss1: 1.146017 Loss2: 1.496204
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.229960 Loss1: 0.737164 Loss2: 1.492796
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.679603 Loss1: 1.685629 Loss2: 1.993974
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.008563 Loss1: 0.540942 Loss2: 1.467621
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.573124 Loss1: 1.114900 Loss2: 1.458223
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.002879 Loss1: 0.535583 Loss2: 1.467295
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.355742 Loss1: 0.888279 Loss2: 1.467463
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.986291 Loss1: 0.499397 Loss2: 1.486894
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.150476 Loss1: 0.681807 Loss2: 1.468669
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.892149 Loss1: 0.408588 Loss2: 1.483560
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.786221 Loss1: 0.299054 Loss2: 1.487166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.836485 Loss1: 0.367156 Loss2: 1.469329 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.857072 Loss1: 0.378844 Loss2: 1.478228 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.919922
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.706745 Loss1: 0.288143 Loss2: 1.418601 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.916667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.624992 Loss1: 1.617728 Loss2: 2.007264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.100147 Loss1: 0.654192 Loss2: 1.445954 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.588638 Loss1: 1.627739 Loss2: 1.960899
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.969784 Loss1: 0.530736 Loss2: 1.439048
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.397357 Loss1: 0.986472 Loss2: 1.410885
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.946746 Loss1: 0.503517 Loss2: 1.443229
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.172276 Loss1: 0.755496 Loss2: 1.416779
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.853394 Loss1: 0.408022 Loss2: 1.445372
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.991757 Loss1: 0.572964 Loss2: 1.418793
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.769482 Loss1: 0.334114 Loss2: 1.435369
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.685041 Loss1: 0.246132 Loss2: 1.438909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.692954 Loss1: 0.258286 Loss2: 1.434667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.703135 Loss1: 0.270528 Loss2: 1.432607 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.931641
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.729338 Loss1: 0.326814 Loss2: 1.402523 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.919792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.703568 Loss1: 1.636614 Loss2: 2.066954 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.283002 Loss1: 0.780891 Loss2: 1.502111 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.131849 Loss1: 0.636660 Loss2: 1.495189
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.859340 Loss1: 1.775437 Loss2: 2.083903
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.633060 Loss1: 1.144736 Loss2: 1.488325 [repeated 2x across cluster]
+DEBUG flwr 2023-10-09 21:46:36,358 | server.py:236 | fit_round 53 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.387072 Loss1: 0.885571 Loss2: 1.501501 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.155079 Loss1: 0.660214 Loss2: 1.494865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.029920 Loss1: 0.537817 Loss2: 1.492103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.945450 Loss1: 0.474529 Loss2: 1.470920 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.875301 Loss1: 0.378553 Loss2: 1.496748
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.852581 Loss1: 0.376917 Loss2: 1.475663
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.769666 Loss1: 0.294127 Loss2: 1.475539
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.741850 Loss1: 0.273790 Loss2: 1.468060
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.704341 Loss1: 0.233888 Loss2: 1.470452
+(DefaultActor pid=3765) >> Training accuracy: 0.923958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.388130 Loss1: 1.432627 Loss2: 1.955503
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.320691 Loss1: 0.916451 Loss2: 1.404240
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.074862 Loss1: 0.681008 Loss2: 1.393854
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.969204 Loss1: 0.568908 Loss2: 1.400296
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.648934 Loss1: 1.668370 Loss2: 1.980564
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.600084 Loss1: 1.135379 Loss2: 1.464705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.245227 Loss1: 0.777707 Loss2: 1.467519 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.091908 Loss1: 0.638841 Loss2: 1.453067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.946893 Loss1: 0.499292 Loss2: 1.447601 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.863137 Loss1: 0.422584 Loss2: 1.440553 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.784899 Loss1: 0.347374 Loss2: 1.437525 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.734489 Loss1: 0.296215 Loss2: 1.438274 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.921875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.836185 Loss1: 1.816167 Loss2: 2.020018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.446014 Loss1: 0.960197 Loss2: 1.485817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.575076 Loss1: 1.530454 Loss2: 2.044622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.543634 Loss1: 1.078129 Loss2: 1.465505 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.202582 Loss1: 0.737513 Loss2: 1.465069 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.789030 Loss1: 0.326004 Loss2: 1.463026 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.884333 Loss1: 0.426972 Loss2: 1.457361 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.875957 Loss1: 0.425292 Loss2: 1.450665 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.913542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.697175 Loss1: 0.251825 Loss2: 1.445350 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.697797 Loss1: 0.246877 Loss2: 1.450920 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.927083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.374118 Loss1: 0.952851 Loss2: 1.421267 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.925604 Loss1: 0.548073 Loss2: 1.377531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.755984 Loss1: 0.374379 Loss2: 1.381605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.721721 Loss1: 0.343692 Loss2: 1.378029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.595389 Loss1: 0.229287 Loss2: 1.366102 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.922917
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 21:46:36,358][flwr][DEBUG] - fit_round 53 received 50 results and 0 failures
+INFO flwr 2023-10-09 21:47:17,782 | server.py:125 | fit progress: (53, 2.385063742296383, {'accuracy': 0.4882}, 122145.56038520999)
+>> Test accuracy: 0.488200
+[2023-10-09 21:47:17,782][flwr][INFO] - fit progress: (53, 2.385063742296383, {'accuracy': 0.4882}, 122145.56038520999)
+DEBUG flwr 2023-10-09 21:47:17,782 | server.py:173 | evaluate_round 53: strategy sampled 50 clients (out of 50)
+[2023-10-09 21:47:17,782][flwr][DEBUG] - evaluate_round 53: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 21:56:27,005 | server.py:187 | evaluate_round 53 received 50 results and 0 failures
+[2023-10-09 21:56:27,005][flwr][DEBUG] - evaluate_round 53 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 21:56:27,005 | server.py:222 | fit_round 54: strategy sampled 50 clients (out of 50)
+[2023-10-09 21:56:27,005][flwr][DEBUG] - fit_round 54: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.816752 Loss1: 1.782007 Loss2: 2.034746
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.583467 Loss1: 1.117697 Loss2: 1.465770
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.333219 Loss1: 0.888765 Loss2: 1.444454
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.089603 Loss1: 0.630571 Loss2: 1.459032
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.573051 Loss1: 1.528375 Loss2: 2.044676
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.466689 Loss1: 1.020514 Loss2: 1.446175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.200960 Loss1: 0.776729 Loss2: 1.424232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.003972 Loss1: 0.572808 Loss2: 1.431164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.881400 Loss1: 0.463864 Loss2: 1.417536 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.778345 Loss1: 0.369387 Loss2: 1.408958 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.931250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.694038 Loss1: 0.281956 Loss2: 1.412083
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.730699 Loss1: 0.314922 Loss2: 1.415776
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.692410 Loss1: 0.277965 Loss2: 1.414446
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.606132 Loss1: 0.202876 Loss2: 1.403257
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.644738 Loss1: 0.242030 Loss2: 1.402708
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.634632 Loss1: 1.616643 Loss2: 2.017990
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.569897 Loss1: 1.124382 Loss2: 1.445515
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.278563 Loss1: 0.839373 Loss2: 1.439190
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.985311 Loss1: 0.542974 Loss2: 1.442337
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.625422 Loss1: 1.638876 Loss2: 1.986545
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.612522 Loss1: 1.137990 Loss2: 1.474532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.248110 Loss1: 0.776507 Loss2: 1.471603 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.971175 Loss1: 0.513319 Loss2: 1.457856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.884751 Loss1: 0.434196 Loss2: 1.450555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.780444 Loss1: 0.336315 Loss2: 1.444129 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.833376 Loss1: 0.387208 Loss2: 1.446168 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.796621 Loss1: 0.332003 Loss2: 1.464617 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.908203
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.609235 Loss1: 1.184099 Loss2: 1.425136 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.979808 Loss1: 0.559337 Loss2: 1.420471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.811226 Loss1: 0.395302 Loss2: 1.415924
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.696354 Loss1: 1.623569 Loss2: 2.072785
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.706619 Loss1: 1.163842 Loss2: 1.542777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.333522 Loss1: 0.807192 Loss2: 1.526330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.031410 Loss1: 0.517038 Loss2: 1.514371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.595806 Loss1: 0.193264 Loss2: 1.402542 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.944196
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.789357 Loss1: 0.303220 Loss2: 1.486137 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.811656 Loss1: 0.313145 Loss2: 1.498511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.851977 Loss1: 0.354211 Loss2: 1.497766 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.909180
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.360129 Loss1: 0.860822 Loss2: 1.499307 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.079348 Loss1: 0.582364 Loss2: 1.496983 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 2.016754 Loss1: 0.510419 Loss2: 1.506335
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.842374 Loss1: 1.803089 Loss2: 2.039285
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.930053 Loss1: 0.440586 Loss2: 1.489467
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.727774 Loss1: 1.230298 Loss2: 1.497477
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.368123 Loss1: 0.900636 Loss2: 1.467487
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.854584 Loss1: 0.362403 Loss2: 1.492181
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.084537 Loss1: 0.624541 Loss2: 1.459997
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.878792 Loss1: 0.384748 Loss2: 1.494045
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.987572 Loss1: 0.538584 Loss2: 1.448988
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.861812 Loss1: 0.362658 Loss2: 1.499154
+(DefaultActor pid=3765) >> Training accuracy: 0.920898
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.852037 Loss1: 0.408535 Loss2: 1.443502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.812814 Loss1: 0.355104 Loss2: 1.457710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.692526 Loss1: 0.235769 Loss2: 1.456757
+(DefaultActor pid=3764) >> Training accuracy: 0.927083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.736777 Loss1: 1.750444 Loss2: 1.986333
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.603951 Loss1: 1.163766 Loss2: 1.440185
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.173658 Loss1: 0.745863 Loss2: 1.427795
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.953343 Loss1: 0.532502 Loss2: 1.420841
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.891831 Loss1: 0.473842 Loss2: 1.417989
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.839068 Loss1: 1.665549 Loss2: 2.173519
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.813871 Loss1: 0.390615 Loss2: 1.423257
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.745552 Loss1: 0.317121 Loss2: 1.428430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.711672 Loss1: 0.288817 Loss2: 1.422854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.707705 Loss1: 0.289613 Loss2: 1.418092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.815730 Loss1: 0.348338 Loss2: 1.467393 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.727055 Loss1: 0.266245 Loss2: 1.460810 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.620427 Loss1: 0.163558 Loss2: 1.456869 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972356
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.711842 Loss1: 1.634123 Loss2: 2.077720
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.603195 Loss1: 1.094168 Loss2: 1.509028
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.265791 Loss1: 0.778642 Loss2: 1.487149
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.033848 Loss1: 0.539719 Loss2: 1.494129
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.681267 Loss1: 1.588864 Loss2: 2.092403
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.495417 Loss1: 1.018421 Loss2: 1.476996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.132664 Loss1: 0.680415 Loss2: 1.452248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.022145 Loss1: 0.578606 Loss2: 1.443540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.894858 Loss1: 0.442010 Loss2: 1.452848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.865245 Loss1: 0.433793 Loss2: 1.431451 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.847618 Loss1: 0.361683 Loss2: 1.485936
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.809858 Loss1: 0.375735 Loss2: 1.434123
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.778589 Loss1: 0.341112 Loss2: 1.437477
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.688895 Loss1: 0.251645 Loss2: 1.437250
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.623999 Loss1: 0.200609 Loss2: 1.423389
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.762363 Loss1: 1.685750 Loss2: 2.076613
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.561901 Loss1: 1.058664 Loss2: 1.503237
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.213024 Loss1: 0.714982 Loss2: 1.498042
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.032217 Loss1: 0.539243 Loss2: 1.492974
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.939087 Loss1: 1.931115 Loss2: 2.007971
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.678430 Loss1: 1.204494 Loss2: 1.473936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.329298 Loss1: 0.826809 Loss2: 1.502489 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.065031 Loss1: 0.605783 Loss2: 1.459248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.010102 Loss1: 0.552268 Loss2: 1.457834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.898860 Loss1: 0.430948 Loss2: 1.467912 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.929167
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.705576 Loss1: 0.227653 Loss2: 1.477923
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.861705 Loss1: 0.405707 Loss2: 1.455998
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.872103 Loss1: 0.417794 Loss2: 1.454309
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.794765 Loss1: 0.333820 Loss2: 1.460945
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.744873 Loss1: 0.290185 Loss2: 1.454688
+(DefaultActor pid=3764) >> Training accuracy: 0.919792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.593150 Loss1: 1.518888 Loss2: 2.074262
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.623959 Loss1: 1.124523 Loss2: 1.499436
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.249842 Loss1: 0.741239 Loss2: 1.508603
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.974867 Loss1: 0.500718 Loss2: 1.474149
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.699029 Loss1: 1.694935 Loss2: 2.004094
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.581132 Loss1: 1.101559 Loss2: 1.479573 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.281028 Loss1: 0.821231 Loss2: 1.459797 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.130680 Loss1: 0.668202 Loss2: 1.462478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.990792 Loss1: 0.538214 Loss2: 1.452578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.846982 Loss1: 0.397169 Loss2: 1.449813 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.912500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.775377 Loss1: 0.309391 Loss2: 1.465986
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.856589 Loss1: 0.415313 Loss2: 1.441276
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.794267 Loss1: 0.341123 Loss2: 1.453144
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.724428 Loss1: 0.277075 Loss2: 1.447353
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.716532 Loss1: 0.285102 Loss2: 1.431429
+(DefaultActor pid=3764) >> Training accuracy: 0.933333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.724465 Loss1: 1.707102 Loss2: 2.017363
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.583682 Loss1: 1.138953 Loss2: 1.444730
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.291139 Loss1: 0.843606 Loss2: 1.447534
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.119452 Loss1: 0.679812 Loss2: 1.439640
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.572706 Loss1: 1.521326 Loss2: 2.051379
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.498948 Loss1: 1.044237 Loss2: 1.454712 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.093063 Loss1: 0.656647 Loss2: 1.436416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.920670 Loss1: 0.496352 Loss2: 1.424318 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.812951 Loss1: 0.399507 Loss2: 1.413445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.772135 Loss1: 0.369257 Loss2: 1.402878 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.934375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.713092 Loss1: 0.279624 Loss2: 1.433468
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.738842 Loss1: 0.335323 Loss2: 1.403519
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.746819 Loss1: 0.328037 Loss2: 1.418782
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.697226 Loss1: 0.284056 Loss2: 1.413169
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.647324 Loss1: 0.245480 Loss2: 1.401844
+(DefaultActor pid=3764) >> Training accuracy: 0.928125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.639267 Loss1: 1.647898 Loss2: 1.991369
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.419071 Loss1: 0.966185 Loss2: 1.452886
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.208094 Loss1: 0.791119 Loss2: 1.416976
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.021632 Loss1: 0.580343 Loss2: 1.441290
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.578655 Loss1: 1.606764 Loss2: 1.971891
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.478103 Loss1: 1.048828 Loss2: 1.429275 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.135832 Loss1: 0.714884 Loss2: 1.420948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.990354 Loss1: 0.579056 Loss2: 1.411298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.929360 Loss1: 0.518039 Loss2: 1.411321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.871598 Loss1: 0.455148 Loss2: 1.416450 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.575333 Loss1: 0.185541 Loss2: 1.389792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.800065 Loss1: 0.386164 Loss2: 1.413901
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.770312 Loss1: 0.365544 Loss2: 1.404768
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.806854 Loss1: 0.381836 Loss2: 1.425018
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.757019 Loss1: 0.340122 Loss2: 1.416897
+(DefaultActor pid=3764) >> Training accuracy: 0.920833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.634371 Loss1: 1.659592 Loss2: 1.974779
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.554950 Loss1: 1.128858 Loss2: 1.426092
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.277085 Loss1: 0.862453 Loss2: 1.414632
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.038533 Loss1: 0.619640 Loss2: 1.418893
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.959456 Loss1: 1.800914 Loss2: 2.158542
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.756584 Loss1: 1.226976 Loss2: 1.529607 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.369075 Loss1: 0.872502 Loss2: 1.496573 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.728903 Loss1: 0.333003 Loss2: 1.395900
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.137102 Loss1: 0.628948 Loss2: 1.508154
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.747209 Loss1: 0.345049 Loss2: 1.402159
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.012237 Loss1: 0.514871 Loss2: 1.497366
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.701027 Loss1: 0.296523 Loss2: 1.404504
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.960684 Loss1: 0.465925 Loss2: 1.494759
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.875596 Loss1: 0.383519 Loss2: 1.492077
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.706663 Loss1: 0.307814 Loss2: 1.398849
+(DefaultActor pid=3765) >> Training accuracy: 0.916667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.777666 Loss1: 0.289743 Loss2: 1.487923 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.677070 Loss1: 1.641406 Loss2: 2.035663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.128441 Loss1: 0.663098 Loss2: 1.465343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.994466 Loss1: 0.524365 Loss2: 1.470101
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.743724 Loss1: 1.625610 Loss2: 2.118114
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.645916 Loss1: 1.114817 Loss2: 1.531100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.222084 Loss1: 0.714502 Loss2: 1.507582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.068086 Loss1: 0.569001 Loss2: 1.499085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.923843 Loss1: 0.419345 Loss2: 1.504498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.865445 Loss1: 0.371676 Loss2: 1.493769 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.943750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.747293 Loss1: 0.275657 Loss2: 1.471636
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.833917 Loss1: 0.347894 Loss2: 1.486023
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.802264 Loss1: 0.313439 Loss2: 1.488825
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.752768 Loss1: 0.259645 Loss2: 1.493123
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.744371 Loss1: 0.251726 Loss2: 1.492645
+(DefaultActor pid=3764) >> Training accuracy: 0.922917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.605926 Loss1: 1.577599 Loss2: 2.028327
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.510172 Loss1: 1.003398 Loss2: 1.506774
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.211153 Loss1: 0.703155 Loss2: 1.507998
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.702882 Loss1: 1.652746 Loss2: 2.050136
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.017569 Loss1: 0.528387 Loss2: 1.489182
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.690689 Loss1: 1.194242 Loss2: 1.496447
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.969865 Loss1: 0.495064 Loss2: 1.474802
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.849719 Loss1: 0.379968 Loss2: 1.469751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.800153 Loss1: 0.335394 Loss2: 1.464759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.817752 Loss1: 0.340493 Loss2: 1.477258 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.810335 Loss1: 0.332446 Loss2: 1.477889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.782854 Loss1: 0.314138 Loss2: 1.468716 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.934570
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.731577 Loss1: 0.279726 Loss2: 1.451851 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.946875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.584148 Loss1: 1.568133 Loss2: 2.016015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.198175 Loss1: 0.725712 Loss2: 1.472463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.969065 Loss1: 0.521348 Loss2: 1.447717
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.710669 Loss1: 1.687655 Loss2: 2.023015
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.878744 Loss1: 0.450702 Loss2: 1.428042
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.567885 Loss1: 1.097082 Loss2: 1.470802
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.833078 Loss1: 0.392899 Loss2: 1.440179
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.310662 Loss1: 0.861581 Loss2: 1.449081
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.729861 Loss1: 0.298975 Loss2: 1.430886
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.140364 Loss1: 0.695688 Loss2: 1.444675
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.725254 Loss1: 0.297129 Loss2: 1.428125
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.025513 Loss1: 0.568132 Loss2: 1.457381
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.701318 Loss1: 0.273730 Loss2: 1.427588
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.819216 Loss1: 0.383519 Loss2: 1.435697
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.692704 Loss1: 0.253996 Loss2: 1.438708
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.828851 Loss1: 0.402075 Loss2: 1.426776
+(DefaultActor pid=3765) >> Training accuracy: 0.943750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.777626 Loss1: 0.340159 Loss2: 1.437467
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.808508 Loss1: 0.368617 Loss2: 1.439890
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.752357 Loss1: 0.308315 Loss2: 1.444042
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.671525 Loss1: 1.646225 Loss2: 2.025300
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.568251 Loss1: 1.085200 Loss2: 1.483051
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.253393 Loss1: 0.774761 Loss2: 1.478632
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.027993 Loss1: 0.562043 Loss2: 1.465950
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.674720 Loss1: 1.620998 Loss2: 2.053722
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.883810 Loss1: 0.427723 Loss2: 1.456087
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.497725 Loss1: 0.996390 Loss2: 1.501335
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.836252 Loss1: 0.381414 Loss2: 1.454838
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.263690 Loss1: 0.807681 Loss2: 1.456009
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.792627 Loss1: 0.335805 Loss2: 1.456822
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.059501 Loss1: 0.583308 Loss2: 1.476193
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.766153 Loss1: 0.308970 Loss2: 1.457183
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.828135 Loss1: 0.372135 Loss2: 1.456000
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.677227 Loss1: 0.228627 Loss2: 1.448599
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.817639 Loss1: 0.373049 Loss2: 1.444590
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.734917 Loss1: 0.288456 Loss2: 1.446461
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.755270 Loss1: 0.305744 Loss2: 1.449526
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.725293 Loss1: 0.289059 Loss2: 1.436234
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.675634 Loss1: 0.234326 Loss2: 1.441308
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.672791 Loss1: 0.237375 Loss2: 1.435416
+(DefaultActor pid=3764) >> Training accuracy: 0.906250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.549789 Loss1: 1.568834 Loss2: 1.980955
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.593286 Loss1: 1.122934 Loss2: 1.470352
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.259012 Loss1: 0.796352 Loss2: 1.462661
+(DefaultActor pid=3764) Epoch: 0 Loss: 4.030767 Loss1: 1.745041 Loss2: 2.285726
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.033575 Loss1: 0.579601 Loss2: 1.453974
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.966064 Loss1: 0.516830 Loss2: 1.449235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.864639 Loss1: 0.404916 Loss2: 1.459722 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.005291 Loss1: 0.473604 Loss2: 1.531687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.887289 Loss1: 0.368808 Loss2: 1.518482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.874439 Loss1: 0.362127 Loss2: 1.512312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.731488 Loss1: 0.287118 Loss2: 1.444371
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.830827 Loss1: 0.325020 Loss2: 1.505807
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.781677 Loss1: 0.277899 Loss2: 1.503778
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.686153 Loss1: 0.246479 Loss2: 1.439675
+(DefaultActor pid=3765) >> Training accuracy: 0.967773
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.531910 Loss1: 1.560431 Loss2: 1.971479 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964844
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.199330 Loss1: 0.729874 Loss2: 1.469456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.602267 Loss1: 1.598584 Loss2: 2.003684
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.006877 Loss1: 0.545292 Loss2: 1.461584
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.488579 Loss1: 0.984249 Loss2: 1.504330
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.898211 Loss1: 0.444701 Loss2: 1.453510
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.151850 Loss1: 0.679113 Loss2: 1.472737
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.907782 Loss1: 0.447866 Loss2: 1.459916
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.945384 Loss1: 0.490187 Loss2: 1.455197
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.847432 Loss1: 0.387867 Loss2: 1.459564
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.798037 Loss1: 0.338135 Loss2: 1.459903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.749142 Loss1: 0.306480 Loss2: 1.442662 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.742765 Loss1: 0.289064 Loss2: 1.453701 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.929228
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.647149 Loss1: 0.212000 Loss2: 1.435149 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.912109
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.719146 Loss1: 1.615090 Loss2: 2.104057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.345898 Loss1: 0.866751 Loss2: 1.479147 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.732227 Loss1: 1.721624 Loss2: 2.010603 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.832022 Loss1: 0.386817 Loss2: 1.445206 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.849693 Loss1: 0.403506 Loss2: 1.446187 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.754190 Loss1: 0.297209 Loss2: 1.456982 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.769471 Loss1: 0.315048 Loss2: 1.454423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.685772 Loss1: 0.230148 Loss2: 1.455624 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963942
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.836911 Loss1: 0.392161 Loss2: 1.444750 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.723510 Loss1: 0.286927 Loss2: 1.436583 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.938542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.724980 Loss1: 0.291828 Loss2: 1.433151
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.801426 Loss1: 1.729491 Loss2: 2.071935
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.569428 Loss1: 1.070075 Loss2: 1.499353
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.328842 Loss1: 0.845190 Loss2: 1.483652
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.080611 Loss1: 0.580134 Loss2: 1.500477
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.040303 Loss1: 0.561597 Loss2: 1.478706
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.505579 Loss1: 1.444891 Loss2: 2.060688
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.524378 Loss1: 1.032866 Loss2: 1.491512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.238598 Loss1: 0.742433 Loss2: 1.496165 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.011760 Loss1: 0.520405 Loss2: 1.491355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.890049 Loss1: 0.424751 Loss2: 1.465298 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.932292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.843860 Loss1: 0.386126 Loss2: 1.457734 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.724842 Loss1: 0.266798 Loss2: 1.458044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.666647 Loss1: 0.224636 Loss2: 1.442011 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.619977 Loss1: 1.155423 Loss2: 1.464554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.035802 Loss1: 0.586824 Loss2: 1.448978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.891251 Loss1: 0.445182 Loss2: 1.446069
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.984993 Loss1: 1.896621 Loss2: 2.088372
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.652196 Loss1: 1.144595 Loss2: 1.507601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.332692 Loss1: 0.835668 Loss2: 1.497024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.085425 Loss1: 0.590377 Loss2: 1.495048 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.742705 Loss1: 0.306245 Loss2: 1.436460
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.931197 Loss1: 0.455226 Loss2: 1.475970
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.735953 Loss1: 0.301155 Loss2: 1.434798
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.916370 Loss1: 0.433311 Loss2: 1.483058
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.822275 Loss1: 0.345326 Loss2: 1.476949
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.787604 Loss1: 0.311554 Loss2: 1.476050
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.865985 Loss1: 0.387821 Loss2: 1.478164
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.790018 Loss1: 0.303643 Loss2: 1.486375
+(DefaultActor pid=3764) >> Training accuracy: 0.891741
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.752852 Loss1: 1.752755 Loss2: 2.000097
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.639472 Loss1: 1.152167 Loss2: 1.487304
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.278962 Loss1: 0.798500 Loss2: 1.480462
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.082508 Loss1: 0.602383 Loss2: 1.480125
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.795931 Loss1: 1.685100 Loss2: 2.110831
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.666760 Loss1: 1.158744 Loss2: 1.508016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.357557 Loss1: 0.839744 Loss2: 1.517813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.069805 Loss1: 0.564960 Loss2: 1.504846 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.921643 Loss1: 0.435070 Loss2: 1.486573 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.793234 Loss1: 0.323379 Loss2: 1.469856
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.902782 Loss1: 0.412828 Loss2: 1.489954
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.774742 Loss1: 0.314994 Loss2: 1.459748
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.896061 Loss1: 0.398458 Loss2: 1.497603
+(DefaultActor pid=3765) >> Training accuracy: 0.936523
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.876088 Loss1: 0.367803 Loss2: 1.508285
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.825613 Loss1: 0.319739 Loss2: 1.505874
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.761743 Loss1: 0.265700 Loss2: 1.496043
+(DefaultActor pid=3764) >> Training accuracy: 0.943750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.662245 Loss1: 1.575435 Loss2: 2.086810
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.647474 Loss1: 1.145659 Loss2: 1.501815
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.276180 Loss1: 0.774349 Loss2: 1.501831
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.042674 Loss1: 0.546364 Loss2: 1.496310
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.747767 Loss1: 1.684344 Loss2: 2.063422
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.592997 Loss1: 1.051056 Loss2: 1.541941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.272055 Loss1: 0.725317 Loss2: 1.546738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.186923 Loss1: 0.651901 Loss2: 1.535022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.070506 Loss1: 0.546223 Loss2: 1.524283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.967516 Loss1: 0.447097 Loss2: 1.520419 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.849822 Loss1: 0.326062 Loss2: 1.523761 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.727976 Loss1: 0.219785 Loss2: 1.508191 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.905273
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.855194 Loss1: 1.820645 Loss2: 2.034549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.253549 Loss1: 0.761304 Loss2: 1.492245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.665090 Loss1: 1.626565 Loss2: 2.038525 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.525074 Loss1: 1.036329 Loss2: 1.488745 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.854704 Loss1: 0.388198 Loss2: 1.466505 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.766729 Loss1: 0.308516 Loss2: 1.458214 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.743043 Loss1: 0.285864 Loss2: 1.457179 [repeated 2x across cluster]
+DEBUG flwr 2023-10-09 22:25:21,915 | server.py:236 | fit_round 54 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.672864 Loss1: 0.221700 Loss2: 1.451165 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.938542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.775943 Loss1: 0.314206 Loss2: 1.461737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.832555 Loss1: 0.352362 Loss2: 1.480193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.948242
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.761160 Loss1: 0.278990 Loss2: 1.482171
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.765002 Loss1: 1.637475 Loss2: 2.127527
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.630785 Loss1: 1.098640 Loss2: 1.532144
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.387834 Loss1: 0.855509 Loss2: 1.532325
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.171984 Loss1: 0.637492 Loss2: 1.534492
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.000367 Loss1: 0.485036 Loss2: 1.515331
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.779612 Loss1: 1.753333 Loss2: 2.026279
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.653015 Loss1: 1.175675 Loss2: 1.477340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.277685 Loss1: 0.820207 Loss2: 1.457478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.052459 Loss1: 0.585101 Loss2: 1.467358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.969911 Loss1: 0.513344 Loss2: 1.456567 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.891497 Loss1: 0.436129 Loss2: 1.455368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.795945 Loss1: 0.338418 Loss2: 1.457527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.765829 Loss1: 0.317203 Loss2: 1.448626 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.923958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.591483 Loss1: 1.135533 Loss2: 1.455950 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.981719 Loss1: 0.571926 Loss2: 1.409793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.873210 Loss1: 0.458498 Loss2: 1.414712
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.689563 Loss1: 1.686409 Loss2: 2.003154
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.690174 Loss1: 1.227197 Loss2: 1.462977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.331839 Loss1: 0.879572 Loss2: 1.452268 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.047227 Loss1: 0.624484 Loss2: 1.422743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.973706 Loss1: 0.549856 Loss2: 1.423850 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.823306 Loss1: 0.403906 Loss2: 1.419401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.794344 Loss1: 0.384218 Loss2: 1.410126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.832596 Loss1: 0.390510 Loss2: 1.442086 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 22:25:21,915][flwr][DEBUG] - fit_round 54 received 50 results and 0 failures
+INFO flwr 2023-10-09 22:26:03,541 | server.py:125 | fit progress: (54, 2.387508314638473, {'accuracy': 0.4907}, 124471.31985545199)
+>> Test accuracy: 0.490700
+[2023-10-09 22:26:03,541][flwr][INFO] - fit progress: (54, 2.387508314638473, {'accuracy': 0.4907}, 124471.31985545199)
+DEBUG flwr 2023-10-09 22:26:03,542 | server.py:173 | evaluate_round 54: strategy sampled 50 clients (out of 50)
+[2023-10-09 22:26:03,542][flwr][DEBUG] - evaluate_round 54: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 22:35:09,793 | server.py:187 | evaluate_round 54 received 50 results and 0 failures
+[2023-10-09 22:35:09,793][flwr][DEBUG] - evaluate_round 54 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 22:35:09,794 | server.py:222 | fit_round 55: strategy sampled 50 clients (out of 50)
+[2023-10-09 22:35:09,794][flwr][DEBUG] - fit_round 55: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.753373 Loss1: 1.766172 Loss2: 1.987201
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.501021 Loss1: 1.053705 Loss2: 1.447316
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.302134 Loss1: 0.865624 Loss2: 1.436510
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.071799 Loss1: 0.625904 Loss2: 1.445895
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.607318 Loss1: 1.546136 Loss2: 2.061182
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.974239 Loss1: 0.548337 Loss2: 1.425902
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.501448 Loss1: 0.988520 Loss2: 1.512929
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.845328 Loss1: 0.427141 Loss2: 1.418187
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.190574 Loss1: 0.716558 Loss2: 1.474016
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.796488 Loss1: 0.380054 Loss2: 1.416434
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.011374 Loss1: 0.541417 Loss2: 1.469957
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.719786 Loss1: 0.304969 Loss2: 1.414816
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.868537 Loss1: 0.423950 Loss2: 1.444587
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.683256 Loss1: 0.273226 Loss2: 1.410030
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.808265 Loss1: 0.362139 Loss2: 1.446126
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.647535 Loss1: 0.244707 Loss2: 1.402829
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.777317 Loss1: 0.324998 Loss2: 1.452319
+(DefaultActor pid=3765) >> Training accuracy: 0.935417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.755574 Loss1: 0.309586 Loss2: 1.445988
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.659600 Loss1: 0.219969 Loss2: 1.439631
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.638329 Loss1: 0.198611 Loss2: 1.439719
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.738528 Loss1: 1.699662 Loss2: 2.038866
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.489803 Loss1: 1.033431 Loss2: 1.456373
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.228875 Loss1: 0.789657 Loss2: 1.439217
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.952062 Loss1: 0.513512 Loss2: 1.438550
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.695102 Loss1: 1.623972 Loss2: 2.071130
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.847425 Loss1: 0.418277 Loss2: 1.429148
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.430887 Loss1: 0.974262 Loss2: 1.456625
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.778925 Loss1: 0.353926 Loss2: 1.424999
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.196552 Loss1: 0.756359 Loss2: 1.440194
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.716426 Loss1: 0.299437 Loss2: 1.416989
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.943271 Loss1: 0.514393 Loss2: 1.428878
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.690496 Loss1: 0.272920 Loss2: 1.417576
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.791074 Loss1: 0.375322 Loss2: 1.415752
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.625772 Loss1: 0.210286 Loss2: 1.415486
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.768869 Loss1: 0.362674 Loss2: 1.406195
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.645550 Loss1: 0.236458 Loss2: 1.409092
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.754449 Loss1: 0.347742 Loss2: 1.406706
+(DefaultActor pid=3765) >> Training accuracy: 0.938542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.685637 Loss1: 0.277720 Loss2: 1.407917
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.592235 Loss1: 0.193989 Loss2: 1.398246
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.591926 Loss1: 0.203590 Loss2: 1.388335
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.806389 Loss1: 1.706931 Loss2: 2.099458
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.404552 Loss1: 0.978123 Loss2: 1.426429
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.232130 Loss1: 0.815929 Loss2: 1.416201
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.960224 Loss1: 0.540276 Loss2: 1.419949
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.566972 Loss1: 1.577664 Loss2: 1.989308
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.806836 Loss1: 0.395015 Loss2: 1.411821 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.763488 Loss1: 0.350804 Loss2: 1.412684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.697686 Loss1: 0.291405 Loss2: 1.406280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.601077 Loss1: 0.203488 Loss2: 1.397589 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.540259 Loss1: 0.148453 Loss2: 1.391807 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955529
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.725833 Loss1: 0.297617 Loss2: 1.428216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.725036 Loss1: 0.291935 Loss2: 1.433101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.691576 Loss1: 0.261981 Loss2: 1.429595
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.727647 Loss1: 1.696488 Loss2: 2.031159
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.613495 Loss1: 1.141282 Loss2: 1.472213
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.317984 Loss1: 0.878680 Loss2: 1.439305
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.090425 Loss1: 0.645058 Loss2: 1.445367
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.934999 Loss1: 0.505728 Loss2: 1.429270
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.415773 Loss1: 1.447668 Loss2: 1.968105
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.755451 Loss1: 0.335445 Loss2: 1.420006
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.741077 Loss1: 0.319159 Loss2: 1.421918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.694944 Loss1: 0.278243 Loss2: 1.416701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.668218 Loss1: 0.259400 Loss2: 1.408818 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.673985 Loss1: 0.258597 Loss2: 1.415388 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.947917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.671213 Loss1: 0.286760 Loss2: 1.384454 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.602347 Loss1: 0.225268 Loss2: 1.377079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.584180 Loss1: 0.208067 Loss2: 1.376113
+(DefaultActor pid=3764) >> Training accuracy: 0.930208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.822140 Loss1: 1.796504 Loss2: 2.025636
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.660366 Loss1: 1.202305 Loss2: 1.458061
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.244989 Loss1: 0.797894 Loss2: 1.447095
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.053084 Loss1: 0.608012 Loss2: 1.445072
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.863619 Loss1: 0.432731 Loss2: 1.430888
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.494966 Loss1: 1.505108 Loss2: 1.989858
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.574767 Loss1: 1.150851 Loss2: 1.423916 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.203436 Loss1: 0.719869 Loss2: 1.483567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.017600 Loss1: 0.560066 Loss2: 1.457534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.917013 Loss1: 0.478198 Loss2: 1.438815 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.943750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.818766 Loss1: 0.382176 Loss2: 1.436590 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.741193 Loss1: 0.323219 Loss2: 1.417974 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.657563 Loss1: 0.242822 Loss2: 1.414741 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.284927 Loss1: 0.853259 Loss2: 1.431668 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.963490 Loss1: 0.511098 Loss2: 1.452392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.817533 Loss1: 0.375444 Loss2: 1.442089 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.757567 Loss1: 0.320529 Loss2: 1.437038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.696637 Loss1: 0.262527 Loss2: 1.434111 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.668682 Loss1: 0.240993 Loss2: 1.427689 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.933594
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.930484 Loss1: 0.454532 Loss2: 1.475952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.819100 Loss1: 0.343816 Loss2: 1.475284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.828147 Loss1: 0.349265 Loss2: 1.478882
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.680080 Loss1: 1.700213 Loss2: 1.979867
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.444032 Loss1: 1.001126 Loss2: 1.442906
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.853383 Loss1: 0.371507 Loss2: 1.481877
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.149789 Loss1: 0.737053 Loss2: 1.412736
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.840752 Loss1: 0.345956 Loss2: 1.494796
+(DefaultActor pid=3764) >> Training accuracy: 0.944336
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.835385 Loss1: 0.439121 Loss2: 1.396264 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.660413 Loss1: 0.267693 Loss2: 1.392720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.655200 Loss1: 0.270421 Loss2: 1.384779
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.543721 Loss1: 1.595422 Loss2: 1.948299
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.592577 Loss1: 0.213234 Loss2: 1.379343
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.437698 Loss1: 1.040273 Loss2: 1.397425
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.545003 Loss1: 0.171725 Loss2: 1.373278
+(DefaultActor pid=3765) >> Training accuracy: 0.923958
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.149552 Loss1: 0.768251 Loss2: 1.381300
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.896210 Loss1: 0.516012 Loss2: 1.380198
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.784458 Loss1: 0.426512 Loss2: 1.357947
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.690473 Loss1: 0.340008 Loss2: 1.350465
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.682657 Loss1: 0.323162 Loss2: 1.359495
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.563144 Loss1: 1.511864 Loss2: 2.051280
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.676200 Loss1: 0.306717 Loss2: 1.369484
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.479692 Loss1: 1.002481 Loss2: 1.477211
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.589760 Loss1: 0.235841 Loss2: 1.353919
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.116304 Loss1: 0.660750 Loss2: 1.455554
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.584035 Loss1: 0.223494 Loss2: 1.360541
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.811063 Loss1: 0.370988 Loss2: 1.440075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.681492 Loss1: 0.261643 Loss2: 1.419848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.698610 Loss1: 0.272247 Loss2: 1.426364
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.898335 Loss1: 1.864556 Loss2: 2.033779
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.718453 Loss1: 0.283460 Loss2: 1.434992
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.614499 Loss1: 1.125854 Loss2: 1.488645
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.704592 Loss1: 0.261973 Loss2: 1.442619
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.253698 Loss1: 0.772366 Loss2: 1.481332
+(DefaultActor pid=3765) >> Training accuracy: 0.910417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.037560 Loss1: 0.573824 Loss2: 1.463736
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.906804 Loss1: 0.451221 Loss2: 1.455583
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.860330 Loss1: 0.397894 Loss2: 1.462436
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.794741 Loss1: 0.350291 Loss2: 1.444450
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.674260 Loss1: 0.224582 Loss2: 1.449678
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.678507 Loss1: 1.656975 Loss2: 2.021532
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.637957 Loss1: 0.206162 Loss2: 1.431794
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.587356 Loss1: 1.098594 Loss2: 1.488762
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.651703 Loss1: 0.220431 Loss2: 1.431271
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.188034 Loss1: 0.712713 Loss2: 1.475321
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.123155 Loss1: 0.644504 Loss2: 1.478651
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.061500 Loss1: 0.563703 Loss2: 1.497797
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.931682 Loss1: 0.446039 Loss2: 1.485643
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.856747 Loss1: 0.383215 Loss2: 1.473532
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.945594 Loss1: 1.886113 Loss2: 2.059482
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.675360 Loss1: 1.173902 Loss2: 1.501458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.777825 Loss1: 0.306719 Loss2: 1.471105
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.285321 Loss1: 0.795470 Loss2: 1.489852
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.792494 Loss1: 0.322337 Loss2: 1.470157
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.119554 Loss1: 0.646615 Loss2: 1.472939
+(DefaultActor pid=3765) >> Training accuracy: 0.964844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.933280 Loss1: 0.462863 Loss2: 1.470418
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.860455 Loss1: 0.407166 Loss2: 1.453288
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.799043 Loss1: 0.345101 Loss2: 1.453942
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.781503 Loss1: 0.321127 Loss2: 1.460376
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.755105 Loss1: 0.303887 Loss2: 1.451218
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.668913 Loss1: 1.691973 Loss2: 1.976939
+(DefaultActor pid=3764) >> Training accuracy: 0.940848
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.185265 Loss1: 0.736491 Loss2: 1.448774 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.942938 Loss1: 0.495105 Loss2: 1.447833 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.840264 Loss1: 0.410410 Loss2: 1.429854
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.601038 Loss1: 1.532413 Loss2: 2.068625
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.783820 Loss1: 0.352939 Loss2: 1.430881
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.604176 Loss1: 1.102343 Loss2: 1.501833
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.337504 Loss1: 0.850400 Loss2: 1.487104
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.693500 Loss1: 0.256143 Loss2: 1.437357
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.127192 Loss1: 0.620891 Loss2: 1.506301
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.673567 Loss1: 0.253115 Loss2: 1.420452
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.075401 Loss1: 0.582670 Loss2: 1.492731
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.711899 Loss1: 0.292806 Loss2: 1.419094
+(DefaultActor pid=3765) >> Training accuracy: 0.893555
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.843238 Loss1: 0.360441 Loss2: 1.482798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.827517 Loss1: 0.352872 Loss2: 1.474645 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.726462 Loss1: 0.240450 Loss2: 1.486012
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.636186 Loss1: 1.573417 Loss2: 2.062770
+(DefaultActor pid=3764) >> Training accuracy: 0.935417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.520486 Loss1: 1.031003 Loss2: 1.489483
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.125381 Loss1: 0.650245 Loss2: 1.475135
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.995121 Loss1: 0.549053 Loss2: 1.446069
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.916633 Loss1: 0.465033 Loss2: 1.451600
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.814897 Loss1: 1.789262 Loss2: 2.025635
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.804247 Loss1: 0.360133 Loss2: 1.444115
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.753133 Loss1: 1.266417 Loss2: 1.486716
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.831654 Loss1: 0.381164 Loss2: 1.450490
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.374291 Loss1: 0.881817 Loss2: 1.492474
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.777571 Loss1: 0.317153 Loss2: 1.460418
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.108436 Loss1: 0.638071 Loss2: 1.470364
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.699147 Loss1: 0.248861 Loss2: 1.450286
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.003721 Loss1: 0.544851 Loss2: 1.458870
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.609144 Loss1: 0.179664 Loss2: 1.429479
+(DefaultActor pid=3765) >> Training accuracy: 0.948958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.880937 Loss1: 0.411493 Loss2: 1.469444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.765272 Loss1: 0.307248 Loss2: 1.458023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.820865 Loss1: 0.360460 Loss2: 1.460405
+(DefaultActor pid=3764) >> Training accuracy: 0.898958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.821228 Loss1: 1.739974 Loss2: 2.081254
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.701334 Loss1: 1.180595 Loss2: 1.520738
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.276516 Loss1: 0.774315 Loss2: 1.502201
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.100497 Loss1: 0.606263 Loss2: 1.494233
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.026491 Loss1: 0.532917 Loss2: 1.493575
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.694271 Loss1: 1.678478 Loss2: 2.015793
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.907575 Loss1: 0.414277 Loss2: 1.493298
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.929731 Loss1: 0.431527 Loss2: 1.498204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.894597 Loss1: 0.395909 Loss2: 1.498688 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.803746 Loss1: 0.302360 Loss2: 1.501387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.809399 Loss1: 0.320731 Loss2: 1.488668 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.933333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.779777 Loss1: 0.329755 Loss2: 1.450023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.720489 Loss1: 0.284621 Loss2: 1.435868 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.735576 Loss1: 0.293151 Loss2: 1.442425
+(DefaultActor pid=3764) >> Training accuracy: 0.925000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.549999 Loss1: 1.461067 Loss2: 2.088932
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.446612 Loss1: 0.949128 Loss2: 1.497484
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.124711 Loss1: 0.636860 Loss2: 1.487852
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.957136 Loss1: 0.471407 Loss2: 1.485728
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.902532 Loss1: 0.431092 Loss2: 1.471440
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.516533 Loss1: 1.539684 Loss2: 1.976850
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.494986 Loss1: 1.022051 Loss2: 1.472935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.128000 Loss1: 0.654511 Loss2: 1.473489 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.924034 Loss1: 0.463687 Loss2: 1.460348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.802046 Loss1: 0.351049 Loss2: 1.450997 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.694369 Loss1: 0.249969 Loss2: 1.444400 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.674417 Loss1: 0.242600 Loss2: 1.431817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.780354 Loss1: 1.767937 Loss2: 2.012418
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.758725 Loss1: 0.318952 Loss2: 1.439773
+(DefaultActor pid=3764) >> Training accuracy: 0.919922
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.204231 Loss1: 0.790284 Loss2: 1.413947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.925893 Loss1: 0.501753 Loss2: 1.424140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.790049 Loss1: 0.374107 Loss2: 1.415942
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.768601 Loss1: 1.646691 Loss2: 2.121909
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.741915 Loss1: 0.329646 Loss2: 1.412269
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.666613 Loss1: 1.185453 Loss2: 1.481159
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.293621 Loss1: 0.818199 Loss2: 1.475422
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.725502 Loss1: 0.322664 Loss2: 1.402838
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.052793 Loss1: 0.568309 Loss2: 1.484484
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.703988 Loss1: 0.293549 Loss2: 1.410439
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.688117 Loss1: 0.272401 Loss2: 1.415716 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.923958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.811216 Loss1: 0.356878 Loss2: 1.454338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.711610 Loss1: 0.264956 Loss2: 1.446654 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.955529
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.639214 Loss1: 1.619964 Loss2: 2.019249 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.289383 Loss1: 0.829072 Loss2: 1.460311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.743992 Loss1: 1.709539 Loss2: 2.034452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.543905 Loss1: 1.082495 Loss2: 1.461409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.168223 Loss1: 0.717811 Loss2: 1.450412 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.074522 Loss1: 0.618497 Loss2: 1.456024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.004241 Loss1: 0.537261 Loss2: 1.466980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.917010 Loss1: 0.457323 Loss2: 1.459688 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.921875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.820118 Loss1: 0.361771 Loss2: 1.458347 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.712208 Loss1: 0.261848 Loss2: 1.450360 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.588871 Loss1: 1.092711 Loss2: 1.496160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.063004 Loss1: 0.572142 Loss2: 1.490862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.857570 Loss1: 1.783563 Loss2: 2.074007
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.945624 Loss1: 0.462974 Loss2: 1.482650
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.898758 Loss1: 0.410571 Loss2: 1.488186 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.792524 Loss1: 0.314932 Loss2: 1.477593 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.815311 Loss1: 0.342294 Loss2: 1.473017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.794428 Loss1: 0.314853 Loss2: 1.479576 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.737252 Loss1: 0.256632 Loss2: 1.480620 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.780205 Loss1: 0.307742 Loss2: 1.472463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.710541 Loss1: 0.232250 Loss2: 1.478291 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.947917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.610746 Loss1: 1.631293 Loss2: 1.979452
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.465969 Loss1: 1.025751 Loss2: 1.440218
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.182772 Loss1: 0.750242 Loss2: 1.432531
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.055416 Loss1: 0.631150 Loss2: 1.424266
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.629649 Loss1: 1.572647 Loss2: 2.057001
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.555165 Loss1: 1.076397 Loss2: 1.478768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.221007 Loss1: 0.729825 Loss2: 1.491182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.029609 Loss1: 0.542783 Loss2: 1.486826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.884657 Loss1: 0.406977 Loss2: 1.477680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.862024 Loss1: 0.397115 Loss2: 1.464910 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.923958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.828269 Loss1: 0.353269 Loss2: 1.475000 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.788383 Loss1: 0.315355 Loss2: 1.473028 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.951042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.592955 Loss1: 1.668807 Loss2: 1.924148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.151011 Loss1: 0.709755 Loss2: 1.441256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.999734 Loss1: 0.587052 Loss2: 1.412682
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.661050 Loss1: 1.688530 Loss2: 1.972519
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.942965 Loss1: 0.518053 Loss2: 1.424912
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.476420 Loss1: 1.028784 Loss2: 1.447636
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.860188 Loss1: 0.444887 Loss2: 1.415301
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.250866 Loss1: 0.814536 Loss2: 1.436330
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.798771 Loss1: 0.380085 Loss2: 1.418686
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.987561 Loss1: 0.554459 Loss2: 1.433102
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.893494 Loss1: 0.487149 Loss2: 1.406345
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.792159 Loss1: 0.368538 Loss2: 1.423621
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.743972 Loss1: 0.344182 Loss2: 1.399790
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.788417 Loss1: 0.362833 Loss2: 1.425583
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.747560 Loss1: 0.363845 Loss2: 1.383715
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.723667 Loss1: 0.308495 Loss2: 1.415172
+(DefaultActor pid=3765) >> Training accuracy: 0.916016
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.583985 Loss1: 0.202097 Loss2: 1.381888 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.564387 Loss1: 1.556549 Loss2: 2.007838 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.211224 Loss1: 0.742199 Loss2: 1.469025 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.965199 Loss1: 0.510119 Loss2: 1.455080
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.625401 Loss1: 1.639061 Loss2: 1.986339
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.857058 Loss1: 0.429424 Loss2: 1.427634
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.499267 Loss1: 1.018932 Loss2: 1.480335
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.800114 Loss1: 0.363479 Loss2: 1.436634
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.171107 Loss1: 0.703632 Loss2: 1.467475
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.800493 Loss1: 0.369547 Loss2: 1.430947
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.008770 Loss1: 0.571846 Loss2: 1.436924
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.785285 Loss1: 0.344385 Loss2: 1.440900
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.844908 Loss1: 0.409683 Loss2: 1.435225
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.760377 Loss1: 0.318222 Loss2: 1.442155
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.872963 Loss1: 0.424978 Loss2: 1.447985
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.706133 Loss1: 0.269138 Loss2: 1.436995
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.868469 Loss1: 0.418632 Loss2: 1.449837
+(DefaultActor pid=3765) >> Training accuracy: 0.921875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.852911 Loss1: 0.407337 Loss2: 1.445574
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.796162 Loss1: 0.343639 Loss2: 1.452523
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.764526 Loss1: 0.310867 Loss2: 1.453659
+(DefaultActor pid=3764) >> Training accuracy: 0.949219
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.837185 Loss1: 1.696414 Loss2: 2.140771
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.682279 Loss1: 1.133958 Loss2: 1.548321
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.315009 Loss1: 0.803067 Loss2: 1.511942
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.125326 Loss1: 0.612096 Loss2: 1.513230
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.638392 Loss1: 1.558386 Loss2: 2.080006
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.534903 Loss1: 1.029249 Loss2: 1.505654 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.208153 Loss1: 0.720136 Loss2: 1.488017 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.036745 Loss1: 0.560223 Loss2: 1.476523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.878453 Loss1: 0.411230 Loss2: 1.467223 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.879366 Loss1: 0.410519 Loss2: 1.468847 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.927083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.810118 Loss1: 0.301394 Loss2: 1.508724
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.835348 Loss1: 0.367992 Loss2: 1.467356
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.832705 Loss1: 0.359034 Loss2: 1.473671
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.806385 Loss1: 0.338395 Loss2: 1.467991
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.750398 Loss1: 0.286150 Loss2: 1.464248
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.803333 Loss1: 1.730997 Loss2: 2.072336
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.615968 Loss1: 1.107088 Loss2: 1.508880
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.240031 Loss1: 0.744714 Loss2: 1.495316
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.064445 Loss1: 0.568918 Loss2: 1.495527
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.637716 Loss1: 1.631334 Loss2: 2.006382
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.756278 Loss1: 1.263838 Loss2: 1.492440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.316307 Loss1: 0.813316 Loss2: 1.502990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.079547 Loss1: 0.619969 Loss2: 1.459578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.987533 Loss1: 0.517476 Loss2: 1.470057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.877086 Loss1: 0.413153 Loss2: 1.463933 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.794326 Loss1: 0.301368 Loss2: 1.492958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.790596 Loss1: 0.336219 Loss2: 1.454377
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.789189 Loss1: 0.336347 Loss2: 1.452843
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.775484 Loss1: 0.319263 Loss2: 1.456221
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.678368 Loss1: 0.221267 Loss2: 1.457101
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.355789 Loss1: 1.421819 Loss2: 1.933970
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.523327 Loss1: 1.078540 Loss2: 1.444787
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.098202 Loss1: 0.658876 Loss2: 1.439326
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.596888 Loss1: 1.578099 Loss2: 2.018789
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.463133 Loss1: 1.009729 Loss2: 1.453404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.297112 Loss1: 0.855137 Loss2: 1.441975 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.044724 Loss1: 0.591648 Loss2: 1.453076 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.773673 Loss1: 0.363094 Loss2: 1.410578
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.921773 Loss1: 0.477043 Loss2: 1.444730
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.840073 Loss1: 0.400627 Loss2: 1.439446
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.684737 Loss1: 0.280253 Loss2: 1.404484
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.777484 Loss1: 0.345487 Loss2: 1.431997
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.677121 Loss1: 0.287014 Loss2: 1.390106
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.717660 Loss1: 0.290420 Loss2: 1.427239
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.591877 Loss1: 0.200680 Loss2: 1.391197
+(DefaultActor pid=3765) >> Training accuracy: 0.941176
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.618130 Loss1: 0.193937 Loss2: 1.424193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.612745 Loss1: 1.585639 Loss2: 2.027106
+DEBUG flwr 2023-10-09 23:03:45,563 | server.py:236 | fit_round 55 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.458169 Loss1: 0.983528 Loss2: 1.474641
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.143406 Loss1: 0.673678 Loss2: 1.469728
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.783602 Loss1: 1.703581 Loss2: 2.080021
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.032042 Loss1: 0.561887 Loss2: 1.470155
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.426031 Loss1: 0.953122 Loss2: 1.472909
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.883929 Loss1: 0.433359 Loss2: 1.450570
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.764207 Loss1: 0.325091 Loss2: 1.439115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.785585 Loss1: 0.345065 Loss2: 1.440520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.794909 Loss1: 0.361630 Loss2: 1.433280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.716194 Loss1: 0.269494 Loss2: 1.446701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.651783 Loss1: 0.226090 Loss2: 1.425693 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.938477
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.659838 Loss1: 0.227504 Loss2: 1.432334 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.956473
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.991141 Loss1: 1.862706 Loss2: 2.128436
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.628699 Loss1: 1.103207 Loss2: 1.525492
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.354515 Loss1: 0.850375 Loss2: 1.504140
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.094541 Loss1: 0.575431 Loss2: 1.519109
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.746328 Loss1: 1.726778 Loss2: 2.019550
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.544702 Loss1: 1.094003 Loss2: 1.450699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.217107 Loss1: 0.787609 Loss2: 1.429498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.938502 Loss1: 0.518910 Loss2: 1.419591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.828126 Loss1: 0.417373 Loss2: 1.410753 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.793437 Loss1: 0.382285 Loss2: 1.411152 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.929688
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.718599 Loss1: 0.305928 Loss2: 1.412671 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.622351 Loss1: 0.213744 Loss2: 1.408607 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.429951 Loss1: 0.985215 Loss2: 1.444737 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.929498 Loss1: 0.495097 Loss2: 1.434402 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.814690 Loss1: 0.396522 Loss2: 1.418168
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.775153 Loss1: 1.671406 Loss2: 2.103747
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.744808 Loss1: 0.318928 Loss2: 1.425879
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.682866 Loss1: 1.132498 Loss2: 1.550368
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.732714 Loss1: 0.313094 Loss2: 1.419620
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.348282 Loss1: 0.786717 Loss2: 1.561565
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.726283 Loss1: 0.308684 Loss2: 1.417598
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.132207 Loss1: 0.597832 Loss2: 1.534375
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.667403 Loss1: 0.249814 Loss2: 1.417589
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.003623 Loss1: 0.483451 Loss2: 1.520172
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.658544 Loss1: 0.243440 Loss2: 1.415105
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.920431 Loss1: 0.400258 Loss2: 1.520173
+(DefaultActor pid=3765) >> Training accuracy: 0.948958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.824570 Loss1: 0.293761 Loss2: 1.530809
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.780243 Loss1: 0.260447 Loss2: 1.519796
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.741984 Loss1: 0.233461 Loss2: 1.508523
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.749335 Loss1: 0.242854 Loss2: 1.506482
+(DefaultActor pid=3764) >> Training accuracy: 0.929167
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 23:03:45,563][flwr][DEBUG] - fit_round 55 received 50 results and 0 failures
+INFO flwr 2023-10-09 23:04:27,636 | server.py:125 | fit progress: (55, 2.378667508832182, {'accuracy': 0.4939}, 126775.4142645)
+>> Test accuracy: 0.493900
+[2023-10-09 23:04:27,636][flwr][INFO] - fit progress: (55, 2.378667508832182, {'accuracy': 0.4939}, 126775.4142645)
+DEBUG flwr 2023-10-09 23:04:27,636 | server.py:173 | evaluate_round 55: strategy sampled 50 clients (out of 50)
+[2023-10-09 23:04:27,636][flwr][DEBUG] - evaluate_round 55: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 23:13:31,942 | server.py:187 | evaluate_round 55 received 50 results and 0 failures
+[2023-10-09 23:13:31,942][flwr][DEBUG] - evaluate_round 55 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 23:13:31,943 | server.py:222 | fit_round 56: strategy sampled 50 clients (out of 50)
+[2023-10-09 23:13:31,943][flwr][DEBUG] - fit_round 56: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.652994 Loss1: 1.701230 Loss2: 1.951764
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.522298 Loss1: 1.067517 Loss2: 1.454782
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.121492 Loss1: 0.677578 Loss2: 1.443914
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.690599 Loss1: 1.657116 Loss2: 2.033483
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.917784 Loss1: 0.477316 Loss2: 1.440468
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.691479 Loss1: 1.202866 Loss2: 1.488614
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.925471 Loss1: 0.492993 Loss2: 1.432479
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.288081 Loss1: 0.806720 Loss2: 1.481361
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.804754 Loss1: 0.356444 Loss2: 1.448310
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.070006 Loss1: 0.592544 Loss2: 1.477462
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.820232 Loss1: 0.383955 Loss2: 1.436277
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.843264 Loss1: 0.391122 Loss2: 1.452142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.738064 Loss1: 0.294040 Loss2: 1.444024 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.653311 Loss1: 0.225097 Loss2: 1.428214 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958008
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.761154 Loss1: 0.303643 Loss2: 1.457511 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.651482 Loss1: 1.644268 Loss2: 2.007214 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.220916 Loss1: 0.775420 Loss2: 1.445496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.034177 Loss1: 0.597294 Loss2: 1.436882
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.509414 Loss1: 1.537761 Loss2: 1.971652
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.869517 Loss1: 0.442490 Loss2: 1.427027
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.431814 Loss1: 0.998203 Loss2: 1.433611
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.810394 Loss1: 0.387533 Loss2: 1.422861
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.081662 Loss1: 0.637014 Loss2: 1.444648
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.742392 Loss1: 0.321895 Loss2: 1.420497
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.954560 Loss1: 0.537550 Loss2: 1.417010
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.679623 Loss1: 0.258473 Loss2: 1.421150
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.840513 Loss1: 0.427430 Loss2: 1.413083
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.668841 Loss1: 0.261068 Loss2: 1.407772
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.746121 Loss1: 0.343823 Loss2: 1.402298
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.696239 Loss1: 0.285315 Loss2: 1.410924
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.692494 Loss1: 0.285130 Loss2: 1.407364
+(DefaultActor pid=3765) >> Training accuracy: 0.935417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.638367 Loss1: 0.241974 Loss2: 1.396393
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.637871 Loss1: 0.230845 Loss2: 1.407026
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.668088 Loss1: 0.265051 Loss2: 1.403037
+(DefaultActor pid=3764) >> Training accuracy: 0.938542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.483932 Loss1: 1.518752 Loss2: 1.965180
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.439459 Loss1: 0.980725 Loss2: 1.458734
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.093915 Loss1: 0.638811 Loss2: 1.455104
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.736065 Loss1: 1.725964 Loss2: 2.010101
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.938843 Loss1: 0.487696 Loss2: 1.451147
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.592978 Loss1: 1.127021 Loss2: 1.465957
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.870321 Loss1: 0.434845 Loss2: 1.435476
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.809556 Loss1: 0.368403 Loss2: 1.441153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.699528 Loss1: 0.268539 Loss2: 1.430989 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.779325 Loss1: 0.353252 Loss2: 1.426073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.666745 Loss1: 0.229205 Loss2: 1.437540 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.653303 Loss1: 0.224406 Loss2: 1.428898 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966912
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.702275 Loss1: 0.271594 Loss2: 1.430681 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.808860 Loss1: 1.704962 Loss2: 2.103898
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.560377 Loss1: 1.066288 Loss2: 1.494088
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.211878 Loss1: 0.764562 Loss2: 1.447316
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.043563 Loss1: 0.584493 Loss2: 1.459070
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.915271 Loss1: 0.451655 Loss2: 1.463616
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.788332 Loss1: 0.335818 Loss2: 1.452513 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.772331 Loss1: 0.334064 Loss2: 1.438267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.711519 Loss1: 0.274461 Loss2: 1.437058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.657582 Loss1: 0.220285 Loss2: 1.437298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.880528 Loss1: 0.431896 Loss2: 1.448633
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.675002 Loss1: 0.238213 Loss2: 1.436789
+(DefaultActor pid=3765) >> Training accuracy: 0.956731
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.771634 Loss1: 0.325509 Loss2: 1.446125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.662448 Loss1: 0.220964 Loss2: 1.441484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.630953 Loss1: 1.638911 Loss2: 1.992042
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.622003 Loss1: 0.196319 Loss2: 1.425684
+(DefaultActor pid=3764) >> Training accuracy: 0.923828
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.150242 Loss1: 0.679128 Loss2: 1.471114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.924789 Loss1: 0.483036 Loss2: 1.441753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.794036 Loss1: 0.353487 Loss2: 1.440549
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.468459 Loss1: 1.506317 Loss2: 1.962142
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.818370 Loss1: 0.375864 Loss2: 1.442506
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.383437 Loss1: 0.968619 Loss2: 1.414818
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.756626 Loss1: 0.305825 Loss2: 1.450801
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.143657 Loss1: 0.726062 Loss2: 1.417596
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.739493 Loss1: 0.296360 Loss2: 1.443133
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.831349 Loss1: 0.432244 Loss2: 1.399104
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.696492 Loss1: 0.247928 Loss2: 1.448564
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.730189 Loss1: 0.355058 Loss2: 1.375131
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.723836 Loss1: 0.346424 Loss2: 1.377412
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.645145 Loss1: 0.273037 Loss2: 1.372108
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.645990 Loss1: 0.271565 Loss2: 1.374425
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.662051 Loss1: 0.286429 Loss2: 1.375622
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.649605 Loss1: 0.278857 Loss2: 1.370747
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.632039 Loss1: 1.520051 Loss2: 2.111988
+(DefaultActor pid=3764) >> Training accuracy: 0.943750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.460195 Loss1: 0.960443 Loss2: 1.499752
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.164333 Loss1: 0.670876 Loss2: 1.493457
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.013456 Loss1: 0.533700 Loss2: 1.479756
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.888459 Loss1: 0.421569 Loss2: 1.466890
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.428299 Loss1: 1.395047 Loss2: 2.033251
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.857331 Loss1: 0.394232 Loss2: 1.463099
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.446925 Loss1: 0.978100 Loss2: 1.468825
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.802818 Loss1: 0.344099 Loss2: 1.458719
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.124296 Loss1: 0.652166 Loss2: 1.472131
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.725340 Loss1: 0.273818 Loss2: 1.451522
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.894567 Loss1: 0.428388 Loss2: 1.466179
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.663141 Loss1: 0.206652 Loss2: 1.456488
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.767416 Loss1: 0.334951 Loss2: 1.432465
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.644688 Loss1: 0.206305 Loss2: 1.438383
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.748570 Loss1: 0.311777 Loss2: 1.436793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.627905 Loss1: 0.193359 Loss2: 1.434546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.667508 Loss1: 1.593822 Loss2: 2.073686
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.628020 Loss1: 0.202032 Loss2: 1.425988
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.236582 Loss1: 0.793367 Loss2: 1.443215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.858820 Loss1: 0.422558 Loss2: 1.436262 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.753052 Loss1: 0.328529 Loss2: 1.424523
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.704942 Loss1: 1.758238 Loss2: 1.946705
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.438149 Loss1: 1.032718 Loss2: 1.405432 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.147813 Loss1: 0.734153 Loss2: 1.413659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.916789 Loss1: 0.504915 Loss2: 1.411874 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.928125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.737156 Loss1: 0.310949 Loss2: 1.426207
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.885331 Loss1: 0.483521 Loss2: 1.401810
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.764079 Loss1: 0.357339 Loss2: 1.406740
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.692851 Loss1: 0.297932 Loss2: 1.394919
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.701248 Loss1: 0.313669 Loss2: 1.387579
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.597163 Loss1: 0.195773 Loss2: 1.401389
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.841733 Loss1: 1.741914 Loss2: 2.099819
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.573442 Loss1: 0.184242 Loss2: 1.389200
+(DefaultActor pid=3764) >> Training accuracy: 0.947917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.316800 Loss1: 0.790865 Loss2: 1.525936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.980314 Loss1: 0.483776 Loss2: 1.496538 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.926545 Loss1: 0.422940 Loss2: 1.503605
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.627323 Loss1: 1.581200 Loss2: 2.046123
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.649077 Loss1: 1.140721 Loss2: 1.508356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.237386 Loss1: 0.705094 Loss2: 1.532293 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.944303 Loss1: 0.470301 Loss2: 1.474002 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.745217 Loss1: 0.269423 Loss2: 1.475794
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.863485 Loss1: 0.411614 Loss2: 1.451870
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.802918 Loss1: 0.350580 Loss2: 1.452338
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.769107 Loss1: 0.310147 Loss2: 1.458960
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.756849 Loss1: 0.295307 Loss2: 1.461541
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.716905 Loss1: 0.263217 Loss2: 1.453688
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.705887 Loss1: 1.655284 Loss2: 2.050603
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.725897 Loss1: 0.277953 Loss2: 1.447944
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.317410 Loss1: 0.837344 Loss2: 1.480067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.893876 Loss1: 0.437998 Loss2: 1.455878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.861354 Loss1: 0.405999 Loss2: 1.455356
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.639114 Loss1: 1.650645 Loss2: 1.988469
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.879806 Loss1: 0.408654 Loss2: 1.471152
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.570844 Loss1: 1.086122 Loss2: 1.484722
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.859822 Loss1: 0.388940 Loss2: 1.470883
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.245336 Loss1: 0.746728 Loss2: 1.498608
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.038698 Loss1: 0.565882 Loss2: 1.472817 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.915625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.760963 Loss1: 0.287718 Loss2: 1.473245
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.003132 Loss1: 0.524418 Loss2: 1.478714
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.850930 Loss1: 0.380992 Loss2: 1.469938
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.747431 Loss1: 0.293258 Loss2: 1.454173
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.747697 Loss1: 0.296737 Loss2: 1.450959
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.759172 Loss1: 0.295002 Loss2: 1.464169
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.633471 Loss1: 1.604813 Loss2: 2.028657
+(DefaultActor pid=3764) >> Training accuracy: 0.954102
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.621668 Loss1: 1.139432 Loss2: 1.482236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.020448 Loss1: 0.558597 Loss2: 1.461851 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.831094 Loss1: 0.367028 Loss2: 1.464066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.774192 Loss1: 0.312508 Loss2: 1.461684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.728898 Loss1: 0.274786 Loss2: 1.454111 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.300360 Loss1: 0.797988 Loss2: 1.502371
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.691511 Loss1: 0.231979 Loss2: 1.459532
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.105718 Loss1: 0.603008 Loss2: 1.502710
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.693981 Loss1: 0.250339 Loss2: 1.443643
+(DefaultActor pid=3765) >> Training accuracy: 0.939583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.816500 Loss1: 0.330173 Loss2: 1.486327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.874230 Loss1: 0.393168 Loss2: 1.481062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.574383 Loss1: 1.547708 Loss2: 2.026675
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.816409 Loss1: 0.322335 Loss2: 1.494074
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.499800 Loss1: 1.035738 Loss2: 1.464062
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.819862 Loss1: 0.331161 Loss2: 1.488701
+(DefaultActor pid=3764) >> Training accuracy: 0.911133
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.951644 Loss1: 0.497028 Loss2: 1.454617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.741788 Loss1: 0.312018 Loss2: 1.429770 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.670633 Loss1: 0.245913 Loss2: 1.424720
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.932898 Loss1: 1.742027 Loss2: 2.190871
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.511924 Loss1: 1.019199 Loss2: 1.492725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.258740 Loss1: 0.801190 Loss2: 1.457549
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.818841 Loss1: 0.390118 Loss2: 1.428723
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.778963 Loss1: 0.334917 Loss2: 1.444046 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.935417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.811412 Loss1: 0.348706 Loss2: 1.462705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.699442 Loss1: 0.256080 Loss2: 1.443362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.666410 Loss1: 0.221276 Loss2: 1.445134 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.954427
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.567391 Loss1: 1.139116 Loss2: 1.428274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.088237 Loss1: 0.653746 Loss2: 1.434492 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.870721 Loss1: 0.444723 Loss2: 1.425998
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.925617 Loss1: 1.839961 Loss2: 2.085657
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.564474 Loss1: 1.099375 Loss2: 1.465099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.287606 Loss1: 0.831035 Loss2: 1.456572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.057608 Loss1: 0.595465 Loss2: 1.462144 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.911920 Loss1: 0.462919 Loss2: 1.449001 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.618095 Loss1: 0.209627 Loss2: 1.408468
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.821674 Loss1: 0.381269 Loss2: 1.440405
+(DefaultActor pid=3765) >> Training accuracy: 0.930208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.752882 Loss1: 0.303126 Loss2: 1.449756
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.722607 Loss1: 0.290104 Loss2: 1.432503
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.723818 Loss1: 0.290673 Loss2: 1.433144
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.741127 Loss1: 0.293241 Loss2: 1.447886
+(DefaultActor pid=3764) >> Training accuracy: 0.909598
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.629797 Loss1: 1.609587 Loss2: 2.020210
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.515402 Loss1: 1.042528 Loss2: 1.472873
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.208796 Loss1: 0.734670 Loss2: 1.474126
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.004448 Loss1: 0.531310 Loss2: 1.473139
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.517535 Loss1: 1.449922 Loss2: 2.067613
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.497835 Loss1: 1.032508 Loss2: 1.465326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.241527 Loss1: 0.746117 Loss2: 1.495410 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.026248 Loss1: 0.558066 Loss2: 1.468182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.890920 Loss1: 0.456935 Loss2: 1.433985 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.846297 Loss1: 0.388753 Loss2: 1.457544 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.931250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.718829 Loss1: 0.282568 Loss2: 1.436261 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.671389 Loss1: 0.242253 Loss2: 1.429136 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.927083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.581841 Loss1: 1.100178 Loss2: 1.481663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.960078 Loss1: 0.497966 Loss2: 1.462113 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.832361 Loss1: 0.377498 Loss2: 1.454863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.813752 Loss1: 0.360925 Loss2: 1.452826 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.884737 Loss1: 0.432070 Loss2: 1.452667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.792436 Loss1: 0.333366 Loss2: 1.459069 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.731848 Loss1: 0.276218 Loss2: 1.455630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.707324 Loss1: 0.264311 Loss2: 1.443013 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.934570
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.773522 Loss1: 0.341735 Loss2: 1.431787 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.684664 Loss1: 0.260038 Loss2: 1.424626 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.607845 Loss1: 1.583152 Loss2: 2.024692
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.475964 Loss1: 1.009230 Loss2: 1.466734
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.188211 Loss1: 0.719406 Loss2: 1.468805
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.006098 Loss1: 0.551884 Loss2: 1.454214
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.793954 Loss1: 1.745291 Loss2: 2.048663
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.654491 Loss1: 1.171707 Loss2: 1.482784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.270617 Loss1: 0.746855 Loss2: 1.523762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.046297 Loss1: 0.553767 Loss2: 1.492530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.015674 Loss1: 0.546313 Loss2: 1.469362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.923062 Loss1: 0.432468 Loss2: 1.490595 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.957292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.852422 Loss1: 0.377867 Loss2: 1.474556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.769946 Loss1: 0.293097 Loss2: 1.476849 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.923958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.678664 Loss1: 1.611840 Loss2: 2.066824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.078660 Loss1: 0.689350 Loss2: 1.389310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.776162 Loss1: 0.386099 Loss2: 1.390063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.719225 Loss1: 0.336124 Loss2: 1.383101 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.677154 Loss1: 0.291036 Loss2: 1.386118 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.585768 Loss1: 0.195249 Loss2: 1.390520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.563295 Loss1: 0.193001 Loss2: 1.370294
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.002251 Loss1: 0.531807 Loss2: 1.470444
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.541696 Loss1: 0.174537 Loss2: 1.367159
+(DefaultActor pid=3765) >> Training accuracy: 0.960337
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.890163 Loss1: 0.443289 Loss2: 1.446874
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.823832 Loss1: 0.368031 Loss2: 1.455801
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.818834 Loss1: 0.358533 Loss2: 1.460301
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.847280 Loss1: 0.379478 Loss2: 1.467803
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.811086 Loss1: 0.334031 Loss2: 1.477055
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.469704 Loss1: 1.481869 Loss2: 1.987835
+(DefaultActor pid=3764) >> Training accuracy: 0.949219
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.420105 Loss1: 0.945284 Loss2: 1.474821 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.999211 Loss1: 0.532072 Loss2: 1.467139 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.810890 Loss1: 0.350133 Loss2: 1.460757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.745095 Loss1: 0.296036 Loss2: 1.449059 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.719548 Loss1: 0.286381 Loss2: 1.433167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.711554 Loss1: 0.273017 Loss2: 1.438536 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.954975 Loss1: 0.518682 Loss2: 1.436293 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.796844 Loss1: 0.342599 Loss2: 1.454245 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.697418 Loss1: 0.269353 Loss2: 1.428065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.644192 Loss1: 0.219766 Loss2: 1.424426
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.758855 Loss1: 1.735854 Loss2: 2.023001
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.637634 Loss1: 1.143422 Loss2: 1.494212
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.303771 Loss1: 0.840891 Loss2: 1.462880
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.026324 Loss1: 0.563956 Loss2: 1.462368
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.986053 Loss1: 0.534476 Loss2: 1.451577
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.665096 Loss1: 1.701487 Loss2: 1.963609
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.863006 Loss1: 0.416278 Loss2: 1.446727
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.710709 Loss1: 1.237903 Loss2: 1.472806
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.787399 Loss1: 0.343161 Loss2: 1.444238
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.385063 Loss1: 0.857937 Loss2: 1.527127
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.751717 Loss1: 0.310847 Loss2: 1.440870
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.004819 Loss1: 0.561511 Loss2: 1.443308
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.757374 Loss1: 0.318497 Loss2: 1.438876
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.972030 Loss1: 0.521817 Loss2: 1.450213
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.709213 Loss1: 0.265803 Loss2: 1.443409
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.800674 Loss1: 0.345760 Loss2: 1.454914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.747833 Loss1: 0.289720 Loss2: 1.458113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.747274 Loss1: 0.297025 Loss2: 1.450249
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.506730 Loss1: 1.563469 Loss2: 1.943261
+(DefaultActor pid=3764) >> Training accuracy: 0.937500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.481769 Loss1: 1.074707 Loss2: 1.407062
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.185584 Loss1: 0.755384 Loss2: 1.430200
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.951712 Loss1: 0.529561 Loss2: 1.422151
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.816756 Loss1: 0.414685 Loss2: 1.402071
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.412427 Loss1: 1.516206 Loss2: 1.896221
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.807235 Loss1: 0.397957 Loss2: 1.409278
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.581816 Loss1: 1.154501 Loss2: 1.427314
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.793241 Loss1: 0.395406 Loss2: 1.397834
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.687398 Loss1: 0.283739 Loss2: 1.403659
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.224252 Loss1: 0.792663 Loss2: 1.431589
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.723604 Loss1: 0.326121 Loss2: 1.397483
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.989730 Loss1: 0.581737 Loss2: 1.407994
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.687494 Loss1: 0.287117 Loss2: 1.400377
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.838995 Loss1: 0.442344 Loss2: 1.396652
+(DefaultActor pid=3765) >> Training accuracy: 0.934375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.815098 Loss1: 0.410775 Loss2: 1.404323
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.813303 Loss1: 0.395641 Loss2: 1.417662
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.757780 Loss1: 0.348865 Loss2: 1.408915
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.660354 Loss1: 0.264879 Loss2: 1.395474
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.593192 Loss1: 1.554046 Loss2: 2.039146
+(DefaultActor pid=3764) >> Training accuracy: 0.951172
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.406657 Loss1: 0.930461 Loss2: 1.476196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.985142 Loss1: 0.538605 Loss2: 1.446537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.778047 Loss1: 0.341349 Loss2: 1.436699 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.781838 Loss1: 0.351738 Loss2: 1.430100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.685583 Loss1: 0.259827 Loss2: 1.425757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.732002 Loss1: 0.301101 Loss2: 1.430901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.684265 Loss1: 0.248854 Loss2: 1.435411 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.921875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.819540 Loss1: 0.378920 Loss2: 1.440620 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.752080 Loss1: 0.313514 Loss2: 1.438565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.620057 Loss1: 1.615659 Loss2: 2.004399 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.941667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.202368 Loss1: 0.761396 Loss2: 1.440972 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.868090 Loss1: 0.440415 Loss2: 1.427675 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.867701 Loss1: 0.445635 Loss2: 1.422065
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.850803 Loss1: 1.797094 Loss2: 2.053709
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.720548 Loss1: 1.202959 Loss2: 1.517589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.273707 Loss1: 0.765680 Loss2: 1.508027 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.076654 Loss1: 0.593734 Loss2: 1.482920 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.671789 Loss1: 0.271658 Loss2: 1.400131
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.131711 Loss1: 0.652896 Loss2: 1.478816
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.998676 Loss1: 0.484151 Loss2: 1.514525
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.947498 Loss1: 0.460147 Loss2: 1.487352
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.895894 Loss1: 0.426910 Loss2: 1.468983
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.795875 Loss1: 0.320220 Loss2: 1.475655
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.597248 Loss1: 1.650637 Loss2: 1.946610
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.740285 Loss1: 0.273557 Loss2: 1.466728
+(DefaultActor pid=3764) >> Training accuracy: 0.936458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.194163 Loss1: 0.762199 Loss2: 1.431964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.810023 Loss1: 0.395529 Loss2: 1.414494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.630472 Loss1: 1.594992 Loss2: 2.035480
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.869306 Loss1: 0.450494 Loss2: 1.418812
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.548008 Loss1: 1.091347 Loss2: 1.456661
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.750471 Loss1: 0.331331 Loss2: 1.419140
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.215310 Loss1: 0.718294 Loss2: 1.497016
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.732050 Loss1: 0.320374 Loss2: 1.411676
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.807817 Loss1: 0.389676 Loss2: 1.418140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.716016 Loss1: 0.275966 Loss2: 1.440050 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.925781
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.709747 Loss1: 0.269106 Loss2: 1.440641 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.617630 Loss1: 0.178849 Loss2: 1.438780 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.960938
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.606104 Loss1: 1.071935 Loss2: 1.534169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.074659 Loss1: 0.553523 Loss2: 1.521137 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.884296 Loss1: 0.370851 Loss2: 1.513445
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.619081 Loss1: 1.631526 Loss2: 1.987555
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.819968 Loss1: 0.329594 Loss2: 1.490374
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.480571 Loss1: 1.035674 Loss2: 1.444897
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.808173 Loss1: 0.307918 Loss2: 1.500255
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.162194 Loss1: 0.716300 Loss2: 1.445894
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.738969 Loss1: 0.247350 Loss2: 1.491620
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.982398 Loss1: 0.547010 Loss2: 1.435388
+DEBUG flwr 2023-10-09 23:42:12,573 | server.py:236 | fit_round 56 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.754610 Loss1: 0.264616 Loss2: 1.489994
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.828722 Loss1: 0.400363 Loss2: 1.428358
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.732791 Loss1: 0.247192 Loss2: 1.485599
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.706508 Loss1: 0.294744 Loss2: 1.411764
+(DefaultActor pid=3765) >> Training accuracy: 0.931250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.710541 Loss1: 0.300388 Loss2: 1.410153
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.686699 Loss1: 0.271996 Loss2: 1.414703
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.628039 Loss1: 0.213580 Loss2: 1.414459
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.614797 Loss1: 0.205885 Loss2: 1.408912
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.580310 Loss1: 1.628773 Loss2: 1.951537
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.406321 Loss1: 0.988133 Loss2: 1.418187
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.155130 Loss1: 0.742000 Loss2: 1.413131
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.946013 Loss1: 0.539790 Loss2: 1.406224
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.824893 Loss1: 0.432727 Loss2: 1.392166
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.764807 Loss1: 0.375102 Loss2: 1.389705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.315952 Loss1: 0.798228 Loss2: 1.517724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.128941 Loss1: 0.609927 Loss2: 1.519014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.972916 Loss1: 0.475339 Loss2: 1.497577 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.851502 Loss1: 0.363276 Loss2: 1.488226 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.915625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.791131 Loss1: 0.306343 Loss2: 1.484789 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.750010 Loss1: 0.268219 Loss2: 1.481791 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.938616
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.671135 Loss1: 1.631420 Loss2: 2.039715
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.599679 Loss1: 1.110484 Loss2: 1.489195
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.371290 Loss1: 0.869155 Loss2: 1.502135
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.065632 Loss1: 0.586698 Loss2: 1.478934
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.596952 Loss1: 1.611043 Loss2: 1.985909
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.527718 Loss1: 1.080112 Loss2: 1.447606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.181404 Loss1: 0.732930 Loss2: 1.448474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.021360 Loss1: 0.585583 Loss2: 1.435777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.913935 Loss1: 0.506397 Loss2: 1.407538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.833392 Loss1: 0.400247 Loss2: 1.433145 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.920833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.727954 Loss1: 0.290040 Loss2: 1.437914
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.794471 Loss1: 0.369926 Loss2: 1.424545
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.812055 Loss1: 0.395661 Loss2: 1.416393
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.803137 Loss1: 0.372381 Loss2: 1.430756
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.666323 Loss1: 0.250190 Loss2: 1.416133
+(DefaultActor pid=3764) >> Training accuracy: 0.947917
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-09 23:42:12,573][flwr][DEBUG] - fit_round 56 received 50 results and 0 failures
+INFO flwr 2023-10-09 23:42:53,929 | server.py:125 | fit progress: (56, 2.3453345260681053, {'accuracy': 0.4999}, 129081.707892689)
+>> Test accuracy: 0.499900
+[2023-10-09 23:42:53,929][flwr][INFO] - fit progress: (56, 2.3453345260681053, {'accuracy': 0.4999}, 129081.707892689)
+DEBUG flwr 2023-10-09 23:42:53,930 | server.py:173 | evaluate_round 56: strategy sampled 50 clients (out of 50)
+[2023-10-09 23:42:53,930][flwr][DEBUG] - evaluate_round 56: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-09 23:52:01,138 | server.py:187 | evaluate_round 56 received 50 results and 0 failures
+[2023-10-09 23:52:01,138][flwr][DEBUG] - evaluate_round 56 received 50 results and 0 failures
+DEBUG flwr 2023-10-09 23:52:01,139 | server.py:222 | fit_round 57: strategy sampled 50 clients (out of 50)
+[2023-10-09 23:52:01,139][flwr][DEBUG] - fit_round 57: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.501036 Loss1: 1.534974 Loss2: 1.966062
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.456984 Loss1: 1.023481 Loss2: 1.433504
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.132692 Loss1: 0.686607 Loss2: 1.446085
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.881050 Loss1: 0.456349 Loss2: 1.424700
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.778287 Loss1: 1.730027 Loss2: 2.048261
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.566606 Loss1: 1.076835 Loss2: 1.489770
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.829794 Loss1: 0.423508 Loss2: 1.406286
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.203989 Loss1: 0.747184 Loss2: 1.456805
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.734412 Loss1: 0.325206 Loss2: 1.409206
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.925204 Loss1: 0.477596 Loss2: 1.447608
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.722640 Loss1: 0.319505 Loss2: 1.403135
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.874402 Loss1: 0.434042 Loss2: 1.440361
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.710023 Loss1: 0.295581 Loss2: 1.414443
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.633261 Loss1: 0.223336 Loss2: 1.409925 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.628499 Loss1: 0.227307 Loss2: 1.401191 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.940430
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.664440 Loss1: 0.230657 Loss2: 1.433782 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.524226 Loss1: 1.588685 Loss2: 1.935541 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.124831 Loss1: 0.701148 Loss2: 1.423682 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.934765 Loss1: 0.519402 Loss2: 1.415363
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.269313 Loss1: 1.335151 Loss2: 1.934163
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.380720 Loss1: 0.950266 Loss2: 1.430454 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.249275 Loss1: 0.794958 Loss2: 1.454317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.987490 Loss1: 0.564556 Loss2: 1.422934 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.859225 Loss1: 0.436104 Loss2: 1.423121 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.593023 Loss1: 0.223965 Loss2: 1.369058 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.923958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.660069 Loss1: 0.254572 Loss2: 1.405497 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.602111 Loss1: 0.204387 Loss2: 1.397724 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.955882
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.583329 Loss1: 1.166838 Loss2: 1.416491 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.966837 Loss1: 0.571940 Loss2: 1.394897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.832892 Loss1: 0.447045 Loss2: 1.385846
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.589257 Loss1: 1.567786 Loss2: 2.021471
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.689973 Loss1: 1.178983 Loss2: 1.510990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.356192 Loss1: 0.785928 Loss2: 1.570264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.993870 Loss1: 0.490375 Loss2: 1.503495 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.922653 Loss1: 0.445670 Loss2: 1.476984 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.939583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.861240 Loss1: 0.376279 Loss2: 1.484961 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.764342 Loss1: 0.292313 Loss2: 1.472029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.665877 Loss1: 0.194641 Loss2: 1.471236 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.649812 Loss1: 1.106367 Loss2: 1.543445 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.031757 Loss1: 0.527702 Loss2: 1.504054 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.520457 Loss1: 1.503122 Loss2: 2.017335
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.988313 Loss1: 0.452483 Loss2: 1.535831
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.340629 Loss1: 0.889966 Loss2: 1.450663
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.943566 Loss1: 0.420694 Loss2: 1.522872
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.088780 Loss1: 0.661030 Loss2: 1.427750
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.867242 Loss1: 0.349386 Loss2: 1.517856
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.891466 Loss1: 0.359270 Loss2: 1.532196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.833355 Loss1: 0.305996 Loss2: 1.527360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.773787 Loss1: 0.264609 Loss2: 1.509178 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.931641
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.680059 Loss1: 0.267232 Loss2: 1.412827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.684889 Loss1: 0.267447 Loss2: 1.417443 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.940625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.483615 Loss1: 1.042636 Loss2: 1.440978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.886310 Loss1: 0.474669 Loss2: 1.411641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.803882 Loss1: 0.407301 Loss2: 1.396582 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.745912 Loss1: 0.336719 Loss2: 1.409193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.694987 Loss1: 0.296440 Loss2: 1.398547 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.701683 Loss1: 0.308653 Loss2: 1.393029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.656700 Loss1: 0.256253 Loss2: 1.400448 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.599419 Loss1: 0.203660 Loss2: 1.395759 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.944792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.602970 Loss1: 0.208825 Loss2: 1.394146 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975446
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.778097 Loss1: 1.706306 Loss2: 2.071791 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.404384 Loss1: 0.839570 Loss2: 1.564814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.165095 Loss1: 0.652058 Loss2: 1.513037
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.725253 Loss1: 1.708053 Loss2: 2.017201
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.979582 Loss1: 0.479298 Loss2: 1.500284
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.552580 Loss1: 1.083515 Loss2: 1.469064
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.861375 Loss1: 0.357959 Loss2: 1.503416
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.182801 Loss1: 0.704902 Loss2: 1.477900
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.847099 Loss1: 0.350444 Loss2: 1.496655
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.982928 Loss1: 0.534992 Loss2: 1.447936
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.866541 Loss1: 0.367508 Loss2: 1.499033
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.874048 Loss1: 0.438425 Loss2: 1.435623
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.812951 Loss1: 0.311798 Loss2: 1.501153
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.823002 Loss1: 0.376413 Loss2: 1.446589
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.823833 Loss1: 0.322361 Loss2: 1.501472
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.779200 Loss1: 0.333945 Loss2: 1.445255
+(DefaultActor pid=3765) >> Training accuracy: 0.925000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.727499 Loss1: 0.294781 Loss2: 1.432718
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.721837 Loss1: 0.280694 Loss2: 1.441142
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.695055 Loss1: 0.251801 Loss2: 1.443254
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.625131 Loss1: 1.590031 Loss2: 2.035100
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.600151 Loss1: 1.094167 Loss2: 1.505984
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.248339 Loss1: 0.729864 Loss2: 1.518475
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.018849 Loss1: 0.526448 Loss2: 1.492400
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.598418 Loss1: 1.533926 Loss2: 2.064492
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.931280 Loss1: 0.450671 Loss2: 1.480609
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.492448 Loss1: 1.001144 Loss2: 1.491303
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.883968 Loss1: 0.397427 Loss2: 1.486541
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.163192 Loss1: 0.675707 Loss2: 1.487485
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.937934 Loss1: 0.471492 Loss2: 1.466442
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.843657 Loss1: 0.353338 Loss2: 1.490319
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.859022 Loss1: 0.405830 Loss2: 1.453192
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.848323 Loss1: 0.356433 Loss2: 1.491890
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.753802 Loss1: 0.304802 Loss2: 1.449000
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.770168 Loss1: 0.278447 Loss2: 1.491720
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.760656 Loss1: 0.313147 Loss2: 1.447509
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.708746 Loss1: 0.226869 Loss2: 1.481877
+(DefaultActor pid=3765) >> Training accuracy: 0.944336
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.675350 Loss1: 0.223054 Loss2: 1.452296 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.670151 Loss1: 1.738519 Loss2: 1.931632 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.139259 Loss1: 0.701094 Loss2: 1.438165 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.042462 Loss1: 0.618877 Loss2: 1.423584
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.559396 Loss1: 1.658640 Loss2: 1.900757
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.865686 Loss1: 0.446171 Loss2: 1.419515
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.419743 Loss1: 0.994568 Loss2: 1.425174
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.788868 Loss1: 0.356582 Loss2: 1.432286
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.151871 Loss1: 0.712372 Loss2: 1.439499
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.819005 Loss1: 0.391325 Loss2: 1.427680
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.985787 Loss1: 0.574882 Loss2: 1.410904
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.772104 Loss1: 0.361112 Loss2: 1.410993
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.851068 Loss1: 0.430529 Loss2: 1.420539
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.729980 Loss1: 0.301999 Loss2: 1.427980
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.766997 Loss1: 0.355059 Loss2: 1.411938
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.727609 Loss1: 0.311465 Loss2: 1.416144
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.702900 Loss1: 0.293917 Loss2: 1.408983
+(DefaultActor pid=3765) >> Training accuracy: 0.943359
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.698921 Loss1: 0.297180 Loss2: 1.401741
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.681156 Loss1: 0.268595 Loss2: 1.412560
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.638058 Loss1: 0.230202 Loss2: 1.407856
+(DefaultActor pid=3764) >> Training accuracy: 0.913086
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.404108 Loss1: 1.428840 Loss2: 1.975269
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.344483 Loss1: 0.922219 Loss2: 1.422264
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.111344 Loss1: 0.673953 Loss2: 1.437391
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.935701 Loss1: 0.520265 Loss2: 1.415435
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.760399 Loss1: 1.734221 Loss2: 2.026178
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.539826 Loss1: 1.059898 Loss2: 1.479928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.335076 Loss1: 0.870564 Loss2: 1.464512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.096684 Loss1: 0.618303 Loss2: 1.478381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.948968 Loss1: 0.499845 Loss2: 1.449123 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.822569 Loss1: 0.373278 Loss2: 1.449291 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.947917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.824795 Loss1: 0.373713 Loss2: 1.451082 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.757939 Loss1: 0.314915 Loss2: 1.443024 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.929167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.562736 Loss1: 1.509953 Loss2: 2.052783 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.255862 Loss1: 0.688460 Loss2: 1.567402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.537902 Loss1: 1.582864 Loss2: 1.955037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.571334 Loss1: 1.142025 Loss2: 1.429309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.226397 Loss1: 0.783984 Loss2: 1.442413 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.970140 Loss1: 0.551099 Loss2: 1.419040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.825346 Loss1: 0.430820 Loss2: 1.394526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.754250 Loss1: 0.356755 Loss2: 1.397495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.892708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.706965 Loss1: 0.318180 Loss2: 1.388785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.660809 Loss1: 0.278716 Loss2: 1.382093 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.587744 Loss1: 1.595236 Loss2: 1.992508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.152526 Loss1: 0.722155 Loss2: 1.430371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.909159 Loss1: 0.476657 Loss2: 1.432502
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.374428 Loss1: 1.420734 Loss2: 1.953695
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.318423 Loss1: 0.899245 Loss2: 1.419178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.058654 Loss1: 0.643365 Loss2: 1.415289 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.931384 Loss1: 0.508109 Loss2: 1.423274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.834718 Loss1: 0.430779 Loss2: 1.403938 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.748916 Loss1: 0.353597 Loss2: 1.395319 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.631350 Loss1: 0.239040 Loss2: 1.392310 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.557627 Loss1: 0.180347 Loss2: 1.377280 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.781558 Loss1: 1.790855 Loss2: 1.990703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.303037 Loss1: 0.822367 Loss2: 1.480669 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.485390 Loss1: 1.500146 Loss2: 1.985244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.428014 Loss1: 0.981077 Loss2: 1.446938 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.193699 Loss1: 0.718261 Loss2: 1.475438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.694913 Loss1: 0.279456 Loss2: 1.415458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.643678 Loss1: 0.229892 Loss2: 1.413786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.660837 Loss1: 0.248354 Loss2: 1.412483 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.934152
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.741184 Loss1: 0.307688 Loss2: 1.433496 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.683926 Loss1: 0.257439 Loss2: 1.426488 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.523025 Loss1: 1.059548 Loss2: 1.463477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.054297 Loss1: 0.579162 Loss2: 1.475135 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.846984 Loss1: 0.407355 Loss2: 1.439630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.740962 Loss1: 0.310634 Loss2: 1.430329 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.809774 Loss1: 0.378446 Loss2: 1.431328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.764250 Loss1: 0.321091 Loss2: 1.443159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.635515 Loss1: 0.200776 Loss2: 1.434739 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.742873 Loss1: 0.328680 Loss2: 1.414192 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.626904 Loss1: 0.224634 Loss2: 1.402270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.492695 Loss1: 1.568690 Loss2: 1.924005 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966346
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.140700 Loss1: 0.757940 Loss2: 1.382760 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.725230 Loss1: 0.342367 Loss2: 1.382863 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.624383 Loss1: 1.630684 Loss2: 1.993699
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.712658 Loss1: 0.341221 Loss2: 1.371437
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.615186 Loss1: 1.146265 Loss2: 1.468921
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.614396 Loss1: 0.257294 Loss2: 1.357102
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.291039 Loss1: 0.791444 Loss2: 1.499594
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.573236 Loss1: 0.216411 Loss2: 1.356825
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.965574 Loss1: 0.502060 Loss2: 1.463515
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.519377 Loss1: 0.163868 Loss2: 1.355509
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.855931 Loss1: 0.405901 Loss2: 1.450030
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.555356 Loss1: 0.202654 Loss2: 1.352703
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.746588 Loss1: 0.287458 Loss2: 1.459130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.740898 Loss1: 0.283258 Loss2: 1.457640 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.886268 Loss1: 1.789988 Loss2: 2.096280
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.706921 Loss1: 0.247900 Loss2: 1.459021
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.200044 Loss1: 0.724754 Loss2: 1.475291 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.960741 Loss1: 0.501506 Loss2: 1.459236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.865234 Loss1: 0.400417 Loss2: 1.464817
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.547922 Loss1: 1.539159 Loss2: 2.008763
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.487745 Loss1: 1.040683 Loss2: 1.447062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.282955 Loss1: 0.815272 Loss2: 1.467683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.058153 Loss1: 0.583622 Loss2: 1.474531 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.868666 Loss1: 0.429760 Loss2: 1.438906 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.774567 Loss1: 0.332597 Loss2: 1.441970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.748372 Loss1: 0.311548 Loss2: 1.436824
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.425218 Loss1: 1.450010 Loss2: 1.975208
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.513024 Loss1: 1.015128 Loss2: 1.497897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.955925 Loss1: 0.489176 Loss2: 1.466749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.816182 Loss1: 0.351897 Loss2: 1.464285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.789850 Loss1: 0.328395 Loss2: 1.461455 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.728122 Loss1: 0.267336 Loss2: 1.460786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.667304 Loss1: 0.212810 Loss2: 1.454494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.738489 Loss1: 0.283351 Loss2: 1.455138 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.927734
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.755689 Loss1: 0.321219 Loss2: 1.434470 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.647370 Loss1: 0.221265 Loss2: 1.426105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.610143 Loss1: 1.598411 Loss2: 2.011731
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.616140 Loss1: 0.198467 Loss2: 1.417673
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.187480 Loss1: 0.701069 Loss2: 1.486411 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.994696 Loss1: 0.518962 Loss2: 1.475734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.830395 Loss1: 0.373181 Loss2: 1.457215
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.508077 Loss1: 1.506424 Loss2: 2.001653
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.816022 Loss1: 0.365062 Loss2: 1.450961
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.553221 Loss1: 1.068324 Loss2: 1.484898
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.807268 Loss1: 0.356604 Loss2: 1.450664
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.304104 Loss1: 0.806351 Loss2: 1.497753
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.739714 Loss1: 0.291216 Loss2: 1.448498
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.085291 Loss1: 0.601200 Loss2: 1.484092
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.730024 Loss1: 0.284359 Loss2: 1.445666
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.918218 Loss1: 0.454579 Loss2: 1.463639
+(DefaultActor pid=3765) >> Training accuracy: 0.954167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.865706 Loss1: 0.415960 Loss2: 1.449746
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.765266 Loss1: 0.308645 Loss2: 1.456621
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.745535 Loss1: 0.286621 Loss2: 1.458913
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.676547 Loss1: 0.227443 Loss2: 1.449104
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.632856 Loss1: 0.184890 Loss2: 1.447965
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.537992 Loss1: 1.613048 Loss2: 1.924944
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.587024 Loss1: 1.154989 Loss2: 1.432035
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.279085 Loss1: 0.803175 Loss2: 1.475910
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.969894 Loss1: 0.543591 Loss2: 1.426303
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.885055 Loss1: 0.463635 Loss2: 1.421420
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.586716 Loss1: 1.541483 Loss2: 2.045233
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.839378 Loss1: 0.419785 Loss2: 1.419592
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.862851 Loss1: 0.442540 Loss2: 1.420311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.765057 Loss1: 0.330304 Loss2: 1.434753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.707674 Loss1: 0.290382 Loss2: 1.417291 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.674307 Loss1: 0.259565 Loss2: 1.414742 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.932617
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.798766 Loss1: 0.348050 Loss2: 1.450716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.779965 Loss1: 0.308731 Loss2: 1.471234 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.912500
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.768313 Loss1: 0.316527 Loss2: 1.451787
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.718915 Loss1: 1.689081 Loss2: 2.029834
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.494040 Loss1: 1.044463 Loss2: 1.449578
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.132365 Loss1: 0.700796 Loss2: 1.431569
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.969744 Loss1: 0.528106 Loss2: 1.441638
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.841619 Loss1: 0.412822 Loss2: 1.428797
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.500077 Loss1: 1.522684 Loss2: 1.977393
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.452482 Loss1: 1.030321 Loss2: 1.422162 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.156154 Loss1: 0.706729 Loss2: 1.449425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.938371 Loss1: 0.507454 Loss2: 1.430918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.852858 Loss1: 0.446467 Loss2: 1.406390 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.927083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.651456 Loss1: 0.232314 Loss2: 1.419141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.810821 Loss1: 0.384731 Loss2: 1.426090
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.770175 Loss1: 0.352297 Loss2: 1.417879
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.718258 Loss1: 0.304318 Loss2: 1.413941
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.632687 Loss1: 0.215813 Loss2: 1.416874
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.666293 Loss1: 0.255126 Loss2: 1.411167
+(DefaultActor pid=3764) >> Training accuracy: 0.907292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.581441 Loss1: 1.614870 Loss2: 1.966571
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.611463 Loss1: 1.164134 Loss2: 1.447329
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.266299 Loss1: 0.770582 Loss2: 1.495716
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.998184 Loss1: 0.545843 Loss2: 1.452341
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.802879 Loss1: 0.373470 Loss2: 1.429409
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.651097 Loss1: 1.657223 Loss2: 1.993874
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.553586 Loss1: 1.071692 Loss2: 1.481894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.223812 Loss1: 0.727127 Loss2: 1.496685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.089817 Loss1: 0.613129 Loss2: 1.476688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.864267 Loss1: 0.398065 Loss2: 1.466202 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.959375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.807223 Loss1: 0.346745 Loss2: 1.460478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.718616 Loss1: 0.262525 Loss2: 1.456091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.678145 Loss1: 0.226701 Loss2: 1.451444 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.414150 Loss1: 0.957612 Loss2: 1.456538 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.905194 Loss1: 0.457351 Loss2: 1.447843 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.731269 Loss1: 0.311931 Loss2: 1.419337
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.753078 Loss1: 1.736561 Loss2: 2.016517
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.510750 Loss1: 1.040056 Loss2: 1.470694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.126163 Loss1: 0.663168 Loss2: 1.462995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.922169 Loss1: 0.475608 Loss2: 1.446561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.906198 Loss1: 0.449073 Loss2: 1.457125 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.857321 Loss1: 0.398490 Loss2: 1.458830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.807245 Loss1: 0.359203 Loss2: 1.448043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.701844 Loss1: 0.262612 Loss2: 1.439232 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.432499 Loss1: 0.984457 Loss2: 1.448042 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.994222 Loss1: 0.547448 Loss2: 1.446775 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.829397 Loss1: 0.394141 Loss2: 1.435256
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.645378 Loss1: 1.662155 Loss2: 1.983224
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.472798 Loss1: 1.043661 Loss2: 1.429137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.125630 Loss1: 0.700969 Loss2: 1.424661 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.941943 Loss1: 0.525380 Loss2: 1.416563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.882823 Loss1: 0.469084 Loss2: 1.413740 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.792379 Loss1: 0.383688 Loss2: 1.408691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.829610 Loss1: 0.413594 Loss2: 1.416016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.701456 Loss1: 0.292379 Loss2: 1.409076 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.936458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.498324 Loss1: 1.016346 Loss2: 1.481979 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.899213 Loss1: 0.450619 Loss2: 1.448594 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.795795 Loss1: 0.352848 Loss2: 1.442946
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.682283 Loss1: 1.609814 Loss2: 2.072469
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.407890 Loss1: 0.973388 Loss2: 1.434502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.776233 Loss1: 0.341613 Loss2: 1.434620
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.174670 Loss1: 0.753240 Loss2: 1.421430
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.946442 Loss1: 0.518608 Loss2: 1.427833
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.774078 Loss1: 0.321073 Loss2: 1.453005
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.884601 Loss1: 0.479782 Loss2: 1.404818
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.783323 Loss1: 0.340394 Loss2: 1.442928
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.825370 Loss1: 0.361457 Loss2: 1.463913 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.943750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.674710 Loss1: 0.260525 Loss2: 1.414185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.578756 Loss1: 0.193908 Loss2: 1.384848 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.954327
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.647289 Loss1: 1.630025 Loss2: 2.017265
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.599539 Loss1: 1.132886 Loss2: 1.466653
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.424611 Loss1: 0.938154 Loss2: 1.486458
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.097497 Loss1: 0.621991 Loss2: 1.475507
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.456313 Loss1: 1.537242 Loss2: 1.919071
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.530087 Loss1: 1.092804 Loss2: 1.437283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.143277 Loss1: 0.721733 Loss2: 1.421543 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 00:20:54,582 | server.py:236 | fit_round 57 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.891341 Loss1: 0.490886 Loss2: 1.400455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.746720 Loss1: 0.356996 Loss2: 1.389724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.692766 Loss1: 0.313632 Loss2: 1.379134 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.944792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.679626 Loss1: 0.288607 Loss2: 1.391019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.689744 Loss1: 0.292808 Loss2: 1.396935 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.938477
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.402110 Loss1: 1.443475 Loss2: 1.958635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.074071 Loss1: 0.666543 Loss2: 1.407528 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.651423 Loss1: 1.575098 Loss2: 2.076326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.494055 Loss1: 0.998649 Loss2: 1.495406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.157695 Loss1: 0.654731 Loss2: 1.502964 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.013370 Loss1: 0.529329 Loss2: 1.484040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.965091 Loss1: 0.489674 Loss2: 1.475417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.912792 Loss1: 0.418845 Loss2: 1.493947 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941406
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.677217 Loss1: 0.294373 Loss2: 1.382844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.793975 Loss1: 0.305974 Loss2: 1.488001
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.776714 Loss1: 0.298942 Loss2: 1.477772
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.694919 Loss1: 0.221974 Loss2: 1.472945
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.660129 Loss1: 0.198368 Loss2: 1.461761
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 00:20:54,582][flwr][DEBUG] - fit_round 57 received 50 results and 0 failures
+INFO flwr 2023-10-10 00:21:36,020 | server.py:125 | fit progress: (57, 2.3549529428299243, {'accuracy': 0.4983}, 131403.798305177)
+>> Test accuracy: 0.498300
+[2023-10-10 00:21:36,020][flwr][INFO] - fit progress: (57, 2.3549529428299243, {'accuracy': 0.4983}, 131403.798305177)
+DEBUG flwr 2023-10-10 00:21:36,020 | server.py:173 | evaluate_round 57: strategy sampled 50 clients (out of 50)
+[2023-10-10 00:21:36,020][flwr][DEBUG] - evaluate_round 57: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 00:30:40,098 | server.py:187 | evaluate_round 57 received 50 results and 0 failures
+[2023-10-10 00:30:40,098][flwr][DEBUG] - evaluate_round 57 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 00:30:40,098 | server.py:222 | fit_round 58: strategy sampled 50 clients (out of 50)
+[2023-10-10 00:30:40,098][flwr][DEBUG] - fit_round 58: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.458826 Loss1: 1.567334 Loss2: 1.891492
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.466060 Loss1: 1.068170 Loss2: 1.397890
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.065189 Loss1: 0.616997 Loss2: 1.448192
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.899287 Loss1: 0.510152 Loss2: 1.389135
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.674814 Loss1: 1.692601 Loss2: 1.982213
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.925376 Loss1: 0.524915 Loss2: 1.400460
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.545214 Loss1: 1.075557 Loss2: 1.469657
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.878743 Loss1: 0.466890 Loss2: 1.411853
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.297775 Loss1: 0.824191 Loss2: 1.473584
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.756528 Loss1: 0.344274 Loss2: 1.412253
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.036887 Loss1: 0.596080 Loss2: 1.440807
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.642542 Loss1: 0.247499 Loss2: 1.395043
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.915653 Loss1: 0.476899 Loss2: 1.438754
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.649129 Loss1: 0.262945 Loss2: 1.386184
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.869272 Loss1: 0.425494 Loss2: 1.443778
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.613237 Loss1: 0.214925 Loss2: 1.398313
+(DefaultActor pid=3765) >> Training accuracy: 0.936458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.777807 Loss1: 0.341159 Loss2: 1.436648
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.731022 Loss1: 0.292597 Loss2: 1.438425
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.736053 Loss1: 0.302704 Loss2: 1.433349
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.682801 Loss1: 0.252475 Loss2: 1.430326
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.596257 Loss1: 1.554472 Loss2: 2.041785
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.618142 Loss1: 1.147376 Loss2: 1.470767
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.327152 Loss1: 0.825428 Loss2: 1.501724
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.066920 Loss1: 0.574853 Loss2: 1.492067
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.501405 Loss1: 1.504483 Loss2: 1.996922
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.848816 Loss1: 0.389652 Loss2: 1.459164
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.412088 Loss1: 0.902613 Loss2: 1.509475
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.123210 Loss1: 0.615715 Loss2: 1.507494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.882359 Loss1: 0.410338 Loss2: 1.472021 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.853223 Loss1: 0.376013 Loss2: 1.477209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.799769 Loss1: 0.317101 Loss2: 1.482667 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.933333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.754757 Loss1: 0.283985 Loss2: 1.470773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.775145 Loss1: 0.304916 Loss2: 1.470229 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.893555
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.624300 Loss1: 1.622859 Loss2: 2.001441 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.279538 Loss1: 0.775909 Loss2: 1.503629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.911036 Loss1: 0.452657 Loss2: 1.458379 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.913002 Loss1: 0.468328 Loss2: 1.444674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.806816 Loss1: 0.349700 Loss2: 1.457116 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.724624 Loss1: 0.272670 Loss2: 1.451954 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.725045 Loss1: 0.283572 Loss2: 1.441474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.699927 Loss1: 0.252076 Loss2: 1.447852 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.912500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.596622 Loss1: 0.214560 Loss2: 1.382062 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.560308 Loss1: 0.169528 Loss2: 1.390780 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.523226 Loss1: 1.105584 Loss2: 1.417642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.032642 Loss1: 0.572939 Loss2: 1.459703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.892871 Loss1: 0.474919 Loss2: 1.417952
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.624137 Loss1: 1.623591 Loss2: 2.000546
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.539139 Loss1: 1.058056 Loss2: 1.481083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.173401 Loss1: 0.655026 Loss2: 1.518375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.944706 Loss1: 0.470791 Loss2: 1.473915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.858707 Loss1: 0.375671 Loss2: 1.483037 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973214
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.811813 Loss1: 0.333524 Loss2: 1.478289 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.689461 Loss1: 0.221804 Loss2: 1.467657 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.684101 Loss1: 0.225255 Loss2: 1.458846
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.346182 Loss1: 1.376118 Loss2: 1.970064
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.306855 Loss1: 0.885916 Loss2: 1.420938
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.086701 Loss1: 0.624571 Loss2: 1.462129
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.850231 Loss1: 0.451282 Loss2: 1.398949
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.857702 Loss1: 0.467083 Loss2: 1.390619
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.686436 Loss1: 1.682424 Loss2: 2.004012
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.821500 Loss1: 0.402529 Loss2: 1.418971
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.610140 Loss1: 1.145675 Loss2: 1.464465
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.660921 Loss1: 0.247075 Loss2: 1.413846
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.139967 Loss1: 0.646588 Loss2: 1.493378
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.588090 Loss1: 0.203773 Loss2: 1.384317
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.987763 Loss1: 0.552583 Loss2: 1.435180
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.553523 Loss1: 0.175285 Loss2: 1.378237
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.992193 Loss1: 0.540742 Loss2: 1.451451
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.538420 Loss1: 0.156951 Loss2: 1.381470
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.785409 Loss1: 0.331378 Loss2: 1.454032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.747662 Loss1: 0.308618 Loss2: 1.439044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.706420 Loss1: 0.260868 Loss2: 1.445552
+(DefaultActor pid=3764) >> Training accuracy: 0.942708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.680623 Loss1: 1.662816 Loss2: 2.017807
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.596606 Loss1: 1.106588 Loss2: 1.490017
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.269826 Loss1: 0.768335 Loss2: 1.501491
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.051164 Loss1: 0.569670 Loss2: 1.481495
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.989501 Loss1: 0.512413 Loss2: 1.477088
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.357275 Loss1: 1.422012 Loss2: 1.935263
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.309768 Loss1: 0.866693 Loss2: 1.443075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.063846 Loss1: 0.596410 Loss2: 1.467436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.917128 Loss1: 0.477033 Loss2: 1.440095 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.832709 Loss1: 0.400659 Loss2: 1.432050 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952148
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.783599 Loss1: 0.345151 Loss2: 1.438449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.757345 Loss1: 0.336235 Loss2: 1.421110 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.593334 Loss1: 0.179255 Loss2: 1.414080 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.949219
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.287653 Loss1: 0.775622 Loss2: 1.512031 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.963888 Loss1: 0.481056 Loss2: 1.482832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.581121 Loss1: 1.597461 Loss2: 1.983660
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.874802 Loss1: 0.382215 Loss2: 1.492587
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.482202 Loss1: 1.004248 Loss2: 1.477953
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.757602 Loss1: 0.272741 Loss2: 1.484861
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.115344 Loss1: 0.650009 Loss2: 1.465336
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.711465 Loss1: 0.243321 Loss2: 1.468143
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.874140 Loss1: 0.435821 Loss2: 1.438319
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.644817 Loss1: 0.183820 Loss2: 1.460996
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.840752 Loss1: 0.403033 Loss2: 1.437719
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.641164 Loss1: 0.178307 Loss2: 1.462857
+(DefaultActor pid=3765) >> Training accuracy: 0.957292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.743604 Loss1: 0.297227 Loss2: 1.446377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.618231 Loss1: 0.198911 Loss2: 1.419320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.696410 Loss1: 0.264750 Loss2: 1.431660
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.549577 Loss1: 1.567132 Loss2: 1.982445
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.546750 Loss1: 1.104779 Loss2: 1.441971
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.182325 Loss1: 0.730554 Loss2: 1.451771
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.950060 Loss1: 0.503415 Loss2: 1.446645
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.876357 Loss1: 0.455978 Loss2: 1.420379
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.674071 Loss1: 1.669153 Loss2: 2.004918
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.752168 Loss1: 0.318206 Loss2: 1.433963
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.741787 Loss1: 0.313980 Loss2: 1.427807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.695032 Loss1: 0.280632 Loss2: 1.414399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.647990 Loss1: 0.231747 Loss2: 1.416244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.691087 Loss1: 0.273146 Loss2: 1.417941 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.932292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.817352 Loss1: 0.343624 Loss2: 1.473728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.731763 Loss1: 0.275942 Loss2: 1.455821 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.690636 Loss1: 0.235165 Loss2: 1.455471
+(DefaultActor pid=3764) >> Training accuracy: 0.932292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.503374 Loss1: 1.543136 Loss2: 1.960238
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.516447 Loss1: 1.071060 Loss2: 1.445387
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.108841 Loss1: 0.653092 Loss2: 1.455749
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.885658 Loss1: 0.474290 Loss2: 1.411369
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.855222 Loss1: 0.455701 Loss2: 1.399521
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.473576 Loss1: 1.506332 Loss2: 1.967244
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.781090 Loss1: 0.359819 Loss2: 1.421271
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.720674 Loss1: 0.321580 Loss2: 1.399094 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.681835 Loss1: 0.284255 Loss2: 1.397580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.662821 Loss1: 0.267879 Loss2: 1.394942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.682774 Loss1: 0.291212 Loss2: 1.391561 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.928125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.643862 Loss1: 0.229268 Loss2: 1.414594 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.592548 Loss1: 0.190414 Loss2: 1.402134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.632866 Loss1: 0.220822 Loss2: 1.412044
+(DefaultActor pid=3764) >> Training accuracy: 0.939583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.521171 Loss1: 1.608537 Loss2: 1.912634
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.536162 Loss1: 1.114460 Loss2: 1.421702
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.199806 Loss1: 0.763172 Loss2: 1.436634
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.932833 Loss1: 0.542706 Loss2: 1.390127
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.829112 Loss1: 0.436903 Loss2: 1.392209
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.603636 Loss1: 1.636707 Loss2: 1.966929
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.731991 Loss1: 0.347460 Loss2: 1.384531
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.668940 Loss1: 0.283594 Loss2: 1.385346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.678296 Loss1: 0.295060 Loss2: 1.383235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.636508 Loss1: 0.253379 Loss2: 1.383129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.644328 Loss1: 0.264623 Loss2: 1.379705 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.803984 Loss1: 0.347970 Loss2: 1.456015 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.665307 Loss1: 0.216999 Loss2: 1.448309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.663920 Loss1: 0.212784 Loss2: 1.451137
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.324739 Loss1: 1.402689 Loss2: 1.922050
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.321797 Loss1: 0.880833 Loss2: 1.440964
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.034958 Loss1: 0.602773 Loss2: 1.432185
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.824139 Loss1: 0.417368 Loss2: 1.406771
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.791698 Loss1: 0.365621 Loss2: 1.426077
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.555076 Loss1: 1.527925 Loss2: 2.027151
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.543241 Loss1: 1.066827 Loss2: 1.476414
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.730897 Loss1: 0.314611 Loss2: 1.416286
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.175888 Loss1: 0.694893 Loss2: 1.480995
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.720918 Loss1: 0.300850 Loss2: 1.420068
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.716402 Loss1: 0.298920 Loss2: 1.417482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.693369 Loss1: 0.269272 Loss2: 1.424098 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.680651 Loss1: 0.264563 Loss2: 1.416088 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941176
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.635671 Loss1: 0.200349 Loss2: 1.435321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.622883 Loss1: 0.186185 Loss2: 1.436697 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.716316 Loss1: 1.771141 Loss2: 1.945175
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.757658 Loss1: 1.253661 Loss2: 1.503997
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.323732 Loss1: 0.818179 Loss2: 1.505554
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.045444 Loss1: 0.564289 Loss2: 1.481155
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.876238 Loss1: 1.724798 Loss2: 2.151440
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.956420 Loss1: 0.485231 Loss2: 1.471189
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.553472 Loss1: 1.091062 Loss2: 1.462410
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.190163 Loss1: 0.718532 Loss2: 1.471631
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.858061 Loss1: 0.388467 Loss2: 1.469594
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.769350 Loss1: 0.305062 Loss2: 1.464288 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.714205 Loss1: 0.266716 Loss2: 1.447489 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.726324 Loss1: 0.268272 Loss2: 1.458053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.745759 Loss1: 0.304536 Loss2: 1.441223 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.913542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.654729 Loss1: 0.218959 Loss2: 1.435769 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.955729
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.503118 Loss1: 1.592731 Loss2: 1.910387
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.563345 Loss1: 1.110101 Loss2: 1.453245
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.193804 Loss1: 0.695358 Loss2: 1.498446
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.018741 Loss1: 0.587587 Loss2: 1.431154
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.431295 Loss1: 1.478989 Loss2: 1.952306
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.883539 Loss1: 0.449749 Loss2: 1.433790
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.392994 Loss1: 0.984241 Loss2: 1.408753
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.788178 Loss1: 0.375051 Loss2: 1.413127
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.151111 Loss1: 0.720969 Loss2: 1.430141
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.699796 Loss1: 0.290694 Loss2: 1.409101
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.877039 Loss1: 0.469743 Loss2: 1.407297
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.721172 Loss1: 0.304369 Loss2: 1.416803
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.830526 Loss1: 0.429354 Loss2: 1.401172
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.720091 Loss1: 0.296718 Loss2: 1.423373
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.842130 Loss1: 0.436698 Loss2: 1.405432
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.652613 Loss1: 0.249394 Loss2: 1.403220
+(DefaultActor pid=3765) >> Training accuracy: 0.933333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.911309 Loss1: 0.478780 Loss2: 1.432529
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.819817 Loss1: 0.402039 Loss2: 1.417778
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.739665 Loss1: 0.336894 Loss2: 1.402771
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.683775 Loss1: 0.280716 Loss2: 1.403058
+(DefaultActor pid=3764) >> Training accuracy: 0.932292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.699539 Loss1: 1.734037 Loss2: 1.965502
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.559470 Loss1: 1.096506 Loss2: 1.462964
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.201529 Loss1: 0.718724 Loss2: 1.482805
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.995548 Loss1: 0.552431 Loss2: 1.443117
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.602040 Loss1: 1.593534 Loss2: 2.008505
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.521947 Loss1: 1.012319 Loss2: 1.509629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.226353 Loss1: 0.697516 Loss2: 1.528837 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.979337 Loss1: 0.483252 Loss2: 1.496085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.857071 Loss1: 0.373016 Loss2: 1.484055 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.821765 Loss1: 0.347724 Loss2: 1.474041 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.905208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.740198 Loss1: 0.262070 Loss2: 1.478128 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.758440 Loss1: 0.280980 Loss2: 1.477460 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.924805
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.337328 Loss1: 0.921728 Loss2: 1.415600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.855275 Loss1: 0.447866 Loss2: 1.407409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.726576 Loss1: 1.638755 Loss2: 2.087821
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.695744 Loss1: 0.304771 Loss2: 1.390973
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.576922 Loss1: 1.033820 Loss2: 1.543102
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.719100 Loss1: 0.331537 Loss2: 1.387563
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.180151 Loss1: 0.620571 Loss2: 1.559580
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.657538 Loss1: 0.267126 Loss2: 1.390411
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.038157 Loss1: 0.522039 Loss2: 1.516118
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.598991 Loss1: 0.212230 Loss2: 1.386761
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.027142 Loss1: 0.504118 Loss2: 1.523024
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.584953 Loss1: 0.206928 Loss2: 1.378025
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.933932 Loss1: 0.392528 Loss2: 1.541404
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.585578 Loss1: 0.196010 Loss2: 1.389568
+(DefaultActor pid=3765) >> Training accuracy: 0.943750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.870450 Loss1: 0.360096 Loss2: 1.510354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.776021 Loss1: 0.254300 Loss2: 1.521721 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.943750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.429321 Loss1: 0.977010 Loss2: 1.452311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.919731 Loss1: 0.483891 Loss2: 1.435839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.810893 Loss1: 0.399735 Loss2: 1.411158
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.636471 Loss1: 1.521080 Loss2: 2.115391
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.744333 Loss1: 0.325788 Loss2: 1.418544
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.504447 Loss1: 0.960760 Loss2: 1.543687
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.692710 Loss1: 0.281931 Loss2: 1.410779
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.143193 Loss1: 0.611200 Loss2: 1.531992
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.660584 Loss1: 0.248604 Loss2: 1.411980
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.970086 Loss1: 0.462980 Loss2: 1.507106
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.684272 Loss1: 0.278507 Loss2: 1.405765
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.853120 Loss1: 0.364950 Loss2: 1.488171
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.618496 Loss1: 0.209268 Loss2: 1.409228
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.809043 Loss1: 0.305302 Loss2: 1.503741
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.768367 Loss1: 0.272095 Loss2: 1.496272
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.778543 Loss1: 0.286460 Loss2: 1.492083
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.742366 Loss1: 0.251963 Loss2: 1.490402
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.720834 Loss1: 0.223227 Loss2: 1.497607
+(DefaultActor pid=3764) >> Training accuracy: 0.916667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.653954 Loss1: 1.630020 Loss2: 2.023934
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.395073 Loss1: 0.971088 Loss2: 1.423985
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.067130 Loss1: 0.683796 Loss2: 1.383334
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.887584 Loss1: 0.489171 Loss2: 1.398413
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.735892 Loss1: 0.354149 Loss2: 1.381743
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.666502 Loss1: 0.296981 Loss2: 1.369521
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.441534 Loss1: 1.498798 Loss2: 1.942736
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.624143 Loss1: 0.254736 Loss2: 1.369407
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.457576 Loss1: 0.972486 Loss2: 1.485091
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.151433 Loss1: 0.668325 Loss2: 1.483108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.995548 Loss1: 0.538630 Loss2: 1.456918 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962740
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.811990 Loss1: 0.351721 Loss2: 1.460268 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.757281 Loss1: 0.306091 Loss2: 1.451189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.746298 Loss1: 1.702282 Loss2: 2.044016
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.714424 Loss1: 0.254090 Loss2: 1.460334
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.705274 Loss1: 1.244077 Loss2: 1.461197
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.673167 Loss1: 0.220641 Loss2: 1.452526
+(DefaultActor pid=3764) >> Training accuracy: 0.935547
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.042248 Loss1: 0.568459 Loss2: 1.473789 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.768872 Loss1: 0.337792 Loss2: 1.431079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.625986 Loss1: 1.608913 Loss2: 2.017073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.492608 Loss1: 0.998194 Loss2: 1.494414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.230473 Loss1: 0.719572 Loss2: 1.510901 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956473
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.876762 Loss1: 0.412565 Loss2: 1.464197 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.794887 Loss1: 0.332649 Loss2: 1.462237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.820554 Loss1: 0.343892 Loss2: 1.476663
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.476360 Loss1: 1.525414 Loss2: 1.950947
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.371524 Loss1: 0.948851 Loss2: 1.422673 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.939583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.738742 Loss1: 0.274840 Loss2: 1.463902
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.091713 Loss1: 0.656885 Loss2: 1.434829
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.951160 Loss1: 0.525410 Loss2: 1.425750
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.878400 Loss1: 0.466462 Loss2: 1.411938
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.805002 Loss1: 0.378248 Loss2: 1.426754
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.703661 Loss1: 0.299342 Loss2: 1.404319
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.399754 Loss1: 1.482602 Loss2: 1.917151
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.651396 Loss1: 0.254603 Loss2: 1.396794
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.643210 Loss1: 0.242062 Loss2: 1.401147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.605047 Loss1: 0.213126 Loss2: 1.391921 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.752379 Loss1: 0.360492 Loss2: 1.391887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.692593 Loss1: 0.303698 Loss2: 1.388895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.649270 Loss1: 0.273215 Loss2: 1.376055
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.386442 Loss1: 1.404811 Loss2: 1.981631
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.368566 Loss1: 0.912787 Loss2: 1.455779 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.941667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.668821 Loss1: 0.281149 Loss2: 1.387672
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.232216 Loss1: 0.754200 Loss2: 1.478016
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.008480 Loss1: 0.564939 Loss2: 1.443541
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.851565 Loss1: 0.416412 Loss2: 1.435153
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.813153 Loss1: 0.385047 Loss2: 1.428106
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.716743 Loss1: 0.295865 Loss2: 1.420878
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.443420 Loss1: 1.464874 Loss2: 1.978546
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.611407 Loss1: 0.202711 Loss2: 1.408696
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.580975 Loss1: 0.179987 Loss2: 1.400988 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.609115 Loss1: 0.210561 Loss2: 1.398554 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.946875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.774754 Loss1: 0.345189 Loss2: 1.429565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.706305 Loss1: 0.280079 Loss2: 1.426226 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.706185 Loss1: 0.284650 Loss2: 1.421535
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.613721 Loss1: 1.545529 Loss2: 2.068192
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.394997 Loss1: 0.954472 Loss2: 1.440525
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.657558 Loss1: 0.223863 Loss2: 1.433695
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.155841 Loss1: 0.729642 Loss2: 1.426199
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.584318 Loss1: 0.163617 Loss2: 1.420701
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.785961 Loss1: 0.361469 Loss2: 1.424492 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.790160 Loss1: 0.354736 Loss2: 1.435424 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.396868 Loss1: 1.469081 Loss2: 1.927787 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.725324 Loss1: 0.295680 Loss2: 1.429643 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941106
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.923923 Loss1: 0.492220 Loss2: 1.431703 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.843303 Loss1: 0.397649 Loss2: 1.445654 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.487710 Loss1: 1.573782 Loss2: 1.913928
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.766639 Loss1: 0.325283 Loss2: 1.441356
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.704669 Loss1: 0.275343 Loss2: 1.429326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.650435 Loss1: 0.219598 Loss2: 1.430837 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.674178 Loss1: 0.243088 Loss2: 1.431090 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957031
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.756163 Loss1: 0.369541 Loss2: 1.386622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.734570 Loss1: 0.327684 Loss2: 1.406887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.583344 Loss1: 1.622332 Loss2: 1.961012 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.929167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.405871 Loss1: 0.988362 Loss2: 1.417509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.891104 Loss1: 0.468508 Loss2: 1.422596 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.776212 Loss1: 0.357289 Loss2: 1.418923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.723342 Loss1: 0.315155 Loss2: 1.408187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.704832 Loss1: 0.303527 Loss2: 1.401306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.654959 Loss1: 0.250937 Loss2: 1.404023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.684732 Loss1: 0.281426 Loss2: 1.403305 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.913542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.798134 Loss1: 0.361707 Loss2: 1.436428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.722385 Loss1: 0.301458 Loss2: 1.420927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.723461 Loss1: 0.292123 Loss2: 1.431339
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.545741 Loss1: 1.615560 Loss2: 1.930181
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.533679 Loss1: 1.077467 Loss2: 1.456212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.985119 Loss1: 0.547400 Loss2: 1.437719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.718486 Loss1: 0.284508 Loss2: 1.433978 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 00:59:19,082 | server.py:236 | fit_round 58 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.704470 Loss1: 0.278615 Loss2: 1.425855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.651618 Loss1: 0.231889 Loss2: 1.419729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.653967 Loss1: 0.230627 Loss2: 1.423340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.677797 Loss1: 0.256408 Loss2: 1.421390 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.952148
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.713454 Loss1: 0.312404 Loss2: 1.401050 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.638133 Loss1: 0.253027 Loss2: 1.385106 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.651894 Loss1: 0.256214 Loss2: 1.395681 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.947266
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.149982 Loss1: 0.696661 Loss2: 1.453321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.808405 Loss1: 0.366086 Loss2: 1.442319 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.751524 Loss1: 0.318710 Loss2: 1.432814
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.441820 Loss1: 1.518625 Loss2: 1.923195
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.731243 Loss1: 0.296226 Loss2: 1.435017
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.490217 Loss1: 1.071037 Loss2: 1.419180
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.712721 Loss1: 0.271407 Loss2: 1.441314
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.141756 Loss1: 0.707631 Loss2: 1.434125
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.818608 Loss1: 0.417526 Loss2: 1.401082
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.718474 Loss1: 0.278617 Loss2: 1.439857
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.818365 Loss1: 0.422773 Loss2: 1.395591
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.669782 Loss1: 0.235044 Loss2: 1.434739
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.723523 Loss1: 0.302411 Loss2: 1.421112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.552222 Loss1: 0.165079 Loss2: 1.387143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.545885 Loss1: 0.166748 Loss2: 1.379137
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.779474 Loss1: 1.744953 Loss2: 2.034521
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.629186 Loss1: 1.167483 Loss2: 1.461703
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.217494 Loss1: 0.737942 Loss2: 1.479551
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.014176 Loss1: 0.573881 Loss2: 1.440294
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.888426 Loss1: 0.452945 Loss2: 1.435481
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.822797 Loss1: 0.366218 Loss2: 1.456580
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.714973 Loss1: 0.288145 Loss2: 1.426829
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.749708 Loss1: 0.319267 Loss2: 1.430441
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.702528 Loss1: 0.268783 Loss2: 1.433746
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.658767 Loss1: 0.238527 Loss2: 1.420240
+(DefaultActor pid=3764) >> Training accuracy: 0.957589
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 00:59:19,082][flwr][DEBUG] - fit_round 58 received 50 results and 0 failures
+INFO flwr 2023-10-10 01:00:00,261 | server.py:125 | fit progress: (58, 2.3542091282792748, {'accuracy': 0.5016}, 133708.03983640502)
+>> Test accuracy: 0.501600
+[2023-10-10 01:00:00,261][flwr][INFO] - fit progress: (58, 2.3542091282792748, {'accuracy': 0.5016}, 133708.03983640502)
+DEBUG flwr 2023-10-10 01:00:00,262 | server.py:173 | evaluate_round 58: strategy sampled 50 clients (out of 50)
+[2023-10-10 01:00:00,262][flwr][DEBUG] - evaluate_round 58: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 01:09:04,862 | server.py:187 | evaluate_round 58 received 50 results and 0 failures
+[2023-10-10 01:09:04,862][flwr][DEBUG] - evaluate_round 58 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 01:09:04,863 | server.py:222 | fit_round 59: strategy sampled 50 clients (out of 50)
+[2023-10-10 01:09:04,863][flwr][DEBUG] - fit_round 59: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.512553 Loss1: 1.483892 Loss2: 2.028662
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.460824 Loss1: 0.977246 Loss2: 1.483578
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.141724 Loss1: 0.645793 Loss2: 1.495930
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.919438 Loss1: 0.453245 Loss2: 1.466193
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.376723 Loss1: 1.390795 Loss2: 1.985929
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.367837 Loss1: 0.921560 Loss2: 1.446278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.083878 Loss1: 0.595846 Loss2: 1.488031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.833841 Loss1: 0.391120 Loss2: 1.442721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.762176 Loss1: 0.341395 Loss2: 1.420781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.723789 Loss1: 0.287558 Loss2: 1.436231 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.597352 Loss1: 0.158291 Loss2: 1.439061
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.660811 Loss1: 0.227901 Loss2: 1.432911
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.658653 Loss1: 0.231643 Loss2: 1.427009
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.619746 Loss1: 0.198516 Loss2: 1.421230
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.668445 Loss1: 0.240461 Loss2: 1.427984
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.528743 Loss1: 1.578802 Loss2: 1.949941
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.625891 Loss1: 1.117517 Loss2: 1.508373
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.242423 Loss1: 0.714653 Loss2: 1.527770
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.044542 Loss1: 0.554100 Loss2: 1.490443
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.547198 Loss1: 1.577066 Loss2: 1.970132
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.986710 Loss1: 0.526401 Loss2: 1.460309
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.537136 Loss1: 1.081466 Loss2: 1.455671
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.189781 Loss1: 0.698379 Loss2: 1.491401
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.886873 Loss1: 0.409292 Loss2: 1.477580
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.979128 Loss1: 0.539401 Loss2: 1.439727
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.843843 Loss1: 0.372931 Loss2: 1.470912
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.888225 Loss1: 0.441774 Loss2: 1.446451
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.848103 Loss1: 0.378315 Loss2: 1.469788
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.864613 Loss1: 0.416414 Loss2: 1.448199
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.730598 Loss1: 0.259363 Loss2: 1.471235
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.652793 Loss1: 0.191433 Loss2: 1.461360 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.943359
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.736913 Loss1: 0.288577 Loss2: 1.448336 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.692562 Loss1: 1.658900 Loss2: 2.033663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.179903 Loss1: 0.623192 Loss2: 1.556711 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.020260 Loss1: 0.521396 Loss2: 1.498864
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.383511 Loss1: 1.469250 Loss2: 1.914261
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.889234 Loss1: 0.369753 Loss2: 1.519481
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.384912 Loss1: 0.954093 Loss2: 1.430819
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.835265 Loss1: 0.337465 Loss2: 1.497800
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.085574 Loss1: 0.640409 Loss2: 1.445165
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.886323 Loss1: 0.377388 Loss2: 1.508935
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.923705 Loss1: 0.515344 Loss2: 1.408362
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.899182 Loss1: 0.379472 Loss2: 1.519710
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.838180 Loss1: 0.410419 Loss2: 1.427761
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.806525 Loss1: 0.290626 Loss2: 1.515899
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.729689 Loss1: 0.321189 Loss2: 1.408499
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.783898 Loss1: 0.271388 Loss2: 1.512510
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.646206 Loss1: 0.244046 Loss2: 1.402160
+(DefaultActor pid=3765) >> Training accuracy: 0.947266
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.698525 Loss1: 0.300357 Loss2: 1.398168
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.737168 Loss1: 0.322274 Loss2: 1.414894
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.699496 Loss1: 0.285404 Loss2: 1.414092
+(DefaultActor pid=3764) >> Training accuracy: 0.893555
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.691733 Loss1: 1.616517 Loss2: 2.075216
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.600787 Loss1: 1.066417 Loss2: 1.534370
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.329878 Loss1: 0.785138 Loss2: 1.544740
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.116497 Loss1: 0.602916 Loss2: 1.513581
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.616650 Loss1: 1.669570 Loss2: 1.947080
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.990745 Loss1: 0.456205 Loss2: 1.534540
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.513994 Loss1: 1.072860 Loss2: 1.441134
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.891587 Loss1: 0.376714 Loss2: 1.514873
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.117452 Loss1: 0.641783 Loss2: 1.475669
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.858507 Loss1: 0.336512 Loss2: 1.521995
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.951966 Loss1: 0.521605 Loss2: 1.430361
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.795074 Loss1: 0.282727 Loss2: 1.512347
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.851994 Loss1: 0.396600 Loss2: 1.455394
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.810741 Loss1: 0.303816 Loss2: 1.506926
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.890839 Loss1: 0.441893 Loss2: 1.448947
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.741302 Loss1: 0.229049 Loss2: 1.512253
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.779517 Loss1: 0.332399 Loss2: 1.447118
+(DefaultActor pid=3765) >> Training accuracy: 0.925000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.738856 Loss1: 0.293071 Loss2: 1.445785
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.726113 Loss1: 0.278771 Loss2: 1.447341
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.650200 Loss1: 0.217468 Loss2: 1.432732
+(DefaultActor pid=3764) >> Training accuracy: 0.947917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.563697 Loss1: 1.614494 Loss2: 1.949203
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.658438 Loss1: 1.113993 Loss2: 1.544445
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.129473 Loss1: 0.621857 Loss2: 1.507616
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.548380 Loss1: 1.648880 Loss2: 1.899500
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.001542 Loss1: 0.533806 Loss2: 1.467735
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.504445 Loss1: 1.093284 Loss2: 1.411161
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.922284 Loss1: 0.440821 Loss2: 1.481463
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.095945 Loss1: 0.671838 Loss2: 1.424107
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.818633 Loss1: 0.357644 Loss2: 1.460989
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.785655 Loss1: 0.323319 Loss2: 1.462336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.748260 Loss1: 0.272863 Loss2: 1.475397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.728148 Loss1: 0.273377 Loss2: 1.454771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.691221 Loss1: 0.236159 Loss2: 1.455063 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.957031
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.697662 Loss1: 0.322198 Loss2: 1.375464 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.938542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.396600 Loss1: 1.480374 Loss2: 1.916225 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.120687 Loss1: 0.596704 Loss2: 1.523983 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.961553 Loss1: 0.551618 Loss2: 1.409936
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.448259 Loss1: 1.443512 Loss2: 2.004748
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.425721 Loss1: 0.957277 Loss2: 1.468444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.161006 Loss1: 0.654655 Loss2: 1.506351 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.913341 Loss1: 0.451599 Loss2: 1.461742 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.821969 Loss1: 0.365708 Loss2: 1.456262 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.759255 Loss1: 0.298380 Loss2: 1.460875 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.627475 Loss1: 0.227267 Loss2: 1.400209
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.719250 Loss1: 0.266912 Loss2: 1.452338
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.729024 Loss1: 0.278729 Loss2: 1.450295
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.711868 Loss1: 0.265998 Loss2: 1.445870
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.690784 Loss1: 0.240235 Loss2: 1.450549
+(DefaultActor pid=3764) >> Training accuracy: 0.941667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.589992 Loss1: 1.558658 Loss2: 2.031334
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.556146 Loss1: 1.058473 Loss2: 1.497673
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.149897 Loss1: 0.636528 Loss2: 1.513369
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.979475 Loss1: 0.492661 Loss2: 1.486814
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.427813 Loss1: 1.482360 Loss2: 1.945452
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.354929 Loss1: 0.926731 Loss2: 1.428197 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.204210 Loss1: 0.738570 Loss2: 1.465640 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.894759 Loss1: 0.487280 Loss2: 1.407479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.763373 Loss1: 0.360395 Loss2: 1.402979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.758845 Loss1: 0.346752 Loss2: 1.412092 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.929167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.735030 Loss1: 0.258417 Loss2: 1.476612
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.699634 Loss1: 0.295226 Loss2: 1.404409
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.639663 Loss1: 0.233582 Loss2: 1.406082
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.670703 Loss1: 0.268576 Loss2: 1.402128
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.667740 Loss1: 0.255214 Loss2: 1.412526
+(DefaultActor pid=3764) >> Training accuracy: 0.935417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.734897 Loss1: 1.704320 Loss2: 2.030577
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.676150 Loss1: 1.187538 Loss2: 1.488612
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.207350 Loss1: 0.694319 Loss2: 1.513031
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.063868 Loss1: 0.588040 Loss2: 1.475828
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.516489 Loss1: 1.557387 Loss2: 1.959102
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.570364 Loss1: 1.117620 Loss2: 1.452744 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.190565 Loss1: 0.681571 Loss2: 1.508994 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.898573 Loss1: 0.461492 Loss2: 1.437080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.840071 Loss1: 0.415508 Loss2: 1.424563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.704061 Loss1: 0.275128 Loss2: 1.428933 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.761467 Loss1: 0.334287 Loss2: 1.427180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.682586 Loss1: 0.255648 Loss2: 1.426938 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.939583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.438478 Loss1: 1.527562 Loss2: 1.910916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.195720 Loss1: 0.702062 Loss2: 1.493658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.952192 Loss1: 0.513556 Loss2: 1.438636
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.591585 Loss1: 1.544221 Loss2: 2.047364
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.618280 Loss1: 1.080496 Loss2: 1.537784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.292642 Loss1: 0.725521 Loss2: 1.567121 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.091616 Loss1: 0.573922 Loss2: 1.517695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.969385 Loss1: 0.450113 Loss2: 1.519271 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.878608 Loss1: 0.366204 Loss2: 1.512403 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.948958
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.608656 Loss1: 0.194088 Loss2: 1.414568
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.802984 Loss1: 0.293524 Loss2: 1.509460
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.758117 Loss1: 0.261334 Loss2: 1.496783
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.721369 Loss1: 0.224011 Loss2: 1.497358
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.697803 Loss1: 0.206978 Loss2: 1.490825
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.656263 Loss1: 1.670360 Loss2: 1.985903
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.615957 Loss1: 1.112399 Loss2: 1.503558
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.134499 Loss1: 0.631064 Loss2: 1.503434
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.741432 Loss1: 1.670260 Loss2: 2.071172
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.995457 Loss1: 0.517053 Loss2: 1.478405
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.948633 Loss1: 0.466444 Loss2: 1.482189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.843684 Loss1: 0.359319 Loss2: 1.484365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.862078 Loss1: 0.443304 Loss2: 1.418775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.775884 Loss1: 0.369856 Loss2: 1.406028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.723520 Loss1: 0.317371 Loss2: 1.406149 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.685788 Loss1: 0.268719 Loss2: 1.417069 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.657215 Loss1: 0.247498 Loss2: 1.409717
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.780859 Loss1: 0.287975 Loss2: 1.492883
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.269753 Loss1: 1.348365 Loss2: 1.921388 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.928385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.110337 Loss1: 0.624524 Loss2: 1.485812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.973352 Loss1: 0.542827 Loss2: 1.430526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.857276 Loss1: 0.409537 Loss2: 1.447739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.729026 Loss1: 0.307782 Loss2: 1.421244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.755597 Loss1: 0.331137 Loss2: 1.424460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.650947 Loss1: 0.224528 Loss2: 1.426419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.658722 Loss1: 0.255201 Loss2: 1.403521 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.627779 Loss1: 0.233445 Loss2: 1.394334 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955882
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.598200 Loss1: 0.200833 Loss2: 1.397367 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.636189 Loss1: 1.726133 Loss2: 1.910056
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.471094 Loss1: 1.045767 Loss2: 1.425327
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.102467 Loss1: 0.671984 Loss2: 1.430482
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.836351 Loss1: 0.441751 Loss2: 1.394600
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.580434 Loss1: 1.588021 Loss2: 1.992413
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.790715 Loss1: 0.389365 Loss2: 1.401349
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.645284 Loss1: 1.168842 Loss2: 1.476442
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.674662 Loss1: 0.284246 Loss2: 1.390416
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.236938 Loss1: 0.697828 Loss2: 1.539111
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.659522 Loss1: 0.275240 Loss2: 1.384282
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.125340 Loss1: 0.658610 Loss2: 1.466730
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.742465 Loss1: 0.346866 Loss2: 1.395599
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.090468 Loss1: 0.581534 Loss2: 1.508935
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.654173 Loss1: 0.255235 Loss2: 1.398938
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.913378 Loss1: 0.421787 Loss2: 1.491591
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.585502 Loss1: 0.204699 Loss2: 1.380803
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.808148 Loss1: 0.344318 Loss2: 1.463830
+(DefaultActor pid=3765) >> Training accuracy: 0.960417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.728806 Loss1: 0.262062 Loss2: 1.466744
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.788481 Loss1: 0.319935 Loss2: 1.468546
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.754525 Loss1: 0.277621 Loss2: 1.476904
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.499882 Loss1: 1.605076 Loss2: 1.894806
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.428676 Loss1: 1.018961 Loss2: 1.409715
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.950972 Loss1: 0.531702 Loss2: 1.419270
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.803025 Loss1: 0.431812 Loss2: 1.371213
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.502872 Loss1: 1.580762 Loss2: 1.922110
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.504256 Loss1: 1.012940 Loss2: 1.491316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.067996 Loss1: 0.578892 Loss2: 1.489105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.927589 Loss1: 0.481646 Loss2: 1.445943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.827225 Loss1: 0.365170 Loss2: 1.462056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.796894 Loss1: 0.343796 Loss2: 1.453099 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.722341 Loss1: 0.267895 Loss2: 1.454446 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.742728 Loss1: 0.282304 Loss2: 1.460424 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.896484
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.574134 Loss1: 1.100854 Loss2: 1.473279 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.994754 Loss1: 0.540249 Loss2: 1.454506 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.915542 Loss1: 0.455057 Loss2: 1.460485
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.449477 Loss1: 1.443484 Loss2: 2.005993
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.814417 Loss1: 0.342107 Loss2: 1.472310
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.460182 Loss1: 0.980522 Loss2: 1.479660
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.757654 Loss1: 0.298519 Loss2: 1.459136
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.136006 Loss1: 0.636103 Loss2: 1.499904
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.886283 Loss1: 0.435169 Loss2: 1.451115 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.809950 Loss1: 0.363856 Loss2: 1.446094 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.700157 Loss1: 0.236263 Loss2: 1.463893
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.694165 Loss1: 0.247637 Loss2: 1.446528
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.718918 Loss1: 0.282949 Loss2: 1.435969
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.755221 Loss1: 0.312745 Loss2: 1.442476
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.738887 Loss1: 0.298848 Loss2: 1.440039
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.678894 Loss1: 0.236396 Loss2: 1.442497
+(DefaultActor pid=3764) >> Training accuracy: 0.955078
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.725103 Loss1: 1.739404 Loss2: 1.985699
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.473030 Loss1: 1.024432 Loss2: 1.448597
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.105948 Loss1: 0.648523 Loss2: 1.457424
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.923791 Loss1: 0.506395 Loss2: 1.417396
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.810686 Loss1: 0.389370 Loss2: 1.421316
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.607982 Loss1: 1.598116 Loss2: 2.009866
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.711385 Loss1: 0.292876 Loss2: 1.418509
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.667211 Loss1: 0.257487 Loss2: 1.409724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.650662 Loss1: 0.238304 Loss2: 1.412359 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.693764 Loss1: 0.275202 Loss2: 1.418562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.588848 Loss1: 0.169666 Loss2: 1.419182 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.691221 Loss1: 0.281677 Loss2: 1.409543 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.624155 Loss1: 0.223623 Loss2: 1.400531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.597486 Loss1: 0.204954 Loss2: 1.392532
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.474444 Loss1: 1.598100 Loss2: 1.876344
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.408408 Loss1: 1.021810 Loss2: 1.386598
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.985817 Loss1: 0.617319 Loss2: 1.368498
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.854468 Loss1: 0.504945 Loss2: 1.349523
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.750011 Loss1: 0.394589 Loss2: 1.355422
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.563020 Loss1: 1.591249 Loss2: 1.971772
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.646095 Loss1: 0.301701 Loss2: 1.344393
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.669197 Loss1: 1.181296 Loss2: 1.487901
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.673411 Loss1: 0.325421 Loss2: 1.347990
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.344830 Loss1: 0.817489 Loss2: 1.527341
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.625086 Loss1: 0.286424 Loss2: 1.338662
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.083768 Loss1: 0.626934 Loss2: 1.456833
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.989752 Loss1: 0.513582 Loss2: 1.476170
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.580720 Loss1: 0.227094 Loss2: 1.353626
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.885928 Loss1: 0.412306 Loss2: 1.473622
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.539816 Loss1: 0.194355 Loss2: 1.345460
+(DefaultActor pid=3765) >> Training accuracy: 0.967773
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.780992 Loss1: 0.312811 Loss2: 1.468181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.668411 Loss1: 0.218478 Loss2: 1.449933 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.927083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.499432 Loss1: 1.006039 Loss2: 1.493393 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.847155 Loss1: 0.396308 Loss2: 1.450847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.786096 Loss1: 0.324269 Loss2: 1.461828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.713564 Loss1: 0.266194 Loss2: 1.447370 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.666143 Loss1: 0.226000 Loss2: 1.440144 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.630716 Loss1: 0.192154 Loss2: 1.438562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.619075 Loss1: 0.183326 Loss2: 1.435748 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.643621 Loss1: 0.217575 Loss2: 1.426046 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.944336
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.698165 Loss1: 0.255296 Loss2: 1.442869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.683054 Loss1: 0.261405 Loss2: 1.421649 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.787538 Loss1: 1.783112 Loss2: 2.004426
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.424968 Loss1: 0.999422 Loss2: 1.425546
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.192643 Loss1: 0.766827 Loss2: 1.425816
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.912862 Loss1: 0.496617 Loss2: 1.416245
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.478810 Loss1: 1.501322 Loss2: 1.977488
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.382780 Loss1: 0.931921 Loss2: 1.450859 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.083601 Loss1: 0.636948 Loss2: 1.446653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.948077 Loss1: 0.509765 Loss2: 1.438312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.808234 Loss1: 0.381237 Loss2: 1.426997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.768137 Loss1: 0.316212 Loss2: 1.451925 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.909598
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.650784 Loss1: 0.234288 Loss2: 1.416496 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.632376 Loss1: 0.212970 Loss2: 1.419406 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.505014 Loss1: 1.049954 Loss2: 1.455060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.873516 Loss1: 0.426480 Loss2: 1.447035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.816080 Loss1: 0.390532 Loss2: 1.425548
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.364622 Loss1: 1.413565 Loss2: 1.951057
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.348579 Loss1: 0.931044 Loss2: 1.417534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.024924 Loss1: 0.613005 Loss2: 1.411919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.839658 Loss1: 0.448975 Loss2: 1.390683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.798376 Loss1: 0.408001 Loss2: 1.390375 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.924107
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.607912 Loss1: 0.218754 Loss2: 1.389158 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.598598 Loss1: 0.220944 Loss2: 1.377654 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.605516 Loss1: 0.226273 Loss2: 1.379243
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.585131 Loss1: 1.582501 Loss2: 2.002630
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.494481 Loss1: 1.045622 Loss2: 1.448859
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.222300 Loss1: 0.769821 Loss2: 1.452479
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.005689 Loss1: 0.570044 Loss2: 1.435646
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.876222 Loss1: 0.447663 Loss2: 1.428559
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.623205 Loss1: 1.636105 Loss2: 1.987101
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.758536 Loss1: 0.334537 Loss2: 1.423999
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.686123 Loss1: 0.265675 Loss2: 1.420448 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.670778 Loss1: 0.255067 Loss2: 1.415712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.694595 Loss1: 0.276341 Loss2: 1.418253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.704965 Loss1: 0.284838 Loss2: 1.420127 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.916667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.849292 Loss1: 0.404655 Loss2: 1.444637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.713107 Loss1: 0.263629 Loss2: 1.449477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.691872 Loss1: 0.259717 Loss2: 1.432155
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.462502 Loss1: 1.512471 Loss2: 1.950031
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.403449 Loss1: 0.981843 Loss2: 1.421606
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.119155 Loss1: 0.694578 Loss2: 1.424577
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.897227 Loss1: 0.494502 Loss2: 1.402724
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.795088 Loss1: 0.397515 Loss2: 1.397573
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.595025 Loss1: 1.666770 Loss2: 1.928255
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.475371 Loss1: 1.035082 Loss2: 1.440289 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.186995 Loss1: 0.732485 Loss2: 1.454510 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.897037 Loss1: 0.468608 Loss2: 1.428430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.807279 Loss1: 0.394756 Loss2: 1.412523 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.746161 Loss1: 0.335862 Loss2: 1.410298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.646109 Loss1: 0.241992 Loss2: 1.404117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.606420 Loss1: 0.209826 Loss2: 1.396593 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.442169 Loss1: 1.023643 Loss2: 1.418526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.981795 Loss1: 0.580979 Loss2: 1.400816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.853701 Loss1: 0.444507 Loss2: 1.409193
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.634217 Loss1: 1.627476 Loss2: 2.006741
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.609559 Loss1: 1.162755 Loss2: 1.446804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.303187 Loss1: 0.814545 Loss2: 1.488642
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.736190 Loss1: 0.342357 Loss2: 1.393833
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.990394 Loss1: 0.531628 Loss2: 1.458766
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.643396 Loss1: 0.238027 Loss2: 1.405369
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.634364 Loss1: 0.238408 Loss2: 1.395956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.626671 Loss1: 0.234853 Loss2: 1.391818 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.950000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.672203 Loss1: 0.246476 Loss2: 1.425727 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.951923
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.626978 Loss1: 1.574868 Loss2: 2.052110 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.085279 Loss1: 0.640042 Loss2: 1.445238 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.620158 Loss1: 1.626901 Loss2: 1.993257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.642762 Loss1: 0.238140 Loss2: 1.404622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.704044 Loss1: 0.294473 Loss2: 1.409571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.655546 Loss1: 0.243014 Loss2: 1.412532 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.629296 Loss1: 0.218121 Loss2: 1.411175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.576690 Loss1: 0.176282 Loss2: 1.400408 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961538
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-10 01:37:53,314 | server.py:236 | fit_round 59 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.745239 Loss1: 0.311948 Loss2: 1.433290 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.695295 Loss1: 0.262840 Loss2: 1.432455 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.651648 Loss1: 0.216117 Loss2: 1.435531
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.390883 Loss1: 1.449385 Loss2: 1.941497
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.354627 Loss1: 0.924471 Loss2: 1.430156
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.047809 Loss1: 0.589833 Loss2: 1.457976
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.809281 Loss1: 0.404452 Loss2: 1.404829
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.687982 Loss1: 0.284263 Loss2: 1.403719
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.675038 Loss1: 1.683155 Loss2: 1.991882
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.587094 Loss1: 1.136312 Loss2: 1.450782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.206834 Loss1: 0.726174 Loss2: 1.480659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.951933 Loss1: 0.504492 Loss2: 1.447441 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.884233 Loss1: 0.440904 Loss2: 1.443328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.636271 Loss1: 0.231362 Loss2: 1.404910
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.802154 Loss1: 0.350109 Loss2: 1.452045
+(DefaultActor pid=3765) >> Training accuracy: 0.936458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.782048 Loss1: 0.332862 Loss2: 1.449185
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.678987 Loss1: 0.244760 Loss2: 1.434227
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.643248 Loss1: 0.215663 Loss2: 1.427584
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.596285 Loss1: 0.170351 Loss2: 1.425933
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.477217 Loss1: 1.521258 Loss2: 1.955959
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.324278 Loss1: 0.913585 Loss2: 1.410692
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.111346 Loss1: 0.670700 Loss2: 1.440646
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.826068 Loss1: 0.451159 Loss2: 1.374909
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.463941 Loss1: 1.509225 Loss2: 1.954716
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.408850 Loss1: 0.949157 Loss2: 1.459693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.111094 Loss1: 0.611056 Loss2: 1.500037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.953186 Loss1: 0.516979 Loss2: 1.436207 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.875680 Loss1: 0.415809 Loss2: 1.459871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.762846 Loss1: 0.318612 Loss2: 1.444234 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.633747 Loss1: 0.203174 Loss2: 1.430573 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.649991 Loss1: 0.222598 Loss2: 1.427393 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 01:37:53,314][flwr][DEBUG] - fit_round 59 received 50 results and 0 failures
+INFO flwr 2023-10-10 01:38:35,167 | server.py:125 | fit progress: (59, 2.3416105348842975, {'accuracy': 0.5068}, 136022.945519467)
+>> Test accuracy: 0.506800
+[2023-10-10 01:38:35,167][flwr][INFO] - fit progress: (59, 2.3416105348842975, {'accuracy': 0.5068}, 136022.945519467)
+DEBUG flwr 2023-10-10 01:38:35,167 | server.py:173 | evaluate_round 59: strategy sampled 50 clients (out of 50)
+[2023-10-10 01:38:35,167][flwr][DEBUG] - evaluate_round 59: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 01:47:37,191 | server.py:187 | evaluate_round 59 received 50 results and 0 failures
+[2023-10-10 01:47:37,191][flwr][DEBUG] - evaluate_round 59 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 01:47:37,192 | server.py:222 | fit_round 60: strategy sampled 50 clients (out of 50)
+[2023-10-10 01:47:37,192][flwr][DEBUG] - fit_round 60: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.591018 Loss1: 1.557674 Loss2: 2.033343
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.573174 Loss1: 1.031042 Loss2: 1.542132
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.235093 Loss1: 0.658286 Loss2: 1.576807
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.010146 Loss1: 0.483423 Loss2: 1.526723
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.368605 Loss1: 1.387827 Loss2: 1.980778
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.934868 Loss1: 0.403980 Loss2: 1.530888
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.293733 Loss1: 0.854592 Loss2: 1.439141
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.857129 Loss1: 0.340881 Loss2: 1.516247
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.049654 Loss1: 0.586050 Loss2: 1.463604
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.788684 Loss1: 0.266473 Loss2: 1.522211
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.912155 Loss1: 0.488811 Loss2: 1.423344
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.726733 Loss1: 0.209900 Loss2: 1.516833
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.819793 Loss1: 0.396205 Loss2: 1.423588
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.716777 Loss1: 0.206534 Loss2: 1.510243
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.785842 Loss1: 0.351839 Loss2: 1.434003
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.695291 Loss1: 0.189033 Loss2: 1.506258
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.722421 Loss1: 0.300275 Loss2: 1.422146
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.712706 Loss1: 0.286975 Loss2: 1.425731
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.649129 Loss1: 0.236434 Loss2: 1.412695
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.638417 Loss1: 0.226933 Loss2: 1.411484
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.602215 Loss1: 1.694100 Loss2: 1.908115
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.555700 Loss1: 1.074570 Loss2: 1.481130
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.249902 Loss1: 0.756300 Loss2: 1.493602
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.992614 Loss1: 0.547217 Loss2: 1.445397
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.272541 Loss1: 1.328680 Loss2: 1.943861
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.358979 Loss1: 0.880452 Loss2: 1.478527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.057229 Loss1: 0.586809 Loss2: 1.470419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.861894 Loss1: 0.421510 Loss2: 1.440384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.884861 Loss1: 0.422888 Loss2: 1.461974 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.822833 Loss1: 0.387009 Loss2: 1.435823 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.936458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.679779 Loss1: 0.247560 Loss2: 1.432219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.639522 Loss1: 0.216404 Loss2: 1.423118 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958984
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.431381 Loss1: 1.412368 Loss2: 2.019013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.137634 Loss1: 0.603398 Loss2: 1.534237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.858894 Loss1: 0.372009 Loss2: 1.486885 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.790790 Loss1: 0.312064 Loss2: 1.478725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.809976 Loss1: 0.331427 Loss2: 1.478549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.805967 Loss1: 0.310057 Loss2: 1.495909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.724585 Loss1: 0.242643 Loss2: 1.481942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.690735 Loss1: 0.213290 Loss2: 1.477445 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.657674 Loss1: 0.210861 Loss2: 1.446813 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.645679 Loss1: 0.205963 Loss2: 1.439716 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.943750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.479837 Loss1: 1.021352 Loss2: 1.458484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.947893 Loss1: 0.492425 Loss2: 1.455468 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.847835 Loss1: 0.400294 Loss2: 1.447541
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.665079 Loss1: 1.669837 Loss2: 1.995242
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.587802 Loss1: 1.101802 Loss2: 1.486001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.220220 Loss1: 0.682035 Loss2: 1.538185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.956585 Loss1: 0.510593 Loss2: 1.445992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.915854 Loss1: 0.459405 Loss2: 1.456448 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.571058 Loss1: 0.151141 Loss2: 1.419917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.853316 Loss1: 0.383582 Loss2: 1.469734
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.779847 Loss1: 0.325383 Loss2: 1.454464
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.766306 Loss1: 0.309808 Loss2: 1.456498
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.734788 Loss1: 0.278972 Loss2: 1.455816
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.670291 Loss1: 0.224190 Loss2: 1.446101
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.470502 Loss1: 1.489856 Loss2: 1.980646
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.339582 Loss1: 0.883063 Loss2: 1.456519
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.997435 Loss1: 0.552054 Loss2: 1.445381
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.923093 Loss1: 0.507967 Loss2: 1.415125
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.820197 Loss1: 0.393223 Loss2: 1.426974
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.434399 Loss1: 1.502907 Loss2: 1.931491
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.488541 Loss1: 1.060528 Loss2: 1.428013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.153130 Loss1: 0.682654 Loss2: 1.470476 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.946245 Loss1: 0.537224 Loss2: 1.409021 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.828071 Loss1: 0.421784 Loss2: 1.406287 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.959375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.627589 Loss1: 0.223546 Loss2: 1.404043
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.766755 Loss1: 0.350946 Loss2: 1.415809
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.721053 Loss1: 0.310412 Loss2: 1.410641
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.665547 Loss1: 0.252742 Loss2: 1.412805
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.579718 Loss1: 0.187692 Loss2: 1.392027
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.608136 Loss1: 0.218154 Loss2: 1.389982
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.538531 Loss1: 1.528406 Loss2: 2.010124
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.436049 Loss1: 1.010004 Loss2: 1.426045
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.216762 Loss1: 0.771133 Loss2: 1.445629
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.908288 Loss1: 0.470080 Loss2: 1.438208
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.835865 Loss1: 0.423110 Loss2: 1.412755
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.709817 Loss1: 0.286517 Loss2: 1.423301
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.486316 Loss1: 1.509245 Loss2: 1.977071
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.338665 Loss1: 0.873978 Loss2: 1.464687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.082963 Loss1: 0.584837 Loss2: 1.498126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.902373 Loss1: 0.444986 Loss2: 1.457387 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.878606
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.731096 Loss1: 0.279018 Loss2: 1.452078 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.643587 Loss1: 0.200628 Loss2: 1.442958 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.702026 Loss1: 0.253877 Loss2: 1.448149
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.880520 Loss1: 1.769917 Loss2: 2.110603
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.661062 Loss1: 0.201835 Loss2: 1.459227
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.786039 Loss1: 1.218822 Loss2: 1.567217
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.366056 Loss1: 0.753631 Loss2: 1.612425
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.034374 Loss1: 0.509880 Loss2: 1.524494
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.976192 Loss1: 0.434927 Loss2: 1.541264
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.865269 Loss1: 0.321571 Loss2: 1.543698
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.890649 Loss1: 0.372465 Loss2: 1.518183
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.444615 Loss1: 1.411127 Loss2: 2.033488
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.835881 Loss1: 0.286628 Loss2: 1.549253
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.469701 Loss1: 0.935244 Loss2: 1.534457
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.162607 Loss1: 0.610078 Loss2: 1.552528 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.960938
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.726204 Loss1: 0.212981 Loss2: 1.513223
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.002385 Loss1: 0.487585 Loss2: 1.514799
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.889982 Loss1: 0.366393 Loss2: 1.523589
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.771988 Loss1: 0.279339 Loss2: 1.492649
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.770850 Loss1: 0.276074 Loss2: 1.494775
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.773040 Loss1: 0.260794 Loss2: 1.512246
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.567468 Loss1: 1.564104 Loss2: 2.003364
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.810095 Loss1: 0.310900 Loss2: 1.499195
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.754885 Loss1: 0.248238 Loss2: 1.506647 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.762396 Loss1: 0.389240 Loss2: 1.373156 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.633358 Loss1: 0.249543 Loss2: 1.383815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.599525 Loss1: 0.228570 Loss2: 1.370954
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.430163 Loss1: 1.514791 Loss2: 1.915372
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.411461 Loss1: 0.962438 Loss2: 1.449024 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978365
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.841958 Loss1: 0.426560 Loss2: 1.415399 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.737415 Loss1: 0.321851 Loss2: 1.415564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.480561 Loss1: 1.577487 Loss2: 1.903075
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.728155 Loss1: 0.316361 Loss2: 1.411794
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.504454 Loss1: 1.088040 Loss2: 1.416414
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.751513 Loss1: 0.321745 Loss2: 1.429768
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.102909 Loss1: 0.651172 Loss2: 1.451736
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.729444 Loss1: 0.302886 Loss2: 1.426557
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.875673 Loss1: 0.487062 Loss2: 1.388611
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.664932 Loss1: 0.239782 Loss2: 1.425149
+(DefaultActor pid=3764) >> Training accuracy: 0.946289
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.691104 Loss1: 0.315212 Loss2: 1.375892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.618946 Loss1: 0.241279 Loss2: 1.377667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.570277 Loss1: 0.193826 Loss2: 1.376451
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.543464 Loss1: 1.599926 Loss2: 1.943538
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.422049 Loss1: 0.954643 Loss2: 1.467406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.973519 Loss1: 0.534984 Loss2: 1.438534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.821878 Loss1: 0.383599 Loss2: 1.438279 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.708031 Loss1: 0.262951 Loss2: 1.445080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.661526 Loss1: 0.232428 Loss2: 1.429098 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.639623 Loss1: 0.206812 Loss2: 1.432811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.600026 Loss1: 0.175462 Loss2: 1.424564 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.931641
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.702100 Loss1: 0.305373 Loss2: 1.396727 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.663986 Loss1: 0.254854 Loss2: 1.409132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.630606 Loss1: 0.227404 Loss2: 1.403202
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.394898 Loss1: 1.476353 Loss2: 1.918546
+(DefaultActor pid=3765) >> Training accuracy: 0.936458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.475029 Loss1: 1.038470 Loss2: 1.436559
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.134874 Loss1: 0.669117 Loss2: 1.465757
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.980562 Loss1: 0.555985 Loss2: 1.424577
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.811325 Loss1: 0.376147 Loss2: 1.435178
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.777998 Loss1: 0.365970 Loss2: 1.412027
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.494171 Loss1: 1.570405 Loss2: 1.923766
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.820585 Loss1: 0.397902 Loss2: 1.422683
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.476138 Loss1: 1.044660 Loss2: 1.431479
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.738387 Loss1: 0.313405 Loss2: 1.424982
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.099438 Loss1: 0.640823 Loss2: 1.458616
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.729526 Loss1: 0.306798 Loss2: 1.422728
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.955688 Loss1: 0.549435 Loss2: 1.406253
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.634595 Loss1: 0.224788 Loss2: 1.409807
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.828918 Loss1: 0.405671 Loss2: 1.423247
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.844507 Loss1: 0.425628 Loss2: 1.418879
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.785988 Loss1: 0.365493 Loss2: 1.420495
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.726780 Loss1: 0.314099 Loss2: 1.412682
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.658360 Loss1: 0.247818 Loss2: 1.410543
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.651781 Loss1: 0.245783 Loss2: 1.405998
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.272278 Loss1: 1.304430 Loss2: 1.967848
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.382754 Loss1: 0.939325 Loss2: 1.443429
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.135522 Loss1: 0.622788 Loss2: 1.512733
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.850233 Loss1: 0.422118 Loss2: 1.428115
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.797266 Loss1: 0.375355 Loss2: 1.421911
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.692119 Loss1: 0.263017 Loss2: 1.429102
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.285503 Loss1: 1.311008 Loss2: 1.974495
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.708404 Loss1: 0.286161 Loss2: 1.422242
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.296370 Loss1: 0.850763 Loss2: 1.445607
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.670245 Loss1: 0.251679 Loss2: 1.418567
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.084778 Loss1: 0.619890 Loss2: 1.464888
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.566544 Loss1: 0.148721 Loss2: 1.417823
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.872979 Loss1: 0.438050 Loss2: 1.434929
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.572212 Loss1: 0.175167 Loss2: 1.397045
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.835514 Loss1: 0.398350 Loss2: 1.437164
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.772514 Loss1: 0.335430 Loss2: 1.437084
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.690846 Loss1: 0.267503 Loss2: 1.423343
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.631499 Loss1: 0.209530 Loss2: 1.421969
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.615971 Loss1: 0.201420 Loss2: 1.414552
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.597798 Loss1: 0.184116 Loss2: 1.413682
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.420544 Loss1: 1.447484 Loss2: 1.973060
+(DefaultActor pid=3765) >> Training accuracy: 0.950000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.418469 Loss1: 0.931082 Loss2: 1.487387
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.117079 Loss1: 0.588977 Loss2: 1.528102
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.984793 Loss1: 0.506878 Loss2: 1.477915
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.884537 Loss1: 0.386718 Loss2: 1.497819
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.481527 Loss1: 1.544364 Loss2: 1.937163
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.811502 Loss1: 0.329196 Loss2: 1.482306
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.843600 Loss1: 0.351596 Loss2: 1.492004 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.838303 Loss1: 0.341771 Loss2: 1.496531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.736123 Loss1: 0.251212 Loss2: 1.484911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.700049 Loss1: 0.216185 Loss2: 1.483864 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964844
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.775803 Loss1: 0.326632 Loss2: 1.449171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.684774 Loss1: 0.244080 Loss2: 1.440694 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.646264 Loss1: 0.203196 Loss2: 1.443069
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.474227 Loss1: 1.564819 Loss2: 1.909408
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.511070 Loss1: 1.046070 Loss2: 1.465000
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.132024 Loss1: 0.649704 Loss2: 1.482320
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.948874 Loss1: 0.527713 Loss2: 1.421161
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.472526 Loss1: 1.510498 Loss2: 1.962027
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.911838 Loss1: 0.456835 Loss2: 1.455004
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.824421 Loss1: 0.375107 Loss2: 1.449314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.867236 Loss1: 0.424591 Loss2: 1.442646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.731699 Loss1: 0.281085 Loss2: 1.450614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.721010 Loss1: 0.297997 Loss2: 1.423013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.676614 Loss1: 0.226667 Loss2: 1.449947 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.942383
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.582711 Loss1: 0.168445 Loss2: 1.414265 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965402
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.500204 Loss1: 1.590294 Loss2: 1.909910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.093507 Loss1: 0.639760 Loss2: 1.453746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.921825 Loss1: 0.526948 Loss2: 1.394877
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.592154 Loss1: 1.634791 Loss2: 1.957363
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.472831 Loss1: 1.017068 Loss2: 1.455763 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.118158 Loss1: 0.628084 Loss2: 1.490074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.866029 Loss1: 0.433952 Loss2: 1.432077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.799776 Loss1: 0.372254 Loss2: 1.427523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.816166 Loss1: 0.374685 Loss2: 1.441480 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.650302 Loss1: 0.256905 Loss2: 1.393397
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.756335 Loss1: 0.312799 Loss2: 1.443537
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.699163 Loss1: 0.267521 Loss2: 1.431642
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.682566 Loss1: 0.255405 Loss2: 1.427161
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.692940 Loss1: 0.247751 Loss2: 1.445190
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.459866 Loss1: 1.514339 Loss2: 1.945527
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.564451 Loss1: 1.151002 Loss2: 1.413449
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.251165 Loss1: 0.763996 Loss2: 1.487169
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.991090 Loss1: 0.570996 Loss2: 1.420094
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.332498 Loss1: 1.421633 Loss2: 1.910865
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.380440 Loss1: 0.929370 Loss2: 1.451069 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.993330 Loss1: 0.554680 Loss2: 1.438650 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.850241 Loss1: 0.435845 Loss2: 1.414396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.550264 Loss1: 0.162089 Loss2: 1.388175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.562633 Loss1: 0.178841 Loss2: 1.383792 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.942708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.750058 Loss1: 0.326189 Loss2: 1.423868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.637053 Loss1: 0.209299 Loss2: 1.427754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.602569 Loss1: 0.194093 Loss2: 1.408476
+(DefaultActor pid=3765) >> Training accuracy: 0.915441
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.625830 Loss1: 1.532811 Loss2: 2.093019
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.650473 Loss1: 1.086903 Loss2: 1.563570
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.348512 Loss1: 0.726421 Loss2: 1.622091
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.116890 Loss1: 0.569124 Loss2: 1.547766
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.875610 Loss1: 0.323765 Loss2: 1.551845
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.411846 Loss1: 1.444986 Loss2: 1.966860
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.805533 Loss1: 0.279675 Loss2: 1.525858
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.459594 Loss1: 0.982011 Loss2: 1.477582
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.790590 Loss1: 0.260335 Loss2: 1.530255
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.044161 Loss1: 0.561030 Loss2: 1.483131
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.770955 Loss1: 0.243359 Loss2: 1.527596
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.810132 Loss1: 0.277171 Loss2: 1.532960
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.894367 Loss1: 0.443113 Loss2: 1.451254
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.756990 Loss1: 0.224972 Loss2: 1.532018
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.881019 Loss1: 0.428969 Loss2: 1.452050
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.757571 Loss1: 0.305590 Loss2: 1.451981
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.761031 Loss1: 0.313225 Loss2: 1.447805
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.730819 Loss1: 0.276453 Loss2: 1.454366
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.673043 Loss1: 0.220868 Loss2: 1.452175
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.453266 Loss1: 1.539683 Loss2: 1.913584
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.654691 Loss1: 0.216804 Loss2: 1.437887
+(DefaultActor pid=3765) >> Training accuracy: 0.944336
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.143917 Loss1: 0.670806 Loss2: 1.473111 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.780540 Loss1: 0.320411 Loss2: 1.460129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.706590 Loss1: 0.272570 Loss2: 1.434021
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.555393 Loss1: 1.609174 Loss2: 1.946218
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.708600 Loss1: 0.275649 Loss2: 1.432952
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.455619 Loss1: 0.995880 Loss2: 1.459740
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.713555 Loss1: 0.266964 Loss2: 1.446591
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.071709 Loss1: 0.602802 Loss2: 1.468907
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.787574 Loss1: 0.337204 Loss2: 1.450370
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.982002 Loss1: 0.549804 Loss2: 1.432198
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.622966 Loss1: 0.177740 Loss2: 1.445226
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.907070 Loss1: 0.457390 Loss2: 1.449680
+(DefaultActor pid=3764) >> Training accuracy: 0.958008
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.827043 Loss1: 0.374476 Loss2: 1.452568
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.785270 Loss1: 0.352582 Loss2: 1.432688
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.718783 Loss1: 0.284943 Loss2: 1.433840
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.735204 Loss1: 0.301405 Loss2: 1.433799
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.460823 Loss1: 1.550155 Loss2: 1.910668
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.731786 Loss1: 0.303229 Loss2: 1.428558
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.190181 Loss1: 0.712362 Loss2: 1.477819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.809550 Loss1: 0.395178 Loss2: 1.414372 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.769902 Loss1: 0.378031 Loss2: 1.391872
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.577201 Loss1: 1.635920 Loss2: 1.941281
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.427786 Loss1: 0.967042 Loss2: 1.460744 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.052666 Loss1: 0.588971 Loss2: 1.463695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.870427 Loss1: 0.434493 Loss2: 1.435935 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.938542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.838026 Loss1: 0.392939 Loss2: 1.445087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.724269 Loss1: 0.285958 Loss2: 1.438310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.606883 Loss1: 0.172371 Loss2: 1.434511 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.581370 Loss1: 0.152927 Loss2: 1.428443 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.286339 Loss1: 0.744185 Loss2: 1.542154 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.863729 Loss1: 0.376366 Loss2: 1.487363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.657668 Loss1: 1.712121 Loss2: 1.945546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.544154 Loss1: 1.138357 Loss2: 1.405798 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.166419 Loss1: 0.743201 Loss2: 1.423218 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.940303 Loss1: 0.556311 Loss2: 1.383992 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.828651 Loss1: 0.444156 Loss2: 1.384495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.688882 Loss1: 0.307421 Loss2: 1.381461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.636437 Loss1: 0.252461 Loss2: 1.383977 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.596325 Loss1: 0.219957 Loss2: 1.376368
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.533762 Loss1: 1.498497 Loss2: 2.035266
+(DefaultActor pid=3765) >> Training accuracy: 0.959821
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.574549 Loss1: 1.040021 Loss2: 1.534528
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.127707 Loss1: 0.590322 Loss2: 1.537385
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.004363 Loss1: 0.513813 Loss2: 1.490549
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.916742 Loss1: 0.411244 Loss2: 1.505498
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.722992 Loss1: 1.597600 Loss2: 2.125392
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.855567 Loss1: 0.358596 Loss2: 1.496971
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.802818 Loss1: 0.309372 Loss2: 1.493446 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.786930 Loss1: 0.295024 Loss2: 1.491907 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.912487 Loss1: 0.457271 Loss2: 1.455217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.789611 Loss1: 0.333696 Loss2: 1.455916 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.947917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.754358 Loss1: 0.307758 Loss2: 1.446600 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.691690 Loss1: 0.230776 Loss2: 1.460914 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.930990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.637563 Loss1: 1.741293 Loss2: 1.896270
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.521618 Loss1: 1.105707 Loss2: 1.415911
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.096877 Loss1: 0.638051 Loss2: 1.458826
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.913776 Loss1: 0.522926 Loss2: 1.390850
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.480711 Loss1: 1.567071 Loss2: 1.913641
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.625090 Loss1: 1.161527 Loss2: 1.463563 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.189042 Loss1: 0.688824 Loss2: 1.500217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.977431 Loss1: 0.543156 Loss2: 1.434275 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.814128 Loss1: 0.364242 Loss2: 1.449886 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.643349 Loss1: 0.222325 Loss2: 1.421024 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.918750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.628857 Loss1: 0.226589 Loss2: 1.402269
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.653786 Loss1: 0.249317 Loss2: 1.404469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.630109 Loss1: 0.217496 Loss2: 1.412613
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.609398 Loss1: 0.200107 Loss2: 1.409291
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.545104 Loss1: 0.138046 Loss2: 1.407058
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.394230 Loss1: 1.447399 Loss2: 1.946830
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.377552 Loss1: 0.943415 Loss2: 1.434137
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.029330 Loss1: 0.573426 Loss2: 1.455904
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.869050 Loss1: 0.442008 Loss2: 1.427043
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.498381 Loss1: 1.541189 Loss2: 1.957192
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.575900 Loss1: 1.110520 Loss2: 1.465380 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.207096 Loss1: 0.686570 Loss2: 1.520526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.947844 Loss1: 0.518808 Loss2: 1.429036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.852454 Loss1: 0.407886 Loss2: 1.444568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.779392 Loss1: 0.331527 Loss2: 1.447865 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.726512 Loss1: 0.292107 Loss2: 1.434405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.605825 Loss1: 0.174974 Loss2: 1.430851 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+DEBUG flwr 2023-10-10 02:16:10,359 | server.py:236 | fit_round 60 received 50 results and 0 failures
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.374302 Loss1: 1.332958 Loss2: 2.041344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.205337 Loss1: 0.666861 Loss2: 1.538476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.579728 Loss1: 1.519577 Loss2: 2.060151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.371007 Loss1: 0.887883 Loss2: 1.483124 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.111014 Loss1: 0.616834 Loss2: 1.494180 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.927242 Loss1: 0.466297 Loss2: 1.460945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.826353 Loss1: 0.379846 Loss2: 1.446507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.776752 Loss1: 0.316868 Loss2: 1.459883 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.737456 Loss1: 0.289013 Loss2: 1.448443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.699231 Loss1: 0.250567 Loss2: 1.448664 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.948958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.443163 Loss1: 1.495850 Loss2: 1.947313 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.128119 Loss1: 0.597409 Loss2: 1.530710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.974671 Loss1: 0.506385 Loss2: 1.468285
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.405445 Loss1: 1.534082 Loss2: 1.871362
+(DefaultActor pid=3764) Epoch: 4 Loss: 2.021409 Loss1: 0.526646 Loss2: 1.494763
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.396758 Loss1: 1.017846 Loss2: 1.378912
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.916531 Loss1: 0.435353 Loss2: 1.481178
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.097982 Loss1: 0.676574 Loss2: 1.421408
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.801881 Loss1: 0.303749 Loss2: 1.498132
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.866265 Loss1: 0.502537 Loss2: 1.363728
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.768292 Loss1: 0.398101 Loss2: 1.370191
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.823428 Loss1: 0.340566 Loss2: 1.482861
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.789901 Loss1: 0.413313 Loss2: 1.376588
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.724997 Loss1: 0.240350 Loss2: 1.484647
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.699286 Loss1: 0.325694 Loss2: 1.373593
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.676847 Loss1: 0.210182 Loss2: 1.466666
+(DefaultActor pid=3764) >> Training accuracy: 0.956055
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.659860 Loss1: 0.277465 Loss2: 1.382395 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.940625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.403273 Loss1: 1.504921 Loss2: 1.898352 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.074720 Loss1: 0.652070 Loss2: 1.422651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.775289 Loss1: 0.404954 Loss2: 1.370335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.635311 Loss1: 0.274886 Loss2: 1.360425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.621175 Loss1: 0.257480 Loss2: 1.363696 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.926042
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 02:16:10,359][flwr][DEBUG] - fit_round 60 received 50 results and 0 failures
+INFO flwr 2023-10-10 02:16:51,856 | server.py:125 | fit progress: (60, 2.347771980891974, {'accuracy': 0.5081}, 138319.634262642)
+>> Test accuracy: 0.508100
+[2023-10-10 02:16:51,856][flwr][INFO] - fit progress: (60, 2.347771980891974, {'accuracy': 0.5081}, 138319.634262642)
+DEBUG flwr 2023-10-10 02:16:51,856 | server.py:173 | evaluate_round 60: strategy sampled 50 clients (out of 50)
+[2023-10-10 02:16:51,856][flwr][DEBUG] - evaluate_round 60: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 02:25:59,976 | server.py:187 | evaluate_round 60 received 50 results and 0 failures
+[2023-10-10 02:25:59,976][flwr][DEBUG] - evaluate_round 60 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 02:25:59,977 | server.py:222 | fit_round 61: strategy sampled 50 clients (out of 50)
+[2023-10-10 02:25:59,977][flwr][DEBUG] - fit_round 61: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.521081 Loss1: 1.555654 Loss2: 1.965427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.155638 Loss1: 0.717498 Loss2: 1.438140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.899818 Loss1: 0.498976 Loss2: 1.400843
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.448607 Loss1: 1.572675 Loss2: 1.875932
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.475314 Loss1: 1.055052 Loss2: 1.420262 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.111981 Loss1: 0.671192 Loss2: 1.440790 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.893759 Loss1: 0.505505 Loss2: 1.388254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.833680 Loss1: 0.422672 Loss2: 1.411008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.802579 Loss1: 0.401625 Loss2: 1.400954 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966518
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.682701 Loss1: 0.292066 Loss2: 1.390635 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.574931 Loss1: 0.183258 Loss2: 1.391673 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.935417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.549164 Loss1: 1.083912 Loss2: 1.465252 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.818087 Loss1: 0.460288 Loss2: 1.357799 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.588834 Loss1: 1.669861 Loss2: 1.918972
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.680210 Loss1: 0.309006 Loss2: 1.371204
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.497326 Loss1: 1.060988 Loss2: 1.436338
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.683656 Loss1: 0.337472 Loss2: 1.346185
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.219182 Loss1: 0.748795 Loss2: 1.470387
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.664510 Loss1: 0.303694 Loss2: 1.360816
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.933436 Loss1: 0.521287 Loss2: 1.412149
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.620130 Loss1: 0.245550 Loss2: 1.374581
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.870006 Loss1: 0.442307 Loss2: 1.427699
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.569637 Loss1: 0.214242 Loss2: 1.355395
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.764446 Loss1: 0.342370 Loss2: 1.422076
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.521748 Loss1: 0.171597 Loss2: 1.350151
+(DefaultActor pid=3765) >> Training accuracy: 0.950000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.684349 Loss1: 0.268712 Loss2: 1.415638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.696722 Loss1: 0.276333 Loss2: 1.420390 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.632916 Loss1: 1.164907 Loss2: 1.468010 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.054976 Loss1: 0.627532 Loss2: 1.427443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.931100 Loss1: 0.468081 Loss2: 1.463019
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.358515 Loss1: 1.510886 Loss2: 1.847629
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.807762 Loss1: 0.375080 Loss2: 1.432682
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.509163 Loss1: 1.063982 Loss2: 1.445181
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.723176 Loss1: 0.293062 Loss2: 1.430114
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.100075 Loss1: 0.669231 Loss2: 1.430844
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.680078 Loss1: 0.258933 Loss2: 1.421145
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.936580 Loss1: 0.524593 Loss2: 1.411987
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.860827 Loss1: 0.449439 Loss2: 1.411389 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.632710 Loss1: 0.214981 Loss2: 1.417729
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.856879 Loss1: 0.438234 Loss2: 1.418645
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.774044 Loss1: 0.358282 Loss2: 1.415762
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.658123 Loss1: 0.257652 Loss2: 1.400471
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.680320 Loss1: 0.278736 Loss2: 1.401585
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.599860 Loss1: 0.198909 Loss2: 1.400951
+(DefaultActor pid=3764) >> Training accuracy: 0.957031
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.229587 Loss1: 1.358254 Loss2: 1.871333
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.303733 Loss1: 0.892472 Loss2: 1.411261
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.049482 Loss1: 0.624199 Loss2: 1.425283
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.866980 Loss1: 0.473499 Loss2: 1.393481
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.735022 Loss1: 0.351490 Loss2: 1.383531
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.611686 Loss1: 1.655294 Loss2: 1.956392
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.665055 Loss1: 0.286093 Loss2: 1.378962
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.494146 Loss1: 1.010395 Loss2: 1.483751
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.612518 Loss1: 0.232656 Loss2: 1.379861
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.150942 Loss1: 0.664703 Loss2: 1.486238
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.585356 Loss1: 0.203303 Loss2: 1.382053
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.974011 Loss1: 0.516279 Loss2: 1.457732
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.541350 Loss1: 0.175976 Loss2: 1.365375
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.884191 Loss1: 0.436571 Loss2: 1.447619
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.515928 Loss1: 0.153508 Loss2: 1.362419
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.658665 Loss1: 0.229202 Loss2: 1.429462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.647349 Loss1: 0.225905 Loss2: 1.421445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.673824 Loss1: 0.229902 Loss2: 1.443923
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.396648 Loss1: 1.516362 Loss2: 1.880286
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.364439 Loss1: 0.943718 Loss2: 1.420721
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.112326 Loss1: 0.668018 Loss2: 1.444309
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.831669 Loss1: 0.427395 Loss2: 1.404274
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.753353 Loss1: 0.343075 Loss2: 1.410279
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.603934 Loss1: 1.707960 Loss2: 1.895974
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.697971 Loss1: 0.303561 Loss2: 1.394409
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.660499 Loss1: 0.258228 Loss2: 1.402270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.669392 Loss1: 0.269329 Loss2: 1.400063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.641835 Loss1: 0.244203 Loss2: 1.397633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.631598 Loss1: 0.237333 Loss2: 1.394265 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.938542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.679286 Loss1: 0.297183 Loss2: 1.382103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.602691 Loss1: 0.218814 Loss2: 1.383877 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.927455
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.477992 Loss1: 1.557068 Loss2: 1.920924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.180868 Loss1: 0.674329 Loss2: 1.506539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.907696 Loss1: 0.440768 Loss2: 1.466929 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.843570 Loss1: 0.391237 Loss2: 1.452333 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.790601 Loss1: 0.335672 Loss2: 1.454929 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.783375 Loss1: 0.319044 Loss2: 1.464332 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.717701 Loss1: 0.267817 Loss2: 1.449884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.713074 Loss1: 0.261610 Loss2: 1.451464 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.940625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.759217 Loss1: 0.356919 Loss2: 1.402298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.730431 Loss1: 0.340477 Loss2: 1.389954 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.664639 Loss1: 0.260015 Loss2: 1.404624
+(DefaultActor pid=3764) >> Training accuracy: 0.924805
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.276958 Loss1: 1.383804 Loss2: 1.893154
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.309834 Loss1: 0.827893 Loss2: 1.481941
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.051283 Loss1: 0.560557 Loss2: 1.490726
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.839468 Loss1: 0.384898 Loss2: 1.454570
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.865265 Loss1: 0.410212 Loss2: 1.455053
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.371475 Loss1: 1.494605 Loss2: 1.876870
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.402556 Loss1: 0.998878 Loss2: 1.403677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.102582 Loss1: 0.685553 Loss2: 1.417029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.870094 Loss1: 0.485854 Loss2: 1.384240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.582734 Loss1: 0.146498 Loss2: 1.436236
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.757754 Loss1: 0.373538 Loss2: 1.384216
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.598313 Loss1: 0.171623 Loss2: 1.426690
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.721278 Loss1: 0.327998 Loss2: 1.393280
+(DefaultActor pid=3765) >> Training accuracy: 0.975586
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.628461 Loss1: 0.246069 Loss2: 1.382392
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.579490 Loss1: 0.202210 Loss2: 1.377279
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.569853 Loss1: 0.197779 Loss2: 1.372074
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.597817 Loss1: 0.227308 Loss2: 1.370509
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.508549 Loss1: 1.601523 Loss2: 1.907026
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.470102 Loss1: 0.984927 Loss2: 1.485176
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.141033 Loss1: 0.675112 Loss2: 1.465921
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.023359 Loss1: 0.562946 Loss2: 1.460412
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.306385 Loss1: 1.469747 Loss2: 1.836637
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.306176 Loss1: 0.940091 Loss2: 1.366085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.075248 Loss1: 0.669263 Loss2: 1.405985 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.994516 Loss1: 0.644185 Loss2: 1.350332 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.811242 Loss1: 0.434554 Loss2: 1.376689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.642581 Loss1: 0.296279 Loss2: 1.346302 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973633
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.539751 Loss1: 0.205087 Loss2: 1.334665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.583935 Loss1: 0.241679 Loss2: 1.342256 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.613021 Loss1: 1.675208 Loss2: 1.937812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.110810 Loss1: 0.653275 Loss2: 1.457535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.480042 Loss1: 1.628371 Loss2: 1.851671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.404691 Loss1: 0.986684 Loss2: 1.418007 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.137590 Loss1: 0.715897 Loss2: 1.421693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.904994 Loss1: 0.511568 Loss2: 1.393426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.817037 Loss1: 0.423809 Loss2: 1.393228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.566497 Loss1: 0.173023 Loss2: 1.393475 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958705
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.700688 Loss1: 0.308903 Loss2: 1.391785 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.692229 Loss1: 0.309129 Loss2: 1.383100 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.413737 Loss1: 0.996714 Loss2: 1.417023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.935464 Loss1: 0.525144 Loss2: 1.410321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.523644 Loss1: 1.500191 Loss2: 2.023452
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.766106 Loss1: 0.353034 Loss2: 1.413073
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.671136 Loss1: 1.101926 Loss2: 1.569210
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.697547 Loss1: 0.303504 Loss2: 1.394043
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.672242 Loss1: 0.280194 Loss2: 1.392049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.661053 Loss1: 0.275773 Loss2: 1.385280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.633900 Loss1: 0.247703 Loss2: 1.386197 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.583334 Loss1: 0.194110 Loss2: 1.389224 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958008
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.840293 Loss1: 0.291684 Loss2: 1.548609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.736326 Loss1: 0.199108 Loss2: 1.537218 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.443271 Loss1: 1.483892 Loss2: 1.959379
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.533184 Loss1: 1.028171 Loss2: 1.505013
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.182679 Loss1: 0.663294 Loss2: 1.519386
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.992891 Loss1: 0.524781 Loss2: 1.468111
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.543156 Loss1: 1.521830 Loss2: 2.021326
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.510755 Loss1: 1.083446 Loss2: 1.427309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.088161 Loss1: 0.597330 Loss2: 1.490830
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.880722 Loss1: 0.410334 Loss2: 1.470388
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.854133 Loss1: 0.450700 Loss2: 1.403432
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.817689 Loss1: 0.337390 Loss2: 1.480299
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.734192 Loss1: 0.259503 Loss2: 1.474690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.695128 Loss1: 0.234273 Loss2: 1.460854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.636186 Loss1: 0.177614 Loss2: 1.458572 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.597254 Loss1: 0.199075 Loss2: 1.398179 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957933
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.483955 Loss1: 1.573820 Loss2: 1.910135
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.387592 Loss1: 0.985780 Loss2: 1.401812
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.102806 Loss1: 0.674674 Loss2: 1.428132
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.889306 Loss1: 0.514043 Loss2: 1.375263
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.327974 Loss1: 1.350515 Loss2: 1.977459
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.820553 Loss1: 0.430934 Loss2: 1.389619
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.467721 Loss1: 0.997676 Loss2: 1.470044
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.702400 Loss1: 0.317679 Loss2: 1.384721
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.196276 Loss1: 0.655478 Loss2: 1.540798
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.639590 Loss1: 0.276276 Loss2: 1.363314
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.929544 Loss1: 0.477561 Loss2: 1.451983
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.682182 Loss1: 0.300600 Loss2: 1.381581
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.793629 Loss1: 0.345887 Loss2: 1.447743
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.643285 Loss1: 0.268517 Loss2: 1.374769
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.756848 Loss1: 0.302569 Loss2: 1.454279
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.594768 Loss1: 0.222816 Loss2: 1.371952
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.724610 Loss1: 0.275951 Loss2: 1.448659
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.669249 Loss1: 0.227835 Loss2: 1.441414
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.669693 Loss1: 0.229717 Loss2: 1.439976
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.653824 Loss1: 0.209315 Loss2: 1.444509
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.398914 Loss1: 1.530798 Loss2: 1.868115
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.504796 Loss1: 1.024407 Loss2: 1.480390
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.177653 Loss1: 0.702117 Loss2: 1.475536
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.428892 Loss1: 1.473044 Loss2: 1.955849
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.966777 Loss1: 0.536791 Loss2: 1.429986
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.347588 Loss1: 0.900974 Loss2: 1.446614
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.908801 Loss1: 0.465861 Loss2: 1.442940
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.025214 Loss1: 0.567457 Loss2: 1.457757
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.713837 Loss1: 0.294564 Loss2: 1.419273
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.811951 Loss1: 0.390120 Loss2: 1.421831
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.636507 Loss1: 0.223718 Loss2: 1.412789
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.738605 Loss1: 0.323271 Loss2: 1.415334
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.614149 Loss1: 0.206165 Loss2: 1.407984
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.572020 Loss1: 0.161171 Loss2: 1.410848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.596434 Loss1: 0.198664 Loss2: 1.397771 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952148
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.606277 Loss1: 0.197312 Loss2: 1.408965 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.931250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.510627 Loss1: 1.567739 Loss2: 1.942888 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.169872 Loss1: 0.682457 Loss2: 1.487414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.048818 Loss1: 0.589263 Loss2: 1.459555
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.440284 Loss1: 1.531857 Loss2: 1.908427
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.873980 Loss1: 0.409387 Loss2: 1.464593
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.472342 Loss1: 1.001393 Loss2: 1.470950
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.780708 Loss1: 0.328923 Loss2: 1.451785
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.083089 Loss1: 0.597337 Loss2: 1.485753
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.667621 Loss1: 0.223191 Loss2: 1.444430
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.980024 Loss1: 0.533446 Loss2: 1.446578
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.698592 Loss1: 0.255370 Loss2: 1.443222
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.801083 Loss1: 0.342368 Loss2: 1.458715
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.638494 Loss1: 0.195186 Loss2: 1.443308
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.690461 Loss1: 0.253708 Loss2: 1.436753
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.659333 Loss1: 0.220575 Loss2: 1.438758
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.721038 Loss1: 0.287108 Loss2: 1.433930
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.652889 Loss1: 0.213410 Loss2: 1.439478
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.698906 Loss1: 0.265751 Loss2: 1.433155
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.666064 Loss1: 0.217944 Loss2: 1.448121
+(DefaultActor pid=3764) >> Training accuracy: 0.928125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.694688 Loss1: 1.696663 Loss2: 1.998025
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.530542 Loss1: 1.053678 Loss2: 1.476863
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.231797 Loss1: 0.744050 Loss2: 1.487746
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.958481 Loss1: 0.518904 Loss2: 1.439577
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.519006 Loss1: 1.582635 Loss2: 1.936370
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.831475 Loss1: 0.378970 Loss2: 1.452504
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.494301 Loss1: 1.008262 Loss2: 1.486038
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.242248 Loss1: 0.776297 Loss2: 1.465951 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.947799 Loss1: 0.500264 Loss2: 1.447535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.805795 Loss1: 0.376006 Loss2: 1.429789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.728718 Loss1: 0.305269 Loss2: 1.423449 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.664666 Loss1: 0.232214 Loss2: 1.432452
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.712585 Loss1: 0.285955 Loss2: 1.426630
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.825253 Loss1: 0.377118 Loss2: 1.448135
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.773077 Loss1: 0.325974 Loss2: 1.447103
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.724520 Loss1: 0.290963 Loss2: 1.433558
+(DefaultActor pid=3764) >> Training accuracy: 0.889583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.679393 Loss1: 1.611004 Loss2: 2.068389
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.459985 Loss1: 1.026570 Loss2: 1.433415
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.125147 Loss1: 0.651284 Loss2: 1.473863
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.915625 Loss1: 0.469463 Loss2: 1.446162
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.845154 Loss1: 0.413007 Loss2: 1.432147
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.801003 Loss1: 0.369991 Loss2: 1.431012 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.695342 Loss1: 0.262194 Loss2: 1.433148
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.374355 Loss1: 0.949865 Loss2: 1.424490
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.914033 Loss1: 0.512156 Loss2: 1.401877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.854295 Loss1: 0.483057 Loss2: 1.371237 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.919271
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.748920 Loss1: 0.379152 Loss2: 1.369768 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.648766 Loss1: 0.286016 Loss2: 1.362750 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.605022 Loss1: 0.258022 Loss2: 1.347000 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.597392 Loss1: 0.233387 Loss2: 1.364005 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957721
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.805113 Loss1: 0.411219 Loss2: 1.393894 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.616251 Loss1: 0.245034 Loss2: 1.371217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.590164 Loss1: 0.225252 Loss2: 1.364913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.616378 Loss1: 0.240264 Loss2: 1.376115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.580267 Loss1: 0.207022 Loss2: 1.373245 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.760981 Loss1: 0.377959 Loss2: 1.383023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.635613 Loss1: 0.268083 Loss2: 1.367529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.571811 Loss1: 0.207214 Loss2: 1.364598
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.391015 Loss1: 1.453631 Loss2: 1.937384
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.363245 Loss1: 0.911999 Loss2: 1.451246 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.038637 Loss1: 0.559733 Loss2: 1.478904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.805321 Loss1: 0.367447 Loss2: 1.437874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.754351 Loss1: 0.327610 Loss2: 1.426742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.681531 Loss1: 0.246700 Loss2: 1.434832 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.638520 Loss1: 0.223593 Loss2: 1.414927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.613930 Loss1: 0.192310 Loss2: 1.421620 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952148
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.774219 Loss1: 0.341062 Loss2: 1.433157 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.668867 Loss1: 0.236840 Loss2: 1.432027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.432563 Loss1: 1.497753 Loss2: 1.934809
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.624016 Loss1: 0.199064 Loss2: 1.424952
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.616046 Loss1: 1.121757 Loss2: 1.494289
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.596836 Loss1: 0.164551 Loss2: 1.432285
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.983760 Loss1: 0.546659 Loss2: 1.437102 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.742617 Loss1: 0.313467 Loss2: 1.429150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.716327 Loss1: 0.283858 Loss2: 1.432469
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.460105 Loss1: 1.561632 Loss2: 1.898472
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.671182 Loss1: 0.231829 Loss2: 1.439353
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.356593 Loss1: 0.940878 Loss2: 1.415715
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.635650 Loss1: 0.209830 Loss2: 1.425820
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.113437 Loss1: 0.646851 Loss2: 1.466585
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.613468 Loss1: 0.186340 Loss2: 1.427128
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.874193 Loss1: 0.459884 Loss2: 1.414308
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.786969 Loss1: 0.369016 Loss2: 1.417953
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.695363 Loss1: 0.285100 Loss2: 1.410262
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.646567 Loss1: 0.236671 Loss2: 1.409896
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.603337 Loss1: 0.199903 Loss2: 1.403433
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.547063 Loss1: 1.604816 Loss2: 1.942247
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.574224 Loss1: 0.178581 Loss2: 1.395643
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.416829 Loss1: 0.971525 Loss2: 1.445304
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.540962 Loss1: 0.143777 Loss2: 1.397184
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.880188 Loss1: 0.468948 Loss2: 1.411240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.702562 Loss1: 0.302436 Loss2: 1.400126 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.642370 Loss1: 0.231314 Loss2: 1.411056
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.339845 Loss1: 1.451160 Loss2: 1.888685
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.629937 Loss1: 0.224034 Loss2: 1.405903
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.386225 Loss1: 0.979464 Loss2: 1.406761
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.606046 Loss1: 0.198842 Loss2: 1.407204
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.065474 Loss1: 0.623239 Loss2: 1.442235
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.601060 Loss1: 0.199956 Loss2: 1.401105
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.840418 Loss1: 0.462255 Loss2: 1.378163
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687307 Loss1: 0.313336 Loss2: 1.373971
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.634572 Loss1: 0.271997 Loss2: 1.362575
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.641249 Loss1: 0.272816 Loss2: 1.368433
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.604510 Loss1: 0.232045 Loss2: 1.372465
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.560289 Loss1: 0.198353 Loss2: 1.361936
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.397966 Loss1: 1.493661 Loss2: 1.904304
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.583927 Loss1: 0.216903 Loss2: 1.367024
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.381209 Loss1: 0.960817 Loss2: 1.420392
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.067893 Loss1: 0.620139 Loss2: 1.447754
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.885717 Loss1: 0.484455 Loss2: 1.401261
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.763806 Loss1: 0.352083 Loss2: 1.411723
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.751160 Loss1: 0.355319 Loss2: 1.395841
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.739138 Loss1: 0.336271 Loss2: 1.402867
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.440134 Loss1: 1.543803 Loss2: 1.896332
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.672242 Loss1: 0.269488 Loss2: 1.402754
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.378266 Loss1: 0.941749 Loss2: 1.436517
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.693540 Loss1: 0.296169 Loss2: 1.397371
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.028241 Loss1: 0.591712 Loss2: 1.436529
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.608637 Loss1: 0.221220 Loss2: 1.387416
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.818391 Loss1: 0.409537 Loss2: 1.408854
+(DefaultActor pid=3765) >> Training accuracy: 0.936458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.707098 Loss1: 0.299170 Loss2: 1.407928
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.714000 Loss1: 0.321513 Loss2: 1.392488
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.698959 Loss1: 0.289141 Loss2: 1.409817
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.662776 Loss1: 0.264119 Loss2: 1.398656
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.728690 Loss1: 0.327679 Loss2: 1.401011
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.394686 Loss1: 1.526072 Loss2: 1.868614
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.659901 Loss1: 0.247133 Loss2: 1.412767
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.325805 Loss1: 0.917255 Loss2: 1.408550
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.114321 Loss1: 0.704976 Loss2: 1.409345
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.845521 Loss1: 0.462967 Loss2: 1.382554
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.772457 Loss1: 0.387003 Loss2: 1.385454
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.765146 Loss1: 0.384876 Loss2: 1.380270
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.312345 Loss1: 1.370098 Loss2: 1.942247
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.403560 Loss1: 0.935152 Loss2: 1.468408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.110723 Loss1: 0.620789 Loss2: 1.489934 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.953236 Loss1: 0.500931 Loss2: 1.452305 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942383
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.583814 Loss1: 0.206246 Loss2: 1.377569
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.831387 Loss1: 0.373925 Loss2: 1.457462
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.759579 Loss1: 0.311858 Loss2: 1.447721
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.720355 Loss1: 0.277824 Loss2: 1.442532
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.721188 Loss1: 0.289496 Loss2: 1.431692
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.689393 Loss1: 0.245071 Loss2: 1.444322
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.497289 Loss1: 1.593853 Loss2: 1.903436
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.686111 Loss1: 0.238908 Loss2: 1.447204
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.175799 Loss1: 0.701944 Loss2: 1.473855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.867857 Loss1: 0.419315 Loss2: 1.448542 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 02:54:36,703 | server.py:236 | fit_round 61 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.774876 Loss1: 0.338069 Loss2: 1.436807
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.372412 Loss1: 1.468446 Loss2: 1.903966
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.490773 Loss1: 1.066048 Loss2: 1.424725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.185743 Loss1: 0.707735 Loss2: 1.478008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.983286 Loss1: 0.553998 Loss2: 1.429288 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.806241 Loss1: 0.354781 Loss2: 1.451460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.788927 Loss1: 0.350049 Loss2: 1.438878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.668380 Loss1: 0.251528 Loss2: 1.416852 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.670542 Loss1: 0.254459 Loss2: 1.416084 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.034811 Loss1: 0.613421 Loss2: 1.421390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.774397 Loss1: 0.381188 Loss2: 1.393209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.683382 Loss1: 0.291208 Loss2: 1.392174
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.266981 Loss1: 1.371932 Loss2: 1.895050
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.248550 Loss1: 0.847433 Loss2: 1.401117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.092860 Loss1: 0.650734 Loss2: 1.442126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.895837 Loss1: 0.496565 Loss2: 1.399273 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.776203 Loss1: 0.370657 Loss2: 1.405547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.629232 Loss1: 0.243118 Loss2: 1.386114 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.660182 Loss1: 0.275569 Loss2: 1.384613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.632652 Loss1: 0.245640 Loss2: 1.387012 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.930208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.201674 Loss1: 0.729939 Loss2: 1.471735 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.777524 Loss1: 0.387182 Loss2: 1.390342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.655280 Loss1: 0.258672 Loss2: 1.396608 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.598268 Loss1: 0.208385 Loss2: 1.389883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.578972 Loss1: 0.195274 Loss2: 1.383698 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.573632 Loss1: 0.195056 Loss2: 1.378576 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972356
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.966176 Loss1: 0.427563 Loss2: 1.538613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.816486 Loss1: 0.284926 Loss2: 1.531560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.787858 Loss1: 0.261760 Loss2: 1.526098 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.945312
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 02:54:36,703][flwr][DEBUG] - fit_round 61 received 50 results and 0 failures
+INFO flwr 2023-10-10 02:55:17,873 | server.py:125 | fit progress: (61, 2.3367708978561548, {'accuracy': 0.51}, 140625.651772201)
+>> Test accuracy: 0.510000
+[2023-10-10 02:55:17,873][flwr][INFO] - fit progress: (61, 2.3367708978561548, {'accuracy': 0.51}, 140625.651772201)
+DEBUG flwr 2023-10-10 02:55:17,874 | server.py:173 | evaluate_round 61: strategy sampled 50 clients (out of 50)
+[2023-10-10 02:55:17,874][flwr][DEBUG] - evaluate_round 61: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 03:04:22,586 | server.py:187 | evaluate_round 61 received 50 results and 0 failures
+[2023-10-10 03:04:22,586][flwr][DEBUG] - evaluate_round 61 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 03:04:22,586 | server.py:222 | fit_round 62: strategy sampled 50 clients (out of 50)
+[2023-10-10 03:04:22,586][flwr][DEBUG] - fit_round 62: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.755579 Loss1: 1.792551 Loss2: 1.963028 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.138409 Loss1: 0.659016 Loss2: 1.479394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.372878 Loss1: 1.449100 Loss2: 1.923778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.295813 Loss1: 0.839930 Loss2: 1.455883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.047529 Loss1: 0.587817 Loss2: 1.459712 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.835167 Loss1: 0.402103 Loss2: 1.433064 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.708856 Loss1: 0.287728 Loss2: 1.421127 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.659558 Loss1: 0.247913 Loss2: 1.411645 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.944196
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.615709 Loss1: 0.188147 Loss2: 1.427562 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.593723 Loss1: 0.184658 Loss2: 1.409065 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.381048 Loss1: 0.896994 Loss2: 1.484054 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.885662 Loss1: 0.441272 Loss2: 1.444390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.828096 Loss1: 0.369466 Loss2: 1.458629
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.347452 Loss1: 1.469538 Loss2: 1.877913
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.776704 Loss1: 0.317016 Loss2: 1.459688
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.517117 Loss1: 1.067969 Loss2: 1.449147
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.723699 Loss1: 0.272221 Loss2: 1.451479
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.126368 Loss1: 0.658730 Loss2: 1.467639
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.665644 Loss1: 0.225666 Loss2: 1.439978
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.994981 Loss1: 0.572711 Loss2: 1.422270
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.640952 Loss1: 0.206253 Loss2: 1.434699
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.848503 Loss1: 0.415202 Loss2: 1.433301
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.662758 Loss1: 0.221711 Loss2: 1.441047
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.809762 Loss1: 0.395243 Loss2: 1.414519
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.746893 Loss1: 0.327451 Loss2: 1.419441
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.677582 Loss1: 0.263944 Loss2: 1.413638
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.701367 Loss1: 0.284780 Loss2: 1.416587
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.670503 Loss1: 0.238716 Loss2: 1.431787
+(DefaultActor pid=3764) >> Training accuracy: 0.929167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.293093 Loss1: 1.328877 Loss2: 1.964216
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.365772 Loss1: 0.836631 Loss2: 1.529141
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.094224 Loss1: 0.572432 Loss2: 1.521792
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.961309 Loss1: 0.455488 Loss2: 1.505821
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.432060 Loss1: 1.475430 Loss2: 1.956630
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.420495 Loss1: 0.947280 Loss2: 1.473216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.154961 Loss1: 0.646816 Loss2: 1.508145 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.781168 Loss1: 0.283967 Loss2: 1.497201
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.003668 Loss1: 0.554862 Loss2: 1.448806
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.862252 Loss1: 0.396868 Loss2: 1.465385
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.688605 Loss1: 0.207026 Loss2: 1.481578
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.803125 Loss1: 0.349631 Loss2: 1.453494
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.704492 Loss1: 0.222067 Loss2: 1.482425
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.746319 Loss1: 0.300130 Loss2: 1.446189
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.754424 Loss1: 0.268719 Loss2: 1.485704
+(DefaultActor pid=3765) >> Training accuracy: 0.956801
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.657989 Loss1: 0.212381 Loss2: 1.445608 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.483839 Loss1: 1.532911 Loss2: 1.950928 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.171965 Loss1: 0.678802 Loss2: 1.493163 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.942112 Loss1: 0.487171 Loss2: 1.454941
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.526768 Loss1: 1.556389 Loss2: 1.970379
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.545262 Loss1: 1.015464 Loss2: 1.529798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.159408 Loss1: 0.620052 Loss2: 1.539356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.002079 Loss1: 0.497149 Loss2: 1.504929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.850356 Loss1: 0.353929 Loss2: 1.496426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.824102 Loss1: 0.333622 Loss2: 1.490480 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.646681 Loss1: 0.204788 Loss2: 1.441893
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.797541 Loss1: 0.293934 Loss2: 1.503607
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.789774 Loss1: 0.300608 Loss2: 1.489165
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.807876 Loss1: 0.317747 Loss2: 1.490130
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.704306 Loss1: 0.210127 Loss2: 1.494179
+(DefaultActor pid=3764) >> Training accuracy: 0.954167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.336641 Loss1: 1.483774 Loss2: 1.852867
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.322430 Loss1: 0.893346 Loss2: 1.429085
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.931809 Loss1: 0.519969 Loss2: 1.411840
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.804153 Loss1: 0.411554 Loss2: 1.392598
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.668209 Loss1: 1.590416 Loss2: 2.077793
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.768472 Loss1: 0.372239 Loss2: 1.396233
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.484278 Loss1: 0.953589 Loss2: 1.530689
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.788717 Loss1: 0.383641 Loss2: 1.405077
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.235810 Loss1: 0.684682 Loss2: 1.551128
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.689828 Loss1: 0.289762 Loss2: 1.400066
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.930852 Loss1: 0.444304 Loss2: 1.486548
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.804590 Loss1: 0.310852 Loss2: 1.493738
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.638431 Loss1: 0.252177 Loss2: 1.386254
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.775249 Loss1: 0.294931 Loss2: 1.480318
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.619525 Loss1: 0.232009 Loss2: 1.387515
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.784824 Loss1: 0.295369 Loss2: 1.489455
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.615672 Loss1: 0.226830 Loss2: 1.388843
+(DefaultActor pid=3765) >> Training accuracy: 0.915039
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.668658 Loss1: 0.194389 Loss2: 1.474269 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.446906 Loss1: 1.510289 Loss2: 1.936617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.228423 Loss1: 0.715969 Loss2: 1.512455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.386457 Loss1: 1.441212 Loss2: 1.945245
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.089117 Loss1: 0.590046 Loss2: 1.499071
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.513353 Loss1: 1.062887 Loss2: 1.450466
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.931644 Loss1: 0.428001 Loss2: 1.503643
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.185826 Loss1: 0.667924 Loss2: 1.517903
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.856258 Loss1: 0.365155 Loss2: 1.491103
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.728467 Loss1: 0.255162 Loss2: 1.473305 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.718772 Loss1: 0.240357 Loss2: 1.478415 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.735506 Loss1: 0.257945 Loss2: 1.477562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.726074 Loss1: 0.252416 Loss2: 1.473659 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942383
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.653698 Loss1: 0.216801 Loss2: 1.436897 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.939583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.408560 Loss1: 1.574896 Loss2: 1.833664 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.094010 Loss1: 0.699236 Loss2: 1.394774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.234470 Loss1: 1.352715 Loss2: 1.881755
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.899869 Loss1: 0.526946 Loss2: 1.372923
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.264843 Loss1: 0.865237 Loss2: 1.399606
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.840701 Loss1: 0.449062 Loss2: 1.391639
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.038381 Loss1: 0.613148 Loss2: 1.425232
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.691718 Loss1: 0.324341 Loss2: 1.367377
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.604117 Loss1: 0.240998 Loss2: 1.363120 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.602002 Loss1: 0.245683 Loss2: 1.356319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.546435 Loss1: 0.187230 Loss2: 1.359204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.555426 Loss1: 0.201717 Loss2: 1.353709 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.950195
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.646120 Loss1: 0.283549 Loss2: 1.362571 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.721484 Loss1: 1.594842 Loss2: 2.126642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.379542 Loss1: 0.761115 Loss2: 1.618427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.485373 Loss1: 1.551143 Loss2: 1.934230 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.611452 Loss1: 1.126216 Loss2: 1.485237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.210281 Loss1: 0.711108 Loss2: 1.499173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.017635 Loss1: 0.567728 Loss2: 1.449906 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.922393 Loss1: 0.451443 Loss2: 1.470949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.804478 Loss1: 0.361888 Loss2: 1.442590 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.728943 Loss1: 0.274470 Loss2: 1.454473 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.659475 Loss1: 0.226279 Loss2: 1.433197 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.516664 Loss1: 1.067884 Loss2: 1.448780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.934836 Loss1: 0.512987 Loss2: 1.421849 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.903772 Loss1: 0.466782 Loss2: 1.436990
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.318513 Loss1: 1.448579 Loss2: 1.869935
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.829867 Loss1: 0.394181 Loss2: 1.435686
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.290404 Loss1: 0.867236 Loss2: 1.423167
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.714581 Loss1: 0.279241 Loss2: 1.435339
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.985245 Loss1: 0.564014 Loss2: 1.421231
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.725008 Loss1: 0.295255 Loss2: 1.429753
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.794662 Loss1: 0.403617 Loss2: 1.391045
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.745939 Loss1: 0.353448 Loss2: 1.392490 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.934375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.620219 Loss1: 0.206604 Loss2: 1.413615
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.739258 Loss1: 0.359068 Loss2: 1.380190
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.722057 Loss1: 0.318833 Loss2: 1.403224
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.684401 Loss1: 0.297878 Loss2: 1.386523
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.635310 Loss1: 0.238808 Loss2: 1.396502
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.539420 Loss1: 0.158659 Loss2: 1.380761
+(DefaultActor pid=3764) >> Training accuracy: 0.944336
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.270889 Loss1: 1.472386 Loss2: 1.798502
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.370645 Loss1: 0.958369 Loss2: 1.412276
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.964660 Loss1: 0.562770 Loss2: 1.401890
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.843552 Loss1: 0.459465 Loss2: 1.384087
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.735972 Loss1: 0.350065 Loss2: 1.385907
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.387847 Loss1: 1.476995 Loss2: 1.910852
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.413175 Loss1: 0.964872 Loss2: 1.448302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.110548 Loss1: 0.639218 Loss2: 1.471330 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.577357 Loss1: 0.218080 Loss2: 1.359276
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.953888 Loss1: 0.528288 Loss2: 1.425599
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.594187 Loss1: 0.220620 Loss2: 1.373566
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.792962 Loss1: 0.357663 Loss2: 1.435299
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.589481 Loss1: 0.218500 Loss2: 1.370981
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.731358 Loss1: 0.312714 Loss2: 1.418644
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.683733 Loss1: 0.258619 Loss2: 1.425114
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.611410 Loss1: 0.197861 Loss2: 1.413548
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.615963 Loss1: 0.207525 Loss2: 1.408439
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.625247 Loss1: 0.218277 Loss2: 1.406970
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.349816 Loss1: 1.460627 Loss2: 1.889189
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.394907 Loss1: 0.977897 Loss2: 1.417010
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.095133 Loss1: 0.634656 Loss2: 1.460476
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.835087 Loss1: 0.432170 Loss2: 1.402917
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.400934 Loss1: 1.444315 Loss2: 1.956619
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.399662 Loss1: 0.934121 Loss2: 1.465541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.128327 Loss1: 0.629694 Loss2: 1.498633 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.938815 Loss1: 0.485351 Loss2: 1.453463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.796865 Loss1: 0.322723 Loss2: 1.474142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.728253 Loss1: 0.283552 Loss2: 1.444701 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.659200 Loss1: 0.222665 Loss2: 1.436535 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.582500 Loss1: 0.157732 Loss2: 1.424768 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.444614 Loss1: 0.978642 Loss2: 1.465973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.890536 Loss1: 0.470783 Loss2: 1.419753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.750573 Loss1: 0.347931 Loss2: 1.402642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.663219 Loss1: 0.278418 Loss2: 1.384800 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.608423 Loss1: 0.216533 Loss2: 1.391890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.790293 Loss1: 0.367631 Loss2: 1.422662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.720541 Loss1: 0.290744 Loss2: 1.429796 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.710547 Loss1: 0.302428 Loss2: 1.408119 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.693988 Loss1: 0.285261 Loss2: 1.408727 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.633367 Loss1: 0.217608 Loss2: 1.415759 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.954427
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.514232 Loss1: 1.646901 Loss2: 1.867331
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.503076 Loss1: 1.098733 Loss2: 1.404343
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.109735 Loss1: 0.671089 Loss2: 1.438646
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.888350 Loss1: 0.502976 Loss2: 1.385374
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.366564 Loss1: 1.492614 Loss2: 1.873950
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.319056 Loss1: 0.879518 Loss2: 1.439539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.052357 Loss1: 0.620724 Loss2: 1.431633 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.888126 Loss1: 0.453508 Loss2: 1.434618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.823447 Loss1: 0.396847 Loss2: 1.426600 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.799370 Loss1: 0.373327 Loss2: 1.426044 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.726846 Loss1: 0.301760 Loss2: 1.425086 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.723952 Loss1: 0.297977 Loss2: 1.425975 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973633
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.273504 Loss1: 1.408756 Loss2: 1.864747 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.047577 Loss1: 0.616661 Loss2: 1.430916 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.413106 Loss1: 1.429808 Loss2: 1.983297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.555924 Loss1: 1.086109 Loss2: 1.469814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.197023 Loss1: 0.673200 Loss2: 1.523823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.921850 Loss1: 0.468018 Loss2: 1.453832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.913674 Loss1: 0.442880 Loss2: 1.470794 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.812024 Loss1: 0.345489 Loss2: 1.466535 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.936458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.705617 Loss1: 0.253008 Loss2: 1.452609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.606205 Loss1: 0.163181 Loss2: 1.443025 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970982
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.632022 Loss1: 1.596730 Loss2: 2.035292 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.012540 Loss1: 0.552878 Loss2: 1.459662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.343701 Loss1: 1.478488 Loss2: 1.865214 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.686502 Loss1: 0.263786 Loss2: 1.422716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.682948 Loss1: 0.265774 Loss2: 1.417174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.671618 Loss1: 0.246471 Loss2: 1.425147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.633092 Loss1: 0.211256 Loss2: 1.421836 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.596083 Loss1: 0.173197 Loss2: 1.422886 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965144
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.663607 Loss1: 0.245536 Loss2: 1.418071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.607950 Loss1: 0.200513 Loss2: 1.407436 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.665606 Loss1: 1.655535 Loss2: 2.010071
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.669027 Loss1: 0.255763 Loss2: 1.413264
+(DefaultActor pid=3764) >> Training accuracy: 0.917969
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.275179 Loss1: 0.705665 Loss2: 1.569515 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 2.060646 Loss1: 0.522344 Loss2: 1.538302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.967406 Loss1: 0.449798 Loss2: 1.517608
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.410270 Loss1: 1.493125 Loss2: 1.917145
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.461389 Loss1: 0.972519 Loss2: 1.488870 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.140218 Loss1: 0.665129 Loss2: 1.475089 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.835970 Loss1: 0.394419 Loss2: 1.441550 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.920833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.777282 Loss1: 0.268930 Loss2: 1.508352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.791169 Loss1: 0.346133 Loss2: 1.445036
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.756921 Loss1: 0.316926 Loss2: 1.439995
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.697395 Loss1: 0.256170 Loss2: 1.441225
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.678575 Loss1: 0.243187 Loss2: 1.435388
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.654954 Loss1: 0.220051 Loss2: 1.434904
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.394473 Loss1: 1.491965 Loss2: 1.902508
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.572224 Loss1: 0.146100 Loss2: 1.426124
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.129640 Loss1: 0.667078 Loss2: 1.462562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.809906 Loss1: 0.379306 Loss2: 1.430600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.757720 Loss1: 0.339563 Loss2: 1.418157
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.435921 Loss1: 1.459715 Loss2: 1.976206
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.720361 Loss1: 0.298982 Loss2: 1.421379
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.384525 Loss1: 0.890856 Loss2: 1.493669
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.712554 Loss1: 0.308519 Loss2: 1.404035
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.148604 Loss1: 0.634169 Loss2: 1.514434
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.642858 Loss1: 0.242909 Loss2: 1.399949
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.954776 Loss1: 0.483095 Loss2: 1.471682
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.617741 Loss1: 0.208613 Loss2: 1.409128
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.872936 Loss1: 0.369892 Loss2: 1.503044
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.751920 Loss1: 0.277847 Loss2: 1.474073
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.657246 Loss1: 0.201191 Loss2: 1.456055
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.682618 Loss1: 0.227931 Loss2: 1.454687
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.652742 Loss1: 0.186095 Loss2: 1.466647
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.639356 Loss1: 0.177890 Loss2: 1.461466
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.399986 Loss1: 1.532226 Loss2: 1.867761
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.452472 Loss1: 0.985793 Loss2: 1.466678
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.076649 Loss1: 0.606015 Loss2: 1.470634
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.933140 Loss1: 0.502694 Loss2: 1.430446
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.803640 Loss1: 0.358882 Loss2: 1.444758
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.298224 Loss1: 1.321741 Loss2: 1.976483
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.379347 Loss1: 0.882354 Loss2: 1.496993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.144105 Loss1: 0.605074 Loss2: 1.539030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.919333 Loss1: 0.450735 Loss2: 1.468598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.821423 Loss1: 0.343798 Loss2: 1.477625 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.673347 Loss1: 0.256651 Loss2: 1.416696
+(DefaultActor pid=3765) >> Training accuracy: 0.916016
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.806122 Loss1: 0.332851 Loss2: 1.473271
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.747314 Loss1: 0.271590 Loss2: 1.475724
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.720083 Loss1: 0.248396 Loss2: 1.471688
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.647638 Loss1: 0.181730 Loss2: 1.465908
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.612757 Loss1: 0.151391 Loss2: 1.461366
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.582286 Loss1: 1.598241 Loss2: 1.984044
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.563127 Loss1: 1.026119 Loss2: 1.537008
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.163281 Loss1: 0.634561 Loss2: 1.528720
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.973206 Loss1: 0.464180 Loss2: 1.509026
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.910194 Loss1: 0.403435 Loss2: 1.506759
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.248722 Loss1: 1.338388 Loss2: 1.910335
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.828538 Loss1: 0.327472 Loss2: 1.501066
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.261307 Loss1: 0.824225 Loss2: 1.437082
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.825573 Loss1: 0.317320 Loss2: 1.508253
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.930808 Loss1: 0.485878 Loss2: 1.444930
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.815854 Loss1: 0.307583 Loss2: 1.508271
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.905136 Loss1: 0.500476 Loss2: 1.404659
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.759591 Loss1: 0.250389 Loss2: 1.509202
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.769563 Loss1: 0.349419 Loss2: 1.420144
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.719022 Loss1: 0.215509 Loss2: 1.503512
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.644565 Loss1: 0.236125 Loss2: 1.408440
+(DefaultActor pid=3765) >> Training accuracy: 0.940625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.576235 Loss1: 0.180629 Loss2: 1.395607
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.578865 Loss1: 0.185342 Loss2: 1.393523
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.542840 Loss1: 0.146029 Loss2: 1.396812
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.555043 Loss1: 0.167842 Loss2: 1.387201
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.498754 Loss1: 1.603589 Loss2: 1.895164
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.501058 Loss1: 1.048486 Loss2: 1.452572
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.065858 Loss1: 0.625512 Loss2: 1.440346
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.887638 Loss1: 0.474409 Loss2: 1.413228
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.771578 Loss1: 0.363513 Loss2: 1.408065
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.736847 Loss1: 0.336859 Loss2: 1.399988 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.672627 Loss1: 0.268391 Loss2: 1.404236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.597251 Loss1: 0.204140 Loss2: 1.393111 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.553982 Loss1: 0.166047 Loss2: 1.387935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.544706 Loss1: 0.154296 Loss2: 1.390410 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.642111 Loss1: 0.242722 Loss2: 1.399389 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.571701 Loss1: 0.165139 Loss2: 1.406562 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.410975 Loss1: 0.977967 Loss2: 1.433008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.848908 Loss1: 0.439993 Loss2: 1.408915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.801016 Loss1: 0.381016 Loss2: 1.420001
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.572068 Loss1: 1.549803 Loss2: 2.022265
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.699017 Loss1: 0.284322 Loss2: 1.414694
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.547561 Loss1: 1.002070 Loss2: 1.545491
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.685318 Loss1: 0.285971 Loss2: 1.399348
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.126189 Loss1: 0.578676 Loss2: 1.547513
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.673598 Loss1: 0.262110 Loss2: 1.411488
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.993821 Loss1: 0.482218 Loss2: 1.511603
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.671462 Loss1: 0.261458 Loss2: 1.410004
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.914071 Loss1: 0.382598 Loss2: 1.531472
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.638037 Loss1: 0.225393 Loss2: 1.412643
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.810796 Loss1: 0.309171 Loss2: 1.501625
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.774763 Loss1: 0.269653 Loss2: 1.505110
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.748450 Loss1: 0.244755 Loss2: 1.503694
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.714057 Loss1: 0.218682 Loss2: 1.495375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.690790 Loss1: 0.197330 Loss2: 1.493460
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.567012 Loss1: 1.539850 Loss2: 2.027162
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.451572 Loss1: 0.975278 Loss2: 1.476294
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.165708 Loss1: 0.659711 Loss2: 1.505997
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.933160 Loss1: 0.472242 Loss2: 1.460918
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.801627 Loss1: 0.348948 Loss2: 1.452679
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.740868 Loss1: 0.272276 Loss2: 1.468591
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.326505 Loss1: 1.447043 Loss2: 1.879462
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.518717 Loss1: 1.065236 Loss2: 1.453481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.135991 Loss1: 0.634988 Loss2: 1.501003 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.888396 Loss1: 0.499256 Loss2: 1.389140 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.906250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.739540 Loss1: 0.349762 Loss2: 1.389778 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.564830 Loss1: 0.173603 Loss2: 1.391227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.687333 Loss1: 0.297270 Loss2: 1.390063
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.214873 Loss1: 1.361943 Loss2: 1.852930
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.325965 Loss1: 0.903817 Loss2: 1.422148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.782624 Loss1: 0.396215 Loss2: 1.386409 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.635284 Loss1: 0.255720 Loss2: 1.379564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.586094 Loss1: 0.208122 Loss2: 1.377972 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.572532 Loss1: 0.191361 Loss2: 1.381171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.522450 Loss1: 0.148624 Loss2: 1.373827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.514379 Loss1: 0.144328 Loss2: 1.370051 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971680
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-10 03:33:10,833 | server.py:236 | fit_round 62 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.680147 Loss1: 0.308588 Loss2: 1.371559 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.619343 Loss1: 0.255269 Loss2: 1.364074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.553843 Loss1: 1.709867 Loss2: 1.843976
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.523898 Loss1: 0.168081 Loss2: 1.355818
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.111575 Loss1: 0.691433 Loss2: 1.420142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.735911 Loss1: 0.352157 Loss2: 1.383754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.686566 Loss1: 0.304979 Loss2: 1.381587
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.384313 Loss1: 1.534637 Loss2: 1.849677
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.644499 Loss1: 0.269721 Loss2: 1.374778
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.383549 Loss1: 0.982794 Loss2: 1.400754
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.614022 Loss1: 0.233110 Loss2: 1.380912
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.090524 Loss1: 0.673024 Loss2: 1.417500
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.557063 Loss1: 0.193130 Loss2: 1.363933
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.874136 Loss1: 0.487132 Loss2: 1.387003
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.535213 Loss1: 0.163008 Loss2: 1.372205
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.698520 Loss1: 0.302085 Loss2: 1.396435
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.660148 Loss1: 0.297698 Loss2: 1.362450
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.588561 Loss1: 0.212496 Loss2: 1.376064
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.565890 Loss1: 0.191437 Loss2: 1.374453
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.546579 Loss1: 0.182335 Loss2: 1.364244
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.545632 Loss1: 0.179930 Loss2: 1.365702
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.480565 Loss1: 1.583876 Loss2: 1.896689
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.450578 Loss1: 1.012685 Loss2: 1.437893
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.081623 Loss1: 0.619812 Loss2: 1.461811
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.975402 Loss1: 0.531933 Loss2: 1.443469
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.875868 Loss1: 0.445007 Loss2: 1.430861
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.320897 Loss1: 1.366942 Loss2: 1.953955
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.708328 Loss1: 0.287687 Loss2: 1.420641
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.356891 Loss1: 0.911423 Loss2: 1.445468
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.678929 Loss1: 0.261201 Loss2: 1.417728
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.060101 Loss1: 0.564943 Loss2: 1.495158
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.725830 Loss1: 0.307411 Loss2: 1.418419
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.858926 Loss1: 0.444887 Loss2: 1.414039
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.668885 Loss1: 0.250026 Loss2: 1.418858
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.812313 Loss1: 0.375357 Loss2: 1.436956
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.640543 Loss1: 0.225630 Loss2: 1.414913
+(DefaultActor pid=3765) >> Training accuracy: 0.943750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.684674 Loss1: 0.271046 Loss2: 1.413628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.606245 Loss1: 0.196261 Loss2: 1.409984 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 03:33:10,833][flwr][DEBUG] - fit_round 62 received 50 results and 0 failures
+INFO flwr 2023-10-10 03:33:52,321 | server.py:125 | fit progress: (62, 2.3237575894346634, {'accuracy': 0.5111}, 142940.099831166)
+>> Test accuracy: 0.511100
+[2023-10-10 03:33:52,321][flwr][INFO] - fit progress: (62, 2.3237575894346634, {'accuracy': 0.5111}, 142940.099831166)
+DEBUG flwr 2023-10-10 03:33:52,322 | server.py:173 | evaluate_round 62: strategy sampled 50 clients (out of 50)
+[2023-10-10 03:33:52,322][flwr][DEBUG] - evaluate_round 62: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 03:43:04,043 | server.py:187 | evaluate_round 62 received 50 results and 0 failures
+[2023-10-10 03:43:04,043][flwr][DEBUG] - evaluate_round 62 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 03:43:04,044 | server.py:222 | fit_round 63: strategy sampled 50 clients (out of 50)
+[2023-10-10 03:43:04,044][flwr][DEBUG] - fit_round 63: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.262390 Loss1: 1.421120 Loss2: 1.841269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.030064 Loss1: 0.621101 Loss2: 1.408963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.789543 Loss1: 0.448905 Loss2: 1.340637
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.415964 Loss1: 1.486789 Loss2: 1.929176
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.310617 Loss1: 0.912890 Loss2: 1.397727 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.592568 Loss1: 0.257868 Loss2: 1.334699
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.008680 Loss1: 0.590738 Loss2: 1.417941
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.598217 Loss1: 0.253942 Loss2: 1.344276
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.800355 Loss1: 0.414611 Loss2: 1.385745
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.668254 Loss1: 0.286516 Loss2: 1.381739
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.598043 Loss1: 0.258066 Loss2: 1.339977
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.684203 Loss1: 0.294758 Loss2: 1.389445
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.574291 Loss1: 0.227072 Loss2: 1.347219
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.629211 Loss1: 0.242880 Loss2: 1.386331
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.581612 Loss1: 0.229483 Loss2: 1.352129
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.586474 Loss1: 0.215532 Loss2: 1.370943 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957589
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.381895 Loss1: 1.493026 Loss2: 1.888869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.092988 Loss1: 0.628195 Loss2: 1.464793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.898138 Loss1: 0.485444 Loss2: 1.412694
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.369833 Loss1: 1.399693 Loss2: 1.970140
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.354149 Loss1: 0.884094 Loss2: 1.470055 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.099642 Loss1: 0.627564 Loss2: 1.472078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.932866 Loss1: 0.494476 Loss2: 1.438390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.782341 Loss1: 0.338023 Loss2: 1.444317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.697887 Loss1: 0.283380 Loss2: 1.414507 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.607077 Loss1: 0.206182 Loss2: 1.400895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.559450 Loss1: 0.161894 Loss2: 1.397556 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.499651 Loss1: 1.525497 Loss2: 1.974154 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.149988 Loss1: 0.648605 Loss2: 1.501382 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.857853 Loss1: 0.399283 Loss2: 1.458570 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.729039 Loss1: 0.289625 Loss2: 1.439414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.742072 Loss1: 0.315684 Loss2: 1.426388 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.659531 Loss1: 0.208099 Loss2: 1.451432 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.598111 Loss1: 0.176397 Loss2: 1.421715 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.562848 Loss1: 0.145101 Loss2: 1.417748 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956731
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.713675 Loss1: 0.261563 Loss2: 1.452112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.589390 Loss1: 0.155687 Loss2: 1.433703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.591882 Loss1: 0.164290 Loss2: 1.427593
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.504554 Loss1: 1.500609 Loss2: 2.003944
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.478784 Loss1: 1.047480 Loss2: 1.431304
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.032612 Loss1: 0.548274 Loss2: 1.484338
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.887519 Loss1: 0.490710 Loss2: 1.396809
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.815161 Loss1: 0.396596 Loss2: 1.418564
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.675777 Loss1: 0.266792 Loss2: 1.408985
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.461833 Loss1: 1.637771 Loss2: 1.824062
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.464524 Loss1: 1.073667 Loss2: 1.390857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.065105 Loss1: 0.665237 Loss2: 1.399869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.532835 Loss1: 0.153104 Loss2: 1.379731 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975962
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.724507 Loss1: 0.347929 Loss2: 1.376578 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.549629 Loss1: 0.196969 Loss2: 1.352660 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.542926 Loss1: 0.188269 Loss2: 1.354656
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.333282 Loss1: 1.476303 Loss2: 1.856979
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.545140 Loss1: 0.189285 Loss2: 1.355855
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.552630 Loss1: 1.094904 Loss2: 1.457726
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.025367 Loss1: 0.578404 Loss2: 1.446963
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.920415 Loss1: 0.504968 Loss2: 1.415447
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.865769 Loss1: 0.433931 Loss2: 1.431838
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.757854 Loss1: 0.333579 Loss2: 1.424276
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.442234 Loss1: 1.536741 Loss2: 1.905494
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.415182 Loss1: 0.952582 Loss2: 1.462601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.045428 Loss1: 0.556180 Loss2: 1.489248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.937169 Loss1: 0.500720 Loss2: 1.436449 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955078
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.616804 Loss1: 0.216603 Loss2: 1.400201
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.842951 Loss1: 0.382470 Loss2: 1.460481
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.822685 Loss1: 0.363380 Loss2: 1.459304
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.757026 Loss1: 0.310911 Loss2: 1.446115
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.677032 Loss1: 0.237517 Loss2: 1.439515
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.712894 Loss1: 0.266356 Loss2: 1.446538
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.369913 Loss1: 1.417160 Loss2: 1.952753
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.293401 Loss1: 0.823650 Loss2: 1.469751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.969158 Loss1: 0.502702 Loss2: 1.466457 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.720408 Loss1: 0.264158 Loss2: 1.456250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.666324 Loss1: 0.215034 Loss2: 1.451290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.612193 Loss1: 0.171296 Loss2: 1.440897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.589480 Loss1: 0.151811 Loss2: 1.437669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.557773 Loss1: 0.128210 Loss2: 1.429563 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.802299 Loss1: 0.340417 Loss2: 1.461882 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.729317 Loss1: 0.269743 Loss2: 1.459574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.433826 Loss1: 1.549314 Loss2: 1.884512 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.043156 Loss1: 0.577511 Loss2: 1.465645 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.884220 Loss1: 0.426456 Loss2: 1.457763 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.808579 Loss1: 0.364326 Loss2: 1.444253
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.393600 Loss1: 1.536421 Loss2: 1.857179
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.813555 Loss1: 0.364759 Loss2: 1.448796
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.448244 Loss1: 1.012734 Loss2: 1.435511
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.180052 Loss1: 0.749868 Loss2: 1.430183
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.692663 Loss1: 0.250368 Loss2: 1.442296
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.969381 Loss1: 0.557949 Loss2: 1.411432
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.629808 Loss1: 0.199293 Loss2: 1.430515
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.837266 Loss1: 0.429547 Loss2: 1.407719
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.593816 Loss1: 0.166019 Loss2: 1.427798
+(DefaultActor pid=3765) >> Training accuracy: 0.961914
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.755956 Loss1: 0.362835 Loss2: 1.393121 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.667016 Loss1: 0.272868 Loss2: 1.394148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.605656 Loss1: 0.220090 Loss2: 1.385566
+(DefaultActor pid=3764) >> Training accuracy: 0.932292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.585346 Loss1: 1.646689 Loss2: 1.938657
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.494072 Loss1: 1.011858 Loss2: 1.482214
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.160522 Loss1: 0.682672 Loss2: 1.477850
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.925777 Loss1: 0.471019 Loss2: 1.454758
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.795016 Loss1: 0.343234 Loss2: 1.451782
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.342250 Loss1: 1.500798 Loss2: 1.841452
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.763968 Loss1: 0.328242 Loss2: 1.435725
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.230644 Loss1: 0.812964 Loss2: 1.417680
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.745260 Loss1: 0.299911 Loss2: 1.445349
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.931085 Loss1: 0.518497 Loss2: 1.412588
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.770299 Loss1: 0.324182 Loss2: 1.446117
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.834281 Loss1: 0.468030 Loss2: 1.366251
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.700421 Loss1: 0.253213 Loss2: 1.447208
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.698303 Loss1: 0.311726 Loss2: 1.386577
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.726553 Loss1: 0.283678 Loss2: 1.442875
+(DefaultActor pid=3765) >> Training accuracy: 0.940625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.657200 Loss1: 0.262278 Loss2: 1.394922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.554603 Loss1: 0.195151 Loss2: 1.359452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.562268 Loss1: 0.204037 Loss2: 1.358231
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.515440 Loss1: 1.539347 Loss2: 1.976093
+(DefaultActor pid=3764) >> Training accuracy: 0.926042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.447609 Loss1: 0.963439 Loss2: 1.484170
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.186543 Loss1: 0.648615 Loss2: 1.537928
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.938998 Loss1: 0.457396 Loss2: 1.481602
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.840536 Loss1: 0.354660 Loss2: 1.485876
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.352199 Loss1: 1.473591 Loss2: 1.878608
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.800608 Loss1: 0.320641 Loss2: 1.479967
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.375815 Loss1: 0.936265 Loss2: 1.439550
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.847792 Loss1: 0.367728 Loss2: 1.480065
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.091083 Loss1: 0.667031 Loss2: 1.424052
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.797737 Loss1: 0.299696 Loss2: 1.498042
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.898343 Loss1: 0.477890 Loss2: 1.420453
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.725472 Loss1: 0.256668 Loss2: 1.468804
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.747376 Loss1: 0.359733 Loss2: 1.387642
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.692346 Loss1: 0.217002 Loss2: 1.475344
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.597423 Loss1: 0.216326 Loss2: 1.381097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.582906 Loss1: 0.202665 Loss2: 1.380242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.543248 Loss1: 0.167557 Loss2: 1.375691
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.528651 Loss1: 1.494105 Loss2: 2.034546
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.491174 Loss1: 0.986018 Loss2: 1.505155
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.185392 Loss1: 0.633161 Loss2: 1.552231
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.050086 Loss1: 0.541863 Loss2: 1.508223
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.896209 Loss1: 0.366847 Loss2: 1.529362
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.795436 Loss1: 0.298852 Loss2: 1.496584
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.301688 Loss1: 1.367239 Loss2: 1.934449
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.749852 Loss1: 0.240479 Loss2: 1.509373
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.451752 Loss1: 0.986514 Loss2: 1.465238
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.672212 Loss1: 0.185829 Loss2: 1.486383
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.171743 Loss1: 0.679765 Loss2: 1.491977
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.655482 Loss1: 0.166281 Loss2: 1.489201
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.927946 Loss1: 0.487002 Loss2: 1.440943
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.703353 Loss1: 0.217132 Loss2: 1.486221
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.827596 Loss1: 0.375920 Loss2: 1.451676
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.707299 Loss1: 0.277464 Loss2: 1.429836
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.683378 Loss1: 0.257549 Loss2: 1.425829
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.702610 Loss1: 0.265546 Loss2: 1.437064
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.670588 Loss1: 0.242562 Loss2: 1.428026
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.425114 Loss1: 1.506411 Loss2: 1.918703
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.622135 Loss1: 0.200871 Loss2: 1.421264
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.101636 Loss1: 0.629552 Loss2: 1.472084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.871947 Loss1: 0.418809 Loss2: 1.453138 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.270385 Loss1: 1.451394 Loss2: 1.818992
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.791596 Loss1: 0.333209 Loss2: 1.458387
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.329029 Loss1: 0.954862 Loss2: 1.374167
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.731012 Loss1: 0.280858 Loss2: 1.450155
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.689357 Loss1: 0.243844 Loss2: 1.445513 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.640200 Loss1: 0.196192 Loss2: 1.444008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.593749 Loss1: 0.161141 Loss2: 1.432609 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.586496 Loss1: 0.232730 Loss2: 1.353766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.562903 Loss1: 0.213839 Loss2: 1.349065 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.514556 Loss1: 0.160307 Loss2: 1.354249
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.528049 Loss1: 1.503417 Loss2: 2.024632
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.407045 Loss1: 0.992171 Loss2: 1.414873
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.196948 Loss1: 0.719135 Loss2: 1.477813
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.931187 Loss1: 0.500831 Loss2: 1.430356
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.806365 Loss1: 0.392243 Loss2: 1.414121
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.742989 Loss1: 0.315929 Loss2: 1.427060
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.441861 Loss1: 1.506601 Loss2: 1.935260
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.672490 Loss1: 0.263677 Loss2: 1.408813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.686595 Loss1: 0.274522 Loss2: 1.412072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.700588 Loss1: 0.276234 Loss2: 1.424354 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.919271
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.963620 Loss1: 0.470588 Loss2: 1.493032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.791378 Loss1: 0.311843 Loss2: 1.479535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.690492 Loss1: 0.224039 Loss2: 1.466454
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.596831 Loss1: 1.673374 Loss2: 1.923457
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.470642 Loss1: 1.024179 Loss2: 1.446463 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.925781
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.690124 Loss1: 0.230398 Loss2: 1.459726
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.091924 Loss1: 0.647319 Loss2: 1.444604
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.949045 Loss1: 0.535800 Loss2: 1.413246
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.890313 Loss1: 0.453690 Loss2: 1.436623
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.790882 Loss1: 0.376652 Loss2: 1.414230
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.736360 Loss1: 0.307579 Loss2: 1.428781
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.368783 Loss1: 1.458094 Loss2: 1.910689
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.695636 Loss1: 0.291379 Loss2: 1.404257
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.312047 Loss1: 0.872814 Loss2: 1.439233
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.653113 Loss1: 0.237935 Loss2: 1.415178
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.158105 Loss1: 0.691023 Loss2: 1.467082
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.595735 Loss1: 0.180312 Loss2: 1.415423
+(DefaultActor pid=3765) >> Training accuracy: 0.960417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.859860 Loss1: 0.429005 Loss2: 1.430856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.627577 Loss1: 0.228860 Loss2: 1.398716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.643555 Loss1: 0.246174 Loss2: 1.397381
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.307069 Loss1: 1.372184 Loss2: 1.934884
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.363048 Loss1: 0.917478 Loss2: 1.445570 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.935417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.610993 Loss1: 0.212436 Loss2: 1.398556
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.100733 Loss1: 0.639366 Loss2: 1.461368
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.888075 Loss1: 0.470895 Loss2: 1.417180
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.767281 Loss1: 0.335344 Loss2: 1.431937
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.693208 Loss1: 0.286901 Loss2: 1.406307
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.647017 Loss1: 0.237010 Loss2: 1.410008
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.251163 Loss1: 1.401234 Loss2: 1.849929
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.662987 Loss1: 0.261217 Loss2: 1.401770
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.585949 Loss1: 0.185196 Loss2: 1.400753
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.342166 Loss1: 0.904554 Loss2: 1.437612
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.561748 Loss1: 0.168237 Loss2: 1.393512
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.003851 Loss1: 0.585044 Loss2: 1.418807
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.809605 Loss1: 0.418327 Loss2: 1.391278
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.696358 Loss1: 0.305379 Loss2: 1.390979
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.658109 Loss1: 0.275126 Loss2: 1.382983
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.612456 Loss1: 0.235434 Loss2: 1.377022
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.290003 Loss1: 1.407502 Loss2: 1.882501
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.249278 Loss1: 0.854871 Loss2: 1.394407 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.971555 Loss1: 0.568658 Loss2: 1.402898 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.935547
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.623256 Loss1: 0.237541 Loss2: 1.385715
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.865871 Loss1: 0.479252 Loss2: 1.386619
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.787132 Loss1: 0.395415 Loss2: 1.391716
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.720009 Loss1: 0.326524 Loss2: 1.393484
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.634158 Loss1: 0.262391 Loss2: 1.371768
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.585835 Loss1: 0.211193 Loss2: 1.374642
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.418299 Loss1: 1.503619 Loss2: 1.914680
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.570147 Loss1: 0.197166 Loss2: 1.372981
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.362206 Loss1: 0.902607 Loss2: 1.459599
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.605208 Loss1: 0.220049 Loss2: 1.385160
+(DefaultActor pid=3765) >> Training accuracy: 0.926042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.932856 Loss1: 0.491644 Loss2: 1.441212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.785922 Loss1: 0.340521 Loss2: 1.445402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.689874 Loss1: 0.250031 Loss2: 1.439842
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.255428 Loss1: 1.377272 Loss2: 1.878156
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.362827 Loss1: 0.881110 Loss2: 1.481717 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.967037 Loss1: 0.507296 Loss2: 1.459741 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.601101 Loss1: 0.165811 Loss2: 1.435290
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.791740 Loss1: 0.362552 Loss2: 1.429188
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.750623 Loss1: 0.317114 Loss2: 1.433509
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.777945 Loss1: 0.333791 Loss2: 1.444154
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.647065 Loss1: 0.217873 Loss2: 1.429191
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.629682 Loss1: 0.211629 Loss2: 1.418053
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.496790 Loss1: 1.568477 Loss2: 1.928313
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.574957 Loss1: 1.085999 Loss2: 1.488958 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972656
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.191050 Loss1: 0.705270 Loss2: 1.485781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.863950 Loss1: 0.417733 Loss2: 1.446217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.616801 Loss1: 0.195841 Loss2: 1.420959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.670739 Loss1: 0.252877 Loss2: 1.417862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.689285 Loss1: 0.266710 Loss2: 1.422575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.651372 Loss1: 0.216565 Loss2: 1.434808 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.939583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.769289 Loss1: 0.382513 Loss2: 1.386776 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.603352 Loss1: 0.237886 Loss2: 1.365466 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.558071 Loss1: 0.188697 Loss2: 1.369374
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.499887 Loss1: 1.609763 Loss2: 1.890124
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.342937 Loss1: 0.926492 Loss2: 1.416444 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.048693 Loss1: 0.600383 Loss2: 1.448309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.749768 Loss1: 0.351215 Loss2: 1.398553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.736512 Loss1: 0.329754 Loss2: 1.406758 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.469714 Loss1: 1.532468 Loss2: 1.937245
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.739863 Loss1: 0.329951 Loss2: 1.409912
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.484008 Loss1: 1.008704 Loss2: 1.475303
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.643239 Loss1: 0.239542 Loss2: 1.403697
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.128354 Loss1: 0.655430 Loss2: 1.472924
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.640946 Loss1: 0.243068 Loss2: 1.397878
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.907806 Loss1: 0.451435 Loss2: 1.456371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.680384 Loss1: 0.247189 Loss2: 1.433195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.635104 Loss1: 0.213586 Loss2: 1.421518
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.380591 Loss1: 1.493539 Loss2: 1.887052
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.407677 Loss1: 0.954916 Loss2: 1.452761 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.938542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.667652 Loss1: 0.231708 Loss2: 1.435944
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.967803 Loss1: 0.522171 Loss2: 1.445632
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.866172 Loss1: 0.450948 Loss2: 1.415224
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.860710 Loss1: 0.434839 Loss2: 1.425871
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.766592 Loss1: 0.331583 Loss2: 1.435009
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.725740 Loss1: 0.302894 Loss2: 1.422845
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.294635 Loss1: 1.388367 Loss2: 1.906268
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.730933 Loss1: 0.314005 Loss2: 1.416928
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.301804 Loss1: 0.843016 Loss2: 1.458789
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.671874 Loss1: 0.252985 Loss2: 1.418889
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.044304 Loss1: 0.593414 Loss2: 1.450889
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.554368 Loss1: 0.149800 Loss2: 1.404569
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.822282 Loss1: 0.396599 Loss2: 1.425684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.643368 Loss1: 0.225740 Loss2: 1.417628 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.616502 Loss1: 0.202539 Loss2: 1.413963
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.431506 Loss1: 1.525375 Loss2: 1.906132
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.564737 Loss1: 1.095657 Loss2: 1.469080 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.954167
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.555188 Loss1: 0.150076 Loss2: 1.405112
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.202055 Loss1: 0.727143 Loss2: 1.474912
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.976845 Loss1: 0.522276 Loss2: 1.454570
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.875389 Loss1: 0.421252 Loss2: 1.454137
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.839863 Loss1: 0.391341 Loss2: 1.448522
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.731909 Loss1: 0.285841 Loss2: 1.446068
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.155624 Loss1: 1.259207 Loss2: 1.896417
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.772936 Loss1: 0.324127 Loss2: 1.448809
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.662786 Loss1: 0.229250 Loss2: 1.433536 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.659786 Loss1: 0.225898 Loss2: 1.433889 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.927083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.752378 Loss1: 0.348239 Loss2: 1.404140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.722577 Loss1: 0.307364 Loss2: 1.415212 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.705868 Loss1: 0.312056 Loss2: 1.393812
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.475789 Loss1: 1.551390 Loss2: 1.924399
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.609299 Loss1: 1.141763 Loss2: 1.467535 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.544852 Loss1: 0.157756 Loss2: 1.387096
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.201372 Loss1: 0.713979 Loss2: 1.487394
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.908732 Loss1: 0.465082 Loss2: 1.443650
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.840923 Loss1: 0.380579 Loss2: 1.460344
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.835873 Loss1: 0.393001 Loss2: 1.442871
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.704257 Loss1: 0.262377 Loss2: 1.441881
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.416832 Loss1: 1.416567 Loss2: 2.000265
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.670992 Loss1: 0.228161 Loss2: 1.442831
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.444102 Loss1: 0.935602 Loss2: 1.508501
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.646889 Loss1: 0.216570 Loss2: 1.430320
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.132859 Loss1: 0.595775 Loss2: 1.537084
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.675216 Loss1: 0.232432 Loss2: 1.442784
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.913936 Loss1: 0.405485 Loss2: 1.508450 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.765441 Loss1: 0.288301 Loss2: 1.477140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.745020 Loss1: 0.255089 Loss2: 1.489931
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.373431 Loss1: 1.482180 Loss2: 1.891251
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.477819 Loss1: 1.004921 Loss2: 1.472898 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.632517 Loss1: 0.173876 Loss2: 1.458641
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.114867 Loss1: 0.635906 Loss2: 1.478960
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.896608 Loss1: 0.486734 Loss2: 1.409874
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.765806 Loss1: 0.342325 Loss2: 1.423481
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.697691 Loss1: 0.286678 Loss2: 1.411014
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.672574 Loss1: 0.270671 Loss2: 1.401903
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.603432 Loss1: 1.633184 Loss2: 1.970248
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.676011 Loss1: 0.269942 Loss2: 1.406069
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.687639 Loss1: 0.278917 Loss2: 1.408722 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.644437 Loss1: 0.234883 Loss2: 1.409554 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.802749 Loss1: 0.334804 Loss2: 1.467944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.769623 Loss1: 0.331160 Loss2: 1.438464 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.771830 Loss1: 0.313216 Loss2: 1.458614
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.555869 Loss1: 1.636354 Loss2: 1.919515
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.442438 Loss1: 1.019856 Loss2: 1.422582 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.730812 Loss1: 0.284571 Loss2: 1.446241
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.051527 Loss1: 0.612600 Loss2: 1.438927
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.870129 Loss1: 0.456387 Loss2: 1.413742
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.777387 Loss1: 0.373061 Loss2: 1.404327
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.733291 Loss1: 0.335208 Loss2: 1.398083
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.744815 Loss1: 0.343851 Loss2: 1.400963
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.670729 Loss1: 0.255497 Loss2: 1.415232
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.412875 Loss1: 1.536262 Loss2: 1.876613
+DEBUG flwr 2023-10-10 04:11:26,766 | server.py:236 | fit_round 63 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.436092 Loss1: 0.991430 Loss2: 1.444663 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.575679 Loss1: 0.184277 Loss2: 1.391402
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.059916 Loss1: 0.611556 Loss2: 1.448361
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.868155 Loss1: 0.459797 Loss2: 1.408358
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.719529 Loss1: 0.313397 Loss2: 1.406132
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.651779 Loss1: 0.242973 Loss2: 1.408806
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.771606 Loss1: 0.373888 Loss2: 1.397718
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.317422 Loss1: 1.512180 Loss2: 1.805241
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.778391 Loss1: 0.344842 Loss2: 1.433549
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.410397 Loss1: 0.972332 Loss2: 1.438064
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.641227 Loss1: 0.230232 Loss2: 1.410994
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.600608 Loss1: 0.205098 Loss2: 1.395510
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.015828 Loss1: 0.631759 Loss2: 1.384069
+(DefaultActor pid=3765) >> Training accuracy: 0.962500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.748629 Loss1: 0.391788 Loss2: 1.356840
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.756919 Loss1: 0.403785 Loss2: 1.353134
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.690111 Loss1: 0.328388 Loss2: 1.361723
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.595919 Loss1: 0.248548 Loss2: 1.347371
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.600730 Loss1: 0.247078 Loss2: 1.353653
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.412438 Loss1: 1.461050 Loss2: 1.951388
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.558145 Loss1: 0.214254 Loss2: 1.343891
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.532886 Loss1: 1.008210 Loss2: 1.524676
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.556612 Loss1: 0.202843 Loss2: 1.353769
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.082166 Loss1: 0.582072 Loss2: 1.500094
+(DefaultActor pid=3764) >> Training accuracy: 0.947266
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.907645 Loss1: 0.438945 Loss2: 1.468701
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.824531 Loss1: 0.338318 Loss2: 1.486213
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.807467 Loss1: 0.327653 Loss2: 1.479814
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.776490 Loss1: 0.301795 Loss2: 1.474695
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.241120 Loss1: 1.339920 Loss2: 1.901200
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.750500 Loss1: 0.273365 Loss2: 1.477135
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.716648 Loss1: 0.248550 Loss2: 1.468098
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.415248 Loss1: 0.929727 Loss2: 1.485522
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.670241 Loss1: 0.196748 Loss2: 1.473493
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.045786 Loss1: 0.596950 Loss2: 1.448836
+(DefaultActor pid=3765) >> Training accuracy: 0.965820
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.822575 Loss1: 0.395600 Loss2: 1.426975
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.750462 Loss1: 0.329971 Loss2: 1.420492
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.742978 Loss1: 0.321444 Loss2: 1.421534
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.690703 Loss1: 0.269384 Loss2: 1.421319
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.316008 Loss1: 1.470613 Loss2: 1.845394
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.306000 Loss1: 0.866741 Loss2: 1.439260 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.040182 Loss1: 0.593260 Loss2: 1.446922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.571513 Loss1: 0.168471 Loss2: 1.403042
+(DefaultActor pid=3764) >> Training accuracy: 0.965074
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.854502 Loss1: 0.446398 Loss2: 1.408104
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.772352 Loss1: 0.354292 Loss2: 1.418061
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.825285 Loss1: 0.414250 Loss2: 1.411034
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.785944 Loss1: 0.364955 Loss2: 1.420989
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.710568 Loss1: 0.299917 Loss2: 1.410651
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.696536 Loss1: 1.708597 Loss2: 1.987940
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.676408 Loss1: 0.262122 Loss2: 1.414285
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.628899 Loss1: 0.228366 Loss2: 1.400533 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.927734
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.860789 Loss1: 0.377112 Loss2: 1.483677 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.694040 Loss1: 0.240873 Loss2: 1.453167 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.663591 Loss1: 0.210344 Loss2: 1.453247 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.955357
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 04:11:26,766][flwr][DEBUG] - fit_round 63 received 50 results and 0 failures
+INFO flwr 2023-10-10 04:12:07,644 | server.py:125 | fit progress: (63, 2.3184024251688022, {'accuracy': 0.517}, 145235.42257321402)
+>> Test accuracy: 0.517000
+[2023-10-10 04:12:07,644][flwr][INFO] - fit progress: (63, 2.3184024251688022, {'accuracy': 0.517}, 145235.42257321402)
+DEBUG flwr 2023-10-10 04:12:07,644 | server.py:173 | evaluate_round 63: strategy sampled 50 clients (out of 50)
+[2023-10-10 04:12:07,644][flwr][DEBUG] - evaluate_round 63: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 04:21:10,351 | server.py:187 | evaluate_round 63 received 50 results and 0 failures
+[2023-10-10 04:21:10,351][flwr][DEBUG] - evaluate_round 63 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 04:21:10,352 | server.py:222 | fit_round 64: strategy sampled 50 clients (out of 50)
+[2023-10-10 04:21:10,352][flwr][DEBUG] - fit_round 64: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.208803 Loss1: 1.352252 Loss2: 1.856551 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.961838 Loss1: 0.541889 Loss2: 1.419949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.414799 Loss1: 1.569210 Loss2: 1.845589
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.824253 Loss1: 0.398180 Loss2: 1.426073
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.452486 Loss1: 1.005056 Loss2: 1.447429
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.768416 Loss1: 0.346190 Loss2: 1.422227
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.089680 Loss1: 0.674365 Loss2: 1.415315
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.694587 Loss1: 0.288127 Loss2: 1.406460
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.674966 Loss1: 0.265861 Loss2: 1.409105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.630674 Loss1: 0.227432 Loss2: 1.403242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.576564 Loss1: 0.174556 Loss2: 1.402009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.599663 Loss1: 0.203253 Loss2: 1.396410 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.954044
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.658757 Loss1: 0.283942 Loss2: 1.374815 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.939453
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.598421 Loss1: 1.566996 Loss2: 2.031425 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.116178 Loss1: 0.616975 Loss2: 1.499203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.955186 Loss1: 0.482394 Loss2: 1.472792
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.346752 Loss1: 1.407413 Loss2: 1.939338
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.764536 Loss1: 0.305032 Loss2: 1.459504
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.390342 Loss1: 0.936873 Loss2: 1.453469
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.691573 Loss1: 0.241801 Loss2: 1.449773
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.123462 Loss1: 0.635845 Loss2: 1.487617
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.693976 Loss1: 0.245659 Loss2: 1.448317
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.877628 Loss1: 0.438024 Loss2: 1.439604
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.662084 Loss1: 0.218074 Loss2: 1.444011
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.777723 Loss1: 0.339412 Loss2: 1.438311
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.677561 Loss1: 0.224950 Loss2: 1.452611
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.699546 Loss1: 0.270982 Loss2: 1.428564
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.662958 Loss1: 0.213289 Loss2: 1.449669
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.653282 Loss1: 0.228841 Loss2: 1.424441
+(DefaultActor pid=3765) >> Training accuracy: 0.947917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.601039 Loss1: 0.185020 Loss2: 1.416019
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.594833 Loss1: 0.191833 Loss2: 1.403001
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.573258 Loss1: 0.161237 Loss2: 1.412021
+(DefaultActor pid=3764) >> Training accuracy: 0.946875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.146866 Loss1: 1.332931 Loss2: 1.813935
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.157519 Loss1: 0.765071 Loss2: 1.392448
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.882586 Loss1: 0.484070 Loss2: 1.398515
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.559350 Loss1: 1.565927 Loss2: 1.993423
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.727489 Loss1: 0.362939 Loss2: 1.364549
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.738363 Loss1: 0.358193 Loss2: 1.380170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.699873 Loss1: 0.332257 Loss2: 1.367616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.595093 Loss1: 0.218092 Loss2: 1.377001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.700165 Loss1: 0.290628 Loss2: 1.409537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.598151 Loss1: 0.198159 Loss2: 1.399992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.567873 Loss1: 0.172255 Loss2: 1.395618 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.542112 Loss1: 0.150639 Loss2: 1.391473 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.274476 Loss1: 1.470014 Loss2: 1.804462 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.057991 Loss1: 0.652802 Loss2: 1.405189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.371926 Loss1: 1.449434 Loss2: 1.922492
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.841408 Loss1: 0.452349 Loss2: 1.389059
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.453133 Loss1: 0.971247 Loss2: 1.481887
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.732907 Loss1: 0.357440 Loss2: 1.375466
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.619629 Loss1: 0.245452 Loss2: 1.374177 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547871 Loss1: 0.184572 Loss2: 1.363299 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.568712 Loss1: 0.202178 Loss2: 1.366534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.614500 Loss1: 0.237464 Loss2: 1.377036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.564156 Loss1: 0.181151 Loss2: 1.383005 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.947266
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.641544 Loss1: 0.192426 Loss2: 1.449118 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.115365 Loss1: 1.209811 Loss2: 1.905554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.117477 Loss1: 0.654900 Loss2: 1.462577 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.916236 Loss1: 0.495574 Loss2: 1.420663
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.624962 Loss1: 1.727943 Loss2: 1.897019
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.755171 Loss1: 0.343346 Loss2: 1.411825
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.458872 Loss1: 0.993355 Loss2: 1.465517
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.725208 Loss1: 0.319858 Loss2: 1.405350
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.055799 Loss1: 0.602482 Loss2: 1.453318
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.649503 Loss1: 0.255032 Loss2: 1.394472
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.871939 Loss1: 0.455304 Loss2: 1.416635
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.572737 Loss1: 0.177614 Loss2: 1.395124
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.856870 Loss1: 0.434438 Loss2: 1.422432
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.611811 Loss1: 0.225376 Loss2: 1.386435
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.821679 Loss1: 0.385612 Loss2: 1.436067
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.569267 Loss1: 0.166699 Loss2: 1.402567
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.741650 Loss1: 0.322415 Loss2: 1.419235
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.680188 Loss1: 0.264425 Loss2: 1.415762
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.654408 Loss1: 0.235652 Loss2: 1.418756
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.604013 Loss1: 0.193817 Loss2: 1.410195
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.359547 Loss1: 1.461585 Loss2: 1.897962
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.492454 Loss1: 1.033062 Loss2: 1.459392
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.050516 Loss1: 0.582630 Loss2: 1.467886
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.891947 Loss1: 0.478158 Loss2: 1.413789
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.437995 Loss1: 1.451765 Loss2: 1.986231
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.796037 Loss1: 0.354982 Loss2: 1.441055
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.475693 Loss1: 0.986527 Loss2: 1.489166
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.694233 Loss1: 0.278346 Loss2: 1.415887
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.103540 Loss1: 0.587931 Loss2: 1.515609
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.886412 Loss1: 0.438357 Loss2: 1.448055
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.687972 Loss1: 0.272811 Loss2: 1.415160
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.772313 Loss1: 0.303852 Loss2: 1.468462
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.693510 Loss1: 0.271116 Loss2: 1.422394
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.744131 Loss1: 0.283646 Loss2: 1.460485
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.652998 Loss1: 0.234962 Loss2: 1.418036
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.585610 Loss1: 0.172119 Loss2: 1.413491 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.635589 Loss1: 0.180803 Loss2: 1.454786 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958705
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.589494 Loss1: 1.656209 Loss2: 1.933285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.195485 Loss1: 0.730417 Loss2: 1.465068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.933238 Loss1: 0.507386 Loss2: 1.425852
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.170636 Loss1: 1.323218 Loss2: 1.847418
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.226903 Loss1: 0.836754 Loss2: 1.390150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.926409 Loss1: 0.529595 Loss2: 1.396815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.745827 Loss1: 0.374516 Loss2: 1.371311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.791338 Loss1: 0.410658 Loss2: 1.380681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.680028 Loss1: 0.302716 Loss2: 1.377312 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967634
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548451 Loss1: 0.189268 Loss2: 1.359183 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.490890 Loss1: 0.135120 Loss2: 1.355770 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.271420 Loss1: 1.391743 Loss2: 1.879677
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.396566 Loss1: 0.923891 Loss2: 1.472674
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.051560 Loss1: 0.598445 Loss2: 1.453115
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.814702 Loss1: 0.401402 Loss2: 1.413300
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.680783 Loss1: 1.537438 Loss2: 2.143345
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.566776 Loss1: 1.037555 Loss2: 1.529221
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.729522 Loss1: 0.312212 Loss2: 1.417311
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.280475 Loss1: 0.704890 Loss2: 1.575586
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.014752 Loss1: 0.490758 Loss2: 1.523995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.861575 Loss1: 0.343740 Loss2: 1.517835 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.584269 Loss1: 0.179939 Loss2: 1.404330 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.595135 Loss1: 0.195183 Loss2: 1.399952 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963867
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.263808 Loss1: 1.373629 Loss2: 1.890179 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.956676 Loss1: 0.552882 Loss2: 1.403794 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.797651 Loss1: 0.405210 Loss2: 1.392441
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.271628 Loss1: 1.405191 Loss2: 1.866437
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.259569 Loss1: 0.870602 Loss2: 1.388967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.979746 Loss1: 0.560972 Loss2: 1.418774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.754082 Loss1: 0.394888 Loss2: 1.359195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.624678 Loss1: 0.256860 Loss2: 1.367818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567175 Loss1: 0.214545 Loss2: 1.352631 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.938542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.605884 Loss1: 0.235531 Loss2: 1.370353
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.548765 Loss1: 0.199703 Loss2: 1.349062
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.608375 Loss1: 0.248203 Loss2: 1.360172
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.570677 Loss1: 0.204352 Loss2: 1.366326
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.553667 Loss1: 0.202574 Loss2: 1.351094
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.327654 Loss1: 1.440341 Loss2: 1.887313
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.344230 Loss1: 0.873752 Loss2: 1.470478
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.023681 Loss1: 0.568004 Loss2: 1.455677
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.867127 Loss1: 0.442510 Loss2: 1.424617
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.370790 Loss1: 1.495808 Loss2: 1.874981
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.764160 Loss1: 0.347971 Loss2: 1.416189
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.496950 Loss1: 1.012149 Loss2: 1.484801
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.717599 Loss1: 0.301389 Loss2: 1.416211
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.135266 Loss1: 0.660992 Loss2: 1.474273
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.672938 Loss1: 0.263095 Loss2: 1.409843
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.879593 Loss1: 0.441056 Loss2: 1.438537
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.630851 Loss1: 0.225909 Loss2: 1.404942
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.811227 Loss1: 0.367040 Loss2: 1.444187
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.595158 Loss1: 0.191925 Loss2: 1.403234
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.792437 Loss1: 0.349200 Loss2: 1.443236
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.602917 Loss1: 0.200095 Loss2: 1.402822
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.709758 Loss1: 0.269222 Loss2: 1.440536
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.704340 Loss1: 0.265413 Loss2: 1.438927
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.663560 Loss1: 0.230267 Loss2: 1.433293
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.625615 Loss1: 0.200184 Loss2: 1.425430
+(DefaultActor pid=3764) >> Training accuracy: 0.958984
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.433984 Loss1: 1.552517 Loss2: 1.881467
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.331148 Loss1: 0.918760 Loss2: 1.412389
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.039476 Loss1: 0.592058 Loss2: 1.447419
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.820578 Loss1: 0.419974 Loss2: 1.400604
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.420405 Loss1: 1.513806 Loss2: 1.906599
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.394712 Loss1: 0.941652 Loss2: 1.453060 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.220928 Loss1: 0.760107 Loss2: 1.460821 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.040360 Loss1: 0.598382 Loss2: 1.441977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.857381 Loss1: 0.423991 Loss2: 1.433390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.801840 Loss1: 0.378746 Loss2: 1.423094 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.929167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.680418 Loss1: 0.262922 Loss2: 1.417496 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.620251 Loss1: 0.213734 Loss2: 1.406517 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.393175 Loss1: 1.546542 Loss2: 1.846632 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.024094 Loss1: 0.585503 Loss2: 1.438591 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.903365 Loss1: 0.486375 Loss2: 1.416990
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.268207 Loss1: 1.378235 Loss2: 1.889973
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.777318 Loss1: 0.358331 Loss2: 1.418987
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.247097 Loss1: 0.832455 Loss2: 1.414642
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.789252 Loss1: 0.369928 Loss2: 1.419325
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.016854 Loss1: 0.587619 Loss2: 1.429235
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.679312 Loss1: 0.259701 Loss2: 1.419611
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.754642 Loss1: 0.389628 Loss2: 1.365015
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.633601 Loss1: 0.264932 Loss2: 1.368668
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.684150 Loss1: 0.274049 Loss2: 1.410101
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.606782 Loss1: 0.244212 Loss2: 1.362571
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.660418 Loss1: 0.238134 Loss2: 1.422283
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.630174 Loss1: 0.264550 Loss2: 1.365625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.605903 Loss1: 0.202637 Loss2: 1.403267
+(DefaultActor pid=3765) >> Training accuracy: 0.963867
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.539275 Loss1: 0.193426 Loss2: 1.345849 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.488122 Loss1: 1.585645 Loss2: 1.902477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.033799 Loss1: 0.589464 Loss2: 1.444334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.864459 Loss1: 0.441925 Loss2: 1.422534
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.410040 Loss1: 1.522555 Loss2: 1.887485
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.737385 Loss1: 0.321005 Loss2: 1.416380
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.436157 Loss1: 1.010388 Loss2: 1.425769
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.746882 Loss1: 0.336388 Loss2: 1.410494
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.071897 Loss1: 0.600803 Loss2: 1.471094
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.644345 Loss1: 0.229839 Loss2: 1.414506
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.877902 Loss1: 0.481574 Loss2: 1.396328
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.683534 Loss1: 0.277379 Loss2: 1.406155
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.785523 Loss1: 0.367452 Loss2: 1.418072
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.614767 Loss1: 0.196134 Loss2: 1.418633
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.700288 Loss1: 0.305275 Loss2: 1.395013
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.564077 Loss1: 0.164475 Loss2: 1.399601
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.678835 Loss1: 0.275352 Loss2: 1.403484
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.633547 Loss1: 0.240032 Loss2: 1.393515
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.579173 Loss1: 0.184176 Loss2: 1.394997
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.564137 Loss1: 0.176309 Loss2: 1.387828
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.374679 Loss1: 1.501028 Loss2: 1.873651
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.463906 Loss1: 1.019947 Loss2: 1.443958
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.179105 Loss1: 0.712755 Loss2: 1.466350
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.940236 Loss1: 0.523844 Loss2: 1.416392
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.252084 Loss1: 1.411346 Loss2: 1.840738
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.815893 Loss1: 0.404043 Loss2: 1.411851
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.340990 Loss1: 0.949658 Loss2: 1.391333
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.751152 Loss1: 0.349640 Loss2: 1.401512
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.035420 Loss1: 0.609100 Loss2: 1.426319
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.730694 Loss1: 0.320035 Loss2: 1.410659
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.824408 Loss1: 0.445536 Loss2: 1.378872
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.674090 Loss1: 0.271736 Loss2: 1.402354
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.674428 Loss1: 0.283149 Loss2: 1.391279
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.627400 Loss1: 0.229167 Loss2: 1.398233
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.643189 Loss1: 0.269599 Loss2: 1.373589
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.561475 Loss1: 0.173598 Loss2: 1.387878
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.683250 Loss1: 0.298493 Loss2: 1.384757
+(DefaultActor pid=3765) >> Training accuracy: 0.943750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.579009 Loss1: 0.199301 Loss2: 1.379708
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.602903 Loss1: 0.231614 Loss2: 1.371289
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.589126 Loss1: 0.215635 Loss2: 1.373491
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.526922 Loss1: 1.616661 Loss2: 1.910261
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.512165 Loss1: 1.039880 Loss2: 1.472284
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.149855 Loss1: 0.664815 Loss2: 1.485040
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.992491 Loss1: 0.532502 Loss2: 1.459989
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.426516 Loss1: 1.544278 Loss2: 1.882237
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.846885 Loss1: 0.381940 Loss2: 1.464944
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.453716 Loss1: 0.985042 Loss2: 1.468673
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.062654 Loss1: 0.632429 Loss2: 1.430225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.888232 Loss1: 0.462500 Loss2: 1.425732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.783145 Loss1: 0.358636 Loss2: 1.424509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.741176 Loss1: 0.322542 Loss2: 1.418634 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.947917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.642759 Loss1: 0.210953 Loss2: 1.431805
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.693349 Loss1: 0.277976 Loss2: 1.415373
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.631998 Loss1: 0.223742 Loss2: 1.408255
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.588463 Loss1: 0.191801 Loss2: 1.396662
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.559744 Loss1: 0.170661 Loss2: 1.389083
+(DefaultActor pid=3764) >> Training accuracy: 0.951042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.507253 Loss1: 1.663039 Loss2: 1.844214
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.639406 Loss1: 1.189321 Loss2: 1.450086
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.100719 Loss1: 0.694588 Loss2: 1.406131
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.866476 Loss1: 0.464207 Loss2: 1.402270
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.691871 Loss1: 1.716603 Loss2: 1.975269
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.559285 Loss1: 1.087310 Loss2: 1.471975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.672134 Loss1: 0.290891 Loss2: 1.381243
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.099514 Loss1: 0.604392 Loss2: 1.495122
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.632990 Loss1: 0.253593 Loss2: 1.379397
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.913655 Loss1: 0.475205 Loss2: 1.438451
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.586928 Loss1: 0.213790 Loss2: 1.373137
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.852033 Loss1: 0.400891 Loss2: 1.451142
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.744721 Loss1: 0.304767 Loss2: 1.439954
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.612363 Loss1: 0.231835 Loss2: 1.380528
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.700917 Loss1: 0.265573 Loss2: 1.435344
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.567730 Loss1: 0.194520 Loss2: 1.373211
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.623767 Loss1: 0.192017 Loss2: 1.431750 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958705
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.487831 Loss1: 1.615229 Loss2: 1.872601 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.067532 Loss1: 0.639761 Loss2: 1.427771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.844650 Loss1: 0.441733 Loss2: 1.402917
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.405280 Loss1: 1.510595 Loss2: 1.894685
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.378305 Loss1: 0.964149 Loss2: 1.414155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.113680 Loss1: 0.651385 Loss2: 1.462295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.855889 Loss1: 0.453683 Loss2: 1.402206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.832195 Loss1: 0.405352 Loss2: 1.426843 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.717675 Loss1: 0.318629 Loss2: 1.399047 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.516396 Loss1: 0.139224 Loss2: 1.377173
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.687190 Loss1: 0.286570 Loss2: 1.400621
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.637389 Loss1: 0.233220 Loss2: 1.404168
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.662217 Loss1: 0.264640 Loss2: 1.397577
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.692093 Loss1: 0.288602 Loss2: 1.403491
+(DefaultActor pid=3764) >> Training accuracy: 0.954167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.376463 Loss1: 1.469915 Loss2: 1.906548
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.333147 Loss1: 0.856016 Loss2: 1.477131
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.078990 Loss1: 0.622123 Loss2: 1.456868
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.846866 Loss1: 0.410447 Loss2: 1.436419
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.221543 Loss1: 1.386716 Loss2: 1.834827
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.486314 Loss1: 1.025589 Loss2: 1.460726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.054207 Loss1: 0.626396 Loss2: 1.427811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.840823 Loss1: 0.418947 Loss2: 1.421876 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.765301 Loss1: 0.363267 Loss2: 1.402034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.683167 Loss1: 0.281380 Loss2: 1.401787 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.957292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.657589 Loss1: 0.255004 Loss2: 1.402585 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.590259 Loss1: 0.188012 Loss2: 1.402247 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.935547
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.398569 Loss1: 1.446529 Loss2: 1.952040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.194892 Loss1: 0.674607 Loss2: 1.520285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.917376 Loss1: 0.436832 Loss2: 1.480544 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.798117 Loss1: 0.321974 Loss2: 1.476143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.818937 Loss1: 0.354598 Loss2: 1.464339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.719329 Loss1: 0.245530 Loss2: 1.473799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.666324 Loss1: 0.205406 Loss2: 1.460918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.682272 Loss1: 0.221589 Loss2: 1.460683 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.938542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.521732 Loss1: 0.201470 Loss2: 1.320262 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.507561 Loss1: 0.193767 Loss2: 1.313793 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.942708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.284667 Loss1: 0.840310 Loss2: 1.444358 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.820082 Loss1: 0.417486 Loss2: 1.402597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.748674 Loss1: 0.318614 Loss2: 1.430060
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.327942 Loss1: 1.419839 Loss2: 1.908102
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.672122 Loss1: 0.261532 Loss2: 1.410590
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.416479 Loss1: 0.982784 Loss2: 1.433695
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.632338 Loss1: 0.234892 Loss2: 1.397445
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.139161 Loss1: 0.674904 Loss2: 1.464256
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.616261 Loss1: 0.209007 Loss2: 1.407254
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.935462 Loss1: 0.514741 Loss2: 1.420721
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.603546 Loss1: 0.204706 Loss2: 1.398840
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.782546 Loss1: 0.356414 Loss2: 1.426132
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.601920 Loss1: 0.198061 Loss2: 1.403859
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.731538 Loss1: 0.315079 Loss2: 1.416459
+(DefaultActor pid=3765) >> Training accuracy: 0.936458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.703802 Loss1: 0.285656 Loss2: 1.418146
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.673374 Loss1: 0.261299 Loss2: 1.412076
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.623995 Loss1: 0.212606 Loss2: 1.411389
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.611477 Loss1: 0.207326 Loss2: 1.404150
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.429097 Loss1: 1.513585 Loss2: 1.915512
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.379764 Loss1: 0.983971 Loss2: 1.395793
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.098985 Loss1: 0.660093 Loss2: 1.438892
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.837958 Loss1: 0.451291 Loss2: 1.386667
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.726331 Loss1: 0.343275 Loss2: 1.383055
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.653131 Loss1: 0.275390 Loss2: 1.377742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.575248 Loss1: 0.195838 Loss2: 1.379409
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.438266 Loss1: 0.964396 Loss2: 1.473870
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.606799 Loss1: 0.235644 Loss2: 1.371155
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.111537 Loss1: 0.662697 Loss2: 1.448840
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.855565 Loss1: 0.451187 Loss2: 1.404378 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.951923
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.818693 Loss1: 0.398839 Loss2: 1.419854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.795474 Loss1: 0.361084 Loss2: 1.434390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.630778 Loss1: 0.219738 Loss2: 1.411040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.632494 Loss1: 0.226505 Loss2: 1.405989 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.110849 Loss1: 0.584537 Loss2: 1.526311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.918979 Loss1: 0.416006 Loss2: 1.502974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.782661 Loss1: 0.289515 Loss2: 1.493145
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.335756 Loss1: 1.495958 Loss2: 1.839798
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.425257 Loss1: 1.004114 Loss2: 1.421143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.111101 Loss1: 0.685683 Loss2: 1.425418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.891825 Loss1: 0.509723 Loss2: 1.382101 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.734366 Loss1: 0.351668 Loss2: 1.382698 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.702646 Loss1: 0.325702 Loss2: 1.376944 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.614730 Loss1: 0.243125 Loss2: 1.371605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.591236 Loss1: 0.215599 Loss2: 1.375636 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.951042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.950433 Loss1: 0.551130 Loss2: 1.399304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.704484 Loss1: 0.327758 Loss2: 1.376725 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 04:50:32,089 | server.py:236 | fit_round 64 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.518837 Loss1: 1.587601 Loss2: 1.931237
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.596188 Loss1: 0.238654 Loss2: 1.357534
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.542823 Loss1: 1.061964 Loss2: 1.480859
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544208 Loss1: 0.180263 Loss2: 1.363945
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.167999 Loss1: 0.675011 Loss2: 1.492987
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.573575 Loss1: 0.213975 Loss2: 1.359600
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.985296 Loss1: 0.528702 Loss2: 1.456594
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.585423 Loss1: 0.217313 Loss2: 1.368110
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.609837 Loss1: 0.230480 Loss2: 1.379357 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.936523
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.744220 Loss1: 0.296331 Loss2: 1.447889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.664352 Loss1: 0.215048 Loss2: 1.449304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.620535 Loss1: 0.181456 Loss2: 1.439079
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.291766 Loss1: 1.390857 Loss2: 1.900910
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.416634 Loss1: 0.945210 Loss2: 1.471424
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.075215 Loss1: 0.630614 Loss2: 1.444601
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.911891 Loss1: 0.467187 Loss2: 1.444704
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.726838 Loss1: 0.306309 Loss2: 1.420529
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.324141 Loss1: 1.332656 Loss2: 1.991486
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.689412 Loss1: 0.266163 Loss2: 1.423249
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.661798 Loss1: 0.249536 Loss2: 1.412262 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.601797 Loss1: 0.188305 Loss2: 1.413493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.618451 Loss1: 0.208938 Loss2: 1.409513 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.599276 Loss1: 0.189662 Loss2: 1.409613 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.674092 Loss1: 0.195034 Loss2: 1.479058 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.655128 Loss1: 0.193048 Loss2: 1.462080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.663602 Loss1: 0.189943 Loss2: 1.473659
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.503108 Loss1: 1.579791 Loss2: 1.923317
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.528353 Loss1: 0.979070 Loss2: 1.549283
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.066242 Loss1: 0.597873 Loss2: 1.468369
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.951473 Loss1: 0.485653 Loss2: 1.465820
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.855444 Loss1: 0.385813 Loss2: 1.469631
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.370746 Loss1: 1.485903 Loss2: 1.884843
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.781596 Loss1: 0.330866 Loss2: 1.450730
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.740052 Loss1: 0.284486 Loss2: 1.455566 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.669097 Loss1: 0.210732 Loss2: 1.458365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.608492 Loss1: 0.175990 Loss2: 1.432502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.590556 Loss1: 0.157892 Loss2: 1.432664 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.635409 Loss1: 0.229836 Loss2: 1.405574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.594987 Loss1: 0.197138 Loss2: 1.397849 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 04:50:32,089][flwr][DEBUG] - fit_round 64 received 50 results and 0 failures
+INFO flwr 2023-10-10 04:51:14,059 | server.py:125 | fit progress: (64, 2.3150291172460244, {'accuracy': 0.5197}, 147581.83765539702)
+>> Test accuracy: 0.519700
+[2023-10-10 04:51:14,059][flwr][INFO] - fit progress: (64, 2.3150291172460244, {'accuracy': 0.5197}, 147581.83765539702)
+DEBUG flwr 2023-10-10 04:51:14,059 | server.py:173 | evaluate_round 64: strategy sampled 50 clients (out of 50)
+[2023-10-10 04:51:14,059][flwr][DEBUG] - evaluate_round 64: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 05:00:15,154 | server.py:187 | evaluate_round 64 received 50 results and 0 failures
+[2023-10-10 05:00:15,154][flwr][DEBUG] - evaluate_round 64 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 05:00:15,154 | server.py:222 | fit_round 65: strategy sampled 50 clients (out of 50)
+[2023-10-10 05:00:15,154][flwr][DEBUG] - fit_round 65: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.368101 Loss1: 1.499589 Loss2: 1.868512 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.975815 Loss1: 0.558605 Loss2: 1.417210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.838668 Loss1: 0.459975 Loss2: 1.378693
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.288523 Loss1: 1.334729 Loss2: 1.953794
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.439390 Loss1: 0.925169 Loss2: 1.514221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.122644 Loss1: 0.621538 Loss2: 1.501106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.870443 Loss1: 0.396918 Loss2: 1.473525 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.809888 Loss1: 0.343779 Loss2: 1.466108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.777223 Loss1: 0.309467 Loss2: 1.467756 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.623769 Loss1: 0.166892 Loss2: 1.456877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.630357 Loss1: 0.178121 Loss2: 1.452236 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969727
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.419371 Loss1: 1.410563 Loss2: 2.008809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.127633 Loss1: 0.668290 Loss2: 1.459342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.885697 Loss1: 0.470996 Loss2: 1.414701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.727919 Loss1: 0.314643 Loss2: 1.413277 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.676451 Loss1: 0.277709 Loss2: 1.398743
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.251313 Loss1: 0.823353 Loss2: 1.427960
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.593392 Loss1: 0.210842 Loss2: 1.382550
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.991288 Loss1: 0.561191 Loss2: 1.430097
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.801303 Loss1: 0.396135 Loss2: 1.405168 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.950721
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.577466 Loss1: 0.197881 Loss2: 1.379585
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.747084 Loss1: 0.342437 Loss2: 1.404647
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.682106 Loss1: 0.281273 Loss2: 1.400833
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.625372 Loss1: 0.241443 Loss2: 1.383929
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.595554 Loss1: 0.216643 Loss2: 1.378911
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.561471 Loss1: 0.180244 Loss2: 1.381227
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.323249 Loss1: 1.428838 Loss2: 1.894411
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.556215 Loss1: 0.175186 Loss2: 1.381029
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.126722 Loss1: 0.653475 Loss2: 1.473248 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.762333 Loss1: 0.349326 Loss2: 1.413008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.668449 Loss1: 0.265658 Loss2: 1.402791
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.211382 Loss1: 1.341255 Loss2: 1.870127
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.287111 Loss1: 0.881916 Loss2: 1.405194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.096967 Loss1: 0.639046 Loss2: 1.457920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.898704 Loss1: 0.513254 Loss2: 1.385451 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.778126 Loss1: 0.374633 Loss2: 1.403492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.579546 Loss1: 0.204738 Loss2: 1.374808 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.557278 Loss1: 0.175422 Loss2: 1.381856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.558914 Loss1: 0.179871 Loss2: 1.379042 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.057386 Loss1: 0.538841 Loss2: 1.518545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.787890 Loss1: 0.312127 Loss2: 1.475763 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.697427 Loss1: 0.221095 Loss2: 1.476332
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.611490 Loss1: 1.540747 Loss2: 2.070743
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.590748 Loss1: 1.016216 Loss2: 1.574531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.141699 Loss1: 0.550820 Loss2: 1.590879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.054509 Loss1: 0.495559 Loss2: 1.558950 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.574648 Loss1: 0.120478 Loss2: 1.454170
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.957744 Loss1: 0.398268 Loss2: 1.559476
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.890006 Loss1: 0.330901 Loss2: 1.559105
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.834073 Loss1: 0.285479 Loss2: 1.548594
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.834434 Loss1: 0.274320 Loss2: 1.560114
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.741647 Loss1: 0.194658 Loss2: 1.546989
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.432278 Loss1: 1.580458 Loss2: 1.851821
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.701686 Loss1: 0.171567 Loss2: 1.530118
+(DefaultActor pid=3764) >> Training accuracy: 0.951042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.051846 Loss1: 0.648901 Loss2: 1.402945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.720774 Loss1: 0.356627 Loss2: 1.364147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.619661 Loss1: 0.267054 Loss2: 1.352607
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.196430 Loss1: 1.263772 Loss2: 1.932658
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.239446 Loss1: 0.800282 Loss2: 1.439164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.008181 Loss1: 0.554170 Loss2: 1.454010 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.865610 Loss1: 0.470742 Loss2: 1.394868 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.821784 Loss1: 0.384899 Loss2: 1.436885 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.672623 Loss1: 0.251718 Loss2: 1.420905 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.579712 Loss1: 0.191760 Loss2: 1.387953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.573509 Loss1: 0.180824 Loss2: 1.392685 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.262067 Loss1: 0.674308 Loss2: 1.587759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.856223 Loss1: 0.326338 Loss2: 1.529885 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.786829 Loss1: 0.279424 Loss2: 1.507405
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.503573 Loss1: 1.564022 Loss2: 1.939551
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.679183 Loss1: 1.111876 Loss2: 1.567308 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.227708 Loss1: 0.731364 Loss2: 1.496344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.109036 Loss1: 0.613097 Loss2: 1.495939 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.900255 Loss1: 0.409922 Loss2: 1.490333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.741801 Loss1: 0.273629 Loss2: 1.468172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.762343 Loss1: 0.294116 Loss2: 1.468227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.227390 Loss1: 1.361826 Loss2: 1.865564
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.694580 Loss1: 0.233491 Loss2: 1.461089
+(DefaultActor pid=3764) >> Training accuracy: 0.941667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.043952 Loss1: 0.615825 Loss2: 1.428127 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.696580 Loss1: 0.308101 Loss2: 1.388479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.617871 Loss1: 0.244136 Loss2: 1.373735
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.455319 Loss1: 1.508828 Loss2: 1.946492
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.509632 Loss1: 0.978441 Loss2: 1.531192 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.151375 Loss1: 0.649070 Loss2: 1.502305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.034148 Loss1: 0.516847 Loss2: 1.517301 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.882885 Loss1: 0.400530 Loss2: 1.482355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.722425 Loss1: 0.251555 Loss2: 1.470871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.727034 Loss1: 0.251217 Loss2: 1.475817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.667672 Loss1: 0.195653 Loss2: 1.472020 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.952148
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.736136 Loss1: 0.373759 Loss2: 1.362377 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.631979 Loss1: 0.267534 Loss2: 1.364445 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.672202 Loss1: 0.304974 Loss2: 1.367227
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.296859 Loss1: 1.427907 Loss2: 1.868953
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.583747 Loss1: 0.217035 Loss2: 1.366712
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.339233 Loss1: 0.896066 Loss2: 1.443168
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.551996 Loss1: 0.189555 Loss2: 1.362441
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.064700 Loss1: 0.614148 Loss2: 1.450552
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.570618 Loss1: 0.209889 Loss2: 1.360729
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.957190 Loss1: 0.518739 Loss2: 1.438451
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.813854 Loss1: 0.384902 Loss2: 1.428952
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.750673 Loss1: 0.337456 Loss2: 1.413216
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.698168 Loss1: 0.280261 Loss2: 1.417907
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.658191 Loss1: 0.242188 Loss2: 1.416004
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.467382 Loss1: 1.500332 Loss2: 1.967050
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.523463 Loss1: 1.000984 Loss2: 1.522478 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.943359
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.169059 Loss1: 0.618007 Loss2: 1.551052 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.870241 Loss1: 0.364091 Loss2: 1.506150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.715451 Loss1: 0.222950 Loss2: 1.492500 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.792066 Loss1: 0.307354 Loss2: 1.484712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.804159 Loss1: 0.305152 Loss2: 1.499007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.754905 Loss1: 0.274612 Loss2: 1.480293 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.896484
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.707582 Loss1: 0.271598 Loss2: 1.435984 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.619887 Loss1: 0.179550 Loss2: 1.440338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.579772 Loss1: 0.154497 Loss2: 1.425274
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.283813 Loss1: 1.463447 Loss2: 1.820366
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.561314 Loss1: 0.134414 Loss2: 1.426900
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.241870 Loss1: 0.851356 Loss2: 1.390514
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.937238 Loss1: 0.520969 Loss2: 1.416269
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.814533 Loss1: 0.436586 Loss2: 1.377947
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.772836 Loss1: 0.377696 Loss2: 1.395140
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.710457 Loss1: 0.329241 Loss2: 1.381216
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.583799 Loss1: 1.617856 Loss2: 1.965943
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.493752 Loss1: 0.983044 Loss2: 1.510708 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.092675 Loss1: 0.561117 Loss2: 1.531558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.016310 Loss1: 0.543692 Loss2: 1.472617 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.951172
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.636513 Loss1: 0.251783 Loss2: 1.384730
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.835155 Loss1: 0.340859 Loss2: 1.494297
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.804831 Loss1: 0.329231 Loss2: 1.475600
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.800482 Loss1: 0.324335 Loss2: 1.476147
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.788116 Loss1: 0.312033 Loss2: 1.476083
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.751239 Loss1: 0.268672 Loss2: 1.482567
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.480498 Loss1: 1.519344 Loss2: 1.961154
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.754772 Loss1: 0.279317 Loss2: 1.475454
+(DefaultActor pid=3764) >> Training accuracy: 0.943750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.141920 Loss1: 0.609255 Loss2: 1.532666 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.855111 Loss1: 0.392099 Loss2: 1.463012 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.843842 Loss1: 0.384367 Loss2: 1.459474
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.311042 Loss1: 1.431942 Loss2: 1.879100
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.279996 Loss1: 0.795269 Loss2: 1.484727 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.976213 Loss1: 0.535176 Loss2: 1.441037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.792662 Loss1: 0.358004 Loss2: 1.434658 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.661709 Loss1: 0.246665 Loss2: 1.415043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.592811 Loss1: 0.185938 Loss2: 1.406873 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.538985 Loss1: 0.142724 Loss2: 1.396262 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.545983 Loss1: 0.148865 Loss2: 1.397117 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958008
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.862938 Loss1: 0.467135 Loss2: 1.395804 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.743316 Loss1: 0.349098 Loss2: 1.394218 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.638462 Loss1: 0.255431 Loss2: 1.383031
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.197636 Loss1: 1.279635 Loss2: 1.918000
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.610628 Loss1: 0.226954 Loss2: 1.383674
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.228968 Loss1: 0.767886 Loss2: 1.461082
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.593212 Loss1: 0.210425 Loss2: 1.382787
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.089845 Loss1: 0.620823 Loss2: 1.469022
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.543517 Loss1: 0.167701 Loss2: 1.375816
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.936560 Loss1: 0.491707 Loss2: 1.444853
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.786044 Loss1: 0.344206 Loss2: 1.441838
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.727896 Loss1: 0.296360 Loss2: 1.431535
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.705785 Loss1: 0.265466 Loss2: 1.440319
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.639662 Loss1: 0.216321 Loss2: 1.423341
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.610300 Loss1: 0.189848 Loss2: 1.420451
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.740223 Loss1: 1.649688 Loss2: 2.090535
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.549487 Loss1: 0.133905 Loss2: 1.415582
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.751676 Loss1: 1.100418 Loss2: 1.651258
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.253098 Loss1: 0.654222 Loss2: 1.598876
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.060719 Loss1: 0.487070 Loss2: 1.573649
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.948032 Loss1: 0.363136 Loss2: 1.584896
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.954702 Loss1: 0.387650 Loss2: 1.567052
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.358301 Loss1: 1.367770 Loss2: 1.990530
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.806520 Loss1: 0.237954 Loss2: 1.568566
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.356265 Loss1: 0.854225 Loss2: 1.502041
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.768752 Loss1: 0.218403 Loss2: 1.550348
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.085187 Loss1: 0.570134 Loss2: 1.515053
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.761183 Loss1: 0.207901 Loss2: 1.553282
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.948991 Loss1: 0.468007 Loss2: 1.480984
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.764092 Loss1: 0.214328 Loss2: 1.549764
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.827887 Loss1: 0.353947 Loss2: 1.473941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.726343 Loss1: 0.257428 Loss2: 1.468915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.507664 Loss1: 1.531272 Loss2: 1.976392
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.683031 Loss1: 0.210285 Loss2: 1.472746
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.484178 Loss1: 1.048073 Loss2: 1.436105
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.701405 Loss1: 0.232914 Loss2: 1.468491
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.864376 Loss1: 0.454429 Loss2: 1.409947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.684295 Loss1: 0.273643 Loss2: 1.410652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.424144 Loss1: 1.526279 Loss2: 1.897866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.390786 Loss1: 0.935561 Loss2: 1.455225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.058070 Loss1: 0.604159 Loss2: 1.453912 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970982
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.805378 Loss1: 0.380807 Loss2: 1.424571 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.720905 Loss1: 0.305390 Loss2: 1.415515 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.663755 Loss1: 0.245301 Loss2: 1.418455
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.546955 Loss1: 1.627178 Loss2: 1.919777
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.551054 Loss1: 1.118206 Loss2: 1.432848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.572485 Loss1: 0.168954 Loss2: 1.403531
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.090332 Loss1: 0.611097 Loss2: 1.479235
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.929957 Loss1: 0.532350 Loss2: 1.397607
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.786189 Loss1: 0.360521 Loss2: 1.425668
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.737255 Loss1: 0.334095 Loss2: 1.403161
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.654306 Loss1: 0.257097 Loss2: 1.397209
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.668979 Loss1: 0.261983 Loss2: 1.406996
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.355373 Loss1: 1.388421 Loss2: 1.966952
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.455841 Loss1: 0.957331 Loss2: 1.498509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.929688
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.118295 Loss1: 0.618673 Loss2: 1.499622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.881529 Loss1: 0.404932 Loss2: 1.476597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.758227 Loss1: 0.291864 Loss2: 1.466363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.803163 Loss1: 0.326824 Loss2: 1.476340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.774381 Loss1: 0.313623 Loss2: 1.460757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.667325 Loss1: 0.195548 Loss2: 1.471777 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.739921 Loss1: 0.345055 Loss2: 1.394865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.621254 Loss1: 0.241106 Loss2: 1.380147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.568091 Loss1: 0.182520 Loss2: 1.385572
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.287320 Loss1: 1.363834 Loss2: 1.923486
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.352435 Loss1: 0.910253 Loss2: 1.442182 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.517299 Loss1: 0.152572 Loss2: 1.364727
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.055212 Loss1: 0.557528 Loss2: 1.497684
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.799611 Loss1: 0.374973 Loss2: 1.424638
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.752943 Loss1: 0.319447 Loss2: 1.433496
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.707285 Loss1: 0.270959 Loss2: 1.436326
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.631857 Loss1: 0.219842 Loss2: 1.412014
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.474244 Loss1: 1.536547 Loss2: 1.937697
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.647809 Loss1: 0.225444 Loss2: 1.422365
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.660285 Loss1: 0.232012 Loss2: 1.428274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.604341 Loss1: 0.183764 Loss2: 1.420577 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.647484 Loss1: 0.241483 Loss2: 1.406001 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.576496 Loss1: 0.192993 Loss2: 1.383503 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.486757 Loss1: 1.363675 Loss2: 2.123083 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980769
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.260307 Loss1: 0.622490 Loss2: 1.637817 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.918027 Loss1: 0.365531 Loss2: 1.552496 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.815831 Loss1: 0.271663 Loss2: 1.544169
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.190210 Loss1: 1.277235 Loss2: 1.912975
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.335277 Loss1: 0.894095 Loss2: 1.441183 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.991325 Loss1: 0.548997 Loss2: 1.442328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.818526 Loss1: 0.402940 Loss2: 1.415586 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.686035 Loss1: 0.160761 Loss2: 1.525274
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.740462 Loss1: 0.329124 Loss2: 1.411339
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.693115 Loss1: 0.291494 Loss2: 1.401621
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.654170 Loss1: 0.248940 Loss2: 1.405229
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.574026 Loss1: 0.170334 Loss2: 1.403692
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.584200 Loss1: 0.183504 Loss2: 1.400697
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.320912 Loss1: 1.449511 Loss2: 1.871401
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.582656 Loss1: 0.184822 Loss2: 1.397834
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.064893 Loss1: 0.625841 Loss2: 1.439051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.749896 Loss1: 0.338385 Loss2: 1.411511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.646716 Loss1: 0.243438 Loss2: 1.403279
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.419682 Loss1: 1.518290 Loss2: 1.901392
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.499435 Loss1: 1.021828 Loss2: 1.477607
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.655530 Loss1: 0.255535 Loss2: 1.399996
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.188431 Loss1: 0.726534 Loss2: 1.461897
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.637773 Loss1: 0.225194 Loss2: 1.412579
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.040942 Loss1: 0.585131 Loss2: 1.455810
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.637601 Loss1: 0.235992 Loss2: 1.401609
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.877235 Loss1: 0.448224 Loss2: 1.429011
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.610292 Loss1: 0.203108 Loss2: 1.407184
+(DefaultActor pid=3764) >> Training accuracy: 0.949219
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.642121 Loss1: 0.216165 Loss2: 1.425957 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.637243 Loss1: 0.214235 Loss2: 1.423009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.590067 Loss1: 0.173538 Loss2: 1.416529
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.417943 Loss1: 1.453937 Loss2: 1.964006
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.490982 Loss1: 0.963278 Loss2: 1.527703
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.085693 Loss1: 0.566327 Loss2: 1.519365
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.939094 Loss1: 0.460758 Loss2: 1.478336
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.833326 Loss1: 0.357969 Loss2: 1.475356
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.463338 Loss1: 1.554920 Loss2: 1.908417
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.804465 Loss1: 0.339703 Loss2: 1.464763
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.683122 Loss1: 0.204040 Loss2: 1.479082 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.646113 Loss1: 0.191350 Loss2: 1.454763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.680377 Loss1: 0.222400 Loss2: 1.457977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.617531 Loss1: 0.148886 Loss2: 1.468645 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.701071 Loss1: 0.303717 Loss2: 1.397354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.624025 Loss1: 0.232295 Loss2: 1.391730 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.599553 Loss1: 0.200368 Loss2: 1.399185
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.310152 Loss1: 1.424718 Loss2: 1.885434
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.377616 Loss1: 0.931048 Loss2: 1.446568
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.060305 Loss1: 0.597939 Loss2: 1.462367
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.866083 Loss1: 0.446973 Loss2: 1.419110
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.751358 Loss1: 0.333791 Loss2: 1.417567
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.492112 Loss1: 1.551556 Loss2: 1.940556
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.422665 Loss1: 0.954522 Loss2: 1.468143
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.764657 Loss1: 0.354179 Loss2: 1.410479
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.071205 Loss1: 0.600821 Loss2: 1.470384
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.696079 Loss1: 0.289074 Loss2: 1.407006
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.921911 Loss1: 0.496467 Loss2: 1.425444
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.611201 Loss1: 0.207408 Loss2: 1.403793
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.825703 Loss1: 0.365853 Loss2: 1.459850
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.607148 Loss1: 0.204461 Loss2: 1.402687
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.701553 Loss1: 0.275568 Loss2: 1.425985
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.630694 Loss1: 0.224562 Loss2: 1.406131
+(DefaultActor pid=3764) >> Training accuracy: 0.959961
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.618774 Loss1: 0.197660 Loss2: 1.421114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.649496 Loss1: 0.233233 Loss2: 1.416263 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.379866 Loss1: 0.929967 Loss2: 1.449899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.055457 Loss1: 0.624853 Loss2: 1.430604 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.873889 Loss1: 0.423812 Loss2: 1.450076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.748439 Loss1: 0.317792 Loss2: 1.430648 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.715696 Loss1: 0.287687 Loss2: 1.428009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.678674 Loss1: 0.260037 Loss2: 1.418637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.658451 Loss1: 0.229808 Loss2: 1.428643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.588561 Loss1: 0.168950 Loss2: 1.419611 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.645566 Loss1: 0.195335 Loss2: 1.450231 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.629774 Loss1: 0.189748 Loss2: 1.440026 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.403057 Loss1: 0.961328 Loss2: 1.441729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.893911 Loss1: 0.482372 Loss2: 1.411539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.772559 Loss1: 0.349069 Loss2: 1.423490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.361704 Loss1: 0.930787 Loss2: 1.430916
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.738842 Loss1: 0.336318 Loss2: 1.402524
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.006262 Loss1: 0.561247 Loss2: 1.445015
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.670004 Loss1: 0.261627 Loss2: 1.408377
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.856663 Loss1: 0.449733 Loss2: 1.406930
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.675180 Loss1: 0.265907 Loss2: 1.409273
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.614333 Loss1: 0.208267 Loss2: 1.406066
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.776519 Loss1: 0.361553 Loss2: 1.414966
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.576697 Loss1: 0.182987 Loss2: 1.393710
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.794284 Loss1: 0.372803 Loss2: 1.421481
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.678933 Loss1: 0.273767 Loss2: 1.405166
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.611089 Loss1: 0.215254 Loss2: 1.395834
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.558573 Loss1: 0.164077 Loss2: 1.394496
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.584033 Loss1: 0.187854 Loss2: 1.396179
+(DefaultActor pid=3765) >> Training accuracy: 0.945312
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.645327 Loss1: 1.539260 Loss2: 2.106067
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.379412 Loss1: 0.915682 Loss2: 1.463729
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.099839 Loss1: 0.622729 Loss2: 1.477110
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.000166 Loss1: 0.505583 Loss2: 1.494583
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.887315 Loss1: 0.431019 Loss2: 1.456295
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.828015 Loss1: 0.371331 Loss2: 1.456684
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.336270 Loss1: 1.337963 Loss2: 1.998307
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.723261 Loss1: 0.260232 Loss2: 1.463029 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 05:28:29,767 | server.py:236 | fit_round 65 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.640795 Loss1: 0.183075 Loss2: 1.457720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.629154 Loss1: 0.179815 Loss2: 1.449339 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.956558 Loss1: 0.420780 Loss2: 1.535778 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.821957 Loss1: 0.309830 Loss2: 1.512127 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.767303 Loss1: 0.257766 Loss2: 1.509538
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.355838 Loss1: 1.451399 Loss2: 1.904439
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.465467 Loss1: 1.012286 Loss2: 1.453181 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.129707 Loss1: 0.629192 Loss2: 1.500515 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.824462 Loss1: 0.378053 Loss2: 1.446409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.725836 Loss1: 0.297526 Loss2: 1.428310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.101285 Loss1: 1.260399 Loss2: 1.840886
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.713121 Loss1: 0.281335 Loss2: 1.431787
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.626678 Loss1: 0.189402 Loss2: 1.437276
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.241159 Loss1: 0.822408 Loss2: 1.418751
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.594139 Loss1: 0.174811 Loss2: 1.419328
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.917307 Loss1: 0.493837 Loss2: 1.423470
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.854473 Loss1: 0.457325 Loss2: 1.397148
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.819312 Loss1: 0.404636 Loss2: 1.414676
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.711246 Loss1: 0.318801 Loss2: 1.392445
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.630517 Loss1: 0.239569 Loss2: 1.390949
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.686354 Loss1: 1.704270 Loss2: 1.982084
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.588254 Loss1: 0.201409 Loss2: 1.386845
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.576429 Loss1: 1.093916 Loss2: 1.482513
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.234839 Loss1: 0.744980 Loss2: 1.489859
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.611153 Loss1: 0.231556 Loss2: 1.379597
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.941972 Loss1: 0.498244 Loss2: 1.443728
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.606831 Loss1: 0.213631 Loss2: 1.393199
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.696584 Loss1: 0.268190 Loss2: 1.428394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.721409 Loss1: 0.281844 Loss2: 1.439565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.711263 Loss1: 0.268840 Loss2: 1.442423 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.943080
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 05:28:29,767][flwr][DEBUG] - fit_round 65 received 50 results and 0 failures
+INFO flwr 2023-10-10 05:29:10,054 | server.py:125 | fit progress: (65, 2.3072678543889102, {'accuracy': 0.5214}, 149857.832570231)
+>> Test accuracy: 0.521400
+[2023-10-10 05:29:10,054][flwr][INFO] - fit progress: (65, 2.3072678543889102, {'accuracy': 0.5214}, 149857.832570231)
+DEBUG flwr 2023-10-10 05:29:10,054 | server.py:173 | evaluate_round 65: strategy sampled 50 clients (out of 50)
+[2023-10-10 05:29:10,054][flwr][DEBUG] - evaluate_round 65: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 05:38:11,548 | server.py:187 | evaluate_round 65 received 50 results and 0 failures
+[2023-10-10 05:38:11,548][flwr][DEBUG] - evaluate_round 65 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 05:38:11,549 | server.py:222 | fit_round 66: strategy sampled 50 clients (out of 50)
+[2023-10-10 05:38:11,549][flwr][DEBUG] - fit_round 66: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.605107 Loss1: 1.538463 Loss2: 2.066644
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.517367 Loss1: 1.073290 Loss2: 1.444076
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.144483 Loss1: 0.619107 Loss2: 1.525377
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.916786 Loss1: 0.466747 Loss2: 1.450039
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.814510 Loss1: 0.369770 Loss2: 1.444740
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.736239 Loss1: 0.278563 Loss2: 1.457676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.685610 Loss1: 0.249583 Loss2: 1.436027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.639505 Loss1: 0.213934 Loss2: 1.425571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.659921 Loss1: 0.231212 Loss2: 1.428709
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.787886 Loss1: 0.416797 Loss2: 1.371090
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.630088 Loss1: 0.192134 Loss2: 1.437954
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.716537 Loss1: 0.336137 Loss2: 1.380400
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.638242 Loss1: 0.262555 Loss2: 1.375687
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.599932 Loss1: 0.237700 Loss2: 1.362232
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.579640 Loss1: 0.214756 Loss2: 1.364884
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.612045 Loss1: 0.242786 Loss2: 1.369259
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.491269 Loss1: 1.501988 Loss2: 1.989281
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.574449 Loss1: 0.213943 Loss2: 1.360506
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.003509 Loss1: 0.547556 Loss2: 1.455953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.723601 Loss1: 0.316506 Loss2: 1.407094 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.642080 Loss1: 0.238201 Loss2: 1.403879 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.572778 Loss1: 0.173183 Loss2: 1.399595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.543048 Loss1: 0.148433 Loss2: 1.394616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.496241 Loss1: 0.101811 Loss2: 1.394431 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985577
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.644975 Loss1: 0.345366 Loss2: 1.299609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486208 Loss1: 0.204364 Loss2: 1.281843 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416210 Loss1: 0.141140 Loss2: 1.275069
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.676186 Loss1: 1.604955 Loss2: 2.071231
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.602671 Loss1: 1.036801 Loss2: 1.565870 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404474 Loss1: 0.145817 Loss2: 1.258657
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.267445 Loss1: 0.725520 Loss2: 1.541925
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.063828 Loss1: 0.527657 Loss2: 1.536170
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.962262 Loss1: 0.457684 Loss2: 1.504578
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.951041 Loss1: 0.434203 Loss2: 1.516838
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.820800 Loss1: 0.309554 Loss2: 1.511246
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.819019 Loss1: 0.318378 Loss2: 1.500641
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.229023 Loss1: 1.373103 Loss2: 1.855920
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.358684 Loss1: 0.897622 Loss2: 1.461062 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973214
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.056276 Loss1: 0.619098 Loss2: 1.437178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.777465 Loss1: 0.355471 Loss2: 1.421993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.666109 Loss1: 0.256098 Loss2: 1.410012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.641895 Loss1: 0.238511 Loss2: 1.403384 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.985998 Loss1: 0.535481 Loss2: 1.450518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.873314 Loss1: 0.470345 Loss2: 1.402969 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.950195
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.676424 Loss1: 0.278398 Loss2: 1.398025 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.602185 Loss1: 0.215934 Loss2: 1.386251 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.555114 Loss1: 0.170884 Loss2: 1.384230
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.300587 Loss1: 1.490599 Loss2: 1.809988
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.570841 Loss1: 0.191653 Loss2: 1.379187
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.281993 Loss1: 0.857374 Loss2: 1.424619
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.967373 Loss1: 0.568788 Loss2: 1.398584
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.811331 Loss1: 0.420487 Loss2: 1.390844
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.747644 Loss1: 0.358086 Loss2: 1.389558
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.752183 Loss1: 0.360805 Loss2: 1.391378
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.297578 Loss1: 1.367182 Loss2: 1.930396
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.423554 Loss1: 0.945472 Loss2: 1.478082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.149048 Loss1: 0.639584 Loss2: 1.509464 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.611882 Loss1: 0.230916 Loss2: 1.380966
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.938168 Loss1: 0.495247 Loss2: 1.442921
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.565407 Loss1: 0.185637 Loss2: 1.379770
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.860609 Loss1: 0.405525 Loss2: 1.455083
+(DefaultActor pid=3764) >> Training accuracy: 0.973633
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.748201 Loss1: 0.303312 Loss2: 1.444889
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.675652 Loss1: 0.246102 Loss2: 1.429550
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.636575 Loss1: 0.202660 Loss2: 1.433915
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.586322 Loss1: 0.163021 Loss2: 1.423300
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.592262 Loss1: 0.174783 Loss2: 1.417479
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.450603 Loss1: 1.557551 Loss2: 1.893052
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.553086 Loss1: 1.052892 Loss2: 1.500194
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.127243 Loss1: 0.690430 Loss2: 1.436812
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.983886 Loss1: 0.549324 Loss2: 1.434561
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.835985 Loss1: 0.405752 Loss2: 1.430232
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.342267 Loss1: 1.552837 Loss2: 1.789430
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.784431 Loss1: 0.360535 Loss2: 1.423897
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.425776 Loss1: 1.019558 Loss2: 1.406218
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.774515 Loss1: 0.357904 Loss2: 1.416611
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.997019 Loss1: 0.631067 Loss2: 1.365951
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.700177 Loss1: 0.282291 Loss2: 1.417886
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.795158 Loss1: 0.435572 Loss2: 1.359585
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.635500 Loss1: 0.227431 Loss2: 1.408070
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.646149 Loss1: 0.302356 Loss2: 1.343792
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.624076 Loss1: 0.212565 Loss2: 1.411511
+(DefaultActor pid=3764) >> Training accuracy: 0.940625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.574903 Loss1: 0.239536 Loss2: 1.335367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489552 Loss1: 0.161683 Loss2: 1.327869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.526923 Loss1: 1.486081 Loss2: 2.040842
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453754 Loss1: 0.134892 Loss2: 1.318862
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.210887 Loss1: 0.657261 Loss2: 1.553626 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.758129 Loss1: 0.274034 Loss2: 1.484095 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.699738 Loss1: 0.230000 Loss2: 1.469738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.684019 Loss1: 0.218922 Loss2: 1.465097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.600392 Loss1: 0.133590 Loss2: 1.466802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.586552 Loss1: 0.131284 Loss2: 1.455268 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967548
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.783644 Loss1: 0.320169 Loss2: 1.463476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.771249 Loss1: 0.324671 Loss2: 1.446579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.843249 Loss1: 0.390202 Loss2: 1.453048
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.410119 Loss1: 1.517430 Loss2: 1.892689
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.410681 Loss1: 0.946707 Loss2: 1.463974 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.946875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.719488 Loss1: 0.262225 Loss2: 1.457263
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.951008 Loss1: 0.522650 Loss2: 1.428357
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.768141 Loss1: 0.354461 Loss2: 1.413680
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.702187 Loss1: 0.288359 Loss2: 1.413827
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.665669 Loss1: 0.263089 Loss2: 1.402581
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.700754 Loss1: 0.297262 Loss2: 1.403492
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.289315 Loss1: 1.397698 Loss2: 1.891617
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.652273 Loss1: 0.236448 Loss2: 1.415825
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.326372 Loss1: 0.896092 Loss2: 1.430280
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.610185 Loss1: 0.210118 Loss2: 1.400068
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.069699 Loss1: 0.614540 Loss2: 1.455158
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.625830 Loss1: 0.216888 Loss2: 1.408943
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.828449 Loss1: 0.410827 Loss2: 1.417623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.682010 Loss1: 0.271895 Loss2: 1.410115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.667516 Loss1: 0.256287 Loss2: 1.411229
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.357141 Loss1: 1.457909 Loss2: 1.899232
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.600265 Loss1: 0.201715 Loss2: 1.398550
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.335837 Loss1: 0.872548 Loss2: 1.463289
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.608592 Loss1: 0.212664 Loss2: 1.395928
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.127711 Loss1: 0.650688 Loss2: 1.477023
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.872748 Loss1: 0.452507 Loss2: 1.420241
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.805541 Loss1: 0.365361 Loss2: 1.440180
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.684933 Loss1: 0.268387 Loss2: 1.416546
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.635880 Loss1: 0.229038 Loss2: 1.406842
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.582187 Loss1: 0.174010 Loss2: 1.408177
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.451107 Loss1: 1.567873 Loss2: 1.883234
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.557907 Loss1: 0.146847 Loss2: 1.411060
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.541480 Loss1: 1.051913 Loss2: 1.489567
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.536664 Loss1: 0.136965 Loss2: 1.399699
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.130192 Loss1: 0.696782 Loss2: 1.433411
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.971487 Loss1: 0.544367 Loss2: 1.427120
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.852114 Loss1: 0.435302 Loss2: 1.416812
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.775367 Loss1: 0.346554 Loss2: 1.428812
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.683488 Loss1: 0.275120 Loss2: 1.408368
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.657362 Loss1: 0.252737 Loss2: 1.404625
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.302453 Loss1: 1.325514 Loss2: 1.976939
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.570517 Loss1: 0.169798 Loss2: 1.400719
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.410597 Loss1: 0.933937 Loss2: 1.476660
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.556877 Loss1: 0.161361 Loss2: 1.395516
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.024298 Loss1: 0.517643 Loss2: 1.506656
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.829807 Loss1: 0.393644 Loss2: 1.436162
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.760637 Loss1: 0.311466 Loss2: 1.449171
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.668958 Loss1: 0.223977 Loss2: 1.444981
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.693857 Loss1: 0.256318 Loss2: 1.437539
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.404985 Loss1: 1.502465 Loss2: 1.902520
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.603266 Loss1: 0.163823 Loss2: 1.439443
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.339212 Loss1: 0.916367 Loss2: 1.422845
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.586093 Loss1: 0.165132 Loss2: 1.420961
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.966249 Loss1: 0.544368 Loss2: 1.421880
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.583392 Loss1: 0.163799 Loss2: 1.419594
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.690233 Loss1: 0.296902 Loss2: 1.393331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.602690 Loss1: 0.226598 Loss2: 1.376091 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.651834 Loss1: 0.273109 Loss2: 1.378725
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.308616 Loss1: 1.424909 Loss2: 1.883707
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.620203 Loss1: 0.225744 Loss2: 1.394459
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.377246 Loss1: 0.948409 Loss2: 1.428837
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.546219 Loss1: 0.164101 Loss2: 1.382118
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.066877 Loss1: 0.614487 Loss2: 1.452389
+(DefaultActor pid=3765) >> Training accuracy: 0.946875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.880826 Loss1: 0.483905 Loss2: 1.396921
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.766541 Loss1: 0.351459 Loss2: 1.415082
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.716102 Loss1: 0.310609 Loss2: 1.405493
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.620648 Loss1: 0.225566 Loss2: 1.395082
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.364252 Loss1: 1.425217 Loss2: 1.939035
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.586554 Loss1: 0.209751 Loss2: 1.376804
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.413455 Loss1: 0.982713 Loss2: 1.430742
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.582635 Loss1: 0.189155 Loss2: 1.393481
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.146781 Loss1: 0.658850 Loss2: 1.487931
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.624055 Loss1: 0.241100 Loss2: 1.382955
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.726390 Loss1: 0.310589 Loss2: 1.415801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.629647 Loss1: 0.230314 Loss2: 1.399333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.286902 Loss1: 1.432288 Loss2: 1.854614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.360514 Loss1: 0.897747 Loss2: 1.462767 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.926339
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.767839 Loss1: 0.380140 Loss2: 1.387698 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.614776 Loss1: 0.242697 Loss2: 1.372079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.188852 Loss1: 1.318829 Loss2: 1.870023
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.566832 Loss1: 0.204372 Loss2: 1.362459
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.177632 Loss1: 0.780587 Loss2: 1.397045
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.549677 Loss1: 0.189576 Loss2: 1.360101
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.056773 Loss1: 0.643286 Loss2: 1.413487
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.559038 Loss1: 0.199274 Loss2: 1.359764
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.845550 Loss1: 0.455201 Loss2: 1.390349
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.544797 Loss1: 0.180237 Loss2: 1.364560
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.568159 Loss1: 0.209147 Loss2: 1.359011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.531071 Loss1: 0.177015 Loss2: 1.354056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488991 Loss1: 0.141784 Loss2: 1.347207
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.396624 Loss1: 1.377069 Loss2: 2.019555
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508861 Loss1: 0.164870 Loss2: 1.343991
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.540867 Loss1: 1.048809 Loss2: 1.492057
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.270399 Loss1: 0.607482 Loss2: 1.662917
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.899970 Loss1: 0.390128 Loss2: 1.509842
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.774561 Loss1: 0.313213 Loss2: 1.461348
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.776064 Loss1: 0.316336 Loss2: 1.459728
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.256044 Loss1: 1.348316 Loss2: 1.907729
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.729407 Loss1: 0.255918 Loss2: 1.473488
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.289907 Loss1: 0.800574 Loss2: 1.489333
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.692265 Loss1: 0.227223 Loss2: 1.465042
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.644313 Loss1: 0.179991 Loss2: 1.464322
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.051061 Loss1: 0.593997 Loss2: 1.457064
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.654176 Loss1: 0.203017 Loss2: 1.451159
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.840612 Loss1: 0.375809 Loss2: 1.464803
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.786220 Loss1: 0.333721 Loss2: 1.452498
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.709597 Loss1: 0.255398 Loss2: 1.454199
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.613923 Loss1: 0.191098 Loss2: 1.422825
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.613356 Loss1: 0.186627 Loss2: 1.426729
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.228505 Loss1: 1.365075 Loss2: 1.863429
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.584895 Loss1: 0.155800 Loss2: 1.429095
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.592518 Loss1: 0.161285 Loss2: 1.431233 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.939453
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.799283 Loss1: 0.422823 Loss2: 1.376460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.624249 Loss1: 0.259080 Loss2: 1.365168 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.566007 Loss1: 0.205285 Loss2: 1.360723
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.131966 Loss1: 1.198235 Loss2: 1.933731
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.164993 Loss1: 0.740013 Loss2: 1.424980 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.011898 Loss1: 0.559268 Loss2: 1.452630 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.515696 Loss1: 0.162421 Loss2: 1.353275
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.953117 Loss1: 0.502453 Loss2: 1.450664
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.823733 Loss1: 0.395782 Loss2: 1.427951
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.721784 Loss1: 0.284744 Loss2: 1.437040
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.621432 Loss1: 0.215997 Loss2: 1.405435
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.614861 Loss1: 0.201921 Loss2: 1.412940
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.395998 Loss1: 1.473122 Loss2: 1.922876
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.600168 Loss1: 0.193703 Loss2: 1.406466
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.560382 Loss1: 0.156956 Loss2: 1.403427 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.954167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.917334 Loss1: 0.458384 Loss2: 1.458950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.754603 Loss1: 0.295371 Loss2: 1.459233 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.685918 Loss1: 0.236122 Loss2: 1.449796
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.434140 Loss1: 1.479894 Loss2: 1.954245
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.319995 Loss1: 0.855512 Loss2: 1.464483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.122352 Loss1: 0.624810 Loss2: 1.497542 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.602638 Loss1: 0.175682 Loss2: 1.426957
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.907848 Loss1: 0.449405 Loss2: 1.458443
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.726712 Loss1: 0.283168 Loss2: 1.443544
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.697206 Loss1: 0.259049 Loss2: 1.438156
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.660319 Loss1: 0.221700 Loss2: 1.438619
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.671683 Loss1: 0.229502 Loss2: 1.442181
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.374055 Loss1: 1.429503 Loss2: 1.944552
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.681334 Loss1: 0.242143 Loss2: 1.439191
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.390830 Loss1: 0.915994 Loss2: 1.474836
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.635615 Loss1: 0.193249 Loss2: 1.442366
+(DefaultActor pid=3765) >> Training accuracy: 0.954167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.920866 Loss1: 0.461201 Loss2: 1.459665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.790617 Loss1: 0.344324 Loss2: 1.446293 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.695799 Loss1: 0.243901 Loss2: 1.451898
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.238990 Loss1: 1.322857 Loss2: 1.916133
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.342115 Loss1: 0.829543 Loss2: 1.512572 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.951101 Loss1: 0.482190 Loss2: 1.468911 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.868494 Loss1: 0.408328 Loss2: 1.460166 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.719129 Loss1: 0.273931 Loss2: 1.445199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.259006 Loss1: 1.429697 Loss2: 1.829309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.383879 Loss1: 0.998125 Loss2: 1.385754 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.080727 Loss1: 0.660265 Loss2: 1.420462 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965074
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.920718 Loss1: 0.533452 Loss2: 1.387266 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.677154 Loss1: 0.299822 Loss2: 1.377333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.558513 Loss1: 0.204770 Loss2: 1.353743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.533838 Loss1: 0.180830 Loss2: 1.353009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.569369 Loss1: 0.210239 Loss2: 1.359130 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.952254 Loss1: 0.472033 Loss2: 1.480222 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.854246 Loss1: 0.351918 Loss2: 1.502328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.390845 Loss1: 1.448961 Loss2: 1.941884 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.480019 Loss1: 0.950613 Loss2: 1.529406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.077293 Loss1: 0.559702 Loss2: 1.517591 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.841734 Loss1: 0.336306 Loss2: 1.505428 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.734765 Loss1: 0.252875 Loss2: 1.481889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.733256 Loss1: 0.257259 Loss2: 1.475997
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.367525 Loss1: 1.427050 Loss2: 1.940476
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.732216 Loss1: 0.245810 Loss2: 1.486405
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.408124 Loss1: 0.939622 Loss2: 1.468502
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.116552 Loss1: 0.607741 Loss2: 1.508810
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.715119 Loss1: 0.232541 Loss2: 1.482578
+(DefaultActor pid=3764) >> Training accuracy: 0.962891
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.822718 Loss1: 0.359919 Loss2: 1.462799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.751775 Loss1: 0.303423 Loss2: 1.448352 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.717052 Loss1: 0.260544 Loss2: 1.456508
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.288250 Loss1: 1.413739 Loss2: 1.874511
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.284816 Loss1: 0.852889 Loss2: 1.431927 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.591771 Loss1: 0.156858 Loss2: 1.434913
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.042123 Loss1: 0.595241 Loss2: 1.446882
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.916353 Loss1: 0.500422 Loss2: 1.415930
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.789538 Loss1: 0.358153 Loss2: 1.431386
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.712573 Loss1: 0.314791 Loss2: 1.397782
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.614781 Loss1: 0.208142 Loss2: 1.406639
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.572570 Loss1: 1.543335 Loss2: 2.029235
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.648090 Loss1: 0.244617 Loss2: 1.403474
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.609086 Loss1: 0.202868 Loss2: 1.406218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.549732 Loss1: 0.157823 Loss2: 1.391909 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.874659 Loss1: 0.372940 Loss2: 1.501718 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.760465 Loss1: 0.263418 Loss2: 1.497047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.241224 Loss1: 1.407294 Loss2: 1.833930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.180845 Loss1: 0.792542 Loss2: 1.388303 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955357
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699049 Loss1: 0.337118 Loss2: 1.361931 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.617216 Loss1: 0.257870 Loss2: 1.359346 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.650435 Loss1: 0.282506 Loss2: 1.367929
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.393373 Loss1: 1.439281 Loss2: 1.954092
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.378942 Loss1: 0.869511 Loss2: 1.509431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.083134 Loss1: 0.576176 Loss2: 1.506958 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.939081 Loss1: 0.444485 Loss2: 1.494597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.764529 Loss1: 0.291945 Loss2: 1.472584 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.693629 Loss1: 0.233036 Loss2: 1.460593 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.687420 Loss1: 0.217484 Loss2: 1.469936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.649047 Loss1: 0.181003 Loss2: 1.468044 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.946289
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.953518 Loss1: 0.477202 Loss2: 1.476316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.806783 Loss1: 0.339234 Loss2: 1.467549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.710478 Loss1: 0.247611 Loss2: 1.462867
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.389568 Loss1: 1.372439 Loss2: 2.017129
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.450434 Loss1: 0.870167 Loss2: 1.580267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.122590 Loss1: 0.557279 Loss2: 1.565311 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958984
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.943210 Loss1: 0.386343 Loss2: 1.556867 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.826907 Loss1: 0.296211 Loss2: 1.530696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.771349 Loss1: 0.236035 Loss2: 1.535313 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.180550 Loss1: 0.798124 Loss2: 1.382426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.924253 Loss1: 0.538036 Loss2: 1.386217 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.949219
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.636495 Loss1: 0.284083 Loss2: 1.352412 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.600677 Loss1: 0.247159 Loss2: 1.353518 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.553622 Loss1: 0.206028 Loss2: 1.347595
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.309367 Loss1: 1.329973 Loss2: 1.979395
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.527698 Loss1: 0.184347 Loss2: 1.343351
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.324341 Loss1: 0.833990 Loss2: 1.490351
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.570650 Loss1: 0.223830 Loss2: 1.346820
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.023725 Loss1: 0.500553 Loss2: 1.523173
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.918613 Loss1: 0.437863 Loss2: 1.480750
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.829823 Loss1: 0.346765 Loss2: 1.483057
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.722785 Loss1: 0.242910 Loss2: 1.479875
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.694973 Loss1: 0.231453 Loss2: 1.463520
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.412753 Loss1: 1.498717 Loss2: 1.914036
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.678462 Loss1: 0.202559 Loss2: 1.475903
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.483812 Loss1: 1.042712 Loss2: 1.441100
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.632911 Loss1: 0.170770 Loss2: 1.462140
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.059823 Loss1: 0.606326 Loss2: 1.453496
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.589384 Loss1: 0.135549 Loss2: 1.453835
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.807535 Loss1: 0.372091 Loss2: 1.435444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.667592 Loss1: 0.243679 Loss2: 1.423913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.612965 Loss1: 0.203507 Loss2: 1.409457
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.355871 Loss1: 1.503996 Loss2: 1.851874
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.629539 Loss1: 0.226167 Loss2: 1.403371
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.475578 Loss1: 1.020209 Loss2: 1.455369
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.583086 Loss1: 0.179901 Loss2: 1.403186
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.989469 Loss1: 0.576911 Loss2: 1.412559
+(DefaultActor pid=3764) >> Training accuracy: 0.946875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.848669 Loss1: 0.450165 Loss2: 1.398504
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.696107 Loss1: 0.309695 Loss2: 1.386413
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.742516 Loss1: 0.354396 Loss2: 1.388120
+DEBUG flwr 2023-10-10 06:06:36,986 | server.py:236 | fit_round 66 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.644080 Loss1: 0.256143 Loss2: 1.387937
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.265004 Loss1: 1.430103 Loss2: 1.834900
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.627945 Loss1: 0.245080 Loss2: 1.382865
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.406692 Loss1: 1.021245 Loss2: 1.385447
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.559525 Loss1: 0.180674 Loss2: 1.378851
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.057420 Loss1: 0.620905 Loss2: 1.436515
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.549918 Loss1: 0.179754 Loss2: 1.370164
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.762520 Loss1: 0.373454 Loss2: 1.389066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.662324 Loss1: 0.300690 Loss2: 1.361633 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.572015 Loss1: 0.193072 Loss2: 1.378942
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.216657 Loss1: 1.366364 Loss2: 1.850292
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.225408 Loss1: 0.843752 Loss2: 1.381656 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.872833 Loss1: 0.508147 Loss2: 1.364686 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.604549 Loss1: 0.266818 Loss2: 1.337731 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.546019 Loss1: 0.219142 Loss2: 1.326877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.551904 Loss1: 0.222655 Loss2: 1.329249 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.532665 Loss1: 0.195481 Loss2: 1.337184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.501111 Loss1: 0.168579 Loss2: 1.332533 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.743812 Loss1: 0.349451 Loss2: 1.394361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.627624 Loss1: 0.242826 Loss2: 1.384799 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.596324 Loss1: 0.208862 Loss2: 1.387462
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.462100 Loss1: 1.514303 Loss2: 1.947798
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.454328 Loss1: 0.962740 Loss2: 1.491588 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.541024 Loss1: 0.157749 Loss2: 1.383275
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.025119 Loss1: 0.513391 Loss2: 1.511728
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.856642 Loss1: 0.396068 Loss2: 1.460575
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.768416 Loss1: 0.295994 Loss2: 1.472422
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.741302 Loss1: 0.283573 Loss2: 1.457729
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.726780 Loss1: 0.256623 Loss2: 1.470157
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.229078 Loss1: 1.346255 Loss2: 1.882823
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.695229 Loss1: 0.232241 Loss2: 1.462988
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.399247 Loss1: 0.931819 Loss2: 1.467428
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.608441 Loss1: 0.149628 Loss2: 1.458813
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.991959 Loss1: 0.512721 Loss2: 1.479237
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.592983 Loss1: 0.147604 Loss2: 1.445379
+(DefaultActor pid=3765) >> Training accuracy: 0.962500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.692789 Loss1: 0.251121 Loss2: 1.441668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.640280 Loss1: 0.207013 Loss2: 1.433267 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.730552 Loss1: 0.282402 Loss2: 1.448150 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.943359
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 06:06:36,986][flwr][DEBUG] - fit_round 66 received 50 results and 0 failures
+INFO flwr 2023-10-10 06:07:18,962 | server.py:125 | fit progress: (66, 2.3170356525780673, {'accuracy': 0.5231}, 152146.740390885)
+>> Test accuracy: 0.523100
+[2023-10-10 06:07:18,962][flwr][INFO] - fit progress: (66, 2.3170356525780673, {'accuracy': 0.5231}, 152146.740390885)
+DEBUG flwr 2023-10-10 06:07:18,962 | server.py:173 | evaluate_round 66: strategy sampled 50 clients (out of 50)
+[2023-10-10 06:07:18,962][flwr][DEBUG] - evaluate_round 66: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 06:16:21,950 | server.py:187 | evaluate_round 66 received 50 results and 0 failures
+[2023-10-10 06:16:21,950][flwr][DEBUG] - evaluate_round 66 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 06:16:21,951 | server.py:222 | fit_round 67: strategy sampled 50 clients (out of 50)
+[2023-10-10 06:16:21,951][flwr][DEBUG] - fit_round 67: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.311802 Loss1: 1.278225 Loss2: 2.033576 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.999931 Loss1: 0.509084 Loss2: 1.490847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.874905 Loss1: 0.420846 Loss2: 1.454059
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.606767 Loss1: 1.628635 Loss2: 1.978132
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.782306 Loss1: 0.324451 Loss2: 1.457855
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.492178 Loss1: 1.023614 Loss2: 1.468564
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.695427 Loss1: 0.255211 Loss2: 1.440216
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.059056 Loss1: 0.585024 Loss2: 1.474032
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.663874 Loss1: 0.225166 Loss2: 1.438708
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.872241 Loss1: 0.435519 Loss2: 1.436723
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.774176 Loss1: 0.342548 Loss2: 1.431628
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.587227 Loss1: 0.156464 Loss2: 1.430763
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.711844 Loss1: 0.285100 Loss2: 1.426745
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.573421 Loss1: 0.151154 Loss2: 1.422267
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.677199 Loss1: 0.249759 Loss2: 1.427441
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.546836 Loss1: 0.123162 Loss2: 1.423674
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.659903 Loss1: 0.235302 Loss2: 1.424602 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.950893
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.154382 Loss1: 1.294351 Loss2: 1.860031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.952151 Loss1: 0.527911 Loss2: 1.424241 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.795868 Loss1: 0.399799 Loss2: 1.396070 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.662552 Loss1: 0.266483 Loss2: 1.396069 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.595160 Loss1: 0.217069 Loss2: 1.378091 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.646862 Loss1: 0.265297 Loss2: 1.381566 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.606272 Loss1: 0.221032 Loss2: 1.385240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.533468 Loss1: 0.151506 Loss2: 1.381962 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.509156 Loss1: 0.132611 Loss2: 1.376544 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981618
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.642647 Loss1: 0.221573 Loss2: 1.421073 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.959961
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.626427 Loss1: 1.657535 Loss2: 1.968893 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.054955 Loss1: 0.582843 Loss2: 1.472112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.513124 Loss1: 1.496717 Loss2: 2.016407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.657126 Loss1: 1.100291 Loss2: 1.556834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.279771 Loss1: 0.728591 Loss2: 1.551181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.079499 Loss1: 0.561496 Loss2: 1.518003 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.896539 Loss1: 0.394630 Loss2: 1.501909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.765013 Loss1: 0.265375 Loss2: 1.499639 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.960938
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.701383 Loss1: 0.223456 Loss2: 1.477927 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.680166 Loss1: 0.201659 Loss2: 1.478508 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.322478 Loss1: 0.818242 Loss2: 1.504236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.889847 Loss1: 0.425926 Loss2: 1.463921 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.814179 Loss1: 0.333316 Loss2: 1.480863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.688021 Loss1: 0.236297 Loss2: 1.451724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.684951 Loss1: 0.228750 Loss2: 1.456200 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.643641 Loss1: 0.200248 Loss2: 1.443394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.673244 Loss1: 0.217442 Loss2: 1.455802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.669413 Loss1: 0.218514 Loss2: 1.450899 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.943359
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.645867 Loss1: 0.173442 Loss2: 1.472425 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.310072 Loss1: 1.434584 Loss2: 1.875488 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.981928 Loss1: 0.577332 Loss2: 1.404595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.831908 Loss1: 0.451668 Loss2: 1.380240
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.415003 Loss1: 1.418534 Loss2: 1.996469
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.460040 Loss1: 0.946059 Loss2: 1.513981 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.113086 Loss1: 0.592780 Loss2: 1.520306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.904805 Loss1: 0.438107 Loss2: 1.466697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.788276 Loss1: 0.307526 Loss2: 1.480751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.842946 Loss1: 0.376905 Loss2: 1.466042 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.567229 Loss1: 0.195230 Loss2: 1.372000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.742219 Loss1: 0.268626 Loss2: 1.473593
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.784859 Loss1: 0.317549 Loss2: 1.467310
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.736376 Loss1: 0.265709 Loss2: 1.470667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.635820 Loss1: 0.177634 Loss2: 1.458186
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.209263 Loss1: 1.334739 Loss2: 1.874525
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.349257 Loss1: 0.910944 Loss2: 1.438313
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.083684 Loss1: 0.627467 Loss2: 1.456217
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.393483 Loss1: 1.498718 Loss2: 1.894766
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.830807 Loss1: 0.407092 Loss2: 1.423715
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.446893 Loss1: 0.993940 Loss2: 1.452953
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.710780 Loss1: 0.294777 Loss2: 1.416002
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.140117 Loss1: 0.665899 Loss2: 1.474218
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.638733 Loss1: 0.238362 Loss2: 1.400370
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.589471 Loss1: 0.188959 Loss2: 1.400512 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.572112 Loss1: 0.173244 Loss2: 1.398868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.624678 Loss1: 0.223544 Loss2: 1.401134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.555447 Loss1: 0.147127 Loss2: 1.408320 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.606231 Loss1: 0.206482 Loss2: 1.399749 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.527338 Loss1: 1.551855 Loss2: 1.975483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.136219 Loss1: 0.627734 Loss2: 1.508484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.891923 Loss1: 0.424267 Loss2: 1.467656
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.403444 Loss1: 1.488708 Loss2: 1.914736
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.808723 Loss1: 0.339803 Loss2: 1.468920
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.348879 Loss1: 0.915537 Loss2: 1.433342
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.748320 Loss1: 0.277082 Loss2: 1.471238
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.056440 Loss1: 0.601972 Loss2: 1.454468
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.738001 Loss1: 0.281037 Loss2: 1.456964
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.831879 Loss1: 0.410153 Loss2: 1.421726
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.708734 Loss1: 0.246183 Loss2: 1.462551
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.742294 Loss1: 0.319527 Loss2: 1.422766
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.700608 Loss1: 0.235516 Loss2: 1.465092
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.693262 Loss1: 0.284825 Loss2: 1.408438
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.710376 Loss1: 0.258147 Loss2: 1.452230
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.660101 Loss1: 0.251215 Loss2: 1.408886
+(DefaultActor pid=3765) >> Training accuracy: 0.944792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.630255 Loss1: 0.222817 Loss2: 1.407438
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.609237 Loss1: 0.199256 Loss2: 1.409981
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.532126 Loss1: 0.131423 Loss2: 1.400704
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.172314 Loss1: 1.303249 Loss2: 1.869066
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.174437 Loss1: 0.783719 Loss2: 1.390718
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.994330 Loss1: 0.582045 Loss2: 1.412285
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.783440 Loss1: 0.394164 Loss2: 1.389277
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.340526 Loss1: 1.403686 Loss2: 1.936840
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.707138 Loss1: 0.337692 Loss2: 1.369446
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.395895 Loss1: 0.930932 Loss2: 1.464963
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.619748 Loss1: 0.248253 Loss2: 1.371495
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.036269 Loss1: 0.559077 Loss2: 1.477192
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.597364 Loss1: 0.235121 Loss2: 1.362243
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.910604 Loss1: 0.476615 Loss2: 1.433989
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.560443 Loss1: 0.196256 Loss2: 1.364187
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.815407 Loss1: 0.360157 Loss2: 1.455250
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.530517 Loss1: 0.180342 Loss2: 1.350175
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.693148 Loss1: 0.259621 Loss2: 1.433527
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.511986 Loss1: 0.156606 Loss2: 1.355379
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.607489 Loss1: 0.188769 Loss2: 1.418720
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.604822 Loss1: 0.195099 Loss2: 1.409723
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.597540 Loss1: 0.188646 Loss2: 1.408893
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.568268 Loss1: 0.155568 Loss2: 1.412700
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.309799 Loss1: 1.432348 Loss2: 1.877452
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.270545 Loss1: 0.863934 Loss2: 1.406612
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.017728 Loss1: 0.559305 Loss2: 1.458423
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.833382 Loss1: 0.443754 Loss2: 1.389628
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.357028 Loss1: 1.381898 Loss2: 1.975131
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.733675 Loss1: 0.310030 Loss2: 1.423644
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.366785 Loss1: 0.858359 Loss2: 1.508426
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.651969 Loss1: 0.259408 Loss2: 1.392561
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.962776 Loss1: 0.481489 Loss2: 1.481287
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.635779 Loss1: 0.242636 Loss2: 1.393143
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.947780 Loss1: 0.465040 Loss2: 1.482739
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.634763 Loss1: 0.234858 Loss2: 1.399905
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.797433 Loss1: 0.323820 Loss2: 1.473613
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.582711 Loss1: 0.188777 Loss2: 1.393934
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.761870 Loss1: 0.298183 Loss2: 1.463687
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.557622 Loss1: 0.166489 Loss2: 1.391133
+(DefaultActor pid=3765) >> Training accuracy: 0.957292
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.724549 Loss1: 0.256536 Loss2: 1.468012
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.636395 Loss1: 0.187349 Loss2: 1.449046
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.641561 Loss1: 0.194355 Loss2: 1.447206
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.600896 Loss1: 0.149839 Loss2: 1.451057
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.325315 Loss1: 1.374988 Loss2: 1.950327
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.381535 Loss1: 0.893868 Loss2: 1.487667
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.083365 Loss1: 0.606665 Loss2: 1.476700
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.869787 Loss1: 0.420859 Loss2: 1.448928
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.156466 Loss1: 1.299933 Loss2: 1.856533
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.224369 Loss1: 0.780653 Loss2: 1.443716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.972775 Loss1: 0.518538 Loss2: 1.454237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.829734 Loss1: 0.406136 Loss2: 1.423598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.676421 Loss1: 0.271351 Loss2: 1.405070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.672316 Loss1: 0.264983 Loss2: 1.407333 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.638646 Loss1: 0.239444 Loss2: 1.399203 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.544226 Loss1: 0.161113 Loss2: 1.383113 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965820
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.253391 Loss1: 1.314003 Loss2: 1.939388 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.988391 Loss1: 0.503593 Loss2: 1.484798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.379797 Loss1: 1.518786 Loss2: 1.861011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.388541 Loss1: 0.979488 Loss2: 1.409053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.134826 Loss1: 0.713567 Loss2: 1.421259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.899364 Loss1: 0.492647 Loss2: 1.406717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.741217 Loss1: 0.352045 Loss2: 1.389172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.676111 Loss1: 0.304502 Loss2: 1.371610 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.626095 Loss1: 0.250368 Loss2: 1.375727 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.555671 Loss1: 0.193770 Loss2: 1.361901 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.447593 Loss1: 1.041492 Loss2: 1.406102 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.874374 Loss1: 0.487694 Loss2: 1.386680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.679313 Loss1: 0.307743 Loss2: 1.371570
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.339871 Loss1: 1.444148 Loss2: 1.895723
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.681648 Loss1: 0.324749 Loss2: 1.356899
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.396690 Loss1: 0.913930 Loss2: 1.482760
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.098110 Loss1: 0.651667 Loss2: 1.446443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.943643 Loss1: 0.489266 Loss2: 1.454377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.794124 Loss1: 0.377865 Loss2: 1.416259 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.669131 Loss1: 0.250271 Loss2: 1.418860 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.579739 Loss1: 0.186258 Loss2: 1.393481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.547090 Loss1: 0.158942 Loss2: 1.388148 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.024891 Loss1: 0.603712 Loss2: 1.421180 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.681066 Loss1: 0.306989 Loss2: 1.374078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.212961 Loss1: 1.394416 Loss2: 1.818545
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.631820 Loss1: 0.270951 Loss2: 1.360870
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.382226 Loss1: 0.952715 Loss2: 1.429511
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.561533 Loss1: 0.211761 Loss2: 1.349772
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.001418 Loss1: 0.604382 Loss2: 1.397036
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.517770 Loss1: 0.162305 Loss2: 1.355465
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.508801 Loss1: 0.162092 Loss2: 1.346709
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.865691 Loss1: 0.460797 Loss2: 1.404894
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.546185 Loss1: 0.195892 Loss2: 1.350293
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.740825 Loss1: 0.359745 Loss2: 1.381080
+(DefaultActor pid=3765) >> Training accuracy: 0.943750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.693264 Loss1: 0.325648 Loss2: 1.367616
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.683097 Loss1: 0.313857 Loss2: 1.369240
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.589495 Loss1: 0.223043 Loss2: 1.366451
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.592975 Loss1: 0.214977 Loss2: 1.377998
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.304201 Loss1: 1.435246 Loss2: 1.868955
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.588019 Loss1: 0.213833 Loss2: 1.374186
+(DefaultActor pid=3764) >> Training accuracy: 0.960938
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.019372 Loss1: 0.579744 Loss2: 1.439628 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.810620 Loss1: 0.391425 Loss2: 1.419196 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.269958 Loss1: 1.432000 Loss2: 1.837958
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.759157 Loss1: 0.360875 Loss2: 1.398282
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.290059 Loss1: 0.933288 Loss2: 1.356771
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.708624 Loss1: 0.306140 Loss2: 1.402484
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.902700 Loss1: 0.548028 Loss2: 1.354671
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.650846 Loss1: 0.248186 Loss2: 1.402660
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.585499 Loss1: 0.195943 Loss2: 1.389556 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.565387 Loss1: 0.178651 Loss2: 1.386736 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.553521 Loss1: 0.231903 Loss2: 1.321618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493477 Loss1: 0.177552 Loss2: 1.315925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.501251 Loss1: 0.182570 Loss2: 1.318681
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.443194 Loss1: 1.533090 Loss2: 1.910104
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.376371 Loss1: 0.942922 Loss2: 1.433449
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.996630 Loss1: 0.552185 Loss2: 1.444445
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.794258 Loss1: 0.398053 Loss2: 1.396205
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.693104 Loss1: 0.273184 Loss2: 1.419920
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.295610 Loss1: 1.390226 Loss2: 1.905384
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.672112 Loss1: 0.282298 Loss2: 1.389814
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.646467 Loss1: 0.242129 Loss2: 1.404338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.618824 Loss1: 0.220087 Loss2: 1.398737 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.582061 Loss1: 0.189724 Loss2: 1.392337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.531811 Loss1: 0.151053 Loss2: 1.380758 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.618612 Loss1: 0.243125 Loss2: 1.375487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.570631 Loss1: 0.200538 Loss2: 1.370092 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.946875
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.569589 Loss1: 0.195680 Loss2: 1.373909
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.213678 Loss1: 1.325269 Loss2: 1.888409
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.174348 Loss1: 0.767689 Loss2: 1.406659
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.926692 Loss1: 0.496684 Loss2: 1.430008
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.761595 Loss1: 0.377133 Loss2: 1.384462
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.626552 Loss1: 0.229800 Loss2: 1.396753
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.169482 Loss1: 1.238697 Loss2: 1.930785
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.297591 Loss1: 0.862075 Loss2: 1.435516 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.928981 Loss1: 0.455921 Loss2: 1.473060 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.832556 Loss1: 0.416877 Loss2: 1.415679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.712635 Loss1: 0.259813 Loss2: 1.452822 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.520732 Loss1: 0.145525 Loss2: 1.375207
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.667039 Loss1: 0.251809 Loss2: 1.415231
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.645035 Loss1: 0.225455 Loss2: 1.419579
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.605019 Loss1: 0.185322 Loss2: 1.419697
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.581188 Loss1: 0.167613 Loss2: 1.413575
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.622812 Loss1: 0.201148 Loss2: 1.421664
+(DefaultActor pid=3764) >> Training accuracy: 0.943750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.379813 Loss1: 1.471206 Loss2: 1.908607
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.329103 Loss1: 0.962512 Loss2: 1.366591
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.022248 Loss1: 0.616134 Loss2: 1.406115
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.816508 Loss1: 0.444868 Loss2: 1.371640
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.705969 Loss1: 0.348701 Loss2: 1.357268
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.660789 Loss1: 0.288094 Loss2: 1.372696
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.538812 Loss1: 1.558070 Loss2: 1.980742
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.533179 Loss1: 0.995484 Loss2: 1.537695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.095200 Loss1: 0.609572 Loss2: 1.485627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.894084 Loss1: 0.410465 Loss2: 1.483619 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972356
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.741877 Loss1: 0.277531 Loss2: 1.464346 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.609472 Loss1: 0.169626 Loss2: 1.439846 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.582353 Loss1: 0.141223 Loss2: 1.441130
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.332201 Loss1: 1.390513 Loss2: 1.941688
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.346658 Loss1: 0.887912 Loss2: 1.458747 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.847555 Loss1: 0.417619 Loss2: 1.429936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.662366 Loss1: 0.233542 Loss2: 1.428823 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.661501 Loss1: 0.237654 Loss2: 1.423848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.594332 Loss1: 0.163104 Loss2: 1.431228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.899713 Loss1: 0.485800 Loss2: 1.413912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.832080 Loss1: 0.420097 Loss2: 1.411983 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.690452 Loss1: 0.265426 Loss2: 1.425025 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.696163 Loss1: 0.279841 Loss2: 1.416322 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.422951 Loss1: 1.461736 Loss2: 1.961215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.080547 Loss1: 0.574813 Loss2: 1.505734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.886254 Loss1: 0.422693 Loss2: 1.463562
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.262215 Loss1: 1.415508 Loss2: 1.846707
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.343880 Loss1: 0.929061 Loss2: 1.414819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.958162 Loss1: 0.539509 Loss2: 1.418652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.789081 Loss1: 0.420914 Loss2: 1.368166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.677475 Loss1: 0.303842 Loss2: 1.373632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.602412 Loss1: 0.232623 Loss2: 1.369789 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.620692 Loss1: 0.248119 Loss2: 1.372573 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.649029 Loss1: 0.276007 Loss2: 1.373022 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.920833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.542169 Loss1: 1.601315 Loss2: 1.940854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.059944 Loss1: 0.592483 Loss2: 1.467461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.197403 Loss1: 1.260177 Loss2: 1.937226 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.393372 Loss1: 0.930639 Loss2: 1.462733 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.104277 Loss1: 0.593517 Loss2: 1.510760 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.916955 Loss1: 0.474834 Loss2: 1.442121 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.716933 Loss1: 0.271114 Loss2: 1.445819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.622079 Loss1: 0.202005 Loss2: 1.420074 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.603439 Loss1: 0.184882 Loss2: 1.418557 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.578962 Loss1: 0.157659 Loss2: 1.421303 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.542393 Loss1: 1.055022 Loss2: 1.487371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.855957 Loss1: 0.401632 Loss2: 1.454325 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.760922 Loss1: 0.298920 Loss2: 1.462002
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.307935 Loss1: 1.378227 Loss2: 1.929708
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.689805 Loss1: 0.230434 Loss2: 1.459371
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.417048 Loss1: 0.946790 Loss2: 1.470258
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.688782 Loss1: 0.238382 Loss2: 1.450399
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.176200 Loss1: 0.685101 Loss2: 1.491099
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.999833 Loss1: 0.552714 Loss2: 1.447120 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.803245 Loss1: 0.355776 Loss2: 1.447469 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958705
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.617885 Loss1: 0.167046 Loss2: 1.450839
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.763152 Loss1: 0.333036 Loss2: 1.430117
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.700194 Loss1: 0.261753 Loss2: 1.438441
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.691529 Loss1: 0.266465 Loss2: 1.425064
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.681210 Loss1: 0.253941 Loss2: 1.427269
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.591829 Loss1: 0.162571 Loss2: 1.429258
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.341247 Loss1: 1.408046 Loss2: 1.933200
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.412491 Loss1: 0.902854 Loss2: 1.509637
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.073052 Loss1: 0.587899 Loss2: 1.485153
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.894070 Loss1: 0.424933 Loss2: 1.469137
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.819536 Loss1: 0.353591 Loss2: 1.465944
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.217498 Loss1: 1.433021 Loss2: 1.784477
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.708390 Loss1: 0.257817 Loss2: 1.450573
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.328924 Loss1: 0.974504 Loss2: 1.354421
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.970029 Loss1: 0.588716 Loss2: 1.381313
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.702713 Loss1: 0.244907 Loss2: 1.457807
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.806210 Loss1: 0.480167 Loss2: 1.326043
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.676855 Loss1: 0.232458 Loss2: 1.444397
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.681967 Loss1: 0.340013 Loss2: 1.341955
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.616053 Loss1: 0.171935 Loss2: 1.444118
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.676083 Loss1: 0.344459 Loss2: 1.331623
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.615696 Loss1: 0.187573 Loss2: 1.428124
+(DefaultActor pid=3765) >> Training accuracy: 0.963867
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.560346 Loss1: 0.240794 Loss2: 1.319552 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.564627 Loss1: 0.242232 Loss2: 1.322396 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.953125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.418985 Loss1: 1.004302 Loss2: 1.414682 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.863987 Loss1: 0.472326 Loss2: 1.391662 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.726217 Loss1: 0.334340 Loss2: 1.391876
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.336018 Loss1: 1.448850 Loss2: 1.887167
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.371522 Loss1: 0.951125 Loss2: 1.420396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.017044 Loss1: 0.580197 Loss2: 1.436847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.586106 Loss1: 0.202891 Loss2: 1.383215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.524985 Loss1: 0.144051 Loss2: 1.380934 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967548
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.653732 Loss1: 0.266149 Loss2: 1.387583 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.602157 Loss1: 0.204296 Loss2: 1.397861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.445393 Loss1: 1.371351 Loss2: 2.074042
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.562378 Loss1: 0.178428 Loss2: 1.383950
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-10 06:45:02,196 | server.py:236 | fit_round 67 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.132732 Loss1: 0.543221 Loss2: 1.589511 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.870300 Loss1: 0.318928 Loss2: 1.551371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.819623 Loss1: 0.287168 Loss2: 1.532455
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.163758 Loss1: 1.247355 Loss2: 1.916403
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.322313 Loss1: 0.895964 Loss2: 1.426350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.066537 Loss1: 0.604305 Loss2: 1.462232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.887371 Loss1: 0.464142 Loss2: 1.423230 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.932292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.777757 Loss1: 0.240126 Loss2: 1.537631
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.735594 Loss1: 0.304266 Loss2: 1.431329
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.643715 Loss1: 0.235886 Loss2: 1.407828
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.672793 Loss1: 0.275567 Loss2: 1.397226
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.615333 Loss1: 0.206185 Loss2: 1.409148
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.596969 Loss1: 0.195372 Loss2: 1.401597
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.260081 Loss1: 1.392550 Loss2: 1.867532
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.560502 Loss1: 0.158932 Loss2: 1.401569
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.976871 Loss1: 0.563325 Loss2: 1.413546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.760592 Loss1: 0.344892 Loss2: 1.415700 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.519872 Loss1: 1.612874 Loss2: 1.906998
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.677020 Loss1: 0.284511 Loss2: 1.392509
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.592125 Loss1: 1.080241 Loss2: 1.511883
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.616390 Loss1: 0.226075 Loss2: 1.390314
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.568182 Loss1: 0.189212 Loss2: 1.378970 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.533984 Loss1: 0.159702 Loss2: 1.374282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.548176 Loss1: 0.171272 Loss2: 1.376904 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.960938
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.648401 Loss1: 0.229515 Loss2: 1.418886 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.624419 Loss1: 0.210098 Loss2: 1.414321 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 06:45:02,196][flwr][DEBUG] - fit_round 67 received 50 results and 0 failures
+INFO flwr 2023-10-10 06:45:44,683 | server.py:125 | fit progress: (67, 2.2957204646957567, {'accuracy': 0.5234}, 154452.462019717)
+>> Test accuracy: 0.523400
+[2023-10-10 06:45:44,683][flwr][INFO] - fit progress: (67, 2.2957204646957567, {'accuracy': 0.5234}, 154452.462019717)
+DEBUG flwr 2023-10-10 06:45:44,684 | server.py:173 | evaluate_round 67: strategy sampled 50 clients (out of 50)
+[2023-10-10 06:45:44,684][flwr][DEBUG] - evaluate_round 67: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 06:54:51,009 | server.py:187 | evaluate_round 67 received 50 results and 0 failures
+[2023-10-10 06:54:51,009][flwr][DEBUG] - evaluate_round 67 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 06:54:51,009 | server.py:222 | fit_round 68: strategy sampled 50 clients (out of 50)
+[2023-10-10 06:54:51,009][flwr][DEBUG] - fit_round 68: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.448779 Loss1: 1.588919 Loss2: 1.859860 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.998602 Loss1: 0.588806 Loss2: 1.409795 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.845527 Loss1: 0.428597 Loss2: 1.416930
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.322443 Loss1: 1.493485 Loss2: 1.828958
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.292485 Loss1: 0.881444 Loss2: 1.411041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.990513 Loss1: 0.603988 Loss2: 1.386526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.801458 Loss1: 0.428100 Loss2: 1.373358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.680642 Loss1: 0.323437 Loss2: 1.357206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.587689 Loss1: 0.242949 Loss2: 1.344740 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.547791 Loss1: 0.161560 Loss2: 1.386231
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.585951 Loss1: 0.237779 Loss2: 1.348173
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.597688 Loss1: 0.253195 Loss2: 1.344493
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.529223 Loss1: 0.189602 Loss2: 1.339621
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.513445 Loss1: 0.177212 Loss2: 1.336232
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.220283 Loss1: 1.355557 Loss2: 1.864725
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.372025 Loss1: 0.898787 Loss2: 1.473239
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.088938 Loss1: 0.616104 Loss2: 1.472834
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.922879 Loss1: 0.485070 Loss2: 1.437809
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.348419 Loss1: 1.361194 Loss2: 1.987224
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.746418 Loss1: 0.317195 Loss2: 1.429223
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.319675 Loss1: 0.798510 Loss2: 1.521166
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.053154 Loss1: 0.525235 Loss2: 1.527919
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.708348 Loss1: 0.277047 Loss2: 1.431301
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.848091 Loss1: 0.363084 Loss2: 1.485007
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.676327 Loss1: 0.256151 Loss2: 1.420177
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.789580 Loss1: 0.295945 Loss2: 1.493635
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.586829 Loss1: 0.173441 Loss2: 1.413388
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.726518 Loss1: 0.240990 Loss2: 1.485528
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.621058 Loss1: 0.207012 Loss2: 1.414046
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.587744 Loss1: 0.180439 Loss2: 1.407305 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958984
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.659699 Loss1: 0.182921 Loss2: 1.476779 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.156062 Loss1: 1.297515 Loss2: 1.858547 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.860634 Loss1: 0.455028 Loss2: 1.405606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.181575 Loss1: 1.335419 Loss2: 1.846156
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.801360 Loss1: 0.390776 Loss2: 1.410584
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.364105 Loss1: 0.934465 Loss2: 1.429640
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.690086 Loss1: 0.281702 Loss2: 1.408384
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.645741 Loss1: 0.255468 Loss2: 1.390273 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.674132 Loss1: 0.265115 Loss2: 1.409017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.653086 Loss1: 0.243231 Loss2: 1.409856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.630567 Loss1: 0.218461 Loss2: 1.412106 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.598880 Loss1: 0.199284 Loss2: 1.399596 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.934570
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.535682 Loss1: 0.166127 Loss2: 1.369556 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.921875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.300385 Loss1: 1.446015 Loss2: 1.854369 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.079667 Loss1: 0.648384 Loss2: 1.431282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.791049 Loss1: 0.385977 Loss2: 1.405072
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.368357 Loss1: 1.471373 Loss2: 1.896985
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.401686 Loss1: 0.952447 Loss2: 1.449239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.140609 Loss1: 0.662359 Loss2: 1.478249 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.923844 Loss1: 0.496800 Loss2: 1.427044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.788959 Loss1: 0.340183 Loss2: 1.448776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.679531 Loss1: 0.266233 Loss2: 1.413297 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.493532 Loss1: 0.122932 Loss2: 1.370600
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.669831 Loss1: 0.254286 Loss2: 1.415545
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.596041 Loss1: 0.181336 Loss2: 1.414705
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.602390 Loss1: 0.192492 Loss2: 1.409898
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.576547 Loss1: 0.177107 Loss2: 1.399440
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.329036 Loss1: 1.503501 Loss2: 1.825535
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.279823 Loss1: 0.829816 Loss2: 1.450007
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.948603 Loss1: 0.551633 Loss2: 1.396970
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.785102 Loss1: 0.398389 Loss2: 1.386712
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.114391 Loss1: 1.241698 Loss2: 1.872694
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.167881 Loss1: 0.778199 Loss2: 1.389682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.935366 Loss1: 0.512526 Loss2: 1.422840 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.706540 Loss1: 0.339784 Loss2: 1.366756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.652006 Loss1: 0.279104 Loss2: 1.372902 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.569784 Loss1: 0.201806 Loss2: 1.367978 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.619254 Loss1: 0.250812 Loss2: 1.368442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.620688 Loss1: 0.248170 Loss2: 1.372518 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.322315 Loss1: 1.456773 Loss2: 1.865542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.045398 Loss1: 0.605162 Loss2: 1.440236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.792656 Loss1: 0.378003 Loss2: 1.414653
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.390156 Loss1: 1.474482 Loss2: 1.915675
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.463531 Loss1: 0.951005 Loss2: 1.512526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.097464 Loss1: 0.628907 Loss2: 1.468557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.874857 Loss1: 0.423916 Loss2: 1.450941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.782747 Loss1: 0.342064 Loss2: 1.440683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.746769 Loss1: 0.305984 Loss2: 1.440786 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.684066 Loss1: 0.240642 Loss2: 1.443424 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.635432 Loss1: 0.214503 Loss2: 1.420929 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.229138 Loss1: 1.387358 Loss2: 1.841780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.017787 Loss1: 0.584875 Loss2: 1.432913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.287278 Loss1: 1.360141 Loss2: 1.927137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.390449 Loss1: 0.869436 Loss2: 1.521013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.081043 Loss1: 0.592195 Loss2: 1.488848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.919110 Loss1: 0.447515 Loss2: 1.471595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.755052 Loss1: 0.303227 Loss2: 1.451825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.560994 Loss1: 0.204102 Loss2: 1.356892 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.655879 Loss1: 0.217305 Loss2: 1.438574 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.562249 Loss1: 0.144115 Loss2: 1.418134 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.952148
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.447548 Loss1: 0.990928 Loss2: 1.456620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.918303 Loss1: 0.489766 Loss2: 1.428536 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.866363 Loss1: 0.402854 Loss2: 1.463509
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.304508 Loss1: 1.456732 Loss2: 1.847776
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.314257 Loss1: 0.926540 Loss2: 1.387718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.007819 Loss1: 0.587213 Loss2: 1.420606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.781671 Loss1: 0.400452 Loss2: 1.381219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.695337 Loss1: 0.296101 Loss2: 1.399236 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.946875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.681253 Loss1: 0.247438 Loss2: 1.433815
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.634850 Loss1: 0.267593 Loss2: 1.367257
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.598176 Loss1: 0.221355 Loss2: 1.376822
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.562369 Loss1: 0.185700 Loss2: 1.376669
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487925 Loss1: 0.129197 Loss2: 1.358728
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491311 Loss1: 0.137352 Loss2: 1.353959
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.385847 Loss1: 1.448913 Loss2: 1.936934
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.567360 Loss1: 1.070615 Loss2: 1.496746
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.225207 Loss1: 0.722894 Loss2: 1.502313
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.931194 Loss1: 0.472427 Loss2: 1.458768
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.765835 Loss1: 0.298700 Loss2: 1.467135
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.301582 Loss1: 1.395635 Loss2: 1.905947
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.207652 Loss1: 0.781098 Loss2: 1.426555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.882152 Loss1: 0.464547 Loss2: 1.417606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.797438 Loss1: 0.399869 Loss2: 1.397569 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.695523 Loss1: 0.293891 Loss2: 1.401633 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.579164 Loss1: 0.137294 Loss2: 1.441870
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.626393 Loss1: 0.227868 Loss2: 1.398525
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.558255 Loss1: 0.171997 Loss2: 1.386258
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.565007 Loss1: 0.187366 Loss2: 1.377641
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.548668 Loss1: 0.164516 Loss2: 1.384152
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.587109 Loss1: 0.202911 Loss2: 1.384199
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.203221 Loss1: 1.417198 Loss2: 1.786023
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.358448 Loss1: 0.985391 Loss2: 1.373057
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.943434 Loss1: 0.569734 Loss2: 1.373700
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.762250 Loss1: 0.425533 Loss2: 1.336717
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.704444 Loss1: 0.345693 Loss2: 1.358751
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.277597 Loss1: 1.439341 Loss2: 1.838256
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.255801 Loss1: 0.840375 Loss2: 1.415426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.977153 Loss1: 0.559702 Loss2: 1.417450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.754996 Loss1: 0.371915 Loss2: 1.383081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.720259 Loss1: 0.335825 Loss2: 1.384434 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.631636 Loss1: 0.242405 Loss2: 1.389231 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.564788 Loss1: 0.196787 Loss2: 1.368001 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.170158 Loss1: 1.344240 Loss2: 1.825918
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.504947 Loss1: 0.140421 Loss2: 1.364526
+(DefaultActor pid=3764) >> Training accuracy: 0.971680
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.989219 Loss1: 0.584409 Loss2: 1.404811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.684224 Loss1: 0.303482 Loss2: 1.380742 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.294170 Loss1: 1.426076 Loss2: 1.868094
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.636784 Loss1: 0.247374 Loss2: 1.389410
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.275522 Loss1: 0.841254 Loss2: 1.434268
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.572559 Loss1: 0.191376 Loss2: 1.381182
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.962760 Loss1: 0.533265 Loss2: 1.429495
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.596186 Loss1: 0.217239 Loss2: 1.378947
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.568617 Loss1: 0.182909 Loss2: 1.385707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508745 Loss1: 0.139648 Loss2: 1.369098 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.549115 Loss1: 0.172934 Loss2: 1.376181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.470801 Loss1: 0.108873 Loss2: 1.361928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491340 Loss1: 0.130349 Loss2: 1.360991
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.248234 Loss1: 1.246230 Loss2: 2.002004
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.411296 Loss1: 0.898377 Loss2: 1.512919
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.193101 Loss1: 0.612812 Loss2: 1.580290
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.074835 Loss1: 0.567284 Loss2: 1.507552
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.959381 Loss1: 0.430609 Loss2: 1.528772
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.367774 Loss1: 1.504360 Loss2: 1.863413
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.797653 Loss1: 0.302948 Loss2: 1.494704
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.422646 Loss1: 0.937067 Loss2: 1.485579
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.720939 Loss1: 0.227615 Loss2: 1.493324
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.078425 Loss1: 0.635995 Loss2: 1.442430
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.630510 Loss1: 0.154620 Loss2: 1.475889
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.841982 Loss1: 0.417219 Loss2: 1.424763
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.599879 Loss1: 0.130513 Loss2: 1.469366
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.574506 Loss1: 0.111312 Loss2: 1.463194
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.753769 Loss1: 0.332654 Loss2: 1.421115
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.677783 Loss1: 0.267617 Loss2: 1.410165
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.662954 Loss1: 0.250865 Loss2: 1.412089
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.647038 Loss1: 0.233235 Loss2: 1.413803
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.607188 Loss1: 0.199212 Loss2: 1.407976
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.233487 Loss1: 1.398183 Loss2: 1.835304
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.625705 Loss1: 0.213118 Loss2: 1.412587
+(DefaultActor pid=3764) >> Training accuracy: 0.965820
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.013283 Loss1: 0.592054 Loss2: 1.421229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.736764 Loss1: 0.351862 Loss2: 1.384903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.649818 Loss1: 0.279360 Loss2: 1.370458
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.276273 Loss1: 1.414050 Loss2: 1.862224
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.613649 Loss1: 0.241078 Loss2: 1.372571
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.321449 Loss1: 0.913430 Loss2: 1.408019
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.545256 Loss1: 0.181176 Loss2: 1.364080
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.011253 Loss1: 0.586666 Loss2: 1.424588
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.497845 Loss1: 0.134661 Loss2: 1.363184
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.804560 Loss1: 0.406122 Loss2: 1.398438
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484958 Loss1: 0.129503 Loss2: 1.355456
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.694661 Loss1: 0.298968 Loss2: 1.395693
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.629907 Loss1: 0.248552 Loss2: 1.381355
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.584712 Loss1: 0.198812 Loss2: 1.385900
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546228 Loss1: 0.171403 Loss2: 1.374825
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.549318 Loss1: 0.165783 Loss2: 1.383536
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.536987 Loss1: 0.164309 Loss2: 1.372678
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.446272 Loss1: 1.528498 Loss2: 1.917774
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.390278 Loss1: 0.928667 Loss2: 1.461610
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.087627 Loss1: 0.611686 Loss2: 1.475942
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.925762 Loss1: 0.493176 Loss2: 1.432585
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.791769 Loss1: 0.357133 Loss2: 1.434636
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.741102 Loss1: 0.324318 Loss2: 1.416783
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.266188 Loss1: 1.391546 Loss2: 1.874641
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.628289 Loss1: 0.219972 Loss2: 1.408317
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.286546 Loss1: 0.868384 Loss2: 1.418162
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.589069 Loss1: 0.180477 Loss2: 1.408592
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.952672 Loss1: 0.523650 Loss2: 1.429022
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.558135 Loss1: 0.163265 Loss2: 1.394870
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.884535 Loss1: 0.499868 Loss2: 1.384667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.528577 Loss1: 0.131233 Loss2: 1.397344
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.773750 Loss1: 0.341986 Loss2: 1.431765
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.673465 Loss1: 0.294513 Loss2: 1.378953
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.607607 Loss1: 0.221587 Loss2: 1.386020
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.608965 Loss1: 0.224688 Loss2: 1.384277
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.547882 Loss1: 0.171439 Loss2: 1.376443
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.414378 Loss1: 1.524212 Loss2: 1.890166
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527897 Loss1: 0.160297 Loss2: 1.367600
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.099103 Loss1: 0.666129 Loss2: 1.432974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.694381 Loss1: 0.317320 Loss2: 1.377061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.361972 Loss1: 1.362014 Loss2: 1.999958 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.262493 Loss1: 0.834253 Loss2: 1.428240 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.038855 Loss1: 0.577288 Loss2: 1.461567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.913763 Loss1: 0.473564 Loss2: 1.440200 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.762434 Loss1: 0.330206 Loss2: 1.432228 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.637317 Loss1: 0.229516 Loss2: 1.407800 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.571834 Loss1: 0.170477 Loss2: 1.401357 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978365
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.530643 Loss1: 0.136623 Loss2: 1.394019
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.101548 Loss1: 1.154868 Loss2: 1.946680
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.361143 Loss1: 0.881145 Loss2: 1.479998
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.071674 Loss1: 0.558021 Loss2: 1.513653
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.807156 Loss1: 0.353319 Loss2: 1.453837
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.771909 Loss1: 0.319160 Loss2: 1.452749
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.341157 Loss1: 1.480196 Loss2: 1.860960
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.452902 Loss1: 1.011929 Loss2: 1.440973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.125628 Loss1: 0.698347 Loss2: 1.427281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.856344 Loss1: 0.444684 Loss2: 1.411660 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.745550 Loss1: 0.358762 Loss2: 1.386788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.622991 Loss1: 0.191835 Loss2: 1.431156
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.703939 Loss1: 0.312400 Loss2: 1.391539
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.654221 Loss1: 0.261021 Loss2: 1.393200
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.668415 Loss1: 0.276164 Loss2: 1.392252
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.618127 Loss1: 0.224672 Loss2: 1.393455
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.612211 Loss1: 0.226034 Loss2: 1.386177
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.210388 Loss1: 1.421683 Loss2: 1.788705
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.328111 Loss1: 0.930351 Loss2: 1.397761
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.089247 Loss1: 0.712163 Loss2: 1.377084
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.823388 Loss1: 0.463180 Loss2: 1.360208
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.724094 Loss1: 0.374904 Loss2: 1.349190
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.390503 Loss1: 1.520500 Loss2: 1.870003
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.289882 Loss1: 0.869982 Loss2: 1.419901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.050839 Loss1: 0.625323 Loss2: 1.425516 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.821025 Loss1: 0.430450 Loss2: 1.390575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.744044 Loss1: 0.346402 Loss2: 1.397642 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.587419 Loss1: 0.238458 Loss2: 1.348961
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.650758 Loss1: 0.273217 Loss2: 1.377540
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.576561 Loss1: 0.204389 Loss2: 1.372172
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.562121 Loss1: 0.190247 Loss2: 1.371875
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.511274 Loss1: 0.145929 Loss2: 1.365345
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.488244 Loss1: 0.128888 Loss2: 1.359356
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.101671 Loss1: 1.277273 Loss2: 1.824398
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.311417 Loss1: 0.848995 Loss2: 1.462421
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.965958 Loss1: 0.539824 Loss2: 1.426134
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.779311 Loss1: 0.369425 Loss2: 1.409886
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.738267 Loss1: 0.330772 Loss2: 1.407495
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.237758 Loss1: 1.380542 Loss2: 1.857216
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.702687 Loss1: 0.284587 Loss2: 1.418100
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.270307 Loss1: 0.852847 Loss2: 1.417460
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.662851 Loss1: 0.267863 Loss2: 1.394987
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.976320 Loss1: 0.552491 Loss2: 1.423829
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.798672 Loss1: 0.407119 Loss2: 1.391553
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.658761 Loss1: 0.246535 Loss2: 1.412226
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.653654 Loss1: 0.271894 Loss2: 1.381760
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.650485 Loss1: 0.246332 Loss2: 1.404153
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.592420 Loss1: 0.226606 Loss2: 1.365814
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.616318 Loss1: 0.206696 Loss2: 1.409621
+(DefaultActor pid=3765) >> Training accuracy: 0.931641
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511727 Loss1: 0.152091 Loss2: 1.359635 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.469916 Loss1: 0.121819 Loss2: 1.348097 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.563038 Loss1: 1.068855 Loss2: 1.494183 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.984683 Loss1: 0.489788 Loss2: 1.494896 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.524947 Loss1: 1.596593 Loss2: 1.928353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.410172 Loss1: 0.984548 Loss2: 1.425624 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.674179 Loss1: 0.202741 Loss2: 1.471437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.622328 Loss1: 0.154464 Loss2: 1.467864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.582030 Loss1: 0.124705 Loss2: 1.457325 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977865
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.608913 Loss1: 0.222916 Loss2: 1.385997 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.574354 Loss1: 0.182342 Loss2: 1.392012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.559956 Loss1: 0.172977 Loss2: 1.386979
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.275436 Loss1: 1.384238 Loss2: 1.891198
+(DefaultActor pid=3764) >> Training accuracy: 0.977679
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.456882 Loss1: 0.944380 Loss2: 1.512502
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.068903 Loss1: 0.627785 Loss2: 1.441118
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.811941 Loss1: 0.369533 Loss2: 1.442409
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.687802 Loss1: 0.257543 Loss2: 1.430258
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.158588 Loss1: 1.292059 Loss2: 1.866529
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.424800 Loss1: 0.959415 Loss2: 1.465385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.922083 Loss1: 0.500421 Loss2: 1.421662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.754936 Loss1: 0.359753 Loss2: 1.395183 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.708148 Loss1: 0.313039 Loss2: 1.395110 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.531235 Loss1: 0.129736 Loss2: 1.401499
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.604342 Loss1: 0.224359 Loss2: 1.379982
+(DefaultActor pid=3765) >> Training accuracy: 0.975586
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.563672 Loss1: 0.192292 Loss2: 1.371380
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.550785 Loss1: 0.185177 Loss2: 1.365608
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.515145 Loss1: 0.144733 Loss2: 1.370412
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.561329 Loss1: 0.187845 Loss2: 1.373484
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.212912 Loss1: 1.338368 Loss2: 1.874544
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.375250 Loss1: 0.910798 Loss2: 1.464452
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.044447 Loss1: 0.585045 Loss2: 1.459402
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.887264 Loss1: 0.448855 Loss2: 1.438409
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.723698 Loss1: 0.284533 Loss2: 1.439165
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.268770 Loss1: 1.412322 Loss2: 1.856448
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.306051 Loss1: 0.889274 Loss2: 1.416777
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.673180 Loss1: 0.255871 Loss2: 1.417309
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.996728 Loss1: 0.573085 Loss2: 1.423643
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.632474 Loss1: 0.210029 Loss2: 1.422445
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.791001 Loss1: 0.390099 Loss2: 1.400901
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.616721 Loss1: 0.195925 Loss2: 1.420796
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.721510 Loss1: 0.331649 Loss2: 1.389862
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.604202 Loss1: 0.186178 Loss2: 1.418024
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.591956 Loss1: 0.172651 Loss2: 1.419305 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.949219
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.585988 Loss1: 0.189246 Loss2: 1.396742 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.562717 Loss1: 0.180657 Loss2: 1.382060 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.299014 Loss1: 1.390707 Loss2: 1.908307
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.264465 Loss1: 0.863092 Loss2: 1.401372
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.945295 Loss1: 0.544886 Loss2: 1.400409
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.763267 Loss1: 0.383699 Loss2: 1.379568
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.368792 Loss1: 1.490180 Loss2: 1.878612
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.351620 Loss1: 0.922905 Loss2: 1.428715 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.982231 Loss1: 0.539264 Loss2: 1.442967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.827808 Loss1: 0.418355 Loss2: 1.409453 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.721920 Loss1: 0.320119 Loss2: 1.401801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.638346 Loss1: 0.234729 Loss2: 1.403617 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548731 Loss1: 0.168778 Loss2: 1.379952 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.500016 Loss1: 0.127217 Loss2: 1.372799 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.250834 Loss1: 0.867921 Loss2: 1.382913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.821008 Loss1: 0.465510 Loss2: 1.355498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.014328 Loss1: 1.209707 Loss2: 1.804622
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.730382 Loss1: 0.377507 Loss2: 1.352876
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.634174 Loss1: 0.290776 Loss2: 1.343399
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.152417 Loss1: 0.742339 Loss2: 1.410077
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.597443 Loss1: 0.254232 Loss2: 1.343211
+DEBUG flwr 2023-10-10 07:23:35,716 | server.py:236 | fit_round 68 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.895045 Loss1: 0.499629 Loss2: 1.395416
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.524579 Loss1: 0.188885 Loss2: 1.335694
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.809632 Loss1: 0.424023 Loss2: 1.385608
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.715106 Loss1: 0.332733 Loss2: 1.382373 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.473764 Loss1: 0.146767 Loss2: 1.326997
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.679950 Loss1: 0.303392 Loss2: 1.376558
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.632012 Loss1: 0.257625 Loss2: 1.374388
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.593842 Loss1: 0.220565 Loss2: 1.373277
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.611059 Loss1: 0.242772 Loss2: 1.368288
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.288831 Loss1: 1.513098 Loss2: 1.775733
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.528663 Loss1: 0.166568 Loss2: 1.362095
+(DefaultActor pid=3764) >> Training accuracy: 0.955882
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.937604 Loss1: 0.578161 Loss2: 1.359442 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.674566 Loss1: 0.344070 Loss2: 1.330496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.602538 Loss1: 0.274388 Loss2: 1.328150
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.403474 Loss1: 1.367784 Loss2: 2.035690
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.520033 Loss1: 0.951352 Loss2: 1.568681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.535833 Loss1: 0.209362 Loss2: 1.326471
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.135179 Loss1: 0.557468 Loss2: 1.577711
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.965409 Loss1: 0.425978 Loss2: 1.539432 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474885 Loss1: 0.157289 Loss2: 1.317596
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.844494 Loss1: 0.309447 Loss2: 1.535047
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.805857 Loss1: 0.285676 Loss2: 1.520181
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.800339 Loss1: 0.272358 Loss2: 1.527981
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.751033 Loss1: 0.234096 Loss2: 1.516937
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.725472 Loss1: 0.206890 Loss2: 1.518581
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.300640 Loss1: 1.465159 Loss2: 1.835480
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.716114 Loss1: 0.204619 Loss2: 1.511496
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.881087 Loss1: 0.510455 Loss2: 1.370632 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.659747 Loss1: 0.330556 Loss2: 1.329191 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.433178 Loss1: 1.471587 Loss2: 1.961591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.451958 Loss1: 1.011988 Loss2: 1.439970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.101660 Loss1: 0.603855 Loss2: 1.497805 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.820872 Loss1: 0.393699 Loss2: 1.427172 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975446
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.495364 Loss1: 0.184164 Loss2: 1.311200
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.709120 Loss1: 0.280604 Loss2: 1.428515
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.670714 Loss1: 0.240273 Loss2: 1.430442
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.628991 Loss1: 0.209174 Loss2: 1.419817
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.659279 Loss1: 0.237567 Loss2: 1.421711
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.602145 Loss1: 0.175940 Loss2: 1.426205
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.588213 Loss1: 0.165987 Loss2: 1.422227
+(DefaultActor pid=3764) >> Training accuracy: 0.969952
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 07:23:35,716][flwr][DEBUG] - fit_round 68 received 50 results and 0 failures
+INFO flwr 2023-10-10 07:24:17,270 | server.py:125 | fit progress: (68, 2.3044485558336154, {'accuracy': 0.5227}, 156765.04836486402)
+>> Test accuracy: 0.522700
+[2023-10-10 07:24:17,270][flwr][INFO] - fit progress: (68, 2.3044485558336154, {'accuracy': 0.5227}, 156765.04836486402)
+DEBUG flwr 2023-10-10 07:24:17,270 | server.py:173 | evaluate_round 68: strategy sampled 50 clients (out of 50)
+[2023-10-10 07:24:17,270][flwr][DEBUG] - evaluate_round 68: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 07:33:24,811 | server.py:187 | evaluate_round 68 received 50 results and 0 failures
+[2023-10-10 07:33:24,811][flwr][DEBUG] - evaluate_round 68 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 07:33:24,812 | server.py:222 | fit_round 69: strategy sampled 50 clients (out of 50)
+[2023-10-10 07:33:24,812][flwr][DEBUG] - fit_round 69: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.373848 Loss1: 1.420487 Loss2: 1.953361
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.402347 Loss1: 0.912187 Loss2: 1.490160
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.047090 Loss1: 0.565633 Loss2: 1.481457
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.951999 Loss1: 0.493622 Loss2: 1.458377
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.831821 Loss1: 0.369443 Loss2: 1.462378 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.752603 Loss1: 0.298691 Loss2: 1.453912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.688104 Loss1: 0.239610 Loss2: 1.448494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.659479 Loss1: 0.213384 Loss2: 1.446096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.614995 Loss1: 0.176732 Loss2: 1.438263 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.587372 Loss1: 0.158193 Loss2: 1.429179 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.650451 Loss1: 0.223803 Loss2: 1.426647 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.372592 Loss1: 1.497731 Loss2: 1.874861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.035918 Loss1: 0.614949 Loss2: 1.420969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.846240 Loss1: 0.451560 Loss2: 1.394680
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.244455 Loss1: 1.365728 Loss2: 1.878727
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.366081 Loss1: 0.888855 Loss2: 1.477227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.053237 Loss1: 0.564745 Loss2: 1.488492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.785216 Loss1: 0.338041 Loss2: 1.447176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.786905 Loss1: 0.334568 Loss2: 1.452337 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.712882 Loss1: 0.262398 Loss2: 1.450485 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.639262 Loss1: 0.201034 Loss2: 1.438228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.551564 Loss1: 0.122663 Loss2: 1.428901 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.955078
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.334028 Loss1: 1.434995 Loss2: 1.899034 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.116380 Loss1: 0.668879 Loss2: 1.447500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.387245 Loss1: 1.473726 Loss2: 1.913520 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.378817 Loss1: 0.885618 Loss2: 1.493199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.052979 Loss1: 0.618829 Loss2: 1.434150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.860870 Loss1: 0.425590 Loss2: 1.435280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.727542 Loss1: 0.305284 Loss2: 1.422258 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.657038 Loss1: 0.256871 Loss2: 1.400167 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.957292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.577464 Loss1: 0.177238 Loss2: 1.400226 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.586254 Loss1: 0.184636 Loss2: 1.401618 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.250165 Loss1: 0.795994 Loss2: 1.454171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.776384 Loss1: 0.362530 Loss2: 1.413855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.668727 Loss1: 0.240979 Loss2: 1.427748
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.419239 Loss1: 1.465608 Loss2: 1.953631
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.615574 Loss1: 0.206224 Loss2: 1.409350
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.399210 Loss1: 0.916817 Loss2: 1.482393
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.584048 Loss1: 0.179888 Loss2: 1.404160
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.173787 Loss1: 0.672774 Loss2: 1.501014
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.594055 Loss1: 0.195343 Loss2: 1.398712
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.984119 Loss1: 0.495980 Loss2: 1.488139
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.565709 Loss1: 0.165958 Loss2: 1.399751
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.828530 Loss1: 0.352972 Loss2: 1.475558
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.583404 Loss1: 0.179080 Loss2: 1.404323
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.805384 Loss1: 0.344695 Loss2: 1.460688
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.736645 Loss1: 0.273747 Loss2: 1.462898
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.685801 Loss1: 0.234615 Loss2: 1.451186
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.673989 Loss1: 0.222174 Loss2: 1.451816
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.632603 Loss1: 0.184313 Loss2: 1.448291
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.316712 Loss1: 1.342389 Loss2: 1.974324
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.340361 Loss1: 0.843021 Loss2: 1.497340
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.071867 Loss1: 0.562819 Loss2: 1.509048
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.854840 Loss1: 0.393153 Loss2: 1.461687
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.734781 Loss1: 0.270639 Loss2: 1.464142
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.415129 Loss1: 0.968298 Loss2: 1.446831 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.039206 Loss1: 0.563023 Loss2: 1.476183 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.868991 Loss1: 0.443002 Loss2: 1.425990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.735544 Loss1: 0.299025 Loss2: 1.436518 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.715508 Loss1: 0.279568 Loss2: 1.435940 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.613569 Loss1: 0.195356 Loss2: 1.418214 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.633702 Loss1: 0.210964 Loss2: 1.422738 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.947545
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.253672 Loss1: 1.289923 Loss2: 1.963748
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.393170 Loss1: 0.886093 Loss2: 1.507077
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.101333 Loss1: 0.606044 Loss2: 1.495289
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.865848 Loss1: 0.393374 Loss2: 1.472473
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.082937 Loss1: 1.226293 Loss2: 1.856644
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.130658 Loss1: 0.699179 Loss2: 1.431479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.923481 Loss1: 0.489958 Loss2: 1.433522 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.739872 Loss1: 0.291789 Loss2: 1.448083 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.700302 Loss1: 0.227472 Loss2: 1.472830 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.620291 Loss1: 0.169367 Loss2: 1.450924 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.608408 Loss1: 0.216560 Loss2: 1.391848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.570186 Loss1: 0.180028 Loss2: 1.390158 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.594580 Loss1: 0.203648 Loss2: 1.390932
+(DefaultActor pid=3764) >> Training accuracy: 0.930147
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.388922 Loss1: 1.429056 Loss2: 1.959866
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.368900 Loss1: 0.879076 Loss2: 1.489824
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.127197 Loss1: 0.622576 Loss2: 1.504620
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.881158 Loss1: 0.414394 Loss2: 1.466764
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.788390 Loss1: 0.326541 Loss2: 1.461849
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.062599 Loss1: 1.234883 Loss2: 1.827717
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.734043 Loss1: 0.280769 Loss2: 1.453274
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.138552 Loss1: 0.752136 Loss2: 1.386416
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.719087 Loss1: 0.269650 Loss2: 1.449436
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.659969 Loss1: 0.215325 Loss2: 1.444644
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.908109 Loss1: 0.500602 Loss2: 1.407507
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.623640 Loss1: 0.182467 Loss2: 1.441173
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.787047 Loss1: 0.425255 Loss2: 1.361792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.589879 Loss1: 0.155235 Loss2: 1.434644
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.674329 Loss1: 0.306841 Loss2: 1.367488
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.635249 Loss1: 0.278916 Loss2: 1.356333
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.612913 Loss1: 0.261608 Loss2: 1.351305
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.537649 Loss1: 0.183440 Loss2: 1.354209
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.526948 Loss1: 0.184083 Loss2: 1.342864
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.435686 Loss1: 1.509219 Loss2: 1.926467
+(DefaultActor pid=3764) >> Training accuracy: 0.975586
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.448004 Loss1: 0.972230 Loss2: 1.475773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.874287 Loss1: 0.431739 Loss2: 1.442548 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.662805 Loss1: 0.235505 Loss2: 1.427300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.616479 Loss1: 0.203552 Loss2: 1.412927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.595405 Loss1: 0.180190 Loss2: 1.415215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.595904 Loss1: 0.174203 Loss2: 1.421701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.574285 Loss1: 0.158048 Loss2: 1.416237 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.724850 Loss1: 0.267984 Loss2: 1.456866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.726920 Loss1: 0.241029 Loss2: 1.485891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.641566 Loss1: 0.176765 Loss2: 1.464801
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.306232 Loss1: 1.404717 Loss2: 1.901515
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.307125 Loss1: 0.832809 Loss2: 1.474316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.788819 Loss1: 0.371615 Loss2: 1.417204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.666125 Loss1: 0.279531 Loss2: 1.386593 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.645546 Loss1: 0.243920 Loss2: 1.401626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.606228 Loss1: 0.206532 Loss2: 1.399696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.546348 Loss1: 0.153102 Loss2: 1.393246 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.577558 Loss1: 0.186515 Loss2: 1.391043 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.608143 Loss1: 0.213514 Loss2: 1.394629 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.555716 Loss1: 0.162422 Loss2: 1.393293 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973558
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527249 Loss1: 0.142886 Loss2: 1.384363
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.283713 Loss1: 1.393098 Loss2: 1.890614
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.360920 Loss1: 0.908121 Loss2: 1.452799
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.063824 Loss1: 0.618645 Loss2: 1.445179
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.818111 Loss1: 0.406499 Loss2: 1.411611
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.755379 Loss1: 0.338787 Loss2: 1.416591
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.302320 Loss1: 1.309655 Loss2: 1.992664
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.456929 Loss1: 0.889609 Loss2: 1.567320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.142915 Loss1: 0.608570 Loss2: 1.534346 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.985385 Loss1: 0.451158 Loss2: 1.534226 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.910873 Loss1: 0.391864 Loss2: 1.519010 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.834992 Loss1: 0.306353 Loss2: 1.528639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.807511 Loss1: 0.294160 Loss2: 1.513351 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.680675 Loss1: 0.186876 Loss2: 1.493799 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.945312
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.008130 Loss1: 0.563267 Loss2: 1.444864 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.758342 Loss1: 0.343453 Loss2: 1.414890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.409973 Loss1: 1.504334 Loss2: 1.905639
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.718957 Loss1: 0.317164 Loss2: 1.401793
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.274558 Loss1: 0.812667 Loss2: 1.461891
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.710192 Loss1: 0.308534 Loss2: 1.401658
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.954299 Loss1: 0.502303 Loss2: 1.451997
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.607536 Loss1: 0.205792 Loss2: 1.401744
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.841883 Loss1: 0.412454 Loss2: 1.429430
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.602627 Loss1: 0.217611 Loss2: 1.385016
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.723036 Loss1: 0.286668 Loss2: 1.436367
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.581382 Loss1: 0.195759 Loss2: 1.385623
+(DefaultActor pid=3765) >> Training accuracy: 0.960417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.643619 Loss1: 0.230431 Loss2: 1.413189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.565414 Loss1: 0.157174 Loss2: 1.408241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527148 Loss1: 0.128857 Loss2: 1.398291
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.291443 Loss1: 1.390001 Loss2: 1.901442
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.227755 Loss1: 0.794130 Loss2: 1.433625
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.934783 Loss1: 0.485352 Loss2: 1.449431
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.828429 Loss1: 0.415238 Loss2: 1.413191
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.767275 Loss1: 0.338383 Loss2: 1.428893
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.410552 Loss1: 1.446748 Loss2: 1.963804
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.687049 Loss1: 0.271992 Loss2: 1.415057
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.409520 Loss1: 0.919821 Loss2: 1.489699
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.654417 Loss1: 0.234641 Loss2: 1.419776
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.061317 Loss1: 0.558991 Loss2: 1.502326
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.616234 Loss1: 0.207030 Loss2: 1.409204
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.981085 Loss1: 0.494905 Loss2: 1.486180
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.626841 Loss1: 0.208627 Loss2: 1.418214
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.815179 Loss1: 0.347601 Loss2: 1.467578
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.566461 Loss1: 0.155178 Loss2: 1.411283
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.672888 Loss1: 0.218435 Loss2: 1.454453 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.593847 Loss1: 0.153622 Loss2: 1.440225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.569977 Loss1: 0.134143 Loss2: 1.435833
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.127670 Loss1: 1.307383 Loss2: 1.820286
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.141645 Loss1: 0.714175 Loss2: 1.427470
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.882552 Loss1: 0.472733 Loss2: 1.409819
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.704791 Loss1: 0.321427 Loss2: 1.383364
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.628717 Loss1: 0.244238 Loss2: 1.384479
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.397312 Loss1: 1.504844 Loss2: 1.892467
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.611201 Loss1: 0.231949 Loss2: 1.379252
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524036 Loss1: 0.153854 Loss2: 1.370181 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500968 Loss1: 0.139849 Loss2: 1.361120 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.546998 Loss1: 0.186206 Loss2: 1.360793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.526798 Loss1: 0.164590 Loss2: 1.362208 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.660926 Loss1: 0.248403 Loss2: 1.412524 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.570920 Loss1: 0.173115 Loss2: 1.397805 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.954167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.264102 Loss1: 0.850711 Loss2: 1.413391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.778062 Loss1: 0.391759 Loss2: 1.386303 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.717001 Loss1: 0.314505 Loss2: 1.402496
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.311818 Loss1: 1.409303 Loss2: 1.902515
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.445202 Loss1: 0.929987 Loss2: 1.515215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.124427 Loss1: 0.643446 Loss2: 1.480980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.954200 Loss1: 0.480526 Loss2: 1.473673 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.755375 Loss1: 0.300846 Loss2: 1.454529 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.685928 Loss1: 0.230831 Loss2: 1.455097 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.647198 Loss1: 0.214501 Loss2: 1.432697 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.154503 Loss1: 1.261364 Loss2: 1.893140
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.644161 Loss1: 0.201673 Loss2: 1.442488
+(DefaultActor pid=3764) >> Training accuracy: 0.967773
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.945867 Loss1: 0.494589 Loss2: 1.451278 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.675799 Loss1: 0.263262 Loss2: 1.412537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.664398 Loss1: 0.265160 Loss2: 1.399238
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.326999 Loss1: 1.394930 Loss2: 1.932069
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.663040 Loss1: 0.253093 Loss2: 1.409947
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.348106 Loss1: 0.897714 Loss2: 1.450392
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.569804 Loss1: 0.173350 Loss2: 1.396454
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.003275 Loss1: 0.541209 Loss2: 1.462066
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.570974 Loss1: 0.178034 Loss2: 1.392940
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.839937 Loss1: 0.409764 Loss2: 1.430172
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.539094 Loss1: 0.146116 Loss2: 1.392978
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.731312 Loss1: 0.299100 Loss2: 1.432212
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.748995 Loss1: 0.312807 Loss2: 1.436188
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.736457 Loss1: 0.297836 Loss2: 1.438621
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.662523 Loss1: 0.240669 Loss2: 1.421854
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.588623 Loss1: 0.176527 Loss2: 1.412096
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.316505 Loss1: 1.384513 Loss2: 1.931993
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.567046 Loss1: 0.152419 Loss2: 1.414627
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.050408 Loss1: 0.549650 Loss2: 1.500757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.763764 Loss1: 0.270095 Loss2: 1.493669 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.244352 Loss1: 1.241112 Loss2: 2.003241
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.695033 Loss1: 0.230502 Loss2: 1.464531
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.382151 Loss1: 0.888341 Loss2: 1.493810
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.724075 Loss1: 0.255975 Loss2: 1.468100
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.107102 Loss1: 0.610587 Loss2: 1.496515
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.761197 Loss1: 0.273438 Loss2: 1.487760
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.910509 Loss1: 0.459472 Loss2: 1.451037
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.688014 Loss1: 0.217265 Loss2: 1.470749
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.685257 Loss1: 0.216271 Loss2: 1.468986 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965820
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.692133 Loss1: 0.249550 Loss2: 1.442583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.555585 Loss1: 0.126695 Loss2: 1.428890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.531796 Loss1: 0.122927 Loss2: 1.408868
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.524767 Loss1: 1.567217 Loss2: 1.957550
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.490684 Loss1: 0.991619 Loss2: 1.499065
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.101103 Loss1: 0.642577 Loss2: 1.458526
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.864613 Loss1: 0.413656 Loss2: 1.450957
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.827747 Loss1: 0.386680 Loss2: 1.441067
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.530921 Loss1: 1.572408 Loss2: 1.958512
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.698392 Loss1: 0.258526 Loss2: 1.439865
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.710056 Loss1: 0.285471 Loss2: 1.424585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.674127 Loss1: 0.250243 Loss2: 1.423883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.615610 Loss1: 0.190265 Loss2: 1.425345 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.620284 Loss1: 0.192331 Loss2: 1.427953 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.605095 Loss1: 0.178969 Loss2: 1.426127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.594811 Loss1: 0.187167 Loss2: 1.407644 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978795
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.242470 Loss1: 1.432290 Loss2: 1.810180 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.959142 Loss1: 0.569370 Loss2: 1.389771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.358495 Loss1: 1.444029 Loss2: 1.914466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.306361 Loss1: 0.878714 Loss2: 1.427647 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.045797 Loss1: 0.581278 Loss2: 1.464519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.804932 Loss1: 0.399833 Loss2: 1.405099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.711732 Loss1: 0.298961 Loss2: 1.412771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647091 Loss1: 0.241157 Loss2: 1.405934 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.653189 Loss1: 0.240042 Loss2: 1.413147 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.602378 Loss1: 0.191637 Loss2: 1.410741 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.267566 Loss1: 0.860986 Loss2: 1.406580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.760049 Loss1: 0.378921 Loss2: 1.381128 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.154271 Loss1: 1.254650 Loss2: 1.899621
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.671659 Loss1: 0.281816 Loss2: 1.389843
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.418882 Loss1: 0.956632 Loss2: 1.462250
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.588039 Loss1: 0.205313 Loss2: 1.382725
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.573261 Loss1: 0.195166 Loss2: 1.378095
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.140123 Loss1: 0.654475 Loss2: 1.485649
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.565415 Loss1: 0.186021 Loss2: 1.379394
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.840180 Loss1: 0.396702 Loss2: 1.443479
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.498099 Loss1: 0.125583 Loss2: 1.372516
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.754042 Loss1: 0.305616 Loss2: 1.448426
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.483559 Loss1: 0.120530 Loss2: 1.363029
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.690646 Loss1: 0.266147 Loss2: 1.424499
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.675128 Loss1: 0.247826 Loss2: 1.427302
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.671529 Loss1: 0.234132 Loss2: 1.437397
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.598708 Loss1: 0.168372 Loss2: 1.430337
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.581425 Loss1: 0.158851 Loss2: 1.422575
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.316493 Loss1: 1.346132 Loss2: 1.970361
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.322171 Loss1: 0.836786 Loss2: 1.485385
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.086043 Loss1: 0.584975 Loss2: 1.501068
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.963662 Loss1: 0.497230 Loss2: 1.466431
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.780713 Loss1: 0.301458 Loss2: 1.479256
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.345012 Loss1: 1.479951 Loss2: 1.865061
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.354580 Loss1: 0.926006 Loss2: 1.428574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.959041 Loss1: 0.548492 Loss2: 1.410549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.753516 Loss1: 0.383833 Loss2: 1.369683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.762408 Loss1: 0.382125 Loss2: 1.380283 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.954167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.667830 Loss1: 0.297562 Loss2: 1.370269 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.489537 Loss1: 0.141549 Loss2: 1.347988 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.526608 Loss1: 0.169039 Loss2: 1.357569 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.272583 Loss1: 0.835659 Loss2: 1.436924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.842020 Loss1: 0.424152 Loss2: 1.417869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.771700 Loss1: 0.349285 Loss2: 1.422415
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.219447 Loss1: 1.317709 Loss2: 1.901738
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.390186 Loss1: 0.941518 Loss2: 1.448668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.151361 Loss1: 0.644277 Loss2: 1.507083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.860867 Loss1: 0.430643 Loss2: 1.430224 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.823260 Loss1: 0.375598 Loss2: 1.447662 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.721006 Loss1: 0.282777 Loss2: 1.438228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.632512 Loss1: 0.212304 Loss2: 1.420208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.639136 Loss1: 0.212545 Loss2: 1.426591 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.363362 Loss1: 0.902801 Loss2: 1.460561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.844963 Loss1: 0.391841 Loss2: 1.453122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.779469 Loss1: 0.313305 Loss2: 1.466165 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.790280 Loss1: 0.311117 Loss2: 1.479163 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.731546 Loss1: 0.259387 Loss2: 1.472159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.674777 Loss1: 0.211487 Loss2: 1.463290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.667830 Loss1: 0.213317 Loss2: 1.454513 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.950893
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.662948 Loss1: 0.273591 Loss2: 1.389357 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.561467 Loss1: 0.186601 Loss2: 1.374866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.498482 Loss1: 0.126454 Loss2: 1.372028
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.454569 Loss1: 1.535973 Loss2: 1.918596
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.493289 Loss1: 0.132260 Loss2: 1.361029
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.395854 Loss1: 0.906027 Loss2: 1.489827
+(DefaultActor pid=3764) >> Training accuracy: 0.960938
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.971419 Loss1: 0.536592 Loss2: 1.434827
+(DefaultActor pid=3765) Epoch: 3 Loss: 2.016770 Loss1: 0.580059 Loss2: 1.436711
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.995961 Loss1: 0.526396 Loss2: 1.469565
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.808521 Loss1: 0.388904 Loss2: 1.419616
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.420875 Loss1: 1.365274 Loss2: 2.055601
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.711108 Loss1: 0.286104 Loss2: 1.425004
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.377037 Loss1: 0.808952 Loss2: 1.568085
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.622934 Loss1: 0.207050 Loss2: 1.415884
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.236713 Loss1: 0.645065 Loss2: 1.591648
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.609150 Loss1: 0.198015 Loss2: 1.411136
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.987656 Loss1: 0.436330 Loss2: 1.551325
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.609103 Loss1: 0.204206 Loss2: 1.404897
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.810147 Loss1: 0.261050 Loss2: 1.549098 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.764240 Loss1: 0.226786 Loss2: 1.537453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.393361 Loss1: 1.445624 Loss2: 1.947737
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.754052 Loss1: 0.230127 Loss2: 1.523925
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.705289 Loss1: 0.178349 Loss2: 1.526940 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.800544 Loss1: 0.454115 Loss2: 1.346430 [repeated 3x across cluster]
+DEBUG flwr 2023-10-10 08:01:53,566 | server.py:236 | fit_round 69 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.601465 Loss1: 0.251145 Loss2: 1.350320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.250461 Loss1: 1.286809 Loss2: 1.963653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.538527 Loss1: 0.196046 Loss2: 1.342481 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.959635
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.804070 Loss1: 0.374856 Loss2: 1.429214 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.625561 Loss1: 0.213138 Loss2: 1.412423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.438631 Loss1: 1.472307 Loss2: 1.966324
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.616866 Loss1: 0.205637 Loss2: 1.411229
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.233629 Loss1: 0.839220 Loss2: 1.394409
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.648597 Loss1: 0.231194 Loss2: 1.417403
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.558130 Loss1: 0.140810 Loss2: 1.417320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529353 Loss1: 0.129679 Loss2: 1.399674 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.566634 Loss1: 0.205733 Loss2: 1.360901 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.536375 Loss1: 0.169784 Loss2: 1.366591 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.521525 Loss1: 0.177404 Loss2: 1.344120
+(DefaultActor pid=3765) >> Training accuracy: 0.965144
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.219767 Loss1: 1.372613 Loss2: 1.847154
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.337599 Loss1: 0.906867 Loss2: 1.430732
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.035381 Loss1: 0.606846 Loss2: 1.428535
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.855587 Loss1: 0.446560 Loss2: 1.409027
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.794122 Loss1: 0.393244 Loss2: 1.400878
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.687904 Loss1: 0.284056 Loss2: 1.403847
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.720438 Loss1: 0.321897 Loss2: 1.398541
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.582803 Loss1: 0.195170 Loss2: 1.387633
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.558573 Loss1: 0.179579 Loss2: 1.378994
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.517771 Loss1: 0.146247 Loss2: 1.371524
+(DefaultActor pid=3764) >> Training accuracy: 0.965820
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 08:01:53,566][flwr][DEBUG] - fit_round 69 received 50 results and 0 failures
+INFO flwr 2023-10-10 08:02:36,713 | server.py:125 | fit progress: (69, 2.284203640949993, {'accuracy': 0.5269}, 159064.49114573802)
+>> Test accuracy: 0.526900
+[2023-10-10 08:02:36,713][flwr][INFO] - fit progress: (69, 2.284203640949993, {'accuracy': 0.5269}, 159064.49114573802)
+DEBUG flwr 2023-10-10 08:02:36,713 | server.py:173 | evaluate_round 69: strategy sampled 50 clients (out of 50)
+[2023-10-10 08:02:36,713][flwr][DEBUG] - evaluate_round 69: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 08:11:42,347 | server.py:187 | evaluate_round 69 received 50 results and 0 failures
+[2023-10-10 08:11:42,347][flwr][DEBUG] - evaluate_round 69 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 08:11:42,348 | server.py:222 | fit_round 70: strategy sampled 50 clients (out of 50)
+[2023-10-10 08:11:42,348][flwr][DEBUG] - fit_round 70: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.274978 Loss1: 1.367127 Loss2: 1.907851
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.386666 Loss1: 0.878949 Loss2: 1.507718
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.955120 Loss1: 0.516671 Loss2: 1.438448
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.837638 Loss1: 0.407437 Loss2: 1.430201
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.316904 Loss1: 1.355468 Loss2: 1.961436
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.305395 Loss1: 0.837145 Loss2: 1.468250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.007396 Loss1: 0.515567 Loss2: 1.491829 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.802524 Loss1: 0.343165 Loss2: 1.459359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.777256 Loss1: 0.318510 Loss2: 1.458746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.725509 Loss1: 0.270493 Loss2: 1.455015 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.587311 Loss1: 0.171590 Loss2: 1.415721
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.705059 Loss1: 0.260233 Loss2: 1.444826
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.682103 Loss1: 0.230606 Loss2: 1.451497
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.632586 Loss1: 0.184747 Loss2: 1.447839
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.675335 Loss1: 0.232204 Loss2: 1.443131
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.548153 Loss1: 1.573119 Loss2: 1.975034
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.386446 Loss1: 0.919699 Loss2: 1.466748
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.133436 Loss1: 0.654918 Loss2: 1.478518
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.850366 Loss1: 0.423486 Loss2: 1.426880
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.008355 Loss1: 1.199329 Loss2: 1.809026
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.024706 Loss1: 0.680172 Loss2: 1.344534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.793906 Loss1: 0.433378 Loss2: 1.360528 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.739646 Loss1: 0.418624 Loss2: 1.321022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.614624 Loss1: 0.275016 Loss2: 1.339608 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.579426 Loss1: 0.260072 Loss2: 1.319354 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.930804
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465789 Loss1: 0.154651 Loss2: 1.311138 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462229 Loss1: 0.150684 Loss2: 1.311545 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.287465 Loss1: 0.866726 Loss2: 1.420740 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765710 Loss1: 0.388534 Loss2: 1.377176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.930844 Loss1: 1.157198 Loss2: 1.773646
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.679595 Loss1: 0.290627 Loss2: 1.388968
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.141411 Loss1: 0.808393 Loss2: 1.333018
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.688813 Loss1: 0.313549 Loss2: 1.375264
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.781101 Loss1: 0.428600 Loss2: 1.352502
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.573292 Loss1: 0.196109 Loss2: 1.377184
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.610738 Loss1: 0.304861 Loss2: 1.305878
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.547268 Loss1: 0.181108 Loss2: 1.366159
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.638906 Loss1: 0.327002 Loss2: 1.311903
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.556077 Loss1: 0.187751 Loss2: 1.368326
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.613595 Loss1: 0.301559 Loss2: 1.312036
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.535024 Loss1: 0.162769 Loss2: 1.372255
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515791 Loss1: 0.211618 Loss2: 1.304173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445175 Loss1: 0.156525 Loss2: 1.288650 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.329352 Loss1: 0.827400 Loss2: 1.501952 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.901603 Loss1: 0.428430 Loss2: 1.473174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.801978 Loss1: 0.330347 Loss2: 1.471631 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.759726 Loss1: 0.290565 Loss2: 1.469162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.736630 Loss1: 0.270040 Loss2: 1.466590 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.850016 Loss1: 0.323177 Loss2: 1.526839 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.790749 Loss1: 0.259937 Loss2: 1.530812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.799440 Loss1: 0.274979 Loss2: 1.524461 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.921875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.714350 Loss1: 0.187491 Loss2: 1.526859 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.533141 Loss1: 1.504381 Loss2: 2.028760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.149708 Loss1: 0.585168 Loss2: 1.564539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.931745 Loss1: 0.436788 Loss2: 1.494957
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.146104 Loss1: 1.235667 Loss2: 1.910437
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.234042 Loss1: 0.775484 Loss2: 1.458558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.970583 Loss1: 0.527498 Loss2: 1.443084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.690139 Loss1: 0.187678 Loss2: 1.502461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.692714 Loss1: 0.216345 Loss2: 1.476369 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.674940 Loss1: 0.187447 Loss2: 1.487493 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.949777
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.660912 Loss1: 0.242195 Loss2: 1.418717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.601201 Loss1: 0.199722 Loss2: 1.401479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.642790 Loss1: 0.234187 Loss2: 1.408603
+(DefaultActor pid=3764) >> Training accuracy: 0.929688
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.353562 Loss1: 1.360854 Loss2: 1.992708
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.296813 Loss1: 0.803199 Loss2: 1.493614
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.010077 Loss1: 0.514004 Loss2: 1.496073
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.777006 Loss1: 0.327624 Loss2: 1.449382
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.778943 Loss1: 0.317963 Loss2: 1.460980
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.249467 Loss1: 1.345730 Loss2: 1.903737
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.651731 Loss1: 0.213802 Loss2: 1.437928
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.654323 Loss1: 0.214286 Loss2: 1.440036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.640209 Loss1: 0.186382 Loss2: 1.453827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.603121 Loss1: 0.163587 Loss2: 1.439534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.579206 Loss1: 0.142264 Loss2: 1.436942 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.624505 Loss1: 0.245005 Loss2: 1.379500 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.520353 Loss1: 0.139118 Loss2: 1.381235 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.277087 Loss1: 0.828987 Loss2: 1.448100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.745625 Loss1: 0.337774 Loss2: 1.407850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.277198 Loss1: 1.389185 Loss2: 1.888013
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.685495 Loss1: 0.280044 Loss2: 1.405451
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.320389 Loss1: 0.871470 Loss2: 1.448919
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.639300 Loss1: 0.233460 Loss2: 1.405840
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.979022 Loss1: 0.514379 Loss2: 1.464643
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.640255 Loss1: 0.238678 Loss2: 1.401577
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.845160 Loss1: 0.419812 Loss2: 1.425348
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.582807 Loss1: 0.172296 Loss2: 1.410510
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.756886 Loss1: 0.333761 Loss2: 1.423125
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.554054 Loss1: 0.150717 Loss2: 1.403337
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.731884 Loss1: 0.305211 Loss2: 1.426673
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.515889 Loss1: 0.115999 Loss2: 1.399890
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.593538 Loss1: 0.188914 Loss2: 1.404624 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.545417 Loss1: 0.144543 Loss2: 1.400874 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.412182 Loss1: 0.930986 Loss2: 1.481196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.911249 Loss1: 0.460547 Loss2: 1.450701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.811219 Loss1: 0.360483 Loss2: 1.450736 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.741863 Loss1: 0.298385 Loss2: 1.443478 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.707253 Loss1: 0.260161 Loss2: 1.447092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.666137 Loss1: 0.226580 Loss2: 1.439557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.632219 Loss1: 0.188740 Loss2: 1.443480 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.633081 Loss1: 0.195737 Loss2: 1.437344 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.933594
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.601839 Loss1: 0.219241 Loss2: 1.382598 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.201279 Loss1: 1.310208 Loss2: 1.891071 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.911913 Loss1: 0.475361 Loss2: 1.436553 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.735439 Loss1: 0.355935 Loss2: 1.379505
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.233506 Loss1: 1.421756 Loss2: 1.811749
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.383791 Loss1: 0.994207 Loss2: 1.389584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.000070 Loss1: 0.605671 Loss2: 1.394399 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.826575 Loss1: 0.468199 Loss2: 1.358376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.652633 Loss1: 0.281217 Loss2: 1.371416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.600910 Loss1: 0.264203 Loss2: 1.336708 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.600375 Loss1: 0.243255 Loss2: 1.357120 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.535230 Loss1: 0.192273 Loss2: 1.342957 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.391818 Loss1: 1.411281 Loss2: 1.980537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.010303 Loss1: 0.476864 Loss2: 1.533439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.254608 Loss1: 1.356525 Loss2: 1.898083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.363166 Loss1: 0.916556 Loss2: 1.446610 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.959316 Loss1: 0.476613 Loss2: 1.482703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.806996 Loss1: 0.388518 Loss2: 1.418478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.707255 Loss1: 0.277530 Loss2: 1.429726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.643593 Loss1: 0.231848 Loss2: 1.411745 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.658234 Loss1: 0.254544 Loss2: 1.403691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.633398 Loss1: 0.220873 Loss2: 1.412524 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.276777 Loss1: 1.344354 Loss2: 1.932423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.939124 Loss1: 0.464934 Loss2: 1.474190 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.179297 Loss1: 1.314197 Loss2: 1.865100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.271376 Loss1: 0.800724 Loss2: 1.470653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.937853 Loss1: 0.513966 Loss2: 1.423887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.764296 Loss1: 0.342857 Loss2: 1.421440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.708554 Loss1: 0.287946 Loss2: 1.420608 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.571867 Loss1: 0.178449 Loss2: 1.393418 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.959375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.641801 Loss1: 0.220976 Loss2: 1.420824 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.558553 Loss1: 0.156912 Loss2: 1.401641 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965820
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.268535 Loss1: 0.852147 Loss2: 1.416388 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.775086 Loss1: 0.393191 Loss2: 1.381895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.223007 Loss1: 1.362292 Loss2: 1.860715
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.704571 Loss1: 0.322986 Loss2: 1.381585
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.257392 Loss1: 0.843803 Loss2: 1.413589
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.705859 Loss1: 0.315934 Loss2: 1.389925
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.975333 Loss1: 0.548598 Loss2: 1.426735
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.688778 Loss1: 0.303916 Loss2: 1.384861
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.628893 Loss1: 0.241376 Loss2: 1.387517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.586548 Loss1: 0.208086 Loss2: 1.378461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.562570 Loss1: 0.188248 Loss2: 1.374322 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.566988 Loss1: 0.196356 Loss2: 1.370632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.558605 Loss1: 0.186845 Loss2: 1.371759 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.186099 Loss1: 1.324660 Loss2: 1.861439
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.257865 Loss1: 0.820177 Loss2: 1.437688
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.035565 Loss1: 0.591235 Loss2: 1.444330
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.858943 Loss1: 0.464441 Loss2: 1.394502
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.110612 Loss1: 1.250660 Loss2: 1.859952
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.687591 Loss1: 0.278870 Loss2: 1.408721
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.282664 Loss1: 0.865389 Loss2: 1.417275
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.946879 Loss1: 0.505569 Loss2: 1.441309
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.560005 Loss1: 0.176063 Loss2: 1.383942
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.766555 Loss1: 0.388186 Loss2: 1.378369
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.596190 Loss1: 0.218728 Loss2: 1.377461
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.722147 Loss1: 0.327658 Loss2: 1.394489
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.579531 Loss1: 0.193913 Loss2: 1.385618
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.655218 Loss1: 0.278800 Loss2: 1.376418
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.601141 Loss1: 0.219014 Loss2: 1.382127
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.581718 Loss1: 0.194567 Loss2: 1.387150 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956055
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.576719 Loss1: 0.204041 Loss2: 1.372678 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.334876 Loss1: 1.444232 Loss2: 1.890644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.064293 Loss1: 0.612282 Loss2: 1.452011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.850846 Loss1: 0.433113 Loss2: 1.417733
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.359776 Loss1: 1.505627 Loss2: 1.854149
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.278517 Loss1: 0.876638 Loss2: 1.401879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.974500 Loss1: 0.561824 Loss2: 1.412676 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.842080 Loss1: 0.451576 Loss2: 1.390503 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.689920 Loss1: 0.313621 Loss2: 1.376299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582185 Loss1: 0.220937 Loss2: 1.361247 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.521466 Loss1: 0.137797 Loss2: 1.383668
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.602515 Loss1: 0.233064 Loss2: 1.369452
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.606066 Loss1: 0.237959 Loss2: 1.368106
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.552854 Loss1: 0.188052 Loss2: 1.364802
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.520795 Loss1: 0.163083 Loss2: 1.357712
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.227334 Loss1: 1.338092 Loss2: 1.889242
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.220088 Loss1: 0.788752 Loss2: 1.431335
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.102124 Loss1: 0.615645 Loss2: 1.486479
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.889366 Loss1: 0.472547 Loss2: 1.416819
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.285924 Loss1: 1.421649 Loss2: 1.864275
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.297908 Loss1: 0.865884 Loss2: 1.432024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.948863 Loss1: 0.514133 Loss2: 1.434730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.825854 Loss1: 0.416754 Loss2: 1.409100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.764907 Loss1: 0.342147 Loss2: 1.422759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.556888 Loss1: 0.164077 Loss2: 1.392810
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.629353 Loss1: 0.229377 Loss2: 1.399976
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.541013 Loss1: 0.143635 Loss2: 1.397378
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.588534 Loss1: 0.185951 Loss2: 1.402583
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.576178 Loss1: 0.186496 Loss2: 1.389682
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.546812 Loss1: 0.149033 Loss2: 1.397779
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.548063 Loss1: 0.154397 Loss2: 1.393666
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.371502 Loss1: 1.517226 Loss2: 1.854275
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.333369 Loss1: 0.894077 Loss2: 1.439292
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.952833 Loss1: 0.531318 Loss2: 1.421515
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.852349 Loss1: 0.462960 Loss2: 1.389388
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.227526 Loss1: 1.358460 Loss2: 1.869066
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.775800 Loss1: 0.378589 Loss2: 1.397211
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.283505 Loss1: 0.851037 Loss2: 1.432468
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.658842 Loss1: 0.281915 Loss2: 1.376927
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.017516 Loss1: 0.560260 Loss2: 1.457256
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.655510 Loss1: 0.272252 Loss2: 1.383259
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.809834 Loss1: 0.411099 Loss2: 1.398736
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.599860 Loss1: 0.231524 Loss2: 1.368335
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.675733 Loss1: 0.266672 Loss2: 1.409061
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.585970 Loss1: 0.221439 Loss2: 1.364531
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.606176 Loss1: 0.221309 Loss2: 1.384867
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.563474 Loss1: 0.190947 Loss2: 1.372527
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.622111 Loss1: 0.229586 Loss2: 1.392526
+(DefaultActor pid=3765) >> Training accuracy: 0.943750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.577824 Loss1: 0.182097 Loss2: 1.395728
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.570386 Loss1: 0.192208 Loss2: 1.378178
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.545257 Loss1: 0.162938 Loss2: 1.382319
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.042616 Loss1: 1.264403 Loss2: 1.778213
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.189333 Loss1: 0.801082 Loss2: 1.388251
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.957004 Loss1: 0.572959 Loss2: 1.384045
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.189837 Loss1: 1.308993 Loss2: 1.880845
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.718592 Loss1: 0.358380 Loss2: 1.360211
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.266454 Loss1: 0.822330 Loss2: 1.444125
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.658354 Loss1: 0.296750 Loss2: 1.361604
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.892565 Loss1: 0.469591 Loss2: 1.422974
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.658948 Loss1: 0.302385 Loss2: 1.356563
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.810790 Loss1: 0.399915 Loss2: 1.410875
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.567936 Loss1: 0.210387 Loss2: 1.357549
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.626178 Loss1: 0.276332 Loss2: 1.349846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.523835 Loss1: 0.170234 Loss2: 1.353601 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.525122 Loss1: 0.177741 Loss2: 1.347381 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962891
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.576594 Loss1: 0.186580 Loss2: 1.390015 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.161592 Loss1: 1.259311 Loss2: 1.902281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.970569 Loss1: 0.515216 Loss2: 1.455353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.812541 Loss1: 0.404471 Loss2: 1.408070
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.338381 Loss1: 1.386007 Loss2: 1.952374
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.500358 Loss1: 0.994605 Loss2: 1.505753 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.176758 Loss1: 0.639483 Loss2: 1.537275 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.897993 Loss1: 0.434662 Loss2: 1.463331 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.781025 Loss1: 0.307799 Loss2: 1.473226 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.705263 Loss1: 0.242132 Loss2: 1.463131 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.557607 Loss1: 0.162820 Loss2: 1.394787
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.696148 Loss1: 0.236456 Loss2: 1.459693
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.664451 Loss1: 0.203398 Loss2: 1.461053
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.698218 Loss1: 0.239595 Loss2: 1.458623
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.687359 Loss1: 0.220410 Loss2: 1.466949
+(DefaultActor pid=3764) >> Training accuracy: 0.954167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.255145 Loss1: 1.388538 Loss2: 1.866608
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.338709 Loss1: 0.888080 Loss2: 1.450629
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.036908 Loss1: 0.593600 Loss2: 1.443308
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.436482 Loss1: 1.505919 Loss2: 1.930563
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.806166 Loss1: 0.386971 Loss2: 1.419194
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.469622 Loss1: 0.962783 Loss2: 1.506839
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.786808 Loss1: 0.381529 Loss2: 1.405280
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.093700 Loss1: 0.615301 Loss2: 1.478399
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.679155 Loss1: 0.261848 Loss2: 1.417307
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.995877 Loss1: 0.522034 Loss2: 1.473843
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.643932 Loss1: 0.243402 Loss2: 1.400530
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.611898 Loss1: 0.205458 Loss2: 1.406440 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.629566 Loss1: 0.227058 Loss2: 1.402508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.590165 Loss1: 0.181033 Loss2: 1.409132 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967773
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.655039 Loss1: 0.213371 Loss2: 1.441669 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.199201 Loss1: 1.311800 Loss2: 1.887400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.963369 Loss1: 0.512822 Loss2: 1.450547 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.775315 Loss1: 0.355512 Loss2: 1.419803
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.125872 Loss1: 1.286479 Loss2: 1.839393
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.160517 Loss1: 0.721621 Loss2: 1.438896 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.930432 Loss1: 0.507465 Loss2: 1.422967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.756569 Loss1: 0.354558 Loss2: 1.402011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.701616 Loss1: 0.307656 Loss2: 1.393960 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.637120 Loss1: 0.241121 Loss2: 1.395999 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.571742 Loss1: 0.186521 Loss2: 1.385220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.509526 Loss1: 0.138297 Loss2: 1.371229 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.207921 Loss1: 1.300685 Loss2: 1.907236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.980032 Loss1: 0.482591 Loss2: 1.497441 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.875297 Loss1: 0.414344 Loss2: 1.460954
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.276130 Loss1: 1.402740 Loss2: 1.873390
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.245424 Loss1: 0.878953 Loss2: 1.366470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.744369 Loss1: 0.299019 Loss2: 1.445350
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.898024 Loss1: 0.508998 Loss2: 1.389026
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.709401 Loss1: 0.352200 Loss2: 1.357201
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.688954 Loss1: 0.228073 Loss2: 1.460881
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.627333 Loss1: 0.185025 Loss2: 1.442307 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.603526 Loss1: 0.165120 Loss2: 1.438406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.584920 Loss1: 0.140320 Loss2: 1.444600 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.467849 Loss1: 0.136980 Loss2: 1.330869 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966346
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.289051 Loss1: 1.419829 Loss2: 1.869222
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.292364 Loss1: 0.834044 Loss2: 1.458320
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.041786 Loss1: 0.597011 Loss2: 1.444775
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.793329 Loss1: 0.385525 Loss2: 1.407804
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.113950 Loss1: 1.221513 Loss2: 1.892437
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.266747 Loss1: 0.770381 Loss2: 1.496366 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.036101 Loss1: 0.568642 Loss2: 1.467459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.891135 Loss1: 0.419657 Loss2: 1.471478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.767873 Loss1: 0.323078 Loss2: 1.444795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.716482 Loss1: 0.273184 Loss2: 1.443298 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.655470 Loss1: 0.219128 Loss2: 1.436342 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.571575 Loss1: 0.149314 Loss2: 1.422261 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978860
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.296682 Loss1: 0.864683 Loss2: 1.431999 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.725373 Loss1: 0.313959 Loss2: 1.411414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.650024 Loss1: 0.230871 Loss2: 1.419153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.600030 Loss1: 0.196837 Loss2: 1.403193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.556796 Loss1: 0.160227 Loss2: 1.396568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.524541 Loss1: 0.137850 Loss2: 1.386691 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476161 Loss1: 0.091665 Loss2: 1.384497 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.570276 Loss1: 0.189815 Loss2: 1.380460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.572696 Loss1: 0.182507 Loss2: 1.390189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.534314 Loss1: 0.153185 Loss2: 1.381130
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.019380 Loss1: 1.201712 Loss2: 1.817669
+(DefaultActor pid=3764) >> Training accuracy: 0.934375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.537589 Loss1: 0.156512 Loss2: 1.381077
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.216959 Loss1: 0.841045 Loss2: 1.375914
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.860920 Loss1: 0.499101 Loss2: 1.361819
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.887694 Loss1: 0.523568 Loss2: 1.364127
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.783897 Loss1: 0.426278 Loss2: 1.357619
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.609066 Loss1: 0.265746 Loss2: 1.343320
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.094049 Loss1: 1.183437 Loss2: 1.910612
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.508707 Loss1: 0.182601 Loss2: 1.326106
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454457 Loss1: 0.131742 Loss2: 1.322714 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 08:40:53,677 | server.py:236 | fit_round 70 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404930 Loss1: 0.095688 Loss2: 1.309241 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400933 Loss1: 0.094718 Loss2: 1.306214 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.648010 Loss1: 0.238185 Loss2: 1.409825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.718435 Loss1: 0.315499 Loss2: 1.402936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.657485 Loss1: 0.231987 Loss2: 1.425498
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.256426 Loss1: 1.460853 Loss2: 1.795572
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.572101 Loss1: 0.157252 Loss2: 1.414849
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.228183 Loss1: 0.881163 Loss2: 1.347019
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.028610 Loss1: 0.668488 Loss2: 1.360122
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.849945 Loss1: 0.518368 Loss2: 1.331578
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.733383 Loss1: 0.391015 Loss2: 1.342368
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.598139 Loss1: 0.279218 Loss2: 1.318921
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.301824 Loss1: 1.433842 Loss2: 1.867982
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.533145 Loss1: 0.220182 Loss2: 1.312963
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.232201 Loss1: 0.807348 Loss2: 1.424853
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.520421 Loss1: 0.213750 Loss2: 1.306671
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.003295 Loss1: 0.562253 Loss2: 1.441041
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509826 Loss1: 0.201843 Loss2: 1.307983
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.900751 Loss1: 0.497413 Loss2: 1.403338
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.538503 Loss1: 0.231626 Loss2: 1.306877
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.712012 Loss1: 0.307931 Loss2: 1.404081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.592977 Loss1: 0.200713 Loss2: 1.392264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.621894 Loss1: 0.236296 Loss2: 1.385598 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 08:40:53,677][flwr][DEBUG] - fit_round 70 received 50 results and 0 failures
+INFO flwr 2023-10-10 08:41:35,096 | server.py:125 | fit progress: (70, 2.281081163654693, {'accuracy': 0.5317}, 161402.874152568)
+>> Test accuracy: 0.531700
+[2023-10-10 08:41:35,096][flwr][INFO] - fit progress: (70, 2.281081163654693, {'accuracy': 0.5317}, 161402.874152568)
+DEBUG flwr 2023-10-10 08:41:35,096 | server.py:173 | evaluate_round 70: strategy sampled 50 clients (out of 50)
+[2023-10-10 08:41:35,096][flwr][DEBUG] - evaluate_round 70: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 08:50:37,056 | server.py:187 | evaluate_round 70 received 50 results and 0 failures
+[2023-10-10 08:50:37,056][flwr][DEBUG] - evaluate_round 70 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 08:50:37,056 | server.py:222 | fit_round 71: strategy sampled 50 clients (out of 50)
+[2023-10-10 08:50:37,056][flwr][DEBUG] - fit_round 71: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.210094 Loss1: 1.284618 Loss2: 1.925476
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.336055 Loss1: 0.948595 Loss2: 1.387461
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.074109 Loss1: 0.605601 Loss2: 1.468508
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.742769 Loss1: 0.356366 Loss2: 1.386403
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.282714 Loss1: 1.310975 Loss2: 1.971739
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.626161 Loss1: 0.227000 Loss2: 1.399161 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.567428 Loss1: 0.185115 Loss2: 1.382313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.574814 Loss1: 0.194708 Loss2: 1.380106 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.511539 Loss1: 0.139124 Loss2: 1.372415 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.493038 Loss1: 0.129397 Loss2: 1.363641 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.654749 Loss1: 0.206541 Loss2: 1.448209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.610599 Loss1: 0.171570 Loss2: 1.439028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.575020 Loss1: 0.140455 Loss2: 1.434565
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.179012 Loss1: 1.342407 Loss2: 1.836605
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.386704 Loss1: 0.921682 Loss2: 1.465022
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.976339 Loss1: 0.548247 Loss2: 1.428092
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.818355 Loss1: 0.411796 Loss2: 1.406559
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.676005 Loss1: 0.273146 Loss2: 1.402859
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.202525 Loss1: 1.309873 Loss2: 1.892652
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.633858 Loss1: 0.245559 Loss2: 1.388299
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.262530 Loss1: 0.843505 Loss2: 1.419024
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.578486 Loss1: 0.187896 Loss2: 1.390590
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.944974 Loss1: 0.515127 Loss2: 1.429847
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.557543 Loss1: 0.175982 Loss2: 1.381561
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.802450 Loss1: 0.411995 Loss2: 1.390454
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.545380 Loss1: 0.164588 Loss2: 1.380792
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.772649 Loss1: 0.373489 Loss2: 1.399160
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.655743 Loss1: 0.263333 Loss2: 1.392410
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.560956 Loss1: 0.177126 Loss2: 1.383829
+(DefaultActor pid=3765) >> Training accuracy: 0.964844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.634299 Loss1: 0.246724 Loss2: 1.387575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527239 Loss1: 0.157863 Loss2: 1.369376 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.368707 Loss1: 0.877617 Loss2: 1.491089 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.770871 Loss1: 0.314711 Loss2: 1.456160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.701587 Loss1: 0.244621 Loss2: 1.456966
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.150718 Loss1: 1.270634 Loss2: 1.880084
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.680041 Loss1: 0.217550 Loss2: 1.462491
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.357953 Loss1: 0.852264 Loss2: 1.505689
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.637988 Loss1: 0.187031 Loss2: 1.450957
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.942277 Loss1: 0.514858 Loss2: 1.427419
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.812645 Loss1: 0.373844 Loss2: 1.438801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.650837 Loss1: 0.245022 Loss2: 1.405815 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.603425 Loss1: 0.204743 Loss2: 1.398681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.537215 Loss1: 0.150603 Loss2: 1.386612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.525028 Loss1: 0.138387 Loss2: 1.386641 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977022
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.994231 Loss1: 0.526627 Loss2: 1.467604 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.772223 Loss1: 0.311332 Loss2: 1.460891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.279569 Loss1: 1.312027 Loss2: 1.967542
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.686107 Loss1: 0.252318 Loss2: 1.433789
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.507719 Loss1: 0.967050 Loss2: 1.540669
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.659802 Loss1: 0.232487 Loss2: 1.427314
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.644309 Loss1: 0.207340 Loss2: 1.436969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.642748 Loss1: 0.215317 Loss2: 1.427431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.656644 Loss1: 0.224016 Loss2: 1.432628 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971680
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.705632 Loss1: 0.240757 Loss2: 1.464875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.613695 Loss1: 0.161188 Loss2: 1.452508 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.591049 Loss1: 0.146712 Loss2: 1.444337
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.328535 Loss1: 1.435591 Loss2: 1.892944
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.330309 Loss1: 0.883944 Loss2: 1.446365
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.997141 Loss1: 0.559151 Loss2: 1.437990
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.782937 Loss1: 0.382516 Loss2: 1.400422
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.647379 Loss1: 0.256249 Loss2: 1.391129
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.706694 Loss1: 1.588303 Loss2: 2.118391
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.490867 Loss1: 0.914474 Loss2: 1.576393 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.212415 Loss1: 0.611507 Loss2: 1.600909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.581146 Loss1: 0.195939 Loss2: 1.385207
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.973040 Loss1: 0.424345 Loss2: 1.548694
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.579333 Loss1: 0.188641 Loss2: 1.390692
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.851433 Loss1: 0.291809 Loss2: 1.559624
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.599514 Loss1: 0.210432 Loss2: 1.389082
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.824020 Loss1: 0.279670 Loss2: 1.544350
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.779715 Loss1: 0.233065 Loss2: 1.546650
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.809055 Loss1: 0.263919 Loss2: 1.545136
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.733312 Loss1: 0.193158 Loss2: 1.540154
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.703504 Loss1: 0.163666 Loss2: 1.539837
+(DefaultActor pid=3764) >> Training accuracy: 0.967634
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.125685 Loss1: 1.215552 Loss2: 1.910133
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.274612 Loss1: 0.833466 Loss2: 1.441146
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.011576 Loss1: 0.547680 Loss2: 1.463896
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.778342 Loss1: 0.372220 Loss2: 1.406122
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.072864 Loss1: 1.240378 Loss2: 1.832486
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.095417 Loss1: 0.726316 Loss2: 1.369101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.911991 Loss1: 0.514616 Loss2: 1.397375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.782964 Loss1: 0.423290 Loss2: 1.359674 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.655020 Loss1: 0.291928 Loss2: 1.363092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.589090 Loss1: 0.240195 Loss2: 1.348896 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.959375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.526566 Loss1: 0.189621 Loss2: 1.336944 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462297 Loss1: 0.129951 Loss2: 1.332347 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.159162 Loss1: 1.253245 Loss2: 1.905917
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.290753 Loss1: 0.813350 Loss2: 1.477404
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.956155 Loss1: 0.485567 Loss2: 1.470588
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.877808 Loss1: 0.443197 Loss2: 1.434611
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.242812 Loss1: 1.391566 Loss2: 1.851246
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.322311 Loss1: 0.855419 Loss2: 1.466892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.988431 Loss1: 0.573180 Loss2: 1.415251 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.854918 Loss1: 0.431376 Loss2: 1.423541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.795176 Loss1: 0.377517 Loss2: 1.417659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.713963 Loss1: 0.295648 Loss2: 1.418316 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975586
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.703820 Loss1: 0.307919 Loss2: 1.395901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.551342 Loss1: 0.160304 Loss2: 1.391038 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971680
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.224211 Loss1: 1.344716 Loss2: 1.879495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.014090 Loss1: 0.549959 Loss2: 1.464131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.185838 Loss1: 1.305763 Loss2: 1.880074 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.355125 Loss1: 0.912635 Loss2: 1.442490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.120030 Loss1: 0.671141 Loss2: 1.448889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.890147 Loss1: 0.468276 Loss2: 1.421870 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.768809 Loss1: 0.369343 Loss2: 1.399466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.694107 Loss1: 0.291109 Loss2: 1.402997 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.600913 Loss1: 0.209737 Loss2: 1.391176 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.581212 Loss1: 0.190234 Loss2: 1.390978 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.166633 Loss1: 1.258496 Loss2: 1.908136
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.299359 Loss1: 0.807626 Loss2: 1.491733
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.968481 Loss1: 0.494887 Loss2: 1.473594
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.809961 Loss1: 0.364144 Loss2: 1.445817
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.431572 Loss1: 1.441120 Loss2: 1.990452
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.394377 Loss1: 0.890109 Loss2: 1.504268 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.059624 Loss1: 0.568526 Loss2: 1.491098 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.947152 Loss1: 0.458903 Loss2: 1.488249 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.589625 Loss1: 0.164567 Loss2: 1.425057
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.843291 Loss1: 0.357817 Loss2: 1.485474
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.558977 Loss1: 0.135851 Loss2: 1.423126
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.802390 Loss1: 0.316945 Loss2: 1.485445
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.536734 Loss1: 0.120370 Loss2: 1.416364
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.677399 Loss1: 0.206326 Loss2: 1.471073
+(DefaultActor pid=3765) >> Training accuracy: 0.977539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.633859 Loss1: 0.167594 Loss2: 1.466264
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.629214 Loss1: 0.170031 Loss2: 1.459183
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.619693 Loss1: 0.159262 Loss2: 1.460430
+(DefaultActor pid=3764) >> Training accuracy: 0.933333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.129571 Loss1: 1.227981 Loss2: 1.901590
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.306580 Loss1: 0.837781 Loss2: 1.468799
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.957025 Loss1: 0.498721 Loss2: 1.458303
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.013172 Loss1: 1.161114 Loss2: 1.852058
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.848515 Loss1: 0.422888 Loss2: 1.425627
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.089404 Loss1: 0.710183 Loss2: 1.379221
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.733745 Loss1: 0.288555 Loss2: 1.445190
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.874018 Loss1: 0.468634 Loss2: 1.405384
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.682528 Loss1: 0.258788 Loss2: 1.423740
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.628518 Loss1: 0.199305 Loss2: 1.429213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.651765 Loss1: 0.230326 Loss2: 1.421438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.620032 Loss1: 0.193259 Loss2: 1.426773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.599893 Loss1: 0.190918 Loss2: 1.408974 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.469831 Loss1: 0.124351 Loss2: 1.345479 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.195832 Loss1: 1.347310 Loss2: 1.848521 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.930451 Loss1: 0.508601 Loss2: 1.421849 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.852847 Loss1: 0.465923 Loss2: 1.386924
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.360484 Loss1: 1.420510 Loss2: 1.939974
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.412070 Loss1: 0.930133 Loss2: 1.481937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.041929 Loss1: 0.562987 Loss2: 1.478941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.915276 Loss1: 0.453651 Loss2: 1.461625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.779773 Loss1: 0.318411 Loss2: 1.461362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.694173 Loss1: 0.260676 Loss2: 1.433497 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.518172 Loss1: 0.144431 Loss2: 1.373741
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.665146 Loss1: 0.219594 Loss2: 1.445552
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.671586 Loss1: 0.233211 Loss2: 1.438375
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.679349 Loss1: 0.226160 Loss2: 1.453189
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.646095 Loss1: 0.203208 Loss2: 1.442887
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.355025 Loss1: 1.397783 Loss2: 1.957242
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.288159 Loss1: 0.923511 Loss2: 1.364648
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.015013 Loss1: 0.592474 Loss2: 1.422538
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.828759 Loss1: 0.432165 Loss2: 1.396594
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.720924 Loss1: 0.358368 Loss2: 1.362557
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.537910 Loss1: 1.010282 Loss2: 1.527628 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.169188 Loss1: 0.666139 Loss2: 1.503049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.494811 Loss1: 0.147303 Loss2: 1.347508 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484566 Loss1: 0.138771 Loss2: 1.345795 [repeated 2x across cluster]
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.649744 Loss1: 0.196628 Loss2: 1.453116 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.674771 Loss1: 0.231023 Loss2: 1.443748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.633945 Loss1: 0.198193 Loss2: 1.435752 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.990011 Loss1: 0.544842 Loss2: 1.445169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.707643 Loss1: 0.283125 Loss2: 1.424518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.696324 Loss1: 0.284299 Loss2: 1.412025
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.381509 Loss1: 1.317934 Loss2: 2.063575
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.429752 Loss1: 0.848924 Loss2: 1.580828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.115365 Loss1: 0.496405 Loss2: 1.618960 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.911954 Loss1: 0.369412 Loss2: 1.542541 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.845979 Loss1: 0.283615 Loss2: 1.562364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.760969 Loss1: 0.204687 Loss2: 1.556282 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.646841 Loss1: 0.119544 Loss2: 1.527297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.612143 Loss1: 0.093539 Loss2: 1.518604 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.992912 Loss1: 0.522228 Loss2: 1.470685 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.792352 Loss1: 0.341602 Loss2: 1.450749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.684153 Loss1: 0.239197 Loss2: 1.444956
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.407897 Loss1: 1.452588 Loss2: 1.955309
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.434003 Loss1: 0.931343 Loss2: 1.502659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.177598 Loss1: 0.656938 Loss2: 1.520660 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.003852 Loss1: 0.532483 Loss2: 1.471368 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.863745 Loss1: 0.390567 Loss2: 1.473178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.717027 Loss1: 0.241564 Loss2: 1.475463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.644212 Loss1: 0.187191 Loss2: 1.457021 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.132096 Loss1: 1.335583 Loss2: 1.796513
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.609820 Loss1: 0.154413 Loss2: 1.455407
+(DefaultActor pid=3764) >> Training accuracy: 0.951042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.986365 Loss1: 0.589883 Loss2: 1.396483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.635777 Loss1: 0.258460 Loss2: 1.377317 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.572931 Loss1: 0.215179 Loss2: 1.357752
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.220426 Loss1: 1.340251 Loss2: 1.880176
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.209884 Loss1: 0.794904 Loss2: 1.414981
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.566631 Loss1: 0.210266 Loss2: 1.356365
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.007130 Loss1: 0.559187 Loss2: 1.447943
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.563594 Loss1: 0.205942 Loss2: 1.357652
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.796054 Loss1: 0.408383 Loss2: 1.387671
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.492259 Loss1: 0.145898 Loss2: 1.346360
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.715647 Loss1: 0.310732 Loss2: 1.404914
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.500409 Loss1: 0.150222 Loss2: 1.350187
+(DefaultActor pid=3765) >> Training accuracy: 0.959961
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.595781 Loss1: 0.214829 Loss2: 1.380952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.532462 Loss1: 0.162758 Loss2: 1.369704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.520814 Loss1: 0.140127 Loss2: 1.380687
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.325646 Loss1: 1.430910 Loss2: 1.894736
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.314672 Loss1: 0.812991 Loss2: 1.501681
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.003643 Loss1: 0.580576 Loss2: 1.423067
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.826386 Loss1: 0.394939 Loss2: 1.431447
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.668149 Loss1: 0.257434 Loss2: 1.410715
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.928960 Loss1: 1.103299 Loss2: 1.825661
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.594723 Loss1: 0.193969 Loss2: 1.400755
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.542208 Loss1: 0.148160 Loss2: 1.394048 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.530294 Loss1: 0.150916 Loss2: 1.379378 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.531458 Loss1: 0.144516 Loss2: 1.386942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.538330 Loss1: 0.145056 Loss2: 1.393274 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545062 Loss1: 0.211184 Loss2: 1.333878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476930 Loss1: 0.147786 Loss2: 1.329144 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460136 Loss1: 0.134264 Loss2: 1.325872
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.286257 Loss1: 1.343579 Loss2: 1.942678
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.281500 Loss1: 0.828836 Loss2: 1.452663
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.895191 Loss1: 0.459333 Loss2: 1.435857
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.726775 Loss1: 0.345678 Loss2: 1.381097
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.770050 Loss1: 0.363326 Loss2: 1.406724
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.174255 Loss1: 1.301284 Loss2: 1.872971
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.216237 Loss1: 0.806204 Loss2: 1.410033 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.931609 Loss1: 0.495387 Loss2: 1.436221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.722449 Loss1: 0.337068 Loss2: 1.385381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.650982 Loss1: 0.264948 Loss2: 1.386035 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.550663 Loss1: 0.177654 Loss2: 1.373009
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.619181 Loss1: 0.237337 Loss2: 1.381844
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.556059 Loss1: 0.186057 Loss2: 1.370002
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548906 Loss1: 0.168671 Loss2: 1.380235
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.537303 Loss1: 0.159611 Loss2: 1.377692
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527045 Loss1: 0.154371 Loss2: 1.372674
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.112807 Loss1: 1.234141 Loss2: 1.878665
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.301924 Loss1: 0.849911 Loss2: 1.452013
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.043251 Loss1: 0.562658 Loss2: 1.480593
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.872292 Loss1: 0.439415 Loss2: 1.432878
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.818153 Loss1: 0.374749 Loss2: 1.443404
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.293538 Loss1: 1.381271 Loss2: 1.912266
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.296233 Loss1: 0.856069 Loss2: 1.440165 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.058968 Loss1: 0.581319 Loss2: 1.477649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.835790 Loss1: 0.412945 Loss2: 1.422845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.748847 Loss1: 0.309631 Loss2: 1.439215 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.699160 Loss1: 0.278764 Loss2: 1.420396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.674052 Loss1: 0.246229 Loss2: 1.427823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.536779 Loss1: 0.129635 Loss2: 1.407144 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.321584 Loss1: 0.898242 Loss2: 1.423342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.805833 Loss1: 0.402113 Loss2: 1.403720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.244162 Loss1: 1.392371 Loss2: 1.851791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.261209 Loss1: 0.855938 Loss2: 1.405270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.642119 Loss1: 0.251923 Loss2: 1.390196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.591340 Loss1: 0.199370 Loss2: 1.391971 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.574969 Loss1: 0.188522 Loss2: 1.386447 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977679
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550901 Loss1: 0.202990 Loss2: 1.347912 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481500 Loss1: 0.142032 Loss2: 1.339468 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.569242 Loss1: 0.221524 Loss2: 1.347718
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.198745 Loss1: 1.360489 Loss2: 1.838257
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.429149 Loss1: 0.980712 Loss2: 1.448436
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.013559 Loss1: 0.567172 Loss2: 1.446387
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.752760 Loss1: 0.371010 Loss2: 1.381750
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.738810 Loss1: 0.346489 Loss2: 1.392321
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.670659 Loss1: 0.280863 Loss2: 1.389796
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.244804 Loss1: 1.367286 Loss2: 1.877519
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.599980 Loss1: 0.222649 Loss2: 1.377331
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.259326 Loss1: 0.849118 Loss2: 1.410208
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.586829 Loss1: 0.203331 Loss2: 1.383499
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.922115 Loss1: 0.507323 Loss2: 1.414792
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.778538 Loss1: 0.383198 Loss2: 1.395339
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.549520 Loss1: 0.181084 Loss2: 1.368436
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.544543 Loss1: 0.175723 Loss2: 1.368820
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.719382 Loss1: 0.324309 Loss2: 1.395073
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.672167 Loss1: 0.284808 Loss2: 1.387360
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.582082 Loss1: 0.194436 Loss2: 1.387646
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.635685 Loss1: 0.250196 Loss2: 1.385488
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.569543 Loss1: 0.191283 Loss2: 1.378261
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.537421 Loss1: 0.152969 Loss2: 1.384452
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.373882 Loss1: 1.451381 Loss2: 1.922501
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.343456 Loss1: 0.851093 Loss2: 1.492363
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.974947 Loss1: 0.498957 Loss2: 1.475989
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.829602 Loss1: 0.388886 Loss2: 1.440717
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.738129 Loss1: 0.297951 Loss2: 1.440178
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.299496 Loss1: 1.307039 Loss2: 1.992457
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.685746 Loss1: 0.261528 Loss2: 1.424218
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.633960 Loss1: 0.204607 Loss2: 1.429353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.639041 Loss1: 0.218896 Loss2: 1.420144 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.633843 Loss1: 0.210508 Loss2: 1.423335 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.611879 Loss1: 0.182260 Loss2: 1.429619 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.600674 Loss1: 0.232795 Loss2: 1.367880 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.476430 Loss1: 0.115627 Loss2: 1.360803 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961538
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.306627 Loss1: 1.365552 Loss2: 1.941075
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.378554 Loss1: 0.878004 Loss2: 1.500550
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.061433 Loss1: 0.559440 Loss2: 1.501993
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.863114 Loss1: 0.402135 Loss2: 1.460979
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.177839 Loss1: 1.272944 Loss2: 1.904895
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.409225 Loss1: 0.894427 Loss2: 1.514798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.057293 Loss1: 0.612630 Loss2: 1.444662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.816756 Loss1: 0.365186 Loss2: 1.451570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.796712 Loss1: 0.358410 Loss2: 1.438302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.697341 Loss1: 0.253706 Loss2: 1.443635 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.614287 Loss1: 0.194165 Loss2: 1.420122 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.560858 Loss1: 0.143001 Loss2: 1.417858 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.326588 Loss1: 0.856665 Loss2: 1.469922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.872515 Loss1: 0.420714 Loss2: 1.451801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.386947 Loss1: 1.469332 Loss2: 1.917614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.290652 Loss1: 0.845522 Loss2: 1.445131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.925816 Loss1: 0.485839 Loss2: 1.439977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.789977 Loss1: 0.383153 Loss2: 1.406823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.671159 Loss1: 0.265136 Loss2: 1.406024 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.937500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.640430 Loss1: 0.238243 Loss2: 1.402187 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.617275 Loss1: 0.225987 Loss2: 1.391288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.590620 Loss1: 0.194730 Loss2: 1.395890
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.349173 Loss1: 1.440407 Loss2: 1.908766
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.375561 Loss1: 0.908585 Loss2: 1.466977
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.004256 Loss1: 0.558816 Loss2: 1.445440
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.819797 Loss1: 0.391319 Loss2: 1.428478
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.746449 Loss1: 0.324392 Loss2: 1.422057
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.714160 Loss1: 0.299474 Loss2: 1.414686
+DEBUG flwr 2023-10-10 09:19:02,090 | server.py:236 | fit_round 71 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.270097 Loss1: 1.373483 Loss2: 1.896615
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.333974 Loss1: 0.842377 Loss2: 1.491597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.051509 Loss1: 0.594085 Loss2: 1.457423 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.939796 Loss1: 0.477496 Loss2: 1.462300 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.934375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.828968 Loss1: 0.378439 Loss2: 1.450529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.752510 Loss1: 0.295289 Loss2: 1.457221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.621704 Loss1: 0.186476 Loss2: 1.435228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.590501 Loss1: 0.163387 Loss2: 1.427114 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971680
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.978392 Loss1: 0.482152 Loss2: 1.496240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.853990 Loss1: 0.383847 Loss2: 1.470143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.225011 Loss1: 1.257716 Loss2: 1.967295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.382132 Loss1: 0.870980 Loss2: 1.511152 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.048049 Loss1: 0.517421 Loss2: 1.530627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.864955 Loss1: 0.387391 Loss2: 1.477563 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.750814 Loss1: 0.286862 Loss2: 1.463952 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.678935 Loss1: 0.209725 Loss2: 1.469211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.624683 Loss1: 0.174403 Loss2: 1.450280 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 09:19:02,090][flwr][DEBUG] - fit_round 71 received 50 results and 0 failures
+INFO flwr 2023-10-10 09:19:44,350 | server.py:125 | fit progress: (71, 2.287786943463091, {'accuracy': 0.5305}, 163692.128101278)
+>> Test accuracy: 0.530500
+[2023-10-10 09:19:44,350][flwr][INFO] - fit progress: (71, 2.287786943463091, {'accuracy': 0.5305}, 163692.128101278)
+DEBUG flwr 2023-10-10 09:19:44,350 | server.py:173 | evaluate_round 71: strategy sampled 50 clients (out of 50)
+[2023-10-10 09:19:44,350][flwr][DEBUG] - evaluate_round 71: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 09:28:51,092 | server.py:187 | evaluate_round 71 received 50 results and 0 failures
+[2023-10-10 09:28:51,092][flwr][DEBUG] - evaluate_round 71 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 09:28:51,092 | server.py:222 | fit_round 72: strategy sampled 50 clients (out of 50)
+[2023-10-10 09:28:51,092][flwr][DEBUG] - fit_round 72: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.968079 Loss1: 1.058500 Loss2: 1.909579
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.202438 Loss1: 0.774802 Loss2: 1.427636
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.913509 Loss1: 0.437428 Loss2: 1.476082
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.770521 Loss1: 0.366931 Loss2: 1.403590
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.253713 Loss1: 1.335396 Loss2: 1.918317
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.314568 Loss1: 0.831132 Loss2: 1.483436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.017792 Loss1: 0.546106 Loss2: 1.471686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.876026 Loss1: 0.427494 Loss2: 1.448532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.759969 Loss1: 0.316917 Loss2: 1.443052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.712450 Loss1: 0.285172 Loss2: 1.427278 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.681765 Loss1: 0.242739 Loss2: 1.439026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.591229 Loss1: 0.163096 Loss2: 1.428133 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.175585 Loss1: 1.296202 Loss2: 1.879383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.911225 Loss1: 0.487707 Loss2: 1.423518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.736410 Loss1: 0.351954 Loss2: 1.384456
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.355738 Loss1: 1.384967 Loss2: 1.970771
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.405942 Loss1: 0.882318 Loss2: 1.523623 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.105478 Loss1: 0.580484 Loss2: 1.524994 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.860491 Loss1: 0.370878 Loss2: 1.489613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.809315 Loss1: 0.331319 Loss2: 1.477996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.685403 Loss1: 0.212701 Loss2: 1.472702 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.493976 Loss1: 0.116767 Loss2: 1.377208
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.641573 Loss1: 0.177660 Loss2: 1.463913
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.628908 Loss1: 0.168137 Loss2: 1.460772
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.623770 Loss1: 0.165016 Loss2: 1.458754
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.596791 Loss1: 0.134470 Loss2: 1.462321
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.525726 Loss1: 1.547060 Loss2: 1.978667
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.379506 Loss1: 0.911116 Loss2: 1.468389
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.020202 Loss1: 0.530050 Loss2: 1.490153
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.865444 Loss1: 0.417766 Loss2: 1.447678
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.184982 Loss1: 1.330813 Loss2: 1.854170
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.189790 Loss1: 0.759595 Loss2: 1.430195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.005971 Loss1: 0.586741 Loss2: 1.419230 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.890008 Loss1: 0.470909 Loss2: 1.419099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.724433 Loss1: 0.328283 Loss2: 1.396150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.638679 Loss1: 0.259087 Loss2: 1.379592 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981027
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.580524 Loss1: 0.202910 Loss2: 1.377614 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.493711 Loss1: 0.128135 Loss2: 1.365576 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.208441 Loss1: 0.852496 Loss2: 1.355944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.747102 Loss1: 0.412321 Loss2: 1.334781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.028047 Loss1: 1.170102 Loss2: 1.857945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500689 Loss1: 0.177975 Loss2: 1.322714 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488452 Loss1: 0.159601 Loss2: 1.328851 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454773 Loss1: 0.135885 Loss2: 1.318887 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442872 Loss1: 0.128641 Loss2: 1.314231 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969952
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.739847 Loss1: 0.361817 Loss2: 1.378030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.598247 Loss1: 0.235500 Loss2: 1.362747 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.527932 Loss1: 0.165433 Loss2: 1.362498
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.257983 Loss1: 1.349127 Loss2: 1.908855
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463342 Loss1: 0.109653 Loss2: 1.353688
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.443582 Loss1: 0.926593 Loss2: 1.516989
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.036491 Loss1: 0.582359 Loss2: 1.454133
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.889191 Loss1: 0.420945 Loss2: 1.468246
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.802552 Loss1: 0.358454 Loss2: 1.444098
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.798554 Loss1: 0.347296 Loss2: 1.451258
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.672469 Loss1: 0.236204 Loss2: 1.436266
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.021737 Loss1: 1.198918 Loss2: 1.822819
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.168279 Loss1: 0.775611 Loss2: 1.392668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.896924 Loss1: 0.454474 Loss2: 1.442450 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.643469 Loss1: 0.212204 Loss2: 1.431266
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.761705 Loss1: 0.372133 Loss2: 1.389571
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.695230 Loss1: 0.310483 Loss2: 1.384747
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.629015 Loss1: 0.241345 Loss2: 1.387669
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.662808 Loss1: 0.282609 Loss2: 1.380200
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.631937 Loss1: 0.239627 Loss2: 1.392310
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.278371 Loss1: 1.409793 Loss2: 1.868578
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.316618 Loss1: 0.861709 Loss2: 1.454910 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958984
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.553927 Loss1: 0.172285 Loss2: 1.381642
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.052242 Loss1: 0.594138 Loss2: 1.458104
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.792862 Loss1: 0.394163 Loss2: 1.398699
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.754273 Loss1: 0.337838 Loss2: 1.416435
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.676366 Loss1: 0.275820 Loss2: 1.400546
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.650626 Loss1: 0.250684 Loss2: 1.399942
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.195758 Loss1: 1.359728 Loss2: 1.836030
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.590518 Loss1: 0.190417 Loss2: 1.400101
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.543140 Loss1: 0.154449 Loss2: 1.388691 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.518243 Loss1: 0.135390 Loss2: 1.382853 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.722910 Loss1: 0.340935 Loss2: 1.381975 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.564311 Loss1: 0.199226 Loss2: 1.365085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.150166 Loss1: 1.307959 Loss2: 1.842207 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.339432 Loss1: 0.877648 Loss2: 1.461784 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.770267 Loss1: 0.350587 Loss2: 1.419680 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.713839 Loss1: 0.303259 Loss2: 1.410580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.673765 Loss1: 0.253322 Loss2: 1.420443
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.220018 Loss1: 1.294380 Loss2: 1.925639
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.575818 Loss1: 0.170440 Loss2: 1.405378
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.172924 Loss1: 0.763863 Loss2: 1.409061
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.594538 Loss1: 0.197030 Loss2: 1.397508
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.998506 Loss1: 0.561913 Loss2: 1.436593
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.817237 Loss1: 0.420268 Loss2: 1.396968
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.592014 Loss1: 0.190209 Loss2: 1.401805
+(DefaultActor pid=3765) >> Training accuracy: 0.959961
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.580841 Loss1: 0.195096 Loss2: 1.385745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.517127 Loss1: 0.144948 Loss2: 1.372180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519081 Loss1: 0.155095 Loss2: 1.363986
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.368782 Loss1: 1.402408 Loss2: 1.966374
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457221 Loss1: 0.093114 Loss2: 1.364107
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.349020 Loss1: 0.830546 Loss2: 1.518474
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.095437 Loss1: 0.583730 Loss2: 1.511706
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.919592 Loss1: 0.422920 Loss2: 1.496672
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.778681 Loss1: 0.297253 Loss2: 1.481428
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.714416 Loss1: 0.236482 Loss2: 1.477934
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.201326 Loss1: 1.297395 Loss2: 1.903930
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.739564 Loss1: 0.252329 Loss2: 1.487235
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.347979 Loss1: 0.889893 Loss2: 1.458086
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.689651 Loss1: 0.214815 Loss2: 1.474836
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.026820 Loss1: 0.548252 Loss2: 1.478568
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.767364 Loss1: 0.294590 Loss2: 1.472774
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.780926 Loss1: 0.352176 Loss2: 1.428751
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.763625 Loss1: 0.269384 Loss2: 1.494241
+(DefaultActor pid=3765) >> Training accuracy: 0.934375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.693447 Loss1: 0.263601 Loss2: 1.429845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.584206 Loss1: 0.163645 Loss2: 1.420561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.576407 Loss1: 0.156378 Loss2: 1.420028
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.287665 Loss1: 1.434742 Loss2: 1.852923
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.547359 Loss1: 0.138104 Loss2: 1.409255
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.380163 Loss1: 0.932873 Loss2: 1.447290
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.929690 Loss1: 0.533550 Loss2: 1.396140
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.729035 Loss1: 0.331156 Loss2: 1.397880
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.597611 Loss1: 0.225387 Loss2: 1.372224
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.546057 Loss1: 0.171046 Loss2: 1.375011
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.101013 Loss1: 1.245104 Loss2: 1.855909
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547253 Loss1: 0.168653 Loss2: 1.378600
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.084779 Loss1: 0.718624 Loss2: 1.366155
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.586831 Loss1: 0.216263 Loss2: 1.370568
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834290 Loss1: 0.430854 Loss2: 1.403435
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.491325 Loss1: 0.114736 Loss2: 1.376589
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.666681 Loss1: 0.325273 Loss2: 1.341408
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.487181 Loss1: 0.124952 Loss2: 1.362229
+(DefaultActor pid=3765) >> Training accuracy: 0.962500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539704 Loss1: 0.205272 Loss2: 1.334432 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.517306 Loss1: 0.179322 Loss2: 1.337984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.554948 Loss1: 0.216005 Loss2: 1.338944
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.243660 Loss1: 1.309321 Loss2: 1.934339
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.531652 Loss1: 0.187029 Loss2: 1.344624
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.368687 Loss1: 0.868068 Loss2: 1.500619
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.026473 Loss1: 0.532886 Loss2: 1.493587
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.865607 Loss1: 0.387220 Loss2: 1.478387
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.765099 Loss1: 0.298547 Loss2: 1.466552
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.709716 Loss1: 0.249138 Loss2: 1.460578
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.993472 Loss1: 1.177388 Loss2: 1.816083
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.163405 Loss1: 0.753340 Loss2: 1.410065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.862604 Loss1: 0.453276 Loss2: 1.409328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.753065 Loss1: 0.370679 Loss2: 1.382386 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961914
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.721779 Loss1: 0.324086 Loss2: 1.397693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.557950 Loss1: 0.187958 Loss2: 1.369993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.478272 Loss1: 0.126614 Loss2: 1.351658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458583 Loss1: 0.107891 Loss2: 1.350693 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.049647 Loss1: 0.595061 Loss2: 1.454586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.677009 Loss1: 0.263393 Loss2: 1.413616 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.214705 Loss1: 1.360105 Loss2: 1.854601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.332416 Loss1: 0.906035 Loss2: 1.426382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.034932 Loss1: 0.608252 Loss2: 1.426680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.795865 Loss1: 0.401749 Loss2: 1.394117 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.701291 Loss1: 0.321072 Loss2: 1.380219 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.606122 Loss1: 0.229442 Loss2: 1.376680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.566031 Loss1: 0.196186 Loss2: 1.369846
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.184279 Loss1: 1.373802 Loss2: 1.810477
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529679 Loss1: 0.159635 Loss2: 1.370044
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.298327 Loss1: 0.883282 Loss2: 1.415045
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.988228 Loss1: 0.592349 Loss2: 1.395879
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.858961 Loss1: 0.469517 Loss2: 1.389444
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.748400 Loss1: 0.360513 Loss2: 1.387887
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.630494 Loss1: 0.263321 Loss2: 1.367173
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.281768 Loss1: 1.450085 Loss2: 1.831684
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.305164 Loss1: 0.918129 Loss2: 1.387034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.043200 Loss1: 0.624833 Loss2: 1.418367 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.795481 Loss1: 0.422647 Loss2: 1.372834 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961914
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484345 Loss1: 0.124794 Loss2: 1.359550
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.664216 Loss1: 0.293420 Loss2: 1.370795
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.594212 Loss1: 0.237780 Loss2: 1.356432
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.572765 Loss1: 0.214142 Loss2: 1.358622
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.557235 Loss1: 0.202399 Loss2: 1.354835
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.576380 Loss1: 0.228473 Loss2: 1.347906
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.285421 Loss1: 1.389978 Loss2: 1.895442
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.523582 Loss1: 0.168270 Loss2: 1.355312
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.104352 Loss1: 0.618759 Loss2: 1.485593 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.873490 Loss1: 0.436973 Loss2: 1.436517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.808447 Loss1: 0.378340 Loss2: 1.430107
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.132263 Loss1: 1.247821 Loss2: 1.884442
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.281742 Loss1: 0.796159 Loss2: 1.485583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.096596 Loss1: 0.613570 Loss2: 1.483026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.921402 Loss1: 0.452930 Loss2: 1.468472 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.798162 Loss1: 0.326231 Loss2: 1.471931 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.678952 Loss1: 0.214812 Loss2: 1.464140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.620319 Loss1: 0.167440 Loss2: 1.452880 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.339339 Loss1: 0.873725 Loss2: 1.465614 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965820
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.859291 Loss1: 0.423597 Loss2: 1.435693 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.773361 Loss1: 0.342372 Loss2: 1.430989 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.665011 Loss1: 0.234188 Loss2: 1.430824
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.253790 Loss1: 1.372085 Loss2: 1.881705
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.256754 Loss1: 0.890178 Loss2: 1.366576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.950621 Loss1: 0.533878 Loss2: 1.416743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.541305 Loss1: 0.138890 Loss2: 1.402415
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.760976 Loss1: 0.390059 Loss2: 1.370918
+(DefaultActor pid=3765) >> Training accuracy: 0.965402
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.645042 Loss1: 0.294317 Loss2: 1.350726
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.548615 Loss1: 0.198896 Loss2: 1.349719
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487848 Loss1: 0.149813 Loss2: 1.338035
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460323 Loss1: 0.126123 Loss2: 1.334200
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464433 Loss1: 0.133904 Loss2: 1.330528
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.173361 Loss1: 1.254983 Loss2: 1.918377
+(DefaultActor pid=3764) >> Training accuracy: 0.972356
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.004535 Loss1: 0.540997 Loss2: 1.463538 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.660962 Loss1: 0.241602 Loss2: 1.419360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.597183 Loss1: 0.195546 Loss2: 1.401637
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.285600 Loss1: 1.342278 Loss2: 1.943322
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.324668 Loss1: 0.840479 Loss2: 1.484189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.041929 Loss1: 0.550812 Loss2: 1.491117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.895586 Loss1: 0.436249 Loss2: 1.459337 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.536422 Loss1: 0.135301 Loss2: 1.401121
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.842899 Loss1: 0.371595 Loss2: 1.471304
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.810299 Loss1: 0.356153 Loss2: 1.454146
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.724015 Loss1: 0.266218 Loss2: 1.457797
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.693405 Loss1: 0.248786 Loss2: 1.444619
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.625044 Loss1: 0.185210 Loss2: 1.439833
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.376087 Loss1: 1.422851 Loss2: 1.953236
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.583584 Loss1: 0.154628 Loss2: 1.428957
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.959962 Loss1: 0.498492 Loss2: 1.461470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.736339 Loss1: 0.296453 Loss2: 1.439886 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.667892 Loss1: 0.229209 Loss2: 1.438683
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.158009 Loss1: 1.275447 Loss2: 1.882562
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.651810 Loss1: 0.216672 Loss2: 1.435139
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.173261 Loss1: 0.780716 Loss2: 1.392545
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.625232 Loss1: 0.189435 Loss2: 1.435796
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.922525 Loss1: 0.516354 Loss2: 1.406170
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.575067 Loss1: 0.147393 Loss2: 1.427674
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.768025 Loss1: 0.402120 Loss2: 1.365905
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.553839 Loss1: 0.135676 Loss2: 1.418163
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.640497 Loss1: 0.273525 Loss2: 1.366972
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.596723 Loss1: 0.233996 Loss2: 1.362727
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.537915 Loss1: 0.187796 Loss2: 1.350118
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.550072 Loss1: 0.193955 Loss2: 1.356116
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.474545 Loss1: 0.121993 Loss2: 1.352552
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.146915 Loss1: 1.238748 Loss2: 1.908166
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.447100 Loss1: 0.106283 Loss2: 1.340817
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.017930 Loss1: 0.565292 Loss2: 1.452638 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.712938 Loss1: 0.295463 Loss2: 1.417476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.655074 Loss1: 0.250323 Loss2: 1.404751
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.141881 Loss1: 1.246758 Loss2: 1.895123
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.254145 Loss1: 0.823592 Loss2: 1.430553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.931449 Loss1: 0.480455 Loss2: 1.450994 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.721690 Loss1: 0.314185 Loss2: 1.407506 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.549763 Loss1: 0.139844 Loss2: 1.409919
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.727194 Loss1: 0.323631 Loss2: 1.403563
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.609494 Loss1: 0.206187 Loss2: 1.403307
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.559526 Loss1: 0.169633 Loss2: 1.389893
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.502214 Loss1: 0.121681 Loss2: 1.380532
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.557903 Loss1: 0.176578 Loss2: 1.381325
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.089361 Loss1: 1.258802 Loss2: 1.830559
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.555548 Loss1: 0.168380 Loss2: 1.387168
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.119540 Loss1: 0.710002 Loss2: 1.409537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.681594 Loss1: 0.291893 Loss2: 1.389701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.368669 Loss1: 1.412750 Loss2: 1.955920
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.634535 Loss1: 0.245097 Loss2: 1.389438
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.484937 Loss1: 0.953222 Loss2: 1.531714
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.576945 Loss1: 0.184913 Loss2: 1.392032
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.089107 Loss1: 0.571776 Loss2: 1.517330
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.514108 Loss1: 0.139087 Loss2: 1.375021
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.899838 Loss1: 0.418541 Loss2: 1.481298
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499088 Loss1: 0.131555 Loss2: 1.367533
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447542 Loss1: 0.086075 Loss2: 1.361467 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.767335 Loss1: 0.298281 Loss2: 1.469054 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.656178 Loss1: 0.188268 Loss2: 1.467910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.674064 Loss1: 0.215616 Loss2: 1.458447
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.279457 Loss1: 1.369811 Loss2: 1.909646
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.251021 Loss1: 0.816205 Loss2: 1.434816
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.995792 Loss1: 0.520474 Loss2: 1.475318
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.802249 Loss1: 0.382201 Loss2: 1.420048
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.733677 Loss1: 0.309505 Loss2: 1.424172
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.094637 Loss1: 1.175749 Loss2: 1.918887
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.678515 Loss1: 0.262751 Loss2: 1.415764
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.669478 Loss1: 0.254859 Loss2: 1.414619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.626971 Loss1: 0.213630 Loss2: 1.413342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.601338 Loss1: 0.189933 Loss2: 1.411405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.620497 Loss1: 0.202531 Loss2: 1.417966 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.651065 Loss1: 0.234989 Loss2: 1.416076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.596184 Loss1: 0.179570 Loss2: 1.416614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.534774 Loss1: 0.131038 Loss2: 1.403736
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.320006 Loss1: 1.403725 Loss2: 1.916281
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.417838 Loss1: 0.880243 Loss2: 1.537595
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.001976 Loss1: 0.536980 Loss2: 1.464996
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.853794 Loss1: 0.373620 Loss2: 1.480174
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.704587 Loss1: 0.253742 Loss2: 1.450845
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.172579 Loss1: 1.287567 Loss2: 1.885012
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.139412 Loss1: 0.661771 Loss2: 1.477641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.923630 Loss1: 0.481717 Loss2: 1.441912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.779202 Loss1: 0.346772 Loss2: 1.432430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.732297 Loss1: 0.305994 Loss2: 1.426303 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961914
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.693585 Loss1: 0.261653 Loss2: 1.431932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.594840 Loss1: 0.175668 Loss2: 1.419172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.352271 Loss1: 1.363540 Loss2: 1.988731 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.943015
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.115670 Loss1: 0.651912 Loss2: 1.463757 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.762036 Loss1: 0.369862 Loss2: 1.392174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547498 Loss1: 0.162641 Loss2: 1.384857 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.512883 Loss1: 0.138896 Loss2: 1.373987
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.289784 Loss1: 1.467230 Loss2: 1.822554
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.334449 Loss1: 0.950163 Loss2: 1.384286 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.992069 Loss1: 0.588952 Loss2: 1.403117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.709471 Loss1: 0.351369 Loss2: 1.358102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.572163 Loss1: 0.227342 Loss2: 1.344821 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.555255 Loss1: 0.215378 Loss2: 1.339877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.553031 Loss1: 0.209895 Loss2: 1.343136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.495354 Loss1: 0.150436 Loss2: 1.344918 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606566 Loss1: 0.269358 Loss2: 1.337208 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485461 Loss1: 0.169312 Loss2: 1.316149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.442555 Loss1: 0.129393 Loss2: 1.313162
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.427796 Loss1: 1.513092 Loss2: 1.914703
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.278309 Loss1: 0.872907 Loss2: 1.405402 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.509536 Loss1: 0.199124 Loss2: 1.310412
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.936065 Loss1: 0.504740 Loss2: 1.431326
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.712426 Loss1: 0.339106 Loss2: 1.373320
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.693612 Loss1: 0.319958 Loss2: 1.373654
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647220 Loss1: 0.262676 Loss2: 1.384544
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.625046 Loss1: 0.253932 Loss2: 1.371115
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.560122 Loss1: 0.186752 Loss2: 1.373370
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.299926 Loss1: 1.427339 Loss2: 1.872588
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.517279 Loss1: 0.157618 Loss2: 1.359661
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.364105 Loss1: 0.916135 Loss2: 1.447970
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.547565 Loss1: 0.184817 Loss2: 1.362748
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.964058 Loss1: 0.544087 Loss2: 1.419972
+(DefaultActor pid=3764) >> Training accuracy: 0.962054
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.808773 Loss1: 0.401232 Loss2: 1.407541
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.803271 Loss1: 0.401830 Loss2: 1.401441
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.680601 Loss1: 0.273911 Loss2: 1.406690
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.657868 Loss1: 0.262221 Loss2: 1.395647
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.483993 Loss1: 1.415152 Loss2: 2.068842
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.659436 Loss1: 0.264263 Loss2: 1.395173
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.442291 Loss1: 0.865956 Loss2: 1.576335
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.616693 Loss1: 0.230969 Loss2: 1.385724
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.092764 Loss1: 0.514623 Loss2: 1.578141
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.571024 Loss1: 0.178858 Loss2: 1.392166
+(DefaultActor pid=3765) >> Training accuracy: 0.947917
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-10 09:57:03,044 | server.py:236 | fit_round 72 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.824148 Loss1: 0.286994 Loss2: 1.537154 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.764744 Loss1: 0.235713 Loss2: 1.529031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.784028 Loss1: 0.240492 Loss2: 1.543536
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.256291 Loss1: 1.311746 Loss2: 1.944545
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.365416 Loss1: 0.881612 Loss2: 1.483804 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.727103 Loss1: 0.197080 Loss2: 1.530023
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.056642 Loss1: 0.551435 Loss2: 1.505207
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.897456 Loss1: 0.428426 Loss2: 1.469030
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.869840 Loss1: 0.380916 Loss2: 1.488924
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.798531 Loss1: 0.319940 Loss2: 1.478592
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.767434 Loss1: 0.293177 Loss2: 1.474257
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.311461 Loss1: 1.366691 Loss2: 1.944771
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.656734 Loss1: 0.191017 Loss2: 1.465717
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.332858 Loss1: 0.856784 Loss2: 1.476073
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.650188 Loss1: 0.185197 Loss2: 1.464991
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.065613 Loss1: 0.553533 Loss2: 1.512080
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.616088 Loss1: 0.163060 Loss2: 1.453028
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.853871 Loss1: 0.364950 Loss2: 1.488921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.718539 Loss1: 0.245772 Loss2: 1.472768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.651796 Loss1: 0.190665 Loss2: 1.461131
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.181539 Loss1: 1.334628 Loss2: 1.846910
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.165426 Loss1: 0.760003 Loss2: 1.405423 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.608374 Loss1: 0.149308 Loss2: 1.459065
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.948359 Loss1: 0.543391 Loss2: 1.404968
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.715783 Loss1: 0.341496 Loss2: 1.374287
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.687018 Loss1: 0.307099 Loss2: 1.379919
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.657249 Loss1: 0.265412 Loss2: 1.391837
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.613677 Loss1: 0.238479 Loss2: 1.375198
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.294492 Loss1: 1.333541 Loss2: 1.960951
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.587382 Loss1: 0.216132 Loss2: 1.371250
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.544756 Loss1: 0.175788 Loss2: 1.368969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.562022 Loss1: 0.200841 Loss2: 1.361180 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.761811 Loss1: 0.288539 Loss2: 1.473272 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.692605 Loss1: 0.241433 Loss2: 1.451171 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.705698 Loss1: 0.255083 Loss2: 1.450615 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 09:57:03,044][flwr][DEBUG] - fit_round 72 received 50 results and 0 failures
+INFO flwr 2023-10-10 09:57:43,564 | server.py:125 | fit progress: (72, 2.2798919079783624, {'accuracy': 0.5342}, 165971.342123449)
+>> Test accuracy: 0.534200
+[2023-10-10 09:57:43,564][flwr][INFO] - fit progress: (72, 2.2798919079783624, {'accuracy': 0.5342}, 165971.342123449)
+DEBUG flwr 2023-10-10 09:57:43,564 | server.py:173 | evaluate_round 72: strategy sampled 50 clients (out of 50)
+[2023-10-10 09:57:43,564][flwr][DEBUG] - evaluate_round 72: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 10:06:48,900 | server.py:187 | evaluate_round 72 received 50 results and 0 failures
+[2023-10-10 10:06:48,900][flwr][DEBUG] - evaluate_round 72 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 10:06:48,901 | server.py:222 | fit_round 73: strategy sampled 50 clients (out of 50)
+[2023-10-10 10:06:48,901][flwr][DEBUG] - fit_round 73: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.123547 Loss1: 1.243335 Loss2: 1.880212 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.849819 Loss1: 0.439233 Loss2: 1.410586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.674467 Loss1: 0.302846 Loss2: 1.371621
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.268794 Loss1: 1.354372 Loss2: 1.914422
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.419835 Loss1: 0.925999 Loss2: 1.493836 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.935088 Loss1: 0.481300 Loss2: 1.453788 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.858021 Loss1: 0.417947 Loss2: 1.440075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.768582 Loss1: 0.325490 Loss2: 1.443092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.683475 Loss1: 0.263220 Loss2: 1.420255 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.957292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.536042 Loss1: 0.181232 Loss2: 1.354810
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.646967 Loss1: 0.222651 Loss2: 1.424315
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.627222 Loss1: 0.209803 Loss2: 1.417419
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.607541 Loss1: 0.184935 Loss2: 1.422606
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.542244 Loss1: 0.129185 Loss2: 1.413059
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.380293 Loss1: 1.489845 Loss2: 1.890448
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.359150 Loss1: 0.877249 Loss2: 1.481901
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.968235 Loss1: 0.505706 Loss2: 1.462529
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.882366 Loss1: 0.447426 Loss2: 1.434941
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.112839 Loss1: 1.244384 Loss2: 1.868454
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.212113 Loss1: 0.786320 Loss2: 1.425793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.948798 Loss1: 0.503893 Loss2: 1.444905 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.839590 Loss1: 0.433947 Loss2: 1.405643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.674829 Loss1: 0.266622 Loss2: 1.408207 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.587353 Loss1: 0.200285 Loss2: 1.387069 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.551406 Loss1: 0.170795 Loss2: 1.380611 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.503976 Loss1: 0.133082 Loss2: 1.370894 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.308996 Loss1: 0.896742 Loss2: 1.412254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.763407 Loss1: 0.379210 Loss2: 1.384197 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.116905 Loss1: 1.261826 Loss2: 1.855079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.228110 Loss1: 0.823990 Loss2: 1.404119 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.005079 Loss1: 0.578482 Loss2: 1.426597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.799290 Loss1: 0.403556 Loss2: 1.395734 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.758910 Loss1: 0.365047 Loss2: 1.393863 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972098
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.596289 Loss1: 0.215516 Loss2: 1.380773 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.489560 Loss1: 0.115260 Loss2: 1.374300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.499628 Loss1: 0.137201 Loss2: 1.362427
+(DefaultActor pid=3764) >> Training accuracy: 0.942708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.158056 Loss1: 1.316102 Loss2: 1.841954
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.125495 Loss1: 0.754910 Loss2: 1.370585
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.891281 Loss1: 0.498487 Loss2: 1.392794
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.704143 Loss1: 0.353595 Loss2: 1.350548
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.612079 Loss1: 0.253429 Loss2: 1.358650
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.418466 Loss1: 1.353713 Loss2: 2.064753
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.598060 Loss1: 0.253863 Loss2: 1.344197
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531473 Loss1: 0.180290 Loss2: 1.351184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.482932 Loss1: 0.143809 Loss2: 1.339123 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477239 Loss1: 0.143017 Loss2: 1.334222 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458125 Loss1: 0.121201 Loss2: 1.336924 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.790495 Loss1: 0.242900 Loss2: 1.547595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.748634 Loss1: 0.212930 Loss2: 1.535704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.750217 Loss1: 0.203717 Loss2: 1.546500
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.041089 Loss1: 1.186254 Loss2: 1.854835
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.207193 Loss1: 0.749259 Loss2: 1.457934
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.890427 Loss1: 0.459311 Loss2: 1.431115
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.763719 Loss1: 0.347256 Loss2: 1.416463
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.668360 Loss1: 0.259711 Loss2: 1.408648
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.503619 Loss1: 1.514842 Loss2: 1.988777
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.661590 Loss1: 0.255384 Loss2: 1.406206
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.439125 Loss1: 0.949080 Loss2: 1.490045
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.087932 Loss1: 0.585787 Loss2: 1.502145
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.631859 Loss1: 0.225723 Loss2: 1.406136
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.911514 Loss1: 0.455689 Loss2: 1.455825
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.594581 Loss1: 0.191611 Loss2: 1.402970
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.533324 Loss1: 0.137584 Loss2: 1.395740 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474048 Loss1: 0.094534 Loss2: 1.379514 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.607198 Loss1: 0.165184 Loss2: 1.442014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.549275 Loss1: 0.130844 Loss2: 1.418431 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970982
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.159777 Loss1: 1.247110 Loss2: 1.912667
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.187863 Loss1: 0.762242 Loss2: 1.425621
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.010250 Loss1: 0.552736 Loss2: 1.457514
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765516 Loss1: 0.354176 Loss2: 1.411339
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.102355 Loss1: 1.199562 Loss2: 1.902793
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.265735 Loss1: 0.776007 Loss2: 1.489729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.887773 Loss1: 0.455128 Loss2: 1.432645 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.786834 Loss1: 0.360453 Loss2: 1.426381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.707800 Loss1: 0.293354 Loss2: 1.414446 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.531930 Loss1: 0.141082 Loss2: 1.390848 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.582478 Loss1: 0.186468 Loss2: 1.396010 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.545310 Loss1: 0.151640 Loss2: 1.393670 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970588
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.202262 Loss1: 0.806840 Loss2: 1.395422 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.700094 Loss1: 0.330181 Loss2: 1.369913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.666016 Loss1: 0.292376 Loss2: 1.373640
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.257647 Loss1: 1.415381 Loss2: 1.842266
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.235546 Loss1: 0.831167 Loss2: 1.404379
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.610119 Loss1: 0.248721 Loss2: 1.361397
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.957518 Loss1: 0.554419 Loss2: 1.403099
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.604012 Loss1: 0.235560 Loss2: 1.368452
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.727732 Loss1: 0.352988 Loss2: 1.374744
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.550387 Loss1: 0.184777 Loss2: 1.365610
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.613815 Loss1: 0.236210 Loss2: 1.377605
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.519155 Loss1: 0.162855 Loss2: 1.356299
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.557292 Loss1: 0.195220 Loss2: 1.362072 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.941406
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487523 Loss1: 0.125962 Loss2: 1.361562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460166 Loss1: 0.117454 Loss2: 1.342713 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.341945 Loss1: 0.867170 Loss2: 1.474775 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.773848 Loss1: 0.334858 Loss2: 1.438990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.742429 Loss1: 0.296918 Loss2: 1.445511 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.702299 Loss1: 0.271371 Loss2: 1.430928 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.698946 Loss1: 0.258131 Loss2: 1.440815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.747144 Loss1: 0.300851 Loss2: 1.446294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.674126 Loss1: 0.244249 Loss2: 1.429877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.638666 Loss1: 0.207223 Loss2: 1.431444 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.932292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.610623 Loss1: 0.231163 Loss2: 1.379460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.496506 Loss1: 0.120866 Loss2: 1.375640 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.380560 Loss1: 0.941427 Loss2: 1.439132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.787086 Loss1: 0.376153 Loss2: 1.410933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.048762 Loss1: 1.244332 Loss2: 1.804430
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.708178 Loss1: 0.292680 Loss2: 1.415497
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.171591 Loss1: 0.745263 Loss2: 1.426328
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.750228 Loss1: 0.353147 Loss2: 1.397081
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.667133 Loss1: 0.257489 Loss2: 1.409645
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.944701 Loss1: 0.559750 Loss2: 1.384951
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.651103 Loss1: 0.250855 Loss2: 1.400249
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.761494 Loss1: 0.392900 Loss2: 1.368594
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.613568 Loss1: 0.219965 Loss2: 1.393603
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.684485 Loss1: 0.327555 Loss2: 1.356929
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.589783 Loss1: 0.203455 Loss2: 1.386328
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.678479 Loss1: 0.312516 Loss2: 1.365963
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.588380 Loss1: 0.235402 Loss2: 1.352978
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.573994 Loss1: 0.210138 Loss2: 1.363855
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.509634 Loss1: 0.167394 Loss2: 1.342240
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.471522 Loss1: 0.134911 Loss2: 1.336611
+(DefaultActor pid=3764) >> Training accuracy: 0.978516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.284747 Loss1: 1.444396 Loss2: 1.840350
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.353063 Loss1: 0.938341 Loss2: 1.414722
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.971647 Loss1: 0.556732 Loss2: 1.414914
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.798769 Loss1: 0.411616 Loss2: 1.387152
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.736401 Loss1: 0.337106 Loss2: 1.399295
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.160307 Loss1: 1.270852 Loss2: 1.889454
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.613721 Loss1: 0.230896 Loss2: 1.382825
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.239364 Loss1: 0.807319 Loss2: 1.432044
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.557546 Loss1: 0.182995 Loss2: 1.374550
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.016969 Loss1: 0.580939 Loss2: 1.436030
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.533957 Loss1: 0.167487 Loss2: 1.366470
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.869397 Loss1: 0.451424 Loss2: 1.417974
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.506756 Loss1: 0.146374 Loss2: 1.360382
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.720497 Loss1: 0.315308 Loss2: 1.405189
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.540317 Loss1: 0.178869 Loss2: 1.361448
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.628902 Loss1: 0.228983 Loss2: 1.399919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.555424 Loss1: 0.167918 Loss2: 1.387505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.544610 Loss1: 0.159778 Loss2: 1.384832
+(DefaultActor pid=3764) >> Training accuracy: 0.947917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.341583 Loss1: 1.394226 Loss2: 1.947357
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.602147 Loss1: 1.084849 Loss2: 1.517298
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.151017 Loss1: 0.642293 Loss2: 1.508724
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.942343 Loss1: 0.461056 Loss2: 1.481287
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.741065 Loss1: 0.277840 Loss2: 1.463225
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.170810 Loss1: 1.287007 Loss2: 1.883803
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.715314 Loss1: 0.274993 Loss2: 1.440320
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.187761 Loss1: 0.720465 Loss2: 1.467295
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.666646 Loss1: 0.215148 Loss2: 1.451497
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.918120 Loss1: 0.472572 Loss2: 1.445548
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.654100 Loss1: 0.208240 Loss2: 1.445860
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.602811 Loss1: 0.164063 Loss2: 1.438748
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.776198 Loss1: 0.349615 Loss2: 1.426583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.589630 Loss1: 0.152184 Loss2: 1.437446
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.693515 Loss1: 0.268957 Loss2: 1.424559
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.669288 Loss1: 0.242443 Loss2: 1.426845
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.597285 Loss1: 0.186162 Loss2: 1.411123
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.580475 Loss1: 0.170043 Loss2: 1.410431
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.591044 Loss1: 0.181872 Loss2: 1.409172
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.382598 Loss1: 1.385013 Loss2: 1.997585
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.565685 Loss1: 0.154718 Loss2: 1.410967
+(DefaultActor pid=3764) >> Training accuracy: 0.970703
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.141783 Loss1: 0.599875 Loss2: 1.541908 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.837807 Loss1: 0.339296 Loss2: 1.498511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.000780 Loss1: 1.149122 Loss2: 1.851658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.096088 Loss1: 0.709173 Loss2: 1.386915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.930884 Loss1: 0.524817 Loss2: 1.406067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.773317 Loss1: 0.393047 Loss2: 1.380270 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.671630 Loss1: 0.297179 Loss2: 1.374451 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.567716 Loss1: 0.211112 Loss2: 1.356604 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.492104 Loss1: 0.146442 Loss2: 1.345663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.467430 Loss1: 0.122173 Loss2: 1.345257 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.841780 Loss1: 0.418473 Loss2: 1.423308 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.695094 Loss1: 0.269028 Loss2: 1.426066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.637893 Loss1: 0.216758 Loss2: 1.421134
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.279787 Loss1: 1.399942 Loss2: 1.879845
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.587235 Loss1: 0.171446 Loss2: 1.415789
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.289660 Loss1: 0.856874 Loss2: 1.432786
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.569625 Loss1: 0.165938 Loss2: 1.403686
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.925444 Loss1: 0.508937 Loss2: 1.416507
+(DefaultActor pid=3765) >> Training accuracy: 0.935268
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.637863 Loss1: 0.228592 Loss2: 1.409271
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.793598 Loss1: 0.405912 Loss2: 1.387686
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.738754 Loss1: 0.345627 Loss2: 1.393128
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.635659 Loss1: 0.264475 Loss2: 1.371184
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.536463 Loss1: 0.164208 Loss2: 1.372255
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.529789 Loss1: 0.165595 Loss2: 1.364194
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.169680 Loss1: 1.321696 Loss2: 1.847985
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.558567 Loss1: 0.193569 Loss2: 1.364997
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.524617 Loss1: 0.147910 Loss2: 1.376707 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.913542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.744167 Loss1: 0.370474 Loss2: 1.373694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.574350 Loss1: 0.212075 Loss2: 1.362274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.594863 Loss1: 0.226843 Loss2: 1.368019
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.197683 Loss1: 1.228219 Loss2: 1.969464
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.207698 Loss1: 0.722535 Loss2: 1.485163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.990264 Loss1: 0.477002 Loss2: 1.513262 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491890 Loss1: 0.146024 Loss2: 1.345867
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.786494 Loss1: 0.328878 Loss2: 1.457616
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.745341 Loss1: 0.282559 Loss2: 1.462782
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.660823 Loss1: 0.207850 Loss2: 1.452974
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.678673 Loss1: 0.231162 Loss2: 1.447511
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.670076 Loss1: 0.208487 Loss2: 1.461589
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.214262 Loss1: 1.317699 Loss2: 1.896563
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.584907 Loss1: 0.141615 Loss2: 1.443292
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.239797 Loss1: 0.798757 Loss2: 1.441040
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.585034 Loss1: 0.146568 Loss2: 1.438466
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.751664 Loss1: 0.351517 Loss2: 1.400148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.656810 Loss1: 0.269501 Loss2: 1.387309 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.562323 Loss1: 0.181621 Loss2: 1.380702
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.155401 Loss1: 1.264644 Loss2: 1.890757
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.239427 Loss1: 0.827617 Loss2: 1.411810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.921902 Loss1: 0.462910 Loss2: 1.458992 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.503180 Loss1: 0.126002 Loss2: 1.377178
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.781567 Loss1: 0.385653 Loss2: 1.395914
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.629577 Loss1: 0.234502 Loss2: 1.395075
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.573262 Loss1: 0.187254 Loss2: 1.386008
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.556417 Loss1: 0.170005 Loss2: 1.386412
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.529843 Loss1: 0.148079 Loss2: 1.381764
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.226465 Loss1: 1.297319 Loss2: 1.929146
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519819 Loss1: 0.139901 Loss2: 1.379918
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.408456 Loss1: 0.920850 Loss2: 1.487606
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.505372 Loss1: 0.137509 Loss2: 1.367862
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.803278 Loss1: 0.369542 Loss2: 1.433736 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.677848 Loss1: 0.258592 Loss2: 1.419256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.636051 Loss1: 0.208026 Loss2: 1.428025
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.129114 Loss1: 1.310023 Loss2: 1.819091
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.253746 Loss1: 0.862247 Loss2: 1.391499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.946487 Loss1: 0.516570 Loss2: 1.429917 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.954167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.560258 Loss1: 0.147339 Loss2: 1.412918
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.718581 Loss1: 0.354618 Loss2: 1.363963
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.621261 Loss1: 0.248655 Loss2: 1.372607
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.592069 Loss1: 0.230768 Loss2: 1.361301
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.562941 Loss1: 0.205903 Loss2: 1.357038
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548168 Loss1: 0.188640 Loss2: 1.359528
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.448672 Loss1: 1.454745 Loss2: 1.993928
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485026 Loss1: 0.140507 Loss2: 1.344519
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460614 Loss1: 0.112311 Loss2: 1.348303 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.844041 Loss1: 0.350235 Loss2: 1.493806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.727682 Loss1: 0.248407 Loss2: 1.479275 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.740322 Loss1: 0.269551 Loss2: 1.470772
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.060417 Loss1: 1.222328 Loss2: 1.838089
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.253069 Loss1: 0.824534 Loss2: 1.428535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.914141 Loss1: 0.501253 Loss2: 1.412888 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.832534 Loss1: 0.437045 Loss2: 1.395489 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.665700 Loss1: 0.284442 Loss2: 1.381258 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.506417 Loss1: 0.134276 Loss2: 1.372142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.475494 Loss1: 0.111876 Loss2: 1.363618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.493962 Loss1: 0.131686 Loss2: 1.362276 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970703
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.667295 Loss1: 0.234710 Loss2: 1.432584 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.617342 Loss1: 0.194749 Loss2: 1.422593 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.588922 Loss1: 0.175728 Loss2: 1.413194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.587371 Loss1: 0.170241 Loss2: 1.417129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.566999 Loss1: 0.153044 Loss2: 1.413955 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.954102
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.793434 Loss1: 0.310399 Loss2: 1.483035 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.740324 Loss1: 0.273667 Loss2: 1.466657 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.428330 Loss1: 1.435310 Loss2: 1.993020 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.186591 Loss1: 0.766325 Loss2: 1.420266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.889337 Loss1: 0.445971 Loss2: 1.443366 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.630921 Loss1: 0.246894 Loss2: 1.384027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.578870 Loss1: 0.202023 Loss2: 1.376847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.537652 Loss1: 0.150227 Loss2: 1.387425
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.263075 Loss1: 1.394387 Loss2: 1.868688
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.202616 Loss1: 0.787756 Loss2: 1.414860 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987981
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.724092 Loss1: 0.334724 Loss2: 1.389368 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.636924 Loss1: 0.250652 Loss2: 1.386273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.573040 Loss1: 0.204654 Loss2: 1.368386
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.367318 Loss1: 1.400108 Loss2: 1.967210
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.296003 Loss1: 0.816078 Loss2: 1.479925 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.988027 Loss1: 0.496044 Loss2: 1.491983 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.842578 Loss1: 0.371691 Loss2: 1.470887 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.732308 Loss1: 0.268835 Loss2: 1.463473 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.680013 Loss1: 0.226014 Loss2: 1.453998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.655039 Loss1: 0.201709 Loss2: 1.453330 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.596143 Loss1: 0.146615 Loss2: 1.449529 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.821304 Loss1: 0.369238 Loss2: 1.452066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.706145 Loss1: 0.252838 Loss2: 1.453307 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.616299 Loss1: 0.170905 Loss2: 1.445394
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.492608 Loss1: 1.446457 Loss2: 2.046152
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.375772 Loss1: 0.959996 Loss2: 1.415776
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.582169 Loss1: 0.141132 Loss2: 1.441037
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.634849 Loss1: 0.199672 Loss2: 1.435178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.638932 Loss1: 0.188752 Loss2: 1.450180 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.723410 Loss1: 0.293657 Loss2: 1.429753 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.625853 Loss1: 0.204663 Loss2: 1.421190 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.179671 Loss1: 1.324667 Loss2: 1.855004 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.997594 Loss1: 0.556362 Loss2: 1.441232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.718191 Loss1: 0.323279 Loss2: 1.394912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.621714 Loss1: 0.242751 Loss2: 1.378963 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.575681 Loss1: 0.201837 Loss2: 1.373844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.506110 Loss1: 0.141685 Loss2: 1.364425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.492337 Loss1: 0.136598 Loss2: 1.355738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441870 Loss1: 0.083522 Loss2: 1.358347 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.842466 Loss1: 0.266555 Loss2: 1.575912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.780312 Loss1: 0.211396 Loss2: 1.568916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.744713 Loss1: 0.183419 Loss2: 1.561294
+(DefaultActor pid=3765) >> Training accuracy: 0.938477
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.369579 Loss1: 1.262939 Loss2: 2.106641
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.394668 Loss1: 0.798412 Loss2: 1.596256
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.098599 Loss1: 0.467610 Loss2: 1.630989
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.988504 Loss1: 0.410045 Loss2: 1.578460
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.931917 Loss1: 0.330648 Loss2: 1.601268
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.125577 Loss1: 1.282310 Loss2: 1.843267
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.792671 Loss1: 0.225404 Loss2: 1.567267
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.201498 Loss1: 0.782524 Loss2: 1.418973
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.725593 Loss1: 0.158078 Loss2: 1.567515
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.912206 Loss1: 0.502185 Loss2: 1.410021
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.748020 Loss1: 0.188675 Loss2: 1.559344
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.741364 Loss1: 0.360092 Loss2: 1.381271
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.699584 Loss1: 0.140236 Loss2: 1.559348
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.684525 Loss1: 0.298962 Loss2: 1.385563
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.660413 Loss1: 0.108294 Loss2: 1.552118
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.554766 Loss1: 0.179207 Loss2: 1.375559 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.492448 Loss1: 0.133748 Loss2: 1.358700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.505969 Loss1: 0.152539 Loss2: 1.353430
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.192329 Loss1: 1.352611 Loss2: 1.839719
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.321108 Loss1: 0.873612 Loss2: 1.447496
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.969011 Loss1: 0.573624 Loss2: 1.395387
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.784566 Loss1: 0.388484 Loss2: 1.396083
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.652670 Loss1: 0.270468 Loss2: 1.382202
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.190247 Loss1: 1.330436 Loss2: 1.859811
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.215051 Loss1: 0.816712 Loss2: 1.398339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.905200 Loss1: 0.475564 Loss2: 1.429637 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.741837 Loss1: 0.373869 Loss2: 1.367968 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 10:35:03,270 | server.py:236 | fit_round 73 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.698442 Loss1: 0.307142 Loss2: 1.391300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.515435 Loss1: 0.148414 Loss2: 1.367021
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564368 Loss1: 0.200098 Loss2: 1.364269
+(DefaultActor pid=3764) >> Training accuracy: 0.960938
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.571774 Loss1: 0.211851 Loss2: 1.359923
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.523187 Loss1: 0.167279 Loss2: 1.355908
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.523534 Loss1: 0.171047 Loss2: 1.352487
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.486932 Loss1: 0.133429 Loss2: 1.353503
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.023220 Loss1: 1.153442 Loss2: 1.869778
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.192844 Loss1: 0.785418 Loss2: 1.407425
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.042488 Loss1: 0.593362 Loss2: 1.449126
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.786249 Loss1: 0.400500 Loss2: 1.385749
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.666709 Loss1: 0.274845 Loss2: 1.391864
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.391137 Loss1: 1.460055 Loss2: 1.931082
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.633771 Loss1: 0.266696 Loss2: 1.367076
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.530642 Loss1: 1.031654 Loss2: 1.498989
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.540375 Loss1: 0.163022 Loss2: 1.377353
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.992985 Loss1: 0.556640 Loss2: 1.436345
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473915 Loss1: 0.113923 Loss2: 1.359992
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.803613 Loss1: 0.377149 Loss2: 1.426463
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493324 Loss1: 0.138751 Loss2: 1.354573
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.743944 Loss1: 0.321606 Loss2: 1.422339
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443201 Loss1: 0.085335 Loss2: 1.357866
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.659675 Loss1: 0.248516 Loss2: 1.411159
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.617204 Loss1: 0.209769 Loss2: 1.407435
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.600158 Loss1: 0.198600 Loss2: 1.401558
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.580919 Loss1: 0.175131 Loss2: 1.405788
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.533173 Loss1: 0.134628 Loss2: 1.398545
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.291699 Loss1: 1.338695 Loss2: 1.953003
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.266905 Loss1: 0.854809 Loss2: 1.412097
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.045230 Loss1: 0.556170 Loss2: 1.489060
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.801404 Loss1: 0.395904 Loss2: 1.405500
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.699496 Loss1: 0.287882 Loss2: 1.411613
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.654661 Loss1: 0.232740 Loss2: 1.421920
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.653369 Loss1: 0.246670 Loss2: 1.406699
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.589291 Loss1: 0.188911 Loss2: 1.400380
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.523636 Loss1: 0.125660 Loss2: 1.397976
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512733 Loss1: 0.125423 Loss2: 1.387310
+(DefaultActor pid=3764) >> Training accuracy: 0.978365
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 10:35:03,270][flwr][DEBUG] - fit_round 73 received 50 results and 0 failures
+INFO flwr 2023-10-10 10:35:45,952 | server.py:125 | fit progress: (73, 2.2661834475331415, {'accuracy': 0.5364}, 168253.73014172702)
+>> Test accuracy: 0.536400
+[2023-10-10 10:35:45,952][flwr][INFO] - fit progress: (73, 2.2661834475331415, {'accuracy': 0.5364}, 168253.73014172702)
+DEBUG flwr 2023-10-10 10:35:45,952 | server.py:173 | evaluate_round 73: strategy sampled 50 clients (out of 50)
+[2023-10-10 10:35:45,952][flwr][DEBUG] - evaluate_round 73: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 10:44:48,060 | server.py:187 | evaluate_round 73 received 50 results and 0 failures
+[2023-10-10 10:44:48,060][flwr][DEBUG] - evaluate_round 73 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 10:44:48,060 | server.py:222 | fit_round 74: strategy sampled 50 clients (out of 50)
+[2023-10-10 10:44:48,060][flwr][DEBUG] - fit_round 74: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.096249 Loss1: 1.230054 Loss2: 1.866195
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.190723 Loss1: 0.775437 Loss2: 1.415286
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.913805 Loss1: 0.484757 Loss2: 1.429048
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.701172 Loss1: 0.325619 Loss2: 1.375553
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.038323 Loss1: 1.201103 Loss2: 1.837221
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.172168 Loss1: 0.766087 Loss2: 1.406081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.908415 Loss1: 0.475867 Loss2: 1.432548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.743183 Loss1: 0.366523 Loss2: 1.376660 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.711718 Loss1: 0.333786 Loss2: 1.377933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.678136 Loss1: 0.314365 Loss2: 1.363771 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.530181 Loss1: 0.174231 Loss2: 1.355950 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441368 Loss1: 0.102360 Loss2: 1.339009 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.271640 Loss1: 0.822002 Loss2: 1.449638 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.747357 Loss1: 0.327530 Loss2: 1.419827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.231487 Loss1: 1.385526 Loss2: 1.845961
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.657110 Loss1: 0.239689 Loss2: 1.417421
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.335575 Loss1: 0.902084 Loss2: 1.433491
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.655920 Loss1: 0.252960 Loss2: 1.402960
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.924583 Loss1: 0.515221 Loss2: 1.409362
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.660885 Loss1: 0.250825 Loss2: 1.410060
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.783064 Loss1: 0.389518 Loss2: 1.393546
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.690281 Loss1: 0.274935 Loss2: 1.415347
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687752 Loss1: 0.297339 Loss2: 1.390413
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.641231 Loss1: 0.238685 Loss2: 1.402546
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.597764 Loss1: 0.223414 Loss2: 1.374350
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.563569 Loss1: 0.150854 Loss2: 1.412715
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.520043 Loss1: 0.151758 Loss2: 1.368285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482596 Loss1: 0.129673 Loss2: 1.352923 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.395471 Loss1: 0.895645 Loss2: 1.499826 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.835105 Loss1: 0.372766 Loss2: 1.462339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.816928 Loss1: 0.357981 Loss2: 1.458948 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.743437 Loss1: 0.282478 Loss2: 1.460959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.653398 Loss1: 0.200891 Loss2: 1.452507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.622908 Loss1: 0.188144 Loss2: 1.434764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.544971 Loss1: 0.109365 Loss2: 1.435606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.554273 Loss1: 0.131055 Loss2: 1.423218 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.574952 Loss1: 0.154495 Loss2: 1.420456 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970982
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.986689 Loss1: 1.105353 Loss2: 1.881336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.892149 Loss1: 0.468941 Loss2: 1.423208 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.679568 Loss1: 0.296684 Loss2: 1.382884
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.322481 Loss1: 1.433292 Loss2: 1.889189
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.618415 Loss1: 0.234945 Loss2: 1.383470
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.289492 Loss1: 0.820801 Loss2: 1.468691
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.540536 Loss1: 0.168573 Loss2: 1.371963
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.957438 Loss1: 0.531782 Loss2: 1.425656
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531300 Loss1: 0.170764 Loss2: 1.360536
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.837715 Loss1: 0.426206 Loss2: 1.411509
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.496988 Loss1: 0.137447 Loss2: 1.359541
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.789755 Loss1: 0.365794 Loss2: 1.423961
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.485416 Loss1: 0.132608 Loss2: 1.352808
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.692337 Loss1: 0.293849 Loss2: 1.398488
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452889 Loss1: 0.098720 Loss2: 1.354169
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.571289 Loss1: 0.165896 Loss2: 1.405393
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.556624 Loss1: 0.173154 Loss2: 1.383470
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.568532 Loss1: 0.187319 Loss2: 1.381213
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.593945 Loss1: 0.197659 Loss2: 1.396287
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.175731 Loss1: 1.277527 Loss2: 1.898204
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.121523 Loss1: 0.703267 Loss2: 1.418256
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.937283 Loss1: 0.499711 Loss2: 1.437572
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.720212 Loss1: 0.304092 Loss2: 1.416120
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.278609 Loss1: 1.397777 Loss2: 1.880832
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.294194 Loss1: 0.822496 Loss2: 1.471698 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.019437 Loss1: 0.562949 Loss2: 1.456488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.829537 Loss1: 0.413512 Loss2: 1.416025 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.723044 Loss1: 0.299654 Loss2: 1.423391 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.645123 Loss1: 0.240514 Loss2: 1.404609 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.959375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.606072 Loss1: 0.213882 Loss2: 1.392190
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.672662 Loss1: 0.264175 Loss2: 1.408487
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.600799 Loss1: 0.188646 Loss2: 1.412153
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.635950 Loss1: 0.220437 Loss2: 1.415513
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.620237 Loss1: 0.211962 Loss2: 1.408275
+(DefaultActor pid=3764) >> Training accuracy: 0.927083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.087554 Loss1: 1.228986 Loss2: 1.858568
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.205077 Loss1: 0.803094 Loss2: 1.401982
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.808333 Loss1: 0.386696 Loss2: 1.421637
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.713420 Loss1: 0.336858 Loss2: 1.376562
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.259902 Loss1: 1.294452 Loss2: 1.965450
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.282139 Loss1: 0.732905 Loss2: 1.549234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.052051 Loss1: 0.524581 Loss2: 1.527470 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.868925 Loss1: 0.374693 Loss2: 1.494232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.777668 Loss1: 0.279461 Loss2: 1.498207 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.700075 Loss1: 0.210752 Loss2: 1.489323 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.648139 Loss1: 0.165009 Loss2: 1.483130 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.573945 Loss1: 0.109104 Loss2: 1.464841 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.339038 Loss1: 0.870241 Loss2: 1.468797 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.729169 Loss1: 0.318634 Loss2: 1.410535 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.673714 Loss1: 0.278151 Loss2: 1.395563
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.983474 Loss1: 1.174905 Loss2: 1.808569
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.582104 Loss1: 0.186257 Loss2: 1.395847
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.053309 Loss1: 0.664498 Loss2: 1.388811
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.828575 Loss1: 0.427515 Loss2: 1.401060 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.690904 Loss1: 0.316620 Loss2: 1.374284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.601387 Loss1: 0.228149 Loss2: 1.373239 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.552175 Loss1: 0.194902 Loss2: 1.357272 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487186 Loss1: 0.141004 Loss2: 1.346181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442998 Loss1: 0.104358 Loss2: 1.338640 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.960938
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.307371 Loss1: 0.831410 Loss2: 1.475960 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.839159 Loss1: 0.377650 Loss2: 1.461509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.228103 Loss1: 1.342020 Loss2: 1.886084 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.279051 Loss1: 0.803392 Loss2: 1.475659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.985806 Loss1: 0.540796 Loss2: 1.445010 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.831230 Loss1: 0.398355 Loss2: 1.432876 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.581328 Loss1: 0.139664 Loss2: 1.441663 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.629568 Loss1: 0.220818 Loss2: 1.408750 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.557568 Loss1: 0.157974 Loss2: 1.399594 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.598158 Loss1: 1.547141 Loss2: 2.051017
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.553897 Loss1: 0.155611 Loss2: 1.398286
+(DefaultActor pid=3764) >> Training accuracy: 0.959961
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.071363 Loss1: 0.536450 Loss2: 1.534913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.752562 Loss1: 0.253603 Loss2: 1.498959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.253929 Loss1: 1.317728 Loss2: 1.936201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.128758 Loss1: 0.748238 Loss2: 1.380520 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.890297 Loss1: 0.459140 Loss2: 1.431157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.683482 Loss1: 0.320235 Loss2: 1.363246 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.630997 Loss1: 0.149393 Loss2: 1.481604
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.659237 Loss1: 0.291909 Loss2: 1.367328
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.622925 Loss1: 0.245101 Loss2: 1.377824
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.537935 Loss1: 0.181331 Loss2: 1.356604
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510189 Loss1: 0.146342 Loss2: 1.363847
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485166 Loss1: 0.125480 Loss2: 1.359687
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487824 Loss1: 0.127007 Loss2: 1.360817
+(DefaultActor pid=3764) >> Training accuracy: 0.975962
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.165707 Loss1: 1.303461 Loss2: 1.862246
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.368980 Loss1: 0.915248 Loss2: 1.453731
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.844387 Loss1: 0.441596 Loss2: 1.402791
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.706443 Loss1: 0.308040 Loss2: 1.398402
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.692178 Loss1: 0.303413 Loss2: 1.388766
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.191104 Loss1: 1.240081 Loss2: 1.951024
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.588049 Loss1: 0.212075 Loss2: 1.375974
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.556997 Loss1: 0.179000 Loss2: 1.377997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.515501 Loss1: 0.142458 Loss2: 1.373043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.507259 Loss1: 0.138038 Loss2: 1.369222 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.492535 Loss1: 0.126077 Loss2: 1.366458 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.574114 Loss1: 0.160140 Loss2: 1.413974 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.527582 Loss1: 0.130463 Loss2: 1.397119 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.498885 Loss1: 0.099117 Loss2: 1.399768
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.162851 Loss1: 1.212886 Loss2: 1.949965
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.305876 Loss1: 0.796717 Loss2: 1.509159
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.077009 Loss1: 0.516273 Loss2: 1.560736
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.839191 Loss1: 0.345275 Loss2: 1.493917
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.718014 Loss1: 0.220522 Loss2: 1.497492
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.397378 Loss1: 1.474338 Loss2: 1.923040
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.690961 Loss1: 0.201749 Loss2: 1.489213
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.280564 Loss1: 0.821050 Loss2: 1.459513
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.637231 Loss1: 0.145218 Loss2: 1.492013
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.975298 Loss1: 0.529302 Loss2: 1.445995
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.616025 Loss1: 0.139771 Loss2: 1.476254
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.804344 Loss1: 0.372549 Loss2: 1.431795
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.685066 Loss1: 0.271558 Loss2: 1.413508
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.571206 Loss1: 0.099612 Loss2: 1.471594
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.686882 Loss1: 0.282996 Loss2: 1.403886
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.597232 Loss1: 0.132502 Loss2: 1.464731
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.562148 Loss1: 0.164382 Loss2: 1.397766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.549827 Loss1: 0.156541 Loss2: 1.393286 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.368164 Loss1: 0.953250 Loss2: 1.414913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.747974 Loss1: 0.394105 Loss2: 1.353870 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.154228 Loss1: 1.209335 Loss2: 1.944893
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.613971 Loss1: 0.271873 Loss2: 1.342098
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.313166 Loss1: 0.849769 Loss2: 1.463397
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.616130 Loss1: 0.283985 Loss2: 1.332145
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.076775 Loss1: 0.566299 Loss2: 1.510476
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.537931 Loss1: 0.191416 Loss2: 1.346515
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.853372 Loss1: 0.398586 Loss2: 1.454786
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.507183 Loss1: 0.178622 Loss2: 1.328561
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.773304 Loss1: 0.323312 Loss2: 1.449992
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.480855 Loss1: 0.162711 Loss2: 1.318144
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.690727 Loss1: 0.251205 Loss2: 1.439522
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445454 Loss1: 0.118581 Loss2: 1.326873
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.672167 Loss1: 0.231444 Loss2: 1.440723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.600871 Loss1: 0.169925 Loss2: 1.430946 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.171267 Loss1: 0.714397 Loss2: 1.456870 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.769309 Loss1: 0.338940 Loss2: 1.430369 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.819577 Loss1: 0.356652 Loss2: 1.462926 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.787999 Loss1: 0.346320 Loss2: 1.441679 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.756648 Loss1: 0.307678 Loss2: 1.448970 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.664864 Loss1: 0.230153 Loss2: 1.434712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.614332 Loss1: 0.187685 Loss2: 1.426648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.635972 Loss1: 0.200769 Loss2: 1.435203 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.949219
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.546342 Loss1: 0.205919 Loss2: 1.340423 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.195783 Loss1: 1.323802 Loss2: 1.871981 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.806092 Loss1: 0.392024 Loss2: 1.414068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.643830 Loss1: 0.287562 Loss2: 1.356268
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.293459 Loss1: 1.346657 Loss2: 1.946802
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.429495 Loss1: 0.942357 Loss2: 1.487138 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.095928 Loss1: 0.576686 Loss2: 1.519242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.875142 Loss1: 0.390204 Loss2: 1.484938 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.851131 Loss1: 0.359542 Loss2: 1.491589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.758741 Loss1: 0.281314 Loss2: 1.477426 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.493182 Loss1: 0.145636 Loss2: 1.347546
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.698849 Loss1: 0.224106 Loss2: 1.474742
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.666237 Loss1: 0.198046 Loss2: 1.468191
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.612543 Loss1: 0.150002 Loss2: 1.462541
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.621981 Loss1: 0.165016 Loss2: 1.456965
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.397708 Loss1: 1.509855 Loss2: 1.887854
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.343559 Loss1: 0.897186 Loss2: 1.446373
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.065463 Loss1: 0.622185 Loss2: 1.443278
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.799080 Loss1: 0.392664 Loss2: 1.406417
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.095354 Loss1: 1.266023 Loss2: 1.829331
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.274414 Loss1: 0.865315 Loss2: 1.409100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.871766 Loss1: 0.515161 Loss2: 1.356605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.756554 Loss1: 0.398335 Loss2: 1.358219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.651387 Loss1: 0.300746 Loss2: 1.350641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.561119 Loss1: 0.225604 Loss2: 1.335515 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.548738 Loss1: 0.215961 Loss2: 1.332777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.505588 Loss1: 0.172835 Loss2: 1.332753 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.153951 Loss1: 1.283494 Loss2: 1.870457 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.899033 Loss1: 0.448665 Loss2: 1.450368 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.700160 Loss1: 0.284759 Loss2: 1.415401 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.689508 Loss1: 0.286106 Loss2: 1.403402 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.584114 Loss1: 0.179457 Loss2: 1.404657 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.605971 Loss1: 0.200414 Loss2: 1.405558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.584610 Loss1: 0.169558 Loss2: 1.415053 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.563056 Loss1: 0.170388 Loss2: 1.392668 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488512 Loss1: 0.160283 Loss2: 1.328229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464917 Loss1: 0.136286 Loss2: 1.328631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428449 Loss1: 0.105072 Loss2: 1.323377
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.182034 Loss1: 1.311115 Loss2: 1.870919
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.173793 Loss1: 0.701913 Loss2: 1.471879
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.873237 Loss1: 0.423353 Loss2: 1.449885
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.767383 Loss1: 0.343781 Loss2: 1.423602
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.676002 Loss1: 0.246154 Loss2: 1.429848
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.915715 Loss1: 1.087824 Loss2: 1.827891
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.193413 Loss1: 0.747367 Loss2: 1.446046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.941529 Loss1: 0.534572 Loss2: 1.406957 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.885669 Loss1: 0.472297 Loss2: 1.413372 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.740179 Loss1: 0.336669 Loss2: 1.403509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962891
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.600467 Loss1: 0.215230 Loss2: 1.385237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511559 Loss1: 0.146864 Loss2: 1.364695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.163485 Loss1: 1.276173 Loss2: 1.887312 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980699
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.004206 Loss1: 0.552223 Loss2: 1.451982 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.729383 Loss1: 0.315977 Loss2: 1.413406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.648915 Loss1: 0.252972 Loss2: 1.395943
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.143371 Loss1: 1.279397 Loss2: 1.863974
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.373416 Loss1: 0.916580 Loss2: 1.456837 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.082477 Loss1: 0.628209 Loss2: 1.454267 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.885407 Loss1: 0.453757 Loss2: 1.431650 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.747356 Loss1: 0.329481 Loss2: 1.417875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.599778 Loss1: 0.196977 Loss2: 1.402801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.537990 Loss1: 0.146099 Loss2: 1.391891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.550979 Loss1: 0.162958 Loss2: 1.388021 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.959961
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.893574 Loss1: 0.457637 Loss2: 1.435937 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.669444 Loss1: 0.248123 Loss2: 1.421321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.375179 Loss1: 1.344915 Loss2: 2.030264
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.614775 Loss1: 0.193075 Loss2: 1.421699
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.605543 Loss1: 0.184328 Loss2: 1.421216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.594363 Loss1: 0.175536 Loss2: 1.418827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.534229 Loss1: 0.117125 Loss2: 1.417104 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.565988 Loss1: 0.163400 Loss2: 1.402588 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.552665 Loss1: 0.162040 Loss2: 1.390626 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962240
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.182044 Loss1: 1.296593 Loss2: 1.885451 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.903063 Loss1: 0.470691 Loss2: 1.432372 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.599954 Loss1: 0.218292 Loss2: 1.381662 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.539394 Loss1: 0.161395 Loss2: 1.378000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480809 Loss1: 0.108770 Loss2: 1.372039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500351 Loss1: 0.134965 Loss2: 1.365386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.549482 Loss1: 0.181890 Loss2: 1.367592 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.563269 Loss1: 0.184939 Loss2: 1.378330 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.960417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.617997 Loss1: 0.216320 Loss2: 1.401677 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.595195 Loss1: 0.202362 Loss2: 1.392834 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.244669 Loss1: 0.818138 Loss2: 1.426531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765375 Loss1: 0.367987 Loss2: 1.397388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.230387 Loss1: 1.341227 Loss2: 1.889160
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.724532 Loss1: 0.313210 Loss2: 1.411322
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.264994 Loss1: 0.882923 Loss2: 1.382072
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.657920 Loss1: 0.266637 Loss2: 1.391284
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.620621 Loss1: 0.216793 Loss2: 1.403828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.514353 Loss1: 0.127326 Loss2: 1.387028 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.551636 Loss1: 0.167911 Loss2: 1.383725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.498865 Loss1: 0.114361 Loss2: 1.384504 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.534439 Loss1: 0.172088 Loss2: 1.362350 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980769
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.353103 Loss1: 1.312023 Loss2: 2.041080 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.038121 Loss1: 0.475583 Loss2: 1.562537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.840479 Loss1: 0.322257 Loss2: 1.518222
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.154182 Loss1: 1.251448 Loss2: 1.902734
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.784047 Loss1: 0.259030 Loss2: 1.525017
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.375349 Loss1: 0.914132 Loss2: 1.461217
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.698401 Loss1: 0.191684 Loss2: 1.506717
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.098062 Loss1: 0.599932 Loss2: 1.498130
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.739215 Loss1: 0.224539 Loss2: 1.514676
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.933918 Loss1: 0.492970 Loss2: 1.440948
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.687680 Loss1: 0.173412 Loss2: 1.514268
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.778315 Loss1: 0.323029 Loss2: 1.455286
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.670284 Loss1: 0.168378 Loss2: 1.501906
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.675810 Loss1: 0.251937 Loss2: 1.423873
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.641385 Loss1: 0.143513 Loss2: 1.497872
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.678149 Loss1: 0.248864 Loss2: 1.429285
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.576780 Loss1: 0.158658 Loss2: 1.418122
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.558982 Loss1: 0.144167 Loss2: 1.414815
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.561241 Loss1: 0.148977 Loss2: 1.412264
+(DefaultActor pid=3764) >> Training accuracy: 0.944792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.154447 Loss1: 1.204241 Loss2: 1.950206
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.171073 Loss1: 0.741133 Loss2: 1.429939
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.000172 Loss1: 0.535673 Loss2: 1.464499
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.810473 Loss1: 0.392735 Loss2: 1.417738
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.171292 Loss1: 1.291123 Loss2: 1.880169
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.676246 Loss1: 0.263060 Loss2: 1.413186
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.215239 Loss1: 0.777419 Loss2: 1.437821
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.637333 Loss1: 0.229071 Loss2: 1.408262
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.916621 Loss1: 0.491839 Loss2: 1.424782
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.584901 Loss1: 0.168390 Loss2: 1.416511
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.780553 Loss1: 0.380911 Loss2: 1.399642
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.544451 Loss1: 0.146521 Loss2: 1.397930
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.635507 Loss1: 0.231652 Loss2: 1.403856
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.541663 Loss1: 0.143181 Loss2: 1.398481
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.601949 Loss1: 0.218420 Loss2: 1.383528
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.513313 Loss1: 0.118829 Loss2: 1.394484
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.576541 Loss1: 0.184062 Loss2: 1.392479
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.558720 Loss1: 0.176718 Loss2: 1.382001
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.514385 Loss1: 0.133323 Loss2: 1.381062
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.502335 Loss1: 0.129338 Loss2: 1.372998
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.397545 Loss1: 1.442843 Loss2: 1.954703
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.361259 Loss1: 0.910370 Loss2: 1.450889
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.042029 Loss1: 0.573633 Loss2: 1.468396
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.846931 Loss1: 0.437376 Loss2: 1.409555
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.111089 Loss1: 1.270053 Loss2: 1.841035
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.251174 Loss1: 0.845054 Loss2: 1.406120 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.006599 Loss1: 0.559873 Loss2: 1.446725 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 11:13:21,113 | server.py:236 | fit_round 74 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699449 Loss1: 0.321923 Loss2: 1.377526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.636631 Loss1: 0.259625 Loss2: 1.377006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.632251 Loss1: 0.249582 Loss2: 1.382669 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.940848
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.529513 Loss1: 0.165013 Loss2: 1.364500 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478325 Loss1: 0.123978 Loss2: 1.354347 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.487732 Loss1: 0.988474 Loss2: 1.499257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.810312 Loss1: 0.365151 Loss2: 1.445162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.773741 Loss1: 0.336857 Loss2: 1.436884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.637043 Loss1: 0.210971 Loss2: 1.426073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.587390 Loss1: 0.176887 Loss2: 1.410503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.577086 Loss1: 0.170963 Loss2: 1.406123 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.568765 Loss1: 0.165683 Loss2: 1.403082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.530241 Loss1: 0.126373 Loss2: 1.403868 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.542769 Loss1: 0.197308 Loss2: 1.345461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.511351 Loss1: 0.171404 Loss2: 1.339947 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 11:13:21,113][flwr][DEBUG] - fit_round 74 received 50 results and 0 failures
+INFO flwr 2023-10-10 11:14:01,896 | server.py:125 | fit progress: (74, 2.2724047929715043, {'accuracy': 0.541}, 170549.674130211)
+>> Test accuracy: 0.541000
+[2023-10-10 11:14:01,896][flwr][INFO] - fit progress: (74, 2.2724047929715043, {'accuracy': 0.541}, 170549.674130211)
+DEBUG flwr 2023-10-10 11:14:01,896 | server.py:173 | evaluate_round 74: strategy sampled 50 clients (out of 50)
+[2023-10-10 11:14:01,896][flwr][DEBUG] - evaluate_round 74: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 11:23:08,976 | server.py:187 | evaluate_round 74 received 50 results and 0 failures
+[2023-10-10 11:23:08,976][flwr][DEBUG] - evaluate_round 74 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 11:23:08,976 | server.py:222 | fit_round 75: strategy sampled 50 clients (out of 50)
+[2023-10-10 11:23:08,976][flwr][DEBUG] - fit_round 75: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.259031 Loss1: 1.360205 Loss2: 1.898826
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.207546 Loss1: 0.762417 Loss2: 1.445129
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.949751 Loss1: 0.520105 Loss2: 1.429646
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.785077 Loss1: 0.361247 Loss2: 1.423830
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.215848 Loss1: 1.358763 Loss2: 1.857085
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.655175 Loss1: 0.257239 Loss2: 1.397936
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.163909 Loss1: 0.753066 Loss2: 1.410844
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.615091 Loss1: 0.219317 Loss2: 1.395774
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.985942 Loss1: 0.565681 Loss2: 1.420262
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.618722 Loss1: 0.230182 Loss2: 1.388539
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.809676 Loss1: 0.434740 Loss2: 1.374936
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.555016 Loss1: 0.172493 Loss2: 1.382523
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.758306 Loss1: 0.366051 Loss2: 1.392255
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.542998 Loss1: 0.157739 Loss2: 1.385259
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.619236 Loss1: 0.252056 Loss2: 1.367179
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.541064 Loss1: 0.159557 Loss2: 1.381508
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.569811 Loss1: 0.209366 Loss2: 1.360444
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510125 Loss1: 0.158424 Loss2: 1.351701
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.455245 Loss1: 0.110096 Loss2: 1.345149
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464072 Loss1: 0.122300 Loss2: 1.341772
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.204881 Loss1: 1.290080 Loss2: 1.914802
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.277091 Loss1: 0.820647 Loss2: 1.456444
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.957338 Loss1: 0.514192 Loss2: 1.443146
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.812253 Loss1: 0.392488 Loss2: 1.419765
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.177030 Loss1: 1.299749 Loss2: 1.877281
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.719517 Loss1: 0.294422 Loss2: 1.425095
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.217530 Loss1: 0.821355 Loss2: 1.396175
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.651274 Loss1: 0.244528 Loss2: 1.406746
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.877749 Loss1: 0.438722 Loss2: 1.439028
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.649441 Loss1: 0.238341 Loss2: 1.411099
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.700745 Loss1: 0.326961 Loss2: 1.373784
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.591459 Loss1: 0.194099 Loss2: 1.397360
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.667899 Loss1: 0.291077 Loss2: 1.376822
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.560061 Loss1: 0.161585 Loss2: 1.398476
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.672261 Loss1: 0.281361 Loss2: 1.390900
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.581719 Loss1: 0.188036 Loss2: 1.393683
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.688149 Loss1: 0.299994 Loss2: 1.388155
+(DefaultActor pid=3765) >> Training accuracy: 0.955208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.580649 Loss1: 0.202261 Loss2: 1.378388
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.547828 Loss1: 0.170115 Loss2: 1.377713
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.552950 Loss1: 0.180715 Loss2: 1.372235
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.273588 Loss1: 1.339896 Loss2: 1.933692
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.357694 Loss1: 0.861313 Loss2: 1.496381
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.004331 Loss1: 0.543457 Loss2: 1.460874
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.843900 Loss1: 0.406867 Loss2: 1.437034
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.058868 Loss1: 1.237213 Loss2: 1.821655
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.268179 Loss1: 0.877169 Loss2: 1.391009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.922755 Loss1: 0.504535 Loss2: 1.418220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.820288 Loss1: 0.448789 Loss2: 1.371499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.683062 Loss1: 0.305955 Loss2: 1.377107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.664714 Loss1: 0.297697 Loss2: 1.367017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.571028 Loss1: 0.161414 Loss2: 1.409614
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.613762 Loss1: 0.251146 Loss2: 1.362616
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.528852 Loss1: 0.179658 Loss2: 1.349193
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493038 Loss1: 0.149714 Loss2: 1.343324
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.500497 Loss1: 0.158168 Loss2: 1.342329
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.219323 Loss1: 1.263781 Loss2: 1.955541
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.275726 Loss1: 0.790831 Loss2: 1.484895
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.959929 Loss1: 0.454554 Loss2: 1.505375
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.786717 Loss1: 0.338633 Loss2: 1.448085
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.366543 Loss1: 1.417113 Loss2: 1.949430
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.339988 Loss1: 0.856732 Loss2: 1.483256 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.055491 Loss1: 0.546896 Loss2: 1.508595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.872809 Loss1: 0.411024 Loss2: 1.461785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.782357 Loss1: 0.301294 Loss2: 1.481064 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.703273 Loss1: 0.244293 Loss2: 1.458980 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.509299 Loss1: 0.092811 Loss2: 1.416487
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.641374 Loss1: 0.182575 Loss2: 1.458798
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.639774 Loss1: 0.193515 Loss2: 1.446259
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.645499 Loss1: 0.201225 Loss2: 1.444274
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.680699 Loss1: 0.227057 Loss2: 1.453642
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.216657 Loss1: 1.392612 Loss2: 1.824045
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.344363 Loss1: 0.926662 Loss2: 1.417701
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.897021 Loss1: 0.499739 Loss2: 1.397282
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.672797 Loss1: 0.320080 Loss2: 1.352716
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.253814 Loss1: 1.331200 Loss2: 1.922614
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593900 Loss1: 0.227645 Loss2: 1.366255
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.333089 Loss1: 0.915737 Loss2: 1.417352
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.583828 Loss1: 0.225760 Loss2: 1.358069
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.105235 Loss1: 0.626765 Loss2: 1.478470
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.549190 Loss1: 0.196941 Loss2: 1.352249
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.799124 Loss1: 0.400878 Loss2: 1.398246
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.750038 Loss1: 0.358617 Loss2: 1.391421
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.512453 Loss1: 0.166804 Loss2: 1.345649
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.672569 Loss1: 0.261679 Loss2: 1.410889
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462888 Loss1: 0.120027 Loss2: 1.342862
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.591867 Loss1: 0.205562 Loss2: 1.386305
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460699 Loss1: 0.129937 Loss2: 1.330762
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.505468 Loss1: 0.135150 Loss2: 1.370319 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962054
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.176828 Loss1: 1.201652 Loss2: 1.975176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.898817 Loss1: 0.443010 Loss2: 1.455807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.792308 Loss1: 0.362591 Loss2: 1.429717
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.035642 Loss1: 1.215164 Loss2: 1.820478
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.199396 Loss1: 0.786653 Loss2: 1.412742 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.868833 Loss1: 0.449300 Loss2: 1.419534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.718747 Loss1: 0.348420 Loss2: 1.370328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.686847 Loss1: 0.296829 Loss2: 1.390018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.604052 Loss1: 0.229781 Loss2: 1.374271 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.567231 Loss1: 0.200406 Loss2: 1.366825 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.515646 Loss1: 0.155467 Loss2: 1.360179 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966797
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.301822 Loss1: 0.793863 Loss2: 1.507959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.845645 Loss1: 0.386942 Loss2: 1.458703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.272987 Loss1: 1.313664 Loss2: 1.959322
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.750804 Loss1: 0.306344 Loss2: 1.444460
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.330760 Loss1: 0.832305 Loss2: 1.498455
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.657141 Loss1: 0.228136 Loss2: 1.429005
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.618407 Loss1: 0.191864 Loss2: 1.426543 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.568203 Loss1: 0.139601 Loss2: 1.428602 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.568857 Loss1: 0.150671 Loss2: 1.418187 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.584932 Loss1: 0.165564 Loss2: 1.419368 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546620 Loss1: 0.113348 Loss2: 1.433272 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.559717 Loss1: 0.133747 Loss2: 1.425970 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.951620 Loss1: 1.119553 Loss2: 1.832067
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.206994 Loss1: 0.802163 Loss2: 1.404831
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.863330 Loss1: 0.476786 Loss2: 1.386545
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.719394 Loss1: 0.356663 Loss2: 1.362731
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.198035 Loss1: 1.260474 Loss2: 1.937561
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.346885 Loss1: 0.827988 Loss2: 1.518897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.991726 Loss1: 0.530906 Loss2: 1.460821 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.777626 Loss1: 0.327075 Loss2: 1.450551 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687320 Loss1: 0.255676 Loss2: 1.431645 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.614109 Loss1: 0.191720 Loss2: 1.422389 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.496158 Loss1: 0.164479 Loss2: 1.331679
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.554156 Loss1: 0.138809 Loss2: 1.415348
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.554646 Loss1: 0.138622 Loss2: 1.416024
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.542287 Loss1: 0.134003 Loss2: 1.408284
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529169 Loss1: 0.114674 Loss2: 1.414495
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.108142 Loss1: 1.240822 Loss2: 1.867320
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.199907 Loss1: 0.783301 Loss2: 1.416605
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.837610 Loss1: 0.429521 Loss2: 1.408088
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765531 Loss1: 0.394848 Loss2: 1.370683
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.322630 Loss1: 1.295047 Loss2: 2.027583
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.273683 Loss1: 0.843867 Loss2: 1.429816 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.904112 Loss1: 0.427491 Loss2: 1.476621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.689046 Loss1: 0.295881 Loss2: 1.393164
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.562538 Loss1: 0.192566 Loss2: 1.369973
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.607721 Loss1: 0.225727 Loss2: 1.381995
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.518707 Loss1: 0.162897 Loss2: 1.355811
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484662 Loss1: 0.130101 Loss2: 1.354561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472304 Loss1: 0.124102 Loss2: 1.348202 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.483784 Loss1: 0.114622 Loss2: 1.369162 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.951923
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.210806 Loss1: 1.299312 Loss2: 1.911494
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.347237 Loss1: 0.894460 Loss2: 1.452777
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.024495 Loss1: 0.516972 Loss2: 1.507523
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.808117 Loss1: 0.378689 Loss2: 1.429428
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.208233 Loss1: 1.290503 Loss2: 1.917730
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.749784 Loss1: 0.295372 Loss2: 1.454412
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.202806 Loss1: 0.807288 Loss2: 1.395519
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.978612 Loss1: 0.532127 Loss2: 1.446485
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.716697 Loss1: 0.276103 Loss2: 1.440594
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.829295 Loss1: 0.438148 Loss2: 1.391147
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.649829 Loss1: 0.209271 Loss2: 1.440559
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.700158 Loss1: 0.300447 Loss2: 1.399711
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.625815 Loss1: 0.183261 Loss2: 1.442553
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.569887 Loss1: 0.145751 Loss2: 1.424136 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.569322 Loss1: 0.141873 Loss2: 1.427450 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.522131 Loss1: 0.151559 Loss2: 1.370572 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972098
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.325967 Loss1: 1.314734 Loss2: 2.011233 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.155024 Loss1: 0.580456 Loss2: 1.574568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.937672 Loss1: 0.413162 Loss2: 1.524510
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.220662 Loss1: 1.334422 Loss2: 1.886240
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.785003 Loss1: 0.252470 Loss2: 1.532533
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.242910 Loss1: 0.833643 Loss2: 1.409267
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.722766 Loss1: 0.228862 Loss2: 1.493904
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.935570 Loss1: 0.511290 Loss2: 1.424279
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.688722 Loss1: 0.200346 Loss2: 1.488376
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.753922 Loss1: 0.365307 Loss2: 1.388615
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.658532 Loss1: 0.167027 Loss2: 1.491505
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.700314 Loss1: 0.310355 Loss2: 1.389959
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.661016 Loss1: 0.166412 Loss2: 1.494605
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647372 Loss1: 0.263413 Loss2: 1.383959
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.702183 Loss1: 0.215337 Loss2: 1.486846
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.599109 Loss1: 0.222672 Loss2: 1.376437
+(DefaultActor pid=3765) >> Training accuracy: 0.954167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.554810 Loss1: 0.183768 Loss2: 1.371042
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.529288 Loss1: 0.173284 Loss2: 1.356004
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529104 Loss1: 0.159510 Loss2: 1.369594
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.221169 Loss1: 1.294506 Loss2: 1.926663
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.296250 Loss1: 0.826382 Loss2: 1.469867
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.021703 Loss1: 0.541666 Loss2: 1.480037
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.852432 Loss1: 0.410907 Loss2: 1.441526
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.091691 Loss1: 1.208584 Loss2: 1.883107
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.735605 Loss1: 0.290333 Loss2: 1.445272
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.149822 Loss1: 0.739989 Loss2: 1.409833
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.680039 Loss1: 0.253221 Loss2: 1.426818
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.964557 Loss1: 0.532529 Loss2: 1.432028
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.634981 Loss1: 0.200691 Loss2: 1.434290
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.816803 Loss1: 0.423980 Loss2: 1.392822
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.610348 Loss1: 0.188040 Loss2: 1.422308
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.750027 Loss1: 0.353785 Loss2: 1.396242
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.596023 Loss1: 0.184602 Loss2: 1.411421
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.702810 Loss1: 0.319922 Loss2: 1.382888
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.545125 Loss1: 0.134567 Loss2: 1.410558
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.625984 Loss1: 0.232821 Loss2: 1.393163
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.556644 Loss1: 0.174990 Loss2: 1.381655
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519107 Loss1: 0.143811 Loss2: 1.375296
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509192 Loss1: 0.137501 Loss2: 1.371691
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.080248 Loss1: 1.294037 Loss2: 1.786211
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.168946 Loss1: 0.819580 Loss2: 1.349367
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.933884 Loss1: 0.545191 Loss2: 1.388694
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.784349 Loss1: 0.448674 Loss2: 1.335674
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.143072 Loss1: 1.181761 Loss2: 1.961311
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.222429 Loss1: 0.757647 Loss2: 1.464782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.993036 Loss1: 0.498655 Loss2: 1.494381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.848149 Loss1: 0.401721 Loss2: 1.446428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.720839 Loss1: 0.264449 Loss2: 1.456390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.656131 Loss1: 0.215546 Loss2: 1.440585 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.492507 Loss1: 0.176203 Loss2: 1.316304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.620927 Loss1: 0.185678 Loss2: 1.435249
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.553944 Loss1: 0.118417 Loss2: 1.435527
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.571516 Loss1: 0.142563 Loss2: 1.428953
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.569690 Loss1: 0.143633 Loss2: 1.426057
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.016694 Loss1: 1.149546 Loss2: 1.867148
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.097884 Loss1: 0.723260 Loss2: 1.374624
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.823532 Loss1: 0.431175 Loss2: 1.392357
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.668943 Loss1: 0.316533 Loss2: 1.352410
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.538690 Loss1: 1.573148 Loss2: 1.965542
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.549523 Loss1: 0.206638 Loss2: 1.342885
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.292170 Loss1: 0.822431 Loss2: 1.469739
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513120 Loss1: 0.177844 Loss2: 1.335277
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.958201 Loss1: 0.474153 Loss2: 1.484048
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.815542 Loss1: 0.378603 Loss2: 1.436939
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495470 Loss1: 0.156157 Loss2: 1.339313
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.747694 Loss1: 0.302768 Loss2: 1.444926
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.555614 Loss1: 0.213676 Loss2: 1.341939
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.717666 Loss1: 0.284552 Loss2: 1.433114
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.579601 Loss1: 0.217970 Loss2: 1.361631
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.515029 Loss1: 0.176499 Loss2: 1.338530 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.619265 Loss1: 0.199743 Loss2: 1.419522 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.954241
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.203816 Loss1: 1.326435 Loss2: 1.877381 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.970965 Loss1: 0.544035 Loss2: 1.426930 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.840619 Loss1: 0.449339 Loss2: 1.391280
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.120582 Loss1: 1.301673 Loss2: 1.818909
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.122853 Loss1: 0.728554 Loss2: 1.394299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.879747 Loss1: 0.491456 Loss2: 1.388291 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.775081 Loss1: 0.403328 Loss2: 1.371753 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.675965 Loss1: 0.306451 Loss2: 1.369514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.687320 Loss1: 0.323390 Loss2: 1.363930 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.605516 Loss1: 0.243200 Loss2: 1.362316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.514407 Loss1: 0.153616 Loss2: 1.360792 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970703
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.978715 Loss1: 1.128073 Loss2: 1.850642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.927181 Loss1: 0.501879 Loss2: 1.425302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617488 Loss1: 0.252509 Loss2: 1.364979 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.622119 Loss1: 0.260175 Loss2: 1.361944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.602871 Loss1: 0.231539 Loss2: 1.371332 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.498089 Loss1: 0.146790 Loss2: 1.351300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499424 Loss1: 0.154385 Loss2: 1.345038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.468080 Loss1: 0.128189 Loss2: 1.339891 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.652798 Loss1: 0.257594 Loss2: 1.395204 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.560580 Loss1: 0.169486 Loss2: 1.391094 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.533679 Loss1: 0.153594 Loss2: 1.380084
+(DefaultActor pid=3764) >> Training accuracy: 0.969727
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.367091 Loss1: 1.410682 Loss2: 1.956408
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.385814 Loss1: 0.886127 Loss2: 1.499688
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.042564 Loss1: 0.546975 Loss2: 1.495589
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.881373 Loss1: 0.413976 Loss2: 1.467397
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.767031 Loss1: 0.287630 Loss2: 1.479401
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.391735 Loss1: 1.378812 Loss2: 2.012923
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.296683 Loss1: 0.898384 Loss2: 1.398299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.132067 Loss1: 0.626225 Loss2: 1.505842
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.693248 Loss1: 0.235427 Loss2: 1.457821
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.650408 Loss1: 0.192089 Loss2: 1.458319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.618376 Loss1: 0.169423 Loss2: 1.448953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.567122 Loss1: 0.122674 Loss2: 1.444448 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.583410 Loss1: 0.191545 Loss2: 1.391865 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962240
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.192438 Loss1: 1.286980 Loss2: 1.905458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.009630 Loss1: 0.548834 Loss2: 1.460796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.836663 Loss1: 0.419604 Loss2: 1.417060
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.337608 Loss1: 1.471179 Loss2: 1.866429
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.275173 Loss1: 0.840400 Loss2: 1.434773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.919201 Loss1: 0.507291 Loss2: 1.411910 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.672276 Loss1: 0.267068 Loss2: 1.405208
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.760231 Loss1: 0.369952 Loss2: 1.390279
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.665124 Loss1: 0.270360 Loss2: 1.394764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.630697 Loss1: 0.251070 Loss2: 1.379627 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.538186 Loss1: 0.149308 Loss2: 1.388878
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.572722 Loss1: 0.193032 Loss2: 1.379690
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.583044 Loss1: 0.211107 Loss2: 1.371936
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.567889 Loss1: 0.188371 Loss2: 1.379518
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.559513 Loss1: 0.181926 Loss2: 1.377586
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.149394 Loss1: 1.255374 Loss2: 1.894020
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.267977 Loss1: 0.815742 Loss2: 1.452236
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.984852 Loss1: 0.530988 Loss2: 1.453864
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.826464 Loss1: 0.387995 Loss2: 1.438469
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.003248 Loss1: 1.149653 Loss2: 1.853595
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.692060 Loss1: 0.257186 Loss2: 1.434874
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.196493 Loss1: 0.794273 Loss2: 1.402220
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.652933 Loss1: 0.235786 Loss2: 1.417147
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.963886 Loss1: 0.539138 Loss2: 1.424748
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.652524 Loss1: 0.232847 Loss2: 1.419677
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.743691 Loss1: 0.355412 Loss2: 1.388279
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.723620 Loss1: 0.295622 Loss2: 1.427999
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.622008 Loss1: 0.242892 Loss2: 1.379116
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.744252 Loss1: 0.302781 Loss2: 1.441472
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.591612 Loss1: 0.217987 Loss2: 1.373625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.614352 Loss1: 0.188850 Loss2: 1.425502
+(DefaultActor pid=3765) >> Training accuracy: 0.963867
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.568899 Loss1: 0.191584 Loss2: 1.377315
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.572915 Loss1: 0.210812 Loss2: 1.362103
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.521149 Loss1: 0.148976 Loss2: 1.372172
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.504042 Loss1: 0.139536 Loss2: 1.364507
+(DefaultActor pid=3764) >> Training accuracy: 0.972656
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.100781 Loss1: 1.231080 Loss2: 1.869701
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.236264 Loss1: 0.749310 Loss2: 1.486954
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.880886 Loss1: 0.433685 Loss2: 1.447201
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.793962 Loss1: 0.353269 Loss2: 1.440693
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.995260 Loss1: 1.149623 Loss2: 1.845637
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.766656 Loss1: 0.318326 Loss2: 1.448330
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.137225 Loss1: 0.719556 Loss2: 1.417669
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.724799 Loss1: 0.287053 Loss2: 1.437746
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.968789 Loss1: 0.546929 Loss2: 1.421860
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.642463 Loss1: 0.212254 Loss2: 1.430208
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.780024 Loss1: 0.367199 Loss2: 1.412825
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.667221 Loss1: 0.231144 Loss2: 1.436076
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.664973 Loss1: 0.281756 Loss2: 1.383217
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.679393 Loss1: 0.245648 Loss2: 1.433745
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.580640 Loss1: 0.198365 Loss2: 1.382274
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.617989 Loss1: 0.178173 Loss2: 1.439816
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500930 Loss1: 0.131732 Loss2: 1.369197
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.528027 Loss1: 0.165158 Loss2: 1.362869
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473187 Loss1: 0.105355 Loss2: 1.367833
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470677 Loss1: 0.114829 Loss2: 1.355848
+(DefaultActor pid=3764) >> Training accuracy: 0.973633
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.191896 Loss1: 1.305551 Loss2: 1.886345
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.249664 Loss1: 0.812778 Loss2: 1.436886
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.873346 Loss1: 0.438538 Loss2: 1.434807
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.691059 Loss1: 0.291706 Loss2: 1.399352
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.159575 Loss1: 1.250148 Loss2: 1.909427
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.153507 Loss1: 0.727588 Loss2: 1.425919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.951368 Loss1: 0.486380 Loss2: 1.464988 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.796140 Loss1: 0.385090 Loss2: 1.411050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.651109 Loss1: 0.231246 Loss2: 1.419863 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.540543 Loss1: 0.142120 Loss2: 1.398423 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.540554 Loss1: 0.152460 Loss2: 1.388093 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.520099 Loss1: 0.127552 Loss2: 1.392547 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.376209 Loss1: 1.384094 Loss2: 1.992115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.030411 Loss1: 0.525182 Loss2: 1.505229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.654684 Loss1: 0.229542 Loss2: 1.425141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.643666 Loss1: 0.212806 Loss2: 1.430861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.622840 Loss1: 0.188806 Loss2: 1.434034 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.592650 Loss1: 0.164454 Loss2: 1.428196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.577533 Loss1: 0.159496 Loss2: 1.418037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.546020 Loss1: 0.129354 Loss2: 1.416666
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.729314 Loss1: 0.297648 Loss2: 1.431666
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-10 11:52:12,986 | server.py:236 | fit_round 75 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.655697 Loss1: 0.235996 Loss2: 1.419701
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.626909 Loss1: 0.208230 Loss2: 1.418679
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.553874 Loss1: 0.150779 Loss2: 1.403095
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.538899 Loss1: 0.137667 Loss2: 1.401232
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.520183 Loss1: 0.125506 Loss2: 1.394676
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.038873 Loss1: 1.247489 Loss2: 1.791384
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.251319 Loss1: 0.850317 Loss2: 1.401001
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.967674 Loss1: 0.565881 Loss2: 1.401793
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.784675 Loss1: 0.415205 Loss2: 1.369470
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.615789 Loss1: 0.255537 Loss2: 1.360252
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.041400 Loss1: 1.147606 Loss2: 1.893794
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.557850 Loss1: 0.218551 Loss2: 1.339299
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.170939 Loss1: 0.706367 Loss2: 1.464572
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500007 Loss1: 0.149257 Loss2: 1.350750
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.836028 Loss1: 0.402790 Loss2: 1.433238
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.528660 Loss1: 0.188974 Loss2: 1.339686
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.695182 Loss1: 0.284794 Loss2: 1.410388
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488002 Loss1: 0.140587 Loss2: 1.347416
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.515271 Loss1: 0.176000 Loss2: 1.339270
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.697396 Loss1: 0.282565 Loss2: 1.414832
+(DefaultActor pid=3765) >> Training accuracy: 0.951172
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.630773 Loss1: 0.212748 Loss2: 1.418025
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.576333 Loss1: 0.175955 Loss2: 1.400378
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.597020 Loss1: 0.194644 Loss2: 1.402377
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.549528 Loss1: 0.142325 Loss2: 1.407203
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.209263 Loss1: 1.342160 Loss2: 1.867103
+(DefaultActor pid=3764) >> Training accuracy: 0.963235
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.501106 Loss1: 0.112999 Loss2: 1.388108
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.337676 Loss1: 0.891690 Loss2: 1.445986
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.000831 Loss1: 0.572504 Loss2: 1.428327
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.729939 Loss1: 0.340728 Loss2: 1.389212
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.709825 Loss1: 0.323100 Loss2: 1.386724
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.619902 Loss1: 0.232681 Loss2: 1.387221
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.637967 Loss1: 0.260375 Loss2: 1.377591
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.260816 Loss1: 1.354795 Loss2: 1.906021
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.573995 Loss1: 0.184837 Loss2: 1.389158
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.336779 Loss1: 0.860271 Loss2: 1.476508
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.573728 Loss1: 0.192269 Loss2: 1.381458
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.081931 Loss1: 0.607659 Loss2: 1.474272
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.587165 Loss1: 0.196184 Loss2: 1.390981
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.878094 Loss1: 0.413907 Loss2: 1.464186
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.814936 Loss1: 0.362823 Loss2: 1.452113
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.730452 Loss1: 0.284651 Loss2: 1.445801
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.645257 Loss1: 0.209950 Loss2: 1.435307
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.632972 Loss1: 0.198823 Loss2: 1.434149
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.595856 Loss1: 0.168664 Loss2: 1.427193
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.392466 Loss1: 1.468100 Loss2: 1.924365
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.585181 Loss1: 0.156635 Loss2: 1.428546
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.455470 Loss1: 0.957173 Loss2: 1.498298
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.085390 Loss1: 0.633637 Loss2: 1.451752
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.920083 Loss1: 0.452006 Loss2: 1.468078
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.789450 Loss1: 0.362935 Loss2: 1.426516
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.718891 Loss1: 0.284517 Loss2: 1.434374
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.730044 Loss1: 0.300399 Loss2: 1.429646
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.183759 Loss1: 1.258734 Loss2: 1.925025
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.702681 Loss1: 0.272907 Loss2: 1.429774
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.353871 Loss1: 0.847352 Loss2: 1.506518
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.630303 Loss1: 0.208179 Loss2: 1.422123
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.953629 Loss1: 0.471526 Loss2: 1.482103
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.566983 Loss1: 0.150470 Loss2: 1.416513
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.905227 Loss1: 0.447075 Loss2: 1.458152
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.740504 Loss1: 0.286916 Loss2: 1.453589
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.721759 Loss1: 0.259138 Loss2: 1.462621
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.623950 Loss1: 0.183875 Loss2: 1.440075
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.575619 Loss1: 0.142583 Loss2: 1.433036
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.624781 Loss1: 0.191672 Loss2: 1.433109
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.604750 Loss1: 0.171624 Loss2: 1.433126
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 11:52:12,986][flwr][DEBUG] - fit_round 75 received 50 results and 0 failures
+INFO flwr 2023-10-10 11:52:54,426 | server.py:125 | fit progress: (75, 2.2630984912665126, {'accuracy': 0.5379}, 172882.20411751402)
+>> Test accuracy: 0.537900
+[2023-10-10 11:52:54,426][flwr][INFO] - fit progress: (75, 2.2630984912665126, {'accuracy': 0.5379}, 172882.20411751402)
+DEBUG flwr 2023-10-10 11:52:54,426 | server.py:173 | evaluate_round 75: strategy sampled 50 clients (out of 50)
+[2023-10-10 11:52:54,426][flwr][DEBUG] - evaluate_round 75: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 12:02:02,633 | server.py:187 | evaluate_round 75 received 50 results and 0 failures
+[2023-10-10 12:02:02,633][flwr][DEBUG] - evaluate_round 75 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 12:02:02,633 | server.py:222 | fit_round 76: strategy sampled 50 clients (out of 50)
+[2023-10-10 12:02:02,633][flwr][DEBUG] - fit_round 76: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.223422 Loss1: 1.340909 Loss2: 1.882514
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.189848 Loss1: 0.707823 Loss2: 1.482025
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.976236 Loss1: 0.532300 Loss2: 1.443936
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.345797 Loss1: 1.332001 Loss2: 2.013796
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.775142 Loss1: 0.346210 Loss2: 1.428932
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.610420 Loss1: 0.204502 Loss2: 1.405918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.573863 Loss1: 0.173712 Loss2: 1.400151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.677625 Loss1: 0.267781 Loss2: 1.409844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.644709 Loss1: 0.245531 Loss2: 1.399178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.648680 Loss1: 0.251663 Loss2: 1.397017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.537949 Loss1: 0.147262 Loss2: 1.390688
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.542685 Loss1: 0.151838 Loss2: 1.390847
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.521676 Loss1: 0.140532 Loss2: 1.381144
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.563494 Loss1: 0.171111 Loss2: 1.392383
+(DefaultActor pid=3765) >> Training accuracy: 0.964844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.119836 Loss1: 1.255567 Loss2: 1.864269 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957031
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.969883 Loss1: 0.515717 Loss2: 1.454166 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.774196 Loss1: 0.351208 Loss2: 1.422987
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.156666 Loss1: 1.295586 Loss2: 1.861081
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.154382 Loss1: 0.739478 Loss2: 1.414904
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.689142 Loss1: 0.264625 Loss2: 1.424517
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.975604 Loss1: 0.533723 Loss2: 1.441881
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.647239 Loss1: 0.237429 Loss2: 1.409811
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.711080 Loss1: 0.314814 Loss2: 1.396265
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.648467 Loss1: 0.233601 Loss2: 1.414866
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.656360 Loss1: 0.263940 Loss2: 1.392420
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.591529 Loss1: 0.186878 Loss2: 1.404651
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.556756 Loss1: 0.158271 Loss2: 1.398485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.544978 Loss1: 0.147048 Loss2: 1.397930 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.542095 Loss1: 0.166667 Loss2: 1.375428 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.996142 Loss1: 1.135226 Loss2: 1.860916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.949637 Loss1: 0.492143 Loss2: 1.457494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.056005 Loss1: 1.217362 Loss2: 1.838643
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.749031 Loss1: 0.331784 Loss2: 1.417248
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.135313 Loss1: 0.726273 Loss2: 1.409040
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.662943 Loss1: 0.244330 Loss2: 1.418613
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.901759 Loss1: 0.471394 Loss2: 1.430365
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569811 Loss1: 0.169203 Loss2: 1.400609
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.519890 Loss1: 0.128233 Loss2: 1.391658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.560956 Loss1: 0.168100 Loss2: 1.392855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.582630 Loss1: 0.180060 Loss2: 1.402570 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.536408 Loss1: 0.138103 Loss2: 1.398305 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.564692 Loss1: 0.187361 Loss2: 1.377331 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.088288 Loss1: 1.276224 Loss2: 1.812063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.935709 Loss1: 0.540139 Loss2: 1.395570 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.763667 Loss1: 0.409417 Loss2: 1.354250
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.263032 Loss1: 1.278794 Loss2: 1.984237
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.724617 Loss1: 0.355136 Loss2: 1.369481
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.374702 Loss1: 0.845420 Loss2: 1.529282
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.541791 Loss1: 0.197031 Loss2: 1.344760
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.103786 Loss1: 0.574628 Loss2: 1.529159
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544683 Loss1: 0.213267 Loss2: 1.331416
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.879967 Loss1: 0.379530 Loss2: 1.500438
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.512849 Loss1: 0.174797 Loss2: 1.338053
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.775176 Loss1: 0.283576 Loss2: 1.491601
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.534052 Loss1: 0.199029 Loss2: 1.335023
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.695239 Loss1: 0.213572 Loss2: 1.481668
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481077 Loss1: 0.149208 Loss2: 1.331869
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.690765 Loss1: 0.211648 Loss2: 1.479116
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.649510 Loss1: 0.174223 Loss2: 1.475286
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.609835 Loss1: 0.140355 Loss2: 1.469481
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.609592 Loss1: 0.148601 Loss2: 1.460991
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.352533 Loss1: 1.495439 Loss2: 1.857094
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.305764 Loss1: 0.862209 Loss2: 1.443555
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.874435 Loss1: 0.473067 Loss2: 1.401367
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.805916 Loss1: 0.392997 Loss2: 1.412919
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.172357 Loss1: 1.270639 Loss2: 1.901718
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.700195 Loss1: 0.307076 Loss2: 1.393120
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.306029 Loss1: 0.860418 Loss2: 1.445611
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.628152 Loss1: 0.249339 Loss2: 1.378813
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.129779 Loss1: 0.628101 Loss2: 1.501678
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.605132 Loss1: 0.222000 Loss2: 1.383132
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.928759 Loss1: 0.494821 Loss2: 1.433938
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.539074 Loss1: 0.166887 Loss2: 1.372188
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.812405 Loss1: 0.356567 Loss2: 1.455838
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.508937 Loss1: 0.143777 Loss2: 1.365161
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.694023 Loss1: 0.262870 Loss2: 1.431153
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.456252 Loss1: 0.094090 Loss2: 1.362162
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.716491 Loss1: 0.291233 Loss2: 1.425258
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.618949 Loss1: 0.189254 Loss2: 1.429695
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.554463 Loss1: 0.147040 Loss2: 1.407423
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.537983 Loss1: 0.133409 Loss2: 1.404574
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.081631 Loss1: 1.274303 Loss2: 1.807327
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.192725 Loss1: 0.789979 Loss2: 1.402746
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.866300 Loss1: 0.462780 Loss2: 1.403520
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.736803 Loss1: 0.360426 Loss2: 1.376377
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.286148 Loss1: 1.409280 Loss2: 1.876868
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.232273 Loss1: 0.787095 Loss2: 1.445178
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.658727 Loss1: 0.280784 Loss2: 1.377943
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.944014 Loss1: 0.529690 Loss2: 1.414324
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.553524 Loss1: 0.187482 Loss2: 1.366042
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.749167 Loss1: 0.344929 Loss2: 1.404238
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.527149 Loss1: 0.172880 Loss2: 1.354269
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.657522 Loss1: 0.271464 Loss2: 1.386058
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.585054 Loss1: 0.220249 Loss2: 1.364805
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.620090 Loss1: 0.255502 Loss2: 1.364588 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.565009 Loss1: 0.195156 Loss2: 1.369852 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.502842 Loss1: 0.131198 Loss2: 1.371644 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.011642 Loss1: 1.116461 Loss2: 1.895181 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.955416 Loss1: 0.505524 Loss2: 1.449892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.792909 Loss1: 0.378457 Loss2: 1.414452
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.028818 Loss1: 1.170646 Loss2: 1.858172
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.129896 Loss1: 0.741509 Loss2: 1.388388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.979359 Loss1: 0.544525 Loss2: 1.434834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.894083 Loss1: 0.502407 Loss2: 1.391677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.780114 Loss1: 0.371942 Loss2: 1.408172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.676103 Loss1: 0.298173 Loss2: 1.377930 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476364 Loss1: 0.102079 Loss2: 1.374285
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.651589 Loss1: 0.265293 Loss2: 1.386296
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.562373 Loss1: 0.189734 Loss2: 1.372639
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.500143 Loss1: 0.137424 Loss2: 1.362719
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.493080 Loss1: 0.135722 Loss2: 1.357358
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.042916 Loss1: 1.214004 Loss2: 1.828912
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.034141 Loss1: 0.694840 Loss2: 1.339301
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.838830 Loss1: 0.460542 Loss2: 1.378288
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.673012 Loss1: 0.341011 Loss2: 1.332001
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.277241 Loss1: 1.290880 Loss2: 1.986361
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.609190 Loss1: 0.282970 Loss2: 1.326220
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.353764 Loss1: 0.797348 Loss2: 1.556416
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.588704 Loss1: 0.248202 Loss2: 1.340502
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.956025 Loss1: 0.447031 Loss2: 1.508994
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.869647 Loss1: 0.372860 Loss2: 1.496786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.528310 Loss1: 0.200457 Loss2: 1.327853
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.750751 Loss1: 0.264661 Loss2: 1.486090
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.471378 Loss1: 0.148340 Loss2: 1.323038
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.755372 Loss1: 0.269191 Loss2: 1.486181
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465967 Loss1: 0.150229 Loss2: 1.315738
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.732601 Loss1: 0.248202 Loss2: 1.484399
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.651928 Loss1: 0.177559 Loss2: 1.474369
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.635400 Loss1: 0.167768 Loss2: 1.467632
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.650683 Loss1: 0.174426 Loss2: 1.476257
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.124413 Loss1: 1.224921 Loss2: 1.899492
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.282705 Loss1: 0.797178 Loss2: 1.485527
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.982774 Loss1: 0.549113 Loss2: 1.433661
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.850226 Loss1: 0.419514 Loss2: 1.430712
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.337293 Loss1: 1.346863 Loss2: 1.990430
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.288101 Loss1: 0.864865 Loss2: 1.423236
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.668188 Loss1: 0.254458 Loss2: 1.413730
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587936 Loss1: 0.197760 Loss2: 1.390176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.558651 Loss1: 0.177016 Loss2: 1.381635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.526583 Loss1: 0.139040 Loss2: 1.387543 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.552974 Loss1: 0.171438 Loss2: 1.381537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491488 Loss1: 0.110927 Loss2: 1.380561 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444962 Loss1: 0.076807 Loss2: 1.368154 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987981
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.293795 Loss1: 1.280458 Loss2: 2.013337
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.325327 Loss1: 0.769626 Loss2: 1.555701
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.024848 Loss1: 0.487309 Loss2: 1.537540
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.873800 Loss1: 0.355604 Loss2: 1.518196
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.020409 Loss1: 1.113821 Loss2: 1.906588
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.152625 Loss1: 0.724641 Loss2: 1.427984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.875891 Loss1: 0.432825 Loss2: 1.443067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.772402 Loss1: 0.360529 Loss2: 1.411872 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.719965 Loss1: 0.297632 Loss2: 1.422333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.613445 Loss1: 0.206529 Loss2: 1.406917 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.591127 Loss1: 0.194652 Loss2: 1.396475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.539217 Loss1: 0.153535 Loss2: 1.385682 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.104255 Loss1: 1.207103 Loss2: 1.897152 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.899709 Loss1: 0.448453 Loss2: 1.451256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.762701 Loss1: 0.353626 Loss2: 1.409075
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.253182 Loss1: 1.334603 Loss2: 1.918579
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.253072 Loss1: 0.831956 Loss2: 1.421116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.081477 Loss1: 0.626485 Loss2: 1.454992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.870065 Loss1: 0.458143 Loss2: 1.411922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.574019 Loss1: 0.178528 Loss2: 1.395491
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.748451 Loss1: 0.342726 Loss2: 1.405725
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.578677 Loss1: 0.178412 Loss2: 1.400266
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.645240 Loss1: 0.261056 Loss2: 1.384185
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.494685 Loss1: 0.100105 Loss2: 1.394580
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.609921 Loss1: 0.224720 Loss2: 1.385200
+(DefaultActor pid=3765) >> Training accuracy: 0.962500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.601234 Loss1: 0.211186 Loss2: 1.390048
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.550660 Loss1: 0.167403 Loss2: 1.383257
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.504473 Loss1: 0.119404 Loss2: 1.385069
+(DefaultActor pid=3764) >> Training accuracy: 0.979911
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.138711 Loss1: 1.257681 Loss2: 1.881030
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.238578 Loss1: 0.825267 Loss2: 1.413311
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.007380 Loss1: 0.560288 Loss2: 1.447092
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765323 Loss1: 0.373162 Loss2: 1.392161
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.337749 Loss1: 1.339825 Loss2: 1.997924
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.275372 Loss1: 0.762266 Loss2: 1.513106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.058255 Loss1: 0.540740 Loss2: 1.517515 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.816223 Loss1: 0.328126 Loss2: 1.488096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.730133 Loss1: 0.248068 Loss2: 1.482066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.700351 Loss1: 0.224046 Loss2: 1.476304 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.585730 Loss1: 0.208256 Loss2: 1.377474
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.686567 Loss1: 0.210527 Loss2: 1.476040
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.641907 Loss1: 0.167390 Loss2: 1.474517
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.632859 Loss1: 0.161781 Loss2: 1.471078
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.574615 Loss1: 0.109948 Loss2: 1.464667
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.093231 Loss1: 1.129467 Loss2: 1.963763
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.145844 Loss1: 0.676589 Loss2: 1.469255
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.902904 Loss1: 0.411327 Loss2: 1.491576
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.784086 Loss1: 0.327342 Loss2: 1.456744
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.160430 Loss1: 1.312640 Loss2: 1.847791
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.210340 Loss1: 0.788378 Loss2: 1.421962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.056847 Loss1: 0.626210 Loss2: 1.430637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.877956 Loss1: 0.470313 Loss2: 1.407643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.746007 Loss1: 0.353569 Loss2: 1.392437 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.599447 Loss1: 0.190749 Loss2: 1.408698 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.541183 Loss1: 0.115739 Loss2: 1.425443
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.548378 Loss1: 0.170017 Loss2: 1.378360
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.533044 Loss1: 0.163309 Loss2: 1.369735
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.542296 Loss1: 0.164829 Loss2: 1.377467
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.508253 Loss1: 0.134316 Loss2: 1.373937
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.515461 Loss1: 1.505389 Loss2: 2.010072
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.382040 Loss1: 0.889090 Loss2: 1.492950
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.006738 Loss1: 0.518834 Loss2: 1.487903
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.890224 Loss1: 0.442852 Loss2: 1.447373
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.137541 Loss1: 1.301968 Loss2: 1.835573
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.187214 Loss1: 0.782054 Loss2: 1.405160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.889790 Loss1: 0.502977 Loss2: 1.386813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.729759 Loss1: 0.361198 Loss2: 1.368561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.631792 Loss1: 0.195566 Loss2: 1.436226 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.599509 Loss1: 0.162419 Loss2: 1.437090 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.946429
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.494601 Loss1: 0.142426 Loss2: 1.352175 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.508443 Loss1: 0.159223 Loss2: 1.349220 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.263581 Loss1: 0.747237 Loss2: 1.516344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.756515 Loss1: 0.280022 Loss2: 1.476493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.302012 Loss1: 1.358315 Loss2: 1.943696
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.683781 Loss1: 0.223930 Loss2: 1.459851
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.289850 Loss1: 0.805580 Loss2: 1.484270
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.637935 Loss1: 0.183810 Loss2: 1.454125
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.004366 Loss1: 0.505201 Loss2: 1.499165
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.656572 Loss1: 0.196713 Loss2: 1.459859
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.648079 Loss1: 0.193141 Loss2: 1.454938 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.679340 Loss1: 0.218969 Loss2: 1.460371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.656019 Loss1: 0.195655 Loss2: 1.460364 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952148
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.622707 Loss1: 0.184977 Loss2: 1.437730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.599438 Loss1: 0.151823 Loss2: 1.447614 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.245301 Loss1: 1.333472 Loss2: 1.911829
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.344180 Loss1: 0.863321 Loss2: 1.480859
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.098195 Loss1: 0.594286 Loss2: 1.503909
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.902041 Loss1: 0.444524 Loss2: 1.457517
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.104866 Loss1: 1.249347 Loss2: 1.855519
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.183379 Loss1: 0.775785 Loss2: 1.407594 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.845663 Loss1: 0.416212 Loss2: 1.429451 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.721848 Loss1: 0.331274 Loss2: 1.390574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.651111 Loss1: 0.244760 Loss2: 1.406351 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582986 Loss1: 0.208578 Loss2: 1.374408 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.582880 Loss1: 0.205989 Loss2: 1.376892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.569198 Loss1: 0.200589 Loss2: 1.368610 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.253874 Loss1: 1.371896 Loss2: 1.881978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.981652 Loss1: 0.544510 Loss2: 1.437142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.360414 Loss1: 1.389467 Loss2: 1.970947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.307846 Loss1: 0.799297 Loss2: 1.508549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.977185 Loss1: 0.478757 Loss2: 1.498428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.897645 Loss1: 0.415294 Loss2: 1.482352 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.744377 Loss1: 0.265585 Loss2: 1.478792 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.679077 Loss1: 0.224519 Loss2: 1.454558 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.639432 Loss1: 0.180548 Loss2: 1.458884 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.629524 Loss1: 0.166814 Loss2: 1.462710 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.943828 Loss1: 1.152647 Loss2: 1.791180
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.188933 Loss1: 0.774600 Loss2: 1.414333
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.816211 Loss1: 0.456436 Loss2: 1.359775
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.660186 Loss1: 0.301402 Loss2: 1.358783
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.209610 Loss1: 1.273873 Loss2: 1.935737
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.153303 Loss1: 0.717753 Loss2: 1.435549
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565997 Loss1: 0.222481 Loss2: 1.343515
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.928970 Loss1: 0.468615 Loss2: 1.460355
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520419 Loss1: 0.184070 Loss2: 1.336349
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.505903 Loss1: 0.172482 Loss2: 1.333422 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471430 Loss1: 0.132828 Loss2: 1.338602 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.486831 Loss1: 0.156580 Loss2: 1.330251 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475664 Loss1: 0.140632 Loss2: 1.335033 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970588
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.506286 Loss1: 0.118993 Loss2: 1.387293 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.107611 Loss1: 1.223570 Loss2: 1.884041
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.202139 Loss1: 0.763339 Loss2: 1.438799
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.849195 Loss1: 0.423247 Loss2: 1.425949
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.518577 Loss1: 1.493142 Loss2: 2.025435
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.675926 Loss1: 0.295033 Loss2: 1.380892
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.400707 Loss1: 0.855409 Loss2: 1.545298
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606572 Loss1: 0.220520 Loss2: 1.386052
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.090910 Loss1: 0.561594 Loss2: 1.529316
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.596536 Loss1: 0.220280 Loss2: 1.376256
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.876297 Loss1: 0.382246 Loss2: 1.494051
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.542884 Loss1: 0.168270 Loss2: 1.374615
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.551785 Loss1: 0.186353 Loss2: 1.365432 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.480763 Loss1: 0.114267 Loss2: 1.366496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.532496 Loss1: 0.168564 Loss2: 1.363932 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952148
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.608772 Loss1: 0.143109 Loss2: 1.465663 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.081066 Loss1: 1.194343 Loss2: 1.886723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.876425 Loss1: 0.442534 Loss2: 1.433891 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.709959 Loss1: 0.346457 Loss2: 1.363502
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.076049 Loss1: 1.242205 Loss2: 1.833844
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.635286 Loss1: 0.261392 Loss2: 1.373894
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.242785 Loss1: 0.777011 Loss2: 1.465774
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.878953 Loss1: 0.458282 Loss2: 1.420671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.770825 Loss1: 0.353980 Loss2: 1.416844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.733921 Loss1: 0.327492 Loss2: 1.406429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.638218 Loss1: 0.239297 Loss2: 1.398921 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.557455 Loss1: 0.170934 Loss2: 1.386521 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.547104 Loss1: 0.158511 Loss2: 1.388592 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970703
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.295870 Loss1: 1.381222 Loss2: 1.914648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.940734 Loss1: 0.498962 Loss2: 1.441772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.678210 Loss1: 0.270788 Loss2: 1.407422 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.626843 Loss1: 0.233594 Loss2: 1.393249 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.599132 Loss1: 0.208810 Loss2: 1.390322 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.554078 Loss1: 0.157689 Loss2: 1.396389 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.524490 Loss1: 0.133036 Loss2: 1.391454 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.527591 Loss1: 0.143145 Loss2: 1.384447 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967634
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.622236 Loss1: 0.228657 Loss2: 1.393579 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.525259 Loss1: 0.144708 Loss2: 1.380551 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.560263 Loss1: 0.191184 Loss2: 1.369079
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.245116 Loss1: 1.286269 Loss2: 1.958846
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.363695 Loss1: 0.883467 Loss2: 1.480228
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.991194 Loss1: 0.494698 Loss2: 1.496497
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.817164 Loss1: 0.368178 Loss2: 1.448986
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.711865 Loss1: 0.260001 Loss2: 1.451863
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.960091 Loss1: 1.180087 Loss2: 1.780004
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.668988 Loss1: 0.226093 Loss2: 1.442895
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.146206 Loss1: 0.739521 Loss2: 1.406685
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.682229 Loss1: 0.230416 Loss2: 1.451813
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.792442 Loss1: 0.425279 Loss2: 1.367163
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.656621 Loss1: 0.211230 Loss2: 1.445391
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.605083 Loss1: 0.167866 Loss2: 1.437217
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.682842 Loss1: 0.330599 Loss2: 1.352242
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.588350 Loss1: 0.156982 Loss2: 1.431368
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.625097 Loss1: 0.265744 Loss2: 1.359353
+(DefaultActor pid=3765) >> Training accuracy: 0.950000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560972 Loss1: 0.219152 Loss2: 1.341821
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.501978 Loss1: 0.155056 Loss2: 1.346922
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477024 Loss1: 0.141288 Loss2: 1.335736
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.456972 Loss1: 0.123493 Loss2: 1.333479
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.116059 Loss1: 1.249051 Loss2: 1.867008
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443704 Loss1: 0.118273 Loss2: 1.325431
+(DefaultActor pid=3764) >> Training accuracy: 0.960938
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.917712 Loss1: 0.501895 Loss2: 1.415817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.600974 Loss1: 0.210986 Loss2: 1.389988 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587263 Loss1: 0.217303 Loss2: 1.369960
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.095833 Loss1: 1.243360 Loss2: 1.852473
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.240732 Loss1: 0.768686 Loss2: 1.472046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.012467 Loss1: 0.575393 Loss2: 1.437074 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 12:31:07,597 | server.py:236 | fit_round 76 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.832601 Loss1: 0.372448 Loss2: 1.460153 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.689138 Loss1: 0.271814 Loss2: 1.417325 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.549364 Loss1: 0.142667 Loss2: 1.406696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.592207 Loss1: 0.189764 Loss2: 1.402443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.203118 Loss1: 0.790663 Loss2: 1.412456 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966797
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.736236 Loss1: 0.380393 Loss2: 1.355843 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.558673 Loss1: 0.208939 Loss2: 1.349734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.580871 Loss1: 0.220020 Loss2: 1.360851
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.329317 Loss1: 1.354435 Loss2: 1.974881
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.519650 Loss1: 0.170640 Loss2: 1.349010
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.307593 Loss1: 0.871957 Loss2: 1.435637
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.959082 Loss1: 0.496269 Loss2: 1.462813
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.481015 Loss1: 0.137553 Loss2: 1.343461
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.486378 Loss1: 0.141635 Loss2: 1.344743 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.637536 Loss1: 0.217066 Loss2: 1.420470 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.616313 Loss1: 0.197687 Loss2: 1.418626 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.522458 Loss1: 0.116981 Loss2: 1.405477 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987981
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.888405 Loss1: 0.453625 Loss2: 1.434780 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.673795 Loss1: 0.276720 Loss2: 1.397076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.164898 Loss1: 1.266137 Loss2: 1.898762
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.580708 Loss1: 0.189957 Loss2: 1.390752
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.363337 Loss1: 0.921192 Loss2: 1.442144
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539739 Loss1: 0.166583 Loss2: 1.373157
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.009017 Loss1: 0.516921 Loss2: 1.492096
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.566753 Loss1: 0.190176 Loss2: 1.376576
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.788003 Loss1: 0.368941 Loss2: 1.419062
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509007 Loss1: 0.130351 Loss2: 1.378656
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.704959 Loss1: 0.279861 Loss2: 1.425097
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.514362 Loss1: 0.140277 Loss2: 1.374085
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.634750 Loss1: 0.223084 Loss2: 1.411666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.592260 Loss1: 0.176737 Loss2: 1.415523 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 12:31:07,597][flwr][DEBUG] - fit_round 76 received 50 results and 0 failures
+INFO flwr 2023-10-10 12:31:48,855 | server.py:125 | fit progress: (76, 2.255889182273572, {'accuracy': 0.5413}, 175216.633607881)
+>> Test accuracy: 0.541300
+[2023-10-10 12:31:48,855][flwr][INFO] - fit progress: (76, 2.255889182273572, {'accuracy': 0.5413}, 175216.633607881)
+DEBUG flwr 2023-10-10 12:31:48,855 | server.py:173 | evaluate_round 76: strategy sampled 50 clients (out of 50)
+[2023-10-10 12:31:48,855][flwr][DEBUG] - evaluate_round 76: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 12:40:54,782 | server.py:187 | evaluate_round 76 received 50 results and 0 failures
+[2023-10-10 12:40:54,782][flwr][DEBUG] - evaluate_round 76 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 12:40:54,783 | server.py:222 | fit_round 77: strategy sampled 50 clients (out of 50)
+[2023-10-10 12:40:54,783][flwr][DEBUG] - fit_round 77: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.918155 Loss1: 1.099646 Loss2: 1.818510 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.915300 Loss1: 0.487453 Loss2: 1.427847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.740982 Loss1: 0.356701 Loss2: 1.384281
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.183848 Loss1: 1.375096 Loss2: 1.808751
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.669212 Loss1: 0.284127 Loss2: 1.385085
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.229108 Loss1: 0.843422 Loss2: 1.385686
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.944424 Loss1: 0.555075 Loss2: 1.389349
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.669933 Loss1: 0.288521 Loss2: 1.381412
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.762207 Loss1: 0.398005 Loss2: 1.364202
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.611588 Loss1: 0.212907 Loss2: 1.398681
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.657838 Loss1: 0.291069 Loss2: 1.366768
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.539920 Loss1: 0.174741 Loss2: 1.365179
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.618691 Loss1: 0.259191 Loss2: 1.359500
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.518541 Loss1: 0.151445 Loss2: 1.367097
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.529094 Loss1: 0.163155 Loss2: 1.365939 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.946289
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.537848 Loss1: 0.179115 Loss2: 1.358734 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.087417 Loss1: 1.178663 Loss2: 1.908755 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.889663 Loss1: 0.432575 Loss2: 1.457088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.751220 Loss1: 0.353604 Loss2: 1.397616
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.185205 Loss1: 1.202266 Loss2: 1.982939
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.696249 Loss1: 0.283875 Loss2: 1.412373
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.212835 Loss1: 0.823426 Loss2: 1.389409
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.903985 Loss1: 0.488434 Loss2: 1.415550
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.619720 Loss1: 0.215831 Loss2: 1.403889
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.560964 Loss1: 0.174242 Loss2: 1.386722 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.555120 Loss1: 0.169117 Loss2: 1.386003 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.518734 Loss1: 0.128497 Loss2: 1.390237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.497654 Loss1: 0.114697 Loss2: 1.382956 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404699 Loss1: 0.062689 Loss2: 1.342010 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.172966 Loss1: 1.320175 Loss2: 1.852790
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.391830 Loss1: 0.943649 Loss2: 1.448181
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.037096 Loss1: 0.593901 Loss2: 1.443195
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.794667 Loss1: 0.378042 Loss2: 1.416625
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.270646 Loss1: 1.336595 Loss2: 1.934050
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.400326 Loss1: 0.920229 Loss2: 1.480097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.097916 Loss1: 0.588972 Loss2: 1.508944 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.879419 Loss1: 0.417959 Loss2: 1.461460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.749817 Loss1: 0.299646 Loss2: 1.450170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.707729 Loss1: 0.264430 Loss2: 1.443299 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.490881 Loss1: 0.114734 Loss2: 1.376146
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.629487 Loss1: 0.194389 Loss2: 1.435098
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.649849 Loss1: 0.219060 Loss2: 1.430790
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.652105 Loss1: 0.225023 Loss2: 1.427082
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.578574 Loss1: 0.150732 Loss2: 1.427842
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.286340 Loss1: 1.389259 Loss2: 1.897081
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.169964 Loss1: 0.744295 Loss2: 1.425668
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.845887 Loss1: 0.411164 Loss2: 1.434723
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.738918 Loss1: 0.344378 Loss2: 1.394540
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.124571 Loss1: 1.175821 Loss2: 1.948750
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.235440 Loss1: 0.738514 Loss2: 1.496926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.930077 Loss1: 0.423164 Loss2: 1.506913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.848974 Loss1: 0.370262 Loss2: 1.478712 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.758428 Loss1: 0.276211 Loss2: 1.482218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.691068 Loss1: 0.239732 Loss2: 1.451336 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.621814 Loss1: 0.163895 Loss2: 1.457920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.571927 Loss1: 0.132447 Loss2: 1.439480 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.978867 Loss1: 1.143556 Loss2: 1.835311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.800975 Loss1: 0.445085 Loss2: 1.355890 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638946 Loss1: 0.312025 Loss2: 1.326921
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.124217 Loss1: 1.227406 Loss2: 1.896812
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.237793 Loss1: 0.806076 Loss2: 1.431717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.903800 Loss1: 0.455293 Loss2: 1.448507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.728317 Loss1: 0.328097 Loss2: 1.400219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.624978 Loss1: 0.217577 Loss2: 1.407401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.589994 Loss1: 0.199447 Loss2: 1.390548 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.563268 Loss1: 0.178826 Loss2: 1.384442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.524869 Loss1: 0.146207 Loss2: 1.378662 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.151751 Loss1: 1.264571 Loss2: 1.887181 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.970265 Loss1: 0.529088 Loss2: 1.441177 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.087995 Loss1: 1.233058 Loss2: 1.854938 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.193844 Loss1: 0.781163 Loss2: 1.412681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.893539 Loss1: 0.456340 Loss2: 1.437199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.700206 Loss1: 0.313474 Loss2: 1.386732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.630714 Loss1: 0.246464 Loss2: 1.384250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.614454 Loss1: 0.229846 Loss2: 1.384608 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.553310 Loss1: 0.178604 Loss2: 1.374707 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.577144 Loss1: 0.200667 Loss2: 1.376476 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.054582 Loss1: 1.171882 Loss2: 1.882700
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.164396 Loss1: 0.748179 Loss2: 1.416216
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.997624 Loss1: 0.537483 Loss2: 1.460140
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.768386 Loss1: 0.365279 Loss2: 1.403108
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.160297 Loss1: 1.243673 Loss2: 1.916624
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.242704 Loss1: 0.790527 Loss2: 1.452177 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.970590 Loss1: 0.517098 Loss2: 1.453492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.755035 Loss1: 0.326675 Loss2: 1.428361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.684935 Loss1: 0.269902 Loss2: 1.415033 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.598819 Loss1: 0.186038 Loss2: 1.412781 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.605218 Loss1: 0.203700 Loss2: 1.401518 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.586274 Loss1: 0.180776 Loss2: 1.405498 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.206447 Loss1: 1.276041 Loss2: 1.930405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.039198 Loss1: 0.542387 Loss2: 1.496811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.872073 Loss1: 0.382378 Loss2: 1.489695
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.257530 Loss1: 1.388504 Loss2: 1.869026
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.265202 Loss1: 0.847925 Loss2: 1.417277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.931158 Loss1: 0.491201 Loss2: 1.439957 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.821023 Loss1: 0.429372 Loss2: 1.391650 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.634357 Loss1: 0.174951 Loss2: 1.459406
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.699806 Loss1: 0.313853 Loss2: 1.385953
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.644388 Loss1: 0.190700 Loss2: 1.453688
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.618739 Loss1: 0.244698 Loss2: 1.374041
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.623088 Loss1: 0.166108 Loss2: 1.456979
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.584763 Loss1: 0.219753 Loss2: 1.365010
+(DefaultActor pid=3765) >> Training accuracy: 0.963867
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.607891 Loss1: 0.235067 Loss2: 1.372824
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.523646 Loss1: 0.160739 Loss2: 1.362907
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529028 Loss1: 0.164827 Loss2: 1.364200
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.124880 Loss1: 1.263735 Loss2: 1.861145
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.216703 Loss1: 0.818709 Loss2: 1.397994
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.922922 Loss1: 0.498496 Loss2: 1.424426
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.821083 Loss1: 0.430918 Loss2: 1.390166
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.251259 Loss1: 1.358466 Loss2: 1.892793
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.698269 Loss1: 0.302462 Loss2: 1.395807
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.317465 Loss1: 0.854769 Loss2: 1.462696
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.571683 Loss1: 0.206584 Loss2: 1.365099
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.927929 Loss1: 0.483653 Loss2: 1.444276
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.626774 Loss1: 0.251539 Loss2: 1.375236
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.757795 Loss1: 0.343023 Loss2: 1.414772
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.590866 Loss1: 0.215140 Loss2: 1.375726
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.681430 Loss1: 0.270739 Loss2: 1.410692
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.565651 Loss1: 0.205815 Loss2: 1.359836
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.635261 Loss1: 0.233932 Loss2: 1.401329
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.558295 Loss1: 0.191946 Loss2: 1.366348
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.622705 Loss1: 0.230768 Loss2: 1.391937
+(DefaultActor pid=3765) >> Training accuracy: 0.933333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.617452 Loss1: 0.221499 Loss2: 1.395953
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.577047 Loss1: 0.174322 Loss2: 1.402726
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.536075 Loss1: 0.142367 Loss2: 1.393708
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.216025 Loss1: 1.418723 Loss2: 1.797301
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.234195 Loss1: 0.860525 Loss2: 1.373670
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.889275 Loss1: 0.528451 Loss2: 1.360824
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.690676 Loss1: 0.355228 Loss2: 1.335448
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.010479 Loss1: 1.141549 Loss2: 1.868930
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.153015 Loss1: 0.740748 Loss2: 1.412267 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.882298 Loss1: 0.476447 Loss2: 1.405851 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.771293 Loss1: 0.378607 Loss2: 1.392686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.599445 Loss1: 0.215524 Loss2: 1.383921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.533973 Loss1: 0.177729 Loss2: 1.356243 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416144 Loss1: 0.110689 Loss2: 1.305455
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.496673 Loss1: 0.135566 Loss2: 1.361107
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467447 Loss1: 0.111409 Loss2: 1.356038
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438972 Loss1: 0.093751 Loss2: 1.345221
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429738 Loss1: 0.089496 Loss2: 1.340242
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.162634 Loss1: 1.271295 Loss2: 1.891338
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.280135 Loss1: 0.826598 Loss2: 1.453536
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.975982 Loss1: 0.524520 Loss2: 1.451462
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.772264 Loss1: 0.348690 Loss2: 1.423574
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.235788 Loss1: 1.264293 Loss2: 1.971495
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.200265 Loss1: 0.714299 Loss2: 1.485966 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.052173 Loss1: 0.530412 Loss2: 1.521762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.822957 Loss1: 0.353440 Loss2: 1.469517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.728458 Loss1: 0.261197 Loss2: 1.467261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.707301 Loss1: 0.252644 Loss2: 1.454657 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.581972 Loss1: 0.187572 Loss2: 1.394400
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.626728 Loss1: 0.169403 Loss2: 1.457326
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.632861 Loss1: 0.187024 Loss2: 1.445837
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.601060 Loss1: 0.153319 Loss2: 1.447741
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.597734 Loss1: 0.150027 Loss2: 1.447707
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.299660 Loss1: 1.370391 Loss2: 1.929269
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.248978 Loss1: 0.818283 Loss2: 1.430694
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.924117 Loss1: 0.463752 Loss2: 1.460365
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.862319 Loss1: 0.454677 Loss2: 1.407642
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.063100 Loss1: 1.192655 Loss2: 1.870445
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.247374 Loss1: 0.840643 Loss2: 1.406730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.902189 Loss1: 0.454237 Loss2: 1.447953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.709260 Loss1: 0.324621 Loss2: 1.384638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.704001 Loss1: 0.314682 Loss2: 1.389319 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.686428 Loss1: 0.304839 Loss2: 1.381588 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969866
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.592774 Loss1: 0.212935 Loss2: 1.379839 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529355 Loss1: 0.144729 Loss2: 1.384625 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.318089 Loss1: 0.837401 Loss2: 1.480688 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.781848 Loss1: 0.323067 Loss2: 1.458781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.709425 Loss1: 0.254710 Loss2: 1.454716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.092395 Loss1: 0.583874 Loss2: 1.508520 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.882463 Loss1: 0.454542 Loss2: 1.427921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.774437 Loss1: 0.343521 Loss2: 1.430916 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.734899 Loss1: 0.281335 Loss2: 1.453564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.670983 Loss1: 0.260202 Loss2: 1.410781
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.578510 Loss1: 0.136833 Loss2: 1.441677
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.545876 Loss1: 0.116793 Loss2: 1.429083 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.020990 Loss1: 1.060532 Loss2: 1.960458 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971354
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.941165 Loss1: 0.445312 Loss2: 1.495853 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.851834 Loss1: 0.368014 Loss2: 1.483819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.704852 Loss1: 0.239972 Loss2: 1.464879 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.647418 Loss1: 0.189560 Loss2: 1.457858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.629439 Loss1: 0.177689 Loss2: 1.451750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.600071 Loss1: 0.148385 Loss2: 1.451686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.589770 Loss1: 0.216005 Loss2: 1.373765 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.545512 Loss1: 0.183157 Loss2: 1.362355 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.515240 Loss1: 0.157900 Loss2: 1.357340 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.101726 Loss1: 1.227340 Loss2: 1.874386
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.235625 Loss1: 0.785332 Loss2: 1.450293
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.932291 Loss1: 0.507357 Loss2: 1.424934
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.803945 Loss1: 0.402157 Loss2: 1.401787
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.291354 Loss1: 1.295454 Loss2: 1.995900
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.251047 Loss1: 0.732390 Loss2: 1.518657 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.018003 Loss1: 0.476115 Loss2: 1.541888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.836353 Loss1: 0.338543 Loss2: 1.497810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.755136 Loss1: 0.264662 Loss2: 1.490474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.709346 Loss1: 0.224453 Loss2: 1.484893 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454700 Loss1: 0.095580 Loss2: 1.359120
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.705327 Loss1: 0.219972 Loss2: 1.485356
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.649822 Loss1: 0.168962 Loss2: 1.480860
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.618566 Loss1: 0.140444 Loss2: 1.478122
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.647176 Loss1: 0.175403 Loss2: 1.471773
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.157553 Loss1: 1.237767 Loss2: 1.919786
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.079449 Loss1: 0.602425 Loss2: 1.477023
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.918323 Loss1: 0.443556 Loss2: 1.474768
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.829579 Loss1: 0.377486 Loss2: 1.452094
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.192056 Loss1: 1.293862 Loss2: 1.898194
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.172351 Loss1: 0.700206 Loss2: 1.472145 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.971152 Loss1: 0.501938 Loss2: 1.469214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.864623 Loss1: 0.420737 Loss2: 1.443885 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.752248 Loss1: 0.307662 Loss2: 1.444586 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.630979 Loss1: 0.210841 Loss2: 1.420138 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.565871 Loss1: 0.151820 Loss2: 1.414051 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.526619 Loss1: 0.123847 Loss2: 1.402772 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965820
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.173231 Loss1: 0.774721 Loss2: 1.398510 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630530 Loss1: 0.272034 Loss2: 1.358496 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.077567 Loss1: 1.114141 Loss2: 1.963426
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.602814 Loss1: 0.252557 Loss2: 1.350257
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.145815 Loss1: 0.667450 Loss2: 1.478365
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587427 Loss1: 0.239398 Loss2: 1.348028
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.904354 Loss1: 0.419854 Loss2: 1.484499
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.561232 Loss1: 0.220245 Loss2: 1.340987
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.745541 Loss1: 0.317713 Loss2: 1.427828
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.504522 Loss1: 0.162338 Loss2: 1.342184
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.679140 Loss1: 0.244373 Loss2: 1.434767
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.492793 Loss1: 0.154971 Loss2: 1.337822
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.623000 Loss1: 0.191200 Loss2: 1.431801
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463236 Loss1: 0.127304 Loss2: 1.335932
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.545787 Loss1: 0.126722 Loss2: 1.419065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.489867 Loss1: 0.081928 Loss2: 1.407940 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.137329 Loss1: 0.699750 Loss2: 1.437579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.703290 Loss1: 0.297723 Loss2: 1.405567 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.644496 Loss1: 0.251175 Loss2: 1.393321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.580371 Loss1: 0.192058 Loss2: 1.388314 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.609790 Loss1: 0.213225 Loss2: 1.396565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.658692 Loss1: 0.248698 Loss2: 1.409994 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.585467 Loss1: 0.198134 Loss2: 1.387332 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.622710 Loss1: 0.224746 Loss2: 1.397964 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.633398 Loss1: 0.173249 Loss2: 1.460150 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.940625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.157859 Loss1: 1.234136 Loss2: 1.923723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.913208 Loss1: 0.427636 Loss2: 1.485572 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.849917 Loss1: 0.375013 Loss2: 1.474904
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.993127 Loss1: 1.224829 Loss2: 1.768299
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.753764 Loss1: 0.276298 Loss2: 1.477466
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.226034 Loss1: 0.857414 Loss2: 1.368620
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.713606 Loss1: 0.250796 Loss2: 1.462810
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.895768 Loss1: 0.552768 Loss2: 1.343000
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.655585 Loss1: 0.187427 Loss2: 1.468158
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.653185 Loss1: 0.329624 Loss2: 1.323561
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.615961 Loss1: 0.154127 Loss2: 1.461834
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541193 Loss1: 0.225793 Loss2: 1.315400
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.587271 Loss1: 0.136794 Loss2: 1.450477
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512962 Loss1: 0.202716 Loss2: 1.310246
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.589711 Loss1: 0.134561 Loss2: 1.455151
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458116 Loss1: 0.149490 Loss2: 1.308626
+(DefaultActor pid=3765) >> Training accuracy: 0.972656
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440557 Loss1: 0.134394 Loss2: 1.306163
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402989 Loss1: 0.104414 Loss2: 1.298575
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421754 Loss1: 0.125809 Loss2: 1.295946
+(DefaultActor pid=3764) >> Training accuracy: 0.973633
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.203796 Loss1: 1.329315 Loss2: 1.874481
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.177711 Loss1: 0.789471 Loss2: 1.388240
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.894464 Loss1: 0.477265 Loss2: 1.417199
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.725351 Loss1: 0.342304 Loss2: 1.383047
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.295854 Loss1: 1.309218 Loss2: 1.986636
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.224520 Loss1: 0.715360 Loss2: 1.509160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.962495 Loss1: 0.431250 Loss2: 1.531245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.874356 Loss1: 0.391288 Loss2: 1.483068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.487042 Loss1: 0.131215 Loss2: 1.355827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.464700 Loss1: 0.115149 Loss2: 1.349551 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.648964 Loss1: 0.173591 Loss2: 1.475372 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.589115 Loss1: 0.131206 Loss2: 1.457909 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.059849 Loss1: 0.632493 Loss2: 1.427356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.724765 Loss1: 0.333461 Loss2: 1.391303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.123167 Loss1: 1.215905 Loss2: 1.907262
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.640975 Loss1: 0.229810 Loss2: 1.411165
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.206743 Loss1: 0.778706 Loss2: 1.428037
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.581665 Loss1: 0.190811 Loss2: 1.390854
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.541525 Loss1: 0.155540 Loss2: 1.385985 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.514993 Loss1: 0.133625 Loss2: 1.381367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489330 Loss1: 0.117245 Loss2: 1.372085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.513882 Loss1: 0.139268 Loss2: 1.374614 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965820
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540898 Loss1: 0.136324 Loss2: 1.404574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.506029 Loss1: 0.116296 Loss2: 1.389733 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.069260 Loss1: 1.263759 Loss2: 1.805502
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.160087 Loss1: 0.786631 Loss2: 1.373455
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.916280 Loss1: 0.513766 Loss2: 1.402514
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.785663 Loss1: 0.429163 Loss2: 1.356500
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.348864 Loss1: 1.461382 Loss2: 1.887482
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.331537 Loss1: 0.896383 Loss2: 1.435155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.958120 Loss1: 0.526147 Loss2: 1.431973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.510902 Loss1: 0.169578 Loss2: 1.341323
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.826272 Loss1: 0.435995 Loss2: 1.390277
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.520741 Loss1: 0.196033 Loss2: 1.324708
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.760087 Loss1: 0.359814 Loss2: 1.400272
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.696551 Loss1: 0.315311 Loss2: 1.381241
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460735 Loss1: 0.135088 Loss2: 1.325647
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.637852 Loss1: 0.247836 Loss2: 1.390016
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477136 Loss1: 0.155735 Loss2: 1.321401
+(DefaultActor pid=3765) >> Training accuracy: 0.962500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.498492 Loss1: 0.133970 Loss2: 1.364523 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978795
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.196869 Loss1: 1.259122 Loss2: 1.937747 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.950332 Loss1: 0.497385 Loss2: 1.452947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.114759 Loss1: 1.175866 Loss2: 1.938892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.622281 Loss1: 0.221738 Loss2: 1.400542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.571235 Loss1: 0.176848 Loss2: 1.394387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.552579 Loss1: 0.164093 Loss2: 1.388486 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.570334 Loss1: 0.183728 Loss2: 1.386606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.504410 Loss1: 0.109175 Loss2: 1.395235 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.506399 Loss1: 0.087789 Loss2: 1.418610 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485151 Loss1: 0.086337 Loss2: 1.398814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456152 Loss1: 0.054503 Loss2: 1.401649
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.166074 Loss1: 1.350856 Loss2: 1.815217
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.220609 Loss1: 0.831450 Loss2: 1.389159
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.886095 Loss1: 0.480389 Loss2: 1.405706
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.732977 Loss1: 0.371576 Loss2: 1.361400
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.666089 Loss1: 0.282325 Loss2: 1.383764
+DEBUG flwr 2023-10-10 13:09:58,999 | server.py:236 | fit_round 77 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.059720 Loss1: 1.244199 Loss2: 1.815522
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.161642 Loss1: 0.787366 Loss2: 1.374276 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.919108 Loss1: 0.512369 Loss2: 1.406739 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.752295 Loss1: 0.399108 Loss2: 1.353188 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.620538 Loss1: 0.266206 Loss2: 1.354332 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.589086 Loss1: 0.236214 Loss2: 1.352872 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.497374 Loss1: 0.155520 Loss2: 1.341854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433297 Loss1: 0.103940 Loss2: 1.329357 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.200055 Loss1: 0.691550 Loss2: 1.508505 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.828379 Loss1: 0.349955 Loss2: 1.478424 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.701860 Loss1: 0.242109 Loss2: 1.459751
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.121098 Loss1: 1.235116 Loss2: 1.885982
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.658956 Loss1: 0.205255 Loss2: 1.453702
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.110851 Loss1: 0.703924 Loss2: 1.406927
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.643943 Loss1: 0.187428 Loss2: 1.456516
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.826253 Loss1: 0.416580 Loss2: 1.409674
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.656674 Loss1: 0.278418 Loss2: 1.378256
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.600894 Loss1: 0.152776 Loss2: 1.448118
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.594114 Loss1: 0.219382 Loss2: 1.374732
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.581192 Loss1: 0.136541 Loss2: 1.444652
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478377 Loss1: 0.124417 Loss2: 1.353960
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.577371 Loss1: 0.143899 Loss2: 1.433472
+(DefaultActor pid=3765) >> Training accuracy: 0.969727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510068 Loss1: 0.149162 Loss2: 1.360905 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444621 Loss1: 0.101759 Loss2: 1.342862 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 13:09:58,999][flwr][DEBUG] - fit_round 77 received 50 results and 0 failures
+INFO flwr 2023-10-10 13:10:39,620 | server.py:125 | fit progress: (77, 2.267773052374014, {'accuracy': 0.5416}, 177547.39856439602)
+>> Test accuracy: 0.541600
+[2023-10-10 13:10:39,620][flwr][INFO] - fit progress: (77, 2.267773052374014, {'accuracy': 0.5416}, 177547.39856439602)
+DEBUG flwr 2023-10-10 13:10:39,620 | server.py:173 | evaluate_round 77: strategy sampled 50 clients (out of 50)
+[2023-10-10 13:10:39,620][flwr][DEBUG] - evaluate_round 77: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 13:19:47,750 | server.py:187 | evaluate_round 77 received 50 results and 0 failures
+[2023-10-10 13:19:47,750][flwr][DEBUG] - evaluate_round 77 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 13:19:47,750 | server.py:222 | fit_round 78: strategy sampled 50 clients (out of 50)
+[2023-10-10 13:19:47,750][flwr][DEBUG] - fit_round 78: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.300586 Loss1: 1.271876 Loss2: 2.028709
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.412655 Loss1: 0.856167 Loss2: 1.556488
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.089703 Loss1: 0.536414 Loss2: 1.553289
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.879215 Loss1: 0.349326 Loss2: 1.529889
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.090203 Loss1: 1.219533 Loss2: 1.870670
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.189735 Loss1: 0.755497 Loss2: 1.434239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.955192 Loss1: 0.502994 Loss2: 1.452198 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.809613 Loss1: 0.372615 Loss2: 1.436998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.698397 Loss1: 0.271234 Loss2: 1.427163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.631018 Loss1: 0.215289 Loss2: 1.415729 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.577164 Loss1: 0.175396 Loss2: 1.401768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519809 Loss1: 0.113236 Loss2: 1.406574 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967773
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.168656 Loss1: 1.280908 Loss2: 1.887748 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.936692 Loss1: 0.474967 Loss2: 1.461724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.192932 Loss1: 1.277126 Loss2: 1.915806 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.195435 Loss1: 0.734892 Loss2: 1.460543 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.960162 Loss1: 0.486900 Loss2: 1.473261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.802194 Loss1: 0.363597 Loss2: 1.438597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.672704 Loss1: 0.238531 Loss2: 1.434174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.639623 Loss1: 0.214156 Loss2: 1.425466 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.631958 Loss1: 0.208082 Loss2: 1.423876 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491446 Loss1: 0.082330 Loss2: 1.409116 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.163690 Loss1: 0.737831 Loss2: 1.425859 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765267 Loss1: 0.366198 Loss2: 1.399069 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.644758 Loss1: 0.252415 Loss2: 1.392343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.578851 Loss1: 0.203799 Loss2: 1.375051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539204 Loss1: 0.159454 Loss2: 1.379750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.553491 Loss1: 0.183152 Loss2: 1.370338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.516282 Loss1: 0.141919 Loss2: 1.374364 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.486582 Loss1: 0.116279 Loss2: 1.370302 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.627032 Loss1: 0.258952 Loss2: 1.368080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.493038 Loss1: 0.142522 Loss2: 1.350517 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.249043 Loss1: 0.741259 Loss2: 1.507784 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.810309 Loss1: 0.344152 Loss2: 1.466157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.074153 Loss1: 1.179013 Loss2: 1.895141
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.709318 Loss1: 0.229329 Loss2: 1.479989
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.328874 Loss1: 0.848397 Loss2: 1.480477
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.654899 Loss1: 0.205406 Loss2: 1.449492
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.017027 Loss1: 0.526377 Loss2: 1.490649
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.639067 Loss1: 0.181471 Loss2: 1.457596
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.651182 Loss1: 0.201444 Loss2: 1.449738
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.784434 Loss1: 0.337304 Loss2: 1.447130
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.629215 Loss1: 0.179146 Loss2: 1.450069
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.709498 Loss1: 0.259218 Loss2: 1.450280
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.595962 Loss1: 0.141460 Loss2: 1.454501
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.639796 Loss1: 0.202720 Loss2: 1.437076
+(DefaultActor pid=3765) >> Training accuracy: 0.950000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.598405 Loss1: 0.166482 Loss2: 1.431923
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.592707 Loss1: 0.167892 Loss2: 1.424815
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.549693 Loss1: 0.122137 Loss2: 1.427557
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.524513 Loss1: 0.106275 Loss2: 1.418238
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.143179 Loss1: 1.248877 Loss2: 1.894302
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.232078 Loss1: 0.769997 Loss2: 1.462081
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.933498 Loss1: 0.468966 Loss2: 1.464532
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.740944 Loss1: 0.321599 Loss2: 1.419346
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.652850 Loss1: 0.233651 Loss2: 1.419199
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.047687 Loss1: 1.177320 Loss2: 1.870367
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.610016 Loss1: 0.205755 Loss2: 1.404260
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.588586 Loss1: 0.177505 Loss2: 1.411081 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.611684 Loss1: 0.206109 Loss2: 1.405575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.551467 Loss1: 0.156947 Loss2: 1.394520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.620108 Loss1: 0.210581 Loss2: 1.409527 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.496818 Loss1: 0.150606 Loss2: 1.346212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428761 Loss1: 0.094182 Loss2: 1.334579 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450584 Loss1: 0.120424 Loss2: 1.330160
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.011446 Loss1: 1.147552 Loss2: 1.863895
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.113627 Loss1: 0.720868 Loss2: 1.392760
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.969550 Loss1: 0.512679 Loss2: 1.456871
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.729380 Loss1: 0.353477 Loss2: 1.375903
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.641131 Loss1: 0.244531 Loss2: 1.396600
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.238699 Loss1: 1.311513 Loss2: 1.927185
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569742 Loss1: 0.199988 Loss2: 1.369754
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.548354 Loss1: 0.186518 Loss2: 1.361836 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.542966 Loss1: 0.180549 Loss2: 1.362417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.675800 Loss1: 0.279900 Loss2: 1.395900 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.611413 Loss1: 0.203740 Loss2: 1.407673 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.528345 Loss1: 0.145057 Loss2: 1.383289 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.514566 Loss1: 0.134466 Loss2: 1.380101 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972356
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.910667 Loss1: 1.078220 Loss2: 1.832447
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.251743 Loss1: 0.797197 Loss2: 1.454546
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.919849 Loss1: 0.496049 Loss2: 1.423800
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.816100 Loss1: 0.382009 Loss2: 1.434091
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.018408 Loss1: 1.197883 Loss2: 1.820525
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.037280 Loss1: 0.672676 Loss2: 1.364604
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.692159 Loss1: 0.288740 Loss2: 1.403420
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.766282 Loss1: 0.383637 Loss2: 1.382644
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.647148 Loss1: 0.250752 Loss2: 1.396396
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.668493 Loss1: 0.320465 Loss2: 1.348028
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.599904 Loss1: 0.205861 Loss2: 1.394043
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.605626 Loss1: 0.248955 Loss2: 1.356671
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.558069 Loss1: 0.167470 Loss2: 1.390600
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.514295 Loss1: 0.130897 Loss2: 1.383398 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.536407 Loss1: 0.154583 Loss2: 1.381824 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966797
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.496274 Loss1: 0.155943 Loss2: 1.340330 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.181916 Loss1: 1.303401 Loss2: 1.878515 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.997757 Loss1: 0.558944 Loss2: 1.438813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.844684 Loss1: 0.431591 Loss2: 1.413094
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.110483 Loss1: 1.198237 Loss2: 1.912245
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.676597 Loss1: 0.282587 Loss2: 1.394010
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.228142 Loss1: 0.767571 Loss2: 1.460572
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.649711 Loss1: 0.256823 Loss2: 1.392889
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.992280 Loss1: 0.564707 Loss2: 1.427573
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.577048 Loss1: 0.186808 Loss2: 1.390240
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.767975 Loss1: 0.354890 Loss2: 1.413085
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.530486 Loss1: 0.151697 Loss2: 1.378789
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.690168 Loss1: 0.288062 Loss2: 1.402106
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.508020 Loss1: 0.135333 Loss2: 1.372687
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.569067 Loss1: 0.189886 Loss2: 1.379181
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.519466 Loss1: 0.142806 Loss2: 1.376660
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.511527 Loss1: 0.133462 Loss2: 1.378065
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.522188 Loss1: 0.148757 Loss2: 1.373431
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454027 Loss1: 0.082142 Loss2: 1.371886
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445362 Loss1: 0.083851 Loss2: 1.361511
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.100594 Loss1: 1.235466 Loss2: 1.865128
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.108395 Loss1: 0.706635 Loss2: 1.401760
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.901520 Loss1: 0.473670 Loss2: 1.427850
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.758757 Loss1: 0.362140 Loss2: 1.396617
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.986304 Loss1: 1.163085 Loss2: 1.823219
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.139955 Loss1: 0.708718 Loss2: 1.431237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.918050 Loss1: 0.507047 Loss2: 1.411003 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.762274 Loss1: 0.366529 Loss2: 1.395746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.645418 Loss1: 0.259746 Loss2: 1.385673 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.596308 Loss1: 0.216573 Loss2: 1.379735 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500046 Loss1: 0.139550 Loss2: 1.360496 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.498390 Loss1: 0.130189 Loss2: 1.368201 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958008
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.274377 Loss1: 0.858051 Loss2: 1.416326 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.703245 Loss1: 0.333050 Loss2: 1.370194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.633094 Loss1: 0.281864 Loss2: 1.351231
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.314157 Loss1: 1.446385 Loss2: 1.867772
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542227 Loss1: 0.193612 Loss2: 1.348615
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.337381 Loss1: 0.875330 Loss2: 1.462050
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.537505 Loss1: 0.195236 Loss2: 1.342269
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.914812 Loss1: 0.476074 Loss2: 1.438738
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.508520 Loss1: 0.175741 Loss2: 1.332778
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.776385 Loss1: 0.370179 Loss2: 1.406207
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456311 Loss1: 0.120320 Loss2: 1.335990
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.738550 Loss1: 0.321934 Loss2: 1.416616
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.468981 Loss1: 0.142012 Loss2: 1.326969
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.620900 Loss1: 0.228004 Loss2: 1.392896
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545223 Loss1: 0.155954 Loss2: 1.389269
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.513467 Loss1: 0.138811 Loss2: 1.374656
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.495505 Loss1: 0.124675 Loss2: 1.370830
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.532748 Loss1: 0.160429 Loss2: 1.372319
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.298274 Loss1: 1.326366 Loss2: 1.971908
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.232307 Loss1: 0.841351 Loss2: 1.390956
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.932416 Loss1: 0.490361 Loss2: 1.442055
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.742649 Loss1: 0.336452 Loss2: 1.406197
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.706968 Loss1: 0.333817 Loss2: 1.373151
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.620979 Loss1: 0.221353 Loss2: 1.399627
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.178141 Loss1: 1.299535 Loss2: 1.878606
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.603137 Loss1: 0.216520 Loss2: 1.386617
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.835410 Loss1: 0.401986 Loss2: 1.433425 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474587 Loss1: 0.113191 Loss2: 1.361396 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981771
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.637001 Loss1: 0.243549 Loss2: 1.393453 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.525352 Loss1: 0.147593 Loss2: 1.377759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.509713 Loss1: 1.484152 Loss2: 2.025561
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.514602 Loss1: 0.139470 Loss2: 1.375132
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.361935 Loss1: 0.839916 Loss2: 1.522019
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.492281 Loss1: 0.116730 Loss2: 1.375551
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.879743 Loss1: 0.390632 Loss2: 1.489111 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.655101 Loss1: 0.181125 Loss2: 1.473977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.190153 Loss1: 1.224517 Loss2: 1.965636 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.229585 Loss1: 0.754984 Loss2: 1.474602 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.060688 Loss1: 0.545114 Loss2: 1.515574 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.695105 Loss1: 0.234137 Loss2: 1.460968 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.630588 Loss1: 0.182681 Loss2: 1.447907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.600328 Loss1: 0.159222 Loss2: 1.441106
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.996273 Loss1: 1.157768 Loss2: 1.838505
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.019837 Loss1: 0.654067 Loss2: 1.365770 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.601122 Loss1: 0.158751 Loss2: 1.442371
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.802890 Loss1: 0.415434 Loss2: 1.387457
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.655073 Loss1: 0.311284 Loss2: 1.343789
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567810 Loss1: 0.226243 Loss2: 1.341567
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458617 Loss1: 0.135779 Loss2: 1.322838
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418585 Loss1: 0.100292 Loss2: 1.318294
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.200073 Loss1: 1.345434 Loss2: 1.854640
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444681 Loss1: 0.130521 Loss2: 1.314160
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.334680 Loss1: 0.909451 Loss2: 1.425230
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438154 Loss1: 0.120642 Loss2: 1.317512
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.987664 Loss1: 0.557969 Loss2: 1.429695
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434519 Loss1: 0.118380 Loss2: 1.316139
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.643931 Loss1: 0.253275 Loss2: 1.390656 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.605870 Loss1: 0.250707 Loss2: 1.355164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.594484 Loss1: 0.223155 Loss2: 1.371329
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.260056 Loss1: 1.313181 Loss2: 1.946875
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.315122 Loss1: 0.802346 Loss2: 1.512775 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509035 Loss1: 0.143917 Loss2: 1.365118
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.022416 Loss1: 0.512892 Loss2: 1.509524
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.831113 Loss1: 0.352778 Loss2: 1.478335
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.806805 Loss1: 0.317505 Loss2: 1.489300
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.760872 Loss1: 0.283438 Loss2: 1.477434
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.710128 Loss1: 0.237418 Loss2: 1.472710
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.289099 Loss1: 1.336767 Loss2: 1.952332
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.726755 Loss1: 0.256842 Loss2: 1.469912
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.737406 Loss1: 0.257507 Loss2: 1.479900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.733252 Loss1: 0.253361 Loss2: 1.479891 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.939583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.748987 Loss1: 0.301918 Loss2: 1.447069 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.600956 Loss1: 0.191747 Loss2: 1.409209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.125798 Loss1: 1.194785 Loss2: 1.931013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.511928 Loss1: 0.117283 Loss2: 1.394645 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.788219 Loss1: 0.315432 Loss2: 1.472787 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.755923 Loss1: 0.297564 Loss2: 1.458360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.095754 Loss1: 1.199226 Loss2: 1.896528
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.721699 Loss1: 0.248516 Loss2: 1.473183
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.159990 Loss1: 0.715145 Loss2: 1.444845
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.628458 Loss1: 0.168522 Loss2: 1.459935
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.862518 Loss1: 0.440813 Loss2: 1.421705
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.587573 Loss1: 0.136365 Loss2: 1.451209
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.591749 Loss1: 0.155083 Loss2: 1.436667 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.570822 Loss1: 0.193364 Loss2: 1.377458 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540026 Loss1: 0.162672 Loss2: 1.377354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.513387 Loss1: 0.142654 Loss2: 1.370733
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.226433 Loss1: 1.289967 Loss2: 1.936466
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.224263 Loss1: 0.752077 Loss2: 1.472186 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.833090 Loss1: 0.383616 Loss2: 1.449474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.743433 Loss1: 0.288400 Loss2: 1.455033 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.712316 Loss1: 0.263498 Loss2: 1.448818 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.681009 Loss1: 0.231730 Loss2: 1.449280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.604807 Loss1: 0.162115 Loss2: 1.442692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.593118 Loss1: 0.163250 Loss2: 1.429868 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.565245 Loss1: 0.153853 Loss2: 1.411392 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548123 Loss1: 0.138738 Loss2: 1.409385 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.084840 Loss1: 1.227045 Loss2: 1.857795 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.930916 Loss1: 0.489161 Loss2: 1.441754 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.639513 Loss1: 0.235502 Loss2: 1.404011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.584516 Loss1: 0.190337 Loss2: 1.394179
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.101348 Loss1: 1.252223 Loss2: 1.849126
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.602783 Loss1: 0.210760 Loss2: 1.392023
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.104681 Loss1: 0.682604 Loss2: 1.422077
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.563066 Loss1: 0.168904 Loss2: 1.394162
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.826176 Loss1: 0.414629 Loss2: 1.411547
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.530077 Loss1: 0.143646 Loss2: 1.386431
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.736709 Loss1: 0.340970 Loss2: 1.395738
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.576236 Loss1: 0.189028 Loss2: 1.387208
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.614965 Loss1: 0.218348 Loss2: 1.396617
+(DefaultActor pid=3765) >> Training accuracy: 0.969727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546979 Loss1: 0.163635 Loss2: 1.383344
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.559965 Loss1: 0.180929 Loss2: 1.379036
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515700 Loss1: 0.131816 Loss2: 1.383884
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.545718 Loss1: 0.163364 Loss2: 1.382354
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.094869 Loss1: 1.183994 Loss2: 1.910875
+(DefaultActor pid=3764) >> Training accuracy: 0.970703
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.344652 Loss1: 0.882077 Loss2: 1.462575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.794586 Loss1: 0.359031 Loss2: 1.435555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.645815 Loss1: 0.222274 Loss2: 1.423541 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.615064 Loss1: 0.195411 Loss2: 1.419654 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.571508 Loss1: 0.148361 Loss2: 1.423147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.513846 Loss1: 0.107716 Loss2: 1.406130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.511367 Loss1: 0.111519 Loss2: 1.399848 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.585780 Loss1: 0.210287 Loss2: 1.375493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501627 Loss1: 0.141935 Loss2: 1.359692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.921831 Loss1: 1.110852 Loss2: 1.810979 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.050841 Loss1: 0.650952 Loss2: 1.399889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.648626 Loss1: 0.282167 Loss2: 1.366458 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.180563 Loss1: 1.267061 Loss2: 1.913502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.324611 Loss1: 0.830186 Loss2: 1.494426 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.878664 Loss1: 0.423551 Loss2: 1.455113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.760330 Loss1: 0.334266 Loss2: 1.426064 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.796754 Loss1: 0.358523 Loss2: 1.438231 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.974265
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474754 Loss1: 0.123099 Loss2: 1.351655
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.721861 Loss1: 0.279809 Loss2: 1.442052
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.649178 Loss1: 0.218664 Loss2: 1.430514
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.617243 Loss1: 0.196059 Loss2: 1.421183
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.597955 Loss1: 0.175309 Loss2: 1.422645
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.581236 Loss1: 0.162255 Loss2: 1.418981
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.966407 Loss1: 1.139082 Loss2: 1.827325
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.042314 Loss1: 0.620904 Loss2: 1.421410
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.849353 Loss1: 0.444603 Loss2: 1.404750
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.675354 Loss1: 0.291927 Loss2: 1.383427
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.976734 Loss1: 1.127934 Loss2: 1.848800
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.671671 Loss1: 0.293676 Loss2: 1.377995
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.180225 Loss1: 0.757099 Loss2: 1.423126
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.589783 Loss1: 0.217803 Loss2: 1.371979
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.917795 Loss1: 0.502013 Loss2: 1.415782
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534764 Loss1: 0.174655 Loss2: 1.360109
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.788818 Loss1: 0.398436 Loss2: 1.390382
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.541860 Loss1: 0.171350 Loss2: 1.370510
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.680749 Loss1: 0.292324 Loss2: 1.388425
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.579870 Loss1: 0.204435 Loss2: 1.375435
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.682494 Loss1: 0.311193 Loss2: 1.371301
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.538970 Loss1: 0.172807 Loss2: 1.366164
+(DefaultActor pid=3765) >> Training accuracy: 0.969727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.573464 Loss1: 0.202767 Loss2: 1.370697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.501205 Loss1: 0.144123 Loss2: 1.357082 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.224099 Loss1: 0.799973 Loss2: 1.424126 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.754027 Loss1: 0.364172 Loss2: 1.389855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.640820 Loss1: 0.247155 Loss2: 1.393665
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.011886 Loss1: 1.133790 Loss2: 1.878096
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.586623 Loss1: 0.196503 Loss2: 1.390119
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.218519 Loss1: 0.793537 Loss2: 1.424982
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544331 Loss1: 0.167067 Loss2: 1.377265
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.922011 Loss1: 0.473041 Loss2: 1.448970
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.502964 Loss1: 0.124086 Loss2: 1.378878
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.711705 Loss1: 0.300749 Loss2: 1.410956
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472808 Loss1: 0.103579 Loss2: 1.369230
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.648543 Loss1: 0.252689 Loss2: 1.395854
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442022 Loss1: 0.075340 Loss2: 1.366682
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.599862 Loss1: 0.214863 Loss2: 1.384999
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.579943 Loss1: 0.189311 Loss2: 1.390631
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.555755 Loss1: 0.176379 Loss2: 1.379376
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.520384 Loss1: 0.136763 Loss2: 1.383620
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512427 Loss1: 0.133732 Loss2: 1.378695
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.139158 Loss1: 1.171376 Loss2: 1.967782
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.179976 Loss1: 0.711853 Loss2: 1.468123
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.932692 Loss1: 0.462148 Loss2: 1.470544
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.731607 Loss1: 0.287639 Loss2: 1.443969
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.663133 Loss1: 0.228402 Loss2: 1.434730
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.654450 Loss1: 0.222749 Loss2: 1.431701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.577986 Loss1: 0.153114 Loss2: 1.424872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.551705 Loss1: 0.127637 Loss2: 1.424067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.540628 Loss1: 0.124571 Loss2: 1.416057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.586466 Loss1: 0.167038 Loss2: 1.419428 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.550727 Loss1: 0.160139 Loss2: 1.390588 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.543872 Loss1: 0.158619 Loss2: 1.385253 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.308659 Loss1: 0.848946 Loss2: 1.459713 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.779334 Loss1: 0.379253 Loss2: 1.400081 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.674160 Loss1: 0.264113 Loss2: 1.410047
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.985019 Loss1: 1.142989 Loss2: 1.842030
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.646865 Loss1: 0.249538 Loss2: 1.397328
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.233713 Loss1: 0.831915 Loss2: 1.401798
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.602860 Loss1: 0.204739 Loss2: 1.398121
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.881140 Loss1: 0.472227 Loss2: 1.408913
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.546389 Loss1: 0.161499 Loss2: 1.384890
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.736346 Loss1: 0.371936 Loss2: 1.364409
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.533442 Loss1: 0.157891 Loss2: 1.375551
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.628603 Loss1: 0.260589 Loss2: 1.368014
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.482979 Loss1: 0.102391 Loss2: 1.380588
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.572169 Loss1: 0.220717 Loss2: 1.351452
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-10 13:48:09,796 | server.py:236 | fit_round 78 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.546313 Loss1: 0.188297 Loss2: 1.358016
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487761 Loss1: 0.143063 Loss2: 1.344697
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.530259 Loss1: 0.189231 Loss2: 1.341027
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.510350 Loss1: 0.157952 Loss2: 1.352398
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.437240 Loss1: 1.439727 Loss2: 1.997512
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.380831 Loss1: 0.901798 Loss2: 1.479033
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.972988 Loss1: 0.472815 Loss2: 1.500173
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.782095 Loss1: 0.349412 Loss2: 1.432684
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.740749 Loss1: 0.295803 Loss2: 1.444946
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.143223 Loss1: 1.227498 Loss2: 1.915725
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.073571 Loss1: 0.702693 Loss2: 1.370878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.849411 Loss1: 0.446884 Loss2: 1.402526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.709958 Loss1: 0.356448 Loss2: 1.353510 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.528952 Loss1: 0.115791 Loss2: 1.413161
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.632124 Loss1: 0.281069 Loss2: 1.351055
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549662 Loss1: 0.195186 Loss2: 1.354477
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508757 Loss1: 0.099953 Loss2: 1.408804
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491137 Loss1: 0.148048 Loss2: 1.343089 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445026 Loss1: 0.116442 Loss2: 1.328583 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965144
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.022568 Loss1: 1.182580 Loss2: 1.839988
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.186047 Loss1: 0.827558 Loss2: 1.358489
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.907181 Loss1: 0.517916 Loss2: 1.389264
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.703409 Loss1: 0.330470 Loss2: 1.372939
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.349568 Loss1: 1.369483 Loss2: 1.980085
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.333295 Loss1: 0.783977 Loss2: 1.549318 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.994049 Loss1: 0.484119 Loss2: 1.509931 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.798678 Loss1: 0.317199 Loss2: 1.481479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.731107 Loss1: 0.250747 Loss2: 1.480361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.656877 Loss1: 0.181071 Loss2: 1.475806 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.622154 Loss1: 0.158799 Loss2: 1.463355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.631159 Loss1: 0.163584 Loss2: 1.467574 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 13:48:09,796][flwr][DEBUG] - fit_round 78 received 50 results and 0 failures
+INFO flwr 2023-10-10 13:48:52,019 | server.py:125 | fit progress: (78, 2.254478170848883, {'accuracy': 0.5425}, 179839.797407425)
+>> Test accuracy: 0.542500
+[2023-10-10 13:48:52,019][flwr][INFO] - fit progress: (78, 2.254478170848883, {'accuracy': 0.5425}, 179839.797407425)
+DEBUG flwr 2023-10-10 13:48:52,019 | server.py:173 | evaluate_round 78: strategy sampled 50 clients (out of 50)
+[2023-10-10 13:48:52,019][flwr][DEBUG] - evaluate_round 78: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 13:57:58,819 | server.py:187 | evaluate_round 78 received 50 results and 0 failures
+[2023-10-10 13:57:58,819][flwr][DEBUG] - evaluate_round 78 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 13:57:58,820 | server.py:222 | fit_round 79: strategy sampled 50 clients (out of 50)
+[2023-10-10 13:57:58,820][flwr][DEBUG] - fit_round 79: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.256388 Loss1: 1.296245 Loss2: 1.960144 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.977891 Loss1: 0.468447 Loss2: 1.509444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.799019 Loss1: 0.329845 Loss2: 1.469174
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.286845 Loss1: 1.346178 Loss2: 1.940667
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.696106 Loss1: 0.227819 Loss2: 1.468287
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.404994 Loss1: 0.911298 Loss2: 1.493696
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.631036 Loss1: 0.190660 Loss2: 1.440377
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.159205 Loss1: 0.660438 Loss2: 1.498767
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.636690 Loss1: 0.196228 Loss2: 1.440462
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.861740 Loss1: 0.384612 Loss2: 1.477129
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.605292 Loss1: 0.159455 Loss2: 1.445838
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.753297 Loss1: 0.303588 Loss2: 1.449709
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.604056 Loss1: 0.163869 Loss2: 1.440187
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.628568 Loss1: 0.175398 Loss2: 1.453170
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.586021 Loss1: 0.154950 Loss2: 1.431071
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.594746 Loss1: 0.163848 Loss2: 1.430898
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.593199 Loss1: 0.163167 Loss2: 1.430032
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.561438 Loss1: 0.128067 Loss2: 1.433371
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.575731 Loss1: 0.143592 Loss2: 1.432138
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.131888 Loss1: 1.251924 Loss2: 1.879964
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.206717 Loss1: 0.783469 Loss2: 1.423247
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.903096 Loss1: 0.439437 Loss2: 1.463659
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.721991 Loss1: 0.320723 Loss2: 1.401269
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.168624 Loss1: 1.258908 Loss2: 1.909716
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.188590 Loss1: 0.737620 Loss2: 1.450971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.886341 Loss1: 0.430343 Loss2: 1.455998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.665415 Loss1: 0.260833 Loss2: 1.404583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.627325 Loss1: 0.223594 Loss2: 1.403730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.655463 Loss1: 0.246090 Loss2: 1.409373 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.524294 Loss1: 0.138120 Loss2: 1.386174
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.601108 Loss1: 0.191810 Loss2: 1.409298
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.593690 Loss1: 0.181320 Loss2: 1.412370
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.564265 Loss1: 0.160736 Loss2: 1.403529
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.534209 Loss1: 0.134611 Loss2: 1.399598
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.022386 Loss1: 1.175336 Loss2: 1.847050
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.073572 Loss1: 0.688970 Loss2: 1.384602
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.807230 Loss1: 0.447269 Loss2: 1.359961
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.696951 Loss1: 0.337796 Loss2: 1.359156
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.822271 Loss1: 1.012691 Loss2: 1.809580
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.985945 Loss1: 0.620289 Loss2: 1.365656 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.757614 Loss1: 0.378715 Loss2: 1.378899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.668430 Loss1: 0.322760 Loss2: 1.345670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.690123 Loss1: 0.332990 Loss2: 1.357133 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.616625 Loss1: 0.269974 Loss2: 1.346651 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.552061 Loss1: 0.212260 Loss2: 1.339800 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499328 Loss1: 0.161630 Loss2: 1.337697 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.170164 Loss1: 1.204499 Loss2: 1.965665 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.982016 Loss1: 0.483216 Loss2: 1.498800 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.877420 Loss1: 0.987753 Loss2: 1.889667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.635862 Loss1: 0.195242 Loss2: 1.440620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.693198 Loss1: 0.246049 Loss2: 1.447150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.683973 Loss1: 0.239192 Loss2: 1.444781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.654164 Loss1: 0.193926 Loss2: 1.460238 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.584467 Loss1: 0.140928 Loss2: 1.443539 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.540304 Loss1: 0.147418 Loss2: 1.392886 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.552940 Loss1: 0.159107 Loss2: 1.393833 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.523204 Loss1: 0.140092 Loss2: 1.383112
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.159612 Loss1: 1.201141 Loss2: 1.958471
+(DefaultActor pid=3764) >> Training accuracy: 0.974265
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.287422 Loss1: 0.796747 Loss2: 1.490675
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.022381 Loss1: 0.500232 Loss2: 1.522149
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.816027 Loss1: 0.343474 Loss2: 1.472553
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.708885 Loss1: 0.242379 Loss2: 1.466506
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.250908 Loss1: 1.304112 Loss2: 1.946796
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.598054 Loss1: 0.143261 Loss2: 1.454794
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.285220 Loss1: 0.753389 Loss2: 1.531832
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.632013 Loss1: 0.188360 Loss2: 1.443653
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.612827 Loss1: 0.166342 Loss2: 1.446485
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.137222 Loss1: 0.636334 Loss2: 1.500888
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.659720 Loss1: 0.206252 Loss2: 1.453467
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.964346 Loss1: 0.443924 Loss2: 1.520422
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.595262 Loss1: 0.152127 Loss2: 1.443135
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.710243 Loss1: 0.254501 Loss2: 1.455742
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.664842 Loss1: 0.202511 Loss2: 1.462332
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.667939 Loss1: 0.208141 Loss2: 1.459798
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.632431 Loss1: 0.175406 Loss2: 1.457024
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.612243 Loss1: 0.161323 Loss2: 1.450920
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.013908 Loss1: 1.119776 Loss2: 1.894132
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.579495 Loss1: 0.136044 Loss2: 1.443451
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.882189 Loss1: 0.434310 Loss2: 1.447879 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.691657 Loss1: 0.257031 Loss2: 1.434626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.668274 Loss1: 0.248998 Loss2: 1.419277
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.238256 Loss1: 1.316648 Loss2: 1.921609
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.344067 Loss1: 0.855190 Loss2: 1.488877
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.644719 Loss1: 0.220980 Loss2: 1.423739
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.057005 Loss1: 0.611701 Loss2: 1.445305
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.630568 Loss1: 0.204357 Loss2: 1.426211
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.902830 Loss1: 0.467527 Loss2: 1.435303
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.624096 Loss1: 0.205522 Loss2: 1.418574
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.799231 Loss1: 0.357469 Loss2: 1.441762
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.564438 Loss1: 0.154110 Loss2: 1.410328
+(DefaultActor pid=3765) >> Training accuracy: 0.954102
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.596163 Loss1: 0.182470 Loss2: 1.413693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.559099 Loss1: 0.156339 Loss2: 1.402760 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.536474 Loss1: 0.140485 Loss2: 1.395989
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.091382 Loss1: 1.288083 Loss2: 1.803299
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.321266 Loss1: 0.853578 Loss2: 1.467688
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.910798 Loss1: 0.530195 Loss2: 1.380604
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.791240 Loss1: 0.403167 Loss2: 1.388073
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.754162 Loss1: 0.370877 Loss2: 1.383285
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.212526 Loss1: 1.339638 Loss2: 1.872888
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.187544 Loss1: 0.763290 Loss2: 1.424254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.955159 Loss1: 0.518375 Loss2: 1.436784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.772547 Loss1: 0.374904 Loss2: 1.397643 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.539598 Loss1: 0.173611 Loss2: 1.365987
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.645266 Loss1: 0.243205 Loss2: 1.402061
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465941 Loss1: 0.111371 Loss2: 1.354569
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.581218 Loss1: 0.202280 Loss2: 1.378938
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.615329 Loss1: 0.240946 Loss2: 1.374383
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.571505 Loss1: 0.184526 Loss2: 1.386979
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.542721 Loss1: 0.167878 Loss2: 1.374842
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527725 Loss1: 0.153353 Loss2: 1.374372
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.060721 Loss1: 1.157405 Loss2: 1.903316
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.128994 Loss1: 0.707005 Loss2: 1.421988
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.938183 Loss1: 0.484935 Loss2: 1.453248
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765080 Loss1: 0.359410 Loss2: 1.405670
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.689686 Loss1: 0.272244 Loss2: 1.417443
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.622465 Loss1: 0.216158 Loss2: 1.406307 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544401 Loss1: 0.148819 Loss2: 1.395582 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.516336 Loss1: 0.127971 Loss2: 1.388365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489859 Loss1: 0.102188 Loss2: 1.387671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.473877 Loss1: 0.088401 Loss2: 1.385476 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.640801 Loss1: 0.247202 Loss2: 1.393599 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.515549 Loss1: 0.155551 Loss2: 1.359998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487364 Loss1: 0.117763 Loss2: 1.369601
+(DefaultActor pid=3764) >> Training accuracy: 0.956055
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.056462 Loss1: 1.031441 Loss2: 2.025021
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.250350 Loss1: 0.734493 Loss2: 1.515857
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.073499 Loss1: 0.503255 Loss2: 1.570244
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.918278 Loss1: 0.411565 Loss2: 1.506713
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.823234 Loss1: 0.299018 Loss2: 1.524216
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.966197 Loss1: 1.145642 Loss2: 1.820555
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.735902 Loss1: 0.235102 Loss2: 1.500800
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.202301 Loss1: 0.761942 Loss2: 1.440359
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.759782 Loss1: 0.255041 Loss2: 1.504740
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.833646 Loss1: 0.460084 Loss2: 1.373563
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.700506 Loss1: 0.201939 Loss2: 1.498567
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.667708 Loss1: 0.293298 Loss2: 1.374410
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.647969 Loss1: 0.160937 Loss2: 1.487032
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.628619 Loss1: 0.140299 Loss2: 1.488320
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.613912 Loss1: 0.247778 Loss2: 1.366134
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.631865 Loss1: 0.275393 Loss2: 1.356472
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.567740 Loss1: 0.196645 Loss2: 1.371095
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.536966 Loss1: 0.187092 Loss2: 1.349875
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.538553 Loss1: 0.187080 Loss2: 1.351473
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.152118 Loss1: 1.261596 Loss2: 1.890521
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464034 Loss1: 0.120509 Loss2: 1.343525
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.914971 Loss1: 0.451915 Loss2: 1.463056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.689668 Loss1: 0.265126 Loss2: 1.424542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562431 Loss1: 0.155393 Loss2: 1.407038
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.255846 Loss1: 1.286940 Loss2: 1.968905
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.377831 Loss1: 0.867180 Loss2: 1.510651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.133119 Loss1: 0.625032 Loss2: 1.508086 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.909775 Loss1: 0.430988 Loss2: 1.478786 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465901 Loss1: 0.084754 Loss2: 1.381147
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.806831 Loss1: 0.320097 Loss2: 1.486734
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.713725 Loss1: 0.238625 Loss2: 1.475100
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.647304 Loss1: 0.194435 Loss2: 1.452869
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.591003 Loss1: 0.142577 Loss2: 1.448426
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.559944 Loss1: 0.119194 Loss2: 1.440750
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.049173 Loss1: 1.209427 Loss2: 1.839746
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.540523 Loss1: 0.097209 Loss2: 1.443314
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.922509 Loss1: 0.493286 Loss2: 1.429224 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.676934 Loss1: 0.268584 Loss2: 1.408350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.575347 Loss1: 0.181967 Loss2: 1.393380
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.065088 Loss1: 1.134579 Loss2: 1.930508
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.555751 Loss1: 0.161506 Loss2: 1.394245
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.192401 Loss1: 0.695504 Loss2: 1.496898
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.530507 Loss1: 0.151355 Loss2: 1.379152
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.907716 Loss1: 0.391149 Loss2: 1.516567
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.540996 Loss1: 0.154961 Loss2: 1.386034
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.837609 Loss1: 0.362187 Loss2: 1.475422
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.535874 Loss1: 0.145105 Loss2: 1.390769
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.745256 Loss1: 0.256141 Loss2: 1.489115
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.673337 Loss1: 0.211638 Loss2: 1.461699
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.633713 Loss1: 0.166101 Loss2: 1.467612
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.618462 Loss1: 0.161504 Loss2: 1.456958
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.600089 Loss1: 0.139338 Loss2: 1.460751
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.084884 Loss1: 1.191712 Loss2: 1.893172
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.191100 Loss1: 0.742092 Loss2: 1.449007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.706613 Loss1: 0.295437 Loss2: 1.411176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569957 Loss1: 0.179011 Loss2: 1.390946 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.553201 Loss1: 0.159655 Loss2: 1.393547 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.552562 Loss1: 0.160921 Loss2: 1.391641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.503733 Loss1: 0.114457 Loss2: 1.389276 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477006 Loss1: 0.104253 Loss2: 1.372754 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.683591 Loss1: 0.227255 Loss2: 1.456336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.635222 Loss1: 0.181617 Loss2: 1.453604 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.556483 Loss1: 0.113618 Loss2: 1.442865
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.182077 Loss1: 1.279051 Loss2: 1.903026
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.099964 Loss1: 0.678797 Loss2: 1.421167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.736782 Loss1: 0.335668 Loss2: 1.401114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.603117 Loss1: 0.205736 Loss2: 1.397381 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547276 Loss1: 0.157524 Loss2: 1.389753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479448 Loss1: 0.104363 Loss2: 1.375085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.471133 Loss1: 0.099743 Loss2: 1.371390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475405 Loss1: 0.108600 Loss2: 1.366804 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.661890 Loss1: 0.222196 Loss2: 1.439694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.648613 Loss1: 0.211174 Loss2: 1.437440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.607755 Loss1: 0.166967 Loss2: 1.440789
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.125778 Loss1: 1.220042 Loss2: 1.905737
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.580002 Loss1: 0.151757 Loss2: 1.428245
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.225674 Loss1: 0.755483 Loss2: 1.470191
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.983985 Loss1: 0.512980 Loss2: 1.471005
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.802889 Loss1: 0.355518 Loss2: 1.447371
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.670902 Loss1: 0.239109 Loss2: 1.431793
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.644304 Loss1: 0.223543 Loss2: 1.420761
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.186478 Loss1: 1.292609 Loss2: 1.893869
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.588893 Loss1: 0.162161 Loss2: 1.426732
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.125847 Loss1: 0.703239 Loss2: 1.422608
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.617957 Loss1: 0.195964 Loss2: 1.421993
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.829906 Loss1: 0.406133 Loss2: 1.423773
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.602006 Loss1: 0.172035 Loss2: 1.429971
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.756657 Loss1: 0.358672 Loss2: 1.397985
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.591810 Loss1: 0.163067 Loss2: 1.428743
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.554558 Loss1: 0.176385 Loss2: 1.378173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.516128 Loss1: 0.148916 Loss2: 1.367213 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.512646 Loss1: 0.144550 Loss2: 1.368096
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.250215 Loss1: 1.263786 Loss2: 1.986429
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.304400 Loss1: 0.933793 Loss2: 1.370607
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.515126 Loss1: 0.145798 Loss2: 1.369327
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.005670 Loss1: 0.539492 Loss2: 1.466178
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.760903 Loss1: 0.380695 Loss2: 1.380207
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631228 Loss1: 0.256559 Loss2: 1.374669
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.645584 Loss1: 0.266308 Loss2: 1.379276
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.619928 Loss1: 0.244511 Loss2: 1.375417
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.538269 Loss1: 0.170454 Loss2: 1.367816
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.038948 Loss1: 1.124839 Loss2: 1.914109
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.150821 Loss1: 0.719815 Loss2: 1.431006 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.960938
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.763082 Loss1: 0.336798 Loss2: 1.426283 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.611345 Loss1: 0.204223 Loss2: 1.407122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.587115 Loss1: 0.179677 Loss2: 1.407438
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.113761 Loss1: 1.233878 Loss2: 1.879883
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.537497 Loss1: 0.139081 Loss2: 1.398415
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.184086 Loss1: 0.760315 Loss2: 1.423771
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501375 Loss1: 0.111678 Loss2: 1.389696
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.896742 Loss1: 0.448499 Loss2: 1.448243
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478541 Loss1: 0.087144 Loss2: 1.391397
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.822721 Loss1: 0.427135 Loss2: 1.395586
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.722600 Loss1: 0.312578 Loss2: 1.410022
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.609779 Loss1: 0.217775 Loss2: 1.392004
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547733 Loss1: 0.155473 Loss2: 1.392260
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.531746 Loss1: 0.160136 Loss2: 1.371610
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.558230 Loss1: 0.180050 Loss2: 1.378180
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.274241 Loss1: 1.275922 Loss2: 1.998319
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508685 Loss1: 0.129692 Loss2: 1.378993
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.345419 Loss1: 0.818508 Loss2: 1.526910
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.000828 Loss1: 0.481712 Loss2: 1.519115
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.791114 Loss1: 0.311505 Loss2: 1.479609
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.757516 Loss1: 0.268930 Loss2: 1.488586
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.663733 Loss1: 0.190111 Loss2: 1.473622
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.121390 Loss1: 1.248340 Loss2: 1.873051
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.649090 Loss1: 0.186170 Loss2: 1.462919
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.095160 Loss1: 0.729076 Loss2: 1.366084
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.615055 Loss1: 0.153581 Loss2: 1.461474
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.932289 Loss1: 0.527779 Loss2: 1.404509
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.592013 Loss1: 0.138891 Loss2: 1.453122
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.623038 Loss1: 0.171641 Loss2: 1.451397 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.646345 Loss1: 0.296700 Loss2: 1.349645 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.579079 Loss1: 0.224222 Loss2: 1.354857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.244717 Loss1: 1.391597 Loss2: 1.853120 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978795
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.870000 Loss1: 0.501202 Loss2: 1.368798 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.626799 Loss1: 0.279706 Loss2: 1.347093 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.057703 Loss1: 1.157995 Loss2: 1.899707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.215858 Loss1: 0.767292 Loss2: 1.448566 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.021451 Loss1: 0.535519 Loss2: 1.485933 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.758463 Loss1: 0.326152 Loss2: 1.432311 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973214
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.626236 Loss1: 0.207315 Loss2: 1.418921 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.660071 Loss1: 0.234412 Loss2: 1.425659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.628821 Loss1: 0.212237 Loss2: 1.416584
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.236516 Loss1: 1.306146 Loss2: 1.930370
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.583311 Loss1: 0.167221 Loss2: 1.416091
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.299724 Loss1: 0.812967 Loss2: 1.486757
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.959767 Loss1: 0.500193 Loss2: 1.459574
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.709514 Loss1: 0.295517 Loss2: 1.413997
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.635777 Loss1: 0.217985 Loss2: 1.417792
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.637623 Loss1: 0.232769 Loss2: 1.404854
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.969434 Loss1: 1.069323 Loss2: 1.900110
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.627085 Loss1: 0.214860 Loss2: 1.412225
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.067120 Loss1: 0.598013 Loss2: 1.469108
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.612185 Loss1: 0.199926 Loss2: 1.412260
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.815230 Loss1: 0.356966 Loss2: 1.458264
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.590302 Loss1: 0.182667 Loss2: 1.407635
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.550428 Loss1: 0.142992 Loss2: 1.407436
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.633517 Loss1: 0.202854 Loss2: 1.430663
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.616277 Loss1: 0.191107 Loss2: 1.425169
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.621534 Loss1: 0.202537 Loss2: 1.418997
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.614625 Loss1: 0.181964 Loss2: 1.432661
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.546310 Loss1: 0.130346 Loss2: 1.415964
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.316585 Loss1: 1.369377 Loss2: 1.947207
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.582606 Loss1: 0.163150 Loss2: 1.419457
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.554564 Loss1: 0.133683 Loss2: 1.420882 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699919 Loss1: 0.299536 Loss2: 1.400383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.577913 Loss1: 0.181011 Loss2: 1.396902 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.148694 Loss1: 1.238793 Loss2: 1.909900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.185614 Loss1: 0.744118 Loss2: 1.441497 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.550846 Loss1: 0.166735 Loss2: 1.384111 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977679
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.709752 Loss1: 0.279903 Loss2: 1.429849 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.687481 Loss1: 0.255125 Loss2: 1.432357 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.617254 Loss1: 0.193159 Loss2: 1.424096
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.052874 Loss1: 1.266479 Loss2: 1.786395
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.574876 Loss1: 0.162813 Loss2: 1.412062
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.232565 Loss1: 0.860821 Loss2: 1.371744
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.551154 Loss1: 0.141726 Loss2: 1.409428
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.902973 Loss1: 0.523327 Loss2: 1.379646
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.711203 Loss1: 0.371825 Loss2: 1.339378
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.617896 Loss1: 0.276541 Loss2: 1.341355
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.569558 Loss1: 0.242807 Loss2: 1.326751
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.512467 Loss1: 0.189250 Loss2: 1.323217
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439244 Loss1: 0.132969 Loss2: 1.306275
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.089782 Loss1: 1.171591 Loss2: 1.918191
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429171 Loss1: 0.125555 Loss2: 1.303616
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.109080 Loss1: 0.698899 Loss2: 1.410181
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433284 Loss1: 0.137298 Loss2: 1.295985
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.838570 Loss1: 0.434612 Loss2: 1.403958
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.636003 Loss1: 0.272980 Loss2: 1.363023
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.576431 Loss1: 0.222939 Loss2: 1.353492
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547120 Loss1: 0.202476 Loss2: 1.344645
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534128 Loss1: 0.182729 Loss2: 1.351399
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.124995 Loss1: 1.112417 Loss2: 2.012578
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.503973 Loss1: 0.157598 Loss2: 1.346375
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.259517 Loss1: 0.755643 Loss2: 1.503874
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.571482 Loss1: 0.218284 Loss2: 1.353198
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.027958 Loss1: 0.466553 Loss2: 1.561405
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.534577 Loss1: 0.192555 Loss2: 1.342022
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.754843 Loss1: 0.265331 Loss2: 1.489512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.630953 Loss1: 0.156705 Loss2: 1.474248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.605483 Loss1: 0.137353 Loss2: 1.468131
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.122766 Loss1: 1.249094 Loss2: 1.873672
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.229514 Loss1: 0.805125 Loss2: 1.424389 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.580447 Loss1: 0.120737 Loss2: 1.459710
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.961113 Loss1: 0.494147 Loss2: 1.466967
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.804053 Loss1: 0.405876 Loss2: 1.398177
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.637746 Loss1: 0.237866 Loss2: 1.399881
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.605279 Loss1: 0.213059 Loss2: 1.392220
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.597838 Loss1: 0.201890 Loss2: 1.395948
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.165029 Loss1: 1.278359 Loss2: 1.886670
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.587342 Loss1: 0.192359 Loss2: 1.394983
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.270836 Loss1: 0.838215 Loss2: 1.432621
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.572056 Loss1: 0.179786 Loss2: 1.392270
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.996376 Loss1: 0.542502 Loss2: 1.453873
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.517311 Loss1: 0.129952 Loss2: 1.387360
+(DefaultActor pid=3765) >> Training accuracy: 0.954167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.676693 Loss1: 0.268376 Loss2: 1.408317 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 14:26:26,108 | server.py:236 | fit_round 79 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.586303 Loss1: 0.196069 Loss2: 1.390233 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.615694 Loss1: 0.215711 Loss2: 1.399983
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.174655 Loss1: 1.235045 Loss2: 1.939610
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.266707 Loss1: 0.799855 Loss2: 1.466851 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.546300 Loss1: 0.151497 Loss2: 1.394802
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.933943 Loss1: 0.448910 Loss2: 1.485033
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.788006 Loss1: 0.355314 Loss2: 1.432692
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.706547 Loss1: 0.251189 Loss2: 1.455358
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.641682 Loss1: 0.209793 Loss2: 1.431889
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.559543 Loss1: 0.141862 Loss2: 1.417681
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.200534 Loss1: 1.227093 Loss2: 1.973441
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.540367 Loss1: 0.129273 Loss2: 1.411094
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.547448 Loss1: 0.137462 Loss2: 1.409986 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.523712 Loss1: 0.117801 Loss2: 1.405911 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.629344 Loss1: 0.205273 Loss2: 1.424071 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548520 Loss1: 0.143096 Loss2: 1.405424 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.144114 Loss1: 1.253209 Loss2: 1.890905 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967548
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.987814 Loss1: 0.538088 Loss2: 1.449726 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.754692 Loss1: 0.323155 Loss2: 1.431537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.204891 Loss1: 1.226347 Loss2: 1.978544
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.675771 Loss1: 0.247559 Loss2: 1.428212
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.229129 Loss1: 0.838179 Loss2: 1.390950
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.647413 Loss1: 0.220243 Loss2: 1.427171
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.632765 Loss1: 0.210194 Loss2: 1.422572 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.645425 Loss1: 0.222619 Loss2: 1.422806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.602573 Loss1: 0.182125 Loss2: 1.420448 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496551 Loss1: 0.140166 Loss2: 1.356385 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434537 Loss1: 0.089528 Loss2: 1.345009 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979567
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.015150 Loss1: 1.165348 Loss2: 1.849803
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.142365 Loss1: 0.739400 Loss2: 1.402965
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.930343 Loss1: 0.499694 Loss2: 1.430649
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.718715 Loss1: 0.344843 Loss2: 1.373873
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.218224 Loss1: 1.369093 Loss2: 1.849131
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.310344 Loss1: 0.869116 Loss2: 1.441229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.980026 Loss1: 0.582994 Loss2: 1.397032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.764230 Loss1: 0.372016 Loss2: 1.392214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.616236 Loss1: 0.244904 Loss2: 1.371333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.575303 Loss1: 0.209635 Loss2: 1.365669 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.558729 Loss1: 0.198705 Loss2: 1.360023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476975 Loss1: 0.131507 Loss2: 1.345467 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 14:26:26,108][flwr][DEBUG] - fit_round 79 received 50 results and 0 failures
+INFO flwr 2023-10-10 14:27:07,462 | server.py:125 | fit progress: (79, 2.2412412355121334, {'accuracy': 0.546}, 182135.241001466)
+>> Test accuracy: 0.546000
+[2023-10-10 14:27:07,462][flwr][INFO] - fit progress: (79, 2.2412412355121334, {'accuracy': 0.546}, 182135.241001466)
+DEBUG flwr 2023-10-10 14:27:07,463 | server.py:173 | evaluate_round 79: strategy sampled 50 clients (out of 50)
+[2023-10-10 14:27:07,463][flwr][DEBUG] - evaluate_round 79: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 14:36:11,090 | server.py:187 | evaluate_round 79 received 50 results and 0 failures
+[2023-10-10 14:36:11,090][flwr][DEBUG] - evaluate_round 79 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 14:36:11,090 | server.py:222 | fit_round 80: strategy sampled 50 clients (out of 50)
+[2023-10-10 14:36:11,090][flwr][DEBUG] - fit_round 80: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.946489 Loss1: 1.061548 Loss2: 1.884941 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.830811 Loss1: 0.406087 Loss2: 1.424724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.662114 Loss1: 0.262915 Loss2: 1.399199
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.110371 Loss1: 1.216273 Loss2: 1.894098
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.240248 Loss1: 0.802024 Loss2: 1.438224 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.922387 Loss1: 0.470606 Loss2: 1.451780 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.723638 Loss1: 0.309327 Loss2: 1.414311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.711100 Loss1: 0.299455 Loss2: 1.411645 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.601666 Loss1: 0.194520 Loss2: 1.407146 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.487629 Loss1: 0.109113 Loss2: 1.378516
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.595592 Loss1: 0.190968 Loss2: 1.404624
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.567561 Loss1: 0.171435 Loss2: 1.396126
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.536964 Loss1: 0.137178 Loss2: 1.399786
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.503335 Loss1: 0.121383 Loss2: 1.381952
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.976228 Loss1: 1.079392 Loss2: 1.896837
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.240009 Loss1: 0.787714 Loss2: 1.452295
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.881017 Loss1: 0.448301 Loss2: 1.432716
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.732805 Loss1: 0.315198 Loss2: 1.417607
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.369958 Loss1: 1.441909 Loss2: 1.928049
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.264858 Loss1: 0.812288 Loss2: 1.452570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.927168 Loss1: 0.488640 Loss2: 1.438528 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.808923 Loss1: 0.389291 Loss2: 1.419632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.688968 Loss1: 0.276653 Loss2: 1.412315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.648933 Loss1: 0.251985 Loss2: 1.396948 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.501280 Loss1: 0.111244 Loss2: 1.390036
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.661899 Loss1: 0.249075 Loss2: 1.412823
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.601153 Loss1: 0.202484 Loss2: 1.398669
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.577121 Loss1: 0.186962 Loss2: 1.390159
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.516623 Loss1: 0.130095 Loss2: 1.386529
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.182581 Loss1: 1.277345 Loss2: 1.905237
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.276930 Loss1: 0.807495 Loss2: 1.469435
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.922908 Loss1: 0.448031 Loss2: 1.474878
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.780332 Loss1: 0.358613 Loss2: 1.421719
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.861485 Loss1: 1.132101 Loss2: 1.729384
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.730023 Loss1: 0.297760 Loss2: 1.432262
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.002986 Loss1: 0.690995 Loss2: 1.311991
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.649172 Loss1: 0.220209 Loss2: 1.428963
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.787585 Loss1: 0.443537 Loss2: 1.344048
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.637826 Loss1: 0.217877 Loss2: 1.419949
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.660705 Loss1: 0.367948 Loss2: 1.292757
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.623805 Loss1: 0.208140 Loss2: 1.415665
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574015 Loss1: 0.269763 Loss2: 1.304253
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.574891 Loss1: 0.157494 Loss2: 1.417397
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582049 Loss1: 0.279390 Loss2: 1.302659
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.524755 Loss1: 0.119426 Loss2: 1.405329
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509660 Loss1: 0.209429 Loss2: 1.300231
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515940 Loss1: 0.230982 Loss2: 1.284959
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.515302 Loss1: 0.225650 Loss2: 1.289652
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438945 Loss1: 0.151247 Loss2: 1.287698
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.954955 Loss1: 1.078758 Loss2: 1.876197
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.048482 Loss1: 0.603412 Loss2: 1.445070
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.793353 Loss1: 0.351724 Loss2: 1.441630
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.645541 Loss1: 0.243144 Loss2: 1.402397
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.993745 Loss1: 1.099459 Loss2: 1.894286
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.634742 Loss1: 0.220820 Loss2: 1.413923
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.166508 Loss1: 0.722897 Loss2: 1.443612
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.995513 Loss1: 0.548180 Loss2: 1.447333
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.615443 Loss1: 0.218389 Loss2: 1.397053
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.786459 Loss1: 0.364172 Loss2: 1.422287
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.631389 Loss1: 0.207785 Loss2: 1.423605
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.700224 Loss1: 0.295347 Loss2: 1.404877
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.587829 Loss1: 0.187636 Loss2: 1.400194
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.660184 Loss1: 0.256802 Loss2: 1.403382
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.566479 Loss1: 0.156660 Loss2: 1.409819
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.600102 Loss1: 0.201113 Loss2: 1.398990
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.536846 Loss1: 0.139646 Loss2: 1.397200
+(DefaultActor pid=3765) >> Training accuracy: 0.974609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.504326 Loss1: 0.128625 Loss2: 1.375701 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.092842 Loss1: 1.199028 Loss2: 1.893814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.017109 Loss1: 0.524996 Loss2: 1.492113 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.820538 Loss1: 0.396705 Loss2: 1.423833
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.007134 Loss1: 1.146541 Loss2: 1.860592
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.146287 Loss1: 0.730421 Loss2: 1.415866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.862506 Loss1: 0.461205 Loss2: 1.401301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.721166 Loss1: 0.342508 Loss2: 1.378658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.582034 Loss1: 0.208255 Loss2: 1.373779 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523155 Loss1: 0.170680 Loss2: 1.352475 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.530769 Loss1: 0.125569 Loss2: 1.405199
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.511024 Loss1: 0.163674 Loss2: 1.347350
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486356 Loss1: 0.140404 Loss2: 1.345952
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444426 Loss1: 0.102779 Loss2: 1.341646
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462078 Loss1: 0.121110 Loss2: 1.340968
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.068509 Loss1: 1.185438 Loss2: 1.883071
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.208564 Loss1: 0.716943 Loss2: 1.491621
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.890545 Loss1: 0.430108 Loss2: 1.460437
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.769302 Loss1: 0.328817 Loss2: 1.440486
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.939014 Loss1: 1.077880 Loss2: 1.861134
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.054806 Loss1: 0.672311 Loss2: 1.382494
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.643867 Loss1: 0.205273 Loss2: 1.438594
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.794703 Loss1: 0.371261 Loss2: 1.423441
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.625029 Loss1: 0.204848 Loss2: 1.420181
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.667685 Loss1: 0.299907 Loss2: 1.367778
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.605588 Loss1: 0.187858 Loss2: 1.417731
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.742029 Loss1: 0.352472 Loss2: 1.389556
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.570189 Loss1: 0.155953 Loss2: 1.414236
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.571380 Loss1: 0.150212 Loss2: 1.421169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.553994 Loss1: 0.139297 Loss2: 1.414696 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479762 Loss1: 0.125265 Loss2: 1.354498 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.096770 Loss1: 1.245625 Loss2: 1.851145 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.895948 Loss1: 0.477829 Loss2: 1.418119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.747894 Loss1: 0.351049 Loss2: 1.396844
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.064037 Loss1: 1.239751 Loss2: 1.824287
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.191513 Loss1: 0.780623 Loss2: 1.410890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.813225 Loss1: 0.431120 Loss2: 1.382105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.725125 Loss1: 0.356986 Loss2: 1.368139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.604788 Loss1: 0.245068 Loss2: 1.359719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.597019 Loss1: 0.249015 Loss2: 1.348004 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.488336 Loss1: 0.115996 Loss2: 1.372341
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.585068 Loss1: 0.212098 Loss2: 1.372970
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518196 Loss1: 0.175374 Loss2: 1.342822
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.557842 Loss1: 0.215041 Loss2: 1.342801
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.524107 Loss1: 0.176803 Loss2: 1.347304
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.385943 Loss1: 1.318093 Loss2: 2.067850
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.226496 Loss1: 0.769948 Loss2: 1.456548
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.114042 Loss1: 0.586197 Loss2: 1.527845
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.870473 Loss1: 0.398406 Loss2: 1.472067
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.715767 Loss1: 0.265002 Loss2: 1.450765
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.661953 Loss1: 0.220045 Loss2: 1.441907 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.860848 Loss1: 0.420376 Loss2: 1.440472 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.724954 Loss1: 0.316613 Loss2: 1.408341 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.558261 Loss1: 0.160771 Loss2: 1.397489 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.517890 Loss1: 0.133200 Loss2: 1.384689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.511526 Loss1: 0.130863 Loss2: 1.380662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.497065 Loss1: 0.117057 Loss2: 1.380008 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988051
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.711714 Loss1: 0.342192 Loss2: 1.369522 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.586862 Loss1: 0.244179 Loss2: 1.342684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.545370 Loss1: 0.196289 Loss2: 1.349082
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.997524 Loss1: 1.214940 Loss2: 1.782584
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.139448 Loss1: 0.751511 Loss2: 1.387937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.862785 Loss1: 0.483766 Loss2: 1.379019 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962891
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448182 Loss1: 0.125902 Loss2: 1.322279
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.712038 Loss1: 0.343188 Loss2: 1.368850
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.656125 Loss1: 0.285999 Loss2: 1.370126
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.569831 Loss1: 0.215912 Loss2: 1.353918
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.517520 Loss1: 0.169443 Loss2: 1.348078
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540397 Loss1: 0.191212 Loss2: 1.349185
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.005763 Loss1: 1.152095 Loss2: 1.853668
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.092158 Loss1: 0.688965 Loss2: 1.403193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966797
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478767 Loss1: 0.131490 Loss2: 1.347278
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.843912 Loss1: 0.440759 Loss2: 1.403154
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.747379 Loss1: 0.365762 Loss2: 1.381617
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.659485 Loss1: 0.269606 Loss2: 1.389879
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.580947 Loss1: 0.207126 Loss2: 1.373822
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514121 Loss1: 0.143694 Loss2: 1.370427
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.063920 Loss1: 1.238899 Loss2: 1.825021
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497074 Loss1: 0.137784 Loss2: 1.359290
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.494359 Loss1: 0.137075 Loss2: 1.357284 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.504789 Loss1: 0.139861 Loss2: 1.364927 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598563 Loss1: 0.232462 Loss2: 1.366101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.574471 Loss1: 0.220266 Loss2: 1.354205 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.506548 Loss1: 0.158505 Loss2: 1.348043
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.960743 Loss1: 1.115938 Loss2: 1.844805
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.963640 Loss1: 0.603137 Loss2: 1.360502 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461129 Loss1: 0.125039 Loss2: 1.336090
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.764366 Loss1: 0.376687 Loss2: 1.387679
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.633392 Loss1: 0.289259 Loss2: 1.344132
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.557094 Loss1: 0.223692 Loss2: 1.333402
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.540183 Loss1: 0.203031 Loss2: 1.337152
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.526960 Loss1: 0.195066 Loss2: 1.331894
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.256164 Loss1: 1.311930 Loss2: 1.944234
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.507533 Loss1: 0.174294 Loss2: 1.333239
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.495478 Loss1: 0.161916 Loss2: 1.333562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448460 Loss1: 0.117581 Loss2: 1.330879 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.758829 Loss1: 0.318290 Loss2: 1.440539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.590897 Loss1: 0.176395 Loss2: 1.414502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.050917 Loss1: 1.167480 Loss2: 1.883437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.124010 Loss1: 0.696781 Loss2: 1.427229 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978795
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.753347 Loss1: 0.344986 Loss2: 1.408360 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.615145 Loss1: 0.216290 Loss2: 1.398854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.536772 Loss1: 0.147399 Loss2: 1.389373
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.115753 Loss1: 1.226139 Loss2: 1.889615
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.527146 Loss1: 0.150045 Loss2: 1.377101
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.180202 Loss1: 0.748566 Loss2: 1.431636
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.508130 Loss1: 0.126414 Loss2: 1.381716
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.946839 Loss1: 0.466548 Loss2: 1.480292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.506663 Loss1: 0.125357 Loss2: 1.381306
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.795724 Loss1: 0.377578 Loss2: 1.418147
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.743139 Loss1: 0.297945 Loss2: 1.445194
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.697560 Loss1: 0.274731 Loss2: 1.422830
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.617291 Loss1: 0.196939 Loss2: 1.420351
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.573714 Loss1: 0.173783 Loss2: 1.399931
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.200451 Loss1: 1.304189 Loss2: 1.896262
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.561985 Loss1: 0.154274 Loss2: 1.407711
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.558735 Loss1: 0.158121 Loss2: 1.400614 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.952083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.695645 Loss1: 0.362003 Loss2: 1.333642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484346 Loss1: 0.158897 Loss2: 1.325449 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450376 Loss1: 0.128053 Loss2: 1.322323 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402387 Loss1: 0.089021 Loss2: 1.313365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392205 Loss1: 0.084034 Loss2: 1.308171 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986779
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.729155 Loss1: 0.339495 Loss2: 1.389661 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549459 Loss1: 0.161441 Loss2: 1.388018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.059168 Loss1: 1.181184 Loss2: 1.877984
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.555195 Loss1: 0.184647 Loss2: 1.370548
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.169730 Loss1: 0.752081 Loss2: 1.417650
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.497402 Loss1: 0.123963 Loss2: 1.373439
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.844692 Loss1: 0.427208 Loss2: 1.417484
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461418 Loss1: 0.108518 Loss2: 1.352900
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623076 Loss1: 0.245254 Loss2: 1.377822
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448034 Loss1: 0.091229 Loss2: 1.356805
+(DefaultActor pid=3764) >> Training accuracy: 0.978516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497138 Loss1: 0.138204 Loss2: 1.358933 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471993 Loss1: 0.114370 Loss2: 1.357622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470871 Loss1: 0.124120 Loss2: 1.346751
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.952740 Loss1: 1.134018 Loss2: 1.818721
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.532437 Loss1: 0.184880 Loss2: 1.347557
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.988764 Loss1: 0.653567 Loss2: 1.335197
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.745374 Loss1: 0.390182 Loss2: 1.355193
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.630983 Loss1: 0.313454 Loss2: 1.317529
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568655 Loss1: 0.240147 Loss2: 1.328508
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.533113 Loss1: 0.214973 Loss2: 1.318140
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.986646 Loss1: 1.146790 Loss2: 1.839856
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480370 Loss1: 0.173128 Loss2: 1.307242
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.269348 Loss1: 0.807599 Loss2: 1.461750
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441618 Loss1: 0.129322 Loss2: 1.312296
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.831042 Loss1: 0.425029 Loss2: 1.406013
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429445 Loss1: 0.129872 Loss2: 1.299573
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.669114 Loss1: 0.279673 Loss2: 1.389441
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398763 Loss1: 0.100537 Loss2: 1.298226
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.571351 Loss1: 0.187600 Loss2: 1.383752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.501671 Loss1: 0.122354 Loss2: 1.379317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.015164 Loss1: 1.172298 Loss2: 1.842866
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.517716 Loss1: 0.147265 Loss2: 1.370451
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.126518 Loss1: 0.698255 Loss2: 1.428263
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.518067 Loss1: 0.144476 Loss2: 1.373591
+(DefaultActor pid=3765) >> Training accuracy: 0.949219
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.734115 Loss1: 0.327686 Loss2: 1.406428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.596830 Loss1: 0.205407 Loss2: 1.391424 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.036635 Loss1: 1.215246 Loss2: 1.821389
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.554162 Loss1: 0.166755 Loss2: 1.387407
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.180548 Loss1: 0.765037 Loss2: 1.415511
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548900 Loss1: 0.163971 Loss2: 1.384929
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.977781 Loss1: 0.573955 Loss2: 1.403826
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519356 Loss1: 0.140503 Loss2: 1.378853
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.502421 Loss1: 0.126939 Loss2: 1.375482 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525635 Loss1: 0.175206 Loss2: 1.350429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480279 Loss1: 0.141412 Loss2: 1.338867 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474033 Loss1: 0.146675 Loss2: 1.327358
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.185446 Loss1: 1.295000 Loss2: 1.890446
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.279179 Loss1: 0.832085 Loss2: 1.447094 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.800558 Loss1: 0.367576 Loss2: 1.432982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.613312 Loss1: 0.195068 Loss2: 1.418244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.077932 Loss1: 1.246594 Loss2: 1.831338
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.638807 Loss1: 0.222010 Loss2: 1.416798
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.179250 Loss1: 0.773693 Loss2: 1.405557
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.559424 Loss1: 0.142416 Loss2: 1.417008
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.841339 Loss1: 0.423406 Loss2: 1.417933
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.537367 Loss1: 0.135708 Loss2: 1.401659
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.755967 Loss1: 0.362875 Loss2: 1.393092
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.590511 Loss1: 0.185708 Loss2: 1.404803
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.554523 Loss1: 0.175308 Loss2: 1.379216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.535043 Loss1: 0.159929 Loss2: 1.375114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477080 Loss1: 0.120777 Loss2: 1.356302
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.203472 Loss1: 1.272917 Loss2: 1.930555
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.473585 Loss1: 0.113342 Loss2: 1.360243
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.200871 Loss1: 0.800034 Loss2: 1.400836
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.936938 Loss1: 0.490463 Loss2: 1.446476
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.805126 Loss1: 0.402268 Loss2: 1.402858
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.712315 Loss1: 0.295186 Loss2: 1.417129
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.554010 Loss1: 0.172154 Loss2: 1.381856
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.542989 Loss1: 0.164635 Loss2: 1.378354
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.514954 Loss1: 0.139077 Loss2: 1.375876
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490433 Loss1: 0.110691 Loss2: 1.379742 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.471554 Loss1: 0.110327 Loss2: 1.361227 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.775949 Loss1: 0.312351 Loss2: 1.463598 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.676929 Loss1: 0.233646 Loss2: 1.443284 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.625608 Loss1: 0.179941 Loss2: 1.445667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.591796 Loss1: 0.161283 Loss2: 1.430513 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.562081 Loss1: 0.128421 Loss2: 1.433660 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.751958 Loss1: 0.265346 Loss2: 1.486613 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.656703 Loss1: 0.171681 Loss2: 1.485022 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.191653 Loss1: 1.240654 Loss2: 1.950998
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.623168 Loss1: 0.144104 Loss2: 1.479064
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.261160 Loss1: 0.821202 Loss2: 1.439958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.598284 Loss1: 0.131340 Loss2: 1.466945
+(DefaultActor pid=3764) >> Training accuracy: 0.964286
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.732980 Loss1: 0.321443 Loss2: 1.411537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.648923 Loss1: 0.244737 Loss2: 1.404186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.050275 Loss1: 1.193413 Loss2: 1.856862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.161441 Loss1: 0.737423 Loss2: 1.424018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.890457 Loss1: 0.465315 Loss2: 1.425141 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981027
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.655773 Loss1: 0.277991 Loss2: 1.377782 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.591611 Loss1: 0.214989 Loss2: 1.376622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.533357 Loss1: 0.157850 Loss2: 1.375506
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.000226 Loss1: 1.128069 Loss2: 1.872156
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.069885 Loss1: 0.683464 Loss2: 1.386421 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457386 Loss1: 0.097956 Loss2: 1.359430
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.889355 Loss1: 0.469237 Loss2: 1.420118
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.721262 Loss1: 0.360177 Loss2: 1.361085
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.584041 Loss1: 0.218627 Loss2: 1.365414
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498207 Loss1: 0.149847 Loss2: 1.348361
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478193 Loss1: 0.132808 Loss2: 1.345385
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.008613 Loss1: 1.206205 Loss2: 1.802407
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439157 Loss1: 0.094197 Loss2: 1.344960
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.217451 Loss1: 0.835553 Loss2: 1.381898
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455697 Loss1: 0.122959 Loss2: 1.332738
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.855555 Loss1: 0.459110 Loss2: 1.396445
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.480948 Loss1: 0.145241 Loss2: 1.335707
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.620860 Loss1: 0.272254 Loss2: 1.348605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.566739 Loss1: 0.233038 Loss2: 1.333701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.492936 Loss1: 0.163030 Loss2: 1.329906
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.156980 Loss1: 1.234901 Loss2: 1.922079
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.205398 Loss1: 0.762989 Loss2: 1.442409 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440064 Loss1: 0.116481 Loss2: 1.323583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.924601 Loss1: 0.488839 Loss2: 1.435763
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.699666 Loss1: 0.303014 Loss2: 1.396652
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593228 Loss1: 0.194491 Loss2: 1.398738
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.534265 Loss1: 0.153324 Loss2: 1.380941
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.567958 Loss1: 0.181916 Loss2: 1.386042
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.066771 Loss1: 1.203830 Loss2: 1.862941
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.575755 Loss1: 0.194297 Loss2: 1.381458
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.178198 Loss1: 0.767001 Loss2: 1.411197
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.473454 Loss1: 0.096951 Loss2: 1.376503
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.897006 Loss1: 0.468568 Loss2: 1.428438
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474011 Loss1: 0.098660 Loss2: 1.375351
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.656380 Loss1: 0.265999 Loss2: 1.390381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.569703 Loss1: 0.185044 Loss2: 1.384659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.587236 Loss1: 0.208047 Loss2: 1.379188
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.100123 Loss1: 1.222510 Loss2: 1.877613
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.169639 Loss1: 0.728605 Loss2: 1.441034 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.019110 Loss1: 0.569415 Loss2: 1.449695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.687620 Loss1: 0.267496 Loss2: 1.420124 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.697116 Loss1: 0.268061 Loss2: 1.429055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.620240 Loss1: 0.213854 Loss2: 1.406386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.587412 Loss1: 0.183814 Loss2: 1.403598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.558763 Loss1: 0.154689 Loss2: 1.404074 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973633
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.625980 Loss1: 0.251034 Loss2: 1.374946 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.547233 Loss1: 0.181677 Loss2: 1.365557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477939 Loss1: 0.121593 Loss2: 1.356346
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.034280 Loss1: 1.111958 Loss2: 1.922323
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.116494 Loss1: 0.667871 Loss2: 1.448623 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465064 Loss1: 0.112218 Loss2: 1.352846
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.866509 Loss1: 0.395961 Loss2: 1.470548
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.731883 Loss1: 0.313938 Loss2: 1.417945
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.636021 Loss1: 0.215018 Loss2: 1.421003
+DEBUG flwr 2023-10-10 15:04:49,444 | server.py:236 | fit_round 80 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.619154 Loss1: 0.200773 Loss2: 1.418381
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.614608 Loss1: 0.203763 Loss2: 1.410845
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.077236 Loss1: 1.259783 Loss2: 1.817452
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.596824 Loss1: 0.191449 Loss2: 1.405375
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.186194 Loss1: 0.765259 Loss2: 1.420935
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.571650 Loss1: 0.167247 Loss2: 1.404403
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.882366 Loss1: 0.494425 Loss2: 1.387942
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.540423 Loss1: 0.139849 Loss2: 1.400573
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.679456 Loss1: 0.310759 Loss2: 1.368697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.527097 Loss1: 0.181772 Loss2: 1.345325 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546608 Loss1: 0.204769 Loss2: 1.341839
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.147678 Loss1: 1.232744 Loss2: 1.914934
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480231 Loss1: 0.131694 Loss2: 1.348537
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.229221 Loss1: 0.763781 Loss2: 1.465440
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.468128 Loss1: 0.134880 Loss2: 1.333248
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.074734 Loss1: 0.589929 Loss2: 1.484805
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.799890 Loss1: 0.361685 Loss2: 1.438204
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.699244 Loss1: 0.265577 Loss2: 1.433666
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.668320 Loss1: 0.247512 Loss2: 1.420808
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.640449 Loss1: 0.217571 Loss2: 1.422877
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.607996 Loss1: 0.190744 Loss2: 1.417252
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.253141 Loss1: 1.312246 Loss2: 1.940896
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.573368 Loss1: 0.158752 Loss2: 1.414616
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.300138 Loss1: 0.841416 Loss2: 1.458722
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.560830 Loss1: 0.148184 Loss2: 1.412646
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.991591 Loss1: 0.533911 Loss2: 1.457680
+(DefaultActor pid=3765) >> Training accuracy: 0.956250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.798755 Loss1: 0.366205 Loss2: 1.432551
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.700522 Loss1: 0.273991 Loss2: 1.426531
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.646462 Loss1: 0.239902 Loss2: 1.406561
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.616457 Loss1: 0.200872 Loss2: 1.415585
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.627019 Loss1: 0.217262 Loss2: 1.409757
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.217052 Loss1: 1.297952 Loss2: 1.919101
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.601149 Loss1: 0.189834 Loss2: 1.411315
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.361627 Loss1: 0.861469 Loss2: 1.500158
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.563649 Loss1: 0.155725 Loss2: 1.407924
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.034240 Loss1: 0.555845 Loss2: 1.478395
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.747593 Loss1: 0.301618 Loss2: 1.445975
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.690276 Loss1: 0.254786 Loss2: 1.435490
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.687030 Loss1: 0.255778 Loss2: 1.431252
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.633550 Loss1: 0.204105 Loss2: 1.429445
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.099192 Loss1: 1.260755 Loss2: 1.838437
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.573952 Loss1: 0.150587 Loss2: 1.423365
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.262585 Loss1: 0.840199 Loss2: 1.422385
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.561306 Loss1: 0.142767 Loss2: 1.418539
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.995170 Loss1: 0.589231 Loss2: 1.405939
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.555468 Loss1: 0.140120 Loss2: 1.415348
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.639124 Loss1: 0.266784 Loss2: 1.372340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.522787 Loss1: 0.166832 Loss2: 1.355955 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466141 Loss1: 0.109813 Loss2: 1.356327 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 15:04:49,444][flwr][DEBUG] - fit_round 80 received 50 results and 0 failures
+INFO flwr 2023-10-10 15:05:31,319 | server.py:125 | fit progress: (80, 2.2419653856716217, {'accuracy': 0.5467}, 184439.09719714)
+>> Test accuracy: 0.546700
+[2023-10-10 15:05:31,319][flwr][INFO] - fit progress: (80, 2.2419653856716217, {'accuracy': 0.5467}, 184439.09719714)
+DEBUG flwr 2023-10-10 15:05:31,319 | server.py:173 | evaluate_round 80: strategy sampled 50 clients (out of 50)
+[2023-10-10 15:05:31,319][flwr][DEBUG] - evaluate_round 80: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 15:14:32,916 | server.py:187 | evaluate_round 80 received 50 results and 0 failures
+[2023-10-10 15:14:32,916][flwr][DEBUG] - evaluate_round 80 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 15:14:32,916 | server.py:222 | fit_round 81: strategy sampled 50 clients (out of 50)
+[2023-10-10 15:14:32,916][flwr][DEBUG] - fit_round 81: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.274305 Loss1: 1.321049 Loss2: 1.953256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.037398 Loss1: 0.542660 Loss2: 1.494737 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.848668 Loss1: 0.376491 Loss2: 1.472177
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.094718 Loss1: 1.265260 Loss2: 1.829457
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.065419 Loss1: 0.684017 Loss2: 1.381403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.817116 Loss1: 0.409035 Loss2: 1.408081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.716123 Loss1: 0.358173 Loss2: 1.357950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.630882 Loss1: 0.272340 Loss2: 1.358542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559711 Loss1: 0.212895 Loss2: 1.346816 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475421 Loss1: 0.134140 Loss2: 1.341281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463906 Loss1: 0.128384 Loss2: 1.335523 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.033650 Loss1: 1.043878 Loss2: 1.989772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.033115 Loss1: 0.502930 Loss2: 1.530185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.881282 Loss1: 0.407575 Loss2: 1.473708
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.088924 Loss1: 1.219411 Loss2: 1.869513
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.109790 Loss1: 0.708554 Loss2: 1.401236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.873049 Loss1: 0.482091 Loss2: 1.390957 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.701225 Loss1: 0.322634 Loss2: 1.378591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.601482 Loss1: 0.243207 Loss2: 1.358275 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.517499 Loss1: 0.167109 Loss2: 1.350390 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484442 Loss1: 0.135640 Loss2: 1.348802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.469535 Loss1: 0.125260 Loss2: 1.344275 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.159521 Loss1: 1.298201 Loss2: 1.861320 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.925734 Loss1: 0.521332 Loss2: 1.404401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.190962 Loss1: 1.225463 Loss2: 1.965500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.138628 Loss1: 0.769747 Loss2: 1.368881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.910170 Loss1: 0.485830 Loss2: 1.424340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.663467 Loss1: 0.310429 Loss2: 1.353038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.610928 Loss1: 0.217493 Loss2: 1.393435
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609880 Loss1: 0.260601 Loss2: 1.349280
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.657535 Loss1: 0.278133 Loss2: 1.379402
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.533728 Loss1: 0.154526 Loss2: 1.379201
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491899 Loss1: 0.123781 Loss2: 1.368118 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519590 Loss1: 0.171436 Loss2: 1.348153 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971154
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.946623 Loss1: 1.066793 Loss2: 1.879830 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.940326 Loss1: 0.468991 Loss2: 1.471335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.099616 Loss1: 1.160286 Loss2: 1.939330
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.719469 Loss1: 0.302196 Loss2: 1.417273
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.336071 Loss1: 0.796486 Loss2: 1.539585
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.612134 Loss1: 0.196901 Loss2: 1.415233
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.976117 Loss1: 0.514492 Loss2: 1.461625
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.554875 Loss1: 0.151170 Loss2: 1.403705
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.578412 Loss1: 0.179278 Loss2: 1.399133 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.583847 Loss1: 0.182834 Loss2: 1.401013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.598309 Loss1: 0.194020 Loss2: 1.404289 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.585348 Loss1: 0.184977 Loss2: 1.400371 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962891
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.585337 Loss1: 0.152917 Loss2: 1.432420 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.977718 Loss1: 1.094067 Loss2: 1.883651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.914669 Loss1: 0.436614 Loss2: 1.478056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.070408 Loss1: 1.187756 Loss2: 1.882652
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.774392 Loss1: 0.326863 Loss2: 1.447529
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.027467 Loss1: 0.607886 Loss2: 1.419580
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.715239 Loss1: 0.266317 Loss2: 1.448923
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.877131 Loss1: 0.423995 Loss2: 1.453136
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.643332 Loss1: 0.199587 Loss2: 1.443745
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.708228 Loss1: 0.303068 Loss2: 1.405159
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.626754 Loss1: 0.194344 Loss2: 1.432409
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.599589 Loss1: 0.159730 Loss2: 1.439858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.607075 Loss1: 0.176204 Loss2: 1.430871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.574263 Loss1: 0.144078 Loss2: 1.430184 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965820
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.536990 Loss1: 0.132094 Loss2: 1.404896 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.264869 Loss1: 1.354699 Loss2: 1.910169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.926438 Loss1: 0.502792 Loss2: 1.423646 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.751341 Loss1: 0.333457 Loss2: 1.417885
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.027377 Loss1: 1.223402 Loss2: 1.803975
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.160692 Loss1: 0.770696 Loss2: 1.389996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.951960 Loss1: 0.546282 Loss2: 1.405678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.803316 Loss1: 0.434695 Loss2: 1.368621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.680867 Loss1: 0.307062 Loss2: 1.373805 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.616107 Loss1: 0.261321 Loss2: 1.354786 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.502640 Loss1: 0.122633 Loss2: 1.380008
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.529566 Loss1: 0.173160 Loss2: 1.356405
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.520248 Loss1: 0.172008 Loss2: 1.348240
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457947 Loss1: 0.116330 Loss2: 1.341616
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470360 Loss1: 0.132898 Loss2: 1.337462
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.161089 Loss1: 1.254578 Loss2: 1.906510
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.266371 Loss1: 0.826098 Loss2: 1.440273
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.017553 Loss1: 0.552478 Loss2: 1.465075
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765900 Loss1: 0.361206 Loss2: 1.404694
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.044292 Loss1: 1.180863 Loss2: 1.863428
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.098329 Loss1: 0.692304 Loss2: 1.406025 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.829019 Loss1: 0.425718 Loss2: 1.403301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.641515 Loss1: 0.272071 Loss2: 1.369444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.575570 Loss1: 0.209753 Loss2: 1.365817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511578 Loss1: 0.154051 Loss2: 1.357528 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459400 Loss1: 0.086331 Loss2: 1.373069
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533470 Loss1: 0.172704 Loss2: 1.360766
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.535480 Loss1: 0.172836 Loss2: 1.362645
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.488024 Loss1: 0.133999 Loss2: 1.354024
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.534358 Loss1: 0.179880 Loss2: 1.354478
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.113102 Loss1: 1.268722 Loss2: 1.844380
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.167322 Loss1: 0.752098 Loss2: 1.415224
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.850812 Loss1: 0.418813 Loss2: 1.432000
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.702141 Loss1: 0.306814 Loss2: 1.395327
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.132456 Loss1: 1.179548 Loss2: 1.952907
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.644313 Loss1: 0.263779 Loss2: 1.380534
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.264712 Loss1: 0.777916 Loss2: 1.486797
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.552624 Loss1: 0.171447 Loss2: 1.381177
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.000960 Loss1: 0.513892 Loss2: 1.487068
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514389 Loss1: 0.148605 Loss2: 1.365784
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.773243 Loss1: 0.311389 Loss2: 1.461853
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.519395 Loss1: 0.144323 Loss2: 1.375072
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.696687 Loss1: 0.246925 Loss2: 1.449762
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.525273 Loss1: 0.154378 Loss2: 1.370894
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.658896 Loss1: 0.222260 Loss2: 1.436635
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.522007 Loss1: 0.147476 Loss2: 1.374531
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.657040 Loss1: 0.217265 Loss2: 1.439775
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.625271 Loss1: 0.196120 Loss2: 1.429151
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.581940 Loss1: 0.146412 Loss2: 1.435528
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.566245 Loss1: 0.139297 Loss2: 1.426948
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.999402 Loss1: 1.136550 Loss2: 1.862852
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.139205 Loss1: 0.723862 Loss2: 1.415343
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.941266 Loss1: 0.485879 Loss2: 1.455388
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.712755 Loss1: 0.326595 Loss2: 1.386160
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.937558 Loss1: 1.099679 Loss2: 1.837879
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.152457 Loss1: 0.745229 Loss2: 1.407228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.969693 Loss1: 0.532223 Loss2: 1.437469 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.839951 Loss1: 0.437861 Loss2: 1.402089 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.653240 Loss1: 0.256662 Loss2: 1.396578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.569599 Loss1: 0.191286 Loss2: 1.378313 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513734 Loss1: 0.143367 Loss2: 1.370367 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.531102 Loss1: 0.158105 Loss2: 1.372997 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.053227 Loss1: 1.181776 Loss2: 1.871451 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731965 Loss1: 0.340480 Loss2: 1.391486 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.310157 Loss1: 1.285844 Loss2: 2.024313 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.195294 Loss1: 0.796523 Loss2: 1.398771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.983205 Loss1: 0.475386 Loss2: 1.507819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.802590 Loss1: 0.386664 Loss2: 1.415926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.759304 Loss1: 0.352867 Loss2: 1.406437
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.510753 Loss1: 0.165117 Loss2: 1.345636
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.570323 Loss1: 0.209583 Loss2: 1.360740 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.520484 Loss1: 0.166659 Loss2: 1.353825 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.951042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.493375 Loss1: 0.114922 Loss2: 1.378453 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.119087 Loss1: 1.253387 Loss2: 1.865701
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.242572 Loss1: 0.834441 Loss2: 1.408131
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.930800 Loss1: 0.489158 Loss2: 1.441642
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.776283 Loss1: 0.377744 Loss2: 1.398539
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.221050 Loss1: 1.326204 Loss2: 1.894846
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.646225 Loss1: 0.247082 Loss2: 1.399143
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.135656 Loss1: 0.769899 Loss2: 1.365757
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.028012 Loss1: 0.612374 Loss2: 1.415637
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.622262 Loss1: 0.230940 Loss2: 1.391322
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.689584 Loss1: 0.320601 Loss2: 1.368983
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.607123 Loss1: 0.209386 Loss2: 1.397736
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.573177 Loss1: 0.185008 Loss2: 1.388169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.534579 Loss1: 0.149941 Loss2: 1.384638 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481026 Loss1: 0.106635 Loss2: 1.374391 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.488548 Loss1: 0.145635 Loss2: 1.342914 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967548
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.030326 Loss1: 1.187300 Loss2: 1.843026
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.195556 Loss1: 0.774805 Loss2: 1.420751
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.991995 Loss1: 0.564838 Loss2: 1.427157
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.153108 Loss1: 1.129350 Loss2: 2.023758
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.803199 Loss1: 0.392638 Loss2: 1.410561
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.276850 Loss1: 0.715845 Loss2: 1.561005
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.664815 Loss1: 0.266476 Loss2: 1.398339
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.962012 Loss1: 0.390971 Loss2: 1.571041
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.609038 Loss1: 0.228104 Loss2: 1.380934
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.832270 Loss1: 0.317104 Loss2: 1.515166
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.554017 Loss1: 0.168760 Loss2: 1.385257
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.579472 Loss1: 0.200500 Loss2: 1.378972 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.555208 Loss1: 0.167699 Loss2: 1.387509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.523235 Loss1: 0.147882 Loss2: 1.375353 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971680
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.626359 Loss1: 0.138234 Loss2: 1.488125 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.993904 Loss1: 1.168344 Loss2: 1.825560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.839701 Loss1: 0.431914 Loss2: 1.407787 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.157167 Loss1: 1.238701 Loss2: 1.918465
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.802362 Loss1: 0.410420 Loss2: 1.391943
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.229280 Loss1: 0.753306 Loss2: 1.475974
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.666886 Loss1: 0.265136 Loss2: 1.401750
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.936489 Loss1: 0.468839 Loss2: 1.467650
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.606501 Loss1: 0.229284 Loss2: 1.377217
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.787529 Loss1: 0.372768 Loss2: 1.414761
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.554968 Loss1: 0.180801 Loss2: 1.374167
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.510669 Loss1: 0.145860 Loss2: 1.364809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482377 Loss1: 0.115685 Loss2: 1.366693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459519 Loss1: 0.103198 Loss2: 1.356321 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.609791 Loss1: 0.191316 Loss2: 1.418476 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.129224 Loss1: 1.263089 Loss2: 1.866135 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.845497 Loss1: 0.435616 Loss2: 1.409881 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.628208 Loss1: 0.261828 Loss2: 1.366380
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.202633 Loss1: 1.255319 Loss2: 1.947314
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.271809 Loss1: 0.783213 Loss2: 1.488596 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.999416 Loss1: 0.498147 Loss2: 1.501270 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.850900 Loss1: 0.386438 Loss2: 1.464463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.717745 Loss1: 0.251401 Loss2: 1.466344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.660310 Loss1: 0.213246 Loss2: 1.447064 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425228 Loss1: 0.081006 Loss2: 1.344222
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.626991 Loss1: 0.176967 Loss2: 1.450024
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.642731 Loss1: 0.193319 Loss2: 1.449411
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.611921 Loss1: 0.173552 Loss2: 1.438369
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.596962 Loss1: 0.152414 Loss2: 1.444548
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.894691 Loss1: 1.009891 Loss2: 1.884800
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.048614 Loss1: 0.605305 Loss2: 1.443309
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.895861 Loss1: 0.454449 Loss2: 1.441411
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.046848 Loss1: 1.090658 Loss2: 1.956190
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.715823 Loss1: 0.299066 Loss2: 1.416756
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.119528 Loss1: 0.656142 Loss2: 1.463385
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.678307 Loss1: 0.271621 Loss2: 1.406686
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.621579 Loss1: 0.213659 Loss2: 1.407920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.588377 Loss1: 0.186849 Loss2: 1.401528 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.567168 Loss1: 0.169011 Loss2: 1.398157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.525524 Loss1: 0.128623 Loss2: 1.396901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.483628 Loss1: 0.097861 Loss2: 1.385767 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989890
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.569197 Loss1: 0.142895 Loss2: 1.426302 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.016394 Loss1: 1.121715 Loss2: 1.894679
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.194883 Loss1: 0.745735 Loss2: 1.449147
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.863460 Loss1: 0.436698 Loss2: 1.426762
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.723709 Loss1: 0.322667 Loss2: 1.401042
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.930253 Loss1: 1.087798 Loss2: 1.842455
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.025051 Loss1: 0.604624 Loss2: 1.420427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.803624 Loss1: 0.378261 Loss2: 1.425363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.647767 Loss1: 0.256483 Loss2: 1.391284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.631863 Loss1: 0.243353 Loss2: 1.388510 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556244 Loss1: 0.176134 Loss2: 1.380110 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.552374 Loss1: 0.173652 Loss2: 1.378721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.504053 Loss1: 0.136461 Loss2: 1.367592 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971680
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.218288 Loss1: 1.220921 Loss2: 1.997367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.948530 Loss1: 0.396607 Loss2: 1.551924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.115154 Loss1: 1.255893 Loss2: 1.859262 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.184554 Loss1: 0.764494 Loss2: 1.420059 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.921188 Loss1: 0.501666 Loss2: 1.419523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.767194 Loss1: 0.375078 Loss2: 1.392116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.749601 Loss1: 0.340604 Loss2: 1.408997 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.598555 Loss1: 0.207998 Loss2: 1.390557 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.547843 Loss1: 0.164702 Loss2: 1.383140 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.525043 Loss1: 0.156834 Loss2: 1.368209 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.157561 Loss1: 0.731273 Loss2: 1.426288 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.733333 Loss1: 0.324135 Loss2: 1.409198 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.663375 Loss1: 0.256351 Loss2: 1.407025
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.126796 Loss1: 1.173020 Loss2: 1.953777
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.156225 Loss1: 0.685123 Loss2: 1.471102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.951657 Loss1: 0.445280 Loss2: 1.506376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.776158 Loss1: 0.321515 Loss2: 1.454644 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687883 Loss1: 0.235538 Loss2: 1.452345 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966518
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.633761 Loss1: 0.190862 Loss2: 1.442899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.692874 Loss1: 0.252879 Loss2: 1.439994 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.544342 Loss1: 0.112834 Loss2: 1.431508 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.109875 Loss1: 0.714903 Loss2: 1.394973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.667985 Loss1: 0.314191 Loss2: 1.353793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.624363 Loss1: 0.271447 Loss2: 1.352916
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.202122 Loss1: 1.238888 Loss2: 1.963234
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.181312 Loss1: 0.712540 Loss2: 1.468772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496801 Loss1: 0.156676 Loss2: 1.340125
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.979688 Loss1: 0.523002 Loss2: 1.456686
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.503260 Loss1: 0.162505 Loss2: 1.340756
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.762333 Loss1: 0.323028 Loss2: 1.439305
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509681 Loss1: 0.170652 Loss2: 1.339029
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.668656 Loss1: 0.264250 Loss2: 1.404406
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.619677 Loss1: 0.215590 Loss2: 1.404088
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476699 Loss1: 0.144365 Loss2: 1.332334
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.560638 Loss1: 0.158415 Loss2: 1.402223 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487892 Loss1: 0.099468 Loss2: 1.388424 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.359979 Loss1: 0.831954 Loss2: 1.528025 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.881654 Loss1: 0.437573 Loss2: 1.444081 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.789060 Loss1: 0.364069 Loss2: 1.424991 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.684163 Loss1: 0.259577 Loss2: 1.424585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.602264 Loss1: 0.184611 Loss2: 1.417652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.516319 Loss1: 0.116380 Loss2: 1.399939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.517546 Loss1: 0.128663 Loss2: 1.388884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.504542 Loss1: 0.113611 Loss2: 1.390931 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.631514 Loss1: 0.173433 Loss2: 1.458081 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.063041 Loss1: 1.230644 Loss2: 1.832397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.984067 Loss1: 0.555640 Loss2: 1.428427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.721374 Loss1: 0.359083 Loss2: 1.362291
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.083791 Loss1: 1.144656 Loss2: 1.939135
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.096215 Loss1: 0.646114 Loss2: 1.450101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.907925 Loss1: 0.452822 Loss2: 1.455102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.783418 Loss1: 0.350172 Loss2: 1.433246 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.683311 Loss1: 0.260732 Loss2: 1.422579 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.649566 Loss1: 0.228356 Loss2: 1.421210 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.516467 Loss1: 0.149655 Loss2: 1.366812
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.604057 Loss1: 0.177035 Loss2: 1.427022
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.551307 Loss1: 0.143392 Loss2: 1.407915
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519463 Loss1: 0.117865 Loss2: 1.401598
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.553327 Loss1: 0.150292 Loss2: 1.403036
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.094748 Loss1: 1.208725 Loss2: 1.886023
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.125181 Loss1: 0.690377 Loss2: 1.434804
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.852983 Loss1: 0.404438 Loss2: 1.448545
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.680301 Loss1: 0.271042 Loss2: 1.409260
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.101094 Loss1: 1.177182 Loss2: 1.923912
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.243111 Loss1: 0.775195 Loss2: 1.467916 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.905432 Loss1: 0.419022 Loss2: 1.486410 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.765978 Loss1: 0.329700 Loss2: 1.436278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.691760 Loss1: 0.250567 Loss2: 1.441193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.593663 Loss1: 0.154463 Loss2: 1.439200 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.530071 Loss1: 0.139982 Loss2: 1.390089
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.579560 Loss1: 0.154935 Loss2: 1.424625
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.582625 Loss1: 0.158581 Loss2: 1.424044
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.570450 Loss1: 0.142838 Loss2: 1.427612
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.543619 Loss1: 0.122669 Loss2: 1.420950
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.271340 Loss1: 1.380449 Loss2: 1.890891
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.164802 Loss1: 0.759945 Loss2: 1.404857
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.908636 Loss1: 0.502725 Loss2: 1.405911
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.746519 Loss1: 0.371974 Loss2: 1.374545
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.340825 Loss1: 1.383264 Loss2: 1.957561
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.257425 Loss1: 0.828163 Loss2: 1.429261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.016293 Loss1: 0.530045 Loss2: 1.486248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.748341 Loss1: 0.340842 Loss2: 1.407499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.624303 Loss1: 0.217813 Loss2: 1.406490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.627609 Loss1: 0.224562 Loss2: 1.403047 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.583510 Loss1: 0.187782 Loss2: 1.395728 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.517774 Loss1: 0.123748 Loss2: 1.394025 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965402
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.912130 Loss1: 1.117943 Loss2: 1.794187
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.128897 Loss1: 0.727798 Loss2: 1.401099
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.841556 Loss1: 0.478877 Loss2: 1.362679
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.699121 Loss1: 0.336115 Loss2: 1.363005
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.080425 Loss1: 1.257457 Loss2: 1.822968
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631527 Loss1: 0.283870 Loss2: 1.347657
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.103302 Loss1: 0.736255 Loss2: 1.367047
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.603756 Loss1: 0.257812 Loss2: 1.345943
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804670 Loss1: 0.420607 Loss2: 1.384063
+DEBUG flwr 2023-10-10 15:43:57,601 | server.py:236 | fit_round 81 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.560299 Loss1: 0.211944 Loss2: 1.348356
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.722007 Loss1: 0.362851 Loss2: 1.359157
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.624781 Loss1: 0.276939 Loss2: 1.347843
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.544848 Loss1: 0.202136 Loss2: 1.342712
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.624833 Loss1: 0.280183 Loss2: 1.344650
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.500896 Loss1: 0.168648 Loss2: 1.332248
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533346 Loss1: 0.186010 Loss2: 1.347337
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.479647 Loss1: 0.144340 Loss2: 1.335307
+(DefaultActor pid=3765) >> Training accuracy: 0.965820
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.465218 Loss1: 0.138519 Loss2: 1.326699 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.179512 Loss1: 1.222240 Loss2: 1.957272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.925923 Loss1: 0.439427 Loss2: 1.486497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.753604 Loss1: 0.281238 Loss2: 1.472366
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.897973 Loss1: 1.047958 Loss2: 1.850015
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.700843 Loss1: 0.234857 Loss2: 1.465986
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.010255 Loss1: 0.619381 Loss2: 1.390873
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.647960 Loss1: 0.197770 Loss2: 1.450190
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.813459 Loss1: 0.396299 Loss2: 1.417160
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.602944 Loss1: 0.156440 Loss2: 1.446505
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.673241 Loss1: 0.300940 Loss2: 1.372301
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.567028 Loss1: 0.123810 Loss2: 1.443218
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.561920 Loss1: 0.185881 Loss2: 1.376039
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.578965 Loss1: 0.138082 Loss2: 1.440882
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.554074 Loss1: 0.203207 Loss2: 1.350867
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.532582 Loss1: 0.087529 Loss2: 1.445053
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.572660 Loss1: 0.199841 Loss2: 1.372818
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.527525 Loss1: 0.158740 Loss2: 1.368786
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.527239 Loss1: 0.161915 Loss2: 1.365323
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.531136 Loss1: 0.171801 Loss2: 1.359334
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 15:43:57,601][flwr][DEBUG] - fit_round 81 received 50 results and 0 failures
+INFO flwr 2023-10-10 15:44:39,875 | server.py:125 | fit progress: (81, 2.2340269601002287, {'accuracy': 0.5476}, 186787.65366741602)
+>> Test accuracy: 0.547600
+[2023-10-10 15:44:39,875][flwr][INFO] - fit progress: (81, 2.2340269601002287, {'accuracy': 0.5476}, 186787.65366741602)
+DEBUG flwr 2023-10-10 15:44:39,875 | server.py:173 | evaluate_round 81: strategy sampled 50 clients (out of 50)
+[2023-10-10 15:44:39,875][flwr][DEBUG] - evaluate_round 81: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 15:53:45,333 | server.py:187 | evaluate_round 81 received 50 results and 0 failures
+[2023-10-10 15:53:45,333][flwr][DEBUG] - evaluate_round 81 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 15:53:45,333 | server.py:222 | fit_round 82: strategy sampled 50 clients (out of 50)
+[2023-10-10 15:53:45,333][flwr][DEBUG] - fit_round 82: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.153625 Loss1: 1.236755 Loss2: 1.916870
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.185194 Loss1: 0.716241 Loss2: 1.468954
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.929168 Loss1: 0.450277 Loss2: 1.478891
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.767782 Loss1: 0.327241 Loss2: 1.440542
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.916289 Loss1: 1.053607 Loss2: 1.862682
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.126444 Loss1: 0.697891 Loss2: 1.428553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.851787 Loss1: 0.407484 Loss2: 1.444303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.764524 Loss1: 0.347075 Loss2: 1.417449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.683036 Loss1: 0.272480 Loss2: 1.410557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.637926 Loss1: 0.232190 Loss2: 1.405736 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.582800 Loss1: 0.183910 Loss2: 1.398890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.526208 Loss1: 0.130914 Loss2: 1.395294 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.166946 Loss1: 1.254433 Loss2: 1.912513 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.089713 Loss1: 0.605504 Loss2: 1.484210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.729710 Loss1: 0.277819 Loss2: 1.451891 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.648099 Loss1: 0.225862 Loss2: 1.422237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.603027 Loss1: 0.176783 Loss2: 1.426245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.562208 Loss1: 0.144123 Loss2: 1.418085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.541668 Loss1: 0.124655 Loss2: 1.417014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.526698 Loss1: 0.118659 Loss2: 1.408038 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.513606 Loss1: 0.166879 Loss2: 1.346726 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.520145 Loss1: 0.168882 Loss2: 1.351262 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.196580 Loss1: 0.780374 Loss2: 1.416205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.803831 Loss1: 0.400754 Loss2: 1.403077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.681823 Loss1: 0.269275 Loss2: 1.412548
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.185851 Loss1: 1.345621 Loss2: 1.840230
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.094048 Loss1: 0.688035 Loss2: 1.406013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.752468 Loss1: 0.355986 Loss2: 1.396482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.670941 Loss1: 0.303124 Loss2: 1.367816 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.602689 Loss1: 0.222093 Loss2: 1.380596 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.545594 Loss1: 0.185035 Loss2: 1.360559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.522651 Loss1: 0.163172 Loss2: 1.359479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.480094 Loss1: 0.136030 Loss2: 1.344064 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.158918 Loss1: 0.717645 Loss2: 1.441273 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.693747 Loss1: 0.294361 Loss2: 1.399386 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.078208 Loss1: 1.201298 Loss2: 1.876911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.119349 Loss1: 0.712135 Loss2: 1.407214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.796234 Loss1: 0.407737 Loss2: 1.388497 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.674572 Loss1: 0.324469 Loss2: 1.350103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569710 Loss1: 0.203693 Loss2: 1.366017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439397 Loss1: 0.111188 Loss2: 1.328209 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448665 Loss1: 0.122539 Loss2: 1.326126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425301 Loss1: 0.105970 Loss2: 1.319331
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.138077 Loss1: 1.193454 Loss2: 1.944623
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.235210 Loss1: 0.733296 Loss2: 1.501913
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.069968 Loss1: 0.543637 Loss2: 1.526331
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.883379 Loss1: 0.412066 Loss2: 1.471313
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.772335 Loss1: 0.289564 Loss2: 1.482770
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.105547 Loss1: 1.230055 Loss2: 1.875492
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.646775 Loss1: 0.183486 Loss2: 1.463289
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.615906 Loss1: 0.166002 Loss2: 1.449904
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.312635 Loss1: 0.820268 Loss2: 1.492367
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.577353 Loss1: 0.129745 Loss2: 1.447608
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.910103 Loss1: 0.453441 Loss2: 1.456662
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.562074 Loss1: 0.112413 Loss2: 1.449661
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.705718 Loss1: 0.285931 Loss2: 1.419788
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.564390 Loss1: 0.127534 Loss2: 1.436856
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.664894 Loss1: 0.245904 Loss2: 1.418991
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.662834 Loss1: 0.236745 Loss2: 1.426089
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.635510 Loss1: 0.203726 Loss2: 1.431783
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568158 Loss1: 0.146308 Loss2: 1.421850
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.520226 Loss1: 0.112271 Loss2: 1.407956
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.226024 Loss1: 1.262703 Loss2: 1.963322
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.297888 Loss1: 0.846162 Loss2: 1.451726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.784150 Loss1: 0.342622 Loss2: 1.441528 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.667614 Loss1: 0.229544 Loss2: 1.438070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.051675 Loss1: 1.197556 Loss2: 1.854119 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.117240 Loss1: 0.720701 Loss2: 1.396540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.912702 Loss1: 0.491978 Loss2: 1.420724 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.630758 Loss1: 0.252647 Loss2: 1.378111 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480770 Loss1: 0.125985 Loss2: 1.354786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501132 Loss1: 0.144414 Loss2: 1.356717
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.002978 Loss1: 1.074006 Loss2: 1.928972
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.076790 Loss1: 0.648479 Loss2: 1.428311 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461996 Loss1: 0.112986 Loss2: 1.349010
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.857570 Loss1: 0.391264 Loss2: 1.466306
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.663666 Loss1: 0.258135 Loss2: 1.405531
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.625996 Loss1: 0.216402 Loss2: 1.409593
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569246 Loss1: 0.168181 Loss2: 1.401065
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.515088 Loss1: 0.124833 Loss2: 1.390256
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.211170 Loss1: 1.277739 Loss2: 1.933431
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.513056 Loss1: 0.123912 Loss2: 1.389144
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.200490 Loss1: 0.770009 Loss2: 1.430481
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474943 Loss1: 0.082354 Loss2: 1.392589
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475137 Loss1: 0.094315 Loss2: 1.380823 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.674567 Loss1: 0.271929 Loss2: 1.402638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.620440 Loss1: 0.214806 Loss2: 1.405634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.546870 Loss1: 0.153366 Loss2: 1.393504 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.505594 Loss1: 0.125775 Loss2: 1.379820 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979911
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.786451 Loss1: 0.370295 Loss2: 1.416156 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.522816 Loss1: 0.143121 Loss2: 1.379695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517420 Loss1: 0.151279 Loss2: 1.366141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.507983 Loss1: 0.142305 Loss2: 1.365678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.528374 Loss1: 0.159164 Loss2: 1.369210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.499726 Loss1: 0.132720 Loss2: 1.367005 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.957031
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.610300 Loss1: 0.215889 Loss2: 1.394411 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.521177 Loss1: 0.143908 Loss2: 1.377269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.088153 Loss1: 1.218258 Loss2: 1.869895 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.891105 Loss1: 0.472442 Loss2: 1.418663 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.597168 Loss1: 0.207141 Loss2: 1.390026 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.536771 Loss1: 0.163780 Loss2: 1.372991
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.025797 Loss1: 1.151694 Loss2: 1.874103
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.119825 Loss1: 0.733132 Loss2: 1.386693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.839047 Loss1: 0.417617 Loss2: 1.421431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.678662 Loss1: 0.311436 Loss2: 1.367226 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.495931 Loss1: 0.126755 Loss2: 1.369176
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.720246 Loss1: 0.326860 Loss2: 1.393386
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.613549 Loss1: 0.241672 Loss2: 1.371877
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.637113 Loss1: 0.266867 Loss2: 1.370245
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.539975 Loss1: 0.165945 Loss2: 1.374030
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476002 Loss1: 0.120856 Loss2: 1.355145
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.218605 Loss1: 1.397155 Loss2: 1.821451
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433246 Loss1: 0.085846 Loss2: 1.347400
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.885757 Loss1: 0.495719 Loss2: 1.390038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.576016 Loss1: 0.239412 Loss2: 1.336603 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.498325 Loss1: 0.179421 Loss2: 1.318904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423419 Loss1: 0.113570 Loss2: 1.309849 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414046 Loss1: 0.110374 Loss2: 1.303672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390988 Loss1: 0.091583 Loss2: 1.299405 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975446
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.698979 Loss1: 0.265184 Loss2: 1.433795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.593545 Loss1: 0.170779 Loss2: 1.422767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.551949 Loss1: 0.139717 Loss2: 1.412232
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.128773 Loss1: 1.265196 Loss2: 1.863577
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.191952 Loss1: 0.772090 Loss2: 1.419862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509260 Loss1: 0.103296 Loss2: 1.405964
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.881472 Loss1: 0.461873 Loss2: 1.419599
+(DefaultActor pid=3764) >> Training accuracy: 0.961914
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.701551 Loss1: 0.320685 Loss2: 1.380866
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605897 Loss1: 0.221636 Loss2: 1.384261
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576980 Loss1: 0.204889 Loss2: 1.372091
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.526095 Loss1: 0.157397 Loss2: 1.368698
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.058881 Loss1: 1.089775 Loss2: 1.969106
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.523730 Loss1: 0.157876 Loss2: 1.365854
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.277323 Loss1: 0.767321 Loss2: 1.510002
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482582 Loss1: 0.116557 Loss2: 1.366025
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.055800 Loss1: 0.534810 Loss2: 1.520990
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484220 Loss1: 0.124169 Loss2: 1.360052
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.723631 Loss1: 0.242966 Loss2: 1.480665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.626418 Loss1: 0.166729 Loss2: 1.459689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.610431 Loss1: 0.162104 Loss2: 1.448326
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.085525 Loss1: 1.193559 Loss2: 1.891967
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.185614 Loss1: 0.737718 Loss2: 1.447896 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.568517 Loss1: 0.134420 Loss2: 1.434097
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.922655 Loss1: 0.469754 Loss2: 1.452901
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.762964 Loss1: 0.336087 Loss2: 1.426877
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.700806 Loss1: 0.272403 Loss2: 1.428403
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.630016 Loss1: 0.214434 Loss2: 1.415582
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517807 Loss1: 0.110700 Loss2: 1.407106
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.520302 Loss1: 0.126946 Loss2: 1.393356
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.194371 Loss1: 1.315487 Loss2: 1.878884
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.532280 Loss1: 0.131704 Loss2: 1.400576
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.116577 Loss1: 0.691370 Loss2: 1.425207
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.489230 Loss1: 0.103203 Loss2: 1.386027
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.867691 Loss1: 0.434455 Loss2: 1.433236
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.776144 Loss1: 0.373129 Loss2: 1.403015
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.692591 Loss1: 0.273676 Loss2: 1.418915
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647944 Loss1: 0.246699 Loss2: 1.401245
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.584959 Loss1: 0.184995 Loss2: 1.399964
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.978011 Loss1: 1.165562 Loss2: 1.812449
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568739 Loss1: 0.178547 Loss2: 1.390192
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.999398 Loss1: 0.636672 Loss2: 1.362727
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.510556 Loss1: 0.120343 Loss2: 1.390213
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.781799 Loss1: 0.404071 Loss2: 1.377728
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487627 Loss1: 0.100264 Loss2: 1.387363
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.554053 Loss1: 0.203178 Loss2: 1.350874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.558150 Loss1: 0.213828 Loss2: 1.344321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.478502 Loss1: 0.137671 Loss2: 1.340831
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.904959 Loss1: 1.080049 Loss2: 1.824910
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.051716 Loss1: 0.650641 Loss2: 1.401075 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.489166 Loss1: 0.156442 Loss2: 1.332724
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834763 Loss1: 0.439663 Loss2: 1.395099
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.770455 Loss1: 0.391575 Loss2: 1.378880
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.670900 Loss1: 0.309048 Loss2: 1.361852
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.643418 Loss1: 0.269300 Loss2: 1.374118
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.568180 Loss1: 0.221985 Loss2: 1.346196
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.233802 Loss1: 1.261673 Loss2: 1.972129
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.528140 Loss1: 0.950466 Loss2: 1.577673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.042286 Loss1: 0.534754 Loss2: 1.507532 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969727
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.488361 Loss1: 0.142812 Loss2: 1.345550
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.880885 Loss1: 0.380581 Loss2: 1.500305
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.775238 Loss1: 0.287916 Loss2: 1.487322
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.737613 Loss1: 0.257585 Loss2: 1.480028
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.765603 Loss1: 0.277195 Loss2: 1.488409
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.655656 Loss1: 0.175707 Loss2: 1.479949
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.199969 Loss1: 1.234014 Loss2: 1.965955
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.620587 Loss1: 0.155784 Loss2: 1.464803
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.579805 Loss1: 0.116511 Loss2: 1.463294 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.962500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.685534 Loss1: 0.296424 Loss2: 1.389110 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513964 Loss1: 0.158268 Loss2: 1.355695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.511600 Loss1: 0.155254 Loss2: 1.356345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.476348 Loss1: 0.124193 Loss2: 1.352155 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.879552 Loss1: 0.485552 Loss2: 1.394001 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.648993 Loss1: 0.284511 Loss2: 1.364482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.574981 Loss1: 0.211773 Loss2: 1.363209
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.036790 Loss1: 1.202283 Loss2: 1.834508
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.577805 Loss1: 0.224979 Loss2: 1.352826
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.231104 Loss1: 0.826699 Loss2: 1.404405
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.506452 Loss1: 0.151792 Loss2: 1.354660
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.849298 Loss1: 0.500169 Loss2: 1.349129
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.480947 Loss1: 0.135281 Loss2: 1.345666
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.740494 Loss1: 0.387174 Loss2: 1.353320
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.610609 Loss1: 0.288477 Loss2: 1.322131
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460612 Loss1: 0.126656 Loss2: 1.333956
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491432 Loss1: 0.178947 Loss2: 1.312485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416016 Loss1: 0.113852 Loss2: 1.302164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398209 Loss1: 0.100399 Loss2: 1.297810
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.976432 Loss1: 1.060378 Loss2: 1.916054
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.049933 Loss1: 0.595327 Loss2: 1.454607
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.848464 Loss1: 0.392860 Loss2: 1.455604
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.720003 Loss1: 0.289000 Loss2: 1.431003
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.657502 Loss1: 0.237583 Loss2: 1.419920
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.608972 Loss1: 0.192539 Loss2: 1.416434
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.913933 Loss1: 1.075746 Loss2: 1.838187
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.606508 Loss1: 0.193450 Loss2: 1.413059
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.060591 Loss1: 0.667679 Loss2: 1.392912
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.570852 Loss1: 0.164273 Loss2: 1.406579
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.855386 Loss1: 0.444001 Loss2: 1.411385
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.549854 Loss1: 0.149126 Loss2: 1.400728
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.666573 Loss1: 0.298719 Loss2: 1.367855
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.526084 Loss1: 0.118768 Loss2: 1.407316
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.577630 Loss1: 0.210330 Loss2: 1.367301
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528818 Loss1: 0.170242 Loss2: 1.358576
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471047 Loss1: 0.117782 Loss2: 1.353265
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460508 Loss1: 0.113958 Loss2: 1.346551
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.477346 Loss1: 0.135842 Loss2: 1.341505
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.477267 Loss1: 0.135432 Loss2: 1.341835
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.042301 Loss1: 1.152881 Loss2: 1.889420
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.146540 Loss1: 0.737979 Loss2: 1.408562
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.931094 Loss1: 0.473604 Loss2: 1.457490
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.747223 Loss1: 0.352043 Loss2: 1.395180
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.682983 Loss1: 0.273728 Loss2: 1.409254
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.580426 Loss1: 0.182950 Loss2: 1.397476
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.927096 Loss1: 1.136719 Loss2: 1.790376
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531547 Loss1: 0.136932 Loss2: 1.394615
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.917881 Loss1: 0.581423 Loss2: 1.336459
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.535620 Loss1: 0.147068 Loss2: 1.388552
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.790066 Loss1: 0.428483 Loss2: 1.361582
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.504161 Loss1: 0.120968 Loss2: 1.383194
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.629926 Loss1: 0.312300 Loss2: 1.317626
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466163 Loss1: 0.090064 Loss2: 1.376099
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535960 Loss1: 0.215167 Loss2: 1.320793
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467281 Loss1: 0.158660 Loss2: 1.308621
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476229 Loss1: 0.174359 Loss2: 1.301870
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457016 Loss1: 0.159511 Loss2: 1.297505
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429842 Loss1: 0.128785 Loss2: 1.301057
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.109909 Loss1: 1.181574 Loss2: 1.928335
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422525 Loss1: 0.127304 Loss2: 1.295221
+(DefaultActor pid=3764) >> Training accuracy: 0.950000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.920260 Loss1: 0.430471 Loss2: 1.489789 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.775550 Loss1: 0.327343 Loss2: 1.448207 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.660129 Loss1: 0.216292 Loss2: 1.443838
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.962380 Loss1: 1.028294 Loss2: 1.934086
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.633054 Loss1: 0.204424 Loss2: 1.428630
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.150305 Loss1: 0.708097 Loss2: 1.442208
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.590585 Loss1: 0.165852 Loss2: 1.424733
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.938529 Loss1: 0.450720 Loss2: 1.487809
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.591022 Loss1: 0.162707 Loss2: 1.428315
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699494 Loss1: 0.284428 Loss2: 1.415066
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.596332 Loss1: 0.172522 Loss2: 1.423810
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.681647 Loss1: 0.246261 Loss2: 1.435386
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.625324 Loss1: 0.202214 Loss2: 1.423110
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.572424 Loss1: 0.165686 Loss2: 1.406738
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.547007 Loss1: 0.143185 Loss2: 1.403822
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.526145 Loss1: 0.124528 Loss2: 1.401617
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.913174 Loss1: 1.044474 Loss2: 1.868701
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.567713 Loss1: 0.164238 Loss2: 1.403475
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.787387 Loss1: 0.380721 Loss2: 1.406666 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.626628 Loss1: 0.260513 Loss2: 1.366115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562645 Loss1: 0.216641 Loss2: 1.346004
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.867980 Loss1: 1.082180 Loss2: 1.785800
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509693 Loss1: 0.164153 Loss2: 1.345540
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.035842 Loss1: 0.700142 Loss2: 1.335701
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469714 Loss1: 0.126942 Loss2: 1.342773
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.850536 Loss1: 0.457016 Loss2: 1.393520
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443943 Loss1: 0.106665 Loss2: 1.337278
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.710207 Loss1: 0.371303 Loss2: 1.338903
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398076 Loss1: 0.068525 Loss2: 1.329551
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.636960 Loss1: 0.286587 Loss2: 1.350372
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.536537 Loss1: 0.206955 Loss2: 1.329583
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.522098 Loss1: 0.196038 Loss2: 1.326061
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484922 Loss1: 0.163823 Loss2: 1.321099
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446011 Loss1: 0.126402 Loss2: 1.319609
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.170472 Loss1: 1.114348 Loss2: 2.056124
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414231 Loss1: 0.100979 Loss2: 1.313252
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.997783 Loss1: 0.463369 Loss2: 1.534414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.747137 Loss1: 0.288602 Loss2: 1.458535 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.616399 Loss1: 0.173554 Loss2: 1.442844 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.595147 Loss1: 0.147635 Loss2: 1.447512 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.619902 Loss1: 0.165988 Loss2: 1.453913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.905236 Loss1: 0.364445 Loss2: 1.540791
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.602152 Loss1: 0.159461 Loss2: 1.442691
+(DefaultActor pid=3765) >> Training accuracy: 0.961538
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.726024 Loss1: 0.230437 Loss2: 1.495586 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.619181 Loss1: 0.153017 Loss2: 1.466164 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.921452 Loss1: 1.061849 Loss2: 1.859603
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.575958 Loss1: 0.114564 Loss2: 1.461394
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.114216 Loss1: 0.714946 Loss2: 1.399269
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.573390 Loss1: 0.118052 Loss2: 1.455338
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.570672 Loss1: 0.108408 Loss2: 1.462264 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.955078
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.645182 Loss1: 0.267862 Loss2: 1.377320 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.498121 Loss1: 0.148165 Loss2: 1.349955 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.035987 Loss1: 1.155657 Loss2: 1.880330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.149910 Loss1: 0.733540 Loss2: 1.416370 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.670717 Loss1: 0.274355 Loss2: 1.396361 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.592544 Loss1: 0.204656 Loss2: 1.387889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.569049 Loss1: 0.186617 Loss2: 1.382432
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.047751 Loss1: 1.192330 Loss2: 1.855421
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.169690 Loss1: 0.775745 Loss2: 1.393945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.911212 Loss1: 0.482097 Loss2: 1.429115 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.703283 Loss1: 0.329720 Loss2: 1.373564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.579208 Loss1: 0.210791 Loss2: 1.368417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.510424 Loss1: 0.151628 Loss2: 1.358797 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449894 Loss1: 0.104177 Loss2: 1.345718 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467109 Loss1: 0.121915 Loss2: 1.345194 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.705193 Loss1: 0.271006 Loss2: 1.434187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.661732 Loss1: 0.225847 Loss2: 1.435885 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.609188 Loss1: 0.174217 Loss2: 1.434971
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.841988 Loss1: 1.024563 Loss2: 1.817425
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.053283 Loss1: 0.625268 Loss2: 1.428014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.859774 Loss1: 0.447043 Loss2: 1.412731 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551522 Loss1: 0.169871 Loss2: 1.381651 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.533810 Loss1: 0.165879 Loss2: 1.367932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.908046 Loss1: 1.085882 Loss2: 1.822164
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.527025 Loss1: 0.163592 Loss2: 1.363432
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.082969 Loss1: 0.652844 Loss2: 1.430125
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.511263 Loss1: 0.139207 Loss2: 1.372057
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.871255 Loss1: 0.466925 Loss2: 1.404330
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.482473 Loss1: 0.110026 Loss2: 1.372447
+(DefaultActor pid=3765) >> Training accuracy: 0.975184
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687116 Loss1: 0.289345 Loss2: 1.397772 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 16:21:57,506 | server.py:236 | fit_round 82 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.525485 Loss1: 0.144683 Loss2: 1.380802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.294049 Loss1: 1.353157 Loss2: 1.940892
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491352 Loss1: 0.125477 Loss2: 1.365875
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.221584 Loss1: 0.772059 Loss2: 1.449525
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497915 Loss1: 0.138136 Loss2: 1.359780
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.050775 Loss1: 0.578041 Loss2: 1.472734
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429772 Loss1: 0.068776 Loss2: 1.360995
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.710616 Loss1: 0.274981 Loss2: 1.435635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.590670 Loss1: 0.178163 Loss2: 1.412507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.603346 Loss1: 0.195873 Loss2: 1.407473
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.041407 Loss1: 1.047872 Loss2: 1.993536
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.568175 Loss1: 0.156354 Loss2: 1.411821
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.202767 Loss1: 0.713458 Loss2: 1.489309
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.538093 Loss1: 0.132506 Loss2: 1.405588
+(DefaultActor pid=3765) >> Training accuracy: 0.960417
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.060174 Loss1: 0.502045 Loss2: 1.558129
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.848149 Loss1: 0.383254 Loss2: 1.464895
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.742755 Loss1: 0.260904 Loss2: 1.481852
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.636088 Loss1: 0.171356 Loss2: 1.464732
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.625205 Loss1: 0.161816 Loss2: 1.463389
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.658834 Loss1: 0.191347 Loss2: 1.467487
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.990366 Loss1: 1.134227 Loss2: 1.856139
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.648800 Loss1: 0.186060 Loss2: 1.462740
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.121632 Loss1: 0.655477 Loss2: 1.466155
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.606465 Loss1: 0.135501 Loss2: 1.470964
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.938547 Loss1: 0.495710 Loss2: 1.442837
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.800058 Loss1: 0.375120 Loss2: 1.424938
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.722017 Loss1: 0.301474 Loss2: 1.420543
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.609826 Loss1: 0.203265 Loss2: 1.406561
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.605558 Loss1: 0.202086 Loss2: 1.403471
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.980541 Loss1: 1.139259 Loss2: 1.841282
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.562407 Loss1: 0.148341 Loss2: 1.414066
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.518463 Loss1: 0.126771 Loss2: 1.391692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.517443 Loss1: 0.127529 Loss2: 1.389914 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.680831 Loss1: 0.288675 Loss2: 1.392156 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.538590 Loss1: 0.174986 Loss2: 1.363604 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.492274 Loss1: 0.129698 Loss2: 1.362575 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 16:21:57,506][flwr][DEBUG] - fit_round 82 received 50 results and 0 failures
+INFO flwr 2023-10-10 16:22:38,908 | server.py:125 | fit progress: (82, 2.2478357490640097, {'accuracy': 0.5485}, 189066.686781659)
+>> Test accuracy: 0.548500
+[2023-10-10 16:22:38,908][flwr][INFO] - fit progress: (82, 2.2478357490640097, {'accuracy': 0.5485}, 189066.686781659)
+DEBUG flwr 2023-10-10 16:22:38,909 | server.py:173 | evaluate_round 82: strategy sampled 50 clients (out of 50)
+[2023-10-10 16:22:38,909][flwr][DEBUG] - evaluate_round 82: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 16:31:40,452 | server.py:187 | evaluate_round 82 received 50 results and 0 failures
+[2023-10-10 16:31:40,452][flwr][DEBUG] - evaluate_round 82 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 16:31:40,452 | server.py:222 | fit_round 83: strategy sampled 50 clients (out of 50)
+[2023-10-10 16:31:40,452][flwr][DEBUG] - fit_round 83: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.146647 Loss1: 1.188510 Loss2: 1.958138 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.010189 Loss1: 0.525772 Loss2: 1.484417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.801587 Loss1: 0.347830 Loss2: 1.453757
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.121623 Loss1: 1.210239 Loss2: 1.911384
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.703829 Loss1: 0.253169 Loss2: 1.450660
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.224671 Loss1: 0.742142 Loss2: 1.482529
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.706298 Loss1: 0.264132 Loss2: 1.442166
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.917511 Loss1: 0.477843 Loss2: 1.439668
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.623745 Loss1: 0.176794 Loss2: 1.446951
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.726454 Loss1: 0.296235 Loss2: 1.430219
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.605589 Loss1: 0.167964 Loss2: 1.437625
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.684959 Loss1: 0.263423 Loss2: 1.421535
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.553633 Loss1: 0.115597 Loss2: 1.438036
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.586532 Loss1: 0.178455 Loss2: 1.408077
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.548968 Loss1: 0.128362 Loss2: 1.420606
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.535622 Loss1: 0.137750 Loss2: 1.397872
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482755 Loss1: 0.091027 Loss2: 1.391728
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479707 Loss1: 0.091255 Loss2: 1.388452
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.524625 Loss1: 0.145803 Loss2: 1.378823
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.043126 Loss1: 1.175933 Loss2: 1.867193
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.242440 Loss1: 0.774436 Loss2: 1.468004
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.957762 Loss1: 0.522789 Loss2: 1.434973
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.106059 Loss1: 1.167431 Loss2: 1.938628
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.763971 Loss1: 0.331315 Loss2: 1.432656
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.223983 Loss1: 0.764390 Loss2: 1.459593
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.678619 Loss1: 0.267705 Loss2: 1.410915
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.914273 Loss1: 0.450880 Loss2: 1.463392
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.593005 Loss1: 0.191093 Loss2: 1.401912
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.629286 Loss1: 0.229886 Loss2: 1.399400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.573146 Loss1: 0.170460 Loss2: 1.402686 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.516988 Loss1: 0.128948 Loss2: 1.388040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.504683 Loss1: 0.113952 Loss2: 1.390731 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971680
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.488113 Loss1: 0.099547 Loss2: 1.388567 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.154518 Loss1: 1.267487 Loss2: 1.887031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.873479 Loss1: 0.427493 Loss2: 1.445986 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.702682 Loss1: 0.309681 Loss2: 1.393001
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.012202 Loss1: 1.137284 Loss2: 1.874918
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.664252 Loss1: 0.270325 Loss2: 1.393927
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.069934 Loss1: 0.675178 Loss2: 1.394756
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.643470 Loss1: 0.256749 Loss2: 1.386721
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.908536 Loss1: 0.484345 Loss2: 1.424191
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.575229 Loss1: 0.185993 Loss2: 1.389236
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.747359 Loss1: 0.354226 Loss2: 1.393133
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.487679 Loss1: 0.114477 Loss2: 1.373202
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.674677 Loss1: 0.287750 Loss2: 1.386927
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472451 Loss1: 0.107822 Loss2: 1.364629
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.604668 Loss1: 0.225590 Loss2: 1.379078
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.470650 Loss1: 0.109719 Loss2: 1.360932
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.562144 Loss1: 0.187130 Loss2: 1.375014
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.535871 Loss1: 0.164416 Loss2: 1.371455
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480318 Loss1: 0.114608 Loss2: 1.365711
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.502599 Loss1: 0.137834 Loss2: 1.364765
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.020696 Loss1: 1.135175 Loss2: 1.885521
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.077519 Loss1: 0.610652 Loss2: 1.466867
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.823541 Loss1: 0.376867 Loss2: 1.446674
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.025413 Loss1: 1.214444 Loss2: 1.810970
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.722134 Loss1: 0.297512 Loss2: 1.424623
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.052713 Loss1: 0.698472 Loss2: 1.354241
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.680422 Loss1: 0.261947 Loss2: 1.418475
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.826020 Loss1: 0.450081 Loss2: 1.375939
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.632876 Loss1: 0.208172 Loss2: 1.424704
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.656518 Loss1: 0.243233 Loss2: 1.413285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.582263 Loss1: 0.171201 Loss2: 1.411062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.566876 Loss1: 0.153873 Loss2: 1.413003 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.509943 Loss1: 0.107623 Loss2: 1.402320 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967773
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425332 Loss1: 0.119788 Loss2: 1.305544 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.007224 Loss1: 1.205045 Loss2: 1.802179 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.830051 Loss1: 0.437880 Loss2: 1.392171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.662215 Loss1: 0.315414 Loss2: 1.346801
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.054770 Loss1: 1.111806 Loss2: 1.942964
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.137216 Loss1: 0.668131 Loss2: 1.469085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.930413 Loss1: 0.429227 Loss2: 1.501186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.823428 Loss1: 0.365804 Loss2: 1.457624 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.765888 Loss1: 0.313511 Loss2: 1.452378 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.654447 Loss1: 0.204550 Loss2: 1.449897 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438174 Loss1: 0.109421 Loss2: 1.328753
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.615239 Loss1: 0.174360 Loss2: 1.440878
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.588052 Loss1: 0.151844 Loss2: 1.436208
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.594733 Loss1: 0.163537 Loss2: 1.431196
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.567786 Loss1: 0.136893 Loss2: 1.430893
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.100627 Loss1: 1.147721 Loss2: 1.952906
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.079637 Loss1: 0.616634 Loss2: 1.463003
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.842891 Loss1: 0.422154 Loss2: 1.420737
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.742869 Loss1: 0.331165 Loss2: 1.411704
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.188536 Loss1: 1.259227 Loss2: 1.929309
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.156139 Loss1: 0.739520 Loss2: 1.416619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.584038 Loss1: 0.192982 Loss2: 1.391055
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.897469 Loss1: 0.435979 Loss2: 1.461490
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524911 Loss1: 0.140141 Loss2: 1.384770
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.720211 Loss1: 0.313291 Loss2: 1.406919
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.530243 Loss1: 0.152957 Loss2: 1.377286
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.662120 Loss1: 0.257117 Loss2: 1.405003
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.634429 Loss1: 0.237358 Loss2: 1.397071
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.515493 Loss1: 0.138449 Loss2: 1.377044
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.582233 Loss1: 0.190731 Loss2: 1.391502
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475594 Loss1: 0.104398 Loss2: 1.371196
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490434 Loss1: 0.114348 Loss2: 1.376086 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965402
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.105046 Loss1: 1.120660 Loss2: 1.984386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.038720 Loss1: 0.506324 Loss2: 1.532396 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.809666 Loss1: 0.329616 Loss2: 1.480050
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.025297 Loss1: 1.104999 Loss2: 1.920298
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.694289 Loss1: 0.220113 Loss2: 1.474176
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.132374 Loss1: 0.675007 Loss2: 1.457366
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.644601 Loss1: 0.187304 Loss2: 1.457296
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.907918 Loss1: 0.439368 Loss2: 1.468550
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.609545 Loss1: 0.152114 Loss2: 1.457431
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.726401 Loss1: 0.308888 Loss2: 1.417513
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.571535 Loss1: 0.124084 Loss2: 1.447451
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.686763 Loss1: 0.256231 Loss2: 1.430531
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.586610 Loss1: 0.131974 Loss2: 1.454636
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.583474 Loss1: 0.172909 Loss2: 1.410566
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.591577 Loss1: 0.141113 Loss2: 1.450464
+(DefaultActor pid=3765) >> Training accuracy: 0.953125
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.629844 Loss1: 0.218769 Loss2: 1.411075
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.600085 Loss1: 0.187546 Loss2: 1.412540
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.627301 Loss1: 0.218391 Loss2: 1.408910
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.573328 Loss1: 0.158197 Loss2: 1.415131
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.223853 Loss1: 1.274877 Loss2: 1.948976
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.141636 Loss1: 0.722297 Loss2: 1.419339
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.927032 Loss1: 0.447348 Loss2: 1.479684
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.760979 Loss1: 0.332454 Loss2: 1.428524
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.968984 Loss1: 1.133313 Loss2: 1.835671
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.625683 Loss1: 0.198671 Loss2: 1.427012 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.622066 Loss1: 0.208851 Loss2: 1.413214 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.581862 Loss1: 0.160553 Loss2: 1.421309 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.556441 Loss1: 0.141158 Loss2: 1.415283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.511543 Loss1: 0.109633 Loss2: 1.401910 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980769
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.565937 Loss1: 0.169023 Loss2: 1.396913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.631573 Loss1: 0.229412 Loss2: 1.402162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.096639 Loss1: 1.132315 Loss2: 1.964324
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.642115 Loss1: 0.231770 Loss2: 1.410345
+(DefaultActor pid=3764) >> Training accuracy: 0.978516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.955232 Loss1: 0.437542 Loss2: 1.517689 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.706161 Loss1: 0.245765 Loss2: 1.460396 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.645262 Loss1: 0.196654 Loss2: 1.448608
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.009175 Loss1: 1.168518 Loss2: 1.840657
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.099822 Loss1: 0.661406 Loss2: 1.438416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.903551 Loss1: 0.487770 Loss2: 1.415781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.753284 Loss1: 0.350656 Loss2: 1.402627 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.633411 Loss1: 0.245128 Loss2: 1.388283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.539165 Loss1: 0.163796 Loss2: 1.375369 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.518457 Loss1: 0.157780 Loss2: 1.360676 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.504637 Loss1: 0.135838 Loss2: 1.368799 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.998327 Loss1: 0.531509 Loss2: 1.466817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.764527 Loss1: 0.319932 Loss2: 1.444595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.679421 Loss1: 0.245283 Loss2: 1.434138
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.879792 Loss1: 0.957574 Loss2: 1.922219
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.072701 Loss1: 0.631079 Loss2: 1.441623 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.927186 Loss1: 0.459181 Loss2: 1.468005 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.595792 Loss1: 0.183246 Loss2: 1.412546
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.787647 Loss1: 0.367048 Loss2: 1.420598
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.552910 Loss1: 0.137884 Loss2: 1.415026
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.657462 Loss1: 0.225044 Loss2: 1.432418
+(DefaultActor pid=3765) >> Training accuracy: 0.962891
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546357 Loss1: 0.141123 Loss2: 1.405234
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.526572 Loss1: 0.132884 Loss2: 1.393688
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.509522 Loss1: 0.115870 Loss2: 1.393651
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487271 Loss1: 0.095535 Loss2: 1.391736
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.475166 Loss1: 0.089350 Loss2: 1.385816
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.016224 Loss1: 1.133919 Loss2: 1.882304
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.268407 Loss1: 0.803685 Loss2: 1.464722
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.904234 Loss1: 0.450093 Loss2: 1.454141
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.782336 Loss1: 0.356600 Loss2: 1.425736
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.668736 Loss1: 0.241752 Loss2: 1.426985
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.667864 Loss1: 0.247301 Loss2: 1.420563
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.904853 Loss1: 1.088119 Loss2: 1.816734
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.566116 Loss1: 0.158239 Loss2: 1.407878
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.188530 Loss1: 0.773189 Loss2: 1.415341
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.549090 Loss1: 0.147041 Loss2: 1.402049
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.867734 Loss1: 0.437011 Loss2: 1.430723
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.707109 Loss1: 0.319544 Loss2: 1.387565 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.549360 Loss1: 0.145846 Loss2: 1.403514
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.628719 Loss1: 0.244357 Loss2: 1.384363
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.632742 Loss1: 0.247305 Loss2: 1.385437
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.581852 Loss1: 0.204232 Loss2: 1.377620
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493388 Loss1: 0.124730 Loss2: 1.368658
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.469522 Loss1: 0.119160 Loss2: 1.350363
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.102138 Loss1: 1.216523 Loss2: 1.885615
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.266182 Loss1: 0.826783 Loss2: 1.439399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.686191 Loss1: 0.293177 Loss2: 1.393015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562859 Loss1: 0.182357 Loss2: 1.380502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512913 Loss1: 0.143753 Loss2: 1.369160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488753 Loss1: 0.124097 Loss2: 1.364656 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441120 Loss1: 0.079019 Loss2: 1.362100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434009 Loss1: 0.082172 Loss2: 1.351838 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.565418 Loss1: 0.192999 Loss2: 1.372419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.550167 Loss1: 0.188448 Loss2: 1.361719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.504209 Loss1: 0.144514 Loss2: 1.359696
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.940094 Loss1: 1.076897 Loss2: 1.863197
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.034806 Loss1: 0.585457 Loss2: 1.449349 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.707681 Loss1: 0.304812 Loss2: 1.402869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.193895 Loss1: 1.243706 Loss2: 1.950189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.234304 Loss1: 0.811192 Loss2: 1.423113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.001595 Loss1: 0.500902 Loss2: 1.500694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.795127 Loss1: 0.372956 Loss2: 1.422170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.530642 Loss1: 0.155577 Loss2: 1.375065
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.659783 Loss1: 0.237722 Loss2: 1.422061
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.683439 Loss1: 0.269634 Loss2: 1.413806
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.514896 Loss1: 0.136036 Loss2: 1.378859
+(DefaultActor pid=3765) >> Training accuracy: 0.982537
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.580661 Loss1: 0.169148 Loss2: 1.411514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.537006 Loss1: 0.133374 Loss2: 1.403632 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978795
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.113955 Loss1: 0.731306 Loss2: 1.382649 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.653075 Loss1: 0.279092 Loss2: 1.373983 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.609632 Loss1: 0.233194 Loss2: 1.376437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530209 Loss1: 0.163086 Loss2: 1.367123 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.878880 Loss1: 0.430982 Loss2: 1.447898
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.527760 Loss1: 0.171073 Loss2: 1.356687
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.720635 Loss1: 0.289918 Loss2: 1.430717
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480350 Loss1: 0.122975 Loss2: 1.357375
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.668525 Loss1: 0.253257 Loss2: 1.415268
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456006 Loss1: 0.104666 Loss2: 1.351340
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428889 Loss1: 0.080650 Loss2: 1.348239
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.607155 Loss1: 0.187007 Loss2: 1.420148
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.552184 Loss1: 0.147568 Loss2: 1.404617
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.504287 Loss1: 0.106772 Loss2: 1.397515
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485348 Loss1: 0.093187 Loss2: 1.392160
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.511406 Loss1: 0.116513 Loss2: 1.394893
+(DefaultActor pid=3764) >> Training accuracy: 0.972656
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.130817 Loss1: 1.301541 Loss2: 1.829276
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.209948 Loss1: 0.810312 Loss2: 1.399635
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.947136 Loss1: 0.552647 Loss2: 1.394489
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.745782 Loss1: 0.352707 Loss2: 1.393075
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.649702 Loss1: 0.287310 Loss2: 1.362392
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.092302 Loss1: 1.243543 Loss2: 1.848760
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.157467 Loss1: 0.780209 Loss2: 1.377257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.956253 Loss1: 0.524071 Loss2: 1.432182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.800658 Loss1: 0.426304 Loss2: 1.374353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.692500 Loss1: 0.305947 Loss2: 1.386553 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.584260 Loss1: 0.227531 Loss2: 1.356730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496662 Loss1: 0.148717 Loss2: 1.347945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.485323 Loss1: 0.125815 Loss2: 1.359508 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.325189 Loss1: 0.747484 Loss2: 1.577705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.894987 Loss1: 0.345077 Loss2: 1.549909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.789756 Loss1: 0.229023 Loss2: 1.560734
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.071345 Loss1: 1.197781 Loss2: 1.873564
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.121031 Loss1: 0.658953 Loss2: 1.462079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.895338 Loss1: 0.435773 Loss2: 1.459564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.800246 Loss1: 0.366145 Loss2: 1.434101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.695270 Loss1: 0.264634 Loss2: 1.430636 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.616642 Loss1: 0.192723 Loss2: 1.423920 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.567039 Loss1: 0.155523 Loss2: 1.411516 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.043016 Loss1: 1.071130 Loss2: 1.971886
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.542428 Loss1: 0.142331 Loss2: 1.400096
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.000167 Loss1: 0.507295 Loss2: 1.492871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.795273 Loss1: 0.337269 Loss2: 1.458004 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.216174 Loss1: 1.284468 Loss2: 1.931706
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.646501 Loss1: 0.188063 Loss2: 1.458438
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.210575 Loss1: 0.773633 Loss2: 1.436942
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.609319 Loss1: 0.168740 Loss2: 1.440579
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.057745 Loss1: 0.590529 Loss2: 1.467216
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.551479 Loss1: 0.120429 Loss2: 1.431050
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.529852 Loss1: 0.110292 Loss2: 1.419560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.521407 Loss1: 0.104580 Loss2: 1.416827 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.586572 Loss1: 0.190170 Loss2: 1.396402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.524761 Loss1: 0.131832 Loss2: 1.392929 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979911
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.494724 Loss1: 0.114159 Loss2: 1.380566
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.007988 Loss1: 1.064399 Loss2: 1.943589
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.011507 Loss1: 0.572302 Loss2: 1.439205
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.832371 Loss1: 0.358351 Loss2: 1.474020
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.714260 Loss1: 0.273323 Loss2: 1.440937
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.692004 Loss1: 0.257355 Loss2: 1.434649
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.103090 Loss1: 1.219198 Loss2: 1.883892
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.295655 Loss1: 0.873206 Loss2: 1.422448 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.864734 Loss1: 0.445829 Loss2: 1.418904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.719563 Loss1: 0.338204 Loss2: 1.381359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.661246 Loss1: 0.271628 Loss2: 1.389618 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.506315 Loss1: 0.099956 Loss2: 1.406360
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.578355 Loss1: 0.202893 Loss2: 1.375462
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550328 Loss1: 0.178513 Loss2: 1.371815
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.514061 Loss1: 0.151246 Loss2: 1.362814
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480323 Loss1: 0.121958 Loss2: 1.358365
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449884 Loss1: 0.098305 Loss2: 1.351579
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.000764 Loss1: 1.151400 Loss2: 1.849364
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.206711 Loss1: 0.787639 Loss2: 1.419072
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.920737 Loss1: 0.498008 Loss2: 1.422730
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.778301 Loss1: 0.387518 Loss2: 1.390783
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.653803 Loss1: 0.262143 Loss2: 1.391659
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.061483 Loss1: 1.208961 Loss2: 1.852522
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.091931 Loss1: 0.707631 Loss2: 1.384300
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.528497 Loss1: 0.154635 Loss2: 1.373862
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.848329 Loss1: 0.414978 Loss2: 1.433350
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475277 Loss1: 0.113559 Loss2: 1.361718
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.643079 Loss1: 0.272124 Loss2: 1.370955
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491528 Loss1: 0.132322 Loss2: 1.359206
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.650586 Loss1: 0.267663 Loss2: 1.382922
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.490376 Loss1: 0.131035 Loss2: 1.359341
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466446 Loss1: 0.112294 Loss2: 1.354152 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501727 Loss1: 0.146096 Loss2: 1.355631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.501236 Loss1: 0.151103 Loss2: 1.350133 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.139682 Loss1: 1.298066 Loss2: 1.841616
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.188089 Loss1: 0.773879 Loss2: 1.414210
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.830619 Loss1: 0.446906 Loss2: 1.383713
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.664970 Loss1: 0.312177 Loss2: 1.352793
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.988570 Loss1: 1.112356 Loss2: 1.876214
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.169674 Loss1: 0.747157 Loss2: 1.422517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.825397 Loss1: 0.401207 Loss2: 1.424190 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.766473 Loss1: 0.382420 Loss2: 1.384053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.634017 Loss1: 0.227117 Loss2: 1.406900 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.588370 Loss1: 0.211650 Loss2: 1.376720 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550601 Loss1: 0.176042 Loss2: 1.374559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467374 Loss1: 0.097707 Loss2: 1.369667 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.004859 Loss1: 1.175371 Loss2: 1.829488 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.847712 Loss1: 0.442149 Loss2: 1.405563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.999805 Loss1: 1.102001 Loss2: 1.897804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.253109 Loss1: 0.772943 Loss2: 1.480166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.863326 Loss1: 0.445698 Loss2: 1.417628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.724688 Loss1: 0.306421 Loss2: 1.418267 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.644497 Loss1: 0.233447 Loss2: 1.411050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.672413 Loss1: 0.264612 Loss2: 1.407801 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.578181 Loss1: 0.179074 Loss2: 1.399107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486658 Loss1: 0.107316 Loss2: 1.379342 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.131605 Loss1: 1.229524 Loss2: 1.902082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.939387 Loss1: 0.484424 Loss2: 1.454963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.809276 Loss1: 0.391243 Loss2: 1.418032
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.225499 Loss1: 1.181676 Loss2: 2.043823
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.252556 Loss1: 0.832222 Loss2: 1.420334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.998887 Loss1: 0.518697 Loss2: 1.480190
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.617265 Loss1: 0.212264 Loss2: 1.405001
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.607449 Loss1: 0.196386 Loss2: 1.411063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.551855 Loss1: 0.146886 Loss2: 1.404969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.520056 Loss1: 0.132431 Loss2: 1.387625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.576105 Loss1: 0.173699 Loss2: 1.402406 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.568947 Loss1: 0.172636 Loss2: 1.396311 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977865
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.192563 Loss1: 1.203844 Loss2: 1.988719
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.092415 Loss1: 0.688233 Loss2: 1.404182
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.876706 Loss1: 0.441273 Loss2: 1.435432
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.681490 Loss1: 0.279570 Loss2: 1.401920
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.937309 Loss1: 1.089691 Loss2: 1.847618
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.536775 Loss1: 0.156591 Loss2: 1.380184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.549034 Loss1: 0.172462 Loss2: 1.376572 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 17:00:31,426 | server.py:236 | fit_round 83 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.558380 Loss1: 0.183197 Loss2: 1.375182 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.505051 Loss1: 0.129598 Loss2: 1.375452 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.496606 Loss1: 0.131588 Loss2: 1.365018 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986779
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.539861 Loss1: 0.170950 Loss2: 1.368911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.483200 Loss1: 0.123805 Loss2: 1.359395 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.514051 Loss1: 0.151051 Loss2: 1.363001
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.088063 Loss1: 1.240325 Loss2: 1.847738
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.201832 Loss1: 0.765214 Loss2: 1.436618
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.809977 Loss1: 0.395452 Loss2: 1.414525
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.747193 Loss1: 0.356055 Loss2: 1.391138
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.692080 Loss1: 0.286498 Loss2: 1.405582
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.087610 Loss1: 1.230136 Loss2: 1.857474
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.242071 Loss1: 0.818049 Loss2: 1.424022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.940978 Loss1: 0.498653 Loss2: 1.442326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.718673 Loss1: 0.336666 Loss2: 1.382007 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.601658 Loss1: 0.209971 Loss2: 1.391688 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528038 Loss1: 0.157536 Loss2: 1.370502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.506432 Loss1: 0.136389 Loss2: 1.370043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.488482 Loss1: 0.118245 Loss2: 1.370237 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.315555 Loss1: 0.850498 Loss2: 1.465057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765005 Loss1: 0.337922 Loss2: 1.427083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.934560 Loss1: 1.050101 Loss2: 1.884459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.059261 Loss1: 0.654998 Loss2: 1.404263 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.791705 Loss1: 0.379824 Loss2: 1.411881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.711774 Loss1: 0.336594 Loss2: 1.375179 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.660364 Loss1: 0.273073 Loss2: 1.387292 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.546310 Loss1: 0.181044 Loss2: 1.365266 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458712 Loss1: 0.108674 Loss2: 1.350038 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 17:00:31,426][flwr][DEBUG] - fit_round 83 received 50 results and 0 failures
+INFO flwr 2023-10-10 17:01:15,237 | server.py:125 | fit progress: (83, 2.2357876489337642, {'accuracy': 0.5508}, 191383.015105836)
+>> Test accuracy: 0.550800
+[2023-10-10 17:01:15,237][flwr][INFO] - fit progress: (83, 2.2357876489337642, {'accuracy': 0.5508}, 191383.015105836)
+DEBUG flwr 2023-10-10 17:01:15,237 | server.py:173 | evaluate_round 83: strategy sampled 50 clients (out of 50)
+[2023-10-10 17:01:15,237][flwr][DEBUG] - evaluate_round 83: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 17:10:19,173 | server.py:187 | evaluate_round 83 received 50 results and 0 failures
+[2023-10-10 17:10:19,173][flwr][DEBUG] - evaluate_round 83 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 17:10:19,173 | server.py:222 | fit_round 84: strategy sampled 50 clients (out of 50)
+[2023-10-10 17:10:19,173][flwr][DEBUG] - fit_round 84: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.076846 Loss1: 1.224578 Loss2: 1.852268 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.865184 Loss1: 0.469992 Loss2: 1.395192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.624753 Loss1: 0.257425 Loss2: 1.367329
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.941911 Loss1: 1.119502 Loss2: 1.822409
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.206439 Loss1: 0.757982 Loss2: 1.448457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.973520 Loss1: 0.557938 Loss2: 1.415582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.783534 Loss1: 0.384495 Loss2: 1.399040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.616578 Loss1: 0.232288 Loss2: 1.384290 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.585610 Loss1: 0.216200 Loss2: 1.369410 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509241 Loss1: 0.137177 Loss2: 1.372063 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467212 Loss1: 0.122444 Loss2: 1.344768 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.113159 Loss1: 1.229103 Loss2: 1.884056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.910179 Loss1: 0.461762 Loss2: 1.448417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.125601 Loss1: 1.197592 Loss2: 1.928009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.257361 Loss1: 0.824011 Loss2: 1.433350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.976209 Loss1: 0.516532 Loss2: 1.459677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.752446 Loss1: 0.349637 Loss2: 1.402809 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.656886 Loss1: 0.251223 Loss2: 1.405663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.586118 Loss1: 0.189005 Loss2: 1.397113 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.516480 Loss1: 0.137933 Loss2: 1.378547
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.542134 Loss1: 0.156688 Loss2: 1.385446
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.535958 Loss1: 0.151051 Loss2: 1.384908
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.511994 Loss1: 0.130741 Loss2: 1.381253
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.484679 Loss1: 0.105634 Loss2: 1.379045
+(DefaultActor pid=3764) >> Training accuracy: 0.977679
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.794070 Loss1: 0.964873 Loss2: 1.829197
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.020192 Loss1: 0.604335 Loss2: 1.415857
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.799007 Loss1: 0.395362 Loss2: 1.403645
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.012422 Loss1: 1.100047 Loss2: 1.912376
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.744986 Loss1: 0.360401 Loss2: 1.384585
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.125139 Loss1: 0.628840 Loss2: 1.496298
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587443 Loss1: 0.205620 Loss2: 1.381823
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.893034 Loss1: 0.399825 Loss2: 1.493209
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.558016 Loss1: 0.180650 Loss2: 1.377365
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.785582 Loss1: 0.317360 Loss2: 1.468222
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539680 Loss1: 0.173559 Loss2: 1.366121
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.498336 Loss1: 0.133807 Loss2: 1.364528 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465927 Loss1: 0.104917 Loss2: 1.361010 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.444963 Loss1: 0.086961 Loss2: 1.358002 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.576702 Loss1: 0.139303 Loss2: 1.437399 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.001256 Loss1: 1.213822 Loss2: 1.787434 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.839003 Loss1: 0.486788 Loss2: 1.352215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.673875 Loss1: 0.337444 Loss2: 1.336431
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.867753 Loss1: 0.996536 Loss2: 1.871217
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.575520 Loss1: 0.233796 Loss2: 1.341724
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.114116 Loss1: 0.695461 Loss2: 1.418654
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506846 Loss1: 0.187548 Loss2: 1.319298
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.876307 Loss1: 0.441490 Loss2: 1.434817
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.540735 Loss1: 0.216877 Loss2: 1.323858
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.801300 Loss1: 0.402294 Loss2: 1.399007
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499479 Loss1: 0.182626 Loss2: 1.316853
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.688088 Loss1: 0.288550 Loss2: 1.399538
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452036 Loss1: 0.139878 Loss2: 1.312158
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.665995 Loss1: 0.276156 Loss2: 1.389839
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447088 Loss1: 0.135583 Loss2: 1.311505
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.568980 Loss1: 0.185959 Loss2: 1.383021
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.552696 Loss1: 0.177486 Loss2: 1.375210
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486288 Loss1: 0.122323 Loss2: 1.363965
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439386 Loss1: 0.082939 Loss2: 1.356447
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.957444 Loss1: 0.994401 Loss2: 1.963043
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.135039 Loss1: 0.688573 Loss2: 1.446466
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.976522 Loss1: 0.480210 Loss2: 1.496312
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.794278 Loss1: 0.338250 Loss2: 1.456028
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.965155 Loss1: 1.012488 Loss2: 1.952666
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.114688 Loss1: 0.654181 Loss2: 1.460507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.945206 Loss1: 0.445621 Loss2: 1.499584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.758146 Loss1: 0.319103 Loss2: 1.439043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.711435 Loss1: 0.269106 Loss2: 1.442329 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.659569 Loss1: 0.224792 Loss2: 1.434777 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.583048 Loss1: 0.149582 Loss2: 1.433467
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.589204 Loss1: 0.152134 Loss2: 1.437071
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.531720 Loss1: 0.106992 Loss2: 1.424728
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.517870 Loss1: 0.103063 Loss2: 1.414807
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.526559 Loss1: 0.113202 Loss2: 1.413357
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.966843 Loss1: 1.128125 Loss2: 1.838718
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.144224 Loss1: 0.738352 Loss2: 1.405872
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.848544 Loss1: 0.456977 Loss2: 1.391567
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.664404 Loss1: 0.296568 Loss2: 1.367835
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.178562 Loss1: 1.192630 Loss2: 1.985932
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.212225 Loss1: 0.723576 Loss2: 1.488649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.927165 Loss1: 0.423690 Loss2: 1.503475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.786073 Loss1: 0.306128 Loss2: 1.479945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.765846 Loss1: 0.284002 Loss2: 1.481844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.674133 Loss1: 0.183318 Loss2: 1.490816 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452231 Loss1: 0.114287 Loss2: 1.337944
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.616059 Loss1: 0.152045 Loss2: 1.464014
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.588170 Loss1: 0.129039 Loss2: 1.459131
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.593387 Loss1: 0.144812 Loss2: 1.448575
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.561155 Loss1: 0.101808 Loss2: 1.459346
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.373978 Loss1: 1.301932 Loss2: 2.072046
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.197896 Loss1: 0.768182 Loss2: 1.429714
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.943169 Loss1: 0.470462 Loss2: 1.472707
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.784776 Loss1: 0.338044 Loss2: 1.446732
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.716798 Loss1: 0.286170 Loss2: 1.430629
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.151271 Loss1: 0.683097 Loss2: 1.468174 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.562062 Loss1: 0.152978 Loss2: 1.409084 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.714166 Loss1: 0.287631 Loss2: 1.426535 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981771
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.678451 Loss1: 0.257369 Loss2: 1.421083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.541638 Loss1: 0.132281 Loss2: 1.409356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.498859 Loss1: 0.108719 Loss2: 1.390141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.481321 Loss1: 0.091058 Loss2: 1.390263 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.747744 Loss1: 0.335294 Loss2: 1.412449 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.630069 Loss1: 0.224127 Loss2: 1.405942 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.824397 Loss1: 0.959872 Loss2: 1.864525
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.616655 Loss1: 0.212433 Loss2: 1.404222
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.009082 Loss1: 0.620064 Loss2: 1.389018
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.527959 Loss1: 0.133234 Loss2: 1.394725
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.818029 Loss1: 0.388384 Loss2: 1.429645
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.539328 Loss1: 0.152505 Loss2: 1.386823
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.480868 Loss1: 0.097262 Loss2: 1.383606 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.563408 Loss1: 0.189790 Loss2: 1.373618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.506938 Loss1: 0.145503 Loss2: 1.361435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.566020 Loss1: 0.197500 Loss2: 1.368520
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.014748 Loss1: 1.082977 Loss2: 1.931771
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.036255 Loss1: 0.609224 Loss2: 1.427032 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.662829 Loss1: 0.253290 Loss2: 1.409539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.605478 Loss1: 0.195576 Loss2: 1.409902 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.574771 Loss1: 0.168818 Loss2: 1.405954 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.103585 Loss1: 0.676047 Loss2: 1.427538
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.546393 Loss1: 0.147808 Loss2: 1.398586
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.808992 Loss1: 0.366237 Loss2: 1.442755
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.526431 Loss1: 0.131980 Loss2: 1.394451
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.766769 Loss1: 0.351740 Loss2: 1.415029
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.524545 Loss1: 0.124455 Loss2: 1.400090
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.612772 Loss1: 0.213120 Loss2: 1.399652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.533619 Loss1: 0.144865 Loss2: 1.388754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.354254 Loss1: 1.397068 Loss2: 1.957187
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.514068 Loss1: 0.121696 Loss2: 1.392372
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.280128 Loss1: 0.779822 Loss2: 1.500306
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.531048 Loss1: 0.143434 Loss2: 1.387614
+(DefaultActor pid=3764) >> Training accuracy: 0.974609
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.796168 Loss1: 0.338407 Loss2: 1.457761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.698608 Loss1: 0.258164 Loss2: 1.440444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.633719 Loss1: 0.186309 Loss2: 1.447410
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.944485 Loss1: 1.071866 Loss2: 1.872619
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.092489 Loss1: 0.695366 Loss2: 1.397123 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.877925 Loss1: 0.456935 Loss2: 1.420990 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.731827 Loss1: 0.348611 Loss2: 1.383216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.571888 Loss1: 0.208652 Loss2: 1.363236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.514582 Loss1: 0.159551 Loss2: 1.355032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448374 Loss1: 0.096643 Loss2: 1.351731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.467637 Loss1: 0.123263 Loss2: 1.344374 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.721617 Loss1: 0.298459 Loss2: 1.423158 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.620102 Loss1: 0.205630 Loss2: 1.414472 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.604813 Loss1: 0.189682 Loss2: 1.415131
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.961956 Loss1: 1.062387 Loss2: 1.899569
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.162810 Loss1: 0.749359 Loss2: 1.413451 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.917354 Loss1: 0.428718 Loss2: 1.488636 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.779083 Loss1: 0.364809 Loss2: 1.414274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.669780 Loss1: 0.250824 Loss2: 1.418956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.634489 Loss1: 0.207869 Loss2: 1.426620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.031550 Loss1: 1.115639 Loss2: 1.915911
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.582502 Loss1: 0.166294 Loss2: 1.416208
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.072337 Loss1: 0.664446 Loss2: 1.407891
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.572748 Loss1: 0.160639 Loss2: 1.412110
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.673998 Loss1: 0.291170 Loss2: 1.382828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569372 Loss1: 0.186955 Loss2: 1.382417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.546567 Loss1: 0.164659 Loss2: 1.381907
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.225653 Loss1: 1.284199 Loss2: 1.941454
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.271813 Loss1: 0.755518 Loss2: 1.516295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.957827 Loss1: 0.467867 Loss2: 1.489959 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.959375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.518109 Loss1: 0.141481 Loss2: 1.376628
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.824302 Loss1: 0.352086 Loss2: 1.472216
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.746117 Loss1: 0.284401 Loss2: 1.461716
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.678760 Loss1: 0.233733 Loss2: 1.445027
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.732845 Loss1: 0.280575 Loss2: 1.452270
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.675375 Loss1: 0.217999 Loss2: 1.457376
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.952974 Loss1: 1.166261 Loss2: 1.786713
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.671329 Loss1: 0.223383 Loss2: 1.447946
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.582070 Loss1: 0.133521 Loss2: 1.448549
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.996102 Loss1: 0.634016 Loss2: 1.362086
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.810414 Loss1: 0.419595 Loss2: 1.390819
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.690150 Loss1: 0.348771 Loss2: 1.341380
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.600626 Loss1: 0.253705 Loss2: 1.346921
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513823 Loss1: 0.181088 Loss2: 1.332735
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.286076 Loss1: 1.314041 Loss2: 1.972035
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.214991 Loss1: 0.769440 Loss2: 1.445551 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.905269 Loss1: 0.440757 Loss2: 1.464513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.706797 Loss1: 0.286982 Loss2: 1.419815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.514750 Loss1: 0.191354 Loss2: 1.323396
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.719674 Loss1: 0.297920 Loss2: 1.421755
+(DefaultActor pid=3765) >> Training accuracy: 0.955078
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.685886 Loss1: 0.238410 Loss2: 1.447476
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.637007 Loss1: 0.210434 Loss2: 1.426574
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.585942 Loss1: 0.169935 Loss2: 1.416006
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.538539 Loss1: 0.121454 Loss2: 1.417085
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529663 Loss1: 0.121306 Loss2: 1.408357
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.198666 Loss1: 1.247840 Loss2: 1.950826
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.214104 Loss1: 0.724417 Loss2: 1.489687
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.015413 Loss1: 0.517639 Loss2: 1.497774
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.821825 Loss1: 0.366833 Loss2: 1.454991
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.757802 Loss1: 0.294640 Loss2: 1.463161
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.070801 Loss1: 1.142726 Loss2: 1.928076
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.690954 Loss1: 0.237427 Loss2: 1.453527
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.140894 Loss1: 0.647625 Loss2: 1.493269
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.688819 Loss1: 0.233107 Loss2: 1.455711
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.873765 Loss1: 0.403397 Loss2: 1.470369
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.643489 Loss1: 0.191167 Loss2: 1.452322
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.750021 Loss1: 0.304167 Loss2: 1.445854
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.614438 Loss1: 0.163503 Loss2: 1.450936
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.632636 Loss1: 0.205040 Loss2: 1.427596
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.547410 Loss1: 0.102849 Loss2: 1.444561
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.542374 Loss1: 0.122617 Loss2: 1.419757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.548925 Loss1: 0.133257 Loss2: 1.415668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.504860 Loss1: 0.099787 Loss2: 1.405073
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.115521 Loss1: 1.196289 Loss2: 1.919231
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.124159 Loss1: 0.647954 Loss2: 1.476204
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.854561 Loss1: 0.380098 Loss2: 1.474463
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.752272 Loss1: 0.310854 Loss2: 1.441418
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.645068 Loss1: 0.197140 Loss2: 1.447928
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.109518 Loss1: 1.243577 Loss2: 1.865941
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.603926 Loss1: 0.166937 Loss2: 1.436989
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.577209 Loss1: 0.154524 Loss2: 1.422685 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.579646 Loss1: 0.156175 Loss2: 1.423471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.573059 Loss1: 0.148103 Loss2: 1.424956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.568390 Loss1: 0.206259 Loss2: 1.362131 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.957292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496103 Loss1: 0.162493 Loss2: 1.333610 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449397 Loss1: 0.120505 Loss2: 1.328892 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977163
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.151008 Loss1: 1.227735 Loss2: 1.923273
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.227312 Loss1: 0.768814 Loss2: 1.458498
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.951265 Loss1: 0.500487 Loss2: 1.450778
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.743960 Loss1: 0.314619 Loss2: 1.429341
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.017915 Loss1: 1.070098 Loss2: 1.947817
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.424644 Loss1: 0.861144 Loss2: 1.563500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.034732 Loss1: 0.495729 Loss2: 1.539002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.909885 Loss1: 0.367914 Loss2: 1.541971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.882032 Loss1: 0.362046 Loss2: 1.519987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.768037 Loss1: 0.263545 Loss2: 1.504492 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.695215 Loss1: 0.192979 Loss2: 1.502236 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.598680 Loss1: 0.105519 Loss2: 1.493161 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.227249 Loss1: 0.845600 Loss2: 1.381649 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694961 Loss1: 0.322696 Loss2: 1.372265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.140050 Loss1: 1.177784 Loss2: 1.962266
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589075 Loss1: 0.216035 Loss2: 1.373040
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.211494 Loss1: 0.758834 Loss2: 1.452659
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.550471 Loss1: 0.186803 Loss2: 1.363668
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.987946 Loss1: 0.492111 Loss2: 1.495834
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511390 Loss1: 0.149639 Loss2: 1.361752
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.798471 Loss1: 0.348977 Loss2: 1.449494
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448205 Loss1: 0.099094 Loss2: 1.349111
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.694723 Loss1: 0.232001 Loss2: 1.462722
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444871 Loss1: 0.103226 Loss2: 1.341645
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647412 Loss1: 0.212710 Loss2: 1.434702
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434891 Loss1: 0.095037 Loss2: 1.339854
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.535538 Loss1: 0.109258 Loss2: 1.426280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.554857 Loss1: 0.130995 Loss2: 1.423863 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.064106 Loss1: 0.677933 Loss2: 1.386173 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.687861 Loss1: 0.320780 Loss2: 1.367081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.909887 Loss1: 1.104941 Loss2: 1.804947
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.641733 Loss1: 0.267170 Loss2: 1.374563
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.131571 Loss1: 0.746627 Loss2: 1.384943
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.598435 Loss1: 0.223920 Loss2: 1.374515
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.886371 Loss1: 0.512336 Loss2: 1.374035
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.567605 Loss1: 0.196013 Loss2: 1.371592
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.700583 Loss1: 0.354365 Loss2: 1.346218
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.564090 Loss1: 0.197556 Loss2: 1.366534
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.594024 Loss1: 0.246798 Loss2: 1.347226
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.503635 Loss1: 0.138054 Loss2: 1.365582
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.625952 Loss1: 0.295477 Loss2: 1.330475
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477356 Loss1: 0.128425 Loss2: 1.348932
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449170 Loss1: 0.131948 Loss2: 1.317222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.477109 Loss1: 0.155497 Loss2: 1.321611 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.216824 Loss1: 0.779554 Loss2: 1.437270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.856384 Loss1: 0.445691 Loss2: 1.410693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.719340 Loss1: 0.293635 Loss2: 1.425705
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.030252 Loss1: 1.127414 Loss2: 1.902838
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.673296 Loss1: 0.272865 Loss2: 1.400431
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.098093 Loss1: 0.651919 Loss2: 1.446174
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.572674 Loss1: 0.180078 Loss2: 1.392596
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.839253 Loss1: 0.384408 Loss2: 1.454845
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.552335 Loss1: 0.163883 Loss2: 1.388452
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.753103 Loss1: 0.334984 Loss2: 1.418120
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.521659 Loss1: 0.145485 Loss2: 1.376174
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.707292 Loss1: 0.276787 Loss2: 1.430505
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.512673 Loss1: 0.126446 Loss2: 1.386227
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.592190 Loss1: 0.179441 Loss2: 1.412748
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.561750 Loss1: 0.154488 Loss2: 1.407262
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.552892 Loss1: 0.151348 Loss2: 1.401543
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.524594 Loss1: 0.125251 Loss2: 1.399344
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.486696 Loss1: 0.094447 Loss2: 1.392248
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.993544 Loss1: 1.141504 Loss2: 1.852040
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.101834 Loss1: 0.699992 Loss2: 1.401841
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.864266 Loss1: 0.435595 Loss2: 1.428671
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.814966 Loss1: 0.433643 Loss2: 1.381322
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.040590 Loss1: 1.141175 Loss2: 1.899415
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.206425 Loss1: 0.775933 Loss2: 1.430492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.007356 Loss1: 0.510882 Loss2: 1.496473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.756034 Loss1: 0.336500 Loss2: 1.419534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.638838 Loss1: 0.217273 Loss2: 1.421565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.588124 Loss1: 0.181380 Loss2: 1.406744 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515189 Loss1: 0.129495 Loss2: 1.385695 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512833 Loss1: 0.122724 Loss2: 1.390109 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.074467 Loss1: 0.640947 Loss2: 1.433521 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.729556 Loss1: 0.331541 Loss2: 1.398015 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.995198 Loss1: 1.182147 Loss2: 1.813051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.066980 Loss1: 0.638908 Loss2: 1.428073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.539876 Loss1: 0.151078 Loss2: 1.388799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.532447 Loss1: 0.133391 Loss2: 1.399055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.504179 Loss1: 0.114392 Loss2: 1.389787 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981971
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.607170 Loss1: 0.223408 Loss2: 1.383762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.523776 Loss1: 0.135620 Loss2: 1.388156 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.495907 Loss1: 0.118476 Loss2: 1.377432
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.946069 Loss1: 1.090139 Loss2: 1.855930
+(DefaultActor pid=3764) >> Training accuracy: 0.972656
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463032 Loss1: 0.098401 Loss2: 1.364631
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.019559 Loss1: 0.654644 Loss2: 1.364914
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.786064 Loss1: 0.406956 Loss2: 1.379108
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.609176 Loss1: 0.249462 Loss2: 1.359714
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567750 Loss1: 0.223232 Loss2: 1.344519
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569026 Loss1: 0.221863 Loss2: 1.347163
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.041751 Loss1: 1.154158 Loss2: 1.887593
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531668 Loss1: 0.183497 Loss2: 1.348171
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.119962 Loss1: 0.672314 Loss2: 1.447648
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451502 Loss1: 0.118238 Loss2: 1.333264
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.884919 Loss1: 0.423212 Loss2: 1.461707
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461335 Loss1: 0.138094 Loss2: 1.323241
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.674712 Loss1: 0.270907 Loss2: 1.403805
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.489577 Loss1: 0.153501 Loss2: 1.336076
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.655919 Loss1: 0.239471 Loss2: 1.416448 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.613634 Loss1: 0.208940 Loss2: 1.404694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.544445 Loss1: 0.140691 Loss2: 1.403754
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.903987 Loss1: 1.129403 Loss2: 1.774584
+(DefaultActor pid=3764) >> Training accuracy: 0.945833
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.497255 Loss1: 0.106643 Loss2: 1.390612
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.128823 Loss1: 0.784142 Loss2: 1.344681
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.879133 Loss1: 0.513127 Loss2: 1.366006
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.724482 Loss1: 0.383459 Loss2: 1.341023
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.698820 Loss1: 0.350277 Loss2: 1.348543
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.568891 Loss1: 0.241598 Loss2: 1.327293
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.988094 Loss1: 1.139797 Loss2: 1.848297
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.601972 Loss1: 0.266721 Loss2: 1.335251
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.537249 Loss1: 0.210704 Loss2: 1.326545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422386 Loss1: 0.105286 Loss2: 1.317101 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 17:38:50,417 | server.py:236 | fit_round 84 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447263 Loss1: 0.141009 Loss2: 1.306254 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531320 Loss1: 0.171490 Loss2: 1.359830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.456297 Loss1: 0.109352 Loss2: 1.346946 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434246 Loss1: 0.093010 Loss2: 1.341236
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.935845 Loss1: 1.007985 Loss2: 1.927861
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.124768 Loss1: 0.623573 Loss2: 1.501195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.755810 Loss1: 0.296618 Loss2: 1.459193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.603665 Loss1: 0.167183 Loss2: 1.436482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.571762 Loss1: 0.137725 Loss2: 1.434037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.567651 Loss1: 0.128548 Loss2: 1.439103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.565264 Loss1: 0.137454 Loss2: 1.427809 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576721 Loss1: 0.219256 Loss2: 1.357465 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513786 Loss1: 0.168354 Loss2: 1.345432 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446701 Loss1: 0.109801 Loss2: 1.336900 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465240 Loss1: 0.123975 Loss2: 1.341265
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.983011 Loss1: 1.121381 Loss2: 1.861630
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.000885 Loss1: 0.604896 Loss2: 1.395990
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.860578 Loss1: 0.426785 Loss2: 1.433792
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.677636 Loss1: 0.284932 Loss2: 1.392705
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.619141 Loss1: 0.226280 Loss2: 1.392860
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.234518 Loss1: 1.284345 Loss2: 1.950173
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.585877 Loss1: 0.193775 Loss2: 1.392102
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544561 Loss1: 0.165412 Loss2: 1.379149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.541146 Loss1: 0.156827 Loss2: 1.384319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469857 Loss1: 0.097301 Loss2: 1.372557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469750 Loss1: 0.102817 Loss2: 1.366933 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.608246 Loss1: 0.190012 Loss2: 1.418233 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.530598 Loss1: 0.121855 Loss2: 1.408743 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.974330
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 17:38:50,417][flwr][DEBUG] - fit_round 84 received 50 results and 0 failures
+INFO flwr 2023-10-10 17:39:33,002 | server.py:125 | fit progress: (84, 2.2436922419185454, {'accuracy': 0.55}, 193680.780604483)
+>> Test accuracy: 0.550000
+[2023-10-10 17:39:33,002][flwr][INFO] - fit progress: (84, 2.2436922419185454, {'accuracy': 0.55}, 193680.780604483)
+DEBUG flwr 2023-10-10 17:39:33,002 | server.py:173 | evaluate_round 84: strategy sampled 50 clients (out of 50)
+[2023-10-10 17:39:33,002][flwr][DEBUG] - evaluate_round 84: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 17:48:36,794 | server.py:187 | evaluate_round 84 received 50 results and 0 failures
+[2023-10-10 17:48:36,794][flwr][DEBUG] - evaluate_round 84 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 17:48:36,794 | server.py:222 | fit_round 85: strategy sampled 50 clients (out of 50)
+[2023-10-10 17:48:36,794][flwr][DEBUG] - fit_round 85: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.800937 Loss1: 0.954712 Loss2: 1.846225
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.041704 Loss1: 0.601705 Loss2: 1.439998
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.827145 Loss1: 0.383457 Loss2: 1.443688
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.946200 Loss1: 1.104253 Loss2: 1.841947
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610122 Loss1: 0.207513 Loss2: 1.402609
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.080785 Loss1: 0.679786 Loss2: 1.400999
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577158 Loss1: 0.174574 Loss2: 1.402584
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.575548 Loss1: 0.188764 Loss2: 1.386783 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528812 Loss1: 0.132048 Loss2: 1.396764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.515457 Loss1: 0.122751 Loss2: 1.392706 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509753 Loss1: 0.126242 Loss2: 1.383510 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.495203 Loss1: 0.115103 Loss2: 1.380100 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.521923 Loss1: 0.181118 Loss2: 1.340806 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.950302 Loss1: 1.083715 Loss2: 1.866587 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.867913 Loss1: 0.414769 Loss2: 1.453144 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.863861 Loss1: 0.975610 Loss2: 1.888251
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.767715 Loss1: 0.349467 Loss2: 1.418248
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.242043 Loss1: 0.783141 Loss2: 1.458902
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.663554 Loss1: 0.250148 Loss2: 1.413405
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.860282 Loss1: 0.412678 Loss2: 1.447604
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.610111 Loss1: 0.202015 Loss2: 1.408095
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.677439 Loss1: 0.269313 Loss2: 1.408126
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.538797 Loss1: 0.135697 Loss2: 1.403099
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499696 Loss1: 0.114881 Loss2: 1.384815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.495903 Loss1: 0.108235 Loss2: 1.387669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.530369 Loss1: 0.138503 Loss2: 1.391866 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.482465 Loss1: 0.113052 Loss2: 1.369413 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.006528 Loss1: 1.138191 Loss2: 1.868337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.940316 Loss1: 0.485529 Loss2: 1.454787 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.843261 Loss1: 0.435898 Loss2: 1.407363
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.092699 Loss1: 1.180276 Loss2: 1.912422
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.745243 Loss1: 0.331577 Loss2: 1.413666
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.113215 Loss1: 0.718621 Loss2: 1.394594
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.862475 Loss1: 0.428517 Loss2: 1.433958
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.608492 Loss1: 0.222458 Loss2: 1.386034
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.659464 Loss1: 0.272174 Loss2: 1.387290
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.587604 Loss1: 0.206115 Loss2: 1.381489
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.602287 Loss1: 0.221386 Loss2: 1.380901
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.511723 Loss1: 0.122128 Loss2: 1.389595
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.498352 Loss1: 0.125045 Loss2: 1.373307 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.517545 Loss1: 0.141297 Loss2: 1.376248 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499249 Loss1: 0.131404 Loss2: 1.367845 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.974330
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.079545 Loss1: 1.220672 Loss2: 1.858873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.916822 Loss1: 0.471513 Loss2: 1.445310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.703237 Loss1: 0.311222 Loss2: 1.392015
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.076035 Loss1: 1.248912 Loss2: 1.827123
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.347309 Loss1: 0.912279 Loss2: 1.435031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.925818 Loss1: 0.524857 Loss2: 1.400961 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.733134 Loss1: 0.346423 Loss2: 1.386711 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.626832 Loss1: 0.254524 Loss2: 1.372307 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.571869 Loss1: 0.203694 Loss2: 1.368175 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.541585 Loss1: 0.162886 Loss2: 1.378699
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486505 Loss1: 0.129166 Loss2: 1.357339
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.497367 Loss1: 0.143221 Loss2: 1.354147
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431026 Loss1: 0.091659 Loss2: 1.339367
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457408 Loss1: 0.120971 Loss2: 1.336437
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.165546 Loss1: 1.231846 Loss2: 1.933700
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.144834 Loss1: 0.773708 Loss2: 1.371126
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.878315 Loss1: 0.451581 Loss2: 1.426734
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654011 Loss1: 0.307272 Loss2: 1.346740
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.991967 Loss1: 1.134557 Loss2: 1.857410
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.512322 Loss1: 0.161186 Loss2: 1.351136 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.453344 Loss1: 0.119389 Loss2: 1.333956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458415 Loss1: 0.133248 Loss2: 1.325167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.415634 Loss1: 0.092942 Loss2: 1.322692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393662 Loss1: 0.080438 Loss2: 1.313224 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.501960 Loss1: 0.149678 Loss2: 1.352282 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.475819 Loss1: 0.128936 Loss2: 1.346883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.477559 Loss1: 0.136597 Loss2: 1.340962
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.936343 Loss1: 1.122945 Loss2: 1.813398
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.252380 Loss1: 0.806648 Loss2: 1.445733
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.892936 Loss1: 0.497844 Loss2: 1.395092
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.724076 Loss1: 0.335825 Loss2: 1.388251
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.658830 Loss1: 0.276614 Loss2: 1.382216
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.046541 Loss1: 1.164579 Loss2: 1.881962
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.580439 Loss1: 0.207848 Loss2: 1.372591
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.112522 Loss1: 0.696260 Loss2: 1.416262
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.558782 Loss1: 0.187384 Loss2: 1.371398
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.829603 Loss1: 0.386297 Loss2: 1.443305
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.669402 Loss1: 0.276049 Loss2: 1.393352
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.560830 Loss1: 0.180478 Loss2: 1.380352
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.621343 Loss1: 0.221785 Loss2: 1.399558
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.522452 Loss1: 0.150829 Loss2: 1.371622
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.568599 Loss1: 0.178652 Loss2: 1.389947
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484436 Loss1: 0.130654 Loss2: 1.353782
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493737 Loss1: 0.118116 Loss2: 1.375622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.502610 Loss1: 0.117481 Loss2: 1.385129 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.856183 Loss1: 0.975341 Loss2: 1.880841
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.004044 Loss1: 0.607095 Loss2: 1.396949
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.757643 Loss1: 0.346208 Loss2: 1.411436
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.631572 Loss1: 0.260296 Loss2: 1.371276
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.115952 Loss1: 1.232714 Loss2: 1.883238
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.229330 Loss1: 0.767719 Loss2: 1.461611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.906994 Loss1: 0.444148 Loss2: 1.462846 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.739861 Loss1: 0.323859 Loss2: 1.416002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.712304 Loss1: 0.280695 Loss2: 1.431609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.613063 Loss1: 0.200701 Loss2: 1.412362 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.564893 Loss1: 0.167584 Loss2: 1.397309 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.540136 Loss1: 0.140240 Loss2: 1.399896 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.995554 Loss1: 0.619482 Loss2: 1.376072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629001 Loss1: 0.286674 Loss2: 1.342327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.032972 Loss1: 1.116288 Loss2: 1.916684
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.542447 Loss1: 0.199028 Loss2: 1.343419
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.220783 Loss1: 0.760904 Loss2: 1.459879
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518686 Loss1: 0.187900 Loss2: 1.330786
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.905313 Loss1: 0.411585 Loss2: 1.493729
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473335 Loss1: 0.147597 Loss2: 1.325739
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.752461 Loss1: 0.319333 Loss2: 1.433128
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438034 Loss1: 0.119618 Loss2: 1.318416
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.721026 Loss1: 0.270100 Loss2: 1.450926
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435599 Loss1: 0.118013 Loss2: 1.317586
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.671270 Loss1: 0.234406 Loss2: 1.436864
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452175 Loss1: 0.135185 Loss2: 1.316990
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.609277 Loss1: 0.177967 Loss2: 1.431310 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.555263 Loss1: 0.128399 Loss2: 1.426864 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.946875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.059179 Loss1: 0.663206 Loss2: 1.395973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.715213 Loss1: 0.341599 Loss2: 1.373613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.657516 Loss1: 0.307685 Loss2: 1.349831 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.566805 Loss1: 0.209182 Loss2: 1.357623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.551241 Loss1: 0.213847 Loss2: 1.337394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500371 Loss1: 0.167506 Loss2: 1.332865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.464758 Loss1: 0.137464 Loss2: 1.327294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.444740 Loss1: 0.119419 Loss2: 1.325321 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467737 Loss1: 0.126614 Loss2: 1.341123 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.973574 Loss1: 1.119286 Loss2: 1.854288 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.818054 Loss1: 0.383592 Loss2: 1.434462 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.735636 Loss1: 0.339191 Loss2: 1.396445
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.939439 Loss1: 1.075607 Loss2: 1.863832
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.660496 Loss1: 0.242445 Loss2: 1.418051
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.074811 Loss1: 0.676615 Loss2: 1.398196
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.862826 Loss1: 0.439091 Loss2: 1.423735
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.570223 Loss1: 0.186275 Loss2: 1.383948
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.748494 Loss1: 0.359535 Loss2: 1.388959
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.527146 Loss1: 0.146307 Loss2: 1.380839
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.742106 Loss1: 0.343587 Loss2: 1.398520
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.531825 Loss1: 0.149332 Loss2: 1.382492
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.638543 Loss1: 0.252995 Loss2: 1.385548
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499926 Loss1: 0.126113 Loss2: 1.373812
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.507525 Loss1: 0.130633 Loss2: 1.376893 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.955078
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501768 Loss1: 0.143621 Loss2: 1.358147 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.954318 Loss1: 1.112017 Loss2: 1.842302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.898816 Loss1: 0.483279 Loss2: 1.415538 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.761560 Loss1: 0.380186 Loss2: 1.381375
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.924363 Loss1: 1.082306 Loss2: 1.842057
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.715479 Loss1: 0.316526 Loss2: 1.398953
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.959399 Loss1: 0.587555 Loss2: 1.371844
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.592246 Loss1: 0.223397 Loss2: 1.368849
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.757744 Loss1: 0.366389 Loss2: 1.391354
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.558086 Loss1: 0.186156 Loss2: 1.371930
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.655166 Loss1: 0.298658 Loss2: 1.356508
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.504456 Loss1: 0.139459 Loss2: 1.364997
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.617059 Loss1: 0.253549 Loss2: 1.363510
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.528021 Loss1: 0.167096 Loss2: 1.360925
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582475 Loss1: 0.229871 Loss2: 1.352604
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.464002 Loss1: 0.104375 Loss2: 1.359627
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.575767 Loss1: 0.223329 Loss2: 1.352438
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.567977 Loss1: 0.204488 Loss2: 1.363489
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.514891 Loss1: 0.166461 Loss2: 1.348430
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441070 Loss1: 0.098255 Loss2: 1.342815
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.218979 Loss1: 1.160913 Loss2: 2.058066
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.223825 Loss1: 0.641717 Loss2: 1.582108
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.029382 Loss1: 0.501827 Loss2: 1.527555
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.864621 Loss1: 0.331775 Loss2: 1.532845
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.005446 Loss1: 1.052731 Loss2: 1.952715
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.059109 Loss1: 0.596022 Loss2: 1.463087 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.897050 Loss1: 0.422887 Loss2: 1.474163 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.622292 Loss1: 0.137451 Loss2: 1.484840
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.775360 Loss1: 0.331117 Loss2: 1.444243
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687344 Loss1: 0.235272 Loss2: 1.452072 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.607629 Loss1: 0.176378 Loss2: 1.431251 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.590165 Loss1: 0.111761 Loss2: 1.478404
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.636830 Loss1: 0.204530 Loss2: 1.432299
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.620413 Loss1: 0.191499 Loss2: 1.428914
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.566641 Loss1: 0.145210 Loss2: 1.421431
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.550516 Loss1: 0.131895 Loss2: 1.418621
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.066520 Loss1: 1.224250 Loss2: 1.842270
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.047430 Loss1: 0.651863 Loss2: 1.395566
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.796491 Loss1: 0.394921 Loss2: 1.401570
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.695760 Loss1: 0.343688 Loss2: 1.352071
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.098346 Loss1: 1.229400 Loss2: 1.868946
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.274683 Loss1: 0.839540 Loss2: 1.435142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.997028 Loss1: 0.550553 Loss2: 1.446475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.749336 Loss1: 0.343413 Loss2: 1.405923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.677988 Loss1: 0.296577 Loss2: 1.381411 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567450 Loss1: 0.185399 Loss2: 1.382050 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424528 Loss1: 0.093854 Loss2: 1.330674
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.568493 Loss1: 0.190856 Loss2: 1.377637
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540262 Loss1: 0.163305 Loss2: 1.376957
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.530118 Loss1: 0.156319 Loss2: 1.373799
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.562248 Loss1: 0.184948 Loss2: 1.377300
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.966248 Loss1: 1.024130 Loss2: 1.942119
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.153373 Loss1: 0.637502 Loss2: 1.515871
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.875141 Loss1: 0.401029 Loss2: 1.474112
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.180561 Loss1: 1.257653 Loss2: 1.922908
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.160599 Loss1: 0.748863 Loss2: 1.411736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.953007 Loss1: 0.498696 Loss2: 1.454312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.578133 Loss1: 0.135723 Loss2: 1.442411
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.762121 Loss1: 0.366266 Loss2: 1.395856
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.664202 Loss1: 0.270181 Loss2: 1.394021
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.587333 Loss1: 0.144594 Loss2: 1.442739
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.583452 Loss1: 0.144222 Loss2: 1.439230 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.550244 Loss1: 0.112383 Loss2: 1.437861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.545569 Loss1: 0.107752 Loss2: 1.437817 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981618
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.874664 Loss1: 1.068254 Loss2: 1.806410 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.894613 Loss1: 0.487693 Loss2: 1.406920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.795201 Loss1: 0.406113 Loss2: 1.389088
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.054402 Loss1: 1.195731 Loss2: 1.858671
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.634132 Loss1: 0.236673 Loss2: 1.397459
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.070148 Loss1: 0.656411 Loss2: 1.413737
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524844 Loss1: 0.166918 Loss2: 1.357926
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.871034 Loss1: 0.456854 Loss2: 1.414180
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.846660 Loss1: 0.449227 Loss2: 1.397433
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511822 Loss1: 0.151005 Loss2: 1.360817
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.627211 Loss1: 0.229972 Loss2: 1.397239
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.517024 Loss1: 0.154126 Loss2: 1.362898
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530205 Loss1: 0.160941 Loss2: 1.369264
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.490062 Loss1: 0.136280 Loss2: 1.353782
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.505873 Loss1: 0.146753 Loss2: 1.359120
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434479 Loss1: 0.084597 Loss2: 1.349882
+(DefaultActor pid=3765) >> Training accuracy: 0.966797
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.455967 Loss1: 0.100225 Loss2: 1.355742 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.899630 Loss1: 1.057869 Loss2: 1.841761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.860746 Loss1: 0.455903 Loss2: 1.404843 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.651785 Loss1: 0.285501 Loss2: 1.366285
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.103906 Loss1: 1.149529 Loss2: 1.954378
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.616054 Loss1: 0.249443 Loss2: 1.366611
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.209292 Loss1: 0.705197 Loss2: 1.504095
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480248 Loss1: 0.137846 Loss2: 1.342402
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.904749 Loss1: 0.402661 Loss2: 1.502088
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495295 Loss1: 0.155584 Loss2: 1.339711
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.788466 Loss1: 0.337790 Loss2: 1.450675
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443543 Loss1: 0.106224 Loss2: 1.337319
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.671572 Loss1: 0.199058 Loss2: 1.472513
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408722 Loss1: 0.082019 Loss2: 1.326703
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.687711 Loss1: 0.243714 Loss2: 1.443996
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420118 Loss1: 0.089727 Loss2: 1.330391
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.671789 Loss1: 0.218109 Loss2: 1.453680
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.621942 Loss1: 0.175727 Loss2: 1.446215
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.575849 Loss1: 0.122790 Loss2: 1.453059
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.563646 Loss1: 0.122288 Loss2: 1.441358
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.135627 Loss1: 1.107585 Loss2: 2.028043
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.284806 Loss1: 0.736788 Loss2: 1.548018
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.957766 Loss1: 0.423340 Loss2: 1.534426
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.755009 Loss1: 0.266636 Loss2: 1.488372
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.286207 Loss1: 1.350538 Loss2: 1.935669
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.724437 Loss1: 0.234784 Loss2: 1.489652
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.186206 Loss1: 0.726657 Loss2: 1.459549
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.657452 Loss1: 0.169763 Loss2: 1.487688
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.929512 Loss1: 0.478429 Loss2: 1.451083
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.722773 Loss1: 0.314984 Loss2: 1.407789
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.645236 Loss1: 0.168615 Loss2: 1.476621
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.637237 Loss1: 0.231272 Loss2: 1.405964
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.603293 Loss1: 0.131308 Loss2: 1.471985
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.578576 Loss1: 0.188665 Loss2: 1.389911
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.574845 Loss1: 0.106287 Loss2: 1.468559
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.547253 Loss1: 0.086916 Loss2: 1.460337 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.477129 Loss1: 0.104175 Loss2: 1.372953 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.960938
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.148312 Loss1: 1.212875 Loss2: 1.935436 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.941356 Loss1: 0.499723 Loss2: 1.441633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561737 Loss1: 0.214929 Loss2: 1.346808 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.090289 Loss1: 0.677999 Loss2: 1.412290 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471850 Loss1: 0.146901 Loss2: 1.324949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.730062 Loss1: 0.331612 Loss2: 1.398450 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975260
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.660818 Loss1: 0.271168 Loss2: 1.389650 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.564625 Loss1: 0.190731 Loss2: 1.373894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.527771 Loss1: 0.157803 Loss2: 1.369967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.500776 Loss1: 0.131545 Loss2: 1.369231 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971680
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.712964 Loss1: 0.337817 Loss2: 1.375147 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.593240 Loss1: 0.219677 Loss2: 1.373563 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.535630 Loss1: 0.186668 Loss2: 1.348962
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.156691 Loss1: 1.208607 Loss2: 1.948083
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.132608 Loss1: 0.715490 Loss2: 1.417117
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497552 Loss1: 0.138365 Loss2: 1.359188
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.910194 Loss1: 0.422627 Loss2: 1.487567
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472882 Loss1: 0.127401 Loss2: 1.345481
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408796 Loss1: 0.071010 Loss2: 1.337786 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.595236 Loss1: 0.183396 Loss2: 1.411840 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510758 Loss1: 0.116411 Loss2: 1.394347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462830 Loss1: 0.076123 Loss2: 1.386707 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985577
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.172372 Loss1: 0.757413 Loss2: 1.414959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.758496 Loss1: 0.382343 Loss2: 1.376153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.076397 Loss1: 1.179676 Loss2: 1.896720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.103014 Loss1: 0.694418 Loss2: 1.408595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.897219 Loss1: 0.432011 Loss2: 1.465208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.719950 Loss1: 0.324128 Loss2: 1.395821 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.651634 Loss1: 0.245048 Loss2: 1.406585 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516902 Loss1: 0.139403 Loss2: 1.377499 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.532584 Loss1: 0.157360 Loss2: 1.375224 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.526919 Loss1: 0.149504 Loss2: 1.377415
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.119815 Loss1: 1.167316 Loss2: 1.952499
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.166577 Loss1: 0.696033 Loss2: 1.470544
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.907733 Loss1: 0.436254 Loss2: 1.471478
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.739502 Loss1: 0.294047 Loss2: 1.445456
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.646923 Loss1: 0.209917 Loss2: 1.437005
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.179399 Loss1: 1.292756 Loss2: 1.886643
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.640431 Loss1: 0.209926 Loss2: 1.430505
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.249306 Loss1: 0.789493 Loss2: 1.459813
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.573501 Loss1: 0.149677 Loss2: 1.423824
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.969661 Loss1: 0.540527 Loss2: 1.429134
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.551089 Loss1: 0.129996 Loss2: 1.421094
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.559308 Loss1: 0.142541 Loss2: 1.416767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.636931 Loss1: 0.215452 Loss2: 1.421479 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.917708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530021 Loss1: 0.130712 Loss2: 1.399309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.525654 Loss1: 0.142050 Loss2: 1.383604 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.530763 Loss1: 0.137576 Loss2: 1.393187
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.854965 Loss1: 1.007962 Loss2: 1.847003
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.150804 Loss1: 0.699709 Loss2: 1.451095
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.891534 Loss1: 0.470291 Loss2: 1.421243
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.756112 Loss1: 0.348494 Loss2: 1.407618
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611881 Loss1: 0.211565 Loss2: 1.400316
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.925604 Loss1: 1.082454 Loss2: 1.843150
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.205508 Loss1: 0.755700 Loss2: 1.449808 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.920778 Loss1: 0.468020 Loss2: 1.452758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.801460 Loss1: 0.386203 Loss2: 1.415256 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.722324 Loss1: 0.304776 Loss2: 1.417548 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.622065 Loss1: 0.215691 Loss2: 1.406375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.524883 Loss1: 0.139922 Loss2: 1.384961 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466094 Loss1: 0.094727 Loss2: 1.371367 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.831286 Loss1: 0.378947 Loss2: 1.452339 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.607640 Loss1: 0.205128 Loss2: 1.402512 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.557044 Loss1: 0.155975 Loss2: 1.401069
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.931377 Loss1: 1.047493 Loss2: 1.883884
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529933 Loss1: 0.146317 Loss2: 1.383616
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.289039 Loss1: 0.849389 Loss2: 1.439650
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480289 Loss1: 0.096179 Loss2: 1.384110
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.892847 Loss1: 0.445031 Loss2: 1.447816
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458804 Loss1: 0.079667 Loss2: 1.379137
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.729994 Loss1: 0.321523 Loss2: 1.408470
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429121 Loss1: 0.058754 Loss2: 1.370367
+DEBUG flwr 2023-10-10 18:17:53,745 | server.py:236 | fit_round 85 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.747203 Loss1: 0.339162 Loss2: 1.408042
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.589259 Loss1: 0.192821 Loss2: 1.396438
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.546109 Loss1: 0.162969 Loss2: 1.383140
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510456 Loss1: 0.129337 Loss2: 1.381120
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.520952 Loss1: 0.147768 Loss2: 1.373185
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.536059 Loss1: 0.159528 Loss2: 1.376530
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.080601 Loss1: 1.175389 Loss2: 1.905212
+(DefaultActor pid=3764) >> Training accuracy: 0.951042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.191175 Loss1: 0.732334 Loss2: 1.458842
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.936827 Loss1: 0.461340 Loss2: 1.475487
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.812477 Loss1: 0.367719 Loss2: 1.444758
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.748502 Loss1: 0.308927 Loss2: 1.439575
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.668894 Loss1: 0.232428 Loss2: 1.436466
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.981593 Loss1: 1.030046 Loss2: 1.951547
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.632896 Loss1: 0.201168 Loss2: 1.431728
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.078302 Loss1: 0.628727 Loss2: 1.449575
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.599550 Loss1: 0.174105 Loss2: 1.425445
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.962696 Loss1: 0.462507 Loss2: 1.500189
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.529160 Loss1: 0.105739 Loss2: 1.423421
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.753905 Loss1: 0.302877 Loss2: 1.451028
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.553738 Loss1: 0.141997 Loss2: 1.411740
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.674429 Loss1: 0.226971 Loss2: 1.447459
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.631807 Loss1: 0.186354 Loss2: 1.445454
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.554357 Loss1: 0.120443 Loss2: 1.433914
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.559272 Loss1: 0.129638 Loss2: 1.429634
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.523930 Loss1: 0.103997 Loss2: 1.419934
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.943089 Loss1: 1.113950 Loss2: 1.829139
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527756 Loss1: 0.108439 Loss2: 1.419318
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.768444 Loss1: 0.361509 Loss2: 1.406934 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.560943 Loss1: 0.201252 Loss2: 1.359691 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542280 Loss1: 0.187673 Loss2: 1.354607
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.036041 Loss1: 1.146256 Loss2: 1.889785
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.557382 Loss1: 0.206804 Loss2: 1.350579
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.204926 Loss1: 0.764123 Loss2: 1.440803
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.562199 Loss1: 0.210437 Loss2: 1.351762
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.910019 Loss1: 0.467515 Loss2: 1.442504
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.519372 Loss1: 0.173403 Loss2: 1.345969
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.792170 Loss1: 0.384511 Loss2: 1.407659
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.471285 Loss1: 0.123086 Loss2: 1.348199
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.678947 Loss1: 0.275957 Loss2: 1.402990
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.589867 Loss1: 0.201619 Loss2: 1.388248
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.547415 Loss1: 0.163629 Loss2: 1.383786
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511675 Loss1: 0.131181 Loss2: 1.380494
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.532399 Loss1: 0.157580 Loss2: 1.374819
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529968 Loss1: 0.152084 Loss2: 1.377884
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 18:17:53,745][flwr][DEBUG] - fit_round 85 received 50 results and 0 failures
+INFO flwr 2023-10-10 18:18:35,399 | server.py:125 | fit progress: (85, 2.2410841007202196, {'accuracy': 0.552}, 196023.177882296)
+>> Test accuracy: 0.552000
+[2023-10-10 18:18:35,399][flwr][INFO] - fit progress: (85, 2.2410841007202196, {'accuracy': 0.552}, 196023.177882296)
+DEBUG flwr 2023-10-10 18:18:35,400 | server.py:173 | evaluate_round 85: strategy sampled 50 clients (out of 50)
+[2023-10-10 18:18:35,400][flwr][DEBUG] - evaluate_round 85: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 18:27:39,110 | server.py:187 | evaluate_round 85 received 50 results and 0 failures
+[2023-10-10 18:27:39,110][flwr][DEBUG] - evaluate_round 85 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 18:27:39,111 | server.py:222 | fit_round 86: strategy sampled 50 clients (out of 50)
+[2023-10-10 18:27:39,111][flwr][DEBUG] - fit_round 86: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.140318 Loss1: 1.270333 Loss2: 1.869985
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.176108 Loss1: 0.781465 Loss2: 1.394643
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.849023 Loss1: 0.436900 Loss2: 1.412122
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.692152 Loss1: 0.335372 Loss2: 1.356781
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.670463 Loss1: 0.285919 Loss2: 1.384544 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.617694 Loss1: 0.268017 Loss2: 1.349677 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544201 Loss1: 0.181340 Loss2: 1.362861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495779 Loss1: 0.153423 Loss2: 1.342356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.732374 Loss1: 0.313687 Loss2: 1.418687
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.506549 Loss1: 0.164520 Loss2: 1.342029
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.639253 Loss1: 0.242032 Loss2: 1.397222
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.490304 Loss1: 0.143774 Loss2: 1.346530
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.575582 Loss1: 0.181434 Loss2: 1.394148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.578316 Loss1: 0.173003 Loss2: 1.405313 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.099565 Loss1: 0.655740 Loss2: 1.443825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.655446 Loss1: 0.250406 Loss2: 1.405039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.598777 Loss1: 0.209109 Loss2: 1.389668
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.082849 Loss1: 1.162817 Loss2: 1.920032
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.568750 Loss1: 0.181023 Loss2: 1.387727
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.183702 Loss1: 0.700405 Loss2: 1.483297
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534038 Loss1: 0.147702 Loss2: 1.386336
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.932178 Loss1: 0.427318 Loss2: 1.504860
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488502 Loss1: 0.117021 Loss2: 1.371481
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.784162 Loss1: 0.348491 Loss2: 1.435671
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484718 Loss1: 0.107286 Loss2: 1.377432
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.745049 Loss1: 0.304238 Loss2: 1.440812
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.480120 Loss1: 0.109343 Loss2: 1.370778
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.746783 Loss1: 0.301795 Loss2: 1.444988
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.695047 Loss1: 0.258589 Loss2: 1.436458
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.619295 Loss1: 0.189401 Loss2: 1.429894
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.554588 Loss1: 0.133703 Loss2: 1.420885
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.508972 Loss1: 0.107050 Loss2: 1.401922
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.978975 Loss1: 1.024959 Loss2: 1.954016
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.055045 Loss1: 0.594785 Loss2: 1.460260
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.900033 Loss1: 0.406303 Loss2: 1.493730
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.741520 Loss1: 0.283532 Loss2: 1.457988
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.717737 Loss1: 0.261566 Loss2: 1.456171
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.620205 Loss1: 0.172917 Loss2: 1.447288 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.597596 Loss1: 0.162633 Loss2: 1.434963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.547915 Loss1: 0.117634 Loss2: 1.430281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.531887 Loss1: 0.112511 Loss2: 1.419376 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.529085 Loss1: 0.102751 Loss2: 1.426334 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.402311 Loss1: 0.090329 Loss2: 1.311983 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370337 Loss1: 0.071687 Loss2: 1.298650 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.126023 Loss1: 0.695988 Loss2: 1.430036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.735021 Loss1: 0.321725 Loss2: 1.413295 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.624257 Loss1: 0.196024 Loss2: 1.428233
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.722584 Loss1: 0.917226 Loss2: 1.805358
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.984746 Loss1: 0.583743 Loss2: 1.401003 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.798530 Loss1: 0.408280 Loss2: 1.390250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.620095 Loss1: 0.240388 Loss2: 1.379707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491130 Loss1: 0.103987 Loss2: 1.387143 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.524420 Loss1: 0.166697 Loss2: 1.357722 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466704 Loss1: 0.119202 Loss2: 1.347502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461881 Loss1: 0.117045 Loss2: 1.344836 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973346
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.884559 Loss1: 0.470601 Loss2: 1.413958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.581154 Loss1: 0.213862 Loss2: 1.367292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.983874 Loss1: 1.133341 Loss2: 1.850534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.229561 Loss1: 0.740890 Loss2: 1.488671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.819181 Loss1: 0.407085 Loss2: 1.412096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.721285 Loss1: 0.304771 Loss2: 1.416513 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.557848 Loss1: 0.166815 Loss2: 1.391033 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481357 Loss1: 0.101545 Loss2: 1.379812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.204708 Loss1: 1.286101 Loss2: 1.918607
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480217 Loss1: 0.101367 Loss2: 1.378850
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.498800 Loss1: 0.118127 Loss2: 1.380673 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969727
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.788792 Loss1: 0.365825 Loss2: 1.422967 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587951 Loss1: 0.182730 Loss2: 1.405221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.128633 Loss1: 1.228426 Loss2: 1.900207 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.279481 Loss1: 0.910370 Loss2: 1.369111 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.877649 Loss1: 0.422631 Loss2: 1.455018 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.698741 Loss1: 0.344132 Loss2: 1.354609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.562390 Loss1: 0.195460 Loss2: 1.366930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469845 Loss1: 0.131613 Loss2: 1.338232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.132133 Loss1: 1.180043 Loss2: 1.952090 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.887831 Loss1: 0.412036 Loss2: 1.475795 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.696635 Loss1: 0.230708 Loss2: 1.465927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.659805 Loss1: 0.202437 Loss2: 1.457368
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.852168 Loss1: 1.027468 Loss2: 1.824700
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.126990 Loss1: 0.673787 Loss2: 1.453203 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.921632 Loss1: 0.483056 Loss2: 1.438576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.747982 Loss1: 0.328565 Loss2: 1.419417 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.652442 Loss1: 0.242060 Loss2: 1.410382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.553926 Loss1: 0.156879 Loss2: 1.397047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.482909 Loss1: 0.101983 Loss2: 1.380927 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.505189 Loss1: 0.124076 Loss2: 1.381114 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.891482 Loss1: 0.415911 Loss2: 1.475571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.653166 Loss1: 0.220822 Loss2: 1.432344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.060435 Loss1: 1.161763 Loss2: 1.898672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.044019 Loss1: 0.631385 Loss2: 1.412634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.858920 Loss1: 0.409337 Loss2: 1.449583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.639119 Loss1: 0.240717 Loss2: 1.398402 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556777 Loss1: 0.153439 Loss2: 1.403338 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.550130 Loss1: 0.166166 Loss2: 1.383964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.293992 Loss1: 1.284787 Loss2: 2.009206 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.888777 Loss1: 0.387104 Loss2: 1.501672 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.763504 Loss1: 0.305887 Loss2: 1.457617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.927293 Loss1: 1.032978 Loss2: 1.894314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.059882 Loss1: 0.657340 Loss2: 1.402542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.799171 Loss1: 0.374192 Loss2: 1.424979 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.538854 Loss1: 0.116943 Loss2: 1.421911 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.974330
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.573552 Loss1: 0.201774 Loss2: 1.371778 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483919 Loss1: 0.115836 Loss2: 1.368083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487697 Loss1: 0.126938 Loss2: 1.360759
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.953130 Loss1: 1.050048 Loss2: 1.903082
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.494218 Loss1: 0.138932 Loss2: 1.355285
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.184933 Loss1: 0.739162 Loss2: 1.445771
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.908962 Loss1: 0.427181 Loss2: 1.481781
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.814323 Loss1: 0.384126 Loss2: 1.430197
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.683379 Loss1: 0.243689 Loss2: 1.439691
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.625978 Loss1: 0.209632 Loss2: 1.416346
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.924332 Loss1: 1.026710 Loss2: 1.897622
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.572280 Loss1: 0.165816 Loss2: 1.406464
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.997553 Loss1: 0.614385 Loss2: 1.383168
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.536103 Loss1: 0.129877 Loss2: 1.406227
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.891541 Loss1: 0.451942 Loss2: 1.439599
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.516275 Loss1: 0.115527 Loss2: 1.400749
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.707782 Loss1: 0.337447 Loss2: 1.370334
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.504930 Loss1: 0.111431 Loss2: 1.393499
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.558951 Loss1: 0.195328 Loss2: 1.363623 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496702 Loss1: 0.133041 Loss2: 1.363662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462200 Loss1: 0.107266 Loss2: 1.354934
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.073827 Loss1: 1.219820 Loss2: 1.854008
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.155574 Loss1: 0.709759 Loss2: 1.445815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.724489 Loss1: 0.313194 Loss2: 1.411294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576092 Loss1: 0.193345 Loss2: 1.382746 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.551713 Loss1: 0.168057 Loss2: 1.383656 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.482451 Loss1: 0.108698 Loss2: 1.373753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443116 Loss1: 0.076808 Loss2: 1.366308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430281 Loss1: 0.066395 Loss2: 1.363886 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502077 Loss1: 0.158645 Loss2: 1.343432 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417661 Loss1: 0.086849 Loss2: 1.330812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.065382 Loss1: 1.183491 Loss2: 1.881892
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.455297 Loss1: 0.133646 Loss2: 1.321651
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.787511 Loss1: 0.346623 Loss2: 1.440888 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.582955 Loss1: 0.181714 Loss2: 1.401241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.044325 Loss1: 1.127147 Loss2: 1.917177
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.591221 Loss1: 0.191352 Loss2: 1.399869
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.191778 Loss1: 0.739928 Loss2: 1.451850
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.597277 Loss1: 0.195103 Loss2: 1.402174
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.973659 Loss1: 0.480980 Loss2: 1.492679
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.569682 Loss1: 0.163629 Loss2: 1.406053
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.817044 Loss1: 0.380173 Loss2: 1.436871
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.558804 Loss1: 0.153289 Loss2: 1.405514
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.497427 Loss1: 0.105992 Loss2: 1.391435 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.586982 Loss1: 0.166063 Loss2: 1.420919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.513874 Loss1: 0.113809 Loss2: 1.400065 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.515585 Loss1: 0.119626 Loss2: 1.395959
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.052319 Loss1: 1.197471 Loss2: 1.854848
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.106842 Loss1: 0.695990 Loss2: 1.410851
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.891222 Loss1: 0.456580 Loss2: 1.434641
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.708818 Loss1: 0.321753 Loss2: 1.387064
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.582496 Loss1: 0.195289 Loss2: 1.387207
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.008967 Loss1: 1.098897 Loss2: 1.910070
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.116235 Loss1: 0.647895 Loss2: 1.468340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.908306 Loss1: 0.431007 Loss2: 1.477299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.783518 Loss1: 0.353844 Loss2: 1.429674 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.701577 Loss1: 0.264661 Loss2: 1.436915 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.959375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647382 Loss1: 0.224848 Loss2: 1.422535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548909 Loss1: 0.151466 Loss2: 1.397443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.548586 Loss1: 0.151568 Loss2: 1.397018 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.226243 Loss1: 0.728475 Loss2: 1.497768 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.835263 Loss1: 0.383479 Loss2: 1.451784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.053141 Loss1: 1.116648 Loss2: 1.936493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.070960 Loss1: 0.681011 Loss2: 1.389949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804125 Loss1: 0.395684 Loss2: 1.408442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.757350 Loss1: 0.381408 Loss2: 1.375943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.630242 Loss1: 0.194191 Loss2: 1.436051
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.572042 Loss1: 0.186340 Loss2: 1.385701
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502157 Loss1: 0.142362 Loss2: 1.359795
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.563636 Loss1: 0.135737 Loss2: 1.427899
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419655 Loss1: 0.076249 Loss2: 1.343405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419535 Loss1: 0.085557 Loss2: 1.333977 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985577
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.134920 Loss1: 1.234275 Loss2: 1.900645
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.174678 Loss1: 0.736140 Loss2: 1.438538
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.776876 Loss1: 0.389936 Loss2: 1.386940
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629821 Loss1: 0.244105 Loss2: 1.385716
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.986023 Loss1: 1.047073 Loss2: 1.938950
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.307706 Loss1: 0.813781 Loss2: 1.493926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.982490 Loss1: 0.476566 Loss2: 1.505924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.820136 Loss1: 0.363610 Loss2: 1.456527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.729848 Loss1: 0.260482 Loss2: 1.469366 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.664943 Loss1: 0.230169 Loss2: 1.434774 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.690163 Loss1: 0.249480 Loss2: 1.440683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.561096 Loss1: 0.128878 Loss2: 1.432218 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.131408 Loss1: 1.209088 Loss2: 1.922320 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.003790 Loss1: 0.527759 Loss2: 1.476031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.804805 Loss1: 0.353539 Loss2: 1.451266
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.974287 Loss1: 1.099049 Loss2: 1.875238
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.194016 Loss1: 0.739413 Loss2: 1.454603 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.881170 Loss1: 0.460854 Loss2: 1.420316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.713523 Loss1: 0.310601 Loss2: 1.402922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.631202 Loss1: 0.242923 Loss2: 1.388279 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.565402 Loss1: 0.177853 Loss2: 1.387549 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.543767 Loss1: 0.162850 Loss2: 1.380917 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.495050 Loss1: 0.123496 Loss2: 1.371554 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.170461 Loss1: 0.698078 Loss2: 1.472383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.751940 Loss1: 0.311952 Loss2: 1.439988 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.982911 Loss1: 1.124208 Loss2: 1.858703
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.674939 Loss1: 0.233717 Loss2: 1.441222
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.101558 Loss1: 0.690153 Loss2: 1.411405
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.640326 Loss1: 0.217940 Loss2: 1.422386
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.872267 Loss1: 0.438886 Loss2: 1.433381
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.566137 Loss1: 0.139852 Loss2: 1.426285
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.725952 Loss1: 0.338309 Loss2: 1.387643
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.529524 Loss1: 0.105745 Loss2: 1.423778
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.626486 Loss1: 0.245505 Loss2: 1.380981
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.543811 Loss1: 0.130039 Loss2: 1.413773
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.584040 Loss1: 0.206688 Loss2: 1.377351
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.530233 Loss1: 0.116214 Loss2: 1.414019
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.495001 Loss1: 0.132543 Loss2: 1.362457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448927 Loss1: 0.096812 Loss2: 1.352114 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.016535 Loss1: 1.095396 Loss2: 1.921139
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.102003 Loss1: 0.604800 Loss2: 1.497203
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.857931 Loss1: 0.382629 Loss2: 1.475302
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.720550 Loss1: 0.270726 Loss2: 1.449824
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.127472 Loss1: 1.230446 Loss2: 1.897026
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.270671 Loss1: 0.803512 Loss2: 1.467160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.972620 Loss1: 0.522537 Loss2: 1.450083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.772661 Loss1: 0.331652 Loss2: 1.441009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.662333 Loss1: 0.243827 Loss2: 1.418505 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.494453 Loss1: 0.082854 Loss2: 1.411598
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.594253 Loss1: 0.183212 Loss2: 1.411042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.486993 Loss1: 0.079563 Loss2: 1.407430
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.547221 Loss1: 0.144198 Loss2: 1.403023
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490699 Loss1: 0.092219 Loss2: 1.398480
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499026 Loss1: 0.111973 Loss2: 1.387052
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.479786 Loss1: 0.093006 Loss2: 1.386780
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.993988 Loss1: 1.078590 Loss2: 1.915398
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.107020 Loss1: 0.656105 Loss2: 1.450915
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.871716 Loss1: 0.398786 Loss2: 1.472930
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.693606 Loss1: 0.277843 Loss2: 1.415762
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.871560 Loss1: 1.093574 Loss2: 1.777986
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.097564 Loss1: 0.698268 Loss2: 1.399296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.832924 Loss1: 0.466771 Loss2: 1.366153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.767144 Loss1: 0.391627 Loss2: 1.375517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.604049 Loss1: 0.254038 Loss2: 1.350011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510023 Loss1: 0.162291 Loss2: 1.347733 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478885 Loss1: 0.146731 Loss2: 1.332154 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432151 Loss1: 0.108971 Loss2: 1.323180 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.970596 Loss1: 1.101645 Loss2: 1.868951 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.859144 Loss1: 0.414649 Loss2: 1.444495 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.906958 Loss1: 1.039525 Loss2: 1.867434 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.931824 Loss1: 0.572296 Loss2: 1.359528 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701808 Loss1: 0.320709 Loss2: 1.381099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.645220 Loss1: 0.309908 Loss2: 1.335313 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.592131 Loss1: 0.240369 Loss2: 1.351762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.565553 Loss1: 0.224695 Loss2: 1.340858 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.521987 Loss1: 0.179208 Loss2: 1.342779 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.520379 Loss1: 0.178255 Loss2: 1.342124 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.955184 Loss1: 1.038752 Loss2: 1.916433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.894033 Loss1: 0.443684 Loss2: 1.450349 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.738227 Loss1: 0.324104 Loss2: 1.414124
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.995747 Loss1: 1.130036 Loss2: 1.865711
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.185991 Loss1: 0.760041 Loss2: 1.425950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.940969 Loss1: 0.497556 Loss2: 1.443413 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.721458 Loss1: 0.317869 Loss2: 1.403589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.643740 Loss1: 0.249498 Loss2: 1.394241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567497 Loss1: 0.183234 Loss2: 1.384263 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.546284 Loss1: 0.166536 Loss2: 1.379748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.489889 Loss1: 0.125435 Loss2: 1.364454 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.084060 Loss1: 1.175128 Loss2: 1.908932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.918908 Loss1: 0.462839 Loss2: 1.456069 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.108981 Loss1: 1.215187 Loss2: 1.893794 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.574513 Loss1: 0.200788 Loss2: 1.373725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.584061 Loss1: 0.197564 Loss2: 1.386497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.533730 Loss1: 0.146384 Loss2: 1.387345 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.555147 Loss1: 0.188915 Loss2: 1.366232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.492432 Loss1: 0.125982 Loss2: 1.366450 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972098
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.611115 Loss1: 0.207426 Loss2: 1.403689 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.526381 Loss1: 0.139873 Loss2: 1.386508 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.027977 Loss1: 0.651643 Loss2: 1.376334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.690495 Loss1: 0.330164 Loss2: 1.360331 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.919832 Loss1: 1.058120 Loss2: 1.861712
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.586099 Loss1: 0.221726 Loss2: 1.364373
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.106318 Loss1: 0.697232 Loss2: 1.409087
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.550877 Loss1: 0.182218 Loss2: 1.368659
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.828238 Loss1: 0.405955 Loss2: 1.422283
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518813 Loss1: 0.170083 Loss2: 1.348730
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.678622 Loss1: 0.306868 Loss2: 1.371754
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457390 Loss1: 0.106461 Loss2: 1.350929
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430875 Loss1: 0.092809 Loss2: 1.338066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446002 Loss1: 0.111351 Loss2: 1.334651 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483082 Loss1: 0.128542 Loss2: 1.354541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439290 Loss1: 0.091306 Loss2: 1.347984 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.206313 Loss1: 0.787053 Loss2: 1.419260 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.837189 Loss1: 0.388848 Loss2: 1.448341 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.073583 Loss1: 1.213796 Loss2: 1.859787 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.130287 Loss1: 0.726676 Loss2: 1.403610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.588832 Loss1: 0.180066 Loss2: 1.408766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.817391 Loss1: 0.421795 Loss2: 1.395597 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972656
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.603306 Loss1: 0.224534 Loss2: 1.378772 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500574 Loss1: 0.129667 Loss2: 1.370907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461975 Loss1: 0.097259 Loss2: 1.364716
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.974263 Loss1: 0.991357 Loss2: 1.982906
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460226 Loss1: 0.107768 Loss2: 1.352458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.335353 Loss1: 0.847355 Loss2: 1.487998
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.045640 Loss1: 0.512737 Loss2: 1.532903
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.878695 Loss1: 0.403655 Loss2: 1.475040
+DEBUG flwr 2023-10-10 18:56:02,170 | server.py:236 | fit_round 86 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.730886 Loss1: 0.256390 Loss2: 1.474496
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.607843 Loss1: 0.155072 Loss2: 1.452771
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.017348 Loss1: 1.124054 Loss2: 1.893294
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.550876 Loss1: 0.112241 Loss2: 1.438635
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.244304 Loss1: 0.730064 Loss2: 1.514240
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.553926 Loss1: 0.120534 Loss2: 1.433392
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.538090 Loss1: 0.105709 Loss2: 1.432382
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.919019 Loss1: 0.461194 Loss2: 1.457825
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.522117 Loss1: 0.090114 Loss2: 1.432003
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.877702 Loss1: 0.420936 Loss2: 1.456767
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.760448 Loss1: 0.320739 Loss2: 1.439709
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.729061 Loss1: 0.274980 Loss2: 1.454081
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.587778 Loss1: 0.159921 Loss2: 1.427857
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.513899 Loss1: 0.098372 Loss2: 1.415528
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.506823 Loss1: 0.096090 Loss2: 1.410733
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.494253 Loss1: 0.084340 Loss2: 1.409912
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 18:56:02,170][flwr][DEBUG] - fit_round 86 received 50 results and 0 failures
+INFO flwr 2023-10-10 18:56:42,398 | server.py:125 | fit progress: (86, 2.234298116863726, {'accuracy': 0.5525}, 198310.17647703202)
+>> Test accuracy: 0.552500
+[2023-10-10 18:56:42,398][flwr][INFO] - fit progress: (86, 2.234298116863726, {'accuracy': 0.5525}, 198310.17647703202)
+DEBUG flwr 2023-10-10 18:56:42,398 | server.py:173 | evaluate_round 86: strategy sampled 50 clients (out of 50)
+[2023-10-10 18:56:42,398][flwr][DEBUG] - evaluate_round 86: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 19:05:46,675 | server.py:187 | evaluate_round 86 received 50 results and 0 failures
+[2023-10-10 19:05:46,675][flwr][DEBUG] - evaluate_round 86 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 19:05:46,675 | server.py:222 | fit_round 87: strategy sampled 50 clients (out of 50)
+[2023-10-10 19:05:46,675][flwr][DEBUG] - fit_round 87: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.123142 Loss1: 1.155201 Loss2: 1.967941
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.334102 Loss1: 0.792193 Loss2: 1.541909
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.999898 Loss1: 0.492751 Loss2: 1.507147
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.826729 Loss1: 0.341843 Loss2: 1.484886
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.237907 Loss1: 1.233943 Loss2: 2.003964
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.052682 Loss1: 0.652435 Loss2: 1.400247
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.790454 Loss1: 0.301870 Loss2: 1.488584
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.805969 Loss1: 0.367733 Loss2: 1.438236
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.636381 Loss1: 0.168893 Loss2: 1.467487
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.598193 Loss1: 0.144110 Loss2: 1.454083 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.597802 Loss1: 0.142650 Loss2: 1.455152 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.608898 Loss1: 0.158259 Loss2: 1.450639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.583480 Loss1: 0.129130 Loss2: 1.454350 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527219 Loss1: 0.158986 Loss2: 1.368232 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973558
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.963377 Loss1: 1.083073 Loss2: 1.880304
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.128378 Loss1: 0.732273 Loss2: 1.396104
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.858283 Loss1: 0.419545 Loss2: 1.438739
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694153 Loss1: 0.318064 Loss2: 1.376090
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.995508 Loss1: 1.157705 Loss2: 1.837804
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568171 Loss1: 0.183584 Loss2: 1.384587
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.171778 Loss1: 0.746189 Loss2: 1.425589
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569931 Loss1: 0.211378 Loss2: 1.358553
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.893239 Loss1: 0.475485 Loss2: 1.417754
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502163 Loss1: 0.134189 Loss2: 1.367973
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.730501 Loss1: 0.339551 Loss2: 1.390950
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.494375 Loss1: 0.134362 Loss2: 1.360014
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.611573 Loss1: 0.220736 Loss2: 1.390837
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484606 Loss1: 0.127206 Loss2: 1.357400
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.562482 Loss1: 0.189794 Loss2: 1.372688
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453533 Loss1: 0.096664 Loss2: 1.356869
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.585929 Loss1: 0.221297 Loss2: 1.364632
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568234 Loss1: 0.199788 Loss2: 1.368446
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.535039 Loss1: 0.168162 Loss2: 1.366877
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.538508 Loss1: 0.177983 Loss2: 1.360525
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.151003 Loss1: 1.255878 Loss2: 1.895124
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.129543 Loss1: 0.698526 Loss2: 1.431017
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.868366 Loss1: 0.423004 Loss2: 1.445362
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.702908 Loss1: 0.299143 Loss2: 1.403765
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.011038 Loss1: 1.189029 Loss2: 1.822009
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605788 Loss1: 0.199626 Loss2: 1.406161
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.167648 Loss1: 0.779409 Loss2: 1.388239
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.589734 Loss1: 0.197212 Loss2: 1.392522
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.865963 Loss1: 0.439676 Loss2: 1.426287
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.592955 Loss1: 0.186713 Loss2: 1.406241
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.717655 Loss1: 0.351234 Loss2: 1.366421
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.515176 Loss1: 0.116668 Loss2: 1.398507
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.653102 Loss1: 0.286410 Loss2: 1.366692
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.502885 Loss1: 0.118459 Loss2: 1.384426
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.601202 Loss1: 0.237629 Loss2: 1.363573
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485755 Loss1: 0.099740 Loss2: 1.386015
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.579076 Loss1: 0.219159 Loss2: 1.359917
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.517145 Loss1: 0.169655 Loss2: 1.347490
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490269 Loss1: 0.150134 Loss2: 1.340135
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465405 Loss1: 0.129868 Loss2: 1.335536
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.996516 Loss1: 1.154056 Loss2: 1.842460
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.103322 Loss1: 0.678339 Loss2: 1.424982
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.845299 Loss1: 0.428060 Loss2: 1.417239
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.871064 Loss1: 1.024639 Loss2: 1.846425
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.666068 Loss1: 0.276350 Loss2: 1.389718
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.121671 Loss1: 0.683827 Loss2: 1.437844
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.614628 Loss1: 0.227037 Loss2: 1.387590
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.822779 Loss1: 0.419962 Loss2: 1.402817
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.581862 Loss1: 0.200834 Loss2: 1.381028
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.785340 Loss1: 0.382393 Loss2: 1.402947
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503257 Loss1: 0.130404 Loss2: 1.372853
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.681038 Loss1: 0.277518 Loss2: 1.403520
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.527316 Loss1: 0.158989 Loss2: 1.368327
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.643249 Loss1: 0.249077 Loss2: 1.394172
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.494682 Loss1: 0.129351 Loss2: 1.365331
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.584859 Loss1: 0.198616 Loss2: 1.386243
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485966 Loss1: 0.118540 Loss2: 1.367426
+(DefaultActor pid=3765) >> Training accuracy: 0.977539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.506063 Loss1: 0.128202 Loss2: 1.377861 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.095399 Loss1: 1.187154 Loss2: 1.908245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.920981 Loss1: 0.448642 Loss2: 1.472339 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.904711 Loss1: 1.042144 Loss2: 1.862567
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.798634 Loss1: 0.324625 Loss2: 1.474009
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.183058 Loss1: 0.749702 Loss2: 1.433356
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.726494 Loss1: 0.265362 Loss2: 1.461132
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.865175 Loss1: 0.422366 Loss2: 1.442809
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.664006 Loss1: 0.205600 Loss2: 1.458406
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.620435 Loss1: 0.170636 Loss2: 1.449799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.603160 Loss1: 0.161307 Loss2: 1.441852 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.584603 Loss1: 0.144926 Loss2: 1.439678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.543380 Loss1: 0.104988 Loss2: 1.438392 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.468591 Loss1: 0.103603 Loss2: 1.364988 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.994748 Loss1: 1.139540 Loss2: 1.855208 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.789413 Loss1: 0.375880 Loss2: 1.413533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.735925 Loss1: 0.361792 Loss2: 1.374133
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.011265 Loss1: 1.149359 Loss2: 1.861907
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.022654 Loss1: 0.606811 Loss2: 1.415843 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.740471 Loss1: 0.318974 Loss2: 1.421497 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.651720 Loss1: 0.264199 Loss2: 1.387521 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.629830 Loss1: 0.226074 Loss2: 1.403756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.639067 Loss1: 0.258119 Loss2: 1.380947 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.515033 Loss1: 0.166794 Loss2: 1.348239
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.611690 Loss1: 0.213262 Loss2: 1.398428
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.564068 Loss1: 0.180133 Loss2: 1.383934
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.504698 Loss1: 0.122267 Loss2: 1.382431
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.467937 Loss1: 0.093200 Loss2: 1.374738
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.853087 Loss1: 1.011718 Loss2: 1.841370
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.085716 Loss1: 0.679890 Loss2: 1.405826
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.855687 Loss1: 0.435491 Loss2: 1.420196
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.766423 Loss1: 0.395498 Loss2: 1.370925
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.827414 Loss1: 0.991093 Loss2: 1.836321
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.938486 Loss1: 0.539637 Loss2: 1.398849 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.832212 Loss1: 0.420771 Loss2: 1.411441 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.710756 Loss1: 0.324094 Loss2: 1.386663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.724654 Loss1: 0.318076 Loss2: 1.406578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.597972 Loss1: 0.216200 Loss2: 1.381772 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.578210 Loss1: 0.193800 Loss2: 1.384409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490243 Loss1: 0.130630 Loss2: 1.359613 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.920994 Loss1: 1.043834 Loss2: 1.877160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.958992 Loss1: 0.499462 Loss2: 1.459530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.880495 Loss1: 1.004879 Loss2: 1.875615 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.133833 Loss1: 0.722001 Loss2: 1.411832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.865467 Loss1: 0.422532 Loss2: 1.442935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.688600 Loss1: 0.300745 Loss2: 1.387855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.689359 Loss1: 0.294580 Loss2: 1.394779 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.635498 Loss1: 0.244813 Loss2: 1.390685 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546851 Loss1: 0.165896 Loss2: 1.380955 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452456 Loss1: 0.092133 Loss2: 1.360323 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.016631 Loss1: 0.617013 Loss2: 1.399618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591104 Loss1: 0.233091 Loss2: 1.358013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517831 Loss1: 0.164222 Loss2: 1.353609 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551895 Loss1: 0.199013 Loss2: 1.352883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.501510 Loss1: 0.141543 Loss2: 1.359968 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495161 Loss1: 0.146518 Loss2: 1.348642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454326 Loss1: 0.106559 Loss2: 1.347767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.514907 Loss1: 0.163692 Loss2: 1.351215
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.482984 Loss1: 0.140583 Loss2: 1.342401
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431093 Loss1: 0.094452 Loss2: 1.336641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396370 Loss1: 0.066421 Loss2: 1.329949 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.175390 Loss1: 0.703898 Loss2: 1.471492 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.891364 Loss1: 0.414005 Loss2: 1.477359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.867849 Loss1: 1.026928 Loss2: 1.840921
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.779388 Loss1: 0.318622 Loss2: 1.460766
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.678558 Loss1: 0.231712 Loss2: 1.446846
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.117276 Loss1: 0.691562 Loss2: 1.425714
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.606772 Loss1: 0.165254 Loss2: 1.441518
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.860973 Loss1: 0.433863 Loss2: 1.427110
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.618968 Loss1: 0.182463 Loss2: 1.436506
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.664006 Loss1: 0.261889 Loss2: 1.402117
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.620638 Loss1: 0.181571 Loss2: 1.439067
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.562916 Loss1: 0.175404 Loss2: 1.387511
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.601799 Loss1: 0.167946 Loss2: 1.433853
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521895 Loss1: 0.142726 Loss2: 1.379169
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487895 Loss1: 0.110260 Loss2: 1.377635
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.463794 Loss1: 0.103565 Loss2: 1.360229
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453574 Loss1: 0.093402 Loss2: 1.360172
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440943 Loss1: 0.084184 Loss2: 1.356759
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.888995 Loss1: 1.068322 Loss2: 1.820673
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.072275 Loss1: 0.683512 Loss2: 1.388763
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.856626 Loss1: 0.462698 Loss2: 1.393928
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.690798 Loss1: 0.332206 Loss2: 1.358592
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.659440 Loss1: 0.296997 Loss2: 1.362444
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.950553 Loss1: 1.008326 Loss2: 1.942227
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576989 Loss1: 0.212047 Loss2: 1.364942
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.202800 Loss1: 0.695831 Loss2: 1.506970
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.543951 Loss1: 0.205604 Loss2: 1.338347
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479655 Loss1: 0.127622 Loss2: 1.352033
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.913789 Loss1: 0.398620 Loss2: 1.515170
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.473060 Loss1: 0.140271 Loss2: 1.332789
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.759216 Loss1: 0.289891 Loss2: 1.469325
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463858 Loss1: 0.124813 Loss2: 1.339045
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.681204 Loss1: 0.208871 Loss2: 1.472333
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.642146 Loss1: 0.174807 Loss2: 1.467339
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.594293 Loss1: 0.138821 Loss2: 1.455472
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568299 Loss1: 0.115452 Loss2: 1.452847
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.540753 Loss1: 0.091620 Loss2: 1.449132
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.815461 Loss1: 1.032986 Loss2: 1.782474
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509437 Loss1: 0.064179 Loss2: 1.445258
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.750223 Loss1: 0.379565 Loss2: 1.370658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.575439 Loss1: 0.229247 Loss2: 1.346192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.500381 Loss1: 0.166007 Loss2: 1.334374
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.882182 Loss1: 1.059409 Loss2: 1.822773
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468873 Loss1: 0.146489 Loss2: 1.322384
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.060609 Loss1: 0.649495 Loss2: 1.411115
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.465153 Loss1: 0.141783 Loss2: 1.323370
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.820805 Loss1: 0.408397 Loss2: 1.412408
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433920 Loss1: 0.116333 Loss2: 1.317587
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.688160 Loss1: 0.304688 Loss2: 1.383472
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421458 Loss1: 0.109481 Loss2: 1.311977
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609279 Loss1: 0.222976 Loss2: 1.386303
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.599012 Loss1: 0.225286 Loss2: 1.373726
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.536291 Loss1: 0.163195 Loss2: 1.373096
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.541074 Loss1: 0.168074 Loss2: 1.373000
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497327 Loss1: 0.134966 Loss2: 1.362361
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.801662 Loss1: 0.933856 Loss2: 1.867806
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.479900 Loss1: 0.122944 Loss2: 1.356955
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.824589 Loss1: 0.407816 Loss2: 1.416773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.581023 Loss1: 0.212503 Loss2: 1.368520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538452 Loss1: 0.186798 Loss2: 1.351654
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.167753 Loss1: 1.176009 Loss2: 1.991744
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.189701 Loss1: 0.717556 Loss2: 1.472144 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.485705 Loss1: 0.122571 Loss2: 1.363134
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.908699 Loss1: 0.410788 Loss2: 1.497911
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.442942 Loss1: 0.094690 Loss2: 1.348253
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.780257 Loss1: 0.326762 Loss2: 1.453495
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.470456 Loss1: 0.127568 Loss2: 1.342888
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.683989 Loss1: 0.222550 Loss2: 1.461439
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.587102 Loss1: 0.141936 Loss2: 1.445166
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.575343 Loss1: 0.140043 Loss2: 1.435300
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.551509 Loss1: 0.120362 Loss2: 1.431147
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.568242 Loss1: 0.141457 Loss2: 1.426785
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.528883 Loss1: 0.098338 Loss2: 1.430545
+(DefaultActor pid=3764) >> Training accuracy: 0.985491
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.023888 Loss1: 1.164051 Loss2: 1.859837
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.182480 Loss1: 0.745989 Loss2: 1.436492
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.879539 Loss1: 0.443832 Loss2: 1.435707
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.813665 Loss1: 0.401034 Loss2: 1.412631
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.648310 Loss1: 0.239305 Loss2: 1.409006
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.004156 Loss1: 1.165693 Loss2: 1.838464
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.593909 Loss1: 0.208395 Loss2: 1.385514
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.563153 Loss1: 0.169944 Loss2: 1.393209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.538040 Loss1: 0.153725 Loss2: 1.384315 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.511626 Loss1: 0.129637 Loss2: 1.381989 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481227 Loss1: 0.098342 Loss2: 1.382885 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.570410 Loss1: 0.177229 Loss2: 1.393182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.491470 Loss1: 0.125921 Loss2: 1.365549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.495117 Loss1: 0.123015 Loss2: 1.372101
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.007217 Loss1: 1.098055 Loss2: 1.909163
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.146294 Loss1: 0.687012 Loss2: 1.459282
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.956629 Loss1: 0.509813 Loss2: 1.446816
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.781765 Loss1: 0.347949 Loss2: 1.433816
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.651409 Loss1: 0.232645 Loss2: 1.418764
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.054601 Loss1: 1.221791 Loss2: 1.832810
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.590951 Loss1: 0.184834 Loss2: 1.406116
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534339 Loss1: 0.143279 Loss2: 1.391060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484868 Loss1: 0.099830 Loss2: 1.385038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.471009 Loss1: 0.095518 Loss2: 1.375491 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.548398 Loss1: 0.202957 Loss2: 1.345441 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.494582 Loss1: 0.168323 Loss2: 1.326260 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410069 Loss1: 0.087959 Loss2: 1.322110 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.919780 Loss1: 1.075036 Loss2: 1.844744
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.038304 Loss1: 0.650102 Loss2: 1.388202
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.826197 Loss1: 0.413628 Loss2: 1.412569
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.738429 Loss1: 0.366313 Loss2: 1.372117
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.796667 Loss1: 0.940556 Loss2: 1.856112
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.146816 Loss1: 0.730568 Loss2: 1.416249 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.923379 Loss1: 0.483669 Loss2: 1.439710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.725566 Loss1: 0.338875 Loss2: 1.386691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558176 Loss1: 0.172286 Loss2: 1.385890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502234 Loss1: 0.137947 Loss2: 1.364288 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483351 Loss1: 0.126070 Loss2: 1.357280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464376 Loss1: 0.121562 Loss2: 1.342814 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.028250 Loss1: 1.112942 Loss2: 1.915308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.024370 Loss1: 0.509755 Loss2: 1.514616 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.114707 Loss1: 1.246896 Loss2: 1.867812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.122982 Loss1: 0.722678 Loss2: 1.400304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.920146 Loss1: 0.507972 Loss2: 1.412174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.703580 Loss1: 0.322149 Loss2: 1.381431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.635479 Loss1: 0.266857 Loss2: 1.368621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527456 Loss1: 0.161496 Loss2: 1.365960 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.520421 Loss1: 0.169593 Loss2: 1.350828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446873 Loss1: 0.100673 Loss2: 1.346200 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.816734 Loss1: 0.920368 Loss2: 1.896367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.905163 Loss1: 0.448370 Loss2: 1.456793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.797993 Loss1: 0.352788 Loss2: 1.445205
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.881670 Loss1: 1.007967 Loss2: 1.873704
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.671139 Loss1: 0.228634 Loss2: 1.442505
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.247844 Loss1: 0.794939 Loss2: 1.452906
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.614954 Loss1: 0.187411 Loss2: 1.427543
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.948901 Loss1: 0.456151 Loss2: 1.492750
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.743749 Loss1: 0.337126 Loss2: 1.406623
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.602411 Loss1: 0.175088 Loss2: 1.427323
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.729014 Loss1: 0.311735 Loss2: 1.417279
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.523061 Loss1: 0.103791 Loss2: 1.419270
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.517825 Loss1: 0.104340 Loss2: 1.413485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474560 Loss1: 0.066766 Loss2: 1.407794 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988971
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.582404 Loss1: 0.184247 Loss2: 1.398157 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.075307 Loss1: 1.041809 Loss2: 2.033498 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.988755 Loss1: 0.486704 Loss2: 1.502051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.806744 Loss1: 0.313740 Loss2: 1.493004
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.866302 Loss1: 1.041500 Loss2: 1.824802
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.724829 Loss1: 0.247780 Loss2: 1.477049
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.186320 Loss1: 0.778429 Loss2: 1.407891
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.714098 Loss1: 0.244855 Loss2: 1.469243
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.847214 Loss1: 0.451944 Loss2: 1.395270
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.636841 Loss1: 0.164394 Loss2: 1.472447
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.691859 Loss1: 0.330291 Loss2: 1.361567
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.583779 Loss1: 0.113637 Loss2: 1.470142
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571952 Loss1: 0.212276 Loss2: 1.359676
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.591436 Loss1: 0.137393 Loss2: 1.454042
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485453 Loss1: 0.143428 Loss2: 1.342025
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.558923 Loss1: 0.099847 Loss2: 1.459076
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436751 Loss1: 0.101051 Loss2: 1.335700
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405182 Loss1: 0.081067 Loss2: 1.324114
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410112 Loss1: 0.096526 Loss2: 1.313586
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387928 Loss1: 0.072271 Loss2: 1.315657
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.059705 Loss1: 1.190905 Loss2: 1.868800
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.107492 Loss1: 0.687858 Loss2: 1.419634
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.852778 Loss1: 0.409224 Loss2: 1.443555
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.224318 Loss1: 1.237630 Loss2: 1.986688
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.667598 Loss1: 0.273136 Loss2: 1.394462
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.638950 Loss1: 0.242188 Loss2: 1.396762 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.613481 Loss1: 0.225387 Loss2: 1.388094 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.622984 Loss1: 0.227304 Loss2: 1.395680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.631885 Loss1: 0.238966 Loss2: 1.392920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.546999 Loss1: 0.165224 Loss2: 1.381775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.545937 Loss1: 0.178028 Loss2: 1.367909 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464608 Loss1: 0.101975 Loss2: 1.362633 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.248846 Loss1: 1.332403 Loss2: 1.916443
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.197725 Loss1: 0.767177 Loss2: 1.430548
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.894033 Loss1: 0.465433 Loss2: 1.428600
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.719393 Loss1: 0.319678 Loss2: 1.399715
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.062884 Loss1: 1.155246 Loss2: 1.907638
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.164075 Loss1: 0.702126 Loss2: 1.461949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.930784 Loss1: 0.469545 Loss2: 1.461238 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.747594 Loss1: 0.323914 Loss2: 1.423679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.666534 Loss1: 0.242561 Loss2: 1.423973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.646152 Loss1: 0.242707 Loss2: 1.403446 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972098
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548619 Loss1: 0.143292 Loss2: 1.405327 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.542084 Loss1: 0.152393 Loss2: 1.389691 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.139492 Loss1: 0.682917 Loss2: 1.456575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.779591 Loss1: 0.318116 Loss2: 1.461475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.728706 Loss1: 0.287547 Loss2: 1.441160
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.923044 Loss1: 1.162544 Loss2: 1.760500
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.050282 Loss1: 0.693360 Loss2: 1.356921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.753329 Loss1: 0.402121 Loss2: 1.351208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699795 Loss1: 0.362548 Loss2: 1.337247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.620637 Loss1: 0.297538 Loss2: 1.323100 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.565489 Loss1: 0.244574 Loss2: 1.320915 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464800 Loss1: 0.150035 Loss2: 1.314765 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437362 Loss1: 0.131949 Loss2: 1.305412
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.892532 Loss1: 1.007908 Loss2: 1.884624
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.137114 Loss1: 0.654127 Loss2: 1.482987
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.848989 Loss1: 0.380705 Loss2: 1.468283
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.786942 Loss1: 0.345216 Loss2: 1.441726
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.687156 Loss1: 0.240960 Loss2: 1.446195
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.997116 Loss1: 1.125269 Loss2: 1.871848
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.132814 Loss1: 0.702264 Loss2: 1.430550 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 19:34:47,276 | server.py:236 | fit_round 87 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.581102 Loss1: 0.159072 Loss2: 1.422030
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.793932 Loss1: 0.382984 Loss2: 1.410948
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.531536 Loss1: 0.111597 Loss2: 1.419939
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.664623 Loss1: 0.280017 Loss2: 1.384605
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.528136 Loss1: 0.115821 Loss2: 1.412315
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.584009 Loss1: 0.209731 Loss2: 1.374278
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.499144 Loss1: 0.091995 Loss2: 1.407149
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556773 Loss1: 0.193355 Loss2: 1.363418
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.529115 Loss1: 0.165309 Loss2: 1.363807
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518849 Loss1: 0.149650 Loss2: 1.369199
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.482090 Loss1: 0.129607 Loss2: 1.352483
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.495949 Loss1: 0.143827 Loss2: 1.352122
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.950017 Loss1: 1.139450 Loss2: 1.810567
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.168611 Loss1: 0.773738 Loss2: 1.394873
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.857307 Loss1: 0.474357 Loss2: 1.382950
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.670003 Loss1: 0.329238 Loss2: 1.340765
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.600205 Loss1: 0.251516 Loss2: 1.348689
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511098 Loss1: 0.181455 Loss2: 1.329643 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450708 Loss1: 0.123751 Loss2: 1.326956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467555 Loss1: 0.145406 Loss2: 1.322149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449415 Loss1: 0.128331 Loss2: 1.321084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433541 Loss1: 0.116628 Loss2: 1.316913 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.541902 Loss1: 0.134564 Loss2: 1.407338 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430696 Loss1: 0.037630 Loss2: 1.393066 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.031865 Loss1: 0.646156 Loss2: 1.385708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623948 Loss1: 0.244926 Loss2: 1.379022 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568421 Loss1: 0.197688 Loss2: 1.370733
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.919731 Loss1: 0.999388 Loss2: 1.920343
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.573846 Loss1: 0.202015 Loss2: 1.371831
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.972387 Loss1: 0.560247 Loss2: 1.412140
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528887 Loss1: 0.159592 Loss2: 1.369295
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.772344 Loss1: 0.337602 Loss2: 1.434742
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.501122 Loss1: 0.137370 Loss2: 1.363752
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.624939 Loss1: 0.227236 Loss2: 1.397704
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499365 Loss1: 0.144810 Loss2: 1.354556
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571660 Loss1: 0.176727 Loss2: 1.394933
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481371 Loss1: 0.122419 Loss2: 1.358951
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.541567 Loss1: 0.149491 Loss2: 1.392076
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530973 Loss1: 0.143977 Loss2: 1.386996
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481857 Loss1: 0.101927 Loss2: 1.379930
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447333 Loss1: 0.077445 Loss2: 1.369888
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430487 Loss1: 0.067069 Loss2: 1.363418
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 19:34:47,276][flwr][DEBUG] - fit_round 87 received 50 results and 0 failures
+INFO flwr 2023-10-10 19:35:29,077 | server.py:125 | fit progress: (87, 2.233296902796712, {'accuracy': 0.5543}, 200636.85539069702)
+>> Test accuracy: 0.554300
+[2023-10-10 19:35:29,077][flwr][INFO] - fit progress: (87, 2.233296902796712, {'accuracy': 0.5543}, 200636.85539069702)
+DEBUG flwr 2023-10-10 19:35:29,077 | server.py:173 | evaluate_round 87: strategy sampled 50 clients (out of 50)
+[2023-10-10 19:35:29,077][flwr][DEBUG] - evaluate_round 87: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 19:44:35,176 | server.py:187 | evaluate_round 87 received 50 results and 0 failures
+[2023-10-10 19:44:35,176][flwr][DEBUG] - evaluate_round 87 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 19:44:35,176 | server.py:222 | fit_round 88: strategy sampled 50 clients (out of 50)
+[2023-10-10 19:44:35,176][flwr][DEBUG] - fit_round 88: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.924679 Loss1: 1.058438 Loss2: 1.866241
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.985379 Loss1: 0.585901 Loss2: 1.399478
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.730162 Loss1: 0.321948 Loss2: 1.408213
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589130 Loss1: 0.224111 Loss2: 1.365019
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.925676 Loss1: 1.106193 Loss2: 1.819483
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.594679 Loss1: 0.233128 Loss2: 1.361551
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.096936 Loss1: 0.716777 Loss2: 1.380160
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.559030 Loss1: 0.197869 Loss2: 1.361161
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.801331 Loss1: 0.404087 Loss2: 1.397244
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.564759 Loss1: 0.205461 Loss2: 1.359298
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.691243 Loss1: 0.324144 Loss2: 1.367099
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.523170 Loss1: 0.154928 Loss2: 1.368243
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557478 Loss1: 0.192982 Loss2: 1.364496
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488764 Loss1: 0.135837 Loss2: 1.352927
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499981 Loss1: 0.152069 Loss2: 1.347912
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.534398 Loss1: 0.175136 Loss2: 1.359262
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.454729 Loss1: 0.114899 Loss2: 1.339830
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439580 Loss1: 0.103174 Loss2: 1.336406
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.469063 Loss1: 0.129777 Loss2: 1.339286
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429986 Loss1: 0.096136 Loss2: 1.333850
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.059348 Loss1: 1.168553 Loss2: 1.890795
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.213468 Loss1: 0.743325 Loss2: 1.470143
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.937880 Loss1: 0.484545 Loss2: 1.453335
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.731087 Loss1: 0.310922 Loss2: 1.420165
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.031033 Loss1: 1.151368 Loss2: 1.879665
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.628199 Loss1: 0.219437 Loss2: 1.408762
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.090553 Loss1: 0.684464 Loss2: 1.406088
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.646099 Loss1: 0.247155 Loss2: 1.398944
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.819964 Loss1: 0.388706 Loss2: 1.431258
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.578778 Loss1: 0.174677 Loss2: 1.404101
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.665902 Loss1: 0.266229 Loss2: 1.399672
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.547448 Loss1: 0.157557 Loss2: 1.389892
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.590123 Loss1: 0.191278 Loss2: 1.398845
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.500371 Loss1: 0.111948 Loss2: 1.388423
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.563507 Loss1: 0.181629 Loss2: 1.381878
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.507049 Loss1: 0.126158 Loss2: 1.380892
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.537972 Loss1: 0.163440 Loss2: 1.374532
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510074 Loss1: 0.130772 Loss2: 1.379302
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.512022 Loss1: 0.140218 Loss2: 1.371804
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.477398 Loss1: 0.107920 Loss2: 1.369477
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.987645 Loss1: 1.106650 Loss2: 1.880995
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.154396 Loss1: 0.739224 Loss2: 1.415172
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.913458 Loss1: 0.446459 Loss2: 1.466999
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.727576 Loss1: 0.313630 Loss2: 1.413946
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.127240 Loss1: 1.252170 Loss2: 1.875071
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.090618 Loss1: 0.707922 Loss2: 1.382697 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.592492 Loss1: 0.195488 Loss2: 1.397004
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.861978 Loss1: 0.437316 Loss2: 1.424663
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547553 Loss1: 0.151026 Loss2: 1.396527
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.617964 Loss1: 0.244529 Loss2: 1.373435
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.510650 Loss1: 0.123354 Loss2: 1.387296
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.566442 Loss1: 0.197947 Loss2: 1.368495
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513632 Loss1: 0.155920 Loss2: 1.357713
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.478860 Loss1: 0.087566 Loss2: 1.391294
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483106 Loss1: 0.130099 Loss2: 1.353008
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461249 Loss1: 0.087041 Loss2: 1.374208
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414382 Loss1: 0.074725 Loss2: 1.339657 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978795
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.914197 Loss1: 1.050302 Loss2: 1.863895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.771447 Loss1: 0.328188 Loss2: 1.443259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.127407 Loss1: 1.286298 Loss2: 1.841108
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.679946 Loss1: 0.271946 Loss2: 1.408000
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.164263 Loss1: 0.747605 Loss2: 1.416657
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.578079 Loss1: 0.170283 Loss2: 1.407796
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.880956 Loss1: 0.443134 Loss2: 1.437822
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.539367 Loss1: 0.143299 Loss2: 1.396068
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.663635 Loss1: 0.275906 Loss2: 1.387729
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516320 Loss1: 0.121073 Loss2: 1.395247
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.510673 Loss1: 0.122041 Loss2: 1.388632 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.510861 Loss1: 0.120330 Loss2: 1.390530 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475149 Loss1: 0.085819 Loss2: 1.389330 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493556 Loss1: 0.123488 Loss2: 1.370068 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.979607 Loss1: 1.114848 Loss2: 1.864759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.829169 Loss1: 0.386071 Loss2: 1.443098 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.748638 Loss1: 0.349836 Loss2: 1.398803
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.131169 Loss1: 1.191256 Loss2: 1.939913
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.683728 Loss1: 0.273605 Loss2: 1.410123
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.182777 Loss1: 0.690839 Loss2: 1.491938
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.567422 Loss1: 0.172325 Loss2: 1.395097
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.856821 Loss1: 0.363301 Loss2: 1.493520
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.562713 Loss1: 0.178884 Loss2: 1.383829
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.719546 Loss1: 0.266859 Loss2: 1.452687
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.545820 Loss1: 0.160792 Loss2: 1.385027
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.659395 Loss1: 0.205163 Loss2: 1.454232
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509865 Loss1: 0.133362 Loss2: 1.376503
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.568097 Loss1: 0.135506 Loss2: 1.432592
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.489800 Loss1: 0.115825 Loss2: 1.373975
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.560172 Loss1: 0.135492 Loss2: 1.424681
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.592213 Loss1: 0.164780 Loss2: 1.427433
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.566269 Loss1: 0.138365 Loss2: 1.427904
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.535365 Loss1: 0.108558 Loss2: 1.426807
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.885275 Loss1: 1.050679 Loss2: 1.834596
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.015959 Loss1: 0.602613 Loss2: 1.413347
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.797019 Loss1: 0.373360 Loss2: 1.423659
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.634698 Loss1: 0.244435 Loss2: 1.390263
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.010803 Loss1: 1.221962 Loss2: 1.788841
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574414 Loss1: 0.196776 Loss2: 1.377638
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.057655 Loss1: 0.660319 Loss2: 1.397336
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538738 Loss1: 0.165843 Loss2: 1.372895
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.728057 Loss1: 0.367597 Loss2: 1.360460
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.606815 Loss1: 0.268718 Loss2: 1.338097
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.526560 Loss1: 0.158515 Loss2: 1.368044
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.578032 Loss1: 0.244573 Loss2: 1.333459
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.518274 Loss1: 0.149801 Loss2: 1.368473
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487183 Loss1: 0.164504 Loss2: 1.322679
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.519789 Loss1: 0.154399 Loss2: 1.365389
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452926 Loss1: 0.136554 Loss2: 1.316373
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.506160 Loss1: 0.141008 Loss2: 1.365152
+(DefaultActor pid=3765) >> Training accuracy: 0.957031
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445704 Loss1: 0.139203 Loss2: 1.306501 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.102656 Loss1: 1.198474 Loss2: 1.904182 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.906662 Loss1: 0.457124 Loss2: 1.449537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.053104 Loss1: 1.139616 Loss2: 1.913488
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.775546 Loss1: 0.319650 Loss2: 1.455896
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.762280 Loss1: 0.332636 Loss2: 1.429643 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.685766 Loss1: 0.244687 Loss2: 1.441079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.641647 Loss1: 0.217095 Loss2: 1.424552 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647444 Loss1: 0.239765 Loss2: 1.407678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.524121 Loss1: 0.153107 Loss2: 1.371014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.512020 Loss1: 0.138605 Loss2: 1.373415 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969727
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.507055 Loss1: 0.139839 Loss2: 1.367216 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978365
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.746783 Loss1: 0.954519 Loss2: 1.792264 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.739735 Loss1: 0.366672 Loss2: 1.373064 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.620986 Loss1: 0.264529 Loss2: 1.356457 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.564386 Loss1: 0.215556 Loss2: 1.348830 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487461 Loss1: 0.145463 Loss2: 1.341998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436375 Loss1: 0.098806 Loss2: 1.337569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448886 Loss1: 0.124248 Loss2: 1.324638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.572865 Loss1: 0.184435 Loss2: 1.388430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.507126 Loss1: 0.134226 Loss2: 1.372900 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970588
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461740 Loss1: 0.097774 Loss2: 1.363966 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.127381 Loss1: 1.256224 Loss2: 1.871157
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.067806 Loss1: 0.698910 Loss2: 1.368896
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.903955 Loss1: 0.487484 Loss2: 1.416471
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.708510 Loss1: 0.355587 Loss2: 1.352923
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.030927 Loss1: 1.206767 Loss2: 1.824160
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.049275 Loss1: 0.648915 Loss2: 1.400360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.800305 Loss1: 0.386823 Loss2: 1.413481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.616303 Loss1: 0.244765 Loss2: 1.371538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571363 Loss1: 0.191602 Loss2: 1.379761 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.566448 Loss1: 0.195412 Loss2: 1.371036 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.522214 Loss1: 0.159650 Loss2: 1.362564 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.477511 Loss1: 0.123413 Loss2: 1.354099 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.127163 Loss1: 0.740246 Loss2: 1.386918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.655328 Loss1: 0.297686 Loss2: 1.357642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.598943 Loss1: 0.243048 Loss2: 1.355895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.549470 Loss1: 0.200911 Loss2: 1.348558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496573 Loss1: 0.156129 Loss2: 1.340444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.478972 Loss1: 0.137372 Loss2: 1.341600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400291 Loss1: 0.070240 Loss2: 1.330051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392577 Loss1: 0.070684 Loss2: 1.321892 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466414 Loss1: 0.127464 Loss2: 1.338950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465016 Loss1: 0.130814 Loss2: 1.334203 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.028283 Loss1: 1.170086 Loss2: 1.858197
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.174607 Loss1: 0.716315 Loss2: 1.458292
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.926926 Loss1: 0.508564 Loss2: 1.418362
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.723461 Loss1: 0.326612 Loss2: 1.396848
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.942908 Loss1: 1.124727 Loss2: 1.818181
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.066403 Loss1: 0.692383 Loss2: 1.374020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.878579 Loss1: 0.487624 Loss2: 1.390955 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.683524 Loss1: 0.314554 Loss2: 1.368970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.547443 Loss1: 0.201298 Loss2: 1.346145 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.535950 Loss1: 0.195644 Loss2: 1.340307 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.528991 Loss1: 0.183526 Loss2: 1.345465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442472 Loss1: 0.113922 Loss2: 1.328549 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.239516 Loss1: 1.177819 Loss2: 2.061697 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.907530 Loss1: 0.386964 Loss2: 1.520566 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.662486 Loss1: 0.209980 Loss2: 1.452506 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.616535 Loss1: 0.163065 Loss2: 1.453470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.594194 Loss1: 0.155087 Loss2: 1.439107 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.542013 Loss1: 0.103857 Loss2: 1.438155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.562172 Loss1: 0.128121 Loss2: 1.434051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.543511 Loss1: 0.114407 Loss2: 1.429104
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.614840 Loss1: 0.184124 Loss2: 1.430716
+(DefaultActor pid=3765) >> Training accuracy: 0.975962
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559362 Loss1: 0.147110 Loss2: 1.412252
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.560962 Loss1: 0.153528 Loss2: 1.407434
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.538951 Loss1: 0.135049 Loss2: 1.403901
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.504931 Loss1: 0.101736 Loss2: 1.403195
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.486556 Loss1: 0.082839 Loss2: 1.403717
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.111843 Loss1: 1.210302 Loss2: 1.901541
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.193866 Loss1: 0.769479 Loss2: 1.424387
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.859907 Loss1: 0.427996 Loss2: 1.431911
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.685432 Loss1: 0.303893 Loss2: 1.381539
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565275 Loss1: 0.176307 Loss2: 1.388968
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.094697 Loss1: 1.162204 Loss2: 1.932493
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524343 Loss1: 0.146569 Loss2: 1.377775
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.283474 Loss1: 0.819319 Loss2: 1.464155
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.526986 Loss1: 0.168455 Loss2: 1.358532
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.977748 Loss1: 0.490931 Loss2: 1.486817
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.519913 Loss1: 0.144469 Loss2: 1.375444
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.811079 Loss1: 0.375170 Loss2: 1.435909
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.504976 Loss1: 0.136512 Loss2: 1.368465
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.661450 Loss1: 0.225451 Loss2: 1.435999
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.492489 Loss1: 0.125155 Loss2: 1.367335
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.556219 Loss1: 0.141558 Loss2: 1.414661 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.598155 Loss1: 0.176072 Loss2: 1.422083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.587890 Loss1: 0.173830 Loss2: 1.414059
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.917050 Loss1: 0.969042 Loss2: 1.948009
+(DefaultActor pid=3764) >> Training accuracy: 0.948958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.093405 Loss1: 0.648109 Loss2: 1.445297
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.885762 Loss1: 0.401842 Loss2: 1.483920
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.689325 Loss1: 0.265182 Loss2: 1.424143
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.680966 Loss1: 0.254504 Loss2: 1.426462
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.646577 Loss1: 0.219505 Loss2: 1.427072
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.870730 Loss1: 1.006810 Loss2: 1.863920
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.575712 Loss1: 0.157376 Loss2: 1.418336
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.949187 Loss1: 0.574272 Loss2: 1.374915
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.513758 Loss1: 0.102828 Loss2: 1.410931
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.734735 Loss1: 0.324586 Loss2: 1.410149
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493601 Loss1: 0.094392 Loss2: 1.399208
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605307 Loss1: 0.251495 Loss2: 1.353812
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.482433 Loss1: 0.085697 Loss2: 1.396737
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555345 Loss1: 0.205155 Loss2: 1.350190
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.507683 Loss1: 0.152491 Loss2: 1.355192
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490023 Loss1: 0.148628 Loss2: 1.341395
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481336 Loss1: 0.137796 Loss2: 1.343540
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448639 Loss1: 0.111640 Loss2: 1.336999
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440113 Loss1: 0.106686 Loss2: 1.333427
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.708071 Loss1: 0.947033 Loss2: 1.761038
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.970412 Loss1: 0.620760 Loss2: 1.349652
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.793418 Loss1: 0.411128 Loss2: 1.382290
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.668389 Loss1: 0.319252 Loss2: 1.349137
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535350 Loss1: 0.195368 Loss2: 1.339981
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.876261 Loss1: 0.968350 Loss2: 1.907911
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503611 Loss1: 0.178984 Loss2: 1.324627
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502556 Loss1: 0.177983 Loss2: 1.324573 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473149 Loss1: 0.144470 Loss2: 1.328680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414948 Loss1: 0.094005 Loss2: 1.320943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398501 Loss1: 0.086294 Loss2: 1.312207 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533628 Loss1: 0.157236 Loss2: 1.376392 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.521300 Loss1: 0.146999 Loss2: 1.374301 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509837 Loss1: 0.133803 Loss2: 1.376034
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.832471 Loss1: 0.991291 Loss2: 1.841180
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.004686 Loss1: 0.632517 Loss2: 1.372168
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.824816 Loss1: 0.413321 Loss2: 1.411495
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.670761 Loss1: 0.308527 Loss2: 1.362234
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587241 Loss1: 0.221551 Loss2: 1.365690
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.058748 Loss1: 1.177874 Loss2: 1.880874
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.154096 Loss1: 0.709780 Loss2: 1.444316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.865241 Loss1: 0.421091 Loss2: 1.444150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.740219 Loss1: 0.332493 Loss2: 1.407726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.629922 Loss1: 0.230612 Loss2: 1.399310 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425671 Loss1: 0.095570 Loss2: 1.330101
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.569588 Loss1: 0.177138 Loss2: 1.392450
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545046 Loss1: 0.157249 Loss2: 1.387797
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.527562 Loss1: 0.136999 Loss2: 1.390563
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.512837 Loss1: 0.127437 Loss2: 1.385400
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460742 Loss1: 0.082155 Loss2: 1.378588
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.830584 Loss1: 1.046883 Loss2: 1.783701
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.987977 Loss1: 0.667710 Loss2: 1.320268
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.785180 Loss1: 0.415404 Loss2: 1.369776
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572942 Loss1: 0.272544 Loss2: 1.300398
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503212 Loss1: 0.198753 Loss2: 1.304459
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.048081 Loss1: 1.164572 Loss2: 1.883510
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.030917 Loss1: 0.584685 Loss2: 1.446232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.757526 Loss1: 0.327279 Loss2: 1.430247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.653142 Loss1: 0.253906 Loss2: 1.399236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.661799 Loss1: 0.260097 Loss2: 1.401702 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.578405 Loss1: 0.177595 Loss2: 1.400810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.520201 Loss1: 0.129471 Loss2: 1.390730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482174 Loss1: 0.103175 Loss2: 1.378999 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.184532 Loss1: 0.744643 Loss2: 1.439889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.866433 Loss1: 0.422642 Loss2: 1.443791 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.628491 Loss1: 0.188763 Loss2: 1.439728 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.550768 Loss1: 0.129868 Loss2: 1.420900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.540411 Loss1: 0.123736 Loss2: 1.416675
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.039900 Loss1: 0.602489 Loss2: 1.437411
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.513065 Loss1: 0.102913 Loss2: 1.410151
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.813840 Loss1: 0.347899 Loss2: 1.465940
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.706849 Loss1: 0.274117 Loss2: 1.432732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.575688 Loss1: 0.154111 Loss2: 1.421577 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.547703 Loss1: 0.133095 Loss2: 1.414608 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.527260 Loss1: 0.121643 Loss2: 1.405617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.496683 Loss1: 0.087935 Loss2: 1.408748 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.965906 Loss1: 0.501947 Loss2: 1.463959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.654613 Loss1: 0.233360 Loss2: 1.421254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.601881 Loss1: 0.180469 Loss2: 1.421412
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.028118 Loss1: 1.157350 Loss2: 1.870768
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.066679 Loss1: 0.666429 Loss2: 1.400250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.878599 Loss1: 0.456407 Loss2: 1.422191 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.538113 Loss1: 0.140966 Loss2: 1.397147
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.748970 Loss1: 0.362361 Loss2: 1.386609
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.660204 Loss1: 0.277549 Loss2: 1.382654
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.568611 Loss1: 0.202471 Loss2: 1.366140
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.492381 Loss1: 0.129741 Loss2: 1.362640
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.479154 Loss1: 0.125755 Loss2: 1.353399
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.808001 Loss1: 1.003740 Loss2: 1.804262
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.478559 Loss1: 0.124854 Loss2: 1.353704
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.094703 Loss1: 0.728134 Loss2: 1.366569
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.473912 Loss1: 0.124985 Loss2: 1.348927
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.636266 Loss1: 0.298955 Loss2: 1.337310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504619 Loss1: 0.173813 Loss2: 1.330806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450569 Loss1: 0.130197 Loss2: 1.320371
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.985547 Loss1: 1.031961 Loss2: 1.953586
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401479 Loss1: 0.083201 Loss2: 1.318277
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.044651 Loss1: 0.567554 Loss2: 1.477097
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.387893 Loss1: 0.077591 Loss2: 1.310302
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.784735 Loss1: 0.324026 Loss2: 1.460709
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378395 Loss1: 0.070046 Loss2: 1.308349
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.718495 Loss1: 0.275172 Loss2: 1.443323
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.625792 Loss1: 0.190502 Loss2: 1.435290
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.576705 Loss1: 0.151766 Loss2: 1.424939
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.565176 Loss1: 0.141895 Loss2: 1.423280
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.590301 Loss1: 0.169122 Loss2: 1.421179
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.535664 Loss1: 0.114428 Loss2: 1.421236
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.854578 Loss1: 1.094672 Loss2: 1.759906
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.520267 Loss1: 0.106648 Loss2: 1.413619
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.006884 Loss1: 0.647021 Loss2: 1.359863
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.782173 Loss1: 0.421704 Loss2: 1.360469
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.622558 Loss1: 0.284579 Loss2: 1.337979
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506734 Loss1: 0.169932 Loss2: 1.336802
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461008 Loss1: 0.141134 Loss2: 1.319875
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.961494 Loss1: 1.044893 Loss2: 1.916602
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512488 Loss1: 0.184692 Loss2: 1.327796
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.482443 Loss1: 0.152653 Loss2: 1.329790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.457635 Loss1: 0.136365 Loss2: 1.321270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474658 Loss1: 0.149585 Loss2: 1.325073 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.670516 Loss1: 0.245461 Loss2: 1.425055 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568190 Loss1: 0.152721 Loss2: 1.415470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.933255 Loss1: 1.100142 Loss2: 1.833112 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.926661 Loss1: 0.508979 Loss2: 1.417682 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.599664 Loss1: 0.237373 Loss2: 1.362292 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508803 Loss1: 0.144495 Loss2: 1.364309
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.869888 Loss1: 1.009125 Loss2: 1.860763
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.114218 Loss1: 0.673568 Loss2: 1.440649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.775237 Loss1: 0.367546 Loss2: 1.407691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.708222 Loss1: 0.311389 Loss2: 1.396832 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.629030 Loss1: 0.243047 Loss2: 1.385983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.537048 Loss1: 0.158340 Loss2: 1.378708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.010090 Loss1: 1.116709 Loss2: 1.893381 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.129866 Loss1: 0.736456 Loss2: 1.393409 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.707885 Loss1: 0.331713 Loss2: 1.376172 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547722 Loss1: 0.169465 Loss2: 1.378257 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 20:13:35,345 | server.py:236 | fit_round 88 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.881189 Loss1: 1.064200 Loss2: 1.816989 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.102759 Loss1: 0.661838 Loss2: 1.440921 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.473257 Loss1: 0.123186 Loss2: 1.350071 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.636834 Loss1: 0.234730 Loss2: 1.402103 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.558262 Loss1: 0.179195 Loss2: 1.379067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.079546 Loss1: 1.262218 Loss2: 1.817328
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.579122 Loss1: 0.187314 Loss2: 1.391808
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501098 Loss1: 0.131290 Loss2: 1.369808 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464048 Loss1: 0.098636 Loss2: 1.365412 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551947 Loss1: 0.190123 Loss2: 1.361824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436011 Loss1: 0.098953 Loss2: 1.337058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414748 Loss1: 0.084639 Loss2: 1.330109
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.847428 Loss1: 1.027014 Loss2: 1.820414
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.095966 Loss1: 0.669663 Loss2: 1.426303 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.807062 Loss1: 0.395113 Loss2: 1.411949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.636690 Loss1: 0.238176 Loss2: 1.398513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.522782 Loss1: 0.142402 Loss2: 1.380380 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486470 Loss1: 0.114660 Loss2: 1.371810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.520954 Loss1: 0.144774 Loss2: 1.376180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487051 Loss1: 0.118819 Loss2: 1.368232 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.639834 Loss1: 0.248016 Loss2: 1.391818 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.586339 Loss1: 0.216842 Loss2: 1.369498 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.574838 Loss1: 0.196459 Loss2: 1.378379
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.888152 Loss1: 1.025784 Loss2: 1.862368
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.973903 Loss1: 0.589385 Loss2: 1.384518 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.548178 Loss1: 0.169726 Loss2: 1.378452
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.758383 Loss1: 0.370598 Loss2: 1.387785
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.622884 Loss1: 0.270180 Loss2: 1.352704
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543298 Loss1: 0.191404 Loss2: 1.351894
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474128 Loss1: 0.129177 Loss2: 1.344951
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.466337 Loss1: 0.132999 Loss2: 1.333338
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471223 Loss1: 0.131079 Loss2: 1.340144
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419486 Loss1: 0.090390 Loss2: 1.329096
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452734 Loss1: 0.128216 Loss2: 1.324518
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 20:13:35,345][flwr][DEBUG] - fit_round 88 received 50 results and 0 failures
+INFO flwr 2023-10-10 20:14:17,254 | server.py:125 | fit progress: (88, 2.2306158236040474, {'accuracy': 0.5567}, 202965.03208381802)
+>> Test accuracy: 0.556700
+[2023-10-10 20:14:17,254][flwr][INFO] - fit progress: (88, 2.2306158236040474, {'accuracy': 0.5567}, 202965.03208381802)
+DEBUG flwr 2023-10-10 20:14:17,254 | server.py:173 | evaluate_round 88: strategy sampled 50 clients (out of 50)
+[2023-10-10 20:14:17,254][flwr][DEBUG] - evaluate_round 88: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 20:23:21,965 | server.py:187 | evaluate_round 88 received 50 results and 0 failures
+[2023-10-10 20:23:21,965][flwr][DEBUG] - evaluate_round 88 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 20:23:21,966 | server.py:222 | fit_round 89: strategy sampled 50 clients (out of 50)
+[2023-10-10 20:23:21,966][flwr][DEBUG] - fit_round 89: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.284616 Loss1: 1.391065 Loss2: 1.893551
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.294996 Loss1: 0.835745 Loss2: 1.459251
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.848403 Loss1: 0.455162 Loss2: 1.393241
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.705968 Loss1: 0.312988 Loss2: 1.392980
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.962718 Loss1: 1.054606 Loss2: 1.908112
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.036971 Loss1: 0.616024 Loss2: 1.420947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.774406 Loss1: 0.313364 Loss2: 1.461043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.670603 Loss1: 0.257021 Loss2: 1.413582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.616991 Loss1: 0.203541 Loss2: 1.413450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.568158 Loss1: 0.155600 Loss2: 1.412558 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981027
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.561626 Loss1: 0.169663 Loss2: 1.391964 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.481254 Loss1: 0.095175 Loss2: 1.386079 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.074215 Loss1: 0.641925 Loss2: 1.432291 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.730555 Loss1: 0.306732 Loss2: 1.423823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.003679 Loss1: 1.151676 Loss2: 1.852002
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.683110 Loss1: 0.292130 Loss2: 1.390980
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.620699 Loss1: 0.224616 Loss2: 1.396083 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.535951 Loss1: 0.151502 Loss2: 1.384449 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474030 Loss1: 0.097124 Loss2: 1.376906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447488 Loss1: 0.082028 Loss2: 1.365460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465796 Loss1: 0.101842 Loss2: 1.363954 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.523072 Loss1: 0.138163 Loss2: 1.384909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512909 Loss1: 0.133079 Loss2: 1.379829 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.018283 Loss1: 1.069561 Loss2: 1.948722
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.159454 Loss1: 0.688018 Loss2: 1.471436
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.911414 Loss1: 0.403805 Loss2: 1.507610
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.782085 Loss1: 0.328959 Loss2: 1.453126
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.758093 Loss1: 0.927284 Loss2: 1.830809
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.976281 Loss1: 0.561628 Loss2: 1.414653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.774856 Loss1: 0.364174 Loss2: 1.410682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636570 Loss1: 0.244272 Loss2: 1.392298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539033 Loss1: 0.162934 Loss2: 1.376100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.536788 Loss1: 0.103819 Loss2: 1.432970 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.524678 Loss1: 0.154396 Loss2: 1.370282 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.496165 Loss1: 0.130968 Loss2: 1.365197 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988051
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.158379 Loss1: 0.701143 Loss2: 1.457236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.727427 Loss1: 0.313968 Loss2: 1.413460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.939039 Loss1: 0.954191 Loss2: 1.984847 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.050054 Loss1: 0.572527 Loss2: 1.477526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.819939 Loss1: 0.320878 Loss2: 1.499061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.778659 Loss1: 0.321911 Loss2: 1.456748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.727313 Loss1: 0.246280 Loss2: 1.481033 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.573751 Loss1: 0.118909 Loss2: 1.454842 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.548608 Loss1: 0.107015 Loss2: 1.441592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529399 Loss1: 0.091446 Loss2: 1.437953
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.948523 Loss1: 1.018491 Loss2: 1.930032
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.170071 Loss1: 0.655315 Loss2: 1.514756
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.972957 Loss1: 0.471335 Loss2: 1.501622
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.819880 Loss1: 0.360971 Loss2: 1.458909
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.747397 Loss1: 0.295898 Loss2: 1.451500
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.967932 Loss1: 1.064886 Loss2: 1.903045
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.127729 Loss1: 0.692309 Loss2: 1.435420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.921094 Loss1: 0.451062 Loss2: 1.470032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.738587 Loss1: 0.320761 Loss2: 1.417825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.655536 Loss1: 0.229223 Loss2: 1.426313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.541386 Loss1: 0.113283 Loss2: 1.428103
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.586168 Loss1: 0.168713 Loss2: 1.417454
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.566095 Loss1: 0.163245 Loss2: 1.402850
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.569794 Loss1: 0.160141 Loss2: 1.409653
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.527056 Loss1: 0.129173 Loss2: 1.397883
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.498121 Loss1: 0.097786 Loss2: 1.400335
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.745839 Loss1: 0.926703 Loss2: 1.819135
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.961502 Loss1: 0.611556 Loss2: 1.349947
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.848420 Loss1: 0.436163 Loss2: 1.412257
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.650734 Loss1: 0.315026 Loss2: 1.335708
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.961542 Loss1: 1.135818 Loss2: 1.825724
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.041368 Loss1: 0.637317 Loss2: 1.404051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.917902 Loss1: 0.505101 Loss2: 1.412801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.757039 Loss1: 0.373685 Loss2: 1.383355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.660768 Loss1: 0.282330 Loss2: 1.378437 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.536261 Loss1: 0.177234 Loss2: 1.359027 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.550110 Loss1: 0.187921 Loss2: 1.362189 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.510002 Loss1: 0.144790 Loss2: 1.365212 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.956250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.107434 Loss1: 0.701897 Loss2: 1.405537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.661550 Loss1: 0.289268 Loss2: 1.372282 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.916942 Loss1: 1.073162 Loss2: 1.843780
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.602281 Loss1: 0.235760 Loss2: 1.366521
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.059048 Loss1: 0.633978 Loss2: 1.425070
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526780 Loss1: 0.167363 Loss2: 1.359417
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524667 Loss1: 0.166370 Loss2: 1.358298
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.867116 Loss1: 0.452726 Loss2: 1.414390
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500358 Loss1: 0.144926 Loss2: 1.355431
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.776557 Loss1: 0.367231 Loss2: 1.409327
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.513349 Loss1: 0.163615 Loss2: 1.349734
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.695429 Loss1: 0.299866 Loss2: 1.395563
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476126 Loss1: 0.116656 Loss2: 1.359469
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.667039 Loss1: 0.272193 Loss2: 1.394846
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.586410 Loss1: 0.199076 Loss2: 1.387334
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546585 Loss1: 0.175306 Loss2: 1.371279
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.508417 Loss1: 0.135795 Loss2: 1.372622
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.507650 Loss1: 0.140296 Loss2: 1.367354
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.184584 Loss1: 1.257342 Loss2: 1.927243
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.153777 Loss1: 0.743237 Loss2: 1.410540
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.902284 Loss1: 0.440475 Loss2: 1.461810
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.689171 Loss1: 0.289334 Loss2: 1.399837
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.608100 Loss1: 0.216805 Loss2: 1.391295
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.870698 Loss1: 1.023141 Loss2: 1.847557
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.553292 Loss1: 0.160602 Loss2: 1.392689
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.545270 Loss1: 0.169182 Loss2: 1.376088
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.010389 Loss1: 0.638906 Loss2: 1.371483
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.514085 Loss1: 0.133948 Loss2: 1.380137
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.798657 Loss1: 0.399897 Loss2: 1.398760
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.457353 Loss1: 0.090877 Loss2: 1.366475
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.658893 Loss1: 0.290923 Loss2: 1.367970
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462745 Loss1: 0.097015 Loss2: 1.365729
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.642196 Loss1: 0.274143 Loss2: 1.368054
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.587343 Loss1: 0.236510 Loss2: 1.350832
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516426 Loss1: 0.164698 Loss2: 1.351729
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.503032 Loss1: 0.153261 Loss2: 1.349771
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460109 Loss1: 0.116586 Loss2: 1.343523
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.115954 Loss1: 1.159281 Loss2: 1.956673
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428586 Loss1: 0.087559 Loss2: 1.341026
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694323 Loss1: 0.323941 Loss2: 1.370383 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.640250 Loss1: 0.279627 Loss2: 1.360623 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.835225 Loss1: 0.982685 Loss2: 1.852540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.011612 Loss1: 0.620180 Loss2: 1.391432 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465958 Loss1: 0.131066 Loss2: 1.334892 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986979
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609962 Loss1: 0.232384 Loss2: 1.377578 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509289 Loss1: 0.145508 Loss2: 1.363781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.827424 Loss1: 1.017068 Loss2: 1.810356
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475271 Loss1: 0.118974 Loss2: 1.356296
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.989775 Loss1: 0.600187 Loss2: 1.389589
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442276 Loss1: 0.092241 Loss2: 1.350034
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.798001 Loss1: 0.407494 Loss2: 1.390507
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444583 Loss1: 0.095081 Loss2: 1.349502
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.637188 Loss1: 0.271955 Loss2: 1.365233 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539019 Loss1: 0.181954 Loss2: 1.357065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.960509 Loss1: 1.098132 Loss2: 1.862378
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464345 Loss1: 0.126896 Loss2: 1.337448
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.019260 Loss1: 0.633054 Loss2: 1.386206
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461862 Loss1: 0.129823 Loss2: 1.332039
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.819507 Loss1: 0.419060 Loss2: 1.400447
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420382 Loss1: 0.083222 Loss2: 1.337161
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.604929 Loss1: 0.229740 Loss2: 1.375189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.554241 Loss1: 0.187782 Loss2: 1.366459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.517828 Loss1: 0.172035 Loss2: 1.345793
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.954241 Loss1: 1.099943 Loss2: 1.854299
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.086990 Loss1: 0.685632 Loss2: 1.401358 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463256 Loss1: 0.119649 Loss2: 1.343608
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.815172 Loss1: 0.403210 Loss2: 1.411962
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.704902 Loss1: 0.329627 Loss2: 1.375276
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.609677 Loss1: 0.225950 Loss2: 1.383727
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.589439 Loss1: 0.230544 Loss2: 1.358895
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.499919 Loss1: 0.139118 Loss2: 1.360801
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.490113 Loss1: 0.131874 Loss2: 1.358239
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.803585 Loss1: 1.046851 Loss2: 1.756734
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477582 Loss1: 0.126282 Loss2: 1.351300
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.082946 Loss1: 0.689165 Loss2: 1.393781
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476950 Loss1: 0.128301 Loss2: 1.348649
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.779940 Loss1: 0.409068 Loss2: 1.370873
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.681170 Loss1: 0.326265 Loss2: 1.354905
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.639212 Loss1: 0.282085 Loss2: 1.357127
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538508 Loss1: 0.198743 Loss2: 1.339765
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.501514 Loss1: 0.171867 Loss2: 1.329647
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.029662 Loss1: 1.156141 Loss2: 1.873520
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.187794 Loss1: 0.730677 Loss2: 1.457117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.862602 Loss1: 0.414436 Loss2: 1.448166 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403404 Loss1: 0.094208 Loss2: 1.309196
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.745274 Loss1: 0.351077 Loss2: 1.394197
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.651543 Loss1: 0.235829 Loss2: 1.415714
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.577307 Loss1: 0.182092 Loss2: 1.395215
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.550345 Loss1: 0.155032 Loss2: 1.395313
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.514228 Loss1: 0.132868 Loss2: 1.381360
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509178 Loss1: 0.129463 Loss2: 1.379715
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.123034 Loss1: 1.167600 Loss2: 1.955434
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.502773 Loss1: 0.124071 Loss2: 1.378702
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.155881 Loss1: 0.664285 Loss2: 1.491596
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.975749 Loss1: 0.476169 Loss2: 1.499580
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.744948 Loss1: 0.273825 Loss2: 1.471123
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.646073 Loss1: 0.194755 Loss2: 1.451318
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.671809 Loss1: 0.213900 Loss2: 1.457909
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.628035 Loss1: 0.186543 Loss2: 1.441492
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.084732 Loss1: 1.205682 Loss2: 1.879050
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.582972 Loss1: 0.144529 Loss2: 1.438443
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.035203 Loss1: 0.600689 Loss2: 1.434514
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.557474 Loss1: 0.126110 Loss2: 1.431364
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.794005 Loss1: 0.376576 Loss2: 1.417429
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.581450 Loss1: 0.153264 Loss2: 1.428185
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.635562 Loss1: 0.253936 Loss2: 1.381625
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.569839 Loss1: 0.179306 Loss2: 1.390534
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.552997 Loss1: 0.170934 Loss2: 1.382063
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502656 Loss1: 0.131544 Loss2: 1.371112
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.472579 Loss1: 0.106833 Loss2: 1.365746
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470131 Loss1: 0.112332 Loss2: 1.357799
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.912631 Loss1: 1.086304 Loss2: 1.826327
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441823 Loss1: 0.084408 Loss2: 1.357414
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.936507 Loss1: 0.567605 Loss2: 1.368902
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.787105 Loss1: 0.383427 Loss2: 1.403678
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.711341 Loss1: 0.341362 Loss2: 1.369980
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.597693 Loss1: 0.229768 Loss2: 1.367925
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.518980 Loss1: 0.171434 Loss2: 1.347546
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509972 Loss1: 0.166131 Loss2: 1.343840
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.906093 Loss1: 1.019908 Loss2: 1.886185
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.504939 Loss1: 0.158673 Loss2: 1.346266
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.037971 Loss1: 0.620501 Loss2: 1.417470
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480430 Loss1: 0.141338 Loss2: 1.339092
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.816070 Loss1: 0.369121 Loss2: 1.446949
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453372 Loss1: 0.112390 Loss2: 1.340982
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.689494 Loss1: 0.292814 Loss2: 1.396681
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606008 Loss1: 0.208685 Loss2: 1.397323
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537316 Loss1: 0.144787 Loss2: 1.392529
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.501581 Loss1: 0.121052 Loss2: 1.380529
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467107 Loss1: 0.099918 Loss2: 1.367189
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.149389 Loss1: 1.234881 Loss2: 1.914508
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472738 Loss1: 0.105758 Loss2: 1.366979
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.287690 Loss1: 0.805240 Loss2: 1.482450
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467635 Loss1: 0.104717 Loss2: 1.362918
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.747129 Loss1: 0.317466 Loss2: 1.429663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.569193 Loss1: 0.160614 Loss2: 1.408579 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545441 Loss1: 0.134607 Loss2: 1.410834
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.847484 Loss1: 0.997389 Loss2: 1.850096
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500531 Loss1: 0.098930 Loss2: 1.401601
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.026872 Loss1: 0.612368 Loss2: 1.414503
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.828826 Loss1: 0.390164 Loss2: 1.438662 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.537850 Loss1: 0.136785 Loss2: 1.401065
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.670218 Loss1: 0.290936 Loss2: 1.379282
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.585084 Loss1: 0.211989 Loss2: 1.373094
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.600972 Loss1: 0.228728 Loss2: 1.372243
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.572137 Loss1: 0.190971 Loss2: 1.381166
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.521152 Loss1: 0.156703 Loss2: 1.364448
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.938284 Loss1: 1.021579 Loss2: 1.916705
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.109906 Loss1: 0.669289 Loss2: 1.440616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.482935 Loss1: 0.130221 Loss2: 1.352715
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.937932 Loss1: 0.449787 Loss2: 1.488146
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.724334 Loss1: 0.288382 Loss2: 1.435952
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.642980 Loss1: 0.218030 Loss2: 1.424950
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582742 Loss1: 0.166092 Loss2: 1.416650
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.606758 Loss1: 0.195429 Loss2: 1.411329
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.063501 Loss1: 1.124663 Loss2: 1.938838
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.551681 Loss1: 0.138818 Loss2: 1.412863
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.195823 Loss1: 0.695540 Loss2: 1.500283
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.522522 Loss1: 0.120253 Loss2: 1.402269
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.972196 Loss1: 0.482444 Loss2: 1.489752
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.486655 Loss1: 0.088203 Loss2: 1.398452
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.647164 Loss1: 0.190892 Loss2: 1.456272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.631977 Loss1: 0.190623 Loss2: 1.441354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.554037 Loss1: 0.124510 Loss2: 1.429527
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.237653 Loss1: 1.304764 Loss2: 1.932889
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.531554 Loss1: 0.103063 Loss2: 1.428491
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.218601 Loss1: 0.729181 Loss2: 1.489420
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.526893 Loss1: 0.106292 Loss2: 1.420601
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.957673 Loss1: 0.470246 Loss2: 1.487426
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.822831 Loss1: 0.368248 Loss2: 1.454582
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.754034 Loss1: 0.300708 Loss2: 1.453326
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.750334 Loss1: 0.294279 Loss2: 1.456055
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.683017 Loss1: 0.231850 Loss2: 1.451167
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.603119 Loss1: 0.169178 Loss2: 1.433942
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.995614 Loss1: 1.124686 Loss2: 1.870928
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.566586 Loss1: 0.137428 Loss2: 1.429158
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.186551 Loss1: 0.745983 Loss2: 1.440568
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.565471 Loss1: 0.137588 Loss2: 1.427883
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.935306 Loss1: 0.517021 Loss2: 1.418285
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.727135 Loss1: 0.326013 Loss2: 1.401122
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.622873 Loss1: 0.250901 Loss2: 1.371971
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.594748 Loss1: 0.220854 Loss2: 1.373894
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.543242 Loss1: 0.179245 Loss2: 1.363997
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.520759 Loss1: 0.165157 Loss2: 1.355602
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.923105 Loss1: 1.010415 Loss2: 1.912690
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482985 Loss1: 0.124940 Loss2: 1.358045
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.091137 Loss1: 0.663832 Loss2: 1.427305
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.470696 Loss1: 0.119695 Loss2: 1.351001
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.902451 Loss1: 0.439789 Loss2: 1.462662
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.738402 Loss1: 0.332395 Loss2: 1.406007
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.615429 Loss1: 0.214383 Loss2: 1.401046
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.601231 Loss1: 0.205141 Loss2: 1.396090
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.522951 Loss1: 0.133398 Loss2: 1.389554
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.002421 Loss1: 1.096286 Loss2: 1.906135
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.495428 Loss1: 0.116495 Loss2: 1.378934
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.151289 Loss1: 0.777102 Loss2: 1.374187
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466584 Loss1: 0.091432 Loss2: 1.375152
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456051 Loss1: 0.088815 Loss2: 1.367235 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.569950 Loss1: 0.200795 Loss2: 1.369155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.535920 Loss1: 0.180363 Loss2: 1.355557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453063 Loss1: 0.098190 Loss2: 1.354873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.530022 Loss1: 0.173545 Loss2: 1.356477 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973558
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.707340 Loss1: 0.301688 Loss2: 1.405652 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.592840 Loss1: 0.203926 Loss2: 1.388914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.579498 Loss1: 0.180106 Loss2: 1.399392
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.946206 Loss1: 1.060717 Loss2: 1.885489
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.013132 Loss1: 0.586188 Loss2: 1.426944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.770789 Loss1: 0.344026 Loss2: 1.426764 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.690407 Loss1: 0.297292 Loss2: 1.393115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.597156 Loss1: 0.199293 Loss2: 1.397863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.540360 Loss1: 0.145646 Loss2: 1.394714 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.530085 Loss1: 0.139260 Loss2: 1.390825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.519527 Loss1: 0.125470 Loss2: 1.394057 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.767177 Loss1: 0.340472 Loss2: 1.426706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.615276 Loss1: 0.202132 Loss2: 1.413143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.846148 Loss1: 1.023668 Loss2: 1.822480 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.991621 Loss1: 0.618853 Loss2: 1.372767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.718539 Loss1: 0.375542 Loss2: 1.342996 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484774 Loss1: 0.156242 Loss2: 1.328532 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.438311 Loss1: 0.121754 Loss2: 1.316558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.406715 Loss1: 0.095627 Loss2: 1.311087
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.130285 Loss1: 1.164630 Loss2: 1.965656
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.380848 Loss1: 0.083104 Loss2: 1.297745
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.198975 Loss1: 0.785252 Loss2: 1.413724
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.895260 Loss1: 0.420336 Loss2: 1.474923
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379927 Loss1: 0.084746 Loss2: 1.295181
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.665225 Loss1: 0.265828 Loss2: 1.399397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497302 Loss1: 0.132602 Loss2: 1.364699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439098 Loss1: 0.085509 Loss2: 1.353589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415275 Loss1: 0.072486 Loss2: 1.342789 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986779
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.791137 Loss1: 0.357795 Loss2: 1.433342 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.629619 Loss1: 0.236155 Loss2: 1.393463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.994622 Loss1: 1.046819 Loss2: 1.947803
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.550577 Loss1: 0.167900 Loss2: 1.382677
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.081294 Loss1: 0.620371 Loss2: 1.460924
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.494485 Loss1: 0.126207 Loss2: 1.368279
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.929038 Loss1: 0.444550 Loss2: 1.484488
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484241 Loss1: 0.114996 Loss2: 1.369244
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.853875 Loss1: 0.392974 Loss2: 1.460901
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.507802 Loss1: 0.137673 Loss2: 1.370129
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.639964 Loss1: 0.205140 Loss2: 1.434825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.600238 Loss1: 0.179035 Loss2: 1.421203 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.560352 Loss1: 0.137036 Loss2: 1.423316
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.058931 Loss1: 1.183438 Loss2: 1.875493
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527331 Loss1: 0.105904 Loss2: 1.421427
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.146751 Loss1: 0.698884 Loss2: 1.447867
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.844032 Loss1: 0.406648 Loss2: 1.437383
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.727789 Loss1: 0.324567 Loss2: 1.403222
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.652911 Loss1: 0.253142 Loss2: 1.399769
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.596842 Loss1: 0.205160 Loss2: 1.391682
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531613 Loss1: 0.139668 Loss2: 1.391945
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.031190 Loss1: 1.131408 Loss2: 1.899782
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.513652 Loss1: 0.136217 Loss2: 1.377435
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.019052 Loss1: 0.595118 Loss2: 1.423934
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.457493 Loss1: 0.081135 Loss2: 1.376357
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.856919 Loss1: 0.415737 Loss2: 1.441182
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440747 Loss1: 0.081756 Loss2: 1.358991
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.698613 Loss1: 0.284646 Loss2: 1.413966
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.690920 Loss1: 0.271047 Loss2: 1.419873
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.656942 Loss1: 0.241196 Loss2: 1.415746
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.611572 Loss1: 0.196564 Loss2: 1.415009
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568197 Loss1: 0.167704 Loss2: 1.400493
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.552330 Loss1: 0.154314 Loss2: 1.398016
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.811822 Loss1: 1.049025 Loss2: 1.762798
+(DefaultActor pid=3764) >> Training accuracy: 0.937500
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.548586 Loss1: 0.148132 Loss2: 1.400454
+DEBUG flwr 2023-10-10 20:51:47,694 | server.py:236 | fit_round 89 received 50 results and 0 failures
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.076919 Loss1: 0.706620 Loss2: 1.370299
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.737626 Loss1: 0.370431 Loss2: 1.367195
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.657270 Loss1: 0.322883 Loss2: 1.334387
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572042 Loss1: 0.228287 Loss2: 1.343755
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564150 Loss1: 0.229695 Loss2: 1.334455
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.755865 Loss1: 0.940258 Loss2: 1.815607
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.950181 Loss1: 0.534797 Loss2: 1.415384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.754197 Loss1: 0.348633 Loss2: 1.405564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.662030 Loss1: 0.272941 Loss2: 1.389089 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569906 Loss1: 0.195558 Loss2: 1.374348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.561909 Loss1: 0.198747 Loss2: 1.363162 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485076 Loss1: 0.125547 Loss2: 1.359529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.506227 Loss1: 0.149792 Loss2: 1.356435 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.817579 Loss1: 0.341466 Loss2: 1.476113 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.675783 Loss1: 0.226057 Loss2: 1.449726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.637909 Loss1: 0.203309 Loss2: 1.434601
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.996172 Loss1: 1.163828 Loss2: 1.832344
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.146949 Loss1: 0.678780 Loss2: 1.468168 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.895793 Loss1: 0.492987 Loss2: 1.402806 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.734365 Loss1: 0.352909 Loss2: 1.381457 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.556204 Loss1: 0.130009 Loss2: 1.426195
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.583375 Loss1: 0.205694 Loss2: 1.377680
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484801 Loss1: 0.125596 Loss2: 1.359205
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438923 Loss1: 0.088066 Loss2: 1.350857
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418684 Loss1: 0.074059 Loss2: 1.344625
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385088 Loss1: 0.053597 Loss2: 1.331491
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.037120 Loss1: 1.170638 Loss2: 1.866481
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390316 Loss1: 0.064861 Loss2: 1.325455
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.795574 Loss1: 0.383837 Loss2: 1.411737 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605818 Loss1: 0.225646 Loss2: 1.380172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.608830 Loss1: 0.240311 Loss2: 1.368519
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.961356 Loss1: 1.024467 Loss2: 1.936889
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.058925 Loss1: 0.626346 Loss2: 1.432579 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.773608 Loss1: 0.311247 Loss2: 1.462361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.672592 Loss1: 0.239448 Loss2: 1.433144 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.498200 Loss1: 0.131561 Loss2: 1.366638
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576011 Loss1: 0.154089 Loss2: 1.421922
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.553646 Loss1: 0.145685 Loss2: 1.407961
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.572100 Loss1: 0.159353 Loss2: 1.412747
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540137 Loss1: 0.124965 Loss2: 1.415172
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.514757 Loss1: 0.109098 Loss2: 1.405658
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.513763 Loss1: 0.108796 Loss2: 1.404967
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 20:51:47,694][flwr][DEBUG] - fit_round 89 received 50 results and 0 failures
+INFO flwr 2023-10-10 20:52:29,505 | server.py:125 | fit progress: (89, 2.2304537201080077, {'accuracy': 0.5568}, 205257.283233468)
+>> Test accuracy: 0.556800
+[2023-10-10 20:52:29,505][flwr][INFO] - fit progress: (89, 2.2304537201080077, {'accuracy': 0.5568}, 205257.283233468)
+DEBUG flwr 2023-10-10 20:52:29,505 | server.py:173 | evaluate_round 89: strategy sampled 50 clients (out of 50)
+[2023-10-10 20:52:29,505][flwr][DEBUG] - evaluate_round 89: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 21:01:38,802 | server.py:187 | evaluate_round 89 received 50 results and 0 failures
+[2023-10-10 21:01:38,802][flwr][DEBUG] - evaluate_round 89 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 21:01:38,803 | server.py:222 | fit_round 90: strategy sampled 50 clients (out of 50)
+[2023-10-10 21:01:38,803][flwr][DEBUG] - fit_round 90: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.810796 Loss1: 0.983006 Loss2: 1.827790
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.053737 Loss1: 0.645279 Loss2: 1.408458
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.871689 Loss1: 0.424789 Loss2: 1.446899
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.872757 Loss1: 1.024199 Loss2: 1.848557
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.963298 Loss1: 0.579538 Loss2: 1.383760 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804612 Loss1: 0.398533 Loss2: 1.406079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.662399 Loss1: 0.296104 Loss2: 1.366295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557995 Loss1: 0.196662 Loss2: 1.361333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.564342 Loss1: 0.211417 Loss2: 1.352925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.547590 Loss1: 0.190406 Loss2: 1.357184 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515261 Loss1: 0.161570 Loss2: 1.353692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446554 Loss1: 0.105643 Loss2: 1.340911 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.188030 Loss1: 1.252146 Loss2: 1.935884
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.209589 Loss1: 0.800563 Loss2: 1.409025
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.935581 Loss1: 0.468874 Loss2: 1.466708
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.671312 Loss1: 0.270696 Loss2: 1.400617
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.899674 Loss1: 1.039624 Loss2: 1.860050
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.583785 Loss1: 0.192467 Loss2: 1.391318 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516648 Loss1: 0.137162 Loss2: 1.379486 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481452 Loss1: 0.101452 Loss2: 1.380000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456440 Loss1: 0.081513 Loss2: 1.374927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451279 Loss1: 0.083597 Loss2: 1.367683 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.562587 Loss1: 0.195474 Loss2: 1.367113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.510184 Loss1: 0.146844 Loss2: 1.363340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527463 Loss1: 0.165534 Loss2: 1.361929
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.775094 Loss1: 1.015748 Loss2: 1.759346
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.998091 Loss1: 0.644659 Loss2: 1.353431
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741511 Loss1: 0.394753 Loss2: 1.346758
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604486 Loss1: 0.290151 Loss2: 1.314334
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.522979 Loss1: 0.209708 Loss2: 1.313271
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.951664 Loss1: 1.085696 Loss2: 1.865967
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.447718 Loss1: 0.149940 Loss2: 1.297778
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420633 Loss1: 0.120729 Loss2: 1.299903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.374394 Loss1: 0.085153 Loss2: 1.289242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371414 Loss1: 0.088897 Loss2: 1.282517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386174 Loss1: 0.104058 Loss2: 1.282116 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474055 Loss1: 0.111668 Loss2: 1.362387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476814 Loss1: 0.118726 Loss2: 1.358088 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465495 Loss1: 0.110104 Loss2: 1.355391
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.965088 Loss1: 1.092194 Loss2: 1.872894
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.029710 Loss1: 0.591406 Loss2: 1.438304
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.813594 Loss1: 0.380188 Loss2: 1.433405
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.697532 Loss1: 0.290060 Loss2: 1.407472
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571518 Loss1: 0.169662 Loss2: 1.401856
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.892451 Loss1: 0.963541 Loss2: 1.928910
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537961 Loss1: 0.146641 Loss2: 1.391321
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.025206 Loss1: 0.541159 Loss2: 1.484047
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.492411 Loss1: 0.097654 Loss2: 1.394757
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.820909 Loss1: 0.337174 Loss2: 1.483735
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499445 Loss1: 0.116395 Loss2: 1.383050
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.757877 Loss1: 0.290509 Loss2: 1.467368
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472223 Loss1: 0.093068 Loss2: 1.379155
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.698440 Loss1: 0.224503 Loss2: 1.473937
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452651 Loss1: 0.069739 Loss2: 1.382912
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.621564 Loss1: 0.170832 Loss2: 1.450732
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.626950 Loss1: 0.178154 Loss2: 1.448796
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.589617 Loss1: 0.142898 Loss2: 1.446719
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.587600 Loss1: 0.147125 Loss2: 1.440476
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.561281 Loss1: 0.124742 Loss2: 1.436539
+(DefaultActor pid=3764) >> Training accuracy: 0.972656
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.984917 Loss1: 1.093389 Loss2: 1.891528
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.045005 Loss1: 0.631552 Loss2: 1.413453
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.795112 Loss1: 0.389206 Loss2: 1.405906
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.691507 Loss1: 0.307379 Loss2: 1.384128
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.594161 Loss1: 0.200231 Loss2: 1.393930
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.875251 Loss1: 1.014292 Loss2: 1.860960
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.051614 Loss1: 0.681963 Loss2: 1.369651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.833008 Loss1: 0.424535 Loss2: 1.408473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.625171 Loss1: 0.275479 Loss2: 1.349692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.603159 Loss1: 0.250889 Loss2: 1.352270 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446474 Loss1: 0.091429 Loss2: 1.355045
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.576313 Loss1: 0.227978 Loss2: 1.348335
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.555393 Loss1: 0.211078 Loss2: 1.344315
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.479176 Loss1: 0.139067 Loss2: 1.340109
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440217 Loss1: 0.110284 Loss2: 1.329933
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414040 Loss1: 0.086314 Loss2: 1.327726
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.872114 Loss1: 0.927886 Loss2: 1.944227
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.154734 Loss1: 0.685204 Loss2: 1.469530
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.813131 Loss1: 0.318548 Loss2: 1.494584
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.670043 Loss1: 0.238399 Loss2: 1.431644
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.626372 Loss1: 0.190334 Loss2: 1.436038
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.597995 Loss1: 0.169723 Loss2: 1.428272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.566875 Loss1: 0.143809 Loss2: 1.423066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.570194 Loss1: 0.148669 Loss2: 1.421526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.510749 Loss1: 0.094627 Loss2: 1.416122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.526641 Loss1: 0.108449 Loss2: 1.418192 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.555498 Loss1: 0.157592 Loss2: 1.397905 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.505727 Loss1: 0.115147 Loss2: 1.390581 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.013049 Loss1: 0.599270 Loss2: 1.413779 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.633118 Loss1: 0.243374 Loss2: 1.389744 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.954426 Loss1: 1.148965 Loss2: 1.805461
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.543875 Loss1: 0.157876 Loss2: 1.385999
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.221795 Loss1: 0.790305 Loss2: 1.431490
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530750 Loss1: 0.160638 Loss2: 1.370112
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.843984 Loss1: 0.488589 Loss2: 1.355395
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480439 Loss1: 0.108183 Loss2: 1.372256
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.707846 Loss1: 0.352949 Loss2: 1.354898
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497289 Loss1: 0.129458 Loss2: 1.367830
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.594112 Loss1: 0.259364 Loss2: 1.334748
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.478686 Loss1: 0.111399 Loss2: 1.367288
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556428 Loss1: 0.226708 Loss2: 1.329719
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449502 Loss1: 0.090716 Loss2: 1.358785
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460275 Loss1: 0.145531 Loss2: 1.314744 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401977 Loss1: 0.091807 Loss2: 1.310170 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.067858 Loss1: 0.670331 Loss2: 1.397528 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654450 Loss1: 0.277542 Loss2: 1.376908 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.949478 Loss1: 1.076277 Loss2: 1.873201
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.569043 Loss1: 0.195971 Loss2: 1.373072
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.066369 Loss1: 0.651622 Loss2: 1.414747
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.536834 Loss1: 0.172845 Loss2: 1.363989
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.826845 Loss1: 0.369465 Loss2: 1.457380
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.555669 Loss1: 0.191424 Loss2: 1.364245
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.690629 Loss1: 0.303696 Loss2: 1.386933
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.482145 Loss1: 0.125598 Loss2: 1.356546
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.611321 Loss1: 0.215857 Loss2: 1.395464
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448667 Loss1: 0.099811 Loss2: 1.348857
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.579361 Loss1: 0.182483 Loss2: 1.396878
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426266 Loss1: 0.081468 Loss2: 1.344798
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.537862 Loss1: 0.147923 Loss2: 1.389939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470550 Loss1: 0.097239 Loss2: 1.373311 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.040853 Loss1: 0.622273 Loss2: 1.418580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.631312 Loss1: 0.237698 Loss2: 1.393614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.730277 Loss1: 0.887293 Loss2: 1.842984
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.573098 Loss1: 0.184564 Loss2: 1.388534
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.957624 Loss1: 0.564873 Loss2: 1.392751
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494126 Loss1: 0.117132 Loss2: 1.376994
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478205 Loss1: 0.110558 Loss2: 1.367647
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.826806 Loss1: 0.400630 Loss2: 1.426176
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.494833 Loss1: 0.125035 Loss2: 1.369799
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.661285 Loss1: 0.280965 Loss2: 1.380320
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.596812 Loss1: 0.213333 Loss2: 1.383479 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.534337 Loss1: 0.165779 Loss2: 1.368558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.476770 Loss1: 0.118010 Loss2: 1.358760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.108090 Loss1: 1.143383 Loss2: 1.964707 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.889855 Loss1: 0.415984 Loss2: 1.473871 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555714 Loss1: 0.168560 Loss2: 1.387154 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491489 Loss1: 0.127300 Loss2: 1.364189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.501670 Loss1: 0.145302 Loss2: 1.356368 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469584 Loss1: 0.113361 Loss2: 1.356223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413337 Loss1: 0.060123 Loss2: 1.353214 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513030 Loss1: 0.139907 Loss2: 1.373123 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510250 Loss1: 0.148336 Loss2: 1.361914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.475365 Loss1: 0.107008 Loss2: 1.368357
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.871808 Loss1: 1.034646 Loss2: 1.837162
+(DefaultActor pid=3764) >> Training accuracy: 0.983259
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457371 Loss1: 0.100792 Loss2: 1.356579
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.100223 Loss1: 0.653871 Loss2: 1.446352
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.849433 Loss1: 0.411977 Loss2: 1.437456
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.729732 Loss1: 0.316920 Loss2: 1.412812
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.621512 Loss1: 0.213779 Loss2: 1.407733
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.572490 Loss1: 0.177937 Loss2: 1.394552
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.930822 Loss1: 1.021268 Loss2: 1.909553
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.079190 Loss1: 0.639208 Loss2: 1.439982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.836225 Loss1: 0.390411 Loss2: 1.445815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.507376 Loss1: 0.118855 Loss2: 1.388521
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.701446 Loss1: 0.296106 Loss2: 1.405340
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.497218 Loss1: 0.114303 Loss2: 1.382915
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.617529 Loss1: 0.211564 Loss2: 1.405965
+(DefaultActor pid=3765) >> Training accuracy: 0.974609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.544306 Loss1: 0.161104 Loss2: 1.383202
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491396 Loss1: 0.114796 Loss2: 1.376600
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501691 Loss1: 0.127916 Loss2: 1.373775
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447709 Loss1: 0.070040 Loss2: 1.377669
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.042783 Loss1: 1.137752 Loss2: 1.905030
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.484348 Loss1: 0.116415 Loss2: 1.367933
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.795214 Loss1: 0.360338 Loss2: 1.434876 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.624447 Loss1: 0.218144 Loss2: 1.406303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.906997 Loss1: 1.005442 Loss2: 1.901555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.046819 Loss1: 0.651627 Loss2: 1.395192 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.850879 Loss1: 0.397358 Loss2: 1.453522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699709 Loss1: 0.313753 Loss2: 1.385955 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981027
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.554265 Loss1: 0.173881 Loss2: 1.380384 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470772 Loss1: 0.102961 Loss2: 1.367812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479284 Loss1: 0.111485 Loss2: 1.367799
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.098942 Loss1: 1.260443 Loss2: 1.838500
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478089 Loss1: 0.110639 Loss2: 1.367451
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.106411 Loss1: 0.685227 Loss2: 1.421184
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.791692 Loss1: 0.400244 Loss2: 1.391448
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.687861 Loss1: 0.318936 Loss2: 1.368925
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.624810 Loss1: 0.253298 Loss2: 1.371512
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.592939 Loss1: 0.221397 Loss2: 1.371541
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.046325 Loss1: 1.197002 Loss2: 1.849323
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.541887 Loss1: 0.192804 Loss2: 1.349083
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.118182 Loss1: 0.700467 Loss2: 1.417715
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486991 Loss1: 0.132854 Loss2: 1.354138
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.784104 Loss1: 0.370773 Loss2: 1.413331
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449497 Loss1: 0.105857 Loss2: 1.343640
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.728117 Loss1: 0.336498 Loss2: 1.391620
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433127 Loss1: 0.096769 Loss2: 1.336358
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.575988 Loss1: 0.190303 Loss2: 1.385686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.536314 Loss1: 0.162682 Loss2: 1.373632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.509051 Loss1: 0.134768 Loss2: 1.374283
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.801802 Loss1: 0.969204 Loss2: 1.832598
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.469418 Loss1: 0.104900 Loss2: 1.364518
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.889502 Loss1: 0.550413 Loss2: 1.339089
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.834113 Loss1: 0.471161 Loss2: 1.362951
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.688412 Loss1: 0.351967 Loss2: 1.336445
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572565 Loss1: 0.239093 Loss2: 1.333472
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492223 Loss1: 0.180687 Loss2: 1.311536
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.069789 Loss1: 1.144976 Loss2: 1.924813
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428341 Loss1: 0.130771 Loss2: 1.297570
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.181820 Loss1: 0.721477 Loss2: 1.460343
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404800 Loss1: 0.102455 Loss2: 1.302345
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.892962 Loss1: 0.406939 Loss2: 1.486023
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.380293 Loss1: 0.087098 Loss2: 1.293195
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.737610 Loss1: 0.309947 Loss2: 1.427662
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378100 Loss1: 0.087445 Loss2: 1.290655
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.637651 Loss1: 0.218745 Loss2: 1.418905 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.603169 Loss1: 0.184210 Loss2: 1.418959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.584536 Loss1: 0.173565 Loss2: 1.410971
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.923039 Loss1: 1.045421 Loss2: 1.877617
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.556602 Loss1: 0.142306 Loss2: 1.414296
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.006953 Loss1: 0.600675 Loss2: 1.406278
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.790585 Loss1: 0.361502 Loss2: 1.429083
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642386 Loss1: 0.252066 Loss2: 1.390319
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593271 Loss1: 0.203184 Loss2: 1.390087
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533893 Loss1: 0.151220 Loss2: 1.382673
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.925746 Loss1: 1.140466 Loss2: 1.785280
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487152 Loss1: 0.116588 Loss2: 1.370564
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.032693 Loss1: 0.669084 Loss2: 1.363609
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491024 Loss1: 0.123987 Loss2: 1.367038
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.751487 Loss1: 0.388833 Loss2: 1.362654
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.486808 Loss1: 0.120467 Loss2: 1.366340
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.639128 Loss1: 0.309671 Loss2: 1.329456
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475194 Loss1: 0.111441 Loss2: 1.363752
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519641 Loss1: 0.191716 Loss2: 1.327925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.403636 Loss1: 0.101218 Loss2: 1.302418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.380866 Loss1: 0.076209 Loss2: 1.304656
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.878904 Loss1: 1.018300 Loss2: 1.860604
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369874 Loss1: 0.074505 Loss2: 1.295369
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.993450 Loss1: 0.604972 Loss2: 1.388478
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.774211 Loss1: 0.368578 Loss2: 1.405633
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.687145 Loss1: 0.313005 Loss2: 1.374140
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.612347 Loss1: 0.237142 Loss2: 1.375205
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547227 Loss1: 0.189855 Loss2: 1.357372
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.156360 Loss1: 1.200861 Loss2: 1.955499
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.499202 Loss1: 0.134679 Loss2: 1.364522
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486224 Loss1: 0.133531 Loss2: 1.352693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.487136 Loss1: 0.136380 Loss2: 1.350756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.478662 Loss1: 0.128588 Loss2: 1.350074 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.660947 Loss1: 0.217994 Loss2: 1.442953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.542433 Loss1: 0.122682 Loss2: 1.419751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.524899 Loss1: 0.113286 Loss2: 1.411613
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.870205 Loss1: 1.034668 Loss2: 1.835537
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.043812 Loss1: 0.637638 Loss2: 1.406175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.732440 Loss1: 0.353516 Loss2: 1.378925 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.601470 Loss1: 0.225622 Loss2: 1.375848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529090 Loss1: 0.160555 Loss2: 1.368535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.852203 Loss1: 0.409039 Loss2: 1.443164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.724259 Loss1: 0.349005 Loss2: 1.375254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.622143 Loss1: 0.242764 Loss2: 1.379379 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487760 Loss1: 0.125745 Loss2: 1.362015 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461468 Loss1: 0.104809 Loss2: 1.356658 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978795
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465664 Loss1: 0.115593 Loss2: 1.350070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.803918 Loss1: 1.003277 Loss2: 1.800641
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.982984 Loss1: 0.615698 Loss2: 1.367286
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.694444 Loss1: 0.342230 Loss2: 1.352215
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.628318 Loss1: 0.298868 Loss2: 1.329450
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.540479 Loss1: 0.199645 Loss2: 1.340833
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.987386 Loss1: 1.085409 Loss2: 1.901977
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.253643 Loss1: 0.808227 Loss2: 1.445416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.976733 Loss1: 0.485860 Loss2: 1.490874 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.740069 Loss1: 0.310784 Loss2: 1.429285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.719620 Loss1: 0.297088 Loss2: 1.422532 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438155 Loss1: 0.126172 Loss2: 1.311984
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.633355 Loss1: 0.211528 Loss2: 1.421828
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.538267 Loss1: 0.136288 Loss2: 1.401979
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.530021 Loss1: 0.130651 Loss2: 1.399370
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.520023 Loss1: 0.120524 Loss2: 1.399499
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491919 Loss1: 0.098865 Loss2: 1.393054
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.120510 Loss1: 1.207250 Loss2: 1.913260
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.297035 Loss1: 0.813480 Loss2: 1.483555
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.945309 Loss1: 0.489617 Loss2: 1.455691
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.752945 Loss1: 0.338965 Loss2: 1.413980
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.640011 Loss1: 0.228676 Loss2: 1.411335
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.895260 Loss1: 1.070382 Loss2: 1.824879
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.030929 Loss1: 0.652252 Loss2: 1.378677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.879923 Loss1: 0.477335 Loss2: 1.402587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.663928 Loss1: 0.299087 Loss2: 1.364841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609742 Loss1: 0.241857 Loss2: 1.367885 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.527295 Loss1: 0.151682 Loss2: 1.375613
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.536969 Loss1: 0.188782 Loss2: 1.348188
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473131 Loss1: 0.121949 Loss2: 1.351182
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453113 Loss1: 0.112894 Loss2: 1.340219
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454855 Loss1: 0.117803 Loss2: 1.337052
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427595 Loss1: 0.093809 Loss2: 1.333786
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.056372 Loss1: 1.170453 Loss2: 1.885919
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.149857 Loss1: 0.683230 Loss2: 1.466627
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.907072 Loss1: 0.470994 Loss2: 1.436078
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.805432 Loss1: 0.381490 Loss2: 1.423941
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631378 Loss1: 0.212538 Loss2: 1.418840
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.931833 Loss1: 1.036718 Loss2: 1.895115
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.105760 Loss1: 0.664699 Loss2: 1.441061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.844175 Loss1: 0.375089 Loss2: 1.469086 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.667591 Loss1: 0.245071 Loss2: 1.422521 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.630193 Loss1: 0.214489 Loss2: 1.415704 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.625882 Loss1: 0.209931 Loss2: 1.415951 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.563132 Loss1: 0.161358 Loss2: 1.401774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.499270 Loss1: 0.103407 Loss2: 1.395864 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.128160 Loss1: 0.613689 Loss2: 1.514472 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.819930 Loss1: 0.342961 Loss2: 1.476969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.696263 Loss1: 0.245639 Loss2: 1.450624
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.813209 Loss1: 1.006946 Loss2: 1.806263
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.032372 Loss1: 0.666567 Loss2: 1.365804
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.602895 Loss1: 0.165289 Loss2: 1.437607
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.813527 Loss1: 0.427854 Loss2: 1.385673
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.557009 Loss1: 0.120616 Loss2: 1.436393
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.686857 Loss1: 0.325673 Loss2: 1.361184
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.543812 Loss1: 0.114790 Loss2: 1.429022
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.667900 Loss1: 0.314871 Loss2: 1.353030
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.549498 Loss1: 0.128489 Loss2: 1.421009
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.532191 Loss1: 0.105753 Loss2: 1.426438 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.960938
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482414 Loss1: 0.143024 Loss2: 1.339390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458677 Loss1: 0.116698 Loss2: 1.341979 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.025418 Loss1: 0.615124 Loss2: 1.410293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.627614 Loss1: 0.238005 Loss2: 1.389610 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.996391 Loss1: 1.084493 Loss2: 1.911898
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.613073 Loss1: 0.233618 Loss2: 1.379455
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.567777 Loss1: 0.189716 Loss2: 1.378061
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.165019 Loss1: 0.660745 Loss2: 1.504274
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514415 Loss1: 0.142339 Loss2: 1.372077
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.906504 Loss1: 0.412196 Loss2: 1.494307
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484833 Loss1: 0.119717 Loss2: 1.365115
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.753153 Loss1: 0.286665 Loss2: 1.466488
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.492194 Loss1: 0.131371 Loss2: 1.360823
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.751746 Loss1: 0.284966 Loss2: 1.466780
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.464449 Loss1: 0.101883 Loss2: 1.362566
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.737639 Loss1: 0.259041 Loss2: 1.478598
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.607060 Loss1: 0.154729 Loss2: 1.452332
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.595319 Loss1: 0.148399 Loss2: 1.446920
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.577965 Loss1: 0.136720 Loss2: 1.441245
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.555344 Loss1: 0.119636 Loss2: 1.435708
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.934253 Loss1: 0.942315 Loss2: 1.991938
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.133403 Loss1: 0.591421 Loss2: 1.541982
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.961151 Loss1: 0.400209 Loss2: 1.560942
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.809226 Loss1: 0.294185 Loss2: 1.515042
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.701362 Loss1: 0.187254 Loss2: 1.514108
+DEBUG flwr 2023-10-10 21:30:05,935 | server.py:236 | fit_round 90 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.079656 Loss1: 1.122970 Loss2: 1.956686
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.681565 Loss1: 0.177951 Loss2: 1.503614
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.140646 Loss1: 0.653240 Loss2: 1.487405
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.648145 Loss1: 0.154832 Loss2: 1.493313
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.945512 Loss1: 0.452026 Loss2: 1.493486
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.750804 Loss1: 0.287366 Loss2: 1.463439
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.603272 Loss1: 0.107745 Loss2: 1.495527
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.686826 Loss1: 0.225939 Loss2: 1.460887
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.607619 Loss1: 0.127778 Loss2: 1.479841
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.676718 Loss1: 0.218159 Loss2: 1.458559
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.620113 Loss1: 0.134483 Loss2: 1.485630
+(DefaultActor pid=3765) >> Training accuracy: 0.965820
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.615485 Loss1: 0.163350 Loss2: 1.452135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.555110 Loss1: 0.112927 Loss2: 1.442183 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.083689 Loss1: 0.694648 Loss2: 1.389040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.620823 Loss1: 0.257542 Loss2: 1.363280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555664 Loss1: 0.209651 Loss2: 1.346013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491509 Loss1: 0.152145 Loss2: 1.339365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467431 Loss1: 0.135858 Loss2: 1.331573 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439441 Loss1: 0.112960 Loss2: 1.326482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.464233 Loss1: 0.144149 Loss2: 1.320084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428198 Loss1: 0.100625 Loss2: 1.327573 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.568893 Loss1: 0.149114 Loss2: 1.419780 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.314325 Loss1: 1.148246 Loss2: 2.166079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.124292 Loss1: 0.531170 Loss2: 1.593122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.744463 Loss1: 0.244664 Loss2: 1.499799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.723416 Loss1: 0.204275 Loss2: 1.519141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.638666 Loss1: 0.143254 Loss2: 1.495412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.612582 Loss1: 0.125251 Loss2: 1.487331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.649892 Loss1: 0.161581 Loss2: 1.488311
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.686269 Loss1: 0.310495 Loss2: 1.375774
+(DefaultActor pid=3765) >> Training accuracy: 0.977865
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.588303 Loss1: 0.102571 Loss2: 1.485731
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.608557 Loss1: 0.251776 Loss2: 1.356781
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.520542 Loss1: 0.172931 Loss2: 1.347611
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471851 Loss1: 0.134064 Loss2: 1.337787
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445844 Loss1: 0.103360 Loss2: 1.342484
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426306 Loss1: 0.093698 Loss2: 1.332608
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409590 Loss1: 0.081725 Loss2: 1.327865
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 21:30:05,935][flwr][DEBUG] - fit_round 90 received 50 results and 0 failures
+INFO flwr 2023-10-10 21:30:47,521 | server.py:125 | fit progress: (90, 2.2210369241504244, {'accuracy': 0.5571}, 207555.299687149)
+>> Test accuracy: 0.557100
+[2023-10-10 21:30:47,521][flwr][INFO] - fit progress: (90, 2.2210369241504244, {'accuracy': 0.5571}, 207555.299687149)
+DEBUG flwr 2023-10-10 21:30:47,521 | server.py:173 | evaluate_round 90: strategy sampled 50 clients (out of 50)
+[2023-10-10 21:30:47,521][flwr][DEBUG] - evaluate_round 90: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 21:39:51,467 | server.py:187 | evaluate_round 90 received 50 results and 0 failures
+[2023-10-10 21:39:51,467][flwr][DEBUG] - evaluate_round 90 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 21:39:51,468 | server.py:222 | fit_round 91: strategy sampled 50 clients (out of 50)
+[2023-10-10 21:39:51,468][flwr][DEBUG] - fit_round 91: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.867967 Loss1: 1.018204 Loss2: 1.849763
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.114334 Loss1: 0.652242 Loss2: 1.462091
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.850079 Loss1: 0.424558 Loss2: 1.425521
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.858704 Loss1: 0.999092 Loss2: 1.859612
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.088812 Loss1: 0.691277 Loss2: 1.397535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.866834 Loss1: 0.418372 Loss2: 1.448462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.719736 Loss1: 0.325113 Loss2: 1.394623 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.664469 Loss1: 0.259439 Loss2: 1.405030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.574134 Loss1: 0.194983 Loss2: 1.379151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490640 Loss1: 0.110943 Loss2: 1.379697 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.474658 Loss1: 0.110613 Loss2: 1.364045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.480128 Loss1: 0.115166 Loss2: 1.364962 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.083119 Loss1: 1.054804 Loss2: 2.028315
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.237600 Loss1: 0.833801 Loss2: 1.403799
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.976208 Loss1: 0.482822 Loss2: 1.493386
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.805945 Loss1: 0.385362 Loss2: 1.420583
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.760926 Loss1: 0.340142 Loss2: 1.420785
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.150359 Loss1: 0.719500 Loss2: 1.430859 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.859140 Loss1: 0.363577 Loss2: 1.495563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.670895 Loss1: 0.253728 Loss2: 1.417167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.518789 Loss1: 0.129976 Loss2: 1.388813 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.537157 Loss1: 0.134577 Loss2: 1.402580 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.500434 Loss1: 0.102774 Loss2: 1.397660 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.774753 Loss1: 0.947862 Loss2: 1.826890
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472873 Loss1: 0.083419 Loss2: 1.389454
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.679510 Loss1: 0.303472 Loss2: 1.376037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534559 Loss1: 0.180455 Loss2: 1.354104 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503422 Loss1: 0.173708 Loss2: 1.329715
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.053397 Loss1: 1.176354 Loss2: 1.877043
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.077658 Loss1: 0.648573 Loss2: 1.429085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.795612 Loss1: 0.372714 Loss2: 1.422898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.676950 Loss1: 0.279568 Loss2: 1.397382 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.952083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431686 Loss1: 0.105065 Loss2: 1.326621
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.619030 Loss1: 0.229561 Loss2: 1.389469
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.535547 Loss1: 0.157212 Loss2: 1.378335
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.544820 Loss1: 0.173017 Loss2: 1.371803
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568104 Loss1: 0.196233 Loss2: 1.371871
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.588078 Loss1: 0.200005 Loss2: 1.388072
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.812205 Loss1: 0.983109 Loss2: 1.829095
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.531226 Loss1: 0.155240 Loss2: 1.375987
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.693360 Loss1: 0.311119 Loss2: 1.382241 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513634 Loss1: 0.158022 Loss2: 1.355612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.447472 Loss1: 0.124325 Loss2: 1.323148
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.938115 Loss1: 1.124995 Loss2: 1.813120
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447114 Loss1: 0.126320 Loss2: 1.320794
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.067360 Loss1: 0.669277 Loss2: 1.398083
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423398 Loss1: 0.101173 Loss2: 1.322225
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.862814 Loss1: 0.474337 Loss2: 1.388478
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409153 Loss1: 0.094396 Loss2: 1.314757
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.721695 Loss1: 0.340727 Loss2: 1.380968
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383821 Loss1: 0.069979 Loss2: 1.313841
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.691865 Loss1: 0.315847 Loss2: 1.376018
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.573676 Loss1: 0.210041 Loss2: 1.363634
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.525507 Loss1: 0.174731 Loss2: 1.350776
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481078 Loss1: 0.132549 Loss2: 1.348529
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450085 Loss1: 0.104372 Loss2: 1.345713
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462489 Loss1: 0.134500 Loss2: 1.327989
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.965909 Loss1: 1.051838 Loss2: 1.914072
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.052428 Loss1: 0.600837 Loss2: 1.451591
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.843361 Loss1: 0.415182 Loss2: 1.428179
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.680249 Loss1: 0.266914 Loss2: 1.413335
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.594106 Loss1: 0.198692 Loss2: 1.395414
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538095 Loss1: 0.143406 Loss2: 1.394689
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.057519 Loss1: 1.173890 Loss2: 1.883630
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528229 Loss1: 0.142255 Loss2: 1.385974
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.043414 Loss1: 0.635561 Loss2: 1.407853
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.501532 Loss1: 0.126322 Loss2: 1.375210
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.916509 Loss1: 0.475003 Loss2: 1.441506
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445452 Loss1: 0.065827 Loss2: 1.379625
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.732315 Loss1: 0.338582 Loss2: 1.393733
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425090 Loss1: 0.056247 Loss2: 1.368843
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.614066 Loss1: 0.215129 Loss2: 1.398937
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.624796 Loss1: 0.235785 Loss2: 1.389011
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.567873 Loss1: 0.182640 Loss2: 1.385233
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.575418 Loss1: 0.193572 Loss2: 1.381846
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.552974 Loss1: 0.167079 Loss2: 1.385895
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.516734 Loss1: 0.131562 Loss2: 1.385173
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.784460 Loss1: 1.027716 Loss2: 1.756744
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.041508 Loss1: 0.683471 Loss2: 1.358036
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731244 Loss1: 0.371235 Loss2: 1.360009
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601194 Loss1: 0.278200 Loss2: 1.322993
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526062 Loss1: 0.201120 Loss2: 1.324942
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.862134 Loss1: 0.993818 Loss2: 1.868316
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475969 Loss1: 0.164245 Loss2: 1.311724
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435304 Loss1: 0.130995 Loss2: 1.304310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430920 Loss1: 0.125038 Loss2: 1.305882 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417430 Loss1: 0.112026 Loss2: 1.305404 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403099 Loss1: 0.095019 Loss2: 1.308079 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480978 Loss1: 0.126550 Loss2: 1.354428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440046 Loss1: 0.094792 Loss2: 1.345254 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429164 Loss1: 0.091937 Loss2: 1.337226
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.945673 Loss1: 1.025114 Loss2: 1.920559
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.135716 Loss1: 0.685838 Loss2: 1.449878
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.903390 Loss1: 0.415770 Loss2: 1.487619
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.696653 Loss1: 0.272584 Loss2: 1.424069
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.644522 Loss1: 0.222137 Loss2: 1.422385
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.953368 Loss1: 1.013299 Loss2: 1.940069
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.045953 Loss1: 0.602529 Loss2: 1.443424 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.797194 Loss1: 0.338952 Loss2: 1.458242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.713275 Loss1: 0.292626 Loss2: 1.420649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.612400 Loss1: 0.192125 Loss2: 1.420274 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.494012 Loss1: 0.101668 Loss2: 1.392344
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531757 Loss1: 0.127268 Loss2: 1.404489
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513467 Loss1: 0.118067 Loss2: 1.395399
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.502256 Loss1: 0.108797 Loss2: 1.393459
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.470506 Loss1: 0.079036 Loss2: 1.391469
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.481370 Loss1: 0.098290 Loss2: 1.383080
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.111561 Loss1: 1.247778 Loss2: 1.863782
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.122225 Loss1: 0.696313 Loss2: 1.425912
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.875389 Loss1: 0.443426 Loss2: 1.431963
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.700111 Loss1: 0.311021 Loss2: 1.389090
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611352 Loss1: 0.229956 Loss2: 1.381395
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.853903 Loss1: 1.016675 Loss2: 1.837229
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.109114 Loss1: 0.686552 Loss2: 1.422562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834924 Loss1: 0.412504 Loss2: 1.422419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.693390 Loss1: 0.301012 Loss2: 1.392378 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.624972 Loss1: 0.237212 Loss2: 1.387760 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567446 Loss1: 0.188105 Loss2: 1.379341 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.542217 Loss1: 0.165581 Loss2: 1.376636 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.492171 Loss1: 0.120159 Loss2: 1.372011 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.830963 Loss1: 0.368348 Loss2: 1.462614 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.656060 Loss1: 0.238813 Loss2: 1.417247 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.560107 Loss1: 0.167102 Loss2: 1.393005
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.680452 Loss1: 0.851990 Loss2: 1.828462
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.885428 Loss1: 0.496275 Loss2: 1.389153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.730030 Loss1: 0.322974 Loss2: 1.407056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475306 Loss1: 0.093239 Loss2: 1.382067 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522244 Loss1: 0.156544 Loss2: 1.365700 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.497762 Loss1: 0.133575 Loss2: 1.364187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453343 Loss1: 0.099006 Loss2: 1.354337 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425887 Loss1: 0.076282 Loss2: 1.349604 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587233 Loss1: 0.171899 Loss2: 1.415333 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.548375 Loss1: 0.151698 Loss2: 1.396677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.142903 Loss1: 1.218340 Loss2: 1.924563
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.514886 Loss1: 0.124133 Loss2: 1.390752
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.531074 Loss1: 0.142026 Loss2: 1.389048 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491314 Loss1: 0.098845 Loss2: 1.392469 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.614343 Loss1: 0.224661 Loss2: 1.389682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.521691 Loss1: 0.142877 Loss2: 1.378814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.942188 Loss1: 1.067212 Loss2: 1.874976 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.046022 Loss1: 0.624876 Loss2: 1.421147 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.608308 Loss1: 0.230843 Loss2: 1.377466 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489251 Loss1: 0.125736 Loss2: 1.363515 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.550635 Loss1: 0.191194 Loss2: 1.359441
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.991315 Loss1: 1.060183 Loss2: 1.931132
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481574 Loss1: 0.108769 Loss2: 1.372806
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.145566 Loss1: 0.675528 Loss2: 1.470038
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.480011 Loss1: 0.124556 Loss2: 1.355455
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.948887 Loss1: 0.437660 Loss2: 1.511227
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469646 Loss1: 0.113150 Loss2: 1.356496
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.863002 Loss1: 0.410622 Loss2: 1.452381
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.699216 Loss1: 0.230683 Loss2: 1.468533
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.642017 Loss1: 0.199222 Loss2: 1.442795
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.607104 Loss1: 0.176861 Loss2: 1.430242
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.601276 Loss1: 0.161873 Loss2: 1.439403
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.945527 Loss1: 1.042376 Loss2: 1.903151
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.561258 Loss1: 0.130712 Loss2: 1.430545
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.147967 Loss1: 0.690923 Loss2: 1.457044
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.553233 Loss1: 0.133168 Loss2: 1.420066
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.663255 Loss1: 0.264755 Loss2: 1.398500 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.549124 Loss1: 0.158325 Loss2: 1.390799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.569662 Loss1: 0.182225 Loss2: 1.387437
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.918841 Loss1: 0.901841 Loss2: 2.017000
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.298025 Loss1: 0.757884 Loss2: 1.540141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.074973 Loss1: 0.507529 Loss2: 1.567444 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.495028 Loss1: 0.120452 Loss2: 1.374576
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.966284 Loss1: 0.453459 Loss2: 1.512825
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.776899 Loss1: 0.277594 Loss2: 1.499305
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.697482 Loss1: 0.220467 Loss2: 1.477015
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.637539 Loss1: 0.154071 Loss2: 1.483469
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.579773 Loss1: 0.116338 Loss2: 1.463435
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.048889 Loss1: 1.100989 Loss2: 1.947900
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.587735 Loss1: 0.133694 Loss2: 1.454041
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.576085 Loss1: 0.115418 Loss2: 1.460667 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.597421 Loss1: 0.197136 Loss2: 1.400285 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.584923 Loss1: 0.188663 Loss2: 1.396260 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.004919 Loss1: 1.063232 Loss2: 1.941687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.323717 Loss1: 0.843019 Loss2: 1.480698 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977163
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.911602 Loss1: 0.418104 Loss2: 1.493499 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.676917 Loss1: 0.217639 Loss2: 1.459278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.643685 Loss1: 0.187769 Loss2: 1.455916
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.954093 Loss1: 1.083838 Loss2: 1.870255
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.002067 Loss1: 0.548783 Loss2: 1.453285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.889517 Loss1: 0.458252 Loss2: 1.431265 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.726370 Loss1: 0.302135 Loss2: 1.424235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.608303 Loss1: 0.216553 Loss2: 1.391749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.511670 Loss1: 0.127675 Loss2: 1.383995 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.491278 Loss1: 0.119284 Loss2: 1.371993 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435551 Loss1: 0.068102 Loss2: 1.367448 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569333 Loss1: 0.211057 Loss2: 1.358276 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483565 Loss1: 0.153863 Loss2: 1.329702 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.896739 Loss1: 1.021429 Loss2: 1.875310
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455820 Loss1: 0.129417 Loss2: 1.326404
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.105353 Loss1: 0.663765 Loss2: 1.441588
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447446 Loss1: 0.126087 Loss2: 1.321359
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.951021 Loss1: 0.498559 Loss2: 1.452462
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403631 Loss1: 0.083372 Loss2: 1.320259
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.658599 Loss1: 0.239349 Loss2: 1.419250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.560727 Loss1: 0.159483 Loss2: 1.401244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.545961 Loss1: 0.141495 Loss2: 1.404467
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.063660 Loss1: 1.207402 Loss2: 1.856258
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.041453 Loss1: 0.638474 Loss2: 1.402979 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.480643 Loss1: 0.094311 Loss2: 1.386332
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.821292 Loss1: 0.429904 Loss2: 1.391388
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.676028 Loss1: 0.298914 Loss2: 1.377114
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598852 Loss1: 0.233302 Loss2: 1.365550
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.534024 Loss1: 0.169913 Loss2: 1.364111
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478417 Loss1: 0.127831 Loss2: 1.350586
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.923552 Loss1: 1.047365 Loss2: 1.876187
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473578 Loss1: 0.130333 Loss2: 1.343245
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.042558 Loss1: 0.637533 Loss2: 1.405025
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425271 Loss1: 0.090969 Loss2: 1.334302
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.821271 Loss1: 0.391697 Loss2: 1.429575
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410317 Loss1: 0.079860 Loss2: 1.330457
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.706819 Loss1: 0.300274 Loss2: 1.406545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.570445 Loss1: 0.196161 Loss2: 1.374284 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.502976 Loss1: 0.132046 Loss2: 1.370930
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.206670 Loss1: 1.207918 Loss2: 1.998752
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.093758 Loss1: 0.685861 Loss2: 1.407897
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.495601 Loss1: 0.134738 Loss2: 1.360862
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.778453 Loss1: 0.339243 Loss2: 1.439210
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.456999 Loss1: 0.092979 Loss2: 1.364020
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.594340 Loss1: 0.228628 Loss2: 1.365712 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465129 Loss1: 0.105168 Loss2: 1.359961 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463062 Loss1: 0.111834 Loss2: 1.351228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.471485 Loss1: 0.117661 Loss2: 1.353824 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973558
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.709112 Loss1: 0.303414 Loss2: 1.405698 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.507542 Loss1: 0.146129 Loss2: 1.361413 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.131501 Loss1: 1.204944 Loss2: 1.926557
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514889 Loss1: 0.166584 Loss2: 1.348305
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.320846 Loss1: 0.820735 Loss2: 1.500111
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.478117 Loss1: 0.124804 Loss2: 1.353313
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.964741 Loss1: 0.500480 Loss2: 1.464261
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.442740 Loss1: 0.098887 Loss2: 1.343853
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.834869 Loss1: 0.366681 Loss2: 1.468188
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407357 Loss1: 0.065232 Loss2: 1.342125
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.661742 Loss1: 0.246087 Loss2: 1.415655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.549473 Loss1: 0.144633 Loss2: 1.404839 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.526359 Loss1: 0.122743 Loss2: 1.403617
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.136371 Loss1: 1.281944 Loss2: 1.854427
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.591989 Loss1: 0.191276 Loss2: 1.400713
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.158194 Loss1: 0.769322 Loss2: 1.388872
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.753865 Loss1: 0.379995 Loss2: 1.373870
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642038 Loss1: 0.308684 Loss2: 1.333354
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574614 Loss1: 0.235691 Loss2: 1.338923
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.483175 Loss1: 0.156235 Loss2: 1.326940
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432069 Loss1: 0.114099 Loss2: 1.317970
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.829207 Loss1: 0.985327 Loss2: 1.843880
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.978466 Loss1: 0.548780 Loss2: 1.429686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.755388 Loss1: 0.318102 Loss2: 1.437286 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.649883 Loss1: 0.261338 Loss2: 1.388545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500331 Loss1: 0.129578 Loss2: 1.370753 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485683 Loss1: 0.119414 Loss2: 1.366269 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.474119 Loss1: 0.110599 Loss2: 1.363520 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.469990 Loss1: 0.107899 Loss2: 1.362092 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.637276 Loss1: 0.298573 Loss2: 1.338703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.529057 Loss1: 0.180561 Loss2: 1.348495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514965 Loss1: 0.175597 Loss2: 1.339368
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.807613 Loss1: 0.946697 Loss2: 1.860916
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.981513 Loss1: 0.596328 Loss2: 1.385186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.776381 Loss1: 0.366926 Loss2: 1.409456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428504 Loss1: 0.102481 Loss2: 1.326023
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.621683 Loss1: 0.249318 Loss2: 1.372365
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.627581 Loss1: 0.255976 Loss2: 1.371605
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531006 Loss1: 0.163147 Loss2: 1.367859
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.526109 Loss1: 0.165685 Loss2: 1.360425
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466147 Loss1: 0.110605 Loss2: 1.355542
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.898502 Loss1: 1.003856 Loss2: 1.894647
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434358 Loss1: 0.085460 Loss2: 1.348898
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.157686 Loss1: 0.663898 Loss2: 1.493788
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422204 Loss1: 0.080756 Loss2: 1.341449
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.705340 Loss1: 0.275878 Loss2: 1.429461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.679538 Loss1: 0.253180 Loss2: 1.426357 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.823379 Loss1: 0.996580 Loss2: 1.826799
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.601662 Loss1: 0.183439 Loss2: 1.418223
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.959542 Loss1: 0.576408 Loss2: 1.383134
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.528996 Loss1: 0.122513 Loss2: 1.406483
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.744847 Loss1: 0.327494 Loss2: 1.417353
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.554165 Loss1: 0.156804 Loss2: 1.397361
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.635563 Loss1: 0.275535 Loss2: 1.360028
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.492336 Loss1: 0.088723 Loss2: 1.403613
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516207 Loss1: 0.159126 Loss2: 1.357081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483016 Loss1: 0.125329 Loss2: 1.357687 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.050230 Loss1: 1.099511 Loss2: 1.950719
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447282 Loss1: 0.101779 Loss2: 1.345503
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.153904 Loss1: 0.650752 Loss2: 1.503152
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.451555 Loss1: 0.112448 Loss2: 1.339107
+(DefaultActor pid=3764) >> Training accuracy: 0.967773
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.772544 Loss1: 0.301529 Loss2: 1.471015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.676967 Loss1: 0.219252 Loss2: 1.457716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.625981 Loss1: 0.169968 Loss2: 1.456013
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.004969 Loss1: 1.151769 Loss2: 1.853199
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.079598 Loss1: 0.672177 Loss2: 1.407421 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.789251 Loss1: 0.359750 Loss2: 1.429501 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.577405 Loss1: 0.130248 Loss2: 1.447157
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.627735 Loss1: 0.254780 Loss2: 1.372955
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.588321 Loss1: 0.206500 Loss2: 1.381821
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.580723 Loss1: 0.208872 Loss2: 1.371851
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513270 Loss1: 0.149306 Loss2: 1.363963
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459119 Loss1: 0.100663 Loss2: 1.358456
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.028089 Loss1: 1.160610 Loss2: 1.867478
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431362 Loss1: 0.084610 Loss2: 1.346752
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413598 Loss1: 0.068179 Loss2: 1.345420 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.700155 Loss1: 0.295521 Loss2: 1.404634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562106 Loss1: 0.177496 Loss2: 1.384610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517549 Loss1: 0.136529 Loss2: 1.381020
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.981592 Loss1: 0.990913 Loss2: 1.990679
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.147480 Loss1: 0.652349 Loss2: 1.495131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.906265 Loss1: 0.389974 Loss2: 1.516291 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.770627 Loss1: 0.294039 Loss2: 1.476588 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.687496 Loss1: 0.224272 Loss2: 1.463224 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.563930 Loss1: 0.108457 Loss2: 1.455473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.584945 Loss1: 0.133557 Loss2: 1.451387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.578227 Loss1: 0.120966 Loss2: 1.457260 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.643218 Loss1: 0.292372 Loss2: 1.350846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514409 Loss1: 0.177587 Loss2: 1.336822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.561626 Loss1: 0.220676 Loss2: 1.340950
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.102399 Loss1: 1.239536 Loss2: 1.862863
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.129156 Loss1: 0.678781 Loss2: 1.450375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.825136 Loss1: 0.442267 Loss2: 1.382869 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.942708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.696049 Loss1: 0.306481 Loss2: 1.389568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.553868 Loss1: 0.189619 Loss2: 1.364250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.480769 Loss1: 0.126807 Loss2: 1.353962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447770 Loss1: 0.099739 Loss2: 1.348031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439665 Loss1: 0.094289 Loss2: 1.345376 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 22:08:20,670 | server.py:236 | fit_round 91 received 50 results and 0 failures
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.733553 Loss1: 0.269153 Loss2: 1.464400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.631232 Loss1: 0.185693 Loss2: 1.445539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.588885 Loss1: 0.143522 Loss2: 1.445363
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.957511 Loss1: 1.065362 Loss2: 1.892150
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.032697 Loss1: 0.623046 Loss2: 1.409651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.886298 Loss1: 0.472381 Loss2: 1.413917 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.752068 Loss1: 0.321859 Loss2: 1.430209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512086 Loss1: 0.133351 Loss2: 1.378735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465448 Loss1: 0.105703 Loss2: 1.359745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452079 Loss1: 0.090288 Loss2: 1.361791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428628 Loss1: 0.072136 Loss2: 1.356492 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.759739 Loss1: 0.360213 Loss2: 1.399526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.565455 Loss1: 0.200288 Loss2: 1.365167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.521179 Loss1: 0.151250 Loss2: 1.369930
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.977539 Loss1: 1.138964 Loss2: 1.838575
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.501523 Loss1: 0.144447 Loss2: 1.357076
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.099006 Loss1: 0.701818 Loss2: 1.397188
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436374 Loss1: 0.087132 Loss2: 1.349242
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.761040 Loss1: 0.354014 Loss2: 1.407026
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.660837 Loss1: 0.302237 Loss2: 1.358600
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420728 Loss1: 0.075344 Loss2: 1.345384
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.570028 Loss1: 0.202670 Loss2: 1.367358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466269 Loss1: 0.116905 Loss2: 1.349364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404162 Loss1: 0.076651 Loss2: 1.327511 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 22:08:20,670][flwr][DEBUG] - fit_round 91 received 50 results and 0 failures
+INFO flwr 2023-10-10 22:09:02,220 | server.py:125 | fit progress: (91, 2.2214489318311403, {'accuracy': 0.5571}, 209849.998660804)
+>> Test accuracy: 0.557100
+[2023-10-10 22:09:02,220][flwr][INFO] - fit progress: (91, 2.2214489318311403, {'accuracy': 0.5571}, 209849.998660804)
+DEBUG flwr 2023-10-10 22:09:02,220 | server.py:173 | evaluate_round 91: strategy sampled 50 clients (out of 50)
+[2023-10-10 22:09:02,220][flwr][DEBUG] - evaluate_round 91: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 22:18:07,786 | server.py:187 | evaluate_round 91 received 50 results and 0 failures
+[2023-10-10 22:18:07,786][flwr][DEBUG] - evaluate_round 91 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 22:18:07,786 | server.py:222 | fit_round 92: strategy sampled 50 clients (out of 50)
+[2023-10-10 22:18:07,786][flwr][DEBUG] - fit_round 92: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.889109 Loss1: 1.072547 Loss2: 1.816562
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.998324 Loss1: 0.561428 Loss2: 1.436896
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.762121 Loss1: 0.360605 Loss2: 1.401516
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.672616 Loss1: 0.271321 Loss2: 1.401295
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.763677 Loss1: 0.995215 Loss2: 1.768462
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.618638 Loss1: 0.231566 Loss2: 1.387072
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.919303 Loss1: 0.560691 Loss2: 1.358612
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.594774 Loss1: 0.209978 Loss2: 1.384796
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.766136 Loss1: 0.405287 Loss2: 1.360849
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.554916 Loss1: 0.171269 Loss2: 1.383647
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.628907 Loss1: 0.283805 Loss2: 1.345101
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.546614 Loss1: 0.167942 Loss2: 1.378672
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.580495 Loss1: 0.235836 Loss2: 1.344659
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.540837 Loss1: 0.160525 Loss2: 1.380312
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530127 Loss1: 0.183963 Loss2: 1.346164
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508140 Loss1: 0.128840 Loss2: 1.379300
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441036 Loss1: 0.107640 Loss2: 1.333395
+(DefaultActor pid=3765) >> Training accuracy: 0.970703
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.402543 Loss1: 0.084227 Loss2: 1.318316
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388489 Loss1: 0.076387 Loss2: 1.312103
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377994 Loss1: 0.072445 Loss2: 1.305549
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.102538 Loss1: 1.135145 Loss2: 1.967393
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.191988 Loss1: 0.771519 Loss2: 1.420469
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.927543 Loss1: 0.431726 Loss2: 1.495817
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694173 Loss1: 0.275578 Loss2: 1.418595
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.682745 Loss1: 0.264188 Loss2: 1.418557
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.640437 Loss1: 0.215304 Loss2: 1.425133 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.572091 Loss1: 0.160896 Loss2: 1.411194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.537303 Loss1: 0.134369 Loss2: 1.402934 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.519368 Loss1: 0.119689 Loss2: 1.399679 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.528761 Loss1: 0.121939 Loss2: 1.406821 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985577
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.515143 Loss1: 0.140782 Loss2: 1.374361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486775 Loss1: 0.124539 Loss2: 1.362236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.508224 Loss1: 0.136327 Loss2: 1.371898
+(DefaultActor pid=3764) >> Training accuracy: 0.955208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.883900 Loss1: 1.064682 Loss2: 1.819218
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.990731 Loss1: 0.611180 Loss2: 1.379551
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.730891 Loss1: 0.349962 Loss2: 1.380929
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623162 Loss1: 0.264468 Loss2: 1.358694
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558987 Loss1: 0.205965 Loss2: 1.353021
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.851548 Loss1: 0.991696 Loss2: 1.859852
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.535067 Loss1: 0.184181 Loss2: 1.350886
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.028156 Loss1: 0.596415 Loss2: 1.431741
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516284 Loss1: 0.171049 Loss2: 1.345235
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452583 Loss1: 0.116880 Loss2: 1.335703
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.759585 Loss1: 0.332682 Loss2: 1.426903
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428029 Loss1: 0.095418 Loss2: 1.332612
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.674814 Loss1: 0.271052 Loss2: 1.403762
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450814 Loss1: 0.121776 Loss2: 1.329038
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.606542 Loss1: 0.208027 Loss2: 1.398515
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.564275 Loss1: 0.173540 Loss2: 1.390735
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500651 Loss1: 0.111843 Loss2: 1.388809
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483828 Loss1: 0.099534 Loss2: 1.384294
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444514 Loss1: 0.071295 Loss2: 1.373219
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.822141 Loss1: 0.987246 Loss2: 1.834896
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445641 Loss1: 0.076542 Loss2: 1.369099
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.708606 Loss1: 0.346763 Loss2: 1.361843 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.502023 Loss1: 0.168311 Loss2: 1.333712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513318 Loss1: 0.194507 Loss2: 1.318812
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.965701 Loss1: 1.117504 Loss2: 1.848197
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.038824 Loss1: 0.628289 Loss2: 1.410535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.860604 Loss1: 0.443422 Loss2: 1.417182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.664817 Loss1: 0.300295 Loss2: 1.364521 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368307 Loss1: 0.067740 Loss2: 1.300567
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598464 Loss1: 0.225343 Loss2: 1.373121
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.550930 Loss1: 0.182307 Loss2: 1.368623
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516048 Loss1: 0.160704 Loss2: 1.355344
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471255 Loss1: 0.116970 Loss2: 1.354286
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447190 Loss1: 0.104577 Loss2: 1.342613
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.886932 Loss1: 0.961395 Loss2: 1.925537
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422601 Loss1: 0.083404 Loss2: 1.339197
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.914803 Loss1: 0.432729 Loss2: 1.482074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.651897 Loss1: 0.219412 Loss2: 1.432485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.578523 Loss1: 0.160475 Loss2: 1.418048
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.013666 Loss1: 1.137602 Loss2: 1.876063
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.013502 Loss1: 0.604303 Loss2: 1.409199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.806948 Loss1: 0.392017 Loss2: 1.414931 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.661850 Loss1: 0.270184 Loss2: 1.391666 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.604796 Loss1: 0.222074 Loss2: 1.382722 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.567313 Loss1: 0.200634 Loss2: 1.366679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.472937 Loss1: 0.112796 Loss2: 1.360141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457408 Loss1: 0.099478 Loss2: 1.357931 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.880657 Loss1: 0.398148 Loss2: 1.482509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.603069 Loss1: 0.166775 Loss2: 1.436294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.568516 Loss1: 0.131344 Loss2: 1.437172
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.942354 Loss1: 1.094338 Loss2: 1.848016
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.102033 Loss1: 0.683406 Loss2: 1.418626 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.821425 Loss1: 0.395263 Loss2: 1.426163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.704479 Loss1: 0.310499 Loss2: 1.393980 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.640120 Loss1: 0.248587 Loss2: 1.391533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.511141 Loss1: 0.139941 Loss2: 1.371199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434554 Loss1: 0.077097 Loss2: 1.357457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453866 Loss1: 0.101103 Loss2: 1.352763 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.757433 Loss1: 0.350024 Loss2: 1.407408 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.617178 Loss1: 0.219775 Loss2: 1.397402 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.578180 Loss1: 0.183359 Loss2: 1.394821
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.889065 Loss1: 1.012141 Loss2: 1.876923
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.588461 Loss1: 0.197319 Loss2: 1.391142
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.036421 Loss1: 0.620630 Loss2: 1.415790
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.511736 Loss1: 0.114757 Loss2: 1.396979
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.829033 Loss1: 0.395095 Loss2: 1.433938
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491684 Loss1: 0.109610 Loss2: 1.382074
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.646480 Loss1: 0.250021 Loss2: 1.396458
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.652269 Loss1: 0.252368 Loss2: 1.399901
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.627057 Loss1: 0.223046 Loss2: 1.404011
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.603587 Loss1: 0.210908 Loss2: 1.392679
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.534125 Loss1: 0.141657 Loss2: 1.392468
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.857231 Loss1: 1.028613 Loss2: 1.828618
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.483288 Loss1: 0.103764 Loss2: 1.379524
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.033679 Loss1: 0.660769 Loss2: 1.372909
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.475891 Loss1: 0.100728 Loss2: 1.375163
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649206 Loss1: 0.282953 Loss2: 1.366253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.563537 Loss1: 0.199430 Loss2: 1.364107 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516745 Loss1: 0.151183 Loss2: 1.365562
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.013156 Loss1: 1.053343 Loss2: 1.959813
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.237600 Loss1: 0.709353 Loss2: 1.528248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.872215 Loss1: 0.391516 Loss2: 1.480699 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449318 Loss1: 0.104287 Loss2: 1.345031
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.776354 Loss1: 0.316071 Loss2: 1.460284
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.666239 Loss1: 0.204424 Loss2: 1.461815
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.580499 Loss1: 0.132775 Loss2: 1.447724
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.561627 Loss1: 0.127985 Loss2: 1.433643
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.523949 Loss1: 0.099595 Loss2: 1.424354
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.093901 Loss1: 1.111562 Loss2: 1.982339
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.511309 Loss1: 0.087556 Loss2: 1.423753
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.524246 Loss1: 0.104794 Loss2: 1.419452 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631484 Loss1: 0.260734 Loss2: 1.370750 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.543926 Loss1: 0.165664 Loss2: 1.378263 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.512838 Loss1: 0.156413 Loss2: 1.356425 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476024 Loss1: 0.119058 Loss2: 1.356965 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986979
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.953492 Loss1: 0.466531 Loss2: 1.486961 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.672483 Loss1: 0.233453 Loss2: 1.439030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.634393 Loss1: 0.220882 Loss2: 1.413511
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.948669 Loss1: 1.107022 Loss2: 1.841646
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.125397 Loss1: 0.741254 Loss2: 1.384143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.790401 Loss1: 0.402516 Loss2: 1.387884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.686435 Loss1: 0.327492 Loss2: 1.358943 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.579302 Loss1: 0.212024 Loss2: 1.367278 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.575735 Loss1: 0.215819 Loss2: 1.359916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472134 Loss1: 0.138122 Loss2: 1.334013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446760 Loss1: 0.114195 Loss2: 1.332565 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.734805 Loss1: 0.385428 Loss2: 1.349376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505054 Loss1: 0.182465 Loss2: 1.322589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487215 Loss1: 0.171234 Loss2: 1.315981
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.023982 Loss1: 1.116506 Loss2: 1.907476
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.148464 Loss1: 0.670895 Loss2: 1.477569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.910025 Loss1: 0.431622 Loss2: 1.478403 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.791458 Loss1: 0.348047 Loss2: 1.443412 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423397 Loss1: 0.123731 Loss2: 1.299666
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.700305 Loss1: 0.261068 Loss2: 1.439236
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.671223 Loss1: 0.248707 Loss2: 1.422516
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.543351 Loss1: 0.120476 Loss2: 1.422876
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.533035 Loss1: 0.124861 Loss2: 1.408174
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.513844 Loss1: 0.101606 Loss2: 1.412238
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.905606 Loss1: 1.014812 Loss2: 1.890794
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.512673 Loss1: 0.103887 Loss2: 1.408787
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.932661 Loss1: 0.445019 Loss2: 1.487643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.670286 Loss1: 0.212040 Loss2: 1.458246 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.651381 Loss1: 0.212070 Loss2: 1.439311
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.881539 Loss1: 1.020164 Loss2: 1.861375
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.115472 Loss1: 0.716401 Loss2: 1.399071
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.636938 Loss1: 0.194111 Loss2: 1.442827
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.779217 Loss1: 0.377595 Loss2: 1.401622
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.638169 Loss1: 0.200238 Loss2: 1.437931
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.712417 Loss1: 0.342947 Loss2: 1.369470
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.588890 Loss1: 0.152700 Loss2: 1.436190
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589560 Loss1: 0.212719 Loss2: 1.376841
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.545122 Loss1: 0.123197 Loss2: 1.421926
+(DefaultActor pid=3764) >> Training accuracy: 0.974609
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.533757 Loss1: 0.171339 Loss2: 1.362418 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462180 Loss1: 0.118854 Loss2: 1.343326 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447309 Loss1: 0.102521 Loss2: 1.344788
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.041857 Loss1: 1.081407 Loss2: 1.960450
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.250613 Loss1: 0.747696 Loss2: 1.502917
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.962131 Loss1: 0.432711 Loss2: 1.529420
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.768626 Loss1: 0.300196 Loss2: 1.468430
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.712072 Loss1: 0.234824 Loss2: 1.477248
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.885486 Loss1: 0.985950 Loss2: 1.899536
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.660622 Loss1: 0.195550 Loss2: 1.465072
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.636923 Loss1: 0.188767 Loss2: 1.448156
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.071998 Loss1: 0.641729 Loss2: 1.430269
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.563865 Loss1: 0.108623 Loss2: 1.455242
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.807323 Loss1: 0.371824 Loss2: 1.435500
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.538979 Loss1: 0.097115 Loss2: 1.441864
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.728587 Loss1: 0.330227 Loss2: 1.398361
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.538391 Loss1: 0.098652 Loss2: 1.439738
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.632539 Loss1: 0.226444 Loss2: 1.406096
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.603960 Loss1: 0.201896 Loss2: 1.402063
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.507452 Loss1: 0.114955 Loss2: 1.392497
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489898 Loss1: 0.103490 Loss2: 1.386408
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.501749 Loss1: 0.121982 Loss2: 1.379768
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.881392 Loss1: 0.983924 Loss2: 1.897468
+(DefaultActor pid=3765) >> Training accuracy: 0.977539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.063314 Loss1: 0.652562 Loss2: 1.410752 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611607 Loss1: 0.211800 Loss2: 1.399807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506016 Loss1: 0.128517 Loss2: 1.377498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.489103 Loss1: 0.112767 Loss2: 1.376336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.503254 Loss1: 0.132523 Loss2: 1.370731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.502783 Loss1: 0.125630 Loss2: 1.377153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487292 Loss1: 0.112974 Loss2: 1.374318 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576139 Loss1: 0.200366 Loss2: 1.375772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.514021 Loss1: 0.145707 Loss2: 1.368314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.955995 Loss1: 1.097426 Loss2: 1.858569 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.146249 Loss1: 0.724270 Loss2: 1.421979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594227 Loss1: 0.225885 Loss2: 1.368342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.561984 Loss1: 0.196024 Loss2: 1.365959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.537130 Loss1: 0.175356 Loss2: 1.361774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472856 Loss1: 0.112412 Loss2: 1.360444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433911 Loss1: 0.079704 Loss2: 1.354208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441482 Loss1: 0.096816 Loss2: 1.344665 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.545748 Loss1: 0.167227 Loss2: 1.378521 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.526668 Loss1: 0.136117 Loss2: 1.390551 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.521876 Loss1: 0.144869 Loss2: 1.377007
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.921708 Loss1: 0.997071 Loss2: 1.924637
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.002887 Loss1: 0.590481 Loss2: 1.412406
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477318 Loss1: 0.098524 Loss2: 1.378793
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.678321 Loss1: 0.279222 Loss2: 1.399099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.597104 Loss1: 0.206964 Loss2: 1.390140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.613468 Loss1: 0.214128 Loss2: 1.399340
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.933643 Loss1: 1.073133 Loss2: 1.860510
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.148081 Loss1: 0.700737 Loss2: 1.447344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.903328 Loss1: 0.464839 Loss2: 1.438489 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.806611 Loss1: 0.385702 Loss2: 1.420909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.594385 Loss1: 0.194476 Loss2: 1.399909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.502531 Loss1: 0.114587 Loss2: 1.387944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.478051 Loss1: 0.103366 Loss2: 1.374685 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462223 Loss1: 0.096391 Loss2: 1.365832 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.627656 Loss1: 0.210525 Loss2: 1.417131 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.532974 Loss1: 0.139232 Loss2: 1.393743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.066726 Loss1: 1.106945 Loss2: 1.959780
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490897 Loss1: 0.104651 Loss2: 1.386246
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.160628 Loss1: 0.681769 Loss2: 1.478859
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448480 Loss1: 0.077207 Loss2: 1.371273
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.945258 Loss1: 0.450102 Loss2: 1.495156
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413899 Loss1: 0.052171 Loss2: 1.361729
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.655238 Loss1: 0.192530 Loss2: 1.462707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.616066 Loss1: 0.180979 Loss2: 1.435086 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.610705 Loss1: 0.167237 Loss2: 1.443469
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.764033 Loss1: 0.906395 Loss2: 1.857638
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.570566 Loss1: 0.127789 Loss2: 1.442776
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.193414 Loss1: 0.767072 Loss2: 1.426342
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.563864 Loss1: 0.128920 Loss2: 1.434944
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.872903 Loss1: 0.425484 Loss2: 1.447420
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.725688 Loss1: 0.307828 Loss2: 1.417860
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687104 Loss1: 0.273783 Loss2: 1.413321
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542944 Loss1: 0.145802 Loss2: 1.397142
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.529546 Loss1: 0.136584 Loss2: 1.392962
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.711333 Loss1: 0.871118 Loss2: 1.840214
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491802 Loss1: 0.105460 Loss2: 1.386342
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.876581 Loss1: 0.478317 Loss2: 1.398265
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446651 Loss1: 0.070976 Loss2: 1.375676
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460646 Loss1: 0.089518 Loss2: 1.371128
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.725695 Loss1: 0.314304 Loss2: 1.411391
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.635489 Loss1: 0.243333 Loss2: 1.392156
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593436 Loss1: 0.210391 Loss2: 1.383045
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.559381 Loss1: 0.177379 Loss2: 1.382002
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.553733 Loss1: 0.178022 Loss2: 1.375711
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.837075 Loss1: 0.972887 Loss2: 1.864188
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.081536 Loss1: 0.666640 Loss2: 1.414895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.541855 Loss1: 0.175155 Loss2: 1.366700
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.906188 Loss1: 0.482971 Loss2: 1.423217
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.552964 Loss1: 0.173151 Loss2: 1.379814
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.818202 Loss1: 0.407270 Loss2: 1.410932
+(DefaultActor pid=3765) >> Training accuracy: 0.977022
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.713746 Loss1: 0.318967 Loss2: 1.394779
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556550 Loss1: 0.167311 Loss2: 1.389239
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.481497 Loss1: 0.120836 Loss2: 1.360661
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471577 Loss1: 0.116234 Loss2: 1.355343
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453601 Loss1: 0.094352 Loss2: 1.359249
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.938722 Loss1: 1.045657 Loss2: 1.893065
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428455 Loss1: 0.075867 Loss2: 1.352588
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.118402 Loss1: 0.694114 Loss2: 1.424288
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.874341 Loss1: 0.422133 Loss2: 1.452209
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.708808 Loss1: 0.316739 Loss2: 1.392069
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.583283 Loss1: 0.180794 Loss2: 1.402489
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537318 Loss1: 0.160612 Loss2: 1.376706
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.103962 Loss1: 1.188205 Loss2: 1.915757
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513845 Loss1: 0.145476 Loss2: 1.368369
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.119919 Loss1: 0.706574 Loss2: 1.413345
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.468724 Loss1: 0.095500 Loss2: 1.373224
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448617 Loss1: 0.087515 Loss2: 1.361102 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434901 Loss1: 0.081036 Loss2: 1.353866 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556177 Loss1: 0.183877 Loss2: 1.372300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.512153 Loss1: 0.136395 Loss2: 1.375758 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.824297 Loss1: 0.943235 Loss2: 1.881062 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979911
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.833652 Loss1: 0.414529 Loss2: 1.419123 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.597189 Loss1: 0.229835 Loss2: 1.367353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516179 Loss1: 0.158811 Loss2: 1.357369
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.973489 Loss1: 1.082194 Loss2: 1.891295
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.026031 Loss1: 0.607324 Loss2: 1.418707 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.816967 Loss1: 0.371117 Loss2: 1.445850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.696493 Loss1: 0.302699 Loss2: 1.393794 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463935 Loss1: 0.123160 Loss2: 1.340775
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.650576 Loss1: 0.250725 Loss2: 1.399851
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.591067 Loss1: 0.196259 Loss2: 1.394808
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508546 Loss1: 0.126006 Loss2: 1.382540
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484823 Loss1: 0.106852 Loss2: 1.377971
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.488751 Loss1: 0.114812 Loss2: 1.373939
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.975123 Loss1: 1.110124 Loss2: 1.864999
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512148 Loss1: 0.131657 Loss2: 1.380491
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.864797 Loss1: 0.407741 Loss2: 1.457056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.702160 Loss1: 0.283449 Loss2: 1.418711 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.616700 Loss1: 0.213420 Loss2: 1.403280
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.729604 Loss1: 0.875473 Loss2: 1.854131
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.935846 Loss1: 0.539817 Loss2: 1.396029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.781303 Loss1: 0.348395 Loss2: 1.432908 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631449 Loss1: 0.252001 Loss2: 1.379448 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.509736 Loss1: 0.121881 Loss2: 1.387854
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.585400 Loss1: 0.201284 Loss2: 1.384116
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.565100 Loss1: 0.191941 Loss2: 1.373159
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497196 Loss1: 0.135100 Loss2: 1.362096
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464733 Loss1: 0.106991 Loss2: 1.357743
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452270 Loss1: 0.096375 Loss2: 1.355895
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.865243 Loss1: 1.011645 Loss2: 1.853598
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420972 Loss1: 0.076543 Loss2: 1.344429
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.854368 Loss1: 0.400704 Loss2: 1.453664 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.638451 Loss1: 0.232769 Loss2: 1.405682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.847711 Loss1: 0.979554 Loss2: 1.868157
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.608849 Loss1: 0.204031 Loss2: 1.404818
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.065074 Loss1: 0.642735 Loss2: 1.422339
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547668 Loss1: 0.145667 Loss2: 1.402001
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497041 Loss1: 0.105869 Loss2: 1.391172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461242 Loss1: 0.082623 Loss2: 1.378619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466499 Loss1: 0.090405 Loss2: 1.376094 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.515968 Loss1: 0.142201 Loss2: 1.373767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452545 Loss1: 0.090985 Loss2: 1.361560 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462803 Loss1: 0.102256 Loss2: 1.360547
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.895589 Loss1: 0.990407 Loss2: 1.905182
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.071470 Loss1: 0.663564 Loss2: 1.407907
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.956785 Loss1: 0.473013 Loss2: 1.483771
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.730910 Loss1: 0.324700 Loss2: 1.406209
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.661596 Loss1: 0.238718 Loss2: 1.422878
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.151986 Loss1: 1.255270 Loss2: 1.896716
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.229323 Loss1: 0.789536 Loss2: 1.439786 [repeated 2x across cluster]
+DEBUG flwr 2023-10-10 22:46:22,633 | server.py:236 | fit_round 92 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.998663 Loss1: 0.544675 Loss2: 1.453989 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.823642 Loss1: 0.397953 Loss2: 1.425689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.683354 Loss1: 0.283105 Loss2: 1.400249 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476402 Loss1: 0.090845 Loss2: 1.385558
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.627123 Loss1: 0.227765 Loss2: 1.399358
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.611755 Loss1: 0.222032 Loss2: 1.389723
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518652 Loss1: 0.130297 Loss2: 1.388355
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458626 Loss1: 0.091729 Loss2: 1.366897
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466392 Loss1: 0.099914 Loss2: 1.366478
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.255228 Loss1: 1.164179 Loss2: 2.091049
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.236739 Loss1: 0.728082 Loss2: 1.508657
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.962396 Loss1: 0.422471 Loss2: 1.539925
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.692520 Loss1: 0.213978 Loss2: 1.478542
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.655147 Loss1: 0.191212 Loss2: 1.463935
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.620433 Loss1: 0.150341 Loss2: 1.470092
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.924096 Loss1: 1.014116 Loss2: 1.909980
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.025473 Loss1: 0.627860 Loss2: 1.397613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.843722 Loss1: 0.397988 Loss2: 1.445734 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612251 Loss1: 0.236443 Loss2: 1.375809 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556125 Loss1: 0.184138 Loss2: 1.371988 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466619 Loss1: 0.109341 Loss2: 1.357278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458304 Loss1: 0.106235 Loss2: 1.352069
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.794437 Loss1: 0.910692 Loss2: 1.883746
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.965769 Loss1: 0.563086 Loss2: 1.402683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.710277 Loss1: 0.308355 Loss2: 1.401922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.625483 Loss1: 0.226515 Loss2: 1.398968 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539715 Loss1: 0.143495 Loss2: 1.396220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.487843 Loss1: 0.099913 Loss2: 1.387931 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.490465 Loss1: 0.103733 Loss2: 1.386733 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.503316 Loss1: 0.122529 Loss2: 1.380787 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.529281 Loss1: 0.150701 Loss2: 1.378581 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.518870 Loss1: 0.155466 Loss2: 1.363404 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977679
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 22:46:22,633][flwr][DEBUG] - fit_round 92 received 50 results and 0 failures
+INFO flwr 2023-10-10 22:47:04,524 | server.py:125 | fit progress: (92, 2.20932971498075, {'accuracy': 0.561}, 212132.302083312)
+>> Test accuracy: 0.561000
+[2023-10-10 22:47:04,524][flwr][INFO] - fit progress: (92, 2.20932971498075, {'accuracy': 0.561}, 212132.302083312)
+DEBUG flwr 2023-10-10 22:47:04,524 | server.py:173 | evaluate_round 92: strategy sampled 50 clients (out of 50)
+[2023-10-10 22:47:04,524][flwr][DEBUG] - evaluate_round 92: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 22:56:12,759 | server.py:187 | evaluate_round 92 received 50 results and 0 failures
+[2023-10-10 22:56:12,759][flwr][DEBUG] - evaluate_round 92 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 22:56:12,760 | server.py:222 | fit_round 93: strategy sampled 50 clients (out of 50)
+[2023-10-10 22:56:12,760][flwr][DEBUG] - fit_round 93: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.716243 Loss1: 0.909041 Loss2: 1.807202 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.734289 Loss1: 0.343208 Loss2: 1.391081 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.668005 Loss1: 0.302522 Loss2: 1.365483
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.875608 Loss1: 1.003338 Loss2: 1.872270
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567956 Loss1: 0.204738 Loss2: 1.363218
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.074874 Loss1: 0.622574 Loss2: 1.452300
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493242 Loss1: 0.137511 Loss2: 1.355731
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.889389 Loss1: 0.439873 Loss2: 1.449515
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455236 Loss1: 0.110153 Loss2: 1.345083
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.772216 Loss1: 0.342019 Loss2: 1.430197
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.468980 Loss1: 0.128619 Loss2: 1.340361
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.728127 Loss1: 0.305265 Loss2: 1.422862
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460593 Loss1: 0.120576 Loss2: 1.340018
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.663186 Loss1: 0.243953 Loss2: 1.419232
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445685 Loss1: 0.108182 Loss2: 1.337503
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.591222 Loss1: 0.190891 Loss2: 1.400331
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.525938 Loss1: 0.126392 Loss2: 1.399546
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501773 Loss1: 0.115137 Loss2: 1.386636
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.474935 Loss1: 0.090506 Loss2: 1.384429
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.918596 Loss1: 1.045376 Loss2: 1.873220
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.059158 Loss1: 0.623015 Loss2: 1.436143
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.807380 Loss1: 0.372454 Loss2: 1.434926
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.745545 Loss1: 0.326542 Loss2: 1.419003
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.645515 Loss1: 0.835572 Loss2: 1.809943
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.862800 Loss1: 0.484562 Loss2: 1.378238 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.729001 Loss1: 0.339984 Loss2: 1.389017 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640862 Loss1: 0.278644 Loss2: 1.362218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539058 Loss1: 0.181630 Loss2: 1.357428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463572 Loss1: 0.118603 Loss2: 1.344969 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400348 Loss1: 0.064880 Loss2: 1.335468 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388517 Loss1: 0.065420 Loss2: 1.323097 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.017610 Loss1: 0.697271 Loss2: 1.320339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.626774 Loss1: 0.300554 Loss2: 1.326220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.819144 Loss1: 1.029654 Loss2: 1.789490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.973944 Loss1: 0.607429 Loss2: 1.366515 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.762401 Loss1: 0.375286 Loss2: 1.387115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433596 Loss1: 0.127804 Loss2: 1.305791 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408323 Loss1: 0.107361 Loss2: 1.300962 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497881 Loss1: 0.163829 Loss2: 1.334052 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487305 Loss1: 0.156099 Loss2: 1.331206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438401 Loss1: 0.112686 Loss2: 1.325715 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969727
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.828197 Loss1: 0.405804 Loss2: 1.422393 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558785 Loss1: 0.184700 Loss2: 1.374085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504891 Loss1: 0.139098 Loss2: 1.365794
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.866247 Loss1: 1.019576 Loss2: 1.846671
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.008700 Loss1: 0.626093 Loss2: 1.382608 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.798450 Loss1: 0.363776 Loss2: 1.434674 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.700035 Loss1: 0.311538 Loss2: 1.388497 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467376 Loss1: 0.100338 Loss2: 1.367038
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.645990 Loss1: 0.248975 Loss2: 1.397015
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.629212 Loss1: 0.241741 Loss2: 1.387471
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.588622 Loss1: 0.194541 Loss2: 1.394081
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.525091 Loss1: 0.147880 Loss2: 1.377211
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467498 Loss1: 0.096764 Loss2: 1.370733
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.817290 Loss1: 0.940397 Loss2: 1.876893
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453111 Loss1: 0.085675 Loss2: 1.367435
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.798437 Loss1: 0.372381 Loss2: 1.426056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.626731 Loss1: 0.249609 Loss2: 1.377122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520169 Loss1: 0.151926 Loss2: 1.368243
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.868146 Loss1: 0.998763 Loss2: 1.869383
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.002765 Loss1: 0.602079 Loss2: 1.400687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.702041 Loss1: 0.276718 Loss2: 1.425323 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586421 Loss1: 0.211951 Loss2: 1.374469 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412749 Loss1: 0.069431 Loss2: 1.343318
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.580804 Loss1: 0.206516 Loss2: 1.374289
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.590917 Loss1: 0.212816 Loss2: 1.378101
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484060 Loss1: 0.105259 Loss2: 1.378801
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460493 Loss1: 0.103012 Loss2: 1.357481
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450314 Loss1: 0.092102 Loss2: 1.358212
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.824944 Loss1: 0.913068 Loss2: 1.911876
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434893 Loss1: 0.081347 Loss2: 1.353546
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.775195 Loss1: 0.327830 Loss2: 1.447365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.614279 Loss1: 0.194644 Loss2: 1.419634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.595724 Loss1: 0.175426 Loss2: 1.420297 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516903 Loss1: 0.105016 Loss2: 1.411887 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497626 Loss1: 0.100809 Loss2: 1.396817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482547 Loss1: 0.083468 Loss2: 1.399079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457873 Loss1: 0.070045 Loss2: 1.387827 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986213
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568236 Loss1: 0.134828 Loss2: 1.433409 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.536158 Loss1: 0.115148 Loss2: 1.421010 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.105022 Loss1: 0.665965 Loss2: 1.439057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.709132 Loss1: 0.306061 Loss2: 1.403071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.913053 Loss1: 1.079855 Loss2: 1.833198
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577881 Loss1: 0.204957 Loss2: 1.372924
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.002894 Loss1: 0.636475 Loss2: 1.366419
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537679 Loss1: 0.167795 Loss2: 1.369885
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.710403 Loss1: 0.339628 Loss2: 1.370776
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.520543 Loss1: 0.156981 Loss2: 1.363562
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.641394 Loss1: 0.291056 Loss2: 1.350338
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.493738 Loss1: 0.125014 Loss2: 1.368723
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.610572 Loss1: 0.256704 Loss2: 1.353868
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.498413 Loss1: 0.139807 Loss2: 1.358606
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.564121 Loss1: 0.224415 Loss2: 1.339706
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460623 Loss1: 0.103567 Loss2: 1.357056
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482058 Loss1: 0.149637 Loss2: 1.332422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433711 Loss1: 0.110804 Loss2: 1.322907 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.103701 Loss1: 0.692118 Loss2: 1.411583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.711686 Loss1: 0.318427 Loss2: 1.393259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.783678 Loss1: 0.956077 Loss2: 1.827602
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.648741 Loss1: 0.253014 Loss2: 1.395727
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.871853 Loss1: 0.515323 Loss2: 1.356530
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542694 Loss1: 0.159857 Loss2: 1.382837
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.698224 Loss1: 0.334802 Loss2: 1.363422
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.507173 Loss1: 0.139454 Loss2: 1.367719
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.598052 Loss1: 0.262711 Loss2: 1.335341
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489050 Loss1: 0.121170 Loss2: 1.367880
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524464 Loss1: 0.201454 Loss2: 1.323011
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454099 Loss1: 0.096738 Loss2: 1.357361
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.445883 Loss1: 0.128257 Loss2: 1.317626
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451409 Loss1: 0.097886 Loss2: 1.353524
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450778 Loss1: 0.131029 Loss2: 1.319749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457398 Loss1: 0.133017 Loss2: 1.324382 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.946767 Loss1: 0.586523 Loss2: 1.360243 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569169 Loss1: 0.226156 Loss2: 1.343014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.570543 Loss1: 0.222720 Loss2: 1.347824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511738 Loss1: 0.172592 Loss2: 1.339146 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428333 Loss1: 0.092615 Loss2: 1.335718 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422641 Loss1: 0.101814 Loss2: 1.320828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419002 Loss1: 0.096719 Loss2: 1.322283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383724 Loss1: 0.067193 Loss2: 1.316531 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.508371 Loss1: 0.157130 Loss2: 1.351240 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431222 Loss1: 0.089609 Loss2: 1.341613 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.966991 Loss1: 0.538501 Loss2: 1.428490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.627958 Loss1: 0.203787 Loss2: 1.424170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605244 Loss1: 0.194359 Loss2: 1.410885 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.573378 Loss1: 0.160156 Loss2: 1.413223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.603692 Loss1: 0.191976 Loss2: 1.411716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.650698 Loss1: 0.227623 Loss2: 1.423075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.598685 Loss1: 0.179896 Loss2: 1.418789 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.560959 Loss1: 0.146585 Loss2: 1.414374 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.526796 Loss1: 0.142274 Loss2: 1.384522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.578309 Loss1: 0.194288 Loss2: 1.384022 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.992327 Loss1: 0.566040 Loss2: 1.426287 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.728291 Loss1: 0.312957 Loss2: 1.415334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.874268 Loss1: 0.914328 Loss2: 1.959940
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617053 Loss1: 0.210445 Loss2: 1.406608
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.140089 Loss1: 0.668258 Loss2: 1.471831
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.595689 Loss1: 0.201449 Loss2: 1.394239
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.898533 Loss1: 0.394022 Loss2: 1.504511
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516951 Loss1: 0.126833 Loss2: 1.390118
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.798197 Loss1: 0.347375 Loss2: 1.450822
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499365 Loss1: 0.122736 Loss2: 1.376630
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.727153 Loss1: 0.257171 Loss2: 1.469982
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470148 Loss1: 0.092676 Loss2: 1.377472
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.620168 Loss1: 0.167097 Loss2: 1.453071
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447306 Loss1: 0.073654 Loss2: 1.373652
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.555861 Loss1: 0.120369 Loss2: 1.435492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.517258 Loss1: 0.089292 Loss2: 1.427966 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.090847 Loss1: 0.657313 Loss2: 1.433534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.639939 Loss1: 0.252656 Loss2: 1.387283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.022700 Loss1: 1.087447 Loss2: 1.935252
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.563566 Loss1: 0.181333 Loss2: 1.382233
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.124931 Loss1: 0.771098 Loss2: 1.353833
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.495106 Loss1: 0.113783 Loss2: 1.381322
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488043 Loss1: 0.115288 Loss2: 1.372755 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436911 Loss1: 0.073893 Loss2: 1.363018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462328 Loss1: 0.098624 Loss2: 1.363703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.478179 Loss1: 0.113234 Loss2: 1.364945 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417798 Loss1: 0.098892 Loss2: 1.318906 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.694290 Loss1: 0.837124 Loss2: 1.857167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.755745 Loss1: 0.319955 Loss2: 1.435790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.718097 Loss1: 0.337248 Loss2: 1.380849
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.012639 Loss1: 1.134312 Loss2: 1.878327
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.040211 Loss1: 0.603169 Loss2: 1.437041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.935367 Loss1: 0.497796 Loss2: 1.437572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.703362 Loss1: 0.277262 Loss2: 1.426100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.582463 Loss1: 0.179314 Loss2: 1.403148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.543390 Loss1: 0.142797 Loss2: 1.400593 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460090 Loss1: 0.096679 Loss2: 1.363411
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485713 Loss1: 0.101005 Loss2: 1.384708
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500324 Loss1: 0.127121 Loss2: 1.373203
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457449 Loss1: 0.079244 Loss2: 1.378205
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445288 Loss1: 0.073874 Loss2: 1.371414
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.891478 Loss1: 0.908659 Loss2: 1.982819
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.057806 Loss1: 0.593494 Loss2: 1.464312
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.884513 Loss1: 0.382694 Loss2: 1.501819
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.735469 Loss1: 0.274411 Loss2: 1.461057
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.062662 Loss1: 1.112284 Loss2: 1.950378
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.228375 Loss1: 0.712491 Loss2: 1.515883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.941141 Loss1: 0.436479 Loss2: 1.504662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.784998 Loss1: 0.331254 Loss2: 1.453743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.713710 Loss1: 0.250097 Loss2: 1.463613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.622614 Loss1: 0.177005 Loss2: 1.445609 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.584647 Loss1: 0.129808 Loss2: 1.454840
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.594708 Loss1: 0.155861 Loss2: 1.438847
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.577125 Loss1: 0.145957 Loss2: 1.431169
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.574511 Loss1: 0.138751 Loss2: 1.435760
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.550525 Loss1: 0.109096 Loss2: 1.441429
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.879921 Loss1: 1.030098 Loss2: 1.849823
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.001236 Loss1: 0.612907 Loss2: 1.388330
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.843978 Loss1: 0.400998 Loss2: 1.442980
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.683835 Loss1: 0.291333 Loss2: 1.392502
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.954231 Loss1: 1.073656 Loss2: 1.880575
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.111996 Loss1: 0.680698 Loss2: 1.431299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.843935 Loss1: 0.401017 Loss2: 1.442918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.713107 Loss1: 0.311320 Loss2: 1.401786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.680829 Loss1: 0.279375 Loss2: 1.401454 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559771 Loss1: 0.170344 Loss2: 1.389427 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.497266 Loss1: 0.133940 Loss2: 1.363326
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509042 Loss1: 0.128524 Loss2: 1.380518
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.479232 Loss1: 0.103980 Loss2: 1.375252
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480627 Loss1: 0.106600 Loss2: 1.374027
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.459613 Loss1: 0.091842 Loss2: 1.367772
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.883031 Loss1: 1.067423 Loss2: 1.815608
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.014020 Loss1: 0.644107 Loss2: 1.369914
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.831999 Loss1: 0.434248 Loss2: 1.397751
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.670492 Loss1: 0.305337 Loss2: 1.365155
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.978359 Loss1: 1.113442 Loss2: 1.864916
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.038479 Loss1: 0.626717 Loss2: 1.411762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.809085 Loss1: 0.391380 Loss2: 1.417705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.671124 Loss1: 0.301224 Loss2: 1.369899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598790 Loss1: 0.220251 Loss2: 1.378539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510523 Loss1: 0.140266 Loss2: 1.370257 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.493795 Loss1: 0.136935 Loss2: 1.356860 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414917 Loss1: 0.067473 Loss2: 1.347444 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.229660 Loss1: 1.162907 Loss2: 2.066753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.996859 Loss1: 0.482455 Loss2: 1.514404 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.652888 Loss1: 0.221578 Loss2: 1.431309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.000215 Loss1: 0.571041 Loss2: 1.429174 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.665367 Loss1: 0.220853 Loss2: 1.444514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.626222 Loss1: 0.243375 Loss2: 1.382847 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.964844
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.514606 Loss1: 0.136318 Loss2: 1.378289 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477724 Loss1: 0.110729 Loss2: 1.366995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438747 Loss1: 0.076815 Loss2: 1.361932
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.974990 Loss1: 1.039005 Loss2: 1.935985
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.119495 Loss1: 0.655122 Loss2: 1.464374 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.758185 Loss1: 0.334321 Loss2: 1.423864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.583985 Loss1: 0.176164 Loss2: 1.407821 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547657 Loss1: 0.155863 Loss2: 1.391794 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.507545 Loss1: 0.118411 Loss2: 1.389134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472873 Loss1: 0.092085 Loss2: 1.380788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467084 Loss1: 0.089610 Loss2: 1.377475 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.538780 Loss1: 0.120820 Loss2: 1.417960 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486468 Loss1: 0.084993 Loss2: 1.401475 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985577
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509990 Loss1: 0.108622 Loss2: 1.401368
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.196707 Loss1: 1.168121 Loss2: 2.028586
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.190877 Loss1: 0.683787 Loss2: 1.507089
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.996576 Loss1: 0.439259 Loss2: 1.557317
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.837648 Loss1: 0.346866 Loss2: 1.490782
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.748447 Loss1: 0.253469 Loss2: 1.494977
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.943291 Loss1: 1.114690 Loss2: 1.828601
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.675911 Loss1: 0.195229 Loss2: 1.480682
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.618929 Loss1: 0.148325 Loss2: 1.470603 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.785406 Loss1: 0.372127 Loss2: 1.413278
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.604176 Loss1: 0.141121 Loss2: 1.463055
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.652596 Loss1: 0.291240 Loss2: 1.361356
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.605478 Loss1: 0.138118 Loss2: 1.467360
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.585837 Loss1: 0.124152 Loss2: 1.461685
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569332 Loss1: 0.199998 Loss2: 1.369335
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.514957 Loss1: 0.157705 Loss2: 1.357252
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.551963 Loss1: 0.190748 Loss2: 1.361215
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.480709 Loss1: 0.129434 Loss2: 1.351275
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454710 Loss1: 0.108411 Loss2: 1.346300
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.473487 Loss1: 0.124617 Loss2: 1.348870
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.878552 Loss1: 1.005070 Loss2: 1.873481
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.936445 Loss1: 0.516207 Loss2: 1.420238
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.783603 Loss1: 0.359960 Loss2: 1.423643
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.667448 Loss1: 0.273903 Loss2: 1.393545
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561539 Loss1: 0.175047 Loss2: 1.386492
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.601049 Loss1: 0.213161 Loss2: 1.387888
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.777669 Loss1: 1.009857 Loss2: 1.767812
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509374 Loss1: 0.132511 Loss2: 1.376863
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.056065 Loss1: 0.667299 Loss2: 1.388766
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.513981 Loss1: 0.139402 Loss2: 1.374579
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.756035 Loss1: 0.386676 Loss2: 1.369359
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.620519 Loss1: 0.283647 Loss2: 1.336871 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.478654 Loss1: 0.116055 Loss2: 1.362599
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521972 Loss1: 0.180542 Loss2: 1.341430
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.505919 Loss1: 0.176455 Loss2: 1.329464
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.505848 Loss1: 0.173603 Loss2: 1.332244
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511616 Loss1: 0.178469 Loss2: 1.333147
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487140 Loss1: 0.156945 Loss2: 1.330195
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.042273 Loss1: 1.203508 Loss2: 1.838765
+(DefaultActor pid=3764) >> Training accuracy: 0.975586
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.074815 Loss1: 0.673280 Loss2: 1.401535 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.741818 Loss1: 0.359574 Loss2: 1.382244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516828 Loss1: 0.150017 Loss2: 1.366811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.499477 Loss1: 0.139426 Loss2: 1.360051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.513204 Loss1: 0.157088 Loss2: 1.356116 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484760 Loss1: 0.129031 Loss2: 1.355729 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.507440 Loss1: 0.157017 Loss2: 1.350423 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523002 Loss1: 0.152447 Loss2: 1.370554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465674 Loss1: 0.104401 Loss2: 1.361272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.013977 Loss1: 1.106912 Loss2: 1.907065 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.249363 Loss1: 0.746095 Loss2: 1.503268 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.732322 Loss1: 0.309846 Loss2: 1.422476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.588923 Loss1: 0.181904 Loss2: 1.407019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.546770 Loss1: 0.132445 Loss2: 1.414324 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.531969 Loss1: 0.133210 Loss2: 1.398759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.505038 Loss1: 0.111860 Loss2: 1.393179 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.471442 Loss1: 0.078718 Loss2: 1.392725 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.503882 Loss1: 0.130584 Loss2: 1.373298 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481711 Loss1: 0.115249 Loss2: 1.366463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439434 Loss1: 0.072685 Loss2: 1.366750
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.010538 Loss1: 1.148237 Loss2: 1.862301
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.200315 Loss1: 0.754726 Loss2: 1.445589
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.890924 Loss1: 0.466005 Loss2: 1.424919
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.697918 Loss1: 0.302573 Loss2: 1.395346
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.622305 Loss1: 0.236011 Loss2: 1.386294
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.660413 Loss1: 0.863704 Loss2: 1.796709
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.591314 Loss1: 0.220517 Loss2: 1.370796
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.949661 Loss1: 0.572032 Loss2: 1.377629
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.570499 Loss1: 0.190296 Loss2: 1.380203
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.757763 Loss1: 0.365664 Loss2: 1.392099
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.548614 Loss1: 0.173342 Loss2: 1.375272
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.506816 Loss1: 0.137614 Loss2: 1.369202
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.686857 Loss1: 0.311512 Loss2: 1.375345
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.503007 Loss1: 0.136018 Loss2: 1.366989
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.631188 Loss1: 0.266401 Loss2: 1.364786
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.543436 Loss1: 0.175110 Loss2: 1.368327
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488308 Loss1: 0.140439 Loss2: 1.347869
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.494542 Loss1: 0.146107 Loss2: 1.348435
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.520952 Loss1: 0.171500 Loss2: 1.349452
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.952077 Loss1: 1.065770 Loss2: 1.886307
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454134 Loss1: 0.104760 Loss2: 1.349374
+(DefaultActor pid=3764) >> Training accuracy: 0.961914
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.852397 Loss1: 0.394768 Loss2: 1.457630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.633517 Loss1: 0.208323 Loss2: 1.425194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.601232 Loss1: 0.172131 Loss2: 1.429102
+DEBUG flwr 2023-10-10 23:24:48,369 | server.py:236 | fit_round 93 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.834095 Loss1: 1.052465 Loss2: 1.781630
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523281 Loss1: 0.103275 Loss2: 1.420006
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.972273 Loss1: 0.595682 Loss2: 1.376591
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470685 Loss1: 0.063106 Loss2: 1.407579
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.731416 Loss1: 0.365002 Loss2: 1.366414
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509749 Loss1: 0.110623 Loss2: 1.399126
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612951 Loss1: 0.268330 Loss2: 1.344622
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477047 Loss1: 0.076278 Loss2: 1.400768
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.578147 Loss1: 0.233680 Loss2: 1.344466
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567235 Loss1: 0.227236 Loss2: 1.339999
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.511692 Loss1: 0.168503 Loss2: 1.343188
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481672 Loss1: 0.145511 Loss2: 1.336161
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.465695 Loss1: 0.137173 Loss2: 1.328522
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.869212 Loss1: 1.013137 Loss2: 1.856075
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438561 Loss1: 0.116312 Loss2: 1.322250
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.777319 Loss1: 0.369334 Loss2: 1.407985 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.628616 Loss1: 0.239722 Loss2: 1.388894 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.567030 Loss1: 0.183113 Loss2: 1.383917
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.890158 Loss1: 1.022410 Loss2: 1.867748
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513652 Loss1: 0.143273 Loss2: 1.370380
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.001154 Loss1: 0.599273 Loss2: 1.401881
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473919 Loss1: 0.105869 Loss2: 1.368050
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.803650 Loss1: 0.371541 Loss2: 1.432109
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.688820 Loss1: 0.298930 Loss2: 1.389890
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.473935 Loss1: 0.111748 Loss2: 1.362186
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.600672 Loss1: 0.214888 Loss2: 1.385783
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441331 Loss1: 0.076387 Loss2: 1.364944
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513554 Loss1: 0.129147 Loss2: 1.384407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.483861 Loss1: 0.116568 Loss2: 1.367292 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-10 23:24:48,369][flwr][DEBUG] - fit_round 93 received 50 results and 0 failures
+INFO flwr 2023-10-10 23:25:29,351 | server.py:125 | fit progress: (93, 2.22590211443246, {'accuracy': 0.5612}, 214437.129805915)
+>> Test accuracy: 0.561200
+[2023-10-10 23:25:29,351][flwr][INFO] - fit progress: (93, 2.22590211443246, {'accuracy': 0.5612}, 214437.129805915)
+DEBUG flwr 2023-10-10 23:25:29,352 | server.py:173 | evaluate_round 93: strategy sampled 50 clients (out of 50)
+[2023-10-10 23:25:29,352][flwr][DEBUG] - evaluate_round 93: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-10 23:34:37,226 | server.py:187 | evaluate_round 93 received 50 results and 0 failures
+[2023-10-10 23:34:37,226][flwr][DEBUG] - evaluate_round 93 received 50 results and 0 failures
+DEBUG flwr 2023-10-10 23:34:37,226 | server.py:222 | fit_round 94: strategy sampled 50 clients (out of 50)
+[2023-10-10 23:34:37,226][flwr][DEBUG] - fit_round 94: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.958269 Loss1: 1.073842 Loss2: 1.884427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.842630 Loss1: 0.426817 Loss2: 1.415813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.851483 Loss1: 0.904900 Loss2: 1.946583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.111795 Loss1: 0.636679 Loss2: 1.475117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.861653 Loss1: 0.383910 Loss2: 1.477743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.775460 Loss1: 0.347849 Loss2: 1.427610 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.664752 Loss1: 0.226244 Loss2: 1.438508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.587440 Loss1: 0.169045 Loss2: 1.418395 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986607
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.508488 Loss1: 0.109804 Loss2: 1.398684 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.519892 Loss1: 0.122615 Loss2: 1.397276 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.057199 Loss1: 0.595562 Loss2: 1.461637 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765773 Loss1: 0.315821 Loss2: 1.449951 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.676306 Loss1: 0.252173 Loss2: 1.424133 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.604731 Loss1: 0.175108 Loss2: 1.429623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.596031 Loss1: 0.180781 Loss2: 1.415250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.534444 Loss1: 0.120905 Loss2: 1.413539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.516725 Loss1: 0.112247 Loss2: 1.404478 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.513049 Loss1: 0.110498 Loss2: 1.402551 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519415 Loss1: 0.170623 Loss2: 1.348792 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.963960 Loss1: 1.079969 Loss2: 1.883991 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.930941 Loss1: 0.470774 Loss2: 1.460168 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.785247 Loss1: 0.347883 Loss2: 1.437365
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.825408 Loss1: 0.941400 Loss2: 1.884008
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.004691 Loss1: 0.615748 Loss2: 1.388943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.828196 Loss1: 0.384850 Loss2: 1.443346 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.647465 Loss1: 0.283197 Loss2: 1.364269 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.577062 Loss1: 0.205767 Loss2: 1.371295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537116 Loss1: 0.174499 Loss2: 1.362617 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472616 Loss1: 0.124067 Loss2: 1.348549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.472840 Loss1: 0.123753 Loss2: 1.349087 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.966412 Loss1: 1.063404 Loss2: 1.903009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.855271 Loss1: 0.399645 Loss2: 1.455627 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.724683 Loss1: 0.304411 Loss2: 1.420272
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.833104 Loss1: 1.001624 Loss2: 1.831480
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.057483 Loss1: 0.662035 Loss2: 1.395448 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.802262 Loss1: 0.409275 Loss2: 1.392986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.642080 Loss1: 0.262486 Loss2: 1.379594 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548851 Loss1: 0.184915 Loss2: 1.363936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542390 Loss1: 0.181277 Loss2: 1.361113 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.495982 Loss1: 0.145845 Loss2: 1.350137 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435556 Loss1: 0.092311 Loss2: 1.343246 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972656
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.150078 Loss1: 0.719528 Loss2: 1.430550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.826479 Loss1: 0.389524 Loss2: 1.436955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.673760 Loss1: 0.262846 Loss2: 1.410914
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.628767 Loss1: 0.864753 Loss2: 1.764014
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.614935 Loss1: 0.211396 Loss2: 1.403539
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.884897 Loss1: 0.491220 Loss2: 1.393677
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.731056 Loss1: 0.381244 Loss2: 1.349812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.626848 Loss1: 0.287089 Loss2: 1.339759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.580150 Loss1: 0.239256 Loss2: 1.340894 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.533093 Loss1: 0.199576 Loss2: 1.333517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484478 Loss1: 0.158340 Loss2: 1.326138 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.089261 Loss1: 1.126336 Loss2: 1.962924 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977022
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.935667 Loss1: 0.467113 Loss2: 1.468554 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.660946 Loss1: 0.224697 Loss2: 1.436249 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.888673 Loss1: 0.977289 Loss2: 1.911384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.021173 Loss1: 0.599988 Loss2: 1.421185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.832034 Loss1: 0.371358 Loss2: 1.460676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.468848 Loss1: 0.072577 Loss2: 1.396271 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.578249 Loss1: 0.178343 Loss2: 1.399906 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.513507 Loss1: 0.125662 Loss2: 1.387845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479230 Loss1: 0.100312 Loss2: 1.378918
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.943806 Loss1: 1.042369 Loss2: 1.901437
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458205 Loss1: 0.087423 Loss2: 1.370782
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.198285 Loss1: 0.710561 Loss2: 1.487723
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.884847 Loss1: 0.416045 Loss2: 1.468803
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.764582 Loss1: 0.312363 Loss2: 1.452220
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.619308 Loss1: 0.188967 Loss2: 1.430341
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.548194 Loss1: 0.132141 Loss2: 1.416054
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.034172 Loss1: 1.136751 Loss2: 1.897420
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.867930 Loss1: 0.466763 Loss2: 1.401167 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.481946 Loss1: 0.082197 Loss2: 1.399749 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.497095 Loss1: 0.098312 Loss2: 1.398783 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970703
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.402330 Loss1: 0.108324 Loss2: 1.294006 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387757 Loss1: 0.100996 Loss2: 1.286762 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985677
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.972547 Loss1: 1.079646 Loss2: 1.892901
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.085659 Loss1: 0.630977 Loss2: 1.454681
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.843523 Loss1: 0.381644 Loss2: 1.461879
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.738246 Loss1: 0.318801 Loss2: 1.419445
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.849761 Loss1: 0.995778 Loss2: 1.853982
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.649051 Loss1: 0.222761 Loss2: 1.426289
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.961358 Loss1: 0.564591 Loss2: 1.396767
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.643462 Loss1: 0.234858 Loss2: 1.408604
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.752066 Loss1: 0.336623 Loss2: 1.415444
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.617726 Loss1: 0.198984 Loss2: 1.418741
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.669375 Loss1: 0.293191 Loss2: 1.376184
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.566451 Loss1: 0.152715 Loss2: 1.413737
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.672677 Loss1: 0.294460 Loss2: 1.378216
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.574787 Loss1: 0.173101 Loss2: 1.401686
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.576217 Loss1: 0.193611 Loss2: 1.382606
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.569739 Loss1: 0.160651 Loss2: 1.409088
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.528959 Loss1: 0.166441 Loss2: 1.362519
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470366 Loss1: 0.116450 Loss2: 1.353915
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430512 Loss1: 0.078871 Loss2: 1.351641
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401870 Loss1: 0.062763 Loss2: 1.339107
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.848204 Loss1: 0.985014 Loss2: 1.863190
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.092802 Loss1: 0.696168 Loss2: 1.396634
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.918795 Loss1: 0.479192 Loss2: 1.439603
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.702029 Loss1: 0.307927 Loss2: 1.394102
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.958895 Loss1: 1.112185 Loss2: 1.846710
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.628530 Loss1: 0.261119 Loss2: 1.367411
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.081956 Loss1: 0.693123 Loss2: 1.388833
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.592274 Loss1: 0.219103 Loss2: 1.373170
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.842163 Loss1: 0.432929 Loss2: 1.409235
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544427 Loss1: 0.184943 Loss2: 1.359484
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.687917 Loss1: 0.312102 Loss2: 1.375815
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484180 Loss1: 0.124663 Loss2: 1.359518
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.573317 Loss1: 0.201210 Loss2: 1.372107
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.471155 Loss1: 0.118403 Loss2: 1.352752
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530625 Loss1: 0.170223 Loss2: 1.360402
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459674 Loss1: 0.110732 Loss2: 1.348942
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508367 Loss1: 0.156532 Loss2: 1.351835
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452451 Loss1: 0.104153 Loss2: 1.348298
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.456607 Loss1: 0.120241 Loss2: 1.336366
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443375 Loss1: 0.100017 Loss2: 1.343358
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.922026 Loss1: 1.050703 Loss2: 1.871323
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.107915 Loss1: 0.704765 Loss2: 1.403150
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.849503 Loss1: 0.429530 Loss2: 1.419973
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.695728 Loss1: 0.313678 Loss2: 1.382049
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.791433 Loss1: 0.964170 Loss2: 1.827262
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.888614 Loss1: 0.542675 Loss2: 1.345939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.728456 Loss1: 0.338006 Loss2: 1.390450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.618995 Loss1: 0.274071 Loss2: 1.344923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524714 Loss1: 0.175603 Loss2: 1.349111 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468626 Loss1: 0.133823 Loss2: 1.334802 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502775 Loss1: 0.170843 Loss2: 1.331931 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.507345 Loss1: 0.169064 Loss2: 1.338282 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.745718 Loss1: 0.891804 Loss2: 1.853914 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.696571 Loss1: 0.236751 Loss2: 1.459819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606047 Loss1: 0.209085 Loss2: 1.396962
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.898366 Loss1: 1.053975 Loss2: 1.844391
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.040745 Loss1: 0.615426 Loss2: 1.425319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513108 Loss1: 0.128731 Loss2: 1.384377
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.813867 Loss1: 0.418001 Loss2: 1.395866
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.586356 Loss1: 0.203453 Loss2: 1.382904
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.629692 Loss1: 0.255284 Loss2: 1.374409
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.577141 Loss1: 0.175483 Loss2: 1.401658
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535170 Loss1: 0.175444 Loss2: 1.359726
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500858 Loss1: 0.149401 Loss2: 1.351458
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.534447 Loss1: 0.146558 Loss2: 1.387890
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460281 Loss1: 0.112823 Loss2: 1.347457
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467634 Loss1: 0.078887 Loss2: 1.388747
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420983 Loss1: 0.086539 Loss2: 1.334443 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.811577 Loss1: 0.930947 Loss2: 1.880630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.828805 Loss1: 0.390186 Loss2: 1.438619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.712788 Loss1: 0.303738 Loss2: 1.409050
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.036584 Loss1: 1.019842 Loss2: 2.016742
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.265473 Loss1: 0.740352 Loss2: 1.525121
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.613535 Loss1: 0.213382 Loss2: 1.400153
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.005646 Loss1: 0.462349 Loss2: 1.543296
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.556952 Loss1: 0.171860 Loss2: 1.385092
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.833513 Loss1: 0.340073 Loss2: 1.493439
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.565355 Loss1: 0.178094 Loss2: 1.387260
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.740119 Loss1: 0.236585 Loss2: 1.503533
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.520668 Loss1: 0.139966 Loss2: 1.380702
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.524635 Loss1: 0.156334 Loss2: 1.368301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.483970 Loss1: 0.111253 Loss2: 1.372717 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.564225 Loss1: 0.097924 Loss2: 1.466301 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.777708 Loss1: 0.897832 Loss2: 1.879876 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.773791 Loss1: 0.344527 Loss2: 1.429264 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.633579 Loss1: 0.247515 Loss2: 1.386064
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.847317 Loss1: 0.916317 Loss2: 1.931001
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.599465 Loss1: 0.216317 Loss2: 1.383147
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.122711 Loss1: 0.684935 Loss2: 1.437775
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518882 Loss1: 0.142102 Loss2: 1.376780
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.887414 Loss1: 0.415649 Loss2: 1.471765
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474328 Loss1: 0.110374 Loss2: 1.363954
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.701594 Loss1: 0.279286 Loss2: 1.422308
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499453 Loss1: 0.132769 Loss2: 1.366684
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.617975 Loss1: 0.188077 Loss2: 1.429897
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453789 Loss1: 0.093853 Loss2: 1.359936
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.544395 Loss1: 0.136805 Loss2: 1.407590
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450064 Loss1: 0.094397 Loss2: 1.355667
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.531490 Loss1: 0.124370 Loss2: 1.407119
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.628876 Loss1: 0.210080 Loss2: 1.418796
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.597953 Loss1: 0.167005 Loss2: 1.430947
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.570567 Loss1: 0.150294 Loss2: 1.420273
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.913732 Loss1: 1.054617 Loss2: 1.859115
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.055107 Loss1: 0.650724 Loss2: 1.404383
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.767244 Loss1: 0.349937 Loss2: 1.417308
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.681641 Loss1: 0.301818 Loss2: 1.379823
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.920598 Loss1: 1.067592 Loss2: 1.853006
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.944584 Loss1: 0.549811 Loss2: 1.394773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.806719 Loss1: 0.399509 Loss2: 1.407210 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.715960 Loss1: 0.333545 Loss2: 1.382415 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.669600 Loss1: 0.288457 Loss2: 1.381143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.548013 Loss1: 0.174179 Loss2: 1.373834 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472365 Loss1: 0.107053 Loss2: 1.365312
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516722 Loss1: 0.146025 Loss2: 1.370697
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440603 Loss1: 0.087240 Loss2: 1.353363
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426744 Loss1: 0.081603 Loss2: 1.345141
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439413 Loss1: 0.098168 Loss2: 1.341245
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.752331 Loss1: 0.976806 Loss2: 1.775526
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.927582 Loss1: 0.574227 Loss2: 1.353355
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736825 Loss1: 0.372197 Loss2: 1.364628
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602101 Loss1: 0.256972 Loss2: 1.345129
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.204316 Loss1: 1.245978 Loss2: 1.958339
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518678 Loss1: 0.181817 Loss2: 1.336861
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.230264 Loss1: 0.774573 Loss2: 1.455690
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.869231 Loss1: 0.411347 Loss2: 1.457884
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473077 Loss1: 0.137235 Loss2: 1.335842
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.681799 Loss1: 0.281004 Loss2: 1.400795
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448504 Loss1: 0.129753 Loss2: 1.318751
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419776 Loss1: 0.098794 Loss2: 1.320982 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395873 Loss1: 0.083632 Loss2: 1.312241 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367929 Loss1: 0.062167 Loss2: 1.305762 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464570 Loss1: 0.087497 Loss2: 1.377074 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.985313 Loss1: 1.062512 Loss2: 1.922801
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.087300 Loss1: 0.624649 Loss2: 1.462651
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.833221 Loss1: 0.362721 Loss2: 1.470500
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.696398 Loss1: 0.266054 Loss2: 1.430344
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.926898 Loss1: 1.010233 Loss2: 1.916664
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.649177 Loss1: 0.215626 Loss2: 1.433550
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.060046 Loss1: 0.591948 Loss2: 1.468097
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.661775 Loss1: 0.238797 Loss2: 1.422978
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.881210 Loss1: 0.390272 Loss2: 1.490938
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.608346 Loss1: 0.180130 Loss2: 1.428216
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.712768 Loss1: 0.283415 Loss2: 1.429353
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.517534 Loss1: 0.104162 Loss2: 1.413372
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.709731 Loss1: 0.256389 Loss2: 1.453341
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489089 Loss1: 0.085894 Loss2: 1.403195
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.638860 Loss1: 0.209128 Loss2: 1.429732
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466137 Loss1: 0.062686 Loss2: 1.403451
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.597911 Loss1: 0.166332 Loss2: 1.431579
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.588730 Loss1: 0.165909 Loss2: 1.422821
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.533035 Loss1: 0.118162 Loss2: 1.414874
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.501033 Loss1: 0.086961 Loss2: 1.414072
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.030054 Loss1: 1.114214 Loss2: 1.915840
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.112307 Loss1: 0.670304 Loss2: 1.442003
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741022 Loss1: 0.325736 Loss2: 1.415287
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.664942 Loss1: 0.266840 Loss2: 1.398103
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.867837 Loss1: 1.014401 Loss2: 1.853436
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.021546 Loss1: 0.621817 Loss2: 1.399729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.852808 Loss1: 0.415998 Loss2: 1.436810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.750581 Loss1: 0.345179 Loss2: 1.405402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.635739 Loss1: 0.223385 Loss2: 1.412353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.548129 Loss1: 0.173361 Loss2: 1.374768 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508900 Loss1: 0.142135 Loss2: 1.366764
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.561735 Loss1: 0.177705 Loss2: 1.384029
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.524702 Loss1: 0.144086 Loss2: 1.380615
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487938 Loss1: 0.118314 Loss2: 1.369624
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.485489 Loss1: 0.112900 Loss2: 1.372589
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.811876 Loss1: 0.991815 Loss2: 1.820061
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.993003 Loss1: 0.573998 Loss2: 1.419005
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.734201 Loss1: 0.342340 Loss2: 1.391861
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.631607 Loss1: 0.258712 Loss2: 1.372896
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.910229 Loss1: 1.018843 Loss2: 1.891387
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566616 Loss1: 0.198679 Loss2: 1.367937
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.067940 Loss1: 0.651608 Loss2: 1.416332
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506624 Loss1: 0.149102 Loss2: 1.357522
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.793169 Loss1: 0.348076 Loss2: 1.445093
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.695562 Loss1: 0.298280 Loss2: 1.397282
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.507933 Loss1: 0.152449 Loss2: 1.355484
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.699992 Loss1: 0.295598 Loss2: 1.404394
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.503813 Loss1: 0.156368 Loss2: 1.347445
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.649191 Loss1: 0.237920 Loss2: 1.411270
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452346 Loss1: 0.105507 Loss2: 1.346839
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.579573 Loss1: 0.190715 Loss2: 1.388858
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.468385 Loss1: 0.121641 Loss2: 1.346744
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.494691 Loss1: 0.107013 Loss2: 1.387678 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.770745 Loss1: 0.888679 Loss2: 1.882066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.843844 Loss1: 0.416277 Loss2: 1.427567 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.689381 Loss1: 0.296086 Loss2: 1.393294
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.953032 Loss1: 0.997676 Loss2: 1.955356
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.010600 Loss1: 0.592504 Loss2: 1.418096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.789062 Loss1: 0.348262 Loss2: 1.440799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.497264 Loss1: 0.129256 Loss2: 1.368008
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.670736 Loss1: 0.256504 Loss2: 1.414232
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.689297 Loss1: 0.277542 Loss2: 1.411756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437561 Loss1: 0.087630 Loss2: 1.349930
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.550137 Loss1: 0.146728 Loss2: 1.403408
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429828 Loss1: 0.079083 Loss2: 1.350745
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504947 Loss1: 0.117782 Loss2: 1.387164
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518422 Loss1: 0.128370 Loss2: 1.390053
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.468708 Loss1: 0.079536 Loss2: 1.389172
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454024 Loss1: 0.072787 Loss2: 1.381237
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.864497 Loss1: 1.005887 Loss2: 1.858610
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.107051 Loss1: 0.684696 Loss2: 1.422355
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.858844 Loss1: 0.450501 Loss2: 1.408342
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.700762 Loss1: 0.300165 Loss2: 1.400597
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.132702 Loss1: 1.137439 Loss2: 1.995264
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.113916 Loss1: 0.686915 Loss2: 1.427001
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.576398 Loss1: 0.193626 Loss2: 1.382772
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.842608 Loss1: 0.388029 Loss2: 1.454578
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.529997 Loss1: 0.155453 Loss2: 1.374544
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470067 Loss1: 0.108062 Loss2: 1.362005 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477705 Loss1: 0.118575 Loss2: 1.359130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439875 Loss1: 0.080010 Loss2: 1.359864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451107 Loss1: 0.100457 Loss2: 1.350650 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450926 Loss1: 0.073173 Loss2: 1.377753 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.041151 Loss1: 1.077606 Loss2: 1.963545
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.155015 Loss1: 0.695191 Loss2: 1.459823
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.966897 Loss1: 0.465950 Loss2: 1.500947
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.810031 Loss1: 0.367794 Loss2: 1.442237
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.043023 Loss1: 1.109749 Loss2: 1.933274
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.746654 Loss1: 0.285631 Loss2: 1.461024
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.118494 Loss1: 0.687815 Loss2: 1.430679
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.649925 Loss1: 0.213335 Loss2: 1.436591
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.922746 Loss1: 0.436503 Loss2: 1.486243
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.635813 Loss1: 0.206329 Loss2: 1.429484
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.715837 Loss1: 0.296075 Loss2: 1.419762
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.628101 Loss1: 0.196914 Loss2: 1.431187
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.652555 Loss1: 0.231638 Loss2: 1.420917
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.586623 Loss1: 0.162049 Loss2: 1.424573
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.604213 Loss1: 0.200593 Loss2: 1.403620
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.531170 Loss1: 0.108718 Loss2: 1.422452
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.549332 Loss1: 0.152766 Loss2: 1.396566
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496479 Loss1: 0.098394 Loss2: 1.398085
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499122 Loss1: 0.117683 Loss2: 1.381439
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448388 Loss1: 0.066907 Loss2: 1.381481
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.855976 Loss1: 0.975996 Loss2: 1.879980
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.984403 Loss1: 0.583090 Loss2: 1.401313
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.833612 Loss1: 0.424716 Loss2: 1.408896
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.708887 Loss1: 0.321759 Loss2: 1.387129
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.039606 Loss1: 1.169901 Loss2: 1.869705
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.100625 Loss1: 0.693844 Loss2: 1.406781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.807868 Loss1: 0.397055 Loss2: 1.410813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.670364 Loss1: 0.289587 Loss2: 1.380776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.552031 Loss1: 0.175105 Loss2: 1.376925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.545343 Loss1: 0.181293 Loss2: 1.364050 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466558 Loss1: 0.127104 Loss2: 1.339454
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533258 Loss1: 0.170785 Loss2: 1.362472
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.530111 Loss1: 0.164067 Loss2: 1.366044
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.559469 Loss1: 0.198308 Loss2: 1.361161
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.514858 Loss1: 0.146721 Loss2: 1.368138
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.943033 Loss1: 1.058482 Loss2: 1.884550
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.077751 Loss1: 0.596566 Loss2: 1.481185
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.840339 Loss1: 0.375619 Loss2: 1.464719
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.698766 Loss1: 0.257454 Loss2: 1.441312
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.038157 Loss1: 1.074971 Loss2: 1.963185
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.144929 Loss1: 0.736519 Loss2: 1.408410
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.622977 Loss1: 0.184423 Loss2: 1.438554
+DEBUG flwr 2023-10-11 00:03:46,361 | server.py:236 | fit_round 94 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.658171 Loss1: 0.236023 Loss2: 1.422148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.614432 Loss1: 0.171637 Loss2: 1.442794 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.637411 Loss1: 0.210623 Loss2: 1.426788 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516809 Loss1: 0.117532 Loss2: 1.399278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.532242 Loss1: 0.142890 Loss2: 1.389353 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973633
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.479831 Loss1: 0.099497 Loss2: 1.380334 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975962
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.041119 Loss1: 1.137238 Loss2: 1.903881
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.173423 Loss1: 0.692209 Loss2: 1.481214
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.876219 Loss1: 0.423264 Loss2: 1.452955
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.793394 Loss1: 0.343003 Loss2: 1.450391
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.069406 Loss1: 1.119702 Loss2: 1.949704
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.651851 Loss1: 0.219009 Loss2: 1.432841
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.192946 Loss1: 0.703196 Loss2: 1.489750
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.616481 Loss1: 0.202247 Loss2: 1.414234
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.880825 Loss1: 0.364350 Loss2: 1.516475
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.546964 Loss1: 0.135336 Loss2: 1.411628
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.763986 Loss1: 0.309029 Loss2: 1.454958
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.559152 Loss1: 0.151974 Loss2: 1.407178
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.694646 Loss1: 0.220715 Loss2: 1.473931
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.576836 Loss1: 0.168211 Loss2: 1.408625
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.588541 Loss1: 0.140912 Loss2: 1.447630
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.537356 Loss1: 0.127650 Loss2: 1.409707
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.541135 Loss1: 0.107659 Loss2: 1.433476
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546361 Loss1: 0.118434 Loss2: 1.427927
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.558717 Loss1: 0.128047 Loss2: 1.430670
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.544676 Loss1: 0.114402 Loss2: 1.430274
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.846647 Loss1: 1.022829 Loss2: 1.823818
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.977326 Loss1: 0.571220 Loss2: 1.406106
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.779660 Loss1: 0.368077 Loss2: 1.411583
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.878867 Loss1: 1.011518 Loss2: 1.867349
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.643775 Loss1: 0.257204 Loss2: 1.386571
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.126799 Loss1: 0.715264 Loss2: 1.411534
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.583234 Loss1: 0.211157 Loss2: 1.372077
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834010 Loss1: 0.405594 Loss2: 1.428416
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.546331 Loss1: 0.168222 Loss2: 1.378109
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.746578 Loss1: 0.348291 Loss2: 1.398287
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.499981 Loss1: 0.133254 Loss2: 1.366727
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568136 Loss1: 0.183884 Loss2: 1.384252
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460967 Loss1: 0.098168 Loss2: 1.362799
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447406 Loss1: 0.090997 Loss2: 1.356409 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437815 Loss1: 0.083485 Loss2: 1.354330 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425903 Loss1: 0.077731 Loss2: 1.348172 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 00:03:46,361][flwr][DEBUG] - fit_round 94 received 50 results and 0 failures
+INFO flwr 2023-10-11 00:04:28,687 | server.py:125 | fit progress: (94, 2.218318693744489, {'accuracy': 0.5643}, 216776.465405886)
+>> Test accuracy: 0.564300
+[2023-10-11 00:04:28,687][flwr][INFO] - fit progress: (94, 2.218318693744489, {'accuracy': 0.5643}, 216776.465405886)
+DEBUG flwr 2023-10-11 00:04:28,687 | server.py:173 | evaluate_round 94: strategy sampled 50 clients (out of 50)
+[2023-10-11 00:04:28,687][flwr][DEBUG] - evaluate_round 94: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 00:13:31,844 | server.py:187 | evaluate_round 94 received 50 results and 0 failures
+[2023-10-11 00:13:31,844][flwr][DEBUG] - evaluate_round 94 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 00:13:31,844 | server.py:222 | fit_round 95: strategy sampled 50 clients (out of 50)
+[2023-10-11 00:13:31,844][flwr][DEBUG] - fit_round 95: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.822954 Loss1: 0.987660 Loss2: 1.835294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.851840 Loss1: 0.417095 Loss2: 1.434745 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.697189 Loss1: 0.333671 Loss2: 1.363517
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.810873 Loss1: 0.957553 Loss2: 1.853320
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.626987 Loss1: 0.248032 Loss2: 1.378956
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.055518 Loss1: 0.666728 Loss2: 1.388790
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.550351 Loss1: 0.193823 Loss2: 1.356527
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.902580 Loss1: 0.449637 Loss2: 1.452943
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483449 Loss1: 0.126118 Loss2: 1.357331
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.648437 Loss1: 0.280998 Loss2: 1.367439
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438745 Loss1: 0.093255 Loss2: 1.345491
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598802 Loss1: 0.216607 Loss2: 1.382195
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423174 Loss1: 0.085252 Loss2: 1.337922
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.577547 Loss1: 0.211477 Loss2: 1.366070
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419505 Loss1: 0.082658 Loss2: 1.336846
+(DefaultActor pid=3765) >> Training accuracy: 0.960417
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485885 Loss1: 0.128270 Loss2: 1.357615
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485801 Loss1: 0.134144 Loss2: 1.351657
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462811 Loss1: 0.112356 Loss2: 1.350455
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456013 Loss1: 0.116190 Loss2: 1.339823
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.004930 Loss1: 1.086794 Loss2: 1.918136
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.117869 Loss1: 0.671846 Loss2: 1.446023
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.860419 Loss1: 0.423764 Loss2: 1.436655
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638038 Loss1: 0.224115 Loss2: 1.413923
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.820917 Loss1: 0.993461 Loss2: 1.827455
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558948 Loss1: 0.166236 Loss2: 1.392712
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.054543 Loss1: 0.680939 Loss2: 1.373604
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530118 Loss1: 0.145563 Loss2: 1.384554
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.822629 Loss1: 0.440894 Loss2: 1.381735
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.522198 Loss1: 0.138523 Loss2: 1.383675
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699464 Loss1: 0.334691 Loss2: 1.364772
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.537906 Loss1: 0.144697 Loss2: 1.393210
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556515 Loss1: 0.209586 Loss2: 1.346930
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.506329 Loss1: 0.118180 Loss2: 1.388149
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.547494 Loss1: 0.206388 Loss2: 1.341105
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477955 Loss1: 0.099306 Loss2: 1.378649
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476641 Loss1: 0.133143 Loss2: 1.343498
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440563 Loss1: 0.117827 Loss2: 1.322737
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406166 Loss1: 0.084126 Loss2: 1.322040
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401974 Loss1: 0.083776 Loss2: 1.318199
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.618934 Loss1: 0.826908 Loss2: 1.792026
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.928462 Loss1: 0.554042 Loss2: 1.374420
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684211 Loss1: 0.307228 Loss2: 1.376983
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.993223 Loss1: 1.073312 Loss2: 1.919911
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591034 Loss1: 0.250717 Loss2: 1.340318
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516313 Loss1: 0.172323 Loss2: 1.343990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.500461 Loss1: 0.168964 Loss2: 1.331497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473145 Loss1: 0.144781 Loss2: 1.328363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433660 Loss1: 0.102073 Loss2: 1.331587 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419850 Loss1: 0.097317 Loss2: 1.322532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.489497 Loss1: 0.115951 Loss2: 1.373546 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460201 Loss1: 0.093477 Loss2: 1.366724 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985491
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.832293 Loss1: 1.006532 Loss2: 1.825761
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.033377 Loss1: 0.667005 Loss2: 1.366372
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.747040 Loss1: 0.371194 Loss2: 1.375846
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583593 Loss1: 0.256171 Loss2: 1.327422
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.874619 Loss1: 0.995445 Loss2: 1.879174
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.973997 Loss1: 0.562322 Loss2: 1.411675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.789163 Loss1: 0.349669 Loss2: 1.439494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.675067 Loss1: 0.282930 Loss2: 1.392137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.613646 Loss1: 0.210574 Loss2: 1.403072 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.590455 Loss1: 0.204119 Loss2: 1.386336 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371039 Loss1: 0.073575 Loss2: 1.297464
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.521133 Loss1: 0.129149 Loss2: 1.391984
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488637 Loss1: 0.106001 Loss2: 1.382636
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449463 Loss1: 0.074482 Loss2: 1.374981
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428235 Loss1: 0.059960 Loss2: 1.368275
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.791327 Loss1: 0.914757 Loss2: 1.876569
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.234569 Loss1: 0.750491 Loss2: 1.484079
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.902513 Loss1: 0.445601 Loss2: 1.456912
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.763659 Loss1: 0.331186 Loss2: 1.432473
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.841867 Loss1: 0.940989 Loss2: 1.900878
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.934780 Loss1: 0.489071 Loss2: 1.445709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.798815 Loss1: 0.339183 Loss2: 1.459632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.679774 Loss1: 0.252492 Loss2: 1.427281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.644187 Loss1: 0.215085 Loss2: 1.429102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.631000 Loss1: 0.216945 Loss2: 1.414055 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484191 Loss1: 0.088575 Loss2: 1.395616
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.569010 Loss1: 0.149264 Loss2: 1.419746
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.555474 Loss1: 0.147624 Loss2: 1.407850
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.537555 Loss1: 0.128115 Loss2: 1.409439
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.499035 Loss1: 0.101703 Loss2: 1.397332
+(DefaultActor pid=3764) >> Training accuracy: 0.966797
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.798308 Loss1: 0.880141 Loss2: 1.918168
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.106989 Loss1: 0.661036 Loss2: 1.445953
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.892801 Loss1: 0.398572 Loss2: 1.494229
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.761386 Loss1: 0.321546 Loss2: 1.439840
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.771107 Loss1: 0.879254 Loss2: 1.891854
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.952181 Loss1: 0.536871 Loss2: 1.415310 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.821113 Loss1: 0.347022 Loss2: 1.474091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.685026 Loss1: 0.284569 Loss2: 1.400458 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687564 Loss1: 0.257101 Loss2: 1.430463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.625360 Loss1: 0.214628 Loss2: 1.410731 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467699 Loss1: 0.065765 Loss2: 1.401934
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.669170 Loss1: 0.248643 Loss2: 1.420526
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.631566 Loss1: 0.220938 Loss2: 1.410628
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.566880 Loss1: 0.158151 Loss2: 1.408728
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.521671 Loss1: 0.122807 Loss2: 1.398864
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.680880 Loss1: 0.919044 Loss2: 1.761836
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.966440 Loss1: 0.595787 Loss2: 1.370653
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.755927 Loss1: 0.377626 Loss2: 1.378301
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599336 Loss1: 0.251088 Loss2: 1.348247
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.997474 Loss1: 1.111733 Loss2: 1.885741
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.104526 Loss1: 0.661055 Loss2: 1.443471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505375 Loss1: 0.176811 Loss2: 1.328564
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.822929 Loss1: 0.367307 Loss2: 1.455622
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467699 Loss1: 0.128718 Loss2: 1.338981
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.747110 Loss1: 0.335492 Loss2: 1.411618
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469898 Loss1: 0.141071 Loss2: 1.328827
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.614624 Loss1: 0.188679 Loss2: 1.425945
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.599546 Loss1: 0.195898 Loss2: 1.403648
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448155 Loss1: 0.109590 Loss2: 1.338566
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.555379 Loss1: 0.145414 Loss2: 1.409965
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442125 Loss1: 0.112318 Loss2: 1.329807
+(DefaultActor pid=3765) >> Training accuracy: 0.952148
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.496698 Loss1: 0.101194 Loss2: 1.395504 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.122241 Loss1: 1.184103 Loss2: 1.938138 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.934152 Loss1: 0.467016 Loss2: 1.467136 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.738123 Loss1: 0.285065 Loss2: 1.453058
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.017528 Loss1: 1.068329 Loss2: 1.949199
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.012711 Loss1: 0.655453 Loss2: 1.357258
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.624448 Loss1: 0.191270 Loss2: 1.433178
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.719002 Loss1: 0.320264 Loss2: 1.398738
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.582777 Loss1: 0.154270 Loss2: 1.428507
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.571751 Loss1: 0.144941 Loss2: 1.426810 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.570552 Loss1: 0.145913 Loss2: 1.424639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.553879 Loss1: 0.136086 Loss2: 1.417793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.522736 Loss1: 0.108856 Loss2: 1.413880 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408305 Loss1: 0.091448 Loss2: 1.316857 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.963155 Loss1: 1.019931 Loss2: 1.943223
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.116606 Loss1: 0.612930 Loss2: 1.503677
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.910607 Loss1: 0.420349 Loss2: 1.490259
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.898854 Loss1: 0.962569 Loss2: 1.936285
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.826756 Loss1: 0.344946 Loss2: 1.481811
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.089267 Loss1: 0.610535 Loss2: 1.478733
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.712198 Loss1: 0.248808 Loss2: 1.463390
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.700944 Loss1: 0.248415 Loss2: 1.452528 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.633813 Loss1: 0.165841 Loss2: 1.467973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.560169 Loss1: 0.113053 Loss2: 1.447116 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509382 Loss1: 0.079159 Loss2: 1.430224 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.487042 Loss1: 0.061655 Loss2: 1.425387 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.526995 Loss1: 0.104682 Loss2: 1.422313 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.731452 Loss1: 0.901742 Loss2: 1.829709 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.810542 Loss1: 0.403810 Loss2: 1.406732 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.679415 Loss1: 0.309119 Loss2: 1.370295
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.834557 Loss1: 0.988284 Loss2: 1.846273
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547206 Loss1: 0.170482 Loss2: 1.376724
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.892324 Loss1: 0.505421 Loss2: 1.386903
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.774542 Loss1: 0.382921 Loss2: 1.391620
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542041 Loss1: 0.181997 Loss2: 1.360044
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.673028 Loss1: 0.311865 Loss2: 1.361163
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481364 Loss1: 0.125659 Loss2: 1.355706
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.536009 Loss1: 0.179994 Loss2: 1.356016
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483375 Loss1: 0.130049 Loss2: 1.353326
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493233 Loss1: 0.152676 Loss2: 1.340557
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461774 Loss1: 0.104156 Loss2: 1.357618
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422771 Loss1: 0.070305 Loss2: 1.352466 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410018 Loss1: 0.082925 Loss2: 1.327093 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.979319 Loss1: 1.082389 Loss2: 1.896929 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.885440 Loss1: 0.421675 Loss2: 1.463765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.752434 Loss1: 0.329356 Loss2: 1.423078
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.804338 Loss1: 0.977725 Loss2: 1.826613
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.936179 Loss1: 0.569812 Loss2: 1.366367 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.807751 Loss1: 0.392149 Loss2: 1.415602 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.600182 Loss1: 0.233596 Loss2: 1.366586 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549349 Loss1: 0.190990 Loss2: 1.358359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542542 Loss1: 0.187741 Loss2: 1.354801 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.521857 Loss1: 0.123219 Loss2: 1.398638
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478418 Loss1: 0.130225 Loss2: 1.348193
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473665 Loss1: 0.128108 Loss2: 1.345558
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444658 Loss1: 0.104396 Loss2: 1.340261
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433779 Loss1: 0.093653 Loss2: 1.340127
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.964091 Loss1: 1.093760 Loss2: 1.870331
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.043904 Loss1: 0.629578 Loss2: 1.414325
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.872447 Loss1: 0.445807 Loss2: 1.426639
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694481 Loss1: 0.303336 Loss2: 1.391145
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.815605 Loss1: 0.951262 Loss2: 1.864342
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.866556 Loss1: 0.462270 Loss2: 1.404286 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.660790 Loss1: 0.273626 Loss2: 1.387164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.628911 Loss1: 0.254318 Loss2: 1.374593 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.581829 Loss1: 0.218188 Loss2: 1.363641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434232 Loss1: 0.085999 Loss2: 1.348233 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467983 Loss1: 0.117737 Loss2: 1.350246 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423718 Loss1: 0.076555 Loss2: 1.347163 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987132
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.063693 Loss1: 0.630160 Loss2: 1.433533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.669063 Loss1: 0.269071 Loss2: 1.399992 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.557900 Loss1: 0.160638 Loss2: 1.397261
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.902986 Loss1: 1.026634 Loss2: 1.876352
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.119285 Loss1: 0.684164 Loss2: 1.435121 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.910454 Loss1: 0.450184 Loss2: 1.460269 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.751461 Loss1: 0.339143 Loss2: 1.412318 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.705989 Loss1: 0.274046 Loss2: 1.431943 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442668 Loss1: 0.076521 Loss2: 1.366147
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.610444 Loss1: 0.205619 Loss2: 1.404825
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.616559 Loss1: 0.215480 Loss2: 1.401079
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.563985 Loss1: 0.174752 Loss2: 1.389233
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.542707 Loss1: 0.152061 Loss2: 1.390646
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.683862 Loss1: 0.265323 Loss2: 1.418539
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.738149 Loss1: 0.903856 Loss2: 1.834293
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.982460 Loss1: 0.603766 Loss2: 1.378694
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.806821 Loss1: 0.392597 Loss2: 1.414224
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642889 Loss1: 0.281995 Loss2: 1.360894
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.584908 Loss1: 0.207789 Loss2: 1.377120
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509357 Loss1: 0.158916 Loss2: 1.350441 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511479 Loss1: 0.161117 Loss2: 1.350362 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495687 Loss1: 0.151242 Loss2: 1.344445 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450694 Loss1: 0.107119 Loss2: 1.343576 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435291 Loss1: 0.099599 Loss2: 1.335692 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530461 Loss1: 0.157709 Loss2: 1.372752 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519071 Loss1: 0.142370 Loss2: 1.376701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.511420 Loss1: 0.147108 Loss2: 1.364312
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.789178 Loss1: 0.943986 Loss2: 1.845192
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.897059 Loss1: 0.541430 Loss2: 1.355629
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.692691 Loss1: 0.318289 Loss2: 1.374402
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555602 Loss1: 0.215991 Loss2: 1.339611
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519373 Loss1: 0.184713 Loss2: 1.334660
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509595 Loss1: 0.183232 Loss2: 1.326362
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.032346 Loss1: 1.064103 Loss2: 1.968243
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441855 Loss1: 0.119365 Loss2: 1.322490
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.122232 Loss1: 0.613578 Loss2: 1.508654
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429198 Loss1: 0.120618 Loss2: 1.308580
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.983249 Loss1: 0.446364 Loss2: 1.536886
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.382741 Loss1: 0.076345 Loss2: 1.306396
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.813576 Loss1: 0.327649 Loss2: 1.485927
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393040 Loss1: 0.091800 Loss2: 1.301240
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.742569 Loss1: 0.249937 Loss2: 1.492632
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.734907 Loss1: 0.251878 Loss2: 1.483028
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.728013 Loss1: 0.243918 Loss2: 1.484095
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.653814 Loss1: 0.168479 Loss2: 1.485335
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.633682 Loss1: 0.159256 Loss2: 1.474427
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.030204 Loss1: 1.112182 Loss2: 1.918022
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.660511 Loss1: 0.187108 Loss2: 1.473403
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.805253 Loss1: 0.349076 Loss2: 1.456177 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587564 Loss1: 0.170217 Loss2: 1.417347 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530381 Loss1: 0.126367 Loss2: 1.404014
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.865062 Loss1: 1.019237 Loss2: 1.845825
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.572387 Loss1: 0.170929 Loss2: 1.401458
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.952119 Loss1: 0.594337 Loss2: 1.357783
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.533864 Loss1: 0.131983 Loss2: 1.401881
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769917 Loss1: 0.378505 Loss2: 1.391412
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488853 Loss1: 0.092368 Loss2: 1.396484
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636893 Loss1: 0.299352 Loss2: 1.337541
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484212 Loss1: 0.085482 Loss2: 1.398730
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556060 Loss1: 0.204409 Loss2: 1.351650
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528470 Loss1: 0.191866 Loss2: 1.336603
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.525050 Loss1: 0.193617 Loss2: 1.331433
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467144 Loss1: 0.140966 Loss2: 1.326179
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451032 Loss1: 0.128056 Loss2: 1.322976
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.974626 Loss1: 1.017650 Loss2: 1.956977
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390404 Loss1: 0.070068 Loss2: 1.320335
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.049840 Loss1: 0.507249 Loss2: 1.542592 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.684325 Loss1: 0.270064 Loss2: 1.414261 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.603443 Loss1: 0.205702 Loss2: 1.397741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479249 Loss1: 0.083368 Loss2: 1.395882 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463596 Loss1: 0.082314 Loss2: 1.381282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.487069 Loss1: 0.111215 Loss2: 1.375854 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.573501 Loss1: 0.194008 Loss2: 1.379494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.524111 Loss1: 0.153918 Loss2: 1.370192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.039775 Loss1: 1.173807 Loss2: 1.865967 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.066302 Loss1: 0.643891 Loss2: 1.422411 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.639771 Loss1: 0.264752 Loss2: 1.375019 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.521511 Loss1: 0.157073 Loss2: 1.364438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490826 Loss1: 0.127702 Loss2: 1.363124
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.815635 Loss1: 0.955311 Loss2: 1.860324
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.919016 Loss1: 0.541770 Loss2: 1.377246 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.765971 Loss1: 0.349882 Loss2: 1.416089 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640013 Loss1: 0.263264 Loss2: 1.376749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503032 Loss1: 0.139543 Loss2: 1.363490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417164 Loss1: 0.075925 Loss2: 1.341239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395958 Loss1: 0.060959 Loss2: 1.334999 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413107 Loss1: 0.079785 Loss2: 1.333323 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.776751 Loss1: 0.264110 Loss2: 1.512641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.698684 Loss1: 0.192210 Loss2: 1.506474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.636348 Loss1: 0.136659 Loss2: 1.499690
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.842322 Loss1: 1.011612 Loss2: 1.830711
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.002303 Loss1: 0.607722 Loss2: 1.394582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.823614 Loss1: 0.415427 Loss2: 1.408187 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.556339 Loss1: 0.077390 Loss2: 1.478949
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.668101 Loss1: 0.306635 Loss2: 1.361466
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.623442 Loss1: 0.260542 Loss2: 1.362901
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582960 Loss1: 0.224705 Loss2: 1.358255
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513380 Loss1: 0.145239 Loss2: 1.368141
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466586 Loss1: 0.122570 Loss2: 1.344016
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.063841 Loss1: 1.094701 Loss2: 1.969140
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420943 Loss1: 0.085043 Loss2: 1.335901
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.063860 Loss1: 0.556325 Loss2: 1.507535
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422384 Loss1: 0.089387 Loss2: 1.332997
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.819856 Loss1: 0.333243 Loss2: 1.486613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.685664 Loss1: 0.220311 Loss2: 1.465352 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.605724 Loss1: 0.143025 Loss2: 1.462700
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.807414 Loss1: 1.032208 Loss2: 1.775207
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.019534 Loss1: 0.614516 Loss2: 1.405018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.731310 Loss1: 0.379712 Loss2: 1.351597 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.597925 Loss1: 0.261136 Loss2: 1.336789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478198 Loss1: 0.154285 Loss2: 1.323912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453420 Loss1: 0.137484 Loss2: 1.315936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395988 Loss1: 0.084500 Loss2: 1.311488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378035 Loss1: 0.070450 Loss2: 1.307585 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.543899 Loss1: 0.160888 Loss2: 1.383011 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478163 Loss1: 0.107974 Loss2: 1.370189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488425 Loss1: 0.122582 Loss2: 1.365843 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462357 Loss1: 0.105153 Loss2: 1.357204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447517 Loss1: 0.090079 Loss2: 1.357438 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.602609 Loss1: 0.172187 Loss2: 1.430422 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.519194 Loss1: 0.111748 Loss2: 1.407445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482687 Loss1: 0.083504 Loss2: 1.399183 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975260
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.809137 Loss1: 0.390446 Loss2: 1.418691 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.569141 Loss1: 0.179143 Loss2: 1.389997 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.991994 Loss1: 1.100382 Loss2: 1.891611
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523292 Loss1: 0.146825 Loss2: 1.376468
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.021283 Loss1: 0.607827 Loss2: 1.413455
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483023 Loss1: 0.117080 Loss2: 1.365943
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.805916 Loss1: 0.387004 Loss2: 1.418912
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457306 Loss1: 0.097519 Loss2: 1.359787
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.727614 Loss1: 0.332333 Loss2: 1.395282
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.446865 Loss1: 0.094641 Loss2: 1.352224
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.632950 Loss1: 0.229511 Loss2: 1.403438
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.559469 Loss1: 0.185054 Loss2: 1.374416
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518986 Loss1: 0.143089 Loss2: 1.375897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463783 Loss1: 0.102011 Loss2: 1.361771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431806 Loss1: 0.073474 Loss2: 1.358331
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.930217 Loss1: 1.107120 Loss2: 1.823097
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.045166 Loss1: 0.613741 Loss2: 1.431425
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.866382 Loss1: 0.455175 Loss2: 1.411207
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.728501 Loss1: 0.333013 Loss2: 1.395488
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.619433 Loss1: 0.236818 Loss2: 1.382614
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.909173 Loss1: 1.147548 Loss2: 1.761626
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.024971 Loss1: 0.682644 Loss2: 1.342326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.797694 Loss1: 0.445447 Loss2: 1.352247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.644372 Loss1: 0.328661 Loss2: 1.315711 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.605529 Loss1: 0.279711 Loss2: 1.325818 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971680
+DEBUG flwr 2023-10-11 00:42:34,448 | server.py:236 | fit_round 95 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435599 Loss1: 0.086387 Loss2: 1.349213
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.532568 Loss1: 0.223262 Loss2: 1.309306
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.492238 Loss1: 0.179881 Loss2: 1.312357
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453016 Loss1: 0.145386 Loss2: 1.307630
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438957 Loss1: 0.142786 Loss2: 1.296172
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428654 Loss1: 0.129348 Loss2: 1.299306
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.991952 Loss1: 1.100472 Loss2: 1.891480
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.016379 Loss1: 0.608633 Loss2: 1.407746
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.877707 Loss1: 0.437612 Loss2: 1.440095
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649455 Loss1: 0.257527 Loss2: 1.391928
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589560 Loss1: 0.199325 Loss2: 1.390235
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.095850 Loss1: 1.198527 Loss2: 1.897323
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.157588 Loss1: 0.753413 Loss2: 1.404176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.925637 Loss1: 0.488370 Loss2: 1.437267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480206 Loss1: 0.112449 Loss2: 1.367757
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.739309 Loss1: 0.366618 Loss2: 1.372691
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.500101 Loss1: 0.129690 Loss2: 1.370410
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.616891 Loss1: 0.220776 Loss2: 1.396115
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467617 Loss1: 0.105975 Loss2: 1.361642
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.553862 Loss1: 0.205040 Loss2: 1.348822
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483023 Loss1: 0.127378 Loss2: 1.355645
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459846 Loss1: 0.119320 Loss2: 1.340526
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436405 Loss1: 0.102039 Loss2: 1.334366
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424070 Loss1: 0.088721 Loss2: 1.335349
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.057047 Loss1: 1.122039 Loss2: 1.935008
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.005792 Loss1: 0.609485 Loss2: 1.396307
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.813355 Loss1: 0.382941 Loss2: 1.430414
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.692810 Loss1: 0.301698 Loss2: 1.391111
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.628564 Loss1: 0.234815 Loss2: 1.393749
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.553556 Loss1: 0.169032 Loss2: 1.384524 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484479 Loss1: 0.109673 Loss2: 1.374806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460161 Loss1: 0.098059 Loss2: 1.362102 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439057 Loss1: 0.082671 Loss2: 1.356386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431543 Loss1: 0.080944 Loss2: 1.350599 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986607
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.532032 Loss1: 0.138967 Loss2: 1.393065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.506105 Loss1: 0.120409 Loss2: 1.385697 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 00:42:34,448][flwr][DEBUG] - fit_round 95 received 50 results and 0 failures
+INFO flwr 2023-10-11 00:43:15,405 | server.py:125 | fit progress: (95, 2.222508845047448, {'accuracy': 0.5634}, 219103.184059672)
+>> Test accuracy: 0.563400
+[2023-10-11 00:43:15,405][flwr][INFO] - fit progress: (95, 2.222508845047448, {'accuracy': 0.5634}, 219103.184059672)
+DEBUG flwr 2023-10-11 00:43:15,406 | server.py:173 | evaluate_round 95: strategy sampled 50 clients (out of 50)
+[2023-10-11 00:43:15,406][flwr][DEBUG] - evaluate_round 95: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 00:52:22,688 | server.py:187 | evaluate_round 95 received 50 results and 0 failures
+[2023-10-11 00:52:22,688][flwr][DEBUG] - evaluate_round 95 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 00:52:22,689 | server.py:222 | fit_round 96: strategy sampled 50 clients (out of 50)
+[2023-10-11 00:52:22,689][flwr][DEBUG] - fit_round 96: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.861972 Loss1: 0.990679 Loss2: 1.871293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.841317 Loss1: 0.402289 Loss2: 1.439028 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.719242 Loss1: 0.324303 Loss2: 1.394939
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.984330 Loss1: 1.128538 Loss2: 1.855792
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.185322 Loss1: 0.741339 Loss2: 1.443983
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.644125 Loss1: 0.241638 Loss2: 1.402487
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.852685 Loss1: 0.440713 Loss2: 1.411972
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.567630 Loss1: 0.185419 Loss2: 1.382211
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.713266 Loss1: 0.310342 Loss2: 1.402923
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528704 Loss1: 0.143923 Loss2: 1.384781
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.587673 Loss1: 0.202409 Loss2: 1.385265
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.466964 Loss1: 0.095989 Loss2: 1.370975
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459889 Loss1: 0.093779 Loss2: 1.366110 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469004 Loss1: 0.107644 Loss2: 1.361360 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.478643 Loss1: 0.120428 Loss2: 1.358215 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.890360 Loss1: 1.033130 Loss2: 1.857230 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.760214 Loss1: 0.343102 Loss2: 1.417112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.710249 Loss1: 0.330731 Loss2: 1.379518
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.924347 Loss1: 0.980556 Loss2: 1.943792
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.569633 Loss1: 0.186750 Loss2: 1.382883
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.009634 Loss1: 0.567278 Loss2: 1.442356
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489177 Loss1: 0.126490 Loss2: 1.362687
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.833846 Loss1: 0.352762 Loss2: 1.481085
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455424 Loss1: 0.096028 Loss2: 1.359396
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.717526 Loss1: 0.284293 Loss2: 1.433233
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480687 Loss1: 0.126511 Loss2: 1.354176
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.606289 Loss1: 0.174813 Loss2: 1.431476
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.479023 Loss1: 0.119475 Loss2: 1.359548
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.583522 Loss1: 0.168702 Loss2: 1.414820
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.479499 Loss1: 0.126224 Loss2: 1.353275
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.575124 Loss1: 0.166424 Loss2: 1.408700
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.531112 Loss1: 0.113029 Loss2: 1.418084
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493668 Loss1: 0.094637 Loss2: 1.399032
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.485911 Loss1: 0.085837 Loss2: 1.400074
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.689856 Loss1: 0.871765 Loss2: 1.818092
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.951668 Loss1: 0.517467 Loss2: 1.434201
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.733310 Loss1: 0.345129 Loss2: 1.388182
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.651085 Loss1: 0.263848 Loss2: 1.387237
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.895328 Loss1: 1.050257 Loss2: 1.845071
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.030297 Loss1: 0.632103 Loss2: 1.398194
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.644021 Loss1: 0.259528 Loss2: 1.384493
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.796379 Loss1: 0.380557 Loss2: 1.415822
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.544945 Loss1: 0.168677 Loss2: 1.376269
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.651782 Loss1: 0.282413 Loss2: 1.369369
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486961 Loss1: 0.120949 Loss2: 1.366012
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.642731 Loss1: 0.268578 Loss2: 1.374154
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438978 Loss1: 0.085652 Loss2: 1.353325
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.415424 Loss1: 0.065611 Loss2: 1.349813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426379 Loss1: 0.080544 Loss2: 1.345834 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.533434 Loss1: 0.173574 Loss2: 1.359860 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.963542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.986517 Loss1: 1.118534 Loss2: 1.867982 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.817043 Loss1: 0.399868 Loss2: 1.417175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.704775 Loss1: 0.318800 Loss2: 1.385975
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.936860 Loss1: 1.108907 Loss2: 1.827953
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.615905 Loss1: 0.227221 Loss2: 1.388684
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.054302 Loss1: 0.654079 Loss2: 1.400222
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587127 Loss1: 0.214711 Loss2: 1.372415
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.900811 Loss1: 0.466767 Loss2: 1.434044
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.566103 Loss1: 0.197839 Loss2: 1.368264
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.710043 Loss1: 0.330694 Loss2: 1.379349
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491293 Loss1: 0.124404 Loss2: 1.366890
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.615279 Loss1: 0.245109 Loss2: 1.370170
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484951 Loss1: 0.135352 Loss2: 1.349599
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530847 Loss1: 0.170592 Loss2: 1.360254
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452279 Loss1: 0.092501 Loss2: 1.359778
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475965 Loss1: 0.121583 Loss2: 1.354382
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469586 Loss1: 0.121495 Loss2: 1.348091
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485433 Loss1: 0.133631 Loss2: 1.351802
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456261 Loss1: 0.109940 Loss2: 1.346321
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.904294 Loss1: 0.923246 Loss2: 1.981048
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.982043 Loss1: 0.520990 Loss2: 1.461053
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.862639 Loss1: 0.369746 Loss2: 1.492892
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.702798 Loss1: 0.252482 Loss2: 1.450316
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.058182 Loss1: 1.130200 Loss2: 1.927982
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605824 Loss1: 0.169868 Loss2: 1.435956
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.089305 Loss1: 0.682164 Loss2: 1.407142
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576676 Loss1: 0.145460 Loss2: 1.431215
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.902331 Loss1: 0.445073 Loss2: 1.457259
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.697556 Loss1: 0.303172 Loss2: 1.394384
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534488 Loss1: 0.111697 Loss2: 1.422791
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.663027 Loss1: 0.259758 Loss2: 1.403269
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.529200 Loss1: 0.110178 Loss2: 1.419022
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.581206 Loss1: 0.189266 Loss2: 1.391940
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.503386 Loss1: 0.085573 Loss2: 1.417813
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.512565 Loss1: 0.096621 Loss2: 1.415944 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462673 Loss1: 0.092422 Loss2: 1.370251 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.861778 Loss1: 1.041789 Loss2: 1.819989 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.779906 Loss1: 0.375400 Loss2: 1.404506 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.621396 Loss1: 0.258041 Loss2: 1.363355
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.849407 Loss1: 0.942642 Loss2: 1.906764
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.032011 Loss1: 0.614186 Loss2: 1.417825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.814544 Loss1: 0.369594 Loss2: 1.444950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.693607 Loss1: 0.295650 Loss2: 1.397957 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.635152 Loss1: 0.226469 Loss2: 1.408684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.543124 Loss1: 0.150946 Loss2: 1.392178 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410298 Loss1: 0.075028 Loss2: 1.335270
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.523960 Loss1: 0.139280 Loss2: 1.384679
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510690 Loss1: 0.126898 Loss2: 1.383792
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.478074 Loss1: 0.103685 Loss2: 1.374389
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449427 Loss1: 0.077298 Loss2: 1.372129
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.859497 Loss1: 0.963733 Loss2: 1.895764
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.090044 Loss1: 0.667123 Loss2: 1.422922
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.819990 Loss1: 0.360294 Loss2: 1.459696
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.720767 Loss1: 0.311211 Loss2: 1.409555
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.949897 Loss1: 1.063438 Loss2: 1.886459
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.653996 Loss1: 0.237934 Loss2: 1.416063
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.030723 Loss1: 0.675726 Loss2: 1.354997
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.798631 Loss1: 0.392750 Loss2: 1.405880
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.612241 Loss1: 0.210092 Loss2: 1.402149
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529651 Loss1: 0.123916 Loss2: 1.405736 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.504522 Loss1: 0.110094 Loss2: 1.394428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489094 Loss1: 0.104022 Loss2: 1.385072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454020 Loss1: 0.079278 Loss2: 1.374742 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433411 Loss1: 0.108078 Loss2: 1.325334 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980769
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.089612 Loss1: 1.155418 Loss2: 1.934193
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.017424 Loss1: 0.608662 Loss2: 1.408762
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.823074 Loss1: 0.378025 Loss2: 1.445048
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629022 Loss1: 0.236629 Loss2: 1.392393
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.984753 Loss1: 1.047618 Loss2: 1.937135
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.000662 Loss1: 0.615990 Loss2: 1.384672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.787770 Loss1: 0.353794 Loss2: 1.433976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.615062 Loss1: 0.243198 Loss2: 1.371864 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558600 Loss1: 0.199047 Loss2: 1.359553 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.485744 Loss1: 0.118230 Loss2: 1.367514
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501561 Loss1: 0.133847 Loss2: 1.367714
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459175 Loss1: 0.089157 Loss2: 1.370017
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.507933 Loss1: 0.151259 Loss2: 1.356674
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546947 Loss1: 0.190749 Loss2: 1.356198
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497726 Loss1: 0.134294 Loss2: 1.363432
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454983 Loss1: 0.105993 Loss2: 1.348990
+(DefaultActor pid=3764) >> Training accuracy: 0.981971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.752916 Loss1: 0.958082 Loss2: 1.794835
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.883102 Loss1: 0.520398 Loss2: 1.362704
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648318 Loss1: 0.295446 Loss2: 1.352872
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.537418 Loss1: 0.220502 Loss2: 1.316917
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.752348 Loss1: 0.885651 Loss2: 1.866697
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.082270 Loss1: 0.685128 Loss2: 1.397142
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487916 Loss1: 0.171309 Loss2: 1.316608
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.845206 Loss1: 0.393907 Loss2: 1.451299
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480020 Loss1: 0.166923 Loss2: 1.313096
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.718550 Loss1: 0.330577 Loss2: 1.387973
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441240 Loss1: 0.135903 Loss2: 1.305337
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.583935 Loss1: 0.178926 Loss2: 1.405009
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414165 Loss1: 0.105882 Loss2: 1.308283
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395896 Loss1: 0.095995 Loss2: 1.299901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452342 Loss1: 0.147981 Loss2: 1.304361 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.517369 Loss1: 0.136406 Loss2: 1.380963 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.832963 Loss1: 1.005777 Loss2: 1.827187 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.685335 Loss1: 0.288676 Loss2: 1.396659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.657386 Loss1: 0.305006 Loss2: 1.352381
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.894545 Loss1: 0.986986 Loss2: 1.907559
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.097350 Loss1: 0.618005 Loss2: 1.479345 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569461 Loss1: 0.213723 Loss2: 1.355738
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.903979 Loss1: 0.411767 Loss2: 1.492212
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.728955 Loss1: 0.284198 Loss2: 1.444758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.694544 Loss1: 0.251535 Loss2: 1.443009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647423 Loss1: 0.205521 Loss2: 1.441902 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457151 Loss1: 0.116358 Loss2: 1.340793
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.611614 Loss1: 0.169520 Loss2: 1.442093
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.597070 Loss1: 0.160875 Loss2: 1.436196
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.567581 Loss1: 0.139433 Loss2: 1.428148
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529573 Loss1: 0.108359 Loss2: 1.421214
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.756410 Loss1: 0.850780 Loss2: 1.905630
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.896959 Loss1: 0.502869 Loss2: 1.394090
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741671 Loss1: 0.325730 Loss2: 1.415941
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.140869 Loss1: 1.139434 Loss2: 2.001436
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629723 Loss1: 0.241909 Loss2: 1.387814
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.590407 Loss1: 0.205417 Loss2: 1.384990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503213 Loss1: 0.133051 Loss2: 1.370162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.498040 Loss1: 0.132557 Loss2: 1.365483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.563603 Loss1: 0.185301 Loss2: 1.378301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449098 Loss1: 0.095652 Loss2: 1.353447 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431060 Loss1: 0.079693 Loss2: 1.351367 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.682263 Loss1: 0.930520 Loss2: 1.751743 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.697286 Loss1: 0.339325 Loss2: 1.357961 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.259885 Loss1: 1.262556 Loss2: 1.997329
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.645839 Loss1: 0.294116 Loss2: 1.351723
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.239852 Loss1: 0.721385 Loss2: 1.518467
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.600887 Loss1: 0.243238 Loss2: 1.357649
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.962596 Loss1: 0.437172 Loss2: 1.525424
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.577641 Loss1: 0.234591 Loss2: 1.343051
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491355 Loss1: 0.153008 Loss2: 1.338347 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499400 Loss1: 0.174527 Loss2: 1.324873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.511793 Loss1: 0.175991 Loss2: 1.335801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452598 Loss1: 0.125410 Loss2: 1.327188 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.974609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.542404 Loss1: 0.102542 Loss2: 1.439862 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.888994 Loss1: 1.030987 Loss2: 1.858006 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.801078 Loss1: 0.377445 Loss2: 1.423634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.849311 Loss1: 1.033983 Loss2: 1.815328
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.618783 Loss1: 0.227766 Loss2: 1.391017
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.996328 Loss1: 0.605381 Loss2: 1.390947
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562491 Loss1: 0.185316 Loss2: 1.377175
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.772610 Loss1: 0.402686 Loss2: 1.369924
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.517496 Loss1: 0.142428 Loss2: 1.375069
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.515114 Loss1: 0.144391 Loss2: 1.370723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.505715 Loss1: 0.131756 Loss2: 1.373960 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472908 Loss1: 0.110016 Loss2: 1.362892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469858 Loss1: 0.107726 Loss2: 1.362131 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476148 Loss1: 0.143938 Loss2: 1.332210 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.776917 Loss1: 0.910151 Loss2: 1.866766 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.794112 Loss1: 0.344355 Loss2: 1.449758 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.687299 Loss1: 0.301464 Loss2: 1.385835
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.915715 Loss1: 1.056556 Loss2: 1.859159
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.635908 Loss1: 0.242705 Loss2: 1.393203
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.037819 Loss1: 0.646645 Loss2: 1.391174
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.536770 Loss1: 0.167514 Loss2: 1.369256
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.786983 Loss1: 0.349902 Loss2: 1.437081
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483689 Loss1: 0.123045 Loss2: 1.360644
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.682686 Loss1: 0.307642 Loss2: 1.375045
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.512635 Loss1: 0.145878 Loss2: 1.366757
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.617973 Loss1: 0.235466 Loss2: 1.382507
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458648 Loss1: 0.098726 Loss2: 1.359922
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.581707 Loss1: 0.197291 Loss2: 1.384416
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420540 Loss1: 0.068650 Loss2: 1.351890
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.554536 Loss1: 0.186360 Loss2: 1.368176
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.499165 Loss1: 0.131521 Loss2: 1.367643
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.541577 Loss1: 0.176410 Loss2: 1.365167
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.499588 Loss1: 0.135326 Loss2: 1.364262
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.824756 Loss1: 0.973680 Loss2: 1.851076
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.067460 Loss1: 0.641356 Loss2: 1.426104
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.761734 Loss1: 0.340515 Loss2: 1.421219
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.633636 Loss1: 0.241690 Loss2: 1.391946
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.227443 Loss1: 1.043379 Loss2: 2.184064
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.591088 Loss1: 0.195082 Loss2: 1.396005
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.370016 Loss1: 0.680472 Loss2: 1.689544
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538196 Loss1: 0.166954 Loss2: 1.371242
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.184090 Loss1: 0.446893 Loss2: 1.737197
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512991 Loss1: 0.143920 Loss2: 1.369071
+(DefaultActor pid=3764) Epoch: 3 Loss: 2.032748 Loss1: 0.370563 Loss2: 1.662185
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481561 Loss1: 0.117253 Loss2: 1.364308
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.944613 Loss1: 0.264126 Loss2: 1.680487
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.475807 Loss1: 0.116428 Loss2: 1.359379
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.871290 Loss1: 0.219990 Loss2: 1.651300
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415684 Loss1: 0.054296 Loss2: 1.361388
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.813845 Loss1: 0.175105 Loss2: 1.638740
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.739844 Loss1: 0.109211 Loss2: 1.630632
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.731516 Loss1: 0.114167 Loss2: 1.617349
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.693149 Loss1: 0.080749 Loss2: 1.612400
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.874216 Loss1: 0.967013 Loss2: 1.907204
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.117784 Loss1: 0.693991 Loss2: 1.423793
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.885875 Loss1: 0.432813 Loss2: 1.453062
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.723955 Loss1: 0.310898 Loss2: 1.413057
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.952359 Loss1: 1.102297 Loss2: 1.850061
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.936292 Loss1: 0.532713 Loss2: 1.403580 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.746224 Loss1: 0.344024 Loss2: 1.402200 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.645836 Loss1: 0.275458 Loss2: 1.370378 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576387 Loss1: 0.209306 Loss2: 1.367081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519589 Loss1: 0.156294 Loss2: 1.363295 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459539 Loss1: 0.085410 Loss2: 1.374129
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484021 Loss1: 0.133523 Loss2: 1.350498
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454007 Loss1: 0.103866 Loss2: 1.350142
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418038 Loss1: 0.072536 Loss2: 1.345502
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410084 Loss1: 0.072041 Loss2: 1.338043
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.628872 Loss1: 0.873504 Loss2: 1.755368
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.841315 Loss1: 0.540856 Loss2: 1.300458
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.689226 Loss1: 0.341981 Loss2: 1.347245
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591237 Loss1: 0.294606 Loss2: 1.296631
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.768561 Loss1: 0.840683 Loss2: 1.927878
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.977698 Loss1: 0.564401 Loss2: 1.413297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.818365 Loss1: 0.346790 Loss2: 1.471574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.688724 Loss1: 0.281166 Loss2: 1.407558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.653144 Loss1: 0.225253 Loss2: 1.427891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.562505 Loss1: 0.159269 Loss2: 1.403236 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380454 Loss1: 0.110940 Loss2: 1.269515
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.557819 Loss1: 0.157957 Loss2: 1.399863
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.505763 Loss1: 0.103893 Loss2: 1.401870
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.532758 Loss1: 0.133186 Loss2: 1.399573
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.498973 Loss1: 0.098859 Loss2: 1.400114
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.965592 Loss1: 1.073552 Loss2: 1.892040
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.041686 Loss1: 0.613023 Loss2: 1.428663
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.773378 Loss1: 0.362987 Loss2: 1.410391
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.676854 Loss1: 0.277139 Loss2: 1.399715
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.635446 Loss1: 0.901202 Loss2: 1.734244
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.973618 Loss1: 0.621988 Loss2: 1.351631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.768428 Loss1: 0.412891 Loss2: 1.355536 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.625140 Loss1: 0.302199 Loss2: 1.322941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.533136 Loss1: 0.220767 Loss2: 1.312369 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484078 Loss1: 0.180915 Loss2: 1.303163 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.394998 Loss1: 0.097357 Loss2: 1.297641 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.346592 Loss1: 0.066600 Loss2: 1.279992 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.033578 Loss1: 0.642884 Loss2: 1.390694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.737218 Loss1: 0.342871 Loss2: 1.394347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.067032 Loss1: 1.130683 Loss2: 1.936348
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.645435 Loss1: 0.268238 Loss2: 1.377197
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.100397 Loss1: 0.665423 Loss2: 1.434974
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.552795 Loss1: 0.185296 Loss2: 1.367499
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.925705 Loss1: 0.452803 Loss2: 1.472902
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.515909 Loss1: 0.148036 Loss2: 1.367873
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.713808 Loss1: 0.295952 Loss2: 1.417856
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467065 Loss1: 0.109553 Loss2: 1.357512
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.628818 Loss1: 0.205955 Loss2: 1.422863
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432234 Loss1: 0.089454 Loss2: 1.342780
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.580931 Loss1: 0.173909 Loss2: 1.407021
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441585 Loss1: 0.098409 Loss2: 1.343176
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.507634 Loss1: 0.112671 Loss2: 1.394963 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.474578 Loss1: 0.089532 Loss2: 1.385046 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.916298 Loss1: 0.502550 Loss2: 1.413748 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587862 Loss1: 0.196993 Loss2: 1.390869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521112 Loss1: 0.144723 Loss2: 1.376389
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.585458 Loss1: 0.829737 Loss2: 1.755722
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.490964 Loss1: 0.120211 Loss2: 1.370753
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.842512 Loss1: 0.507612 Loss2: 1.334900
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684056 Loss1: 0.330768 Loss2: 1.353288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.638047 Loss1: 0.305870 Loss2: 1.332177 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544127 Loss1: 0.216275 Loss2: 1.327852 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513917 Loss1: 0.186143 Loss2: 1.327774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482309 Loss1: 0.166596 Loss2: 1.315713 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.856458 Loss1: 1.005970 Loss2: 1.850488 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981618
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.795691 Loss1: 0.356817 Loss2: 1.438873 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.588667 Loss1: 0.202606 Loss2: 1.386061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.517686 Loss1: 0.147852 Loss2: 1.369834
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.935313 Loss1: 1.035336 Loss2: 1.899977
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481771 Loss1: 0.118426 Loss2: 1.363345
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.055486 Loss1: 0.638267 Loss2: 1.417219
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457696 Loss1: 0.100317 Loss2: 1.357380
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.830871 Loss1: 0.365384 Loss2: 1.465487
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.479125 Loss1: 0.117568 Loss2: 1.361557
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.710906 Loss1: 0.292323 Loss2: 1.418584
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.503103 Loss1: 0.145822 Loss2: 1.357281
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598508 Loss1: 0.169999 Loss2: 1.428509
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.574310 Loss1: 0.170235 Loss2: 1.404076
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.527229 Loss1: 0.122712 Loss2: 1.404517
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.534534 Loss1: 0.143116 Loss2: 1.391418
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.522407 Loss1: 0.124896 Loss2: 1.397511
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.177670 Loss1: 1.233525 Loss2: 1.944145
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.538010 Loss1: 0.131124 Loss2: 1.406886
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.894133 Loss1: 0.434313 Loss2: 1.459820 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.615348 Loss1: 0.204939 Loss2: 1.410409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.022524 Loss1: 1.078441 Loss2: 1.944084 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.161945 Loss1: 0.672414 Loss2: 1.489531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.796382 Loss1: 0.380659 Loss2: 1.415723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.727540 Loss1: 0.295406 Loss2: 1.432134 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560759 Loss1: 0.156092 Loss2: 1.404666 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465965 Loss1: 0.080034 Loss2: 1.385931 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499170 Loss1: 0.115471 Loss2: 1.383700
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.733284 Loss1: 0.900160 Loss2: 1.833124
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460574 Loss1: 0.078705 Loss2: 1.381869
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.152701 Loss1: 0.718852 Loss2: 1.433849
+DEBUG flwr 2023-10-11 01:21:32,481 | server.py:236 | fit_round 96 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.788774 Loss1: 0.372534 Loss2: 1.416239
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.678606 Loss1: 0.282020 Loss2: 1.396585
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.576647 Loss1: 0.184751 Loss2: 1.391896
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.556978 Loss1: 0.174347 Loss2: 1.382631
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.982371 Loss1: 1.099676 Loss2: 1.882694
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.126630 Loss1: 0.701248 Loss2: 1.425382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.914270 Loss1: 0.441812 Loss2: 1.472459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.707214 Loss1: 0.291028 Loss2: 1.416186 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481043 Loss1: 0.105494 Loss2: 1.375549
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.643994 Loss1: 0.228598 Loss2: 1.415396
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.586936 Loss1: 0.183871 Loss2: 1.403066
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.568223 Loss1: 0.170347 Loss2: 1.397877
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.560340 Loss1: 0.151835 Loss2: 1.408506
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.549117 Loss1: 0.162755 Loss2: 1.386361
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.682698 Loss1: 0.834256 Loss2: 1.848441
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.530551 Loss1: 0.139445 Loss2: 1.391106
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.740172 Loss1: 0.320165 Loss2: 1.420007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577835 Loss1: 0.201600 Loss2: 1.376235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538009 Loss1: 0.172622 Loss2: 1.365387
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.789730 Loss1: 1.000118 Loss2: 1.789612
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.890901 Loss1: 0.529467 Loss2: 1.361433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677907 Loss1: 0.308533 Loss2: 1.369374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.608386 Loss1: 0.257588 Loss2: 1.350799 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513090 Loss1: 0.170272 Loss2: 1.342818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435490 Loss1: 0.109460 Loss2: 1.326030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405427 Loss1: 0.089103 Loss2: 1.316324 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430232 Loss1: 0.114234 Loss2: 1.315998 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647931 Loss1: 0.279645 Loss2: 1.368286 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.540293 Loss1: 0.172400 Loss2: 1.367894 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.507869 Loss1: 0.143265 Loss2: 1.364603
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.806608 Loss1: 1.008162 Loss2: 1.798446
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.463365 Loss1: 0.106578 Loss2: 1.356787
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.021193 Loss1: 0.592799 Loss2: 1.428395
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465835 Loss1: 0.113029 Loss2: 1.352806
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.792132 Loss1: 0.391865 Loss2: 1.400267
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426220 Loss1: 0.084153 Loss2: 1.342067
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.660535 Loss1: 0.269397 Loss2: 1.391138
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.632110 Loss1: 0.254056 Loss2: 1.378053
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.581391 Loss1: 0.204762 Loss2: 1.376629
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516595 Loss1: 0.146458 Loss2: 1.370137
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.492214 Loss1: 0.130084 Loss2: 1.362130
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487352 Loss1: 0.120139 Loss2: 1.367213
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.504399 Loss1: 0.143926 Loss2: 1.360473
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 01:21:32,481][flwr][DEBUG] - fit_round 96 received 50 results and 0 failures
+INFO flwr 2023-10-11 01:22:13,443 | server.py:125 | fit progress: (96, 2.205584313351506, {'accuracy': 0.5652}, 221441.221870161)
+>> Test accuracy: 0.565200
+[2023-10-11 01:22:13,443][flwr][INFO] - fit progress: (96, 2.205584313351506, {'accuracy': 0.5652}, 221441.221870161)
+DEBUG flwr 2023-10-11 01:22:13,444 | server.py:173 | evaluate_round 96: strategy sampled 50 clients (out of 50)
+[2023-10-11 01:22:13,444][flwr][DEBUG] - evaluate_round 96: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 01:31:20,981 | server.py:187 | evaluate_round 96 received 50 results and 0 failures
+[2023-10-11 01:31:20,981][flwr][DEBUG] - evaluate_round 96 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 01:31:20,981 | server.py:222 | fit_round 97: strategy sampled 50 clients (out of 50)
+[2023-10-11 01:31:20,981][flwr][DEBUG] - fit_round 97: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.067949 Loss1: 1.154553 Loss2: 1.913396
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.064240 Loss1: 0.672277 Loss2: 1.391963
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.885521 Loss1: 0.446770 Loss2: 1.438751
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.688976 Loss1: 0.308943 Loss2: 1.380033
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.824553 Loss1: 0.978364 Loss2: 1.846190
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.073387 Loss1: 0.652526 Loss2: 1.420861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.878587 Loss1: 0.438048 Loss2: 1.440539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430106 Loss1: 0.077452 Loss2: 1.352654 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428275 Loss1: 0.081306 Loss2: 1.346969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413755 Loss1: 0.073421 Loss2: 1.340334 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.572598 Loss1: 0.178216 Loss2: 1.394381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487610 Loss1: 0.110173 Loss2: 1.377438 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.476721 Loss1: 0.104716 Loss2: 1.372004
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.941494 Loss1: 1.079815 Loss2: 1.861680
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.051745 Loss1: 0.632767 Loss2: 1.418978
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.782821 Loss1: 0.386856 Loss2: 1.395964
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.691665 Loss1: 0.310610 Loss2: 1.381055
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.629187 Loss1: 0.236632 Loss2: 1.392555
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.795795 Loss1: 0.960750 Loss2: 1.835045
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.561389 Loss1: 0.201344 Loss2: 1.360046
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496157 Loss1: 0.131684 Loss2: 1.364473 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.431117 Loss1: 0.078818 Loss2: 1.352299 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411344 Loss1: 0.065334 Loss2: 1.346010 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396577 Loss1: 0.060238 Loss2: 1.336339 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.551715 Loss1: 0.188989 Loss2: 1.362726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431090 Loss1: 0.091327 Loss2: 1.339763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419213 Loss1: 0.078531 Loss2: 1.340682
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.864949 Loss1: 1.059603 Loss2: 1.805345
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.008753 Loss1: 0.642378 Loss2: 1.366375
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.744149 Loss1: 0.374645 Loss2: 1.369504
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.619145 Loss1: 0.271209 Loss2: 1.347936
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.493127 Loss1: 0.156006 Loss2: 1.337121
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.754117 Loss1: 0.875510 Loss2: 1.878607
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471284 Loss1: 0.148475 Loss2: 1.322809
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.988432 Loss1: 0.531179 Loss2: 1.457253
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418299 Loss1: 0.101931 Loss2: 1.316369
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.852251 Loss1: 0.379605 Loss2: 1.472646
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401267 Loss1: 0.093573 Loss2: 1.307694
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377610 Loss1: 0.077045 Loss2: 1.300565
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.745459 Loss1: 0.292094 Loss2: 1.453365
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377897 Loss1: 0.072930 Loss2: 1.304967
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.690907 Loss1: 0.244313 Loss2: 1.446594
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.613766 Loss1: 0.178183 Loss2: 1.435583
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.538524 Loss1: 0.109675 Loss2: 1.428850
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.513380 Loss1: 0.095118 Loss2: 1.418262
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.540845 Loss1: 0.121791 Loss2: 1.419054
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.956355 Loss1: 1.060407 Loss2: 1.895948
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.524797 Loss1: 0.106223 Loss2: 1.418574
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.784171 Loss1: 0.344385 Loss2: 1.439786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.594306 Loss1: 0.183848 Loss2: 1.410458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.579797 Loss1: 0.181895 Loss2: 1.397901
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.809999 Loss1: 0.923597 Loss2: 1.886401
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.939472 Loss1: 0.536307 Loss2: 1.403166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.763077 Loss1: 0.330956 Loss2: 1.432121 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.679749 Loss1: 0.294350 Loss2: 1.385398 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.672765 Loss1: 0.275742 Loss2: 1.397023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.531918 Loss1: 0.152152 Loss2: 1.379766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497862 Loss1: 0.123792 Loss2: 1.374069 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464297 Loss1: 0.098932 Loss2: 1.365365 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.780209 Loss1: 0.382356 Loss2: 1.397852 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.620812 Loss1: 0.247555 Loss2: 1.373257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.557657 Loss1: 0.204878 Loss2: 1.352778
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.748737 Loss1: 0.845683 Loss2: 1.903055
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.894706 Loss1: 0.499476 Loss2: 1.395231 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.782195 Loss1: 0.342146 Loss2: 1.440049 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.683987 Loss1: 0.291393 Loss2: 1.392594 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418991 Loss1: 0.087988 Loss2: 1.331004
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.614406 Loss1: 0.209444 Loss2: 1.404962
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.558189 Loss1: 0.172562 Loss2: 1.385627
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.531578 Loss1: 0.141279 Loss2: 1.390299
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490817 Loss1: 0.109886 Loss2: 1.380931
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464330 Loss1: 0.091449 Loss2: 1.372881
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.777164 Loss1: 0.919999 Loss2: 1.857166
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452368 Loss1: 0.085030 Loss2: 1.367338
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.823443 Loss1: 0.402529 Loss2: 1.420914 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.576813 Loss1: 0.200042 Loss2: 1.376771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537102 Loss1: 0.167143 Loss2: 1.369959
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.700250 Loss1: 0.815123 Loss2: 1.885127
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.988489 Loss1: 0.565336 Loss2: 1.423152 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.776636 Loss1: 0.347457 Loss2: 1.429179 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.671913 Loss1: 0.249591 Loss2: 1.422322 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.589103 Loss1: 0.185429 Loss2: 1.403674 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487370 Loss1: 0.098339 Loss2: 1.389031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.862269 Loss1: 0.952600 Loss2: 1.909669
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462789 Loss1: 0.082025 Loss2: 1.380763
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452646 Loss1: 0.077830 Loss2: 1.374816 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972426
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.666654 Loss1: 0.256554 Loss2: 1.410100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.585065 Loss1: 0.178119 Loss2: 1.406946 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.959606 Loss1: 1.031588 Loss2: 1.928018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.141182 Loss1: 0.650643 Loss2: 1.490539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.888534 Loss1: 0.406608 Loss2: 1.481926 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.653611 Loss1: 0.211551 Loss2: 1.442060 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.528568 Loss1: 0.103065 Loss2: 1.425502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510912 Loss1: 0.095891 Loss2: 1.415022
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.009204 Loss1: 1.067452 Loss2: 1.941752
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.064848 Loss1: 0.643257 Loss2: 1.421591 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.473095 Loss1: 0.068850 Loss2: 1.404246
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.847953 Loss1: 0.377853 Loss2: 1.470100
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.682804 Loss1: 0.268630 Loss2: 1.414174
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.600264 Loss1: 0.186367 Loss2: 1.413897
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.563018 Loss1: 0.158045 Loss2: 1.404973
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.543338 Loss1: 0.142493 Loss2: 1.400845
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.542410 Loss1: 0.143701 Loss2: 1.398709
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.850593 Loss1: 0.991052 Loss2: 1.859540
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.917729 Loss1: 0.519402 Loss2: 1.398327 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477287 Loss1: 0.094080 Loss2: 1.383207
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.820962 Loss1: 0.379078 Loss2: 1.441884
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.782094 Loss1: 0.395830 Loss2: 1.386264
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.632012 Loss1: 0.230970 Loss2: 1.401042
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.599837 Loss1: 0.221340 Loss2: 1.378497
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.580017 Loss1: 0.202907 Loss2: 1.377110
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.063351 Loss1: 1.078898 Loss2: 1.984452
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546573 Loss1: 0.164225 Loss2: 1.382348
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.535036 Loss1: 0.159703 Loss2: 1.375333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.474160 Loss1: 0.102132 Loss2: 1.372028 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.565538 Loss1: 0.197193 Loss2: 1.368346 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.476122 Loss1: 0.126072 Loss2: 1.350050 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461993 Loss1: 0.121722 Loss2: 1.340270 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.801149 Loss1: 0.348021 Loss2: 1.453128 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.719348 Loss1: 0.272773 Loss2: 1.446575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.671465 Loss1: 0.825845 Loss2: 1.845620
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.670801 Loss1: 0.247016 Loss2: 1.423785
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.973832 Loss1: 0.584825 Loss2: 1.389007
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.596394 Loss1: 0.171373 Loss2: 1.425021
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.812313 Loss1: 0.369391 Loss2: 1.442922
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.567020 Loss1: 0.155363 Loss2: 1.411657
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647792 Loss1: 0.273479 Loss2: 1.374313
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.530338 Loss1: 0.120784 Loss2: 1.409555
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.679343 Loss1: 0.283338 Loss2: 1.396005
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.529851 Loss1: 0.129349 Loss2: 1.400502
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525905 Loss1: 0.154946 Loss2: 1.370959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462759 Loss1: 0.110328 Loss2: 1.352431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411775 Loss1: 0.061627 Loss2: 1.350148
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.953798 Loss1: 1.117495 Loss2: 1.836304
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.009736 Loss1: 0.594096 Loss2: 1.415640
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.852098 Loss1: 0.442091 Loss2: 1.410007
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.790134 Loss1: 0.394256 Loss2: 1.395878
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.738430 Loss1: 0.334097 Loss2: 1.404332
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.853291 Loss1: 1.006465 Loss2: 1.846826
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.653944 Loss1: 0.265810 Loss2: 1.388134
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.023615 Loss1: 0.578480 Loss2: 1.445136
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.559319 Loss1: 0.187407 Loss2: 1.371912
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.513036 Loss1: 0.142373 Loss2: 1.370663
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.811381 Loss1: 0.376379 Loss2: 1.435002
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.475564 Loss1: 0.114478 Loss2: 1.361085
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.702533 Loss1: 0.303416 Loss2: 1.399118
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472892 Loss1: 0.118653 Loss2: 1.354239
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.637008 Loss1: 0.234516 Loss2: 1.402492
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.594825 Loss1: 0.203365 Loss2: 1.391460
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.554852 Loss1: 0.163865 Loss2: 1.390987
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.541318 Loss1: 0.148627 Loss2: 1.392692
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.557253 Loss1: 0.159121 Loss2: 1.398132
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.894229 Loss1: 0.956316 Loss2: 1.937913
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485252 Loss1: 0.103282 Loss2: 1.381970
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.973671 Loss1: 0.470557 Loss2: 1.503113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.737084 Loss1: 0.270051 Loss2: 1.467033 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647487 Loss1: 0.201702 Loss2: 1.445785
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.720589 Loss1: 0.868822 Loss2: 1.851767
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.847918 Loss1: 0.448706 Loss2: 1.399212 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684396 Loss1: 0.286802 Loss2: 1.397594 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.615261 Loss1: 0.234150 Loss2: 1.381111 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547382 Loss1: 0.170430 Loss2: 1.376952 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473166 Loss1: 0.109047 Loss2: 1.364118 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431380 Loss1: 0.075561 Loss2: 1.355819
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.893446 Loss1: 1.084614 Loss2: 1.808832
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.030447 Loss1: 0.659303 Loss2: 1.371145
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416283 Loss1: 0.064505 Loss2: 1.351778
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.638779 Loss1: 0.292228 Loss2: 1.346552 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519061 Loss1: 0.186289 Loss2: 1.332773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545390 Loss1: 0.205105 Loss2: 1.340285
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.766742 Loss1: 1.006789 Loss2: 1.759953
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470130 Loss1: 0.132603 Loss2: 1.337527
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.932364 Loss1: 0.630280 Loss2: 1.302084
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453720 Loss1: 0.128465 Loss2: 1.325254
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.698822 Loss1: 0.374164 Loss2: 1.324658
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434603 Loss1: 0.105440 Loss2: 1.329162
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569970 Loss1: 0.284900 Loss2: 1.285070
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.501092 Loss1: 0.207829 Loss2: 1.293263
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.453074 Loss1: 0.177487 Loss2: 1.275587
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404479 Loss1: 0.129133 Loss2: 1.275345
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394824 Loss1: 0.127888 Loss2: 1.266936
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.358116 Loss1: 0.090808 Loss2: 1.267308
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.796647 Loss1: 0.947730 Loss2: 1.848917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.345380 Loss1: 0.084736 Loss2: 1.260644
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.933294 Loss1: 0.548337 Loss2: 1.384957
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.752798 Loss1: 0.328473 Loss2: 1.424325
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.715108 Loss1: 0.354169 Loss2: 1.360939
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.623732 Loss1: 0.240873 Loss2: 1.382859
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.571459 Loss1: 0.216706 Loss2: 1.354753
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518642 Loss1: 0.156215 Loss2: 1.362427
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.922939 Loss1: 0.988815 Loss2: 1.934124
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466537 Loss1: 0.119227 Loss2: 1.347311
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.139813 Loss1: 0.679281 Loss2: 1.460532
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.455627 Loss1: 0.116063 Loss2: 1.339564
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.866046 Loss1: 0.357619 Loss2: 1.508427
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.451441 Loss1: 0.121234 Loss2: 1.330207
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.761301 Loss1: 0.317275 Loss2: 1.444027
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.700294 Loss1: 0.232553 Loss2: 1.467741
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.585373 Loss1: 0.155094 Loss2: 1.430279
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.560764 Loss1: 0.133310 Loss2: 1.427454
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.583285 Loss1: 0.154381 Loss2: 1.428904
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.722511 Loss1: 0.893115 Loss2: 1.829396
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.571270 Loss1: 0.148215 Loss2: 1.423055
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.554463 Loss1: 0.127418 Loss2: 1.427046
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.937809 Loss1: 0.541649 Loss2: 1.396160
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.699199 Loss1: 0.271065 Loss2: 1.428134
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619386 Loss1: 0.242760 Loss2: 1.376626
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549566 Loss1: 0.163699 Loss2: 1.385867
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531753 Loss1: 0.155262 Loss2: 1.376491
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.945948 Loss1: 1.005600 Loss2: 1.940348
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.523685 Loss1: 0.149332 Loss2: 1.374353
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.562459 Loss1: 0.180334 Loss2: 1.382125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.590736 Loss1: 0.210902 Loss2: 1.379834 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.646808 Loss1: 0.238193 Loss2: 1.408615 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964844
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514667 Loss1: 0.126456 Loss2: 1.388211 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470502 Loss1: 0.088914 Loss2: 1.381588 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.950205 Loss1: 0.991163 Loss2: 1.959041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.993213 Loss1: 0.452586 Loss2: 1.540627 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.691509 Loss1: 0.874112 Loss2: 1.817397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.940429 Loss1: 0.585386 Loss2: 1.355043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.729205 Loss1: 0.320143 Loss2: 1.409061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.603846 Loss1: 0.264815 Loss2: 1.339031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526609 Loss1: 0.184221 Loss2: 1.342387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.532098 Loss1: 0.189621 Loss2: 1.342477 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441951 Loss1: 0.110789 Loss2: 1.331162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385695 Loss1: 0.068802 Loss2: 1.316893 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.965610 Loss1: 1.117622 Loss2: 1.847987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726065 Loss1: 0.327616 Loss2: 1.398449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.598264 Loss1: 0.237976 Loss2: 1.360287
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.195703 Loss1: 1.257553 Loss2: 1.938150
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.116622 Loss1: 0.663195 Loss2: 1.453427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.820343 Loss1: 0.378376 Loss2: 1.441967 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.729567 Loss1: 0.308764 Loss2: 1.420803 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.651898 Loss1: 0.248469 Loss2: 1.403429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447828 Loss1: 0.108351 Loss2: 1.339477
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.553014 Loss1: 0.156914 Loss2: 1.396099
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414540 Loss1: 0.081935 Loss2: 1.332605
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511696 Loss1: 0.120414 Loss2: 1.391282
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497085 Loss1: 0.112350 Loss2: 1.384735
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458304 Loss1: 0.076893 Loss2: 1.381411
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438565 Loss1: 0.064845 Loss2: 1.373721
+(DefaultActor pid=3765) >> Training accuracy: 0.975446
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.894579 Loss1: 1.034838 Loss2: 1.859742
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.993601 Loss1: 0.608814 Loss2: 1.384787
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.808755 Loss1: 0.398507 Loss2: 1.410247
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.661102 Loss1: 0.281631 Loss2: 1.379472
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.895110 Loss1: 1.008312 Loss2: 1.886798
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.594730 Loss1: 0.224148 Loss2: 1.370583
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.986739 Loss1: 0.552954 Loss2: 1.433785
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506245 Loss1: 0.144267 Loss2: 1.361978
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.858909 Loss1: 0.391242 Loss2: 1.467667
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443291 Loss1: 0.088460 Loss2: 1.354831
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.745659 Loss1: 0.323857 Loss2: 1.421802
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439130 Loss1: 0.094942 Loss2: 1.344187
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.647812 Loss1: 0.208858 Loss2: 1.438954
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438434 Loss1: 0.096500 Loss2: 1.341934
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.573938 Loss1: 0.149313 Loss2: 1.424625
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436941 Loss1: 0.091846 Loss2: 1.345095
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.541856 Loss1: 0.134690 Loss2: 1.407166
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.524543 Loss1: 0.121654 Loss2: 1.402889
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.513077 Loss1: 0.101347 Loss2: 1.411731
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.525223 Loss1: 0.123034 Loss2: 1.402189
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.784867 Loss1: 0.945267 Loss2: 1.839599
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.822223 Loss1: 0.452872 Loss2: 1.369352
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.679811 Loss1: 0.304492 Loss2: 1.375319
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.587425 Loss1: 0.243211 Loss2: 1.344215
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.971087 Loss1: 1.083745 Loss2: 1.887342
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.004593 Loss1: 0.574033 Loss2: 1.430560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.759559 Loss1: 0.341000 Loss2: 1.418559 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.657511 Loss1: 0.265338 Loss2: 1.392173 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.578760 Loss1: 0.196140 Loss2: 1.382620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.535965 Loss1: 0.158642 Loss2: 1.377323 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379448 Loss1: 0.056013 Loss2: 1.323434
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488213 Loss1: 0.113003 Loss2: 1.375210
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484201 Loss1: 0.121600 Loss2: 1.362601
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.480086 Loss1: 0.118417 Loss2: 1.361669
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.480481 Loss1: 0.111566 Loss2: 1.368916
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.684053 Loss1: 0.885209 Loss2: 1.798844
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.898615 Loss1: 0.533408 Loss2: 1.365207
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.739010 Loss1: 0.333259 Loss2: 1.405751
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619307 Loss1: 0.264467 Loss2: 1.354841
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.788159 Loss1: 0.843002 Loss2: 1.945157
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549468 Loss1: 0.187521 Loss2: 1.361947
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.107503 Loss1: 0.645109 Loss2: 1.462394
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523417 Loss1: 0.176606 Loss2: 1.346811
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.889467 Loss1: 0.402559 Loss2: 1.486908
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.773738 Loss1: 0.318292 Loss2: 1.455446
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448587 Loss1: 0.107958 Loss2: 1.340629
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.682542 Loss1: 0.240554 Loss2: 1.441989
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461599 Loss1: 0.127192 Loss2: 1.334406
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.638419 Loss1: 0.204770 Loss2: 1.433649
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426629 Loss1: 0.092339 Loss2: 1.334290
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.586662 Loss1: 0.154371 Loss2: 1.432291
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403811 Loss1: 0.082273 Loss2: 1.321537
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.524086 Loss1: 0.097481 Loss2: 1.426606 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.956096 Loss1: 1.082172 Loss2: 1.873924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.797714 Loss1: 0.365734 Loss2: 1.431980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.644136 Loss1: 0.255971 Loss2: 1.388164
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.959974 Loss1: 1.148501 Loss2: 1.811474
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.934579 Loss1: 0.535615 Loss2: 1.398965 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.761715 Loss1: 0.420737 Loss2: 1.340978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.598507 Loss1: 0.255366 Loss2: 1.343141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.509019 Loss1: 0.188482 Loss2: 1.320536 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437399 Loss1: 0.115457 Loss2: 1.321942 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419963 Loss1: 0.065197 Loss2: 1.354767
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402496 Loss1: 0.094046 Loss2: 1.308450
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378669 Loss1: 0.082368 Loss2: 1.296301
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396596 Loss1: 0.100349 Loss2: 1.296247
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408180 Loss1: 0.111446 Loss2: 1.296734
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.851114 Loss1: 0.970010 Loss2: 1.881104
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.056680 Loss1: 0.596260 Loss2: 1.460420
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.816919 Loss1: 0.389324 Loss2: 1.427595
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.090329 Loss1: 1.087394 Loss2: 2.002935
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.688619 Loss1: 0.268843 Loss2: 1.419776
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.984541 Loss1: 0.576380 Loss2: 1.408161
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.652265 Loss1: 0.246697 Loss2: 1.405567
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567354 Loss1: 0.158722 Loss2: 1.408632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.556521 Loss1: 0.166625 Loss2: 1.389896 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.540879 Loss1: 0.155908 Loss2: 1.384971 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461539 Loss1: 0.093733 Loss2: 1.367806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495299 Loss1: 0.129345 Loss2: 1.365954 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428800 Loss1: 0.075756 Loss2: 1.353045 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.843295 Loss1: 1.014745 Loss2: 1.828550
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.012922 Loss1: 0.619422 Loss2: 1.393500
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.788952 Loss1: 0.360236 Loss2: 1.428716
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.660937 Loss1: 0.295551 Loss2: 1.365386
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.987249 Loss1: 1.063177 Loss2: 1.924071
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.622188 Loss1: 0.232454 Loss2: 1.389734
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.072092 Loss1: 0.631693 Loss2: 1.440399
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524162 Loss1: 0.154064 Loss2: 1.370097
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.880034 Loss1: 0.419583 Loss2: 1.460451
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456623 Loss1: 0.107118 Loss2: 1.349505
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.759078 Loss1: 0.333221 Loss2: 1.425857
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472352 Loss1: 0.123651 Loss2: 1.348701
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.616136 Loss1: 0.195377 Loss2: 1.420759
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458726 Loss1: 0.113618 Loss2: 1.345109
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526351 Loss1: 0.125753 Loss2: 1.400598
+DEBUG flwr 2023-10-11 02:00:27,690 | server.py:236 | fit_round 97 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449128 Loss1: 0.109452 Loss2: 1.339677
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503298 Loss1: 0.110562 Loss2: 1.392735
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474443 Loss1: 0.086495 Loss2: 1.387948
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465451 Loss1: 0.084208 Loss2: 1.381243
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.473863 Loss1: 0.095377 Loss2: 1.378485
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.917522 Loss1: 1.011192 Loss2: 1.906330
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.207788 Loss1: 0.757917 Loss2: 1.449871
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.865987 Loss1: 0.388172 Loss2: 1.477814
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.715751 Loss1: 0.289849 Loss2: 1.425901
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.994353 Loss1: 1.052377 Loss2: 1.941976
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.981018 Loss1: 0.539179 Loss2: 1.441839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.748174 Loss1: 0.311596 Loss2: 1.436578 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.668587 Loss1: 0.258978 Loss2: 1.409609 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.597165 Loss1: 0.200616 Loss2: 1.396549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.541901 Loss1: 0.148250 Loss2: 1.393651 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.522340 Loss1: 0.134958 Loss2: 1.387382
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534362 Loss1: 0.151734 Loss2: 1.382628
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.502592 Loss1: 0.127343 Loss2: 1.375248
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.503378 Loss1: 0.128726 Loss2: 1.374652
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477984 Loss1: 0.098999 Loss2: 1.378985
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.889258 Loss1: 0.968776 Loss2: 1.920482
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.056073 Loss1: 0.607966 Loss2: 1.448106
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834591 Loss1: 0.359742 Loss2: 1.474849
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.752873 Loss1: 0.305279 Loss2: 1.447594
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.808914 Loss1: 0.952057 Loss2: 1.856857
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.988971 Loss1: 0.561490 Loss2: 1.427480 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.796318 Loss1: 0.369042 Loss2: 1.427276 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.711322 Loss1: 0.314790 Loss2: 1.396531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.618606 Loss1: 0.221093 Loss2: 1.397513 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.571224 Loss1: 0.181563 Loss2: 1.389661 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.508132 Loss1: 0.119755 Loss2: 1.388378 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442740 Loss1: 0.078473 Loss2: 1.364267 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.905566 Loss1: 0.526674 Loss2: 1.378891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.650177 Loss1: 0.290692 Loss2: 1.359485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.514059 Loss1: 0.160924 Loss2: 1.353135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468225 Loss1: 0.125066 Loss2: 1.343159 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458880 Loss1: 0.117373 Loss2: 1.341507 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 02:00:27,690][flwr][DEBUG] - fit_round 97 received 50 results and 0 failures
+INFO flwr 2023-10-11 02:01:11,210 | server.py:125 | fit progress: (97, 2.2052834205353222, {'accuracy': 0.5673}, 223778.98901012202)
+>> Test accuracy: 0.567300
+[2023-10-11 02:01:11,210][flwr][INFO] - fit progress: (97, 2.2052834205353222, {'accuracy': 0.5673}, 223778.98901012202)
+DEBUG flwr 2023-10-11 02:01:11,211 | server.py:173 | evaluate_round 97: strategy sampled 50 clients (out of 50)
+[2023-10-11 02:01:11,211][flwr][DEBUG] - evaluate_round 97: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 02:10:21,087 | server.py:187 | evaluate_round 97 received 50 results and 0 failures
+[2023-10-11 02:10:21,087][flwr][DEBUG] - evaluate_round 97 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 02:10:21,088 | server.py:222 | fit_round 98: strategy sampled 50 clients (out of 50)
+[2023-10-11 02:10:21,088][flwr][DEBUG] - fit_round 98: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.811126 Loss1: 0.915043 Loss2: 1.896082
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.006571 Loss1: 0.558173 Loss2: 1.448398
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.850102 Loss1: 0.366221 Loss2: 1.483881
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.668043 Loss1: 0.236848 Loss2: 1.431195
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.953009 Loss1: 1.068865 Loss2: 1.884144
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.636035 Loss1: 0.211694 Loss2: 1.424341
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.980677 Loss1: 0.587586 Loss2: 1.393091
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.552202 Loss1: 0.125684 Loss2: 1.426518
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.714900 Loss1: 0.295003 Loss2: 1.419897
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525498 Loss1: 0.115633 Loss2: 1.409865
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.621039 Loss1: 0.243275 Loss2: 1.377765
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.482159 Loss1: 0.075520 Loss2: 1.406639
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.628710 Loss1: 0.240087 Loss2: 1.388623
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.487840 Loss1: 0.090946 Loss2: 1.396894
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546553 Loss1: 0.174966 Loss2: 1.371587
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485478 Loss1: 0.092370 Loss2: 1.393108
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.505673 Loss1: 0.137138 Loss2: 1.368534
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487213 Loss1: 0.124688 Loss2: 1.362525
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.512129 Loss1: 0.147979 Loss2: 1.364150
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440257 Loss1: 0.077506 Loss2: 1.362751
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.981365 Loss1: 1.090837 Loss2: 1.890528
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.219940 Loss1: 0.721192 Loss2: 1.498748
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.829186 Loss1: 0.396751 Loss2: 1.432435
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.765808 Loss1: 0.332237 Loss2: 1.433571
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.743724 Loss1: 0.892525 Loss2: 1.851199
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.171798 Loss1: 0.708723 Loss2: 1.463075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.840934 Loss1: 0.406306 Loss2: 1.434628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.659074 Loss1: 0.231320 Loss2: 1.427753 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.579243 Loss1: 0.171063 Loss2: 1.408180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542956 Loss1: 0.143820 Loss2: 1.399136 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.570477 Loss1: 0.174295 Loss2: 1.396182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.556853 Loss1: 0.148595 Loss2: 1.408258 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971680
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.742889 Loss1: 0.899860 Loss2: 1.843029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.686480 Loss1: 0.295011 Loss2: 1.391470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594404 Loss1: 0.234577 Loss2: 1.359827
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.143268 Loss1: 1.175781 Loss2: 1.967487
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.099151 Loss1: 0.681250 Loss2: 1.417900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511010 Loss1: 0.152321 Loss2: 1.358689
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.798120 Loss1: 0.348234 Loss2: 1.449885
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.643795 Loss1: 0.258547 Loss2: 1.385248
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523161 Loss1: 0.171633 Loss2: 1.351527
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.478866 Loss1: 0.125601 Loss2: 1.353265 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451142 Loss1: 0.117084 Loss2: 1.334058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441145 Loss1: 0.101146 Loss2: 1.339999 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973633
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440034 Loss1: 0.085714 Loss2: 1.354320 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.043390 Loss1: 1.178085 Loss2: 1.865305
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.088396 Loss1: 0.683960 Loss2: 1.404436
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.895669 Loss1: 0.433423 Loss2: 1.462246
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.728685 Loss1: 0.338873 Loss2: 1.389812
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.820915 Loss1: 0.976990 Loss2: 1.843925
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.663884 Loss1: 0.278119 Loss2: 1.385765
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.014382 Loss1: 0.600204 Loss2: 1.414178
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.577469 Loss1: 0.194950 Loss2: 1.382520
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.806617 Loss1: 0.363998 Loss2: 1.442619
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490642 Loss1: 0.127316 Loss2: 1.363326
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.658537 Loss1: 0.279610 Loss2: 1.378927
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499180 Loss1: 0.142137 Loss2: 1.357044
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.572921 Loss1: 0.187170 Loss2: 1.385751
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.511645 Loss1: 0.149424 Loss2: 1.362221
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556963 Loss1: 0.189353 Loss2: 1.367610
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460847 Loss1: 0.101060 Loss2: 1.359787
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.582455 Loss1: 0.206751 Loss2: 1.375704
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548376 Loss1: 0.170739 Loss2: 1.377637
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.506679 Loss1: 0.140763 Loss2: 1.365916
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.475964 Loss1: 0.109013 Loss2: 1.366952
+(DefaultActor pid=3764) >> Training accuracy: 0.960417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.756134 Loss1: 0.900291 Loss2: 1.855843
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.000528 Loss1: 0.603304 Loss2: 1.397225
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.862674 Loss1: 0.408504 Loss2: 1.454170
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.669344 Loss1: 0.287357 Loss2: 1.381987
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.899412 Loss1: 0.972654 Loss2: 1.926758
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.162400 Loss1: 0.671771 Loss2: 1.490629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.943102 Loss1: 0.483152 Loss2: 1.459949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.720172 Loss1: 0.266851 Loss2: 1.453321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.600977 Loss1: 0.165927 Loss2: 1.435050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.570022 Loss1: 0.144524 Loss2: 1.425498 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442669 Loss1: 0.088093 Loss2: 1.354575
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.556296 Loss1: 0.138936 Loss2: 1.417360
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.532263 Loss1: 0.117358 Loss2: 1.414904
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.510884 Loss1: 0.106906 Loss2: 1.403977
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.542198 Loss1: 0.136038 Loss2: 1.406160
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.726804 Loss1: 0.946129 Loss2: 1.780676
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.870130 Loss1: 0.514062 Loss2: 1.356069
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.753848 Loss1: 0.380249 Loss2: 1.373598
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.747886 Loss1: 0.932119 Loss2: 1.815766
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.676879 Loss1: 0.325859 Loss2: 1.351020
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.971993 Loss1: 0.610251 Loss2: 1.361742
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577726 Loss1: 0.228081 Loss2: 1.349646
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.673253 Loss1: 0.293289 Loss2: 1.379964
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.552485 Loss1: 0.210288 Loss2: 1.342197
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579436 Loss1: 0.250139 Loss2: 1.329297
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488399 Loss1: 0.152024 Loss2: 1.336375
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432010 Loss1: 0.104104 Loss2: 1.327906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417625 Loss1: 0.094155 Loss2: 1.323470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416818 Loss1: 0.092382 Loss2: 1.324436 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973633
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.474743 Loss1: 0.147854 Loss2: 1.326889 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.826435 Loss1: 0.938067 Loss2: 1.888368 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.749760 Loss1: 0.315896 Loss2: 1.433864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.632240 Loss1: 0.246550 Loss2: 1.385690
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.787085 Loss1: 0.952324 Loss2: 1.834761
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.924369 Loss1: 0.539083 Loss2: 1.385286 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.774088 Loss1: 0.369678 Loss2: 1.404410 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640757 Loss1: 0.277537 Loss2: 1.363220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521811 Loss1: 0.154113 Loss2: 1.367698 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495363 Loss1: 0.139692 Loss2: 1.355671 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427022 Loss1: 0.063016 Loss2: 1.364006
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445820 Loss1: 0.089708 Loss2: 1.356112
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.478544 Loss1: 0.138882 Loss2: 1.339662
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464157 Loss1: 0.113022 Loss2: 1.351136
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.459661 Loss1: 0.116425 Loss2: 1.343235
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.766877 Loss1: 0.904062 Loss2: 1.862815
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.954504 Loss1: 0.575721 Loss2: 1.378784
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.761624 Loss1: 0.341167 Loss2: 1.420457
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638447 Loss1: 0.275040 Loss2: 1.363407
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.036254 Loss1: 1.153904 Loss2: 1.882349
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571981 Loss1: 0.203229 Loss2: 1.368752
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.062714 Loss1: 0.682335 Loss2: 1.380380
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498366 Loss1: 0.144732 Loss2: 1.353633
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.900256 Loss1: 0.500205 Loss2: 1.400051
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.679333 Loss1: 0.325415 Loss2: 1.353918
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476955 Loss1: 0.129076 Loss2: 1.347879
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.599600 Loss1: 0.237145 Loss2: 1.362455
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440447 Loss1: 0.089934 Loss2: 1.350514
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.541842 Loss1: 0.195997 Loss2: 1.345845
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417897 Loss1: 0.077639 Loss2: 1.340258
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390893 Loss1: 0.056888 Loss2: 1.334005 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436319 Loss1: 0.109023 Loss2: 1.327296 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.939732
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.788058 Loss1: 0.970608 Loss2: 1.817450 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.745973 Loss1: 0.361724 Loss2: 1.384248 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638587 Loss1: 0.280834 Loss2: 1.357753
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.825882 Loss1: 0.973367 Loss2: 1.852514
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.988576 Loss1: 0.604601 Loss2: 1.383976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769477 Loss1: 0.337761 Loss2: 1.431716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631018 Loss1: 0.259159 Loss2: 1.371858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.579927 Loss1: 0.206888 Loss2: 1.373039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523213 Loss1: 0.156696 Loss2: 1.366518 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423325 Loss1: 0.101614 Loss2: 1.321711
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470143 Loss1: 0.108141 Loss2: 1.362001
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467275 Loss1: 0.110613 Loss2: 1.356662
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466731 Loss1: 0.115927 Loss2: 1.350804
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466217 Loss1: 0.114495 Loss2: 1.351722
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.896325 Loss1: 1.027084 Loss2: 1.869241
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.954178 Loss1: 0.577174 Loss2: 1.377004
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.751849 Loss1: 0.366636 Loss2: 1.385212
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586681 Loss1: 0.214668 Loss2: 1.372013
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.782936 Loss1: 0.918689 Loss2: 1.864247
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.025963 Loss1: 0.630849 Loss2: 1.395114 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.866931 Loss1: 0.405603 Loss2: 1.461328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.707795 Loss1: 0.324913 Loss2: 1.382882 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.641658 Loss1: 0.234341 Loss2: 1.407316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.563842 Loss1: 0.181786 Loss2: 1.382057 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405109 Loss1: 0.073927 Loss2: 1.331181
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.540486 Loss1: 0.155927 Loss2: 1.384559
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487167 Loss1: 0.114014 Loss2: 1.373153
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493628 Loss1: 0.129003 Loss2: 1.364625
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450975 Loss1: 0.085719 Loss2: 1.365256
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.790624 Loss1: 0.883687 Loss2: 1.906937
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.909199 Loss1: 0.503499 Loss2: 1.405700
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.758213 Loss1: 0.315069 Loss2: 1.443144
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.626250 Loss1: 0.229393 Loss2: 1.396857
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.861106 Loss1: 0.975656 Loss2: 1.885450
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.919932 Loss1: 0.513875 Loss2: 1.406057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727141 Loss1: 0.292745 Loss2: 1.434397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.622291 Loss1: 0.233148 Loss2: 1.389144 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.607998 Loss1: 0.221610 Loss2: 1.386388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.580677 Loss1: 0.187004 Loss2: 1.393672 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.579068 Loss1: 0.193682 Loss2: 1.385386 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.470950 Loss1: 0.101707 Loss2: 1.369242 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.616628 Loss1: 0.813796 Loss2: 1.802833 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.727071 Loss1: 0.341500 Loss2: 1.385571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.632434 Loss1: 0.313019 Loss2: 1.319415
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.891657 Loss1: 1.053078 Loss2: 1.838578
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.026353 Loss1: 0.608423 Loss2: 1.417930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.756093 Loss1: 0.382858 Loss2: 1.373235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.683326 Loss1: 0.320132 Loss2: 1.363194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.654202 Loss1: 0.277406 Loss2: 1.376797 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.563515 Loss1: 0.200548 Loss2: 1.362967 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.362045 Loss1: 0.073950 Loss2: 1.288096
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.527352 Loss1: 0.169441 Loss2: 1.357911
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491978 Loss1: 0.126602 Loss2: 1.365376
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.495019 Loss1: 0.147320 Loss2: 1.347699
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464039 Loss1: 0.110412 Loss2: 1.353626
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.822679 Loss1: 0.966488 Loss2: 1.856191
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.983105 Loss1: 0.609048 Loss2: 1.374058
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.800523 Loss1: 0.377345 Loss2: 1.423178
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.683729 Loss1: 0.309705 Loss2: 1.374025
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.954235 Loss1: 1.080610 Loss2: 1.873624
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.959000 Loss1: 0.574496 Loss2: 1.384504 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.737832 Loss1: 0.338720 Loss2: 1.399112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.625882 Loss1: 0.266064 Loss2: 1.359818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.573890 Loss1: 0.211495 Loss2: 1.362395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527100 Loss1: 0.170812 Loss2: 1.356288 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.462245 Loss1: 0.110791 Loss2: 1.351454 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426022 Loss1: 0.095258 Loss2: 1.330764 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.127923 Loss1: 1.126823 Loss2: 2.001100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.954369 Loss1: 0.470969 Loss2: 1.483400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.645793 Loss1: 0.232332 Loss2: 1.413461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.565184 Loss1: 0.179825 Loss2: 1.385359 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.503825 Loss1: 0.117004 Loss2: 1.386822 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.754256 Loss1: 0.350179 Loss2: 1.404077 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.574809 Loss1: 0.181390 Loss2: 1.393418 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500086 Loss1: 0.126529 Loss2: 1.373558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.861071 Loss1: 1.004314 Loss2: 1.856756
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449992 Loss1: 0.077142 Loss2: 1.372850
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.921867 Loss1: 0.535103 Loss2: 1.386764
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446108 Loss1: 0.086621 Loss2: 1.359487
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606861 Loss1: 0.229343 Loss2: 1.377518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.590484 Loss1: 0.218091 Loss2: 1.372393 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517571 Loss1: 0.154866 Loss2: 1.362705
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.806962 Loss1: 1.033505 Loss2: 1.773457
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.929555 Loss1: 0.572923 Loss2: 1.356632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.733378 Loss1: 0.371358 Loss2: 1.362019 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454300 Loss1: 0.096531 Loss2: 1.357768
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.671089 Loss1: 0.338805 Loss2: 1.332283
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554271 Loss1: 0.219647 Loss2: 1.334625
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511900 Loss1: 0.197810 Loss2: 1.314090
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452923 Loss1: 0.140430 Loss2: 1.312493
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411309 Loss1: 0.104700 Loss2: 1.306608
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.801718 Loss1: 0.937656 Loss2: 1.864061
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397906 Loss1: 0.099889 Loss2: 1.298017
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.880174 Loss1: 0.510714 Loss2: 1.369460
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401777 Loss1: 0.106117 Loss2: 1.295659
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.684978 Loss1: 0.309021 Loss2: 1.375957 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.553478 Loss1: 0.172773 Loss2: 1.380705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.482664 Loss1: 0.131536 Loss2: 1.351128
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.957862 Loss1: 1.094590 Loss2: 1.863272
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.105464 Loss1: 0.675739 Loss2: 1.429725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.800545 Loss1: 0.370497 Loss2: 1.430049 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400870 Loss1: 0.059623 Loss2: 1.341248
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640207 Loss1: 0.241652 Loss2: 1.398555
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.601387 Loss1: 0.204668 Loss2: 1.396718
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.543046 Loss1: 0.166565 Loss2: 1.376482
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.507043 Loss1: 0.130167 Loss2: 1.376876
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460370 Loss1: 0.085339 Loss2: 1.375031
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.916436 Loss1: 1.066359 Loss2: 1.850077
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434663 Loss1: 0.074874 Loss2: 1.359789
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431526 Loss1: 0.079073 Loss2: 1.352453 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.670782 Loss1: 0.328683 Loss2: 1.342099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504343 Loss1: 0.160277 Loss2: 1.344066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.478143 Loss1: 0.159888 Loss2: 1.318256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401723 Loss1: 0.097627 Loss2: 1.304096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395808 Loss1: 0.091811 Loss2: 1.303997 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.753395 Loss1: 0.319893 Loss2: 1.433502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.636962 Loss1: 0.205644 Loss2: 1.431318 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.949630 Loss1: 1.048175 Loss2: 1.901455 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.130034 Loss1: 0.706341 Loss2: 1.423693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.893436 Loss1: 0.438290 Loss2: 1.455146 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.600072 Loss1: 0.201314 Loss2: 1.398757 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.546685 Loss1: 0.157391 Loss2: 1.389294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.504914 Loss1: 0.119972 Loss2: 1.384942
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.940721 Loss1: 1.035171 Loss2: 1.905550
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.149580 Loss1: 0.770638 Loss2: 1.378942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442815 Loss1: 0.068080 Loss2: 1.374734
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.893365 Loss1: 0.439238 Loss2: 1.454127
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.679593 Loss1: 0.312655 Loss2: 1.366937
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548208 Loss1: 0.178126 Loss2: 1.370082
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490183 Loss1: 0.134492 Loss2: 1.355691
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471067 Loss1: 0.121518 Loss2: 1.349549
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427205 Loss1: 0.082573 Loss2: 1.344633
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.691497 Loss1: 0.862178 Loss2: 1.829319
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.019584 Loss1: 0.583353 Loss2: 1.436231 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.794272 Loss1: 0.378016 Loss2: 1.416256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.541492 Loss1: 0.165778 Loss2: 1.375714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.666474 Loss1: 0.881227 Loss2: 1.785247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.859667 Loss1: 0.508811 Loss2: 1.350856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.704791 Loss1: 0.322568 Loss2: 1.382223 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.589568 Loss1: 0.245114 Loss2: 1.344454 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989890
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549242 Loss1: 0.200612 Loss2: 1.348629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.479470 Loss1: 0.133296 Loss2: 1.346174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433867 Loss1: 0.108546 Loss2: 1.325321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414822 Loss1: 0.093881 Loss2: 1.320940 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973633
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.774199 Loss1: 0.372277 Loss2: 1.401922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611223 Loss1: 0.234816 Loss2: 1.376406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.541576 Loss1: 0.169554 Loss2: 1.372021
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.658385 Loss1: 0.823363 Loss2: 1.835022
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.882463 Loss1: 0.505634 Loss2: 1.376828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671682 Loss1: 0.277137 Loss2: 1.394545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553267 Loss1: 0.201213 Loss2: 1.352055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449482 Loss1: 0.099849 Loss2: 1.349633
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505490 Loss1: 0.157856 Loss2: 1.347634
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.475188 Loss1: 0.135848 Loss2: 1.339340
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.462072 Loss1: 0.121655 Loss2: 1.340416
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434205 Loss1: 0.102437 Loss2: 1.331768
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415049 Loss1: 0.084208 Loss2: 1.330841
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.984946 Loss1: 1.109085 Loss2: 1.875862
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397210 Loss1: 0.069759 Loss2: 1.327451
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.763113 Loss1: 0.357257 Loss2: 1.405856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.597338 Loss1: 0.228877 Loss2: 1.368461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.662244 Loss1: 0.865879 Loss2: 1.796365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.996734 Loss1: 0.585186 Loss2: 1.411548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.806993 Loss1: 0.415735 Loss2: 1.391259 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397532 Loss1: 0.057486 Loss2: 1.340047 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994420
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.557783 Loss1: 0.189639 Loss2: 1.368144 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.507885 Loss1: 0.146166 Loss2: 1.361719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.494747 Loss1: 0.133110 Loss2: 1.361637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.451343 Loss1: 0.091879 Loss2: 1.359464 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.571070 Loss1: 0.210246 Loss2: 1.360824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506053 Loss1: 0.143899 Loss2: 1.362154 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.809934 Loss1: 0.947041 Loss2: 1.862893 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.992623 Loss1: 0.591812 Loss2: 1.400812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.873057 Loss1: 0.418803 Loss2: 1.454254 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.729775 Loss1: 0.319239 Loss2: 1.410536 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.541454 Loss1: 0.158759 Loss2: 1.382696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.505794 Loss1: 0.134526 Loss2: 1.371268
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.927377 Loss1: 0.989256 Loss2: 1.938120
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.020006 Loss1: 0.564836 Loss2: 1.455171 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.798770 Loss1: 0.389017 Loss2: 1.409753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.600422 Loss1: 0.200350 Loss2: 1.400073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.557968 Loss1: 0.156026 Loss2: 1.401942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480933 Loss1: 0.101695 Loss2: 1.379238 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477589 Loss1: 0.102432 Loss2: 1.375156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.492270 Loss1: 0.116444 Loss2: 1.375826 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.663967 Loss1: 0.224748 Loss2: 1.439219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.581545 Loss1: 0.155399 Loss2: 1.426146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540027 Loss1: 0.127835 Loss2: 1.412192
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.790859 Loss1: 0.928902 Loss2: 1.861957
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.985098 Loss1: 0.557564 Loss2: 1.427533 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-11 02:39:11,326 | server.py:236 | fit_round 98 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.720096 Loss1: 0.305741 Loss2: 1.414355 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.559709 Loss1: 0.155082 Loss2: 1.404627 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509337 Loss1: 0.113722 Loss2: 1.395615
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.796061 Loss1: 0.969095 Loss2: 1.826965
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.532212 Loss1: 0.139460 Loss2: 1.392751
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.917925 Loss1: 0.546524 Loss2: 1.371401
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.746691 Loss1: 0.346060 Loss2: 1.400632
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.511808 Loss1: 0.119039 Loss2: 1.392769
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605430 Loss1: 0.254040 Loss2: 1.351389
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467733 Loss1: 0.084310 Loss2: 1.383423
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.515278 Loss1: 0.165226 Loss2: 1.350052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450525 Loss1: 0.107555 Loss2: 1.342970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428312 Loss1: 0.096030 Loss2: 1.332282
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.029267 Loss1: 1.180666 Loss2: 1.848601
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430893 Loss1: 0.097259 Loss2: 1.333634
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.066447 Loss1: 0.617826 Loss2: 1.448621
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.818285 Loss1: 0.418756 Loss2: 1.399529
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.685361 Loss1: 0.290038 Loss2: 1.395322
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.695124 Loss1: 0.289756 Loss2: 1.405367
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.606865 Loss1: 0.211582 Loss2: 1.395284
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.627501 Loss1: 0.831532 Loss2: 1.795969
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528993 Loss1: 0.163558 Loss2: 1.365435
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471268 Loss1: 0.104394 Loss2: 1.366873
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.872966 Loss1: 0.490402 Loss2: 1.382563
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454538 Loss1: 0.096356 Loss2: 1.358182
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.752462 Loss1: 0.357027 Loss2: 1.395435
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465770 Loss1: 0.112247 Loss2: 1.353523
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631507 Loss1: 0.254517 Loss2: 1.376990
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569342 Loss1: 0.213156 Loss2: 1.356187
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.488441 Loss1: 0.135514 Loss2: 1.352927
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439442 Loss1: 0.097302 Loss2: 1.342140
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429231 Loss1: 0.092460 Loss2: 1.336771
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410901 Loss1: 0.084852 Loss2: 1.326049
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418913 Loss1: 0.095495 Loss2: 1.323418
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 02:39:11,326][flwr][DEBUG] - fit_round 98 received 50 results and 0 failures
+INFO flwr 2023-10-11 02:39:53,371 | server.py:125 | fit progress: (98, 2.1971591758651856, {'accuracy': 0.5667}, 226101.149762787)
+>> Test accuracy: 0.566700
+[2023-10-11 02:39:53,371][flwr][INFO] - fit progress: (98, 2.1971591758651856, {'accuracy': 0.5667}, 226101.149762787)
+DEBUG flwr 2023-10-11 02:39:53,372 | server.py:173 | evaluate_round 98: strategy sampled 50 clients (out of 50)
+[2023-10-11 02:39:53,372][flwr][DEBUG] - evaluate_round 98: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 02:49:01,799 | server.py:187 | evaluate_round 98 received 50 results and 0 failures
+[2023-10-11 02:49:01,799][flwr][DEBUG] - evaluate_round 98 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 02:49:01,799 | server.py:222 | fit_round 99: strategy sampled 50 clients (out of 50)
+[2023-10-11 02:49:01,799][flwr][DEBUG] - fit_round 99: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.920294 Loss1: 1.009401 Loss2: 1.910893
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.068249 Loss1: 0.610990 Loss2: 1.457258
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.956522 Loss1: 0.455532 Loss2: 1.500990
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.752265 Loss1: 0.306590 Loss2: 1.445675
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.862420 Loss1: 0.981099 Loss2: 1.881321
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.642954 Loss1: 0.197482 Loss2: 1.445472
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.020170 Loss1: 0.595395 Loss2: 1.424775
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.612932 Loss1: 0.180536 Loss2: 1.432397
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.795809 Loss1: 0.349631 Loss2: 1.446178
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.584284 Loss1: 0.152135 Loss2: 1.432150
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.661857 Loss1: 0.262198 Loss2: 1.399659
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.592246 Loss1: 0.166417 Loss2: 1.425829
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.578316 Loss1: 0.182539 Loss2: 1.395777
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.555286 Loss1: 0.132400 Loss2: 1.422886
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.583955 Loss1: 0.195747 Loss2: 1.388208
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.589515 Loss1: 0.166387 Loss2: 1.423128
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.551447 Loss1: 0.157261 Loss2: 1.394186
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515585 Loss1: 0.136339 Loss2: 1.379246
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.511642 Loss1: 0.126090 Loss2: 1.385553
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464780 Loss1: 0.086760 Loss2: 1.378020
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.991654 Loss1: 1.022762 Loss2: 1.968892
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.991371 Loss1: 0.624802 Loss2: 1.366569
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.792068 Loss1: 0.388685 Loss2: 1.403383
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.657397 Loss1: 0.265743 Loss2: 1.391654
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.597961 Loss1: 0.225380 Loss2: 1.372580
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.565263 Loss1: 0.188490 Loss2: 1.376774 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.538351 Loss1: 0.168174 Loss2: 1.370178 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.490866 Loss1: 0.132799 Loss2: 1.358068
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727142 Loss1: 0.330229 Loss2: 1.396913
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636885 Loss1: 0.255759 Loss2: 1.381126 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.606852 Loss1: 0.220713 Loss2: 1.386139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486972 Loss1: 0.120934 Loss2: 1.366037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443954 Loss1: 0.096021 Loss2: 1.347933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454356 Loss1: 0.110507 Loss2: 1.343850 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.690561 Loss1: 0.239273 Loss2: 1.451289 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.579642 Loss1: 0.136227 Loss2: 1.443415 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534506 Loss1: 0.097549 Loss2: 1.436957
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.842263 Loss1: 0.928661 Loss2: 1.913602
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.549104 Loss1: 0.116807 Loss2: 1.432297
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.037449 Loss1: 0.566219 Loss2: 1.471230
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.511269 Loss1: 0.081497 Loss2: 1.429772
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.925240 Loss1: 0.436576 Loss2: 1.488664
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491360 Loss1: 0.068242 Loss2: 1.423118
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.850548 Loss1: 0.377707 Loss2: 1.472842
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.755384 Loss1: 0.288648 Loss2: 1.466736
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.705698 Loss1: 0.234883 Loss2: 1.470815
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.661826 Loss1: 0.209916 Loss2: 1.451910
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.611830 Loss1: 0.149672 Loss2: 1.462158
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.631874 Loss1: 0.780715 Loss2: 1.851159
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.922127 Loss1: 0.540055 Loss2: 1.382072 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.744968 Loss1: 0.309427 Loss2: 1.435542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593423 Loss1: 0.215010 Loss2: 1.378414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528725 Loss1: 0.161601 Loss2: 1.367124 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.517628 Loss1: 0.152806 Loss2: 1.364822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.502660 Loss1: 0.139879 Loss2: 1.362780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.548838 Loss1: 0.178201 Loss2: 1.370637 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527078 Loss1: 0.143408 Loss2: 1.383670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490540 Loss1: 0.112207 Loss2: 1.378333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486974 Loss1: 0.116049 Loss2: 1.370924
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.746856 Loss1: 0.915707 Loss2: 1.831149
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.008088 Loss1: 0.626693 Loss2: 1.381395 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470751 Loss1: 0.105409 Loss2: 1.365341
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.903924 Loss1: 0.470113 Loss2: 1.433811
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.714912 Loss1: 0.337114 Loss2: 1.377799
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.634306 Loss1: 0.259469 Loss2: 1.374836
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516253 Loss1: 0.159943 Loss2: 1.356310
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503547 Loss1: 0.151939 Loss2: 1.351607
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.056085 Loss1: 1.151646 Loss2: 1.904439
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.466953 Loss1: 0.119991 Loss2: 1.346961
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.471628 Loss1: 0.131845 Loss2: 1.339784 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474581 Loss1: 0.129085 Loss2: 1.345497 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574478 Loss1: 0.190782 Loss2: 1.383697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.496773 Loss1: 0.147857 Loss2: 1.348917 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.892997 Loss1: 1.042964 Loss2: 1.850033 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.062708 Loss1: 0.659299 Loss2: 1.403409 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982143
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.641509 Loss1: 0.253538 Loss2: 1.387971 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.605533 Loss1: 0.215322 Loss2: 1.390212 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.555177 Loss1: 0.169762 Loss2: 1.385415
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.977999 Loss1: 1.025333 Loss2: 1.952666
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.073390 Loss1: 0.651736 Loss2: 1.421654
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.501636 Loss1: 0.129755 Loss2: 1.371880
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.861904 Loss1: 0.370579 Loss2: 1.491325
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.491977 Loss1: 0.119489 Loss2: 1.372488
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.494322 Loss1: 0.123326 Loss2: 1.370996 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.628559 Loss1: 0.188463 Loss2: 1.440096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.539486 Loss1: 0.131577 Loss2: 1.407909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.467463 Loss1: 0.072116 Loss2: 1.395347 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.952844 Loss1: 0.484402 Loss2: 1.468442 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.684121 Loss1: 0.249315 Loss2: 1.434806 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.929756 Loss1: 1.101253 Loss2: 1.828503
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.667394 Loss1: 0.246747 Loss2: 1.420646
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.937936 Loss1: 0.548008 Loss2: 1.389928
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.614263 Loss1: 0.183052 Loss2: 1.431211
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.774240 Loss1: 0.390678 Loss2: 1.383563
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.560341 Loss1: 0.149205 Loss2: 1.411136
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.621586 Loss1: 0.263710 Loss2: 1.357876
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.524882 Loss1: 0.117727 Loss2: 1.407155
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.601715 Loss1: 0.247839 Loss2: 1.353876
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.511239 Loss1: 0.113516 Loss2: 1.397722
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.538573 Loss1: 0.185388 Loss2: 1.353184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446897 Loss1: 0.107791 Loss2: 1.339105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423017 Loss1: 0.085990 Loss2: 1.337026
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.819767 Loss1: 0.949694 Loss2: 1.870073
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.034495 Loss1: 0.622934 Loss2: 1.411561
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.874163 Loss1: 0.450730 Loss2: 1.423432
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.726195 Loss1: 0.329674 Loss2: 1.396520
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.654608 Loss1: 0.252133 Loss2: 1.402475
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.969649 Loss1: 1.052888 Loss2: 1.916761
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.622055 Loss1: 0.231548 Loss2: 1.390507
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.611029 Loss1: 0.217164 Loss2: 1.393866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.520523 Loss1: 0.129665 Loss2: 1.390858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.457931 Loss1: 0.085581 Loss2: 1.372350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472876 Loss1: 0.113899 Loss2: 1.358977 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516010 Loss1: 0.149390 Loss2: 1.366620 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404445 Loss1: 0.053507 Loss2: 1.350938 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.772355 Loss1: 0.978562 Loss2: 1.793793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716061 Loss1: 0.338576 Loss2: 1.377485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562189 Loss1: 0.216291 Loss2: 1.345897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.923957 Loss1: 0.500387 Loss2: 1.423570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.871852 Loss1: 0.423954 Loss2: 1.447898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.643721 Loss1: 0.218643 Loss2: 1.425078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.593245 Loss1: 0.188364 Loss2: 1.404881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.561297 Loss1: 0.149260 Loss2: 1.412037 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.517696 Loss1: 0.122229 Loss2: 1.395468 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.482848 Loss1: 0.095738 Loss2: 1.387110 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.831810 Loss1: 0.979266 Loss2: 1.852544 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.788129 Loss1: 0.380246 Loss2: 1.407883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.730204 Loss1: 0.885394 Loss2: 1.844810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.030654 Loss1: 0.641383 Loss2: 1.389271 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.787843 Loss1: 0.344142 Loss2: 1.443701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.690319 Loss1: 0.310140 Loss2: 1.380179 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.593422 Loss1: 0.201876 Loss2: 1.391546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.547965 Loss1: 0.173847 Loss2: 1.374118 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.580304 Loss1: 0.191183 Loss2: 1.389121 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.480878 Loss1: 0.120303 Loss2: 1.360576 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.876273 Loss1: 0.919788 Loss2: 1.956485
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.128045 Loss1: 0.657004 Loss2: 1.471041
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.863827 Loss1: 0.343718 Loss2: 1.520110
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.704849 Loss1: 0.261603 Loss2: 1.443245
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.862983 Loss1: 0.938930 Loss2: 1.924053
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.002378 Loss1: 0.542598 Loss2: 1.459780 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.798348 Loss1: 0.327496 Loss2: 1.470852 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.710328 Loss1: 0.273031 Loss2: 1.437297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609930 Loss1: 0.171497 Loss2: 1.438434 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.585371 Loss1: 0.168326 Loss2: 1.417045 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530434 Loss1: 0.115189 Loss2: 1.415244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501208 Loss1: 0.098123 Loss2: 1.403085 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.750848 Loss1: 0.885264 Loss2: 1.865583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.779768 Loss1: 0.350975 Loss2: 1.428793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.695459 Loss1: 0.314091 Loss2: 1.381368
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.838823 Loss1: 1.000268 Loss2: 1.838555
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.908938 Loss1: 0.524762 Loss2: 1.384176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.743414 Loss1: 0.332078 Loss2: 1.411337 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.601819 Loss1: 0.227364 Loss2: 1.374455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576249 Loss1: 0.211419 Loss2: 1.364830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.557411 Loss1: 0.186679 Loss2: 1.370731 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.479144 Loss1: 0.126071 Loss2: 1.353073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439965 Loss1: 0.090242 Loss2: 1.349722 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.955693 Loss1: 1.058318 Loss2: 1.897374 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.737042 Loss1: 0.315884 Loss2: 1.421158 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.993434 Loss1: 1.100304 Loss2: 1.893129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.170028 Loss1: 0.718566 Loss2: 1.451462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.832732 Loss1: 0.387696 Loss2: 1.445037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.776411 Loss1: 0.365768 Loss2: 1.410642 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.701789 Loss1: 0.287488 Loss2: 1.414301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.595699 Loss1: 0.194411 Loss2: 1.401288 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.531725 Loss1: 0.143548 Loss2: 1.388177 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.469087 Loss1: 0.080309 Loss2: 1.388777 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.795993 Loss1: 0.951458 Loss2: 1.844535
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.018617 Loss1: 0.575770 Loss2: 1.442848
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.832782 Loss1: 0.416601 Loss2: 1.416181
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.723601 Loss1: 0.312901 Loss2: 1.410701
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.782383 Loss1: 0.886263 Loss2: 1.896120
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.991012 Loss1: 0.563293 Loss2: 1.427719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.842155 Loss1: 0.382622 Loss2: 1.459533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.527805 Loss1: 0.134255 Loss2: 1.393550
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.760618 Loss1: 0.329850 Loss2: 1.430768
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.622835 Loss1: 0.242696 Loss2: 1.380139
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.636013 Loss1: 0.225298 Loss2: 1.410715
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.506703 Loss1: 0.109396 Loss2: 1.397307
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.562802 Loss1: 0.154684 Loss2: 1.408117
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.532239 Loss1: 0.136507 Loss2: 1.395732
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.488560 Loss1: 0.118046 Loss2: 1.370514
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.531014 Loss1: 0.142092 Loss2: 1.388922 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.872509 Loss1: 1.048570 Loss2: 1.823939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.683881 Loss1: 0.309579 Loss2: 1.374302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596811 Loss1: 0.239753 Loss2: 1.357058
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.870359 Loss1: 0.994712 Loss2: 1.875648
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546646 Loss1: 0.197133 Loss2: 1.349513
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.942661 Loss1: 0.541793 Loss2: 1.400867
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519460 Loss1: 0.173964 Loss2: 1.345495
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.785827 Loss1: 0.362992 Loss2: 1.422835
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.471744 Loss1: 0.135207 Loss2: 1.336537
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.688835 Loss1: 0.314142 Loss2: 1.374693
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411488 Loss1: 0.084341 Loss2: 1.327147
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.648968 Loss1: 0.259334 Loss2: 1.389634
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421968 Loss1: 0.101547 Loss2: 1.320421
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.564961 Loss1: 0.195367 Loss2: 1.369595
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398511 Loss1: 0.080660 Loss2: 1.317851
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469938 Loss1: 0.114984 Loss2: 1.354955
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485876 Loss1: 0.135812 Loss2: 1.350064
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486032 Loss1: 0.132674 Loss2: 1.353358
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456333 Loss1: 0.110172 Loss2: 1.346161
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.775231 Loss1: 0.885230 Loss2: 1.890002
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.960477 Loss1: 0.557941 Loss2: 1.402535
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.808023 Loss1: 0.361503 Loss2: 1.446520
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649602 Loss1: 0.255338 Loss2: 1.394264
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.014919 Loss1: 1.040864 Loss2: 1.974055
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.123416 Loss1: 0.627747 Loss2: 1.495669 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.919017 Loss1: 0.417976 Loss2: 1.501040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.763724 Loss1: 0.280804 Loss2: 1.482920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.705174 Loss1: 0.241669 Loss2: 1.463505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.662687 Loss1: 0.208426 Loss2: 1.454261 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453568 Loss1: 0.080947 Loss2: 1.372622
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.614894 Loss1: 0.166432 Loss2: 1.448462
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.552760 Loss1: 0.114736 Loss2: 1.438024
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.500826 Loss1: 0.069829 Loss2: 1.430997
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491718 Loss1: 0.073749 Loss2: 1.417970
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.765486 Loss1: 0.911729 Loss2: 1.853757
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.866819 Loss1: 0.493616 Loss2: 1.373204
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.707867 Loss1: 0.309021 Loss2: 1.398847
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.625394 Loss1: 0.266867 Loss2: 1.358526
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.905715 Loss1: 0.983132 Loss2: 1.922584
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.552640 Loss1: 0.186429 Loss2: 1.366211
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.103592 Loss1: 0.667773 Loss2: 1.435819
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476855 Loss1: 0.125809 Loss2: 1.351046
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.871234 Loss1: 0.385687 Loss2: 1.485547
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440260 Loss1: 0.099764 Loss2: 1.340496
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.797149 Loss1: 0.349993 Loss2: 1.447157
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.442585 Loss1: 0.103046 Loss2: 1.339539
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.658246 Loss1: 0.212529 Loss2: 1.445717
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.420430 Loss1: 0.089455 Loss2: 1.330974
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.571999 Loss1: 0.142898 Loss2: 1.429101
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440480 Loss1: 0.110618 Loss2: 1.329862
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.582217 Loss1: 0.162086 Loss2: 1.420132
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.533523 Loss1: 0.111116 Loss2: 1.422407
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.518071 Loss1: 0.101873 Loss2: 1.416198
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482552 Loss1: 0.070964 Loss2: 1.411588
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.789828 Loss1: 0.968981 Loss2: 1.820848
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.051203 Loss1: 0.663708 Loss2: 1.387495
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.823077 Loss1: 0.411435 Loss2: 1.411641
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642089 Loss1: 0.286246 Loss2: 1.355843
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.775320 Loss1: 0.946248 Loss2: 1.829072
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.991607 Loss1: 0.602301 Loss2: 1.389306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.767564 Loss1: 0.374733 Loss2: 1.392831 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.639282 Loss1: 0.263777 Loss2: 1.375505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.617032 Loss1: 0.250534 Loss2: 1.366498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.532583 Loss1: 0.167755 Loss2: 1.364828 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.504681 Loss1: 0.154845 Loss2: 1.349836 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463624 Loss1: 0.112048 Loss2: 1.351577 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.934520 Loss1: 0.527243 Loss2: 1.407278 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.648445 Loss1: 0.267516 Loss2: 1.380929 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.592178 Loss1: 0.195736 Loss2: 1.396442
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.910073 Loss1: 0.957553 Loss2: 1.952521
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514608 Loss1: 0.141816 Loss2: 1.372792
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.078217 Loss1: 0.592480 Loss2: 1.485737
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.499755 Loss1: 0.138021 Loss2: 1.361733
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.888198 Loss1: 0.376431 Loss2: 1.511768
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459042 Loss1: 0.104397 Loss2: 1.354645
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.804120 Loss1: 0.333744 Loss2: 1.470376
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435203 Loss1: 0.084373 Loss2: 1.350830
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.719226 Loss1: 0.234841 Loss2: 1.484386
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437249 Loss1: 0.095812 Loss2: 1.341437
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.616538 Loss1: 0.154907 Loss2: 1.461631
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.607018 Loss1: 0.158813 Loss2: 1.448205
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.578041 Loss1: 0.130746 Loss2: 1.447295
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.585081 Loss1: 0.137639 Loss2: 1.447442
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.542961 Loss1: 0.103581 Loss2: 1.439380
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.840110 Loss1: 0.972870 Loss2: 1.867240
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.049545 Loss1: 0.600785 Loss2: 1.448759
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.916783 Loss1: 0.471039 Loss2: 1.445743
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.821125 Loss1: 0.385055 Loss2: 1.436070
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.698917 Loss1: 0.884073 Loss2: 1.814843
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.967950 Loss1: 0.569641 Loss2: 1.398309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.770639 Loss1: 0.342891 Loss2: 1.427748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631127 Loss1: 0.256569 Loss2: 1.374558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549312 Loss1: 0.170093 Loss2: 1.379219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521117 Loss1: 0.156216 Loss2: 1.364901 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473216 Loss1: 0.125642 Loss2: 1.347575 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431508 Loss1: 0.087992 Loss2: 1.343516 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.048795 Loss1: 0.672105 Loss2: 1.376690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.667280 Loss1: 0.302216 Loss2: 1.365064 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.906159 Loss1: 1.044460 Loss2: 1.861699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.070582 Loss1: 0.656630 Loss2: 1.413952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.894577 Loss1: 0.469233 Loss2: 1.425344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.695447 Loss1: 0.315075 Loss2: 1.380372 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.499108 Loss1: 0.138987 Loss2: 1.360121 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975962
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494499 Loss1: 0.128289 Loss2: 1.366210 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464693 Loss1: 0.109999 Loss2: 1.354694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.476205 Loss1: 0.116924 Loss2: 1.359281
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.683834 Loss1: 0.836329 Loss2: 1.847505
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.829675 Loss1: 0.432313 Loss2: 1.397362
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684788 Loss1: 0.257036 Loss2: 1.427751
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558850 Loss1: 0.175420 Loss2: 1.383430
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534189 Loss1: 0.163382 Loss2: 1.370807
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.720690 Loss1: 0.817819 Loss2: 1.902871
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564038 Loss1: 0.187160 Loss2: 1.376878
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531709 Loss1: 0.152672 Loss2: 1.379038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.538723 Loss1: 0.171297 Loss2: 1.367427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.517910 Loss1: 0.137992 Loss2: 1.379918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.500582 Loss1: 0.130400 Loss2: 1.370181 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966797
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.466750 Loss1: 0.083843 Loss2: 1.382907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450312 Loss1: 0.077738 Loss2: 1.372573 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436597 Loss1: 0.067296 Loss2: 1.369301
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.960640 Loss1: 1.104006 Loss2: 1.856634
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.957211 Loss1: 0.572344 Loss2: 1.384868
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.726792 Loss1: 0.333680 Loss2: 1.393112
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582168 Loss1: 0.223059 Loss2: 1.359109
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544614 Loss1: 0.192295 Loss2: 1.352320
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.678042 Loss1: 0.892832 Loss2: 1.785210
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.838670 Loss1: 0.527052 Loss2: 1.311618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.638721 Loss1: 0.279668 Loss2: 1.359053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555629 Loss1: 0.251647 Loss2: 1.303981 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.517125 Loss1: 0.206975 Loss2: 1.310150 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503637 Loss1: 0.205067 Loss2: 1.298569 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376141 Loss1: 0.087244 Loss2: 1.288897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376502 Loss1: 0.095884 Loss2: 1.280618 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.056565 Loss1: 0.659574 Loss2: 1.396991 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 03:17:44,020 | server.py:236 | fit_round 99 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642346 Loss1: 0.271398 Loss2: 1.370948 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481209 Loss1: 0.133480 Loss2: 1.347729 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518437 Loss1: 0.168481 Loss2: 1.349956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.487434 Loss1: 0.136537 Loss2: 1.350897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.479641 Loss1: 0.131898 Loss2: 1.347742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447871 Loss1: 0.104499 Loss2: 1.343373 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444181 Loss1: 0.071756 Loss2: 1.372425 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454458 Loss1: 0.090039 Loss2: 1.364419 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.596046 Loss1: 0.766634 Loss2: 1.829412
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435019 Loss1: 0.077457 Loss2: 1.357561
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.763871 Loss1: 0.360554 Loss2: 1.403317 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531517 Loss1: 0.166647 Loss2: 1.364869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523620 Loss1: 0.166853 Loss2: 1.356766 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466954 Loss1: 0.116082 Loss2: 1.350872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456192 Loss1: 0.113876 Loss2: 1.342316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.481938 Loss1: 0.136521 Loss2: 1.345417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435759 Loss1: 0.090217 Loss2: 1.345542 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986213
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518990 Loss1: 0.123729 Loss2: 1.395261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.511009 Loss1: 0.113974 Loss2: 1.397035 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 03:17:44,020][flwr][DEBUG] - fit_round 99 received 50 results and 0 failures
+INFO flwr 2023-10-11 03:18:26,620 | server.py:125 | fit progress: (99, 2.2024877379877497, {'accuracy': 0.5666}, 228414.399005038)
+>> Test accuracy: 0.566600
+[2023-10-11 03:18:26,620][flwr][INFO] - fit progress: (99, 2.2024877379877497, {'accuracy': 0.5666}, 228414.399005038)
+DEBUG flwr 2023-10-11 03:18:26,621 | server.py:173 | evaluate_round 99: strategy sampled 50 clients (out of 50)
+[2023-10-11 03:18:26,621][flwr][DEBUG] - evaluate_round 99: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 03:27:29,176 | server.py:187 | evaluate_round 99 received 50 results and 0 failures
+[2023-10-11 03:27:29,176][flwr][DEBUG] - evaluate_round 99 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 03:27:29,176 | server.py:222 | fit_round 100: strategy sampled 50 clients (out of 50)
+[2023-10-11 03:27:29,176][flwr][DEBUG] - fit_round 100: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.865358 Loss1: 1.041977 Loss2: 1.823381 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.809730 Loss1: 0.401109 Loss2: 1.408621 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638140 Loss1: 0.298792 Loss2: 1.339348
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.125170 Loss1: 1.181259 Loss2: 1.943911
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.103731 Loss1: 0.674316 Loss2: 1.429415 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.909857 Loss1: 0.437087 Loss2: 1.472770 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.724176 Loss1: 0.325762 Loss2: 1.398414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.603326 Loss1: 0.191396 Loss2: 1.411930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546531 Loss1: 0.145327 Loss2: 1.401204 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533816 Loss1: 0.143148 Loss2: 1.390668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.503468 Loss1: 0.116130 Loss2: 1.387338 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967634
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.892776 Loss1: 1.051836 Loss2: 1.840939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.779324 Loss1: 0.371040 Loss2: 1.408283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629292 Loss1: 0.239964 Loss2: 1.389328
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.016863 Loss1: 1.027504 Loss2: 1.989359
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.233814 Loss1: 0.682749 Loss2: 1.551065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.913386 Loss1: 0.397899 Loss2: 1.515487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.798393 Loss1: 0.301357 Loss2: 1.497036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.688534 Loss1: 0.198287 Loss2: 1.490247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.605623 Loss1: 0.128292 Loss2: 1.477331 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.501618 Loss1: 0.128568 Loss2: 1.373050
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.554079 Loss1: 0.093138 Loss2: 1.460941
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.559961 Loss1: 0.109307 Loss2: 1.450654
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.574590 Loss1: 0.115651 Loss2: 1.458939
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.537640 Loss1: 0.083305 Loss2: 1.454335
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.831510 Loss1: 0.965167 Loss2: 1.866344
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.964435 Loss1: 0.577272 Loss2: 1.387163
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.793172 Loss1: 0.376868 Loss2: 1.416304
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.665181 Loss1: 0.281076 Loss2: 1.384105
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.855543 Loss1: 0.972666 Loss2: 1.882877
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.036593 Loss1: 0.635104 Loss2: 1.401489 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.790148 Loss1: 0.349927 Loss2: 1.440221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.661073 Loss1: 0.271975 Loss2: 1.389097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.641637 Loss1: 0.251603 Loss2: 1.390033 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.640745 Loss1: 0.250391 Loss2: 1.390354 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.542713 Loss1: 0.152301 Loss2: 1.390412 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.471286 Loss1: 0.098358 Loss2: 1.372928 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.929802 Loss1: 1.007783 Loss2: 1.922019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.886746 Loss1: 0.393478 Loss2: 1.493268 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.784964 Loss1: 0.331431 Loss2: 1.453533
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.955674 Loss1: 1.053151 Loss2: 1.902523
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.109245 Loss1: 0.678741 Loss2: 1.430504 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.839602 Loss1: 0.383168 Loss2: 1.456433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.700423 Loss1: 0.279627 Loss2: 1.420796 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.566225 Loss1: 0.151778 Loss2: 1.414446 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523696 Loss1: 0.120763 Loss2: 1.402933 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494179 Loss1: 0.098729 Loss2: 1.395449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487084 Loss1: 0.098603 Loss2: 1.388481 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.785661 Loss1: 0.706717 Loss2: 2.078944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 2.031012 Loss1: 0.404759 Loss2: 1.626253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.908460 Loss1: 0.344627 Loss2: 1.563833
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.692580 Loss1: 0.899209 Loss2: 1.793371
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.793507 Loss1: 0.465777 Loss2: 1.327730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.663604 Loss1: 0.311826 Loss2: 1.351777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.656847 Loss1: 0.339944 Loss2: 1.316903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568106 Loss1: 0.225969 Loss2: 1.342137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.448142 Loss1: 0.149086 Loss2: 1.299056 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435410 Loss1: 0.132088 Loss2: 1.303322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366850 Loss1: 0.071601 Loss2: 1.295249 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.795898 Loss1: 0.944658 Loss2: 1.851240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.866403 Loss1: 0.431372 Loss2: 1.435031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.782098 Loss1: 0.345274 Loss2: 1.436825
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.673525 Loss1: 0.822526 Loss2: 1.850999
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.923038 Loss1: 0.486903 Loss2: 1.436134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.747512 Loss1: 0.310968 Loss2: 1.436544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.641596 Loss1: 0.241039 Loss2: 1.400557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550169 Loss1: 0.151789 Loss2: 1.398379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.558799 Loss1: 0.169621 Loss2: 1.389178 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500900 Loss1: 0.111992 Loss2: 1.388908 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442270 Loss1: 0.066230 Loss2: 1.376041 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.655594 Loss1: 0.817781 Loss2: 1.837812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.788778 Loss1: 0.384246 Loss2: 1.404531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.789741 Loss1: 0.924844 Loss2: 1.864898 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475991 Loss1: 0.145837 Loss2: 1.330154 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426378 Loss1: 0.103814 Loss2: 1.322565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430906 Loss1: 0.104362 Loss2: 1.326544 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400963 Loss1: 0.082351 Loss2: 1.318612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397327 Loss1: 0.086677 Loss2: 1.310650 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.553516 Loss1: 0.161895 Loss2: 1.391621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.491167 Loss1: 0.105106 Loss2: 1.386061 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.473365 Loss1: 0.092054 Loss2: 1.381311
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.886520 Loss1: 1.028781 Loss2: 1.857739
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.045082 Loss1: 0.626347 Loss2: 1.418735
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.861702 Loss1: 0.414435 Loss2: 1.447266
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.696883 Loss1: 0.310850 Loss2: 1.386033
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.652966 Loss1: 0.249029 Loss2: 1.403937
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.888682 Loss1: 1.034065 Loss2: 1.854617
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.116871 Loss1: 0.698024 Loss2: 1.418847 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.777855 Loss1: 0.335080 Loss2: 1.442776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.663093 Loss1: 0.278023 Loss2: 1.385071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.607053 Loss1: 0.209644 Loss2: 1.397409 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537631 Loss1: 0.154554 Loss2: 1.383076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.517305 Loss1: 0.143165 Loss2: 1.374140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.474396 Loss1: 0.107037 Loss2: 1.367359 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.926376 Loss1: 0.507919 Loss2: 1.418457 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.657582 Loss1: 0.255903 Loss2: 1.401679 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631811 Loss1: 0.222414 Loss2: 1.409396
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.799379 Loss1: 0.866754 Loss2: 1.932625
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.579055 Loss1: 0.178775 Loss2: 1.400281
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.942540 Loss1: 0.489979 Loss2: 1.452561
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509392 Loss1: 0.119123 Loss2: 1.390269
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.772051 Loss1: 0.295498 Loss2: 1.476553
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.485144 Loss1: 0.106411 Loss2: 1.378733
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.672201 Loss1: 0.240542 Loss2: 1.431659
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.481912 Loss1: 0.103892 Loss2: 1.378019
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.610226 Loss1: 0.175957 Loss2: 1.434269
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477625 Loss1: 0.096149 Loss2: 1.381475
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.631733 Loss1: 0.214258 Loss2: 1.417475
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.624095 Loss1: 0.188535 Loss2: 1.435560
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.565613 Loss1: 0.151935 Loss2: 1.413679
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.577047 Loss1: 0.161280 Loss2: 1.415767
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.574735 Loss1: 0.165127 Loss2: 1.409608
+(DefaultActor pid=3764) >> Training accuracy: 0.960938
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.780057 Loss1: 0.895952 Loss2: 1.884105
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.840328 Loss1: 0.453477 Loss2: 1.386850
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.735338 Loss1: 0.335745 Loss2: 1.399593
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.593187 Loss1: 0.216548 Loss2: 1.376639
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544979 Loss1: 0.179705 Loss2: 1.365274
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.686370 Loss1: 0.891767 Loss2: 1.794602
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.964519 Loss1: 0.599852 Loss2: 1.364667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.667200 Loss1: 0.300919 Loss2: 1.366281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540709 Loss1: 0.205084 Loss2: 1.335626 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495189 Loss1: 0.161927 Loss2: 1.333261 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469802 Loss1: 0.123055 Loss2: 1.346747
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.462449 Loss1: 0.142893 Loss2: 1.319556
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423346 Loss1: 0.103199 Loss2: 1.320147
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431368 Loss1: 0.112694 Loss2: 1.318675
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420665 Loss1: 0.106923 Loss2: 1.313742
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413202 Loss1: 0.101367 Loss2: 1.311834
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.805851 Loss1: 0.967962 Loss2: 1.837889
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.004764 Loss1: 0.586858 Loss2: 1.417905
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.827899 Loss1: 0.399387 Loss2: 1.428512
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.628337 Loss1: 0.245077 Loss2: 1.383260
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.624621 Loss1: 0.230328 Loss2: 1.394293
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509318 Loss1: 0.133237 Loss2: 1.376082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483331 Loss1: 0.114226 Loss2: 1.369104 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449340 Loss1: 0.083075 Loss2: 1.366265 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423530 Loss1: 0.074177 Loss2: 1.349353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421419 Loss1: 0.074591 Loss2: 1.346827 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500119 Loss1: 0.183283 Loss2: 1.316836 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422872 Loss1: 0.112197 Loss2: 1.310674 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.954167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.135157 Loss1: 0.715587 Loss2: 1.419570 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.653054 Loss1: 0.278822 Loss2: 1.374232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.942953 Loss1: 0.973168 Loss2: 1.969785
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606181 Loss1: 0.254347 Loss2: 1.351834
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502774 Loss1: 0.166231 Loss2: 1.336543 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479791 Loss1: 0.145401 Loss2: 1.334389 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.465825 Loss1: 0.137475 Loss2: 1.328350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436836 Loss1: 0.108193 Loss2: 1.328644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396739 Loss1: 0.074509 Loss2: 1.322230 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480964 Loss1: 0.099479 Loss2: 1.381485 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986779
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.825128 Loss1: 0.954339 Loss2: 1.870789 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.759102 Loss1: 0.324823 Loss2: 1.434279 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.616936 Loss1: 0.214639 Loss2: 1.402297
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.784372 Loss1: 0.932460 Loss2: 1.851912
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.554204 Loss1: 0.156686 Loss2: 1.397518
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.936563 Loss1: 0.551532 Loss2: 1.385031
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519390 Loss1: 0.133934 Loss2: 1.385456
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.819764 Loss1: 0.387432 Loss2: 1.432332
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472790 Loss1: 0.092012 Loss2: 1.380779
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.600280 Loss1: 0.223944 Loss2: 1.376336
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440159 Loss1: 0.066126 Loss2: 1.374033
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518188 Loss1: 0.140973 Loss2: 1.377215
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430240 Loss1: 0.060788 Loss2: 1.369452
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491430 Loss1: 0.133876 Loss2: 1.357554
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437856 Loss1: 0.073545 Loss2: 1.364311
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458619 Loss1: 0.100507 Loss2: 1.358113
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447416 Loss1: 0.094035 Loss2: 1.353381
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449120 Loss1: 0.102904 Loss2: 1.346216
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427070 Loss1: 0.081260 Loss2: 1.345810
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.865250 Loss1: 1.028010 Loss2: 1.837241
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.914002 Loss1: 0.596548 Loss2: 1.317454
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.874457 Loss1: 0.491236 Loss2: 1.383221
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.614816 Loss1: 0.278515 Loss2: 1.336301
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.607068 Loss1: 0.793986 Loss2: 1.813082
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471604 Loss1: 0.151478 Loss2: 1.320126 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.437136 Loss1: 0.127356 Loss2: 1.309780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.387730 Loss1: 0.084824 Loss2: 1.302906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.370575 Loss1: 0.076112 Loss2: 1.294463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.325925 Loss1: 0.038263 Loss2: 1.287662 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.507926 Loss1: 0.189534 Loss2: 1.318392 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460276 Loss1: 0.141303 Loss2: 1.318973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404126 Loss1: 0.086547 Loss2: 1.317579
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.662885 Loss1: 0.816201 Loss2: 1.846684
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.920977 Loss1: 0.513211 Loss2: 1.407766
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.779997 Loss1: 0.339694 Loss2: 1.440304
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.658019 Loss1: 0.260987 Loss2: 1.397032
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.597181 Loss1: 0.198378 Loss2: 1.398803
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.821530 Loss1: 0.940888 Loss2: 1.880642
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587698 Loss1: 0.200382 Loss2: 1.387315
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.960941 Loss1: 0.535896 Loss2: 1.425045
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.772306 Loss1: 0.314408 Loss2: 1.457898
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.559764 Loss1: 0.169819 Loss2: 1.389945
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.655768 Loss1: 0.241346 Loss2: 1.414422
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495011 Loss1: 0.111309 Loss2: 1.383702
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.503247 Loss1: 0.124376 Loss2: 1.378871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484408 Loss1: 0.101504 Loss2: 1.382904 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991728
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546895 Loss1: 0.151733 Loss2: 1.395162 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.510308 Loss1: 0.125268 Loss2: 1.385040 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.970833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.825334 Loss1: 0.941523 Loss2: 1.883811
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.851374 Loss1: 0.460485 Loss2: 1.390888
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645137 Loss1: 0.265255 Loss2: 1.379882
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.674823 Loss1: 0.313656 Loss2: 1.361167
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.823833 Loss1: 0.985251 Loss2: 1.838582
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.986929 Loss1: 0.610835 Loss2: 1.376095 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804069 Loss1: 0.392825 Loss2: 1.411244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.700190 Loss1: 0.330394 Loss2: 1.369795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571746 Loss1: 0.202772 Loss2: 1.368974 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546262 Loss1: 0.190679 Loss2: 1.355583 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445838 Loss1: 0.104500 Loss2: 1.341338
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494794 Loss1: 0.144933 Loss2: 1.349861
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462138 Loss1: 0.119834 Loss2: 1.342304
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462720 Loss1: 0.123741 Loss2: 1.338980
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427936 Loss1: 0.090346 Loss2: 1.337590
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.742399 Loss1: 0.933715 Loss2: 1.808684
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.913290 Loss1: 0.512397 Loss2: 1.400893
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.710263 Loss1: 0.302156 Loss2: 1.408108
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638455 Loss1: 0.269577 Loss2: 1.368878
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.745975 Loss1: 0.900253 Loss2: 1.845723
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568830 Loss1: 0.194467 Loss2: 1.374363
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.968316 Loss1: 0.615903 Loss2: 1.352413
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508540 Loss1: 0.150349 Loss2: 1.358191
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.758166 Loss1: 0.359269 Loss2: 1.398897
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.608073 Loss1: 0.260480 Loss2: 1.347593
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473073 Loss1: 0.120707 Loss2: 1.352366
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519052 Loss1: 0.164605 Loss2: 1.354447
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436542 Loss1: 0.086188 Loss2: 1.350354
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.454178 Loss1: 0.121734 Loss2: 1.332445
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462018 Loss1: 0.118305 Loss2: 1.343713
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436937 Loss1: 0.109598 Loss2: 1.327338
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.468540 Loss1: 0.118472 Loss2: 1.350068
+(DefaultActor pid=3765) >> Training accuracy: 0.974609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425244 Loss1: 0.107771 Loss2: 1.317473 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.752205 Loss1: 0.906099 Loss2: 1.846105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.735001 Loss1: 0.326941 Loss2: 1.408060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647337 Loss1: 0.275501 Loss2: 1.371836
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.737414 Loss1: 0.937425 Loss2: 1.799988
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.131864 Loss1: 0.715216 Loss2: 1.416647 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.770358 Loss1: 0.384842 Loss2: 1.385516 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.682941 Loss1: 0.333700 Loss2: 1.349241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.588861 Loss1: 0.230001 Loss2: 1.358860 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.562276 Loss1: 0.221409 Loss2: 1.340866 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437083 Loss1: 0.086676 Loss2: 1.350406
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474170 Loss1: 0.145498 Loss2: 1.328671
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462290 Loss1: 0.144759 Loss2: 1.317531
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412533 Loss1: 0.092985 Loss2: 1.319548
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407290 Loss1: 0.092748 Loss2: 1.314542
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.754004 Loss1: 1.021838 Loss2: 1.732166
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.922737 Loss1: 0.591295 Loss2: 1.331442
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.680894 Loss1: 0.347660 Loss2: 1.333233
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520365 Loss1: 0.215387 Loss2: 1.304978
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.860129 Loss1: 1.026396 Loss2: 1.833732
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.029715 Loss1: 0.646027 Loss2: 1.383689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.774044 Loss1: 0.404415 Loss2: 1.369629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.596102 Loss1: 0.240679 Loss2: 1.355423 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.533686 Loss1: 0.183145 Loss2: 1.350542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474967 Loss1: 0.140542 Loss2: 1.334424 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361546 Loss1: 0.095489 Loss2: 1.266057
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437108 Loss1: 0.108949 Loss2: 1.328160
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422497 Loss1: 0.090633 Loss2: 1.331863
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418092 Loss1: 0.094576 Loss2: 1.323515
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390429 Loss1: 0.072163 Loss2: 1.318266
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.724673 Loss1: 0.872084 Loss2: 1.852590
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.851297 Loss1: 0.499330 Loss2: 1.351968
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.739664 Loss1: 0.341718 Loss2: 1.397946
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.603659 Loss1: 0.246417 Loss2: 1.357242
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.796461 Loss1: 0.967999 Loss2: 1.828462
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.016785 Loss1: 0.624442 Loss2: 1.392343 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.813676 Loss1: 0.422520 Loss2: 1.391156 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.737489 Loss1: 0.346818 Loss2: 1.390671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.621715 Loss1: 0.257045 Loss2: 1.364670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529249 Loss1: 0.175297 Loss2: 1.353952 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419389 Loss1: 0.097992 Loss2: 1.321397
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.503758 Loss1: 0.155642 Loss2: 1.348116
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.514794 Loss1: 0.163960 Loss2: 1.350835
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.489346 Loss1: 0.146033 Loss2: 1.343313
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482636 Loss1: 0.141401 Loss2: 1.341235
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.754057 Loss1: 0.848558 Loss2: 1.905499
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.944494 Loss1: 0.538726 Loss2: 1.405767
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.750351 Loss1: 0.302967 Loss2: 1.447385
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.712466 Loss1: 0.315760 Loss2: 1.396706
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.857655 Loss1: 1.023994 Loss2: 1.833661
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.965438 Loss1: 0.559519 Loss2: 1.405920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.765472 Loss1: 0.342530 Loss2: 1.422942 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.653637 Loss1: 0.267037 Loss2: 1.386600 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.620562 Loss1: 0.233064 Loss2: 1.387498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556876 Loss1: 0.170804 Loss2: 1.386072 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.513884 Loss1: 0.135626 Loss2: 1.378258 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.486901 Loss1: 0.126157 Loss2: 1.360744 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.977897 Loss1: 0.551414 Loss2: 1.426483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.727290 Loss1: 0.301560 Loss2: 1.425730 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.659972 Loss1: 0.232527 Loss2: 1.427445
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.948692 Loss1: 1.105387 Loss2: 1.843305
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.605692 Loss1: 0.199969 Loss2: 1.405723
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.037040 Loss1: 0.660856 Loss2: 1.376184
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.552878 Loss1: 0.141487 Loss2: 1.411391
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.805707 Loss1: 0.413827 Loss2: 1.391880
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500797 Loss1: 0.110061 Loss2: 1.390736
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.626840 Loss1: 0.285018 Loss2: 1.341822
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465815 Loss1: 0.074242 Loss2: 1.391574
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.534171 Loss1: 0.193662 Loss2: 1.340509
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.504107 Loss1: 0.113727 Loss2: 1.390379
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538164 Loss1: 0.196889 Loss2: 1.341275
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472307 Loss1: 0.137440 Loss2: 1.334866
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445584 Loss1: 0.123654 Loss2: 1.321930
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439956 Loss1: 0.119852 Loss2: 1.320104
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401599 Loss1: 0.080418 Loss2: 1.321181
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.980199 Loss1: 1.048878 Loss2: 1.931322
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.010150 Loss1: 0.557372 Loss2: 1.452778
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.813693 Loss1: 0.343718 Loss2: 1.469975
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694482 Loss1: 0.256112 Loss2: 1.438370
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.911904 Loss1: 1.007605 Loss2: 1.904299
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.090237 Loss1: 0.706300 Loss2: 1.383937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.859053 Loss1: 0.431933 Loss2: 1.427119 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.653562 Loss1: 0.293199 Loss2: 1.360363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554277 Loss1: 0.173961 Loss2: 1.380316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494912 Loss1: 0.136335 Loss2: 1.358577 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443411 Loss1: 0.096774 Loss2: 1.346637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430653 Loss1: 0.097712 Loss2: 1.332941 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 03:56:20,067 | server.py:236 | fit_round 100 received 50 results and 0 failures
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.697562 Loss1: 0.869867 Loss2: 1.827694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.714948 Loss1: 0.281153 Loss2: 1.433795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.883447 Loss1: 0.978281 Loss2: 1.905166
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649821 Loss1: 0.257568 Loss2: 1.392253
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.001560 Loss1: 0.593429 Loss2: 1.408130
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611336 Loss1: 0.202175 Loss2: 1.409160
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.709711 Loss1: 0.283114 Loss2: 1.426597
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523561 Loss1: 0.146041 Loss2: 1.377520
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.645048 Loss1: 0.247484 Loss2: 1.397564
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.551072 Loss1: 0.167641 Loss2: 1.383431
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500784 Loss1: 0.115829 Loss2: 1.384956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.495092 Loss1: 0.117432 Loss2: 1.377660 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.493356 Loss1: 0.127404 Loss2: 1.365952 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.477439 Loss1: 0.103095 Loss2: 1.374344 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.083631 Loss1: 1.063814 Loss2: 2.019817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.954320 Loss1: 0.458660 Loss2: 1.495660 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.669941 Loss1: 0.274176 Loss2: 1.395765 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.945961 Loss1: 0.555915 Loss2: 1.390046 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.543415 Loss1: 0.150660 Loss2: 1.392754 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.677806 Loss1: 0.297226 Loss2: 1.380579 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528605 Loss1: 0.171256 Loss2: 1.357349 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.509108 Loss1: 0.148664 Loss2: 1.360444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470216 Loss1: 0.116048 Loss2: 1.354169 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975586
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 03:56:20,067][flwr][DEBUG] - fit_round 100 received 50 results and 0 failures
+INFO flwr 2023-10-11 03:57:02,805 | server.py:125 | fit progress: (100, 2.2043136573447204, {'accuracy': 0.5667}, 230730.583792349)
+>> Test accuracy: 0.566700
+[2023-10-11 03:57:02,805][flwr][INFO] - fit progress: (100, 2.2043136573447204, {'accuracy': 0.5667}, 230730.583792349)
+DEBUG flwr 2023-10-11 03:57:02,806 | server.py:173 | evaluate_round 100: strategy sampled 50 clients (out of 50)
+[2023-10-11 03:57:02,806][flwr][DEBUG] - evaluate_round 100: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 04:06:07,050 | server.py:187 | evaluate_round 100 received 50 results and 0 failures
+[2023-10-11 04:06:07,050][flwr][DEBUG] - evaluate_round 100 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 04:06:07,050 | server.py:222 | fit_round 101: strategy sampled 50 clients (out of 50)
+[2023-10-11 04:06:07,050][flwr][DEBUG] - fit_round 101: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.744296 Loss1: 0.912524 Loss2: 1.831771
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.051886 Loss1: 0.660426 Loss2: 1.391460
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.887061 Loss1: 0.506079 Loss2: 1.380981
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647543 Loss1: 0.281962 Loss2: 1.365581
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.044514 Loss1: 1.107124 Loss2: 1.937390
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.583577 Loss1: 0.224629 Loss2: 1.358948
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.090629 Loss1: 0.621181 Loss2: 1.469448
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503713 Loss1: 0.167327 Loss2: 1.336385
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.902232 Loss1: 0.413073 Loss2: 1.489158
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483504 Loss1: 0.147393 Loss2: 1.336111
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.735154 Loss1: 0.299224 Loss2: 1.435930
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.472663 Loss1: 0.142652 Loss2: 1.330011
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.670471 Loss1: 0.225102 Loss2: 1.445369
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418248 Loss1: 0.087884 Loss2: 1.330363
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.606315 Loss1: 0.175635 Loss2: 1.430680
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393663 Loss1: 0.074047 Loss2: 1.319615
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.601663 Loss1: 0.176877 Loss2: 1.424785
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.579821 Loss1: 0.149898 Loss2: 1.429923
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.558529 Loss1: 0.141819 Loss2: 1.416711
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.506564 Loss1: 0.095023 Loss2: 1.411541
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.875908 Loss1: 0.960230 Loss2: 1.915677
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.987915 Loss1: 0.544034 Loss2: 1.443880
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.862320 Loss1: 0.373675 Loss2: 1.488645
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.769950 Loss1: 0.347514 Loss2: 1.422436
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.951340 Loss1: 1.064259 Loss2: 1.887081
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.073302 Loss1: 0.621506 Loss2: 1.451795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.732234 Loss1: 0.293335 Loss2: 1.438899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.570634 Loss1: 0.178185 Loss2: 1.392449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554355 Loss1: 0.155332 Loss2: 1.399023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498776 Loss1: 0.117403 Loss2: 1.381373 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452552 Loss1: 0.060968 Loss2: 1.391584
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.524017 Loss1: 0.138703 Loss2: 1.385314
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490964 Loss1: 0.104555 Loss2: 1.386409
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466001 Loss1: 0.088932 Loss2: 1.377069
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464365 Loss1: 0.092669 Loss2: 1.371696
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.889437 Loss1: 0.979626 Loss2: 1.909811
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.186245 Loss1: 0.726601 Loss2: 1.459644
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.978718 Loss1: 0.523650 Loss2: 1.455069
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.721412 Loss1: 0.311788 Loss2: 1.409624
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.823518 Loss1: 0.960426 Loss2: 1.863092
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.958850 Loss1: 0.569099 Loss2: 1.389751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.715034 Loss1: 0.301130 Loss2: 1.413904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.638495 Loss1: 0.262747 Loss2: 1.375748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527497 Loss1: 0.161052 Loss2: 1.366445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503161 Loss1: 0.137229 Loss2: 1.365932 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468424 Loss1: 0.121102 Loss2: 1.347322 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434043 Loss1: 0.091547 Loss2: 1.342496 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.060896 Loss1: 0.639332 Loss2: 1.421564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.667690 Loss1: 0.271468 Loss2: 1.396222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.981256 Loss1: 0.997413 Loss2: 1.983843
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.573750 Loss1: 0.176415 Loss2: 1.397335
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.244380 Loss1: 0.740963 Loss2: 1.503417
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551148 Loss1: 0.163502 Loss2: 1.387646
+(DefaultActor pid=3764) Epoch: 2 Loss: 2.031981 Loss1: 0.529255 Loss2: 1.502726
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.526485 Loss1: 0.140540 Loss2: 1.385945
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.815049 Loss1: 0.329904 Loss2: 1.485145
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.502945 Loss1: 0.122594 Loss2: 1.380351
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.771786 Loss1: 0.310886 Loss2: 1.460900
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.475346 Loss1: 0.096632 Loss2: 1.378714
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.696220 Loss1: 0.223681 Loss2: 1.472539
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.479621 Loss1: 0.108179 Loss2: 1.371442
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.581321 Loss1: 0.137471 Loss2: 1.443851 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.519210 Loss1: 0.092110 Loss2: 1.427100 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.960722 Loss1: 0.557641 Loss2: 1.403081 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654172 Loss1: 0.278072 Loss2: 1.376099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.780731 Loss1: 0.920762 Loss2: 1.859968
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.599741 Loss1: 0.211894 Loss2: 1.387847
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.947659 Loss1: 0.522082 Loss2: 1.425576
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.589672 Loss1: 0.211971 Loss2: 1.377701
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804656 Loss1: 0.387248 Loss2: 1.417408
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.530078 Loss1: 0.161839 Loss2: 1.368239
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.657989 Loss1: 0.276482 Loss2: 1.381508
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.510712 Loss1: 0.142738 Loss2: 1.367974
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.575843 Loss1: 0.204010 Loss2: 1.371833
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.495572 Loss1: 0.129960 Loss2: 1.365612
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503541 Loss1: 0.145435 Loss2: 1.358106
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454720 Loss1: 0.095710 Loss2: 1.359010
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469176 Loss1: 0.108358 Loss2: 1.360818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419457 Loss1: 0.070666 Loss2: 1.348791 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.992556 Loss1: 0.580892 Loss2: 1.411664 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.640699 Loss1: 0.256199 Loss2: 1.384499 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566465 Loss1: 0.183487 Loss2: 1.382977
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.863954 Loss1: 0.910729 Loss2: 1.953225
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505544 Loss1: 0.139420 Loss2: 1.366124
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.052345 Loss1: 0.541916 Loss2: 1.510429
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.878956 Loss1: 0.354001 Loss2: 1.524955 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.741775 Loss1: 0.269453 Loss2: 1.472322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.729806 Loss1: 0.248748 Loss2: 1.481058 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.644084 Loss1: 0.176356 Loss2: 1.467728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.570921 Loss1: 0.116613 Loss2: 1.454308 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.524161 Loss1: 0.077079 Loss2: 1.447082 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.924748 Loss1: 0.568803 Loss2: 1.355944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601942 Loss1: 0.250193 Loss2: 1.351749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.822456 Loss1: 0.980757 Loss2: 1.841699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.022287 Loss1: 0.583362 Loss2: 1.438924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.773385 Loss1: 0.355418 Loss2: 1.417967 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433405 Loss1: 0.120420 Loss2: 1.312985 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400570 Loss1: 0.079940 Loss2: 1.320630 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491920 Loss1: 0.125865 Loss2: 1.366055 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448150 Loss1: 0.089195 Loss2: 1.358955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.552975 Loss1: 0.777049 Loss2: 1.775926
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440627 Loss1: 0.083663 Loss2: 1.356965
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.680885 Loss1: 0.317012 Loss2: 1.363873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524830 Loss1: 0.192530 Loss2: 1.332300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.888582 Loss1: 0.992235 Loss2: 1.896347
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492551 Loss1: 0.157149 Loss2: 1.335402
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448802 Loss1: 0.128788 Loss2: 1.320014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404961 Loss1: 0.086560 Loss2: 1.318401 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372558 Loss1: 0.065499 Loss2: 1.307058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355157 Loss1: 0.054116 Loss2: 1.301042 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993566
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.480082 Loss1: 0.099699 Loss2: 1.380383 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405610 Loss1: 0.042047 Loss2: 1.363563 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.999729 Loss1: 0.579008 Loss2: 1.420721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.662340 Loss1: 0.265869 Loss2: 1.396471 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.814213 Loss1: 0.922293 Loss2: 1.891920
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547646 Loss1: 0.161770 Loss2: 1.385876
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.090234 Loss1: 0.648544 Loss2: 1.441690
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.496124 Loss1: 0.119021 Loss2: 1.377103
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.820345 Loss1: 0.349080 Loss2: 1.471266
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485370 Loss1: 0.109652 Loss2: 1.375718
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452981 Loss1: 0.091639 Loss2: 1.361342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463511 Loss1: 0.096637 Loss2: 1.366874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443022 Loss1: 0.082590 Loss2: 1.360433 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.519574 Loss1: 0.115004 Loss2: 1.404570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.467268 Loss1: 0.084417 Loss2: 1.382851 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.009587 Loss1: 1.036230 Loss2: 1.973357
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.018373 Loss1: 0.564589 Loss2: 1.453784
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.782795 Loss1: 0.305544 Loss2: 1.477251
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.708496 Loss1: 0.270494 Loss2: 1.438002
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.789021 Loss1: 0.908675 Loss2: 1.880346
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.618814 Loss1: 0.199714 Loss2: 1.419100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.627836 Loss1: 0.198600 Loss2: 1.429237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.567899 Loss1: 0.139345 Loss2: 1.428554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.571336 Loss1: 0.159004 Loss2: 1.412331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.568645 Loss1: 0.147323 Loss2: 1.421322 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986607
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.514904 Loss1: 0.160888 Loss2: 1.354016 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439893 Loss1: 0.102061 Loss2: 1.337832 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.911000 Loss1: 0.501385 Loss2: 1.409615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647597 Loss1: 0.254460 Loss2: 1.393136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.788256 Loss1: 0.941081 Loss2: 1.847175
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.633927 Loss1: 0.245415 Loss2: 1.388512
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.937613 Loss1: 0.538425 Loss2: 1.399188
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.563515 Loss1: 0.176584 Loss2: 1.386931
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.760056 Loss1: 0.338295 Loss2: 1.421761
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529593 Loss1: 0.151118 Loss2: 1.378474
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.482548 Loss1: 0.111684 Loss2: 1.370864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509439 Loss1: 0.139056 Loss2: 1.370383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.497349 Loss1: 0.124895 Loss2: 1.372454 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436752 Loss1: 0.082157 Loss2: 1.354595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391232 Loss1: 0.050121 Loss2: 1.341111 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.901065 Loss1: 0.986161 Loss2: 1.914905
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.016354 Loss1: 0.564580 Loss2: 1.451774
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.857341 Loss1: 0.395502 Loss2: 1.461839
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.673501 Loss1: 0.243143 Loss2: 1.430358
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.994484 Loss1: 1.021949 Loss2: 1.972535
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.044178 Loss1: 0.563100 Loss2: 1.481078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.911050 Loss1: 0.418642 Loss2: 1.492408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.774295 Loss1: 0.304078 Loss2: 1.470217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.709497 Loss1: 0.251540 Loss2: 1.457956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.678432 Loss1: 0.219790 Loss2: 1.458642 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.639206 Loss1: 0.175799 Loss2: 1.463407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.576181 Loss1: 0.135056 Loss2: 1.441125 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.731933 Loss1: 0.798159 Loss2: 1.933774 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.833170 Loss1: 0.365629 Loss2: 1.467541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.829856 Loss1: 0.971909 Loss2: 1.857947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.071838 Loss1: 0.642645 Loss2: 1.429193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834799 Loss1: 0.419458 Loss2: 1.415341 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.658039 Loss1: 0.269103 Loss2: 1.388937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554849 Loss1: 0.174023 Loss2: 1.380826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539509 Loss1: 0.168470 Loss2: 1.371040 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510484 Loss1: 0.134094 Loss2: 1.376389 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.469990 Loss1: 0.114644 Loss2: 1.355345 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.862088 Loss1: 1.043716 Loss2: 1.818373
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.952160 Loss1: 0.595859 Loss2: 1.356301
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.742614 Loss1: 0.357076 Loss2: 1.385538
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576142 Loss1: 0.234931 Loss2: 1.341211
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.813290 Loss1: 0.919983 Loss2: 1.893307
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.884280 Loss1: 0.478326 Loss2: 1.405955 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.751484 Loss1: 0.327310 Loss2: 1.424173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.648238 Loss1: 0.248226 Loss2: 1.400012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548618 Loss1: 0.154064 Loss2: 1.394554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538139 Loss1: 0.151629 Loss2: 1.386510 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488699 Loss1: 0.116962 Loss2: 1.371737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.488878 Loss1: 0.110540 Loss2: 1.378338 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.966667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.097999 Loss1: 1.036717 Loss2: 2.061282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.910987 Loss1: 0.407674 Loss2: 1.503313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.632715 Loss1: 0.189946 Loss2: 1.442770 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.565911 Loss1: 0.134020 Loss2: 1.431891 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524511 Loss1: 0.107934 Loss2: 1.416577 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.524961 Loss1: 0.108254 Loss2: 1.416706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.629065 Loss1: 0.260598 Loss2: 1.368467
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.535260 Loss1: 0.121003 Loss2: 1.414257
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.502621 Loss1: 0.093222 Loss2: 1.409399
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554513 Loss1: 0.197324 Loss2: 1.357189
+(DefaultActor pid=3765) >> Training accuracy: 0.986779
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492424 Loss1: 0.149219 Loss2: 1.343205
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472174 Loss1: 0.128958 Loss2: 1.343216
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421639 Loss1: 0.088276 Loss2: 1.333364
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396654 Loss1: 0.070813 Loss2: 1.325841
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395409 Loss1: 0.080162 Loss2: 1.315248
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.825948 Loss1: 0.972839 Loss2: 1.853109
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.923512 Loss1: 0.540652 Loss2: 1.382859
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.732100 Loss1: 0.332837 Loss2: 1.399263
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.615942 Loss1: 0.257174 Loss2: 1.358768
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566159 Loss1: 0.212094 Loss2: 1.354065
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.787964 Loss1: 0.983254 Loss2: 1.804710
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537350 Loss1: 0.195067 Loss2: 1.342283
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.015157 Loss1: 0.650851 Loss2: 1.364307
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.507564 Loss1: 0.159605 Loss2: 1.347959
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.763674 Loss1: 0.384537 Loss2: 1.379137
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.442525 Loss1: 0.113804 Loss2: 1.328721
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588936 Loss1: 0.252408 Loss2: 1.336528
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453544 Loss1: 0.125933 Loss2: 1.327611
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503942 Loss1: 0.174967 Loss2: 1.328975
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406591 Loss1: 0.077743 Loss2: 1.328848
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450777 Loss1: 0.137945 Loss2: 1.312833 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402589 Loss1: 0.088199 Loss2: 1.314390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418498 Loss1: 0.114021 Loss2: 1.304477
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.716159 Loss1: 0.884763 Loss2: 1.831395
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.992164 Loss1: 0.580546 Loss2: 1.411618
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.766432 Loss1: 0.365325 Loss2: 1.401107
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.633856 Loss1: 0.251859 Loss2: 1.381997
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.625364 Loss1: 0.249164 Loss2: 1.376200
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.718042 Loss1: 0.827034 Loss2: 1.891008
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.216402 Loss1: 0.720347 Loss2: 1.496055 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.940335 Loss1: 0.481473 Loss2: 1.458861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.775730 Loss1: 0.321622 Loss2: 1.454108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.683717 Loss1: 0.253715 Loss2: 1.430002 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.688033 Loss1: 0.254296 Loss2: 1.433736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.573854 Loss1: 0.157910 Loss2: 1.415944 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.557470 Loss1: 0.143652 Loss2: 1.413818 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965820
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.845334 Loss1: 0.450016 Loss2: 1.395318 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565202 Loss1: 0.178887 Loss2: 1.386315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.778662 Loss1: 0.938196 Loss2: 1.840466
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498929 Loss1: 0.119471 Loss2: 1.379457
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.988444 Loss1: 0.605481 Loss2: 1.382963
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428858 Loss1: 0.060353 Loss2: 1.368505
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.827034 Loss1: 0.418576 Loss2: 1.408458
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432749 Loss1: 0.083056 Loss2: 1.349693
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636231 Loss1: 0.268250 Loss2: 1.367981
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432654 Loss1: 0.082230 Loss2: 1.350424
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569969 Loss1: 0.197226 Loss2: 1.372743
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421332 Loss1: 0.070200 Loss2: 1.351132
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485147 Loss1: 0.132230 Loss2: 1.352917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486140 Loss1: 0.131565 Loss2: 1.354575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465418 Loss1: 0.111119 Loss2: 1.354299
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.976176 Loss1: 1.098761 Loss2: 1.877415
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.064292 Loss1: 0.675401 Loss2: 1.388891
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.866788 Loss1: 0.459474 Loss2: 1.407315
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.645278 Loss1: 0.293085 Loss2: 1.352193
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587842 Loss1: 0.235750 Loss2: 1.352093
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511175 Loss1: 0.171603 Loss2: 1.339571
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.844578 Loss1: 0.996568 Loss2: 1.848010
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441754 Loss1: 0.112877 Loss2: 1.328878
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.968098 Loss1: 0.589027 Loss2: 1.379071
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.739980 Loss1: 0.323694 Loss2: 1.416286 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.607422 Loss1: 0.246031 Loss2: 1.361391 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386709 Loss1: 0.071701 Loss2: 1.315008
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539740 Loss1: 0.173662 Loss2: 1.366078
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480701 Loss1: 0.129327 Loss2: 1.351374
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461528 Loss1: 0.116047 Loss2: 1.345481
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428376 Loss1: 0.083088 Loss2: 1.345288
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429241 Loss1: 0.090786 Loss2: 1.338454
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.764333 Loss1: 0.946255 Loss2: 1.818078
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427328 Loss1: 0.092022 Loss2: 1.335306
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.812685 Loss1: 0.386196 Loss2: 1.426489 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.570647 Loss1: 0.209258 Loss2: 1.361389 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.546128 Loss1: 0.187513 Loss2: 1.358615
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.624147 Loss1: 0.794854 Loss2: 1.829294
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448496 Loss1: 0.100777 Loss2: 1.347718
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.951377 Loss1: 0.577106 Loss2: 1.374271
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432646 Loss1: 0.096662 Loss2: 1.335984
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.829478 Loss1: 0.404553 Loss2: 1.424925
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395285 Loss1: 0.070235 Loss2: 1.325050
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.707245 Loss1: 0.337772 Loss2: 1.369473
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404887 Loss1: 0.081935 Loss2: 1.322952
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.604782 Loss1: 0.225618 Loss2: 1.379164
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.520854 Loss1: 0.168019 Loss2: 1.352835
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487934 Loss1: 0.139223 Loss2: 1.348710
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417367 Loss1: 0.075998 Loss2: 1.341369
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431069 Loss1: 0.098941 Loss2: 1.332128
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.837961 Loss1: 0.953135 Loss2: 1.884826
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408609 Loss1: 0.077879 Loss2: 1.330730
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.769126 Loss1: 0.330512 Loss2: 1.438614 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.620738 Loss1: 0.207069 Loss2: 1.413670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576892 Loss1: 0.186370 Loss2: 1.390521
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.919137 Loss1: 0.963564 Loss2: 1.955573
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.017845 Loss1: 0.612112 Loss2: 1.405733
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.561101 Loss1: 0.168676 Loss2: 1.392424
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488201 Loss1: 0.104239 Loss2: 1.383962 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465553 Loss1: 0.093074 Loss2: 1.372479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451360 Loss1: 0.076014 Loss2: 1.375346 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487861 Loss1: 0.094590 Loss2: 1.393271 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466906 Loss1: 0.078763 Loss2: 1.388143 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.826839 Loss1: 0.940922 Loss2: 1.885917
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.986047 Loss1: 0.512691 Loss2: 1.473356
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.812431 Loss1: 0.386002 Loss2: 1.426428
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.670708 Loss1: 0.231239 Loss2: 1.439469
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.647923 Loss1: 0.897630 Loss2: 1.750294
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.940621 Loss1: 0.570905 Loss2: 1.369716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.733983 Loss1: 0.371674 Loss2: 1.362308 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594164 Loss1: 0.249439 Loss2: 1.344725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513565 Loss1: 0.178866 Loss2: 1.334699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468590 Loss1: 0.146601 Loss2: 1.321989 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443269 Loss1: 0.126881 Loss2: 1.316388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366204 Loss1: 0.056559 Loss2: 1.309645 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.971501 Loss1: 1.021032 Loss2: 1.950468 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.973667 Loss1: 0.480813 Loss2: 1.492854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.607659 Loss1: 0.192142 Loss2: 1.415517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525803 Loss1: 0.126275 Loss2: 1.399528 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514213 Loss1: 0.122886 Loss2: 1.391326 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474353 Loss1: 0.091120 Loss2: 1.383234 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.506820 Loss1: 0.127551 Loss2: 1.379270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465319 Loss1: 0.081861 Loss2: 1.383458 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404141 Loss1: 0.072359 Loss2: 1.331782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397533 Loss1: 0.078923 Loss2: 1.318610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.067097 Loss1: 1.092464 Loss2: 1.974633
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417258 Loss1: 0.095614 Loss2: 1.321643
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-11 04:34:47,253 | server.py:236 | fit_round 101 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.682455 Loss1: 0.302801 Loss2: 1.379655 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.543860 Loss1: 0.170562 Loss2: 1.373298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.788187 Loss1: 0.905787 Loss2: 1.882400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.507823 Loss1: 0.150339 Loss2: 1.357483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508621 Loss1: 0.154953 Loss2: 1.353668 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977865
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.583937 Loss1: 0.189465 Loss2: 1.394471 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504272 Loss1: 0.140209 Loss2: 1.364062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.591979 Loss1: 0.787478 Loss2: 1.804501
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.498951 Loss1: 0.135040 Loss2: 1.363911
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.877970 Loss1: 0.535088 Loss2: 1.342883
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447345 Loss1: 0.082159 Loss2: 1.365186
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731618 Loss1: 0.338659 Loss2: 1.392959
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419515 Loss1: 0.064093 Loss2: 1.355422
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498797 Loss1: 0.165968 Loss2: 1.332830 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406851 Loss1: 0.093070 Loss2: 1.313781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.387950 Loss1: 0.078095 Loss2: 1.309856
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.877919 Loss1: 1.018030 Loss2: 1.859889
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.022277 Loss1: 0.616359 Loss2: 1.405918 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367689 Loss1: 0.063866 Loss2: 1.303822
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.831575 Loss1: 0.375989 Loss2: 1.455586
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.719532 Loss1: 0.326082 Loss2: 1.393450
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.628084 Loss1: 0.218526 Loss2: 1.409559
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.596059 Loss1: 0.198213 Loss2: 1.397846
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.549153 Loss1: 0.157236 Loss2: 1.391917
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.512488 Loss1: 0.120814 Loss2: 1.391674
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.508291 Loss1: 0.128700 Loss2: 1.379591
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491259 Loss1: 0.109937 Loss2: 1.381322
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 04:34:47,253][flwr][DEBUG] - fit_round 101 received 50 results and 0 failures
+INFO flwr 2023-10-11 04:35:28,895 | server.py:125 | fit progress: (101, 2.207833128044019, {'accuracy': 0.5696}, 233036.673442934)
+>> Test accuracy: 0.569600
+[2023-10-11 04:35:28,895][flwr][INFO] - fit progress: (101, 2.207833128044019, {'accuracy': 0.5696}, 233036.673442934)
+DEBUG flwr 2023-10-11 04:35:28,895 | server.py:173 | evaluate_round 101: strategy sampled 50 clients (out of 50)
+[2023-10-11 04:35:28,895][flwr][DEBUG] - evaluate_round 101: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 04:44:35,805 | server.py:187 | evaluate_round 101 received 50 results and 0 failures
+[2023-10-11 04:44:35,805][flwr][DEBUG] - evaluate_round 101 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 04:44:35,805 | server.py:222 | fit_round 102: strategy sampled 50 clients (out of 50)
+[2023-10-11 04:44:35,805][flwr][DEBUG] - fit_round 102: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.928432 Loss1: 0.976942 Loss2: 1.951490
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.086212 Loss1: 0.612961 Loss2: 1.473251
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.884005 Loss1: 0.390936 Loss2: 1.493069
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.781752 Loss1: 0.317450 Loss2: 1.464303
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.855217 Loss1: 1.116873 Loss2: 1.738345
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.946189 Loss1: 0.621552 Loss2: 1.324638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671153 Loss1: 0.352387 Loss2: 1.318766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556522 Loss1: 0.263476 Loss2: 1.293045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.466119 Loss1: 0.181865 Loss2: 1.284254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.393824 Loss1: 0.116220 Loss2: 1.277604 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.527975 Loss1: 0.096546 Loss2: 1.431429
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.421824 Loss1: 0.148674 Loss2: 1.273150
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368303 Loss1: 0.102824 Loss2: 1.265479
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372003 Loss1: 0.109119 Loss2: 1.262884
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.343794 Loss1: 0.082972 Loss2: 1.260822
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.983633 Loss1: 1.088248 Loss2: 1.895385
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.079556 Loss1: 0.647815 Loss2: 1.431741
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.793204 Loss1: 0.350713 Loss2: 1.442491
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.753307 Loss1: 0.347589 Loss2: 1.405717
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.789617 Loss1: 0.984243 Loss2: 1.805373
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.897412 Loss1: 0.504149 Loss2: 1.393264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.659812 Loss1: 0.267058 Loss2: 1.392754 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584859 Loss1: 0.225291 Loss2: 1.359568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535422 Loss1: 0.180464 Loss2: 1.354958 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478967 Loss1: 0.143154 Loss2: 1.335813 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448552 Loss1: 0.113672 Loss2: 1.334880 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444267 Loss1: 0.112294 Loss2: 1.331973 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.970851 Loss1: 0.616134 Loss2: 1.354716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606549 Loss1: 0.276953 Loss2: 1.329597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.504384 Loss1: 0.168599 Loss2: 1.335784
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.786036 Loss1: 0.986221 Loss2: 1.799815
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.441317 Loss1: 0.124707 Loss2: 1.316610
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.842442 Loss1: 0.480193 Loss2: 1.362249
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419049 Loss1: 0.110545 Loss2: 1.308505
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.735753 Loss1: 0.339003 Loss2: 1.396750
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.402885 Loss1: 0.090931 Loss2: 1.311953
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563259 Loss1: 0.223714 Loss2: 1.339546
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451330 Loss1: 0.147222 Loss2: 1.304108
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549441 Loss1: 0.201362 Loss2: 1.348079
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407566 Loss1: 0.097263 Loss2: 1.310303
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512278 Loss1: 0.175346 Loss2: 1.336932
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460122 Loss1: 0.130635 Loss2: 1.329487
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460281 Loss1: 0.136392 Loss2: 1.323889
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423316 Loss1: 0.098911 Loss2: 1.324405
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417132 Loss1: 0.107457 Loss2: 1.309675
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.832458 Loss1: 0.993463 Loss2: 1.838995
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.042195 Loss1: 0.659836 Loss2: 1.382359
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.775064 Loss1: 0.361156 Loss2: 1.413908
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.597862 Loss1: 0.238803 Loss2: 1.359058
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.512712 Loss1: 0.148277 Loss2: 1.364435
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518753 Loss1: 0.170434 Loss2: 1.348319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.492915 Loss1: 0.142006 Loss2: 1.350908 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450692 Loss1: 0.099065 Loss2: 1.351627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.610982 Loss1: 0.232145 Loss2: 1.378837 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542698 Loss1: 0.166055 Loss2: 1.376643 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443055 Loss1: 0.089367 Loss2: 1.353688 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408223 Loss1: 0.062595 Loss2: 1.345628 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983259
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.835288 Loss1: 0.965321 Loss2: 1.869967
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.057672 Loss1: 0.616764 Loss2: 1.440907
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.784140 Loss1: 0.362078 Loss2: 1.422062
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.700439 Loss1: 0.274262 Loss2: 1.426177
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.660808 Loss1: 0.832984 Loss2: 1.827823
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.816929 Loss1: 0.409554 Loss2: 1.407375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.698368 Loss1: 0.291455 Loss2: 1.406913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.575741 Loss1: 0.190605 Loss2: 1.385135 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.548404 Loss1: 0.155907 Loss2: 1.392497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.506471 Loss1: 0.118070 Loss2: 1.388401 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427739 Loss1: 0.067336 Loss2: 1.360403 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423271 Loss1: 0.069538 Loss2: 1.353733 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.960007 Loss1: 0.531531 Loss2: 1.428476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.754222 Loss1: 0.323878 Loss2: 1.430345 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.689087 Loss1: 0.256452 Loss2: 1.432635
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.689914 Loss1: 0.796679 Loss2: 1.893236
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.945661 Loss1: 0.531206 Loss2: 1.414456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.765062 Loss1: 0.326960 Loss2: 1.438102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.666564 Loss1: 0.269442 Loss2: 1.397122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.623777 Loss1: 0.213560 Loss2: 1.410218 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.569741 Loss1: 0.169659 Loss2: 1.400082 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.492742 Loss1: 0.112515 Loss2: 1.380227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.459067 Loss1: 0.086832 Loss2: 1.372235
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.931655 Loss1: 0.969127 Loss2: 1.962528
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.050487 Loss1: 0.584605 Loss2: 1.465882
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.776298 Loss1: 0.323462 Loss2: 1.452836
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.665643 Loss1: 0.248216 Loss2: 1.417427
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.649132 Loss1: 0.229656 Loss2: 1.419476
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.545722 Loss1: 0.137898 Loss2: 1.407823
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.791239 Loss1: 0.862196 Loss2: 1.929043
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523113 Loss1: 0.120856 Loss2: 1.402257
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.958770 Loss1: 0.530863 Loss2: 1.427906
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.530543 Loss1: 0.130515 Loss2: 1.400027
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.800115 Loss1: 0.334229 Loss2: 1.465885
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.536441 Loss1: 0.138288 Loss2: 1.398153
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.633711 Loss1: 0.210596 Loss2: 1.423115
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.513969 Loss1: 0.112956 Loss2: 1.401013
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.586160 Loss1: 0.171290 Loss2: 1.414870
+(DefaultActor pid=3765) >> Training accuracy: 0.964583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559584 Loss1: 0.149699 Loss2: 1.409886
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.525792 Loss1: 0.112231 Loss2: 1.413561
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.524705 Loss1: 0.114928 Loss2: 1.409777
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519725 Loss1: 0.111231 Loss2: 1.408494
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.500448 Loss1: 0.098788 Loss2: 1.401660
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.719877 Loss1: 0.883968 Loss2: 1.835909
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.026691 Loss1: 0.588821 Loss2: 1.437870
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.713842 Loss1: 0.297567 Loss2: 1.416274
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.618837 Loss1: 0.227484 Loss2: 1.391353
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606215 Loss1: 0.222171 Loss2: 1.384044
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.770898 Loss1: 0.858608 Loss2: 1.912290
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.527004 Loss1: 0.142118 Loss2: 1.384886
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.504376 Loss1: 0.119176 Loss2: 1.385201 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.475050 Loss1: 0.096743 Loss2: 1.378307 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450912 Loss1: 0.082892 Loss2: 1.368020 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435918 Loss1: 0.071652 Loss2: 1.364266 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.525184 Loss1: 0.126070 Loss2: 1.399113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.474934 Loss1: 0.086526 Loss2: 1.388408 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465215 Loss1: 0.081198 Loss2: 1.384018
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.794338 Loss1: 0.902952 Loss2: 1.891386
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.885263 Loss1: 0.481297 Loss2: 1.403966
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.781568 Loss1: 0.340445 Loss2: 1.441123
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.614457 Loss1: 0.218705 Loss2: 1.395752
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.599044 Loss1: 0.208711 Loss2: 1.390333
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.642653 Loss1: 0.760040 Loss2: 1.882613
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.898360 Loss1: 0.496819 Loss2: 1.401541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.717529 Loss1: 0.301850 Loss2: 1.415680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581161 Loss1: 0.205084 Loss2: 1.376077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569941 Loss1: 0.189194 Loss2: 1.380747 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.480509 Loss1: 0.110469 Loss2: 1.370040
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499112 Loss1: 0.135562 Loss2: 1.363550
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476187 Loss1: 0.112334 Loss2: 1.363853
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.503996 Loss1: 0.141134 Loss2: 1.362863
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481594 Loss1: 0.122348 Loss2: 1.359246
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464125 Loss1: 0.106471 Loss2: 1.357654
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.764244 Loss1: 0.853480 Loss2: 1.910764
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.843065 Loss1: 0.428237 Loss2: 1.414829
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.744254 Loss1: 0.296758 Loss2: 1.447495
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.605530 Loss1: 0.199329 Loss2: 1.406201
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.588941 Loss1: 0.194358 Loss2: 1.394583
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.927526 Loss1: 1.057248 Loss2: 1.870278
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.970492 Loss1: 0.587012 Loss2: 1.383480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.676498 Loss1: 0.279055 Loss2: 1.397443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.570475 Loss1: 0.209730 Loss2: 1.360745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545821 Loss1: 0.190498 Loss2: 1.355323 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.950000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.541155 Loss1: 0.153625 Loss2: 1.387530
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.514948 Loss1: 0.156308 Loss2: 1.358639
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463499 Loss1: 0.106835 Loss2: 1.356664
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424926 Loss1: 0.083904 Loss2: 1.341022
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416750 Loss1: 0.079344 Loss2: 1.337406
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428628 Loss1: 0.096084 Loss2: 1.332544
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.713290 Loss1: 0.857714 Loss2: 1.855575
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.973277 Loss1: 0.573688 Loss2: 1.399589
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.800418 Loss1: 0.355119 Loss2: 1.445299
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.677615 Loss1: 0.290128 Loss2: 1.387488
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.649983 Loss1: 0.248530 Loss2: 1.401453
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.930855 Loss1: 1.047043 Loss2: 1.883812
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.037027 Loss1: 0.612881 Loss2: 1.424146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.851242 Loss1: 0.402491 Loss2: 1.448751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.687621 Loss1: 0.284539 Loss2: 1.403082 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.657820 Loss1: 0.253774 Loss2: 1.404046 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.586734 Loss1: 0.193425 Loss2: 1.393309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.503585 Loss1: 0.113394 Loss2: 1.390191 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470497 Loss1: 0.095377 Loss2: 1.375120 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.141718 Loss1: 0.745057 Loss2: 1.396661 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.657969 Loss1: 0.297011 Loss2: 1.360958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.585394 Loss1: 0.234399 Loss2: 1.350996
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.655869 Loss1: 0.819478 Loss2: 1.836391
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.871962 Loss1: 0.454371 Loss2: 1.417591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.694462 Loss1: 0.295370 Loss2: 1.399093 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434609 Loss1: 0.111091 Loss2: 1.323519 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416085 Loss1: 0.093337 Loss2: 1.322748 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.607638 Loss1: 0.210233 Loss2: 1.397405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496269 Loss1: 0.122933 Loss2: 1.373336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479108 Loss1: 0.108292 Loss2: 1.370816
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.668064 Loss1: 0.910550 Loss2: 1.757514
+(DefaultActor pid=3764) >> Training accuracy: 0.991728
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433016 Loss1: 0.069327 Loss2: 1.363689
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.875748 Loss1: 0.505427 Loss2: 1.370321
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.746065 Loss1: 0.364355 Loss2: 1.381710
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.611563 Loss1: 0.256707 Loss2: 1.354856
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562236 Loss1: 0.213730 Loss2: 1.348506
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506934 Loss1: 0.162838 Loss2: 1.344096
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.739308 Loss1: 0.900100 Loss2: 1.839208
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.075171 Loss1: 0.659632 Loss2: 1.415539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.797379 Loss1: 0.344068 Loss2: 1.453311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640937 Loss1: 0.249800 Loss2: 1.391137 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571907 Loss1: 0.185347 Loss2: 1.386560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.499685 Loss1: 0.118045 Loss2: 1.381641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.491554 Loss1: 0.128933 Loss2: 1.362621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464536 Loss1: 0.094803 Loss2: 1.369733 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973633
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563555 Loss1: 0.215602 Loss2: 1.347952 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488558 Loss1: 0.154550 Loss2: 1.334009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461353 Loss1: 0.127069 Loss2: 1.334283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469102 Loss1: 0.132283 Loss2: 1.336819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474180 Loss1: 0.134171 Loss2: 1.340008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437277 Loss1: 0.104245 Loss2: 1.333032 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467902 Loss1: 0.105807 Loss2: 1.362095 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480001 Loss1: 0.116712 Loss2: 1.363289 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979567
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453975 Loss1: 0.091553 Loss2: 1.362422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.772098 Loss1: 0.909725 Loss2: 1.862373
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.857651 Loss1: 0.470454 Loss2: 1.387197
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667022 Loss1: 0.253020 Loss2: 1.414002
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629648 Loss1: 0.250360 Loss2: 1.379288
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.538487 Loss1: 0.163521 Loss2: 1.374966
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.832661 Loss1: 0.976773 Loss2: 1.855888
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.078185 Loss1: 0.670400 Loss2: 1.407785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.805898 Loss1: 0.349565 Loss2: 1.456333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.707661 Loss1: 0.318496 Loss2: 1.389165 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.636508 Loss1: 0.232789 Loss2: 1.403719 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.572500 Loss1: 0.180654 Loss2: 1.391846 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477431 Loss1: 0.105926 Loss2: 1.371505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.432104 Loss1: 0.071458 Loss2: 1.360646 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.042014 Loss1: 0.611315 Loss2: 1.430699 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.665158 Loss1: 0.264835 Loss2: 1.400324 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.564545 Loss1: 0.167226 Loss2: 1.397320
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.661206 Loss1: 0.868616 Loss2: 1.792590
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.907245 Loss1: 0.559243 Loss2: 1.348001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.738605 Loss1: 0.350273 Loss2: 1.388332 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.624188 Loss1: 0.285273 Loss2: 1.338915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609465 Loss1: 0.253145 Loss2: 1.356321 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490518 Loss1: 0.152375 Loss2: 1.338142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429933 Loss1: 0.102807 Loss2: 1.327125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421302 Loss1: 0.106518 Loss2: 1.314784 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.009732 Loss1: 0.596935 Loss2: 1.412797 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.697186 Loss1: 0.304480 Loss2: 1.392706 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.620446 Loss1: 0.217259 Loss2: 1.403188
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.770992 Loss1: 0.901965 Loss2: 1.869027
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551302 Loss1: 0.177257 Loss2: 1.374045
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.943741 Loss1: 0.579999 Loss2: 1.363741
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513300 Loss1: 0.132343 Loss2: 1.380958
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.772060 Loss1: 0.343523 Loss2: 1.428537
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.697395 Loss1: 0.334137 Loss2: 1.363258
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480521 Loss1: 0.109838 Loss2: 1.370684
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570679 Loss1: 0.204026 Loss2: 1.366653
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454304 Loss1: 0.093954 Loss2: 1.360351
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516724 Loss1: 0.164557 Loss2: 1.352167
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460204 Loss1: 0.097647 Loss2: 1.362556
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452949 Loss1: 0.110435 Loss2: 1.342514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418825 Loss1: 0.083990 Loss2: 1.334835 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.136138 Loss1: 0.685446 Loss2: 1.450691 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.680011 Loss1: 0.277927 Loss2: 1.402084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606119 Loss1: 0.199332 Loss2: 1.406787
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.862197 Loss1: 0.984361 Loss2: 1.877836
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.069039 Loss1: 0.639451 Loss2: 1.429588 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.842253 Loss1: 0.399277 Loss2: 1.442976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699172 Loss1: 0.280866 Loss2: 1.418305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.610186 Loss1: 0.200170 Loss2: 1.410017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.507026 Loss1: 0.114642 Loss2: 1.392384 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443935 Loss1: 0.064512 Loss2: 1.379423 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458213 Loss1: 0.086227 Loss2: 1.371986
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.948135 Loss1: 1.056916 Loss2: 1.891219
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.050700 Loss1: 0.685434 Loss2: 1.365266
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.872687 Loss1: 0.431047 Loss2: 1.441640
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.684062 Loss1: 0.324351 Loss2: 1.359711
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.586455 Loss1: 0.220607 Loss2: 1.365848
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.550133 Loss1: 0.189712 Loss2: 1.360421
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.731357 Loss1: 0.844938 Loss2: 1.886420
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.988648 Loss1: 0.584138 Loss2: 1.404510 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413662 Loss1: 0.068842 Loss2: 1.344820 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383443 Loss1: 0.050365 Loss2: 1.333077 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500079 Loss1: 0.127626 Loss2: 1.372454 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423707 Loss1: 0.072354 Loss2: 1.351352 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.856248 Loss1: 1.006973 Loss2: 1.849275
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414870 Loss1: 0.068014 Loss2: 1.346856
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.961005 Loss1: 0.571834 Loss2: 1.389171
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401884 Loss1: 0.057231 Loss2: 1.344653
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.627149 Loss1: 0.261904 Loss2: 1.365245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473228 Loss1: 0.119688 Loss2: 1.353540 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447598 Loss1: 0.101502 Loss2: 1.346096
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.789690 Loss1: 0.971402 Loss2: 1.818287
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.983184 Loss1: 0.578600 Loss2: 1.404583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.745519 Loss1: 0.369114 Loss2: 1.376405 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.733751 Loss1: 0.348597 Loss2: 1.385154 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.593569 Loss1: 0.231095 Loss2: 1.362474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461253 Loss1: 0.110798 Loss2: 1.350455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431300 Loss1: 0.089444 Loss2: 1.341856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409423 Loss1: 0.075297 Loss2: 1.334126 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.635978 Loss1: 0.204549 Loss2: 1.431429 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523969 Loss1: 0.124265 Loss2: 1.399704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.903593 Loss1: 0.984518 Loss2: 1.919076
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.493125 Loss1: 0.099196 Loss2: 1.393929
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.086203 Loss1: 0.630670 Loss2: 1.455533
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.481737 Loss1: 0.093879 Loss2: 1.387858
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.881100 Loss1: 0.398675 Loss2: 1.482425
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476774 Loss1: 0.086749 Loss2: 1.390025
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.706301 Loss1: 0.264418 Loss2: 1.441883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.592418 Loss1: 0.168203 Loss2: 1.424215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.571300 Loss1: 0.150174 Loss2: 1.421125
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.755893 Loss1: 0.902554 Loss2: 1.853339
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.950743 Loss1: 0.565643 Loss2: 1.385100 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.488345 Loss1: 0.081130 Loss2: 1.407214
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.710599 Loss1: 0.302877 Loss2: 1.407722
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.611467 Loss1: 0.244611 Loss2: 1.366856
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.564517 Loss1: 0.196059 Loss2: 1.368458
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.548732 Loss1: 0.187585 Loss2: 1.361147
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486497 Loss1: 0.134879 Loss2: 1.351618
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.695839 Loss1: 0.796960 Loss2: 1.898879
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.533268 Loss1: 0.187756 Loss2: 1.345512
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.965399 Loss1: 0.565381 Loss2: 1.400017
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.515548 Loss1: 0.143167 Loss2: 1.372381
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.835441 Loss1: 0.389227 Loss2: 1.446214
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481704 Loss1: 0.127382 Loss2: 1.354322
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.622428 Loss1: 0.220789 Loss2: 1.401638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467597 Loss1: 0.095851 Loss2: 1.371746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464033 Loss1: 0.099520 Loss2: 1.364513
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.951060 Loss1: 1.070621 Loss2: 1.880439
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.107095 Loss1: 0.665403 Loss2: 1.441692 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426602 Loss1: 0.066669 Loss2: 1.359933
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.801576 Loss1: 0.377079 Loss2: 1.424497
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.774917 Loss1: 0.352428 Loss2: 1.422489
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.637035 Loss1: 0.230668 Loss2: 1.406367
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569907 Loss1: 0.180190 Loss2: 1.389718
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.548527 Loss1: 0.156658 Loss2: 1.391870
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.045884 Loss1: 0.968217 Loss2: 2.077667
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.533143 Loss1: 0.147097 Loss2: 1.386046
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.560537 Loss1: 0.169421 Loss2: 1.391116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.754150 Loss1: 0.243032 Loss2: 1.511118 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.708294 Loss1: 0.243042 Loss2: 1.465252 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.598941 Loss1: 0.147543 Loss2: 1.451398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.558670 Loss1: 0.123827 Loss2: 1.434843 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972656
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.795262 Loss1: 0.350436 Loss2: 1.444826 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.578438 Loss1: 0.175892 Loss2: 1.402545 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 05:12:54,725 | server.py:236 | fit_round 102 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.527990 Loss1: 0.133799 Loss2: 1.394191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528592 Loss1: 0.130644 Loss2: 1.397948 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.504574 Loss1: 0.117916 Loss2: 1.386659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.483303 Loss1: 0.101260 Loss2: 1.382043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458763 Loss1: 0.086577 Loss2: 1.372186 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.523044 Loss1: 0.137763 Loss2: 1.385281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.505187 Loss1: 0.134751 Loss2: 1.370435 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978795
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.477954 Loss1: 0.102282 Loss2: 1.375672
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.761586 Loss1: 0.889076 Loss2: 1.872510
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.062294 Loss1: 0.659824 Loss2: 1.402470
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.904285 Loss1: 0.454222 Loss2: 1.450062
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.707213 Loss1: 0.329084 Loss2: 1.378129
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.643346 Loss1: 0.244110 Loss2: 1.399236
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.574490 Loss1: 0.823834 Loss2: 1.750657
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.847290 Loss1: 0.506508 Loss2: 1.340782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.581214 Loss1: 0.243449 Loss2: 1.337765 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565353 Loss1: 0.262037 Loss2: 1.303316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.561084 Loss1: 0.235665 Loss2: 1.325419 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.948958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494875 Loss1: 0.170484 Loss2: 1.324390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400904 Loss1: 0.102218 Loss2: 1.298686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367405 Loss1: 0.079186 Loss2: 1.288218 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 05:12:54,725][flwr][DEBUG] - fit_round 102 received 50 results and 0 failures
+INFO flwr 2023-10-11 05:13:36,032 | server.py:125 | fit progress: (102, 2.1906376145899107, {'accuracy': 0.5702}, 235323.810206367)
+>> Test accuracy: 0.570200
+[2023-10-11 05:13:36,032][flwr][INFO] - fit progress: (102, 2.1906376145899107, {'accuracy': 0.5702}, 235323.810206367)
+DEBUG flwr 2023-10-11 05:13:36,032 | server.py:173 | evaluate_round 102: strategy sampled 50 clients (out of 50)
+[2023-10-11 05:13:36,032][flwr][DEBUG] - evaluate_round 102: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 05:22:42,238 | server.py:187 | evaluate_round 102 received 50 results and 0 failures
+[2023-10-11 05:22:42,238][flwr][DEBUG] - evaluate_round 102 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 05:22:42,238 | server.py:222 | fit_round 103: strategy sampled 50 clients (out of 50)
+[2023-10-11 05:22:42,238][flwr][DEBUG] - fit_round 103: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.778473 Loss1: 0.895703 Loss2: 1.882769
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.984881 Loss1: 0.565067 Loss2: 1.419814
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.825053 Loss1: 0.355582 Loss2: 1.469472
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.671543 Loss1: 0.281071 Loss2: 1.390472
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.658888 Loss1: 0.860656 Loss2: 1.798233
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.630841 Loss1: 0.215935 Loss2: 1.414906
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.893371 Loss1: 0.549050 Loss2: 1.344321
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562335 Loss1: 0.169559 Loss2: 1.392776
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.729622 Loss1: 0.367857 Loss2: 1.361765
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531204 Loss1: 0.144171 Loss2: 1.387032
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.613511 Loss1: 0.282354 Loss2: 1.331157
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.508402 Loss1: 0.128831 Loss2: 1.379571
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543508 Loss1: 0.203414 Loss2: 1.340094
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.502312 Loss1: 0.126414 Loss2: 1.375898
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529257 Loss1: 0.200792 Loss2: 1.328465
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.497196 Loss1: 0.117843 Loss2: 1.379353
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.466103 Loss1: 0.144479 Loss2: 1.321624
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475692 Loss1: 0.147056 Loss2: 1.328636
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.411426 Loss1: 0.096785 Loss2: 1.314641
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375977 Loss1: 0.067103 Loss2: 1.308874
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.708051 Loss1: 0.910217 Loss2: 1.797834
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.979968 Loss1: 0.583873 Loss2: 1.396095
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.698597 Loss1: 0.303891 Loss2: 1.394706
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.785885 Loss1: 0.873678 Loss2: 1.912208
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.646556 Loss1: 0.265486 Loss2: 1.381070
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.961224 Loss1: 0.546577 Loss2: 1.414647
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.607838 Loss1: 0.234792 Loss2: 1.373047
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.780919 Loss1: 0.349225 Loss2: 1.431694
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.559918 Loss1: 0.198714 Loss2: 1.361204
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479478 Loss1: 0.120062 Loss2: 1.359415 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469537 Loss1: 0.118414 Loss2: 1.351123 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443965 Loss1: 0.097420 Loss2: 1.346545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.488996 Loss1: 0.142131 Loss2: 1.346865 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.437567 Loss1: 0.065607 Loss2: 1.371959 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.744469 Loss1: 0.873984 Loss2: 1.870485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.835691 Loss1: 0.397201 Loss2: 1.438491 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.668885 Loss1: 0.271324 Loss2: 1.397561
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.875699 Loss1: 0.951780 Loss2: 1.923919
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.614429 Loss1: 0.229602 Loss2: 1.384827
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.089184 Loss1: 0.630483 Loss2: 1.458701
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.608492 Loss1: 0.209505 Loss2: 1.398987
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.851796 Loss1: 0.396864 Loss2: 1.454932
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544336 Loss1: 0.164711 Loss2: 1.379624
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.687334 Loss1: 0.267653 Loss2: 1.419681
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.521387 Loss1: 0.143960 Loss2: 1.377427
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.622448 Loss1: 0.205772 Loss2: 1.416676
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.473348 Loss1: 0.108634 Loss2: 1.364714
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.545571 Loss1: 0.147995 Loss2: 1.397576
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476606 Loss1: 0.114655 Loss2: 1.361950
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504477 Loss1: 0.106228 Loss2: 1.398249
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481399 Loss1: 0.098293 Loss2: 1.383106
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458156 Loss1: 0.079527 Loss2: 1.378629
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.495960 Loss1: 0.112395 Loss2: 1.383564
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.785780 Loss1: 0.922601 Loss2: 1.863179
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.950958 Loss1: 0.545674 Loss2: 1.405284
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.729954 Loss1: 0.321230 Loss2: 1.408724
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623395 Loss1: 0.226268 Loss2: 1.397127
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.848704 Loss1: 1.001167 Loss2: 1.847536
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.957633 Loss1: 0.546621 Loss2: 1.411012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726608 Loss1: 0.336973 Loss2: 1.389635 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564164 Loss1: 0.202424 Loss2: 1.361740 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.531892 Loss1: 0.175689 Loss2: 1.356203 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486734 Loss1: 0.131904 Loss2: 1.354830 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467363 Loss1: 0.120601 Loss2: 1.346761 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414222 Loss1: 0.072801 Loss2: 1.341421 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.774053 Loss1: 0.932449 Loss2: 1.841604 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.824808 Loss1: 0.389401 Loss2: 1.435407 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.576943 Loss1: 0.199295 Loss2: 1.377648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504208 Loss1: 0.136250 Loss2: 1.367958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528139 Loss1: 0.163991 Loss2: 1.364148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473815 Loss1: 0.120018 Loss2: 1.353798 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.468678 Loss1: 0.118861 Loss2: 1.349817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467331 Loss1: 0.116359 Loss2: 1.350971 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448803 Loss1: 0.098857 Loss2: 1.349946 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440807 Loss1: 0.099067 Loss2: 1.341741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406239 Loss1: 0.070207 Loss2: 1.336032
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.757039 Loss1: 0.853913 Loss2: 1.903126
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.012985 Loss1: 0.551007 Loss2: 1.461978
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.820019 Loss1: 0.354534 Loss2: 1.465485
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.715269 Loss1: 0.279918 Loss2: 1.435352
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.719226 Loss1: 0.284424 Loss2: 1.434801
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.858191 Loss1: 1.052296 Loss2: 1.805895
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.013693 Loss1: 0.631801 Loss2: 1.381892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.683782 Loss1: 0.237932 Loss2: 1.445850
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.781034 Loss1: 0.398044 Loss2: 1.382990
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.601725 Loss1: 0.172920 Loss2: 1.428805
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.645329 Loss1: 0.290395 Loss2: 1.354934
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.522502 Loss1: 0.108541 Loss2: 1.413961
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509173 Loss1: 0.161540 Loss2: 1.347633
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491757 Loss1: 0.165578 Loss2: 1.326179
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.511940 Loss1: 0.099180 Loss2: 1.412760
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.492876 Loss1: 0.165176 Loss2: 1.327700 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.507592 Loss1: 0.168089 Loss2: 1.339503 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.098708 Loss1: 0.628849 Loss2: 1.469859 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.713051 Loss1: 0.273894 Loss2: 1.439157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.682828 Loss1: 0.902060 Loss2: 1.780768
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617803 Loss1: 0.178578 Loss2: 1.439225
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.573982 Loss1: 0.153031 Loss2: 1.420951
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.894746 Loss1: 0.532369 Loss2: 1.362377
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523666 Loss1: 0.114585 Loss2: 1.409080
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.756838 Loss1: 0.382280 Loss2: 1.374558
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.515276 Loss1: 0.099377 Loss2: 1.415899
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.725425 Loss1: 0.368491 Loss2: 1.356935
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482077 Loss1: 0.074321 Loss2: 1.407756
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.580158 Loss1: 0.228162 Loss2: 1.351996
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481301 Loss1: 0.079703 Loss2: 1.401598
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502878 Loss1: 0.168460 Loss2: 1.334418
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438290 Loss1: 0.112775 Loss2: 1.325515
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449209 Loss1: 0.128371 Loss2: 1.320838
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398873 Loss1: 0.080227 Loss2: 1.318646
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404583 Loss1: 0.092854 Loss2: 1.311729
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.809791 Loss1: 0.927178 Loss2: 1.882613
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.910294 Loss1: 0.520310 Loss2: 1.389983
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.790002 Loss1: 0.373691 Loss2: 1.416311
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.661636 Loss1: 0.279541 Loss2: 1.382095
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568003 Loss1: 0.194320 Loss2: 1.373683
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.676527 Loss1: 0.820969 Loss2: 1.855558
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504558 Loss1: 0.139600 Loss2: 1.364958
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467441 Loss1: 0.105843 Loss2: 1.361598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451735 Loss1: 0.099012 Loss2: 1.352723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432507 Loss1: 0.082345 Loss2: 1.350161 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426932 Loss1: 0.084915 Loss2: 1.342017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464380 Loss1: 0.111085 Loss2: 1.353295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449754 Loss1: 0.103651 Loss2: 1.346103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409230 Loss1: 0.070705 Loss2: 1.338525
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.773676 Loss1: 0.864401 Loss2: 1.909275
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.943623 Loss1: 0.525875 Loss2: 1.417748
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.851858 Loss1: 0.392999 Loss2: 1.458860
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.775786 Loss1: 0.340333 Loss2: 1.435453
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.665720 Loss1: 0.227227 Loss2: 1.438493
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.827393 Loss1: 0.916977 Loss2: 1.910416
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.536620 Loss1: 0.126726 Loss2: 1.409894
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.521833 Loss1: 0.113215 Loss2: 1.408618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.519606 Loss1: 0.116204 Loss2: 1.403403 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484433 Loss1: 0.091476 Loss2: 1.392957 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.473443 Loss1: 0.087728 Loss2: 1.385715 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.502871 Loss1: 0.124529 Loss2: 1.378342 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463946 Loss1: 0.091954 Loss2: 1.371992 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982143
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.964070 Loss1: 0.598212 Loss2: 1.365859 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630856 Loss1: 0.275708 Loss2: 1.355148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.581138 Loss1: 0.228784 Loss2: 1.352354
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.679612 Loss1: 0.812161 Loss2: 1.867451
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.876511 Loss1: 0.450399 Loss2: 1.426112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671844 Loss1: 0.244939 Loss2: 1.426904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.632252 Loss1: 0.227841 Loss2: 1.404412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454073 Loss1: 0.120589 Loss2: 1.333484 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.492391 Loss1: 0.108142 Loss2: 1.384249 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466424 Loss1: 0.091530 Loss2: 1.374894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439119 Loss1: 0.075775 Loss2: 1.363344 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.742249 Loss1: 0.312271 Loss2: 1.429977 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536310 Loss1: 0.163210 Loss2: 1.373100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.528608 Loss1: 0.166925 Loss2: 1.361682
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.977162 Loss1: 1.099367 Loss2: 1.877795
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.981649 Loss1: 0.598066 Loss2: 1.383583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.705095 Loss1: 0.323461 Loss2: 1.381634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458681 Loss1: 0.101226 Loss2: 1.357455
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.589983 Loss1: 0.237994 Loss2: 1.351989
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485401 Loss1: 0.127084 Loss2: 1.358317
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543227 Loss1: 0.192529 Loss2: 1.350698
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496075 Loss1: 0.149966 Loss2: 1.346109
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480581 Loss1: 0.149817 Loss2: 1.330764
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446148 Loss1: 0.113233 Loss2: 1.332915
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451279 Loss1: 0.119908 Loss2: 1.331371
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410973 Loss1: 0.084321 Loss2: 1.326653
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.891768 Loss1: 1.049885 Loss2: 1.841882
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.996178 Loss1: 0.587973 Loss2: 1.408205
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.820578 Loss1: 0.413256 Loss2: 1.407323
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.627694 Loss1: 0.253338 Loss2: 1.374356
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536191 Loss1: 0.164677 Loss2: 1.371515
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.986550 Loss1: 0.993036 Loss2: 1.993514
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487691 Loss1: 0.137440 Loss2: 1.350251
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486535 Loss1: 0.134583 Loss2: 1.351952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.628109 Loss1: 0.222892 Loss2: 1.405217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.682247 Loss1: 0.306634 Loss2: 1.375614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.581476 Loss1: 0.186426 Loss2: 1.395049 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.537794 Loss1: 0.155031 Loss2: 1.382763 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.539926 Loss1: 0.164632 Loss2: 1.375294 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975260
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.854307 Loss1: 0.936383 Loss2: 1.917924
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.957556 Loss1: 0.536389 Loss2: 1.421167
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.765369 Loss1: 0.352435 Loss2: 1.412934
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.677555 Loss1: 0.278610 Loss2: 1.398945
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.723021 Loss1: 0.928496 Loss2: 1.794525
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.944473 Loss1: 0.582064 Loss2: 1.362409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.751870 Loss1: 0.361134 Loss2: 1.390736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.621420 Loss1: 0.278428 Loss2: 1.342992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518553 Loss1: 0.173694 Loss2: 1.344859 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473650 Loss1: 0.151765 Loss2: 1.321885 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438875 Loss1: 0.091387 Loss2: 1.347488
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472597 Loss1: 0.144922 Loss2: 1.327675
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442137 Loss1: 0.119314 Loss2: 1.322824
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434107 Loss1: 0.116986 Loss2: 1.317121
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.447845 Loss1: 0.124153 Loss2: 1.323692
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.855621 Loss1: 0.947515 Loss2: 1.908105
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.053661 Loss1: 0.638362 Loss2: 1.415298
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.786824 Loss1: 0.339824 Loss2: 1.447000
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629211 Loss1: 0.232991 Loss2: 1.396220
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.772614 Loss1: 0.902213 Loss2: 1.870401
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561167 Loss1: 0.174156 Loss2: 1.387011
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.995111 Loss1: 0.587709 Loss2: 1.407403
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524023 Loss1: 0.141287 Loss2: 1.382736
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.767498 Loss1: 0.332248 Loss2: 1.435250
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480107 Loss1: 0.102179 Loss2: 1.377928
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.691660 Loss1: 0.301196 Loss2: 1.390464
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457816 Loss1: 0.087620 Loss2: 1.370197
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.592651 Loss1: 0.194180 Loss2: 1.398471
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447418 Loss1: 0.080526 Loss2: 1.366893
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.505141 Loss1: 0.131170 Loss2: 1.373972
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420271 Loss1: 0.058677 Loss2: 1.361594
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487197 Loss1: 0.114786 Loss2: 1.372411
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466494 Loss1: 0.104356 Loss2: 1.362138
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433093 Loss1: 0.077331 Loss2: 1.355762
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457043 Loss1: 0.101218 Loss2: 1.355825
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.790784 Loss1: 0.960080 Loss2: 1.830704
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.059812 Loss1: 0.659020 Loss2: 1.400792
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.833672 Loss1: 0.408052 Loss2: 1.425620
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.726046 Loss1: 0.330693 Loss2: 1.395353
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.763550 Loss1: 0.891026 Loss2: 1.872524
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.049129 Loss1: 0.630809 Loss2: 1.418320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.811616 Loss1: 0.360304 Loss2: 1.451311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.691687 Loss1: 0.285659 Loss2: 1.406027 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.612211 Loss1: 0.220222 Loss2: 1.391989 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529990 Loss1: 0.144669 Loss2: 1.385321 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410484 Loss1: 0.077410 Loss2: 1.333074
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.538154 Loss1: 0.153249 Loss2: 1.384905
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.533327 Loss1: 0.153241 Loss2: 1.380086
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.488106 Loss1: 0.106731 Loss2: 1.381376
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458595 Loss1: 0.094023 Loss2: 1.364572
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.933082 Loss1: 0.986974 Loss2: 1.946108
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.939131 Loss1: 0.572654 Loss2: 1.366477
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.775103 Loss1: 0.383903 Loss2: 1.391200
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.680975 Loss1: 0.291203 Loss2: 1.389772
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.706641 Loss1: 0.893299 Loss2: 1.813342
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.521067 Loss1: 0.159132 Loss2: 1.361935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479816 Loss1: 0.129762 Loss2: 1.350053 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440366 Loss1: 0.097116 Loss2: 1.343250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399038 Loss1: 0.070921 Loss2: 1.328117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421887 Loss1: 0.095537 Loss2: 1.326350 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508062 Loss1: 0.163589 Loss2: 1.344474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387618 Loss1: 0.059164 Loss2: 1.328454 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387939 Loss1: 0.063290 Loss2: 1.324649
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.684788 Loss1: 0.757894 Loss2: 1.926894
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.015103 Loss1: 0.572010 Loss2: 1.443093
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.912010 Loss1: 0.446854 Loss2: 1.465156
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.714232 Loss1: 0.290086 Loss2: 1.424146
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631535 Loss1: 0.207276 Loss2: 1.424259
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.805001 Loss1: 0.946110 Loss2: 1.858890
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.631950 Loss1: 0.211909 Loss2: 1.420041
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.521795 Loss1: 0.115776 Loss2: 1.406019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.731073 Loss1: 0.293878 Loss2: 1.437195
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.465362 Loss1: 0.074650 Loss2: 1.390712
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.614374 Loss1: 0.214908 Loss2: 1.399466
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459567 Loss1: 0.070563 Loss2: 1.389004
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.604118 Loss1: 0.203589 Loss2: 1.400528
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438206 Loss1: 0.057665 Loss2: 1.380541
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.562756 Loss1: 0.167593 Loss2: 1.395163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.484537 Loss1: 0.101558 Loss2: 1.382979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.523746 Loss1: 0.141048 Loss2: 1.382699 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.889403 Loss1: 0.428247 Loss2: 1.461156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558987 Loss1: 0.162600 Loss2: 1.396388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.828596 Loss1: 0.981486 Loss2: 1.847110 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.988772 Loss1: 0.586234 Loss2: 1.402538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.798074 Loss1: 0.360709 Loss2: 1.437365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.712195 Loss1: 0.339362 Loss2: 1.372833 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.663544 Loss1: 0.273406 Loss2: 1.390138 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.495356 Loss1: 0.134206 Loss2: 1.361150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.504060 Loss1: 0.139116 Loss2: 1.364944 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.501487 Loss1: 0.140539 Loss2: 1.360949 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.765504 Loss1: 0.370407 Loss2: 1.395097 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.585721 Loss1: 0.221755 Loss2: 1.363966 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530721 Loss1: 0.168452 Loss2: 1.362268
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.725635 Loss1: 0.851662 Loss2: 1.873973
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.939896 Loss1: 0.540539 Loss2: 1.399357 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677601 Loss1: 0.243312 Loss2: 1.434289 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574043 Loss1: 0.207089 Loss2: 1.366955 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405973 Loss1: 0.067540 Loss2: 1.338433
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.586796 Loss1: 0.212354 Loss2: 1.374442
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519076 Loss1: 0.149455 Loss2: 1.369621
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.537422 Loss1: 0.174620 Loss2: 1.362802
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488881 Loss1: 0.118044 Loss2: 1.370837
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.468499 Loss1: 0.110457 Loss2: 1.358043
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.618830 Loss1: 0.778691 Loss2: 1.840139
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450166 Loss1: 0.095369 Loss2: 1.354797
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.754801 Loss1: 0.361725 Loss2: 1.393075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524624 Loss1: 0.184180 Loss2: 1.340443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.474429 Loss1: 0.137770 Loss2: 1.336660
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.594986 Loss1: 0.764860 Loss2: 1.830125
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.835201 Loss1: 0.450049 Loss2: 1.385153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.758272 Loss1: 0.342012 Loss2: 1.416260 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.677104 Loss1: 0.283835 Loss2: 1.393269 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.605575 Loss1: 0.215647 Loss2: 1.389928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509507 Loss1: 0.130417 Loss2: 1.379091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464519 Loss1: 0.098703 Loss2: 1.365816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.139578 Loss1: 0.678803 Loss2: 1.460774 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694690 Loss1: 0.262294 Loss2: 1.432396 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.574346 Loss1: 0.148730 Loss2: 1.425616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.548519 Loss1: 0.132553 Loss2: 1.415966
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.716334 Loss1: 0.864740 Loss2: 1.851594
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.902349 Loss1: 0.510580 Loss2: 1.391768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.704125 Loss1: 0.293658 Loss2: 1.410467 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.520132 Loss1: 0.116888 Loss2: 1.403244
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.570603 Loss1: 0.209374 Loss2: 1.361229
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543913 Loss1: 0.176929 Loss2: 1.366983
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446511 Loss1: 0.095868 Loss2: 1.350643
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470609 Loss1: 0.135144 Loss2: 1.335464
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445122 Loss1: 0.103709 Loss2: 1.341412
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420699 Loss1: 0.081141 Loss2: 1.339558
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.668728 Loss1: 0.787831 Loss2: 1.880897
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440541 Loss1: 0.100603 Loss2: 1.339938
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.055638 Loss1: 0.612691 Loss2: 1.442947
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.827480 Loss1: 0.347147 Loss2: 1.480333
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.707366 Loss1: 0.279942 Loss2: 1.427424
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611470 Loss1: 0.175699 Loss2: 1.435771
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.541995 Loss1: 0.121979 Loss2: 1.420016
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.754368 Loss1: 1.001920 Loss2: 1.752448
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.543833 Loss1: 0.122833 Loss2: 1.420999
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.541935 Loss1: 0.124278 Loss2: 1.417658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509517 Loss1: 0.094043 Loss2: 1.415474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.544026 Loss1: 0.135839 Loss2: 1.408187 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506216 Loss1: 0.203640 Loss2: 1.302576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.361478 Loss1: 0.075329 Loss2: 1.286149 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.348207 Loss1: 0.073762 Loss2: 1.274445
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.898558 Loss1: 1.033794 Loss2: 1.864764
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.029130 Loss1: 0.600672 Loss2: 1.428458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606758 Loss1: 0.220184 Loss2: 1.386574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.517985 Loss1: 0.148735 Loss2: 1.369251 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490732 Loss1: 0.122590 Loss2: 1.368142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460534 Loss1: 0.093878 Loss2: 1.366656 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 05:51:14,294 | server.py:236 | fit_round 103 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458041 Loss1: 0.099865 Loss2: 1.358176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445934 Loss1: 0.088851 Loss2: 1.357083 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582448 Loss1: 0.134210 Loss2: 1.448237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.524496 Loss1: 0.090087 Loss2: 1.434409 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.886841 Loss1: 1.045462 Loss2: 1.841380 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.937579 Loss1: 0.550044 Loss2: 1.387535 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602380 Loss1: 0.238482 Loss2: 1.363898 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484455 Loss1: 0.126906 Loss2: 1.357549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456160 Loss1: 0.101734 Loss2: 1.354426 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439366 Loss1: 0.096172 Loss2: 1.343194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.693834 Loss1: 0.297701 Loss2: 1.396132 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.592135 Loss1: 0.205758 Loss2: 1.386377 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.575533 Loss1: 0.195463 Loss2: 1.380070 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501724 Loss1: 0.113135 Loss2: 1.388589 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.793088 Loss1: 0.944942 Loss2: 1.848146 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.770591 Loss1: 0.373928 Loss2: 1.396663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.630631 Loss1: 0.244809 Loss2: 1.385822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.548419 Loss1: 0.175107 Loss2: 1.373312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.482140 Loss1: 0.122896 Loss2: 1.359244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489854 Loss1: 0.135604 Loss2: 1.354250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439692 Loss1: 0.088800 Loss2: 1.350892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431049 Loss1: 0.075406 Loss2: 1.355643 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470952 Loss1: 0.111178 Loss2: 1.359774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421518 Loss1: 0.069773 Loss2: 1.351745 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994485
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 05:51:14,294][flwr][DEBUG] - fit_round 103 received 50 results and 0 failures
+INFO flwr 2023-10-11 05:51:55,181 | server.py:125 | fit progress: (103, 2.201942985431074, {'accuracy': 0.5712}, 237622.959630363)
+>> Test accuracy: 0.571200
+[2023-10-11 05:51:55,181][flwr][INFO] - fit progress: (103, 2.201942985431074, {'accuracy': 0.5712}, 237622.959630363)
+DEBUG flwr 2023-10-11 05:51:55,181 | server.py:173 | evaluate_round 103: strategy sampled 50 clients (out of 50)
+[2023-10-11 05:51:55,181][flwr][DEBUG] - evaluate_round 103: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 06:00:59,470 | server.py:187 | evaluate_round 103 received 50 results and 0 failures
+[2023-10-11 06:00:59,470][flwr][DEBUG] - evaluate_round 103 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 06:00:59,470 | server.py:222 | fit_round 104: strategy sampled 50 clients (out of 50)
+[2023-10-11 06:00:59,470][flwr][DEBUG] - fit_round 104: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.929769 Loss1: 1.089953 Loss2: 1.839816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.778159 Loss1: 0.376161 Loss2: 1.401998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.624868 Loss1: 0.240768 Loss2: 1.384101
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.718823 Loss1: 0.893453 Loss2: 1.825371
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.588538 Loss1: 0.218441 Loss2: 1.370097
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.888609 Loss1: 0.471504 Loss2: 1.417105
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.707965 Loss1: 0.302139 Loss2: 1.405825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.628171 Loss1: 0.235501 Loss2: 1.392670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.564284 Loss1: 0.185507 Loss2: 1.378776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537724 Loss1: 0.158402 Loss2: 1.379322 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509229 Loss1: 0.140904 Loss2: 1.368326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434402 Loss1: 0.075849 Loss2: 1.358553 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.690898 Loss1: 0.837425 Loss2: 1.853473 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.784061 Loss1: 0.356451 Loss2: 1.427610 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.725726 Loss1: 0.871246 Loss2: 1.854479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.026553 Loss1: 0.596700 Loss2: 1.429853 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524825 Loss1: 0.149821 Loss2: 1.375004 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.517952 Loss1: 0.145651 Loss2: 1.372301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451671 Loss1: 0.086507 Loss2: 1.365164 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.503740 Loss1: 0.148903 Loss2: 1.354837 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441773 Loss1: 0.064741 Loss2: 1.377032 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436204 Loss1: 0.071873 Loss2: 1.364331 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.761285 Loss1: 0.429230 Loss2: 1.332055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573364 Loss1: 0.237757 Loss2: 1.335608 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.853183 Loss1: 0.987976 Loss2: 1.865207
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526718 Loss1: 0.178386 Loss2: 1.348331
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.848622 Loss1: 0.483172 Loss2: 1.365450
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461563 Loss1: 0.135089 Loss2: 1.326474
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.452311 Loss1: 0.128828 Loss2: 1.323483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422649 Loss1: 0.099413 Loss2: 1.323236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441580 Loss1: 0.123103 Loss2: 1.318477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419798 Loss1: 0.099157 Loss2: 1.320641 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977022
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386360 Loss1: 0.059535 Loss2: 1.326825 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.746722 Loss1: 0.839966 Loss2: 1.906757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.829136 Loss1: 0.413165 Loss2: 1.415970 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.686053 Loss1: 0.290471 Loss2: 1.395583
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.810319 Loss1: 0.908167 Loss2: 1.902152
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.989713 Loss1: 0.560711 Loss2: 1.429002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.905022 Loss1: 0.404192 Loss2: 1.500830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.667451 Loss1: 0.240741 Loss2: 1.426711 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687668 Loss1: 0.261786 Loss2: 1.425883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.595328 Loss1: 0.165351 Loss2: 1.429976 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.520333 Loss1: 0.149751 Loss2: 1.370582
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.558890 Loss1: 0.142201 Loss2: 1.416689
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.514124 Loss1: 0.104770 Loss2: 1.409354
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.483628 Loss1: 0.079282 Loss2: 1.404346
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.518299 Loss1: 0.119063 Loss2: 1.399237
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.101655 Loss1: 1.121042 Loss2: 1.980613
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.060622 Loss1: 0.684713 Loss2: 1.375909
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.728349 Loss1: 0.297405 Loss2: 1.430944
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.611137 Loss1: 0.236411 Loss2: 1.374727
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565626 Loss1: 0.205613 Loss2: 1.360013
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564917 Loss1: 0.178726 Loss2: 1.386191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490287 Loss1: 0.134913 Loss2: 1.355375 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451221 Loss1: 0.104167 Loss2: 1.347055
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.808428 Loss1: 0.379107 Loss2: 1.429321
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699006 Loss1: 0.318920 Loss2: 1.380086 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.610170 Loss1: 0.224750 Loss2: 1.385420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.544609 Loss1: 0.177939 Loss2: 1.366670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.945013 Loss1: 0.984729 Loss2: 1.960284 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.040679 Loss1: 0.650166 Loss2: 1.390513 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.667853 Loss1: 0.286202 Loss2: 1.381651 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.585631 Loss1: 0.188232 Loss2: 1.397400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452925 Loss1: 0.081835 Loss2: 1.371090 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450207 Loss1: 0.088180 Loss2: 1.362027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418324 Loss1: 0.061168 Loss2: 1.357156 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998798
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514636 Loss1: 0.200247 Loss2: 1.314388 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440299 Loss1: 0.128447 Loss2: 1.311852 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.604726 Loss1: 0.831388 Loss2: 1.773338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377382 Loss1: 0.084148 Loss2: 1.293234 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987981
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.584315 Loss1: 0.248300 Loss2: 1.336014 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493413 Loss1: 0.164307 Loss2: 1.329105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.584119 Loss1: 0.826173 Loss2: 1.757946
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.482546 Loss1: 0.151267 Loss2: 1.331279
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.740122 Loss1: 0.396017 Loss2: 1.344106
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422184 Loss1: 0.091581 Loss2: 1.330604
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.634141 Loss1: 0.271677 Loss2: 1.362464
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.382489 Loss1: 0.064722 Loss2: 1.317766
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532301 Loss1: 0.212699 Loss2: 1.319601
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369396 Loss1: 0.055684 Loss2: 1.313711
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.448639 Loss1: 0.126707 Loss2: 1.321932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411096 Loss1: 0.099725 Loss2: 1.311371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.810571 Loss1: 0.965911 Loss2: 1.844660
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.353892 Loss1: 0.047472 Loss2: 1.306420
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.916267 Loss1: 0.508624 Loss2: 1.407642
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.359201 Loss1: 0.059142 Loss2: 1.300060
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.641785 Loss1: 0.265106 Loss2: 1.376679 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.548608 Loss1: 0.178068 Loss2: 1.370540 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.526573 Loss1: 0.157037 Loss2: 1.369536
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.629911 Loss1: 0.724449 Loss2: 1.905462
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.921997 Loss1: 0.519122 Loss2: 1.402875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.815821 Loss1: 0.368625 Loss2: 1.447196 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.746269 Loss1: 0.339592 Loss2: 1.406677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530746 Loss1: 0.132483 Loss2: 1.398263 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.497782 Loss1: 0.111587 Loss2: 1.386195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.777244 Loss1: 0.878248 Loss2: 1.898996
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450525 Loss1: 0.068745 Loss2: 1.381780
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.932468 Loss1: 0.523210 Loss2: 1.409258
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434002 Loss1: 0.066515 Loss2: 1.367487
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.548563 Loss1: 0.172233 Loss2: 1.376329 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471822 Loss1: 0.101844 Loss2: 1.369978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512607 Loss1: 0.152766 Loss2: 1.359841
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.941034 Loss1: 0.948062 Loss2: 1.992972
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471972 Loss1: 0.105514 Loss2: 1.366459
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.027841 Loss1: 0.577259 Loss2: 1.450583
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.476063 Loss1: 0.111968 Loss2: 1.364095
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.802205 Loss1: 0.318629 Loss2: 1.483577
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460581 Loss1: 0.105925 Loss2: 1.354656
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.689004 Loss1: 0.252740 Loss2: 1.436264
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.671108 Loss1: 0.232378 Loss2: 1.438730
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.594372 Loss1: 0.159973 Loss2: 1.434399
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.556494 Loss1: 0.132402 Loss2: 1.424092
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.553197 Loss1: 0.131208 Loss2: 1.421989
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.553916 Loss1: 0.128876 Loss2: 1.425039
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.589504 Loss1: 0.730792 Loss2: 1.858712
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.518162 Loss1: 0.097540 Loss2: 1.420622
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.798974 Loss1: 0.424519 Loss2: 1.374455
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.780502 Loss1: 0.362436 Loss2: 1.418066
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.688314 Loss1: 0.314024 Loss2: 1.374290
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593428 Loss1: 0.216227 Loss2: 1.377201
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509379 Loss1: 0.151187 Loss2: 1.358192
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.692379 Loss1: 0.864702 Loss2: 1.827677
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.465472 Loss1: 0.108383 Loss2: 1.357088
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.475499 Loss1: 0.127315 Loss2: 1.348184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431938 Loss1: 0.079821 Loss2: 1.352117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391387 Loss1: 0.050565 Loss2: 1.340823 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495047 Loss1: 0.164277 Loss2: 1.330771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437780 Loss1: 0.113634 Loss2: 1.324146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418197 Loss1: 0.104797 Loss2: 1.313400
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.706690 Loss1: 0.896019 Loss2: 1.810671
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.897868 Loss1: 0.513407 Loss2: 1.384460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.579280 Loss1: 0.215730 Loss2: 1.363550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542911 Loss1: 0.181278 Loss2: 1.361633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503582 Loss1: 0.143929 Loss2: 1.359653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489314 Loss1: 0.131724 Loss2: 1.357590 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459389 Loss1: 0.112870 Loss2: 1.346519 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481341 Loss1: 0.135178 Loss2: 1.346163 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.622505 Loss1: 0.165437 Loss2: 1.457068 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.588925 Loss1: 0.124017 Loss2: 1.464908 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.559962 Loss1: 0.115155 Loss2: 1.444807
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.889019 Loss1: 0.988227 Loss2: 1.900792
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.539367 Loss1: 0.095798 Loss2: 1.443569
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.967107 Loss1: 0.526593 Loss2: 1.440513
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.816274 Loss1: 0.353653 Loss2: 1.462621
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.644974 Loss1: 0.224647 Loss2: 1.420327
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.615130 Loss1: 0.192390 Loss2: 1.422740
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.545727 Loss1: 0.135874 Loss2: 1.409852
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.620211 Loss1: 0.809055 Loss2: 1.811156
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524135 Loss1: 0.113848 Loss2: 1.410286
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.512890 Loss1: 0.109946 Loss2: 1.402945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.541884 Loss1: 0.139543 Loss2: 1.402341 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.497135 Loss1: 0.092183 Loss2: 1.404952 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519824 Loss1: 0.177353 Loss2: 1.342471 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433110 Loss1: 0.109072 Loss2: 1.324038 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418122 Loss1: 0.089427 Loss2: 1.328695
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.773461 Loss1: 0.888949 Loss2: 1.884512
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405309 Loss1: 0.083092 Loss2: 1.322217
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.959385 Loss1: 0.576333 Loss2: 1.383052
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.693917 Loss1: 0.279231 Loss2: 1.414687
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540742 Loss1: 0.183230 Loss2: 1.357512
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488223 Loss1: 0.127442 Loss2: 1.360780
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458246 Loss1: 0.109488 Loss2: 1.348758
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.727638 Loss1: 0.875728 Loss2: 1.851910
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450346 Loss1: 0.107212 Loss2: 1.343134
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452234 Loss1: 0.111361 Loss2: 1.340873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444284 Loss1: 0.098707 Loss2: 1.345577 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413070 Loss1: 0.078024 Loss2: 1.335046 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465538 Loss1: 0.101421 Loss2: 1.364117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484492 Loss1: 0.121585 Loss2: 1.362906 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.456094 Loss1: 0.098073 Loss2: 1.358021
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.840011 Loss1: 0.956074 Loss2: 1.883937
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446767 Loss1: 0.088441 Loss2: 1.358326
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.936156 Loss1: 0.562070 Loss2: 1.374086
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.725992 Loss1: 0.317383 Loss2: 1.408609
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.616907 Loss1: 0.249564 Loss2: 1.367342
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.585153 Loss1: 0.205461 Loss2: 1.379691
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.575943 Loss1: 0.208756 Loss2: 1.367187
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.734505 Loss1: 0.810565 Loss2: 1.923941
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502536 Loss1: 0.140842 Loss2: 1.361694
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.056045 Loss1: 0.607389 Loss2: 1.448656
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461139 Loss1: 0.102594 Loss2: 1.358545
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.813152 Loss1: 0.368554 Loss2: 1.444598
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443640 Loss1: 0.094338 Loss2: 1.349302
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.724615 Loss1: 0.315271 Loss2: 1.409344
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418352 Loss1: 0.075111 Loss2: 1.343241
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567981 Loss1: 0.167518 Loss2: 1.400463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548319 Loss1: 0.157480 Loss2: 1.390840 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497309 Loss1: 0.110430 Loss2: 1.386879
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.840712 Loss1: 0.966047 Loss2: 1.874665
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463777 Loss1: 0.087655 Loss2: 1.376122
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.941817 Loss1: 0.527247 Loss2: 1.414570
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.839926 Loss1: 0.428150 Loss2: 1.411776
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.640132 Loss1: 0.236932 Loss2: 1.403200
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.541234 Loss1: 0.148996 Loss2: 1.392238
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513122 Loss1: 0.136761 Loss2: 1.376361
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.087577 Loss1: 1.088814 Loss2: 1.998763
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475192 Loss1: 0.095393 Loss2: 1.379799
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.233568 Loss1: 0.717562 Loss2: 1.516006
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437138 Loss1: 0.069717 Loss2: 1.367421
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421441 Loss1: 0.059789 Loss2: 1.361652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417094 Loss1: 0.059693 Loss2: 1.357401 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.620882 Loss1: 0.162779 Loss2: 1.458103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.547861 Loss1: 0.103571 Loss2: 1.444290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.767671 Loss1: 0.928852 Loss2: 1.838819 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.791507 Loss1: 0.372784 Loss2: 1.418723 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514076 Loss1: 0.159600 Loss2: 1.354477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491214 Loss1: 0.145704 Loss2: 1.345510
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.892426 Loss1: 0.957826 Loss2: 1.934599
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.031595 Loss1: 0.587831 Loss2: 1.443764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.881015 Loss1: 0.386322 Loss2: 1.494693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.673894 Loss1: 0.244304 Loss2: 1.429590 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424941 Loss1: 0.089139 Loss2: 1.335803
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.649039 Loss1: 0.210555 Loss2: 1.438483
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.568769 Loss1: 0.140532 Loss2: 1.428238
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.520320 Loss1: 0.115562 Loss2: 1.404758
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.521876 Loss1: 0.114546 Loss2: 1.407331
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490446 Loss1: 0.084423 Loss2: 1.406023
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.750814 Loss1: 0.879788 Loss2: 1.871026
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.518825 Loss1: 0.118787 Loss2: 1.400038
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716995 Loss1: 0.298863 Loss2: 1.418131 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561954 Loss1: 0.181620 Loss2: 1.380333 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492724 Loss1: 0.121332 Loss2: 1.371392
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.763051 Loss1: 0.867418 Loss2: 1.895633
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.028097 Loss1: 0.594387 Loss2: 1.433710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.846045 Loss1: 0.411336 Loss2: 1.434709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.662571 Loss1: 0.255858 Loss2: 1.406713 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421440 Loss1: 0.068655 Loss2: 1.352785
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.593735 Loss1: 0.195384 Loss2: 1.398351
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531463 Loss1: 0.137475 Loss2: 1.393988
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485914 Loss1: 0.101927 Loss2: 1.383987
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438707 Loss1: 0.067476 Loss2: 1.371231
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432170 Loss1: 0.067196 Loss2: 1.364974
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.658638 Loss1: 0.795391 Loss2: 1.863247
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454613 Loss1: 0.089096 Loss2: 1.365517
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.887054 Loss1: 0.414903 Loss2: 1.472151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.591186 Loss1: 0.200046 Loss2: 1.391140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564686 Loss1: 0.189771 Loss2: 1.374914
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.795498 Loss1: 0.949985 Loss2: 1.845513
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.914478 Loss1: 0.551743 Loss2: 1.362735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.705294 Loss1: 0.310103 Loss2: 1.395191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488535 Loss1: 0.120297 Loss2: 1.368239
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573440 Loss1: 0.218320 Loss2: 1.355120
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434558 Loss1: 0.075245 Loss2: 1.359312
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.561303 Loss1: 0.205657 Loss2: 1.355645
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472600 Loss1: 0.118790 Loss2: 1.353810
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465458 Loss1: 0.124181 Loss2: 1.341278
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426006 Loss1: 0.087130 Loss2: 1.338875
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408702 Loss1: 0.076976 Loss2: 1.331727
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.790197 Loss1: 0.889249 Loss2: 1.900948
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378695 Loss1: 0.052688 Loss2: 1.326007
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.851714 Loss1: 0.353647 Loss2: 1.498067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.748652 Loss1: 0.282144 Loss2: 1.466508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.750366 Loss1: 0.864818 Loss2: 1.885548
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.670322 Loss1: 0.203348 Loss2: 1.466974
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.059931 Loss1: 0.629318 Loss2: 1.430614
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.669099 Loss1: 0.217806 Loss2: 1.451293
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834629 Loss1: 0.375242 Loss2: 1.459387
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.588576 Loss1: 0.135625 Loss2: 1.452951
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.541116 Loss1: 0.101393 Loss2: 1.439723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.522854 Loss1: 0.087335 Loss2: 1.435520 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.542272 Loss1: 0.137333 Loss2: 1.404939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.482369 Loss1: 0.096203 Loss2: 1.386166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465255 Loss1: 0.083899 Loss2: 1.381356
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.952828 Loss1: 1.037583 Loss2: 1.915245
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.112304 Loss1: 0.623443 Loss2: 1.488860
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.809877 Loss1: 0.353191 Loss2: 1.456686
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.734412 Loss1: 0.287113 Loss2: 1.447299
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.717836 Loss1: 0.257401 Loss2: 1.460435
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.873568 Loss1: 1.004145 Loss2: 1.869423
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.704629 Loss1: 0.259523 Loss2: 1.445106
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.634212 Loss1: 0.195460 Loss2: 1.438751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.567909 Loss1: 0.139702 Loss2: 1.428207 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.541126 Loss1: 0.118882 Loss2: 1.422244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508761 Loss1: 0.094850 Loss2: 1.413911 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474557 Loss1: 0.122089 Loss2: 1.352468 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.472517 Loss1: 0.121578 Loss2: 1.350939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431507 Loss1: 0.083845 Loss2: 1.347662
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.809931 Loss1: 0.900398 Loss2: 1.909534
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.997577 Loss1: 0.561209 Loss2: 1.436368
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.846085 Loss1: 0.386527 Loss2: 1.459559
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.684934 Loss1: 0.273724 Loss2: 1.411210
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.664557 Loss1: 0.254476 Loss2: 1.410081
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.028210 Loss1: 1.054620 Loss2: 1.973591
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.592707 Loss1: 0.185061 Loss2: 1.407646
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.546025 Loss1: 0.145169 Loss2: 1.400856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.524202 Loss1: 0.129186 Loss2: 1.395016 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499632 Loss1: 0.116664 Loss2: 1.382968 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.480870 Loss1: 0.095322 Loss2: 1.385547 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.598545 Loss1: 0.149495 Loss2: 1.449050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.559867 Loss1: 0.117604 Loss2: 1.442263 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.532698 Loss1: 0.092164 Loss2: 1.440533
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.940954 Loss1: 1.044640 Loss2: 1.896314
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.037365 Loss1: 0.620215 Loss2: 1.417150
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.764661 Loss1: 0.325852 Loss2: 1.438809
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.662450 Loss1: 0.257184 Loss2: 1.405266
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.607765 Loss1: 0.202498 Loss2: 1.405267
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.776649 Loss1: 1.005936 Loss2: 1.770713
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.584673 Loss1: 0.184827 Loss2: 1.399846
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.593624 Loss1: 0.188155 Loss2: 1.405469 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.561323 Loss1: 0.154138 Loss2: 1.407185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.550501 Loss1: 0.158753 Loss2: 1.391749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.522489 Loss1: 0.125046 Loss2: 1.397444 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.432470 Loss1: 0.122880 Loss2: 1.309590 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423841 Loss1: 0.122628 Loss2: 1.301213 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.359756 Loss1: 0.060525 Loss2: 1.299231
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.703370 Loss1: 0.872428 Loss2: 1.830943
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.842005 Loss1: 0.492982 Loss2: 1.349023
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.677691 Loss1: 0.281919 Loss2: 1.395772
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533914 Loss1: 0.182718 Loss2: 1.351196
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491772 Loss1: 0.154856 Loss2: 1.336916
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.027748 Loss1: 1.113328 Loss2: 1.914420
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.496417 Loss1: 0.157270 Loss2: 1.339147
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.429387 Loss1: 0.095241 Loss2: 1.334146 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420998 Loss1: 0.094899 Loss2: 1.326099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394061 Loss1: 0.074437 Loss2: 1.319624 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398569 Loss1: 0.081720 Loss2: 1.316849 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.506117 Loss1: 0.139949 Loss2: 1.366168 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434354 Loss1: 0.081003 Loss2: 1.353351 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+DEBUG flwr 2023-10-11 06:29:21,456 | server.py:236 | fit_round 104 received 50 results and 0 failures
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.954503 Loss1: 0.555921 Loss2: 1.398581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.680183 Loss1: 0.295338 Loss2: 1.384844 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.627717 Loss1: 0.216198 Loss2: 1.411519
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.774576 Loss1: 0.873182 Loss2: 1.901394
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.560921 Loss1: 0.175738 Loss2: 1.385183
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.951285 Loss1: 0.518336 Loss2: 1.432950
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.477004 Loss1: 0.097181 Loss2: 1.379823
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.941515 Loss1: 0.468616 Loss2: 1.472899
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469518 Loss1: 0.101010 Loss2: 1.368508
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.632019 Loss1: 0.215939 Loss2: 1.416080
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450251 Loss1: 0.085207 Loss2: 1.365044
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.611231 Loss1: 0.200567 Loss2: 1.410664
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443684 Loss1: 0.079942 Loss2: 1.363742
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537212 Loss1: 0.128235 Loss2: 1.408977
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530752 Loss1: 0.129681 Loss2: 1.401070
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511767 Loss1: 0.116579 Loss2: 1.395188
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476092 Loss1: 0.084809 Loss2: 1.391283
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.527442 Loss1: 0.133922 Loss2: 1.393520
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.798455 Loss1: 0.944892 Loss2: 1.853563
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.952215 Loss1: 0.566389 Loss2: 1.385827
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.766361 Loss1: 0.342248 Loss2: 1.424114
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.650531 Loss1: 0.278118 Loss2: 1.372412
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544962 Loss1: 0.155168 Loss2: 1.389794
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.852797 Loss1: 0.961987 Loss2: 1.890810
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.055749 Loss1: 0.586346 Loss2: 1.469403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.814418 Loss1: 0.355623 Loss2: 1.458795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.685898 Loss1: 0.247684 Loss2: 1.438214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.634807 Loss1: 0.210249 Loss2: 1.424558 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.604893 Loss1: 0.175715 Loss2: 1.429178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.557029 Loss1: 0.140706 Loss2: 1.416323 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.576039 Loss1: 0.156629 Loss2: 1.419410 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 06:29:21,456][flwr][DEBUG] - fit_round 104 received 50 results and 0 failures
+INFO flwr 2023-10-11 06:30:03,449 | server.py:125 | fit progress: (104, 2.207348462110891, {'accuracy': 0.5699}, 239911.22773559002)
+>> Test accuracy: 0.569900
+[2023-10-11 06:30:03,449][flwr][INFO] - fit progress: (104, 2.207348462110891, {'accuracy': 0.5699}, 239911.22773559002)
+DEBUG flwr 2023-10-11 06:30:03,450 | server.py:173 | evaluate_round 104: strategy sampled 50 clients (out of 50)
+[2023-10-11 06:30:03,450][flwr][DEBUG] - evaluate_round 104: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 06:39:07,247 | server.py:187 | evaluate_round 104 received 50 results and 0 failures
+[2023-10-11 06:39:07,247][flwr][DEBUG] - evaluate_round 104 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 06:39:07,248 | server.py:222 | fit_round 105: strategy sampled 50 clients (out of 50)
+[2023-10-11 06:39:07,248][flwr][DEBUG] - fit_round 105: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.704990 Loss1: 0.810588 Loss2: 1.894402
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.069108 Loss1: 0.651020 Loss2: 1.418088
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.893941 Loss1: 0.417519 Loss2: 1.476422
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.679731 Loss1: 0.277142 Loss2: 1.402589
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.946470 Loss1: 1.064075 Loss2: 1.882395
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.588677 Loss1: 0.184592 Loss2: 1.404085
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.023537 Loss1: 0.636189 Loss2: 1.387348
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587663 Loss1: 0.195733 Loss2: 1.391930
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.827323 Loss1: 0.432653 Loss2: 1.394670
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.533449 Loss1: 0.130003 Loss2: 1.403447
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.667838 Loss1: 0.299752 Loss2: 1.368086
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548310 Loss1: 0.190905 Loss2: 1.357404
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489236 Loss1: 0.107316 Loss2: 1.381920
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.533683 Loss1: 0.185082 Loss2: 1.348601
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488186 Loss1: 0.104900 Loss2: 1.383287
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.482994 Loss1: 0.131317 Loss2: 1.351677
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.557399 Loss1: 0.163761 Loss2: 1.393638
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432047 Loss1: 0.099475 Loss2: 1.332572 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.023920 Loss1: 1.063371 Loss2: 1.960549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.794615 Loss1: 0.338917 Loss2: 1.455698 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.832292 Loss1: 0.926246 Loss2: 1.906046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.900784 Loss1: 0.481937 Loss2: 1.418847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.497296 Loss1: 0.096930 Loss2: 1.400367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.509120 Loss1: 0.117272 Loss2: 1.391848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.475780 Loss1: 0.079219 Loss2: 1.396561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.470490 Loss1: 0.083929 Loss2: 1.386561 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485727 Loss1: 0.116196 Loss2: 1.369531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405110 Loss1: 0.045449 Loss2: 1.359662 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405960 Loss1: 0.052912 Loss2: 1.353048
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.767476 Loss1: 0.939723 Loss2: 1.827753
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.904753 Loss1: 0.534596 Loss2: 1.370157
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.713313 Loss1: 0.317154 Loss2: 1.396159
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.626237 Loss1: 0.264235 Loss2: 1.362002
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.608280 Loss1: 0.247753 Loss2: 1.360527
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.746541 Loss1: 0.888535 Loss2: 1.858006
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.882995 Loss1: 0.485912 Loss2: 1.397083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.796249 Loss1: 0.350730 Loss2: 1.445519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.647579 Loss1: 0.261922 Loss2: 1.385656 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.668227 Loss1: 0.261786 Loss2: 1.406441 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421018 Loss1: 0.083870 Loss2: 1.337149
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.562436 Loss1: 0.166671 Loss2: 1.395765
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.526629 Loss1: 0.147625 Loss2: 1.379004
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.530575 Loss1: 0.151002 Loss2: 1.379573
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480955 Loss1: 0.098207 Loss2: 1.382748
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466201 Loss1: 0.097050 Loss2: 1.369151
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.077102 Loss1: 1.086885 Loss2: 1.990217
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.982613 Loss1: 0.610976 Loss2: 1.371637
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.764072 Loss1: 0.339007 Loss2: 1.425065
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.656068 Loss1: 0.272231 Loss2: 1.383837
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.564359 Loss1: 0.194835 Loss2: 1.369524
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.501228 Loss1: 0.134191 Loss2: 1.367037
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.699628 Loss1: 0.830177 Loss2: 1.869451
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446434 Loss1: 0.096506 Loss2: 1.349928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.730772 Loss1: 0.299198 Loss2: 1.431574 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.589442 Loss1: 0.205308 Loss2: 1.384134 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502087 Loss1: 0.129451 Loss2: 1.372636 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460392 Loss1: 0.092247 Loss2: 1.368145
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.659922 Loss1: 0.830964 Loss2: 1.828958
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.808105 Loss1: 0.463254 Loss2: 1.344851 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.520559 Loss1: 0.152726 Loss2: 1.367833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.617539 Loss1: 0.243030 Loss2: 1.374509
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562894 Loss1: 0.231637 Loss2: 1.331258
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544424 Loss1: 0.206226 Loss2: 1.338197
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506720 Loss1: 0.171508 Loss2: 1.335212
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456621 Loss1: 0.125179 Loss2: 1.331441
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.943531 Loss1: 0.968392 Loss2: 1.975139
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415076 Loss1: 0.089940 Loss2: 1.325136
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402406 Loss1: 0.081820 Loss2: 1.320586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404699 Loss1: 0.086623 Loss2: 1.318075 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491796 Loss1: 0.140122 Loss2: 1.351674 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.525596 Loss1: 0.173561 Loss2: 1.352036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.477093 Loss1: 0.117162 Loss2: 1.359931
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.731036 Loss1: 0.839107 Loss2: 1.891929
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.984097 Loss1: 0.574550 Loss2: 1.409546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.598663 Loss1: 0.212366 Loss2: 1.386296 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.563550 Loss1: 0.171588 Loss2: 1.391962 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.545025 Loss1: 0.154724 Loss2: 1.390301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.511097 Loss1: 0.131367 Loss2: 1.379730 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451280 Loss1: 0.082112 Loss2: 1.369169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454922 Loss1: 0.088569 Loss2: 1.366353 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.536079 Loss1: 0.124903 Loss2: 1.411176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.506982 Loss1: 0.113785 Loss2: 1.393196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.587563 Loss1: 0.790587 Loss2: 1.796976
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.529306 Loss1: 0.125203 Loss2: 1.404103
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.852802 Loss1: 0.509684 Loss2: 1.343118
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.510878 Loss1: 0.119773 Loss2: 1.391106
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.603526 Loss1: 0.267204 Loss2: 1.336322 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503006 Loss1: 0.159473 Loss2: 1.343533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509154 Loss1: 0.179625 Loss2: 1.329529
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.705705 Loss1: 0.859212 Loss2: 1.846493
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.026424 Loss1: 0.627550 Loss2: 1.398875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.762127 Loss1: 0.329828 Loss2: 1.432299 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378877 Loss1: 0.062733 Loss2: 1.316144
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.601972 Loss1: 0.236638 Loss2: 1.365334
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.494224 Loss1: 0.127501 Loss2: 1.366723
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484781 Loss1: 0.131404 Loss2: 1.353376
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491182 Loss1: 0.140591 Loss2: 1.350592
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.463829 Loss1: 0.110784 Loss2: 1.353045
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.946464 Loss1: 1.013604 Loss2: 1.932860
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453324 Loss1: 0.108642 Loss2: 1.344682
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.471490 Loss1: 0.122584 Loss2: 1.348906 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.686097 Loss1: 0.305166 Loss2: 1.380931 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.588252 Loss1: 0.195622 Loss2: 1.392630 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.755654 Loss1: 0.850016 Loss2: 1.905638 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.478763 Loss1: 0.116547 Loss2: 1.362216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461982 Loss1: 0.104572 Loss2: 1.357409 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.592790 Loss1: 0.201813 Loss2: 1.390977 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.515008 Loss1: 0.130692 Loss2: 1.384315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488599 Loss1: 0.115908 Loss2: 1.372691
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.788957 Loss1: 0.900982 Loss2: 1.887975
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.500570 Loss1: 0.122348 Loss2: 1.378222
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.955209 Loss1: 0.541935 Loss2: 1.413274
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472142 Loss1: 0.096166 Loss2: 1.375976
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.739542 Loss1: 0.293578 Loss2: 1.445964
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.770011 Loss1: 0.355973 Loss2: 1.414039
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.632920 Loss1: 0.201664 Loss2: 1.431256
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.617506 Loss1: 0.212738 Loss2: 1.404768
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.555559 Loss1: 0.153116 Loss2: 1.402443
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.886381 Loss1: 1.022106 Loss2: 1.864275
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461556 Loss1: 0.075501 Loss2: 1.386055
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.949571 Loss1: 0.549323 Loss2: 1.400248
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469746 Loss1: 0.093394 Loss2: 1.376352
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.753188 Loss1: 0.344263 Loss2: 1.408925
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443538 Loss1: 0.067546 Loss2: 1.375992
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.578280 Loss1: 0.200684 Loss2: 1.377596 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502185 Loss1: 0.146390 Loss2: 1.355795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493246 Loss1: 0.121524 Loss2: 1.371722
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.689228 Loss1: 0.832705 Loss2: 1.856523
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444711 Loss1: 0.088457 Loss2: 1.356254
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.918539 Loss1: 0.494224 Loss2: 1.424315
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426221 Loss1: 0.080163 Loss2: 1.346058
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.856939 Loss1: 0.407687 Loss2: 1.449252
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.683847 Loss1: 0.264542 Loss2: 1.419306
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.680277 Loss1: 0.268407 Loss2: 1.411870
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.566831 Loss1: 0.155003 Loss2: 1.411828
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531997 Loss1: 0.136946 Loss2: 1.395051
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.636753 Loss1: 0.825539 Loss2: 1.811213
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.866632 Loss1: 0.491746 Loss2: 1.374887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677018 Loss1: 0.276728 Loss2: 1.400290 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.620160 Loss1: 0.267350 Loss2: 1.352811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522930 Loss1: 0.168371 Loss2: 1.354559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477849 Loss1: 0.126913 Loss2: 1.350936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476990 Loss1: 0.137556 Loss2: 1.339434 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690251 Loss1: 0.330161 Loss2: 1.360090 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.578854 Loss1: 0.231349 Loss2: 1.347505 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458797 Loss1: 0.132479 Loss2: 1.326317 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453437 Loss1: 0.137059 Loss2: 1.316379
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.919884 Loss1: 0.971513 Loss2: 1.948370
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.416040 Loss1: 0.095975 Loss2: 1.320065
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.104415 Loss1: 0.557777 Loss2: 1.546639
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395940 Loss1: 0.081364 Loss2: 1.314576
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.927699 Loss1: 0.428862 Loss2: 1.498837
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.787505 Loss1: 0.285938 Loss2: 1.501567
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.692919 Loss1: 0.214828 Loss2: 1.478092
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.672874 Loss1: 0.188139 Loss2: 1.484735
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.580356 Loss1: 0.111222 Loss2: 1.469134
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.881915 Loss1: 1.013494 Loss2: 1.868422
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.592142 Loss1: 0.130844 Loss2: 1.461298
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.566506 Loss1: 0.103687 Loss2: 1.462819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.558737 Loss1: 0.104695 Loss2: 1.454042 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566239 Loss1: 0.186163 Loss2: 1.380076 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472724 Loss1: 0.115256 Loss2: 1.357467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.768805 Loss1: 0.925720 Loss2: 1.843085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.914322 Loss1: 0.533584 Loss2: 1.380738 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604530 Loss1: 0.235991 Loss2: 1.368539 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473870 Loss1: 0.111413 Loss2: 1.362458 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473063 Loss1: 0.115433 Loss2: 1.357631
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.642672 Loss1: 0.804628 Loss2: 1.838044
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.864674 Loss1: 0.504747 Loss2: 1.359927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.704026 Loss1: 0.323389 Loss2: 1.380636 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450565 Loss1: 0.100488 Loss2: 1.350077
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.622844 Loss1: 0.286444 Loss2: 1.336400
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516515 Loss1: 0.167550 Loss2: 1.348965
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508086 Loss1: 0.181380 Loss2: 1.326706
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460023 Loss1: 0.140417 Loss2: 1.319606
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423172 Loss1: 0.100140 Loss2: 1.323032
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.858226 Loss1: 1.007586 Loss2: 1.850640
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407911 Loss1: 0.097837 Loss2: 1.310074
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401420 Loss1: 0.091933 Loss2: 1.309487 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.670115 Loss1: 0.282089 Loss2: 1.388026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529941 Loss1: 0.173524 Loss2: 1.356417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.462725 Loss1: 0.110026 Loss2: 1.352699
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.762939 Loss1: 0.920167 Loss2: 1.842772
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.969079 Loss1: 0.585862 Loss2: 1.383217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.796796 Loss1: 0.375200 Loss2: 1.421596 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638257 Loss1: 0.272490 Loss2: 1.365767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.496816 Loss1: 0.138364 Loss2: 1.358452 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411443 Loss1: 0.066889 Loss2: 1.344554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398661 Loss1: 0.065201 Loss2: 1.333460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420147 Loss1: 0.084267 Loss2: 1.335881 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604599 Loss1: 0.221971 Loss2: 1.382627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521599 Loss1: 0.147257 Loss2: 1.374342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.752581 Loss1: 0.922792 Loss2: 1.829789 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.008750 Loss1: 0.579379 Loss2: 1.429371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.742175 Loss1: 0.328567 Loss2: 1.413607 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.596853 Loss1: 0.223509 Loss2: 1.373344 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.541121 Loss1: 0.165137 Loss2: 1.375984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.723652 Loss1: 0.898760 Loss2: 1.824891
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.551347 Loss1: 0.173815 Loss2: 1.377532
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.538232 Loss1: 0.170027 Loss2: 1.368205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445051 Loss1: 0.082056 Loss2: 1.362994 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.546661 Loss1: 0.177156 Loss2: 1.369505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516669 Loss1: 0.163872 Loss2: 1.352797 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.917166 Loss1: 0.967588 Loss2: 1.949578 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.932726 Loss1: 0.472707 Loss2: 1.460019 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.831482 Loss1: 0.357508 Loss2: 1.473973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.685543 Loss1: 0.246217 Loss2: 1.439326 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.555581 Loss1: 0.146061 Loss2: 1.409520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.533203 Loss1: 0.126803 Loss2: 1.406400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.978778 Loss1: 0.533884 Loss2: 1.444894
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.522859 Loss1: 0.123764 Loss2: 1.399095
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.526830 Loss1: 0.130416 Loss2: 1.396414
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.722990 Loss1: 0.292011 Loss2: 1.430979
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.671496 Loss1: 0.257320 Loss2: 1.414176
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556176 Loss1: 0.146538 Loss2: 1.409638
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500154 Loss1: 0.105885 Loss2: 1.394269
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504581 Loss1: 0.115171 Loss2: 1.389411
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.924492 Loss1: 1.041299 Loss2: 1.883193
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.046729 Loss1: 0.627222 Loss2: 1.419507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.772991 Loss1: 0.335077 Loss2: 1.437914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487753 Loss1: 0.102510 Loss2: 1.385243
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.679815 Loss1: 0.291375 Loss2: 1.388440
+(DefaultActor pid=3764) >> Training accuracy: 0.988971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.596011 Loss1: 0.199968 Loss2: 1.396043
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.512338 Loss1: 0.136740 Loss2: 1.375597
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525103 Loss1: 0.148822 Loss2: 1.376282
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.541751 Loss1: 0.173962 Loss2: 1.367789
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.739211 Loss1: 0.947689 Loss2: 1.791522
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.467232 Loss1: 0.092495 Loss2: 1.374737
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.906157 Loss1: 0.564928 Loss2: 1.341230
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443857 Loss1: 0.082155 Loss2: 1.361702
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611317 Loss1: 0.285887 Loss2: 1.325431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528709 Loss1: 0.197222 Loss2: 1.331487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.510045 Loss1: 0.195295 Loss2: 1.314750
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.750069 Loss1: 0.908323 Loss2: 1.841746
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.893305 Loss1: 0.524871 Loss2: 1.368434 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.709701 Loss1: 0.305890 Loss2: 1.403811 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436388 Loss1: 0.124771 Loss2: 1.311617
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573112 Loss1: 0.220707 Loss2: 1.352406
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519604 Loss1: 0.165437 Loss2: 1.354167
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462041 Loss1: 0.115636 Loss2: 1.346405
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419952 Loss1: 0.087457 Loss2: 1.332495
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.397179 Loss1: 0.068172 Loss2: 1.329007
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.562031 Loss1: 0.800603 Loss2: 1.761428
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394377 Loss1: 0.071028 Loss2: 1.323349
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.788553 Loss1: 0.482742 Loss2: 1.305811
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386620 Loss1: 0.058847 Loss2: 1.327773
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530378 Loss1: 0.228015 Loss2: 1.302363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.405185 Loss1: 0.120540 Loss2: 1.284645 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.394624 Loss1: 0.114186 Loss2: 1.280438
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.582866 Loss1: 0.801826 Loss2: 1.781040
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.825640 Loss1: 0.474071 Loss2: 1.351568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.702964 Loss1: 0.310616 Loss2: 1.392347 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.338423 Loss1: 0.061566 Loss2: 1.276857
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.593469 Loss1: 0.247823 Loss2: 1.345646
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589094 Loss1: 0.240826 Loss2: 1.348269
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.540089 Loss1: 0.180092 Loss2: 1.359997
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534935 Loss1: 0.187654 Loss2: 1.347281
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467354 Loss1: 0.126182 Loss2: 1.341172
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.886000 Loss1: 0.965419 Loss2: 1.920582
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.061805 Loss1: 0.601800 Loss2: 1.460004 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391238 Loss1: 0.066332 Loss2: 1.324905
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.897626 Loss1: 0.409226 Loss2: 1.488400
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.717917 Loss1: 0.283551 Loss2: 1.434366
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.641832 Loss1: 0.205972 Loss2: 1.435859
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.577736 Loss1: 0.157617 Loss2: 1.420119
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.577783 Loss1: 0.162339 Loss2: 1.415444
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.858696 Loss1: 0.940419 Loss2: 1.918277
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.534707 Loss1: 0.115284 Loss2: 1.419423
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.510737 Loss1: 0.105204 Loss2: 1.405533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.522519 Loss1: 0.117940 Loss2: 1.404578 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.590310 Loss1: 0.213274 Loss2: 1.377035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.537687 Loss1: 0.167224 Loss2: 1.370463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.631922 Loss1: 0.794199 Loss2: 1.837723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463603 Loss1: 0.098984 Loss2: 1.364619 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.625341 Loss1: 0.244334 Loss2: 1.381006 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523680 Loss1: 0.148261 Loss2: 1.375418 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.636315 Loss1: 0.803002 Loss2: 1.833313
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448154 Loss1: 0.077069 Loss2: 1.371084
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437348 Loss1: 0.080451 Loss2: 1.356898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433106 Loss1: 0.084493 Loss2: 1.348613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405803 Loss1: 0.059835 Loss2: 1.345968 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.474748 Loss1: 0.126552 Loss2: 1.348196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433276 Loss1: 0.091239 Loss2: 1.342038 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.779556 Loss1: 0.862169 Loss2: 1.917387 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.831407 Loss1: 0.344247 Loss2: 1.487160 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.665901 Loss1: 0.229604 Loss2: 1.436297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.573921 Loss1: 0.166262 Loss2: 1.407659
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.665933 Loss1: 0.765220 Loss2: 1.900714
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.030780 Loss1: 0.573490 Loss2: 1.457290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.823376 Loss1: 0.330635 Loss2: 1.492741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.744329 Loss1: 0.308422 Loss2: 1.435908 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.667436 Loss1: 0.216123 Loss2: 1.451313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.564610 Loss1: 0.137593 Loss2: 1.427017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.581205 Loss1: 0.156628 Loss2: 1.424577 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.537841 Loss1: 0.114856 Loss2: 1.422985 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.652492 Loss1: 0.271572 Loss2: 1.380920 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484906 Loss1: 0.122626 Loss2: 1.362281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472776 Loss1: 0.114929 Loss2: 1.357847
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.685378 Loss1: 0.844962 Loss2: 1.840415
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470305 Loss1: 0.114261 Loss2: 1.356044
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.875090 Loss1: 0.475087 Loss2: 1.400003
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479162 Loss1: 0.120018 Loss2: 1.359145
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.695350 Loss1: 0.297876 Loss2: 1.397475
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431353 Loss1: 0.086180 Loss2: 1.345173
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.643353 Loss1: 0.252664 Loss2: 1.390689
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.550515 Loss1: 0.181604 Loss2: 1.368911
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.527273 Loss1: 0.157649 Loss2: 1.369624
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480302 Loss1: 0.117425 Loss2: 1.362877
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499371 Loss1: 0.140764 Loss2: 1.358607
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.808688 Loss1: 0.867330 Loss2: 1.941358
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.056901 Loss1: 0.578706 Loss2: 1.478194 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.786904 Loss1: 0.321438 Loss2: 1.465466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.644267 Loss1: 0.193193 Loss2: 1.451074 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488796 Loss1: 0.071741 Loss2: 1.417054 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483311 Loss1: 0.072579 Loss2: 1.410733 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.478270 Loss1: 0.075037 Loss2: 1.403233 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.507427 Loss1: 0.102489 Loss2: 1.404938 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.663261 Loss1: 0.242050 Loss2: 1.421212 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 07:07:33,861 | server.py:236 | fit_round 105 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496215 Loss1: 0.101309 Loss2: 1.394906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461179 Loss1: 0.074135 Loss2: 1.387044
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.733099 Loss1: 0.837265 Loss2: 1.895835
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.985977 Loss1: 0.577941 Loss2: 1.408036 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.920637 Loss1: 0.426664 Loss2: 1.493972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.644741 Loss1: 0.212155 Loss2: 1.432586 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.560722 Loss1: 0.164839 Loss2: 1.395883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482661 Loss1: 0.088239 Loss2: 1.394422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448939 Loss1: 0.065391 Loss2: 1.383548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420560 Loss1: 0.042602 Loss2: 1.377958 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567793 Loss1: 0.163537 Loss2: 1.404256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509547 Loss1: 0.133383 Loss2: 1.376164 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491366 Loss1: 0.107410 Loss2: 1.383956
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.736929 Loss1: 0.907590 Loss2: 1.829338
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.107040 Loss1: 0.722161 Loss2: 1.384879 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.839277 Loss1: 0.407305 Loss2: 1.431973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.632637 Loss1: 0.234836 Loss2: 1.397801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.529429 Loss1: 0.163497 Loss2: 1.365932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452651 Loss1: 0.097636 Loss2: 1.355015 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 07:07:33,861][flwr][DEBUG] - fit_round 105 received 50 results and 0 failures
+INFO flwr 2023-10-11 07:08:15,351 | server.py:125 | fit progress: (105, 2.1982187847740735, {'accuracy': 0.5705}, 242203.12937831102)
+>> Test accuracy: 0.570500
+[2023-10-11 07:08:15,351][flwr][INFO] - fit progress: (105, 2.1982187847740735, {'accuracy': 0.5705}, 242203.12937831102)
+DEBUG flwr 2023-10-11 07:08:15,351 | server.py:173 | evaluate_round 105: strategy sampled 50 clients (out of 50)
+[2023-10-11 07:08:15,351][flwr][DEBUG] - evaluate_round 105: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 07:17:20,498 | server.py:187 | evaluate_round 105 received 50 results and 0 failures
+[2023-10-11 07:17:20,498][flwr][DEBUG] - evaluate_round 105 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 07:17:20,498 | server.py:222 | fit_round 106: strategy sampled 50 clients (out of 50)
+[2023-10-11 07:17:20,498][flwr][DEBUG] - fit_round 106: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.719383 Loss1: 0.873246 Loss2: 1.846137 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.808833 Loss1: 0.391621 Loss2: 1.417212 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.679863 Loss1: 0.304549 Loss2: 1.375314
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.581107 Loss1: 0.711345 Loss2: 1.869762
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.898700 Loss1: 0.476638 Loss2: 1.422062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.709759 Loss1: 0.284840 Loss2: 1.424919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.618965 Loss1: 0.226300 Loss2: 1.392665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.591367 Loss1: 0.190864 Loss2: 1.400503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463362 Loss1: 0.118972 Loss2: 1.344390 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.552439 Loss1: 0.170360 Loss2: 1.382079 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.488161 Loss1: 0.118431 Loss2: 1.369730 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981618
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.817077 Loss1: 0.376038 Loss2: 1.441039 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571664 Loss1: 0.177968 Loss2: 1.393697 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.517979 Loss1: 0.140757 Loss2: 1.377222
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.580638 Loss1: 0.789933 Loss2: 1.790705
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.939013 Loss1: 0.546607 Loss2: 1.392406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.729735 Loss1: 0.353089 Loss2: 1.376646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.597682 Loss1: 0.233456 Loss2: 1.364226 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469161 Loss1: 0.127546 Loss2: 1.341615 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452982 Loss1: 0.116937 Loss2: 1.336044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420584 Loss1: 0.094226 Loss2: 1.326358
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.635485 Loss1: 0.882923 Loss2: 1.752562
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374238 Loss1: 0.048615 Loss2: 1.325623
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.869455 Loss1: 0.498386 Loss2: 1.371068
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.712113 Loss1: 0.359066 Loss2: 1.353047
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580256 Loss1: 0.240237 Loss2: 1.340019
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514315 Loss1: 0.187947 Loss2: 1.326369
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459618 Loss1: 0.141494 Loss2: 1.318123
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.019321 Loss1: 1.025829 Loss2: 1.993492
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.029341 Loss1: 0.643933 Loss2: 1.385408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.791418 Loss1: 0.331469 Loss2: 1.459949
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384713 Loss1: 0.086620 Loss2: 1.298093
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.365033 Loss1: 0.065884 Loss2: 1.299149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.341302 Loss1: 0.046383 Loss2: 1.294920 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485329 Loss1: 0.119578 Loss2: 1.365751 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452489 Loss1: 0.083621 Loss2: 1.368867 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985677
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.750201 Loss1: 0.871702 Loss2: 1.878499
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.978582 Loss1: 0.570247 Loss2: 1.408336
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.794353 Loss1: 0.361308 Loss2: 1.433045
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.734804 Loss1: 0.328501 Loss2: 1.406302
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.894283 Loss1: 1.037969 Loss2: 1.856314
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.626401 Loss1: 0.219000 Loss2: 1.407402
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.990637 Loss1: 0.601661 Loss2: 1.388976
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515321 Loss1: 0.128958 Loss2: 1.386363
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.863441 Loss1: 0.429412 Loss2: 1.434029
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.510692 Loss1: 0.125381 Loss2: 1.385312
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.668874 Loss1: 0.284547 Loss2: 1.384327
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484916 Loss1: 0.106279 Loss2: 1.378637
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.553483 Loss1: 0.180992 Loss2: 1.372491
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.457611 Loss1: 0.079753 Loss2: 1.377858
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500114 Loss1: 0.142167 Loss2: 1.357947
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459293 Loss1: 0.086529 Loss2: 1.372763
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.489745 Loss1: 0.135270 Loss2: 1.354476
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472172 Loss1: 0.120175 Loss2: 1.351996
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461579 Loss1: 0.114334 Loss2: 1.347245
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461567 Loss1: 0.115229 Loss2: 1.346338
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.867564 Loss1: 0.973087 Loss2: 1.894477
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.000503 Loss1: 0.564306 Loss2: 1.436197
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.805578 Loss1: 0.356123 Loss2: 1.449455
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.711128 Loss1: 0.273715 Loss2: 1.437413
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.719978 Loss1: 0.885742 Loss2: 1.834237
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589965 Loss1: 0.174484 Loss2: 1.415481
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.946780 Loss1: 0.578327 Loss2: 1.368453
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.554552 Loss1: 0.150160 Loss2: 1.404392
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.725278 Loss1: 0.311801 Loss2: 1.413477
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.489800 Loss1: 0.094540 Loss2: 1.395260
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.583530 Loss1: 0.226406 Loss2: 1.357125
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.498861 Loss1: 0.103094 Loss2: 1.395766
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542137 Loss1: 0.177143 Loss2: 1.364994
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.466659 Loss1: 0.075364 Loss2: 1.391295
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511240 Loss1: 0.158250 Loss2: 1.352991
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.468770 Loss1: 0.077964 Loss2: 1.390806
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478224 Loss1: 0.129266 Loss2: 1.348958
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434196 Loss1: 0.090758 Loss2: 1.343438
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453093 Loss1: 0.113989 Loss2: 1.339104
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444916 Loss1: 0.111581 Loss2: 1.333336
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.855876 Loss1: 1.010367 Loss2: 1.845510
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.098573 Loss1: 0.635877 Loss2: 1.462696
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.787689 Loss1: 0.360848 Loss2: 1.426841
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.677283 Loss1: 0.279709 Loss2: 1.397575
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.906005 Loss1: 0.993213 Loss2: 1.912792
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.934660 Loss1: 0.568397 Loss2: 1.366263 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.575343 Loss1: 0.193114 Loss2: 1.382228
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.819693 Loss1: 0.412319 Loss2: 1.407375
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538190 Loss1: 0.181097 Loss2: 1.357093
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.501155 Loss1: 0.125878 Loss2: 1.375277
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.489764 Loss1: 0.154531 Loss2: 1.335233
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441726 Loss1: 0.077742 Loss2: 1.363984
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443632 Loss1: 0.082594 Loss2: 1.361038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466968 Loss1: 0.107195 Loss2: 1.359774 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.360186 Loss1: 0.044487 Loss2: 1.315699 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986779
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.606765 Loss1: 0.802558 Loss2: 1.804207
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.843033 Loss1: 0.460311 Loss2: 1.382722
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.723000 Loss1: 0.325515 Loss2: 1.397485
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.788520 Loss1: 0.962299 Loss2: 1.826221
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.648874 Loss1: 0.267718 Loss2: 1.381156
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.979585 Loss1: 0.569025 Loss2: 1.410560
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572718 Loss1: 0.194424 Loss2: 1.378294
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.723419 Loss1: 0.322148 Loss2: 1.401271
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.575627 Loss1: 0.207096 Loss2: 1.368531
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509615 Loss1: 0.142923 Loss2: 1.366692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.527556 Loss1: 0.163396 Loss2: 1.364160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.531247 Loss1: 0.151497 Loss2: 1.379750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.498701 Loss1: 0.138847 Loss2: 1.359854 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412081 Loss1: 0.064347 Loss2: 1.347734 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.617991 Loss1: 0.791429 Loss2: 1.826562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690920 Loss1: 0.309246 Loss2: 1.381674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557395 Loss1: 0.207184 Loss2: 1.350211
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.760818 Loss1: 0.967145 Loss2: 1.793673
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491754 Loss1: 0.139383 Loss2: 1.352371
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.895232 Loss1: 0.515665 Loss2: 1.379566
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515203 Loss1: 0.168593 Loss2: 1.346610
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.692275 Loss1: 0.319065 Loss2: 1.373210
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.553129 Loss1: 0.204675 Loss2: 1.348454
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.548716 Loss1: 0.209558 Loss2: 1.339158
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489279 Loss1: 0.134540 Loss2: 1.354740
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.490436 Loss1: 0.160827 Loss2: 1.329608
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470046 Loss1: 0.127273 Loss2: 1.342774
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460427 Loss1: 0.133264 Loss2: 1.327163
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431248 Loss1: 0.092154 Loss2: 1.339094
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451189 Loss1: 0.124637 Loss2: 1.326552
+(DefaultActor pid=3765) >> Training accuracy: 0.970703
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420718 Loss1: 0.103620 Loss2: 1.317098
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400404 Loss1: 0.089141 Loss2: 1.311262
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411747 Loss1: 0.100514 Loss2: 1.311233
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.576430 Loss1: 0.784952 Loss2: 1.791478
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.861582 Loss1: 0.496482 Loss2: 1.365101
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.718761 Loss1: 0.338281 Loss2: 1.380479
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.669360 Loss1: 0.308994 Loss2: 1.360366
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.759008 Loss1: 0.874392 Loss2: 1.884616
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.581051 Loss1: 0.225753 Loss2: 1.355298
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.016590 Loss1: 0.564028 Loss2: 1.452563
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.740687 Loss1: 0.272472 Loss2: 1.468216
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477108 Loss1: 0.135484 Loss2: 1.341624
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.677716 Loss1: 0.260624 Loss2: 1.417092
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448857 Loss1: 0.118823 Loss2: 1.330034
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.603205 Loss1: 0.183720 Loss2: 1.419485
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405529 Loss1: 0.087076 Loss2: 1.318453
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.561431 Loss1: 0.159264 Loss2: 1.402167
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.393069 Loss1: 0.076031 Loss2: 1.317039
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378828 Loss1: 0.065165 Loss2: 1.313663 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479269 Loss1: 0.089483 Loss2: 1.389786 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.744429 Loss1: 0.881474 Loss2: 1.862955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.807373 Loss1: 0.343607 Loss2: 1.463766 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.660490 Loss1: 0.263861 Loss2: 1.396628
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.807575 Loss1: 0.871629 Loss2: 1.935946
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.596076 Loss1: 0.197144 Loss2: 1.398932
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.002714 Loss1: 0.560042 Loss2: 1.442671
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.541454 Loss1: 0.151243 Loss2: 1.390210
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.781108 Loss1: 0.304091 Loss2: 1.477017
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.545728 Loss1: 0.159794 Loss2: 1.385934
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.625021 Loss1: 0.199996 Loss2: 1.425025
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.541154 Loss1: 0.155188 Loss2: 1.385966
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.623741 Loss1: 0.197513 Loss2: 1.426229
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.496746 Loss1: 0.116676 Loss2: 1.380070
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549655 Loss1: 0.135912 Loss2: 1.413743
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.503047 Loss1: 0.129504 Loss2: 1.373544
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487060 Loss1: 0.090072 Loss2: 1.396988
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461799 Loss1: 0.072207 Loss2: 1.389592
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450560 Loss1: 0.062038 Loss2: 1.388522
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482250 Loss1: 0.099945 Loss2: 1.382305
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.761076 Loss1: 0.941490 Loss2: 1.819586
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.965421 Loss1: 0.595870 Loss2: 1.369551
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.735942 Loss1: 0.351971 Loss2: 1.383971
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580789 Loss1: 0.236323 Loss2: 1.344466
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.922890 Loss1: 0.974934 Loss2: 1.947956
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.501851 Loss1: 0.166980 Loss2: 1.334871
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.972471 Loss1: 0.538899 Loss2: 1.433572
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465415 Loss1: 0.137379 Loss2: 1.328035
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.796260 Loss1: 0.332853 Loss2: 1.463407
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444588 Loss1: 0.120804 Loss2: 1.323785
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.644671 Loss1: 0.230422 Loss2: 1.414249
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433660 Loss1: 0.113650 Loss2: 1.320009
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.668707 Loss1: 0.243451 Loss2: 1.425257
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396501 Loss1: 0.079204 Loss2: 1.317297
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559478 Loss1: 0.149063 Loss2: 1.410416
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363392 Loss1: 0.051026 Loss2: 1.312366
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550359 Loss1: 0.145474 Loss2: 1.404885
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.534804 Loss1: 0.127802 Loss2: 1.407001
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.483192 Loss1: 0.089722 Loss2: 1.393471
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.480000 Loss1: 0.086388 Loss2: 1.393612
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.657924 Loss1: 0.785469 Loss2: 1.872455
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.021080 Loss1: 0.582556 Loss2: 1.438523
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.819188 Loss1: 0.353536 Loss2: 1.465652
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.725125 Loss1: 0.836214 Loss2: 1.888911
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.735963 Loss1: 0.308623 Loss2: 1.427340
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.930396 Loss1: 0.542836 Loss2: 1.387560
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.629621 Loss1: 0.202023 Loss2: 1.427598
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.729017 Loss1: 0.295684 Loss2: 1.433334
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.561344 Loss1: 0.151348 Loss2: 1.409996
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509758 Loss1: 0.106893 Loss2: 1.402865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.505793 Loss1: 0.105154 Loss2: 1.400639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.497139 Loss1: 0.095040 Loss2: 1.402099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508759 Loss1: 0.110167 Loss2: 1.398592 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389797 Loss1: 0.047251 Loss2: 1.342547 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.705057 Loss1: 0.797804 Loss2: 1.907254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.663704 Loss1: 0.255509 Loss2: 1.408196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623362 Loss1: 0.237210 Loss2: 1.386153
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.699927 Loss1: 0.863754 Loss2: 1.836173
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572487 Loss1: 0.174575 Loss2: 1.397912
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.813694 Loss1: 0.482931 Loss2: 1.330763
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504504 Loss1: 0.124086 Loss2: 1.380418
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.672418 Loss1: 0.321972 Loss2: 1.350446
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.506559 Loss1: 0.124244 Loss2: 1.382316
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.542011 Loss1: 0.221479 Loss2: 1.320532
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457832 Loss1: 0.085703 Loss2: 1.372129
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543607 Loss1: 0.222367 Loss2: 1.321240
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453686 Loss1: 0.082026 Loss2: 1.371660
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467698 Loss1: 0.147065 Loss2: 1.320633
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426985 Loss1: 0.058767 Loss2: 1.368219
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441809 Loss1: 0.137406 Loss2: 1.304404
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389343 Loss1: 0.085755 Loss2: 1.303588
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368295 Loss1: 0.071113 Loss2: 1.297182
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.359224 Loss1: 0.063338 Loss2: 1.295886
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.586211 Loss1: 0.826892 Loss2: 1.759318
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.937881 Loss1: 0.579029 Loss2: 1.358852
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.705829 Loss1: 0.336866 Loss2: 1.368963
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574291 Loss1: 0.233931 Loss2: 1.340359
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.783342 Loss1: 0.846637 Loss2: 1.936705
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487883 Loss1: 0.149284 Loss2: 1.338599
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.997828 Loss1: 0.549500 Loss2: 1.448328
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461267 Loss1: 0.134821 Loss2: 1.326446
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.858524 Loss1: 0.354996 Loss2: 1.503527
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460829 Loss1: 0.139461 Loss2: 1.321368
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.732524 Loss1: 0.280590 Loss2: 1.451935
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.712111 Loss1: 0.244626 Loss2: 1.467485
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449082 Loss1: 0.127217 Loss2: 1.321865
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.633837 Loss1: 0.189203 Loss2: 1.444634
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413871 Loss1: 0.096874 Loss2: 1.316997
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.560247 Loss1: 0.126007 Loss2: 1.434239
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415682 Loss1: 0.106040 Loss2: 1.309642
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490401 Loss1: 0.073895 Loss2: 1.416506 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.616833 Loss1: 0.790230 Loss2: 1.826603 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.808494 Loss1: 0.390102 Loss2: 1.418392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.671251 Loss1: 0.277967 Loss2: 1.393284
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.794779 Loss1: 0.947627 Loss2: 1.847152
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.039352 Loss1: 0.658415 Loss2: 1.380937
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.586432 Loss1: 0.196197 Loss2: 1.390235
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.754081 Loss1: 0.355669 Loss2: 1.398412
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.546066 Loss1: 0.157203 Loss2: 1.388864
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576921 Loss1: 0.223560 Loss2: 1.353361
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502394 Loss1: 0.116509 Loss2: 1.385885
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528591 Loss1: 0.178351 Loss2: 1.350240
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.533894 Loss1: 0.158080 Loss2: 1.375814
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470657 Loss1: 0.091541 Loss2: 1.379116 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447004 Loss1: 0.083617 Loss2: 1.363387 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422372 Loss1: 0.087655 Loss2: 1.334717 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.747718 Loss1: 0.860929 Loss2: 1.886790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.714616 Loss1: 0.270998 Loss2: 1.443618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578382 Loss1: 0.206869 Loss2: 1.371513
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.694024 Loss1: 0.819559 Loss2: 1.874466
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.007254 Loss1: 0.588567 Loss2: 1.418687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.823268 Loss1: 0.385611 Loss2: 1.437657 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.663310 Loss1: 0.256137 Loss2: 1.407172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.615595 Loss1: 0.203851 Loss2: 1.411744 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.561144 Loss1: 0.160722 Loss2: 1.400422 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431987 Loss1: 0.082053 Loss2: 1.349934
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.548573 Loss1: 0.155543 Loss2: 1.393030
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540234 Loss1: 0.140576 Loss2: 1.399658
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.559088 Loss1: 0.168913 Loss2: 1.390175
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512729 Loss1: 0.120539 Loss2: 1.392190
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.988695 Loss1: 1.028070 Loss2: 1.960626
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.079815 Loss1: 0.645657 Loss2: 1.434158
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.870628 Loss1: 0.379892 Loss2: 1.490736
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.769561 Loss1: 0.337484 Loss2: 1.432077
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.900066 Loss1: 1.004183 Loss2: 1.895883
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.051585 Loss1: 0.690253 Loss2: 1.361332 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.843545 Loss1: 0.404896 Loss2: 1.438649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.628891 Loss1: 0.277240 Loss2: 1.351651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467074 Loss1: 0.077473 Loss2: 1.389601
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527459 Loss1: 0.171438 Loss2: 1.356021
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487068 Loss1: 0.137145 Loss2: 1.349923
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452906 Loss1: 0.072481 Loss2: 1.380425
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437142 Loss1: 0.103431 Loss2: 1.333710
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.455406 Loss1: 0.073402 Loss2: 1.382004
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369279 Loss1: 0.046215 Loss2: 1.323064 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980769
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.786265 Loss1: 0.895386 Loss2: 1.890879 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.815136 Loss1: 0.361997 Loss2: 1.453139 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623717 Loss1: 0.211883 Loss2: 1.411834
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.765027 Loss1: 0.879497 Loss2: 1.885529
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562027 Loss1: 0.158205 Loss2: 1.403822
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.005609 Loss1: 0.600708 Loss2: 1.404901
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514134 Loss1: 0.114566 Loss2: 1.399568
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.871887 Loss1: 0.442839 Loss2: 1.429048
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478281 Loss1: 0.088222 Loss2: 1.390059
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.730533 Loss1: 0.334507 Loss2: 1.396026
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462560 Loss1: 0.085993 Loss2: 1.376567
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.627490 Loss1: 0.235955 Loss2: 1.391535
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453701 Loss1: 0.074587 Loss2: 1.379114
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.583727 Loss1: 0.198116 Loss2: 1.385611
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440492 Loss1: 0.062232 Loss2: 1.378260
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.532617 Loss1: 0.154774 Loss2: 1.377843
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488268 Loss1: 0.125801 Loss2: 1.362467
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.496177 Loss1: 0.130664 Loss2: 1.365513
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.493485 Loss1: 0.137283 Loss2: 1.356202
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.006208 Loss1: 1.024740 Loss2: 1.981467
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.011751 Loss1: 0.564082 Loss2: 1.447669
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.791686 Loss1: 0.308383 Loss2: 1.483303
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.722026 Loss1: 0.283370 Loss2: 1.438656
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.814094 Loss1: 0.805362 Loss2: 2.008732
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.990108 Loss1: 0.471761 Loss2: 1.518347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.918800 Loss1: 0.359488 Loss2: 1.559312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.779144 Loss1: 0.267867 Loss2: 1.511277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.685960 Loss1: 0.185401 Loss2: 1.500559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.713583 Loss1: 0.213079 Loss2: 1.500504 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.611763 Loss1: 0.120921 Loss2: 1.490842 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.598440 Loss1: 0.123190 Loss2: 1.475250 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.962853 Loss1: 0.561855 Loss2: 1.400998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.609895 Loss1: 0.226640 Loss2: 1.383255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567606 Loss1: 0.194018 Loss2: 1.373588
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.752776 Loss1: 0.912273 Loss2: 1.840503
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481920 Loss1: 0.109106 Loss2: 1.372814
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.021759 Loss1: 0.627212 Loss2: 1.394547
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.494668 Loss1: 0.139831 Loss2: 1.354838
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.702970 Loss1: 0.304563 Loss2: 1.398406
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448307 Loss1: 0.092518 Loss2: 1.355790
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592480 Loss1: 0.236928 Loss2: 1.355552
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429444 Loss1: 0.076446 Loss2: 1.352997
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570876 Loss1: 0.203182 Loss2: 1.367694
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410304 Loss1: 0.068492 Loss2: 1.341812
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560012 Loss1: 0.194729 Loss2: 1.365283
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500596 Loss1: 0.140804 Loss2: 1.359792
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500315 Loss1: 0.145936 Loss2: 1.354379
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.477270 Loss1: 0.131365 Loss2: 1.345905
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419130 Loss1: 0.079043 Loss2: 1.340086
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.849237 Loss1: 0.916454 Loss2: 1.932783
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.008522 Loss1: 0.571907 Loss2: 1.436615
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.848689 Loss1: 0.375519 Loss2: 1.473170
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.660516 Loss1: 0.252361 Loss2: 1.408155
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587494 Loss1: 0.179041 Loss2: 1.408454
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.536109 Loss1: 0.144749 Loss2: 1.391360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503427 Loss1: 0.117957 Loss2: 1.385470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499366 Loss1: 0.116253 Loss2: 1.383114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.471589 Loss1: 0.091202 Loss2: 1.380387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422378 Loss1: 0.053290 Loss2: 1.369088 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.538452 Loss1: 0.158584 Loss2: 1.379868 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.523719 Loss1: 0.135491 Loss2: 1.388228 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.750744 Loss1: 0.413008 Loss2: 1.337736 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582510 Loss1: 0.246081 Loss2: 1.336429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491279 Loss1: 0.156951 Loss2: 1.334328
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.718926 Loss1: 0.892843 Loss2: 1.826083
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.868134 Loss1: 0.494219 Loss2: 1.373915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.650805 Loss1: 0.267340 Loss2: 1.383465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.589803 Loss1: 0.237429 Loss2: 1.352374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.538458 Loss1: 0.182812 Loss2: 1.355647 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-11 07:46:30,212 | server.py:236 | fit_round 106 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485911 Loss1: 0.137252 Loss2: 1.348659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.479621 Loss1: 0.130933 Loss2: 1.348687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431946 Loss1: 0.094620 Loss2: 1.337327 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.040227 Loss1: 0.573639 Loss2: 1.466589 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.740335 Loss1: 0.292892 Loss2: 1.447443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.655548 Loss1: 0.202224 Loss2: 1.453324
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.679121 Loss1: 0.861176 Loss2: 1.817946
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.933680 Loss1: 0.583668 Loss2: 1.350012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.728893 Loss1: 0.328846 Loss2: 1.400047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.614868 Loss1: 0.269769 Loss2: 1.345099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571198 Loss1: 0.210888 Loss2: 1.360310 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508519 Loss1: 0.169115 Loss2: 1.339403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471883 Loss1: 0.126865 Loss2: 1.345017 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393917 Loss1: 0.072040 Loss2: 1.321877 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.923340 Loss1: 0.539426 Loss2: 1.383914 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561617 Loss1: 0.204486 Loss2: 1.357131 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470996 Loss1: 0.124108 Loss2: 1.346888
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.857841 Loss1: 0.992248 Loss2: 1.865593
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.994435 Loss1: 0.559708 Loss2: 1.434726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.844190 Loss1: 0.389630 Loss2: 1.454561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.661089 Loss1: 0.251598 Loss2: 1.409491 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.597398 Loss1: 0.193058 Loss2: 1.404340 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.526964 Loss1: 0.135458 Loss2: 1.391506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.499338 Loss1: 0.118058 Loss2: 1.381281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.504809 Loss1: 0.127338 Loss2: 1.377470 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.923978 Loss1: 0.567928 Loss2: 1.356049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572797 Loss1: 0.243322 Loss2: 1.329474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.495619 Loss1: 0.172330 Loss2: 1.323289
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.601179 Loss1: 0.738553 Loss2: 1.862626
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.886657 Loss1: 0.498963 Loss2: 1.387694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769048 Loss1: 0.347155 Loss2: 1.421892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.625730 Loss1: 0.244252 Loss2: 1.381478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.572881 Loss1: 0.195902 Loss2: 1.376978 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350604 Loss1: 0.061520 Loss2: 1.289084
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.570626 Loss1: 0.195297 Loss2: 1.375330
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.555607 Loss1: 0.177713 Loss2: 1.377893
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.538960 Loss1: 0.171888 Loss2: 1.367072
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.522147 Loss1: 0.159621 Loss2: 1.362526
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.493553 Loss1: 0.125960 Loss2: 1.367593
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 07:46:30,212][flwr][DEBUG] - fit_round 106 received 50 results and 0 failures
+INFO flwr 2023-10-11 07:47:11,216 | server.py:125 | fit progress: (106, 2.199014896401963, {'accuracy': 0.5705}, 244538.994493704)
+>> Test accuracy: 0.570500
+[2023-10-11 07:47:11,216][flwr][INFO] - fit progress: (106, 2.199014896401963, {'accuracy': 0.5705}, 244538.994493704)
+DEBUG flwr 2023-10-11 07:47:11,216 | server.py:173 | evaluate_round 106: strategy sampled 50 clients (out of 50)
+[2023-10-11 07:47:11,216][flwr][DEBUG] - evaluate_round 106: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 07:56:17,161 | server.py:187 | evaluate_round 106 received 50 results and 0 failures
+[2023-10-11 07:56:17,161][flwr][DEBUG] - evaluate_round 106 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 07:56:17,161 | server.py:222 | fit_round 107: strategy sampled 50 clients (out of 50)
+[2023-10-11 07:56:17,161][flwr][DEBUG] - fit_round 107: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.766414 Loss1: 0.902424 Loss2: 1.863990
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.910279 Loss1: 0.506617 Loss2: 1.403663
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.685939 Loss1: 0.270829 Loss2: 1.415110
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.615692 Loss1: 0.235571 Loss2: 1.380121
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.788242 Loss1: 0.839147 Loss2: 1.949094
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.613665 Loss1: 0.226594 Loss2: 1.387071
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.060491 Loss1: 0.591637 Loss2: 1.468854
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.605230 Loss1: 0.217655 Loss2: 1.387575
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.925142 Loss1: 0.376536 Loss2: 1.548606
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.598023 Loss1: 0.216094 Loss2: 1.381929
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.760335 Loss1: 0.287417 Loss2: 1.472918
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.601856 Loss1: 0.208863 Loss2: 1.392993
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.690734 Loss1: 0.213428 Loss2: 1.477306
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.539379 Loss1: 0.160926 Loss2: 1.378453
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.649275 Loss1: 0.183198 Loss2: 1.466077
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.521018 Loss1: 0.141144 Loss2: 1.379874
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.650549 Loss1: 0.181756 Loss2: 1.468793
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.605611 Loss1: 0.149473 Loss2: 1.456138
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.561800 Loss1: 0.112154 Loss2: 1.449646
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.578131 Loss1: 0.130767 Loss2: 1.447364
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.797069 Loss1: 0.925394 Loss2: 1.871674
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.991525 Loss1: 0.613997 Loss2: 1.377528
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.797412 Loss1: 0.356804 Loss2: 1.440608
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.612620 Loss1: 0.236081 Loss2: 1.376538
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.722763 Loss1: 0.915961 Loss2: 1.806803
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.576737 Loss1: 0.199345 Loss2: 1.377393
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.975021 Loss1: 0.574571 Loss2: 1.400450
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.687349 Loss1: 0.322948 Loss2: 1.364401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561801 Loss1: 0.219347 Loss2: 1.342455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516109 Loss1: 0.168730 Loss2: 1.347379 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.471720 Loss1: 0.119975 Loss2: 1.351745
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474877 Loss1: 0.152455 Loss2: 1.322422
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432308 Loss1: 0.080389 Loss2: 1.351920
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443591 Loss1: 0.121964 Loss2: 1.321627
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439590 Loss1: 0.116423 Loss2: 1.323167
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454207 Loss1: 0.137875 Loss2: 1.316332
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392415 Loss1: 0.078436 Loss2: 1.313979
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.853533 Loss1: 0.920054 Loss2: 1.933479
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.928134 Loss1: 0.492775 Loss2: 1.435359
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.748074 Loss1: 0.297025 Loss2: 1.451048
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.605824 Loss1: 0.179897 Loss2: 1.425927
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.772966 Loss1: 0.882691 Loss2: 1.890275
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.018306 Loss1: 0.548594 Loss2: 1.469712 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.760774 Loss1: 0.322815 Loss2: 1.437959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.734793 Loss1: 0.302589 Loss2: 1.432204 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.687437 Loss1: 0.260170 Loss2: 1.427267 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.633191 Loss1: 0.212051 Loss2: 1.421141 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.575659 Loss1: 0.164294 Loss2: 1.411365 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.496538 Loss1: 0.093748 Loss2: 1.402790 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.837756 Loss1: 0.458136 Loss2: 1.379620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.616777 Loss1: 0.248917 Loss2: 1.367860 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.643702 Loss1: 0.262975 Loss2: 1.380727 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547473 Loss1: 0.180995 Loss2: 1.366477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.521446 Loss1: 0.159290 Loss2: 1.362155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461399 Loss1: 0.101527 Loss2: 1.359872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419523 Loss1: 0.070513 Loss2: 1.349010 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406366 Loss1: 0.066182 Loss2: 1.340184 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386554 Loss1: 0.060911 Loss2: 1.325644 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.858656 Loss1: 0.988724 Loss2: 1.869932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.732912 Loss1: 0.324022 Loss2: 1.408890 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.600154 Loss1: 0.230245 Loss2: 1.369908
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.823074 Loss1: 0.937030 Loss2: 1.886044
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.009439 Loss1: 0.582267 Loss2: 1.427172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.848635 Loss1: 0.396747 Loss2: 1.451888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.669848 Loss1: 0.262277 Loss2: 1.407571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570189 Loss1: 0.167923 Loss2: 1.402265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538927 Loss1: 0.145217 Loss2: 1.393710 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432768 Loss1: 0.090470 Loss2: 1.342298
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.506041 Loss1: 0.120855 Loss2: 1.385186
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490416 Loss1: 0.106323 Loss2: 1.384094
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.494563 Loss1: 0.114727 Loss2: 1.379836
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449691 Loss1: 0.072500 Loss2: 1.377191
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.827855 Loss1: 0.918805 Loss2: 1.909049
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.980835 Loss1: 0.544748 Loss2: 1.436087
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.765609 Loss1: 0.311185 Loss2: 1.454424
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.673274 Loss1: 0.255493 Loss2: 1.417781
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.742522 Loss1: 0.858865 Loss2: 1.883658
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.916873 Loss1: 0.466575 Loss2: 1.450298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.768739 Loss1: 0.314056 Loss2: 1.454683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.624380 Loss1: 0.191219 Loss2: 1.433160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557922 Loss1: 0.148525 Loss2: 1.409397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521980 Loss1: 0.115014 Loss2: 1.406966 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475000 Loss1: 0.079789 Loss2: 1.395211 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465124 Loss1: 0.080574 Loss2: 1.384550 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.951937 Loss1: 0.589894 Loss2: 1.362043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561459 Loss1: 0.206067 Loss2: 1.355392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.500944 Loss1: 0.145421 Loss2: 1.355523
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.609296 Loss1: 0.754379 Loss2: 1.854916
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.811636 Loss1: 0.432857 Loss2: 1.378779 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.743800 Loss1: 0.332751 Loss2: 1.411049 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577420 Loss1: 0.210829 Loss2: 1.366591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.547553 Loss1: 0.185196 Loss2: 1.362356 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390999 Loss1: 0.067481 Loss2: 1.323518
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473736 Loss1: 0.111349 Loss2: 1.362386
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453636 Loss1: 0.100343 Loss2: 1.353293
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431160 Loss1: 0.083266 Loss2: 1.347894
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422199 Loss1: 0.075466 Loss2: 1.346732
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405906 Loss1: 0.065118 Loss2: 1.340788
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.659253 Loss1: 0.806855 Loss2: 1.852398
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.918957 Loss1: 0.540709 Loss2: 1.378248
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.826169 Loss1: 0.394631 Loss2: 1.431538
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.643331 Loss1: 0.269550 Loss2: 1.373781
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.595128 Loss1: 0.230433 Loss2: 1.364695
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.748445 Loss1: 0.926511 Loss2: 1.821934
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.967529 Loss1: 0.575201 Loss2: 1.392328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.778159 Loss1: 0.374451 Loss2: 1.403708 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.685287 Loss1: 0.312207 Loss2: 1.373081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.584947 Loss1: 0.208667 Loss2: 1.376280 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516095 Loss1: 0.159870 Loss2: 1.356225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472106 Loss1: 0.118641 Loss2: 1.353465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405251 Loss1: 0.067356 Loss2: 1.337895 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.797612 Loss1: 0.321311 Loss2: 1.476302 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.576966 Loss1: 0.171184 Loss2: 1.405782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.894403 Loss1: 0.999062 Loss2: 1.895341
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.517945 Loss1: 0.121996 Loss2: 1.395948
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.077135 Loss1: 0.633251 Loss2: 1.443884
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.506603 Loss1: 0.117193 Loss2: 1.389410
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.846676 Loss1: 0.389040 Loss2: 1.457636
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489525 Loss1: 0.099903 Loss2: 1.389622
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.752355 Loss1: 0.325115 Loss2: 1.427240
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456756 Loss1: 0.081016 Loss2: 1.375740
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.665612 Loss1: 0.244540 Loss2: 1.421072
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434277 Loss1: 0.059422 Loss2: 1.374855
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.531345 Loss1: 0.131409 Loss2: 1.399935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466393 Loss1: 0.077037 Loss2: 1.389356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.468347 Loss1: 0.081266 Loss2: 1.387081
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.683380 Loss1: 0.809020 Loss2: 1.874360
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.999564 Loss1: 0.602584 Loss2: 1.396980
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.785660 Loss1: 0.334276 Loss2: 1.451384
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.644379 Loss1: 0.245864 Loss2: 1.398515
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.581796 Loss1: 0.175335 Loss2: 1.406461
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.802380 Loss1: 0.968047 Loss2: 1.834333
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.539159 Loss1: 0.136885 Loss2: 1.402274
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.901162 Loss1: 0.536854 Loss2: 1.364308
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.526911 Loss1: 0.145548 Loss2: 1.381363
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.738144 Loss1: 0.336444 Loss2: 1.401700
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.505104 Loss1: 0.114091 Loss2: 1.391012
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.681463 Loss1: 0.321144 Loss2: 1.360319
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.487319 Loss1: 0.108756 Loss2: 1.378562
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.583703 Loss1: 0.197784 Loss2: 1.385918
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.489107 Loss1: 0.118752 Loss2: 1.370355
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474196 Loss1: 0.124073 Loss2: 1.350124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452305 Loss1: 0.119377 Loss2: 1.332928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428662 Loss1: 0.100889 Loss2: 1.327773
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.836734 Loss1: 0.904927 Loss2: 1.931808
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.012272 Loss1: 0.575309 Loss2: 1.436963
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.863321 Loss1: 0.383216 Loss2: 1.480105
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.748083 Loss1: 0.313100 Loss2: 1.434983
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.620940 Loss1: 0.201812 Loss2: 1.419128
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.900802 Loss1: 0.950232 Loss2: 1.950570
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.626133 Loss1: 0.214116 Loss2: 1.412017
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.999029 Loss1: 0.520407 Loss2: 1.478622
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528610 Loss1: 0.116027 Loss2: 1.412583
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.839148 Loss1: 0.326744 Loss2: 1.512404
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.509809 Loss1: 0.108736 Loss2: 1.401073
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.716906 Loss1: 0.259575 Loss2: 1.457331
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493154 Loss1: 0.094117 Loss2: 1.399037
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.685826 Loss1: 0.232487 Loss2: 1.453339
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.522717 Loss1: 0.128074 Loss2: 1.394643
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.551372 Loss1: 0.112210 Loss2: 1.439163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.502842 Loss1: 0.081771 Loss2: 1.421070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.488686 Loss1: 0.070945 Loss2: 1.417741
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.691016 Loss1: 0.897594 Loss2: 1.793422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.927838 Loss1: 0.588403 Loss2: 1.339435
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.744332 Loss1: 0.351389 Loss2: 1.392942
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.618183 Loss1: 0.289639 Loss2: 1.328544
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.613442 Loss1: 0.259543 Loss2: 1.353899
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.749341 Loss1: 0.782344 Loss2: 1.966997
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520718 Loss1: 0.178169 Loss2: 1.342549
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.988536 Loss1: 0.544384 Loss2: 1.444152
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502488 Loss1: 0.167401 Loss2: 1.335086
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.855773 Loss1: 0.355677 Loss2: 1.500095
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434742 Loss1: 0.103546 Loss2: 1.331196
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.727621 Loss1: 0.268079 Loss2: 1.459542
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.426785 Loss1: 0.108545 Loss2: 1.318239
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.655131 Loss1: 0.199355 Loss2: 1.455776
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393685 Loss1: 0.079614 Loss2: 1.314071
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.566252 Loss1: 0.130559 Loss2: 1.435694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.530090 Loss1: 0.101312 Loss2: 1.428777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.578053 Loss1: 0.138585 Loss2: 1.439468
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.758152 Loss1: 0.917261 Loss2: 1.840891
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.990195 Loss1: 0.589947 Loss2: 1.400248
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731735 Loss1: 0.318027 Loss2: 1.413708
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.597635 Loss1: 0.225986 Loss2: 1.371649
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561416 Loss1: 0.186583 Loss2: 1.374833
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.880611 Loss1: 0.969199 Loss2: 1.911413
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.568002 Loss1: 0.202312 Loss2: 1.365691
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.552821 Loss1: 0.180997 Loss2: 1.371824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469507 Loss1: 0.111911 Loss2: 1.357596 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469126 Loss1: 0.115432 Loss2: 1.353694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448370 Loss1: 0.102119 Loss2: 1.346251 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453994 Loss1: 0.083535 Loss2: 1.370459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457074 Loss1: 0.102773 Loss2: 1.354301 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.675840 Loss1: 0.810199 Loss2: 1.865641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.698467 Loss1: 0.297268 Loss2: 1.401199 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546729 Loss1: 0.191667 Loss2: 1.355062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503158 Loss1: 0.151727 Loss2: 1.351432 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486655 Loss1: 0.138756 Loss2: 1.347899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436581 Loss1: 0.097509 Loss2: 1.339072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394436 Loss1: 0.065340 Loss2: 1.329096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391577 Loss1: 0.063095 Loss2: 1.328482 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397182 Loss1: 0.108419 Loss2: 1.288763 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.355176 Loss1: 0.072193 Loss2: 1.282983 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.941417 Loss1: 0.500048 Loss2: 1.441369 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576970 Loss1: 0.196093 Loss2: 1.380877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.727789 Loss1: 0.891236 Loss2: 1.836553
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.570619 Loss1: 0.204093 Loss2: 1.366526
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.955039 Loss1: 0.564580 Loss2: 1.390458
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.571999 Loss1: 0.194061 Loss2: 1.377938
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.538390 Loss1: 0.170426 Loss2: 1.367963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432033 Loss1: 0.076595 Loss2: 1.355438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396905 Loss1: 0.053700 Loss2: 1.343206 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407987 Loss1: 0.068638 Loss2: 1.339349 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475612 Loss1: 0.139176 Loss2: 1.336437 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367883 Loss1: 0.053380 Loss2: 1.314503 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.742228 Loss1: 0.790433 Loss2: 1.951795
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.023226 Loss1: 0.556480 Loss2: 1.466745
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.870978 Loss1: 0.389436 Loss2: 1.481541
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.736187 Loss1: 0.306531 Loss2: 1.429656
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.539500 Loss1: 0.662926 Loss2: 1.876574
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.876098 Loss1: 0.459467 Loss2: 1.416631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.744032 Loss1: 0.300950 Loss2: 1.443082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497533 Loss1: 0.086919 Loss2: 1.410614 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.498692 Loss1: 0.088493 Loss2: 1.410200 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474225 Loss1: 0.066958 Loss2: 1.407267 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.554993 Loss1: 0.146961 Loss2: 1.408032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.498347 Loss1: 0.109034 Loss2: 1.389313 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.488204 Loss1: 0.101903 Loss2: 1.386300
+(DefaultActor pid=3764) >> Training accuracy: 0.985294
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.590924 Loss1: 0.752374 Loss2: 1.838550
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.919220 Loss1: 0.507107 Loss2: 1.412113
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.727174 Loss1: 0.287328 Loss2: 1.439846
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.678717 Loss1: 0.291130 Loss2: 1.387587
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.638480 Loss1: 0.240483 Loss2: 1.397997
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.018255 Loss1: 1.012078 Loss2: 2.006178
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.959713 Loss1: 0.577640 Loss2: 1.382073
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.557651 Loss1: 0.178130 Loss2: 1.379521
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.505126 Loss1: 0.125373 Loss2: 1.379752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.465145 Loss1: 0.087375 Loss2: 1.377770 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560895 Loss1: 0.188998 Loss2: 1.371897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.525996 Loss1: 0.141371 Loss2: 1.384625 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481428 Loss1: 0.113394 Loss2: 1.368034 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.711645 Loss1: 0.868850 Loss2: 1.842795 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.754824 Loss1: 0.324912 Loss2: 1.429912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.657200 Loss1: 0.298456 Loss2: 1.358744
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.693488 Loss1: 0.865141 Loss2: 1.828347
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605954 Loss1: 0.230201 Loss2: 1.375753
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831543 Loss1: 0.485388 Loss2: 1.346154
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537543 Loss1: 0.182145 Loss2: 1.355398
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677654 Loss1: 0.287270 Loss2: 1.390384
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516222 Loss1: 0.168173 Loss2: 1.348049
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562847 Loss1: 0.236444 Loss2: 1.326403
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500147 Loss1: 0.149392 Loss2: 1.350755
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518334 Loss1: 0.184819 Loss2: 1.333516
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427814 Loss1: 0.078367 Loss2: 1.349447
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.448419 Loss1: 0.124757 Loss2: 1.323663
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406500 Loss1: 0.072737 Loss2: 1.333763
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480653 Loss1: 0.162962 Loss2: 1.317691
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428890 Loss1: 0.107883 Loss2: 1.321007
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.468748 Loss1: 0.155610 Loss2: 1.313138
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408264 Loss1: 0.082365 Loss2: 1.325899
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.947324 Loss1: 1.041965 Loss2: 1.905359
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.948418 Loss1: 0.507858 Loss2: 1.440560
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.761091 Loss1: 0.333521 Loss2: 1.427570
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604302 Loss1: 0.212874 Loss2: 1.391428
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.859113 Loss1: 0.886888 Loss2: 1.972225
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.933767 Loss1: 0.550478 Loss2: 1.383289
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.539183 Loss1: 0.154128 Loss2: 1.385055
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491094 Loss1: 0.110122 Loss2: 1.380972 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451361 Loss1: 0.080705 Loss2: 1.370656 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456496 Loss1: 0.091557 Loss2: 1.364939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452616 Loss1: 0.089274 Loss2: 1.363343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449395 Loss1: 0.084505 Loss2: 1.364891 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419535 Loss1: 0.075024 Loss2: 1.344511 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.685502 Loss1: 0.863094 Loss2: 1.822407
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.816555 Loss1: 0.465649 Loss2: 1.350906
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.653278 Loss1: 0.273725 Loss2: 1.379553
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556825 Loss1: 0.221249 Loss2: 1.335576
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.881167 Loss1: 1.015393 Loss2: 1.865774
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.958491 Loss1: 0.603258 Loss2: 1.355233
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498987 Loss1: 0.151830 Loss2: 1.347157
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472692 Loss1: 0.139955 Loss2: 1.332736 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446782 Loss1: 0.118875 Loss2: 1.327907 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467052 Loss1: 0.137089 Loss2: 1.329963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455836 Loss1: 0.131278 Loss2: 1.324557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414074 Loss1: 0.087546 Loss2: 1.326528 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384807 Loss1: 0.056933 Loss2: 1.327874 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.678893 Loss1: 0.843250 Loss2: 1.835643
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.914848 Loss1: 0.538406 Loss2: 1.376442
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.681504 Loss1: 0.293527 Loss2: 1.387977
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.593612 Loss1: 0.237655 Loss2: 1.355957
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.615047 Loss1: 0.828004 Loss2: 1.787043
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.855330 Loss1: 0.492926 Loss2: 1.362405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.737987 Loss1: 0.383252 Loss2: 1.354735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619313 Loss1: 0.266769 Loss2: 1.352544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.474203 Loss1: 0.145345 Loss2: 1.328858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.488261 Loss1: 0.162551 Loss2: 1.325709 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433116 Loss1: 0.115545 Loss2: 1.317571 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389445 Loss1: 0.081607 Loss2: 1.307839 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.880745 Loss1: 0.521907 Loss2: 1.358838 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.560901 Loss1: 0.203729 Loss2: 1.357173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.818817 Loss1: 0.887272 Loss2: 1.931544
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498403 Loss1: 0.162240 Loss2: 1.336163
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.081341 Loss1: 0.538404 Loss2: 1.542937
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505164 Loss1: 0.164657 Loss2: 1.340507
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.790162 Loss1: 0.278594 Loss2: 1.511568
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476209 Loss1: 0.139786 Loss2: 1.336424
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425320 Loss1: 0.093390 Loss2: 1.331929
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.741368 Loss1: 0.252670 Loss2: 1.488699
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405943 Loss1: 0.082343 Loss2: 1.323600
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.688995 Loss1: 0.206919 Loss2: 1.482076
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390869 Loss1: 0.075352 Loss2: 1.315517
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.725236 Loss1: 0.234986 Loss2: 1.490250
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.692200 Loss1: 0.207051 Loss2: 1.485149
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.649920 Loss1: 0.179499 Loss2: 1.470422
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.598466 Loss1: 0.125667 Loss2: 1.472799
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.579445 Loss1: 0.114831 Loss2: 1.464613
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.918558 Loss1: 0.976944 Loss2: 1.941615
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.008056 Loss1: 0.609118 Loss2: 1.398938
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.825021 Loss1: 0.385384 Loss2: 1.439637
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.707570 Loss1: 0.317770 Loss2: 1.389800
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606741 Loss1: 0.205297 Loss2: 1.401443
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.669233 Loss1: 0.800197 Loss2: 1.869036
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.570688 Loss1: 0.186066 Loss2: 1.384622
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.928640 Loss1: 0.493540 Loss2: 1.435100
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.492446 Loss1: 0.105171 Loss2: 1.387275
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447699 Loss1: 0.076096 Loss2: 1.371603
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.792961 Loss1: 0.345453 Loss2: 1.447508
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460955 Loss1: 0.094273 Loss2: 1.366682
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.705162 Loss1: 0.285155 Loss2: 1.420007
+DEBUG flwr 2023-10-11 08:25:10,956 | server.py:236 | fit_round 107 received 50 results and 0 failures
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436026 Loss1: 0.070344 Loss2: 1.365682
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.658209 Loss1: 0.241436 Loss2: 1.416773
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.626922 Loss1: 0.213907 Loss2: 1.413015
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.610583 Loss1: 0.198524 Loss2: 1.412059
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.542032 Loss1: 0.133147 Loss2: 1.408885
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.465125 Loss1: 0.072858 Loss2: 1.392267
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.686520 Loss1: 0.868705 Loss2: 1.817815
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.790162 Loss1: 0.451497 Loss2: 1.338665 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.609554 Loss1: 0.269939 Loss2: 1.339615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513052 Loss1: 0.171181 Loss2: 1.341871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.687288 Loss1: 0.852066 Loss2: 1.835221
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445734 Loss1: 0.111101 Loss2: 1.334633
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.903550 Loss1: 0.515893 Loss2: 1.387658
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443480 Loss1: 0.118347 Loss2: 1.325133
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.694471 Loss1: 0.295898 Loss2: 1.398572
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419699 Loss1: 0.096666 Loss2: 1.323033
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.608665 Loss1: 0.241919 Loss2: 1.366746
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375419 Loss1: 0.059426 Loss2: 1.315993
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.594161 Loss1: 0.232244 Loss2: 1.361917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490529 Loss1: 0.132948 Loss2: 1.357581 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436790 Loss1: 0.092058 Loss2: 1.344732
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.635210 Loss1: 0.775320 Loss2: 1.859890
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416838 Loss1: 0.077600 Loss2: 1.339237
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.853377 Loss1: 0.491152 Loss2: 1.362225
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700552 Loss1: 0.305859 Loss2: 1.394694
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.552167 Loss1: 0.199873 Loss2: 1.352294
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462765 Loss1: 0.111275 Loss2: 1.351490
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450865 Loss1: 0.112017 Loss2: 1.338848
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.941283 Loss1: 1.050043 Loss2: 1.891241
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.465582 Loss1: 0.127110 Loss2: 1.338472
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.442920 Loss1: 0.103293 Loss2: 1.339627 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441073 Loss1: 0.104126 Loss2: 1.336947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415950 Loss1: 0.087075 Loss2: 1.328876 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.499309 Loss1: 0.136376 Loss2: 1.362933 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409594 Loss1: 0.062038 Loss2: 1.347556 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 08:25:10,956][flwr][DEBUG] - fit_round 107 received 50 results and 0 failures
+INFO flwr 2023-10-11 08:25:52,279 | server.py:125 | fit progress: (107, 2.205110744546397, {'accuracy': 0.5746}, 246860.058023771)
+>> Test accuracy: 0.574600
+[2023-10-11 08:25:52,279][flwr][INFO] - fit progress: (107, 2.205110744546397, {'accuracy': 0.5746}, 246860.058023771)
+DEBUG flwr 2023-10-11 08:25:52,280 | server.py:173 | evaluate_round 107: strategy sampled 50 clients (out of 50)
+[2023-10-11 08:25:52,280][flwr][DEBUG] - evaluate_round 107: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 08:34:53,870 | server.py:187 | evaluate_round 107 received 50 results and 0 failures
+[2023-10-11 08:34:53,870][flwr][DEBUG] - evaluate_round 107 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 08:34:53,871 | server.py:222 | fit_round 108: strategy sampled 50 clients (out of 50)
+[2023-10-11 08:34:53,871][flwr][DEBUG] - fit_round 108: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.616460 Loss1: 0.780404 Loss2: 1.836056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.719982 Loss1: 0.339141 Loss2: 1.380841 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.628162 Loss1: 0.268513 Loss2: 1.359649
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.754510 Loss1: 0.934771 Loss2: 1.819739
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562181 Loss1: 0.206497 Loss2: 1.355684
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.963870 Loss1: 0.548888 Loss2: 1.414982
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.692600 Loss1: 0.316666 Loss2: 1.375935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594481 Loss1: 0.226177 Loss2: 1.368304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.559496 Loss1: 0.199710 Loss2: 1.359785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.550992 Loss1: 0.188200 Loss2: 1.362792 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.559940 Loss1: 0.201951 Loss2: 1.357988 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.494807 Loss1: 0.135392 Loss2: 1.359415 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.762000 Loss1: 0.914111 Loss2: 1.847889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.737267 Loss1: 0.328328 Loss2: 1.408940 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.614278 Loss1: 0.815951 Loss2: 1.798327 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.567321 Loss1: 0.199160 Loss2: 1.368162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517330 Loss1: 0.146830 Loss2: 1.370500 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.492963 Loss1: 0.129010 Loss2: 1.363953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493171 Loss1: 0.137157 Loss2: 1.356014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472630 Loss1: 0.119986 Loss2: 1.352644 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430108 Loss1: 0.103020 Loss2: 1.327088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407821 Loss1: 0.089462 Loss2: 1.318359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397294 Loss1: 0.082255 Loss2: 1.315038
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.814358 Loss1: 0.946581 Loss2: 1.867777
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.902065 Loss1: 0.487316 Loss2: 1.414749
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.747400 Loss1: 0.312172 Loss2: 1.435228
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578463 Loss1: 0.207621 Loss2: 1.370842
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606874 Loss1: 0.228953 Loss2: 1.377921
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.794423 Loss1: 0.831259 Loss2: 1.963164
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.023626 Loss1: 0.551241 Loss2: 1.472386 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.789224 Loss1: 0.304963 Loss2: 1.484261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.712947 Loss1: 0.266148 Loss2: 1.446798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.668633 Loss1: 0.201128 Loss2: 1.467505 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.596583 Loss1: 0.163362 Loss2: 1.433221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.514205 Loss1: 0.083969 Loss2: 1.430235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.545409 Loss1: 0.123162 Loss2: 1.422247 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.919721 Loss1: 0.515818 Loss2: 1.403903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610411 Loss1: 0.219521 Loss2: 1.390890 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617472 Loss1: 0.226177 Loss2: 1.391295
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.759476 Loss1: 0.870254 Loss2: 1.889222
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.943181 Loss1: 0.530440 Loss2: 1.412741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.754449 Loss1: 0.310822 Loss2: 1.443627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612549 Loss1: 0.217365 Loss2: 1.395185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.582689 Loss1: 0.191184 Loss2: 1.391505 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506965 Loss1: 0.123796 Loss2: 1.383169 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491703 Loss1: 0.114331 Loss2: 1.377372 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433106 Loss1: 0.071353 Loss2: 1.361753 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.959648 Loss1: 0.606173 Loss2: 1.353476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.605668 Loss1: 0.282419 Loss2: 1.323249 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.785266 Loss1: 0.978523 Loss2: 1.806743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.899508 Loss1: 0.534134 Loss2: 1.365375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.740879 Loss1: 0.331340 Loss2: 1.409539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.645097 Loss1: 0.288576 Loss2: 1.356520 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574070 Loss1: 0.203773 Loss2: 1.370297 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455740 Loss1: 0.119458 Loss2: 1.336282 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396335 Loss1: 0.072185 Loss2: 1.324149 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387393 Loss1: 0.065935 Loss2: 1.321458
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.703381 Loss1: 0.840504 Loss2: 1.862877
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.000611 Loss1: 0.553975 Loss2: 1.446636
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.782385 Loss1: 0.331484 Loss2: 1.450901
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.661380 Loss1: 0.236659 Loss2: 1.424722
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.609766 Loss1: 0.191068 Loss2: 1.418698
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.709651 Loss1: 0.820319 Loss2: 1.889331
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.945408 Loss1: 0.535257 Loss2: 1.410151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.823064 Loss1: 0.359231 Loss2: 1.463832 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481969 Loss1: 0.085444 Loss2: 1.396524
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.649384 Loss1: 0.241324 Loss2: 1.408060
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.522363 Loss1: 0.127902 Loss2: 1.394461
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.604887 Loss1: 0.198786 Loss2: 1.406101
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472648 Loss1: 0.083452 Loss2: 1.389197
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539306 Loss1: 0.138257 Loss2: 1.401049
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518142 Loss1: 0.127053 Loss2: 1.391089
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.522904 Loss1: 0.132372 Loss2: 1.390532
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476720 Loss1: 0.094760 Loss2: 1.381960
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452003 Loss1: 0.073273 Loss2: 1.378729
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.747550 Loss1: 0.934254 Loss2: 1.813296
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.025334 Loss1: 0.639301 Loss2: 1.386033
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.775187 Loss1: 0.373622 Loss2: 1.401565
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.626252 Loss1: 0.271471 Loss2: 1.354780
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.651863 Loss1: 0.765954 Loss2: 1.885909
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454177 Loss1: 0.114138 Loss2: 1.340039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434295 Loss1: 0.106373 Loss2: 1.327922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433106 Loss1: 0.104107 Loss2: 1.328999 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411567 Loss1: 0.080233 Loss2: 1.331334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435913 Loss1: 0.109720 Loss2: 1.326193 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.534114 Loss1: 0.160902 Loss2: 1.373213 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.495531 Loss1: 0.126875 Loss2: 1.368656 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460130 Loss1: 0.090974 Loss2: 1.369157
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.685864 Loss1: 0.906339 Loss2: 1.779524
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.704566 Loss1: 0.383417 Loss2: 1.321149
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684908 Loss1: 0.340293 Loss2: 1.344615
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554827 Loss1: 0.233175 Loss2: 1.321652
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.469048 Loss1: 0.161163 Loss2: 1.307885
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.722492 Loss1: 0.870230 Loss2: 1.852262
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.409464 Loss1: 0.109884 Loss2: 1.299579
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.380791 Loss1: 0.087965 Loss2: 1.292825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.353645 Loss1: 0.065114 Loss2: 1.288531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.358283 Loss1: 0.074949 Loss2: 1.283333 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.348503 Loss1: 0.070252 Loss2: 1.278250 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508304 Loss1: 0.137859 Loss2: 1.370445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454484 Loss1: 0.093928 Loss2: 1.360556 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457427 Loss1: 0.097116 Loss2: 1.360311
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.684985 Loss1: 0.929742 Loss2: 1.755243
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.930268 Loss1: 0.557253 Loss2: 1.373016
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700277 Loss1: 0.358674 Loss2: 1.341603
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.545492 Loss1: 0.231323 Loss2: 1.314169
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475964 Loss1: 0.153592 Loss2: 1.322372
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.834198 Loss1: 0.945878 Loss2: 1.888320
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.062735 Loss1: 0.634426 Loss2: 1.428309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.839889 Loss1: 0.386179 Loss2: 1.453710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.687186 Loss1: 0.262310 Loss2: 1.424875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.647549 Loss1: 0.239788 Loss2: 1.407761 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.339837 Loss1: 0.054173 Loss2: 1.285664
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.599645 Loss1: 0.189926 Loss2: 1.409719
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550268 Loss1: 0.146858 Loss2: 1.403410
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.502731 Loss1: 0.109371 Loss2: 1.393361
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461915 Loss1: 0.081880 Loss2: 1.380035
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470371 Loss1: 0.094496 Loss2: 1.375875
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.761017 Loss1: 0.877211 Loss2: 1.883806
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.896775 Loss1: 0.502583 Loss2: 1.394192
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.745915 Loss1: 0.320995 Loss2: 1.424921
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.640582 Loss1: 0.260377 Loss2: 1.380205
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.612274 Loss1: 0.209145 Loss2: 1.403128
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.672212 Loss1: 0.838350 Loss2: 1.833862
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.925685 Loss1: 0.553902 Loss2: 1.371783 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.750798 Loss1: 0.354962 Loss2: 1.395837 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.617310 Loss1: 0.240091 Loss2: 1.377219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.596114 Loss1: 0.224277 Loss2: 1.371837 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549300 Loss1: 0.191828 Loss2: 1.357472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452830 Loss1: 0.104313 Loss2: 1.348518 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491049 Loss1: 0.149856 Loss2: 1.341193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.970323 Loss1: 0.569589 Loss2: 1.400734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.626119 Loss1: 0.236385 Loss2: 1.389734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.507035 Loss1: 0.132906 Loss2: 1.374129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470709 Loss1: 0.104958 Loss2: 1.365751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441871 Loss1: 0.084860 Loss2: 1.357011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421530 Loss1: 0.067186 Loss2: 1.354344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461580 Loss1: 0.108292 Loss2: 1.353288 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986779
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468165 Loss1: 0.148459 Loss2: 1.319706 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394817 Loss1: 0.083132 Loss2: 1.311685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374778 Loss1: 0.074215 Loss2: 1.300563
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.857520 Loss1: 0.937895 Loss2: 1.919625
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.945410 Loss1: 0.561401 Loss2: 1.384008
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.749913 Loss1: 0.328417 Loss2: 1.421496
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.718367 Loss1: 0.315679 Loss2: 1.402688
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605133 Loss1: 0.217758 Loss2: 1.387375
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.570289 Loss1: 0.191592 Loss2: 1.378697
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.906667 Loss1: 1.019368 Loss2: 1.887299
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.971534 Loss1: 0.595788 Loss2: 1.375746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.857398 Loss1: 0.437444 Loss2: 1.419953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390234 Loss1: 0.039166 Loss2: 1.351068 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.550189 Loss1: 0.197190 Loss2: 1.353000 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487312 Loss1: 0.143581 Loss2: 1.343731 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.780672 Loss1: 0.831938 Loss2: 1.948733 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.964488 Loss1: 0.499867 Loss2: 1.464621 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.672794 Loss1: 0.225885 Loss2: 1.446909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547014 Loss1: 0.112773 Loss2: 1.434241 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547297 Loss1: 0.119502 Loss2: 1.427794 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.530907 Loss1: 0.099661 Loss2: 1.431245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.511202 Loss1: 0.087347 Loss2: 1.423855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.523931 Loss1: 0.105978 Loss2: 1.417953 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.575147 Loss1: 0.190704 Loss2: 1.384443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462152 Loss1: 0.091117 Loss2: 1.371035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.662099 Loss1: 0.814415 Loss2: 1.847684 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.654106 Loss1: 0.260122 Loss2: 1.393984 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.520746 Loss1: 0.140284 Loss2: 1.380462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.798436 Loss1: 0.950008 Loss2: 1.848427
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514940 Loss1: 0.132322 Loss2: 1.382618
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.980744 Loss1: 0.563023 Loss2: 1.417721
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472846 Loss1: 0.098678 Loss2: 1.374169
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.757964 Loss1: 0.331997 Loss2: 1.425966
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.506464 Loss1: 0.140328 Loss2: 1.366135
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636660 Loss1: 0.246351 Loss2: 1.390309
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460308 Loss1: 0.086564 Loss2: 1.373744
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.520692 Loss1: 0.142929 Loss2: 1.377763 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963867
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478901 Loss1: 0.113108 Loss2: 1.365793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466789 Loss1: 0.101817 Loss2: 1.364972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443871 Loss1: 0.086492 Loss2: 1.357379
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.804656 Loss1: 0.900681 Loss2: 1.903975
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.977523 Loss1: 0.564842 Loss2: 1.412681
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.831685 Loss1: 0.370942 Loss2: 1.460743
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.675584 Loss1: 0.285171 Loss2: 1.390413
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.637358 Loss1: 0.228221 Loss2: 1.409138
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.654474 Loss1: 0.833850 Loss2: 1.820624
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.922461 Loss1: 0.520956 Loss2: 1.401505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769071 Loss1: 0.356291 Loss2: 1.412780 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.641981 Loss1: 0.254310 Loss2: 1.387672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.562161 Loss1: 0.184868 Loss2: 1.377293 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504599 Loss1: 0.132554 Loss2: 1.372045 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462325 Loss1: 0.106838 Loss2: 1.355487 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.830026 Loss1: 0.883772 Loss2: 1.946254
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436969 Loss1: 0.084233 Loss2: 1.352736
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.838326 Loss1: 0.350198 Loss2: 1.488128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.688012 Loss1: 0.244545 Loss2: 1.443466 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.595595 Loss1: 0.166771 Loss2: 1.428824
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.491539 Loss1: 0.712176 Loss2: 1.779363
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.781201 Loss1: 0.433712 Loss2: 1.347488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.664903 Loss1: 0.296329 Loss2: 1.368574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567650 Loss1: 0.228632 Loss2: 1.339018 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455447 Loss1: 0.128851 Loss2: 1.326596 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442420 Loss1: 0.119390 Loss2: 1.323030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434709 Loss1: 0.112579 Loss2: 1.322130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400919 Loss1: 0.085219 Loss2: 1.315700 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977941
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.792299 Loss1: 0.380309 Loss2: 1.411990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.603373 Loss1: 0.194240 Loss2: 1.409133 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.759187 Loss1: 0.884879 Loss2: 1.874308 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.913408 Loss1: 0.529957 Loss2: 1.383452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.698935 Loss1: 0.273535 Loss2: 1.425400 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541781 Loss1: 0.157276 Loss2: 1.384504 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478664 Loss1: 0.130712 Loss2: 1.347952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464645 Loss1: 0.110332 Loss2: 1.354312
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.819273 Loss1: 0.953768 Loss2: 1.865505
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.954092 Loss1: 0.558004 Loss2: 1.396088 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.804068 Loss1: 0.380388 Loss2: 1.423680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.549842 Loss1: 0.163700 Loss2: 1.386143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525996 Loss1: 0.153132 Loss2: 1.372864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.493435 Loss1: 0.132888 Loss2: 1.360547 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454062 Loss1: 0.093140 Loss2: 1.360922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476266 Loss1: 0.113596 Loss2: 1.362670 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560255 Loss1: 0.148472 Loss2: 1.411782 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518275 Loss1: 0.112276 Loss2: 1.405999 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.468839 Loss1: 0.068467 Loss2: 1.400372
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.791207 Loss1: 0.937789 Loss2: 1.853418
+(DefaultActor pid=3764) >> Training accuracy: 0.994420
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444372 Loss1: 0.052630 Loss2: 1.391742
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.963619 Loss1: 0.584319 Loss2: 1.379300
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.793106 Loss1: 0.380120 Loss2: 1.412986
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694354 Loss1: 0.326275 Loss2: 1.368079
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551253 Loss1: 0.191367 Loss2: 1.359886
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502575 Loss1: 0.158942 Loss2: 1.343633
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.757302 Loss1: 0.746598 Loss2: 2.010704
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486753 Loss1: 0.145743 Loss2: 1.341010
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.088732 Loss1: 0.574619 Loss2: 1.514113
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469411 Loss1: 0.130122 Loss2: 1.339289
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.878364 Loss1: 0.326536 Loss2: 1.551828
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452427 Loss1: 0.117790 Loss2: 1.334636
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.788567 Loss1: 0.276569 Loss2: 1.511998
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427052 Loss1: 0.094800 Loss2: 1.332253
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.637782 Loss1: 0.155910 Loss2: 1.481871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.585561 Loss1: 0.114823 Loss2: 1.470738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.560371 Loss1: 0.094670 Loss2: 1.465701
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.777949 Loss1: 0.887639 Loss2: 1.890309
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.549325 Loss1: 0.084229 Loss2: 1.465097
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.846034 Loss1: 0.465916 Loss2: 1.380118
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.708628 Loss1: 0.313218 Loss2: 1.395409
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583237 Loss1: 0.200435 Loss2: 1.382803
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.552019 Loss1: 0.187377 Loss2: 1.364642
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.510247 Loss1: 0.143293 Loss2: 1.366954
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.606844 Loss1: 0.733184 Loss2: 1.873661
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523983 Loss1: 0.162970 Loss2: 1.361012
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.815909 Loss1: 0.419809 Loss2: 1.396100
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454084 Loss1: 0.090718 Loss2: 1.363367
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.719145 Loss1: 0.292351 Loss2: 1.426794
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434163 Loss1: 0.081530 Loss2: 1.352633
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.642236 Loss1: 0.261770 Loss2: 1.380465
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429308 Loss1: 0.086484 Loss2: 1.342824
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480570 Loss1: 0.104254 Loss2: 1.376317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435785 Loss1: 0.075217 Loss2: 1.360568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428710 Loss1: 0.072733 Loss2: 1.355977
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.902983 Loss1: 0.917253 Loss2: 1.985731
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420118 Loss1: 0.065435 Loss2: 1.354683
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.915451 Loss1: 0.442836 Loss2: 1.472615
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.880978 Loss1: 0.400128 Loss2: 1.480850
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.687302 Loss1: 0.236928 Loss2: 1.450374
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.612612 Loss1: 0.174274 Loss2: 1.438338
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.585149 Loss1: 0.144866 Loss2: 1.440283
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.730275 Loss1: 0.941215 Loss2: 1.789060
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547014 Loss1: 0.128608 Loss2: 1.418406
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.917169 Loss1: 0.521541 Loss2: 1.395627
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.506251 Loss1: 0.088365 Loss2: 1.417886
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.638133 Loss1: 0.289126 Loss2: 1.349007
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.535808 Loss1: 0.120053 Loss2: 1.415755
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.583120 Loss1: 0.247605 Loss2: 1.335515
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.537408 Loss1: 0.120037 Loss2: 1.417371
+(DefaultActor pid=3765) >> Training accuracy: 0.960417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468898 Loss1: 0.143514 Loss2: 1.325384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432848 Loss1: 0.112006 Loss2: 1.320841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421337 Loss1: 0.104203 Loss2: 1.317134
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.706252 Loss1: 0.847656 Loss2: 1.858595
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409877 Loss1: 0.098608 Loss2: 1.311269
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.858002 Loss1: 0.483492 Loss2: 1.374509
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.764887 Loss1: 0.325038 Loss2: 1.439849
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586586 Loss1: 0.226620 Loss2: 1.359966
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.583093 Loss1: 0.212486 Loss2: 1.370606
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.554046 Loss1: 0.189636 Loss2: 1.364409
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451614 Loss1: 0.099550 Loss2: 1.352064
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.739449 Loss1: 0.860161 Loss2: 1.879288
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460704 Loss1: 0.114784 Loss2: 1.345920
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.928975 Loss1: 0.535945 Loss2: 1.393030
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452913 Loss1: 0.110131 Loss2: 1.342781
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.697265 Loss1: 0.261010 Loss2: 1.436255
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406294 Loss1: 0.068649 Loss2: 1.337645
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.633185 Loss1: 0.241060 Loss2: 1.392125
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.572125 Loss1: 0.178104 Loss2: 1.394021
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.580059 Loss1: 0.186316 Loss2: 1.393743
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.526213 Loss1: 0.141812 Loss2: 1.384401
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.528636 Loss1: 0.138268 Loss2: 1.390368
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.533530 Loss1: 0.151666 Loss2: 1.381864
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.531765 Loss1: 0.725979 Loss2: 1.805787
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.484983 Loss1: 0.103382 Loss2: 1.381602
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.961188 Loss1: 0.584494 Loss2: 1.376694
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.748716 Loss1: 0.322788 Loss2: 1.425928
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.640062 Loss1: 0.266709 Loss2: 1.373353
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.650422 Loss1: 0.253997 Loss2: 1.396425
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.579567 Loss1: 0.200255 Loss2: 1.379312
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.645333 Loss1: 0.851980 Loss2: 1.793353
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.558066 Loss1: 0.182722 Loss2: 1.375344
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.511608 Loss1: 0.141191 Loss2: 1.370417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.467456 Loss1: 0.101778 Loss2: 1.365678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445582 Loss1: 0.089986 Loss2: 1.355595 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.457820 Loss1: 0.114987 Loss2: 1.342833 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412424 Loss1: 0.082239 Loss2: 1.330185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.906999 Loss1: 0.925857 Loss2: 1.981142
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429828 Loss1: 0.097849 Loss2: 1.331979
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385690 Loss1: 0.058036 Loss2: 1.327654 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587237 Loss1: 0.204311 Loss2: 1.382926 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.553756 Loss1: 0.184868 Loss2: 1.368888 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 09:03:22,411 | server.py:236 | fit_round 108 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.868328 Loss1: 1.011181 Loss2: 1.857147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438049 Loss1: 0.073824 Loss2: 1.364225 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586731 Loss1: 0.225764 Loss2: 1.360966 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.534119 Loss1: 0.182468 Loss2: 1.351650 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.662099 Loss1: 0.855724 Loss2: 1.806375
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.506067 Loss1: 0.156467 Loss2: 1.349601
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.006967 Loss1: 0.629659 Loss2: 1.377308
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449600 Loss1: 0.104497 Loss2: 1.345104
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.781728 Loss1: 0.360565 Loss2: 1.421163
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408829 Loss1: 0.072566 Loss2: 1.336263
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576940 Loss1: 0.223381 Loss2: 1.353558
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386841 Loss1: 0.056127 Loss2: 1.330714
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442240 Loss1: 0.110009 Loss2: 1.332231 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.396014 Loss1: 0.076261 Loss2: 1.319754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.387287 Loss1: 0.069770 Loss2: 1.317517
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.580112 Loss1: 0.768044 Loss2: 1.812067
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359721 Loss1: 0.048307 Loss2: 1.311414
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.897932 Loss1: 0.516559 Loss2: 1.381373
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726231 Loss1: 0.303399 Loss2: 1.422833
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.663599 Loss1: 0.282173 Loss2: 1.381427
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.577915 Loss1: 0.195364 Loss2: 1.382551
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521777 Loss1: 0.140078 Loss2: 1.381699
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486730 Loss1: 0.115272 Loss2: 1.371458
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.476417 Loss1: 0.115627 Loss2: 1.360789
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442965 Loss1: 0.083701 Loss2: 1.359264
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453010 Loss1: 0.097535 Loss2: 1.355475
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 09:03:22,411][flwr][DEBUG] - fit_round 108 received 50 results and 0 failures
+INFO flwr 2023-10-11 09:04:03,728 | server.py:125 | fit progress: (108, 2.2027086655552774, {'accuracy': 0.5734}, 249151.506683776)
+>> Test accuracy: 0.573400
+[2023-10-11 09:04:03,728][flwr][INFO] - fit progress: (108, 2.2027086655552774, {'accuracy': 0.5734}, 249151.506683776)
+DEBUG flwr 2023-10-11 09:04:03,728 | server.py:173 | evaluate_round 108: strategy sampled 50 clients (out of 50)
+[2023-10-11 09:04:03,728][flwr][DEBUG] - evaluate_round 108: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 09:13:05,386 | server.py:187 | evaluate_round 108 received 50 results and 0 failures
+[2023-10-11 09:13:05,386][flwr][DEBUG] - evaluate_round 108 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 09:13:05,386 | server.py:222 | fit_round 109: strategy sampled 50 clients (out of 50)
+[2023-10-11 09:13:05,386][flwr][DEBUG] - fit_round 109: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.735014 Loss1: 0.928740 Loss2: 1.806274
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.895976 Loss1: 0.535552 Loss2: 1.360425
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.843333 Loss1: 0.445985 Loss2: 1.397348
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.757091 Loss1: 0.393862 Loss2: 1.363229
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.626719 Loss1: 0.259940 Loss2: 1.366779 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537218 Loss1: 0.191623 Loss2: 1.345595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.510461 Loss1: 0.157710 Loss2: 1.352752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477421 Loss1: 0.136300 Loss2: 1.341120 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.475883 Loss1: 0.145936 Loss2: 1.329947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408786 Loss1: 0.080697 Loss2: 1.328089 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490263 Loss1: 0.081569 Loss2: 1.408694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.488618 Loss1: 0.089050 Loss2: 1.399568 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.986026 Loss1: 0.575228 Loss2: 1.410798 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.634668 Loss1: 0.228665 Loss2: 1.406002 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.579751 Loss1: 0.178701 Loss2: 1.401050 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.550198 Loss1: 0.156619 Loss2: 1.393579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512738 Loss1: 0.127649 Loss2: 1.385090 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.524916 Loss1: 0.144017 Loss2: 1.380898 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484162 Loss1: 0.100051 Loss2: 1.384111 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448914 Loss1: 0.070560 Loss2: 1.378354 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.516333 Loss1: 0.138199 Loss2: 1.378135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435216 Loss1: 0.067373 Loss2: 1.367843 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.891627 Loss1: 0.456063 Loss2: 1.435564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.636508 Loss1: 0.212666 Loss2: 1.423842 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.575609 Loss1: 0.148465 Loss2: 1.427144 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523484 Loss1: 0.117328 Loss2: 1.406156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495752 Loss1: 0.091014 Loss2: 1.404738 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470477 Loss1: 0.072019 Loss2: 1.398458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.466009 Loss1: 0.071631 Loss2: 1.394378 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465552 Loss1: 0.073953 Loss2: 1.391599 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460273 Loss1: 0.097988 Loss2: 1.362285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442626 Loss1: 0.096267 Loss2: 1.346359 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.642431 Loss1: 0.838720 Loss2: 1.803712
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.792803 Loss1: 0.435176 Loss2: 1.357627
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638854 Loss1: 0.261839 Loss2: 1.377015
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.501133 Loss1: 0.181700 Loss2: 1.319433
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.795428 Loss1: 0.866949 Loss2: 1.928479
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.942240 Loss1: 0.499022 Loss2: 1.443218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804828 Loss1: 0.329979 Loss2: 1.474849 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.699748 Loss1: 0.270044 Loss2: 1.429704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.626838 Loss1: 0.188156 Loss2: 1.438682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.617595 Loss1: 0.193273 Loss2: 1.424321 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.547180 Loss1: 0.117569 Loss2: 1.429610 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.518899 Loss1: 0.111545 Loss2: 1.407354 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 3.035384 Loss1: 1.027527 Loss2: 2.007858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.867946 Loss1: 0.418057 Loss2: 1.449890 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.582387 Loss1: 0.199706 Loss2: 1.382681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.527881 Loss1: 0.136133 Loss2: 1.391748 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.497847 Loss1: 0.123569 Loss2: 1.374278 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461278 Loss1: 0.091604 Loss2: 1.369673
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.768272 Loss1: 0.324621 Loss2: 1.443652
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.646799 Loss1: 0.209627 Loss2: 1.437172 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557717 Loss1: 0.139055 Loss2: 1.418662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.569638 Loss1: 0.153189 Loss2: 1.416450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.531016 Loss1: 0.120559 Loss2: 1.410457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.510903 Loss1: 0.101570 Loss2: 1.409333 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.720543 Loss1: 0.255334 Loss2: 1.465209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.619603 Loss1: 0.183684 Loss2: 1.435918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.650221 Loss1: 0.817439 Loss2: 1.832782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.952987 Loss1: 0.548228 Loss2: 1.404758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.755955 Loss1: 0.340470 Loss2: 1.415485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.629909 Loss1: 0.255473 Loss2: 1.374435 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.534760 Loss1: 0.159654 Loss2: 1.375105 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422870 Loss1: 0.071031 Loss2: 1.351839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.826087 Loss1: 0.897012 Loss2: 1.929075
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401459 Loss1: 0.058512 Loss2: 1.342947
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398677 Loss1: 0.059067 Loss2: 1.339610 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.732389 Loss1: 0.271123 Loss2: 1.461266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.592387 Loss1: 0.154338 Loss2: 1.438049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.548747 Loss1: 0.119617 Loss2: 1.429129
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.787998 Loss1: 0.911706 Loss2: 1.876292
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.960482 Loss1: 0.557943 Loss2: 1.402539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.751287 Loss1: 0.350157 Loss2: 1.401130 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.486099 Loss1: 0.071444 Loss2: 1.414655
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.621712 Loss1: 0.253743 Loss2: 1.367969
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530156 Loss1: 0.152284 Loss2: 1.377872
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502429 Loss1: 0.147964 Loss2: 1.354465
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436181 Loss1: 0.085295 Loss2: 1.350887
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416092 Loss1: 0.072851 Loss2: 1.343241
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.811635 Loss1: 0.953381 Loss2: 1.858254
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420973 Loss1: 0.081271 Loss2: 1.339702
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.940420 Loss1: 0.560930 Loss2: 1.379490
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397411 Loss1: 0.063789 Loss2: 1.333622
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.585922 Loss1: 0.218689 Loss2: 1.367233 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478533 Loss1: 0.133610 Loss2: 1.344924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448784 Loss1: 0.098593 Loss2: 1.350191
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.856124 Loss1: 0.929479 Loss2: 1.926645
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.982374 Loss1: 0.521767 Loss2: 1.460607 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.799176 Loss1: 0.310186 Loss2: 1.488990 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421509 Loss1: 0.088996 Loss2: 1.332514
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.658496 Loss1: 0.225568 Loss2: 1.432928
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.596493 Loss1: 0.166032 Loss2: 1.430461
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.517656 Loss1: 0.098629 Loss2: 1.419027
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.527888 Loss1: 0.118785 Loss2: 1.409103
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.524650 Loss1: 0.109472 Loss2: 1.415178
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.830528 Loss1: 0.940993 Loss2: 1.889535
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480149 Loss1: 0.073403 Loss2: 1.406746
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.966514 Loss1: 0.591516 Loss2: 1.374998
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.507911 Loss1: 0.105641 Loss2: 1.402269
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.621856 Loss1: 0.261140 Loss2: 1.360716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.496952 Loss1: 0.136817 Loss2: 1.360135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.770307 Loss1: 0.923057 Loss2: 1.847250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.874035 Loss1: 0.454843 Loss2: 1.419191 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.722734 Loss1: 0.302766 Loss2: 1.419969 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979911
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.619436 Loss1: 0.226111 Loss2: 1.393325 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.519505 Loss1: 0.137854 Loss2: 1.381651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.986661 Loss1: 0.993152 Loss2: 1.993510
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.476546 Loss1: 0.104903 Loss2: 1.371643
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.517862 Loss1: 0.142786 Loss2: 1.375075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462805 Loss1: 0.088504 Loss2: 1.374301 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525531 Loss1: 0.137294 Loss2: 1.388237 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.475436 Loss1: 0.106754 Loss2: 1.368682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.772084 Loss1: 0.846688 Loss2: 1.925396 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979567
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.851186 Loss1: 0.375673 Loss2: 1.475512 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.604862 Loss1: 0.198761 Loss2: 1.406101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.547411 Loss1: 0.159627 Loss2: 1.387784
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.771311 Loss1: 0.884073 Loss2: 1.887238
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.948958 Loss1: 0.499459 Loss2: 1.449499 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.749076 Loss1: 0.292140 Loss2: 1.456936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.672836 Loss1: 0.256024 Loss2: 1.416812 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.577902 Loss1: 0.161825 Loss2: 1.416077 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486992 Loss1: 0.079206 Loss2: 1.407786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465665 Loss1: 0.068201 Loss2: 1.397464
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.491622 Loss1: 0.670380 Loss2: 1.821242
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475035 Loss1: 0.085370 Loss2: 1.389665
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.824485 Loss1: 0.479129 Loss2: 1.345356
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.697054 Loss1: 0.307971 Loss2: 1.389083
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619551 Loss1: 0.270306 Loss2: 1.349245
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543835 Loss1: 0.186544 Loss2: 1.357291
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461530 Loss1: 0.117461 Loss2: 1.344068
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412568 Loss1: 0.074474 Loss2: 1.338094
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.517894 Loss1: 0.721271 Loss2: 1.796624
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.886360 Loss1: 0.506466 Loss2: 1.379895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.764653 Loss1: 0.348109 Loss2: 1.416545 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388998 Loss1: 0.069257 Loss2: 1.319742
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.696723 Loss1: 0.310888 Loss2: 1.385835
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.645432 Loss1: 0.254386 Loss2: 1.391045
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.663244 Loss1: 0.275206 Loss2: 1.388038
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.530387 Loss1: 0.147724 Loss2: 1.382663
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477290 Loss1: 0.112197 Loss2: 1.365093
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.755424 Loss1: 0.906559 Loss2: 1.848866
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.017140 Loss1: 0.614021 Loss2: 1.403119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462120 Loss1: 0.104540 Loss2: 1.357580
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.849241 Loss1: 0.422584 Loss2: 1.426657
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.644730 Loss1: 0.254854 Loss2: 1.389876
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519996 Loss1: 0.143793 Loss2: 1.376203
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485554 Loss1: 0.123416 Loss2: 1.362138
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.481571 Loss1: 0.119313 Loss2: 1.362257
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448527 Loss1: 0.086053 Loss2: 1.362474
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.587671 Loss1: 0.748418 Loss2: 1.839254
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460821 Loss1: 0.110544 Loss2: 1.350277
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.808604 Loss1: 0.415949 Loss2: 1.392654
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457326 Loss1: 0.103851 Loss2: 1.353475
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731276 Loss1: 0.292027 Loss2: 1.439248
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601105 Loss1: 0.211857 Loss2: 1.389248
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567334 Loss1: 0.179525 Loss2: 1.387809
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524642 Loss1: 0.144842 Loss2: 1.379800
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514368 Loss1: 0.136453 Loss2: 1.377914
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.754601 Loss1: 0.886663 Loss2: 1.867938
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.920174 Loss1: 0.511991 Loss2: 1.408182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.841785 Loss1: 0.399017 Loss2: 1.442768 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481793 Loss1: 0.115275 Loss2: 1.366517
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.621488 Loss1: 0.240970 Loss2: 1.380517
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.601507 Loss1: 0.210037 Loss2: 1.391470
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.555222 Loss1: 0.179567 Loss2: 1.375655
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.505272 Loss1: 0.135588 Loss2: 1.369683
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486518 Loss1: 0.118925 Loss2: 1.367593
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.681403 Loss1: 0.828500 Loss2: 1.852903
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.891204 Loss1: 0.514467 Loss2: 1.376737 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655296 Loss1: 0.255407 Loss2: 1.399889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.500261 Loss1: 0.156742 Loss2: 1.343519 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481775 Loss1: 0.148206 Loss2: 1.333569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.502437 Loss1: 0.162367 Loss2: 1.340070 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452616 Loss1: 0.120116 Loss2: 1.332500 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.470986 Loss1: 0.134073 Loss2: 1.336913 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530863 Loss1: 0.170325 Loss2: 1.360538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468918 Loss1: 0.122163 Loss2: 1.346755 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.642523 Loss1: 0.836506 Loss2: 1.806017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.927561 Loss1: 0.565002 Loss2: 1.362559 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.738312 Loss1: 0.331454 Loss2: 1.406858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.659940 Loss1: 0.292241 Loss2: 1.367699 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.506404 Loss1: 0.164168 Loss2: 1.342236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454713 Loss1: 0.115723 Loss2: 1.338990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460256 Loss1: 0.130355 Loss2: 1.329901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402027 Loss1: 0.070945 Loss2: 1.331082 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527841 Loss1: 0.160535 Loss2: 1.367307 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494194 Loss1: 0.129283 Loss2: 1.364911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470476 Loss1: 0.109700 Loss2: 1.360776
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.732210 Loss1: 0.917312 Loss2: 1.814898
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.840527 Loss1: 0.527093 Loss2: 1.313434 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425421 Loss1: 0.071011 Loss2: 1.354410
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.657867 Loss1: 0.305200 Loss2: 1.352667
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513557 Loss1: 0.210436 Loss2: 1.303121
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.455823 Loss1: 0.145285 Loss2: 1.310538
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449302 Loss1: 0.157412 Loss2: 1.291890
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.399363 Loss1: 0.109037 Loss2: 1.290327
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.376026 Loss1: 0.089424 Loss2: 1.286602
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.561247 Loss1: 0.747557 Loss2: 1.813690
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.871018 Loss1: 0.473763 Loss2: 1.397255 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979911
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.723772 Loss1: 0.308801 Loss2: 1.414971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544411 Loss1: 0.174389 Loss2: 1.370021 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494419 Loss1: 0.136436 Loss2: 1.357984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452293 Loss1: 0.096295 Loss2: 1.355998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448263 Loss1: 0.089357 Loss2: 1.358906 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.486015 Loss1: 0.140109 Loss2: 1.345907 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465328 Loss1: 0.146752 Loss2: 1.318576 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393442 Loss1: 0.082801 Loss2: 1.310641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372933 Loss1: 0.073181 Loss2: 1.299752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391423 Loss1: 0.086200 Loss2: 1.305223 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557341 Loss1: 0.190840 Loss2: 1.366501 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485315 Loss1: 0.123239 Loss2: 1.362076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462595 Loss1: 0.122525 Loss2: 1.340070
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.733834 Loss1: 0.864570 Loss2: 1.869264
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.953302 Loss1: 0.556860 Loss2: 1.396442 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575874 Loss1: 0.211314 Loss2: 1.364559 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470451 Loss1: 0.112464 Loss2: 1.357987 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455879 Loss1: 0.116791 Loss2: 1.339088
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.797656 Loss1: 0.928389 Loss2: 1.869267
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.972269 Loss1: 0.567817 Loss2: 1.404452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.830597 Loss1: 0.384475 Loss2: 1.446122 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431978 Loss1: 0.092730 Loss2: 1.339248
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.698269 Loss1: 0.283239 Loss2: 1.415030
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.562884 Loss1: 0.181254 Loss2: 1.381630
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494154 Loss1: 0.120990 Loss2: 1.373164
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463613 Loss1: 0.095197 Loss2: 1.368416
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441719 Loss1: 0.083134 Loss2: 1.358584
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.707839 Loss1: 0.848601 Loss2: 1.859238
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442536 Loss1: 0.083454 Loss2: 1.359081
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438142 Loss1: 0.080591 Loss2: 1.357551 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610445 Loss1: 0.245118 Loss2: 1.365328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494308 Loss1: 0.138977 Loss2: 1.355331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456916 Loss1: 0.111059 Loss2: 1.345857
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.907958 Loss1: 1.085085 Loss2: 1.822872
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.940411 Loss1: 0.596536 Loss2: 1.343875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671751 Loss1: 0.314843 Loss2: 1.356907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.494635 Loss1: 0.184944 Loss2: 1.309691 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.440613 Loss1: 0.140205 Loss2: 1.300408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389804 Loss1: 0.098738 Loss2: 1.291066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.364041 Loss1: 0.073828 Loss2: 1.290214
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.542179 Loss1: 0.693445 Loss2: 1.848734
+(DefaultActor pid=3764) >> Training accuracy: 0.994420
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.341670 Loss1: 0.053138 Loss2: 1.288532
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.906405 Loss1: 0.548474 Loss2: 1.357931
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.696032 Loss1: 0.297132 Loss2: 1.398900
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.592152 Loss1: 0.237931 Loss2: 1.354221
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566251 Loss1: 0.209327 Loss2: 1.356924
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494559 Loss1: 0.138298 Loss2: 1.356261
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.558192 Loss1: 0.770388 Loss2: 1.787804
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464455 Loss1: 0.124374 Loss2: 1.340081
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.784158 Loss1: 0.455838 Loss2: 1.328320
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.416592 Loss1: 0.084068 Loss2: 1.332524
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.705203 Loss1: 0.336459 Loss2: 1.368744
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417401 Loss1: 0.085381 Loss2: 1.332020
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604410 Loss1: 0.274822 Loss2: 1.329587
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396379 Loss1: 0.065324 Loss2: 1.331055
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452861 Loss1: 0.142155 Loss2: 1.310706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376887 Loss1: 0.074918 Loss2: 1.301969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.351054 Loss1: 0.052979 Loss2: 1.298076
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.673041 Loss1: 0.777475 Loss2: 1.895566
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.860769 Loss1: 0.474881 Loss2: 1.385889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.568602 Loss1: 0.197471 Loss2: 1.371132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491648 Loss1: 0.125583 Loss2: 1.366065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.623896 Loss1: 0.753359 Loss2: 1.870536
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460636 Loss1: 0.100407 Loss2: 1.360229
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.910640 Loss1: 0.524882 Loss2: 1.385759
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445219 Loss1: 0.089246 Loss2: 1.355973
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.744610 Loss1: 0.291900 Loss2: 1.452710
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447716 Loss1: 0.099405 Loss2: 1.348312
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619493 Loss1: 0.239217 Loss2: 1.380276
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430159 Loss1: 0.077552 Loss2: 1.352607
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.507349 Loss1: 0.132547 Loss2: 1.374802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.451964 Loss1: 0.092317 Loss2: 1.359646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473014 Loss1: 0.113722 Loss2: 1.359292
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.682593 Loss1: 0.811344 Loss2: 1.871249
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438465 Loss1: 0.078171 Loss2: 1.360294
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.846587 Loss1: 0.455580 Loss2: 1.391007
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.739362 Loss1: 0.309960 Loss2: 1.429402
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649376 Loss1: 0.266902 Loss2: 1.382474
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.643655 Loss1: 0.244998 Loss2: 1.398657
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.550466 Loss1: 0.151981 Loss2: 1.398485
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.709163 Loss1: 0.855451 Loss2: 1.853711
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524908 Loss1: 0.142864 Loss2: 1.382044
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491560 Loss1: 0.116964 Loss2: 1.374596
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.932245 Loss1: 0.516672 Loss2: 1.415572
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.467102 Loss1: 0.082891 Loss2: 1.384211
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.712970 Loss1: 0.286780 Loss2: 1.426189
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442646 Loss1: 0.075659 Loss2: 1.366987
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.647203 Loss1: 0.256142 Loss2: 1.391061
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574275 Loss1: 0.178900 Loss2: 1.395375
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513970 Loss1: 0.136411 Loss2: 1.377559
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513972 Loss1: 0.143818 Loss2: 1.370154
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464186 Loss1: 0.085839 Loss2: 1.378347
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.557304 Loss1: 0.746940 Loss2: 1.810364
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444210 Loss1: 0.080947 Loss2: 1.363263
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452626 Loss1: 0.088981 Loss2: 1.363645 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649983 Loss1: 0.288491 Loss2: 1.361492 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525813 Loss1: 0.158988 Loss2: 1.366825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.520482 Loss1: 0.159913 Loss2: 1.360568
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.694558 Loss1: 0.794694 Loss2: 1.899864
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.001271 Loss1: 0.562469 Loss2: 1.438802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.758906 Loss1: 0.292251 Loss2: 1.466655 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.693443 Loss1: 0.259353 Loss2: 1.434090 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.526732 Loss1: 0.115217 Loss2: 1.411515 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.513302 Loss1: 0.102897 Loss2: 1.410405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.503175 Loss1: 0.103424 Loss2: 1.399751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512337 Loss1: 0.112372 Loss2: 1.399965 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581720 Loss1: 0.222911 Loss2: 1.358809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502051 Loss1: 0.154722 Loss2: 1.347329 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456768 Loss1: 0.109007 Loss2: 1.347761
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.590914 Loss1: 0.748668 Loss2: 1.842246
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.905346 Loss1: 0.464749 Loss2: 1.440597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727636 Loss1: 0.303041 Loss2: 1.424595 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 09:41:25,648 | server.py:236 | fit_round 109 received 50 results and 0 failures
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.668586 Loss1: 0.264515 Loss2: 1.404071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.541717 Loss1: 0.152755 Loss2: 1.388962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461510 Loss1: 0.092785 Loss2: 1.368725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476052 Loss1: 0.103350 Loss2: 1.372702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.484730 Loss1: 0.113823 Loss2: 1.370907 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965820
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577724 Loss1: 0.184963 Loss2: 1.392761 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488665 Loss1: 0.117052 Loss2: 1.371613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.524301 Loss1: 0.144542 Loss2: 1.379759
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.531778 Loss1: 0.735661 Loss2: 1.796117
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.798864 Loss1: 0.418261 Loss2: 1.380603 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.639675 Loss1: 0.269085 Loss2: 1.370590 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.533990 Loss1: 0.178791 Loss2: 1.355199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456339 Loss1: 0.111158 Loss2: 1.345181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441693 Loss1: 0.101305 Loss2: 1.340387 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992647
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 09:41:25,648][flwr][DEBUG] - fit_round 109 received 50 results and 0 failures
+INFO flwr 2023-10-11 09:42:09,239 | server.py:125 | fit progress: (109, 2.2131336215205084, {'accuracy': 0.5739}, 251437.017252407)
+>> Test accuracy: 0.573900
+[2023-10-11 09:42:09,239][flwr][INFO] - fit progress: (109, 2.2131336215205084, {'accuracy': 0.5739}, 251437.017252407)
+DEBUG flwr 2023-10-11 09:42:09,239 | server.py:173 | evaluate_round 109: strategy sampled 50 clients (out of 50)
+[2023-10-11 09:42:09,239][flwr][DEBUG] - evaluate_round 109: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 09:51:16,347 | server.py:187 | evaluate_round 109 received 50 results and 0 failures
+[2023-10-11 09:51:16,347][flwr][DEBUG] - evaluate_round 109 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 09:51:16,347 | server.py:222 | fit_round 110: strategy sampled 50 clients (out of 50)
+[2023-10-11 09:51:16,347][flwr][DEBUG] - fit_round 110: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.742540 Loss1: 0.820059 Loss2: 1.922481 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736004 Loss1: 0.269109 Loss2: 1.466895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629098 Loss1: 0.206557 Loss2: 1.422541
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.858670 Loss1: 1.018418 Loss2: 1.840252
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589384 Loss1: 0.157475 Loss2: 1.431909
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.027905 Loss1: 0.641047 Loss2: 1.386858
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538986 Loss1: 0.123992 Loss2: 1.414994
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.779959 Loss1: 0.377697 Loss2: 1.402262
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.507550 Loss1: 0.095114 Loss2: 1.412436
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604329 Loss1: 0.246944 Loss2: 1.357385
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484357 Loss1: 0.074910 Loss2: 1.409447
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512026 Loss1: 0.144162 Loss2: 1.367863
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.481271 Loss1: 0.074509 Loss2: 1.406762
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467536 Loss1: 0.123328 Loss2: 1.344209
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472576 Loss1: 0.070887 Loss2: 1.401689
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476980 Loss1: 0.128760 Loss2: 1.348220
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466403 Loss1: 0.124964 Loss2: 1.341439
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462181 Loss1: 0.125438 Loss2: 1.336742
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410127 Loss1: 0.074533 Loss2: 1.335594
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.543573 Loss1: 0.761912 Loss2: 1.781661
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.907069 Loss1: 0.538353 Loss2: 1.368716
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.755008 Loss1: 0.368205 Loss2: 1.386803
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.738078 Loss1: 0.907994 Loss2: 1.830084
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610612 Loss1: 0.259214 Loss2: 1.351398
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.884586 Loss1: 0.514257 Loss2: 1.370329
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.545041 Loss1: 0.177800 Loss2: 1.367240
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.736053 Loss1: 0.324996 Loss2: 1.411057
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520805 Loss1: 0.189030 Loss2: 1.331776
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.560341 Loss1: 0.193943 Loss2: 1.366399
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.519398 Loss1: 0.180463 Loss2: 1.338935
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471601 Loss1: 0.137670 Loss2: 1.333931 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444352 Loss1: 0.114616 Loss2: 1.329736 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403121 Loss1: 0.077370 Loss2: 1.325751 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420388 Loss1: 0.077007 Loss2: 1.343381 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.847872 Loss1: 0.917755 Loss2: 1.930117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.908887 Loss1: 0.421023 Loss2: 1.487865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.680876 Loss1: 0.230761 Loss2: 1.450115
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.735318 Loss1: 0.881997 Loss2: 1.853321
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.818549 Loss1: 0.434302 Loss2: 1.384248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.688447 Loss1: 0.297729 Loss2: 1.390719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.601389 Loss1: 0.229941 Loss2: 1.371449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475028 Loss1: 0.123792 Loss2: 1.351236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459102 Loss1: 0.112530 Loss2: 1.346572 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472361 Loss1: 0.065696 Loss2: 1.406665
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435436 Loss1: 0.096916 Loss2: 1.338521
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425391 Loss1: 0.086461 Loss2: 1.338930
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381585 Loss1: 0.046396 Loss2: 1.335189
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409094 Loss1: 0.080616 Loss2: 1.328479
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.599345 Loss1: 0.694930 Loss2: 1.904415
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.944405 Loss1: 0.522916 Loss2: 1.421489
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.819202 Loss1: 0.348188 Loss2: 1.471014
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.700702 Loss1: 0.287808 Loss2: 1.412894
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.830695 Loss1: 0.984474 Loss2: 1.846221
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.956462 Loss1: 0.549362 Loss2: 1.407099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.795765 Loss1: 0.375653 Loss2: 1.420112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.685288 Loss1: 0.287632 Loss2: 1.397655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.634253 Loss1: 0.245119 Loss2: 1.389134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.591468 Loss1: 0.211743 Loss2: 1.379725 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447015 Loss1: 0.069385 Loss2: 1.377630
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.553008 Loss1: 0.166214 Loss2: 1.386794
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540915 Loss1: 0.170217 Loss2: 1.370698
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481514 Loss1: 0.110057 Loss2: 1.371457
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434250 Loss1: 0.071913 Loss2: 1.362337
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.847983 Loss1: 0.900654 Loss2: 1.947329
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.916648 Loss1: 0.482343 Loss2: 1.434305
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.796791 Loss1: 0.321609 Loss2: 1.475182
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.637665 Loss1: 0.212408 Loss2: 1.425257
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.652169 Loss1: 0.748719 Loss2: 1.903449
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.969694 Loss1: 0.504820 Loss2: 1.464874 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.793198 Loss1: 0.309057 Loss2: 1.484141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.687476 Loss1: 0.244316 Loss2: 1.443160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.651063 Loss1: 0.197537 Loss2: 1.453526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.598591 Loss1: 0.168824 Loss2: 1.429767 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.522203 Loss1: 0.089592 Loss2: 1.432611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497779 Loss1: 0.078892 Loss2: 1.418887 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.935818 Loss1: 0.962087 Loss2: 1.973731 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.828051 Loss1: 0.349580 Loss2: 1.478471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617466 Loss1: 0.205757 Loss2: 1.411709 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.603476 Loss1: 0.189538 Loss2: 1.413938 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.805388 Loss1: 0.362687 Loss2: 1.442701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.647031 Loss1: 0.270698 Loss2: 1.376333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492857 Loss1: 0.121418 Loss2: 1.371439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471631 Loss1: 0.111227 Loss2: 1.360405 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978795
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425249 Loss1: 0.079493 Loss2: 1.345756 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381821 Loss1: 0.047259 Loss2: 1.334562 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.840888 Loss1: 0.896143 Loss2: 1.944744
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.994498 Loss1: 0.512313 Loss2: 1.482185
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.802025 Loss1: 0.297217 Loss2: 1.504808
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.729081 Loss1: 0.264382 Loss2: 1.464699
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.654214 Loss1: 0.771394 Loss2: 1.882820
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.684531 Loss1: 0.222128 Loss2: 1.462404
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.057388 Loss1: 0.639235 Loss2: 1.418153
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.614123 Loss1: 0.157402 Loss2: 1.456721
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.873847 Loss1: 0.399209 Loss2: 1.474637
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.676179 Loss1: 0.271461 Loss2: 1.404718
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.596306 Loss1: 0.151338 Loss2: 1.444968
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.613064 Loss1: 0.199295 Loss2: 1.413770
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.571084 Loss1: 0.120887 Loss2: 1.450197
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.533218 Loss1: 0.138150 Loss2: 1.395068
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.549257 Loss1: 0.115625 Loss2: 1.433633
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.514036 Loss1: 0.127052 Loss2: 1.386985
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.522057 Loss1: 0.093975 Loss2: 1.428082
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501145 Loss1: 0.110042 Loss2: 1.391103 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.883025 Loss1: 0.977962 Loss2: 1.905063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.782403 Loss1: 0.330890 Loss2: 1.451512 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.659057 Loss1: 0.249242 Loss2: 1.409815
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.874737 Loss1: 0.948431 Loss2: 1.926306
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.607051 Loss1: 0.193241 Loss2: 1.413810
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.076544 Loss1: 0.583304 Loss2: 1.493240
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526094 Loss1: 0.128399 Loss2: 1.397694
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834661 Loss1: 0.374238 Loss2: 1.460422
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529000 Loss1: 0.141759 Loss2: 1.387241
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.659197 Loss1: 0.231286 Loss2: 1.427911
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.527953 Loss1: 0.137108 Loss2: 1.390845
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.587084 Loss1: 0.163972 Loss2: 1.423112
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.533909 Loss1: 0.145857 Loss2: 1.388052
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542886 Loss1: 0.133783 Loss2: 1.409103
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.547208 Loss1: 0.148812 Loss2: 1.398396
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550628 Loss1: 0.150872 Loss2: 1.399755
+(DefaultActor pid=3765) >> Training accuracy: 0.961458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.559302 Loss1: 0.149026 Loss2: 1.410276
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.509979 Loss1: 0.104194 Loss2: 1.405785
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.513396 Loss1: 0.109249 Loss2: 1.404147
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.878585 Loss1: 0.916491 Loss2: 1.962094
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.974114 Loss1: 0.546502 Loss2: 1.427612
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.798470 Loss1: 0.308722 Loss2: 1.489748
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.730882 Loss1: 0.305848 Loss2: 1.425034
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.775695 Loss1: 0.878206 Loss2: 1.897489
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.023199 Loss1: 0.592824 Loss2: 1.430375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.831250 Loss1: 0.386105 Loss2: 1.445145 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.732342 Loss1: 0.301456 Loss2: 1.430886 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.664361 Loss1: 0.241732 Loss2: 1.422629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.551631 Loss1: 0.146167 Loss2: 1.405463 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972098
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486421 Loss1: 0.094595 Loss2: 1.391826 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466278 Loss1: 0.082195 Loss2: 1.384083 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.870020 Loss1: 0.465826 Loss2: 1.404193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.612962 Loss1: 0.226248 Loss2: 1.386714 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.533802 Loss1: 0.149121 Loss2: 1.384681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515662 Loss1: 0.133256 Loss2: 1.382406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.471219 Loss1: 0.100586 Loss2: 1.370633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444333 Loss1: 0.073953 Loss2: 1.370380 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445685 Loss1: 0.080332 Loss2: 1.365353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451924 Loss1: 0.092477 Loss2: 1.359448 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469795 Loss1: 0.121037 Loss2: 1.348757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466408 Loss1: 0.122318 Loss2: 1.344090 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.965625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.967103 Loss1: 0.551607 Loss2: 1.415496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.658977 Loss1: 0.255882 Loss2: 1.403095 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.690308 Loss1: 0.861568 Loss2: 1.828740
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.608791 Loss1: 0.202511 Loss2: 1.406281
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.914056 Loss1: 0.526375 Loss2: 1.387681
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.557239 Loss1: 0.161795 Loss2: 1.395444
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.760475 Loss1: 0.332250 Loss2: 1.428226
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.557066 Loss1: 0.165265 Loss2: 1.391801
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.580281 Loss1: 0.216417 Loss2: 1.363864
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.537604 Loss1: 0.136850 Loss2: 1.400755
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485877 Loss1: 0.127654 Loss2: 1.358223
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.524174 Loss1: 0.131359 Loss2: 1.392814
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499233 Loss1: 0.143674 Loss2: 1.355559
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491347 Loss1: 0.104825 Loss2: 1.386522
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433894 Loss1: 0.092457 Loss2: 1.341437 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431741 Loss1: 0.090806 Loss2: 1.340935 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.905413 Loss1: 0.513984 Loss2: 1.391429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572550 Loss1: 0.202257 Loss2: 1.370293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555124 Loss1: 0.187066 Loss2: 1.368057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.528431 Loss1: 0.160583 Loss2: 1.367848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514442 Loss1: 0.153216 Loss2: 1.361227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.465147 Loss1: 0.098204 Loss2: 1.366943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462324 Loss1: 0.109001 Loss2: 1.353323 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451833 Loss1: 0.096763 Loss2: 1.355070 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455435 Loss1: 0.110152 Loss2: 1.345282 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452827 Loss1: 0.108403 Loss2: 1.344424 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.605406 Loss1: 0.799978 Loss2: 1.805427
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.904189 Loss1: 0.509789 Loss2: 1.394400
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.748690 Loss1: 0.334976 Loss2: 1.413714
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606314 Loss1: 0.231199 Loss2: 1.375115
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.780903 Loss1: 0.884776 Loss2: 1.896127
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.980223 Loss1: 0.539922 Loss2: 1.440301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.824892 Loss1: 0.352619 Loss2: 1.472273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.741489 Loss1: 0.320216 Loss2: 1.421273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.611138 Loss1: 0.178001 Loss2: 1.433136 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474965 Loss1: 0.120247 Loss2: 1.354717
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.600493 Loss1: 0.184676 Loss2: 1.415817
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454348 Loss1: 0.103860 Loss2: 1.350488
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.552776 Loss1: 0.134003 Loss2: 1.418773
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.517181 Loss1: 0.105422 Loss2: 1.411759
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.491930 Loss1: 0.088032 Loss2: 1.403899
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491200 Loss1: 0.097396 Loss2: 1.393804
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.643213 Loss1: 0.799931 Loss2: 1.843283
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.791032 Loss1: 0.443867 Loss2: 1.347164
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.661655 Loss1: 0.278164 Loss2: 1.383491
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580335 Loss1: 0.253217 Loss2: 1.327119
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.667656 Loss1: 0.762618 Loss2: 1.905038
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.939266 Loss1: 0.534753 Loss2: 1.404513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.828160 Loss1: 0.367486 Loss2: 1.460675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.703840 Loss1: 0.290574 Loss2: 1.413267 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.681675 Loss1: 0.266224 Loss2: 1.415450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.600738 Loss1: 0.183848 Loss2: 1.416890 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439902 Loss1: 0.121135 Loss2: 1.318767
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545835 Loss1: 0.152902 Loss2: 1.392933
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454751 Loss1: 0.066491 Loss2: 1.388260
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452504 Loss1: 0.075210 Loss2: 1.377294
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444398 Loss1: 0.071989 Loss2: 1.372409
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.811582 Loss1: 0.897390 Loss2: 1.914192
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.967399 Loss1: 0.530878 Loss2: 1.436521
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.833929 Loss1: 0.380206 Loss2: 1.453723
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.660836 Loss1: 0.233773 Loss2: 1.427063
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.679157 Loss1: 0.777365 Loss2: 1.901792
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.870770 Loss1: 0.485373 Loss2: 1.385397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682349 Loss1: 0.271896 Loss2: 1.410452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584758 Loss1: 0.217481 Loss2: 1.367277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571949 Loss1: 0.194374 Loss2: 1.377575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559903 Loss1: 0.188327 Loss2: 1.371576 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446278 Loss1: 0.063106 Loss2: 1.383172
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494627 Loss1: 0.132526 Loss2: 1.362102
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484448 Loss1: 0.117308 Loss2: 1.367141
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440704 Loss1: 0.076949 Loss2: 1.363755
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417152 Loss1: 0.066773 Loss2: 1.350379
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.644525 Loss1: 0.802925 Loss2: 1.841600
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.786785 Loss1: 0.387830 Loss2: 1.398954
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.730050 Loss1: 0.311881 Loss2: 1.418169
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.627626 Loss1: 0.233027 Loss2: 1.394598
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.479798 Loss1: 0.703524 Loss2: 1.776274
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605260 Loss1: 0.204785 Loss2: 1.400475
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.818875 Loss1: 0.473046 Loss2: 1.345829
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.611827 Loss1: 0.216089 Loss2: 1.395738
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.766979 Loss1: 0.383205 Loss2: 1.383774
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529610 Loss1: 0.138293 Loss2: 1.391317
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.624247 Loss1: 0.278106 Loss2: 1.346141
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474834 Loss1: 0.096065 Loss2: 1.378769
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556634 Loss1: 0.206833 Loss2: 1.349801
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.467070 Loss1: 0.093565 Loss2: 1.373504
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516196 Loss1: 0.173255 Loss2: 1.342941
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438006 Loss1: 0.070403 Loss2: 1.367603
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447203 Loss1: 0.115442 Loss2: 1.331761
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448437 Loss1: 0.120552 Loss2: 1.327885
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428748 Loss1: 0.104637 Loss2: 1.324111
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392451 Loss1: 0.071073 Loss2: 1.321378
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.717342 Loss1: 0.810972 Loss2: 1.906370
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.963316 Loss1: 0.529482 Loss2: 1.433834
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.794217 Loss1: 0.338364 Loss2: 1.455853
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.621626 Loss1: 0.203732 Loss2: 1.417893
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.609515 Loss1: 0.750150 Loss2: 1.859365
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.955545 Loss1: 0.496824 Loss2: 1.458721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.689100 Loss1: 0.259012 Loss2: 1.430087 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.585329 Loss1: 0.182593 Loss2: 1.402737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.538540 Loss1: 0.139197 Loss2: 1.399342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502442 Loss1: 0.117392 Loss2: 1.385050 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.505509 Loss1: 0.108557 Loss2: 1.396952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436893 Loss1: 0.061398 Loss2: 1.375495 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.615887 Loss1: 0.792323 Loss2: 1.823564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.755614 Loss1: 0.355194 Loss2: 1.400420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518858 Loss1: 0.173611 Loss2: 1.345247 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498987 Loss1: 0.166364 Loss2: 1.332623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525754 Loss1: 0.192005 Loss2: 1.333749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483036 Loss1: 0.150557 Loss2: 1.332480 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.563804 Loss1: 0.219889 Loss2: 1.343915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465170 Loss1: 0.119454 Loss2: 1.345716 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.542867 Loss1: 0.158573 Loss2: 1.384294 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461735 Loss1: 0.100318 Loss2: 1.361417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448217 Loss1: 0.083382 Loss2: 1.364835
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.924079 Loss1: 0.987389 Loss2: 1.936690
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.154603 Loss1: 0.642368 Loss2: 1.512235
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.795288 Loss1: 0.354342 Loss2: 1.440946
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.713449 Loss1: 0.280514 Loss2: 1.432935
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617402 Loss1: 0.176794 Loss2: 1.440609
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.483880 Loss1: 0.723652 Loss2: 1.760228
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.608352 Loss1: 0.192671 Loss2: 1.415680
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.546411 Loss1: 0.125601 Loss2: 1.420810 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483907 Loss1: 0.072098 Loss2: 1.411810 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482106 Loss1: 0.086389 Loss2: 1.395717 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485717 Loss1: 0.087650 Loss2: 1.398067 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.466957 Loss1: 0.172379 Loss2: 1.294578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413931 Loss1: 0.115017 Loss2: 1.298914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385681 Loss1: 0.092339 Loss2: 1.293342
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.700309 Loss1: 0.861175 Loss2: 1.839134
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.984330 Loss1: 0.590432 Loss2: 1.393898
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.765261 Loss1: 0.357774 Loss2: 1.407486
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.661561 Loss1: 0.281519 Loss2: 1.380042
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572505 Loss1: 0.195460 Loss2: 1.377045
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.567173 Loss1: 0.744717 Loss2: 1.822456
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488133 Loss1: 0.126265 Loss2: 1.361869
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480791 Loss1: 0.126249 Loss2: 1.354542
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.884195 Loss1: 0.494524 Loss2: 1.389671
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437732 Loss1: 0.082776 Loss2: 1.354956
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726418 Loss1: 0.339367 Loss2: 1.387050
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436717 Loss1: 0.091209 Loss2: 1.345508
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605914 Loss1: 0.243472 Loss2: 1.362442
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395844 Loss1: 0.054447 Loss2: 1.341398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.510990 Loss1: 0.155465 Loss2: 1.355525
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530923 Loss1: 0.177977 Loss2: 1.352945
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.562319 Loss1: 0.197889 Loss2: 1.364430
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.506125 Loss1: 0.154384 Loss2: 1.351741
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449976 Loss1: 0.100479 Loss2: 1.349496
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.761086 Loss1: 0.899642 Loss2: 1.861443
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413558 Loss1: 0.078137 Loss2: 1.335421
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.956831 Loss1: 0.522316 Loss2: 1.434515
+(DefaultActor pid=3764) >> Training accuracy: 0.993566
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.744496 Loss1: 0.332627 Loss2: 1.411869
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.568123 Loss1: 0.184830 Loss2: 1.383293
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577124 Loss1: 0.182099 Loss2: 1.395026
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518793 Loss1: 0.135856 Loss2: 1.382937
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.710548 Loss1: 0.871527 Loss2: 1.839020
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.494327 Loss1: 0.123201 Loss2: 1.371127
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464911 Loss1: 0.088346 Loss2: 1.376565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.475707 Loss1: 0.112559 Loss2: 1.363148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450981 Loss1: 0.080517 Loss2: 1.370464 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465245 Loss1: 0.116688 Loss2: 1.348557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440636 Loss1: 0.106649 Loss2: 1.333987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432650 Loss1: 0.100135 Loss2: 1.332516
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.709567 Loss1: 0.814172 Loss2: 1.895396
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.867075 Loss1: 0.478369 Loss2: 1.388705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.705369 Loss1: 0.313355 Loss2: 1.392014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.573222 Loss1: 0.181914 Loss2: 1.391308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.537786 Loss1: 0.157079 Loss2: 1.380706 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.540152 Loss1: 0.153562 Loss2: 1.386590 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484441 Loss1: 0.106771 Loss2: 1.377670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436691 Loss1: 0.070978 Loss2: 1.365713 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468436 Loss1: 0.111783 Loss2: 1.356654 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.463649 Loss1: 0.106881 Loss2: 1.356768 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.891839 Loss1: 0.994446 Loss2: 1.897394 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.812439 Loss1: 0.372494 Loss2: 1.439944 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544389 Loss1: 0.171356 Loss2: 1.373033 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498162 Loss1: 0.126208 Loss2: 1.371954
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.087434 Loss1: 0.991554 Loss2: 2.095880
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.087932 Loss1: 0.619892 Loss2: 1.468040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.918522 Loss1: 0.403123 Loss2: 1.515399
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467339 Loss1: 0.111224 Loss2: 1.356114
+DEBUG flwr 2023-10-11 10:19:55,773 | server.py:236 | fit_round 110 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436874 Loss1: 0.090654 Loss2: 1.346220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407179 Loss1: 0.066447 Loss2: 1.340732 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.561563 Loss1: 0.112676 Loss2: 1.448886 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.534631 Loss1: 0.095015 Loss2: 1.439617 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983073
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.069952 Loss1: 0.649366 Loss2: 1.420586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.704538 Loss1: 0.286874 Loss2: 1.417664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.655692 Loss1: 0.840341 Loss2: 1.815351
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.588129 Loss1: 0.164180 Loss2: 1.423949
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.543939 Loss1: 0.134840 Loss2: 1.409099
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.847866 Loss1: 0.511482 Loss2: 1.336384
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.696937 Loss1: 0.328497 Loss2: 1.368440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.583027 Loss1: 0.250045 Loss2: 1.332983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542583 Loss1: 0.208317 Loss2: 1.334266 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983173
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452132 Loss1: 0.129517 Loss2: 1.322615 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392718 Loss1: 0.075840 Loss2: 1.316878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403994 Loss1: 0.098282 Loss2: 1.305712
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.645197 Loss1: 0.779096 Loss2: 1.866101
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.837073 Loss1: 0.462014 Loss2: 1.375058
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.682445 Loss1: 0.277364 Loss2: 1.405081
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596624 Loss1: 0.224750 Loss2: 1.371874
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.627929 Loss1: 0.236321 Loss2: 1.391607
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.005488 Loss1: 0.920352 Loss2: 2.085136
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.535318 Loss1: 0.164608 Loss2: 1.370710
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495985 Loss1: 0.138222 Loss2: 1.357763 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464835 Loss1: 0.110816 Loss2: 1.354019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448910 Loss1: 0.099188 Loss2: 1.349722 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406897 Loss1: 0.066151 Loss2: 1.340745 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.654869 Loss1: 0.138441 Loss2: 1.516428 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.646633 Loss1: 0.142185 Loss2: 1.504448 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 10:19:55,773][flwr][DEBUG] - fit_round 110 received 50 results and 0 failures
+INFO flwr 2023-10-11 10:20:36,510 | server.py:125 | fit progress: (110, 2.194226622581482, {'accuracy': 0.5761}, 253744.288186102)
+>> Test accuracy: 0.576100
+[2023-10-11 10:20:36,510][flwr][INFO] - fit progress: (110, 2.194226622581482, {'accuracy': 0.5761}, 253744.288186102)
+DEBUG flwr 2023-10-11 10:20:36,510 | server.py:173 | evaluate_round 110: strategy sampled 50 clients (out of 50)
+[2023-10-11 10:20:36,510][flwr][DEBUG] - evaluate_round 110: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 10:29:39,966 | server.py:187 | evaluate_round 110 received 50 results and 0 failures
+[2023-10-11 10:29:39,966][flwr][DEBUG] - evaluate_round 110 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 10:29:39,967 | server.py:222 | fit_round 111: strategy sampled 50 clients (out of 50)
+[2023-10-11 10:29:39,967][flwr][DEBUG] - fit_round 111: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.644603 Loss1: 0.806579 Loss2: 1.838023
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.960231 Loss1: 0.565252 Loss2: 1.394979
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.799988 Loss1: 0.393496 Loss2: 1.406493
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.648741 Loss1: 0.275376 Loss2: 1.373365
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.741061 Loss1: 0.851490 Loss2: 1.889571
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.080960 Loss1: 0.595806 Loss2: 1.485153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.833065 Loss1: 0.392137 Loss2: 1.440928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.748289 Loss1: 0.288838 Loss2: 1.459451 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.645848 Loss1: 0.227483 Loss2: 1.418365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.598296 Loss1: 0.182253 Loss2: 1.416043 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.562135 Loss1: 0.153593 Loss2: 1.408542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.506914 Loss1: 0.111733 Loss2: 1.395181 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.476160 Loss1: 0.082604 Loss2: 1.393556
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.868336 Loss1: 0.904864 Loss2: 1.963472
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.951499 Loss1: 0.508881 Loss2: 1.442617
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.733518 Loss1: 0.270133 Loss2: 1.463385
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.639582 Loss1: 0.211807 Loss2: 1.427775
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.601094 Loss1: 0.178563 Loss2: 1.422531
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.822445 Loss1: 0.901233 Loss2: 1.921212
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.961749 Loss1: 0.639663 Loss2: 1.322085
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.534762 Loss1: 0.121831 Loss2: 1.412931
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.520609 Loss1: 0.119756 Loss2: 1.400853 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456558 Loss1: 0.064072 Loss2: 1.392485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463862 Loss1: 0.067934 Loss2: 1.395929 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466834 Loss1: 0.072654 Loss2: 1.394180 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396042 Loss1: 0.084852 Loss2: 1.311190 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986979
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.631184 Loss1: 0.763788 Loss2: 1.867396 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.750600 Loss1: 0.306300 Loss2: 1.444300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.687297 Loss1: 0.280438 Loss2: 1.406859
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.554095 Loss1: 0.710889 Loss2: 1.843205
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.771788 Loss1: 0.419401 Loss2: 1.352387
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589389 Loss1: 0.186566 Loss2: 1.402824
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.707497 Loss1: 0.312608 Loss2: 1.394889
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.529135 Loss1: 0.127360 Loss2: 1.401775
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563124 Loss1: 0.212895 Loss2: 1.350229
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.492070 Loss1: 0.104580 Loss2: 1.387490
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.531461 Loss1: 0.180872 Loss2: 1.350590
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486042 Loss1: 0.104936 Loss2: 1.381106
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.491317 Loss1: 0.112253 Loss2: 1.379064 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.507601 Loss1: 0.127087 Loss2: 1.380514 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972656
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414149 Loss1: 0.079723 Loss2: 1.334425 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.647404 Loss1: 0.779391 Loss2: 1.868012 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.686781 Loss1: 0.267604 Loss2: 1.419176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582019 Loss1: 0.219726 Loss2: 1.362293
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.660527 Loss1: 0.849819 Loss2: 1.810708
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.894857 Loss1: 0.522862 Loss2: 1.371995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.700482 Loss1: 0.303000 Loss2: 1.397482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.634678 Loss1: 0.288206 Loss2: 1.346472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.668669 Loss1: 0.290794 Loss2: 1.377875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.566628 Loss1: 0.216537 Loss2: 1.350091 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408668 Loss1: 0.075295 Loss2: 1.333373
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.514813 Loss1: 0.166768 Loss2: 1.348045
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467649 Loss1: 0.126724 Loss2: 1.340925
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424343 Loss1: 0.089334 Loss2: 1.335008
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393307 Loss1: 0.074722 Loss2: 1.318585
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.654197 Loss1: 0.772333 Loss2: 1.881864
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.871819 Loss1: 0.485601 Loss2: 1.386218
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.695830 Loss1: 0.275216 Loss2: 1.420614
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.593608 Loss1: 0.220828 Loss2: 1.372780
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.626058 Loss1: 0.767632 Loss2: 1.858426
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.989331 Loss1: 0.557063 Loss2: 1.432268 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.776515 Loss1: 0.310538 Loss2: 1.465977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.696381 Loss1: 0.270253 Loss2: 1.426127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.653232 Loss1: 0.226161 Loss2: 1.427071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.578476 Loss1: 0.165415 Loss2: 1.413061 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.532512 Loss1: 0.133644 Loss2: 1.398867 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.481609 Loss1: 0.084318 Loss2: 1.397291 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.731162 Loss1: 0.429202 Loss2: 1.301961 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599341 Loss1: 0.307479 Loss2: 1.291862 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510135 Loss1: 0.215136 Loss2: 1.294998
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.514337 Loss1: 0.730622 Loss2: 1.783715
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.464469 Loss1: 0.174679 Loss2: 1.289790
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.705313 Loss1: 0.359838 Loss2: 1.345476
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383955 Loss1: 0.109127 Loss2: 1.274827
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.582009 Loss1: 0.227328 Loss2: 1.354681
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.365071 Loss1: 0.095546 Loss2: 1.269524
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586396 Loss1: 0.244674 Loss2: 1.341722
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.551111 Loss1: 0.205989 Loss2: 1.345121 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.322555 Loss1: 0.060000 Loss2: 1.262555
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500916 Loss1: 0.158571 Loss2: 1.342344
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473152 Loss1: 0.146889 Loss2: 1.326263
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465048 Loss1: 0.136071 Loss2: 1.328977
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429485 Loss1: 0.101499 Loss2: 1.327986
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428760 Loss1: 0.104769 Loss2: 1.323992
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.665651 Loss1: 0.825819 Loss2: 1.839832
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.927730 Loss1: 0.548673 Loss2: 1.379057
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.859768 Loss1: 0.404354 Loss2: 1.455415
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.651426 Loss1: 0.277095 Loss2: 1.374331
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.559478 Loss1: 0.184007 Loss2: 1.375470
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.807056 Loss1: 0.951548 Loss2: 1.855508
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524497 Loss1: 0.160790 Loss2: 1.363707
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464681 Loss1: 0.103528 Loss2: 1.361153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.468273 Loss1: 0.115507 Loss2: 1.352765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424995 Loss1: 0.077409 Loss2: 1.347586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445120 Loss1: 0.092101 Loss2: 1.353020 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427984 Loss1: 0.103833 Loss2: 1.324151 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375022 Loss1: 0.059569 Loss2: 1.315453 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.825429 Loss1: 0.469710 Loss2: 1.355719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578540 Loss1: 0.228113 Loss2: 1.350427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.483926 Loss1: 0.141120 Loss2: 1.342806
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.740515 Loss1: 0.855137 Loss2: 1.885378
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.044340 Loss1: 0.619346 Loss2: 1.424994 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.765214 Loss1: 0.328380 Loss2: 1.436834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.615917 Loss1: 0.213921 Loss2: 1.401996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.560916 Loss1: 0.156683 Loss2: 1.404232 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393451 Loss1: 0.079590 Loss2: 1.313861
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.548425 Loss1: 0.160958 Loss2: 1.387467
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484495 Loss1: 0.099803 Loss2: 1.384692
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477093 Loss1: 0.100903 Loss2: 1.376190
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.471399 Loss1: 0.093091 Loss2: 1.378308
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446634 Loss1: 0.072732 Loss2: 1.373902
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.607923 Loss1: 0.769621 Loss2: 1.838302
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.835692 Loss1: 0.427272 Loss2: 1.408420
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655466 Loss1: 0.263115 Loss2: 1.392350
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551771 Loss1: 0.182610 Loss2: 1.369161
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518844 Loss1: 0.155249 Loss2: 1.363595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458903 Loss1: 0.101209 Loss2: 1.357694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456647 Loss1: 0.100578 Loss2: 1.356068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447994 Loss1: 0.099875 Loss2: 1.348119 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.554889 Loss1: 0.172717 Loss2: 1.382172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.505335 Loss1: 0.130800 Loss2: 1.374535 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996324
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.459903 Loss1: 0.099829 Loss2: 1.360074 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.818585 Loss1: 0.975649 Loss2: 1.842936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.721143 Loss1: 0.321856 Loss2: 1.399287 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578926 Loss1: 0.236065 Loss2: 1.342861
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.737043 Loss1: 0.869127 Loss2: 1.867915
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.560193 Loss1: 0.220779 Loss2: 1.339414
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.946350 Loss1: 0.526872 Loss2: 1.419478
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458614 Loss1: 0.125144 Loss2: 1.333470
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.736884 Loss1: 0.294201 Loss2: 1.442683
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.429901 Loss1: 0.110716 Loss2: 1.319185
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.648079 Loss1: 0.254305 Loss2: 1.393774
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.396134 Loss1: 0.083124 Loss2: 1.313010
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.592319 Loss1: 0.179976 Loss2: 1.412343
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.367583 Loss1: 0.059093 Loss2: 1.308490
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.517695 Loss1: 0.127572 Loss2: 1.390123
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350882 Loss1: 0.050200 Loss2: 1.300683
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486772 Loss1: 0.107210 Loss2: 1.379562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452213 Loss1: 0.076006 Loss2: 1.376207
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430031 Loss1: 0.063726 Loss2: 1.366305
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421324 Loss1: 0.063242 Loss2: 1.358083
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.609619 Loss1: 0.789765 Loss2: 1.819854
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.788931 Loss1: 0.437252 Loss2: 1.351680
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.668595 Loss1: 0.298447 Loss2: 1.370148
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.609943 Loss1: 0.264727 Loss2: 1.345216
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.524014 Loss1: 0.728823 Loss2: 1.795191
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.808743 Loss1: 0.473353 Loss2: 1.335390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.700693 Loss1: 0.313962 Loss2: 1.386731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.580923 Loss1: 0.237339 Loss2: 1.343583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512905 Loss1: 0.177927 Loss2: 1.334977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449681 Loss1: 0.123670 Loss2: 1.326010 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428207 Loss1: 0.103018 Loss2: 1.325189
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436337 Loss1: 0.110611 Loss2: 1.325726
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414959 Loss1: 0.093317 Loss2: 1.321642
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439605 Loss1: 0.128021 Loss2: 1.311583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418636 Loss1: 0.098417 Loss2: 1.320219
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.600364 Loss1: 0.778432 Loss2: 1.821932
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.829308 Loss1: 0.419963 Loss2: 1.409344
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.734795 Loss1: 0.325720 Loss2: 1.409075
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.841770 Loss1: 0.919251 Loss2: 1.922519
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.645397 Loss1: 0.244919 Loss2: 1.400479
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631444 Loss1: 0.236874 Loss2: 1.394571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.565177 Loss1: 0.176596 Loss2: 1.388581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.510764 Loss1: 0.129406 Loss2: 1.381359 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484647 Loss1: 0.115994 Loss2: 1.368653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443777 Loss1: 0.079527 Loss2: 1.364250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481299 Loss1: 0.108045 Loss2: 1.373255 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445888 Loss1: 0.082704 Loss2: 1.363184 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983259
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.837718 Loss1: 0.937018 Loss2: 1.900700
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.893724 Loss1: 0.464453 Loss2: 1.429270
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.748384 Loss1: 0.306067 Loss2: 1.442317
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.652920 Loss1: 0.259389 Loss2: 1.393531
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.670463 Loss1: 0.789723 Loss2: 1.880740
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.618483 Loss1: 0.205792 Loss2: 1.412690
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.978857 Loss1: 0.542106 Loss2: 1.436751
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.601702 Loss1: 0.209161 Loss2: 1.392541
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.869711 Loss1: 0.403848 Loss2: 1.465863
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.563505 Loss1: 0.163896 Loss2: 1.399609
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.822438 Loss1: 0.381099 Loss2: 1.441339
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481285 Loss1: 0.094153 Loss2: 1.387132
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.730154 Loss1: 0.285791 Loss2: 1.444363
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.473105 Loss1: 0.096523 Loss2: 1.376582
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.701612 Loss1: 0.267328 Loss2: 1.434283
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469559 Loss1: 0.093128 Loss2: 1.376431
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.549674 Loss1: 0.136981 Loss2: 1.412693
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.536319 Loss1: 0.125614 Loss2: 1.410705
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.489469 Loss1: 0.093700 Loss2: 1.395768
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482428 Loss1: 0.093091 Loss2: 1.389337
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.910031 Loss1: 0.967479 Loss2: 1.942552
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.898700 Loss1: 0.547329 Loss2: 1.351372
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.686965 Loss1: 0.290724 Loss2: 1.396242
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549231 Loss1: 0.200785 Loss2: 1.348446
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527757 Loss1: 0.195292 Loss2: 1.332465
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487406 Loss1: 0.144581 Loss2: 1.342825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462462 Loss1: 0.134098 Loss2: 1.328364 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414861 Loss1: 0.094943 Loss2: 1.319917 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421026 Loss1: 0.102054 Loss2: 1.318972 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377834 Loss1: 0.063375 Loss2: 1.314459
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543901 Loss1: 0.188078 Loss2: 1.355823
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479292 Loss1: 0.138268 Loss2: 1.341024
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.477944 Loss1: 0.137641 Loss2: 1.340304
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441724 Loss1: 0.101268 Loss2: 1.340456
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410713 Loss1: 0.079691 Loss2: 1.331022
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.635401 Loss1: 0.821828 Loss2: 1.813573
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390934 Loss1: 0.064356 Loss2: 1.326578
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.775814 Loss1: 0.373705 Loss2: 1.402109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.539059 Loss1: 0.192419 Loss2: 1.346640 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478513 Loss1: 0.144366 Loss2: 1.334147
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.669092 Loss1: 0.844222 Loss2: 1.824870
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.879908 Loss1: 0.522020 Loss2: 1.357888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.706722 Loss1: 0.311831 Loss2: 1.394891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538621 Loss1: 0.191231 Loss2: 1.347390 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364872 Loss1: 0.051337 Loss2: 1.313535
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484245 Loss1: 0.140642 Loss2: 1.343603
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468223 Loss1: 0.136739 Loss2: 1.331484
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450509 Loss1: 0.110530 Loss2: 1.339980
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424905 Loss1: 0.099155 Loss2: 1.325750
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389912 Loss1: 0.069110 Loss2: 1.320802
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.920926 Loss1: 0.965022 Loss2: 1.955904
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377897 Loss1: 0.058024 Loss2: 1.319873
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.769313 Loss1: 0.275650 Loss2: 1.493663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.684918 Loss1: 0.237693 Loss2: 1.447225 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.615253 Loss1: 0.175787 Loss2: 1.439466
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.729476 Loss1: 0.856520 Loss2: 1.872956
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.979869 Loss1: 0.554595 Loss2: 1.425274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.863106 Loss1: 0.393279 Loss2: 1.469827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.751640 Loss1: 0.355224 Loss2: 1.396416 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609799 Loss1: 0.206671 Loss2: 1.403128 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502495 Loss1: 0.116382 Loss2: 1.386113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445405 Loss1: 0.081250 Loss2: 1.364155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452968 Loss1: 0.084633 Loss2: 1.368336 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741249 Loss1: 0.287554 Loss2: 1.453695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.560274 Loss1: 0.165676 Loss2: 1.394598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.521424 Loss1: 0.142537 Loss2: 1.378887
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.757647 Loss1: 0.906932 Loss2: 1.850715
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.913106 Loss1: 0.525522 Loss2: 1.387584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.720848 Loss1: 0.303948 Loss2: 1.416900 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592414 Loss1: 0.226040 Loss2: 1.366375 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.537996 Loss1: 0.171865 Loss2: 1.366131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.519994 Loss1: 0.163137 Loss2: 1.356857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.465699 Loss1: 0.111390 Loss2: 1.354309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434554 Loss1: 0.088930 Loss2: 1.345623 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690886 Loss1: 0.291596 Loss2: 1.399290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536808 Loss1: 0.170620 Loss2: 1.366188 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.482590 Loss1: 0.117725 Loss2: 1.364865
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.711488 Loss1: 0.860069 Loss2: 1.851419
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434957 Loss1: 0.080265 Loss2: 1.354692
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.912744 Loss1: 0.509918 Loss2: 1.402827
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423149 Loss1: 0.079067 Loss2: 1.344081
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.655885 Loss1: 0.251439 Loss2: 1.404446
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.642541 Loss1: 0.267257 Loss2: 1.375284
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402216 Loss1: 0.057402 Loss2: 1.344814
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512588 Loss1: 0.133606 Loss2: 1.378981
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407744 Loss1: 0.071268 Loss2: 1.336476
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476487 Loss1: 0.117035 Loss2: 1.359452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436700 Loss1: 0.085571 Loss2: 1.351130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409978 Loss1: 0.064704 Loss2: 1.345274
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.637557 Loss1: 0.866179 Loss2: 1.771378
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.938235 Loss1: 0.563676 Loss2: 1.374559
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.683202 Loss1: 0.318756 Loss2: 1.364446
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589989 Loss1: 0.243618 Loss2: 1.346371
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468872 Loss1: 0.141215 Loss2: 1.327657
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.869798 Loss1: 0.937375 Loss2: 1.932423
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.166756 Loss1: 0.699865 Loss2: 1.466891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.850087 Loss1: 0.340017 Loss2: 1.510070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.732870 Loss1: 0.284870 Loss2: 1.448000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407321 Loss1: 0.092496 Loss2: 1.314826
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.656472 Loss1: 0.198803 Loss2: 1.457669
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373104 Loss1: 0.068712 Loss2: 1.304391
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.579406 Loss1: 0.141648 Loss2: 1.437758
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.526482 Loss1: 0.099272 Loss2: 1.427210
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486604 Loss1: 0.064270 Loss2: 1.422333
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473939 Loss1: 0.063473 Loss2: 1.410465
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450516 Loss1: 0.042015 Loss2: 1.408501
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.557534 Loss1: 0.748130 Loss2: 1.809404
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.765876 Loss1: 0.393868 Loss2: 1.372008
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.586000 Loss1: 0.211032 Loss2: 1.374968
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534402 Loss1: 0.189062 Loss2: 1.345340
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.783851 Loss1: 0.895076 Loss2: 1.888774
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.972074 Loss1: 0.570516 Loss2: 1.401558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.744254 Loss1: 0.287225 Loss2: 1.457030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.660861 Loss1: 0.269253 Loss2: 1.391609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.610067 Loss1: 0.208184 Loss2: 1.401882 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527471 Loss1: 0.138714 Loss2: 1.388756 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485987 Loss1: 0.105830 Loss2: 1.380157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443589 Loss1: 0.073637 Loss2: 1.369952 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.722773 Loss1: 0.877310 Loss2: 1.845463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736844 Loss1: 0.316480 Loss2: 1.420364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.651894 Loss1: 0.814288 Loss2: 1.837606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.813204 Loss1: 0.468615 Loss2: 1.344589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.630073 Loss1: 0.261271 Loss2: 1.368802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551245 Loss1: 0.219964 Loss2: 1.331281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574105 Loss1: 0.238530 Loss2: 1.335575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522997 Loss1: 0.179476 Loss2: 1.343521 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.573647 Loss1: 0.230323 Loss2: 1.343324 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410063 Loss1: 0.083668 Loss2: 1.326394 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.980606 Loss1: 0.621984 Loss2: 1.358622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602266 Loss1: 0.241609 Loss2: 1.360657 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.765328 Loss1: 0.827528 Loss2: 1.937800 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.943752 Loss1: 0.514358 Loss2: 1.429393 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.805724 Loss1: 0.330936 Loss2: 1.474788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399237 Loss1: 0.067525 Loss2: 1.331712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397713 Loss1: 0.066815 Loss2: 1.330899 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.558566 Loss1: 0.151757 Loss2: 1.406809 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490775 Loss1: 0.101817 Loss2: 1.388958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.633019 Loss1: 0.839742 Loss2: 1.793276
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.483505 Loss1: 0.091310 Loss2: 1.392195
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.719310 Loss1: 0.338437 Loss2: 1.380874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574335 Loss1: 0.230200 Loss2: 1.344135 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.517391 Loss1: 0.182943 Loss2: 1.334448
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.776544 Loss1: 0.851688 Loss2: 1.924856
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464936 Loss1: 0.137750 Loss2: 1.327186
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.964286 Loss1: 0.529564 Loss2: 1.434721
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474486 Loss1: 0.153654 Loss2: 1.320832
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.745717 Loss1: 0.282220 Loss2: 1.463497
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448335 Loss1: 0.122116 Loss2: 1.326219
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.651808 Loss1: 0.238826 Loss2: 1.412982
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393470 Loss1: 0.079638 Loss2: 1.313832
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.630132 Loss1: 0.201846 Loss2: 1.428286
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.557421 Loss1: 0.153045 Loss2: 1.404376
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.527055 Loss1: 0.129510 Loss2: 1.397546
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510227 Loss1: 0.107014 Loss2: 1.403213
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486201 Loss1: 0.093528 Loss2: 1.392673
+DEBUG flwr 2023-10-11 10:58:31,459 | server.py:236 | fit_round 111 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.471704 Loss1: 0.083509 Loss2: 1.388195
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.813048 Loss1: 0.940248 Loss2: 1.872800
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.873320 Loss1: 0.444704 Loss2: 1.428615
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.687117 Loss1: 0.279429 Loss2: 1.407689
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604715 Loss1: 0.216085 Loss2: 1.388630
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589067 Loss1: 0.199274 Loss2: 1.389792
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.577980 Loss1: 0.788964 Loss2: 1.789016
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537534 Loss1: 0.153553 Loss2: 1.383981
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.859608 Loss1: 0.494739 Loss2: 1.364869
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511679 Loss1: 0.143756 Loss2: 1.367923
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.739537 Loss1: 0.344152 Loss2: 1.395385
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500848 Loss1: 0.123945 Loss2: 1.376902
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574692 Loss1: 0.234681 Loss2: 1.340011
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.497893 Loss1: 0.131623 Loss2: 1.366270
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502243 Loss1: 0.167015 Loss2: 1.335228
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.492800 Loss1: 0.122003 Loss2: 1.370797
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427203 Loss1: 0.099233 Loss2: 1.327969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386155 Loss1: 0.073088 Loss2: 1.313067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351449 Loss1: 0.044697 Loss2: 1.306753
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.796029 Loss1: 0.858770 Loss2: 1.937259
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.023874 Loss1: 0.573292 Loss2: 1.450582
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.870766 Loss1: 0.372512 Loss2: 1.498254
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.725325 Loss1: 0.281634 Loss2: 1.443690
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.668231 Loss1: 0.217662 Loss2: 1.450568
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.565019 Loss1: 0.123229 Loss2: 1.441790
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.778941 Loss1: 0.922713 Loss2: 1.856228
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534740 Loss1: 0.105175 Loss2: 1.429565
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.965857 Loss1: 0.545288 Loss2: 1.420569
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480338 Loss1: 0.062875 Loss2: 1.417462
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.760502 Loss1: 0.356406 Loss2: 1.404096
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.495471 Loss1: 0.087412 Loss2: 1.408059
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.698880 Loss1: 0.316345 Loss2: 1.382535
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.471421 Loss1: 0.061402 Loss2: 1.410019
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.592410 Loss1: 0.213606 Loss2: 1.378804
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539680 Loss1: 0.167534 Loss2: 1.372145
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446744 Loss1: 0.091970 Loss2: 1.354773
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418182 Loss1: 0.069364 Loss2: 1.348818
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426997 Loss1: 0.084626 Loss2: 1.342370
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411450 Loss1: 0.070965 Loss2: 1.340485
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 10:58:31,459][flwr][DEBUG] - fit_round 111 received 50 results and 0 failures
+INFO flwr 2023-10-11 10:59:13,095 | server.py:125 | fit progress: (111, 2.1984924363632934, {'accuracy': 0.5772}, 256060.873497265)
+>> Test accuracy: 0.577200
+[2023-10-11 10:59:13,095][flwr][INFO] - fit progress: (111, 2.1984924363632934, {'accuracy': 0.5772}, 256060.873497265)
+DEBUG flwr 2023-10-11 10:59:13,095 | server.py:173 | evaluate_round 111: strategy sampled 50 clients (out of 50)
+[2023-10-11 10:59:13,095][flwr][DEBUG] - evaluate_round 111: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 11:08:18,545 | server.py:187 | evaluate_round 111 received 50 results and 0 failures
+[2023-10-11 11:08:18,545][flwr][DEBUG] - evaluate_round 111 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 11:08:18,546 | server.py:222 | fit_round 112: strategy sampled 50 clients (out of 50)
+[2023-10-11 11:08:18,546][flwr][DEBUG] - fit_round 112: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.625170 Loss1: 0.780257 Loss2: 1.844913
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.900276 Loss1: 0.494278 Loss2: 1.405998
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.718017 Loss1: 0.285741 Loss2: 1.432276
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.662390 Loss1: 0.811975 Loss2: 1.850415
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.903011 Loss1: 0.504598 Loss2: 1.398413 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.739361 Loss1: 0.314279 Loss2: 1.425081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.616010 Loss1: 0.220980 Loss2: 1.395030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.526764 Loss1: 0.138153 Loss2: 1.388611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469066 Loss1: 0.101719 Loss2: 1.367347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430137 Loss1: 0.072702 Loss2: 1.357435 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428847 Loss1: 0.072612 Loss2: 1.356235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423284 Loss1: 0.072507 Loss2: 1.350776 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.654681 Loss1: 0.816230 Loss2: 1.838451
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.836888 Loss1: 0.479546 Loss2: 1.357342
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.717899 Loss1: 0.295541 Loss2: 1.422358
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.598524 Loss1: 0.246992 Loss2: 1.351533
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.624416 Loss1: 0.778230 Loss2: 1.846187
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544357 Loss1: 0.184248 Loss2: 1.360110
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.901142 Loss1: 0.525699 Loss2: 1.375443
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.535349 Loss1: 0.169213 Loss2: 1.366135
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.731689 Loss1: 0.337271 Loss2: 1.394418
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502182 Loss1: 0.154562 Loss2: 1.347619
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.654053 Loss1: 0.274205 Loss2: 1.379849
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457748 Loss1: 0.112111 Loss2: 1.345637
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.603469 Loss1: 0.220747 Loss2: 1.382722
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450026 Loss1: 0.111968 Loss2: 1.338058
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.497137 Loss1: 0.130393 Loss2: 1.366744
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429039 Loss1: 0.093065 Loss2: 1.335974
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475367 Loss1: 0.113853 Loss2: 1.361513
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443573 Loss1: 0.088278 Loss2: 1.355295
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467249 Loss1: 0.115123 Loss2: 1.352126
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448451 Loss1: 0.101082 Loss2: 1.347369
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.696867 Loss1: 0.874744 Loss2: 1.822123
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.960682 Loss1: 0.583547 Loss2: 1.377135
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.810135 Loss1: 0.398533 Loss2: 1.411602
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.635056 Loss1: 0.255318 Loss2: 1.379738
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.678946 Loss1: 0.822950 Loss2: 1.855997
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525333 Loss1: 0.167982 Loss2: 1.357352
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.113456 Loss1: 0.694695 Loss2: 1.418761
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485607 Loss1: 0.133765 Loss2: 1.351842
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.900445 Loss1: 0.454075 Loss2: 1.446370
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461764 Loss1: 0.113364 Loss2: 1.348399
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.714369 Loss1: 0.328726 Loss2: 1.385642
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459978 Loss1: 0.123716 Loss2: 1.336262
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.629114 Loss1: 0.243282 Loss2: 1.385832
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412100 Loss1: 0.078776 Loss2: 1.333324
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.532264 Loss1: 0.158420 Loss2: 1.373844
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370509 Loss1: 0.042659 Loss2: 1.327850
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460696 Loss1: 0.103541 Loss2: 1.357156
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414766 Loss1: 0.070529 Loss2: 1.344237
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416419 Loss1: 0.069147 Loss2: 1.347271
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394842 Loss1: 0.054930 Loss2: 1.339912
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.709875 Loss1: 0.840425 Loss2: 1.869450
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.857771 Loss1: 0.464465 Loss2: 1.393306
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690203 Loss1: 0.271931 Loss2: 1.418271
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574747 Loss1: 0.195691 Loss2: 1.379057
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.657909 Loss1: 0.811598 Loss2: 1.846311
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.868545 Loss1: 0.484337 Loss2: 1.384208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.665352 Loss1: 0.251329 Loss2: 1.414023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554252 Loss1: 0.187712 Loss2: 1.366540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.551905 Loss1: 0.177179 Loss2: 1.374726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560263 Loss1: 0.179957 Loss2: 1.380307 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448427 Loss1: 0.088660 Loss2: 1.359767
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.522410 Loss1: 0.144699 Loss2: 1.377710
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.519524 Loss1: 0.151361 Loss2: 1.368163
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476439 Loss1: 0.111050 Loss2: 1.365389
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478119 Loss1: 0.113191 Loss2: 1.364928
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.872837 Loss1: 0.941579 Loss2: 1.931257
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.976020 Loss1: 0.559409 Loss2: 1.416611
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.881103 Loss1: 0.426185 Loss2: 1.454918
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.667459 Loss1: 0.261406 Loss2: 1.406053
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.805161 Loss1: 0.840056 Loss2: 1.965104
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.875829 Loss1: 0.430745 Loss2: 1.445084 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.766632 Loss1: 0.290932 Loss2: 1.475700 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.630666 Loss1: 0.200908 Loss2: 1.429759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.624021 Loss1: 0.186721 Loss2: 1.437300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.595063 Loss1: 0.162896 Loss2: 1.432168 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.536959 Loss1: 0.111523 Loss2: 1.425436 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.481959 Loss1: 0.070760 Loss2: 1.411200 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.873349 Loss1: 0.507609 Loss2: 1.365741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587544 Loss1: 0.246272 Loss2: 1.341272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525367 Loss1: 0.175791 Loss2: 1.349576 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451060 Loss1: 0.123780 Loss2: 1.327280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422083 Loss1: 0.098460 Loss2: 1.323624 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404975 Loss1: 0.080774 Loss2: 1.324201 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394134 Loss1: 0.076891 Loss2: 1.317243 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383509 Loss1: 0.073015 Loss2: 1.310495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.528376 Loss1: 0.111134 Loss2: 1.417242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.513103 Loss1: 0.119001 Loss2: 1.394102 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.868792 Loss1: 0.481188 Loss2: 1.387604 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.622068 Loss1: 0.258553 Loss2: 1.363516 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.570355 Loss1: 0.204056 Loss2: 1.366299
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.600480 Loss1: 0.739006 Loss2: 1.861474
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.571724 Loss1: 0.214728 Loss2: 1.356996
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.866312 Loss1: 0.445668 Loss2: 1.420644
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.699654 Loss1: 0.294808 Loss2: 1.404845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581236 Loss1: 0.194541 Loss2: 1.386695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.547803 Loss1: 0.164477 Loss2: 1.383326 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529597 Loss1: 0.159396 Loss2: 1.370201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482787 Loss1: 0.124149 Loss2: 1.358638 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.715372 Loss1: 0.862179 Loss2: 1.853193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987132
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731339 Loss1: 0.333058 Loss2: 1.398281 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.563528 Loss1: 0.192928 Loss2: 1.370600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463710 Loss1: 0.102582 Loss2: 1.361127
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.910330 Loss1: 1.006841 Loss2: 1.903489
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462974 Loss1: 0.119558 Loss2: 1.343416
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.007366 Loss1: 0.612042 Loss2: 1.395324
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426560 Loss1: 0.078233 Loss2: 1.348327
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.734924 Loss1: 0.338244 Loss2: 1.396680
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.646151 Loss1: 0.277103 Loss2: 1.369048
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.410793 Loss1: 0.073099 Loss2: 1.337694
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544430 Loss1: 0.184977 Loss2: 1.359453
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401713 Loss1: 0.065865 Loss2: 1.335848
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415998 Loss1: 0.074775 Loss2: 1.341223 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381685 Loss1: 0.048580 Loss2: 1.333104 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388432 Loss1: 0.061195 Loss2: 1.327238
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.776366 Loss1: 0.883320 Loss2: 1.893046
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.995722 Loss1: 0.581792 Loss2: 1.413930
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.826503 Loss1: 0.376097 Loss2: 1.450406
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654400 Loss1: 0.247459 Loss2: 1.406941
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631093 Loss1: 0.222275 Loss2: 1.408818
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.805061 Loss1: 0.863972 Loss2: 1.941089
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.872691 Loss1: 0.495516 Loss2: 1.377175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490003 Loss1: 0.096279 Loss2: 1.393724
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677096 Loss1: 0.279958 Loss2: 1.397137
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599387 Loss1: 0.225421 Loss2: 1.373967
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461595 Loss1: 0.079267 Loss2: 1.382327
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463113 Loss1: 0.084888 Loss2: 1.378225 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.464640 Loss1: 0.091725 Loss2: 1.372916 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427407 Loss1: 0.073911 Loss2: 1.353496 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443783 Loss1: 0.106339 Loss2: 1.337444 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987981
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.558630 Loss1: 0.760328 Loss2: 1.798302
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.834750 Loss1: 0.439973 Loss2: 1.394777
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.670365 Loss1: 0.287386 Loss2: 1.382979
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.696198 Loss1: 0.853516 Loss2: 1.842683
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599725 Loss1: 0.228628 Loss2: 1.371097
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.938471 Loss1: 0.560781 Loss2: 1.377690
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544336 Loss1: 0.185843 Loss2: 1.358493
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.830852 Loss1: 0.402858 Loss2: 1.427994
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484424 Loss1: 0.125573 Loss2: 1.358851
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433693 Loss1: 0.087494 Loss2: 1.346199 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424657 Loss1: 0.082905 Loss2: 1.341751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.415202 Loss1: 0.079645 Loss2: 1.335557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422825 Loss1: 0.083555 Loss2: 1.339270 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439593 Loss1: 0.106135 Loss2: 1.333458 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.693744 Loss1: 0.839525 Loss2: 1.854220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638687 Loss1: 0.266448 Loss2: 1.372239 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562751 Loss1: 0.212189 Loss2: 1.350562
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.651840 Loss1: 0.757360 Loss2: 1.894480
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.529006 Loss1: 0.170220 Loss2: 1.358786
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.908107 Loss1: 0.516905 Loss2: 1.391203
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.510215 Loss1: 0.158239 Loss2: 1.351976
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.794005 Loss1: 0.331623 Loss2: 1.462382
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425688 Loss1: 0.084222 Loss2: 1.341466
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591065 Loss1: 0.209134 Loss2: 1.381931
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.431616 Loss1: 0.102047 Loss2: 1.329569
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.532114 Loss1: 0.147935 Loss2: 1.384179
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384828 Loss1: 0.057044 Loss2: 1.327784
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506417 Loss1: 0.121861 Loss2: 1.384556
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375625 Loss1: 0.055090 Loss2: 1.320535
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470279 Loss1: 0.099323 Loss2: 1.370956
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462256 Loss1: 0.092131 Loss2: 1.370125
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448727 Loss1: 0.086661 Loss2: 1.362066
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430008 Loss1: 0.073287 Loss2: 1.356720
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.580659 Loss1: 0.728472 Loss2: 1.852187
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.806424 Loss1: 0.388005 Loss2: 1.418419
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.695606 Loss1: 0.262061 Loss2: 1.433544
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.612953 Loss1: 0.211587 Loss2: 1.401366
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.796933 Loss1: 0.927160 Loss2: 1.869773
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.564296 Loss1: 0.163896 Loss2: 1.400399
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.911617 Loss1: 0.498352 Loss2: 1.413265
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.532040 Loss1: 0.135261 Loss2: 1.396779
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.731166 Loss1: 0.302757 Loss2: 1.428409
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.654867 Loss1: 0.259050 Loss2: 1.395817
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.543210 Loss1: 0.152570 Loss2: 1.390640
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.575382 Loss1: 0.183230 Loss2: 1.392152
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.532976 Loss1: 0.143644 Loss2: 1.389333
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531984 Loss1: 0.150475 Loss2: 1.381509
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482883 Loss1: 0.103196 Loss2: 1.379687
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.521902 Loss1: 0.138532 Loss2: 1.383369
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452321 Loss1: 0.078252 Loss2: 1.374069
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481501 Loss1: 0.108436 Loss2: 1.373065 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.699097 Loss1: 0.782999 Loss2: 1.916098 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.889567 Loss1: 0.418295 Loss2: 1.471272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.663267 Loss1: 0.274857 Loss2: 1.388410
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.796311 Loss1: 0.945288 Loss2: 1.851023
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.985358 Loss1: 0.555669 Loss2: 1.429689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769965 Loss1: 0.357554 Loss2: 1.412411 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636413 Loss1: 0.244047 Loss2: 1.392366 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.533030 Loss1: 0.153859 Loss2: 1.379171 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.507192 Loss1: 0.136569 Loss2: 1.370622 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445902 Loss1: 0.083429 Loss2: 1.362473
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502054 Loss1: 0.137468 Loss2: 1.364586
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.507568 Loss1: 0.142148 Loss2: 1.365420
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481000 Loss1: 0.118177 Loss2: 1.362823
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438742 Loss1: 0.082592 Loss2: 1.356150
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.656983 Loss1: 0.810313 Loss2: 1.846671
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.944004 Loss1: 0.574482 Loss2: 1.369522
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.778557 Loss1: 0.346088 Loss2: 1.432468
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.703407 Loss1: 0.334249 Loss2: 1.369158
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.657002 Loss1: 0.708609 Loss2: 1.948393
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.965959 Loss1: 0.507908 Loss2: 1.458050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.830484 Loss1: 0.312032 Loss2: 1.518452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.780058 Loss1: 0.318388 Loss2: 1.461670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.693105 Loss1: 0.221209 Loss2: 1.471895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.651756 Loss1: 0.203713 Loss2: 1.448043 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415724 Loss1: 0.073807 Loss2: 1.341917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550664 Loss1: 0.099966 Loss2: 1.450698
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510740 Loss1: 0.075433 Loss2: 1.435308
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481439 Loss1: 0.052142 Loss2: 1.429297
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.492923 Loss1: 0.077164 Loss2: 1.415759
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.788611 Loss1: 0.790320 Loss2: 1.998291
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.984282 Loss1: 0.461854 Loss2: 1.522428
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.820788 Loss1: 0.298642 Loss2: 1.522146
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.750670 Loss1: 0.898685 Loss2: 1.851985
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.724496 Loss1: 0.230454 Loss2: 1.494042
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.848208 Loss1: 0.460055 Loss2: 1.388153
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.739266 Loss1: 0.243402 Loss2: 1.495864
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.751781 Loss1: 0.325795 Loss2: 1.425987
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.696400 Loss1: 0.190715 Loss2: 1.505685
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.675346 Loss1: 0.287968 Loss2: 1.387378
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.654829 Loss1: 0.175557 Loss2: 1.479273
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.631346 Loss1: 0.149330 Loss2: 1.482015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.572869 Loss1: 0.100538 Loss2: 1.472331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.547291 Loss1: 0.080074 Loss2: 1.467217 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445391 Loss1: 0.083707 Loss2: 1.361684 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.625875 Loss1: 0.766645 Loss2: 1.859230 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.733812 Loss1: 0.323553 Loss2: 1.410259 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.665098 Loss1: 0.274133 Loss2: 1.390964
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.679312 Loss1: 0.810538 Loss2: 1.868773
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.584662 Loss1: 0.205284 Loss2: 1.379379
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.926826 Loss1: 0.516117 Loss2: 1.410709
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520345 Loss1: 0.158199 Loss2: 1.362145
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769495 Loss1: 0.323141 Loss2: 1.446354
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478643 Loss1: 0.115096 Loss2: 1.363548
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.660700 Loss1: 0.274774 Loss2: 1.385926
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453353 Loss1: 0.095014 Loss2: 1.358339
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.723450 Loss1: 0.297658 Loss2: 1.425792
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434250 Loss1: 0.082405 Loss2: 1.351845
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.599779 Loss1: 0.204213 Loss2: 1.395566
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417211 Loss1: 0.071211 Loss2: 1.346000
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.612538 Loss1: 0.212779 Loss2: 1.399760
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.573190 Loss1: 0.166926 Loss2: 1.406263
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.528251 Loss1: 0.143193 Loss2: 1.385058
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.521785 Loss1: 0.142563 Loss2: 1.379222
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.917858 Loss1: 0.944397 Loss2: 1.973460
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.911008 Loss1: 0.422800 Loss2: 1.488208
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.782903 Loss1: 0.295516 Loss2: 1.487387
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654157 Loss1: 0.195950 Loss2: 1.458207
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.753000 Loss1: 0.810106 Loss2: 1.942894
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.652774 Loss1: 0.196470 Loss2: 1.456304
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.953335 Loss1: 0.516520 Loss2: 1.436814
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.613372 Loss1: 0.151246 Loss2: 1.462125
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.859115 Loss1: 0.340395 Loss2: 1.518720
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.612309 Loss1: 0.153939 Loss2: 1.458370
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.666541 Loss1: 0.235444 Loss2: 1.431097
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.569683 Loss1: 0.127081 Loss2: 1.442601
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.590497 Loss1: 0.158274 Loss2: 1.432223
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.529640 Loss1: 0.091060 Loss2: 1.438581
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.557895 Loss1: 0.129129 Loss2: 1.428767
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.530381 Loss1: 0.099544 Loss2: 1.430837
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518532 Loss1: 0.101272 Loss2: 1.417260
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.509432 Loss1: 0.096265 Loss2: 1.413167
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.482755 Loss1: 0.073269 Loss2: 1.409486
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465146 Loss1: 0.058586 Loss2: 1.406560
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.673271 Loss1: 0.853187 Loss2: 1.820083
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.850699 Loss1: 0.475244 Loss2: 1.375455
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.760565 Loss1: 0.352176 Loss2: 1.408389
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.707521 Loss1: 0.340307 Loss2: 1.367215
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.563170 Loss1: 0.732128 Loss2: 1.831043
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.920215 Loss1: 0.514735 Loss2: 1.405480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.791580 Loss1: 0.361292 Loss2: 1.430288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.680994 Loss1: 0.289231 Loss2: 1.391762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598343 Loss1: 0.208536 Loss2: 1.389807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559221 Loss1: 0.170728 Loss2: 1.388493 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.517260 Loss1: 0.133003 Loss2: 1.384257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438703 Loss1: 0.078725 Loss2: 1.359977 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.578725 Loss1: 0.728657 Loss2: 1.850068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.754145 Loss1: 0.329872 Loss2: 1.424273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.739711 Loss1: 0.905538 Loss2: 1.834173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.890519 Loss1: 0.497706 Loss2: 1.392813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.691045 Loss1: 0.306541 Loss2: 1.384504 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.600312 Loss1: 0.234020 Loss2: 1.366292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509455 Loss1: 0.149535 Loss2: 1.359920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481678 Loss1: 0.127749 Loss2: 1.353929 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447222 Loss1: 0.108260 Loss2: 1.338962 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424552 Loss1: 0.087060 Loss2: 1.337491 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.812172 Loss1: 0.456695 Loss2: 1.355478 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599814 Loss1: 0.247350 Loss2: 1.352464 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.557916 Loss1: 0.204616 Loss2: 1.353299 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503997 Loss1: 0.142042 Loss2: 1.361955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.482232 Loss1: 0.131647 Loss2: 1.350585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458693 Loss1: 0.114768 Loss2: 1.343924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453891 Loss1: 0.106515 Loss2: 1.347376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500631 Loss1: 0.151895 Loss2: 1.348736 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438549 Loss1: 0.087875 Loss2: 1.350674 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987981
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.942136 Loss1: 0.871125 Loss2: 2.071011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.923973 Loss1: 0.418485 Loss2: 1.505488 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.793065 Loss1: 0.337833 Loss2: 1.455232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.690338 Loss1: 0.216141 Loss2: 1.474197 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.559588 Loss1: 0.131081 Loss2: 1.428507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.530902 Loss1: 0.111401 Loss2: 1.419501
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.722538 Loss1: 0.316599 Loss2: 1.405939
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.527110 Loss1: 0.104420 Loss2: 1.422690
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.642637 Loss1: 0.266282 Loss2: 1.376354
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.545716 Loss1: 0.129256 Loss2: 1.416460
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.623322 Loss1: 0.237112 Loss2: 1.386210
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538511 Loss1: 0.159974 Loss2: 1.378537
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.505799 Loss1: 0.135581 Loss2: 1.370219
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467939 Loss1: 0.102503 Loss2: 1.365436
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451354 Loss1: 0.096180 Loss2: 1.355174
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.747009 Loss1: 0.879779 Loss2: 1.867230
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427781 Loss1: 0.075678 Loss2: 1.352103
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.805355 Loss1: 0.351309 Loss2: 1.454046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.662505 Loss1: 0.237090 Loss2: 1.425414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.540179 Loss1: 0.135287 Loss2: 1.404892
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.759804 Loss1: 0.893423 Loss2: 1.866381
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.881777 Loss1: 0.475922 Loss2: 1.405856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.720717 Loss1: 0.295366 Loss2: 1.425351 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599828 Loss1: 0.227929 Loss2: 1.371899 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.480108 Loss1: 0.097439 Loss2: 1.382668
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563032 Loss1: 0.180353 Loss2: 1.382679
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503564 Loss1: 0.129194 Loss2: 1.374370
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.477512 Loss1: 0.104872 Loss2: 1.372639
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501950 Loss1: 0.130820 Loss2: 1.371129
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436140 Loss1: 0.075264 Loss2: 1.360877
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.706711 Loss1: 0.795638 Loss2: 1.911073
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448190 Loss1: 0.090283 Loss2: 1.357907
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.873502 Loss1: 0.419030 Loss2: 1.454472 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617687 Loss1: 0.211930 Loss2: 1.405757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.601469 Loss1: 0.189855 Loss2: 1.411614
+DEBUG flwr 2023-10-11 11:37:32,543 | server.py:236 | fit_round 112 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.652854 Loss1: 0.853927 Loss2: 1.798928
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.865788 Loss1: 0.490428 Loss2: 1.375360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682903 Loss1: 0.300298 Loss2: 1.382605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.602864 Loss1: 0.257119 Loss2: 1.345746 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.552797 Loss1: 0.189293 Loss2: 1.363505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.547756 Loss1: 0.191031 Loss2: 1.356725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421864 Loss1: 0.086861 Loss2: 1.335003 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406268 Loss1: 0.079474 Loss2: 1.326794 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.605864 Loss1: 0.248697 Loss2: 1.357167 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516839 Loss1: 0.166410 Loss2: 1.350428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490901 Loss1: 0.133482 Loss2: 1.357419
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.663716 Loss1: 0.800804 Loss2: 1.862911
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444138 Loss1: 0.104403 Loss2: 1.339735
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.878163 Loss1: 0.480044 Loss2: 1.398119
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421378 Loss1: 0.086678 Loss2: 1.334700
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.733302 Loss1: 0.311685 Loss2: 1.421617
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403738 Loss1: 0.070024 Loss2: 1.333714
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.695074 Loss1: 0.309581 Loss2: 1.385493
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.565083 Loss1: 0.177785 Loss2: 1.387298
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496582 Loss1: 0.120613 Loss2: 1.375969
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.492660 Loss1: 0.127335 Loss2: 1.365325
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445590 Loss1: 0.079186 Loss2: 1.366405
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.787199 Loss1: 0.837305 Loss2: 1.949894
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430151 Loss1: 0.070440 Loss2: 1.359711
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.050317 Loss1: 0.620520 Loss2: 1.429797
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422133 Loss1: 0.067088 Loss2: 1.355045
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623241 Loss1: 0.207837 Loss2: 1.415404 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.574475 Loss1: 0.162084 Loss2: 1.412390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.663893 Loss1: 0.858947 Loss2: 1.804946 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.935563 Loss1: 0.510503 Loss2: 1.425061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.502825 Loss1: 0.116450 Loss2: 1.386375 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981027
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548291 Loss1: 0.180389 Loss2: 1.367901 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508955 Loss1: 0.154671 Loss2: 1.354284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410101 Loss1: 0.067276 Loss2: 1.342825 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 11:37:32,543][flwr][DEBUG] - fit_round 112 received 50 results and 0 failures
+INFO flwr 2023-10-11 11:38:15,479 | server.py:125 | fit progress: (112, 2.2133301115645385, {'accuracy': 0.5783}, 258403.2577254)
+>> Test accuracy: 0.578300
+[2023-10-11 11:38:15,479][flwr][INFO] - fit progress: (112, 2.2133301115645385, {'accuracy': 0.5783}, 258403.2577254)
+DEBUG flwr 2023-10-11 11:38:15,480 | server.py:173 | evaluate_round 112: strategy sampled 50 clients (out of 50)
+[2023-10-11 11:38:15,480][flwr][DEBUG] - evaluate_round 112: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 11:47:21,954 | server.py:187 | evaluate_round 112 received 50 results and 0 failures
+[2023-10-11 11:47:21,954][flwr][DEBUG] - evaluate_round 112 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 11:47:21,955 | server.py:222 | fit_round 113: strategy sampled 50 clients (out of 50)
+[2023-10-11 11:47:21,955][flwr][DEBUG] - fit_round 113: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.535149 Loss1: 0.685920 Loss2: 1.849229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.720501 Loss1: 0.308383 Loss2: 1.412119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601953 Loss1: 0.237817 Loss2: 1.364136
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.621870 Loss1: 0.799286 Loss2: 1.822585
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514071 Loss1: 0.156590 Loss2: 1.357481
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.017334 Loss1: 0.589831 Loss2: 1.427504
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.760750 Loss1: 0.354607 Loss2: 1.406143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.668075 Loss1: 0.275316 Loss2: 1.392759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.594842 Loss1: 0.207279 Loss2: 1.387563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.603486 Loss1: 0.218609 Loss2: 1.384877 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.551277 Loss1: 0.174005 Loss2: 1.377272 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.469339 Loss1: 0.104597 Loss2: 1.364741 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.652808 Loss1: 0.815613 Loss2: 1.837195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.607111 Loss1: 0.220628 Loss2: 1.386483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490648 Loss1: 0.137510 Loss2: 1.353137 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457030 Loss1: 0.124832 Loss2: 1.332198 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.465328 Loss1: 0.127962 Loss2: 1.337366 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414620 Loss1: 0.083882 Loss2: 1.330738 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400405 Loss1: 0.075725 Loss2: 1.324680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381626 Loss1: 0.063159 Loss2: 1.318468 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.654959 Loss1: 0.229909 Loss2: 1.425050 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491218 Loss1: 0.086706 Loss2: 1.404511 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.936727 Loss1: 0.553885 Loss2: 1.382841 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.761277 Loss1: 0.340194 Loss2: 1.421083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.686238 Loss1: 0.798165 Loss2: 1.888073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476187 Loss1: 0.104666 Loss2: 1.371522 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458165 Loss1: 0.100796 Loss2: 1.357369 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440510 Loss1: 0.085924 Loss2: 1.354586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434981 Loss1: 0.080455 Loss2: 1.354525 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985577
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538534 Loss1: 0.140595 Loss2: 1.397939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.476423 Loss1: 0.091915 Loss2: 1.384509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454299 Loss1: 0.080283 Loss2: 1.374016
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.743872 Loss1: 0.854733 Loss2: 1.889138
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.833346 Loss1: 0.451384 Loss2: 1.381962 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.584751 Loss1: 0.215280 Loss2: 1.369471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471881 Loss1: 0.114886 Loss2: 1.356995 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451024 Loss1: 0.098501 Loss2: 1.352523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439493 Loss1: 0.087613 Loss2: 1.351880 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418565 Loss1: 0.075995 Loss2: 1.342571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389729 Loss1: 0.055004 Loss2: 1.334725 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467663 Loss1: 0.112380 Loss2: 1.355284 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409286 Loss1: 0.070487 Loss2: 1.338799 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387001 Loss1: 0.053017 Loss2: 1.333983
+(DefaultActor pid=3764) >> Training accuracy: 0.983259
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.409292 Loss1: 0.607152 Loss2: 1.802140
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.833012 Loss1: 0.467367 Loss2: 1.365645
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.657912 Loss1: 0.279897 Loss2: 1.378015
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.612103 Loss1: 0.250084 Loss2: 1.362019
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.543884 Loss1: 0.193789 Loss2: 1.350096
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.665189 Loss1: 0.762041 Loss2: 1.903148
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.517256 Loss1: 0.164293 Loss2: 1.352963
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.962609 Loss1: 0.533859 Loss2: 1.428750
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.799055 Loss1: 0.331289 Loss2: 1.467766
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.494950 Loss1: 0.150936 Loss2: 1.344013
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.695593 Loss1: 0.291028 Loss2: 1.404566
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.490106 Loss1: 0.149949 Loss2: 1.340157
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.643501 Loss1: 0.226396 Loss2: 1.417105
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458296 Loss1: 0.107454 Loss2: 1.350842
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430410 Loss1: 0.084438 Loss2: 1.345972 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975184
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.534790 Loss1: 0.136942 Loss2: 1.397848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.500497 Loss1: 0.112037 Loss2: 1.388460 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.869610 Loss1: 0.476919 Loss2: 1.392692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.665953 Loss1: 0.276201 Loss2: 1.389752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.612035 Loss1: 0.224538 Loss2: 1.387497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.567937 Loss1: 0.170560 Loss2: 1.397377 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502577 Loss1: 0.124766 Loss2: 1.377811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484386 Loss1: 0.107901 Loss2: 1.376485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424250 Loss1: 0.063099 Loss2: 1.361151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403143 Loss1: 0.049613 Loss2: 1.353530 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432624 Loss1: 0.107486 Loss2: 1.325138 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392985 Loss1: 0.074472 Loss2: 1.318514 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.578901 Loss1: 0.778707 Loss2: 1.800194
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.838122 Loss1: 0.461074 Loss2: 1.377049
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.613849 Loss1: 0.217837 Loss2: 1.396012
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.603632 Loss1: 0.244101 Loss2: 1.359531
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.750937 Loss1: 0.866241 Loss2: 1.884697
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.985869 Loss1: 0.551156 Loss2: 1.434713 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.755717 Loss1: 0.301527 Loss2: 1.454190 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.649000 Loss1: 0.243854 Loss2: 1.405146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568893 Loss1: 0.153218 Loss2: 1.415675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524384 Loss1: 0.125609 Loss2: 1.398775 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427837 Loss1: 0.086326 Loss2: 1.341511
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.477941 Loss1: 0.089087 Loss2: 1.388854
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475194 Loss1: 0.097340 Loss2: 1.377854
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462593 Loss1: 0.082534 Loss2: 1.380060
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436374 Loss1: 0.061224 Loss2: 1.375150
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.670111 Loss1: 0.789848 Loss2: 1.880263
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.925949 Loss1: 0.531853 Loss2: 1.394096
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.752468 Loss1: 0.303880 Loss2: 1.448588
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.625290 Loss1: 0.237874 Loss2: 1.387416
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.611181 Loss1: 0.760642 Loss2: 1.850540
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.971189 Loss1: 0.554750 Loss2: 1.416439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.807447 Loss1: 0.375328 Loss2: 1.432119 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565453 Loss1: 0.190738 Loss2: 1.374715 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523868 Loss1: 0.155686 Loss2: 1.368182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.525956 Loss1: 0.163754 Loss2: 1.362202 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.492707 Loss1: 0.129437 Loss2: 1.363270 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445611 Loss1: 0.103278 Loss2: 1.342334 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.649439 Loss1: 0.828780 Loss2: 1.820658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.742281 Loss1: 0.342607 Loss2: 1.399674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.714989 Loss1: 0.345651 Loss2: 1.369338
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.907644 Loss1: 1.033968 Loss2: 1.873676
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.970371 Loss1: 0.557049 Loss2: 1.413322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.841044 Loss1: 0.397958 Loss2: 1.443086 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.673400 Loss1: 0.280608 Loss2: 1.392792 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570315 Loss1: 0.175281 Loss2: 1.395034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.583770 Loss1: 0.197760 Loss2: 1.386011 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509348 Loss1: 0.132361 Loss2: 1.376987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485851 Loss1: 0.115771 Loss2: 1.370080 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.711380 Loss1: 0.838361 Loss2: 1.873019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.762596 Loss1: 0.330730 Loss2: 1.431866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.688718 Loss1: 0.797684 Loss2: 1.891034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.964762 Loss1: 0.525350 Loss2: 1.439412 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769839 Loss1: 0.316778 Loss2: 1.453061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.797865 Loss1: 0.358415 Loss2: 1.439449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.681912 Loss1: 0.242435 Loss2: 1.439477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582484 Loss1: 0.166384 Loss2: 1.416101 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.530466 Loss1: 0.129292 Loss2: 1.401174 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.500725 Loss1: 0.105225 Loss2: 1.395500 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.825315 Loss1: 0.474917 Loss2: 1.350398 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.634343 Loss1: 0.295193 Loss2: 1.339150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.542356 Loss1: 0.190696 Loss2: 1.351660 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525085 Loss1: 0.186554 Loss2: 1.338530 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.492426 Loss1: 0.149922 Loss2: 1.342504 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409078 Loss1: 0.077401 Loss2: 1.331677 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.415622 Loss1: 0.095971 Loss2: 1.319651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.394606 Loss1: 0.073821 Loss2: 1.320784 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491638 Loss1: 0.117636 Loss2: 1.374002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429504 Loss1: 0.065686 Loss2: 1.363819 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.792159 Loss1: 0.374642 Loss2: 1.417518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.615608 Loss1: 0.221297 Loss2: 1.394311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.982489 Loss1: 0.609955 Loss2: 1.372534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.880174 Loss1: 0.421064 Loss2: 1.459110 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.661532 Loss1: 0.264797 Loss2: 1.396735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598220 Loss1: 0.205139 Loss2: 1.393081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.594954 Loss1: 0.191834 Loss2: 1.403121
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462808 Loss1: 0.089727 Loss2: 1.373081
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439036 Loss1: 0.074219 Loss2: 1.364816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416704 Loss1: 0.057994 Loss2: 1.358710 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.625961 Loss1: 0.784131 Loss2: 1.841830 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.728939 Loss1: 0.303188 Loss2: 1.425752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.650125 Loss1: 0.275392 Loss2: 1.374733
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.726734 Loss1: 0.914719 Loss2: 1.812015
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.559381 Loss1: 0.188607 Loss2: 1.370774
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.950765 Loss1: 0.559456 Loss2: 1.391308
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465157 Loss1: 0.105080 Loss2: 1.360077
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726952 Loss1: 0.347321 Loss2: 1.379631
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422576 Loss1: 0.069802 Loss2: 1.352774
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.593817 Loss1: 0.260728 Loss2: 1.333089
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413240 Loss1: 0.060533 Loss2: 1.352707
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.613347 Loss1: 0.249933 Loss2: 1.363414
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418237 Loss1: 0.074391 Loss2: 1.343846
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504902 Loss1: 0.176037 Loss2: 1.328865
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436033 Loss1: 0.097107 Loss2: 1.338925
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438196 Loss1: 0.114454 Loss2: 1.323742
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431072 Loss1: 0.107890 Loss2: 1.323182
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406038 Loss1: 0.091302 Loss2: 1.314736
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390929 Loss1: 0.082849 Loss2: 1.308080
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.595832 Loss1: 0.728790 Loss2: 1.867042
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.944631 Loss1: 0.541833 Loss2: 1.402798
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.751668 Loss1: 0.317801 Loss2: 1.433867
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638602 Loss1: 0.239349 Loss2: 1.399252
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.671218 Loss1: 0.845858 Loss2: 1.825360
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.870397 Loss1: 0.503491 Loss2: 1.366906 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.695943 Loss1: 0.291873 Loss2: 1.404070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550399 Loss1: 0.201943 Loss2: 1.348457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508355 Loss1: 0.161538 Loss2: 1.346817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506889 Loss1: 0.159693 Loss2: 1.347195 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450873 Loss1: 0.083143 Loss2: 1.367730
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497250 Loss1: 0.144955 Loss2: 1.352295
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437741 Loss1: 0.098515 Loss2: 1.339226
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426062 Loss1: 0.095843 Loss2: 1.330218
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390472 Loss1: 0.064457 Loss2: 1.326015
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.672033 Loss1: 0.849247 Loss2: 1.822786
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.887583 Loss1: 0.531024 Loss2: 1.356559
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.657918 Loss1: 0.282153 Loss2: 1.375765
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555110 Loss1: 0.222093 Loss2: 1.333017
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.794037 Loss1: 0.853039 Loss2: 1.940998
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536138 Loss1: 0.194274 Loss2: 1.341865
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.979810 Loss1: 0.589274 Loss2: 1.390536
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834773 Loss1: 0.395167 Loss2: 1.439607
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492506 Loss1: 0.153509 Loss2: 1.338996
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.628614 Loss1: 0.212441 Loss2: 1.416172
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425051 Loss1: 0.101418 Loss2: 1.323633
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.396391 Loss1: 0.080184 Loss2: 1.316207 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.370757 Loss1: 0.058280 Loss2: 1.312477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360690 Loss1: 0.054941 Loss2: 1.305750 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.459901 Loss1: 0.089058 Loss2: 1.370843 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.613792 Loss1: 0.772156 Loss2: 1.841636
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.736748 Loss1: 0.383740 Loss2: 1.353009
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716393 Loss1: 0.331299 Loss2: 1.385095
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581870 Loss1: 0.233767 Loss2: 1.348104
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.791778 Loss1: 0.816137 Loss2: 1.975642
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503125 Loss1: 0.159786 Loss2: 1.343339
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.996050 Loss1: 0.543646 Loss2: 1.452403
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470174 Loss1: 0.138888 Loss2: 1.331286
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.851558 Loss1: 0.343695 Loss2: 1.507863
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416465 Loss1: 0.089963 Loss2: 1.326502
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.804246 Loss1: 0.357319 Loss2: 1.446927
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.743856 Loss1: 0.265571 Loss2: 1.478286
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451526 Loss1: 0.130199 Loss2: 1.321327
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.608724 Loss1: 0.166806 Loss2: 1.441918
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434864 Loss1: 0.111041 Loss2: 1.323823
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.578457 Loss1: 0.141243 Loss2: 1.437215
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421402 Loss1: 0.096102 Loss2: 1.325300
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.512067 Loss1: 0.088331 Loss2: 1.423736 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.846986 Loss1: 0.968267 Loss2: 1.878718 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.775896 Loss1: 0.343585 Loss2: 1.432311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.665841 Loss1: 0.275934 Loss2: 1.389907
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.768014 Loss1: 0.801159 Loss2: 1.966855
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.583644 Loss1: 0.190589 Loss2: 1.393055
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.930812 Loss1: 0.486612 Loss2: 1.444201
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519985 Loss1: 0.142589 Loss2: 1.377396
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.702165 Loss1: 0.252124 Loss2: 1.450041
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466970 Loss1: 0.100701 Loss2: 1.366269
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588903 Loss1: 0.176606 Loss2: 1.412297
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440918 Loss1: 0.081723 Loss2: 1.359196
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.510868 Loss1: 0.099562 Loss2: 1.411305
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423315 Loss1: 0.076068 Loss2: 1.347247
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538703 Loss1: 0.140622 Loss2: 1.398081
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441420 Loss1: 0.094614 Loss2: 1.346806
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488598 Loss1: 0.092545 Loss2: 1.396052
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.479972 Loss1: 0.087491 Loss2: 1.392481
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.498998 Loss1: 0.105850 Loss2: 1.393148
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.511751 Loss1: 0.118084 Loss2: 1.393667
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.640421 Loss1: 0.801666 Loss2: 1.838755
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.801441 Loss1: 0.407402 Loss2: 1.394039
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.726888 Loss1: 0.312499 Loss2: 1.414389
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.640775 Loss1: 0.744051 Loss2: 1.896724
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.667664 Loss1: 0.290402 Loss2: 1.377262
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.837673 Loss1: 0.444789 Loss2: 1.392884
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.655941 Loss1: 0.264525 Loss2: 1.391416
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538442 Loss1: 0.168864 Loss2: 1.369579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.477664 Loss1: 0.114261 Loss2: 1.363403 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460870 Loss1: 0.107727 Loss2: 1.353143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427157 Loss1: 0.078027 Loss2: 1.349131 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422344 Loss1: 0.078580 Loss2: 1.343764 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451618 Loss1: 0.085493 Loss2: 1.366125 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.696497 Loss1: 0.856448 Loss2: 1.840049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.625953 Loss1: 0.253744 Loss2: 1.372209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535692 Loss1: 0.200163 Loss2: 1.335529
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.569237 Loss1: 0.747469 Loss2: 1.821768
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.939654 Loss1: 0.516320 Loss2: 1.423335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.738953 Loss1: 0.298796 Loss2: 1.440157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.664415 Loss1: 0.266234 Loss2: 1.398181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543284 Loss1: 0.144332 Loss2: 1.398952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.540116 Loss1: 0.159678 Loss2: 1.380437 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518887 Loss1: 0.142629 Loss2: 1.376258 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.455084 Loss1: 0.085667 Loss2: 1.369416 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.815287 Loss1: 0.851966 Loss2: 1.963320 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.871276 Loss1: 0.360066 Loss2: 1.511210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.665339 Loss1: 0.201981 Loss2: 1.463358 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.637643 Loss1: 0.180239 Loss2: 1.457405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.600274 Loss1: 0.157012 Loss2: 1.443262 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.608529 Loss1: 0.146737 Loss2: 1.461792 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.560398 Loss1: 0.120196 Loss2: 1.440202 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.524544 Loss1: 0.096114 Loss2: 1.428431 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.519682 Loss1: 0.159976 Loss2: 1.359706 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470340 Loss1: 0.101945 Loss2: 1.368395 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.981661 Loss1: 0.594630 Loss2: 1.387031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.657673 Loss1: 0.284083 Loss2: 1.373589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.695567 Loss1: 0.784278 Loss2: 1.911290
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.554390 Loss1: 0.168418 Loss2: 1.385972
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.995589 Loss1: 0.566960 Loss2: 1.428629
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518928 Loss1: 0.168879 Loss2: 1.350049
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.755313 Loss1: 0.287148 Loss2: 1.468166
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495452 Loss1: 0.145847 Loss2: 1.349606
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.689577 Loss1: 0.263394 Loss2: 1.426183
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.463452 Loss1: 0.113205 Loss2: 1.350248
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.591803 Loss1: 0.158807 Loss2: 1.432996
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423675 Loss1: 0.078879 Loss2: 1.344797
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523934 Loss1: 0.108200 Loss2: 1.415735
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416627 Loss1: 0.081697 Loss2: 1.334930
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493460 Loss1: 0.085927 Loss2: 1.407534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454215 Loss1: 0.059362 Loss2: 1.394853 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.898974 Loss1: 0.537178 Loss2: 1.361796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587029 Loss1: 0.231426 Loss2: 1.355602 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566445 Loss1: 0.213745 Loss2: 1.352700
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.568946 Loss1: 0.765422 Loss2: 1.803525
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.844197 Loss1: 0.476695 Loss2: 1.367502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.631181 Loss1: 0.267754 Loss2: 1.363427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.608452 Loss1: 0.272865 Loss2: 1.335587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527848 Loss1: 0.185006 Loss2: 1.342842 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995536
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433110 Loss1: 0.110794 Loss2: 1.322316 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386980 Loss1: 0.074765 Loss2: 1.312215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.645806 Loss1: 0.848784 Loss2: 1.797022
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405666 Loss1: 0.099235 Loss2: 1.306432
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.659833 Loss1: 0.285049 Loss2: 1.374784 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475342 Loss1: 0.150093 Loss2: 1.325249 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.420056 Loss1: 0.103111 Loss2: 1.316944
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.528551 Loss1: 0.694789 Loss2: 1.833762
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.806012 Loss1: 0.453658 Loss2: 1.352354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684717 Loss1: 0.278237 Loss2: 1.406480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588088 Loss1: 0.226006 Loss2: 1.362082 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.329405 Loss1: 0.040760 Loss2: 1.288646
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.583191 Loss1: 0.210541 Loss2: 1.372650
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510619 Loss1: 0.141563 Loss2: 1.369056
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475632 Loss1: 0.123089 Loss2: 1.352543
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449611 Loss1: 0.106173 Loss2: 1.343438
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428548 Loss1: 0.085523 Loss2: 1.343025
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.585689 Loss1: 0.706152 Loss2: 1.879537
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412054 Loss1: 0.068053 Loss2: 1.344001
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700042 Loss1: 0.257443 Loss2: 1.442599 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499714 Loss1: 0.099741 Loss2: 1.399972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.784023 Loss1: 0.904706 Loss2: 1.879316
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485782 Loss1: 0.090068 Loss2: 1.395714
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.925135 Loss1: 0.533635 Loss2: 1.391500
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474002 Loss1: 0.087699 Loss2: 1.386302
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.711663 Loss1: 0.283923 Loss2: 1.427740
+DEBUG flwr 2023-10-11 12:16:05,021 | server.py:236 | fit_round 113 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464926 Loss1: 0.079588 Loss2: 1.385339
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611675 Loss1: 0.225369 Loss2: 1.386306
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.467622 Loss1: 0.082363 Loss2: 1.385259
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466226 Loss1: 0.081583 Loss2: 1.384643 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.477665 Loss1: 0.119143 Loss2: 1.358521 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.471890 Loss1: 0.119449 Loss2: 1.352440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456634 Loss1: 0.094342 Loss2: 1.362292
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.658354 Loss1: 0.799375 Loss2: 1.858979
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.041120 Loss1: 0.597627 Loss2: 1.443494
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.728138 Loss1: 0.299420 Loss2: 1.428718
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647613 Loss1: 0.255028 Loss2: 1.392585
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577332 Loss1: 0.171933 Loss2: 1.405398
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.761746 Loss1: 0.888564 Loss2: 1.873182
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.947252 Loss1: 0.522625 Loss2: 1.424627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.820862 Loss1: 0.346314 Loss2: 1.474547 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.562737 Loss1: 0.176894 Loss2: 1.385843
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.666809 Loss1: 0.260471 Loss2: 1.406338
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.581321 Loss1: 0.189603 Loss2: 1.391718
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.626724 Loss1: 0.211787 Loss2: 1.414937
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.490339 Loss1: 0.091326 Loss2: 1.399013
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.525148 Loss1: 0.134035 Loss2: 1.391113
+(DefaultActor pid=3765) >> Training accuracy: 0.971680
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.551477 Loss1: 0.156943 Loss2: 1.394534
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.514011 Loss1: 0.119225 Loss2: 1.394785
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.504274 Loss1: 0.118780 Loss2: 1.385494
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.541090 Loss1: 0.157846 Loss2: 1.383244
+(DefaultActor pid=3764) >> Training accuracy: 0.957292
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 12:16:05,021][flwr][DEBUG] - fit_round 113 received 50 results and 0 failures
+INFO flwr 2023-10-11 12:16:45,585 | server.py:125 | fit progress: (113, 2.1971218222246383, {'accuracy': 0.579}, 260713.36380087602)
+>> Test accuracy: 0.579000
+[2023-10-11 12:16:45,585][flwr][INFO] - fit progress: (113, 2.1971218222246383, {'accuracy': 0.579}, 260713.36380087602)
+DEBUG flwr 2023-10-11 12:16:45,586 | server.py:173 | evaluate_round 113: strategy sampled 50 clients (out of 50)
+[2023-10-11 12:16:45,586][flwr][DEBUG] - evaluate_round 113: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 12:25:47,515 | server.py:187 | evaluate_round 113 received 50 results and 0 failures
+[2023-10-11 12:25:47,515][flwr][DEBUG] - evaluate_round 113 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 12:25:47,515 | server.py:222 | fit_round 114: strategy sampled 50 clients (out of 50)
+[2023-10-11 12:25:47,515][flwr][DEBUG] - fit_round 114: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.515881 Loss1: 0.730689 Loss2: 1.785192
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.782824 Loss1: 0.409773 Loss2: 1.373052
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.601986 Loss1: 0.233279 Loss2: 1.368707
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.727789 Loss1: 0.843824 Loss2: 1.883965
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518830 Loss1: 0.185178 Loss2: 1.333653
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.946829 Loss1: 0.502373 Loss2: 1.444456
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507323 Loss1: 0.170659 Loss2: 1.336665
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.752552 Loss1: 0.320132 Loss2: 1.432420
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493653 Loss1: 0.164283 Loss2: 1.329370
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611729 Loss1: 0.199483 Loss2: 1.412246
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.421679 Loss1: 0.099330 Loss2: 1.322349
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556481 Loss1: 0.151283 Loss2: 1.405198
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450674 Loss1: 0.125664 Loss2: 1.325011
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384010 Loss1: 0.066519 Loss2: 1.317490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.349179 Loss1: 0.042432 Loss2: 1.306748 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.500806 Loss1: 0.105625 Loss2: 1.395181 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.639243 Loss1: 0.850193 Loss2: 1.789051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.632671 Loss1: 0.271845 Loss2: 1.360826 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526072 Loss1: 0.202614 Loss2: 1.323457
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.615019 Loss1: 0.812283 Loss2: 1.802737
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492952 Loss1: 0.164946 Loss2: 1.328005
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.852892 Loss1: 0.492569 Loss2: 1.360323
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449862 Loss1: 0.126257 Loss2: 1.323606
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635801 Loss1: 0.262161 Loss2: 1.373640
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406877 Loss1: 0.087502 Loss2: 1.319374
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509461 Loss1: 0.169732 Loss2: 1.339729
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403011 Loss1: 0.093797 Loss2: 1.309214
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.482761 Loss1: 0.147990 Loss2: 1.334772
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379960 Loss1: 0.074375 Loss2: 1.305585
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466945 Loss1: 0.136173 Loss2: 1.330772
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374227 Loss1: 0.073565 Loss2: 1.300663
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.411589 Loss1: 0.090559 Loss2: 1.321031
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.384384 Loss1: 0.067478 Loss2: 1.316906
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381965 Loss1: 0.071635 Loss2: 1.310330
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381630 Loss1: 0.073219 Loss2: 1.308411
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.835527 Loss1: 0.916414 Loss2: 1.919113
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.887931 Loss1: 0.512686 Loss2: 1.375244
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.755337 Loss1: 0.344363 Loss2: 1.410975
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.639510 Loss1: 0.259668 Loss2: 1.379842
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.664647 Loss1: 0.697567 Loss2: 1.967080
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538225 Loss1: 0.152142 Loss2: 1.386083 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491648 Loss1: 0.130998 Loss2: 1.360650 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448157 Loss1: 0.096820 Loss2: 1.351338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437452 Loss1: 0.088481 Loss2: 1.348971 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.444966 Loss1: 0.096558 Loss2: 1.348408 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987981
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.569906 Loss1: 0.147845 Loss2: 1.422061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497539 Loss1: 0.076871 Loss2: 1.420668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.498646 Loss1: 0.093469 Loss2: 1.405177
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.607631 Loss1: 0.758852 Loss2: 1.848779
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.900850 Loss1: 0.538301 Loss2: 1.362549
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.711006 Loss1: 0.293081 Loss2: 1.417926
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.613212 Loss1: 0.256910 Loss2: 1.356302
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499472 Loss1: 0.130159 Loss2: 1.369314
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.564792 Loss1: 0.762814 Loss2: 1.801978
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457536 Loss1: 0.109388 Loss2: 1.348148
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.817850 Loss1: 0.436271 Loss2: 1.381579
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435282 Loss1: 0.097872 Loss2: 1.337410
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.745633 Loss1: 0.359456 Loss2: 1.386177
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418782 Loss1: 0.082247 Loss2: 1.336535
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.587749 Loss1: 0.228509 Loss2: 1.359241
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408706 Loss1: 0.074964 Loss2: 1.333743
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428262 Loss1: 0.102255 Loss2: 1.326007
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.560550 Loss1: 0.204800 Loss2: 1.355749
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487397 Loss1: 0.145486 Loss2: 1.341911
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444386 Loss1: 0.109186 Loss2: 1.335201
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411297 Loss1: 0.083169 Loss2: 1.328127
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394843 Loss1: 0.077791 Loss2: 1.317052
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.712713 Loss1: 0.896328 Loss2: 1.816386
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388958 Loss1: 0.069101 Loss2: 1.319857
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.755241 Loss1: 0.354254 Loss2: 1.400987 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506532 Loss1: 0.144241 Loss2: 1.362291 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.447194 Loss1: 0.110918 Loss2: 1.336276
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.537920 Loss1: 0.715265 Loss2: 1.822655
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.819077 Loss1: 0.472624 Loss2: 1.346453 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669136 Loss1: 0.293619 Loss2: 1.375517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631810 Loss1: 0.294996 Loss2: 1.336814 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.590008 Loss1: 0.236612 Loss2: 1.353396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451286 Loss1: 0.124077 Loss2: 1.327209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416793 Loss1: 0.104026 Loss2: 1.312767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388441 Loss1: 0.072160 Loss2: 1.316281 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.708922 Loss1: 0.309529 Loss2: 1.399393 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.600928 Loss1: 0.217789 Loss2: 1.383140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523454 Loss1: 0.148373 Loss2: 1.375081
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.625208 Loss1: 0.757047 Loss2: 1.868161
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.925572 Loss1: 0.529855 Loss2: 1.395717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.724149 Loss1: 0.272668 Loss2: 1.451481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.659160 Loss1: 0.271374 Loss2: 1.387787 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.603980 Loss1: 0.210932 Loss2: 1.393048 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.537994 Loss1: 0.157159 Loss2: 1.380835 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.456100 Loss1: 0.085806 Loss2: 1.370294 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461517 Loss1: 0.098164 Loss2: 1.363352 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.693609 Loss1: 0.278352 Loss2: 1.415257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.660608 Loss1: 0.277780 Loss2: 1.382828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.571397 Loss1: 0.193356 Loss2: 1.378041
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.697122 Loss1: 0.832556 Loss2: 1.864566
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.879742 Loss1: 0.501084 Loss2: 1.378659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.733136 Loss1: 0.302690 Loss2: 1.430446 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.634298 Loss1: 0.253439 Loss2: 1.380859 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.596111 Loss1: 0.195757 Loss2: 1.400354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480213 Loss1: 0.108484 Loss2: 1.371729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458806 Loss1: 0.099164 Loss2: 1.359643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426226 Loss1: 0.061130 Loss2: 1.365095 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.742888 Loss1: 0.356439 Loss2: 1.386449 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.632504 Loss1: 0.262274 Loss2: 1.370230 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.507832 Loss1: 0.163099 Loss2: 1.344732
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.748189 Loss1: 0.858605 Loss2: 1.889584
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.882473 Loss1: 0.473834 Loss2: 1.408639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.799241 Loss1: 0.358516 Loss2: 1.440724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.676120 Loss1: 0.270809 Loss2: 1.405311 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.566000 Loss1: 0.166243 Loss2: 1.399756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504232 Loss1: 0.119160 Loss2: 1.385072 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461076 Loss1: 0.088159 Loss2: 1.372917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437915 Loss1: 0.066711 Loss2: 1.371204 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.607017 Loss1: 0.231559 Loss2: 1.375458 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497692 Loss1: 0.140374 Loss2: 1.357318 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.715808 Loss1: 0.880291 Loss2: 1.835517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.853075 Loss1: 0.471905 Loss2: 1.381170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386534 Loss1: 0.048859 Loss2: 1.337675 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548955 Loss1: 0.166714 Loss2: 1.382241 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473172 Loss1: 0.118235 Loss2: 1.354936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434210 Loss1: 0.085649 Loss2: 1.348561
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.747664 Loss1: 0.863097 Loss2: 1.884567
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414988 Loss1: 0.069402 Loss2: 1.345586
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.893553 Loss1: 0.503716 Loss2: 1.389837
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407493 Loss1: 0.064140 Loss2: 1.343352
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.734004 Loss1: 0.304821 Loss2: 1.429182
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.628294 Loss1: 0.243063 Loss2: 1.385231
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.618172 Loss1: 0.225597 Loss2: 1.392575
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.567245 Loss1: 0.164961 Loss2: 1.402284
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.536418 Loss1: 0.156230 Loss2: 1.380189
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.516246 Loss1: 0.130029 Loss2: 1.386217
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.546737 Loss1: 0.753476 Loss2: 1.793261
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.471046 Loss1: 0.092899 Loss2: 1.378147
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.837122 Loss1: 0.471060 Loss2: 1.366062
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439030 Loss1: 0.071207 Loss2: 1.367822
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682286 Loss1: 0.292785 Loss2: 1.389502
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550325 Loss1: 0.200249 Loss2: 1.350076
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.572729 Loss1: 0.211199 Loss2: 1.361530
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513706 Loss1: 0.166170 Loss2: 1.347536
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458702 Loss1: 0.117441 Loss2: 1.341260
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.754574 Loss1: 0.824474 Loss2: 1.930100
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.910812 Loss1: 0.490521 Loss2: 1.420291 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.773880 Loss1: 0.311737 Loss2: 1.462143 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397237 Loss1: 0.070680 Loss2: 1.326558
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642591 Loss1: 0.248715 Loss2: 1.393876
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.604536 Loss1: 0.184464 Loss2: 1.420073
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.550281 Loss1: 0.154272 Loss2: 1.396009
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516926 Loss1: 0.123790 Loss2: 1.393136
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488231 Loss1: 0.099852 Loss2: 1.388379
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.640328 Loss1: 0.788227 Loss2: 1.852101
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.507891 Loss1: 0.119327 Loss2: 1.388563
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476226 Loss1: 0.090807 Loss2: 1.385419 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.627288 Loss1: 0.254546 Loss2: 1.372742 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529869 Loss1: 0.154598 Loss2: 1.375271 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.541014 Loss1: 0.165167 Loss2: 1.375847
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.571858 Loss1: 0.737884 Loss2: 1.833974
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.857249 Loss1: 0.479202 Loss2: 1.378046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.733211 Loss1: 0.323508 Loss2: 1.409703 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450102 Loss1: 0.098211 Loss2: 1.351891
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.810191 Loss1: 0.421995 Loss2: 1.388195
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.718053 Loss1: 0.327499 Loss2: 1.390554
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.580701 Loss1: 0.215376 Loss2: 1.365326
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456498 Loss1: 0.096311 Loss2: 1.360187
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423145 Loss1: 0.074405 Loss2: 1.348740
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.708622 Loss1: 0.886330 Loss2: 1.822293
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409248 Loss1: 0.071749 Loss2: 1.337499
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.933209 Loss1: 0.555258 Loss2: 1.377951
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.382797 Loss1: 0.049195 Loss2: 1.333602
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.575093 Loss1: 0.238003 Loss2: 1.337091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500606 Loss1: 0.164912 Loss2: 1.335694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.489396 Loss1: 0.152301 Loss2: 1.337095
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.866368 Loss1: 0.976353 Loss2: 1.890015
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.032131 Loss1: 0.611320 Loss2: 1.420811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.836335 Loss1: 0.397017 Loss2: 1.439317 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424615 Loss1: 0.094566 Loss2: 1.330048
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.681014 Loss1: 0.273830 Loss2: 1.407184
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589806 Loss1: 0.194293 Loss2: 1.395514
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562769 Loss1: 0.175422 Loss2: 1.387347
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511515 Loss1: 0.129162 Loss2: 1.382353
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477543 Loss1: 0.106813 Loss2: 1.370730
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.683218 Loss1: 0.877859 Loss2: 1.805359
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.485227 Loss1: 0.113090 Loss2: 1.372137
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477431 Loss1: 0.111266 Loss2: 1.366165 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.639898 Loss1: 0.296501 Loss2: 1.343397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.454926 Loss1: 0.130541 Loss2: 1.324385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.403497 Loss1: 0.083003 Loss2: 1.320494
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.754297 Loss1: 0.840430 Loss2: 1.913867
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.044782 Loss1: 0.572280 Loss2: 1.472502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.918324 Loss1: 0.411081 Loss2: 1.507243 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.816640 Loss1: 0.351863 Loss2: 1.464777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.579826 Loss1: 0.140559 Loss2: 1.439267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.525744 Loss1: 0.103443 Loss2: 1.422301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.502398 Loss1: 0.076831 Loss2: 1.425568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.470007 Loss1: 0.051712 Loss2: 1.418295 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.656193 Loss1: 0.265514 Loss2: 1.390678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.551856 Loss1: 0.184658 Loss2: 1.367199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.535516 Loss1: 0.164683 Loss2: 1.370833
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.471815 Loss1: 0.655450 Loss2: 1.816365
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.860822 Loss1: 0.510585 Loss2: 1.350236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.737018 Loss1: 0.327118 Loss2: 1.409900 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.588016 Loss1: 0.244107 Loss2: 1.343909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488267 Loss1: 0.154415 Loss2: 1.333852 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435430 Loss1: 0.099828 Loss2: 1.335602 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433136 Loss1: 0.110361 Loss2: 1.322774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.923725 Loss1: 0.490938 Loss2: 1.432787
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422208 Loss1: 0.092835 Loss2: 1.329373
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574433 Loss1: 0.167686 Loss2: 1.406748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.533354 Loss1: 0.140018 Loss2: 1.393336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.620993 Loss1: 0.832936 Loss2: 1.788057
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.501574 Loss1: 0.107918 Loss2: 1.393655
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.905670 Loss1: 0.540481 Loss2: 1.365189
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.495556 Loss1: 0.108333 Loss2: 1.387223
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.670908 Loss1: 0.286933 Loss2: 1.383974
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473206 Loss1: 0.087062 Loss2: 1.386144
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581264 Loss1: 0.233256 Loss2: 1.348008
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.468866 Loss1: 0.087107 Loss2: 1.381759
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506057 Loss1: 0.174803 Loss2: 1.331254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440674 Loss1: 0.114025 Loss2: 1.326649 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.393281 Loss1: 0.069971 Loss2: 1.323309
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.649312 Loss1: 0.849184 Loss2: 1.800128
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.920081 Loss1: 0.522030 Loss2: 1.398051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586062 Loss1: 0.213397 Loss2: 1.372664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473366 Loss1: 0.117882 Loss2: 1.355485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451804 Loss1: 0.102041 Loss2: 1.349763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461227 Loss1: 0.111626 Loss2: 1.349601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427772 Loss1: 0.085003 Loss2: 1.342769 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406282 Loss1: 0.074381 Loss2: 1.331901 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509847 Loss1: 0.136255 Loss2: 1.373592 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445811 Loss1: 0.084723 Loss2: 1.361088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430363 Loss1: 0.075065 Loss2: 1.355298 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.791976 Loss1: 0.389740 Loss2: 1.402236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550173 Loss1: 0.218061 Loss2: 1.332112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.692883 Loss1: 0.847103 Loss2: 1.845780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.858587 Loss1: 0.474420 Loss2: 1.384167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736848 Loss1: 0.322799 Loss2: 1.414049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575639 Loss1: 0.202338 Loss2: 1.373301 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506572 Loss1: 0.143123 Loss2: 1.363449 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.515621 Loss1: 0.153508 Loss2: 1.362112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.490700 Loss1: 0.123314 Loss2: 1.367386
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.688667 Loss1: 0.776247 Loss2: 1.912421
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441097 Loss1: 0.090833 Loss2: 1.350264
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.020572 Loss1: 0.599870 Loss2: 1.420703
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.834590 Loss1: 0.373262 Loss2: 1.461328
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.718465 Loss1: 0.301491 Loss2: 1.416974
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.636667 Loss1: 0.220054 Loss2: 1.416613
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.605081 Loss1: 0.193132 Loss2: 1.411949
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.729925 Loss1: 0.857071 Loss2: 1.872854
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.528888 Loss1: 0.121018 Loss2: 1.407870
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.480760 Loss1: 0.091948 Loss2: 1.388812
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.048763 Loss1: 0.597215 Loss2: 1.451549
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460092 Loss1: 0.077671 Loss2: 1.382421
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.787274 Loss1: 0.370755 Loss2: 1.416519
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448610 Loss1: 0.071374 Loss2: 1.377236
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.628611 Loss1: 0.207975 Loss2: 1.420636
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.570782 Loss1: 0.177631 Loss2: 1.393151
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.529928 Loss1: 0.135380 Loss2: 1.394548
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.499715 Loss1: 0.122966 Loss2: 1.376749
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451228 Loss1: 0.078827 Loss2: 1.372401
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.758347 Loss1: 0.877909 Loss2: 1.880438
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408904 Loss1: 0.048489 Loss2: 1.360415
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435731 Loss1: 0.081244 Loss2: 1.354487 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.560543 Loss1: 0.198578 Loss2: 1.361965 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.457063 Loss1: 0.105497 Loss2: 1.351566 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.811598 Loss1: 0.906724 Loss2: 1.904874
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462870 Loss1: 0.112322 Loss2: 1.350548
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.893612 Loss1: 0.512516 Loss2: 1.381096
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415329 Loss1: 0.076942 Loss2: 1.338387
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.755926 Loss1: 0.333643 Loss2: 1.422283
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423823 Loss1: 0.090046 Loss2: 1.333778
+(DefaultActor pid=3764) >> Training accuracy: 0.979911
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567964 Loss1: 0.187062 Loss2: 1.380902 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.552216 Loss1: 0.181503 Loss2: 1.370713 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488906 Loss1: 0.109052 Loss2: 1.379854
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.381660 Loss1: 0.678753 Loss2: 1.702907
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.784918 Loss1: 0.474985 Loss2: 1.309933 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.574812 Loss1: 0.238682 Loss2: 1.336130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.454327 Loss1: 0.164281 Loss2: 1.290046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.363049 Loss1: 0.084733 Loss2: 1.278316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396634 Loss1: 0.131479 Loss2: 1.265155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.686944 Loss1: 0.311600 Loss2: 1.375344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554952 Loss1: 0.212010 Loss2: 1.342943 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975586
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475964 Loss1: 0.140057 Loss2: 1.335908 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422276 Loss1: 0.097487 Loss2: 1.324789 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408525 Loss1: 0.088308 Loss2: 1.320218
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.482616 Loss1: 0.637054 Loss2: 1.845563
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.839865 Loss1: 0.433636 Loss2: 1.406229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.641948 Loss1: 0.235575 Loss2: 1.406373 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523540 Loss1: 0.140317 Loss2: 1.383224 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494628 Loss1: 0.112880 Loss2: 1.381749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467987 Loss1: 0.087323 Loss2: 1.380663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580167 Loss1: 0.209586 Loss2: 1.370581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536010 Loss1: 0.160186 Loss2: 1.375824 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991728
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432240 Loss1: 0.078919 Loss2: 1.353322 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434335 Loss1: 0.087114 Loss2: 1.347221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.779298 Loss1: 0.869723 Loss2: 1.909575
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435738 Loss1: 0.091929 Loss2: 1.343809
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.771810 Loss1: 0.333749 Loss2: 1.438061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.606421 Loss1: 0.190266 Loss2: 1.416155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.577889 Loss1: 0.167265 Loss2: 1.410625
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.746893 Loss1: 0.928554 Loss2: 1.818339
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.536351 Loss1: 0.134789 Loss2: 1.401562
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.938478 Loss1: 0.539755 Loss2: 1.398723
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481937 Loss1: 0.085855 Loss2: 1.396082
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656300 Loss1: 0.265835 Loss2: 1.390465
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461608 Loss1: 0.076624 Loss2: 1.384984
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.571382 Loss1: 0.214227 Loss2: 1.357155
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453495 Loss1: 0.071861 Loss2: 1.381634
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521199 Loss1: 0.157066 Loss2: 1.364133
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502519 Loss1: 0.152115 Loss2: 1.350404
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474980 Loss1: 0.123752 Loss2: 1.351228
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.463873 Loss1: 0.118363 Loss2: 1.345510
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427956 Loss1: 0.082508 Loss2: 1.345448
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.652171 Loss1: 0.799010 Loss2: 1.853161
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405494 Loss1: 0.068134 Loss2: 1.337360
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.681754 Loss1: 0.295048 Loss2: 1.386706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558190 Loss1: 0.202673 Loss2: 1.355517 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481286 Loss1: 0.135165 Loss2: 1.346121
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.614372 Loss1: 0.753096 Loss2: 1.861275
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.840402 Loss1: 0.436107 Loss2: 1.404296 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.653696 Loss1: 0.237178 Loss2: 1.416518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583556 Loss1: 0.194996 Loss2: 1.388560 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 12:54:13,365 | server.py:236 | fit_round 114 received 50 results and 0 failures
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.590767 Loss1: 0.190646 Loss2: 1.400121 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466374 Loss1: 0.092258 Loss2: 1.374116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.886900 Loss1: 0.934332 Loss2: 1.952568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.975137 Loss1: 0.613978 Loss2: 1.361160 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.965820
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.683479 Loss1: 0.298916 Loss2: 1.384563 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.557674 Loss1: 0.187535 Loss2: 1.370140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458625 Loss1: 0.119488 Loss2: 1.339137 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.636966 Loss1: 0.777049 Loss2: 1.859917 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400543 Loss1: 0.074218 Loss2: 1.326325
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.812432 Loss1: 0.455369 Loss2: 1.357063
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.660898 Loss1: 0.259971 Loss2: 1.400927
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.494031 Loss1: 0.142496 Loss2: 1.351535
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475410 Loss1: 0.131309 Loss2: 1.344101
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451300 Loss1: 0.109752 Loss2: 1.341548
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.636243 Loss1: 0.834989 Loss2: 1.801254
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410505 Loss1: 0.076284 Loss2: 1.334221
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.791644 Loss1: 0.462304 Loss2: 1.329340
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424552 Loss1: 0.088481 Loss2: 1.336070
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.652331 Loss1: 0.303580 Loss2: 1.348751
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431644 Loss1: 0.099483 Loss2: 1.332161
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.614646 Loss1: 0.299005 Loss2: 1.315641
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414547 Loss1: 0.083741 Loss2: 1.330806
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474306 Loss1: 0.164169 Loss2: 1.310137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.384280 Loss1: 0.090636 Loss2: 1.293644 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.349569 Loss1: 0.069405 Loss2: 1.280164 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 12:54:13,365][flwr][DEBUG] - fit_round 114 received 50 results and 0 failures
+INFO flwr 2023-10-11 12:54:54,932 | server.py:125 | fit progress: (114, 2.199429733303789, {'accuracy': 0.5787}, 263002.71083863097)
+>> Test accuracy: 0.578700
+[2023-10-11 12:54:54,932][flwr][INFO] - fit progress: (114, 2.199429733303789, {'accuracy': 0.5787}, 263002.71083863097)
+DEBUG flwr 2023-10-11 12:54:54,933 | server.py:173 | evaluate_round 114: strategy sampled 50 clients (out of 50)
+[2023-10-11 12:54:54,933][flwr][DEBUG] - evaluate_round 114: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 13:03:59,677 | server.py:187 | evaluate_round 114 received 50 results and 0 failures
+[2023-10-11 13:03:59,677][flwr][DEBUG] - evaluate_round 114 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 13:03:59,677 | server.py:222 | fit_round 115: strategy sampled 50 clients (out of 50)
+[2023-10-11 13:03:59,677][flwr][DEBUG] - fit_round 115: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.706543 Loss1: 0.799641 Loss2: 1.906902
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.973225 Loss1: 0.529686 Loss2: 1.443540
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.828500 Loss1: 0.346591 Loss2: 1.481909
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.707411 Loss1: 0.835424 Loss2: 1.871987
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.739395 Loss1: 0.307880 Loss2: 1.431515
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.984538 Loss1: 0.575936 Loss2: 1.408602
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.619951 Loss1: 0.177834 Loss2: 1.442116
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.846611 Loss1: 0.390517 Loss2: 1.456094
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.561945 Loss1: 0.139712 Loss2: 1.422233
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631932 Loss1: 0.236767 Loss2: 1.395164
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529939 Loss1: 0.106633 Loss2: 1.423306
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477308 Loss1: 0.067980 Loss2: 1.409328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482842 Loss1: 0.080333 Loss2: 1.402509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457568 Loss1: 0.064713 Loss2: 1.392855 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.470887 Loss1: 0.098574 Loss2: 1.372313 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.784849 Loss1: 0.895917 Loss2: 1.888932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644984 Loss1: 0.286148 Loss2: 1.358836 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.681092 Loss1: 0.852407 Loss2: 1.828686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.784316 Loss1: 0.435526 Loss2: 1.348790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383794 Loss1: 0.090559 Loss2: 1.293235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382035 Loss1: 0.094873 Loss2: 1.287162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.349688 Loss1: 0.066426 Loss2: 1.283262 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.339198 Loss1: 0.062496 Loss2: 1.276701 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995192
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443588 Loss1: 0.115538 Loss2: 1.328051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409276 Loss1: 0.097101 Loss2: 1.312175 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386816 Loss1: 0.075242 Loss2: 1.311573
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.524501 Loss1: 0.643478 Loss2: 1.881023
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.844453 Loss1: 0.439519 Loss2: 1.404934
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.767602 Loss1: 0.310590 Loss2: 1.457012
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.612664 Loss1: 0.209875 Loss2: 1.402789
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.584357 Loss1: 0.178402 Loss2: 1.405955
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.644576 Loss1: 0.809240 Loss2: 1.835336
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.917530 Loss1: 0.560957 Loss2: 1.356573 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.766014 Loss1: 0.369101 Loss2: 1.396914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.613197 Loss1: 0.260089 Loss2: 1.353109 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522626 Loss1: 0.167179 Loss2: 1.355447 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.444829 Loss1: 0.069606 Loss2: 1.375223
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485909 Loss1: 0.150427 Loss2: 1.335482
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446810 Loss1: 0.110410 Loss2: 1.336400
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406678 Loss1: 0.076534 Loss2: 1.330144
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419796 Loss1: 0.098562 Loss2: 1.321234
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441685 Loss1: 0.115596 Loss2: 1.326089
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.872049 Loss1: 1.008672 Loss2: 1.863377
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.001344 Loss1: 0.591325 Loss2: 1.410019
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.756606 Loss1: 0.331848 Loss2: 1.424758
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.628149 Loss1: 0.243350 Loss2: 1.384799
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.584640 Loss1: 0.198079 Loss2: 1.386561
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.731190 Loss1: 0.879962 Loss2: 1.851228
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537742 Loss1: 0.167576 Loss2: 1.370166
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458211 Loss1: 0.097146 Loss2: 1.361065 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451155 Loss1: 0.090514 Loss2: 1.360642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447056 Loss1: 0.095289 Loss2: 1.351767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451681 Loss1: 0.103664 Loss2: 1.348017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419115 Loss1: 0.081980 Loss2: 1.337135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381501 Loss1: 0.056214 Loss2: 1.325287 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391489 Loss1: 0.066106 Loss2: 1.325383
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.634072 Loss1: 0.756471 Loss2: 1.877601
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.918810 Loss1: 0.514632 Loss2: 1.404178
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.754066 Loss1: 0.286907 Loss2: 1.467159
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.600826 Loss1: 0.203942 Loss2: 1.396884
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.580067 Loss1: 0.178344 Loss2: 1.401723
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.688068 Loss1: 0.815707 Loss2: 1.872361
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.995121 Loss1: 0.561530 Loss2: 1.433592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.748036 Loss1: 0.298369 Loss2: 1.449666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.698209 Loss1: 0.282982 Loss2: 1.415227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.620752 Loss1: 0.194562 Loss2: 1.426190 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.511488 Loss1: 0.111104 Loss2: 1.400383 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.469788 Loss1: 0.080190 Loss2: 1.389598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458029 Loss1: 0.078156 Loss2: 1.379873
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.856198 Loss1: 0.885784 Loss2: 1.970413
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.062555 Loss1: 0.569351 Loss2: 1.493204
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.872825 Loss1: 0.358653 Loss2: 1.514172
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.717449 Loss1: 0.243176 Loss2: 1.474273
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.674638 Loss1: 0.204496 Loss2: 1.470142
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.835296 Loss1: 0.982002 Loss2: 1.853293
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.597890 Loss1: 0.138318 Loss2: 1.459572
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.579468 Loss1: 0.125394 Loss2: 1.454074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.560963 Loss1: 0.110150 Loss2: 1.450814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.566696 Loss1: 0.118075 Loss2: 1.448621 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.552427 Loss1: 0.102260 Loss2: 1.450166 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.462083 Loss1: 0.127528 Loss2: 1.334554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443865 Loss1: 0.112322 Loss2: 1.331543 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406400 Loss1: 0.080159 Loss2: 1.326240
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.785225 Loss1: 0.900530 Loss2: 1.884694
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.861669 Loss1: 0.470981 Loss2: 1.390688
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.722735 Loss1: 0.320969 Loss2: 1.401766
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.648707 Loss1: 0.275219 Loss2: 1.373488
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567673 Loss1: 0.187787 Loss2: 1.379886
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.767630 Loss1: 0.847711 Loss2: 1.919919
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530894 Loss1: 0.158557 Loss2: 1.372337
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466159 Loss1: 0.105271 Loss2: 1.360887 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426892 Loss1: 0.075483 Loss2: 1.351409 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399833 Loss1: 0.056053 Loss2: 1.343780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420175 Loss1: 0.078155 Loss2: 1.342020 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457281 Loss1: 0.091452 Loss2: 1.365829 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.476215 Loss1: 0.114575 Loss2: 1.361640 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.942758 Loss1: 0.536212 Loss2: 1.406546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580959 Loss1: 0.197810 Loss2: 1.383148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.557022 Loss1: 0.177063 Loss2: 1.379958
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.729932 Loss1: 0.869213 Loss2: 1.860720
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520596 Loss1: 0.150913 Loss2: 1.369682
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.867420 Loss1: 0.463927 Loss2: 1.403493
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486126 Loss1: 0.120970 Loss2: 1.365156
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.686892 Loss1: 0.251496 Loss2: 1.435395
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488716 Loss1: 0.130220 Loss2: 1.358496
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.641906 Loss1: 0.254292 Loss2: 1.387614
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.466046 Loss1: 0.106747 Loss2: 1.359299
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568494 Loss1: 0.173326 Loss2: 1.395168
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428513 Loss1: 0.074666 Loss2: 1.353847
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.563997 Loss1: 0.188543 Loss2: 1.375455
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518475 Loss1: 0.140271 Loss2: 1.378203
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484735 Loss1: 0.109988 Loss2: 1.374747
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501238 Loss1: 0.128593 Loss2: 1.372645
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458340 Loss1: 0.088036 Loss2: 1.370303
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.594256 Loss1: 0.727575 Loss2: 1.866680
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.819046 Loss1: 0.429073 Loss2: 1.389973
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.749645 Loss1: 0.304867 Loss2: 1.444779
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.644293 Loss1: 0.248760 Loss2: 1.395533
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.584720 Loss1: 0.186275 Loss2: 1.398445
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.531491 Loss1: 0.142345 Loss2: 1.389146 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.558034 Loss1: 0.169332 Loss2: 1.388702 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.527521 Loss1: 0.130248 Loss2: 1.397273 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.485839 Loss1: 0.110485 Loss2: 1.375353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477506 Loss1: 0.098321 Loss2: 1.379185 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405488 Loss1: 0.046575 Loss2: 1.358913 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411393 Loss1: 0.067788 Loss2: 1.343606 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.866648 Loss1: 0.455589 Loss2: 1.411059 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.646270 Loss1: 0.240399 Loss2: 1.405871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.545963 Loss1: 0.143533 Loss2: 1.402430
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.601943 Loss1: 0.721115 Loss2: 1.880828
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471368 Loss1: 0.098387 Loss2: 1.372981
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.858997 Loss1: 0.431890 Loss2: 1.427106
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466785 Loss1: 0.098887 Loss2: 1.367898
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.785332 Loss1: 0.294120 Loss2: 1.491212
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.692454 Loss1: 0.268380 Loss2: 1.424075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.647560 Loss1: 0.196530 Loss2: 1.451030 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426629 Loss1: 0.068352 Loss2: 1.358277
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.669902 Loss1: 0.243841 Loss2: 1.426061
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.605519 Loss1: 0.166134 Loss2: 1.439385
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.545915 Loss1: 0.132661 Loss2: 1.413254
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.498769 Loss1: 0.096831 Loss2: 1.401939
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470847 Loss1: 0.076414 Loss2: 1.394432
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.686903 Loss1: 0.852613 Loss2: 1.834290
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.807465 Loss1: 0.441003 Loss2: 1.366462
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.704972 Loss1: 0.303018 Loss2: 1.401954
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.634234 Loss1: 0.279305 Loss2: 1.354929
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.607436 Loss1: 0.235799 Loss2: 1.371637
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.570957 Loss1: 0.742686 Loss2: 1.828271
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.864862 Loss1: 0.506822 Loss2: 1.358040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.680315 Loss1: 0.295443 Loss2: 1.384872 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.547022 Loss1: 0.201161 Loss2: 1.345861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483049 Loss1: 0.137573 Loss2: 1.345477 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474414 Loss1: 0.142786 Loss2: 1.331628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427023 Loss1: 0.099957 Loss2: 1.327066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386034 Loss1: 0.063505 Loss2: 1.322530 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.920220 Loss1: 0.484483 Loss2: 1.435737 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594996 Loss1: 0.184635 Loss2: 1.410361 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536036 Loss1: 0.132175 Loss2: 1.403860
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.799689 Loss1: 0.902542 Loss2: 1.897147
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.932008 Loss1: 0.495360 Loss2: 1.436649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769779 Loss1: 0.321610 Loss2: 1.448169 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.645266 Loss1: 0.226744 Loss2: 1.418522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.631476 Loss1: 0.215687 Loss2: 1.415789 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450597 Loss1: 0.071140 Loss2: 1.379456
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.571642 Loss1: 0.159525 Loss2: 1.412116
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502363 Loss1: 0.098782 Loss2: 1.403581
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462764 Loss1: 0.072351 Loss2: 1.390414
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438293 Loss1: 0.059153 Loss2: 1.379140
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428508 Loss1: 0.056014 Loss2: 1.372494
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.734325 Loss1: 0.832615 Loss2: 1.901710
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.882507 Loss1: 0.473114 Loss2: 1.409393
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.757257 Loss1: 0.312865 Loss2: 1.444391
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.631970 Loss1: 0.236526 Loss2: 1.395444
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.550448 Loss1: 0.148572 Loss2: 1.401875
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.900982 Loss1: 0.901793 Loss2: 1.999189
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.950092 Loss1: 0.575578 Loss2: 1.374514
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564601 Loss1: 0.175738 Loss2: 1.388863
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.780060 Loss1: 0.341420 Loss2: 1.438640
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453099 Loss1: 0.086558 Loss2: 1.366541 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455724 Loss1: 0.092325 Loss2: 1.363399 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.564187 Loss1: 0.184761 Loss2: 1.379426 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445254 Loss1: 0.082083 Loss2: 1.363171 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.470888 Loss1: 0.625987 Loss2: 1.844902 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.688195 Loss1: 0.258528 Loss2: 1.429667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.625444 Loss1: 0.246599 Loss2: 1.378846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593336 Loss1: 0.204758 Loss2: 1.388579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.581222 Loss1: 0.193379 Loss2: 1.387843 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491777 Loss1: 0.109311 Loss2: 1.382466 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483707 Loss1: 0.112429 Loss2: 1.371278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.557537 Loss1: 0.163458 Loss2: 1.394079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540045 Loss1: 0.150073 Loss2: 1.389972 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462911 Loss1: 0.088990 Loss2: 1.373922 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.493429 Loss1: 0.657171 Loss2: 1.836258
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.753590 Loss1: 0.378370 Loss2: 1.375220
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.625937 Loss1: 0.214684 Loss2: 1.411253
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.603461 Loss1: 0.735885 Loss2: 1.867576
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569567 Loss1: 0.211242 Loss2: 1.358325
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.879531 Loss1: 0.499805 Loss2: 1.379726
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519458 Loss1: 0.148024 Loss2: 1.371435
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472581 Loss1: 0.106963 Loss2: 1.365618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486204 Loss1: 0.132615 Loss2: 1.353589 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483930 Loss1: 0.128664 Loss2: 1.355266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452852 Loss1: 0.101469 Loss2: 1.351383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446248 Loss1: 0.089670 Loss2: 1.356577 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460875 Loss1: 0.105126 Loss2: 1.355749 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.797192 Loss1: 0.932270 Loss2: 1.864922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.758018 Loss1: 0.342866 Loss2: 1.415153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629037 Loss1: 0.230368 Loss2: 1.398669
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.734491 Loss1: 0.905626 Loss2: 1.828865
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.982402 Loss1: 0.607845 Loss2: 1.374557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.735199 Loss1: 0.345598 Loss2: 1.389601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562746 Loss1: 0.214053 Loss2: 1.348693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503595 Loss1: 0.156411 Loss2: 1.347185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474352 Loss1: 0.138026 Loss2: 1.336326 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406161 Loss1: 0.056938 Loss2: 1.349223
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.466386 Loss1: 0.127442 Loss2: 1.338944
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446000 Loss1: 0.112272 Loss2: 1.333728
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397925 Loss1: 0.074521 Loss2: 1.323405
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378016 Loss1: 0.061195 Loss2: 1.316820
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.652248 Loss1: 0.809602 Loss2: 1.842646
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.941810 Loss1: 0.525833 Loss2: 1.415977
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684567 Loss1: 0.258280 Loss2: 1.426286
+(DefaultActor pid=3764) Epoch: 0 Loss: 3.027996 Loss1: 1.042986 Loss2: 1.985010
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528313 Loss1: 0.158790 Loss2: 1.369524
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.057008 Loss1: 0.569313 Loss2: 1.487695
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480060 Loss1: 0.110352 Loss2: 1.369707
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.467685 Loss1: 0.109041 Loss2: 1.358644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484975 Loss1: 0.123427 Loss2: 1.361547 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461501 Loss1: 0.102116 Loss2: 1.359385 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493127 Loss1: 0.139860 Loss2: 1.353267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431088 Loss1: 0.078215 Loss2: 1.352873 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.502566 Loss1: 0.083288 Loss2: 1.419277 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.542877 Loss1: 0.755750 Loss2: 1.787126
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.869050 Loss1: 0.498074 Loss2: 1.370976
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.675081 Loss1: 0.284412 Loss2: 1.390669
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604623 Loss1: 0.251153 Loss2: 1.353470
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.673859 Loss1: 0.763597 Loss2: 1.910261
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.528263 Loss1: 0.172465 Loss2: 1.355798
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.865894 Loss1: 0.457027 Loss2: 1.408867
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.783437 Loss1: 0.342005 Loss2: 1.441431
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489738 Loss1: 0.144949 Loss2: 1.344789
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.623410 Loss1: 0.212059 Loss2: 1.411351
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490595 Loss1: 0.145912 Loss2: 1.344683
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.600515 Loss1: 0.191930 Loss2: 1.408585
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467348 Loss1: 0.127058 Loss2: 1.340290
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528468 Loss1: 0.126000 Loss2: 1.402467
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434781 Loss1: 0.095565 Loss2: 1.339216
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393204 Loss1: 0.065979 Loss2: 1.327225 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467536 Loss1: 0.084982 Loss2: 1.382555 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.586153 Loss1: 0.720480 Loss2: 1.865673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716360 Loss1: 0.291581 Loss2: 1.424779 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.641648 Loss1: 0.740899 Loss2: 1.900749
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.631068 Loss1: 0.244118 Loss2: 1.386949
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.868881 Loss1: 0.471339 Loss2: 1.397542
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.542393 Loss1: 0.143548 Loss2: 1.398844
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.807977 Loss1: 0.362933 Loss2: 1.445045
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551957 Loss1: 0.175402 Loss2: 1.376555
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.691375 Loss1: 0.282455 Loss2: 1.408920
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.499021 Loss1: 0.123348 Loss2: 1.375673
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488140 Loss1: 0.115617 Loss2: 1.372523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429497 Loss1: 0.070869 Loss2: 1.358627 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443084 Loss1: 0.081127 Loss2: 1.361957 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462536 Loss1: 0.083725 Loss2: 1.378811 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.544555 Loss1: 0.589251 Loss2: 1.955304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.862796 Loss1: 0.363781 Loss2: 1.499015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.793698 Loss1: 0.339525 Loss2: 1.454173
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.861724 Loss1: 0.934062 Loss2: 1.927662
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.655342 Loss1: 0.200343 Loss2: 1.454999
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.979636 Loss1: 0.577193 Loss2: 1.402443
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.812403 Loss1: 0.349054 Loss2: 1.463349
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.606271 Loss1: 0.164070 Loss2: 1.442201
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.620213 Loss1: 0.224922 Loss2: 1.395291
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.616420 Loss1: 0.177206 Loss2: 1.439214
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.567287 Loss1: 0.173802 Loss2: 1.393485
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.553415 Loss1: 0.114132 Loss2: 1.439283
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.485343 Loss1: 0.057986 Loss2: 1.427357 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.523117 Loss1: 0.105629 Loss2: 1.417488 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446680 Loss1: 0.087671 Loss2: 1.359009 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.675022 Loss1: 0.880121 Loss2: 1.794902 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.729599 Loss1: 0.352861 Loss2: 1.376738 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.643935 Loss1: 0.297817 Loss2: 1.346117
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.543431 Loss1: 0.741499 Loss2: 1.801933
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.523442 Loss1: 0.183071 Loss2: 1.340370
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.777629 Loss1: 0.455672 Loss2: 1.321956
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.464103 Loss1: 0.136420 Loss2: 1.327682
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.628954 Loss1: 0.260613 Loss2: 1.368342
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450958 Loss1: 0.134219 Loss2: 1.316740
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.522978 Loss1: 0.211613 Loss2: 1.311366
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446616 Loss1: 0.128444 Loss2: 1.318172
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476399 Loss1: 0.153735 Loss2: 1.322664
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391168 Loss1: 0.079107 Loss2: 1.312061
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.399740 Loss1: 0.095597 Loss2: 1.304143
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366563 Loss1: 0.060310 Loss2: 1.306253
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.398334 Loss1: 0.099703 Loss2: 1.298631
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.355128 Loss1: 0.054126 Loss2: 1.301002
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.333798 Loss1: 0.043201 Loss2: 1.290597
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.341263 Loss1: 0.054174 Loss2: 1.287088
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.816547 Loss1: 0.876904 Loss2: 1.939643
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.040250 Loss1: 0.594000 Loss2: 1.446250
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.803885 Loss1: 0.366578 Loss2: 1.437307
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.621085 Loss1: 0.223185 Loss2: 1.397900
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.758792 Loss1: 0.892585 Loss2: 1.866207
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.539868 Loss1: 0.143657 Loss2: 1.396211
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.907465 Loss1: 0.528555 Loss2: 1.378910
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.532612 Loss1: 0.137200 Loss2: 1.395412
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.721958 Loss1: 0.301640 Loss2: 1.420318
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503660 Loss1: 0.115624 Loss2: 1.388036
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.663120 Loss1: 0.304820 Loss2: 1.358301
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462734 Loss1: 0.087587 Loss2: 1.375147
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.593229 Loss1: 0.208617 Loss2: 1.384611
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436513 Loss1: 0.062908 Loss2: 1.373605
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.532114 Loss1: 0.170467 Loss2: 1.361647
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429797 Loss1: 0.064905 Loss2: 1.364891
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467179 Loss1: 0.111123 Loss2: 1.356057
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433542 Loss1: 0.087860 Loss2: 1.345682
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401111 Loss1: 0.066959 Loss2: 1.334151
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397176 Loss1: 0.066475 Loss2: 1.330702
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.491092 Loss1: 0.711156 Loss2: 1.779936
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.737305 Loss1: 0.385033 Loss2: 1.352272
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.637091 Loss1: 0.264348 Loss2: 1.372743
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.647490 Loss1: 0.805040 Loss2: 1.842450
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524352 Loss1: 0.174576 Loss2: 1.349777
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.051488 Loss1: 0.645728 Loss2: 1.405760
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514795 Loss1: 0.171729 Loss2: 1.343066
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.865808 Loss1: 0.408346 Loss2: 1.457462
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514500 Loss1: 0.170972 Loss2: 1.343528
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.740538 Loss1: 0.358282 Loss2: 1.382257
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.519273 Loss1: 0.167633 Loss2: 1.351640
+DEBUG flwr 2023-10-11 13:33:09,447 | server.py:236 | fit_round 115 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477922 Loss1: 0.134040 Loss2: 1.343882 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499704 Loss1: 0.161028 Loss2: 1.338676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443907 Loss1: 0.107613 Loss2: 1.336294 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973633
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449889 Loss1: 0.093940 Loss2: 1.355949 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.576477 Loss1: 0.737924 Loss2: 1.838553 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.646444 Loss1: 0.229756 Loss2: 1.416689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.615592 Loss1: 0.785595 Loss2: 1.829997
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555047 Loss1: 0.188152 Loss2: 1.366895
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.823690 Loss1: 0.462387 Loss2: 1.361303
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547651 Loss1: 0.177954 Loss2: 1.369697
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.707280 Loss1: 0.312956 Loss2: 1.394323
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.512733 Loss1: 0.144397 Loss2: 1.368336
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604061 Loss1: 0.242948 Loss2: 1.361113
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500855 Loss1: 0.139781 Loss2: 1.361074
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.475788 Loss1: 0.110255 Loss2: 1.365533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477916 Loss1: 0.122566 Loss2: 1.355350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.477433 Loss1: 0.120207 Loss2: 1.357226 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423772 Loss1: 0.090840 Loss2: 1.332931 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.659897 Loss1: 0.812618 Loss2: 1.847279 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.785663 Loss1: 0.379800 Loss2: 1.405863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.650685 Loss1: 0.272981 Loss2: 1.377704
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.812332 Loss1: 0.886143 Loss2: 1.926189
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.923248 Loss1: 0.524690 Loss2: 1.398559
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.575856 Loss1: 0.199180 Loss2: 1.376676
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463368 Loss1: 0.106587 Loss2: 1.356781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445660 Loss1: 0.099399 Loss2: 1.346261 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409027 Loss1: 0.067998 Loss2: 1.341028 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406788 Loss1: 0.068168 Loss2: 1.338620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374314 Loss1: 0.046891 Loss2: 1.327422 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472171 Loss1: 0.099427 Loss2: 1.372743 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978365
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 13:33:09,447][flwr][DEBUG] - fit_round 115 received 50 results and 0 failures
+INFO flwr 2023-10-11 13:33:50,426 | server.py:125 | fit progress: (115, 2.1888895084301883, {'accuracy': 0.5787}, 265338.20431725896)
+>> Test accuracy: 0.578700
+[2023-10-11 13:33:50,426][flwr][INFO] - fit progress: (115, 2.1888895084301883, {'accuracy': 0.5787}, 265338.20431725896)
+DEBUG flwr 2023-10-11 13:33:50,426 | server.py:173 | evaluate_round 115: strategy sampled 50 clients (out of 50)
+[2023-10-11 13:33:50,426][flwr][DEBUG] - evaluate_round 115: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 13:42:55,317 | server.py:187 | evaluate_round 115 received 50 results and 0 failures
+[2023-10-11 13:42:55,317][flwr][DEBUG] - evaluate_round 115 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 13:42:55,318 | server.py:222 | fit_round 116: strategy sampled 50 clients (out of 50)
+[2023-10-11 13:42:55,318][flwr][DEBUG] - fit_round 116: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.906143 Loss1: 0.907155 Loss2: 1.998989
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.946148 Loss1: 0.578437 Loss2: 1.367711
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.813106 Loss1: 0.381679 Loss2: 1.431427
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.650857 Loss1: 0.247068 Loss2: 1.403789
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.623057 Loss1: 0.258060 Loss2: 1.364997
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551842 Loss1: 0.177523 Loss2: 1.374319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529006 Loss1: 0.164012 Loss2: 1.364995 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500520 Loss1: 0.147403 Loss2: 1.353118
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682597 Loss1: 0.276488 Loss2: 1.406109
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472270 Loss1: 0.110440 Loss2: 1.361830
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576947 Loss1: 0.222620 Loss2: 1.354327
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521167 Loss1: 0.166698 Loss2: 1.354468 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.429173 Loss1: 0.090722 Loss2: 1.338450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414879 Loss1: 0.080673 Loss2: 1.334206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403497 Loss1: 0.073032 Loss2: 1.330465 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.786802 Loss1: 0.368635 Loss2: 1.418167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.608275 Loss1: 0.236921 Loss2: 1.371354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551689 Loss1: 0.199494 Loss2: 1.352195
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.655852 Loss1: 0.796243 Loss2: 1.859609
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.832066 Loss1: 0.448205 Loss2: 1.383861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.765758 Loss1: 0.330871 Loss2: 1.434887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640294 Loss1: 0.268875 Loss2: 1.371419 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563155 Loss1: 0.168741 Loss2: 1.394414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.429675 Loss1: 0.069190 Loss2: 1.360485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400586 Loss1: 0.052198 Loss2: 1.348388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405677 Loss1: 0.063293 Loss2: 1.342384 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.711623 Loss1: 0.290822 Loss2: 1.420801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521616 Loss1: 0.130871 Loss2: 1.390745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.651928 Loss1: 0.725951 Loss2: 1.925977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.007794 Loss1: 0.591866 Loss2: 1.415928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.748248 Loss1: 0.278738 Loss2: 1.469510 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.638270 Loss1: 0.226578 Loss2: 1.411692 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.564428 Loss1: 0.150738 Loss2: 1.413690 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485482 Loss1: 0.077757 Loss2: 1.407725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.486164 Loss1: 0.709748 Loss2: 1.776416 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.775791 Loss1: 0.447399 Loss2: 1.328392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542612 Loss1: 0.207739 Loss2: 1.334874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.447377 Loss1: 0.130041 Loss2: 1.317336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409695 Loss1: 0.104389 Loss2: 1.305306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.402999 Loss1: 0.096180 Loss2: 1.306819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.359604 Loss1: 0.059561 Loss2: 1.300043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361228 Loss1: 0.066140 Loss2: 1.295088 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443049 Loss1: 0.096155 Loss2: 1.346895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442564 Loss1: 0.102516 Loss2: 1.340048 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.667056 Loss1: 0.826016 Loss2: 1.841040 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.699489 Loss1: 0.290060 Loss2: 1.409429 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587957 Loss1: 0.213274 Loss2: 1.374682 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530608 Loss1: 0.171749 Loss2: 1.358858
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.633628 Loss1: 0.738329 Loss2: 1.895299
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.847694 Loss1: 0.461692 Loss2: 1.386002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.835026 Loss1: 0.376868 Loss2: 1.458158 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.703043 Loss1: 0.328512 Loss2: 1.374531 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.645663 Loss1: 0.248838 Loss2: 1.396825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513276 Loss1: 0.142005 Loss2: 1.371271 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412047 Loss1: 0.060577 Loss2: 1.351470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.510568 Loss1: 0.659932 Loss2: 1.850636
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403048 Loss1: 0.061859 Loss2: 1.341189
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.800005 Loss1: 0.340565 Loss2: 1.459440 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.607970 Loss1: 0.188828 Loss2: 1.419143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.561427 Loss1: 0.161534 Loss2: 1.399893
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.618448 Loss1: 0.662465 Loss2: 1.955983
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.960737 Loss1: 0.514305 Loss2: 1.446432 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469099 Loss1: 0.081034 Loss2: 1.388065
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.768286 Loss1: 0.262877 Loss2: 1.505408
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434643 Loss1: 0.055436 Loss2: 1.379207
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.737307 Loss1: 0.282009 Loss2: 1.455298
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454321 Loss1: 0.084244 Loss2: 1.370078
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.658933 Loss1: 0.200134 Loss2: 1.458798
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.612378 Loss1: 0.163717 Loss2: 1.448662
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.565849 Loss1: 0.120061 Loss2: 1.445788
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.593323 Loss1: 0.150341 Loss2: 1.442982
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.600266 Loss1: 0.158800 Loss2: 1.441466
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.534948 Loss1: 0.093800 Loss2: 1.441148
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.553920 Loss1: 0.756984 Loss2: 1.796936
+(DefaultActor pid=3764) >> Training accuracy: 0.962500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.770356 Loss1: 0.435693 Loss2: 1.334663
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655330 Loss1: 0.275427 Loss2: 1.379903
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488823 Loss1: 0.162589 Loss2: 1.326234
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.500506 Loss1: 0.179400 Loss2: 1.321106
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.508403 Loss1: 0.645774 Loss2: 1.862629
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478698 Loss1: 0.152776 Loss2: 1.325922
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455470 Loss1: 0.131813 Loss2: 1.323657
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.922021 Loss1: 0.512889 Loss2: 1.409132
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435742 Loss1: 0.116766 Loss2: 1.318975
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.724880 Loss1: 0.256727 Loss2: 1.468153
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424663 Loss1: 0.111473 Loss2: 1.313189
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.685219 Loss1: 0.275945 Loss2: 1.409274
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407336 Loss1: 0.091900 Loss2: 1.315436
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.703147 Loss1: 0.277868 Loss2: 1.425280
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.667207 Loss1: 0.252869 Loss2: 1.414338
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.576737 Loss1: 0.158500 Loss2: 1.418237
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.575723 Loss1: 0.174081 Loss2: 1.401642
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.525798 Loss1: 0.122202 Loss2: 1.403596
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.701300 Loss1: 0.848624 Loss2: 1.852676
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.869923 Loss1: 0.494953 Loss2: 1.374970 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578765 Loss1: 0.218024 Loss2: 1.360741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484278 Loss1: 0.121610 Loss2: 1.362668 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446473 Loss1: 0.094259 Loss2: 1.352214 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425733 Loss1: 0.080860 Loss2: 1.344873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477343 Loss1: 0.127986 Loss2: 1.349357 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427836 Loss1: 0.076594 Loss2: 1.351242 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.544297 Loss1: 0.145673 Loss2: 1.398624 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493958 Loss1: 0.110069 Loss2: 1.383889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460636 Loss1: 0.082377 Loss2: 1.378259
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.635285 Loss1: 0.851794 Loss2: 1.783491
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.789548 Loss1: 0.450429 Loss2: 1.339119
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.680920 Loss1: 0.310924 Loss2: 1.369997
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.615798 Loss1: 0.286638 Loss2: 1.329160
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.550820 Loss1: 0.212349 Loss2: 1.338471
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.660704 Loss1: 0.778330 Loss2: 1.882374
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.531529 Loss1: 0.206775 Loss2: 1.324754
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.865152 Loss1: 0.480224 Loss2: 1.384928
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.497219 Loss1: 0.171321 Loss2: 1.325898
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684719 Loss1: 0.291652 Loss2: 1.393067
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454586 Loss1: 0.136783 Loss2: 1.317803
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592966 Loss1: 0.238304 Loss2: 1.354662
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431530 Loss1: 0.113635 Loss2: 1.317895
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.480297 Loss1: 0.128930 Loss2: 1.351368
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411355 Loss1: 0.107322 Loss2: 1.304033
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434447 Loss1: 0.098581 Loss2: 1.335866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390482 Loss1: 0.061711 Loss2: 1.328771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.371784 Loss1: 0.045948 Loss2: 1.325835
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.562258 Loss1: 0.727144 Loss2: 1.835113
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.871267 Loss1: 0.501509 Loss2: 1.369759
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.768372 Loss1: 0.342450 Loss2: 1.425922
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.686091 Loss1: 0.303791 Loss2: 1.382301
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.599935 Loss1: 0.216157 Loss2: 1.383778
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538356 Loss1: 0.172688 Loss2: 1.365667
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.928969 Loss1: 0.983294 Loss2: 1.945675
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529773 Loss1: 0.173750 Loss2: 1.356024
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.080188 Loss1: 0.619494 Loss2: 1.460695
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.496773 Loss1: 0.134119 Loss2: 1.362655
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.870652 Loss1: 0.379318 Loss2: 1.491334
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465187 Loss1: 0.109552 Loss2: 1.355635
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.754738 Loss1: 0.309361 Loss2: 1.445376
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448095 Loss1: 0.099042 Loss2: 1.349053
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.674177 Loss1: 0.218153 Loss2: 1.456023
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.572360 Loss1: 0.146121 Loss2: 1.426239
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.526671 Loss1: 0.100443 Loss2: 1.426228
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.492110 Loss1: 0.073986 Loss2: 1.418124
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485544 Loss1: 0.074973 Loss2: 1.410571
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472723 Loss1: 0.071667 Loss2: 1.401056
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.571861 Loss1: 0.757691 Loss2: 1.814171
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.912784 Loss1: 0.539654 Loss2: 1.373130
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.762376 Loss1: 0.361322 Loss2: 1.401055
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649335 Loss1: 0.283960 Loss2: 1.365375
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524031 Loss1: 0.166460 Loss2: 1.357571
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462691 Loss1: 0.126524 Loss2: 1.336167
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.710974 Loss1: 0.822220 Loss2: 1.888754
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428128 Loss1: 0.095969 Loss2: 1.332159
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.964372 Loss1: 0.533590 Loss2: 1.430782
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417461 Loss1: 0.088709 Loss2: 1.328752
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.861786 Loss1: 0.375645 Loss2: 1.486141
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398444 Loss1: 0.075046 Loss2: 1.323399
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.714248 Loss1: 0.287867 Loss2: 1.426381
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370335 Loss1: 0.052704 Loss2: 1.317631
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.625644 Loss1: 0.194789 Loss2: 1.430855
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.586960 Loss1: 0.169375 Loss2: 1.417586
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504427 Loss1: 0.090719 Loss2: 1.413708
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518204 Loss1: 0.114303 Loss2: 1.403901
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.514194 Loss1: 0.110235 Loss2: 1.403959
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.480122 Loss1: 0.082404 Loss2: 1.397718
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.644557 Loss1: 0.824895 Loss2: 1.819662
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.801972 Loss1: 0.445137 Loss2: 1.356835
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.668891 Loss1: 0.277209 Loss2: 1.391682
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513556 Loss1: 0.171442 Loss2: 1.342115
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.489293 Loss1: 0.148055 Loss2: 1.341238
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.467162 Loss1: 0.124797 Loss2: 1.342365
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.571294 Loss1: 0.676843 Loss2: 1.894450
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476621 Loss1: 0.145946 Loss2: 1.330675
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.994924 Loss1: 0.519149 Loss2: 1.475776
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477349 Loss1: 0.142913 Loss2: 1.334436
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.760041 Loss1: 0.260131 Loss2: 1.499910
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612278 Loss1: 0.177591 Loss2: 1.434687 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.958333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440616 Loss1: 0.110167 Loss2: 1.330449
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.650522 Loss1: 0.202906 Loss2: 1.447616
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.602971 Loss1: 0.165141 Loss2: 1.437830
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533018 Loss1: 0.104745 Loss2: 1.428272
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.554421 Loss1: 0.123641 Loss2: 1.430780
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485335 Loss1: 0.060283 Loss2: 1.425052
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.687279 Loss1: 0.772140 Loss2: 1.915139
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.910777 Loss1: 0.486647 Loss2: 1.424129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.676742 Loss1: 0.264600 Loss2: 1.412141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562584 Loss1: 0.159797 Loss2: 1.402788 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525161 Loss1: 0.127350 Loss2: 1.397811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484393 Loss1: 0.091423 Loss2: 1.392970 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488122 Loss1: 0.098993 Loss2: 1.389129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.517792 Loss1: 0.125748 Loss2: 1.392044 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458030 Loss1: 0.136267 Loss2: 1.321764 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434064 Loss1: 0.130392 Loss2: 1.303672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.798477 Loss1: 0.892885 Loss2: 1.905592
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409264 Loss1: 0.105040 Loss2: 1.304224
+(DefaultActor pid=3764) >> Training accuracy: 0.985491
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.760049 Loss1: 0.325553 Loss2: 1.434496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.552154 Loss1: 0.179142 Loss2: 1.373012 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542826 Loss1: 0.173553 Loss2: 1.369273
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.645646 Loss1: 0.787828 Loss2: 1.857818
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831101 Loss1: 0.417142 Loss2: 1.413959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.621712 Loss1: 0.196463 Loss2: 1.425249 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567074 Loss1: 0.170442 Loss2: 1.396632 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535593 Loss1: 0.144617 Loss2: 1.390977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.522497 Loss1: 0.131778 Loss2: 1.390719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460164 Loss1: 0.078830 Loss2: 1.381335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430405 Loss1: 0.057169 Loss2: 1.373236 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.780300 Loss1: 0.371993 Loss2: 1.408308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562492 Loss1: 0.187906 Loss2: 1.374585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520132 Loss1: 0.147495 Loss2: 1.372637
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.632309 Loss1: 0.784671 Loss2: 1.847638
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.822812 Loss1: 0.443867 Loss2: 1.378945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.742976 Loss1: 0.318572 Loss2: 1.424405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.667129 Loss1: 0.286758 Loss2: 1.380371 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.464029 Loss1: 0.114350 Loss2: 1.349679
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.616563 Loss1: 0.221764 Loss2: 1.394799
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527124 Loss1: 0.153842 Loss2: 1.373282
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464710 Loss1: 0.094022 Loss2: 1.370688
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434360 Loss1: 0.069620 Loss2: 1.364741
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424999 Loss1: 0.068976 Loss2: 1.356023
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.671257 Loss1: 0.840121 Loss2: 1.831136
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422804 Loss1: 0.069273 Loss2: 1.353531
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.761526 Loss1: 0.359914 Loss2: 1.401613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572385 Loss1: 0.199074 Loss2: 1.373310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.535473 Loss1: 0.171440 Loss2: 1.364033
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.531611 Loss1: 0.751089 Loss2: 1.780522
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.802278 Loss1: 0.469846 Loss2: 1.332432 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.708267 Loss1: 0.356174 Loss2: 1.352093 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.607577 Loss1: 0.277166 Loss2: 1.330411 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427961 Loss1: 0.080846 Loss2: 1.347114
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542167 Loss1: 0.228514 Loss2: 1.313654
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495666 Loss1: 0.175312 Loss2: 1.320354
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.421304 Loss1: 0.114555 Loss2: 1.306749
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416498 Loss1: 0.115878 Loss2: 1.300620
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395030 Loss1: 0.101610 Loss2: 1.293420
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.689188 Loss1: 0.863210 Loss2: 1.825978
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364842 Loss1: 0.074106 Loss2: 1.290736
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.704884 Loss1: 0.291161 Loss2: 1.413724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526184 Loss1: 0.159122 Loss2: 1.367062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.423375 Loss1: 0.612750 Loss2: 1.810624 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.789123 Loss1: 0.461357 Loss2: 1.327766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633333 Loss1: 0.269176 Loss2: 1.364157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529834 Loss1: 0.198164 Loss2: 1.331670 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512258 Loss1: 0.186701 Loss2: 1.325557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.547200 Loss1: 0.227139 Loss2: 1.320061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391224 Loss1: 0.072504 Loss2: 1.318720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364780 Loss1: 0.058230 Loss2: 1.306550 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.735900 Loss1: 0.308261 Loss2: 1.427639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567403 Loss1: 0.198553 Loss2: 1.368850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.509814 Loss1: 0.772631 Loss2: 1.737183 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.792215 Loss1: 0.459891 Loss2: 1.332324 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.594348 Loss1: 0.242899 Loss2: 1.351449 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431037 Loss1: 0.094044 Loss2: 1.336993 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452316 Loss1: 0.141394 Loss2: 1.310922 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.380066 Loss1: 0.082239 Loss2: 1.297827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.729136 Loss1: 0.867813 Loss2: 1.861323
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381120 Loss1: 0.087881 Loss2: 1.293239
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.867943 Loss1: 0.476943 Loss2: 1.391000
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406511 Loss1: 0.107466 Loss2: 1.299046
+(DefaultActor pid=3764) >> Training accuracy: 0.964844
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.660875 Loss1: 0.273094 Loss2: 1.387781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523863 Loss1: 0.147464 Loss2: 1.376399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488620 Loss1: 0.120353 Loss2: 1.368267
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.703100 Loss1: 0.776449 Loss2: 1.926651
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.847065 Loss1: 0.425239 Loss2: 1.421826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.705764 Loss1: 0.253768 Loss2: 1.451997 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383448 Loss1: 0.032420 Loss2: 1.351028
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631904 Loss1: 0.222468 Loss2: 1.409436
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556342 Loss1: 0.143563 Loss2: 1.412779
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503960 Loss1: 0.096765 Loss2: 1.407194
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.498356 Loss1: 0.108076 Loss2: 1.390280
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471416 Loss1: 0.081393 Loss2: 1.390024
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.720768 Loss1: 0.876189 Loss2: 1.844579
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441312 Loss1: 0.057424 Loss2: 1.383887
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.953036 Loss1: 0.551518 Loss2: 1.401518
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.459338 Loss1: 0.077550 Loss2: 1.381788
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606067 Loss1: 0.224146 Loss2: 1.381921 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516263 Loss1: 0.141646 Loss2: 1.374617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469305 Loss1: 0.103667 Loss2: 1.365638
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.420432 Loss1: 0.603865 Loss2: 1.816567
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.760698 Loss1: 0.391001 Loss2: 1.369698 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.667877 Loss1: 0.275089 Loss2: 1.392788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577200 Loss1: 0.214509 Loss2: 1.362691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486877 Loss1: 0.130853 Loss2: 1.356023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500000 Loss1: 0.151167 Loss2: 1.348833 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.808293 Loss1: 0.400067 Loss2: 1.408225 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.663220 Loss1: 0.235725 Loss2: 1.427495 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983456
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491519 Loss1: 0.111784 Loss2: 1.379735 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.498336 Loss1: 0.116859 Loss2: 1.381476 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.798855 Loss1: 0.863344 Loss2: 1.935511
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483181 Loss1: 0.106984 Loss2: 1.376196
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.909495 Loss1: 0.489252 Loss2: 1.420244
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.464761 Loss1: 0.088700 Loss2: 1.376061
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428476 Loss1: 0.065940 Loss2: 1.362536 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.607076 Loss1: 0.201370 Loss2: 1.405706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516014 Loss1: 0.127909 Loss2: 1.388105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.897331 Loss1: 0.911932 Loss2: 1.985399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.892091 Loss1: 0.488977 Loss2: 1.403114 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.624990 Loss1: 0.223646 Loss2: 1.401344 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506538 Loss1: 0.130372 Loss2: 1.376166 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436810 Loss1: 0.073750 Loss2: 1.363060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411944 Loss1: 0.058080 Loss2: 1.353864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420473 Loss1: 0.067228 Loss2: 1.353244 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.491393 Loss1: 0.162519 Loss2: 1.328874 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444336 Loss1: 0.117300 Loss2: 1.327036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.607876 Loss1: 0.829306 Loss2: 1.778571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.828564 Loss1: 0.471696 Loss2: 1.356867 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655402 Loss1: 0.288574 Loss2: 1.366827 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516179 Loss1: 0.177193 Loss2: 1.338986 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469746 Loss1: 0.148830 Loss2: 1.320916 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.763848 Loss1: 0.796293 Loss2: 1.967555
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439630 Loss1: 0.121387 Loss2: 1.318243
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.882029 Loss1: 0.440745 Loss2: 1.441284
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427034 Loss1: 0.110808 Loss2: 1.316226
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.753847 Loss1: 0.292033 Loss2: 1.461813
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403657 Loss1: 0.089554 Loss2: 1.314103
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.605869 Loss1: 0.171040 Loss2: 1.434829 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.539264 Loss1: 0.115059 Loss2: 1.424204 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.507728 Loss1: 0.083084 Loss2: 1.424644
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.531088 Loss1: 0.731426 Loss2: 1.799662
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.840338 Loss1: 0.470035 Loss2: 1.370303 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.475749 Loss1: 0.060092 Loss2: 1.415657
+DEBUG flwr 2023-10-11 14:11:11,621 | server.py:236 | fit_round 116 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648944 Loss1: 0.252509 Loss2: 1.396435
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570818 Loss1: 0.210096 Loss2: 1.360722
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.596006 Loss1: 0.224684 Loss2: 1.371322
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.581220 Loss1: 0.210137 Loss2: 1.371082
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.521324 Loss1: 0.157249 Loss2: 1.364075
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.662196 Loss1: 0.793559 Loss2: 1.868636
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.895674 Loss1: 0.525052 Loss2: 1.370622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.746348 Loss1: 0.328713 Loss2: 1.417635 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435607 Loss1: 0.088842 Loss2: 1.346765
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.658760 Loss1: 0.279826 Loss2: 1.378933
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.567504 Loss1: 0.187539 Loss2: 1.379966
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472174 Loss1: 0.108830 Loss2: 1.363344
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440756 Loss1: 0.091265 Loss2: 1.349492
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398101 Loss1: 0.053913 Loss2: 1.344187
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.885538 Loss1: 0.992652 Loss2: 1.892886
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383540 Loss1: 0.044496 Loss2: 1.339044
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374352 Loss1: 0.038103 Loss2: 1.336249 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604365 Loss1: 0.243879 Loss2: 1.360487 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462291 Loss1: 0.111572 Loss2: 1.350719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389610 Loss1: 0.059084 Loss2: 1.330526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388643 Loss1: 0.063955 Loss2: 1.324688 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387783 Loss1: 0.071913 Loss2: 1.315869 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577209 Loss1: 0.223781 Loss2: 1.353428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468861 Loss1: 0.124948 Loss2: 1.343914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425261 Loss1: 0.088379 Loss2: 1.336882 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406180 Loss1: 0.073426 Loss2: 1.332754 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 14:11:11,621][flwr][DEBUG] - fit_round 116 received 50 results and 0 failures
+INFO flwr 2023-10-11 14:11:52,821 | server.py:125 | fit progress: (116, 2.2013852497259268, {'accuracy': 0.5796}, 267620.599708241)
+>> Test accuracy: 0.579600
+[2023-10-11 14:11:52,821][flwr][INFO] - fit progress: (116, 2.2013852497259268, {'accuracy': 0.5796}, 267620.599708241)
+DEBUG flwr 2023-10-11 14:11:52,821 | server.py:173 | evaluate_round 116: strategy sampled 50 clients (out of 50)
+[2023-10-11 14:11:52,821][flwr][DEBUG] - evaluate_round 116: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 14:20:55,356 | server.py:187 | evaluate_round 116 received 50 results and 0 failures
+[2023-10-11 14:20:55,356][flwr][DEBUG] - evaluate_round 116 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 14:20:55,357 | server.py:222 | fit_round 117: strategy sampled 50 clients (out of 50)
+[2023-10-11 14:20:55,357][flwr][DEBUG] - fit_round 117: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.568193 Loss1: 0.641608 Loss2: 1.926586
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.815686 Loss1: 0.391947 Loss2: 1.423738
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.691189 Loss1: 0.232786 Loss2: 1.458403
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562930 Loss1: 0.148521 Loss2: 1.414409
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.564264 Loss1: 0.776131 Loss2: 1.788133
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.874353 Loss1: 0.531635 Loss2: 1.342718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.837331 Loss1: 0.431067 Loss2: 1.406264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.615142 Loss1: 0.275143 Loss2: 1.339999 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.566318 Loss1: 0.213824 Loss2: 1.352494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470320 Loss1: 0.138593 Loss2: 1.331728 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.497416 Loss1: 0.097561 Loss2: 1.399856
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458783 Loss1: 0.137194 Loss2: 1.321589
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472069 Loss1: 0.142661 Loss2: 1.329408
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438429 Loss1: 0.114772 Loss2: 1.323656
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378562 Loss1: 0.063359 Loss2: 1.315203
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.777227 Loss1: 0.894554 Loss2: 1.882674
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.911899 Loss1: 0.540224 Loss2: 1.371675
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.709425 Loss1: 0.305080 Loss2: 1.404345
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.595853 Loss1: 0.229235 Loss2: 1.366618
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.622775 Loss1: 0.761224 Loss2: 1.861551
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.805667 Loss1: 0.424736 Loss2: 1.380932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.676093 Loss1: 0.291781 Loss2: 1.384312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.664877 Loss1: 0.293007 Loss2: 1.371869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541611 Loss1: 0.181677 Loss2: 1.359934 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405047 Loss1: 0.073902 Loss2: 1.331145 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434113 Loss1: 0.091702 Loss2: 1.342411 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398121 Loss1: 0.070012 Loss2: 1.328108 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.846313 Loss1: 0.475282 Loss2: 1.371032 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573895 Loss1: 0.225877 Loss2: 1.348018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.535959 Loss1: 0.695110 Loss2: 1.840849
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.542053 Loss1: 0.172883 Loss2: 1.369170
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.886735 Loss1: 0.463410 Loss2: 1.423325
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463333 Loss1: 0.116831 Loss2: 1.346501
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.430612 Loss1: 0.094592 Loss2: 1.336020
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.781471 Loss1: 0.336897 Loss2: 1.444574
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.381969 Loss1: 0.054780 Loss2: 1.327189
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.665919 Loss1: 0.251900 Loss2: 1.414019
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.376597 Loss1: 0.051545 Loss2: 1.325051
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.640167 Loss1: 0.220082 Loss2: 1.420085
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368601 Loss1: 0.047695 Loss2: 1.320906
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.563940 Loss1: 0.160333 Loss2: 1.403606
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.535972 Loss1: 0.129639 Loss2: 1.406333
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.557184 Loss1: 0.164953 Loss2: 1.392232
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.507183 Loss1: 0.112089 Loss2: 1.395094
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478793 Loss1: 0.085281 Loss2: 1.393513
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.632616 Loss1: 0.774047 Loss2: 1.858569
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.923869 Loss1: 0.501725 Loss2: 1.422144
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.699644 Loss1: 0.275313 Loss2: 1.424331
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.577871 Loss1: 0.198428 Loss2: 1.379443
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.549319 Loss1: 0.153152 Loss2: 1.396167
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.733577 Loss1: 0.896728 Loss2: 1.836849
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.919356 Loss1: 0.540385 Loss2: 1.378971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701440 Loss1: 0.302069 Loss2: 1.399371 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631528 Loss1: 0.271161 Loss2: 1.360368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521979 Loss1: 0.170017 Loss2: 1.351961 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465309 Loss1: 0.125564 Loss2: 1.339745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418247 Loss1: 0.088740 Loss2: 1.329508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401721 Loss1: 0.081942 Loss2: 1.319779 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.847311 Loss1: 0.480478 Loss2: 1.366834 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629393 Loss1: 0.269387 Loss2: 1.360006 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.602320 Loss1: 0.221975 Loss2: 1.380345
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.703371 Loss1: 0.846006 Loss2: 1.857365
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.851291 Loss1: 0.435497 Loss2: 1.415794 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.784046 Loss1: 0.356548 Loss2: 1.427498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.662129 Loss1: 0.266449 Loss2: 1.395680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.602380 Loss1: 0.191637 Loss2: 1.410744 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527618 Loss1: 0.154284 Loss2: 1.373334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488301 Loss1: 0.118281 Loss2: 1.370020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430260 Loss1: 0.062979 Loss2: 1.367281 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.767347 Loss1: 0.413950 Loss2: 1.353397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.673426 Loss1: 0.312435 Loss2: 1.360991 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534605 Loss1: 0.179278 Loss2: 1.355327
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.605446 Loss1: 0.778261 Loss2: 1.827185
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.914630 Loss1: 0.549565 Loss2: 1.365064 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.785032 Loss1: 0.354923 Loss2: 1.430108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.598457 Loss1: 0.224881 Loss2: 1.373576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521510 Loss1: 0.156072 Loss2: 1.365438 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474152 Loss1: 0.115303 Loss2: 1.358849 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427487 Loss1: 0.082719 Loss2: 1.344768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424134 Loss1: 0.090528 Loss2: 1.333606 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.835634 Loss1: 0.422048 Loss2: 1.413586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.631056 Loss1: 0.225925 Loss2: 1.405132 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.562436 Loss1: 0.733695 Loss2: 1.828741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.717111 Loss1: 0.397735 Loss2: 1.319376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.637424 Loss1: 0.296415 Loss2: 1.341009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529784 Loss1: 0.215567 Loss2: 1.314218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557142 Loss1: 0.235579 Loss2: 1.321563 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.401360 Loss1: 0.105125 Loss2: 1.296235 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.351343 Loss1: 0.059953 Loss2: 1.291390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.335476 Loss1: 0.057135 Loss2: 1.278341
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.650672 Loss1: 0.787297 Loss2: 1.863375
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.899019 Loss1: 0.474818 Loss2: 1.424200
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.796743 Loss1: 0.331154 Loss2: 1.465589
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654757 Loss1: 0.250137 Loss2: 1.404620
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606615 Loss1: 0.189408 Loss2: 1.417207
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.733625 Loss1: 0.854655 Loss2: 1.878970
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.891354 Loss1: 0.511827 Loss2: 1.379527 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.515028 Loss1: 0.123157 Loss2: 1.391871
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670127 Loss1: 0.266926 Loss2: 1.403201
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491692 Loss1: 0.095381 Loss2: 1.396311
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.590479 Loss1: 0.224093 Loss2: 1.366386
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447722 Loss1: 0.062820 Loss2: 1.384903
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550119 Loss1: 0.175712 Loss2: 1.374407
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447164 Loss1: 0.066032 Loss2: 1.381132
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.483750 Loss1: 0.116588 Loss2: 1.367162
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447985 Loss1: 0.095798 Loss2: 1.352186
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420538 Loss1: 0.074710 Loss2: 1.345827
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.411516 Loss1: 0.069188 Loss2: 1.342328
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394196 Loss1: 0.060084 Loss2: 1.334112
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.452539 Loss1: 0.648882 Loss2: 1.803657
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.745505 Loss1: 0.406251 Loss2: 1.339254
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.676293 Loss1: 0.296024 Loss2: 1.380269
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547653 Loss1: 0.206771 Loss2: 1.340883
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.574108 Loss1: 0.783522 Loss2: 1.790586
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.867141 Loss1: 0.520474 Loss2: 1.346667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.706765 Loss1: 0.339380 Loss2: 1.367385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.583169 Loss1: 0.247453 Loss2: 1.335716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530292 Loss1: 0.186747 Loss2: 1.343545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459607 Loss1: 0.132322 Loss2: 1.327285 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412252 Loss1: 0.091140 Loss2: 1.321112 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376765 Loss1: 0.066534 Loss2: 1.310231 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.974380 Loss1: 0.560566 Loss2: 1.413814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.663390 Loss1: 0.272319 Loss2: 1.391071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.520161 Loss1: 0.691232 Loss2: 1.828930
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.666710 Loss1: 0.260831 Loss2: 1.405879
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.787752 Loss1: 0.437886 Loss2: 1.349866
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562345 Loss1: 0.174342 Loss2: 1.388004
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.675548 Loss1: 0.283732 Loss2: 1.391816
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.533007 Loss1: 0.145368 Loss2: 1.387639
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.533246 Loss1: 0.184748 Loss2: 1.348498
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449738 Loss1: 0.070561 Loss2: 1.379177
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460441 Loss1: 0.122118 Loss2: 1.338323
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458136 Loss1: 0.093446 Loss2: 1.364690
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459364 Loss1: 0.131566 Loss2: 1.327797
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.488343 Loss1: 0.121444 Loss2: 1.366900
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406106 Loss1: 0.086787 Loss2: 1.319320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404010 Loss1: 0.084724 Loss2: 1.319286 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.853987 Loss1: 0.458914 Loss2: 1.395072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.621471 Loss1: 0.239759 Loss2: 1.381712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.538753 Loss1: 0.157439 Loss2: 1.381314
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.777070 Loss1: 0.900856 Loss2: 1.876214
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.848006 Loss1: 0.494865 Loss2: 1.353141
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502090 Loss1: 0.135173 Loss2: 1.366917
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.703509 Loss1: 0.294052 Loss2: 1.409458
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512268 Loss1: 0.144916 Loss2: 1.367352
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564955 Loss1: 0.218710 Loss2: 1.346245
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.493993 Loss1: 0.119504 Loss2: 1.374489
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.475738 Loss1: 0.114228 Loss2: 1.361510 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.456802 Loss1: 0.097860 Loss2: 1.358942 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.377932 Loss1: 0.056655 Loss2: 1.321277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379858 Loss1: 0.072381 Loss2: 1.307477 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.795220 Loss1: 0.853159 Loss2: 1.942060
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.928019 Loss1: 0.529948 Loss2: 1.398071
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.757951 Loss1: 0.323724 Loss2: 1.434226
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.615213 Loss1: 0.201039 Loss2: 1.414174
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558848 Loss1: 0.168833 Loss2: 1.390015
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.527601 Loss1: 0.138434 Loss2: 1.389166 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483578 Loss1: 0.099644 Loss2: 1.383933 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461853 Loss1: 0.086781 Loss2: 1.375072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.468831 Loss1: 0.090396 Loss2: 1.378435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440732 Loss1: 0.070219 Loss2: 1.370513 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.477428 Loss1: 0.099235 Loss2: 1.378193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449949 Loss1: 0.081246 Loss2: 1.368703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420839 Loss1: 0.052697 Loss2: 1.368142
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.535629 Loss1: 0.686284 Loss2: 1.849345
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.732965 Loss1: 0.331788 Loss2: 1.401177
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.631619 Loss1: 0.230207 Loss2: 1.401412
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540952 Loss1: 0.166708 Loss2: 1.374244
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498084 Loss1: 0.125038 Loss2: 1.373046
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.599483 Loss1: 0.736245 Loss2: 1.863238
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.507312 Loss1: 0.135271 Loss2: 1.372042
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.864037 Loss1: 0.474337 Loss2: 1.389700
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.764233 Loss1: 0.332589 Loss2: 1.431644
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463626 Loss1: 0.092201 Loss2: 1.371425
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.600105 Loss1: 0.216548 Loss2: 1.383556
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.442851 Loss1: 0.080466 Loss2: 1.362385
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529205 Loss1: 0.151272 Loss2: 1.377933
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433370 Loss1: 0.070147 Loss2: 1.363223
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429736 Loss1: 0.074239 Loss2: 1.355497 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991728
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483724 Loss1: 0.118764 Loss2: 1.364960 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436555 Loss1: 0.084079 Loss2: 1.352476 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.533993 Loss1: 0.715819 Loss2: 1.818174
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.890307 Loss1: 0.478052 Loss2: 1.412256
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.686842 Loss1: 0.270042 Loss2: 1.416800
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594803 Loss1: 0.220914 Loss2: 1.373889
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.671431 Loss1: 0.890148 Loss2: 1.781282
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.875077 Loss1: 0.545112 Loss2: 1.329965 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.761153 Loss1: 0.377979 Loss2: 1.383174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.505949 Loss1: 0.134987 Loss2: 1.370963
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.638690 Loss1: 0.301349 Loss2: 1.337341
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451834 Loss1: 0.093740 Loss2: 1.358094
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.547475 Loss1: 0.213366 Loss2: 1.334109
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438835 Loss1: 0.085331 Loss2: 1.353504
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480985 Loss1: 0.160793 Loss2: 1.320192
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460660 Loss1: 0.146907 Loss2: 1.313753
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442101 Loss1: 0.094924 Loss2: 1.347176
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388539 Loss1: 0.083016 Loss2: 1.305523 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.617290 Loss1: 0.838571 Loss2: 1.778718 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.737844 Loss1: 0.378726 Loss2: 1.359118 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.609786 Loss1: 0.287741 Loss2: 1.322045
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.868957 Loss1: 0.930359 Loss2: 1.938598
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.917138 Loss1: 0.547691 Loss2: 1.369447
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484510 Loss1: 0.169874 Loss2: 1.314635
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.423507 Loss1: 0.129824 Loss2: 1.293683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.438009 Loss1: 0.144267 Loss2: 1.293742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389371 Loss1: 0.101922 Loss2: 1.287448 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.361294 Loss1: 0.079287 Loss2: 1.282007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.348716 Loss1: 0.069119 Loss2: 1.279597 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356994 Loss1: 0.040185 Loss2: 1.316809 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.800038 Loss1: 0.856251 Loss2: 1.943787
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.021114 Loss1: 0.686522 Loss2: 1.334592
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.815841 Loss1: 0.385634 Loss2: 1.430207
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.650424 Loss1: 0.290068 Loss2: 1.360356
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.582411 Loss1: 0.234787 Loss2: 1.347624
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542018 Loss1: 0.175707 Loss2: 1.366311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.533557 Loss1: 0.186500 Loss2: 1.347056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446316 Loss1: 0.105792 Loss2: 1.340524 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397224 Loss1: 0.072593 Loss2: 1.324631
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605699 Loss1: 0.256205 Loss2: 1.349495
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380563 Loss1: 0.057641 Loss2: 1.322922
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527199 Loss1: 0.179999 Loss2: 1.347199
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431932 Loss1: 0.091566 Loss2: 1.340366
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416506 Loss1: 0.086512 Loss2: 1.329994
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.403727 Loss1: 0.080442 Loss2: 1.323284
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400455 Loss1: 0.079666 Loss2: 1.320789
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.539083 Loss1: 0.666686 Loss2: 1.872397
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381287 Loss1: 0.067670 Loss2: 1.313617
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716207 Loss1: 0.286968 Loss2: 1.429240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.595130 Loss1: 0.186555 Loss2: 1.408575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542789 Loss1: 0.156113 Loss2: 1.386677
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.628388 Loss1: 0.842583 Loss2: 1.785805
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.742497 Loss1: 0.414667 Loss2: 1.327830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.637540 Loss1: 0.282065 Loss2: 1.355475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.495467 Loss1: 0.171049 Loss2: 1.324418 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514646 Loss1: 0.192910 Loss2: 1.321736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455592 Loss1: 0.143987 Loss2: 1.311605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386514 Loss1: 0.088382 Loss2: 1.298132 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375754 Loss1: 0.079239 Loss2: 1.296515 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.664785 Loss1: 0.300301 Loss2: 1.364485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.469403 Loss1: 0.152394 Loss2: 1.317009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.632015 Loss1: 0.821003 Loss2: 1.811013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.878284 Loss1: 0.524669 Loss2: 1.353615 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.648169 Loss1: 0.240761 Loss2: 1.407408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567053 Loss1: 0.223504 Loss2: 1.343550 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523423 Loss1: 0.175856 Loss2: 1.347567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426261 Loss1: 0.098408 Loss2: 1.327853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402389 Loss1: 0.078459 Loss2: 1.323930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398543 Loss1: 0.078455 Loss2: 1.320088 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.766065 Loss1: 0.313292 Loss2: 1.452773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589638 Loss1: 0.169979 Loss2: 1.419659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.549577 Loss1: 0.139877 Loss2: 1.409700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.513768 Loss1: 0.111073 Loss2: 1.402695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469061 Loss1: 0.074522 Loss2: 1.394539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445511 Loss1: 0.056005 Loss2: 1.389506 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.566443 Loss1: 0.212164 Loss2: 1.354280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.454026 Loss1: 0.110816 Loss2: 1.343211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439493 Loss1: 0.116594 Loss2: 1.322899
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.723122 Loss1: 0.851440 Loss2: 1.871682
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.999138 Loss1: 0.553360 Loss2: 1.445778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405278 Loss1: 0.082614 Loss2: 1.322663
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.862237 Loss1: 0.415102 Loss2: 1.447136
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.746087 Loss1: 0.341547 Loss2: 1.404540
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.635218 Loss1: 0.218510 Loss2: 1.416708
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533769 Loss1: 0.144905 Loss2: 1.388864
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503981 Loss1: 0.119115 Loss2: 1.384866
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.822654 Loss1: 0.845018 Loss2: 1.977636
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453923 Loss1: 0.079059 Loss2: 1.374865
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.089523 Loss1: 0.579165 Loss2: 1.510357
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428891 Loss1: 0.056840 Loss2: 1.372051
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.868721 Loss1: 0.344436 Loss2: 1.524285
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446570 Loss1: 0.086124 Loss2: 1.360446
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.700432 Loss1: 0.221804 Loss2: 1.478628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.566124 Loss1: 0.108528 Loss2: 1.457596 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.537077 Loss1: 0.092068 Loss2: 1.445008
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.570451 Loss1: 0.792519 Loss2: 1.777933
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.891516 Loss1: 0.511151 Loss2: 1.380366 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.497474 Loss1: 0.067027 Loss2: 1.430447
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.662373 Loss1: 0.277223 Loss2: 1.385151
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575684 Loss1: 0.231445 Loss2: 1.344240
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.528933 Loss1: 0.184922 Loss2: 1.344011
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487034 Loss1: 0.147355 Loss2: 1.339678
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446441 Loss1: 0.109121 Loss2: 1.337320
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.451292 Loss1: 0.641656 Loss2: 1.809636
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.890806 Loss1: 0.499044 Loss2: 1.391762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.770780 Loss1: 0.354447 Loss2: 1.416332 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640242 Loss1: 0.257926 Loss2: 1.382317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506121 Loss1: 0.144751 Loss2: 1.361370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481410 Loss1: 0.134961 Loss2: 1.346449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444647 Loss1: 0.099398 Loss2: 1.345248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460872 Loss1: 0.121436 Loss2: 1.339436 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.608105 Loss1: 0.230236 Loss2: 1.377869 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475190 Loss1: 0.124646 Loss2: 1.350545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461348 Loss1: 0.113179 Loss2: 1.348169
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.751714 Loss1: 0.828208 Loss2: 1.923506
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438734 Loss1: 0.095676 Loss2: 1.343057
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.009237 Loss1: 0.548926 Loss2: 1.460311
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412583 Loss1: 0.074908 Loss2: 1.337676
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.794420 Loss1: 0.324707 Loss2: 1.469714
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.652412 Loss1: 0.214646 Loss2: 1.437766
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.599334 Loss1: 0.167328 Loss2: 1.432006
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.544095 Loss1: 0.123006 Loss2: 1.421089
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.551654 Loss1: 0.134554 Loss2: 1.417100
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.661863 Loss1: 0.802773 Loss2: 1.859090
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.536362 Loss1: 0.124465 Loss2: 1.411897
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.789555 Loss1: 0.399290 Loss2: 1.390266
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.495279 Loss1: 0.087970 Loss2: 1.407310
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.669478 Loss1: 0.250164 Loss2: 1.419313
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512678 Loss1: 0.106106 Loss2: 1.406572
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.584386 Loss1: 0.196564 Loss2: 1.387823 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523418 Loss1: 0.152044 Loss2: 1.371374 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453030 Loss1: 0.091554 Loss2: 1.361476
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.566797 Loss1: 0.688020 Loss2: 1.878777
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.870537 Loss1: 0.432983 Loss2: 1.437554 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432321 Loss1: 0.077946 Loss2: 1.354375
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.739816 Loss1: 0.272323 Loss2: 1.467493
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.568118 Loss1: 0.157183 Loss2: 1.410935
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558424 Loss1: 0.144566 Loss2: 1.413858
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.532297 Loss1: 0.129755 Loss2: 1.402542
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.534336 Loss1: 0.132090 Loss2: 1.402245
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.660779 Loss1: 0.867745 Loss2: 1.793035
+DEBUG flwr 2023-10-11 14:49:24,532 | server.py:236 | fit_round 117 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.813195 Loss1: 0.458065 Loss2: 1.355130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.671754 Loss1: 0.306142 Loss2: 1.365611 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.538747 Loss1: 0.130882 Loss2: 1.407865
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556855 Loss1: 0.229506 Loss2: 1.327349
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.500410 Loss1: 0.170516 Loss2: 1.329894
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.418047 Loss1: 0.098728 Loss2: 1.319319
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402482 Loss1: 0.085600 Loss2: 1.316883
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.385845 Loss1: 0.074758 Loss2: 1.311087
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.648640 Loss1: 0.815523 Loss2: 1.833117
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.360702 Loss1: 0.059924 Loss2: 1.300779
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.891974 Loss1: 0.491490 Loss2: 1.400484
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.347598 Loss1: 0.048315 Loss2: 1.299283
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.655150 Loss1: 0.269035 Loss2: 1.386115 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485573 Loss1: 0.117883 Loss2: 1.367690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.490663 Loss1: 0.689233 Loss2: 1.801429
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475921 Loss1: 0.114734 Loss2: 1.361186
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.754383 Loss1: 0.414124 Loss2: 1.340259
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448964 Loss1: 0.088364 Loss2: 1.360600
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638759 Loss1: 0.259691 Loss2: 1.379068
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418008 Loss1: 0.071543 Loss2: 1.346465
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524736 Loss1: 0.190486 Loss2: 1.334250
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442559 Loss1: 0.094588 Loss2: 1.347971
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459422 Loss1: 0.130758 Loss2: 1.328665 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421378 Loss1: 0.103067 Loss2: 1.318311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437632 Loss1: 0.109791 Loss2: 1.327841
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.524471 Loss1: 0.727555 Loss2: 1.796916
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404824 Loss1: 0.087325 Loss2: 1.317499
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.757020 Loss1: 0.398510 Loss2: 1.358510
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588829 Loss1: 0.222943 Loss2: 1.365886
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550393 Loss1: 0.208866 Loss2: 1.341527
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.459888 Loss1: 0.122414 Loss2: 1.337474
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464576 Loss1: 0.142918 Loss2: 1.321659
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.454102 Loss1: 0.119416 Loss2: 1.334686
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397797 Loss1: 0.080437 Loss2: 1.317360
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395079 Loss1: 0.076167 Loss2: 1.318913
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404277 Loss1: 0.091694 Loss2: 1.312583
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 14:49:24,532][flwr][DEBUG] - fit_round 117 received 50 results and 0 failures
+INFO flwr 2023-10-11 14:50:04,664 | server.py:125 | fit progress: (117, 2.210739805104253, {'accuracy': 0.5816}, 269912.44244217797)
+>> Test accuracy: 0.581600
+[2023-10-11 14:50:04,664][flwr][INFO] - fit progress: (117, 2.210739805104253, {'accuracy': 0.5816}, 269912.44244217797)
+DEBUG flwr 2023-10-11 14:50:04,664 | server.py:173 | evaluate_round 117: strategy sampled 50 clients (out of 50)
+[2023-10-11 14:50:04,664][flwr][DEBUG] - evaluate_round 117: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 14:59:10,074 | server.py:187 | evaluate_round 117 received 50 results and 0 failures
+[2023-10-11 14:59:10,074][flwr][DEBUG] - evaluate_round 117 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 14:59:10,075 | server.py:222 | fit_round 118: strategy sampled 50 clients (out of 50)
+[2023-10-11 14:59:10,075][flwr][DEBUG] - fit_round 118: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.580240 Loss1: 0.706629 Loss2: 1.873611
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.861077 Loss1: 0.491414 Loss2: 1.369662
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.699847 Loss1: 0.292896 Loss2: 1.406950
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602700 Loss1: 0.232118 Loss2: 1.370582
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555010 Loss1: 0.189774 Loss2: 1.365236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451632 Loss1: 0.090973 Loss2: 1.360659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433989 Loss1: 0.091126 Loss2: 1.342863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.412793 Loss1: 0.069935 Loss2: 1.342858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461742 Loss1: 0.125001 Loss2: 1.336741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452654 Loss1: 0.107217 Loss2: 1.345437 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368115 Loss1: 0.037804 Loss2: 1.330311 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.713885 Loss1: 0.837827 Loss2: 1.876058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.774318 Loss1: 0.317303 Loss2: 1.457015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630606 Loss1: 0.237980 Loss2: 1.392626
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.762843 Loss1: 0.861057 Loss2: 1.901786
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.943683 Loss1: 0.517977 Loss2: 1.425706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.753268 Loss1: 0.273308 Loss2: 1.479960 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.683947 Loss1: 0.261658 Loss2: 1.422289 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.616810 Loss1: 0.194883 Loss2: 1.421927 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549245 Loss1: 0.129288 Loss2: 1.419957 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466492 Loss1: 0.093987 Loss2: 1.372505
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.542162 Loss1: 0.134449 Loss2: 1.407713
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.557010 Loss1: 0.140488 Loss2: 1.416523
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.508051 Loss1: 0.109476 Loss2: 1.398575
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454883 Loss1: 0.060883 Loss2: 1.394000
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.839929 Loss1: 0.928654 Loss2: 1.911275
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.030738 Loss1: 0.608249 Loss2: 1.422490
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.782372 Loss1: 0.334507 Loss2: 1.447865
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.627634 Loss1: 0.227450 Loss2: 1.400184
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.756795 Loss1: 0.847226 Loss2: 1.909569
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.085728 Loss1: 0.653756 Loss2: 1.431972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.817171 Loss1: 0.328609 Loss2: 1.488561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.644303 Loss1: 0.221255 Loss2: 1.423048 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430089 Loss1: 0.066933 Loss2: 1.363156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403793 Loss1: 0.047652 Loss2: 1.356141 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995536
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.558409 Loss1: 0.141555 Loss2: 1.416854 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.490385 Loss1: 0.095740 Loss2: 1.394644 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.896501 Loss1: 0.552636 Loss2: 1.343864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591597 Loss1: 0.272090 Loss2: 1.319508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.483755 Loss1: 0.155063 Loss2: 1.328691
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.709368 Loss1: 0.825768 Loss2: 1.883600
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456713 Loss1: 0.143129 Loss2: 1.313584
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.805481 Loss1: 0.406528 Loss2: 1.398952
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448758 Loss1: 0.140630 Loss2: 1.308127
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.790290 Loss1: 0.336268 Loss2: 1.454022
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405103 Loss1: 0.097984 Loss2: 1.307118
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.705075 Loss1: 0.296640 Loss2: 1.408435
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394553 Loss1: 0.091884 Loss2: 1.302669
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.665163 Loss1: 0.241943 Loss2: 1.423221
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372425 Loss1: 0.078582 Loss2: 1.293843
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.628222 Loss1: 0.212075 Loss2: 1.416146
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.531490 Loss1: 0.130417 Loss2: 1.401073
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.509812 Loss1: 0.120113 Loss2: 1.389700
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.470233 Loss1: 0.082167 Loss2: 1.388066
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463462 Loss1: 0.079819 Loss2: 1.383643
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.616520 Loss1: 0.791916 Loss2: 1.824605
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.850084 Loss1: 0.498815 Loss2: 1.351269
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.742372 Loss1: 0.341440 Loss2: 1.400932
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561696 Loss1: 0.208473 Loss2: 1.353223
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498016 Loss1: 0.155795 Loss2: 1.342221
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.761795 Loss1: 0.860787 Loss2: 1.901008
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468013 Loss1: 0.124350 Loss2: 1.343663
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.842776 Loss1: 0.441263 Loss2: 1.401513
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425920 Loss1: 0.092598 Loss2: 1.333322
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.703392 Loss1: 0.289916 Loss2: 1.413475
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426701 Loss1: 0.092927 Loss2: 1.333774
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.601145 Loss1: 0.215486 Loss2: 1.385658
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406562 Loss1: 0.075176 Loss2: 1.331386
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519155 Loss1: 0.141818 Loss2: 1.377337
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381222 Loss1: 0.052672 Loss2: 1.328550
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472455 Loss1: 0.106244 Loss2: 1.366211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.442773 Loss1: 0.075999 Loss2: 1.366774
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446547 Loss1: 0.095741 Loss2: 1.350807
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429687 Loss1: 0.080821 Loss2: 1.348866
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399066 Loss1: 0.051434 Loss2: 1.347633
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.765943 Loss1: 0.885316 Loss2: 1.880627
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.907842 Loss1: 0.553681 Loss2: 1.354161
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.657524 Loss1: 0.235942 Loss2: 1.421582
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.507498 Loss1: 0.163718 Loss2: 1.343781
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.523706 Loss1: 0.182060 Loss2: 1.341647
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.640581 Loss1: 0.745348 Loss2: 1.895234
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481863 Loss1: 0.131446 Loss2: 1.350417
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451793 Loss1: 0.111170 Loss2: 1.340623
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.775818 Loss1: 0.385166 Loss2: 1.390652
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441686 Loss1: 0.114082 Loss2: 1.327604
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.664243 Loss1: 0.243204 Loss2: 1.421039
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574536 Loss1: 0.179085 Loss2: 1.395451 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.947115
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529704 Loss1: 0.141055 Loss2: 1.388649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460553 Loss1: 0.087117 Loss2: 1.373436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.455675 Loss1: 0.099960 Loss2: 1.355714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436975 Loss1: 0.082005 Loss2: 1.354970 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635125 Loss1: 0.246512 Loss2: 1.388613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506082 Loss1: 0.143726 Loss2: 1.362357 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.528480 Loss1: 0.178319 Loss2: 1.350161
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.897000 Loss1: 0.919708 Loss2: 1.977292
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467654 Loss1: 0.111225 Loss2: 1.356429
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.959132 Loss1: 0.532671 Loss2: 1.426461
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.766115 Loss1: 0.290006 Loss2: 1.476110
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436029 Loss1: 0.090205 Loss2: 1.345824
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.708145 Loss1: 0.285065 Loss2: 1.423080
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430480 Loss1: 0.090630 Loss2: 1.339849
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430978 Loss1: 0.094345 Loss2: 1.336633 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.557322 Loss1: 0.152982 Loss2: 1.404340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499222 Loss1: 0.095804 Loss2: 1.403418 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.451426 Loss1: 0.057650 Loss2: 1.393776
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.691332 Loss1: 0.807114 Loss2: 1.884218
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.831003 Loss1: 0.430089 Loss2: 1.400914
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.668134 Loss1: 0.242371 Loss2: 1.425763
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629160 Loss1: 0.230509 Loss2: 1.398651
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617123 Loss1: 0.208832 Loss2: 1.408291
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.778193 Loss1: 0.909717 Loss2: 1.868476
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.934614 Loss1: 0.523561 Loss2: 1.411053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.722517 Loss1: 0.313774 Loss2: 1.408743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.580268 Loss1: 0.207375 Loss2: 1.372893 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505245 Loss1: 0.132481 Loss2: 1.372764 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439334 Loss1: 0.069026 Loss2: 1.370308
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470025 Loss1: 0.112295 Loss2: 1.357729
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.432437 Loss1: 0.078344 Loss2: 1.354093
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405060 Loss1: 0.056954 Loss2: 1.348106
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423430 Loss1: 0.079774 Loss2: 1.343656
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410794 Loss1: 0.069747 Loss2: 1.341046
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.555925 Loss1: 0.725166 Loss2: 1.830758
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.906222 Loss1: 0.522904 Loss2: 1.383318
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.689502 Loss1: 0.277310 Loss2: 1.412192
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.669133 Loss1: 0.305419 Loss2: 1.363714
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587977 Loss1: 0.215312 Loss2: 1.372665
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.753050 Loss1: 0.916619 Loss2: 1.836431
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.886230 Loss1: 0.546694 Loss2: 1.339536 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495758 Loss1: 0.138629 Loss2: 1.357129
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701601 Loss1: 0.323404 Loss2: 1.378196
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449411 Loss1: 0.090386 Loss2: 1.359025
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573336 Loss1: 0.259217 Loss2: 1.314120
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419339 Loss1: 0.077415 Loss2: 1.341924
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524821 Loss1: 0.190141 Loss2: 1.334680
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513124 Loss1: 0.186845 Loss2: 1.326279
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406349 Loss1: 0.075087 Loss2: 1.331262
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430474 Loss1: 0.123018 Loss2: 1.307456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375768 Loss1: 0.076041 Loss2: 1.299726 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987723
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.903957 Loss1: 0.480968 Loss2: 1.422989 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.639649 Loss1: 0.220622 Loss2: 1.419027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.600046 Loss1: 0.179449 Loss2: 1.420597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.556976 Loss1: 0.151918 Loss2: 1.405057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.519803 Loss1: 0.117947 Loss2: 1.401856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.475774 Loss1: 0.086035 Loss2: 1.389739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459482 Loss1: 0.074141 Loss2: 1.385341 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.468414 Loss1: 0.085453 Loss2: 1.382961 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501574 Loss1: 0.127539 Loss2: 1.374035 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.823190 Loss1: 0.881831 Loss2: 1.941360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741995 Loss1: 0.271367 Loss2: 1.470629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.645751 Loss1: 0.225204 Loss2: 1.420547
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.497707 Loss1: 0.612544 Loss2: 1.885163
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.850129 Loss1: 0.463166 Loss2: 1.386963 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.768456 Loss1: 0.322473 Loss2: 1.445983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.651127 Loss1: 0.265706 Loss2: 1.385421 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.649073 Loss1: 0.240776 Loss2: 1.408297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.659631 Loss1: 0.252211 Loss2: 1.407420 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.471662 Loss1: 0.073786 Loss2: 1.397876
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.596649 Loss1: 0.195963 Loss2: 1.400686
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.519803 Loss1: 0.130944 Loss2: 1.388859
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.484331 Loss1: 0.099740 Loss2: 1.384590
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.473214 Loss1: 0.100848 Loss2: 1.372367
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.397514 Loss1: 0.609256 Loss2: 1.788258
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.792694 Loss1: 0.438820 Loss2: 1.353874
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.698557 Loss1: 0.292308 Loss2: 1.406249
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.840624 Loss1: 0.929598 Loss2: 1.911025
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580048 Loss1: 0.220128 Loss2: 1.359920
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555281 Loss1: 0.191503 Loss2: 1.363778 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508495 Loss1: 0.161784 Loss2: 1.346711 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.471600 Loss1: 0.123712 Loss2: 1.347889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501720 Loss1: 0.177590 Loss2: 1.324130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.499958 Loss1: 0.168486 Loss2: 1.331473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435821 Loss1: 0.113192 Loss2: 1.322629 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409913 Loss1: 0.091321 Loss2: 1.318592 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.670900 Loss1: 0.809865 Loss2: 1.861036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667319 Loss1: 0.260103 Loss2: 1.407216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.590634 Loss1: 0.210039 Loss2: 1.380595
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.523924 Loss1: 0.655808 Loss2: 1.868116
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.835642 Loss1: 0.428141 Loss2: 1.407501 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.673251 Loss1: 0.243580 Loss2: 1.429671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599854 Loss1: 0.199573 Loss2: 1.400281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.532586 Loss1: 0.137972 Loss2: 1.394614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521070 Loss1: 0.127945 Loss2: 1.393125 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480999 Loss1: 0.094761 Loss2: 1.386239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454424 Loss1: 0.079080 Loss2: 1.375344 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.969727
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.485513 Loss1: 0.654668 Loss2: 1.830845 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.566037 Loss1: 0.200486 Loss2: 1.365552 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.682443 Loss1: 0.710522 Loss2: 1.971921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.011471 Loss1: 0.535006 Loss2: 1.476465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.908268 Loss1: 0.375924 Loss2: 1.532344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.768713 Loss1: 0.283046 Loss2: 1.485667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.668257 Loss1: 0.194318 Loss2: 1.473939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.617226 Loss1: 0.159298 Loss2: 1.457928 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.586250 Loss1: 0.134834 Loss2: 1.451416 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.551718 Loss1: 0.109935 Loss2: 1.441783 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.729752 Loss1: 0.389201 Loss2: 1.340552 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.532308 Loss1: 0.192717 Loss2: 1.339591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.599219 Loss1: 0.783689 Loss2: 1.815530
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.529785 Loss1: 0.188915 Loss2: 1.340870
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.877070 Loss1: 0.500735 Loss2: 1.376335
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498927 Loss1: 0.164493 Loss2: 1.334435
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.692787 Loss1: 0.290081 Loss2: 1.402706
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428802 Loss1: 0.099377 Loss2: 1.329425
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591934 Loss1: 0.227873 Loss2: 1.364061
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444331 Loss1: 0.119548 Loss2: 1.324783
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512141 Loss1: 0.143127 Loss2: 1.369013
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399504 Loss1: 0.075034 Loss2: 1.324470
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476409 Loss1: 0.129869 Loss2: 1.346540
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381835 Loss1: 0.062744 Loss2: 1.319091
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419282 Loss1: 0.085783 Loss2: 1.333499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373562 Loss1: 0.053884 Loss2: 1.319678 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.827062 Loss1: 0.472576 Loss2: 1.354485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555222 Loss1: 0.215420 Loss2: 1.339802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.677393 Loss1: 0.816350 Loss2: 1.861043
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515780 Loss1: 0.170013 Loss2: 1.345767
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.888130 Loss1: 0.502671 Loss2: 1.385460
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492289 Loss1: 0.154309 Loss2: 1.337981
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727454 Loss1: 0.314428 Loss2: 1.413026
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449228 Loss1: 0.117552 Loss2: 1.331676
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555923 Loss1: 0.177006 Loss2: 1.378916
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426622 Loss1: 0.100072 Loss2: 1.326550
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507730 Loss1: 0.139367 Loss2: 1.368363
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414596 Loss1: 0.088227 Loss2: 1.326369
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503281 Loss1: 0.145230 Loss2: 1.358050
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385220 Loss1: 0.061278 Loss2: 1.323942
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448896 Loss1: 0.095034 Loss2: 1.353862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399889 Loss1: 0.053932 Loss2: 1.345958 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.868673 Loss1: 0.477384 Loss2: 1.391289 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589775 Loss1: 0.220952 Loss2: 1.368823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.605936 Loss1: 0.746838 Loss2: 1.859098
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.532631 Loss1: 0.159358 Loss2: 1.373273
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.809706 Loss1: 0.425645 Loss2: 1.384062
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503002 Loss1: 0.136589 Loss2: 1.366413
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701338 Loss1: 0.278019 Loss2: 1.423319
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463380 Loss1: 0.104858 Loss2: 1.358523
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579957 Loss1: 0.202977 Loss2: 1.376980
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434792 Loss1: 0.085904 Loss2: 1.348888
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.536370 Loss1: 0.165459 Loss2: 1.370910
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427264 Loss1: 0.079162 Loss2: 1.348102
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504979 Loss1: 0.143821 Loss2: 1.361158
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441272 Loss1: 0.096907 Loss2: 1.344365
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417922 Loss1: 0.071052 Loss2: 1.346869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443622 Loss1: 0.102482 Loss2: 1.341141 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.829145 Loss1: 0.466203 Loss2: 1.362942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694871 Loss1: 0.321456 Loss2: 1.373415 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.484110 Loss1: 0.635980 Loss2: 1.848129
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572076 Loss1: 0.195655 Loss2: 1.376420
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.819102 Loss1: 0.407202 Loss2: 1.411900
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.522627 Loss1: 0.156745 Loss2: 1.365882
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484515 Loss1: 0.125322 Loss2: 1.359192
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.675326 Loss1: 0.242382 Loss2: 1.432945
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464973 Loss1: 0.109935 Loss2: 1.355039
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605162 Loss1: 0.205788 Loss2: 1.399374
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428183 Loss1: 0.078948 Loss2: 1.349236
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.581389 Loss1: 0.169087 Loss2: 1.412302
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425921 Loss1: 0.085548 Loss2: 1.340373
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.507125 Loss1: 0.106647 Loss2: 1.400478
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473329 Loss1: 0.083093 Loss2: 1.390236
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436250 Loss1: 0.056244 Loss2: 1.380005
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439725 Loss1: 0.065515 Loss2: 1.374210
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438068 Loss1: 0.067667 Loss2: 1.370401
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.607895 Loss1: 0.783804 Loss2: 1.824090
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.722122 Loss1: 0.366751 Loss2: 1.355371
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656837 Loss1: 0.289950 Loss2: 1.366887
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540207 Loss1: 0.190999 Loss2: 1.349208
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514325 Loss1: 0.169146 Loss2: 1.345179
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.459313 Loss1: 0.720139 Loss2: 1.739174
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492012 Loss1: 0.157374 Loss2: 1.334638
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.756291 Loss1: 0.443470 Loss2: 1.312821
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469841 Loss1: 0.138256 Loss2: 1.331585
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684236 Loss1: 0.336861 Loss2: 1.347375
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452548 Loss1: 0.122424 Loss2: 1.330125
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398925 Loss1: 0.074503 Loss2: 1.324422
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528217 Loss1: 0.216886 Loss2: 1.311331
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399576 Loss1: 0.079276 Loss2: 1.320301
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509911 Loss1: 0.195416 Loss2: 1.314495
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474504 Loss1: 0.169669 Loss2: 1.304834
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428136 Loss1: 0.121810 Loss2: 1.306326
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.385708 Loss1: 0.089210 Loss2: 1.296498
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.345691 Loss1: 0.061170 Loss2: 1.284521
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.727882 Loss1: 0.817721 Loss2: 1.910161
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.905025 Loss1: 0.468911 Loss2: 1.436114 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649522 Loss1: 0.215960 Loss2: 1.433562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.600495 Loss1: 0.172823 Loss2: 1.427671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.538261 Loss1: 0.109841 Loss2: 1.428420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.795647 Loss1: 0.409070 Loss2: 1.386577
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491489 Loss1: 0.072446 Loss2: 1.419043
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.639524 Loss1: 0.255577 Loss2: 1.383947
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460351 Loss1: 0.053274 Loss2: 1.407077
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.473883 Loss1: 0.077548 Loss2: 1.396335
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604542 Loss1: 0.233580 Loss2: 1.370962
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.559683 Loss1: 0.188634 Loss2: 1.371049
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531576 Loss1: 0.167480 Loss2: 1.364096
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475745 Loss1: 0.121369 Loss2: 1.354376
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433855 Loss1: 0.088422 Loss2: 1.345433
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.644833 Loss1: 0.790511 Loss2: 1.854322
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.932923 Loss1: 0.519948 Loss2: 1.412975 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993566
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418313 Loss1: 0.076923 Loss2: 1.341390
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.833373 Loss1: 0.392777 Loss2: 1.440596
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.700114 Loss1: 0.290438 Loss2: 1.409676
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.583203 Loss1: 0.182707 Loss2: 1.400496
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547566 Loss1: 0.166175 Loss2: 1.381391
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481850 Loss1: 0.099884 Loss2: 1.381966
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.631893 Loss1: 0.810753 Loss2: 1.821141
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.900554 Loss1: 0.511171 Loss2: 1.389382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.715118 Loss1: 0.325837 Loss2: 1.389280 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419428 Loss1: 0.055401 Loss2: 1.364027
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604042 Loss1: 0.234390 Loss2: 1.369652
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.566598 Loss1: 0.209253 Loss2: 1.357345
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464183 Loss1: 0.110863 Loss2: 1.353320
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447353 Loss1: 0.102488 Loss2: 1.344865
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423099 Loss1: 0.086681 Loss2: 1.336418
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.527206 Loss1: 0.715079 Loss2: 1.812126
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406094 Loss1: 0.073029 Loss2: 1.333065
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.885324 Loss1: 0.486766 Loss2: 1.398557
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377040 Loss1: 0.051437 Loss2: 1.325603
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.663331 Loss1: 0.287191 Loss2: 1.376140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.589415 Loss1: 0.208958 Loss2: 1.380457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.712248 Loss1: 0.832523 Loss2: 1.879725
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.505865 Loss1: 0.132679 Loss2: 1.373187
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.989070 Loss1: 0.573658 Loss2: 1.415412
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452994 Loss1: 0.091698 Loss2: 1.361296
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411515 Loss1: 0.057222 Loss2: 1.354293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401842 Loss1: 0.058555 Loss2: 1.343287 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.533041 Loss1: 0.152690 Loss2: 1.380351 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458089 Loss1: 0.086740 Loss2: 1.371350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.650583 Loss1: 0.786015 Loss2: 1.864568 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.983645 Loss1: 0.553062 Loss2: 1.430582 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.695885 Loss1: 0.290300 Loss2: 1.405585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.555635 Loss1: 0.160951 Loss2: 1.394684 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 15:27:43,747 | server.py:236 | fit_round 118 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.510100 Loss1: 0.121361 Loss2: 1.388739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460764 Loss1: 0.082978 Loss2: 1.377785 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455251 Loss1: 0.087153 Loss2: 1.368098 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440322 Loss1: 0.074678 Loss2: 1.365643 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.609576 Loss1: 0.152538 Loss2: 1.457039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.612717 Loss1: 0.162082 Loss2: 1.450635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.497380 Loss1: 0.711139 Loss2: 1.786241 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.653406 Loss1: 0.255602 Loss2: 1.397804 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.489192 Loss1: 0.143575 Loss2: 1.345616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506469 Loss1: 0.169188 Loss2: 1.337281
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.572660 Loss1: 0.743347 Loss2: 1.829313
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448093 Loss1: 0.104567 Loss2: 1.343526
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.821786 Loss1: 0.470281 Loss2: 1.351506
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.699132 Loss1: 0.315091 Loss2: 1.384040
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437252 Loss1: 0.105594 Loss2: 1.331658
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.570729 Loss1: 0.239326 Loss2: 1.331403
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444464 Loss1: 0.113780 Loss2: 1.330685
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518560 Loss1: 0.165439 Loss2: 1.353121
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418996 Loss1: 0.089217 Loss2: 1.329779
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395177 Loss1: 0.080621 Loss2: 1.314557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366409 Loss1: 0.066379 Loss2: 1.300029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.353456 Loss1: 0.055535 Loss2: 1.297921
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.969248 Loss1: 0.922547 Loss2: 2.046701
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.039157 Loss1: 0.535872 Loss2: 1.503285
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.817601 Loss1: 0.273952 Loss2: 1.543649
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.730119 Loss1: 0.250256 Loss2: 1.479863
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.648691 Loss1: 0.151643 Loss2: 1.497048
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.769530 Loss1: 0.798428 Loss2: 1.971102
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.580555 Loss1: 0.111142 Loss2: 1.469412
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.588798 Loss1: 0.120239 Loss2: 1.468559 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.580453 Loss1: 0.110730 Loss2: 1.469723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.551267 Loss1: 0.086174 Loss2: 1.465093 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559557 Loss1: 0.178327 Loss2: 1.381230 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482779 Loss1: 0.142097 Loss2: 1.340682 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396794 Loss1: 0.064355 Loss2: 1.332439 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 15:27:43,747][flwr][DEBUG] - fit_round 118 received 50 results and 0 failures
+INFO flwr 2023-10-11 15:28:25,629 | server.py:125 | fit progress: (118, 2.2009005855066706, {'accuracy': 0.5821}, 272213.407915922)
+>> Test accuracy: 0.582100
+[2023-10-11 15:28:25,629][flwr][INFO] - fit progress: (118, 2.2009005855066706, {'accuracy': 0.5821}, 272213.407915922)
+DEBUG flwr 2023-10-11 15:28:25,630 | server.py:173 | evaluate_round 118: strategy sampled 50 clients (out of 50)
+[2023-10-11 15:28:25,630][flwr][DEBUG] - evaluate_round 118: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 15:37:27,804 | server.py:187 | evaluate_round 118 received 50 results and 0 failures
+[2023-10-11 15:37:27,804][flwr][DEBUG] - evaluate_round 118 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 15:37:27,805 | server.py:222 | fit_round 119: strategy sampled 50 clients (out of 50)
+[2023-10-11 15:37:27,805][flwr][DEBUG] - fit_round 119: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.511798 Loss1: 0.678606 Loss2: 1.833192
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.796235 Loss1: 0.436231 Loss2: 1.360004
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.633239 Loss1: 0.234414 Loss2: 1.398825
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534812 Loss1: 0.184769 Loss2: 1.350043
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.739259 Loss1: 0.880668 Loss2: 1.858591
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498993 Loss1: 0.151424 Loss2: 1.347570
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.982554 Loss1: 0.623804 Loss2: 1.358750
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470060 Loss1: 0.132030 Loss2: 1.338030
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.711197 Loss1: 0.309582 Loss2: 1.401615
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581511 Loss1: 0.229792 Loss2: 1.351719
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431108 Loss1: 0.097233 Loss2: 1.333875
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.551866 Loss1: 0.207335 Loss2: 1.344531
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425542 Loss1: 0.086970 Loss2: 1.338572
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.535934 Loss1: 0.188074 Loss2: 1.347860
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398646 Loss1: 0.076309 Loss2: 1.322337
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399364 Loss1: 0.075997 Loss2: 1.323366 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394099 Loss1: 0.064748 Loss2: 1.329350 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.569602 Loss1: 0.706879 Loss2: 1.862723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.734374 Loss1: 0.289265 Loss2: 1.445109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596568 Loss1: 0.202813 Loss2: 1.393755
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.636744 Loss1: 0.735146 Loss2: 1.901597
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.520666 Loss1: 0.124493 Loss2: 1.396174
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.887004 Loss1: 0.441693 Loss2: 1.445311
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461055 Loss1: 0.079763 Loss2: 1.381292
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.716927 Loss1: 0.275740 Loss2: 1.441187
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.454645 Loss1: 0.088062 Loss2: 1.366583
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594279 Loss1: 0.171354 Loss2: 1.422925
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469292 Loss1: 0.098201 Loss2: 1.371091
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.531030 Loss1: 0.119942 Loss2: 1.411088
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.457014 Loss1: 0.087919 Loss2: 1.369095
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472126 Loss1: 0.068901 Loss2: 1.403225
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439919 Loss1: 0.078589 Loss2: 1.361330
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471514 Loss1: 0.078943 Loss2: 1.392571
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.476550 Loss1: 0.084576 Loss2: 1.391974
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476954 Loss1: 0.081789 Loss2: 1.395165
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439637 Loss1: 0.050118 Loss2: 1.389519
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.605750 Loss1: 0.776244 Loss2: 1.829506
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.872924 Loss1: 0.517277 Loss2: 1.355647
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.694121 Loss1: 0.288293 Loss2: 1.405828
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.579421 Loss1: 0.235625 Loss2: 1.343795
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.425499 Loss1: 0.596049 Loss2: 1.829450
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.769449 Loss1: 0.389902 Loss2: 1.379547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.673697 Loss1: 0.266070 Loss2: 1.407628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567693 Loss1: 0.203308 Loss2: 1.364384 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452280 Loss1: 0.130812 Loss2: 1.321468 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407450 Loss1: 0.091598 Loss2: 1.315853 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.457761 Loss1: 0.103609 Loss2: 1.354151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426769 Loss1: 0.076103 Loss2: 1.350666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430842 Loss1: 0.086371 Loss2: 1.344471
+(DefaultActor pid=3764) >> Training accuracy: 0.988051
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.504211 Loss1: 0.658452 Loss2: 1.845759
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.853019 Loss1: 0.492867 Loss2: 1.360152
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.762870 Loss1: 0.334349 Loss2: 1.428521
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582882 Loss1: 0.212940 Loss2: 1.369943
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524756 Loss1: 0.162512 Loss2: 1.362244
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.579213 Loss1: 0.728379 Loss2: 1.850834
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499231 Loss1: 0.144235 Loss2: 1.354996
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.883069 Loss1: 0.458075 Loss2: 1.424994
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470834 Loss1: 0.115623 Loss2: 1.355211
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.465520 Loss1: 0.114934 Loss2: 1.350586
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.732641 Loss1: 0.292995 Loss2: 1.439646
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436861 Loss1: 0.091179 Loss2: 1.345682
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.641975 Loss1: 0.237114 Loss2: 1.404862
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395904 Loss1: 0.053794 Loss2: 1.342109
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570117 Loss1: 0.156518 Loss2: 1.413600
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.564796 Loss1: 0.167319 Loss2: 1.397477
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.546982 Loss1: 0.152365 Loss2: 1.394617
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.527869 Loss1: 0.131871 Loss2: 1.395998
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.506789 Loss1: 0.114367 Loss2: 1.392422
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.622469 Loss1: 0.811516 Loss2: 1.810954
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.848009 Loss1: 0.487980 Loss2: 1.360029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555415 Loss1: 0.220347 Loss2: 1.335068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494842 Loss1: 0.156622 Loss2: 1.338220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425233 Loss1: 0.105581 Loss2: 1.319652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.392935 Loss1: 0.079272 Loss2: 1.313664 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.383781 Loss1: 0.078610 Loss2: 1.305171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361548 Loss1: 0.063213 Loss2: 1.298335 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.553667 Loss1: 0.179148 Loss2: 1.374519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464070 Loss1: 0.109779 Loss2: 1.354290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.567247 Loss1: 0.730703 Loss2: 1.836544 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.812418 Loss1: 0.430334 Loss2: 1.382084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558006 Loss1: 0.193566 Loss2: 1.364440 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.474881 Loss1: 0.116183 Loss2: 1.358698 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434669 Loss1: 0.086184 Loss2: 1.348485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434431 Loss1: 0.090564 Loss2: 1.343867 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550489 Loss1: 0.165084 Loss2: 1.385405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.594665 Loss1: 0.228675 Loss2: 1.365990 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508000 Loss1: 0.135792 Loss2: 1.372207 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427262 Loss1: 0.080155 Loss2: 1.347107 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.635691 Loss1: 0.840016 Loss2: 1.795675 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.686392 Loss1: 0.308523 Loss2: 1.377869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462363 Loss1: 0.146730 Loss2: 1.315632 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.406857 Loss1: 0.095934 Loss2: 1.310923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406246 Loss1: 0.100010 Loss2: 1.306236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.359271 Loss1: 0.058920 Loss2: 1.300351 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.353626 Loss1: 0.059233 Loss2: 1.294394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.357141 Loss1: 0.061984 Loss2: 1.295157 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374584 Loss1: 0.067196 Loss2: 1.307387 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356125 Loss1: 0.062015 Loss2: 1.294110 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.968403 Loss1: 0.570967 Loss2: 1.397436 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.696006 Loss1: 0.317242 Loss2: 1.378764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.622625 Loss1: 0.727683 Loss2: 1.894942
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.610264 Loss1: 0.225020 Loss2: 1.385244
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.806838 Loss1: 0.429844 Loss2: 1.376995
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.600748 Loss1: 0.228827 Loss2: 1.371921
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635185 Loss1: 0.259744 Loss2: 1.375441
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539634 Loss1: 0.168948 Loss2: 1.370685
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592278 Loss1: 0.226003 Loss2: 1.366275
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477365 Loss1: 0.121342 Loss2: 1.356023
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539150 Loss1: 0.163955 Loss2: 1.375196
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450133 Loss1: 0.097071 Loss2: 1.353062
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528145 Loss1: 0.173425 Loss2: 1.354720
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422176 Loss1: 0.076621 Loss2: 1.345555
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.476793 Loss1: 0.127161 Loss2: 1.349632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439540 Loss1: 0.093504 Loss2: 1.346037 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.804714 Loss1: 0.492006 Loss2: 1.312708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.644370 Loss1: 0.285782 Loss2: 1.358588 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564885 Loss1: 0.231847 Loss2: 1.333039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441341 Loss1: 0.116523 Loss2: 1.324817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.379613 Loss1: 0.079793 Loss2: 1.299820 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401154 Loss1: 0.101037 Loss2: 1.300117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373828 Loss1: 0.076246 Loss2: 1.297582 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490058 Loss1: 0.124133 Loss2: 1.365924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.532456 Loss1: 0.162887 Loss2: 1.369570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446920 Loss1: 0.080579 Loss2: 1.366341
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.704517 Loss1: 0.808004 Loss2: 1.896513
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.455578 Loss1: 0.101155 Loss2: 1.354423
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.864373 Loss1: 0.508857 Loss2: 1.355516
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690265 Loss1: 0.285728 Loss2: 1.404537
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.537213 Loss1: 0.183215 Loss2: 1.353998
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.552984 Loss1: 0.193589 Loss2: 1.359395
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513066 Loss1: 0.152378 Loss2: 1.360689
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467450 Loss1: 0.124944 Loss2: 1.342505
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.707313 Loss1: 0.850947 Loss2: 1.856366
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.867041 Loss1: 0.464716 Loss2: 1.402325 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.703760 Loss1: 0.266620 Loss2: 1.437140 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.583250 Loss1: 0.203687 Loss2: 1.379563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499877 Loss1: 0.125666 Loss2: 1.374211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443799 Loss1: 0.073915 Loss2: 1.369883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416590 Loss1: 0.060132 Loss2: 1.356458 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424525 Loss1: 0.067391 Loss2: 1.357134 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562252 Loss1: 0.195154 Loss2: 1.367099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.501629 Loss1: 0.139054 Loss2: 1.362575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.613807 Loss1: 0.771021 Loss2: 1.842786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.877491 Loss1: 0.512260 Loss2: 1.365231 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.693165 Loss1: 0.279925 Loss2: 1.413240 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529015 Loss1: 0.170289 Loss2: 1.358726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455713 Loss1: 0.106956 Loss2: 1.348757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422149 Loss1: 0.078489 Loss2: 1.343660 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389571 Loss1: 0.053573 Loss2: 1.335998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400720 Loss1: 0.071198 Loss2: 1.329521 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.600008 Loss1: 0.257684 Loss2: 1.342323 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484890 Loss1: 0.144511 Loss2: 1.340379 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448509 Loss1: 0.112451 Loss2: 1.336058
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.621892 Loss1: 0.711518 Loss2: 1.910374
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.776614 Loss1: 0.380363 Loss2: 1.396251 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.715694 Loss1: 0.297182 Loss2: 1.418512 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576479 Loss1: 0.190116 Loss2: 1.386363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464827 Loss1: 0.086856 Loss2: 1.377971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440536 Loss1: 0.070116 Loss2: 1.370420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445465 Loss1: 0.078519 Loss2: 1.366947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408380 Loss1: 0.051631 Loss2: 1.356749 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554647 Loss1: 0.237685 Loss2: 1.316962 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.446545 Loss1: 0.143405 Loss2: 1.303140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.507913 Loss1: 0.688852 Loss2: 1.819061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.837004 Loss1: 0.451269 Loss2: 1.385735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.746423 Loss1: 0.321019 Loss2: 1.425404 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.564014 Loss1: 0.187692 Loss2: 1.376322 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.536015 Loss1: 0.169326 Loss2: 1.366690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.650362 Loss1: 0.833345 Loss2: 1.817017
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472114 Loss1: 0.109691 Loss2: 1.362423
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.944875 Loss1: 0.610618 Loss2: 1.334258
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452812 Loss1: 0.092626 Loss2: 1.360186
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.770141 Loss1: 0.365456 Loss2: 1.404685
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450640 Loss1: 0.099097 Loss2: 1.351543
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.523270 Loss1: 0.194575 Loss2: 1.328695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473451 Loss1: 0.167912 Loss2: 1.305538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.770136 Loss1: 0.898443 Loss2: 1.871693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.015381 Loss1: 0.586877 Loss2: 1.428504 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.616071 Loss1: 0.245613 Loss2: 1.370458 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492638 Loss1: 0.138500 Loss2: 1.354138 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473003 Loss1: 0.118201 Loss2: 1.354802
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.681213 Loss1: 0.848071 Loss2: 1.833142
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419473 Loss1: 0.074345 Loss2: 1.345128
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.849838 Loss1: 0.489911 Loss2: 1.359927
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394156 Loss1: 0.060277 Loss2: 1.333880
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.705859 Loss1: 0.317503 Loss2: 1.388357
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382599 Loss1: 0.054794 Loss2: 1.327804
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.617140 Loss1: 0.276210 Loss2: 1.340929
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534882 Loss1: 0.180922 Loss2: 1.353960
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473956 Loss1: 0.138210 Loss2: 1.335746
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419477 Loss1: 0.088922 Loss2: 1.330555
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398698 Loss1: 0.077677 Loss2: 1.321022
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.578387 Loss1: 0.683626 Loss2: 1.894760
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378197 Loss1: 0.062079 Loss2: 1.316118
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.886094 Loss1: 0.478561 Loss2: 1.407533
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355305 Loss1: 0.047427 Loss2: 1.307878
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.638989 Loss1: 0.240208 Loss2: 1.398781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.483478 Loss1: 0.095832 Loss2: 1.387647 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475149 Loss1: 0.093011 Loss2: 1.382138
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.603228 Loss1: 0.810617 Loss2: 1.792611
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426774 Loss1: 0.055843 Loss2: 1.370932
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.840050 Loss1: 0.489983 Loss2: 1.350067
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405426 Loss1: 0.039814 Loss2: 1.365611
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.708524 Loss1: 0.321503 Loss2: 1.387021
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389358 Loss1: 0.030446 Loss2: 1.358912
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.590044 Loss1: 0.256075 Loss2: 1.333968
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507545 Loss1: 0.173773 Loss2: 1.333772
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.439003 Loss1: 0.114997 Loss2: 1.324006
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426390 Loss1: 0.110462 Loss2: 1.315928
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.355415 Loss1: 0.048356 Loss2: 1.307058
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.357204 Loss1: 0.058591 Loss2: 1.298612
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.606196 Loss1: 0.820581 Loss2: 1.785615
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360297 Loss1: 0.064913 Loss2: 1.295384
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.778179 Loss1: 0.442598 Loss2: 1.335581
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614855 Loss1: 0.254185 Loss2: 1.360669
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.569213 Loss1: 0.244034 Loss2: 1.325179
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.486402 Loss1: 0.157700 Loss2: 1.328702
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441641 Loss1: 0.122189 Loss2: 1.319452
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392615 Loss1: 0.084657 Loss2: 1.307958
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.692750 Loss1: 0.833358 Loss2: 1.859392
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376240 Loss1: 0.073882 Loss2: 1.302358
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.886001 Loss1: 0.490344 Loss2: 1.395656
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376210 Loss1: 0.078644 Loss2: 1.297566
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.653877 Loss1: 0.240529 Loss2: 1.413348
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.342866 Loss1: 0.053586 Loss2: 1.289279
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.631154 Loss1: 0.257722 Loss2: 1.373432
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571762 Loss1: 0.191651 Loss2: 1.380111
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506713 Loss1: 0.145731 Loss2: 1.360982
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.520337 Loss1: 0.160420 Loss2: 1.359917
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.490711 Loss1: 0.118005 Loss2: 1.372705
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.823990 Loss1: 0.858099 Loss2: 1.965891
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463896 Loss1: 0.108783 Loss2: 1.355113
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463961 Loss1: 0.107817 Loss2: 1.356144 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543520 Loss1: 0.137783 Loss2: 1.405737 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.526235 Loss1: 0.130457 Loss2: 1.395778 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.691308 Loss1: 0.798166 Loss2: 1.893142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458335 Loss1: 0.066661 Loss2: 1.391674 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604985 Loss1: 0.173749 Loss2: 1.431236 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503369 Loss1: 0.088679 Loss2: 1.414690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.498733 Loss1: 0.092774 Loss2: 1.405959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.485002 Loss1: 0.081574 Loss2: 1.403428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474027 Loss1: 0.070906 Loss2: 1.403121 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458042 Loss1: 0.061142 Loss2: 1.396900 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.540521 Loss1: 0.142916 Loss2: 1.397605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.505958 Loss1: 0.116681 Loss2: 1.389277 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.551105 Loss1: 0.679182 Loss2: 1.871924 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.707622 Loss1: 0.279757 Loss2: 1.427865 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.489745 Loss1: 0.116315 Loss2: 1.373430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506858 Loss1: 0.142032 Loss2: 1.364826
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.477287 Loss1: 0.681475 Loss2: 1.795813
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.793306 Loss1: 0.430825 Loss2: 1.362481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.599504 Loss1: 0.219499 Loss2: 1.380004 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.522682 Loss1: 0.196811 Loss2: 1.325871 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.443975 Loss1: 0.111692 Loss2: 1.332283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450740 Loss1: 0.115792 Loss2: 1.334948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409072 Loss1: 0.093591 Loss2: 1.315481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428415 Loss1: 0.117428 Loss2: 1.310987 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551261 Loss1: 0.195988 Loss2: 1.355273 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472229 Loss1: 0.121732 Loss2: 1.350497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441636 Loss1: 0.095312 Loss2: 1.346324
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.524314 Loss1: 0.670164 Loss2: 1.854150
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409072 Loss1: 0.069462 Loss2: 1.339609
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.812127 Loss1: 0.434667 Loss2: 1.377460
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390283 Loss1: 0.065334 Loss2: 1.324950
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.663393 Loss1: 0.261203 Loss2: 1.402190
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377151 Loss1: 0.052822 Loss2: 1.324329
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551265 Loss1: 0.184863 Loss2: 1.366401
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500721 Loss1: 0.142828 Loss2: 1.357893
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477067 Loss1: 0.115677 Loss2: 1.361391
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.409866 Loss1: 0.067429 Loss2: 1.342438
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.403758 Loss1: 0.064474 Loss2: 1.339284
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.875580 Loss1: 0.959589 Loss2: 1.915990
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405723 Loss1: 0.066779 Loss2: 1.338944
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.879185 Loss1: 0.445641 Loss2: 1.433544
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401496 Loss1: 0.060573 Loss2: 1.340924
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596612 Loss1: 0.189329 Loss2: 1.407283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.495880 Loss1: 0.105726 Loss2: 1.390153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529634 Loss1: 0.143280 Loss2: 1.386353
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.631960 Loss1: 0.778345 Loss2: 1.853616
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484341 Loss1: 0.102413 Loss2: 1.381929
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.927789 Loss1: 0.544273 Loss2: 1.383516
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469393 Loss1: 0.095882 Loss2: 1.373511
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.823475 Loss1: 0.367754 Loss2: 1.455721
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439391 Loss1: 0.066140 Loss2: 1.373250
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.675564 Loss1: 0.298900 Loss2: 1.376664
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.652140 Loss1: 0.253297 Loss2: 1.398843
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542280 Loss1: 0.163886 Loss2: 1.378393
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455020 Loss1: 0.095997 Loss2: 1.359023
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467187 Loss1: 0.105220 Loss2: 1.361967
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412923 Loss1: 0.065293 Loss2: 1.347631
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.497593 Loss1: 0.642801 Loss2: 1.854792
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392348 Loss1: 0.049973 Loss2: 1.342375
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.736087 Loss1: 0.367950 Loss2: 1.368137
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.666489 Loss1: 0.264057 Loss2: 1.402432
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599881 Loss1: 0.238972 Loss2: 1.360910
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.529278 Loss1: 0.167109 Loss2: 1.362169
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.596347 Loss1: 0.230384 Loss2: 1.365963
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525162 Loss1: 0.162398 Loss2: 1.362764
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.489511 Loss1: 0.674756 Loss2: 1.814755
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.835269 Loss1: 0.447760 Loss2: 1.387509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.697679 Loss1: 0.277995 Loss2: 1.419683 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430617 Loss1: 0.087423 Loss2: 1.343194
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.603941 Loss1: 0.218132 Loss2: 1.385809
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.552396 Loss1: 0.173245 Loss2: 1.379150
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531795 Loss1: 0.152136 Loss2: 1.379659
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473547 Loss1: 0.108255 Loss2: 1.365292
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427385 Loss1: 0.070210 Loss2: 1.357174
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.517104 Loss1: 0.685031 Loss2: 1.832073
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.783476 Loss1: 0.413142 Loss2: 1.370334 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376593 Loss1: 0.032694 Loss2: 1.343899
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.668336 Loss1: 0.272418 Loss2: 1.395919
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549445 Loss1: 0.197106 Loss2: 1.352339
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507927 Loss1: 0.168206 Loss2: 1.339721
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530869 Loss1: 0.187693 Loss2: 1.343177
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469419 Loss1: 0.129475 Loss2: 1.339944
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448373 Loss1: 0.115216 Loss2: 1.333158
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.717942 Loss1: 0.876609 Loss2: 1.841333
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453972 Loss1: 0.122805 Loss2: 1.331168
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.847328 Loss1: 0.443477 Loss2: 1.403851
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427328 Loss1: 0.094724 Loss2: 1.332604
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.723954 Loss1: 0.318083 Loss2: 1.405871
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.655624 Loss1: 0.273481 Loss2: 1.382143
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558302 Loss1: 0.180466 Loss2: 1.377836
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.489008 Loss1: 0.126466 Loss2: 1.362542
+DEBUG flwr 2023-10-11 16:06:01,233 | server.py:236 | fit_round 119 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438512 Loss1: 0.072448 Loss2: 1.366064
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.496632 Loss1: 0.662381 Loss2: 1.834251
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427502 Loss1: 0.074102 Loss2: 1.353400
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.859501 Loss1: 0.483032 Loss2: 1.376469
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404023 Loss1: 0.058881 Loss2: 1.345142
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.751116 Loss1: 0.320756 Loss2: 1.430359
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396847 Loss1: 0.054915 Loss2: 1.341932
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.616041 Loss1: 0.222048 Loss2: 1.393993 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481879 Loss1: 0.114733 Loss2: 1.367146 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460223 Loss1: 0.097362 Loss2: 1.362861
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.721104 Loss1: 0.849982 Loss2: 1.871122
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458470 Loss1: 0.104795 Loss2: 1.353675
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.866940 Loss1: 0.478284 Loss2: 1.388656
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449001 Loss1: 0.100650 Loss2: 1.348351
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.697376 Loss1: 0.314519 Loss2: 1.382857
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543055 Loss1: 0.173906 Loss2: 1.369149
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497755 Loss1: 0.150414 Loss2: 1.347341
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476312 Loss1: 0.132700 Loss2: 1.343611
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.393669 Loss1: 0.055869 Loss2: 1.337800
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.601103 Loss1: 0.762530 Loss2: 1.838573
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408785 Loss1: 0.076279 Loss2: 1.332506
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.902571 Loss1: 0.515679 Loss2: 1.386892
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404127 Loss1: 0.076563 Loss2: 1.327564
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.668795 Loss1: 0.262759 Loss2: 1.406036
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366009 Loss1: 0.044023 Loss2: 1.321986
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.599823 Loss1: 0.220483 Loss2: 1.379340 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467552 Loss1: 0.106254 Loss2: 1.361298 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486737 Loss1: 0.130923 Loss2: 1.355814
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.540321 Loss1: 0.721312 Loss2: 1.819009
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428179 Loss1: 0.079321 Loss2: 1.348858
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.872309 Loss1: 0.511192 Loss2: 1.361117
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421667 Loss1: 0.076713 Loss2: 1.344954
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.717286 Loss1: 0.299008 Loss2: 1.418278
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.544123 Loss1: 0.188604 Loss2: 1.355519
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544801 Loss1: 0.188988 Loss2: 1.355813
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479744 Loss1: 0.123756 Loss2: 1.355987
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497164 Loss1: 0.152218 Loss2: 1.344946
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473420 Loss1: 0.130068 Loss2: 1.343351
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.498798 Loss1: 0.157355 Loss2: 1.341443
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431154 Loss1: 0.090136 Loss2: 1.341018
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 16:06:01,233][flwr][DEBUG] - fit_round 119 received 50 results and 0 failures
+INFO flwr 2023-10-11 16:06:41,589 | server.py:125 | fit progress: (119, 2.20066045934019, {'accuracy': 0.5794}, 274509.36708156497)
+>> Test accuracy: 0.579400
+[2023-10-11 16:06:41,589][flwr][INFO] - fit progress: (119, 2.20066045934019, {'accuracy': 0.5794}, 274509.36708156497)
+DEBUG flwr 2023-10-11 16:06:41,589 | server.py:173 | evaluate_round 119: strategy sampled 50 clients (out of 50)
+[2023-10-11 16:06:41,589][flwr][DEBUG] - evaluate_round 119: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 16:15:48,017 | server.py:187 | evaluate_round 119 received 50 results and 0 failures
+[2023-10-11 16:15:48,017][flwr][DEBUG] - evaluate_round 119 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 16:15:48,018 | server.py:222 | fit_round 120: strategy sampled 50 clients (out of 50)
+[2023-10-11 16:15:48,018][flwr][DEBUG] - fit_round 120: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.554561 Loss1: 0.712557 Loss2: 1.842004
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.747517 Loss1: 0.382555 Loss2: 1.364962
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.654988 Loss1: 0.253666 Loss2: 1.401322
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563171 Loss1: 0.201377 Loss2: 1.361794
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.552059 Loss1: 0.684097 Loss2: 1.867962
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.926081 Loss1: 0.534709 Loss2: 1.391371 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.742841 Loss1: 0.287152 Loss2: 1.455689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.645522 Loss1: 0.255101 Loss2: 1.390421 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.582292 Loss1: 0.193384 Loss2: 1.388909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.547747 Loss1: 0.173621 Loss2: 1.374126 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385449 Loss1: 0.054846 Loss2: 1.330603
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459573 Loss1: 0.096064 Loss2: 1.363509
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433903 Loss1: 0.074981 Loss2: 1.358922
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399599 Loss1: 0.045354 Loss2: 1.354244
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410741 Loss1: 0.069421 Loss2: 1.341320
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.864972 Loss1: 0.959897 Loss2: 1.905075
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.838676 Loss1: 0.476404 Loss2: 1.362271
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.729278 Loss1: 0.324598 Loss2: 1.404679
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591587 Loss1: 0.225111 Loss2: 1.366475
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.620886 Loss1: 0.771422 Loss2: 1.849463
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.853738 Loss1: 0.486500 Loss2: 1.367238 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.648647 Loss1: 0.236560 Loss2: 1.412088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534386 Loss1: 0.184754 Loss2: 1.349632 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456194 Loss1: 0.108689 Loss2: 1.347505 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412498 Loss1: 0.078153 Loss2: 1.334344 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.456009 Loss1: 0.113829 Loss2: 1.342180 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392222 Loss1: 0.060226 Loss2: 1.331996 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.889907 Loss1: 0.467350 Loss2: 1.422557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630071 Loss1: 0.220610 Loss2: 1.409461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566294 Loss1: 0.150324 Loss2: 1.415970
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.624544 Loss1: 0.826012 Loss2: 1.798532
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.893498 Loss1: 0.529718 Loss2: 1.363779 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671256 Loss1: 0.297191 Loss2: 1.374064 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605525 Loss1: 0.260371 Loss2: 1.345154 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530701 Loss1: 0.177510 Loss2: 1.353192 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981027
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433344 Loss1: 0.099855 Loss2: 1.333489 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453481 Loss1: 0.132518 Loss2: 1.320963 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396947 Loss1: 0.070213 Loss2: 1.326735
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.789241 Loss1: 0.878304 Loss2: 1.910936
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.922282 Loss1: 0.484168 Loss2: 1.438115
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.758632 Loss1: 0.294055 Loss2: 1.464577
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.685717 Loss1: 0.253569 Loss2: 1.432147
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.661909 Loss1: 0.217442 Loss2: 1.444467
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.746588 Loss1: 0.895664 Loss2: 1.850924
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.555107 Loss1: 0.137763 Loss2: 1.417344
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.545737 Loss1: 0.132199 Loss2: 1.413539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.518082 Loss1: 0.098003 Loss2: 1.420080 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488370 Loss1: 0.078066 Loss2: 1.410304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476618 Loss1: 0.070164 Loss2: 1.406454 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441415 Loss1: 0.085098 Loss2: 1.356317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397587 Loss1: 0.060191 Loss2: 1.337395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380779 Loss1: 0.044256 Loss2: 1.336523
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.545850 Loss1: 0.698580 Loss2: 1.847270
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.826584 Loss1: 0.455465 Loss2: 1.371119
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.711356 Loss1: 0.326630 Loss2: 1.384725
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.625058 Loss1: 0.246762 Loss2: 1.378296
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.596796 Loss1: 0.237335 Loss2: 1.359460
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.770742 Loss1: 0.845369 Loss2: 1.925373
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.527778 Loss1: 0.162554 Loss2: 1.365224
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479533 Loss1: 0.137748 Loss2: 1.341785 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417015 Loss1: 0.079919 Loss2: 1.337096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384916 Loss1: 0.062296 Loss2: 1.322620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392726 Loss1: 0.067113 Loss2: 1.325613 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.558787 Loss1: 0.151405 Loss2: 1.407381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.518226 Loss1: 0.112711 Loss2: 1.405515 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.502450 Loss1: 0.106117 Loss2: 1.396333
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.581641 Loss1: 0.716968 Loss2: 1.864673
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.857438 Loss1: 0.488111 Loss2: 1.369327
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690037 Loss1: 0.270023 Loss2: 1.420014
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551552 Loss1: 0.189962 Loss2: 1.361591
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499374 Loss1: 0.137843 Loss2: 1.361531
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.568966 Loss1: 0.718679 Loss2: 1.850287
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468102 Loss1: 0.119498 Loss2: 1.348604
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.821765 Loss1: 0.420894 Loss2: 1.400871
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472729 Loss1: 0.130933 Loss2: 1.341796
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404650 Loss1: 0.061909 Loss2: 1.342741
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.646776 Loss1: 0.222343 Loss2: 1.424433
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.383700 Loss1: 0.049368 Loss2: 1.334331
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581868 Loss1: 0.184390 Loss2: 1.397478
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367409 Loss1: 0.041204 Loss2: 1.326206
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558086 Loss1: 0.160720 Loss2: 1.397366
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.518062 Loss1: 0.129862 Loss2: 1.388200
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550293 Loss1: 0.163974 Loss2: 1.386319
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493892 Loss1: 0.107376 Loss2: 1.386515
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.468935 Loss1: 0.083442 Loss2: 1.385494
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.539310 Loss1: 0.671324 Loss2: 1.867986
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426510 Loss1: 0.055928 Loss2: 1.370582
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645051 Loss1: 0.218652 Loss2: 1.426399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.523444 Loss1: 0.123793 Loss2: 1.399651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.501908 Loss1: 0.116543 Loss2: 1.385365
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.743778 Loss1: 0.750951 Loss2: 1.992828
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456192 Loss1: 0.070758 Loss2: 1.385434
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.890782 Loss1: 0.429942 Loss2: 1.460840
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473585 Loss1: 0.095757 Loss2: 1.377828
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.822842 Loss1: 0.316487 Loss2: 1.506355
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.678633 Loss1: 0.228374 Loss2: 1.450259
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.491463 Loss1: 0.109871 Loss2: 1.381592
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.613916 Loss1: 0.164248 Loss2: 1.449668
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458669 Loss1: 0.080019 Loss2: 1.378650
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.531350 Loss1: 0.090021 Loss2: 1.441330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.484176 Loss1: 0.065778 Loss2: 1.418398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.471787 Loss1: 0.058537 Loss2: 1.413249
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.516428 Loss1: 0.681405 Loss2: 1.835023
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.756256 Loss1: 0.395508 Loss2: 1.360748
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648529 Loss1: 0.259330 Loss2: 1.389199
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554078 Loss1: 0.192147 Loss2: 1.361931
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526402 Loss1: 0.173226 Loss2: 1.353176
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.656911 Loss1: 0.773687 Loss2: 1.883224
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.428620 Loss1: 0.080705 Loss2: 1.347915
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414005 Loss1: 0.077099 Loss2: 1.336906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400594 Loss1: 0.069988 Loss2: 1.330606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.383987 Loss1: 0.052507 Loss2: 1.331480 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383723 Loss1: 0.062879 Loss2: 1.320844 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485107 Loss1: 0.131112 Loss2: 1.353996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457891 Loss1: 0.106347 Loss2: 1.351544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431257 Loss1: 0.086699 Loss2: 1.344558
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.738486 Loss1: 0.804295 Loss2: 1.934192
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.952334 Loss1: 0.499259 Loss2: 1.453074
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.816731 Loss1: 0.312508 Loss2: 1.504223
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.665390 Loss1: 0.217625 Loss2: 1.447765
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587877 Loss1: 0.143179 Loss2: 1.444698
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.575552 Loss1: 0.749793 Loss2: 1.825760
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.572150 Loss1: 0.136802 Loss2: 1.435348
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529254 Loss1: 0.101077 Loss2: 1.428177 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.493543 Loss1: 0.071434 Loss2: 1.422109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493364 Loss1: 0.079664 Loss2: 1.413700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.471954 Loss1: 0.057901 Loss2: 1.414052 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434900 Loss1: 0.087968 Loss2: 1.346932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416522 Loss1: 0.072456 Loss2: 1.344066 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407163 Loss1: 0.066180 Loss2: 1.340983
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.758289 Loss1: 0.877406 Loss2: 1.880884
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.909653 Loss1: 0.507566 Loss2: 1.402088
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.711284 Loss1: 0.278110 Loss2: 1.433174
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.621246 Loss1: 0.232588 Loss2: 1.388658
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.563859 Loss1: 0.174005 Loss2: 1.389854
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.534544 Loss1: 0.692615 Loss2: 1.841929
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.749480 Loss1: 0.401154 Loss2: 1.348326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.696952 Loss1: 0.287396 Loss2: 1.409556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.570759 Loss1: 0.224583 Loss2: 1.346176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.537072 Loss1: 0.192379 Loss2: 1.344694 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472004 Loss1: 0.122038 Loss2: 1.349966 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441652 Loss1: 0.103932 Loss2: 1.337720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370993 Loss1: 0.051184 Loss2: 1.319809 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.955524 Loss1: 0.547744 Loss2: 1.407780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589619 Loss1: 0.208457 Loss2: 1.381162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536940 Loss1: 0.154365 Loss2: 1.382575
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.557548 Loss1: 0.717378 Loss2: 1.840170
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.924057 Loss1: 0.549938 Loss2: 1.374119 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804680 Loss1: 0.358271 Loss2: 1.446409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599052 Loss1: 0.235612 Loss2: 1.363440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550095 Loss1: 0.173091 Loss2: 1.377004 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446160 Loss1: 0.085035 Loss2: 1.361125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.550689 Loss1: 0.187326 Loss2: 1.363362
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444235 Loss1: 0.080330 Loss2: 1.363905
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406908 Loss1: 0.055911 Loss2: 1.350996
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399427 Loss1: 0.054939 Loss2: 1.344489
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380788 Loss1: 0.045710 Loss2: 1.335078
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.641776 Loss1: 0.805990 Loss2: 1.835786
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.822747 Loss1: 0.486211 Loss2: 1.336536
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.701576 Loss1: 0.306289 Loss2: 1.395288
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511045 Loss1: 0.177262 Loss2: 1.333783
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479682 Loss1: 0.157685 Loss2: 1.321997
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.446800 Loss1: 0.657102 Loss2: 1.789698
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472215 Loss1: 0.144458 Loss2: 1.327757
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441319 Loss1: 0.121780 Loss2: 1.319539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.720913 Loss1: 0.327609 Loss2: 1.393304
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.466476 Loss1: 0.146577 Loss2: 1.319899
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419800 Loss1: 0.097190 Loss2: 1.322610
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.624474 Loss1: 0.246290 Loss2: 1.378184
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378660 Loss1: 0.068801 Loss2: 1.309859
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496721 Loss1: 0.137257 Loss2: 1.359464
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441411 Loss1: 0.099070 Loss2: 1.342341
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441289 Loss1: 0.107014 Loss2: 1.334274
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418844 Loss1: 0.085155 Loss2: 1.333689
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396800 Loss1: 0.074397 Loss2: 1.322403
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.640191 Loss1: 0.755328 Loss2: 1.884863
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.909933 Loss1: 0.501782 Loss2: 1.408151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694389 Loss1: 0.294963 Loss2: 1.399427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.558898 Loss1: 0.169494 Loss2: 1.389404 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512248 Loss1: 0.129323 Loss2: 1.382925 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488853 Loss1: 0.107394 Loss2: 1.381459 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.426441 Loss1: 0.065535 Loss2: 1.360906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409414 Loss1: 0.047144 Loss2: 1.362270 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.644750 Loss1: 0.204086 Loss2: 1.440664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.540846 Loss1: 0.113336 Loss2: 1.427510 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.593707 Loss1: 0.795091 Loss2: 1.798616 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.742636 Loss1: 0.352137 Loss2: 1.390499 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558761 Loss1: 0.219333 Loss2: 1.339428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515159 Loss1: 0.174380 Loss2: 1.340779
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.752432 Loss1: 0.835412 Loss2: 1.917020
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.012707 Loss1: 0.633102 Loss2: 1.379605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.828468 Loss1: 0.394641 Loss2: 1.433827
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481712 Loss1: 0.153608 Loss2: 1.328104
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.659120 Loss1: 0.276943 Loss2: 1.382176
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419659 Loss1: 0.098190 Loss2: 1.321470
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405317 Loss1: 0.085742 Loss2: 1.319574 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460719 Loss1: 0.096878 Loss2: 1.363841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404450 Loss1: 0.055571 Loss2: 1.348879 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.687900 Loss1: 0.771884 Loss2: 1.916016 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.811696 Loss1: 0.338919 Loss2: 1.472777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.814744 Loss1: 0.879144 Loss2: 1.935600 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.887633 Loss1: 0.465948 Loss2: 1.421685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.693925 Loss1: 0.247049 Loss2: 1.446876 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.607536 Loss1: 0.213284 Loss2: 1.394252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555429 Loss1: 0.148639 Loss2: 1.406789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529199 Loss1: 0.138058 Loss2: 1.391141 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458478 Loss1: 0.078537 Loss2: 1.379941 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449908 Loss1: 0.074950 Loss2: 1.374957 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.863767 Loss1: 0.463589 Loss2: 1.400178 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.616496 Loss1: 0.235024 Loss2: 1.381472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.729576 Loss1: 0.785150 Loss2: 1.944426
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562679 Loss1: 0.178553 Loss2: 1.384126
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.500978 Loss1: 0.123463 Loss2: 1.377515 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455884 Loss1: 0.095807 Loss2: 1.360077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436704 Loss1: 0.076763 Loss2: 1.359941 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.426185 Loss1: 0.070650 Loss2: 1.355535 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404834 Loss1: 0.050991 Loss2: 1.353843 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387712 Loss1: 0.048964 Loss2: 1.338748 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.481905 Loss1: 0.730385 Loss2: 1.751520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615246 Loss1: 0.269281 Loss2: 1.345966 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522780 Loss1: 0.212793 Loss2: 1.309987
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.520937 Loss1: 0.706121 Loss2: 1.814816
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.799156 Loss1: 0.415603 Loss2: 1.383553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629768 Loss1: 0.232869 Loss2: 1.396899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543089 Loss1: 0.189658 Loss2: 1.353431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493685 Loss1: 0.139098 Loss2: 1.354587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477430 Loss1: 0.120078 Loss2: 1.357352 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451416 Loss1: 0.108475 Loss2: 1.342941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399916 Loss1: 0.065583 Loss2: 1.334333 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.574380 Loss1: 0.693573 Loss2: 1.880807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.714194 Loss1: 0.276040 Loss2: 1.438153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.658107 Loss1: 0.778820 Loss2: 1.879287 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.859490 Loss1: 0.470557 Loss2: 1.388932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.757501 Loss1: 0.332640 Loss2: 1.424861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.673682 Loss1: 0.287076 Loss2: 1.386607 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.582987 Loss1: 0.190934 Loss2: 1.392053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512207 Loss1: 0.145893 Loss2: 1.366315 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.444140 Loss1: 0.079285 Loss2: 1.364855 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407428 Loss1: 0.058856 Loss2: 1.348572 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.771031 Loss1: 0.435578 Loss2: 1.335454 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.525462 Loss1: 0.190757 Loss2: 1.334705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498346 Loss1: 0.171863 Loss2: 1.326483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.428253 Loss1: 0.106405 Loss2: 1.321848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410978 Loss1: 0.100000 Loss2: 1.310979 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.387795 Loss1: 0.074182 Loss2: 1.313613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.393867 Loss1: 0.087014 Loss2: 1.306853 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.362196 Loss1: 0.061477 Loss2: 1.300718 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445528 Loss1: 0.098322 Loss2: 1.347206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423591 Loss1: 0.087655 Loss2: 1.335936 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) 
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.716694 Loss1: 0.405360 Loss2: 1.311334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562464 Loss1: 0.257657 Loss2: 1.304808 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.481142 Loss1: 0.169802 Loss2: 1.311341 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.394783 Loss1: 0.099287 Loss2: 1.295496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.396877 Loss1: 0.109097 Loss2: 1.287780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380046 Loss1: 0.090642 Loss2: 1.289404 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.347737 Loss1: 0.065222 Loss2: 1.282515 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.323035 Loss1: 0.048442 Loss2: 1.274592 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451374 Loss1: 0.082899 Loss2: 1.368475 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.546657 Loss1: 0.718315 Loss2: 1.828342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.719446 Loss1: 0.276318 Loss2: 1.443128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563307 Loss1: 0.177764 Loss2: 1.385543
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.567898 Loss1: 0.733495 Loss2: 1.834403
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499805 Loss1: 0.122926 Loss2: 1.376879
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.929176 Loss1: 0.526054 Loss2: 1.403122
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461865 Loss1: 0.089450 Loss2: 1.372415
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.785606 Loss1: 0.357001 Loss2: 1.428604
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424792 Loss1: 0.060618 Loss2: 1.364173
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.695721 Loss1: 0.313101 Loss2: 1.382620
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441327 Loss1: 0.088481 Loss2: 1.352846
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.587030 Loss1: 0.201334 Loss2: 1.385695
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458301 Loss1: 0.098284 Loss2: 1.360017
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472697 Loss1: 0.112429 Loss2: 1.360268
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461038 Loss1: 0.091228 Loss2: 1.369810
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459341 Loss1: 0.109757 Loss2: 1.349584
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447823 Loss1: 0.102085 Loss2: 1.345737
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430207 Loss1: 0.080841 Loss2: 1.349366
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.455534 Loss1: 0.104693 Loss2: 1.350841
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.495809 Loss1: 0.655923 Loss2: 1.839886
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.768210 Loss1: 0.416020 Loss2: 1.352190
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.646534 Loss1: 0.253326 Loss2: 1.393208
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574457 Loss1: 0.216747 Loss2: 1.357711
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.735078 Loss1: 0.857361 Loss2: 1.877717
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.877267 Loss1: 0.483918 Loss2: 1.393349 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.707121 Loss1: 0.274809 Loss2: 1.432312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584152 Loss1: 0.200418 Loss2: 1.383734 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529353 Loss1: 0.151112 Loss2: 1.378241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508258 Loss1: 0.129040 Loss2: 1.379218 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473293 Loss1: 0.094734 Loss2: 1.378559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430922 Loss1: 0.067146 Loss2: 1.363776 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.472787 Loss1: 0.674287 Loss2: 1.798500 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684789 Loss1: 0.280243 Loss2: 1.404546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586881 Loss1: 0.216611 Loss2: 1.370270
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.578563 Loss1: 0.747099 Loss2: 1.831464
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.779906 Loss1: 0.434991 Loss2: 1.344914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.639863 Loss1: 0.269678 Loss2: 1.370185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500965 Loss1: 0.148383 Loss2: 1.352581
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509240 Loss1: 0.185124 Loss2: 1.324116
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452639 Loss1: 0.096417 Loss2: 1.356222
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.456763 Loss1: 0.130768 Loss2: 1.325995
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493187 Loss1: 0.139758 Loss2: 1.353429
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473103 Loss1: 0.152060 Loss2: 1.321043
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439316 Loss1: 0.088200 Loss2: 1.351117
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423369 Loss1: 0.108503 Loss2: 1.314866
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406012 Loss1: 0.105182 Loss2: 1.300830
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418892 Loss1: 0.117153 Loss2: 1.301739
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376102 Loss1: 0.070682 Loss2: 1.305420
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.903521 Loss1: 0.835337 Loss2: 2.068184
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.057379 Loss1: 0.631251 Loss2: 1.426128
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.919259 Loss1: 0.410561 Loss2: 1.508699
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.785374 Loss1: 0.336591 Loss2: 1.448783
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.659504 Loss1: 0.230853 Loss2: 1.428651
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.838873 Loss1: 0.475489 Loss2: 1.363384 [repeated 3x across cluster]
+DEBUG flwr 2023-10-11 16:44:04,487 | server.py:236 | fit_round 120 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.657822 Loss1: 0.304274 Loss2: 1.353548 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.487386 Loss1: 0.079082 Loss2: 1.408304 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983073
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.503254 Loss1: 0.094248 Loss2: 1.409006 [repeated 2x across cluster]
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425916 Loss1: 0.117371 Loss2: 1.308545 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368632 Loss1: 0.067041 Loss2: 1.301591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351883 Loss1: 0.054450 Loss2: 1.297434 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.628851 Loss1: 0.224911 Loss2: 1.403940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544295 Loss1: 0.173597 Loss2: 1.370698 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503792 Loss1: 0.142502 Loss2: 1.361290
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.593604 Loss1: 0.675284 Loss2: 1.918320
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.818228 Loss1: 0.401996 Loss2: 1.416232
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442994 Loss1: 0.087512 Loss2: 1.355482
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.718564 Loss1: 0.261596 Loss2: 1.456969
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429069 Loss1: 0.078591 Loss2: 1.350478
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.694222 Loss1: 0.280118 Loss2: 1.414104
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450574 Loss1: 0.106669 Loss2: 1.343905
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438805 Loss1: 0.095149 Loss2: 1.343656 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988971
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.525107 Loss1: 0.123021 Loss2: 1.402085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444765 Loss1: 0.057959 Loss2: 1.386806 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 16:44:04,487][flwr][DEBUG] - fit_round 120 received 50 results and 0 failures
+INFO flwr 2023-10-11 16:44:46,252 | server.py:125 | fit progress: (120, 2.2117004977247587, {'accuracy': 0.5816}, 276794.030562)
+>> Test accuracy: 0.581600
+[2023-10-11 16:44:46,252][flwr][INFO] - fit progress: (120, 2.2117004977247587, {'accuracy': 0.5816}, 276794.030562)
+DEBUG flwr 2023-10-11 16:44:46,252 | server.py:173 | evaluate_round 120: strategy sampled 50 clients (out of 50)
+[2023-10-11 16:44:46,252][flwr][DEBUG] - evaluate_round 120: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 16:53:51,591 | server.py:187 | evaluate_round 120 received 50 results and 0 failures
+[2023-10-11 16:53:51,591][flwr][DEBUG] - evaluate_round 120 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 16:53:51,592 | server.py:222 | fit_round 121: strategy sampled 50 clients (out of 50)
+[2023-10-11 16:53:51,592][flwr][DEBUG] - fit_round 121: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.677700 Loss1: 0.799180 Loss2: 1.878521 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.865612 Loss1: 0.392350 Loss2: 1.473262 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.449436 Loss1: 0.617994 Loss2: 1.831442
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647401 Loss1: 0.210317 Loss2: 1.437084
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.733803 Loss1: 0.351450 Loss2: 1.382353
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.554066 Loss1: 0.133143 Loss2: 1.420923
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622260 Loss1: 0.229019 Loss2: 1.393241
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520568 Loss1: 0.118081 Loss2: 1.402486
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529648 Loss1: 0.158611 Loss2: 1.371037
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485859 Loss1: 0.086521 Loss2: 1.399338
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507339 Loss1: 0.138483 Loss2: 1.368856
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481097 Loss1: 0.084695 Loss2: 1.396402
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468331 Loss1: 0.105092 Loss2: 1.363239
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489869 Loss1: 0.093036 Loss2: 1.396833
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437068 Loss1: 0.081339 Loss2: 1.355729
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.471418 Loss1: 0.079360 Loss2: 1.392058
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429496 Loss1: 0.080218 Loss2: 1.349278 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.524345 Loss1: 0.700670 Loss2: 1.823675 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.865229 Loss1: 0.428180 Loss2: 1.437049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599609 Loss1: 0.238315 Loss2: 1.361294
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.596027 Loss1: 0.766395 Loss2: 1.829632
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.881952 Loss1: 0.528127 Loss2: 1.353824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670395 Loss1: 0.291018 Loss2: 1.379377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.602937 Loss1: 0.266822 Loss2: 1.336115 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529661 Loss1: 0.180291 Loss2: 1.349370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485474 Loss1: 0.149086 Loss2: 1.336389 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374831 Loss1: 0.045456 Loss2: 1.329375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433809 Loss1: 0.114245 Loss2: 1.319563
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400511 Loss1: 0.082705 Loss2: 1.317806
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383788 Loss1: 0.070069 Loss2: 1.313719
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379655 Loss1: 0.066325 Loss2: 1.313330
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.826197 Loss1: 0.829002 Loss2: 1.997195
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.903871 Loss1: 0.529037 Loss2: 1.374834
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.775985 Loss1: 0.354814 Loss2: 1.421172
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.707353 Loss1: 0.304699 Loss2: 1.402655
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.625549 Loss1: 0.239112 Loss2: 1.386437
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831260 Loss1: 0.449658 Loss2: 1.381602 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.639732 Loss1: 0.226340 Loss2: 1.413392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477844 Loss1: 0.111176 Loss2: 1.366668 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986979
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449170 Loss1: 0.080275 Loss2: 1.368895 [repeated 2x across cluster]
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.531443 Loss1: 0.163697 Loss2: 1.367746 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461901 Loss1: 0.110295 Loss2: 1.351606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.472546 Loss1: 0.636769 Loss2: 1.835777
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433063 Loss1: 0.084546 Loss2: 1.348517
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.765860 Loss1: 0.347083 Loss2: 1.418777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.559112 Loss1: 0.174693 Loss2: 1.384419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.752709 Loss1: 0.822677 Loss2: 1.930033
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.510213 Loss1: 0.139638 Loss2: 1.370575
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.929647 Loss1: 0.500681 Loss2: 1.428966
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461427 Loss1: 0.089627 Loss2: 1.371800
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.782242 Loss1: 0.322589 Loss2: 1.459653
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422605 Loss1: 0.070711 Loss2: 1.351894
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.639479 Loss1: 0.220851 Loss2: 1.418629
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400222 Loss1: 0.050138 Loss2: 1.350083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374308 Loss1: 0.034252 Loss2: 1.340056 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.546430 Loss1: 0.144367 Loss2: 1.402063 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519611 Loss1: 0.120796 Loss2: 1.398814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.474016 Loss1: 0.085684 Loss2: 1.388331
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.657975 Loss1: 0.800098 Loss2: 1.857877
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.764820 Loss1: 0.397484 Loss2: 1.367336
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.657020 Loss1: 0.261014 Loss2: 1.396005
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517526 Loss1: 0.169520 Loss2: 1.348006
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521050 Loss1: 0.170007 Loss2: 1.351043
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.476615 Loss1: 0.657780 Loss2: 1.818836
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.512444 Loss1: 0.160229 Loss2: 1.352214
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.859618 Loss1: 0.454324 Loss2: 1.405294
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.501167 Loss1: 0.142599 Loss2: 1.358568
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.686890 Loss1: 0.270588 Loss2: 1.416302
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.476028 Loss1: 0.126991 Loss2: 1.349037
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.578365 Loss1: 0.192319 Loss2: 1.386046
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422330 Loss1: 0.087327 Loss2: 1.335003
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429335 Loss1: 0.088126 Loss2: 1.341209
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.532730 Loss1: 0.154703 Loss2: 1.378027
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.517373 Loss1: 0.148871 Loss2: 1.368501
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464865 Loss1: 0.100566 Loss2: 1.364298
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429957 Loss1: 0.069336 Loss2: 1.360621
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442003 Loss1: 0.089351 Loss2: 1.352652
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.583347 Loss1: 0.773292 Loss2: 1.810056
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420611 Loss1: 0.063680 Loss2: 1.356932
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736689 Loss1: 0.325329 Loss2: 1.411360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.583532 Loss1: 0.224499 Loss2: 1.359032 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520717 Loss1: 0.160541 Loss2: 1.360177
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.670022 Loss1: 0.853064 Loss2: 1.816958
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.027181 Loss1: 0.629632 Loss2: 1.397549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.739517 Loss1: 0.346703 Loss2: 1.392814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550146 Loss1: 0.202937 Loss2: 1.347209 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497042 Loss1: 0.149100 Loss2: 1.347942 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464088 Loss1: 0.133987 Loss2: 1.330101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386446 Loss1: 0.066503 Loss2: 1.319943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380755 Loss1: 0.069134 Loss2: 1.311621 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.553772 Loss1: 0.196019 Loss2: 1.357753 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438233 Loss1: 0.105192 Loss2: 1.333041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.738293 Loss1: 0.893057 Loss2: 1.845236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.912673 Loss1: 0.519436 Loss2: 1.393238 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.768091 Loss1: 0.346538 Loss2: 1.421553 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.573148 Loss1: 0.195095 Loss2: 1.378053 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464409 Loss1: 0.104154 Loss2: 1.360255 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458321 Loss1: 0.100641 Loss2: 1.357680
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.765492 Loss1: 0.960689 Loss2: 1.804803
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.786006 Loss1: 0.433164 Loss2: 1.352843 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.641468 Loss1: 0.293606 Loss2: 1.347862 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461667 Loss1: 0.131564 Loss2: 1.330104 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.364923 Loss1: 0.056695 Loss2: 1.308228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.359953 Loss1: 0.058711 Loss2: 1.301242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.360880 Loss1: 0.064546 Loss2: 1.296334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.349692 Loss1: 0.059050 Loss2: 1.290641 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484933 Loss1: 0.128802 Loss2: 1.356131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.503851 Loss1: 0.162844 Loss2: 1.341007 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455874 Loss1: 0.109507 Loss2: 1.346366
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.897664 Loss1: 1.023799 Loss2: 1.873865
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.903654 Loss1: 0.532913 Loss2: 1.370741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.727412 Loss1: 0.322692 Loss2: 1.404720 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535347 Loss1: 0.190596 Loss2: 1.344751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.423132 Loss1: 0.101226 Loss2: 1.321906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.408058 Loss1: 0.081241 Loss2: 1.326816
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.738528 Loss1: 0.839189 Loss2: 1.899339
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.837238 Loss1: 0.458754 Loss2: 1.378484
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.393999 Loss1: 0.073812 Loss2: 1.320188
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.747828 Loss1: 0.342592 Loss2: 1.405236
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395124 Loss1: 0.084238 Loss2: 1.310886
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555676 Loss1: 0.192307 Loss2: 1.363370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.514243 Loss1: 0.139360 Loss2: 1.374883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461770 Loss1: 0.104393 Loss2: 1.357376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443314 Loss1: 0.083559 Loss2: 1.359756 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499620 Loss1: 0.185202 Loss2: 1.314418 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.391007 Loss1: 0.093733 Loss2: 1.297274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.360551 Loss1: 0.072216 Loss2: 1.288336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.355057 Loss1: 0.071437 Loss2: 1.283620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373576 Loss1: 0.085460 Loss2: 1.288117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412520 Loss1: 0.123125 Loss2: 1.289395 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504109 Loss1: 0.171834 Loss2: 1.332275 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414777 Loss1: 0.098093 Loss2: 1.316684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409055 Loss1: 0.085863 Loss2: 1.323192
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.583810 Loss1: 0.709099 Loss2: 1.874711
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379720 Loss1: 0.059540 Loss2: 1.320180
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.816866 Loss1: 0.436397 Loss2: 1.380469
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.699487 Loss1: 0.284920 Loss2: 1.414567
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580425 Loss1: 0.201530 Loss2: 1.378895
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514268 Loss1: 0.153692 Loss2: 1.360576
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513327 Loss1: 0.136047 Loss2: 1.377280
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.636532 Loss1: 0.834449 Loss2: 1.802082
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459917 Loss1: 0.103768 Loss2: 1.356148
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.806985 Loss1: 0.447770 Loss2: 1.359215
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404038 Loss1: 0.055378 Loss2: 1.348661
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.676656 Loss1: 0.294172 Loss2: 1.382484
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432252 Loss1: 0.086047 Loss2: 1.346205
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562281 Loss1: 0.221042 Loss2: 1.341239
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400022 Loss1: 0.059672 Loss2: 1.340349
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443105 Loss1: 0.114300 Loss2: 1.328804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.385532 Loss1: 0.072264 Loss2: 1.313268 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372254 Loss1: 0.067037 Loss2: 1.305217
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.802452 Loss1: 0.863469 Loss2: 1.938983
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367801 Loss1: 0.063928 Loss2: 1.303873
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.032048 Loss1: 0.610643 Loss2: 1.421405
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.795376 Loss1: 0.324654 Loss2: 1.470721
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.645826 Loss1: 0.241690 Loss2: 1.404135
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.620637 Loss1: 0.206292 Loss2: 1.414344
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511858 Loss1: 0.115011 Loss2: 1.396847
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484039 Loss1: 0.096090 Loss2: 1.387949
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.517645 Loss1: 0.734055 Loss2: 1.783590
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.750100 Loss1: 0.432819 Loss2: 1.317281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.644127 Loss1: 0.298763 Loss2: 1.345365 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520359 Loss1: 0.210351 Loss2: 1.310009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.438412 Loss1: 0.129571 Loss2: 1.308841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420222 Loss1: 0.122244 Loss2: 1.297977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373717 Loss1: 0.083003 Loss2: 1.290714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372135 Loss1: 0.081021 Loss2: 1.291115 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.570613 Loss1: 0.159363 Loss2: 1.411251 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511198 Loss1: 0.115643 Loss2: 1.395556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.489208 Loss1: 0.688130 Loss2: 1.801078
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489095 Loss1: 0.087223 Loss2: 1.401872
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452726 Loss1: 0.059401 Loss2: 1.393325
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.778896 Loss1: 0.434958 Loss2: 1.343938
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.444066 Loss1: 0.061112 Loss2: 1.382954
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.652520 Loss1: 0.285684 Loss2: 1.366837
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.569873 Loss1: 0.228210 Loss2: 1.341663
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500072 Loss1: 0.158397 Loss2: 1.341674
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510082 Loss1: 0.180833 Loss2: 1.329248
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441826 Loss1: 0.112431 Loss2: 1.329395
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.665729 Loss1: 0.816436 Loss2: 1.849292
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407317 Loss1: 0.079764 Loss2: 1.327553
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.884108 Loss1: 0.498567 Loss2: 1.385541
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.382990 Loss1: 0.066562 Loss2: 1.316428
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.720932 Loss1: 0.283847 Loss2: 1.437085
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465808 Loss1: 0.145838 Loss2: 1.319970
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565253 Loss1: 0.193119 Loss2: 1.372134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447172 Loss1: 0.091722 Loss2: 1.355450 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422120 Loss1: 0.078230 Loss2: 1.343890
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.657383 Loss1: 0.740732 Loss2: 1.916652
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394905 Loss1: 0.056112 Loss2: 1.338794
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.855768 Loss1: 0.426434 Loss2: 1.429334
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396809 Loss1: 0.061789 Loss2: 1.335020
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.723247 Loss1: 0.254196 Loss2: 1.469050
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584065 Loss1: 0.182921 Loss2: 1.401144
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516360 Loss1: 0.109361 Loss2: 1.406999
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485767 Loss1: 0.088400 Loss2: 1.397367
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.449423 Loss1: 0.068889 Loss2: 1.380534
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434344 Loss1: 0.061081 Loss2: 1.373263
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.627243 Loss1: 0.760818 Loss2: 1.866425
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414389 Loss1: 0.049993 Loss2: 1.364396
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.967347 Loss1: 0.522426 Loss2: 1.444921
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399546 Loss1: 0.038431 Loss2: 1.361115
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.804982 Loss1: 0.352111 Loss2: 1.452871
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.692467 Loss1: 0.273071 Loss2: 1.419396
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605279 Loss1: 0.198785 Loss2: 1.406494
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.581844 Loss1: 0.173014 Loss2: 1.408830
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544812 Loss1: 0.135455 Loss2: 1.409357
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.805135 Loss1: 0.881908 Loss2: 1.923227
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.020528 Loss1: 0.552348 Loss2: 1.468180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.725830 Loss1: 0.266553 Loss2: 1.459277 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.471603 Loss1: 0.083910 Loss2: 1.387693
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.620717 Loss1: 0.194653 Loss2: 1.426064
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.525113 Loss1: 0.110751 Loss2: 1.414362
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538339 Loss1: 0.140556 Loss2: 1.397783
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.520586 Loss1: 0.119447 Loss2: 1.401138
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.527339 Loss1: 0.125553 Loss2: 1.401786
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.490347 Loss1: 0.632533 Loss2: 1.857815
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.489181 Loss1: 0.083370 Loss2: 1.405811
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.800256 Loss1: 0.422841 Loss2: 1.377415
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.501018 Loss1: 0.098862 Loss2: 1.402156
+(DefaultActor pid=3764) >> Training accuracy: 0.969792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.659900 Loss1: 0.288747 Loss2: 1.371153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.556813 Loss1: 0.185910 Loss2: 1.370903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.465819 Loss1: 0.103154 Loss2: 1.362665
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.462731 Loss1: 0.690846 Loss2: 1.771886
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.850179 Loss1: 0.497760 Loss2: 1.352419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.708100 Loss1: 0.295563 Loss2: 1.412537 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.603579 Loss1: 0.264492 Loss2: 1.339087 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.433715 Loss1: 0.099793 Loss2: 1.333922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409576 Loss1: 0.088666 Loss2: 1.320909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395325 Loss1: 0.076982 Loss2: 1.318344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412513 Loss1: 0.095074 Loss2: 1.317439 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.648410 Loss1: 0.221432 Loss2: 1.426978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.553609 Loss1: 0.131933 Loss2: 1.421676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518178 Loss1: 0.108879 Loss2: 1.409299
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.459658 Loss1: 0.620100 Loss2: 1.839558
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.819905 Loss1: 0.421470 Loss2: 1.398435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.658903 Loss1: 0.261443 Loss2: 1.397460 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.552779 Loss1: 0.178520 Loss2: 1.374259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.526442 Loss1: 0.144060 Loss2: 1.382381 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.606140 Loss1: 0.747096 Loss2: 1.859044 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.913853 Loss1: 0.531158 Loss2: 1.382695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.746923 Loss1: 0.310486 Loss2: 1.436438 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.971507
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.624172 Loss1: 0.249365 Loss2: 1.374807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508042 Loss1: 0.140560 Loss2: 1.367483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479073 Loss1: 0.116644 Loss2: 1.362429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444708 Loss1: 0.087645 Loss2: 1.357062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426906 Loss1: 0.073879 Loss2: 1.353027 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.659261 Loss1: 0.239518 Loss2: 1.419743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.574587 Loss1: 0.157662 Loss2: 1.416925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.514439 Loss1: 0.108719 Loss2: 1.405720
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.599655 Loss1: 0.727956 Loss2: 1.871699
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.938525 Loss1: 0.496357 Loss2: 1.442168 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.766474 Loss1: 0.311135 Loss2: 1.455339 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566588 Loss1: 0.146389 Loss2: 1.420199 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518377 Loss1: 0.122441 Loss2: 1.395936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495028 Loss1: 0.098901 Loss2: 1.396127
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.517447 Loss1: 0.741163 Loss2: 1.776284
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.778075 Loss1: 0.436645 Loss2: 1.341430
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.466141 Loss1: 0.073141 Loss2: 1.393000
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653703 Loss1: 0.295011 Loss2: 1.358691
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451626 Loss1: 0.064673 Loss2: 1.386952
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508765 Loss1: 0.176713 Loss2: 1.332052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463088 Loss1: 0.148693 Loss2: 1.314395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.393679 Loss1: 0.084311 Loss2: 1.309368
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.538828 Loss1: 0.763889 Loss2: 1.774939
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.777537 Loss1: 0.429355 Loss2: 1.348182 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.363235 Loss1: 0.063839 Loss2: 1.299396
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.627963 Loss1: 0.267994 Loss2: 1.359969
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562522 Loss1: 0.224342 Loss2: 1.338180
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.548915 Loss1: 0.214448 Loss2: 1.334466
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.479902 Loss1: 0.154064 Loss2: 1.325838
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444249 Loss1: 0.120914 Loss2: 1.323335
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.641209 Loss1: 0.818933 Loss2: 1.822276
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.840973 Loss1: 0.475688 Loss2: 1.365285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.693626 Loss1: 0.300471 Loss2: 1.393156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372366 Loss1: 0.071415 Loss2: 1.300951
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.623955 Loss1: 0.262858 Loss2: 1.361096
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.611359 Loss1: 0.240488 Loss2: 1.370871
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.505251 Loss1: 0.160603 Loss2: 1.344648
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438744 Loss1: 0.098559 Loss2: 1.340185
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417090 Loss1: 0.084148 Loss2: 1.332942
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.506393 Loss1: 0.699275 Loss2: 1.807118
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401435 Loss1: 0.076131 Loss2: 1.325305
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.819532 Loss1: 0.482159 Loss2: 1.337373
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429025 Loss1: 0.103021 Loss2: 1.326004
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.644065 Loss1: 0.301743 Loss2: 1.342322 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.557235 Loss1: 0.212564 Loss2: 1.344671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.482021 Loss1: 0.146317 Loss2: 1.335704
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.594633 Loss1: 0.761761 Loss2: 1.832872
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.496259 Loss1: 0.168143 Loss2: 1.328116
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.747749 Loss1: 0.384794 Loss2: 1.362956
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456374 Loss1: 0.120165 Loss2: 1.336209
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642921 Loss1: 0.261201 Loss2: 1.381720
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374203 Loss1: 0.056916 Loss2: 1.317287
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554655 Loss1: 0.193580 Loss2: 1.361075
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524116 Loss1: 0.168005 Loss2: 1.356112
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501452 Loss1: 0.142858 Loss2: 1.358594
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434416 Loss1: 0.078946 Loss2: 1.355470
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418348 Loss1: 0.081635 Loss2: 1.336713
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395500 Loss1: 0.062182 Loss2: 1.333318
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.621520 Loss1: 0.800287 Loss2: 1.821233
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386124 Loss1: 0.062126 Loss2: 1.323998
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.762796 Loss1: 0.405813 Loss2: 1.356983
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.703228 Loss1: 0.342286 Loss2: 1.360941
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601075 Loss1: 0.257308 Loss2: 1.343767
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491155 Loss1: 0.156120 Loss2: 1.335036
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459530 Loss1: 0.140018 Loss2: 1.319512
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450781 Loss1: 0.131438 Loss2: 1.319344
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.489995 Loss1: 0.731089 Loss2: 1.758906
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437409 Loss1: 0.114554 Loss2: 1.322855
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.770118 Loss1: 0.453882 Loss2: 1.316235
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391300 Loss1: 0.079843 Loss2: 1.311457
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.658584 Loss1: 0.297732 Loss2: 1.360852
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374577 Loss1: 0.062281 Loss2: 1.312296
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.582086 Loss1: 0.267464 Loss2: 1.314621
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541232 Loss1: 0.214019 Loss2: 1.327214
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461186 Loss1: 0.152037 Loss2: 1.309150
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437295 Loss1: 0.133457 Loss2: 1.303837
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386917 Loss1: 0.089147 Loss2: 1.297770
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.378158 Loss1: 0.088879 Loss2: 1.289279
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.549716 Loss1: 0.698100 Loss2: 1.851616
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356353 Loss1: 0.071050 Loss2: 1.285303
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.779331 Loss1: 0.419439 Loss2: 1.359892
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.666476 Loss1: 0.278032 Loss2: 1.388443
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.607530 Loss1: 0.255181 Loss2: 1.352349
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535585 Loss1: 0.185084 Loss2: 1.350500
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.495795 Loss1: 0.140723 Loss2: 1.355072
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442286 Loss1: 0.106778 Loss2: 1.335509
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.444860 Loss1: 0.572549 Loss2: 1.872311
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432897 Loss1: 0.098885 Loss2: 1.334012
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.832440 Loss1: 0.454513 Loss2: 1.377927
+DEBUG flwr 2023-10-11 17:22:07,102 | server.py:236 | fit_round 121 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408593 Loss1: 0.076609 Loss2: 1.331984
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.732984 Loss1: 0.320522 Loss2: 1.412462
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391134 Loss1: 0.062681 Loss2: 1.328452
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.616674 Loss1: 0.216282 Loss2: 1.400392
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563356 Loss1: 0.178237 Loss2: 1.385119
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556838 Loss1: 0.179258 Loss2: 1.377581
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467282 Loss1: 0.082637 Loss2: 1.384646
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441765 Loss1: 0.074019 Loss2: 1.367746
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.405797 Loss1: 0.592954 Loss2: 1.812843
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413590 Loss1: 0.052529 Loss2: 1.361060
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.710354 Loss1: 0.365512 Loss2: 1.344843
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395863 Loss1: 0.039726 Loss2: 1.356137
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547689 Loss1: 0.197814 Loss2: 1.349875 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487341 Loss1: 0.143948 Loss2: 1.343394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446610 Loss1: 0.102451 Loss2: 1.344158
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.551722 Loss1: 0.674335 Loss2: 1.877387
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.813147 Loss1: 0.420936 Loss2: 1.392211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.698530 Loss1: 0.283260 Loss2: 1.415270 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.362004 Loss1: 0.035780 Loss2: 1.326224
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.617236 Loss1: 0.252226 Loss2: 1.365010
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.608813 Loss1: 0.222252 Loss2: 1.386561
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484624 Loss1: 0.115207 Loss2: 1.369416
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473888 Loss1: 0.118547 Loss2: 1.355341
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430742 Loss1: 0.076099 Loss2: 1.354642
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.613134 Loss1: 0.735571 Loss2: 1.877563
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415864 Loss1: 0.064240 Loss2: 1.351624
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431924 Loss1: 0.089778 Loss2: 1.342147 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.620937 Loss1: 0.239033 Loss2: 1.381903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505826 Loss1: 0.134515 Loss2: 1.371311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467336 Loss1: 0.093627 Loss2: 1.373710
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.488430 Loss1: 0.666771 Loss2: 1.821659
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.899270 Loss1: 0.535381 Loss2: 1.363888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.772830 Loss1: 0.360949 Loss2: 1.411881 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.609069 Loss1: 0.240440 Loss2: 1.368629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502792 Loss1: 0.160827 Loss2: 1.341964 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486385 Loss1: 0.148882 Loss2: 1.337503 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403259 Loss1: 0.069998 Loss2: 1.333261 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 17:22:07,102][flwr][DEBUG] - fit_round 121 received 50 results and 0 failures
+INFO flwr 2023-10-11 17:22:47,444 | server.py:125 | fit progress: (121, 2.2130620091106183, {'accuracy': 0.5797}, 279075.222091128)
+>> Test accuracy: 0.579700
+[2023-10-11 17:22:47,444][flwr][INFO] - fit progress: (121, 2.2130620091106183, {'accuracy': 0.5797}, 279075.222091128)
+DEBUG flwr 2023-10-11 17:22:47,444 | server.py:173 | evaluate_round 121: strategy sampled 50 clients (out of 50)
+[2023-10-11 17:22:47,444][flwr][DEBUG] - evaluate_round 121: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 17:31:52,201 | server.py:187 | evaluate_round 121 received 50 results and 0 failures
+[2023-10-11 17:31:52,201][flwr][DEBUG] - evaluate_round 121 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 17:31:52,201 | server.py:222 | fit_round 122: strategy sampled 50 clients (out of 50)
+[2023-10-11 17:31:52,201][flwr][DEBUG] - fit_round 122: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.617665 Loss1: 0.797766 Loss2: 1.819899
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.880841 Loss1: 0.526684 Loss2: 1.354157
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.683406 Loss1: 0.297566 Loss2: 1.385839
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.552487 Loss1: 0.214271 Loss2: 1.338215
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.574964 Loss1: 0.762329 Loss2: 1.812636
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487005 Loss1: 0.150376 Loss2: 1.336629
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.883872 Loss1: 0.518764 Loss2: 1.365109
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442006 Loss1: 0.119407 Loss2: 1.322600
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726590 Loss1: 0.321036 Loss2: 1.405553
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410289 Loss1: 0.090232 Loss2: 1.320056
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.658915 Loss1: 0.299671 Loss2: 1.359244
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.399866 Loss1: 0.081351 Loss2: 1.318516
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.588850 Loss1: 0.229736 Loss2: 1.359114
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374621 Loss1: 0.063634 Loss2: 1.310987
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511548 Loss1: 0.161130 Loss2: 1.350418
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377565 Loss1: 0.064936 Loss2: 1.312630
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440668 Loss1: 0.103785 Loss2: 1.336883
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442155 Loss1: 0.107935 Loss2: 1.334220
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394135 Loss1: 0.067957 Loss2: 1.326178
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377932 Loss1: 0.059865 Loss2: 1.318067
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.553439 Loss1: 0.667307 Loss2: 1.886132
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.960394 Loss1: 0.501749 Loss2: 1.458646
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.828219 Loss1: 0.344242 Loss2: 1.483977
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.686701 Loss1: 0.242514 Loss2: 1.444187
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.708153 Loss1: 0.771803 Loss2: 1.936350
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.823690 Loss1: 0.402985 Loss2: 1.420704
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.665573 Loss1: 0.215481 Loss2: 1.450092
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.693036 Loss1: 0.257459 Loss2: 1.435578
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.645452 Loss1: 0.209189 Loss2: 1.436264
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.672549 Loss1: 0.268738 Loss2: 1.403811
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.566744 Loss1: 0.131032 Loss2: 1.435712
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.573176 Loss1: 0.169291 Loss2: 1.403884
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.528753 Loss1: 0.104843 Loss2: 1.423910
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.508376 Loss1: 0.089214 Loss2: 1.419162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.506628 Loss1: 0.091968 Loss2: 1.414659 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485645 Loss1: 0.104036 Loss2: 1.381609 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.473796 Loss1: 0.694952 Loss2: 1.778844 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.612283 Loss1: 0.235322 Loss2: 1.376961 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.611824 Loss1: 0.263078 Loss2: 1.348746
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.695613 Loss1: 0.838738 Loss2: 1.856875
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.891393 Loss1: 0.515371 Loss2: 1.376022
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.533481 Loss1: 0.183165 Loss2: 1.350316
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.844539 Loss1: 0.409488 Loss2: 1.435051
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498453 Loss1: 0.160286 Loss2: 1.338167
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.785122 Loss1: 0.397291 Loss2: 1.387831
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.438608 Loss1: 0.103531 Loss2: 1.335077
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.790538 Loss1: 0.357847 Loss2: 1.432691
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451028 Loss1: 0.121157 Loss2: 1.329871
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425292 Loss1: 0.098866 Loss2: 1.326426 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430155 Loss1: 0.108368 Loss2: 1.321788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418940 Loss1: 0.073084 Loss2: 1.345856 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.595001 Loss1: 0.732467 Loss2: 1.862534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.771795 Loss1: 0.346763 Loss2: 1.425032 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580275 Loss1: 0.199076 Loss2: 1.381199
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.692608 Loss1: 0.744285 Loss2: 1.948323
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.553976 Loss1: 0.166897 Loss2: 1.387079
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.941160 Loss1: 0.494471 Loss2: 1.446688
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491279 Loss1: 0.114855 Loss2: 1.376424
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.910981 Loss1: 0.427106 Loss2: 1.483875
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.465920 Loss1: 0.099009 Loss2: 1.366911
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.737089 Loss1: 0.285190 Loss2: 1.451898
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.468215 Loss1: 0.104248 Loss2: 1.363968
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.622794 Loss1: 0.194626 Loss2: 1.428168
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454234 Loss1: 0.092116 Loss2: 1.362118
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.579166 Loss1: 0.151758 Loss2: 1.427409
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457075 Loss1: 0.096613 Loss2: 1.360462
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518180 Loss1: 0.099267 Loss2: 1.418913
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.504099 Loss1: 0.092802 Loss2: 1.411297
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.500640 Loss1: 0.091304 Loss2: 1.409336
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472467 Loss1: 0.065215 Loss2: 1.407253
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.511622 Loss1: 0.692917 Loss2: 1.818705
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.848930 Loss1: 0.484605 Loss2: 1.364325
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.682421 Loss1: 0.282624 Loss2: 1.399797
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513853 Loss1: 0.168946 Loss2: 1.344907
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.497625 Loss1: 0.633369 Loss2: 1.864256
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.790123 Loss1: 0.425012 Loss2: 1.365111 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.660949 Loss1: 0.252601 Loss2: 1.408348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.569425 Loss1: 0.197109 Loss2: 1.372315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515267 Loss1: 0.154236 Loss2: 1.361030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470234 Loss1: 0.107853 Loss2: 1.362382 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414996 Loss1: 0.084320 Loss2: 1.330677
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464264 Loss1: 0.113339 Loss2: 1.350925
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437848 Loss1: 0.092420 Loss2: 1.345428
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426353 Loss1: 0.081960 Loss2: 1.344393
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427716 Loss1: 0.080166 Loss2: 1.347551
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.593666 Loss1: 0.728499 Loss2: 1.865167
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.904070 Loss1: 0.505814 Loss2: 1.398255
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.729849 Loss1: 0.314195 Loss2: 1.415653
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606292 Loss1: 0.215815 Loss2: 1.390478
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.496314 Loss1: 0.645642 Loss2: 1.850672
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.784964 Loss1: 0.440432 Loss2: 1.344531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.699702 Loss1: 0.300079 Loss2: 1.399622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.571800 Loss1: 0.228335 Loss2: 1.343465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545893 Loss1: 0.199150 Loss2: 1.346743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496039 Loss1: 0.153300 Loss2: 1.342740 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392956 Loss1: 0.050661 Loss2: 1.342295
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438353 Loss1: 0.103570 Loss2: 1.334783
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417632 Loss1: 0.085064 Loss2: 1.332568
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394408 Loss1: 0.066175 Loss2: 1.328234
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373432 Loss1: 0.053595 Loss2: 1.319836
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.454509 Loss1: 0.647745 Loss2: 1.806763
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.783359 Loss1: 0.455706 Loss2: 1.327654
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.600570 Loss1: 0.248128 Loss2: 1.352442
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.553963 Loss1: 0.230792 Loss2: 1.323171
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.601145 Loss1: 0.714664 Loss2: 1.886481
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.858366 Loss1: 0.451182 Loss2: 1.407184
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516583 Loss1: 0.186561 Loss2: 1.330022
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.754301 Loss1: 0.287591 Loss2: 1.466711
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437132 Loss1: 0.121307 Loss2: 1.315825
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.639825 Loss1: 0.243904 Loss2: 1.395921
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410642 Loss1: 0.101412 Loss2: 1.309230
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598648 Loss1: 0.190021 Loss2: 1.408626
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410478 Loss1: 0.105792 Loss2: 1.304687
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403597 Loss1: 0.095050 Loss2: 1.308547 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385009 Loss1: 0.082461 Loss2: 1.302548 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493474 Loss1: 0.106276 Loss2: 1.387198 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.617290 Loss1: 0.732311 Loss2: 1.884978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.727886 Loss1: 0.292005 Loss2: 1.435881 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.614415 Loss1: 0.219563 Loss2: 1.394852
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.441214 Loss1: 0.648526 Loss2: 1.792688
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.824624 Loss1: 0.458575 Loss2: 1.366049 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.681502 Loss1: 0.290320 Loss2: 1.391182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.596809 Loss1: 0.250196 Loss2: 1.346612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.517897 Loss1: 0.159451 Loss2: 1.358447 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472205 Loss1: 0.131469 Loss2: 1.340736 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468106 Loss1: 0.133077 Loss2: 1.335029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423751 Loss1: 0.091686 Loss2: 1.332066 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975586
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.445134 Loss1: 0.669882 Loss2: 1.775252 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.636652 Loss1: 0.266598 Loss2: 1.370055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.600140 Loss1: 0.254215 Loss2: 1.345926
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.727131 Loss1: 0.865427 Loss2: 1.861704
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.985673 Loss1: 0.580917 Loss2: 1.404757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.753948 Loss1: 0.315962 Loss2: 1.437987 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460943 Loss1: 0.129319 Loss2: 1.331625
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.665705 Loss1: 0.296706 Loss2: 1.368999
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438169 Loss1: 0.105611 Loss2: 1.332558
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544512 Loss1: 0.166742 Loss2: 1.377770
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549451 Loss1: 0.190756 Loss2: 1.358695
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.387136 Loss1: 0.068524 Loss2: 1.318612
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530438 Loss1: 0.170137 Loss2: 1.360301
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365390 Loss1: 0.049663 Loss2: 1.315727
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409411 Loss1: 0.073647 Loss2: 1.335763 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.616606 Loss1: 0.768960 Loss2: 1.847646 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.681061 Loss1: 0.271936 Loss2: 1.409125 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599002 Loss1: 0.224795 Loss2: 1.374207
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.500550 Loss1: 0.732043 Loss2: 1.768507
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.768591 Loss1: 0.430539 Loss2: 1.338052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.659600 Loss1: 0.280884 Loss2: 1.378717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554415 Loss1: 0.221158 Loss2: 1.333256 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469215 Loss1: 0.139906 Loss2: 1.329309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421899 Loss1: 0.093201 Loss2: 1.328698 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.400946 Loss1: 0.083946 Loss2: 1.317000 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385387 Loss1: 0.077794 Loss2: 1.307593 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958008
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.695658 Loss1: 0.683029 Loss2: 2.012629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.891567 Loss1: 0.334687 Loss2: 1.556880 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.719628 Loss1: 0.861770 Loss2: 1.857858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.866988 Loss1: 0.469671 Loss2: 1.397317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.704970 Loss1: 0.295186 Loss2: 1.409784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.621889 Loss1: 0.243187 Loss2: 1.378702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523440 Loss1: 0.139221 Loss2: 1.384219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471248 Loss1: 0.100924 Loss2: 1.370324 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450626 Loss1: 0.094480 Loss2: 1.356146 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411515 Loss1: 0.063569 Loss2: 1.347946 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.877379 Loss1: 0.485925 Loss2: 1.391454 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583905 Loss1: 0.204767 Loss2: 1.379137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.545773 Loss1: 0.720551 Loss2: 1.825222
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515519 Loss1: 0.131099 Loss2: 1.384420
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.931506 Loss1: 0.541622 Loss2: 1.389885
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462830 Loss1: 0.098056 Loss2: 1.364775
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442680 Loss1: 0.083211 Loss2: 1.359469
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804555 Loss1: 0.382613 Loss2: 1.421941
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.431874 Loss1: 0.075137 Loss2: 1.356738
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.735539 Loss1: 0.353021 Loss2: 1.382518
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431816 Loss1: 0.076717 Loss2: 1.355099
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.562479 Loss1: 0.190124 Loss2: 1.372355
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400199 Loss1: 0.053347 Loss2: 1.346853
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484564 Loss1: 0.128796 Loss2: 1.355768
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464397 Loss1: 0.119163 Loss2: 1.345234
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436259 Loss1: 0.092741 Loss2: 1.343518
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425969 Loss1: 0.090775 Loss2: 1.335194
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405676 Loss1: 0.070248 Loss2: 1.335428
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.662622 Loss1: 0.709172 Loss2: 1.953450
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.925288 Loss1: 0.477779 Loss2: 1.447509
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.767143 Loss1: 0.270522 Loss2: 1.496621
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.659153 Loss1: 0.217286 Loss2: 1.441867
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.629142 Loss1: 0.192602 Loss2: 1.436540
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.660206 Loss1: 0.754991 Loss2: 1.905216
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.757156 Loss1: 0.360803 Loss2: 1.396353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.697375 Loss1: 0.277980 Loss2: 1.419395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.570172 Loss1: 0.190835 Loss2: 1.379337 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.537272 Loss1: 0.153820 Loss2: 1.383452 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481395 Loss1: 0.060124 Loss2: 1.421271
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504764 Loss1: 0.132488 Loss2: 1.372275
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452128 Loss1: 0.086893 Loss2: 1.365235
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432944 Loss1: 0.074779 Loss2: 1.358165
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396419 Loss1: 0.044615 Loss2: 1.351804
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382984 Loss1: 0.031944 Loss2: 1.351041
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.559956 Loss1: 0.718836 Loss2: 1.841121
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.713708 Loss1: 0.378333 Loss2: 1.335375
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.581218 Loss1: 0.226362 Loss2: 1.354856
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491453 Loss1: 0.163427 Loss2: 1.328027
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480087 Loss1: 0.153796 Loss2: 1.326291
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.901932 Loss1: 0.898112 Loss2: 2.003820
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.951815 Loss1: 0.568535 Loss2: 1.383280
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455976 Loss1: 0.134426 Loss2: 1.321549
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.430012 Loss1: 0.116451 Loss2: 1.313561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434344 Loss1: 0.118972 Loss2: 1.315372 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409277 Loss1: 0.092301 Loss2: 1.316976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437854 Loss1: 0.077384 Loss2: 1.360471 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443676 Loss1: 0.092173 Loss2: 1.351504 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977865
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.683478 Loss1: 0.844500 Loss2: 1.838978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.670185 Loss1: 0.277738 Loss2: 1.392447 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.584553 Loss1: 0.220857 Loss2: 1.363697
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.686539 Loss1: 0.791104 Loss2: 1.895434
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547309 Loss1: 0.186387 Loss2: 1.360922
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.824104 Loss1: 0.410295 Loss2: 1.413809
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.517098 Loss1: 0.157889 Loss2: 1.359209
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.723171 Loss1: 0.308999 Loss2: 1.414172
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.454345 Loss1: 0.106370 Loss2: 1.347976
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.676104 Loss1: 0.270647 Loss2: 1.405457
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447756 Loss1: 0.108556 Loss2: 1.339201
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.564000 Loss1: 0.172968 Loss2: 1.391032
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401995 Loss1: 0.067777 Loss2: 1.334218
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.551167 Loss1: 0.168951 Loss2: 1.382216
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419853 Loss1: 0.091711 Loss2: 1.328142
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.552051 Loss1: 0.162910 Loss2: 1.389142
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477252 Loss1: 0.100721 Loss2: 1.376530
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.524253 Loss1: 0.145973 Loss2: 1.378280
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.513227 Loss1: 0.128427 Loss2: 1.384799
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.749110 Loss1: 0.865989 Loss2: 1.883120
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.941220 Loss1: 0.538127 Loss2: 1.403093
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.689483 Loss1: 0.277728 Loss2: 1.411754
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498081 Loss1: 0.141363 Loss2: 1.356719
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.688132 Loss1: 0.771377 Loss2: 1.916755
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.925128 Loss1: 0.511744 Loss2: 1.413384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.773044 Loss1: 0.304583 Loss2: 1.468460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.717849 Loss1: 0.303737 Loss2: 1.414112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.589720 Loss1: 0.170508 Loss2: 1.419212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546643 Loss1: 0.134658 Loss2: 1.411985 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379753 Loss1: 0.047322 Loss2: 1.332431
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.498360 Loss1: 0.101582 Loss2: 1.396778
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466539 Loss1: 0.075439 Loss2: 1.391100
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436142 Loss1: 0.048376 Loss2: 1.387766
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433593 Loss1: 0.056405 Loss2: 1.377188
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.871454 Loss1: 0.879871 Loss2: 1.991583
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.889525 Loss1: 0.482178 Loss2: 1.407347
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647847 Loss1: 0.232073 Loss2: 1.415775
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558509 Loss1: 0.168798 Loss2: 1.389711
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.626354 Loss1: 0.746034 Loss2: 1.880320
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.549268 Loss1: 0.162294 Loss2: 1.386974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486857 Loss1: 0.101243 Loss2: 1.385614 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.466910 Loss1: 0.097910 Loss2: 1.369000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438102 Loss1: 0.073387 Loss2: 1.364715 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412191 Loss1: 0.053609 Loss2: 1.358582 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463579 Loss1: 0.108698 Loss2: 1.354881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444042 Loss1: 0.092988 Loss2: 1.351054 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429574 Loss1: 0.086331 Loss2: 1.343243
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.741175 Loss1: 0.844404 Loss2: 1.896771
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.903941 Loss1: 0.523792 Loss2: 1.380149
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.657925 Loss1: 0.244594 Loss2: 1.413331
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569687 Loss1: 0.198147 Loss2: 1.371540
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503772 Loss1: 0.141036 Loss2: 1.362736
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.565749 Loss1: 0.722668 Loss2: 1.843080
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463008 Loss1: 0.101847 Loss2: 1.361161
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.810455 Loss1: 0.419678 Loss2: 1.390777
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451532 Loss1: 0.104814 Loss2: 1.346718
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670925 Loss1: 0.255106 Loss2: 1.415820
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427169 Loss1: 0.078709 Loss2: 1.348460
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414517 Loss1: 0.074063 Loss2: 1.340454
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576160 Loss1: 0.203941 Loss2: 1.372219
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371340 Loss1: 0.042523 Loss2: 1.328816
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554600 Loss1: 0.177243 Loss2: 1.377357
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512189 Loss1: 0.153066 Loss2: 1.359123
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478135 Loss1: 0.115159 Loss2: 1.362976
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443117 Loss1: 0.089735 Loss2: 1.353382
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413944 Loss1: 0.066421 Loss2: 1.347523
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.397342 Loss1: 0.554101 Loss2: 1.843241
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.775405 Loss1: 0.386369 Loss2: 1.389035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536810 Loss1: 0.151422 Loss2: 1.385389 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466478 Loss1: 0.089356 Loss2: 1.377122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479137 Loss1: 0.103433 Loss2: 1.375704 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.482491 Loss1: 0.114565 Loss2: 1.367926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.645553 Loss1: 0.286980 Loss2: 1.358573 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522791 Loss1: 0.152299 Loss2: 1.370492 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485256 Loss1: 0.141885 Loss2: 1.343370 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449467 Loss1: 0.113328 Loss2: 1.336139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414031 Loss1: 0.088138 Loss2: 1.325893
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.358174 Loss1: 0.556312 Loss2: 1.801861
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.782277 Loss1: 0.419284 Loss2: 1.362993
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.659751 Loss1: 0.257278 Loss2: 1.402473
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563841 Loss1: 0.200489 Loss2: 1.363352
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479587 Loss1: 0.124213 Loss2: 1.355373
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.705837 Loss1: 0.831251 Loss2: 1.874587
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.849098 Loss1: 0.500203 Loss2: 1.348895
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460704 Loss1: 0.116368 Loss2: 1.344336
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.786418 Loss1: 0.365993 Loss2: 1.420425
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.423235 Loss1: 0.080981 Loss2: 1.342253
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391827 Loss1: 0.059576 Loss2: 1.332251 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373970 Loss1: 0.047289 Loss2: 1.326682 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395266 Loss1: 0.074727 Loss2: 1.320538 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990809
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433566 Loss1: 0.107452 Loss2: 1.326114 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.717585 Loss1: 0.781795 Loss2: 1.935790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.799844 Loss1: 0.394571 Loss2: 1.405273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.635172 Loss1: 0.725431 Loss2: 1.909740 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.855316 Loss1: 0.447107 Loss2: 1.408210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513972 Loss1: 0.133436 Loss2: 1.380536 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483249 Loss1: 0.111312 Loss2: 1.371938 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451513 Loss1: 0.089373 Loss2: 1.362141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428203 Loss1: 0.070873 Loss2: 1.357330 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995192
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.549251 Loss1: 0.144784 Loss2: 1.404467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.491856 Loss1: 0.098637 Loss2: 1.393219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465143 Loss1: 0.073627 Loss2: 1.391516
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.752353 Loss1: 0.853537 Loss2: 1.898816
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.120621 Loss1: 0.669887 Loss2: 1.450733
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.765841 Loss1: 0.316708 Loss2: 1.449133
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649127 Loss1: 0.243155 Loss2: 1.405972
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.621882 Loss1: 0.201128 Loss2: 1.420754
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.594701 Loss1: 0.788458 Loss2: 1.806243
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.529706 Loss1: 0.139600 Loss2: 1.390105
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487083 Loss1: 0.106017 Loss2: 1.381066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444720 Loss1: 0.069762 Loss2: 1.374958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430664 Loss1: 0.060638 Loss2: 1.370025 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410847 Loss1: 0.049090 Loss2: 1.361757 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425223 Loss1: 0.091452 Loss2: 1.333771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431644 Loss1: 0.114955 Loss2: 1.316688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410382 Loss1: 0.095975 Loss2: 1.314408
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.567548 Loss1: 0.770550 Loss2: 1.796997
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.749186 Loss1: 0.410414 Loss2: 1.338771
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.626869 Loss1: 0.266016 Loss2: 1.360854
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.689317 Loss1: 0.342999 Loss2: 1.346318
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565860 Loss1: 0.215450 Loss2: 1.350410
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.508724 Loss1: 0.686321 Loss2: 1.822403
+DEBUG flwr 2023-10-11 18:00:49,744 | server.py:236 | fit_round 122 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.725038 Loss1: 0.390533 Loss2: 1.334505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.658885 Loss1: 0.300034 Loss2: 1.358850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567376 Loss1: 0.239362 Loss2: 1.328015 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.468289 Loss1: 0.143164 Loss2: 1.325125 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429323 Loss1: 0.117578 Loss2: 1.311745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421021 Loss1: 0.109484 Loss2: 1.311537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394662 Loss1: 0.086831 Loss2: 1.307831 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.887665 Loss1: 0.432434 Loss2: 1.455232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.687263 Loss1: 0.225820 Loss2: 1.461442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.708564 Loss1: 0.841030 Loss2: 1.867534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.958181 Loss1: 0.519854 Loss2: 1.438326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.706146 Loss1: 0.278626 Loss2: 1.427521 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.685324 Loss1: 0.269513 Loss2: 1.415811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598197 Loss1: 0.177482 Loss2: 1.420715 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.578190 Loss1: 0.168711 Loss2: 1.409478 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.502500 Loss1: 0.115974 Loss2: 1.386525 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.525432 Loss1: 0.123737 Loss2: 1.401695
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.864253 Loss1: 0.873850 Loss2: 1.990403
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.001077 Loss1: 0.551350 Loss2: 1.449727
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.794371 Loss1: 0.296019 Loss2: 1.498352
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.671719 Loss1: 0.239214 Loss2: 1.432506
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.620980 Loss1: 0.184759 Loss2: 1.436221
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.555605 Loss1: 0.128413 Loss2: 1.427192
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.631341 Loss1: 0.771355 Loss2: 1.859986
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.573975 Loss1: 0.153499 Loss2: 1.420475
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.833377 Loss1: 0.457676 Loss2: 1.375701
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.541176 Loss1: 0.127392 Loss2: 1.413784
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669571 Loss1: 0.266084 Loss2: 1.403487
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588370 Loss1: 0.223041 Loss2: 1.365329 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.503296 Loss1: 0.093926 Loss2: 1.409370
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512354 Loss1: 0.154255 Loss2: 1.358100
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516371 Loss1: 0.157093 Loss2: 1.359278
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.522489 Loss1: 0.155906 Loss2: 1.366583
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485085 Loss1: 0.127816 Loss2: 1.357269
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441490 Loss1: 0.092436 Loss2: 1.349054
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437586 Loss1: 0.096490 Loss2: 1.341096
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 18:00:49,744][flwr][DEBUG] - fit_round 122 received 50 results and 0 failures
+INFO flwr 2023-10-11 18:01:30,831 | server.py:125 | fit progress: (122, 2.2014924769584363, {'accuracy': 0.5839}, 281398.609859069)
+>> Test accuracy: 0.583900
+[2023-10-11 18:01:30,831][flwr][INFO] - fit progress: (122, 2.2014924769584363, {'accuracy': 0.5839}, 281398.609859069)
+DEBUG flwr 2023-10-11 18:01:30,832 | server.py:173 | evaluate_round 122: strategy sampled 50 clients (out of 50)
+[2023-10-11 18:01:30,832][flwr][DEBUG] - evaluate_round 122: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 18:10:42,537 | server.py:187 | evaluate_round 122 received 50 results and 0 failures
+[2023-10-11 18:10:42,537][flwr][DEBUG] - evaluate_round 122 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 18:10:42,538 | server.py:222 | fit_round 123: strategy sampled 50 clients (out of 50)
+[2023-10-11 18:10:42,538][flwr][DEBUG] - fit_round 123: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.588495 Loss1: 0.694753 Loss2: 1.893742
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.821124 Loss1: 0.406362 Loss2: 1.414763
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.710857 Loss1: 0.251138 Loss2: 1.459719
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.632009 Loss1: 0.211875 Loss2: 1.420134
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.870830 Loss1: 0.493933 Loss2: 1.376897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.687880 Loss1: 0.278308 Loss2: 1.409572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.673437 Loss1: 0.303057 Loss2: 1.370381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.580271 Loss1: 0.202025 Loss2: 1.378245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482522 Loss1: 0.123934 Loss2: 1.358587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437302 Loss1: 0.085815 Loss2: 1.351487 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433674 Loss1: 0.093693 Loss2: 1.339981 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.537901 Loss1: 0.675398 Loss2: 1.862503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.767156 Loss1: 0.326606 Loss2: 1.440550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.641430 Loss1: 0.264592 Loss2: 1.376838
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.507419 Loss1: 0.695191 Loss2: 1.812228
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.809719 Loss1: 0.474445 Loss2: 1.335274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.665442 Loss1: 0.286734 Loss2: 1.378708 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565389 Loss1: 0.232267 Loss2: 1.333122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513566 Loss1: 0.181764 Loss2: 1.331802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.453458 Loss1: 0.134714 Loss2: 1.318745 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438074 Loss1: 0.082060 Loss2: 1.356014
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450702 Loss1: 0.135409 Loss2: 1.315294
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429381 Loss1: 0.109425 Loss2: 1.319956
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408108 Loss1: 0.093423 Loss2: 1.314685
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410607 Loss1: 0.100865 Loss2: 1.309741
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.559499 Loss1: 0.723886 Loss2: 1.835613
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.850791 Loss1: 0.482784 Loss2: 1.368007
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.686313 Loss1: 0.283404 Loss2: 1.402909
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.544541 Loss1: 0.189152 Loss2: 1.355388
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.625534 Loss1: 0.778807 Loss2: 1.846727
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.858401 Loss1: 0.478833 Loss2: 1.379568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669369 Loss1: 0.261323 Loss2: 1.408047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611067 Loss1: 0.249278 Loss2: 1.361789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503062 Loss1: 0.132712 Loss2: 1.370350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455355 Loss1: 0.107875 Loss2: 1.347481 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400757 Loss1: 0.060113 Loss2: 1.340644
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441233 Loss1: 0.096025 Loss2: 1.345208
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415624 Loss1: 0.074460 Loss2: 1.341164
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388117 Loss1: 0.051191 Loss2: 1.336926
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380652 Loss1: 0.054862 Loss2: 1.325790
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.558077 Loss1: 0.733023 Loss2: 1.825054
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.894855 Loss1: 0.491600 Loss2: 1.403255
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.708183 Loss1: 0.308347 Loss2: 1.399836
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.662128 Loss1: 0.275712 Loss2: 1.386416
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.678732 Loss1: 0.797772 Loss2: 1.880959
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.884823 Loss1: 0.464510 Loss2: 1.420312
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.608224 Loss1: 0.226953 Loss2: 1.381270
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.758052 Loss1: 0.307123 Loss2: 1.450930
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.561520 Loss1: 0.186616 Loss2: 1.374904
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.654824 Loss1: 0.256833 Loss2: 1.397991
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524676 Loss1: 0.158411 Loss2: 1.366265
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.559212 Loss1: 0.160387 Loss2: 1.398825
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458359 Loss1: 0.093452 Loss2: 1.364907
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411080 Loss1: 0.060380 Loss2: 1.350700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402275 Loss1: 0.057485 Loss2: 1.344790 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452940 Loss1: 0.080036 Loss2: 1.372904 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.555903 Loss1: 0.733032 Loss2: 1.822871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.691042 Loss1: 0.287238 Loss2: 1.403804 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.619440 Loss1: 0.261257 Loss2: 1.358184
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.702600 Loss1: 0.793879 Loss2: 1.908721
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.881359 Loss1: 0.521807 Loss2: 1.359552
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531602 Loss1: 0.164302 Loss2: 1.367299
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.678424 Loss1: 0.305586 Loss2: 1.372838
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.482441 Loss1: 0.133825 Loss2: 1.348617
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470401 Loss1: 0.129958 Loss2: 1.340444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437053 Loss1: 0.095409 Loss2: 1.341644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.446272 Loss1: 0.113900 Loss2: 1.332373 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406339 Loss1: 0.071389 Loss2: 1.334949 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.346286 Loss1: 0.050297 Loss2: 1.295989 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.588012 Loss1: 0.683638 Loss2: 1.904374
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.840804 Loss1: 0.397558 Loss2: 1.443246
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.677386 Loss1: 0.209199 Loss2: 1.468187
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610805 Loss1: 0.189290 Loss2: 1.421515
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.585161 Loss1: 0.695748 Loss2: 1.889413
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.804998 Loss1: 0.421337 Loss2: 1.383660
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.573967 Loss1: 0.150390 Loss2: 1.423576
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669005 Loss1: 0.236863 Loss2: 1.432142
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569443 Loss1: 0.152017 Loss2: 1.417426
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591118 Loss1: 0.212626 Loss2: 1.378492
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.559212 Loss1: 0.141981 Loss2: 1.417231
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569196 Loss1: 0.188544 Loss2: 1.380652
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.496347 Loss1: 0.077426 Loss2: 1.418920
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448247 Loss1: 0.049900 Loss2: 1.398346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.471735 Loss1: 0.078898 Loss2: 1.392837 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447177 Loss1: 0.084487 Loss2: 1.362690 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.779609 Loss1: 0.801693 Loss2: 1.977915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.769567 Loss1: 0.288730 Loss2: 1.480837 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.646041 Loss1: 0.209406 Loss2: 1.436635
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.702457 Loss1: 0.799238 Loss2: 1.903219
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.853859 Loss1: 0.430639 Loss2: 1.423220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.776659 Loss1: 0.325061 Loss2: 1.451598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.630813 Loss1: 0.210498 Loss2: 1.420315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557853 Loss1: 0.138631 Loss2: 1.419223 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.564298 Loss1: 0.157224 Loss2: 1.407074 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998884
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468159 Loss1: 0.072763 Loss2: 1.395396 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.455736 Loss1: 0.074506 Loss2: 1.381230 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.639127 Loss1: 0.317142 Loss2: 1.321986 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520113 Loss1: 0.213366 Loss2: 1.306747 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.482202 Loss1: 0.619381 Loss2: 1.862821
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491391 Loss1: 0.171613 Loss2: 1.319778
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.763633 Loss1: 0.362573 Loss2: 1.401061
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.399192 Loss1: 0.091114 Loss2: 1.308079
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.378417 Loss1: 0.079770 Loss2: 1.298648
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684969 Loss1: 0.250800 Loss2: 1.434169
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394386 Loss1: 0.093394 Loss2: 1.300992
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619455 Loss1: 0.216387 Loss2: 1.403068
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381811 Loss1: 0.086672 Loss2: 1.295139
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.577384 Loss1: 0.175834 Loss2: 1.401550
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359905 Loss1: 0.062833 Loss2: 1.297071
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.532015 Loss1: 0.134555 Loss2: 1.397461
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.498585 Loss1: 0.119105 Loss2: 1.379479
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.498250 Loss1: 0.114555 Loss2: 1.383695
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490715 Loss1: 0.108366 Loss2: 1.382348
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465815 Loss1: 0.087720 Loss2: 1.378095
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.668061 Loss1: 0.810523 Loss2: 1.857538
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.980134 Loss1: 0.585231 Loss2: 1.394903
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.837555 Loss1: 0.400475 Loss2: 1.437079
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630408 Loss1: 0.244179 Loss2: 1.386229
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535045 Loss1: 0.141251 Loss2: 1.393794
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.641438 Loss1: 0.700364 Loss2: 1.941074
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480092 Loss1: 0.116349 Loss2: 1.363743
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424969 Loss1: 0.067274 Loss2: 1.357695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439238 Loss1: 0.082659 Loss2: 1.356580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443198 Loss1: 0.094163 Loss2: 1.349034 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447784 Loss1: 0.091735 Loss2: 1.356049 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.579797 Loss1: 0.158886 Loss2: 1.420911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.528469 Loss1: 0.110114 Loss2: 1.418355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509163 Loss1: 0.098279 Loss2: 1.410884
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.469354 Loss1: 0.625475 Loss2: 1.843879
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.789283 Loss1: 0.428811 Loss2: 1.360473
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.712536 Loss1: 0.290062 Loss2: 1.422474
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527637 Loss1: 0.174582 Loss2: 1.353055
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518031 Loss1: 0.165878 Loss2: 1.352153
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.592506 Loss1: 0.720528 Loss2: 1.871979
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.860059 Loss1: 0.468437 Loss2: 1.391622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.683743 Loss1: 0.259640 Loss2: 1.424102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.568601 Loss1: 0.200900 Loss2: 1.367702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.479971 Loss1: 0.112400 Loss2: 1.367572 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427232 Loss1: 0.077792 Loss2: 1.349440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446520 Loss1: 0.097504 Loss2: 1.349016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433419 Loss1: 0.086878 Loss2: 1.346541 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.649384 Loss1: 0.326766 Loss2: 1.322618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.470131 Loss1: 0.161789 Loss2: 1.308341 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.695268 Loss1: 0.746587 Loss2: 1.948681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.962083 Loss1: 0.458734 Loss2: 1.503349 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.826674 Loss1: 0.312606 Loss2: 1.514068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.327515 Loss1: 0.049355 Loss2: 1.278160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.313954 Loss1: 0.042558 Loss2: 1.271396 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.622019 Loss1: 0.156605 Loss2: 1.465415 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.578021 Loss1: 0.123917 Loss2: 1.454103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.518287 Loss1: 0.073451 Loss2: 1.444836 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.652807 Loss1: 0.254188 Loss2: 1.398619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.493965 Loss1: 0.116050 Loss2: 1.377915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.467594 Loss1: 0.099041 Loss2: 1.368553
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.569623 Loss1: 0.617430 Loss2: 1.952193
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831206 Loss1: 0.400667 Loss2: 1.430539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.828660 Loss1: 0.319060 Loss2: 1.509601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.710084 Loss1: 0.273452 Loss2: 1.436632 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.671629 Loss1: 0.221554 Loss2: 1.450075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.639566 Loss1: 0.204191 Loss2: 1.435374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.522208 Loss1: 0.097700 Loss2: 1.424509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.505803 Loss1: 0.091593 Loss2: 1.414210 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644596 Loss1: 0.289631 Loss2: 1.354965 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.483255 Loss1: 0.162584 Loss2: 1.320672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.429133 Loss1: 0.122466 Loss2: 1.306667
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.600431 Loss1: 0.775967 Loss2: 1.824465
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.837166 Loss1: 0.462889 Loss2: 1.374277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633365 Loss1: 0.236230 Loss2: 1.397135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526335 Loss1: 0.176217 Loss2: 1.350117 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.346735 Loss1: 0.060842 Loss2: 1.285893
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.572138 Loss1: 0.220756 Loss2: 1.351382
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527179 Loss1: 0.176506 Loss2: 1.350674
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494556 Loss1: 0.138879 Loss2: 1.355676
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415882 Loss1: 0.076718 Loss2: 1.339164
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403436 Loss1: 0.069029 Loss2: 1.334408
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.527746 Loss1: 0.734325 Loss2: 1.793420
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413523 Loss1: 0.083759 Loss2: 1.329763
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638797 Loss1: 0.283004 Loss2: 1.355793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488237 Loss1: 0.157753 Loss2: 1.330484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.414370 Loss1: 0.095579 Loss2: 1.318791
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.739695 Loss1: 0.922420 Loss2: 1.817275
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.836346 Loss1: 0.498370 Loss2: 1.337976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.620552 Loss1: 0.259458 Loss2: 1.361095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.368061 Loss1: 0.068494 Loss2: 1.299567
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584700 Loss1: 0.280236 Loss2: 1.304464
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363775 Loss1: 0.066760 Loss2: 1.297015
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.464400 Loss1: 0.152987 Loss2: 1.311413
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.445229 Loss1: 0.137877 Loss2: 1.307352
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446156 Loss1: 0.141016 Loss2: 1.305140
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.380740 Loss1: 0.080217 Loss2: 1.300523
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354490 Loss1: 0.066588 Loss2: 1.287902
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356608 Loss1: 0.071510 Loss2: 1.285099
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.643262 Loss1: 0.810163 Loss2: 1.833099
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.897368 Loss1: 0.529844 Loss2: 1.367524
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.773927 Loss1: 0.354892 Loss2: 1.419035
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591425 Loss1: 0.228621 Loss2: 1.362804
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.596256 Loss1: 0.228432 Loss2: 1.367824
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.605921 Loss1: 0.716848 Loss2: 1.889073
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.768034 Loss1: 0.362072 Loss2: 1.405962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.679230 Loss1: 0.246419 Loss2: 1.432811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540181 Loss1: 0.150146 Loss2: 1.390035 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530122 Loss1: 0.149981 Loss2: 1.380142 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.563673 Loss1: 0.181864 Loss2: 1.381809 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.492437 Loss1: 0.120779 Loss2: 1.371658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462368 Loss1: 0.093716 Loss2: 1.368651 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.968028 Loss1: 0.547064 Loss2: 1.420964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.613173 Loss1: 0.227656 Loss2: 1.385517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571311 Loss1: 0.180746 Loss2: 1.390565
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.520658 Loss1: 0.756425 Loss2: 1.764233
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.799336 Loss1: 0.473800 Loss2: 1.325535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.611533 Loss1: 0.272737 Loss2: 1.338795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.501058 Loss1: 0.183409 Loss2: 1.317649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.412408 Loss1: 0.110664 Loss2: 1.301744 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427851 Loss1: 0.075392 Loss2: 1.352460
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.400800 Loss1: 0.108309 Loss2: 1.292491
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425384 Loss1: 0.128112 Loss2: 1.297272
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.370793 Loss1: 0.079973 Loss2: 1.290820
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.352025 Loss1: 0.070514 Loss2: 1.281512
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.345374 Loss1: 0.068390 Loss2: 1.276984
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.890472 Loss1: 0.886588 Loss2: 2.003884
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.924634 Loss1: 0.553453 Loss2: 1.371181
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.758323 Loss1: 0.322682 Loss2: 1.435641
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606416 Loss1: 0.210806 Loss2: 1.395611
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534072 Loss1: 0.161122 Loss2: 1.372950
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.486352 Loss1: 0.115155 Loss2: 1.371197
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.401624 Loss1: 0.663487 Loss2: 1.738137
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449711 Loss1: 0.096471 Loss2: 1.353239 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437576 Loss1: 0.079350 Loss2: 1.358226 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433992 Loss1: 0.079439 Loss2: 1.354553 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.468995 Loss1: 0.159094 Loss2: 1.309901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406461 Loss1: 0.104610 Loss2: 1.301851 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.365617 Loss1: 0.075413 Loss2: 1.290204
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.515599 Loss1: 0.701111 Loss2: 1.814488
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392748 Loss1: 0.108873 Loss2: 1.283875
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.806359 Loss1: 0.432615 Loss2: 1.373744
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351528 Loss1: 0.063528 Loss2: 1.288000
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.596413 Loss1: 0.201136 Loss2: 1.395278
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547836 Loss1: 0.201095 Loss2: 1.346741
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479478 Loss1: 0.122239 Loss2: 1.357239
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.428554 Loss1: 0.087010 Loss2: 1.341544
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432778 Loss1: 0.087159 Loss2: 1.345619
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.535428 Loss1: 0.673481 Loss2: 1.861948
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.849028 Loss1: 0.471835 Loss2: 1.377193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.647049 Loss1: 0.241009 Loss2: 1.406040 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398752 Loss1: 0.070319 Loss2: 1.328433
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577265 Loss1: 0.211749 Loss2: 1.365516
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491206 Loss1: 0.126599 Loss2: 1.364607
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461923 Loss1: 0.109413 Loss2: 1.352510
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451337 Loss1: 0.101421 Loss2: 1.349916
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485626 Loss1: 0.133399 Loss2: 1.352227
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.409723 Loss1: 0.615660 Loss2: 1.794063
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447096 Loss1: 0.098299 Loss2: 1.348797
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.809738 Loss1: 0.437337 Loss2: 1.372401
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400322 Loss1: 0.056610 Loss2: 1.343711
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.645129 Loss1: 0.286188 Loss2: 1.358942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497591 Loss1: 0.140181 Loss2: 1.357410 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466929 Loss1: 0.114513 Loss2: 1.352416
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.419955 Loss1: 0.580705 Loss2: 1.839250
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.750352 Loss1: 0.392546 Loss2: 1.357807
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423826 Loss1: 0.078561 Loss2: 1.345265
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.685524 Loss1: 0.274888 Loss2: 1.410636
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403823 Loss1: 0.070875 Loss2: 1.332948
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559867 Loss1: 0.205648 Loss2: 1.354219
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390302 Loss1: 0.060511 Loss2: 1.329791
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466694 Loss1: 0.112301 Loss2: 1.354393 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447820 Loss1: 0.101789 Loss2: 1.346030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436389 Loss1: 0.096665 Loss2: 1.339724
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.426014 Loss1: 0.642161 Loss2: 1.783853
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401312 Loss1: 0.067688 Loss2: 1.333624
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.675061 Loss1: 0.370768 Loss2: 1.304294
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.527669 Loss1: 0.198889 Loss2: 1.328780
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496637 Loss1: 0.204375 Loss2: 1.292262
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516719 Loss1: 0.217201 Loss2: 1.299518
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.474801 Loss1: 0.181815 Loss2: 1.292987
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.477570 Loss1: 0.630367 Loss2: 1.847203
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.363499 Loss1: 0.077531 Loss2: 1.285967
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.859862 Loss1: 0.462679 Loss2: 1.397183
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.337792 Loss1: 0.062505 Loss2: 1.275287
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.326263 Loss1: 0.060518 Loss2: 1.265746
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.751286 Loss1: 0.306918 Loss2: 1.444368
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.326242 Loss1: 0.063981 Loss2: 1.262261
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.689324 Loss1: 0.296501 Loss2: 1.392823
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.602904 Loss1: 0.203300 Loss2: 1.399605
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.592152 Loss1: 0.201796 Loss2: 1.390355
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.538626 Loss1: 0.150371 Loss2: 1.388255
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568336 Loss1: 0.180585 Loss2: 1.387751
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.689053 Loss1: 0.790308 Loss2: 1.898745
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.526389 Loss1: 0.144860 Loss2: 1.381529
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456608 Loss1: 0.086778 Loss2: 1.369830 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587737 Loss1: 0.193373 Loss2: 1.394364 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505597 Loss1: 0.131237 Loss2: 1.374360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476323 Loss1: 0.107578 Loss2: 1.368745
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.511863 Loss1: 0.665409 Loss2: 1.846453
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.789576 Loss1: 0.437009 Loss2: 1.352567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.661127 Loss1: 0.267195 Loss2: 1.393932 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584467 Loss1: 0.232050 Loss2: 1.352417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482764 Loss1: 0.137597 Loss2: 1.345167 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470248 Loss1: 0.124593 Loss2: 1.345655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419310 Loss1: 0.084172 Loss2: 1.335137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453129 Loss1: 0.115791 Loss2: 1.337338 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451114 Loss1: 0.110083 Loss2: 1.341031 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.471707 Loss1: 0.131078 Loss2: 1.340629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.584292 Loss1: 0.699831 Loss2: 1.884461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.902699 Loss1: 0.494722 Loss2: 1.407977 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981971
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.617996 Loss1: 0.230089 Loss2: 1.387907 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508104 Loss1: 0.111520 Loss2: 1.396584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487603 Loss1: 0.109802 Loss2: 1.377801
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.465413 Loss1: 0.618053 Loss2: 1.847360
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.922338 Loss1: 0.570080 Loss2: 1.352258 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.805494 Loss1: 0.359888 Loss2: 1.445605 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.648094 Loss1: 0.294599 Loss2: 1.353495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576774 Loss1: 0.204633 Loss2: 1.372141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464721 Loss1: 0.109228 Loss2: 1.355493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433932 Loss1: 0.091001 Loss2: 1.342931 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389884 Loss1: 0.055610 Loss2: 1.334273 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577311 Loss1: 0.205397 Loss2: 1.371914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522840 Loss1: 0.163509 Loss2: 1.359331 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471054 Loss1: 0.114659 Loss2: 1.356395
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.592649 Loss1: 0.756943 Loss2: 1.835707
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469182 Loss1: 0.119298 Loss2: 1.349884
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.745529 Loss1: 0.382642 Loss2: 1.362887
+DEBUG flwr 2023-10-11 18:39:25,526 | server.py:236 | fit_round 123 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644450 Loss1: 0.250100 Loss2: 1.394350
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415318 Loss1: 0.070271 Loss2: 1.345047
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520440 Loss1: 0.169594 Loss2: 1.350846
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393293 Loss1: 0.058653 Loss2: 1.334640
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452237 Loss1: 0.105881 Loss2: 1.346356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.476736 Loss1: 0.133380 Loss2: 1.343356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489635 Loss1: 0.153526 Loss2: 1.336109
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.615167 Loss1: 0.750967 Loss2: 1.864200
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463467 Loss1: 0.118373 Loss2: 1.345094
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.992882 Loss1: 0.582643 Loss2: 1.410239
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.758224 Loss1: 0.355712 Loss2: 1.402512
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.670407 Loss1: 0.297625 Loss2: 1.372783
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563388 Loss1: 0.185687 Loss2: 1.377701
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473905 Loss1: 0.133074 Loss2: 1.340832
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.622294 Loss1: 0.795193 Loss2: 1.827101
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425273 Loss1: 0.086337 Loss2: 1.338936
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.822560 Loss1: 0.451359 Loss2: 1.371201
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411690 Loss1: 0.082539 Loss2: 1.329151
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.640967 Loss1: 0.230651 Loss2: 1.410316
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384035 Loss1: 0.062228 Loss2: 1.321806
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535230 Loss1: 0.169014 Loss2: 1.366215
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369540 Loss1: 0.052165 Loss2: 1.317376
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.532772 Loss1: 0.154798 Loss2: 1.377973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.514166 Loss1: 0.147424 Loss2: 1.366742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.504953 Loss1: 0.145683 Loss2: 1.359271
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.763370 Loss1: 0.842590 Loss2: 1.920780
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491728 Loss1: 0.129124 Loss2: 1.362604
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.908797 Loss1: 0.493387 Loss2: 1.415410
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.827775 Loss1: 0.366282 Loss2: 1.461493
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.644017 Loss1: 0.239915 Loss2: 1.404102
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.594437 Loss1: 0.194447 Loss2: 1.399990
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.497267 Loss1: 0.110717 Loss2: 1.386550
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471261 Loss1: 0.089056 Loss2: 1.382206
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441937 Loss1: 0.069821 Loss2: 1.372116
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446082 Loss1: 0.075385 Loss2: 1.370698
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441497 Loss1: 0.073801 Loss2: 1.367695
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 18:39:25,526][flwr][DEBUG] - fit_round 123 received 50 results and 0 failures
+INFO flwr 2023-10-11 18:40:05,828 | server.py:125 | fit progress: (123, 2.200183067268457, {'accuracy': 0.5825}, 283713.606855665)
+>> Test accuracy: 0.582500
+[2023-10-11 18:40:05,828][flwr][INFO] - fit progress: (123, 2.200183067268457, {'accuracy': 0.5825}, 283713.606855665)
+DEBUG flwr 2023-10-11 18:40:05,829 | server.py:173 | evaluate_round 123: strategy sampled 50 clients (out of 50)
+[2023-10-11 18:40:05,829][flwr][DEBUG] - evaluate_round 123: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 18:49:10,229 | server.py:187 | evaluate_round 123 received 50 results and 0 failures
+[2023-10-11 18:49:10,229][flwr][DEBUG] - evaluate_round 123 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 18:49:10,229 | server.py:222 | fit_round 124: strategy sampled 50 clients (out of 50)
+[2023-10-11 18:49:10,229][flwr][DEBUG] - fit_round 124: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.466810 Loss1: 0.590347 Loss2: 1.876463
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.818023 Loss1: 0.394176 Loss2: 1.423846
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.697503 Loss1: 0.236396 Loss2: 1.461107
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.648653 Loss1: 0.773769 Loss2: 1.874884
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.845338 Loss1: 0.464186 Loss2: 1.381152 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.667636 Loss1: 0.244924 Loss2: 1.422712 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.589451 Loss1: 0.223480 Loss2: 1.365971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512155 Loss1: 0.139927 Loss2: 1.372228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481202 Loss1: 0.122643 Loss2: 1.358559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455723 Loss1: 0.104048 Loss2: 1.351675 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429238 Loss1: 0.075009 Loss2: 1.354228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461252 Loss1: 0.109281 Loss2: 1.351972 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.416135 Loss1: 0.561326 Loss2: 1.854808
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.750519 Loss1: 0.345499 Loss2: 1.405020
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.643979 Loss1: 0.219765 Loss2: 1.424213
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.883994 Loss1: 0.921664 Loss2: 1.962330
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.969261 Loss1: 0.539666 Loss2: 1.429595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.776150 Loss1: 0.297054 Loss2: 1.479097 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.563895 Loss1: 0.168449 Loss2: 1.395446
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.658896 Loss1: 0.247890 Loss2: 1.411006
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.629309 Loss1: 0.205007 Loss2: 1.424302
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481668 Loss1: 0.095721 Loss2: 1.385948
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.592731 Loss1: 0.182164 Loss2: 1.410567
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.494208 Loss1: 0.114745 Loss2: 1.379463
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433720 Loss1: 0.058103 Loss2: 1.375617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418324 Loss1: 0.048772 Loss2: 1.369552 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996324
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487726 Loss1: 0.094877 Loss2: 1.392849 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.872298 Loss1: 0.850970 Loss2: 2.021329
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.910518 Loss1: 0.518336 Loss2: 1.392182
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.697717 Loss1: 0.271130 Loss2: 1.426587
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.673790 Loss1: 0.271456 Loss2: 1.402334
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631258 Loss1: 0.239398 Loss2: 1.391861
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.820647 Loss1: 0.462832 Loss2: 1.357815 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489033 Loss1: 0.104581 Loss2: 1.384452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.571044 Loss1: 0.236832 Loss2: 1.334212 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.537035 Loss1: 0.213232 Loss2: 1.323803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480847 Loss1: 0.160636 Loss2: 1.320211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415936 Loss1: 0.110679 Loss2: 1.305257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377460 Loss1: 0.070286 Loss2: 1.307174 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.643744 Loss1: 0.238133 Loss2: 1.405611 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.455734 Loss1: 0.111648 Loss2: 1.344086 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438067 Loss1: 0.098841 Loss2: 1.339226
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.623450 Loss1: 0.737195 Loss2: 1.886254
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.890216 Loss1: 0.485455 Loss2: 1.404761 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.709241 Loss1: 0.278401 Loss2: 1.430839 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.544534 Loss1: 0.154865 Loss2: 1.389669 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.525753 Loss1: 0.139766 Loss2: 1.385987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460650 Loss1: 0.097315 Loss2: 1.363336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438962 Loss1: 0.078641 Loss2: 1.360321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453903 Loss1: 0.094078 Loss2: 1.359826 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.921354 Loss1: 0.422330 Loss2: 1.499023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.683812 Loss1: 0.250429 Loss2: 1.433383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.608385 Loss1: 0.190034 Loss2: 1.418350
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.492141 Loss1: 0.662841 Loss2: 1.829301
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.813887 Loss1: 0.447054 Loss2: 1.366832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.664097 Loss1: 0.253592 Loss2: 1.410506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612747 Loss1: 0.261605 Loss2: 1.351142 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555235 Loss1: 0.189071 Loss2: 1.366165 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.424376 Loss1: 0.089154 Loss2: 1.335222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390770 Loss1: 0.067198 Loss2: 1.323572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405823 Loss1: 0.082811 Loss2: 1.323012 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.740567 Loss1: 0.285978 Loss2: 1.454589 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.675506 Loss1: 0.234364 Loss2: 1.441142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.588848 Loss1: 0.184110 Loss2: 1.404737
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.417604 Loss1: 0.653893 Loss2: 1.763711
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.732968 Loss1: 0.394958 Loss2: 1.338010 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.577922 Loss1: 0.225589 Loss2: 1.352333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563522 Loss1: 0.244137 Loss2: 1.319385 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.590153 Loss1: 0.264271 Loss2: 1.325883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422538 Loss1: 0.106846 Loss2: 1.315693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354285 Loss1: 0.058772 Loss2: 1.295513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356017 Loss1: 0.069886 Loss2: 1.286131 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564353 Loss1: 0.183205 Loss2: 1.381148 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516371 Loss1: 0.140947 Loss2: 1.375424 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.708341 Loss1: 0.708262 Loss2: 2.000078
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.489932 Loss1: 0.118120 Loss2: 1.371812
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.986017 Loss1: 0.489383 Loss2: 1.496634
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.472726 Loss1: 0.095669 Loss2: 1.377058
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.888917 Loss1: 0.330471 Loss2: 1.558446
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437182 Loss1: 0.068540 Loss2: 1.368642
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.712109 Loss1: 0.222214 Loss2: 1.489896
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435626 Loss1: 0.073211 Loss2: 1.362415
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.624731 Loss1: 0.138494 Loss2: 1.486237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.583014 Loss1: 0.111525 Loss2: 1.471488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.580746 Loss1: 0.106132 Loss2: 1.474613
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.504243 Loss1: 0.660690 Loss2: 1.843553
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.533193 Loss1: 0.063387 Loss2: 1.469806
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.779697 Loss1: 0.382377 Loss2: 1.397321
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.669062 Loss1: 0.241883 Loss2: 1.427179
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.592136 Loss1: 0.202615 Loss2: 1.389521
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514997 Loss1: 0.121404 Loss2: 1.393594
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493231 Loss1: 0.120098 Loss2: 1.373133
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.689846 Loss1: 0.855599 Loss2: 1.834247
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.834536 Loss1: 0.443480 Loss2: 1.391057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604787 Loss1: 0.209337 Loss2: 1.395450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592762 Loss1: 0.229179 Loss2: 1.363583 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438757 Loss1: 0.077893 Loss2: 1.360864
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544033 Loss1: 0.171887 Loss2: 1.372146
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484473 Loss1: 0.128707 Loss2: 1.355766
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478687 Loss1: 0.128792 Loss2: 1.349895
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445009 Loss1: 0.093901 Loss2: 1.351108
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405443 Loss1: 0.067344 Loss2: 1.338099
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.767783 Loss1: 0.881606 Loss2: 1.886176
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386260 Loss1: 0.054493 Loss2: 1.331767
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.692157 Loss1: 0.266551 Loss2: 1.425605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.563737 Loss1: 0.166530 Loss2: 1.397207 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519577 Loss1: 0.128887 Loss2: 1.390690
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.582042 Loss1: 0.731667 Loss2: 1.850376
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476215 Loss1: 0.093550 Loss2: 1.382665
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.795494 Loss1: 0.467877 Loss2: 1.327618
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.709360 Loss1: 0.332170 Loss2: 1.377190
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486484 Loss1: 0.110714 Loss2: 1.375771
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557498 Loss1: 0.224202 Loss2: 1.333296
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456414 Loss1: 0.083235 Loss2: 1.373179
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516999 Loss1: 0.188485 Loss2: 1.328514
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463833 Loss1: 0.088311 Loss2: 1.375522
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445315 Loss1: 0.126082 Loss2: 1.319233 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.365519 Loss1: 0.062316 Loss2: 1.303203 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.360035 Loss1: 0.063482 Loss2: 1.296553
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.433001 Loss1: 0.665017 Loss2: 1.767984
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.767166 Loss1: 0.424278 Loss2: 1.342888
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.669768 Loss1: 0.307773 Loss2: 1.361994
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563345 Loss1: 0.242691 Loss2: 1.320654
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.504906 Loss1: 0.173125 Loss2: 1.331781
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.543732 Loss1: 0.665110 Loss2: 1.878622
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471266 Loss1: 0.152238 Loss2: 1.319028
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.935582 Loss1: 0.534451 Loss2: 1.401131
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.398235 Loss1: 0.084788 Loss2: 1.313447
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.799481 Loss1: 0.344070 Loss2: 1.455410
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.685100 Loss1: 0.291098 Loss2: 1.394002
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.395358 Loss1: 0.088007 Loss2: 1.307351
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.853239 Loss1: 0.423389 Loss2: 1.429850
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386122 Loss1: 0.082482 Loss2: 1.303640
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.660715 Loss1: 0.256363 Loss2: 1.404351
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387034 Loss1: 0.081213 Loss2: 1.305821
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.530297 Loss1: 0.141957 Loss2: 1.388340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461881 Loss1: 0.090753 Loss2: 1.371128 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.637578 Loss1: 0.320915 Loss2: 1.316663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506675 Loss1: 0.192305 Loss2: 1.314370 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484008 Loss1: 0.177545 Loss2: 1.306463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519874 Loss1: 0.193814 Loss2: 1.326059 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426501 Loss1: 0.105941 Loss2: 1.320560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.396733 Loss1: 0.097039 Loss2: 1.299695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.514978 Loss1: 0.118847 Loss2: 1.396131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475964 Loss1: 0.099686 Loss2: 1.376278 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431763 Loss1: 0.064321 Loss2: 1.367443 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.612990 Loss1: 0.803452 Loss2: 1.809538 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.678888 Loss1: 0.285305 Loss2: 1.393583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.597548 Loss1: 0.249660 Loss2: 1.347887
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.492410 Loss1: 0.729468 Loss2: 1.762942
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.743512 Loss1: 0.417432 Loss2: 1.326081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.644102 Loss1: 0.296688 Loss2: 1.347414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.515388 Loss1: 0.195956 Loss2: 1.319431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.444986 Loss1: 0.125511 Loss2: 1.319475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.381499 Loss1: 0.079618 Loss2: 1.301882 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.370715 Loss1: 0.068168 Loss2: 1.302547 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.336520 Loss1: 0.049300 Loss2: 1.287219 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.832390 Loss1: 0.465669 Loss2: 1.366721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.530699 Loss1: 0.167479 Loss2: 1.363220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.498074 Loss1: 0.686503 Loss2: 1.811570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.815363 Loss1: 0.441152 Loss2: 1.374211 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450012 Loss1: 0.107004 Loss2: 1.343008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440431 Loss1: 0.099372 Loss2: 1.341059 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405506 Loss1: 0.075792 Loss2: 1.329713 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416101 Loss1: 0.076210 Loss2: 1.339891 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390880 Loss1: 0.057482 Loss2: 1.333398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400731 Loss1: 0.069240 Loss2: 1.331491 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.723921 Loss1: 0.312488 Loss2: 1.411433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.467608 Loss1: 0.129186 Loss2: 1.338422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.673646 Loss1: 0.790085 Loss2: 1.883560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.893981 Loss1: 0.493545 Loss2: 1.400436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701773 Loss1: 0.285156 Loss2: 1.416617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591805 Loss1: 0.207729 Loss2: 1.384076 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499218 Loss1: 0.131315 Loss2: 1.367903 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417429 Loss1: 0.058909 Loss2: 1.358520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.598972 Loss1: 0.806027 Loss2: 1.792945 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.653930 Loss1: 0.290950 Loss2: 1.362980 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448848 Loss1: 0.134882 Loss2: 1.313966 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.390950 Loss1: 0.080801 Loss2: 1.310149
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.760131 Loss1: 0.810325 Loss2: 1.949807
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.892424 Loss1: 0.487412 Loss2: 1.405011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.792931 Loss1: 0.325516 Loss2: 1.467415 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.359775 Loss1: 0.066042 Loss2: 1.293734
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.719576 Loss1: 0.312475 Loss2: 1.407101
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.353363 Loss1: 0.065919 Loss2: 1.287444
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.616263 Loss1: 0.188549 Loss2: 1.427713
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.587130 Loss1: 0.184343 Loss2: 1.402786
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.523122 Loss1: 0.121331 Loss2: 1.401790
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.495958 Loss1: 0.097099 Loss2: 1.398858
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466669 Loss1: 0.076600 Loss2: 1.390068
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436250 Loss1: 0.057106 Loss2: 1.379144
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.913006 Loss1: 0.924472 Loss2: 1.988534
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.911721 Loss1: 0.447749 Loss2: 1.463972
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.747103 Loss1: 0.291292 Loss2: 1.455811
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.671500 Loss1: 0.233706 Loss2: 1.437794
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.629306 Loss1: 0.193295 Loss2: 1.436011
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564413 Loss1: 0.135283 Loss2: 1.429130
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.548949 Loss1: 0.663386 Loss2: 1.885562
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.555192 Loss1: 0.131296 Loss2: 1.423896
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.797660 Loss1: 0.418586 Loss2: 1.379074
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.504768 Loss1: 0.096767 Loss2: 1.408002
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.654215 Loss1: 0.229079 Loss2: 1.425136
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499066 Loss1: 0.087051 Loss2: 1.412015
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.580487 Loss1: 0.212284 Loss2: 1.368204
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453816 Loss1: 0.047624 Loss2: 1.406191
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501625 Loss1: 0.132924 Loss2: 1.368701
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466210 Loss1: 0.105937 Loss2: 1.360273
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485345 Loss1: 0.131965 Loss2: 1.353380
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455810 Loss1: 0.106053 Loss2: 1.349758
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433690 Loss1: 0.077931 Loss2: 1.355759
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437723 Loss1: 0.096448 Loss2: 1.341275
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.745414 Loss1: 0.834403 Loss2: 1.911011
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.833155 Loss1: 0.443147 Loss2: 1.390008
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634249 Loss1: 0.209250 Loss2: 1.424999
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.566771 Loss1: 0.198335 Loss2: 1.368436
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513534 Loss1: 0.146772 Loss2: 1.366762
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.559867 Loss1: 0.758937 Loss2: 1.800930
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515585 Loss1: 0.147576 Loss2: 1.368009
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503827 Loss1: 0.132839 Loss2: 1.370988
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.827306 Loss1: 0.483770 Loss2: 1.343536
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484539 Loss1: 0.117403 Loss2: 1.367136
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.741443 Loss1: 0.349066 Loss2: 1.392377
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456955 Loss1: 0.097585 Loss2: 1.359369
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595828 Loss1: 0.239621 Loss2: 1.356206
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421243 Loss1: 0.066228 Loss2: 1.355016
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491905 Loss1: 0.153857 Loss2: 1.338047
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455862 Loss1: 0.118818 Loss2: 1.337045
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412273 Loss1: 0.088424 Loss2: 1.323849
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.403878 Loss1: 0.086153 Loss2: 1.317726
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423040 Loss1: 0.107353 Loss2: 1.315686
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.476248 Loss1: 0.659220 Loss2: 1.817028
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400201 Loss1: 0.090378 Loss2: 1.309823
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595823 Loss1: 0.226816 Loss2: 1.369007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.501408 Loss1: 0.163536 Loss2: 1.337872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444056 Loss1: 0.104040 Loss2: 1.340016
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.673783 Loss1: 0.769003 Loss2: 1.904780
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.858599 Loss1: 0.436765 Loss2: 1.421834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.768402 Loss1: 0.304516 Loss2: 1.463886 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.656763 Loss1: 0.238489 Loss2: 1.418274 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576916 Loss1: 0.147992 Loss2: 1.428924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.523676 Loss1: 0.117863 Loss2: 1.405813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462911 Loss1: 0.071388 Loss2: 1.391522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457238 Loss1: 0.070410 Loss2: 1.386828 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.728047 Loss1: 0.289371 Loss2: 1.438676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.495797 Loss1: 0.125573 Loss2: 1.370224 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466946 Loss1: 0.108400 Loss2: 1.358546
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.629068 Loss1: 0.743364 Loss2: 1.885704
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.906072 Loss1: 0.507707 Loss2: 1.398365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.832661 Loss1: 0.372570 Loss2: 1.460090 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.674822 Loss1: 0.275318 Loss2: 1.399504 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.620676 Loss1: 0.213549 Loss2: 1.407127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497232 Loss1: 0.113170 Loss2: 1.384062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439202 Loss1: 0.063455 Loss2: 1.375747 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436660 Loss1: 0.066553 Loss2: 1.370107 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.663272 Loss1: 0.249905 Loss2: 1.413367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506872 Loss1: 0.139099 Loss2: 1.367772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.486750 Loss1: 0.125799 Loss2: 1.360951
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.576710 Loss1: 0.666882 Loss2: 1.909827
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.896263 Loss1: 0.488544 Loss2: 1.407719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.744879 Loss1: 0.287127 Loss2: 1.457752 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.737208 Loss1: 0.312468 Loss2: 1.424740 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.624769 Loss1: 0.195725 Loss2: 1.429044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518974 Loss1: 0.108428 Loss2: 1.410546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.494185 Loss1: 0.098572 Loss2: 1.395613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449659 Loss1: 0.056407 Loss2: 1.393252 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.621357 Loss1: 0.212761 Loss2: 1.408596 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.552135 Loss1: 0.173326 Loss2: 1.378809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.549995 Loss1: 0.177319 Loss2: 1.372675
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.571343 Loss1: 0.677699 Loss2: 1.893643
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.497997 Loss1: 0.125204 Loss2: 1.372793
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.807592 Loss1: 0.433075 Loss2: 1.374517
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.490505 Loss1: 0.131903 Loss2: 1.358602
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.655800 Loss1: 0.264438 Loss2: 1.391362
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595514 Loss1: 0.220206 Loss2: 1.375308
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.466477 Loss1: 0.105256 Loss2: 1.361221
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544598 Loss1: 0.171663 Loss2: 1.372936
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447042 Loss1: 0.090159 Loss2: 1.356883
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.544120 Loss1: 0.167774 Loss2: 1.376346 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442121 Loss1: 0.089848 Loss2: 1.352273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408848 Loss1: 0.064044 Loss2: 1.344804
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.402522 Loss1: 0.607394 Loss2: 1.795127
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.758669 Loss1: 0.416305 Loss2: 1.342364
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.720909 Loss1: 0.332178 Loss2: 1.388731
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.661379 Loss1: 0.320193 Loss2: 1.341185
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.581056 Loss1: 0.227037 Loss2: 1.354018
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.671214 Loss1: 0.780609 Loss2: 1.890605
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.799634 Loss1: 0.407101 Loss2: 1.392534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.732392 Loss1: 0.299828 Loss2: 1.432563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636375 Loss1: 0.246825 Loss2: 1.389550 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576991 Loss1: 0.185391 Loss2: 1.391600 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375345 Loss1: 0.075008 Loss2: 1.300337
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.497458 Loss1: 0.118598 Loss2: 1.378860
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494552 Loss1: 0.120985 Loss2: 1.373566
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468934 Loss1: 0.092031 Loss2: 1.376903
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438254 Loss1: 0.076494 Loss2: 1.361760
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411429 Loss1: 0.056300 Loss2: 1.355129
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.577351 Loss1: 0.663912 Loss2: 1.913439
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.773627 Loss1: 0.363958 Loss2: 1.409669
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.696396 Loss1: 0.251926 Loss2: 1.444471
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.607652 Loss1: 0.202804 Loss2: 1.404848
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.550284 Loss1: 0.153058 Loss2: 1.397226
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.556925 Loss1: 0.706817 Loss2: 1.850108
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524849 Loss1: 0.125056 Loss2: 1.399792
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.789644 Loss1: 0.403482 Loss2: 1.386162
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517238 Loss1: 0.124905 Loss2: 1.392333
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.751240 Loss1: 0.318963 Loss2: 1.432277
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450197 Loss1: 0.061747 Loss2: 1.388450
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.571619 Loss1: 0.187959 Loss2: 1.383660
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433614 Loss1: 0.051592 Loss2: 1.382022
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516607 Loss1: 0.143113 Loss2: 1.373494
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417742 Loss1: 0.044266 Loss2: 1.373476
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490584 Loss1: 0.120603 Loss2: 1.369981
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490388 Loss1: 0.118798 Loss2: 1.371590
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.534064 Loss1: 0.166965 Loss2: 1.367100
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.475999 Loss1: 0.102669 Loss2: 1.373330
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446600 Loss1: 0.082524 Loss2: 1.364077
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.781466 Loss1: 0.656282 Loss2: 2.125184
+(DefaultActor pid=3765) Epoch: 1 Loss: 2.012204 Loss1: 0.440305 Loss2: 1.571899
+DEBUG flwr 2023-10-11 19:17:58,049 | server.py:236 | fit_round 124 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.885147 Loss1: 0.261764 Loss2: 1.623383
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.722750 Loss1: 0.161280 Loss2: 1.561470
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.723190 Loss1: 0.802633 Loss2: 1.920556
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.870977 Loss1: 0.505539 Loss2: 1.365439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.713213 Loss1: 0.324039 Loss2: 1.389173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554973 Loss1: 0.197434 Loss2: 1.357539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.482957 Loss1: 0.146933 Loss2: 1.336023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427500 Loss1: 0.092138 Loss2: 1.335362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.403456 Loss1: 0.077953 Loss2: 1.325503 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363206 Loss1: 0.047283 Loss2: 1.315923 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.553373 Loss1: 0.718394 Loss2: 1.834979 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.733021 Loss1: 0.324172 Loss2: 1.408849 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.624329 Loss1: 0.252285 Loss2: 1.372044
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.737385 Loss1: 0.813416 Loss2: 1.923970
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.002066 Loss1: 0.552929 Loss2: 1.449137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.764445 Loss1: 0.267083 Loss2: 1.497362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612109 Loss1: 0.177965 Loss2: 1.434144 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563863 Loss1: 0.132488 Loss2: 1.431375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530078 Loss1: 0.110731 Loss2: 1.419348 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415337 Loss1: 0.075047 Loss2: 1.340290
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.529349 Loss1: 0.114750 Loss2: 1.414599
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.499417 Loss1: 0.090766 Loss2: 1.408651
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490111 Loss1: 0.079807 Loss2: 1.410304
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487399 Loss1: 0.080675 Loss2: 1.406724
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 19:17:58,049][flwr][DEBUG] - fit_round 124 received 50 results and 0 failures
+INFO flwr 2023-10-11 19:18:40,644 | server.py:125 | fit progress: (124, 2.2044151994747856, {'accuracy': 0.5851}, 286028.422550948)
+>> Test accuracy: 0.585100
+[2023-10-11 19:18:40,644][flwr][INFO] - fit progress: (124, 2.2044151994747856, {'accuracy': 0.5851}, 286028.422550948)
+DEBUG flwr 2023-10-11 19:18:40,644 | server.py:173 | evaluate_round 124: strategy sampled 50 clients (out of 50)
+[2023-10-11 19:18:40,644][flwr][DEBUG] - evaluate_round 124: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 19:27:45,219 | server.py:187 | evaluate_round 124 received 50 results and 0 failures
+[2023-10-11 19:27:45,219][flwr][DEBUG] - evaluate_round 124 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 19:27:45,219 | server.py:222 | fit_round 125: strategy sampled 50 clients (out of 50)
+[2023-10-11 19:27:45,219][flwr][DEBUG] - fit_round 125: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.443535 Loss1: 0.654620 Loss2: 1.788914
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.730356 Loss1: 0.421198 Loss2: 1.309157
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645501 Loss1: 0.286574 Loss2: 1.358928
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.584707 Loss1: 0.271466 Loss2: 1.313240
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.773465 Loss1: 0.857707 Loss2: 1.915758
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.857536 Loss1: 0.467886 Loss2: 1.389650 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440176 Loss1: 0.136827 Loss2: 1.303349
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.683834 Loss1: 0.250008 Loss2: 1.433825
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.371115 Loss1: 0.072279 Loss2: 1.298836
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586978 Loss1: 0.210763 Loss2: 1.376215
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394865 Loss1: 0.099602 Loss2: 1.295263
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.586160 Loss1: 0.201607 Loss2: 1.384553
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.557441 Loss1: 0.174841 Loss2: 1.382600
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.415563 Loss1: 0.118536 Loss2: 1.297027
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483574 Loss1: 0.114093 Loss2: 1.369480
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391461 Loss1: 0.103323 Loss2: 1.288138
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461007 Loss1: 0.100234 Loss2: 1.360773 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982143
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.669436 Loss1: 0.831685 Loss2: 1.837751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.726755 Loss1: 0.315899 Loss2: 1.410857 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.568081 Loss1: 0.216183 Loss2: 1.351898
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.534529 Loss1: 0.702047 Loss2: 1.832482
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.767979 Loss1: 0.401245 Loss2: 1.366733 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.693065 Loss1: 0.294398 Loss2: 1.398667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.607623 Loss1: 0.238941 Loss2: 1.368682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528458 Loss1: 0.162746 Loss2: 1.365711 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486489 Loss1: 0.120949 Loss2: 1.365540 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372598 Loss1: 0.055457 Loss2: 1.317141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447824 Loss1: 0.098001 Loss2: 1.349823
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413590 Loss1: 0.068412 Loss2: 1.345178
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385854 Loss1: 0.049268 Loss2: 1.336585
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370414 Loss1: 0.040660 Loss2: 1.329754
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.589670 Loss1: 0.772898 Loss2: 1.816772
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.721571 Loss1: 0.372125 Loss2: 1.349445
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.587100 Loss1: 0.213880 Loss2: 1.373220
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513435 Loss1: 0.178380 Loss2: 1.335055
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.548981 Loss1: 0.744627 Loss2: 1.804354
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.884791 Loss1: 0.537308 Loss2: 1.347484 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.796064 Loss1: 0.379332 Loss2: 1.416733 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.648889 Loss1: 0.307717 Loss2: 1.341173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.584157 Loss1: 0.219877 Loss2: 1.364280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528151 Loss1: 0.193236 Loss2: 1.334914 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.362341 Loss1: 0.050096 Loss2: 1.312244
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.506545 Loss1: 0.160771 Loss2: 1.345775
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.463567 Loss1: 0.123142 Loss2: 1.340425
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.411652 Loss1: 0.085955 Loss2: 1.325698
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438031 Loss1: 0.114380 Loss2: 1.323650
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.443483 Loss1: 0.574607 Loss2: 1.868876
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784992 Loss1: 0.397989 Loss2: 1.387003
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.787041 Loss1: 0.337915 Loss2: 1.449125
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.620185 Loss1: 0.225591 Loss2: 1.394594
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.735906 Loss1: 0.873610 Loss2: 1.862295
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.837596 Loss1: 0.480587 Loss2: 1.357009
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534934 Loss1: 0.132340 Loss2: 1.402594
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.664623 Loss1: 0.266027 Loss2: 1.398596
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491447 Loss1: 0.108777 Loss2: 1.382671
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562191 Loss1: 0.227553 Loss2: 1.334639
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461528 Loss1: 0.086083 Loss2: 1.375446
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452408 Loss1: 0.080903 Loss2: 1.371505 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438645 Loss1: 0.071011 Loss2: 1.367634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430314 Loss1: 0.063994 Loss2: 1.366320 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389596 Loss1: 0.077387 Loss2: 1.312209 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.764146 Loss1: 0.890226 Loss2: 1.873919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.639865 Loss1: 0.232052 Loss2: 1.407813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572278 Loss1: 0.195176 Loss2: 1.377102
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.579813 Loss1: 0.767075 Loss2: 1.812739
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518458 Loss1: 0.143352 Loss2: 1.375107
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.833159 Loss1: 0.495962 Loss2: 1.337198
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477996 Loss1: 0.113282 Loss2: 1.364714
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.692934 Loss1: 0.307197 Loss2: 1.385737
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456873 Loss1: 0.100467 Loss2: 1.356406
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528965 Loss1: 0.197973 Loss2: 1.330992
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.487747 Loss1: 0.137565 Loss2: 1.350183
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446639 Loss1: 0.121883 Loss2: 1.324756
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425589 Loss1: 0.074008 Loss2: 1.351582
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.425059 Loss1: 0.112670 Loss2: 1.312389
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413576 Loss1: 0.066899 Loss2: 1.346677
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.394258 Loss1: 0.085322 Loss2: 1.308936
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.404970 Loss1: 0.103604 Loss2: 1.301366
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392325 Loss1: 0.084722 Loss2: 1.307602
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352510 Loss1: 0.055481 Loss2: 1.297029
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.567178 Loss1: 0.733618 Loss2: 1.833560
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.811979 Loss1: 0.450699 Loss2: 1.361281
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.631142 Loss1: 0.234595 Loss2: 1.396547
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.609083 Loss1: 0.255610 Loss2: 1.353473
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.694014 Loss1: 0.820183 Loss2: 1.873831
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.852334 Loss1: 0.445797 Loss2: 1.406536 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.686506 Loss1: 0.262730 Loss2: 1.423776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.655100 Loss1: 0.273941 Loss2: 1.381159 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.566676 Loss1: 0.170686 Loss2: 1.395991 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.535821 Loss1: 0.158155 Loss2: 1.377666 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399223 Loss1: 0.070563 Loss2: 1.328661
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.479597 Loss1: 0.110516 Loss2: 1.369081
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468916 Loss1: 0.105459 Loss2: 1.363457
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447898 Loss1: 0.089497 Loss2: 1.358401
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424278 Loss1: 0.067248 Loss2: 1.357030
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.507398 Loss1: 0.611150 Loss2: 1.896247
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.855710 Loss1: 0.464440 Loss2: 1.391270
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.737352 Loss1: 0.286920 Loss2: 1.450432
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.609069 Loss1: 0.217200 Loss2: 1.391869
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.585615 Loss1: 0.706772 Loss2: 1.878843
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.762780 Loss1: 0.371606 Loss2: 1.391174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.692611 Loss1: 0.260714 Loss2: 1.431897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.613158 Loss1: 0.216701 Loss2: 1.396457 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.670372 Loss1: 0.273807 Loss2: 1.396565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.585152 Loss1: 0.173621 Loss2: 1.411531 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432544 Loss1: 0.069009 Loss2: 1.363535
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.534083 Loss1: 0.148513 Loss2: 1.385570
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.533435 Loss1: 0.146465 Loss2: 1.386969
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.534704 Loss1: 0.144010 Loss2: 1.390694
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.528193 Loss1: 0.140851 Loss2: 1.387342
+(DefaultActor pid=3764) >> Training accuracy: 0.929167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.620158 Loss1: 0.785471 Loss2: 1.834687
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.917949 Loss1: 0.522451 Loss2: 1.395498
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700593 Loss1: 0.295602 Loss2: 1.404991
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.639273 Loss1: 0.277257 Loss2: 1.362016
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.736079 Loss1: 0.868316 Loss2: 1.867763
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514040 Loss1: 0.147024 Loss2: 1.367017
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.844029 Loss1: 0.485678 Loss2: 1.358352
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.675233 Loss1: 0.274875 Loss2: 1.400358
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.467170 Loss1: 0.104763 Loss2: 1.362406
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567695 Loss1: 0.217472 Loss2: 1.350223
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.454577 Loss1: 0.100587 Loss2: 1.353990
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.498343 Loss1: 0.151456 Loss2: 1.346886
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471519 Loss1: 0.117153 Loss2: 1.354365
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412160 Loss1: 0.068092 Loss2: 1.344068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387301 Loss1: 0.050143 Loss2: 1.337158 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414322 Loss1: 0.092038 Loss2: 1.322284 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.683760 Loss1: 0.774371 Loss2: 1.909389 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.735065 Loss1: 0.279875 Loss2: 1.455190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642926 Loss1: 0.250137 Loss2: 1.392789
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.517285 Loss1: 0.739420 Loss2: 1.777865
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.810643 Loss1: 0.475171 Loss2: 1.335472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.607831 Loss1: 0.240114 Loss2: 1.367717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.517515 Loss1: 0.208452 Loss2: 1.309063 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.466304 Loss1: 0.154700 Loss2: 1.311604 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473204 Loss1: 0.159948 Loss2: 1.313256 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484211 Loss1: 0.114257 Loss2: 1.369954
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.449394 Loss1: 0.139674 Loss2: 1.309719
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397047 Loss1: 0.092289 Loss2: 1.304759
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363769 Loss1: 0.066126 Loss2: 1.297643
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400981 Loss1: 0.109388 Loss2: 1.291592
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.592136 Loss1: 0.720941 Loss2: 1.871195
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.777612 Loss1: 0.383163 Loss2: 1.394449
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.682580 Loss1: 0.256525 Loss2: 1.426055
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596006 Loss1: 0.207019 Loss2: 1.388987
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.776048 Loss1: 0.812133 Loss2: 1.963915
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.891280 Loss1: 0.535401 Loss2: 1.355879
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546008 Loss1: 0.149652 Loss2: 1.396356
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505607 Loss1: 0.121253 Loss2: 1.384354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.519570 Loss1: 0.138350 Loss2: 1.381220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.522665 Loss1: 0.140050 Loss2: 1.382616 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469538 Loss1: 0.119565 Loss2: 1.349973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.480654 Loss1: 0.131415 Loss2: 1.349239 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425971 Loss1: 0.081414 Loss2: 1.344557 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.647362 Loss1: 0.798667 Loss2: 1.848695
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.746515 Loss1: 0.376157 Loss2: 1.370358
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.662969 Loss1: 0.272480 Loss2: 1.390488
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534035 Loss1: 0.173135 Loss2: 1.360900
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.693331 Loss1: 0.765793 Loss2: 1.927538
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.894848 Loss1: 0.445005 Loss2: 1.449842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.729290 Loss1: 0.285489 Loss2: 1.443801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612243 Loss1: 0.195767 Loss2: 1.416476 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576756 Loss1: 0.175473 Loss2: 1.401284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524172 Loss1: 0.127835 Loss2: 1.396337 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430961 Loss1: 0.082882 Loss2: 1.348079
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480653 Loss1: 0.095682 Loss2: 1.384971
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452557 Loss1: 0.066904 Loss2: 1.385653
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439382 Loss1: 0.065990 Loss2: 1.373392
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417314 Loss1: 0.047081 Loss2: 1.370233
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.713344 Loss1: 0.789838 Loss2: 1.923505
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.882818 Loss1: 0.494399 Loss2: 1.388419
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.745288 Loss1: 0.331964 Loss2: 1.413324
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.598976 Loss1: 0.181769 Loss2: 1.417208
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562252 Loss1: 0.176426 Loss2: 1.385825
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551253 Loss1: 0.162440 Loss2: 1.388813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484489 Loss1: 0.102808 Loss2: 1.381681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443072 Loss1: 0.070160 Loss2: 1.372911 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451788 Loss1: 0.095084 Loss2: 1.356703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450601 Loss1: 0.082615 Loss2: 1.367986 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.410421 Loss1: 0.083539 Loss2: 1.326882 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362342 Loss1: 0.046012 Loss2: 1.316330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348575 Loss1: 0.041118 Loss2: 1.307457
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.430959 Loss1: 0.578115 Loss2: 1.852844
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.837201 Loss1: 0.436013 Loss2: 1.401188
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.694922 Loss1: 0.268780 Loss2: 1.426142
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.685136 Loss1: 0.284338 Loss2: 1.400798
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.684376 Loss1: 0.254625 Loss2: 1.429751
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.506693 Loss1: 0.691550 Loss2: 1.815143
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.572802 Loss1: 0.165609 Loss2: 1.407193
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.526392 Loss1: 0.129516 Loss2: 1.396877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.492558 Loss1: 0.103797 Loss2: 1.388761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472871 Loss1: 0.081432 Loss2: 1.391439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466325 Loss1: 0.082080 Loss2: 1.384245 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451499 Loss1: 0.097049 Loss2: 1.354450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404684 Loss1: 0.059461 Loss2: 1.345223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.610424 Loss1: 0.723084 Loss2: 1.887340
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399813 Loss1: 0.062068 Loss2: 1.337745
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.770041 Loss1: 0.331675 Loss2: 1.438366 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519311 Loss1: 0.124949 Loss2: 1.394363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.510230 Loss1: 0.133294 Loss2: 1.376936
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.672948 Loss1: 0.664282 Loss2: 2.008666
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513903 Loss1: 0.128009 Loss2: 1.385894
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.893027 Loss1: 0.410978 Loss2: 1.482050
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495132 Loss1: 0.113570 Loss2: 1.381562
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.796711 Loss1: 0.268443 Loss2: 1.528267
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451385 Loss1: 0.079835 Loss2: 1.371550
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.701215 Loss1: 0.227050 Loss2: 1.474165
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424487 Loss1: 0.057178 Loss2: 1.367310
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.738389 Loss1: 0.255976 Loss2: 1.482412
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.723810 Loss1: 0.220706 Loss2: 1.503105
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.651298 Loss1: 0.174211 Loss2: 1.477087
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.603990 Loss1: 0.134145 Loss2: 1.469845
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.560335 Loss1: 0.104353 Loss2: 1.455982
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.595425 Loss1: 0.828536 Loss2: 1.766888
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.562365 Loss1: 0.107179 Loss2: 1.455185
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.549496 Loss1: 0.252050 Loss2: 1.297446 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.408908 Loss1: 0.139304 Loss2: 1.269605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.361921 Loss1: 0.100792 Loss2: 1.261129
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.470962 Loss1: 0.621438 Loss2: 1.849525
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.370000 Loss1: 0.117628 Loss2: 1.252372
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.702137 Loss1: 0.299855 Loss2: 1.402282
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.338107 Loss1: 0.081316 Loss2: 1.256791
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.597181 Loss1: 0.193108 Loss2: 1.404073
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.522955 Loss1: 0.148352 Loss2: 1.374603 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.289688 Loss1: 0.042330 Loss2: 1.247359
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496078 Loss1: 0.119946 Loss2: 1.376132
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485887 Loss1: 0.115205 Loss2: 1.370682
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430697 Loss1: 0.068707 Loss2: 1.361990
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438794 Loss1: 0.080010 Loss2: 1.358783
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467830 Loss1: 0.107059 Loss2: 1.360771
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.485134 Loss1: 0.676166 Loss2: 1.808967
+(DefaultActor pid=3764) >> Training accuracy: 0.977539
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.910588 Loss1: 0.505440 Loss2: 1.405148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.637130 Loss1: 0.239985 Loss2: 1.397145 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497505 Loss1: 0.122276 Loss2: 1.375229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479093 Loss1: 0.104587 Loss2: 1.374506 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.463795 Loss1: 0.100143 Loss2: 1.363652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430054 Loss1: 0.071329 Loss2: 1.358725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400844 Loss1: 0.049614 Loss2: 1.351230 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.482718 Loss1: 0.107082 Loss2: 1.375636 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.500514 Loss1: 0.127091 Loss2: 1.373423 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462758 Loss1: 0.085261 Loss2: 1.377496 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634678 Loss1: 0.197803 Loss2: 1.436874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.560705 Loss1: 0.159816 Loss2: 1.400890 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.541345 Loss1: 0.136405 Loss2: 1.404941
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.516123 Loss1: 0.674082 Loss2: 1.842042
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.515678 Loss1: 0.116324 Loss2: 1.399354
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.855317 Loss1: 0.439564 Loss2: 1.415754
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.534992 Loss1: 0.135879 Loss2: 1.399113
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769353 Loss1: 0.318529 Loss2: 1.450824
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482143 Loss1: 0.086598 Loss2: 1.395545
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.691349 Loss1: 0.293082 Loss2: 1.398267
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.473519 Loss1: 0.084738 Loss2: 1.388780
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609290 Loss1: 0.208868 Loss2: 1.400422
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.552539 Loss1: 0.151835 Loss2: 1.400705
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.527999 Loss1: 0.142892 Loss2: 1.385107
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.528378 Loss1: 0.138886 Loss2: 1.389492
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.465829 Loss1: 0.082924 Loss2: 1.382905
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.666652 Loss1: 0.780839 Loss2: 1.885813
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.910419 Loss1: 0.483295 Loss2: 1.427124 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576720 Loss1: 0.194592 Loss2: 1.382128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478086 Loss1: 0.113188 Loss2: 1.364899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476474 Loss1: 0.114418 Loss2: 1.362056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449752 Loss1: 0.091313 Loss2: 1.358439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453812 Loss1: 0.098247 Loss2: 1.355564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447233 Loss1: 0.087445 Loss2: 1.359788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.534388 Loss1: 0.091617 Loss2: 1.442771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.499877 Loss1: 0.075443 Loss2: 1.424434 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.344288 Loss1: 0.585807 Loss2: 1.758481 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.605165 Loss1: 0.285585 Loss2: 1.319581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.448694 Loss1: 0.141274 Loss2: 1.307420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.634365 Loss1: 0.757333 Loss2: 1.877032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831294 Loss1: 0.455558 Loss2: 1.375736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.634300 Loss1: 0.269516 Loss2: 1.364784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604956 Loss1: 0.252555 Loss2: 1.352401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.593058 Loss1: 0.243657 Loss2: 1.349401 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988051
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361105 Loss1: 0.073358 Loss2: 1.287747
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.526702 Loss1: 0.172311 Loss2: 1.354391
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448036 Loss1: 0.107559 Loss2: 1.340477
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435607 Loss1: 0.092817 Loss2: 1.342790
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413820 Loss1: 0.087503 Loss2: 1.326317
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365785 Loss1: 0.047434 Loss2: 1.318351
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.447479 Loss1: 0.619099 Loss2: 1.828380
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.701858 Loss1: 0.361265 Loss2: 1.340593
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656521 Loss1: 0.290502 Loss2: 1.366019
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518902 Loss1: 0.180187 Loss2: 1.338715
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492417 Loss1: 0.163542 Loss2: 1.328875
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.513703 Loss1: 0.696139 Loss2: 1.817564
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504264 Loss1: 0.162368 Loss2: 1.341896
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.879491 Loss1: 0.514212 Loss2: 1.365280
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439858 Loss1: 0.110103 Loss2: 1.329755
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670381 Loss1: 0.274455 Loss2: 1.395927
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592415 Loss1: 0.244368 Loss2: 1.348047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515194 Loss1: 0.153896 Loss2: 1.361298 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386371 Loss1: 0.071012 Loss2: 1.315360
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446161 Loss1: 0.105388 Loss2: 1.340773
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425870 Loss1: 0.095166 Loss2: 1.330704
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.402953 Loss1: 0.073138 Loss2: 1.329815
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.375751 Loss1: 0.056599 Loss2: 1.319152
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.353278 Loss1: 0.040926 Loss2: 1.312352
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.556310 Loss1: 0.746182 Loss2: 1.810129
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.861586 Loss1: 0.509934 Loss2: 1.351652
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.694812 Loss1: 0.288768 Loss2: 1.406044
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533677 Loss1: 0.188994 Loss2: 1.344682
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480015 Loss1: 0.136109 Loss2: 1.343906
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.595967 Loss1: 0.755899 Loss2: 1.840068
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442697 Loss1: 0.113649 Loss2: 1.329048
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.834943 Loss1: 0.455016 Loss2: 1.379927
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.407292 Loss1: 0.083668 Loss2: 1.323624
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.723430 Loss1: 0.305814 Loss2: 1.417616
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411241 Loss1: 0.086882 Loss2: 1.324359
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636343 Loss1: 0.262986 Loss2: 1.373357
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423862 Loss1: 0.103761 Loss2: 1.320101
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.708423 Loss1: 0.319683 Loss2: 1.388740
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399794 Loss1: 0.079786 Loss2: 1.320008
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.526828 Loss1: 0.154085 Loss2: 1.372743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.456828 Loss1: 0.103254 Loss2: 1.353574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438644 Loss1: 0.086152 Loss2: 1.352492
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.695199 Loss1: 0.753900 Loss2: 1.941299
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.962132 Loss1: 0.504825 Loss2: 1.457307
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.779176 Loss1: 0.269303 Loss2: 1.509873
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647270 Loss1: 0.203056 Loss2: 1.444214
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.625421 Loss1: 0.172542 Loss2: 1.452879
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.812923 Loss1: 0.826814 Loss2: 1.986109
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.554246 Loss1: 0.116091 Loss2: 1.438155
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.538844 Loss1: 0.107667 Loss2: 1.431177 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.518952 Loss1: 0.097188 Loss2: 1.421764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.514979 Loss1: 0.086298 Loss2: 1.428681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.482947 Loss1: 0.062801 Loss2: 1.420146 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431585 Loss1: 0.074819 Loss2: 1.356766 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458823 Loss1: 0.100307 Loss2: 1.358516 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.580237 Loss1: 0.722077 Loss2: 1.858160
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.921303 Loss1: 0.526735 Loss2: 1.394567
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.701342 Loss1: 0.301798 Loss2: 1.399544
+DEBUG flwr 2023-10-11 19:56:44,136 | server.py:236 | fit_round 125 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.640326 Loss1: 0.281214 Loss2: 1.359112
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.568251 Loss1: 0.688993 Loss2: 1.879258
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.891278 Loss1: 0.483415 Loss2: 1.407863 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.754474 Loss1: 0.308849 Loss2: 1.445625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.596627 Loss1: 0.209369 Loss2: 1.387257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535304 Loss1: 0.144246 Loss2: 1.391059 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487329 Loss1: 0.109276 Loss2: 1.378053 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413032 Loss1: 0.072967 Loss2: 1.340065
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474035 Loss1: 0.106681 Loss2: 1.367354
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445301 Loss1: 0.078708 Loss2: 1.366593
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426694 Loss1: 0.067020 Loss2: 1.359674
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434779 Loss1: 0.078058 Loss2: 1.356720
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.661679 Loss1: 0.787220 Loss2: 1.874459
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.730982 Loss1: 0.329963 Loss2: 1.401018
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.649976 Loss1: 0.251339 Loss2: 1.398637
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.560467 Loss1: 0.181674 Loss2: 1.378794
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.571357 Loss1: 0.722489 Loss2: 1.848867
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.874796 Loss1: 0.488921 Loss2: 1.385875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727277 Loss1: 0.289628 Loss2: 1.437649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.587819 Loss1: 0.209329 Loss2: 1.378490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.622508 Loss1: 0.220041 Loss2: 1.402467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499882 Loss1: 0.114978 Loss2: 1.384904 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417692 Loss1: 0.066858 Loss2: 1.350834
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.495760 Loss1: 0.123168 Loss2: 1.372592
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467884 Loss1: 0.098093 Loss2: 1.369790
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450037 Loss1: 0.085522 Loss2: 1.364514
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422405 Loss1: 0.063771 Loss2: 1.358634
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.428784 Loss1: 0.655038 Loss2: 1.773746
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.679881 Loss1: 0.375336 Loss2: 1.304545
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.571767 Loss1: 0.240874 Loss2: 1.330892
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.503047 Loss1: 0.206636 Loss2: 1.296411
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.474169 Loss1: 0.639692 Loss2: 1.834477
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.849768 Loss1: 0.463120 Loss2: 1.386648 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.728493 Loss1: 0.280598 Loss2: 1.447895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526245 Loss1: 0.149040 Loss2: 1.377206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515162 Loss1: 0.141853 Loss2: 1.373309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487564 Loss1: 0.120090 Loss2: 1.367474 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.451017 Loss1: 0.086291 Loss2: 1.364727 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405422 Loss1: 0.052920 Loss2: 1.352502 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 19:56:44,136][flwr][DEBUG] - fit_round 125 received 50 results and 0 failures
+INFO flwr 2023-10-11 19:57:25,673 | server.py:125 | fit progress: (125, 2.2017363711667897, {'accuracy': 0.5871}, 288353.45192956197)
+>> Test accuracy: 0.587100
+[2023-10-11 19:57:25,673][flwr][INFO] - fit progress: (125, 2.2017363711667897, {'accuracy': 0.5871}, 288353.45192956197)
+DEBUG flwr 2023-10-11 19:57:25,674 | server.py:173 | evaluate_round 125: strategy sampled 50 clients (out of 50)
+[2023-10-11 19:57:25,674][flwr][DEBUG] - evaluate_round 125: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 20:06:31,840 | server.py:187 | evaluate_round 125 received 50 results and 0 failures
+[2023-10-11 20:06:31,840][flwr][DEBUG] - evaluate_round 125 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 20:06:31,841 | server.py:222 | fit_round 126: strategy sampled 50 clients (out of 50)
+[2023-10-11 20:06:31,841][flwr][DEBUG] - fit_round 126: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.576233 Loss1: 0.737990 Loss2: 1.838243
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.758718 Loss1: 0.408260 Loss2: 1.350458
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645540 Loss1: 0.266268 Loss2: 1.379272
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.501947 Loss1: 0.162962 Loss2: 1.338986
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.438728 Loss1: 0.626583 Loss2: 1.812145
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.692581 Loss1: 0.356973 Loss2: 1.335608 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.574129 Loss1: 0.196592 Loss2: 1.377536 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.590264 Loss1: 0.248974 Loss2: 1.341290 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514117 Loss1: 0.162264 Loss2: 1.351853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486287 Loss1: 0.146203 Loss2: 1.340084 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.353976 Loss1: 0.045063 Loss2: 1.308913
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448255 Loss1: 0.115142 Loss2: 1.333112
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419721 Loss1: 0.087875 Loss2: 1.331846
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435119 Loss1: 0.113681 Loss2: 1.321438
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417398 Loss1: 0.093756 Loss2: 1.323642
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.566994 Loss1: 0.736652 Loss2: 1.830343
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.753369 Loss1: 0.377566 Loss2: 1.375804
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.659102 Loss1: 0.268109 Loss2: 1.390993
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.446415 Loss1: 0.584788 Loss2: 1.861627
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574309 Loss1: 0.219490 Loss2: 1.354819
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.823802 Loss1: 0.424000 Loss2: 1.399802
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.469061 Loss1: 0.118196 Loss2: 1.350865
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.712865 Loss1: 0.265763 Loss2: 1.447102
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475950 Loss1: 0.128557 Loss2: 1.347393
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.706162 Loss1: 0.312446 Loss2: 1.393716
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434797 Loss1: 0.099182 Loss2: 1.335616
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.597999 Loss1: 0.170301 Loss2: 1.427698
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432087 Loss1: 0.096683 Loss2: 1.335404
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538103 Loss1: 0.151898 Loss2: 1.386205
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399350 Loss1: 0.068727 Loss2: 1.330623
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.506557 Loss1: 0.115878 Loss2: 1.390679
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418944 Loss1: 0.093045 Loss2: 1.325898
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480395 Loss1: 0.099619 Loss2: 1.380776 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.603346 Loss1: 0.779272 Loss2: 1.824073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.665549 Loss1: 0.278051 Loss2: 1.387498 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.603689 Loss1: 0.253216 Loss2: 1.350472
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.693368 Loss1: 0.881772 Loss2: 1.811595
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.995763 Loss1: 0.558471 Loss2: 1.437292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.713724 Loss1: 0.335841 Loss2: 1.377883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.635941 Loss1: 0.269077 Loss2: 1.366864 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.594517 Loss1: 0.229790 Loss2: 1.364727 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.526301 Loss1: 0.181231 Loss2: 1.345070 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383778 Loss1: 0.056250 Loss2: 1.327527
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437304 Loss1: 0.099924 Loss2: 1.337380
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422715 Loss1: 0.090102 Loss2: 1.332613
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434120 Loss1: 0.102368 Loss2: 1.331752
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390500 Loss1: 0.059179 Loss2: 1.331320
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.964240 Loss1: 0.588465 Loss2: 1.375775 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.684814 Loss1: 0.279397 Loss2: 1.405418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.421508 Loss1: 0.624141 Loss2: 1.797367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484132 Loss1: 0.122516 Loss2: 1.361617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.507527 Loss1: 0.139974 Loss2: 1.367553 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.506473 Loss1: 0.135850 Loss2: 1.370623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451886 Loss1: 0.086881 Loss2: 1.365006 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.526876 Loss1: 0.162205 Loss2: 1.364671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424317 Loss1: 0.082018 Loss2: 1.342299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415847 Loss1: 0.075376 Loss2: 1.340471
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.539658 Loss1: 0.695641 Loss2: 1.844016
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415274 Loss1: 0.080876 Loss2: 1.334399
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.876511 Loss1: 0.476806 Loss2: 1.399706
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.729818 Loss1: 0.318676 Loss2: 1.411141
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.637369 Loss1: 0.243999 Loss2: 1.393370
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577472 Loss1: 0.193748 Loss2: 1.383724
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.543235 Loss1: 0.171455 Loss2: 1.371780
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.511715 Loss1: 0.666403 Loss2: 1.845312
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.506254 Loss1: 0.137594 Loss2: 1.368660
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452853 Loss1: 0.088874 Loss2: 1.363978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459807 Loss1: 0.100274 Loss2: 1.359533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438717 Loss1: 0.081605 Loss2: 1.357112 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.462793 Loss1: 0.123741 Loss2: 1.339052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381236 Loss1: 0.060656 Loss2: 1.320580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.558490 Loss1: 0.690075 Loss2: 1.868414 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.896186 Loss1: 0.462722 Loss2: 1.433464 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.619297 Loss1: 0.203394 Loss2: 1.415903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.633035 Loss1: 0.721014 Loss2: 1.912021 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.939519 Loss1: 0.505569 Loss2: 1.433950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.775895 Loss1: 0.297263 Loss2: 1.478631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.725024 Loss1: 0.291280 Loss2: 1.433744 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.628057 Loss1: 0.199593 Loss2: 1.428464 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.528187 Loss1: 0.124466 Loss2: 1.403721 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463945 Loss1: 0.076680 Loss2: 1.387265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478164 Loss1: 0.092045 Loss2: 1.386118
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.553133 Loss1: 0.735864 Loss2: 1.817269
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.780567 Loss1: 0.417174 Loss2: 1.363393
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.651324 Loss1: 0.264544 Loss2: 1.386781
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582880 Loss1: 0.234121 Loss2: 1.348758
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507381 Loss1: 0.155135 Loss2: 1.352246
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.493239 Loss1: 0.668339 Loss2: 1.824899
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.755032 Loss1: 0.415139 Loss2: 1.339892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.667549 Loss1: 0.290805 Loss2: 1.376744 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.525095 Loss1: 0.196360 Loss2: 1.328735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.498235 Loss1: 0.168276 Loss2: 1.329959 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400435 Loss1: 0.074802 Loss2: 1.325632
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479262 Loss1: 0.149193 Loss2: 1.330069
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.462233 Loss1: 0.144279 Loss2: 1.317955
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.403353 Loss1: 0.080352 Loss2: 1.323001
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413811 Loss1: 0.095551 Loss2: 1.318260
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402607 Loss1: 0.089541 Loss2: 1.313067
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.648821 Loss1: 0.772100 Loss2: 1.876720
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.961799 Loss1: 0.556402 Loss2: 1.405397
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.762426 Loss1: 0.307111 Loss2: 1.455315
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.670340 Loss1: 0.265529 Loss2: 1.404811
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.606847 Loss1: 0.200291 Loss2: 1.406556
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.539589 Loss1: 0.712893 Loss2: 1.826696
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.560505 Loss1: 0.174194 Loss2: 1.386311
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.698966 Loss1: 0.359301 Loss2: 1.339665
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518984 Loss1: 0.134498 Loss2: 1.384487
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.557853 Loss1: 0.196146 Loss2: 1.361707
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495586 Loss1: 0.120150 Loss2: 1.375436
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520846 Loss1: 0.186164 Loss2: 1.334682
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461326 Loss1: 0.087186 Loss2: 1.374140
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.444367 Loss1: 0.107929 Loss2: 1.336438
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431927 Loss1: 0.063507 Loss2: 1.368420
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431814 Loss1: 0.109807 Loss2: 1.322008
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444662 Loss1: 0.118883 Loss2: 1.325779
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471583 Loss1: 0.145405 Loss2: 1.326179
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.492869 Loss1: 0.157765 Loss2: 1.335104
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434740 Loss1: 0.104955 Loss2: 1.329785
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.846644 Loss1: 0.845763 Loss2: 2.000882
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.890285 Loss1: 0.426706 Loss2: 1.463579
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.742048 Loss1: 0.267860 Loss2: 1.474189
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606773 Loss1: 0.167983 Loss2: 1.438790
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525510 Loss1: 0.092093 Loss2: 1.433417
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509141 Loss1: 0.085762 Loss2: 1.423379 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491997 Loss1: 0.073422 Loss2: 1.418575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.472251 Loss1: 0.060449 Loss2: 1.411802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.483922 Loss1: 0.081511 Loss2: 1.402410 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462467 Loss1: 0.057332 Loss2: 1.405135 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.529910 Loss1: 0.081726 Loss2: 1.448185 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.475076 Loss1: 0.037617 Loss2: 1.437459 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.968935 Loss1: 0.564095 Loss2: 1.404840 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.632924 Loss1: 0.248815 Loss2: 1.384109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567422 Loss1: 0.181060 Loss2: 1.386362
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.694652 Loss1: 0.777791 Loss2: 1.916861
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.544479 Loss1: 0.168263 Loss2: 1.376216
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.814107 Loss1: 0.401890 Loss2: 1.412217
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485988 Loss1: 0.110984 Loss2: 1.375005
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635201 Loss1: 0.190676 Loss2: 1.444525
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456615 Loss1: 0.095710 Loss2: 1.360905
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595869 Loss1: 0.195260 Loss2: 1.400609
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444594 Loss1: 0.083905 Loss2: 1.360689
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.581909 Loss1: 0.183454 Loss2: 1.398455
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390078 Loss1: 0.042159 Loss2: 1.347919
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522263 Loss1: 0.116176 Loss2: 1.406087
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480012 Loss1: 0.082967 Loss2: 1.397045
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465195 Loss1: 0.075357 Loss2: 1.389838
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432895 Loss1: 0.048340 Loss2: 1.384555
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419471 Loss1: 0.042207 Loss2: 1.377264
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.503084 Loss1: 0.675934 Loss2: 1.827150
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.758215 Loss1: 0.411220 Loss2: 1.346995
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.701078 Loss1: 0.309468 Loss2: 1.391610
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500626 Loss1: 0.160638 Loss2: 1.339988
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.538635 Loss1: 0.200986 Loss2: 1.337648
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488863 Loss1: 0.140963 Loss2: 1.347899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436138 Loss1: 0.112429 Loss2: 1.323709 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.396105 Loss1: 0.077686 Loss2: 1.318419 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371159 Loss1: 0.060115 Loss2: 1.311044 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350204 Loss1: 0.048428 Loss2: 1.301777 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.505212 Loss1: 0.130329 Loss2: 1.374882 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.455773 Loss1: 0.092807 Loss2: 1.362966 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.858417 Loss1: 0.470740 Loss2: 1.387677 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649431 Loss1: 0.259479 Loss2: 1.389951 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558558 Loss1: 0.160137 Loss2: 1.398421
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.554114 Loss1: 0.693040 Loss2: 1.861074
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499131 Loss1: 0.116805 Loss2: 1.382325
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.850839 Loss1: 0.465090 Loss2: 1.385748
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478005 Loss1: 0.108440 Loss2: 1.369565
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.662352 Loss1: 0.237677 Loss2: 1.424674
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.502845 Loss1: 0.135170 Loss2: 1.367675
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.616693 Loss1: 0.231901 Loss2: 1.384792
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.491447 Loss1: 0.112164 Loss2: 1.379283
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514521 Loss1: 0.132382 Loss2: 1.382140
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435050 Loss1: 0.066832 Loss2: 1.368218
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.475226 Loss1: 0.112420 Loss2: 1.362806
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.432802 Loss1: 0.080377 Loss2: 1.352425
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433443 Loss1: 0.078768 Loss2: 1.354675
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416613 Loss1: 0.069500 Loss2: 1.347113
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414620 Loss1: 0.069582 Loss2: 1.345038
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.652483 Loss1: 0.794655 Loss2: 1.857828
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.859810 Loss1: 0.472427 Loss2: 1.387383
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716220 Loss1: 0.286431 Loss2: 1.429789
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602782 Loss1: 0.232795 Loss2: 1.369988
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551370 Loss1: 0.171860 Loss2: 1.379510
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.529841 Loss1: 0.161880 Loss2: 1.367961 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486982 Loss1: 0.124357 Loss2: 1.362625 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.478200 Loss1: 0.117872 Loss2: 1.360328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423863 Loss1: 0.072257 Loss2: 1.351607 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421308 Loss1: 0.072933 Loss2: 1.348375 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494053 Loss1: 0.107308 Loss2: 1.386745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.503449 Loss1: 0.115433 Loss2: 1.388016 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.420911 Loss1: 0.571382 Loss2: 1.849529
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.451935 Loss1: 0.063236 Loss2: 1.388699
+(DefaultActor pid=3764) >> Training accuracy: 0.990809
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655162 Loss1: 0.258564 Loss2: 1.396598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.538820 Loss1: 0.182248 Loss2: 1.356573 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468756 Loss1: 0.111261 Loss2: 1.357495
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.527094 Loss1: 0.652678 Loss2: 1.874416
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.827060 Loss1: 0.391150 Loss2: 1.435910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.724422 Loss1: 0.269608 Loss2: 1.454815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.629331 Loss1: 0.213189 Loss2: 1.416141 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501951 Loss1: 0.097154 Loss2: 1.404796 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434604 Loss1: 0.042554 Loss2: 1.392051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439165 Loss1: 0.053471 Loss2: 1.385694
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.404572 Loss1: 0.628497 Loss2: 1.776074
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425572 Loss1: 0.043831 Loss2: 1.381741
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.741664 Loss1: 0.402912 Loss2: 1.338752
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573865 Loss1: 0.200699 Loss2: 1.373165
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.507935 Loss1: 0.182561 Loss2: 1.325374
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490135 Loss1: 0.161662 Loss2: 1.328473
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458423 Loss1: 0.132144 Loss2: 1.326279
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.726102 Loss1: 0.846014 Loss2: 1.880088
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.438317 Loss1: 0.116099 Loss2: 1.322218
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.386763 Loss1: 0.065789 Loss2: 1.320974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.367095 Loss1: 0.056770 Loss2: 1.310325 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.345234 Loss1: 0.038527 Loss2: 1.306707 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.453493 Loss1: 0.087205 Loss2: 1.366287 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483651 Loss1: 0.115871 Loss2: 1.367780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.634151 Loss1: 0.766309 Loss2: 1.867842 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.688493 Loss1: 0.274683 Loss2: 1.413810 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.549706 Loss1: 0.172501 Loss2: 1.377205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508424 Loss1: 0.151624 Loss2: 1.356799
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.702889 Loss1: 0.775048 Loss2: 1.927840
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.914064 Loss1: 0.532431 Loss2: 1.381633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462153 Loss1: 0.124656 Loss2: 1.337497
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.832541 Loss1: 0.373484 Loss2: 1.459057
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.689266 Loss1: 0.285762 Loss2: 1.403503
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407697 Loss1: 0.066478 Loss2: 1.341219
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.582988 Loss1: 0.194992 Loss2: 1.387996
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410155 Loss1: 0.075403 Loss2: 1.334753
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516517 Loss1: 0.140438 Loss2: 1.376079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442638 Loss1: 0.074550 Loss2: 1.368088 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.575639 Loss1: 0.685059 Loss2: 1.890581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.810867 Loss1: 0.331230 Loss2: 1.479637 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.645711 Loss1: 0.224419 Loss2: 1.421292 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.591227 Loss1: 0.190305 Loss2: 1.400922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.515908 Loss1: 0.127189 Loss2: 1.388720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.490906 Loss1: 0.101709 Loss2: 1.389197 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460463 Loss1: 0.082603 Loss2: 1.377860 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469435 Loss1: 0.096383 Loss2: 1.373052 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.517701 Loss1: 0.116715 Loss2: 1.400986 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.531535 Loss1: 0.127176 Loss2: 1.404359 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.856308 Loss1: 0.468775 Loss2: 1.387533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.532314 Loss1: 0.158133 Loss2: 1.374181 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.501449 Loss1: 0.129078 Loss2: 1.372371
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.753703 Loss1: 0.848536 Loss2: 1.905167
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449723 Loss1: 0.084580 Loss2: 1.365143
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831128 Loss1: 0.442497 Loss2: 1.388631
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458223 Loss1: 0.099413 Loss2: 1.358810
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653713 Loss1: 0.277777 Loss2: 1.375937
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459217 Loss1: 0.099258 Loss2: 1.359959
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.585176 Loss1: 0.231194 Loss2: 1.353982
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423620 Loss1: 0.069941 Loss2: 1.353679
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509432 Loss1: 0.156640 Loss2: 1.352792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412570 Loss1: 0.061782 Loss2: 1.350789
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467587 Loss1: 0.128848 Loss2: 1.338739
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450885 Loss1: 0.115647 Loss2: 1.335238
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422450 Loss1: 0.085927 Loss2: 1.336523
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385227 Loss1: 0.062700 Loss2: 1.322528
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384963 Loss1: 0.063311 Loss2: 1.321652
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.727946 Loss1: 0.849127 Loss2: 1.878819
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.872898 Loss1: 0.511907 Loss2: 1.360991
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.794145 Loss1: 0.370876 Loss2: 1.423269
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.579518 Loss1: 0.225508 Loss2: 1.354010
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.573423 Loss1: 0.221743 Loss2: 1.351680
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.682797 Loss1: 0.785840 Loss2: 1.896957
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509522 Loss1: 0.144503 Loss2: 1.365019
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448809 Loss1: 0.107114 Loss2: 1.341695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.468175 Loss1: 0.134561 Loss2: 1.333615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.426903 Loss1: 0.097574 Loss2: 1.329329 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396896 Loss1: 0.073153 Loss2: 1.323743 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463863 Loss1: 0.099761 Loss2: 1.364103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422160 Loss1: 0.074030 Loss2: 1.348131 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385201 Loss1: 0.039117 Loss2: 1.346084
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.407709 Loss1: 0.597306 Loss2: 1.810403
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.705992 Loss1: 0.378479 Loss2: 1.327513
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.620700 Loss1: 0.250855 Loss2: 1.369845
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526376 Loss1: 0.197666 Loss2: 1.328710
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.523153 Loss1: 0.199185 Loss2: 1.323967
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.561553 Loss1: 0.683331 Loss2: 1.878221
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456206 Loss1: 0.133751 Loss2: 1.322454
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831774 Loss1: 0.437528 Loss2: 1.394246
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402556 Loss1: 0.096547 Loss2: 1.306009
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.703274 Loss1: 0.280136 Loss2: 1.423138
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.369942 Loss1: 0.067706 Loss2: 1.302236
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.623322 Loss1: 0.236326 Loss2: 1.386996
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395453 Loss1: 0.096563 Loss2: 1.298890
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.606637 Loss1: 0.206119 Loss2: 1.400518
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375142 Loss1: 0.069617 Loss2: 1.305525
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542601 Loss1: 0.153953 Loss2: 1.388648
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490085 Loss1: 0.111946 Loss2: 1.378138
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470723 Loss1: 0.105020 Loss2: 1.365702
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464880 Loss1: 0.097563 Loss2: 1.367317
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446903 Loss1: 0.079340 Loss2: 1.367563
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.515608 Loss1: 0.633791 Loss2: 1.881817
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.912815 Loss1: 0.537647 Loss2: 1.375168
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.807549 Loss1: 0.331665 Loss2: 1.475884
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.632011 Loss1: 0.262714 Loss2: 1.369297
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.603952 Loss1: 0.212615 Loss2: 1.391337
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.555047 Loss1: 0.167393 Loss2: 1.387654 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473493 Loss1: 0.106234 Loss2: 1.367259 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439327 Loss1: 0.077356 Loss2: 1.361971 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413413 Loss1: 0.058235 Loss2: 1.355179 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407803 Loss1: 0.060140 Loss2: 1.347663 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485048 Loss1: 0.117053 Loss2: 1.367995 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414556 Loss1: 0.058728 Loss2: 1.355827 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.936945 Loss1: 0.552629 Loss2: 1.384315 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580682 Loss1: 0.205631 Loss2: 1.375050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.650185 Loss1: 0.729755 Loss2: 1.920430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.959645 Loss1: 0.510039 Loss2: 1.449606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421062 Loss1: 0.070189 Loss2: 1.350873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.393459 Loss1: 0.048075 Loss2: 1.345383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414607 Loss1: 0.075849 Loss2: 1.338759 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.541367 Loss1: 0.117472 Loss2: 1.423895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490014 Loss1: 0.082355 Loss2: 1.407659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.629200 Loss1: 0.790299 Loss2: 1.838901 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644126 Loss1: 0.264353 Loss2: 1.379772 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536617 Loss1: 0.188099 Loss2: 1.348518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.482897 Loss1: 0.150011 Loss2: 1.332886
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.751527 Loss1: 0.825127 Loss2: 1.926400
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.947000 Loss1: 0.515550 Loss2: 1.431449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.811136 Loss1: 0.332213 Loss2: 1.478922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.711525 Loss1: 0.283842 Loss2: 1.427684 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.344993 Loss1: 0.039235 Loss2: 1.305758
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.643640 Loss1: 0.204556 Loss2: 1.439084
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.620827 Loss1: 0.201564 Loss2: 1.419264
+DEBUG flwr 2023-10-11 20:34:53,769 | server.py:236 | fit_round 126 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.565481 Loss1: 0.151177 Loss2: 1.414305
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.549699 Loss1: 0.139182 Loss2: 1.410517
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.549709 Loss1: 0.139748 Loss2: 1.409961
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.559834 Loss1: 0.727963 Loss2: 1.831872
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.504092 Loss1: 0.097287 Loss2: 1.406805
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700980 Loss1: 0.275516 Loss2: 1.425464 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514027 Loss1: 0.149247 Loss2: 1.364779 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492748 Loss1: 0.144663 Loss2: 1.348085
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.401730 Loss1: 0.620403 Loss2: 1.781327
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.725184 Loss1: 0.383448 Loss2: 1.341736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.662588 Loss1: 0.291541 Loss2: 1.371047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529670 Loss1: 0.182996 Loss2: 1.346673 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.458272 Loss1: 0.121178 Loss2: 1.337094 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.417345 Loss1: 0.098010 Loss2: 1.319335 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.696757 Loss1: 0.765834 Loss2: 1.930923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.924863 Loss1: 0.521899 Loss2: 1.402964 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610065 Loss1: 0.205370 Loss2: 1.404695 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551112 Loss1: 0.150764 Loss2: 1.400348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.508168 Loss1: 0.678223 Loss2: 1.829945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.779188 Loss1: 0.402686 Loss2: 1.376502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416617 Loss1: 0.055817 Loss2: 1.360800 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515618 Loss1: 0.148752 Loss2: 1.366865 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476086 Loss1: 0.115443 Loss2: 1.360643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394448 Loss1: 0.055389 Loss2: 1.339059 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 20:34:53,769][flwr][DEBUG] - fit_round 126 received 50 results and 0 failures
+INFO flwr 2023-10-11 20:35:35,315 | server.py:125 | fit progress: (126, 2.198208836701731, {'accuracy': 0.5847}, 290643.09371704096)
+>> Test accuracy: 0.584700
+[2023-10-11 20:35:35,315][flwr][INFO] - fit progress: (126, 2.198208836701731, {'accuracy': 0.5847}, 290643.09371704096)
+DEBUG flwr 2023-10-11 20:35:35,316 | server.py:173 | evaluate_round 126: strategy sampled 50 clients (out of 50)
+[2023-10-11 20:35:35,316][flwr][DEBUG] - evaluate_round 126: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 20:44:42,301 | server.py:187 | evaluate_round 126 received 50 results and 0 failures
+[2023-10-11 20:44:42,301][flwr][DEBUG] - evaluate_round 126 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 20:44:42,301 | server.py:222 | fit_round 127: strategy sampled 50 clients (out of 50)
+[2023-10-11 20:44:42,301][flwr][DEBUG] - fit_round 127: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.525568 Loss1: 0.699176 Loss2: 1.826392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.704776 Loss1: 0.277625 Loss2: 1.427151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564030 Loss1: 0.202734 Loss2: 1.361296
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.522604 Loss1: 0.730080 Loss2: 1.792524
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574586 Loss1: 0.204529 Loss2: 1.370057
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.774397 Loss1: 0.434035 Loss2: 1.340363
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.512585 Loss1: 0.142214 Loss2: 1.370371
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.665365 Loss1: 0.311559 Loss2: 1.353806
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500436 Loss1: 0.135085 Loss2: 1.365350
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.522490 Loss1: 0.198099 Loss2: 1.324391
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440150 Loss1: 0.087518 Loss2: 1.352632
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.457749 Loss1: 0.134025 Loss2: 1.323724
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.446388 Loss1: 0.095746 Loss2: 1.350642
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.410850 Loss1: 0.103747 Loss2: 1.307102
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428935 Loss1: 0.080479 Loss2: 1.348456
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.369155 Loss1: 0.064745 Loss2: 1.304410
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368963 Loss1: 0.071027 Loss2: 1.297936
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.375224 Loss1: 0.071662 Loss2: 1.303562
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385644 Loss1: 0.091263 Loss2: 1.294381
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.388922 Loss1: 0.599842 Loss2: 1.789080
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.727892 Loss1: 0.373078 Loss2: 1.354814
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.662982 Loss1: 0.276420 Loss2: 1.386562
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.633195 Loss1: 0.753618 Loss2: 1.879577
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558511 Loss1: 0.190521 Loss2: 1.367991
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.820743 Loss1: 0.471857 Loss2: 1.348886
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519525 Loss1: 0.161409 Loss2: 1.358116
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463247 Loss1: 0.113947 Loss2: 1.349301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448926 Loss1: 0.104583 Loss2: 1.344343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.468472 Loss1: 0.126933 Loss2: 1.341539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.457287 Loss1: 0.108597 Loss2: 1.348690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429382 Loss1: 0.090178 Loss2: 1.339204 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391892 Loss1: 0.066557 Loss2: 1.325336 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982143
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.611079 Loss1: 0.710618 Loss2: 1.900461
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.795369 Loss1: 0.387256 Loss2: 1.408112
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.681706 Loss1: 0.254608 Loss2: 1.427098
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604827 Loss1: 0.203275 Loss2: 1.401552
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.636918 Loss1: 0.770781 Loss2: 1.866137
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574346 Loss1: 0.168127 Loss2: 1.406218
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.855455 Loss1: 0.435981 Loss2: 1.419473
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526094 Loss1: 0.119064 Loss2: 1.407030
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.640359 Loss1: 0.247684 Loss2: 1.392675
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516014 Loss1: 0.123630 Loss2: 1.392384
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528040 Loss1: 0.167632 Loss2: 1.360409
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484193 Loss1: 0.095480 Loss2: 1.388714
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.457500 Loss1: 0.099057 Loss2: 1.358443
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463986 Loss1: 0.081157 Loss2: 1.382829
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.435263 Loss1: 0.088302 Loss2: 1.346960
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.455837 Loss1: 0.073839 Loss2: 1.381998
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.396457 Loss1: 0.065242 Loss2: 1.331215
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388214 Loss1: 0.060505 Loss2: 1.327709
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366551 Loss1: 0.042348 Loss2: 1.324204
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352390 Loss1: 0.032659 Loss2: 1.319731
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.547452 Loss1: 0.658024 Loss2: 1.889428
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751920 Loss1: 0.335823 Loss2: 1.416097
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.605812 Loss1: 0.177164 Loss2: 1.428648
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.569390 Loss1: 0.667282 Loss2: 1.902109
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582365 Loss1: 0.184518 Loss2: 1.397848
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.025953 Loss1: 0.595612 Loss2: 1.430340
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.557206 Loss1: 0.149015 Loss2: 1.408191
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.730824 Loss1: 0.262968 Loss2: 1.467856
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513852 Loss1: 0.120785 Loss2: 1.393067
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480575 Loss1: 0.089371 Loss2: 1.391204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.487382 Loss1: 0.102084 Loss2: 1.385298 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.496193 Loss1: 0.111694 Loss2: 1.384499 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.488167 Loss1: 0.094356 Loss2: 1.393811 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973633
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.474505 Loss1: 0.085263 Loss2: 1.389243 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.540514 Loss1: 0.712497 Loss2: 1.828017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.661631 Loss1: 0.266057 Loss2: 1.395574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541474 Loss1: 0.184577 Loss2: 1.356897
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.395909 Loss1: 0.596814 Loss2: 1.799095
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521053 Loss1: 0.163312 Loss2: 1.357741
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.706806 Loss1: 0.378947 Loss2: 1.327859
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.469830 Loss1: 0.119203 Loss2: 1.350627
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636123 Loss1: 0.270532 Loss2: 1.365591
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476432 Loss1: 0.136655 Loss2: 1.339776
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498197 Loss1: 0.174923 Loss2: 1.323273
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432821 Loss1: 0.091115 Loss2: 1.341706
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.438033 Loss1: 0.114739 Loss2: 1.323294
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414621 Loss1: 0.080801 Loss2: 1.333820
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.406639 Loss1: 0.093880 Loss2: 1.312759
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390543 Loss1: 0.059892 Loss2: 1.330651
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.382916 Loss1: 0.076479 Loss2: 1.306437
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376567 Loss1: 0.074878 Loss2: 1.301689
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371295 Loss1: 0.074451 Loss2: 1.296844
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.349724 Loss1: 0.050309 Loss2: 1.299415
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.491964 Loss1: 0.626872 Loss2: 1.865093
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.801854 Loss1: 0.426374 Loss2: 1.375480
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.675787 Loss1: 0.270769 Loss2: 1.405018
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.578701 Loss1: 0.746275 Loss2: 1.832426
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.577871 Loss1: 0.203237 Loss2: 1.374635
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.799041 Loss1: 0.479267 Loss2: 1.319774
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546422 Loss1: 0.169365 Loss2: 1.377057
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488776 Loss1: 0.124356 Loss2: 1.364420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439545 Loss1: 0.081460 Loss2: 1.358085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456777 Loss1: 0.102289 Loss2: 1.354488 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413855 Loss1: 0.061024 Loss2: 1.352831 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410852 Loss1: 0.061295 Loss2: 1.349557 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382757 Loss1: 0.086601 Loss2: 1.296155 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987981
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.719044 Loss1: 0.788659 Loss2: 1.930385
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.843193 Loss1: 0.465439 Loss2: 1.377755
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.669224 Loss1: 0.250214 Loss2: 1.419009
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.509262 Loss1: 0.135288 Loss2: 1.373974
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.586657 Loss1: 0.748724 Loss2: 1.837933
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.802780 Loss1: 0.442253 Loss2: 1.360528 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.655973 Loss1: 0.265436 Loss2: 1.390537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526673 Loss1: 0.180793 Loss2: 1.345881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501826 Loss1: 0.149815 Loss2: 1.352011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444794 Loss1: 0.107091 Loss2: 1.337704 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432733 Loss1: 0.099849 Loss2: 1.332884 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413323 Loss1: 0.088550 Loss2: 1.324773 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.715510 Loss1: 0.335338 Loss2: 1.380172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.471584 Loss1: 0.112607 Loss2: 1.358977 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.425916 Loss1: 0.073029 Loss2: 1.352887
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.579105 Loss1: 0.695877 Loss2: 1.883227
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.424408 Loss1: 0.074316 Loss2: 1.350092
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.800975 Loss1: 0.406808 Loss2: 1.394166
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.430722 Loss1: 0.090320 Loss2: 1.340402
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.714343 Loss1: 0.268462 Loss2: 1.445881
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418584 Loss1: 0.073044 Loss2: 1.345541
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.560579 Loss1: 0.200633 Loss2: 1.359947
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418179 Loss1: 0.078486 Loss2: 1.339693
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502654 Loss1: 0.125797 Loss2: 1.376857
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403833 Loss1: 0.060772 Loss2: 1.343061
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521416 Loss1: 0.153964 Loss2: 1.367452
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.442848 Loss1: 0.084175 Loss2: 1.358673
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441750 Loss1: 0.083445 Loss2: 1.358305
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402790 Loss1: 0.054491 Loss2: 1.348299
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398543 Loss1: 0.052240 Loss2: 1.346304
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.545948 Loss1: 0.667091 Loss2: 1.878858
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.788416 Loss1: 0.350036 Loss2: 1.438380
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.652706 Loss1: 0.227278 Loss2: 1.425429
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569287 Loss1: 0.163011 Loss2: 1.406276
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.549522 Loss1: 0.754477 Loss2: 1.795044
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.701076 Loss1: 0.351614 Loss2: 1.349462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.640570 Loss1: 0.262881 Loss2: 1.377689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.568953 Loss1: 0.216242 Loss2: 1.352711 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.553436 Loss1: 0.207313 Loss2: 1.346122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501921 Loss1: 0.150261 Loss2: 1.351660 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430663 Loss1: 0.093018 Loss2: 1.337645 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389775 Loss1: 0.073820 Loss2: 1.315955 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.722820 Loss1: 0.346398 Loss2: 1.376422 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.645723 Loss1: 0.271541 Loss2: 1.374182 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.599501 Loss1: 0.221965 Loss2: 1.377535
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.571355 Loss1: 0.742934 Loss2: 1.828420
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.574016 Loss1: 0.200560 Loss2: 1.373456
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.830961 Loss1: 0.412030 Loss2: 1.418931
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502255 Loss1: 0.137446 Loss2: 1.364809
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.660452 Loss1: 0.264675 Loss2: 1.395777
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538590 Loss1: 0.160901 Loss2: 1.377689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516817 Loss1: 0.145518 Loss2: 1.371299 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452273 Loss1: 0.097517 Loss2: 1.354755
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480126 Loss1: 0.111433 Loss2: 1.368693
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464226 Loss1: 0.109502 Loss2: 1.354724
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471110 Loss1: 0.114097 Loss2: 1.357013
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.475435 Loss1: 0.110478 Loss2: 1.364958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458819 Loss1: 0.101465 Loss2: 1.357353
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.491673 Loss1: 0.672935 Loss2: 1.818738
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.739653 Loss1: 0.379696 Loss2: 1.359957
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.619428 Loss1: 0.242157 Loss2: 1.377271
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564408 Loss1: 0.219252 Loss2: 1.345157
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.570800 Loss1: 0.218728 Loss2: 1.352072
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.408853 Loss1: 0.565349 Loss2: 1.843504
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.726245 Loss1: 0.341613 Loss2: 1.384632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578678 Loss1: 0.175907 Loss2: 1.402771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.548021 Loss1: 0.178281 Loss2: 1.369740 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.453530 Loss1: 0.086966 Loss2: 1.366564 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367168 Loss1: 0.048011 Loss2: 1.319157
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443586 Loss1: 0.081306 Loss2: 1.362281
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436996 Loss1: 0.083230 Loss2: 1.353766
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423756 Loss1: 0.063762 Loss2: 1.359995
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395106 Loss1: 0.045989 Loss2: 1.349117
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407579 Loss1: 0.061879 Loss2: 1.345701
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.592214 Loss1: 0.752417 Loss2: 1.839798
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.835691 Loss1: 0.456641 Loss2: 1.379051
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.715236 Loss1: 0.280220 Loss2: 1.435017
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555928 Loss1: 0.183989 Loss2: 1.371939
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.540804 Loss1: 0.173176 Loss2: 1.367628
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.532104 Loss1: 0.678629 Loss2: 1.853475
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454420 Loss1: 0.090768 Loss2: 1.363653
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442271 Loss1: 0.089013 Loss2: 1.353258 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425223 Loss1: 0.080917 Loss2: 1.344306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459496 Loss1: 0.118732 Loss2: 1.340764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443738 Loss1: 0.084422 Loss2: 1.359315 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431900 Loss1: 0.091318 Loss2: 1.340581 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432261 Loss1: 0.094379 Loss2: 1.337882 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422737 Loss1: 0.087809 Loss2: 1.334928
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.612245 Loss1: 0.828738 Loss2: 1.783507
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.771228 Loss1: 0.411924 Loss2: 1.359304
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.627638 Loss1: 0.256612 Loss2: 1.371027
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.567145 Loss1: 0.238830 Loss2: 1.328315
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514651 Loss1: 0.174057 Loss2: 1.340594
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.752710 Loss1: 0.802247 Loss2: 1.950463
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514264 Loss1: 0.192539 Loss2: 1.321726
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449959 Loss1: 0.124136 Loss2: 1.325824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453440 Loss1: 0.133254 Loss2: 1.320186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541174 Loss1: 0.169075 Loss2: 1.372098 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.488198 Loss1: 0.130500 Loss2: 1.357698 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466951 Loss1: 0.122096 Loss2: 1.344855 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443359 Loss1: 0.101970 Loss2: 1.341389 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.548158 Loss1: 0.684940 Loss2: 1.863218
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.747742 Loss1: 0.376106 Loss2: 1.371636
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.746824 Loss1: 0.325425 Loss2: 1.421399
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.607054 Loss1: 0.242438 Loss2: 1.364616
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.517987 Loss1: 0.659869 Loss2: 1.858118
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571559 Loss1: 0.195910 Loss2: 1.375649
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.775040 Loss1: 0.405750 Loss2: 1.369291
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505489 Loss1: 0.137655 Loss2: 1.367834
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635522 Loss1: 0.230791 Loss2: 1.404731
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469275 Loss1: 0.115424 Loss2: 1.353851
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577904 Loss1: 0.217150 Loss2: 1.360754
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474155 Loss1: 0.124630 Loss2: 1.349524
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550739 Loss1: 0.174327 Loss2: 1.376412
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451070 Loss1: 0.097681 Loss2: 1.353389
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472281 Loss1: 0.112090 Loss2: 1.360191
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411411 Loss1: 0.068435 Loss2: 1.342976
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438297 Loss1: 0.087438 Loss2: 1.350858
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442303 Loss1: 0.093921 Loss2: 1.348382
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425553 Loss1: 0.082403 Loss2: 1.343150
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.432706 Loss1: 0.083537 Loss2: 1.349169
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.526477 Loss1: 0.706844 Loss2: 1.819633
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.849426 Loss1: 0.489375 Loss2: 1.360051
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667547 Loss1: 0.268779 Loss2: 1.398769
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587707 Loss1: 0.237017 Loss2: 1.350689
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.532842 Loss1: 0.699631 Loss2: 1.833210
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.775182 Loss1: 0.409518 Loss2: 1.365663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670994 Loss1: 0.264531 Loss2: 1.406462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535234 Loss1: 0.175785 Loss2: 1.359449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484852 Loss1: 0.129702 Loss2: 1.355150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492116 Loss1: 0.142274 Loss2: 1.349843 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413932 Loss1: 0.079845 Loss2: 1.334087
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468012 Loss1: 0.122120 Loss2: 1.345892
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436232 Loss1: 0.086979 Loss2: 1.349253
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432986 Loss1: 0.092879 Loss2: 1.340106
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399608 Loss1: 0.065571 Loss2: 1.334038
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.409688 Loss1: 0.589763 Loss2: 1.819925
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.886273 Loss1: 0.500986 Loss2: 1.385288
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.746138 Loss1: 0.294607 Loss2: 1.451531
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.681420 Loss1: 0.278469 Loss2: 1.402951
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.607254 Loss1: 0.698314 Loss2: 1.908940
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.692520 Loss1: 0.278386 Loss2: 1.414135
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.823869 Loss1: 0.384971 Loss2: 1.438898
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.546523 Loss1: 0.155322 Loss2: 1.391201
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.796057 Loss1: 0.345619 Loss2: 1.450438
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.712115 Loss1: 0.276313 Loss2: 1.435802
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487714 Loss1: 0.112972 Loss2: 1.374742
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.644906 Loss1: 0.215317 Loss2: 1.429589
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453578 Loss1: 0.081337 Loss2: 1.372241
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.576494 Loss1: 0.157985 Loss2: 1.418509
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425191 Loss1: 0.061060 Loss2: 1.364131
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.512843 Loss1: 0.114665 Loss2: 1.398178
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399740 Loss1: 0.045756 Loss2: 1.353984
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.471502 Loss1: 0.078059 Loss2: 1.393443 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.621932 Loss1: 0.806512 Loss2: 1.815420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.623633 Loss1: 0.255018 Loss2: 1.368615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518446 Loss1: 0.187366 Loss2: 1.331081
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.488589 Loss1: 0.643935 Loss2: 1.844654
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.775718 Loss1: 0.429285 Loss2: 1.346433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.637590 Loss1: 0.259464 Loss2: 1.378126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.593398 Loss1: 0.257796 Loss2: 1.335602 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518272 Loss1: 0.177349 Loss2: 1.340923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485107 Loss1: 0.144742 Loss2: 1.340365 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419554 Loss1: 0.103983 Loss2: 1.315571
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455895 Loss1: 0.132739 Loss2: 1.323156
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427368 Loss1: 0.101384 Loss2: 1.325984
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406962 Loss1: 0.086454 Loss2: 1.320508
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386103 Loss1: 0.066598 Loss2: 1.319505
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.486230 Loss1: 0.632899 Loss2: 1.853331
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.686131 Loss1: 0.316887 Loss2: 1.369244
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.588544 Loss1: 0.208653 Loss2: 1.379891
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.483198 Loss1: 0.113632 Loss2: 1.369566
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.347094 Loss1: 0.577029 Loss2: 1.770065
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.657782 Loss1: 0.332352 Loss2: 1.325430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578696 Loss1: 0.247899 Loss2: 1.330797 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441848 Loss1: 0.097861 Loss2: 1.343987 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401852 Loss1: 0.068446 Loss2: 1.333406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412927 Loss1: 0.080498 Loss2: 1.332428 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.396621 Loss1: 0.093291 Loss2: 1.303330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.344359 Loss1: 0.049448 Loss2: 1.294911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.332020 Loss1: 0.037739 Loss2: 1.294281
+(DefaultActor pid=3764) >> Training accuracy: 0.992647
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.685624 Loss1: 0.832882 Loss2: 1.852742
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.931668 Loss1: 0.546634 Loss2: 1.385033
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736615 Loss1: 0.293398 Loss2: 1.443217
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642269 Loss1: 0.258760 Loss2: 1.383510
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.569786 Loss1: 0.183738 Loss2: 1.386048
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.816197 Loss1: 0.860159 Loss2: 1.956038
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542498 Loss1: 0.164307 Loss2: 1.378192
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502445 Loss1: 0.126509 Loss2: 1.375936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.596909 Loss1: 0.213113 Loss2: 1.383796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440082 Loss1: 0.079012 Loss2: 1.361070 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425870 Loss1: 0.072500 Loss2: 1.353369 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415895 Loss1: 0.073412 Loss2: 1.342483 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993490
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.451524 Loss1: 0.633015 Loss2: 1.818509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.702818 Loss1: 0.290865 Loss2: 1.411953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.446068 Loss1: 0.664555 Loss2: 1.781513
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.661721 Loss1: 0.284099 Loss2: 1.377622
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.747495 Loss1: 0.428603 Loss2: 1.318892
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.542184 Loss1: 0.169116 Loss2: 1.373067
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701289 Loss1: 0.345397 Loss2: 1.355892
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480992 Loss1: 0.121537 Loss2: 1.359455
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456098 Loss1: 0.101390 Loss2: 1.354708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423676 Loss1: 0.079777 Loss2: 1.343899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394180 Loss1: 0.054344 Loss2: 1.339836 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390184 Loss1: 0.054224 Loss2: 1.335959 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.342403 Loss1: 0.057498 Loss2: 1.284905 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.586543 Loss1: 0.733782 Loss2: 1.852761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.773950 Loss1: 0.337017 Loss2: 1.436933 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.543209 Loss1: 0.171752 Loss2: 1.371457
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.626346 Loss1: 0.691718 Loss2: 1.934628
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.869441 Loss1: 0.430819 Loss2: 1.438623 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.739338 Loss1: 0.259478 Loss2: 1.479860 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.623263 Loss1: 0.191425 Loss2: 1.431837 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.605948 Loss1: 0.170756 Loss2: 1.435192 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.532587 Loss1: 0.110911 Loss2: 1.421676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389619 Loss1: 0.043801 Loss2: 1.345818
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.546946 Loss1: 0.136701 Loss2: 1.410245
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.541299 Loss1: 0.115327 Loss2: 1.425971
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487086 Loss1: 0.081977 Loss2: 1.405109
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462664 Loss1: 0.065653 Loss2: 1.397011
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.679601 Loss1: 0.714680 Loss2: 1.964922
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.916598 Loss1: 0.442068 Loss2: 1.474530
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.932825 Loss1: 0.403574 Loss2: 1.529250
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.791474 Loss1: 0.321959 Loss2: 1.469515
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.900264 Loss1: 0.902181 Loss2: 1.998084
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.913716 Loss1: 0.424550 Loss2: 1.489166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.738939 Loss1: 0.249272 Loss2: 1.489667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.702344 Loss1: 0.238104 Loss2: 1.464240 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.637334 Loss1: 0.179437 Loss2: 1.457897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.580629 Loss1: 0.133737 Loss2: 1.446892 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.559813 Loss1: 0.122004 Loss2: 1.437809 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.488691 Loss1: 0.061078 Loss2: 1.427613 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.467722 Loss1: 0.697763 Loss2: 1.769959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.590594 Loss1: 0.250602 Loss2: 1.339992 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.530815 Loss1: 0.223596 Loss2: 1.307219
+DEBUG flwr 2023-10-11 21:13:44,827 | server.py:236 | fit_round 127 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.460217 Loss1: 0.661284 Loss2: 1.798933
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.747319 Loss1: 0.424829 Loss2: 1.322490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.644970 Loss1: 0.280853 Loss2: 1.364117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557768 Loss1: 0.238293 Loss2: 1.319475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.499112 Loss1: 0.178704 Loss2: 1.320408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478849 Loss1: 0.164047 Loss2: 1.314802 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.329741 Loss1: 0.058064 Loss2: 1.271677
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434313 Loss1: 0.127961 Loss2: 1.306353
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421475 Loss1: 0.112863 Loss2: 1.308612
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384420 Loss1: 0.083080 Loss2: 1.301340
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352183 Loss1: 0.055823 Loss2: 1.296360
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.546857 Loss1: 0.748664 Loss2: 1.798193
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.742865 Loss1: 0.410195 Loss2: 1.332670
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638664 Loss1: 0.269992 Loss2: 1.368672
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508790 Loss1: 0.186319 Loss2: 1.322471
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.615725 Loss1: 0.743072 Loss2: 1.872653
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.886542 Loss1: 0.475978 Loss2: 1.410564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.722081 Loss1: 0.304641 Loss2: 1.417440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612807 Loss1: 0.221611 Loss2: 1.391197 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545683 Loss1: 0.162173 Loss2: 1.383510 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469131 Loss1: 0.094753 Loss2: 1.374379 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390506 Loss1: 0.089648 Loss2: 1.300858
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.454962 Loss1: 0.088095 Loss2: 1.366866
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434667 Loss1: 0.076878 Loss2: 1.357789
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416930 Loss1: 0.062375 Loss2: 1.354555
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387581 Loss1: 0.039892 Loss2: 1.347689
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.705236 Loss1: 0.813036 Loss2: 1.892200
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.827418 Loss1: 0.455776 Loss2: 1.371643
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.721607 Loss1: 0.299908 Loss2: 1.421699
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.678312 Loss1: 0.306047 Loss2: 1.372265
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.476340 Loss1: 0.671620 Loss2: 1.804720
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.793600 Loss1: 0.447539 Loss2: 1.346061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.652223 Loss1: 0.261661 Loss2: 1.390561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573282 Loss1: 0.235391 Loss2: 1.337891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.538007 Loss1: 0.177951 Loss2: 1.360056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421366 Loss1: 0.077362 Loss2: 1.344004 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454800 Loss1: 0.127046 Loss2: 1.327753 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.432969 Loss1: 0.104101 Loss2: 1.328868 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 21:13:44,827][flwr][DEBUG] - fit_round 127 received 50 results and 0 failures
+INFO flwr 2023-10-11 21:14:26,756 | server.py:125 | fit progress: (127, 2.2097703696439823, {'accuracy': 0.5875}, 292974.534956929)
+>> Test accuracy: 0.587500
+[2023-10-11 21:14:26,756][flwr][INFO] - fit progress: (127, 2.2097703696439823, {'accuracy': 0.5875}, 292974.534956929)
+DEBUG flwr 2023-10-11 21:14:26,757 | server.py:173 | evaluate_round 127: strategy sampled 50 clients (out of 50)
+[2023-10-11 21:14:26,757][flwr][DEBUG] - evaluate_round 127: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 21:23:34,535 | server.py:187 | evaluate_round 127 received 50 results and 0 failures
+[2023-10-11 21:23:34,535][flwr][DEBUG] - evaluate_round 127 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 21:23:34,536 | server.py:222 | fit_round 128: strategy sampled 50 clients (out of 50)
+[2023-10-11 21:23:34,536][flwr][DEBUG] - fit_round 128: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.457145 Loss1: 0.592992 Loss2: 1.864153
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.740107 Loss1: 0.381462 Loss2: 1.358645
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684749 Loss1: 0.298709 Loss2: 1.386039
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630691 Loss1: 0.270325 Loss2: 1.360366
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.514857 Loss1: 0.639855 Loss2: 1.875002
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.710278 Loss1: 0.352222 Loss2: 1.358056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.533394 Loss1: 0.167793 Loss2: 1.365601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.479986 Loss1: 0.133776 Loss2: 1.346210 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.425680 Loss1: 0.095264 Loss2: 1.330416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439483 Loss1: 0.110740 Loss2: 1.328743 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399198 Loss1: 0.068355 Loss2: 1.330843
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.418855 Loss1: 0.092970 Loss2: 1.325885
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.402230 Loss1: 0.078687 Loss2: 1.323543
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403571 Loss1: 0.082191 Loss2: 1.321380
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395150 Loss1: 0.075231 Loss2: 1.319919
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.698664 Loss1: 0.806294 Loss2: 1.892370
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.931724 Loss1: 0.549523 Loss2: 1.382201
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.712732 Loss1: 0.308686 Loss2: 1.404046
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542855 Loss1: 0.181516 Loss2: 1.361339
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.535912 Loss1: 0.654387 Loss2: 1.881526
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.787063 Loss1: 0.410693 Loss2: 1.376370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.626533 Loss1: 0.214445 Loss2: 1.412088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.593168 Loss1: 0.227149 Loss2: 1.366019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.584997 Loss1: 0.190212 Loss2: 1.394784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510711 Loss1: 0.140384 Loss2: 1.370327 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404741 Loss1: 0.068339 Loss2: 1.336402
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459269 Loss1: 0.096758 Loss2: 1.362511
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485957 Loss1: 0.119556 Loss2: 1.366401
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435746 Loss1: 0.073839 Loss2: 1.361907
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408378 Loss1: 0.054504 Loss2: 1.353874
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.502385 Loss1: 0.621097 Loss2: 1.881288
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.792468 Loss1: 0.392532 Loss2: 1.399936
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.683247 Loss1: 0.250462 Loss2: 1.432785
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.632108 Loss1: 0.238549 Loss2: 1.393559
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.623689 Loss1: 0.733241 Loss2: 1.890447
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.834862 Loss1: 0.440041 Loss2: 1.394822
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547525 Loss1: 0.156487 Loss2: 1.391037
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.654709 Loss1: 0.231180 Loss2: 1.423529
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.522184 Loss1: 0.139071 Loss2: 1.383114
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.587081 Loss1: 0.198099 Loss2: 1.388982
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488963 Loss1: 0.109498 Loss2: 1.379464
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.562847 Loss1: 0.178919 Loss2: 1.383928
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.503731 Loss1: 0.132385 Loss2: 1.371346
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462757 Loss1: 0.090045 Loss2: 1.372712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434297 Loss1: 0.064334 Loss2: 1.369964 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425568 Loss1: 0.059672 Loss2: 1.365896 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.445466 Loss1: 0.658848 Loss2: 1.786618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.770239 Loss1: 0.369782 Loss2: 1.400457 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563382 Loss1: 0.210612 Loss2: 1.352770
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.679342 Loss1: 0.703270 Loss2: 1.976072
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.947680 Loss1: 0.479574 Loss2: 1.468107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769516 Loss1: 0.266877 Loss2: 1.502639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.704770 Loss1: 0.246358 Loss2: 1.458412 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.654563 Loss1: 0.197932 Loss2: 1.456632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.585427 Loss1: 0.125930 Loss2: 1.459497 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381380 Loss1: 0.056791 Loss2: 1.324589
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.555797 Loss1: 0.109401 Loss2: 1.446395
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.545187 Loss1: 0.104570 Loss2: 1.440617
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501496 Loss1: 0.060368 Loss2: 1.441128
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512011 Loss1: 0.085687 Loss2: 1.426324
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.570832 Loss1: 0.755925 Loss2: 1.814907
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.820339 Loss1: 0.454271 Loss2: 1.366068
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700533 Loss1: 0.305230 Loss2: 1.395303
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581844 Loss1: 0.215335 Loss2: 1.366509
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.487336 Loss1: 0.670425 Loss2: 1.816911
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.774962 Loss1: 0.395900 Loss2: 1.379062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.714845 Loss1: 0.291770 Loss2: 1.423075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.656980 Loss1: 0.282700 Loss2: 1.374280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509000 Loss1: 0.135491 Loss2: 1.373510 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467175 Loss1: 0.106616 Loss2: 1.360560 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465571 Loss1: 0.110783 Loss2: 1.354789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424652 Loss1: 0.077925 Loss2: 1.346727 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.437678 Loss1: 0.614608 Loss2: 1.823070 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573115 Loss1: 0.210657 Loss2: 1.362458 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.460666 Loss1: 0.598237 Loss2: 1.862429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.781811 Loss1: 0.420456 Loss2: 1.361355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.696635 Loss1: 0.286792 Loss2: 1.409842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573494 Loss1: 0.213964 Loss2: 1.359530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518103 Loss1: 0.160209 Loss2: 1.357894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449843 Loss1: 0.101331 Loss2: 1.348512 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415180 Loss1: 0.076099 Loss2: 1.339082 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408708 Loss1: 0.074380 Loss2: 1.334329 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.851912 Loss1: 0.455220 Loss2: 1.396692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.619481 Loss1: 0.224738 Loss2: 1.394743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.603948 Loss1: 0.212364 Loss2: 1.391584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.822405 Loss1: 0.347010 Loss2: 1.475396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.692349 Loss1: 0.242296 Loss2: 1.450053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.623609 Loss1: 0.204046 Loss2: 1.419563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.596268 Loss1: 0.161651 Loss2: 1.434617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.528770 Loss1: 0.111674 Loss2: 1.417096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427231 Loss1: 0.066993 Loss2: 1.360237
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481174 Loss1: 0.073634 Loss2: 1.407540
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454943 Loss1: 0.050586 Loss2: 1.404358
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427387 Loss1: 0.032298 Loss2: 1.395090
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.618432 Loss1: 0.683665 Loss2: 1.934767
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.878939 Loss1: 0.434254 Loss2: 1.444685
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.724597 Loss1: 0.236776 Loss2: 1.487821
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594899 Loss1: 0.168953 Loss2: 1.425946
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.583620 Loss1: 0.154836 Loss2: 1.428785 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.583266 Loss1: 0.157271 Loss2: 1.425996 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511038 Loss1: 0.092371 Loss2: 1.418668 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488662 Loss1: 0.079577 Loss2: 1.409085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469426 Loss1: 0.064141 Loss2: 1.405284 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.465706 Loss1: 0.061781 Loss2: 1.403924 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.482255 Loss1: 0.116499 Loss2: 1.365756 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.448857 Loss1: 0.601511 Loss2: 1.847346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561011 Loss1: 0.173876 Loss2: 1.387135 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535627 Loss1: 0.192267 Loss2: 1.343360
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.541740 Loss1: 0.733898 Loss2: 1.807842
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.755957 Loss1: 0.408457 Loss2: 1.347500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605576 Loss1: 0.237777 Loss2: 1.367800 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.489698 Loss1: 0.156748 Loss2: 1.332950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441012 Loss1: 0.111505 Loss2: 1.329507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.436760 Loss1: 0.113035 Loss2: 1.323724 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433812 Loss1: 0.117052 Loss2: 1.316760 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368545 Loss1: 0.055617 Loss2: 1.312928 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.676958 Loss1: 0.779155 Loss2: 1.897802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.682794 Loss1: 0.232408 Loss2: 1.450387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.810037 Loss1: 0.845503 Loss2: 1.964534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.892444 Loss1: 0.539811 Loss2: 1.352633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.575454 Loss1: 0.162556 Loss2: 1.412897
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.778894 Loss1: 0.356020 Loss2: 1.422874
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.646583 Loss1: 0.270477 Loss2: 1.376106
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.506801 Loss1: 0.109447 Loss2: 1.397354
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469387 Loss1: 0.076988 Loss2: 1.392399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.442088 Loss1: 0.053387 Loss2: 1.388701 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426142 Loss1: 0.050983 Loss2: 1.375159 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431763 Loss1: 0.101690 Loss2: 1.330073 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.648186 Loss1: 0.773649 Loss2: 1.874537
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.889604 Loss1: 0.465565 Loss2: 1.424039
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716735 Loss1: 0.277776 Loss2: 1.438959
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.616724 Loss1: 0.211708 Loss2: 1.405017
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.528421 Loss1: 0.627701 Loss2: 1.900720
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565232 Loss1: 0.154246 Loss2: 1.410986
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.807308 Loss1: 0.401240 Loss2: 1.406068
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519456 Loss1: 0.125129 Loss2: 1.394328
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.679958 Loss1: 0.214470 Loss2: 1.465487
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.504287 Loss1: 0.115997 Loss2: 1.388289
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.580573 Loss1: 0.182680 Loss2: 1.397894
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474778 Loss1: 0.085504 Loss2: 1.389274
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.547872 Loss1: 0.149769 Loss2: 1.398103
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.481473 Loss1: 0.103396 Loss2: 1.378077
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522954 Loss1: 0.119602 Loss2: 1.403352
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448349 Loss1: 0.069815 Loss2: 1.378533
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478509 Loss1: 0.086772 Loss2: 1.391738
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.444364 Loss1: 0.059672 Loss2: 1.384691
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.437396 Loss1: 0.059564 Loss2: 1.377832
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436753 Loss1: 0.065759 Loss2: 1.370995
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.672187 Loss1: 0.811346 Loss2: 1.860841
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.823299 Loss1: 0.474546 Loss2: 1.348753
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.751101 Loss1: 0.348437 Loss2: 1.402664
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557637 Loss1: 0.209977 Loss2: 1.347660
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.479191 Loss1: 0.618673 Loss2: 1.860518
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.775946 Loss1: 0.357841 Loss2: 1.418105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.703837 Loss1: 0.272291 Loss2: 1.431546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425551 Loss1: 0.102360 Loss2: 1.323192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394373 Loss1: 0.066936 Loss2: 1.327437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372287 Loss1: 0.056456 Loss2: 1.315831 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.539138 Loss1: 0.139237 Loss2: 1.399902 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463480 Loss1: 0.083901 Loss2: 1.379579 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453295 Loss1: 0.077245 Loss2: 1.376050
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.446730 Loss1: 0.559461 Loss2: 1.887269
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.785255 Loss1: 0.360302 Loss2: 1.424953
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.636669 Loss1: 0.190846 Loss2: 1.445824
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564504 Loss1: 0.152902 Loss2: 1.411603
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.606678 Loss1: 0.717434 Loss2: 1.889244
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561473 Loss1: 0.142306 Loss2: 1.419166
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831857 Loss1: 0.431314 Loss2: 1.400543
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.594306 Loss1: 0.172350 Loss2: 1.421956
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.594634 Loss1: 0.168729 Loss2: 1.425905 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.552178 Loss1: 0.140470 Loss2: 1.411708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.593604 Loss1: 0.185282 Loss2: 1.408321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.576452 Loss1: 0.159250 Loss2: 1.417201 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985294
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.459763 Loss1: 0.106243 Loss2: 1.353520 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.521215 Loss1: 0.665523 Loss2: 1.855692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.695111 Loss1: 0.261681 Loss2: 1.433431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581974 Loss1: 0.209466 Loss2: 1.372508
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.551577 Loss1: 0.700467 Loss2: 1.851111
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.869201 Loss1: 0.487672 Loss2: 1.381529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.743939 Loss1: 0.301941 Loss2: 1.441998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631577 Loss1: 0.252315 Loss2: 1.379261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.559755 Loss1: 0.161134 Loss2: 1.398621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493453 Loss1: 0.115007 Loss2: 1.378446 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.538767 Loss1: 0.169135 Loss2: 1.369631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458105 Loss1: 0.088204 Loss2: 1.369901 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.479424 Loss1: 0.621662 Loss2: 1.857763 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.646622 Loss1: 0.231164 Loss2: 1.415458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583892 Loss1: 0.221480 Loss2: 1.362412
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.672429 Loss1: 0.811451 Loss2: 1.860978
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.872166 Loss1: 0.496970 Loss2: 1.375196 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.698037 Loss1: 0.287261 Loss2: 1.410776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605887 Loss1: 0.233096 Loss2: 1.372791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563526 Loss1: 0.186867 Loss2: 1.376659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.517413 Loss1: 0.148204 Loss2: 1.369209 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500244 Loss1: 0.134175 Loss2: 1.366068 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422995 Loss1: 0.077854 Loss2: 1.345141 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.489986 Loss1: 0.643624 Loss2: 1.846362 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.605542 Loss1: 0.209487 Loss2: 1.396055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529596 Loss1: 0.156211 Loss2: 1.373385
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.505705 Loss1: 0.690495 Loss2: 1.815210
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.817369 Loss1: 0.463791 Loss2: 1.353578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.704276 Loss1: 0.300973 Loss2: 1.403303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559657 Loss1: 0.213478 Loss2: 1.346179 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569008 Loss1: 0.209519 Loss2: 1.359489 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528539 Loss1: 0.179891 Loss2: 1.348648 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396709 Loss1: 0.057217 Loss2: 1.339492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.521119 Loss1: 0.177258 Loss2: 1.343862
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443302 Loss1: 0.098469 Loss2: 1.344833
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425861 Loss1: 0.092116 Loss2: 1.333745
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404862 Loss1: 0.074162 Loss2: 1.330700
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.616349 Loss1: 0.721311 Loss2: 1.895038
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.879437 Loss1: 0.485823 Loss2: 1.393614
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.689891 Loss1: 0.252023 Loss2: 1.437868
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559955 Loss1: 0.178406 Loss2: 1.381549
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.469028 Loss1: 0.662263 Loss2: 1.806766
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.745479 Loss1: 0.400085 Loss2: 1.345394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608304 Loss1: 0.235168 Loss2: 1.373136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588266 Loss1: 0.242051 Loss2: 1.346214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508106 Loss1: 0.154336 Loss2: 1.353770 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.505375 Loss1: 0.163173 Loss2: 1.342202 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441737 Loss1: 0.074100 Loss2: 1.367638
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.399374 Loss1: 0.062264 Loss2: 1.337110
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372667 Loss1: 0.050567 Loss2: 1.322100
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.358180 Loss1: 0.043794 Loss2: 1.314386
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.341480 Loss1: 0.030980 Loss2: 1.310499
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.546399 Loss1: 0.624151 Loss2: 1.922248
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.776258 Loss1: 0.360347 Loss2: 1.415911
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.664822 Loss1: 0.212610 Loss2: 1.452212
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.537402 Loss1: 0.130824 Loss2: 1.406578
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.487927 Loss1: 0.656193 Loss2: 1.831734
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.833750 Loss1: 0.488193 Loss2: 1.345557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.691419 Loss1: 0.299107 Loss2: 1.392311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.560737 Loss1: 0.216156 Loss2: 1.344581 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541859 Loss1: 0.191150 Loss2: 1.350709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476286 Loss1: 0.129024 Loss2: 1.347263 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449171 Loss1: 0.063452 Loss2: 1.385719
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450197 Loss1: 0.111432 Loss2: 1.338764
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413131 Loss1: 0.074428 Loss2: 1.338703
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397035 Loss1: 0.067654 Loss2: 1.329381
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403975 Loss1: 0.077537 Loss2: 1.326438
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.457047 Loss1: 0.634843 Loss2: 1.822204
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.800699 Loss1: 0.440926 Loss2: 1.359773
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.725182 Loss1: 0.325146 Loss2: 1.400036
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.593545 Loss1: 0.242832 Loss2: 1.350713
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.819115 Loss1: 0.868279 Loss2: 1.950836
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.934795 Loss1: 0.517613 Loss2: 1.417182
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.543500 Loss1: 0.184498 Loss2: 1.359002
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.801034 Loss1: 0.336411 Loss2: 1.464624
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462202 Loss1: 0.120572 Loss2: 1.341631
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.679840 Loss1: 0.271393 Loss2: 1.408447
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439777 Loss1: 0.106264 Loss2: 1.333512
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417182 Loss1: 0.085439 Loss2: 1.331743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399750 Loss1: 0.074367 Loss2: 1.325383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375685 Loss1: 0.059629 Loss2: 1.316056 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.490446 Loss1: 0.098368 Loss2: 1.392078 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.974330
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.647190 Loss1: 0.792684 Loss2: 1.854506
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.919922 Loss1: 0.511513 Loss2: 1.408409
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.734725 Loss1: 0.295209 Loss2: 1.439517
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604768 Loss1: 0.222731 Loss2: 1.382036
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.458236 Loss1: 0.665113 Loss2: 1.793123
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.848956 Loss1: 0.473475 Loss2: 1.375481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.650182 Loss1: 0.263175 Loss2: 1.387006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.566345 Loss1: 0.205849 Loss2: 1.360496 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.498079 Loss1: 0.149094 Loss2: 1.348985 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500317 Loss1: 0.147874 Loss2: 1.352443 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467615 Loss1: 0.115389 Loss2: 1.352226 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.437052 Loss1: 0.096679 Loss2: 1.340373 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.670936 Loss1: 0.776161 Loss2: 1.894775 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731442 Loss1: 0.274283 Loss2: 1.457159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.541033 Loss1: 0.166093 Loss2: 1.374939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.500376 Loss1: 0.125101 Loss2: 1.375274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.492964 Loss1: 0.124994 Loss2: 1.367971 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445009 Loss1: 0.083719 Loss2: 1.361290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436199 Loss1: 0.083653 Loss2: 1.352546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.435261 Loss1: 0.103367 Loss2: 1.331894
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460734 Loss1: 0.110290 Loss2: 1.350444
+(DefaultActor pid=3765) >> Training accuracy: 0.986607
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.382907 Loss1: 0.068170 Loss2: 1.314736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.365722 Loss1: 0.054077 Loss2: 1.311645 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.464883 Loss1: 0.644707 Loss2: 1.820176
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.346503 Loss1: 0.045556 Loss2: 1.300947
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.663582 Loss1: 0.261971 Loss2: 1.401610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.605423 Loss1: 0.205862 Loss2: 1.399561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.534708 Loss1: 0.156412 Loss2: 1.378295
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.655724 Loss1: 0.737884 Loss2: 1.917839
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.822979 Loss1: 0.407442 Loss2: 1.415537
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503700 Loss1: 0.127734 Loss2: 1.375967
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684153 Loss1: 0.227316 Loss2: 1.456837
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.496162 Loss1: 0.124775 Loss2: 1.371388
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.648059 Loss1: 0.247736 Loss2: 1.400323
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.467890 Loss1: 0.101684 Loss2: 1.366206
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.626958 Loss1: 0.191151 Loss2: 1.435807
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438277 Loss1: 0.075307 Loss2: 1.362970
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.571052 Loss1: 0.158772 Loss2: 1.412279 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.513607 Loss1: 0.110193 Loss2: 1.403414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465847 Loss1: 0.072460 Loss2: 1.393387
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.676710 Loss1: 0.726765 Loss2: 1.949946
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.940606 Loss1: 0.552116 Loss2: 1.388490
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741007 Loss1: 0.300488 Loss2: 1.440519
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474813 Loss1: 0.104281 Loss2: 1.370532
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478252 Loss1: 0.119407 Loss2: 1.358845
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442084 Loss1: 0.089334 Loss2: 1.352750
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.546415 Loss1: 0.675617 Loss2: 1.870798
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.817019 Loss1: 0.445645 Loss2: 1.371374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.704633 Loss1: 0.270967 Loss2: 1.433666 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431165 Loss1: 0.098871 Loss2: 1.332294 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.580328 Loss1: 0.196929 Loss2: 1.383399 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483622 Loss1: 0.116816 Loss2: 1.366806 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458125 Loss1: 0.100527 Loss2: 1.357598
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.658333 Loss1: 0.762173 Loss2: 1.896160
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421235 Loss1: 0.065913 Loss2: 1.355322
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.748565 Loss1: 0.364348 Loss2: 1.384217
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.669219 Loss1: 0.259133 Loss2: 1.410087
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547529 Loss1: 0.163455 Loss2: 1.384074
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.493754 Loss1: 0.117076 Loss2: 1.376678
+DEBUG flwr 2023-10-11 21:52:37,010 | server.py:236 | fit_round 128 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504110 Loss1: 0.131150 Loss2: 1.372960
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.517564 Loss1: 0.693552 Loss2: 1.824012
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462316 Loss1: 0.093045 Loss2: 1.369271
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.842515 Loss1: 0.482005 Loss2: 1.360510
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458625 Loss1: 0.099377 Loss2: 1.359248
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.695520 Loss1: 0.282553 Loss2: 1.412966
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445284 Loss1: 0.086115 Loss2: 1.359169
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.596991 Loss1: 0.229823 Loss2: 1.367168
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445111 Loss1: 0.086964 Loss2: 1.358147
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464798 Loss1: 0.113092 Loss2: 1.351706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408759 Loss1: 0.070339 Loss2: 1.338420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401410 Loss1: 0.067127 Loss2: 1.334283
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.447289 Loss1: 0.635136 Loss2: 1.812153
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387262 Loss1: 0.058935 Loss2: 1.328327
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.722744 Loss1: 0.398375 Loss2: 1.324370
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.669089 Loss1: 0.296582 Loss2: 1.372507
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575768 Loss1: 0.240059 Loss2: 1.335709
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.538083 Loss1: 0.188135 Loss2: 1.349947
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511212 Loss1: 0.184948 Loss2: 1.326264
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.420519 Loss1: 0.589128 Loss2: 1.831390
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490496 Loss1: 0.164720 Loss2: 1.325776
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443899 Loss1: 0.115011 Loss2: 1.328888
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.795650 Loss1: 0.393608 Loss2: 1.402042
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403134 Loss1: 0.086434 Loss2: 1.316700
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.649472 Loss1: 0.239787 Loss2: 1.409685
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375074 Loss1: 0.063755 Loss2: 1.311319
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.546858 Loss1: 0.172033 Loss2: 1.374825
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522910 Loss1: 0.143924 Loss2: 1.378986
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471096 Loss1: 0.102940 Loss2: 1.368156
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465296 Loss1: 0.104325 Loss2: 1.360972
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408663 Loss1: 0.048077 Loss2: 1.360586
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389846 Loss1: 0.039717 Loss2: 1.350129
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383956 Loss1: 0.039235 Loss2: 1.344721
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 21:52:37,010][flwr][DEBUG] - fit_round 128 received 50 results and 0 failures
+INFO flwr 2023-10-11 21:53:18,684 | server.py:125 | fit progress: (128, 2.208596081969837, {'accuracy': 0.5867}, 295306.462969138)
+>> Test accuracy: 0.586700
+[2023-10-11 21:53:18,684][flwr][INFO] - fit progress: (128, 2.208596081969837, {'accuracy': 0.5867}, 295306.462969138)
+DEBUG flwr 2023-10-11 21:53:18,685 | server.py:173 | evaluate_round 128: strategy sampled 50 clients (out of 50)
+[2023-10-11 21:53:18,685][flwr][DEBUG] - evaluate_round 128: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 22:02:27,797 | server.py:187 | evaluate_round 128 received 50 results and 0 failures
+[2023-10-11 22:02:27,797][flwr][DEBUG] - evaluate_round 128 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 22:02:27,798 | server.py:222 | fit_round 129: strategy sampled 50 clients (out of 50)
+[2023-10-11 22:02:27,798][flwr][DEBUG] - fit_round 129: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.518712 Loss1: 0.678186 Loss2: 1.840526
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.940663 Loss1: 0.502480 Loss2: 1.438183
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.712715 Loss1: 0.296847 Loss2: 1.415868
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.515229 Loss1: 0.631178 Loss2: 1.884051
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.636201 Loss1: 0.241714 Loss2: 1.394487
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.830140 Loss1: 0.429910 Loss2: 1.400230
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.601612 Loss1: 0.190828 Loss2: 1.410783
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.616354 Loss1: 0.183798 Loss2: 1.432557
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.529311 Loss1: 0.144349 Loss2: 1.384962
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.537873 Loss1: 0.150713 Loss2: 1.387159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.504848 Loss1: 0.123847 Loss2: 1.381000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.501278 Loss1: 0.120183 Loss2: 1.381095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449457 Loss1: 0.075658 Loss2: 1.373799 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402788 Loss1: 0.046446 Loss2: 1.356342 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.556007 Loss1: 0.695549 Loss2: 1.860457 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.709806 Loss1: 0.287285 Loss2: 1.422522 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589688 Loss1: 0.186822 Loss2: 1.402866
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.388919 Loss1: 0.566007 Loss2: 1.822912
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.774089 Loss1: 0.394758 Loss2: 1.379330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.666223 Loss1: 0.257475 Loss2: 1.408747 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.609002 Loss1: 0.241527 Loss2: 1.367475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576627 Loss1: 0.204124 Loss2: 1.372503 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512733 Loss1: 0.146953 Loss2: 1.365781 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.495285 Loss1: 0.130628 Loss2: 1.364658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415843 Loss1: 0.069824 Loss2: 1.346019 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.441551 Loss1: 0.627996 Loss2: 1.813555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.578705 Loss1: 0.204899 Loss2: 1.373807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.450815 Loss1: 0.127570 Loss2: 1.323245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.423937 Loss1: 0.104447 Loss2: 1.319489 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432974 Loss1: 0.111704 Loss2: 1.321270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.390663 Loss1: 0.075105 Loss2: 1.315558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609768 Loss1: 0.231260 Loss2: 1.378508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354838 Loss1: 0.055117 Loss2: 1.299720 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481981 Loss1: 0.125632 Loss2: 1.356349 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410513 Loss1: 0.066823 Loss2: 1.343690 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.830588 Loss1: 0.412570 Loss2: 1.418017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.592433 Loss1: 0.200244 Loss2: 1.392189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.438599 Loss1: 0.578651 Loss2: 1.859948
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513434 Loss1: 0.120490 Loss2: 1.392944
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.743877 Loss1: 0.371662 Loss2: 1.372215
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494073 Loss1: 0.118780 Loss2: 1.375293
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677487 Loss1: 0.266799 Loss2: 1.410687
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447476 Loss1: 0.077823 Loss2: 1.369652
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550658 Loss1: 0.194158 Loss2: 1.356499
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432484 Loss1: 0.071894 Loss2: 1.360590
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.547426 Loss1: 0.190248 Loss2: 1.357178
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412015 Loss1: 0.054295 Loss2: 1.357719
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537327 Loss1: 0.172141 Loss2: 1.365186
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421391 Loss1: 0.064563 Loss2: 1.356828
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439546 Loss1: 0.091013 Loss2: 1.348533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426466 Loss1: 0.079116 Loss2: 1.347350 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.742513 Loss1: 0.391489 Loss2: 1.351024 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526200 Loss1: 0.171211 Loss2: 1.354990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.626018 Loss1: 0.758874 Loss2: 1.867144
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488868 Loss1: 0.142327 Loss2: 1.346541
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.829807 Loss1: 0.446231 Loss2: 1.383576
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.483252 Loss1: 0.132365 Loss2: 1.350887
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682713 Loss1: 0.266869 Loss2: 1.415844
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469562 Loss1: 0.130563 Loss2: 1.338999
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567803 Loss1: 0.187203 Loss2: 1.380600
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413366 Loss1: 0.082140 Loss2: 1.331226
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509428 Loss1: 0.131858 Loss2: 1.377570
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402581 Loss1: 0.070930 Loss2: 1.331651
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474893 Loss1: 0.105607 Loss2: 1.369286
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381541 Loss1: 0.052161 Loss2: 1.329380
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461059 Loss1: 0.096724 Loss2: 1.364335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396246 Loss1: 0.049391 Loss2: 1.346854 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.847260 Loss1: 0.492742 Loss2: 1.354518 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563053 Loss1: 0.223203 Loss2: 1.339850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.716619 Loss1: 0.784529 Loss2: 1.932089
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484724 Loss1: 0.149179 Loss2: 1.335544
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.962130 Loss1: 0.478622 Loss2: 1.483507
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470802 Loss1: 0.141077 Loss2: 1.329725
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.754922 Loss1: 0.290708 Loss2: 1.464214
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441112 Loss1: 0.106396 Loss2: 1.334716
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.647236 Loss1: 0.216449 Loss2: 1.430787
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432293 Loss1: 0.111068 Loss2: 1.321226
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570113 Loss1: 0.133796 Loss2: 1.436317
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411777 Loss1: 0.093952 Loss2: 1.317825
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.525804 Loss1: 0.112056 Loss2: 1.413748
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439490 Loss1: 0.119495 Loss2: 1.319995
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.534079 Loss1: 0.119668 Loss2: 1.414411 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.518516 Loss1: 0.109511 Loss2: 1.409004 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.829905 Loss1: 0.477289 Loss2: 1.352616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.590913 Loss1: 0.234203 Loss2: 1.356710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.533546 Loss1: 0.654804 Loss2: 1.878741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.794198 Loss1: 0.413848 Loss2: 1.380350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.602442 Loss1: 0.199708 Loss2: 1.402734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401950 Loss1: 0.082328 Loss2: 1.319622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390012 Loss1: 0.067756 Loss2: 1.322257 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508345 Loss1: 0.129764 Loss2: 1.378581 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497543 Loss1: 0.122644 Loss2: 1.374899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.508964 Loss1: 0.710744 Loss2: 1.798220
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434823 Loss1: 0.076184 Loss2: 1.358639
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.697328 Loss1: 0.318098 Loss2: 1.379230 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.512029 Loss1: 0.180265 Loss2: 1.331764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.761479 Loss1: 0.750039 Loss2: 2.011440
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.469062 Loss1: 0.137494 Loss2: 1.331568
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472389 Loss1: 0.158693 Loss2: 1.313696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471646 Loss1: 0.151263 Loss2: 1.320383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417211 Loss1: 0.103536 Loss2: 1.313675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522038 Loss1: 0.129540 Loss2: 1.392498 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518915 Loss1: 0.132212 Loss2: 1.386703 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509961 Loss1: 0.136465 Loss2: 1.373496 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977865
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.520480 Loss1: 0.723981 Loss2: 1.796499
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.868052 Loss1: 0.503125 Loss2: 1.364927
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.715911 Loss1: 0.341771 Loss2: 1.374141
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.568279 Loss1: 0.212653 Loss2: 1.355626
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.475301 Loss1: 0.635028 Loss2: 1.840272
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.841457 Loss1: 0.468690 Loss2: 1.372767
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462109 Loss1: 0.129034 Loss2: 1.333076
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.747352 Loss1: 0.300074 Loss2: 1.447278
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.411990 Loss1: 0.093404 Loss2: 1.318586
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.633955 Loss1: 0.255866 Loss2: 1.378089
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.417256 Loss1: 0.100780 Loss2: 1.316475
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.607678 Loss1: 0.223663 Loss2: 1.384015
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404094 Loss1: 0.085873 Loss2: 1.318221
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.362615 Loss1: 0.049315 Loss2: 1.313300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.341429 Loss1: 0.037734 Loss2: 1.303695 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462511 Loss1: 0.102477 Loss2: 1.360034 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.536294 Loss1: 0.720131 Loss2: 1.816163 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.738191 Loss1: 0.331425 Loss2: 1.406765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.621466 Loss1: 0.257734 Loss2: 1.363732
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.697977 Loss1: 0.732640 Loss2: 1.965337
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.906958 Loss1: 0.444097 Loss2: 1.462862 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.827027 Loss1: 0.317679 Loss2: 1.509349 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.748244 Loss1: 0.287286 Loss2: 1.460958 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.656855 Loss1: 0.192216 Loss2: 1.464639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582939 Loss1: 0.137255 Loss2: 1.445684 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.376239 Loss1: 0.053518 Loss2: 1.322721
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.592747 Loss1: 0.148257 Loss2: 1.444490
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.547058 Loss1: 0.099229 Loss2: 1.447829
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490124 Loss1: 0.057218 Loss2: 1.432906
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.519682 Loss1: 0.097693 Loss2: 1.421989
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.404905 Loss1: 0.563318 Loss2: 1.841587
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.728652 Loss1: 0.371670 Loss2: 1.356981
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.712616 Loss1: 0.301505 Loss2: 1.411111
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581849 Loss1: 0.220945 Loss2: 1.360904
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.449161 Loss1: 0.691218 Loss2: 1.757944
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.706775 Loss1: 0.411600 Loss2: 1.295175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.602880 Loss1: 0.255796 Loss2: 1.347084 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523490 Loss1: 0.229133 Loss2: 1.294357 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.448636 Loss1: 0.152666 Loss2: 1.295970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423959 Loss1: 0.131215 Loss2: 1.292743 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392645 Loss1: 0.051220 Loss2: 1.341425
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.379666 Loss1: 0.094300 Loss2: 1.285366
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.357651 Loss1: 0.084052 Loss2: 1.273599
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379441 Loss1: 0.103898 Loss2: 1.275543
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.333801 Loss1: 0.055695 Loss2: 1.278107
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.692603 Loss1: 0.730783 Loss2: 1.961820
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.900400 Loss1: 0.517006 Loss2: 1.383394
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.768771 Loss1: 0.327447 Loss2: 1.441324
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604258 Loss1: 0.230081 Loss2: 1.374178
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.523599 Loss1: 0.695404 Loss2: 1.828194
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.571038 Loss1: 0.183134 Loss2: 1.387903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485986 Loss1: 0.128157 Loss2: 1.357829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460550 Loss1: 0.104056 Loss2: 1.356493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433243 Loss1: 0.081844 Loss2: 1.351399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403640 Loss1: 0.059427 Loss2: 1.344213 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408051 Loss1: 0.082072 Loss2: 1.325979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407379 Loss1: 0.091018 Loss2: 1.316361 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414028 Loss1: 0.100013 Loss2: 1.314016
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.567471 Loss1: 0.727380 Loss2: 1.840091
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.870574 Loss1: 0.498262 Loss2: 1.372312
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.706128 Loss1: 0.307374 Loss2: 1.398754
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570773 Loss1: 0.213033 Loss2: 1.357739
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503002 Loss1: 0.146157 Loss2: 1.356845
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.537519 Loss1: 0.685570 Loss2: 1.851949
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.907645 Loss1: 0.522913 Loss2: 1.384732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.759798 Loss1: 0.306603 Loss2: 1.453195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.590869 Loss1: 0.220049 Loss2: 1.370820 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541637 Loss1: 0.160498 Loss2: 1.381139 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.520507 Loss1: 0.156078 Loss2: 1.364429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457128 Loss1: 0.102712 Loss2: 1.354416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401280 Loss1: 0.060631 Loss2: 1.340649 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.929383 Loss1: 0.517821 Loss2: 1.411562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.639630 Loss1: 0.235316 Loss2: 1.404314 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564919 Loss1: 0.172703 Loss2: 1.392216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472780 Loss1: 0.097676 Loss2: 1.375103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439619 Loss1: 0.067189 Loss2: 1.372431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419304 Loss1: 0.058974 Loss2: 1.360330 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407197 Loss1: 0.052362 Loss2: 1.354834 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994420
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512992 Loss1: 0.131586 Loss2: 1.381406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.524976 Loss1: 0.148946 Loss2: 1.376030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485393 Loss1: 0.113754 Loss2: 1.371639
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.560460 Loss1: 0.727083 Loss2: 1.833377
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454884 Loss1: 0.091956 Loss2: 1.362928
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.915540 Loss1: 0.487969 Loss2: 1.427571
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.726124 Loss1: 0.322267 Loss2: 1.403857
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.611476 Loss1: 0.223326 Loss2: 1.388150
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568893 Loss1: 0.188106 Loss2: 1.380787
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525319 Loss1: 0.160401 Loss2: 1.364918
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.706511 Loss1: 0.771639 Loss2: 1.934872
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.883764 Loss1: 0.477720 Loss2: 1.406044 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430267 Loss1: 0.074000 Loss2: 1.356267
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.769286 Loss1: 0.310884 Loss2: 1.458402
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411288 Loss1: 0.058196 Loss2: 1.353093
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.627141 Loss1: 0.219105 Loss2: 1.408036
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574112 Loss1: 0.177316 Loss2: 1.396796
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403182 Loss1: 0.057106 Loss2: 1.346076
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.526214 Loss1: 0.140538 Loss2: 1.385676 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479411 Loss1: 0.097325 Loss2: 1.382086 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465708 Loss1: 0.090288 Loss2: 1.375420
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.500787 Loss1: 0.698839 Loss2: 1.801948
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.677093 Loss1: 0.330152 Loss2: 1.346941
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.570118 Loss1: 0.215633 Loss2: 1.354484
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580058 Loss1: 0.243648 Loss2: 1.336410
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535643 Loss1: 0.193073 Loss2: 1.342570
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.531836 Loss1: 0.672710 Loss2: 1.859127
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.834407 Loss1: 0.442097 Loss2: 1.392310
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.479037 Loss1: 0.154010 Loss2: 1.325028
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726695 Loss1: 0.286671 Loss2: 1.440025
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446764 Loss1: 0.122363 Loss2: 1.324401
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.655367 Loss1: 0.253973 Loss2: 1.401393
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422182 Loss1: 0.100771 Loss2: 1.321410
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.644975 Loss1: 0.234025 Loss2: 1.410951
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.382127 Loss1: 0.069696 Loss2: 1.312431
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378406 Loss1: 0.072484 Loss2: 1.305922 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.495403 Loss1: 0.104226 Loss2: 1.391177 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.480896 Loss1: 0.097280 Loss2: 1.383615 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.610244 Loss1: 0.747227 Loss2: 1.863017
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.847596 Loss1: 0.470321 Loss2: 1.377275
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.750116 Loss1: 0.325420 Loss2: 1.424696
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630589 Loss1: 0.261534 Loss2: 1.369055
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.632985 Loss1: 0.744870 Loss2: 1.888115
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.898432 Loss1: 0.493214 Loss2: 1.405218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.747536 Loss1: 0.302426 Loss2: 1.445109 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.709835 Loss1: 0.291327 Loss2: 1.418508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.659517 Loss1: 0.233563 Loss2: 1.425953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516172 Loss1: 0.119365 Loss2: 1.396807 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454157 Loss1: 0.071100 Loss2: 1.383057 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398790 Loss1: 0.039682 Loss2: 1.359109 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.710373 Loss1: 0.346010 Loss2: 1.364363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.585063 Loss1: 0.215675 Loss2: 1.369388 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546246 Loss1: 0.181974 Loss2: 1.364271
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.405563 Loss1: 0.596890 Loss2: 1.808673
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509699 Loss1: 0.130465 Loss2: 1.379234
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.703351 Loss1: 0.351839 Loss2: 1.351511
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.452313 Loss1: 0.105114 Loss2: 1.347198
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.586053 Loss1: 0.208728 Loss2: 1.377325
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417538 Loss1: 0.073795 Loss2: 1.343742
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581949 Loss1: 0.237056 Loss2: 1.344893
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403720 Loss1: 0.064328 Loss2: 1.339392
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.472738 Loss1: 0.131707 Loss2: 1.341031
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392282 Loss1: 0.056845 Loss2: 1.335437
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.440713 Loss1: 0.111090 Loss2: 1.329623
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425567 Loss1: 0.096387 Loss2: 1.329179
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407286 Loss1: 0.090488 Loss2: 1.316799
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376199 Loss1: 0.064714 Loss2: 1.311486
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365221 Loss1: 0.058285 Loss2: 1.306936
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.529858 Loss1: 0.652549 Loss2: 1.877308
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.834287 Loss1: 0.426422 Loss2: 1.407866
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.706208 Loss1: 0.247678 Loss2: 1.458530
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649947 Loss1: 0.248554 Loss2: 1.401393
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631540 Loss1: 0.209696 Loss2: 1.421845
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.513050 Loss1: 0.610515 Loss2: 1.902535
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569362 Loss1: 0.159825 Loss2: 1.409537
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.707000 Loss1: 0.317867 Loss2: 1.389133
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.625126 Loss1: 0.222533 Loss2: 1.402593
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516696 Loss1: 0.115517 Loss2: 1.401179
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.568620 Loss1: 0.184366 Loss2: 1.384254
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460147 Loss1: 0.068032 Loss2: 1.392116
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.547213 Loss1: 0.157146 Loss2: 1.390067
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458390 Loss1: 0.068723 Loss2: 1.389667
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508560 Loss1: 0.132458 Loss2: 1.376102
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457200 Loss1: 0.069660 Loss2: 1.387540
+(DefaultActor pid=3765) >> Training accuracy: 0.975586
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484684 Loss1: 0.106921 Loss2: 1.377763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444460 Loss1: 0.076723 Loss2: 1.367736 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.733519 Loss1: 0.360719 Loss2: 1.372800 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.546370 Loss1: 0.192722 Loss2: 1.353648 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.584732 Loss1: 0.748889 Loss2: 1.835843
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515482 Loss1: 0.164105 Loss2: 1.351376
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.895358 Loss1: 0.538452 Loss2: 1.356906
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.428048 Loss1: 0.084833 Loss2: 1.343215
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.767402 Loss1: 0.356221 Loss2: 1.411180
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410178 Loss1: 0.073177 Loss2: 1.337001
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.622957 Loss1: 0.266982 Loss2: 1.355975
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420746 Loss1: 0.089873 Loss2: 1.330873
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539100 Loss1: 0.184202 Loss2: 1.354898
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406702 Loss1: 0.077533 Loss2: 1.329169
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512146 Loss1: 0.164574 Loss2: 1.347572
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393592 Loss1: 0.070883 Loss2: 1.322709
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430308 Loss1: 0.098191 Loss2: 1.332117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402749 Loss1: 0.076795 Loss2: 1.325953 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.745839 Loss1: 0.405261 Loss2: 1.340579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533091 Loss1: 0.189652 Loss2: 1.343439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.478610 Loss1: 0.651381 Loss2: 1.827229
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.482536 Loss1: 0.136746 Loss2: 1.345790
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.806051 Loss1: 0.402336 Loss2: 1.403715
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491232 Loss1: 0.143355 Loss2: 1.347877
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.471535 Loss1: 0.138356 Loss2: 1.333179
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.665410 Loss1: 0.243160 Loss2: 1.422249
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483420 Loss1: 0.139368 Loss2: 1.344052
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591457 Loss1: 0.200561 Loss2: 1.390896
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.513868 Loss1: 0.164898 Loss2: 1.348970
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571191 Loss1: 0.173959 Loss2: 1.397232
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426402 Loss1: 0.088547 Loss2: 1.337856
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.547016 Loss1: 0.161651 Loss2: 1.385365
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516350 Loss1: 0.132526 Loss2: 1.383824
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461683 Loss1: 0.086042 Loss2: 1.375641
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438351 Loss1: 0.066559 Loss2: 1.371792
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421584 Loss1: 0.054179 Loss2: 1.367405
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.506146 Loss1: 0.661928 Loss2: 1.844219
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.795467 Loss1: 0.428076 Loss2: 1.367391
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.699141 Loss1: 0.293483 Loss2: 1.405658
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.519734 Loss1: 0.176716 Loss2: 1.343018
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471762 Loss1: 0.134470 Loss2: 1.337292
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.667530 Loss1: 0.767848 Loss2: 1.899682
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.767453 Loss1: 0.398966 Loss2: 1.368487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.731234 Loss1: 0.324476 Loss2: 1.406758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588118 Loss1: 0.211417 Loss2: 1.376702 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377198 Loss1: 0.067424 Loss2: 1.309774
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.486225 Loss1: 0.128551 Loss2: 1.357674
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372046 Loss1: 0.063558 Loss2: 1.308488
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449773 Loss1: 0.087816 Loss2: 1.361957
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.413471 Loss1: 0.068649 Loss2: 1.344822
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407296 Loss1: 0.071266 Loss2: 1.336030
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412636 Loss1: 0.074721 Loss2: 1.337915
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406951 Loss1: 0.072351 Loss2: 1.334600
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.570546 Loss1: 0.742524 Loss2: 1.828023
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.829170 Loss1: 0.452009 Loss2: 1.377161
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.743224 Loss1: 0.332553 Loss2: 1.410671
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.614765 Loss1: 0.265827 Loss2: 1.348937
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.533326 Loss1: 0.179870 Loss2: 1.353456
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451501 Loss1: 0.116674 Loss2: 1.334827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.407040 Loss1: 0.081903 Loss2: 1.325137 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410314 Loss1: 0.090330 Loss2: 1.319984 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 22:31:04,741 | server.py:236 | fit_round 129 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371743 Loss1: 0.046834 Loss2: 1.324908 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388551 Loss1: 0.070828 Loss2: 1.317723 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.466557 Loss1: 0.104246 Loss2: 1.362311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.470563 Loss1: 0.106364 Loss2: 1.364199 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.590712 Loss1: 0.744784 Loss2: 1.845928
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445453 Loss1: 0.084571 Loss2: 1.360882
+(DefaultActor pid=3764) >> Training accuracy: 0.988051
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.660246 Loss1: 0.272595 Loss2: 1.387652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492489 Loss1: 0.148638 Loss2: 1.343851 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462250 Loss1: 0.127843 Loss2: 1.334407
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.577791 Loss1: 0.708759 Loss2: 1.869032
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479897 Loss1: 0.150357 Loss2: 1.329540
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.839116 Loss1: 0.461381 Loss2: 1.377735
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437659 Loss1: 0.114916 Loss2: 1.322743
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.685002 Loss1: 0.269634 Loss2: 1.415369
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419152 Loss1: 0.097684 Loss2: 1.321468
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.552931 Loss1: 0.179588 Loss2: 1.373343
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393001 Loss1: 0.072798 Loss2: 1.320203
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500921 Loss1: 0.134863 Loss2: 1.366059
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499214 Loss1: 0.140875 Loss2: 1.358339
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504202 Loss1: 0.147366 Loss2: 1.356836
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458644 Loss1: 0.098722 Loss2: 1.359921
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433099 Loss1: 0.084687 Loss2: 1.348412
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.517604 Loss1: 0.613859 Loss2: 1.903745
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389231 Loss1: 0.045043 Loss2: 1.344189
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.753815 Loss1: 0.287390 Loss2: 1.466425 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.615629 Loss1: 0.183706 Loss2: 1.431922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.555434 Loss1: 0.129253 Loss2: 1.426181
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.615862 Loss1: 0.722800 Loss2: 1.893062
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525865 Loss1: 0.115944 Loss2: 1.409921
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.808602 Loss1: 0.397072 Loss2: 1.411530
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.524417 Loss1: 0.116773 Loss2: 1.407645
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682090 Loss1: 0.245405 Loss2: 1.436686
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499824 Loss1: 0.095176 Loss2: 1.404648
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.650913 Loss1: 0.248768 Loss2: 1.402145
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.486106 Loss1: 0.088021 Loss2: 1.398085
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.551300 Loss1: 0.152896 Loss2: 1.398403
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529854 Loss1: 0.138630 Loss2: 1.391224
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491963 Loss1: 0.101166 Loss2: 1.390797
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486866 Loss1: 0.099025 Loss2: 1.387841
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479563 Loss1: 0.098787 Loss2: 1.380777
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429446 Loss1: 0.048728 Loss2: 1.380718
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 22:31:04,741][flwr][DEBUG] - fit_round 129 received 50 results and 0 failures
+INFO flwr 2023-10-11 22:31:46,998 | server.py:125 | fit progress: (129, 2.204728903480993, {'accuracy': 0.5893}, 297614.776855937)
+>> Test accuracy: 0.589300
+[2023-10-11 22:31:46,998][flwr][INFO] - fit progress: (129, 2.204728903480993, {'accuracy': 0.5893}, 297614.776855937)
+DEBUG flwr 2023-10-11 22:31:46,999 | server.py:173 | evaluate_round 129: strategy sampled 50 clients (out of 50)
+[2023-10-11 22:31:46,999][flwr][DEBUG] - evaluate_round 129: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 22:40:50,713 | server.py:187 | evaluate_round 129 received 50 results and 0 failures
+[2023-10-11 22:40:50,713][flwr][DEBUG] - evaluate_round 129 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 22:40:50,713 | server.py:222 | fit_round 130: strategy sampled 50 clients (out of 50)
+[2023-10-11 22:40:50,713][flwr][DEBUG] - fit_round 130: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.800936 Loss1: 0.895898 Loss2: 1.905039
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.866630 Loss1: 0.481495 Loss2: 1.385135
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.735574 Loss1: 0.313371 Loss2: 1.422203
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606793 Loss1: 0.243740 Loss2: 1.363053
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555570 Loss1: 0.177702 Loss2: 1.377867 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511968 Loss1: 0.149180 Loss2: 1.362788 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.624508 Loss1: 0.229659 Loss2: 1.394849
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.452821 Loss1: 0.095938 Loss2: 1.356883
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458855 Loss1: 0.102463 Loss2: 1.356392
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.477053 Loss1: 0.120772 Loss2: 1.356280
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428772 Loss1: 0.078316 Loss2: 1.350456
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475624 Loss1: 0.130082 Loss2: 1.345541
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401955 Loss1: 0.062848 Loss2: 1.339107
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447422 Loss1: 0.100943 Loss2: 1.346479
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430806 Loss1: 0.092670 Loss2: 1.338136
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390223 Loss1: 0.052982 Loss2: 1.337241
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394325 Loss1: 0.058729 Loss2: 1.335596
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369590 Loss1: 0.046499 Loss2: 1.323092
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.528725 Loss1: 0.660917 Loss2: 1.867808
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.749085 Loss1: 0.364108 Loss2: 1.384976
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.737746 Loss1: 0.312173 Loss2: 1.425573
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.600053 Loss1: 0.228200 Loss2: 1.371852
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547958 Loss1: 0.159127 Loss2: 1.388831
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.548324 Loss1: 0.727884 Loss2: 1.820440
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516218 Loss1: 0.145552 Loss2: 1.370665
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474878 Loss1: 0.100944 Loss2: 1.373933 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464955 Loss1: 0.097520 Loss2: 1.367435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423963 Loss1: 0.061120 Loss2: 1.362843 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423096 Loss1: 0.066942 Loss2: 1.356154 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408497 Loss1: 0.081508 Loss2: 1.326989 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369110 Loss1: 0.041897 Loss2: 1.327213 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388302 Loss1: 0.065515 Loss2: 1.322787
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.560879 Loss1: 0.665283 Loss2: 1.895596
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.885195 Loss1: 0.499592 Loss2: 1.385603
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.759004 Loss1: 0.291624 Loss2: 1.467380
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.579639 Loss1: 0.205245 Loss2: 1.374394
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.508340 Loss1: 0.136982 Loss2: 1.371358
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.584582 Loss1: 0.707951 Loss2: 1.876631
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.796103 Loss1: 0.384139 Loss2: 1.411964 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.663103 Loss1: 0.252032 Loss2: 1.411071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.597645 Loss1: 0.207236 Loss2: 1.390409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545330 Loss1: 0.153551 Loss2: 1.391779 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465962 Loss1: 0.094395 Loss2: 1.371567 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449025 Loss1: 0.084565 Loss2: 1.364460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.723035 Loss1: 0.685104 Loss2: 2.037931
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436577 Loss1: 0.075825 Loss2: 1.360752
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.836822 Loss1: 0.280687 Loss2: 1.556136 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.717462 Loss1: 0.201398 Loss2: 1.516064 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.716759 Loss1: 0.187617 Loss2: 1.529142
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.566513 Loss1: 0.666135 Loss2: 1.900378
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.790142 Loss1: 0.408263 Loss2: 1.381879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.724029 Loss1: 0.316358 Loss2: 1.407671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619094 Loss1: 0.240291 Loss2: 1.378803 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.612455 Loss1: 0.112350 Loss2: 1.500105
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529870 Loss1: 0.164837 Loss2: 1.365033
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481439 Loss1: 0.121461 Loss2: 1.359978
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.462751 Loss1: 0.109550 Loss2: 1.353202
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.456499 Loss1: 0.101741 Loss2: 1.354759
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438170 Loss1: 0.083877 Loss2: 1.354293
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.611675 Loss1: 0.759573 Loss2: 1.852102
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393324 Loss1: 0.050914 Loss2: 1.342409
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.780342 Loss1: 0.356796 Loss2: 1.423546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506669 Loss1: 0.151607 Loss2: 1.355062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455767 Loss1: 0.113700 Loss2: 1.342067
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.423144 Loss1: 0.579351 Loss2: 1.843793
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.734669 Loss1: 0.375584 Loss2: 1.359085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.654622 Loss1: 0.248266 Loss2: 1.406356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.628223 Loss1: 0.275212 Loss2: 1.353011 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.613440 Loss1: 0.229012 Loss2: 1.384428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445222 Loss1: 0.093739 Loss2: 1.351483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381018 Loss1: 0.046789 Loss2: 1.334229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367272 Loss1: 0.038922 Loss2: 1.328350 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.572068 Loss1: 0.191176 Loss2: 1.380892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476890 Loss1: 0.133816 Loss2: 1.343074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460412 Loss1: 0.121212 Loss2: 1.339199
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.643005 Loss1: 0.769906 Loss2: 1.873100
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441545 Loss1: 0.104980 Loss2: 1.336564
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.802474 Loss1: 0.422692 Loss2: 1.379782
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419172 Loss1: 0.091172 Loss2: 1.328000
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622487 Loss1: 0.228728 Loss2: 1.393759
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419919 Loss1: 0.096871 Loss2: 1.323048
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498064 Loss1: 0.128834 Loss2: 1.369230
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.486675 Loss1: 0.127893 Loss2: 1.358783
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436242 Loss1: 0.112740 Loss2: 1.323502
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.424890 Loss1: 0.081072 Loss2: 1.343818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400642 Loss1: 0.060687 Loss2: 1.339955 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399536 Loss1: 0.065842 Loss2: 1.333694
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.740295 Loss1: 0.828452 Loss2: 1.911842
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.750289 Loss1: 0.406253 Loss2: 1.344036
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.558356 Loss1: 0.199669 Loss2: 1.358687
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.509045 Loss1: 0.168423 Loss2: 1.340622
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.464655 Loss1: 0.142593 Loss2: 1.322062
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.404143 Loss1: 0.082208 Loss2: 1.321935
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.513500 Loss1: 0.638670 Loss2: 1.874830
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.811326 Loss1: 0.428458 Loss2: 1.382868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394271 Loss1: 0.088596 Loss2: 1.305675 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400935 Loss1: 0.094810 Loss2: 1.306125 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997596
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.639060 Loss1: 0.240445 Loss2: 1.398614 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.507298 Loss1: 0.129625 Loss2: 1.377672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.557148 Loss1: 0.687430 Loss2: 1.869718
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467918 Loss1: 0.094364 Loss2: 1.373554
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.914702 Loss1: 0.520554 Loss2: 1.394148
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456079 Loss1: 0.100589 Loss2: 1.355490
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.648093 Loss1: 0.254224 Loss2: 1.393869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.567591 Loss1: 0.181396 Loss2: 1.386194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.535659 Loss1: 0.154291 Loss2: 1.381367
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.607832 Loss1: 0.793921 Loss2: 1.813911
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.783839 Loss1: 0.443821 Loss2: 1.340018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.683689 Loss1: 0.288902 Loss2: 1.394786 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449212 Loss1: 0.082367 Loss2: 1.366845
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561260 Loss1: 0.227470 Loss2: 1.333790
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503867 Loss1: 0.169072 Loss2: 1.334795
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479281 Loss1: 0.144027 Loss2: 1.335254
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440742 Loss1: 0.105397 Loss2: 1.335345
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.391565 Loss1: 0.071217 Loss2: 1.320348
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.394338 Loss1: 0.598563 Loss2: 1.795775
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385602 Loss1: 0.072856 Loss2: 1.312747
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.702825 Loss1: 0.347565 Loss2: 1.355259
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415959 Loss1: 0.103072 Loss2: 1.312887
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527110 Loss1: 0.189088 Loss2: 1.338022 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.435944 Loss1: 0.104567 Loss2: 1.331376 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.430396 Loss1: 0.099973 Loss2: 1.330423
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.455593 Loss1: 0.635226 Loss2: 1.820367
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.744030 Loss1: 0.406732 Loss2: 1.337298
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.397324 Loss1: 0.070576 Loss2: 1.326748
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.631454 Loss1: 0.258818 Loss2: 1.372636
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401618 Loss1: 0.077877 Loss2: 1.323741
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.549198 Loss1: 0.200768 Loss2: 1.348429
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378161 Loss1: 0.063358 Loss2: 1.314803
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461291 Loss1: 0.127242 Loss2: 1.334049 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409397 Loss1: 0.083827 Loss2: 1.325570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410523 Loss1: 0.079655 Loss2: 1.330867
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.537705 Loss1: 0.716588 Loss2: 1.821116
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370095 Loss1: 0.049766 Loss2: 1.320329
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.710369 Loss1: 0.370689 Loss2: 1.339680
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.620743 Loss1: 0.252424 Loss2: 1.368319
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.560007 Loss1: 0.224843 Loss2: 1.335164
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.520099 Loss1: 0.170483 Loss2: 1.349616
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515423 Loss1: 0.179988 Loss2: 1.335435
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484351 Loss1: 0.147747 Loss2: 1.336604
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.330119 Loss1: 0.550274 Loss2: 1.779845
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445647 Loss1: 0.109388 Loss2: 1.336259
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.665856 Loss1: 0.331106 Loss2: 1.334750
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454437 Loss1: 0.127881 Loss2: 1.326556
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.646556 Loss1: 0.259221 Loss2: 1.387335
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428194 Loss1: 0.105125 Loss2: 1.323069
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519737 Loss1: 0.182913 Loss2: 1.336824
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491552 Loss1: 0.140035 Loss2: 1.351516
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495597 Loss1: 0.159338 Loss2: 1.336258
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484399 Loss1: 0.140834 Loss2: 1.343566
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405133 Loss1: 0.077500 Loss2: 1.327633
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.635190 Loss1: 0.762894 Loss2: 1.872296
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400640 Loss1: 0.078269 Loss2: 1.322372
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381694 Loss1: 0.060487 Loss2: 1.321207 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594429 Loss1: 0.239844 Loss2: 1.354585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494180 Loss1: 0.148663 Loss2: 1.345517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.553018 Loss1: 0.199469 Loss2: 1.353549
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.448996 Loss1: 0.641685 Loss2: 1.807310
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.707631 Loss1: 0.376189 Loss2: 1.331442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.596347 Loss1: 0.215997 Loss2: 1.380350 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441588 Loss1: 0.096965 Loss2: 1.344624
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.501409 Loss1: 0.167274 Loss2: 1.334135
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.490045 Loss1: 0.158471 Loss2: 1.331574
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474121 Loss1: 0.145135 Loss2: 1.328985
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446528 Loss1: 0.114728 Loss2: 1.331800
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415587 Loss1: 0.095433 Loss2: 1.320154
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.732040 Loss1: 0.784511 Loss2: 1.947529
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420167 Loss1: 0.096617 Loss2: 1.323550
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.838141 Loss1: 0.357251 Loss2: 1.480890
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384463 Loss1: 0.072360 Loss2: 1.312104
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586041 Loss1: 0.139780 Loss2: 1.446261 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.590462 Loss1: 0.144741 Loss2: 1.445721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.530043 Loss1: 0.091510 Loss2: 1.438533
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.435813 Loss1: 0.616407 Loss2: 1.819406
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.792946 Loss1: 0.453480 Loss2: 1.339466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.646452 Loss1: 0.247800 Loss2: 1.398652 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.521699 Loss1: 0.089051 Loss2: 1.432648
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554626 Loss1: 0.221869 Loss2: 1.332757
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491651 Loss1: 0.145724 Loss2: 1.345927
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439122 Loss1: 0.115066 Loss2: 1.324056
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.411901 Loss1: 0.086589 Loss2: 1.325313
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.362924 Loss1: 0.051207 Loss2: 1.311717
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.595059 Loss1: 0.695928 Loss2: 1.899132
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373005 Loss1: 0.065074 Loss2: 1.307931
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.859069 Loss1: 0.447920 Loss2: 1.411149
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.335230 Loss1: 0.031825 Loss2: 1.303404
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557597 Loss1: 0.151539 Loss2: 1.406058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503074 Loss1: 0.112764 Loss2: 1.390310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.493402 Loss1: 0.106777 Loss2: 1.386626
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.730956 Loss1: 0.796588 Loss2: 1.934367
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.974803 Loss1: 0.577856 Loss2: 1.396947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462316 Loss1: 0.084441 Loss2: 1.377875
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.741922 Loss1: 0.287308 Loss2: 1.454613
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.490654 Loss1: 0.115843 Loss2: 1.374811
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.626820 Loss1: 0.234887 Loss2: 1.391933
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.575919 Loss1: 0.170695 Loss2: 1.405224
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503130 Loss1: 0.115857 Loss2: 1.387272
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465702 Loss1: 0.083313 Loss2: 1.382389
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453093 Loss1: 0.085278 Loss2: 1.367815
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433762 Loss1: 0.066505 Loss2: 1.367257
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.556454 Loss1: 0.702484 Loss2: 1.853971
+(DefaultActor pid=3764) >> Training accuracy: 0.997768
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.908381 Loss1: 0.507663 Loss2: 1.400719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.597947 Loss1: 0.218000 Loss2: 1.379947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516586 Loss1: 0.126249 Loss2: 1.390337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459384 Loss1: 0.088364 Loss2: 1.371020 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.428039 Loss1: 0.066739 Loss2: 1.361299 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408486 Loss1: 0.053394 Loss2: 1.355092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433280 Loss1: 0.076730 Loss2: 1.356551 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496797 Loss1: 0.126883 Loss2: 1.369913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438473 Loss1: 0.082110 Loss2: 1.356363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.433494 Loss1: 0.638326 Loss2: 1.795168 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.632265 Loss1: 0.255815 Loss2: 1.376450 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499731 Loss1: 0.144039 Loss2: 1.355692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.556333 Loss1: 0.695220 Loss2: 1.861113
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480855 Loss1: 0.125970 Loss2: 1.354885
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.847221 Loss1: 0.466770 Loss2: 1.380451
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.437335 Loss1: 0.096361 Loss2: 1.340974
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411869 Loss1: 0.073986 Loss2: 1.337883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388474 Loss1: 0.057896 Loss2: 1.330578 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373657 Loss1: 0.049679 Loss2: 1.323978 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488654 Loss1: 0.133441 Loss2: 1.355213 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.478361 Loss1: 0.120547 Loss2: 1.357815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466118 Loss1: 0.113774 Loss2: 1.352345
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.587065 Loss1: 0.753587 Loss2: 1.833478
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.851610 Loss1: 0.481293 Loss2: 1.370317
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.694208 Loss1: 0.279850 Loss2: 1.414358
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516696 Loss1: 0.166810 Loss2: 1.349886
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.458827 Loss1: 0.106682 Loss2: 1.352145
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.451482 Loss1: 0.656414 Loss2: 1.795068
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.690710 Loss1: 0.343153 Loss2: 1.347556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568689 Loss1: 0.198564 Loss2: 1.370125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534660 Loss1: 0.199912 Loss2: 1.334749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.467546 Loss1: 0.133240 Loss2: 1.334306 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463557 Loss1: 0.133649 Loss2: 1.329907 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433728 Loss1: 0.104753 Loss2: 1.328975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.644864 Loss1: 0.710357 Loss2: 1.934508
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414702 Loss1: 0.096991 Loss2: 1.317711
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.717871 Loss1: 0.242371 Loss2: 1.475499 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.585150 Loss1: 0.142103 Loss2: 1.443047 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.553338 Loss1: 0.122509 Loss2: 1.430828
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.596948 Loss1: 0.795440 Loss2: 1.801508
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.571011 Loss1: 0.144100 Loss2: 1.426911
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.771202 Loss1: 0.417515 Loss2: 1.353687
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.510434 Loss1: 0.083541 Loss2: 1.426893
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.689863 Loss1: 0.310486 Loss2: 1.379377
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.521409 Loss1: 0.101361 Loss2: 1.420048
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562264 Loss1: 0.222861 Loss2: 1.339403
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.517237 Loss1: 0.103892 Loss2: 1.413345
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496983 Loss1: 0.151353 Loss2: 1.345630
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465487 Loss1: 0.142700 Loss2: 1.322787
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404489 Loss1: 0.080718 Loss2: 1.323772
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400400 Loss1: 0.085694 Loss2: 1.314706
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.359106 Loss1: 0.049759 Loss2: 1.309346
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.565260 Loss1: 0.739326 Loss2: 1.825934
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.338458 Loss1: 0.034649 Loss2: 1.303808
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.691071 Loss1: 0.274174 Loss2: 1.416897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.603419 Loss1: 0.229399 Loss2: 1.374020 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.521637 Loss1: 0.168896 Loss2: 1.352741
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.410646 Loss1: 0.596950 Loss2: 1.813696
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.734194 Loss1: 0.372621 Loss2: 1.361573 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.652379 Loss1: 0.258907 Loss2: 1.393472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.621571 Loss1: 0.254399 Loss2: 1.367172 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.632721 Loss1: 0.251346 Loss2: 1.381375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.527904 Loss1: 0.155959 Loss2: 1.371945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414267 Loss1: 0.067449 Loss2: 1.346818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395374 Loss1: 0.056750 Loss2: 1.338624 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630512 Loss1: 0.259775 Loss2: 1.370737 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.521827 Loss1: 0.156580 Loss2: 1.365247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.739626 Loss1: 0.781127 Loss2: 1.958499
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.530917 Loss1: 0.166473 Loss2: 1.364445
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483878 Loss1: 0.122396 Loss2: 1.361481 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.475260 Loss1: 0.123078 Loss2: 1.352182 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445031 Loss1: 0.091007 Loss2: 1.354024 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.482340 Loss1: 0.130839 Loss2: 1.351500 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415767 Loss1: 0.069998 Loss2: 1.345770 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.449190 Loss1: 0.619373 Loss2: 1.829817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.642955 Loss1: 0.256595 Loss2: 1.386360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484863 Loss1: 0.150046 Loss2: 1.334817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462041 Loss1: 0.129418 Loss2: 1.332623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434967 Loss1: 0.110622 Loss2: 1.324345 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398407 Loss1: 0.073008 Loss2: 1.325399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.368857 Loss1: 0.060506 Loss2: 1.308350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.343823 Loss1: 0.033612 Loss2: 1.310211 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381263 Loss1: 0.068072 Loss2: 1.313191 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357404 Loss1: 0.049616 Loss2: 1.307788 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.794749 Loss1: 0.450950 Loss2: 1.343799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.577797 Loss1: 0.235764 Loss2: 1.342033 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525699 Loss1: 0.173785 Loss2: 1.351914
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.570061 Loss1: 0.746353 Loss2: 1.823709
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485882 Loss1: 0.147172 Loss2: 1.338710
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.747731 Loss1: 0.430346 Loss2: 1.317385
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467771 Loss1: 0.143520 Loss2: 1.324250
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.621512 Loss1: 0.262683 Loss2: 1.358829
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555603 Loss1: 0.228195 Loss2: 1.327408
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420052 Loss1: 0.087775 Loss2: 1.332277
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493378 Loss1: 0.178392 Loss2: 1.314986
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391851 Loss1: 0.074235 Loss2: 1.317616
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.448805 Loss1: 0.130856 Loss2: 1.317949
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375989 Loss1: 0.064419 Loss2: 1.311570
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389480 Loss1: 0.091543 Loss2: 1.297937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.338557 Loss1: 0.052286 Loss2: 1.286272 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.507395 Loss1: 0.677291 Loss2: 1.830104
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.797884 Loss1: 0.422472 Loss2: 1.375412
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647605 Loss1: 0.252412 Loss2: 1.395193
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570253 Loss1: 0.207691 Loss2: 1.362562
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.512804 Loss1: 0.702134 Loss2: 1.810670
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.733529 Loss1: 0.386571 Loss2: 1.346959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.615385 Loss1: 0.236991 Loss2: 1.378394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497751 Loss1: 0.171968 Loss2: 1.325782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.478412 Loss1: 0.143515 Loss2: 1.334897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.432153 Loss1: 0.109149 Loss2: 1.323005 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.400322 Loss1: 0.094355 Loss2: 1.305967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.353185 Loss1: 0.055862 Loss2: 1.297323 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.519270 Loss1: 0.638986 Loss2: 1.880285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.666356 Loss1: 0.241800 Loss2: 1.424556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.707440 Loss1: 0.784356 Loss2: 1.923084 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.951014 Loss1: 0.567036 Loss2: 1.383978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.759893 Loss1: 0.298874 Loss2: 1.461019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496045 Loss1: 0.109591 Loss2: 1.386453
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564711 Loss1: 0.175307 Loss2: 1.389405
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503307 Loss1: 0.134180 Loss2: 1.369127
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.490522 Loss1: 0.104347 Loss2: 1.386175
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.488000 Loss1: 0.111120 Loss2: 1.376880
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461960 Loss1: 0.083339 Loss2: 1.378621
+DEBUG flwr 2023-10-11 23:09:16,659 | server.py:236 | fit_round 130 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427644 Loss1: 0.050666 Loss2: 1.376978 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445370 Loss1: 0.085004 Loss2: 1.360367 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.321627 Loss1: 0.520111 Loss2: 1.801517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.707103 Loss1: 0.302135 Loss2: 1.404968 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.617693 Loss1: 0.722675 Loss2: 1.895018
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.539448 Loss1: 0.184531 Loss2: 1.354917
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.522953 Loss1: 0.174153 Loss2: 1.348800 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465450 Loss1: 0.123541 Loss2: 1.341909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419110 Loss1: 0.086204 Loss2: 1.332906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.377378 Loss1: 0.055478 Loss2: 1.321900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379953 Loss1: 0.060203 Loss2: 1.319750 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460908 Loss1: 0.084329 Loss2: 1.376580 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992647
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.486790 Loss1: 0.107748 Loss2: 1.379042 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.620290 Loss1: 0.726050 Loss2: 1.894240
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.842931 Loss1: 0.405603 Loss2: 1.437328
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.703500 Loss1: 0.239792 Loss2: 1.463708
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.643113 Loss1: 0.775656 Loss2: 1.867457
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.608521 Loss1: 0.192132 Loss2: 1.416389
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831783 Loss1: 0.458173 Loss2: 1.373610
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.553328 Loss1: 0.132676 Loss2: 1.420652
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.740018 Loss1: 0.324195 Loss2: 1.415823
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542660 Loss1: 0.130997 Loss2: 1.411663
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611673 Loss1: 0.235032 Loss2: 1.376641
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516480 Loss1: 0.100515 Loss2: 1.415964
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.527153 Loss1: 0.119163 Loss2: 1.407991 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465607 Loss1: 0.062009 Loss2: 1.403598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463814 Loss1: 0.070421 Loss2: 1.393394 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410587 Loss1: 0.065663 Loss2: 1.344923 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 23:09:16,659][flwr][DEBUG] - fit_round 130 received 50 results and 0 failures
+INFO flwr 2023-10-11 23:09:57,233 | server.py:125 | fit progress: (130, 2.2075375242355153, {'accuracy': 0.59}, 299905.01109869697)
+>> Test accuracy: 0.590000
+[2023-10-11 23:09:57,233][flwr][INFO] - fit progress: (130, 2.2075375242355153, {'accuracy': 0.59}, 299905.01109869697)
+DEBUG flwr 2023-10-11 23:09:57,233 | server.py:173 | evaluate_round 130: strategy sampled 50 clients (out of 50)
+[2023-10-11 23:09:57,233][flwr][DEBUG] - evaluate_round 130: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 23:18:59,952 | server.py:187 | evaluate_round 130 received 50 results and 0 failures
+[2023-10-11 23:18:59,952][flwr][DEBUG] - evaluate_round 130 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 23:18:59,953 | server.py:222 | fit_round 131: strategy sampled 50 clients (out of 50)
+[2023-10-11 23:18:59,953][flwr][DEBUG] - fit_round 131: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.655447 Loss1: 0.741645 Loss2: 1.913802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.639519 Loss1: 0.189385 Loss2: 1.450134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575051 Loss1: 0.157119 Loss2: 1.417932
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.596476 Loss1: 0.747446 Loss2: 1.849030
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.579809 Loss1: 0.162617 Loss2: 1.417192
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.723144 Loss1: 0.365974 Loss2: 1.357170
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547536 Loss1: 0.128478 Loss2: 1.419058
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600007 Loss1: 0.230904 Loss2: 1.369103
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511143 Loss1: 0.101059 Loss2: 1.410085
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.502456 Loss1: 0.159896 Loss2: 1.342560
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.498117 Loss1: 0.091330 Loss2: 1.406787
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.504488 Loss1: 0.161807 Loss2: 1.342681
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.495805 Loss1: 0.094175 Loss2: 1.401630
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450952 Loss1: 0.111278 Loss2: 1.339675
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.479402 Loss1: 0.075204 Loss2: 1.404198
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419000 Loss1: 0.092460 Loss2: 1.326540
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428781 Loss1: 0.100377 Loss2: 1.328404
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444868 Loss1: 0.111755 Loss2: 1.333113
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390287 Loss1: 0.063630 Loss2: 1.326657
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.414217 Loss1: 0.588850 Loss2: 1.825367
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.851522 Loss1: 0.495036 Loss2: 1.356487
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.699647 Loss1: 0.317143 Loss2: 1.382503
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573163 Loss1: 0.249471 Loss2: 1.323692
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.595285 Loss1: 0.712859 Loss2: 1.882426
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.832670 Loss1: 0.458416 Loss2: 1.374254
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.538283 Loss1: 0.206169 Loss2: 1.332114
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614618 Loss1: 0.214013 Loss2: 1.400605
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473574 Loss1: 0.156140 Loss2: 1.317434
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573481 Loss1: 0.210704 Loss2: 1.362777
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.399753 Loss1: 0.091881 Loss2: 1.307873
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.376656 Loss1: 0.074862 Loss2: 1.301794 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381632 Loss1: 0.083686 Loss2: 1.297946 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423631 Loss1: 0.125257 Loss2: 1.298374 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406833 Loss1: 0.062342 Loss2: 1.344491 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994420
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.501044 Loss1: 0.655360 Loss2: 1.845684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.704701 Loss1: 0.275093 Loss2: 1.429608 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.653301 Loss1: 0.289382 Loss2: 1.363919
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.545647 Loss1: 0.706387 Loss2: 1.839260
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593877 Loss1: 0.213778 Loss2: 1.380099
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.720653 Loss1: 0.352984 Loss2: 1.367669
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491259 Loss1: 0.130050 Loss2: 1.361209
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605386 Loss1: 0.232468 Loss2: 1.372917
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434864 Loss1: 0.071475 Loss2: 1.363389
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599619 Loss1: 0.246698 Loss2: 1.352921
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425014 Loss1: 0.082471 Loss2: 1.342543
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522419 Loss1: 0.164132 Loss2: 1.358287
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455055 Loss1: 0.113913 Loss2: 1.341142
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494158 Loss1: 0.147796 Loss2: 1.346362
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463867 Loss1: 0.113397 Loss2: 1.350469
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446500 Loss1: 0.107955 Loss2: 1.338545
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396701 Loss1: 0.059854 Loss2: 1.336847
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408914 Loss1: 0.082093 Loss2: 1.326821
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361877 Loss1: 0.036858 Loss2: 1.325019
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.520774 Loss1: 0.601928 Loss2: 1.918846
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.783756 Loss1: 0.380591 Loss2: 1.403164
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.739026 Loss1: 0.281825 Loss2: 1.457201
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.643433 Loss1: 0.239344 Loss2: 1.404088
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.708355 Loss1: 0.683639 Loss2: 2.024715
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.667645 Loss1: 0.243644 Loss2: 1.424001
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.946440 Loss1: 0.451038 Loss2: 1.495401
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.596745 Loss1: 0.191270 Loss2: 1.405476
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804574 Loss1: 0.265716 Loss2: 1.538857
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.654336 Loss1: 0.235937 Loss2: 1.418399
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.708254 Loss1: 0.204386 Loss2: 1.503868
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.547979 Loss1: 0.139829 Loss2: 1.408151
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.674163 Loss1: 0.177459 Loss2: 1.496704
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.492673 Loss1: 0.093084 Loss2: 1.399590
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.647987 Loss1: 0.145094 Loss2: 1.502893
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476239 Loss1: 0.086286 Loss2: 1.389953
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.597949 Loss1: 0.102223 Loss2: 1.495726
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.558430 Loss1: 0.085313 Loss2: 1.473117
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.536553 Loss1: 0.056851 Loss2: 1.479702
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.542902 Loss1: 0.072645 Loss2: 1.470256
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.531740 Loss1: 0.599636 Loss2: 1.932104
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.899102 Loss1: 0.472555 Loss2: 1.426547
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.792196 Loss1: 0.302158 Loss2: 1.490038
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.653138 Loss1: 0.239056 Loss2: 1.414082
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.692781 Loss1: 0.801759 Loss2: 1.891022
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.633811 Loss1: 0.211658 Loss2: 1.422154
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.877030 Loss1: 0.492772 Loss2: 1.384259
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.559408 Loss1: 0.143870 Loss2: 1.415538
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633180 Loss1: 0.240247 Loss2: 1.392934
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.594653 Loss1: 0.190124 Loss2: 1.404529
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.547821 Loss1: 0.189050 Loss2: 1.358771
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.567703 Loss1: 0.155916 Loss2: 1.411787
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476084 Loss1: 0.125319 Loss2: 1.350766
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.522046 Loss1: 0.124206 Loss2: 1.397841
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.428048 Loss1: 0.088745 Loss2: 1.339303
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.534427 Loss1: 0.130479 Loss2: 1.403948
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.413582 Loss1: 0.078729 Loss2: 1.334852
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414923 Loss1: 0.088266 Loss2: 1.326657
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397997 Loss1: 0.066948 Loss2: 1.331049
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378858 Loss1: 0.053380 Loss2: 1.325477
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.699188 Loss1: 0.769592 Loss2: 1.929596
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.845011 Loss1: 0.414518 Loss2: 1.430493
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684179 Loss1: 0.222376 Loss2: 1.461804
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.571334 Loss1: 0.151208 Loss2: 1.420126
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.547495 Loss1: 0.695535 Loss2: 1.851960
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.801933 Loss1: 0.432262 Loss2: 1.369671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.706128 Loss1: 0.277207 Loss2: 1.428921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574404 Loss1: 0.206385 Loss2: 1.368019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.578422 Loss1: 0.196370 Loss2: 1.382052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567924 Loss1: 0.190848 Loss2: 1.377075 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.493818 Loss1: 0.102742 Loss2: 1.391076
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.515522 Loss1: 0.138961 Loss2: 1.376560
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452012 Loss1: 0.084663 Loss2: 1.367349
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412272 Loss1: 0.058573 Loss2: 1.353699
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392734 Loss1: 0.047413 Loss2: 1.345320
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.903734 Loss1: 0.835387 Loss2: 2.068347
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.958015 Loss1: 0.506195 Loss2: 1.451820
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731828 Loss1: 0.236740 Loss2: 1.495088
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.646155 Loss1: 0.201123 Loss2: 1.445032
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.514191 Loss1: 0.632003 Loss2: 1.882189
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.718892 Loss1: 0.336554 Loss2: 1.382338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509452 Loss1: 0.085247 Loss2: 1.424205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.523990 Loss1: 0.100873 Loss2: 1.423117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.487849 Loss1: 0.075190 Loss2: 1.412658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476824 Loss1: 0.065750 Loss2: 1.411074 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419809 Loss1: 0.057658 Loss2: 1.362151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400200 Loss1: 0.047753 Loss2: 1.352447 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386410 Loss1: 0.039104 Loss2: 1.347306
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.643246 Loss1: 0.727253 Loss2: 1.915994
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.913276 Loss1: 0.540826 Loss2: 1.372450
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.715015 Loss1: 0.283007 Loss2: 1.432008
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.641260 Loss1: 0.261879 Loss2: 1.379381
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.567795 Loss1: 0.183362 Loss2: 1.384433
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.501723 Loss1: 0.124839 Loss2: 1.376884
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.686646 Loss1: 0.794631 Loss2: 1.892015
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.780503 Loss1: 0.421736 Loss2: 1.358768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.617561 Loss1: 0.226939 Loss2: 1.390622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.503267 Loss1: 0.157392 Loss2: 1.345874 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.453346 Loss1: 0.115281 Loss2: 1.338065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.424181 Loss1: 0.094355 Loss2: 1.329825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.382398 Loss1: 0.065224 Loss2: 1.317174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387497 Loss1: 0.067622 Loss2: 1.319874
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.425699 Loss1: 0.647424 Loss2: 1.778275
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.819293 Loss1: 0.460127 Loss2: 1.359166
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684889 Loss1: 0.299190 Loss2: 1.385699
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526298 Loss1: 0.193883 Loss2: 1.332415
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.467571 Loss1: 0.130534 Loss2: 1.337036
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.481967 Loss1: 0.650983 Loss2: 1.830984
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.399668 Loss1: 0.081486 Loss2: 1.318182
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.398539 Loss1: 0.083216 Loss2: 1.315323 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394111 Loss1: 0.088340 Loss2: 1.305771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.393851 Loss1: 0.083002 Loss2: 1.310848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.376527 Loss1: 0.071678 Loss2: 1.304849 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484250 Loss1: 0.126491 Loss2: 1.357760 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462971 Loss1: 0.112448 Loss2: 1.350523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415537 Loss1: 0.069334 Loss2: 1.346204
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.427714 Loss1: 0.614710 Loss2: 1.813004
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.773687 Loss1: 0.439608 Loss2: 1.334079
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.654490 Loss1: 0.273280 Loss2: 1.381210
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.539197 Loss1: 0.195657 Loss2: 1.343539
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503345 Loss1: 0.165786 Loss2: 1.337559
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.620448 Loss1: 0.730352 Loss2: 1.890095
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.806042 Loss1: 0.413294 Loss2: 1.392748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.687310 Loss1: 0.240413 Loss2: 1.446897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.617545 Loss1: 0.226003 Loss2: 1.391542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544069 Loss1: 0.146891 Loss2: 1.397179 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360601 Loss1: 0.056269 Loss2: 1.304332
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531598 Loss1: 0.145074 Loss2: 1.386525
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.462171 Loss1: 0.086181 Loss2: 1.375990
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442551 Loss1: 0.075674 Loss2: 1.366877
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429510 Loss1: 0.065219 Loss2: 1.364291
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429019 Loss1: 0.065280 Loss2: 1.363739
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.645707 Loss1: 0.734312 Loss2: 1.911395
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.746400 Loss1: 0.344836 Loss2: 1.401564
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.631535 Loss1: 0.213144 Loss2: 1.418391
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574695 Loss1: 0.192981 Loss2: 1.381714
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.541611 Loss1: 0.162338 Loss2: 1.379273
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.565656 Loss1: 0.647981 Loss2: 1.917674
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.822958 Loss1: 0.342488 Loss2: 1.480470 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.695160 Loss1: 0.239002 Loss2: 1.456158 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.646602 Loss1: 0.209053 Loss2: 1.437549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.567560 Loss1: 0.134327 Loss2: 1.433233 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549290 Loss1: 0.127160 Loss2: 1.422130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.509755 Loss1: 0.091714 Loss2: 1.418041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458773 Loss1: 0.054873 Loss2: 1.403901 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736122 Loss1: 0.294389 Loss2: 1.441733 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587862 Loss1: 0.194008 Loss2: 1.393854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551241 Loss1: 0.164103 Loss2: 1.387137
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.429551 Loss1: 0.669896 Loss2: 1.759655
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518356 Loss1: 0.132920 Loss2: 1.385436
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.832261 Loss1: 0.484216 Loss2: 1.348044
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479735 Loss1: 0.103527 Loss2: 1.376207
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.648719 Loss1: 0.276922 Loss2: 1.371797
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448377 Loss1: 0.081502 Loss2: 1.366875
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.484835 Loss1: 0.157014 Loss2: 1.327821
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436799 Loss1: 0.074857 Loss2: 1.361942
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.422367 Loss1: 0.111130 Loss2: 1.311237
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439355 Loss1: 0.131832 Loss2: 1.307522
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.421814 Loss1: 0.115840 Loss2: 1.305974
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.387021 Loss1: 0.085529 Loss2: 1.301492
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381849 Loss1: 0.080776 Loss2: 1.301074
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.794551 Loss1: 0.750987 Loss2: 2.043564
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.346210 Loss1: 0.057822 Loss2: 1.288388
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.823520 Loss1: 0.242949 Loss2: 1.580571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.732509 Loss1: 0.190713 Loss2: 1.541796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.719112 Loss1: 0.187470 Loss2: 1.531642
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.428803 Loss1: 0.581483 Loss2: 1.847320
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.772499 Loss1: 0.407580 Loss2: 1.364919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.639727 Loss1: 0.241828 Loss2: 1.397899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.623928 Loss1: 0.250951 Loss2: 1.372977 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.608160 Loss1: 0.231816 Loss2: 1.376344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497200 Loss1: 0.132685 Loss2: 1.364515 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454543 Loss1: 0.109507 Loss2: 1.345035 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442194 Loss1: 0.094851 Loss2: 1.347343 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.634832 Loss1: 0.250945 Loss2: 1.383887 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533386 Loss1: 0.175829 Loss2: 1.357557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.428117 Loss1: 0.093080 Loss2: 1.335036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427618 Loss1: 0.093828 Loss2: 1.333790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419233 Loss1: 0.087688 Loss2: 1.331544 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983073
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.566011 Loss1: 0.189792 Loss2: 1.376219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464045 Loss1: 0.103639 Loss2: 1.360406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.406611 Loss1: 0.588794 Loss2: 1.817817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.725080 Loss1: 0.382813 Loss2: 1.342267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.621295 Loss1: 0.234792 Loss2: 1.386503 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529267 Loss1: 0.179043 Loss2: 1.350224 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498424 Loss1: 0.146808 Loss2: 1.351616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414970 Loss1: 0.084357 Loss2: 1.330613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400731 Loss1: 0.072039 Loss2: 1.328692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.394692 Loss1: 0.069447 Loss2: 1.325245 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557981 Loss1: 0.170953 Loss2: 1.387028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499353 Loss1: 0.109862 Loss2: 1.389490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.613654 Loss1: 0.749817 Loss2: 1.863837 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.836630 Loss1: 0.464456 Loss2: 1.372174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.696455 Loss1: 0.282220 Loss2: 1.414235 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.585666 Loss1: 0.214430 Loss2: 1.371236 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503986 Loss1: 0.134792 Loss2: 1.369194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460198 Loss1: 0.103858 Loss2: 1.356339
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.525611 Loss1: 0.700592 Loss2: 1.825019
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.802310 Loss1: 0.446144 Loss2: 1.356166 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433528 Loss1: 0.081866 Loss2: 1.351662
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726073 Loss1: 0.323794 Loss2: 1.402278
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636482 Loss1: 0.265864 Loss2: 1.370619
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.608458 Loss1: 0.240257 Loss2: 1.368201
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504615 Loss1: 0.142016 Loss2: 1.362599
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478089 Loss1: 0.130689 Loss2: 1.347400
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.434957 Loss1: 0.642450 Loss2: 1.792508
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.476744 Loss1: 0.125238 Loss2: 1.351506
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473585 Loss1: 0.125590 Loss2: 1.347996
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.746988 Loss1: 0.398044 Loss2: 1.348944
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452551 Loss1: 0.106886 Loss2: 1.345664
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.613052 Loss1: 0.235392 Loss2: 1.377660
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527439 Loss1: 0.196802 Loss2: 1.330636
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.502939 Loss1: 0.159823 Loss2: 1.343117
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.501489 Loss1: 0.173506 Loss2: 1.327983
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467809 Loss1: 0.145793 Loss2: 1.322016
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.390704 Loss1: 0.566913 Loss2: 1.823790
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.788441 Loss1: 0.420150 Loss2: 1.368290 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.637949 Loss1: 0.235267 Loss2: 1.402682 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581910 Loss1: 0.227514 Loss2: 1.354396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470859 Loss1: 0.116482 Loss2: 1.354377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400241 Loss1: 0.061574 Loss2: 1.338667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425737 Loss1: 0.084118 Loss2: 1.341619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.681457 Loss1: 0.257939 Loss2: 1.423519 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975586
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555098 Loss1: 0.166674 Loss2: 1.388424 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459142 Loss1: 0.088815 Loss2: 1.370327 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448081 Loss1: 0.076891 Loss2: 1.371189
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.395191 Loss1: 0.596168 Loss2: 1.799024
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435689 Loss1: 0.064722 Loss2: 1.370967
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.747535 Loss1: 0.386744 Loss2: 1.360791
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430323 Loss1: 0.067559 Loss2: 1.362763
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.690707 Loss1: 0.286452 Loss2: 1.404256
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.582186 Loss1: 0.226401 Loss2: 1.355785
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.540087 Loss1: 0.180981 Loss2: 1.359106
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519669 Loss1: 0.161298 Loss2: 1.358370
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486453 Loss1: 0.130976 Loss2: 1.355476
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.399231 Loss1: 0.548805 Loss2: 1.850426
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.718156 Loss1: 0.368029 Loss2: 1.350127 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.664093 Loss1: 0.266918 Loss2: 1.397175 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374979 Loss1: 0.046157 Loss2: 1.328821
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547401 Loss1: 0.189426 Loss2: 1.357975
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.512857 Loss1: 0.163934 Loss2: 1.348923
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475994 Loss1: 0.120493 Loss2: 1.355501
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464798 Loss1: 0.119530 Loss2: 1.345269
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429923 Loss1: 0.083532 Loss2: 1.346390
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.381397 Loss1: 0.528582 Loss2: 1.852816
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408499 Loss1: 0.068903 Loss2: 1.339595
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.771271 Loss1: 0.378380 Loss2: 1.392892
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414686 Loss1: 0.078139 Loss2: 1.336547
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.585723 Loss1: 0.201662 Loss2: 1.384062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.545747 Loss1: 0.155316 Loss2: 1.390430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.561334 Loss1: 0.174638 Loss2: 1.386697
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.550110 Loss1: 0.676292 Loss2: 1.873819
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.727577 Loss1: 0.369778 Loss2: 1.357800
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485187 Loss1: 0.107754 Loss2: 1.377433
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.569762 Loss1: 0.192104 Loss2: 1.377658
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.437997 Loss1: 0.067298 Loss2: 1.370699
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.507676 Loss1: 0.154267 Loss2: 1.353409
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412271 Loss1: 0.053533 Loss2: 1.358737
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.568443 Loss1: 0.209863 Loss2: 1.358580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.431841 Loss1: 0.092494 Loss2: 1.339347 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412613 Loss1: 0.079196 Loss2: 1.333416
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.434871 Loss1: 0.621201 Loss2: 1.813670
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.803046 Loss1: 0.420143 Loss2: 1.382903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.587495 Loss1: 0.210340 Loss2: 1.377155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513281 Loss1: 0.147179 Loss2: 1.366101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500916 Loss1: 0.129427 Loss2: 1.371488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484680 Loss1: 0.118730 Loss2: 1.365950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.492156 Loss1: 0.127851 Loss2: 1.364305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416668 Loss1: 0.057541 Loss2: 1.359128 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523584 Loss1: 0.153291 Loss2: 1.370293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443064 Loss1: 0.087580 Loss2: 1.355484 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.521542 Loss1: 0.691366 Loss2: 1.830175
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460806 Loss1: 0.107495 Loss2: 1.353311
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.827271 Loss1: 0.466657 Loss2: 1.360614
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430937 Loss1: 0.078719 Loss2: 1.352218
+(DefaultActor pid=3765) >> Training accuracy: 0.987132
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554158 Loss1: 0.192179 Loss2: 1.361978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427902 Loss1: 0.086817 Loss2: 1.341085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428867 Loss1: 0.096196 Loss2: 1.332671
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.562532 Loss1: 0.666217 Loss2: 1.896315
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.861249 Loss1: 0.459860 Loss2: 1.401390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.672473 Loss1: 0.245429 Loss2: 1.427044 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.353134 Loss1: 0.037226 Loss2: 1.315907
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554378 Loss1: 0.167642 Loss2: 1.386736
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515197 Loss1: 0.127031 Loss2: 1.388166
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485177 Loss1: 0.112148 Loss2: 1.373029
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464890 Loss1: 0.094707 Loss2: 1.370183
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462878 Loss1: 0.086943 Loss2: 1.375935
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.489221 Loss1: 0.674302 Loss2: 1.814919
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.483667 Loss1: 0.110769 Loss2: 1.372898
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.855215 Loss1: 0.489477 Loss2: 1.365738
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420819 Loss1: 0.052966 Loss2: 1.367852
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.598531 Loss1: 0.247175 Loss2: 1.351356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481544 Loss1: 0.134012 Loss2: 1.347533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431872 Loss1: 0.091639 Loss2: 1.340232
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.466264 Loss1: 0.655468 Loss2: 1.810796
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.737839 Loss1: 0.377984 Loss2: 1.359855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.668206 Loss1: 0.268232 Loss2: 1.399974 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583500 Loss1: 0.216542 Loss2: 1.366958 [repeated 2x across cluster]
+DEBUG flwr 2023-10-11 23:47:38,409 | server.py:236 | fit_round 131 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438124 Loss1: 0.085785 Loss2: 1.352339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423021 Loss1: 0.083676 Loss2: 1.339345 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402561 Loss1: 0.070618 Loss2: 1.331943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.672240 Loss1: 0.255190 Loss2: 1.417050 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518979 Loss1: 0.147128 Loss2: 1.371851 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455180 Loss1: 0.104915 Loss2: 1.350265 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.489630 Loss1: 0.639223 Loss2: 1.850407 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.800525 Loss1: 0.436594 Loss2: 1.363932 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.477148 Loss1: 0.137941 Loss2: 1.339207 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.422774 Loss1: 0.094135 Loss2: 1.328639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.556248 Loss1: 0.709541 Loss2: 1.846708 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.813435 Loss1: 0.436887 Loss2: 1.376548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.693746 Loss1: 0.264896 Loss2: 1.428850 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514975 Loss1: 0.142493 Loss2: 1.372482 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430389 Loss1: 0.077021 Loss2: 1.353368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412389 Loss1: 0.063516 Loss2: 1.348874
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.508610 Loss1: 0.665301 Loss2: 1.843309
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.755356 Loss1: 0.387861 Loss2: 1.367495 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656179 Loss1: 0.265088 Loss2: 1.391091 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515240 Loss1: 0.164742 Loss2: 1.350497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450774 Loss1: 0.113346 Loss2: 1.337427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419913 Loss1: 0.093523 Loss2: 1.326390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398615 Loss1: 0.076163 Loss2: 1.322452 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406401 Loss1: 0.085038 Loss2: 1.321363 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548908 Loss1: 0.181138 Loss2: 1.367770 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488081 Loss1: 0.115162 Loss2: 1.372919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480838 Loss1: 0.128306 Loss2: 1.352532 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-11 23:47:38,409][flwr][DEBUG] - fit_round 131 received 50 results and 0 failures
+INFO flwr 2023-10-11 23:48:19,830 | server.py:125 | fit progress: (131, 2.212314099168625, {'accuracy': 0.5878}, 302207.60819046496)
+>> Test accuracy: 0.587800
+[2023-10-11 23:48:19,830][flwr][INFO] - fit progress: (131, 2.212314099168625, {'accuracy': 0.5878}, 302207.60819046496)
+DEBUG flwr 2023-10-11 23:48:19,830 | server.py:173 | evaluate_round 131: strategy sampled 50 clients (out of 50)
+[2023-10-11 23:48:19,830][flwr][DEBUG] - evaluate_round 131: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-11 23:57:23,062 | server.py:187 | evaluate_round 131 received 50 results and 0 failures
+[2023-10-11 23:57:23,062][flwr][DEBUG] - evaluate_round 131 received 50 results and 0 failures
+DEBUG flwr 2023-10-11 23:57:23,063 | server.py:222 | fit_round 132: strategy sampled 50 clients (out of 50)
+[2023-10-11 23:57:23,063][flwr][DEBUG] - fit_round 132: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.573851 Loss1: 0.692672 Loss2: 1.881179 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690794 Loss1: 0.269910 Loss2: 1.420884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587510 Loss1: 0.212556 Loss2: 1.374955
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.562005 Loss1: 0.660835 Loss2: 1.901170
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.528557 Loss1: 0.149665 Loss2: 1.378892
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.811514 Loss1: 0.378983 Loss2: 1.432531
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513099 Loss1: 0.140849 Loss2: 1.372250
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701455 Loss1: 0.228951 Loss2: 1.472504
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447403 Loss1: 0.080379 Loss2: 1.367024
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.608665 Loss1: 0.192196 Loss2: 1.416469
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429088 Loss1: 0.068547 Loss2: 1.360541
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.595399 Loss1: 0.164591 Loss2: 1.430807
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445888 Loss1: 0.090631 Loss2: 1.355258
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524717 Loss1: 0.111541 Loss2: 1.413176
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427225 Loss1: 0.068755 Loss2: 1.358470
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.535854 Loss1: 0.129087 Loss2: 1.406767
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.522576 Loss1: 0.109054 Loss2: 1.413523
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497852 Loss1: 0.092395 Loss2: 1.405456
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.490743 Loss1: 0.096791 Loss2: 1.393952
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.454506 Loss1: 0.603796 Loss2: 1.850710
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.781761 Loss1: 0.424862 Loss2: 1.356899
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.732683 Loss1: 0.310726 Loss2: 1.421956
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557654 Loss1: 0.211505 Loss2: 1.346149
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.330679 Loss1: 0.532985 Loss2: 1.797694
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.494990 Loss1: 0.146456 Loss2: 1.348534
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.692945 Loss1: 0.332517 Loss2: 1.360428
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592182 Loss1: 0.207465 Loss2: 1.384717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.506265 Loss1: 0.160344 Loss2: 1.345921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549059 Loss1: 0.194026 Loss2: 1.355033 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530686 Loss1: 0.180343 Loss2: 1.350343 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.528817 Loss1: 0.175380 Loss2: 1.353437 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444482 Loss1: 0.098302 Loss2: 1.346181 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.680507 Loss1: 0.775732 Loss2: 1.904776 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.682669 Loss1: 0.271377 Loss2: 1.411292 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.509185 Loss1: 0.135014 Loss2: 1.374171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472674 Loss1: 0.108357 Loss2: 1.364317 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460459 Loss1: 0.106413 Loss2: 1.354046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419348 Loss1: 0.066789 Loss2: 1.352559 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397595 Loss1: 0.054726 Loss2: 1.342869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370382 Loss1: 0.030434 Loss2: 1.339948 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445563 Loss1: 0.113393 Loss2: 1.332170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369705 Loss1: 0.046623 Loss2: 1.323082 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377749 Loss1: 0.061557 Loss2: 1.316192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.741776 Loss1: 0.817254 Loss2: 1.924522
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.892703 Loss1: 0.513849 Loss2: 1.378853
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.747019 Loss1: 0.338705 Loss2: 1.408314
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649169 Loss1: 0.255708 Loss2: 1.393460
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589741 Loss1: 0.204094 Loss2: 1.385647
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493869 Loss1: 0.122416 Loss2: 1.371454
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.462598 Loss1: 0.635286 Loss2: 1.827312
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.673015 Loss1: 0.341525 Loss2: 1.331490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608690 Loss1: 0.245668 Loss2: 1.363022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526038 Loss1: 0.194742 Loss2: 1.331296 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997596
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477167 Loss1: 0.146809 Loss2: 1.330358 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390972 Loss1: 0.081801 Loss2: 1.309171 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.360109 Loss1: 0.056481 Loss2: 1.303628
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.539800 Loss1: 0.631380 Loss2: 1.908420
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.325888 Loss1: 0.033695 Loss2: 1.292193
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.824097 Loss1: 0.423557 Loss2: 1.400540
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.753007 Loss1: 0.300974 Loss2: 1.452033
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602317 Loss1: 0.213515 Loss2: 1.388802
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513293 Loss1: 0.123125 Loss2: 1.390169
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478125 Loss1: 0.094506 Loss2: 1.383619
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.532382 Loss1: 0.676638 Loss2: 1.855745
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450124 Loss1: 0.080891 Loss2: 1.369234
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.771085 Loss1: 0.368209 Loss2: 1.402876
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415792 Loss1: 0.055216 Loss2: 1.360575
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.648384 Loss1: 0.235813 Loss2: 1.412570
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409201 Loss1: 0.054508 Loss2: 1.354693
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397444 Loss1: 0.044206 Loss2: 1.353238
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.590029 Loss1: 0.207481 Loss2: 1.382547
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.551086 Loss1: 0.159039 Loss2: 1.392047
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.541140 Loss1: 0.158213 Loss2: 1.382927
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483400 Loss1: 0.106303 Loss2: 1.377097
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.479851 Loss1: 0.105883 Loss2: 1.373967
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.836623 Loss1: 0.798403 Loss2: 2.038220
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463186 Loss1: 0.088337 Loss2: 1.374849
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437870 Loss1: 0.071922 Loss2: 1.365948 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.582972 Loss1: 0.164256 Loss2: 1.418716 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.541387 Loss1: 0.130773 Loss2: 1.410614 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472517 Loss1: 0.075513 Loss2: 1.397004 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440967 Loss1: 0.057036 Loss2: 1.383931 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.616085 Loss1: 0.248357 Loss2: 1.367728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545071 Loss1: 0.186587 Loss2: 1.358484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.799134 Loss1: 0.879127 Loss2: 1.920007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.828935 Loss1: 0.448545 Loss2: 1.380390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741497 Loss1: 0.331511 Loss2: 1.409986 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569617 Loss1: 0.193211 Loss2: 1.376407 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370370 Loss1: 0.046315 Loss2: 1.324055
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.545768 Loss1: 0.179252 Loss2: 1.366515
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.521141 Loss1: 0.145868 Loss2: 1.375274
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467934 Loss1: 0.102947 Loss2: 1.364987
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.455675 Loss1: 0.100022 Loss2: 1.355652
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453304 Loss1: 0.099134 Loss2: 1.354170
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427488 Loss1: 0.073429 Loss2: 1.354059
+(DefaultActor pid=3765) >> Training accuracy: 0.983259
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.675024 Loss1: 0.747759 Loss2: 1.927266
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.785889 Loss1: 0.377312 Loss2: 1.408577
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.706619 Loss1: 0.269367 Loss2: 1.437252
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.668264 Loss1: 0.253871 Loss2: 1.414393
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.580302 Loss1: 0.169704 Loss2: 1.410598
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.522106 Loss1: 0.710119 Loss2: 1.811988
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.526397 Loss1: 0.127378 Loss2: 1.399019
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.711244 Loss1: 0.366628 Loss2: 1.344617
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487592 Loss1: 0.098461 Loss2: 1.389130
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.637912 Loss1: 0.268148 Loss2: 1.369764
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486649 Loss1: 0.095053 Loss2: 1.391596
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.553491 Loss1: 0.213434 Loss2: 1.340057
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457588 Loss1: 0.075327 Loss2: 1.382261
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527030 Loss1: 0.183075 Loss2: 1.343955
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441638 Loss1: 0.064409 Loss2: 1.377229
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459910 Loss1: 0.131807 Loss2: 1.328103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.392281 Loss1: 0.076940 Loss2: 1.315341 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364772 Loss1: 0.052354 Loss2: 1.312418
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.698767 Loss1: 0.726529 Loss2: 1.972238
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.970354 Loss1: 0.486820 Loss2: 1.483534
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.838415 Loss1: 0.317073 Loss2: 1.521342
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.738804 Loss1: 0.265117 Loss2: 1.473687
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.692390 Loss1: 0.220616 Loss2: 1.471775
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.564193 Loss1: 0.704082 Loss2: 1.860110
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.583096 Loss1: 0.117904 Loss2: 1.465192
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.575106 Loss1: 0.117799 Loss2: 1.457307 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.560322 Loss1: 0.110700 Loss2: 1.449622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.525807 Loss1: 0.076263 Loss2: 1.449543 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.514842 Loss1: 0.078685 Loss2: 1.436157 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.954167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476045 Loss1: 0.103050 Loss2: 1.372994 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433663 Loss1: 0.074348 Loss2: 1.359315 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414186 Loss1: 0.060025 Loss2: 1.354161
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.654144 Loss1: 0.788410 Loss2: 1.865734
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.836048 Loss1: 0.459472 Loss2: 1.376576
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.717969 Loss1: 0.299057 Loss2: 1.418912
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.585679 Loss1: 0.212988 Loss2: 1.372691
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.562643 Loss1: 0.189430 Loss2: 1.373213
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.357620 Loss1: 0.572018 Loss2: 1.785603
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495554 Loss1: 0.132400 Loss2: 1.363154
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488877 Loss1: 0.127382 Loss2: 1.361495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.630610 Loss1: 0.255051 Loss2: 1.375558
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469334 Loss1: 0.112171 Loss2: 1.357163
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529226 Loss1: 0.188141 Loss2: 1.341085
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486236 Loss1: 0.126048 Loss2: 1.360188
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487123 Loss1: 0.138491 Loss2: 1.348632
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452118 Loss1: 0.096652 Loss2: 1.355466
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410631 Loss1: 0.075808 Loss2: 1.334823 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391934 Loss1: 0.066621 Loss2: 1.325312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378417 Loss1: 0.059397 Loss2: 1.319021
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.549217 Loss1: 0.701074 Loss2: 1.848143
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.821085 Loss1: 0.467292 Loss2: 1.353793
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.702012 Loss1: 0.302780 Loss2: 1.399232
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567301 Loss1: 0.203957 Loss2: 1.363344
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550536 Loss1: 0.187036 Loss2: 1.363500
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484819 Loss1: 0.128954 Loss2: 1.355865
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.495644 Loss1: 0.704424 Loss2: 1.791220
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463714 Loss1: 0.115288 Loss2: 1.348425
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.762262 Loss1: 0.410203 Loss2: 1.352059
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429694 Loss1: 0.088288 Loss2: 1.341406
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.679837 Loss1: 0.275680 Loss2: 1.404157
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454717 Loss1: 0.116849 Loss2: 1.337868
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.567810 Loss1: 0.220083 Loss2: 1.347727
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439601 Loss1: 0.097856 Loss2: 1.341745
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.539491 Loss1: 0.189587 Loss2: 1.349904
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475573 Loss1: 0.132432 Loss2: 1.343141
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461999 Loss1: 0.135553 Loss2: 1.326446
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418135 Loss1: 0.095490 Loss2: 1.322646
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.387949 Loss1: 0.064005 Loss2: 1.323944
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.632380 Loss1: 0.636354 Loss2: 1.996026
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363718 Loss1: 0.052987 Loss2: 1.310731
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.919414 Loss1: 0.404341 Loss2: 1.515073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.627462 Loss1: 0.162013 Loss2: 1.465449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.579565 Loss1: 0.136562 Loss2: 1.443003
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.421045 Loss1: 0.582557 Loss2: 1.838489
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.767843 Loss1: 0.374063 Loss2: 1.393780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.728639 Loss1: 0.293456 Loss2: 1.435183 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642274 Loss1: 0.254429 Loss2: 1.387845 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.622032 Loss1: 0.213302 Loss2: 1.408730 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479861 Loss1: 0.107940 Loss2: 1.371921 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444249 Loss1: 0.085842 Loss2: 1.358407 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448421 Loss1: 0.087509 Loss2: 1.360912 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.623819 Loss1: 0.237335 Loss2: 1.386484 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519787 Loss1: 0.132584 Loss2: 1.387203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.603607 Loss1: 0.744459 Loss2: 1.859147
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446615 Loss1: 0.077175 Loss2: 1.369440
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.871362 Loss1: 0.495528 Loss2: 1.375835
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429141 Loss1: 0.062002 Loss2: 1.367139
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.745468 Loss1: 0.325636 Loss2: 1.419831
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410201 Loss1: 0.048980 Loss2: 1.361222
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.700098 Loss1: 0.318941 Loss2: 1.381157
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393550 Loss1: 0.038271 Loss2: 1.355279
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.539100 Loss1: 0.171262 Loss2: 1.367838 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439177 Loss1: 0.088680 Loss2: 1.350496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.376592 Loss1: 0.033007 Loss2: 1.343586
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.584692 Loss1: 0.773101 Loss2: 1.811590
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.362248 Loss1: 0.030889 Loss2: 1.331359
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.744123 Loss1: 0.398480 Loss2: 1.345644
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.627317 Loss1: 0.268309 Loss2: 1.359008
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528609 Loss1: 0.188287 Loss2: 1.340322
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.470337 Loss1: 0.131052 Loss2: 1.339286
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431270 Loss1: 0.101715 Loss2: 1.329555
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.424743 Loss1: 0.524044 Loss2: 1.900698
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.421751 Loss1: 0.097044 Loss2: 1.324708
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.815190 Loss1: 0.431034 Loss2: 1.384156
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425132 Loss1: 0.109140 Loss2: 1.315993
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.771703 Loss1: 0.311658 Loss2: 1.460045
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402320 Loss1: 0.081543 Loss2: 1.320777
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.634374 Loss1: 0.233725 Loss2: 1.400648
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386900 Loss1: 0.068700 Loss2: 1.318200
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492294 Loss1: 0.104224 Loss2: 1.388069 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.476542 Loss1: 0.097889 Loss2: 1.378653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440107 Loss1: 0.068740 Loss2: 1.371367
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.630629 Loss1: 0.766467 Loss2: 1.864162
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427531 Loss1: 0.059242 Loss2: 1.368289
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.870277 Loss1: 0.478108 Loss2: 1.392170
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.688987 Loss1: 0.247814 Loss2: 1.441173
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579672 Loss1: 0.201041 Loss2: 1.378630
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.595404 Loss1: 0.208019 Loss2: 1.387385
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.564586 Loss1: 0.190498 Loss2: 1.374088
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.578751 Loss1: 0.675163 Loss2: 1.903588
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550156 Loss1: 0.173570 Loss2: 1.376586
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.867336 Loss1: 0.455876 Loss2: 1.411460
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483635 Loss1: 0.109505 Loss2: 1.374130
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.760077 Loss1: 0.292948 Loss2: 1.467129
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453944 Loss1: 0.093710 Loss2: 1.360233
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654401 Loss1: 0.245854 Loss2: 1.408546
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444227 Loss1: 0.087204 Loss2: 1.357023
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.559005 Loss1: 0.151079 Loss2: 1.407926 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477337 Loss1: 0.088648 Loss2: 1.388689 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.475253 Loss1: 0.095814 Loss2: 1.379439
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.495767 Loss1: 0.690128 Loss2: 1.805639
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426846 Loss1: 0.047959 Loss2: 1.378888
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.737838 Loss1: 0.412873 Loss2: 1.324965
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642813 Loss1: 0.268055 Loss2: 1.374758
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537661 Loss1: 0.209691 Loss2: 1.327969
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.463619 Loss1: 0.117586 Loss2: 1.346033
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485807 Loss1: 0.160176 Loss2: 1.325630
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.403740 Loss1: 0.088639 Loss2: 1.315101
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.443096 Loss1: 0.622087 Loss2: 1.821008
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.403077 Loss1: 0.088132 Loss2: 1.314945
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.707595 Loss1: 0.363026 Loss2: 1.344569
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395783 Loss1: 0.081166 Loss2: 1.314617
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.593001 Loss1: 0.222953 Loss2: 1.370048
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405813 Loss1: 0.095161 Loss2: 1.310651
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520837 Loss1: 0.174226 Loss2: 1.346611
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.481312 Loss1: 0.142023 Loss2: 1.339290
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.490375 Loss1: 0.145708 Loss2: 1.344667
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476151 Loss1: 0.135985 Loss2: 1.340166
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429291 Loss1: 0.098003 Loss2: 1.331287
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.551214 Loss1: 0.621503 Loss2: 1.929711
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405739 Loss1: 0.082006 Loss2: 1.323733
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.804704 Loss1: 0.378776 Loss2: 1.425928
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412359 Loss1: 0.088770 Loss2: 1.323589
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619311 Loss1: 0.180777 Loss2: 1.438534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560281 Loss1: 0.125720 Loss2: 1.434561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.570020 Loss1: 0.147334 Loss2: 1.422686
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.587279 Loss1: 0.715036 Loss2: 1.872243
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.760036 Loss1: 0.342947 Loss2: 1.417089 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.671257 Loss1: 0.245044 Loss2: 1.426214 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.597587 Loss1: 0.188242 Loss2: 1.409345 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.507286 Loss1: 0.110770 Loss2: 1.396517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425854 Loss1: 0.037969 Loss2: 1.387886 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409358 Loss1: 0.033967 Loss2: 1.375392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397609 Loss1: 0.026719 Loss2: 1.370890 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553925 Loss1: 0.191200 Loss2: 1.362725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.436835 Loss1: 0.080959 Loss2: 1.355876 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.410205 Loss1: 0.060920 Loss2: 1.349285
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.517969 Loss1: 0.659209 Loss2: 1.858760
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.836557 Loss1: 0.457224 Loss2: 1.379333 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.770715 Loss1: 0.317356 Loss2: 1.453358 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381885 Loss1: 0.046382 Loss2: 1.335502
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.622271 Loss1: 0.240423 Loss2: 1.381848
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574638 Loss1: 0.181350 Loss2: 1.393288
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513897 Loss1: 0.148339 Loss2: 1.365558
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463796 Loss1: 0.099087 Loss2: 1.364709
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469617 Loss1: 0.106168 Loss2: 1.363449
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.694711 Loss1: 0.812615 Loss2: 1.882096
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419001 Loss1: 0.066299 Loss2: 1.352701
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402703 Loss1: 0.059378 Loss2: 1.343324 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523176 Loss1: 0.177570 Loss2: 1.345605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471710 Loss1: 0.121182 Loss2: 1.350528 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.435160 Loss1: 0.575133 Loss2: 1.860027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.834228 Loss1: 0.472570 Loss2: 1.361658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398428 Loss1: 0.073777 Loss2: 1.324650 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561690 Loss1: 0.187434 Loss2: 1.374256 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486165 Loss1: 0.138454 Loss2: 1.347711 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446733 Loss1: 0.100233 Loss2: 1.346500
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.488385 Loss1: 0.655331 Loss2: 1.833054
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396779 Loss1: 0.060041 Loss2: 1.336738
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.734740 Loss1: 0.351632 Loss2: 1.383107
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420294 Loss1: 0.086210 Loss2: 1.334084
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629399 Loss1: 0.229681 Loss2: 1.399718
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554390 Loss1: 0.189915 Loss2: 1.364475
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513716 Loss1: 0.151248 Loss2: 1.362469
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477285 Loss1: 0.126830 Loss2: 1.350455
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439549 Loss1: 0.088318 Loss2: 1.351232
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.625868 Loss1: 0.759328 Loss2: 1.866539
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.781500 Loss1: 0.409375 Loss2: 1.372125 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624117 Loss1: 0.219507 Loss2: 1.404609 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382234 Loss1: 0.046936 Loss2: 1.335298
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541217 Loss1: 0.179509 Loss2: 1.361708
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461302 Loss1: 0.110195 Loss2: 1.351107
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466032 Loss1: 0.118482 Loss2: 1.347549
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449413 Loss1: 0.103639 Loss2: 1.345774
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424200 Loss1: 0.085988 Loss2: 1.338212
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.512834 Loss1: 0.620859 Loss2: 1.891975
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.376437 Loss1: 0.044132 Loss2: 1.332305
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.760748 Loss1: 0.347704 Loss2: 1.413045
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360095 Loss1: 0.033241 Loss2: 1.326854
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.637610 Loss1: 0.220654 Loss2: 1.416956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.614686 Loss1: 0.184413 Loss2: 1.430273 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.641795 Loss1: 0.682804 Loss2: 1.958990
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.583025 Loss1: 0.170746 Loss2: 1.412280
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.822255 Loss1: 0.369572 Loss2: 1.452683
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.546021 Loss1: 0.138832 Loss2: 1.407189
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.769731 Loss1: 0.285011 Loss2: 1.484719
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493794 Loss1: 0.100367 Loss2: 1.393427
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.497361 Loss1: 0.106408 Loss2: 1.390953 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.559422 Loss1: 0.126687 Loss2: 1.432735 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.587950 Loss1: 0.155967 Loss2: 1.431983 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.543427 Loss1: 0.109005 Loss2: 1.434422
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.271235 Loss1: 0.482001 Loss2: 1.789234
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.714964 Loss1: 0.365036 Loss2: 1.349928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.514357 Loss1: 0.170535 Loss2: 1.343822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.695626 Loss1: 0.809045 Loss2: 1.886582 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.773305 Loss1: 0.450726 Loss2: 1.322579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.715437 Loss1: 0.320825 Loss2: 1.394612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381872 Loss1: 0.053957 Loss2: 1.327916
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594457 Loss1: 0.273288 Loss2: 1.321169
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534933 Loss1: 0.212254 Loss2: 1.322679
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363843 Loss1: 0.041751 Loss2: 1.322092
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362571 Loss1: 0.043194 Loss2: 1.319377 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385007 Loss1: 0.086372 Loss2: 1.298635 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.600286 Loss1: 0.710243 Loss2: 1.890044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.660765 Loss1: 0.240755 Loss2: 1.420010 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.566957 Loss1: 0.207835 Loss2: 1.359123
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.618613 Loss1: 0.758524 Loss2: 1.860089
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.812197 Loss1: 0.412321 Loss2: 1.399876 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.705683 Loss1: 0.272526 Loss2: 1.433156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.595950 Loss1: 0.213095 Loss2: 1.382855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.570432 Loss1: 0.180630 Loss2: 1.389802 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 00:25:47,411 | server.py:236 | fit_round 132 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.522388 Loss1: 0.139363 Loss2: 1.383025 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402075 Loss1: 0.060814 Loss2: 1.341261
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490165 Loss1: 0.114047 Loss2: 1.376118
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495734 Loss1: 0.126578 Loss2: 1.369156
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499087 Loss1: 0.124293 Loss2: 1.374794
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434783 Loss1: 0.070479 Loss2: 1.364305
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.485087 Loss1: 0.665422 Loss2: 1.819665
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.791004 Loss1: 0.437547 Loss2: 1.353457
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.752932 Loss1: 0.349862 Loss2: 1.403070
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595732 Loss1: 0.239361 Loss2: 1.356371
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.417858 Loss1: 0.583253 Loss2: 1.834606
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.781364 Loss1: 0.404632 Loss2: 1.376732 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.665631 Loss1: 0.245418 Loss2: 1.420213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541471 Loss1: 0.166545 Loss2: 1.374926 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517281 Loss1: 0.141001 Loss2: 1.376279 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.500934 Loss1: 0.135398 Loss2: 1.365536 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534340 Loss1: 0.164361 Loss2: 1.369978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499750 Loss1: 0.137094 Loss2: 1.362656 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.437673 Loss1: 0.687596 Loss2: 1.750077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.544203 Loss1: 0.214112 Loss2: 1.330091 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.497162 Loss1: 0.633703 Loss2: 1.863459 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.790085 Loss1: 0.432186 Loss2: 1.357899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648631 Loss1: 0.249646 Loss2: 1.398985 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561457 Loss1: 0.200460 Loss2: 1.360997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519914 Loss1: 0.154289 Loss2: 1.365625 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.495320 Loss1: 0.123492 Loss2: 1.371828 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424520 Loss1: 0.078587 Loss2: 1.345932 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404775 Loss1: 0.064968 Loss2: 1.339806 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.892778 Loss1: 0.443506 Loss2: 1.449273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.678226 Loss1: 0.226903 Loss2: 1.451322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.591210 Loss1: 0.150873 Loss2: 1.440337 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515550 Loss1: 0.086090 Loss2: 1.429461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.525129 Loss1: 0.101966 Loss2: 1.423163 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 00:25:47,411][flwr][DEBUG] - fit_round 132 received 50 results and 0 failures
+INFO flwr 2023-10-12 00:26:29,342 | server.py:125 | fit progress: (132, 2.2114045372405373, {'accuracy': 0.5901}, 304497.120365695)
+>> Test accuracy: 0.590100
+[2023-10-12 00:26:29,342][flwr][INFO] - fit progress: (132, 2.2114045372405373, {'accuracy': 0.5901}, 304497.120365695)
+DEBUG flwr 2023-10-12 00:26:29,342 | server.py:173 | evaluate_round 132: strategy sampled 50 clients (out of 50)
+[2023-10-12 00:26:29,342][flwr][DEBUG] - evaluate_round 132: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 00:35:31,724 | server.py:187 | evaluate_round 132 received 50 results and 0 failures
+[2023-10-12 00:35:31,724][flwr][DEBUG] - evaluate_round 132 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 00:35:31,724 | server.py:222 | fit_round 133: strategy sampled 50 clients (out of 50)
+[2023-10-12 00:35:31,724][flwr][DEBUG] - fit_round 133: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.501698 Loss1: 0.663436 Loss2: 1.838262
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.812044 Loss1: 0.440654 Loss2: 1.371390
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667290 Loss1: 0.266884 Loss2: 1.400407
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.550483 Loss1: 0.197517 Loss2: 1.352966
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.501815 Loss1: 0.657143 Loss2: 1.844672
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.678148 Loss1: 0.328239 Loss2: 1.349908 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.549544 Loss1: 0.178998 Loss2: 1.370547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.508907 Loss1: 0.161266 Loss2: 1.347641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491329 Loss1: 0.153290 Loss2: 1.338039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.415112 Loss1: 0.082013 Loss2: 1.333099 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365198 Loss1: 0.041714 Loss2: 1.323483
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.393249 Loss1: 0.067916 Loss2: 1.325332
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376266 Loss1: 0.055387 Loss2: 1.320879
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374142 Loss1: 0.059965 Loss2: 1.314176
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367266 Loss1: 0.055391 Loss2: 1.311876
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.736110 Loss1: 0.737989 Loss2: 1.998121
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.944799 Loss1: 0.545102 Loss2: 1.399697
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.757923 Loss1: 0.282216 Loss2: 1.475707
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556055 Loss1: 0.164930 Loss2: 1.391125
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516050 Loss1: 0.136318 Loss2: 1.379732
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450749 Loss1: 0.071821 Loss2: 1.378927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440748 Loss1: 0.068513 Loss2: 1.372235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437002 Loss1: 0.071836 Loss2: 1.365166 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.420731 Loss1: 0.055153 Loss2: 1.365578 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395216 Loss1: 0.041709 Loss2: 1.353508 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497524 Loss1: 0.115768 Loss2: 1.381757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454058 Loss1: 0.082594 Loss2: 1.371464 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418493 Loss1: 0.058469 Loss2: 1.360024
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.494995 Loss1: 0.648693 Loss2: 1.846302
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.744181 Loss1: 0.400516 Loss2: 1.343665
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647194 Loss1: 0.282101 Loss2: 1.365094
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561885 Loss1: 0.226819 Loss2: 1.335066
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.501909 Loss1: 0.169000 Loss2: 1.332909
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.555014 Loss1: 0.639120 Loss2: 1.915894
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.483287 Loss1: 0.161767 Loss2: 1.321521
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.780181 Loss1: 0.362763 Loss2: 1.417418
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422152 Loss1: 0.103995 Loss2: 1.318157
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.753923 Loss1: 0.279385 Loss2: 1.474538
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.359641 Loss1: 0.050076 Loss2: 1.309566
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599359 Loss1: 0.182020 Loss2: 1.417339
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.339130 Loss1: 0.038090 Loss2: 1.301040
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.532379 Loss1: 0.128138 Loss2: 1.404240
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.329652 Loss1: 0.037633 Loss2: 1.292019
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.550438 Loss1: 0.136062 Loss2: 1.414375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485913 Loss1: 0.087421 Loss2: 1.398491 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487888 Loss1: 0.092546 Loss2: 1.395342
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.571861 Loss1: 0.711223 Loss2: 1.860637
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.797046 Loss1: 0.411067 Loss2: 1.385979
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.643780 Loss1: 0.227996 Loss2: 1.415784
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533010 Loss1: 0.151253 Loss2: 1.381758
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551118 Loss1: 0.163173 Loss2: 1.387945
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.499406 Loss1: 0.619722 Loss2: 1.879683
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448079 Loss1: 0.075497 Loss2: 1.372581
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.861533 Loss1: 0.460855 Loss2: 1.400678
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441800 Loss1: 0.077185 Loss2: 1.364616
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.776337 Loss1: 0.324890 Loss2: 1.451447
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467678 Loss1: 0.107006 Loss2: 1.360672
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.715316 Loss1: 0.291403 Loss2: 1.423913
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435212 Loss1: 0.077259 Loss2: 1.357953
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.647727 Loss1: 0.212992 Loss2: 1.434735
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438495 Loss1: 0.084166 Loss2: 1.354329
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.535812 Loss1: 0.134752 Loss2: 1.401060 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441796 Loss1: 0.054874 Loss2: 1.386922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434821 Loss1: 0.058481 Loss2: 1.376341
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.437939 Loss1: 0.602746 Loss2: 1.835193
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.912506 Loss1: 0.499787 Loss2: 1.412719
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.742343 Loss1: 0.302298 Loss2: 1.440044
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.696064 Loss1: 0.291597 Loss2: 1.404467
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.641701 Loss1: 0.249255 Loss2: 1.392446
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.759124 Loss1: 0.810670 Loss2: 1.948454
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.540283 Loss1: 0.160002 Loss2: 1.380282
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.845031 Loss1: 0.494877 Loss2: 1.350154
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.731721 Loss1: 0.333843 Loss2: 1.397878
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547584 Loss1: 0.163461 Loss2: 1.384123
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.509548 Loss1: 0.135054 Loss2: 1.374495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448080 Loss1: 0.082080 Loss2: 1.366000 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412021 Loss1: 0.072845 Loss2: 1.339176 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369990 Loss1: 0.039661 Loss2: 1.330329 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.464367 Loss1: 0.701322 Loss2: 1.763044 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.583777 Loss1: 0.254232 Loss2: 1.329545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.460976 Loss1: 0.161067 Loss2: 1.299910
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.394435 Loss1: 0.546042 Loss2: 1.848392
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.717004 Loss1: 0.314738 Loss2: 1.402266 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633047 Loss1: 0.207203 Loss2: 1.425843 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.601826 Loss1: 0.212928 Loss2: 1.388898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520932 Loss1: 0.127168 Loss2: 1.393764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.497584 Loss1: 0.115628 Loss2: 1.381956 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451923 Loss1: 0.074975 Loss2: 1.376948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421288 Loss1: 0.059485 Loss2: 1.361803 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.958984
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.494216 Loss1: 0.682822 Loss2: 1.811394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648054 Loss1: 0.225615 Loss2: 1.422439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524303 Loss1: 0.153806 Loss2: 1.370497
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.560204 Loss1: 0.700604 Loss2: 1.859600
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.758173 Loss1: 0.396869 Loss2: 1.361304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455249 Loss1: 0.097558 Loss2: 1.357691
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.645457 Loss1: 0.253322 Loss2: 1.392135
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440676 Loss1: 0.085823 Loss2: 1.354853
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.590224 Loss1: 0.233342 Loss2: 1.356882
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483127 Loss1: 0.119027 Loss2: 1.364100
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.561857 Loss1: 0.194276 Loss2: 1.367580
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450459 Loss1: 0.096761 Loss2: 1.353698
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516334 Loss1: 0.166515 Loss2: 1.349819
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.449803 Loss1: 0.095812 Loss2: 1.353991
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416591 Loss1: 0.070297 Loss2: 1.346294
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403583 Loss1: 0.069668 Loss2: 1.333915 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.452073 Loss1: 0.641983 Loss2: 1.810090 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634189 Loss1: 0.260829 Loss2: 1.373360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.493269 Loss1: 0.159453 Loss2: 1.333816
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.508062 Loss1: 0.635578 Loss2: 1.872485
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471981 Loss1: 0.147540 Loss2: 1.324442
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.846331 Loss1: 0.455048 Loss2: 1.391283
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445224 Loss1: 0.118437 Loss2: 1.326788
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.812011 Loss1: 0.361413 Loss2: 1.450598
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.430239 Loss1: 0.116904 Loss2: 1.313335
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.683160 Loss1: 0.290137 Loss2: 1.393024
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.407706 Loss1: 0.087701 Loss2: 1.320006
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.630392 Loss1: 0.219946 Loss2: 1.410445
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386491 Loss1: 0.071980 Loss2: 1.314510
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530304 Loss1: 0.146631 Loss2: 1.383673
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365426 Loss1: 0.056861 Loss2: 1.308565
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470112 Loss1: 0.095123 Loss2: 1.374989
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442848 Loss1: 0.078245 Loss2: 1.364603
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421820 Loss1: 0.063637 Loss2: 1.358183
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406214 Loss1: 0.052389 Loss2: 1.353825
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.433855 Loss1: 0.601012 Loss2: 1.832844
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.728567 Loss1: 0.388859 Loss2: 1.339707
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.723077 Loss1: 0.332512 Loss2: 1.390565
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.635953 Loss1: 0.274313 Loss2: 1.361640
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.633339 Loss1: 0.744144 Loss2: 1.889195
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593548 Loss1: 0.229016 Loss2: 1.364532
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.802419 Loss1: 0.432273 Loss2: 1.370146
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.512334 Loss1: 0.157788 Loss2: 1.354547
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.687133 Loss1: 0.281808 Loss2: 1.405324
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.632841 Loss1: 0.262856 Loss2: 1.369985
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487097 Loss1: 0.141089 Loss2: 1.346008
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.551116 Loss1: 0.183449 Loss2: 1.367667
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.442445 Loss1: 0.102392 Loss2: 1.340053
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481913 Loss1: 0.125290 Loss2: 1.356624
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424767 Loss1: 0.095810 Loss2: 1.328957
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412984 Loss1: 0.077506 Loss2: 1.335478 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.380104 Loss1: 0.043669 Loss2: 1.336435 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.540190 Loss1: 0.681528 Loss2: 1.858662 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.819139 Loss1: 0.349214 Loss2: 1.469925 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.677228 Loss1: 0.276533 Loss2: 1.400695
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.380629 Loss1: 0.553116 Loss2: 1.827513
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.616959 Loss1: 0.217385 Loss2: 1.399575
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.782425 Loss1: 0.390634 Loss2: 1.391791
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.695232 Loss1: 0.275368 Loss2: 1.419864 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.587272 Loss1: 0.195857 Loss2: 1.391415 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.552487 Loss1: 0.166201 Loss2: 1.386286 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.525736 Loss1: 0.142639 Loss2: 1.383098 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.503392 Loss1: 0.132918 Loss2: 1.370474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444542 Loss1: 0.081777 Loss2: 1.362765 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.483983 Loss1: 0.688296 Loss2: 1.795687 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.653883 Loss1: 0.294316 Loss2: 1.359567 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574193 Loss1: 0.233241 Loss2: 1.340952
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.461264 Loss1: 0.628084 Loss2: 1.833180
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.722450 Loss1: 0.350121 Loss2: 1.372329 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.625865 Loss1: 0.217636 Loss2: 1.408228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556531 Loss1: 0.191413 Loss2: 1.365118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545153 Loss1: 0.188469 Loss2: 1.356684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421206 Loss1: 0.108021 Loss2: 1.313185
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477383 Loss1: 0.117023 Loss2: 1.360361
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406526 Loss1: 0.093207 Loss2: 1.313319
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458119 Loss1: 0.104744 Loss2: 1.353375
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446101 Loss1: 0.097559 Loss2: 1.348543
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405227 Loss1: 0.058439 Loss2: 1.346788
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385027 Loss1: 0.046635 Loss2: 1.338392
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.371819 Loss1: 0.490943 Loss2: 1.880876
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.759246 Loss1: 0.389617 Loss2: 1.369629
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731301 Loss1: 0.287228 Loss2: 1.444074
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.661700 Loss1: 0.275434 Loss2: 1.386266
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.520922 Loss1: 0.642170 Loss2: 1.878752
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.634642 Loss1: 0.236571 Loss2: 1.398070
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.880800 Loss1: 0.494145 Loss2: 1.386655
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.568202 Loss1: 0.179351 Loss2: 1.388851
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727201 Loss1: 0.264975 Loss2: 1.462227
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514677 Loss1: 0.137179 Loss2: 1.377497
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.624679 Loss1: 0.235564 Loss2: 1.389115
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479186 Loss1: 0.106973 Loss2: 1.372213
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.632857 Loss1: 0.218676 Loss2: 1.414181
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443093 Loss1: 0.076631 Loss2: 1.366462
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556146 Loss1: 0.162579 Loss2: 1.393567
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437477 Loss1: 0.073459 Loss2: 1.364018
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545365 Loss1: 0.159980 Loss2: 1.385384
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485023 Loss1: 0.098445 Loss2: 1.386578
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460886 Loss1: 0.088661 Loss2: 1.372224
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.506092 Loss1: 0.133094 Loss2: 1.372998
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.416558 Loss1: 0.570255 Loss2: 1.846303
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.659683 Loss1: 0.299583 Loss2: 1.360100
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594306 Loss1: 0.210094 Loss2: 1.384212
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.531760 Loss1: 0.168922 Loss2: 1.362838
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.348304 Loss1: 0.527264 Loss2: 1.821040
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.700345 Loss1: 0.347962 Loss2: 1.352383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629236 Loss1: 0.237639 Loss2: 1.391596 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555923 Loss1: 0.197628 Loss2: 1.358295 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378872 Loss1: 0.042968 Loss2: 1.335904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381758 Loss1: 0.048587 Loss2: 1.333171 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470877 Loss1: 0.121235 Loss2: 1.349642 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400309 Loss1: 0.059859 Loss2: 1.340450 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994485
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.835302 Loss1: 0.463799 Loss2: 1.371503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547274 Loss1: 0.183646 Loss2: 1.363629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.653557 Loss1: 0.759149 Loss2: 1.894408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.773402 Loss1: 0.364097 Loss2: 1.409304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.693356 Loss1: 0.244958 Loss2: 1.448398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584579 Loss1: 0.190415 Loss2: 1.394164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542265 Loss1: 0.149137 Loss2: 1.393128 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498249 Loss1: 0.106811 Loss2: 1.391438 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446669 Loss1: 0.063421 Loss2: 1.383248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417444 Loss1: 0.050992 Loss2: 1.366452 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.829824 Loss1: 0.449169 Loss2: 1.380655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606769 Loss1: 0.231989 Loss2: 1.374779 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.533271 Loss1: 0.155865 Loss2: 1.377405
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.404065 Loss1: 0.599859 Loss2: 1.804206
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.697523 Loss1: 0.370646 Loss2: 1.326876 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451950 Loss1: 0.091638 Loss2: 1.360312
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.625267 Loss1: 0.276527 Loss2: 1.348740
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448802 Loss1: 0.089892 Loss2: 1.358910
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.542968 Loss1: 0.216425 Loss2: 1.326544
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435097 Loss1: 0.081629 Loss2: 1.353469
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556262 Loss1: 0.229021 Loss2: 1.327241
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419490 Loss1: 0.065414 Loss2: 1.354076
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471545 Loss1: 0.152600 Loss2: 1.318944
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408824 Loss1: 0.098423 Loss2: 1.310401
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381910 Loss1: 0.081847 Loss2: 1.300062
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371190 Loss1: 0.073073 Loss2: 1.298118
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348426 Loss1: 0.055464 Loss2: 1.292962
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.574823 Loss1: 0.714956 Loss2: 1.859867
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.868911 Loss1: 0.481449 Loss2: 1.387462
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.726480 Loss1: 0.286537 Loss2: 1.439943
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610707 Loss1: 0.220205 Loss2: 1.390502
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.496169 Loss1: 0.624908 Loss2: 1.871261
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.721719 Loss1: 0.357580 Loss2: 1.364139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.640168 Loss1: 0.241885 Loss2: 1.398283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.568372 Loss1: 0.204013 Loss2: 1.364359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.481487 Loss1: 0.121531 Loss2: 1.359956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449765 Loss1: 0.099016 Loss2: 1.350749 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.387377 Loss1: 0.058142 Loss2: 1.329235 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377900 Loss1: 0.050128 Loss2: 1.327772 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.829283 Loss1: 0.476480 Loss2: 1.352803 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526627 Loss1: 0.196412 Loss2: 1.330216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.377587 Loss1: 0.536912 Loss2: 1.840675
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479141 Loss1: 0.136928 Loss2: 1.342212
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.723791 Loss1: 0.359053 Loss2: 1.364738
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433364 Loss1: 0.106833 Loss2: 1.326531
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.639689 Loss1: 0.230397 Loss2: 1.409293
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.405329 Loss1: 0.082995 Loss2: 1.322334
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538393 Loss1: 0.170453 Loss2: 1.367941
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384162 Loss1: 0.068393 Loss2: 1.315769
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.486189 Loss1: 0.120858 Loss2: 1.365331
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.366867 Loss1: 0.061498 Loss2: 1.305369
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504144 Loss1: 0.137254 Loss2: 1.366891
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.348644 Loss1: 0.050471 Loss2: 1.298174
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470673 Loss1: 0.105068 Loss2: 1.365606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463106 Loss1: 0.106032 Loss2: 1.357074 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.882768 Loss1: 0.460539 Loss2: 1.422230 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642157 Loss1: 0.232597 Loss2: 1.409559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.530524 Loss1: 0.679836 Loss2: 1.850688
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611374 Loss1: 0.190012 Loss2: 1.421362
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.800117 Loss1: 0.413221 Loss2: 1.386895
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.570266 Loss1: 0.155562 Loss2: 1.414704
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.672966 Loss1: 0.250909 Loss2: 1.422057
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.540410 Loss1: 0.133798 Loss2: 1.406612
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562531 Loss1: 0.187673 Loss2: 1.374859
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.528979 Loss1: 0.125173 Loss2: 1.403806
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508145 Loss1: 0.133311 Loss2: 1.374834
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.486273 Loss1: 0.084253 Loss2: 1.402020
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492869 Loss1: 0.123133 Loss2: 1.369736
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466352 Loss1: 0.074007 Loss2: 1.392345
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484338 Loss1: 0.123803 Loss2: 1.360535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427237 Loss1: 0.071215 Loss2: 1.356022 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.912992 Loss1: 0.529756 Loss2: 1.383236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589901 Loss1: 0.230659 Loss2: 1.359242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.544410 Loss1: 0.630530 Loss2: 1.913880
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.569791 Loss1: 0.197332 Loss2: 1.372459
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.823718 Loss1: 0.417171 Loss2: 1.406547
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504088 Loss1: 0.143506 Loss2: 1.360582
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.773895 Loss1: 0.307290 Loss2: 1.466605
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470838 Loss1: 0.118511 Loss2: 1.352327
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.614649 Loss1: 0.208696 Loss2: 1.405953
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450032 Loss1: 0.102202 Loss2: 1.347830
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568403 Loss1: 0.163589 Loss2: 1.404815
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414362 Loss1: 0.072803 Loss2: 1.341559
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527429 Loss1: 0.126969 Loss2: 1.400460
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380874 Loss1: 0.045574 Loss2: 1.335300
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515642 Loss1: 0.125731 Loss2: 1.389912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.511279 Loss1: 0.124834 Loss2: 1.386444 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.759091 Loss1: 0.394766 Loss2: 1.364324 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569342 Loss1: 0.220753 Loss2: 1.348589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.647013 Loss1: 0.774336 Loss2: 1.872677
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473337 Loss1: 0.120868 Loss2: 1.352469
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.782819 Loss1: 0.418198 Loss2: 1.364621
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437894 Loss1: 0.098114 Loss2: 1.339780
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402405 Loss1: 0.068169 Loss2: 1.334236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.360377 Loss1: 0.036399 Loss2: 1.323978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.364332 Loss1: 0.045902 Loss2: 1.318430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369264 Loss1: 0.055970 Loss2: 1.313294 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424518 Loss1: 0.083150 Loss2: 1.341368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409097 Loss1: 0.077492 Loss2: 1.331605 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.586812 Loss1: 0.773679 Loss2: 1.813133
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784938 Loss1: 0.450210 Loss2: 1.334728
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.590417 Loss1: 0.240440 Loss2: 1.349977
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.525439 Loss1: 0.203239 Loss2: 1.322200
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.319651 Loss1: 0.531743 Loss2: 1.787908
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.726706 Loss1: 0.397660 Loss2: 1.329047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.581877 Loss1: 0.199785 Loss2: 1.382092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.569908 Loss1: 0.235115 Loss2: 1.334793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.525144 Loss1: 0.183623 Loss2: 1.341521 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494736 Loss1: 0.154659 Loss2: 1.340077 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450677 Loss1: 0.118942 Loss2: 1.331735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501970 Loss1: 0.175271 Loss2: 1.326699 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.959961
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.558359 Loss1: 0.701341 Loss2: 1.857018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611208 Loss1: 0.212386 Loss2: 1.398822 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.741852 Loss1: 0.842235 Loss2: 1.899617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.804076 Loss1: 0.431654 Loss2: 1.372421 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.683296 Loss1: 0.269593 Loss2: 1.413703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.580674 Loss1: 0.189633 Loss2: 1.391040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.559764 Loss1: 0.190180 Loss2: 1.369584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519276 Loss1: 0.137456 Loss2: 1.381820 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474055 Loss1: 0.111929 Loss2: 1.362126
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380788 Loss1: 0.045471 Loss2: 1.335317
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410369 Loss1: 0.061545 Loss2: 1.348825 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.404926 Loss1: 0.597772 Loss2: 1.807153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.719013 Loss1: 0.306304 Loss2: 1.412708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.641258 Loss1: 0.269003 Loss2: 1.372256
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.466950 Loss1: 0.641965 Loss2: 1.824985
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536873 Loss1: 0.154098 Loss2: 1.382776
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.829450 Loss1: 0.429655 Loss2: 1.399795
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461642 Loss1: 0.094447 Loss2: 1.367194
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604337 Loss1: 0.205811 Loss2: 1.398527
+DEBUG flwr 2023-10-12 01:04:22,989 | server.py:236 | fit_round 133 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469065 Loss1: 0.112543 Loss2: 1.356522
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.525910 Loss1: 0.169835 Loss2: 1.356074
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434053 Loss1: 0.084798 Loss2: 1.349256
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493676 Loss1: 0.137188 Loss2: 1.356488
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419739 Loss1: 0.068299 Loss2: 1.351440
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464240 Loss1: 0.112043 Loss2: 1.352197
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415150 Loss1: 0.073735 Loss2: 1.341414
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422672 Loss1: 0.078435 Loss2: 1.344236
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414824 Loss1: 0.074211 Loss2: 1.340613
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389797 Loss1: 0.058430 Loss2: 1.331368
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405707 Loss1: 0.074268 Loss2: 1.331439
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.546006 Loss1: 0.658702 Loss2: 1.887304
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.771561 Loss1: 0.367416 Loss2: 1.404145
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.717797 Loss1: 0.277855 Loss2: 1.439942
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.626842 Loss1: 0.240530 Loss2: 1.386312
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.317210 Loss1: 0.542938 Loss2: 1.774273
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.672852 Loss1: 0.370353 Loss2: 1.302499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.648397 Loss1: 0.276377 Loss2: 1.372020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523505 Loss1: 0.226089 Loss2: 1.297416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.511472 Loss1: 0.198507 Loss2: 1.312965 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486785 Loss1: 0.192993 Loss2: 1.293792 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429174 Loss1: 0.068927 Loss2: 1.360248
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425933 Loss1: 0.126758 Loss2: 1.299175
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386788 Loss1: 0.098236 Loss2: 1.288552
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366015 Loss1: 0.087691 Loss2: 1.278324
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366151 Loss1: 0.088076 Loss2: 1.278076
+(DefaultActor pid=3764) >> Training accuracy: 0.951042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.542829 Loss1: 0.699767 Loss2: 1.843062
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.754112 Loss1: 0.373162 Loss2: 1.380949
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.640568 Loss1: 0.240311 Loss2: 1.400257
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551568 Loss1: 0.185107 Loss2: 1.366461
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.611098 Loss1: 0.703034 Loss2: 1.908064
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531069 Loss1: 0.160249 Loss2: 1.370820
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.853755 Loss1: 0.500310 Loss2: 1.353445
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.685076 Loss1: 0.282767 Loss2: 1.402310
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505710 Loss1: 0.140277 Loss2: 1.365433
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538278 Loss1: 0.185702 Loss2: 1.352575
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475396 Loss1: 0.113658 Loss2: 1.361739
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487713 Loss1: 0.144203 Loss2: 1.343510
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470905 Loss1: 0.114215 Loss2: 1.356690
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477604 Loss1: 0.124109 Loss2: 1.353495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.455525 Loss1: 0.101432 Loss2: 1.354092 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401439 Loss1: 0.070059 Loss2: 1.331380 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997768
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 01:04:22,989][flwr][DEBUG] - fit_round 133 received 50 results and 0 failures
+INFO flwr 2023-10-12 01:05:05,544 | server.py:125 | fit progress: (133, 2.2147254682958315, {'accuracy': 0.5918}, 306813.32244291797)
+>> Test accuracy: 0.591800
+[2023-10-12 01:05:05,544][flwr][INFO] - fit progress: (133, 2.2147254682958315, {'accuracy': 0.5918}, 306813.32244291797)
+DEBUG flwr 2023-10-12 01:05:05,544 | server.py:173 | evaluate_round 133: strategy sampled 50 clients (out of 50)
+[2023-10-12 01:05:05,544][flwr][DEBUG] - evaluate_round 133: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 01:14:14,076 | server.py:187 | evaluate_round 133 received 50 results and 0 failures
+[2023-10-12 01:14:14,076][flwr][DEBUG] - evaluate_round 133 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 01:14:14,076 | server.py:222 | fit_round 134: strategy sampled 50 clients (out of 50)
+[2023-10-12 01:14:14,076][flwr][DEBUG] - fit_round 134: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.417288 Loss1: 0.620175 Loss2: 1.797113 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.685492 Loss1: 0.289476 Loss2: 1.396016 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554907 Loss1: 0.222733 Loss2: 1.332174
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.738351 Loss1: 0.801204 Loss2: 1.937147
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.752648 Loss1: 0.431566 Loss2: 1.321082
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547834 Loss1: 0.211554 Loss2: 1.336280
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445506 Loss1: 0.119099 Loss2: 1.326406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440420 Loss1: 0.120279 Loss2: 1.320141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.408607 Loss1: 0.096604 Loss2: 1.312003 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441762 Loss1: 0.127484 Loss2: 1.314278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400030 Loss1: 0.087220 Loss2: 1.312809 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397847 Loss1: 0.094928 Loss2: 1.302919 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.509235 Loss1: 0.659343 Loss2: 1.849892
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.807895 Loss1: 0.405477 Loss2: 1.402418
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.681457 Loss1: 0.262061 Loss2: 1.419396
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.619441 Loss1: 0.224721 Loss2: 1.394720
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.541080 Loss1: 0.633200 Loss2: 1.907880
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.573508 Loss1: 0.169080 Loss2: 1.404428
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.914840 Loss1: 0.433164 Loss2: 1.481675
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526829 Loss1: 0.140776 Loss2: 1.386053
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.821959 Loss1: 0.311125 Loss2: 1.510834
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.489651 Loss1: 0.105580 Loss2: 1.384071
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.669620 Loss1: 0.204829 Loss2: 1.464792
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452331 Loss1: 0.080853 Loss2: 1.371479
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.685593 Loss1: 0.205250 Loss2: 1.480343
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.471493 Loss1: 0.103556 Loss2: 1.367937
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.588088 Loss1: 0.136912 Loss2: 1.451176
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425810 Loss1: 0.060451 Loss2: 1.365360
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.578252 Loss1: 0.123991 Loss2: 1.454261
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511815 Loss1: 0.071884 Loss2: 1.439930
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.522621 Loss1: 0.090919 Loss2: 1.431703
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466292 Loss1: 0.038049 Loss2: 1.428242
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.630299 Loss1: 0.733650 Loss2: 1.896649
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.825412 Loss1: 0.443766 Loss2: 1.381646
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635996 Loss1: 0.253090 Loss2: 1.382906
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580506 Loss1: 0.228612 Loss2: 1.351894
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.492262 Loss1: 0.638842 Loss2: 1.853420
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.795120 Loss1: 0.434906 Loss2: 1.360214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.694333 Loss1: 0.293993 Loss2: 1.400340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.578178 Loss1: 0.230834 Loss2: 1.347344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502988 Loss1: 0.152385 Loss2: 1.350603 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461391 Loss1: 0.112213 Loss2: 1.349179 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.343056 Loss1: 0.027069 Loss2: 1.315987
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450916 Loss1: 0.111607 Loss2: 1.339309
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448954 Loss1: 0.103336 Loss2: 1.345618
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410270 Loss1: 0.081524 Loss2: 1.328746
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422267 Loss1: 0.089374 Loss2: 1.332894
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.524616 Loss1: 0.566165 Loss2: 1.958451
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.816138 Loss1: 0.349756 Loss2: 1.466382
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.844111 Loss1: 0.330189 Loss2: 1.513922
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.713209 Loss1: 0.250934 Loss2: 1.462274
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.610876 Loss1: 0.742823 Loss2: 1.868053
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.653644 Loss1: 0.177341 Loss2: 1.476303
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.843329 Loss1: 0.466826 Loss2: 1.376503
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.609129 Loss1: 0.151998 Loss2: 1.457131
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.705856 Loss1: 0.278619 Loss2: 1.427237
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.593343 Loss1: 0.127614 Loss2: 1.465729
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543341 Loss1: 0.173906 Loss2: 1.369436
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.536822 Loss1: 0.086834 Loss2: 1.449988
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520365 Loss1: 0.149312 Loss2: 1.371054
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446649 Loss1: 0.090321 Loss2: 1.356328
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.523165 Loss1: 0.079202 Loss2: 1.443963
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436872 Loss1: 0.088871 Loss2: 1.348001
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.491973 Loss1: 0.055789 Loss2: 1.436184
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431514 Loss1: 0.088581 Loss2: 1.342933 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.624679 Loss1: 0.758426 Loss2: 1.866254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.677498 Loss1: 0.250127 Loss2: 1.427370 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.607709 Loss1: 0.231372 Loss2: 1.376337
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.567776 Loss1: 0.724262 Loss2: 1.843513
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517281 Loss1: 0.123835 Loss2: 1.393446
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.810279 Loss1: 0.438216 Loss2: 1.372063
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511807 Loss1: 0.139019 Loss2: 1.372788
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671328 Loss1: 0.269768 Loss2: 1.401560
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470873 Loss1: 0.102144 Loss2: 1.368729
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.624832 Loss1: 0.260188 Loss2: 1.364644
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456570 Loss1: 0.088111 Loss2: 1.368459
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508837 Loss1: 0.140706 Loss2: 1.368131
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396924 Loss1: 0.044616 Loss2: 1.352309
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.483398 Loss1: 0.129624 Loss2: 1.353774
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398879 Loss1: 0.049732 Loss2: 1.349147
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463471 Loss1: 0.110340 Loss2: 1.353131
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435956 Loss1: 0.090109 Loss2: 1.345847
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429037 Loss1: 0.087779 Loss2: 1.341258
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396688 Loss1: 0.064140 Loss2: 1.332548
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.355004 Loss1: 0.596227 Loss2: 1.758776
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.698765 Loss1: 0.363463 Loss2: 1.335301
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.571036 Loss1: 0.217588 Loss2: 1.353448
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.363535 Loss1: 0.566564 Loss2: 1.796971
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527993 Loss1: 0.196563 Loss2: 1.331430
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.771096 Loss1: 0.415645 Loss2: 1.355451
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480795 Loss1: 0.151845 Loss2: 1.328950
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677117 Loss1: 0.271982 Loss2: 1.405135
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.453778 Loss1: 0.126282 Loss2: 1.327496
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595053 Loss1: 0.243690 Loss2: 1.351363
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466840 Loss1: 0.140120 Loss2: 1.326720
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492863 Loss1: 0.131869 Loss2: 1.360994
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456510 Loss1: 0.129990 Loss2: 1.326520
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447399 Loss1: 0.104032 Loss2: 1.343367
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409276 Loss1: 0.080285 Loss2: 1.328991
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453858 Loss1: 0.111864 Loss2: 1.341994
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419035 Loss1: 0.099802 Loss2: 1.319234
+(DefaultActor pid=3765) >> Training accuracy: 0.973633
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363125 Loss1: 0.043202 Loss2: 1.319923 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.382802 Loss1: 0.557191 Loss2: 1.825610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.681165 Loss1: 0.281659 Loss2: 1.399506 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.612073 Loss1: 0.233638 Loss2: 1.378435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514855 Loss1: 0.139662 Loss2: 1.375193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.490309 Loss1: 0.126337 Loss2: 1.363972 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444382 Loss1: 0.080505 Loss2: 1.363877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435613 Loss1: 0.082599 Loss2: 1.353014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.493344 Loss1: 0.124100 Loss2: 1.369244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501777 Loss1: 0.140959 Loss2: 1.360818 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994485
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425713 Loss1: 0.073544 Loss2: 1.352169 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.485964 Loss1: 0.627088 Loss2: 1.858876
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.769218 Loss1: 0.396460 Loss2: 1.372758
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.689889 Loss1: 0.271166 Loss2: 1.418724
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654552 Loss1: 0.268108 Loss2: 1.386444
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.630948 Loss1: 0.724573 Loss2: 1.906375
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.920704 Loss1: 0.553002 Loss2: 1.367702
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.622727 Loss1: 0.243533 Loss2: 1.379195
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.579877 Loss1: 0.195882 Loss2: 1.383996 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.508515 Loss1: 0.134137 Loss2: 1.374378 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.492135 Loss1: 0.126740 Loss2: 1.365395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.487582 Loss1: 0.123761 Loss2: 1.363821 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429844 Loss1: 0.073843 Loss2: 1.356000 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390277 Loss1: 0.055928 Loss2: 1.334350 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.427226 Loss1: 0.602446 Loss2: 1.824780
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.682936 Loss1: 0.355261 Loss2: 1.327676
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635251 Loss1: 0.263838 Loss2: 1.371413
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.565322 Loss1: 0.237840 Loss2: 1.327482
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.511814 Loss1: 0.551820 Loss2: 1.959993
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526080 Loss1: 0.196392 Loss2: 1.329688
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.821891 Loss1: 0.378363 Loss2: 1.443528
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492577 Loss1: 0.154732 Loss2: 1.337845
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.717738 Loss1: 0.224916 Loss2: 1.492822
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409895 Loss1: 0.093719 Loss2: 1.316176
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.633354 Loss1: 0.186027 Loss2: 1.447327
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.412644 Loss1: 0.102813 Loss2: 1.309831
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.624098 Loss1: 0.173280 Loss2: 1.450818
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458756 Loss1: 0.141132 Loss2: 1.317624
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.587654 Loss1: 0.144705 Loss2: 1.442948
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419833 Loss1: 0.109221 Loss2: 1.310613
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.556594 Loss1: 0.116803 Loss2: 1.439792
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.532621 Loss1: 0.099411 Loss2: 1.433209
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.484832 Loss1: 0.059630 Loss2: 1.425201
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.469414 Loss1: 0.055151 Loss2: 1.414263
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.533823 Loss1: 0.712935 Loss2: 1.820888
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.713313 Loss1: 0.373495 Loss2: 1.339818
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.601159 Loss1: 0.216908 Loss2: 1.384251
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569893 Loss1: 0.235961 Loss2: 1.333932
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.471313 Loss1: 0.648702 Loss2: 1.822611
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.730420 Loss1: 0.372906 Loss2: 1.357514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.672919 Loss1: 0.262738 Loss2: 1.410180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537868 Loss1: 0.180768 Loss2: 1.357100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.489991 Loss1: 0.127472 Loss2: 1.362518 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463261 Loss1: 0.104381 Loss2: 1.358880 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.405067 Loss1: 0.060152 Loss2: 1.344916 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401539 Loss1: 0.060377 Loss2: 1.341163 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.474502 Loss1: 0.600225 Loss2: 1.874277 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.689260 Loss1: 0.253203 Loss2: 1.436057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573401 Loss1: 0.189651 Loss2: 1.383750
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.513933 Loss1: 0.633698 Loss2: 1.880235
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.739835 Loss1: 0.366105 Loss2: 1.373730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604276 Loss1: 0.201511 Loss2: 1.402765 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.512208 Loss1: 0.151857 Loss2: 1.360352 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.559374 Loss1: 0.193807 Loss2: 1.365567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485088 Loss1: 0.116791 Loss2: 1.368298 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452587 Loss1: 0.100456 Loss2: 1.352130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384870 Loss1: 0.043502 Loss2: 1.341368 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.509758 Loss1: 0.619515 Loss2: 1.890243 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.677577 Loss1: 0.220937 Loss2: 1.456640 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.585635 Loss1: 0.177382 Loss2: 1.408253
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.676392 Loss1: 0.750272 Loss2: 1.926120
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.533287 Loss1: 0.130967 Loss2: 1.402319
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.800145 Loss1: 0.451073 Loss2: 1.349073
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.657077 Loss1: 0.275179 Loss2: 1.381898
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.528445 Loss1: 0.134502 Loss2: 1.393943
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500884 Loss1: 0.098956 Loss2: 1.401928 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.509228 Loss1: 0.114106 Loss2: 1.395122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.483045 Loss1: 0.088468 Loss2: 1.394578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372130 Loss1: 0.059270 Loss2: 1.312860 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.371267 Loss1: 0.064739 Loss2: 1.306528 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.508885 Loss1: 0.672139 Loss2: 1.836746
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.668256 Loss1: 0.315320 Loss2: 1.352936
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.631898 Loss1: 0.258362 Loss2: 1.373536
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599523 Loss1: 0.255923 Loss2: 1.343600
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.458564 Loss1: 0.655957 Loss2: 1.802607
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492024 Loss1: 0.148225 Loss2: 1.343799
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.784019 Loss1: 0.452802 Loss2: 1.331217
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452742 Loss1: 0.112921 Loss2: 1.339821
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568495 Loss1: 0.195981 Loss2: 1.372514
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408339 Loss1: 0.079102 Loss2: 1.329237
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.451624 Loss1: 0.125110 Loss2: 1.326514
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435998 Loss1: 0.103016 Loss2: 1.332982
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.445108 Loss1: 0.127470 Loss2: 1.317639
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414307 Loss1: 0.083153 Loss2: 1.331154
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439436 Loss1: 0.117607 Loss2: 1.321829
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411964 Loss1: 0.087690 Loss2: 1.324274
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440295 Loss1: 0.117299 Loss2: 1.322996
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413204 Loss1: 0.094229 Loss2: 1.318975
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404038 Loss1: 0.092404 Loss2: 1.311633
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.360810 Loss1: 0.048788 Loss2: 1.312022
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.582341 Loss1: 0.698531 Loss2: 1.883810
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.837750 Loss1: 0.439026 Loss2: 1.398724
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.673806 Loss1: 0.230725 Loss2: 1.443081
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.571308 Loss1: 0.185395 Loss2: 1.385913
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.655407 Loss1: 0.796090 Loss2: 1.859317
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.723988 Loss1: 0.365161 Loss2: 1.358826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.606305 Loss1: 0.214374 Loss2: 1.391930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559914 Loss1: 0.206832 Loss2: 1.353081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518358 Loss1: 0.169458 Loss2: 1.348900 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459334 Loss1: 0.115809 Loss2: 1.343525 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432370 Loss1: 0.068613 Loss2: 1.363758
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408800 Loss1: 0.070235 Loss2: 1.338565
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.391680 Loss1: 0.062117 Loss2: 1.329563
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374078 Loss1: 0.050311 Loss2: 1.323767
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366759 Loss1: 0.049824 Loss2: 1.316935
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.454284 Loss1: 0.648860 Loss2: 1.805425
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.725323 Loss1: 0.352029 Loss2: 1.373294
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.592296 Loss1: 0.204343 Loss2: 1.387953
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.512837 Loss1: 0.155638 Loss2: 1.357199
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.488711 Loss1: 0.606321 Loss2: 1.882390
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.483551 Loss1: 0.124701 Loss2: 1.358849
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.907238 Loss1: 0.496389 Loss2: 1.410849
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.737316 Loss1: 0.322730 Loss2: 1.414585
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487522 Loss1: 0.133687 Loss2: 1.353835
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631703 Loss1: 0.252369 Loss2: 1.379334
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424376 Loss1: 0.080230 Loss2: 1.344146
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.525183 Loss1: 0.145730 Loss2: 1.379454
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413471 Loss1: 0.074613 Loss2: 1.338858
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485731 Loss1: 0.132133 Loss2: 1.353597
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404455 Loss1: 0.074456 Loss2: 1.329999
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462237 Loss1: 0.128676 Loss2: 1.333561 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388764 Loss1: 0.053570 Loss2: 1.335194 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.414680 Loss1: 0.604681 Loss2: 1.809999 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.699455 Loss1: 0.280476 Loss2: 1.418980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.501743 Loss1: 0.670356 Loss2: 1.831386
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.605213 Loss1: 0.230623 Loss2: 1.374590
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.796505 Loss1: 0.419809 Loss2: 1.376697
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546862 Loss1: 0.162982 Loss2: 1.383880
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.624706 Loss1: 0.214146 Loss2: 1.410560
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489816 Loss1: 0.121694 Loss2: 1.368122
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.544083 Loss1: 0.173577 Loss2: 1.370506
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464262 Loss1: 0.097672 Loss2: 1.366590
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439295 Loss1: 0.087823 Loss2: 1.351471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440684 Loss1: 0.087000 Loss2: 1.353684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414235 Loss1: 0.064969 Loss2: 1.349266 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415051 Loss1: 0.071997 Loss2: 1.343054 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.561569 Loss1: 0.670409 Loss2: 1.891160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.757829 Loss1: 0.308062 Loss2: 1.449767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638537 Loss1: 0.225234 Loss2: 1.413302
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.383431 Loss1: 0.545810 Loss2: 1.837622
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558549 Loss1: 0.151936 Loss2: 1.406613
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.788639 Loss1: 0.435554 Loss2: 1.353084
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488321 Loss1: 0.096511 Loss2: 1.391810
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592630 Loss1: 0.211896 Loss2: 1.380735
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463664 Loss1: 0.076283 Loss2: 1.387381
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.503199 Loss1: 0.151549 Loss2: 1.351650
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.476391 Loss1: 0.095323 Loss2: 1.381068
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.470070 Loss1: 0.126492 Loss2: 1.343579
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499001 Loss1: 0.120102 Loss2: 1.378898
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499205 Loss1: 0.152426 Loss2: 1.346779
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472958 Loss1: 0.085183 Loss2: 1.387774
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440437 Loss1: 0.091278 Loss2: 1.349160
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414517 Loss1: 0.076692 Loss2: 1.337825
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398440 Loss1: 0.063853 Loss2: 1.334588
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367932 Loss1: 0.043831 Loss2: 1.324101
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.527832 Loss1: 0.653866 Loss2: 1.873965
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.763444 Loss1: 0.402303 Loss2: 1.361141
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573964 Loss1: 0.179831 Loss2: 1.394133
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.532077 Loss1: 0.181001 Loss2: 1.351077
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.666736 Loss1: 0.745150 Loss2: 1.921586
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.898107 Loss1: 0.510493 Loss2: 1.387613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468299 Loss1: 0.118636 Loss2: 1.349662
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.721742 Loss1: 0.284193 Loss2: 1.437549
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480753 Loss1: 0.131536 Loss2: 1.349216
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581468 Loss1: 0.191685 Loss2: 1.389782
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439662 Loss1: 0.090321 Loss2: 1.349341
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.534431 Loss1: 0.139762 Loss2: 1.394669
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486457 Loss1: 0.104587 Loss2: 1.381870
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436102 Loss1: 0.092785 Loss2: 1.343317
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472422 Loss1: 0.098444 Loss2: 1.373978
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430682 Loss1: 0.092043 Loss2: 1.338639
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417677 Loss1: 0.053287 Loss2: 1.364390 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998884
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.641033 Loss1: 0.753437 Loss2: 1.887596 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.801129 Loss1: 0.380666 Loss2: 1.420463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.707412 Loss1: 0.275594 Loss2: 1.431818
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.599012 Loss1: 0.741107 Loss2: 1.857905
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.872142 Loss1: 0.460008 Loss2: 1.412134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.674221 Loss1: 0.266316 Loss2: 1.407906 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.603133 Loss1: 0.216550 Loss2: 1.386583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.538520 Loss1: 0.141929 Loss2: 1.396591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492951 Loss1: 0.112577 Loss2: 1.380374 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409329 Loss1: 0.045866 Loss2: 1.363464
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459253 Loss1: 0.091976 Loss2: 1.367276
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439384 Loss1: 0.079740 Loss2: 1.359644
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435210 Loss1: 0.077631 Loss2: 1.357579
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442345 Loss1: 0.088579 Loss2: 1.353766
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.537923 Loss1: 0.722900 Loss2: 1.815023
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.806789 Loss1: 0.468676 Loss2: 1.338114
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684221 Loss1: 0.307364 Loss2: 1.376857
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.585223 Loss1: 0.242809 Loss2: 1.342414
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.567155 Loss1: 0.717268 Loss2: 1.849886
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534057 Loss1: 0.200310 Loss2: 1.333747
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.829748 Loss1: 0.449368 Loss2: 1.380381
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.659562 Loss1: 0.261204 Loss2: 1.398358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.632779 Loss1: 0.267065 Loss2: 1.365715 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.562323 Loss1: 0.196535 Loss2: 1.365788 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502319 Loss1: 0.141526 Loss2: 1.360793 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381101 Loss1: 0.069118 Loss2: 1.311983
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.454679 Loss1: 0.106032 Loss2: 1.348648
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420124 Loss1: 0.079032 Loss2: 1.341092
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391792 Loss1: 0.056738 Loss2: 1.335054
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369658 Loss1: 0.042143 Loss2: 1.327515
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.472632 Loss1: 0.661150 Loss2: 1.811482
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.715620 Loss1: 0.384445 Loss2: 1.331175
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.621061 Loss1: 0.251397 Loss2: 1.369664
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.560221 Loss1: 0.223227 Loss2: 1.336994
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.420467 Loss1: 0.564821 Loss2: 1.855646
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527813 Loss1: 0.190545 Loss2: 1.337268
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.722910 Loss1: 0.312674 Loss2: 1.410236
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.646850 Loss1: 0.229238 Loss2: 1.417612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564043 Loss1: 0.172154 Loss2: 1.391890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555664 Loss1: 0.165731 Loss2: 1.389933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513504 Loss1: 0.123347 Loss2: 1.390156 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440271 Loss1: 0.059838 Loss2: 1.380433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423843 Loss1: 0.054846 Loss2: 1.368997 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.636277 Loss1: 0.773439 Loss2: 1.862838 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594367 Loss1: 0.202543 Loss2: 1.391824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.469043 Loss1: 0.615380 Loss2: 1.853663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.780779 Loss1: 0.399126 Loss2: 1.381653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.624821 Loss1: 0.212360 Loss2: 1.412461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537006 Loss1: 0.180744 Loss2: 1.356263 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.468764 Loss1: 0.113741 Loss2: 1.355024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444052 Loss1: 0.093831 Loss2: 1.350221 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423028 Loss1: 0.086347 Loss2: 1.336680 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394099 Loss1: 0.060780 Loss2: 1.333319 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+DEBUG flwr 2023-10-12 01:43:58,990 | server.py:236 | fit_round 134 received 50 results and 0 failures
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.506670 Loss1: 0.685971 Loss2: 1.820699
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.780868 Loss1: 0.431188 Loss2: 1.349680
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.676143 Loss1: 0.281499 Loss2: 1.394644
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575182 Loss1: 0.219106 Loss2: 1.356076
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.502941 Loss1: 0.677573 Loss2: 1.825367
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.788744 Loss1: 0.426647 Loss2: 1.362097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.708046 Loss1: 0.273344 Loss2: 1.434702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.572431 Loss1: 0.219153 Loss2: 1.353278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.583164 Loss1: 0.214259 Loss2: 1.368906 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485381 Loss1: 0.120254 Loss2: 1.365127 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468863 Loss1: 0.114498 Loss2: 1.354365 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.473163 Loss1: 0.127526 Loss2: 1.345637 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.734394 Loss1: 0.360200 Loss2: 1.374194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497468 Loss1: 0.137666 Loss2: 1.359802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517174 Loss1: 0.153713 Loss2: 1.363461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484981 Loss1: 0.120187 Loss2: 1.364793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463266 Loss1: 0.108331 Loss2: 1.354935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426749 Loss1: 0.072103 Loss2: 1.354646 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394906 Loss1: 0.046850 Loss2: 1.348056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401420 Loss1: 0.062010 Loss2: 1.339410 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453638 Loss1: 0.112436 Loss2: 1.341202 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967634
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.727180 Loss1: 0.759023 Loss2: 1.968157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.762680 Loss1: 0.279037 Loss2: 1.483643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.663898 Loss1: 0.757890 Loss2: 1.906008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.782274 Loss1: 0.407289 Loss2: 1.374984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601530 Loss1: 0.203185 Loss2: 1.398345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543674 Loss1: 0.169476 Loss2: 1.374198 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.470730 Loss1: 0.104770 Loss2: 1.365961 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461744 Loss1: 0.078908 Loss2: 1.382835 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428445 Loss1: 0.082405 Loss2: 1.346039 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411759 Loss1: 0.065436 Loss2: 1.346322 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 01:43:58,990][flwr][DEBUG] - fit_round 134 received 50 results and 0 failures
+INFO flwr 2023-10-12 01:44:40,461 | server.py:125 | fit progress: (134, 2.212735759754912, {'accuracy': 0.5886}, 309188.239732421)
+>> Test accuracy: 0.588600
+[2023-10-12 01:44:40,461][flwr][INFO] - fit progress: (134, 2.212735759754912, {'accuracy': 0.5886}, 309188.239732421)
+DEBUG flwr 2023-10-12 01:44:40,461 | server.py:173 | evaluate_round 134: strategy sampled 50 clients (out of 50)
+[2023-10-12 01:44:40,461][flwr][DEBUG] - evaluate_round 134: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 01:53:44,878 | server.py:187 | evaluate_round 134 received 50 results and 0 failures
+[2023-10-12 01:53:44,878][flwr][DEBUG] - evaluate_round 134 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 01:53:44,879 | server.py:222 | fit_round 135: strategy sampled 50 clients (out of 50)
+[2023-10-12 01:53:44,879][flwr][DEBUG] - fit_round 135: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.785606 Loss1: 0.815206 Loss2: 1.970400
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.839257 Loss1: 0.411414 Loss2: 1.427843
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.745391 Loss1: 0.282733 Loss2: 1.462658
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586909 Loss1: 0.167369 Loss2: 1.419540
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.590139 Loss1: 0.642976 Loss2: 1.947163
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.969752 Loss1: 0.521016 Loss2: 1.448735 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.861999 Loss1: 0.329120 Loss2: 1.532879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.675765 Loss1: 0.233892 Loss2: 1.441874 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.676048 Loss1: 0.213154 Loss2: 1.462894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.583509 Loss1: 0.140841 Loss2: 1.442669 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515314 Loss1: 0.079518 Loss2: 1.435796 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.484540 Loss1: 0.073649 Loss2: 1.410891 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.867561 Loss1: 0.481590 Loss2: 1.385971 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.626852 Loss1: 0.194759 Loss2: 1.432093 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.557838 Loss1: 0.165200 Loss2: 1.392639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.809220 Loss1: 0.436537 Loss2: 1.372683 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.746370 Loss1: 0.349990 Loss2: 1.396379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539056 Loss1: 0.182211 Loss2: 1.356846 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456146 Loss1: 0.120466 Loss2: 1.335680 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399591 Loss1: 0.073910 Loss2: 1.325681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408359 Loss1: 0.088458 Loss2: 1.319901
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.548087 Loss1: 0.697130 Loss2: 1.850957
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369263 Loss1: 0.048946 Loss2: 1.320318
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.836885 Loss1: 0.483069 Loss2: 1.353815
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.612710 Loss1: 0.211694 Loss2: 1.401016
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.595755 Loss1: 0.250870 Loss2: 1.344885
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566266 Loss1: 0.202524 Loss2: 1.363742
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.482531 Loss1: 0.138589 Loss2: 1.343942
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478625 Loss1: 0.136834 Loss2: 1.341791
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.408449 Loss1: 0.542171 Loss2: 1.866278
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444509 Loss1: 0.102208 Loss2: 1.342301
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.787427 Loss1: 0.370354 Loss2: 1.417072
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395396 Loss1: 0.065697 Loss2: 1.329699
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727759 Loss1: 0.291557 Loss2: 1.436202
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361995 Loss1: 0.039278 Loss2: 1.322717
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.614667 Loss1: 0.216448 Loss2: 1.398219
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.579812 Loss1: 0.174106 Loss2: 1.405706
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537883 Loss1: 0.137449 Loss2: 1.400434
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545109 Loss1: 0.149111 Loss2: 1.395998
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.508935 Loss1: 0.116903 Loss2: 1.392031
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.560333 Loss1: 0.695234 Loss2: 1.865099
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.775555 Loss1: 0.376696 Loss2: 1.398859 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.660833 Loss1: 0.241600 Loss2: 1.419234 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.493085 Loss1: 0.111748 Loss2: 1.381337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441438 Loss1: 0.075116 Loss2: 1.366322 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413558 Loss1: 0.053008 Loss2: 1.360549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406969 Loss1: 0.049401 Loss2: 1.357568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393016 Loss1: 0.038638 Loss2: 1.354378 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.615198 Loss1: 0.197846 Loss2: 1.417352 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.589057 Loss1: 0.185627 Loss2: 1.403430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.540712 Loss1: 0.114383 Loss2: 1.426329 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472769 Loss1: 0.078684 Loss2: 1.394085 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558980 Loss1: 0.162515 Loss2: 1.396465 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.496324 Loss1: 0.102310 Loss2: 1.394014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488360 Loss1: 0.101985 Loss2: 1.386374
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.448538 Loss1: 0.599180 Loss2: 1.849359
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.694894 Loss1: 0.346796 Loss2: 1.348099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.602880 Loss1: 0.215885 Loss2: 1.386995 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536411 Loss1: 0.186628 Loss2: 1.349783 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.557256 Loss1: 0.196059 Loss2: 1.361197 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398612 Loss1: 0.059275 Loss2: 1.339337 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388748 Loss1: 0.057883 Loss2: 1.330866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372150 Loss1: 0.044891 Loss2: 1.327259 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.548533 Loss1: 0.185190 Loss2: 1.363343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.474712 Loss1: 0.107727 Loss2: 1.366984 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.438891 Loss1: 0.088617 Loss2: 1.350274
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.531678 Loss1: 0.690838 Loss2: 1.840840
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.779187 Loss1: 0.411643 Loss2: 1.367545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.720238 Loss1: 0.310542 Loss2: 1.409695 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604907 Loss1: 0.245863 Loss2: 1.359043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523653 Loss1: 0.169606 Loss2: 1.354047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440804 Loss1: 0.096223 Loss2: 1.344581 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385382 Loss1: 0.049386 Loss2: 1.335996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391015 Loss1: 0.064467 Loss2: 1.326547 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.575060 Loss1: 0.177171 Loss2: 1.397889 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484296 Loss1: 0.089633 Loss2: 1.394663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.452012 Loss1: 0.589486 Loss2: 1.862526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.827059 Loss1: 0.458835 Loss2: 1.368225 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521907 Loss1: 0.168863 Loss2: 1.353044 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486370 Loss1: 0.131749 Loss2: 1.354621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460324 Loss1: 0.110728 Loss2: 1.349596
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.593670 Loss1: 0.680942 Loss2: 1.912728
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411271 Loss1: 0.075411 Loss2: 1.335860
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.910646 Loss1: 0.491703 Loss2: 1.418943
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386548 Loss1: 0.051843 Loss2: 1.334705
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.730337 Loss1: 0.262954 Loss2: 1.467384
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384335 Loss1: 0.050684 Loss2: 1.333651
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606415 Loss1: 0.189413 Loss2: 1.417003
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.588939 Loss1: 0.169646 Loss2: 1.419294
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513962 Loss1: 0.107124 Loss2: 1.406838
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486048 Loss1: 0.087283 Loss2: 1.398765
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461609 Loss1: 0.069471 Loss2: 1.392139
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462691 Loss1: 0.071049 Loss2: 1.391642
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.507823 Loss1: 0.641272 Loss2: 1.866551
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442471 Loss1: 0.057762 Loss2: 1.384709
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.745073 Loss1: 0.372629 Loss2: 1.372444
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.612528 Loss1: 0.214173 Loss2: 1.398355
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.522975 Loss1: 0.161923 Loss2: 1.361051
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.479040 Loss1: 0.121945 Loss2: 1.357095
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470459 Loss1: 0.114256 Loss2: 1.356203
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431364 Loss1: 0.086613 Loss2: 1.344751
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.442719 Loss1: 0.616492 Loss2: 1.826227
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430176 Loss1: 0.091384 Loss2: 1.338793
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.869871 Loss1: 0.519744 Loss2: 1.350127
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431198 Loss1: 0.087977 Loss2: 1.343221
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.734983 Loss1: 0.342133 Loss2: 1.392850
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438492 Loss1: 0.090856 Loss2: 1.347636
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610421 Loss1: 0.269529 Loss2: 1.340892
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.511923 Loss1: 0.162262 Loss2: 1.349662
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468428 Loss1: 0.139813 Loss2: 1.328616
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436363 Loss1: 0.115383 Loss2: 1.320980
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405344 Loss1: 0.084981 Loss2: 1.320362
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.601484 Loss1: 0.743253 Loss2: 1.858231
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371511 Loss1: 0.060684 Loss2: 1.310826
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.815889 Loss1: 0.417994 Loss2: 1.397895
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358777 Loss1: 0.057260 Loss2: 1.301517
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.593626 Loss1: 0.203906 Loss2: 1.389720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.544588 Loss1: 0.163991 Loss2: 1.380596 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.512315 Loss1: 0.130815 Loss2: 1.381500
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.585452 Loss1: 0.657019 Loss2: 1.928433
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.891439 Loss1: 0.446173 Loss2: 1.445266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.750480 Loss1: 0.258872 Loss2: 1.491608 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.627481 Loss1: 0.201223 Loss2: 1.426257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514578 Loss1: 0.087409 Loss2: 1.427170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.472700 Loss1: 0.068575 Loss2: 1.404125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.353183 Loss1: 0.557193 Loss2: 1.795990
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461401 Loss1: 0.058468 Loss2: 1.402933
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.705484 Loss1: 0.350247 Loss2: 1.355237
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448654 Loss1: 0.050144 Loss2: 1.398510
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.518385 Loss1: 0.176677 Loss2: 1.341708 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.430889 Loss1: 0.099074 Loss2: 1.331815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.490143 Loss1: 0.658070 Loss2: 1.832072
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.397734 Loss1: 0.071001 Loss2: 1.326733
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.809164 Loss1: 0.444306 Loss2: 1.364858
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390448 Loss1: 0.069554 Loss2: 1.320894
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.764339 Loss1: 0.306412 Loss2: 1.457927
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.361476 Loss1: 0.047351 Loss2: 1.314124
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.638026 Loss1: 0.270452 Loss2: 1.367574
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356704 Loss1: 0.050217 Loss2: 1.306487
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505865 Loss1: 0.143739 Loss2: 1.362126 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440663 Loss1: 0.087539 Loss2: 1.353124 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422864 Loss1: 0.077455 Loss2: 1.345410
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.327929 Loss1: 0.477253 Loss2: 1.850676
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.735109 Loss1: 0.349451 Loss2: 1.385658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.607771 Loss1: 0.225201 Loss2: 1.382571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.547887 Loss1: 0.164907 Loss2: 1.382981 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.757776 Loss1: 0.374836 Loss2: 1.382940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.671174 Loss1: 0.233414 Loss2: 1.437760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.617290 Loss1: 0.251577 Loss2: 1.365713 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.585686 Loss1: 0.192560 Loss2: 1.393126 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997243
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478721 Loss1: 0.101022 Loss2: 1.377699 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446569 Loss1: 0.091032 Loss2: 1.355537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.478136 Loss1: 0.118083 Loss2: 1.360053 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.725479 Loss1: 0.369499 Loss2: 1.355980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539707 Loss1: 0.185137 Loss2: 1.354570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.477973 Loss1: 0.127851 Loss2: 1.350122
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.589185 Loss1: 0.702399 Loss2: 1.886786
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.782124 Loss1: 0.418563 Loss2: 1.363561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.697080 Loss1: 0.280181 Loss2: 1.416898 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551722 Loss1: 0.186638 Loss2: 1.365084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.508155 Loss1: 0.143261 Loss2: 1.364894 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.464581 Loss1: 0.108834 Loss2: 1.355746 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409709 Loss1: 0.069799 Loss2: 1.339910 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371382 Loss1: 0.044662 Loss2: 1.326720 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.880520 Loss1: 0.476473 Loss2: 1.404046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.617851 Loss1: 0.215718 Loss2: 1.402133 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571973 Loss1: 0.163723 Loss2: 1.408250
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.498595 Loss1: 0.610225 Loss2: 1.888370
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.736256 Loss1: 0.355777 Loss2: 1.380479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.751072 Loss1: 0.294763 Loss2: 1.456309 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594544 Loss1: 0.208510 Loss2: 1.386034 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.573937 Loss1: 0.186053 Loss2: 1.387884 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454970 Loss1: 0.078025 Loss2: 1.376945
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518853 Loss1: 0.130730 Loss2: 1.388123
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461100 Loss1: 0.088452 Loss2: 1.372648
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439700 Loss1: 0.066392 Loss2: 1.373308
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447286 Loss1: 0.081985 Loss2: 1.365301
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429541 Loss1: 0.063811 Loss2: 1.365730
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.415592 Loss1: 0.584351 Loss2: 1.831241
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.746833 Loss1: 0.405076 Loss2: 1.341757
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.655681 Loss1: 0.264942 Loss2: 1.390739
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584023 Loss1: 0.237379 Loss2: 1.346644
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545409 Loss1: 0.199767 Loss2: 1.345642
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.384019 Loss1: 0.554016 Loss2: 1.830003
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.769567 Loss1: 0.423141 Loss2: 1.346426 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615150 Loss1: 0.235215 Loss2: 1.379935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.585944 Loss1: 0.241494 Loss2: 1.344451 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490690 Loss1: 0.141128 Loss2: 1.349562 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415471 Loss1: 0.087360 Loss2: 1.328111
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.464085 Loss1: 0.124469 Loss2: 1.339616
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.454059 Loss1: 0.114116 Loss2: 1.339943
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462019 Loss1: 0.134701 Loss2: 1.327318
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405173 Loss1: 0.074607 Loss2: 1.330566
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399877 Loss1: 0.075696 Loss2: 1.324181
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.524071 Loss1: 0.685373 Loss2: 1.838698
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.773681 Loss1: 0.415230 Loss2: 1.358451
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.646056 Loss1: 0.255122 Loss2: 1.390934
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576907 Loss1: 0.220695 Loss2: 1.356213
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508072 Loss1: 0.162390 Loss2: 1.345682
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.345439 Loss1: 0.560824 Loss2: 1.784615
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456606 Loss1: 0.118853 Loss2: 1.337753
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445033 Loss1: 0.109503 Loss2: 1.335530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435193 Loss1: 0.099283 Loss2: 1.335910 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520197 Loss1: 0.169603 Loss2: 1.350594
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486832 Loss1: 0.152195 Loss2: 1.334637
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527910 Loss1: 0.178472 Loss2: 1.349438
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.432964 Loss1: 0.102741 Loss2: 1.330223
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419360 Loss1: 0.081061 Loss2: 1.338299 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385038 Loss1: 0.057384 Loss2: 1.327654 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.444708 Loss1: 0.612375 Loss2: 1.832333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358815 Loss1: 0.042510 Loss2: 1.316305
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.646675 Loss1: 0.256659 Loss2: 1.390016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548337 Loss1: 0.194267 Loss2: 1.354070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.489966 Loss1: 0.141513 Loss2: 1.348453
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.544620 Loss1: 0.697498 Loss2: 1.847122
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.701521 Loss1: 0.337127 Loss2: 1.364394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.605826 Loss1: 0.213006 Loss2: 1.392820 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527835 Loss1: 0.169192 Loss2: 1.358643 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439233 Loss1: 0.110389 Loss2: 1.328843
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503061 Loss1: 0.141996 Loss2: 1.361065
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476919 Loss1: 0.123086 Loss2: 1.353833
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435502 Loss1: 0.088950 Loss2: 1.346553
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447296 Loss1: 0.105263 Loss2: 1.342034
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423195 Loss1: 0.079045 Loss2: 1.344150
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.394786 Loss1: 0.584277 Loss2: 1.810509
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404624 Loss1: 0.063527 Loss2: 1.341097
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.621493 Loss1: 0.247066 Loss2: 1.374427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508897 Loss1: 0.173213 Loss2: 1.335684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.528335 Loss1: 0.642888 Loss2: 1.885447
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473121 Loss1: 0.141841 Loss2: 1.331280
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.893530 Loss1: 0.458669 Loss2: 1.434861
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.414755 Loss1: 0.085292 Loss2: 1.329463
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.825644 Loss1: 0.349829 Loss2: 1.475815
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.391316 Loss1: 0.070972 Loss2: 1.320345
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.616686 Loss1: 0.203497 Loss2: 1.413189
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379397 Loss1: 0.060754 Loss2: 1.318643
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.623299 Loss1: 0.198911 Loss2: 1.424388
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.343926 Loss1: 0.031560 Loss2: 1.312366
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529582 Loss1: 0.117275 Loss2: 1.412307 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.498703 Loss1: 0.101688 Loss2: 1.397016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.454833 Loss1: 0.669801 Loss2: 1.785032
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.502433 Loss1: 0.101397 Loss2: 1.401036
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642752 Loss1: 0.287422 Loss2: 1.355330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.499598 Loss1: 0.158163 Loss2: 1.341435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.419720 Loss1: 0.107094 Loss2: 1.312626
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.424244 Loss1: 0.593742 Loss2: 1.830502
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.697014 Loss1: 0.377486 Loss2: 1.319529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.670439 Loss1: 0.308661 Loss2: 1.361777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599570 Loss1: 0.275489 Loss2: 1.324081 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476163 Loss1: 0.155885 Loss2: 1.320279 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.368413 Loss1: 0.077539 Loss2: 1.290874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.366819 Loss1: 0.083026 Loss2: 1.283794 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.480923 Loss1: 0.677171 Loss2: 1.803752
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.332211 Loss1: 0.049608 Loss2: 1.282602
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.603681 Loss1: 0.231513 Loss2: 1.372168 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.429362 Loss1: 0.102119 Loss2: 1.327243 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473194 Loss1: 0.147264 Loss2: 1.325930
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.506882 Loss1: 0.662073 Loss2: 1.844809
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447167 Loss1: 0.123645 Loss2: 1.323522
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.696898 Loss1: 0.328324 Loss2: 1.368574
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466027 Loss1: 0.144469 Loss2: 1.321558
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615262 Loss1: 0.227437 Loss2: 1.387825
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435570 Loss1: 0.117565 Loss2: 1.318006
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533227 Loss1: 0.171552 Loss2: 1.361675
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424850 Loss1: 0.105095 Loss2: 1.319755
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498072 Loss1: 0.132784 Loss2: 1.365288
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468890 Loss1: 0.115754 Loss2: 1.353136
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443485 Loss1: 0.092977 Loss2: 1.350508
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413292 Loss1: 0.061831 Loss2: 1.351461
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453338 Loss1: 0.108126 Loss2: 1.345212
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.461632 Loss1: 0.596893 Loss2: 1.864740
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433129 Loss1: 0.083796 Loss2: 1.349333
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.703338 Loss1: 0.283840 Loss2: 1.419498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509187 Loss1: 0.133641 Loss2: 1.375546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455376 Loss1: 0.086267 Loss2: 1.369109
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.454632 Loss1: 0.633333 Loss2: 1.821298
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.797725 Loss1: 0.436618 Loss2: 1.361107 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656733 Loss1: 0.264798 Loss2: 1.391936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581718 Loss1: 0.233741 Loss2: 1.347978 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406368 Loss1: 0.061930 Loss2: 1.344438
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525188 Loss1: 0.173188 Loss2: 1.351999
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503927 Loss1: 0.158611 Loss2: 1.345316
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462738 Loss1: 0.125534 Loss2: 1.337205
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447879 Loss1: 0.107280 Loss2: 1.340599
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405515 Loss1: 0.077438 Loss2: 1.328077
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.474734 Loss1: 0.644375 Loss2: 1.830359
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361936 Loss1: 0.041889 Loss2: 1.320047
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671206 Loss1: 0.242391 Loss2: 1.428815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.552068 Loss1: 0.162740 Loss2: 1.389328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.580987 Loss1: 0.648876 Loss2: 1.932110
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499815 Loss1: 0.123620 Loss2: 1.376195
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.992958 Loss1: 0.534962 Loss2: 1.457996
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455327 Loss1: 0.078370 Loss2: 1.376956
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.797921 Loss1: 0.275626 Loss2: 1.522296
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.451002 Loss1: 0.087235 Loss2: 1.363767
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.753108 Loss1: 0.302623 Loss2: 1.450485
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424952 Loss1: 0.065336 Loss2: 1.359616
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425846 Loss1: 0.066195 Loss2: 1.359651 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.546558 Loss1: 0.101064 Loss2: 1.445494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.518052 Loss1: 0.084661 Loss2: 1.433391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.512510 Loss1: 0.085654 Loss2: 1.426856
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.457300 Loss1: 0.611248 Loss2: 1.846053
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.804795 Loss1: 0.436244 Loss2: 1.368551
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578089 Loss1: 0.174194 Loss2: 1.403895
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559466 Loss1: 0.210803 Loss2: 1.348662
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516806 Loss1: 0.157301 Loss2: 1.359505
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.607656 Loss1: 0.761381 Loss2: 1.846276
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476562 Loss1: 0.140493 Loss2: 1.336069
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470978 Loss1: 0.131650 Loss2: 1.339328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.474078 Loss1: 0.139677 Loss2: 1.334400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434423 Loss1: 0.100772 Loss2: 1.333651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416222 Loss1: 0.084516 Loss2: 1.331705 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394147 Loss1: 0.097453 Loss2: 1.296694 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.351369 Loss1: 0.067156 Loss2: 1.284213 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994420
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.782806 Loss1: 0.363453 Loss2: 1.419353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581901 Loss1: 0.161739 Loss2: 1.420163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.537341 Loss1: 0.124577 Loss2: 1.412764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.547685 Loss1: 0.134801 Loss2: 1.412884 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533598 Loss1: 0.118944 Loss2: 1.414653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.504704 Loss1: 0.097724 Loss2: 1.406980 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 02:22:08,427 | server.py:236 | fit_round 135 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490282 Loss1: 0.091401 Loss2: 1.398881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452161 Loss1: 0.051941 Loss2: 1.400221 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461444 Loss1: 0.088547 Loss2: 1.372897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419790 Loss1: 0.057640 Loss2: 1.362150 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.432886 Loss1: 0.634798 Loss2: 1.798088
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.741773 Loss1: 0.392899 Loss2: 1.348874
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588582 Loss1: 0.200794 Loss2: 1.387789
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.587299 Loss1: 0.241191 Loss2: 1.346109
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.680434 Loss1: 0.786407 Loss2: 1.894027
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492941 Loss1: 0.152864 Loss2: 1.340078
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.836635 Loss1: 0.472474 Loss2: 1.364162
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.650818 Loss1: 0.262450 Loss2: 1.388368
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467186 Loss1: 0.129273 Loss2: 1.337913
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434401 Loss1: 0.103972 Loss2: 1.330429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390732 Loss1: 0.069816 Loss2: 1.320916 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372014 Loss1: 0.051755 Loss2: 1.320259 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394140 Loss1: 0.062787 Loss2: 1.331354 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365969 Loss1: 0.043001 Loss2: 1.322969 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.677208 Loss1: 0.771412 Loss2: 1.905796
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.914726 Loss1: 0.533063 Loss2: 1.381663
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.876558 Loss1: 0.403539 Loss2: 1.473019
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604775 Loss1: 0.229602 Loss2: 1.375173
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568623 Loss1: 0.180345 Loss2: 1.388278
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469430 Loss1: 0.098930 Loss2: 1.370500
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472817 Loss1: 0.117339 Loss2: 1.355479
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473975 Loss1: 0.120443 Loss2: 1.353532
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424305 Loss1: 0.072546 Loss2: 1.351759
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394959 Loss1: 0.053692 Loss2: 1.341267
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 02:22:08,427][flwr][DEBUG] - fit_round 135 received 50 results and 0 failures
+INFO flwr 2023-10-12 02:22:50,554 | server.py:125 | fit progress: (135, 2.2166092506231974, {'accuracy': 0.5909}, 311478.33283696795)
+>> Test accuracy: 0.590900
+[2023-10-12 02:22:50,554][flwr][INFO] - fit progress: (135, 2.2166092506231974, {'accuracy': 0.5909}, 311478.33283696795)
+DEBUG flwr 2023-10-12 02:22:50,555 | server.py:173 | evaluate_round 135: strategy sampled 50 clients (out of 50)
+[2023-10-12 02:22:50,555][flwr][DEBUG] - evaluate_round 135: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 02:31:56,688 | server.py:187 | evaluate_round 135 received 50 results and 0 failures
+[2023-10-12 02:31:56,688][flwr][DEBUG] - evaluate_round 135 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 02:31:56,688 | server.py:222 | fit_round 136: strategy sampled 50 clients (out of 50)
+[2023-10-12 02:31:56,688][flwr][DEBUG] - fit_round 136: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.431467 Loss1: 0.532830 Loss2: 1.898638
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.818669 Loss1: 0.400566 Loss2: 1.418103
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.720135 Loss1: 0.257127 Loss2: 1.463008
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.605790 Loss1: 0.177286 Loss2: 1.428505
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551012 Loss1: 0.132345 Loss2: 1.418667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.531163 Loss1: 0.120581 Loss2: 1.410583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495566 Loss1: 0.089336 Loss2: 1.406229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489298 Loss1: 0.088324 Loss2: 1.400974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.480232 Loss1: 0.078598 Loss2: 1.401634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462063 Loss1: 0.068526 Loss2: 1.393537 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432568 Loss1: 0.100844 Loss2: 1.331724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414058 Loss1: 0.081773 Loss2: 1.332285 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.763717 Loss1: 0.384591 Loss2: 1.379126 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542644 Loss1: 0.180773 Loss2: 1.361871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.425544 Loss1: 0.639530 Loss2: 1.786014
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.500070 Loss1: 0.133611 Loss2: 1.366459
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.731695 Loss1: 0.393677 Loss2: 1.338017
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.479541 Loss1: 0.116738 Loss2: 1.362803
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.595055 Loss1: 0.234102 Loss2: 1.360954
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491874 Loss1: 0.138965 Loss2: 1.352909
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498246 Loss1: 0.158213 Loss2: 1.340033
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489830 Loss1: 0.134779 Loss2: 1.355050
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.463264 Loss1: 0.135496 Loss2: 1.327768
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452218 Loss1: 0.100151 Loss2: 1.352067
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516763 Loss1: 0.188463 Loss2: 1.328300
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430447 Loss1: 0.083181 Loss2: 1.347266
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460753 Loss1: 0.137253 Loss2: 1.323499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396789 Loss1: 0.076495 Loss2: 1.320294 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.675290 Loss1: 0.342098 Loss2: 1.333192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.538831 Loss1: 0.212538 Loss2: 1.326293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.477926 Loss1: 0.144698 Loss2: 1.333228
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.680321 Loss1: 0.715921 Loss2: 1.964400
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433538 Loss1: 0.115191 Loss2: 1.318347
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.940398 Loss1: 0.519884 Loss2: 1.420514
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804251 Loss1: 0.317086 Loss2: 1.487166
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419837 Loss1: 0.103199 Loss2: 1.316638
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.645393 Loss1: 0.235371 Loss2: 1.410022
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389687 Loss1: 0.076096 Loss2: 1.313590
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.649434 Loss1: 0.218330 Loss2: 1.431104
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381256 Loss1: 0.077997 Loss2: 1.303259
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354189 Loss1: 0.057246 Loss2: 1.296943 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485825 Loss1: 0.077621 Loss2: 1.408204 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478879 Loss1: 0.085613 Loss2: 1.393266 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975446
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.726459 Loss1: 0.781648 Loss2: 1.944811
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.799527 Loss1: 0.440268 Loss2: 1.359259
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.687774 Loss1: 0.279394 Loss2: 1.408380
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535523 Loss1: 0.170485 Loss2: 1.365038
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484232 Loss1: 0.136177 Loss2: 1.348055
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460573 Loss1: 0.108885 Loss2: 1.351688 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422644 Loss1: 0.076895 Loss2: 1.345748 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411186 Loss1: 0.074705 Loss2: 1.336481 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.380908 Loss1: 0.050489 Loss2: 1.330418 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377322 Loss1: 0.052764 Loss2: 1.324558 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986779
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500243 Loss1: 0.110941 Loss2: 1.389302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.489061 Loss1: 0.102754 Loss2: 1.386306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456204 Loss1: 0.077184 Loss2: 1.379020
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.535793 Loss1: 0.641821 Loss2: 1.893972
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.818745 Loss1: 0.401794 Loss2: 1.416951
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.703537 Loss1: 0.256647 Loss2: 1.446890
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.669102 Loss1: 0.255871 Loss2: 1.413231
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.584655 Loss1: 0.165294 Loss2: 1.419361
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.286662 Loss1: 0.510935 Loss2: 1.775727
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523753 Loss1: 0.122194 Loss2: 1.401559
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653754 Loss1: 0.332938 Loss2: 1.320816
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496927 Loss1: 0.099457 Loss2: 1.397470
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.550189 Loss1: 0.196377 Loss2: 1.353811
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.494125 Loss1: 0.101344 Loss2: 1.392781
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444419 Loss1: 0.056077 Loss2: 1.388342
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.473899 Loss1: 0.160790 Loss2: 1.313109
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432075 Loss1: 0.049030 Loss2: 1.383045
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469053 Loss1: 0.157435 Loss2: 1.311619
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449062 Loss1: 0.135479 Loss2: 1.313583
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.396465 Loss1: 0.095771 Loss2: 1.300694
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397844 Loss1: 0.101466 Loss2: 1.296378
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379608 Loss1: 0.085475 Loss2: 1.294133
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.643427 Loss1: 0.744103 Loss2: 1.899324
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384712 Loss1: 0.088774 Loss2: 1.295938
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.823885 Loss1: 0.352112 Loss2: 1.471772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.608492 Loss1: 0.194080 Loss2: 1.414412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511648 Loss1: 0.115613 Loss2: 1.396035
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.495991 Loss1: 0.638858 Loss2: 1.857133
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.728153 Loss1: 0.370403 Loss2: 1.357750 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.626801 Loss1: 0.247957 Loss2: 1.378844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.541942 Loss1: 0.198242 Loss2: 1.343700 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.482851 Loss1: 0.095633 Loss2: 1.387218
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497014 Loss1: 0.141661 Loss2: 1.355353
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487183 Loss1: 0.146182 Loss2: 1.341002
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425793 Loss1: 0.088095 Loss2: 1.337698
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415487 Loss1: 0.083778 Loss2: 1.331709
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400450 Loss1: 0.072778 Loss2: 1.327672
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.445030 Loss1: 0.649384 Loss2: 1.795646
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388471 Loss1: 0.061032 Loss2: 1.327439
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.755361 Loss1: 0.350405 Loss2: 1.404956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571633 Loss1: 0.218239 Loss2: 1.353394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.435522 Loss1: 0.633812 Loss2: 1.801710
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487315 Loss1: 0.139650 Loss2: 1.347665
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.713657 Loss1: 0.340389 Loss2: 1.373268
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408364 Loss1: 0.083307 Loss2: 1.325057
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.647637 Loss1: 0.264793 Loss2: 1.382843
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418934 Loss1: 0.095709 Loss2: 1.323225
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.632393 Loss1: 0.263533 Loss2: 1.368860
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419242 Loss1: 0.098702 Loss2: 1.320540
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.540733 Loss1: 0.176564 Loss2: 1.364169
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403891 Loss1: 0.080728 Loss2: 1.323163
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470232 Loss1: 0.113640 Loss2: 1.356592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423767 Loss1: 0.083044 Loss2: 1.340723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.482909 Loss1: 0.666513 Loss2: 1.816395
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389198 Loss1: 0.054144 Loss2: 1.335053
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599374 Loss1: 0.224856 Loss2: 1.374519 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.459591 Loss1: 0.118647 Loss2: 1.340944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458632 Loss1: 0.117409 Loss2: 1.341223
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.553340 Loss1: 0.701962 Loss2: 1.851378
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.771185 Loss1: 0.399230 Loss2: 1.371956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.569987 Loss1: 0.180803 Loss2: 1.389184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481237 Loss1: 0.123728 Loss2: 1.357509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422963 Loss1: 0.085357 Loss2: 1.337606
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.453035 Loss1: 0.101750 Loss2: 1.351285
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452585 Loss1: 0.097485 Loss2: 1.355100
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450218 Loss1: 0.107865 Loss2: 1.342353
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424263 Loss1: 0.079362 Loss2: 1.344901
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391217 Loss1: 0.058368 Loss2: 1.332849
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.447644 Loss1: 0.586172 Loss2: 1.861472
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398820 Loss1: 0.068822 Loss2: 1.329997
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667651 Loss1: 0.275530 Loss2: 1.392121 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506226 Loss1: 0.145548 Loss2: 1.360678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485468 Loss1: 0.120807 Loss2: 1.364660
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.564720 Loss1: 0.705443 Loss2: 1.859277
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440682 Loss1: 0.085829 Loss2: 1.354853
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.816954 Loss1: 0.489947 Loss2: 1.327007
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.751570 Loss1: 0.346786 Loss2: 1.404784
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451376 Loss1: 0.105519 Loss2: 1.345857
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.602995 Loss1: 0.248866 Loss2: 1.354129
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455053 Loss1: 0.101444 Loss2: 1.353609
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.564687 Loss1: 0.197162 Loss2: 1.367525
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462791 Loss1: 0.103761 Loss2: 1.359031
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468130 Loss1: 0.115384 Loss2: 1.352746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401555 Loss1: 0.074807 Loss2: 1.326747 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376470 Loss1: 0.053469 Loss2: 1.323001
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.718864 Loss1: 0.714181 Loss2: 2.004684
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.857432 Loss1: 0.496690 Loss2: 1.360742
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.711676 Loss1: 0.299317 Loss2: 1.412359
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.663867 Loss1: 0.256357 Loss2: 1.407510
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.559268 Loss1: 0.194627 Loss2: 1.364641
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547570 Loss1: 0.175590 Loss2: 1.371980
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.505334 Loss1: 0.138318 Loss2: 1.367016
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.770477 Loss1: 0.382733 Loss2: 1.387744 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604166 Loss1: 0.197985 Loss2: 1.406181 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503094 Loss1: 0.134185 Loss2: 1.368909 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476748 Loss1: 0.110056 Loss2: 1.366692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470027 Loss1: 0.112163 Loss2: 1.357864
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.529065 Loss1: 0.625844 Loss2: 1.903221
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784348 Loss1: 0.375167 Loss2: 1.409181 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452819 Loss1: 0.100191 Loss2: 1.352628
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.713440 Loss1: 0.260306 Loss2: 1.453134
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654329 Loss1: 0.244475 Loss2: 1.409854
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.623765 Loss1: 0.220692 Loss2: 1.403073
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.556619 Loss1: 0.143619 Loss2: 1.413000
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488535 Loss1: 0.087693 Loss2: 1.400842
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.377205 Loss1: 0.558678 Loss2: 1.818527
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462234 Loss1: 0.076014 Loss2: 1.386220
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455231 Loss1: 0.069263 Loss2: 1.385968 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450783 Loss1: 0.066892 Loss2: 1.383890 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571675 Loss1: 0.221350 Loss2: 1.350325 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464442 Loss1: 0.124892 Loss2: 1.339550 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389031 Loss1: 0.060006 Loss2: 1.329025
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.401766 Loss1: 0.589319 Loss2: 1.812448
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.843900 Loss1: 0.419314 Loss2: 1.424585 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645610 Loss1: 0.264561 Loss2: 1.381049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499813 Loss1: 0.127591 Loss2: 1.372221 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.429896 Loss1: 0.074671 Loss2: 1.355226 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.455489 Loss1: 0.107941 Loss2: 1.347548 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399229 Loss1: 0.048999 Loss2: 1.350230 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.560521 Loss1: 0.211713 Loss2: 1.348808 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.418415 Loss1: 0.083814 Loss2: 1.334601 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376547 Loss1: 0.054842 Loss2: 1.321705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389876 Loss1: 0.075081 Loss2: 1.314794
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.555635 Loss1: 0.698694 Loss2: 1.856941
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383710 Loss1: 0.069590 Loss2: 1.314120
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.832389 Loss1: 0.453812 Loss2: 1.378577
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.707646 Loss1: 0.270727 Loss2: 1.436920
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556920 Loss1: 0.180917 Loss2: 1.376003
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593184 Loss1: 0.214361 Loss2: 1.378824
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489304 Loss1: 0.116516 Loss2: 1.372788
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.436197 Loss1: 0.652785 Loss2: 1.783411
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426963 Loss1: 0.071408 Loss2: 1.355555
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.724267 Loss1: 0.399830 Loss2: 1.324437
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.397102 Loss1: 0.050081 Loss2: 1.347021
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.615249 Loss1: 0.252222 Loss2: 1.363027
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378030 Loss1: 0.037320 Loss2: 1.340711
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523381 Loss1: 0.202274 Loss2: 1.321107
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375128 Loss1: 0.042164 Loss2: 1.332964
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.432386 Loss1: 0.113396 Loss2: 1.318990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.353366 Loss1: 0.058695 Loss2: 1.294671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.333875 Loss1: 0.037733 Loss2: 1.296143
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.288042 Loss1: 0.482744 Loss2: 1.805298
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.340780 Loss1: 0.052101 Loss2: 1.288679
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.640479 Loss1: 0.280488 Loss2: 1.359992
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647240 Loss1: 0.253517 Loss2: 1.393724
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564896 Loss1: 0.203666 Loss2: 1.361230
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524808 Loss1: 0.160903 Loss2: 1.363905
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465542 Loss1: 0.099777 Loss2: 1.365765
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.257270 Loss1: 0.519655 Loss2: 1.737615
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.716884 Loss1: 0.411839 Loss2: 1.305045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636964 Loss1: 0.287638 Loss2: 1.349326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.493487 Loss1: 0.185772 Loss2: 1.307715 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.490303 Loss1: 0.173389 Loss2: 1.316914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.353744 Loss1: 0.063981 Loss2: 1.289764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.343176 Loss1: 0.061639 Loss2: 1.281537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.350588 Loss1: 0.071130 Loss2: 1.279458 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583273 Loss1: 0.189202 Loss2: 1.394070 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547074 Loss1: 0.134182 Loss2: 1.412892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.315236 Loss1: 0.571436 Loss2: 1.743800
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525991 Loss1: 0.130368 Loss2: 1.395623
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.681471 Loss1: 0.354762 Loss2: 1.326709
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.509784 Loss1: 0.111638 Loss2: 1.398145
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592817 Loss1: 0.231252 Loss2: 1.361565
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.475968 Loss1: 0.088358 Loss2: 1.387610
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461820 Loss1: 0.083066 Loss2: 1.378754
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.489325 Loss1: 0.169890 Loss2: 1.319436
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500740 Loss1: 0.181233 Loss2: 1.319508
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.454865 Loss1: 0.133709 Loss2: 1.321156
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441991 Loss1: 0.130413 Loss2: 1.311578
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.401561 Loss1: 0.095375 Loss2: 1.306186
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.634384 Loss1: 0.806838 Loss2: 1.827545
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373719 Loss1: 0.063759 Loss2: 1.309960
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.729647 Loss1: 0.376243 Loss2: 1.353405
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352884 Loss1: 0.055334 Loss2: 1.297550
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.494539 Loss1: 0.172320 Loss2: 1.322219 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.399240 Loss1: 0.090079 Loss2: 1.309160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395647 Loss1: 0.090404 Loss2: 1.305243
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.535082 Loss1: 0.641893 Loss2: 1.893189
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.818883 Loss1: 0.422890 Loss2: 1.395993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682522 Loss1: 0.248424 Loss2: 1.434098 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.348951 Loss1: 0.061250 Loss2: 1.287702
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573223 Loss1: 0.195239 Loss2: 1.377984
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495360 Loss1: 0.114944 Loss2: 1.380417
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466953 Loss1: 0.101278 Loss2: 1.365675
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416960 Loss1: 0.059831 Loss2: 1.357130
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398055 Loss1: 0.044408 Loss2: 1.353647
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.549879 Loss1: 0.727150 Loss2: 1.822729
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392343 Loss1: 0.047922 Loss2: 1.344421
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400202 Loss1: 0.056137 Loss2: 1.344065 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559405 Loss1: 0.222763 Loss2: 1.336642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.490743 Loss1: 0.153318 Loss2: 1.337425 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436850 Loss1: 0.100407 Loss2: 1.336443
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.504766 Loss1: 0.670912 Loss2: 1.833854
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.885282 Loss1: 0.505841 Loss2: 1.379442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.650993 Loss1: 0.242953 Loss2: 1.408040 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.548680 Loss1: 0.183337 Loss2: 1.365342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443699 Loss1: 0.086621 Loss2: 1.357078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426306 Loss1: 0.079060 Loss2: 1.347246 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422090 Loss1: 0.087177 Loss2: 1.334913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395654 Loss1: 0.063298 Loss2: 1.332356 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549453 Loss1: 0.202326 Loss2: 1.347127 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.421287 Loss1: 0.085703 Loss2: 1.335584 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.391173 Loss1: 0.064180 Loss2: 1.326993
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.528793 Loss1: 0.704202 Loss2: 1.824591
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.850307 Loss1: 0.502806 Loss2: 1.347500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.687887 Loss1: 0.279110 Loss2: 1.408776 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.351216 Loss1: 0.038280 Loss2: 1.312936
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523781 Loss1: 0.181127 Loss2: 1.342654
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515793 Loss1: 0.162721 Loss2: 1.353073
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519049 Loss1: 0.167867 Loss2: 1.351182
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458432 Loss1: 0.117166 Loss2: 1.341266
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406994 Loss1: 0.073353 Loss2: 1.333641
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.434694 Loss1: 0.621257 Loss2: 1.813438
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407830 Loss1: 0.079481 Loss2: 1.328349
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388384 Loss1: 0.064629 Loss2: 1.323754 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599169 Loss1: 0.245736 Loss2: 1.353433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525826 Loss1: 0.153811 Loss2: 1.372015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500509 Loss1: 0.147595 Loss2: 1.352914
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.299890 Loss1: 0.509364 Loss2: 1.790526
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.715623 Loss1: 0.353135 Loss2: 1.362488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614331 Loss1: 0.230577 Loss2: 1.383754 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520288 Loss1: 0.160821 Loss2: 1.359467 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447952 Loss1: 0.111611 Loss2: 1.336341 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.444046 Loss1: 0.109202 Loss2: 1.334844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408898 Loss1: 0.084092 Loss2: 1.324805 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369505 Loss1: 0.046948 Loss2: 1.322557 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994485
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503492 Loss1: 0.141426 Loss2: 1.362066 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.487803 Loss1: 0.127212 Loss2: 1.360591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.677015 Loss1: 0.742502 Loss2: 1.934512
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418957 Loss1: 0.072940 Loss2: 1.346017
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397477 Loss1: 0.061076 Loss2: 1.336401 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.630273 Loss1: 0.230760 Loss2: 1.399513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529223 Loss1: 0.133804 Loss2: 1.395420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487477 Loss1: 0.107729 Loss2: 1.379747 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436550 Loss1: 0.069445 Loss2: 1.367105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403102 Loss1: 0.038939 Loss2: 1.364163 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.655659 Loss1: 0.216151 Loss2: 1.439508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.627540 Loss1: 0.175867 Loss2: 1.451673 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.387369 Loss1: 0.589570 Loss2: 1.797799 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.643670 Loss1: 0.312536 Loss2: 1.331134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.616463 Loss1: 0.247270 Loss2: 1.369193 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.527942 Loss1: 0.191430 Loss2: 1.336512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431299 Loss1: 0.107764 Loss2: 1.323535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.383989 Loss1: 0.073539 Loss2: 1.310451 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.364845 Loss1: 0.062920 Loss2: 1.301925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352917 Loss1: 0.051807 Loss2: 1.301110 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.548499 Loss1: 0.161296 Loss2: 1.387203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513819 Loss1: 0.124691 Loss2: 1.389128 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.560647 Loss1: 0.654809 Loss2: 1.905838 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.859027 Loss1: 0.454158 Loss2: 1.404869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.707866 Loss1: 0.263618 Loss2: 1.444248 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591986 Loss1: 0.202616 Loss2: 1.389370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490265 Loss1: 0.121382 Loss2: 1.368883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.506612 Loss1: 0.134070 Loss2: 1.372542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467879 Loss1: 0.095091 Loss2: 1.372789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458208 Loss1: 0.097801 Loss2: 1.360407 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.501244 Loss1: 0.149785 Loss2: 1.351459 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458831 Loss1: 0.115636 Loss2: 1.343196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.457396 Loss1: 0.118113 Loss2: 1.339283
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.411543 Loss1: 0.639287 Loss2: 1.772256
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.682610 Loss1: 0.363783 Loss2: 1.318827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.566406 Loss1: 0.207431 Loss2: 1.358974 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.515589 Loss1: 0.196411 Loss2: 1.319178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.392772 Loss1: 0.091163 Loss2: 1.301609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.332554 Loss1: 0.039561 Loss2: 1.292993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.327485 Loss1: 0.042763 Loss2: 1.284722 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 03:00:13,617 | server.py:236 | fit_round 136 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.329531 Loss1: 0.045747 Loss2: 1.283784 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.661025 Loss1: 0.262798 Loss2: 1.398227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499356 Loss1: 0.105961 Loss2: 1.393395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.367305 Loss1: 0.531189 Loss2: 1.836117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.789716 Loss1: 0.402568 Loss2: 1.387148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.699059 Loss1: 0.257475 Loss2: 1.441584 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.564271 Loss1: 0.171659 Loss2: 1.392612 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.513273 Loss1: 0.137709 Loss2: 1.375564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.381044 Loss1: 0.472970 Loss2: 1.908074
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.495814 Loss1: 0.115904 Loss2: 1.379910
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.773993 Loss1: 0.370381 Loss2: 1.403612
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453683 Loss1: 0.089757 Loss2: 1.363926
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647631 Loss1: 0.223049 Loss2: 1.424582
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440308 Loss1: 0.075316 Loss2: 1.364992
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517570 Loss1: 0.123088 Loss2: 1.394482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484299 Loss1: 0.097846 Loss2: 1.386453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471881 Loss1: 0.084976 Loss2: 1.386905
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.635856 Loss1: 0.756034 Loss2: 1.879822
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.832813 Loss1: 0.464031 Loss2: 1.368782 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416521 Loss1: 0.038991 Loss2: 1.377530
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.722592 Loss1: 0.290671 Loss2: 1.431922
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528052 Loss1: 0.168461 Loss2: 1.359591
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506924 Loss1: 0.146828 Loss2: 1.360096
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449271 Loss1: 0.093519 Loss2: 1.355752
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439914 Loss1: 0.093723 Loss2: 1.346191
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416270 Loss1: 0.077191 Loss2: 1.339079
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379225 Loss1: 0.046532 Loss2: 1.332693
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374936 Loss1: 0.045060 Loss2: 1.329875
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 03:00:13,617][flwr][DEBUG] - fit_round 136 received 50 results and 0 failures
+INFO flwr 2023-10-12 03:00:54,580 | server.py:125 | fit progress: (136, 2.2225120815987025, {'accuracy': 0.5923}, 313762.358835965)
+>> Test accuracy: 0.592300
+[2023-10-12 03:00:54,580][flwr][INFO] - fit progress: (136, 2.2225120815987025, {'accuracy': 0.5923}, 313762.358835965)
+DEBUG flwr 2023-10-12 03:00:54,581 | server.py:173 | evaluate_round 136: strategy sampled 50 clients (out of 50)
+[2023-10-12 03:00:54,581][flwr][DEBUG] - evaluate_round 136: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 03:09:58,664 | server.py:187 | evaluate_round 136 received 50 results and 0 failures
+[2023-10-12 03:09:58,664][flwr][DEBUG] - evaluate_round 136 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 03:09:58,664 | server.py:222 | fit_round 137: strategy sampled 50 clients (out of 50)
+[2023-10-12 03:09:58,664][flwr][DEBUG] - fit_round 137: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.485478 Loss1: 0.622519 Loss2: 1.862959
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.857813 Loss1: 0.449963 Loss2: 1.407850
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.709538 Loss1: 0.251030 Loss2: 1.458508
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.491607 Loss1: 0.598024 Loss2: 1.893582
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601883 Loss1: 0.200457 Loss2: 1.401426
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.816381 Loss1: 0.419041 Loss2: 1.397341
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.602217 Loss1: 0.190620 Loss2: 1.411598
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.728677 Loss1: 0.263409 Loss2: 1.465268
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.559724 Loss1: 0.154980 Loss2: 1.404744
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.595585 Loss1: 0.190959 Loss2: 1.404625 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.581717 Loss1: 0.171735 Loss2: 1.409982 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.501870 Loss1: 0.107323 Loss2: 1.394548 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.522512 Loss1: 0.132019 Loss2: 1.390493 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.508699 Loss1: 0.131013 Loss2: 1.377686 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.543137 Loss1: 0.659005 Loss2: 1.884132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.711468 Loss1: 0.281006 Loss2: 1.430462 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606764 Loss1: 0.222341 Loss2: 1.384423
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.605643 Loss1: 0.691514 Loss2: 1.914129
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525909 Loss1: 0.146978 Loss2: 1.378931
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.846389 Loss1: 0.415281 Loss2: 1.431108
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454386 Loss1: 0.085534 Loss2: 1.368852
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.681330 Loss1: 0.251800 Loss2: 1.429530
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434984 Loss1: 0.075012 Loss2: 1.359972
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591532 Loss1: 0.178433 Loss2: 1.413099
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414225 Loss1: 0.058739 Loss2: 1.355486
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.567941 Loss1: 0.161507 Loss2: 1.406434
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390464 Loss1: 0.041217 Loss2: 1.349246
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.551164 Loss1: 0.144212 Loss2: 1.406952
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386795 Loss1: 0.045136 Loss2: 1.341659
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508886 Loss1: 0.111720 Loss2: 1.397166
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482517 Loss1: 0.093336 Loss2: 1.389181
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490061 Loss1: 0.099817 Loss2: 1.390244
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.481336 Loss1: 0.094778 Loss2: 1.386558
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.593595 Loss1: 0.717868 Loss2: 1.875727
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.740742 Loss1: 0.407779 Loss2: 1.332963
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.619969 Loss1: 0.255573 Loss2: 1.364396
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541344 Loss1: 0.180511 Loss2: 1.360834
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519010 Loss1: 0.180528 Loss2: 1.338482
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448296 Loss1: 0.111648 Loss2: 1.336648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424362 Loss1: 0.096679 Loss2: 1.327683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391851 Loss1: 0.068974 Loss2: 1.322877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390032 Loss1: 0.066654 Loss2: 1.323378 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373115 Loss1: 0.060167 Loss2: 1.312948 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986779
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433541 Loss1: 0.073325 Loss2: 1.360216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379347 Loss1: 0.036622 Loss2: 1.342726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385486 Loss1: 0.051296 Loss2: 1.334190
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.738395 Loss1: 0.784175 Loss2: 1.954221
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.856870 Loss1: 0.402117 Loss2: 1.454753
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.728249 Loss1: 0.251708 Loss2: 1.476542
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.644286 Loss1: 0.205804 Loss2: 1.438482
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.596763 Loss1: 0.158360 Loss2: 1.438403
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.788576 Loss1: 0.778307 Loss2: 2.010270
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.910861 Loss1: 0.527907 Loss2: 1.382953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.905953 Loss1: 0.398999 Loss2: 1.506954
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.504059 Loss1: 0.080981 Loss2: 1.423078
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497807 Loss1: 0.080544 Loss2: 1.417263 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482682 Loss1: 0.069432 Loss2: 1.413249 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466896 Loss1: 0.058785 Loss2: 1.408111 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433771 Loss1: 0.067349 Loss2: 1.366422 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990885
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.364864 Loss1: 0.599558 Loss2: 1.765306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561812 Loss1: 0.200883 Loss2: 1.360929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.414300 Loss1: 0.556360 Loss2: 1.857940
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.509002 Loss1: 0.193622 Loss2: 1.315380
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.686686 Loss1: 0.320362 Loss2: 1.366324
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.421215 Loss1: 0.101533 Loss2: 1.319682
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.645210 Loss1: 0.255126 Loss2: 1.390084
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.374669 Loss1: 0.071350 Loss2: 1.303319
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567127 Loss1: 0.202126 Loss2: 1.365000
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.355021 Loss1: 0.063365 Loss2: 1.291655
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389990 Loss1: 0.093721 Loss2: 1.296269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390716 Loss1: 0.094520 Loss2: 1.296196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377001 Loss1: 0.083764 Loss2: 1.293237 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.483213 Loss1: 0.121184 Loss2: 1.362029 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.402185 Loss1: 0.561148 Loss2: 1.841038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.664733 Loss1: 0.245299 Loss2: 1.419434 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.455432 Loss1: 0.600920 Loss2: 1.854512
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.585322 Loss1: 0.206930 Loss2: 1.378392
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.815019 Loss1: 0.445360 Loss2: 1.369659
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.563044 Loss1: 0.174352 Loss2: 1.388692
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.632638 Loss1: 0.218007 Loss2: 1.414631
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471999 Loss1: 0.098033 Loss2: 1.373966
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.430322 Loss1: 0.066264 Loss2: 1.364057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414036 Loss1: 0.056033 Loss2: 1.358003 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422811 Loss1: 0.072202 Loss2: 1.350609 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409720 Loss1: 0.057779 Loss2: 1.351941 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396142 Loss1: 0.058717 Loss2: 1.337425 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.281973 Loss1: 0.494861 Loss2: 1.787112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.597450 Loss1: 0.236867 Loss2: 1.360583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488300 Loss1: 0.153958 Loss2: 1.334343
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.452554 Loss1: 0.614457 Loss2: 1.838097
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.847190 Loss1: 0.459048 Loss2: 1.388142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.743536 Loss1: 0.327272 Loss2: 1.416264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557423 Loss1: 0.190404 Loss2: 1.367019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476188 Loss1: 0.112564 Loss2: 1.363624 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444590 Loss1: 0.089547 Loss2: 1.355043 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423885 Loss1: 0.074621 Loss2: 1.349264 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390558 Loss1: 0.054939 Loss2: 1.335619 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.827919 Loss1: 0.405831 Loss2: 1.422088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.682141 Loss1: 0.284239 Loss2: 1.397903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.599405 Loss1: 0.192932 Loss2: 1.406472
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.603547 Loss1: 0.718801 Loss2: 1.884745
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.554329 Loss1: 0.162947 Loss2: 1.391382
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.823492 Loss1: 0.458416 Loss2: 1.365076
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544671 Loss1: 0.152671 Loss2: 1.392000
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.644802 Loss1: 0.232611 Loss2: 1.412192
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526209 Loss1: 0.163349 Loss2: 1.362860
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500494 Loss1: 0.119383 Loss2: 1.381111
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514253 Loss1: 0.155876 Loss2: 1.358377
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477677 Loss1: 0.097674 Loss2: 1.380003
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477081 Loss1: 0.120575 Loss2: 1.356506
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458759 Loss1: 0.078261 Loss2: 1.380499
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423922 Loss1: 0.080242 Loss2: 1.343680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417854 Loss1: 0.082145 Loss2: 1.335709 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987723
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.523688 Loss1: 0.659828 Loss2: 1.863860
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.889290 Loss1: 0.481971 Loss2: 1.407319
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.711506 Loss1: 0.268625 Loss2: 1.442881
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.622225 Loss1: 0.223115 Loss2: 1.399110
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.474279 Loss1: 0.661173 Loss2: 1.813106
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.694341 Loss1: 0.356914 Loss2: 1.337427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.567798 Loss1: 0.200742 Loss2: 1.367056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519483 Loss1: 0.185317 Loss2: 1.334166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.440437 Loss1: 0.099905 Loss2: 1.340532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.435717 Loss1: 0.108092 Loss2: 1.327625 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.388445 Loss1: 0.066708 Loss2: 1.321738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.351584 Loss1: 0.044618 Loss2: 1.306966 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.414739 Loss1: 0.554819 Loss2: 1.859920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684405 Loss1: 0.262020 Loss2: 1.422384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.360866 Loss1: 0.557322 Loss2: 1.803544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.832002 Loss1: 0.496437 Loss2: 1.335565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684408 Loss1: 0.273713 Loss2: 1.410695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.675277 Loss1: 0.335741 Loss2: 1.339536 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.603430 Loss1: 0.235615 Loss2: 1.367815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539947 Loss1: 0.193828 Loss2: 1.346119 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.489011 Loss1: 0.158814 Loss2: 1.330198 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420694 Loss1: 0.092033 Loss2: 1.328661 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.750186 Loss1: 0.434711 Loss2: 1.315475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508185 Loss1: 0.204543 Loss2: 1.303642 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.509996 Loss1: 0.658399 Loss2: 1.851597
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.496278 Loss1: 0.180839 Loss2: 1.315439
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.921633 Loss1: 0.517581 Loss2: 1.404052
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.395717 Loss1: 0.097755 Loss2: 1.297962
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.787259 Loss1: 0.337856 Loss2: 1.449404
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.393317 Loss1: 0.101886 Loss2: 1.291431
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.658746 Loss1: 0.263609 Loss2: 1.395137
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382939 Loss1: 0.097246 Loss2: 1.285693
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.572850 Loss1: 0.170818 Loss2: 1.402031
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395758 Loss1: 0.105748 Loss2: 1.290009
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542461 Loss1: 0.168073 Loss2: 1.374389
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.341512 Loss1: 0.055849 Loss2: 1.285663
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464019 Loss1: 0.097505 Loss2: 1.366514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400018 Loss1: 0.052624 Loss2: 1.347395 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784418 Loss1: 0.393634 Loss2: 1.390784 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.543784 Loss1: 0.150324 Loss2: 1.393460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.586825 Loss1: 0.703239 Loss2: 1.883586
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.533770 Loss1: 0.146427 Loss2: 1.387344
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.743525 Loss1: 0.346917 Loss2: 1.396608
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471950 Loss1: 0.087440 Loss2: 1.384511
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.610836 Loss1: 0.193053 Loss2: 1.417783
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446159 Loss1: 0.068742 Loss2: 1.377417
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538930 Loss1: 0.155967 Loss2: 1.382964
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429045 Loss1: 0.058876 Loss2: 1.370169
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.533038 Loss1: 0.150390 Loss2: 1.382648
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452024 Loss1: 0.086509 Loss2: 1.365515
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481967 Loss1: 0.103551 Loss2: 1.378417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426594 Loss1: 0.059409 Loss2: 1.367185
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420730 Loss1: 0.059968 Loss2: 1.360762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425208 Loss1: 0.071659 Loss2: 1.353549 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.292760 Loss1: 0.519031 Loss2: 1.773729
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.728690 Loss1: 0.399698 Loss2: 1.328992
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.585931 Loss1: 0.201601 Loss2: 1.384329
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484253 Loss1: 0.151216 Loss2: 1.333038
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.546986 Loss1: 0.640234 Loss2: 1.906752
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.764091 Loss1: 0.345852 Loss2: 1.418239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.712068 Loss1: 0.263928 Loss2: 1.448140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.647329 Loss1: 0.231924 Loss2: 1.415405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448630 Loss1: 0.113532 Loss2: 1.335097
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.599203 Loss1: 0.185044 Loss2: 1.414159
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482840 Loss1: 0.151498 Loss2: 1.331342
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529276 Loss1: 0.126017 Loss2: 1.403259
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438909 Loss1: 0.097887 Loss2: 1.341021
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.506248 Loss1: 0.105918 Loss2: 1.400330
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453010 Loss1: 0.059256 Loss2: 1.393754
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438129 Loss1: 0.055172 Loss2: 1.382957
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426952 Loss1: 0.049369 Loss2: 1.377583
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.360135 Loss1: 0.551340 Loss2: 1.808795
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784911 Loss1: 0.450284 Loss2: 1.334627
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655106 Loss1: 0.287819 Loss2: 1.367288
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581336 Loss1: 0.235611 Loss2: 1.345725
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.505923 Loss1: 0.663940 Loss2: 1.841983
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.793842 Loss1: 0.424846 Loss2: 1.368996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669844 Loss1: 0.280283 Loss2: 1.389562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.494082 Loss1: 0.154091 Loss2: 1.339992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500722 Loss1: 0.154227 Loss2: 1.346495 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443664 Loss1: 0.110293 Loss2: 1.333371 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368267 Loss1: 0.071896 Loss2: 1.296371
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444165 Loss1: 0.115444 Loss2: 1.328721
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418582 Loss1: 0.090284 Loss2: 1.328298
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395093 Loss1: 0.073170 Loss2: 1.321923
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392872 Loss1: 0.067198 Loss2: 1.325674
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.487299 Loss1: 0.624883 Loss2: 1.862416
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.744701 Loss1: 0.396056 Loss2: 1.348644
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736871 Loss1: 0.325112 Loss2: 1.411759
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.543960 Loss1: 0.192854 Loss2: 1.351106
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.474728 Loss1: 0.676453 Loss2: 1.798275
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653858 Loss1: 0.329459 Loss2: 1.324400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572390 Loss1: 0.217265 Loss2: 1.355125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.486104 Loss1: 0.161260 Loss2: 1.324843 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450707 Loss1: 0.130176 Loss2: 1.320531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439911 Loss1: 0.121811 Loss2: 1.318100 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419442 Loss1: 0.114177 Loss2: 1.305265 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392480 Loss1: 0.084537 Loss2: 1.307943 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.789597 Loss1: 0.377590 Loss2: 1.412007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596156 Loss1: 0.186108 Loss2: 1.410048 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571104 Loss1: 0.157786 Loss2: 1.413318
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.455940 Loss1: 0.573962 Loss2: 1.881978
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.510764 Loss1: 0.109831 Loss2: 1.400933
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.782846 Loss1: 0.390570 Loss2: 1.392276
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469942 Loss1: 0.075708 Loss2: 1.394234
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701595 Loss1: 0.241106 Loss2: 1.460489
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427227 Loss1: 0.048792 Loss2: 1.378436
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604742 Loss1: 0.211493 Loss2: 1.393249
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428967 Loss1: 0.052984 Loss2: 1.375983
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.561048 Loss1: 0.161773 Loss2: 1.399275
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412106 Loss1: 0.041530 Loss2: 1.370576
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537872 Loss1: 0.151480 Loss2: 1.386392
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.514444 Loss1: 0.125221 Loss2: 1.389223
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.514608 Loss1: 0.133145 Loss2: 1.381462
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501770 Loss1: 0.119386 Loss2: 1.382384
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449126 Loss1: 0.064641 Loss2: 1.384485
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.309808 Loss1: 0.512789 Loss2: 1.797019
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.738193 Loss1: 0.379900 Loss2: 1.358292
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.711461 Loss1: 0.307765 Loss2: 1.403695
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.612364 Loss1: 0.247208 Loss2: 1.365155
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.560833 Loss1: 0.689095 Loss2: 1.871738
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.787614 Loss1: 0.411677 Loss2: 1.375937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.652770 Loss1: 0.236705 Loss2: 1.416065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.600550 Loss1: 0.230687 Loss2: 1.369863 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543191 Loss1: 0.168047 Loss2: 1.375144 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556846 Loss1: 0.196663 Loss2: 1.360183 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418934 Loss1: 0.083231 Loss2: 1.335702
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.535653 Loss1: 0.168138 Loss2: 1.367515
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488287 Loss1: 0.130036 Loss2: 1.358252
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481257 Loss1: 0.116541 Loss2: 1.364716
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482488 Loss1: 0.123959 Loss2: 1.358529
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.550030 Loss1: 0.692732 Loss2: 1.857298
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784477 Loss1: 0.397162 Loss2: 1.387315
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.743821 Loss1: 0.318684 Loss2: 1.425137
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586953 Loss1: 0.209494 Loss2: 1.377459
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.548852 Loss1: 0.615719 Loss2: 1.933132
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.792101 Loss1: 0.365736 Loss2: 1.426364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.689533 Loss1: 0.247742 Loss2: 1.441792 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.615754 Loss1: 0.204285 Loss2: 1.411469 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.577214 Loss1: 0.168701 Loss2: 1.408513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512632 Loss1: 0.107857 Loss2: 1.404775 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388993 Loss1: 0.049535 Loss2: 1.339458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472646 Loss1: 0.084124 Loss2: 1.388522
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501748 Loss1: 0.118461 Loss2: 1.383286
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.470665 Loss1: 0.084935 Loss2: 1.385730
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452415 Loss1: 0.071681 Loss2: 1.380733
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.609728 Loss1: 0.692529 Loss2: 1.917199
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.843118 Loss1: 0.424650 Loss2: 1.418467
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.803578 Loss1: 0.337424 Loss2: 1.466154
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586451 Loss1: 0.175714 Loss2: 1.410737
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.679830 Loss1: 0.749912 Loss2: 1.929919
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.706949 Loss1: 0.365430 Loss2: 1.341519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.626963 Loss1: 0.253719 Loss2: 1.373243
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533876 Loss1: 0.130793 Loss2: 1.403083
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.517442 Loss1: 0.174943 Loss2: 1.342499
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.493138 Loss1: 0.096712 Loss2: 1.396426
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456549 Loss1: 0.063627 Loss2: 1.392922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429547 Loss1: 0.044616 Loss2: 1.384931 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418987 Loss1: 0.040967 Loss2: 1.378020 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.336223 Loss1: 0.035308 Loss2: 1.300914 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.468766 Loss1: 0.642240 Loss2: 1.826527
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.717290 Loss1: 0.376696 Loss2: 1.340594
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624837 Loss1: 0.251482 Loss2: 1.373355
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523682 Loss1: 0.185596 Loss2: 1.338085
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.603416 Loss1: 0.694217 Loss2: 1.909199
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.721521 Loss1: 0.318837 Loss2: 1.402684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.623482 Loss1: 0.206190 Loss2: 1.417292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565673 Loss1: 0.184308 Loss2: 1.381366 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543173 Loss1: 0.158228 Loss2: 1.384944 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.558039 Loss1: 0.166597 Loss2: 1.391442 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.330348 Loss1: 0.032171 Loss2: 1.298177
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.543705 Loss1: 0.157748 Loss2: 1.385957
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477102 Loss1: 0.098271 Loss2: 1.378831
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432719 Loss1: 0.067222 Loss2: 1.365497
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424044 Loss1: 0.066986 Loss2: 1.357058
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.524342 Loss1: 0.683059 Loss2: 1.841283
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.735975 Loss1: 0.371479 Loss2: 1.364495
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.657713 Loss1: 0.266356 Loss2: 1.391357
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562300 Loss1: 0.194712 Loss2: 1.367587
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.548207 Loss1: 0.643270 Loss2: 1.904937
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507666 Loss1: 0.149513 Loss2: 1.358153
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.863991 Loss1: 0.475660 Loss2: 1.388332
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475307 Loss1: 0.121910 Loss2: 1.353397
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.697256 Loss1: 0.280968 Loss2: 1.416288
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447287 Loss1: 0.098498 Loss2: 1.348789
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581960 Loss1: 0.192185 Loss2: 1.389775
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419906 Loss1: 0.078034 Loss2: 1.341873
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522315 Loss1: 0.151823 Loss2: 1.370492
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394575 Loss1: 0.061820 Loss2: 1.332755
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499813 Loss1: 0.127110 Loss2: 1.372704
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426696 Loss1: 0.095236 Loss2: 1.331460
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436930 Loss1: 0.078397 Loss2: 1.358532
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442912 Loss1: 0.088213 Loss2: 1.354700
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441037 Loss1: 0.087103 Loss2: 1.353934
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406241 Loss1: 0.058150 Loss2: 1.348092
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.515564 Loss1: 0.595262 Loss2: 1.920302
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.797184 Loss1: 0.357029 Loss2: 1.440155
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.671982 Loss1: 0.212136 Loss2: 1.459847
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.608225 Loss1: 0.732155 Loss2: 1.876069
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.608927 Loss1: 0.194860 Loss2: 1.414066
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.867519 Loss1: 0.501575 Loss2: 1.365944
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.591514 Loss1: 0.165573 Loss2: 1.425941
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576046 Loss1: 0.159391 Loss2: 1.416655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518829 Loss1: 0.108780 Loss2: 1.410050 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470652 Loss1: 0.076036 Loss2: 1.394616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469477 Loss1: 0.079263 Loss2: 1.390214 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.486023 Loss1: 0.096904 Loss2: 1.389119 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370274 Loss1: 0.038822 Loss2: 1.331452 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997768
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.587482 Loss1: 0.637756 Loss2: 1.949726
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.858460 Loss1: 0.416929 Loss2: 1.441531
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.754219 Loss1: 0.274060 Loss2: 1.480158
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.668722 Loss1: 0.246299 Loss2: 1.422423
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.616638 Loss1: 0.722100 Loss2: 1.894538
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.845149 Loss1: 0.441649 Loss2: 1.403501 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.777217 Loss1: 0.327761 Loss2: 1.449456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.653474 Loss1: 0.251412 Loss2: 1.402063 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 03:39:08,208 | server.py:236 | fit_round 137 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.623179 Loss1: 0.219223 Loss2: 1.403955 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.604489 Loss1: 0.202523 Loss2: 1.401967 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.515446 Loss1: 0.113042 Loss2: 1.402403
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.558539 Loss1: 0.154341 Loss2: 1.404198
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.533356 Loss1: 0.142197 Loss2: 1.391159
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.478738 Loss1: 0.095750 Loss2: 1.382987
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450109 Loss1: 0.070358 Loss2: 1.379750
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.503532 Loss1: 0.613654 Loss2: 1.889878
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.942731 Loss1: 0.459814 Loss2: 1.482916
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.695455 Loss1: 0.233978 Loss2: 1.461477
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623067 Loss1: 0.194788 Loss2: 1.428279
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.469885 Loss1: 0.634412 Loss2: 1.835473
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617774 Loss1: 0.177245 Loss2: 1.440529
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.797458 Loss1: 0.442497 Loss2: 1.354960
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.551621 Loss1: 0.122002 Loss2: 1.429619
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.708372 Loss1: 0.302825 Loss2: 1.405546
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518030 Loss1: 0.098728 Loss2: 1.419302
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528423 Loss1: 0.174274 Loss2: 1.354149
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497977 Loss1: 0.159484 Loss2: 1.338493
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.543148 Loss1: 0.126381 Loss2: 1.416767
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465833 Loss1: 0.128694 Loss2: 1.337139
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.522229 Loss1: 0.101790 Loss2: 1.420439
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480003 Loss1: 0.147158 Loss2: 1.332844
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.493419 Loss1: 0.082523 Loss2: 1.410896
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432725 Loss1: 0.092932 Loss2: 1.339793 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.301758 Loss1: 0.455408 Loss2: 1.846350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.623355 Loss1: 0.202673 Loss2: 1.420682 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586792 Loss1: 0.207662 Loss2: 1.379131 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.595110 Loss1: 0.204865 Loss2: 1.390245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576983 Loss1: 0.189403 Loss2: 1.387580 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488689 Loss1: 0.100009 Loss2: 1.388681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454500 Loss1: 0.078966 Loss2: 1.375534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429073 Loss1: 0.061402 Loss2: 1.367671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416472 Loss1: 0.053632 Loss2: 1.362840 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995404
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.411833 Loss1: 0.073575 Loss2: 1.338258 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 03:39:08,208][flwr][DEBUG] - fit_round 137 received 50 results and 0 failures
+INFO flwr 2023-10-12 03:39:50,476 | server.py:125 | fit progress: (137, 2.2192010569115417, {'accuracy': 0.5942}, 316098.254376675)
+>> Test accuracy: 0.594200
+[2023-10-12 03:39:50,476][flwr][INFO] - fit progress: (137, 2.2192010569115417, {'accuracy': 0.5942}, 316098.254376675)
+DEBUG flwr 2023-10-12 03:39:50,476 | server.py:173 | evaluate_round 137: strategy sampled 50 clients (out of 50)
+[2023-10-12 03:39:50,476][flwr][DEBUG] - evaluate_round 137: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 03:48:54,994 | server.py:187 | evaluate_round 137 received 50 results and 0 failures
+[2023-10-12 03:48:54,994][flwr][DEBUG] - evaluate_round 137 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 03:48:54,995 | server.py:222 | fit_round 138: strategy sampled 50 clients (out of 50)
+[2023-10-12 03:48:54,995][flwr][DEBUG] - fit_round 138: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.601519 Loss1: 0.637777 Loss2: 1.963742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.696500 Loss1: 0.219139 Loss2: 1.477361 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594755 Loss1: 0.163003 Loss2: 1.431753
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.427397 Loss1: 0.570787 Loss2: 1.856610
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.800804 Loss1: 0.372318 Loss2: 1.428486 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.662058 Loss1: 0.234226 Loss2: 1.427832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592049 Loss1: 0.202280 Loss2: 1.389769 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515050 Loss1: 0.120748 Loss2: 1.394301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512932 Loss1: 0.129853 Loss2: 1.383079 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443080 Loss1: 0.074651 Loss2: 1.368429 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433674 Loss1: 0.064017 Loss2: 1.369658 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.780473 Loss1: 0.339718 Loss2: 1.440755 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.625981 Loss1: 0.173675 Loss2: 1.452306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.446745 Loss1: 0.601353 Loss2: 1.845392
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.630615 Loss1: 0.175511 Loss2: 1.455105
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.764470 Loss1: 0.409035 Loss2: 1.355435
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.571610 Loss1: 0.126653 Loss2: 1.444958
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682581 Loss1: 0.273608 Loss2: 1.408973
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.558975 Loss1: 0.121475 Loss2: 1.437500
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.516773 Loss1: 0.083200 Loss2: 1.433573 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.497708 Loss1: 0.074581 Loss2: 1.423127 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481254 Loss1: 0.059238 Loss2: 1.422017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439104 Loss1: 0.097570 Loss2: 1.341534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411227 Loss1: 0.083291 Loss2: 1.327936 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.786201 Loss1: 0.434530 Loss2: 1.351670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630294 Loss1: 0.241859 Loss2: 1.388435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.307707 Loss1: 0.512679 Loss2: 1.795028 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.543734 Loss1: 0.164696 Loss2: 1.379038 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.632223 Loss1: 0.261894 Loss2: 1.370328 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396451 Loss1: 0.059043 Loss2: 1.337407 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.451606 Loss1: 0.110935 Loss2: 1.340670 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435342 Loss1: 0.106330 Loss2: 1.329012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394551 Loss1: 0.065445 Loss2: 1.329106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402388 Loss1: 0.079427 Loss2: 1.322961 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990809
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.550932 Loss1: 0.151322 Loss2: 1.399610 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.521249 Loss1: 0.123148 Loss2: 1.398102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.652270 Loss1: 0.696082 Loss2: 1.956188
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.496344 Loss1: 0.094747 Loss2: 1.401597
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.890742 Loss1: 0.486649 Loss2: 1.404093
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.498096 Loss1: 0.097750 Loss2: 1.400346
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462991 Loss1: 0.065998 Loss2: 1.396992 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571270 Loss1: 0.164612 Loss2: 1.406658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484088 Loss1: 0.086478 Loss2: 1.397610 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.389169 Loss1: 0.580445 Loss2: 1.808724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.636882 Loss1: 0.310881 Loss2: 1.326001 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.464574 Loss1: 0.133993 Loss2: 1.330581 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463387 Loss1: 0.136489 Loss2: 1.326899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.429620 Loss1: 0.104909 Loss2: 1.324710
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.444581 Loss1: 0.625034 Loss2: 1.819546
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.820012 Loss1: 0.449651 Loss2: 1.370361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.616376 Loss1: 0.220464 Loss2: 1.395912 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543099 Loss1: 0.181382 Loss2: 1.361716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447042 Loss1: 0.102031 Loss2: 1.345012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469979 Loss1: 0.118241 Loss2: 1.351738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449389 Loss1: 0.106740 Loss2: 1.342648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.704924 Loss1: 0.260736 Loss2: 1.444188 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572506 Loss1: 0.182650 Loss2: 1.389856 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.508317 Loss1: 0.141489 Loss2: 1.366827 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.522050 Loss1: 0.147788 Loss2: 1.374262
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.742213 Loss1: 0.721607 Loss2: 2.020606
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.777062 Loss1: 0.375556 Loss2: 1.401506 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977679
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.528190 Loss1: 0.153215 Loss2: 1.374975
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.678006 Loss1: 0.239981 Loss2: 1.438025
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.614026 Loss1: 0.199717 Loss2: 1.414309
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.531553 Loss1: 0.132277 Loss2: 1.399276
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.520570 Loss1: 0.120810 Loss2: 1.399759
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483659 Loss1: 0.098215 Loss2: 1.385444
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465372 Loss1: 0.084271 Loss2: 1.381101
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.525872 Loss1: 0.676869 Loss2: 1.849003
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.727637 Loss1: 0.372695 Loss2: 1.354943 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.624732 Loss1: 0.258836 Loss2: 1.365896 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473518 Loss1: 0.116457 Loss2: 1.357061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433875 Loss1: 0.083977 Loss2: 1.349898
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.459497 Loss1: 0.630630 Loss2: 1.828867
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.720798 Loss1: 0.377001 Loss2: 1.343798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.630509 Loss1: 0.227349 Loss2: 1.403160 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536151 Loss1: 0.195035 Loss2: 1.341116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.520234 Loss1: 0.172299 Loss2: 1.347935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435976 Loss1: 0.105107 Loss2: 1.330869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423156 Loss1: 0.092829 Loss2: 1.330327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397421 Loss1: 0.072671 Loss2: 1.324750 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522733 Loss1: 0.120730 Loss2: 1.402002 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468548 Loss1: 0.101340 Loss2: 1.367207 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.452238 Loss1: 0.088841 Loss2: 1.363397
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.417944 Loss1: 0.637927 Loss2: 1.780018
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630798 Loss1: 0.317993 Loss2: 1.312804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.550048 Loss1: 0.214130 Loss2: 1.335918 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.474395 Loss1: 0.160102 Loss2: 1.314293 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423574 Loss1: 0.116062 Loss2: 1.307512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417394 Loss1: 0.122408 Loss2: 1.294986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381963 Loss1: 0.086141 Loss2: 1.295821 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367276 Loss1: 0.075286 Loss2: 1.291991 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581390 Loss1: 0.240472 Loss2: 1.340919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493752 Loss1: 0.153378 Loss2: 1.340374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.560251 Loss1: 0.722949 Loss2: 1.837302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.754147 Loss1: 0.381893 Loss2: 1.372254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604393 Loss1: 0.212768 Loss2: 1.391624 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.590398 Loss1: 0.227049 Loss2: 1.363349 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.481790 Loss1: 0.127573 Loss2: 1.354217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440452 Loss1: 0.089190 Loss2: 1.351262
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.610874 Loss1: 0.743229 Loss2: 1.867645
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.685763 Loss1: 0.347566 Loss2: 1.338196 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384350 Loss1: 0.049740 Loss2: 1.334610
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551455 Loss1: 0.207375 Loss2: 1.344080
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517149 Loss1: 0.181373 Loss2: 1.335776
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.439546 Loss1: 0.115035 Loss2: 1.324511
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.425691 Loss1: 0.109360 Loss2: 1.316331
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406667 Loss1: 0.093475 Loss2: 1.313192
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.424559 Loss1: 0.623736 Loss2: 1.800823
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403179 Loss1: 0.095561 Loss2: 1.307619
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391524 Loss1: 0.086695 Loss2: 1.304829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379251 Loss1: 0.077670 Loss2: 1.301581 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496117 Loss1: 0.142793 Loss2: 1.353323 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415903 Loss1: 0.079375 Loss2: 1.336527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.383466 Loss1: 0.058154 Loss2: 1.325312
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.514304 Loss1: 0.663756 Loss2: 1.850547
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.816161 Loss1: 0.449916 Loss2: 1.366244 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.661038 Loss1: 0.246377 Loss2: 1.414661 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462674 Loss1: 0.095628 Loss2: 1.367046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478750 Loss1: 0.124945 Loss2: 1.353805 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489743 Loss1: 0.134935 Loss2: 1.354809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441576 Loss1: 0.093725 Loss2: 1.347851 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413928 Loss1: 0.067804 Loss2: 1.346124 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497651 Loss1: 0.139901 Loss2: 1.357750 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.429031 Loss1: 0.083506 Loss2: 1.345525 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400672 Loss1: 0.056969 Loss2: 1.343703
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.465346 Loss1: 0.613303 Loss2: 1.852043
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.810876 Loss1: 0.405843 Loss2: 1.405033 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.679084 Loss1: 0.231130 Loss2: 1.447954 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.495830 Loss1: 0.113149 Loss2: 1.382680 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458886 Loss1: 0.087048 Loss2: 1.371838 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461500 Loss1: 0.092103 Loss2: 1.369397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438399 Loss1: 0.071656 Loss2: 1.366743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416637 Loss1: 0.053332 Loss2: 1.363305 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495876 Loss1: 0.133447 Loss2: 1.362429 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399264 Loss1: 0.055595 Loss2: 1.343670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406801 Loss1: 0.070257 Loss2: 1.336544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388919 Loss1: 0.053204 Loss2: 1.335715 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.720272 Loss1: 0.337066 Loss2: 1.383206 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502428 Loss1: 0.144380 Loss2: 1.358049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419038 Loss1: 0.086252 Loss2: 1.332785 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421073 Loss1: 0.096149 Loss2: 1.324924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.382579 Loss1: 0.058755 Loss2: 1.323824 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995192
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559833 Loss1: 0.238274 Loss2: 1.321560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476856 Loss1: 0.150635 Loss2: 1.326222 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.541434 Loss1: 0.619428 Loss2: 1.922006 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.796449 Loss1: 0.363361 Loss2: 1.433088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.745905 Loss1: 0.295689 Loss2: 1.450215 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.639497 Loss1: 0.218106 Loss2: 1.421391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.539259 Loss1: 0.121790 Loss2: 1.417469 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.519646 Loss1: 0.108027 Loss2: 1.411618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489741 Loss1: 0.086049 Loss2: 1.403692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447042 Loss1: 0.050766 Loss2: 1.396276 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.483251 Loss1: 0.172883 Loss2: 1.310368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.412267 Loss1: 0.112350 Loss2: 1.299917 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.450389 Loss1: 0.652177 Loss2: 1.798212 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.786002 Loss1: 0.447002 Loss2: 1.339000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.646629 Loss1: 0.261829 Loss2: 1.384801 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.532904 Loss1: 0.196602 Loss2: 1.336302 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443868 Loss1: 0.118409 Loss2: 1.325459 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.402952 Loss1: 0.079288 Loss2: 1.323664
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.462311 Loss1: 0.611705 Loss2: 1.850606
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.741634 Loss1: 0.372409 Loss2: 1.369225 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375015 Loss1: 0.066520 Loss2: 1.308495
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636579 Loss1: 0.239086 Loss2: 1.397493
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540104 Loss1: 0.176978 Loss2: 1.363125
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505648 Loss1: 0.151113 Loss2: 1.354536
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511136 Loss1: 0.151175 Loss2: 1.359960
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.511394 Loss1: 0.156327 Loss2: 1.355067
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.359743 Loss1: 0.527218 Loss2: 1.832525
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.526614 Loss1: 0.168031 Loss2: 1.358583
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.487636 Loss1: 0.139744 Loss2: 1.347892
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.633651 Loss1: 0.250204 Loss2: 1.383447
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439622 Loss1: 0.092235 Loss2: 1.347387
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574168 Loss1: 0.175253 Loss2: 1.398915
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535103 Loss1: 0.158710 Loss2: 1.376393
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472648 Loss1: 0.095168 Loss2: 1.377480
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444958 Loss1: 0.085911 Loss2: 1.359047
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451473 Loss1: 0.086903 Loss2: 1.364570
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.454875 Loss1: 0.616816 Loss2: 1.838058
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414489 Loss1: 0.053130 Loss2: 1.361359
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427290 Loss1: 0.078339 Loss2: 1.348951 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400340 Loss1: 0.048952 Loss2: 1.351388 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519782 Loss1: 0.154141 Loss2: 1.365642 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.517543 Loss1: 0.155396 Loss2: 1.362147 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.502377 Loss1: 0.147334 Loss2: 1.355043
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.457296 Loss1: 0.513960 Loss2: 1.943337
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.785763 Loss1: 0.337122 Loss2: 1.448641 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.720344 Loss1: 0.230883 Loss2: 1.489461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.651684 Loss1: 0.194607 Loss2: 1.457078 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.519278 Loss1: 0.080054 Loss2: 1.439223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.513814 Loss1: 0.085447 Loss2: 1.428367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.492346 Loss1: 0.061706 Loss2: 1.430640 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458346 Loss1: 0.040933 Loss2: 1.417413 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569024 Loss1: 0.175780 Loss2: 1.393244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451150 Loss1: 0.082191 Loss2: 1.368959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423640 Loss1: 0.059022 Loss2: 1.364618
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.384650 Loss1: 0.541594 Loss2: 1.843056
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.776794 Loss1: 0.421994 Loss2: 1.354801 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656524 Loss1: 0.251212 Loss2: 1.405312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471634 Loss1: 0.130776 Loss2: 1.340858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433464 Loss1: 0.100173 Loss2: 1.333291 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.304882 Loss1: 0.507145 Loss2: 1.797738
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438366 Loss1: 0.109211 Loss2: 1.329155
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.805854 Loss1: 0.428362 Loss2: 1.377493
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395898 Loss1: 0.065567 Loss2: 1.330331
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.683449 Loss1: 0.269988 Loss2: 1.413461
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386059 Loss1: 0.065333 Loss2: 1.320726
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.504003 Loss1: 0.138054 Loss2: 1.365949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450297 Loss1: 0.106480 Loss2: 1.343817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415553 Loss1: 0.078790 Loss2: 1.336763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396176 Loss1: 0.063532 Loss2: 1.332644 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411222 Loss1: 0.080874 Loss2: 1.330348 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494967 Loss1: 0.117842 Loss2: 1.377124 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484410 Loss1: 0.118817 Loss2: 1.365593 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455672 Loss1: 0.094913 Loss2: 1.360759
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.351185 Loss1: 0.542763 Loss2: 1.808422
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466700 Loss1: 0.097417 Loss2: 1.369283
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.714156 Loss1: 0.374467 Loss2: 1.339689
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653023 Loss1: 0.278644 Loss2: 1.374379
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530402 Loss1: 0.185164 Loss2: 1.345237
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.488359 Loss1: 0.141224 Loss2: 1.347135
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443620 Loss1: 0.097208 Loss2: 1.346411
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.533517 Loss1: 0.710975 Loss2: 1.822542
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.396850 Loss1: 0.074647 Loss2: 1.322202
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.739523 Loss1: 0.376701 Loss2: 1.362822
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412927 Loss1: 0.089558 Loss2: 1.323370
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.584282 Loss1: 0.207376 Loss2: 1.376905
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374263 Loss1: 0.054835 Loss2: 1.319428
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.532850 Loss1: 0.190441 Loss2: 1.342409
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.342756 Loss1: 0.035117 Loss2: 1.307639
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452016 Loss1: 0.107003 Loss2: 1.345013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.392555 Loss1: 0.069830 Loss2: 1.322725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391486 Loss1: 0.069340 Loss2: 1.322146
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.522140 Loss1: 0.625212 Loss2: 1.896928
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367175 Loss1: 0.054938 Loss2: 1.312237
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.761256 Loss1: 0.350558 Loss2: 1.410698
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.707614 Loss1: 0.259994 Loss2: 1.447620
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.632195 Loss1: 0.221391 Loss2: 1.410804
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.577202 Loss1: 0.164467 Loss2: 1.412735
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531984 Loss1: 0.130396 Loss2: 1.401588
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.430394 Loss1: 0.598093 Loss2: 1.832302
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.723598 Loss1: 0.376859 Loss2: 1.346739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611416 Loss1: 0.214936 Loss2: 1.396479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508156 Loss1: 0.164185 Loss2: 1.343971 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441916 Loss1: 0.055111 Loss2: 1.386805
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492051 Loss1: 0.151647 Loss2: 1.340404
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463297 Loss1: 0.111747 Loss2: 1.351551
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.417927 Loss1: 0.083526 Loss2: 1.334401
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400918 Loss1: 0.074604 Loss2: 1.326314
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388943 Loss1: 0.071556 Loss2: 1.317387
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.379656 Loss1: 0.544768 Loss2: 1.834888
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368219 Loss1: 0.057253 Loss2: 1.310966
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622881 Loss1: 0.236696 Loss2: 1.386185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484976 Loss1: 0.141304 Loss2: 1.343672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491726 Loss1: 0.157974 Loss2: 1.333752
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.538139 Loss1: 0.695501 Loss2: 1.842638
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.768854 Loss1: 0.350615 Loss2: 1.418239 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594536 Loss1: 0.190903 Loss2: 1.403632 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.584691 Loss1: 0.200509 Loss2: 1.384182 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.530566 Loss1: 0.145143 Loss2: 1.385423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.560049 Loss1: 0.174923 Loss2: 1.385126 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484777 Loss1: 0.105047 Loss2: 1.379730 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475134 Loss1: 0.096038 Loss2: 1.379096 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559148 Loss1: 0.198138 Loss2: 1.361010 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.489629 Loss1: 0.127211 Loss2: 1.362418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.424543 Loss1: 0.071185 Loss2: 1.353358
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.570741 Loss1: 0.628174 Loss2: 1.942567
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399938 Loss1: 0.058490 Loss2: 1.341448
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.810092 Loss1: 0.380223 Loss2: 1.429870
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373225 Loss1: 0.045103 Loss2: 1.328122
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736777 Loss1: 0.273990 Loss2: 1.462787
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361877 Loss1: 0.034480 Loss2: 1.327398
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642057 Loss1: 0.206788 Loss2: 1.435269
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.598565 Loss1: 0.167401 Loss2: 1.431164
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542183 Loss1: 0.117229 Loss2: 1.424954
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512910 Loss1: 0.099402 Loss2: 1.413508
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.509675 Loss1: 0.095813 Loss2: 1.413862
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.464161 Loss1: 0.056674 Loss2: 1.407487
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.432171 Loss1: 0.592608 Loss2: 1.839563
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466354 Loss1: 0.063335 Loss2: 1.403018
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.686975 Loss1: 0.323058 Loss2: 1.363917
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.616989 Loss1: 0.223224 Loss2: 1.393765
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563901 Loss1: 0.186574 Loss2: 1.377327
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524258 Loss1: 0.159698 Loss2: 1.364560
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503604 Loss1: 0.136366 Loss2: 1.367238
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.462862 Loss1: 0.100407 Loss2: 1.362455
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.462954 Loss1: 0.621484 Loss2: 1.841469
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450639 Loss1: 0.097932 Loss2: 1.352707
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.777661 Loss1: 0.416930 Loss2: 1.360732
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.471793 Loss1: 0.116900 Loss2: 1.354893
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634475 Loss1: 0.239414 Loss2: 1.395062
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437480 Loss1: 0.081425 Loss2: 1.356055
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490401 Loss1: 0.143352 Loss2: 1.347048
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.459261 Loss1: 0.120637 Loss2: 1.338624
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.430695 Loss1: 0.098086 Loss2: 1.332609
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.421081 Loss1: 0.091296 Loss2: 1.329785
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.412766 Loss1: 0.083589 Loss2: 1.329177
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430032 Loss1: 0.107140 Loss2: 1.322892
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.586308 Loss1: 0.764085 Loss2: 1.822223
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384957 Loss1: 0.063820 Loss2: 1.321137
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.733328 Loss1: 0.399251 Loss2: 1.334077
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.613403 Loss1: 0.254963 Loss2: 1.358440
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584024 Loss1: 0.251119 Loss2: 1.332905
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485438 Loss1: 0.156979 Loss2: 1.328459
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431186 Loss1: 0.114674 Loss2: 1.316512
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.379259 Loss1: 0.071193 Loss2: 1.308066
+DEBUG flwr 2023-10-12 04:17:10,386 | server.py:236 | fit_round 138 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.370420 Loss1: 0.566634 Loss2: 1.803785
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.401131 Loss1: 0.101099 Loss2: 1.300033
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.670508 Loss1: 0.321707 Loss2: 1.348802
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434963 Loss1: 0.127287 Loss2: 1.307676
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.607684 Loss1: 0.234542 Loss2: 1.373142
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421996 Loss1: 0.115314 Loss2: 1.306682
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.538440 Loss1: 0.192688 Loss2: 1.345752
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.552127 Loss1: 0.193730 Loss2: 1.358397
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.486538 Loss1: 0.146108 Loss2: 1.340429
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444635 Loss1: 0.106859 Loss2: 1.337777
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446866 Loss1: 0.104385 Loss2: 1.342481
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.502665 Loss1: 0.682362 Loss2: 1.820302
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.785194 Loss1: 0.434382 Loss2: 1.350811 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.708432 Loss1: 0.313664 Loss2: 1.394768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554122 Loss1: 0.191449 Loss2: 1.362673 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485195 Loss1: 0.148334 Loss2: 1.336861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.382459 Loss1: 0.056201 Loss2: 1.326258 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 04:17:10,386][flwr][DEBUG] - fit_round 138 received 50 results and 0 failures
+INFO flwr 2023-10-12 04:17:50,641 | server.py:125 | fit progress: (138, 2.2246322159569103, {'accuracy': 0.5925}, 318378.419111409)
+>> Test accuracy: 0.592500
+[2023-10-12 04:17:50,641][flwr][INFO] - fit progress: (138, 2.2246322159569103, {'accuracy': 0.5925}, 318378.419111409)
+DEBUG flwr 2023-10-12 04:17:50,641 | server.py:173 | evaluate_round 138: strategy sampled 50 clients (out of 50)
+[2023-10-12 04:17:50,641][flwr][DEBUG] - evaluate_round 138: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 04:26:57,586 | server.py:187 | evaluate_round 138 received 50 results and 0 failures
+[2023-10-12 04:26:57,586][flwr][DEBUG] - evaluate_round 138 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 04:26:57,587 | server.py:222 | fit_round 139: strategy sampled 50 clients (out of 50)
+[2023-10-12 04:26:57,587][flwr][DEBUG] - fit_round 139: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.522356 Loss1: 0.647756 Loss2: 1.874600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.776681 Loss1: 0.330577 Loss2: 1.446104 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.613739 Loss1: 0.226457 Loss2: 1.387281
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.469907 Loss1: 0.610251 Loss2: 1.859656
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.828833 Loss1: 0.440938 Loss2: 1.387895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.724543 Loss1: 0.284154 Loss2: 1.440390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.662874 Loss1: 0.271504 Loss2: 1.391371 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568984 Loss1: 0.173934 Loss2: 1.395050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511147 Loss1: 0.133345 Loss2: 1.377802 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502200 Loss1: 0.125205 Loss2: 1.376995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.475381 Loss1: 0.087070 Loss2: 1.388311 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.364029 Loss1: 0.499986 Loss2: 1.864043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.702309 Loss1: 0.279594 Loss2: 1.422715 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.497475 Loss1: 0.584479 Loss2: 1.912995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.878076 Loss1: 0.460171 Loss2: 1.417904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.762776 Loss1: 0.282802 Loss2: 1.479973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591552 Loss1: 0.179945 Loss2: 1.411607 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.566551 Loss1: 0.158477 Loss2: 1.408074 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493550 Loss1: 0.094586 Loss2: 1.398964 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424506 Loss1: 0.045029 Loss2: 1.379477 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415348 Loss1: 0.050391 Loss2: 1.364958 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.868505 Loss1: 0.486933 Loss2: 1.381571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570370 Loss1: 0.183233 Loss2: 1.387137 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518908 Loss1: 0.138130 Loss2: 1.380778
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.498089 Loss1: 0.603560 Loss2: 1.894530
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.762372 Loss1: 0.374005 Loss2: 1.388366 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.659519 Loss1: 0.214991 Loss2: 1.444527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579130 Loss1: 0.194042 Loss2: 1.385088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542961 Loss1: 0.158262 Loss2: 1.384699 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986607
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549162 Loss1: 0.156803 Loss2: 1.392359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433529 Loss1: 0.062071 Loss2: 1.371458 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419369 Loss1: 0.051634 Loss2: 1.367735 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.711962 Loss1: 0.337605 Loss2: 1.374357 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491454 Loss1: 0.133818 Loss2: 1.357636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472499 Loss1: 0.118854 Loss2: 1.353645
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.434508 Loss1: 0.555348 Loss2: 1.879161
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.453904 Loss1: 0.096719 Loss2: 1.357185
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.769182 Loss1: 0.389406 Loss2: 1.379777
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439625 Loss1: 0.085968 Loss2: 1.353658
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.658663 Loss1: 0.234793 Loss2: 1.423870
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538917 Loss1: 0.167863 Loss2: 1.371054
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473946 Loss1: 0.120116 Loss2: 1.353831
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502620 Loss1: 0.131255 Loss2: 1.371365
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.497693 Loss1: 0.140268 Loss2: 1.357425
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513416 Loss1: 0.140569 Loss2: 1.372847
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429556 Loss1: 0.076797 Loss2: 1.352758
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417808 Loss1: 0.059984 Loss2: 1.357824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408834 Loss1: 0.061174 Loss2: 1.347660 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.772763 Loss1: 0.378013 Loss2: 1.394749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606929 Loss1: 0.222382 Loss2: 1.384546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566064 Loss1: 0.174753 Loss2: 1.391311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488075 Loss1: 0.113943 Loss2: 1.374133 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468705 Loss1: 0.104839 Loss2: 1.363866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440839 Loss1: 0.073467 Loss2: 1.367372 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433097 Loss1: 0.077189 Loss2: 1.355908 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426581 Loss1: 0.069394 Loss2: 1.357186 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389948 Loss1: 0.059383 Loss2: 1.330564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395871 Loss1: 0.074658 Loss2: 1.321213 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.811680 Loss1: 0.481343 Loss2: 1.330337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572929 Loss1: 0.248422 Loss2: 1.324507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.569482 Loss1: 0.243218 Loss2: 1.326265
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.289978 Loss1: 0.449874 Loss2: 1.840104
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.675209 Loss1: 0.303955 Loss2: 1.371254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.381863 Loss1: 0.079132 Loss2: 1.302732 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.357823 Loss1: 0.053479 Loss2: 1.304344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350923 Loss1: 0.056046 Loss2: 1.294877 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986779
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490717 Loss1: 0.126148 Loss2: 1.364569 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414438 Loss1: 0.057223 Loss2: 1.357215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407840 Loss1: 0.059739 Loss2: 1.348101
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.567103 Loss1: 0.669583 Loss2: 1.897520
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.882402 Loss1: 0.449846 Loss2: 1.432557
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414308 Loss1: 0.064974 Loss2: 1.349334
+(DefaultActor pid=3764) >> Training accuracy: 0.990809
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.658414 Loss1: 0.276770 Loss2: 1.381645 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.479679 Loss1: 0.110724 Loss2: 1.368954 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450071 Loss1: 0.086374 Loss2: 1.363697
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.454644 Loss1: 0.610225 Loss2: 1.844419
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.767582 Loss1: 0.410137 Loss2: 1.357445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.686392 Loss1: 0.253896 Loss2: 1.432496 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356977 Loss1: 0.020175 Loss2: 1.336801
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579081 Loss1: 0.218239 Loss2: 1.360842
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550724 Loss1: 0.184512 Loss2: 1.366212
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521861 Loss1: 0.144534 Loss2: 1.377326
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455029 Loss1: 0.106268 Loss2: 1.348761
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447569 Loss1: 0.106965 Loss2: 1.340605
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.364397 Loss1: 0.561980 Loss2: 1.802417
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425581 Loss1: 0.082249 Loss2: 1.343331
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.639413 Loss1: 0.307514 Loss2: 1.331899
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405450 Loss1: 0.068863 Loss2: 1.336588
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576399 Loss1: 0.241183 Loss2: 1.335216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481245 Loss1: 0.149691 Loss2: 1.331554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.477324 Loss1: 0.147343 Loss2: 1.329981
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.473538 Loss1: 0.618785 Loss2: 1.854753
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409648 Loss1: 0.087269 Loss2: 1.322378
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.721390 Loss1: 0.359096 Loss2: 1.362294
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388320 Loss1: 0.074785 Loss2: 1.313535
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.706809 Loss1: 0.312046 Loss2: 1.394763
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400950 Loss1: 0.089134 Loss2: 1.311816
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550984 Loss1: 0.191611 Loss2: 1.359373
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530715 Loss1: 0.172716 Loss2: 1.357999
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510793 Loss1: 0.155519 Loss2: 1.355274
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465266 Loss1: 0.121006 Loss2: 1.344260
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422133 Loss1: 0.080036 Loss2: 1.342097
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399270 Loss1: 0.069201 Loss2: 1.330069
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.326448 Loss1: 0.496268 Loss2: 1.830180
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385748 Loss1: 0.057931 Loss2: 1.327817
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.686063 Loss1: 0.304320 Loss2: 1.381743
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.579835 Loss1: 0.176908 Loss2: 1.402926
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517727 Loss1: 0.150978 Loss2: 1.366749
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.482677 Loss1: 0.121526 Loss2: 1.361151
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454878 Loss1: 0.098356 Loss2: 1.356521
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.345976 Loss1: 0.565025 Loss2: 1.780951
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444345 Loss1: 0.094537 Loss2: 1.349809
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.685290 Loss1: 0.352162 Loss2: 1.333127
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447400 Loss1: 0.095484 Loss2: 1.351916
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608697 Loss1: 0.238381 Loss2: 1.370316
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445722 Loss1: 0.092204 Loss2: 1.353518
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.465648 Loss1: 0.151508 Loss2: 1.314140
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417514 Loss1: 0.068169 Loss2: 1.349344
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.448826 Loss1: 0.128703 Loss2: 1.320122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434553 Loss1: 0.120369 Loss2: 1.314184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.474085 Loss1: 0.645329 Loss2: 1.828757
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408468 Loss1: 0.100232 Loss2: 1.308236
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751292 Loss1: 0.385877 Loss2: 1.365415
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393690 Loss1: 0.088214 Loss2: 1.305476
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.539492 Loss1: 0.179577 Loss2: 1.359915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.427552 Loss1: 0.072714 Loss2: 1.354839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.396926 Loss1: 0.059212 Loss2: 1.337714
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.596215 Loss1: 0.643662 Loss2: 1.952553
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.823167 Loss1: 0.500294 Loss2: 1.322873
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394567 Loss1: 0.061980 Loss2: 1.332587
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386527 Loss1: 0.054592 Loss2: 1.331935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370024 Loss1: 0.047338 Loss2: 1.322686 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.413901 Loss1: 0.075548 Loss2: 1.338354 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362695 Loss1: 0.046502 Loss2: 1.316193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.339419 Loss1: 0.028247 Loss2: 1.311172
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.506325 Loss1: 0.656381 Loss2: 1.849944
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.681448 Loss1: 0.319377 Loss2: 1.362071
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.643210 Loss1: 0.247223 Loss2: 1.395987
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551159 Loss1: 0.195390 Loss2: 1.355769
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544429 Loss1: 0.178430 Loss2: 1.366000
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.569208 Loss1: 0.701083 Loss2: 1.868125
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.846219 Loss1: 0.457433 Loss2: 1.388786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.705828 Loss1: 0.270736 Loss2: 1.435092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.552187 Loss1: 0.183521 Loss2: 1.368666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528753 Loss1: 0.145083 Loss2: 1.383671 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405591 Loss1: 0.080823 Loss2: 1.324768
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496546 Loss1: 0.125192 Loss2: 1.371354
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485541 Loss1: 0.120305 Loss2: 1.365236
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491066 Loss1: 0.121072 Loss2: 1.369994
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.494946 Loss1: 0.129574 Loss2: 1.365373
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442610 Loss1: 0.074577 Loss2: 1.368032
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.375608 Loss1: 0.585662 Loss2: 1.789947
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.669700 Loss1: 0.324701 Loss2: 1.344999
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.570193 Loss1: 0.207857 Loss2: 1.362336
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.538624 Loss1: 0.196799 Loss2: 1.341825
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.501004 Loss1: 0.605762 Loss2: 1.895241
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490299 Loss1: 0.149129 Loss2: 1.341170
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.735560 Loss1: 0.350972 Loss2: 1.384589
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433084 Loss1: 0.100646 Loss2: 1.332438
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.722997 Loss1: 0.300261 Loss2: 1.422736
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428195 Loss1: 0.097223 Loss2: 1.330972
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612905 Loss1: 0.237092 Loss2: 1.375812
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.390735 Loss1: 0.068597 Loss2: 1.322138
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379659 Loss1: 0.062645 Loss2: 1.317014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387571 Loss1: 0.075176 Loss2: 1.312394 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483044 Loss1: 0.102410 Loss2: 1.380634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460609 Loss1: 0.095438 Loss2: 1.365172 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.449777 Loss1: 0.618427 Loss2: 1.831350
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.737162 Loss1: 0.380761 Loss2: 1.356401
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.641805 Loss1: 0.247081 Loss2: 1.394724
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.507560 Loss1: 0.152439 Loss2: 1.355121
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.444278 Loss1: 0.626243 Loss2: 1.818035
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.839711 Loss1: 0.501387 Loss2: 1.338324 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.649728 Loss1: 0.245961 Loss2: 1.403767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523785 Loss1: 0.180865 Loss2: 1.342921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487213 Loss1: 0.150981 Loss2: 1.336232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443117 Loss1: 0.105010 Loss2: 1.338107 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423091 Loss1: 0.097811 Loss2: 1.325279 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376561 Loss1: 0.058001 Loss2: 1.318561 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.378686 Loss1: 0.523706 Loss2: 1.854980
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.753223 Loss1: 0.348108 Loss2: 1.405115
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.706568 Loss1: 0.270413 Loss2: 1.436155
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.655874 Loss1: 0.240315 Loss2: 1.415559
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.612329 Loss1: 0.736394 Loss2: 1.875936
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.607766 Loss1: 0.199072 Loss2: 1.408694
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.768327 Loss1: 0.413808 Loss2: 1.354519
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.599472 Loss1: 0.225827 Loss2: 1.373645
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.566476 Loss1: 0.150356 Loss2: 1.416120
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509364 Loss1: 0.112935 Loss2: 1.396429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471717 Loss1: 0.079136 Loss2: 1.392581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.468574 Loss1: 0.082430 Loss2: 1.386144 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400566 Loss1: 0.069522 Loss2: 1.331044 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394654 Loss1: 0.073867 Loss2: 1.320788 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.452504 Loss1: 0.602809 Loss2: 1.849695
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.755973 Loss1: 0.407486 Loss2: 1.348488
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.623997 Loss1: 0.224295 Loss2: 1.399703
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575128 Loss1: 0.225187 Loss2: 1.349942
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.477727 Loss1: 0.586601 Loss2: 1.891126
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.816706 Loss1: 0.417554 Loss2: 1.399152 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.695964 Loss1: 0.247902 Loss2: 1.448062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574305 Loss1: 0.184330 Loss2: 1.389975 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520590 Loss1: 0.131997 Loss2: 1.388592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.518311 Loss1: 0.132968 Loss2: 1.385344 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364382 Loss1: 0.042873 Loss2: 1.321509
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.489423 Loss1: 0.111265 Loss2: 1.378158
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472141 Loss1: 0.092811 Loss2: 1.379329
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.459408 Loss1: 0.088813 Loss2: 1.370595
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.458198 Loss1: 0.088131 Loss2: 1.370067
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.451484 Loss1: 0.634862 Loss2: 1.816623
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.793753 Loss1: 0.449590 Loss2: 1.344163
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.641062 Loss1: 0.235390 Loss2: 1.405672
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.525015 Loss1: 0.178959 Loss2: 1.346056
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.566239 Loss1: 0.679204 Loss2: 1.887036
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.838459 Loss1: 0.439818 Loss2: 1.398641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.740124 Loss1: 0.297373 Loss2: 1.442751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.610297 Loss1: 0.219107 Loss2: 1.391190 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.578363 Loss1: 0.182586 Loss2: 1.395776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.545523 Loss1: 0.156394 Loss2: 1.389129 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420421 Loss1: 0.090242 Loss2: 1.330179
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.532050 Loss1: 0.146822 Loss2: 1.385227
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.489571 Loss1: 0.107421 Loss2: 1.382150
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466004 Loss1: 0.089208 Loss2: 1.376795
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440177 Loss1: 0.070998 Loss2: 1.369178
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.310235 Loss1: 0.503048 Loss2: 1.807187
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.707260 Loss1: 0.380840 Loss2: 1.326420
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.587639 Loss1: 0.221833 Loss2: 1.365806
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.479100 Loss1: 0.152562 Loss2: 1.326539
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.475187 Loss1: 0.619774 Loss2: 1.855413
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.766144 Loss1: 0.390914 Loss2: 1.375230 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.644731 Loss1: 0.232470 Loss2: 1.412261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595658 Loss1: 0.219272 Loss2: 1.376385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.565252 Loss1: 0.187676 Loss2: 1.377576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.557567 Loss1: 0.178137 Loss2: 1.379430 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363600 Loss1: 0.060462 Loss2: 1.303138
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.515544 Loss1: 0.144791 Loss2: 1.370753
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475864 Loss1: 0.103730 Loss2: 1.372134
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431791 Loss1: 0.066083 Loss2: 1.365709
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420781 Loss1: 0.062035 Loss2: 1.358746
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.521036 Loss1: 0.578500 Loss2: 1.942536
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.846690 Loss1: 0.445212 Loss2: 1.401478
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.698111 Loss1: 0.246115 Loss2: 1.451996
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551548 Loss1: 0.156546 Loss2: 1.395002
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.584980 Loss1: 0.666147 Loss2: 1.918832
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.847633 Loss1: 0.422939 Loss2: 1.424694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.760053 Loss1: 0.293552 Loss2: 1.466500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604992 Loss1: 0.176059 Loss2: 1.428933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558452 Loss1: 0.141766 Loss2: 1.416687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.518627 Loss1: 0.104413 Loss2: 1.414214 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483390 Loss1: 0.078537 Loss2: 1.404854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454109 Loss1: 0.062749 Loss2: 1.391360 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.816082 Loss1: 0.792934 Loss2: 2.023148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.932394 Loss1: 0.396716 Loss2: 1.535678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.635732 Loss1: 0.164643 Loss2: 1.471088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587236 Loss1: 0.135694 Loss2: 1.451542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.558042 Loss1: 0.102722 Loss2: 1.455320 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.549474 Loss1: 0.102731 Loss2: 1.446743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.514913 Loss1: 0.077491 Loss2: 1.437422 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508890 Loss1: 0.075106 Loss2: 1.433784 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478523 Loss1: 0.104442 Loss2: 1.374081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449889 Loss1: 0.091320 Loss2: 1.358569 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445875 Loss1: 0.083312 Loss2: 1.362563
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.390618 Loss1: 0.583186 Loss2: 1.807432
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.790851 Loss1: 0.398938 Loss2: 1.391913
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.652819 Loss1: 0.242904 Loss2: 1.409914
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.597449 Loss1: 0.231093 Loss2: 1.366356
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.545146 Loss1: 0.168061 Loss2: 1.377085
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.617830 Loss1: 0.753770 Loss2: 1.864060
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.735851 Loss1: 0.401611 Loss2: 1.334240
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481818 Loss1: 0.121964 Loss2: 1.359854
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.534345 Loss1: 0.174966 Loss2: 1.359379
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436089 Loss1: 0.085616 Loss2: 1.350472
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.412137 Loss1: 0.066005 Loss2: 1.346131 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397088 Loss1: 0.055222 Loss2: 1.341866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400494 Loss1: 0.068980 Loss2: 1.331514 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.358350 Loss1: 0.045950 Loss2: 1.312400 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.471715 Loss1: 0.612320 Loss2: 1.859395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591334 Loss1: 0.180628 Loss2: 1.410705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517647 Loss1: 0.158030 Loss2: 1.359617
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.380853 Loss1: 0.541931 Loss2: 1.838922
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612603 Loss1: 0.266599 Loss2: 1.346004 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.582858 Loss1: 0.227056 Loss2: 1.355803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478637 Loss1: 0.136910 Loss2: 1.341727 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.489919 Loss1: 0.158874 Loss2: 1.331045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420842 Loss1: 0.084913 Loss2: 1.335929 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403760 Loss1: 0.055031 Loss2: 1.348729
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406545 Loss1: 0.078216 Loss2: 1.328329
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.377948 Loss1: 0.058008 Loss2: 1.319940
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370485 Loss1: 0.055407 Loss2: 1.315078
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362398 Loss1: 0.047816 Loss2: 1.314582
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.353161 Loss1: 0.540504 Loss2: 1.812657
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.731595 Loss1: 0.397285 Loss2: 1.334310
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.650869 Loss1: 0.278192 Loss2: 1.372677
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500483 Loss1: 0.168058 Loss2: 1.332424
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.522015 Loss1: 0.659145 Loss2: 1.862870
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.849845 Loss1: 0.419470 Loss2: 1.430375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.799898 Loss1: 0.352105 Loss2: 1.447793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.656185 Loss1: 0.234739 Loss2: 1.421447 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576196 Loss1: 0.166897 Loss2: 1.409299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501106 Loss1: 0.109361 Loss2: 1.391744 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427070 Loss1: 0.054578 Loss2: 1.372492 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393176 Loss1: 0.027652 Loss2: 1.365524 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.688185 Loss1: 0.358544 Loss2: 1.329641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467356 Loss1: 0.150413 Loss2: 1.316944 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.528563 Loss1: 0.694022 Loss2: 1.834541
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448720 Loss1: 0.124290 Loss2: 1.324430
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.810976 Loss1: 0.417089 Loss2: 1.393887
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.425655 Loss1: 0.109727 Loss2: 1.315929
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.660691 Loss1: 0.255908 Loss2: 1.404783
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404575 Loss1: 0.089732 Loss2: 1.314843
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557070 Loss1: 0.203103 Loss2: 1.353968
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.368011 Loss1: 0.054760 Loss2: 1.313251
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391726 Loss1: 0.087334 Loss2: 1.304391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398258 Loss1: 0.089041 Loss2: 1.309217 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973633
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457577 Loss1: 0.111104 Loss2: 1.346473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378090 Loss1: 0.042436 Loss2: 1.335654 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.838215 Loss1: 0.447059 Loss2: 1.391156 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 04:55:41,879 | server.py:236 | fit_round 139 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.613794 Loss1: 0.230299 Loss2: 1.383495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.556313 Loss1: 0.166244 Loss2: 1.390070 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518568 Loss1: 0.145219 Loss2: 1.373349 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531144 Loss1: 0.157422 Loss2: 1.373722 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.496665 Loss1: 0.123514 Loss2: 1.373151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.480577 Loss1: 0.123328 Loss2: 1.357249 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425106 Loss1: 0.062144 Loss2: 1.362963 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445229 Loss1: 0.069492 Loss2: 1.375737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397081 Loss1: 0.042032 Loss2: 1.355048 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.957777 Loss1: 0.569596 Loss2: 1.388181 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.758868 Loss1: 0.355412 Loss2: 1.403456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.576883 Loss1: 0.193800 Loss2: 1.383082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485672 Loss1: 0.117859 Loss2: 1.367814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483533 Loss1: 0.125387 Loss2: 1.358146 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460904 Loss1: 0.106927 Loss2: 1.353977 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459629 Loss1: 0.110940 Loss2: 1.348689 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434623 Loss1: 0.091702 Loss2: 1.342921 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486095 Loss1: 0.094329 Loss2: 1.391767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429561 Loss1: 0.050406 Loss2: 1.379155 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 04:55:41,879][flwr][DEBUG] - fit_round 139 received 50 results and 0 failures
+INFO flwr 2023-10-12 04:56:24,401 | server.py:125 | fit progress: (139, 2.2211843315785686, {'accuracy': 0.5932}, 320692.179992624)
+>> Test accuracy: 0.593200
+[2023-10-12 04:56:24,401][flwr][INFO] - fit progress: (139, 2.2211843315785686, {'accuracy': 0.5932}, 320692.179992624)
+DEBUG flwr 2023-10-12 04:56:24,402 | server.py:173 | evaluate_round 139: strategy sampled 50 clients (out of 50)
+[2023-10-12 04:56:24,402][flwr][DEBUG] - evaluate_round 139: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 05:05:31,838 | server.py:187 | evaluate_round 139 received 50 results and 0 failures
+[2023-10-12 05:05:31,838][flwr][DEBUG] - evaluate_round 139 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 05:05:31,838 | server.py:222 | fit_round 140: strategy sampled 50 clients (out of 50)
+[2023-10-12 05:05:31,838][flwr][DEBUG] - fit_round 140: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.605426 Loss1: 0.689924 Loss2: 1.915502
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.806395 Loss1: 0.452169 Loss2: 1.354226
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.661636 Loss1: 0.276274 Loss2: 1.385362
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572756 Loss1: 0.196644 Loss2: 1.376112
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.545873 Loss1: 0.199825 Loss2: 1.346047
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525945 Loss1: 0.169249 Loss2: 1.356695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.506048 Loss1: 0.145983 Loss2: 1.360066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459630 Loss1: 0.106401 Loss2: 1.353229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.442361 Loss1: 0.096171 Loss2: 1.346190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421560 Loss1: 0.082097 Loss2: 1.339463 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985577
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500528 Loss1: 0.134104 Loss2: 1.366424 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431227 Loss1: 0.079842 Loss2: 1.351384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397237 Loss1: 0.048975 Loss2: 1.348262
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.490742 Loss1: 0.632355 Loss2: 1.858388
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.777977 Loss1: 0.397341 Loss2: 1.380636
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700156 Loss1: 0.281753 Loss2: 1.418403
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.568380 Loss1: 0.182309 Loss2: 1.386072
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513593 Loss1: 0.134288 Loss2: 1.379305
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.389057 Loss1: 0.568894 Loss2: 1.820163
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475274 Loss1: 0.095243 Loss2: 1.380031
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449344 Loss1: 0.081873 Loss2: 1.367470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430792 Loss1: 0.067491 Loss2: 1.363301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428941 Loss1: 0.073122 Loss2: 1.355819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388694 Loss1: 0.032624 Loss2: 1.356070 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440407 Loss1: 0.099816 Loss2: 1.340591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390294 Loss1: 0.059583 Loss2: 1.330711 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372183 Loss1: 0.048100 Loss2: 1.324083
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.485745 Loss1: 0.613445 Loss2: 1.872300
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.726511 Loss1: 0.359416 Loss2: 1.367095
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.617705 Loss1: 0.221725 Loss2: 1.395981
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535088 Loss1: 0.177806 Loss2: 1.357282
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491260 Loss1: 0.130422 Loss2: 1.360838
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.373487 Loss1: 0.485397 Loss2: 1.888090
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461708 Loss1: 0.112261 Loss2: 1.349447
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447980 Loss1: 0.112093 Loss2: 1.335888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.702830 Loss1: 0.264932 Loss2: 1.437898
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.406364 Loss1: 0.066689 Loss2: 1.339675
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.652461 Loss1: 0.255305 Loss2: 1.397157
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388533 Loss1: 0.048906 Loss2: 1.339627
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548029 Loss1: 0.134195 Loss2: 1.413834
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385087 Loss1: 0.056744 Loss2: 1.328343
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483532 Loss1: 0.090241 Loss2: 1.393292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443631 Loss1: 0.064978 Loss2: 1.378653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.560487 Loss1: 0.664703 Loss2: 1.895784
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435579 Loss1: 0.058118 Loss2: 1.377462
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.604135 Loss1: 0.204361 Loss2: 1.399773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.493358 Loss1: 0.135898 Loss2: 1.357460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437964 Loss1: 0.082377 Loss2: 1.355587
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.588439 Loss1: 0.710597 Loss2: 1.877843
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.810603 Loss1: 0.415103 Loss2: 1.395500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.717229 Loss1: 0.277477 Loss2: 1.439752 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.616427 Loss1: 0.232754 Loss2: 1.383673 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356229 Loss1: 0.031433 Loss2: 1.324796
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.546361 Loss1: 0.153642 Loss2: 1.392718
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486654 Loss1: 0.112189 Loss2: 1.374465
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450680 Loss1: 0.080224 Loss2: 1.370456
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432191 Loss1: 0.067031 Loss2: 1.365160
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428914 Loss1: 0.070386 Loss2: 1.358527
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.417632 Loss1: 0.603208 Loss2: 1.814425
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415572 Loss1: 0.059481 Loss2: 1.356091
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.621259 Loss1: 0.237530 Loss2: 1.383729 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527304 Loss1: 0.181180 Loss2: 1.346125 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476136 Loss1: 0.126435 Loss2: 1.349701
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.519748 Loss1: 0.609860 Loss2: 1.909888
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.740097 Loss1: 0.379797 Loss2: 1.360300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.638493 Loss1: 0.250617 Loss2: 1.387877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557487 Loss1: 0.171318 Loss2: 1.386170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406509 Loss1: 0.079169 Loss2: 1.327340
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505830 Loss1: 0.143956 Loss2: 1.361874
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446268 Loss1: 0.082514 Loss2: 1.363753
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.401305 Loss1: 0.048720 Loss2: 1.352585
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390144 Loss1: 0.045735 Loss2: 1.344409
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396349 Loss1: 0.054756 Loss2: 1.341594
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394889 Loss1: 0.054535 Loss2: 1.340354
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.468492 Loss1: 0.580896 Loss2: 1.887596
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.804631 Loss1: 0.408879 Loss2: 1.395752
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.701159 Loss1: 0.257531 Loss2: 1.443627
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602967 Loss1: 0.195790 Loss2: 1.407177
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.579080 Loss1: 0.173671 Loss2: 1.405408
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.483837 Loss1: 0.633845 Loss2: 1.849993
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547212 Loss1: 0.142326 Loss2: 1.404886
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.773977 Loss1: 0.420942 Loss2: 1.353035
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486090 Loss1: 0.091504 Loss2: 1.394586
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.632175 Loss1: 0.215687 Loss2: 1.416489
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.468278 Loss1: 0.084860 Loss2: 1.383418
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.466442 Loss1: 0.129685 Loss2: 1.336757
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445867 Loss1: 0.063569 Loss2: 1.382299
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.478895 Loss1: 0.137474 Loss2: 1.341421
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436580 Loss1: 0.062009 Loss2: 1.374571
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.418270 Loss1: 0.085728 Loss2: 1.332542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371901 Loss1: 0.049674 Loss2: 1.322227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383986 Loss1: 0.067475 Loss2: 1.316511
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.688965 Loss1: 0.787972 Loss2: 1.900993
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.791276 Loss1: 0.426969 Loss2: 1.364307
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.677318 Loss1: 0.264521 Loss2: 1.412797
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.545990 Loss1: 0.191482 Loss2: 1.354508
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507709 Loss1: 0.153246 Loss2: 1.354463
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445902 Loss1: 0.088844 Loss2: 1.357059
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.674738 Loss1: 0.710194 Loss2: 1.964544
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.772865 Loss1: 0.435754 Loss2: 1.337111
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426601 Loss1: 0.085416 Loss2: 1.341185
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448780 Loss1: 0.109087 Loss2: 1.339694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404941 Loss1: 0.062777 Loss2: 1.342163 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385280 Loss1: 0.055088 Loss2: 1.330192 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430726 Loss1: 0.083053 Loss2: 1.347673 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405616 Loss1: 0.072039 Loss2: 1.333577 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.708568 Loss1: 0.339137 Loss2: 1.369431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524096 Loss1: 0.162307 Loss2: 1.361789 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.457230 Loss1: 0.102874 Loss2: 1.354356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445312 Loss1: 0.093536 Loss2: 1.351776 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443208 Loss1: 0.094592 Loss2: 1.348616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421850 Loss1: 0.077776 Loss2: 1.344075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438942 Loss1: 0.102816 Loss2: 1.336127 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408786 Loss1: 0.074482 Loss2: 1.334304 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464733 Loss1: 0.109313 Loss2: 1.355420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406753 Loss1: 0.070892 Loss2: 1.335861 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.854649 Loss1: 0.473591 Loss2: 1.381058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580049 Loss1: 0.203759 Loss2: 1.376290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499820 Loss1: 0.120524 Loss2: 1.379296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.771889 Loss1: 0.365414 Loss2: 1.406475
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491741 Loss1: 0.119381 Loss2: 1.372359
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642784 Loss1: 0.205946 Loss2: 1.436838
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449197 Loss1: 0.078020 Loss2: 1.371177
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.598390 Loss1: 0.187318 Loss2: 1.411072
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439808 Loss1: 0.078699 Loss2: 1.361109
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.410255 Loss1: 0.054011 Loss2: 1.356244
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550248 Loss1: 0.140444 Loss2: 1.409805
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386957 Loss1: 0.040497 Loss2: 1.346460
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496465 Loss1: 0.096086 Loss2: 1.400379
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.498927 Loss1: 0.111016 Loss2: 1.387911
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466984 Loss1: 0.075887 Loss2: 1.391096
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448843 Loss1: 0.066157 Loss2: 1.382686
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437992 Loss1: 0.059588 Loss2: 1.378404
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.461246 Loss1: 0.669529 Loss2: 1.791717
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.687211 Loss1: 0.361289 Loss2: 1.325922
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.641857 Loss1: 0.282644 Loss2: 1.359213
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.525372 Loss1: 0.202267 Loss2: 1.323105
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.458681 Loss1: 0.130715 Loss2: 1.327966
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.403970 Loss1: 0.606924 Loss2: 1.797046
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.660667 Loss1: 0.308658 Loss2: 1.352009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.625601 Loss1: 0.239523 Loss2: 1.386078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.570942 Loss1: 0.222046 Loss2: 1.348896 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487364 Loss1: 0.141824 Loss2: 1.345540 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.497997 Loss1: 0.152829 Loss2: 1.345168 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442476 Loss1: 0.101914 Loss2: 1.340562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410042 Loss1: 0.082091 Loss2: 1.327951 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.735884 Loss1: 0.318884 Loss2: 1.417000 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521677 Loss1: 0.165844 Loss2: 1.355833 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.610410 Loss1: 0.674813 Loss2: 1.935598
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462915 Loss1: 0.114407 Loss2: 1.348508
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436552 Loss1: 0.100427 Loss2: 1.336125 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419033 Loss1: 0.079322 Loss2: 1.339711 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.393646 Loss1: 0.061505 Loss2: 1.332141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378148 Loss1: 0.054907 Loss2: 1.323241 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390618 Loss1: 0.051047 Loss2: 1.339571 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361247 Loss1: 0.032535 Loss2: 1.328712 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.394819 Loss1: 0.561954 Loss2: 1.832865
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.617398 Loss1: 0.238392 Loss2: 1.379007
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.572315 Loss1: 0.183711 Loss2: 1.388603
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508106 Loss1: 0.137835 Loss2: 1.370272
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.611306 Loss1: 0.769122 Loss2: 1.842183
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.690536 Loss1: 0.352798 Loss2: 1.337738 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524214 Loss1: 0.159062 Loss2: 1.365152
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.582585 Loss1: 0.215852 Loss2: 1.366733
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516157 Loss1: 0.155640 Loss2: 1.360516
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.518253 Loss1: 0.187141 Loss2: 1.331113
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.455677 Loss1: 0.089516 Loss2: 1.366161
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.454115 Loss1: 0.124542 Loss2: 1.329573
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.403520 Loss1: 0.085497 Loss2: 1.318023
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.415912 Loss1: 0.063012 Loss2: 1.352901
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395840 Loss1: 0.087796 Loss2: 1.308044
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392148 Loss1: 0.043246 Loss2: 1.348902
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391931 Loss1: 0.086397 Loss2: 1.305533 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.482250 Loss1: 0.620479 Loss2: 1.861771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.720420 Loss1: 0.276723 Loss2: 1.443697 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.686337 Loss1: 0.301383 Loss2: 1.384955
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.278327 Loss1: 0.452319 Loss2: 1.826008
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.751257 Loss1: 0.378688 Loss2: 1.372569 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671686 Loss1: 0.262082 Loss2: 1.409604 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.560740 Loss1: 0.201999 Loss2: 1.358741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.560869 Loss1: 0.188403 Loss2: 1.372465 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416114 Loss1: 0.056599 Loss2: 1.359515 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.474170 Loss1: 0.117518 Loss2: 1.356652 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393690 Loss1: 0.049722 Loss2: 1.343968 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992647
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.653285 Loss1: 0.317030 Loss2: 1.336255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476380 Loss1: 0.151578 Loss2: 1.324801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462407 Loss1: 0.137199 Loss2: 1.325208
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.461105 Loss1: 0.621303 Loss2: 1.839801
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450790 Loss1: 0.129585 Loss2: 1.321205
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.705075 Loss1: 0.344041 Loss2: 1.361035
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481099 Loss1: 0.153560 Loss2: 1.327539
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.681520 Loss1: 0.288497 Loss2: 1.393023
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456968 Loss1: 0.133108 Loss2: 1.323860
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.533614 Loss1: 0.177284 Loss2: 1.356331
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518563 Loss1: 0.161529 Loss2: 1.357034
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397900 Loss1: 0.084010 Loss2: 1.313890
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506424 Loss1: 0.157864 Loss2: 1.348561
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384986 Loss1: 0.072174 Loss2: 1.312812
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471428 Loss1: 0.117882 Loss2: 1.353546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400234 Loss1: 0.058912 Loss2: 1.341321 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.787160 Loss1: 0.378474 Loss2: 1.408685 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.619936 Loss1: 0.206598 Loss2: 1.413337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.530976 Loss1: 0.124211 Loss2: 1.406765
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.410114 Loss1: 0.564814 Loss2: 1.845299
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514499 Loss1: 0.117434 Loss2: 1.397065
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.741973 Loss1: 0.381044 Loss2: 1.360929
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487240 Loss1: 0.102572 Loss2: 1.384668
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.641026 Loss1: 0.233776 Loss2: 1.407251
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471867 Loss1: 0.088068 Loss2: 1.383799
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521468 Loss1: 0.169394 Loss2: 1.352074
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.473423 Loss1: 0.087580 Loss2: 1.385843
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512700 Loss1: 0.161868 Loss2: 1.350832
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472194 Loss1: 0.094648 Loss2: 1.377545
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512668 Loss1: 0.166384 Loss2: 1.346284
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.515993 Loss1: 0.165547 Loss2: 1.350447
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.474062 Loss1: 0.124766 Loss2: 1.349297
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445498 Loss1: 0.103696 Loss2: 1.341802
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449867 Loss1: 0.102538 Loss2: 1.347329
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.579560 Loss1: 0.637351 Loss2: 1.942210
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.781972 Loss1: 0.354361 Loss2: 1.427611
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.660186 Loss1: 0.220212 Loss2: 1.439974
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.664239 Loss1: 0.261252 Loss2: 1.402987
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571570 Loss1: 0.153510 Loss2: 1.418060
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519147 Loss1: 0.122479 Loss2: 1.396669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.569404 Loss1: 0.169796 Loss2: 1.399607 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.560336 Loss1: 0.155039 Loss2: 1.405297 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.536569 Loss1: 0.122866 Loss2: 1.413703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.488763 Loss1: 0.098033 Loss2: 1.390730 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425344 Loss1: 0.061669 Loss2: 1.363675 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398464 Loss1: 0.050307 Loss2: 1.348157 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.744418 Loss1: 0.365817 Loss2: 1.378601 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554982 Loss1: 0.191693 Loss2: 1.363289 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507605 Loss1: 0.134366 Loss2: 1.373239
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.475483 Loss1: 0.653785 Loss2: 1.821698
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.819622 Loss1: 0.456559 Loss2: 1.363062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.648893 Loss1: 0.225997 Loss2: 1.422896 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.547241 Loss1: 0.202023 Loss2: 1.345219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501274 Loss1: 0.150292 Loss2: 1.350982 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.478806 Loss1: 0.122193 Loss2: 1.356613
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469036 Loss1: 0.124459 Loss2: 1.344576
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444316 Loss1: 0.100461 Loss2: 1.343855
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.444470 Loss1: 0.106556 Loss2: 1.337914
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421655 Loss1: 0.080916 Loss2: 1.340739
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397492 Loss1: 0.066588 Loss2: 1.330905
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.258566 Loss1: 0.416188 Loss2: 1.842379
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.692196 Loss1: 0.348194 Loss2: 1.344001
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.577227 Loss1: 0.217037 Loss2: 1.360189
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.595165 Loss1: 0.242423 Loss2: 1.352741
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.530069 Loss1: 0.178411 Loss2: 1.351658
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.635565 Loss1: 0.725369 Loss2: 1.910196
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.873403 Loss1: 0.452137 Loss2: 1.421266 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.751645 Loss1: 0.288920 Loss2: 1.462724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.582178 Loss1: 0.167143 Loss2: 1.415035 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529998 Loss1: 0.115113 Loss2: 1.414885 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539103 Loss1: 0.132509 Loss2: 1.406593 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.527398 Loss1: 0.126169 Loss2: 1.401229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.505109 Loss1: 0.106749 Loss2: 1.398360 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.703503 Loss1: 0.372263 Loss2: 1.331240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523709 Loss1: 0.183374 Loss2: 1.340336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.435415 Loss1: 0.103405 Loss2: 1.332010
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.466755 Loss1: 0.600540 Loss2: 1.866216
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.411224 Loss1: 0.080910 Loss2: 1.330314
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.755508 Loss1: 0.370847 Loss2: 1.384661
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.656272 Loss1: 0.243908 Loss2: 1.412364
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426367 Loss1: 0.100908 Loss2: 1.325458
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.582333 Loss1: 0.211737 Loss2: 1.370596
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405458 Loss1: 0.082315 Loss2: 1.323143
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.551155 Loss1: 0.162523 Loss2: 1.388632
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381413 Loss1: 0.066274 Loss2: 1.315140
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538252 Loss1: 0.162455 Loss2: 1.375797
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354828 Loss1: 0.043048 Loss2: 1.311780
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470958 Loss1: 0.094583 Loss2: 1.376375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413104 Loss1: 0.058621 Loss2: 1.354483 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.761896 Loss1: 0.371989 Loss2: 1.389906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.550784 Loss1: 0.166810 Loss2: 1.383974 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.476816 Loss1: 0.576942 Loss2: 1.899874
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.504625 Loss1: 0.125983 Loss2: 1.378642
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.788834 Loss1: 0.403654 Loss2: 1.385180
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489535 Loss1: 0.120892 Loss2: 1.368643
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.694034 Loss1: 0.260528 Loss2: 1.433506
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474370 Loss1: 0.105631 Loss2: 1.368740
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.644738 Loss1: 0.257704 Loss2: 1.387034
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467092 Loss1: 0.104374 Loss2: 1.362718
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529252 Loss1: 0.142555 Loss2: 1.386697
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455513 Loss1: 0.081714 Loss2: 1.373800
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491088 Loss1: 0.119496 Loss2: 1.371592
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449583 Loss1: 0.084744 Loss2: 1.364839
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415499 Loss1: 0.059604 Loss2: 1.355894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386612 Loss1: 0.041290 Loss2: 1.345322 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.817842 Loss1: 0.458282 Loss2: 1.359560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559114 Loss1: 0.195780 Loss2: 1.363334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568866 Loss1: 0.187704 Loss2: 1.381162
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.467412 Loss1: 0.623619 Loss2: 1.843793
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491744 Loss1: 0.133651 Loss2: 1.358094
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.772076 Loss1: 0.388658 Loss2: 1.383418
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.676751 Loss1: 0.248307 Loss2: 1.428443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.542220 Loss1: 0.166449 Loss2: 1.375771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507877 Loss1: 0.133242 Loss2: 1.374635 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490607 Loss1: 0.123618 Loss2: 1.366988 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459461 Loss1: 0.088017 Loss2: 1.371444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.455838 Loss1: 0.096398 Loss2: 1.359440 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.741851 Loss1: 0.390157 Loss2: 1.351694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.510010 Loss1: 0.163207 Loss2: 1.346804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.357784 Loss1: 0.496765 Loss2: 1.861019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.738021 Loss1: 0.351909 Loss2: 1.386113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.620589 Loss1: 0.195914 Loss2: 1.424676 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.609916 Loss1: 0.216105 Loss2: 1.393811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512808 Loss1: 0.119574 Loss2: 1.393234 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498968 Loss1: 0.117096 Loss2: 1.381872 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472108 Loss1: 0.096650 Loss2: 1.375459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417891 Loss1: 0.051785 Loss2: 1.366105 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.829927 Loss1: 0.441927 Loss2: 1.388000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647608 Loss1: 0.244630 Loss2: 1.402978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.647586 Loss1: 0.240970 Loss2: 1.406616
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.370324 Loss1: 0.528804 Loss2: 1.841520
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.735494 Loss1: 0.377660 Loss2: 1.357833 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682878 Loss1: 0.286611 Loss2: 1.396267 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.596557 Loss1: 0.230488 Loss2: 1.366069 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519836 Loss1: 0.161047 Loss2: 1.358789 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419105 Loss1: 0.057099 Loss2: 1.362006
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.457574 Loss1: 0.103014 Loss2: 1.354559
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426982 Loss1: 0.079702 Loss2: 1.347280
+DEBUG flwr 2023-10-12 05:33:54,001 | server.py:236 | fit_round 140 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415590 Loss1: 0.079765 Loss2: 1.335825
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406097 Loss1: 0.066120 Loss2: 1.339977
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390716 Loss1: 0.056680 Loss2: 1.334036
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.417383 Loss1: 0.569954 Loss2: 1.847429
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.708783 Loss1: 0.348099 Loss2: 1.360685
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568049 Loss1: 0.175942 Loss2: 1.392108
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491270 Loss1: 0.133750 Loss2: 1.357521
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506261 Loss1: 0.152602 Loss2: 1.353659
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.297677 Loss1: 0.527423 Loss2: 1.770254
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.688972 Loss1: 0.361644 Loss2: 1.327328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.596305 Loss1: 0.228970 Loss2: 1.367335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.494311 Loss1: 0.166345 Loss2: 1.327965 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505404 Loss1: 0.175507 Loss2: 1.329897 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452105 Loss1: 0.133176 Loss2: 1.318929 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370107 Loss1: 0.064543 Loss2: 1.305564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375884 Loss1: 0.070879 Loss2: 1.305005
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.334592 Loss1: 0.548561 Loss2: 1.786032
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.707262 Loss1: 0.362101 Loss2: 1.345161
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.554377 Loss1: 0.183820 Loss2: 1.370557
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.531933 Loss1: 0.191716 Loss2: 1.340217
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490760 Loss1: 0.151898 Loss2: 1.338861
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.407403 Loss1: 0.566509 Loss2: 1.840894
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519151 Loss1: 0.186100 Loss2: 1.333052
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.734945 Loss1: 0.354702 Loss2: 1.380243
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460674 Loss1: 0.126171 Loss2: 1.334504
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.639412 Loss1: 0.209188 Loss2: 1.430223
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461003 Loss1: 0.130593 Loss2: 1.330410
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.629796 Loss1: 0.243227 Loss2: 1.386569
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422393 Loss1: 0.098315 Loss2: 1.324078
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.532499 Loss1: 0.136135 Loss2: 1.396364
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392291 Loss1: 0.072760 Loss2: 1.319532
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491505 Loss1: 0.109846 Loss2: 1.381659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417648 Loss1: 0.049552 Loss2: 1.368096 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 05:33:54,001][flwr][DEBUG] - fit_round 140 received 50 results and 0 failures
+INFO flwr 2023-10-12 05:34:37,053 | server.py:125 | fit progress: (140, 2.210895585747192, {'accuracy': 0.5928}, 322984.83112155297)
+>> Test accuracy: 0.592800
+[2023-10-12 05:34:37,053][flwr][INFO] - fit progress: (140, 2.210895585747192, {'accuracy': 0.5928}, 322984.83112155297)
+DEBUG flwr 2023-10-12 05:34:37,053 | server.py:173 | evaluate_round 140: strategy sampled 50 clients (out of 50)
+[2023-10-12 05:34:37,053][flwr][DEBUG] - evaluate_round 140: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 05:43:37,505 | server.py:187 | evaluate_round 140 received 50 results and 0 failures
+[2023-10-12 05:43:37,505][flwr][DEBUG] - evaluate_round 140 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 05:43:37,506 | server.py:222 | fit_round 141: strategy sampled 50 clients (out of 50)
+[2023-10-12 05:43:37,506][flwr][DEBUG] - fit_round 141: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.639876 Loss1: 0.646545 Loss2: 1.993331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.705954 Loss1: 0.300646 Loss2: 1.405308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.556240 Loss1: 0.177169 Loss2: 1.379071 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.521738 Loss1: 0.142967 Loss2: 1.378771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474054 Loss1: 0.096240 Loss2: 1.377814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451480 Loss1: 0.087686 Loss2: 1.363794
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.522813 Loss1: 0.160524 Loss2: 1.362289
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436098 Loss1: 0.070709 Loss2: 1.365389
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.472181 Loss1: 0.128893 Loss2: 1.343288
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.426551 Loss1: 0.087612 Loss2: 1.338939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.401178 Loss1: 0.074972 Loss2: 1.326206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397387 Loss1: 0.068108 Loss2: 1.329279 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387318 Loss1: 0.060846 Loss2: 1.326472 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587442 Loss1: 0.188866 Loss2: 1.398576 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515767 Loss1: 0.132635 Loss2: 1.383132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.489762 Loss1: 0.111827 Loss2: 1.377935
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.335909 Loss1: 0.528954 Loss2: 1.806956
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471497 Loss1: 0.090475 Loss2: 1.381022
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.661227 Loss1: 0.298691 Loss2: 1.362536
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447805 Loss1: 0.074267 Loss2: 1.373538
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.584636 Loss1: 0.212748 Loss2: 1.371888
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436185 Loss1: 0.065956 Loss2: 1.370228
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530344 Loss1: 0.178334 Loss2: 1.352010
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.461597 Loss1: 0.114721 Loss2: 1.346876
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.434846 Loss1: 0.094679 Loss2: 1.340166
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395328 Loss1: 0.069249 Loss2: 1.326079
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382291 Loss1: 0.058359 Loss2: 1.323931
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.555931 Loss1: 0.634863 Loss2: 1.921068
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.870145 Loss1: 0.430260 Loss2: 1.439885
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366623 Loss1: 0.047015 Loss2: 1.319609
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.705009 Loss1: 0.231157 Loss2: 1.473853
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381662 Loss1: 0.067429 Loss2: 1.314233
+(DefaultActor pid=3764) >> Training accuracy: 0.991728
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536429 Loss1: 0.106768 Loss2: 1.429661 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518520 Loss1: 0.101911 Loss2: 1.416609 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483925 Loss1: 0.070942 Loss2: 1.412984
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.630963 Loss1: 0.701831 Loss2: 1.929132
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.784116 Loss1: 0.391955 Loss2: 1.392160
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.512849 Loss1: 0.109302 Loss2: 1.403547
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.672891 Loss1: 0.249334 Loss2: 1.423557
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.493681 Loss1: 0.090103 Loss2: 1.403578
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528543 Loss1: 0.137889 Loss2: 1.390655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461498 Loss1: 0.081785 Loss2: 1.379713 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491516 Loss1: 0.121911 Loss2: 1.369606
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.386760 Loss1: 0.561860 Loss2: 1.824900
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.752528 Loss1: 0.386084 Loss2: 1.366445 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.504885 Loss1: 0.146852 Loss2: 1.358032 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437883 Loss1: 0.091869 Loss2: 1.346013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.454537 Loss1: 0.553390 Loss2: 1.901147
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439705 Loss1: 0.096066 Loss2: 1.343639
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.728936 Loss1: 0.338432 Loss2: 1.390504
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.431907 Loss1: 0.090215 Loss2: 1.341692
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633955 Loss1: 0.222472 Loss2: 1.411483
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404971 Loss1: 0.059602 Loss2: 1.345370
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.593829 Loss1: 0.193710 Loss2: 1.400119
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447735 Loss1: 0.113478 Loss2: 1.334257
+(DefaultActor pid=3765) >> Training accuracy: 0.978516
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.526011 Loss1: 0.140982 Loss2: 1.385029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455642 Loss1: 0.074781 Loss2: 1.380861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426871 Loss1: 0.058889 Loss2: 1.367982
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.495883 Loss1: 0.663786 Loss2: 1.832097
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406525 Loss1: 0.045518 Loss2: 1.361006
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.756044 Loss1: 0.406005 Loss2: 1.350038
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638040 Loss1: 0.241519 Loss2: 1.396521
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541082 Loss1: 0.186781 Loss2: 1.354301
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.489196 Loss1: 0.143762 Loss2: 1.345434
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449228 Loss1: 0.109471 Loss2: 1.339757
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.418561 Loss1: 0.568086 Loss2: 1.850475
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.427114 Loss1: 0.096495 Loss2: 1.330619
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.753317 Loss1: 0.356001 Loss2: 1.397316
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425295 Loss1: 0.094829 Loss2: 1.330466
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.631014 Loss1: 0.197359 Loss2: 1.433655
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405080 Loss1: 0.077100 Loss2: 1.327979
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.547672 Loss1: 0.149613 Loss2: 1.398059
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384630 Loss1: 0.064127 Loss2: 1.320503
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480520 Loss1: 0.095703 Loss2: 1.384817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421144 Loss1: 0.043507 Loss2: 1.377637 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.522753 Loss1: 0.655934 Loss2: 1.866819
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433178 Loss1: 0.061101 Loss2: 1.372077
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.872504 Loss1: 0.479271 Loss2: 1.393233
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417997 Loss1: 0.044921 Loss2: 1.373076
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.636363 Loss1: 0.253299 Loss2: 1.383064 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502221 Loss1: 0.129756 Loss2: 1.372465 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.498573 Loss1: 0.133426 Loss2: 1.365147
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.403294 Loss1: 0.578394 Loss2: 1.824900
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.690036 Loss1: 0.326345 Loss2: 1.363690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.637375 Loss1: 0.228246 Loss2: 1.409130 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573997 Loss1: 0.202850 Loss2: 1.371147 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.533745 Loss1: 0.158648 Loss2: 1.375097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.520869 Loss1: 0.152626 Loss2: 1.368243 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.656554 Loss1: 0.317256 Loss2: 1.339298 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551219 Loss1: 0.182319 Loss2: 1.368900 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.460175 Loss1: 0.131334 Loss2: 1.328841 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409349 Loss1: 0.080369 Loss2: 1.328980 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.390025 Loss1: 0.069984 Loss2: 1.320041
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.551790 Loss1: 0.700285 Loss2: 1.851505
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.364554 Loss1: 0.052466 Loss2: 1.312088
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.819462 Loss1: 0.437239 Loss2: 1.382222
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.339994 Loss1: 0.030726 Loss2: 1.309267
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.681873 Loss1: 0.252436 Loss2: 1.429437
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.549085 Loss1: 0.177245 Loss2: 1.371840
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528303 Loss1: 0.169732 Loss2: 1.358571
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472758 Loss1: 0.113090 Loss2: 1.359668
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428752 Loss1: 0.076391 Loss2: 1.352362
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440609 Loss1: 0.095169 Loss2: 1.345439
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.315187 Loss1: 0.504877 Loss2: 1.810310
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420674 Loss1: 0.081453 Loss2: 1.339222
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.727075 Loss1: 0.364516 Loss2: 1.362559
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382972 Loss1: 0.046944 Loss2: 1.336027
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.622475 Loss1: 0.212592 Loss2: 1.409883
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562777 Loss1: 0.210614 Loss2: 1.352163
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.573696 Loss1: 0.214731 Loss2: 1.358965
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533985 Loss1: 0.159090 Loss2: 1.374896
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513010 Loss1: 0.156700 Loss2: 1.356310
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.540424 Loss1: 0.706451 Loss2: 1.833973
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.813194 Loss1: 0.430571 Loss2: 1.382622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.708018 Loss1: 0.303435 Loss2: 1.404583 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422531 Loss1: 0.078205 Loss2: 1.344326
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532017 Loss1: 0.165564 Loss2: 1.366454
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503417 Loss1: 0.144321 Loss2: 1.359096
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492665 Loss1: 0.141171 Loss2: 1.351494
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416668 Loss1: 0.069902 Loss2: 1.346767
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411012 Loss1: 0.070876 Loss2: 1.340136
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.470332 Loss1: 0.576042 Loss2: 1.894290
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385163 Loss1: 0.052788 Loss2: 1.332375
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.749572 Loss1: 0.360313 Loss2: 1.389259
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.371543 Loss1: 0.043271 Loss2: 1.328271
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.667967 Loss1: 0.272730 Loss2: 1.395236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.550498 Loss1: 0.157594 Loss2: 1.392904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524829 Loss1: 0.136543 Loss2: 1.388285
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.424879 Loss1: 0.614511 Loss2: 1.810367
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.525854 Loss1: 0.141717 Loss2: 1.384136
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.670428 Loss1: 0.332094 Loss2: 1.338334
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.483824 Loss1: 0.103356 Loss2: 1.380468
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.645355 Loss1: 0.278009 Loss2: 1.367346
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.464974 Loss1: 0.091263 Loss2: 1.373711
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555983 Loss1: 0.220687 Loss2: 1.335297
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.461740 Loss1: 0.132273 Loss2: 1.329468
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.417362 Loss1: 0.091749 Loss2: 1.325614
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.424905 Loss1: 0.105087 Loss2: 1.319819
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397639 Loss1: 0.081526 Loss2: 1.316113
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.512749 Loss1: 0.654066 Loss2: 1.858683
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368716 Loss1: 0.058483 Loss2: 1.310234
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.824535 Loss1: 0.428940 Loss2: 1.395595
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.355791 Loss1: 0.052686 Loss2: 1.303105
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.653238 Loss1: 0.254846 Loss2: 1.398392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503607 Loss1: 0.105584 Loss2: 1.398023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463922 Loss1: 0.087976 Loss2: 1.375946
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.551729 Loss1: 0.662062 Loss2: 1.889667
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445695 Loss1: 0.075951 Loss2: 1.369744
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.701771 Loss1: 0.337689 Loss2: 1.364082
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411374 Loss1: 0.044046 Loss2: 1.367328
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.567995 Loss1: 0.193426 Loss2: 1.374569
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408903 Loss1: 0.048514 Loss2: 1.360389
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.484000 Loss1: 0.142184 Loss2: 1.341816
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.435870 Loss1: 0.093114 Loss2: 1.342756
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444693 Loss1: 0.106822 Loss2: 1.337871
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467412 Loss1: 0.122577 Loss2: 1.344835
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427238 Loss1: 0.089437 Loss2: 1.337801
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.453472 Loss1: 0.608280 Loss2: 1.845193
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398672 Loss1: 0.063081 Loss2: 1.335591
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.783403 Loss1: 0.404823 Loss2: 1.378580
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393123 Loss1: 0.059455 Loss2: 1.333668
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.699756 Loss1: 0.306545 Loss2: 1.393211 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562809 Loss1: 0.183766 Loss2: 1.379043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503632 Loss1: 0.123863 Loss2: 1.379769
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.465728 Loss1: 0.653184 Loss2: 1.812544
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479510 Loss1: 0.116533 Loss2: 1.362977
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.748271 Loss1: 0.406980 Loss2: 1.341291
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458482 Loss1: 0.096604 Loss2: 1.361879
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.638004 Loss1: 0.246653 Loss2: 1.391351
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417822 Loss1: 0.059063 Loss2: 1.358759
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520070 Loss1: 0.173742 Loss2: 1.346328
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485349 Loss1: 0.147606 Loss2: 1.337743
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455660 Loss1: 0.112886 Loss2: 1.342774
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480531 Loss1: 0.145933 Loss2: 1.334598
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.410963 Loss1: 0.086416 Loss2: 1.324547
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.525868 Loss1: 0.668211 Loss2: 1.857657
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412419 Loss1: 0.093607 Loss2: 1.318812
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.696869 Loss1: 0.358951 Loss2: 1.337918
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415991 Loss1: 0.087760 Loss2: 1.328231
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.521412 Loss1: 0.183552 Loss2: 1.337860 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449607 Loss1: 0.127219 Loss2: 1.322389 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.365729 Loss1: 0.540800 Loss2: 1.824929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.651842 Loss1: 0.312536 Loss2: 1.339306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.612277 Loss1: 0.233547 Loss2: 1.378731 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.511257 Loss1: 0.168500 Loss2: 1.342756 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456579 Loss1: 0.115462 Loss2: 1.341117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398825 Loss1: 0.069400 Loss2: 1.329425
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.393940 Loss1: 0.611779 Loss2: 1.782161
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.671038 Loss1: 0.333441 Loss2: 1.337597 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.570464 Loss1: 0.215704 Loss2: 1.354760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.438571 Loss1: 0.110709 Loss2: 1.327862 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.386088 Loss1: 0.078083 Loss2: 1.308005 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.373510 Loss1: 0.070490 Loss2: 1.303020 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.358719 Loss1: 0.064144 Loss2: 1.294575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.346754 Loss1: 0.055024 Loss2: 1.291730 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524469 Loss1: 0.156230 Loss2: 1.368239 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427367 Loss1: 0.082033 Loss2: 1.345334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.381693 Loss1: 0.550513 Loss2: 1.831180
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392302 Loss1: 0.051998 Loss2: 1.340304
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406821 Loss1: 0.070830 Loss2: 1.335991
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.720165 Loss1: 0.349313 Loss2: 1.370853
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.621079 Loss1: 0.203960 Loss2: 1.417119
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570666 Loss1: 0.207567 Loss2: 1.363099
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527790 Loss1: 0.146373 Loss2: 1.381417
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471079 Loss1: 0.107953 Loss2: 1.363127
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.599618 Loss1: 0.700848 Loss2: 1.898770
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420575 Loss1: 0.062561 Loss2: 1.358014
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.397678 Loss1: 0.053254 Loss2: 1.344423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.365024 Loss1: 0.024097 Loss2: 1.340927 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.525592 Loss1: 0.159697 Loss2: 1.365895 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468301 Loss1: 0.109807 Loss2: 1.358494 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.459092 Loss1: 0.099913 Loss2: 1.359178 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.390189 Loss1: 0.546614 Loss2: 1.843574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.613061 Loss1: 0.222170 Loss2: 1.390891 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510338 Loss1: 0.152824 Loss2: 1.357515 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459420 Loss1: 0.100601 Loss2: 1.358819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431233 Loss1: 0.084484 Loss2: 1.346750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417843 Loss1: 0.075118 Loss2: 1.342726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412213 Loss1: 0.066308 Loss2: 1.345906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416281 Loss1: 0.074819 Loss2: 1.341461 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.395390 Loss1: 0.061103 Loss2: 1.334287 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408354 Loss1: 0.089834 Loss2: 1.318519 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.755147 Loss1: 0.309489 Loss2: 1.445658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.662999 Loss1: 0.220265 Loss2: 1.442734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.650035 Loss1: 0.198052 Loss2: 1.451983
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.493778 Loss1: 0.657250 Loss2: 1.836527
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.742622 Loss1: 0.369482 Loss2: 1.373140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.595333 Loss1: 0.183522 Loss2: 1.411811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526598 Loss1: 0.171198 Loss2: 1.355400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509311 Loss1: 0.153848 Loss2: 1.355463 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485324 Loss1: 0.061951 Loss2: 1.423373
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.488579 Loss1: 0.127670 Loss2: 1.360909
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445073 Loss1: 0.091851 Loss2: 1.353222
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423713 Loss1: 0.078544 Loss2: 1.345169
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427455 Loss1: 0.085758 Loss2: 1.341697
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409549 Loss1: 0.069353 Loss2: 1.340195
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.513412 Loss1: 0.614096 Loss2: 1.899316
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.782593 Loss1: 0.379651 Loss2: 1.402942
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700321 Loss1: 0.252740 Loss2: 1.447580
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.652964 Loss1: 0.264435 Loss2: 1.388528
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546003 Loss1: 0.145863 Loss2: 1.400139
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.500001 Loss1: 0.676077 Loss2: 1.823923
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.745859 Loss1: 0.399126 Loss2: 1.346732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.668230 Loss1: 0.276886 Loss2: 1.391344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595448 Loss1: 0.241846 Loss2: 1.353601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544516 Loss1: 0.197021 Loss2: 1.347495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419436 Loss1: 0.050353 Loss2: 1.369082
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.534899 Loss1: 0.180179 Loss2: 1.354720
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465440 Loss1: 0.117629 Loss2: 1.347811
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416952 Loss1: 0.080836 Loss2: 1.336115
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445190 Loss1: 0.115946 Loss2: 1.329243
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400711 Loss1: 0.073243 Loss2: 1.327468
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.434786 Loss1: 0.577585 Loss2: 1.857201
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.814693 Loss1: 0.413724 Loss2: 1.400969
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.744261 Loss1: 0.299629 Loss2: 1.444632
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.661170 Loss1: 0.259606 Loss2: 1.401564
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546320 Loss1: 0.150518 Loss2: 1.395802
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.385880 Loss1: 0.511541 Loss2: 1.874340
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499339 Loss1: 0.112386 Loss2: 1.386953
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.712887 Loss1: 0.351255 Loss2: 1.361631
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451035 Loss1: 0.075023 Loss2: 1.376012
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684416 Loss1: 0.269067 Loss2: 1.415350
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424378 Loss1: 0.062679 Loss2: 1.361700
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.545974 Loss1: 0.188098 Loss2: 1.357875
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406764 Loss1: 0.046490 Loss2: 1.360274
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555569 Loss1: 0.195541 Loss2: 1.360028
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406431 Loss1: 0.053798 Loss2: 1.352633
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.592125 Loss1: 0.229304 Loss2: 1.362821
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.554602 Loss1: 0.180950 Loss2: 1.373652
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464423 Loss1: 0.099286 Loss2: 1.365137
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415632 Loss1: 0.067937 Loss2: 1.347695
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389381 Loss1: 0.049045 Loss2: 1.340336
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.493154 Loss1: 0.656989 Loss2: 1.836165
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.777525 Loss1: 0.426570 Loss2: 1.350954
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.689168 Loss1: 0.285303 Loss2: 1.403865
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573649 Loss1: 0.211888 Loss2: 1.361761
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.360259 Loss1: 0.537350 Loss2: 1.822909
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.678458 Loss1: 0.344780 Loss2: 1.333678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.576229 Loss1: 0.199470 Loss2: 1.376759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.504430 Loss1: 0.180522 Loss2: 1.323909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.480907 Loss1: 0.151510 Loss2: 1.329398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.432171 Loss1: 0.101808 Loss2: 1.330363 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.356285 Loss1: 0.046561 Loss2: 1.309725 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.323798 Loss1: 0.026158 Loss2: 1.297639 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.774467 Loss1: 0.404425 Loss2: 1.370042 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561386 Loss1: 0.202173 Loss2: 1.359213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.496420 Loss1: 0.134191 Loss2: 1.362229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.446433 Loss1: 0.094613 Loss2: 1.351820 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431575 Loss1: 0.089919 Loss2: 1.341656 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.406701 Loss1: 0.066204 Loss2: 1.340498 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397876 Loss1: 0.067551 Loss2: 1.330325 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400739 Loss1: 0.071159 Loss2: 1.329580 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387831 Loss1: 0.074104 Loss2: 1.313727 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.491048 Loss1: 0.600294 Loss2: 1.890754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.730257 Loss1: 0.287672 Loss2: 1.442585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.680071 Loss1: 0.283040 Loss2: 1.397032
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.594341 Loss1: 0.689924 Loss2: 1.904416
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574760 Loss1: 0.178388 Loss2: 1.396372
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.798775 Loss1: 0.433985 Loss2: 1.364791
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494491 Loss1: 0.110541 Loss2: 1.383950
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.703038 Loss1: 0.275221 Loss2: 1.427816
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564911 Loss1: 0.183798 Loss2: 1.381113
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491088 Loss1: 0.124399 Loss2: 1.366689
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.504852 Loss1: 0.137775 Loss2: 1.367077
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464083 Loss1: 0.086485 Loss2: 1.377598
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508905 Loss1: 0.135728 Loss2: 1.373177
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449472 Loss1: 0.083923 Loss2: 1.365549
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438979 Loss1: 0.077212 Loss2: 1.361767 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388502 Loss1: 0.043060 Loss2: 1.345442 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.327399 Loss1: 0.493011 Loss2: 1.834388 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599050 Loss1: 0.234001 Loss2: 1.365049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476115 Loss1: 0.131662 Loss2: 1.344453
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.441354 Loss1: 0.559184 Loss2: 1.882171
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.804305 Loss1: 0.417635 Loss2: 1.386670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.666761 Loss1: 0.225025 Loss2: 1.441736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.542005 Loss1: 0.157511 Loss2: 1.384493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505129 Loss1: 0.121821 Loss2: 1.383309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521290 Loss1: 0.131429 Loss2: 1.389861 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366614 Loss1: 0.056602 Loss2: 1.310012
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.481831 Loss1: 0.102723 Loss2: 1.379108
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470926 Loss1: 0.096009 Loss2: 1.374917
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447974 Loss1: 0.074474 Loss2: 1.373500
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456885 Loss1: 0.083316 Loss2: 1.373569
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.641737 Loss1: 0.706116 Loss2: 1.935620
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.738648 Loss1: 0.387560 Loss2: 1.351089
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.581905 Loss1: 0.214466 Loss2: 1.367439
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.485391 Loss1: 0.136758 Loss2: 1.348633
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.432016 Loss1: 0.626570 Loss2: 1.805445
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.441647 Loss1: 0.107446 Loss2: 1.334201 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420784 Loss1: 0.096107 Loss2: 1.324677 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.377063 Loss1: 0.056367 Loss2: 1.320696 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 06:11:59,436 | server.py:236 | fit_round 141 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371777 Loss1: 0.055790 Loss2: 1.315988 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359382 Loss1: 0.052803 Loss2: 1.306579 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431904 Loss1: 0.121155 Loss2: 1.310749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381122 Loss1: 0.081679 Loss2: 1.299442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.340376 Loss1: 0.045871 Loss2: 1.294505
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.459018 Loss1: 0.631565 Loss2: 1.827453
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.768871 Loss1: 0.417110 Loss2: 1.351762
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582284 Loss1: 0.180701 Loss2: 1.401583
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513061 Loss1: 0.164400 Loss2: 1.348661
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.465862 Loss1: 0.111730 Loss2: 1.354132
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.413760 Loss1: 0.623322 Loss2: 1.790438
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478641 Loss1: 0.135692 Loss2: 1.342948
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464979 Loss1: 0.117720 Loss2: 1.347259 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438188 Loss1: 0.091110 Loss2: 1.347077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398691 Loss1: 0.066446 Loss2: 1.332245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377371 Loss1: 0.046260 Loss2: 1.331111 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.420087 Loss1: 0.139395 Loss2: 1.280691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.361990 Loss1: 0.086698 Loss2: 1.275292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.331253 Loss1: 0.067182 Loss2: 1.264071
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.460980 Loss1: 0.584377 Loss2: 1.876603
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.726049 Loss1: 0.353855 Loss2: 1.372194
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.626435 Loss1: 0.219519 Loss2: 1.406916
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513187 Loss1: 0.154314 Loss2: 1.358873
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.454671 Loss1: 0.098355 Loss2: 1.356315
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.309757 Loss1: 0.492370 Loss2: 1.817387
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455714 Loss1: 0.106863 Loss2: 1.348851
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.457135 Loss1: 0.110483 Loss2: 1.346652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.712405 Loss1: 0.280242 Loss2: 1.432162
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439889 Loss1: 0.096689 Loss2: 1.343200
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.622767 Loss1: 0.242691 Loss2: 1.380076
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397285 Loss1: 0.060667 Loss2: 1.336618
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507059 Loss1: 0.123058 Loss2: 1.384001
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396620 Loss1: 0.068468 Loss2: 1.328152
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427490 Loss1: 0.064801 Loss2: 1.362689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401668 Loss1: 0.052665 Loss2: 1.349004 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 06:11:59,436][flwr][DEBUG] - fit_round 141 received 50 results and 0 failures
+INFO flwr 2023-10-12 06:12:40,690 | server.py:125 | fit progress: (141, 2.2251683026076123, {'accuracy': 0.5956}, 325268.468418965)
+>> Test accuracy: 0.595600
+[2023-10-12 06:12:40,690][flwr][INFO] - fit progress: (141, 2.2251683026076123, {'accuracy': 0.5956}, 325268.468418965)
+DEBUG flwr 2023-10-12 06:12:40,690 | server.py:173 | evaluate_round 141: strategy sampled 50 clients (out of 50)
+[2023-10-12 06:12:40,690][flwr][DEBUG] - evaluate_round 141: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 06:21:43,473 | server.py:187 | evaluate_round 141 received 50 results and 0 failures
+[2023-10-12 06:21:43,473][flwr][DEBUG] - evaluate_round 141 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 06:21:43,474 | server.py:222 | fit_round 142: strategy sampled 50 clients (out of 50)
+[2023-10-12 06:21:43,474][flwr][DEBUG] - fit_round 142: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.373143 Loss1: 0.550470 Loss2: 1.822673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.636669 Loss1: 0.232665 Loss2: 1.404004 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522729 Loss1: 0.152452 Loss2: 1.370277
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.357071 Loss1: 0.559454 Loss2: 1.797617
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.485852 Loss1: 0.114785 Loss2: 1.371067
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.687208 Loss1: 0.329198 Loss2: 1.358010
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470025 Loss1: 0.108317 Loss2: 1.361709
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.637647 Loss1: 0.250825 Loss2: 1.386822
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488679 Loss1: 0.126142 Loss2: 1.362538
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521148 Loss1: 0.166737 Loss2: 1.354411
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454769 Loss1: 0.097775 Loss2: 1.356994
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.486828 Loss1: 0.131757 Loss2: 1.355071
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429673 Loss1: 0.073448 Loss2: 1.356225
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446356 Loss1: 0.098468 Loss2: 1.347888
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431025 Loss1: 0.083157 Loss2: 1.347868
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473912 Loss1: 0.133524 Loss2: 1.340388
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432952 Loss1: 0.082637 Loss2: 1.350314
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.411826 Loss1: 0.072325 Loss2: 1.339501
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.371383 Loss1: 0.034296 Loss2: 1.337086
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.627747 Loss1: 0.715366 Loss2: 1.912381
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.961996 Loss1: 0.509981 Loss2: 1.452014
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.823368 Loss1: 0.318749 Loss2: 1.504620
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.634780 Loss1: 0.196559 Loss2: 1.438221
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.500149 Loss1: 0.622540 Loss2: 1.877609
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.719919 Loss1: 0.363545 Loss2: 1.356375
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.607794 Loss1: 0.173225 Loss2: 1.434569
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.649128 Loss1: 0.251429 Loss2: 1.397700
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.601269 Loss1: 0.174397 Loss2: 1.426871
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.544959 Loss1: 0.178611 Loss2: 1.366347
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531463 Loss1: 0.109389 Loss2: 1.422074
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.492416 Loss1: 0.081288 Loss2: 1.411128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456601 Loss1: 0.051456 Loss2: 1.405145 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453804 Loss1: 0.061864 Loss2: 1.391940 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381164 Loss1: 0.048511 Loss2: 1.332653 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.457006 Loss1: 0.640789 Loss2: 1.816217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.632329 Loss1: 0.249748 Loss2: 1.382581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467464 Loss1: 0.140231 Loss2: 1.327233
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.576761 Loss1: 0.645360 Loss2: 1.931401
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.467832 Loss1: 0.136615 Loss2: 1.331217
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.844583 Loss1: 0.430863 Loss2: 1.413720
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458199 Loss1: 0.122178 Loss2: 1.336020
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.731178 Loss1: 0.285670 Loss2: 1.445508
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433845 Loss1: 0.106464 Loss2: 1.327381
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.643908 Loss1: 0.241966 Loss2: 1.401942
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436449 Loss1: 0.114289 Loss2: 1.322160
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.583600 Loss1: 0.172396 Loss2: 1.411204
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.382714 Loss1: 0.064615 Loss2: 1.318100
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504222 Loss1: 0.108915 Loss2: 1.395307
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372766 Loss1: 0.062970 Loss2: 1.309795
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484147 Loss1: 0.092351 Loss2: 1.391796
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460591 Loss1: 0.075236 Loss2: 1.385356
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419215 Loss1: 0.050385 Loss2: 1.368830
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421398 Loss1: 0.059301 Loss2: 1.362097
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.452315 Loss1: 0.621245 Loss2: 1.831069
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.674677 Loss1: 0.331620 Loss2: 1.343056
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.663561 Loss1: 0.279365 Loss2: 1.384196
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558291 Loss1: 0.210900 Loss2: 1.347391
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.322793 Loss1: 0.499531 Loss2: 1.823262
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.709553 Loss1: 0.340211 Loss2: 1.369342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590967 Loss1: 0.187200 Loss2: 1.403766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559045 Loss1: 0.205501 Loss2: 1.353544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.533647 Loss1: 0.159076 Loss2: 1.374571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446112 Loss1: 0.093696 Loss2: 1.352417 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412684 Loss1: 0.077407 Loss2: 1.335277 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399811 Loss1: 0.066892 Loss2: 1.332920 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.735766 Loss1: 0.350790 Loss2: 1.384977 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563365 Loss1: 0.182834 Loss2: 1.380532 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472869 Loss1: 0.106481 Loss2: 1.366387
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.369992 Loss1: 0.493024 Loss2: 1.876968
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.464198 Loss1: 0.095476 Loss2: 1.368722
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.738604 Loss1: 0.343447 Loss2: 1.395157
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450612 Loss1: 0.090316 Loss2: 1.360296
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.704796 Loss1: 0.266634 Loss2: 1.438162
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.585385 Loss1: 0.189067 Loss2: 1.396317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527075 Loss1: 0.136421 Loss2: 1.390654 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498096 Loss1: 0.112481 Loss2: 1.385616 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458327 Loss1: 0.079307 Loss2: 1.379021 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431254 Loss1: 0.063523 Loss2: 1.367730 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.792591 Loss1: 0.402788 Loss2: 1.389802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.618013 Loss1: 0.222467 Loss2: 1.395545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.534752 Loss1: 0.640734 Loss2: 1.894018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.746919 Loss1: 0.366039 Loss2: 1.380880 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.675276 Loss1: 0.247285 Loss2: 1.427991 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537703 Loss1: 0.160329 Loss2: 1.377375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506903 Loss1: 0.134364 Loss2: 1.372539 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438817 Loss1: 0.090508 Loss2: 1.348308 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434281 Loss1: 0.082155 Loss2: 1.352126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440125 Loss1: 0.087401 Loss2: 1.352724
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.238077 Loss1: 0.464031 Loss2: 1.774046
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.640705 Loss1: 0.315957 Loss2: 1.324748
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.569368 Loss1: 0.211813 Loss2: 1.357555
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.475228 Loss1: 0.147928 Loss2: 1.327300
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.426810 Loss1: 0.096958 Loss2: 1.329851
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.623647 Loss1: 0.714912 Loss2: 1.908736
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.752890 Loss1: 0.377939 Loss2: 1.374951
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.435219 Loss1: 0.112745 Loss2: 1.322475
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435399 Loss1: 0.113206 Loss2: 1.322193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425863 Loss1: 0.104750 Loss2: 1.321113 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381540 Loss1: 0.062064 Loss2: 1.319476 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.511321 Loss1: 0.145398 Loss2: 1.365923 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427944 Loss1: 0.078659 Loss2: 1.349286 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.403246 Loss1: 0.601926 Loss2: 1.801321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.602069 Loss1: 0.236208 Loss2: 1.365861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.487079 Loss1: 0.153356 Loss2: 1.333723
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.387701 Loss1: 0.518796 Loss2: 1.868905
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470948 Loss1: 0.143997 Loss2: 1.326951
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.735691 Loss1: 0.361423 Loss2: 1.374267
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.420704 Loss1: 0.102351 Loss2: 1.318352
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.607561 Loss1: 0.202177 Loss2: 1.405384
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402782 Loss1: 0.087719 Loss2: 1.315063
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563086 Loss1: 0.184811 Loss2: 1.378275
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389203 Loss1: 0.078240 Loss2: 1.310963
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505055 Loss1: 0.138675 Loss2: 1.366380
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.368627 Loss1: 0.060971 Loss2: 1.307656
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.544854 Loss1: 0.171069 Loss2: 1.373785
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359368 Loss1: 0.058668 Loss2: 1.300700
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488296 Loss1: 0.114943 Loss2: 1.373352
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486094 Loss1: 0.111131 Loss2: 1.374964
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463384 Loss1: 0.103556 Loss2: 1.359828
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422548 Loss1: 0.066845 Loss2: 1.355703
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.425697 Loss1: 0.582809 Loss2: 1.842888
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.783693 Loss1: 0.428111 Loss2: 1.355582
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.709078 Loss1: 0.281513 Loss2: 1.427565
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581629 Loss1: 0.218056 Loss2: 1.363574
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.442773 Loss1: 0.576171 Loss2: 1.866602
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.766868 Loss1: 0.402736 Loss2: 1.364132 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.703800 Loss1: 0.283831 Loss2: 1.419969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.623563 Loss1: 0.253458 Loss2: 1.370105 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.504352 Loss1: 0.127571 Loss2: 1.376780 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479692 Loss1: 0.119279 Loss2: 1.360413 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417689 Loss1: 0.083187 Loss2: 1.334503
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.442128 Loss1: 0.080851 Loss2: 1.361277
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417746 Loss1: 0.068050 Loss2: 1.349695
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396606 Loss1: 0.056756 Loss2: 1.339850
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368262 Loss1: 0.031640 Loss2: 1.336623
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.401750 Loss1: 0.557688 Loss2: 1.844062
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.796362 Loss1: 0.438888 Loss2: 1.357474
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655014 Loss1: 0.251258 Loss2: 1.403756
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586291 Loss1: 0.236372 Loss2: 1.349919
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.495083 Loss1: 0.717440 Loss2: 1.777643
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.511344 Loss1: 0.165743 Loss2: 1.345601
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653173 Loss1: 0.334481 Loss2: 1.318693
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451057 Loss1: 0.120456 Loss2: 1.330601
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636166 Loss1: 0.300785 Loss2: 1.335381
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.392404 Loss1: 0.067629 Loss2: 1.324774
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530678 Loss1: 0.212364 Loss2: 1.318314
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.368903 Loss1: 0.046498 Loss2: 1.322405
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475442 Loss1: 0.153673 Loss2: 1.321769
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.346524 Loss1: 0.037269 Loss2: 1.309255
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.415069 Loss1: 0.115930 Loss2: 1.299139
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.324625 Loss1: 0.026630 Loss2: 1.297994
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.366010 Loss1: 0.071089 Loss2: 1.294920
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.343197 Loss1: 0.052469 Loss2: 1.290728
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.333741 Loss1: 0.054625 Loss2: 1.279117
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.316824 Loss1: 0.040641 Loss2: 1.276183
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.410453 Loss1: 0.633082 Loss2: 1.777371
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.771858 Loss1: 0.433830 Loss2: 1.338028
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.626134 Loss1: 0.251470 Loss2: 1.374665
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513962 Loss1: 0.180838 Loss2: 1.333124
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.416090 Loss1: 0.590258 Loss2: 1.825832
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.723856 Loss1: 0.383760 Loss2: 1.340097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.619202 Loss1: 0.241531 Loss2: 1.377671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563549 Loss1: 0.210083 Loss2: 1.353467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515034 Loss1: 0.168358 Loss2: 1.346676 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455313 Loss1: 0.114627 Loss2: 1.340686 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.405846 Loss1: 0.076747 Loss2: 1.329099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371563 Loss1: 0.045476 Loss2: 1.326087 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.594151 Loss1: 0.698872 Loss2: 1.895279 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.680169 Loss1: 0.242525 Loss2: 1.437644 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.382400 Loss1: 0.567259 Loss2: 1.815141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.613364 Loss1: 0.279334 Loss2: 1.334029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.623449 Loss1: 0.257774 Loss2: 1.365676 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534109 Loss1: 0.193719 Loss2: 1.340390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.458218 Loss1: 0.130795 Loss2: 1.327423 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421716 Loss1: 0.087216 Loss2: 1.334500 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395440 Loss1: 0.073318 Loss2: 1.322122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374366 Loss1: 0.056546 Loss2: 1.317820 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.436497 Loss1: 0.584022 Loss2: 1.852474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.649162 Loss1: 0.246724 Loss2: 1.402437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523129 Loss1: 0.167151 Loss2: 1.355978
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.553959 Loss1: 0.712624 Loss2: 1.841335
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.840763 Loss1: 0.470870 Loss2: 1.369894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.766621 Loss1: 0.334418 Loss2: 1.432203 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.625823 Loss1: 0.250107 Loss2: 1.375716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569060 Loss1: 0.193729 Loss2: 1.375332 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.544972 Loss1: 0.170265 Loss2: 1.374707 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486384 Loss1: 0.128268 Loss2: 1.358116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454939 Loss1: 0.101686 Loss2: 1.353253 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.319894 Loss1: 0.443533 Loss2: 1.876361 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.676125 Loss1: 0.263027 Loss2: 1.413098 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562722 Loss1: 0.173913 Loss2: 1.388810
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.442189 Loss1: 0.593118 Loss2: 1.849071
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.777338 Loss1: 0.403865 Loss2: 1.373473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601799 Loss1: 0.193351 Loss2: 1.408448 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535471 Loss1: 0.171470 Loss2: 1.364002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.439529 Loss1: 0.082054 Loss2: 1.357475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431817 Loss1: 0.085725 Loss2: 1.346092 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413940 Loss1: 0.057697 Loss2: 1.356243
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423205 Loss1: 0.077944 Loss2: 1.345261
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.387627 Loss1: 0.045880 Loss2: 1.341747
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371337 Loss1: 0.040558 Loss2: 1.330779
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.363452 Loss1: 0.036738 Loss2: 1.326713
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.652062 Loss1: 0.711245 Loss2: 1.940817
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.876201 Loss1: 0.522014 Loss2: 1.354187
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656625 Loss1: 0.233783 Loss2: 1.422843
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536278 Loss1: 0.176790 Loss2: 1.359488
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513859 Loss1: 0.158765 Loss2: 1.355094
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471081 Loss1: 0.110735 Loss2: 1.360346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436404 Loss1: 0.087218 Loss2: 1.349186 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.399374 Loss1: 0.055755 Loss2: 1.343619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374620 Loss1: 0.041808 Loss2: 1.332811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389219 Loss1: 0.061808 Loss2: 1.327411 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986779
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447972 Loss1: 0.097680 Loss2: 1.350292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418682 Loss1: 0.076447 Loss2: 1.342235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411254 Loss1: 0.072220 Loss2: 1.339034
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.576999 Loss1: 0.696795 Loss2: 1.880205
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.735835 Loss1: 0.379359 Loss2: 1.356476
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.575088 Loss1: 0.200956 Loss2: 1.374132
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526223 Loss1: 0.179618 Loss2: 1.346605
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490074 Loss1: 0.140592 Loss2: 1.349482
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.384686 Loss1: 0.536133 Loss2: 1.848553
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459855 Loss1: 0.122336 Loss2: 1.337519
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442315 Loss1: 0.108565 Loss2: 1.333750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449250 Loss1: 0.124820 Loss2: 1.324429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408656 Loss1: 0.078929 Loss2: 1.329728 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385444 Loss1: 0.061605 Loss2: 1.323839 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435684 Loss1: 0.102996 Loss2: 1.332689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426622 Loss1: 0.099195 Loss2: 1.327427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424935 Loss1: 0.090390 Loss2: 1.334545
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.521059 Loss1: 0.611818 Loss2: 1.909241
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.855646 Loss1: 0.438112 Loss2: 1.417534
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.808367 Loss1: 0.318706 Loss2: 1.489661
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.672713 Loss1: 0.246021 Loss2: 1.426693
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.622681 Loss1: 0.185666 Loss2: 1.437015
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.467338 Loss1: 0.622232 Loss2: 1.845106
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.770216 Loss1: 0.399089 Loss2: 1.371127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.662883 Loss1: 0.257915 Loss2: 1.404968 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567474 Loss1: 0.197769 Loss2: 1.369705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493486 Loss1: 0.129469 Loss2: 1.364017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470326 Loss1: 0.114311 Loss2: 1.356015 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416218 Loss1: 0.070947 Loss2: 1.345271 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394442 Loss1: 0.059801 Loss2: 1.334640 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.795088 Loss1: 0.436614 Loss2: 1.358474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556239 Loss1: 0.194150 Loss2: 1.362089 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.652157 Loss1: 0.811779 Loss2: 1.840378 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.812025 Loss1: 0.467042 Loss2: 1.344983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.704911 Loss1: 0.331330 Loss2: 1.373581 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557595 Loss1: 0.229566 Loss2: 1.328029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.416436 Loss1: 0.088760 Loss2: 1.327676 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397002 Loss1: 0.068884 Loss2: 1.328118
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.417866 Loss1: 0.111775 Loss2: 1.306090
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.391548 Loss1: 0.089575 Loss2: 1.301972
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372359 Loss1: 0.070297 Loss2: 1.302062
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.339960 Loss1: 0.047017 Loss2: 1.292943
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365877 Loss1: 0.079266 Loss2: 1.286611
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.430476 Loss1: 0.580373 Loss2: 1.850103
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.777209 Loss1: 0.389221 Loss2: 1.387987
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.738286 Loss1: 0.323492 Loss2: 1.414793
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.600821 Loss1: 0.213715 Loss2: 1.387106
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.564707 Loss1: 0.650629 Loss2: 1.914078
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.828854 Loss1: 0.388355 Loss2: 1.440499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.735089 Loss1: 0.263135 Loss2: 1.471954 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.695735 Loss1: 0.274966 Loss2: 1.420769 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.599510 Loss1: 0.163971 Loss2: 1.435539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.541067 Loss1: 0.120332 Loss2: 1.420735 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419135 Loss1: 0.063307 Loss2: 1.355828
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509923 Loss1: 0.102919 Loss2: 1.407004
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467617 Loss1: 0.062855 Loss2: 1.404762
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466860 Loss1: 0.068589 Loss2: 1.398271
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435206 Loss1: 0.047843 Loss2: 1.387364
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.436034 Loss1: 0.569947 Loss2: 1.866087
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.767772 Loss1: 0.378008 Loss2: 1.389764
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.691940 Loss1: 0.257826 Loss2: 1.434115
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601359 Loss1: 0.211620 Loss2: 1.389740
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.360855 Loss1: 0.564177 Loss2: 1.796678
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.717398 Loss1: 0.353581 Loss2: 1.363817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.609932 Loss1: 0.225322 Loss2: 1.384610 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526672 Loss1: 0.173007 Loss2: 1.353665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522168 Loss1: 0.165251 Loss2: 1.356918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494390 Loss1: 0.136663 Loss2: 1.357726 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459469 Loss1: 0.109627 Loss2: 1.349841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.393230 Loss1: 0.059405 Loss2: 1.333825 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.596759 Loss1: 0.740306 Loss2: 1.856452 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.639046 Loss1: 0.220126 Loss2: 1.418921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.578167 Loss1: 0.716456 Loss2: 1.861711 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.725366 Loss1: 0.339377 Loss2: 1.385989 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670468 Loss1: 0.251643 Loss2: 1.418826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599444 Loss1: 0.219049 Loss2: 1.380396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519782 Loss1: 0.138870 Loss2: 1.380911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471901 Loss1: 0.099154 Loss2: 1.372747 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423982 Loss1: 0.068830 Loss2: 1.355151 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410577 Loss1: 0.063499 Loss2: 1.347078 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.726847 Loss1: 0.356327 Loss2: 1.370520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.567893 Loss1: 0.189293 Loss2: 1.378600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499161 Loss1: 0.129577 Loss2: 1.369584 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462799 Loss1: 0.102144 Loss2: 1.360656 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455267 Loss1: 0.096211 Loss2: 1.359055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429743 Loss1: 0.085823 Loss2: 1.343920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407608 Loss1: 0.067223 Loss2: 1.340385 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434079 Loss1: 0.091317 Loss2: 1.342762 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.475142 Loss1: 0.089210 Loss2: 1.385932 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.263007 Loss1: 0.412765 Loss2: 1.850242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.607251 Loss1: 0.197581 Loss2: 1.409670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.609744 Loss1: 0.228964 Loss2: 1.380780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.622780 Loss1: 0.206288 Loss2: 1.416492 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.577345 Loss1: 0.183908 Loss2: 1.393436 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.521527 Loss1: 0.116822 Loss2: 1.404705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488975 Loss1: 0.110178 Loss2: 1.378796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443322 Loss1: 0.066999 Loss2: 1.376323 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451843 Loss1: 0.083075 Loss2: 1.368768 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.393616 Loss1: 0.051405 Loss2: 1.342210 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.367902 Loss1: 0.525899 Loss2: 1.842003 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.628662 Loss1: 0.231544 Loss2: 1.397118 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 06:50:21,139 | server.py:236 | fit_round 142 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.718213 Loss1: 0.747387 Loss2: 1.970826
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573489 Loss1: 0.214800 Loss2: 1.358689
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.816808 Loss1: 0.457280 Loss2: 1.359528
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.545135 Loss1: 0.184136 Loss2: 1.361000
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465295 Loss1: 0.110961 Loss2: 1.354334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440321 Loss1: 0.094342 Loss2: 1.345978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.483457 Loss1: 0.118714 Loss2: 1.364743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427414 Loss1: 0.072836 Loss2: 1.354578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.403901 Loss1: 0.057962 Loss2: 1.345939 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385577 Loss1: 0.047793 Loss2: 1.337784 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.578686 Loss1: 0.691917 Loss2: 1.886769
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.836442 Loss1: 0.468989 Loss2: 1.367453
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.705561 Loss1: 0.296374 Loss2: 1.409187
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591489 Loss1: 0.222045 Loss2: 1.369444
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.588640 Loss1: 0.674603 Loss2: 1.914037
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.748963 Loss1: 0.352593 Loss2: 1.396369 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.716944 Loss1: 0.286597 Loss2: 1.430347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605245 Loss1: 0.201982 Loss2: 1.403263 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506517 Loss1: 0.118606 Loss2: 1.387911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467284 Loss1: 0.095294 Loss2: 1.371989 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407660 Loss1: 0.050406 Loss2: 1.357254 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411853 Loss1: 0.062786 Loss2: 1.349066 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 06:50:21,139][flwr][DEBUG] - fit_round 142 received 50 results and 0 failures
+INFO flwr 2023-10-12 06:51:03,693 | server.py:125 | fit progress: (142, 2.223556954068498, {'accuracy': 0.5928}, 327571.471800928)
+>> Test accuracy: 0.592800
+[2023-10-12 06:51:03,693][flwr][INFO] - fit progress: (142, 2.223556954068498, {'accuracy': 0.5928}, 327571.471800928)
+DEBUG flwr 2023-10-12 06:51:03,694 | server.py:173 | evaluate_round 142: strategy sampled 50 clients (out of 50)
+[2023-10-12 06:51:03,694][flwr][DEBUG] - evaluate_round 142: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 07:00:09,959 | server.py:187 | evaluate_round 142 received 50 results and 0 failures
+[2023-10-12 07:00:09,959][flwr][DEBUG] - evaluate_round 142 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 07:00:09,959 | server.py:222 | fit_round 143: strategy sampled 50 clients (out of 50)
+[2023-10-12 07:00:09,959][flwr][DEBUG] - fit_round 143: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.482705 Loss1: 0.590210 Loss2: 1.892495
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.810926 Loss1: 0.396204 Loss2: 1.414723
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.686284 Loss1: 0.258978 Loss2: 1.427306
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.612892 Loss1: 0.214761 Loss2: 1.398131
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.469528 Loss1: 0.640543 Loss2: 1.828986
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.539264 Loss1: 0.136501 Loss2: 1.402763
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.719088 Loss1: 0.365228 Loss2: 1.353861
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494324 Loss1: 0.104669 Loss2: 1.389655
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.681203 Loss1: 0.285294 Loss2: 1.395909
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500489 Loss1: 0.120524 Loss2: 1.379964
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.560093 Loss1: 0.210953 Loss2: 1.349140
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462390 Loss1: 0.079498 Loss2: 1.382892
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.532445 Loss1: 0.178001 Loss2: 1.354443
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441155 Loss1: 0.060136 Loss2: 1.381019
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490856 Loss1: 0.138379 Loss2: 1.352478
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454794 Loss1: 0.079920 Loss2: 1.374874
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465365 Loss1: 0.123170 Loss2: 1.342194
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437796 Loss1: 0.093544 Loss2: 1.344252
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425424 Loss1: 0.090527 Loss2: 1.334898
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406543 Loss1: 0.079419 Loss2: 1.327124
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.450474 Loss1: 0.599791 Loss2: 1.850682
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.715450 Loss1: 0.347228 Loss2: 1.368222
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634918 Loss1: 0.239160 Loss2: 1.395758
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.530863 Loss1: 0.166608 Loss2: 1.364255
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.442645 Loss1: 0.557353 Loss2: 1.885292
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.520388 Loss1: 0.154171 Loss2: 1.366218
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659083 Loss1: 0.281116 Loss2: 1.377967
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491426 Loss1: 0.125922 Loss2: 1.365504
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.643954 Loss1: 0.230698 Loss2: 1.413255
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478846 Loss1: 0.125315 Loss2: 1.353531
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523197 Loss1: 0.145311 Loss2: 1.377886
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.526652 Loss1: 0.161640 Loss2: 1.365011
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557591 Loss1: 0.182226 Loss2: 1.375365
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.479524 Loss1: 0.117401 Loss2: 1.362123
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556788 Loss1: 0.177843 Loss2: 1.378945
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450767 Loss1: 0.095132 Loss2: 1.355635
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.498524 Loss1: 0.124727 Loss2: 1.373797
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470343 Loss1: 0.105386 Loss2: 1.364957
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458968 Loss1: 0.091527 Loss2: 1.367442
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417044 Loss1: 0.056497 Loss2: 1.360547
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.354854 Loss1: 0.569593 Loss2: 1.785261
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.613883 Loss1: 0.279038 Loss2: 1.334845
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.566235 Loss1: 0.206389 Loss2: 1.359846
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.637675 Loss1: 0.724037 Loss2: 1.913638
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484089 Loss1: 0.157350 Loss2: 1.326739
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472424 Loss1: 0.142504 Loss2: 1.329920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433188 Loss1: 0.104282 Loss2: 1.328906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.369306 Loss1: 0.055501 Loss2: 1.313805 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484279 Loss1: 0.115653 Loss2: 1.368626 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.421799 Loss1: 0.057980 Loss2: 1.363818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430260 Loss1: 0.074353 Loss2: 1.355906 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401776 Loss1: 0.063164 Loss2: 1.338613 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987981
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.629956 Loss1: 0.752873 Loss2: 1.877083
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784021 Loss1: 0.440435 Loss2: 1.343586
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.652821 Loss1: 0.273091 Loss2: 1.379731
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490985 Loss1: 0.140006 Loss2: 1.350979
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.464990 Loss1: 0.671516 Loss2: 1.793474
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.765434 Loss1: 0.435058 Loss2: 1.330376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.567311 Loss1: 0.220178 Loss2: 1.347133 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523474 Loss1: 0.213689 Loss2: 1.309785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.462947 Loss1: 0.140568 Loss2: 1.322379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450820 Loss1: 0.139489 Loss2: 1.311331 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995536
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427863 Loss1: 0.111212 Loss2: 1.316651 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370014 Loss1: 0.077187 Loss2: 1.292826 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.927666 Loss1: 0.505497 Loss2: 1.422169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.614265 Loss1: 0.226310 Loss2: 1.387955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561536 Loss1: 0.174630 Loss2: 1.386906
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.589615 Loss1: 0.638859 Loss2: 1.950755
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.877340 Loss1: 0.414810 Loss2: 1.462530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.784528 Loss1: 0.290673 Loss2: 1.493855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.680503 Loss1: 0.234841 Loss2: 1.445662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.618268 Loss1: 0.173174 Loss2: 1.445094 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979911
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.551577 Loss1: 0.115841 Loss2: 1.435736 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.472953 Loss1: 0.056097 Loss2: 1.416855 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470419 Loss1: 0.055516 Loss2: 1.414903
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.467796 Loss1: 0.656276 Loss2: 1.811520
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.790769 Loss1: 0.448514 Loss2: 1.342255
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.665526 Loss1: 0.267636 Loss2: 1.397890
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.515338 Loss1: 0.179037 Loss2: 1.336301
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535229 Loss1: 0.193800 Loss2: 1.341429
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.345598 Loss1: 0.510140 Loss2: 1.835458
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.720873 Loss1: 0.383766 Loss2: 1.337108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.716956 Loss1: 0.325334 Loss2: 1.391622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550605 Loss1: 0.200012 Loss2: 1.350593 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.459421 Loss1: 0.118509 Loss2: 1.340912 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.438108 Loss1: 0.101020 Loss2: 1.337088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441750 Loss1: 0.117048 Loss2: 1.324702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397056 Loss1: 0.075253 Loss2: 1.321803 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.855626 Loss1: 0.447825 Loss2: 1.407801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.514846 Loss1: 0.125514 Loss2: 1.389333 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490317 Loss1: 0.114274 Loss2: 1.376044
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.478160 Loss1: 0.621413 Loss2: 1.856747
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.814712 Loss1: 0.434442 Loss2: 1.380270 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.678011 Loss1: 0.242417 Loss2: 1.435595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561142 Loss1: 0.190104 Loss2: 1.371038 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556324 Loss1: 0.170410 Loss2: 1.385913 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435629 Loss1: 0.070155 Loss2: 1.365474
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493698 Loss1: 0.117016 Loss2: 1.376681
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455180 Loss1: 0.085078 Loss2: 1.370102
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443554 Loss1: 0.088237 Loss2: 1.355317
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410971 Loss1: 0.055090 Loss2: 1.355881
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414208 Loss1: 0.065362 Loss2: 1.348846
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.330902 Loss1: 0.486177 Loss2: 1.844725
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.733776 Loss1: 0.380347 Loss2: 1.353429
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.651127 Loss1: 0.263582 Loss2: 1.387545
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511464 Loss1: 0.156496 Loss2: 1.354968
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.447798 Loss1: 0.106178 Loss2: 1.341619
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465011 Loss1: 0.125569 Loss2: 1.339442 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484549 Loss1: 0.142062 Loss2: 1.342486 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426906 Loss1: 0.087196 Loss2: 1.339709 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408804 Loss1: 0.078514 Loss2: 1.330291 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380744 Loss1: 0.051319 Loss2: 1.329425 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428956 Loss1: 0.098976 Loss2: 1.329980 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395453 Loss1: 0.078701 Loss2: 1.316752 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.716811 Loss1: 0.376955 Loss2: 1.339856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497834 Loss1: 0.169523 Loss2: 1.328311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507577 Loss1: 0.175847 Loss2: 1.331730
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.290524 Loss1: 0.430347 Loss2: 1.860177
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.679285 Loss1: 0.283796 Loss2: 1.395490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.603269 Loss1: 0.195576 Loss2: 1.407693 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.558617 Loss1: 0.169075 Loss2: 1.389542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522862 Loss1: 0.130807 Loss2: 1.392054 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484635 Loss1: 0.101884 Loss2: 1.382751 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447322 Loss1: 0.070850 Loss2: 1.376472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408821 Loss1: 0.041788 Loss2: 1.367033 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997243
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.641077 Loss1: 0.239438 Loss2: 1.401639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.509356 Loss1: 0.152273 Loss2: 1.357083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.481585 Loss1: 0.639075 Loss2: 1.842510 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.746439 Loss1: 0.386327 Loss2: 1.360112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.651466 Loss1: 0.257560 Loss2: 1.393906 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565765 Loss1: 0.209457 Loss2: 1.356308 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472901 Loss1: 0.131483 Loss2: 1.341418 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.395115 Loss1: 0.069070 Loss2: 1.326045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408454 Loss1: 0.081607 Loss2: 1.326847
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.540601 Loss1: 0.657792 Loss2: 1.882809
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.447783 Loss1: 0.113092 Loss2: 1.334691
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.816880 Loss1: 0.378238 Loss2: 1.438642
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.712474 Loss1: 0.262667 Loss2: 1.449807
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575357 Loss1: 0.151119 Loss2: 1.424238
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.552655 Loss1: 0.135807 Loss2: 1.416849
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481684 Loss1: 0.080903 Loss2: 1.400781
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.574880 Loss1: 0.618225 Loss2: 1.956655
+(DefaultActor pid=3764) Epoch: 1 Loss: 2.022330 Loss1: 0.555320 Loss2: 1.467009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484214 Loss1: 0.086963 Loss2: 1.397251
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.875459 Loss1: 0.350867 Loss2: 1.524592
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438085 Loss1: 0.045550 Loss2: 1.392535
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.788514 Loss1: 0.318247 Loss2: 1.470267
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425123 Loss1: 0.043201 Loss2: 1.381922
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.680161 Loss1: 0.186716 Loss2: 1.493445
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.636553 Loss1: 0.162703 Loss2: 1.473850
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.613017 Loss1: 0.156235 Loss2: 1.456782
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.619123 Loss1: 0.159695 Loss2: 1.459427
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.563392 Loss1: 0.107947 Loss2: 1.455445
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.450959 Loss1: 0.585550 Loss2: 1.865408
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.549007 Loss1: 0.098334 Loss2: 1.450673
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.587793 Loss1: 0.194698 Loss2: 1.393095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.481296 Loss1: 0.130087 Loss2: 1.351209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.495424 Loss1: 0.146805 Loss2: 1.348618
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.412457 Loss1: 0.588636 Loss2: 1.823821
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486376 Loss1: 0.122477 Loss2: 1.363899
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.739325 Loss1: 0.359301 Loss2: 1.380024
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477438 Loss1: 0.128700 Loss2: 1.348739
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.758054 Loss1: 0.337935 Loss2: 1.420119
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430399 Loss1: 0.085102 Loss2: 1.345297
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.630512 Loss1: 0.246866 Loss2: 1.383646
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411552 Loss1: 0.076479 Loss2: 1.335073
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.517838 Loss1: 0.143511 Loss2: 1.374328
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461894 Loss1: 0.108017 Loss2: 1.353877
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434444 Loss1: 0.079638 Loss2: 1.354806
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397359 Loss1: 0.053731 Loss2: 1.343628
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399598 Loss1: 0.059959 Loss2: 1.339639
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.334866 Loss1: 0.490305 Loss2: 1.844562
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.711146 Loss1: 0.362371 Loss2: 1.348775 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.662119 Loss1: 0.295811 Loss2: 1.366308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511117 Loss1: 0.153444 Loss2: 1.357673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470957 Loss1: 0.118276 Loss2: 1.352681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446422 Loss1: 0.097618 Loss2: 1.348804 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411251 Loss1: 0.069593 Loss2: 1.341659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406531 Loss1: 0.070822 Loss2: 1.335709 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.598871 Loss1: 0.193543 Loss2: 1.405328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.474263 Loss1: 0.082920 Loss2: 1.391344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.600256 Loss1: 0.714870 Loss2: 1.885386
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.471577 Loss1: 0.086600 Loss2: 1.384977
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751717 Loss1: 0.399890 Loss2: 1.351827
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.479978 Loss1: 0.094236 Loss2: 1.385743
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534937 Loss1: 0.184919 Loss2: 1.350018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.486738 Loss1: 0.133260 Loss2: 1.353478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.413111 Loss1: 0.596483 Loss2: 1.816628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.697519 Loss1: 0.340953 Loss2: 1.356566 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601975 Loss1: 0.227019 Loss2: 1.374955 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505129 Loss1: 0.155391 Loss2: 1.349738 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453024 Loss1: 0.115279 Loss2: 1.337745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413637 Loss1: 0.080652 Loss2: 1.332984
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.588479 Loss1: 0.674276 Loss2: 1.914203
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.821231 Loss1: 0.394415 Loss2: 1.426817 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.713486 Loss1: 0.266393 Loss2: 1.447094 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551603 Loss1: 0.146771 Loss2: 1.404832 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481967 Loss1: 0.095911 Loss2: 1.386056 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456015 Loss1: 0.078808 Loss2: 1.377207 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437602 Loss1: 0.066115 Loss2: 1.371486 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405534 Loss1: 0.038849 Loss2: 1.366685 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502565 Loss1: 0.136492 Loss2: 1.366073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426883 Loss1: 0.069671 Loss2: 1.357212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418364 Loss1: 0.069942 Loss2: 1.348422
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.636045 Loss1: 0.639568 Loss2: 1.996476
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.795236 Loss1: 0.428620 Loss2: 1.366616
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392629 Loss1: 0.047987 Loss2: 1.344642
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.646546 Loss1: 0.263013 Loss2: 1.383534
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376816 Loss1: 0.041010 Loss2: 1.335806
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534060 Loss1: 0.166063 Loss2: 1.367997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476633 Loss1: 0.115290 Loss2: 1.361343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448952 Loss1: 0.092396 Loss2: 1.356556 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.494427 Loss1: 0.139252 Loss2: 1.355174 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985677
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598132 Loss1: 0.216930 Loss2: 1.381202 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.451459 Loss1: 0.100006 Loss2: 1.351452 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.526723 Loss1: 0.705093 Loss2: 1.821630
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467724 Loss1: 0.117926 Loss2: 1.349799
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.765681 Loss1: 0.380063 Loss2: 1.385618
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470380 Loss1: 0.121018 Loss2: 1.349361
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644721 Loss1: 0.246661 Loss2: 1.398060
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496135 Loss1: 0.142606 Loss2: 1.353528
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642825 Loss1: 0.252435 Loss2: 1.390390
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467995 Loss1: 0.120231 Loss2: 1.347764
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.582299 Loss1: 0.195426 Loss2: 1.386873
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452933 Loss1: 0.110698 Loss2: 1.342234
+(DefaultActor pid=3764) >> Training accuracy: 0.974609
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.453698 Loss1: 0.099678 Loss2: 1.354020 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450510 Loss1: 0.104375 Loss2: 1.346135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.435246 Loss1: 0.590467 Loss2: 1.844779
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427789 Loss1: 0.078555 Loss2: 1.349234
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642637 Loss1: 0.245045 Loss2: 1.397592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.532720 Loss1: 0.154113 Loss2: 1.378607 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460208 Loss1: 0.102358 Loss2: 1.357851
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.435190 Loss1: 0.540298 Loss2: 1.894892
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.775619 Loss1: 0.346138 Loss2: 1.429481 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.681484 Loss1: 0.214456 Loss2: 1.467028 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.653761 Loss1: 0.219044 Loss2: 1.434718 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.612934 Loss1: 0.169012 Loss2: 1.443922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.530021 Loss1: 0.105813 Loss2: 1.424208 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.482589 Loss1: 0.067123 Loss2: 1.415466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.766104 Loss1: 0.392557 Loss2: 1.373547 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556826 Loss1: 0.185060 Loss2: 1.371766 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.438232 Loss1: 0.079444 Loss2: 1.358787 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416740 Loss1: 0.071014 Loss2: 1.345726
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.362959 Loss1: 0.522407 Loss2: 1.840552
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.683637 Loss1: 0.306780 Loss2: 1.376857 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.592185 Loss1: 0.196818 Loss2: 1.395366 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.609169 Loss1: 0.237340 Loss2: 1.371829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443155 Loss1: 0.081356 Loss2: 1.361799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423961 Loss1: 0.084687 Loss2: 1.339274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428243 Loss1: 0.085465 Loss2: 1.342778 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377105 Loss1: 0.041077 Loss2: 1.336028 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.999023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.476965 Loss1: 0.127271 Loss2: 1.349694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.407726 Loss1: 0.075808 Loss2: 1.331918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.518492 Loss1: 0.676358 Loss2: 1.842134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.766420 Loss1: 0.369246 Loss2: 1.397174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.663136 Loss1: 0.241393 Loss2: 1.421744 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517901 Loss1: 0.139098 Loss2: 1.378803 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434724 Loss1: 0.077007 Loss2: 1.357717 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.402343 Loss1: 0.049925 Loss2: 1.352418
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.430816 Loss1: 0.550917 Loss2: 1.879899
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391857 Loss1: 0.045456 Loss2: 1.346401
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.658552 Loss1: 0.278704 Loss2: 1.379848
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366337 Loss1: 0.028423 Loss2: 1.337914
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.630839 Loss1: 0.220983 Loss2: 1.409857
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.508410 Loss1: 0.135486 Loss2: 1.372924
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528040 Loss1: 0.160972 Loss2: 1.367068
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496955 Loss1: 0.126060 Loss2: 1.370895
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.501272 Loss1: 0.140977 Loss2: 1.360295
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.497112 Loss1: 0.619026 Loss2: 1.878086
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458294 Loss1: 0.097880 Loss2: 1.360414
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.725306 Loss1: 0.341862 Loss2: 1.383445
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425850 Loss1: 0.070431 Loss2: 1.355419
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.689828 Loss1: 0.273344 Loss2: 1.416484
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422409 Loss1: 0.067213 Loss2: 1.355196
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.601548 Loss1: 0.191461 Loss2: 1.410087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517343 Loss1: 0.145447 Loss2: 1.371897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486207 Loss1: 0.119931 Loss2: 1.366276
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.271771 Loss1: 0.487352 Loss2: 1.784420
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.723920 Loss1: 0.373215 Loss2: 1.350705 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434333 Loss1: 0.077617 Loss2: 1.356716
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.647153 Loss1: 0.232281 Loss2: 1.414872
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535582 Loss1: 0.182504 Loss2: 1.353078
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.477374 Loss1: 0.131726 Loss2: 1.345647
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.436085 Loss1: 0.094316 Loss2: 1.341770
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435990 Loss1: 0.098570 Loss2: 1.337420
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.438036 Loss1: 0.585315 Loss2: 1.852721
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.746280 Loss1: 0.397202 Loss2: 1.349078 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.660543 Loss1: 0.261534 Loss2: 1.399009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427033 Loss1: 0.089844 Loss2: 1.337189
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589514 Loss1: 0.233273 Loss2: 1.356241
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574496 Loss1: 0.204669 Loss2: 1.369827
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499639 Loss1: 0.148825 Loss2: 1.350814
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.489837 Loss1: 0.134405 Loss2: 1.355431
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425585 Loss1: 0.083877 Loss2: 1.341707
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.508850 Loss1: 0.670532 Loss2: 1.838318
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434596 Loss1: 0.097660 Loss2: 1.336936
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831352 Loss1: 0.455326 Loss2: 1.376025
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445937 Loss1: 0.102588 Loss2: 1.343349
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.603049 Loss1: 0.225319 Loss2: 1.377730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487530 Loss1: 0.127711 Loss2: 1.359819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427224 Loss1: 0.072002 Loss2: 1.355222
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.474912 Loss1: 0.655109 Loss2: 1.819804
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421111 Loss1: 0.074077 Loss2: 1.347034
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.877831 Loss1: 0.506322 Loss2: 1.371509
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395521 Loss1: 0.051728 Loss2: 1.343793
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.747227 Loss1: 0.305406 Loss2: 1.441821
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407105 Loss1: 0.069289 Loss2: 1.337815
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.624252 Loss1: 0.253075 Loss2: 1.371177
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.629019 Loss1: 0.246398 Loss2: 1.382622
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515116 Loss1: 0.148835 Loss2: 1.366281
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491450 Loss1: 0.131624 Loss2: 1.359825
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425041 Loss1: 0.070690 Loss2: 1.354351
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.476932 Loss1: 0.631349 Loss2: 1.845583
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452035 Loss1: 0.102851 Loss2: 1.349185
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.951491 Loss1: 0.551356 Loss2: 1.400135
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435722 Loss1: 0.091378 Loss2: 1.344344
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.648902 Loss1: 0.280597 Loss2: 1.368305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446042 Loss1: 0.092028 Loss2: 1.354014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.413912 Loss1: 0.074487 Loss2: 1.339425
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.543727 Loss1: 0.698478 Loss2: 1.845249
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.792478 Loss1: 0.420409 Loss2: 1.372069 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 07:28:41,524 | server.py:236 | fit_round 143 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.657136 Loss1: 0.260672 Loss2: 1.396464 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390845 Loss1: 0.056751 Loss2: 1.334093
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569460 Loss1: 0.212876 Loss2: 1.356584
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471867 Loss1: 0.118461 Loss2: 1.353406
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.429210 Loss1: 0.089648 Loss2: 1.339562
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419669 Loss1: 0.083406 Loss2: 1.336263
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.392386 Loss1: 0.062570 Loss2: 1.329816
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.374247 Loss1: 0.518257 Loss2: 1.855990
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378859 Loss1: 0.055157 Loss2: 1.323702
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.783334 Loss1: 0.423291 Loss2: 1.360043
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377207 Loss1: 0.061514 Loss2: 1.315693
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567781 Loss1: 0.200969 Loss2: 1.366812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529868 Loss1: 0.167897 Loss2: 1.361971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423191 Loss1: 0.071657 Loss2: 1.351534
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.398865 Loss1: 0.573690 Loss2: 1.825175
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.642428 Loss1: 0.315069 Loss2: 1.327359 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.660555 Loss1: 0.308986 Loss2: 1.351569 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375729 Loss1: 0.045617 Loss2: 1.330112
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559480 Loss1: 0.227982 Loss2: 1.331498
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521842 Loss1: 0.183194 Loss2: 1.338648
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.436179 Loss1: 0.112446 Loss2: 1.323732
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451638 Loss1: 0.135936 Loss2: 1.315702
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410932 Loss1: 0.097299 Loss2: 1.313633
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.666727 Loss1: 0.731026 Loss2: 1.935700
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.762793 Loss1: 0.423199 Loss2: 1.339595 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399152 Loss1: 0.087255 Loss2: 1.311897
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590694 Loss1: 0.220520 Loss2: 1.370174
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529592 Loss1: 0.184544 Loss2: 1.345049
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508965 Loss1: 0.178036 Loss2: 1.330929
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443390 Loss1: 0.112214 Loss2: 1.331176
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.400293 Loss1: 0.078733 Loss2: 1.321560
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412218 Loss1: 0.092243 Loss2: 1.319976
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385439 Loss1: 0.071825 Loss2: 1.313614
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.363027 Loss1: 0.053108 Loss2: 1.309919
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 07:28:41,524][flwr][DEBUG] - fit_round 143 received 50 results and 0 failures
+INFO flwr 2023-10-12 07:29:23,708 | server.py:125 | fit progress: (143, 2.2164862047369107, {'accuracy': 0.5926}, 329871.486398289)
+>> Test accuracy: 0.592600
+[2023-10-12 07:29:23,708][flwr][INFO] - fit progress: (143, 2.2164862047369107, {'accuracy': 0.5926}, 329871.486398289)
+DEBUG flwr 2023-10-12 07:29:23,708 | server.py:173 | evaluate_round 143: strategy sampled 50 clients (out of 50)
+[2023-10-12 07:29:23,708][flwr][DEBUG] - evaluate_round 143: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 07:38:29,300 | server.py:187 | evaluate_round 143 received 50 results and 0 failures
+[2023-10-12 07:38:29,300][flwr][DEBUG] - evaluate_round 143 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 07:38:29,301 | server.py:222 | fit_round 144: strategy sampled 50 clients (out of 50)
+[2023-10-12 07:38:29,301][flwr][DEBUG] - fit_round 144: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.540999 Loss1: 0.616980 Loss2: 1.924019
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.814660 Loss1: 0.379194 Loss2: 1.435466
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690154 Loss1: 0.220678 Loss2: 1.469476
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.664771 Loss1: 0.218725 Loss2: 1.446046
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.386895 Loss1: 0.536240 Loss2: 1.850655
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.620090 Loss1: 0.229017 Loss2: 1.391073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633204 Loss1: 0.239157 Loss2: 1.394047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.490555 Loss1: 0.112634 Loss2: 1.377922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.474315 Loss1: 0.112333 Loss2: 1.361982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463111 Loss1: 0.094704 Loss2: 1.368407 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.449880 Loss1: 0.081687 Loss2: 1.368193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429234 Loss1: 0.071562 Loss2: 1.357672 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.370708 Loss1: 0.545440 Loss2: 1.825268 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634206 Loss1: 0.236139 Loss2: 1.398066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.387528 Loss1: 0.623146 Loss2: 1.764382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.661972 Loss1: 0.352057 Loss2: 1.309915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.562395 Loss1: 0.213245 Loss2: 1.349150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.463071 Loss1: 0.156745 Loss2: 1.306326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.416294 Loss1: 0.111857 Loss2: 1.304437 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.408207 Loss1: 0.109925 Loss2: 1.298282 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386377 Loss1: 0.048848 Loss2: 1.337529
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404648 Loss1: 0.108427 Loss2: 1.296221
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.354433 Loss1: 0.059073 Loss2: 1.295359
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.357159 Loss1: 0.069834 Loss2: 1.287325
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.355659 Loss1: 0.071139 Loss2: 1.284519
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.707109 Loss1: 0.686543 Loss2: 2.020566
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.841569 Loss1: 0.463733 Loss2: 1.377836
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.671747 Loss1: 0.247217 Loss2: 1.424530
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.733593 Loss1: 0.316018 Loss2: 1.417574
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.610258 Loss1: 0.211022 Loss2: 1.399237
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.558162 Loss1: 0.164774 Loss2: 1.393387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438042 Loss1: 0.062299 Loss2: 1.375743 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.568461 Loss1: 0.191639 Loss2: 1.376822 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527408 Loss1: 0.153492 Loss2: 1.373917 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467601 Loss1: 0.100379 Loss2: 1.367222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441585 Loss1: 0.087732 Loss2: 1.353853
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.450607 Loss1: 0.637135 Loss2: 1.813472
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.739148 Loss1: 0.400849 Loss2: 1.338299
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416369 Loss1: 0.059690 Loss2: 1.356680
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575261 Loss1: 0.236578 Loss2: 1.338683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506625 Loss1: 0.165963 Loss2: 1.340663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439462 Loss1: 0.103656 Loss2: 1.335806
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.465616 Loss1: 0.620640 Loss2: 1.844976
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423604 Loss1: 0.093372 Loss2: 1.330233
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.797435 Loss1: 0.432455 Loss2: 1.364980
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399910 Loss1: 0.082268 Loss2: 1.317642
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677976 Loss1: 0.267423 Loss2: 1.410553
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383615 Loss1: 0.072067 Loss2: 1.311549
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.617346 Loss1: 0.245104 Loss2: 1.372242
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497323 Loss1: 0.130843 Loss2: 1.366480
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499012 Loss1: 0.138927 Loss2: 1.360085
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459504 Loss1: 0.104593 Loss2: 1.354911
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412433 Loss1: 0.064056 Loss2: 1.348377
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.588847 Loss1: 0.690706 Loss2: 1.898141
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392403 Loss1: 0.054960 Loss2: 1.337443
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751902 Loss1: 0.397028 Loss2: 1.354875
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405765 Loss1: 0.070399 Loss2: 1.335366
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587294 Loss1: 0.207961 Loss2: 1.379334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.453493 Loss1: 0.105739 Loss2: 1.347754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.407246 Loss1: 0.071277 Loss2: 1.335969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373824 Loss1: 0.041209 Loss2: 1.332615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.347838 Loss1: 0.031631 Loss2: 1.316207 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.494917 Loss1: 0.156747 Loss2: 1.338170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423063 Loss1: 0.096845 Loss2: 1.326218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406584 Loss1: 0.084766 Loss2: 1.321818
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.294680 Loss1: 0.471028 Loss2: 1.823652
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.619387 Loss1: 0.283132 Loss2: 1.336255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.550813 Loss1: 0.189334 Loss2: 1.361479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395822 Loss1: 0.085923 Loss2: 1.309898
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489706 Loss1: 0.153731 Loss2: 1.335974
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.441652 Loss1: 0.110045 Loss2: 1.331608
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437291 Loss1: 0.102710 Loss2: 1.334580
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424411 Loss1: 0.096391 Loss2: 1.328020
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417365 Loss1: 0.082736 Loss2: 1.334628
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422147 Loss1: 0.093865 Loss2: 1.328282
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.335606 Loss1: 0.528293 Loss2: 1.807312
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387422 Loss1: 0.066162 Loss2: 1.321260
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.681241 Loss1: 0.351148 Loss2: 1.330092
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.596970 Loss1: 0.233299 Loss2: 1.363670
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.491533 Loss1: 0.159179 Loss2: 1.332354
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.467089 Loss1: 0.146067 Loss2: 1.321022
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456950 Loss1: 0.123834 Loss2: 1.333115
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416950 Loss1: 0.094195 Loss2: 1.322755
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.362545 Loss1: 0.548366 Loss2: 1.814178
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388681 Loss1: 0.077251 Loss2: 1.311429
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.692946 Loss1: 0.382589 Loss2: 1.310357
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404363 Loss1: 0.096664 Loss2: 1.307698
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.621743 Loss1: 0.235247 Loss2: 1.386496
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.359343 Loss1: 0.049344 Loss2: 1.309999
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516213 Loss1: 0.200991 Loss2: 1.315222
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443598 Loss1: 0.128388 Loss2: 1.315211
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.415947 Loss1: 0.107509 Loss2: 1.308438
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.381802 Loss1: 0.087774 Loss2: 1.294029
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.371398 Loss1: 0.074428 Loss2: 1.296970
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.430662 Loss1: 0.556648 Loss2: 1.874013
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.370255 Loss1: 0.081484 Loss2: 1.288770
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.732934 Loss1: 0.353035 Loss2: 1.379899
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.333964 Loss1: 0.048335 Loss2: 1.285629
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.616071 Loss1: 0.236152 Loss2: 1.379918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.514079 Loss1: 0.147878 Loss2: 1.366201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476454 Loss1: 0.121123 Loss2: 1.355332
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.492014 Loss1: 0.637428 Loss2: 1.854587
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459551 Loss1: 0.100965 Loss2: 1.358586
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.860984 Loss1: 0.488263 Loss2: 1.372720
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445480 Loss1: 0.093341 Loss2: 1.352139
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.671356 Loss1: 0.239701 Loss2: 1.431655
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437427 Loss1: 0.096534 Loss2: 1.340893
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499878 Loss1: 0.126855 Loss2: 1.373023
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462308 Loss1: 0.100083 Loss2: 1.362225
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468704 Loss1: 0.110221 Loss2: 1.358483
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432020 Loss1: 0.075328 Loss2: 1.356692
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400528 Loss1: 0.046911 Loss2: 1.353617
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.426495 Loss1: 0.583043 Loss2: 1.843453
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.370059 Loss1: 0.027222 Loss2: 1.342837
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.674630 Loss1: 0.310062 Loss2: 1.364569
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367981 Loss1: 0.033009 Loss2: 1.334972
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567066 Loss1: 0.206175 Loss2: 1.360891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.428308 Loss1: 0.081379 Loss2: 1.346929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.402567 Loss1: 0.064872 Loss2: 1.337695
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.335503 Loss1: 0.521802 Loss2: 1.813700
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421434 Loss1: 0.091220 Loss2: 1.330214
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.689922 Loss1: 0.345918 Loss2: 1.344004
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402528 Loss1: 0.073913 Loss2: 1.328615
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.602778 Loss1: 0.214488 Loss2: 1.388290
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391107 Loss1: 0.064886 Loss2: 1.326221
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520902 Loss1: 0.170595 Loss2: 1.350308
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492914 Loss1: 0.143054 Loss2: 1.349860
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476413 Loss1: 0.129724 Loss2: 1.346688
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467005 Loss1: 0.123561 Loss2: 1.343444
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426197 Loss1: 0.084462 Loss2: 1.341735
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401891 Loss1: 0.063308 Loss2: 1.338583
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.325177 Loss1: 0.523637 Loss2: 1.801540
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364611 Loss1: 0.037219 Loss2: 1.327392
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.715113 Loss1: 0.357960 Loss2: 1.357153
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600047 Loss1: 0.203833 Loss2: 1.396213
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540227 Loss1: 0.175312 Loss2: 1.364916
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501272 Loss1: 0.138094 Loss2: 1.363178
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466786 Loss1: 0.112219 Loss2: 1.354567
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.521317 Loss1: 0.646601 Loss2: 1.874716
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471221 Loss1: 0.115707 Loss2: 1.355515
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462392 Loss1: 0.109946 Loss2: 1.352447 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424875 Loss1: 0.081187 Loss2: 1.343688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398115 Loss1: 0.059376 Loss2: 1.338738 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488343 Loss1: 0.115009 Loss2: 1.373334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419113 Loss1: 0.055439 Loss2: 1.363673 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.446387 Loss1: 0.596994 Loss2: 1.849393 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629058 Loss1: 0.237721 Loss2: 1.391338 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460297 Loss1: 0.118142 Loss2: 1.342155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.411342 Loss1: 0.084549 Loss2: 1.326793
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.463864 Loss1: 0.626728 Loss2: 1.837136
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.721485 Loss1: 0.400711 Loss2: 1.320773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667322 Loss1: 0.287101 Loss2: 1.380221 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549576 Loss1: 0.236615 Loss2: 1.312961 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367770 Loss1: 0.060112 Loss2: 1.307658
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.459858 Loss1: 0.127908 Loss2: 1.331951
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416184 Loss1: 0.105293 Loss2: 1.310891
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395277 Loss1: 0.091556 Loss2: 1.303720
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.362027 Loss1: 0.063146 Loss2: 1.298881
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.353984 Loss1: 0.058305 Loss2: 1.295680
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.653863 Loss1: 0.679410 Loss2: 1.974453
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.324039 Loss1: 0.036320 Loss2: 1.287719
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.743463 Loss1: 0.275255 Loss2: 1.468207 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569732 Loss1: 0.156069 Loss2: 1.413663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.348311 Loss1: 0.487638 Loss2: 1.860673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.677770 Loss1: 0.320459 Loss2: 1.357311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.627882 Loss1: 0.223004 Loss2: 1.404878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460523 Loss1: 0.066714 Loss2: 1.393809 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499362 Loss1: 0.139649 Loss2: 1.359713 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467656 Loss1: 0.118185 Loss2: 1.349471 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.641014 Loss1: 0.739025 Loss2: 1.901988
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493192 Loss1: 0.136576 Loss2: 1.356616
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428047 Loss1: 0.079511 Loss2: 1.348536 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.661035 Loss1: 0.301058 Loss2: 1.359977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.534981 Loss1: 0.163037 Loss2: 1.371944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.474923 Loss1: 0.561563 Loss2: 1.913360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.858597 Loss1: 0.452956 Loss2: 1.405641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406711 Loss1: 0.062567 Loss2: 1.344144 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.548443 Loss1: 0.146625 Loss2: 1.401818 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500140 Loss1: 0.109880 Loss2: 1.390260 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471192 Loss1: 0.081608 Loss2: 1.389584
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.741817 Loss1: 0.672240 Loss2: 2.069577
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436718 Loss1: 0.051350 Loss2: 1.385368
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.957891 Loss1: 0.417831 Loss2: 1.540060
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457437 Loss1: 0.081998 Loss2: 1.375440
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.826725 Loss1: 0.222645 Loss2: 1.604080
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.752408 Loss1: 0.226093 Loss2: 1.526314
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.709374 Loss1: 0.173926 Loss2: 1.535448
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.699383 Loss1: 0.169360 Loss2: 1.530023
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.642102 Loss1: 0.118478 Loss2: 1.523624
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.377178 Loss1: 0.551509 Loss2: 1.825669
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.634477 Loss1: 0.119250 Loss2: 1.515227
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.692514 Loss1: 0.362068 Loss2: 1.330446
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.604830 Loss1: 0.095886 Loss2: 1.508944
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615138 Loss1: 0.230828 Loss2: 1.384310
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.568149 Loss1: 0.069876 Loss2: 1.498273
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.442085 Loss1: 0.110026 Loss2: 1.332058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443692 Loss1: 0.123156 Loss2: 1.320536 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473716 Loss1: 0.144934 Loss2: 1.328782
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.222002 Loss1: 0.477196 Loss2: 1.744806
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.676514 Loss1: 0.369657 Loss2: 1.306857 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416630 Loss1: 0.106239 Loss2: 1.310391
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.644322 Loss1: 0.287551 Loss2: 1.356772
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.504213 Loss1: 0.176610 Loss2: 1.327603
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.464236 Loss1: 0.137404 Loss2: 1.326832
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420261 Loss1: 0.106878 Loss2: 1.313382
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448589 Loss1: 0.141570 Loss2: 1.307019
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.509738 Loss1: 0.630686 Loss2: 1.879052
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399132 Loss1: 0.087949 Loss2: 1.311183
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.710081 Loss1: 0.327589 Loss2: 1.382492
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394854 Loss1: 0.090825 Loss2: 1.304028
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.658908 Loss1: 0.240120 Loss2: 1.418788
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405931 Loss1: 0.104787 Loss2: 1.301144
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.565223 Loss1: 0.184342 Loss2: 1.380881
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.532132 Loss1: 0.145138 Loss2: 1.386994
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466426 Loss1: 0.096637 Loss2: 1.369789
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446440 Loss1: 0.085513 Loss2: 1.360927
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433807 Loss1: 0.069698 Loss2: 1.364109
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.571436 Loss1: 0.689304 Loss2: 1.882132
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413752 Loss1: 0.059919 Loss2: 1.353833
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.848313 Loss1: 0.438563 Loss2: 1.409751
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383285 Loss1: 0.037447 Loss2: 1.345838
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.578195 Loss1: 0.181010 Loss2: 1.397184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537923 Loss1: 0.153388 Loss2: 1.384535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474312 Loss1: 0.092918 Loss2: 1.381394
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.485369 Loss1: 0.553064 Loss2: 1.932305
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454826 Loss1: 0.081234 Loss2: 1.373592
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.706800 Loss1: 0.307778 Loss2: 1.399022
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433222 Loss1: 0.065090 Loss2: 1.368132
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647947 Loss1: 0.222857 Loss2: 1.425090
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431888 Loss1: 0.071655 Loss2: 1.360233
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541558 Loss1: 0.132276 Loss2: 1.409282
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547030 Loss1: 0.164770 Loss2: 1.382259
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497641 Loss1: 0.113810 Loss2: 1.383832
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467913 Loss1: 0.089188 Loss2: 1.378725
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459940 Loss1: 0.085348 Loss2: 1.374592
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456176 Loss1: 0.086228 Loss2: 1.369948
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.343830 Loss1: 0.484796 Loss2: 1.859034
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466307 Loss1: 0.095921 Loss2: 1.370386
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.728435 Loss1: 0.369322 Loss2: 1.359113
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642692 Loss1: 0.244228 Loss2: 1.398464
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561256 Loss1: 0.201443 Loss2: 1.359814
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507556 Loss1: 0.148971 Loss2: 1.358585
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469706 Loss1: 0.118480 Loss2: 1.351226
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.471542 Loss1: 0.603323 Loss2: 1.868219
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451537 Loss1: 0.102288 Loss2: 1.349249
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.835741 Loss1: 0.475949 Loss2: 1.359791
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435668 Loss1: 0.092213 Loss2: 1.343455
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645342 Loss1: 0.224042 Loss2: 1.421301
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396665 Loss1: 0.056549 Loss2: 1.340116
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586962 Loss1: 0.223486 Loss2: 1.363476
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367667 Loss1: 0.034459 Loss2: 1.333208
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473193 Loss1: 0.101197 Loss2: 1.371996 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447518 Loss1: 0.106234 Loss2: 1.341285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401539 Loss1: 0.064517 Loss2: 1.337021
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.464058 Loss1: 0.604545 Loss2: 1.859512
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.899710 Loss1: 0.530839 Loss2: 1.368871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640923 Loss1: 0.267776 Loss2: 1.373147 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.541512 Loss1: 0.177364 Loss2: 1.364148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.507106 Loss1: 0.142277 Loss2: 1.364829 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459500 Loss1: 0.097794 Loss2: 1.361706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443723 Loss1: 0.089869 Loss2: 1.353853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464964 Loss1: 0.120058 Loss2: 1.344906 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456813 Loss1: 0.133896 Loss2: 1.322917 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456098 Loss1: 0.124684 Loss2: 1.331414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421792 Loss1: 0.104521 Loss2: 1.317271
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.487764 Loss1: 0.604794 Loss2: 1.882970
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.831791 Loss1: 0.433815 Loss2: 1.397977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576533 Loss1: 0.176020 Loss2: 1.400512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546794 Loss1: 0.149255 Loss2: 1.397539 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.541437 Loss1: 0.150284 Loss2: 1.391153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.544741 Loss1: 0.148270 Loss2: 1.396471 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.476488 Loss1: 0.087816 Loss2: 1.388672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.469090 Loss1: 0.150383 Loss2: 1.318707
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491015 Loss1: 0.109805 Loss2: 1.381210
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.374655 Loss1: 0.071651 Loss2: 1.303004 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.356033 Loss1: 0.061021 Loss2: 1.295012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.440170 Loss1: 0.547853 Loss2: 1.892317
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.342149 Loss1: 0.052328 Loss2: 1.289821
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.732237 Loss1: 0.332503 Loss2: 1.399734
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.317729 Loss1: 0.035110 Loss2: 1.282619
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.548484 Loss1: 0.155564 Loss2: 1.392920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500221 Loss1: 0.114653 Loss2: 1.385568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.525997 Loss1: 0.139718 Loss2: 1.386279
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.471063 Loss1: 0.623868 Loss2: 1.847195
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511939 Loss1: 0.124970 Loss2: 1.386969
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.733277 Loss1: 0.363083 Loss2: 1.370194
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463419 Loss1: 0.078107 Loss2: 1.385313
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.607443 Loss1: 0.215254 Loss2: 1.392189
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454012 Loss1: 0.071214 Loss2: 1.382799
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.560373 Loss1: 0.187195 Loss2: 1.373178
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476179 Loss1: 0.114850 Loss2: 1.361328
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461947 Loss1: 0.099033 Loss2: 1.362914
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431865 Loss1: 0.080129 Loss2: 1.351736
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435264 Loss1: 0.083242 Loss2: 1.352022
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.381566 Loss1: 0.528412 Loss2: 1.853154
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433788 Loss1: 0.086314 Loss2: 1.347474
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401296 Loss1: 0.058952 Loss2: 1.342344 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.674467 Loss1: 0.304048 Loss2: 1.370419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486412 Loss1: 0.137374 Loss2: 1.349038 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427071 Loss1: 0.081196 Loss2: 1.345875
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.386106 Loss1: 0.541766 Loss2: 1.844339
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.775922 Loss1: 0.396458 Loss2: 1.379464 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.665704 Loss1: 0.245572 Loss2: 1.420132 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557987 Loss1: 0.179420 Loss2: 1.378568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481796 Loss1: 0.113107 Loss2: 1.368689 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448608 Loss1: 0.087626 Loss2: 1.360982 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432162 Loss1: 0.076688 Loss2: 1.355474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.534887 Loss1: 0.164161 Loss2: 1.370726 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.399715 Loss1: 0.065422 Loss2: 1.334293 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.388104 Loss1: 0.058194 Loss2: 1.329911 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.510778 Loss1: 0.603352 Loss2: 1.907425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372429 Loss1: 0.046565 Loss2: 1.325863 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604734 Loss1: 0.191272 Loss2: 1.413462 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.590985 Loss1: 0.173982 Loss2: 1.417002 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.535883 Loss1: 0.128476 Loss2: 1.407407
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.184061 Loss1: 0.404640 Loss2: 1.779422
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.490933 Loss1: 0.090672 Loss2: 1.400261
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653371 Loss1: 0.330216 Loss2: 1.323156
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.548280 Loss1: 0.190712 Loss2: 1.357567 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448677 Loss1: 0.056156 Loss2: 1.392521
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497367 Loss1: 0.178914 Loss2: 1.318453
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524355 Loss1: 0.197633 Loss2: 1.326722
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468546 Loss1: 0.146405 Loss2: 1.322141
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.418650 Loss1: 0.103914 Loss2: 1.314736
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400099 Loss1: 0.094462 Loss2: 1.305638
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.403006 Loss1: 0.489770 Loss2: 1.913236
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.747218 Loss1: 0.354547 Loss2: 1.392671
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370734 Loss1: 0.068998 Loss2: 1.301736
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.670104 Loss1: 0.246334 Loss2: 1.423770
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.344561 Loss1: 0.045018 Loss2: 1.299544
+(DefaultActor pid=3764) >> Training accuracy: 0.995404
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.632594 Loss1: 0.226077 Loss2: 1.406517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.532826 Loss1: 0.137880 Loss2: 1.394945 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 08:06:56,576 | server.py:236 | fit_round 144 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458552 Loss1: 0.076339 Loss2: 1.382213
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.389856 Loss1: 0.545673 Loss2: 1.844183
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.650262 Loss1: 0.295399 Loss2: 1.354863 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453007 Loss1: 0.080583 Loss2: 1.372424
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565637 Loss1: 0.187599 Loss2: 1.378038
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.493579 Loss1: 0.140457 Loss2: 1.353122
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485081 Loss1: 0.139594 Loss2: 1.345487
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447231 Loss1: 0.102114 Loss2: 1.345117
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436328 Loss1: 0.092163 Loss2: 1.344164
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.533794 Loss1: 0.664908 Loss2: 1.868887
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430466 Loss1: 0.092370 Loss2: 1.338097
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376685 Loss1: 0.043750 Loss2: 1.332935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392926 Loss1: 0.066281 Loss2: 1.326645 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.537242 Loss1: 0.157501 Loss2: 1.379741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488422 Loss1: 0.116244 Loss2: 1.372179 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441990 Loss1: 0.085833 Loss2: 1.356158
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.549750 Loss1: 0.694729 Loss2: 1.855021
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.799057 Loss1: 0.430483 Loss2: 1.368574 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.963542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.464172 Loss1: 0.102839 Loss2: 1.361334
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726227 Loss1: 0.286381 Loss2: 1.439845
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563163 Loss1: 0.196533 Loss2: 1.366630
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515271 Loss1: 0.155465 Loss2: 1.359806
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.440218 Loss1: 0.082395 Loss2: 1.357823
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425237 Loss1: 0.079351 Loss2: 1.345886
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.349944 Loss1: 0.528801 Loss2: 1.821143
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419215 Loss1: 0.071382 Loss2: 1.347833
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.757181 Loss1: 0.383254 Loss2: 1.373927
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402445 Loss1: 0.058486 Loss2: 1.343959
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655336 Loss1: 0.258475 Loss2: 1.396861
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424152 Loss1: 0.082642 Loss2: 1.341510
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484273 Loss1: 0.116940 Loss2: 1.367332 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.393473 Loss1: 0.049100 Loss2: 1.344373 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.359165 Loss1: 0.547683 Loss2: 1.811482
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.375704 Loss1: 0.039505 Loss2: 1.336199
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.361796 Loss1: 0.036545 Loss2: 1.325251 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373286 Loss1: 0.053224 Loss2: 1.320062 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506788 Loss1: 0.159769 Loss2: 1.347018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433333 Loss1: 0.103310 Loss2: 1.330023 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408054 Loss1: 0.080316 Loss2: 1.327738 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 08:06:56,576][flwr][DEBUG] - fit_round 144 received 50 results and 0 failures
+INFO flwr 2023-10-12 08:07:37,517 | server.py:125 | fit progress: (144, 2.2120984548958726, {'accuracy': 0.5939}, 332165.295221584)
+>> Test accuracy: 0.593900
+[2023-10-12 08:07:37,517][flwr][INFO] - fit progress: (144, 2.2120984548958726, {'accuracy': 0.5939}, 332165.295221584)
+DEBUG flwr 2023-10-12 08:07:37,517 | server.py:173 | evaluate_round 144: strategy sampled 50 clients (out of 50)
+[2023-10-12 08:07:37,517][flwr][DEBUG] - evaluate_round 144: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 08:16:38,589 | server.py:187 | evaluate_round 144 received 50 results and 0 failures
+[2023-10-12 08:16:38,589][flwr][DEBUG] - evaluate_round 144 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 08:16:38,590 | server.py:222 | fit_round 145: strategy sampled 50 clients (out of 50)
+[2023-10-12 08:16:38,590][flwr][DEBUG] - fit_round 145: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.467809 Loss1: 0.619768 Loss2: 1.848041 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.616755 Loss1: 0.233312 Loss2: 1.383444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.530572 Loss1: 0.186984 Loss2: 1.343588
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.445451 Loss1: 0.522399 Loss2: 1.923052
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468728 Loss1: 0.124673 Loss2: 1.344055
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.757074 Loss1: 0.344255 Loss2: 1.412819
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456089 Loss1: 0.120998 Loss2: 1.335091
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.666770 Loss1: 0.216127 Loss2: 1.450643
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418713 Loss1: 0.086018 Loss2: 1.332695
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.606179 Loss1: 0.188111 Loss2: 1.418068
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403630 Loss1: 0.079509 Loss2: 1.324121
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541346 Loss1: 0.128288 Loss2: 1.413059
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.380817 Loss1: 0.058156 Loss2: 1.322661
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.507058 Loss1: 0.102751 Loss2: 1.404307
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368043 Loss1: 0.046581 Loss2: 1.321462
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.489596 Loss1: 0.090050 Loss2: 1.399546
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.474487 Loss1: 0.082895 Loss2: 1.391592
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.455659 Loss1: 0.064249 Loss2: 1.391410
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424743 Loss1: 0.042090 Loss2: 1.382653
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.447451 Loss1: 0.573547 Loss2: 1.873904
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.782068 Loss1: 0.402659 Loss2: 1.379409
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.743979 Loss1: 0.297458 Loss2: 1.446520
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.652312 Loss1: 0.273156 Loss2: 1.379156
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.420618 Loss1: 0.558677 Loss2: 1.861941
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.601424 Loss1: 0.267646 Loss2: 1.333777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588396 Loss1: 0.245767 Loss2: 1.342629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564533 Loss1: 0.228128 Loss2: 1.336405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.488791 Loss1: 0.150302 Loss2: 1.338489 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491241 Loss1: 0.155980 Loss2: 1.335261 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413563 Loss1: 0.054767 Loss2: 1.358796
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444140 Loss1: 0.115517 Loss2: 1.328622
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388346 Loss1: 0.070835 Loss2: 1.317511
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388536 Loss1: 0.075006 Loss2: 1.313529
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395648 Loss1: 0.085146 Loss2: 1.310503
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.324889 Loss1: 0.501141 Loss2: 1.823748
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.679608 Loss1: 0.309425 Loss2: 1.370183
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.590460 Loss1: 0.199961 Loss2: 1.390500
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547046 Loss1: 0.180951 Loss2: 1.366095
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.505518 Loss1: 0.646303 Loss2: 1.859216
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.541784 Loss1: 0.167940 Loss2: 1.373844
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.885516 Loss1: 0.478193 Loss2: 1.407323
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533570 Loss1: 0.167413 Loss2: 1.366157
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.676195 Loss1: 0.225235 Loss2: 1.450961
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484781 Loss1: 0.120747 Loss2: 1.364034
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581439 Loss1: 0.185874 Loss2: 1.395565
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.553049 Loss1: 0.162319 Loss2: 1.390729
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.524364 Loss1: 0.158000 Loss2: 1.366364
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.535397 Loss1: 0.145880 Loss2: 1.389517
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.446973 Loss1: 0.090763 Loss2: 1.356210
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.489860 Loss1: 0.110758 Loss2: 1.379102
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403949 Loss1: 0.053693 Loss2: 1.350257
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490828 Loss1: 0.121717 Loss2: 1.369111 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.513393 Loss1: 0.621958 Loss2: 1.891435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.664022 Loss1: 0.242118 Loss2: 1.421904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578508 Loss1: 0.189111 Loss2: 1.389397
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.476005 Loss1: 0.581582 Loss2: 1.894423
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.906707 Loss1: 0.481864 Loss2: 1.424842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.852759 Loss1: 0.379031 Loss2: 1.473728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599593 Loss1: 0.189683 Loss2: 1.409910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520239 Loss1: 0.111908 Loss2: 1.408332 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508625 Loss1: 0.114121 Loss2: 1.394504 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494307 Loss1: 0.101143 Loss2: 1.393164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452550 Loss1: 0.067464 Loss2: 1.385085 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454162 Loss1: 0.072359 Loss2: 1.381803
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.440326 Loss1: 0.625812 Loss2: 1.814513
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.802705 Loss1: 0.415511 Loss2: 1.387194
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.623463 Loss1: 0.218320 Loss2: 1.405143
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.487141 Loss1: 0.120323 Loss2: 1.366819
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.369209 Loss1: 0.504695 Loss2: 1.864513
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506613 Loss1: 0.144299 Loss2: 1.362314
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471123 Loss1: 0.109115 Loss2: 1.362008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448635 Loss1: 0.093459 Loss2: 1.355176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421841 Loss1: 0.071667 Loss2: 1.350174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417474 Loss1: 0.069593 Loss2: 1.347881 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404090 Loss1: 0.059571 Loss2: 1.344518 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449508 Loss1: 0.090521 Loss2: 1.358987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408665 Loss1: 0.064331 Loss2: 1.344334 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.416503 Loss1: 0.602059 Loss2: 1.814444
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.760484 Loss1: 0.410688 Loss2: 1.349795
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.621821 Loss1: 0.221392 Loss2: 1.400429
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.636645 Loss1: 0.274141 Loss2: 1.362504
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.376155 Loss1: 0.495788 Loss2: 1.880367
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.689259 Loss1: 0.306674 Loss2: 1.382585 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.613853 Loss1: 0.225439 Loss2: 1.388414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559444 Loss1: 0.174657 Loss2: 1.384786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512141 Loss1: 0.134412 Loss2: 1.377729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.457787 Loss1: 0.088505 Loss2: 1.369282 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.462848 Loss1: 0.094243 Loss2: 1.368606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440992 Loss1: 0.077016 Loss2: 1.363976 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.430679 Loss1: 0.594068 Loss2: 1.836611 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.666404 Loss1: 0.245862 Loss2: 1.420543 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524716 Loss1: 0.150105 Loss2: 1.374611
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.448155 Loss1: 0.600638 Loss2: 1.847517
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.788843 Loss1: 0.436067 Loss2: 1.352776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.717003 Loss1: 0.290877 Loss2: 1.426126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520466 Loss1: 0.173849 Loss2: 1.346617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.479935 Loss1: 0.129689 Loss2: 1.350246 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446873 Loss1: 0.098814 Loss2: 1.348058 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.442611 Loss1: 0.106173 Loss2: 1.336438 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428715 Loss1: 0.096642 Loss2: 1.332073 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.426952 Loss1: 0.550925 Loss2: 1.876027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667100 Loss1: 0.259037 Loss2: 1.408063 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.570058 Loss1: 0.672161 Loss2: 1.897897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.804369 Loss1: 0.417340 Loss2: 1.387028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600849 Loss1: 0.208107 Loss2: 1.392742 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536926 Loss1: 0.168861 Loss2: 1.368065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455250 Loss1: 0.093115 Loss2: 1.362135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444660 Loss1: 0.092324 Loss2: 1.352336 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.413371 Loss1: 0.068781 Loss2: 1.344589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390196 Loss1: 0.056960 Loss2: 1.333236 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.199565 Loss1: 0.432819 Loss2: 1.766746 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582851 Loss1: 0.227182 Loss2: 1.355669 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.385985 Loss1: 0.532534 Loss2: 1.853450
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.503246 Loss1: 0.187880 Loss2: 1.315366
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.767370 Loss1: 0.396354 Loss2: 1.371017
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.460206 Loss1: 0.133934 Loss2: 1.326272
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.390579 Loss1: 0.088464 Loss2: 1.302115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383331 Loss1: 0.083941 Loss2: 1.299390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.357466 Loss1: 0.060030 Loss2: 1.297437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.349357 Loss1: 0.055215 Loss2: 1.294142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.324000 Loss1: 0.033749 Loss2: 1.290251 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997243
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431217 Loss1: 0.071945 Loss2: 1.359271 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.730133 Loss1: 0.695613 Loss2: 2.034520
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.785487 Loss1: 0.399978 Loss2: 1.385508
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.757798 Loss1: 0.332383 Loss2: 1.425415
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.707216 Loss1: 0.258462 Loss2: 1.448754
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.560004 Loss1: 0.163241 Loss2: 1.396762
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.561774 Loss1: 0.166464 Loss2: 1.395310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.550965 Loss1: 0.138923 Loss2: 1.412041 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445277 Loss1: 0.055706 Loss2: 1.389572 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445017 Loss1: 0.063318 Loss2: 1.381699 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438240 Loss1: 0.061176 Loss2: 1.377064 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.424709 Loss1: 0.087660 Loss2: 1.337049 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386402 Loss1: 0.049805 Loss2: 1.336598 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.276846 Loss1: 0.426528 Loss2: 1.850318 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599232 Loss1: 0.197479 Loss2: 1.401753 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.322592 Loss1: 0.470663 Loss2: 1.851929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.721265 Loss1: 0.363996 Loss2: 1.357268 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.688617 Loss1: 0.279469 Loss2: 1.409148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.607654 Loss1: 0.254849 Loss2: 1.352805 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568791 Loss1: 0.202237 Loss2: 1.366554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512474 Loss1: 0.151143 Loss2: 1.361331 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.489334 Loss1: 0.145008 Loss2: 1.344326 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428230 Loss1: 0.085688 Loss2: 1.342542 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.760036 Loss1: 0.370327 Loss2: 1.389710 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599781 Loss1: 0.209682 Loss2: 1.390099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517962 Loss1: 0.128469 Loss2: 1.389493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481664 Loss1: 0.108061 Loss2: 1.373602 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491548 Loss1: 0.119397 Loss2: 1.372152 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470396 Loss1: 0.106504 Loss2: 1.363893 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451038 Loss1: 0.092481 Loss2: 1.358557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429135 Loss1: 0.070057 Loss2: 1.359078 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.503932 Loss1: 0.104230 Loss2: 1.399702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463157 Loss1: 0.075372 Loss2: 1.387785 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.777342 Loss1: 0.389089 Loss2: 1.388253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580202 Loss1: 0.198154 Loss2: 1.382048 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.504484 Loss1: 0.122456 Loss2: 1.382028
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.440642 Loss1: 0.534984 Loss2: 1.905658
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489889 Loss1: 0.121718 Loss2: 1.368171
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.815537 Loss1: 0.402027 Loss2: 1.413510
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441446 Loss1: 0.075349 Loss2: 1.366097
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727942 Loss1: 0.262922 Loss2: 1.465020
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.590983 Loss1: 0.179175 Loss2: 1.411808 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.595239 Loss1: 0.180241 Loss2: 1.414998 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466544 Loss1: 0.108189 Loss2: 1.358355
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559320 Loss1: 0.148751 Loss2: 1.410569
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502137 Loss1: 0.097187 Loss2: 1.404950
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.489131 Loss1: 0.092503 Loss2: 1.396628
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442454 Loss1: 0.054698 Loss2: 1.387756
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.480986 Loss1: 0.096994 Loss2: 1.383992
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.426640 Loss1: 0.583902 Loss2: 1.842737
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.700196 Loss1: 0.349657 Loss2: 1.350539
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.781752 Loss1: 0.368009 Loss2: 1.413743
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599950 Loss1: 0.242524 Loss2: 1.357426
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536614 Loss1: 0.179552 Loss2: 1.357062
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.367064 Loss1: 0.551829 Loss2: 1.815235
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505666 Loss1: 0.153006 Loss2: 1.352660
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.710058 Loss1: 0.365680 Loss2: 1.344377
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.454821 Loss1: 0.108277 Loss2: 1.346543
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563517 Loss1: 0.189300 Loss2: 1.374218
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411974 Loss1: 0.072578 Loss2: 1.339396
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468502 Loss1: 0.135437 Loss2: 1.333065
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396512 Loss1: 0.063026 Loss2: 1.333487
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.447275 Loss1: 0.117297 Loss2: 1.329978
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374871 Loss1: 0.046810 Loss2: 1.328061
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.417336 Loss1: 0.091901 Loss2: 1.325435
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.384186 Loss1: 0.055532 Loss2: 1.328654
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368075 Loss1: 0.048082 Loss2: 1.319993
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.367055 Loss1: 0.049470 Loss2: 1.317585
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374856 Loss1: 0.058771 Loss2: 1.316085
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.339107 Loss1: 0.526559 Loss2: 1.812548
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.733514 Loss1: 0.358498 Loss2: 1.375017
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741565 Loss1: 0.314806 Loss2: 1.426759
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.646194 Loss1: 0.266664 Loss2: 1.379530
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.553880 Loss1: 0.669251 Loss2: 1.884629
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.596197 Loss1: 0.208896 Loss2: 1.387300
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493654 Loss1: 0.118923 Loss2: 1.374731 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424838 Loss1: 0.067463 Loss2: 1.357375 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422203 Loss1: 0.070084 Loss2: 1.352119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404722 Loss1: 0.053276 Loss2: 1.351446 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428509 Loss1: 0.081488 Loss2: 1.347021 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401016 Loss1: 0.068435 Loss2: 1.332581 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978795
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.333985 Loss1: 0.517225 Loss2: 1.816760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656760 Loss1: 0.235019 Loss2: 1.421740 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.533923 Loss1: 0.664729 Loss2: 1.869194
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575647 Loss1: 0.200249 Loss2: 1.375398
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.794310 Loss1: 0.405187 Loss2: 1.389123
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.545704 Loss1: 0.164881 Loss2: 1.380823
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635288 Loss1: 0.221515 Loss2: 1.413773
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485588 Loss1: 0.103927 Loss2: 1.381661
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530577 Loss1: 0.160228 Loss2: 1.370349
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478042 Loss1: 0.107084 Loss2: 1.370958
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454728 Loss1: 0.083645 Loss2: 1.371083 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414799 Loss1: 0.056317 Loss2: 1.358482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387004 Loss1: 0.036124 Loss2: 1.350880 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414617 Loss1: 0.068530 Loss2: 1.346087 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.579060 Loss1: 0.738884 Loss2: 1.840176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.666277 Loss1: 0.258685 Loss2: 1.407592 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490212 Loss1: 0.153497 Loss2: 1.336715
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.497243 Loss1: 0.567660 Loss2: 1.929583
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451630 Loss1: 0.115196 Loss2: 1.336433
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.726389 Loss1: 0.320463 Loss2: 1.405926
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.441489 Loss1: 0.114164 Loss2: 1.327325
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.736342 Loss1: 0.287295 Loss2: 1.449047
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416566 Loss1: 0.091133 Loss2: 1.325433
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559738 Loss1: 0.162633 Loss2: 1.397105
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401560 Loss1: 0.084735 Loss2: 1.316825
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524022 Loss1: 0.129505 Loss2: 1.394517
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377321 Loss1: 0.068977 Loss2: 1.308344
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528746 Loss1: 0.138907 Loss2: 1.389838
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369652 Loss1: 0.062715 Loss2: 1.306937
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.482499 Loss1: 0.100116 Loss2: 1.382383
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.444706 Loss1: 0.061500 Loss2: 1.383206
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447006 Loss1: 0.069656 Loss2: 1.377349
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422675 Loss1: 0.049501 Loss2: 1.373174
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.484974 Loss1: 0.609583 Loss2: 1.875390
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.732496 Loss1: 0.336460 Loss2: 1.396036
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.672680 Loss1: 0.228376 Loss2: 1.444304
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557441 Loss1: 0.165531 Loss2: 1.391910
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.396948 Loss1: 0.541253 Loss2: 1.855695
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.582285 Loss1: 0.182838 Loss2: 1.399447
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.701350 Loss1: 0.341992 Loss2: 1.359358
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505025 Loss1: 0.114322 Loss2: 1.390703
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.607193 Loss1: 0.203953 Loss2: 1.403239
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443317 Loss1: 0.067800 Loss2: 1.375517
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564673 Loss1: 0.199520 Loss2: 1.365153
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.407613 Loss1: 0.034780 Loss2: 1.372833
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.546296 Loss1: 0.185844 Loss2: 1.360452
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.389807 Loss1: 0.031187 Loss2: 1.358621
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504410 Loss1: 0.128768 Loss2: 1.375642
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390133 Loss1: 0.043573 Loss2: 1.346560
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484781 Loss1: 0.124370 Loss2: 1.360411
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.456525 Loss1: 0.106548 Loss2: 1.349977
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446529 Loss1: 0.095218 Loss2: 1.351311
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427613 Loss1: 0.076473 Loss2: 1.351140
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.512941 Loss1: 0.658101 Loss2: 1.854841
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.694009 Loss1: 0.343870 Loss2: 1.350139
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.637651 Loss1: 0.245198 Loss2: 1.392452
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559895 Loss1: 0.200639 Loss2: 1.359256
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.345597 Loss1: 0.508631 Loss2: 1.836966
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498132 Loss1: 0.138020 Loss2: 1.360113
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.651261 Loss1: 0.306020 Loss2: 1.345241
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.422930 Loss1: 0.074723 Loss2: 1.348208
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.581786 Loss1: 0.204135 Loss2: 1.377652
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446420 Loss1: 0.106628 Loss2: 1.339792
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492485 Loss1: 0.139529 Loss2: 1.352956
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413658 Loss1: 0.074956 Loss2: 1.338702
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.479906 Loss1: 0.138001 Loss2: 1.341905
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423255 Loss1: 0.088321 Loss2: 1.334933
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467653 Loss1: 0.109956 Loss2: 1.357697
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404580 Loss1: 0.065567 Loss2: 1.339013
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439080 Loss1: 0.096606 Loss2: 1.342474
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420953 Loss1: 0.092684 Loss2: 1.328269
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401127 Loss1: 0.067516 Loss2: 1.333611
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399143 Loss1: 0.075258 Loss2: 1.323884
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.432636 Loss1: 0.589360 Loss2: 1.843276
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.748010 Loss1: 0.382062 Loss2: 1.365948
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648510 Loss1: 0.237558 Loss2: 1.410952
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.546720 Loss1: 0.178965 Loss2: 1.367755
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.230854 Loss1: 0.476219 Loss2: 1.754635
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.645220 Loss1: 0.330484 Loss2: 1.314736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.580651 Loss1: 0.247288 Loss2: 1.333363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.495721 Loss1: 0.174442 Loss2: 1.321279 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.433565 Loss1: 0.126548 Loss2: 1.307017 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.438520 Loss1: 0.129967 Loss2: 1.308553 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.945833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.424654 Loss1: 0.120736 Loss2: 1.303918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.340952 Loss1: 0.050489 Loss2: 1.290463 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.488485 Loss1: 0.603499 Loss2: 1.884986 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.625695 Loss1: 0.255259 Loss2: 1.370436 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.474103 Loss1: 0.134296 Loss2: 1.339807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477164 Loss1: 0.133924 Loss2: 1.343239 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464794 Loss1: 0.134171 Loss2: 1.330622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.406372 Loss1: 0.076563 Loss2: 1.329810 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373417 Loss1: 0.052034 Loss2: 1.321383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.353849 Loss1: 0.039937 Loss2: 1.313913 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.520622 Loss1: 0.152743 Loss2: 1.367879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443303 Loss1: 0.085113 Loss2: 1.358189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417534 Loss1: 0.070200 Loss2: 1.347333
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.429323 Loss1: 0.585370 Loss2: 1.843954
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.710138 Loss1: 0.358030 Loss2: 1.352108
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563168 Loss1: 0.181703 Loss2: 1.381465
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518575 Loss1: 0.171145 Loss2: 1.347430
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471463 Loss1: 0.129101 Loss2: 1.342362
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.654519 Loss1: 0.709188 Loss2: 1.945331
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449303 Loss1: 0.111108 Loss2: 1.338195
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.392427 Loss1: 0.067270 Loss2: 1.325157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380909 Loss1: 0.058675 Loss2: 1.322234 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372412 Loss1: 0.052600 Loss2: 1.319812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356446 Loss1: 0.040095 Loss2: 1.316351 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.613777 Loss1: 0.166965 Loss2: 1.446812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.551721 Loss1: 0.108331 Loss2: 1.443391 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.538607 Loss1: 0.095062 Loss2: 1.443545
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.298768 Loss1: 0.499736 Loss2: 1.799032
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.627367 Loss1: 0.279420 Loss2: 1.347946
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.531462 Loss1: 0.170580 Loss2: 1.360882
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.457616 Loss1: 0.130631 Loss2: 1.326985
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.485199 Loss1: 0.159323 Loss2: 1.325876
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.362453 Loss1: 0.490133 Loss2: 1.872321
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442701 Loss1: 0.112682 Loss2: 1.330019
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.748600 Loss1: 0.366789 Loss2: 1.381811
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.412595 Loss1: 0.089747 Loss2: 1.322848
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.681329 Loss1: 0.243597 Loss2: 1.437732
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409789 Loss1: 0.088783 Loss2: 1.321006
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.552443 Loss1: 0.166608 Loss2: 1.385835
+DEBUG flwr 2023-10-12 08:45:28,048 | server.py:236 | fit_round 145 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519160 Loss1: 0.143195 Loss2: 1.375964
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409132 Loss1: 0.096107 Loss2: 1.313025
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513986 Loss1: 0.143648 Loss2: 1.370337
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428891 Loss1: 0.115930 Loss2: 1.312961
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459488 Loss1: 0.096304 Loss2: 1.363184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.479345 Loss1: 0.108602 Loss2: 1.370743 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.748598 Loss1: 0.383358 Loss2: 1.365240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511969 Loss1: 0.129280 Loss2: 1.382689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.512377 Loss1: 0.582075 Loss2: 1.930302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.726564 Loss1: 0.300875 Loss2: 1.425688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.738378 Loss1: 0.283574 Loss2: 1.454804 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394619 Loss1: 0.055406 Loss2: 1.339213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395678 Loss1: 0.062612 Loss2: 1.333066 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985577
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533842 Loss1: 0.125044 Loss2: 1.408799 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485699 Loss1: 0.077956 Loss2: 1.407743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.419544 Loss1: 0.614643 Loss2: 1.804901
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478262 Loss1: 0.078767 Loss2: 1.399494
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.665538 Loss1: 0.279382 Loss2: 1.386156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.482905 Loss1: 0.152675 Loss2: 1.330230 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433405 Loss1: 0.113065 Loss2: 1.320340
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.405436 Loss1: 0.538982 Loss2: 1.866454
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.398625 Loss1: 0.085563 Loss2: 1.313063
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.658164 Loss1: 0.286997 Loss2: 1.371167
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410197 Loss1: 0.109270 Loss2: 1.300927
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.548796 Loss1: 0.148934 Loss2: 1.399861
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456994 Loss1: 0.142122 Loss2: 1.314873
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.480792 Loss1: 0.115604 Loss2: 1.365188
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425511 Loss1: 0.106229 Loss2: 1.319282
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.473733 Loss1: 0.114490 Loss2: 1.359243
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455604 Loss1: 0.098945 Loss2: 1.356659
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440105 Loss1: 0.085521 Loss2: 1.354585
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406421 Loss1: 0.055304 Loss2: 1.351117
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404559 Loss1: 0.058392 Loss2: 1.346167
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401556 Loss1: 0.058121 Loss2: 1.343435
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 08:45:28,048][flwr][DEBUG] - fit_round 145 received 50 results and 0 failures
+INFO flwr 2023-10-12 08:46:09,613 | server.py:125 | fit progress: (145, 2.2184009710059, {'accuracy': 0.596}, 334477.391319445)
+>> Test accuracy: 0.596000
+[2023-10-12 08:46:09,613][flwr][INFO] - fit progress: (145, 2.2184009710059, {'accuracy': 0.596}, 334477.391319445)
+DEBUG flwr 2023-10-12 08:46:09,613 | server.py:173 | evaluate_round 145: strategy sampled 50 clients (out of 50)
+[2023-10-12 08:46:09,613][flwr][DEBUG] - evaluate_round 145: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 08:55:18,111 | server.py:187 | evaluate_round 145 received 50 results and 0 failures
+[2023-10-12 08:55:18,111][flwr][DEBUG] - evaluate_round 145 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 08:55:18,112 | server.py:222 | fit_round 146: strategy sampled 50 clients (out of 50)
+[2023-10-12 08:55:18,112][flwr][DEBUG] - fit_round 146: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.391963 Loss1: 0.575907 Loss2: 1.816057
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.672821 Loss1: 0.338091 Loss2: 1.334729
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599669 Loss1: 0.224509 Loss2: 1.375160
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.454731 Loss1: 0.115464 Loss2: 1.339267
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.447475 Loss1: 0.112062 Loss2: 1.335413 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.411699 Loss1: 0.082746 Loss2: 1.328953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395633 Loss1: 0.071546 Loss2: 1.324087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.366995 Loss1: 0.053389 Loss2: 1.313607 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.363362 Loss1: 0.054224 Loss2: 1.309137 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.345377 Loss1: 0.039908 Loss2: 1.305469 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.367105 Loss1: 0.050857 Loss2: 1.316248 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.446362 Loss1: 0.482934 Loss2: 1.963428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741104 Loss1: 0.233739 Loss2: 1.507365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.359480 Loss1: 0.521954 Loss2: 1.837526
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.626133 Loss1: 0.147949 Loss2: 1.478184
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.622859 Loss1: 0.290222 Loss2: 1.332636
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577309 Loss1: 0.111864 Loss2: 1.465445
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.544829 Loss1: 0.190744 Loss2: 1.354085
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.549534 Loss1: 0.083228 Loss2: 1.466306
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.467292 Loss1: 0.139089 Loss2: 1.328203
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.552232 Loss1: 0.096055 Loss2: 1.456177
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.553767 Loss1: 0.097678 Loss2: 1.456089 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.516792 Loss1: 0.063667 Loss2: 1.453125 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.481736 Loss1: 0.036335 Loss2: 1.445401 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371589 Loss1: 0.062241 Loss2: 1.309348 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.398775 Loss1: 0.572471 Loss2: 1.826304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.545019 Loss1: 0.182316 Loss2: 1.362703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563743 Loss1: 0.224028 Loss2: 1.339715
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.515204 Loss1: 0.627086 Loss2: 1.888118
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.496863 Loss1: 0.142447 Loss2: 1.354415
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.734082 Loss1: 0.405027 Loss2: 1.329055
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.645268 Loss1: 0.274028 Loss2: 1.371239
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463973 Loss1: 0.124269 Loss2: 1.339705
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.511389 Loss1: 0.177005 Loss2: 1.334384
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419702 Loss1: 0.090846 Loss2: 1.328856
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409418 Loss1: 0.081009 Loss2: 1.328409 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394437 Loss1: 0.073599 Loss2: 1.320838 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361228 Loss1: 0.045727 Loss2: 1.315502 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376081 Loss1: 0.074286 Loss2: 1.301796 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967548
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.269877 Loss1: 0.454762 Loss2: 1.815114
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.598257 Loss1: 0.272921 Loss2: 1.325335
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.515016 Loss1: 0.172604 Loss2: 1.342412
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511546 Loss1: 0.173995 Loss2: 1.337550
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.521461 Loss1: 0.645927 Loss2: 1.875535
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461599 Loss1: 0.132570 Loss2: 1.329029
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.711970 Loss1: 0.314883 Loss2: 1.397086
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.403615 Loss1: 0.074632 Loss2: 1.328984
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598221 Loss1: 0.183072 Loss2: 1.415149
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.382918 Loss1: 0.066290 Loss2: 1.316628
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.531279 Loss1: 0.140681 Loss2: 1.390599
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.371299 Loss1: 0.059425 Loss2: 1.311873
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.553872 Loss1: 0.163883 Loss2: 1.389989
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.369539 Loss1: 0.062350 Loss2: 1.307189
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494121 Loss1: 0.105414 Loss2: 1.388707
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.357190 Loss1: 0.050302 Loss2: 1.306887
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500104 Loss1: 0.120957 Loss2: 1.379147
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461013 Loss1: 0.086318 Loss2: 1.374695
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454837 Loss1: 0.081464 Loss2: 1.373373
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435478 Loss1: 0.067562 Loss2: 1.367916
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.405781 Loss1: 0.547062 Loss2: 1.858719
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.631542 Loss1: 0.272077 Loss2: 1.359465
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.559633 Loss1: 0.190323 Loss2: 1.369310
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.473436 Loss1: 0.120000 Loss2: 1.353436
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.432016 Loss1: 0.576545 Loss2: 1.855472
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.459927 Loss1: 0.114526 Loss2: 1.345400
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.688805 Loss1: 0.335824 Loss2: 1.352980
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.439260 Loss1: 0.084891 Loss2: 1.354369
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.555474 Loss1: 0.182718 Loss2: 1.372756
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.417840 Loss1: 0.070574 Loss2: 1.347266
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.546697 Loss1: 0.205247 Loss2: 1.341450
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.390971 Loss1: 0.056258 Loss2: 1.334713
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496585 Loss1: 0.151410 Loss2: 1.345175
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403435 Loss1: 0.068304 Loss2: 1.335132
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443199 Loss1: 0.111498 Loss2: 1.331700
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395546 Loss1: 0.061533 Loss2: 1.334013
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392509 Loss1: 0.070765 Loss2: 1.321744
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.377827 Loss1: 0.056290 Loss2: 1.321537
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.353197 Loss1: 0.039719 Loss2: 1.313477
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370881 Loss1: 0.061233 Loss2: 1.309649
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.345570 Loss1: 0.557833 Loss2: 1.787737
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.632102 Loss1: 0.312524 Loss2: 1.319578
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.534075 Loss1: 0.179457 Loss2: 1.354619
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.453111 Loss1: 0.133528 Loss2: 1.319583
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.380063 Loss1: 0.567520 Loss2: 1.812543
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.614452 Loss1: 0.277102 Loss2: 1.337350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.522538 Loss1: 0.155369 Loss2: 1.367169 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.479322 Loss1: 0.143281 Loss2: 1.336041 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.468887 Loss1: 0.139522 Loss2: 1.329365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465369 Loss1: 0.123202 Loss2: 1.342167 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.327133 Loss1: 0.039843 Loss2: 1.287290
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469807 Loss1: 0.132800 Loss2: 1.337007
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473516 Loss1: 0.137630 Loss2: 1.335886
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485215 Loss1: 0.143601 Loss2: 1.341613
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431323 Loss1: 0.091689 Loss2: 1.339634
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.538141 Loss1: 0.666968 Loss2: 1.871173
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.841444 Loss1: 0.431171 Loss2: 1.410274
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.728007 Loss1: 0.275863 Loss2: 1.452144
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572096 Loss1: 0.171395 Loss2: 1.400701
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.359005 Loss1: 0.529912 Loss2: 1.829093
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.724898 Loss1: 0.340932 Loss2: 1.383966 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.558910 Loss1: 0.145576 Loss2: 1.413334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526376 Loss1: 0.148261 Loss2: 1.378115 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555149 Loss1: 0.168046 Loss2: 1.387104 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501658 Loss1: 0.120820 Loss2: 1.380837 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450294 Loss1: 0.072022 Loss2: 1.378271 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419403 Loss1: 0.055732 Loss2: 1.363671 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.807612 Loss1: 0.423045 Loss2: 1.384567 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.627644 Loss1: 0.241155 Loss2: 1.386489 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.586569 Loss1: 0.667698 Loss2: 1.918871
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.602429 Loss1: 0.211427 Loss2: 1.391002
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.534587 Loss1: 0.149290 Loss2: 1.385297 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.547293 Loss1: 0.171104 Loss2: 1.376189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.558065 Loss1: 0.168612 Loss2: 1.389454 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469388 Loss1: 0.101005 Loss2: 1.368383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448477 Loss1: 0.089760 Loss2: 1.358717 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.378448 Loss1: 0.043875 Loss2: 1.334573 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.446684 Loss1: 0.644699 Loss2: 1.801984 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.557157 Loss1: 0.181756 Loss2: 1.375401 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500327 Loss1: 0.156954 Loss2: 1.343373
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.514464 Loss1: 0.582193 Loss2: 1.932271
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484148 Loss1: 0.146728 Loss2: 1.337419
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.757267 Loss1: 0.343073 Loss2: 1.414193
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.539500 Loss1: 0.203236 Loss2: 1.336263
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.760257 Loss1: 0.296832 Loss2: 1.463425
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451786 Loss1: 0.106311 Loss2: 1.345475
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.656757 Loss1: 0.233271 Loss2: 1.423486
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420593 Loss1: 0.091265 Loss2: 1.329328
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.565842 Loss1: 0.147110 Loss2: 1.418732
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403180 Loss1: 0.078110 Loss2: 1.325070
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.517785 Loss1: 0.112373 Loss2: 1.405412
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377146 Loss1: 0.059731 Loss2: 1.317415
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488748 Loss1: 0.089200 Loss2: 1.399548
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472031 Loss1: 0.076600 Loss2: 1.395431
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461125 Loss1: 0.071966 Loss2: 1.389159
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442680 Loss1: 0.057270 Loss2: 1.385410
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.349419 Loss1: 0.563646 Loss2: 1.785773
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.735494 Loss1: 0.411940 Loss2: 1.323554
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.675043 Loss1: 0.300913 Loss2: 1.374131
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.341355 Loss1: 0.514437 Loss2: 1.826918
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549005 Loss1: 0.228452 Loss2: 1.320553
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527634 Loss1: 0.185485 Loss2: 1.342149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460317 Loss1: 0.141128 Loss2: 1.319189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433758 Loss1: 0.119911 Loss2: 1.313846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.407594 Loss1: 0.105982 Loss2: 1.301612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418852 Loss1: 0.114309 Loss2: 1.304543 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411232 Loss1: 0.102933 Loss2: 1.308299 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407706 Loss1: 0.069427 Loss2: 1.338279 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.235759 Loss1: 0.479117 Loss2: 1.756641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568758 Loss1: 0.212073 Loss2: 1.356685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.503348 Loss1: 0.667339 Loss2: 1.836008
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488468 Loss1: 0.156008 Loss2: 1.332461
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.743624 Loss1: 0.401287 Loss2: 1.342338
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.445385 Loss1: 0.121450 Loss2: 1.323935
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556525 Loss1: 0.206139 Loss2: 1.350386
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.446935 Loss1: 0.127564 Loss2: 1.319372
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.483738 Loss1: 0.160163 Loss2: 1.323576
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473196 Loss1: 0.155413 Loss2: 1.317783
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405330 Loss1: 0.084210 Loss2: 1.321120 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373116 Loss1: 0.070982 Loss2: 1.302133 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361787 Loss1: 0.056508 Loss2: 1.305279 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.327793 Loss1: 0.033215 Loss2: 1.294578 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.386754 Loss1: 0.574189 Loss2: 1.812565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.659466 Loss1: 0.272783 Loss2: 1.386683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.660134 Loss1: 0.304824 Loss2: 1.355310
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.353123 Loss1: 0.532803 Loss2: 1.820319
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.628964 Loss1: 0.266221 Loss2: 1.362744 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556406 Loss1: 0.175552 Loss2: 1.380854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.504649 Loss1: 0.152006 Loss2: 1.352643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.478292 Loss1: 0.126874 Loss2: 1.351418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463014 Loss1: 0.114698 Loss2: 1.348316 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436129 Loss1: 0.090147 Loss2: 1.345982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402587 Loss1: 0.067534 Loss2: 1.335053 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.660479 Loss1: 0.663629 Loss2: 1.996850 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634787 Loss1: 0.194293 Loss2: 1.440494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551189 Loss1: 0.129604 Loss2: 1.421585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.565025 Loss1: 0.134554 Loss2: 1.430471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.521231 Loss1: 0.105817 Loss2: 1.415415 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497028 Loss1: 0.082448 Loss2: 1.414579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.483309 Loss1: 0.077505 Loss2: 1.405803 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.473938 Loss1: 0.069883 Loss2: 1.404055 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475225 Loss1: 0.133170 Loss2: 1.342055 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409641 Loss1: 0.076690 Loss2: 1.332951 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440417 Loss1: 0.113194 Loss2: 1.327224
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.416422 Loss1: 0.551876 Loss2: 1.864546
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.634355 Loss1: 0.290644 Loss2: 1.343711
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.529030 Loss1: 0.173942 Loss2: 1.355088
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508692 Loss1: 0.165471 Loss2: 1.343221
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492517 Loss1: 0.156281 Loss2: 1.336235
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.415851 Loss1: 0.579707 Loss2: 1.836144
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444931 Loss1: 0.101088 Loss2: 1.343842
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.716095 Loss1: 0.357619 Loss2: 1.358476
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467031 Loss1: 0.139807 Loss2: 1.327225
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.666432 Loss1: 0.284478 Loss2: 1.381955
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460909 Loss1: 0.122393 Loss2: 1.338516
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553656 Loss1: 0.200627 Loss2: 1.353028
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417611 Loss1: 0.086436 Loss2: 1.331175
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.537132 Loss1: 0.175961 Loss2: 1.361171
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371221 Loss1: 0.048360 Loss2: 1.322861
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483598 Loss1: 0.130284 Loss2: 1.353314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436359 Loss1: 0.094771 Loss2: 1.341589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410238 Loss1: 0.073624 Loss2: 1.336614
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.375591 Loss1: 0.560352 Loss2: 1.815239
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622294 Loss1: 0.293436 Loss2: 1.328858
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.507460 Loss1: 0.151723 Loss2: 1.355736
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.470361 Loss1: 0.156714 Loss2: 1.313647
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.494672 Loss1: 0.176054 Loss2: 1.318618
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.600459 Loss1: 0.706289 Loss2: 1.894170
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442944 Loss1: 0.114900 Loss2: 1.328044
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.799373 Loss1: 0.384220 Loss2: 1.415152
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469449 Loss1: 0.150228 Loss2: 1.319222
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.630219 Loss1: 0.188043 Loss2: 1.442176
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425080 Loss1: 0.105237 Loss2: 1.319843
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592401 Loss1: 0.201251 Loss2: 1.391151
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441873 Loss1: 0.125465 Loss2: 1.316408
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.619582 Loss1: 0.209286 Loss2: 1.410295
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421686 Loss1: 0.102115 Loss2: 1.319571
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.553117 Loss1: 0.163932 Loss2: 1.389185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480980 Loss1: 0.095519 Loss2: 1.385461 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435838 Loss1: 0.052743 Loss2: 1.383095
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.302283 Loss1: 0.429508 Loss2: 1.872776
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.667885 Loss1: 0.296070 Loss2: 1.371815
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.701417 Loss1: 0.294691 Loss2: 1.406726
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.566700 Loss1: 0.186830 Loss2: 1.379870
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561963 Loss1: 0.195301 Loss2: 1.366662
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530214 Loss1: 0.151333 Loss2: 1.378881
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.271884 Loss1: 0.437579 Loss2: 1.834306
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.510101 Loss1: 0.138505 Loss2: 1.371596
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.753081 Loss1: 0.374407 Loss2: 1.378674
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.699817 Loss1: 0.287251 Loss2: 1.412566 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631641 Loss1: 0.258349 Loss2: 1.373292 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539261 Loss1: 0.166510 Loss2: 1.372751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.479060 Loss1: 0.121444 Loss2: 1.357616 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.469959 Loss1: 0.121935 Loss2: 1.348023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.733472 Loss1: 0.387444 Loss2: 1.346028 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994485
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528114 Loss1: 0.175931 Loss2: 1.352183 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499804 Loss1: 0.141880 Loss2: 1.357924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.457185 Loss1: 0.115811 Loss2: 1.341374 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445630 Loss1: 0.102502 Loss2: 1.343128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401962 Loss1: 0.060212 Loss2: 1.341750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379219 Loss1: 0.045076 Loss2: 1.334143 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466872 Loss1: 0.090950 Loss2: 1.375923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446249 Loss1: 0.084820 Loss2: 1.361429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.345154 Loss1: 0.535820 Loss2: 1.809334
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422543 Loss1: 0.064119 Loss2: 1.358425
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.743979 Loss1: 0.395393 Loss2: 1.348586
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431647 Loss1: 0.074201 Loss2: 1.357446
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556156 Loss1: 0.210674 Loss2: 1.345481 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.574172 Loss1: 0.213361 Loss2: 1.360812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.493855 Loss1: 0.147141 Loss2: 1.346715
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.387350 Loss1: 0.565337 Loss2: 1.822013
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.716015 Loss1: 0.380261 Loss2: 1.335754 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.655697 Loss1: 0.261181 Loss2: 1.394516 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381629 Loss1: 0.057573 Loss2: 1.324055
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554476 Loss1: 0.219527 Loss2: 1.334950
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487860 Loss1: 0.157346 Loss2: 1.330513
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421224 Loss1: 0.098740 Loss2: 1.322483
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.383338 Loss1: 0.074462 Loss2: 1.308877
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.367653 Loss1: 0.061069 Loss2: 1.306584
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.474039 Loss1: 0.567456 Loss2: 1.906583
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381472 Loss1: 0.080408 Loss2: 1.301064
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348188 Loss1: 0.048684 Loss2: 1.299504 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.565386 Loss1: 0.155858 Loss2: 1.409529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.546001 Loss1: 0.146986 Loss2: 1.399015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.501395 Loss1: 0.108503 Loss2: 1.392892
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.566637 Loss1: 0.696733 Loss2: 1.869904
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446664 Loss1: 0.064144 Loss2: 1.382521
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.768147 Loss1: 0.424918 Loss2: 1.343229
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425239 Loss1: 0.046829 Loss2: 1.378411
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.626265 Loss1: 0.235734 Loss2: 1.390531
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.542654 Loss1: 0.193286 Loss2: 1.349368
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410795 Loss1: 0.037611 Loss2: 1.373184
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.435624 Loss1: 0.093622 Loss2: 1.342002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418386 Loss1: 0.093927 Loss2: 1.324459 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.374637 Loss1: 0.593610 Loss2: 1.781027 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.610428 Loss1: 0.298632 Loss2: 1.311796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.469696 Loss1: 0.146976 Loss2: 1.322720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463638 Loss1: 0.150796 Loss2: 1.312842 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432374 Loss1: 0.121284 Loss2: 1.311090 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440628 Loss1: 0.121644 Loss2: 1.318984 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417040 Loss1: 0.115099 Loss2: 1.301941 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364267 Loss1: 0.062407 Loss2: 1.301861 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.571668 Loss1: 0.180678 Loss2: 1.390990 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.488805 Loss1: 0.115092 Loss2: 1.373714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441868 Loss1: 0.069444 Loss2: 1.372424
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.367880 Loss1: 0.503889 Loss2: 1.863991
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.704233 Loss1: 0.321533 Loss2: 1.382700
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591171 Loss1: 0.178872 Loss2: 1.412300
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527495 Loss1: 0.150829 Loss2: 1.376666
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506294 Loss1: 0.130691 Loss2: 1.375603
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.536932 Loss1: 0.648417 Loss2: 1.888516
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.495817 Loss1: 0.123703 Loss2: 1.372113
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.806461 Loss1: 0.440388 Loss2: 1.366074
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466538 Loss1: 0.106433 Loss2: 1.360105
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.431611 Loss1: 0.068690 Loss2: 1.362921 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414917 Loss1: 0.064582 Loss2: 1.350336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384259 Loss1: 0.032365 Loss2: 1.351894 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480973 Loss1: 0.133838 Loss2: 1.347135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418026 Loss1: 0.076666 Loss2: 1.341360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408745 Loss1: 0.075182 Loss2: 1.333563
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.537182 Loss1: 0.628504 Loss2: 1.908678
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.878605 Loss1: 0.471948 Loss2: 1.406657
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.767831 Loss1: 0.287384 Loss2: 1.480446
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.634864 Loss1: 0.204451 Loss2: 1.430413
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572243 Loss1: 0.159149 Loss2: 1.413094
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.315188 Loss1: 0.526733 Loss2: 1.788455
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.522162 Loss1: 0.110324 Loss2: 1.411838
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.682146 Loss1: 0.342962 Loss2: 1.339183
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.508026 Loss1: 0.112438 Loss2: 1.395587
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.631028 Loss1: 0.260054 Loss2: 1.370973
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457917 Loss1: 0.066808 Loss2: 1.391109
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.575929 Loss1: 0.223701 Loss2: 1.352228
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463078 Loss1: 0.074224 Loss2: 1.388854
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424641 Loss1: 0.042643 Loss2: 1.381998
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.538028 Loss1: 0.187665 Loss2: 1.350363
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.515977 Loss1: 0.165942 Loss2: 1.350035
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464317 Loss1: 0.125750 Loss2: 1.338567
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438837 Loss1: 0.106260 Loss2: 1.332577
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409586 Loss1: 0.076013 Loss2: 1.333573
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.473004 Loss1: 0.616583 Loss2: 1.856421
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386903 Loss1: 0.065528 Loss2: 1.321375
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.643720 Loss1: 0.223335 Loss2: 1.420386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.493001 Loss1: 0.158876 Loss2: 1.334125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.617796 Loss1: 0.707527 Loss2: 1.910269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417244 Loss1: 0.078058 Loss2: 1.339186 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394176 Loss1: 0.068397 Loss2: 1.325779 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404462 Loss1: 0.082816 Loss2: 1.321646 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-12 09:24:18,695 | server.py:236 | fit_round 146 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.399204 Loss1: 0.103188 Loss2: 1.296016 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373922 Loss1: 0.077079 Loss2: 1.296843 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983073
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.369745 Loss1: 0.503802 Loss2: 1.865943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.642817 Loss1: 0.236721 Loss2: 1.406096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.447164 Loss1: 0.597375 Loss2: 1.849788 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.705031 Loss1: 0.328495 Loss2: 1.376537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.658676 Loss1: 0.244763 Loss2: 1.413913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.545685 Loss1: 0.174955 Loss2: 1.370730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523803 Loss1: 0.156986 Loss2: 1.366817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486162 Loss1: 0.113233 Loss2: 1.372929 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465819 Loss1: 0.110801 Loss2: 1.355018 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406521 Loss1: 0.058811 Loss2: 1.347710 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.617855 Loss1: 0.303835 Loss2: 1.314020 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524254 Loss1: 0.213542 Loss2: 1.310712 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.517277 Loss1: 0.642919 Loss2: 1.874358
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472610 Loss1: 0.164341 Loss2: 1.308269
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.771449 Loss1: 0.391551 Loss2: 1.379898
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.407289 Loss1: 0.108892 Loss2: 1.298396
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.640619 Loss1: 0.228678 Loss2: 1.411941
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.385572 Loss1: 0.093974 Loss2: 1.291598
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.548631 Loss1: 0.172005 Loss2: 1.376625
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.357780 Loss1: 0.072872 Loss2: 1.284908
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491730 Loss1: 0.121443 Loss2: 1.370286
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.347961 Loss1: 0.064097 Loss2: 1.283864
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474927 Loss1: 0.113339 Loss2: 1.361589
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.329549 Loss1: 0.051092 Loss2: 1.278456
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422622 Loss1: 0.075490 Loss2: 1.347132 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410270 Loss1: 0.071095 Loss2: 1.339174 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 09:24:18,695][flwr][DEBUG] - fit_round 146 received 50 results and 0 failures
+INFO flwr 2023-10-12 09:24:59,817 | server.py:125 | fit progress: (146, 2.2189620870370836, {'accuracy': 0.5958}, 336807.595103479)
+>> Test accuracy: 0.595800
+[2023-10-12 09:24:59,817][flwr][INFO] - fit progress: (146, 2.2189620870370836, {'accuracy': 0.5958}, 336807.595103479)
+DEBUG flwr 2023-10-12 09:24:59,817 | server.py:173 | evaluate_round 146: strategy sampled 50 clients (out of 50)
+[2023-10-12 09:24:59,817][flwr][DEBUG] - evaluate_round 146: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 09:34:05,961 | server.py:187 | evaluate_round 146 received 50 results and 0 failures
+[2023-10-12 09:34:05,961][flwr][DEBUG] - evaluate_round 146 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 09:34:05,962 | server.py:222 | fit_round 147: strategy sampled 50 clients (out of 50)
+[2023-10-12 09:34:05,962][flwr][DEBUG] - fit_round 147: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.439478 Loss1: 0.647178 Loss2: 1.792300
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.739556 Loss1: 0.416941 Loss2: 1.322615
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618374 Loss1: 0.258735 Loss2: 1.359639
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516649 Loss1: 0.194620 Loss2: 1.322029
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.390286 Loss1: 0.525887 Loss2: 1.864400
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484017 Loss1: 0.156576 Loss2: 1.327442
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.642569 Loss1: 0.303594 Loss2: 1.338975
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515143 Loss1: 0.198389 Loss2: 1.316754
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608381 Loss1: 0.234781 Loss2: 1.373600
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.471092 Loss1: 0.153617 Loss2: 1.317475
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.512014 Loss1: 0.170759 Loss2: 1.341255
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458683 Loss1: 0.150714 Loss2: 1.307969
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.451767 Loss1: 0.113924 Loss2: 1.337844
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373054 Loss1: 0.064655 Loss2: 1.308399
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.414986 Loss1: 0.072866 Loss2: 1.342121
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.352746 Loss1: 0.054816 Loss2: 1.297931
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.414580 Loss1: 0.090591 Loss2: 1.323989
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397300 Loss1: 0.074712 Loss2: 1.322588
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373888 Loss1: 0.054144 Loss2: 1.319743
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.349167 Loss1: 0.034225 Loss2: 1.314942
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.396828 Loss1: 0.562697 Loss2: 1.834132
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.754116 Loss1: 0.403048 Loss2: 1.351068
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.577223 Loss1: 0.185835 Loss2: 1.391388
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.472034 Loss1: 0.124207 Loss2: 1.347827
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.306248 Loss1: 0.496178 Loss2: 1.810070
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.717176 Loss1: 0.390792 Loss2: 1.326384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677595 Loss1: 0.296692 Loss2: 1.380903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519623 Loss1: 0.187559 Loss2: 1.332064 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493724 Loss1: 0.161692 Loss2: 1.332031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459494 Loss1: 0.125851 Loss2: 1.333643 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385229 Loss1: 0.055134 Loss2: 1.330095
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.397016 Loss1: 0.079459 Loss2: 1.317557
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.370179 Loss1: 0.063924 Loss2: 1.306255
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381833 Loss1: 0.081424 Loss2: 1.300409
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367295 Loss1: 0.060096 Loss2: 1.307199
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.366598 Loss1: 0.560462 Loss2: 1.806136
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.693067 Loss1: 0.343614 Loss2: 1.349453
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.585633 Loss1: 0.197084 Loss2: 1.388549
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511064 Loss1: 0.168955 Loss2: 1.342109
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.388262 Loss1: 0.598216 Loss2: 1.790046
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.696851 Loss1: 0.365893 Loss2: 1.330958 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.624492 Loss1: 0.263308 Loss2: 1.361184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.515659 Loss1: 0.185407 Loss2: 1.330252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476541 Loss1: 0.141541 Loss2: 1.334999 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423046 Loss1: 0.105005 Loss2: 1.318041 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381734 Loss1: 0.052799 Loss2: 1.328935
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.407492 Loss1: 0.091372 Loss2: 1.316121
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372656 Loss1: 0.061726 Loss2: 1.310930
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371552 Loss1: 0.070684 Loss2: 1.300868
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.329891 Loss1: 0.036859 Loss2: 1.293032
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.202803 Loss1: 0.435325 Loss2: 1.767478
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.700650 Loss1: 0.363853 Loss2: 1.336797
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.583243 Loss1: 0.219169 Loss2: 1.364074
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.570411 Loss1: 0.630636 Loss2: 1.939774
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489983 Loss1: 0.163898 Loss2: 1.326085
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.773862 Loss1: 0.350635 Loss2: 1.423227
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.458412 Loss1: 0.124794 Loss2: 1.333617
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.766008 Loss1: 0.295842 Loss2: 1.470165
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442921 Loss1: 0.117462 Loss2: 1.325459
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.706137 Loss1: 0.270814 Loss2: 1.435323
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466167 Loss1: 0.139992 Loss2: 1.326175
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464551 Loss1: 0.135683 Loss2: 1.328868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433262 Loss1: 0.112741 Loss2: 1.320521 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424506 Loss1: 0.111946 Loss2: 1.312559 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.503817 Loss1: 0.102369 Loss2: 1.401448 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.226434 Loss1: 0.456432 Loss2: 1.770002 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.606523 Loss1: 0.220463 Loss2: 1.386060 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.533094 Loss1: 0.626955 Loss2: 1.906139
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520098 Loss1: 0.168427 Loss2: 1.351671
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.711004 Loss1: 0.310652 Loss2: 1.400352
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.505622 Loss1: 0.144328 Loss2: 1.361295
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588239 Loss1: 0.179428 Loss2: 1.408811
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444304 Loss1: 0.098917 Loss2: 1.345387
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586218 Loss1: 0.191666 Loss2: 1.394552
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418483 Loss1: 0.085033 Loss2: 1.333450
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393951 Loss1: 0.065652 Loss2: 1.328299 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404429 Loss1: 0.076526 Loss2: 1.327903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380573 Loss1: 0.056699 Loss2: 1.323874 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467728 Loss1: 0.092421 Loss2: 1.375307 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.394360 Loss1: 0.508376 Loss2: 1.885984 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.675670 Loss1: 0.235872 Loss2: 1.439798 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.566104 Loss1: 0.162956 Loss2: 1.403147
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.475559 Loss1: 0.566125 Loss2: 1.909434
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.684439 Loss1: 0.271980 Loss2: 1.412460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.618994 Loss1: 0.203496 Loss2: 1.415499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.603480 Loss1: 0.190415 Loss2: 1.413065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.599633 Loss1: 0.183439 Loss2: 1.416194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.590221 Loss1: 0.194053 Loss2: 1.396169 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412236 Loss1: 0.036061 Loss2: 1.376175
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.544342 Loss1: 0.141971 Loss2: 1.402371
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.524816 Loss1: 0.126725 Loss2: 1.398090
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.515409 Loss1: 0.123160 Loss2: 1.392248
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487280 Loss1: 0.099145 Loss2: 1.388136
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.694927 Loss1: 0.785432 Loss2: 1.909495
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.811983 Loss1: 0.431721 Loss2: 1.380262
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.664738 Loss1: 0.256024 Loss2: 1.408714
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.538378 Loss1: 0.179850 Loss2: 1.358527
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.439816 Loss1: 0.589027 Loss2: 1.850789
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.751907 Loss1: 0.364444 Loss2: 1.387463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.643844 Loss1: 0.230136 Loss2: 1.413707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436815 Loss1: 0.093119 Loss2: 1.343696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432521 Loss1: 0.091646 Loss2: 1.340875 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404290 Loss1: 0.058510 Loss2: 1.345780 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468142 Loss1: 0.101498 Loss2: 1.366644 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449390 Loss1: 0.080418 Loss2: 1.368972 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434371 Loss1: 0.075819 Loss2: 1.358552
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.281976 Loss1: 0.473434 Loss2: 1.808542
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.660212 Loss1: 0.297082 Loss2: 1.363130
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.584377 Loss1: 0.215242 Loss2: 1.369135
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.543992 Loss1: 0.194774 Loss2: 1.349218
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462034 Loss1: 0.114075 Loss2: 1.347959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465493 Loss1: 0.128904 Loss2: 1.336589 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414977 Loss1: 0.082700 Loss2: 1.332278 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403296 Loss1: 0.074935 Loss2: 1.328360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406285 Loss1: 0.080744 Loss2: 1.325540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.521441 Loss1: 0.201098 Loss2: 1.320343 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983456
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404834 Loss1: 0.090938 Loss2: 1.313896 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.374654 Loss1: 0.544741 Loss2: 1.829913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.602582 Loss1: 0.212280 Loss2: 1.390302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523340 Loss1: 0.176094 Loss2: 1.347246
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.300048 Loss1: 0.538552 Loss2: 1.761497
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461969 Loss1: 0.113967 Loss2: 1.348002
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.662438 Loss1: 0.334152 Loss2: 1.328286
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.663673 Loss1: 0.292332 Loss2: 1.371341 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.513218 Loss1: 0.189192 Loss2: 1.324026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507106 Loss1: 0.177234 Loss2: 1.329872 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.415553 Loss1: 0.108872 Loss2: 1.306681 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395764 Loss1: 0.095362 Loss2: 1.300403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.350294 Loss1: 0.059675 Loss2: 1.290619 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.412117 Loss1: 0.541216 Loss2: 1.870901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741785 Loss1: 0.258807 Loss2: 1.482977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.399310 Loss1: 0.609472 Loss2: 1.789838 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.639611 Loss1: 0.320215 Loss2: 1.319396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.519765 Loss1: 0.152509 Loss2: 1.367255 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.443516 Loss1: 0.128605 Loss2: 1.314911 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.430521 Loss1: 0.111477 Loss2: 1.319044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.394008 Loss1: 0.076311 Loss2: 1.317696 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.482241 Loss1: 0.086291 Loss2: 1.395950
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.356726 Loss1: 0.052053 Loss2: 1.304674
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386932 Loss1: 0.092467 Loss2: 1.294465
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.375597 Loss1: 0.078776 Loss2: 1.296821
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365398 Loss1: 0.059449 Loss2: 1.305949
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.434551 Loss1: 0.605132 Loss2: 1.829419
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.729483 Loss1: 0.365922 Loss2: 1.363561
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.602335 Loss1: 0.218334 Loss2: 1.384002
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.501935 Loss1: 0.144330 Loss2: 1.357605
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.482792 Loss1: 0.570902 Loss2: 1.911889
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.467495 Loss1: 0.114693 Loss2: 1.352802
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.771892 Loss1: 0.342301 Loss2: 1.429591
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416675 Loss1: 0.074266 Loss2: 1.342409
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670846 Loss1: 0.205835 Loss2: 1.465011
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.405791 Loss1: 0.068966 Loss2: 1.336825
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540602 Loss1: 0.121009 Loss2: 1.419593
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.386507 Loss1: 0.054221 Loss2: 1.332287
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.584445 Loss1: 0.168287 Loss2: 1.416158
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374076 Loss1: 0.048700 Loss2: 1.325376
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567091 Loss1: 0.145559 Loss2: 1.421532
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354693 Loss1: 0.031735 Loss2: 1.322958
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487839 Loss1: 0.076180 Loss2: 1.411659
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471245 Loss1: 0.073577 Loss2: 1.397668
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454438 Loss1: 0.060646 Loss2: 1.393791
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438939 Loss1: 0.046844 Loss2: 1.392095
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.270034 Loss1: 0.442183 Loss2: 1.827851
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.688816 Loss1: 0.345878 Loss2: 1.342938
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.584357 Loss1: 0.203807 Loss2: 1.380550
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.546648 Loss1: 0.188404 Loss2: 1.358245
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.432038 Loss1: 0.600901 Loss2: 1.831136
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.736853 Loss1: 0.386545 Loss2: 1.350308 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590436 Loss1: 0.209199 Loss2: 1.381236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.513464 Loss1: 0.165750 Loss2: 1.347714 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394814 Loss1: 0.069657 Loss2: 1.325157
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483416 Loss1: 0.138477 Loss2: 1.344939
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479081 Loss1: 0.138685 Loss2: 1.340396 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385012 Loss1: 0.071342 Loss2: 1.313670
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426092 Loss1: 0.086486 Loss2: 1.339606
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425131 Loss1: 0.101170 Loss2: 1.323961
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434241 Loss1: 0.103210 Loss2: 1.331031
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438992 Loss1: 0.100534 Loss2: 1.338458
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.449643 Loss1: 0.568490 Loss2: 1.881152
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.731711 Loss1: 0.345235 Loss2: 1.386476
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.677737 Loss1: 0.250222 Loss2: 1.427514
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.633469 Loss1: 0.242827 Loss2: 1.390642
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.580040 Loss1: 0.668171 Loss2: 1.911869
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.808819 Loss1: 0.405234 Loss2: 1.403586 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.664235 Loss1: 0.236832 Loss2: 1.427403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595937 Loss1: 0.196917 Loss2: 1.399020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501356 Loss1: 0.115619 Loss2: 1.385737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.489488 Loss1: 0.112487 Loss2: 1.377001 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389283 Loss1: 0.037666 Loss2: 1.351618
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.466267 Loss1: 0.091438 Loss2: 1.374829
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418245 Loss1: 0.054565 Loss2: 1.363680
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407037 Loss1: 0.051825 Loss2: 1.355213
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.432930 Loss1: 0.075995 Loss2: 1.356935
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.540996 Loss1: 0.587422 Loss2: 1.953574
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.837180 Loss1: 0.383053 Loss2: 1.454127
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.718096 Loss1: 0.222190 Loss2: 1.495906
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.629410 Loss1: 0.190895 Loss2: 1.438514
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.579330 Loss1: 0.649821 Loss2: 1.929509
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.867367 Loss1: 0.472360 Loss2: 1.395007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.548997 Loss1: 0.120539 Loss2: 1.428457
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.703385 Loss1: 0.244595 Loss2: 1.458791
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.541849 Loss1: 0.119253 Loss2: 1.422596
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.560189 Loss1: 0.164089 Loss2: 1.396100
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.516840 Loss1: 0.097926 Loss2: 1.418914
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514299 Loss1: 0.125219 Loss2: 1.389080
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470628 Loss1: 0.087465 Loss2: 1.383163
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.490330 Loss1: 0.072321 Loss2: 1.418009
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472095 Loss1: 0.097289 Loss2: 1.374807
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457265 Loss1: 0.043306 Loss2: 1.413958
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423969 Loss1: 0.052649 Loss2: 1.371321 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.423124 Loss1: 0.528722 Loss2: 1.894403 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.732768 Loss1: 0.295360 Loss2: 1.437408 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.592532 Loss1: 0.210789 Loss2: 1.381743
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.419044 Loss1: 0.519541 Loss2: 1.899503
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.563638 Loss1: 0.164902 Loss2: 1.398736
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.738800 Loss1: 0.341209 Loss2: 1.397591
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.544862 Loss1: 0.163429 Loss2: 1.381433
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.634243 Loss1: 0.213237 Loss2: 1.421006
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462610 Loss1: 0.082283 Loss2: 1.380327
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.544655 Loss1: 0.151962 Loss2: 1.392693
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473018 Loss1: 0.094289 Loss2: 1.378729
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.531096 Loss1: 0.145645 Loss2: 1.385451
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459970 Loss1: 0.087042 Loss2: 1.372928
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492242 Loss1: 0.108639 Loss2: 1.383603
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436857 Loss1: 0.067134 Loss2: 1.369722
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474418 Loss1: 0.096626 Loss2: 1.377792
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.463284 Loss1: 0.086267 Loss2: 1.377017
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453548 Loss1: 0.080741 Loss2: 1.372807
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428579 Loss1: 0.060839 Loss2: 1.367740
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.441080 Loss1: 0.560447 Loss2: 1.880633
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751926 Loss1: 0.370748 Loss2: 1.381178
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.640389 Loss1: 0.221428 Loss2: 1.418961
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529679 Loss1: 0.154002 Loss2: 1.375677
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.467268 Loss1: 0.645639 Loss2: 1.821629
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.712233 Loss1: 0.365850 Loss2: 1.346383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.626366 Loss1: 0.239583 Loss2: 1.386784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577426 Loss1: 0.222453 Loss2: 1.354973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491458 Loss1: 0.147870 Loss2: 1.343588 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.458700 Loss1: 0.117914 Loss2: 1.340786 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405557 Loss1: 0.058905 Loss2: 1.346652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426256 Loss1: 0.096387 Loss2: 1.329869
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408393 Loss1: 0.077443 Loss2: 1.330950
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363629 Loss1: 0.050865 Loss2: 1.312764
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351622 Loss1: 0.044349 Loss2: 1.307274
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.379907 Loss1: 0.494109 Loss2: 1.885799
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.700899 Loss1: 0.306560 Loss2: 1.394339
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.674436 Loss1: 0.230234 Loss2: 1.444202
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602516 Loss1: 0.198981 Loss2: 1.403535
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.615672 Loss1: 0.636758 Loss2: 1.978914
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.794492 Loss1: 0.408184 Loss2: 1.386308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.563378 Loss1: 0.161495 Loss2: 1.401883
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.747284 Loss1: 0.328877 Loss2: 1.418407
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.721464 Loss1: 0.293396 Loss2: 1.428068
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523308 Loss1: 0.127348 Loss2: 1.395959
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.615067 Loss1: 0.217976 Loss2: 1.397091
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.500207 Loss1: 0.108400 Loss2: 1.391807
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.479215 Loss1: 0.099172 Loss2: 1.380043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474102 Loss1: 0.100547 Loss2: 1.373554 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438035 Loss1: 0.071718 Loss2: 1.366316 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.413618 Loss1: 0.591206 Loss2: 1.822412
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.685985 Loss1: 0.353895 Loss2: 1.332090
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635439 Loss1: 0.262483 Loss2: 1.372956
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489960 Loss1: 0.159636 Loss2: 1.330324
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.488662 Loss1: 0.625078 Loss2: 1.863583
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.722749 Loss1: 0.398817 Loss2: 1.323932
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.450181 Loss1: 0.125917 Loss2: 1.324265
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564708 Loss1: 0.213822 Loss2: 1.350886
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443392 Loss1: 0.121692 Loss2: 1.321700
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395825 Loss1: 0.072814 Loss2: 1.323011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.359054 Loss1: 0.050479 Loss2: 1.308575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.359205 Loss1: 0.056940 Loss2: 1.302265 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354661 Loss1: 0.057925 Loss2: 1.296737 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368818 Loss1: 0.062437 Loss2: 1.306381 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.397043 Loss1: 0.573185 Loss2: 1.823858
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751137 Loss1: 0.396905 Loss2: 1.354233
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.753769 Loss1: 0.345274 Loss2: 1.408495
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.622626 Loss1: 0.262958 Loss2: 1.359668
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.467540 Loss1: 0.611770 Loss2: 1.855770
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.560295 Loss1: 0.183421 Loss2: 1.376874
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.731035 Loss1: 0.365230 Loss2: 1.365805
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518781 Loss1: 0.161655 Loss2: 1.357126
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.606925 Loss1: 0.200369 Loss2: 1.406556
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.526193 Loss1: 0.168442 Loss2: 1.357751
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588886 Loss1: 0.228706 Loss2: 1.360181
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.478367 Loss1: 0.125605 Loss2: 1.352762
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.551572 Loss1: 0.181120 Loss2: 1.370451
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419850 Loss1: 0.070607 Loss2: 1.349242
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471636 Loss1: 0.124094 Loss2: 1.347542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409200 Loss1: 0.066278 Loss2: 1.342922
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463142 Loss1: 0.121960 Loss2: 1.341181
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414369 Loss1: 0.074104 Loss2: 1.340265
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394107 Loss1: 0.061097 Loss2: 1.333010
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375721 Loss1: 0.047758 Loss2: 1.327963
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.742326 Loss1: 0.742260 Loss2: 2.000066
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.746486 Loss1: 0.381564 Loss2: 1.364922
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.617143 Loss1: 0.239683 Loss2: 1.377460
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.519000 Loss1: 0.142094 Loss2: 1.376906
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448837 Loss1: 0.091487 Loss2: 1.357350
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.441297 Loss1: 0.090913 Loss2: 1.350384 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.412862 Loss1: 0.064605 Loss2: 1.348256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380785 Loss1: 0.041429 Loss2: 1.339356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381394 Loss1: 0.053550 Loss2: 1.327844
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553914 Loss1: 0.187148 Loss2: 1.366766
+(DefaultActor pid=3765) >> Training accuracy: 0.976562
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389040 Loss1: 0.063010 Loss2: 1.326030
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530191 Loss1: 0.163623 Loss2: 1.366568
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477852 Loss1: 0.113720 Loss2: 1.364133
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.432773 Loss1: 0.083370 Loss2: 1.349403
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425656 Loss1: 0.084368 Loss2: 1.341288
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406189 Loss1: 0.066407 Loss2: 1.339782
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.379882 Loss1: 0.521097 Loss2: 1.858785
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385522 Loss1: 0.048033 Loss2: 1.337489
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.683322 Loss1: 0.241815 Loss2: 1.441507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517915 Loss1: 0.120910 Loss2: 1.397005 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473239 Loss1: 0.091370 Loss2: 1.381870
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.412889 Loss1: 0.516899 Loss2: 1.895990
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449370 Loss1: 0.072352 Loss2: 1.377018
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.619485 Loss1: 0.233891 Loss2: 1.385593
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.683984 Loss1: 0.267241 Loss2: 1.416743
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427277 Loss1: 0.050720 Loss2: 1.376557
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.643246 Loss1: 0.253078 Loss2: 1.390168
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406331 Loss1: 0.039659 Loss2: 1.366672
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.609121 Loss1: 0.206323 Loss2: 1.402798
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393046 Loss1: 0.036836 Loss2: 1.356210
+(DefaultActor pid=3765) >> Training accuracy: 0.998047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.496537 Loss1: 0.113837 Loss2: 1.382699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497278 Loss1: 0.115933 Loss2: 1.381346 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438804 Loss1: 0.070228 Loss2: 1.368577
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.403127 Loss1: 0.500058 Loss2: 1.903069
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.724658 Loss1: 0.332140 Loss2: 1.392517
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.626083 Loss1: 0.209885 Loss2: 1.416198
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564800 Loss1: 0.176079 Loss2: 1.388721
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524273 Loss1: 0.130546 Loss2: 1.393727
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.370823 Loss1: 0.596311 Loss2: 1.774513
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515673 Loss1: 0.125695 Loss2: 1.389978
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.702789 Loss1: 0.368935 Loss2: 1.333855
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495178 Loss1: 0.111485 Loss2: 1.383693
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.685914 Loss1: 0.290854 Loss2: 1.395060
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454770 Loss1: 0.081398 Loss2: 1.373372
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581164 Loss1: 0.241217 Loss2: 1.339948
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463991 Loss1: 0.086167 Loss2: 1.377824
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521836 Loss1: 0.190986 Loss2: 1.330850
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434499 Loss1: 0.064464 Loss2: 1.370035
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-12 10:03:15,266 | server.py:236 | fit_round 147 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.394007 Loss1: 0.076047 Loss2: 1.317960 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.411035 Loss1: 0.106922 Loss2: 1.304113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379304 Loss1: 0.071304 Loss2: 1.308001
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.320208 Loss1: 0.461667 Loss2: 1.858542
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.720447 Loss1: 0.339605 Loss2: 1.380842
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.673410 Loss1: 0.245440 Loss2: 1.427970
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.607835 Loss1: 0.229618 Loss2: 1.378217
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.591256 Loss1: 0.207616 Loss2: 1.383639
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.554277 Loss1: 0.619649 Loss2: 1.934627
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.923271 Loss1: 0.516448 Loss2: 1.406823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.776896 Loss1: 0.271572 Loss2: 1.505325 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477424 Loss1: 0.098789 Loss2: 1.378635
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.632182 Loss1: 0.221948 Loss2: 1.410233
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450259 Loss1: 0.078378 Loss2: 1.371881
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.559534 Loss1: 0.140855 Loss2: 1.418680
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501853 Loss1: 0.100419 Loss2: 1.401433
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427257 Loss1: 0.062559 Loss2: 1.364698
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464000 Loss1: 0.079207 Loss2: 1.384793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430866 Loss1: 0.056534 Loss2: 1.374333 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.636653 Loss1: 0.314225 Loss2: 1.322428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.493181 Loss1: 0.172299 Loss2: 1.320881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.442427 Loss1: 0.593463 Loss2: 1.848964
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468472 Loss1: 0.142465 Loss2: 1.326007
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.725733 Loss1: 0.364279 Loss2: 1.361454
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470819 Loss1: 0.147603 Loss2: 1.323216
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642356 Loss1: 0.237163 Loss2: 1.405193
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.411734 Loss1: 0.089862 Loss2: 1.321872
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586145 Loss1: 0.218988 Loss2: 1.367157
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409837 Loss1: 0.093591 Loss2: 1.316246
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.564303 Loss1: 0.196478 Loss2: 1.367825
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396757 Loss1: 0.085440 Loss2: 1.311317
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.489106 Loss1: 0.117821 Loss2: 1.371284
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397663 Loss1: 0.083573 Loss2: 1.314090
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488557 Loss1: 0.123736 Loss2: 1.364821 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464017 Loss1: 0.106249 Loss2: 1.357768 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.669644 Loss1: 0.310079 Loss2: 1.359565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535626 Loss1: 0.174825 Loss2: 1.360801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503270 Loss1: 0.142742 Loss2: 1.360527 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449057 Loss1: 0.097735 Loss2: 1.351323 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456055 Loss1: 0.106720 Loss2: 1.349335 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.431523 Loss1: 0.084883 Loss2: 1.346640 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444506 Loss1: 0.097403 Loss2: 1.347103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401907 Loss1: 0.056781 Loss2: 1.345126 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459210 Loss1: 0.099907 Loss2: 1.359303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403070 Loss1: 0.053092 Loss2: 1.349978 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 10:03:15,266][flwr][DEBUG] - fit_round 147 received 50 results and 0 failures
+INFO flwr 2023-10-12 10:03:56,846 | server.py:125 | fit progress: (147, 2.237486937365974, {'accuracy': 0.5946}, 339144.62492308597)
+>> Test accuracy: 0.594600
+[2023-10-12 10:03:56,846][flwr][INFO] - fit progress: (147, 2.237486937365974, {'accuracy': 0.5946}, 339144.62492308597)
+DEBUG flwr 2023-10-12 10:03:56,847 | server.py:173 | evaluate_round 147: strategy sampled 50 clients (out of 50)
+[2023-10-12 10:03:56,847][flwr][DEBUG] - evaluate_round 147: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 10:13:05,012 | server.py:187 | evaluate_round 147 received 50 results and 0 failures
+[2023-10-12 10:13:05,012][flwr][DEBUG] - evaluate_round 147 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 10:13:05,012 | server.py:222 | fit_round 148: strategy sampled 50 clients (out of 50)
+[2023-10-12 10:13:05,012][flwr][DEBUG] - fit_round 148: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.546072 Loss1: 0.662736 Loss2: 1.883335
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.818955 Loss1: 0.417863 Loss2: 1.401092
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.711597 Loss1: 0.278420 Loss2: 1.433177
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.568763 Loss1: 0.177440 Loss2: 1.391323
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.520358 Loss1: 0.626275 Loss2: 1.894083
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.676621 Loss1: 0.293454 Loss2: 1.383167 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.663379 Loss1: 0.244206 Loss2: 1.419173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540413 Loss1: 0.156125 Loss2: 1.384287 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500917 Loss1: 0.122234 Loss2: 1.378682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470557 Loss1: 0.092678 Loss2: 1.377879 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453959 Loss1: 0.084879 Loss2: 1.369079 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445567 Loss1: 0.080784 Loss2: 1.364783 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.355027 Loss1: 0.533068 Loss2: 1.821959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.543277 Loss1: 0.169793 Loss2: 1.373484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.538225 Loss1: 0.201163 Loss2: 1.337062
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.436426 Loss1: 0.566692 Loss2: 1.869734
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.769554 Loss1: 0.381465 Loss2: 1.388088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450091 Loss1: 0.113531 Loss2: 1.336560
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671843 Loss1: 0.262508 Loss2: 1.409335
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443929 Loss1: 0.109536 Loss2: 1.334394
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.544374 Loss1: 0.167669 Loss2: 1.376705
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403677 Loss1: 0.075713 Loss2: 1.327963
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529613 Loss1: 0.142967 Loss2: 1.386647
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492958 Loss1: 0.120778 Loss2: 1.372180
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399820 Loss1: 0.080831 Loss2: 1.318988
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.482561 Loss1: 0.111814 Loss2: 1.370747
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423718 Loss1: 0.099842 Loss2: 1.323876
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.471525 Loss1: 0.107045 Loss2: 1.364480 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.265436 Loss1: 0.437991 Loss2: 1.827444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.578533 Loss1: 0.191272 Loss2: 1.387260 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.627089 Loss1: 0.665890 Loss2: 1.961199
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.571430 Loss1: 0.210419 Loss2: 1.361011
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.528395 Loss1: 0.153925 Loss2: 1.374470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526658 Loss1: 0.156819 Loss2: 1.369839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486468 Loss1: 0.121375 Loss2: 1.365094 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522523 Loss1: 0.115669 Loss2: 1.406854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469345 Loss1: 0.066248 Loss2: 1.403097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453764 Loss1: 0.067209 Loss2: 1.386556 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447314 Loss1: 0.062609 Loss2: 1.384704 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.478357 Loss1: 0.631486 Loss2: 1.846871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.650854 Loss1: 0.242952 Loss2: 1.407901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606564 Loss1: 0.238413 Loss2: 1.368151
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.519102 Loss1: 0.646046 Loss2: 1.873056
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547651 Loss1: 0.181409 Loss2: 1.366242
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.738124 Loss1: 0.329440 Loss2: 1.408684
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465565 Loss1: 0.103758 Loss2: 1.361808
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.637611 Loss1: 0.209732 Loss2: 1.427880
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.454210 Loss1: 0.103694 Loss2: 1.350516
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611229 Loss1: 0.214630 Loss2: 1.396599
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451677 Loss1: 0.097466 Loss2: 1.354211
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523032 Loss1: 0.125252 Loss2: 1.397780
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459529 Loss1: 0.110419 Loss2: 1.349110
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.534067 Loss1: 0.149247 Loss2: 1.384820
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433013 Loss1: 0.086695 Loss2: 1.346318
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508978 Loss1: 0.130046 Loss2: 1.378932
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.509661 Loss1: 0.127266 Loss2: 1.382395
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.492731 Loss1: 0.116679 Loss2: 1.376052
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464798 Loss1: 0.087147 Loss2: 1.377651
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.411979 Loss1: 0.580514 Loss2: 1.831464
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.723916 Loss1: 0.361498 Loss2: 1.362418
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647659 Loss1: 0.262727 Loss2: 1.384932
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.641397 Loss1: 0.269411 Loss2: 1.371986
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.596798 Loss1: 0.676110 Loss2: 1.920688
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.826159 Loss1: 0.432314 Loss2: 1.393846 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684749 Loss1: 0.257746 Loss2: 1.427003 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472292 Loss1: 0.121683 Loss2: 1.350609
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.578953 Loss1: 0.205037 Loss2: 1.373916
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411471 Loss1: 0.071150 Loss2: 1.340321
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535054 Loss1: 0.160247 Loss2: 1.374807
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373685 Loss1: 0.043494 Loss2: 1.330191
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519440 Loss1: 0.141358 Loss2: 1.378082
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480946 Loss1: 0.108887 Loss2: 1.372059
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367804 Loss1: 0.043160 Loss2: 1.324644
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454592 Loss1: 0.093400 Loss2: 1.361192 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.311372 Loss1: 0.510300 Loss2: 1.801072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.657311 Loss1: 0.278292 Loss2: 1.379019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563169 Loss1: 0.216240 Loss2: 1.346929
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.351283 Loss1: 0.528860 Loss2: 1.822423
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.684438 Loss1: 0.352108 Loss2: 1.332329 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.579232 Loss1: 0.218734 Loss2: 1.360499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509271 Loss1: 0.192088 Loss2: 1.317183 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495349 Loss1: 0.176408 Loss2: 1.318941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478162 Loss1: 0.159971 Loss2: 1.318191 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.348423 Loss1: 0.044808 Loss2: 1.303615
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422571 Loss1: 0.117187 Loss2: 1.305384
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.379943 Loss1: 0.076125 Loss2: 1.303818
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389637 Loss1: 0.090972 Loss2: 1.298665
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.346586 Loss1: 0.053597 Loss2: 1.292988
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.292697 Loss1: 0.474654 Loss2: 1.818043
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.657759 Loss1: 0.340989 Loss2: 1.316770
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.577568 Loss1: 0.217221 Loss2: 1.360346
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496862 Loss1: 0.166622 Loss2: 1.330240
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.441140 Loss1: 0.563106 Loss2: 1.878034
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.839652 Loss1: 0.454659 Loss2: 1.384993 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454464 Loss1: 0.130288 Loss2: 1.324176
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670938 Loss1: 0.215486 Loss2: 1.455452
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619550 Loss1: 0.235493 Loss2: 1.384057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563671 Loss1: 0.164331 Loss2: 1.399340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495182 Loss1: 0.110976 Loss2: 1.384205 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358094 Loss1: 0.056415 Loss2: 1.301679
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445149 Loss1: 0.072751 Loss2: 1.372399
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407710 Loss1: 0.047143 Loss2: 1.360567
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396682 Loss1: 0.039053 Loss2: 1.357629
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381206 Loss1: 0.030990 Loss2: 1.350216
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.416658 Loss1: 0.504117 Loss2: 1.912541
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.758195 Loss1: 0.345794 Loss2: 1.412402
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716579 Loss1: 0.276697 Loss2: 1.439882
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596493 Loss1: 0.186483 Loss2: 1.410010
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.347093 Loss1: 0.525118 Loss2: 1.821975
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.701640 Loss1: 0.345512 Loss2: 1.356129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.553966 Loss1: 0.176972 Loss2: 1.376994 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526367 Loss1: 0.175155 Loss2: 1.351212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475435 Loss1: 0.124699 Loss2: 1.350736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455662 Loss1: 0.112499 Loss2: 1.343164 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.379244 Loss1: 0.050291 Loss2: 1.328954 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378066 Loss1: 0.053272 Loss2: 1.324794 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.748245 Loss1: 0.405649 Loss2: 1.342596 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498424 Loss1: 0.158844 Loss2: 1.339580 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.444859 Loss1: 0.561286 Loss2: 1.883573
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484967 Loss1: 0.148468 Loss2: 1.336499
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.742464 Loss1: 0.355731 Loss2: 1.386733
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454783 Loss1: 0.120418 Loss2: 1.334365
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.700106 Loss1: 0.283086 Loss2: 1.417021
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414427 Loss1: 0.090463 Loss2: 1.323964
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.547234 Loss1: 0.168631 Loss2: 1.378603
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384318 Loss1: 0.064504 Loss2: 1.319814
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.477514 Loss1: 0.115260 Loss2: 1.362255
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385957 Loss1: 0.071056 Loss2: 1.314902
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.414110 Loss1: 0.058444 Loss2: 1.355667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.353719 Loss1: 0.040749 Loss2: 1.312970
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.401083 Loss1: 0.058577 Loss2: 1.342506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378174 Loss1: 0.038929 Loss2: 1.339244 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.714393 Loss1: 0.323580 Loss2: 1.390813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578326 Loss1: 0.177383 Loss2: 1.400942 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.506852 Loss1: 0.667944 Loss2: 1.838907
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.596376 Loss1: 0.196340 Loss2: 1.400036
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.825726 Loss1: 0.466168 Loss2: 1.359558
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.583018 Loss1: 0.179075 Loss2: 1.403943
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671390 Loss1: 0.265438 Loss2: 1.405952
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531147 Loss1: 0.127358 Loss2: 1.403789
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563020 Loss1: 0.202845 Loss2: 1.360174
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.510730 Loss1: 0.111452 Loss2: 1.399278
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.546468 Loss1: 0.191342 Loss2: 1.355126
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.487841 Loss1: 0.097592 Loss2: 1.390249
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508910 Loss1: 0.157007 Loss2: 1.351903
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457367 Loss1: 0.075025 Loss2: 1.382341
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.456845 Loss1: 0.120779 Loss2: 1.336066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435621 Loss1: 0.105392 Loss2: 1.330229 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.582041 Loss1: 0.234868 Loss2: 1.347174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.437453 Loss1: 0.105089 Loss2: 1.332363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.442547 Loss1: 0.616625 Loss2: 1.825922
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.434680 Loss1: 0.111759 Loss2: 1.322921
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.417257 Loss1: 0.089395 Loss2: 1.327862 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.387440 Loss1: 0.065199 Loss2: 1.322241 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.363511 Loss1: 0.054407 Loss2: 1.309103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.380612 Loss1: 0.077434 Loss2: 1.303178 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356438 Loss1: 0.053599 Loss2: 1.302839 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.357791 Loss1: 0.064991 Loss2: 1.292801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362693 Loss1: 0.075513 Loss2: 1.287180 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.338710 Loss1: 0.570151 Loss2: 1.768559
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.719149 Loss1: 0.403304 Loss2: 1.315844
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656720 Loss1: 0.281524 Loss2: 1.375196
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529149 Loss1: 0.205738 Loss2: 1.323411
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.457449 Loss1: 0.597590 Loss2: 1.859859
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.758316 Loss1: 0.366494 Loss2: 1.391822 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669592 Loss1: 0.240081 Loss2: 1.429511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591377 Loss1: 0.210614 Loss2: 1.380764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.582068 Loss1: 0.187192 Loss2: 1.394876 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484838 Loss1: 0.108359 Loss2: 1.376478 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491417 Loss1: 0.120300 Loss2: 1.371118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402236 Loss1: 0.046367 Loss2: 1.355868 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.380467 Loss1: 0.514404 Loss2: 1.866063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644417 Loss1: 0.234500 Loss2: 1.409917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.403169 Loss1: 0.527275 Loss2: 1.875893 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.728653 Loss1: 0.344340 Loss2: 1.384312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.668831 Loss1: 0.246299 Loss2: 1.422533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.589624 Loss1: 0.198337 Loss2: 1.391287 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.629386 Loss1: 0.231947 Loss2: 1.397439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.573195 Loss1: 0.179775 Loss2: 1.393420 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.504587 Loss1: 0.113734 Loss2: 1.390854 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442216 Loss1: 0.071960 Loss2: 1.370256 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.847996 Loss1: 0.424346 Loss2: 1.423651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.636840 Loss1: 0.235503 Loss2: 1.401336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.359753 Loss1: 0.528972 Loss2: 1.830781
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.636481 Loss1: 0.222202 Loss2: 1.414279
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.573766 Loss1: 0.256676 Loss2: 1.317090
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.544941 Loss1: 0.149201 Loss2: 1.395740
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.512413 Loss1: 0.189152 Loss2: 1.323261
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528944 Loss1: 0.139826 Loss2: 1.389118
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523988 Loss1: 0.210487 Loss2: 1.313501
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481130 Loss1: 0.099412 Loss2: 1.381718
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.490268 Loss1: 0.177208 Loss2: 1.313060
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450725 Loss1: 0.076555 Loss2: 1.374170
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.398572 Loss1: 0.093618 Loss2: 1.304954
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421261 Loss1: 0.053928 Loss2: 1.367333
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.353129 Loss1: 0.060960 Loss2: 1.292169 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.328231 Loss1: 0.046300 Loss2: 1.281930 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.742185 Loss1: 0.344626 Loss2: 1.397558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583875 Loss1: 0.176829 Loss2: 1.407046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555399 Loss1: 0.149806 Loss2: 1.405593
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.356504 Loss1: 0.460148 Loss2: 1.896356
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488616 Loss1: 0.089725 Loss2: 1.398892
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.805551 Loss1: 0.374122 Loss2: 1.431429
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.501949 Loss1: 0.110067 Loss2: 1.391882
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727297 Loss1: 0.254416 Loss2: 1.472881
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.493055 Loss1: 0.107401 Loss2: 1.385654
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640990 Loss1: 0.212154 Loss2: 1.428836
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.667435 Loss1: 0.237706 Loss2: 1.429729 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.534263 Loss1: 0.131472 Loss2: 1.402791
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.563747 Loss1: 0.124354 Loss2: 1.439394
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533784 Loss1: 0.109474 Loss2: 1.424311
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511155 Loss1: 0.096848 Loss2: 1.414307
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.495798 Loss1: 0.075914 Loss2: 1.419883
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.474521 Loss1: 0.063927 Loss2: 1.410594
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.502129 Loss1: 0.628483 Loss2: 1.873646
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.762855 Loss1: 0.367090 Loss2: 1.395764
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.685335 Loss1: 0.255446 Loss2: 1.429889
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587110 Loss1: 0.199259 Loss2: 1.387850
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.548659 Loss1: 0.154939 Loss2: 1.393720
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.504680 Loss1: 0.651076 Loss2: 1.853604
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.730949 Loss1: 0.374229 Loss2: 1.356720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605663 Loss1: 0.226338 Loss2: 1.379324 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487161 Loss1: 0.146846 Loss2: 1.340315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487995 Loss1: 0.147055 Loss2: 1.340940 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.458915 Loss1: 0.114514 Loss2: 1.344401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400842 Loss1: 0.073127 Loss2: 1.327715 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354321 Loss1: 0.040948 Loss2: 1.313372 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645726 Loss1: 0.213171 Loss2: 1.432555 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.431057 Loss1: 0.088891 Loss2: 1.342166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.637309 Loss1: 0.641907 Loss2: 1.995403 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.776652 Loss1: 0.399958 Loss2: 1.376695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.755589 Loss1: 0.356238 Loss2: 1.399350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595437 Loss1: 0.180229 Loss2: 1.415208 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377059 Loss1: 0.041242 Loss2: 1.335817
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.526873 Loss1: 0.152949 Loss2: 1.373924
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.518248 Loss1: 0.138635 Loss2: 1.379613
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.539799 Loss1: 0.159633 Loss2: 1.380167
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.474895 Loss1: 0.097268 Loss2: 1.377627
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421735 Loss1: 0.056981 Loss2: 1.364754
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407774 Loss1: 0.047103 Loss2: 1.360671
+(DefaultActor pid=3764) >> Training accuracy: 0.998698
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.384930 Loss1: 0.529922 Loss2: 1.855007
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.785662 Loss1: 0.429121 Loss2: 1.356541
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624222 Loss1: 0.222414 Loss2: 1.401808
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.579820 Loss1: 0.222110 Loss2: 1.357710
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.482629 Loss1: 0.131788 Loss2: 1.350841 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459397 Loss1: 0.123094 Loss2: 1.336304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433811 Loss1: 0.100022 Loss2: 1.333789 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403353 Loss1: 0.070887 Loss2: 1.332467 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381151 Loss1: 0.057136 Loss2: 1.324014 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.376774 Loss1: 0.057129 Loss2: 1.319644 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409963 Loss1: 0.088240 Loss2: 1.321722 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.344745 Loss1: 0.044158 Loss2: 1.300587 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.438854 Loss1: 0.626698 Loss2: 1.812155
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.697336 Loss1: 0.362336 Loss2: 1.334999
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624889 Loss1: 0.256062 Loss2: 1.368827
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513223 Loss1: 0.180674 Loss2: 1.332549
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.360114 Loss1: 0.570660 Loss2: 1.789454
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.715768 Loss1: 0.362343 Loss2: 1.353425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.619846 Loss1: 0.234189 Loss2: 1.385657 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509432 Loss1: 0.163110 Loss2: 1.346321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506462 Loss1: 0.160424 Loss2: 1.346038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390243 Loss1: 0.080726 Loss2: 1.309517 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431349 Loss1: 0.095504 Loss2: 1.335845 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366351 Loss1: 0.049260 Loss2: 1.317092 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.639849 Loss1: 0.293419 Loss2: 1.346430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.512110 Loss1: 0.161619 Loss2: 1.350490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.483307 Loss1: 0.580734 Loss2: 1.902574
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.505558 Loss1: 0.150093 Loss2: 1.355465
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.791483 Loss1: 0.408903 Loss2: 1.382580
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450077 Loss1: 0.104216 Loss2: 1.345860
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416518 Loss1: 0.076580 Loss2: 1.339938 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403929 Loss1: 0.065659 Loss2: 1.338270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405519 Loss1: 0.072029 Loss2: 1.333490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410275 Loss1: 0.079040 Loss2: 1.331234 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.393086 Loss1: 0.039394 Loss2: 1.353692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380078 Loss1: 0.040637 Loss2: 1.339441 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.457212 Loss1: 0.567919 Loss2: 1.889292
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.799031 Loss1: 0.396068 Loss2: 1.402963
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.640846 Loss1: 0.184694 Loss2: 1.456152
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533711 Loss1: 0.132967 Loss2: 1.400744
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.361636 Loss1: 0.540770 Loss2: 1.820866
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.760903 Loss1: 0.403920 Loss2: 1.356983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.652377 Loss1: 0.251947 Loss2: 1.400430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507926 Loss1: 0.158848 Loss2: 1.349077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507804 Loss1: 0.158654 Loss2: 1.349150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.483427 Loss1: 0.131850 Loss2: 1.351577 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.457615 Loss1: 0.111202 Loss2: 1.346413 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379398 Loss1: 0.050054 Loss2: 1.329344 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.497772 Loss1: 0.650539 Loss2: 1.847233 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.640770 Loss1: 0.221748 Loss2: 1.419022 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.510668 Loss1: 0.154548 Loss2: 1.356121
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.462028 Loss1: 0.611742 Loss2: 1.850286
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.740638 Loss1: 0.411365 Loss2: 1.329273 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.611458 Loss1: 0.229684 Loss2: 1.381774 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484096 Loss1: 0.136119 Loss2: 1.347978
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.505814 Loss1: 0.171011 Loss2: 1.334804
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.455790 Loss1: 0.108573 Loss2: 1.347216
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441781 Loss1: 0.120034 Loss2: 1.321747
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.416716 Loss1: 0.070282 Loss2: 1.346434
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479621 Loss1: 0.153119 Loss2: 1.326502
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441648 Loss1: 0.120265 Loss2: 1.321383
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435048 Loss1: 0.102131 Loss2: 1.332917
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383620 Loss1: 0.072498 Loss2: 1.311122 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.300506 Loss1: 0.464023 Loss2: 1.836483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.612420 Loss1: 0.183913 Loss2: 1.428507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.461945 Loss1: 0.611995 Loss2: 1.849950
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.635502 Loss1: 0.240334 Loss2: 1.395168
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.732680 Loss1: 0.376168 Loss2: 1.356512
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.563774 Loss1: 0.162893 Loss2: 1.400881
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.544170 Loss1: 0.150579 Loss2: 1.393591 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511851 Loss1: 0.119144 Loss2: 1.392707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474223 Loss1: 0.089350 Loss2: 1.384874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440709 Loss1: 0.062353 Loss2: 1.378356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418286 Loss1: 0.047507 Loss2: 1.370780 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.378037 Loss1: 0.039775 Loss2: 1.338263 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.397477 Loss1: 0.505844 Loss2: 1.891633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700054 Loss1: 0.251372 Loss2: 1.448682 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 10:42:12,207 | server.py:236 | fit_round 148 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.620781 Loss1: 0.223527 Loss2: 1.397254
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.347067 Loss1: 0.572688 Loss2: 1.774380
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.643800 Loss1: 0.310641 Loss2: 1.333159 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.580764 Loss1: 0.224123 Loss2: 1.356640 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.485067 Loss1: 0.154740 Loss2: 1.330326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.426544 Loss1: 0.095311 Loss2: 1.331234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.389108 Loss1: 0.070352 Loss2: 1.318756 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.376856 Loss1: 0.062177 Loss2: 1.314679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363791 Loss1: 0.058582 Loss2: 1.305209 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.226983 Loss1: 0.438136 Loss2: 1.788846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.632237 Loss1: 0.266766 Loss2: 1.365471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502352 Loss1: 0.169360 Loss2: 1.332992
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.390174 Loss1: 0.536622 Loss2: 1.853552
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.635246 Loss1: 0.304754 Loss2: 1.330493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437776 Loss1: 0.112972 Loss2: 1.324804
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.490584 Loss1: 0.137956 Loss2: 1.352629
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390859 Loss1: 0.076139 Loss2: 1.314720
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.436961 Loss1: 0.101785 Loss2: 1.335176
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.421471 Loss1: 0.099157 Loss2: 1.322314
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.371686 Loss1: 0.058878 Loss2: 1.312808
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.433210 Loss1: 0.110949 Loss2: 1.322261
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.344299 Loss1: 0.040642 Loss2: 1.303657
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.347243 Loss1: 0.048005 Loss2: 1.299238 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995404
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.360848 Loss1: 0.053614 Loss2: 1.307233 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 10:42:12,207][flwr][DEBUG] - fit_round 148 received 50 results and 0 failures
+INFO flwr 2023-10-12 10:42:53,108 | server.py:125 | fit progress: (148, 2.2302481027456897, {'accuracy': 0.5964}, 341480.886121305)
+>> Test accuracy: 0.596400
+[2023-10-12 10:42:53,108][flwr][INFO] - fit progress: (148, 2.2302481027456897, {'accuracy': 0.5964}, 341480.886121305)
+DEBUG flwr 2023-10-12 10:42:53,108 | server.py:173 | evaluate_round 148: strategy sampled 50 clients (out of 50)
+[2023-10-12 10:42:53,108][flwr][DEBUG] - evaluate_round 148: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 10:51:56,443 | server.py:187 | evaluate_round 148 received 50 results and 0 failures
+[2023-10-12 10:51:56,443][flwr][DEBUG] - evaluate_round 148 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 10:51:56,443 | server.py:222 | fit_round 149: strategy sampled 50 clients (out of 50)
+[2023-10-12 10:51:56,443][flwr][DEBUG] - fit_round 149: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.220751 Loss1: 0.449531 Loss2: 1.771220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.536025 Loss1: 0.182320 Loss2: 1.353705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.533065 Loss1: 0.625213 Loss2: 1.907853
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.492035 Loss1: 0.169268 Loss2: 1.322767
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516236 Loss1: 0.175567 Loss2: 1.340669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506649 Loss1: 0.176465 Loss2: 1.330184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443458 Loss1: 0.115429 Loss2: 1.328029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420283 Loss1: 0.103006 Loss2: 1.317277 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402028 Loss1: 0.082984 Loss2: 1.319044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424660 Loss1: 0.083665 Loss2: 1.340995 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418492 Loss1: 0.079077 Loss2: 1.339415 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.435300 Loss1: 0.534976 Loss2: 1.900324
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.810591 Loss1: 0.412572 Loss2: 1.398019
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716156 Loss1: 0.269841 Loss2: 1.446315
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578787 Loss1: 0.186385 Loss2: 1.392402
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.326373 Loss1: 0.567152 Loss2: 1.759220
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.665553 Loss1: 0.367370 Loss2: 1.298183 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.577603 Loss1: 0.235743 Loss2: 1.341860 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.448046 Loss1: 0.166482 Loss2: 1.281563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.398858 Loss1: 0.117013 Loss2: 1.281845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.394034 Loss1: 0.114441 Loss2: 1.279593 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450348 Loss1: 0.070045 Loss2: 1.380304
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.333447 Loss1: 0.058170 Loss2: 1.275277
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.329303 Loss1: 0.064035 Loss2: 1.265268
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.311739 Loss1: 0.050327 Loss2: 1.261412
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.293055 Loss1: 0.042156 Loss2: 1.250899
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.495402 Loss1: 0.599931 Loss2: 1.895471
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.738603 Loss1: 0.352067 Loss2: 1.386536
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.637325 Loss1: 0.208978 Loss2: 1.428348
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.595586 Loss1: 0.211333 Loss2: 1.384253
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.430724 Loss1: 0.518711 Loss2: 1.912013
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.684575 Loss1: 0.327412 Loss2: 1.357163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669011 Loss1: 0.291826 Loss2: 1.377186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496870 Loss1: 0.141273 Loss2: 1.355597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484997 Loss1: 0.136371 Loss2: 1.348626 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.451143 Loss1: 0.096471 Loss2: 1.354671 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433168 Loss1: 0.064293 Loss2: 1.368875
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.442413 Loss1: 0.101245 Loss2: 1.341168
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424582 Loss1: 0.084417 Loss2: 1.340165
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433891 Loss1: 0.088589 Loss2: 1.345302
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388478 Loss1: 0.053599 Loss2: 1.334880
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.441836 Loss1: 0.544531 Loss2: 1.897304
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.755583 Loss1: 0.371991 Loss2: 1.383592
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.641753 Loss1: 0.220601 Loss2: 1.421152
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549894 Loss1: 0.164915 Loss2: 1.384979
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.568501 Loss1: 0.560345 Loss2: 2.008157
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.930603 Loss1: 0.423927 Loss2: 1.506676 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.822559 Loss1: 0.258577 Loss2: 1.563982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.703246 Loss1: 0.199835 Loss2: 1.503411 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.667974 Loss1: 0.161718 Loss2: 1.506256 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.650772 Loss1: 0.152563 Loss2: 1.498209 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410973 Loss1: 0.058012 Loss2: 1.352961
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.606518 Loss1: 0.117064 Loss2: 1.489454
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.588539 Loss1: 0.096774 Loss2: 1.491765
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.572093 Loss1: 0.086295 Loss2: 1.485797
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.558091 Loss1: 0.076585 Loss2: 1.481506
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.298732 Loss1: 0.437744 Loss2: 1.860988
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.671943 Loss1: 0.270722 Loss2: 1.401222
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690159 Loss1: 0.253729 Loss2: 1.436429
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.525206 Loss1: 0.635941 Loss2: 1.889264
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602653 Loss1: 0.198522 Loss2: 1.404131
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.928385 Loss1: 0.508791 Loss2: 1.419594
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589096 Loss1: 0.185146 Loss2: 1.403951
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.728037 Loss1: 0.272184 Loss2: 1.455853
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.583732 Loss1: 0.182975 Loss2: 1.400757
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.534812 Loss1: 0.132271 Loss2: 1.402540 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.526864 Loss1: 0.126993 Loss2: 1.399871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.478142 Loss1: 0.081100 Loss2: 1.397042 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446291 Loss1: 0.064665 Loss2: 1.381626 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.437834 Loss1: 0.060253 Loss2: 1.377581 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.676436 Loss1: 0.716643 Loss2: 1.959793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615946 Loss1: 0.204817 Loss2: 1.411129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.609792 Loss1: 0.670055 Loss2: 1.939737 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444510 Loss1: 0.090146 Loss2: 1.354364 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422974 Loss1: 0.077303 Loss2: 1.345671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393888 Loss1: 0.063216 Loss2: 1.330672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373955 Loss1: 0.042160 Loss2: 1.331795 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.382978 Loss1: 0.057775 Loss2: 1.325204 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396145 Loss1: 0.085506 Loss2: 1.310639 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980469
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.412563 Loss1: 0.548451 Loss2: 1.864112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.575828 Loss1: 0.172903 Loss2: 1.402925 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497805 Loss1: 0.145030 Loss2: 1.352775
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.314790 Loss1: 0.506201 Loss2: 1.808588
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.713530 Loss1: 0.351227 Loss2: 1.362303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.724648 Loss1: 0.299796 Loss2: 1.424852 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553162 Loss1: 0.195628 Loss2: 1.357534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520488 Loss1: 0.157784 Loss2: 1.362704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506891 Loss1: 0.147644 Loss2: 1.359247 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.489387 Loss1: 0.136917 Loss2: 1.352470 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452399 Loss1: 0.105698 Loss2: 1.346700 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.441714 Loss1: 0.538474 Loss2: 1.903240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.570240 Loss1: 0.163006 Loss2: 1.407234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.362499 Loss1: 0.528359 Loss2: 1.834140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.721881 Loss1: 0.386724 Loss2: 1.335157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.606266 Loss1: 0.223372 Loss2: 1.382894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.475279 Loss1: 0.135450 Loss2: 1.339828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.454388 Loss1: 0.118467 Loss2: 1.335921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.437381 Loss1: 0.105929 Loss2: 1.331452 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452438 Loss1: 0.125459 Loss2: 1.326979 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407482 Loss1: 0.080959 Loss2: 1.326522 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.795051 Loss1: 0.417324 Loss2: 1.377726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554017 Loss1: 0.182762 Loss2: 1.371255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484655 Loss1: 0.114918 Loss2: 1.369737
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.446386 Loss1: 0.601120 Loss2: 1.845265
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448840 Loss1: 0.091413 Loss2: 1.357427
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.726867 Loss1: 0.365779 Loss2: 1.361087
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462068 Loss1: 0.108859 Loss2: 1.353209
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.613902 Loss1: 0.204683 Loss2: 1.409219
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421323 Loss1: 0.066053 Loss2: 1.355270
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521490 Loss1: 0.163358 Loss2: 1.358132
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405730 Loss1: 0.053746 Loss2: 1.351984
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.472346 Loss1: 0.109849 Loss2: 1.362496
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403643 Loss1: 0.059379 Loss2: 1.344263
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429475 Loss1: 0.077945 Loss2: 1.351530
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422644 Loss1: 0.076024 Loss2: 1.346621
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397525 Loss1: 0.052280 Loss2: 1.345245
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395750 Loss1: 0.062236 Loss2: 1.333515
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397235 Loss1: 0.061363 Loss2: 1.335872
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.298004 Loss1: 0.500675 Loss2: 1.797329
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.578483 Loss1: 0.257448 Loss2: 1.321034
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.510478 Loss1: 0.169775 Loss2: 1.340702
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.441768 Loss1: 0.120276 Loss2: 1.321492
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.240414 Loss1: 0.474558 Loss2: 1.765856
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449687 Loss1: 0.138316 Loss2: 1.311371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.411885 Loss1: 0.100377 Loss2: 1.311508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380635 Loss1: 0.073889 Loss2: 1.306746 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.347596 Loss1: 0.052620 Loss2: 1.294975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.335095 Loss1: 0.044439 Loss2: 1.290656 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433234 Loss1: 0.101438 Loss2: 1.331796 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438947 Loss1: 0.114162 Loss2: 1.324785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445379 Loss1: 0.119938 Loss2: 1.325441
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.482635 Loss1: 0.632085 Loss2: 1.850550
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.777510 Loss1: 0.420151 Loss2: 1.357359
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638802 Loss1: 0.249462 Loss2: 1.389340
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527226 Loss1: 0.179920 Loss2: 1.347307
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514403 Loss1: 0.167597 Loss2: 1.346806
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.330554 Loss1: 0.494116 Loss2: 1.836438
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476498 Loss1: 0.127681 Loss2: 1.348817
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434515 Loss1: 0.100445 Loss2: 1.334070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.618694 Loss1: 0.210911 Loss2: 1.407783
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434172 Loss1: 0.099829 Loss2: 1.334344
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535128 Loss1: 0.161658 Loss2: 1.373471
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407104 Loss1: 0.071795 Loss2: 1.335310
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.462717 Loss1: 0.098950 Loss2: 1.363767
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383264 Loss1: 0.063794 Loss2: 1.319470
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404740 Loss1: 0.055892 Loss2: 1.348849 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373723 Loss1: 0.032775 Loss2: 1.340948 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.396693 Loss1: 0.559379 Loss2: 1.837314
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364303 Loss1: 0.031547 Loss2: 1.332756
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.613545 Loss1: 0.226306 Loss2: 1.387240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.441703 Loss1: 0.105291 Loss2: 1.336412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.426146 Loss1: 0.089961 Loss2: 1.336185
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.617152 Loss1: 0.691169 Loss2: 1.925982
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.413156 Loss1: 0.084178 Loss2: 1.328977
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.676414 Loss1: 0.295229 Loss2: 1.381185
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601204 Loss1: 0.207550 Loss2: 1.393654
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.386031 Loss1: 0.064699 Loss2: 1.321332
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536917 Loss1: 0.154642 Loss2: 1.382275
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.368967 Loss1: 0.052304 Loss2: 1.316663
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528128 Loss1: 0.155442 Loss2: 1.372686
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393184 Loss1: 0.075446 Loss2: 1.317738
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453844 Loss1: 0.089589 Loss2: 1.364255 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.474239 Loss1: 0.114933 Loss2: 1.359306 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972098
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441504 Loss1: 0.083325 Loss2: 1.358179
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.334150 Loss1: 0.549235 Loss2: 1.784914
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.645555 Loss1: 0.304850 Loss2: 1.340705
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.548720 Loss1: 0.192382 Loss2: 1.356338
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.460597 Loss1: 0.144621 Loss2: 1.315976
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.436803 Loss1: 0.122747 Loss2: 1.314056
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.362534 Loss1: 0.544179 Loss2: 1.818355
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443962 Loss1: 0.134562 Loss2: 1.309400
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659091 Loss1: 0.323431 Loss2: 1.335660
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431937 Loss1: 0.114411 Loss2: 1.317526
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.513220 Loss1: 0.151220 Loss2: 1.362000
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.475477 Loss1: 0.145833 Loss2: 1.329644
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.365495 Loss1: 0.061609 Loss2: 1.303886
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529766 Loss1: 0.188993 Loss2: 1.340773
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.365806 Loss1: 0.063346 Loss2: 1.302460
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494937 Loss1: 0.154564 Loss2: 1.340373
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355943 Loss1: 0.057560 Loss2: 1.298383
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429341 Loss1: 0.092439 Loss2: 1.336902 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384747 Loss1: 0.064770 Loss2: 1.319977 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.695834 Loss1: 0.357564 Loss2: 1.338270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.492578 Loss1: 0.165778 Loss2: 1.326800 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.452563 Loss1: 0.134573 Loss2: 1.317990
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.448185 Loss1: 0.567922 Loss2: 1.880263
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445821 Loss1: 0.128304 Loss2: 1.317517
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.682134 Loss1: 0.304654 Loss2: 1.377481
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420436 Loss1: 0.109238 Loss2: 1.311199
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.671277 Loss1: 0.251929 Loss2: 1.419348
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.388785 Loss1: 0.079643 Loss2: 1.309142
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573780 Loss1: 0.194346 Loss2: 1.379434
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.382502 Loss1: 0.070701 Loss2: 1.311802
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502968 Loss1: 0.124825 Loss2: 1.378142
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354580 Loss1: 0.052639 Loss2: 1.301941
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.453186 Loss1: 0.090950 Loss2: 1.362236
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.492371 Loss1: 0.130076 Loss2: 1.362295
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470707 Loss1: 0.096262 Loss2: 1.374444
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428434 Loss1: 0.073625 Loss2: 1.354809
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406734 Loss1: 0.058162 Loss2: 1.348571
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.471235 Loss1: 0.620877 Loss2: 1.850358
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.732246 Loss1: 0.357833 Loss2: 1.374414
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.654899 Loss1: 0.243045 Loss2: 1.411854
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604000 Loss1: 0.225939 Loss2: 1.378061
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.360526 Loss1: 0.555183 Loss2: 1.805343
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.709841 Loss1: 0.365257 Loss2: 1.344585 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.599575 Loss1: 0.200943 Loss2: 1.398632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540777 Loss1: 0.190068 Loss2: 1.350710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.511678 Loss1: 0.166561 Loss2: 1.345117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.535738 Loss1: 0.173859 Loss2: 1.361879 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429525 Loss1: 0.087455 Loss2: 1.342070 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424206 Loss1: 0.090562 Loss2: 1.333644 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.687012 Loss1: 0.330758 Loss2: 1.356254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491726 Loss1: 0.133581 Loss2: 1.358145 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.404249 Loss1: 0.571682 Loss2: 1.832566
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499244 Loss1: 0.153467 Loss2: 1.345777
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.735941 Loss1: 0.381445 Loss2: 1.354496
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.441978 Loss1: 0.094586 Loss2: 1.347392
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.617050 Loss1: 0.219705 Loss2: 1.397345
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418058 Loss1: 0.085136 Loss2: 1.332923
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519690 Loss1: 0.171988 Loss2: 1.347702
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419118 Loss1: 0.089728 Loss2: 1.329391
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476602 Loss1: 0.127019 Loss2: 1.349584
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396086 Loss1: 0.064005 Loss2: 1.332081
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466096 Loss1: 0.119622 Loss2: 1.346474
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387267 Loss1: 0.056246 Loss2: 1.331022
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413900 Loss1: 0.082715 Loss2: 1.331185 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382876 Loss1: 0.056718 Loss2: 1.326159 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.712392 Loss1: 0.344289 Loss2: 1.368103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551925 Loss1: 0.189114 Loss2: 1.362812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.509948 Loss1: 0.147561 Loss2: 1.362387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458941 Loss1: 0.108459 Loss2: 1.350482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410934 Loss1: 0.069639 Loss2: 1.341294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.381531 Loss1: 0.043003 Loss2: 1.338529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377432 Loss1: 0.047348 Loss2: 1.330084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378264 Loss1: 0.047286 Loss2: 1.330978 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.339105 Loss1: 0.035727 Loss2: 1.303378 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.410802 Loss1: 0.532317 Loss2: 1.878485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609962 Loss1: 0.207354 Loss2: 1.402608 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540658 Loss1: 0.172546 Loss2: 1.368112
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.334641 Loss1: 0.458150 Loss2: 1.876490
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653882 Loss1: 0.253380 Loss2: 1.400502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.668321 Loss1: 0.242394 Loss2: 1.425926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599416 Loss1: 0.188578 Loss2: 1.410838 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.605473 Loss1: 0.203233 Loss2: 1.402240 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.545469 Loss1: 0.138799 Loss2: 1.406670 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.578099 Loss1: 0.181593 Loss2: 1.396506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.504236 Loss1: 0.118362 Loss2: 1.385874 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979492
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.460055 Loss1: 0.659023 Loss2: 1.801032 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.643565 Loss1: 0.270877 Loss2: 1.372688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.294860 Loss1: 0.455486 Loss2: 1.839375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.698959 Loss1: 0.349368 Loss2: 1.349590 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629731 Loss1: 0.236113 Loss2: 1.393618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574921 Loss1: 0.207604 Loss2: 1.367317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509334 Loss1: 0.139128 Loss2: 1.370206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504939 Loss1: 0.136274 Loss2: 1.368665 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420911 Loss1: 0.066887 Loss2: 1.354023 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398905 Loss1: 0.058136 Loss2: 1.340769 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.779447 Loss1: 0.437385 Loss2: 1.342061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529168 Loss1: 0.206466 Loss2: 1.322702 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.458273 Loss1: 0.134992 Loss2: 1.323281
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.307286 Loss1: 0.453081 Loss2: 1.854205
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.725239 Loss1: 0.330706 Loss2: 1.394533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.674875 Loss1: 0.224838 Loss2: 1.450036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550151 Loss1: 0.142675 Loss2: 1.407476 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.504300 Loss1: 0.109081 Loss2: 1.395219 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997768
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.534551 Loss1: 0.143349 Loss2: 1.391202 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.468119 Loss1: 0.082138 Loss2: 1.385981 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.356836 Loss1: 0.505814 Loss2: 1.851022
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.475552 Loss1: 0.092394 Loss2: 1.383158
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.580292 Loss1: 0.203361 Loss2: 1.376931 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.486781 Loss1: 0.148112 Loss2: 1.338670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.439471 Loss1: 0.096149 Loss2: 1.343321
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.437164 Loss1: 0.626377 Loss2: 1.810787
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416706 Loss1: 0.083844 Loss2: 1.332862
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.767780 Loss1: 0.420927 Loss2: 1.346853
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410718 Loss1: 0.077386 Loss2: 1.333332
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.733661 Loss1: 0.327862 Loss2: 1.405800
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414356 Loss1: 0.087944 Loss2: 1.326412
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.583959 Loss1: 0.226042 Loss2: 1.357917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387628 Loss1: 0.060539 Loss2: 1.327089
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541083 Loss1: 0.186639 Loss2: 1.354444
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.505208 Loss1: 0.152377 Loss2: 1.352831
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.492102 Loss1: 0.148997 Loss2: 1.343105
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465628 Loss1: 0.128654 Loss2: 1.336975
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427469 Loss1: 0.095592 Loss2: 1.331878
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.412346 Loss1: 0.535861 Loss2: 1.876485
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383715 Loss1: 0.058509 Loss2: 1.325206
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.670623 Loss1: 0.228182 Loss2: 1.442441 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.590776 Loss1: 0.187135 Loss2: 1.403641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.389422 Loss1: 0.473895 Loss2: 1.915526
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519604 Loss1: 0.120967 Loss2: 1.398637
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491963 Loss1: 0.105861 Loss2: 1.386101
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.688972 Loss1: 0.292140 Loss2: 1.396832
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491208 Loss1: 0.103578 Loss2: 1.387631
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.693725 Loss1: 0.250447 Loss2: 1.443279
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.532582 Loss1: 0.148070 Loss2: 1.384512
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551838 Loss1: 0.142106 Loss2: 1.409732
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.510070 Loss1: 0.120343 Loss2: 1.389727
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524373 Loss1: 0.131549 Loss2: 1.392825
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503592 Loss1: 0.105094 Loss2: 1.398498
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474679 Loss1: 0.083229 Loss2: 1.391451
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450113 Loss1: 0.066685 Loss2: 1.383428
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426557 Loss1: 0.053956 Loss2: 1.372601
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.532077 Loss1: 0.653547 Loss2: 1.878530
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423052 Loss1: 0.051563 Loss2: 1.371489
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.728542 Loss1: 0.291334 Loss2: 1.437209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.530622 Loss1: 0.133119 Loss2: 1.397503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485315 Loss1: 0.083987 Loss2: 1.401328
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.286844 Loss1: 0.446802 Loss2: 1.840042
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.637434 Loss1: 0.265096 Loss2: 1.372338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.545651 Loss1: 0.154070 Loss2: 1.391581 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496211 Loss1: 0.131964 Loss2: 1.364247 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493208 Loss1: 0.121980 Loss2: 1.371228 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426350 Loss1: 0.072824 Loss2: 1.353526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404108 Loss1: 0.052021 Loss2: 1.352088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402643 Loss1: 0.059704 Loss2: 1.342939 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989890
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-12 11:20:37,912 | server.py:236 | fit_round 149 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547272 Loss1: 0.203213 Loss2: 1.344059 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440453 Loss1: 0.098789 Loss2: 1.341664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.523177 Loss1: 0.647112 Loss2: 1.876065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.706311 Loss1: 0.353388 Loss2: 1.352923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.647130 Loss1: 0.264437 Loss2: 1.382693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400107 Loss1: 0.081366 Loss2: 1.318741
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573360 Loss1: 0.185941 Loss2: 1.387419
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.534182 Loss1: 0.182098 Loss2: 1.352084
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511358 Loss1: 0.145123 Loss2: 1.366235
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484355 Loss1: 0.124757 Loss2: 1.359598
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423796 Loss1: 0.077134 Loss2: 1.346662
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408526 Loss1: 0.069302 Loss2: 1.339225
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.376987 Loss1: 0.578705 Loss2: 1.798282
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.590602 Loss1: 0.197905 Loss2: 1.392697 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.459087 Loss1: 0.118316 Loss2: 1.340771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443075 Loss1: 0.116664 Loss2: 1.326410
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.403003 Loss1: 0.530634 Loss2: 1.872369
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.417675 Loss1: 0.090673 Loss2: 1.327002
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.753977 Loss1: 0.379190 Loss2: 1.374787
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414413 Loss1: 0.090343 Loss2: 1.324070
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.593975 Loss1: 0.184971 Loss2: 1.409003
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427780 Loss1: 0.109612 Loss2: 1.318168
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.545980 Loss1: 0.177689 Loss2: 1.368291
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428596 Loss1: 0.101907 Loss2: 1.326689
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.472693 Loss1: 0.111010 Loss2: 1.361683
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465289 Loss1: 0.104144 Loss2: 1.361144
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423863 Loss1: 0.066686 Loss2: 1.357178
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428142 Loss1: 0.080935 Loss2: 1.347207
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409058 Loss1: 0.062639 Loss2: 1.346419
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389810 Loss1: 0.049370 Loss2: 1.340440
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 11:20:37,912][flwr][DEBUG] - fit_round 149 received 50 results and 0 failures
+INFO flwr 2023-10-12 11:21:20,012 | server.py:125 | fit progress: (149, 2.23768986547336, {'accuracy': 0.5952}, 343787.790518002)
+>> Test accuracy: 0.595200
+[2023-10-12 11:21:20,012][flwr][INFO] - fit progress: (149, 2.23768986547336, {'accuracy': 0.5952}, 343787.790518002)
+DEBUG flwr 2023-10-12 11:21:20,012 | server.py:173 | evaluate_round 149: strategy sampled 50 clients (out of 50)
+[2023-10-12 11:21:20,012][flwr][DEBUG] - evaluate_round 149: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 11:30:25,431 | server.py:187 | evaluate_round 149 received 50 results and 0 failures
+[2023-10-12 11:30:25,431][flwr][DEBUG] - evaluate_round 149 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 11:30:25,431 | server.py:222 | fit_round 150: strategy sampled 50 clients (out of 50)
+[2023-10-12 11:30:25,431][flwr][DEBUG] - fit_round 150: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.403502 Loss1: 0.502800 Loss2: 1.900702
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.693667 Loss1: 0.291619 Loss2: 1.402048
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.666317 Loss1: 0.244674 Loss2: 1.421643
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.584682 Loss1: 0.185758 Loss2: 1.398924
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.522502 Loss1: 0.131177 Loss2: 1.391325 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499598 Loss1: 0.113156 Loss2: 1.386442 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464070 Loss1: 0.079125 Loss2: 1.384945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454768 Loss1: 0.073178 Loss2: 1.381590 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.442592 Loss1: 0.073487 Loss2: 1.369105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429613 Loss1: 0.058839 Loss2: 1.370774 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401015 Loss1: 0.071136 Loss2: 1.329880 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.390162 Loss1: 0.499812 Loss2: 1.890350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.675948 Loss1: 0.251715 Loss2: 1.424232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.507304 Loss1: 0.140236 Loss2: 1.367068
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.397544 Loss1: 0.587945 Loss2: 1.809599
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491038 Loss1: 0.129349 Loss2: 1.361689
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.773985 Loss1: 0.427968 Loss2: 1.346017
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.482529 Loss1: 0.126492 Loss2: 1.356037
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.665253 Loss1: 0.280174 Loss2: 1.385079
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481356 Loss1: 0.121238 Loss2: 1.360117
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526596 Loss1: 0.191274 Loss2: 1.335323
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447930 Loss1: 0.093739 Loss2: 1.354191
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554542 Loss1: 0.205092 Loss2: 1.349451
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.389587 Loss1: 0.047570 Loss2: 1.342017
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471125 Loss1: 0.137774 Loss2: 1.333351
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374259 Loss1: 0.031562 Loss2: 1.342697
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443138 Loss1: 0.109505 Loss2: 1.333633
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407215 Loss1: 0.088268 Loss2: 1.318947
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390383 Loss1: 0.077967 Loss2: 1.312416
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377848 Loss1: 0.072230 Loss2: 1.305618
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.384415 Loss1: 0.530760 Loss2: 1.853655
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.721673 Loss1: 0.361094 Loss2: 1.360579
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.669309 Loss1: 0.275871 Loss2: 1.393438
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591278 Loss1: 0.228231 Loss2: 1.363046
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.653741 Loss1: 0.722061 Loss2: 1.931680
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.512883 Loss1: 0.145073 Loss2: 1.367810
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.942917 Loss1: 0.554605 Loss2: 1.388312
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.804167 Loss1: 0.346922 Loss2: 1.457245
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526183 Loss1: 0.163982 Loss2: 1.362202
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.658963 Loss1: 0.279769 Loss2: 1.379194
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480325 Loss1: 0.123561 Loss2: 1.356764
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.551117 Loss1: 0.162738 Loss2: 1.388379
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429547 Loss1: 0.080640 Loss2: 1.348908
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499235 Loss1: 0.127717 Loss2: 1.371518
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396195 Loss1: 0.058030 Loss2: 1.338166
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390308 Loss1: 0.056856 Loss2: 1.333452 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410486 Loss1: 0.046157 Loss2: 1.364328 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994420
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.438238 Loss1: 0.636322 Loss2: 1.801916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.580592 Loss1: 0.233444 Loss2: 1.347148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467528 Loss1: 0.139256 Loss2: 1.328272
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.435037 Loss1: 0.553221 Loss2: 1.881815
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.779647 Loss1: 0.406948 Loss2: 1.372699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.697511 Loss1: 0.277671 Loss2: 1.419841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.552024 Loss1: 0.189262 Loss2: 1.362762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.498960 Loss1: 0.125898 Loss2: 1.373062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476739 Loss1: 0.116655 Loss2: 1.360083 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.342527 Loss1: 0.055773 Loss2: 1.286754
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461412 Loss1: 0.112496 Loss2: 1.348916
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425301 Loss1: 0.078941 Loss2: 1.346360
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415664 Loss1: 0.072691 Loss2: 1.342973
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380249 Loss1: 0.047062 Loss2: 1.333187
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.730549 Loss1: 0.730222 Loss2: 2.000327
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.763932 Loss1: 0.392402 Loss2: 1.371530
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.665857 Loss1: 0.266292 Loss2: 1.399564
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.597603 Loss1: 0.190344 Loss2: 1.407259
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.545221 Loss1: 0.169762 Loss2: 1.375459
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524568 Loss1: 0.148284 Loss2: 1.376283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487686 Loss1: 0.111525 Loss2: 1.376161 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444547 Loss1: 0.079690 Loss2: 1.364857 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.387147 Loss1: 0.036567 Loss2: 1.350580
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537540 Loss1: 0.175870 Loss2: 1.361670
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381982 Loss1: 0.039147 Loss2: 1.342835
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.490346 Loss1: 0.132369 Loss2: 1.357977
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491259 Loss1: 0.131674 Loss2: 1.359585
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426508 Loss1: 0.079095 Loss2: 1.347413
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.401546 Loss1: 0.059971 Loss2: 1.341575
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403618 Loss1: 0.071865 Loss2: 1.331753
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.572318 Loss1: 0.652281 Loss2: 1.920037
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368756 Loss1: 0.037819 Loss2: 1.330937
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.640632 Loss1: 0.245502 Loss2: 1.395130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.532614 Loss1: 0.168513 Loss2: 1.364100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.365867 Loss1: 0.568310 Loss2: 1.797557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.750510 Loss1: 0.384583 Loss2: 1.365928 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445463 Loss1: 0.092698 Loss2: 1.352764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453679 Loss1: 0.100770 Loss2: 1.352909 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.433364 Loss1: 0.101916 Loss2: 1.331448 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427151 Loss1: 0.100043 Loss2: 1.327108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397750 Loss1: 0.072823 Loss2: 1.324927 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390776 Loss1: 0.070387 Loss2: 1.320389 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.505228 Loss1: 0.132828 Loss2: 1.372400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.521783 Loss1: 0.138172 Loss2: 1.383611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.258982 Loss1: 0.468528 Loss2: 1.790454 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.689505 Loss1: 0.344955 Loss2: 1.344550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427468 Loss1: 0.071882 Loss2: 1.355585 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.470807 Loss1: 0.138257 Loss2: 1.332550 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.420215 Loss1: 0.099408 Loss2: 1.320807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.225711 Loss1: 0.443198 Loss2: 1.782513
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374696 Loss1: 0.055636 Loss2: 1.319060
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.335204 Loss1: 0.023223 Loss2: 1.311981
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.660776 Loss1: 0.322195 Loss2: 1.338581
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.340167 Loss1: 0.034131 Loss2: 1.306036
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568934 Loss1: 0.210257 Loss2: 1.358677
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517910 Loss1: 0.188153 Loss2: 1.329757
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471074 Loss1: 0.145284 Loss2: 1.325791
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433433 Loss1: 0.109941 Loss2: 1.323492
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445579 Loss1: 0.130300 Loss2: 1.315280
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.265581 Loss1: 0.438342 Loss2: 1.827239
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.618816 Loss1: 0.289256 Loss2: 1.329560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379715 Loss1: 0.071476 Loss2: 1.308239
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.550259 Loss1: 0.198869 Loss2: 1.351390
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375532 Loss1: 0.070218 Loss2: 1.305314
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.427805 Loss1: 0.096105 Loss2: 1.331700
+(DefaultActor pid=3765) >> Training accuracy: 0.983456
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460821 Loss1: 0.141925 Loss2: 1.318896
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.432856 Loss1: 0.110971 Loss2: 1.321885
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395155 Loss1: 0.078277 Loss2: 1.316878
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381727 Loss1: 0.062835 Loss2: 1.318892
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399274 Loss1: 0.090087 Loss2: 1.309187
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.370606 Loss1: 0.506950 Loss2: 1.863656
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348253 Loss1: 0.039888 Loss2: 1.308365
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.752905 Loss1: 0.373974 Loss2: 1.378931
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.659573 Loss1: 0.242092 Loss2: 1.417481
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540031 Loss1: 0.160609 Loss2: 1.379422
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.486497 Loss1: 0.107460 Loss2: 1.379038
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454209 Loss1: 0.092412 Loss2: 1.361797
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.367166 Loss1: 0.504801 Loss2: 1.862365
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467064 Loss1: 0.102161 Loss2: 1.364903
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.792888 Loss1: 0.425264 Loss2: 1.367624
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456804 Loss1: 0.100826 Loss2: 1.355978
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614657 Loss1: 0.197762 Loss2: 1.416895
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451630 Loss1: 0.098166 Loss2: 1.353465
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.569771 Loss1: 0.213829 Loss2: 1.355943
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.455673 Loss1: 0.102631 Loss2: 1.353041
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.536854 Loss1: 0.167814 Loss2: 1.369040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496147 Loss1: 0.139520 Loss2: 1.356627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452046 Loss1: 0.095368 Loss2: 1.356678
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.323916 Loss1: 0.510330 Loss2: 1.813586
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448487 Loss1: 0.092126 Loss2: 1.356361
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.635967 Loss1: 0.295058 Loss2: 1.340910
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.562207 Loss1: 0.189659 Loss2: 1.372547
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.434689 Loss1: 0.098631 Loss2: 1.336058
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.445415 Loss1: 0.112860 Loss2: 1.332554
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.387768 Loss1: 0.060435 Loss2: 1.327333
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.400943 Loss1: 0.513432 Loss2: 1.887512
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.371057 Loss1: 0.057079 Loss2: 1.313978
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.692847 Loss1: 0.312870 Loss2: 1.379976
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.363023 Loss1: 0.049012 Loss2: 1.314012
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.620398 Loss1: 0.210100 Loss2: 1.410299
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.346568 Loss1: 0.031930 Loss2: 1.314639
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554875 Loss1: 0.163132 Loss2: 1.391742
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368683 Loss1: 0.065329 Loss2: 1.303354
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510797 Loss1: 0.128902 Loss2: 1.381895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.497379 Loss1: 0.116552 Loss2: 1.380827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422724 Loss1: 0.053713 Loss2: 1.369011
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.460822 Loss1: 0.615423 Loss2: 1.845399
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.705351 Loss1: 0.309898 Loss2: 1.395453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574816 Loss1: 0.185071 Loss2: 1.389745 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525208 Loss1: 0.132937 Loss2: 1.392271 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513792 Loss1: 0.129380 Loss2: 1.384412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.466921 Loss1: 0.095228 Loss2: 1.371694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.472160 Loss1: 0.096220 Loss2: 1.375940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452737 Loss1: 0.078463 Loss2: 1.374273 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461729 Loss1: 0.119570 Loss2: 1.342159 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369557 Loss1: 0.050897 Loss2: 1.318660 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352628 Loss1: 0.043014 Loss2: 1.309614
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.525916 Loss1: 0.636061 Loss2: 1.889856
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.740406 Loss1: 0.347919 Loss2: 1.392487
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.646373 Loss1: 0.226498 Loss2: 1.419875
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563434 Loss1: 0.172896 Loss2: 1.390538
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.530301 Loss1: 0.145163 Loss2: 1.385138
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497672 Loss1: 0.118036 Loss2: 1.379636
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.406348 Loss1: 0.539388 Loss2: 1.866959
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473404 Loss1: 0.097089 Loss2: 1.376315
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.786486 Loss1: 0.416153 Loss2: 1.370333
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.482726 Loss1: 0.102243 Loss2: 1.380483
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.645104 Loss1: 0.219312 Loss2: 1.425792
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430453 Loss1: 0.063598 Loss2: 1.366855
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562288 Loss1: 0.194653 Loss2: 1.367635
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435713 Loss1: 0.075639 Loss2: 1.360074
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491976 Loss1: 0.118549 Loss2: 1.373427
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455446 Loss1: 0.093359 Loss2: 1.362087
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453277 Loss1: 0.093484 Loss2: 1.359793
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417470 Loss1: 0.067688 Loss2: 1.349782
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412888 Loss1: 0.065703 Loss2: 1.347185
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383383 Loss1: 0.037323 Loss2: 1.346060
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.263452 Loss1: 0.450101 Loss2: 1.813351
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572968 Loss1: 0.220091 Loss2: 1.352877
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.593899 Loss1: 0.223970 Loss2: 1.369929
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591632 Loss1: 0.230581 Loss2: 1.361051
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.493617 Loss1: 0.126369 Loss2: 1.367248
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.393380 Loss1: 0.551775 Loss2: 1.841605
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.435784 Loss1: 0.095661 Loss2: 1.340123
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406766 Loss1: 0.072094 Loss2: 1.334672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378489 Loss1: 0.047465 Loss2: 1.331024 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.355986 Loss1: 0.034259 Loss2: 1.321727 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.339907 Loss1: 0.020748 Loss2: 1.319159 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.552171 Loss1: 0.193674 Loss2: 1.358497 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442466 Loss1: 0.089264 Loss2: 1.353202 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392422 Loss1: 0.052499 Loss2: 1.339923
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.380179 Loss1: 0.543032 Loss2: 1.837146
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.739478 Loss1: 0.364287 Loss2: 1.375191
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.601936 Loss1: 0.207034 Loss2: 1.394902
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557637 Loss1: 0.193642 Loss2: 1.363995
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.559156 Loss1: 0.186750 Loss2: 1.372406
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.423379 Loss1: 0.517919 Loss2: 1.905461
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.710617 Loss1: 0.306628 Loss2: 1.403989 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.686193 Loss1: 0.247197 Loss2: 1.438996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636390 Loss1: 0.217909 Loss2: 1.418481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570026 Loss1: 0.158524 Loss2: 1.411502 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.543149 Loss1: 0.130233 Loss2: 1.412917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.489769 Loss1: 0.093491 Loss2: 1.396277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427931 Loss1: 0.036299 Loss2: 1.391631 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.729387 Loss1: 0.368981 Loss2: 1.360405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542547 Loss1: 0.168790 Loss2: 1.373757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472767 Loss1: 0.120429 Loss2: 1.352338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467465 Loss1: 0.123956 Loss2: 1.343508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421350 Loss1: 0.081857 Loss2: 1.339493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397335 Loss1: 0.067266 Loss2: 1.330068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375865 Loss1: 0.045890 Loss2: 1.329975 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484993 Loss1: 0.138425 Loss2: 1.346567 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387455 Loss1: 0.053033 Loss2: 1.334422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.363253 Loss1: 0.037578 Loss2: 1.325675 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741264 Loss1: 0.284766 Loss2: 1.456498 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547621 Loss1: 0.153826 Loss2: 1.393795 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504519 Loss1: 0.110756 Loss2: 1.393763
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.363257 Loss1: 0.513661 Loss2: 1.849596
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.730117 Loss1: 0.344774 Loss2: 1.385344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604320 Loss1: 0.194045 Loss2: 1.410275 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.546160 Loss1: 0.168552 Loss2: 1.377609 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485094 Loss1: 0.111505 Loss2: 1.373589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.413178 Loss1: 0.059783 Loss2: 1.353395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402136 Loss1: 0.063058 Loss2: 1.339078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394923 Loss1: 0.057857 Loss2: 1.337066 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.509646 Loss1: 0.145462 Loss2: 1.364184 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452196 Loss1: 0.101047 Loss2: 1.351149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460827 Loss1: 0.113151 Loss2: 1.347676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415890 Loss1: 0.067768 Loss2: 1.348122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417185 Loss1: 0.070361 Loss2: 1.346824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.394313 Loss1: 0.054230 Loss2: 1.340082 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447324 Loss1: 0.121499 Loss2: 1.325825 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403763 Loss1: 0.079822 Loss2: 1.323941 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401233 Loss1: 0.083656 Loss2: 1.317577
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.425292 Loss1: 0.571285 Loss2: 1.854007
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.781478 Loss1: 0.417222 Loss2: 1.364257
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.662187 Loss1: 0.241027 Loss2: 1.421159
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.514489 Loss1: 0.151141 Loss2: 1.363348
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462897 Loss1: 0.114327 Loss2: 1.348571
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.418382 Loss1: 0.536528 Loss2: 1.881853
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.745604 Loss1: 0.355679 Loss2: 1.389925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.599492 Loss1: 0.176073 Loss2: 1.423419 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551251 Loss1: 0.162940 Loss2: 1.388311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.488845 Loss1: 0.105912 Loss2: 1.382933 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384588 Loss1: 0.051801 Loss2: 1.332786
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487056 Loss1: 0.113316 Loss2: 1.373740
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.492216 Loss1: 0.111083 Loss2: 1.381133
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482192 Loss1: 0.101404 Loss2: 1.380788
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.478928 Loss1: 0.109669 Loss2: 1.369259
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453993 Loss1: 0.087862 Loss2: 1.366131
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.293408 Loss1: 0.505873 Loss2: 1.787535
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.620522 Loss1: 0.288837 Loss2: 1.331685
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595643 Loss1: 0.232138 Loss2: 1.363505
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.507976 Loss1: 0.175496 Loss2: 1.332479
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513307 Loss1: 0.180400 Loss2: 1.332906
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.533301 Loss1: 0.668129 Loss2: 1.865172
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.818465 Loss1: 0.439030 Loss2: 1.379435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682410 Loss1: 0.249910 Loss2: 1.432500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586009 Loss1: 0.217467 Loss2: 1.368541 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.571152 Loss1: 0.188384 Loss2: 1.382768 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484274 Loss1: 0.121762 Loss2: 1.362512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411925 Loss1: 0.063195 Loss2: 1.348730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373221 Loss1: 0.035604 Loss2: 1.337617 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.670024 Loss1: 0.291531 Loss2: 1.378493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.531247 Loss1: 0.155849 Loss2: 1.375398 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503812 Loss1: 0.136528 Loss2: 1.367285
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.247503 Loss1: 0.437598 Loss2: 1.809905
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.583879 Loss1: 0.237061 Loss2: 1.346818 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.528664 Loss1: 0.157630 Loss2: 1.371034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.512087 Loss1: 0.148510 Loss2: 1.363578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530276 Loss1: 0.168314 Loss2: 1.361962 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.498445 Loss1: 0.138618 Loss2: 1.359827 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418710 Loss1: 0.072733 Loss2: 1.345977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420716 Loss1: 0.075605 Loss2: 1.345111
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.375641 Loss1: 0.535205 Loss2: 1.840437
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.768719 Loss1: 0.412891 Loss2: 1.355828
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690207 Loss1: 0.274631 Loss2: 1.415576
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564462 Loss1: 0.199971 Loss2: 1.364491
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.563377 Loss1: 0.191129 Loss2: 1.372248
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509976 Loss1: 0.148448 Loss2: 1.361528
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.266764 Loss1: 0.439951 Loss2: 1.826814
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464921 Loss1: 0.108125 Loss2: 1.356796
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.679741 Loss1: 0.322055 Loss2: 1.357686
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461962 Loss1: 0.101384 Loss2: 1.360578
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.687537 Loss1: 0.280077 Loss2: 1.407460
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577730 Loss1: 0.198571 Loss2: 1.379159 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383317 Loss1: 0.044830 Loss2: 1.338487
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530384 Loss1: 0.159341 Loss2: 1.371043
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496726 Loss1: 0.122114 Loss2: 1.374612
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460326 Loss1: 0.096327 Loss2: 1.363999
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432852 Loss1: 0.071986 Loss2: 1.360867
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414255 Loss1: 0.061815 Loss2: 1.352440
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.409962 Loss1: 0.479198 Loss2: 1.930763
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388869 Loss1: 0.040881 Loss2: 1.347987
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.807304 Loss1: 0.342016 Loss2: 1.465289
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.822488 Loss1: 0.307293 Loss2: 1.515195
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.643990 Loss1: 0.182422 Loss2: 1.461569
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.582348 Loss1: 0.122787 Loss2: 1.459561
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.545454 Loss1: 0.094426 Loss2: 1.451028
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.694193 Loss1: 0.684553 Loss2: 2.009640
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.511031 Loss1: 0.068095 Loss2: 1.442936
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.835064 Loss1: 0.447720 Loss2: 1.387344
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.702590 Loss1: 0.268922 Loss2: 1.433667
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.510597 Loss1: 0.068475 Loss2: 1.442121
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509927 Loss1: 0.074492 Loss2: 1.435434 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.484170 Loss1: 0.051688 Loss2: 1.432481 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433522 Loss1: 0.057816 Loss2: 1.375706 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394705 Loss1: 0.034940 Loss2: 1.359765 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.769338 Loss1: 0.376371 Loss2: 1.392967 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.652584 Loss1: 0.233184 Loss2: 1.419400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568044 Loss1: 0.172577 Loss2: 1.395467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.716577 Loss1: 0.343593 Loss2: 1.372984
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.528449 Loss1: 0.131112 Loss2: 1.397337
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.603907 Loss1: 0.202030 Loss2: 1.401877
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478275 Loss1: 0.092095 Loss2: 1.386180
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539981 Loss1: 0.177013 Loss2: 1.362968
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459782 Loss1: 0.080810 Loss2: 1.378972
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430485 Loss1: 0.058579 Loss2: 1.371906
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484864 Loss1: 0.121580 Loss2: 1.363285
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404330 Loss1: 0.034128 Loss2: 1.370202
+DEBUG flwr 2023-10-12 11:58:53,801 | server.py:236 | fit_round 150 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493686 Loss1: 0.142215 Loss2: 1.351471
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471275 Loss1: 0.120118 Loss2: 1.351157
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462167 Loss1: 0.113351 Loss2: 1.348816
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451682 Loss1: 0.099760 Loss2: 1.351922
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418110 Loss1: 0.071551 Loss2: 1.346559
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.487037 Loss1: 0.611709 Loss2: 1.875327
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.841296 Loss1: 0.440099 Loss2: 1.401198
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.627741 Loss1: 0.204263 Loss2: 1.423478
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540253 Loss1: 0.170409 Loss2: 1.369843
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513449 Loss1: 0.139365 Loss2: 1.374084
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.407007 Loss1: 0.495838 Loss2: 1.911169
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.697548 Loss1: 0.300780 Loss2: 1.396767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600956 Loss1: 0.185975 Loss2: 1.414982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.558122 Loss1: 0.163015 Loss2: 1.395108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484296 Loss1: 0.107978 Loss2: 1.376318 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.458320 Loss1: 0.078883 Loss2: 1.379437 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436012 Loss1: 0.075847 Loss2: 1.360166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395522 Loss1: 0.042846 Loss2: 1.352676 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.786443 Loss1: 0.403569 Loss2: 1.382874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.580483 Loss1: 0.194154 Loss2: 1.386329 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536246 Loss1: 0.156913 Loss2: 1.379332
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.387294 Loss1: 0.501522 Loss2: 1.885772
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.678077 Loss1: 0.290765 Loss2: 1.387312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598397 Loss1: 0.178964 Loss2: 1.419433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536226 Loss1: 0.154038 Loss2: 1.382188 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485055 Loss1: 0.112152 Loss2: 1.372903 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414685 Loss1: 0.064067 Loss2: 1.350618
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516848 Loss1: 0.145283 Loss2: 1.371565
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530638 Loss1: 0.146256 Loss2: 1.384382
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.480997 Loss1: 0.105406 Loss2: 1.375592
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.459397 Loss1: 0.089632 Loss2: 1.369764
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449641 Loss1: 0.076760 Loss2: 1.372881
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 11:58:53,801][flwr][DEBUG] - fit_round 150 received 50 results and 0 failures
+INFO flwr 2023-10-12 11:59:35,575 | server.py:125 | fit progress: (150, 2.2385582904846144, {'accuracy': 0.5956}, 346083.35339983396)
+>> Test accuracy: 0.595600
+[2023-10-12 11:59:35,575][flwr][INFO] - fit progress: (150, 2.2385582904846144, {'accuracy': 0.5956}, 346083.35339983396)
+DEBUG flwr 2023-10-12 11:59:35,575 | server.py:173 | evaluate_round 150: strategy sampled 50 clients (out of 50)
+[2023-10-12 11:59:35,575][flwr][DEBUG] - evaluate_round 150: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 12:08:36,595 | server.py:187 | evaluate_round 150 received 50 results and 0 failures
+[2023-10-12 12:08:36,595][flwr][DEBUG] - evaluate_round 150 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 12:08:36,596 | server.py:222 | fit_round 151: strategy sampled 50 clients (out of 50)
+[2023-10-12 12:08:36,596][flwr][DEBUG] - fit_round 151: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.408101 Loss1: 0.560553 Loss2: 1.847549
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.761150 Loss1: 0.393327 Loss2: 1.367824
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609584 Loss1: 0.203242 Loss2: 1.406342
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557224 Loss1: 0.200772 Loss2: 1.356452
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.386249 Loss1: 0.536195 Loss2: 1.850054
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.783634 Loss1: 0.418226 Loss2: 1.365408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.613723 Loss1: 0.224429 Loss2: 1.389294 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.545549 Loss1: 0.188671 Loss2: 1.356878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.582037 Loss1: 0.222015 Loss2: 1.360022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.517809 Loss1: 0.150417 Loss2: 1.367392 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367554 Loss1: 0.040001 Loss2: 1.327553
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453609 Loss1: 0.102277 Loss2: 1.351332
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417412 Loss1: 0.071927 Loss2: 1.345485
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413426 Loss1: 0.073559 Loss2: 1.339867
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399338 Loss1: 0.068250 Loss2: 1.331088
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.584236 Loss1: 0.646018 Loss2: 1.938219
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.795836 Loss1: 0.408608 Loss2: 1.387228
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716531 Loss1: 0.278786 Loss2: 1.437744
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.647755 Loss1: 0.222178 Loss2: 1.425577
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.310204 Loss1: 0.527361 Loss2: 1.782844
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587026 Loss1: 0.184049 Loss2: 1.402976 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481220 Loss1: 0.092494 Loss2: 1.388726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464182 Loss1: 0.089192 Loss2: 1.374990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452153 Loss1: 0.073550 Loss2: 1.378603 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425459 Loss1: 0.053851 Loss2: 1.371608 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.380259 Loss1: 0.077915 Loss2: 1.302344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354642 Loss1: 0.063133 Loss2: 1.291509 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.324261 Loss1: 0.038743 Loss2: 1.285519
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.311934 Loss1: 0.531696 Loss2: 1.780238
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.719784 Loss1: 0.394999 Loss2: 1.324785
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.676259 Loss1: 0.302180 Loss2: 1.374079
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.600609 Loss1: 0.258758 Loss2: 1.341851
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525992 Loss1: 0.178749 Loss2: 1.347243
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.300680 Loss1: 0.487695 Loss2: 1.812985
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473690 Loss1: 0.144933 Loss2: 1.328757
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442991 Loss1: 0.117814 Loss2: 1.325178 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415942 Loss1: 0.096521 Loss2: 1.319421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391840 Loss1: 0.073610 Loss2: 1.318231 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354091 Loss1: 0.049066 Loss2: 1.305025 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425554 Loss1: 0.082640 Loss2: 1.342914 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.377182 Loss1: 0.048192 Loss2: 1.328990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364994 Loss1: 0.039580 Loss2: 1.325414
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.324598 Loss1: 0.488713 Loss2: 1.835886
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.756887 Loss1: 0.372260 Loss2: 1.384628
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.630417 Loss1: 0.213692 Loss2: 1.416726
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547166 Loss1: 0.176420 Loss2: 1.370745
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.467117 Loss1: 0.101492 Loss2: 1.365625
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.430651 Loss1: 0.632035 Loss2: 1.798615
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.839256 Loss1: 0.515255 Loss2: 1.324002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.771071 Loss1: 0.358126 Loss2: 1.412945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592073 Loss1: 0.266409 Loss2: 1.325664 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.389508 Loss1: 0.050373 Loss2: 1.339135
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527532 Loss1: 0.170962 Loss2: 1.356571
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399857 Loss1: 0.062169 Loss2: 1.337688
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461517 Loss1: 0.142728 Loss2: 1.318789
+(DefaultActor pid=3765) >> Training accuracy: 0.974609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423694 Loss1: 0.104988 Loss2: 1.318706
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422509 Loss1: 0.101120 Loss2: 1.321389
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405460 Loss1: 0.094513 Loss2: 1.310947
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372540 Loss1: 0.066995 Loss2: 1.305546
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.376117 Loss1: 0.522390 Loss2: 1.853727
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.647915 Loss1: 0.283515 Loss2: 1.364401
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591753 Loss1: 0.192341 Loss2: 1.399412
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518644 Loss1: 0.150877 Loss2: 1.367767
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471640 Loss1: 0.112600 Loss2: 1.359040
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442011 Loss1: 0.092710 Loss2: 1.349302 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408144 Loss1: 0.066237 Loss2: 1.341907 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.396526 Loss1: 0.056576 Loss2: 1.339950 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384115 Loss1: 0.049065 Loss2: 1.335049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368661 Loss1: 0.036666 Loss2: 1.331995 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.375175 Loss1: 0.076744 Loss2: 1.298431 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.339997 Loss1: 0.053341 Loss2: 1.286655 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.618821 Loss1: 0.243619 Loss2: 1.375202 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.538060 Loss1: 0.166227 Loss2: 1.371833 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491208 Loss1: 0.119580 Loss2: 1.371629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455923 Loss1: 0.089174 Loss2: 1.366750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467732 Loss1: 0.109695 Loss2: 1.358037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491261 Loss1: 0.124585 Loss2: 1.366676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419881 Loss1: 0.062863 Loss2: 1.357019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405214 Loss1: 0.053328 Loss2: 1.351886 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996324
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.515718 Loss1: 0.177353 Loss2: 1.338365 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.968750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.296023 Loss1: 0.498663 Loss2: 1.797360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551843 Loss1: 0.205601 Loss2: 1.346242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482068 Loss1: 0.150732 Loss2: 1.331337
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.502423 Loss1: 0.606807 Loss2: 1.895616
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.777581 Loss1: 0.398733 Loss2: 1.378848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.721665 Loss1: 0.292332 Loss2: 1.429333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579394 Loss1: 0.187790 Loss2: 1.391604 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.350589 Loss1: 0.058102 Loss2: 1.292488
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.517441 Loss1: 0.136383 Loss2: 1.381058
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.356526 Loss1: 0.066145 Loss2: 1.290381
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470802 Loss1: 0.099228 Loss2: 1.371573
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.331398 Loss1: 0.044101 Loss2: 1.287297
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447732 Loss1: 0.077021 Loss2: 1.370711
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406794 Loss1: 0.044699 Loss2: 1.362095
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389991 Loss1: 0.040642 Loss2: 1.349349
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399122 Loss1: 0.048409 Loss2: 1.350714
+(DefaultActor pid=3764) >> Training accuracy: 0.979911
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.339456 Loss1: 0.551691 Loss2: 1.787765
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.753744 Loss1: 0.418704 Loss2: 1.335040
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.584077 Loss1: 0.215964 Loss2: 1.368113
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582056 Loss1: 0.240343 Loss2: 1.341714
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.291198 Loss1: 0.525052 Loss2: 1.766146
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.663931 Loss1: 0.341119 Loss2: 1.322811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.611648 Loss1: 0.245354 Loss2: 1.366295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.504903 Loss1: 0.181479 Loss2: 1.323424 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.470519 Loss1: 0.154159 Loss2: 1.316359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.395544 Loss1: 0.082334 Loss2: 1.313209 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.367329 Loss1: 0.061262 Loss2: 1.306067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376696 Loss1: 0.079425 Loss2: 1.297271 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.566034 Loss1: 0.585476 Loss2: 1.980558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.736185 Loss1: 0.252631 Loss2: 1.483554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.631892 Loss1: 0.175586 Loss2: 1.456306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.582300 Loss1: 0.141827 Loss2: 1.440473 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.542322 Loss1: 0.100597 Loss2: 1.441725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.551222 Loss1: 0.120366 Loss2: 1.430856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.523901 Loss1: 0.096118 Loss2: 1.427783 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.514764 Loss1: 0.091420 Loss2: 1.423344 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455982 Loss1: 0.082302 Loss2: 1.373680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461621 Loss1: 0.095391 Loss2: 1.366230 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461171 Loss1: 0.085966 Loss2: 1.375205
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.343509 Loss1: 0.559759 Loss2: 1.783750
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.607807 Loss1: 0.297993 Loss2: 1.309814
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.499857 Loss1: 0.170631 Loss2: 1.329225
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.413149 Loss1: 0.113836 Loss2: 1.299314
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.409678 Loss1: 0.115709 Loss2: 1.293969
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.364388 Loss1: 0.565054 Loss2: 1.799334
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.760474 Loss1: 0.406283 Loss2: 1.354191 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670180 Loss1: 0.278289 Loss2: 1.391890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528116 Loss1: 0.189353 Loss2: 1.338763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.465654 Loss1: 0.126527 Loss2: 1.339127 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449831 Loss1: 0.123433 Loss2: 1.326398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372578 Loss1: 0.054971 Loss2: 1.317607 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362392 Loss1: 0.048539 Loss2: 1.313854 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.703538 Loss1: 0.370319 Loss2: 1.333220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511580 Loss1: 0.172069 Loss2: 1.339511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.528678 Loss1: 0.662529 Loss2: 1.866149 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.742324 Loss1: 0.378961 Loss2: 1.363363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.597817 Loss1: 0.221449 Loss2: 1.376368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.578542 Loss1: 0.230781 Loss2: 1.347761 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475446 Loss1: 0.127262 Loss2: 1.348184 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.398632 Loss1: 0.072296 Loss2: 1.326336 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403789 Loss1: 0.083796 Loss2: 1.319993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373610 Loss1: 0.058856 Loss2: 1.314754
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.378270 Loss1: 0.531038 Loss2: 1.847232
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.816624 Loss1: 0.435869 Loss2: 1.380755
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.749127 Loss1: 0.311944 Loss2: 1.437183
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573547 Loss1: 0.196290 Loss2: 1.377257
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.511116 Loss1: 0.138251 Loss2: 1.372865
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.434280 Loss1: 0.592639 Loss2: 1.841640
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448793 Loss1: 0.086713 Loss2: 1.362080
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469180 Loss1: 0.109800 Loss2: 1.359380 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495943 Loss1: 0.133048 Loss2: 1.362895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447264 Loss1: 0.090008 Loss2: 1.357256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434509 Loss1: 0.081491 Loss2: 1.353018 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416727 Loss1: 0.077690 Loss2: 1.339037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401416 Loss1: 0.068920 Loss2: 1.332496 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416587 Loss1: 0.089984 Loss2: 1.326603
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.246264 Loss1: 0.441519 Loss2: 1.804745
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.618193 Loss1: 0.296231 Loss2: 1.321962
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.598749 Loss1: 0.237325 Loss2: 1.361423
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506558 Loss1: 0.172428 Loss2: 1.334130
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.438679 Loss1: 0.116097 Loss2: 1.322582
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.414213 Loss1: 0.536519 Loss2: 1.877694
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.390665 Loss1: 0.080497 Loss2: 1.310168
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406800 Loss1: 0.106224 Loss2: 1.300576 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.375674 Loss1: 0.070769 Loss2: 1.304905 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.360694 Loss1: 0.061861 Loss2: 1.298833 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.338231 Loss1: 0.040303 Loss2: 1.297928 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485678 Loss1: 0.109612 Loss2: 1.376066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441345 Loss1: 0.074894 Loss2: 1.366451 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482359 Loss1: 0.124684 Loss2: 1.357675
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.399650 Loss1: 0.556960 Loss2: 1.842691
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.757853 Loss1: 0.358833 Loss2: 1.399019
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.707946 Loss1: 0.258321 Loss2: 1.449625
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569187 Loss1: 0.183942 Loss2: 1.385244
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568321 Loss1: 0.176015 Loss2: 1.392306
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.438891 Loss1: 0.525856 Loss2: 1.913036
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525529 Loss1: 0.131037 Loss2: 1.394492
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.766089 Loss1: 0.353949 Loss2: 1.412140
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.556707 Loss1: 0.164797 Loss2: 1.391910
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.767124 Loss1: 0.304248 Loss2: 1.462876
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594560 Loss1: 0.184185 Loss2: 1.410375
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.519788 Loss1: 0.119263 Loss2: 1.400525
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555915 Loss1: 0.147561 Loss2: 1.408354
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493407 Loss1: 0.111203 Loss2: 1.382204
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.548517 Loss1: 0.147311 Loss2: 1.401206
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462081 Loss1: 0.079304 Loss2: 1.382777
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500135 Loss1: 0.111143 Loss2: 1.388992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423565 Loss1: 0.043468 Loss2: 1.380097 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.739698 Loss1: 0.372553 Loss2: 1.367145 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535295 Loss1: 0.162495 Loss2: 1.372800 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498261 Loss1: 0.136431 Loss2: 1.361830 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504320 Loss1: 0.140083 Loss2: 1.364237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474448 Loss1: 0.106688 Loss2: 1.367760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441596 Loss1: 0.080129 Loss2: 1.361467 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429194 Loss1: 0.074604 Loss2: 1.354590 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.585707 Loss1: 0.183089 Loss2: 1.402618
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405716 Loss1: 0.057008 Loss2: 1.348709
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.504786 Loss1: 0.131206 Loss2: 1.373581 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462029 Loss1: 0.082154 Loss2: 1.379875 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.837033 Loss1: 0.397010 Loss2: 1.440023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.593146 Loss1: 0.187788 Loss2: 1.405358 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544575 Loss1: 0.132525 Loss2: 1.412050 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533876 Loss1: 0.125747 Loss2: 1.408130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.498419 Loss1: 0.099047 Loss2: 1.399371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.463509 Loss1: 0.071136 Loss2: 1.392373 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.451470 Loss1: 0.060498 Loss2: 1.390972 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.463498 Loss1: 0.074020 Loss2: 1.389478 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426132 Loss1: 0.070930 Loss2: 1.355202 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.308835 Loss1: 0.523175 Loss2: 1.785660 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609843 Loss1: 0.236319 Loss2: 1.373524 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534165 Loss1: 0.210796 Loss2: 1.323370
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.328779 Loss1: 0.521749 Loss2: 1.807030
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526169 Loss1: 0.194642 Loss2: 1.331528
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.590866 Loss1: 0.255965 Loss2: 1.334901
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448025 Loss1: 0.125902 Loss2: 1.322123
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.478405 Loss1: 0.139735 Loss2: 1.338670
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402366 Loss1: 0.088114 Loss2: 1.314253
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.443794 Loss1: 0.122527 Loss2: 1.321267
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437382 Loss1: 0.125213 Loss2: 1.312169
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.430452 Loss1: 0.119737 Loss2: 1.310715
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386999 Loss1: 0.077396 Loss2: 1.309603
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427500 Loss1: 0.108678 Loss2: 1.318822
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.376634 Loss1: 0.072720 Loss2: 1.303914
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.388727 Loss1: 0.075367 Loss2: 1.313360
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.370179 Loss1: 0.064322 Loss2: 1.305857
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.356373 Loss1: 0.055524 Loss2: 1.300849
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365197 Loss1: 0.067133 Loss2: 1.298064
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.617015 Loss1: 0.731381 Loss2: 1.885634
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.606358 Loss1: 0.294954 Loss2: 1.311404
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.544699 Loss1: 0.219839 Loss2: 1.324860
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.442142 Loss1: 0.125279 Loss2: 1.316863
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.459267 Loss1: 0.589216 Loss2: 1.870051
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465938 Loss1: 0.166774 Loss2: 1.299164 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415808 Loss1: 0.115170 Loss2: 1.300638 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.372745 Loss1: 0.080588 Loss2: 1.292157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.356354 Loss1: 0.067337 Loss2: 1.289016 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.328878 Loss1: 0.046960 Loss2: 1.281918 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439925 Loss1: 0.086197 Loss2: 1.353728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412273 Loss1: 0.069272 Loss2: 1.343000 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391953 Loss1: 0.054180 Loss2: 1.337773
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.193232 Loss1: 0.417272 Loss2: 1.775960
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.601845 Loss1: 0.271488 Loss2: 1.330357
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.570086 Loss1: 0.207300 Loss2: 1.362786
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536246 Loss1: 0.196521 Loss2: 1.339725
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480360 Loss1: 0.145857 Loss2: 1.334502
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.505027 Loss1: 0.609114 Loss2: 1.895913
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.436042 Loss1: 0.104440 Loss2: 1.331602
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.868633 Loss1: 0.452406 Loss2: 1.416227
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432674 Loss1: 0.106780 Loss2: 1.325894
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.688596 Loss1: 0.232543 Loss2: 1.456053
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404080 Loss1: 0.081926 Loss2: 1.322154
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.591422 Loss1: 0.192825 Loss2: 1.398597
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385318 Loss1: 0.067670 Loss2: 1.317648
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527055 Loss1: 0.122761 Loss2: 1.404294
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498550 Loss1: 0.105988 Loss2: 1.392562
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393573 Loss1: 0.079278 Loss2: 1.314295
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467014 Loss1: 0.080376 Loss2: 1.386638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435915 Loss1: 0.055979 Loss2: 1.379935 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.626876 Loss1: 0.290351 Loss2: 1.336525 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.469847 Loss1: 0.131864 Loss2: 1.337983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.451037 Loss1: 0.615133 Loss2: 1.835904
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.427539 Loss1: 0.098682 Loss2: 1.328858
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.752948 Loss1: 0.399321 Loss2: 1.353627
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.392712 Loss1: 0.070403 Loss2: 1.322310
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614635 Loss1: 0.212306 Loss2: 1.402329
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.377676 Loss1: 0.060288 Loss2: 1.317388
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540532 Loss1: 0.191227 Loss2: 1.349305
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.356611 Loss1: 0.047965 Loss2: 1.308647
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513630 Loss1: 0.170931 Loss2: 1.342699
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.344145 Loss1: 0.038999 Loss2: 1.305146
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480153 Loss1: 0.137108 Loss2: 1.343045
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.328003 Loss1: 0.022792 Loss2: 1.305211
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440854 Loss1: 0.104797 Loss2: 1.336057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382785 Loss1: 0.061196 Loss2: 1.321589 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.679450 Loss1: 0.328043 Loss2: 1.351408 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542750 Loss1: 0.198374 Loss2: 1.344376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.225260 Loss1: 0.453310 Loss2: 1.771950
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503074 Loss1: 0.164281 Loss2: 1.338793
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.590326 Loss1: 0.253099 Loss2: 1.337227
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445420 Loss1: 0.117164 Loss2: 1.328255
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.513297 Loss1: 0.149468 Loss2: 1.363829
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420356 Loss1: 0.089857 Loss2: 1.330499
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419591 Loss1: 0.094926 Loss2: 1.324665
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529103 Loss1: 0.192595 Loss2: 1.336508
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.389494 Loss1: 0.068239 Loss2: 1.321255
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.480287 Loss1: 0.137746 Loss2: 1.342541
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354091 Loss1: 0.042673 Loss2: 1.311418
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465380 Loss1: 0.136624 Loss2: 1.328757
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448617 Loss1: 0.120434 Loss2: 1.328183
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426704 Loss1: 0.099739 Loss2: 1.326966
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.355718 Loss1: 0.038615 Loss2: 1.317104
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351257 Loss1: 0.041665 Loss2: 1.309592
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.349935 Loss1: 0.503451 Loss2: 1.846484
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.739060 Loss1: 0.340713 Loss2: 1.398347
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.679123 Loss1: 0.236069 Loss2: 1.443054
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.620851 Loss1: 0.210882 Loss2: 1.409969
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.509995 Loss1: 0.576119 Loss2: 1.933877
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.554542 Loss1: 0.134187 Loss2: 1.420355
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.543225 Loss1: 0.143766 Loss2: 1.399459 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.497452 Loss1: 0.092559 Loss2: 1.404893 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529042 Loss1: 0.196624 Loss2: 1.332418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482108 Loss1: 0.144827 Loss2: 1.337282 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467795 Loss1: 0.133775 Loss2: 1.334019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427584 Loss1: 0.100437 Loss2: 1.327148 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373772 Loss1: 0.058013 Loss2: 1.315759 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.264007 Loss1: 0.489111 Loss2: 1.774897
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.678175 Loss1: 0.342826 Loss2: 1.335350
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.549931 Loss1: 0.187646 Loss2: 1.362285
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.438346 Loss1: 0.545641 Loss2: 1.892705
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.538875 Loss1: 0.203457 Loss2: 1.335419
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.693990 Loss1: 0.315053 Loss2: 1.378938
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478343 Loss1: 0.128672 Loss2: 1.349671
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.408545 Loss1: 0.072671 Loss2: 1.335874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415710 Loss1: 0.087954 Loss2: 1.327756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420271 Loss1: 0.088552 Loss2: 1.331719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394699 Loss1: 0.067707 Loss2: 1.326993 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410660 Loss1: 0.087617 Loss2: 1.323043 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.999023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451336 Loss1: 0.086913 Loss2: 1.364423 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-12 12:37:31,319 | server.py:236 | fit_round 151 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.362476 Loss1: 0.544815 Loss2: 1.817661 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.614615 Loss1: 0.234973 Loss2: 1.379642 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.530323 Loss1: 0.192762 Loss2: 1.337561
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.442149 Loss1: 0.525407 Loss2: 1.916742
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.757993 Loss1: 0.350920 Loss2: 1.407073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.621264 Loss1: 0.179401 Loss2: 1.441863 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528074 Loss1: 0.124404 Loss2: 1.403670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.474030 Loss1: 0.077703 Loss2: 1.396327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472631 Loss1: 0.085948 Loss2: 1.386684 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367270 Loss1: 0.043158 Loss2: 1.324112
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441681 Loss1: 0.054940 Loss2: 1.386741
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425730 Loss1: 0.050198 Loss2: 1.375532
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440950 Loss1: 0.062210 Loss2: 1.378739
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431242 Loss1: 0.050128 Loss2: 1.381114
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.471228 Loss1: 0.644307 Loss2: 1.826921
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.638149 Loss1: 0.320299 Loss2: 1.317850
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.589087 Loss1: 0.248334 Loss2: 1.340753
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517248 Loss1: 0.195565 Loss2: 1.321683
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.517345 Loss1: 0.605322 Loss2: 1.912023
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.795819 Loss1: 0.379158 Loss2: 1.416661 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.750549 Loss1: 0.262503 Loss2: 1.488045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.632195 Loss1: 0.223334 Loss2: 1.408861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.534398 Loss1: 0.125605 Loss2: 1.408793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396331 Loss1: 0.102177 Loss2: 1.294154 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969866
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458787 Loss1: 0.070523 Loss2: 1.388264 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.483792 Loss1: 0.100572 Loss2: 1.383221 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 12:37:31,319][flwr][DEBUG] - fit_round 151 received 50 results and 0 failures
+INFO flwr 2023-10-12 12:38:13,715 | server.py:125 | fit progress: (151, 2.2425448860223303, {'accuracy': 0.5966}, 348401.49327949)
+>> Test accuracy: 0.596600
+[2023-10-12 12:38:13,715][flwr][INFO] - fit progress: (151, 2.2425448860223303, {'accuracy': 0.5966}, 348401.49327949)
+DEBUG flwr 2023-10-12 12:38:13,715 | server.py:173 | evaluate_round 151: strategy sampled 50 clients (out of 50)
+[2023-10-12 12:38:13,715][flwr][DEBUG] - evaluate_round 151: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 12:47:21,319 | server.py:187 | evaluate_round 151 received 50 results and 0 failures
+[2023-10-12 12:47:21,319][flwr][DEBUG] - evaluate_round 151 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 12:47:21,319 | server.py:222 | fit_round 152: strategy sampled 50 clients (out of 50)
+[2023-10-12 12:47:21,319][flwr][DEBUG] - fit_round 152: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.651230 Loss1: 0.659511 Loss2: 1.991718
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.793533 Loss1: 0.426721 Loss2: 1.366811
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.660700 Loss1: 0.259889 Loss2: 1.400811
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.606571 Loss1: 0.214880 Loss2: 1.391690
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525602 Loss1: 0.155602 Loss2: 1.370000
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.747036 Loss1: 0.376578 Loss2: 1.370458 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437388 Loss1: 0.085084 Loss2: 1.352304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.514405 Loss1: 0.143035 Loss2: 1.371370 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495690 Loss1: 0.125455 Loss2: 1.370235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441042 Loss1: 0.082006 Loss2: 1.359037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402859 Loss1: 0.047229 Loss2: 1.355630 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384658 Loss1: 0.037358 Loss2: 1.347299 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.468521 Loss1: 0.164643 Loss2: 1.303878 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457025 Loss1: 0.160458 Loss2: 1.296567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.385430 Loss1: 0.501956 Loss2: 1.883473
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402357 Loss1: 0.103555 Loss2: 1.298802
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.597400 Loss1: 0.228137 Loss2: 1.369263
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380962 Loss1: 0.090655 Loss2: 1.290307
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.523039 Loss1: 0.156260 Loss2: 1.366778
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385016 Loss1: 0.097509 Loss2: 1.287507
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.451518 Loss1: 0.088924 Loss2: 1.362594
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407555 Loss1: 0.118378 Loss2: 1.289177
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.430909 Loss1: 0.089423 Loss2: 1.341486 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418389 Loss1: 0.087030 Loss2: 1.331359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404044 Loss1: 0.072726 Loss2: 1.331318
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.359818 Loss1: 0.550214 Loss2: 1.809604
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430930 Loss1: 0.092070 Loss2: 1.338861
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.610927 Loss1: 0.286319 Loss2: 1.324607
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526780 Loss1: 0.182367 Loss2: 1.344413
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.455214 Loss1: 0.141606 Loss2: 1.313609
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.403488 Loss1: 0.094730 Loss2: 1.308759
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473363 Loss1: 0.162310 Loss2: 1.311053
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.527772 Loss1: 0.646228 Loss2: 1.881543
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464639 Loss1: 0.126492 Loss2: 1.338147
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.752937 Loss1: 0.354182 Loss2: 1.398754
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439422 Loss1: 0.128723 Loss2: 1.310700
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.683285 Loss1: 0.260394 Loss2: 1.422891
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418857 Loss1: 0.114763 Loss2: 1.304095
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.549316 Loss1: 0.152252 Loss2: 1.397063
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402200 Loss1: 0.094909 Loss2: 1.307291
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502185 Loss1: 0.122950 Loss2: 1.379235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450204 Loss1: 0.088061 Loss2: 1.362143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427475 Loss1: 0.064862 Loss2: 1.362613
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.319790 Loss1: 0.465669 Loss2: 1.854121
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.656975 Loss1: 0.288957 Loss2: 1.368018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573143 Loss1: 0.200394 Loss2: 1.372749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497039 Loss1: 0.120731 Loss2: 1.376309 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539837 Loss1: 0.179402 Loss2: 1.360435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.494727 Loss1: 0.130169 Loss2: 1.364558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432051 Loss1: 0.078111 Loss2: 1.353939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413934 Loss1: 0.067158 Loss2: 1.346776 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546910 Loss1: 0.124924 Loss2: 1.421986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.513087 Loss1: 0.095251 Loss2: 1.417836 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.506836 Loss1: 0.095649 Loss2: 1.411187
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.390103 Loss1: 0.551926 Loss2: 1.838177
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464303 Loss1: 0.059803 Loss2: 1.404500
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.699423 Loss1: 0.349772 Loss2: 1.349652
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.700258 Loss1: 0.295527 Loss2: 1.404731
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570460 Loss1: 0.208312 Loss2: 1.362148
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.587926 Loss1: 0.222434 Loss2: 1.365492
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.534191 Loss1: 0.167874 Loss2: 1.366317
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451216 Loss1: 0.105812 Loss2: 1.345405
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.344622 Loss1: 0.474481 Loss2: 1.870141
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401446 Loss1: 0.058923 Loss2: 1.342523
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.663636 Loss1: 0.315974 Loss2: 1.347662
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403513 Loss1: 0.067727 Loss2: 1.335786
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.634989 Loss1: 0.234079 Loss2: 1.400910
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411970 Loss1: 0.079259 Loss2: 1.332711
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567735 Loss1: 0.215878 Loss2: 1.351857
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513842 Loss1: 0.159125 Loss2: 1.354717
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479657 Loss1: 0.126406 Loss2: 1.353251
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434492 Loss1: 0.089484 Loss2: 1.345008
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406462 Loss1: 0.066609 Loss2: 1.339853
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396839 Loss1: 0.065157 Loss2: 1.331681
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.304292 Loss1: 0.491942 Loss2: 1.812350
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367565 Loss1: 0.040889 Loss2: 1.326675
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.657267 Loss1: 0.326371 Loss2: 1.330896
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.653366 Loss1: 0.273913 Loss2: 1.379454
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561737 Loss1: 0.201727 Loss2: 1.360011
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468296 Loss1: 0.122325 Loss2: 1.345971
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.419253 Loss1: 0.083570 Loss2: 1.335683
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.533485 Loss1: 0.639335 Loss2: 1.894150
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.413178 Loss1: 0.083500 Loss2: 1.329678
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.815400 Loss1: 0.423900 Loss2: 1.391500
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.379558 Loss1: 0.051154 Loss2: 1.328404
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.744657 Loss1: 0.295951 Loss2: 1.448706
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.375866 Loss1: 0.052086 Loss2: 1.323781
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579122 Loss1: 0.188464 Loss2: 1.390658
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390703 Loss1: 0.072417 Loss2: 1.318286
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500728 Loss1: 0.119175 Loss2: 1.381553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458507 Loss1: 0.085583 Loss2: 1.372924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479200 Loss1: 0.110172 Loss2: 1.369028
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.453560 Loss1: 0.560460 Loss2: 1.893100
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460527 Loss1: 0.086583 Loss2: 1.373944
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.701059 Loss1: 0.314290 Loss2: 1.386770
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618155 Loss1: 0.210735 Loss2: 1.407420
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.571398 Loss1: 0.202049 Loss2: 1.369349
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.542440 Loss1: 0.167226 Loss2: 1.375214
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506272 Loss1: 0.137756 Loss2: 1.368516
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444404 Loss1: 0.086946 Loss2: 1.357458
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.476679 Loss1: 0.581962 Loss2: 1.894717
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427877 Loss1: 0.078754 Loss2: 1.349123
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.694317 Loss1: 0.318456 Loss2: 1.375861
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.420185 Loss1: 0.071976 Loss2: 1.348209
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670342 Loss1: 0.255645 Loss2: 1.414697
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402186 Loss1: 0.060330 Loss2: 1.341856
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576776 Loss1: 0.186489 Loss2: 1.390287
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.553607 Loss1: 0.173094 Loss2: 1.380513
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.568431 Loss1: 0.180619 Loss2: 1.387813
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.514314 Loss1: 0.133954 Loss2: 1.380360
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483615 Loss1: 0.108307 Loss2: 1.375307
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.469802 Loss1: 0.097393 Loss2: 1.372410
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.470287 Loss1: 0.589942 Loss2: 1.880346
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.469415 Loss1: 0.090929 Loss2: 1.378486
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.721354 Loss1: 0.342767 Loss2: 1.378587
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610830 Loss1: 0.192967 Loss2: 1.417863
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564458 Loss1: 0.186957 Loss2: 1.377501
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534990 Loss1: 0.150661 Loss2: 1.384329
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.453965 Loss1: 0.081532 Loss2: 1.372433
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.406232 Loss1: 0.548709 Loss2: 1.857523
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440817 Loss1: 0.079150 Loss2: 1.361667
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.780594 Loss1: 0.410481 Loss2: 1.370112
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417578 Loss1: 0.061361 Loss2: 1.356217
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614121 Loss1: 0.208233 Loss2: 1.405888
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424129 Loss1: 0.065574 Loss2: 1.358556
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498676 Loss1: 0.133765 Loss2: 1.364911
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391699 Loss1: 0.046127 Loss2: 1.345572
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478952 Loss1: 0.119924 Loss2: 1.359028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.456644 Loss1: 0.106973 Loss2: 1.349670 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422985 Loss1: 0.068644 Loss2: 1.354341
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.373639 Loss1: 0.567691 Loss2: 1.805949
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401069 Loss1: 0.052491 Loss2: 1.348578
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.661826 Loss1: 0.327323 Loss2: 1.334503
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.585712 Loss1: 0.238692 Loss2: 1.347020
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467790 Loss1: 0.140471 Loss2: 1.327319
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.391318 Loss1: 0.069094 Loss2: 1.322224
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.381296 Loss1: 0.073108 Loss2: 1.308189
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.369162 Loss1: 0.067424 Loss2: 1.301738
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.417609 Loss1: 0.520862 Loss2: 1.896747
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.373361 Loss1: 0.072135 Loss2: 1.301227
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.793586 Loss1: 0.360619 Loss2: 1.432967
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.376236 Loss1: 0.077229 Loss2: 1.299006
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.657579 Loss1: 0.194684 Loss2: 1.462895
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.336560 Loss1: 0.044619 Loss2: 1.291941
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540815 Loss1: 0.114423 Loss2: 1.426392
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.540006 Loss1: 0.120967 Loss2: 1.419039
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.514562 Loss1: 0.095436 Loss2: 1.419126
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504633 Loss1: 0.095918 Loss2: 1.408715
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452557 Loss1: 0.042803 Loss2: 1.409753
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453762 Loss1: 0.054738 Loss2: 1.399024
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.493958 Loss1: 0.681950 Loss2: 1.812009
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441050 Loss1: 0.048629 Loss2: 1.392421
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.716522 Loss1: 0.355976 Loss2: 1.360546
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595674 Loss1: 0.215030 Loss2: 1.380644
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.519154 Loss1: 0.175971 Loss2: 1.343183
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.464479 Loss1: 0.119928 Loss2: 1.344551
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.432781 Loss1: 0.098589 Loss2: 1.334193
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.379827 Loss1: 0.459506 Loss2: 1.920321
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422042 Loss1: 0.091124 Loss2: 1.330918
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.740546 Loss1: 0.323006 Loss2: 1.417540
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.408658 Loss1: 0.075949 Loss2: 1.332709
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.704954 Loss1: 0.253728 Loss2: 1.451226
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397879 Loss1: 0.075989 Loss2: 1.321890
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.612591 Loss1: 0.188021 Loss2: 1.424570
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426645 Loss1: 0.103229 Loss2: 1.323416
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527792 Loss1: 0.111840 Loss2: 1.415952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481959 Loss1: 0.078424 Loss2: 1.403535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462396 Loss1: 0.062608 Loss2: 1.399788
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.418470 Loss1: 0.539489 Loss2: 1.878980
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444588 Loss1: 0.055995 Loss2: 1.388593
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.811562 Loss1: 0.428022 Loss2: 1.383540
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.703289 Loss1: 0.254957 Loss2: 1.448332
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572124 Loss1: 0.176344 Loss2: 1.395779
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.610307 Loss1: 0.203892 Loss2: 1.406414
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.541901 Loss1: 0.148732 Loss2: 1.393169
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.478564 Loss1: 0.559418 Loss2: 1.919146
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.554183 Loss1: 0.167963 Loss2: 1.386220
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486637 Loss1: 0.092961 Loss2: 1.393676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424019 Loss1: 0.050537 Loss2: 1.373482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404522 Loss1: 0.036994 Loss2: 1.367529 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499700 Loss1: 0.124124 Loss2: 1.375577 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.494180 Loss1: 0.127536 Loss2: 1.366643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430795 Loss1: 0.066396 Loss2: 1.364399 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.636477 Loss1: 0.201238 Loss2: 1.435239 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.594659 Loss1: 0.183268 Loss2: 1.411391 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.463992 Loss1: 0.581314 Loss2: 1.882678
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.624620 Loss1: 0.207441 Loss2: 1.417179
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.707237 Loss1: 0.343250 Loss2: 1.363987
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.536693 Loss1: 0.135123 Loss2: 1.401570
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.466355 Loss1: 0.066291 Loss2: 1.400065 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448670 Loss1: 0.061385 Loss2: 1.387285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453424 Loss1: 0.073317 Loss2: 1.380107 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447223 Loss1: 0.105964 Loss2: 1.341259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446508 Loss1: 0.096643 Loss2: 1.349865 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429851 Loss1: 0.092345 Loss2: 1.337506
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.456508 Loss1: 0.604789 Loss2: 1.851719
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.741320 Loss1: 0.405660 Loss2: 1.335660
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656496 Loss1: 0.265617 Loss2: 1.390879
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517813 Loss1: 0.178333 Loss2: 1.339480
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.456297 Loss1: 0.127483 Loss2: 1.328814
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.486041 Loss1: 0.598241 Loss2: 1.887800
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.402068 Loss1: 0.077793 Loss2: 1.324275
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.798591 Loss1: 0.388662 Loss2: 1.409929
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.394043 Loss1: 0.077660 Loss2: 1.316383
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.374960 Loss1: 0.066372 Loss2: 1.308587
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.686647 Loss1: 0.232086 Loss2: 1.454561
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.387556 Loss1: 0.079714 Loss2: 1.307843
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.664153 Loss1: 0.269186 Loss2: 1.394967
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387906 Loss1: 0.083979 Loss2: 1.303927
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.641687 Loss1: 0.219136 Loss2: 1.422551
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560648 Loss1: 0.167053 Loss2: 1.393594
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.496294 Loss1: 0.107170 Loss2: 1.389125
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.451631 Loss1: 0.066457 Loss2: 1.385174
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439987 Loss1: 0.070954 Loss2: 1.369033
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415503 Loss1: 0.048223 Loss2: 1.367280
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.272902 Loss1: 0.458255 Loss2: 1.814647
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.646203 Loss1: 0.291825 Loss2: 1.354378
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.585006 Loss1: 0.201528 Loss2: 1.383478
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.480142 Loss1: 0.128996 Loss2: 1.351146
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443677 Loss1: 0.097035 Loss2: 1.346642
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.412019 Loss1: 0.524465 Loss2: 1.887554
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.726745 Loss1: 0.345133 Loss2: 1.381611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.702842 Loss1: 0.282591 Loss2: 1.420251 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595368 Loss1: 0.211839 Loss2: 1.383530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.564451 Loss1: 0.176491 Loss2: 1.387960 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410144 Loss1: 0.074594 Loss2: 1.335550
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502985 Loss1: 0.126406 Loss2: 1.376579
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.507774 Loss1: 0.129392 Loss2: 1.378382
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438332 Loss1: 0.067820 Loss2: 1.370513
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422735 Loss1: 0.055225 Loss2: 1.367510
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387683 Loss1: 0.030839 Loss2: 1.356844
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.449997 Loss1: 0.563119 Loss2: 1.886878
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.744936 Loss1: 0.358837 Loss2: 1.386099
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615728 Loss1: 0.200379 Loss2: 1.415349
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.568966 Loss1: 0.192364 Loss2: 1.376602
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.508082 Loss1: 0.121984 Loss2: 1.386098
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.490745 Loss1: 0.116237 Loss2: 1.374508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448830 Loss1: 0.075733 Loss2: 1.373098 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453775 Loss1: 0.084677 Loss2: 1.369097 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465024 Loss1: 0.098401 Loss2: 1.366622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529601 Loss1: 0.138106 Loss2: 1.391495
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405788 Loss1: 0.043645 Loss2: 1.362143
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.512170 Loss1: 0.121170 Loss2: 1.390999 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454715 Loss1: 0.072109 Loss2: 1.382605 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448760 Loss1: 0.074857 Loss2: 1.373904 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989890
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.674863 Loss1: 0.266367 Loss2: 1.408496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506043 Loss1: 0.149125 Loss2: 1.356918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484784 Loss1: 0.138837 Loss2: 1.345948
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.330621 Loss1: 0.484250 Loss2: 1.846371
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.740988 Loss1: 0.385982 Loss2: 1.355005 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.647384 Loss1: 0.233866 Loss2: 1.413518 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536577 Loss1: 0.176384 Loss2: 1.360193 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.481982 Loss1: 0.129184 Loss2: 1.352798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436346 Loss1: 0.090534 Loss2: 1.345813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388755 Loss1: 0.054275 Loss2: 1.334480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400145 Loss1: 0.068144 Loss2: 1.332001 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609008 Loss1: 0.263269 Loss2: 1.345739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475276 Loss1: 0.157642 Loss2: 1.317634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.411680 Loss1: 0.505726 Loss2: 1.905954 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.727576 Loss1: 0.345288 Loss2: 1.382288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.623729 Loss1: 0.197007 Loss2: 1.426723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555420 Loss1: 0.182686 Loss2: 1.372733 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477108 Loss1: 0.096171 Loss2: 1.380937 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449046 Loss1: 0.094580 Loss2: 1.354466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415104 Loss1: 0.062920 Loss2: 1.352184
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.457615 Loss1: 0.614212 Loss2: 1.843403
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.723902 Loss1: 0.363532 Loss2: 1.360370 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533184 Loss1: 0.176610 Loss2: 1.356575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462161 Loss1: 0.115760 Loss2: 1.346401 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463850 Loss1: 0.125386 Loss2: 1.338464 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426889 Loss1: 0.086933 Loss2: 1.339956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.462890 Loss1: 0.165086 Loss2: 1.297804
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417915 Loss1: 0.086866 Loss2: 1.331049
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.429232 Loss1: 0.151330 Loss2: 1.277902
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381644 Loss1: 0.049675 Loss2: 1.331969
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.326440 Loss1: 0.058183 Loss2: 1.268257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.302015 Loss1: 0.048578 Loss2: 1.253438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.319672 Loss1: 0.473006 Loss2: 1.846666
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.306396 Loss1: 0.058028 Loss2: 1.248368
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.640708 Loss1: 0.256962 Loss2: 1.383746
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.298350 Loss1: 0.053711 Loss2: 1.244639
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506229 Loss1: 0.135181 Loss2: 1.371048 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480610 Loss1: 0.114094 Loss2: 1.366516 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.419702 Loss1: 0.549147 Loss2: 1.870555
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.494302 Loss1: 0.123523 Loss2: 1.370779
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.694678 Loss1: 0.327393 Loss2: 1.367284
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432802 Loss1: 0.062236 Loss2: 1.370566
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.591242 Loss1: 0.207487 Loss2: 1.383756
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403673 Loss1: 0.042681 Loss2: 1.360993
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507000 Loss1: 0.148452 Loss2: 1.358549
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381768 Loss1: 0.028295 Loss2: 1.353473
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477741 Loss1: 0.118912 Loss2: 1.358829 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.395182 Loss1: 0.053158 Loss2: 1.342024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389677 Loss1: 0.049566 Loss2: 1.340112
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.376305 Loss1: 0.548765 Loss2: 1.827539
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.674719 Loss1: 0.320606 Loss2: 1.354113 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.548850 Loss1: 0.196634 Loss2: 1.352216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443835 Loss1: 0.099161 Loss2: 1.344675 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422493 Loss1: 0.089491 Loss2: 1.333002 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389131 Loss1: 0.064844 Loss2: 1.324287 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444531 Loss1: 0.119054 Loss2: 1.325477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437684 Loss1: 0.096421 Loss2: 1.341263 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530618 Loss1: 0.109236 Loss2: 1.421382 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.505089 Loss1: 0.095471 Loss2: 1.409617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.603898 Loss1: 0.646845 Loss2: 1.957053
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470530 Loss1: 0.068840 Loss2: 1.401690
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.838767 Loss1: 0.350748 Loss2: 1.488019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.615927 Loss1: 0.191931 Loss2: 1.423996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.368802 Loss1: 0.535011 Loss2: 1.833790 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.622183 Loss1: 0.282881 Loss2: 1.339302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588561 Loss1: 0.221213 Loss2: 1.367348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.501179 Loss1: 0.153380 Loss2: 1.347799 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.497593 Loss1: 0.159110 Loss2: 1.338483 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412725 Loss1: 0.086474 Loss2: 1.326251 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427161 Loss1: 0.098301 Loss2: 1.328861
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.364659 Loss1: 0.467256 Loss2: 1.897402
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.657282 Loss1: 0.249415 Loss2: 1.407867 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563240 Loss1: 0.159918 Loss2: 1.403322 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.560806 Loss1: 0.155068 Loss2: 1.405739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.506332 Loss1: 0.115085 Loss2: 1.391247 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.543332 Loss1: 0.146854 Loss2: 1.396478 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.509928 Loss1: 0.120101 Loss2: 1.389828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.486239 Loss1: 0.104122 Loss2: 1.382117 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440344 Loss1: 0.106790 Loss2: 1.333554 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408354 Loss1: 0.073203 Loss2: 1.335151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391633 Loss1: 0.064477 Loss2: 1.327156
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.416583 Loss1: 0.574679 Loss2: 1.841904
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.738457 Loss1: 0.357952 Loss2: 1.380504
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.642267 Loss1: 0.251428 Loss2: 1.390839
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526241 Loss1: 0.162367 Loss2: 1.363874
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.539546 Loss1: 0.178194 Loss2: 1.361352
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.579991 Loss1: 0.642716 Loss2: 1.937275
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.474915 Loss1: 0.111743 Loss2: 1.363173
+DEBUG flwr 2023-10-12 13:15:40,621 | server.py:236 | fit_round 152 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474375 Loss1: 0.122431 Loss2: 1.351944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419886 Loss1: 0.073741 Loss2: 1.346145 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407237 Loss1: 0.067081 Loss2: 1.340156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409438 Loss1: 0.074780 Loss2: 1.334658 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382840 Loss1: 0.065156 Loss2: 1.317684 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383309 Loss1: 0.075520 Loss2: 1.307789 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.769057 Loss1: 0.417960 Loss2: 1.351097 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586932 Loss1: 0.210885 Loss2: 1.376047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.430049 Loss1: 0.535284 Loss2: 1.894765 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.697514 Loss1: 0.303084 Loss2: 1.394429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.645078 Loss1: 0.226230 Loss2: 1.418847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385408 Loss1: 0.059717 Loss2: 1.325691 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387862 Loss1: 0.060998 Loss2: 1.326864 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530588 Loss1: 0.137044 Loss2: 1.393544 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467074 Loss1: 0.084550 Loss2: 1.382524 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421295 Loss1: 0.054719 Loss2: 1.366576
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.372429 Loss1: 0.483905 Loss2: 1.888524
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.732988 Loss1: 0.318101 Loss2: 1.414886
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.661865 Loss1: 0.204067 Loss2: 1.457798
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535675 Loss1: 0.122723 Loss2: 1.412952
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531488 Loss1: 0.127704 Loss2: 1.403784
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.343088 Loss1: 0.519204 Loss2: 1.823884
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516611 Loss1: 0.106580 Loss2: 1.410030
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.494135 Loss1: 0.101334 Loss2: 1.392801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481894 Loss1: 0.080775 Loss2: 1.401119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.468719 Loss1: 0.074308 Loss2: 1.394412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459868 Loss1: 0.071926 Loss2: 1.387942 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.383040 Loss1: 0.049608 Loss2: 1.333432 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.342222 Loss1: 0.024778 Loss2: 1.317444 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 13:15:40,621][flwr][DEBUG] - fit_round 152 received 50 results and 0 failures
+INFO flwr 2023-10-12 13:16:22,588 | server.py:125 | fit progress: (152, 2.2384562981776157, {'accuracy': 0.5963}, 350690.366838993)
+>> Test accuracy: 0.596300
+[2023-10-12 13:16:22,588][flwr][INFO] - fit progress: (152, 2.2384562981776157, {'accuracy': 0.5963}, 350690.366838993)
+DEBUG flwr 2023-10-12 13:16:22,589 | server.py:173 | evaluate_round 152: strategy sampled 50 clients (out of 50)
+[2023-10-12 13:16:22,589][flwr][DEBUG] - evaluate_round 152: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 13:25:24,835 | server.py:187 | evaluate_round 152 received 50 results and 0 failures
+[2023-10-12 13:25:24,835][flwr][DEBUG] - evaluate_round 152 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 13:25:24,836 | server.py:222 | fit_round 153: strategy sampled 50 clients (out of 50)
+[2023-10-12 13:25:24,836][flwr][DEBUG] - fit_round 153: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.632526 Loss1: 0.652354 Loss2: 1.980173 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.675564 Loss1: 0.288148 Loss2: 1.387416 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.522478 Loss1: 0.160526 Loss2: 1.361953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488491 Loss1: 0.131740 Loss2: 1.356751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440138 Loss1: 0.087116 Loss2: 1.353022 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414429 Loss1: 0.061312 Loss2: 1.353117
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.602628 Loss1: 0.201584 Loss2: 1.401044
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.485761 Loss1: 0.116055 Loss2: 1.369705 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998698
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.465046 Loss1: 0.104751 Loss2: 1.360295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.388065 Loss1: 0.044318 Loss2: 1.343747 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404054 Loss1: 0.061018 Loss2: 1.343036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398721 Loss1: 0.056708 Loss2: 1.342013 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582122 Loss1: 0.208548 Loss2: 1.373574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.482684 Loss1: 0.145507 Loss2: 1.337178 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472786 Loss1: 0.133974 Loss2: 1.338812
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.406466 Loss1: 0.485417 Loss2: 1.921049
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.751206 Loss1: 0.327682 Loss2: 1.423523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.673503 Loss1: 0.215372 Loss2: 1.458130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.668779 Loss1: 0.232995 Loss2: 1.435784 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.598179 Loss1: 0.171390 Loss2: 1.426788 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.524360 Loss1: 0.104731 Loss2: 1.419629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.501155 Loss1: 0.094598 Loss2: 1.406557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478637 Loss1: 0.071075 Loss2: 1.407562 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.598975 Loss1: 0.177906 Loss2: 1.421069 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480144 Loss1: 0.072375 Loss2: 1.407768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.374971 Loss1: 0.584633 Loss2: 1.790338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.685967 Loss1: 0.356747 Loss2: 1.329220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.545879 Loss1: 0.187026 Loss2: 1.358853 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995192
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.430415 Loss1: 0.116105 Loss2: 1.314311 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.380238 Loss1: 0.069866 Loss2: 1.310373 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.360593 Loss1: 0.060211 Loss2: 1.300382
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.281173 Loss1: 0.473851 Loss2: 1.807322
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.587097 Loss1: 0.254756 Loss2: 1.332341 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.553175 Loss1: 0.191831 Loss2: 1.361343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503591 Loss1: 0.159370 Loss2: 1.344221 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431169 Loss1: 0.103614 Loss2: 1.327555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.363869 Loss1: 0.039176 Loss2: 1.324693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.338425 Loss1: 0.029735 Loss2: 1.308690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.335061 Loss1: 0.030707 Loss2: 1.304353 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482871 Loss1: 0.142901 Loss2: 1.339969 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436049 Loss1: 0.097131 Loss2: 1.338918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.424436 Loss1: 0.512659 Loss2: 1.911777
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405793 Loss1: 0.078527 Loss2: 1.327265
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784198 Loss1: 0.374755 Loss2: 1.409443
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388244 Loss1: 0.064328 Loss2: 1.323917
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541118 Loss1: 0.133689 Loss2: 1.407429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.549143 Loss1: 0.152889 Loss2: 1.396255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.548787 Loss1: 0.139470 Loss2: 1.409317
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.249482 Loss1: 0.419398 Loss2: 1.830084
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.628587 Loss1: 0.265669 Loss2: 1.362917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572416 Loss1: 0.182911 Loss2: 1.389506 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.482453 Loss1: 0.121518 Loss2: 1.360935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.442362 Loss1: 0.082858 Loss2: 1.359504 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.289658 Loss1: 0.501515 Loss2: 1.788143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.845610 Loss1: 0.531992 Loss2: 1.313618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.695789 Loss1: 0.298967 Loss2: 1.396823 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995404
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.552041 Loss1: 0.241321 Loss2: 1.310720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463734 Loss1: 0.164214 Loss2: 1.299520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.406231 Loss1: 0.110548 Loss2: 1.295684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.418240 Loss1: 0.532345 Loss2: 1.885895
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.383573 Loss1: 0.089333 Loss2: 1.294240
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.672031 Loss1: 0.293788 Loss2: 1.378243
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369054 Loss1: 0.077958 Loss2: 1.291095
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.512261 Loss1: 0.128602 Loss2: 1.383659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482208 Loss1: 0.119449 Loss2: 1.362759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461166 Loss1: 0.096542 Loss2: 1.364624
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.392917 Loss1: 0.562802 Loss2: 1.830115
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.694148 Loss1: 0.337150 Loss2: 1.356997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.689714 Loss1: 0.267526 Loss2: 1.422187 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402628 Loss1: 0.045805 Loss2: 1.356823
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536969 Loss1: 0.179166 Loss2: 1.357803
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546230 Loss1: 0.187215 Loss2: 1.359015
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.532667 Loss1: 0.166347 Loss2: 1.366319
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.499790 Loss1: 0.145068 Loss2: 1.354722
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.477555 Loss1: 0.118225 Loss2: 1.359330
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.479117 Loss1: 0.601114 Loss2: 1.878003
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417780 Loss1: 0.067548 Loss2: 1.350232
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404566 Loss1: 0.065238 Loss2: 1.339327 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500905 Loss1: 0.158985 Loss2: 1.341920 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459069 Loss1: 0.122575 Loss2: 1.336494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437977 Loss1: 0.099539 Loss2: 1.338438
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.579927 Loss1: 0.636148 Loss2: 1.943779
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.820323 Loss1: 0.422620 Loss2: 1.397703 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410252 Loss1: 0.073081 Loss2: 1.337171
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.678735 Loss1: 0.231925 Loss2: 1.446810
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.614289 Loss1: 0.210303 Loss2: 1.403986
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.570887 Loss1: 0.177122 Loss2: 1.393765
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516554 Loss1: 0.116243 Loss2: 1.400311
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.465221 Loss1: 0.079117 Loss2: 1.386105
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.466907 Loss1: 0.085434 Loss2: 1.381474
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.282405 Loss1: 0.476938 Loss2: 1.805467
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469030 Loss1: 0.087486 Loss2: 1.381544
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.634963 Loss1: 0.324458 Loss2: 1.310505
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442707 Loss1: 0.067647 Loss2: 1.375059
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.524562 Loss1: 0.181527 Loss2: 1.343034
+(DefaultActor pid=3765) >> Training accuracy: 0.994420
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.473860 Loss1: 0.160369 Loss2: 1.313490
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.429238 Loss1: 0.114211 Loss2: 1.315027
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420966 Loss1: 0.107904 Loss2: 1.313062
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.374200 Loss1: 0.074182 Loss2: 1.300018
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.352120 Loss1: 0.055476 Loss2: 1.296643
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.297878 Loss1: 0.504452 Loss2: 1.793426
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354517 Loss1: 0.063038 Loss2: 1.291478
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572784 Loss1: 0.260231 Loss2: 1.312553
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.333735 Loss1: 0.046869 Loss2: 1.286866
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.577557 Loss1: 0.228931 Loss2: 1.348626
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.519151 Loss1: 0.190834 Loss2: 1.328317
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.455769 Loss1: 0.138347 Loss2: 1.317422
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444765 Loss1: 0.126617 Loss2: 1.318148
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475058 Loss1: 0.166561 Loss2: 1.308497
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.494835 Loss1: 0.166238 Loss2: 1.328597
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.370588 Loss1: 0.557456 Loss2: 1.813132
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435959 Loss1: 0.122693 Loss2: 1.313266
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.664163 Loss1: 0.328105 Loss2: 1.336058
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388332 Loss1: 0.079066 Loss2: 1.309266
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.575972 Loss1: 0.209951 Loss2: 1.366021
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.464217 Loss1: 0.141180 Loss2: 1.323037
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.438073 Loss1: 0.121129 Loss2: 1.316944
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459978 Loss1: 0.143458 Loss2: 1.316520
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415626 Loss1: 0.099550 Loss2: 1.316076
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374440 Loss1: 0.066480 Loss2: 1.307961
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.424776 Loss1: 0.570110 Loss2: 1.854666
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.701389 Loss1: 0.342901 Loss2: 1.358488
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.375580 Loss1: 0.076991 Loss2: 1.298589
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.562985 Loss1: 0.180750 Loss2: 1.382235
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354317 Loss1: 0.050471 Loss2: 1.303846
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536207 Loss1: 0.166913 Loss2: 1.369294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.437576 Loss1: 0.086958 Loss2: 1.350618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.428967 Loss1: 0.078073 Loss2: 1.350894
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.496491 Loss1: 0.591380 Loss2: 1.905111
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396377 Loss1: 0.052611 Loss2: 1.343766
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.691030 Loss1: 0.328382 Loss2: 1.362649
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.741148 Loss1: 0.344948 Loss2: 1.396201
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389404 Loss1: 0.054096 Loss2: 1.335308
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515065 Loss1: 0.155765 Loss2: 1.359300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478975 Loss1: 0.123888 Loss2: 1.355088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.286539 Loss1: 0.448565 Loss2: 1.837973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.723750 Loss1: 0.344511 Loss2: 1.379239 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.646631 Loss1: 0.256701 Loss2: 1.389930 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505542 Loss1: 0.120415 Loss2: 1.385127 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466514 Loss1: 0.083211 Loss2: 1.383303
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.282894 Loss1: 0.532576 Loss2: 1.750318
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444811 Loss1: 0.073672 Loss2: 1.371139
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.669101 Loss1: 0.353098 Loss2: 1.316003
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449835 Loss1: 0.080876 Loss2: 1.368959
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.609604 Loss1: 0.250000 Loss2: 1.359604
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461625 Loss1: 0.092571 Loss2: 1.369054
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.466663 Loss1: 0.154660 Loss2: 1.312003
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.437988 Loss1: 0.122998 Loss2: 1.314990
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.406687 Loss1: 0.092382 Loss2: 1.314306
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426393 Loss1: 0.121890 Loss2: 1.304503
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411182 Loss1: 0.100603 Loss2: 1.310579
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.399417 Loss1: 0.506174 Loss2: 1.893242
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445527 Loss1: 0.139862 Loss2: 1.305666
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405660 Loss1: 0.100150 Loss2: 1.305511 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522386 Loss1: 0.143789 Loss2: 1.378597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444899 Loss1: 0.086740 Loss2: 1.358159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409279 Loss1: 0.054503 Loss2: 1.354776
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.475350 Loss1: 0.637212 Loss2: 1.838138
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.749461 Loss1: 0.385808 Loss2: 1.363653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604038 Loss1: 0.196565 Loss2: 1.407473 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561885 Loss1: 0.204012 Loss2: 1.357874 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496578 Loss1: 0.148765 Loss2: 1.347813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488222 Loss1: 0.140889 Loss2: 1.347333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440040 Loss1: 0.097823 Loss2: 1.342217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389708 Loss1: 0.054648 Loss2: 1.335060 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.553415 Loss1: 0.203181 Loss2: 1.350234 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477843 Loss1: 0.126290 Loss2: 1.351553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.249226 Loss1: 0.468477 Loss2: 1.780749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.632800 Loss1: 0.303028 Loss2: 1.329772 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.518338 Loss1: 0.165877 Loss2: 1.352461 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.419118 Loss1: 0.096425 Loss2: 1.322693 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.398384 Loss1: 0.085236 Loss2: 1.313148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.343908 Loss1: 0.523544 Loss2: 1.820364
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381530 Loss1: 0.067546 Loss2: 1.313983
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.648475 Loss1: 0.303301 Loss2: 1.345174
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388406 Loss1: 0.078497 Loss2: 1.309909
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551730 Loss1: 0.190950 Loss2: 1.360780
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362262 Loss1: 0.053961 Loss2: 1.308301
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.438281 Loss1: 0.120000 Loss2: 1.318281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.396156 Loss1: 0.081911 Loss2: 1.314245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.368120 Loss1: 0.060308 Loss2: 1.307811
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.393856 Loss1: 0.549978 Loss2: 1.843878
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.645953 Loss1: 0.292747 Loss2: 1.353206 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636758 Loss1: 0.244724 Loss2: 1.392034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.499572 Loss1: 0.149989 Loss2: 1.349583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445335 Loss1: 0.101295 Loss2: 1.344040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420011 Loss1: 0.081715 Loss2: 1.338296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428792 Loss1: 0.100062 Loss2: 1.328730 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400810 Loss1: 0.068990 Loss2: 1.331819 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465462 Loss1: 0.117127 Loss2: 1.348335 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.386972 Loss1: 0.061877 Loss2: 1.325096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371533 Loss1: 0.052396 Loss2: 1.319137
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.404246 Loss1: 0.549075 Loss2: 1.855170
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366854 Loss1: 0.053755 Loss2: 1.313099
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.589509 Loss1: 0.259250 Loss2: 1.330259
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.491344 Loss1: 0.160105 Loss2: 1.331239
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.427328 Loss1: 0.115852 Loss2: 1.311476
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.424153 Loss1: 0.117396 Loss2: 1.306756
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.387091 Loss1: 0.075770 Loss2: 1.311321
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.368391 Loss1: 0.441862 Loss2: 1.926528
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.393021 Loss1: 0.086360 Loss2: 1.306661
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.375802 Loss1: 0.071808 Loss2: 1.303994 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376218 Loss1: 0.078596 Loss2: 1.297622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.358067 Loss1: 0.064979 Loss2: 1.293088 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524590 Loss1: 0.113407 Loss2: 1.411183 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474980 Loss1: 0.077356 Loss2: 1.397624 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425802 Loss1: 0.046403 Loss2: 1.379399
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.286224 Loss1: 0.483382 Loss2: 1.802841
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612020 Loss1: 0.289275 Loss2: 1.322745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.504242 Loss1: 0.180444 Loss2: 1.323798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502550 Loss1: 0.164253 Loss2: 1.338297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416230 Loss1: 0.095312 Loss2: 1.320919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.384349 Loss1: 0.070601 Loss2: 1.313748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372896 Loss1: 0.063870 Loss2: 1.309026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.345205 Loss1: 0.042740 Loss2: 1.302465 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.510183 Loss1: 0.148054 Loss2: 1.362129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497384 Loss1: 0.145405 Loss2: 1.351979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.260447 Loss1: 0.481030 Loss2: 1.779417 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.689799 Loss1: 0.371521 Loss2: 1.318278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.510797 Loss1: 0.182523 Loss2: 1.328274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.408075 Loss1: 0.087529 Loss2: 1.320547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.403678 Loss1: 0.090981 Loss2: 1.312697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389330 Loss1: 0.079340 Loss2: 1.309991 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369069 Loss1: 0.056560 Loss2: 1.312509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381786 Loss1: 0.082959 Loss2: 1.298826 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480458 Loss1: 0.099298 Loss2: 1.381160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499840 Loss1: 0.115840 Loss2: 1.384001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.587350 Loss1: 0.600941 Loss2: 1.986409 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.820374 Loss1: 0.305365 Loss2: 1.515009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.661860 Loss1: 0.168808 Loss2: 1.493052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.620189 Loss1: 0.134254 Loss2: 1.485935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.582512 Loss1: 0.093168 Loss2: 1.489344 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.548296 Loss1: 0.059437 Loss2: 1.488858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.544113 Loss1: 0.070023 Loss2: 1.474089 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509454 Loss1: 0.046785 Loss2: 1.462669 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.589595 Loss1: 0.181424 Loss2: 1.408172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489239 Loss1: 0.098120 Loss2: 1.391119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477103 Loss1: 0.079418 Loss2: 1.397685
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.426641 Loss1: 0.550840 Loss2: 1.875801
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.731016 Loss1: 0.364572 Loss2: 1.366444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.546180 Loss1: 0.182902 Loss2: 1.363278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.458599 Loss1: 0.102170 Loss2: 1.356429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439680 Loss1: 0.097493 Loss2: 1.342187 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.737882 Loss1: 0.387977 Loss2: 1.349905
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423708 Loss1: 0.080058 Loss2: 1.343650
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.550040 Loss1: 0.179759 Loss2: 1.370281
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409748 Loss1: 0.067572 Loss2: 1.342176
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.471437 Loss1: 0.144200 Loss2: 1.327236
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380722 Loss1: 0.045885 Loss2: 1.334838
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.415477 Loss1: 0.092692 Loss2: 1.322786 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.353446 Loss1: 0.042472 Loss2: 1.310974 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.302165 Loss1: 0.463229 Loss2: 1.838936
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.367617 Loss1: 0.068280 Loss2: 1.299337
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.743497 Loss1: 0.376893 Loss2: 1.366604
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367458 Loss1: 0.065837 Loss2: 1.301620
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.636949 Loss1: 0.260738 Loss2: 1.376211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.518501 Loss1: 0.159095 Loss2: 1.359406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497114 Loss1: 0.127173 Loss2: 1.369941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450131 Loss1: 0.095170 Loss2: 1.354961 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427139 Loss1: 0.078134 Loss2: 1.349005 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417454 Loss1: 0.078406 Loss2: 1.339048 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514575 Loss1: 0.074939 Loss2: 1.439636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.485685 Loss1: 0.062676 Loss2: 1.423009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.448419 Loss1: 0.569699 Loss2: 1.878719 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.655119 Loss1: 0.201069 Loss2: 1.454050 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.553127 Loss1: 0.151177 Loss2: 1.401950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.535149 Loss1: 0.137807 Loss2: 1.397343
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.423228 Loss1: 0.582082 Loss2: 1.841146
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.670796 Loss1: 0.308702 Loss2: 1.362095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.651396 Loss1: 0.267124 Loss2: 1.384272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554201 Loss1: 0.199097 Loss2: 1.355104 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.485790 Loss1: 0.133667 Loss2: 1.352123 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460092 Loss1: 0.117219 Loss2: 1.342874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398937 Loss1: 0.070517 Loss2: 1.328420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384106 Loss1: 0.056873 Loss2: 1.327233 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.619726 Loss1: 0.233733 Loss2: 1.385993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492274 Loss1: 0.137359 Loss2: 1.354915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494047 Loss1: 0.143165 Loss2: 1.350881
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.291530 Loss1: 0.461757 Loss2: 1.829773
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.726669 Loss1: 0.338259 Loss2: 1.388410 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611427 Loss1: 0.192485 Loss2: 1.418942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557770 Loss1: 0.183221 Loss2: 1.374548 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503949 Loss1: 0.115189 Loss2: 1.388760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.430787 Loss1: 0.067335 Loss2: 1.363453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379921 Loss1: 0.030205 Loss2: 1.349716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374651 Loss1: 0.025453 Loss2: 1.349197 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478167 Loss1: 0.175488 Loss2: 1.302680 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.386009 Loss1: 0.097109 Loss2: 1.288900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.566369 Loss1: 0.566974 Loss2: 1.999396
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.369111 Loss1: 0.085216 Loss2: 1.283894
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.869082 Loss1: 0.381867 Loss2: 1.487215
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.350876 Loss1: 0.074290 Loss2: 1.276587
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.831428 Loss1: 0.293388 Loss2: 1.538040
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.309661 Loss1: 0.038453 Loss2: 1.271208
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.654769 Loss1: 0.162195 Loss2: 1.492574
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.312681 Loss1: 0.041430 Loss2: 1.271250
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.578056 Loss1: 0.097313 Loss2: 1.480743 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.594702 Loss1: 0.121672 Loss2: 1.473030 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.558322 Loss1: 0.086078 Loss2: 1.472245
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.366465 Loss1: 0.525944 Loss2: 1.840521
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.515602 Loss1: 0.051840 Loss2: 1.463762
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.668597 Loss1: 0.318339 Loss2: 1.350257
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.638626 Loss1: 0.228599 Loss2: 1.410027
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540751 Loss1: 0.179633 Loss2: 1.361118
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513647 Loss1: 0.162934 Loss2: 1.350714
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500221 Loss1: 0.139975 Loss2: 1.360246
+DEBUG flwr 2023-10-12 13:53:48,276 | server.py:236 | fit_round 153 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.405337 Loss1: 0.575100 Loss2: 1.830238
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465313 Loss1: 0.117616 Loss2: 1.347698
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.669386 Loss1: 0.322429 Loss2: 1.346956
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.444524 Loss1: 0.104494 Loss2: 1.340030
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.633418 Loss1: 0.250072 Loss2: 1.383347
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436857 Loss1: 0.093153 Loss2: 1.343704
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554691 Loss1: 0.187766 Loss2: 1.366926
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.504823 Loss1: 0.163625 Loss2: 1.341198
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530037 Loss1: 0.162751 Loss2: 1.367287 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450752 Loss1: 0.102884 Loss2: 1.347868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400781 Loss1: 0.060335 Loss2: 1.340445
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.395409 Loss1: 0.501850 Loss2: 1.893559
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389088 Loss1: 0.058565 Loss2: 1.330523
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.744131 Loss1: 0.364509 Loss2: 1.379622
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.717520 Loss1: 0.298435 Loss2: 1.419085
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.602615 Loss1: 0.220628 Loss2: 1.381987
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569018 Loss1: 0.181902 Loss2: 1.387116
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.507542 Loss1: 0.127697 Loss2: 1.379844
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491835 Loss1: 0.121137 Loss2: 1.370698
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439155 Loss1: 0.071004 Loss2: 1.368151
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419147 Loss1: 0.056251 Loss2: 1.362897
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394651 Loss1: 0.042356 Loss2: 1.352295
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 13:53:48,276][flwr][DEBUG] - fit_round 153 received 50 results and 0 failures
+INFO flwr 2023-10-12 13:54:29,172 | server.py:125 | fit progress: (153, 2.248646311485729, {'accuracy': 0.595}, 352976.950533177)
+>> Test accuracy: 0.595000
+[2023-10-12 13:54:29,172][flwr][INFO] - fit progress: (153, 2.248646311485729, {'accuracy': 0.595}, 352976.950533177)
+DEBUG flwr 2023-10-12 13:54:29,172 | server.py:173 | evaluate_round 153: strategy sampled 50 clients (out of 50)
+[2023-10-12 13:54:29,172][flwr][DEBUG] - evaluate_round 153: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 14:03:33,955 | server.py:187 | evaluate_round 153 received 50 results and 0 failures
+[2023-10-12 14:03:33,955][flwr][DEBUG] - evaluate_round 153 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 14:03:33,956 | server.py:222 | fit_round 154: strategy sampled 50 clients (out of 50)
+[2023-10-12 14:03:33,956][flwr][DEBUG] - fit_round 154: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.138247 Loss1: 0.390402 Loss2: 1.747845
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.555261 Loss1: 0.244661 Loss2: 1.310601
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.488559 Loss1: 0.151811 Loss2: 1.336748
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.391332 Loss1: 0.494566 Loss2: 1.896766
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.744568 Loss1: 0.355877 Loss2: 1.388691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.710768 Loss1: 0.252853 Loss2: 1.457916 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592606 Loss1: 0.189863 Loss2: 1.402743 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.613281 Loss1: 0.214674 Loss2: 1.398608 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560132 Loss1: 0.158481 Loss2: 1.401651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.560568 Loss1: 0.162446 Loss2: 1.398121 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518926 Loss1: 0.118207 Loss2: 1.400719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.463939 Loss1: 0.082409 Loss2: 1.381530 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.448733 Loss1: 0.549822 Loss2: 1.898911
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.637962 Loss1: 0.271918 Loss2: 1.366045
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.564738 Loss1: 0.181621 Loss2: 1.383117
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.495996 Loss1: 0.119217 Loss2: 1.376779
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.251483 Loss1: 0.456494 Loss2: 1.794989
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.736313 Loss1: 0.376420 Loss2: 1.359893 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669897 Loss1: 0.254480 Loss2: 1.415417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535726 Loss1: 0.175158 Loss2: 1.360568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518821 Loss1: 0.156289 Loss2: 1.362532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512607 Loss1: 0.152438 Loss2: 1.360169 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448662 Loss1: 0.093359 Loss2: 1.355304 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426886 Loss1: 0.087753 Loss2: 1.339133 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.675357 Loss1: 0.299821 Loss2: 1.375536 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520927 Loss1: 0.153695 Loss2: 1.367232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534239 Loss1: 0.158798 Loss2: 1.375441
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.280256 Loss1: 0.444260 Loss2: 1.835996
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.483829 Loss1: 0.113607 Loss2: 1.370222
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.667961 Loss1: 0.285601 Loss2: 1.382360
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461597 Loss1: 0.099429 Loss2: 1.362168
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.665921 Loss1: 0.241830 Loss2: 1.424091
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556721 Loss1: 0.170149 Loss2: 1.386573 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.510009 Loss1: 0.126442 Loss2: 1.383567 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424136 Loss1: 0.069153 Loss2: 1.354984
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516707 Loss1: 0.130263 Loss2: 1.386444
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485652 Loss1: 0.104249 Loss2: 1.381403
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453336 Loss1: 0.080482 Loss2: 1.372854
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440943 Loss1: 0.068890 Loss2: 1.372054
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411617 Loss1: 0.043286 Loss2: 1.368330
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.349322 Loss1: 0.520791 Loss2: 1.828531
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.656344 Loss1: 0.317877 Loss2: 1.338467
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.590702 Loss1: 0.207714 Loss2: 1.382988
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.545661 Loss1: 0.196996 Loss2: 1.348666
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510213 Loss1: 0.160589 Loss2: 1.349624
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.538072 Loss1: 0.607914 Loss2: 1.930158
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444774 Loss1: 0.105571 Loss2: 1.339203
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.427280 Loss1: 0.096533 Loss2: 1.330746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574842 Loss1: 0.189435 Loss2: 1.385407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.536653 Loss1: 0.201458 Loss2: 1.335195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494899 Loss1: 0.160435 Loss2: 1.334464 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412612 Loss1: 0.085475 Loss2: 1.327138 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377549 Loss1: 0.057801 Loss2: 1.319748 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.399443 Loss1: 0.539537 Loss2: 1.859906
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.706901 Loss1: 0.341438 Loss2: 1.365464
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667029 Loss1: 0.270541 Loss2: 1.396487
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.548432 Loss1: 0.177886 Loss2: 1.370546
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.346149 Loss1: 0.469624 Loss2: 1.876525
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515012 Loss1: 0.153243 Loss2: 1.361770
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.656367 Loss1: 0.278426 Loss2: 1.377941
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.467803 Loss1: 0.114789 Loss2: 1.353014
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590435 Loss1: 0.187843 Loss2: 1.402592
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.500098 Loss1: 0.127237 Loss2: 1.372861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.466956 Loss1: 0.101076 Loss2: 1.365880 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450689 Loss1: 0.080430 Loss2: 1.370259 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408721 Loss1: 0.078572 Loss2: 1.330149
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419095 Loss1: 0.063051 Loss2: 1.356045
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426759 Loss1: 0.071244 Loss2: 1.355514
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401043 Loss1: 0.051778 Loss2: 1.349266
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388625 Loss1: 0.040043 Loss2: 1.348582
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.232303 Loss1: 0.452973 Loss2: 1.779330
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.601779 Loss1: 0.279661 Loss2: 1.322117
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.567198 Loss1: 0.209192 Loss2: 1.358006
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.557690 Loss1: 0.622495 Loss2: 1.935195
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497701 Loss1: 0.161130 Loss2: 1.336571
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.857872 Loss1: 0.461966 Loss2: 1.395906
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.457406 Loss1: 0.125677 Loss2: 1.331729
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.428108 Loss1: 0.105117 Loss2: 1.322991 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.427747 Loss1: 0.107836 Loss2: 1.319912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410026 Loss1: 0.089996 Loss2: 1.320030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.524331 Loss1: 0.132539 Loss2: 1.391792 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465150 Loss1: 0.076529 Loss2: 1.388621 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422780 Loss1: 0.051324 Loss2: 1.371456 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.540703 Loss1: 0.627024 Loss2: 1.913679
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.686943 Loss1: 0.356889 Loss2: 1.330054
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563568 Loss1: 0.212875 Loss2: 1.350694
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506701 Loss1: 0.174156 Loss2: 1.332545
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.227473 Loss1: 0.389165 Loss2: 1.838308
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437202 Loss1: 0.117666 Loss2: 1.319536 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.372863 Loss1: 0.062847 Loss2: 1.310017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.366815 Loss1: 0.061675 Loss2: 1.305140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.360525 Loss1: 0.059437 Loss2: 1.301087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.337432 Loss1: 0.038695 Loss2: 1.298737
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507237 Loss1: 0.127359 Loss2: 1.379878
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.488007 Loss1: 0.113895 Loss2: 1.374112
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458709 Loss1: 0.085514 Loss2: 1.373195
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468212 Loss1: 0.092156 Loss2: 1.376057
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453865 Loss1: 0.082804 Loss2: 1.371061
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.378868 Loss1: 0.541432 Loss2: 1.837437
+(DefaultActor pid=3764) >> Training accuracy: 0.995404
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446469 Loss1: 0.083243 Loss2: 1.363226
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.651245 Loss1: 0.300653 Loss2: 1.350592
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.600357 Loss1: 0.220751 Loss2: 1.379606
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.567884 Loss1: 0.195385 Loss2: 1.372499
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.495531 Loss1: 0.140844 Loss2: 1.354687
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473187 Loss1: 0.117664 Loss2: 1.355522
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.382468 Loss1: 0.497428 Loss2: 1.885040
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450464 Loss1: 0.107506 Loss2: 1.342959
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414390 Loss1: 0.075279 Loss2: 1.339111 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396967 Loss1: 0.067640 Loss2: 1.329328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377040 Loss1: 0.052260 Loss2: 1.324780 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485498 Loss1: 0.113485 Loss2: 1.372013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.456178 Loss1: 0.107788 Loss2: 1.348390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.470213 Loss1: 0.117330 Loss2: 1.352882
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.417538 Loss1: 0.546215 Loss2: 1.871323
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427866 Loss1: 0.074990 Loss2: 1.352876
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.792454 Loss1: 0.389186 Loss2: 1.403268
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.710670 Loss1: 0.263473 Loss2: 1.447197
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594601 Loss1: 0.200465 Loss2: 1.394136
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.592445 Loss1: 0.198956 Loss2: 1.393489
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.539827 Loss1: 0.147518 Loss2: 1.392308
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.503956 Loss1: 0.601910 Loss2: 1.902046
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.510189 Loss1: 0.115886 Loss2: 1.394304
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453759 Loss1: 0.076406 Loss2: 1.377353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445539 Loss1: 0.072386 Loss2: 1.373153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422271 Loss1: 0.062123 Loss2: 1.360148 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469296 Loss1: 0.103579 Loss2: 1.365716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418728 Loss1: 0.058706 Loss2: 1.360022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370224 Loss1: 0.027997 Loss2: 1.342227 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.669112 Loss1: 0.248423 Loss2: 1.420689 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558143 Loss1: 0.164350 Loss2: 1.393793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509163 Loss1: 0.131795 Loss2: 1.377369
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.389204 Loss1: 0.563536 Loss2: 1.825669
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468159 Loss1: 0.098254 Loss2: 1.369904
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.743435 Loss1: 0.419032 Loss2: 1.324403
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435858 Loss1: 0.073048 Loss2: 1.362810
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.665233 Loss1: 0.282129 Loss2: 1.383104
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441974 Loss1: 0.083274 Loss2: 1.358700
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.527678 Loss1: 0.199212 Loss2: 1.328466
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422646 Loss1: 0.067039 Loss2: 1.355607
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.494484 Loss1: 0.158545 Loss2: 1.335939
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.457937 Loss1: 0.132638 Loss2: 1.325299
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.399930 Loss1: 0.085570 Loss2: 1.314360
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386236 Loss1: 0.076374 Loss2: 1.309862
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.357252 Loss1: 0.048913 Loss2: 1.308339
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.570834 Loss1: 0.726476 Loss2: 1.844358
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357445 Loss1: 0.057314 Loss2: 1.300131
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656873 Loss1: 0.258014 Loss2: 1.398858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.548247 Loss1: 0.181659 Loss2: 1.366588 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461214 Loss1: 0.110491 Loss2: 1.350723
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.342109 Loss1: 0.462356 Loss2: 1.879753
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.705472 Loss1: 0.338855 Loss2: 1.366617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572653 Loss1: 0.170403 Loss2: 1.402250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478202 Loss1: 0.127588 Loss2: 1.350614 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435536 Loss1: 0.105711 Loss2: 1.329824
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.540355 Loss1: 0.189207 Loss2: 1.351148
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546738 Loss1: 0.162812 Loss2: 1.383926
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.457930 Loss1: 0.107976 Loss2: 1.349954
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417837 Loss1: 0.069423 Loss2: 1.348414
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449235 Loss1: 0.104468 Loss2: 1.344767
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.384636 Loss1: 0.532976 Loss2: 1.851659
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439662 Loss1: 0.092062 Loss2: 1.347600
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667075 Loss1: 0.243797 Loss2: 1.423278 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.469488 Loss1: 0.118346 Loss2: 1.351143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.483308 Loss1: 0.130705 Loss2: 1.352602
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.331145 Loss1: 0.560019 Loss2: 1.771126
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.727630 Loss1: 0.422136 Loss2: 1.305494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.576218 Loss1: 0.218165 Loss2: 1.358052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.495034 Loss1: 0.192038 Loss2: 1.302996 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412448 Loss1: 0.072675 Loss2: 1.339773
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.453530 Loss1: 0.151275 Loss2: 1.302255
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485063 Loss1: 0.174887 Loss2: 1.310176
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416057 Loss1: 0.110991 Loss2: 1.305066
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376470 Loss1: 0.079852 Loss2: 1.296617
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.352984 Loss1: 0.065630 Loss2: 1.287354
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.353868 Loss1: 0.478968 Loss2: 1.874899
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.353998 Loss1: 0.068566 Loss2: 1.285432
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624716 Loss1: 0.183769 Loss2: 1.440947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.496753 Loss1: 0.104545 Loss2: 1.392208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.544184 Loss1: 0.634968 Loss2: 1.909216
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480622 Loss1: 0.097640 Loss2: 1.382982
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.753129 Loss1: 0.392377 Loss2: 1.360752
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539682 Loss1: 0.149082 Loss2: 1.390600
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.654181 Loss1: 0.252203 Loss2: 1.401978
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488729 Loss1: 0.091545 Loss2: 1.397184
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462086 Loss1: 0.078957 Loss2: 1.383130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453007 Loss1: 0.070834 Loss2: 1.382173 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472177 Loss1: 0.126204 Loss2: 1.345973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410183 Loss1: 0.074795 Loss2: 1.335388 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395206 Loss1: 0.064042 Loss2: 1.331164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.455432 Loss1: 0.609639 Loss2: 1.845794
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.734582 Loss1: 0.365951 Loss2: 1.368632
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.612267 Loss1: 0.203599 Loss2: 1.408668
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.483390 Loss1: 0.129184 Loss2: 1.354206
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.456430 Loss1: 0.101484 Loss2: 1.354946
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.321321 Loss1: 0.473994 Loss2: 1.847328
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.701278 Loss1: 0.359615 Loss2: 1.341663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.682580 Loss1: 0.281582 Loss2: 1.400998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.604420 Loss1: 0.244438 Loss2: 1.359982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.553069 Loss1: 0.192324 Loss2: 1.360745 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.540399 Loss1: 0.182717 Loss2: 1.357682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408530 Loss1: 0.071101 Loss2: 1.337429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374586 Loss1: 0.050063 Loss2: 1.324523 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.717585 Loss1: 0.316353 Loss2: 1.401232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.603403 Loss1: 0.195190 Loss2: 1.408213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.592932 Loss1: 0.182522 Loss2: 1.410410
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.333215 Loss1: 0.521558 Loss2: 1.811657
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.677827 Loss1: 0.337069 Loss2: 1.340758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.552158 Loss1: 0.191895 Loss2: 1.360263 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520518 Loss1: 0.190693 Loss2: 1.329825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.490573 Loss1: 0.152130 Loss2: 1.338443 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.397240 Loss1: 0.082707 Loss2: 1.314533 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402314 Loss1: 0.090013 Loss2: 1.312301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369606 Loss1: 0.059211 Loss2: 1.310395
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.447653 Loss1: 0.518607 Loss2: 1.929046
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.866312 Loss1: 0.438495 Loss2: 1.427817
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.795615 Loss1: 0.301659 Loss2: 1.493956
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.728004 Loss1: 0.280692 Loss2: 1.447312
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.632564 Loss1: 0.184883 Loss2: 1.447680
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.581944 Loss1: 0.154120 Loss2: 1.427824
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.287352 Loss1: 0.470841 Loss2: 1.816512
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.564622 Loss1: 0.134000 Loss2: 1.430623
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.668875 Loss1: 0.332787 Loss2: 1.336089
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.536521 Loss1: 0.120943 Loss2: 1.415579
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.626429 Loss1: 0.251142 Loss2: 1.375287
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.497970 Loss1: 0.081938 Loss2: 1.416033
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562634 Loss1: 0.221210 Loss2: 1.341425
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.470239 Loss1: 0.059026 Loss2: 1.411213
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502715 Loss1: 0.154307 Loss2: 1.348408
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482161 Loss1: 0.140463 Loss2: 1.341697
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419948 Loss1: 0.084023 Loss2: 1.335925
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.401749 Loss1: 0.076238 Loss2: 1.325511
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392908 Loss1: 0.077314 Loss2: 1.315594
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369172 Loss1: 0.052179 Loss2: 1.316994
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.252896 Loss1: 0.461971 Loss2: 1.790926
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.613718 Loss1: 0.288811 Loss2: 1.324907
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.572873 Loss1: 0.211039 Loss2: 1.361834
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.532275 Loss1: 0.203079 Loss2: 1.329196
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551683 Loss1: 0.218910 Loss2: 1.332773
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.513584 Loss1: 0.599215 Loss2: 1.914369
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.482918 Loss1: 0.146162 Loss2: 1.336756
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432957 Loss1: 0.111879 Loss2: 1.321078 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420077 Loss1: 0.103415 Loss2: 1.316662 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396721 Loss1: 0.083049 Loss2: 1.313672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466253 Loss1: 0.106956 Loss2: 1.359296 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398490 Loss1: 0.051661 Loss2: 1.346830 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436963 Loss1: 0.099693 Loss2: 1.337270 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.452148 Loss1: 0.578404 Loss2: 1.873744
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.785702 Loss1: 0.350554 Loss2: 1.435148
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.639761 Loss1: 0.205288 Loss2: 1.434473
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.565322 Loss1: 0.163921 Loss2: 1.401401
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.521130 Loss1: 0.574216 Loss2: 1.946914
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.890616 Loss1: 0.459243 Loss2: 1.431372 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.756378 Loss1: 0.261604 Loss2: 1.494774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.617677 Loss1: 0.180723 Loss2: 1.436954 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.619905 Loss1: 0.178754 Loss2: 1.441151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567916 Loss1: 0.137091 Loss2: 1.430825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388416 Loss1: 0.023400 Loss2: 1.365016
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.539562 Loss1: 0.118048 Loss2: 1.421514
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.517137 Loss1: 0.098099 Loss2: 1.419037
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481018 Loss1: 0.062920 Loss2: 1.418098
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.480747 Loss1: 0.079247 Loss2: 1.401500
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.305967 Loss1: 0.527410 Loss2: 1.778557
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.717443 Loss1: 0.362328 Loss2: 1.355115
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.587895 Loss1: 0.215143 Loss2: 1.372751
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.357163 Loss1: 0.496500 Loss2: 1.860663
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.543943 Loss1: 0.198105 Loss2: 1.345838
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.638732 Loss1: 0.280045 Loss2: 1.358687
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473134 Loss1: 0.126087 Loss2: 1.347047
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.587898 Loss1: 0.208626 Loss2: 1.379272
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443652 Loss1: 0.103362 Loss2: 1.340291
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.549211 Loss1: 0.194212 Loss2: 1.354999
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.393599 Loss1: 0.066853 Loss2: 1.326747
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.369193 Loss1: 0.043957 Loss2: 1.325236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.360134 Loss1: 0.046626 Loss2: 1.313509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.341256 Loss1: 0.033224 Loss2: 1.308032 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399727 Loss1: 0.070524 Loss2: 1.329204 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.359668 Loss1: 0.462866 Loss2: 1.896802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.747361 Loss1: 0.252190 Loss2: 1.495171 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.453043 Loss1: 0.667189 Loss2: 1.785854
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.689788 Loss1: 0.234230 Loss2: 1.455559
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.723932 Loss1: 0.388552 Loss2: 1.335380
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.678406 Loss1: 0.217643 Loss2: 1.460763
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633748 Loss1: 0.260053 Loss2: 1.373695
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.628701 Loss1: 0.174255 Loss2: 1.454447
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509563 Loss1: 0.188029 Loss2: 1.321535
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.620520 Loss1: 0.168486 Loss2: 1.452034
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.568168 Loss1: 0.121997 Loss2: 1.446172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.508749 Loss1: 0.070495 Loss2: 1.438254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.502858 Loss1: 0.070256 Loss2: 1.432602 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366954 Loss1: 0.072136 Loss2: 1.294818 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.272306 Loss1: 0.472024 Loss2: 1.800282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.628392 Loss1: 0.233570 Loss2: 1.394822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520097 Loss1: 0.194557 Loss2: 1.325540
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.385548 Loss1: 0.524308 Loss2: 1.861241
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.699382 Loss1: 0.336436 Loss2: 1.362946 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.618679 Loss1: 0.227159 Loss2: 1.391520 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562929 Loss1: 0.176207 Loss2: 1.386722 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519689 Loss1: 0.156377 Loss2: 1.363312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499049 Loss1: 0.140881 Loss2: 1.358168 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359816 Loss1: 0.052653 Loss2: 1.307163
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436336 Loss1: 0.078734 Loss2: 1.357602
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422850 Loss1: 0.080286 Loss2: 1.342565
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399426 Loss1: 0.055984 Loss2: 1.343442
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385375 Loss1: 0.047601 Loss2: 1.337775
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.314888 Loss1: 0.423050 Loss2: 1.891838
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.672482 Loss1: 0.285057 Loss2: 1.387425
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.650640 Loss1: 0.244870 Loss2: 1.405770
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.548629 Loss1: 0.150387 Loss2: 1.398242
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.377551 Loss1: 0.513996 Loss2: 1.863555
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.721380 Loss1: 0.352726 Loss2: 1.368655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.615389 Loss1: 0.213587 Loss2: 1.401802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521038 Loss1: 0.152940 Loss2: 1.368098 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487743 Loss1: 0.125375 Loss2: 1.362368 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479272 Loss1: 0.124219 Loss2: 1.355053 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415090 Loss1: 0.049754 Loss2: 1.365336
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473490 Loss1: 0.120099 Loss2: 1.353392
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458268 Loss1: 0.108375 Loss2: 1.349893
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435047 Loss1: 0.087598 Loss2: 1.347450
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422920 Loss1: 0.077300 Loss2: 1.345620
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.436348 Loss1: 0.532730 Loss2: 1.903618
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.976560 Loss1: 0.543206 Loss2: 1.433353
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.757370 Loss1: 0.302603 Loss2: 1.454767
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.630738 Loss1: 0.230503 Loss2: 1.400235
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.298630 Loss1: 0.461986 Loss2: 1.836645
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.662743 Loss1: 0.326765 Loss2: 1.335978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622822 Loss1: 0.234610 Loss2: 1.388212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.508637 Loss1: 0.158316 Loss2: 1.350321 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 14:32:21,168 | server.py:236 | fit_round 154 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509184 Loss1: 0.157548 Loss2: 1.351635 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450770 Loss1: 0.105368 Loss2: 1.345402 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408045 Loss1: 0.051281 Loss2: 1.356763
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.414344 Loss1: 0.075790 Loss2: 1.338554
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460775 Loss1: 0.129440 Loss2: 1.331335
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.474760 Loss1: 0.142172 Loss2: 1.332588
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437355 Loss1: 0.087363 Loss2: 1.349992
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.375111 Loss1: 0.510639 Loss2: 1.864472
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.715088 Loss1: 0.345400 Loss2: 1.369688
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611322 Loss1: 0.201447 Loss2: 1.409875
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555331 Loss1: 0.175867 Loss2: 1.379464
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.367232 Loss1: 0.499558 Loss2: 1.867673
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.691294 Loss1: 0.312315 Loss2: 1.378979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.645099 Loss1: 0.225816 Loss2: 1.419284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.516237 Loss1: 0.136591 Loss2: 1.379646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.517620 Loss1: 0.144113 Loss2: 1.373507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476703 Loss1: 0.094065 Loss2: 1.382638 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407183 Loss1: 0.052759 Loss2: 1.354424
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484021 Loss1: 0.117005 Loss2: 1.367015
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473619 Loss1: 0.101613 Loss2: 1.372006
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452454 Loss1: 0.082972 Loss2: 1.369482
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.509118 Loss1: 0.143340 Loss2: 1.365779
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.293757 Loss1: 0.447858 Loss2: 1.845899
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.654759 Loss1: 0.311437 Loss2: 1.343322
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595099 Loss1: 0.216240 Loss2: 1.378859
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.495614 Loss1: 0.134173 Loss2: 1.361441
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.401776 Loss1: 0.512053 Loss2: 1.889723
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.705830 Loss1: 0.310297 Loss2: 1.395532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.673183 Loss1: 0.237302 Loss2: 1.435881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534587 Loss1: 0.140208 Loss2: 1.394379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527597 Loss1: 0.144407 Loss2: 1.383190 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.509663 Loss1: 0.118254 Loss2: 1.391409 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.479195 Loss1: 0.098949 Loss2: 1.380246 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448721 Loss1: 0.076909 Loss2: 1.371813 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 14:32:21,168][flwr][DEBUG] - fit_round 154 received 50 results and 0 failures
+INFO flwr 2023-10-12 14:33:01,217 | server.py:125 | fit progress: (154, 2.24084857896494, {'accuracy': 0.5961}, 355288.995210152)
+>> Test accuracy: 0.596100
+[2023-10-12 14:33:01,217][flwr][INFO] - fit progress: (154, 2.24084857896494, {'accuracy': 0.5961}, 355288.995210152)
+DEBUG flwr 2023-10-12 14:33:01,217 | server.py:173 | evaluate_round 154: strategy sampled 50 clients (out of 50)
+[2023-10-12 14:33:01,217][flwr][DEBUG] - evaluate_round 154: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 14:42:06,296 | server.py:187 | evaluate_round 154 received 50 results and 0 failures
+[2023-10-12 14:42:06,296][flwr][DEBUG] - evaluate_round 154 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 14:42:06,297 | server.py:222 | fit_round 155: strategy sampled 50 clients (out of 50)
+[2023-10-12 14:42:06,297][flwr][DEBUG] - fit_round 155: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.378869 Loss1: 0.476945 Loss2: 1.901923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.665786 Loss1: 0.215034 Loss2: 1.450752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.567361 Loss1: 0.170312 Loss2: 1.397049
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.436190 Loss1: 0.567335 Loss2: 1.868854
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510138 Loss1: 0.119317 Loss2: 1.390822
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.718313 Loss1: 0.392533 Loss2: 1.325781
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.549249 Loss1: 0.194686 Loss2: 1.354563
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477696 Loss1: 0.095289 Loss2: 1.382408
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520620 Loss1: 0.172060 Loss2: 1.348559
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479729 Loss1: 0.105316 Loss2: 1.374413
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439243 Loss1: 0.064631 Loss2: 1.374612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430648 Loss1: 0.062367 Loss2: 1.368281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418771 Loss1: 0.052420 Loss2: 1.366351 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387713 Loss1: 0.075474 Loss2: 1.312239 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.422281 Loss1: 0.515482 Loss2: 1.906800
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.703307 Loss1: 0.293894 Loss2: 1.409413
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618879 Loss1: 0.202179 Loss2: 1.416700
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559273 Loss1: 0.155500 Loss2: 1.403774
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.229832 Loss1: 0.418984 Loss2: 1.810847
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503893 Loss1: 0.103996 Loss2: 1.399898
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.608073 Loss1: 0.283518 Loss2: 1.324555
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466181 Loss1: 0.077946 Loss2: 1.388235
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.513520 Loss1: 0.173492 Loss2: 1.340028
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464640 Loss1: 0.080927 Loss2: 1.383712
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.447525 Loss1: 0.123350 Loss2: 1.324176
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471046 Loss1: 0.090229 Loss2: 1.380818
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.427343 Loss1: 0.115769 Loss2: 1.311574
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.442877 Loss1: 0.060326 Loss2: 1.382551
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.385775 Loss1: 0.074129 Loss2: 1.311646
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437598 Loss1: 0.064941 Loss2: 1.372657
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406185 Loss1: 0.100496 Loss2: 1.305689
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.373025 Loss1: 0.065321 Loss2: 1.307704
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362384 Loss1: 0.056717 Loss2: 1.305667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367929 Loss1: 0.066475 Loss2: 1.301454
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.326608 Loss1: 0.510310 Loss2: 1.816298
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.788049 Loss1: 0.455399 Loss2: 1.332651
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638590 Loss1: 0.239885 Loss2: 1.398705
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491928 Loss1: 0.161666 Loss2: 1.330261
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.325670 Loss1: 0.473140 Loss2: 1.852530
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.670950 Loss1: 0.323111 Loss2: 1.347839 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.539823 Loss1: 0.170654 Loss2: 1.369170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539180 Loss1: 0.193149 Loss2: 1.346031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496088 Loss1: 0.152847 Loss2: 1.343241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.552119 Loss1: 0.195009 Loss2: 1.357109 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389065 Loss1: 0.073595 Loss2: 1.315470
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.466742 Loss1: 0.112156 Loss2: 1.354586
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457252 Loss1: 0.117252 Loss2: 1.340000
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458012 Loss1: 0.116232 Loss2: 1.341780
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418867 Loss1: 0.081938 Loss2: 1.336929
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.327252 Loss1: 0.524753 Loss2: 1.802500
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.616962 Loss1: 0.282026 Loss2: 1.334937
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.538729 Loss1: 0.178692 Loss2: 1.360037
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.495601 Loss1: 0.159143 Loss2: 1.336459
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.368966 Loss1: 0.502275 Loss2: 1.866692
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443277 Loss1: 0.113571 Loss2: 1.329706
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.757678 Loss1: 0.397680 Loss2: 1.359998
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438053 Loss1: 0.105685 Loss2: 1.332369
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605649 Loss1: 0.209970 Loss2: 1.395680
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441412 Loss1: 0.113823 Loss2: 1.327589
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.541421 Loss1: 0.170273 Loss2: 1.371147
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491667 Loss1: 0.128279 Loss2: 1.363388
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409142 Loss1: 0.077772 Loss2: 1.331371
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.458406 Loss1: 0.094807 Loss2: 1.363599
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.359037 Loss1: 0.035916 Loss2: 1.323121
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426799 Loss1: 0.081863 Loss2: 1.344936
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370627 Loss1: 0.060054 Loss2: 1.310574
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383097 Loss1: 0.044173 Loss2: 1.338924 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.482827 Loss1: 0.604305 Loss2: 1.878522 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.639311 Loss1: 0.237358 Loss2: 1.401953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.509585 Loss1: 0.148783 Loss2: 1.360803
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.449503 Loss1: 0.523504 Loss2: 1.926000
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.467047 Loss1: 0.109118 Loss2: 1.357930
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.837227 Loss1: 0.415910 Loss2: 1.421317
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.439541 Loss1: 0.084254 Loss2: 1.355287
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.726951 Loss1: 0.262770 Loss2: 1.464181
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.417444 Loss1: 0.073307 Loss2: 1.344137
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586938 Loss1: 0.173151 Loss2: 1.413788
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401975 Loss1: 0.058839 Loss2: 1.343136
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527350 Loss1: 0.110144 Loss2: 1.417207
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394434 Loss1: 0.058104 Loss2: 1.336331
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495467 Loss1: 0.081254 Loss2: 1.414214
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386601 Loss1: 0.052584 Loss2: 1.334017
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472376 Loss1: 0.074745 Loss2: 1.397631
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439322 Loss1: 0.046475 Loss2: 1.392846
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430081 Loss1: 0.042104 Loss2: 1.387977
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422746 Loss1: 0.042672 Loss2: 1.380074
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.293595 Loss1: 0.472801 Loss2: 1.820795
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.678430 Loss1: 0.345946 Loss2: 1.332484
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624274 Loss1: 0.235369 Loss2: 1.388904
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528087 Loss1: 0.183441 Loss2: 1.344647
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.362308 Loss1: 0.521873 Loss2: 1.840435
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.456206 Loss1: 0.110279 Loss2: 1.345927
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.744623 Loss1: 0.378563 Loss2: 1.366060
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492243 Loss1: 0.152575 Loss2: 1.339668
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.666345 Loss1: 0.261130 Loss2: 1.405215
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.498122 Loss1: 0.154845 Loss2: 1.343278
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.566621 Loss1: 0.187967 Loss2: 1.378654
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451047 Loss1: 0.096984 Loss2: 1.354063
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.561799 Loss1: 0.190633 Loss2: 1.371165
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.436641 Loss1: 0.104488 Loss2: 1.332153
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506155 Loss1: 0.133394 Loss2: 1.372761
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.456253 Loss1: 0.124284 Loss2: 1.331969
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.482611 Loss1: 0.122491 Loss2: 1.360120
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460374 Loss1: 0.096608 Loss2: 1.363765
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446148 Loss1: 0.088425 Loss2: 1.357722
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409402 Loss1: 0.066019 Loss2: 1.343383
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.602303 Loss1: 0.600118 Loss2: 2.002185
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.759582 Loss1: 0.392362 Loss2: 1.367220
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.651862 Loss1: 0.260064 Loss2: 1.391798
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.632715 Loss1: 0.216386 Loss2: 1.416329
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561524 Loss1: 0.187377 Loss2: 1.374147
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.763721 Loss1: 0.412799 Loss2: 1.350922 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635610 Loss1: 0.225501 Loss2: 1.410109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437987 Loss1: 0.075958 Loss2: 1.362029 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404910 Loss1: 0.051563 Loss2: 1.353347 [repeated 2x across cluster]
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434741 Loss1: 0.102031 Loss2: 1.332710 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414413 Loss1: 0.077563 Loss2: 1.336850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397714 Loss1: 0.064717 Loss2: 1.332997 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645341 Loss1: 0.245747 Loss2: 1.399594 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565978 Loss1: 0.207675 Loss2: 1.358303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.464808 Loss1: 0.575141 Loss2: 1.889667
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484708 Loss1: 0.130798 Loss2: 1.353910
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.731824 Loss1: 0.340853 Loss2: 1.390972
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432425 Loss1: 0.098334 Loss2: 1.334091
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669687 Loss1: 0.247814 Loss2: 1.421872
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.407016 Loss1: 0.073596 Loss2: 1.333420
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.382262 Loss1: 0.056870 Loss2: 1.325392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385581 Loss1: 0.068126 Loss2: 1.317455 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.442941 Loss1: 0.067014 Loss2: 1.375927 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423362 Loss1: 0.067738 Loss2: 1.355624 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408888 Loss1: 0.057343 Loss2: 1.351545
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.300895 Loss1: 0.481076 Loss2: 1.819819
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.628377 Loss1: 0.268149 Loss2: 1.360228
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.668126 Loss1: 0.280411 Loss2: 1.387715
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551465 Loss1: 0.181902 Loss2: 1.369563
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517603 Loss1: 0.157183 Loss2: 1.360420
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.360011 Loss1: 0.495426 Loss2: 1.864585
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.624695 Loss1: 0.258673 Loss2: 1.366022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608445 Loss1: 0.222692 Loss2: 1.385753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413919 Loss1: 0.063199 Loss2: 1.350720
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498122 Loss1: 0.139049 Loss2: 1.359072
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394662 Loss1: 0.054813 Loss2: 1.339849
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520869 Loss1: 0.161781 Loss2: 1.359087
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395090 Loss1: 0.058198 Loss2: 1.336892
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.532420 Loss1: 0.166643 Loss2: 1.365778
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469110 Loss1: 0.108096 Loss2: 1.361014
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466819 Loss1: 0.110669 Loss2: 1.356151
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428617 Loss1: 0.077525 Loss2: 1.351092
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402759 Loss1: 0.052238 Loss2: 1.350521
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.614000 Loss1: 0.638550 Loss2: 1.975450
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.714698 Loss1: 0.342848 Loss2: 1.371850
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638896 Loss1: 0.229838 Loss2: 1.409058
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557305 Loss1: 0.171921 Loss2: 1.385384
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.467599 Loss1: 0.107385 Loss2: 1.360214
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.428718 Loss1: 0.072186 Loss2: 1.356531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426146 Loss1: 0.073948 Loss2: 1.352198
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.680109 Loss1: 0.296650 Loss2: 1.383460
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421193 Loss1: 0.069907 Loss2: 1.351286
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604142 Loss1: 0.208622 Loss2: 1.395520
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.506075 Loss1: 0.124660 Loss2: 1.381415 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.478553 Loss1: 0.116195 Loss2: 1.362358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.493286 Loss1: 0.129149 Loss2: 1.364136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429023 Loss1: 0.073451 Loss2: 1.355572 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421894 Loss1: 0.065776 Loss2: 1.356118 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.708804 Loss1: 0.321875 Loss2: 1.386930 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478586 Loss1: 0.138979 Loss2: 1.339607 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439716 Loss1: 0.106040 Loss2: 1.333676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418303 Loss1: 0.098411 Loss2: 1.319892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418512 Loss1: 0.092329 Loss2: 1.326184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387746 Loss1: 0.065896 Loss2: 1.321850 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.607661 Loss1: 0.205341 Loss2: 1.402320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.536983 Loss1: 0.139847 Loss2: 1.397136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496889 Loss1: 0.115486 Loss2: 1.381403
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.410571 Loss1: 0.536082 Loss2: 1.874489
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450114 Loss1: 0.067834 Loss2: 1.382279
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.813157 Loss1: 0.425652 Loss2: 1.387505
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412396 Loss1: 0.043152 Loss2: 1.369244
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.716431 Loss1: 0.257132 Loss2: 1.459299
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.645059 Loss1: 0.244860 Loss2: 1.400199
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.619130 Loss1: 0.211799 Loss2: 1.407331
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.579428 Loss1: 0.172313 Loss2: 1.407115
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.533811 Loss1: 0.141265 Loss2: 1.392546
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.391678 Loss1: 0.524923 Loss2: 1.866754
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.543969 Loss1: 0.150279 Loss2: 1.393690
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.752610 Loss1: 0.384627 Loss2: 1.367982
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.496580 Loss1: 0.101966 Loss2: 1.394614
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601062 Loss1: 0.186059 Loss2: 1.415003
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458701 Loss1: 0.075884 Loss2: 1.382818
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556843 Loss1: 0.178490 Loss2: 1.378353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.482832 Loss1: 0.111463 Loss2: 1.371369 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477955 Loss1: 0.122492 Loss2: 1.355464
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.443623 Loss1: 0.564093 Loss2: 1.879530
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445195 Loss1: 0.087164 Loss2: 1.358031
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784954 Loss1: 0.397523 Loss2: 1.387432
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.455160 Loss1: 0.102870 Loss2: 1.352290
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.721368 Loss1: 0.282865 Loss2: 1.438503
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.619929 Loss1: 0.208714 Loss2: 1.411215
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.621261 Loss1: 0.216729 Loss2: 1.404532
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569418 Loss1: 0.158546 Loss2: 1.410872
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513210 Loss1: 0.117649 Loss2: 1.395562
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.278393 Loss1: 0.420466 Loss2: 1.857927
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483145 Loss1: 0.096279 Loss2: 1.386867
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.466575 Loss1: 0.083868 Loss2: 1.382707
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.672542 Loss1: 0.278331 Loss2: 1.394211
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423625 Loss1: 0.049632 Loss2: 1.373993
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.551470 Loss1: 0.135892 Loss2: 1.415579
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.500102 Loss1: 0.119347 Loss2: 1.380755
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556173 Loss1: 0.168610 Loss2: 1.387563
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460994 Loss1: 0.075276 Loss2: 1.385718
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412885 Loss1: 0.046724 Loss2: 1.366161
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.339328 Loss1: 0.495141 Loss2: 1.844187
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415987 Loss1: 0.053120 Loss2: 1.362867
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408822 Loss1: 0.050886 Loss2: 1.357936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431337 Loss1: 0.076544 Loss2: 1.354793 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516475 Loss1: 0.146832 Loss2: 1.369643 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476490 Loss1: 0.115538 Loss2: 1.360951 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446039 Loss1: 0.075746 Loss2: 1.370292
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.447886 Loss1: 0.563475 Loss2: 1.884410
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.712983 Loss1: 0.351841 Loss2: 1.361142 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.621370 Loss1: 0.218060 Loss2: 1.403311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516406 Loss1: 0.158359 Loss2: 1.358047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434998 Loss1: 0.085319 Loss2: 1.349679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421725 Loss1: 0.080849 Loss2: 1.340876 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387402 Loss1: 0.052370 Loss2: 1.335032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379091 Loss1: 0.048284 Loss2: 1.330807 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492251 Loss1: 0.138007 Loss2: 1.354244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.400996 Loss1: 0.059084 Loss2: 1.341912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404866 Loss1: 0.065555 Loss2: 1.339310
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.381022 Loss1: 0.537889 Loss2: 1.843133
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.699742 Loss1: 0.349207 Loss2: 1.350535 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608331 Loss1: 0.222676 Loss2: 1.385655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539597 Loss1: 0.173646 Loss2: 1.365950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508212 Loss1: 0.145633 Loss2: 1.362579 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493190 Loss1: 0.142066 Loss2: 1.351124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404578 Loss1: 0.065291 Loss2: 1.339288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378432 Loss1: 0.049817 Loss2: 1.328616 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521345 Loss1: 0.159002 Loss2: 1.362343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.486205 Loss1: 0.122378 Loss2: 1.363826 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484087 Loss1: 0.127856 Loss2: 1.356232
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.397837 Loss1: 0.528184 Loss2: 1.869653
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.711882 Loss1: 0.321838 Loss2: 1.390044 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390754 Loss1: 0.043201 Loss2: 1.347553
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614543 Loss1: 0.204027 Loss2: 1.410516
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555288 Loss1: 0.162047 Loss2: 1.393241
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542124 Loss1: 0.150622 Loss2: 1.391502
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.567567 Loss1: 0.177995 Loss2: 1.389571
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.555829 Loss1: 0.156701 Loss2: 1.399128
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.316415 Loss1: 0.439184 Loss2: 1.877232
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.522710 Loss1: 0.132093 Loss2: 1.390617
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499142 Loss1: 0.109273 Loss2: 1.389869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.470247 Loss1: 0.092370 Loss2: 1.377877 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527255 Loss1: 0.156437 Loss2: 1.370818 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467267 Loss1: 0.106676 Loss2: 1.360591 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450794 Loss1: 0.091117 Loss2: 1.359677
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.611270 Loss1: 0.684962 Loss2: 1.926308
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.799597 Loss1: 0.405307 Loss2: 1.394290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395293 Loss1: 0.049088 Loss2: 1.346205
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.686688 Loss1: 0.254913 Loss2: 1.431774
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536805 Loss1: 0.155710 Loss2: 1.381095
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522989 Loss1: 0.142058 Loss2: 1.380931
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.509386 Loss1: 0.120666 Loss2: 1.388720
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471667 Loss1: 0.094084 Loss2: 1.377584
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414210 Loss1: 0.044179 Loss2: 1.370031
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.369112 Loss1: 0.508060 Loss2: 1.861052
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.755136 Loss1: 0.390931 Loss2: 1.364206 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.649914 Loss1: 0.229136 Loss2: 1.420778 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.494194 Loss1: 0.137186 Loss2: 1.357008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431695 Loss1: 0.090108 Loss2: 1.341587 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436100 Loss1: 0.097845 Loss2: 1.338255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445938 Loss1: 0.101320 Loss2: 1.344618 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430471 Loss1: 0.081490 Loss2: 1.348981 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550062 Loss1: 0.145954 Loss2: 1.404108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476749 Loss1: 0.088806 Loss2: 1.387943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.322435 Loss1: 0.433999 Loss2: 1.888436 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.890798 Loss1: 0.478770 Loss2: 1.412028 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.809269 Loss1: 0.318902 Loss2: 1.490367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.595239 Loss1: 0.182655 Loss2: 1.412585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.494735 Loss1: 0.087826 Loss2: 1.406910 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459166 Loss1: 0.072284 Loss2: 1.386883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435302 Loss1: 0.053506 Loss2: 1.381795 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449789 Loss1: 0.067497 Loss2: 1.382292 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456790 Loss1: 0.113411 Loss2: 1.343379 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447613 Loss1: 0.105820 Loss2: 1.341793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409631 Loss1: 0.079403 Loss2: 1.330228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433417 Loss1: 0.099666 Loss2: 1.333751 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.431785 Loss1: 0.125215 Loss2: 1.306569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.381304 Loss1: 0.072426 Loss2: 1.308878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.316546 Loss1: 0.447878 Loss2: 1.868668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.718238 Loss1: 0.319453 Loss2: 1.398785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.603098 Loss1: 0.168511 Loss2: 1.434587 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535695 Loss1: 0.133963 Loss2: 1.401731 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530828 Loss1: 0.141016 Loss2: 1.389812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.263877 Loss1: 0.514569 Loss2: 1.749308
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.498078 Loss1: 0.101445 Loss2: 1.396633
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.616028 Loss1: 0.326456 Loss2: 1.289572
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461737 Loss1: 0.073353 Loss2: 1.388384
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.485292 Loss1: 0.168531 Loss2: 1.316761
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453484 Loss1: 0.073785 Loss2: 1.379698
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.406090 Loss1: 0.120092 Loss2: 1.285998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402980 Loss1: 0.129432 Loss2: 1.273548 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.334615 Loss1: 0.063380 Loss2: 1.271236
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.403559 Loss1: 0.535403 Loss2: 1.868155
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.307411 Loss1: 0.043235 Loss2: 1.264176
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.706246 Loss1: 0.338023 Loss2: 1.368223
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.289819 Loss1: 0.037343 Loss2: 1.252476
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605733 Loss1: 0.213931 Loss2: 1.391802
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538748 Loss1: 0.171142 Loss2: 1.367605
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.482648 Loss1: 0.127013 Loss2: 1.355635
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465512 Loss1: 0.108911 Loss2: 1.356601
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431207 Loss1: 0.080641 Loss2: 1.350566
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.296978 Loss1: 0.431287 Loss2: 1.865691
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441655 Loss1: 0.093179 Loss2: 1.348477
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.790284 Loss1: 0.428075 Loss2: 1.362209
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431345 Loss1: 0.083024 Loss2: 1.348321
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.654897 Loss1: 0.244189 Loss2: 1.410709
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392208 Loss1: 0.052060 Loss2: 1.340148
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544778 Loss1: 0.171846 Loss2: 1.372932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.452300 Loss1: 0.096494 Loss2: 1.355806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439808 Loss1: 0.088503 Loss2: 1.351305
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.176130 Loss1: 0.425300 Loss2: 1.750831
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.618978 Loss1: 0.307508 Loss2: 1.311470 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.498484 Loss1: 0.155142 Loss2: 1.343342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.420385 Loss1: 0.101103 Loss2: 1.319282 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.389078 Loss1: 0.085204 Loss2: 1.303874 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.393016 Loss1: 0.094549 Loss2: 1.298468 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394212 Loss1: 0.088080 Loss2: 1.306132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.566590 Loss1: 0.162699 Loss2: 1.403891 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516222 Loss1: 0.109705 Loss2: 1.406517 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452074 Loss1: 0.061461 Loss2: 1.390613 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 15:10:40,756 | server.py:236 | fit_round 155 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.478911 Loss1: 0.090064 Loss2: 1.388847
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.482957 Loss1: 0.552619 Loss2: 1.930338
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.810308 Loss1: 0.397540 Loss2: 1.412768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519832 Loss1: 0.113461 Loss2: 1.406371 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.525369 Loss1: 0.133427 Loss2: 1.391941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.499778 Loss1: 0.107766 Loss2: 1.392011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.494894 Loss1: 0.106041 Loss2: 1.388853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481113 Loss1: 0.090906 Loss2: 1.390208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487390 Loss1: 0.091878 Loss2: 1.395511 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497008 Loss1: 0.101141 Loss2: 1.395866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462135 Loss1: 0.082780 Loss2: 1.379355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.169090 Loss1: 0.389058 Loss2: 1.780032 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.570570 Loss1: 0.216682 Loss2: 1.353888 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.420162 Loss1: 0.103316 Loss2: 1.316845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.399735 Loss1: 0.084558 Loss2: 1.315177 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.376472 Loss1: 0.074652 Loss2: 1.301819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396437 Loss1: 0.097223 Loss2: 1.299214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425955 Loss1: 0.113248 Loss2: 1.312707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.564002 Loss1: 0.176612 Loss2: 1.387390 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988051
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.523073 Loss1: 0.137531 Loss2: 1.385542 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429265 Loss1: 0.061348 Loss2: 1.367918 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.664195 Loss1: 0.330800 Loss2: 1.333395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.506487 Loss1: 0.164298 Loss2: 1.342189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.422162 Loss1: 0.093950 Loss2: 1.328213 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374951 Loss1: 0.058988 Loss2: 1.315962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.339269 Loss1: 0.035167 Loss2: 1.304102 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 15:10:40,756][flwr][DEBUG] - fit_round 155 received 50 results and 0 failures
+INFO flwr 2023-10-12 15:11:22,006 | server.py:125 | fit progress: (155, 2.2388751522039834, {'accuracy': 0.5988}, 357589.78495004296)
+>> Test accuracy: 0.598800
+[2023-10-12 15:11:22,006][flwr][INFO] - fit progress: (155, 2.2388751522039834, {'accuracy': 0.5988}, 357589.78495004296)
+DEBUG flwr 2023-10-12 15:11:22,007 | server.py:173 | evaluate_round 155: strategy sampled 50 clients (out of 50)
+[2023-10-12 15:11:22,007][flwr][DEBUG] - evaluate_round 155: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 15:20:26,416 | server.py:187 | evaluate_round 155 received 50 results and 0 failures
+[2023-10-12 15:20:26,416][flwr][DEBUG] - evaluate_round 155 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 15:20:26,417 | server.py:222 | fit_round 156: strategy sampled 50 clients (out of 50)
+[2023-10-12 15:20:26,417][flwr][DEBUG] - fit_round 156: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.429997 Loss1: 0.551551 Loss2: 1.878447
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.639817 Loss1: 0.261043 Loss2: 1.378774
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.604604 Loss1: 0.225012 Loss2: 1.379592
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.539861 Loss1: 0.170469 Loss2: 1.369392
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.347775 Loss1: 0.511610 Loss2: 1.836165
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535146 Loss1: 0.172136 Loss2: 1.363010
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.750215 Loss1: 0.379830 Loss2: 1.370385
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461290 Loss1: 0.099753 Loss2: 1.361537
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635557 Loss1: 0.222464 Loss2: 1.413094
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420600 Loss1: 0.074618 Loss2: 1.345982
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532191 Loss1: 0.162762 Loss2: 1.369429
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382827 Loss1: 0.040830 Loss2: 1.341997
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.482289 Loss1: 0.120930 Loss2: 1.361359
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.363839 Loss1: 0.030331 Loss2: 1.333508
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481697 Loss1: 0.130356 Loss2: 1.351341
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366065 Loss1: 0.039795 Loss2: 1.326270
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.477722 Loss1: 0.124960 Loss2: 1.352761
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422393 Loss1: 0.073212 Loss2: 1.349182
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424677 Loss1: 0.081927 Loss2: 1.342750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385322 Loss1: 0.042798 Loss2: 1.342524
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.548437 Loss1: 0.633609 Loss2: 1.914828
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.771984 Loss1: 0.400300 Loss2: 1.371684
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.612039 Loss1: 0.223414 Loss2: 1.388625
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569944 Loss1: 0.181158 Loss2: 1.388786
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.403738 Loss1: 0.550645 Loss2: 1.853093
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.507555 Loss1: 0.142086 Loss2: 1.365469 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458086 Loss1: 0.093780 Loss2: 1.364306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421088 Loss1: 0.074945 Loss2: 1.346143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407844 Loss1: 0.067324 Loss2: 1.340520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393974 Loss1: 0.060753 Loss2: 1.333221 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.432549 Loss1: 0.110570 Loss2: 1.321979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384906 Loss1: 0.071475 Loss2: 1.313431 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364807 Loss1: 0.056074 Loss2: 1.308733
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.392448 Loss1: 0.522562 Loss2: 1.869887
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.731652 Loss1: 0.362497 Loss2: 1.369155
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.629434 Loss1: 0.222229 Loss2: 1.407204
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.602565 Loss1: 0.231999 Loss2: 1.370567
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.537903 Loss1: 0.155461 Loss2: 1.382442
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.303592 Loss1: 0.485413 Loss2: 1.818179
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.705463 Loss1: 0.367557 Loss2: 1.337906 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.609958 Loss1: 0.227796 Loss2: 1.382163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523177 Loss1: 0.193026 Loss2: 1.330151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.454150 Loss1: 0.124039 Loss2: 1.330111 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.426724 Loss1: 0.104528 Loss2: 1.322197 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.353178 Loss1: 0.046361 Loss2: 1.306817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.313341 Loss1: 0.027566 Loss2: 1.285775 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.764687 Loss1: 0.402290 Loss2: 1.362397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551039 Loss1: 0.183044 Loss2: 1.367995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.337396 Loss1: 0.485000 Loss2: 1.852396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.741993 Loss1: 0.366272 Loss2: 1.375721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.765059 Loss1: 0.311696 Loss2: 1.453363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.668717 Loss1: 0.276560 Loss2: 1.392156 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570457 Loss1: 0.171993 Loss2: 1.398464 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531067 Loss1: 0.140358 Loss2: 1.390709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458598 Loss1: 0.088628 Loss2: 1.369970 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419722 Loss1: 0.059272 Loss2: 1.360450 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.650223 Loss1: 0.305244 Loss2: 1.344978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502904 Loss1: 0.150359 Loss2: 1.352545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.213526 Loss1: 0.452178 Loss2: 1.761347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.713436 Loss1: 0.377589 Loss2: 1.335847 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590894 Loss1: 0.212719 Loss2: 1.378175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573031 Loss1: 0.226698 Loss2: 1.346333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.534453 Loss1: 0.177151 Loss2: 1.357302 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395763 Loss1: 0.065732 Loss2: 1.330032 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.353900 Loss1: 0.040171 Loss2: 1.313729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.342358 Loss1: 0.030781 Loss2: 1.311577 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594849 Loss1: 0.152202 Loss2: 1.442647 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.545466 Loss1: 0.145664 Loss2: 1.399802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502873 Loss1: 0.103599 Loss2: 1.399274
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.418679 Loss1: 0.470016 Loss2: 1.948663
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.813955 Loss1: 0.376935 Loss2: 1.437021 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.733601 Loss1: 0.246473 Loss2: 1.487128 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.633496 Loss1: 0.195289 Loss2: 1.438207 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424818 Loss1: 0.043989 Loss2: 1.380829
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.600937 Loss1: 0.163252 Loss2: 1.437685
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.535701 Loss1: 0.098198 Loss2: 1.437503
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.519151 Loss1: 0.091053 Loss2: 1.428099
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510329 Loss1: 0.091056 Loss2: 1.419274
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473186 Loss1: 0.059303 Loss2: 1.413883
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.477709 Loss1: 0.591239 Loss2: 1.886470
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.474920 Loss1: 0.067511 Loss2: 1.407409
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.654395 Loss1: 0.213572 Loss2: 1.440822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527515 Loss1: 0.141694 Loss2: 1.385821 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537756 Loss1: 0.144005 Loss2: 1.393750
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.376222 Loss1: 0.522103 Loss2: 1.854118
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.693313 Loss1: 0.329801 Loss2: 1.363511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563137 Loss1: 0.165758 Loss2: 1.397379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555023 Loss1: 0.185778 Loss2: 1.369245 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443263 Loss1: 0.070258 Loss2: 1.373004
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495447 Loss1: 0.123297 Loss2: 1.372150
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461827 Loss1: 0.096890 Loss2: 1.364937
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455425 Loss1: 0.099007 Loss2: 1.356418
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454995 Loss1: 0.101554 Loss2: 1.353441
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429689 Loss1: 0.076331 Loss2: 1.353358
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.316180 Loss1: 0.435880 Loss2: 1.880300
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390892 Loss1: 0.043235 Loss2: 1.347657
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.583285 Loss1: 0.167882 Loss2: 1.415403 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507866 Loss1: 0.132870 Loss2: 1.374996 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473815 Loss1: 0.108726 Loss2: 1.365089
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.411696 Loss1: 0.543035 Loss2: 1.868661
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.729776 Loss1: 0.362135 Loss2: 1.367641 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636120 Loss1: 0.236958 Loss2: 1.399163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573930 Loss1: 0.195763 Loss2: 1.378167 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.498847 Loss1: 0.121720 Loss2: 1.377128
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.494944 Loss1: 0.133613 Loss2: 1.361330
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.438555 Loss1: 0.071534 Loss2: 1.367021
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.403279 Loss1: 0.055974 Loss2: 1.347305
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389720 Loss1: 0.043663 Loss2: 1.346056
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401579 Loss1: 0.060335 Loss2: 1.341245
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.617955 Loss1: 0.652061 Loss2: 1.965894
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398918 Loss1: 0.061199 Loss2: 1.337719
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.673628 Loss1: 0.256639 Loss2: 1.416989 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.449290 Loss1: 0.098133 Loss2: 1.351157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415560 Loss1: 0.066427 Loss2: 1.349134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.392045 Loss1: 0.050835 Loss2: 1.341211 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.383028 Loss1: 0.045812 Loss2: 1.337216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381027 Loss1: 0.047386 Loss2: 1.333641 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997596
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512818 Loss1: 0.131340 Loss2: 1.381479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460856 Loss1: 0.079793 Loss2: 1.381063 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453173 Loss1: 0.085144 Loss2: 1.368029
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.439562 Loss1: 0.582677 Loss2: 1.856885
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422366 Loss1: 0.060829 Loss2: 1.361538
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.746198 Loss1: 0.358517 Loss2: 1.387681
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417048 Loss1: 0.062248 Loss2: 1.354800
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.625520 Loss1: 0.203752 Loss2: 1.421768
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524011 Loss1: 0.163072 Loss2: 1.360939
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498758 Loss1: 0.127171 Loss2: 1.371587
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.486521 Loss1: 0.125785 Loss2: 1.360736
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416460 Loss1: 0.059493 Loss2: 1.356967
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.400299 Loss1: 0.582203 Loss2: 1.818096
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436207 Loss1: 0.081648 Loss2: 1.354559
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.743422 Loss1: 0.406626 Loss2: 1.336797
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.416049 Loss1: 0.061600 Loss2: 1.354450
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.557249 Loss1: 0.178499 Loss2: 1.378750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403439 Loss1: 0.060690 Loss2: 1.342749
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.432132 Loss1: 0.103161 Loss2: 1.328971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.489714 Loss1: 0.178135 Loss2: 1.311579 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440294 Loss1: 0.115107 Loss2: 1.325188
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.356818 Loss1: 0.516239 Loss2: 1.840579
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422635 Loss1: 0.111000 Loss2: 1.311635
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.679973 Loss1: 0.335700 Loss2: 1.344273
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406635 Loss1: 0.095924 Loss2: 1.310711
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.651129 Loss1: 0.255688 Loss2: 1.395441
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.608763 Loss1: 0.256143 Loss2: 1.352620
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.530520 Loss1: 0.181312 Loss2: 1.349208
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460660 Loss1: 0.104254 Loss2: 1.356406
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434477 Loss1: 0.101221 Loss2: 1.333255
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461905 Loss1: 0.126654 Loss2: 1.335251
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.334172 Loss1: 0.500348 Loss2: 1.833824
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434429 Loss1: 0.098270 Loss2: 1.336159
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.611078 Loss1: 0.275504 Loss2: 1.335574
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423321 Loss1: 0.089362 Loss2: 1.333959
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.575698 Loss1: 0.211768 Loss2: 1.363929
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496059 Loss1: 0.154652 Loss2: 1.341407
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460509 Loss1: 0.121752 Loss2: 1.338757
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421046 Loss1: 0.090858 Loss2: 1.330187
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.391691 Loss1: 0.071436 Loss2: 1.320255
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388567 Loss1: 0.070506 Loss2: 1.318061
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.371728 Loss1: 0.528483 Loss2: 1.843245
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370151 Loss1: 0.064484 Loss2: 1.305667
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.746875 Loss1: 0.383841 Loss2: 1.363034
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.344867 Loss1: 0.039535 Loss2: 1.305332
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.628887 Loss1: 0.208608 Loss2: 1.420279
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520248 Loss1: 0.160507 Loss2: 1.359742
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507596 Loss1: 0.140977 Loss2: 1.366619
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.501421 Loss1: 0.137423 Loss2: 1.363998
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475481 Loss1: 0.116208 Loss2: 1.359273
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.402505 Loss1: 0.545256 Loss2: 1.857249
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430929 Loss1: 0.083514 Loss2: 1.347415
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.682085 Loss1: 0.331936 Loss2: 1.350148
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414509 Loss1: 0.068460 Loss2: 1.346049
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.621623 Loss1: 0.226326 Loss2: 1.395297
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443137 Loss1: 0.093830 Loss2: 1.349307
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549306 Loss1: 0.180499 Loss2: 1.368807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464617 Loss1: 0.103781 Loss2: 1.360836 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453651 Loss1: 0.102470 Loss2: 1.351181
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.428554 Loss1: 0.579203 Loss2: 1.849351
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439434 Loss1: 0.094668 Loss2: 1.344766
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.772688 Loss1: 0.389742 Loss2: 1.382947
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454034 Loss1: 0.102512 Loss2: 1.351521
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.631232 Loss1: 0.215248 Loss2: 1.415984
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549069 Loss1: 0.180760 Loss2: 1.368309
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565579 Loss1: 0.182924 Loss2: 1.382655
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502187 Loss1: 0.132405 Loss2: 1.369783
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.494029 Loss1: 0.134080 Loss2: 1.359949
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447459 Loss1: 0.094638 Loss2: 1.352821
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.242686 Loss1: 0.434820 Loss2: 1.807866
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418404 Loss1: 0.068064 Loss2: 1.350341
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.622712 Loss1: 0.270192 Loss2: 1.352519
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374298 Loss1: 0.035972 Loss2: 1.338326
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.509165 Loss1: 0.143893 Loss2: 1.365272
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.477878 Loss1: 0.142662 Loss2: 1.335216
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.430335 Loss1: 0.091785 Loss2: 1.338551
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.419296 Loss1: 0.087438 Loss2: 1.331858
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392829 Loss1: 0.070383 Loss2: 1.322446
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.246943 Loss1: 0.428827 Loss2: 1.818116
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.599943 Loss1: 0.284065 Loss2: 1.315877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561907 Loss1: 0.215267 Loss2: 1.346640 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423506 Loss1: 0.100441 Loss2: 1.323065
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496352 Loss1: 0.161988 Loss2: 1.334364
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451224 Loss1: 0.136259 Loss2: 1.314965
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.417430 Loss1: 0.100383 Loss2: 1.317047
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.381843 Loss1: 0.076320 Loss2: 1.305523
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.374556 Loss1: 0.068399 Loss2: 1.306157
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.248154 Loss1: 0.425489 Loss2: 1.822664
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447233 Loss1: 0.137990 Loss2: 1.309243
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412348 Loss1: 0.108762 Loss2: 1.303587
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.598178 Loss1: 0.247079 Loss2: 1.351099
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563382 Loss1: 0.203501 Loss2: 1.359881
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539355 Loss1: 0.187957 Loss2: 1.351397
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455455 Loss1: 0.113014 Loss2: 1.342441
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427992 Loss1: 0.093695 Loss2: 1.334297
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.282163 Loss1: 0.493352 Loss2: 1.788812
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.681781 Loss1: 0.372926 Loss2: 1.308855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424865 Loss1: 0.095339 Loss2: 1.329526
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.565190 Loss1: 0.215491 Loss2: 1.349699
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401338 Loss1: 0.073612 Loss2: 1.327726
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.481996 Loss1: 0.161701 Loss2: 1.320295
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463102 Loss1: 0.146386 Loss2: 1.316716
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376995 Loss1: 0.059990 Loss2: 1.317005
+(DefaultActor pid=3764) >> Training accuracy: 0.994485
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.417334 Loss1: 0.112168 Loss2: 1.305166 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402335 Loss1: 0.095660 Loss2: 1.306674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354435 Loss1: 0.048536 Loss2: 1.305899
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.453674 Loss1: 0.562275 Loss2: 1.891399
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.718583 Loss1: 0.358709 Loss2: 1.359875
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.596761 Loss1: 0.194193 Loss2: 1.402567
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.548824 Loss1: 0.176875 Loss2: 1.371949
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524235 Loss1: 0.164645 Loss2: 1.359590
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476879 Loss1: 0.120532 Loss2: 1.356347
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.332680 Loss1: 0.497754 Loss2: 1.834926
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.612131 Loss1: 0.269444 Loss2: 1.342687 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561273 Loss1: 0.200467 Loss2: 1.360806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498042 Loss1: 0.141316 Loss2: 1.356725 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.444102 Loss1: 0.099491 Loss2: 1.344612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.372760 Loss1: 0.041392 Loss2: 1.331369 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.345728 Loss1: 0.027414 Loss2: 1.318314 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358830 Loss1: 0.045213 Loss2: 1.313617 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.687996 Loss1: 0.239983 Loss2: 1.448013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.531058 Loss1: 0.116281 Loss2: 1.414777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.496458 Loss1: 0.095357 Loss2: 1.401101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.472072 Loss1: 0.085719 Loss2: 1.386353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457964 Loss1: 0.073909 Loss2: 1.384055 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442961 Loss1: 0.058772 Loss2: 1.384189 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.552713 Loss1: 0.144083 Loss2: 1.408630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512977 Loss1: 0.112975 Loss2: 1.400001 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473536 Loss1: 0.067828 Loss2: 1.405708
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.335363 Loss1: 0.489328 Loss2: 1.846035
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.442656 Loss1: 0.049330 Loss2: 1.393326
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.715209 Loss1: 0.365990 Loss2: 1.349219
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428400 Loss1: 0.038497 Loss2: 1.389903
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.613053 Loss1: 0.218189 Loss2: 1.394865
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539056 Loss1: 0.182851 Loss2: 1.356205
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.532553 Loss1: 0.172304 Loss2: 1.360250
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.497481 Loss1: 0.143229 Loss2: 1.354252
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443816 Loss1: 0.096216 Loss2: 1.347600
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414308 Loss1: 0.068909 Loss2: 1.345399
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.449888 Loss1: 0.497472 Loss2: 1.952416
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440640 Loss1: 0.103124 Loss2: 1.337516
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751053 Loss1: 0.305090 Loss2: 1.445963
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392722 Loss1: 0.060610 Loss2: 1.332112
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.685986 Loss1: 0.218224 Loss2: 1.467762
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.651883 Loss1: 0.204236 Loss2: 1.447647
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.659997 Loss1: 0.207033 Loss2: 1.452964
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.569443 Loss1: 0.123720 Loss2: 1.445724
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523872 Loss1: 0.091221 Loss2: 1.432650
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.483814 Loss1: 0.581317 Loss2: 1.902497
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.485224 Loss1: 0.065520 Loss2: 1.419704
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474388 Loss1: 0.053586 Loss2: 1.420803
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.803833 Loss1: 0.367084 Loss2: 1.436749
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.456681 Loss1: 0.043049 Loss2: 1.413631
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.644634 Loss1: 0.198855 Loss2: 1.445779
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573256 Loss1: 0.158305 Loss2: 1.414950
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.553067 Loss1: 0.143520 Loss2: 1.409547
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493831 Loss1: 0.092539 Loss2: 1.401291
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.505592 Loss1: 0.110726 Loss2: 1.394865
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.302112 Loss1: 0.495466 Loss2: 1.806646
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.691244 Loss1: 0.330270 Loss2: 1.360974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610327 Loss1: 0.220032 Loss2: 1.390295 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506182 Loss1: 0.160913 Loss2: 1.345270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476046 Loss1: 0.128222 Loss2: 1.347824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439201 Loss1: 0.102615 Loss2: 1.336586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405700 Loss1: 0.069562 Loss2: 1.336138 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398777 Loss1: 0.066376 Loss2: 1.332401 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.431229 Loss1: 0.103666 Loss2: 1.327563 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415621 Loss1: 0.096814 Loss2: 1.318807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.394957 Loss1: 0.079683 Loss2: 1.315274
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.447678 Loss1: 0.565561 Loss2: 1.882117
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.356977 Loss1: 0.048182 Loss2: 1.308795
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.825776 Loss1: 0.421010 Loss2: 1.404767
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.321396 Loss1: 0.022527 Loss2: 1.298869
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.658656 Loss1: 0.219886 Loss2: 1.438770
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.525972 Loss1: 0.129170 Loss2: 1.396802
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.500265 Loss1: 0.115867 Loss2: 1.384398
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491475 Loss1: 0.112137 Loss2: 1.379338
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.525699 Loss1: 0.146261 Loss2: 1.379439
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.600294 Loss1: 0.648656 Loss2: 1.951639
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483598 Loss1: 0.109514 Loss2: 1.374084
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.410864 Loss1: 0.046551 Loss2: 1.364314 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405532 Loss1: 0.048003 Loss2: 1.357529 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.426899 Loss1: 0.106899 Loss2: 1.319999 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418096 Loss1: 0.093104 Loss2: 1.324992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402483 Loss1: 0.081128 Loss2: 1.321355 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.514684 Loss1: 0.146421 Loss2: 1.368263 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473072 Loss1: 0.139278 Loss2: 1.333795 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416811 Loss1: 0.087973 Loss2: 1.328838
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.292009 Loss1: 0.471116 Loss2: 1.820894
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.421615 Loss1: 0.094280 Loss2: 1.327335
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.683645 Loss1: 0.316371 Loss2: 1.367275
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.385711 Loss1: 0.059857 Loss2: 1.325854
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564413 Loss1: 0.170042 Loss2: 1.394372
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537519 Loss1: 0.177934 Loss2: 1.359585 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370176 Loss1: 0.057325 Loss2: 1.312851
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518776 Loss1: 0.144836 Loss2: 1.373940
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486699 Loss1: 0.128345 Loss2: 1.358354
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458742 Loss1: 0.096032 Loss2: 1.362711
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415505 Loss1: 0.062356 Loss2: 1.353148
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409324 Loss1: 0.061805 Loss2: 1.347518
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.188558 Loss1: 0.437672 Loss2: 1.750886
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.588081 Loss1: 0.280382 Loss2: 1.307698 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.456655 Loss1: 0.144702 Loss2: 1.311953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.446002 Loss1: 0.140637 Loss2: 1.305365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.403597 Loss1: 0.092331 Loss2: 1.311266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398405 Loss1: 0.092671 Loss2: 1.305734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.347401 Loss1: 0.049887 Loss2: 1.297514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.349651 Loss1: 0.057774 Loss2: 1.291878 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.534953 Loss1: 0.159291 Loss2: 1.375663 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.459313 Loss1: 0.090472 Loss2: 1.368841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448154 Loss1: 0.086895 Loss2: 1.361259
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.299732 Loss1: 0.486124 Loss2: 1.813608
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.713228 Loss1: 0.350861 Loss2: 1.362367
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.617667 Loss1: 0.214770 Loss2: 1.402897
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.538606 Loss1: 0.177259 Loss2: 1.361347
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461635 Loss1: 0.109974 Loss2: 1.351661
+DEBUG flwr 2023-10-12 15:48:54,176 | server.py:236 | fit_round 156 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.240408 Loss1: 0.403579 Loss2: 1.836828
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471239 Loss1: 0.120067 Loss2: 1.351172
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.666602 Loss1: 0.304054 Loss2: 1.362548
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418179 Loss1: 0.078156 Loss2: 1.340023
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.583403 Loss1: 0.190657 Loss2: 1.392746
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424983 Loss1: 0.087555 Loss2: 1.337428
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.508406 Loss1: 0.153811 Loss2: 1.354595
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407382 Loss1: 0.073926 Loss2: 1.333457
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492158 Loss1: 0.132574 Loss2: 1.359584
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397371 Loss1: 0.070065 Loss2: 1.327306
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.449193 Loss1: 0.102549 Loss2: 1.346644 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391466 Loss1: 0.053283 Loss2: 1.338184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.287712 Loss1: 0.442550 Loss2: 1.845162
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401783 Loss1: 0.062166 Loss2: 1.339617
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.584545 Loss1: 0.202179 Loss2: 1.382367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490493 Loss1: 0.128910 Loss2: 1.361583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481533 Loss1: 0.119144 Loss2: 1.362389
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.586007 Loss1: 0.652547 Loss2: 1.933460
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.841324 Loss1: 0.451972 Loss2: 1.389352
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441047 Loss1: 0.084605 Loss2: 1.356441
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653715 Loss1: 0.207667 Loss2: 1.446048
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432194 Loss1: 0.076668 Loss2: 1.355526
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555450 Loss1: 0.174223 Loss2: 1.381227
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408864 Loss1: 0.061109 Loss2: 1.347755
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.382004 Loss1: 0.041573 Loss2: 1.340431 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.523290 Loss1: 0.153507 Loss2: 1.369783 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.493373 Loss1: 0.118812 Loss2: 1.374562 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982143
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465397 Loss1: 0.092183 Loss2: 1.373214
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.351488 Loss1: 0.504318 Loss2: 1.847170
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.793470 Loss1: 0.433672 Loss2: 1.359798
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.695121 Loss1: 0.264338 Loss2: 1.430783
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561397 Loss1: 0.218642 Loss2: 1.342755
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526241 Loss1: 0.178945 Loss2: 1.347296
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.422544 Loss1: 0.600720 Loss2: 1.821824
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.737646 Loss1: 0.394747 Loss2: 1.342899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.712807 Loss1: 0.307408 Loss2: 1.405399 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497238 Loss1: 0.158900 Loss2: 1.338338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.498825 Loss1: 0.160244 Loss2: 1.338581 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354019 Loss1: 0.045054 Loss2: 1.308965
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429560 Loss1: 0.094812 Loss2: 1.334748
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.396335 Loss1: 0.069163 Loss2: 1.327172
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.379389 Loss1: 0.055376 Loss2: 1.324013
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386379 Loss1: 0.067839 Loss2: 1.318539
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357815 Loss1: 0.045969 Loss2: 1.311846
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 15:48:54,176][flwr][DEBUG] - fit_round 156 received 50 results and 0 failures
+INFO flwr 2023-10-12 15:49:37,514 | server.py:125 | fit progress: (156, 2.2486510133971804, {'accuracy': 0.5976}, 359885.292311408)
+>> Test accuracy: 0.597600
+[2023-10-12 15:49:37,514][flwr][INFO] - fit progress: (156, 2.2486510133971804, {'accuracy': 0.5976}, 359885.292311408)
+DEBUG flwr 2023-10-12 15:49:37,514 | server.py:173 | evaluate_round 156: strategy sampled 50 clients (out of 50)
+[2023-10-12 15:49:37,514][flwr][DEBUG] - evaluate_round 156: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 15:58:40,820 | server.py:187 | evaluate_round 156 received 50 results and 0 failures
+[2023-10-12 15:58:40,820][flwr][DEBUG] - evaluate_round 156 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 15:58:40,821 | server.py:222 | fit_round 157: strategy sampled 50 clients (out of 50)
+[2023-10-12 15:58:40,821][flwr][DEBUG] - fit_round 157: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.445224 Loss1: 0.556040 Loss2: 1.889184
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.723500 Loss1: 0.376111 Loss2: 1.347388
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.576604 Loss1: 0.191790 Loss2: 1.384815
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547592 Loss1: 0.190320 Loss2: 1.357272
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.305553 Loss1: 0.469798 Loss2: 1.835755
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.603719 Loss1: 0.251906 Loss2: 1.351813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.536656 Loss1: 0.167608 Loss2: 1.369049 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.473824 Loss1: 0.120757 Loss2: 1.353067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.451532 Loss1: 0.106039 Loss2: 1.345493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.433346 Loss1: 0.088783 Loss2: 1.344563 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382698 Loss1: 0.049602 Loss2: 1.333096 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377355 Loss1: 0.051118 Loss2: 1.326237 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.565445 Loss1: 0.239004 Loss2: 1.326440 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.495459 Loss1: 0.161813 Loss2: 1.333646 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470210 Loss1: 0.132884 Loss2: 1.337326 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470885 Loss1: 0.138750 Loss2: 1.332135 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449768 Loss1: 0.115314 Loss2: 1.334453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418130 Loss1: 0.089807 Loss2: 1.328323 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408603 Loss1: 0.081630 Loss2: 1.326973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391789 Loss1: 0.070257 Loss2: 1.321532 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436917 Loss1: 0.072585 Loss2: 1.364332 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.254869 Loss1: 0.428783 Loss2: 1.826086 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.552366 Loss1: 0.174418 Loss2: 1.377948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.305311 Loss1: 0.494458 Loss2: 1.810854
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524555 Loss1: 0.160205 Loss2: 1.364350
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.600709 Loss1: 0.281143 Loss2: 1.319566
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.501612 Loss1: 0.143609 Loss2: 1.358003
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.555066 Loss1: 0.202752 Loss2: 1.352313
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470250 Loss1: 0.107156 Loss2: 1.363094
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507183 Loss1: 0.178131 Loss2: 1.329052
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458705 Loss1: 0.100531 Loss2: 1.358173
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.407394 Loss1: 0.060589 Loss2: 1.346805 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405361 Loss1: 0.062191 Loss2: 1.343171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385836 Loss1: 0.045659 Loss2: 1.340177 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374203 Loss1: 0.074126 Loss2: 1.300077 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.183526 Loss1: 0.388169 Loss2: 1.795356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.513151 Loss1: 0.190504 Loss2: 1.322646 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474814 Loss1: 0.136996 Loss2: 1.337818
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.379449 Loss1: 0.518876 Loss2: 1.860574
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.415050 Loss1: 0.106491 Loss2: 1.308559
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.702310 Loss1: 0.336085 Loss2: 1.366225
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.391060 Loss1: 0.084841 Loss2: 1.306219
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.569882 Loss1: 0.170691 Loss2: 1.399190
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.375792 Loss1: 0.064647 Loss2: 1.311145
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.514420 Loss1: 0.142991 Loss2: 1.371429
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.349130 Loss1: 0.053475 Loss2: 1.295654
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530931 Loss1: 0.164962 Loss2: 1.365969
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.352423 Loss1: 0.063296 Loss2: 1.289127
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500525 Loss1: 0.128075 Loss2: 1.372450
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.319358 Loss1: 0.029180 Loss2: 1.290178
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472224 Loss1: 0.100597 Loss2: 1.371627
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.522445 Loss1: 0.156486 Loss2: 1.365959
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461069 Loss1: 0.096509 Loss2: 1.364560
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419004 Loss1: 0.060790 Loss2: 1.358214
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.509955 Loss1: 0.618871 Loss2: 1.891084
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.760437 Loss1: 0.361556 Loss2: 1.398881
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.661861 Loss1: 0.229029 Loss2: 1.432833
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549990 Loss1: 0.155545 Loss2: 1.394446
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.500722 Loss1: 0.594182 Loss2: 1.906540
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.688787 Loss1: 0.300539 Loss2: 1.388247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590570 Loss1: 0.193499 Loss2: 1.397071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537648 Loss1: 0.149996 Loss2: 1.387652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520669 Loss1: 0.138076 Loss2: 1.382592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.428625 Loss1: 0.057236 Loss2: 1.371389 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389717 Loss1: 0.034780 Loss2: 1.354936
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406602 Loss1: 0.047170 Loss2: 1.359432
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409009 Loss1: 0.056023 Loss2: 1.352986
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387060 Loss1: 0.041335 Loss2: 1.345725
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393135 Loss1: 0.044780 Loss2: 1.348355
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.399865 Loss1: 0.463776 Loss2: 1.936089
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.717293 Loss1: 0.296608 Loss2: 1.420684
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.650374 Loss1: 0.199961 Loss2: 1.450412
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.635734 Loss1: 0.204773 Loss2: 1.430962
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.346035 Loss1: 0.526927 Loss2: 1.819108
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.589550 Loss1: 0.260388 Loss2: 1.329162 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.519234 Loss1: 0.180023 Loss2: 1.339211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.459447 Loss1: 0.130229 Loss2: 1.329219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.430100 Loss1: 0.114715 Loss2: 1.315385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.396277 Loss1: 0.079160 Loss2: 1.317117 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.377260 Loss1: 0.069543 Loss2: 1.307716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.365226 Loss1: 0.061534 Loss2: 1.303692 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.479348 Loss1: 0.540385 Loss2: 1.938963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.680474 Loss1: 0.223751 Loss2: 1.456723 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594535 Loss1: 0.155002 Loss2: 1.439533
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.395101 Loss1: 0.529064 Loss2: 1.866037
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.660966 Loss1: 0.298320 Loss2: 1.362646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572372 Loss1: 0.188850 Loss2: 1.383522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.493024 Loss1: 0.126629 Loss2: 1.366395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.464619 Loss1: 0.109984 Loss2: 1.354635 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.410649 Loss1: 0.061261 Loss2: 1.349388 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.967708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.501508 Loss1: 0.095014 Loss2: 1.406494
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.411922 Loss1: 0.074366 Loss2: 1.337557
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390176 Loss1: 0.061991 Loss2: 1.328185
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388582 Loss1: 0.058444 Loss2: 1.330139
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373700 Loss1: 0.046269 Loss2: 1.327432
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.315720 Loss1: 0.443408 Loss2: 1.872312
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.680969 Loss1: 0.280208 Loss2: 1.400761
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.625991 Loss1: 0.209826 Loss2: 1.416165
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.551872 Loss1: 0.631909 Loss2: 1.919963
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.600249 Loss1: 0.197747 Loss2: 1.402502
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611688 Loss1: 0.197570 Loss2: 1.414117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.556145 Loss1: 0.164509 Loss2: 1.391636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.527648 Loss1: 0.133250 Loss2: 1.394398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476241 Loss1: 0.138427 Loss2: 1.337814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436191 Loss1: 0.101495 Loss2: 1.334696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420923 Loss1: 0.100010 Loss2: 1.320913 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443415 Loss1: 0.078716 Loss2: 1.364699
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363221 Loss1: 0.045113 Loss2: 1.318109
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394937 Loss1: 0.089353 Loss2: 1.305584
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.290831 Loss1: 0.480339 Loss2: 1.810492
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.714783 Loss1: 0.370317 Loss2: 1.344465
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.583553 Loss1: 0.208350 Loss2: 1.375202
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498251 Loss1: 0.172759 Loss2: 1.325493
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.243224 Loss1: 0.413554 Loss2: 1.829671
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463742 Loss1: 0.118591 Loss2: 1.345151
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.629379 Loss1: 0.264579 Loss2: 1.364800
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.400420 Loss1: 0.082229 Loss2: 1.318190
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.569436 Loss1: 0.173745 Loss2: 1.395691
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422344 Loss1: 0.105549 Loss2: 1.316796
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481669 Loss1: 0.125710 Loss2: 1.355959
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.386066 Loss1: 0.076712 Loss2: 1.309353
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.490627 Loss1: 0.125065 Loss2: 1.365562
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.350931 Loss1: 0.045910 Loss2: 1.305021
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466219 Loss1: 0.110815 Loss2: 1.355404
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356351 Loss1: 0.051938 Loss2: 1.304414
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440927 Loss1: 0.089042 Loss2: 1.351885
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482700 Loss1: 0.129059 Loss2: 1.353641
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457021 Loss1: 0.100665 Loss2: 1.356356
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.432213 Loss1: 0.079771 Loss2: 1.352442
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.315962 Loss1: 0.477849 Loss2: 1.838113
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.654494 Loss1: 0.265205 Loss2: 1.389289
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.608450 Loss1: 0.191140 Loss2: 1.417310
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555317 Loss1: 0.168635 Loss2: 1.386681
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.357881 Loss1: 0.507314 Loss2: 1.850566
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.511178 Loss1: 0.126182 Loss2: 1.384996
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.708051 Loss1: 0.341738 Loss2: 1.366313
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598400 Loss1: 0.209606 Loss2: 1.388794
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480734 Loss1: 0.103184 Loss2: 1.377550
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.504192 Loss1: 0.139752 Loss2: 1.364440
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472970 Loss1: 0.104688 Loss2: 1.368282
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.477160 Loss1: 0.123703 Loss2: 1.353457
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449260 Loss1: 0.080364 Loss2: 1.368896
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450227 Loss1: 0.095820 Loss2: 1.354407
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465967 Loss1: 0.098392 Loss2: 1.367575
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432129 Loss1: 0.065727 Loss2: 1.366401 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420493 Loss1: 0.071247 Loss2: 1.349246 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.206958 Loss1: 0.402766 Loss2: 1.804191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574195 Loss1: 0.205123 Loss2: 1.369073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.265530 Loss1: 0.454541 Loss2: 1.810989
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517823 Loss1: 0.169412 Loss2: 1.348410
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.694368 Loss1: 0.376581 Loss2: 1.317787
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461196 Loss1: 0.128234 Loss2: 1.332962
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455649 Loss1: 0.116794 Loss2: 1.338855 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416639 Loss1: 0.085239 Loss2: 1.331400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422323 Loss1: 0.096984 Loss2: 1.325339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377846 Loss1: 0.054651 Loss2: 1.323194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385940 Loss1: 0.063743 Loss2: 1.322197 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370967 Loss1: 0.071660 Loss2: 1.299307 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.424759 Loss1: 0.590785 Loss2: 1.833974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.670082 Loss1: 0.268719 Loss2: 1.401363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558480 Loss1: 0.196146 Loss2: 1.362334
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.277574 Loss1: 0.421384 Loss2: 1.856190
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.580996 Loss1: 0.215353 Loss2: 1.365643
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.660678 Loss1: 0.310596 Loss2: 1.350082
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.510610 Loss1: 0.138327 Loss2: 1.372283
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.612478 Loss1: 0.212576 Loss2: 1.399902
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496012 Loss1: 0.131860 Loss2: 1.364153
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554481 Loss1: 0.197994 Loss2: 1.356486
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.489430 Loss1: 0.134289 Loss2: 1.355142
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.538602 Loss1: 0.181714 Loss2: 1.356888
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437732 Loss1: 0.080822 Loss2: 1.356910
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477505 Loss1: 0.124907 Loss2: 1.352598
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421794 Loss1: 0.083883 Loss2: 1.337912
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455726 Loss1: 0.108100 Loss2: 1.347625
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449125 Loss1: 0.102644 Loss2: 1.346481
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413046 Loss1: 0.072071 Loss2: 1.340975
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402593 Loss1: 0.066362 Loss2: 1.336231
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.313654 Loss1: 0.384566 Loss2: 1.929088
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.718063 Loss1: 0.303185 Loss2: 1.414879
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635434 Loss1: 0.203035 Loss2: 1.432399
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.588694 Loss1: 0.162110 Loss2: 1.426584
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.285162 Loss1: 0.446475 Loss2: 1.838687
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589240 Loss1: 0.176441 Loss2: 1.412799
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.631251 Loss1: 0.298411 Loss2: 1.332840
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.575015 Loss1: 0.151148 Loss2: 1.423867
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600175 Loss1: 0.229925 Loss2: 1.370250
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.527263 Loss1: 0.107702 Loss2: 1.419561
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.495715 Loss1: 0.155280 Loss2: 1.340434
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.522634 Loss1: 0.103201 Loss2: 1.419433
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.445800 Loss1: 0.122046 Loss2: 1.323754
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.458814 Loss1: 0.051029 Loss2: 1.407785
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423136 Loss1: 0.096763 Loss2: 1.326372
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466378 Loss1: 0.066949 Loss2: 1.399428
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.378787 Loss1: 0.064359 Loss2: 1.314429
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.364545 Loss1: 0.053138 Loss2: 1.311407
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.356790 Loss1: 0.051799 Loss2: 1.304991
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.345297 Loss1: 0.042103 Loss2: 1.303194
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.507113 Loss1: 0.585427 Loss2: 1.921686
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.739231 Loss1: 0.355660 Loss2: 1.383571
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.631595 Loss1: 0.220581 Loss2: 1.411015
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596142 Loss1: 0.192002 Loss2: 1.404140
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.307335 Loss1: 0.512219 Loss2: 1.795116
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.690401 Loss1: 0.365851 Loss2: 1.324550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.491707 Loss1: 0.109203 Loss2: 1.382504 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462453 Loss1: 0.091814 Loss2: 1.370639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453393 Loss1: 0.085940 Loss2: 1.367453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431750 Loss1: 0.061413 Loss2: 1.370337 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436885 Loss1: 0.119527 Loss2: 1.317358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371660 Loss1: 0.062705 Loss2: 1.308955 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.355316 Loss1: 0.050334 Loss2: 1.304981
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.314778 Loss1: 0.519703 Loss2: 1.795074
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.676582 Loss1: 0.363751 Loss2: 1.312831
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.584080 Loss1: 0.220749 Loss2: 1.363331
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482908 Loss1: 0.165617 Loss2: 1.317290
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.429472 Loss1: 0.109606 Loss2: 1.319866
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.532911 Loss1: 0.614673 Loss2: 1.918238
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.729269 Loss1: 0.354919 Loss2: 1.374350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635245 Loss1: 0.227844 Loss2: 1.407401 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.383200 Loss1: 0.084817 Loss2: 1.298384
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.582920 Loss1: 0.193132 Loss2: 1.389788
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.368902 Loss1: 0.064148 Loss2: 1.304754
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530492 Loss1: 0.158020 Loss2: 1.372472
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471424 Loss1: 0.102289 Loss2: 1.369135
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.340393 Loss1: 0.045532 Loss2: 1.294860
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427272 Loss1: 0.065328 Loss2: 1.361944 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382584 Loss1: 0.038441 Loss2: 1.344143 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.608133 Loss1: 0.309212 Loss2: 1.298922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.462169 Loss1: 0.150140 Loss2: 1.312029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.432241 Loss1: 0.137694 Loss2: 1.294547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.811023 Loss1: 0.404310 Loss2: 1.406713
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.414558 Loss1: 0.112607 Loss2: 1.301951
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.665806 Loss1: 0.234393 Loss2: 1.431413
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.396400 Loss1: 0.095982 Loss2: 1.300418
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640270 Loss1: 0.218878 Loss2: 1.421392
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.356454 Loss1: 0.063958 Loss2: 1.292497
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371055 Loss1: 0.082354 Loss2: 1.288701
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.559358 Loss1: 0.155064 Loss2: 1.404294
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.333396 Loss1: 0.045989 Loss2: 1.287406
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531308 Loss1: 0.132756 Loss2: 1.398553
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470204 Loss1: 0.077983 Loss2: 1.392222
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442389 Loss1: 0.058125 Loss2: 1.384264
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436272 Loss1: 0.058875 Loss2: 1.377398
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.462242 Loss1: 0.089777 Loss2: 1.372465
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.328598 Loss1: 0.489723 Loss2: 1.838875
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.664697 Loss1: 0.301361 Loss2: 1.363335
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599018 Loss1: 0.200706 Loss2: 1.398312
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499401 Loss1: 0.150091 Loss2: 1.349311
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.536762 Loss1: 0.174888 Loss2: 1.361874
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.669630 Loss1: 0.593938 Loss2: 2.075692
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.836734 Loss1: 0.413466 Loss2: 1.423267
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518311 Loss1: 0.153475 Loss2: 1.364836
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473545 Loss1: 0.113896 Loss2: 1.359649 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427847 Loss1: 0.080128 Loss2: 1.347719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.466188 Loss1: 0.123781 Loss2: 1.342407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.527950 Loss1: 0.104926 Loss2: 1.423024 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.519987 Loss1: 0.112443 Loss2: 1.407544 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.328688 Loss1: 0.482737 Loss2: 1.845951 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.531841 Loss1: 0.169602 Loss2: 1.362240 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491140 Loss1: 0.137805 Loss2: 1.353334
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.360830 Loss1: 0.501756 Loss2: 1.859074
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.656010 Loss1: 0.310477 Loss2: 1.345533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.640593 Loss1: 0.242663 Loss2: 1.397930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564558 Loss1: 0.205907 Loss2: 1.358651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505341 Loss1: 0.151478 Loss2: 1.353863 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467604 Loss1: 0.109474 Loss2: 1.358130 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370120 Loss1: 0.035866 Loss2: 1.334254
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446184 Loss1: 0.100146 Loss2: 1.346039
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457889 Loss1: 0.115315 Loss2: 1.342574
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462275 Loss1: 0.117268 Loss2: 1.345007
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409370 Loss1: 0.065878 Loss2: 1.343492
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.569735 Loss1: 0.636132 Loss2: 1.933602
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.784523 Loss1: 0.370247 Loss2: 1.414275
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.633759 Loss1: 0.200786 Loss2: 1.432973
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591396 Loss1: 0.188304 Loss2: 1.403092
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.401794 Loss1: 0.580027 Loss2: 1.821766
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.486905 Loss1: 0.066651 Loss2: 1.420254
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.647681 Loss1: 0.322922 Loss2: 1.324759
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497138 Loss1: 0.115269 Loss2: 1.381869
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.532100 Loss1: 0.188970 Loss2: 1.343131
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.474366 Loss1: 0.154095 Loss2: 1.320271
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483132 Loss1: 0.096161 Loss2: 1.386971
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.432288 Loss1: 0.126205 Loss2: 1.306083
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433171 Loss1: 0.052020 Loss2: 1.381151
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.379307 Loss1: 0.082214 Loss2: 1.297093
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419036 Loss1: 0.044382 Loss2: 1.374654
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414035 Loss1: 0.043413 Loss2: 1.370622 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.382226 Loss1: 0.095670 Loss2: 1.286557 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.331967 Loss1: 0.480332 Loss2: 1.851636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624347 Loss1: 0.238840 Loss2: 1.385507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.624695 Loss1: 0.249629 Loss2: 1.375066
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.231919 Loss1: 0.411521 Loss2: 1.820398
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.661229 Loss1: 0.306313 Loss2: 1.354915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.594611 Loss1: 0.209411 Loss2: 1.385200 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605436 Loss1: 0.252114 Loss2: 1.353322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.488113 Loss1: 0.130669 Loss2: 1.357444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366457 Loss1: 0.048994 Loss2: 1.317463 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420419 Loss1: 0.079633 Loss2: 1.340786 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416814 Loss1: 0.084267 Loss2: 1.332548 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982537
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.715627 Loss1: 0.344141 Loss2: 1.371486 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524059 Loss1: 0.159377 Loss2: 1.364682 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507321 Loss1: 0.151230 Loss2: 1.356091
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.446260 Loss1: 0.589455 Loss2: 1.856805
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.828979 Loss1: 0.452285 Loss2: 1.376694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636956 Loss1: 0.217321 Loss2: 1.419635 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530316 Loss1: 0.168354 Loss2: 1.361962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.470052 Loss1: 0.102670 Loss2: 1.367381 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446580 Loss1: 0.088095 Loss2: 1.358485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415128 Loss1: 0.071875 Loss2: 1.343253 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394355 Loss1: 0.056402 Loss2: 1.337952 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.600355 Loss1: 0.250565 Loss2: 1.349790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535670 Loss1: 0.166750 Loss2: 1.368920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525373 Loss1: 0.182714 Loss2: 1.342659
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.316597 Loss1: 0.474053 Loss2: 1.842544
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630057 Loss1: 0.282923 Loss2: 1.347133 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.555268 Loss1: 0.188050 Loss2: 1.367218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.515501 Loss1: 0.161301 Loss2: 1.354200 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484603 Loss1: 0.135483 Loss2: 1.349119 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406836 Loss1: 0.066148 Loss2: 1.340689
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427502 Loss1: 0.082156 Loss2: 1.345345
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436869 Loss1: 0.098469 Loss2: 1.338400
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457833 Loss1: 0.114412 Loss2: 1.343421
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408429 Loss1: 0.067402 Loss2: 1.341027
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406146 Loss1: 0.069788 Loss2: 1.336359
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.364329 Loss1: 0.515988 Loss2: 1.848341
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.734394 Loss1: 0.368053 Loss2: 1.366341
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.597546 Loss1: 0.189897 Loss2: 1.407649
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551292 Loss1: 0.184843 Loss2: 1.366449
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492074 Loss1: 0.114655 Loss2: 1.377419
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.341031 Loss1: 0.519299 Loss2: 1.821732
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.687991 Loss1: 0.316829 Loss2: 1.371162 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 16:27:53,474 | server.py:236 | fit_round 157 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.638557 Loss1: 0.245750 Loss2: 1.392807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.584880 Loss1: 0.211398 Loss2: 1.373482 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563597 Loss1: 0.191226 Loss2: 1.372372 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.534073 Loss1: 0.164899 Loss2: 1.369174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443697 Loss1: 0.083772 Loss2: 1.359925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381638 Loss1: 0.038966 Loss2: 1.342672 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.783935 Loss1: 0.263197 Loss2: 1.520738 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611955 Loss1: 0.156255 Loss2: 1.455700 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.338731 Loss1: 0.539727 Loss2: 1.799003
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.664311 Loss1: 0.209214 Loss2: 1.455097
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.699918 Loss1: 0.353788 Loss2: 1.346131
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.580206 Loss1: 0.131913 Loss2: 1.448293
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.664187 Loss1: 0.271636 Loss2: 1.392551
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.570019 Loss1: 0.125303 Loss2: 1.444716
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.504489 Loss1: 0.158295 Loss2: 1.346194
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.569382 Loss1: 0.119453 Loss2: 1.449929
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.465602 Loss1: 0.132070 Loss2: 1.333532
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.539700 Loss1: 0.099238 Loss2: 1.440462
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458611 Loss1: 0.123728 Loss2: 1.334883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383348 Loss1: 0.070045 Loss2: 1.313302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390992 Loss1: 0.075686 Loss2: 1.315306
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.377231 Loss1: 0.581477 Loss2: 1.795755
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.650971 Loss1: 0.315219 Loss2: 1.335752
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.560998 Loss1: 0.189251 Loss2: 1.371747
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498248 Loss1: 0.167011 Loss2: 1.331237
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.413102 Loss1: 0.081260 Loss2: 1.331842
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.449037 Loss1: 0.594100 Loss2: 1.854937
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.409544 Loss1: 0.092964 Loss2: 1.316580
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.758824 Loss1: 0.394158 Loss2: 1.364666
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.393185 Loss1: 0.083281 Loss2: 1.309904
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642672 Loss1: 0.238530 Loss2: 1.404142
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.388370 Loss1: 0.077080 Loss2: 1.311290
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556090 Loss1: 0.198631 Loss2: 1.357459
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409878 Loss1: 0.094336 Loss2: 1.315543
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495658 Loss1: 0.139682 Loss2: 1.355975
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396671 Loss1: 0.081632 Loss2: 1.315039
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.497956 Loss1: 0.149863 Loss2: 1.348092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419399 Loss1: 0.071788 Loss2: 1.347610 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 16:27:53,474][flwr][DEBUG] - fit_round 157 received 50 results and 0 failures
+INFO flwr 2023-10-12 16:28:36,026 | server.py:125 | fit progress: (157, 2.243841856051558, {'accuracy': 0.6005}, 362223.804914811)
+>> Test accuracy: 0.600500
+[2023-10-12 16:28:36,026][flwr][INFO] - fit progress: (157, 2.243841856051558, {'accuracy': 0.6005}, 362223.804914811)
+DEBUG flwr 2023-10-12 16:28:36,027 | server.py:173 | evaluate_round 157: strategy sampled 50 clients (out of 50)
+[2023-10-12 16:28:36,027][flwr][DEBUG] - evaluate_round 157: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 16:37:42,171 | server.py:187 | evaluate_round 157 received 50 results and 0 failures
+[2023-10-12 16:37:42,171][flwr][DEBUG] - evaluate_round 157 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 16:37:42,172 | server.py:222 | fit_round 158: strategy sampled 50 clients (out of 50)
+[2023-10-12 16:37:42,172][flwr][DEBUG] - fit_round 158: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.289313 Loss1: 0.473308 Loss2: 1.816005 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.625995 Loss1: 0.219490 Loss2: 1.406505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.401960 Loss1: 0.512164 Loss2: 1.889796
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547245 Loss1: 0.163419 Loss2: 1.383826
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.617099 Loss1: 0.242554 Loss2: 1.374545
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.504250 Loss1: 0.128757 Loss2: 1.375493
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492259 Loss1: 0.117500 Loss2: 1.374759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473413 Loss1: 0.094441 Loss2: 1.378972 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458129 Loss1: 0.095264 Loss2: 1.362866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440250 Loss1: 0.078182 Loss2: 1.362068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.444040 Loss1: 0.083285 Loss2: 1.360755 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.467974 Loss1: 0.110739 Loss2: 1.357235 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.484167 Loss1: 0.545297 Loss2: 1.938870 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.602910 Loss1: 0.186874 Loss2: 1.416035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.634739 Loss1: 0.226521 Loss2: 1.408217
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.240782 Loss1: 0.414895 Loss2: 1.825888
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.636088 Loss1: 0.268572 Loss2: 1.367516 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.586725 Loss1: 0.190504 Loss2: 1.396221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.502655 Loss1: 0.145568 Loss2: 1.357087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.515067 Loss1: 0.137399 Loss2: 1.377668 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.478743 Loss1: 0.100394 Loss2: 1.378350 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368920 Loss1: 0.035926 Loss2: 1.332994 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365448 Loss1: 0.040076 Loss2: 1.325372 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.612780 Loss1: 0.299136 Loss2: 1.313644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.495326 Loss1: 0.179354 Loss2: 1.315973 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498304 Loss1: 0.181616 Loss2: 1.316688
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.438979 Loss1: 0.579218 Loss2: 1.859761
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.697770 Loss1: 0.380467 Loss2: 1.317303 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473305 Loss1: 0.154997 Loss2: 1.318307
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556365 Loss1: 0.200059 Loss2: 1.356306
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.392329 Loss1: 0.081446 Loss2: 1.310883
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.473424 Loss1: 0.142163 Loss2: 1.331261
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.392200 Loss1: 0.087461 Loss2: 1.304739
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.443949 Loss1: 0.129437 Loss2: 1.314512
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.387001 Loss1: 0.071354 Loss2: 1.315647
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393486 Loss1: 0.095707 Loss2: 1.297779
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.357260 Loss1: 0.057628 Loss2: 1.299632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.329277 Loss1: 0.034875 Loss2: 1.294402 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.342800 Loss1: 0.472461 Loss2: 1.870339
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.722260 Loss1: 0.337635 Loss2: 1.384625
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.663201 Loss1: 0.230706 Loss2: 1.432495
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599119 Loss1: 0.198501 Loss2: 1.400619
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.335383 Loss1: 0.490056 Loss2: 1.845327
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.677617 Loss1: 0.307043 Loss2: 1.370574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.685972 Loss1: 0.272489 Loss2: 1.413483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565701 Loss1: 0.186052 Loss2: 1.379649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505914 Loss1: 0.141679 Loss2: 1.364235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494817 Loss1: 0.121929 Loss2: 1.372889 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406693 Loss1: 0.059016 Loss2: 1.347677 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410287 Loss1: 0.065523 Loss2: 1.344764 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.296906 Loss1: 0.473852 Loss2: 1.823053
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.665410 Loss1: 0.300652 Loss2: 1.364758
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.557895 Loss1: 0.163285 Loss2: 1.394610
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536837 Loss1: 0.174594 Loss2: 1.362243
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.367057 Loss1: 0.498115 Loss2: 1.868942
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.698252 Loss1: 0.327383 Loss2: 1.370869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.615717 Loss1: 0.192758 Loss2: 1.422959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.610583 Loss1: 0.230955 Loss2: 1.379628 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405854 Loss1: 0.062619 Loss2: 1.343235
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550924 Loss1: 0.170910 Loss2: 1.380014
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408150 Loss1: 0.065909 Loss2: 1.342241
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504007 Loss1: 0.125054 Loss2: 1.378953
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398912 Loss1: 0.060504 Loss2: 1.338408
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.454687 Loss1: 0.094001 Loss2: 1.360686
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435024 Loss1: 0.077852 Loss2: 1.357172
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399182 Loss1: 0.043539 Loss2: 1.355643
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383832 Loss1: 0.037449 Loss2: 1.346383
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.206078 Loss1: 0.437592 Loss2: 1.768485
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.654132 Loss1: 0.321657 Loss2: 1.332475
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.581880 Loss1: 0.215313 Loss2: 1.366567
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.284839 Loss1: 0.446901 Loss2: 1.837938
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484222 Loss1: 0.154917 Loss2: 1.329305
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630589 Loss1: 0.289703 Loss2: 1.340887
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.408789 Loss1: 0.087587 Loss2: 1.321203
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605366 Loss1: 0.231964 Loss2: 1.373402
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450870 Loss1: 0.127950 Loss2: 1.322920
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431331 Loss1: 0.107648 Loss2: 1.323683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404134 Loss1: 0.084444 Loss2: 1.319690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391845 Loss1: 0.079413 Loss2: 1.312431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377492 Loss1: 0.062753 Loss2: 1.314739 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421464 Loss1: 0.095396 Loss2: 1.326068 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.322684 Loss1: 0.535228 Loss2: 1.787456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.600695 Loss1: 0.248783 Loss2: 1.351912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.464393 Loss1: 0.153666 Loss2: 1.310727
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.432257 Loss1: 0.522172 Loss2: 1.910085
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.425322 Loss1: 0.114118 Loss2: 1.311205
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.643770 Loss1: 0.281365 Loss2: 1.362404
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.387699 Loss1: 0.081158 Loss2: 1.306541
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.585398 Loss1: 0.203509 Loss2: 1.381888
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.379054 Loss1: 0.080578 Loss2: 1.298476
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.573254 Loss1: 0.201368 Loss2: 1.371886
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378627 Loss1: 0.080948 Loss2: 1.297679
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518505 Loss1: 0.153760 Loss2: 1.364746
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.332482 Loss1: 0.043060 Loss2: 1.289422
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470313 Loss1: 0.110065 Loss2: 1.360249
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.338832 Loss1: 0.055346 Loss2: 1.283486
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428282 Loss1: 0.077227 Loss2: 1.351055
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434076 Loss1: 0.091385 Loss2: 1.342691
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421246 Loss1: 0.080937 Loss2: 1.340310
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.432849 Loss1: 0.089768 Loss2: 1.343081
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.238357 Loss1: 0.402862 Loss2: 1.835495
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.591244 Loss1: 0.221428 Loss2: 1.369816
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.593524 Loss1: 0.193477 Loss2: 1.400047
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.355356 Loss1: 0.461774 Loss2: 1.893582
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517235 Loss1: 0.154555 Loss2: 1.362681
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516314 Loss1: 0.138815 Loss2: 1.377499 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511488 Loss1: 0.142037 Loss2: 1.369451 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459347 Loss1: 0.093723 Loss2: 1.365625 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436179 Loss1: 0.077413 Loss2: 1.358767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400019 Loss1: 0.052199 Loss2: 1.347820 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452209 Loss1: 0.073915 Loss2: 1.378294 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992647
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429346 Loss1: 0.062879 Loss2: 1.366467 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.472138 Loss1: 0.560654 Loss2: 1.911485
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.797428 Loss1: 0.414718 Loss2: 1.382710
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.679210 Loss1: 0.257884 Loss2: 1.421325
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.613300 Loss1: 0.227759 Loss2: 1.385541
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.276717 Loss1: 0.406362 Loss2: 1.870355
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.679282 Loss1: 0.269300 Loss2: 1.409983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.658359 Loss1: 0.210279 Loss2: 1.448080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619294 Loss1: 0.203888 Loss2: 1.415406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.593938 Loss1: 0.165826 Loss2: 1.428113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.589524 Loss1: 0.167711 Loss2: 1.421813 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518013 Loss1: 0.102304 Loss2: 1.415709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.472987 Loss1: 0.069010 Loss2: 1.403978 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.448532 Loss1: 0.574411 Loss2: 1.874121 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.588841 Loss1: 0.189439 Loss2: 1.399402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.329426 Loss1: 0.480141 Loss2: 1.849286 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.652284 Loss1: 0.298040 Loss2: 1.354244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.589783 Loss1: 0.210253 Loss2: 1.379530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.542244 Loss1: 0.175933 Loss2: 1.366310 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.533401 Loss1: 0.185287 Loss2: 1.348114 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464084 Loss1: 0.115232 Loss2: 1.348852 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412069 Loss1: 0.077377 Loss2: 1.334693 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384609 Loss1: 0.058238 Loss2: 1.326371 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.690692 Loss1: 0.347231 Loss2: 1.343460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491266 Loss1: 0.162793 Loss2: 1.328473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.393876 Loss1: 0.549444 Loss2: 1.844432
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.423141 Loss1: 0.092424 Loss2: 1.330717
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.648415 Loss1: 0.291521 Loss2: 1.356894
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442711 Loss1: 0.119577 Loss2: 1.323134
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.551349 Loss1: 0.169912 Loss2: 1.381437
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.388164 Loss1: 0.072225 Loss2: 1.315939
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.451253 Loss1: 0.105200 Loss2: 1.346053
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.370448 Loss1: 0.056396 Loss2: 1.314052
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.433682 Loss1: 0.088485 Loss2: 1.345197
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.346389 Loss1: 0.041740 Loss2: 1.304649
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.428430 Loss1: 0.085906 Loss2: 1.342524
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.324457 Loss1: 0.029687 Loss2: 1.294770
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414517 Loss1: 0.080712 Loss2: 1.333805 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408545 Loss1: 0.077862 Loss2: 1.330682 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.698460 Loss1: 0.309822 Loss2: 1.388639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.515049 Loss1: 0.140551 Loss2: 1.374498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.293404 Loss1: 0.469770 Loss2: 1.823635
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499536 Loss1: 0.129075 Loss2: 1.370460
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.660324 Loss1: 0.334573 Loss2: 1.325751
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.492972 Loss1: 0.122758 Loss2: 1.370214
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.584622 Loss1: 0.225060 Loss2: 1.359562
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490310 Loss1: 0.121021 Loss2: 1.369289
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.517482 Loss1: 0.181988 Loss2: 1.335494
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.487960 Loss1: 0.119775 Loss2: 1.368185
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.471269 Loss1: 0.149791 Loss2: 1.321477
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437181 Loss1: 0.068923 Loss2: 1.368258
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.418410 Loss1: 0.097977 Loss2: 1.320433
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417300 Loss1: 0.057057 Loss2: 1.360243
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396754 Loss1: 0.088085 Loss2: 1.308669 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.340444 Loss1: 0.042995 Loss2: 1.297449 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.696154 Loss1: 0.339986 Loss2: 1.356168 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513271 Loss1: 0.158547 Loss2: 1.354724 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506964 Loss1: 0.147206 Loss2: 1.359759
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.382545 Loss1: 0.565014 Loss2: 1.817532
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.753842 Loss1: 0.416575 Loss2: 1.337268 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564640 Loss1: 0.176456 Loss2: 1.388184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.494843 Loss1: 0.164249 Loss2: 1.330594 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.430548 Loss1: 0.101063 Loss2: 1.329484 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.346991 Loss1: 0.021043 Loss2: 1.325948
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421676 Loss1: 0.106684 Loss2: 1.314992
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419704 Loss1: 0.105632 Loss2: 1.314072
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429188 Loss1: 0.107646 Loss2: 1.321542
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449137 Loss1: 0.136350 Loss2: 1.312787
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414146 Loss1: 0.102649 Loss2: 1.311497
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.325990 Loss1: 0.537202 Loss2: 1.788788
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.667683 Loss1: 0.345809 Loss2: 1.321874
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.579771 Loss1: 0.218304 Loss2: 1.361467
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528727 Loss1: 0.208289 Loss2: 1.320437
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.457808 Loss1: 0.137696 Loss2: 1.320112
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.290714 Loss1: 0.444293 Loss2: 1.846421
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.716620 Loss1: 0.346683 Loss2: 1.369937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.646871 Loss1: 0.252399 Loss2: 1.394472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.582768 Loss1: 0.209408 Loss2: 1.373360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.538722 Loss1: 0.158071 Loss2: 1.380652 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423288 Loss1: 0.071871 Loss2: 1.351416 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379713 Loss1: 0.045489 Loss2: 1.334224 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369748 Loss1: 0.044347 Loss2: 1.325401
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.594153 Loss1: 0.710890 Loss2: 1.883263
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.805323 Loss1: 0.406082 Loss2: 1.399241
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.606754 Loss1: 0.214047 Loss2: 1.392707
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562550 Loss1: 0.200180 Loss2: 1.362370
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526792 Loss1: 0.163133 Loss2: 1.363659
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.577988 Loss1: 0.595644 Loss2: 1.982344
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454419 Loss1: 0.096566 Loss2: 1.357853
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.806282 Loss1: 0.384887 Loss2: 1.421395
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460657 Loss1: 0.115640 Loss2: 1.345017
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.733197 Loss1: 0.264018 Loss2: 1.469179
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423841 Loss1: 0.074467 Loss2: 1.349373
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406133 Loss1: 0.069102 Loss2: 1.337031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371985 Loss1: 0.040202 Loss2: 1.331784 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.543053 Loss1: 0.134800 Loss2: 1.408253 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454243 Loss1: 0.057859 Loss2: 1.396384 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440600 Loss1: 0.050921 Loss2: 1.389679
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.408853 Loss1: 0.540038 Loss2: 1.868815
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.712993 Loss1: 0.322889 Loss2: 1.390104
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.640444 Loss1: 0.208781 Loss2: 1.431663
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.637425 Loss1: 0.242213 Loss2: 1.395212
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519977 Loss1: 0.119841 Loss2: 1.400136
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.604223 Loss1: 0.655570 Loss2: 1.948653
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.763719 Loss1: 0.375009 Loss2: 1.388710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622627 Loss1: 0.224732 Loss2: 1.397894
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490572 Loss1: 0.117036 Loss2: 1.373535
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555304 Loss1: 0.143626 Loss2: 1.411678
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.494467 Loss1: 0.116189 Loss2: 1.378278
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433274 Loss1: 0.058080 Loss2: 1.375194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436014 Loss1: 0.068868 Loss2: 1.367146 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419673 Loss1: 0.069564 Loss2: 1.350110 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.319089 Loss1: 0.417770 Loss2: 1.901319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.708252 Loss1: 0.227674 Loss2: 1.480578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.205312 Loss1: 0.413250 Loss2: 1.792063
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.649715 Loss1: 0.228260 Loss2: 1.421455
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.694257 Loss1: 0.356313 Loss2: 1.337944
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.538166 Loss1: 0.113693 Loss2: 1.424473
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.632924 Loss1: 0.238030 Loss2: 1.394894
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497070 Loss1: 0.078890 Loss2: 1.418180
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492731 Loss1: 0.153672 Loss2: 1.339059
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517968 Loss1: 0.109919 Loss2: 1.408049
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.448025 Loss1: 0.106614 Loss2: 1.341411
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495528 Loss1: 0.088209 Loss2: 1.407318
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450901 Loss1: 0.122629 Loss2: 1.328272
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.479450 Loss1: 0.068352 Loss2: 1.411098
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461678 Loss1: 0.130016 Loss2: 1.331662
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469822 Loss1: 0.064049 Loss2: 1.405774
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412230 Loss1: 0.078624 Loss2: 1.333606 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.400398 Loss1: 0.525478 Loss2: 1.874920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.653620 Loss1: 0.230666 Loss2: 1.422954 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549838 Loss1: 0.150138 Loss2: 1.399700
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.229828 Loss1: 0.452161 Loss2: 1.777667
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547150 Loss1: 0.150544 Loss2: 1.396606
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.529645 Loss1: 0.228402 Loss2: 1.301244
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.544747 Loss1: 0.149315 Loss2: 1.395431
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.472845 Loss1: 0.166265 Loss2: 1.306580
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.518271 Loss1: 0.128031 Loss2: 1.390240
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.423095 Loss1: 0.132116 Loss2: 1.290979
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484443 Loss1: 0.098026 Loss2: 1.386417
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.472901 Loss1: 0.173421 Loss2: 1.299480
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456007 Loss1: 0.075307 Loss2: 1.380700
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421608 Loss1: 0.122213 Loss2: 1.299396
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437511 Loss1: 0.065276 Loss2: 1.372235
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392495 Loss1: 0.110772 Loss2: 1.281723
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.358855 Loss1: 0.074952 Loss2: 1.283903
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371708 Loss1: 0.092212 Loss2: 1.279496
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375519 Loss1: 0.095797 Loss2: 1.279722
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.305426 Loss1: 0.454398 Loss2: 1.851028
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.692037 Loss1: 0.336189 Loss2: 1.355848
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.584912 Loss1: 0.186131 Loss2: 1.398781
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547000 Loss1: 0.178536 Loss2: 1.368464
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.642354 Loss1: 0.598826 Loss2: 2.043528
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.792618 Loss1: 0.398041 Loss2: 1.394577
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.489014 Loss1: 0.131507 Loss2: 1.357507
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.796098 Loss1: 0.329906 Loss2: 1.466192
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410733 Loss1: 0.066563 Loss2: 1.344169 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401634 Loss1: 0.058779 Loss2: 1.342856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.568199 Loss1: 0.148566 Loss2: 1.419633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373815 Loss1: 0.043241 Loss2: 1.330575 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444297 Loss1: 0.057041 Loss2: 1.387256 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997396
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.353667 Loss1: 0.522991 Loss2: 1.830677
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.700031 Loss1: 0.362808 Loss2: 1.337223
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.691859 Loss1: 0.307290 Loss2: 1.384569
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.595329 Loss1: 0.245331 Loss2: 1.349998
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.424833 Loss1: 0.541776 Loss2: 1.883057
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.776960 Loss1: 0.391434 Loss2: 1.385526 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670437 Loss1: 0.222660 Loss2: 1.447777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565749 Loss1: 0.169623 Loss2: 1.396125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.568480 Loss1: 0.170544 Loss2: 1.397936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510122 Loss1: 0.114586 Loss2: 1.395536 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403167 Loss1: 0.087576 Loss2: 1.315590
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455823 Loss1: 0.075132 Loss2: 1.380691
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438035 Loss1: 0.065312 Loss2: 1.372723
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425102 Loss1: 0.059787 Loss2: 1.365315
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417781 Loss1: 0.051119 Loss2: 1.366662
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.303588 Loss1: 0.465375 Loss2: 1.838213
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.635988 Loss1: 0.272685 Loss2: 1.363303
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.562533 Loss1: 0.166048 Loss2: 1.396485
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559492 Loss1: 0.196385 Loss2: 1.363108
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.333375 Loss1: 0.498986 Loss2: 1.834389
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574891 Loss1: 0.204329 Loss2: 1.370563
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.660299 Loss1: 0.313226 Loss2: 1.347073
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.545083 Loss1: 0.174708 Loss2: 1.370376
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.583930 Loss1: 0.203784 Loss2: 1.380145
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509974 Loss1: 0.158957 Loss2: 1.351017
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469844 Loss1: 0.104213 Loss2: 1.365631
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483262 Loss1: 0.130192 Loss2: 1.353070
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458634 Loss1: 0.101635 Loss2: 1.356999
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492468 Loss1: 0.139829 Loss2: 1.352639
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448882 Loss1: 0.092701 Loss2: 1.356181
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423447 Loss1: 0.081121 Loss2: 1.342326
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422916 Loss1: 0.070761 Loss2: 1.352155
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366385 Loss1: 0.034919 Loss2: 1.331466 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.265704 Loss1: 0.464823 Loss2: 1.800882 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.520908 Loss1: 0.191717 Loss2: 1.329192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.503493 Loss1: 0.193359 Loss2: 1.310134
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.281962 Loss1: 0.433201 Loss2: 1.848761
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.578746 Loss1: 0.243392 Loss2: 1.335354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.483765 Loss1: 0.139809 Loss2: 1.343956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.453261 Loss1: 0.105624 Loss2: 1.347637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.413435 Loss1: 0.074276 Loss2: 1.339160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.388539 Loss1: 0.057670 Loss2: 1.330869 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.367027 Loss1: 0.043048 Loss2: 1.323979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.343592 Loss1: 0.029272 Loss2: 1.314320 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.543091 Loss1: 0.623194 Loss2: 1.919898 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647853 Loss1: 0.277146 Loss2: 1.370707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.465820 Loss1: 0.129455 Loss2: 1.336365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438632 Loss1: 0.099517 Loss2: 1.339116 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.400764 Loss1: 0.068410 Loss2: 1.332354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391223 Loss1: 0.075364 Loss2: 1.315859 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385136 Loss1: 0.066515 Loss2: 1.318621 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364703 Loss1: 0.051202 Loss2: 1.313502
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.464845 Loss1: 0.114020 Loss2: 1.350825
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+DEBUG flwr 2023-10-12 17:06:27,327 | server.py:236 | fit_round 158 received 50 results and 0 failures
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464382 Loss1: 0.128160 Loss2: 1.336221
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467214 Loss1: 0.129765 Loss2: 1.337449
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420980 Loss1: 0.088516 Loss2: 1.332464
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385587 Loss1: 0.059527 Loss2: 1.326059
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380547 Loss1: 0.064769 Loss2: 1.315777
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.447858 Loss1: 0.545547 Loss2: 1.902310
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.872249 Loss1: 0.454459 Loss2: 1.417790
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.714792 Loss1: 0.261015 Loss2: 1.453777
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610680 Loss1: 0.192396 Loss2: 1.418284
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.537130 Loss1: 0.132799 Loss2: 1.404331
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.383612 Loss1: 0.498149 Loss2: 1.885463
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516899 Loss1: 0.110623 Loss2: 1.406276
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.693379 Loss1: 0.286672 Loss2: 1.406707
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495624 Loss1: 0.101242 Loss2: 1.394382
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.620217 Loss1: 0.179168 Loss2: 1.441049
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.463185 Loss1: 0.074730 Loss2: 1.388455
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538181 Loss1: 0.136297 Loss2: 1.401884
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.446815 Loss1: 0.065080 Loss2: 1.381735
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469747 Loss1: 0.064657 Loss2: 1.405090
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.475277 Loss1: 0.093902 Loss2: 1.381375
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) 
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437185 Loss1: 0.048010 Loss2: 1.389175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428514 Loss1: 0.054767 Loss2: 1.373748 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415649 Loss1: 0.039492 Loss2: 1.376157
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.391259 Loss1: 0.470147 Loss2: 1.921112
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.688863 Loss1: 0.270819 Loss2: 1.418043
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568983 Loss1: 0.134965 Loss2: 1.434018
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572788 Loss1: 0.159071 Loss2: 1.413717
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.580992 Loss1: 0.168090 Loss2: 1.412902
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.374695 Loss1: 0.490235 Loss2: 1.884460
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.550299 Loss1: 0.132993 Loss2: 1.417306
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.672157 Loss1: 0.290251 Loss2: 1.381906
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.537309 Loss1: 0.119561 Loss2: 1.417747
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.641220 Loss1: 0.216047 Loss2: 1.425173
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.507608 Loss1: 0.094788 Loss2: 1.412819
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.632010 Loss1: 0.236440 Loss2: 1.395570
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.531300 Loss1: 0.125340 Loss2: 1.405960
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.634619 Loss1: 0.224207 Loss2: 1.410412
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.508198 Loss1: 0.098173 Loss2: 1.410025
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.499942 Loss1: 0.110377 Loss2: 1.389566 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.437039 Loss1: 0.061561 Loss2: 1.375478 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 17:06:27,327][flwr][DEBUG] - fit_round 158 received 50 results and 0 failures
+INFO flwr 2023-10-12 17:07:09,409 | server.py:125 | fit progress: (158, 2.2473408779778037, {'accuracy': 0.6015}, 364537.187364186)
+>> Test accuracy: 0.601500
+[2023-10-12 17:07:09,409][flwr][INFO] - fit progress: (158, 2.2473408779778037, {'accuracy': 0.6015}, 364537.187364186)
+DEBUG flwr 2023-10-12 17:07:09,409 | server.py:173 | evaluate_round 158: strategy sampled 50 clients (out of 50)
+[2023-10-12 17:07:09,409][flwr][DEBUG] - evaluate_round 158: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 17:16:14,041 | server.py:187 | evaluate_round 158 received 50 results and 0 failures
+[2023-10-12 17:16:14,041][flwr][DEBUG] - evaluate_round 158 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 17:16:14,042 | server.py:222 | fit_round 159: strategy sampled 50 clients (out of 50)
+[2023-10-12 17:16:14,042][flwr][DEBUG] - fit_round 159: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.338345 Loss1: 0.431808 Loss2: 1.906537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615430 Loss1: 0.208778 Loss2: 1.406652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.566468 Loss1: 0.170348 Loss2: 1.396120
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.422885 Loss1: 0.613199 Loss2: 1.809686
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.495597 Loss1: 0.108133 Loss2: 1.387464
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.694463 Loss1: 0.366233 Loss2: 1.328230
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459333 Loss1: 0.083712 Loss2: 1.375621
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565836 Loss1: 0.204107 Loss2: 1.361729
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445166 Loss1: 0.076852 Loss2: 1.368314
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.582493 Loss1: 0.242866 Loss2: 1.339628
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420403 Loss1: 0.054329 Loss2: 1.366075
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500630 Loss1: 0.156159 Loss2: 1.344472
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.392766 Loss1: 0.037548 Loss2: 1.355218
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.424821 Loss1: 0.100371 Loss2: 1.324450
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393327 Loss1: 0.038600 Loss2: 1.354728
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.402159 Loss1: 0.080396 Loss2: 1.321763
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.395900 Loss1: 0.080461 Loss2: 1.315440
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387709 Loss1: 0.077347 Loss2: 1.310362
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379332 Loss1: 0.069409 Loss2: 1.309923
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.332687 Loss1: 0.502521 Loss2: 1.830166
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.669016 Loss1: 0.326892 Loss2: 1.342124
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.576848 Loss1: 0.185929 Loss2: 1.390919
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601732 Loss1: 0.258625 Loss2: 1.343107
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.380224 Loss1: 0.516601 Loss2: 1.863623
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.753170 Loss1: 0.369216 Loss2: 1.383953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578797 Loss1: 0.158436 Loss2: 1.420361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551533 Loss1: 0.180168 Loss2: 1.371365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505588 Loss1: 0.132605 Loss2: 1.372983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481688 Loss1: 0.112920 Loss2: 1.368768 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404106 Loss1: 0.068808 Loss2: 1.335298
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484347 Loss1: 0.116738 Loss2: 1.367609
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434749 Loss1: 0.078593 Loss2: 1.356156
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431562 Loss1: 0.074959 Loss2: 1.356603
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430426 Loss1: 0.081785 Loss2: 1.348641
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.187999 Loss1: 0.438124 Loss2: 1.749875
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.586493 Loss1: 0.284831 Loss2: 1.301662
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.522662 Loss1: 0.178625 Loss2: 1.344038
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.411552 Loss1: 0.108803 Loss2: 1.302749
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.376155 Loss1: 0.518389 Loss2: 1.857767
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.682319 Loss1: 0.319529 Loss2: 1.362790
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.428622 Loss1: 0.128730 Loss2: 1.299892
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.610146 Loss1: 0.223114 Loss2: 1.387032
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.386909 Loss1: 0.083478 Loss2: 1.303431
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.516868 Loss1: 0.152963 Loss2: 1.363905
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.356142 Loss1: 0.064078 Loss2: 1.292064
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.468671 Loss1: 0.108360 Loss2: 1.360311
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.358277 Loss1: 0.070119 Loss2: 1.288158
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.334167 Loss1: 0.052812 Loss2: 1.281355 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.344567 Loss1: 0.065110 Loss2: 1.279458 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396945 Loss1: 0.062224 Loss2: 1.334721 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.329177 Loss1: 0.455096 Loss2: 1.874080 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624186 Loss1: 0.229603 Loss2: 1.394582 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551739 Loss1: 0.171230 Loss2: 1.380509
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.391312 Loss1: 0.550710 Loss2: 1.840603
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.740785 Loss1: 0.376574 Loss2: 1.364211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605221 Loss1: 0.213878 Loss2: 1.391343 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535667 Loss1: 0.179565 Loss2: 1.356102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508986 Loss1: 0.154079 Loss2: 1.354907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477140 Loss1: 0.123356 Loss2: 1.353784 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368279 Loss1: 0.030473 Loss2: 1.337806
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425120 Loss1: 0.082968 Loss2: 1.342152
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.373576 Loss1: 0.039078 Loss2: 1.334498
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.353637 Loss1: 0.031410 Loss2: 1.322227
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.339158 Loss1: 0.023115 Loss2: 1.316043
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.506381 Loss1: 0.561230 Loss2: 1.945151
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.776967 Loss1: 0.338273 Loss2: 1.438694
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.683765 Loss1: 0.206679 Loss2: 1.477086
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582737 Loss1: 0.148624 Loss2: 1.434113
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.303763 Loss1: 0.529036 Loss2: 1.774727
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.628209 Loss1: 0.296911 Loss2: 1.331299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.493194 Loss1: 0.147260 Loss2: 1.345933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.437702 Loss1: 0.115465 Loss2: 1.322237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.412648 Loss1: 0.101896 Loss2: 1.310751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.385442 Loss1: 0.074145 Loss2: 1.311297 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.375333 Loss1: 0.072098 Loss2: 1.303236 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.350777 Loss1: 0.050825 Loss2: 1.299952 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.749629 Loss1: 0.363711 Loss2: 1.385919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.592796 Loss1: 0.202941 Loss2: 1.389855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.325188 Loss1: 0.496132 Loss2: 1.829056
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.548224 Loss1: 0.161939 Loss2: 1.386285
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.538649 Loss1: 0.154680 Loss2: 1.383969 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456172 Loss1: 0.085786 Loss2: 1.370386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420872 Loss1: 0.051902 Loss2: 1.368970 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414640 Loss1: 0.056380 Loss2: 1.358260 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404036 Loss1: 0.044957 Loss2: 1.359079 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399315 Loss1: 0.060664 Loss2: 1.338651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378695 Loss1: 0.048922 Loss2: 1.329774 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751971 Loss1: 0.366882 Loss2: 1.385089 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557568 Loss1: 0.174887 Loss2: 1.382681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.567416 Loss1: 0.577685 Loss2: 1.989730
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.477287 Loss1: 0.100727 Loss2: 1.376560
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481799 Loss1: 0.106433 Loss2: 1.375367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466660 Loss1: 0.100200 Loss2: 1.366460 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.408309 Loss1: 0.054615 Loss2: 1.353693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424585 Loss1: 0.074666 Loss2: 1.349919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412968 Loss1: 0.071560 Loss2: 1.341408 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400939 Loss1: 0.038793 Loss2: 1.362146 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.376251 Loss1: 0.527913 Loss2: 1.848337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563041 Loss1: 0.159932 Loss2: 1.403110 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516151 Loss1: 0.160843 Loss2: 1.355308
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.414660 Loss1: 0.475947 Loss2: 1.938713
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.679534 Loss1: 0.274503 Loss2: 1.405031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.678634 Loss1: 0.249092 Loss2: 1.429542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.614498 Loss1: 0.205820 Loss2: 1.408678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558938 Loss1: 0.154218 Loss2: 1.404720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523840 Loss1: 0.126456 Loss2: 1.397384 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372134 Loss1: 0.039477 Loss2: 1.332657
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533288 Loss1: 0.138290 Loss2: 1.394999
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.530627 Loss1: 0.133508 Loss2: 1.397119
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.538923 Loss1: 0.143949 Loss2: 1.394974
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.507872 Loss1: 0.112605 Loss2: 1.395267
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.448918 Loss1: 0.557641 Loss2: 1.891277
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.746628 Loss1: 0.343068 Loss2: 1.403561
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.659692 Loss1: 0.213552 Loss2: 1.446140
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572468 Loss1: 0.164561 Loss2: 1.407906
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.216752 Loss1: 0.455358 Loss2: 1.761394
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.622730 Loss1: 0.325332 Loss2: 1.297397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.527513 Loss1: 0.201057 Loss2: 1.326456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.409532 Loss1: 0.115712 Loss2: 1.293820 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.383372 Loss1: 0.093316 Loss2: 1.290056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.386994 Loss1: 0.097455 Loss2: 1.289539 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427382 Loss1: 0.055129 Loss2: 1.372253
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.379394 Loss1: 0.092662 Loss2: 1.286732
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.354831 Loss1: 0.071598 Loss2: 1.283232
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.331767 Loss1: 0.057936 Loss2: 1.273831
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.317538 Loss1: 0.048176 Loss2: 1.269363
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.293906 Loss1: 0.469776 Loss2: 1.824131
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.679346 Loss1: 0.335011 Loss2: 1.344335
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.576712 Loss1: 0.201950 Loss2: 1.374762
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484057 Loss1: 0.141254 Loss2: 1.342802
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.177043 Loss1: 0.398496 Loss2: 1.778547
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.701070 Loss1: 0.351502 Loss2: 1.349568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.648052 Loss1: 0.260420 Loss2: 1.387632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534837 Loss1: 0.185394 Loss2: 1.349443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.466195 Loss1: 0.125510 Loss2: 1.340684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446860 Loss1: 0.108947 Loss2: 1.337913 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400124 Loss1: 0.067341 Loss2: 1.332783 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383075 Loss1: 0.056277 Loss2: 1.326798 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.642279 Loss1: 0.240223 Loss2: 1.402055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533745 Loss1: 0.133810 Loss2: 1.399934 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.504998 Loss1: 0.108961 Loss2: 1.396037
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.318210 Loss1: 0.508480 Loss2: 1.809730
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.495239 Loss1: 0.102664 Loss2: 1.392575
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.672346 Loss1: 0.325301 Loss2: 1.347044
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441283 Loss1: 0.060015 Loss2: 1.381268
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.582981 Loss1: 0.204689 Loss2: 1.378292
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427177 Loss1: 0.053982 Loss2: 1.373195
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487301 Loss1: 0.149501 Loss2: 1.337800
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.458781 Loss1: 0.122640 Loss2: 1.336140 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415089 Loss1: 0.043018 Loss2: 1.372072
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.462029 Loss1: 0.122677 Loss2: 1.339352
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467758 Loss1: 0.128851 Loss2: 1.338907
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445422 Loss1: 0.115618 Loss2: 1.329804
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400769 Loss1: 0.073567 Loss2: 1.327202
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379027 Loss1: 0.059642 Loss2: 1.319384
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.317686 Loss1: 0.463774 Loss2: 1.853912
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.715240 Loss1: 0.364641 Loss2: 1.350599
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618549 Loss1: 0.208678 Loss2: 1.409870
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516815 Loss1: 0.160793 Loss2: 1.356022
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.459115 Loss1: 0.107562 Loss2: 1.351553
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.405501 Loss1: 0.577430 Loss2: 1.828071
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.731001 Loss1: 0.367471 Loss2: 1.363531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.618378 Loss1: 0.220890 Loss2: 1.397487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.541795 Loss1: 0.182767 Loss2: 1.359028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.552551 Loss1: 0.196727 Loss2: 1.355825 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494956 Loss1: 0.138998 Loss2: 1.355958 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.478996 Loss1: 0.135554 Loss2: 1.343442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446258 Loss1: 0.100606 Loss2: 1.345653 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.628667 Loss1: 0.265997 Loss2: 1.362670 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581656 Loss1: 0.229117 Loss2: 1.352539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.590714 Loss1: 0.231546 Loss2: 1.359167
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.392702 Loss1: 0.585629 Loss2: 1.807073
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.652474 Loss1: 0.324837 Loss2: 1.327637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.561609 Loss1: 0.200111 Loss2: 1.361498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.488025 Loss1: 0.163003 Loss2: 1.325022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.461508 Loss1: 0.138408 Loss2: 1.323101 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.434215 Loss1: 0.113005 Loss2: 1.321209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.378004 Loss1: 0.062782 Loss2: 1.315222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376075 Loss1: 0.074410 Loss2: 1.301665 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.663423 Loss1: 0.331075 Loss2: 1.332348 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556082 Loss1: 0.222166 Loss2: 1.333915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480287 Loss1: 0.144456 Loss2: 1.335831
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.251893 Loss1: 0.414576 Loss2: 1.837317
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.629165 Loss1: 0.290376 Loss2: 1.338789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636422 Loss1: 0.249556 Loss2: 1.386866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.527484 Loss1: 0.181195 Loss2: 1.346288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501555 Loss1: 0.160421 Loss2: 1.341134 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371757 Loss1: 0.048672 Loss2: 1.323085
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461757 Loss1: 0.119996 Loss2: 1.341761
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455783 Loss1: 0.125444 Loss2: 1.330339
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408221 Loss1: 0.077267 Loss2: 1.330954
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384735 Loss1: 0.063676 Loss2: 1.321059
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385359 Loss1: 0.064016 Loss2: 1.321343
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.497352 Loss1: 0.650302 Loss2: 1.847050
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.700658 Loss1: 0.371181 Loss2: 1.329478
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.548815 Loss1: 0.193189 Loss2: 1.355627
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.468865 Loss1: 0.148115 Loss2: 1.320749
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.421123 Loss1: 0.110590 Loss2: 1.310533
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.319991 Loss1: 0.494727 Loss2: 1.825264
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.419132 Loss1: 0.111324 Loss2: 1.307808
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.401178 Loss1: 0.085580 Loss2: 1.315598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629650 Loss1: 0.221192 Loss2: 1.408458
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.397220 Loss1: 0.087396 Loss2: 1.309824
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398918 Loss1: 0.095404 Loss2: 1.303515
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611768 Loss1: 0.238201 Loss2: 1.373567
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.352465 Loss1: 0.050813 Loss2: 1.301652
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.534314 Loss1: 0.158669 Loss2: 1.375645
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478623 Loss1: 0.113586 Loss2: 1.365037
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446205 Loss1: 0.090106 Loss2: 1.356099
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419276 Loss1: 0.069635 Loss2: 1.349641
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418572 Loss1: 0.075009 Loss2: 1.343563
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.530367 Loss1: 0.557933 Loss2: 1.972435
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430829 Loss1: 0.085362 Loss2: 1.345467
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.686635 Loss1: 0.261183 Loss2: 1.425452
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.731713 Loss1: 0.291417 Loss2: 1.440296
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623849 Loss1: 0.179758 Loss2: 1.444091
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531661 Loss1: 0.114709 Loss2: 1.416951
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516833 Loss1: 0.102831 Loss2: 1.414003
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524882 Loss1: 0.113954 Loss2: 1.410928
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.483845 Loss1: 0.559390 Loss2: 1.924455
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.482080 Loss1: 0.083112 Loss2: 1.398968
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.693073 Loss1: 0.313456 Loss2: 1.379617
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653823 Loss1: 0.266230 Loss2: 1.387593
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470628 Loss1: 0.067535 Loss2: 1.403093
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.618094 Loss1: 0.205815 Loss2: 1.412279
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440665 Loss1: 0.041716 Loss2: 1.398949
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495007 Loss1: 0.116506 Loss2: 1.378501 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433830 Loss1: 0.073113 Loss2: 1.360717 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.494628 Loss1: 0.567442 Loss2: 1.927186 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975962
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.649211 Loss1: 0.294180 Loss2: 1.355031 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555141 Loss1: 0.204109 Loss2: 1.351032 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.551356 Loss1: 0.204269 Loss2: 1.347086 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.491586 Loss1: 0.145838 Loss2: 1.345748
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.189580 Loss1: 0.391967 Loss2: 1.797612
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.446474 Loss1: 0.119723 Loss2: 1.326750
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.692248 Loss1: 0.331160 Loss2: 1.361087
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.581124 Loss1: 0.198555 Loss2: 1.382568 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.471358 Loss1: 0.127988 Loss2: 1.343370 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.384347 Loss1: 0.478347 Loss2: 1.906000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.716112 Loss1: 0.319792 Loss2: 1.396321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.629828 Loss1: 0.205024 Loss2: 1.424803 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.579669 Loss1: 0.176691 Loss2: 1.402978 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991728
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517089 Loss1: 0.129645 Loss2: 1.387443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456046 Loss1: 0.072797 Loss2: 1.383249 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449241 Loss1: 0.072387 Loss2: 1.376854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427332 Loss1: 0.059734 Loss2: 1.367598 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.525929 Loss1: 0.175165 Loss2: 1.350764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.434635 Loss1: 0.115369 Loss2: 1.319265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427306 Loss1: 0.108064 Loss2: 1.319241
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.286098 Loss1: 0.449780 Loss2: 1.836319
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.609428 Loss1: 0.265092 Loss2: 1.344335 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.571375 Loss1: 0.203690 Loss2: 1.367685 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.461405 Loss1: 0.112870 Loss2: 1.348535 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354787 Loss1: 0.059403 Loss2: 1.295385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448558 Loss1: 0.112798 Loss2: 1.335760
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444773 Loss1: 0.103698 Loss2: 1.341076
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416824 Loss1: 0.081439 Loss2: 1.335385
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.407881 Loss1: 0.076486 Loss2: 1.331395
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378431 Loss1: 0.056935 Loss2: 1.321496
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.378564 Loss1: 0.517587 Loss2: 1.860977
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355884 Loss1: 0.038799 Loss2: 1.317085
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.579043 Loss1: 0.209311 Loss2: 1.369731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469435 Loss1: 0.134969 Loss2: 1.334466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423324 Loss1: 0.089838 Loss2: 1.333487
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.242535 Loss1: 0.402432 Loss2: 1.840103
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622855 Loss1: 0.245803 Loss2: 1.377052 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563301 Loss1: 0.166824 Loss2: 1.396477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.493646 Loss1: 0.123995 Loss2: 1.369651 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.481811 Loss1: 0.123388 Loss2: 1.358423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490852 Loss1: 0.114230 Loss2: 1.376622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419757 Loss1: 0.055156 Loss2: 1.364601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.666199 Loss1: 0.315963 Loss2: 1.350235 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594069 Loss1: 0.237005 Loss2: 1.357064 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511355 Loss1: 0.159762 Loss2: 1.351593 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464062 Loss1: 0.117180 Loss2: 1.346882
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.469856 Loss1: 0.549379 Loss2: 1.920478
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.444282 Loss1: 0.100396 Loss2: 1.343886
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.685283 Loss1: 0.274592 Loss2: 1.410691
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422769 Loss1: 0.085728 Loss2: 1.337041
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609334 Loss1: 0.191587 Loss2: 1.417747
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421236 Loss1: 0.085317 Loss2: 1.335919
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.568097 Loss1: 0.168268 Loss2: 1.399828
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.530610 Loss1: 0.131704 Loss2: 1.398906
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.531182 Loss1: 0.137247 Loss2: 1.393935
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456293 Loss1: 0.065374 Loss2: 1.390919
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425774 Loss1: 0.042316 Loss2: 1.383458
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.442321 Loss1: 0.574322 Loss2: 1.867999
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422499 Loss1: 0.046721 Loss2: 1.375778
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.699251 Loss1: 0.322317 Loss2: 1.376934
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413207 Loss1: 0.044991 Loss2: 1.368216
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530218 Loss1: 0.163862 Loss2: 1.366355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479568 Loss1: 0.116548 Loss2: 1.363020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455068 Loss1: 0.096689 Loss2: 1.358378
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.147074 Loss1: 0.395776 Loss2: 1.751298
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.558658 Loss1: 0.251606 Loss2: 1.307052 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.543410 Loss1: 0.196273 Loss2: 1.347137 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.452232 Loss1: 0.134568 Loss2: 1.317664 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462858 Loss1: 0.144459 Loss2: 1.318400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467440 Loss1: 0.156432 Loss2: 1.311007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438965 Loss1: 0.116042 Loss2: 1.322922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426613 Loss1: 0.112665 Loss2: 1.313948 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970703
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516827 Loss1: 0.151785 Loss2: 1.365042 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459272 Loss1: 0.099340 Loss2: 1.359932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432728 Loss1: 0.078481 Loss2: 1.354248
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.372664 Loss1: 0.516868 Loss2: 1.855796
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.706056 Loss1: 0.336008 Loss2: 1.370049 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.601282 Loss1: 0.198272 Loss2: 1.403010 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.485913 Loss1: 0.123025 Loss2: 1.362888 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440289 Loss1: 0.090099 Loss2: 1.350189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423269 Loss1: 0.073881 Loss2: 1.349388 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386281 Loss1: 0.043143 Loss2: 1.343138 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380520 Loss1: 0.044562 Loss2: 1.335958 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.435595 Loss1: 0.101736 Loss2: 1.333859 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.381713 Loss1: 0.065079 Loss2: 1.316634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.378931 Loss1: 0.528532 Loss2: 1.850399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.624038 Loss1: 0.276469 Loss2: 1.347570 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-12 17:44:58,334 | server.py:236 | fit_round 159 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.519548 Loss1: 0.167513 Loss2: 1.352035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506862 Loss1: 0.169565 Loss2: 1.337297 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445479 Loss1: 0.099166 Loss2: 1.346313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404108 Loss1: 0.070062 Loss2: 1.334045 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397966 Loss1: 0.070064 Loss2: 1.327901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392897 Loss1: 0.066137 Loss2: 1.326759 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460217 Loss1: 0.149211 Loss2: 1.311006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.396605 Loss1: 0.078404 Loss2: 1.318201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405627 Loss1: 0.093274 Loss2: 1.312353
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.355811 Loss1: 0.499488 Loss2: 1.856323
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.662534 Loss1: 0.286739 Loss2: 1.375795
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409838 Loss1: 0.103539 Loss2: 1.306299
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591997 Loss1: 0.195270 Loss2: 1.396727
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378700 Loss1: 0.064733 Loss2: 1.313966
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524228 Loss1: 0.153753 Loss2: 1.370475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.453088 Loss1: 0.092921 Loss2: 1.360167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401272 Loss1: 0.046075 Loss2: 1.355197
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.290333 Loss1: 0.469694 Loss2: 1.820639
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.678731 Loss1: 0.319770 Loss2: 1.358961 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.655037 Loss1: 0.237610 Loss2: 1.417427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.452365 Loss1: 0.115736 Loss2: 1.336629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431091 Loss1: 0.101927 Loss2: 1.329164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424182 Loss1: 0.096219 Loss2: 1.327963 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 17:44:58,334][flwr][DEBUG] - fit_round 159 received 50 results and 0 failures
+INFO flwr 2023-10-12 17:45:39,229 | server.py:125 | fit progress: (159, 2.244518861222191, {'accuracy': 0.6021}, 366847.008015419)
+>> Test accuracy: 0.602100
+[2023-10-12 17:45:39,229][flwr][INFO] - fit progress: (159, 2.244518861222191, {'accuracy': 0.6021}, 366847.008015419)
+DEBUG flwr 2023-10-12 17:45:39,230 | server.py:173 | evaluate_round 159: strategy sampled 50 clients (out of 50)
+[2023-10-12 17:45:39,230][flwr][DEBUG] - evaluate_round 159: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 17:54:44,009 | server.py:187 | evaluate_round 159 received 50 results and 0 failures
+[2023-10-12 17:54:44,009][flwr][DEBUG] - evaluate_round 159 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 17:54:44,010 | server.py:222 | fit_round 160: strategy sampled 50 clients (out of 50)
+[2023-10-12 17:54:44,010][flwr][DEBUG] - fit_round 160: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.370840 Loss1: 0.523599 Loss2: 1.847242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.708797 Loss1: 0.283447 Loss2: 1.425350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.615136 Loss1: 0.237217 Loss2: 1.377918
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.324577 Loss1: 0.507573 Loss2: 1.817005
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.655248 Loss1: 0.328962 Loss2: 1.326285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604775 Loss1: 0.240796 Loss2: 1.363979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509495 Loss1: 0.178279 Loss2: 1.331216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518528 Loss1: 0.175033 Loss2: 1.343495 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484162 Loss1: 0.140547 Loss2: 1.343614 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.394934 Loss1: 0.048519 Loss2: 1.346415
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406731 Loss1: 0.078185 Loss2: 1.328546
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389557 Loss1: 0.070436 Loss2: 1.319121
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.352441 Loss1: 0.043087 Loss2: 1.309354
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374441 Loss1: 0.070356 Loss2: 1.304085
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.430091 Loss1: 0.570859 Loss2: 1.859232
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.697094 Loss1: 0.330621 Loss2: 1.366472
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.676216 Loss1: 0.266857 Loss2: 1.409359
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.532337 Loss1: 0.166093 Loss2: 1.366243
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.290266 Loss1: 0.509298 Loss2: 1.780968
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.554798 Loss1: 0.183964 Loss2: 1.370835
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659849 Loss1: 0.350366 Loss2: 1.309483
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526461 Loss1: 0.158544 Loss2: 1.367917
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.566741 Loss1: 0.218117 Loss2: 1.348624
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446136 Loss1: 0.088555 Loss2: 1.357581
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.488071 Loss1: 0.182468 Loss2: 1.305603
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.416412 Loss1: 0.062623 Loss2: 1.353789
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.473136 Loss1: 0.164053 Loss2: 1.309083
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.382625 Loss1: 0.045633 Loss2: 1.336993
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.425970 Loss1: 0.114641 Loss2: 1.311329
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359101 Loss1: 0.030974 Loss2: 1.328127
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433671 Loss1: 0.139570 Loss2: 1.294101
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428584 Loss1: 0.126419 Loss2: 1.302165
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370453 Loss1: 0.072563 Loss2: 1.297889
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348177 Loss1: 0.059218 Loss2: 1.288958
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.363092 Loss1: 0.495452 Loss2: 1.867640
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.633475 Loss1: 0.289768 Loss2: 1.343707
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624071 Loss1: 0.248493 Loss2: 1.375578
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528440 Loss1: 0.179844 Loss2: 1.348596
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.403457 Loss1: 0.541267 Loss2: 1.862190
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.509220 Loss1: 0.165646 Loss2: 1.343574
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.750725 Loss1: 0.377376 Loss2: 1.373349
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455065 Loss1: 0.115347 Loss2: 1.339718
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.654976 Loss1: 0.239710 Loss2: 1.415266
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435334 Loss1: 0.104393 Loss2: 1.330941
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.533238 Loss1: 0.163046 Loss2: 1.370192
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384273 Loss1: 0.054877 Loss2: 1.329397
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520413 Loss1: 0.150865 Loss2: 1.369548
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.366403 Loss1: 0.050097 Loss2: 1.316307
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452290 Loss1: 0.092981 Loss2: 1.359309
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365428 Loss1: 0.055009 Loss2: 1.310419
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.389018 Loss1: 0.041609 Loss2: 1.347410
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382253 Loss1: 0.044578 Loss2: 1.337676
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.382717 Loss1: 0.051371 Loss2: 1.331346
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351640 Loss1: 0.021150 Loss2: 1.330490
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.346285 Loss1: 0.512432 Loss2: 1.833853
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.635782 Loss1: 0.295954 Loss2: 1.339828
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595200 Loss1: 0.230142 Loss2: 1.365058
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.577047 Loss1: 0.222516 Loss2: 1.354532
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.224477 Loss1: 0.435202 Loss2: 1.789275
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.669318 Loss1: 0.321219 Loss2: 1.348099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.638661 Loss1: 0.248459 Loss2: 1.390203 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534977 Loss1: 0.192044 Loss2: 1.342933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.473743 Loss1: 0.123840 Loss2: 1.349903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449094 Loss1: 0.109415 Loss2: 1.339679 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.395075 Loss1: 0.068112 Loss2: 1.326963 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.337292 Loss1: 0.021690 Loss2: 1.315602 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.999023
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.612629 Loss1: 0.249855 Loss2: 1.362773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524003 Loss1: 0.160291 Loss2: 1.363712 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.393338 Loss1: 0.514377 Loss2: 1.878961
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527923 Loss1: 0.155691 Loss2: 1.372232
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.727889 Loss1: 0.338649 Loss2: 1.389240
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473271 Loss1: 0.108803 Loss2: 1.364467
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446924 Loss1: 0.097267 Loss2: 1.349657 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461304 Loss1: 0.115283 Loss2: 1.346021 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450033 Loss1: 0.097938 Loss2: 1.352095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434608 Loss1: 0.085229 Loss2: 1.349380 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971680
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461369 Loss1: 0.084201 Loss2: 1.377168 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427631 Loss1: 0.051219 Loss2: 1.376412 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.326908 Loss1: 0.479521 Loss2: 1.847387
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.676163 Loss1: 0.312814 Loss2: 1.363349
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.641678 Loss1: 0.241267 Loss2: 1.400410
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564416 Loss1: 0.201830 Loss2: 1.362586
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.337674 Loss1: 0.422138 Loss2: 1.915536
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.683660 Loss1: 0.291794 Loss2: 1.391865 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.646058 Loss1: 0.221955 Loss2: 1.424103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.631437 Loss1: 0.229121 Loss2: 1.402316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545641 Loss1: 0.143154 Loss2: 1.402487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521632 Loss1: 0.126267 Loss2: 1.395365 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451429 Loss1: 0.073017 Loss2: 1.378412 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443613 Loss1: 0.077676 Loss2: 1.365936 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.390905 Loss1: 0.523846 Loss2: 1.867060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645236 Loss1: 0.219086 Loss2: 1.426150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564094 Loss1: 0.195974 Loss2: 1.368120
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.259045 Loss1: 0.490663 Loss2: 1.768382
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.668727 Loss1: 0.366514 Loss2: 1.302213 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.524667 Loss1: 0.181987 Loss2: 1.342680 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.533165 Loss1: 0.218655 Loss2: 1.314510 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455720 Loss1: 0.141573 Loss2: 1.314147 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431226 Loss1: 0.116773 Loss2: 1.314452 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384802 Loss1: 0.039560 Loss2: 1.345242
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440802 Loss1: 0.136726 Loss2: 1.304076
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399362 Loss1: 0.088331 Loss2: 1.311031
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.353134 Loss1: 0.057856 Loss2: 1.295278
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.347168 Loss1: 0.049741 Loss2: 1.297427
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.533785 Loss1: 0.599722 Loss2: 1.934063
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.737676 Loss1: 0.354365 Loss2: 1.383310
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644631 Loss1: 0.257215 Loss2: 1.387416
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.567032 Loss1: 0.153278 Loss2: 1.413754
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.363889 Loss1: 0.522743 Loss2: 1.841146
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466777 Loss1: 0.095563 Loss2: 1.371215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431276 Loss1: 0.065292 Loss2: 1.365984 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421747 Loss1: 0.065363 Loss2: 1.356384 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411792 Loss1: 0.062931 Loss2: 1.348861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399007 Loss1: 0.055002 Loss2: 1.344005 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426726 Loss1: 0.088945 Loss2: 1.337782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.360798 Loss1: 0.042862 Loss2: 1.317936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.349316 Loss1: 0.032853 Loss2: 1.316463
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.331084 Loss1: 0.505948 Loss2: 1.825136
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.612568 Loss1: 0.289539 Loss2: 1.323029
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561722 Loss1: 0.185695 Loss2: 1.376026
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.479075 Loss1: 0.151670 Loss2: 1.327404
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475589 Loss1: 0.147770 Loss2: 1.327819
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.420743 Loss1: 0.563153 Loss2: 1.857590
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.824393 Loss1: 0.436037 Loss2: 1.388356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.683363 Loss1: 0.234018 Loss2: 1.449345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551167 Loss1: 0.168609 Loss2: 1.382558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.543789 Loss1: 0.166582 Loss2: 1.377207 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463581 Loss1: 0.087351 Loss2: 1.376230 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424921 Loss1: 0.063175 Loss2: 1.361746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406707 Loss1: 0.059195 Loss2: 1.347512 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.665692 Loss1: 0.297410 Loss2: 1.368282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558852 Loss1: 0.189229 Loss2: 1.369623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.505344 Loss1: 0.139935 Loss2: 1.365409
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.464576 Loss1: 0.597003 Loss2: 1.867573
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473446 Loss1: 0.113800 Loss2: 1.359646
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.714377 Loss1: 0.380712 Loss2: 1.333666
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445684 Loss1: 0.090234 Loss2: 1.355450
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.595047 Loss1: 0.221520 Loss2: 1.373527
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.525355 Loss1: 0.183382 Loss2: 1.341973
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435769 Loss1: 0.090749 Loss2: 1.345020
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.499558 Loss1: 0.168710 Loss2: 1.330848
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409034 Loss1: 0.067019 Loss2: 1.342015
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443223 Loss1: 0.108686 Loss2: 1.334537
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386978 Loss1: 0.049653 Loss2: 1.337326
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.395089 Loss1: 0.075275 Loss2: 1.319815 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357360 Loss1: 0.051504 Loss2: 1.305856 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.326104 Loss1: 0.486158 Loss2: 1.839946
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.676368 Loss1: 0.313181 Loss2: 1.363186
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609684 Loss1: 0.205758 Loss2: 1.403926
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524750 Loss1: 0.170587 Loss2: 1.354163
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.595715 Loss1: 0.691684 Loss2: 1.904032
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.677944 Loss1: 0.342766 Loss2: 1.335177
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.505964 Loss1: 0.142640 Loss2: 1.363324
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445874 Loss1: 0.098631 Loss2: 1.347243 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446950 Loss1: 0.105395 Loss2: 1.341555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425111 Loss1: 0.081736 Loss2: 1.343375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.378538 Loss1: 0.079163 Loss2: 1.299375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.383240 Loss1: 0.075223 Loss2: 1.308017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.344152 Loss1: 0.051713 Loss2: 1.292439 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986779
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.311482 Loss1: 0.457515 Loss2: 1.853967
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.713230 Loss1: 0.336819 Loss2: 1.376411
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635885 Loss1: 0.206375 Loss2: 1.429510
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.359493 Loss1: 0.467527 Loss2: 1.891966
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508560 Loss1: 0.129808 Loss2: 1.378752
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.728603 Loss1: 0.344376 Loss2: 1.384227
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503328 Loss1: 0.132101 Loss2: 1.371227
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.646047 Loss1: 0.213771 Loss2: 1.432276
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519408 Loss1: 0.141813 Loss2: 1.377595
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537501 Loss1: 0.151503 Loss2: 1.385998
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.499777 Loss1: 0.133113 Loss2: 1.366664
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473251 Loss1: 0.104779 Loss2: 1.368471 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418022 Loss1: 0.054981 Loss2: 1.363041 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414129 Loss1: 0.055758 Loss2: 1.358371 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405474 Loss1: 0.051289 Loss2: 1.354185 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.235678 Loss1: 0.382598 Loss2: 1.853080 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.629200 Loss1: 0.196823 Loss2: 1.432377 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508670 Loss1: 0.125357 Loss2: 1.383313
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.245456 Loss1: 0.353625 Loss2: 1.891831
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.520615 Loss1: 0.143271 Loss2: 1.377344
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.647920 Loss1: 0.238570 Loss2: 1.409350
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.506002 Loss1: 0.121402 Loss2: 1.384600
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614951 Loss1: 0.180114 Loss2: 1.434837
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.493541 Loss1: 0.119782 Loss2: 1.373760
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.566767 Loss1: 0.162331 Loss2: 1.404435
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545929 Loss1: 0.132030 Loss2: 1.413899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491023 Loss1: 0.092846 Loss2: 1.398177 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407252 Loss1: 0.042079 Loss2: 1.365174
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.521524 Loss1: 0.125171 Loss2: 1.396352
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484620 Loss1: 0.085539 Loss2: 1.399081
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485451 Loss1: 0.093930 Loss2: 1.391521
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.464361 Loss1: 0.070062 Loss2: 1.394299
+(DefaultActor pid=3764) >> Training accuracy: 0.992647
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.667079 Loss1: 0.319563 Loss2: 1.347516 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524464 Loss1: 0.177103 Loss2: 1.347361 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478729 Loss1: 0.135660 Loss2: 1.343069
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.372477 Loss1: 0.522009 Loss2: 1.850468
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468787 Loss1: 0.122864 Loss2: 1.345922
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.684799 Loss1: 0.317855 Loss2: 1.366943
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415938 Loss1: 0.073794 Loss2: 1.342144
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.666209 Loss1: 0.265563 Loss2: 1.400646
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400743 Loss1: 0.073693 Loss2: 1.327050
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577641 Loss1: 0.199824 Loss2: 1.377817
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401297 Loss1: 0.071837 Loss2: 1.329461
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501929 Loss1: 0.134016 Loss2: 1.367913
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.362751 Loss1: 0.036597 Loss2: 1.326154
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.483806 Loss1: 0.123607 Loss2: 1.360198
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460855 Loss1: 0.110857 Loss2: 1.349997
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431264 Loss1: 0.079979 Loss2: 1.351285
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389642 Loss1: 0.049855 Loss2: 1.339788
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392199 Loss1: 0.061009 Loss2: 1.331191
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.123228 Loss1: 0.356559 Loss2: 1.766669
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.565835 Loss1: 0.246067 Loss2: 1.319767
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.464035 Loss1: 0.134924 Loss2: 1.329111
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.468810 Loss1: 0.161319 Loss2: 1.307492
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.463540 Loss1: 0.615145 Loss2: 1.848395
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.794037 Loss1: 0.460240 Loss2: 1.333797 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.652592 Loss1: 0.242682 Loss2: 1.409910 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.399288 Loss1: 0.087988 Loss2: 1.311300
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565172 Loss1: 0.237621 Loss2: 1.327551
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391784 Loss1: 0.082687 Loss2: 1.309098
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515558 Loss1: 0.163907 Loss2: 1.351652
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.435486 Loss1: 0.104427 Loss2: 1.331059
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378384 Loss1: 0.074187 Loss2: 1.304197
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433102 Loss1: 0.109701 Loss2: 1.323401
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384241 Loss1: 0.079776 Loss2: 1.304465
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400647 Loss1: 0.090953 Loss2: 1.309694 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982143
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.591286 Loss1: 0.609050 Loss2: 1.982236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.699990 Loss1: 0.284961 Loss2: 1.415030 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510982 Loss1: 0.146799 Loss2: 1.364183 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.706470 Loss1: 0.349977 Loss2: 1.356493 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.507101 Loss1: 0.124183 Loss2: 1.382917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551765 Loss1: 0.191690 Loss2: 1.360076 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990885
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550151 Loss1: 0.188228 Loss2: 1.361923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428400 Loss1: 0.076576 Loss2: 1.351823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373728 Loss1: 0.044065 Loss2: 1.329663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.358927 Loss1: 0.035011 Loss2: 1.323916 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.999023
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.493679 Loss1: 0.159363 Loss2: 1.334316 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.420535 Loss1: 0.092058 Loss2: 1.328477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.485690 Loss1: 0.599058 Loss2: 1.886632
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.385632 Loss1: 0.061484 Loss2: 1.324148
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.732956 Loss1: 0.386434 Loss2: 1.346522
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.344654 Loss1: 0.035476 Loss2: 1.309178
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.656425 Loss1: 0.257751 Loss2: 1.398673
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.337769 Loss1: 0.028623 Loss2: 1.309146
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.321375 Loss1: 0.022949 Loss2: 1.298426 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447674 Loss1: 0.112688 Loss2: 1.334986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442305 Loss1: 0.108292 Loss2: 1.334013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.378981 Loss1: 0.541169 Loss2: 1.837812 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.529273 Loss1: 0.160938 Loss2: 1.368335 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.446957 Loss1: 0.110521 Loss2: 1.336436 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466049 Loss1: 0.126556 Loss2: 1.339493
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.386517 Loss1: 0.466241 Loss2: 1.920276
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415331 Loss1: 0.078300 Loss2: 1.337031
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.762009 Loss1: 0.355217 Loss2: 1.406792
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.388293 Loss1: 0.060673 Loss2: 1.327619
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653641 Loss1: 0.191078 Loss2: 1.462563
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374901 Loss1: 0.051661 Loss2: 1.323240
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579640 Loss1: 0.174315 Loss2: 1.405326
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.344531 Loss1: 0.027408 Loss2: 1.317123
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569492 Loss1: 0.155358 Loss2: 1.414134
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.515424 Loss1: 0.097392 Loss2: 1.418032
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.503367 Loss1: 0.099083 Loss2: 1.404283
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477963 Loss1: 0.073938 Loss2: 1.404025
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450409 Loss1: 0.055915 Loss2: 1.394494
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.283547 Loss1: 0.450179 Loss2: 1.833368
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.459741 Loss1: 0.070011 Loss2: 1.389730
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.546765 Loss1: 0.194556 Loss2: 1.352209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.431003 Loss1: 0.100946 Loss2: 1.330057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.411147 Loss1: 0.089656 Loss2: 1.321491
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.356998 Loss1: 0.470513 Loss2: 1.886485
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.842365 Loss1: 0.409950 Loss2: 1.432416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.717368 Loss1: 0.249058 Loss2: 1.468310 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.667753 Loss1: 0.229218 Loss2: 1.438534 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.595172 Loss1: 0.152143 Loss2: 1.443029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.540806 Loss1: 0.114630 Loss2: 1.426176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499053 Loss1: 0.083913 Loss2: 1.415140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.473142 Loss1: 0.068879 Loss2: 1.404264 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.567586 Loss1: 0.158543 Loss2: 1.409043 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491370 Loss1: 0.095574 Loss2: 1.395797 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.366354 Loss1: 0.484484 Loss2: 1.881870
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.501597 Loss1: 0.116495 Loss2: 1.385101
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.680755 Loss1: 0.298454 Loss2: 1.382301
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.496149 Loss1: 0.107661 Loss2: 1.388488
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543513 Loss1: 0.131462 Loss2: 1.412052
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454677 Loss1: 0.074902 Loss2: 1.379775
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492567 Loss1: 0.123190 Loss2: 1.369377
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434978 Loss1: 0.055207 Loss2: 1.379771
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450485 Loss1: 0.084201 Loss2: 1.366284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443397 Loss1: 0.084153 Loss2: 1.359244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431665 Loss1: 0.071895 Loss2: 1.359771
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.279264 Loss1: 0.467429 Loss2: 1.811835
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401360 Loss1: 0.047221 Loss2: 1.354139
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.685564 Loss1: 0.359331 Loss2: 1.326234
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.556921 Loss1: 0.173691 Loss2: 1.383230
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516815 Loss1: 0.181678 Loss2: 1.335137
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479743 Loss1: 0.137724 Loss2: 1.342018
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.427868 Loss1: 0.098342 Loss2: 1.329526
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.319089 Loss1: 0.454881 Loss2: 1.864208
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.391703 Loss1: 0.070610 Loss2: 1.321094
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.670167 Loss1: 0.298258 Loss2: 1.371908
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384044 Loss1: 0.072639 Loss2: 1.311406
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.531005 Loss1: 0.141818 Loss2: 1.389187
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.366326 Loss1: 0.056125 Loss2: 1.310201
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.456484 Loss1: 0.081051 Loss2: 1.375433
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350060 Loss1: 0.046338 Loss2: 1.303723
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429528 Loss1: 0.075123 Loss2: 1.354406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416452 Loss1: 0.064986 Loss2: 1.351466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415165 Loss1: 0.067229 Loss2: 1.347936
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.214081 Loss1: 0.388084 Loss2: 1.825997
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394970 Loss1: 0.046824 Loss2: 1.348146
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.656214 Loss1: 0.283487 Loss2: 1.372727
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563278 Loss1: 0.155878 Loss2: 1.407400
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488522 Loss1: 0.109425 Loss2: 1.379097
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.469749 Loss1: 0.101843 Loss2: 1.367906
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438838 Loss1: 0.072498 Loss2: 1.366340
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.298077 Loss1: 0.494006 Loss2: 1.804071
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.666514 Loss1: 0.353921 Loss2: 1.312593 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636625 Loss1: 0.259198 Loss2: 1.377427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.466160 Loss1: 0.148921 Loss2: 1.317239 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998047
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400966 Loss1: 0.049585 Loss2: 1.351382
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.458326 Loss1: 0.148408 Loss2: 1.309919
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.412268 Loss1: 0.100800 Loss2: 1.311468
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425965 Loss1: 0.116149 Loss2: 1.309816
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.384785 Loss1: 0.088449 Loss2: 1.296336
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.339586 Loss1: 0.043846 Loss2: 1.295740
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.351935 Loss1: 0.498483 Loss2: 1.853452
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.328160 Loss1: 0.038913 Loss2: 1.289246
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.567877 Loss1: 0.179987 Loss2: 1.387890 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448593 Loss1: 0.085500 Loss2: 1.363093 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480071 Loss1: 0.119014 Loss2: 1.361057
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.370137 Loss1: 0.507996 Loss2: 1.862140
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440745 Loss1: 0.081704 Loss2: 1.359041
+DEBUG flwr 2023-10-12 18:23:27,711 | server.py:236 | fit_round 160 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.662879 Loss1: 0.292297 Loss2: 1.370582
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448483 Loss1: 0.094148 Loss2: 1.354335
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.597539 Loss1: 0.194308 Loss2: 1.403231
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437305 Loss1: 0.087888 Loss2: 1.349417
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520528 Loss1: 0.138399 Loss2: 1.382129
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443111 Loss1: 0.095829 Loss2: 1.347282
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492159 Loss1: 0.122294 Loss2: 1.369865
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482745 Loss1: 0.114384 Loss2: 1.368361
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.510993 Loss1: 0.146436 Loss2: 1.364557
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446362 Loss1: 0.082445 Loss2: 1.363917
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449219 Loss1: 0.090219 Loss2: 1.359000
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.414348 Loss1: 0.568315 Loss2: 1.846033
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434354 Loss1: 0.080116 Loss2: 1.354238
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561064 Loss1: 0.179024 Loss2: 1.382040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.421263 Loss1: 0.085922 Loss2: 1.335341 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.414993 Loss1: 0.081780 Loss2: 1.333213
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.255477 Loss1: 0.442124 Loss2: 1.813353
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.401868 Loss1: 0.076956 Loss2: 1.324912
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.637817 Loss1: 0.291607 Loss2: 1.346210
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432341 Loss1: 0.110001 Loss2: 1.322341
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568809 Loss1: 0.191169 Loss2: 1.377640
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399050 Loss1: 0.076814 Loss2: 1.322236
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507191 Loss1: 0.162146 Loss2: 1.345045
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375554 Loss1: 0.061589 Loss2: 1.313965
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.468567 Loss1: 0.123339 Loss2: 1.345228
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446069 Loss1: 0.106780 Loss2: 1.339289
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.418975 Loss1: 0.081845 Loss2: 1.337129
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.377673 Loss1: 0.047623 Loss2: 1.330050
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392348 Loss1: 0.070880 Loss2: 1.321468
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.496490 Loss1: 0.569479 Loss2: 1.927011
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391280 Loss1: 0.067145 Loss2: 1.324135
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.631431 Loss1: 0.204076 Loss2: 1.427354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611396 Loss1: 0.199969 Loss2: 1.411427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.592772 Loss1: 0.165268 Loss2: 1.427505
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.321253 Loss1: 0.422460 Loss2: 1.898794
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.559806 Loss1: 0.138766 Loss2: 1.421040
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.676548 Loss1: 0.294138 Loss2: 1.382410
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.507665 Loss1: 0.100212 Loss2: 1.407453
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.631877 Loss1: 0.201833 Loss2: 1.430044
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453588 Loss1: 0.055561 Loss2: 1.398027
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509345 Loss1: 0.115784 Loss2: 1.393561
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.444337 Loss1: 0.053737 Loss2: 1.390600
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.474287 Loss1: 0.089848 Loss2: 1.384440
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502033 Loss1: 0.114380 Loss2: 1.387653
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488800 Loss1: 0.107532 Loss2: 1.381268
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454061 Loss1: 0.075380 Loss2: 1.378681
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418751 Loss1: 0.044681 Loss2: 1.374069
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410413 Loss1: 0.037945 Loss2: 1.372468
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 18:23:27,711][flwr][DEBUG] - fit_round 160 received 50 results and 0 failures
+INFO flwr 2023-10-12 18:24:09,395 | server.py:125 | fit progress: (160, 2.2541892732294224, {'accuracy': 0.6005}, 369157.174040303)
+>> Test accuracy: 0.600500
+[2023-10-12 18:24:09,395][flwr][INFO] - fit progress: (160, 2.2541892732294224, {'accuracy': 0.6005}, 369157.174040303)
+DEBUG flwr 2023-10-12 18:24:09,396 | server.py:173 | evaluate_round 160: strategy sampled 50 clients (out of 50)
+[2023-10-12 18:24:09,396][flwr][DEBUG] - evaluate_round 160: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 18:33:16,953 | server.py:187 | evaluate_round 160 received 50 results and 0 failures
+[2023-10-12 18:33:16,953][flwr][DEBUG] - evaluate_round 160 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 18:33:16,954 | server.py:222 | fit_round 161: strategy sampled 50 clients (out of 50)
+[2023-10-12 18:33:16,954][flwr][DEBUG] - fit_round 161: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.526520 Loss1: 0.635193 Loss2: 1.891327
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.629250 Loss1: 0.332634 Loss2: 1.296616
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563797 Loss1: 0.247182 Loss2: 1.316615
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.478115 Loss1: 0.150651 Loss2: 1.327464
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443371 Loss1: 0.137395 Loss2: 1.305976
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.335278 Loss1: 0.505356 Loss2: 1.829922
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442514 Loss1: 0.135131 Loss2: 1.307383
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592744 Loss1: 0.203255 Loss2: 1.389488 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.355223 Loss1: 0.065000 Loss2: 1.290223 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.333232 Loss1: 0.049611 Loss2: 1.283620 [repeated 2x across cluster]
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426080 Loss1: 0.085287 Loss2: 1.340793 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373194 Loss1: 0.040082 Loss2: 1.333113 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.340720 Loss1: 0.466614 Loss2: 1.874105
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352639 Loss1: 0.026699 Loss2: 1.325940
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.562026 Loss1: 0.160036 Loss2: 1.401990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468812 Loss1: 0.105909 Loss2: 1.362904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.425277 Loss1: 0.067564 Loss2: 1.357713
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.367000 Loss1: 0.495057 Loss2: 1.871943
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.719379 Loss1: 0.302634 Loss2: 1.416745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.634156 Loss1: 0.216990 Loss2: 1.417166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.547416 Loss1: 0.155939 Loss2: 1.391477 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461563 Loss1: 0.073553 Loss2: 1.388010 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431719 Loss1: 0.053314 Loss2: 1.378404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416078 Loss1: 0.045949 Loss2: 1.370129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417178 Loss1: 0.054393 Loss2: 1.362786 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995404
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559494 Loss1: 0.174877 Loss2: 1.384617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476844 Loss1: 0.100779 Loss2: 1.376065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.383713 Loss1: 0.545793 Loss2: 1.837920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.677916 Loss1: 0.343014 Loss2: 1.334902 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588878 Loss1: 0.217096 Loss2: 1.371783 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.440996 Loss1: 0.106836 Loss2: 1.334160 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.413477 Loss1: 0.092627 Loss2: 1.320850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.387677 Loss1: 0.069351 Loss2: 1.318326
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.290706 Loss1: 0.477559 Loss2: 1.813147
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.629416 Loss1: 0.276231 Loss2: 1.353185 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526580 Loss1: 0.159758 Loss2: 1.366822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.456348 Loss1: 0.117653 Loss2: 1.338696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415524 Loss1: 0.077096 Loss2: 1.338428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380171 Loss1: 0.052609 Loss2: 1.327563 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.367290 Loss1: 0.041181 Loss2: 1.326109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.345506 Loss1: 0.030128 Loss2: 1.315378 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524807 Loss1: 0.128071 Loss2: 1.396736 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.479212 Loss1: 0.093225 Loss2: 1.385987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486000 Loss1: 0.103137 Loss2: 1.382863
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.372112 Loss1: 0.460622 Loss2: 1.911489
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.483343 Loss1: 0.097908 Loss2: 1.385435
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.666736 Loss1: 0.261225 Loss2: 1.405511
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.637231 Loss1: 0.203929 Loss2: 1.433302
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.577572 Loss1: 0.156778 Loss2: 1.420793
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.538067 Loss1: 0.119179 Loss2: 1.418888
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.535098 Loss1: 0.122705 Loss2: 1.412393
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.576184 Loss1: 0.162636 Loss2: 1.413549
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.139122 Loss1: 0.371673 Loss2: 1.767449
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.617938 Loss1: 0.298930 Loss2: 1.319008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.580509 Loss1: 0.228425 Loss2: 1.352084 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.492280 Loss1: 0.084785 Loss2: 1.407495
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.506925 Loss1: 0.191269 Loss2: 1.315655
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.420438 Loss1: 0.110808 Loss2: 1.309630
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423763 Loss1: 0.120657 Loss2: 1.303106
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.385666 Loss1: 0.084541 Loss2: 1.301125
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368436 Loss1: 0.069831 Loss2: 1.298605
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.199049 Loss1: 0.414439 Loss2: 1.784610
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.618615 Loss1: 0.270561 Loss2: 1.348053 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.999023
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.340254 Loss1: 0.049711 Loss2: 1.290543
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.544999 Loss1: 0.177428 Loss2: 1.367571
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.468420 Loss1: 0.123370 Loss2: 1.345049
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.442502 Loss1: 0.102715 Loss2: 1.339787
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.425940 Loss1: 0.096678 Loss2: 1.329263
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422896 Loss1: 0.099242 Loss2: 1.323654
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.653300 Loss1: 0.667072 Loss2: 1.986229
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.758257 Loss1: 0.322192 Loss2: 1.436065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.724383 Loss1: 0.248869 Loss2: 1.475514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.362529 Loss1: 0.040763 Loss2: 1.321766
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.600519 Loss1: 0.169954 Loss2: 1.430566
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554150 Loss1: 0.122425 Loss2: 1.431724
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513270 Loss1: 0.090566 Loss2: 1.422704
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.492639 Loss1: 0.078593 Loss2: 1.414046
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470318 Loss1: 0.067498 Loss2: 1.402820
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461587 Loss1: 0.059234 Loss2: 1.402353
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.169292 Loss1: 0.383602 Loss2: 1.785690
+(DefaultActor pid=3764) >> Training accuracy: 0.994420
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.544063 Loss1: 0.239300 Loss2: 1.304763 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.475642 Loss1: 0.159170 Loss2: 1.316472 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.388497 Loss1: 0.094824 Loss2: 1.293673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.377347 Loss1: 0.075418 Loss2: 1.301929 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.347724 Loss1: 0.058320 Loss2: 1.289404 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.350222 Loss1: 0.066137 Loss2: 1.284085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.338853 Loss1: 0.054781 Loss2: 1.284072 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.434331 Loss1: 0.116643 Loss2: 1.317688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390330 Loss1: 0.085231 Loss2: 1.305099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395756 Loss1: 0.091632 Loss2: 1.304125
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.476454 Loss1: 0.591924 Loss2: 1.884530
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387838 Loss1: 0.088769 Loss2: 1.299068
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.687415 Loss1: 0.342812 Loss2: 1.344603
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573584 Loss1: 0.190151 Loss2: 1.383433
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490791 Loss1: 0.141721 Loss2: 1.349070
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461968 Loss1: 0.119816 Loss2: 1.342152
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.424871 Loss1: 0.084578 Loss2: 1.340293
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428121 Loss1: 0.092163 Loss2: 1.335957
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.300798 Loss1: 0.432313 Loss2: 1.868485
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.765671 Loss1: 0.395806 Loss2: 1.369865 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.609020 Loss1: 0.175432 Loss2: 1.433588 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594704 Loss1: 0.219958 Loss2: 1.374746 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.483255 Loss1: 0.109778 Loss2: 1.373477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443333 Loss1: 0.077898 Loss2: 1.365434 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433481 Loss1: 0.074842 Loss2: 1.358639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405110 Loss1: 0.049140 Loss2: 1.355970 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496420 Loss1: 0.172025 Loss2: 1.324395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444015 Loss1: 0.118156 Loss2: 1.325860 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426685 Loss1: 0.101962 Loss2: 1.324723
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.329097 Loss1: 0.492765 Loss2: 1.836332
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.648373 Loss1: 0.303052 Loss2: 1.345321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.591572 Loss1: 0.205678 Loss2: 1.385894 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562132 Loss1: 0.206298 Loss2: 1.355834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479722 Loss1: 0.120057 Loss2: 1.359666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409173 Loss1: 0.068931 Loss2: 1.340242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370450 Loss1: 0.041032 Loss2: 1.329418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370323 Loss1: 0.046073 Loss2: 1.324250 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523326 Loss1: 0.149678 Loss2: 1.373648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.464792 Loss1: 0.101361 Loss2: 1.363431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462883 Loss1: 0.101173 Loss2: 1.361709
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.331795 Loss1: 0.444942 Loss2: 1.886854
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.688676 Loss1: 0.276294 Loss2: 1.412382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.693342 Loss1: 0.240656 Loss2: 1.452685 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.608788 Loss1: 0.189878 Loss2: 1.418910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510473 Loss1: 0.105606 Loss2: 1.404867 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.340195 Loss1: 0.480487 Loss2: 1.859708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.703948 Loss1: 0.333145 Loss2: 1.370804 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.671347 Loss1: 0.239681 Loss2: 1.431665 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.557546 Loss1: 0.182746 Loss2: 1.374800 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456430 Loss1: 0.087237 Loss2: 1.369193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497236 Loss1: 0.130581 Loss2: 1.366655
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.393321 Loss1: 0.554910 Loss2: 1.838411
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.724616 Loss1: 0.370962 Loss2: 1.353654 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458187 Loss1: 0.093690 Loss2: 1.364498
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.684866 Loss1: 0.268415 Loss2: 1.416451
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595903 Loss1: 0.241820 Loss2: 1.354083
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.628527 Loss1: 0.248960 Loss2: 1.379567
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.544893 Loss1: 0.174080 Loss2: 1.370813
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500737 Loss1: 0.143341 Loss2: 1.357396
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.167823 Loss1: 0.397805 Loss2: 1.770018
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449835 Loss1: 0.090013 Loss2: 1.359822
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451848 Loss1: 0.100178 Loss2: 1.351671
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.654579 Loss1: 0.331428 Loss2: 1.323151
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421693 Loss1: 0.074492 Loss2: 1.347201
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595498 Loss1: 0.225137 Loss2: 1.370361
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556340 Loss1: 0.224635 Loss2: 1.331705
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.458833 Loss1: 0.121231 Loss2: 1.337602
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.421559 Loss1: 0.095913 Loss2: 1.325647
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.437748 Loss1: 0.121697 Loss2: 1.316051
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.332410 Loss1: 0.520344 Loss2: 1.812066
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389204 Loss1: 0.073275 Loss2: 1.315929
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.354939 Loss1: 0.044589 Loss2: 1.310350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.342887 Loss1: 0.042355 Loss2: 1.300532 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502469 Loss1: 0.145810 Loss2: 1.356658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472437 Loss1: 0.130157 Loss2: 1.342280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.405010 Loss1: 0.501482 Loss2: 1.903528 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.696523 Loss1: 0.300823 Loss2: 1.395700 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541935 Loss1: 0.137744 Loss2: 1.404192 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.539613 Loss1: 0.143205 Loss2: 1.396408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.228980 Loss1: 0.390182 Loss2: 1.838798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.626837 Loss1: 0.296182 Loss2: 1.330656 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622023 Loss1: 0.262690 Loss2: 1.359333 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.969792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.566056 Loss1: 0.215041 Loss2: 1.351015 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.448801 Loss1: 0.106835 Loss2: 1.341967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409917 Loss1: 0.085985 Loss2: 1.323932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385539 Loss1: 0.070057 Loss2: 1.315481 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357933 Loss1: 0.045650 Loss2: 1.312283 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.532531 Loss1: 0.179123 Loss2: 1.353408 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440299 Loss1: 0.083773 Loss2: 1.356526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.391727 Loss1: 0.048890 Loss2: 1.342836
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.564614 Loss1: 0.592901 Loss2: 1.971713
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.764532 Loss1: 0.380012 Loss2: 1.384520 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.706676 Loss1: 0.304885 Loss2: 1.401791
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398901 Loss1: 0.062637 Loss2: 1.336264
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565217 Loss1: 0.165392 Loss2: 1.399824
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396238 Loss1: 0.061414 Loss2: 1.334824
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476176 Loss1: 0.108008 Loss2: 1.368168 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428309 Loss1: 0.070791 Loss2: 1.357519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370428 Loss1: 0.033448 Loss2: 1.336980 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.567289 Loss1: 0.213889 Loss2: 1.353400 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.446575 Loss1: 0.128290 Loss2: 1.318285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475392 Loss1: 0.146538 Loss2: 1.328854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433866 Loss1: 0.113689 Loss2: 1.320177 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378899 Loss1: 0.063872 Loss2: 1.315027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.365177 Loss1: 0.057821 Loss2: 1.307356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.345329 Loss1: 0.043322 Loss2: 1.302007 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404103 Loss1: 0.089737 Loss2: 1.314366 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402896 Loss1: 0.094110 Loss2: 1.308785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373952 Loss1: 0.068296 Loss2: 1.305656
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.460698 Loss1: 0.591109 Loss2: 1.869589
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.707153 Loss1: 0.330538 Loss2: 1.376616
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.642289 Loss1: 0.234635 Loss2: 1.407653
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522022 Loss1: 0.148288 Loss2: 1.373734
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487224 Loss1: 0.118335 Loss2: 1.368889
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.559650 Loss1: 0.563817 Loss2: 1.995833
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461520 Loss1: 0.101058 Loss2: 1.360463
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455987 Loss1: 0.098820 Loss2: 1.357167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423844 Loss1: 0.072369 Loss2: 1.351475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414734 Loss1: 0.066100 Loss2: 1.348634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414552 Loss1: 0.067013 Loss2: 1.347539 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516921 Loss1: 0.090871 Loss2: 1.426049 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480796 Loss1: 0.067083 Loss2: 1.413713 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472555 Loss1: 0.062560 Loss2: 1.409994
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.257933 Loss1: 0.441784 Loss2: 1.816149
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.716813 Loss1: 0.349102 Loss2: 1.367712
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.614160 Loss1: 0.190748 Loss2: 1.423411
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570978 Loss1: 0.217399 Loss2: 1.353580
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498860 Loss1: 0.130139 Loss2: 1.368720
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.325895 Loss1: 0.486426 Loss2: 1.839469
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523781 Loss1: 0.170439 Loss2: 1.353342
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.649946 Loss1: 0.319070 Loss2: 1.330876
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590880 Loss1: 0.215490 Loss2: 1.375390
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.504026 Loss1: 0.137141 Loss2: 1.366885
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538253 Loss1: 0.200339 Loss2: 1.337914
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462574 Loss1: 0.110966 Loss2: 1.351608
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.466047 Loss1: 0.127252 Loss2: 1.338795
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.497195 Loss1: 0.146195 Loss2: 1.350999
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.417696 Loss1: 0.094981 Loss2: 1.322715
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440641 Loss1: 0.082160 Loss2: 1.358481
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.392441 Loss1: 0.084204 Loss2: 1.308237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367072 Loss1: 0.057843 Loss2: 1.309229 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.959375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.668746 Loss1: 0.327207 Loss2: 1.341539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534507 Loss1: 0.190893 Loss2: 1.343614 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.559997 Loss1: 0.195774 Loss2: 1.364223
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.163946 Loss1: 0.384661 Loss2: 1.779286
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463530 Loss1: 0.118355 Loss2: 1.345175
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.618806 Loss1: 0.285969 Loss2: 1.332837
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435854 Loss1: 0.101811 Loss2: 1.334043
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635997 Loss1: 0.259032 Loss2: 1.376966
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419296 Loss1: 0.080490 Loss2: 1.338806
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519470 Loss1: 0.179763 Loss2: 1.339707
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406218 Loss1: 0.077214 Loss2: 1.329004
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521312 Loss1: 0.178596 Loss2: 1.342716
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385531 Loss1: 0.060514 Loss2: 1.325018
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494444 Loss1: 0.151459 Loss2: 1.342985
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428243 Loss1: 0.095174 Loss2: 1.333069
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406846 Loss1: 0.080927 Loss2: 1.325919
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.377575 Loss1: 0.059263 Loss2: 1.318311
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370570 Loss1: 0.050372 Loss2: 1.320198
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.285394 Loss1: 0.398972 Loss2: 1.886422
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.639465 Loss1: 0.269044 Loss2: 1.370420
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.587412 Loss1: 0.189375 Loss2: 1.398037
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527752 Loss1: 0.147369 Loss2: 1.380383
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510013 Loss1: 0.135938 Loss2: 1.374074
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.303824 Loss1: 0.522820 Loss2: 1.781004
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461646 Loss1: 0.095059 Loss2: 1.366587
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446917 Loss1: 0.082197 Loss2: 1.364720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427420 Loss1: 0.068270 Loss2: 1.359150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411735 Loss1: 0.055656 Loss2: 1.356079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391186 Loss1: 0.044442 Loss2: 1.346744 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415629 Loss1: 0.114276 Loss2: 1.301352 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420732 Loss1: 0.121592 Loss2: 1.299139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352264 Loss1: 0.063124 Loss2: 1.289140
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.220909 Loss1: 0.399952 Loss2: 1.820957
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.627754 Loss1: 0.306599 Loss2: 1.321155
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591540 Loss1: 0.240151 Loss2: 1.351388
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523246 Loss1: 0.188381 Loss2: 1.334865
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490711 Loss1: 0.159416 Loss2: 1.331294
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.373473 Loss1: 0.489265 Loss2: 1.884208
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.744531 Loss1: 0.360868 Loss2: 1.383662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.643257 Loss1: 0.214596 Loss2: 1.428662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574462 Loss1: 0.185635 Loss2: 1.388826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501101 Loss1: 0.122067 Loss2: 1.379034 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.458268 Loss1: 0.081509 Loss2: 1.376759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423828 Loss1: 0.063759 Loss2: 1.360069 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422095 Loss1: 0.066597 Loss2: 1.355498 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.615421 Loss1: 0.215805 Loss2: 1.399616 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502907 Loss1: 0.104834 Loss2: 1.398073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.528526 Loss1: 0.136205 Loss2: 1.392321
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.267793 Loss1: 0.427452 Loss2: 1.840340
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.660738 Loss1: 0.307847 Loss2: 1.352891 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462369 Loss1: 0.076088 Loss2: 1.386281
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598334 Loss1: 0.219858 Loss2: 1.378476
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462285 Loss1: 0.081603 Loss2: 1.380682
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530852 Loss1: 0.175751 Loss2: 1.355101
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445346 Loss1: 0.068960 Loss2: 1.376386
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495440 Loss1: 0.150583 Loss2: 1.344857
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432883 Loss1: 0.056875 Loss2: 1.376008
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.462076 Loss1: 0.122058 Loss2: 1.340018
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.449708 Loss1: 0.116803 Loss2: 1.332905
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453040 Loss1: 0.118127 Loss2: 1.334913
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419234 Loss1: 0.086796 Loss2: 1.332438
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425999 Loss1: 0.095820 Loss2: 1.330179
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.476332 Loss1: 0.512977 Loss2: 1.963355
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.796182 Loss1: 0.376687 Loss2: 1.419495
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.740995 Loss1: 0.281283 Loss2: 1.459713
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.708990 Loss1: 0.274712 Loss2: 1.434278
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.711561 Loss1: 0.254444 Loss2: 1.457117
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.587536 Loss1: 0.168633 Loss2: 1.418903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.535418 Loss1: 0.119736 Loss2: 1.415682 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.481955 Loss1: 0.072083 Loss2: 1.409872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443864 Loss1: 0.042279 Loss2: 1.401585 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429732 Loss1: 0.037363 Loss2: 1.392368 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415835 Loss1: 0.084445 Loss2: 1.331390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398013 Loss1: 0.073977 Loss2: 1.324036 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414523 Loss1: 0.084571 Loss2: 1.329952
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.385471 Loss1: 0.549795 Loss2: 1.835676
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.642145 Loss1: 0.284811 Loss2: 1.357335
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599439 Loss1: 0.206697 Loss2: 1.392743
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482160 Loss1: 0.116770 Loss2: 1.365390
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443672 Loss1: 0.088601 Loss2: 1.355071
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.226629 Loss1: 0.439161 Loss2: 1.787468
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.431270 Loss1: 0.081828 Loss2: 1.349442
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406405 Loss1: 0.065151 Loss2: 1.341255 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.612924 Loss1: 0.222881 Loss2: 1.390043
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418582 Loss1: 0.079784 Loss2: 1.338799
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.545818 Loss1: 0.195114 Loss2: 1.350704
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406960 Loss1: 0.065887 Loss2: 1.341072
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503735 Loss1: 0.156955 Loss2: 1.346780
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371297 Loss1: 0.038240 Loss2: 1.333057
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456021 Loss1: 0.119885 Loss2: 1.336135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388348 Loss1: 0.062358 Loss2: 1.325989 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.298810 Loss1: 0.460930 Loss2: 1.837879
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.353546 Loss1: 0.031764 Loss2: 1.321782
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582498 Loss1: 0.204270 Loss2: 1.378228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.572689 Loss1: 0.219775 Loss2: 1.352914 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509992 Loss1: 0.148252 Loss2: 1.361740
+DEBUG flwr 2023-10-12 19:01:42,900 | server.py:236 | fit_round 161 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.425790 Loss1: 0.518017 Loss2: 1.907773
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470497 Loss1: 0.120580 Loss2: 1.349917
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.681069 Loss1: 0.315198 Loss2: 1.365871
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614592 Loss1: 0.220725 Loss2: 1.393866
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470915 Loss1: 0.124622 Loss2: 1.346293
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.410567 Loss1: 0.064104 Loss2: 1.346463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392457 Loss1: 0.061433 Loss2: 1.331024 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473072 Loss1: 0.095367 Loss2: 1.377705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438439 Loss1: 0.074819 Loss2: 1.363621 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.385465 Loss1: 0.505550 Loss2: 1.879916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647465 Loss1: 0.238710 Loss2: 1.408755 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.497661 Loss1: 0.608403 Loss2: 1.889258 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.705841 Loss1: 0.340608 Loss2: 1.365232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.678780 Loss1: 0.273604 Loss2: 1.405176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563890 Loss1: 0.188733 Loss2: 1.375157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528956 Loss1: 0.166869 Loss2: 1.362087 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441592 Loss1: 0.083083 Loss2: 1.358509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420280 Loss1: 0.072088 Loss2: 1.348193 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404697 Loss1: 0.064167 Loss2: 1.340530 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 19:01:42,900][flwr][DEBUG] - fit_round 161 received 50 results and 0 failures
+INFO flwr 2023-10-12 19:02:24,438 | server.py:125 | fit progress: (161, 2.2532033196653423, {'accuracy': 0.6029}, 371452.21645676397)
+>> Test accuracy: 0.602900
+[2023-10-12 19:02:24,438][flwr][INFO] - fit progress: (161, 2.2532033196653423, {'accuracy': 0.6029}, 371452.21645676397)
+DEBUG flwr 2023-10-12 19:02:24,438 | server.py:173 | evaluate_round 161: strategy sampled 50 clients (out of 50)
+[2023-10-12 19:02:24,438][flwr][DEBUG] - evaluate_round 161: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 19:11:28,807 | server.py:187 | evaluate_round 161 received 50 results and 0 failures
+[2023-10-12 19:11:28,807][flwr][DEBUG] - evaluate_round 161 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 19:11:28,807 | server.py:222 | fit_round 162: strategy sampled 50 clients (out of 50)
+[2023-10-12 19:11:28,807][flwr][DEBUG] - fit_round 162: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.602624 Loss1: 0.692091 Loss2: 1.910534
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.730924 Loss1: 0.367264 Loss2: 1.363660
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.605269 Loss1: 0.230488 Loss2: 1.374782
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.545348 Loss1: 0.170319 Loss2: 1.375029
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.226651 Loss1: 0.430661 Loss2: 1.795990
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.435824 Loss1: 0.098901 Loss2: 1.336922 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402681 Loss1: 0.071311 Loss2: 1.331370 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.399159 Loss1: 0.070502 Loss2: 1.328657 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388903 Loss1: 0.070634 Loss2: 1.318269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381961 Loss1: 0.060044 Loss2: 1.321916 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458058 Loss1: 0.100169 Loss2: 1.357889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398515 Loss1: 0.056946 Loss2: 1.341569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.319209 Loss1: 0.444584 Loss2: 1.874625
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390015 Loss1: 0.054382 Loss2: 1.335633
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573939 Loss1: 0.193184 Loss2: 1.380755 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.469309 Loss1: 0.103886 Loss2: 1.365423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.479659 Loss1: 0.121061 Loss2: 1.358598
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.283383 Loss1: 0.491117 Loss2: 1.792266
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451483 Loss1: 0.088366 Loss2: 1.363117
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.587558 Loss1: 0.302071 Loss2: 1.285488
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429432 Loss1: 0.072448 Loss2: 1.356984
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.502674 Loss1: 0.200535 Loss2: 1.302139
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405570 Loss1: 0.056645 Loss2: 1.348924
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468338 Loss1: 0.166786 Loss2: 1.301552
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.436055 Loss1: 0.150465 Loss2: 1.285590
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403835 Loss1: 0.061104 Loss2: 1.342731
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.401521 Loss1: 0.120591 Loss2: 1.280930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.348327 Loss1: 0.077877 Loss2: 1.270451 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994420
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.314156 Loss1: 0.046457 Loss2: 1.267699
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.277658 Loss1: 0.446857 Loss2: 1.830801
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.648198 Loss1: 0.292580 Loss2: 1.355619
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.556686 Loss1: 0.174631 Loss2: 1.382055
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499987 Loss1: 0.151329 Loss2: 1.348658
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471231 Loss1: 0.129279 Loss2: 1.341952
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.373366 Loss1: 0.514772 Loss2: 1.858594
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.631394 Loss1: 0.258415 Loss2: 1.372979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601876 Loss1: 0.211428 Loss2: 1.390447 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555984 Loss1: 0.174825 Loss2: 1.381158 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.540615 Loss1: 0.154721 Loss2: 1.385894 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559425 Loss1: 0.175902 Loss2: 1.383524 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440660 Loss1: 0.077802 Loss2: 1.362858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401512 Loss1: 0.041547 Loss2: 1.359964 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.603176 Loss1: 0.262140 Loss2: 1.341037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.452981 Loss1: 0.121961 Loss2: 1.331020 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.454998 Loss1: 0.125525 Loss2: 1.329473
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.220825 Loss1: 0.375020 Loss2: 1.845805
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.615202 Loss1: 0.260151 Loss2: 1.355050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.599841 Loss1: 0.222912 Loss2: 1.376928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.548769 Loss1: 0.187626 Loss2: 1.361143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539959 Loss1: 0.185512 Loss2: 1.354447 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355728 Loss1: 0.049952 Loss2: 1.305775
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493791 Loss1: 0.142835 Loss2: 1.350956
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465533 Loss1: 0.103412 Loss2: 1.362121
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439946 Loss1: 0.088147 Loss2: 1.351799
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430376 Loss1: 0.083642 Loss2: 1.346734
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414802 Loss1: 0.069473 Loss2: 1.345328
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.388301 Loss1: 0.494899 Loss2: 1.893401
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.647305 Loss1: 0.264009 Loss2: 1.383296
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.608026 Loss1: 0.205388 Loss2: 1.402639
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551318 Loss1: 0.155914 Loss2: 1.395404
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491617 Loss1: 0.117624 Loss2: 1.373993
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.505647 Loss1: 0.639589 Loss2: 1.866058
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.676499 Loss1: 0.336004 Loss2: 1.340495 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635358 Loss1: 0.272375 Loss2: 1.362982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519466 Loss1: 0.189416 Loss2: 1.330051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408612 Loss1: 0.053041 Loss2: 1.355571
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507515 Loss1: 0.181311 Loss2: 1.326204
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385217 Loss1: 0.032443 Loss2: 1.352773
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.438752 Loss1: 0.109033 Loss2: 1.329719
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406903 Loss1: 0.088874 Loss2: 1.318029
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.401841 Loss1: 0.086409 Loss2: 1.315432
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370361 Loss1: 0.063714 Loss2: 1.306647
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364765 Loss1: 0.062988 Loss2: 1.301777
+(DefaultActor pid=3764) >> Training accuracy: 0.994420
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.395135 Loss1: 0.523528 Loss2: 1.871606
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.678777 Loss1: 0.297326 Loss2: 1.381451
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644098 Loss1: 0.236231 Loss2: 1.407867
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542354 Loss1: 0.167275 Loss2: 1.375079
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.384221 Loss1: 0.525334 Loss2: 1.858887
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.778370 Loss1: 0.393772 Loss2: 1.384597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.720467 Loss1: 0.273369 Loss2: 1.447099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.589239 Loss1: 0.202161 Loss2: 1.387078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.565150 Loss1: 0.166866 Loss2: 1.398283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492932 Loss1: 0.114389 Loss2: 1.378543 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425378 Loss1: 0.060958 Loss2: 1.364420 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409962 Loss1: 0.058234 Loss2: 1.351728 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.632080 Loss1: 0.251903 Loss2: 1.380177 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542827 Loss1: 0.161361 Loss2: 1.381467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.376338 Loss1: 0.534419 Loss2: 1.841919
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.486550 Loss1: 0.103459 Loss2: 1.383090
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.733348 Loss1: 0.369615 Loss2: 1.363733
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458897 Loss1: 0.082138 Loss2: 1.376759
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.675150 Loss1: 0.259446 Loss2: 1.415704
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455586 Loss1: 0.086443 Loss2: 1.369143
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.558089 Loss1: 0.185647 Loss2: 1.372442
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436415 Loss1: 0.064789 Loss2: 1.371626
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.479496 Loss1: 0.118805 Loss2: 1.360691
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425237 Loss1: 0.060553 Loss2: 1.364685
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.434134 Loss1: 0.079111 Loss2: 1.355023
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452649 Loss1: 0.086599 Loss2: 1.366050
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408336 Loss1: 0.069306 Loss2: 1.339030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378788 Loss1: 0.044999 Loss2: 1.333789 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.612401 Loss1: 0.287393 Loss2: 1.325008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476922 Loss1: 0.150742 Loss2: 1.326180 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.485417 Loss1: 0.156050 Loss2: 1.329367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.429952 Loss1: 0.102973 Loss2: 1.326979 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420923 Loss1: 0.105324 Loss2: 1.315600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425180 Loss1: 0.108833 Loss2: 1.316347 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.369964 Loss1: 0.049605 Loss2: 1.320360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365841 Loss1: 0.056029 Loss2: 1.309812 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389872 Loss1: 0.037294 Loss2: 1.352577 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367221 Loss1: 0.030553 Loss2: 1.336667 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.152470 Loss1: 0.356741 Loss2: 1.795728
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.645351 Loss1: 0.291268 Loss2: 1.354083
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610660 Loss1: 0.222072 Loss2: 1.388588
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517792 Loss1: 0.148723 Loss2: 1.369069
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.357847 Loss1: 0.510135 Loss2: 1.847712
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.715135 Loss1: 0.348483 Loss2: 1.366652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.680281 Loss1: 0.247604 Loss2: 1.432677 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464578 Loss1: 0.102516 Loss2: 1.362062
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550945 Loss1: 0.186892 Loss2: 1.364053
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443836 Loss1: 0.087960 Loss2: 1.355876
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492248 Loss1: 0.139605 Loss2: 1.352642
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424377 Loss1: 0.073603 Loss2: 1.350774
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446110 Loss1: 0.091218 Loss2: 1.354892
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411238 Loss1: 0.061700 Loss2: 1.349538
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433842 Loss1: 0.086107 Loss2: 1.347735
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414019 Loss1: 0.074982 Loss2: 1.339036
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396992 Loss1: 0.058515 Loss2: 1.338477
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373071 Loss1: 0.040541 Loss2: 1.332530
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.231599 Loss1: 0.429923 Loss2: 1.801676
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.601793 Loss1: 0.273789 Loss2: 1.328004
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.546778 Loss1: 0.181319 Loss2: 1.365459
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.468476 Loss1: 0.134928 Loss2: 1.333547
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.355937 Loss1: 0.532910 Loss2: 1.823027
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.445584 Loss1: 0.116437 Loss2: 1.329147
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.685561 Loss1: 0.344193 Loss2: 1.341368
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454259 Loss1: 0.121844 Loss2: 1.332415
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.660173 Loss1: 0.269077 Loss2: 1.391096
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.403602 Loss1: 0.082138 Loss2: 1.321465
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536329 Loss1: 0.181748 Loss2: 1.354581
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.412071 Loss1: 0.090065 Loss2: 1.322006
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.471807 Loss1: 0.125032 Loss2: 1.346775
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.368932 Loss1: 0.050685 Loss2: 1.318247
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512889 Loss1: 0.161549 Loss2: 1.351340
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350365 Loss1: 0.038040 Loss2: 1.312325
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.495799 Loss1: 0.148388 Loss2: 1.347411
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419580 Loss1: 0.083306 Loss2: 1.336274
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432991 Loss1: 0.093141 Loss2: 1.339851
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438798 Loss1: 0.103465 Loss2: 1.335333
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.525970 Loss1: 0.573768 Loss2: 1.952201
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.646663 Loss1: 0.321712 Loss2: 1.324951
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.633092 Loss1: 0.290956 Loss2: 1.342136
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570207 Loss1: 0.183800 Loss2: 1.386407
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514050 Loss1: 0.178338 Loss2: 1.335712
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.510215 Loss1: 0.170951 Loss2: 1.339263 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.454066 Loss1: 0.103758 Loss2: 1.350307 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.397727 Loss1: 0.068709 Loss2: 1.329018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.370225 Loss1: 0.050109 Loss2: 1.320116
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574768 Loss1: 0.167574 Loss2: 1.407194
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350708 Loss1: 0.031548 Loss2: 1.319160
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.537372 Loss1: 0.131826 Loss2: 1.405545
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.556729 Loss1: 0.155644 Loss2: 1.401085
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.506214 Loss1: 0.118872 Loss2: 1.387341
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.489560 Loss1: 0.101712 Loss2: 1.387848
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.459226 Loss1: 0.084049 Loss2: 1.375177
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.412171 Loss1: 0.496817 Loss2: 1.915355
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460243 Loss1: 0.080902 Loss2: 1.379341
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.619367 Loss1: 0.173161 Loss2: 1.446206 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611910 Loss1: 0.194391 Loss2: 1.417520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.548416 Loss1: 0.134030 Loss2: 1.414386
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.457888 Loss1: 0.600061 Loss2: 1.857827
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612766 Loss1: 0.317861 Loss2: 1.294905
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490916 Loss1: 0.088637 Loss2: 1.402279
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.472992 Loss1: 0.074916 Loss2: 1.398076 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449812 Loss1: 0.055311 Loss2: 1.394501 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.456185 Loss1: 0.071220 Loss2: 1.384964 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.334924 Loss1: 0.079731 Loss2: 1.255193 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.336046 Loss1: 0.083511 Loss2: 1.252535 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.614126 Loss1: 0.313557 Loss2: 1.300569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.454173 Loss1: 0.152537 Loss2: 1.301636 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.555850 Loss1: 0.605800 Loss2: 1.950050
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.422264 Loss1: 0.132489 Loss2: 1.289775
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.793107 Loss1: 0.365766 Loss2: 1.427340
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.376469 Loss1: 0.086603 Loss2: 1.289866
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.719019 Loss1: 0.267246 Loss2: 1.451772
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.358277 Loss1: 0.075418 Loss2: 1.282858
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.623815 Loss1: 0.215017 Loss2: 1.408798
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.363757 Loss1: 0.084620 Loss2: 1.279137
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574520 Loss1: 0.144382 Loss2: 1.430138
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403186 Loss1: 0.121420 Loss2: 1.281766
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.548112 Loss1: 0.141922 Loss2: 1.406190
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.348248 Loss1: 0.067328 Loss2: 1.280920
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457908 Loss1: 0.066159 Loss2: 1.391749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427822 Loss1: 0.041092 Loss2: 1.386730 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.654234 Loss1: 0.320928 Loss2: 1.333306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.486607 Loss1: 0.166466 Loss2: 1.320141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.465549 Loss1: 0.133845 Loss2: 1.331704 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.405008 Loss1: 0.086321 Loss2: 1.318687 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.400332 Loss1: 0.085876 Loss2: 1.314456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404820 Loss1: 0.087509 Loss2: 1.317312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.351805 Loss1: 0.044259 Loss2: 1.307546 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359890 Loss1: 0.055660 Loss2: 1.304230 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390790 Loss1: 0.096945 Loss2: 1.293845 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.243804 Loss1: 0.411334 Loss2: 1.832470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.629437 Loss1: 0.224226 Loss2: 1.405211 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528876 Loss1: 0.161134 Loss2: 1.367743
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.235815 Loss1: 0.413141 Loss2: 1.822674
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.514698 Loss1: 0.154243 Loss2: 1.360455
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.593470 Loss1: 0.242781 Loss2: 1.350689
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491132 Loss1: 0.130298 Loss2: 1.360834
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.462607 Loss1: 0.104681 Loss2: 1.357926
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.417431 Loss1: 0.069615 Loss2: 1.347817
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.469440 Loss1: 0.136245 Loss2: 1.333195
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.436785 Loss1: 0.098837 Loss2: 1.337948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446745 Loss1: 0.115996 Loss2: 1.330749 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379721 Loss1: 0.043521 Loss2: 1.336200
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437449 Loss1: 0.106811 Loss2: 1.330638
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.378684 Loss1: 0.048833 Loss2: 1.329851
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370981 Loss1: 0.048593 Loss2: 1.322387
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375072 Loss1: 0.056737 Loss2: 1.318335
+(DefaultActor pid=3764) >> Training accuracy: 0.990809
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.594210 Loss1: 0.239859 Loss2: 1.354350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527100 Loss1: 0.166733 Loss2: 1.360367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475506 Loss1: 0.111754 Loss2: 1.363752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472172 Loss1: 0.110750 Loss2: 1.361421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466277 Loss1: 0.109212 Loss2: 1.357065 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435657 Loss1: 0.087083 Loss2: 1.348574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405838 Loss1: 0.057159 Loss2: 1.348679 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421625 Loss1: 0.078923 Loss2: 1.342702 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390594 Loss1: 0.044256 Loss2: 1.346338 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.253183 Loss1: 0.447480 Loss2: 1.805703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611199 Loss1: 0.257369 Loss2: 1.353830 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.458806 Loss1: 0.137513 Loss2: 1.321293
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.430207 Loss1: 0.513078 Loss2: 1.917129
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.402440 Loss1: 0.094906 Loss2: 1.307535
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.722510 Loss1: 0.306201 Loss2: 1.416309
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.408021 Loss1: 0.104605 Loss2: 1.303416
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.729916 Loss1: 0.269165 Loss2: 1.460751
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383411 Loss1: 0.083308 Loss2: 1.300102
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556943 Loss1: 0.145795 Loss2: 1.411148
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.365435 Loss1: 0.067802 Loss2: 1.297633
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539954 Loss1: 0.124719 Loss2: 1.415235
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372278 Loss1: 0.075644 Loss2: 1.296634
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516620 Loss1: 0.108234 Loss2: 1.408386
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358333 Loss1: 0.068470 Loss2: 1.289863
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486279 Loss1: 0.083095 Loss2: 1.403184
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490411 Loss1: 0.093505 Loss2: 1.396906
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463481 Loss1: 0.077016 Loss2: 1.386465
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.451921 Loss1: 0.064119 Loss2: 1.387802
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.290555 Loss1: 0.453072 Loss2: 1.837484
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.571825 Loss1: 0.234603 Loss2: 1.337222
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.565356 Loss1: 0.223809 Loss2: 1.341547
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.495392 Loss1: 0.147034 Loss2: 1.348358
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.457709 Loss1: 0.554670 Loss2: 1.903040
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.707155 Loss1: 0.304505 Loss2: 1.402650 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.570459 Loss1: 0.167092 Loss2: 1.403367 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.524668 Loss1: 0.144470 Loss2: 1.380198 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493012 Loss1: 0.105935 Loss2: 1.387077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.462226 Loss1: 0.089706 Loss2: 1.372520 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384506 Loss1: 0.067500 Loss2: 1.317006
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.466131 Loss1: 0.093398 Loss2: 1.372733
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428545 Loss1: 0.066968 Loss2: 1.361577
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403972 Loss1: 0.045776 Loss2: 1.358196
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391169 Loss1: 0.037894 Loss2: 1.353276
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.429379 Loss1: 0.539738 Loss2: 1.889641
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.774809 Loss1: 0.381934 Loss2: 1.392875
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.765194 Loss1: 0.315126 Loss2: 1.450068
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694107 Loss1: 0.293151 Loss2: 1.400956
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.282055 Loss1: 0.446619 Loss2: 1.835436
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.654733 Loss1: 0.276112 Loss2: 1.378621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.569361 Loss1: 0.167516 Loss2: 1.401845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.552107 Loss1: 0.180659 Loss2: 1.371448 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528554 Loss1: 0.151828 Loss2: 1.376726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522772 Loss1: 0.140004 Loss2: 1.382768 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.481282 Loss1: 0.102692 Loss2: 1.378591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416897 Loss1: 0.054468 Loss2: 1.362429 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.485284 Loss1: 0.515182 Loss2: 1.970101 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.653393 Loss1: 0.166200 Loss2: 1.487193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.300948 Loss1: 0.498084 Loss2: 1.802864 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.711676 Loss1: 0.387084 Loss2: 1.324592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.597587 Loss1: 0.219541 Loss2: 1.378046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.531811 Loss1: 0.199466 Loss2: 1.332346 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542642 Loss1: 0.218282 Loss2: 1.324360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459501 Loss1: 0.127614 Loss2: 1.331887 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388902 Loss1: 0.074061 Loss2: 1.314841 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361710 Loss1: 0.060874 Loss2: 1.300836 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.662846 Loss1: 0.333944 Loss2: 1.328902 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488651 Loss1: 0.152821 Loss2: 1.335830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.234151 Loss1: 0.411851 Loss2: 1.822300
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.434105 Loss1: 0.107764 Loss2: 1.326342
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.569284 Loss1: 0.261078 Loss2: 1.308207
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443807 Loss1: 0.119852 Loss2: 1.323955
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.461712 Loss1: 0.128165 Loss2: 1.333547
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.403614 Loss1: 0.085144 Loss2: 1.318470
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.403687 Loss1: 0.095692 Loss2: 1.307995
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404688 Loss1: 0.085717 Loss2: 1.318970
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.363311 Loss1: 0.079223 Loss2: 1.284089
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.389731 Loss1: 0.069979 Loss2: 1.319752
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.367717 Loss1: 0.079271 Loss2: 1.288446
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361692 Loss1: 0.050363 Loss2: 1.311329
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431336 Loss1: 0.127219 Loss2: 1.304117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364517 Loss1: 0.075588 Loss2: 1.288929 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.717618 Loss1: 0.300904 Loss2: 1.416714 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.577586 Loss1: 0.164742 Loss2: 1.412845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.280506 Loss1: 0.479441 Loss2: 1.801065
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.575887 Loss1: 0.160234 Loss2: 1.415653
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515576 Loss1: 0.103334 Loss2: 1.412242
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.678547 Loss1: 0.338041 Loss2: 1.340505
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466163 Loss1: 0.070614 Loss2: 1.395549
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.553563 Loss1: 0.186788 Loss2: 1.366775
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.472400 Loss1: 0.083193 Loss2: 1.389207
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.484486 Loss1: 0.152469 Loss2: 1.332017
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444270 Loss1: 0.054194 Loss2: 1.390076
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446996 Loss1: 0.120531 Loss2: 1.326465
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457570 Loss1: 0.077137 Loss2: 1.380433
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429415 Loss1: 0.105552 Loss2: 1.323863
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419812 Loss1: 0.098987 Loss2: 1.320825
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418119 Loss1: 0.095235 Loss2: 1.322885
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394309 Loss1: 0.072776 Loss2: 1.321534
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407024 Loss1: 0.094391 Loss2: 1.312633
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.318733 Loss1: 0.481399 Loss2: 1.837334
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.651438 Loss1: 0.308210 Loss2: 1.343228
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.608433 Loss1: 0.210766 Loss2: 1.397667
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.486220 Loss1: 0.128998 Loss2: 1.357222
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476193 Loss1: 0.129800 Loss2: 1.346393
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.557782 Loss1: 0.630130 Loss2: 1.927652
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.740568 Loss1: 0.357563 Loss2: 1.383004 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.649490 Loss1: 0.234506 Loss2: 1.414984 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.504787 Loss1: 0.156823 Loss2: 1.347965
+DEBUG flwr 2023-10-12 19:40:12,582 | server.py:236 | fit_round 162 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.615773 Loss1: 0.224382 Loss2: 1.391391
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433402 Loss1: 0.086352 Loss2: 1.347050
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.560360 Loss1: 0.167358 Loss2: 1.393001
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397280 Loss1: 0.056907 Loss2: 1.340373
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539928 Loss1: 0.138575 Loss2: 1.401353
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487588 Loss1: 0.107993 Loss2: 1.379596
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483015 Loss1: 0.096590 Loss2: 1.386425
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422853 Loss1: 0.050020 Loss2: 1.372833
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412449 Loss1: 0.050988 Loss2: 1.361461
+(DefaultActor pid=3764) >> Training accuracy: 0.994420
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.255636 Loss1: 0.488725 Loss2: 1.766911
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.637320 Loss1: 0.300505 Loss2: 1.336815
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610557 Loss1: 0.246184 Loss2: 1.364373
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.480258 Loss1: 0.146373 Loss2: 1.333885
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.263098 Loss1: 0.463680 Loss2: 1.799417
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.611901 Loss1: 0.261675 Loss2: 1.350226 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.497250 Loss1: 0.136873 Loss2: 1.360378 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.453547 Loss1: 0.112508 Loss2: 1.341039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.415101 Loss1: 0.080556 Loss2: 1.334545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.415190 Loss1: 0.078886 Loss2: 1.336303 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.393293 Loss1: 0.064970 Loss2: 1.328323 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.345385 Loss1: 0.031979 Loss2: 1.313406 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.324195 Loss1: 0.435935 Loss2: 1.888260 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563061 Loss1: 0.174354 Loss2: 1.388707 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.335592 Loss1: 0.454990 Loss2: 1.880602 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.679563 Loss1: 0.300056 Loss2: 1.379507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.607802 Loss1: 0.187558 Loss2: 1.420244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521697 Loss1: 0.138836 Loss2: 1.382861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.517249 Loss1: 0.142156 Loss2: 1.375093 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476685 Loss1: 0.093388 Loss2: 1.383296 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437687 Loss1: 0.065392 Loss2: 1.372296 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401246 Loss1: 0.038867 Loss2: 1.362379 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 19:40:12,582][flwr][DEBUG] - fit_round 162 received 50 results and 0 failures
+INFO flwr 2023-10-12 19:40:53,988 | server.py:125 | fit progress: (162, 2.2612200133716716, {'accuracy': 0.6036}, 373761.76673800097)
+>> Test accuracy: 0.603600
+[2023-10-12 19:40:53,988][flwr][INFO] - fit progress: (162, 2.2612200133716716, {'accuracy': 0.6036}, 373761.76673800097)
+DEBUG flwr 2023-10-12 19:40:53,989 | server.py:173 | evaluate_round 162: strategy sampled 50 clients (out of 50)
+[2023-10-12 19:40:53,989][flwr][DEBUG] - evaluate_round 162: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 19:50:01,998 | server.py:187 | evaluate_round 162 received 50 results and 0 failures
+[2023-10-12 19:50:01,998][flwr][DEBUG] - evaluate_round 162 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 19:50:01,998 | server.py:222 | fit_round 163: strategy sampled 50 clients (out of 50)
+[2023-10-12 19:50:01,998][flwr][DEBUG] - fit_round 163: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.384640 Loss1: 0.531320 Loss2: 1.853320
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.638172 Loss1: 0.285489 Loss2: 1.352683
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.580421 Loss1: 0.191166 Loss2: 1.389254
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536816 Loss1: 0.183022 Loss2: 1.353794
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.311966 Loss1: 0.482309 Loss2: 1.829657
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.819920 Loss1: 0.432433 Loss2: 1.387487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.710991 Loss1: 0.275587 Loss2: 1.435404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.578772 Loss1: 0.202492 Loss2: 1.376280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.504798 Loss1: 0.124695 Loss2: 1.380102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449860 Loss1: 0.082263 Loss2: 1.367596 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388800 Loss1: 0.064522 Loss2: 1.324278
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463963 Loss1: 0.104708 Loss2: 1.359255
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432131 Loss1: 0.071992 Loss2: 1.360139
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421139 Loss1: 0.072807 Loss2: 1.348331
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444586 Loss1: 0.095162 Loss2: 1.349424
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.181567 Loss1: 0.403429 Loss2: 1.778138
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.666768 Loss1: 0.324960 Loss2: 1.341808
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.544823 Loss1: 0.166072 Loss2: 1.378751
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.338022 Loss1: 0.459095 Loss2: 1.878928
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506174 Loss1: 0.168350 Loss2: 1.337824
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.616843 Loss1: 0.234816 Loss2: 1.382027
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.465359 Loss1: 0.113785 Loss2: 1.351574
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.574817 Loss1: 0.178524 Loss2: 1.396293
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449979 Loss1: 0.112056 Loss2: 1.337923
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.430380 Loss1: 0.090319 Loss2: 1.340061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421445 Loss1: 0.095326 Loss2: 1.326120 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449272 Loss1: 0.113689 Loss2: 1.335583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404171 Loss1: 0.072477 Loss2: 1.331694 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409418 Loss1: 0.052721 Loss2: 1.356697 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.324678 Loss1: 0.495527 Loss2: 1.829151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.593287 Loss1: 0.201593 Loss2: 1.391694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.565427 Loss1: 0.587277 Loss2: 1.978150
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502202 Loss1: 0.165207 Loss2: 1.336995
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.761424 Loss1: 0.376859 Loss2: 1.384564
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488046 Loss1: 0.155200 Loss2: 1.332846
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470245 Loss1: 0.134125 Loss2: 1.336120 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.413563 Loss1: 0.085070 Loss2: 1.328493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.383585 Loss1: 0.065675 Loss2: 1.317911 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379063 Loss1: 0.068574 Loss2: 1.310489 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383128 Loss1: 0.072874 Loss2: 1.310254 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446203 Loss1: 0.092042 Loss2: 1.354161 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.433403 Loss1: 0.547784 Loss2: 1.885619
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.692903 Loss1: 0.341412 Loss2: 1.351491
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.571115 Loss1: 0.178713 Loss2: 1.392402
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527973 Loss1: 0.165042 Loss2: 1.362932
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.417777 Loss1: 0.524548 Loss2: 1.893229
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.727309 Loss1: 0.341953 Loss2: 1.385356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727956 Loss1: 0.282064 Loss2: 1.445892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.638002 Loss1: 0.237583 Loss2: 1.400420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.610739 Loss1: 0.192470 Loss2: 1.418269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383032 Loss1: 0.051924 Loss2: 1.331108 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468462 Loss1: 0.086558 Loss2: 1.381904 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429074 Loss1: 0.057817 Loss2: 1.371257 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.673496 Loss1: 0.292436 Loss2: 1.381060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513578 Loss1: 0.131241 Loss2: 1.382337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.520802 Loss1: 0.139625 Loss2: 1.381177
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.300505 Loss1: 0.472745 Loss2: 1.827760
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498682 Loss1: 0.119584 Loss2: 1.379098
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.672172 Loss1: 0.327356 Loss2: 1.344816
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481352 Loss1: 0.100000 Loss2: 1.381352
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.555141 Loss1: 0.182038 Loss2: 1.373103
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.475828 Loss1: 0.102713 Loss2: 1.373115
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468896 Loss1: 0.134494 Loss2: 1.334402
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441728 Loss1: 0.068391 Loss2: 1.373337
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.438713 Loss1: 0.100622 Loss2: 1.338091
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409601 Loss1: 0.045704 Loss2: 1.363897
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.388645 Loss1: 0.059005 Loss2: 1.329639
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392571 Loss1: 0.070215 Loss2: 1.322355
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424928 Loss1: 0.103734 Loss2: 1.321194
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407072 Loss1: 0.086590 Loss2: 1.320482
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386993 Loss1: 0.063731 Loss2: 1.323262
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.434464 Loss1: 0.555503 Loss2: 1.878960
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.673687 Loss1: 0.315333 Loss2: 1.358354
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611619 Loss1: 0.217914 Loss2: 1.393705
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476922 Loss1: 0.126548 Loss2: 1.350373
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.486184 Loss1: 0.143587 Loss2: 1.342597
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.378244 Loss1: 0.539677 Loss2: 1.838567
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.773968 Loss1: 0.409174 Loss2: 1.364795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.687556 Loss1: 0.248892 Loss2: 1.438664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535990 Loss1: 0.169288 Loss2: 1.366702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539313 Loss1: 0.175179 Loss2: 1.364133 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.510161 Loss1: 0.136757 Loss2: 1.373404 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.474044 Loss1: 0.120531 Loss2: 1.353513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461508 Loss1: 0.103945 Loss2: 1.357563
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.581972 Loss1: 0.581808 Loss2: 2.000165
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.840129 Loss1: 0.463825 Loss2: 1.376304
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.751331 Loss1: 0.286369 Loss2: 1.464962
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.610045 Loss1: 0.197186 Loss2: 1.412859
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.644075 Loss1: 0.256910 Loss2: 1.387166
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514840 Loss1: 0.114379 Loss2: 1.400461
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470866 Loss1: 0.092800 Loss2: 1.378066
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.716599 Loss1: 0.316353 Loss2: 1.400246 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.715798 Loss1: 0.247065 Loss2: 1.468733 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.599331 Loss1: 0.168310 Loss2: 1.431021 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.558139 Loss1: 0.148760 Loss2: 1.409379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.496065 Loss1: 0.089790 Loss2: 1.406275
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.115943 Loss1: 0.376095 Loss2: 1.739848
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.477718 Loss1: 0.180532 Loss2: 1.297186 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.495823 Loss1: 0.178059 Loss2: 1.317764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.399421 Loss1: 0.116615 Loss2: 1.282806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.357277 Loss1: 0.073806 Loss2: 1.283472 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380926 Loss1: 0.101368 Loss2: 1.279559 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.346917 Loss1: 0.063267 Loss2: 1.283650 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.334126 Loss1: 0.055047 Loss2: 1.279079 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500912 Loss1: 0.156878 Loss2: 1.344034 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452769 Loss1: 0.111969 Loss2: 1.340800 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443001 Loss1: 0.114812 Loss2: 1.328189
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.217764 Loss1: 0.416063 Loss2: 1.801701
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460107 Loss1: 0.125361 Loss2: 1.334746
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.602088 Loss1: 0.255150 Loss2: 1.346938
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.532276 Loss1: 0.180571 Loss2: 1.351705
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498230 Loss1: 0.151449 Loss2: 1.346781
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.495508 Loss1: 0.157671 Loss2: 1.337837
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450159 Loss1: 0.110607 Loss2: 1.339552
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.281050 Loss1: 0.443361 Loss2: 1.837689
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630712 Loss1: 0.281636 Loss2: 1.349076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.583726 Loss1: 0.206794 Loss2: 1.376932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523725 Loss1: 0.167468 Loss2: 1.356257 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364967 Loss1: 0.039987 Loss2: 1.324980
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.431548 Loss1: 0.079774 Loss2: 1.351774
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.422635 Loss1: 0.083926 Loss2: 1.338709
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395261 Loss1: 0.063445 Loss2: 1.331816
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405614 Loss1: 0.075990 Loss2: 1.329624
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403964 Loss1: 0.081247 Loss2: 1.322717
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.341549 Loss1: 0.407409 Loss2: 1.934140
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421666 Loss1: 0.092990 Loss2: 1.328675
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.597020 Loss1: 0.199974 Loss2: 1.397046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.687734 Loss1: 0.277561 Loss2: 1.410172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.535897 Loss1: 0.139462 Loss2: 1.396435
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.351431 Loss1: 0.451363 Loss2: 1.900068
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.736883 Loss1: 0.336993 Loss2: 1.399890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.639140 Loss1: 0.200900 Loss2: 1.438241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.560814 Loss1: 0.151246 Loss2: 1.409568 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.468431 Loss1: 0.085746 Loss2: 1.382684
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.494472 Loss1: 0.098349 Loss2: 1.396123
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510000 Loss1: 0.119990 Loss2: 1.390009
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500201 Loss1: 0.105253 Loss2: 1.394948
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.524157 Loss1: 0.135315 Loss2: 1.388842
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.517056 Loss1: 0.114798 Loss2: 1.402258
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.344344 Loss1: 0.496064 Loss2: 1.848280
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442881 Loss1: 0.056144 Loss2: 1.386737
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.622684 Loss1: 0.227260 Loss2: 1.395424 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.436289 Loss1: 0.098427 Loss2: 1.337863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.421697 Loss1: 0.088488 Loss2: 1.333209
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.275021 Loss1: 0.430252 Loss2: 1.844769
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.641990 Loss1: 0.261932 Loss2: 1.380058 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.616497 Loss1: 0.208220 Loss2: 1.408276 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.541945 Loss1: 0.163764 Loss2: 1.378181 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447682 Loss1: 0.084779 Loss2: 1.362903 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414850 Loss1: 0.054686 Loss2: 1.360164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404649 Loss1: 0.055210 Loss2: 1.349439
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.426095 Loss1: 0.554635 Loss2: 1.871460
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.720590 Loss1: 0.353672 Loss2: 1.366917
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417280 Loss1: 0.066733 Loss2: 1.350548
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488583 Loss1: 0.131589 Loss2: 1.356994 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.406095 Loss1: 0.063231 Loss2: 1.342864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.392588 Loss1: 0.053530 Loss2: 1.339058
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.274306 Loss1: 0.446801 Loss2: 1.827506
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382703 Loss1: 0.049784 Loss2: 1.332918
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.665980 Loss1: 0.324032 Loss2: 1.341948
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399832 Loss1: 0.068576 Loss2: 1.331256
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.593784 Loss1: 0.209646 Loss2: 1.384138
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386248 Loss1: 0.053347 Loss2: 1.332901
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519434 Loss1: 0.167378 Loss2: 1.352056
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475484 Loss1: 0.136680 Loss2: 1.338804
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444301 Loss1: 0.097900 Loss2: 1.346402
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.421324 Loss1: 0.090591 Loss2: 1.330733
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438224 Loss1: 0.100506 Loss2: 1.337719
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398403 Loss1: 0.063248 Loss2: 1.335155
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.300880 Loss1: 0.452203 Loss2: 1.848676
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381090 Loss1: 0.057425 Loss2: 1.323665
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.728154 Loss1: 0.337348 Loss2: 1.390806
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.649628 Loss1: 0.220479 Loss2: 1.429149
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573577 Loss1: 0.178880 Loss2: 1.394697
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561188 Loss1: 0.159199 Loss2: 1.401989
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520530 Loss1: 0.130786 Loss2: 1.389744
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.248938 Loss1: 0.373710 Loss2: 1.875228
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659698 Loss1: 0.255800 Loss2: 1.403898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.582013 Loss1: 0.161341 Loss2: 1.420672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556657 Loss1: 0.154162 Loss2: 1.402495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.590100 Loss1: 0.189378 Loss2: 1.400722 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472745 Loss1: 0.081177 Loss2: 1.391568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.320740 Loss1: 0.490603 Loss2: 1.830136 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.680429 Loss1: 0.344007 Loss2: 1.336423 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994485
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474425 Loss1: 0.128526 Loss2: 1.345899 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487212 Loss1: 0.145738 Loss2: 1.341474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439166 Loss1: 0.113105 Loss2: 1.326061
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.351928 Loss1: 0.555130 Loss2: 1.796798
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.682637 Loss1: 0.359430 Loss2: 1.323207 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.591889 Loss1: 0.237323 Loss2: 1.354566 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.959375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496540 Loss1: 0.166090 Loss2: 1.330450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494446 Loss1: 0.161240 Loss2: 1.333206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.387994 Loss1: 0.076253 Loss2: 1.311741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385415 Loss1: 0.078558 Loss2: 1.306857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366501 Loss1: 0.065431 Loss2: 1.301071 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518150 Loss1: 0.179627 Loss2: 1.338523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449105 Loss1: 0.117145 Loss2: 1.331960 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.229337 Loss1: 0.434497 Loss2: 1.794840 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.669426 Loss1: 0.346215 Loss2: 1.323211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.658304 Loss1: 0.266744 Loss2: 1.391560 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469477 Loss1: 0.133729 Loss2: 1.335748 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.381119 Loss1: 0.056107 Loss2: 1.325012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374034 Loss1: 0.063396 Loss2: 1.310638
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.412753 Loss1: 0.562473 Loss2: 1.850279
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.672812 Loss1: 0.343393 Loss2: 1.329419 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.564981 Loss1: 0.203222 Loss2: 1.361759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.411881 Loss1: 0.086741 Loss2: 1.325140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.377086 Loss1: 0.070112 Loss2: 1.306974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.364861 Loss1: 0.057076 Loss2: 1.307785 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.342034 Loss1: 0.041380 Loss2: 1.300654 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350067 Loss1: 0.052603 Loss2: 1.297464 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506962 Loss1: 0.117218 Loss2: 1.389744 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445677 Loss1: 0.071658 Loss2: 1.374019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441448 Loss1: 0.068143 Loss2: 1.373306
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.341082 Loss1: 0.504316 Loss2: 1.836766
+(DefaultActor pid=3764) >> Training accuracy: 0.997768
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411924 Loss1: 0.045686 Loss2: 1.366237
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.733160 Loss1: 0.380879 Loss2: 1.352282
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.636539 Loss1: 0.240386 Loss2: 1.396153
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.543353 Loss1: 0.191218 Loss2: 1.352135
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.504982 Loss1: 0.146372 Loss2: 1.358610
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465974 Loss1: 0.115463 Loss2: 1.350511
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.167215 Loss1: 0.372563 Loss2: 1.794652
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462280 Loss1: 0.121115 Loss2: 1.341165
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.626063 Loss1: 0.277253 Loss2: 1.348810
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398999 Loss1: 0.063675 Loss2: 1.335324
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.503717 Loss1: 0.131740 Loss2: 1.371977
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431026 Loss1: 0.099140 Loss2: 1.331886
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.465642 Loss1: 0.124964 Loss2: 1.340678
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372080 Loss1: 0.046332 Loss2: 1.325749
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.445149 Loss1: 0.105148 Loss2: 1.340001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442101 Loss1: 0.102798 Loss2: 1.339303 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.357866 Loss1: 0.497673 Loss2: 1.860193
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435159 Loss1: 0.095843 Loss2: 1.339317
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424012 Loss1: 0.079983 Loss2: 1.344029 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596649 Loss1: 0.219354 Loss2: 1.377295 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514035 Loss1: 0.126022 Loss2: 1.388013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474644 Loss1: 0.109726 Loss2: 1.364918
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.304833 Loss1: 0.519418 Loss2: 1.785415
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.647931 Loss1: 0.317105 Loss2: 1.330826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598759 Loss1: 0.228242 Loss2: 1.370518 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400835 Loss1: 0.046661 Loss2: 1.354173
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.462532 Loss1: 0.128737 Loss2: 1.333795
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450002 Loss1: 0.119220 Loss2: 1.330783
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.407245 Loss1: 0.078787 Loss2: 1.328458
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.371936 Loss1: 0.059553 Loss2: 1.312382
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.380630 Loss1: 0.067935 Loss2: 1.312695
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.369883 Loss1: 0.515037 Loss2: 1.854846
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.355984 Loss1: 0.047109 Loss2: 1.308875
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.336523 Loss1: 0.026780 Loss2: 1.309743 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526391 Loss1: 0.143118 Loss2: 1.383273 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440061 Loss1: 0.080953 Loss2: 1.359108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435836 Loss1: 0.087024 Loss2: 1.348813
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.220436 Loss1: 0.403249 Loss2: 1.817187
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.594582 Loss1: 0.247904 Loss2: 1.346677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.538597 Loss1: 0.175151 Loss2: 1.363446 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405789 Loss1: 0.076962 Loss2: 1.328827
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.466191 Loss1: 0.135916 Loss2: 1.330275
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.416254 Loss1: 0.087165 Loss2: 1.329090
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.415919 Loss1: 0.093651 Loss2: 1.322268
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426410 Loss1: 0.106282 Loss2: 1.320127
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372437 Loss1: 0.066127 Loss2: 1.306310
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.289739 Loss1: 0.483614 Loss2: 1.806126
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.346455 Loss1: 0.040558 Loss2: 1.305897
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.337789 Loss1: 0.034607 Loss2: 1.303182 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.446094 Loss1: 0.102604 Loss2: 1.343490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.428157 Loss1: 0.115699 Loss2: 1.312458 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460427 Loss1: 0.136810 Loss2: 1.323617
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.275661 Loss1: 0.470932 Loss2: 1.804729
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.638963 Loss1: 0.303610 Loss2: 1.335353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592374 Loss1: 0.219030 Loss2: 1.373345 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374698 Loss1: 0.072731 Loss2: 1.301967
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563759 Loss1: 0.206907 Loss2: 1.356852
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519411 Loss1: 0.170568 Loss2: 1.348844
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476975 Loss1: 0.115487 Loss2: 1.361489
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.421611 Loss1: 0.086995 Loss2: 1.334616
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412118 Loss1: 0.076852 Loss2: 1.335266
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.292183 Loss1: 0.447004 Loss2: 1.845179
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384533 Loss1: 0.050326 Loss2: 1.334207
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370904 Loss1: 0.045934 Loss2: 1.324970 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513520 Loss1: 0.161528 Loss2: 1.351991 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.435353 Loss1: 0.090956 Loss2: 1.344397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.398619 Loss1: 0.060406 Loss2: 1.338213
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.382206 Loss1: 0.553426 Loss2: 1.828780
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.731652 Loss1: 0.378760 Loss2: 1.352892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.728559 Loss1: 0.308822 Loss2: 1.419737 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.617268 Loss1: 0.242785 Loss2: 1.374483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472429 Loss1: 0.107632 Loss2: 1.364798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405946 Loss1: 0.061439 Loss2: 1.344506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408107 Loss1: 0.071779 Loss2: 1.336329 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.733108 Loss1: 0.334561 Loss2: 1.398547
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408340 Loss1: 0.074964 Loss2: 1.333376
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575389 Loss1: 0.198587 Loss2: 1.376802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456660 Loss1: 0.088932 Loss2: 1.367728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.348807 Loss1: 0.514839 Loss2: 1.833967
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406044 Loss1: 0.047282 Loss2: 1.358763
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612477 Loss1: 0.274088 Loss2: 1.338388
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401684 Loss1: 0.048501 Loss2: 1.353183
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572569 Loss1: 0.218118 Loss2: 1.354451
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396808 Loss1: 0.052915 Loss2: 1.343893
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379965 Loss1: 0.037189 Loss2: 1.342775 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.457832 Loss1: 0.125793 Loss2: 1.332038 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453106 Loss1: 0.123314 Loss2: 1.329792 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426942 Loss1: 0.097287 Loss2: 1.329655
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.469698 Loss1: 0.598421 Loss2: 1.871277
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379933 Loss1: 0.057588 Loss2: 1.322345
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.594463 Loss1: 0.251911 Loss2: 1.342551
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.605272 Loss1: 0.246288 Loss2: 1.358983
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506141 Loss1: 0.129433 Loss2: 1.376708
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468168 Loss1: 0.124776 Loss2: 1.343392
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459485 Loss1: 0.121619 Loss2: 1.337866
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436358 Loss1: 0.092424 Loss2: 1.343934
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378998 Loss1: 0.040388 Loss2: 1.338610
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.360562 Loss1: 0.034611 Loss2: 1.325951 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380332 Loss1: 0.061166 Loss2: 1.319166 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492864 Loss1: 0.138972 Loss2: 1.353892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.417282 Loss1: 0.075189 Loss2: 1.342092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.304338 Loss1: 0.472389 Loss2: 1.831949 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.753325 Loss1: 0.368091 Loss2: 1.385234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361495 Loss1: 0.034964 Loss2: 1.326531 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-12 20:18:38,185 | server.py:236 | fit_round 163 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507663 Loss1: 0.122408 Loss2: 1.385255 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478210 Loss1: 0.107949 Loss2: 1.370261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.348777 Loss1: 0.491512 Loss2: 1.857265
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434588 Loss1: 0.069794 Loss2: 1.364795
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.719448 Loss1: 0.348976 Loss2: 1.370471
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439946 Loss1: 0.075868 Loss2: 1.364078
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428014 Loss1: 0.073660 Loss2: 1.354354 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.561223 Loss1: 0.204092 Loss2: 1.357132 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.496462 Loss1: 0.137539 Loss2: 1.358924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449705 Loss1: 0.087472 Loss2: 1.362234
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.297432 Loss1: 0.457815 Loss2: 1.839617
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.676778 Loss1: 0.326569 Loss2: 1.350209 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.586243 Loss1: 0.188394 Loss2: 1.397850 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478582 Loss1: 0.129423 Loss2: 1.349159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410061 Loss1: 0.077674 Loss2: 1.332387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384144 Loss1: 0.055330 Loss2: 1.328814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390152 Loss1: 0.063769 Loss2: 1.326383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600900 Loss1: 0.243885 Loss2: 1.357015
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368878 Loss1: 0.049798 Loss2: 1.319080
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503874 Loss1: 0.159124 Loss2: 1.344750 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461168 Loss1: 0.121924 Loss2: 1.339244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362695 Loss1: 0.046961 Loss2: 1.315735 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 20:18:38,185][flwr][DEBUG] - fit_round 163 received 50 results and 0 failures
+INFO flwr 2023-10-12 20:19:18,934 | server.py:125 | fit progress: (163, 2.254364108125242, {'accuracy': 0.6001}, 376066.71229861496)
+>> Test accuracy: 0.600100
+[2023-10-12 20:19:18,934][flwr][INFO] - fit progress: (163, 2.254364108125242, {'accuracy': 0.6001}, 376066.71229861496)
+DEBUG flwr 2023-10-12 20:19:18,934 | server.py:173 | evaluate_round 163: strategy sampled 50 clients (out of 50)
+[2023-10-12 20:19:18,934][flwr][DEBUG] - evaluate_round 163: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 20:28:25,350 | server.py:187 | evaluate_round 163 received 50 results and 0 failures
+[2023-10-12 20:28:25,350][flwr][DEBUG] - evaluate_round 163 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 20:28:25,351 | server.py:222 | fit_round 164: strategy sampled 50 clients (out of 50)
+[2023-10-12 20:28:25,351][flwr][DEBUG] - fit_round 164: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.335590 Loss1: 0.462985 Loss2: 1.872604 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.613316 Loss1: 0.191351 Loss2: 1.421965 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549644 Loss1: 0.176474 Loss2: 1.373170
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.382404 Loss1: 0.485102 Loss2: 1.897301
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.670794 Loss1: 0.254867 Loss2: 1.415927 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564065 Loss1: 0.134712 Loss2: 1.429353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.511896 Loss1: 0.108790 Loss2: 1.403106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495003 Loss1: 0.095137 Loss2: 1.399866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481351 Loss1: 0.086175 Loss2: 1.395176 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459199 Loss1: 0.065811 Loss2: 1.393387 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438935 Loss1: 0.050229 Loss2: 1.388706 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.629204 Loss1: 0.287804 Loss2: 1.341400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.515603 Loss1: 0.159153 Loss2: 1.356450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.485690 Loss1: 0.575208 Loss2: 1.910482
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.483103 Loss1: 0.148046 Loss2: 1.335057
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.650077 Loss1: 0.270719 Loss2: 1.379359
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443014 Loss1: 0.105547 Loss2: 1.337467
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487903 Loss1: 0.154564 Loss2: 1.333339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432011 Loss1: 0.093194 Loss2: 1.338817 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401349 Loss1: 0.069333 Loss2: 1.332016 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390763 Loss1: 0.065912 Loss2: 1.324851 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428072 Loss1: 0.071369 Loss2: 1.356703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409501 Loss1: 0.058569 Loss2: 1.350933 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.536221 Loss1: 0.590187 Loss2: 1.946034
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.688669 Loss1: 0.348079 Loss2: 1.340589
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.642948 Loss1: 0.275431 Loss2: 1.367516
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587103 Loss1: 0.196811 Loss2: 1.390292
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.582797 Loss1: 0.240329 Loss2: 1.342468
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513338 Loss1: 0.154268 Loss2: 1.359070 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440341 Loss1: 0.097338 Loss2: 1.343003 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413153 Loss1: 0.085057 Loss2: 1.328096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395169 Loss1: 0.067697 Loss2: 1.327473
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.489529 Loss1: 0.135335 Loss2: 1.354193
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405403 Loss1: 0.084028 Loss2: 1.321375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492413 Loss1: 0.135892 Loss2: 1.356521
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493334 Loss1: 0.137014 Loss2: 1.356320
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453790 Loss1: 0.111197 Loss2: 1.342592
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432600 Loss1: 0.094030 Loss2: 1.338571
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420772 Loss1: 0.079937 Loss2: 1.340835
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.331990 Loss1: 0.479890 Loss2: 1.852100
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426036 Loss1: 0.091378 Loss2: 1.334658
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.598261 Loss1: 0.186738 Loss2: 1.411523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.497074 Loss1: 0.129718 Loss2: 1.367356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.414006 Loss1: 0.563533 Loss2: 1.850473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.698940 Loss1: 0.342005 Loss2: 1.356935 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629943 Loss1: 0.238844 Loss2: 1.391099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.500845 Loss1: 0.146004 Loss2: 1.354841 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.498309 Loss1: 0.144068 Loss2: 1.354241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.400649 Loss1: 0.063597 Loss2: 1.337052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373731 Loss1: 0.051263 Loss2: 1.322468 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357716 Loss1: 0.038921 Loss2: 1.318795 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.497031 Loss1: 0.155535 Loss2: 1.341496 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.449438 Loss1: 0.130520 Loss2: 1.318918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.282419 Loss1: 0.492741 Loss2: 1.789678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630634 Loss1: 0.314304 Loss2: 1.316330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.575089 Loss1: 0.215049 Loss2: 1.360039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.493255 Loss1: 0.175538 Loss2: 1.317717 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.402350 Loss1: 0.090155 Loss2: 1.312196 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.365431 Loss1: 0.061387 Loss2: 1.304045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.348993 Loss1: 0.052402 Loss2: 1.296591
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.204653 Loss1: 0.438530 Loss2: 1.766123
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.327894 Loss1: 0.037825 Loss2: 1.290069
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.591149 Loss1: 0.269875 Loss2: 1.321274
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.535311 Loss1: 0.183405 Loss2: 1.351906
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496432 Loss1: 0.181665 Loss2: 1.314767
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490988 Loss1: 0.155774 Loss2: 1.335213
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452921 Loss1: 0.136328 Loss2: 1.316593
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.335551 Loss1: 0.464699 Loss2: 1.870852
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.625697 Loss1: 0.256338 Loss2: 1.369359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556858 Loss1: 0.164835 Loss2: 1.392023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374194 Loss1: 0.068935 Loss2: 1.305259
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520822 Loss1: 0.149833 Loss2: 1.370989
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360637 Loss1: 0.061911 Loss2: 1.298726
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512194 Loss1: 0.146768 Loss2: 1.365426
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.540437 Loss1: 0.172483 Loss2: 1.367954
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509687 Loss1: 0.128337 Loss2: 1.381349
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462078 Loss1: 0.093179 Loss2: 1.368899
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418131 Loss1: 0.049553 Loss2: 1.368578
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392483 Loss1: 0.041735 Loss2: 1.350748
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.313295 Loss1: 0.433077 Loss2: 1.880218
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.642509 Loss1: 0.266026 Loss2: 1.376483
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.524168 Loss1: 0.121510 Loss2: 1.402659
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484896 Loss1: 0.108632 Loss2: 1.376264
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492434 Loss1: 0.125734 Loss2: 1.366700
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476045 Loss1: 0.109383 Loss2: 1.366662
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.501601 Loss1: 0.633595 Loss2: 1.868005
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.720708 Loss1: 0.397841 Loss2: 1.322867
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455255 Loss1: 0.089158 Loss2: 1.366097
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653072 Loss1: 0.297909 Loss2: 1.355162
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426083 Loss1: 0.075363 Loss2: 1.350720
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540053 Loss1: 0.219749 Loss2: 1.320305
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439893 Loss1: 0.078971 Loss2: 1.360922
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419146 Loss1: 0.058109 Loss2: 1.361037 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441680 Loss1: 0.126631 Loss2: 1.315048 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.380653 Loss1: 0.078205 Loss2: 1.302448 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357458 Loss1: 0.058816 Loss2: 1.298642
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.170830 Loss1: 0.357465 Loss2: 1.813365
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.569034 Loss1: 0.220874 Loss2: 1.348160
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.543846 Loss1: 0.181908 Loss2: 1.361938
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.492346 Loss1: 0.136382 Loss2: 1.355963
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471756 Loss1: 0.132566 Loss2: 1.339191
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.339754 Loss1: 0.423603 Loss2: 1.916151
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.483760 Loss1: 0.127197 Loss2: 1.356563
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.708701 Loss1: 0.305491 Loss2: 1.403210
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445139 Loss1: 0.106392 Loss2: 1.338746
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.617692 Loss1: 0.188977 Loss2: 1.428715
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543081 Loss1: 0.142258 Loss2: 1.400822
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441356 Loss1: 0.091916 Loss2: 1.349440
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512640 Loss1: 0.124898 Loss2: 1.387742
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408004 Loss1: 0.065709 Loss2: 1.342296
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.545204 Loss1: 0.155712 Loss2: 1.389493
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399524 Loss1: 0.064229 Loss2: 1.335295
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461259 Loss1: 0.077212 Loss2: 1.384047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420232 Loss1: 0.044646 Loss2: 1.375586 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.606916 Loss1: 0.264879 Loss2: 1.342037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523285 Loss1: 0.169823 Loss2: 1.353461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.486573 Loss1: 0.145291 Loss2: 1.341282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450985 Loss1: 0.109357 Loss2: 1.341628 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.407586 Loss1: 0.076816 Loss2: 1.330770 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394193 Loss1: 0.066017 Loss2: 1.328176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.366544 Loss1: 0.046463 Loss2: 1.320082 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372770 Loss1: 0.058976 Loss2: 1.313793 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.505835 Loss1: 0.065079 Loss2: 1.440756 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.210382 Loss1: 0.365467 Loss2: 1.844916 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611856 Loss1: 0.179464 Loss2: 1.432392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527549 Loss1: 0.142213 Loss2: 1.385337
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.426220 Loss1: 0.548544 Loss2: 1.877677
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.597305 Loss1: 0.245617 Loss2: 1.351689
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.529021 Loss1: 0.141629 Loss2: 1.387392
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.571020 Loss1: 0.182557 Loss2: 1.388463
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487373 Loss1: 0.101519 Loss2: 1.385854
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.474117 Loss1: 0.118167 Loss2: 1.355950
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.489639 Loss1: 0.106960 Loss2: 1.382680
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.472222 Loss1: 0.124323 Loss2: 1.347900
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.466343 Loss1: 0.085020 Loss2: 1.381323
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448295 Loss1: 0.071784 Loss2: 1.376511 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415255 Loss1: 0.040621 Loss2: 1.374635 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381211 Loss1: 0.051580 Loss2: 1.329632 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.224557 Loss1: 0.384060 Loss2: 1.840497 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.638631 Loss1: 0.211803 Loss2: 1.426828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.340107 Loss1: 0.470528 Loss2: 1.869579
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.532766 Loss1: 0.158451 Loss2: 1.374315
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.687381 Loss1: 0.314186 Loss2: 1.373194
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547672 Loss1: 0.161680 Loss2: 1.385992
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608079 Loss1: 0.197858 Loss2: 1.410221
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498913 Loss1: 0.105793 Loss2: 1.393120
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576547 Loss1: 0.201760 Loss2: 1.374787
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475745 Loss1: 0.101627 Loss2: 1.374117
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471392 Loss1: 0.103507 Loss2: 1.367885 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423680 Loss1: 0.055128 Loss2: 1.368552 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414284 Loss1: 0.054807 Loss2: 1.359477 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.456703 Loss1: 0.098479 Loss2: 1.358224 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.361146 Loss1: 0.480749 Loss2: 1.880397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635157 Loss1: 0.227578 Loss2: 1.407579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.571323 Loss1: 0.184300 Loss2: 1.387024
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.456040 Loss1: 0.554745 Loss2: 1.901295
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526180 Loss1: 0.138294 Loss2: 1.387887
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.775162 Loss1: 0.378130 Loss2: 1.397032
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516071 Loss1: 0.136117 Loss2: 1.379954
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670128 Loss1: 0.230482 Loss2: 1.439645
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461143 Loss1: 0.088989 Loss2: 1.372154
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.607423 Loss1: 0.204679 Loss2: 1.402744
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440170 Loss1: 0.073394 Loss2: 1.366776
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.597496 Loss1: 0.184941 Loss2: 1.412555
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434031 Loss1: 0.071474 Loss2: 1.362557
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.543102 Loss1: 0.138204 Loss2: 1.404897
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410993 Loss1: 0.051279 Loss2: 1.359714
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.530171 Loss1: 0.133588 Loss2: 1.396582
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493957 Loss1: 0.092949 Loss2: 1.401009
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.466001 Loss1: 0.081520 Loss2: 1.384481
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.487686 Loss1: 0.110326 Loss2: 1.377359
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.358347 Loss1: 0.529556 Loss2: 1.828792
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.674688 Loss1: 0.328017 Loss2: 1.346671
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.592373 Loss1: 0.209101 Loss2: 1.383272
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523938 Loss1: 0.178879 Loss2: 1.345059
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.273429 Loss1: 0.526122 Loss2: 1.747307
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.591879 Loss1: 0.293368 Loss2: 1.298511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.506497 Loss1: 0.174146 Loss2: 1.332351 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.502055 Loss1: 0.208522 Loss2: 1.293534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.417762 Loss1: 0.127287 Loss2: 1.290475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.403349 Loss1: 0.113327 Loss2: 1.290022 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409898 Loss1: 0.071098 Loss2: 1.338800
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.332087 Loss1: 0.050908 Loss2: 1.281180
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.318358 Loss1: 0.050477 Loss2: 1.267880
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.309337 Loss1: 0.042963 Loss2: 1.266373
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.303400 Loss1: 0.041742 Loss2: 1.261658
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.495497 Loss1: 0.598727 Loss2: 1.896770
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.665648 Loss1: 0.319929 Loss2: 1.345719
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648995 Loss1: 0.257820 Loss2: 1.391174
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498396 Loss1: 0.147896 Loss2: 1.350499
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476711 Loss1: 0.150807 Loss2: 1.325904
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451044 Loss1: 0.118454 Loss2: 1.332590 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414845 Loss1: 0.092408 Loss2: 1.322437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423541 Loss1: 0.093892 Loss2: 1.329649 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411469 Loss1: 0.092247 Loss2: 1.319222 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373015 Loss1: 0.056819 Loss2: 1.316195 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.379538 Loss1: 0.076119 Loss2: 1.303418 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.350631 Loss1: 0.059125 Loss2: 1.291507 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622329 Loss1: 0.302212 Loss2: 1.320117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.449476 Loss1: 0.130282 Loss2: 1.319194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.433822 Loss1: 0.117843 Loss2: 1.315978
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.277460 Loss1: 0.467790 Loss2: 1.809670
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455544 Loss1: 0.129444 Loss2: 1.326100
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612687 Loss1: 0.298177 Loss2: 1.314510
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.421733 Loss1: 0.104938 Loss2: 1.316794
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.525783 Loss1: 0.187276 Loss2: 1.338507
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424822 Loss1: 0.109375 Loss2: 1.315447
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.474499 Loss1: 0.152813 Loss2: 1.321686
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373831 Loss1: 0.054661 Loss2: 1.319170
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.467304 Loss1: 0.150327 Loss2: 1.316976
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395640 Loss1: 0.088827 Loss2: 1.306813
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427394 Loss1: 0.106650 Loss2: 1.320743
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.403159 Loss1: 0.095157 Loss2: 1.308002
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.359220 Loss1: 0.052578 Loss2: 1.306642
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.336086 Loss1: 0.039390 Loss2: 1.296696
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.324872 Loss1: 0.033440 Loss2: 1.291432
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.286597 Loss1: 0.446890 Loss2: 1.839708
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.592992 Loss1: 0.251815 Loss2: 1.341177
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.559781 Loss1: 0.192601 Loss2: 1.367180
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.509755 Loss1: 0.153347 Loss2: 1.356408
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468105 Loss1: 0.130075 Loss2: 1.338030
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442401 Loss1: 0.096206 Loss2: 1.346196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.465165 Loss1: 0.135535 Loss2: 1.329631 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438275 Loss1: 0.102160 Loss2: 1.336115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412287 Loss1: 0.077487 Loss2: 1.334801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403935 Loss1: 0.070638 Loss2: 1.333297 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397246 Loss1: 0.069122 Loss2: 1.328124 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381659 Loss1: 0.063703 Loss2: 1.317956 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.576099 Loss1: 0.263182 Loss2: 1.312917 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500758 Loss1: 0.173343 Loss2: 1.327415 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516162 Loss1: 0.202230 Loss2: 1.313932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480879 Loss1: 0.161243 Loss2: 1.319636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441053 Loss1: 0.117838 Loss2: 1.323215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454042 Loss1: 0.146371 Loss2: 1.307671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406154 Loss1: 0.093101 Loss2: 1.313052 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373844 Loss1: 0.066322 Loss2: 1.307522 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428554 Loss1: 0.068873 Loss2: 1.359681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418887 Loss1: 0.062911 Loss2: 1.355976 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.714676 Loss1: 0.351115 Loss2: 1.363561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498388 Loss1: 0.139281 Loss2: 1.359107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.243484 Loss1: 0.408875 Loss2: 1.834608
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488901 Loss1: 0.131526 Loss2: 1.357375
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.686819 Loss1: 0.348951 Loss2: 1.337868
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459352 Loss1: 0.107032 Loss2: 1.352320
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.597386 Loss1: 0.201713 Loss2: 1.395672
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424484 Loss1: 0.083301 Loss2: 1.341184
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526021 Loss1: 0.186519 Loss2: 1.339502
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422387 Loss1: 0.078121 Loss2: 1.344266
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527543 Loss1: 0.175055 Loss2: 1.352488
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.375421 Loss1: 0.038493 Loss2: 1.336928
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.415460 Loss1: 0.082090 Loss2: 1.333370
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350979 Loss1: 0.023965 Loss2: 1.327013
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417552 Loss1: 0.104405 Loss2: 1.313147 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351572 Loss1: 0.045882 Loss2: 1.305691 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.755497 Loss1: 0.371845 Loss2: 1.383651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.642724 Loss1: 0.254765 Loss2: 1.387959 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.566517 Loss1: 0.170127 Loss2: 1.396390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.534889 Loss1: 0.160242 Loss2: 1.374647 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.507915 Loss1: 0.128934 Loss2: 1.378982 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451415 Loss1: 0.083549 Loss2: 1.367866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438863 Loss1: 0.068740 Loss2: 1.370122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412342 Loss1: 0.054834 Loss2: 1.357509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.520752 Loss1: 0.148689 Loss2: 1.372063 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.309839 Loss1: 0.439413 Loss2: 1.870426 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.498734 Loss1: 0.145249 Loss2: 1.353485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.480055 Loss1: 0.135093 Loss2: 1.344962
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.401039 Loss1: 0.538731 Loss2: 1.862308
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.716719 Loss1: 0.350842 Loss2: 1.365877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.745143 Loss1: 0.316379 Loss2: 1.428765 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.586921 Loss1: 0.206395 Loss2: 1.380525 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.575174 Loss1: 0.194423 Loss2: 1.380751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542539 Loss1: 0.159206 Loss2: 1.383333 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404631 Loss1: 0.080406 Loss2: 1.324225
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.519834 Loss1: 0.139955 Loss2: 1.379879
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488025 Loss1: 0.114347 Loss2: 1.373678
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448189 Loss1: 0.081210 Loss2: 1.366979
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420261 Loss1: 0.061103 Loss2: 1.359159
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.363236 Loss1: 0.523503 Loss2: 1.839733
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.609405 Loss1: 0.259882 Loss2: 1.349524
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.541751 Loss1: 0.178583 Loss2: 1.363168
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.448742 Loss1: 0.097657 Loss2: 1.351085
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.405694 Loss1: 0.495653 Loss2: 1.910041
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.435710 Loss1: 0.102910 Loss2: 1.332800
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.700669 Loss1: 0.311029 Loss2: 1.389640
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.424615 Loss1: 0.090274 Loss2: 1.334341
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.657407 Loss1: 0.218992 Loss2: 1.438415
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445184 Loss1: 0.115990 Loss2: 1.329194
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.568919 Loss1: 0.162648 Loss2: 1.406271
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400446 Loss1: 0.078619 Loss2: 1.321827
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549409 Loss1: 0.150575 Loss2: 1.398833
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421754 Loss1: 0.097941 Loss2: 1.323813
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.575400 Loss1: 0.174682 Loss2: 1.400717
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.444183 Loss1: 0.118898 Loss2: 1.325285
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.493242 Loss1: 0.101265 Loss2: 1.391976
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448936 Loss1: 0.059642 Loss2: 1.389293
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440509 Loss1: 0.060408 Loss2: 1.380101
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410266 Loss1: 0.033070 Loss2: 1.377197
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.178753 Loss1: 0.360833 Loss2: 1.817920
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.551914 Loss1: 0.184561 Loss2: 1.367354
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.566561 Loss1: 0.181065 Loss2: 1.385496
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.264377 Loss1: 0.415940 Loss2: 1.848437
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563630 Loss1: 0.197073 Loss2: 1.366558
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.621313 Loss1: 0.278022 Loss2: 1.343292
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503783 Loss1: 0.136704 Loss2: 1.367079
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489307 Loss1: 0.121090 Loss2: 1.368217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514314 Loss1: 0.150564 Loss2: 1.363750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467336 Loss1: 0.110448 Loss2: 1.356888 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413255 Loss1: 0.056045 Loss2: 1.357210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391105 Loss1: 0.046404 Loss2: 1.344701 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996324
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365319 Loss1: 0.051088 Loss2: 1.314231 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.475046 Loss1: 0.587667 Loss2: 1.887379
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.723652 Loss1: 0.330765 Loss2: 1.392887
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.660096 Loss1: 0.230000 Loss2: 1.430096
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555795 Loss1: 0.170215 Loss2: 1.385579
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.221226 Loss1: 0.383561 Loss2: 1.837665
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.697357 Loss1: 0.310773 Loss2: 1.386584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.567820 Loss1: 0.161305 Loss2: 1.406515 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.491601 Loss1: 0.127158 Loss2: 1.364443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491588 Loss1: 0.126172 Loss2: 1.365416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504212 Loss1: 0.135539 Loss2: 1.368674 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.541872 Loss1: 0.174067 Loss2: 1.367805 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 20:57:20,922 | server.py:236 | fit_round 164 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473286 Loss1: 0.117372 Loss2: 1.355915 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.344679 Loss1: 0.527689 Loss2: 1.816990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568972 Loss1: 0.200332 Loss2: 1.368640 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.355499 Loss1: 0.502122 Loss2: 1.853377 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.670525 Loss1: 0.309464 Loss2: 1.361061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.613425 Loss1: 0.203462 Loss2: 1.409963 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551007 Loss1: 0.182801 Loss2: 1.368206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519031 Loss1: 0.158201 Loss2: 1.360830 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486749 Loss1: 0.124430 Loss2: 1.362319 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397742 Loss1: 0.059482 Loss2: 1.338260 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379548 Loss1: 0.048579 Loss2: 1.330969 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.657176 Loss1: 0.290838 Loss2: 1.366339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.584677 Loss1: 0.198532 Loss2: 1.386145 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.303735 Loss1: 0.488726 Loss2: 1.815009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443592 Loss1: 0.099162 Loss2: 1.344430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447005 Loss1: 0.111922 Loss2: 1.335083 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404800 Loss1: 0.071193 Loss2: 1.333607 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380922 Loss1: 0.054701 Loss2: 1.326222 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986779
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450328 Loss1: 0.095046 Loss2: 1.355282 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420999 Loss1: 0.075759 Loss2: 1.345241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406143 Loss1: 0.068838 Loss2: 1.337305 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 20:57:20,922][flwr][DEBUG] - fit_round 164 received 50 results and 0 failures
+INFO flwr 2023-10-12 20:58:02,362 | server.py:125 | fit progress: (164, 2.246745443191772, {'accuracy': 0.6016}, 378390.14048832597)
+>> Test accuracy: 0.601600
+[2023-10-12 20:58:02,362][flwr][INFO] - fit progress: (164, 2.246745443191772, {'accuracy': 0.6016}, 378390.14048832597)
+DEBUG flwr 2023-10-12 20:58:02,362 | server.py:173 | evaluate_round 164: strategy sampled 50 clients (out of 50)
+[2023-10-12 20:58:02,362][flwr][DEBUG] - evaluate_round 164: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 21:07:07,276 | server.py:187 | evaluate_round 164 received 50 results and 0 failures
+[2023-10-12 21:07:07,276][flwr][DEBUG] - evaluate_round 164 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 21:07:07,277 | server.py:222 | fit_round 165: strategy sampled 50 clients (out of 50)
+[2023-10-12 21:07:07,277][flwr][DEBUG] - fit_round 165: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.317514 Loss1: 0.477396 Loss2: 1.840118
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.656635 Loss1: 0.276828 Loss2: 1.379807
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.671522 Loss1: 0.243973 Loss2: 1.427549
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.598090 Loss1: 0.220685 Loss2: 1.377405
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.314536 Loss1: 0.436719 Loss2: 1.877817
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544031 Loss1: 0.160585 Loss2: 1.383446
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.565131 Loss1: 0.155949 Loss2: 1.409181
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565077 Loss1: 0.163635 Loss2: 1.401442 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532332 Loss1: 0.127053 Loss2: 1.405279 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.478095 Loss1: 0.085876 Loss2: 1.392220 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447375 Loss1: 0.064410 Loss2: 1.382965 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431607 Loss1: 0.052189 Loss2: 1.379418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435640 Loss1: 0.060213 Loss2: 1.375428 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.368531 Loss1: 0.467678 Loss2: 1.900853 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.632967 Loss1: 0.236865 Loss2: 1.396102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.485840 Loss1: 0.575478 Loss2: 1.910362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.713924 Loss1: 0.338003 Loss2: 1.375922 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564693 Loss1: 0.169338 Loss2: 1.395355 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.577387 Loss1: 0.208109 Loss2: 1.369279
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.495832 Loss1: 0.113888 Loss2: 1.381944
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.439165 Loss1: 0.080308 Loss2: 1.358858
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.498536 Loss1: 0.138154 Loss2: 1.360381
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443135 Loss1: 0.083985 Loss2: 1.359149
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463899 Loss1: 0.112314 Loss2: 1.351586
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447165 Loss1: 0.091783 Loss2: 1.355382 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376882 Loss1: 0.033376 Loss2: 1.343506 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985577
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.390023 Loss1: 0.440988 Loss2: 1.949035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.590919 Loss1: 0.155426 Loss2: 1.435493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511433 Loss1: 0.097263 Loss2: 1.414170
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.105785 Loss1: 0.344170 Loss2: 1.761615
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.576234 Loss1: 0.258212 Loss2: 1.318022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.545206 Loss1: 0.183497 Loss2: 1.361709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.447023 Loss1: 0.130533 Loss2: 1.316490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.445338 Loss1: 0.122283 Loss2: 1.323055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469685 Loss1: 0.082732 Loss2: 1.386954 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398069 Loss1: 0.090596 Loss2: 1.307473 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379977 Loss1: 0.078594 Loss2: 1.301382 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996324
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.668129 Loss1: 0.310712 Loss2: 1.357417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522085 Loss1: 0.171056 Loss2: 1.351029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473398 Loss1: 0.126166 Loss2: 1.347232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.427649 Loss1: 0.088740 Loss2: 1.338908 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437547 Loss1: 0.106874 Loss2: 1.330673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395740 Loss1: 0.065714 Loss2: 1.330026 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404881 Loss1: 0.083049 Loss2: 1.321833 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483003 Loss1: 0.113355 Loss2: 1.369648 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385501 Loss1: 0.036636 Loss2: 1.348865 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405509 Loss1: 0.062896 Loss2: 1.342614 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.571230 Loss1: 0.227076 Loss2: 1.344154 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475311 Loss1: 0.155924 Loss2: 1.319387 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.409785 Loss1: 0.106930 Loss2: 1.302855
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.273808 Loss1: 0.431619 Loss2: 1.842189
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.651009 Loss1: 0.278508 Loss2: 1.372501 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.593893 Loss1: 0.194754 Loss2: 1.399139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.524175 Loss1: 0.162548 Loss2: 1.361626 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.445805 Loss1: 0.079745 Loss2: 1.366060 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440822 Loss1: 0.086954 Loss2: 1.353868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.304925 Loss1: 0.398311 Loss2: 1.906614
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429592 Loss1: 0.076291 Loss2: 1.353301
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.663028 Loss1: 0.268631 Loss2: 1.394397
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424411 Loss1: 0.077868 Loss2: 1.346543
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583551 Loss1: 0.167392 Loss2: 1.416158 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516243 Loss1: 0.119478 Loss2: 1.396764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.507493 Loss1: 0.101957 Loss2: 1.405535
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.300133 Loss1: 0.462452 Loss2: 1.837681
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.523763 Loss1: 0.123764 Loss2: 1.399999
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.616945 Loss1: 0.267637 Loss2: 1.349308
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.502916 Loss1: 0.100257 Loss2: 1.402659
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.514387 Loss1: 0.148159 Loss2: 1.366228
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.495544 Loss1: 0.098005 Loss2: 1.397539
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.489765 Loss1: 0.144485 Loss2: 1.345280
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441396 Loss1: 0.104396 Loss2: 1.337000
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.422220 Loss1: 0.087899 Loss2: 1.334320
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.398618 Loss1: 0.069489 Loss2: 1.329129
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398339 Loss1: 0.078313 Loss2: 1.320026
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.355556 Loss1: 0.511183 Loss2: 1.844373
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.378280 Loss1: 0.057018 Loss2: 1.321262
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.721185 Loss1: 0.358961 Loss2: 1.362224
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381087 Loss1: 0.061099 Loss2: 1.319988
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576788 Loss1: 0.208985 Loss2: 1.367803 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470221 Loss1: 0.107126 Loss2: 1.363094 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442584 Loss1: 0.092464 Loss2: 1.350120
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.167399 Loss1: 0.361980 Loss2: 1.805418
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.580255 Loss1: 0.237395 Loss2: 1.342860 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.536310 Loss1: 0.170984 Loss2: 1.365327 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414994 Loss1: 0.075958 Loss2: 1.339036
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.471489 Loss1: 0.131932 Loss2: 1.339557
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.470441 Loss1: 0.128486 Loss2: 1.341955
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.435389 Loss1: 0.094224 Loss2: 1.341165
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412133 Loss1: 0.074735 Loss2: 1.337399
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382148 Loss1: 0.050742 Loss2: 1.331406
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.219717 Loss1: 0.389617 Loss2: 1.830100
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.599382 Loss1: 0.236064 Loss2: 1.363318 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.999023
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.579618 Loss1: 0.195140 Loss2: 1.384478 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463386 Loss1: 0.101711 Loss2: 1.361674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464506 Loss1: 0.111252 Loss2: 1.353254 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461804 Loss1: 0.107412 Loss2: 1.354392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427414 Loss1: 0.084196 Loss2: 1.343218 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420141 Loss1: 0.080180 Loss2: 1.339961 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.394570 Loss1: 0.066095 Loss2: 1.328475 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390356 Loss1: 0.063153 Loss2: 1.327203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.324402 Loss1: 0.479423 Loss2: 1.844978
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368531 Loss1: 0.048851 Loss2: 1.319680
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.677940 Loss1: 0.325243 Loss2: 1.352697
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.358477 Loss1: 0.044912 Loss2: 1.313565
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498938 Loss1: 0.144952 Loss2: 1.353985 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.469081 Loss1: 0.114401 Loss2: 1.354681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449069 Loss1: 0.104945 Loss2: 1.344123
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.207868 Loss1: 0.398336 Loss2: 1.809532
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.595620 Loss1: 0.277615 Loss2: 1.318006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.650108 Loss1: 0.265163 Loss2: 1.384944 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386272 Loss1: 0.056459 Loss2: 1.329813
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492982 Loss1: 0.155859 Loss2: 1.337123
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.477509 Loss1: 0.155814 Loss2: 1.321694
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459899 Loss1: 0.123816 Loss2: 1.336083
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404189 Loss1: 0.087845 Loss2: 1.316345
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411699 Loss1: 0.096815 Loss2: 1.314884
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.386295 Loss1: 0.471056 Loss2: 1.915239
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385825 Loss1: 0.073150 Loss2: 1.312675
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398765 Loss1: 0.090129 Loss2: 1.308636 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.565092 Loss1: 0.151276 Loss2: 1.413816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497025 Loss1: 0.089612 Loss2: 1.407412 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463291 Loss1: 0.066070 Loss2: 1.397220
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.233329 Loss1: 0.447500 Loss2: 1.785829
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.620733 Loss1: 0.289036 Loss2: 1.331697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608136 Loss1: 0.245932 Loss2: 1.362204 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.511889 Loss1: 0.171427 Loss2: 1.340462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443616 Loss1: 0.111635 Loss2: 1.331982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.395411 Loss1: 0.069479 Loss2: 1.325932 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379650 Loss1: 0.060459 Loss2: 1.319192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.503320 Loss1: 0.166555 Loss2: 1.336765 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.433113 Loss1: 0.110111 Loss2: 1.323002 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473479 Loss1: 0.150709 Loss2: 1.322770 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424598 Loss1: 0.110531 Loss2: 1.314067
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.303015 Loss1: 0.413339 Loss2: 1.889676
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424462 Loss1: 0.107447 Loss2: 1.317015
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.591012 Loss1: 0.204541 Loss2: 1.386471
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.435486 Loss1: 0.119733 Loss2: 1.315753
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.531812 Loss1: 0.150308 Loss2: 1.381504
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.494983 Loss1: 0.113393 Loss2: 1.381590
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505318 Loss1: 0.128636 Loss2: 1.376682
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.515983 Loss1: 0.140244 Loss2: 1.375739
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533236 Loss1: 0.150553 Loss2: 1.382683
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.562311 Loss1: 0.179753 Loss2: 1.382558
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.271197 Loss1: 0.442550 Loss2: 1.828647
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.507191 Loss1: 0.119630 Loss2: 1.387561
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.691042 Loss1: 0.328745 Loss2: 1.362296
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.505226 Loss1: 0.122445 Loss2: 1.382781
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574257 Loss1: 0.182654 Loss2: 1.391603
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529626 Loss1: 0.168742 Loss2: 1.360884
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.483147 Loss1: 0.117713 Loss2: 1.365434
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514305 Loss1: 0.152435 Loss2: 1.361871
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.430299 Loss1: 0.073062 Loss2: 1.357237
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418762 Loss1: 0.075844 Loss2: 1.342918
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.358655 Loss1: 0.498288 Loss2: 1.860367
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408719 Loss1: 0.062598 Loss2: 1.346121
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.648023 Loss1: 0.292389 Loss2: 1.355634
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407697 Loss1: 0.068159 Loss2: 1.339538
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.547371 Loss1: 0.164915 Loss2: 1.382456
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519780 Loss1: 0.165189 Loss2: 1.354590
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502462 Loss1: 0.139442 Loss2: 1.363020
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469957 Loss1: 0.118511 Loss2: 1.351446
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419218 Loss1: 0.072695 Loss2: 1.346523
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447115 Loss1: 0.109559 Loss2: 1.337556
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.177105 Loss1: 0.378784 Loss2: 1.798321
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416747 Loss1: 0.072095 Loss2: 1.344652
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.589349 Loss1: 0.233522 Loss2: 1.355827
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407918 Loss1: 0.071841 Loss2: 1.336077
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.558703 Loss1: 0.181274 Loss2: 1.377429
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498248 Loss1: 0.149916 Loss2: 1.348332
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.422978 Loss1: 0.079509 Loss2: 1.343469
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.432015 Loss1: 0.087605 Loss2: 1.344410
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410522 Loss1: 0.071061 Loss2: 1.339461
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.258570 Loss1: 0.417462 Loss2: 1.841108
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.399894 Loss1: 0.065585 Loss2: 1.334308
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397548 Loss1: 0.068118 Loss2: 1.329430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370021 Loss1: 0.045155 Loss2: 1.324866 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469921 Loss1: 0.148539 Loss2: 1.321383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435426 Loss1: 0.112033 Loss2: 1.323393 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.590767 Loss1: 0.531812 Loss2: 2.058955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.671392 Loss1: 0.247484 Loss2: 1.423908 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.632442 Loss1: 0.218759 Loss2: 1.413682 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.531240 Loss1: 0.115089 Loss2: 1.416152 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.554991 Loss1: 0.147947 Loss2: 1.407045 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990885
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485759 Loss1: 0.073363 Loss2: 1.412396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.546175 Loss1: 0.157815 Loss2: 1.388360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505331 Loss1: 0.154292 Loss2: 1.351040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464088 Loss1: 0.107685 Loss2: 1.356403
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.342042 Loss1: 0.519414 Loss2: 1.822627
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.414344 Loss1: 0.071700 Loss2: 1.342644
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.680775 Loss1: 0.336546 Loss2: 1.344230
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.363404 Loss1: 0.031089 Loss2: 1.332315
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.588705 Loss1: 0.212402 Loss2: 1.376302
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490910 Loss1: 0.154625 Loss2: 1.336285
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.352999 Loss1: 0.028902 Loss2: 1.324097
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451560 Loss1: 0.122770 Loss2: 1.328790
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357782 Loss1: 0.041505 Loss2: 1.316276
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.400538 Loss1: 0.071217 Loss2: 1.329321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.362667 Loss1: 0.046083 Loss2: 1.316584 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356033 Loss1: 0.044739 Loss2: 1.311294
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.542953 Loss1: 0.600063 Loss2: 1.942890
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.644787 Loss1: 0.287749 Loss2: 1.357038
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.548376 Loss1: 0.186963 Loss2: 1.361412
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.544059 Loss1: 0.176119 Loss2: 1.367941
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528500 Loss1: 0.175507 Loss2: 1.352993
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477282 Loss1: 0.113743 Loss2: 1.363538
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.280727 Loss1: 0.385637 Loss2: 1.895091
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.707917 Loss1: 0.314245 Loss2: 1.393672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426089 Loss1: 0.087749 Loss2: 1.338340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395952 Loss1: 0.060206 Loss2: 1.335746 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502255 Loss1: 0.105019 Loss2: 1.397236 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443098 Loss1: 0.062362 Loss2: 1.380736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.355499 Loss1: 0.458375 Loss2: 1.897125
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422802 Loss1: 0.048556 Loss2: 1.374246
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.720468 Loss1: 0.326299 Loss2: 1.394169
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413316 Loss1: 0.041738 Loss2: 1.371578
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535686 Loss1: 0.136278 Loss2: 1.399409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512078 Loss1: 0.115751 Loss2: 1.396327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.475964 Loss1: 0.084620 Loss2: 1.391344
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.383044 Loss1: 0.561617 Loss2: 1.821426
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.498663 Loss1: 0.107182 Loss2: 1.391482
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.693175 Loss1: 0.336266 Loss2: 1.356909
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454103 Loss1: 0.064705 Loss2: 1.389398
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615754 Loss1: 0.207320 Loss2: 1.408435
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441810 Loss1: 0.062083 Loss2: 1.379727
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574981 Loss1: 0.220184 Loss2: 1.354797
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517426 Loss1: 0.151706 Loss2: 1.365720
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503251 Loss1: 0.139898 Loss2: 1.363353
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415275 Loss1: 0.070868 Loss2: 1.344407
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.416435 Loss1: 0.074952 Loss2: 1.341483
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.399784 Loss1: 0.533383 Loss2: 1.866401
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378472 Loss1: 0.042041 Loss2: 1.336431
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.688489 Loss1: 0.317129 Loss2: 1.371360
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.376655 Loss1: 0.047854 Loss2: 1.328801
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.489124 Loss1: 0.121239 Loss2: 1.367885 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429842 Loss1: 0.078436 Loss2: 1.351406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408589 Loss1: 0.062279 Loss2: 1.346310
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.321378 Loss1: 0.467608 Loss2: 1.853770
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.595423 Loss1: 0.241860 Loss2: 1.353562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561378 Loss1: 0.176345 Loss2: 1.385033 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388650 Loss1: 0.055743 Loss2: 1.332907
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573464 Loss1: 0.214387 Loss2: 1.359077
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535713 Loss1: 0.174733 Loss2: 1.360980
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514847 Loss1: 0.143408 Loss2: 1.371439
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490353 Loss1: 0.131026 Loss2: 1.359327
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462759 Loss1: 0.111358 Loss2: 1.351402
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.199656 Loss1: 0.443047 Loss2: 1.756609
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.420216 Loss1: 0.069803 Loss2: 1.350414
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396654 Loss1: 0.060732 Loss2: 1.335922
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.607047 Loss1: 0.293860 Loss2: 1.313188
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588986 Loss1: 0.235149 Loss2: 1.353837
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.503772 Loss1: 0.186476 Loss2: 1.317296
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503818 Loss1: 0.182538 Loss2: 1.321280
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459883 Loss1: 0.133952 Loss2: 1.325931
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.290927 Loss1: 0.489422 Loss2: 1.801505
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416724 Loss1: 0.106613 Loss2: 1.310111
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.375125 Loss1: 0.070248 Loss2: 1.304877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.378683 Loss1: 0.083216 Loss2: 1.295467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.338005 Loss1: 0.044731 Loss2: 1.293274 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.375761 Loss1: 0.067367 Loss2: 1.308395 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.328868 Loss1: 0.033388 Loss2: 1.295480 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.397648 Loss1: 0.509900 Loss2: 1.887748 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.698685 Loss1: 0.312615 Loss2: 1.386070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.518749 Loss1: 0.146440 Loss2: 1.372310 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464552 Loss1: 0.093525 Loss2: 1.371026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441460 Loss1: 0.075579 Loss2: 1.365881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435015 Loss1: 0.071822 Loss2: 1.363193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.577434 Loss1: 0.198439 Loss2: 1.378995
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419672 Loss1: 0.058036 Loss2: 1.361637
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398796 Loss1: 0.047640 Loss2: 1.351156 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.397051 Loss1: 0.065316 Loss2: 1.331735 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.377977 Loss1: 0.054126 Loss2: 1.323850 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.352856 Loss1: 0.034627 Loss2: 1.318229
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.366050 Loss1: 0.482077 Loss2: 1.883972
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.739729 Loss1: 0.366548 Loss2: 1.373181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553754 Loss1: 0.178302 Loss2: 1.375452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.454716 Loss1: 0.091182 Loss2: 1.363534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.409179 Loss1: 0.053630 Loss2: 1.355548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386245 Loss1: 0.039577 Loss2: 1.346668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371441 Loss1: 0.029839 Loss2: 1.341601 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365264 Loss1: 0.030395 Loss2: 1.334869 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537689 Loss1: 0.162842 Loss2: 1.374847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449327 Loss1: 0.088546 Loss2: 1.360782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.308116 Loss1: 0.511758 Loss2: 1.796358 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568978 Loss1: 0.187512 Loss2: 1.381466 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485014 Loss1: 0.148477 Loss2: 1.336537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420852 Loss1: 0.084174 Loss2: 1.336678
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.273812 Loss1: 0.432227 Loss2: 1.841584
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.569785 Loss1: 0.228494 Loss2: 1.341290 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.524282 Loss1: 0.178510 Loss2: 1.345772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.461990 Loss1: 0.128519 Loss2: 1.333471 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.440473 Loss1: 0.115603 Loss2: 1.324871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.386134 Loss1: 0.063858 Loss2: 1.322276 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422403 Loss1: 0.100451 Loss2: 1.321952 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374763 Loss1: 0.055328 Loss2: 1.319436 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622613 Loss1: 0.201703 Loss2: 1.420910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503832 Loss1: 0.140154 Loss2: 1.363678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542656 Loss1: 0.171157 Loss2: 1.371498
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.396373 Loss1: 0.497752 Loss2: 1.898621
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.663795 Loss1: 0.277117 Loss2: 1.386678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.620162 Loss1: 0.200910 Loss2: 1.419252 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586024 Loss1: 0.196366 Loss2: 1.389658 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.967708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.542397 Loss1: 0.145426 Loss2: 1.396971 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.503861 Loss1: 0.126546 Loss2: 1.377315 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449171 Loss1: 0.076943 Loss2: 1.372227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441988 Loss1: 0.072963 Loss2: 1.369026 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.734419 Loss1: 0.288143 Loss2: 1.446277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.611087 Loss1: 0.213133 Loss2: 1.397954 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.540260 Loss1: 0.150093 Loss2: 1.390167
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.417231 Loss1: 0.513366 Loss2: 1.903865
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.731425 Loss1: 0.372730 Loss2: 1.358695 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.650162 Loss1: 0.240008 Loss2: 1.410155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576897 Loss1: 0.201575 Loss2: 1.375322 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.550865 Loss1: 0.174064 Loss2: 1.376801 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415000 Loss1: 0.061265 Loss2: 1.353735 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 21:35:37,455 | server.py:236 | fit_round 165 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411073 Loss1: 0.070355 Loss2: 1.340718 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375491 Loss1: 0.037159 Loss2: 1.338333
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.528817 Loss1: 0.635737 Loss2: 1.893080
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.714096 Loss1: 0.336341 Loss2: 1.377755
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600194 Loss1: 0.205808 Loss2: 1.394386
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487833 Loss1: 0.129751 Loss2: 1.358082
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455685 Loss1: 0.109541 Loss2: 1.346144
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.417136 Loss1: 0.070970 Loss2: 1.346166
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.404440 Loss1: 0.517767 Loss2: 1.886673
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.644723 Loss1: 0.270860 Loss2: 1.373863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.622043 Loss1: 0.212025 Loss2: 1.410018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.530423 Loss1: 0.162573 Loss2: 1.367850 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997768
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.497918 Loss1: 0.135826 Loss2: 1.362092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416251 Loss1: 0.065308 Loss2: 1.350942 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429977 Loss1: 0.082062 Loss2: 1.347915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402507 Loss1: 0.053570 Loss2: 1.348936 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543252 Loss1: 0.191755 Loss2: 1.351497 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491547 Loss1: 0.173420 Loss2: 1.318127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427780 Loss1: 0.112426 Loss2: 1.315354 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391662 Loss1: 0.080420 Loss2: 1.311242 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 21:35:37,455][flwr][DEBUG] - fit_round 165 received 50 results and 0 failures
+INFO flwr 2023-10-12 21:36:18,414 | server.py:125 | fit progress: (165, 2.264890566420631, {'accuracy': 0.6029}, 380686.19234982197)
+>> Test accuracy: 0.602900
+[2023-10-12 21:36:18,414][flwr][INFO] - fit progress: (165, 2.264890566420631, {'accuracy': 0.6029}, 380686.19234982197)
+DEBUG flwr 2023-10-12 21:36:18,414 | server.py:173 | evaluate_round 165: strategy sampled 50 clients (out of 50)
+[2023-10-12 21:36:18,414][flwr][DEBUG] - evaluate_round 165: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 21:45:25,013 | server.py:187 | evaluate_round 165 received 50 results and 0 failures
+[2023-10-12 21:45:25,013][flwr][DEBUG] - evaluate_round 165 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 21:45:25,014 | server.py:222 | fit_round 166: strategy sampled 50 clients (out of 50)
+[2023-10-12 21:45:25,014][flwr][DEBUG] - fit_round 166: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.343892 Loss1: 0.455765 Loss2: 1.888128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.559987 Loss1: 0.154160 Loss2: 1.405828 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508862 Loss1: 0.118094 Loss2: 1.390768
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.312016 Loss1: 0.448645 Loss2: 1.863371
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.626595 Loss1: 0.259954 Loss2: 1.366640 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460878 Loss1: 0.077268 Loss2: 1.383610
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.528342 Loss1: 0.136463 Loss2: 1.391879
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.482826 Loss1: 0.101874 Loss2: 1.380953
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520564 Loss1: 0.146246 Loss2: 1.374317
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502261 Loss1: 0.128442 Loss2: 1.373819 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439563 Loss1: 0.071239 Loss2: 1.368324
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452645 Loss1: 0.083857 Loss2: 1.368787
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417233 Loss1: 0.052609 Loss2: 1.364624
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444106 Loss1: 0.082640 Loss2: 1.361466
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450813 Loss1: 0.091753 Loss2: 1.359061
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431328 Loss1: 0.077585 Loss2: 1.353743
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453493 Loss1: 0.100098 Loss2: 1.353395
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.228002 Loss1: 0.371911 Loss2: 1.856090
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.565114 Loss1: 0.213702 Loss2: 1.351412
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.519700 Loss1: 0.169895 Loss2: 1.349804
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502005 Loss1: 0.141543 Loss2: 1.360462
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.305657 Loss1: 0.434672 Loss2: 1.870986
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.640762 Loss1: 0.278162 Loss2: 1.362600 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.623312 Loss1: 0.216585 Loss2: 1.406727 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543418 Loss1: 0.175969 Loss2: 1.367449 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.526583 Loss1: 0.155987 Loss2: 1.370596 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493931 Loss1: 0.118828 Loss2: 1.375103 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408571 Loss1: 0.072526 Loss2: 1.336045
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463330 Loss1: 0.098835 Loss2: 1.364495
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442181 Loss1: 0.084869 Loss2: 1.357313
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429725 Loss1: 0.074529 Loss2: 1.355196
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427862 Loss1: 0.075847 Loss2: 1.352015
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.355259 Loss1: 0.485756 Loss2: 1.869502
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.712887 Loss1: 0.342365 Loss2: 1.370522
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.581729 Loss1: 0.166990 Loss2: 1.414739
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.521122 Loss1: 0.149679 Loss2: 1.371443
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.241971 Loss1: 0.453889 Loss2: 1.788081
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.682450 Loss1: 0.364322 Loss2: 1.318128 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.594679 Loss1: 0.213169 Loss2: 1.381510 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.476814 Loss1: 0.152402 Loss2: 1.324412 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.464002 Loss1: 0.146404 Loss2: 1.317598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.445111 Loss1: 0.120439 Loss2: 1.324672 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399068 Loss1: 0.048794 Loss2: 1.350274
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.405164 Loss1: 0.094963 Loss2: 1.310202
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.392921 Loss1: 0.081967 Loss2: 1.310954
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397025 Loss1: 0.096964 Loss2: 1.300061
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382242 Loss1: 0.081528 Loss2: 1.300714
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.323155 Loss1: 0.532248 Loss2: 1.790907
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.699871 Loss1: 0.380586 Loss2: 1.319285
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615640 Loss1: 0.249902 Loss2: 1.365738
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.458909 Loss1: 0.156545 Loss2: 1.302364
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.334553 Loss1: 0.499128 Loss2: 1.835424
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659632 Loss1: 0.278610 Loss2: 1.381022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.615898 Loss1: 0.208366 Loss2: 1.407532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535687 Loss1: 0.167187 Loss2: 1.368500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.498983 Loss1: 0.113624 Loss2: 1.385359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460586 Loss1: 0.094578 Loss2: 1.366009 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443129 Loss1: 0.085071 Loss2: 1.358057 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443217 Loss1: 0.089720 Loss2: 1.353497 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.604780 Loss1: 0.231206 Loss2: 1.373575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.481413 Loss1: 0.112204 Loss2: 1.369209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473042 Loss1: 0.119416 Loss2: 1.353626
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.279015 Loss1: 0.469876 Loss2: 1.809139
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.467848 Loss1: 0.107754 Loss2: 1.360094
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.654178 Loss1: 0.303520 Loss2: 1.350658
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451005 Loss1: 0.093171 Loss2: 1.357834
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.584810 Loss1: 0.206769 Loss2: 1.378040
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535320 Loss1: 0.189728 Loss2: 1.345592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530061 Loss1: 0.159801 Loss2: 1.370261 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408270 Loss1: 0.060336 Loss2: 1.347933
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.525701 Loss1: 0.166667 Loss2: 1.359034
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.449583 Loss1: 0.095051 Loss2: 1.354533
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.401280 Loss1: 0.065651 Loss2: 1.335630
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424460 Loss1: 0.088679 Loss2: 1.335781
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.451663 Loss1: 0.115699 Loss2: 1.335964
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.226943 Loss1: 0.339157 Loss2: 1.887785
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.629531 Loss1: 0.222990 Loss2: 1.406541
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.553200 Loss1: 0.140242 Loss2: 1.412958
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526155 Loss1: 0.128053 Loss2: 1.398102
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.386532 Loss1: 0.496227 Loss2: 1.890305
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.496461 Loss1: 0.102508 Loss2: 1.393952
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.694110 Loss1: 0.319435 Loss2: 1.374675
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.490420 Loss1: 0.101498 Loss2: 1.388922
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.638363 Loss1: 0.232736 Loss2: 1.405627
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.510704 Loss1: 0.111343 Loss2: 1.399361
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.532452 Loss1: 0.132897 Loss2: 1.399555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474572 Loss1: 0.073078 Loss2: 1.401494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.493794 Loss1: 0.100150 Loss2: 1.393643 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988971
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441124 Loss1: 0.094500 Loss2: 1.346624 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.259635 Loss1: 0.404448 Loss2: 1.855187 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634083 Loss1: 0.233044 Loss2: 1.401040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.487672 Loss1: 0.129594 Loss2: 1.358078
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.294938 Loss1: 0.405776 Loss2: 1.889162
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.669777 Loss1: 0.256664 Loss2: 1.413112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605190 Loss1: 0.160255 Loss2: 1.444936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.560194 Loss1: 0.140831 Loss2: 1.419363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520329 Loss1: 0.107563 Loss2: 1.412766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476409 Loss1: 0.072415 Loss2: 1.403994 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456428 Loss1: 0.061043 Loss2: 1.395385 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461732 Loss1: 0.068514 Loss2: 1.393218 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.198654 Loss1: 0.387216 Loss2: 1.811438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.555608 Loss1: 0.191474 Loss2: 1.364134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.270251 Loss1: 0.436174 Loss2: 1.834077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.617431 Loss1: 0.282952 Loss2: 1.334479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588407 Loss1: 0.221600 Loss2: 1.366807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.516142 Loss1: 0.167688 Loss2: 1.348454 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485376 Loss1: 0.143061 Loss2: 1.342314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.413331 Loss1: 0.074386 Loss2: 1.338944 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378153 Loss1: 0.043003 Loss2: 1.335151
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408754 Loss1: 0.083880 Loss2: 1.324874
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407070 Loss1: 0.085121 Loss2: 1.321949
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384363 Loss1: 0.066007 Loss2: 1.318356
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386689 Loss1: 0.073577 Loss2: 1.313112
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.417888 Loss1: 0.514163 Loss2: 1.903725
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.700683 Loss1: 0.346545 Loss2: 1.354138
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.592323 Loss1: 0.192199 Loss2: 1.400124
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.512101 Loss1: 0.149416 Loss2: 1.362684
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.245226 Loss1: 0.418269 Loss2: 1.826957
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.589796 Loss1: 0.254721 Loss2: 1.335075 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.575961 Loss1: 0.201749 Loss2: 1.374212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.472763 Loss1: 0.127324 Loss2: 1.345439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378359 Loss1: 0.041867 Loss2: 1.336492 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364734 Loss1: 0.034574 Loss2: 1.330160 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450326 Loss1: 0.123354 Loss2: 1.326972 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374885 Loss1: 0.050650 Loss2: 1.324235 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.594273 Loss1: 0.241517 Loss2: 1.352757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467406 Loss1: 0.116068 Loss2: 1.351338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.380120 Loss1: 0.494090 Loss2: 1.886030
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468746 Loss1: 0.120809 Loss2: 1.347937
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.490112 Loss1: 0.137733 Loss2: 1.352379 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496405 Loss1: 0.141407 Loss2: 1.354998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425682 Loss1: 0.074007 Loss2: 1.351676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448905 Loss1: 0.101876 Loss2: 1.347029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410032 Loss1: 0.067359 Loss2: 1.342673 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422677 Loss1: 0.052014 Loss2: 1.370663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404955 Loss1: 0.044242 Loss2: 1.360713 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.316897 Loss1: 0.503839 Loss2: 1.813059
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.596110 Loss1: 0.271382 Loss2: 1.324727
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574842 Loss1: 0.215596 Loss2: 1.359246
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484868 Loss1: 0.143846 Loss2: 1.341022
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.333085 Loss1: 0.511892 Loss2: 1.821193
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.669315 Loss1: 0.311775 Loss2: 1.357540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.596799 Loss1: 0.205301 Loss2: 1.391497 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561573 Loss1: 0.201742 Loss2: 1.359831 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.525611 Loss1: 0.170054 Loss2: 1.355557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484044 Loss1: 0.124230 Loss2: 1.359815 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415660 Loss1: 0.067818 Loss2: 1.347842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391204 Loss1: 0.057568 Loss2: 1.333636 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.244977 Loss1: 0.441095 Loss2: 1.803883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.490391 Loss1: 0.136511 Loss2: 1.353880 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.436629 Loss1: 0.124899 Loss2: 1.311729
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.457163 Loss1: 0.528907 Loss2: 1.928256
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.706498 Loss1: 0.341054 Loss2: 1.365444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.582030 Loss1: 0.192907 Loss2: 1.389123 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565621 Loss1: 0.188366 Loss2: 1.377255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393287 Loss1: 0.085134 Loss2: 1.308153
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558902 Loss1: 0.184753 Loss2: 1.374150
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381412 Loss1: 0.075615 Loss2: 1.305797
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.574127 Loss1: 0.174819 Loss2: 1.399308
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508809 Loss1: 0.147410 Loss2: 1.361399
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378327 Loss1: 0.075882 Loss2: 1.302445
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451378 Loss1: 0.092496 Loss2: 1.358882 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.337917 Loss1: 0.518468 Loss2: 1.819449 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.534968 Loss1: 0.190537 Loss2: 1.344431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.486155 Loss1: 0.146912 Loss2: 1.339242
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.269733 Loss1: 0.437568 Loss2: 1.832166
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443967 Loss1: 0.117897 Loss2: 1.326070
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.614613 Loss1: 0.283175 Loss2: 1.331438
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.405902 Loss1: 0.078026 Loss2: 1.327877
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.537718 Loss1: 0.185628 Loss2: 1.352089
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.385103 Loss1: 0.067541 Loss2: 1.317562
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567872 Loss1: 0.206288 Loss2: 1.361584
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391827 Loss1: 0.070472 Loss2: 1.321355
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.540138 Loss1: 0.194031 Loss2: 1.346107
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384568 Loss1: 0.065383 Loss2: 1.319185
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490107 Loss1: 0.130543 Loss2: 1.359564
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369204 Loss1: 0.055764 Loss2: 1.313440
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446904 Loss1: 0.106160 Loss2: 1.340744
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433578 Loss1: 0.103882 Loss2: 1.329696
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443827 Loss1: 0.109147 Loss2: 1.334680
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400245 Loss1: 0.069603 Loss2: 1.330641
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.204682 Loss1: 0.416959 Loss2: 1.787724
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.678237 Loss1: 0.331086 Loss2: 1.347152
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.604100 Loss1: 0.215382 Loss2: 1.388717
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.532860 Loss1: 0.603859 Loss2: 1.929002
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.469862 Loss1: 0.126345 Loss2: 1.343517
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.455915 Loss1: 0.121447 Loss2: 1.334468 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.524970 Loss1: 0.166028 Loss2: 1.358941 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.474458 Loss1: 0.159132 Loss2: 1.315326 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.366857 Loss1: 0.043007 Loss2: 1.323850 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400423 Loss1: 0.092005 Loss2: 1.308417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374623 Loss1: 0.068744 Loss2: 1.305879 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.360769 Loss1: 0.502849 Loss2: 1.857920 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.608242 Loss1: 0.197567 Loss2: 1.410675 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520249 Loss1: 0.152192 Loss2: 1.368056
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.298691 Loss1: 0.449855 Loss2: 1.848836
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.744874 Loss1: 0.381967 Loss2: 1.362907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.573809 Loss1: 0.171874 Loss2: 1.401934 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528884 Loss1: 0.175006 Loss2: 1.353877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.481538 Loss1: 0.117413 Loss2: 1.364125 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459584 Loss1: 0.106508 Loss2: 1.353076 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411408 Loss1: 0.066750 Loss2: 1.344658
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452074 Loss1: 0.098241 Loss2: 1.353832
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419169 Loss1: 0.074320 Loss2: 1.344850
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419846 Loss1: 0.074216 Loss2: 1.345630
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400267 Loss1: 0.062030 Loss2: 1.338237
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.291838 Loss1: 0.400803 Loss2: 1.891035
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.688282 Loss1: 0.294649 Loss2: 1.393632
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.605837 Loss1: 0.178845 Loss2: 1.426992
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561476 Loss1: 0.158754 Loss2: 1.402722
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.446125 Loss1: 0.541415 Loss2: 1.904710
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.712920 Loss1: 0.312224 Loss2: 1.400696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622623 Loss1: 0.202744 Loss2: 1.419879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.570758 Loss1: 0.170629 Loss2: 1.400129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.511878 Loss1: 0.120113 Loss2: 1.391766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491890 Loss1: 0.108454 Loss2: 1.383435 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.489821 Loss1: 0.109835 Loss2: 1.379987 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423302 Loss1: 0.055116 Loss2: 1.368186 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.497832 Loss1: 0.603851 Loss2: 1.893981
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.655887 Loss1: 0.284821 Loss2: 1.371067
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.501880 Loss1: 0.131100 Loss2: 1.370780
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.454473 Loss1: 0.100519 Loss2: 1.353955
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.431398 Loss1: 0.519482 Loss2: 1.911917
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.687923 Loss1: 0.337444 Loss2: 1.350479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.589946 Loss1: 0.225038 Loss2: 1.364907 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.401083 Loss1: 0.067911 Loss2: 1.333172
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551911 Loss1: 0.193924 Loss2: 1.357988
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496737 Loss1: 0.157255 Loss2: 1.339482
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.390327 Loss1: 0.057227 Loss2: 1.333100
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372808 Loss1: 0.046064 Loss2: 1.326744 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360157 Loss1: 0.031257 Loss2: 1.328900 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403135 Loss1: 0.073112 Loss2: 1.330023 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.448267 Loss1: 0.553071 Loss2: 1.895196 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.668528 Loss1: 0.241439 Loss2: 1.427090 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535794 Loss1: 0.165634 Loss2: 1.370160
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.334088 Loss1: 0.436761 Loss2: 1.897328
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.717224 Loss1: 0.330657 Loss2: 1.386566 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635625 Loss1: 0.214159 Loss2: 1.421466 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537022 Loss1: 0.141472 Loss2: 1.395550 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513206 Loss1: 0.136079 Loss2: 1.377127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508766 Loss1: 0.121447 Loss2: 1.387319 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.474457 Loss1: 0.097354 Loss2: 1.377102 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424326 Loss1: 0.063265 Loss2: 1.361061 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.571732 Loss1: 0.225276 Loss2: 1.346456 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.510920 Loss1: 0.154531 Loss2: 1.356389 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499204 Loss1: 0.146735 Loss2: 1.352469 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487353 Loss1: 0.126723 Loss2: 1.360630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451388 Loss1: 0.104882 Loss2: 1.346506 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.455437 Loss1: 0.111398 Loss2: 1.344039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425265 Loss1: 0.082527 Loss2: 1.342738 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402808 Loss1: 0.062129 Loss2: 1.340679 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391715 Loss1: 0.049446 Loss2: 1.342269 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.248856 Loss1: 0.420750 Loss2: 1.828106 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.683341 Loss1: 0.265301 Loss2: 1.418041 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.539205 Loss1: 0.198279 Loss2: 1.340925
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.287805 Loss1: 0.500057 Loss2: 1.787748
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.654566 Loss1: 0.335012 Loss2: 1.319555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.577032 Loss1: 0.227196 Loss2: 1.349836 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.479678 Loss1: 0.159705 Loss2: 1.319973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.417600 Loss1: 0.105238 Loss2: 1.312362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.419347 Loss1: 0.110829 Loss2: 1.308518 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.382259 Loss1: 0.061395 Loss2: 1.320864
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.407285 Loss1: 0.099301 Loss2: 1.307984
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412729 Loss1: 0.105594 Loss2: 1.307134
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381324 Loss1: 0.073779 Loss2: 1.307546
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370369 Loss1: 0.073969 Loss2: 1.296400
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.510109 Loss1: 0.571035 Loss2: 1.939074
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.800208 Loss1: 0.366912 Loss2: 1.433295
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690123 Loss1: 0.210377 Loss2: 1.479746
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.617210 Loss1: 0.194940 Loss2: 1.422270
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.307473 Loss1: 0.440574 Loss2: 1.866900
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.574704 Loss1: 0.231734 Loss2: 1.342969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.539073 Loss1: 0.179546 Loss2: 1.359527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.450917 Loss1: 0.100007 Loss2: 1.350910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.395296 Loss1: 0.069876 Loss2: 1.325420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.424579 Loss1: 0.099782 Loss2: 1.324797 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392734 Loss1: 0.066373 Loss2: 1.326362 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.361369 Loss1: 0.046217 Loss2: 1.315152 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.221769 Loss1: 0.381556 Loss2: 1.840213 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.501897 Loss1: 0.133391 Loss2: 1.368506 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.456902 Loss1: 0.098880 Loss2: 1.358022
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.401011 Loss1: 0.497854 Loss2: 1.903157
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.422119 Loss1: 0.075176 Loss2: 1.346942
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.670172 Loss1: 0.288862 Loss2: 1.381310
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.404495 Loss1: 0.059055 Loss2: 1.345440
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.662143 Loss1: 0.251449 Loss2: 1.410694
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409062 Loss1: 0.071035 Loss2: 1.338027
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520932 Loss1: 0.142068 Loss2: 1.378864
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418565 Loss1: 0.076781 Loss2: 1.341784
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506377 Loss1: 0.136065 Loss2: 1.370312
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474586 Loss1: 0.097572 Loss2: 1.377014
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396436 Loss1: 0.055743 Loss2: 1.340693
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.414002 Loss1: 0.052152 Loss2: 1.361851
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373394 Loss1: 0.035491 Loss2: 1.337904
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397181 Loss1: 0.049729 Loss2: 1.347452 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.518380 Loss1: 0.558503 Loss2: 1.959877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.608371 Loss1: 0.207187 Loss2: 1.401184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.215610 Loss1: 0.355643 Loss2: 1.859967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.579172 Loss1: 0.214853 Loss2: 1.364319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426179 Loss1: 0.073852 Loss2: 1.352326 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419528 Loss1: 0.078390 Loss2: 1.341138 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384273 Loss1: 0.044132 Loss2: 1.340141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377732 Loss1: 0.048542 Loss2: 1.329189 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995192
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430749 Loss1: 0.068392 Loss2: 1.362357 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.447092 Loss1: 0.088565 Loss2: 1.358528 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412586 Loss1: 0.057474 Loss2: 1.355112
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.246424 Loss1: 0.407774 Loss2: 1.838650
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.623475 Loss1: 0.260813 Loss2: 1.362661
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.565350 Loss1: 0.169997 Loss2: 1.395352
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511354 Loss1: 0.151655 Loss2: 1.359699
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515566 Loss1: 0.150746 Loss2: 1.364820
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.311076 Loss1: 0.467981 Loss2: 1.843095
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.648944 Loss1: 0.309992 Loss2: 1.338952
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456170 Loss1: 0.097090 Loss2: 1.359081
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.542788 Loss1: 0.171313 Loss2: 1.371475
+DEBUG flwr 2023-10-12 22:14:38,884 | server.py:236 | fit_round 166 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.438489 Loss1: 0.087636 Loss2: 1.350853
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492349 Loss1: 0.143595 Loss2: 1.348755
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.408792 Loss1: 0.060188 Loss2: 1.348604
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500816 Loss1: 0.158848 Loss2: 1.341968
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400775 Loss1: 0.059809 Loss2: 1.340966
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379371 Loss1: 0.042307 Loss2: 1.337064 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.999023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446943 Loss1: 0.107426 Loss2: 1.339516 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403013 Loss1: 0.071526 Loss2: 1.331487 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.681287 Loss1: 0.312339 Loss2: 1.368949 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.466718 Loss1: 0.110053 Loss2: 1.356665 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.444966 Loss1: 0.087603 Loss2: 1.357363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458095 Loss1: 0.104696 Loss2: 1.353399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449428 Loss1: 0.100277 Loss2: 1.349151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457696 Loss1: 0.099697 Loss2: 1.358000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407575 Loss1: 0.053685 Loss2: 1.353889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402422 Loss1: 0.064283 Loss2: 1.338139 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413303 Loss1: 0.057168 Loss2: 1.356135 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412841 Loss1: 0.065576 Loss2: 1.347265 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 22:14:38,884][flwr][DEBUG] - fit_round 166 received 50 results and 0 failures
+INFO flwr 2023-10-12 22:15:20,286 | server.py:125 | fit progress: (166, 2.2735640943621673, {'accuracy': 0.6028}, 383028.06479311496)
+>> Test accuracy: 0.602800
+[2023-10-12 22:15:20,286][flwr][INFO] - fit progress: (166, 2.2735640943621673, {'accuracy': 0.6028}, 383028.06479311496)
+DEBUG flwr 2023-10-12 22:15:20,287 | server.py:173 | evaluate_round 166: strategy sampled 50 clients (out of 50)
+[2023-10-12 22:15:20,287][flwr][DEBUG] - evaluate_round 166: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 22:24:24,139 | server.py:187 | evaluate_round 166 received 50 results and 0 failures
+[2023-10-12 22:24:24,139][flwr][DEBUG] - evaluate_round 166 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 22:24:24,139 | server.py:222 | fit_round 167: strategy sampled 50 clients (out of 50)
+[2023-10-12 22:24:24,139][flwr][DEBUG] - fit_round 167: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.300072 Loss1: 0.456071 Loss2: 1.844001
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.620255 Loss1: 0.260268 Loss2: 1.359987
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.521334 Loss1: 0.139540 Loss2: 1.381794
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.514715 Loss1: 0.156000 Loss2: 1.358715
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.402719 Loss1: 0.507377 Loss2: 1.895342
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.543585 Loss1: 0.176657 Loss2: 1.366928
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.668880 Loss1: 0.309978 Loss2: 1.358902
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455187 Loss1: 0.093547 Loss2: 1.361640
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642615 Loss1: 0.244649 Loss2: 1.397966
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.525967 Loss1: 0.161239 Loss2: 1.364728
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.490230 Loss1: 0.135988 Loss2: 1.354242
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524020 Loss1: 0.164424 Loss2: 1.359596
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427595 Loss1: 0.081583 Loss2: 1.346012
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530371 Loss1: 0.168229 Loss2: 1.362142
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.416131 Loss1: 0.073220 Loss2: 1.342912
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422674 Loss1: 0.076463 Loss2: 1.346211 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458923 Loss1: 0.107927 Loss2: 1.350995 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.350761 Loss1: 0.481714 Loss2: 1.869047 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.623876 Loss1: 0.197633 Loss2: 1.426243 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542537 Loss1: 0.163409 Loss2: 1.379127
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.364156 Loss1: 0.493373 Loss2: 1.870782
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.613784 Loss1: 0.240628 Loss2: 1.373156 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.529830 Loss1: 0.148832 Loss2: 1.380998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497723 Loss1: 0.137330 Loss2: 1.360394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.488536 Loss1: 0.130896 Loss2: 1.357640 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460695 Loss1: 0.092401 Loss2: 1.368294 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450592 Loss1: 0.086937 Loss2: 1.363655
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437979 Loss1: 0.085522 Loss2: 1.352457
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408660 Loss1: 0.063964 Loss2: 1.344696
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408147 Loss1: 0.066882 Loss2: 1.341265
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410446 Loss1: 0.077930 Loss2: 1.332516
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.336565 Loss1: 0.475211 Loss2: 1.861354
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.797165 Loss1: 0.407082 Loss2: 1.390083
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.714823 Loss1: 0.257211 Loss2: 1.457612
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589125 Loss1: 0.202522 Loss2: 1.386603
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.344303 Loss1: 0.481394 Loss2: 1.862909
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.650491 Loss1: 0.277710 Loss2: 1.372782 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.582499 Loss1: 0.204411 Loss2: 1.378088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551771 Loss1: 0.176467 Loss2: 1.375304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.498199 Loss1: 0.133617 Loss2: 1.364582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493467 Loss1: 0.129393 Loss2: 1.364074 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379252 Loss1: 0.027669 Loss2: 1.351583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428180 Loss1: 0.076685 Loss2: 1.351495
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419867 Loss1: 0.073366 Loss2: 1.346500
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412075 Loss1: 0.067558 Loss2: 1.344517
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411785 Loss1: 0.067297 Loss2: 1.344488
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.250349 Loss1: 0.477909 Loss2: 1.772440
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.576346 Loss1: 0.266659 Loss2: 1.309686
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.478144 Loss1: 0.143345 Loss2: 1.334799
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.455560 Loss1: 0.146102 Loss2: 1.309459
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.249948 Loss1: 0.403978 Loss2: 1.845970
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.602848 Loss1: 0.248561 Loss2: 1.354288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.628635 Loss1: 0.254813 Loss2: 1.373823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.518149 Loss1: 0.154374 Loss2: 1.363776 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523705 Loss1: 0.159978 Loss2: 1.363727 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476519 Loss1: 0.109609 Loss2: 1.366910 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456711 Loss1: 0.108153 Loss2: 1.348557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420608 Loss1: 0.076443 Loss2: 1.344164 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.398672 Loss1: 0.518075 Loss2: 1.880598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.630261 Loss1: 0.236521 Loss2: 1.393740 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.507054 Loss1: 0.153603 Loss2: 1.353451
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.457666 Loss1: 0.590166 Loss2: 1.867499
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.777495 Loss1: 0.429159 Loss2: 1.348337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.418805 Loss1: 0.073280 Loss2: 1.345524
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.624253 Loss1: 0.217922 Loss2: 1.406332
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481979 Loss1: 0.125840 Loss2: 1.356138
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416180 Loss1: 0.083325 Loss2: 1.332855
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.395851 Loss1: 0.067778 Loss2: 1.328073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371461 Loss1: 0.047382 Loss2: 1.324079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377226 Loss1: 0.054386 Loss2: 1.322840 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.346189 Loss1: 0.036931 Loss2: 1.309258 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.260192 Loss1: 0.410865 Loss2: 1.849327
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.602632 Loss1: 0.248360 Loss2: 1.354272
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.577964 Loss1: 0.202519 Loss2: 1.375445
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524546 Loss1: 0.169094 Loss2: 1.355453
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.148540 Loss1: 0.370969 Loss2: 1.777571
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.614326 Loss1: 0.293986 Loss2: 1.320340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.561762 Loss1: 0.181940 Loss2: 1.379823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.483741 Loss1: 0.161366 Loss2: 1.322375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.432169 Loss1: 0.105771 Loss2: 1.326397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421498 Loss1: 0.101662 Loss2: 1.319836 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.410553 Loss1: 0.102249 Loss2: 1.308304 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370539 Loss1: 0.072182 Loss2: 1.298358 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.343245 Loss1: 0.480736 Loss2: 1.862509 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599020 Loss1: 0.212383 Loss2: 1.386637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.513497 Loss1: 0.539955 Loss2: 1.973542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.794516 Loss1: 0.424604 Loss2: 1.369913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.640149 Loss1: 0.224524 Loss2: 1.415625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576914 Loss1: 0.191240 Loss2: 1.385675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.562160 Loss1: 0.190130 Loss2: 1.372030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490910 Loss1: 0.111221 Loss2: 1.379689
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427572 Loss1: 0.078149 Loss2: 1.349423
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445285 Loss1: 0.086730 Loss2: 1.358555
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407326 Loss1: 0.061844 Loss2: 1.345482
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432308 Loss1: 0.079237 Loss2: 1.353071 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.352538 Loss1: 0.450809 Loss2: 1.901729 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648094 Loss1: 0.244369 Loss2: 1.403726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559283 Loss1: 0.163595 Loss2: 1.395688
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.295446 Loss1: 0.458644 Loss2: 1.836802
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.714470 Loss1: 0.329064 Loss2: 1.385406 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605210 Loss1: 0.196391 Loss2: 1.408819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519053 Loss1: 0.141849 Loss2: 1.377204 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450596 Loss1: 0.086787 Loss2: 1.363809 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.418348 Loss1: 0.060764 Loss2: 1.357584 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.401142 Loss1: 0.053670 Loss2: 1.347472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374837 Loss1: 0.034177 Loss2: 1.340660 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.292024 Loss1: 0.456797 Loss2: 1.835228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568088 Loss1: 0.170460 Loss2: 1.397629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.530705 Loss1: 0.168618 Loss2: 1.362086 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471873 Loss1: 0.109220 Loss2: 1.362652 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.452206 Loss1: 0.098607 Loss2: 1.353599 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405586 Loss1: 0.061825 Loss2: 1.343762 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398620 Loss1: 0.061468 Loss2: 1.337151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405608 Loss1: 0.073102 Loss2: 1.332506 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.375568 Loss1: 0.057596 Loss2: 1.317972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.367214 Loss1: 0.057791 Loss2: 1.309422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.355778 Loss1: 0.046266 Loss2: 1.309511
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.272625 Loss1: 0.461189 Loss2: 1.811436
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.651324 Loss1: 0.328524 Loss2: 1.322800
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.564958 Loss1: 0.181208 Loss2: 1.383750
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.458513 Loss1: 0.141887 Loss2: 1.316627
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.436858 Loss1: 0.115560 Loss2: 1.321298
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.322495 Loss1: 0.459481 Loss2: 1.863014
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.418868 Loss1: 0.099320 Loss2: 1.319548
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.401604 Loss1: 0.092854 Loss2: 1.308750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.365695 Loss1: 0.058023 Loss2: 1.307672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378550 Loss1: 0.071645 Loss2: 1.306905 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366258 Loss1: 0.060842 Loss2: 1.305416 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441532 Loss1: 0.091375 Loss2: 1.350156 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410628 Loss1: 0.070899 Loss2: 1.339729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401623 Loss1: 0.060093 Loss2: 1.341530
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.275941 Loss1: 0.441762 Loss2: 1.834179
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.657015 Loss1: 0.316820 Loss2: 1.340195
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574909 Loss1: 0.200880 Loss2: 1.374029
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.486016 Loss1: 0.133881 Loss2: 1.352135
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.434664 Loss1: 0.098820 Loss2: 1.335844
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.217351 Loss1: 0.422528 Loss2: 1.794823
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449625 Loss1: 0.116544 Loss2: 1.333081
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.417370 Loss1: 0.091465 Loss2: 1.325905 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378511 Loss1: 0.052945 Loss2: 1.325566 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.369636 Loss1: 0.049391 Loss2: 1.320245 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365833 Loss1: 0.053658 Loss2: 1.312175 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.410836 Loss1: 0.105399 Loss2: 1.305437 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.380334 Loss1: 0.078844 Loss2: 1.301490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.350309 Loss1: 0.054745 Loss2: 1.295564
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.435927 Loss1: 0.504749 Loss2: 1.931178
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.747347 Loss1: 0.325376 Loss2: 1.421971
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635698 Loss1: 0.184894 Loss2: 1.450804
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.611580 Loss1: 0.185281 Loss2: 1.426298
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.512081 Loss1: 0.092693 Loss2: 1.419387
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.239721 Loss1: 0.432182 Loss2: 1.807539
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511774 Loss1: 0.104300 Loss2: 1.407474
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.504908 Loss1: 0.095634 Loss2: 1.409274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.538283 Loss1: 0.181979 Loss2: 1.356305
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.472926 Loss1: 0.072018 Loss2: 1.400909
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450451 Loss1: 0.055233 Loss2: 1.395218 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446471 Loss1: 0.056778 Loss2: 1.389693 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.396912 Loss1: 0.084447 Loss2: 1.312465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374078 Loss1: 0.066399 Loss2: 1.307679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.334585 Loss1: 0.033081 Loss2: 1.301504
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.513157 Loss1: 0.497430 Loss2: 2.015727
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.735154 Loss1: 0.362624 Loss2: 1.372530
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.679291 Loss1: 0.267044 Loss2: 1.412246
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576601 Loss1: 0.138806 Loss2: 1.437795
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527089 Loss1: 0.128837 Loss2: 1.398253
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.545261 Loss1: 0.159754 Loss2: 1.385507
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.535184 Loss1: 0.135348 Loss2: 1.399836
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.626430 Loss1: 0.267052 Loss2: 1.359378 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.687337 Loss1: 0.284351 Loss2: 1.402986 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985677
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523495 Loss1: 0.154251 Loss2: 1.369244 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.449211 Loss1: 0.087995 Loss2: 1.361217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436366 Loss1: 0.080985 Loss2: 1.355381
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.277702 Loss1: 0.399435 Loss2: 1.878267
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.638635 Loss1: 0.267695 Loss2: 1.370940 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400655 Loss1: 0.059425 Loss2: 1.341230
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.584912 Loss1: 0.176513 Loss2: 1.408399
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.656126 Loss1: 0.257762 Loss2: 1.398364
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.553980 Loss1: 0.156170 Loss2: 1.397810
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508906 Loss1: 0.123789 Loss2: 1.385117
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487553 Loss1: 0.098813 Loss2: 1.388740
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.333113 Loss1: 0.476362 Loss2: 1.856751
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480272 Loss1: 0.098147 Loss2: 1.382125
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.728421 Loss1: 0.350739 Loss2: 1.377681
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.487733 Loss1: 0.111583 Loss2: 1.376150
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.728785 Loss1: 0.265847 Loss2: 1.462939
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457078 Loss1: 0.085102 Loss2: 1.371976
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.639908 Loss1: 0.224817 Loss2: 1.415090 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.551027 Loss1: 0.157617 Loss2: 1.393410 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501316 Loss1: 0.114883 Loss2: 1.386434
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.293076 Loss1: 0.421308 Loss2: 1.871768
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.646968 Loss1: 0.290131 Loss2: 1.356837 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445099 Loss1: 0.066846 Loss2: 1.378253
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.566911 Loss1: 0.184862 Loss2: 1.382049
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.590345 Loss1: 0.227495 Loss2: 1.362850
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.620753 Loss1: 0.218413 Loss2: 1.402341
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511055 Loss1: 0.154803 Loss2: 1.356252
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433599 Loss1: 0.079697 Loss2: 1.353901
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.233410 Loss1: 0.396018 Loss2: 1.837393
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410103 Loss1: 0.064938 Loss2: 1.345165
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.611347 Loss1: 0.276187 Loss2: 1.335159
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413250 Loss1: 0.072605 Loss2: 1.340646
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.547332 Loss1: 0.177389 Loss2: 1.369944
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387209 Loss1: 0.047965 Loss2: 1.339244
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.471961 Loss1: 0.131666 Loss2: 1.340295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.442456 Loss1: 0.106491 Loss2: 1.335965 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399965 Loss1: 0.068375 Loss2: 1.331590
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.427283 Loss1: 0.516988 Loss2: 1.910295
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.653107 Loss1: 0.280500 Loss2: 1.372607 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401706 Loss1: 0.078812 Loss2: 1.322893
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.651525 Loss1: 0.263821 Loss2: 1.387704
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589088 Loss1: 0.196739 Loss2: 1.392348
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.598381 Loss1: 0.234493 Loss2: 1.363888
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471575 Loss1: 0.100396 Loss2: 1.371180
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440299 Loss1: 0.084412 Loss2: 1.355887
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400825 Loss1: 0.052631 Loss2: 1.348194
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.364070 Loss1: 0.525605 Loss2: 1.838465
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.676176 Loss1: 0.334317 Loss2: 1.341859 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983259
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429398 Loss1: 0.089816 Loss2: 1.339583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.643865 Loss1: 0.240131 Loss2: 1.403734
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526362 Loss1: 0.166439 Loss2: 1.359923
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.471590 Loss1: 0.117232 Loss2: 1.354357
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503508 Loss1: 0.150124 Loss2: 1.353384
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.529143 Loss1: 0.176279 Loss2: 1.352863
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.292838 Loss1: 0.425735 Loss2: 1.867103
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511193 Loss1: 0.142946 Loss2: 1.368247
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499801 Loss1: 0.155845 Loss2: 1.343956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.459515 Loss1: 0.106136 Loss2: 1.353379 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480917 Loss1: 0.128967 Loss2: 1.351949 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409662 Loss1: 0.066878 Loss2: 1.342783 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.402886 Loss1: 0.064926 Loss2: 1.337961
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.334003 Loss1: 0.455136 Loss2: 1.878867
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.643180 Loss1: 0.273802 Loss2: 1.369378 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386714 Loss1: 0.051127 Loss2: 1.335587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.593880 Loss1: 0.186920 Loss2: 1.406959
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534862 Loss1: 0.154024 Loss2: 1.380838
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492340 Loss1: 0.112144 Loss2: 1.380196
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465643 Loss1: 0.088667 Loss2: 1.376977
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.500729 Loss1: 0.127345 Loss2: 1.373384
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.289630 Loss1: 0.446008 Loss2: 1.843622
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460371 Loss1: 0.087801 Loss2: 1.372570
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415334 Loss1: 0.050288 Loss2: 1.365046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397711 Loss1: 0.045767 Loss2: 1.351944 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.502531 Loss1: 0.157778 Loss2: 1.344753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408327 Loss1: 0.075345 Loss2: 1.332982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.305536 Loss1: 0.492645 Loss2: 1.812892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.620825 Loss1: 0.304862 Loss2: 1.315963 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565481 Loss1: 0.218851 Loss2: 1.346630 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455947 Loss1: 0.135239 Loss2: 1.320708 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.385130 Loss1: 0.071122 Loss2: 1.314008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388225 Loss1: 0.085616 Loss2: 1.302609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370475 Loss1: 0.067700 Loss2: 1.302775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362788 Loss1: 0.065375 Loss2: 1.297413 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.439878 Loss1: 0.121038 Loss2: 1.318840 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383013 Loss1: 0.082847 Loss2: 1.300167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.367522 Loss1: 0.072887 Loss2: 1.294635
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.209397 Loss1: 0.318813 Loss2: 1.890584
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.658586 Loss1: 0.237965 Loss2: 1.420622 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.549409 Loss1: 0.121714 Loss2: 1.427695 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542783 Loss1: 0.121891 Loss2: 1.420892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.521887 Loss1: 0.104200 Loss2: 1.417687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518255 Loss1: 0.098500 Loss2: 1.419755 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.515206 Loss1: 0.099920 Loss2: 1.415287 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.492017 Loss1: 0.077519 Loss2: 1.414498 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983456
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.376429 Loss1: 0.088703 Loss2: 1.287726 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.331826 Loss1: 0.063482 Loss2: 1.268343 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.320681 Loss1: 0.056563 Loss2: 1.264118
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.350985 Loss1: 0.486501 Loss2: 1.864484
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.674170 Loss1: 0.307928 Loss2: 1.366242
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614990 Loss1: 0.218648 Loss2: 1.396342
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540353 Loss1: 0.174628 Loss2: 1.365725
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497361 Loss1: 0.131105 Loss2: 1.366256
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.298333 Loss1: 0.452850 Loss2: 1.845482
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490515 Loss1: 0.128519 Loss2: 1.361996
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.642971 Loss1: 0.284088 Loss2: 1.358883
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485998 Loss1: 0.119621 Loss2: 1.366377
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610950 Loss1: 0.217480 Loss2: 1.393470
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475969 Loss1: 0.111140 Loss2: 1.364830
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549417 Loss1: 0.185241 Loss2: 1.364177
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441514 Loss1: 0.086269 Loss2: 1.355245
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499305 Loss1: 0.138865 Loss2: 1.360439
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442770 Loss1: 0.088888 Loss2: 1.353882
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462080 Loss1: 0.117235 Loss2: 1.344845 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413150 Loss1: 0.060467 Loss2: 1.352684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384978 Loss1: 0.043545 Loss2: 1.341433
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.179571 Loss1: 0.353898 Loss2: 1.825673
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.681866 Loss1: 0.320020 Loss2: 1.361846
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629848 Loss1: 0.230889 Loss2: 1.398960
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538275 Loss1: 0.160812 Loss2: 1.377464
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.536521 Loss1: 0.163066 Loss2: 1.373454
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.227940 Loss1: 0.417733 Loss2: 1.810206
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530102 Loss1: 0.158128 Loss2: 1.371974
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509393 Loss1: 0.132178 Loss2: 1.377215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486012 Loss1: 0.119680 Loss2: 1.366333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444792 Loss1: 0.082938 Loss2: 1.361854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416110 Loss1: 0.058165 Loss2: 1.357945 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418228 Loss1: 0.081943 Loss2: 1.336285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379453 Loss1: 0.055304 Loss2: 1.324149 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383376 Loss1: 0.056570 Loss2: 1.326806
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.238171 Loss1: 0.447286 Loss2: 1.790885
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.614329 Loss1: 0.287192 Loss2: 1.327137
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.559282 Loss1: 0.197678 Loss2: 1.361604
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.502849 Loss1: 0.176133 Loss2: 1.326715
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.436359 Loss1: 0.107325 Loss2: 1.329033
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.370976 Loss1: 0.523055 Loss2: 1.847921
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.633766 Loss1: 0.271196 Loss2: 1.362569
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439393 Loss1: 0.112315 Loss2: 1.327078
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.556547 Loss1: 0.177289 Loss2: 1.379258
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404349 Loss1: 0.083913 Loss2: 1.320436
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541931 Loss1: 0.184375 Loss2: 1.357556
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389682 Loss1: 0.077051 Loss2: 1.312631
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.505052 Loss1: 0.142764 Loss2: 1.362288
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.335388 Loss1: 0.029925 Loss2: 1.305463
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480668 Loss1: 0.121088 Loss2: 1.359580
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.322692 Loss1: 0.027953 Loss2: 1.294739
+(DefaultActor pid=3764) >> Training accuracy: 0.999023
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456878 Loss1: 0.095403 Loss2: 1.361475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398081 Loss1: 0.050991 Loss2: 1.347089 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.186506 Loss1: 0.393722 Loss2: 1.792784
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612013 Loss1: 0.264356 Loss2: 1.347657
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.534254 Loss1: 0.163623 Loss2: 1.370631
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520600 Loss1: 0.184663 Loss2: 1.335937
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.207850 Loss1: 0.377343 Loss2: 1.830506
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.588279 Loss1: 0.243143 Loss2: 1.345136 [repeated 2x across cluster]
+DEBUG flwr 2023-10-12 22:53:02,257 | server.py:236 | fit_round 167 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.535803 Loss1: 0.171338 Loss2: 1.364465 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518091 Loss1: 0.176407 Loss2: 1.341684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.508694 Loss1: 0.162995 Loss2: 1.345699 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477666 Loss1: 0.128731 Loss2: 1.348934 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367093 Loss1: 0.049910 Loss2: 1.317183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485065 Loss1: 0.134739 Loss2: 1.350326
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434074 Loss1: 0.096333 Loss2: 1.337741
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433689 Loss1: 0.102715 Loss2: 1.330974
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411048 Loss1: 0.080396 Loss2: 1.330651
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.797144 Loss1: 0.431768 Loss2: 1.365376 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536820 Loss1: 0.174933 Loss2: 1.361887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500625 Loss1: 0.140775 Loss2: 1.359850
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.277203 Loss1: 0.434634 Loss2: 1.842569
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490399 Loss1: 0.130483 Loss2: 1.359916
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.735197 Loss1: 0.350220 Loss2: 1.384978
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.623915 Loss1: 0.198916 Loss2: 1.424998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589982 Loss1: 0.199007 Loss2: 1.390975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.554889 Loss1: 0.154863 Loss2: 1.400026 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.492920 Loss1: 0.113612 Loss2: 1.379308 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444450 Loss1: 0.071350 Loss2: 1.373100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418328 Loss1: 0.055223 Loss2: 1.363105
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.287120 Loss1: 0.414894 Loss2: 1.872226
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.684173 Loss1: 0.267689 Loss2: 1.416484
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.637886 Loss1: 0.199437 Loss2: 1.438450
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.578303 Loss1: 0.170102 Loss2: 1.408200
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.587975 Loss1: 0.165808 Loss2: 1.422167
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.564704 Loss1: 0.152732 Loss2: 1.411972
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.543159 Loss1: 0.136865 Loss2: 1.406294
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.538228 Loss1: 0.126014 Loss2: 1.412214
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.524905 Loss1: 0.115087 Loss2: 1.409818
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.515328 Loss1: 0.108344 Loss2: 1.406983
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 22:53:02,257][flwr][DEBUG] - fit_round 167 received 50 results and 0 failures
+INFO flwr 2023-10-12 22:53:44,190 | server.py:125 | fit progress: (167, 2.2618117819959744, {'accuracy': 0.6034}, 385331.968352496)
+>> Test accuracy: 0.603400
+[2023-10-12 22:53:44,190][flwr][INFO] - fit progress: (167, 2.2618117819959744, {'accuracy': 0.6034}, 385331.968352496)
+DEBUG flwr 2023-10-12 22:53:44,190 | server.py:173 | evaluate_round 167: strategy sampled 50 clients (out of 50)
+[2023-10-12 22:53:44,190][flwr][DEBUG] - evaluate_round 167: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 23:02:50,075 | server.py:187 | evaluate_round 167 received 50 results and 0 failures
+[2023-10-12 23:02:50,075][flwr][DEBUG] - evaluate_round 167 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 23:02:50,076 | server.py:222 | fit_round 168: strategy sampled 50 clients (out of 50)
+[2023-10-12 23:02:50,076][flwr][DEBUG] - fit_round 168: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.253372 Loss1: 0.477824 Loss2: 1.775548
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622301 Loss1: 0.317462 Loss2: 1.304840
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.585747 Loss1: 0.219457 Loss2: 1.366290
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534789 Loss1: 0.224923 Loss2: 1.309866
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.334661 Loss1: 0.495947 Loss2: 1.838713
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463326 Loss1: 0.146071 Loss2: 1.317255
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.625032 Loss1: 0.270060 Loss2: 1.354972
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448709 Loss1: 0.138972 Loss2: 1.309738
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.599609 Loss1: 0.212114 Loss2: 1.387496
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402143 Loss1: 0.091488 Loss2: 1.310654
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.527709 Loss1: 0.162226 Loss2: 1.365483
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380753 Loss1: 0.078437 Loss2: 1.302316
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509655 Loss1: 0.151396 Loss2: 1.358259
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.345440 Loss1: 0.051843 Loss2: 1.293596
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.462270 Loss1: 0.105001 Loss2: 1.357270
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.323503 Loss1: 0.034127 Loss2: 1.289376
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469302 Loss1: 0.124613 Loss2: 1.344689
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434188 Loss1: 0.081882 Loss2: 1.352307
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425335 Loss1: 0.078879 Loss2: 1.346456
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391628 Loss1: 0.053083 Loss2: 1.338546
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.368550 Loss1: 0.462423 Loss2: 1.906127
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.769289 Loss1: 0.362974 Loss2: 1.406315
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.664457 Loss1: 0.228951 Loss2: 1.435507
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582049 Loss1: 0.173530 Loss2: 1.408518
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.293547 Loss1: 0.476383 Loss2: 1.817165
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.679437 Loss1: 0.339047 Loss2: 1.340389 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.560064 Loss1: 0.192376 Loss2: 1.367688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.482660 Loss1: 0.146983 Loss2: 1.335677 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.447810 Loss1: 0.112907 Loss2: 1.334903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.418077 Loss1: 0.092337 Loss2: 1.325740 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441597 Loss1: 0.058729 Loss2: 1.382868
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416516 Loss1: 0.093780 Loss2: 1.322736
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.379749 Loss1: 0.055082 Loss2: 1.324668
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372260 Loss1: 0.055540 Loss2: 1.316720
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372410 Loss1: 0.060197 Loss2: 1.312213
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.348561 Loss1: 0.448280 Loss2: 1.900281
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.701040 Loss1: 0.294791 Loss2: 1.406249
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.678831 Loss1: 0.232296 Loss2: 1.446535
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536075 Loss1: 0.128606 Loss2: 1.407469
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.291889 Loss1: 0.481511 Loss2: 1.810377
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.742546 Loss1: 0.385826 Loss2: 1.356720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.663110 Loss1: 0.249793 Loss2: 1.413317 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.524023 Loss1: 0.163911 Loss2: 1.360112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535172 Loss1: 0.181936 Loss2: 1.353236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.475659 Loss1: 0.122574 Loss2: 1.353086 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428489 Loss1: 0.089027 Loss2: 1.339462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430763 Loss1: 0.093044 Loss2: 1.337720 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.472043 Loss1: 0.542805 Loss2: 1.929239 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.583054 Loss1: 0.162553 Loss2: 1.420500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.396480 Loss1: 0.531251 Loss2: 1.865229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.689950 Loss1: 0.343187 Loss2: 1.346763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.595306 Loss1: 0.225315 Loss2: 1.369991 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467092 Loss1: 0.103836 Loss2: 1.363256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.464571 Loss1: 0.100819 Loss2: 1.363752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.479019 Loss1: 0.120267 Loss2: 1.358752 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.377376 Loss1: 0.052856 Loss2: 1.324520 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.360219 Loss1: 0.042482 Loss2: 1.317737 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.696768 Loss1: 0.334689 Loss2: 1.362079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.577289 Loss1: 0.210243 Loss2: 1.367046 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.215631 Loss1: 0.398228 Loss2: 1.817402
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.512392 Loss1: 0.144295 Loss2: 1.368097
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.628682 Loss1: 0.279429 Loss2: 1.349253
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.496574 Loss1: 0.130554 Loss2: 1.366019
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.620816 Loss1: 0.250975 Loss2: 1.369841
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487089 Loss1: 0.127207 Loss2: 1.359882
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.533113 Loss1: 0.178215 Loss2: 1.354898
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.512424 Loss1: 0.153349 Loss2: 1.359074
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.494861 Loss1: 0.146883 Loss2: 1.347978
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449363 Loss1: 0.096263 Loss2: 1.353100
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.445099 Loss1: 0.101421 Loss2: 1.343678
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405743 Loss1: 0.060099 Loss2: 1.345644
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415726 Loss1: 0.069595 Loss2: 1.346131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391938 Loss1: 0.065221 Loss2: 1.326716 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.692094 Loss1: 0.366647 Loss2: 1.325447 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.565929 Loss1: 0.191484 Loss2: 1.374445 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.425764 Loss1: 0.101834 Loss2: 1.323930 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436094 Loss1: 0.112667 Loss2: 1.323427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.397221 Loss1: 0.080198 Loss2: 1.317023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373648 Loss1: 0.068088 Loss2: 1.305560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399841 Loss1: 0.094586 Loss2: 1.305255 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.458478 Loss1: 0.086816 Loss2: 1.371662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475628 Loss1: 0.104373 Loss2: 1.371255 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428442 Loss1: 0.063202 Loss2: 1.365240
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.210154 Loss1: 0.389821 Loss2: 1.820333
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412347 Loss1: 0.054579 Loss2: 1.357769
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.570482 Loss1: 0.216186 Loss2: 1.354296
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.525213 Loss1: 0.148710 Loss2: 1.376503
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527844 Loss1: 0.167475 Loss2: 1.360369
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492303 Loss1: 0.132252 Loss2: 1.360051
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457749 Loss1: 0.103117 Loss2: 1.354631
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.251585 Loss1: 0.367300 Loss2: 1.884286
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.683412 Loss1: 0.303138 Loss2: 1.380274 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.585005 Loss1: 0.166506 Loss2: 1.418500 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540428 Loss1: 0.152203 Loss2: 1.388224 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379868 Loss1: 0.042302 Loss2: 1.337566
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523823 Loss1: 0.135325 Loss2: 1.388498
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499682 Loss1: 0.116115 Loss2: 1.383567
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437044 Loss1: 0.062606 Loss2: 1.374438
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433409 Loss1: 0.060069 Loss2: 1.373340
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431642 Loss1: 0.062768 Loss2: 1.368873
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435297 Loss1: 0.065853 Loss2: 1.369444
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.303363 Loss1: 0.501753 Loss2: 1.801610
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.626622 Loss1: 0.304741 Loss2: 1.321881
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.522195 Loss1: 0.174857 Loss2: 1.347338
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.446334 Loss1: 0.124048 Loss2: 1.322285
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.412658 Loss1: 0.097585 Loss2: 1.315074
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463099 Loss1: 0.149832 Loss2: 1.313267
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.353810 Loss1: 0.472429 Loss2: 1.881381
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.427098 Loss1: 0.112725 Loss2: 1.314373
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.663205 Loss1: 0.282046 Loss2: 1.381158
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.395632 Loss1: 0.082801 Loss2: 1.312831
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.658198 Loss1: 0.250465 Loss2: 1.407733
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399665 Loss1: 0.091159 Loss2: 1.308506
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557026 Loss1: 0.171284 Loss2: 1.385742
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360581 Loss1: 0.049136 Loss2: 1.311446
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528612 Loss1: 0.145820 Loss2: 1.382792
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.530425 Loss1: 0.155524 Loss2: 1.374902
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.499129 Loss1: 0.114177 Loss2: 1.384952
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.535562 Loss1: 0.162072 Loss2: 1.373490
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462493 Loss1: 0.087501 Loss2: 1.374992
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.513614 Loss1: 0.573681 Loss2: 1.939932
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426140 Loss1: 0.059966 Loss2: 1.366173
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610914 Loss1: 0.239967 Loss2: 1.370948 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468674 Loss1: 0.107428 Loss2: 1.361246 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.413627 Loss1: 0.061413 Loss2: 1.352215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.392695 Loss1: 0.057253 Loss2: 1.335442 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.370104 Loss1: 0.038244 Loss2: 1.331860 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387659 Loss1: 0.058155 Loss2: 1.329504 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.480104 Loss1: 0.106385 Loss2: 1.373719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467335 Loss1: 0.101952 Loss2: 1.365383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464573 Loss1: 0.102026 Loss2: 1.362547
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.282434 Loss1: 0.441070 Loss2: 1.841364
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.617330 Loss1: 0.264052 Loss2: 1.353279 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.613916 Loss1: 0.224903 Loss2: 1.389013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.483157 Loss1: 0.137446 Loss2: 1.345710 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449360 Loss1: 0.103061 Loss2: 1.346298 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.416829 Loss1: 0.075677 Loss2: 1.341152 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396731 Loss1: 0.061578 Loss2: 1.335153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.562359 Loss1: 0.196528 Loss2: 1.365832
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390625 Loss1: 0.056477 Loss2: 1.334149
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487367 Loss1: 0.146133 Loss2: 1.341234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.405956 Loss1: 0.072951 Loss2: 1.333005 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.351615 Loss1: 0.525725 Loss2: 1.825890
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.367389 Loss1: 0.045697 Loss2: 1.321692
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.602109 Loss1: 0.262909 Loss2: 1.339201
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366247 Loss1: 0.049138 Loss2: 1.317109
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.511345 Loss1: 0.166140 Loss2: 1.345205
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367027 Loss1: 0.047894 Loss2: 1.319133
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.386829 Loss1: 0.070854 Loss2: 1.315976 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.407093 Loss1: 0.092564 Loss2: 1.314530 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.402069 Loss1: 0.089142 Loss2: 1.312927
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.486656 Loss1: 0.586817 Loss2: 1.899839
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408768 Loss1: 0.089396 Loss2: 1.319372
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.667165 Loss1: 0.344765 Loss2: 1.322399
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.603753 Loss1: 0.237370 Loss2: 1.366383
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399215 Loss1: 0.075731 Loss2: 1.323484
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483813 Loss1: 0.171554 Loss2: 1.312259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.384176 Loss1: 0.080654 Loss2: 1.303522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.355755 Loss1: 0.059109 Loss2: 1.296646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364050 Loss1: 0.074593 Loss2: 1.289457 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.531926 Loss1: 0.192121 Loss2: 1.339805 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468575 Loss1: 0.130875 Loss2: 1.337700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458288 Loss1: 0.116046 Loss2: 1.342242
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.342693 Loss1: 0.476552 Loss2: 1.866141
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418300 Loss1: 0.084557 Loss2: 1.333744
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.715670 Loss1: 0.338721 Loss2: 1.376949
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421302 Loss1: 0.095129 Loss2: 1.326173
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.727735 Loss1: 0.304726 Loss2: 1.423009
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390035 Loss1: 0.065456 Loss2: 1.324579
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.607699 Loss1: 0.228268 Loss2: 1.379431
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503321 Loss1: 0.130880 Loss2: 1.372441
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472246 Loss1: 0.101242 Loss2: 1.371004
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459863 Loss1: 0.098700 Loss2: 1.361163
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427265 Loss1: 0.073241 Loss2: 1.354023
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406336 Loss1: 0.050410 Loss2: 1.355927
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.205404 Loss1: 0.382054 Loss2: 1.823350
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384818 Loss1: 0.038465 Loss2: 1.346353
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.566246 Loss1: 0.236709 Loss2: 1.329536
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.490318 Loss1: 0.162027 Loss2: 1.328291
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.418741 Loss1: 0.094107 Loss2: 1.324634
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.403701 Loss1: 0.098688 Loss2: 1.305013
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.385707 Loss1: 0.080915 Loss2: 1.304792
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.374833 Loss1: 0.523841 Loss2: 1.850992
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.381711 Loss1: 0.079698 Loss2: 1.302013
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.361469 Loss1: 0.055578 Loss2: 1.305891 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374945 Loss1: 0.069452 Loss2: 1.305493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350456 Loss1: 0.047679 Loss2: 1.302777 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420072 Loss1: 0.069038 Loss2: 1.351034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407420 Loss1: 0.074474 Loss2: 1.332946 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399354 Loss1: 0.065730 Loss2: 1.333623
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.468014 Loss1: 0.527405 Loss2: 1.940609
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388759 Loss1: 0.056594 Loss2: 1.332165
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.734670 Loss1: 0.336202 Loss2: 1.398467
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.698292 Loss1: 0.257477 Loss2: 1.440815
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.588525 Loss1: 0.179652 Loss2: 1.408873
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555953 Loss1: 0.167589 Loss2: 1.388363
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525401 Loss1: 0.126396 Loss2: 1.399005
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495368 Loss1: 0.096104 Loss2: 1.399264
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.287196 Loss1: 0.406974 Loss2: 1.880222
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.478213 Loss1: 0.096923 Loss2: 1.381291
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.621948 Loss1: 0.242551 Loss2: 1.379397
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448576 Loss1: 0.067398 Loss2: 1.381178
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.659857 Loss1: 0.251229 Loss2: 1.408628
+(DefaultActor pid=3765) >> Training accuracy: 0.995536
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430102 Loss1: 0.054767 Loss2: 1.375335
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.563239 Loss1: 0.169259 Loss2: 1.393981
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.560197 Loss1: 0.178297 Loss2: 1.381899
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512213 Loss1: 0.125318 Loss2: 1.386895
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.510024 Loss1: 0.129723 Loss2: 1.380301
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.530651 Loss1: 0.150151 Loss2: 1.380501
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.228593 Loss1: 0.359645 Loss2: 1.868948
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.491509 Loss1: 0.114155 Loss2: 1.377355
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.475328 Loss1: 0.098465 Loss2: 1.376863 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484541 Loss1: 0.122601 Loss2: 1.361940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.435511 Loss1: 0.080390 Loss2: 1.355121 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443959 Loss1: 0.095157 Loss2: 1.348802
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.489529 Loss1: 0.565487 Loss2: 1.924042
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.856265 Loss1: 0.422506 Loss2: 1.433759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.776780 Loss1: 0.289825 Loss2: 1.486955 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.653680 Loss1: 0.223573 Loss2: 1.430107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.518258 Loss1: 0.101775 Loss2: 1.416483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457214 Loss1: 0.048019 Loss2: 1.409195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.250242 Loss1: 0.455932 Loss2: 1.794310
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457582 Loss1: 0.058155 Loss2: 1.399427
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572330 Loss1: 0.263444 Loss2: 1.308885
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482464 Loss1: 0.087832 Loss2: 1.394633
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.441081 Loss1: 0.135424 Loss2: 1.305657 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.397725 Loss1: 0.094832 Loss2: 1.302893 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.357889 Loss1: 0.060142 Loss2: 1.297747
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.169017 Loss1: 0.362853 Loss2: 1.806164
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.644043 Loss1: 0.298918 Loss2: 1.345126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.612450 Loss1: 0.222863 Loss2: 1.389587 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611181 Loss1: 0.244967 Loss2: 1.366215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524503 Loss1: 0.167309 Loss2: 1.357195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435368 Loss1: 0.095514 Loss2: 1.339854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415044 Loss1: 0.080436 Loss2: 1.334608 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368231 Loss1: 0.042282 Loss2: 1.325949 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507646 Loss1: 0.138315 Loss2: 1.369331 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435303 Loss1: 0.073089 Loss2: 1.362214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.184549 Loss1: 0.353977 Loss2: 1.830572
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415125 Loss1: 0.060566 Loss2: 1.354559
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.605106 Loss1: 0.240704 Loss2: 1.364402
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399987 Loss1: 0.054486 Loss2: 1.345501
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439518 Loss1: 0.090259 Loss2: 1.349258
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.584187 Loss1: 0.188261 Loss2: 1.395925
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.518941 Loss1: 0.160999 Loss2: 1.357942
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507753 Loss1: 0.141330 Loss2: 1.366423
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.505320 Loss1: 0.140105 Loss2: 1.365216
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473608 Loss1: 0.103990 Loss2: 1.369618
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.225838 Loss1: 0.414739 Loss2: 1.811099
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455871 Loss1: 0.094263 Loss2: 1.361608
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.595823 Loss1: 0.228167 Loss2: 1.367657
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.471818 Loss1: 0.109257 Loss2: 1.362561
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.530370 Loss1: 0.135345 Loss2: 1.395026
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435731 Loss1: 0.077114 Loss2: 1.358617
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.477192 Loss1: 0.114906 Loss2: 1.362286 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420398 Loss1: 0.062171 Loss2: 1.358227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.193503 Loss1: 0.451672 Loss2: 1.741831
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404184 Loss1: 0.056456 Loss2: 1.347728
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.582935 Loss1: 0.281853 Loss2: 1.301082
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409886 Loss1: 0.059230 Loss2: 1.350656
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.487840 Loss1: 0.166331 Loss2: 1.321509
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410245 Loss1: 0.068746 Loss2: 1.341499
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.406091 Loss1: 0.108242 Loss2: 1.297849 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.410617 Loss1: 0.116958 Loss2: 1.293659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.306883 Loss1: 0.499873 Loss2: 1.807010
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.353707 Loss1: 0.066725 Loss2: 1.286983
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.685703 Loss1: 0.309829 Loss2: 1.375874
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.344689 Loss1: 0.061734 Loss2: 1.282955
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.549472 Loss1: 0.185931 Loss2: 1.363540
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.342051 Loss1: 0.056837 Loss2: 1.285214
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.450945 Loss1: 0.109904 Loss2: 1.341041 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434817 Loss1: 0.098014 Loss2: 1.336803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.407542 Loss1: 0.491791 Loss2: 1.915751
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.407905 Loss1: 0.072719 Loss2: 1.335186
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377177 Loss1: 0.055332 Loss2: 1.321844 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363751 Loss1: 0.041892 Loss2: 1.321859 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.536512 Loss1: 0.157727 Loss2: 1.378785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480163 Loss1: 0.105554 Loss2: 1.374609 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.333582 Loss1: 0.421417 Loss2: 1.912165 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.681579 Loss1: 0.295176 Loss2: 1.386403 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987723
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586518 Loss1: 0.199540 Loss2: 1.386978 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.548279 Loss1: 0.160995 Loss2: 1.387284 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481334 Loss1: 0.105773 Loss2: 1.375561
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.276471 Loss1: 0.372147 Loss2: 1.904324
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443914 Loss1: 0.060796 Loss2: 1.383118
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.751533 Loss1: 0.341724 Loss2: 1.409809
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414004 Loss1: 0.051234 Loss2: 1.362770
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.599291 Loss1: 0.150775 Loss2: 1.448515
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400619 Loss1: 0.037237 Loss2: 1.363382
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538493 Loss1: 0.128329 Loss2: 1.410163
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516898 Loss1: 0.109412 Loss2: 1.407486
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524707 Loss1: 0.117092 Loss2: 1.407616
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.510631 Loss1: 0.099578 Loss2: 1.411054
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470711 Loss1: 0.065074 Loss2: 1.405637
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.472505 Loss1: 0.078585 Loss2: 1.393920
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.296513 Loss1: 0.460914 Loss2: 1.835599
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.465317 Loss1: 0.070703 Loss2: 1.394614
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.665625 Loss1: 0.312040 Loss2: 1.353585
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.637107 Loss1: 0.261412 Loss2: 1.375695
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.543799 Loss1: 0.187006 Loss2: 1.356793
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.442748 Loss1: 0.088857 Loss2: 1.353892
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433535 Loss1: 0.095690 Loss2: 1.337845
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.264417 Loss1: 0.433008 Loss2: 1.831409
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434454 Loss1: 0.099737 Loss2: 1.334717
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.407221 Loss1: 0.071804 Loss2: 1.335417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.376939 Loss1: 0.055245 Loss2: 1.321694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358302 Loss1: 0.041862 Loss2: 1.316440 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.411986 Loss1: 0.083737 Loss2: 1.328250 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381421 Loss1: 0.067796 Loss2: 1.313625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.355651 Loss1: 0.046613 Loss2: 1.309039
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.464600 Loss1: 0.588610 Loss2: 1.875990
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.710072 Loss1: 0.324079 Loss2: 1.385993 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497369 Loss1: 0.131405 Loss2: 1.365964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458119 Loss1: 0.093727 Loss2: 1.364391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428450 Loss1: 0.071793 Loss2: 1.356657 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447866 Loss1: 0.092950 Loss2: 1.354915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421518 Loss1: 0.070726 Loss2: 1.350793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402824 Loss1: 0.055570 Loss2: 1.347254 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.391941 Loss1: 0.078148 Loss2: 1.313793 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396224 Loss1: 0.084173 Loss2: 1.312051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.365436 Loss1: 0.057586 Loss2: 1.307850
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.329817 Loss1: 0.435505 Loss2: 1.894312
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.632604 Loss1: 0.276919 Loss2: 1.355686 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.603961 Loss1: 0.238432 Loss2: 1.365529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508608 Loss1: 0.143193 Loss2: 1.365414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459761 Loss1: 0.110256 Loss2: 1.349505 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419076 Loss1: 0.072836 Loss2: 1.346239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.573938 Loss1: 0.227418 Loss2: 1.346519
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390778 Loss1: 0.050190 Loss2: 1.340588
+DEBUG flwr 2023-10-12 23:31:25,177 | server.py:236 | fit_round 168 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.494400 Loss1: 0.179760 Loss2: 1.314640
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378990 Loss1: 0.040314 Loss2: 1.338676
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.417437 Loss1: 0.120392 Loss2: 1.297045 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.355477 Loss1: 0.064961 Loss2: 1.290516 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.346073 Loss1: 0.057927 Loss2: 1.288146
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.202280 Loss1: 0.360971 Loss2: 1.841309
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.347190 Loss1: 0.062812 Loss2: 1.284378
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.708406 Loss1: 0.330033 Loss2: 1.378373
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.596143 Loss1: 0.188097 Loss2: 1.408046
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540021 Loss1: 0.161154 Loss2: 1.378867
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.452851 Loss1: 0.085158 Loss2: 1.367693
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454511 Loss1: 0.088115 Loss2: 1.366396
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.098461 Loss1: 0.331567 Loss2: 1.766893
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478788 Loss1: 0.119668 Loss2: 1.359120
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433298 Loss1: 0.068325 Loss2: 1.364973
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.548370 Loss1: 0.231262 Loss2: 1.317109
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.420104 Loss1: 0.073430 Loss2: 1.346674
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.504235 Loss1: 0.171884 Loss2: 1.332351
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397491 Loss1: 0.052675 Loss2: 1.344816
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.448794 Loss1: 0.129181 Loss2: 1.319613
+(DefaultActor pid=3765) >> Training accuracy: 0.979492
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450163 Loss1: 0.139163 Loss2: 1.311000
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420310 Loss1: 0.108183 Loss2: 1.312127
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.373723 Loss1: 0.066003 Loss2: 1.307719
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.360432 Loss1: 0.064126 Loss2: 1.296306
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.244198 Loss1: 0.433307 Loss2: 1.810890
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.701087 Loss1: 0.364773 Loss2: 1.336314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.339692 Loss1: 0.051394 Loss2: 1.288298
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.651641 Loss1: 0.264370 Loss2: 1.387271
+(DefaultActor pid=3764) >> Training accuracy: 0.998162
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.572684 Loss1: 0.212149 Loss2: 1.360536
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544733 Loss1: 0.185557 Loss2: 1.359177
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.496250 Loss1: 0.146318 Loss2: 1.349931
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475916 Loss1: 0.136344 Loss2: 1.339571
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.319601 Loss1: 0.429461 Loss2: 1.890140
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.428899 Loss1: 0.093214 Loss2: 1.335686
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.674691 Loss1: 0.289299 Loss2: 1.385392
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413769 Loss1: 0.079391 Loss2: 1.334377
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.619827 Loss1: 0.205052 Loss2: 1.414774
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421055 Loss1: 0.090801 Loss2: 1.330254
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.556610 Loss1: 0.156920 Loss2: 1.399690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.503913 Loss1: 0.118122 Loss2: 1.385791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452608 Loss1: 0.079994 Loss2: 1.372614 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-12 23:31:25,177][flwr][DEBUG] - fit_round 168 received 50 results and 0 failures
+INFO flwr 2023-10-12 23:32:06,757 | server.py:125 | fit progress: (168, 2.2627456317694423, {'accuracy': 0.604}, 387634.535789812)
+>> Test accuracy: 0.604000
+[2023-10-12 23:32:06,757][flwr][INFO] - fit progress: (168, 2.2627456317694423, {'accuracy': 0.604}, 387634.535789812)
+DEBUG flwr 2023-10-12 23:32:06,758 | server.py:173 | evaluate_round 168: strategy sampled 50 clients (out of 50)
+[2023-10-12 23:32:06,758][flwr][DEBUG] - evaluate_round 168: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-12 23:41:14,708 | server.py:187 | evaluate_round 168 received 50 results and 0 failures
+[2023-10-12 23:41:14,708][flwr][DEBUG] - evaluate_round 168 received 50 results and 0 failures
+DEBUG flwr 2023-10-12 23:41:14,708 | server.py:222 | fit_round 169: strategy sampled 50 clients (out of 50)
+[2023-10-12 23:41:14,708][flwr][DEBUG] - fit_round 169: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.322958 Loss1: 0.505384 Loss2: 1.817574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.538757 Loss1: 0.192529 Loss2: 1.346228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474863 Loss1: 0.153734 Loss2: 1.321129
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.271811 Loss1: 0.420814 Loss2: 1.850997
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.685874 Loss1: 0.331881 Loss2: 1.353993 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.583935 Loss1: 0.204931 Loss2: 1.379004 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.502646 Loss1: 0.130407 Loss2: 1.372238 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521195 Loss1: 0.167468 Loss2: 1.353728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512985 Loss1: 0.150277 Loss2: 1.362708 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.345136 Loss1: 0.043682 Loss2: 1.301455
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.457785 Loss1: 0.111218 Loss2: 1.346568
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415076 Loss1: 0.070710 Loss2: 1.344366
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413448 Loss1: 0.073793 Loss2: 1.339655
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422887 Loss1: 0.083734 Loss2: 1.339154
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.258517 Loss1: 0.416270 Loss2: 1.842248
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.642901 Loss1: 0.302909 Loss2: 1.339992
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591520 Loss1: 0.217333 Loss2: 1.374187
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500307 Loss1: 0.149914 Loss2: 1.350393
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.307564 Loss1: 0.425720 Loss2: 1.881844
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.749695 Loss1: 0.357250 Loss2: 1.392445 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653631 Loss1: 0.202922 Loss2: 1.450709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553981 Loss1: 0.156129 Loss2: 1.397853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507041 Loss1: 0.117706 Loss2: 1.389335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479437 Loss1: 0.085653 Loss2: 1.393783 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374266 Loss1: 0.054099 Loss2: 1.320167
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456991 Loss1: 0.076479 Loss2: 1.380512
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425442 Loss1: 0.054757 Loss2: 1.370685
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421664 Loss1: 0.057158 Loss2: 1.364506
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433997 Loss1: 0.071697 Loss2: 1.362300
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.110002 Loss1: 0.299314 Loss2: 1.810687
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.538096 Loss1: 0.190118 Loss2: 1.347979
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.494595 Loss1: 0.148795 Loss2: 1.345800
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.413303 Loss1: 0.536270 Loss2: 1.877033
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.437644 Loss1: 0.081908 Loss2: 1.355736
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.684677 Loss1: 0.306444 Loss2: 1.378232
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.418860 Loss1: 0.080414 Loss2: 1.338446
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.581159 Loss1: 0.187603 Loss2: 1.393555
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.419933 Loss1: 0.083331 Loss2: 1.336602
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414614 Loss1: 0.080669 Loss2: 1.333946 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401300 Loss1: 0.068756 Loss2: 1.332544 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394799 Loss1: 0.058739 Loss2: 1.336060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415356 Loss1: 0.079826 Loss2: 1.335530 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977941
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409985 Loss1: 0.068518 Loss2: 1.341466 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.224501 Loss1: 0.402984 Loss2: 1.821517
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622296 Loss1: 0.264642 Loss2: 1.357654
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.556375 Loss1: 0.175524 Loss2: 1.380851
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.114129 Loss1: 0.336655 Loss2: 1.777473
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499254 Loss1: 0.129141 Loss2: 1.370114
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.603306 Loss1: 0.268261 Loss2: 1.335045
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577224 Loss1: 0.212325 Loss2: 1.364899
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578107 Loss1: 0.200941 Loss2: 1.377166
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533541 Loss1: 0.155757 Loss2: 1.377783
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.463746 Loss1: 0.122603 Loss2: 1.341143
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.527041 Loss1: 0.167398 Loss2: 1.359643
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.433328 Loss1: 0.101878 Loss2: 1.331450
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483061 Loss1: 0.125820 Loss2: 1.357241
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.418357 Loss1: 0.085377 Loss2: 1.332980
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.499359 Loss1: 0.143635 Loss2: 1.355724
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423169 Loss1: 0.094450 Loss2: 1.328719
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450533 Loss1: 0.094387 Loss2: 1.356145
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391342 Loss1: 0.066609 Loss2: 1.324733 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.466919 Loss1: 0.515476 Loss2: 1.951443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.816677 Loss1: 0.276612 Loss2: 1.540065 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.679660 Loss1: 0.219878 Loss2: 1.459782
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.644857 Loss1: 0.620238 Loss2: 2.024618
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.745990 Loss1: 0.346738 Loss2: 1.399252
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.687866 Loss1: 0.232849 Loss2: 1.455017
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.706873 Loss1: 0.287305 Loss2: 1.419569
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.590283 Loss1: 0.140061 Loss2: 1.450222 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.592756 Loss1: 0.145452 Loss2: 1.447303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.505360 Loss1: 0.115919 Loss2: 1.389441 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.556886 Loss1: 0.109235 Loss2: 1.447651 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424227 Loss1: 0.040720 Loss2: 1.383507 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993490
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.324346 Loss1: 0.497651 Loss2: 1.826695
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.706635 Loss1: 0.365241 Loss2: 1.341395
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618785 Loss1: 0.228876 Loss2: 1.389910
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541690 Loss1: 0.194910 Loss2: 1.346780
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.333452 Loss1: 0.491318 Loss2: 1.842134
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.610366 Loss1: 0.304588 Loss2: 1.305779
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480689 Loss1: 0.139416 Loss2: 1.341273
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563105 Loss1: 0.240545 Loss2: 1.322560
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463316 Loss1: 0.118394 Loss2: 1.344922
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404874 Loss1: 0.072996 Loss2: 1.331879 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.392616 Loss1: 0.062336 Loss2: 1.330281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.365270 Loss1: 0.044263 Loss2: 1.321007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368269 Loss1: 0.051690 Loss2: 1.316579 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386347 Loss1: 0.083296 Loss2: 1.303051 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.278562 Loss1: 0.495442 Loss2: 1.783119
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622281 Loss1: 0.305194 Loss2: 1.317087
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.547330 Loss1: 0.184824 Loss2: 1.362506
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.424139 Loss1: 0.104815 Loss2: 1.319324
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.314230 Loss1: 0.403354 Loss2: 1.910877
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.698600 Loss1: 0.263310 Loss2: 1.435290 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.594398 Loss1: 0.137849 Loss2: 1.456549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574172 Loss1: 0.148817 Loss2: 1.425355 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513410 Loss1: 0.089102 Loss2: 1.424308 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.489081 Loss1: 0.070744 Loss2: 1.418337 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486804 Loss1: 0.079311 Loss2: 1.407493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479911 Loss1: 0.073532 Loss2: 1.406378 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.317581 Loss1: 0.407522 Loss2: 1.910059 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.607923 Loss1: 0.179812 Loss2: 1.428111 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.544537 Loss1: 0.619969 Loss2: 1.924567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.814046 Loss1: 0.404218 Loss2: 1.409829 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.721144 Loss1: 0.290953 Loss2: 1.430190 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.598160 Loss1: 0.201583 Loss2: 1.396577 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.494660 Loss1: 0.104327 Loss2: 1.390334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443131 Loss1: 0.072002 Loss2: 1.371129 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.411542 Loss1: 0.049385 Loss2: 1.362157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398822 Loss1: 0.048067 Loss2: 1.350756 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.342754 Loss1: 0.487021 Loss2: 1.855733 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.503305 Loss1: 0.126377 Loss2: 1.376927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474230 Loss1: 0.128689 Loss2: 1.345540
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.366086 Loss1: 0.520212 Loss2: 1.845874
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.447286 Loss1: 0.106722 Loss2: 1.340565
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.775891 Loss1: 0.403045 Loss2: 1.372846
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.424380 Loss1: 0.089089 Loss2: 1.335290
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.662880 Loss1: 0.241892 Loss2: 1.420987
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.388273 Loss1: 0.055194 Loss2: 1.333079
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.566105 Loss1: 0.199201 Loss2: 1.366904
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.367677 Loss1: 0.038507 Loss2: 1.329170
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.584059 Loss1: 0.203073 Loss2: 1.380986
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.362271 Loss1: 0.042017 Loss2: 1.320254
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452961 Loss1: 0.085730 Loss2: 1.367230
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359610 Loss1: 0.041015 Loss2: 1.318595
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.420583 Loss1: 0.074681 Loss2: 1.345902
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398884 Loss1: 0.056906 Loss2: 1.341977
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.380581 Loss1: 0.045315 Loss2: 1.335266
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385982 Loss1: 0.056778 Loss2: 1.329204
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.207854 Loss1: 0.394008 Loss2: 1.813846
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.593273 Loss1: 0.263748 Loss2: 1.329525
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.473810 Loss1: 0.117934 Loss2: 1.355877
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.454807 Loss1: 0.121320 Loss2: 1.333486
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.367960 Loss1: 0.434058 Loss2: 1.933902
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.739198 Loss1: 0.323072 Loss2: 1.416126 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.681672 Loss1: 0.225554 Loss2: 1.456118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579861 Loss1: 0.162989 Loss2: 1.416872 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.560029 Loss1: 0.154906 Loss2: 1.405123 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511102 Loss1: 0.103823 Loss2: 1.407279 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389205 Loss1: 0.072588 Loss2: 1.316617
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.493745 Loss1: 0.097847 Loss2: 1.395898
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460965 Loss1: 0.072595 Loss2: 1.388370
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460619 Loss1: 0.077440 Loss2: 1.383179
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439949 Loss1: 0.057305 Loss2: 1.382644
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.256271 Loss1: 0.420118 Loss2: 1.836153
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.595368 Loss1: 0.244068 Loss2: 1.351300
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.663916 Loss1: 0.259072 Loss2: 1.404844
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524683 Loss1: 0.167546 Loss2: 1.357138
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.253657 Loss1: 0.469436 Loss2: 1.784221
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478794 Loss1: 0.122918 Loss2: 1.355876
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.631818 Loss1: 0.332676 Loss2: 1.299142
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461088 Loss1: 0.102617 Loss2: 1.358471
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.546561 Loss1: 0.202516 Loss2: 1.344044
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418437 Loss1: 0.069357 Loss2: 1.349080
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.454981 Loss1: 0.145855 Loss2: 1.309127
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424902 Loss1: 0.079255 Loss2: 1.345647
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.407052 Loss1: 0.103522 Loss2: 1.303530
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423450 Loss1: 0.082478 Loss2: 1.340972
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.386726 Loss1: 0.092759 Loss2: 1.293966
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417097 Loss1: 0.075950 Loss2: 1.341147
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.364733 Loss1: 0.074552 Loss2: 1.290181
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.352019 Loss1: 0.066245 Loss2: 1.285774
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354806 Loss1: 0.073824 Loss2: 1.280982
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.336645 Loss1: 0.062288 Loss2: 1.274357
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.248187 Loss1: 0.358309 Loss2: 1.889878
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.637395 Loss1: 0.259918 Loss2: 1.377477
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618451 Loss1: 0.237072 Loss2: 1.381379
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604297 Loss1: 0.192248 Loss2: 1.412049
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.346765 Loss1: 0.480200 Loss2: 1.866566
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.532810 Loss1: 0.148770 Loss2: 1.384040
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.715560 Loss1: 0.341939 Loss2: 1.373622
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497485 Loss1: 0.119574 Loss2: 1.377911
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.678441 Loss1: 0.261117 Loss2: 1.417324
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487196 Loss1: 0.109297 Loss2: 1.377899
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.565918 Loss1: 0.185103 Loss2: 1.380814
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419832 Loss1: 0.052686 Loss2: 1.367146
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523218 Loss1: 0.147625 Loss2: 1.375593
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435519 Loss1: 0.077217 Loss2: 1.358303
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493812 Loss1: 0.127794 Loss2: 1.366018
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396896 Loss1: 0.033784 Loss2: 1.363112
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452423 Loss1: 0.093668 Loss2: 1.358755
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411947 Loss1: 0.064801 Loss2: 1.347146
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.380444 Loss1: 0.037756 Loss2: 1.342688
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365402 Loss1: 0.032888 Loss2: 1.332514
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.299549 Loss1: 0.426444 Loss2: 1.873105
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.689568 Loss1: 0.295297 Loss2: 1.394271
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645209 Loss1: 0.206060 Loss2: 1.439149
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.557158 Loss1: 0.594298 Loss2: 1.962860
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502242 Loss1: 0.115198 Loss2: 1.387044
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475780 Loss1: 0.095177 Loss2: 1.380602 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445260 Loss1: 0.065198 Loss2: 1.380061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443448 Loss1: 0.068774 Loss2: 1.374674 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.507155 Loss1: 0.128829 Loss2: 1.378326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.523153 Loss1: 0.151594 Loss2: 1.371559 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.510857 Loss1: 0.132082 Loss2: 1.378775 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407576 Loss1: 0.046307 Loss2: 1.361268 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.426456 Loss1: 0.517393 Loss2: 1.909063
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.757847 Loss1: 0.377792 Loss2: 1.380055
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.654551 Loss1: 0.235255 Loss2: 1.419296
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574022 Loss1: 0.202494 Loss2: 1.371528
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.336759 Loss1: 0.523190 Loss2: 1.813569
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.607353 Loss1: 0.262438 Loss2: 1.344915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.584322 Loss1: 0.214326 Loss2: 1.369997 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.456952 Loss1: 0.119815 Loss2: 1.337137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487506 Loss1: 0.154133 Loss2: 1.333374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472991 Loss1: 0.130050 Loss2: 1.342941 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391250 Loss1: 0.051562 Loss2: 1.339688
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426149 Loss1: 0.102658 Loss2: 1.323492
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413453 Loss1: 0.084598 Loss2: 1.328855
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363178 Loss1: 0.046496 Loss2: 1.316682
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369724 Loss1: 0.062942 Loss2: 1.306782
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.304547 Loss1: 0.438132 Loss2: 1.866416
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.696029 Loss1: 0.319139 Loss2: 1.376890
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.627194 Loss1: 0.205117 Loss2: 1.422076
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508763 Loss1: 0.132708 Loss2: 1.376055
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.241287 Loss1: 0.432172 Loss2: 1.809116
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.440796 Loss1: 0.080146 Loss2: 1.360650
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.577448 Loss1: 0.243332 Loss2: 1.334116
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.494953 Loss1: 0.130386 Loss2: 1.364567
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.514986 Loss1: 0.168284 Loss2: 1.346702
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436191 Loss1: 0.077928 Loss2: 1.358263
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.459554 Loss1: 0.116893 Loss2: 1.342662
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422774 Loss1: 0.068814 Loss2: 1.353960
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.422537 Loss1: 0.096132 Loss2: 1.326405
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440968 Loss1: 0.091994 Loss2: 1.348974
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455926 Loss1: 0.135741 Loss2: 1.320185
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416708 Loss1: 0.060752 Loss2: 1.355956
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.411342 Loss1: 0.083513 Loss2: 1.327829
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.377948 Loss1: 0.058920 Loss2: 1.319028
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.357741 Loss1: 0.046309 Loss2: 1.311432
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356944 Loss1: 0.051537 Loss2: 1.305407
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.215744 Loss1: 0.356698 Loss2: 1.859046
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.633943 Loss1: 0.246215 Loss2: 1.387728
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.558173 Loss1: 0.145570 Loss2: 1.412603
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.293857 Loss1: 0.420760 Loss2: 1.873097
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547784 Loss1: 0.170504 Loss2: 1.377281
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.651889 Loss1: 0.289115 Loss2: 1.362774
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503197 Loss1: 0.120755 Loss2: 1.382442
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.550337 Loss1: 0.165386 Loss2: 1.384951
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.469349 Loss1: 0.091614 Loss2: 1.377735
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478273 Loss1: 0.111440 Loss2: 1.366833 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.476298 Loss1: 0.098056 Loss2: 1.378242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413212 Loss1: 0.049153 Loss2: 1.364059 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409110 Loss1: 0.051093 Loss2: 1.358017 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397684 Loss1: 0.045765 Loss2: 1.351919 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.281688 Loss1: 0.441947 Loss2: 1.839741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.533008 Loss1: 0.142412 Loss2: 1.390597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.348565 Loss1: 0.485954 Loss2: 1.862611
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499590 Loss1: 0.126703 Loss2: 1.372887
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.739223 Loss1: 0.345455 Loss2: 1.393767
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480533 Loss1: 0.104975 Loss2: 1.375559
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.683752 Loss1: 0.257277 Loss2: 1.426475
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458938 Loss1: 0.092631 Loss2: 1.366307
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445874 Loss1: 0.085048 Loss2: 1.360825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436249 Loss1: 0.077843 Loss2: 1.358406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438626 Loss1: 0.076458 Loss2: 1.362168 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405702 Loss1: 0.053395 Loss2: 1.352307 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421733 Loss1: 0.059632 Loss2: 1.362101 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.114787 Loss1: 0.304334 Loss2: 1.810453 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.630389 Loss1: 0.224569 Loss2: 1.405820 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.197931 Loss1: 0.391165 Loss2: 1.806767
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.577755 Loss1: 0.218196 Loss2: 1.359559
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.646744 Loss1: 0.323653 Loss2: 1.323090
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.542646 Loss1: 0.178979 Loss2: 1.363667
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.535787 Loss1: 0.183915 Loss2: 1.351873
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513187 Loss1: 0.156800 Loss2: 1.356387
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.475350 Loss1: 0.148067 Loss2: 1.327283
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.540146 Loss1: 0.176277 Loss2: 1.363869
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.502914 Loss1: 0.136971 Loss2: 1.365943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449457 Loss1: 0.101493 Loss2: 1.347964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413827 Loss1: 0.072455 Loss2: 1.341372 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371052 Loss1: 0.071555 Loss2: 1.299497 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.266470 Loss1: 0.461437 Loss2: 1.805033 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.564113 Loss1: 0.198332 Loss2: 1.365782 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.509788 Loss1: 0.166596 Loss2: 1.343192
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.168752 Loss1: 0.343173 Loss2: 1.825579
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.549813 Loss1: 0.192086 Loss2: 1.357728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.479745 Loss1: 0.118987 Loss2: 1.360758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.457585 Loss1: 0.117505 Loss2: 1.340080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.432617 Loss1: 0.090906 Loss2: 1.341710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.405979 Loss1: 0.070091 Loss2: 1.335888 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.393241 Loss1: 0.067713 Loss2: 1.325528 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363080 Loss1: 0.037057 Loss2: 1.326023 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.315543 Loss1: 0.467479 Loss2: 1.848064 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644572 Loss1: 0.262421 Loss2: 1.382151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.304975 Loss1: 0.446655 Loss2: 1.858320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.677959 Loss1: 0.311278 Loss2: 1.366681 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.654504 Loss1: 0.242106 Loss2: 1.412398 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.660932 Loss1: 0.284776 Loss2: 1.376156 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570720 Loss1: 0.187266 Loss2: 1.383455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491900 Loss1: 0.131610 Loss2: 1.360291 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439667 Loss1: 0.087511 Loss2: 1.352156 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388286 Loss1: 0.045565 Loss2: 1.342722 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.643721 Loss1: 0.301829 Loss2: 1.341892 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499474 Loss1: 0.147159 Loss2: 1.352315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.350809 Loss1: 0.494594 Loss2: 1.856214
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.464982 Loss1: 0.123087 Loss2: 1.341896
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445368 Loss1: 0.107812 Loss2: 1.337556 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419263 Loss1: 0.083967 Loss2: 1.335296 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.397799 Loss1: 0.063415 Loss2: 1.334383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379010 Loss1: 0.051982 Loss2: 1.327028 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387737 Loss1: 0.061950 Loss2: 1.325788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411982 Loss1: 0.088569 Loss2: 1.323413 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364666 Loss1: 0.052340 Loss2: 1.312326 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994420
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.281369 Loss1: 0.470007 Loss2: 1.811362
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.632423 Loss1: 0.301897 Loss2: 1.330526
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.539896 Loss1: 0.177334 Loss2: 1.362562
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.470343 Loss1: 0.140144 Loss2: 1.330199
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.347824 Loss1: 0.474287 Loss2: 1.873538
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.665057 Loss1: 0.298912 Loss2: 1.366145 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592123 Loss1: 0.204721 Loss2: 1.387402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.506024 Loss1: 0.141141 Loss2: 1.364883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450380 Loss1: 0.092613 Loss2: 1.357767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431185 Loss1: 0.079139 Loss2: 1.352047 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381613 Loss1: 0.070568 Loss2: 1.311046
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.414502 Loss1: 0.070006 Loss2: 1.344496
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386930 Loss1: 0.041472 Loss2: 1.345458
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.378498 Loss1: 0.042109 Loss2: 1.336388
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375419 Loss1: 0.042476 Loss2: 1.332942
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.284850 Loss1: 0.452271 Loss2: 1.832578
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.595923 Loss1: 0.262904 Loss2: 1.333019
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594012 Loss1: 0.220103 Loss2: 1.373909
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.441553 Loss1: 0.109173 Loss2: 1.332380
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.339600 Loss1: 0.455079 Loss2: 1.884521
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659427 Loss1: 0.267529 Loss2: 1.391899 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.571352 Loss1: 0.157346 Loss2: 1.414006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.510824 Loss1: 0.124969 Loss2: 1.385854 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.473228 Loss1: 0.097651 Loss2: 1.375577 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 00:10:42,908 | server.py:236 | fit_round 169 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501321 Loss1: 0.125186 Loss2: 1.376135 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469119 Loss1: 0.097333 Loss2: 1.371785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413490 Loss1: 0.043765 Loss2: 1.369724 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.231011 Loss1: 0.380021 Loss2: 1.850990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.613162 Loss1: 0.206321 Loss2: 1.406841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.317530 Loss1: 0.461134 Loss2: 1.856396
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.579661 Loss1: 0.185629 Loss2: 1.394032
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.596767 Loss1: 0.266840 Loss2: 1.329927
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544746 Loss1: 0.146049 Loss2: 1.398697
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502681 Loss1: 0.106204 Loss2: 1.396477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468034 Loss1: 0.084026 Loss2: 1.384008 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454487 Loss1: 0.079755 Loss2: 1.374733 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453735 Loss1: 0.076323 Loss2: 1.377413 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453909 Loss1: 0.080840 Loss2: 1.373070 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372246 Loss1: 0.067563 Loss2: 1.304683 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.315980 Loss1: 0.437346 Loss2: 1.878634
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.708193 Loss1: 0.365667 Loss2: 1.342525
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.593417 Loss1: 0.214588 Loss2: 1.378829
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.543428 Loss1: 0.200214 Loss2: 1.343215
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.351698 Loss1: 0.474637 Loss2: 1.877061
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510268 Loss1: 0.152704 Loss2: 1.357564
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.650336 Loss1: 0.268669 Loss2: 1.381667
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514291 Loss1: 0.180301 Loss2: 1.333989
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.603831 Loss1: 0.204209 Loss2: 1.399622
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440134 Loss1: 0.087079 Loss2: 1.353055
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520995 Loss1: 0.136957 Loss2: 1.384039
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419770 Loss1: 0.088396 Loss2: 1.331374
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509316 Loss1: 0.131385 Loss2: 1.377931
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386525 Loss1: 0.059966 Loss2: 1.326559
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499113 Loss1: 0.121098 Loss2: 1.378015
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367671 Loss1: 0.047696 Loss2: 1.319975
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452925 Loss1: 0.083590 Loss2: 1.369334
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446358 Loss1: 0.081800 Loss2: 1.364558
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433540 Loss1: 0.072718 Loss2: 1.360822
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422726 Loss1: 0.061110 Loss2: 1.361616
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 00:10:42,908][flwr][DEBUG] - fit_round 169 received 50 results and 0 failures
+INFO flwr 2023-10-13 00:11:25,013 | server.py:125 | fit progress: (169, 2.2763475324399174, {'accuracy': 0.6043}, 389992.79194534)
+>> Test accuracy: 0.604300
+[2023-10-13 00:11:25,013][flwr][INFO] - fit progress: (169, 2.2763475324399174, {'accuracy': 0.6043}, 389992.79194534)
+DEBUG flwr 2023-10-13 00:11:25,014 | server.py:173 | evaluate_round 169: strategy sampled 50 clients (out of 50)
+[2023-10-13 00:11:25,014][flwr][DEBUG] - evaluate_round 169: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 00:20:30,365 | server.py:187 | evaluate_round 169 received 50 results and 0 failures
+[2023-10-13 00:20:30,365][flwr][DEBUG] - evaluate_round 169 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 00:20:30,366 | server.py:222 | fit_round 170: strategy sampled 50 clients (out of 50)
+[2023-10-13 00:20:30,366][flwr][DEBUG] - fit_round 170: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.381073 Loss1: 0.528030 Loss2: 1.853044
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.664291 Loss1: 0.335554 Loss2: 1.328737
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.598451 Loss1: 0.222522 Loss2: 1.375930
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.501364 Loss1: 0.160115 Loss2: 1.341248
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.278654 Loss1: 0.418864 Loss2: 1.859789
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.614582 Loss1: 0.255176 Loss2: 1.359407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.539865 Loss1: 0.158529 Loss2: 1.381336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.505946 Loss1: 0.148590 Loss2: 1.357356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.442965 Loss1: 0.090575 Loss2: 1.352390 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.437715 Loss1: 0.095860 Loss2: 1.341855 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994420
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432237 Loss1: 0.090221 Loss2: 1.342016 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411395 Loss1: 0.074379 Loss2: 1.337016 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.721000 Loss1: 0.369604 Loss2: 1.351396 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517967 Loss1: 0.158311 Loss2: 1.359656 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.294053 Loss1: 0.445798 Loss2: 1.848255
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448394 Loss1: 0.092107 Loss2: 1.356287
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.650274 Loss1: 0.300934 Loss2: 1.349340
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456777 Loss1: 0.098049 Loss2: 1.358728
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.551378 Loss1: 0.162168 Loss2: 1.389210
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449593 Loss1: 0.099085 Loss2: 1.350508
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496974 Loss1: 0.132930 Loss2: 1.364044
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429251 Loss1: 0.083346 Loss2: 1.345905
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.512252 Loss1: 0.157141 Loss2: 1.355112
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432072 Loss1: 0.082961 Loss2: 1.349110
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.457923 Loss1: 0.113700 Loss2: 1.344223
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415067 Loss1: 0.072554 Loss2: 1.342514
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.466321 Loss1: 0.128134 Loss2: 1.338187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391147 Loss1: 0.055552 Loss2: 1.335596 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.659995 Loss1: 0.294964 Loss2: 1.365031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547264 Loss1: 0.193652 Loss2: 1.353612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525187 Loss1: 0.146996 Loss2: 1.378191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.536376 Loss1: 0.173646 Loss2: 1.362730 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.505012 Loss1: 0.138385 Loss2: 1.366628 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.498963 Loss1: 0.136806 Loss2: 1.362157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.496554 Loss1: 0.133543 Loss2: 1.363011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476577 Loss1: 0.115690 Loss2: 1.360888 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441601 Loss1: 0.081460 Loss2: 1.360141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416234 Loss1: 0.065929 Loss2: 1.350304 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.202423 Loss1: 0.369345 Loss2: 1.833078
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.597055 Loss1: 0.243060 Loss2: 1.353994
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.490136 Loss1: 0.126203 Loss2: 1.363933
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474956 Loss1: 0.124514 Loss2: 1.350442
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.304588 Loss1: 0.422961 Loss2: 1.881628
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443784 Loss1: 0.098990 Loss2: 1.344794
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.646542 Loss1: 0.285333 Loss2: 1.361208
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.399054 Loss1: 0.061074 Loss2: 1.337980
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.521089 Loss1: 0.142373 Loss2: 1.378716
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.475010 Loss1: 0.122227 Loss2: 1.352783
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.400583 Loss1: 0.067048 Loss2: 1.333534
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.445559 Loss1: 0.101340 Loss2: 1.344219
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378697 Loss1: 0.044860 Loss2: 1.333837
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431120 Loss1: 0.084489 Loss2: 1.346631
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396801 Loss1: 0.067029 Loss2: 1.329772
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.371849 Loss1: 0.042398 Loss2: 1.329451
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380599 Loss1: 0.051113 Loss2: 1.329485
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.375340 Loss1: 0.052891 Loss2: 1.322449 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.368804 Loss1: 0.496846 Loss2: 1.871958 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582793 Loss1: 0.196915 Loss2: 1.385878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540248 Loss1: 0.168711 Loss2: 1.371537
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.321785 Loss1: 0.526080 Loss2: 1.795706
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.494640 Loss1: 0.134438 Loss2: 1.360202
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.637253 Loss1: 0.310998 Loss2: 1.326255
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477787 Loss1: 0.109989 Loss2: 1.367797
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.538335 Loss1: 0.150864 Loss2: 1.387470
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474310 Loss1: 0.113972 Loss2: 1.360339
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.469561 Loss1: 0.143479 Loss2: 1.326082
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452798 Loss1: 0.093934 Loss2: 1.358864
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.456027 Loss1: 0.129379 Loss2: 1.326648
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453775 Loss1: 0.096255 Loss2: 1.357520
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455060 Loss1: 0.122682 Loss2: 1.332378
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413066 Loss1: 0.060605 Loss2: 1.352461
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408488 Loss1: 0.087453 Loss2: 1.321035
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408308 Loss1: 0.089898 Loss2: 1.318410
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374935 Loss1: 0.055079 Loss2: 1.319856
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395013 Loss1: 0.082517 Loss2: 1.312497
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.366431 Loss1: 0.519313 Loss2: 1.847118
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.658690 Loss1: 0.300656 Loss2: 1.358034
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.643186 Loss1: 0.220913 Loss2: 1.422273
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527191 Loss1: 0.170268 Loss2: 1.356923
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.348089 Loss1: 0.475151 Loss2: 1.872938
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.626633 Loss1: 0.269486 Loss2: 1.357147 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.557506 Loss1: 0.170294 Loss2: 1.387212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.499089 Loss1: 0.146601 Loss2: 1.352488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485891 Loss1: 0.137814 Loss2: 1.348077 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.424894 Loss1: 0.077934 Loss2: 1.346960 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396351 Loss1: 0.067861 Loss2: 1.328490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435505 Loss1: 0.101673 Loss2: 1.333832
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407059 Loss1: 0.067026 Loss2: 1.340033
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.380094 Loss1: 0.050248 Loss2: 1.329847
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366614 Loss1: 0.037970 Loss2: 1.328644
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.257672 Loss1: 0.389909 Loss2: 1.867763
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.681868 Loss1: 0.284913 Loss2: 1.396955
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.605591 Loss1: 0.183173 Loss2: 1.422418
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.343056 Loss1: 0.420314 Loss2: 1.922741
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.531082 Loss1: 0.141314 Loss2: 1.389768
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.735194 Loss1: 0.321140 Loss2: 1.414055
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479034 Loss1: 0.083786 Loss2: 1.395248
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701593 Loss1: 0.245831 Loss2: 1.455762
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451043 Loss1: 0.065275 Loss2: 1.385768
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.614088 Loss1: 0.191715 Loss2: 1.422373
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463196 Loss1: 0.083808 Loss2: 1.379389
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430257 Loss1: 0.056924 Loss2: 1.373333 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431732 Loss1: 0.063491 Loss2: 1.368241 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417612 Loss1: 0.049280 Loss2: 1.368332 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457708 Loss1: 0.048097 Loss2: 1.409612 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.316916 Loss1: 0.471312 Loss2: 1.845605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.616091 Loss1: 0.239531 Loss2: 1.376560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.582206 Loss1: 0.215326 Loss2: 1.366881
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.316868 Loss1: 0.461893 Loss2: 1.854975
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.577991 Loss1: 0.211332 Loss2: 1.366659
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653260 Loss1: 0.286867 Loss2: 1.366392
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466315 Loss1: 0.115785 Loss2: 1.350530
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563668 Loss1: 0.165767 Loss2: 1.397901
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461167 Loss1: 0.113386 Loss2: 1.347781
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.482761 Loss1: 0.113192 Loss2: 1.369569
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417681 Loss1: 0.072634 Loss2: 1.345047
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.449887 Loss1: 0.092522 Loss2: 1.357365
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417892 Loss1: 0.081014 Loss2: 1.336878
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444769 Loss1: 0.085023 Loss2: 1.359745
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389276 Loss1: 0.059966 Loss2: 1.329310
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412188 Loss1: 0.061686 Loss2: 1.350502
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399438 Loss1: 0.050650 Loss2: 1.348788
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.393743 Loss1: 0.047302 Loss2: 1.346441
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388514 Loss1: 0.046982 Loss2: 1.341532
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.336359 Loss1: 0.447751 Loss2: 1.888608
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.656615 Loss1: 0.279192 Loss2: 1.377423
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.602041 Loss1: 0.191690 Loss2: 1.410351
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.515562 Loss1: 0.128182 Loss2: 1.387379
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.234796 Loss1: 0.339611 Loss2: 1.895185
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473922 Loss1: 0.102198 Loss2: 1.371724
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.664545 Loss1: 0.276847 Loss2: 1.387698
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444372 Loss1: 0.072442 Loss2: 1.371930
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622261 Loss1: 0.200563 Loss2: 1.421699
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424137 Loss1: 0.062816 Loss2: 1.361320
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.552216 Loss1: 0.154095 Loss2: 1.398121
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432029 Loss1: 0.071965 Loss2: 1.360064
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516586 Loss1: 0.124349 Loss2: 1.392237
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432993 Loss1: 0.073447 Loss2: 1.359546
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499510 Loss1: 0.113736 Loss2: 1.385774
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418996 Loss1: 0.062561 Loss2: 1.356435
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468087 Loss1: 0.085196 Loss2: 1.382891
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431217 Loss1: 0.059668 Loss2: 1.371549
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424896 Loss1: 0.057559 Loss2: 1.367337
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.508639 Loss1: 0.135395 Loss2: 1.373245
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.373067 Loss1: 0.420059 Loss2: 1.953008
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.690909 Loss1: 0.252160 Loss2: 1.438749
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.639991 Loss1: 0.182225 Loss2: 1.457766
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586442 Loss1: 0.144268 Loss2: 1.442174
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.329475 Loss1: 0.453459 Loss2: 1.876016
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.706951 Loss1: 0.317828 Loss2: 1.389122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629332 Loss1: 0.199888 Loss2: 1.429443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555376 Loss1: 0.157069 Loss2: 1.398306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518854 Loss1: 0.140189 Loss2: 1.378665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.489215 Loss1: 0.100841 Loss2: 1.388374 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.501755 Loss1: 0.080714 Loss2: 1.421041
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.459963 Loss1: 0.081286 Loss2: 1.378677
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459845 Loss1: 0.089080 Loss2: 1.370765
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448250 Loss1: 0.079468 Loss2: 1.368782
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453335 Loss1: 0.085922 Loss2: 1.367412
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.324678 Loss1: 0.473998 Loss2: 1.850681
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.700605 Loss1: 0.338089 Loss2: 1.362516
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.718149 Loss1: 0.294499 Loss2: 1.423650
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542458 Loss1: 0.164820 Loss2: 1.377638
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.223182 Loss1: 0.382114 Loss2: 1.841069
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.604503 Loss1: 0.240310 Loss2: 1.364193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.547300 Loss1: 0.163130 Loss2: 1.384170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478659 Loss1: 0.114616 Loss2: 1.364043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.462472 Loss1: 0.103425 Loss2: 1.359047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469480 Loss1: 0.111189 Loss2: 1.358290 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433396 Loss1: 0.072338 Loss2: 1.361057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414277 Loss1: 0.069001 Loss2: 1.345276 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.455584 Loss1: 0.109069 Loss2: 1.346516
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.380965 Loss1: 0.482022 Loss2: 1.898944
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.721067 Loss1: 0.328758 Loss2: 1.392309
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.647649 Loss1: 0.227202 Loss2: 1.420447
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547742 Loss1: 0.155204 Loss2: 1.392538
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547638 Loss1: 0.160308 Loss2: 1.387330
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.312027 Loss1: 0.507939 Loss2: 1.804088
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477245 Loss1: 0.085516 Loss2: 1.391729
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612832 Loss1: 0.268581 Loss2: 1.344251
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446357 Loss1: 0.070043 Loss2: 1.376314
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.542273 Loss1: 0.197659 Loss2: 1.344614
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.490822 Loss1: 0.153036 Loss2: 1.337785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.453676 Loss1: 0.110145 Loss2: 1.343531 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445428 Loss1: 0.070330 Loss2: 1.375098
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463289 Loss1: 0.132486 Loss2: 1.330803
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416512 Loss1: 0.089816 Loss2: 1.326696
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409443 Loss1: 0.087972 Loss2: 1.321471
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415700 Loss1: 0.101043 Loss2: 1.314657
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.426706 Loss1: 0.107210 Loss2: 1.319496
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.192364 Loss1: 0.342191 Loss2: 1.850173
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.693216 Loss1: 0.346158 Loss2: 1.347058
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.674341 Loss1: 0.277123 Loss2: 1.397217
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556341 Loss1: 0.190958 Loss2: 1.365383
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461074 Loss1: 0.102016 Loss2: 1.359058
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.447159 Loss1: 0.538947 Loss2: 1.908212
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449517 Loss1: 0.098680 Loss2: 1.350837
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.410520 Loss1: 0.067959 Loss2: 1.342561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398081 Loss1: 0.058030 Loss2: 1.340051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.375910 Loss1: 0.040289 Loss2: 1.335622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369663 Loss1: 0.042688 Loss2: 1.326976 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429253 Loss1: 0.077023 Loss2: 1.352230 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374938 Loss1: 0.033249 Loss2: 1.341690 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.688462 Loss1: 0.374270 Loss2: 1.314192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511501 Loss1: 0.151239 Loss2: 1.360262 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445161 Loss1: 0.124763 Loss2: 1.320398 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414075 Loss1: 0.089901 Loss2: 1.324174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.383303 Loss1: 0.080501 Loss2: 1.302803 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.346176 Loss1: 0.044408 Loss2: 1.301768 [repeated 2x across cluster]
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459165 Loss1: 0.106721 Loss2: 1.352445 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454840 Loss1: 0.108983 Loss2: 1.345857 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.318705 Loss1: 0.495629 Loss2: 1.823076
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417684 Loss1: 0.068773 Loss2: 1.348911
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.615713 Loss1: 0.266948 Loss2: 1.348765
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418514 Loss1: 0.073890 Loss2: 1.344624
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482868 Loss1: 0.135199 Loss2: 1.347668 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416864 Loss1: 0.089053 Loss2: 1.327811 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424111 Loss1: 0.094405 Loss2: 1.329706
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.197223 Loss1: 0.350996 Loss2: 1.846226
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.595731 Loss1: 0.225516 Loss2: 1.370214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.546905 Loss1: 0.163022 Loss2: 1.383883 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.514191 Loss1: 0.149885 Loss2: 1.364306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470293 Loss1: 0.103566 Loss2: 1.366726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.087939 Loss1: 0.334447 Loss2: 1.753492 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.575654 Loss1: 0.271084 Loss2: 1.304571 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.513579 Loss1: 0.191248 Loss2: 1.322331 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995404
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.400177 Loss1: 0.108585 Loss2: 1.291592 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.366190 Loss1: 0.077443 Loss2: 1.288747 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384717 Loss1: 0.093341 Loss2: 1.291376
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.252345 Loss1: 0.385905 Loss2: 1.866440
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.591804 Loss1: 0.231284 Loss2: 1.360521
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402133 Loss1: 0.108300 Loss2: 1.293833
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.554093 Loss1: 0.186165 Loss2: 1.367927
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391514 Loss1: 0.099322 Loss2: 1.292192
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.480671 Loss1: 0.119012 Loss2: 1.361660 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433694 Loss1: 0.081939 Loss2: 1.351755 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389122 Loss1: 0.038751 Loss2: 1.350371
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.346515 Loss1: 0.482788 Loss2: 1.863727
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.411145 Loss1: 0.072399 Loss2: 1.338746
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.645983 Loss1: 0.304210 Loss2: 1.341774
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425717 Loss1: 0.082156 Loss2: 1.343561
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551946 Loss1: 0.194801 Loss2: 1.357146
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542073 Loss1: 0.194916 Loss2: 1.347156
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.442756 Loss1: 0.098877 Loss2: 1.343878
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.405398 Loss1: 0.077450 Loss2: 1.327948
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383978 Loss1: 0.059802 Loss2: 1.324176
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.200968 Loss1: 0.377126 Loss2: 1.823842
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.360553 Loss1: 0.044582 Loss2: 1.315971
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.564187 Loss1: 0.225624 Loss2: 1.338563
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.375271 Loss1: 0.061433 Loss2: 1.313838
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578500 Loss1: 0.220797 Loss2: 1.357703
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371512 Loss1: 0.057133 Loss2: 1.314379
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.427825 Loss1: 0.092093 Loss2: 1.335732 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.376328 Loss1: 0.049995 Loss2: 1.326333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.394361 Loss1: 0.073573 Loss2: 1.320788
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.165693 Loss1: 0.355977 Loss2: 1.809716
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.680185 Loss1: 0.322714 Loss2: 1.357471 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352644 Loss1: 0.039198 Loss2: 1.313446
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618933 Loss1: 0.223023 Loss2: 1.395910
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551930 Loss1: 0.180076 Loss2: 1.371854
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574960 Loss1: 0.204042 Loss2: 1.370918
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472352 Loss1: 0.091044 Loss2: 1.381307
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441995 Loss1: 0.086478 Loss2: 1.355517
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.639500 Loss1: 0.603836 Loss2: 2.035663
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413950 Loss1: 0.060967 Loss2: 1.352983
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.869558 Loss1: 0.422573 Loss2: 1.446985
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.752888 Loss1: 0.264870 Loss2: 1.488018
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404877 Loss1: 0.057796 Loss2: 1.347082
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404444 Loss1: 0.060987 Loss2: 1.343457 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.559244 Loss1: 0.117097 Loss2: 1.442147 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501399 Loss1: 0.077670 Loss2: 1.423729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.492016 Loss1: 0.070424 Loss2: 1.421592 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979567
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.542765 Loss1: 0.169863 Loss2: 1.372901 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.477966 Loss1: 0.117786 Loss2: 1.360180 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.428238 Loss1: 0.071737 Loss2: 1.356501 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402032 Loss1: 0.056694 Loss2: 1.345338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.381608 Loss1: 0.044047 Loss2: 1.337562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.364221 Loss1: 0.031156 Loss2: 1.333065 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379608 Loss1: 0.053581 Loss2: 1.326027 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458398 Loss1: 0.088177 Loss2: 1.370222 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.477657 Loss1: 0.113601 Loss2: 1.364056 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983259
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.627361 Loss1: 0.284168 Loss2: 1.343192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527276 Loss1: 0.171328 Loss2: 1.355948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.286630 Loss1: 0.462829 Loss2: 1.823801 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.718264 Loss1: 0.367415 Loss2: 1.350849 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393969 Loss1: 0.080814 Loss2: 1.313155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.349181 Loss1: 0.036591 Loss2: 1.312589 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350243 Loss1: 0.043637 Loss2: 1.306607 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.442473 Loss1: 0.087938 Loss2: 1.354534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.393626 Loss1: 0.064757 Loss2: 1.328869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.075781 Loss1: 0.353238 Loss2: 1.722543 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.536901 Loss1: 0.208302 Loss2: 1.328599 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.450454 Loss1: 0.143922 Loss2: 1.306532 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.418594 Loss1: 0.121190 Loss2: 1.297404 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.398536 Loss1: 0.108808 Loss2: 1.289728 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382641 Loss1: 0.087762 Loss2: 1.294879 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.357016 Loss1: 0.078697 Loss2: 1.278319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.340876 Loss1: 0.063468 Loss2: 1.277408 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456092 Loss1: 0.132425 Loss2: 1.323668 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399540 Loss1: 0.080079 Loss2: 1.319461 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354515 Loss1: 0.042110 Loss2: 1.312405
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.338177 Loss1: 0.426180 Loss2: 1.911997
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.746212 Loss1: 0.333320 Loss2: 1.412892
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618742 Loss1: 0.171364 Loss2: 1.447378
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.583944 Loss1: 0.160908 Loss2: 1.423036
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.543223 Loss1: 0.132908 Loss2: 1.410315
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.211151 Loss1: 0.400966 Loss2: 1.810185
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.565174 Loss1: 0.249243 Loss2: 1.315931 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.486797 Loss1: 0.154439 Loss2: 1.332358 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.428191 Loss1: 0.118576 Loss2: 1.309615 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.415759 Loss1: 0.108937 Loss2: 1.306822 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437622 Loss1: 0.049673 Loss2: 1.387949
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.391343 Loss1: 0.081851 Loss2: 1.309492
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406729 Loss1: 0.106364 Loss2: 1.300365
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.380545 Loss1: 0.080549 Loss2: 1.299997
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386422 Loss1: 0.088399 Loss2: 1.298023
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369373 Loss1: 0.074920 Loss2: 1.294453
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.223853 Loss1: 0.416035 Loss2: 1.807818
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.607380 Loss1: 0.247358 Loss2: 1.360023
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.577673 Loss1: 0.188656 Loss2: 1.389018
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.530063 Loss1: 0.176892 Loss2: 1.353172
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.254838 Loss1: 0.425397 Loss2: 1.829442
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.648055 Loss1: 0.312071 Loss2: 1.335984 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.566080 Loss1: 0.196300 Loss2: 1.369780 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.505359 Loss1: 0.161938 Loss2: 1.343422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.489965 Loss1: 0.152708 Loss2: 1.337257 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431555 Loss1: 0.097336 Loss2: 1.334219 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+DEBUG flwr 2023-10-13 00:49:13,185 | server.py:236 | fit_round 170 received 50 results and 0 failures
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392195 Loss1: 0.072230 Loss2: 1.319965 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.365933 Loss1: 0.043651 Loss2: 1.322282 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.340518 Loss1: 0.517232 Loss2: 1.823286 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.690640 Loss1: 0.287909 Loss2: 1.402731 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527441 Loss1: 0.195837 Loss2: 1.331603
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.386574 Loss1: 0.516071 Loss2: 1.870503
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.627176 Loss1: 0.276219 Loss2: 1.350957 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.541380 Loss1: 0.163729 Loss2: 1.377651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.460020 Loss1: 0.122698 Loss2: 1.337323 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.415933 Loss1: 0.086536 Loss2: 1.329396 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.396459 Loss1: 0.070450 Loss2: 1.326009 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.381116 Loss1: 0.061221 Loss2: 1.319894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354447 Loss1: 0.045292 Loss2: 1.309156 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.361114 Loss1: 0.466246 Loss2: 1.894868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.603116 Loss1: 0.174755 Loss2: 1.428361 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.276768 Loss1: 0.448648 Loss2: 1.828120 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.552485 Loss1: 0.224687 Loss2: 1.327798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.550766 Loss1: 0.203416 Loss2: 1.347350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534848 Loss1: 0.181725 Loss2: 1.353122 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.425586 Loss1: 0.106512 Loss2: 1.319073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.436635 Loss1: 0.119889 Loss2: 1.316746 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.389339 Loss1: 0.072487 Loss2: 1.316853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.367893 Loss1: 0.060693 Loss2: 1.307200 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 00:49:13,185][flwr][DEBUG] - fit_round 170 received 50 results and 0 failures
+INFO flwr 2023-10-13 00:49:55,800 | server.py:125 | fit progress: (170, 2.275574233966133, {'accuracy': 0.6037}, 392303.578070072)
+>> Test accuracy: 0.603700
+[2023-10-13 00:49:55,800][flwr][INFO] - fit progress: (170, 2.275574233966133, {'accuracy': 0.6037}, 392303.578070072)
+DEBUG flwr 2023-10-13 00:49:55,800 | server.py:173 | evaluate_round 170: strategy sampled 50 clients (out of 50)
+[2023-10-13 00:49:55,800][flwr][DEBUG] - evaluate_round 170: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 00:59:02,057 | server.py:187 | evaluate_round 170 received 50 results and 0 failures
+[2023-10-13 00:59:02,057][flwr][DEBUG] - evaluate_round 170 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 00:59:02,058 | server.py:222 | fit_round 171: strategy sampled 50 clients (out of 50)
+[2023-10-13 00:59:02,058][flwr][DEBUG] - fit_round 171: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.335487 Loss1: 0.443906 Loss2: 1.891581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591003 Loss1: 0.146861 Loss2: 1.444141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.306609 Loss1: 0.454723 Loss2: 1.851886
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.544153 Loss1: 0.144535 Loss2: 1.399618
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.632591 Loss1: 0.279213 Loss2: 1.353378
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.557761 Loss1: 0.155248 Loss2: 1.402513
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.630599 Loss1: 0.244609 Loss2: 1.385990
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.545162 Loss1: 0.141579 Loss2: 1.403583
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538919 Loss1: 0.168613 Loss2: 1.370307
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483913 Loss1: 0.082996 Loss2: 1.400917
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.511787 Loss1: 0.153088 Loss2: 1.358700
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474636 Loss1: 0.084876 Loss2: 1.389761
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470250 Loss1: 0.085964 Loss2: 1.384286 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.457147 Loss1: 0.066988 Loss2: 1.390158 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422574 Loss1: 0.075727 Loss2: 1.346847 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.194453 Loss1: 0.335254 Loss2: 1.859199 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609959 Loss1: 0.177525 Loss2: 1.432434 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551078 Loss1: 0.162497 Loss2: 1.388581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525461 Loss1: 0.132250 Loss2: 1.393211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.698784 Loss1: 0.217701 Loss2: 1.481083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.626838 Loss1: 0.148173 Loss2: 1.478665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.579740 Loss1: 0.113278 Loss2: 1.466462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.572314 Loss1: 0.118361 Loss2: 1.453952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.567068 Loss1: 0.109710 Loss2: 1.457358 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453974 Loss1: 0.075520 Loss2: 1.378454
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.582519 Loss1: 0.123509 Loss2: 1.459010
+(DefaultActor pid=3765) >> Training accuracy: 0.986213
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.541123 Loss1: 0.083721 Loss2: 1.457402
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.249180 Loss1: 0.404578 Loss2: 1.844603
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.565253 Loss1: 0.239305 Loss2: 1.325948
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.482598 Loss1: 0.134390 Loss2: 1.348209
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.460849 Loss1: 0.131001 Loss2: 1.329847
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.277462 Loss1: 0.414900 Loss2: 1.862562
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.421290 Loss1: 0.104859 Loss2: 1.316431
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.682820 Loss1: 0.311421 Loss2: 1.371400
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.406331 Loss1: 0.092547 Loss2: 1.313784
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.610211 Loss1: 0.211445 Loss2: 1.398766
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414257 Loss1: 0.104411 Loss2: 1.309846
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.545238 Loss1: 0.183497 Loss2: 1.361741
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405421 Loss1: 0.093926 Loss2: 1.311495
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520751 Loss1: 0.155056 Loss2: 1.365695
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406632 Loss1: 0.099674 Loss2: 1.306958
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519115 Loss1: 0.138332 Loss2: 1.380783
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401502 Loss1: 0.093310 Loss2: 1.308192
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448106 Loss1: 0.090495 Loss2: 1.357611
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473493 Loss1: 0.117700 Loss2: 1.355794
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449933 Loss1: 0.092764 Loss2: 1.357169
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399609 Loss1: 0.052531 Loss2: 1.347078
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.238580 Loss1: 0.399904 Loss2: 1.838677
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.582073 Loss1: 0.239254 Loss2: 1.342819
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.495462 Loss1: 0.145067 Loss2: 1.350395
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.472970 Loss1: 0.124021 Loss2: 1.348949
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.242532 Loss1: 0.391830 Loss2: 1.850702
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.620239 Loss1: 0.265849 Loss2: 1.354389 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.591481 Loss1: 0.227473 Loss2: 1.364008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.520122 Loss1: 0.155048 Loss2: 1.365073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502935 Loss1: 0.145148 Loss2: 1.357787 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.453779 Loss1: 0.099251 Loss2: 1.354528 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406601 Loss1: 0.071421 Loss2: 1.335181
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433550 Loss1: 0.089339 Loss2: 1.344211
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396214 Loss1: 0.053442 Loss2: 1.342773
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408174 Loss1: 0.076936 Loss2: 1.331238
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409583 Loss1: 0.072351 Loss2: 1.337231
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.156689 Loss1: 0.418383 Loss2: 1.738306
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.632918 Loss1: 0.347436 Loss2: 1.285481
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618161 Loss1: 0.263426 Loss2: 1.354734
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490578 Loss1: 0.197504 Loss2: 1.293074
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.252432 Loss1: 0.417333 Loss2: 1.835099
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659581 Loss1: 0.303691 Loss2: 1.355890 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.597934 Loss1: 0.199084 Loss2: 1.398850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.515254 Loss1: 0.151404 Loss2: 1.363850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.489535 Loss1: 0.133530 Loss2: 1.356005 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498845 Loss1: 0.134499 Loss2: 1.364345 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441484 Loss1: 0.091596 Loss2: 1.349888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435370 Loss1: 0.086759 Loss2: 1.348612 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.253823 Loss1: 0.392681 Loss2: 1.861142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.575048 Loss1: 0.208119 Loss2: 1.366929 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520632 Loss1: 0.164690 Loss2: 1.355942
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.398096 Loss1: 0.535517 Loss2: 1.862578
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480507 Loss1: 0.140105 Loss2: 1.340402
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.588194 Loss1: 0.254276 Loss2: 1.333918
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460672 Loss1: 0.120958 Loss2: 1.339714
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592097 Loss1: 0.233722 Loss2: 1.358376
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.407309 Loss1: 0.072365 Loss2: 1.334944
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.479343 Loss1: 0.145640 Loss2: 1.333703
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.539537 Loss1: 0.214302 Loss2: 1.325235
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434532 Loss1: 0.102526 Loss2: 1.332006
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512595 Loss1: 0.178165 Loss2: 1.334430
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429209 Loss1: 0.094715 Loss2: 1.334494
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471894 Loss1: 0.138977 Loss2: 1.332916
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430105 Loss1: 0.095627 Loss2: 1.334478
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412902 Loss1: 0.094956 Loss2: 1.317946 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.557300 Loss1: 0.531302 Loss2: 2.025998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.691212 Loss1: 0.285708 Loss2: 1.405504 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.563956 Loss1: 0.166587 Loss2: 1.397369 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.618960 Loss1: 0.259292 Loss2: 1.359668 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.487585 Loss1: 0.095148 Loss2: 1.392438 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.542496 Loss1: 0.166942 Loss2: 1.375554 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997396
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.586670 Loss1: 0.182135 Loss2: 1.404535 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453255 Loss1: 0.085153 Loss2: 1.368102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444289 Loss1: 0.079146 Loss2: 1.365143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441843 Loss1: 0.085387 Loss2: 1.356456 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.469384 Loss1: 0.132438 Loss2: 1.336945 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.396735 Loss1: 0.074942 Loss2: 1.321792 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.378756 Loss1: 0.064143 Loss2: 1.314613
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.368145 Loss1: 0.413062 Loss2: 1.955083
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.333558 Loss1: 0.027948 Loss2: 1.305611
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.750899 Loss1: 0.316877 Loss2: 1.434022
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.333320 Loss1: 0.034846 Loss2: 1.298474
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.672692 Loss1: 0.173523 Loss2: 1.499169
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.329296 Loss1: 0.032009 Loss2: 1.297287
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.593736 Loss1: 0.153624 Loss2: 1.440112
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.588496 Loss1: 0.146482 Loss2: 1.442014
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539305 Loss1: 0.101342 Loss2: 1.437963
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508723 Loss1: 0.077600 Loss2: 1.431123
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485958 Loss1: 0.064327 Loss2: 1.421631
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.525638 Loss1: 0.104760 Loss2: 1.420877
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.159092 Loss1: 0.379539 Loss2: 1.779553
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.484119 Loss1: 0.067636 Loss2: 1.416483
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622113 Loss1: 0.294871 Loss2: 1.327242
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.542858 Loss1: 0.172207 Loss2: 1.370651
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.456376 Loss1: 0.135147 Loss2: 1.321229
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.432616 Loss1: 0.112011 Loss2: 1.320605
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.411677 Loss1: 0.092948 Loss2: 1.318728
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.276714 Loss1: 0.438402 Loss2: 1.838312
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.707953 Loss1: 0.326256 Loss2: 1.381697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.611421 Loss1: 0.185991 Loss2: 1.425430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526712 Loss1: 0.154255 Loss2: 1.372457 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.480958 Loss1: 0.101227 Loss2: 1.379731 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426775 Loss1: 0.065717 Loss2: 1.361059 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389001 Loss1: 0.045026 Loss2: 1.343975 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362840 Loss1: 0.027876 Loss2: 1.334964 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.619930 Loss1: 0.195239 Loss2: 1.424691 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493789 Loss1: 0.092789 Loss2: 1.401000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464210 Loss1: 0.068617 Loss2: 1.395593
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.334384 Loss1: 0.443690 Loss2: 1.890694
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.724138 Loss1: 0.337965 Loss2: 1.386173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.736212 Loss1: 0.285582 Loss2: 1.450629 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.632195 Loss1: 0.219747 Loss2: 1.412448 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464030 Loss1: 0.075519 Loss2: 1.388511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433010 Loss1: 0.055850 Loss2: 1.377160
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.139921 Loss1: 0.344732 Loss2: 1.795189
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.561004 Loss1: 0.224063 Loss2: 1.336941 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.501587 Loss1: 0.148054 Loss2: 1.353534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.512838 Loss1: 0.171657 Loss2: 1.341181 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404173 Loss1: 0.080329 Loss2: 1.323844 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384289 Loss1: 0.064103 Loss2: 1.320186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.673708 Loss1: 0.213696 Loss2: 1.460012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.615334 Loss1: 0.178478 Loss2: 1.436856 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527811 Loss1: 0.109781 Loss2: 1.418029 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.502457 Loss1: 0.091870 Loss2: 1.410587 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.477589 Loss1: 0.074523 Loss2: 1.403066
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.489860 Loss1: 0.537712 Loss2: 1.952148
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.718744 Loss1: 0.362816 Loss2: 1.355927
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457530 Loss1: 0.059793 Loss2: 1.397737
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.505943 Loss1: 0.150946 Loss2: 1.354997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.459438 Loss1: 0.106423 Loss2: 1.353016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.359222 Loss1: 0.436481 Loss2: 1.922741 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401349 Loss1: 0.067430 Loss2: 1.333919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405775 Loss1: 0.077445 Loss2: 1.328330 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.534684 Loss1: 0.111148 Loss2: 1.423536 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.517492 Loss1: 0.095328 Loss2: 1.422164 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.258611 Loss1: 0.466651 Loss2: 1.791960
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484065 Loss1: 0.066780 Loss2: 1.417285
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.591920 Loss1: 0.269525 Loss2: 1.322396
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479081 Loss1: 0.062043 Loss2: 1.417038
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.506759 Loss1: 0.166416 Loss2: 1.340342
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450976 Loss1: 0.040417 Loss2: 1.410559
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.407791 Loss1: 0.105093 Loss2: 1.302697 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.377887 Loss1: 0.084586 Loss2: 1.293301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.333313 Loss1: 0.050283 Loss2: 1.283030
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.304507 Loss1: 0.444517 Loss2: 1.859991
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.767426 Loss1: 0.371623 Loss2: 1.395803 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.665772 Loss1: 0.216555 Loss2: 1.449217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.596825 Loss1: 0.188398 Loss2: 1.408427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490727 Loss1: 0.106747 Loss2: 1.383981 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473361 Loss1: 0.088511 Loss2: 1.384850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479695 Loss1: 0.098749 Loss2: 1.380945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410219 Loss1: 0.036906 Loss2: 1.373313 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511918 Loss1: 0.107899 Loss2: 1.404019 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447965 Loss1: 0.065039 Loss2: 1.382926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.280077 Loss1: 0.424501 Loss2: 1.855576
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433542 Loss1: 0.057203 Loss2: 1.376339
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.696214 Loss1: 0.332590 Loss2: 1.363624
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437459 Loss1: 0.061148 Loss2: 1.376311
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.438795 Loss1: 0.079801 Loss2: 1.358995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456262 Loss1: 0.099892 Loss2: 1.356370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451666 Loss1: 0.100931 Loss2: 1.350735
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.273316 Loss1: 0.459875 Loss2: 1.813440
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460049 Loss1: 0.107339 Loss2: 1.352710
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.614145 Loss1: 0.286734 Loss2: 1.327412
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421477 Loss1: 0.076454 Loss2: 1.345024
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.589000 Loss1: 0.204315 Loss2: 1.384684
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398251 Loss1: 0.059379 Loss2: 1.338872
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489278 Loss1: 0.162759 Loss2: 1.326518
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479649 Loss1: 0.154427 Loss2: 1.325222
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.423833 Loss1: 0.096079 Loss2: 1.327754
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395189 Loss1: 0.077233 Loss2: 1.317956
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384976 Loss1: 0.072499 Loss2: 1.312477
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395852 Loss1: 0.081261 Loss2: 1.314591
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.244902 Loss1: 0.374163 Loss2: 1.870739
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378781 Loss1: 0.071992 Loss2: 1.306788
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.577093 Loss1: 0.226092 Loss2: 1.351001
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.535066 Loss1: 0.176817 Loss2: 1.358249
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523548 Loss1: 0.146935 Loss2: 1.376613
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483714 Loss1: 0.136843 Loss2: 1.346871
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.434046 Loss1: 0.088241 Loss2: 1.345805
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.398192 Loss1: 0.053077 Loss2: 1.345116
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.296232 Loss1: 0.500870 Loss2: 1.795362
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381410 Loss1: 0.045272 Loss2: 1.336138
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.628208 Loss1: 0.321501 Loss2: 1.306707
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362646 Loss1: 0.032011 Loss2: 1.330636
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551321 Loss1: 0.201344 Loss2: 1.349977
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354305 Loss1: 0.027556 Loss2: 1.326750
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.598318 Loss1: 0.260429 Loss2: 1.337889
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534245 Loss1: 0.197725 Loss2: 1.336520
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.483590 Loss1: 0.146911 Loss2: 1.336679
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.412908 Loss1: 0.097557 Loss2: 1.315350
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.406924 Loss1: 0.090490 Loss2: 1.316435
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.360012 Loss1: 0.050675 Loss2: 1.309337
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.193313 Loss1: 0.336000 Loss2: 1.857313
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.342664 Loss1: 0.039297 Loss2: 1.303367
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.582160 Loss1: 0.212276 Loss2: 1.369883
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556127 Loss1: 0.171592 Loss2: 1.384535
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.533166 Loss1: 0.155333 Loss2: 1.377833
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.517453 Loss1: 0.141789 Loss2: 1.375664
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478519 Loss1: 0.103725 Loss2: 1.374794
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.396868 Loss1: 0.443197 Loss2: 1.953671
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490698 Loss1: 0.127204 Loss2: 1.363494
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411389 Loss1: 0.049160 Loss2: 1.362229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410620 Loss1: 0.056739 Loss2: 1.353881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403519 Loss1: 0.057168 Loss2: 1.346351 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.514556 Loss1: 0.103363 Loss2: 1.411194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461508 Loss1: 0.057322 Loss2: 1.404185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441886 Loss1: 0.044690 Loss2: 1.397197
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.476313 Loss1: 0.578872 Loss2: 1.897441
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437937 Loss1: 0.047792 Loss2: 1.390145
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.723792 Loss1: 0.363292 Loss2: 1.360500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.630910 Loss1: 0.247542 Loss2: 1.383368
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553577 Loss1: 0.163166 Loss2: 1.390411
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.468679 Loss1: 0.109559 Loss2: 1.359120
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.417976 Loss1: 0.069584 Loss2: 1.348392
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.398099 Loss1: 0.053096 Loss2: 1.345003
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.408266 Loss1: 0.509519 Loss2: 1.898748
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.643266 Loss1: 0.289846 Loss2: 1.353420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.626403 Loss1: 0.239143 Loss2: 1.387260 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998798
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.477179 Loss1: 0.114845 Loss2: 1.362334 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439231 Loss1: 0.081040 Loss2: 1.358191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452088 Loss1: 0.096603 Loss2: 1.355485
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.347625 Loss1: 0.433982 Loss2: 1.913642
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.723407 Loss1: 0.291125 Loss2: 1.432282 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.697724 Loss1: 0.233427 Loss2: 1.464296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.560290 Loss1: 0.124024 Loss2: 1.436266 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.516163 Loss1: 0.096775 Loss2: 1.419388 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491922 Loss1: 0.074893 Loss2: 1.417029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.492125 Loss1: 0.077492 Loss2: 1.414633 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454956 Loss1: 0.044714 Loss2: 1.410243 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.432898 Loss1: 0.087768 Loss2: 1.345130 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.402612 Loss1: 0.075889 Loss2: 1.326723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.289584 Loss1: 0.414097 Loss2: 1.875487
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.365717 Loss1: 0.039054 Loss2: 1.326663
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.343689 Loss1: 0.026902 Loss2: 1.316787
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653403 Loss1: 0.258312 Loss2: 1.395091
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600871 Loss1: 0.194052 Loss2: 1.406819
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557723 Loss1: 0.164312 Loss2: 1.393411
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.533694 Loss1: 0.143367 Loss2: 1.390327
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537778 Loss1: 0.137708 Loss2: 1.400070
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.267433 Loss1: 0.414140 Loss2: 1.853293
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.479641 Loss1: 0.094805 Loss2: 1.384836
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450876 Loss1: 0.070242 Loss2: 1.380634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431709 Loss1: 0.057616 Loss2: 1.374093 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453158 Loss1: 0.081542 Loss2: 1.371616 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460136 Loss1: 0.087580 Loss2: 1.372555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424793 Loss1: 0.074935 Loss2: 1.349858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.306887 Loss1: 0.414212 Loss2: 1.892674 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.650873 Loss1: 0.210058 Loss2: 1.440815 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.538618 Loss1: 0.151621 Loss2: 1.386997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.359786 Loss1: 0.465698 Loss2: 1.894088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.841464 Loss1: 0.446371 Loss2: 1.395093 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.750547 Loss1: 0.284615 Loss2: 1.465932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.594946 Loss1: 0.201787 Loss2: 1.393159 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.556893 Loss1: 0.156343 Loss2: 1.400550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488117 Loss1: 0.105260 Loss2: 1.382858 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.416328 Loss1: 0.046228 Loss2: 1.370100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.394644 Loss1: 0.035930 Loss2: 1.358714 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.503757 Loss1: 0.170204 Loss2: 1.333553 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441982 Loss1: 0.128789 Loss2: 1.313193 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446343 Loss1: 0.124475 Loss2: 1.321868
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.298825 Loss1: 0.442321 Loss2: 1.856504
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.610354 Loss1: 0.253054 Loss2: 1.357300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.607791 Loss1: 0.223019 Loss2: 1.384772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524400 Loss1: 0.163447 Loss2: 1.360953 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.540363 Loss1: 0.180309 Loss2: 1.360054 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433294 Loss1: 0.087530 Loss2: 1.345765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385870 Loss1: 0.051241 Loss2: 1.334629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366011 Loss1: 0.037953 Loss2: 1.328058 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.567648 Loss1: 0.186212 Loss2: 1.381436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506893 Loss1: 0.143702 Loss2: 1.363192 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.497390 Loss1: 0.134779 Loss2: 1.362610
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.270949 Loss1: 0.432149 Loss2: 1.838800
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.637939 Loss1: 0.289336 Loss2: 1.348603 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.598201 Loss1: 0.216782 Loss2: 1.381419 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581221 Loss1: 0.212402 Loss2: 1.368819 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525462 Loss1: 0.178885 Loss2: 1.346577 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.423746 Loss1: 0.086883 Loss2: 1.336863 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.393777 Loss1: 0.064279 Loss2: 1.329498 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380539 Loss1: 0.054027 Loss2: 1.326513 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.573955 Loss1: 0.193789 Loss2: 1.380167 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.499715 Loss1: 0.137797 Loss2: 1.361918 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.551088 Loss1: 0.179053 Loss2: 1.372035
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.273974 Loss1: 0.421347 Loss2: 1.852627
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.568346 Loss1: 0.233320 Loss2: 1.335026 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.479840 Loss1: 0.137028 Loss2: 1.342812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.438891 Loss1: 0.103607 Loss2: 1.335284 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.421840 Loss1: 0.097113 Loss2: 1.324727 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.374168 Loss1: 0.058285 Loss2: 1.315883 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 01:27:47,557 | server.py:236 | fit_round 171 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381505 Loss1: 0.066024 Loss2: 1.315481 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.394796 Loss1: 0.079728 Loss2: 1.315068 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.546066 Loss1: 0.180338 Loss2: 1.365728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500225 Loss1: 0.147247 Loss2: 1.352979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421420 Loss1: 0.079875 Loss2: 1.341545
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.405007 Loss1: 0.506303 Loss2: 1.898704
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.682005 Loss1: 0.299315 Loss2: 1.382690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.666771 Loss1: 0.252943 Loss2: 1.413829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.567017 Loss1: 0.182065 Loss2: 1.384952 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362750 Loss1: 0.040617 Loss2: 1.322133
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524509 Loss1: 0.142931 Loss2: 1.381578
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476718 Loss1: 0.101587 Loss2: 1.375130
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418993 Loss1: 0.059316 Loss2: 1.359677
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417126 Loss1: 0.056277 Loss2: 1.360849
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390146 Loss1: 0.038534 Loss2: 1.351613
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.399865 Loss1: 0.541918 Loss2: 1.857947
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.376434 Loss1: 0.027323 Loss2: 1.349110
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629102 Loss1: 0.217312 Loss2: 1.411790 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.478235 Loss1: 0.113823 Loss2: 1.364412 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425243 Loss1: 0.067667 Loss2: 1.357576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433992 Loss1: 0.081873 Loss2: 1.352119 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 01:27:47,557][flwr][DEBUG] - fit_round 171 received 50 results and 0 failures
+INFO flwr 2023-10-13 01:28:28,887 | server.py:125 | fit progress: (171, 2.2737315053376146, {'accuracy': 0.6067}, 394616.66555445397)
+>> Test accuracy: 0.606700
+[2023-10-13 01:28:28,887][flwr][INFO] - fit progress: (171, 2.2737315053376146, {'accuracy': 0.6067}, 394616.66555445397)
+DEBUG flwr 2023-10-13 01:28:28,887 | server.py:173 | evaluate_round 171: strategy sampled 50 clients (out of 50)
+[2023-10-13 01:28:28,887][flwr][DEBUG] - evaluate_round 171: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 01:37:33,269 | server.py:187 | evaluate_round 171 received 50 results and 0 failures
+[2023-10-13 01:37:33,269][flwr][DEBUG] - evaluate_round 171 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 01:37:33,269 | server.py:222 | fit_round 172: strategy sampled 50 clients (out of 50)
+[2023-10-13 01:37:33,269][flwr][DEBUG] - fit_round 172: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.214392 Loss1: 0.365235 Loss2: 1.849157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.558037 Loss1: 0.161128 Loss2: 1.396909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.225112 Loss1: 0.415776 Loss2: 1.809337
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.503342 Loss1: 0.112057 Loss2: 1.391286
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.530272 Loss1: 0.211535 Loss2: 1.318737
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479777 Loss1: 0.102052 Loss2: 1.377725
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.497699 Loss1: 0.168879 Loss2: 1.328820
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473943 Loss1: 0.096060 Loss2: 1.377883
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.426638 Loss1: 0.103860 Loss2: 1.322777
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458285 Loss1: 0.084523 Loss2: 1.373762
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454012 Loss1: 0.081925 Loss2: 1.372087 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438347 Loss1: 0.065193 Loss2: 1.373154 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426053 Loss1: 0.060673 Loss2: 1.365381 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.361415 Loss1: 0.072863 Loss2: 1.288552 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.144352 Loss1: 0.348044 Loss2: 1.796308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.481290 Loss1: 0.138632 Loss2: 1.342657 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.324666 Loss1: 0.459365 Loss2: 1.865300
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.446403 Loss1: 0.123394 Loss2: 1.323008
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.416166 Loss1: 0.103740 Loss2: 1.312426 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.403635 Loss1: 0.081417 Loss2: 1.322218 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.421497 Loss1: 0.107613 Loss2: 1.313883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415221 Loss1: 0.098264 Loss2: 1.316956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430498 Loss1: 0.110509 Loss2: 1.319989 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429191 Loss1: 0.062792 Loss2: 1.366399 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983456
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414876 Loss1: 0.055038 Loss2: 1.359838 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.241366 Loss1: 0.396845 Loss2: 1.844521
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.585283 Loss1: 0.239784 Loss2: 1.345499
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.552762 Loss1: 0.184091 Loss2: 1.368671
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.503242 Loss1: 0.146907 Loss2: 1.356335
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.368414 Loss1: 0.494875 Loss2: 1.873539
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.745458 Loss1: 0.368515 Loss2: 1.376943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605036 Loss1: 0.216680 Loss2: 1.388356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.544607 Loss1: 0.173361 Loss2: 1.371247 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.467775 Loss1: 0.102627 Loss2: 1.365148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.436447 Loss1: 0.079102 Loss2: 1.357345 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401330 Loss1: 0.071076 Loss2: 1.330254
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412987 Loss1: 0.063417 Loss2: 1.349570
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.402618 Loss1: 0.059087 Loss2: 1.343530
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383691 Loss1: 0.045222 Loss2: 1.338469
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393038 Loss1: 0.056815 Loss2: 1.336223
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.189939 Loss1: 0.407168 Loss2: 1.782772
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.585765 Loss1: 0.277965 Loss2: 1.307800
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.478669 Loss1: 0.156176 Loss2: 1.322493
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.442816 Loss1: 0.137120 Loss2: 1.305695
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.255634 Loss1: 0.461516 Loss2: 1.794119
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.607847 Loss1: 0.287652 Loss2: 1.320195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543116 Loss1: 0.180532 Loss2: 1.362584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.418489 Loss1: 0.101933 Loss2: 1.316555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.411576 Loss1: 0.100163 Loss2: 1.311413 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.392192 Loss1: 0.080129 Loss2: 1.312063 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.354899 Loss1: 0.050893 Loss2: 1.304006 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.332669 Loss1: 0.039811 Loss2: 1.292858 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.698617 Loss1: 0.328472 Loss2: 1.370144 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513013 Loss1: 0.154108 Loss2: 1.358905 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531937 Loss1: 0.173037 Loss2: 1.358900
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.386827 Loss1: 0.496394 Loss2: 1.890433
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477068 Loss1: 0.112206 Loss2: 1.364862
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.734576 Loss1: 0.347539 Loss2: 1.387038
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425321 Loss1: 0.072169 Loss2: 1.353152
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.611564 Loss1: 0.185834 Loss2: 1.425730
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.406692 Loss1: 0.059786 Loss2: 1.346906
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.575337 Loss1: 0.187918 Loss2: 1.387420
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.392033 Loss1: 0.054310 Loss2: 1.337723
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.582646 Loss1: 0.193874 Loss2: 1.388772
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387368 Loss1: 0.046940 Loss2: 1.340428
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.560102 Loss1: 0.158073 Loss2: 1.402029
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486708 Loss1: 0.106212 Loss2: 1.380496
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.494401 Loss1: 0.117315 Loss2: 1.377086
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473917 Loss1: 0.095687 Loss2: 1.378229
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439742 Loss1: 0.070604 Loss2: 1.369137
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.142247 Loss1: 0.362636 Loss2: 1.779610
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.602363 Loss1: 0.263674 Loss2: 1.338689
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.559702 Loss1: 0.179278 Loss2: 1.380424
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.486833 Loss1: 0.141241 Loss2: 1.345592
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.230158 Loss1: 0.407623 Loss2: 1.822535
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612243 Loss1: 0.294691 Loss2: 1.317552 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.540507 Loss1: 0.196857 Loss2: 1.343650 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.452039 Loss1: 0.129639 Loss2: 1.322400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.405781 Loss1: 0.094273 Loss2: 1.311509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.406864 Loss1: 0.091148 Loss2: 1.315716 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406216 Loss1: 0.073369 Loss2: 1.332846
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.376778 Loss1: 0.073149 Loss2: 1.303629
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372372 Loss1: 0.074123 Loss2: 1.298249
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376643 Loss1: 0.079054 Loss2: 1.297589
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.358316 Loss1: 0.065565 Loss2: 1.292751
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.595305 Loss1: 0.644383 Loss2: 1.950921
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.715519 Loss1: 0.360454 Loss2: 1.355065
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568959 Loss1: 0.183123 Loss2: 1.385835
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.479004 Loss1: 0.131933 Loss2: 1.347071
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.245875 Loss1: 0.414754 Loss2: 1.831121
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.633247 Loss1: 0.291469 Loss2: 1.341778 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418690 Loss1: 0.078379 Loss2: 1.340311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.383616 Loss1: 0.055731 Loss2: 1.327886 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390466 Loss1: 0.068404 Loss2: 1.322062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408249 Loss1: 0.091553 Loss2: 1.316696 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463002 Loss1: 0.108263 Loss2: 1.354739 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388464 Loss1: 0.053830 Loss2: 1.334634 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385913 Loss1: 0.054241 Loss2: 1.331672
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.229142 Loss1: 0.390850 Loss2: 1.838293
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.588174 Loss1: 0.244017 Loss2: 1.344156
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568669 Loss1: 0.207059 Loss2: 1.361610
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.477536 Loss1: 0.119426 Loss2: 1.358110
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.429691 Loss1: 0.085844 Loss2: 1.343847
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.375132 Loss1: 0.487994 Loss2: 1.887139
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.666060 Loss1: 0.276728 Loss2: 1.389332 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.559780 Loss1: 0.155315 Loss2: 1.404465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530583 Loss1: 0.150527 Loss2: 1.380056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469894 Loss1: 0.094584 Loss2: 1.375311 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.475960 Loss1: 0.104022 Loss2: 1.371937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437034 Loss1: 0.068715 Loss2: 1.368319 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446949 Loss1: 0.086261 Loss2: 1.360687 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.553707 Loss1: 0.219101 Loss2: 1.334606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562474 Loss1: 0.208221 Loss2: 1.354253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487154 Loss1: 0.140675 Loss2: 1.346479
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.247585 Loss1: 0.359460 Loss2: 1.888125
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.648502 Loss1: 0.270046 Loss2: 1.378456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564458 Loss1: 0.165926 Loss2: 1.398532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.512752 Loss1: 0.129290 Loss2: 1.383462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529089 Loss1: 0.158887 Loss2: 1.370202 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.518691 Loss1: 0.132124 Loss2: 1.386567 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.444667 Loss1: 0.074839 Loss2: 1.369828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434219 Loss1: 0.076510 Loss2: 1.357709 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.571067 Loss1: 0.267801 Loss2: 1.303266 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498777 Loss1: 0.182617 Loss2: 1.316160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.447997 Loss1: 0.135459 Loss2: 1.312538
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.192961 Loss1: 0.398559 Loss2: 1.794402
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.634890 Loss1: 0.290602 Loss2: 1.344288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.500077 Loss1: 0.124250 Loss2: 1.375827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478887 Loss1: 0.137790 Loss2: 1.341097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.451992 Loss1: 0.112898 Loss2: 1.339094 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.398695 Loss1: 0.065031 Loss2: 1.333664 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379342 Loss1: 0.060810 Loss2: 1.318532 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.294251 Loss1: 0.483014 Loss2: 1.811238
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374805 Loss1: 0.054892 Loss2: 1.319913
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.581025 Loss1: 0.203522 Loss2: 1.377503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.539277 Loss1: 0.211226 Loss2: 1.328051 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450394 Loss1: 0.112563 Loss2: 1.337831
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.267498 Loss1: 0.402659 Loss2: 1.864839
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.393292 Loss1: 0.077298 Loss2: 1.315995
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.582999 Loss1: 0.224880 Loss2: 1.358120
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.349084 Loss1: 0.038645 Loss2: 1.310439
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.553696 Loss1: 0.191595 Loss2: 1.362101
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.328902 Loss1: 0.032521 Loss2: 1.296380
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555503 Loss1: 0.193101 Loss2: 1.362402
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.322214 Loss1: 0.028036 Loss2: 1.294179
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.482386 Loss1: 0.121860 Loss2: 1.360526
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420148 Loss1: 0.072967 Loss2: 1.347182
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.396334 Loss1: 0.059191 Loss2: 1.337143
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398513 Loss1: 0.065003 Loss2: 1.333509
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398295 Loss1: 0.070029 Loss2: 1.328267
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389017 Loss1: 0.053341 Loss2: 1.335677
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.268968 Loss1: 0.422733 Loss2: 1.846235
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.683493 Loss1: 0.316501 Loss2: 1.366992
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.666431 Loss1: 0.236262 Loss2: 1.430169
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601093 Loss1: 0.229098 Loss2: 1.371995
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.565857 Loss1: 0.186821 Loss2: 1.379036
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.408724 Loss1: 0.497578 Loss2: 1.911146
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.500318 Loss1: 0.132504 Loss2: 1.367813
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.477812 Loss1: 0.117499 Loss2: 1.360314 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434453 Loss1: 0.078455 Loss2: 1.355998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460660 Loss1: 0.109443 Loss2: 1.351217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459864 Loss1: 0.113013 Loss2: 1.346851 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414159 Loss1: 0.067318 Loss2: 1.346841 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.381913 Loss1: 0.048363 Loss2: 1.333550 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.646992 Loss1: 0.308186 Loss2: 1.338806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.464384 Loss1: 0.116380 Loss2: 1.348004 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.444385 Loss1: 0.112411 Loss2: 1.331974 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.423039 Loss1: 0.087869 Loss2: 1.335170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408577 Loss1: 0.076254 Loss2: 1.332323 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398962 Loss1: 0.067433 Loss2: 1.331529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433251 Loss1: 0.100244 Loss2: 1.333007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381662 Loss1: 0.053394 Loss2: 1.328268 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392868 Loss1: 0.034348 Loss2: 1.358520 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.239329 Loss1: 0.406835 Loss2: 1.832494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.552756 Loss1: 0.193348 Loss2: 1.359409 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491356 Loss1: 0.131212 Loss2: 1.360145
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.275477 Loss1: 0.440100 Loss2: 1.835377
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.634074 Loss1: 0.277297 Loss2: 1.356777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.546849 Loss1: 0.167337 Loss2: 1.379512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478724 Loss1: 0.123787 Loss2: 1.354937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502878 Loss1: 0.150719 Loss2: 1.352158 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500667 Loss1: 0.140179 Loss2: 1.360488 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445497 Loss1: 0.109347 Loss2: 1.336150
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455398 Loss1: 0.114009 Loss2: 1.341389
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470948 Loss1: 0.123760 Loss2: 1.347188
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.444023 Loss1: 0.098493 Loss2: 1.345531
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410900 Loss1: 0.069249 Loss2: 1.341651
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.117408 Loss1: 0.345376 Loss2: 1.772033
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.554323 Loss1: 0.240339 Loss2: 1.313984
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.562709 Loss1: 0.210126 Loss2: 1.352583
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541221 Loss1: 0.224248 Loss2: 1.316973
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.294016 Loss1: 0.432879 Loss2: 1.861137
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.748515 Loss1: 0.358368 Loss2: 1.390147
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513364 Loss1: 0.179193 Loss2: 1.334172
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.685448 Loss1: 0.221598 Loss2: 1.463850
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489490 Loss1: 0.167175 Loss2: 1.322314
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561973 Loss1: 0.181782 Loss2: 1.380191
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461725 Loss1: 0.145805 Loss2: 1.315920
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542523 Loss1: 0.149165 Loss2: 1.393359
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418630 Loss1: 0.105876 Loss2: 1.312754
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390439 Loss1: 0.087737 Loss2: 1.302703 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356912 Loss1: 0.054717 Loss2: 1.302195 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439343 Loss1: 0.067960 Loss2: 1.371384 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.171549 Loss1: 0.360303 Loss2: 1.811246 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.473751 Loss1: 0.102552 Loss2: 1.371199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.214603 Loss1: 0.367580 Loss2: 1.847023
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.443024 Loss1: 0.097906 Loss2: 1.345118
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.626913 Loss1: 0.273665 Loss2: 1.353248
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.437719 Loss1: 0.091741 Loss2: 1.345978
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.420229 Loss1: 0.075632 Loss2: 1.344597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428100 Loss1: 0.084976 Loss2: 1.343124 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404420 Loss1: 0.065199 Loss2: 1.339221 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402710 Loss1: 0.063764 Loss2: 1.338946 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389593 Loss1: 0.055612 Loss2: 1.333981 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433354 Loss1: 0.094683 Loss2: 1.338672 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.395035 Loss1: 0.520689 Loss2: 1.874346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.636247 Loss1: 0.222434 Loss2: 1.413812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.521994 Loss1: 0.567668 Loss2: 1.954326
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.562431 Loss1: 0.197330 Loss2: 1.365101
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521480 Loss1: 0.151071 Loss2: 1.370409 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451435 Loss1: 0.087088 Loss2: 1.364346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435607 Loss1: 0.081286 Loss2: 1.354321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499165 Loss1: 0.151856 Loss2: 1.347309 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406126 Loss1: 0.064019 Loss2: 1.342107 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399373 Loss1: 0.058167 Loss2: 1.341206 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.402313 Loss1: 0.492291 Loss2: 1.910022 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990885
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573249 Loss1: 0.172085 Loss2: 1.401164 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523026 Loss1: 0.133066 Loss2: 1.389960
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.281928 Loss1: 0.483442 Loss2: 1.798486
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.682876 Loss1: 0.349475 Loss2: 1.333401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622935 Loss1: 0.239564 Loss2: 1.383371 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528865 Loss1: 0.187239 Loss2: 1.341627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502611 Loss1: 0.155136 Loss2: 1.347476 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500259 Loss1: 0.167355 Loss2: 1.332904 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381069 Loss1: 0.029855 Loss2: 1.351214
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433655 Loss1: 0.104531 Loss2: 1.329125
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.385122 Loss1: 0.059195 Loss2: 1.325926
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.382447 Loss1: 0.063891 Loss2: 1.318556
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394456 Loss1: 0.076070 Loss2: 1.318386
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.285267 Loss1: 0.404296 Loss2: 1.880970
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.566922 Loss1: 0.209427 Loss2: 1.357495
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.517634 Loss1: 0.159431 Loss2: 1.358203
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513607 Loss1: 0.140289 Loss2: 1.373318
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.069545 Loss1: 0.359323 Loss2: 1.710222
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.506944 Loss1: 0.245292 Loss2: 1.261652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.476938 Loss1: 0.189197 Loss2: 1.287741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.470265 Loss1: 0.191555 Loss2: 1.278710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.419371 Loss1: 0.150069 Loss2: 1.269303 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.374247 Loss1: 0.102598 Loss2: 1.271649 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.322685 Loss1: 0.062834 Loss2: 1.259851 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.307643 Loss1: 0.051028 Loss2: 1.256615 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.356486 Loss1: 0.463252 Loss2: 1.893235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655659 Loss1: 0.203672 Loss2: 1.451987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.313043 Loss1: 0.487412 Loss2: 1.825631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.698082 Loss1: 0.355719 Loss2: 1.342363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.549722 Loss1: 0.171274 Loss2: 1.378448 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.506208 Loss1: 0.165765 Loss2: 1.340443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501232 Loss1: 0.157596 Loss2: 1.343637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.504406 Loss1: 0.161247 Loss2: 1.343158 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458047 Loss1: 0.126709 Loss2: 1.331337 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367545 Loss1: 0.044504 Loss2: 1.323042 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.655885 Loss1: 0.313751 Loss2: 1.342134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529323 Loss1: 0.165345 Loss2: 1.363978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475012 Loss1: 0.138840 Loss2: 1.336171
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.171411 Loss1: 0.382395 Loss2: 1.789015
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.586508 Loss1: 0.239427 Loss2: 1.347081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.627906 Loss1: 0.247475 Loss2: 1.380431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.524796 Loss1: 0.168940 Loss2: 1.355856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429984 Loss1: 0.102695 Loss2: 1.327289 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451548 Loss1: 0.097384 Loss2: 1.354163 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460215 Loss1: 0.116113 Loss2: 1.344101 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.102106 Loss1: 0.388181 Loss2: 1.713925
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410296 Loss1: 0.065202 Loss2: 1.345093
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.410911 Loss1: 0.133416 Loss2: 1.277495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.332129 Loss1: 0.080673 Loss2: 1.251455 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.342793 Loss1: 0.099551 Loss2: 1.243242
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.362272 Loss1: 0.457499 Loss2: 1.904773
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.311209 Loss1: 0.066099 Loss2: 1.245110
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.742021 Loss1: 0.393928 Loss2: 1.348094
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.302877 Loss1: 0.064783 Loss2: 1.238094
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.644204 Loss1: 0.229539 Loss2: 1.414665
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.313028 Loss1: 0.072862 Loss2: 1.240165
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.533468 Loss1: 0.161379 Loss2: 1.372089
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475141 Loss1: 0.114553 Loss2: 1.360588
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.275541 Loss1: 0.037317 Loss2: 1.238224
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.421066 Loss1: 0.070745 Loss2: 1.350321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415117 Loss1: 0.076073 Loss2: 1.339043 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398384 Loss1: 0.055476 Loss2: 1.342909
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.209437 Loss1: 0.424168 Loss2: 1.785269
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.600950 Loss1: 0.282990 Loss2: 1.317959
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.506499 Loss1: 0.181192 Loss2: 1.325307
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.439191 Loss1: 0.120602 Loss2: 1.318589
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.436375 Loss1: 0.127540 Loss2: 1.308835
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.357813 Loss1: 0.429585 Loss2: 1.928229
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.691545 Loss1: 0.258787 Loss2: 1.432759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.649581 Loss1: 0.200830 Loss2: 1.448751 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.597166 Loss1: 0.153821 Loss2: 1.443345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.583121 Loss1: 0.165673 Loss2: 1.417449 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549437 Loss1: 0.122844 Loss2: 1.426593 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486144 Loss1: 0.075078 Loss2: 1.411066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.492443 Loss1: 0.086806 Loss2: 1.405637 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.698253 Loss1: 0.292387 Loss2: 1.405866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.550987 Loss1: 0.150735 Loss2: 1.400251 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.505856 Loss1: 0.107900 Loss2: 1.397956
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.408741 Loss1: 0.513066 Loss2: 1.895675
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.666470 Loss1: 0.279281 Loss2: 1.387189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565465 Loss1: 0.185662 Loss2: 1.379803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530782 Loss1: 0.153739 Loss2: 1.377043 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.549250 Loss1: 0.173197 Loss2: 1.376054 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 02:06:34,321 | server.py:236 | fit_round 172 received 50 results and 0 failures
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501997 Loss1: 0.127939 Loss2: 1.374058 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440581 Loss1: 0.079517 Loss2: 1.361064 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397360 Loss1: 0.048130 Loss2: 1.349231 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.683835 Loss1: 0.284302 Loss2: 1.399532 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525374 Loss1: 0.171526 Loss2: 1.353848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473430 Loss1: 0.129122 Loss2: 1.344308
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.315409 Loss1: 0.478316 Loss2: 1.837093
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449341 Loss1: 0.111976 Loss2: 1.337365
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653696 Loss1: 0.308933 Loss2: 1.344763
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568363 Loss1: 0.179475 Loss2: 1.388888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497829 Loss1: 0.146287 Loss2: 1.351542 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455174 Loss1: 0.105451 Loss2: 1.349723 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.417860 Loss1: 0.088296 Loss2: 1.329565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435214 Loss1: 0.100581 Loss2: 1.334633 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 02:06:34,321][flwr][DEBUG] - fit_round 172 received 50 results and 0 failures
+INFO flwr 2023-10-13 02:07:15,038 | server.py:125 | fit progress: (172, 2.27676078781914, {'accuracy': 0.6068}, 396942.816128839)
+>> Test accuracy: 0.606800
+[2023-10-13 02:07:15,038][flwr][INFO] - fit progress: (172, 2.27676078781914, {'accuracy': 0.6068}, 396942.816128839)
+DEBUG flwr 2023-10-13 02:07:15,038 | server.py:173 | evaluate_round 172: strategy sampled 50 clients (out of 50)
+[2023-10-13 02:07:15,038][flwr][DEBUG] - evaluate_round 172: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 02:16:22,112 | server.py:187 | evaluate_round 172 received 50 results and 0 failures
+[2023-10-13 02:16:22,112][flwr][DEBUG] - evaluate_round 172 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 02:16:22,112 | server.py:222 | fit_round 173: strategy sampled 50 clients (out of 50)
+[2023-10-13 02:16:22,112][flwr][DEBUG] - fit_round 173: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.228823 Loss1: 0.370823 Loss2: 1.858000 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.515252 Loss1: 0.136114 Loss2: 1.379138 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.461290 Loss1: 0.108244 Loss2: 1.353046
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.249642 Loss1: 0.347229 Loss2: 1.902413
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.438328 Loss1: 0.097135 Loss2: 1.341193
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.662446 Loss1: 0.270695 Loss2: 1.391751
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.390846 Loss1: 0.058818 Loss2: 1.332028
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.642712 Loss1: 0.211270 Loss2: 1.431442
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.417333 Loss1: 0.084539 Loss2: 1.332794
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.702589 Loss1: 0.286829 Loss2: 1.415761
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441825 Loss1: 0.111535 Loss2: 1.330290
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.581531 Loss1: 0.166074 Loss2: 1.415457
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417539 Loss1: 0.077104 Loss2: 1.340436
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.549766 Loss1: 0.146242 Loss2: 1.403524
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420198 Loss1: 0.084556 Loss2: 1.335641
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545704 Loss1: 0.141429 Loss2: 1.404275
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469427 Loss1: 0.082597 Loss2: 1.386829
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464099 Loss1: 0.086257 Loss2: 1.377842
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415930 Loss1: 0.039682 Loss2: 1.376248
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.129898 Loss1: 0.308459 Loss2: 1.821439
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.560038 Loss1: 0.214606 Loss2: 1.345432
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.489450 Loss1: 0.123629 Loss2: 1.365821
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.327069 Loss1: 0.441885 Loss2: 1.885184
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.548630 Loss1: 0.206444 Loss2: 1.342186
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.589392 Loss1: 0.228328 Loss2: 1.361064
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473632 Loss1: 0.124733 Loss2: 1.348899
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565298 Loss1: 0.183930 Loss2: 1.381367
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.453952 Loss1: 0.114515 Loss2: 1.339437
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431871 Loss1: 0.105067 Loss2: 1.326804 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400514 Loss1: 0.065168 Loss2: 1.335346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378951 Loss1: 0.051530 Loss2: 1.327421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368268 Loss1: 0.047113 Loss2: 1.321155 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383116 Loss1: 0.049819 Loss2: 1.333297 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.305216 Loss1: 0.453060 Loss2: 1.852156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618655 Loss1: 0.235054 Loss2: 1.383601 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559917 Loss1: 0.192415 Loss2: 1.367502
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.251810 Loss1: 0.403787 Loss2: 1.848023
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507722 Loss1: 0.148519 Loss2: 1.359203
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.629902 Loss1: 0.265273 Loss2: 1.364629
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461741 Loss1: 0.108038 Loss2: 1.353703
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563383 Loss1: 0.187485 Loss2: 1.375898
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459328 Loss1: 0.109183 Loss2: 1.350145
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577328 Loss1: 0.193735 Loss2: 1.383593
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423683 Loss1: 0.077441 Loss2: 1.346242
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555123 Loss1: 0.183032 Loss2: 1.372091
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422528 Loss1: 0.081247 Loss2: 1.341281
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523705 Loss1: 0.157453 Loss2: 1.366253
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378290 Loss1: 0.046083 Loss2: 1.332208
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463708 Loss1: 0.095569 Loss2: 1.368139
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445414 Loss1: 0.083883 Loss2: 1.361531
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405444 Loss1: 0.052183 Loss2: 1.353261
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372383 Loss1: 0.028861 Loss2: 1.343522
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.199244 Loss1: 0.306830 Loss2: 1.892414
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.647145 Loss1: 0.231291 Loss2: 1.415854
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.640363 Loss1: 0.195292 Loss2: 1.445071
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.221136 Loss1: 0.354876 Loss2: 1.866260
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.604998 Loss1: 0.181712 Loss2: 1.423286
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.594509 Loss1: 0.208381 Loss2: 1.386128
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.604910 Loss1: 0.178946 Loss2: 1.425964
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520888 Loss1: 0.104205 Loss2: 1.416683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514698 Loss1: 0.104417 Loss2: 1.410281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.471993 Loss1: 0.062621 Loss2: 1.409372 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423768 Loss1: 0.023293 Loss2: 1.400475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420870 Loss1: 0.032373 Loss2: 1.388497 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994485
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480364 Loss1: 0.101400 Loss2: 1.378964 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.276789 Loss1: 0.362517 Loss2: 1.914272 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.632820 Loss1: 0.234504 Loss2: 1.398316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578577 Loss1: 0.184189 Loss2: 1.394388
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.326640 Loss1: 0.405361 Loss2: 1.921278
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.666851 Loss1: 0.262023 Loss2: 1.404828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633406 Loss1: 0.178394 Loss2: 1.455012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537380 Loss1: 0.124624 Loss2: 1.412757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522372 Loss1: 0.120013 Loss2: 1.402359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.525290 Loss1: 0.119782 Loss2: 1.405508 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484789 Loss1: 0.078310 Loss2: 1.406478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458431 Loss1: 0.065774 Loss2: 1.392657 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.251919 Loss1: 0.437154 Loss2: 1.814765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.545262 Loss1: 0.197194 Loss2: 1.348067 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.521281 Loss1: 0.186649 Loss2: 1.334632
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.412577 Loss1: 0.509978 Loss2: 1.902599
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.662709 Loss1: 0.310029 Loss2: 1.352680
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.502499 Loss1: 0.179468 Loss2: 1.323031
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.415677 Loss1: 0.091709 Loss2: 1.323968 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.401901 Loss1: 0.089468 Loss2: 1.312433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.399248 Loss1: 0.089990 Loss2: 1.309258 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384337 Loss1: 0.071603 Loss2: 1.312734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.362143 Loss1: 0.056008 Loss2: 1.306135 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402586 Loss1: 0.062048 Loss2: 1.340539 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.400763 Loss1: 0.448129 Loss2: 1.952634
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.706096 Loss1: 0.279938 Loss2: 1.426158
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.650325 Loss1: 0.196121 Loss2: 1.454204
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581474 Loss1: 0.150642 Loss2: 1.430833
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.255799 Loss1: 0.420267 Loss2: 1.835532
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.626316 Loss1: 0.309330 Loss2: 1.316986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.570792 Loss1: 0.188988 Loss2: 1.381804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.548298 Loss1: 0.216852 Loss2: 1.331446 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.592805 Loss1: 0.236050 Loss2: 1.356755 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539829 Loss1: 0.189228 Loss2: 1.350601 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458907 Loss1: 0.057801 Loss2: 1.401106
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502224 Loss1: 0.172613 Loss2: 1.329611
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428930 Loss1: 0.097910 Loss2: 1.331021
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389108 Loss1: 0.068794 Loss2: 1.320314
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370243 Loss1: 0.055737 Loss2: 1.314505
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.273957 Loss1: 0.413004 Loss2: 1.860953
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.658726 Loss1: 0.308321 Loss2: 1.350405
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561110 Loss1: 0.177767 Loss2: 1.383344
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.505690 Loss1: 0.149069 Loss2: 1.356621
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.306393 Loss1: 0.433763 Loss2: 1.872630
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659778 Loss1: 0.296369 Loss2: 1.363408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.652701 Loss1: 0.242205 Loss2: 1.410495 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559792 Loss1: 0.192475 Loss2: 1.367316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484765 Loss1: 0.121081 Loss2: 1.363684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496167 Loss1: 0.131009 Loss2: 1.365158 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446069 Loss1: 0.085970 Loss2: 1.360099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402273 Loss1: 0.057532 Loss2: 1.344741 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.303073 Loss1: 0.438572 Loss2: 1.864502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.686169 Loss1: 0.244061 Loss2: 1.442108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601814 Loss1: 0.212021 Loss2: 1.389793
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.230693 Loss1: 0.361658 Loss2: 1.869035
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.633936 Loss1: 0.247784 Loss2: 1.386152 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.587626 Loss1: 0.164877 Loss2: 1.422749 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.547011 Loss1: 0.153618 Loss2: 1.393393 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500449 Loss1: 0.111987 Loss2: 1.388462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463328 Loss1: 0.075091 Loss2: 1.388237 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447084 Loss1: 0.068748 Loss2: 1.378336 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428496 Loss1: 0.049231 Loss2: 1.379264 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.645507 Loss1: 0.268377 Loss2: 1.377129 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.471969 Loss1: 0.097210 Loss2: 1.374759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.438711 Loss1: 0.073832 Loss2: 1.364879
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.355209 Loss1: 0.427852 Loss2: 1.927357
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416847 Loss1: 0.051128 Loss2: 1.365719
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.665789 Loss1: 0.270905 Loss2: 1.394884
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.437277 Loss1: 0.084908 Loss2: 1.352369
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.623485 Loss1: 0.192580 Loss2: 1.430905
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415012 Loss1: 0.060693 Loss2: 1.354319
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.583733 Loss1: 0.167505 Loss2: 1.416228
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.383800 Loss1: 0.029809 Loss2: 1.353991
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.580422 Loss1: 0.170673 Loss2: 1.409748
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381701 Loss1: 0.033755 Loss2: 1.347945
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513056 Loss1: 0.097301 Loss2: 1.415755
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509330 Loss1: 0.114664 Loss2: 1.394665
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459678 Loss1: 0.064712 Loss2: 1.394966
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.477250 Loss1: 0.086391 Loss2: 1.390859
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442547 Loss1: 0.056592 Loss2: 1.385955
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.212115 Loss1: 0.359239 Loss2: 1.852876
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.682353 Loss1: 0.297057 Loss2: 1.385296
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.655872 Loss1: 0.225612 Loss2: 1.430259
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542871 Loss1: 0.146503 Loss2: 1.396369
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.140867 Loss1: 0.359984 Loss2: 1.780884
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.528087 Loss1: 0.218073 Loss2: 1.310014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.470329 Loss1: 0.152943 Loss2: 1.317387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.455174 Loss1: 0.141824 Loss2: 1.313350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.432061 Loss1: 0.122158 Loss2: 1.309903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.400845 Loss1: 0.091987 Loss2: 1.308857 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372496 Loss1: 0.065527 Loss2: 1.306969 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.319192 Loss1: 0.024022 Loss2: 1.295170 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.999023
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.672394 Loss1: 0.343930 Loss2: 1.328464 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.448857 Loss1: 0.126790 Loss2: 1.322067 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.246072 Loss1: 0.367143 Loss2: 1.878929
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.428458 Loss1: 0.098801 Loss2: 1.329657
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.600941 Loss1: 0.224541 Loss2: 1.376400
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.401696 Loss1: 0.083398 Loss2: 1.318298
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565969 Loss1: 0.169924 Loss2: 1.396046
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.378031 Loss1: 0.069061 Loss2: 1.308970
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.499249 Loss1: 0.120194 Loss2: 1.379056
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.360329 Loss1: 0.058012 Loss2: 1.302317
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446917 Loss1: 0.086554 Loss2: 1.360362
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.349209 Loss1: 0.048195 Loss2: 1.301014
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427457 Loss1: 0.068711 Loss2: 1.358746
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.338174 Loss1: 0.045623 Loss2: 1.292551
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412146 Loss1: 0.061824 Loss2: 1.350322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391731 Loss1: 0.045350 Loss2: 1.346381 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.582414 Loss1: 0.240243 Loss2: 1.342171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.538956 Loss1: 0.173724 Loss2: 1.365233 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521248 Loss1: 0.172051 Loss2: 1.349197
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.308543 Loss1: 0.456036 Loss2: 1.852507
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.467202 Loss1: 0.120946 Loss2: 1.346256
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.641427 Loss1: 0.290577 Loss2: 1.350850
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442556 Loss1: 0.101667 Loss2: 1.340889
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.587201 Loss1: 0.186272 Loss2: 1.400929
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425209 Loss1: 0.087242 Loss2: 1.337967
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.590694 Loss1: 0.223750 Loss2: 1.366943
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.392736 Loss1: 0.062226 Loss2: 1.330511
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574543 Loss1: 0.200791 Loss2: 1.373752
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380448 Loss1: 0.054796 Loss2: 1.325651
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501201 Loss1: 0.127408 Loss2: 1.373794
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.479818 Loss1: 0.115888 Loss2: 1.363930
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.463019 Loss1: 0.102830 Loss2: 1.360189
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431129 Loss1: 0.075144 Loss2: 1.355985
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388323 Loss1: 0.048071 Loss2: 1.340252
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.284684 Loss1: 0.458580 Loss2: 1.826104
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.679661 Loss1: 0.324346 Loss2: 1.355315
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.560760 Loss1: 0.175750 Loss2: 1.385010
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.483521 Loss1: 0.143035 Loss2: 1.340486
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.477801 Loss1: 0.140083 Loss2: 1.337718
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.426807 Loss1: 0.089711 Loss2: 1.337096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390641 Loss1: 0.061018 Loss2: 1.329623 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414643 Loss1: 0.094985 Loss2: 1.319658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.362659 Loss1: 0.040636 Loss2: 1.322023 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.362133 Loss1: 0.047535 Loss2: 1.314598 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483582 Loss1: 0.110441 Loss2: 1.373141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452556 Loss1: 0.080700 Loss2: 1.371856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448094 Loss1: 0.082607 Loss2: 1.365487
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.255635 Loss1: 0.420517 Loss2: 1.835118
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.623098 Loss1: 0.282845 Loss2: 1.340253
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609404 Loss1: 0.231012 Loss2: 1.378392
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528859 Loss1: 0.182212 Loss2: 1.346647
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.439265 Loss1: 0.093973 Loss2: 1.345291
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.435014 Loss1: 0.093846 Loss2: 1.341168
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.314669 Loss1: 0.433532 Loss2: 1.881138
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439581 Loss1: 0.106893 Loss2: 1.332688
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.700336 Loss1: 0.322394 Loss2: 1.377942
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400113 Loss1: 0.067543 Loss2: 1.332570
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.619387 Loss1: 0.210207 Loss2: 1.409181
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377281 Loss1: 0.048424 Loss2: 1.328856
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.566811 Loss1: 0.177227 Loss2: 1.389584
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368427 Loss1: 0.046251 Loss2: 1.322176
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507913 Loss1: 0.136393 Loss2: 1.371520
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478491 Loss1: 0.112210 Loss2: 1.366281
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447228 Loss1: 0.077856 Loss2: 1.369372
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428609 Loss1: 0.061453 Loss2: 1.367156
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402944 Loss1: 0.048859 Loss2: 1.354084
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406717 Loss1: 0.060099 Loss2: 1.346617
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.244397 Loss1: 0.386394 Loss2: 1.858003
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.648888 Loss1: 0.294210 Loss2: 1.354677
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.548510 Loss1: 0.165021 Loss2: 1.383489
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.485024 Loss1: 0.119929 Loss2: 1.365095
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468174 Loss1: 0.118413 Loss2: 1.349762
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.501881 Loss1: 0.145910 Loss2: 1.355971
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.268787 Loss1: 0.440644 Loss2: 1.828143
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653703 Loss1: 0.279014 Loss2: 1.374690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.615898 Loss1: 0.215199 Loss2: 1.400699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576725 Loss1: 0.202073 Loss2: 1.374652 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522238 Loss1: 0.142383 Loss2: 1.379855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450134 Loss1: 0.090417 Loss2: 1.359716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419437 Loss1: 0.064929 Loss2: 1.354508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402558 Loss1: 0.051713 Loss2: 1.350846 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.452796 Loss1: 0.138638 Loss2: 1.314158 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.427682 Loss1: 0.117539 Loss2: 1.310143 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438526 Loss1: 0.119507 Loss2: 1.319019
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.241275 Loss1: 0.451807 Loss2: 1.789468
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.617677 Loss1: 0.292132 Loss2: 1.325545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568769 Loss1: 0.230419 Loss2: 1.338350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.502775 Loss1: 0.174089 Loss2: 1.328686 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358541 Loss1: 0.058005 Loss2: 1.300536
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455038 Loss1: 0.119464 Loss2: 1.335574
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443194 Loss1: 0.123184 Loss2: 1.320010
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419219 Loss1: 0.095909 Loss2: 1.323309
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.370445 Loss1: 0.059335 Loss2: 1.311110
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394039 Loss1: 0.085162 Loss2: 1.308877
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.290400 Loss1: 0.426898 Loss2: 1.863502
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.339215 Loss1: 0.039943 Loss2: 1.299272
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594931 Loss1: 0.207799 Loss2: 1.387132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.504620 Loss1: 0.138106 Loss2: 1.366514 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.496363 Loss1: 0.130228 Loss2: 1.366136
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.221806 Loss1: 0.403163 Loss2: 1.818643
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.454563 Loss1: 0.091905 Loss2: 1.362658
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.636580 Loss1: 0.309179 Loss2: 1.327402
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462386 Loss1: 0.098849 Loss2: 1.363536
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.554334 Loss1: 0.194668 Loss2: 1.359667
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.415318 Loss1: 0.059999 Loss2: 1.355319
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468633 Loss1: 0.127883 Loss2: 1.340750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.444832 Loss1: 0.090280 Loss2: 1.354552
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.444854 Loss1: 0.112876 Loss2: 1.331978
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456562 Loss1: 0.131181 Loss2: 1.325381
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453040 Loss1: 0.118946 Loss2: 1.334095
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424119 Loss1: 0.098089 Loss2: 1.326029
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383055 Loss1: 0.059526 Loss2: 1.323530
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.298282 Loss1: 0.436128 Loss2: 1.862154
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372549 Loss1: 0.052094 Loss2: 1.320455
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.617456 Loss1: 0.197354 Loss2: 1.420102 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534849 Loss1: 0.142220 Loss2: 1.392629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533935 Loss1: 0.143039 Loss2: 1.390895
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.292544 Loss1: 0.445174 Loss2: 1.847370
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463351 Loss1: 0.087614 Loss2: 1.375737
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.640108 Loss1: 0.313918 Loss2: 1.326190
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601957 Loss1: 0.234915 Loss2: 1.367042
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449756 Loss1: 0.082450 Loss2: 1.367306
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.448851 Loss1: 0.115460 Loss2: 1.333391
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438721 Loss1: 0.068280 Loss2: 1.370441
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.407290 Loss1: 0.088988 Loss2: 1.318302
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439106 Loss1: 0.073802 Loss2: 1.365304
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.367041 Loss1: 0.060233 Loss2: 1.306807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.351539 Loss1: 0.052775 Loss2: 1.298764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376656 Loss1: 0.079294 Loss2: 1.297362
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.437290 Loss1: 0.536750 Loss2: 1.900540
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.729552 Loss1: 0.370770 Loss2: 1.358781
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595921 Loss1: 0.202178 Loss2: 1.393742
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.465437 Loss1: 0.109250 Loss2: 1.356188
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.444851 Loss1: 0.094071 Loss2: 1.350780
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.401011 Loss1: 0.056651 Loss2: 1.344360
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.512823 Loss1: 0.517739 Loss2: 1.995084
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.727269 Loss1: 0.328212 Loss2: 1.399057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.617157 Loss1: 0.208094 Loss2: 1.409064 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.511299 Loss1: 0.108380 Loss2: 1.402919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397001 Loss1: 0.069367 Loss2: 1.327633
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495638 Loss1: 0.124937 Loss2: 1.370701
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470982 Loss1: 0.102472 Loss2: 1.368510
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453834 Loss1: 0.083760 Loss2: 1.370074
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453775 Loss1: 0.089695 Loss2: 1.364081
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422557 Loss1: 0.056290 Loss2: 1.366266
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379106 Loss1: 0.023086 Loss2: 1.356021
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.304279 Loss1: 0.418539 Loss2: 1.885740
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.714787 Loss1: 0.343147 Loss2: 1.371640
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634820 Loss1: 0.206987 Loss2: 1.427833
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573570 Loss1: 0.187274 Loss2: 1.386296
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531808 Loss1: 0.147746 Loss2: 1.384061
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.291808 Loss1: 0.392441 Loss2: 1.899367
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.722425 Loss1: 0.343236 Loss2: 1.379189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.611100 Loss1: 0.187961 Loss2: 1.423139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.577033 Loss1: 0.194055 Loss2: 1.382978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550365 Loss1: 0.151412 Loss2: 1.398953 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516963 Loss1: 0.124951 Loss2: 1.392012 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493802 Loss1: 0.111964 Loss2: 1.381838 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.452086 Loss1: 0.076470 Loss2: 1.375616 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.632344 Loss1: 0.280034 Loss2: 1.352310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533797 Loss1: 0.180481 Loss2: 1.353316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.467260 Loss1: 0.117812 Loss2: 1.349447
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.200760 Loss1: 0.400475 Loss2: 1.800285
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.525074 Loss1: 0.215670 Loss2: 1.309404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.519922 Loss1: 0.208846 Loss2: 1.311076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.437742 Loss1: 0.116647 Loss2: 1.321095 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.393133 Loss1: 0.093689 Loss2: 1.299444 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.386492 Loss1: 0.090060 Loss2: 1.296432 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.392125 Loss1: 0.096446 Loss2: 1.295679 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.312473 Loss1: 0.026663 Loss2: 1.285810 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.581731 Loss1: 0.235714 Loss2: 1.346017 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517549 Loss1: 0.151735 Loss2: 1.365813 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 02:44:52,111 | server.py:236 | fit_round 173 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.493705 Loss1: 0.504816 Loss2: 1.988889 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.673455 Loss1: 0.306412 Loss2: 1.367043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.391330 Loss1: 0.058702 Loss2: 1.332628
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.602709 Loss1: 0.230591 Loss2: 1.372118
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539798 Loss1: 0.143326 Loss2: 1.396473
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.397543 Loss1: 0.067517 Loss2: 1.330026
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402383 Loss1: 0.072904 Loss2: 1.329479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426293 Loss1: 0.101873 Loss2: 1.324421 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428646 Loss1: 0.072723 Loss2: 1.355923 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985677
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.469408 Loss1: 0.553554 Loss2: 1.915854 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574862 Loss1: 0.158858 Loss2: 1.416004 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.512688 Loss1: 0.129496 Loss2: 1.383192
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.426078 Loss1: 0.522225 Loss2: 1.903853
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.714241 Loss1: 0.356256 Loss2: 1.357985 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633171 Loss1: 0.220985 Loss2: 1.412187 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523353 Loss1: 0.153686 Loss2: 1.369667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.454688 Loss1: 0.097419 Loss2: 1.357269 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.424448 Loss1: 0.067497 Loss2: 1.356951 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408108 Loss1: 0.059924 Loss2: 1.348184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397638 Loss1: 0.064048 Loss2: 1.333591 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.394851 Loss1: 0.470484 Loss2: 1.924366 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.771152 Loss1: 0.257649 Loss2: 1.513503 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.624144 Loss1: 0.183873 Loss2: 1.440271
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.207071 Loss1: 0.331836 Loss2: 1.875234
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.605240 Loss1: 0.247455 Loss2: 1.357785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.571575 Loss1: 0.191240 Loss2: 1.380335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554738 Loss1: 0.174167 Loss2: 1.380571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.564500 Loss1: 0.194463 Loss2: 1.370038 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478391 Loss1: 0.106274 Loss2: 1.372117 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.464444 Loss1: 0.057162 Loss2: 1.407282
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471425 Loss1: 0.100149 Loss2: 1.371276
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467947 Loss1: 0.103091 Loss2: 1.364856
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424975 Loss1: 0.057863 Loss2: 1.367112
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405422 Loss1: 0.050094 Loss2: 1.355328
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 02:44:52,111][flwr][DEBUG] - fit_round 173 received 50 results and 0 failures
+INFO flwr 2023-10-13 02:45:34,454 | server.py:125 | fit progress: (173, 2.277818728559695, {'accuracy': 0.6063}, 399242.232187888)
+>> Test accuracy: 0.606300
+[2023-10-13 02:45:34,454][flwr][INFO] - fit progress: (173, 2.277818728559695, {'accuracy': 0.6063}, 399242.232187888)
+DEBUG flwr 2023-10-13 02:45:34,454 | server.py:173 | evaluate_round 173: strategy sampled 50 clients (out of 50)
+[2023-10-13 02:45:34,454][flwr][DEBUG] - evaluate_round 173: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 02:54:37,975 | server.py:187 | evaluate_round 173 received 50 results and 0 failures
+[2023-10-13 02:54:37,975][flwr][DEBUG] - evaluate_round 173 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 02:54:37,976 | server.py:222 | fit_round 174: strategy sampled 50 clients (out of 50)
+[2023-10-13 02:54:37,976][flwr][DEBUG] - fit_round 174: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.244869 Loss1: 0.404431 Loss2: 1.840437
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.705238 Loss1: 0.349820 Loss2: 1.355417
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.741721 Loss1: 0.298569 Loss2: 1.443152
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.593132 Loss1: 0.236513 Loss2: 1.356620
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.288203 Loss1: 0.458088 Loss2: 1.830114
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.545754 Loss1: 0.203502 Loss2: 1.342252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.515159 Loss1: 0.173228 Loss2: 1.341930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.465614 Loss1: 0.134399 Loss2: 1.331214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.457557 Loss1: 0.128246 Loss2: 1.329311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472387 Loss1: 0.144859 Loss2: 1.327527 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371622 Loss1: 0.041715 Loss2: 1.329907
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395954 Loss1: 0.076395 Loss2: 1.319559
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389862 Loss1: 0.070423 Loss2: 1.319439
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376657 Loss1: 0.061425 Loss2: 1.315232
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377852 Loss1: 0.068737 Loss2: 1.309115
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.176901 Loss1: 0.326808 Loss2: 1.850094
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.598411 Loss1: 0.225274 Loss2: 1.373137
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.510457 Loss1: 0.127077 Loss2: 1.383380
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491087 Loss1: 0.117763 Loss2: 1.373324
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.199855 Loss1: 0.377692 Loss2: 1.822163
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475668 Loss1: 0.116200 Loss2: 1.359468
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.672809 Loss1: 0.338289 Loss2: 1.334520
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443008 Loss1: 0.082226 Loss2: 1.360783
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.626279 Loss1: 0.244181 Loss2: 1.382098
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.412444 Loss1: 0.052686 Loss2: 1.359758
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521649 Loss1: 0.179681 Loss2: 1.341969
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423094 Loss1: 0.067172 Loss2: 1.355923
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555476 Loss1: 0.209424 Loss2: 1.346052
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405120 Loss1: 0.049288 Loss2: 1.355832
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490087 Loss1: 0.141507 Loss2: 1.348579
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.397796 Loss1: 0.050095 Loss2: 1.347701
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439381 Loss1: 0.106579 Loss2: 1.332802
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386910 Loss1: 0.062454 Loss2: 1.324456
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386064 Loss1: 0.055945 Loss2: 1.330119
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369774 Loss1: 0.048937 Loss2: 1.320837
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.327817 Loss1: 0.489806 Loss2: 1.838011
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.566847 Loss1: 0.227630 Loss2: 1.339217
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.482781 Loss1: 0.141607 Loss2: 1.341174
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.460128 Loss1: 0.132121 Loss2: 1.328007
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.394161 Loss1: 0.491483 Loss2: 1.902678
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.758295 Loss1: 0.399625 Loss2: 1.358670
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562455 Loss1: 0.230731 Loss2: 1.331724
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669498 Loss1: 0.236997 Loss2: 1.432501
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468579 Loss1: 0.124435 Loss2: 1.344144
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.596636 Loss1: 0.221800 Loss2: 1.374835
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.411711 Loss1: 0.088016 Loss2: 1.323696
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.364171 Loss1: 0.046277 Loss2: 1.317894 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.358125 Loss1: 0.048126 Loss2: 1.309998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.342671 Loss1: 0.037875 Loss2: 1.304796 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376534 Loss1: 0.041484 Loss2: 1.335049 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.232433 Loss1: 0.401525 Loss2: 1.830908 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563939 Loss1: 0.193708 Loss2: 1.370231 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541548 Loss1: 0.176584 Loss2: 1.364964
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.107666 Loss1: 0.345140 Loss2: 1.762526
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.513275 Loss1: 0.158378 Loss2: 1.354897
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.491828 Loss1: 0.211875 Loss2: 1.279953
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499820 Loss1: 0.145493 Loss2: 1.354327
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.487547 Loss1: 0.198661 Loss2: 1.288886
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.465576 Loss1: 0.113827 Loss2: 1.351749
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496111 Loss1: 0.186278 Loss2: 1.309833
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427055 Loss1: 0.078469 Loss2: 1.348586
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.389122 Loss1: 0.099163 Loss2: 1.289959
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.387760 Loss1: 0.048580 Loss2: 1.339180
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.344642 Loss1: 0.064672 Loss2: 1.279971
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.382222 Loss1: 0.049730 Loss2: 1.332493
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.390548 Loss1: 0.109482 Loss2: 1.281066
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.369987 Loss1: 0.089595 Loss2: 1.280392
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.351978 Loss1: 0.078060 Loss2: 1.273918
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351524 Loss1: 0.074300 Loss2: 1.277224
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.165618 Loss1: 0.420037 Loss2: 1.745581
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572647 Loss1: 0.276073 Loss2: 1.296575
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.528127 Loss1: 0.197186 Loss2: 1.330940
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.445219 Loss1: 0.137751 Loss2: 1.307468
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.147175 Loss1: 0.382967 Loss2: 1.764208
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.569925 Loss1: 0.261956 Loss2: 1.307969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.562374 Loss1: 0.221503 Loss2: 1.340872 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519941 Loss1: 0.168859 Loss2: 1.351082 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460954 Loss1: 0.142883 Loss2: 1.318072 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452465 Loss1: 0.132553 Loss2: 1.319912 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388797 Loss1: 0.070258 Loss2: 1.318538 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348619 Loss1: 0.051041 Loss2: 1.297579 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.602605 Loss1: 0.224728 Loss2: 1.377877 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489916 Loss1: 0.116846 Loss2: 1.373071 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.454955 Loss1: 0.092309 Loss2: 1.362647 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.421306 Loss1: 0.062182 Loss2: 1.359123 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404558 Loss1: 0.053382 Loss2: 1.351176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393748 Loss1: 0.053199 Loss2: 1.340549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.392162 Loss1: 0.050353 Loss2: 1.341809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396064 Loss1: 0.055641 Loss2: 1.340423 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389549 Loss1: 0.043823 Loss2: 1.345726 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361376 Loss1: 0.025365 Loss2: 1.336011 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.171084 Loss1: 0.373407 Loss2: 1.797677
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.561206 Loss1: 0.261495 Loss2: 1.299711
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.531766 Loss1: 0.204219 Loss2: 1.327547
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.431433 Loss1: 0.119902 Loss2: 1.311531
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.191744 Loss1: 0.375735 Loss2: 1.816009
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.558150 Loss1: 0.199328 Loss2: 1.358822 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.530148 Loss1: 0.147186 Loss2: 1.382962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.469516 Loss1: 0.107487 Loss2: 1.362029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.464770 Loss1: 0.112136 Loss2: 1.352635 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470283 Loss1: 0.114364 Loss2: 1.355919 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437989 Loss1: 0.086237 Loss2: 1.351752 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425362 Loss1: 0.077364 Loss2: 1.347999 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.559136 Loss1: 0.255153 Loss2: 1.303982 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.459590 Loss1: 0.138244 Loss2: 1.321347 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.421562 Loss1: 0.117518 Loss2: 1.304044
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.239605 Loss1: 0.454804 Loss2: 1.784801
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.372005 Loss1: 0.070384 Loss2: 1.301620
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.564492 Loss1: 0.252437 Loss2: 1.312055
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.371783 Loss1: 0.076828 Loss2: 1.294954
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.544882 Loss1: 0.207100 Loss2: 1.337782
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.355437 Loss1: 0.063058 Loss2: 1.292380
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.422033 Loss1: 0.105699 Loss2: 1.316334
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.362265 Loss1: 0.068849 Loss2: 1.293416
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.404354 Loss1: 0.095021 Loss2: 1.309333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.326263 Loss1: 0.040540 Loss2: 1.285723
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.359317 Loss1: 0.051364 Loss2: 1.307953
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.337026 Loss1: 0.041769 Loss2: 1.295256
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.315083 Loss1: 0.028588 Loss2: 1.286495
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.309491 Loss1: 0.031037 Loss2: 1.278454
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.299720 Loss1: 0.026854 Loss2: 1.272866
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.398246 Loss1: 0.513173 Loss2: 1.885073
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.540915 Loss1: 0.228040 Loss2: 1.312875
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.465622 Loss1: 0.159989 Loss2: 1.305633
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.440724 Loss1: 0.128418 Loss2: 1.312306
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.400337 Loss1: 0.109425 Loss2: 1.290912
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.360693 Loss1: 0.074392 Loss2: 1.286301
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.235768 Loss1: 0.408041 Loss2: 1.827727
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.343207 Loss1: 0.061673 Loss2: 1.281534
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.651257 Loss1: 0.301536 Loss2: 1.349721
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.607139 Loss1: 0.219012 Loss2: 1.388127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532899 Loss1: 0.179524 Loss2: 1.353375 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997596
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498743 Loss1: 0.146913 Loss2: 1.351830 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439025 Loss1: 0.102042 Loss2: 1.336982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442533 Loss1: 0.099373 Loss2: 1.343160
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.272140 Loss1: 0.447165 Loss2: 1.824975
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.670455 Loss1: 0.308315 Loss2: 1.362141 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576804 Loss1: 0.208915 Loss2: 1.367889 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480796 Loss1: 0.129883 Loss2: 1.350913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478675 Loss1: 0.117399 Loss2: 1.361276 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.474442 Loss1: 0.124923 Loss2: 1.349519 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399410 Loss1: 0.052164 Loss2: 1.347246 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396821 Loss1: 0.061158 Loss2: 1.335663 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452273 Loss1: 0.108358 Loss2: 1.343915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427931 Loss1: 0.091631 Loss2: 1.336300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.281706 Loss1: 0.377959 Loss2: 1.903746 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574059 Loss1: 0.175340 Loss2: 1.398719 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.447905 Loss1: 0.073287 Loss2: 1.374617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.405298 Loss1: 0.041215 Loss2: 1.364083
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.361425 Loss1: 0.469381 Loss2: 1.892044
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.778714 Loss1: 0.376958 Loss2: 1.401756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.691151 Loss1: 0.239373 Loss2: 1.451778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.619137 Loss1: 0.209078 Loss2: 1.410059 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.620962 Loss1: 0.207075 Loss2: 1.413887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.573571 Loss1: 0.160532 Loss2: 1.413039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.550605 Loss1: 0.143981 Loss2: 1.406624 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.490499 Loss1: 0.087892 Loss2: 1.402607 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.717233 Loss1: 0.254696 Loss2: 1.462537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.548815 Loss1: 0.153720 Loss2: 1.395095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504801 Loss1: 0.103826 Loss2: 1.400975
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.233438 Loss1: 0.400983 Loss2: 1.832454
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.609076 Loss1: 0.270172 Loss2: 1.338904 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.553090 Loss1: 0.190848 Loss2: 1.362242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.510327 Loss1: 0.166996 Loss2: 1.343331 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459740 Loss1: 0.073176 Loss2: 1.386564
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541897 Loss1: 0.198102 Loss2: 1.343795
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508415 Loss1: 0.146597 Loss2: 1.361818
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.517605 Loss1: 0.171709 Loss2: 1.345896
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424545 Loss1: 0.077416 Loss2: 1.347129
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431633 Loss1: 0.096071 Loss2: 1.335562
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.218946 Loss1: 0.390068 Loss2: 1.828878
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.384637 Loss1: 0.054822 Loss2: 1.329815
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594409 Loss1: 0.184676 Loss2: 1.409732 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.481463 Loss1: 0.105841 Loss2: 1.375622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471986 Loss1: 0.095343 Loss2: 1.376643
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.427605 Loss1: 0.530031 Loss2: 1.897574
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.658229 Loss1: 0.302662 Loss2: 1.355567
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455773 Loss1: 0.088641 Loss2: 1.367133
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.428709 Loss1: 0.061290 Loss2: 1.367419 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424341 Loss1: 0.060246 Loss2: 1.364095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405672 Loss1: 0.047832 Loss2: 1.357840 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442007 Loss1: 0.094840 Loss2: 1.347167 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413950 Loss1: 0.078807 Loss2: 1.335143 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.317527 Loss1: 0.477306 Loss2: 1.840221
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.680235 Loss1: 0.328661 Loss2: 1.351573
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.540979 Loss1: 0.160267 Loss2: 1.380712
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.493181 Loss1: 0.144199 Loss2: 1.348982
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.228922 Loss1: 0.403868 Loss2: 1.825054
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.646999 Loss1: 0.313571 Loss2: 1.333428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.602320 Loss1: 0.232471 Loss2: 1.369848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.603393 Loss1: 0.249083 Loss2: 1.354310 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483877 Loss1: 0.138992 Loss2: 1.344886 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468727 Loss1: 0.137017 Loss2: 1.331710 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430321 Loss1: 0.095344 Loss2: 1.334976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368268 Loss1: 0.048723 Loss2: 1.319545 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.312128 Loss1: 0.397285 Loss2: 1.914843 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573125 Loss1: 0.155933 Loss2: 1.417192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.504052 Loss1: 0.111702 Loss2: 1.392350
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.289953 Loss1: 0.426120 Loss2: 1.863833
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.452331 Loss1: 0.074778 Loss2: 1.377553
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.650094 Loss1: 0.327259 Loss2: 1.322835
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454210 Loss1: 0.079153 Loss2: 1.375057
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.599003 Loss1: 0.234487 Loss2: 1.364516
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444007 Loss1: 0.065960 Loss2: 1.378047
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.491694 Loss1: 0.137724 Loss2: 1.353970
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501198 Loss1: 0.170667 Loss2: 1.330531
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405654 Loss1: 0.044120 Loss2: 1.361534
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469918 Loss1: 0.128108 Loss2: 1.341810
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.410326 Loss1: 0.053349 Loss2: 1.356977
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404973 Loss1: 0.069108 Loss2: 1.335865
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404178 Loss1: 0.051568 Loss2: 1.352610
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381697 Loss1: 0.062977 Loss2: 1.318720 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.167570 Loss1: 0.345709 Loss2: 1.821861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.504578 Loss1: 0.136617 Loss2: 1.367961 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.319633 Loss1: 0.442738 Loss2: 1.876895
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.473641 Loss1: 0.113815 Loss2: 1.359825
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.687981 Loss1: 0.300903 Loss2: 1.387078
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478011 Loss1: 0.112902 Loss2: 1.365109
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592164 Loss1: 0.183139 Loss2: 1.409025
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.431780 Loss1: 0.078101 Loss2: 1.353679
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564304 Loss1: 0.185093 Loss2: 1.379211
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485804 Loss1: 0.136819 Loss2: 1.348985
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418762 Loss1: 0.074362 Loss2: 1.344400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431803 Loss1: 0.087377 Loss2: 1.344426 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403817 Loss1: 0.065842 Loss2: 1.337975 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421670 Loss1: 0.057956 Loss2: 1.363714 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.277872 Loss1: 0.424286 Loss2: 1.853586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635946 Loss1: 0.220290 Loss2: 1.415656 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.575114 Loss1: 0.202219 Loss2: 1.372895
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.310592 Loss1: 0.483244 Loss2: 1.827347
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.537653 Loss1: 0.160294 Loss2: 1.377359
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.669316 Loss1: 0.314444 Loss2: 1.354872
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.537352 Loss1: 0.163320 Loss2: 1.374033
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.615991 Loss1: 0.227074 Loss2: 1.388917
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.507366 Loss1: 0.139315 Loss2: 1.368051
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534965 Loss1: 0.181994 Loss2: 1.352970
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.455961 Loss1: 0.094481 Loss2: 1.361480
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.561569 Loss1: 0.211013 Loss2: 1.350556
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.426043 Loss1: 0.071801 Loss2: 1.354242
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510163 Loss1: 0.146443 Loss2: 1.363720
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424959 Loss1: 0.075149 Loss2: 1.349811
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456358 Loss1: 0.105626 Loss2: 1.350732
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414325 Loss1: 0.073870 Loss2: 1.340455
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379955 Loss1: 0.041939 Loss2: 1.338015
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370743 Loss1: 0.042112 Loss2: 1.328630
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.237708 Loss1: 0.434041 Loss2: 1.803667
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.644447 Loss1: 0.288754 Loss2: 1.355694
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.556734 Loss1: 0.175717 Loss2: 1.381017
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.450749 Loss1: 0.497837 Loss2: 1.952911
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497391 Loss1: 0.149825 Loss2: 1.347566
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479977 Loss1: 0.134594 Loss2: 1.345383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.414412 Loss1: 0.071375 Loss2: 1.343037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497468 Loss1: 0.155922 Loss2: 1.341546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.488215 Loss1: 0.147737 Loss2: 1.340477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.510339 Loss1: 0.152045 Loss2: 1.358295 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388922 Loss1: 0.058520 Loss2: 1.330402
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490674 Loss1: 0.141486 Loss2: 1.349188
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.494881 Loss1: 0.148890 Loss2: 1.345991
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369574 Loss1: 0.043335 Loss2: 1.326239
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.244109 Loss1: 0.396407 Loss2: 1.847702 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990885
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.580807 Loss1: 0.191106 Loss2: 1.389700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.519279 Loss1: 0.140841 Loss2: 1.378438
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.154918 Loss1: 0.363458 Loss2: 1.791459
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.525908 Loss1: 0.175217 Loss2: 1.350690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.486171 Loss1: 0.136165 Loss2: 1.350006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.462628 Loss1: 0.126231 Loss2: 1.336397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.525799 Loss1: 0.173792 Loss2: 1.352008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450796 Loss1: 0.111647 Loss2: 1.339149 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431610 Loss1: 0.101768 Loss2: 1.329842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370250 Loss1: 0.047802 Loss2: 1.322448 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.091369 Loss1: 0.321302 Loss2: 1.770068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.508471 Loss1: 0.157205 Loss2: 1.351266 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.177351 Loss1: 0.381640 Loss2: 1.795711 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.587321 Loss1: 0.270392 Loss2: 1.316929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.509439 Loss1: 0.163492 Loss2: 1.345947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.506544 Loss1: 0.192604 Loss2: 1.313940 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.453151 Loss1: 0.133413 Loss2: 1.319738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.383274 Loss1: 0.071378 Loss2: 1.311896 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.378113 Loss1: 0.074462 Loss2: 1.303651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.341078 Loss1: 0.043956 Loss2: 1.297122 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.275980 Loss1: 0.489956 Loss2: 1.786024 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.564088 Loss1: 0.220568 Loss2: 1.343520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517554 Loss1: 0.183269 Loss2: 1.334285
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.330228 Loss1: 0.488230 Loss2: 1.841999
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.598509 Loss1: 0.255906 Loss2: 1.342603 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.559701 Loss1: 0.188112 Loss2: 1.371589 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.500582 Loss1: 0.134767 Loss2: 1.365815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435894 Loss1: 0.119664 Loss2: 1.316230
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487568 Loss1: 0.137158 Loss2: 1.350410
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371068 Loss1: 0.067767 Loss2: 1.303301
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421451 Loss1: 0.072842 Loss2: 1.348609
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363936 Loss1: 0.058200 Loss2: 1.305736
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.385982 Loss1: 0.049529 Loss2: 1.336453
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374302 Loss1: 0.042898 Loss2: 1.331405
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362855 Loss1: 0.038738 Loss2: 1.324117
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.349904 Loss1: 0.029660 Loss2: 1.320244
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.006617 Loss1: 0.257205 Loss2: 1.749412
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.467673 Loss1: 0.158404 Loss2: 1.309269
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.443332 Loss1: 0.129944 Loss2: 1.313389
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.249291 Loss1: 0.448638 Loss2: 1.800652
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.580764 Loss1: 0.262908 Loss2: 1.317855 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.482925 Loss1: 0.149550 Loss2: 1.333375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.442841 Loss1: 0.125355 Loss2: 1.317486 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.379912 Loss1: 0.079223 Loss2: 1.300688
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.427260 Loss1: 0.110709 Loss2: 1.316551
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.395514 Loss1: 0.095487 Loss2: 1.300027
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.402424 Loss1: 0.086742 Loss2: 1.315682
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.386931 Loss1: 0.076800 Loss2: 1.310132
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.337660 Loss1: 0.040253 Loss2: 1.297407
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.384162 Loss1: 0.084798 Loss2: 1.299364
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.331710 Loss1: 0.047525 Loss2: 1.284185
+(DefaultActor pid=3765) >> Training accuracy: 0.993566
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.371888 Loss1: 0.074390 Loss2: 1.297498 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.329629 Loss1: 0.484873 Loss2: 1.844756
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.587842 Loss1: 0.244382 Loss2: 1.343460
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.529347 Loss1: 0.169148 Loss2: 1.360198
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.537897 Loss1: 0.187670 Loss2: 1.350227
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.310670 Loss1: 0.392004 Loss2: 1.918666
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470899 Loss1: 0.124148 Loss2: 1.346751
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.665757 Loss1: 0.277748 Loss2: 1.388009
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484762 Loss1: 0.138621 Loss2: 1.346140
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572670 Loss1: 0.162954 Loss2: 1.409716
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436407 Loss1: 0.102272 Loss2: 1.334135
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.527961 Loss1: 0.145892 Loss2: 1.382069
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425914 Loss1: 0.093438 Loss2: 1.332476
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514672 Loss1: 0.128965 Loss2: 1.385707
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437992 Loss1: 0.105561 Loss2: 1.332431
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.544480 Loss1: 0.161331 Loss2: 1.383149
+DEBUG flwr 2023-10-13 03:23:49,504 | server.py:236 | fit_round 174 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445371 Loss1: 0.110133 Loss2: 1.335238
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518147 Loss1: 0.130359 Loss2: 1.387788
+(DefaultActor pid=3765) >> Training accuracy: 0.966667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.501566 Loss1: 0.120025 Loss2: 1.381541
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454669 Loss1: 0.078609 Loss2: 1.376060
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420465 Loss1: 0.051933 Loss2: 1.368533
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.427831 Loss1: 0.510732 Loss2: 1.917100
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.745759 Loss1: 0.362008 Loss2: 1.383751
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644437 Loss1: 0.233648 Loss2: 1.410789
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540068 Loss1: 0.139918 Loss2: 1.400150
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.228368 Loss1: 0.407173 Loss2: 1.821194
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.571278 Loss1: 0.242957 Loss2: 1.328321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.502062 Loss1: 0.158830 Loss2: 1.343232 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.490340 Loss1: 0.156575 Loss2: 1.333765 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.456343 Loss1: 0.128698 Loss2: 1.327644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432390 Loss1: 0.074903 Loss2: 1.357487 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981027
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396557 Loss1: 0.075097 Loss2: 1.321461 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365136 Loss1: 0.050755 Loss2: 1.314381 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.660425 Loss1: 0.267937 Loss2: 1.392488 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.553468 Loss1: 0.160295 Loss2: 1.393173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.100277 Loss1: 0.332858 Loss2: 1.767419
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544612 Loss1: 0.159850 Loss2: 1.384762
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.517593 Loss1: 0.130181 Loss2: 1.387412
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.587022 Loss1: 0.265064 Loss2: 1.321958
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488976 Loss1: 0.102755 Loss2: 1.386221
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.524305 Loss1: 0.162085 Loss2: 1.362220
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460171 Loss1: 0.089138 Loss2: 1.371033
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481408 Loss1: 0.156903 Loss2: 1.324505
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.446349 Loss1: 0.076208 Loss2: 1.370140
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.459599 Loss1: 0.131466 Loss2: 1.328133
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411377 Loss1: 0.048979 Loss2: 1.362397
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471681 Loss1: 0.143841 Loss2: 1.327840
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450408 Loss1: 0.122257 Loss2: 1.328151
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437758 Loss1: 0.119764 Loss2: 1.317994
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435706 Loss1: 0.113787 Loss2: 1.321919
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408815 Loss1: 0.092296 Loss2: 1.316520
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 03:23:49,504][flwr][DEBUG] - fit_round 174 received 50 results and 0 failures
+INFO flwr 2023-10-13 03:24:30,497 | server.py:125 | fit progress: (174, 2.2803944634934203, {'accuracy': 0.6048}, 401578.275854018)
+>> Test accuracy: 0.604800
+[2023-10-13 03:24:30,497][flwr][INFO] - fit progress: (174, 2.2803944634934203, {'accuracy': 0.6048}, 401578.275854018)
+DEBUG flwr 2023-10-13 03:24:30,498 | server.py:173 | evaluate_round 174: strategy sampled 50 clients (out of 50)
+[2023-10-13 03:24:30,498][flwr][DEBUG] - evaluate_round 174: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 03:33:34,337 | server.py:187 | evaluate_round 174 received 50 results and 0 failures
+[2023-10-13 03:33:34,337][flwr][DEBUG] - evaluate_round 174 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 03:33:34,337 | server.py:222 | fit_round 175: strategy sampled 50 clients (out of 50)
+[2023-10-13 03:33:34,337][flwr][DEBUG] - fit_round 175: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.270808 Loss1: 0.488092 Loss2: 1.782716
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.618834 Loss1: 0.315748 Loss2: 1.303087
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.592273 Loss1: 0.223723 Loss2: 1.368550
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467045 Loss1: 0.154687 Loss2: 1.312358
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.302559 Loss1: 0.361764 Loss2: 1.940795
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.497138 Loss1: 0.181862 Loss2: 1.315276
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.666372 Loss1: 0.250082 Loss2: 1.416290
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.391463 Loss1: 0.077703 Loss2: 1.313760
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.612905 Loss1: 0.166214 Loss2: 1.446691
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415527 Loss1: 0.113368 Loss2: 1.302160
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588099 Loss1: 0.152273 Loss2: 1.435826
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.355627 Loss1: 0.056812 Loss2: 1.298815
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522922 Loss1: 0.103320 Loss2: 1.419602
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.362249 Loss1: 0.066659 Loss2: 1.295590
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531556 Loss1: 0.115523 Loss2: 1.416033
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.338494 Loss1: 0.047746 Loss2: 1.290748
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483565 Loss1: 0.069252 Loss2: 1.414313
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.451330 Loss1: 0.051688 Loss2: 1.399642
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460271 Loss1: 0.059176 Loss2: 1.401095
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.436621 Loss1: 0.038746 Loss2: 1.397875
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.234462 Loss1: 0.400243 Loss2: 1.834219
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.624392 Loss1: 0.275098 Loss2: 1.349294
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.667701 Loss1: 0.272848 Loss2: 1.394853
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.505620 Loss1: 0.157427 Loss2: 1.348193
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.275586 Loss1: 0.345542 Loss2: 1.930044
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.644547 Loss1: 0.245122 Loss2: 1.399425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.643980 Loss1: 0.223130 Loss2: 1.420850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555298 Loss1: 0.138990 Loss2: 1.416308 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528738 Loss1: 0.127571 Loss2: 1.401166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.526195 Loss1: 0.126508 Loss2: 1.399687 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421362 Loss1: 0.088011 Loss2: 1.333351
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494353 Loss1: 0.097766 Loss2: 1.396587
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481831 Loss1: 0.089259 Loss2: 1.392573
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.491266 Loss1: 0.094382 Loss2: 1.396884
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.455258 Loss1: 0.064559 Loss2: 1.390699
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.354060 Loss1: 0.413585 Loss2: 1.940474
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.696064 Loss1: 0.280864 Loss2: 1.415200
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.600107 Loss1: 0.151440 Loss2: 1.448666
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555299 Loss1: 0.140054 Loss2: 1.415245
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.267806 Loss1: 0.483474 Loss2: 1.784332
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.607557 Loss1: 0.289599 Loss2: 1.317958 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.493707 Loss1: 0.150343 Loss2: 1.343364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478164 Loss1: 0.162067 Loss2: 1.316097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.419404 Loss1: 0.103666 Loss2: 1.315738 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461742 Loss1: 0.143954 Loss2: 1.317788 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442276 Loss1: 0.054650 Loss2: 1.387626
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455089 Loss1: 0.141665 Loss2: 1.313424
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419200 Loss1: 0.103783 Loss2: 1.315416
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417626 Loss1: 0.108093 Loss2: 1.309533
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410380 Loss1: 0.097706 Loss2: 1.312674
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.185255 Loss1: 0.340079 Loss2: 1.845176
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.608553 Loss1: 0.263195 Loss2: 1.345358
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.572604 Loss1: 0.205028 Loss2: 1.367576
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489411 Loss1: 0.133214 Loss2: 1.356198
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.281332 Loss1: 0.420443 Loss2: 1.860889
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.683150 Loss1: 0.294873 Loss2: 1.388277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635241 Loss1: 0.218051 Loss2: 1.417190 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561933 Loss1: 0.168436 Loss2: 1.393497 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518129 Loss1: 0.131732 Loss2: 1.386397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495080 Loss1: 0.101490 Loss2: 1.393590 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427705 Loss1: 0.056572 Loss2: 1.371133 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409690 Loss1: 0.044740 Loss2: 1.364951 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.670764 Loss1: 0.287320 Loss2: 1.383445 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474741 Loss1: 0.108586 Loss2: 1.366155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.414112 Loss1: 0.070454 Loss2: 1.343658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390498 Loss1: 0.051491 Loss2: 1.339007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378222 Loss1: 0.047459 Loss2: 1.330762 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399951 Loss1: 0.073434 Loss2: 1.326517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368306 Loss1: 0.041480 Loss2: 1.326825 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.445858 Loss1: 0.090224 Loss2: 1.355633 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449859 Loss1: 0.101204 Loss2: 1.348654 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423454 Loss1: 0.072127 Loss2: 1.351328
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.146752 Loss1: 0.340489 Loss2: 1.806263
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404454 Loss1: 0.057682 Loss2: 1.346772
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.596534 Loss1: 0.290855 Loss2: 1.305679
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526533 Loss1: 0.188364 Loss2: 1.338170
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518422 Loss1: 0.188812 Loss2: 1.329610
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.474212 Loss1: 0.149415 Loss2: 1.324797
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462298 Loss1: 0.144261 Loss2: 1.318037
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.521090 Loss1: 0.199556 Loss2: 1.321535
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.218477 Loss1: 0.354665 Loss2: 1.863812
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457010 Loss1: 0.118653 Loss2: 1.338356
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.652655 Loss1: 0.267332 Loss2: 1.385323
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398900 Loss1: 0.078742 Loss2: 1.320157
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.560928 Loss1: 0.144078 Loss2: 1.416850
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.394936 Loss1: 0.079449 Loss2: 1.315487
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519769 Loss1: 0.139253 Loss2: 1.380516
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506163 Loss1: 0.125293 Loss2: 1.380870
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.495727 Loss1: 0.120009 Loss2: 1.375718
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463482 Loss1: 0.081544 Loss2: 1.381938
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455266 Loss1: 0.085373 Loss2: 1.369893
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.209571 Loss1: 0.430118 Loss2: 1.779453
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.550460 Loss1: 0.238187 Loss2: 1.312273 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.418779 Loss1: 0.052079 Loss2: 1.366700
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.480601 Loss1: 0.158100 Loss2: 1.322500
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.441818 Loss1: 0.128700 Loss2: 1.313119
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.413948 Loss1: 0.110884 Loss2: 1.303064
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.385721 Loss1: 0.085674 Loss2: 1.300047
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416908 Loss1: 0.123463 Loss2: 1.293445
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.184147 Loss1: 0.322047 Loss2: 1.862100
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.373733 Loss1: 0.081672 Loss2: 1.292062
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.358179 Loss1: 0.070038 Loss2: 1.288141 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.575104 Loss1: 0.160703 Loss2: 1.414401
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.334737 Loss1: 0.046078 Loss2: 1.288659
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.489262 Loss1: 0.107448 Loss2: 1.381814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473951 Loss1: 0.096180 Loss2: 1.377771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.265409 Loss1: 0.414035 Loss2: 1.851373
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493545 Loss1: 0.116563 Loss2: 1.376981
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.584084 Loss1: 0.227747 Loss2: 1.356337
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.456766 Loss1: 0.078588 Loss2: 1.378178
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.533611 Loss1: 0.149602 Loss2: 1.384010
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445029 Loss1: 0.074937 Loss2: 1.370092
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.482041 Loss1: 0.117410 Loss2: 1.364631 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448383 Loss1: 0.091983 Loss2: 1.356400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409853 Loss1: 0.063262 Loss2: 1.346591
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.327235 Loss1: 0.484758 Loss2: 1.842477
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.647251 Loss1: 0.285993 Loss2: 1.361258 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418298 Loss1: 0.078535 Loss2: 1.339763
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608200 Loss1: 0.223344 Loss2: 1.384856
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497905 Loss1: 0.138244 Loss2: 1.359661
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.477996 Loss1: 0.126775 Loss2: 1.351221
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447345 Loss1: 0.093142 Loss2: 1.354203
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.410880 Loss1: 0.070534 Loss2: 1.340347
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.291219 Loss1: 0.433147 Loss2: 1.858072
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386992 Loss1: 0.058457 Loss2: 1.328535
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.671924 Loss1: 0.310229 Loss2: 1.361695
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.412711 Loss1: 0.078792 Loss2: 1.333918
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.605489 Loss1: 0.199433 Loss2: 1.406057
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450303 Loss1: 0.108551 Loss2: 1.341752
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.477391 Loss1: 0.109120 Loss2: 1.368271 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473364 Loss1: 0.111068 Loss2: 1.362296 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458268 Loss1: 0.097530 Loss2: 1.360738
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.255017 Loss1: 0.430303 Loss2: 1.824714
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.673047 Loss1: 0.346309 Loss2: 1.326739 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564068 Loss1: 0.197453 Loss2: 1.366615 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.433155 Loss1: 0.104425 Loss2: 1.328729 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.356745 Loss1: 0.049755 Loss2: 1.306990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.357956 Loss1: 0.056485 Loss2: 1.301471 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.336850 Loss1: 0.040183 Loss2: 1.296666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.318594 Loss1: 0.028145 Loss2: 1.290449 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.436728 Loss1: 0.112778 Loss2: 1.323949 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395499 Loss1: 0.068246 Loss2: 1.327252 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.367530 Loss1: 0.051176 Loss2: 1.316354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367655 Loss1: 0.046574 Loss2: 1.321082 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.549413 Loss1: 0.165528 Loss2: 1.383885 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.518262 Loss1: 0.122948 Loss2: 1.395315 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.445101 Loss1: 0.499500 Loss2: 1.945602
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463406 Loss1: 0.088699 Loss2: 1.374707
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.701264 Loss1: 0.320659 Loss2: 1.380605
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491225 Loss1: 0.119510 Loss2: 1.371715
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624749 Loss1: 0.215746 Loss2: 1.409003
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428744 Loss1: 0.059775 Loss2: 1.368970
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433718 Loss1: 0.072709 Loss2: 1.361008 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.579446 Loss1: 0.171298 Loss2: 1.408148 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.498754 Loss1: 0.121912 Loss2: 1.376842 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.303144 Loss1: 0.419987 Loss2: 1.883157 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578523 Loss1: 0.186394 Loss2: 1.392129 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530438 Loss1: 0.174320 Loss2: 1.356118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487245 Loss1: 0.133198 Loss2: 1.354047
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.210028 Loss1: 0.426516 Loss2: 1.783512
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.619704 Loss1: 0.306891 Loss2: 1.312813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.528787 Loss1: 0.179926 Loss2: 1.348861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.463764 Loss1: 0.143727 Loss2: 1.320037 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463376 Loss1: 0.142434 Loss2: 1.320943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.376809 Loss1: 0.070775 Loss2: 1.306034 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.324345 Loss1: 0.032002 Loss2: 1.292342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.327174 Loss1: 0.037809 Loss2: 1.289365 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.569887 Loss1: 0.166016 Loss2: 1.403871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.463671 Loss1: 0.091637 Loss2: 1.372034 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441522 Loss1: 0.071950 Loss2: 1.369573
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.367518 Loss1: 0.509197 Loss2: 1.858322
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.651047 Loss1: 0.309907 Loss2: 1.341139 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.527918 Loss1: 0.162044 Loss2: 1.365874 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498857 Loss1: 0.159503 Loss2: 1.339355 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482541 Loss1: 0.107148 Loss2: 1.375392
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.458250 Loss1: 0.123299 Loss2: 1.334951
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.413852 Loss1: 0.081570 Loss2: 1.332282
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.378947 Loss1: 0.057446 Loss2: 1.321501
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.383756 Loss1: 0.064596 Loss2: 1.319160
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.359920 Loss1: 0.044802 Loss2: 1.315117
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.289381 Loss1: 0.405011 Loss2: 1.884370
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388055 Loss1: 0.080930 Loss2: 1.307125
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.582377 Loss1: 0.178449 Loss2: 1.403928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529900 Loss1: 0.161072 Loss2: 1.368828 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.521564 Loss1: 0.143374 Loss2: 1.378190
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.412002 Loss1: 0.502752 Loss2: 1.909250
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.707046 Loss1: 0.340198 Loss2: 1.366848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573141 Loss1: 0.182603 Loss2: 1.390539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482497 Loss1: 0.129404 Loss2: 1.353094 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472640 Loss1: 0.118435 Loss2: 1.354205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395436 Loss1: 0.062532 Loss2: 1.332904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371279 Loss1: 0.047998 Loss2: 1.323282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379069 Loss1: 0.055579 Loss2: 1.323491 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.613825 Loss1: 0.188381 Loss2: 1.425445 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486912 Loss1: 0.099283 Loss2: 1.387629 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.191025 Loss1: 0.332255 Loss2: 1.858770 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.714075 Loss1: 0.323632 Loss2: 1.390443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419416 Loss1: 0.053477 Loss2: 1.365938 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992788
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518253 Loss1: 0.124475 Loss2: 1.393778 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.493078 Loss1: 0.105146 Loss2: 1.387931 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.126650 Loss1: 0.299124 Loss2: 1.827526
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.511120 Loss1: 0.114099 Loss2: 1.397021
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.554217 Loss1: 0.203248 Loss2: 1.350968
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484886 Loss1: 0.090254 Loss2: 1.394632
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.495275 Loss1: 0.129398 Loss2: 1.365877
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454383 Loss1: 0.067690 Loss2: 1.386694
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.426015 Loss1: 0.084587 Loss2: 1.341428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.418025 Loss1: 0.070588 Loss2: 1.347437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.362685 Loss1: 0.530009 Loss2: 1.832676
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412247 Loss1: 0.072927 Loss2: 1.339320
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.724818 Loss1: 0.387200 Loss2: 1.337618
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425102 Loss1: 0.095318 Loss2: 1.329784
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.656108 Loss1: 0.244006 Loss2: 1.412102
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411433 Loss1: 0.074085 Loss2: 1.337348
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526693 Loss1: 0.179523 Loss2: 1.347170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473638 Loss1: 0.130558 Loss2: 1.343080 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438390 Loss1: 0.103398 Loss2: 1.334992
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.188555 Loss1: 0.389291 Loss2: 1.799264
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.576981 Loss1: 0.243209 Loss2: 1.333772 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.549162 Loss1: 0.183757 Loss2: 1.365404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.425695 Loss1: 0.102519 Loss2: 1.323176 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419289 Loss1: 0.095021 Loss2: 1.324268 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421066 Loss1: 0.099232 Loss2: 1.321834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415371 Loss1: 0.093109 Loss2: 1.322262 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413450 Loss1: 0.092852 Loss2: 1.320598 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.482250 Loss1: 0.103546 Loss2: 1.378704 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.416439 Loss1: 0.059395 Loss2: 1.357044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.268110 Loss1: 0.414474 Loss2: 1.853636
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402223 Loss1: 0.044055 Loss2: 1.358167
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.652066 Loss1: 0.300495 Loss2: 1.351571
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396251 Loss1: 0.047136 Loss2: 1.349115
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.518425 Loss1: 0.161994 Loss2: 1.356431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480149 Loss1: 0.120296 Loss2: 1.359853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437123 Loss1: 0.075553 Loss2: 1.361570
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.260801 Loss1: 0.448486 Loss2: 1.812315
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.547951 Loss1: 0.239375 Loss2: 1.308575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.481014 Loss1: 0.149479 Loss2: 1.331536 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390281 Loss1: 0.051183 Loss2: 1.339098
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.439530 Loss1: 0.117201 Loss2: 1.322329
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.412950 Loss1: 0.111128 Loss2: 1.301821
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.377641 Loss1: 0.073124 Loss2: 1.304517
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.363306 Loss1: 0.062880 Loss2: 1.300426
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.370453 Loss1: 0.073022 Loss2: 1.297431
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.307478 Loss1: 0.456197 Loss2: 1.851281
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.357834 Loss1: 0.060139 Loss2: 1.297695
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.344007 Loss1: 0.055590 Loss2: 1.288417 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496330 Loss1: 0.133647 Loss2: 1.362683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498589 Loss1: 0.137091 Loss2: 1.361498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460113 Loss1: 0.106101 Loss2: 1.354012
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.205951 Loss1: 0.361150 Loss2: 1.844801
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.640310 Loss1: 0.267046 Loss2: 1.373264 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.603068 Loss1: 0.208629 Loss2: 1.394439 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.441582 Loss1: 0.077231 Loss2: 1.364351 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.438597 Loss1: 0.077498 Loss2: 1.361099 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420710 Loss1: 0.069463 Loss2: 1.351247
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.134323 Loss1: 0.311119 Loss2: 1.823204
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418164 Loss1: 0.066683 Loss2: 1.351482
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.563711 Loss1: 0.220418 Loss2: 1.343293
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403025 Loss1: 0.051712 Loss2: 1.351313
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.511288 Loss1: 0.149311 Loss2: 1.361977
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481725 Loss1: 0.132107 Loss2: 1.349618
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.462522 Loss1: 0.118708 Loss2: 1.343815
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420509 Loss1: 0.077725 Loss2: 1.342784
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.429746 Loss1: 0.092313 Loss2: 1.337432
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.304504 Loss1: 0.431638 Loss2: 1.872866
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.582397 Loss1: 0.238469 Loss2: 1.343928
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414879 Loss1: 0.079489 Loss2: 1.335390
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.511782 Loss1: 0.162822 Loss2: 1.348960
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394661 Loss1: 0.063225 Loss2: 1.331435
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407753 Loss1: 0.084905 Loss2: 1.322848 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990809
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484907 Loss1: 0.143273 Loss2: 1.341634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435570 Loss1: 0.099304 Loss2: 1.336267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372628 Loss1: 0.050132 Loss2: 1.322496 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653346 Loss1: 0.240881 Loss2: 1.412465 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544246 Loss1: 0.166694 Loss2: 1.377552 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.582429 Loss1: 0.179323 Loss2: 1.403106
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.222913 Loss1: 0.383169 Loss2: 1.839744
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.714348 Loss1: 0.337084 Loss2: 1.377264 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.584449 Loss1: 0.164760 Loss2: 1.419689 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529077 Loss1: 0.160867 Loss2: 1.368210 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568688 Loss1: 0.182415 Loss2: 1.386273 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516737 Loss1: 0.141985 Loss2: 1.374752 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447157 Loss1: 0.080770 Loss2: 1.366387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.763956 Loss1: 0.393592 Loss2: 1.370365 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551316 Loss1: 0.153576 Loss2: 1.397739 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.578445 Loss1: 0.185743 Loss2: 1.392702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.518008 Loss1: 0.139428 Loss2: 1.378580 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449141 Loss1: 0.076757 Loss2: 1.372384 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.428419 Loss1: 0.058156 Loss2: 1.370263 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993490
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520720 Loss1: 0.110703 Loss2: 1.410018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487186 Loss1: 0.079293 Loss2: 1.407894 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495631 Loss1: 0.085372 Loss2: 1.410259
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.206763 Loss1: 0.432237 Loss2: 1.774526
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460948 Loss1: 0.057419 Loss2: 1.403529
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.606005 Loss1: 0.307598 Loss2: 1.298407
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.466547 Loss1: 0.064210 Loss2: 1.402337
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.527863 Loss1: 0.205405 Loss2: 1.322458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474480 Loss1: 0.076858 Loss2: 1.397622
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.488840 Loss1: 0.185948 Loss2: 1.302892
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446885 Loss1: 0.138104 Loss2: 1.308781
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421689 Loss1: 0.123322 Loss2: 1.298367
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.378100 Loss1: 0.082787 Loss2: 1.295313
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.369787 Loss1: 0.081402 Loss2: 1.288385
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.360096 Loss1: 0.075333 Loss2: 1.284763
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.258230 Loss1: 0.381670 Loss2: 1.876560
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.316539 Loss1: 0.042255 Loss2: 1.274284
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.663793 Loss1: 0.280895 Loss2: 1.382899
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551429 Loss1: 0.149699 Loss2: 1.401730
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536131 Loss1: 0.155514 Loss2: 1.380617
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476414 Loss1: 0.106743 Loss2: 1.369671
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485853 Loss1: 0.114244 Loss2: 1.371609
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441838 Loss1: 0.074249 Loss2: 1.367590
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.253583 Loss1: 0.431242 Loss2: 1.822342
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449727 Loss1: 0.090622 Loss2: 1.359105
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653863 Loss1: 0.318949 Loss2: 1.334914
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409599 Loss1: 0.048286 Loss2: 1.361314
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601742 Loss1: 0.224694 Loss2: 1.377048
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425034 Loss1: 0.069304 Loss2: 1.355730
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.476164 Loss1: 0.128174 Loss2: 1.347990
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.438281 Loss1: 0.101686 Loss2: 1.336596
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.398522 Loss1: 0.068697 Loss2: 1.329825
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.384847 Loss1: 0.068548 Loss2: 1.316299
+DEBUG flwr 2023-10-13 04:01:47,123 | server.py:236 | fit_round 175 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.349065 Loss1: 0.033569 Loss2: 1.315496
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.327160 Loss1: 0.020947 Loss2: 1.306213
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.295048 Loss1: 0.430823 Loss2: 1.864225
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.324283 Loss1: 0.027021 Loss2: 1.297261
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.643439 Loss1: 0.274003 Loss2: 1.369436
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.565511 Loss1: 0.174337 Loss2: 1.391174
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.493046 Loss1: 0.117763 Loss2: 1.375283
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.469397 Loss1: 0.105468 Loss2: 1.363929
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.424859 Loss1: 0.062979 Loss2: 1.361880
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.388322 Loss1: 0.035464 Loss2: 1.352858
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.169132 Loss1: 0.380409 Loss2: 1.788724
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391922 Loss1: 0.046111 Loss2: 1.345811
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.588078 Loss1: 0.284923 Loss2: 1.303156
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379110 Loss1: 0.036275 Loss2: 1.342836
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.546165 Loss1: 0.207249 Loss2: 1.338916
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369471 Loss1: 0.033375 Loss2: 1.336095
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507732 Loss1: 0.193817 Loss2: 1.313915
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485451 Loss1: 0.167648 Loss2: 1.317803
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.435855 Loss1: 0.119782 Loss2: 1.316074
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.382345 Loss1: 0.078278 Loss2: 1.304068
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.369164 Loss1: 0.065222 Loss2: 1.303942
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.341526 Loss1: 0.047206 Loss2: 1.294320
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.236759 Loss1: 0.413544 Loss2: 1.823215
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.335130 Loss1: 0.044385 Loss2: 1.290746
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.584948 Loss1: 0.240991 Loss2: 1.343957
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.546413 Loss1: 0.184088 Loss2: 1.362325
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489527 Loss1: 0.138021 Loss2: 1.351506
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470609 Loss1: 0.123904 Loss2: 1.346705
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440107 Loss1: 0.104042 Loss2: 1.336065
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.232005 Loss1: 0.421683 Loss2: 1.810322
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447129 Loss1: 0.110118 Loss2: 1.337011
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.638535 Loss1: 0.314982 Loss2: 1.323554
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447115 Loss1: 0.109639 Loss2: 1.337476
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.610898 Loss1: 0.254377 Loss2: 1.356522
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438778 Loss1: 0.105544 Loss2: 1.333233
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509108 Loss1: 0.184935 Loss2: 1.324173
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405638 Loss1: 0.071934 Loss2: 1.333704
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.550925 Loss1: 0.216204 Loss2: 1.334722 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445890 Loss1: 0.126863 Loss2: 1.319028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383649 Loss1: 0.070411 Loss2: 1.313238 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 04:01:47,123][flwr][DEBUG] - fit_round 175 received 50 results and 0 failures
+INFO flwr 2023-10-13 04:02:28,339 | server.py:125 | fit progress: (175, 2.274364816304594, {'accuracy': 0.6093}, 403856.117812133)
+>> Test accuracy: 0.609300
+[2023-10-13 04:02:28,339][flwr][INFO] - fit progress: (175, 2.274364816304594, {'accuracy': 0.6093}, 403856.117812133)
+DEBUG flwr 2023-10-13 04:02:28,340 | server.py:173 | evaluate_round 175: strategy sampled 50 clients (out of 50)
+[2023-10-13 04:02:28,340][flwr][DEBUG] - evaluate_round 175: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 04:11:32,737 | server.py:187 | evaluate_round 175 received 50 results and 0 failures
+[2023-10-13 04:11:32,737][flwr][DEBUG] - evaluate_round 175 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 04:11:32,738 | server.py:222 | fit_round 176: strategy sampled 50 clients (out of 50)
+[2023-10-13 04:11:32,738][flwr][DEBUG] - fit_round 176: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.170290 Loss1: 0.358709 Loss2: 1.811581
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.516624 Loss1: 0.183547 Loss2: 1.333077
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.440048 Loss1: 0.112371 Loss2: 1.327677
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.453652 Loss1: 0.126275 Loss2: 1.327376
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.256428 Loss1: 0.464357 Loss2: 1.792070
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.621105 Loss1: 0.286483 Loss2: 1.334622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556050 Loss1: 0.178967 Loss2: 1.377083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.467025 Loss1: 0.116683 Loss2: 1.350342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509585 Loss1: 0.165360 Loss2: 1.344225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503621 Loss1: 0.153117 Loss2: 1.350505 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.337311 Loss1: 0.038668 Loss2: 1.298643
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452368 Loss1: 0.111953 Loss2: 1.340416
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411402 Loss1: 0.073117 Loss2: 1.338285
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372953 Loss1: 0.039798 Loss2: 1.333155
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.355299 Loss1: 0.031457 Loss2: 1.323842
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.360517 Loss1: 0.476504 Loss2: 1.884013
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.659602 Loss1: 0.267756 Loss2: 1.391846
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.676188 Loss1: 0.240093 Loss2: 1.436095
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.553713 Loss1: 0.145179 Loss2: 1.408534
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.279482 Loss1: 0.417777 Loss2: 1.861705
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.768148 Loss1: 0.373109 Loss2: 1.395039 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.766637 Loss1: 0.304473 Loss2: 1.462164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.603101 Loss1: 0.195943 Loss2: 1.407159 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.555395 Loss1: 0.148469 Loss2: 1.406926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492973 Loss1: 0.094674 Loss2: 1.398299 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.441062 Loss1: 0.062967 Loss2: 1.378096
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490902 Loss1: 0.096226 Loss2: 1.394676
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468465 Loss1: 0.082252 Loss2: 1.386213
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.471899 Loss1: 0.092637 Loss2: 1.379262
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437760 Loss1: 0.059466 Loss2: 1.378293
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.258640 Loss1: 0.451851 Loss2: 1.806789
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.645302 Loss1: 0.330897 Loss2: 1.314405
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.567917 Loss1: 0.194716 Loss2: 1.373201
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.469875 Loss1: 0.143258 Loss2: 1.326616
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.348707 Loss1: 0.484238 Loss2: 1.864470
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.668805 Loss1: 0.315377 Loss2: 1.353428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.571609 Loss1: 0.192502 Loss2: 1.379107 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.512455 Loss1: 0.151073 Loss2: 1.361382 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476909 Loss1: 0.131858 Loss2: 1.345051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456826 Loss1: 0.107659 Loss2: 1.349167 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430580 Loss1: 0.100762 Loss2: 1.329819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363804 Loss1: 0.037147 Loss2: 1.326657 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.285933 Loss1: 0.449490 Loss2: 1.836443 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.531912 Loss1: 0.150835 Loss2: 1.381077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534870 Loss1: 0.188996 Loss2: 1.345874
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.184729 Loss1: 0.342665 Loss2: 1.842065
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.655092 Loss1: 0.262479 Loss2: 1.392612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.612879 Loss1: 0.179475 Loss2: 1.433404 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.558543 Loss1: 0.175624 Loss2: 1.382919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.547182 Loss1: 0.139723 Loss2: 1.407459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.533802 Loss1: 0.140111 Loss2: 1.393692 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.503880 Loss1: 0.112917 Loss2: 1.390963 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.483981 Loss1: 0.100019 Loss2: 1.383961 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.643745 Loss1: 0.318115 Loss2: 1.325630 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522452 Loss1: 0.195403 Loss2: 1.327049 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.489932 Loss1: 0.151907 Loss2: 1.338025
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.176268 Loss1: 0.359960 Loss2: 1.816307
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.648673 Loss1: 0.272360 Loss2: 1.376313 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588743 Loss1: 0.172535 Loss2: 1.416208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.633957 Loss1: 0.261212 Loss2: 1.372745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.586913 Loss1: 0.185263 Loss2: 1.401649 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.488959 Loss1: 0.110787 Loss2: 1.378172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439248 Loss1: 0.071155 Loss2: 1.368093 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391486 Loss1: 0.038934 Loss2: 1.352553 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.627301 Loss1: 0.221988 Loss2: 1.405313 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479738 Loss1: 0.091321 Loss2: 1.388417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.236790 Loss1: 0.381260 Loss2: 1.855530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.670510 Loss1: 0.302434 Loss2: 1.368076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.610258 Loss1: 0.211251 Loss2: 1.399007 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380113 Loss1: 0.023398 Loss2: 1.356715 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997596
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498571 Loss1: 0.128468 Loss2: 1.370102 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.404721 Loss1: 0.050417 Loss2: 1.354304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.285044 Loss1: 0.386022 Loss2: 1.899022
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373994 Loss1: 0.025984 Loss2: 1.348009
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.672385 Loss1: 0.286309 Loss2: 1.386076
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.350777 Loss1: 0.013221 Loss2: 1.337557
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.508585 Loss1: 0.122133 Loss2: 1.386452 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.483386 Loss1: 0.105258 Loss2: 1.378128 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447080 Loss1: 0.071446 Loss2: 1.375634
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.137759 Loss1: 0.341440 Loss2: 1.796319
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.582810 Loss1: 0.266609 Loss2: 1.316201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563483 Loss1: 0.225363 Loss2: 1.338119 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423553 Loss1: 0.063356 Loss2: 1.360197
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.452111 Loss1: 0.108205 Loss2: 1.343906
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.427800 Loss1: 0.113680 Loss2: 1.314121
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.381372 Loss1: 0.068422 Loss2: 1.312950
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395928 Loss1: 0.083088 Loss2: 1.312840
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374615 Loss1: 0.070118 Loss2: 1.304497
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.198431 Loss1: 0.343346 Loss2: 1.855086
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.353855 Loss1: 0.053413 Loss2: 1.300443
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.603511 Loss1: 0.242730 Loss2: 1.360781
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.332291 Loss1: 0.036710 Loss2: 1.295581
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.483873 Loss1: 0.120113 Loss2: 1.363760 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438685 Loss1: 0.089619 Loss2: 1.349066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450091 Loss1: 0.107946 Loss2: 1.342146
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.173190 Loss1: 0.356233 Loss2: 1.816958
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.535931 Loss1: 0.212349 Loss2: 1.323583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.450922 Loss1: 0.121453 Loss2: 1.329468 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389081 Loss1: 0.052896 Loss2: 1.336185
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.427158 Loss1: 0.108984 Loss2: 1.318174
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.414870 Loss1: 0.106151 Loss2: 1.308719
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.475249 Loss1: 0.164134 Loss2: 1.311115
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.397545 Loss1: 0.084692 Loss2: 1.312854
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418143 Loss1: 0.107333 Loss2: 1.310810
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405887 Loss1: 0.086836 Loss2: 1.319052
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.231468 Loss1: 0.383402 Loss2: 1.848065
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397226 Loss1: 0.084012 Loss2: 1.313214
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.623658 Loss1: 0.228863 Loss2: 1.394795
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561747 Loss1: 0.157701 Loss2: 1.404047
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502368 Loss1: 0.113469 Loss2: 1.388899
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.482874 Loss1: 0.106527 Loss2: 1.376346
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.464801 Loss1: 0.087893 Loss2: 1.376908
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.197296 Loss1: 0.411789 Loss2: 1.785507
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464088 Loss1: 0.093350 Loss2: 1.370738
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.596793 Loss1: 0.251521 Loss2: 1.345272
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426448 Loss1: 0.049871 Loss2: 1.376577
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.519054 Loss1: 0.157186 Loss2: 1.361868
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.416939 Loss1: 0.052684 Loss2: 1.364255
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.508152 Loss1: 0.163152 Loss2: 1.345000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411780 Loss1: 0.055044 Loss2: 1.356736
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463823 Loss1: 0.117000 Loss2: 1.346823 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427364 Loss1: 0.089444 Loss2: 1.337920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395346 Loss1: 0.067751 Loss2: 1.327596
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.253146 Loss1: 0.459800 Loss2: 1.793347
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.651340 Loss1: 0.329938 Loss2: 1.321402
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382277 Loss1: 0.056232 Loss2: 1.326045
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.469101 Loss1: 0.155308 Loss2: 1.313794 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416896 Loss1: 0.106755 Loss2: 1.310140 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442746 Loss1: 0.133836 Loss2: 1.308910
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.230937 Loss1: 0.428585 Loss2: 1.802352
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.395694 Loss1: 0.085560 Loss2: 1.310133
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.596470 Loss1: 0.287002 Loss2: 1.309469
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372731 Loss1: 0.072436 Loss2: 1.300295
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.467831 Loss1: 0.138882 Loss2: 1.328949
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.342099 Loss1: 0.051475 Loss2: 1.290624
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.426544 Loss1: 0.112165 Loss2: 1.314379
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.404683 Loss1: 0.096619 Loss2: 1.308064
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.388419 Loss1: 0.084003 Loss2: 1.304417
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.350512 Loss1: 0.047015 Loss2: 1.303497
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.340241 Loss1: 0.049905 Loss2: 1.290336
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.282526 Loss1: 0.434037 Loss2: 1.848489
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.343712 Loss1: 0.058345 Loss2: 1.285366
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.638069 Loss1: 0.277748 Loss2: 1.360320
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.313480 Loss1: 0.026366 Loss2: 1.287114
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524484 Loss1: 0.166067 Loss2: 1.358417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460247 Loss1: 0.098493 Loss2: 1.361754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.423205 Loss1: 0.071303 Loss2: 1.351902
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.228603 Loss1: 0.383801 Loss2: 1.844802
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.388156 Loss1: 0.053162 Loss2: 1.334995
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.547448 Loss1: 0.211315 Loss2: 1.336133
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388233 Loss1: 0.052654 Loss2: 1.335579
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590292 Loss1: 0.229686 Loss2: 1.360606
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377627 Loss1: 0.048176 Loss2: 1.329451
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.525405 Loss1: 0.177610 Loss2: 1.347795
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.489710 Loss1: 0.160801 Loss2: 1.328909
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444757 Loss1: 0.103926 Loss2: 1.340830
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445640 Loss1: 0.108804 Loss2: 1.336837
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421582 Loss1: 0.094361 Loss2: 1.327221
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.304375 Loss1: 0.470149 Loss2: 1.834227
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.380113 Loss1: 0.058532 Loss2: 1.321581
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.584669 Loss1: 0.249759 Loss2: 1.334910
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.363299 Loss1: 0.044937 Loss2: 1.318362
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490260 Loss1: 0.145025 Loss2: 1.345236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.479867 Loss1: 0.138333 Loss2: 1.341534 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.457548 Loss1: 0.123373 Loss2: 1.334174
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.267792 Loss1: 0.395298 Loss2: 1.872494
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.606118 Loss1: 0.251780 Loss2: 1.354338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.532871 Loss1: 0.163355 Loss2: 1.369517 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378017 Loss1: 0.058490 Loss2: 1.319528
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.572642 Loss1: 0.194938 Loss2: 1.377704
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.548008 Loss1: 0.190127 Loss2: 1.357881
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460274 Loss1: 0.111205 Loss2: 1.349069
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.402279 Loss1: 0.056422 Loss2: 1.345857
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388249 Loss1: 0.050236 Loss2: 1.338013
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.172564 Loss1: 0.424527 Loss2: 1.748038
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369264 Loss1: 0.036659 Loss2: 1.332605
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351415 Loss1: 0.025066 Loss2: 1.326349
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.561913 Loss1: 0.258535 Loss2: 1.303378
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.519247 Loss1: 0.190439 Loss2: 1.328808
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.431601 Loss1: 0.119279 Loss2: 1.312322
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.423496 Loss1: 0.119379 Loss2: 1.304117
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.368456 Loss1: 0.068367 Loss2: 1.300089
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.074522 Loss1: 0.337049 Loss2: 1.737473
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.378463 Loss1: 0.091732 Loss2: 1.286731
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.494725 Loss1: 0.203230 Loss2: 1.291495
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.346282 Loss1: 0.063838 Loss2: 1.282444
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.410453 Loss1: 0.110532 Loss2: 1.299921
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.333023 Loss1: 0.057230 Loss2: 1.275794
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.436905 Loss1: 0.145101 Loss2: 1.291803
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.318702 Loss1: 0.043600 Loss2: 1.275103
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.393576 Loss1: 0.104256 Loss2: 1.289320 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.343388 Loss1: 0.056610 Loss2: 1.286778 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.434032 Loss1: 0.550658 Loss2: 1.883374
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.346929 Loss1: 0.064982 Loss2: 1.281946
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.307371 Loss1: 0.029652 Loss2: 1.277719 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.469488 Loss1: 0.120305 Loss2: 1.349183 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456840 Loss1: 0.124859 Loss2: 1.331981 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.376525 Loss1: 0.515245 Loss2: 1.861280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.665023 Loss1: 0.351071 Loss2: 1.313952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572042 Loss1: 0.207667 Loss2: 1.364375 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986607
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.462883 Loss1: 0.136401 Loss2: 1.326482 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.386517 Loss1: 0.063322 Loss2: 1.323195 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.387851 Loss1: 0.073608 Loss2: 1.314243
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.414718 Loss1: 0.538321 Loss2: 1.876397
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.668070 Loss1: 0.333168 Loss2: 1.334902 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998884
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.347564 Loss1: 0.040334 Loss2: 1.307231
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.547898 Loss1: 0.207827 Loss2: 1.340071
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.519463 Loss1: 0.168182 Loss2: 1.351281
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.456050 Loss1: 0.127908 Loss2: 1.328142
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.420737 Loss1: 0.093017 Loss2: 1.327719
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.394717 Loss1: 0.075823 Loss2: 1.318893
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404789 Loss1: 0.085273 Loss2: 1.319516
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.216630 Loss1: 0.353518 Loss2: 1.863112
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.596950 Loss1: 0.233951 Loss2: 1.362999 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497290 Loss1: 0.117287 Loss2: 1.380003 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484209 Loss1: 0.122452 Loss2: 1.361757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453530 Loss1: 0.089369 Loss2: 1.364160
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.433711 Loss1: 0.525226 Loss2: 1.908485
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.583995 Loss1: 0.210323 Loss2: 1.373672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.515449 Loss1: 0.139445 Loss2: 1.376004 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394347 Loss1: 0.052057 Loss2: 1.342289
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490723 Loss1: 0.118723 Loss2: 1.372000
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451079 Loss1: 0.094827 Loss2: 1.356252
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.419875 Loss1: 0.071503 Loss2: 1.348372
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.438699 Loss1: 0.084013 Loss2: 1.354686
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.408584 Loss1: 0.061369 Loss2: 1.347216
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.357420 Loss1: 0.450502 Loss2: 1.906918
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.411279 Loss1: 0.061759 Loss2: 1.349520
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.691331 Loss1: 0.301839 Loss2: 1.389492
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395678 Loss1: 0.054008 Loss2: 1.341670
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481742 Loss1: 0.102445 Loss2: 1.379298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.400093 Loss1: 0.036333 Loss2: 1.363759 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406213 Loss1: 0.055729 Loss2: 1.350485
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.166756 Loss1: 0.384847 Loss2: 1.781908
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.568706 Loss1: 0.253537 Loss2: 1.315169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.468045 Loss1: 0.137317 Loss2: 1.330728 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.441623 Loss1: 0.131102 Loss2: 1.310521 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.392820 Loss1: 0.088546 Loss2: 1.304274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.360825 Loss1: 0.070289 Loss2: 1.290536 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.368992 Loss1: 0.077673 Loss2: 1.291319 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364760 Loss1: 0.074308 Loss2: 1.290453 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508398 Loss1: 0.116087 Loss2: 1.392311 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461314 Loss1: 0.071573 Loss2: 1.389741 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420374 Loss1: 0.046317 Loss2: 1.374057
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.308139 Loss1: 0.474798 Loss2: 1.833341
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402967 Loss1: 0.031292 Loss2: 1.371675
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.678309 Loss1: 0.333490 Loss2: 1.344819
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399069 Loss1: 0.032680 Loss2: 1.366389
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.583783 Loss1: 0.172875 Loss2: 1.410908
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498550 Loss1: 0.159258 Loss2: 1.339291
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.454417 Loss1: 0.109732 Loss2: 1.344685
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.381333 Loss1: 0.048260 Loss2: 1.333073
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.389156 Loss1: 0.065145 Loss2: 1.324012
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.379942 Loss1: 0.061729 Loss2: 1.318213
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.148604 Loss1: 0.291161 Loss2: 1.857443
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.358604 Loss1: 0.044672 Loss2: 1.313932
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.542922 Loss1: 0.157699 Loss2: 1.385223
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.353843 Loss1: 0.044123 Loss2: 1.309720
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.493227 Loss1: 0.102647 Loss2: 1.390579
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487424 Loss1: 0.116622 Loss2: 1.370802
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.444549 Loss1: 0.071368 Loss2: 1.373181
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474011 Loss1: 0.100664 Loss2: 1.373347
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443430 Loss1: 0.072441 Loss2: 1.370990
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.303321 Loss1: 0.418883 Loss2: 1.884437
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.629183 Loss1: 0.261733 Loss2: 1.367450
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438621 Loss1: 0.066818 Loss2: 1.371803
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.579103 Loss1: 0.181339 Loss2: 1.397764
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.428120 Loss1: 0.055406 Loss2: 1.372713
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511263 Loss1: 0.133037 Loss2: 1.378225
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412875 Loss1: 0.049255 Loss2: 1.363620
+(DefaultActor pid=3764) >> Training accuracy: 0.995404
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476100 Loss1: 0.108873 Loss2: 1.367227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404345 Loss1: 0.047028 Loss2: 1.357317 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.376714 Loss1: 0.026004 Loss2: 1.350710
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.188944 Loss1: 0.359174 Loss2: 1.829770
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.728086 Loss1: 0.362599 Loss2: 1.365487 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.551457 Loss1: 0.183403 Loss2: 1.368054 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485838 Loss1: 0.109186 Loss2: 1.376652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437649 Loss1: 0.074322 Loss2: 1.363327 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.691265 Loss1: 0.228956 Loss2: 1.462308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576545 Loss1: 0.190910 Loss2: 1.385635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.529110 Loss1: 0.137090 Loss2: 1.392021 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472862 Loss1: 0.087818 Loss2: 1.385044 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425644 Loss1: 0.051467 Loss2: 1.374176 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.270611 Loss1: 0.418885 Loss2: 1.851725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.593746 Loss1: 0.209362 Loss2: 1.384385 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.473199 Loss1: 0.516366 Loss2: 1.956833 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.663525 Loss1: 0.332131 Loss2: 1.331393 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.586287 Loss1: 0.238160 Loss2: 1.348127 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.479438 Loss1: 0.117204 Loss2: 1.362233 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.416943 Loss1: 0.088770 Loss2: 1.328174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442634 Loss1: 0.122289 Loss2: 1.320345
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.434631 Loss1: 0.080783 Loss2: 1.353849
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.429584 Loss1: 0.109335 Loss2: 1.320249
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424773 Loss1: 0.073768 Loss2: 1.351005
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408671 Loss1: 0.082833 Loss2: 1.325838 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.260898 Loss1: 0.409725 Loss2: 1.851173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.603242 Loss1: 0.198488 Loss2: 1.404754 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532311 Loss1: 0.164119 Loss2: 1.368192
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.378301 Loss1: 0.485893 Loss2: 1.892408
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.505156 Loss1: 0.141650 Loss2: 1.363505
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.726349 Loss1: 0.316423 Loss2: 1.409926
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476775 Loss1: 0.117962 Loss2: 1.358813
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610539 Loss1: 0.191211 Loss2: 1.419328
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508682 Loss1: 0.150224 Loss2: 1.358458
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.503430 Loss1: 0.114619 Loss2: 1.388811
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.458036 Loss1: 0.095217 Loss2: 1.362820
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480466 Loss1: 0.099305 Loss2: 1.381161
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396170 Loss1: 0.053569 Loss2: 1.342602
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451195 Loss1: 0.068297 Loss2: 1.382898
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402283 Loss1: 0.063168 Loss2: 1.339116
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416343 Loss1: 0.048913 Loss2: 1.367430
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.446558 Loss1: 0.077867 Loss2: 1.368691
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425327 Loss1: 0.059537 Loss2: 1.365790
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405121 Loss1: 0.048232 Loss2: 1.356889
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.311515 Loss1: 0.453042 Loss2: 1.858473
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.654213 Loss1: 0.289604 Loss2: 1.364609
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.535126 Loss1: 0.144589 Loss2: 1.390537
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.495655 Loss1: 0.134598 Loss2: 1.361056
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.313189 Loss1: 0.401829 Loss2: 1.911360
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.658398 Loss1: 0.259705 Loss2: 1.398693 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.625148 Loss1: 0.198948 Loss2: 1.426200 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563498 Loss1: 0.155420 Loss2: 1.408079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.522737 Loss1: 0.119372 Loss2: 1.403365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470769 Loss1: 0.075020 Loss2: 1.395749 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.479689 Loss1: 0.106825 Loss2: 1.372864
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.464370 Loss1: 0.072315 Loss2: 1.392055
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427913 Loss1: 0.042811 Loss2: 1.385102
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.420059 Loss1: 0.043617 Loss2: 1.376442
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411740 Loss1: 0.039516 Loss2: 1.372224
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+DEBUG flwr 2023-10-13 04:40:01,874 | server.py:236 | fit_round 176 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.455792 Loss1: 0.506271 Loss2: 1.949522
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.716343 Loss1: 0.304765 Loss2: 1.411578
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.620461 Loss1: 0.170978 Loss2: 1.449483
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543648 Loss1: 0.130066 Loss2: 1.413581
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.118188 Loss1: 0.283003 Loss2: 1.835185
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.561708 Loss1: 0.191629 Loss2: 1.370079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.569061 Loss1: 0.192577 Loss2: 1.376484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526930 Loss1: 0.139503 Loss2: 1.387427 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519462 Loss1: 0.148016 Loss2: 1.371445 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508437 Loss1: 0.117510 Loss2: 1.390927 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495077 Loss1: 0.114192 Loss2: 1.380885 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440338 Loss1: 0.080220 Loss2: 1.360117 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.210669 Loss1: 0.407608 Loss2: 1.803061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.485984 Loss1: 0.144710 Loss2: 1.341274 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.310801 Loss1: 0.473210 Loss2: 1.837590 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.665165 Loss1: 0.317091 Loss2: 1.348074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.597263 Loss1: 0.226021 Loss2: 1.371242 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490055 Loss1: 0.144660 Loss2: 1.345396 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478120 Loss1: 0.141539 Loss2: 1.336581 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.419456 Loss1: 0.080450 Loss2: 1.339005 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410959 Loss1: 0.084511 Loss2: 1.326448 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367434 Loss1: 0.045787 Loss2: 1.321647 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.650593 Loss1: 0.270795 Loss2: 1.379798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532414 Loss1: 0.152184 Loss2: 1.380230 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473996 Loss1: 0.098161 Loss2: 1.375835 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.444713 Loss1: 0.083774 Loss2: 1.360939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413132 Loss1: 0.054079 Loss2: 1.359053 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 04:40:01,874][flwr][DEBUG] - fit_round 176 received 50 results and 0 failures
+INFO flwr 2023-10-13 04:40:42,106 | server.py:125 | fit progress: (176, 2.2847310698832186, {'accuracy': 0.6073}, 406149.884959183)
+>> Test accuracy: 0.607300
+[2023-10-13 04:40:42,106][flwr][INFO] - fit progress: (176, 2.2847310698832186, {'accuracy': 0.6073}, 406149.884959183)
+DEBUG flwr 2023-10-13 04:40:42,107 | server.py:173 | evaluate_round 176: strategy sampled 50 clients (out of 50)
+[2023-10-13 04:40:42,107][flwr][DEBUG] - evaluate_round 176: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 04:49:44,719 | server.py:187 | evaluate_round 176 received 50 results and 0 failures
+[2023-10-13 04:49:44,719][flwr][DEBUG] - evaluate_round 176 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 04:49:44,719 | server.py:222 | fit_round 177: strategy sampled 50 clients (out of 50)
+[2023-10-13 04:49:44,719][flwr][DEBUG] - fit_round 177: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.117091 Loss1: 0.338446 Loss2: 1.778645
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.556786 Loss1: 0.224746 Loss2: 1.332040
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.520462 Loss1: 0.164888 Loss2: 1.355573
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.329108 Loss1: 0.467118 Loss2: 1.861990
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.512839 Loss1: 0.161003 Loss2: 1.351836
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.672503 Loss1: 0.294029 Loss2: 1.378474
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.466050 Loss1: 0.119651 Loss2: 1.346399
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.580015 Loss1: 0.165610 Loss2: 1.414404
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462442 Loss1: 0.123611 Loss2: 1.338832
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.505920 Loss1: 0.139919 Loss2: 1.366001
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.501164 Loss1: 0.155051 Loss2: 1.346114
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.485031 Loss1: 0.140522 Loss2: 1.344508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462242 Loss1: 0.110402 Loss2: 1.351840 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438515 Loss1: 0.099683 Loss2: 1.338832 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372458 Loss1: 0.037574 Loss2: 1.334884 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.289880 Loss1: 0.516681 Loss2: 1.773199 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.581005 Loss1: 0.219771 Loss2: 1.361234 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534950 Loss1: 0.219131 Loss2: 1.315819
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.179853 Loss1: 0.393738 Loss2: 1.786115
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.575659 Loss1: 0.236826 Loss2: 1.338833 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.535185 Loss1: 0.172249 Loss2: 1.362936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.467213 Loss1: 0.136064 Loss2: 1.331149 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.426117 Loss1: 0.096398 Loss2: 1.329719 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.371604 Loss1: 0.050335 Loss2: 1.321269 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.345910 Loss1: 0.038528 Loss2: 1.307382 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.353521 Loss1: 0.050512 Loss2: 1.303008 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.591600 Loss1: 0.268255 Loss2: 1.323345 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.503199 Loss1: 0.166889 Loss2: 1.336310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.455234 Loss1: 0.127333 Loss2: 1.327901
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.270680 Loss1: 0.414861 Loss2: 1.855819
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457135 Loss1: 0.128698 Loss2: 1.328436
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.700104 Loss1: 0.327819 Loss2: 1.372286
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.394111 Loss1: 0.075020 Loss2: 1.319091
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.631477 Loss1: 0.201134 Loss2: 1.430344
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.377881 Loss1: 0.061913 Loss2: 1.315968
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.547700 Loss1: 0.174021 Loss2: 1.373679
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.369407 Loss1: 0.062535 Loss2: 1.306872
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509225 Loss1: 0.138038 Loss2: 1.371187
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.348374 Loss1: 0.047845 Loss2: 1.300530
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481513 Loss1: 0.112713 Loss2: 1.368800
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.457182 Loss1: 0.096232 Loss2: 1.360950
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412807 Loss1: 0.058996 Loss2: 1.353811
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415173 Loss1: 0.063758 Loss2: 1.351415
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394165 Loss1: 0.054574 Loss2: 1.339591
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.210480 Loss1: 0.381470 Loss2: 1.829010
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572045 Loss1: 0.249485 Loss2: 1.322560
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.495749 Loss1: 0.152176 Loss2: 1.343573
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.452456 Loss1: 0.122449 Loss2: 1.330007
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.403887 Loss1: 0.075104 Loss2: 1.328783
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.410242 Loss1: 0.440968 Loss2: 1.969274
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.648994 Loss1: 0.303094 Loss2: 1.345901
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.386354 Loss1: 0.062226 Loss2: 1.324128
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.377116 Loss1: 0.061617 Loss2: 1.315499 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.362061 Loss1: 0.050135 Loss2: 1.311926 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378948 Loss1: 0.068293 Loss2: 1.310655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463629 Loss1: 0.096589 Loss2: 1.367040 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403337 Loss1: 0.053701 Loss2: 1.349636 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.331720 Loss1: 0.385345 Loss2: 1.946375 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.749427 Loss1: 0.265651 Loss2: 1.483776 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.690998 Loss1: 0.256621 Loss2: 1.434376
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.222830 Loss1: 0.434928 Loss2: 1.787902
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.616562 Loss1: 0.179330 Loss2: 1.437232
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.575019 Loss1: 0.271107 Loss2: 1.303913
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.576274 Loss1: 0.138071 Loss2: 1.438203
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.547416 Loss1: 0.200814 Loss2: 1.346602
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.530872 Loss1: 0.108151 Loss2: 1.422721
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.452867 Loss1: 0.130535 Loss2: 1.322332
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.531635 Loss1: 0.107202 Loss2: 1.424433
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.419659 Loss1: 0.103910 Loss2: 1.315748
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.526198 Loss1: 0.112133 Loss2: 1.414065
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.406693 Loss1: 0.092893 Loss2: 1.313800
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.517351 Loss1: 0.107400 Loss2: 1.409950
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392177 Loss1: 0.082664 Loss2: 1.309513
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.380744 Loss1: 0.071164 Loss2: 1.309580
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374648 Loss1: 0.077823 Loss2: 1.296826
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.329316 Loss1: 0.040606 Loss2: 1.288710
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.245002 Loss1: 0.352579 Loss2: 1.892424
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.679267 Loss1: 0.276167 Loss2: 1.403100
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610408 Loss1: 0.168751 Loss2: 1.441657
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547776 Loss1: 0.149289 Loss2: 1.398487
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.234936 Loss1: 0.381982 Loss2: 1.852955
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525067 Loss1: 0.125831 Loss2: 1.399236
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.589723 Loss1: 0.237150 Loss2: 1.352574
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526820 Loss1: 0.124715 Loss2: 1.402105
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.488411 Loss1: 0.132283 Loss2: 1.356128
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.456122 Loss1: 0.098696 Loss2: 1.357426
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.540314 Loss1: 0.139612 Loss2: 1.400702
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.414322 Loss1: 0.076417 Loss2: 1.337905
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.541393 Loss1: 0.144472 Loss2: 1.396921
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420638 Loss1: 0.082587 Loss2: 1.338050
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.534719 Loss1: 0.137524 Loss2: 1.397195
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.467483 Loss1: 0.129869 Loss2: 1.337615
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.505399 Loss1: 0.111987 Loss2: 1.393412
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379541 Loss1: 0.041960 Loss2: 1.337581 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.434275 Loss1: 0.535662 Loss2: 1.898613 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573182 Loss1: 0.225413 Loss2: 1.347768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.304018 Loss1: 0.442244 Loss2: 1.861774 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416634 Loss1: 0.106718 Loss2: 1.309915 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.369929 Loss1: 0.065534 Loss2: 1.304394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.359173 Loss1: 0.059427 Loss2: 1.299746 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.341395 Loss1: 0.047685 Loss2: 1.293710 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.341999 Loss1: 0.052276 Loss2: 1.289723 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.382472 Loss1: 0.057493 Loss2: 1.324979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.336417 Loss1: 0.027485 Loss2: 1.308932 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.346207 Loss1: 0.042611 Loss2: 1.303596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.263516 Loss1: 0.424200 Loss2: 1.839316
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.660231 Loss1: 0.309451 Loss2: 1.350780
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.660810 Loss1: 0.261614 Loss2: 1.399197
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570157 Loss1: 0.199874 Loss2: 1.370283
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498980 Loss1: 0.144016 Loss2: 1.354964
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.204966 Loss1: 0.396120 Loss2: 1.808846
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.600414 Loss1: 0.272502 Loss2: 1.327912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.508328 Loss1: 0.163275 Loss2: 1.345053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.473538 Loss1: 0.151030 Loss2: 1.322509 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.449256 Loss1: 0.127300 Loss2: 1.321957 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.352109 Loss1: 0.026207 Loss2: 1.325902
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443568 Loss1: 0.117145 Loss2: 1.326423
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422634 Loss1: 0.101074 Loss2: 1.321560
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.383649 Loss1: 0.065216 Loss2: 1.318433
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354635 Loss1: 0.044810 Loss2: 1.309825
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364730 Loss1: 0.057250 Loss2: 1.307480
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.275443 Loss1: 0.454281 Loss2: 1.821162
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.603899 Loss1: 0.263388 Loss2: 1.340511
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.536920 Loss1: 0.172663 Loss2: 1.364257
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529584 Loss1: 0.183330 Loss2: 1.346254
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.450658 Loss1: 0.112240 Loss2: 1.338418
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.311392 Loss1: 0.438242 Loss2: 1.873149
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.657676 Loss1: 0.297376 Loss2: 1.360301 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598399 Loss1: 0.186233 Loss2: 1.412166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507444 Loss1: 0.142625 Loss2: 1.364819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.537497 Loss1: 0.178474 Loss2: 1.359023 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.520597 Loss1: 0.147606 Loss2: 1.372992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475968 Loss1: 0.119071 Loss2: 1.356897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453933 Loss1: 0.099969 Loss2: 1.353964 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.713469 Loss1: 0.326914 Loss2: 1.386555 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574375 Loss1: 0.188741 Loss2: 1.385634 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510041 Loss1: 0.133222 Loss2: 1.376819
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.370804 Loss1: 0.463575 Loss2: 1.907229
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.762771 Loss1: 0.397079 Loss2: 1.365692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633159 Loss1: 0.220349 Loss2: 1.412810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.545476 Loss1: 0.147424 Loss2: 1.398052
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451552 Loss1: 0.082879 Loss2: 1.368673
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.567348 Loss1: 0.199204 Loss2: 1.368144
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447704 Loss1: 0.080572 Loss2: 1.367132
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.446728 Loss1: 0.085219 Loss2: 1.361509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437782 Loss1: 0.077260 Loss2: 1.360522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393960 Loss1: 0.040107 Loss2: 1.353853 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.293368 Loss1: 0.423052 Loss2: 1.870316
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.656123 Loss1: 0.260804 Loss2: 1.395320
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.616568 Loss1: 0.199413 Loss2: 1.417156
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533525 Loss1: 0.134344 Loss2: 1.399181
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.254928 Loss1: 0.363513 Loss2: 1.891415
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.679749 Loss1: 0.280965 Loss2: 1.398784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.623437 Loss1: 0.172701 Loss2: 1.450736 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.608819 Loss1: 0.208843 Loss2: 1.399976 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.643399 Loss1: 0.228504 Loss2: 1.414895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.515377 Loss1: 0.108384 Loss2: 1.406993 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418426 Loss1: 0.052399 Loss2: 1.366028
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.545137 Loss1: 0.155749 Loss2: 1.389388
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.478014 Loss1: 0.087461 Loss2: 1.390552
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460088 Loss1: 0.071472 Loss2: 1.388616
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431381 Loss1: 0.054223 Loss2: 1.377157
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.381244 Loss1: 0.472063 Loss2: 1.909181
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.683662 Loss1: 0.322044 Loss2: 1.361618
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648936 Loss1: 0.246692 Loss2: 1.402244
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.530583 Loss1: 0.166539 Loss2: 1.364044
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.198301 Loss1: 0.365400 Loss2: 1.832901
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.606636 Loss1: 0.272456 Loss2: 1.334179 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.555001 Loss1: 0.193914 Loss2: 1.361088 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.476163 Loss1: 0.122462 Loss2: 1.353701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.444732 Loss1: 0.111445 Loss2: 1.333287 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450884 Loss1: 0.117982 Loss2: 1.332902 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382553 Loss1: 0.052982 Loss2: 1.329571 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361855 Loss1: 0.046548 Loss2: 1.315306 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572662 Loss1: 0.207723 Loss2: 1.364939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.477152 Loss1: 0.102284 Loss2: 1.374869 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.460540 Loss1: 0.100366 Loss2: 1.360174 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440794 Loss1: 0.073758 Loss2: 1.367036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419589 Loss1: 0.061941 Loss2: 1.357648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404762 Loss1: 0.051176 Loss2: 1.353586 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428838 Loss1: 0.068931 Loss2: 1.359907 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404974 Loss1: 0.048186 Loss2: 1.356789 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416807 Loss1: 0.077902 Loss2: 1.338905 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413536 Loss1: 0.072584 Loss2: 1.340952 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.629621 Loss1: 0.306496 Loss2: 1.323125 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518768 Loss1: 0.181616 Loss2: 1.337152 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.474048 Loss1: 0.137352 Loss2: 1.336696
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.259024 Loss1: 0.444705 Loss2: 1.814319
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.592178 Loss1: 0.261978 Loss2: 1.330200 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.532597 Loss1: 0.182583 Loss2: 1.350013 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.452537 Loss1: 0.127310 Loss2: 1.325227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.429831 Loss1: 0.112501 Loss2: 1.317330 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415280 Loss1: 0.095539 Loss2: 1.319741 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384012 Loss1: 0.074796 Loss2: 1.309216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354980 Loss1: 0.051763 Loss2: 1.303217
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.148386 Loss1: 0.332490 Loss2: 1.815896
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.563694 Loss1: 0.213113 Loss2: 1.350581
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.536852 Loss1: 0.175788 Loss2: 1.361064
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526646 Loss1: 0.165686 Loss2: 1.360960
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484915 Loss1: 0.141073 Loss2: 1.343842
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.216204 Loss1: 0.412099 Loss2: 1.804104
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.610213 Loss1: 0.267350 Loss2: 1.342863 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592683 Loss1: 0.207348 Loss2: 1.385336 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498266 Loss1: 0.145574 Loss2: 1.352692 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495240 Loss1: 0.141189 Loss2: 1.354052 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.350185 Loss1: 0.023531 Loss2: 1.326654
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.454685 Loss1: 0.104803 Loss2: 1.349882
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.418952 Loss1: 0.083391 Loss2: 1.335560
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.402055 Loss1: 0.073968 Loss2: 1.328087
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398442 Loss1: 0.070027 Loss2: 1.328416
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367465 Loss1: 0.044278 Loss2: 1.323187
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.162582 Loss1: 0.354537 Loss2: 1.808045
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.640718 Loss1: 0.282844 Loss2: 1.357873
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.622771 Loss1: 0.219378 Loss2: 1.403392
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.510992 Loss1: 0.146082 Loss2: 1.364910
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.554463 Loss1: 0.175642 Loss2: 1.378821
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.161002 Loss1: 0.303116 Loss2: 1.857886
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.637712 Loss1: 0.252739 Loss2: 1.384974 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588606 Loss1: 0.188801 Loss2: 1.399805 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.516644 Loss1: 0.124013 Loss2: 1.392631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530647 Loss1: 0.147023 Loss2: 1.383624 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388766 Loss1: 0.042952 Loss2: 1.345813
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487798 Loss1: 0.096580 Loss2: 1.391218
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450860 Loss1: 0.072234 Loss2: 1.378626
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455355 Loss1: 0.075163 Loss2: 1.380192
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442376 Loss1: 0.070588 Loss2: 1.371788
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.448630 Loss1: 0.074894 Loss2: 1.373736
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.275255 Loss1: 0.430387 Loss2: 1.844869
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.638900 Loss1: 0.295776 Loss2: 1.343124
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526514 Loss1: 0.152439 Loss2: 1.374075
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528723 Loss1: 0.184495 Loss2: 1.344229
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451179 Loss1: 0.105797 Loss2: 1.345382
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.169468 Loss1: 0.350726 Loss2: 1.818741
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.570045 Loss1: 0.249781 Loss2: 1.320264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.486190 Loss1: 0.147891 Loss2: 1.338299 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.443172 Loss1: 0.112480 Loss2: 1.330691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.438547 Loss1: 0.112236 Loss2: 1.326311 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.404833 Loss1: 0.087578 Loss2: 1.317255 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455973 Loss1: 0.130391 Loss2: 1.325582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435441 Loss1: 0.118285 Loss2: 1.317156 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.978125
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.738366 Loss1: 0.332014 Loss2: 1.406353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.648617 Loss1: 0.232853 Loss2: 1.415764 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.309624 Loss1: 0.453709 Loss2: 1.855915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.664802 Loss1: 0.306309 Loss2: 1.358493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.548068 Loss1: 0.159628 Loss2: 1.388440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.506688 Loss1: 0.149464 Loss2: 1.357225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469382 Loss1: 0.123689 Loss2: 1.345693 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422195 Loss1: 0.075868 Loss2: 1.346326 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.411091 Loss1: 0.083172 Loss2: 1.327919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376512 Loss1: 0.045177 Loss2: 1.331335
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.371567 Loss1: 0.490233 Loss2: 1.881334
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.659236 Loss1: 0.314332 Loss2: 1.344903
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.644735 Loss1: 0.239898 Loss2: 1.404837
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558721 Loss1: 0.207117 Loss2: 1.351604
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531790 Loss1: 0.179516 Loss2: 1.352274
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.211199 Loss1: 0.373794 Loss2: 1.837406
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.681224 Loss1: 0.335815 Loss2: 1.345408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568904 Loss1: 0.195643 Loss2: 1.373261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530508 Loss1: 0.171075 Loss2: 1.359433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.463516 Loss1: 0.123175 Loss2: 1.340341 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.438265 Loss1: 0.099318 Loss2: 1.338947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389105 Loss1: 0.061617 Loss2: 1.327488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388921 Loss1: 0.067783 Loss2: 1.321138 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.588835 Loss1: 0.251189 Loss2: 1.337647 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511466 Loss1: 0.165073 Loss2: 1.346392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472213 Loss1: 0.132540 Loss2: 1.339673
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.170335 Loss1: 0.367310 Loss2: 1.803024
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.577140 Loss1: 0.254680 Loss2: 1.322460 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543491 Loss1: 0.197376 Loss2: 1.346115 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.515691 Loss1: 0.169126 Loss2: 1.346565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.525733 Loss1: 0.187299 Loss2: 1.338434 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487050 Loss1: 0.146795 Loss2: 1.340254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.442275 Loss1: 0.107922 Loss2: 1.334353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434412 Loss1: 0.104611 Loss2: 1.329801 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.680529 Loss1: 0.327548 Loss2: 1.352981 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506229 Loss1: 0.131900 Loss2: 1.374330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.355253 Loss1: 0.469926 Loss2: 1.885327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.678311 Loss1: 0.322095 Loss2: 1.356216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.596677 Loss1: 0.209671 Loss2: 1.387006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.546018 Loss1: 0.169243 Loss2: 1.376775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.479178 Loss1: 0.125030 Loss2: 1.354148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.419101 Loss1: 0.064119 Loss2: 1.354982 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389309 Loss1: 0.055200 Loss2: 1.334109 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.350141 Loss1: 0.025480 Loss2: 1.324661 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.568548 Loss1: 0.242741 Loss2: 1.325807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.443745 Loss1: 0.114033 Loss2: 1.329712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.431460 Loss1: 0.110627 Loss2: 1.320833
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.263078 Loss1: 0.400846 Loss2: 1.862232
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.388965 Loss1: 0.072149 Loss2: 1.316817
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.712462 Loss1: 0.347832 Loss2: 1.364630
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383828 Loss1: 0.070043 Loss2: 1.313785
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.584405 Loss1: 0.183803 Loss2: 1.400602
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.357584 Loss1: 0.049845 Loss2: 1.307739
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496115 Loss1: 0.117214 Loss2: 1.378901
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425976 Loss1: 0.119505 Loss2: 1.306471
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.520783 Loss1: 0.159251 Loss2: 1.361532
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370364 Loss1: 0.059262 Loss2: 1.311102
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476194 Loss1: 0.114747 Loss2: 1.361448
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455299 Loss1: 0.098179 Loss2: 1.357121
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429948 Loss1: 0.071251 Loss2: 1.358698
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449259 Loss1: 0.092224 Loss2: 1.357035
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439653 Loss1: 0.087851 Loss2: 1.351803
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.143110 Loss1: 0.365647 Loss2: 1.777462
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.595407 Loss1: 0.268595 Loss2: 1.326812
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.509656 Loss1: 0.156283 Loss2: 1.353373
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.454789 Loss1: 0.126046 Loss2: 1.328743
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.317963 Loss1: 0.462110 Loss2: 1.855853
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.642637 Loss1: 0.275910 Loss2: 1.366727 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590208 Loss1: 0.191570 Loss2: 1.398637 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478994 Loss1: 0.109009 Loss2: 1.369985 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.362038 Loss1: 0.049151 Loss2: 1.312887
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476882 Loss1: 0.115969 Loss2: 1.360914
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.333736 Loss1: 0.033263 Loss2: 1.300473
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.422132 Loss1: 0.060697 Loss2: 1.361435
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.329326 Loss1: 0.032043 Loss2: 1.297284
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434309 Loss1: 0.086773 Loss2: 1.347537
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.393530 Loss1: 0.044391 Loss2: 1.349139
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.380401 Loss1: 0.040193 Loss2: 1.340208
+DEBUG flwr 2023-10-13 05:18:23,011 | server.py:236 | fit_round 177 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374209 Loss1: 0.033647 Loss2: 1.340562
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.320010 Loss1: 0.433429 Loss2: 1.886581
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.600746 Loss1: 0.228928 Loss2: 1.371818
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.503340 Loss1: 0.125099 Loss2: 1.378242
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.445964 Loss1: 0.081120 Loss2: 1.364845
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.314921 Loss1: 0.461162 Loss2: 1.853759
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.434981 Loss1: 0.079238 Loss2: 1.355743
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.579335 Loss1: 0.253357 Loss2: 1.325978
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.435692 Loss1: 0.077119 Loss2: 1.358573
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.552899 Loss1: 0.211806 Loss2: 1.341092
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390500 Loss1: 0.040686 Loss2: 1.349814
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.457945 Loss1: 0.133731 Loss2: 1.324214
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.374264 Loss1: 0.035918 Loss2: 1.338346
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.463333 Loss1: 0.141549 Loss2: 1.321784
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384145 Loss1: 0.048480 Loss2: 1.335664
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.391651 Loss1: 0.079930 Loss2: 1.311721
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380777 Loss1: 0.044510 Loss2: 1.336266
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.366319 Loss1: 0.056566 Loss2: 1.309753
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.341416 Loss1: 0.036837 Loss2: 1.304579
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.323067 Loss1: 0.025116 Loss2: 1.297950
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.327711 Loss1: 0.038995 Loss2: 1.288716
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.132054 Loss1: 0.298821 Loss2: 1.833233
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.616124 Loss1: 0.261461 Loss2: 1.354663
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.587948 Loss1: 0.200820 Loss2: 1.387128
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.110666 Loss1: 0.330279 Loss2: 1.780387
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.473798 Loss1: 0.115273 Loss2: 1.358525
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.445898 Loss1: 0.105418 Loss2: 1.340480 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.420161 Loss1: 0.076401 Loss2: 1.343761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.398396 Loss1: 0.062597 Loss2: 1.335799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.376151 Loss1: 0.043613 Loss2: 1.332538 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.375043 Loss1: 0.053559 Loss2: 1.321484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366524 Loss1: 0.040189 Loss2: 1.326335 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994485
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354621 Loss1: 0.056779 Loss2: 1.297842 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 05:18:23,011][flwr][DEBUG] - fit_round 177 received 50 results and 0 failures
+INFO flwr 2023-10-13 05:19:04,964 | server.py:125 | fit progress: (177, 2.2940372186727798, {'accuracy': 0.6069}, 408452.7422024)
+>> Test accuracy: 0.606900
+[2023-10-13 05:19:04,964][flwr][INFO] - fit progress: (177, 2.2940372186727798, {'accuracy': 0.6069}, 408452.7422024)
+DEBUG flwr 2023-10-13 05:19:04,964 | server.py:173 | evaluate_round 177: strategy sampled 50 clients (out of 50)
+[2023-10-13 05:19:04,964][flwr][DEBUG] - evaluate_round 177: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 05:28:09,864 | server.py:187 | evaluate_round 177 received 50 results and 0 failures
+[2023-10-13 05:28:09,864][flwr][DEBUG] - evaluate_round 177 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 05:28:09,865 | server.py:222 | fit_round 178: strategy sampled 50 clients (out of 50)
+[2023-10-13 05:28:09,865][flwr][DEBUG] - fit_round 178: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.201988 Loss1: 0.363664 Loss2: 1.838324 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.500116 Loss1: 0.143046 Loss2: 1.357070 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.459070 Loss1: 0.116699 Loss2: 1.342371
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.110972 Loss1: 0.339066 Loss2: 1.771905
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.545231 Loss1: 0.230566 Loss2: 1.314665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.536833 Loss1: 0.197555 Loss2: 1.339278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.461655 Loss1: 0.132235 Loss2: 1.329420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.435044 Loss1: 0.117397 Loss2: 1.317647 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447854 Loss1: 0.120328 Loss2: 1.327526 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427153 Loss1: 0.105888 Loss2: 1.321265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414928 Loss1: 0.101618 Loss2: 1.313310 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.144887 Loss1: 0.356440 Loss2: 1.788448 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.570396 Loss1: 0.209706 Loss2: 1.360690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.445271 Loss1: 0.508752 Loss2: 1.936519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.589599 Loss1: 0.215785 Loss2: 1.373813 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.553235 Loss1: 0.184423 Loss2: 1.368812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.505921 Loss1: 0.126656 Loss2: 1.379264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501266 Loss1: 0.134708 Loss2: 1.366557 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397138 Loss1: 0.071544 Loss2: 1.325594
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490170 Loss1: 0.128044 Loss2: 1.362126
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480649 Loss1: 0.115499 Loss2: 1.365149
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366367 Loss1: 0.045161 Loss2: 1.321206
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432739 Loss1: 0.078720 Loss2: 1.354019 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.294385 Loss1: 0.403157 Loss2: 1.891228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595256 Loss1: 0.192223 Loss2: 1.403032 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.570159 Loss1: 0.161326 Loss2: 1.408833
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.241395 Loss1: 0.389660 Loss2: 1.851735
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.605536 Loss1: 0.269109 Loss2: 1.336427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.513426 Loss1: 0.142415 Loss2: 1.371011 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.471471 Loss1: 0.125764 Loss2: 1.345707 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524959 Loss1: 0.185369 Loss2: 1.339590 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446619 Loss1: 0.104490 Loss2: 1.342129 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416926 Loss1: 0.080187 Loss2: 1.336739 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387924 Loss1: 0.058183 Loss2: 1.329741 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.286542 Loss1: 0.415740 Loss2: 1.870802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.648285 Loss1: 0.234501 Loss2: 1.413784 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.550529 Loss1: 0.163395 Loss2: 1.387134
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.249140 Loss1: 0.375199 Loss2: 1.873940
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653402 Loss1: 0.307071 Loss2: 1.346331 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.537957 Loss1: 0.170526 Loss2: 1.367431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.474391 Loss1: 0.134483 Loss2: 1.339908 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.451847 Loss1: 0.106871 Loss2: 1.344975 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.400577 Loss1: 0.067441 Loss2: 1.333136 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414889 Loss1: 0.057432 Loss2: 1.357456
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.414426 Loss1: 0.081138 Loss2: 1.333287
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.395576 Loss1: 0.067452 Loss2: 1.328124
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381596 Loss1: 0.056710 Loss2: 1.324886
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408808 Loss1: 0.089401 Loss2: 1.319407
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.197888 Loss1: 0.401438 Loss2: 1.796450
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.548728 Loss1: 0.222776 Loss2: 1.325952
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.494965 Loss1: 0.153819 Loss2: 1.341145
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.437386 Loss1: 0.116978 Loss2: 1.320408
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.355628 Loss1: 0.495760 Loss2: 1.859868
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.676864 Loss1: 0.292333 Loss2: 1.384531 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.716876 Loss1: 0.286917 Loss2: 1.429959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559335 Loss1: 0.171028 Loss2: 1.388307 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563457 Loss1: 0.182128 Loss2: 1.381328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.520689 Loss1: 0.135486 Loss2: 1.385204 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378881 Loss1: 0.062588 Loss2: 1.316293
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464900 Loss1: 0.087444 Loss2: 1.377456
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419930 Loss1: 0.059670 Loss2: 1.360260
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402457 Loss1: 0.044572 Loss2: 1.357885
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401603 Loss1: 0.050219 Loss2: 1.351384
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.320722 Loss1: 0.386922 Loss2: 1.933800
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.668454 Loss1: 0.266293 Loss2: 1.402161
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.673967 Loss1: 0.233721 Loss2: 1.440246
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.597658 Loss1: 0.172770 Loss2: 1.424887
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.272585 Loss1: 0.433390 Loss2: 1.839194
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589209 Loss1: 0.175408 Loss2: 1.413801
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.564191 Loss1: 0.230112 Loss2: 1.334079
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.597477 Loss1: 0.157332 Loss2: 1.440145
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605640 Loss1: 0.256487 Loss2: 1.349153
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.530733 Loss1: 0.122928 Loss2: 1.407806
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521012 Loss1: 0.163094 Loss2: 1.357918
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479690 Loss1: 0.078100 Loss2: 1.401590
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.536079 Loss1: 0.200717 Loss2: 1.335362
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.464056 Loss1: 0.067672 Loss2: 1.396384
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480795 Loss1: 0.139120 Loss2: 1.341676
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438821 Loss1: 0.055991 Loss2: 1.382830
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.402922 Loss1: 0.081985 Loss2: 1.320937
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415201 Loss1: 0.100786 Loss2: 1.314414
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363800 Loss1: 0.048573 Loss2: 1.315227
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.355235 Loss1: 0.054409 Loss2: 1.300826
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.159340 Loss1: 0.372747 Loss2: 1.786593
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.599935 Loss1: 0.265504 Loss2: 1.334430
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563304 Loss1: 0.192938 Loss2: 1.370366
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527650 Loss1: 0.184563 Loss2: 1.343087
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.258409 Loss1: 0.418413 Loss2: 1.839996
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510430 Loss1: 0.173638 Loss2: 1.336792
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.618430 Loss1: 0.272889 Loss2: 1.345541
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556717 Loss1: 0.201942 Loss2: 1.354775
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476540 Loss1: 0.130340 Loss2: 1.346199
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521438 Loss1: 0.162516 Loss2: 1.358922
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420850 Loss1: 0.095978 Loss2: 1.324871
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.494574 Loss1: 0.145287 Loss2: 1.349287
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414645 Loss1: 0.089980 Loss2: 1.324665
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441512 Loss1: 0.094932 Loss2: 1.346580
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384062 Loss1: 0.062976 Loss2: 1.321085
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355882 Loss1: 0.037847 Loss2: 1.318035 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394377 Loss1: 0.060480 Loss2: 1.333897 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.088995 Loss1: 0.361100 Loss2: 1.727895 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.471467 Loss1: 0.165230 Loss2: 1.306237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.447758 Loss1: 0.157577 Loss2: 1.290181
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.199876 Loss1: 0.397063 Loss2: 1.802813
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.404700 Loss1: 0.110194 Loss2: 1.294506
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.629875 Loss1: 0.316800 Loss2: 1.313075
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.538530 Loss1: 0.193526 Loss2: 1.345004
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.404341 Loss1: 0.125213 Loss2: 1.279128
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496336 Loss1: 0.174992 Loss2: 1.321343
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383001 Loss1: 0.096575 Loss2: 1.286426
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.499854 Loss1: 0.174783 Loss2: 1.325071
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.350148 Loss1: 0.067465 Loss2: 1.282683
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443920 Loss1: 0.126326 Loss2: 1.317594
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379391 Loss1: 0.100554 Loss2: 1.278837
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.387570 Loss1: 0.082697 Loss2: 1.304873
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.330276 Loss1: 0.051810 Loss2: 1.278466
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.340748 Loss1: 0.050056 Loss2: 1.290692 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.242006 Loss1: 0.372891 Loss2: 1.869115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.498137 Loss1: 0.111366 Loss2: 1.386771 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467913 Loss1: 0.102921 Loss2: 1.364992
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.173261 Loss1: 0.316951 Loss2: 1.856310
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448373 Loss1: 0.096264 Loss2: 1.352108
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.576268 Loss1: 0.220198 Loss2: 1.356070
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456267 Loss1: 0.100925 Loss2: 1.355342
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.497283 Loss1: 0.142782 Loss2: 1.354501
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390170 Loss1: 0.046056 Loss2: 1.344113
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.461576 Loss1: 0.117327 Loss2: 1.344249
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.383628 Loss1: 0.041817 Loss2: 1.341811
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.420754 Loss1: 0.078557 Loss2: 1.342197
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408756 Loss1: 0.072725 Loss2: 1.336032
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427198 Loss1: 0.089474 Loss2: 1.337724
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399067 Loss1: 0.055589 Loss2: 1.343478
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.401406 Loss1: 0.065845 Loss2: 1.335561
+(DefaultActor pid=3765) >> Training accuracy: 0.978125
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386059 Loss1: 0.059285 Loss2: 1.326773
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396357 Loss1: 0.068792 Loss2: 1.327565
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366011 Loss1: 0.033321 Loss2: 1.332690
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.239542 Loss1: 0.403758 Loss2: 1.835784
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.637046 Loss1: 0.291062 Loss2: 1.345983
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.557309 Loss1: 0.190024 Loss2: 1.367285
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496004 Loss1: 0.135692 Loss2: 1.360311
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.306687 Loss1: 0.451982 Loss2: 1.854705
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.632016 Loss1: 0.272678 Loss2: 1.359337 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.549340 Loss1: 0.163810 Loss2: 1.385530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.499185 Loss1: 0.141863 Loss2: 1.357322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.482637 Loss1: 0.131495 Loss2: 1.351142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.458041 Loss1: 0.104877 Loss2: 1.353164 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.472190 Loss1: 0.127280 Loss2: 1.344910
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441465 Loss1: 0.093526 Loss2: 1.347939
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.404757 Loss1: 0.063846 Loss2: 1.340911
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398299 Loss1: 0.057652 Loss2: 1.340647
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366370 Loss1: 0.029227 Loss2: 1.337143
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.117108 Loss1: 0.343758 Loss2: 1.773351
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.548908 Loss1: 0.230843 Loss2: 1.318065
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.550313 Loss1: 0.205494 Loss2: 1.344818
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.459388 Loss1: 0.130715 Loss2: 1.328673
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.257104 Loss1: 0.371076 Loss2: 1.886028
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463070 Loss1: 0.140319 Loss2: 1.322751
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.557373 Loss1: 0.205870 Loss2: 1.351503
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.509676 Loss1: 0.150358 Loss2: 1.359318
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.403576 Loss1: 0.084018 Loss2: 1.319558
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.477807 Loss1: 0.130514 Loss2: 1.347293
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404191 Loss1: 0.095318 Loss2: 1.308873
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.433940 Loss1: 0.092377 Loss2: 1.341563
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.357117 Loss1: 0.048596 Loss2: 1.308521
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.402610 Loss1: 0.064961 Loss2: 1.337650
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.355869 Loss1: 0.051634 Loss2: 1.304235
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.351413 Loss1: 0.053013 Loss2: 1.298400 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414474 Loss1: 0.078610 Loss2: 1.335864 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.304476 Loss1: 0.408114 Loss2: 1.896362 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.706193 Loss1: 0.234034 Loss2: 1.472160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573378 Loss1: 0.164612 Loss2: 1.408767
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.550090 Loss1: 0.555656 Loss2: 1.994434
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.692412 Loss1: 0.307033 Loss2: 1.385379 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.574688 Loss1: 0.177447 Loss2: 1.397241
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.539201 Loss1: 0.126926 Loss2: 1.412275
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532333 Loss1: 0.136851 Loss2: 1.395482
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.549391 Loss1: 0.145554 Loss2: 1.403837
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.502816 Loss1: 0.101168 Loss2: 1.401648 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493921 Loss1: 0.099097 Loss2: 1.394825 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.471790 Loss1: 0.085211 Loss2: 1.386578 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388977 Loss1: 0.032552 Loss2: 1.356426 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995192
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.120452 Loss1: 0.299373 Loss2: 1.821079
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.625328 Loss1: 0.275036 Loss2: 1.350292
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582508 Loss1: 0.190508 Loss2: 1.392000
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502299 Loss1: 0.146390 Loss2: 1.355908
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.284268 Loss1: 0.423849 Loss2: 1.860419
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630154 Loss1: 0.281572 Loss2: 1.348583
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.501716 Loss1: 0.145736 Loss2: 1.355980
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.557542 Loss1: 0.179990 Loss2: 1.377552
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460578 Loss1: 0.109564 Loss2: 1.351014
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521021 Loss1: 0.160523 Loss2: 1.360498
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428405 Loss1: 0.082907 Loss2: 1.345498
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475764 Loss1: 0.127032 Loss2: 1.348732
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426955 Loss1: 0.083412 Loss2: 1.343543
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438146 Loss1: 0.097635 Loss2: 1.340511 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416272 Loss1: 0.074117 Loss2: 1.342155 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413086 Loss1: 0.074756 Loss2: 1.338330 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.353118 Loss1: 0.448210 Loss2: 1.904909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.539530 Loss1: 0.168778 Loss2: 1.370753 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540733 Loss1: 0.168943 Loss2: 1.371790
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.152173 Loss1: 0.345080 Loss2: 1.807093
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.553300 Loss1: 0.203343 Loss2: 1.349957 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.560932 Loss1: 0.198743 Loss2: 1.362189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507534 Loss1: 0.153062 Loss2: 1.354472 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541812 Loss1: 0.190512 Loss2: 1.351300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516764 Loss1: 0.162730 Loss2: 1.354033 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444254 Loss1: 0.100753 Loss2: 1.343501 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407109 Loss1: 0.076682 Loss2: 1.330427 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.360988 Loss1: 0.481590 Loss2: 1.879398 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.646052 Loss1: 0.276207 Loss2: 1.369844 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.611189 Loss1: 0.244005 Loss2: 1.367184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.580273 Loss1: 0.208070 Loss2: 1.372203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.530827 Loss1: 0.160632 Loss2: 1.370195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486517 Loss1: 0.130995 Loss2: 1.355521 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469942 Loss1: 0.120162 Loss2: 1.349780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405778 Loss1: 0.056132 Loss2: 1.349646 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994420
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408700 Loss1: 0.071576 Loss2: 1.337124 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.349975 Loss1: 0.027712 Loss2: 1.322262 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.341730 Loss1: 0.028039 Loss2: 1.313691
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.170509 Loss1: 0.356997 Loss2: 1.813512
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.576713 Loss1: 0.217836 Loss2: 1.358877
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.560601 Loss1: 0.169163 Loss2: 1.391438
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500280 Loss1: 0.136113 Loss2: 1.364167
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480895 Loss1: 0.118078 Loss2: 1.362817
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.182178 Loss1: 0.369936 Loss2: 1.812242
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.453968 Loss1: 0.087377 Loss2: 1.366591
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.522463 Loss1: 0.204555 Loss2: 1.317908
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461574 Loss1: 0.109118 Loss2: 1.352456
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.498608 Loss1: 0.170429 Loss2: 1.328179
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.504163 Loss1: 0.171658 Loss2: 1.332505
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.417321 Loss1: 0.056799 Loss2: 1.360522
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.471971 Loss1: 0.151786 Loss2: 1.320185
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414587 Loss1: 0.064619 Loss2: 1.349968
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460717 Loss1: 0.137736 Loss2: 1.322982
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378330 Loss1: 0.034303 Loss2: 1.344027
+(DefaultActor pid=3765) >> Training accuracy: 0.999023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418096 Loss1: 0.102301 Loss2: 1.315795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382310 Loss1: 0.073759 Loss2: 1.308551 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.591397 Loss1: 0.248131 Loss2: 1.343267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.519725 Loss1: 0.169089 Loss2: 1.350636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478872 Loss1: 0.133936 Loss2: 1.344936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473398 Loss1: 0.120622 Loss2: 1.352776 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474066 Loss1: 0.124415 Loss2: 1.349651 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439833 Loss1: 0.096442 Loss2: 1.343390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.442969 Loss1: 0.105932 Loss2: 1.337037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406287 Loss1: 0.075796 Loss2: 1.330491 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420517 Loss1: 0.074117 Loss2: 1.346400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375900 Loss1: 0.040595 Loss2: 1.335306 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.682952 Loss1: 0.320866 Loss2: 1.362086 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576833 Loss1: 0.202650 Loss2: 1.374183 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470615 Loss1: 0.103813 Loss2: 1.366802 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455214 Loss1: 0.098967 Loss2: 1.356247 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425286 Loss1: 0.070930 Loss2: 1.354356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441033 Loss1: 0.094426 Loss2: 1.346607 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454050 Loss1: 0.107395 Loss2: 1.346655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413179 Loss1: 0.066797 Loss2: 1.346383 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464062 Loss1: 0.083456 Loss2: 1.380606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402806 Loss1: 0.037027 Loss2: 1.365779 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.668066 Loss1: 0.291850 Loss2: 1.376217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557603 Loss1: 0.162006 Loss2: 1.395597 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524880 Loss1: 0.141452 Loss2: 1.383428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475639 Loss1: 0.096011 Loss2: 1.379627 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439137 Loss1: 0.068233 Loss2: 1.370904 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443579 Loss1: 0.074873 Loss2: 1.368706 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418090 Loss1: 0.058511 Loss2: 1.359579 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420250 Loss1: 0.064939 Loss2: 1.355310 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382254 Loss1: 0.056013 Loss2: 1.326240 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.350788 Loss1: 0.031645 Loss2: 1.319143 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.266986 Loss1: 0.416186 Loss2: 1.850800
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.630827 Loss1: 0.263440 Loss2: 1.367387
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610154 Loss1: 0.211708 Loss2: 1.398446
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534890 Loss1: 0.170467 Loss2: 1.364423
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.445103 Loss1: 0.512622 Loss2: 1.932481
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.703427 Loss1: 0.320961 Loss2: 1.382467 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460721 Loss1: 0.100243 Loss2: 1.360478
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.749611 Loss1: 0.327655 Loss2: 1.421956
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.601781 Loss1: 0.209661 Loss2: 1.392120
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458554 Loss1: 0.099560 Loss2: 1.358994
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557700 Loss1: 0.177656 Loss2: 1.380044
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440699 Loss1: 0.084666 Loss2: 1.356034
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419199 Loss1: 0.067738 Loss2: 1.351461 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391880 Loss1: 0.049325 Loss2: 1.342555 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.474944 Loss1: 0.102760 Loss2: 1.372184 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.191031 Loss1: 0.353800 Loss2: 1.837231 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.536738 Loss1: 0.148330 Loss2: 1.388409 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.268299 Loss1: 0.424946 Loss2: 1.843353
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.521246 Loss1: 0.150413 Loss2: 1.370834
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.643569 Loss1: 0.301439 Loss2: 1.342130
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.500649 Loss1: 0.137699 Loss2: 1.362950
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.528535 Loss1: 0.146632 Loss2: 1.381903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539847 Loss1: 0.157055 Loss2: 1.382792 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.513765 Loss1: 0.138282 Loss2: 1.375484 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488156 Loss1: 0.115437 Loss2: 1.372719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.469133 Loss1: 0.100010 Loss2: 1.369123 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.974609
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452538 Loss1: 0.104001 Loss2: 1.348537 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.181425 Loss1: 0.325208 Loss2: 1.856217 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.555296 Loss1: 0.157192 Loss2: 1.398104 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.487685 Loss1: 0.106791 Loss2: 1.380894 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.481088 Loss1: 0.107721 Loss2: 1.373367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437441 Loss1: 0.062120 Loss2: 1.375320 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422278 Loss1: 0.058763 Loss2: 1.363515 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437117 Loss1: 0.077372 Loss2: 1.359745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.360963 Loss1: 0.050182 Loss2: 1.310781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.343341 Loss1: 0.037702 Loss2: 1.305638 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995404
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.335751 Loss1: 0.037042 Loss2: 1.298709 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.270093 Loss1: 0.423481 Loss2: 1.846612
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.656929 Loss1: 0.304590 Loss2: 1.352339
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.543249 Loss1: 0.158071 Loss2: 1.385178
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.505823 Loss1: 0.144681 Loss2: 1.361142
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.327192 Loss1: 0.466997 Loss2: 1.860195
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.625207 Loss1: 0.311476 Loss2: 1.313731 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.436658 Loss1: 0.086335 Loss2: 1.350323
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.561362 Loss1: 0.218355 Loss2: 1.343007
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.386651 Loss1: 0.042708 Loss2: 1.343943
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496148 Loss1: 0.166814 Loss2: 1.329334
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441972 Loss1: 0.118968 Loss2: 1.323004
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391863 Loss1: 0.058828 Loss2: 1.333035
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.410186 Loss1: 0.092815 Loss2: 1.317371
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.363085 Loss1: 0.031620 Loss2: 1.331466
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.371443 Loss1: 0.063211 Loss2: 1.308232
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363595 Loss1: 0.039194 Loss2: 1.324401
+DEBUG flwr 2023-10-13 05:57:46,315 | server.py:236 | fit_round 178 received 50 results and 0 failures
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397845 Loss1: 0.089627 Loss2: 1.308219 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.974330
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.232180 Loss1: 0.397314 Loss2: 1.834866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561106 Loss1: 0.199287 Loss2: 1.361819 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.438812 Loss1: 0.471132 Loss2: 1.967680
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.504641 Loss1: 0.171908 Loss2: 1.332733
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.452201 Loss1: 0.119026 Loss2: 1.333175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.430821 Loss1: 0.097699 Loss2: 1.333122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409834 Loss1: 0.079700 Loss2: 1.330133 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470960 Loss1: 0.124625 Loss2: 1.346335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484852 Loss1: 0.134695 Loss2: 1.350157 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438332 Loss1: 0.093368 Loss2: 1.344965 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413163 Loss1: 0.088362 Loss2: 1.324801 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.377881 Loss1: 0.433037 Loss2: 1.944844
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.726980 Loss1: 0.288634 Loss2: 1.438346
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.664300 Loss1: 0.189053 Loss2: 1.475247
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.567078 Loss1: 0.134049 Loss2: 1.433029
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.203454 Loss1: 0.336128 Loss2: 1.867326
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.569502 Loss1: 0.218382 Loss2: 1.351120 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.499530 Loss1: 0.147465 Loss2: 1.352065 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.482398 Loss1: 0.123474 Loss2: 1.358923 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441365 Loss1: 0.092217 Loss2: 1.349148 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.422719 Loss1: 0.080340 Loss2: 1.342379 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.447904 Loss1: 0.043657 Loss2: 1.404246
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428878 Loss1: 0.086846 Loss2: 1.342032
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390074 Loss1: 0.052681 Loss2: 1.337393
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373936 Loss1: 0.042108 Loss2: 1.331829
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377308 Loss1: 0.045129 Loss2: 1.332179
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 05:57:46,315][flwr][DEBUG] - fit_round 178 received 50 results and 0 failures
+INFO flwr 2023-10-13 05:58:28,897 | server.py:125 | fit progress: (178, 2.2912714462310744, {'accuracy': 0.6079}, 410816.67539118696)
+>> Test accuracy: 0.607900
+[2023-10-13 05:58:28,897][flwr][INFO] - fit progress: (178, 2.2912714462310744, {'accuracy': 0.6079}, 410816.67539118696)
+DEBUG flwr 2023-10-13 05:58:28,897 | server.py:173 | evaluate_round 178: strategy sampled 50 clients (out of 50)
+[2023-10-13 05:58:28,897][flwr][DEBUG] - evaluate_round 178: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 06:07:37,631 | server.py:187 | evaluate_round 178 received 50 results and 0 failures
+[2023-10-13 06:07:37,631][flwr][DEBUG] - evaluate_round 178 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 06:07:37,632 | server.py:222 | fit_round 179: strategy sampled 50 clients (out of 50)
+[2023-10-13 06:07:37,632][flwr][DEBUG] - fit_round 179: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.266425 Loss1: 0.414109 Loss2: 1.852315
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.584580 Loss1: 0.213875 Loss2: 1.370706
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.510690 Loss1: 0.138652 Loss2: 1.372038
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502929 Loss1: 0.131545 Loss2: 1.371385
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.253272 Loss1: 0.385019 Loss2: 1.868253
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.460963 Loss1: 0.104443 Loss2: 1.356519
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.590211 Loss1: 0.216362 Loss2: 1.373849
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451777 Loss1: 0.098207 Loss2: 1.353571
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565950 Loss1: 0.173800 Loss2: 1.392150
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.445193 Loss1: 0.094510 Loss2: 1.350683
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.508789 Loss1: 0.128353 Loss2: 1.380436
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.412168 Loss1: 0.061007 Loss2: 1.351162
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.443972 Loss1: 0.085363 Loss2: 1.358609
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408985 Loss1: 0.062754 Loss2: 1.346231
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450744 Loss1: 0.093902 Loss2: 1.356843
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381007 Loss1: 0.039073 Loss2: 1.341935
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469808 Loss1: 0.120764 Loss2: 1.349043
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.495844 Loss1: 0.121572 Loss2: 1.374273
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438933 Loss1: 0.085366 Loss2: 1.353567
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407650 Loss1: 0.057935 Loss2: 1.349715
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.304376 Loss1: 0.462434 Loss2: 1.841942
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.722911 Loss1: 0.357156 Loss2: 1.365756
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595798 Loss1: 0.182252 Loss2: 1.413546
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.471866 Loss1: 0.122123 Loss2: 1.349743
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.363618 Loss1: 0.494289 Loss2: 1.869329
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.662030 Loss1: 0.340118 Loss2: 1.321912
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.493566 Loss1: 0.137814 Loss2: 1.355752
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.535132 Loss1: 0.198292 Loss2: 1.336840
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.401045 Loss1: 0.054052 Loss2: 1.346993
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.396150 Loss1: 0.063202 Loss2: 1.332948 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.379193 Loss1: 0.050287 Loss2: 1.328906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374324 Loss1: 0.048364 Loss2: 1.325960 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360132 Loss1: 0.042606 Loss2: 1.317526 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368338 Loss1: 0.055195 Loss2: 1.313144 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985577
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.420359 Loss1: 0.450568 Loss2: 1.969791
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.721646 Loss1: 0.310848 Loss2: 1.410798
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.682034 Loss1: 0.221293 Loss2: 1.460740
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.599215 Loss1: 0.162853 Loss2: 1.436362
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.149985 Loss1: 0.347231 Loss2: 1.802754
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.591749 Loss1: 0.246574 Loss2: 1.345174 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.494933 Loss1: 0.127175 Loss2: 1.367758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.472851 Loss1: 0.121230 Loss2: 1.351622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459090 Loss1: 0.062464 Loss2: 1.396626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454606 Loss1: 0.064693 Loss2: 1.389913 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415888 Loss1: 0.075173 Loss2: 1.340715 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.457168 Loss1: 0.110714 Loss2: 1.346455 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.583737 Loss1: 0.240838 Loss2: 1.342899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.494714 Loss1: 0.170137 Loss2: 1.324577 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.258626 Loss1: 0.456003 Loss2: 1.802623
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463407 Loss1: 0.126557 Loss2: 1.336850
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.628739 Loss1: 0.312230 Loss2: 1.316509
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.386618 Loss1: 0.067281 Loss2: 1.319338
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.361018 Loss1: 0.053806 Loss2: 1.307212 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.379452 Loss1: 0.070521 Loss2: 1.308930 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.352863 Loss1: 0.051342 Loss2: 1.301521 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.346725 Loss1: 0.044441 Loss2: 1.302284 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991728
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372785 Loss1: 0.083511 Loss2: 1.289274 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.302173 Loss1: 0.433427 Loss2: 1.868747 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.590378 Loss1: 0.190434 Loss2: 1.399945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.493859 Loss1: 0.140581 Loss2: 1.353279
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.216075 Loss1: 0.371185 Loss2: 1.844890
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.580250 Loss1: 0.234328 Loss2: 1.345921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.506933 Loss1: 0.148500 Loss2: 1.358433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.488691 Loss1: 0.134393 Loss2: 1.354298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.443329 Loss1: 0.098559 Loss2: 1.344771 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.419418 Loss1: 0.081896 Loss2: 1.337522 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.383863 Loss1: 0.054343 Loss2: 1.329519 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370219 Loss1: 0.047219 Loss2: 1.323000 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.716706 Loss1: 0.311517 Loss2: 1.405189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529366 Loss1: 0.120562 Loss2: 1.408804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.318996 Loss1: 0.456466 Loss2: 1.862530
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.489777 Loss1: 0.089855 Loss2: 1.399922
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.603087 Loss1: 0.256713 Loss2: 1.346374
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470557 Loss1: 0.074515 Loss2: 1.396042
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.511969 Loss1: 0.130709 Loss2: 1.381260
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.454013 Loss1: 0.067028 Loss2: 1.386985
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.486361 Loss1: 0.142018 Loss2: 1.344343
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.451868 Loss1: 0.066938 Loss2: 1.384930
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.442192 Loss1: 0.103150 Loss2: 1.339042
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429948 Loss1: 0.049574 Loss2: 1.380374
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.416889 Loss1: 0.079986 Loss2: 1.336902
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413880 Loss1: 0.038861 Loss2: 1.375018
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419918 Loss1: 0.081026 Loss2: 1.338892 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414195 Loss1: 0.076792 Loss2: 1.337403 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.643349 Loss1: 0.266613 Loss2: 1.376736 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497474 Loss1: 0.125803 Loss2: 1.371671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.511881 Loss1: 0.142837 Loss2: 1.369044 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.476314 Loss1: 0.105498 Loss2: 1.370816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.452735 Loss1: 0.082922 Loss2: 1.369813 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438874 Loss1: 0.073474 Loss2: 1.365399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456970 Loss1: 0.091167 Loss2: 1.365803 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431762 Loss1: 0.074601 Loss2: 1.357161 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413624 Loss1: 0.047639 Loss2: 1.365986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416813 Loss1: 0.056738 Loss2: 1.360075 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.628899 Loss1: 0.278571 Loss2: 1.350328 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516251 Loss1: 0.155345 Loss2: 1.360906 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.498144 Loss1: 0.146341 Loss2: 1.351803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.629853 Loss1: 0.270993 Loss2: 1.358860
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475429 Loss1: 0.114707 Loss2: 1.360723
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.545899 Loss1: 0.147089 Loss2: 1.398810
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447294 Loss1: 0.093706 Loss2: 1.353588
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557346 Loss1: 0.197353 Loss2: 1.359994
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418235 Loss1: 0.068748 Loss2: 1.349486
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418469 Loss1: 0.072757 Loss2: 1.345712
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.466117 Loss1: 0.094603 Loss2: 1.371514
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411663 Loss1: 0.063250 Loss2: 1.348413
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.398378 Loss1: 0.049746 Loss2: 1.348632
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.375168 Loss1: 0.033274 Loss2: 1.341894
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.360928 Loss1: 0.030635 Loss2: 1.330293
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.350527 Loss1: 0.027800 Loss2: 1.322726
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352897 Loss1: 0.034585 Loss2: 1.318313
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.302960 Loss1: 0.431037 Loss2: 1.871923
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.611959 Loss1: 0.230442 Loss2: 1.381517
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.550316 Loss1: 0.164380 Loss2: 1.385935
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.514989 Loss1: 0.145663 Loss2: 1.369327
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470726 Loss1: 0.103045 Loss2: 1.367682
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.270607 Loss1: 0.375860 Loss2: 1.894747
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.624318 Loss1: 0.243368 Loss2: 1.380950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.610907 Loss1: 0.193768 Loss2: 1.417140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.534956 Loss1: 0.150381 Loss2: 1.384575 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502313 Loss1: 0.110216 Loss2: 1.392097 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392852 Loss1: 0.046331 Loss2: 1.346521
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480054 Loss1: 0.096066 Loss2: 1.383988
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464231 Loss1: 0.090202 Loss2: 1.374029
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437920 Loss1: 0.064929 Loss2: 1.372991
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422296 Loss1: 0.053883 Loss2: 1.368413
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413535 Loss1: 0.051865 Loss2: 1.361670
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.231257 Loss1: 0.349799 Loss2: 1.881458
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.611681 Loss1: 0.240267 Loss2: 1.371414
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.569372 Loss1: 0.175521 Loss2: 1.393851
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523071 Loss1: 0.133605 Loss2: 1.389466
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475252 Loss1: 0.098471 Loss2: 1.376781
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.178364 Loss1: 0.375244 Loss2: 1.803121
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.579742 Loss1: 0.238932 Loss2: 1.340810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.549395 Loss1: 0.181798 Loss2: 1.367597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507871 Loss1: 0.160586 Loss2: 1.347285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.504111 Loss1: 0.158341 Loss2: 1.345770 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480478 Loss1: 0.130987 Loss2: 1.349492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424826 Loss1: 0.090858 Loss2: 1.333967 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.333453 Loss1: 0.437112 Loss2: 1.896341 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.665286 Loss1: 0.231598 Loss2: 1.433688 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.530731 Loss1: 0.126563 Loss2: 1.404169 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487423 Loss1: 0.105727 Loss2: 1.381696
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.168510 Loss1: 0.362162 Loss2: 1.806348
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459685 Loss1: 0.083865 Loss2: 1.375820
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.610888 Loss1: 0.258249 Loss2: 1.352639
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.533710 Loss1: 0.152702 Loss2: 1.381008 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.442289 Loss1: 0.108690 Loss2: 1.333600 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408874 Loss1: 0.045233 Loss2: 1.363641
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.422989 Loss1: 0.085762 Loss2: 1.337227
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.434163 Loss1: 0.103629 Loss2: 1.330534
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.397557 Loss1: 0.075833 Loss2: 1.321724
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.387857 Loss1: 0.065563 Loss2: 1.322294
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.375679 Loss1: 0.060779 Loss2: 1.314899
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.239463 Loss1: 0.425111 Loss2: 1.814352
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379873 Loss1: 0.064803 Loss2: 1.315070
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.547977 Loss1: 0.224211 Loss2: 1.323765
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.522012 Loss1: 0.174168 Loss2: 1.347843
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.434815 Loss1: 0.102693 Loss2: 1.332122
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.416705 Loss1: 0.097074 Loss2: 1.319631
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.381441 Loss1: 0.065674 Loss2: 1.315767
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.348027 Loss1: 0.476156 Loss2: 1.871871
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.350262 Loss1: 0.039899 Loss2: 1.310363
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.760749 Loss1: 0.384850 Loss2: 1.375899
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.345500 Loss1: 0.042386 Loss2: 1.303114
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.757422 Loss1: 0.287024 Loss2: 1.470397
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.330054 Loss1: 0.035352 Loss2: 1.294702
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.602833 Loss1: 0.209093 Loss2: 1.393740
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.326169 Loss1: 0.030789 Loss2: 1.295380
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.507875 Loss1: 0.117987 Loss2: 1.389888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430044 Loss1: 0.061007 Loss2: 1.369037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464989 Loss1: 0.102928 Loss2: 1.362061
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.142319 Loss1: 0.361986 Loss2: 1.780334
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444889 Loss1: 0.083617 Loss2: 1.361273
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.557377 Loss1: 0.257031 Loss2: 1.300346
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.499364 Loss1: 0.182105 Loss2: 1.317258
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499794 Loss1: 0.173405 Loss2: 1.326389
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.456579 Loss1: 0.142282 Loss2: 1.314297
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.421191 Loss1: 0.104970 Loss2: 1.316221
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.188304 Loss1: 0.375168 Loss2: 1.813135
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.405438 Loss1: 0.095684 Loss2: 1.309754
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612191 Loss1: 0.282183 Loss2: 1.330008
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.365178 Loss1: 0.063028 Loss2: 1.302150
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.537018 Loss1: 0.166734 Loss2: 1.370284
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.351760 Loss1: 0.054011 Loss2: 1.297750
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.465842 Loss1: 0.128215 Loss2: 1.337627
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368549 Loss1: 0.071666 Loss2: 1.296883
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.414341 Loss1: 0.096027 Loss2: 1.318314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.380946 Loss1: 0.064123 Loss2: 1.316824 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.360272 Loss1: 0.056473 Loss2: 1.303799
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.295047 Loss1: 0.464051 Loss2: 1.830996
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.332574 Loss1: 0.033037 Loss2: 1.299536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.654228 Loss1: 0.323530 Loss2: 1.330699
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.637315 Loss1: 0.266813 Loss2: 1.370501
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511083 Loss1: 0.181578 Loss2: 1.329505
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.457397 Loss1: 0.127186 Loss2: 1.330210
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.392872 Loss1: 0.072849 Loss2: 1.320022
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.195571 Loss1: 0.365950 Loss2: 1.829621
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.398081 Loss1: 0.094631 Loss2: 1.303450
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.358333 Loss1: 0.059716 Loss2: 1.298618
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.577109 Loss1: 0.220126 Loss2: 1.356984
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374174 Loss1: 0.067812 Loss2: 1.306362
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.482268 Loss1: 0.126486 Loss2: 1.355783
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368057 Loss1: 0.067945 Loss2: 1.300112
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496823 Loss1: 0.147812 Loss2: 1.349011
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.488977 Loss1: 0.146723 Loss2: 1.342255
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.532204 Loss1: 0.177797 Loss2: 1.354408
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.421698 Loss1: 0.084581 Loss2: 1.337116
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412170 Loss1: 0.083543 Loss2: 1.328627
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.438053 Loss1: 0.527513 Loss2: 1.910540
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386785 Loss1: 0.061563 Loss2: 1.325221
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370140 Loss1: 0.047752 Loss2: 1.322388 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.456486 Loss1: 0.143778 Loss2: 1.312708 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.371980 Loss1: 0.063955 Loss2: 1.308024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.359357 Loss1: 0.470448 Loss2: 1.888908 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.703413 Loss1: 0.307147 Loss2: 1.396266 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.501383 Loss1: 0.110457 Loss2: 1.390926 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441535 Loss1: 0.056666 Loss2: 1.384868 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435319 Loss1: 0.063500 Loss2: 1.371820
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.317894 Loss1: 0.468273 Loss2: 1.849620
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420461 Loss1: 0.053686 Loss2: 1.366775
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.639828 Loss1: 0.287410 Loss2: 1.352418
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403042 Loss1: 0.035238 Loss2: 1.367804
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.543432 Loss1: 0.170897 Loss2: 1.372535
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394578 Loss1: 0.031982 Loss2: 1.362596
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516332 Loss1: 0.159843 Loss2: 1.356489
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491489 Loss1: 0.145748 Loss2: 1.345742
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465017 Loss1: 0.110781 Loss2: 1.354237
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425620 Loss1: 0.083200 Loss2: 1.342419
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393274 Loss1: 0.061824 Loss2: 1.331450
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373361 Loss1: 0.046849 Loss2: 1.326513
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.326592 Loss1: 0.456034 Loss2: 1.870558
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.353545 Loss1: 0.031802 Loss2: 1.321743
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.707634 Loss1: 0.357720 Loss2: 1.349915
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.630165 Loss1: 0.221624 Loss2: 1.408541
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.525392 Loss1: 0.176008 Loss2: 1.349384
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501499 Loss1: 0.154053 Loss2: 1.347446
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.418467 Loss1: 0.076437 Loss2: 1.342030
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.174134 Loss1: 0.388832 Loss2: 1.785303
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416427 Loss1: 0.080077 Loss2: 1.336350
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.379198 Loss1: 0.052500 Loss2: 1.326698 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387677 Loss1: 0.066139 Loss2: 1.321537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366399 Loss1: 0.047590 Loss2: 1.318809 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416737 Loss1: 0.093872 Loss2: 1.322864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.360330 Loss1: 0.050309 Loss2: 1.310022 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.351548 Loss1: 0.047608 Loss2: 1.303940
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.217896 Loss1: 0.365147 Loss2: 1.852749
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.722354 Loss1: 0.323757 Loss2: 1.398598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537530 Loss1: 0.144293 Loss2: 1.393237 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.489296 Loss1: 0.509045 Loss2: 1.980250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751666 Loss1: 0.380587 Loss2: 1.371079 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.748974 Loss1: 0.312407 Loss2: 1.436567 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549298 Loss1: 0.130432 Loss2: 1.418866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.529876 Loss1: 0.152249 Loss2: 1.377627
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.452703 Loss1: 0.070806 Loss2: 1.381896
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446982 Loss1: 0.069314 Loss2: 1.377668 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.442750 Loss1: 0.078396 Loss2: 1.364355 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.142221 Loss1: 0.369918 Loss2: 1.772302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.521607 Loss1: 0.156828 Loss2: 1.364778 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.295455 Loss1: 0.492155 Loss2: 1.803300
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.455260 Loss1: 0.131937 Loss2: 1.323323
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.613382 Loss1: 0.283634 Loss2: 1.329748
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.440879 Loss1: 0.110697 Loss2: 1.330182
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455083 Loss1: 0.124256 Loss2: 1.330827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415898 Loss1: 0.096335 Loss2: 1.319563 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421895 Loss1: 0.099780 Loss2: 1.322116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453414 Loss1: 0.127980 Loss2: 1.325434 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423717 Loss1: 0.094643 Loss2: 1.329075 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.363075 Loss1: 0.057182 Loss2: 1.305894 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.186679 Loss1: 0.386108 Loss2: 1.800571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.567892 Loss1: 0.209420 Loss2: 1.358473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.488848 Loss1: 0.154857 Loss2: 1.333992
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.333310 Loss1: 0.395831 Loss2: 1.937479
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.713566 Loss1: 0.304296 Loss2: 1.409270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.661891 Loss1: 0.203461 Loss2: 1.458430 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.601432 Loss1: 0.190410 Loss2: 1.411022 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.537116 Loss1: 0.123104 Loss2: 1.414012 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524825 Loss1: 0.107438 Loss2: 1.417387 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351822 Loss1: 0.044554 Loss2: 1.307268
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475926 Loss1: 0.077561 Loss2: 1.398365
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479022 Loss1: 0.080662 Loss2: 1.398361
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433800 Loss1: 0.045491 Loss2: 1.388309
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434948 Loss1: 0.047160 Loss2: 1.387787
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.184096 Loss1: 0.403574 Loss2: 1.780522
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.618827 Loss1: 0.314744 Loss2: 1.304082
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588938 Loss1: 0.249812 Loss2: 1.339126
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.471029 Loss1: 0.152312 Loss2: 1.318717
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.264411 Loss1: 0.393369 Loss2: 1.871042
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.636883 Loss1: 0.268484 Loss2: 1.368399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.587230 Loss1: 0.189630 Loss2: 1.397600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534436 Loss1: 0.150659 Loss2: 1.383777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.575006 Loss1: 0.204666 Loss2: 1.370339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.562404 Loss1: 0.176260 Loss2: 1.386144 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496971 Loss1: 0.128288 Loss2: 1.368683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455618 Loss1: 0.087454 Loss2: 1.368164 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.239169 Loss1: 0.389612 Loss2: 1.849556 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600780 Loss1: 0.213343 Loss2: 1.387437 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.228370 Loss1: 0.394492 Loss2: 1.833878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.561532 Loss1: 0.203235 Loss2: 1.358297 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.524466 Loss1: 0.147207 Loss2: 1.377259 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.419196 Loss1: 0.066262 Loss2: 1.352933 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.418205 Loss1: 0.071087 Loss2: 1.347118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409518 Loss1: 0.070738 Loss2: 1.338780 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382687 Loss1: 0.050004 Loss2: 1.332683 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371351 Loss1: 0.040057 Loss2: 1.331294 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.654144 Loss1: 0.267110 Loss2: 1.387035 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.657153 Loss1: 0.268327 Loss2: 1.388826 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558913 Loss1: 0.156304 Loss2: 1.402609
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.291896 Loss1: 0.447356 Loss2: 1.844540
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.647708 Loss1: 0.316622 Loss2: 1.331086 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.493886 Loss1: 0.107239 Loss2: 1.386646
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.588002 Loss1: 0.231766 Loss2: 1.356235
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.490077 Loss1: 0.106323 Loss2: 1.383754
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526834 Loss1: 0.178668 Loss2: 1.348166
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.438568 Loss1: 0.109575 Loss2: 1.328994
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.489854 Loss1: 0.116046 Loss2: 1.373808
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442580 Loss1: 0.114933 Loss2: 1.327647
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.481633 Loss1: 0.096334 Loss2: 1.385299
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.374602 Loss1: 0.061773 Loss2: 1.312829 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.346334 Loss1: 0.039929 Loss2: 1.306405 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.294406 Loss1: 0.459723 Loss2: 1.834683
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.639042 Loss1: 0.293608 Loss2: 1.345434
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564382 Loss1: 0.188965 Loss2: 1.375417
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497086 Loss1: 0.157060 Loss2: 1.340026
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.241368 Loss1: 0.393325 Loss2: 1.848042
+DEBUG flwr 2023-10-13 06:36:20,724 | server.py:236 | fit_round 179 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.559068 Loss1: 0.226717 Loss2: 1.332352 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.512712 Loss1: 0.168632 Loss2: 1.344081 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476429 Loss1: 0.137028 Loss2: 1.339401 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.432267 Loss1: 0.111421 Loss2: 1.320846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.401919 Loss1: 0.080267 Loss2: 1.321652 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.382399 Loss1: 0.066360 Loss2: 1.316038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391484 Loss1: 0.077395 Loss2: 1.314089 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.232715 Loss1: 0.442106 Loss2: 1.790609 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.520645 Loss1: 0.163160 Loss2: 1.357485 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.459861 Loss1: 0.132547 Loss2: 1.327314
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.308646 Loss1: 0.450917 Loss2: 1.857729
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.534632 Loss1: 0.188430 Loss2: 1.346203 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.509574 Loss1: 0.160604 Loss2: 1.348970 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540623 Loss1: 0.197872 Loss2: 1.342751 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.512721 Loss1: 0.156796 Loss2: 1.355924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.518872 Loss1: 0.170830 Loss2: 1.348042 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.508676 Loss1: 0.139288 Loss2: 1.369389 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414575 Loss1: 0.075046 Loss2: 1.339530 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.231771 Loss1: 0.348954 Loss2: 1.882817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608773 Loss1: 0.162619 Loss2: 1.446153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513155 Loss1: 0.102872 Loss2: 1.410283 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.473990 Loss1: 0.081206 Loss2: 1.392784 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450128 Loss1: 0.063661 Loss2: 1.386467 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 06:36:20,724][flwr][DEBUG] - fit_round 179 received 50 results and 0 failures
+INFO flwr 2023-10-13 06:37:01,303 | server.py:125 | fit progress: (179, 2.289638937662204, {'accuracy': 0.6098}, 413129.08199989)
+>> Test accuracy: 0.609800
+[2023-10-13 06:37:01,303][flwr][INFO] - fit progress: (179, 2.289638937662204, {'accuracy': 0.6098}, 413129.08199989)
+DEBUG flwr 2023-10-13 06:37:01,304 | server.py:173 | evaluate_round 179: strategy sampled 50 clients (out of 50)
+[2023-10-13 06:37:01,304][flwr][DEBUG] - evaluate_round 179: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 06:46:06,569 | server.py:187 | evaluate_round 179 received 50 results and 0 failures
+[2023-10-13 06:46:06,569][flwr][DEBUG] - evaluate_round 179 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 06:46:06,569 | server.py:222 | fit_round 180: strategy sampled 50 clients (out of 50)
+[2023-10-13 06:46:06,569][flwr][DEBUG] - fit_round 180: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.257761 Loss1: 0.407385 Loss2: 1.850376 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.493978 Loss1: 0.176831 Loss2: 1.317147 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.426828 Loss1: 0.094389 Loss2: 1.332439
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.319501 Loss1: 0.462983 Loss2: 1.856518
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.540133 Loss1: 0.204123 Loss2: 1.336010 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.500260 Loss1: 0.141829 Loss2: 1.358431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.473979 Loss1: 0.129512 Loss2: 1.344467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.444219 Loss1: 0.114547 Loss2: 1.329672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.406547 Loss1: 0.076215 Loss2: 1.330332 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415291 Loss1: 0.092256 Loss2: 1.323035 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442737 Loss1: 0.115859 Loss2: 1.326878 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.961458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.650228 Loss1: 0.249012 Loss2: 1.401216 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.585927 Loss1: 0.172863 Loss2: 1.413063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527216 Loss1: 0.127212 Loss2: 1.400004
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.139031 Loss1: 0.309317 Loss2: 1.829714
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.554426 Loss1: 0.145735 Loss2: 1.408690
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.565986 Loss1: 0.209186 Loss2: 1.356800
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.522096 Loss1: 0.121870 Loss2: 1.400226
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.554489 Loss1: 0.173633 Loss2: 1.380856
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.515962 Loss1: 0.153648 Loss2: 1.362313 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.459084 Loss1: 0.097403 Loss2: 1.361680 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447578 Loss1: 0.091319 Loss2: 1.356259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424303 Loss1: 0.067479 Loss2: 1.356824 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.355660 Loss1: 0.454527 Loss2: 1.901133 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991728
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.601790 Loss1: 0.239700 Loss2: 1.362090 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517342 Loss1: 0.157040 Loss2: 1.360302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.256637 Loss1: 0.432955 Loss2: 1.823683 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.627694 Loss1: 0.302182 Loss2: 1.325512 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425796 Loss1: 0.062373 Loss2: 1.363423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440753 Loss1: 0.093100 Loss2: 1.347652 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983259
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.453678 Loss1: 0.121826 Loss2: 1.331852 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431696 Loss1: 0.101995 Loss2: 1.329702 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.346398 Loss1: 0.418297 Loss2: 1.928100
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433570 Loss1: 0.112524 Loss2: 1.321046
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.728746 Loss1: 0.313850 Loss2: 1.414895
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403304 Loss1: 0.083834 Loss2: 1.319470
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.552850 Loss1: 0.138415 Loss2: 1.414435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460889 Loss1: 0.062340 Loss2: 1.398549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496771 Loss1: 0.106443 Loss2: 1.390328
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.169387 Loss1: 0.332793 Loss2: 1.836594
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445311 Loss1: 0.047191 Loss2: 1.398120
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.602656 Loss1: 0.243044 Loss2: 1.359613
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.449296 Loss1: 0.064316 Loss2: 1.384980
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578622 Loss1: 0.182567 Loss2: 1.396055
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440240 Loss1: 0.057866 Loss2: 1.382374
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539253 Loss1: 0.173053 Loss2: 1.366200
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.457696 Loss1: 0.099685 Loss2: 1.358012
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439995 Loss1: 0.084088 Loss2: 1.355907
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456848 Loss1: 0.104192 Loss2: 1.352656
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453846 Loss1: 0.105730 Loss2: 1.348115
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.310241 Loss1: 0.383609 Loss2: 1.926631
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416071 Loss1: 0.062414 Loss2: 1.353657
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389887 Loss1: 0.042835 Loss2: 1.347052 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542457 Loss1: 0.129074 Loss2: 1.413383 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533569 Loss1: 0.138987 Loss2: 1.394582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.230867 Loss1: 0.358480 Loss2: 1.872386 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.567159 Loss1: 0.203481 Loss2: 1.363678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.551058 Loss1: 0.190086 Loss2: 1.360972 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.527864 Loss1: 0.153766 Loss2: 1.374098 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.547108 Loss1: 0.170240 Loss2: 1.376869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445428 Loss1: 0.087371 Loss2: 1.358057 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426710 Loss1: 0.072925 Loss2: 1.353785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433599 Loss1: 0.082770 Loss2: 1.350828 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549131 Loss1: 0.188657 Loss2: 1.360474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449551 Loss1: 0.085448 Loss2: 1.364103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.246174 Loss1: 0.410329 Loss2: 1.835846 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.596023 Loss1: 0.249743 Loss2: 1.346280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564849 Loss1: 0.191284 Loss2: 1.373565 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.480581 Loss1: 0.136489 Loss2: 1.344091 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438335 Loss1: 0.095794 Loss2: 1.342540 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419094 Loss1: 0.087661 Loss2: 1.331433
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.213867 Loss1: 0.351237 Loss2: 1.862630
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.684041 Loss1: 0.326861 Loss2: 1.357180 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.407217 Loss1: 0.074849 Loss2: 1.332367
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.624715 Loss1: 0.230479 Loss2: 1.394235
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517521 Loss1: 0.155459 Loss2: 1.362062
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.432823 Loss1: 0.085129 Loss2: 1.347693
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466154 Loss1: 0.117450 Loss2: 1.348704
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439197 Loss1: 0.098203 Loss2: 1.340995
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.327169 Loss1: 0.464212 Loss2: 1.862957
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424670 Loss1: 0.083423 Loss2: 1.341247
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399114 Loss1: 0.063905 Loss2: 1.335209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387350 Loss1: 0.062337 Loss2: 1.325013 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.529756 Loss1: 0.159674 Loss2: 1.370082 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.564820 Loss1: 0.202474 Loss2: 1.362346 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.516602 Loss1: 0.146033 Loss2: 1.370569
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.231271 Loss1: 0.370808 Loss2: 1.860463
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.610435 Loss1: 0.248551 Loss2: 1.361884 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582902 Loss1: 0.199496 Loss2: 1.383406 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517358 Loss1: 0.140637 Loss2: 1.376720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448460 Loss1: 0.088560 Loss2: 1.359900 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.168099 Loss1: 0.320791 Loss2: 1.847308
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483748 Loss1: 0.123179 Loss2: 1.360569
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.594438 Loss1: 0.231570 Loss2: 1.362868
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.467377 Loss1: 0.098749 Loss2: 1.368627
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.551071 Loss1: 0.169195 Loss2: 1.381876
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.429122 Loss1: 0.072882 Loss2: 1.356239
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455600 Loss1: 0.091731 Loss2: 1.363869 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448866 Loss1: 0.095565 Loss2: 1.353301 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.365383 Loss1: 0.464125 Loss2: 1.901257
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450650 Loss1: 0.092999 Loss2: 1.357651
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.704283 Loss1: 0.298174 Loss2: 1.406109
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443371 Loss1: 0.094671 Loss2: 1.348700
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635796 Loss1: 0.202767 Loss2: 1.433029
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.442894 Loss1: 0.093764 Loss2: 1.349130
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.569211 Loss1: 0.161748 Loss2: 1.407462 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.524808 Loss1: 0.133203 Loss2: 1.391605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.495798 Loss1: 0.104267 Loss2: 1.391531
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.268770 Loss1: 0.329005 Loss2: 1.939765
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630344 Loss1: 0.206313 Loss2: 1.424030 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432455 Loss1: 0.056628 Loss2: 1.375827
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.622894 Loss1: 0.192165 Loss2: 1.430728
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.639929 Loss1: 0.190863 Loss2: 1.449065
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.565274 Loss1: 0.126757 Loss2: 1.438517
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.545588 Loss1: 0.130720 Loss2: 1.414868
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490957 Loss1: 0.075436 Loss2: 1.415521
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.199586 Loss1: 0.404758 Loss2: 1.794828
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.519694 Loss1: 0.109840 Loss2: 1.409854
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.598958 Loss1: 0.264914 Loss2: 1.334045
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.470278 Loss1: 0.062903 Loss2: 1.407376
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.511462 Loss1: 0.161432 Loss2: 1.350030
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.451223 Loss1: 0.049904 Loss2: 1.401319
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448590 Loss1: 0.127335 Loss2: 1.321255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418457 Loss1: 0.086876 Loss2: 1.331581 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.296658 Loss1: 0.441915 Loss2: 1.854742
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.402047 Loss1: 0.070822 Loss2: 1.331225
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.582657 Loss1: 0.232427 Loss2: 1.350230
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.352757 Loss1: 0.033465 Loss2: 1.319292
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.353461 Loss1: 0.039585 Loss2: 1.313876 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.436326 Loss1: 0.098641 Loss2: 1.337685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406864 Loss1: 0.074433 Loss2: 1.332431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430059 Loss1: 0.098117 Loss2: 1.331942
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.199690 Loss1: 0.389231 Loss2: 1.810459
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.573764 Loss1: 0.254831 Loss2: 1.318933 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.564789 Loss1: 0.207413 Loss2: 1.357376 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.386342 Loss1: 0.072949 Loss2: 1.313393 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.396567 Loss1: 0.093030 Loss2: 1.303537 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.374625 Loss1: 0.070573 Loss2: 1.304053 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.354468 Loss1: 0.057037 Loss2: 1.297431 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369108 Loss1: 0.069749 Loss2: 1.299359 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495044 Loss1: 0.132570 Loss2: 1.362474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430866 Loss1: 0.074228 Loss2: 1.356638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.423314 Loss1: 0.068121 Loss2: 1.355193
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.233404 Loss1: 0.357952 Loss2: 1.875452
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.670465 Loss1: 0.270571 Loss2: 1.399894 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610599 Loss1: 0.166251 Loss2: 1.444348 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.485806 Loss1: 0.085672 Loss2: 1.400134 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459085 Loss1: 0.077024 Loss2: 1.382062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470746 Loss1: 0.086299 Loss2: 1.384447 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448400 Loss1: 0.067745 Loss2: 1.380655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439051 Loss1: 0.059141 Loss2: 1.379909 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421308 Loss1: 0.082242 Loss2: 1.339066 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.385591 Loss1: 0.056632 Loss2: 1.328959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400618 Loss1: 0.078625 Loss2: 1.321993
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.256324 Loss1: 0.424156 Loss2: 1.832168
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351272 Loss1: 0.030980 Loss2: 1.320292
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.617740 Loss1: 0.265968 Loss2: 1.351772
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.567213 Loss1: 0.186460 Loss2: 1.380753
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569187 Loss1: 0.203232 Loss2: 1.365955
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.538643 Loss1: 0.173774 Loss2: 1.364869
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516033 Loss1: 0.158556 Loss2: 1.357477
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.453142 Loss1: 0.090110 Loss2: 1.363032
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.366367 Loss1: 0.438877 Loss2: 1.927491
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.428812 Loss1: 0.082417 Loss2: 1.346395
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.667865 Loss1: 0.275346 Loss2: 1.392520
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429500 Loss1: 0.087392 Loss2: 1.342108
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.669465 Loss1: 0.247275 Loss2: 1.422190
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405592 Loss1: 0.071161 Loss2: 1.334431
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.603425 Loss1: 0.200921 Loss2: 1.402504
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.584395 Loss1: 0.173953 Loss2: 1.410442
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506935 Loss1: 0.109527 Loss2: 1.397409
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470503 Loss1: 0.088551 Loss2: 1.381952
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437440 Loss1: 0.049424 Loss2: 1.388016
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433903 Loss1: 0.060531 Loss2: 1.373372
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.201687 Loss1: 0.375613 Loss2: 1.826074
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434347 Loss1: 0.061882 Loss2: 1.372465
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.627177 Loss1: 0.277864 Loss2: 1.349313
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574601 Loss1: 0.199744 Loss2: 1.374857
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542183 Loss1: 0.182113 Loss2: 1.360069
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475612 Loss1: 0.121312 Loss2: 1.354300
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487942 Loss1: 0.135136 Loss2: 1.352806
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.157578 Loss1: 0.346679 Loss2: 1.810899
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.553194 Loss1: 0.194527 Loss2: 1.358667 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.510125 Loss1: 0.148175 Loss2: 1.361950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.471428 Loss1: 0.120214 Loss2: 1.351213 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521269 Loss1: 0.170613 Loss2: 1.350656 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.442646 Loss1: 0.097590 Loss2: 1.345056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398532 Loss1: 0.060666 Loss2: 1.337866 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373373 Loss1: 0.042042 Loss2: 1.331332 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.622568 Loss1: 0.184376 Loss2: 1.438192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461238 Loss1: 0.086027 Loss2: 1.375210 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.379815 Loss1: 0.449035 Loss2: 1.930780 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.683472 Loss1: 0.309889 Loss2: 1.373584 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.583725 Loss1: 0.189325 Loss2: 1.394400 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.518398 Loss1: 0.129459 Loss2: 1.388939 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430973 Loss1: 0.076705 Loss2: 1.354268
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.462840 Loss1: 0.098087 Loss2: 1.364752
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.457843 Loss1: 0.093751 Loss2: 1.364092
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461377 Loss1: 0.098676 Loss2: 1.362701
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446251 Loss1: 0.086390 Loss2: 1.359861
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453099 Loss1: 0.099080 Loss2: 1.354019
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441291 Loss1: 0.086127 Loss2: 1.355164
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.201818 Loss1: 0.366190 Loss2: 1.835628
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.552327 Loss1: 0.205912 Loss2: 1.346415
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.456752 Loss1: 0.111173 Loss2: 1.345579
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.435601 Loss1: 0.091340 Loss2: 1.344261
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.398631 Loss1: 0.069627 Loss2: 1.329004
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.397415 Loss1: 0.075550 Loss2: 1.321865
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.309634 Loss1: 0.401419 Loss2: 1.908215
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.379271 Loss1: 0.056163 Loss2: 1.323108
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.690976 Loss1: 0.302545 Loss2: 1.388431
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380313 Loss1: 0.059117 Loss2: 1.321196
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.613596 Loss1: 0.183782 Loss2: 1.429815
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372740 Loss1: 0.057397 Loss2: 1.315343
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.559069 Loss1: 0.148795 Loss2: 1.410274
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364794 Loss1: 0.050638 Loss2: 1.314156
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518306 Loss1: 0.123928 Loss2: 1.394378
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.510686 Loss1: 0.120507 Loss2: 1.390178
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460720 Loss1: 0.073720 Loss2: 1.387000
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.463200 Loss1: 0.077063 Loss2: 1.386137
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433128 Loss1: 0.049461 Loss2: 1.383666
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.238952 Loss1: 0.372116 Loss2: 1.866836
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427515 Loss1: 0.058519 Loss2: 1.368996
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.616212 Loss1: 0.243249 Loss2: 1.372963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503188 Loss1: 0.152176 Loss2: 1.351011 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449044 Loss1: 0.116832 Loss2: 1.332211
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.225950 Loss1: 0.364659 Loss2: 1.861291
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404517 Loss1: 0.072969 Loss2: 1.331548
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.639614 Loss1: 0.283173 Loss2: 1.356441
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.365053 Loss1: 0.043292 Loss2: 1.321761
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568156 Loss1: 0.189903 Loss2: 1.378253
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.357200 Loss1: 0.042231 Loss2: 1.314968
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529177 Loss1: 0.162120 Loss2: 1.367057
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.343790 Loss1: 0.033097 Loss2: 1.310693
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.480975 Loss1: 0.125720 Loss2: 1.355254
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463803 Loss1: 0.112024 Loss2: 1.351778
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.457074 Loss1: 0.103633 Loss2: 1.353441
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.459848 Loss1: 0.114085 Loss2: 1.345763
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424714 Loss1: 0.082498 Loss2: 1.342216
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403734 Loss1: 0.066109 Loss2: 1.337625
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.274068 Loss1: 0.329538 Loss2: 1.944530
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.801325 Loss1: 0.342848 Loss2: 1.458477
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.675607 Loss1: 0.173088 Loss2: 1.502519
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.581900 Loss1: 0.118031 Loss2: 1.463869
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.560403 Loss1: 0.108813 Loss2: 1.451590
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.186938 Loss1: 0.327400 Loss2: 1.859538
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.560571 Loss1: 0.103782 Loss2: 1.456789
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.544918 Loss1: 0.082998 Loss2: 1.461919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.522049 Loss1: 0.067820 Loss2: 1.454229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.488743 Loss1: 0.042897 Loss2: 1.445846 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.467690 Loss1: 0.033248 Loss2: 1.434442 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476411 Loss1: 0.135209 Loss2: 1.341202 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410429 Loss1: 0.072197 Loss2: 1.338232 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383924 Loss1: 0.055205 Loss2: 1.328719
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.201262 Loss1: 0.389431 Loss2: 1.811832
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.513183 Loss1: 0.205800 Loss2: 1.307382
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.517924 Loss1: 0.189755 Loss2: 1.328170
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.422159 Loss1: 0.114465 Loss2: 1.307693
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.398672 Loss1: 0.103895 Loss2: 1.294777
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.232971 Loss1: 0.380336 Loss2: 1.852635
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.677669 Loss1: 0.321267 Loss2: 1.356402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563474 Loss1: 0.159819 Loss2: 1.403655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507881 Loss1: 0.142793 Loss2: 1.365089 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.494480 Loss1: 0.130293 Loss2: 1.364186 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.330210 Loss1: 0.042132 Loss2: 1.288078
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.458161 Loss1: 0.099626 Loss2: 1.358535
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.407629 Loss1: 0.063503 Loss2: 1.344126
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389143 Loss1: 0.045255 Loss2: 1.343888
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405702 Loss1: 0.063757 Loss2: 1.341946
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377313 Loss1: 0.044035 Loss2: 1.333278
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.215260 Loss1: 0.416078 Loss2: 1.799182
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.578914 Loss1: 0.262046 Loss2: 1.316868
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.527268 Loss1: 0.178887 Loss2: 1.348382
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.448555 Loss1: 0.123388 Loss2: 1.325168
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487307 Loss1: 0.162337 Loss2: 1.324969
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.233450 Loss1: 0.402798 Loss2: 1.830651
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.487245 Loss1: 0.155700 Loss2: 1.331545
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.611776 Loss1: 0.274032 Loss2: 1.337744
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431093 Loss1: 0.108232 Loss2: 1.322861
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.558238 Loss1: 0.202171 Loss2: 1.356067
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394756 Loss1: 0.071743 Loss2: 1.323012
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498510 Loss1: 0.146134 Loss2: 1.352376
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427163 Loss1: 0.107937 Loss2: 1.319227
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.436988 Loss1: 0.110682 Loss2: 1.326306
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398606 Loss1: 0.080430 Loss2: 1.318176
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420410 Loss1: 0.092567 Loss2: 1.327843
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.378095 Loss1: 0.055569 Loss2: 1.322525
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390121 Loss1: 0.070702 Loss2: 1.319419
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369074 Loss1: 0.052108 Loss2: 1.316966
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356695 Loss1: 0.046486 Loss2: 1.310210
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.490329 Loss1: 0.528978 Loss2: 1.961351
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.631018 Loss1: 0.266613 Loss2: 1.364406
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.566702 Loss1: 0.202425 Loss2: 1.364277
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.487616 Loss1: 0.112461 Loss2: 1.375155
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.422817 Loss1: 0.076148 Loss2: 1.346670
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452764 Loss1: 0.111051 Loss2: 1.341713
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.076021 Loss1: 0.312125 Loss2: 1.763896
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451084 Loss1: 0.106046 Loss2: 1.345038
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.513303 Loss1: 0.201198 Loss2: 1.312105
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.503077 Loss1: 0.183141 Loss2: 1.319936 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.517646 Loss1: 0.195721 Loss2: 1.321925 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.397760 Loss1: 0.084221 Loss2: 1.313539 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.336368 Loss1: 0.045906 Loss2: 1.290462 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.292165 Loss1: 0.396737 Loss2: 1.895428
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.320427 Loss1: 0.032339 Loss2: 1.288088
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.603943 Loss1: 0.222784 Loss2: 1.381159
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.309646 Loss1: 0.025675 Loss2: 1.283970
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516011 Loss1: 0.125486 Loss2: 1.390525 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.500706 Loss1: 0.114156 Loss2: 1.386551 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513418 Loss1: 0.133778 Loss2: 1.379641
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.279782 Loss1: 0.406785 Loss2: 1.872996
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.610538 Loss1: 0.259074 Loss2: 1.351465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608728 Loss1: 0.222560 Loss2: 1.386168 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466580 Loss1: 0.099465 Loss2: 1.367115
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.456017 Loss1: 0.101507 Loss2: 1.354510
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.444393 Loss1: 0.101376 Loss2: 1.343017
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.399334 Loss1: 0.057478 Loss2: 1.341856
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.380925 Loss1: 0.047934 Loss2: 1.332992
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.355139 Loss1: 0.032793 Loss2: 1.322347
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.224236 Loss1: 0.379462 Loss2: 1.844774
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.352951 Loss1: 0.029019 Loss2: 1.323932
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.662402 Loss1: 0.326025 Loss2: 1.336378
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370846 Loss1: 0.050198 Loss2: 1.320648
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.515044 Loss1: 0.147595 Loss2: 1.367448 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462510 Loss1: 0.114446 Loss2: 1.348064 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439252 Loss1: 0.097966 Loss2: 1.341286
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.217613 Loss1: 0.379412 Loss2: 1.838201
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.625984 Loss1: 0.240517 Loss2: 1.385467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.559312 Loss1: 0.144362 Loss2: 1.414951 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 07:14:10,680 | server.py:236 | fit_round 180 received 50 results and 0 failures
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562056 Loss1: 0.171544 Loss2: 1.390512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.542522 Loss1: 0.148283 Loss2: 1.394239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.467818 Loss1: 0.080165 Loss2: 1.387652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460622 Loss1: 0.078322 Loss2: 1.382300 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472212 Loss1: 0.094087 Loss2: 1.378125 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471021 Loss1: 0.138571 Loss2: 1.332450 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.421836 Loss1: 0.089518 Loss2: 1.332318 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.227897 Loss1: 0.352854 Loss2: 1.875043
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.395484 Loss1: 0.067399 Loss2: 1.328086
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.539834 Loss1: 0.185556 Loss2: 1.354278
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378326 Loss1: 0.055099 Loss2: 1.323227
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.573873 Loss1: 0.216413 Loss2: 1.357461
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378927 Loss1: 0.060557 Loss2: 1.318370
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.437984 Loss1: 0.105207 Loss2: 1.332777 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392149 Loss1: 0.060819 Loss2: 1.331331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.415758 Loss1: 0.498065 Loss2: 1.917693
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390021 Loss1: 0.069430 Loss2: 1.320590
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.696357 Loss1: 0.333743 Loss2: 1.362614
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374791 Loss1: 0.051841 Loss2: 1.322950
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380613 Loss1: 0.063606 Loss2: 1.317007 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443343 Loss1: 0.092761 Loss2: 1.350582 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426932 Loss1: 0.085369 Loss2: 1.341562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.544354 Loss1: 0.497747 Loss2: 2.046607 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.700226 Loss1: 0.316157 Loss2: 1.384069 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.554352 Loss1: 0.136144 Loss2: 1.418208 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494216 Loss1: 0.111937 Loss2: 1.382278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438453 Loss1: 0.060613 Loss2: 1.377840 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417442 Loss1: 0.058596 Loss2: 1.358846 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 07:14:10,680][flwr][DEBUG] - fit_round 180 received 50 results and 0 failures
+INFO flwr 2023-10-13 07:14:52,168 | server.py:125 | fit progress: (180, 2.291451721145703, {'accuracy': 0.609}, 415399.94684953)
+>> Test accuracy: 0.609000
+[2023-10-13 07:14:52,168][flwr][INFO] - fit progress: (180, 2.291451721145703, {'accuracy': 0.609}, 415399.94684953)
+DEBUG flwr 2023-10-13 07:14:52,169 | server.py:173 | evaluate_round 180: strategy sampled 50 clients (out of 50)
+[2023-10-13 07:14:52,169][flwr][DEBUG] - evaluate_round 180: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 07:24:01,804 | server.py:187 | evaluate_round 180 received 50 results and 0 failures
+[2023-10-13 07:24:01,804][flwr][DEBUG] - evaluate_round 180 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 07:24:01,804 | server.py:222 | fit_round 181: strategy sampled 50 clients (out of 50)
+[2023-10-13 07:24:01,804][flwr][DEBUG] - fit_round 181: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.055213 Loss1: 0.307115 Loss2: 1.748098
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.520971 Loss1: 0.214537 Loss2: 1.306434
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.481422 Loss1: 0.162592 Loss2: 1.318829
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.259465 Loss1: 0.387961 Loss2: 1.871504
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.411431 Loss1: 0.105868 Loss2: 1.305563
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.633491 Loss1: 0.256592 Loss2: 1.376899
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.414876 Loss1: 0.117526 Loss2: 1.297350
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.577436 Loss1: 0.176182 Loss2: 1.401254
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.381526 Loss1: 0.077754 Loss2: 1.303771
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.388259 Loss1: 0.091378 Loss2: 1.296881 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.388888 Loss1: 0.090095 Loss2: 1.298793 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.341965 Loss1: 0.050869 Loss2: 1.291095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.341183 Loss1: 0.050330 Loss2: 1.290853 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.420694 Loss1: 0.065567 Loss2: 1.355126 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.255461 Loss1: 0.390123 Loss2: 1.865338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.633043 Loss1: 0.232610 Loss2: 1.400433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540091 Loss1: 0.176298 Loss2: 1.363793
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.135402 Loss1: 0.387890 Loss2: 1.747512
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.483438 Loss1: 0.122527 Loss2: 1.360911
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.510789 Loss1: 0.242442 Loss2: 1.268347
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.471254 Loss1: 0.114765 Loss2: 1.356490
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.426938 Loss1: 0.136815 Loss2: 1.290123
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428240 Loss1: 0.075140 Loss2: 1.353100
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.405678 Loss1: 0.134984 Loss2: 1.270694
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389122 Loss1: 0.043168 Loss2: 1.345954
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.355823 Loss1: 0.085485 Loss2: 1.270338
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399318 Loss1: 0.057649 Loss2: 1.341669
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.375498 Loss1: 0.106216 Loss2: 1.269282
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385060 Loss1: 0.049134 Loss2: 1.335926
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.345318 Loss1: 0.079807 Loss2: 1.265511
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.324185 Loss1: 0.062044 Loss2: 1.262142
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.287651 Loss1: 0.033022 Loss2: 1.254629
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.321848 Loss1: 0.069471 Loss2: 1.252377
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.272396 Loss1: 0.383378 Loss2: 1.889018
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.684376 Loss1: 0.300417 Loss2: 1.383959
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.585888 Loss1: 0.181738 Loss2: 1.404150
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569388 Loss1: 0.180888 Loss2: 1.388500
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.272052 Loss1: 0.445837 Loss2: 1.826216
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.580198 Loss1: 0.186771 Loss2: 1.393428
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.581963 Loss1: 0.236540 Loss2: 1.345423
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519749 Loss1: 0.127163 Loss2: 1.392586
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.469642 Loss1: 0.113499 Loss2: 1.356143
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.493503 Loss1: 0.119445 Loss2: 1.374059
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.426813 Loss1: 0.084017 Loss2: 1.342796
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449636 Loss1: 0.070144 Loss2: 1.379493
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.399333 Loss1: 0.071778 Loss2: 1.327555
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452672 Loss1: 0.076092 Loss2: 1.376580
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.397226 Loss1: 0.069312 Loss2: 1.327914
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414406 Loss1: 0.041870 Loss2: 1.372536
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422201 Loss1: 0.092782 Loss2: 1.329419
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388437 Loss1: 0.057825 Loss2: 1.330612
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.378017 Loss1: 0.054423 Loss2: 1.323594
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376448 Loss1: 0.063989 Loss2: 1.312459
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.266059 Loss1: 0.369103 Loss2: 1.896957
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.686842 Loss1: 0.290277 Loss2: 1.396565
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.632301 Loss1: 0.203521 Loss2: 1.428780
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554647 Loss1: 0.138826 Loss2: 1.415821
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.214189 Loss1: 0.375045 Loss2: 1.839145
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503442 Loss1: 0.102554 Loss2: 1.400889
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.577622 Loss1: 0.234175 Loss2: 1.343447
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.499335 Loss1: 0.099238 Loss2: 1.400096
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.633498 Loss1: 0.270001 Loss2: 1.363497
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.494645 Loss1: 0.098954 Loss2: 1.395691
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576836 Loss1: 0.224461 Loss2: 1.352375
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470673 Loss1: 0.076097 Loss2: 1.394575
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.565503 Loss1: 0.219808 Loss2: 1.345695
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.490980 Loss1: 0.096852 Loss2: 1.394127
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.509589 Loss1: 0.159640 Loss2: 1.349949
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461791 Loss1: 0.069079 Loss2: 1.392712
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.501473 Loss1: 0.160696 Loss2: 1.340778
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428304 Loss1: 0.088325 Loss2: 1.339978
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421420 Loss1: 0.092863 Loss2: 1.328557
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368248 Loss1: 0.040101 Loss2: 1.328148
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.226789 Loss1: 0.368879 Loss2: 1.857910
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572261 Loss1: 0.252654 Loss2: 1.319607
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.535560 Loss1: 0.197695 Loss2: 1.337865
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496257 Loss1: 0.157124 Loss2: 1.339133
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.281966 Loss1: 0.429962 Loss2: 1.852004
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.629938 Loss1: 0.286588 Loss2: 1.343350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543604 Loss1: 0.166310 Loss2: 1.377295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.441885 Loss1: 0.096627 Loss2: 1.345258 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.437220 Loss1: 0.100585 Loss2: 1.336635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427861 Loss1: 0.104102 Loss2: 1.323759
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.426032 Loss1: 0.091093 Loss2: 1.334939
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384339 Loss1: 0.071835 Loss2: 1.312504
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.381590 Loss1: 0.048692 Loss2: 1.332898
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390470 Loss1: 0.062266 Loss2: 1.328204
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390503 Loss1: 0.063976 Loss2: 1.326528
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398491 Loss1: 0.073475 Loss2: 1.325016
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.226126 Loss1: 0.390991 Loss2: 1.835134
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.599196 Loss1: 0.254552 Loss2: 1.344644
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.570849 Loss1: 0.203646 Loss2: 1.367202
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.503375 Loss1: 0.158084 Loss2: 1.345291
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.419994 Loss1: 0.542567 Loss2: 1.877427
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.582715 Loss1: 0.255515 Loss2: 1.327200
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.459358 Loss1: 0.110599 Loss2: 1.348760
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437634 Loss1: 0.097895 Loss2: 1.339739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395523 Loss1: 0.060248 Loss2: 1.335275 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.368097 Loss1: 0.042936 Loss2: 1.325161 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.361309 Loss1: 0.039047 Loss2: 1.322262 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.376451 Loss1: 0.061875 Loss2: 1.314576 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354145 Loss1: 0.041037 Loss2: 1.313108 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.153582 Loss1: 0.363319 Loss2: 1.790263
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.624071 Loss1: 0.296090 Loss2: 1.327981
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.553508 Loss1: 0.199600 Loss2: 1.353908
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489215 Loss1: 0.145851 Loss2: 1.343364
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.154739 Loss1: 0.381302 Loss2: 1.773438
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443258 Loss1: 0.122319 Loss2: 1.320939
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.578984 Loss1: 0.247750 Loss2: 1.331234
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.529106 Loss1: 0.179177 Loss2: 1.349929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492287 Loss1: 0.163587 Loss2: 1.328701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.445057 Loss1: 0.121722 Loss2: 1.323335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.410788 Loss1: 0.087221 Loss2: 1.323567 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.398727 Loss1: 0.081673 Loss2: 1.317055 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379979 Loss1: 0.071393 Loss2: 1.308586 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.291876 Loss1: 0.398422 Loss2: 1.893454 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.520800 Loss1: 0.125528 Loss2: 1.395271 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.505714 Loss1: 0.116538 Loss2: 1.389176
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.355530 Loss1: 0.463662 Loss2: 1.891868
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.636188 Loss1: 0.280921 Loss2: 1.355268 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568549 Loss1: 0.189459 Loss2: 1.379090 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.486898 Loss1: 0.132000 Loss2: 1.354898 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446357 Loss1: 0.099892 Loss2: 1.346465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.409628 Loss1: 0.065237 Loss2: 1.344391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405685 Loss1: 0.052131 Loss2: 1.353555
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416275 Loss1: 0.080416 Loss2: 1.335860
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.392865 Loss1: 0.061312 Loss2: 1.331553
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385715 Loss1: 0.060999 Loss2: 1.324716
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373205 Loss1: 0.049918 Loss2: 1.323287
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.379927 Loss1: 0.439970 Loss2: 1.939957
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.657944 Loss1: 0.283453 Loss2: 1.374492
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.623799 Loss1: 0.215432 Loss2: 1.408367
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.525606 Loss1: 0.124083 Loss2: 1.401523
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.213216 Loss1: 0.345260 Loss2: 1.867957
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.636729 Loss1: 0.246009 Loss2: 1.390720 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.656684 Loss1: 0.222789 Loss2: 1.433894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.611800 Loss1: 0.199958 Loss2: 1.411841 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433824 Loss1: 0.070810 Loss2: 1.363015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410679 Loss1: 0.046203 Loss2: 1.364476 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449670 Loss1: 0.060635 Loss2: 1.389035 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440299 Loss1: 0.063028 Loss2: 1.377272 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.489513 Loss1: 0.193201 Loss2: 1.296313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.428050 Loss1: 0.123610 Loss2: 1.304440 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.373212 Loss1: 0.089442 Loss2: 1.283770
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.222044 Loss1: 0.337253 Loss2: 1.884791
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.587168 Loss1: 0.219408 Loss2: 1.367760 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.586284 Loss1: 0.201426 Loss2: 1.384858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.509360 Loss1: 0.134647 Loss2: 1.374713 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476629 Loss1: 0.116721 Loss2: 1.359909 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460415 Loss1: 0.097784 Loss2: 1.362631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426187 Loss1: 0.071380 Loss2: 1.354807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391949 Loss1: 0.040473 Loss2: 1.351476 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.533315 Loss1: 0.197442 Loss2: 1.335873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.452380 Loss1: 0.107230 Loss2: 1.345150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476830 Loss1: 0.146185 Loss2: 1.330645
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.350859 Loss1: 0.447454 Loss2: 1.903405
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.414929 Loss1: 0.076718 Loss2: 1.338212
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.619202 Loss1: 0.209328 Loss2: 1.409874
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404385 Loss1: 0.073878 Loss2: 1.330507
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.607733 Loss1: 0.172967 Loss2: 1.434766
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382160 Loss1: 0.056281 Loss2: 1.325879
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528485 Loss1: 0.123142 Loss2: 1.405344
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497692 Loss1: 0.104921 Loss2: 1.392771
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386301 Loss1: 0.064558 Loss2: 1.321743
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.503573 Loss1: 0.110362 Loss2: 1.393210
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367585 Loss1: 0.049954 Loss2: 1.317631
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453258 Loss1: 0.066755 Loss2: 1.386503 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424846 Loss1: 0.052378 Loss2: 1.372468 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.544378 Loss1: 0.232499 Loss2: 1.311879 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.450182 Loss1: 0.133974 Loss2: 1.316209 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.401367 Loss1: 0.092130 Loss2: 1.309237
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.364795 Loss1: 0.504478 Loss2: 1.860317
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.435749 Loss1: 0.135790 Loss2: 1.299958
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.586953 Loss1: 0.240035 Loss2: 1.346917
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.509583 Loss1: 0.142724 Loss2: 1.366859 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492622 Loss1: 0.140517 Loss2: 1.352104 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460357 Loss1: 0.121673 Loss2: 1.338684 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.442328 Loss1: 0.091185 Loss2: 1.351143 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439145 Loss1: 0.108252 Loss2: 1.330893 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366873 Loss1: 0.044735 Loss2: 1.322138 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.604130 Loss1: 0.287460 Loss2: 1.316669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.465276 Loss1: 0.140069 Loss2: 1.325208 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.438253 Loss1: 0.123104 Loss2: 1.315148
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.369836 Loss1: 0.502914 Loss2: 1.866922
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659973 Loss1: 0.302725 Loss2: 1.357248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.568897 Loss1: 0.185203 Loss2: 1.383694 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481607 Loss1: 0.139612 Loss2: 1.341995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450522 Loss1: 0.115689 Loss2: 1.334834 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420707 Loss1: 0.088496 Loss2: 1.332211 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386689 Loss1: 0.065026 Loss2: 1.321664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.338662 Loss1: 0.030477 Loss2: 1.308186 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.534161 Loss1: 0.222499 Loss2: 1.311662 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.412845 Loss1: 0.097414 Loss2: 1.315432 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.393622 Loss1: 0.094805 Loss2: 1.298816
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.290169 Loss1: 0.394168 Loss2: 1.896001
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.614519 Loss1: 0.226977 Loss2: 1.387542 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556723 Loss1: 0.160813 Loss2: 1.395910 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.477508 Loss1: 0.089072 Loss2: 1.388436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.468023 Loss1: 0.094319 Loss2: 1.373704 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461283 Loss1: 0.081831 Loss2: 1.379452 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.403414 Loss1: 0.040785 Loss2: 1.362629 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402227 Loss1: 0.047803 Loss2: 1.354424 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.564269 Loss1: 0.220164 Loss2: 1.344106 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500561 Loss1: 0.138592 Loss2: 1.361968 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463842 Loss1: 0.125647 Loss2: 1.338195
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.268189 Loss1: 0.398724 Loss2: 1.869465
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.674224 Loss1: 0.310039 Loss2: 1.364185 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404929 Loss1: 0.065564 Loss2: 1.339365
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.708259 Loss1: 0.291357 Loss2: 1.416902
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398480 Loss1: 0.065358 Loss2: 1.333121
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.640763 Loss1: 0.254332 Loss2: 1.386431
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396624 Loss1: 0.066252 Loss2: 1.330372
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557281 Loss1: 0.175064 Loss2: 1.382217
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371908 Loss1: 0.045720 Loss2: 1.326187
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.497160 Loss1: 0.111605 Loss2: 1.385555
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443971 Loss1: 0.075213 Loss2: 1.368758
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431163 Loss1: 0.065576 Loss2: 1.365588
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.437481 Loss1: 0.079956 Loss2: 1.357524
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450763 Loss1: 0.091913 Loss2: 1.358850
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.249240 Loss1: 0.407272 Loss2: 1.841968
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.552465 Loss1: 0.199222 Loss2: 1.353243
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.514308 Loss1: 0.147752 Loss2: 1.366556
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.468913 Loss1: 0.116501 Loss2: 1.352412
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476768 Loss1: 0.132781 Loss2: 1.343986
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.250247 Loss1: 0.384472 Loss2: 1.865774
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.633033 Loss1: 0.267245 Loss2: 1.365788 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.518064 Loss1: 0.131482 Loss2: 1.386582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.482443 Loss1: 0.102808 Loss2: 1.379636 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503254 Loss1: 0.142726 Loss2: 1.360528 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365446 Loss1: 0.040667 Loss2: 1.324779
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473144 Loss1: 0.110229 Loss2: 1.362915
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406364 Loss1: 0.051216 Loss2: 1.355147
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406696 Loss1: 0.055354 Loss2: 1.351341
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419672 Loss1: 0.070966 Loss2: 1.348706
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416118 Loss1: 0.066961 Loss2: 1.349158
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.263783 Loss1: 0.451287 Loss2: 1.812496
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.708972 Loss1: 0.366809 Loss2: 1.342163
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609651 Loss1: 0.207842 Loss2: 1.401810
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498808 Loss1: 0.156684 Loss2: 1.342124
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488704 Loss1: 0.145630 Loss2: 1.343074
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.328559 Loss1: 0.446551 Loss2: 1.882008
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.646770 Loss1: 0.267310 Loss2: 1.379459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598778 Loss1: 0.197259 Loss2: 1.401519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556312 Loss1: 0.173923 Loss2: 1.382389 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493627 Loss1: 0.129600 Loss2: 1.364027 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368133 Loss1: 0.046246 Loss2: 1.321887
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498751 Loss1: 0.135741 Loss2: 1.363011
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.487062 Loss1: 0.125171 Loss2: 1.361892
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435289 Loss1: 0.072847 Loss2: 1.362442
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.423612 Loss1: 0.068044 Loss2: 1.355568
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401199 Loss1: 0.053045 Loss2: 1.348154
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.272994 Loss1: 0.436773 Loss2: 1.836222
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.654619 Loss1: 0.307222 Loss2: 1.347398
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.652631 Loss1: 0.247835 Loss2: 1.404796
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518023 Loss1: 0.154366 Loss2: 1.363656
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.529359 Loss1: 0.175738 Loss2: 1.353621
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.266601 Loss1: 0.416002 Loss2: 1.850598
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.667126 Loss1: 0.306491 Loss2: 1.360635 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.641261 Loss1: 0.237106 Loss2: 1.404156 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556793 Loss1: 0.188613 Loss2: 1.368180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497840 Loss1: 0.137563 Loss2: 1.360278 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.950000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.457002 Loss1: 0.099659 Loss2: 1.357343 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.388479 Loss1: 0.049215 Loss2: 1.339264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372291 Loss1: 0.036817 Loss2: 1.335474 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.576070 Loss1: 0.217877 Loss2: 1.358193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.443863 Loss1: 0.092331 Loss2: 1.351532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.235829 Loss1: 0.423879 Loss2: 1.811950
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.412888 Loss1: 0.063378 Loss2: 1.349510
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.587649 Loss1: 0.243794 Loss2: 1.343855
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.383914 Loss1: 0.045098 Loss2: 1.338816
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572493 Loss1: 0.198969 Loss2: 1.373524
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419566 Loss1: 0.086470 Loss2: 1.333096
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.395262 Loss1: 0.055981 Loss2: 1.339282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412381 Loss1: 0.075931 Loss2: 1.336450 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415919 Loss1: 0.074789 Loss2: 1.341131 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992647
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434585 Loss1: 0.091933 Loss2: 1.342652 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375959 Loss1: 0.044429 Loss2: 1.331530 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.137204 Loss1: 0.348021 Loss2: 1.789183
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.551310 Loss1: 0.227106 Loss2: 1.324205
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.650849 Loss1: 0.277727 Loss2: 1.373123
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541775 Loss1: 0.209991 Loss2: 1.331784
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.292976 Loss1: 0.415031 Loss2: 1.877945
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.672344 Loss1: 0.304657 Loss2: 1.367687 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.553584 Loss1: 0.149274 Loss2: 1.404310 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487099 Loss1: 0.121606 Loss2: 1.365493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.385167 Loss1: 0.072311 Loss2: 1.312856
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493802 Loss1: 0.128705 Loss2: 1.365097
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.380495 Loss1: 0.070832 Loss2: 1.309663
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476450 Loss1: 0.103584 Loss2: 1.372866
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395008 Loss1: 0.088674 Loss2: 1.306334
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435298 Loss1: 0.078664 Loss2: 1.356633
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449976 Loss1: 0.091939 Loss2: 1.358036
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417907 Loss1: 0.068324 Loss2: 1.349583
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466469 Loss1: 0.112373 Loss2: 1.354095
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.343747 Loss1: 0.444275 Loss2: 1.899472
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.605900 Loss1: 0.232009 Loss2: 1.373891
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.564338 Loss1: 0.171613 Loss2: 1.392725
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517320 Loss1: 0.142615 Loss2: 1.374705
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.420829 Loss1: 0.483474 Loss2: 1.937355
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.757206 Loss1: 0.426713 Loss2: 1.330493
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.505952 Loss1: 0.141663 Loss2: 1.364288
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.574536 Loss1: 0.202084 Loss2: 1.372452
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466023 Loss1: 0.103057 Loss2: 1.362965 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445883 Loss1: 0.083592 Loss2: 1.362292 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.418796 Loss1: 0.082321 Loss2: 1.336476 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413525 Loss1: 0.053564 Loss2: 1.359961 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373710 Loss1: 0.061793 Loss2: 1.311917 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986979
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.513209 Loss1: 0.517727 Loss2: 1.995482
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.749562 Loss1: 0.351205 Loss2: 1.398357
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.651386 Loss1: 0.229599 Loss2: 1.421787
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587343 Loss1: 0.173832 Loss2: 1.413511
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.216630 Loss1: 0.391231 Loss2: 1.825399
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.472094 Loss1: 0.076064 Loss2: 1.396030 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476547 Loss1: 0.097611 Loss2: 1.378937 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435927 Loss1: 0.060031 Loss2: 1.375896 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455218 Loss1: 0.081028 Loss2: 1.374190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431708 Loss1: 0.066512 Loss2: 1.365196 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433333 Loss1: 0.094117 Loss2: 1.339215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417544 Loss1: 0.087953 Loss2: 1.329591 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382798 Loss1: 0.049166 Loss2: 1.333631
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.181192 Loss1: 0.353584 Loss2: 1.827609
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.651210 Loss1: 0.285982 Loss2: 1.365228
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635200 Loss1: 0.223530 Loss2: 1.411670
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529661 Loss1: 0.165481 Loss2: 1.364180
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492433 Loss1: 0.120289 Loss2: 1.372144
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.143293 Loss1: 0.328856 Loss2: 1.814437
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.490782 Loss1: 0.130515 Loss2: 1.360268
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.598853 Loss1: 0.253608 Loss2: 1.345245
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458728 Loss1: 0.104979 Loss2: 1.353749
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.541179 Loss1: 0.162192 Loss2: 1.378986
+DEBUG flwr 2023-10-13 07:53:02,607 | server.py:236 | fit_round 181 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404807 Loss1: 0.051548 Loss2: 1.353259
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.479873 Loss1: 0.143202 Loss2: 1.336671
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398133 Loss1: 0.052996 Loss2: 1.345137
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.457656 Loss1: 0.122538 Loss2: 1.335117
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374478 Loss1: 0.034536 Loss2: 1.339941
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.433325 Loss1: 0.093942 Loss2: 1.339383
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441534 Loss1: 0.107328 Loss2: 1.334206
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434697 Loss1: 0.101937 Loss2: 1.332760
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409266 Loss1: 0.079988 Loss2: 1.329277
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365676 Loss1: 0.041045 Loss2: 1.324631
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.382029 Loss1: 0.439561 Loss2: 1.942468
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.708606 Loss1: 0.279241 Loss2: 1.429366
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.603697 Loss1: 0.149854 Loss2: 1.453843
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561946 Loss1: 0.131560 Loss2: 1.430386
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.561729 Loss1: 0.131260 Loss2: 1.430469
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.191933 Loss1: 0.399365 Loss2: 1.792568
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.528143 Loss1: 0.210842 Loss2: 1.317302 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.472699 Loss1: 0.147130 Loss2: 1.325569 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.427075 Loss1: 0.114571 Loss2: 1.312505 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.379194 Loss1: 0.084665 Loss2: 1.294529 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.390276 Loss1: 0.096934 Loss2: 1.293342 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.336511 Loss1: 0.047982 Loss2: 1.288529 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.327160 Loss1: 0.045608 Loss2: 1.281553 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.593852 Loss1: 0.216978 Loss2: 1.376875 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522238 Loss1: 0.142958 Loss2: 1.379280 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.185983 Loss1: 0.363380 Loss2: 1.822603 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.598348 Loss1: 0.237371 Loss2: 1.360977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.523362 Loss1: 0.152015 Loss2: 1.371347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.466410 Loss1: 0.111076 Loss2: 1.355334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476968 Loss1: 0.132043 Loss2: 1.344925 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450530 Loss1: 0.094131 Loss2: 1.356399 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449837 Loss1: 0.108302 Loss2: 1.341534 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 07:53:02,607][flwr][DEBUG] - fit_round 181 received 50 results and 0 failures
+INFO flwr 2023-10-13 07:53:43,836 | server.py:125 | fit progress: (181, 2.3034834168589535, {'accuracy': 0.6087}, 417731.61495577596)
+>> Test accuracy: 0.608700
+[2023-10-13 07:53:43,836][flwr][INFO] - fit progress: (181, 2.3034834168589535, {'accuracy': 0.6087}, 417731.61495577596)
+DEBUG flwr 2023-10-13 07:53:43,837 | server.py:173 | evaluate_round 181: strategy sampled 50 clients (out of 50)
+[2023-10-13 07:53:43,837][flwr][DEBUG] - evaluate_round 181: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 08:02:53,395 | server.py:187 | evaluate_round 181 received 50 results and 0 failures
+[2023-10-13 08:02:53,395][flwr][DEBUG] - evaluate_round 181 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 08:02:53,396 | server.py:222 | fit_round 182: strategy sampled 50 clients (out of 50)
+[2023-10-13 08:02:53,396][flwr][DEBUG] - fit_round 182: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.283660 Loss1: 0.429136 Loss2: 1.854523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611377 Loss1: 0.207606 Loss2: 1.403772 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534026 Loss1: 0.163386 Loss2: 1.370639
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.151537 Loss1: 0.333499 Loss2: 1.818038
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503794 Loss1: 0.143439 Loss2: 1.360355
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.570800 Loss1: 0.252676 Loss2: 1.318124
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.475693 Loss1: 0.114494 Loss2: 1.361200
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.491118 Loss1: 0.153248 Loss2: 1.337870
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424085 Loss1: 0.067734 Loss2: 1.356351
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.428501 Loss1: 0.095210 Loss2: 1.333292
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433835 Loss1: 0.077011 Loss2: 1.356823
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.370818 Loss1: 0.055734 Loss2: 1.315084
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414634 Loss1: 0.065017 Loss2: 1.349617
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.378533 Loss1: 0.070736 Loss2: 1.307797
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393640 Loss1: 0.046127 Loss2: 1.347513
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.360796 Loss1: 0.053626 Loss2: 1.307170
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.325921 Loss1: 0.026795 Loss2: 1.299126
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.318342 Loss1: 0.024269 Loss2: 1.294073
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.322574 Loss1: 0.032832 Loss2: 1.289741
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.273443 Loss1: 0.387672 Loss2: 1.885771
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.659437 Loss1: 0.297570 Loss2: 1.361867
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.560295 Loss1: 0.171407 Loss2: 1.388888
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474619 Loss1: 0.121622 Loss2: 1.352996
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.146025 Loss1: 0.360925 Loss2: 1.785100
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.606901 Loss1: 0.299351 Loss2: 1.307550 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.494098 Loss1: 0.169007 Loss2: 1.325091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.422011 Loss1: 0.101964 Loss2: 1.320047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.409479 Loss1: 0.103901 Loss2: 1.305578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.400132 Loss1: 0.093578 Loss2: 1.306554 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.366705 Loss1: 0.036366 Loss2: 1.330339
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.382900 Loss1: 0.079181 Loss2: 1.303719
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.350702 Loss1: 0.051491 Loss2: 1.299210
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.337859 Loss1: 0.045990 Loss2: 1.291869
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.335602 Loss1: 0.045066 Loss2: 1.290537
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.187932 Loss1: 0.338311 Loss2: 1.849621
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.577913 Loss1: 0.209715 Loss2: 1.368198
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.523645 Loss1: 0.134136 Loss2: 1.389509
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.463801 Loss1: 0.091805 Loss2: 1.371995
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.289108 Loss1: 0.387007 Loss2: 1.902101
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468776 Loss1: 0.110570 Loss2: 1.358206
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.592546 Loss1: 0.206507 Loss2: 1.386038
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481839 Loss1: 0.118295 Loss2: 1.363544
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.555343 Loss1: 0.159456 Loss2: 1.395886
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.474426 Loss1: 0.087674 Loss2: 1.386752
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.513268 Loss1: 0.141916 Loss2: 1.371352
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.448788 Loss1: 0.077382 Loss2: 1.371406
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.529601 Loss1: 0.154547 Loss2: 1.375055
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456997 Loss1: 0.087444 Loss2: 1.369552
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470608 Loss1: 0.100812 Loss2: 1.369796
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445320 Loss1: 0.075968 Loss2: 1.369352
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421444 Loss1: 0.060724 Loss2: 1.360720
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431938 Loss1: 0.066298 Loss2: 1.365640 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.197878 Loss1: 0.415883 Loss2: 1.781995 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.466637 Loss1: 0.144992 Loss2: 1.321645 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476534 Loss1: 0.165784 Loss2: 1.310750
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.123308 Loss1: 0.311325 Loss2: 1.811982
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.553957 Loss1: 0.241618 Loss2: 1.312339 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.464528 Loss1: 0.148457 Loss2: 1.316071 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.431653 Loss1: 0.115146 Loss2: 1.316507 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.413322 Loss1: 0.111611 Loss2: 1.301710 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.345247 Loss1: 0.053094 Loss2: 1.292152
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.377265 Loss1: 0.069110 Loss2: 1.308155
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.336871 Loss1: 0.048688 Loss2: 1.288183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.394740 Loss1: 0.086227 Loss2: 1.308512
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426395 Loss1: 0.123234 Loss2: 1.303161
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410128 Loss1: 0.107157 Loss2: 1.302971
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388724 Loss1: 0.083207 Loss2: 1.305517
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.365878 Loss1: 0.490785 Loss2: 1.875093
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.643019 Loss1: 0.284302 Loss2: 1.358717
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.566989 Loss1: 0.167504 Loss2: 1.399485
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.544250 Loss1: 0.181673 Loss2: 1.362576
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.285990 Loss1: 0.424241 Loss2: 1.861749
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.629219 Loss1: 0.253622 Loss2: 1.375597 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.550106 Loss1: 0.172768 Loss2: 1.377338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.514222 Loss1: 0.136215 Loss2: 1.378007 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513421 Loss1: 0.146292 Loss2: 1.367130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.451082 Loss1: 0.081601 Loss2: 1.369481 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403626 Loss1: 0.061041 Loss2: 1.342585
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460617 Loss1: 0.099609 Loss2: 1.361008
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421138 Loss1: 0.066772 Loss2: 1.354366
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426231 Loss1: 0.070338 Loss2: 1.355893
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.460504 Loss1: 0.106777 Loss2: 1.353727
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.218701 Loss1: 0.345031 Loss2: 1.873670
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.657515 Loss1: 0.260942 Loss2: 1.396573
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.564826 Loss1: 0.149887 Loss2: 1.414939
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.286219 Loss1: 0.436442 Loss2: 1.849777
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522070 Loss1: 0.130476 Loss2: 1.391594
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.613183 Loss1: 0.299062 Loss2: 1.314121
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.471106 Loss1: 0.084609 Loss2: 1.386497
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462271 Loss1: 0.077551 Loss2: 1.384720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443976 Loss1: 0.066658 Loss2: 1.377318 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.454323 Loss1: 0.075822 Loss2: 1.378501 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445512 Loss1: 0.067996 Loss2: 1.377516 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451515 Loss1: 0.074558 Loss2: 1.376958 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.358095 Loss1: 0.047146 Loss2: 1.310949 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.128542 Loss1: 0.310149 Loss2: 1.818393
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.599995 Loss1: 0.244409 Loss2: 1.355586
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551718 Loss1: 0.163745 Loss2: 1.387973
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.346506 Loss1: 0.465369 Loss2: 1.881137
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488008 Loss1: 0.133337 Loss2: 1.354671
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.620238 Loss1: 0.256510 Loss2: 1.363728
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443744 Loss1: 0.090990 Loss2: 1.352754
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451376 Loss1: 0.102037 Loss2: 1.349339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415964 Loss1: 0.067278 Loss2: 1.348687 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410165 Loss1: 0.069481 Loss2: 1.340684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400547 Loss1: 0.066255 Loss2: 1.334293 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363273 Loss1: 0.028085 Loss2: 1.335187 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382582 Loss1: 0.052752 Loss2: 1.329830 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.225577 Loss1: 0.375742 Loss2: 1.849835
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.684812 Loss1: 0.308542 Loss2: 1.376270
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618973 Loss1: 0.203682 Loss2: 1.415291
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.303259 Loss1: 0.407799 Loss2: 1.895460
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.552943 Loss1: 0.176884 Loss2: 1.376059
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659458 Loss1: 0.279547 Loss2: 1.379911
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518589 Loss1: 0.133033 Loss2: 1.385556
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.589778 Loss1: 0.184192 Loss2: 1.405586
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.526880 Loss1: 0.149116 Loss2: 1.377764
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458357 Loss1: 0.087837 Loss2: 1.370519 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425890 Loss1: 0.067576 Loss2: 1.358313 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422929 Loss1: 0.073737 Loss2: 1.349193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396619 Loss1: 0.053890 Loss2: 1.342729 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425725 Loss1: 0.060976 Loss2: 1.364749 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.169718 Loss1: 0.300396 Loss2: 1.869321 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.512337 Loss1: 0.118277 Loss2: 1.394060 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.473577 Loss1: 0.087346 Loss2: 1.386231
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.522439 Loss1: 0.522125 Loss2: 2.000314
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.751303 Loss1: 0.362971 Loss2: 1.388332
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.429584 Loss1: 0.067254 Loss2: 1.362330
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.647696 Loss1: 0.239193 Loss2: 1.408503
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438718 Loss1: 0.070242 Loss2: 1.368476
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426301 Loss1: 0.063191 Loss2: 1.363111 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403941 Loss1: 0.047298 Loss2: 1.356644 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417744 Loss1: 0.060337 Loss2: 1.357407 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392163 Loss1: 0.040461 Loss2: 1.351702 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416897 Loss1: 0.062012 Loss2: 1.354884 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.240385 Loss1: 0.382352 Loss2: 1.858033
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.612410 Loss1: 0.262261 Loss2: 1.350149
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.516934 Loss1: 0.148659 Loss2: 1.368275
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.466481 Loss1: 0.110820 Loss2: 1.355661
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.273163 Loss1: 0.393227 Loss2: 1.879936
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448569 Loss1: 0.106097 Loss2: 1.342472
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.619600 Loss1: 0.246925 Loss2: 1.372674
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.436403 Loss1: 0.094513 Loss2: 1.341890
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.520017 Loss1: 0.118331 Loss2: 1.401687
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.405165 Loss1: 0.066917 Loss2: 1.338248
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.480308 Loss1: 0.103895 Loss2: 1.376413
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378154 Loss1: 0.048840 Loss2: 1.329314
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.443408 Loss1: 0.079362 Loss2: 1.364047
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372186 Loss1: 0.042650 Loss2: 1.329536
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.478839 Loss1: 0.114624 Loss2: 1.364215
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367401 Loss1: 0.039300 Loss2: 1.328101
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.490152 Loss1: 0.122063 Loss2: 1.368089
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.562389 Loss1: 0.176990 Loss2: 1.385399
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.503710 Loss1: 0.117668 Loss2: 1.386042
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.466687 Loss1: 0.086369 Loss2: 1.380318
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.268703 Loss1: 0.402444 Loss2: 1.866259
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.641209 Loss1: 0.289904 Loss2: 1.351305
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.579049 Loss1: 0.198650 Loss2: 1.380399
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496955 Loss1: 0.131015 Loss2: 1.365939
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.169141 Loss1: 0.370122 Loss2: 1.799020
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.557560 Loss1: 0.242180 Loss2: 1.315380 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.507371 Loss1: 0.158248 Loss2: 1.349123 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.466125 Loss1: 0.136071 Loss2: 1.330054 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.423812 Loss1: 0.105727 Loss2: 1.318085 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.398305 Loss1: 0.088091 Loss2: 1.310215 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388331 Loss1: 0.050272 Loss2: 1.338060
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.408564 Loss1: 0.095445 Loss2: 1.313119
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421773 Loss1: 0.107074 Loss2: 1.314699
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371534 Loss1: 0.062158 Loss2: 1.309375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361591 Loss1: 0.053152 Loss2: 1.308439
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.424874 Loss1: 0.485264 Loss2: 1.939610
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.689763 Loss1: 0.274042 Loss2: 1.415721
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.695469 Loss1: 0.237602 Loss2: 1.457866
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.714836 Loss1: 0.283434 Loss2: 1.431401
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.241819 Loss1: 0.434462 Loss2: 1.807357
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.632439 Loss1: 0.285154 Loss2: 1.347286 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.516284 Loss1: 0.161921 Loss2: 1.354363 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.473718 Loss1: 0.151152 Loss2: 1.322566 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.477620 Loss1: 0.141584 Loss2: 1.336036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423989 Loss1: 0.096861 Loss2: 1.327128 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.402300 Loss1: 0.085226 Loss2: 1.317074 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362579 Loss1: 0.052493 Loss2: 1.310087 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.161485 Loss1: 0.349515 Loss2: 1.811971 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.637117 Loss1: 0.263429 Loss2: 1.373688 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.218896 Loss1: 0.392657 Loss2: 1.826238 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.690623 Loss1: 0.367902 Loss2: 1.322721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.670509 Loss1: 0.272611 Loss2: 1.397897 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.502422 Loss1: 0.174857 Loss2: 1.327565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469694 Loss1: 0.141594 Loss2: 1.328100 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444846 Loss1: 0.114072 Loss2: 1.330774 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372113 Loss1: 0.061455 Loss2: 1.310658 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.349903 Loss1: 0.040867 Loss2: 1.309036 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.168880 Loss1: 0.262719 Loss2: 1.906161
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.673814 Loss1: 0.248656 Loss2: 1.425158
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.663844 Loss1: 0.198182 Loss2: 1.465662
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.565607 Loss1: 0.123684 Loss2: 1.441922
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.160229 Loss1: 0.335237 Loss2: 1.824993
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.617517 Loss1: 0.178649 Loss2: 1.438868
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.581869 Loss1: 0.218023 Loss2: 1.363846
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.551077 Loss1: 0.162547 Loss2: 1.388529
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523471 Loss1: 0.089917 Loss2: 1.433554
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.517156 Loss1: 0.154961 Loss2: 1.362194
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.509816 Loss1: 0.084183 Loss2: 1.425633
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492058 Loss1: 0.122615 Loss2: 1.369443
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.516345 Loss1: 0.093322 Loss2: 1.423023
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.428517 Loss1: 0.063618 Loss2: 1.364900
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.480661 Loss1: 0.063824 Loss2: 1.416837
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.399341 Loss1: 0.050785 Loss2: 1.348555
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.494181 Loss1: 0.078826 Loss2: 1.415355
+(DefaultActor pid=3765) >> Training accuracy: 0.991728
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.375099 Loss1: 0.035944 Loss2: 1.339155 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.305729 Loss1: 0.375587 Loss2: 1.930142 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610698 Loss1: 0.180020 Loss2: 1.430678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.531638 Loss1: 0.126055 Loss2: 1.405584
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.164458 Loss1: 0.366430 Loss2: 1.798028
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.569522 Loss1: 0.221688 Loss2: 1.347834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.527797 Loss1: 0.167741 Loss2: 1.360056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481405 Loss1: 0.142565 Loss2: 1.338841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460689 Loss1: 0.127460 Loss2: 1.333228 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.399610 Loss1: 0.067708 Loss2: 1.331902 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.401007 Loss1: 0.077218 Loss2: 1.323789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.342079 Loss1: 0.031058 Loss2: 1.311021 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.291117 Loss1: 0.388837 Loss2: 1.902280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.543512 Loss1: 0.168664 Loss2: 1.374848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.185373 Loss1: 0.390345 Loss2: 1.795028 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.626255 Loss1: 0.311581 Loss2: 1.314674 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.531256 Loss1: 0.192899 Loss2: 1.338357 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.489215 Loss1: 0.180716 Loss2: 1.308499 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450500 Loss1: 0.130234 Loss2: 1.320266 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471176 Loss1: 0.157177 Loss2: 1.313999 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428388 Loss1: 0.101989 Loss2: 1.326399 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362050 Loss1: 0.061482 Loss2: 1.300568 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.687774 Loss1: 0.355150 Loss2: 1.332625 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.603041 Loss1: 0.260112 Loss2: 1.342929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.315911 Loss1: 0.439279 Loss2: 1.876631
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.539623 Loss1: 0.182439 Loss2: 1.357183
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.690233 Loss1: 0.304862 Loss2: 1.385370
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.501520 Loss1: 0.146977 Loss2: 1.354544
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.629222 Loss1: 0.208286 Loss2: 1.420936
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.452789 Loss1: 0.107803 Loss2: 1.344986
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540470 Loss1: 0.156146 Loss2: 1.384324
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411346 Loss1: 0.068074 Loss2: 1.343272
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.489144 Loss1: 0.106893 Loss2: 1.382251
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404582 Loss1: 0.075824 Loss2: 1.328759
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.499718 Loss1: 0.123342 Loss2: 1.376376
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.382094 Loss1: 0.056412 Loss2: 1.325683
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440321 Loss1: 0.074252 Loss2: 1.366069 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408717 Loss1: 0.052978 Loss2: 1.355739 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.552492 Loss1: 0.207175 Loss2: 1.345316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496637 Loss1: 0.131338 Loss2: 1.365300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463343 Loss1: 0.110462 Loss2: 1.352882 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.427918 Loss1: 0.085969 Loss2: 1.341948 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415851 Loss1: 0.075052 Loss2: 1.340799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426287 Loss1: 0.088505 Loss2: 1.337782 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.416390 Loss1: 0.076900 Loss2: 1.339490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413238 Loss1: 0.075187 Loss2: 1.338051 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977539
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.462978 Loss1: 0.115952 Loss2: 1.347026 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419631 Loss1: 0.077370 Loss2: 1.342261 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.260061 Loss1: 0.384057 Loss2: 1.876004
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.618709 Loss1: 0.261230 Loss2: 1.357479
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.546809 Loss1: 0.169944 Loss2: 1.376865
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.463781 Loss1: 0.094970 Loss2: 1.368811
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.275173 Loss1: 0.408295 Loss2: 1.866878
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.542814 Loss1: 0.169157 Loss2: 1.373657 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.520762 Loss1: 0.144200 Loss2: 1.376562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.488327 Loss1: 0.118872 Loss2: 1.369456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.457372 Loss1: 0.104427 Loss2: 1.352945 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.448878 Loss1: 0.093457 Loss2: 1.355420 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.346225 Loss1: 0.017708 Loss2: 1.328517
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.432260 Loss1: 0.079107 Loss2: 1.353153
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427171 Loss1: 0.081986 Loss2: 1.345185
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400697 Loss1: 0.062308 Loss2: 1.338390
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415073 Loss1: 0.079751 Loss2: 1.335322
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.220437 Loss1: 0.433329 Loss2: 1.787108
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.629344 Loss1: 0.300716 Loss2: 1.328628
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.572714 Loss1: 0.195929 Loss2: 1.376785
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518469 Loss1: 0.172242 Loss2: 1.346228
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.191362 Loss1: 0.391879 Loss2: 1.799482
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.568923 Loss1: 0.266483 Loss2: 1.302441 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.505973 Loss1: 0.184958 Loss2: 1.321016 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.434914 Loss1: 0.121315 Loss2: 1.313600 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.396207 Loss1: 0.091535 Loss2: 1.304672 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.392972 Loss1: 0.097249 Loss2: 1.295722 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.339678 Loss1: 0.036437 Loss2: 1.303241
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.381357 Loss1: 0.085074 Loss2: 1.296283
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368265 Loss1: 0.070837 Loss2: 1.297428
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370685 Loss1: 0.081219 Loss2: 1.289466
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348639 Loss1: 0.057482 Loss2: 1.291157
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.451492 Loss1: 0.518729 Loss2: 1.932763
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.652526 Loss1: 0.284177 Loss2: 1.368349
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599556 Loss1: 0.210231 Loss2: 1.389325
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589994 Loss1: 0.209865 Loss2: 1.380129
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.245124 Loss1: 0.383972 Loss2: 1.861152
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.634066 Loss1: 0.276717 Loss2: 1.357348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.579452 Loss1: 0.200547 Loss2: 1.378905 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.465541 Loss1: 0.109369 Loss2: 1.356172 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422875 Loss1: 0.065348 Loss2: 1.357527 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405737 Loss1: 0.060845 Loss2: 1.344892 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.383048 Loss1: 0.054845 Loss2: 1.328204 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.371516 Loss1: 0.056814 Loss2: 1.314702 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.540547 Loss1: 0.234686 Loss2: 1.305861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.531341 Loss1: 0.195046 Loss2: 1.336295 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.469567 Loss1: 0.150261 Loss2: 1.319306 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.401390 Loss1: 0.082688 Loss2: 1.318702 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391996 Loss1: 0.083277 Loss2: 1.308718 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.368920 Loss1: 0.068309 Loss2: 1.300611 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.335157 Loss1: 0.035876 Loss2: 1.299280
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492121 Loss1: 0.111213 Loss2: 1.380908
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487827 Loss1: 0.108698 Loss2: 1.379129
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468739 Loss1: 0.086971 Loss2: 1.381769
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469487 Loss1: 0.096960 Loss2: 1.372527
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.499403 Loss1: 0.127841 Loss2: 1.371562
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.248699 Loss1: 0.412937 Loss2: 1.835763
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.522714 Loss1: 0.225324 Loss2: 1.297389 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.503409 Loss1: 0.124605 Loss2: 1.378804
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.454174 Loss1: 0.143829 Loss2: 1.310345
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.445422 Loss1: 0.141802 Loss2: 1.303620
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.453682 Loss1: 0.150698 Loss2: 1.302985
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.424212 Loss1: 0.123617 Loss2: 1.300595
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395775 Loss1: 0.104479 Loss2: 1.291296
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.262075 Loss1: 0.389617 Loss2: 1.872458
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.371102 Loss1: 0.077813 Loss2: 1.293289
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379415 Loss1: 0.088938 Loss2: 1.290477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.339469 Loss1: 0.054699 Loss2: 1.284771 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.481804 Loss1: 0.126977 Loss2: 1.354826 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 08:32:01,406 | server.py:236 | fit_round 182 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.506657 Loss1: 0.153049 Loss2: 1.353608 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469027 Loss1: 0.112648 Loss2: 1.356379
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.394626 Loss1: 0.471854 Loss2: 1.922772
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.635928 Loss1: 0.268822 Loss2: 1.367106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409238 Loss1: 0.067892 Loss2: 1.341346
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.541202 Loss1: 0.173796 Loss2: 1.367406
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.487487 Loss1: 0.114174 Loss2: 1.373313
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.439585 Loss1: 0.084938 Loss2: 1.354647
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.405437 Loss1: 0.057333 Loss2: 1.348105
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.424712 Loss1: 0.076913 Loss2: 1.347799
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409328 Loss1: 0.061375 Loss2: 1.347953
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.341125 Loss1: 0.392863 Loss2: 1.948262
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.677398 Loss1: 0.247605 Loss2: 1.429793 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.634765 Loss1: 0.191169 Loss2: 1.443595 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.589437 Loss1: 0.136777 Loss2: 1.452661 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.511615 Loss1: 0.086113 Loss2: 1.425501
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.287622 Loss1: 0.464200 Loss2: 1.823423
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485542 Loss1: 0.066412 Loss2: 1.419130
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.588262 Loss1: 0.264053 Loss2: 1.324210
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480871 Loss1: 0.069668 Loss2: 1.411203
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.533515 Loss1: 0.174704 Loss2: 1.358811
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444023 Loss1: 0.037548 Loss2: 1.406475
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498096 Loss1: 0.163646 Loss2: 1.334449
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.417888 Loss1: 0.090660 Loss2: 1.327229
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.389062 Loss1: 0.067214 Loss2: 1.321849
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.381326 Loss1: 0.067866 Loss2: 1.313460
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.354600 Loss1: 0.050522 Loss2: 1.304078
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.348337 Loss1: 0.047721 Loss2: 1.300616
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.194022 Loss1: 0.292650 Loss2: 1.901373
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.328395 Loss1: 0.030242 Loss2: 1.298153
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.632356 Loss1: 0.235331 Loss2: 1.397025
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601168 Loss1: 0.193639 Loss2: 1.407529
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498905 Loss1: 0.100271 Loss2: 1.398635
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.504646 Loss1: 0.121139 Loss2: 1.383507
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500672 Loss1: 0.110005 Loss2: 1.390667
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480492 Loss1: 0.096236 Loss2: 1.384256
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445738 Loss1: 0.065243 Loss2: 1.380495
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450801 Loss1: 0.075575 Loss2: 1.375226
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429147 Loss1: 0.055922 Loss2: 1.373225
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 08:32:01,406][flwr][DEBUG] - fit_round 182 received 50 results and 0 failures
+INFO flwr 2023-10-13 08:32:42,255 | server.py:125 | fit progress: (182, 2.2981419727063406, {'accuracy': 0.6113}, 420070.033974992)
+>> Test accuracy: 0.611300
+[2023-10-13 08:32:42,255][flwr][INFO] - fit progress: (182, 2.2981419727063406, {'accuracy': 0.6113}, 420070.033974992)
+DEBUG flwr 2023-10-13 08:32:42,256 | server.py:173 | evaluate_round 182: strategy sampled 50 clients (out of 50)
+[2023-10-13 08:32:42,256][flwr][DEBUG] - evaluate_round 182: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 08:41:43,569 | server.py:187 | evaluate_round 182 received 50 results and 0 failures
+[2023-10-13 08:41:43,569][flwr][DEBUG] - evaluate_round 182 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 08:41:43,570 | server.py:222 | fit_round 183: strategy sampled 50 clients (out of 50)
+[2023-10-13 08:41:43,570][flwr][DEBUG] - fit_round 183: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.185188 Loss1: 0.405870 Loss2: 1.779318
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.564587 Loss1: 0.249417 Loss2: 1.315170
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526629 Loss1: 0.192360 Loss2: 1.334269
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.460706 Loss1: 0.141355 Loss2: 1.319351
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451803 Loss1: 0.138140 Loss2: 1.313663 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.426083 Loss1: 0.116128 Loss2: 1.309954 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.378578 Loss1: 0.072062 Loss2: 1.306516 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.343833 Loss1: 0.047718 Loss2: 1.296115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.335858 Loss1: 0.045971 Loss2: 1.289887 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.352207 Loss1: 0.062038 Loss2: 1.290169 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434297 Loss1: 0.093683 Loss2: 1.340614 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401380 Loss1: 0.075394 Loss2: 1.325987 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.542967 Loss1: 0.219932 Loss2: 1.323035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.398819 Loss1: 0.073339 Loss2: 1.325480 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.365353 Loss1: 0.065992 Loss2: 1.299361
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.238560 Loss1: 0.347830 Loss2: 1.890730
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.360505 Loss1: 0.060845 Loss2: 1.299659
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.707802 Loss1: 0.319075 Loss2: 1.388727
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.327318 Loss1: 0.036191 Loss2: 1.291127
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.612224 Loss1: 0.180361 Loss2: 1.431863
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.328403 Loss1: 0.038061 Loss2: 1.290342
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550604 Loss1: 0.158643 Loss2: 1.391961
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509704 Loss1: 0.113037 Loss2: 1.396667
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.335864 Loss1: 0.051986 Loss2: 1.283878
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.351658 Loss1: 0.067407 Loss2: 1.284251
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.474364 Loss1: 0.080124 Loss2: 1.394240
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.470684 Loss1: 0.083617 Loss2: 1.387068
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468789 Loss1: 0.087090 Loss2: 1.381698
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.462058 Loss1: 0.083546 Loss2: 1.378512
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433060 Loss1: 0.054252 Loss2: 1.378808
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.286151 Loss1: 0.409026 Loss2: 1.877125
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.618022 Loss1: 0.236333 Loss2: 1.381689
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.630193 Loss1: 0.218249 Loss2: 1.411944
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559648 Loss1: 0.163829 Loss2: 1.395818
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.419133 Loss1: 0.508497 Loss2: 1.910636
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.693454 Loss1: 0.338795 Loss2: 1.354659 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.560691 Loss1: 0.186366 Loss2: 1.374324 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.500348 Loss1: 0.123974 Loss2: 1.376374 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446033 Loss1: 0.092469 Loss2: 1.353565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427674 Loss1: 0.081076 Loss2: 1.346598 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433106 Loss1: 0.084226 Loss2: 1.348880 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373082 Loss1: 0.035608 Loss2: 1.337473 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.303279 Loss1: 0.359415 Loss2: 1.943864 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610946 Loss1: 0.144565 Loss2: 1.466381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.411924 Loss1: 0.491082 Loss2: 1.920842
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574627 Loss1: 0.138417 Loss2: 1.436211
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.560117 Loss1: 0.227605 Loss2: 1.332511
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527307 Loss1: 0.106351 Loss2: 1.420956
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.533474 Loss1: 0.109503 Loss2: 1.423971 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.549657 Loss1: 0.126669 Loss2: 1.422988 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.485951 Loss1: 0.066346 Loss2: 1.419605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470598 Loss1: 0.056583 Loss2: 1.414015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.462787 Loss1: 0.055880 Loss2: 1.406908 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.340525 Loss1: 0.044783 Loss2: 1.295742 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.190574 Loss1: 0.350027 Loss2: 1.840547
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.547232 Loss1: 0.217070 Loss2: 1.330161
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568789 Loss1: 0.220723 Loss2: 1.348067
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556483 Loss1: 0.209082 Loss2: 1.347401
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.246552 Loss1: 0.411446 Loss2: 1.835106
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.618442 Loss1: 0.272204 Loss2: 1.346238 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.560286 Loss1: 0.187707 Loss2: 1.372578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576032 Loss1: 0.221396 Loss2: 1.354636 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491453 Loss1: 0.132938 Loss2: 1.358514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449342 Loss1: 0.099850 Loss2: 1.349492 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.376822 Loss1: 0.062617 Loss2: 1.314205
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443562 Loss1: 0.100328 Loss2: 1.343234
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455477 Loss1: 0.114836 Loss2: 1.340641
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422414 Loss1: 0.076866 Loss2: 1.345548
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.444605 Loss1: 0.101327 Loss2: 1.343278
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.259710 Loss1: 0.351843 Loss2: 1.907867
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.584012 Loss1: 0.174383 Loss2: 1.409629
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.524879 Loss1: 0.111791 Loss2: 1.413087
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496758 Loss1: 0.090987 Loss2: 1.405771
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.169072 Loss1: 0.366308 Loss2: 1.802764
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517735 Loss1: 0.120105 Loss2: 1.397630
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.507448 Loss1: 0.178331 Loss2: 1.329118
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503987 Loss1: 0.104489 Loss2: 1.399498
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.551632 Loss1: 0.211957 Loss2: 1.339675
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.516312 Loss1: 0.181451 Loss2: 1.334862
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.487651 Loss1: 0.088588 Loss2: 1.399063
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.430199 Loss1: 0.104854 Loss2: 1.325345
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.514490 Loss1: 0.107048 Loss2: 1.407442
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.389339 Loss1: 0.074109 Loss2: 1.315230
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.460141 Loss1: 0.063287 Loss2: 1.396853
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416976 Loss1: 0.106409 Loss2: 1.310566
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443438 Loss1: 0.053441 Loss2: 1.389997
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.336002 Loss1: 0.035086 Loss2: 1.300916 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.174893 Loss1: 0.327827 Loss2: 1.847066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.549924 Loss1: 0.171483 Loss2: 1.378441 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.178640 Loss1: 0.341839 Loss2: 1.836802
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.547038 Loss1: 0.169419 Loss2: 1.377619
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.623045 Loss1: 0.245444 Loss2: 1.377601
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.537817 Loss1: 0.176873 Loss2: 1.360945
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565668 Loss1: 0.162742 Loss2: 1.402926
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448558 Loss1: 0.081580 Loss2: 1.366978
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.472672 Loss1: 0.092328 Loss2: 1.380344
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443489 Loss1: 0.085147 Loss2: 1.358342
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.435191 Loss1: 0.062238 Loss2: 1.372953
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433078 Loss1: 0.080376 Loss2: 1.352702
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449489 Loss1: 0.078871 Loss2: 1.370617
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428675 Loss1: 0.078041 Loss2: 1.350635
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444919 Loss1: 0.075851 Loss2: 1.369069
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.425076 Loss1: 0.073596 Loss2: 1.351479
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.461152 Loss1: 0.098016 Loss2: 1.363136 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.202721 Loss1: 0.363295 Loss2: 1.839426 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.472673 Loss1: 0.115782 Loss2: 1.356891 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.423791 Loss1: 0.082577 Loss2: 1.341214
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.197344 Loss1: 0.357041 Loss2: 1.840303
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.432723 Loss1: 0.103585 Loss2: 1.329138
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.508775 Loss1: 0.173831 Loss2: 1.334944
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.411651 Loss1: 0.082900 Loss2: 1.328751
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.502930 Loss1: 0.154927 Loss2: 1.348003
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.389054 Loss1: 0.070138 Loss2: 1.318916
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.461855 Loss1: 0.114687 Loss2: 1.347168
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.386217 Loss1: 0.063195 Loss2: 1.323022
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.432794 Loss1: 0.097519 Loss2: 1.335274
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.355347 Loss1: 0.037453 Loss2: 1.317894
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.412330 Loss1: 0.078223 Loss2: 1.334107
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374788 Loss1: 0.058573 Loss2: 1.316215
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.399465 Loss1: 0.074687 Loss2: 1.324778
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.356037 Loss1: 0.036751 Loss2: 1.319286
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.352015 Loss1: 0.037177 Loss2: 1.314838
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.337479 Loss1: 0.024607 Loss2: 1.312872
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.194882 Loss1: 0.375512 Loss2: 1.819370
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.604962 Loss1: 0.292801 Loss2: 1.312160
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.511762 Loss1: 0.170361 Loss2: 1.341401
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489160 Loss1: 0.166677 Loss2: 1.322483
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.265789 Loss1: 0.391654 Loss2: 1.874136
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451358 Loss1: 0.135887 Loss2: 1.315471
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.601940 Loss1: 0.217650 Loss2: 1.384290
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.408660 Loss1: 0.088338 Loss2: 1.320321
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.619512 Loss1: 0.214478 Loss2: 1.405034
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383269 Loss1: 0.074012 Loss2: 1.309257
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528780 Loss1: 0.121229 Loss2: 1.407550
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.352505 Loss1: 0.055084 Loss2: 1.297421
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524850 Loss1: 0.139056 Loss2: 1.385794
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.325426 Loss1: 0.032392 Loss2: 1.293034
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464848 Loss1: 0.077601 Loss2: 1.387246
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.326527 Loss1: 0.032270 Loss2: 1.294257
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478969 Loss1: 0.104174 Loss2: 1.374795
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438521 Loss1: 0.066558 Loss2: 1.371964
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424563 Loss1: 0.053640 Loss2: 1.370923
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399471 Loss1: 0.035777 Loss2: 1.363694
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.285835 Loss1: 0.431181 Loss2: 1.854654
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622299 Loss1: 0.274712 Loss2: 1.347586
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.578329 Loss1: 0.205001 Loss2: 1.373328
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540631 Loss1: 0.181358 Loss2: 1.359273
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.335877 Loss1: 0.456487 Loss2: 1.879390
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.657538 Loss1: 0.310360 Loss2: 1.347178
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.528994 Loss1: 0.168307 Loss2: 1.360687
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.627187 Loss1: 0.243642 Loss2: 1.383545
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515358 Loss1: 0.155893 Loss2: 1.359465
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.531099 Loss1: 0.160567 Loss2: 1.370533
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517962 Loss1: 0.152971 Loss2: 1.364991
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437026 Loss1: 0.087873 Loss2: 1.349153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422931 Loss1: 0.081648 Loss2: 1.341283 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404358 Loss1: 0.064938 Loss2: 1.339420 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385154 Loss1: 0.058134 Loss2: 1.327020 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987723
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.250111 Loss1: 0.407779 Loss2: 1.842331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.471871 Loss1: 0.123594 Loss2: 1.348277 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.413240 Loss1: 0.070096 Loss2: 1.343144
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.236376 Loss1: 0.387691 Loss2: 1.848685
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.573139 Loss1: 0.216880 Loss2: 1.356259 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543128 Loss1: 0.173516 Loss2: 1.369611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526058 Loss1: 0.164420 Loss2: 1.361638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.447240 Loss1: 0.101622 Loss2: 1.345617 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.442470 Loss1: 0.098395 Loss2: 1.344075 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360852 Loss1: 0.038112 Loss2: 1.322740
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468589 Loss1: 0.120151 Loss2: 1.348439
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408642 Loss1: 0.066926 Loss2: 1.341716
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388866 Loss1: 0.046010 Loss2: 1.342856
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397753 Loss1: 0.062549 Loss2: 1.335205
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.248546 Loss1: 0.465818 Loss2: 1.782728
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.566691 Loss1: 0.268229 Loss2: 1.298462
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.499875 Loss1: 0.168938 Loss2: 1.330936
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.417828 Loss1: 0.099338 Loss2: 1.318489
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.227025 Loss1: 0.376377 Loss2: 1.850648
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.591521 Loss1: 0.241553 Loss2: 1.349968 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.514252 Loss1: 0.149197 Loss2: 1.365055 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.469906 Loss1: 0.116126 Loss2: 1.353780 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.436434 Loss1: 0.092790 Loss2: 1.343643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.408555 Loss1: 0.073317 Loss2: 1.335237 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374692 Loss1: 0.083131 Loss2: 1.291562
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415722 Loss1: 0.084204 Loss2: 1.331518
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420013 Loss1: 0.083806 Loss2: 1.336207
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385625 Loss1: 0.053440 Loss2: 1.332185
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367834 Loss1: 0.039087 Loss2: 1.328747
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.224909 Loss1: 0.436787 Loss2: 1.788122
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.575651 Loss1: 0.264509 Loss2: 1.311143
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.573754 Loss1: 0.230210 Loss2: 1.343544
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.480136 Loss1: 0.152266 Loss2: 1.327870
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.357949 Loss1: 0.486502 Loss2: 1.871447
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.474433 Loss1: 0.161478 Loss2: 1.312955
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.610692 Loss1: 0.267734 Loss2: 1.342958
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.393023 Loss1: 0.071412 Loss2: 1.321611
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.523306 Loss1: 0.158537 Loss2: 1.364768
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390557 Loss1: 0.083396 Loss2: 1.307162
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.490300 Loss1: 0.148372 Loss2: 1.341928
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.444109 Loss1: 0.103511 Loss2: 1.340599
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.368000 Loss1: 0.069235 Loss2: 1.298765
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.418331 Loss1: 0.080608 Loss2: 1.337724
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.346414 Loss1: 0.050767 Loss2: 1.295647
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437504 Loss1: 0.106124 Loss2: 1.331381
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.325439 Loss1: 0.034659 Loss2: 1.290780
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407285 Loss1: 0.073061 Loss2: 1.334224 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.294680 Loss1: 0.427498 Loss2: 1.867182 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.604707 Loss1: 0.210033 Loss2: 1.394673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.552011 Loss1: 0.185199 Loss2: 1.366813
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.270023 Loss1: 0.410752 Loss2: 1.859271
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.469385 Loss1: 0.110866 Loss2: 1.358519
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.643874 Loss1: 0.275933 Loss2: 1.367941
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452207 Loss1: 0.091291 Loss2: 1.360916
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604063 Loss1: 0.179862 Loss2: 1.424202
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415098 Loss1: 0.069825 Loss2: 1.345273
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.558339 Loss1: 0.187904 Loss2: 1.370435
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.443910 Loss1: 0.089601 Loss2: 1.354309
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550634 Loss1: 0.179601 Loss2: 1.371033
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439573 Loss1: 0.083181 Loss2: 1.356391
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496551 Loss1: 0.118000 Loss2: 1.378551
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424183 Loss1: 0.073375 Loss2: 1.350808
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452113 Loss1: 0.092640 Loss2: 1.359473
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437953 Loss1: 0.075222 Loss2: 1.362731
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.419063 Loss1: 0.066764 Loss2: 1.352299
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400826 Loss1: 0.047058 Loss2: 1.353768
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.259113 Loss1: 0.426974 Loss2: 1.832139
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.704989 Loss1: 0.344856 Loss2: 1.360133
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.641408 Loss1: 0.222446 Loss2: 1.418961
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.505893 Loss1: 0.130437 Loss2: 1.375456
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.197463 Loss1: 0.339081 Loss2: 1.858382
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.678079 Loss1: 0.288429 Loss2: 1.389651 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.593554 Loss1: 0.164383 Loss2: 1.429170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519442 Loss1: 0.146262 Loss2: 1.373180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.464701 Loss1: 0.093441 Loss2: 1.371260 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.449974 Loss1: 0.078867 Loss2: 1.371107 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413440 Loss1: 0.051581 Loss2: 1.361860 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388691 Loss1: 0.039064 Loss2: 1.349627 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.595880 Loss1: 0.247690 Loss2: 1.348190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.461559 Loss1: 0.118460 Loss2: 1.343099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.294296 Loss1: 0.418137 Loss2: 1.876158
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451665 Loss1: 0.106383 Loss2: 1.345282
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468735 Loss1: 0.120289 Loss2: 1.348446 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.418705 Loss1: 0.077857 Loss2: 1.340848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401547 Loss1: 0.063098 Loss2: 1.338449 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.382336 Loss1: 0.049537 Loss2: 1.332799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383323 Loss1: 0.056730 Loss2: 1.326593 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397275 Loss1: 0.059545 Loss2: 1.337730 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.236916 Loss1: 0.375478 Loss2: 1.861438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.560554 Loss1: 0.156754 Loss2: 1.403800 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.548030 Loss1: 0.174033 Loss2: 1.373997
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.233662 Loss1: 0.373250 Loss2: 1.860411
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.654301 Loss1: 0.293730 Loss2: 1.360571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.560625 Loss1: 0.170822 Loss2: 1.389803 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.508736 Loss1: 0.144171 Loss2: 1.364565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.452456 Loss1: 0.096741 Loss2: 1.355714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443711 Loss1: 0.087133 Loss2: 1.356578 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374412 Loss1: 0.028426 Loss2: 1.345986
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434214 Loss1: 0.090374 Loss2: 1.343840
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390203 Loss1: 0.050324 Loss2: 1.339880
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395013 Loss1: 0.061096 Loss2: 1.333918
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.377709 Loss1: 0.044003 Loss2: 1.333706
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.294399 Loss1: 0.475606 Loss2: 1.818793
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.617529 Loss1: 0.289634 Loss2: 1.327895
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.636732 Loss1: 0.264941 Loss2: 1.371792
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.552368 Loss1: 0.214583 Loss2: 1.337785
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.180810 Loss1: 0.367344 Loss2: 1.813466
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468881 Loss1: 0.129483 Loss2: 1.339398
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630911 Loss1: 0.292360 Loss2: 1.338551
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.421901 Loss1: 0.095425 Loss2: 1.326477
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.546038 Loss1: 0.188514 Loss2: 1.357524
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422291 Loss1: 0.105366 Loss2: 1.316925
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.438798 Loss1: 0.108154 Loss2: 1.330644
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.383604 Loss1: 0.067189 Loss2: 1.316415
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450276 Loss1: 0.123282 Loss2: 1.326993
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.349137 Loss1: 0.037683 Loss2: 1.311454
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.445196 Loss1: 0.118280 Loss2: 1.326916
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.329085 Loss1: 0.027331 Loss2: 1.301754
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.414934 Loss1: 0.101357 Loss2: 1.313578
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.402453 Loss1: 0.084543 Loss2: 1.317910
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381763 Loss1: 0.065671 Loss2: 1.316093
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354732 Loss1: 0.045514 Loss2: 1.309219
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.219416 Loss1: 0.391551 Loss2: 1.827865
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.614158 Loss1: 0.275502 Loss2: 1.338656
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.519540 Loss1: 0.158032 Loss2: 1.361508
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488570 Loss1: 0.149417 Loss2: 1.339154
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.040371 Loss1: 0.276451 Loss2: 1.763920
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.533088 Loss1: 0.215652 Loss2: 1.317436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.459272 Loss1: 0.135041 Loss2: 1.324231 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.410106 Loss1: 0.096538 Loss2: 1.313568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.365375 Loss1: 0.040836 Loss2: 1.324539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.343354 Loss1: 0.025006 Loss2: 1.318348 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.358980 Loss1: 0.065836 Loss2: 1.293144 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.322166 Loss1: 0.034956 Loss2: 1.287210 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994485
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.588238 Loss1: 0.218519 Loss2: 1.369719 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.494056 Loss1: 0.115841 Loss2: 1.378215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.460908 Loss1: 0.097068 Loss2: 1.363840
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.188849 Loss1: 0.367406 Loss2: 1.821443
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.624549 Loss1: 0.259857 Loss2: 1.364691 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564790 Loss1: 0.168727 Loss2: 1.396063 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468516 Loss1: 0.103072 Loss2: 1.365443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.417242 Loss1: 0.068534 Loss2: 1.348707 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400384 Loss1: 0.044900 Loss2: 1.355484
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.397542 Loss1: 0.054017 Loss2: 1.343525
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.399496 Loss1: 0.064975 Loss2: 1.334521
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.384710 Loss1: 0.051489 Loss2: 1.333220
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395621 Loss1: 0.064042 Loss2: 1.331579
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397443 Loss1: 0.064688 Loss2: 1.332755
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.259556 Loss1: 0.434265 Loss2: 1.825291
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.710894 Loss1: 0.362534 Loss2: 1.348360
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.662147 Loss1: 0.254611 Loss2: 1.407536
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.545751 Loss1: 0.186705 Loss2: 1.359046
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491390 Loss1: 0.129292 Loss2: 1.362098
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.179691 Loss1: 0.320685 Loss2: 1.859006
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452282 Loss1: 0.094206 Loss2: 1.358076
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435703 Loss1: 0.091646 Loss2: 1.344057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425737 Loss1: 0.078718 Loss2: 1.347019 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413614 Loss1: 0.077671 Loss2: 1.335943 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415232 Loss1: 0.075239 Loss2: 1.339993 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456409 Loss1: 0.103525 Loss2: 1.352884 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421014 Loss1: 0.069650 Loss2: 1.351364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390448 Loss1: 0.043953 Loss2: 1.346495
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.109225 Loss1: 0.318101 Loss2: 1.791124
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.559506 Loss1: 0.227138 Loss2: 1.332367
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.558094 Loss1: 0.198653 Loss2: 1.359441
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500083 Loss1: 0.160920 Loss2: 1.339163
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473062 Loss1: 0.138397 Loss2: 1.334665
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.154758 Loss1: 0.268761 Loss2: 1.885997
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.538152 Loss1: 0.159905 Loss2: 1.378248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.491578 Loss1: 0.120369 Loss2: 1.371209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530785 Loss1: 0.146816 Loss2: 1.383969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.467996 Loss1: 0.096705 Loss2: 1.371291 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358144 Loss1: 0.040492 Loss2: 1.317652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461141 Loss1: 0.094770 Loss2: 1.366371
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448305 Loss1: 0.073515 Loss2: 1.374789
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439240 Loss1: 0.072296 Loss2: 1.366943
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422563 Loss1: 0.056236 Loss2: 1.366327
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403689 Loss1: 0.041599 Loss2: 1.362090
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+DEBUG flwr 2023-10-13 09:10:49,204 | server.py:236 | fit_round 183 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.454402 Loss1: 0.535829 Loss2: 1.918573
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.635164 Loss1: 0.317837 Loss2: 1.317326
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.543393 Loss1: 0.207724 Loss2: 1.335669
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489287 Loss1: 0.142639 Loss2: 1.346648
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.438950 Loss1: 0.123696 Loss2: 1.315254
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.392388 Loss1: 0.082184 Loss2: 1.310204
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.309582 Loss1: 0.410495 Loss2: 1.899087
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.627856 Loss1: 0.231734 Loss2: 1.396122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.337731 Loss1: 0.045740 Loss2: 1.291992 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.328192 Loss1: 0.035236 Loss2: 1.292956 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490099 Loss1: 0.109422 Loss2: 1.380677 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481190 Loss1: 0.104115 Loss2: 1.377075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.302358 Loss1: 0.432303 Loss2: 1.870055
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453751 Loss1: 0.075419 Loss2: 1.378332
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.575784 Loss1: 0.222447 Loss2: 1.353337
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.447027 Loss1: 0.076215 Loss2: 1.370813
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.441405 Loss1: 0.101330 Loss2: 1.340074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.413445 Loss1: 0.084108 Loss2: 1.329337 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408892 Loss1: 0.073394 Loss2: 1.335498
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.185434 Loss1: 0.371476 Loss2: 1.813957
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.654016 Loss1: 0.326225 Loss2: 1.327790 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.614503 Loss1: 0.219831 Loss2: 1.394672 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.357209 Loss1: 0.039831 Loss2: 1.317378
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532448 Loss1: 0.184307 Loss2: 1.348141
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503292 Loss1: 0.155604 Loss2: 1.347688
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506188 Loss1: 0.152741 Loss2: 1.353447
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.448771 Loss1: 0.111417 Loss2: 1.337354
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.403835 Loss1: 0.068049 Loss2: 1.335786
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.225828 Loss1: 0.353117 Loss2: 1.872712
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.361394 Loss1: 0.034637 Loss2: 1.326757
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354686 Loss1: 0.038592 Loss2: 1.316094 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.436375 Loss1: 0.065713 Loss2: 1.370662 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.410316 Loss1: 0.062831 Loss2: 1.347485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.407013 Loss1: 0.065081 Loss2: 1.341931
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.160182 Loss1: 0.351584 Loss2: 1.808597
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.575367 Loss1: 0.242479 Loss2: 1.332888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.507917 Loss1: 0.155653 Loss2: 1.352264 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.464814 Loss1: 0.134012 Loss2: 1.330802 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.433842 Loss1: 0.106835 Loss2: 1.327007 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389631 Loss1: 0.075479 Loss2: 1.314152 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369081 Loss1: 0.064222 Loss2: 1.304860 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 09:10:49,204][flwr][DEBUG] - fit_round 183 received 50 results and 0 failures
+INFO flwr 2023-10-13 09:11:30,238 | server.py:125 | fit progress: (183, 2.3107704240293168, {'accuracy': 0.6095}, 422398.016980325)
+>> Test accuracy: 0.609500
+[2023-10-13 09:11:30,238][flwr][INFO] - fit progress: (183, 2.3107704240293168, {'accuracy': 0.6095}, 422398.016980325)
+DEBUG flwr 2023-10-13 09:11:30,239 | server.py:173 | evaluate_round 183: strategy sampled 50 clients (out of 50)
+[2023-10-13 09:11:30,239][flwr][DEBUG] - evaluate_round 183: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 09:20:34,104 | server.py:187 | evaluate_round 183 received 50 results and 0 failures
+[2023-10-13 09:20:34,104][flwr][DEBUG] - evaluate_round 183 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 09:20:34,104 | server.py:222 | fit_round 184: strategy sampled 50 clients (out of 50)
+[2023-10-13 09:20:34,104][flwr][DEBUG] - fit_round 184: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.198657 Loss1: 0.372254 Loss2: 1.826403
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.625395 Loss1: 0.299363 Loss2: 1.326032
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.511443 Loss1: 0.147102 Loss2: 1.364342
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490037 Loss1: 0.155776 Loss2: 1.334260
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.192223 Loss1: 0.342465 Loss2: 1.849758
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.466104 Loss1: 0.137983 Loss2: 1.328120
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.644637 Loss1: 0.292961 Loss2: 1.351675
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457810 Loss1: 0.134597 Loss2: 1.323213
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.571596 Loss1: 0.178175 Loss2: 1.393422
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434995 Loss1: 0.112754 Loss2: 1.322241
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.515529 Loss1: 0.155985 Loss2: 1.359545
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427298 Loss1: 0.104613 Loss2: 1.322685
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492495 Loss1: 0.133495 Loss2: 1.359000
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.361611 Loss1: 0.045988 Loss2: 1.315623
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464832 Loss1: 0.114215 Loss2: 1.350617
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.342380 Loss1: 0.037096 Loss2: 1.305283
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438974 Loss1: 0.091141 Loss2: 1.347834
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412203 Loss1: 0.070978 Loss2: 1.341225
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366589 Loss1: 0.027497 Loss2: 1.339092
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369521 Loss1: 0.039922 Loss2: 1.329600
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.367545 Loss1: 0.454659 Loss2: 1.912886
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.697477 Loss1: 0.356980 Loss2: 1.340497
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634283 Loss1: 0.234644 Loss2: 1.399640
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516642 Loss1: 0.131617 Loss2: 1.385025
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.154864 Loss1: 0.334717 Loss2: 1.820147
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.429537 Loss1: 0.076126 Loss2: 1.353410 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458789 Loss1: 0.112134 Loss2: 1.346655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427134 Loss1: 0.079231 Loss2: 1.347903 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443814 Loss1: 0.095145 Loss2: 1.348669 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450308 Loss1: 0.103979 Loss2: 1.346328 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.419498 Loss1: 0.083666 Loss2: 1.335832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396501 Loss1: 0.056318 Loss2: 1.340183 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.170696 Loss1: 0.327369 Loss2: 1.843327
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403955 Loss1: 0.066849 Loss2: 1.337107
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.486678 Loss1: 0.129738 Loss2: 1.356940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.465220 Loss1: 0.120787 Loss2: 1.344432 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457026 Loss1: 0.114705 Loss2: 1.342321
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.214853 Loss1: 0.342261 Loss2: 1.872592
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.684860 Loss1: 0.309155 Loss2: 1.375705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.606837 Loss1: 0.179430 Loss2: 1.427407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556099 Loss1: 0.174751 Loss2: 1.381348 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368243 Loss1: 0.041069 Loss2: 1.327174
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523517 Loss1: 0.144080 Loss2: 1.379437
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469463 Loss1: 0.097213 Loss2: 1.372251
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.454999 Loss1: 0.087045 Loss2: 1.367954
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445213 Loss1: 0.079283 Loss2: 1.365930
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440910 Loss1: 0.078727 Loss2: 1.362184
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.111964 Loss1: 0.291134 Loss2: 1.820830
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415087 Loss1: 0.053240 Loss2: 1.361848
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.470584 Loss1: 0.120020 Loss2: 1.350565 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.429645 Loss1: 0.092909 Loss2: 1.336735 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.417977 Loss1: 0.081319 Loss2: 1.336659 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468675 Loss1: 0.123336 Loss2: 1.345339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430301 Loss1: 0.085742 Loss2: 1.344559 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390161 Loss1: 0.052994 Loss2: 1.337167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380653 Loss1: 0.044020 Loss2: 1.336633 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998162
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.380288 Loss1: 0.058257 Loss2: 1.322032 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364113 Loss1: 0.054172 Loss2: 1.309941 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.653980 Loss1: 0.286098 Loss2: 1.367882 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.623507 Loss1: 0.248685 Loss2: 1.374822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.594512 Loss1: 0.199622 Loss2: 1.394890 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491038 Loss1: 0.104316 Loss2: 1.386721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.493862 Loss1: 0.123336 Loss2: 1.370526 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486267 Loss1: 0.111086 Loss2: 1.375182 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424909 Loss1: 0.065945 Loss2: 1.358964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410266 Loss1: 0.052610 Loss2: 1.357656 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397332 Loss1: 0.074827 Loss2: 1.322504 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.215373 Loss1: 0.399290 Loss2: 1.816083 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.480696 Loss1: 0.145330 Loss2: 1.335366 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.458987 Loss1: 0.123726 Loss2: 1.335261
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.221988 Loss1: 0.419875 Loss2: 1.802113
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.603891 Loss1: 0.277158 Loss2: 1.326733 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.541715 Loss1: 0.167185 Loss2: 1.374530 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.422641 Loss1: 0.096619 Loss2: 1.326022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.422440 Loss1: 0.101554 Loss2: 1.320886 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.412354 Loss1: 0.091664 Loss2: 1.320690 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.330400 Loss1: 0.034967 Loss2: 1.295433
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.400130 Loss1: 0.080500 Loss2: 1.319630
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.383707 Loss1: 0.066480 Loss2: 1.317227
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366137 Loss1: 0.057261 Loss2: 1.308876
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.342116 Loss1: 0.040473 Loss2: 1.301643
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.293255 Loss1: 0.415015 Loss2: 1.878240
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.670386 Loss1: 0.283672 Loss2: 1.386714
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.585826 Loss1: 0.181858 Loss2: 1.403967
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.565994 Loss1: 0.172953 Loss2: 1.393041
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.301675 Loss1: 0.393465 Loss2: 1.908210
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.620766 Loss1: 0.269489 Loss2: 1.351277 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.581516 Loss1: 0.201565 Loss2: 1.379951 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595882 Loss1: 0.198929 Loss2: 1.396953 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.503191 Loss1: 0.128511 Loss2: 1.374680
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514162 Loss1: 0.140671 Loss2: 1.373490
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.452610 Loss1: 0.075371 Loss2: 1.377239
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480393 Loss1: 0.105721 Loss2: 1.374672
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409571 Loss1: 0.042056 Loss2: 1.367515
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440603 Loss1: 0.073011 Loss2: 1.367592
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407187 Loss1: 0.051798 Loss2: 1.355389
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388125 Loss1: 0.036753 Loss2: 1.351372
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.389659 Loss1: 0.044812 Loss2: 1.344846
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.277816 Loss1: 0.368671 Loss2: 1.909145
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.629450 Loss1: 0.239983 Loss2: 1.389467
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.646434 Loss1: 0.239947 Loss2: 1.406487
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.571992 Loss1: 0.161384 Loss2: 1.410608
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.151213 Loss1: 0.359776 Loss2: 1.791436
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.552634 Loss1: 0.243109 Loss2: 1.309525 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.486546 Loss1: 0.170451 Loss2: 1.316095 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.424423 Loss1: 0.105597 Loss2: 1.318825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.407092 Loss1: 0.100267 Loss2: 1.306825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.389234 Loss1: 0.086242 Loss2: 1.302993 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438535 Loss1: 0.066512 Loss2: 1.372023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.375878 Loss1: 0.070563 Loss2: 1.305315
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.394511 Loss1: 0.100804 Loss2: 1.293708
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362527 Loss1: 0.056485 Loss2: 1.306042
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376414 Loss1: 0.079190 Loss2: 1.297224
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.211427 Loss1: 0.339785 Loss2: 1.871642
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.590083 Loss1: 0.209474 Loss2: 1.380610
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563541 Loss1: 0.170906 Loss2: 1.392635
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482860 Loss1: 0.087601 Loss2: 1.395259
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.248567 Loss1: 0.413841 Loss2: 1.834726
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612240 Loss1: 0.274255 Loss2: 1.337986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.537270 Loss1: 0.164513 Loss2: 1.372757 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.427422 Loss1: 0.088241 Loss2: 1.339180 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.415330 Loss1: 0.089967 Loss2: 1.325364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.391515 Loss1: 0.064020 Loss2: 1.327496 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450746 Loss1: 0.073571 Loss2: 1.377175
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.381807 Loss1: 0.059898 Loss2: 1.321910
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.398010 Loss1: 0.076381 Loss2: 1.321630
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397880 Loss1: 0.073797 Loss2: 1.324083
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382913 Loss1: 0.062166 Loss2: 1.320747
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.264274 Loss1: 0.437171 Loss2: 1.827103
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.648556 Loss1: 0.301598 Loss2: 1.346959
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.569705 Loss1: 0.205513 Loss2: 1.364191
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.478635 Loss1: 0.130253 Loss2: 1.348382
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.278168 Loss1: 0.459901 Loss2: 1.818266
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.606694 Loss1: 0.262179 Loss2: 1.344514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601997 Loss1: 0.219685 Loss2: 1.382312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543970 Loss1: 0.186074 Loss2: 1.357896 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493182 Loss1: 0.150934 Loss2: 1.342248 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467121 Loss1: 0.116626 Loss2: 1.350495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450640 Loss1: 0.117103 Loss2: 1.333537 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403135 Loss1: 0.071890 Loss2: 1.331245 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989258
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.559930 Loss1: 0.195746 Loss2: 1.364184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.470416 Loss1: 0.124523 Loss2: 1.345893 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.056140 Loss1: 0.313560 Loss2: 1.742580
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.427307 Loss1: 0.087000 Loss2: 1.340307
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.483631 Loss1: 0.179170 Loss2: 1.304462
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.402327 Loss1: 0.066130 Loss2: 1.336197
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.500607 Loss1: 0.161710 Loss2: 1.338897
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.453296 Loss1: 0.119191 Loss2: 1.334104
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.414552 Loss1: 0.111193 Loss2: 1.303358
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398581 Loss1: 0.058339 Loss2: 1.340243
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.376934 Loss1: 0.084824 Loss2: 1.292110
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412383 Loss1: 0.079052 Loss2: 1.333331
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.349697 Loss1: 0.053607 Loss2: 1.296090
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421669 Loss1: 0.093532 Loss2: 1.328137
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.321731 Loss1: 0.043317 Loss2: 1.278415 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.296840 Loss1: 0.026807 Loss2: 1.270033 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.699080 Loss1: 0.352103 Loss2: 1.346977 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499922 Loss1: 0.154360 Loss2: 1.345561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499082 Loss1: 0.156598 Loss2: 1.342484
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.302085 Loss1: 0.465668 Loss2: 1.836417
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.507617 Loss1: 0.161671 Loss2: 1.345946
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.638753 Loss1: 0.290437 Loss2: 1.348316
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485046 Loss1: 0.138786 Loss2: 1.346260
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.620493 Loss1: 0.222697 Loss2: 1.397796
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438490 Loss1: 0.103082 Loss2: 1.335408
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574224 Loss1: 0.220259 Loss2: 1.353965
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407615 Loss1: 0.073946 Loss2: 1.333669
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.457230 Loss1: 0.105989 Loss2: 1.351241
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384188 Loss1: 0.061926 Loss2: 1.322262
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455093 Loss1: 0.116970 Loss2: 1.338123
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.394412 Loss1: 0.062323 Loss2: 1.332089
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405853 Loss1: 0.073935 Loss2: 1.331918
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379270 Loss1: 0.054966 Loss2: 1.324305
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380522 Loss1: 0.061378 Loss2: 1.319144
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.159904 Loss1: 0.353821 Loss2: 1.806083
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.579105 Loss1: 0.254221 Loss2: 1.324884
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.521395 Loss1: 0.163943 Loss2: 1.357452
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500001 Loss1: 0.157469 Loss2: 1.342532
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.337682 Loss1: 0.472051 Loss2: 1.865631
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.573733 Loss1: 0.220069 Loss2: 1.353664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.515769 Loss1: 0.150972 Loss2: 1.364798 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.476386 Loss1: 0.131030 Loss2: 1.345356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495663 Loss1: 0.141882 Loss2: 1.353781 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.473465 Loss1: 0.124498 Loss2: 1.348967 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.421374 Loss1: 0.075049 Loss2: 1.346324 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391178 Loss1: 0.050386 Loss2: 1.340792 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.575731 Loss1: 0.229919 Loss2: 1.345812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.487019 Loss1: 0.133742 Loss2: 1.353277 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492196 Loss1: 0.145520 Loss2: 1.346676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503653 Loss1: 0.144080 Loss2: 1.359572 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479682 Loss1: 0.119203 Loss2: 1.360479 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452834 Loss1: 0.104569 Loss2: 1.348264 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.463968 Loss1: 0.111750 Loss2: 1.352218 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422891 Loss1: 0.076589 Loss2: 1.346302 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.410912 Loss1: 0.074753 Loss2: 1.336159 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.347019 Loss1: 0.024049 Loss2: 1.322970 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.656084 Loss1: 0.301032 Loss2: 1.355052 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.510033 Loss1: 0.140070 Loss2: 1.369963 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.481841 Loss1: 0.133666 Loss2: 1.348175
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.163247 Loss1: 0.347255 Loss2: 1.815992
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.571405 Loss1: 0.240303 Loss2: 1.331101 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543753 Loss1: 0.190979 Loss2: 1.352774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.511764 Loss1: 0.165068 Loss2: 1.346696 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446094 Loss1: 0.109995 Loss2: 1.336100 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989955
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.403158 Loss1: 0.078520 Loss2: 1.324638 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362866 Loss1: 0.048333 Loss2: 1.314533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366108 Loss1: 0.053918 Loss2: 1.312189
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.146249 Loss1: 0.323643 Loss2: 1.822606
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.604782 Loss1: 0.263100 Loss2: 1.341681
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.459205 Loss1: 0.098654 Loss2: 1.360550
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.408911 Loss1: 0.069306 Loss2: 1.339605
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.392706 Loss1: 0.068678 Loss2: 1.324028
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.157177 Loss1: 0.325239 Loss2: 1.831938
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.408315 Loss1: 0.082118 Loss2: 1.326197
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.559432 Loss1: 0.218441 Loss2: 1.340990
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.376133 Loss1: 0.046580 Loss2: 1.329553
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.541614 Loss1: 0.187811 Loss2: 1.353804
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.373547 Loss1: 0.055837 Loss2: 1.317709
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481631 Loss1: 0.118750 Loss2: 1.362881
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384499 Loss1: 0.069161 Loss2: 1.315338
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446031 Loss1: 0.108112 Loss2: 1.337919
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412196 Loss1: 0.092509 Loss2: 1.319687
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.370952 Loss1: 0.043425 Loss2: 1.327528 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.360313 Loss1: 0.040810 Loss2: 1.319503 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.360431 Loss1: 0.043337 Loss2: 1.317094
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.217751 Loss1: 0.364082 Loss2: 1.853669
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.627155 Loss1: 0.266930 Loss2: 1.360224
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.570102 Loss1: 0.181289 Loss2: 1.388814
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.600390 Loss1: 0.227518 Loss2: 1.372872
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.662886 Loss1: 0.270394 Loss2: 1.392492
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.194082 Loss1: 0.367611 Loss2: 1.826471
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.535699 Loss1: 0.160428 Loss2: 1.375271
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.558127 Loss1: 0.216278 Loss2: 1.341849
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.532809 Loss1: 0.163539 Loss2: 1.369270
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.514034 Loss1: 0.165946 Loss2: 1.348089
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.475426 Loss1: 0.101690 Loss2: 1.373736
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.484514 Loss1: 0.126740 Loss2: 1.357774
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423250 Loss1: 0.064586 Loss2: 1.358664
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.442993 Loss1: 0.106965 Loss2: 1.336028
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402047 Loss1: 0.050190 Loss2: 1.351857
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.379124 Loss1: 0.056328 Loss2: 1.322796 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.367883 Loss1: 0.053545 Loss2: 1.314338 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362287 Loss1: 0.053054 Loss2: 1.309233
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.213594 Loss1: 0.367695 Loss2: 1.845899
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.560329 Loss1: 0.235241 Loss2: 1.325088
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.508529 Loss1: 0.160508 Loss2: 1.348022
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498474 Loss1: 0.162953 Loss2: 1.335520
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.416345 Loss1: 0.087491 Loss2: 1.328854
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.270130 Loss1: 0.422650 Loss2: 1.847480
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.369055 Loss1: 0.051298 Loss2: 1.317757
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.591913 Loss1: 0.232863 Loss2: 1.359050
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.370162 Loss1: 0.061153 Loss2: 1.309009
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.604031 Loss1: 0.226720 Loss2: 1.377311
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.379235 Loss1: 0.067937 Loss2: 1.311299
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588694 Loss1: 0.204677 Loss2: 1.384018
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.366239 Loss1: 0.058192 Loss2: 1.308047
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.507965 Loss1: 0.144955 Loss2: 1.363010
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.347777 Loss1: 0.044594 Loss2: 1.303183
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.484785 Loss1: 0.125529 Loss2: 1.359256 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410009 Loss1: 0.064591 Loss2: 1.345418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.371918 Loss1: 0.031654 Loss2: 1.340264
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.215855 Loss1: 0.346923 Loss2: 1.868933
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.598861 Loss1: 0.224138 Loss2: 1.374723
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.543601 Loss1: 0.162203 Loss2: 1.381398
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517341 Loss1: 0.134298 Loss2: 1.383044
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.486784 Loss1: 0.119308 Loss2: 1.367476
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.430468 Loss1: 0.070690 Loss2: 1.359777
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.165380 Loss1: 0.346458 Loss2: 1.818922
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.427900 Loss1: 0.069312 Loss2: 1.358587
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.673537 Loss1: 0.298537 Loss2: 1.375000
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401799 Loss1: 0.049686 Loss2: 1.352112
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.621221 Loss1: 0.206701 Loss2: 1.414521
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400300 Loss1: 0.047894 Loss2: 1.352407
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.549292 Loss1: 0.173243 Loss2: 1.376049
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409323 Loss1: 0.063795 Loss2: 1.345528
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.550425 Loss1: 0.167398 Loss2: 1.383027
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496476 Loss1: 0.120625 Loss2: 1.375851
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.471386 Loss1: 0.104987 Loss2: 1.366399
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452818 Loss1: 0.088580 Loss2: 1.364238
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.503650 Loss1: 0.144235 Loss2: 1.359416
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.372601 Loss1: 0.479045 Loss2: 1.893555
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.598812 Loss1: 0.229317 Loss2: 1.369494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491539 Loss1: 0.113316 Loss2: 1.378223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.426918 Loss1: 0.059373 Loss2: 1.367545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408683 Loss1: 0.054619 Loss2: 1.354064 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410686 Loss1: 0.059643 Loss2: 1.351043 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409008 Loss1: 0.056144 Loss2: 1.352864 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.488085 Loss1: 0.128075 Loss2: 1.360010 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453805 Loss1: 0.088359 Loss2: 1.365446 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417258 Loss1: 0.071646 Loss2: 1.345612 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.321642 Loss1: 0.399963 Loss2: 1.921678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.525678 Loss1: 0.146192 Loss2: 1.379485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472136 Loss1: 0.108469 Loss2: 1.363667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.497251 Loss1: 0.128212 Loss2: 1.369039 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476190 Loss1: 0.107157 Loss2: 1.369033 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433648 Loss1: 0.064106 Loss2: 1.369542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.454704 Loss1: 0.087778 Loss2: 1.366927 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.437069 Loss1: 0.070964 Loss2: 1.366105 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.365227 Loss1: 0.045223 Loss2: 1.320004 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368938 Loss1: 0.053421 Loss2: 1.315517 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.975000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.552241 Loss1: 0.225173 Loss2: 1.327068 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.473510 Loss1: 0.129034 Loss2: 1.344477 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519877 Loss1: 0.177729 Loss2: 1.342148
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.281198 Loss1: 0.396827 Loss2: 1.884371
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.643391 Loss1: 0.278279 Loss2: 1.365112
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.541633 Loss1: 0.187812 Loss2: 1.353821
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.550152 Loss1: 0.158025 Loss2: 1.392127
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.481828 Loss1: 0.130743 Loss2: 1.351085
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481814 Loss1: 0.132443 Loss2: 1.349372
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434791 Loss1: 0.089029 Loss2: 1.345762
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.440870 Loss1: 0.083318 Loss2: 1.357552
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465430 Loss1: 0.128311 Loss2: 1.337119
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399621 Loss1: 0.064050 Loss2: 1.335571 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433703 Loss1: 0.096820 Loss2: 1.336883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.419937 Loss1: 0.086707 Loss2: 1.333230 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.620068 Loss1: 0.241226 Loss2: 1.378842 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484348 Loss1: 0.110160 Loss2: 1.374188 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.481687 Loss1: 0.111867 Loss2: 1.369820
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.202444 Loss1: 0.343554 Loss2: 1.858890
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473396 Loss1: 0.100498 Loss2: 1.372897
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.579286 Loss1: 0.223011 Loss2: 1.356275
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420615 Loss1: 0.061520 Loss2: 1.359095
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543818 Loss1: 0.170721 Loss2: 1.373097
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.388431 Loss1: 0.031615 Loss2: 1.356816
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.456268 Loss1: 0.086751 Loss2: 1.369517
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386504 Loss1: 0.039094 Loss2: 1.347410
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.448376 Loss1: 0.101387 Loss2: 1.346990
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387750 Loss1: 0.046155 Loss2: 1.341594
+DEBUG flwr 2023-10-13 09:48:58,822 | server.py:236 | fit_round 184 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.436912 Loss1: 0.093670 Loss2: 1.343242
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436592 Loss1: 0.092184 Loss2: 1.344407
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422125 Loss1: 0.087448 Loss2: 1.334677
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451598 Loss1: 0.114252 Loss2: 1.337346
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454525 Loss1: 0.104347 Loss2: 1.350178
+(DefaultActor pid=3764) >> Training accuracy: 0.971875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.256007 Loss1: 0.368155 Loss2: 1.887852
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.670335 Loss1: 0.283852 Loss2: 1.386483
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.612025 Loss1: 0.184920 Loss2: 1.427105
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536776 Loss1: 0.135137 Loss2: 1.401639
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.463613 Loss1: 0.482522 Loss2: 1.981091
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.657578 Loss1: 0.298487 Loss2: 1.359092 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.576019 Loss1: 0.213256 Loss2: 1.362763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.518869 Loss1: 0.131395 Loss2: 1.387474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.462826 Loss1: 0.100965 Loss2: 1.361861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427722 Loss1: 0.073791 Loss2: 1.353931
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.443737 Loss1: 0.065887 Loss2: 1.377850
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453889 Loss1: 0.080006 Loss2: 1.373882 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.432328 Loss1: 0.081591 Loss2: 1.350737 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.401736 Loss1: 0.464630 Loss2: 1.937106
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.754249 Loss1: 0.355541 Loss2: 1.398707
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.636121 Loss1: 0.201301 Loss2: 1.434820
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.618382 Loss1: 0.227766 Loss2: 1.390616
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.227357 Loss1: 0.364058 Loss2: 1.863299
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.605430 Loss1: 0.239818 Loss2: 1.365612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.595585 Loss1: 0.220107 Loss2: 1.375478 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.531228 Loss1: 0.157428 Loss2: 1.373800 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506938 Loss1: 0.149631 Loss2: 1.357307 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441469 Loss1: 0.088645 Loss2: 1.352824 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.379470 Loss1: 0.038409 Loss2: 1.341060 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401220 Loss1: 0.072188 Loss2: 1.329032 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 09:48:58,822][flwr][DEBUG] - fit_round 184 received 50 results and 0 failures
+INFO flwr 2023-10-13 09:49:40,817 | server.py:125 | fit progress: (184, 2.313936873937186, {'accuracy': 0.6104}, 424688.596004301)
+>> Test accuracy: 0.610400
+[2023-10-13 09:49:40,817][flwr][INFO] - fit progress: (184, 2.313936873937186, {'accuracy': 0.6104}, 424688.596004301)
+DEBUG flwr 2023-10-13 09:49:40,818 | server.py:173 | evaluate_round 184: strategy sampled 50 clients (out of 50)
+[2023-10-13 09:49:40,818][flwr][DEBUG] - evaluate_round 184: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 09:58:44,423 | server.py:187 | evaluate_round 184 received 50 results and 0 failures
+[2023-10-13 09:58:44,423][flwr][DEBUG] - evaluate_round 184 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 09:58:44,424 | server.py:222 | fit_round 185: strategy sampled 50 clients (out of 50)
+[2023-10-13 09:58:44,424][flwr][DEBUG] - fit_round 185: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.475192 Loss1: 0.529156 Loss2: 1.946036
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.647571 Loss1: 0.305100 Loss2: 1.342471
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.601481 Loss1: 0.250879 Loss2: 1.350602
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.481783 Loss1: 0.118756 Loss2: 1.363027
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.441785 Loss1: 0.108605 Loss2: 1.333180
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.426714 Loss1: 0.101053 Loss2: 1.325661 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.380807 Loss1: 0.055103 Loss2: 1.325704 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.352949 Loss1: 0.037204 Loss2: 1.315745 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.329984 Loss1: 0.020065 Loss2: 1.309918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.328445 Loss1: 0.025422 Loss2: 1.303023 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991587
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.409275 Loss1: 0.074114 Loss2: 1.335161 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.349618 Loss1: 0.022510 Loss2: 1.327107 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361234 Loss1: 0.040942 Loss2: 1.320292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.137348 Loss1: 0.363035 Loss2: 1.774313
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.564696 Loss1: 0.237756 Loss2: 1.326940
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.498207 Loss1: 0.154062 Loss2: 1.344144
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.480300 Loss1: 0.157459 Loss2: 1.322841
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443273 Loss1: 0.125107 Loss2: 1.318165
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.270821 Loss1: 0.325460 Loss2: 1.945360
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.398224 Loss1: 0.075882 Loss2: 1.322343
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.689315 Loss1: 0.275438 Loss2: 1.413877
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.647059 Loss1: 0.197400 Loss2: 1.449658
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.387972 Loss1: 0.076309 Loss2: 1.311663
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567542 Loss1: 0.124769 Loss2: 1.442773
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449553 Loss1: 0.128906 Loss2: 1.320648
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515495 Loss1: 0.099742 Loss2: 1.415754
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402468 Loss1: 0.081340 Loss2: 1.321129
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512875 Loss1: 0.098219 Loss2: 1.414656
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439649 Loss1: 0.122738 Loss2: 1.316911
+(DefaultActor pid=3765) >> Training accuracy: 0.975586
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.492585 Loss1: 0.079344 Loss2: 1.413241 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.430807 Loss1: 0.030766 Loss2: 1.400041 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.751989 Loss1: 0.343044 Loss2: 1.408945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586966 Loss1: 0.173423 Loss2: 1.413543 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.519612 Loss1: 0.121140 Loss2: 1.398473 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.496720 Loss1: 0.100780 Loss2: 1.395940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514998 Loss1: 0.108889 Loss2: 1.406109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480358 Loss1: 0.080866 Loss2: 1.399493 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444331 Loss1: 0.052727 Loss2: 1.391604 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421205 Loss1: 0.038411 Loss2: 1.382794 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427264 Loss1: 0.089293 Loss2: 1.337971 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396815 Loss1: 0.062101 Loss2: 1.334714 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.680974 Loss1: 0.315880 Loss2: 1.365094 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558332 Loss1: 0.184032 Loss2: 1.374300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535099 Loss1: 0.160277 Loss2: 1.374822
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.150325 Loss1: 0.389116 Loss2: 1.761209
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503904 Loss1: 0.129793 Loss2: 1.374110
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.508960 Loss1: 0.210694 Loss2: 1.298266
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425338 Loss1: 0.061793 Loss2: 1.363545
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.485265 Loss1: 0.162928 Loss2: 1.322337
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.420108 Loss1: 0.117240 Loss2: 1.302868 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.416181 Loss1: 0.117934 Loss2: 1.298247 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.403750 Loss1: 0.101345 Loss2: 1.302405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.362195 Loss1: 0.063054 Loss2: 1.299140 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.335257 Loss1: 0.048725 Loss2: 1.286532 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.571599 Loss1: 0.257164 Loss2: 1.314435 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.387628 Loss1: 0.072818 Loss2: 1.314811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.424642 Loss1: 0.475225 Loss2: 1.949416 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.739869 Loss1: 0.343163 Loss2: 1.396706 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.617828 Loss1: 0.185900 Loss2: 1.431928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.599543 Loss1: 0.197921 Loss2: 1.401622 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.585787 Loss1: 0.171771 Loss2: 1.414016 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537573 Loss1: 0.121798 Loss2: 1.415775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477024 Loss1: 0.083780 Loss2: 1.393244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411122 Loss1: 0.032006 Loss2: 1.379117 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.600914 Loss1: 0.218336 Loss2: 1.382577 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.523178 Loss1: 0.136036 Loss2: 1.387142 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.547838 Loss1: 0.503199 Loss2: 2.044639
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531619 Loss1: 0.158903 Loss2: 1.372716
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484312 Loss1: 0.108197 Loss2: 1.376115 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.539172 Loss1: 0.163645 Loss2: 1.375527 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.484499 Loss1: 0.107825 Loss2: 1.376674 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.540889 Loss1: 0.130184 Loss2: 1.410705 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.544868 Loss1: 0.136323 Loss2: 1.408545 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.457431 Loss1: 0.069273 Loss2: 1.388158 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.291461 Loss1: 0.397181 Loss2: 1.894280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.604039 Loss1: 0.178075 Loss2: 1.425964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535944 Loss1: 0.156159 Loss2: 1.379785
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.368905 Loss1: 0.448015 Loss2: 1.920891
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.533994 Loss1: 0.145569 Loss2: 1.388425
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.631659 Loss1: 0.240596 Loss2: 1.391062
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592500 Loss1: 0.204359 Loss2: 1.388141
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505990 Loss1: 0.116259 Loss2: 1.389731
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561870 Loss1: 0.155890 Loss2: 1.405979
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461431 Loss1: 0.087546 Loss2: 1.373885
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.495837 Loss1: 0.120832 Loss2: 1.375005
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429902 Loss1: 0.054705 Loss2: 1.375197
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397422 Loss1: 0.036387 Loss2: 1.361034 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426750 Loss1: 0.069330 Loss2: 1.357420 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.486262 Loss1: 0.104942 Loss2: 1.381320 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985491
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.168345 Loss1: 0.344067 Loss2: 1.824277 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.507973 Loss1: 0.170752 Loss2: 1.337222 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.437122 Loss1: 0.100961 Loss2: 1.336160
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.265688 Loss1: 0.427481 Loss2: 1.838207
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.418874 Loss1: 0.108831 Loss2: 1.310043
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.621348 Loss1: 0.280527 Loss2: 1.340820
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433502 Loss1: 0.125325 Loss2: 1.308177
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.566073 Loss1: 0.186860 Loss2: 1.379213
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414597 Loss1: 0.092821 Loss2: 1.321776
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498896 Loss1: 0.154541 Loss2: 1.344354
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413647 Loss1: 0.102534 Loss2: 1.311113
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.501296 Loss1: 0.161172 Loss2: 1.340124
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388618 Loss1: 0.075023 Loss2: 1.313595
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.438380 Loss1: 0.099074 Loss2: 1.339306
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355362 Loss1: 0.049998 Loss2: 1.305364
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.428880 Loss1: 0.098271 Loss2: 1.330609
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381234 Loss1: 0.048712 Loss2: 1.332522
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.403966 Loss1: 0.079276 Loss2: 1.324690
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379673 Loss1: 0.056156 Loss2: 1.323517
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.218713 Loss1: 0.355331 Loss2: 1.863382
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.589874 Loss1: 0.231841 Loss2: 1.358033
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.600589 Loss1: 0.201040 Loss2: 1.399549
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.614031 Loss1: 0.226331 Loss2: 1.387700
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.040742 Loss1: 0.256593 Loss2: 1.784149
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.478148 Loss1: 0.154004 Loss2: 1.324145 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.423716 Loss1: 0.104514 Loss2: 1.319202 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.406733 Loss1: 0.093169 Loss2: 1.313564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.412366 Loss1: 0.100922 Loss2: 1.311444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.368825 Loss1: 0.053361 Loss2: 1.315464 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.336387 Loss1: 0.036810 Loss2: 1.299577 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.320573 Loss1: 0.031056 Loss2: 1.289517 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995404
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.602710 Loss1: 0.255608 Loss2: 1.347102 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.543328 Loss1: 0.175761 Loss2: 1.367566 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.479120 Loss1: 0.117116 Loss2: 1.362003
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.212863 Loss1: 0.363157 Loss2: 1.849706
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.617879 Loss1: 0.260745 Loss2: 1.357134 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.515565 Loss1: 0.132628 Loss2: 1.382937 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.503036 Loss1: 0.142982 Loss2: 1.360053 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483146 Loss1: 0.126026 Loss2: 1.357120 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422137 Loss1: 0.073337 Loss2: 1.348799
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470126 Loss1: 0.111598 Loss2: 1.358529
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.384999 Loss1: 0.037311 Loss2: 1.347687
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376171 Loss1: 0.034929 Loss2: 1.341242
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.387724 Loss1: 0.053271 Loss2: 1.334453
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380592 Loss1: 0.049463 Loss2: 1.331128
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.264046 Loss1: 0.406036 Loss2: 1.858010
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.576071 Loss1: 0.216095 Loss2: 1.359975
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.540497 Loss1: 0.171189 Loss2: 1.369308
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.532895 Loss1: 0.160976 Loss2: 1.371920
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.495392 Loss1: 0.136921 Loss2: 1.358471
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.144762 Loss1: 0.323987 Loss2: 1.820774
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.511605 Loss1: 0.148929 Loss2: 1.362676
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.571810 Loss1: 0.257514 Loss2: 1.314297
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450822 Loss1: 0.093778 Loss2: 1.357044
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.463911 Loss1: 0.121815 Loss2: 1.342097
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460460 Loss1: 0.096463 Loss2: 1.363997
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.470702 Loss1: 0.153274 Loss2: 1.317428
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439598 Loss1: 0.092664 Loss2: 1.346934
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.481328 Loss1: 0.160133 Loss2: 1.321194
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.424541 Loss1: 0.076807 Loss2: 1.347734
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443781 Loss1: 0.115097 Loss2: 1.328684
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.451244 Loss1: 0.133202 Loss2: 1.318042
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.417372 Loss1: 0.100077 Loss2: 1.317295
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383131 Loss1: 0.066693 Loss2: 1.316437
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.345276 Loss1: 0.038106 Loss2: 1.307170
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.247462 Loss1: 0.411162 Loss2: 1.836300
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.642684 Loss1: 0.295549 Loss2: 1.347134
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.535086 Loss1: 0.153739 Loss2: 1.381347
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.475674 Loss1: 0.125855 Loss2: 1.349819
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.441940 Loss1: 0.092948 Loss2: 1.348992
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.122081 Loss1: 0.314298 Loss2: 1.807782
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.550213 Loss1: 0.201620 Loss2: 1.348593 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.510642 Loss1: 0.153130 Loss2: 1.357512 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.451548 Loss1: 0.110203 Loss2: 1.341345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.420209 Loss1: 0.081405 Loss2: 1.338804 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.412186 Loss1: 0.073041 Loss2: 1.339145 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416107 Loss1: 0.088212 Loss2: 1.327896 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378903 Loss1: 0.043399 Loss2: 1.335504 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.724629 Loss1: 0.358449 Loss2: 1.366180 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558665 Loss1: 0.180593 Loss2: 1.378072 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.266326 Loss1: 0.374572 Loss2: 1.891755 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.697152 Loss1: 0.293992 Loss2: 1.403160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.606420 Loss1: 0.158994 Loss2: 1.447427 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.650303 Loss1: 0.240878 Loss2: 1.409425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.553984 Loss1: 0.142347 Loss2: 1.411637 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386155 Loss1: 0.044037 Loss2: 1.342118
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.576680 Loss1: 0.167666 Loss2: 1.409014
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.493157 Loss1: 0.089860 Loss2: 1.403297
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493174 Loss1: 0.103657 Loss2: 1.389517
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.518741 Loss1: 0.124869 Loss2: 1.393872
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482505 Loss1: 0.090235 Loss2: 1.392270
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.229582 Loss1: 0.343291 Loss2: 1.886291
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.632288 Loss1: 0.227899 Loss2: 1.404389
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.581640 Loss1: 0.167701 Loss2: 1.413939
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542552 Loss1: 0.135872 Loss2: 1.406681
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.499877 Loss1: 0.108181 Loss2: 1.391696
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.211311 Loss1: 0.341320 Loss2: 1.869990
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.480536 Loss1: 0.085042 Loss2: 1.395494
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630019 Loss1: 0.254530 Loss2: 1.375489
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468474 Loss1: 0.079430 Loss2: 1.389045
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.586039 Loss1: 0.195279 Loss2: 1.390760
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.527817 Loss1: 0.141272 Loss2: 1.386546
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486781 Loss1: 0.094490 Loss2: 1.392291
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574715 Loss1: 0.202455 Loss2: 1.372260
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461284 Loss1: 0.070348 Loss2: 1.390936
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.577838 Loss1: 0.182964 Loss2: 1.394874
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448995 Loss1: 0.064551 Loss2: 1.384444
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.464322 Loss1: 0.088576 Loss2: 1.375745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.412615 Loss1: 0.053985 Loss2: 1.358630 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.673631 Loss1: 0.275266 Loss2: 1.398365 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522589 Loss1: 0.120669 Loss2: 1.401920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.463291 Loss1: 0.084423 Loss2: 1.378868 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.509625 Loss1: 0.126428 Loss2: 1.383197 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473551 Loss1: 0.091704 Loss2: 1.381847 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440117 Loss1: 0.060278 Loss2: 1.379839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413574 Loss1: 0.040718 Loss2: 1.372856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393896 Loss1: 0.031894 Loss2: 1.362002 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376637 Loss1: 0.043307 Loss2: 1.333331 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378719 Loss1: 0.054914 Loss2: 1.323805 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.146609 Loss1: 0.292395 Loss2: 1.854215
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.567525 Loss1: 0.193632 Loss2: 1.373893
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526785 Loss1: 0.146570 Loss2: 1.380215
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502158 Loss1: 0.137980 Loss2: 1.364178
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.333542 Loss1: 0.445286 Loss2: 1.888255
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.574350 Loss1: 0.240992 Loss2: 1.333358 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493991 Loss1: 0.128516 Loss2: 1.365475
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.538558 Loss1: 0.180487 Loss2: 1.358071
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467806 Loss1: 0.106720 Loss2: 1.361086
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.501376 Loss1: 0.143707 Loss2: 1.357669
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.510221 Loss1: 0.176125 Loss2: 1.334096
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429491 Loss1: 0.065089 Loss2: 1.364403
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450518 Loss1: 0.111579 Loss2: 1.338939
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.424821 Loss1: 0.074957 Loss2: 1.349865
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408172 Loss1: 0.056144 Loss2: 1.352028 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376437 Loss1: 0.060245 Loss2: 1.316193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988839
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.266404 Loss1: 0.387565 Loss2: 1.878839 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.587726 Loss1: 0.187806 Loss2: 1.399919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516303 Loss1: 0.160790 Loss2: 1.355513
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.206666 Loss1: 0.410616 Loss2: 1.796050
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.527313 Loss1: 0.226684 Loss2: 1.300628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.522292 Loss1: 0.193326 Loss2: 1.328966 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.465003 Loss1: 0.154535 Loss2: 1.310468 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.415644 Loss1: 0.106174 Loss2: 1.309470 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.434320 Loss1: 0.126910 Loss2: 1.307410 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432260 Loss1: 0.076610 Loss2: 1.355650
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404690 Loss1: 0.097167 Loss2: 1.307523
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416081 Loss1: 0.112260 Loss2: 1.303821
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.367440 Loss1: 0.067545 Loss2: 1.299896
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.353105 Loss1: 0.056209 Loss2: 1.296896
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.143297 Loss1: 0.378910 Loss2: 1.764387
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.576836 Loss1: 0.279192 Loss2: 1.297645
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.567093 Loss1: 0.226007 Loss2: 1.341086
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476339 Loss1: 0.175571 Loss2: 1.300768
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.248843 Loss1: 0.415782 Loss2: 1.833061
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.697767 Loss1: 0.345457 Loss2: 1.352309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.570125 Loss1: 0.175269 Loss2: 1.394856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468414 Loss1: 0.124025 Loss2: 1.344389 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.444628 Loss1: 0.109078 Loss2: 1.335550 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.399148 Loss1: 0.063584 Loss2: 1.335564 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.326109 Loss1: 0.043134 Loss2: 1.282975
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.390837 Loss1: 0.063012 Loss2: 1.327825
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.375171 Loss1: 0.049949 Loss2: 1.325222
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371324 Loss1: 0.048476 Loss2: 1.322847
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365164 Loss1: 0.049169 Loss2: 1.315995
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.185374 Loss1: 0.391204 Loss2: 1.794170
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.624376 Loss1: 0.310001 Loss2: 1.314375
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.478711 Loss1: 0.144748 Loss2: 1.333963
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482909 Loss1: 0.161464 Loss2: 1.321445
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.260296 Loss1: 0.392259 Loss2: 1.868037
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.649419 Loss1: 0.302619 Loss2: 1.346800 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.530451 Loss1: 0.168914 Loss2: 1.361537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.460779 Loss1: 0.111633 Loss2: 1.349146 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.428797 Loss1: 0.097841 Loss2: 1.330956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446003 Loss1: 0.114819 Loss2: 1.331184 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.393578 Loss1: 0.061558 Loss2: 1.332020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384276 Loss1: 0.062828 Loss2: 1.321449 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.131586 Loss1: 0.342363 Loss2: 1.789223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.520203 Loss1: 0.182809 Loss2: 1.337394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.492049 Loss1: 0.165518 Loss2: 1.326531
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.120214 Loss1: 0.299219 Loss2: 1.820995
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630732 Loss1: 0.263276 Loss2: 1.367456 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.574202 Loss1: 0.172412 Loss2: 1.401790 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.450911 Loss1: 0.085016 Loss2: 1.365895 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.449794 Loss1: 0.090039 Loss2: 1.359754 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.422262 Loss1: 0.066604 Loss2: 1.355658 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.999023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.335404 Loss1: 0.032261 Loss2: 1.303143
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.397431 Loss1: 0.043975 Loss2: 1.353456
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396983 Loss1: 0.052763 Loss2: 1.344219
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404326 Loss1: 0.065586 Loss2: 1.338739
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404130 Loss1: 0.061012 Loss2: 1.343118
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.674359 Loss1: 0.312328 Loss2: 1.362031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516511 Loss1: 0.139434 Loss2: 1.377077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488774 Loss1: 0.127017 Loss2: 1.361757 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.447604 Loss1: 0.075971 Loss2: 1.371633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426331 Loss1: 0.069138 Loss2: 1.357193 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441468 Loss1: 0.096293 Loss2: 1.345175 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450231 Loss1: 0.099901 Loss2: 1.350330 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409521 Loss1: 0.059574 Loss2: 1.349947 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399165 Loss1: 0.072397 Loss2: 1.326768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.359745 Loss1: 0.034886 Loss2: 1.324859 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.504279 Loss1: 0.164991 Loss2: 1.339288 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.439074 Loss1: 0.102640 Loss2: 1.336435 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.256535 Loss1: 0.390109 Loss2: 1.866426
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.412623 Loss1: 0.082577 Loss2: 1.330045
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.616275 Loss1: 0.277894 Loss2: 1.338381
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.377271 Loss1: 0.052874 Loss2: 1.324397
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.558984 Loss1: 0.192959 Loss2: 1.366025
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.370399 Loss1: 0.050188 Loss2: 1.320211
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492535 Loss1: 0.141096 Loss2: 1.351439
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401125 Loss1: 0.082215 Loss2: 1.318910
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475055 Loss1: 0.140843 Loss2: 1.334213
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409112 Loss1: 0.090181 Loss2: 1.318931
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447006 Loss1: 0.110090 Loss2: 1.336915
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379377 Loss1: 0.053853 Loss2: 1.325524
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446348 Loss1: 0.100581 Loss2: 1.345767 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396695 Loss1: 0.064788 Loss2: 1.331907 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.556266 Loss1: 0.202735 Loss2: 1.353531 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.458958 Loss1: 0.105632 Loss2: 1.353325 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.428832 Loss1: 0.098847 Loss2: 1.329985 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.406372 Loss1: 0.081310 Loss2: 1.325063 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.412649 Loss1: 0.081056 Loss2: 1.331592 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.370728 Loss1: 0.037140 Loss2: 1.333589 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405304 Loss1: 0.086356 Loss2: 1.318948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.464874 Loss1: 0.100938 Loss2: 1.363936 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404441 Loss1: 0.055408 Loss2: 1.349032 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.328013 Loss1: 0.414486 Loss2: 1.913527 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 10:27:38,759 | server.py:236 | fit_round 185 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.569659 Loss1: 0.142701 Loss2: 1.426957 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573438 Loss1: 0.162849 Loss2: 1.410590
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.159029 Loss1: 0.347487 Loss2: 1.811542
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534675 Loss1: 0.131256 Loss2: 1.403418
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.530086 Loss1: 0.207067 Loss2: 1.323019
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488535 Loss1: 0.086438 Loss2: 1.402097
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.490378 Loss1: 0.157421 Loss2: 1.332957
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484185 Loss1: 0.087403 Loss2: 1.396782
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.465309 Loss1: 0.142292 Loss2: 1.323017
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.488965 Loss1: 0.095125 Loss2: 1.393840
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.415760 Loss1: 0.100941 Loss2: 1.314819
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.502971 Loss1: 0.099754 Loss2: 1.403217
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463616 Loss1: 0.148759 Loss2: 1.314858
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.516864 Loss1: 0.119692 Loss2: 1.397172
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.379234 Loss1: 0.066702 Loss2: 1.312532
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.394866 Loss1: 0.086384 Loss2: 1.308482
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.344739 Loss1: 0.042134 Loss2: 1.302605
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397560 Loss1: 0.100030 Loss2: 1.297530
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.148682 Loss1: 0.294512 Loss2: 1.854170
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.562201 Loss1: 0.204286 Loss2: 1.357914
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.524706 Loss1: 0.160873 Loss2: 1.363834
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.179003 Loss1: 0.373542 Loss2: 1.805461
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.479947 Loss1: 0.115719 Loss2: 1.364228
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.628254 Loss1: 0.285936 Loss2: 1.342318
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.439580 Loss1: 0.085915 Loss2: 1.353666
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.498357 Loss1: 0.139929 Loss2: 1.358429
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451316 Loss1: 0.103436 Loss2: 1.347880
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404383 Loss1: 0.057008 Loss2: 1.347375 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.383033 Loss1: 0.043553 Loss2: 1.339480 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.387076 Loss1: 0.057481 Loss2: 1.329595 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420807 Loss1: 0.083451 Loss2: 1.337356 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384017 Loss1: 0.063815 Loss2: 1.320201 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 10:27:38,759][flwr][DEBUG] - fit_round 185 received 50 results and 0 failures
+INFO flwr 2023-10-13 10:28:19,874 | server.py:125 | fit progress: (185, 2.306797981262207, {'accuracy': 0.6091}, 427007.65254263097)
+>> Test accuracy: 0.609100
+[2023-10-13 10:28:19,874][flwr][INFO] - fit progress: (185, 2.306797981262207, {'accuracy': 0.6091}, 427007.65254263097)
+DEBUG flwr 2023-10-13 10:28:19,874 | server.py:173 | evaluate_round 185: strategy sampled 50 clients (out of 50)
+[2023-10-13 10:28:19,874][flwr][DEBUG] - evaluate_round 185: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 10:37:29,077 | server.py:187 | evaluate_round 185 received 50 results and 0 failures
+[2023-10-13 10:37:29,077][flwr][DEBUG] - evaluate_round 185 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 10:37:29,077 | server.py:222 | fit_round 186: strategy sampled 50 clients (out of 50)
+[2023-10-13 10:37:29,077][flwr][DEBUG] - fit_round 186: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.227668 Loss1: 0.369802 Loss2: 1.857866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599522 Loss1: 0.194869 Loss2: 1.404653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502821 Loss1: 0.137835 Loss2: 1.364986
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.222112 Loss1: 0.363083 Loss2: 1.859029
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.508664 Loss1: 0.153647 Loss2: 1.355017
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.583407 Loss1: 0.244463 Loss2: 1.338944
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524923 Loss1: 0.164571 Loss2: 1.360352
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.557447 Loss1: 0.181739 Loss2: 1.375708
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514548 Loss1: 0.153847 Loss2: 1.360702
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.523585 Loss1: 0.163602 Loss2: 1.359983
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.458306 Loss1: 0.100475 Loss2: 1.357831
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.448091 Loss1: 0.099020 Loss2: 1.349071
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431501 Loss1: 0.090993 Loss2: 1.340508
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444531 Loss1: 0.098196 Loss2: 1.346335
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408781 Loss1: 0.065010 Loss2: 1.343772
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.399406 Loss1: 0.059125 Loss2: 1.340281
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.407207 Loss1: 0.069890 Loss2: 1.337318
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.417102 Loss1: 0.077241 Loss2: 1.339861
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391278 Loss1: 0.057244 Loss2: 1.334033
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.105437 Loss1: 0.365772 Loss2: 1.739665
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.502668 Loss1: 0.220688 Loss2: 1.281980
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.502788 Loss1: 0.202923 Loss2: 1.299864
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.459732 Loss1: 0.152248 Loss2: 1.307483
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.361101 Loss1: 0.430741 Loss2: 1.930360
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.353443 Loss1: 0.069959 Loss2: 1.283484
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.613029 Loss1: 0.246000 Loss2: 1.367029
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.569447 Loss1: 0.201014 Loss2: 1.368433
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.346125 Loss1: 0.074668 Loss2: 1.271457
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.522710 Loss1: 0.129254 Loss2: 1.393455
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.321292 Loss1: 0.051590 Loss2: 1.269702
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.322583 Loss1: 0.056348 Loss2: 1.266235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.314943 Loss1: 0.053627 Loss2: 1.261316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.280684 Loss1: 0.026299 Loss2: 1.254385 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.383901 Loss1: 0.039674 Loss2: 1.344227 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998798
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.312868 Loss1: 0.464131 Loss2: 1.848737
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.658659 Loss1: 0.343466 Loss2: 1.315193
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.522039 Loss1: 0.186356 Loss2: 1.335683
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.475242 Loss1: 0.152572 Loss2: 1.322670
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.215604 Loss1: 0.369308 Loss2: 1.846296
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.643551 Loss1: 0.293844 Loss2: 1.349707 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.608670 Loss1: 0.216623 Loss2: 1.392047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.581528 Loss1: 0.217440 Loss2: 1.364088 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.363305 Loss1: 0.063202 Loss2: 1.300104 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360941 Loss1: 0.066334 Loss2: 1.294606 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438749 Loss1: 0.089255 Loss2: 1.349494 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422168 Loss1: 0.084932 Loss2: 1.337236 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.593773 Loss1: 0.230622 Loss2: 1.363150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558417 Loss1: 0.177450 Loss2: 1.380967 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535376 Loss1: 0.170656 Loss2: 1.364720
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.244848 Loss1: 0.381636 Loss2: 1.863212
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.598606 Loss1: 0.243400 Loss2: 1.355207 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.554354 Loss1: 0.176822 Loss2: 1.377532 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.535622 Loss1: 0.171306 Loss2: 1.364316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460513 Loss1: 0.108436 Loss2: 1.352077 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423925 Loss1: 0.067270 Loss2: 1.356656
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439948 Loss1: 0.096323 Loss2: 1.343625
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406273 Loss1: 0.068675 Loss2: 1.337598
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400438 Loss1: 0.066813 Loss2: 1.333626
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370716 Loss1: 0.041054 Loss2: 1.329662
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376085 Loss1: 0.046803 Loss2: 1.329283
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.272981 Loss1: 0.406166 Loss2: 1.866815
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.607033 Loss1: 0.239130 Loss2: 1.367903
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.514825 Loss1: 0.131080 Loss2: 1.383745
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474067 Loss1: 0.107048 Loss2: 1.367020
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472573 Loss1: 0.122893 Loss2: 1.349680
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.248370 Loss1: 0.391731 Loss2: 1.856640
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.636408 Loss1: 0.264524 Loss2: 1.371885 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543775 Loss1: 0.143127 Loss2: 1.400648 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.508822 Loss1: 0.139726 Loss2: 1.369096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497004 Loss1: 0.123669 Loss2: 1.373335 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456993 Loss1: 0.085709 Loss2: 1.371284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449796 Loss1: 0.092325 Loss2: 1.357471 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.514988 Loss1: 0.145998 Loss2: 1.368990 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976562
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591022 Loss1: 0.223657 Loss2: 1.367365 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515594 Loss1: 0.177474 Loss2: 1.338119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456482 Loss1: 0.109113 Loss2: 1.347369
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.352890 Loss1: 0.477020 Loss2: 1.875870
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.681688 Loss1: 0.308118 Loss2: 1.373570 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.636874 Loss1: 0.222291 Loss2: 1.414583 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.595902 Loss1: 0.206106 Loss2: 1.389796 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994420
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461922 Loss1: 0.093110 Loss2: 1.368812 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443456 Loss1: 0.082206 Loss2: 1.361249 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425126 Loss1: 0.071058 Loss2: 1.354068
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.076427 Loss1: 0.301597 Loss2: 1.774830
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427317 Loss1: 0.079185 Loss2: 1.348132
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.496744 Loss1: 0.181086 Loss2: 1.315658
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.579166 Loss1: 0.234584 Loss2: 1.344582
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502623 Loss1: 0.155749 Loss2: 1.346874
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.436867 Loss1: 0.105874 Loss2: 1.330993
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.419478 Loss1: 0.091728 Loss2: 1.327750
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.402619 Loss1: 0.477481 Loss2: 1.925138
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.411815 Loss1: 0.087574 Loss2: 1.324241
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.685166 Loss1: 0.343795 Loss2: 1.341372
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598768 Loss1: 0.220632 Loss2: 1.378136
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.418240 Loss1: 0.092792 Loss2: 1.325449
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395370 Loss1: 0.076457 Loss2: 1.318913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373176 Loss1: 0.058217 Loss2: 1.314959 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.456011 Loss1: 0.125256 Loss2: 1.330755 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361920 Loss1: 0.043229 Loss2: 1.318690 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987981
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.606507 Loss1: 0.276682 Loss2: 1.329826 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.449839 Loss1: 0.113285 Loss2: 1.336554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.436000 Loss1: 0.112275 Loss2: 1.323725 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.419307 Loss1: 0.088526 Loss2: 1.330781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435687 Loss1: 0.110530 Loss2: 1.325158 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403824 Loss1: 0.073899 Loss2: 1.329925 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377264 Loss1: 0.055704 Loss2: 1.321560 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359007 Loss1: 0.041996 Loss2: 1.317011 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483227 Loss1: 0.100076 Loss2: 1.383151 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427079 Loss1: 0.050740 Loss2: 1.376339 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.675944 Loss1: 0.366942 Loss2: 1.309002 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528314 Loss1: 0.175875 Loss2: 1.352439 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.227926 Loss1: 0.380670 Loss2: 1.847256 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.364728 Loss1: 0.054485 Loss2: 1.310243 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.584902 Loss1: 0.182444 Loss2: 1.402459 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487169 Loss1: 0.127520 Loss2: 1.359650 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485490 Loss1: 0.129143 Loss2: 1.356346 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447718 Loss1: 0.101699 Loss2: 1.346019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421573 Loss1: 0.070183 Loss2: 1.351390
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.210053 Loss1: 0.337425 Loss2: 1.872628
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415458 Loss1: 0.074818 Loss2: 1.340640
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.552822 Loss1: 0.199678 Loss2: 1.353143
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.513202 Loss1: 0.151863 Loss2: 1.361339
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467234 Loss1: 0.108950 Loss2: 1.358285
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.494334 Loss1: 0.143004 Loss2: 1.351330
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477334 Loss1: 0.124182 Loss2: 1.353151
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.209016 Loss1: 0.321060 Loss2: 1.887956
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432802 Loss1: 0.084192 Loss2: 1.348610
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.642776 Loss1: 0.243917 Loss2: 1.398859
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422176 Loss1: 0.080844 Loss2: 1.341333
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563176 Loss1: 0.143777 Loss2: 1.419399
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412626 Loss1: 0.070501 Loss2: 1.342125
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421732 Loss1: 0.081377 Loss2: 1.340355
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.571528 Loss1: 0.164851 Loss2: 1.406677
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513096 Loss1: 0.119763 Loss2: 1.393333
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479108 Loss1: 0.085604 Loss2: 1.393504
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.489190 Loss1: 0.099622 Loss2: 1.389568
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449489 Loss1: 0.065894 Loss2: 1.383595
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.175001 Loss1: 0.303981 Loss2: 1.871020
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422296 Loss1: 0.037585 Loss2: 1.384710
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.664887 Loss1: 0.271545 Loss2: 1.393342
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403341 Loss1: 0.027479 Loss2: 1.375862
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.614238 Loss1: 0.203027 Loss2: 1.411211 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.577349 Loss1: 0.157493 Loss2: 1.419856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.122484 Loss1: 0.343387 Loss2: 1.779096
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.530377 Loss1: 0.117648 Loss2: 1.412729
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.502761 Loss1: 0.204031 Loss2: 1.298730
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.542479 Loss1: 0.139480 Loss2: 1.402999
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.568230 Loss1: 0.153994 Loss2: 1.414236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.474867 Loss1: 0.074629 Loss2: 1.400238 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.364914 Loss1: 0.074923 Loss2: 1.289991 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.369283 Loss1: 0.077476 Loss2: 1.291807 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368100 Loss1: 0.081216 Loss2: 1.286884
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.350564 Loss1: 0.421884 Loss2: 1.928680
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.685102 Loss1: 0.294353 Loss2: 1.390749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.545147 Loss1: 0.145492 Loss2: 1.399655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478279 Loss1: 0.095004 Loss2: 1.383276 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.455323 Loss1: 0.073214 Loss2: 1.382109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427008 Loss1: 0.054235 Loss2: 1.372773 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.420864 Loss1: 0.051165 Loss2: 1.369699 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415214 Loss1: 0.047976 Loss2: 1.367237 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.395873 Loss1: 0.092293 Loss2: 1.303580 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.364204 Loss1: 0.065216 Loss2: 1.298988 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.339823 Loss1: 0.047657 Loss2: 1.292166
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.306717 Loss1: 0.437392 Loss2: 1.869325
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.317933 Loss1: 0.028934 Loss2: 1.288999
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.580577 Loss1: 0.236905 Loss2: 1.343673
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.547710 Loss1: 0.188955 Loss2: 1.358755
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524389 Loss1: 0.171046 Loss2: 1.353342
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476717 Loss1: 0.127008 Loss2: 1.349710
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.431865 Loss1: 0.091944 Loss2: 1.339921
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.126500 Loss1: 0.329877 Loss2: 1.796623
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.439085 Loss1: 0.103946 Loss2: 1.335139
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420933 Loss1: 0.089294 Loss2: 1.331638 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.535007 Loss1: 0.145600 Loss2: 1.389407
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397595 Loss1: 0.066371 Loss2: 1.331224
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.466367 Loss1: 0.117988 Loss2: 1.348379
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381129 Loss1: 0.057751 Loss2: 1.323379
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450375 Loss1: 0.101748 Loss2: 1.348627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441864 Loss1: 0.096862 Loss2: 1.345002 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.108470 Loss1: 0.263118 Loss2: 1.845352
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.407467 Loss1: 0.062738 Loss2: 1.344729
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.559349 Loss1: 0.190009 Loss2: 1.369340
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409791 Loss1: 0.069559 Loss2: 1.340232
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488080 Loss1: 0.127149 Loss2: 1.360932 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.445528 Loss1: 0.082614 Loss2: 1.362914 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426365 Loss1: 0.072468 Loss2: 1.353897 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419188 Loss1: 0.067481 Loss2: 1.351707 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412584 Loss1: 0.063817 Loss2: 1.348767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399593 Loss1: 0.050446 Loss2: 1.349147 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993566
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.413826 Loss1: 0.067036 Loss2: 1.346790 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419997 Loss1: 0.074058 Loss2: 1.345939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369973 Loss1: 0.030866 Loss2: 1.339107
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.116699 Loss1: 0.323478 Loss2: 1.793221
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.536478 Loss1: 0.229222 Loss2: 1.307255 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.459823 Loss1: 0.145785 Loss2: 1.314038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.378354 Loss1: 0.069446 Loss2: 1.308908 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.384570 Loss1: 0.075674 Loss2: 1.308896 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.349618 Loss1: 0.043598 Loss2: 1.306020 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.347297 Loss1: 0.049460 Loss2: 1.297837 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.351750 Loss1: 0.055987 Loss2: 1.295763 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.448065 Loss1: 0.098063 Loss2: 1.350002 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449228 Loss1: 0.107670 Loss2: 1.341558 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422852 Loss1: 0.078793 Loss2: 1.344059
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.238460 Loss1: 0.392533 Loss2: 1.845927
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.626749 Loss1: 0.269856 Loss2: 1.356893 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497293 Loss1: 0.135242 Loss2: 1.362052 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449485 Loss1: 0.101427 Loss2: 1.348057 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440657 Loss1: 0.093404 Loss2: 1.347252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.632897 Loss1: 0.285079 Loss2: 1.347818
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.457407 Loss1: 0.108955 Loss2: 1.348453
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.559297 Loss1: 0.175258 Loss2: 1.384039
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.481178 Loss1: 0.126260 Loss2: 1.354917
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.488645 Loss1: 0.136879 Loss2: 1.351766
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458310 Loss1: 0.110642 Loss2: 1.347668
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.393587 Loss1: 0.055403 Loss2: 1.338184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396048 Loss1: 0.070044 Loss2: 1.326004 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.218785 Loss1: 0.358190 Loss2: 1.860595
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391567 Loss1: 0.063252 Loss2: 1.328315
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.633670 Loss1: 0.267545 Loss2: 1.366125
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.398880 Loss1: 0.076012 Loss2: 1.322868
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511992 Loss1: 0.139979 Loss2: 1.372013 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.529722 Loss1: 0.148476 Loss2: 1.381246 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.471790 Loss1: 0.094152 Loss2: 1.377639
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.202312 Loss1: 0.369298 Loss2: 1.833014
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.518793 Loss1: 0.182298 Loss2: 1.336495 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407544 Loss1: 0.045576 Loss2: 1.361968
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.502184 Loss1: 0.152860 Loss2: 1.349324
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406737 Loss1: 0.051549 Loss2: 1.355187
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.447573 Loss1: 0.109983 Loss2: 1.337590
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.461356 Loss1: 0.125904 Loss2: 1.335452
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431562 Loss1: 0.092438 Loss2: 1.339123
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453910 Loss1: 0.121957 Loss2: 1.331953
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422082 Loss1: 0.092763 Loss2: 1.329319
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399931 Loss1: 0.075186 Loss2: 1.324745
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.197214 Loss1: 0.362537 Loss2: 1.834677
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.403559 Loss1: 0.070476 Loss2: 1.333083
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622950 Loss1: 0.289248 Loss2: 1.333702
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.600182 Loss1: 0.232976 Loss2: 1.367205
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.521873 Loss1: 0.158049 Loss2: 1.363824
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.444327 Loss1: 0.103839 Loss2: 1.340488
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438455 Loss1: 0.103896 Loss2: 1.334560
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447446 Loss1: 0.110628 Loss2: 1.336818
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.233575 Loss1: 0.389013 Loss2: 1.844563
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415051 Loss1: 0.084399 Loss2: 1.330652
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.632264 Loss1: 0.285623 Loss2: 1.346641
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379048 Loss1: 0.047974 Loss2: 1.331074
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.513273 Loss1: 0.144536 Loss2: 1.368737
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386472 Loss1: 0.065556 Loss2: 1.320916
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481376 Loss1: 0.127035 Loss2: 1.354341
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.437389 Loss1: 0.097582 Loss2: 1.339807
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.414611 Loss1: 0.083973 Loss2: 1.330637
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.426068 Loss1: 0.095818 Loss2: 1.330250
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405640 Loss1: 0.073003 Loss2: 1.332638
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409845 Loss1: 0.081254 Loss2: 1.328591
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.229158 Loss1: 0.370011 Loss2: 1.859147
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423242 Loss1: 0.102796 Loss2: 1.320446
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.636905 Loss1: 0.274795 Loss2: 1.362111
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.600007 Loss1: 0.196214 Loss2: 1.403793
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.563265 Loss1: 0.186915 Loss2: 1.376350
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.505961 Loss1: 0.130299 Loss2: 1.375662
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.479999 Loss1: 0.107639 Loss2: 1.372359
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.126906 Loss1: 0.313888 Loss2: 1.813018
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462383 Loss1: 0.092504 Loss2: 1.369879
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.498346 Loss1: 0.192841 Loss2: 1.305504
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439899 Loss1: 0.079954 Loss2: 1.359946
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.471176 Loss1: 0.166059 Loss2: 1.305117
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425663 Loss1: 0.073987 Loss2: 1.351676
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.460518 Loss1: 0.147948 Loss2: 1.312570
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401410 Loss1: 0.048868 Loss2: 1.352542
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.479291 Loss1: 0.173247 Loss2: 1.306044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424397 Loss1: 0.115698 Loss2: 1.308699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.382466 Loss1: 0.078051 Loss2: 1.304415
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.190316 Loss1: 0.355427 Loss2: 1.834889
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.345420 Loss1: 0.049708 Loss2: 1.295712
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.532015 Loss1: 0.205809 Loss2: 1.326206
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.450141 Loss1: 0.119274 Loss2: 1.330867
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.462895 Loss1: 0.131950 Loss2: 1.330946
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.421276 Loss1: 0.100339 Loss2: 1.320937
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.404150 Loss1: 0.088092 Loss2: 1.316057
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.053140 Loss1: 0.279058 Loss2: 1.774082
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.368099 Loss1: 0.059832 Loss2: 1.308267
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.508356 Loss1: 0.210771 Loss2: 1.297585
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.359914 Loss1: 0.058757 Loss2: 1.301158
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.394621 Loss1: 0.096821 Loss2: 1.297800
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.369572 Loss1: 0.064606 Loss2: 1.304967
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.357800 Loss1: 0.065211 Loss2: 1.292588
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358610 Loss1: 0.057748 Loss2: 1.300862
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.341781 Loss1: 0.062708 Loss2: 1.279073 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.341638 Loss1: 0.060533 Loss2: 1.281106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.323891 Loss1: 0.049402 Loss2: 1.274489
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.283863 Loss1: 0.415070 Loss2: 1.868794
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.288058 Loss1: 0.017996 Loss2: 1.270063
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.607313 Loss1: 0.230598 Loss2: 1.376714
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.549366 Loss1: 0.160883 Loss2: 1.388483
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517463 Loss1: 0.149438 Loss2: 1.368025
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.465138 Loss1: 0.099904 Loss2: 1.365235
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.434821 Loss1: 0.068507 Loss2: 1.366314
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.345692 Loss1: 0.433309 Loss2: 1.912383
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468370 Loss1: 0.115543 Loss2: 1.352828
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456950 Loss1: 0.094155 Loss2: 1.362796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.502269 Loss1: 0.140823 Loss2: 1.361446 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439785 Loss1: 0.085235 Loss2: 1.354550 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484254 Loss1: 0.115945 Loss2: 1.368309 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427692 Loss1: 0.074271 Loss2: 1.353420 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408178 Loss1: 0.056317 Loss2: 1.351861 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.558339 Loss1: 0.160233 Loss2: 1.398106 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510925 Loss1: 0.140830 Loss2: 1.370095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.528347 Loss1: 0.149141 Loss2: 1.379206
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.335459 Loss1: 0.400282 Loss2: 1.935177
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480066 Loss1: 0.107623 Loss2: 1.372443
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.624903 Loss1: 0.238013 Loss2: 1.386890
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445900 Loss1: 0.076311 Loss2: 1.369590
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.648726 Loss1: 0.215459 Loss2: 1.433267
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.456967 Loss1: 0.089864 Loss2: 1.367104
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.641428 Loss1: 0.236781 Loss2: 1.404647
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401783 Loss1: 0.033355 Loss2: 1.368428
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.620454 Loss1: 0.202523 Loss2: 1.417931
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.527490 Loss1: 0.128809 Loss2: 1.398680
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.469815 Loss1: 0.080213 Loss2: 1.389602
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.491285 Loss1: 0.105192 Loss2: 1.386093
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.484467 Loss1: 0.092759 Loss2: 1.391708
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.147760 Loss1: 0.333091 Loss2: 1.814669
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.478320 Loss1: 0.092674 Loss2: 1.385646
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.537980 Loss1: 0.180372 Loss2: 1.357608 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.449328 Loss1: 0.131889 Loss2: 1.317439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.407598 Loss1: 0.082193 Loss2: 1.325405
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.131664 Loss1: 0.315781 Loss2: 1.815883
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.532602 Loss1: 0.178556 Loss2: 1.354047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.512094 Loss1: 0.151654 Loss2: 1.360439 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 11:06:03,529 | server.py:236 | fit_round 186 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.499011 Loss1: 0.148384 Loss2: 1.350627 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535951 Loss1: 0.174813 Loss2: 1.361138 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440175 Loss1: 0.084260 Loss2: 1.355915 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389569 Loss1: 0.044807 Loss2: 1.344762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.386921 Loss1: 0.052465 Loss2: 1.334456 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.534457 Loss1: 0.150710 Loss2: 1.383747 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461459 Loss1: 0.102758 Loss2: 1.358700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460685 Loss1: 0.101330 Loss2: 1.359355
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.276626 Loss1: 0.406259 Loss2: 1.870367
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.610913 Loss1: 0.245818 Loss2: 1.365095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441214 Loss1: 0.088813 Loss2: 1.352401
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.570735 Loss1: 0.185748 Loss2: 1.384987
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.418015 Loss1: 0.065826 Loss2: 1.352189
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.589178 Loss1: 0.205414 Loss2: 1.383764
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402113 Loss1: 0.055028 Loss2: 1.347085
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557359 Loss1: 0.179005 Loss2: 1.378354
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524931 Loss1: 0.148766 Loss2: 1.376165
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480162 Loss1: 0.107676 Loss2: 1.372486
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457288 Loss1: 0.093549 Loss2: 1.363739
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446506 Loss1: 0.083969 Loss2: 1.362537
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.127112 Loss1: 0.302930 Loss2: 1.824181
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435689 Loss1: 0.073794 Loss2: 1.361895
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.466222 Loss1: 0.143858 Loss2: 1.322364 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.384751 Loss1: 0.069966 Loss2: 1.314785 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.372293 Loss1: 0.063694 Loss2: 1.308599
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.299141 Loss1: 0.443143 Loss2: 1.855998
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441202 Loss1: 0.135669 Loss2: 1.305533
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.749066 Loss1: 0.371539 Loss2: 1.377527
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.428256 Loss1: 0.106894 Loss2: 1.321361
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.634806 Loss1: 0.214841 Loss2: 1.419966
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.375241 Loss1: 0.059141 Loss2: 1.316100
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.569091 Loss1: 0.196230 Loss2: 1.372862
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370112 Loss1: 0.057635 Loss2: 1.312477
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.557721 Loss1: 0.175737 Loss2: 1.381984
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.489466 Loss1: 0.119987 Loss2: 1.369479
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.411216 Loss1: 0.055567 Loss2: 1.355649
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.390338 Loss1: 0.042419 Loss2: 1.347919
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401267 Loss1: 0.062526 Loss2: 1.338741
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368225 Loss1: 0.031701 Loss2: 1.336525
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 11:06:03,529][flwr][DEBUG] - fit_round 186 received 50 results and 0 failures
+INFO flwr 2023-10-13 11:06:45,183 | server.py:125 | fit progress: (186, 2.3054927869345816, {'accuracy': 0.6097}, 429312.96133268997)
+>> Test accuracy: 0.609700
+[2023-10-13 11:06:45,183][flwr][INFO] - fit progress: (186, 2.3054927869345816, {'accuracy': 0.6097}, 429312.96133268997)
+DEBUG flwr 2023-10-13 11:06:45,183 | server.py:173 | evaluate_round 186: strategy sampled 50 clients (out of 50)
+[2023-10-13 11:06:45,183][flwr][DEBUG] - evaluate_round 186: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 11:15:51,073 | server.py:187 | evaluate_round 186 received 50 results and 0 failures
+[2023-10-13 11:15:51,073][flwr][DEBUG] - evaluate_round 186 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 11:15:51,073 | server.py:222 | fit_round 187: strategy sampled 50 clients (out of 50)
+[2023-10-13 11:15:51,073][flwr][DEBUG] - fit_round 187: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.336178 Loss1: 0.427350 Loss2: 1.908828
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.738245 Loss1: 0.326493 Loss2: 1.411751
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.578871 Loss1: 0.156192 Loss2: 1.422679
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.473635 Loss1: 0.466272 Loss2: 2.007363
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.638479 Loss1: 0.271163 Loss2: 1.367316 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.617815 Loss1: 0.232816 Loss2: 1.384998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594230 Loss1: 0.186596 Loss2: 1.407634 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.540711 Loss1: 0.168177 Loss2: 1.372534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528211 Loss1: 0.144558 Loss2: 1.383653
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483004 Loss1: 0.088084 Loss2: 1.394920
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.484209 Loss1: 0.092511 Loss2: 1.391697 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.451298 Loss1: 0.064869 Loss2: 1.386429 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454975 Loss1: 0.080890 Loss2: 1.374085 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983073
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.166681 Loss1: 0.334901 Loss2: 1.831780
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.588103 Loss1: 0.257545 Loss2: 1.330558
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.577657 Loss1: 0.187894 Loss2: 1.389763
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498366 Loss1: 0.143788 Loss2: 1.354578
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.232063 Loss1: 0.407795 Loss2: 1.824269
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473838 Loss1: 0.129139 Loss2: 1.344699
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.550588 Loss1: 0.219442 Loss2: 1.331146
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.552332 Loss1: 0.203028 Loss2: 1.349304
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.484963 Loss1: 0.149414 Loss2: 1.335549
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.489116 Loss1: 0.133063 Loss2: 1.356053
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.507574 Loss1: 0.178357 Loss2: 1.329218
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445077 Loss1: 0.098779 Loss2: 1.346298
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.445406 Loss1: 0.112716 Loss2: 1.332689
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440010 Loss1: 0.093667 Loss2: 1.346343
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431813 Loss1: 0.104025 Loss2: 1.327787
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423073 Loss1: 0.077284 Loss2: 1.345789
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440575 Loss1: 0.113238 Loss2: 1.327337
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386892 Loss1: 0.066454 Loss2: 1.320438
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427947 Loss1: 0.105303 Loss2: 1.322644
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413138 Loss1: 0.094111 Loss2: 1.319028
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.173522 Loss1: 0.349033 Loss2: 1.824490
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.561903 Loss1: 0.226446 Loss2: 1.335457
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.514099 Loss1: 0.177668 Loss2: 1.336431
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.448683 Loss1: 0.097202 Loss2: 1.351481
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.246111 Loss1: 0.391043 Loss2: 1.855068
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.412357 Loss1: 0.086235 Loss2: 1.326122
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.645475 Loss1: 0.286010 Loss2: 1.359465
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.389712 Loss1: 0.069700 Loss2: 1.320012
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578884 Loss1: 0.181219 Loss2: 1.397665
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.516238 Loss1: 0.157888 Loss2: 1.358350 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.379436 Loss1: 0.061981 Loss2: 1.317454
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493451 Loss1: 0.135928 Loss2: 1.357524
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.369158 Loss1: 0.054709 Loss2: 1.314449
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446819 Loss1: 0.092629 Loss2: 1.354190
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.343864 Loss1: 0.033780 Loss2: 1.310084
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.390146 Loss1: 0.049297 Loss2: 1.340850
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.373099 Loss1: 0.039475 Loss2: 1.333624
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.361475 Loss1: 0.033642 Loss2: 1.327833
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.341356 Loss1: 0.018763 Loss2: 1.322593
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.170430 Loss1: 0.346081 Loss2: 1.824350
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.544513 Loss1: 0.214247 Loss2: 1.330266
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.521264 Loss1: 0.171880 Loss2: 1.349384
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484939 Loss1: 0.146506 Loss2: 1.338433
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.221868 Loss1: 0.358749 Loss2: 1.863119
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.573207 Loss1: 0.212835 Loss2: 1.360372 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.535562 Loss1: 0.157399 Loss2: 1.378163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.475143 Loss1: 0.107025 Loss2: 1.368118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.475508 Loss1: 0.115860 Loss2: 1.359648 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.418545 Loss1: 0.064723 Loss2: 1.353822 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388999 Loss1: 0.070601 Loss2: 1.318398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431378 Loss1: 0.080609 Loss2: 1.350769
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.410889 Loss1: 0.062041 Loss2: 1.348849
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.389799 Loss1: 0.045443 Loss2: 1.344356
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365629 Loss1: 0.028059 Loss2: 1.337570
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.352379 Loss1: 0.432728 Loss2: 1.919651
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.610379 Loss1: 0.236339 Loss2: 1.374040
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.481217 Loss1: 0.098658 Loss2: 1.382559
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.449479 Loss1: 0.086788 Loss2: 1.362692
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.230863 Loss1: 0.375085 Loss2: 1.855777
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.657735 Loss1: 0.307155 Loss2: 1.350580 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572702 Loss1: 0.184884 Loss2: 1.387818 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469412 Loss1: 0.112218 Loss2: 1.357195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.447079 Loss1: 0.079222 Loss2: 1.367857 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.470901 Loss1: 0.114908 Loss2: 1.355993 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986607
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399969 Loss1: 0.065249 Loss2: 1.334720 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.363909 Loss1: 0.030733 Loss2: 1.333176 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.571033 Loss1: 0.248926 Loss2: 1.322107 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.433968 Loss1: 0.099395 Loss2: 1.334574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.176118 Loss1: 0.313078 Loss2: 1.863040 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.635367 Loss1: 0.267530 Loss2: 1.367837 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.354263 Loss1: 0.051231 Loss2: 1.303032 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.346167 Loss1: 0.045129 Loss2: 1.301038 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.346699 Loss1: 0.052915 Loss2: 1.293785 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.462276 Loss1: 0.099867 Loss2: 1.362408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.475484 Loss1: 0.118172 Loss2: 1.357312 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.408681 Loss1: 0.405310 Loss2: 2.003372 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.743273 Loss1: 0.224003 Loss2: 1.519270 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.640293 Loss1: 0.150689 Loss2: 1.489604 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.633029 Loss1: 0.143726 Loss2: 1.489303
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.157821 Loss1: 0.374904 Loss2: 1.782918
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.615691 Loss1: 0.300791 Loss2: 1.314900 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.582273 Loss1: 0.223110 Loss2: 1.359163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521227 Loss1: 0.196037 Loss2: 1.325190 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.522871 Loss1: 0.061030 Loss2: 1.461841
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446962 Loss1: 0.130669 Loss2: 1.316293
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.407337 Loss1: 0.096261 Loss2: 1.311076
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.381634 Loss1: 0.074081 Loss2: 1.307554
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374581 Loss1: 0.071555 Loss2: 1.303026
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.371341 Loss1: 0.072084 Loss2: 1.299257
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.297222 Loss1: 0.455821 Loss2: 1.841400
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354575 Loss1: 0.061477 Loss2: 1.293099
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551304 Loss1: 0.220626 Loss2: 1.330678 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.423490 Loss1: 0.119278 Loss2: 1.304212 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.391169 Loss1: 0.088126 Loss2: 1.303043
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.315591 Loss1: 0.434488 Loss2: 1.881103
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.595602 Loss1: 0.255805 Loss2: 1.339797 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.522107 Loss1: 0.168998 Loss2: 1.353109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404065 Loss1: 0.101839 Loss2: 1.302226
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.448178 Loss1: 0.107839 Loss2: 1.340339
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.352094 Loss1: 0.062271 Loss2: 1.289823
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.423214 Loss1: 0.094527 Loss2: 1.328686
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.398920 Loss1: 0.069789 Loss2: 1.329132
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.394278 Loss1: 0.072552 Loss2: 1.321726
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.415706 Loss1: 0.093067 Loss2: 1.322639
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.393027 Loss1: 0.073322 Loss2: 1.319704
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393946 Loss1: 0.074740 Loss2: 1.319206
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.225323 Loss1: 0.411990 Loss2: 1.813333
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.557404 Loss1: 0.235121 Loss2: 1.322283
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.601954 Loss1: 0.238361 Loss2: 1.363594
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.507220 Loss1: 0.161585 Loss2: 1.345635
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472442 Loss1: 0.137265 Loss2: 1.335178
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.202421 Loss1: 0.400857 Loss2: 1.801563
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.410369 Loss1: 0.076117 Loss2: 1.334253
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.392556 Loss1: 0.076930 Loss2: 1.315626 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.371742 Loss1: 0.055317 Loss2: 1.316425 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374052 Loss1: 0.059345 Loss2: 1.314706 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.353926 Loss1: 0.046883 Loss2: 1.307043 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.369024 Loss1: 0.056334 Loss2: 1.312690 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.353463 Loss1: 0.045000 Loss2: 1.308463 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.337268 Loss1: 0.036495 Loss2: 1.300773
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.451874 Loss1: 0.473545 Loss2: 1.978329
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.720768 Loss1: 0.318573 Loss2: 1.402195
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.665144 Loss1: 0.230647 Loss2: 1.434497
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.615148 Loss1: 0.175485 Loss2: 1.439664
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531094 Loss1: 0.124200 Loss2: 1.406894
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.549238 Loss1: 0.143107 Loss2: 1.406131
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.229488 Loss1: 0.348479 Loss2: 1.881009
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.508229 Loss1: 0.101038 Loss2: 1.407192
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.604779 Loss1: 0.239904 Loss2: 1.364875
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.480483 Loss1: 0.084068 Loss2: 1.396416
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.621471 Loss1: 0.203452 Loss2: 1.418019
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.503991 Loss1: 0.122565 Loss2: 1.381426 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417541 Loss1: 0.036498 Loss2: 1.381043
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496293 Loss1: 0.123427 Loss2: 1.372866
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.529534 Loss1: 0.150322 Loss2: 1.379212
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.458416 Loss1: 0.091824 Loss2: 1.366592
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448535 Loss1: 0.083751 Loss2: 1.364783
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445832 Loss1: 0.085964 Loss2: 1.359868
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.167338 Loss1: 0.312898 Loss2: 1.854440
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434635 Loss1: 0.070848 Loss2: 1.363787
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551288 Loss1: 0.154788 Loss2: 1.396500 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.483177 Loss1: 0.110363 Loss2: 1.372814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.334591 Loss1: 0.453470 Loss2: 1.881120
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470046 Loss1: 0.102142 Loss2: 1.367905
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.637224 Loss1: 0.263561 Loss2: 1.373663
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472512 Loss1: 0.106767 Loss2: 1.365745
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601238 Loss1: 0.196078 Loss2: 1.405160
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.439127 Loss1: 0.074811 Loss2: 1.364316
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444605 Loss1: 0.087225 Loss2: 1.357381 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416523 Loss1: 0.061025 Loss2: 1.355498 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981445
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427356 Loss1: 0.057661 Loss2: 1.369695 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390157 Loss1: 0.037713 Loss2: 1.352444 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.410327 Loss1: 0.060926 Loss2: 1.349401
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.118546 Loss1: 0.305628 Loss2: 1.812917
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.528186 Loss1: 0.180702 Loss2: 1.347483
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.477567 Loss1: 0.123535 Loss2: 1.354032
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.453892 Loss1: 0.110150 Loss2: 1.343742
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.464014 Loss1: 0.118513 Loss2: 1.345501
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.075173 Loss1: 0.303538 Loss2: 1.771635
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.493517 Loss1: 0.179725 Loss2: 1.313792 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.478111 Loss1: 0.155194 Loss2: 1.322917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.495350 Loss1: 0.175831 Loss2: 1.319519 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497054 Loss1: 0.177515 Loss2: 1.319539 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.442184 Loss1: 0.130572 Loss2: 1.311612 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.365320 Loss1: 0.064997 Loss2: 1.300322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379867 Loss1: 0.083179 Loss2: 1.296688
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.255626 Loss1: 0.406638 Loss2: 1.848987
+(DefaultActor pid=3764) >> Training accuracy: 0.988971
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.613056 Loss1: 0.281701 Loss2: 1.331356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513230 Loss1: 0.169517 Loss2: 1.343713 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468689 Loss1: 0.122487 Loss2: 1.346202 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461541 Loss1: 0.126517 Loss2: 1.335024 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453853 Loss1: 0.124162 Loss2: 1.329692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.467107 Loss1: 0.135041 Loss2: 1.332066 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419344 Loss1: 0.084833 Loss2: 1.334512 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.566869 Loss1: 0.156367 Loss2: 1.410502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511982 Loss1: 0.103739 Loss2: 1.408243 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.482841 Loss1: 0.078060 Loss2: 1.404781
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.159419 Loss1: 0.359804 Loss2: 1.799615
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.543072 Loss1: 0.232509 Loss2: 1.310564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.432381 Loss1: 0.117105 Loss2: 1.315275 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.383406 Loss1: 0.076594 Loss2: 1.306812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.361345 Loss1: 0.066255 Loss2: 1.295091 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.340729 Loss1: 0.045368 Loss2: 1.295361 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.331698 Loss1: 0.041224 Loss2: 1.290474 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.305491 Loss1: 0.023459 Loss2: 1.282032 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.453473 Loss1: 0.085378 Loss2: 1.368095 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.395524 Loss1: 0.050917 Loss2: 1.344607 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.377788 Loss1: 0.036645 Loss2: 1.341143
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.250496 Loss1: 0.387985 Loss2: 1.862511
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.657228 Loss1: 0.295179 Loss2: 1.362050 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528455 Loss1: 0.161400 Loss2: 1.367055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448693 Loss1: 0.094216 Loss2: 1.354477 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.081353 Loss1: 0.309522 Loss2: 1.771831
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.421983 Loss1: 0.067484 Loss2: 1.354499
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.489257 Loss1: 0.195957 Loss2: 1.293300
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415183 Loss1: 0.065051 Loss2: 1.350132
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.445085 Loss1: 0.138652 Loss2: 1.306433
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390944 Loss1: 0.044800 Loss2: 1.346145
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.407538 Loss1: 0.068933 Loss2: 1.338605
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.428392 Loss1: 0.123278 Loss2: 1.305114
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.457266 Loss1: 0.159050 Loss2: 1.298216
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.490827 Loss1: 0.190619 Loss2: 1.300208
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.457789 Loss1: 0.140782 Loss2: 1.317008
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429869 Loss1: 0.130460 Loss2: 1.299409
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.093099 Loss1: 0.369227 Loss2: 1.723871
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391267 Loss1: 0.086029 Loss2: 1.305238
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.478520 Loss1: 0.187797 Loss2: 1.290723
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351102 Loss1: 0.059884 Loss2: 1.291218
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.384502 Loss1: 0.107298 Loss2: 1.277204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.354874 Loss1: 0.080390 Loss2: 1.274484 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.198384 Loss1: 0.362341 Loss2: 1.836044
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.337267 Loss1: 0.071664 Loss2: 1.265603
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.648307 Loss1: 0.294622 Loss2: 1.353685
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.345828 Loss1: 0.076714 Loss2: 1.269114
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.305337 Loss1: 0.038645 Loss2: 1.266692 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.310457 Loss1: 0.048597 Loss2: 1.261860 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465526 Loss1: 0.106160 Loss2: 1.359366 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424236 Loss1: 0.081910 Loss2: 1.342327 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397520 Loss1: 0.055984 Loss2: 1.341536
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.253481 Loss1: 0.349939 Loss2: 1.903542
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.703079 Loss1: 0.315218 Loss2: 1.387861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.510295 Loss1: 0.120283 Loss2: 1.390012 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.482410 Loss1: 0.094842 Loss2: 1.387567 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.475901 Loss1: 0.095515 Loss2: 1.380386 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.462438 Loss1: 0.083287 Loss2: 1.379151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444982 Loss1: 0.071032 Loss2: 1.373950 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418975 Loss1: 0.054889 Loss2: 1.364086 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508931 Loss1: 0.126537 Loss2: 1.382394 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.479136 Loss1: 0.101062 Loss2: 1.378074 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.201323 Loss1: 0.335943 Loss2: 1.865380 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.972917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.590759 Loss1: 0.233687 Loss2: 1.357072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.470830 Loss1: 0.110570 Loss2: 1.360259 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.447516 Loss1: 0.098455 Loss2: 1.349061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431922 Loss1: 0.086560 Loss2: 1.345362 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445265 Loss1: 0.100280 Loss2: 1.344985 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.450331 Loss1: 0.129947 Loss2: 1.320384
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427414 Loss1: 0.073325 Loss2: 1.354088
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.431406 Loss1: 0.113866 Loss2: 1.317540
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420274 Loss1: 0.072884 Loss2: 1.347390
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.355917 Loss1: 0.057530 Loss2: 1.298387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.317132 Loss1: 0.033428 Loss2: 1.283705 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.280808 Loss1: 0.375140 Loss2: 1.905669
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.315449 Loss1: 0.036107 Loss2: 1.279343
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.633932 Loss1: 0.250332 Loss2: 1.383601
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.315802 Loss1: 0.034637 Loss2: 1.281165
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511106 Loss1: 0.122176 Loss2: 1.388930 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465752 Loss1: 0.093319 Loss2: 1.372433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.420146 Loss1: 0.053075 Loss2: 1.367071
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.275876 Loss1: 0.387060 Loss2: 1.888816
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.588672 Loss1: 0.238098 Loss2: 1.350574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.609092 Loss1: 0.219925 Loss2: 1.389167 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417295 Loss1: 0.059354 Loss2: 1.357941
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.538423 Loss1: 0.163220 Loss2: 1.375203
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.479888 Loss1: 0.125233 Loss2: 1.354655
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.442018 Loss1: 0.089018 Loss2: 1.353000
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.417796 Loss1: 0.065692 Loss2: 1.352103
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406672 Loss1: 0.064081 Loss2: 1.342591
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.102478 Loss1: 0.299615 Loss2: 1.802863
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379057 Loss1: 0.040033 Loss2: 1.339024
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.567860 Loss1: 0.223239 Loss2: 1.344621
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365869 Loss1: 0.037719 Loss2: 1.328150
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.454243 Loss1: 0.104088 Loss2: 1.350155 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.484097 Loss1: 0.129869 Loss2: 1.354228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449865 Loss1: 0.103104 Loss2: 1.346762 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.432090 Loss1: 0.088369 Loss2: 1.343721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391343 Loss1: 0.050431 Loss2: 1.340912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.438289 Loss1: 0.109534 Loss2: 1.328755 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.432747 Loss1: 0.102646 Loss2: 1.330100 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400020 Loss1: 0.072816 Loss2: 1.327204 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990385
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.179263 Loss1: 0.334472 Loss2: 1.844791 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.473643 Loss1: 0.128944 Loss2: 1.344700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.420366 Loss1: 0.102271 Loss2: 1.318095 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443611 Loss1: 0.122127 Loss2: 1.321483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.403919 Loss1: 0.078800 Loss2: 1.325119 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400781 Loss1: 0.082685 Loss2: 1.318096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.390957 Loss1: 0.070556 Loss2: 1.320401 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.368033 Loss1: 0.053543 Loss2: 1.314491 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.568368 Loss1: 0.095926 Loss2: 1.472442 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.498213 Loss1: 0.042687 Loss2: 1.455526 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.660971 Loss1: 0.261110 Loss2: 1.399861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.560400 Loss1: 0.149787 Loss2: 1.410613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.126808 Loss1: 0.340767 Loss2: 1.786041
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568208 Loss1: 0.157731 Loss2: 1.410477
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.497864 Loss1: 0.208666 Loss2: 1.289198
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.540205 Loss1: 0.126557 Loss2: 1.413648
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.457502 Loss1: 0.143077 Loss2: 1.314425
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.489709 Loss1: 0.086913 Loss2: 1.402796
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.443292 Loss1: 0.148733 Loss2: 1.294559
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.497862 Loss1: 0.098726 Loss2: 1.399136
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.469924 Loss1: 0.072068 Loss2: 1.397856 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.478442 Loss1: 0.084490 Loss2: 1.393953 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374083 Loss1: 0.080227 Loss2: 1.293856 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.319630 Loss1: 0.039124 Loss2: 1.280506 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.330343 Loss1: 0.452016 Loss2: 1.878328
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.658823 Loss1: 0.276077 Loss2: 1.382746
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599689 Loss1: 0.184565 Loss2: 1.415123
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.544022 Loss1: 0.163106 Loss2: 1.380917
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.164938 Loss1: 0.318556 Loss2: 1.846381
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.570005 Loss1: 0.200377 Loss2: 1.369628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.487760 Loss1: 0.103926 Loss2: 1.383833 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.477363 Loss1: 0.110574 Loss2: 1.366790 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.478063 Loss1: 0.119378 Loss2: 1.358685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.453189 Loss1: 0.088594 Loss2: 1.364594 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426558 Loss1: 0.069686 Loss2: 1.356872 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396737 Loss1: 0.046746 Loss2: 1.349991 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 11:44:30,134 | server.py:236 | fit_round 187 received 50 results and 0 failures
+(DefaultActor pid=3764) >> Training accuracy: 0.990234
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.519019 Loss1: 0.187859 Loss2: 1.331160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.444159 Loss1: 0.112128 Loss2: 1.332030 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.220397 Loss1: 0.334188 Loss2: 1.886208
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.452721 Loss1: 0.127324 Loss2: 1.325397
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.698510 Loss1: 0.323550 Loss2: 1.374960
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.391338 Loss1: 0.063651 Loss2: 1.327687
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432984 Loss1: 0.108059 Loss2: 1.324924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450101 Loss1: 0.121992 Loss2: 1.328109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.450036 Loss1: 0.112505 Loss2: 1.337532 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399124 Loss1: 0.067166 Loss2: 1.331958 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.500493 Loss1: 0.114364 Loss2: 1.386129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.453066 Loss1: 0.072488 Loss2: 1.380578 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.244684 Loss1: 0.336050 Loss2: 1.908634
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.684115 Loss1: 0.305199 Loss2: 1.378917
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.645790 Loss1: 0.201639 Loss2: 1.444151
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549086 Loss1: 0.152247 Loss2: 1.396839
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.185681 Loss1: 0.371577 Loss2: 1.814104
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.597352 Loss1: 0.264805 Loss2: 1.332547 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.515622 Loss1: 0.159386 Loss2: 1.356235 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.546544 Loss1: 0.206372 Loss2: 1.340172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509889 Loss1: 0.155648 Loss2: 1.354242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.466386 Loss1: 0.128383 Loss2: 1.338002 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431873 Loss1: 0.089552 Loss2: 1.342322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385146 Loss1: 0.056405 Loss2: 1.328741 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 11:44:30,134][flwr][DEBUG] - fit_round 187 received 50 results and 0 failures
+INFO flwr 2023-10-13 11:45:10,982 | server.py:125 | fit progress: (187, 2.303106297890599, {'accuracy': 0.6109}, 431618.760375392)
+>> Test accuracy: 0.610900
+[2023-10-13 11:45:10,982][flwr][INFO] - fit progress: (187, 2.303106297890599, {'accuracy': 0.6109}, 431618.760375392)
+DEBUG flwr 2023-10-13 11:45:10,982 | server.py:173 | evaluate_round 187: strategy sampled 50 clients (out of 50)
+[2023-10-13 11:45:10,982][flwr][DEBUG] - evaluate_round 187: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 11:54:10,259 | server.py:187 | evaluate_round 187 received 50 results and 0 failures
+[2023-10-13 11:54:10,259][flwr][DEBUG] - evaluate_round 187 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 11:54:10,259 | server.py:222 | fit_round 188: strategy sampled 50 clients (out of 50)
+[2023-10-13 11:54:10,259][flwr][DEBUG] - fit_round 188: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.161443 Loss1: 0.308743 Loss2: 1.852700 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.597154 Loss1: 0.205320 Loss2: 1.391834 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.521249 Loss1: 0.140936 Loss2: 1.380313
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.370034 Loss1: 0.415784 Loss2: 1.954250
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.589548 Loss1: 0.264448 Loss2: 1.325101
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515079 Loss1: 0.150135 Loss2: 1.364943
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.447257 Loss1: 0.081630 Loss2: 1.365627 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.469906 Loss1: 0.111920 Loss2: 1.357986 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456811 Loss1: 0.096693 Loss2: 1.360118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.477290 Loss1: 0.136414 Loss2: 1.340876 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.470306 Loss1: 0.134550 Loss2: 1.335757 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406719 Loss1: 0.064373 Loss2: 1.342346 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.091355 Loss1: 0.286589 Loss2: 1.804765
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.590734 Loss1: 0.268853 Loss2: 1.321881
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.458391 Loss1: 0.109574 Loss2: 1.348817
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.411684 Loss1: 0.083995 Loss2: 1.327688
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.147898 Loss1: 0.314290 Loss2: 1.833608
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.380086 Loss1: 0.060608 Loss2: 1.319478
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.582703 Loss1: 0.209874 Loss2: 1.372829
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.373415 Loss1: 0.059614 Loss2: 1.313800
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.545515 Loss1: 0.162950 Loss2: 1.382564
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395162 Loss1: 0.083138 Loss2: 1.312024
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.483969 Loss1: 0.111844 Loss2: 1.372125
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384417 Loss1: 0.074537 Loss2: 1.309880
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.486639 Loss1: 0.127243 Loss2: 1.359396
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.365882 Loss1: 0.057048 Loss2: 1.308834
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.496921 Loss1: 0.133603 Loss2: 1.363318
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356780 Loss1: 0.050853 Loss2: 1.305927
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433265 Loss1: 0.071432 Loss2: 1.361833
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431060 Loss1: 0.074046 Loss2: 1.357014
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441135 Loss1: 0.085921 Loss2: 1.355214
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425651 Loss1: 0.066310 Loss2: 1.359341
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.285809 Loss1: 0.409188 Loss2: 1.876621
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.589578 Loss1: 0.221360 Loss2: 1.368218
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.572809 Loss1: 0.175938 Loss2: 1.396871
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489267 Loss1: 0.116613 Loss2: 1.372654
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.149851 Loss1: 0.304900 Loss2: 1.844951
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.523354 Loss1: 0.189789 Loss2: 1.333565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.438935 Loss1: 0.101057 Loss2: 1.337878 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.391535 Loss1: 0.063685 Loss2: 1.327850 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.368660 Loss1: 0.057989 Loss2: 1.310671 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.346263 Loss1: 0.035292 Loss2: 1.310972 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.976042
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439158 Loss1: 0.083598 Loss2: 1.355561
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.364253 Loss1: 0.061267 Loss2: 1.302986
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.344802 Loss1: 0.043666 Loss2: 1.301136
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373961 Loss1: 0.077158 Loss2: 1.296804
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348072 Loss1: 0.046484 Loss2: 1.301588
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.413851 Loss1: 0.450707 Loss2: 1.963145
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.744878 Loss1: 0.311379 Loss2: 1.433500
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.724691 Loss1: 0.249611 Loss2: 1.475080
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.627863 Loss1: 0.190584 Loss2: 1.437279
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.490515 Loss1: 0.510736 Loss2: 1.979778
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.581500 Loss1: 0.141889 Loss2: 1.439611
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.657785 Loss1: 0.284015 Loss2: 1.373770
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.553867 Loss1: 0.167302 Loss2: 1.386565
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.578639 Loss1: 0.153761 Loss2: 1.424878
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.502884 Loss1: 0.065335 Loss2: 1.437549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483121 Loss1: 0.062248 Loss2: 1.420873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474400 Loss1: 0.063229 Loss2: 1.411170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460507 Loss1: 0.046234 Loss2: 1.414274 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395233 Loss1: 0.040626 Loss2: 1.354607 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989183
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.131675 Loss1: 0.303915 Loss2: 1.827760
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.561416 Loss1: 0.210129 Loss2: 1.351288
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.535126 Loss1: 0.168985 Loss2: 1.366141
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.485232 Loss1: 0.113458 Loss2: 1.371775
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.177746 Loss1: 0.394057 Loss2: 1.783689
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.496428 Loss1: 0.146945 Loss2: 1.349483
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.553524 Loss1: 0.258064 Loss2: 1.295460
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.501729 Loss1: 0.163316 Loss2: 1.338413
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.540379 Loss1: 0.176355 Loss2: 1.364024
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.436674 Loss1: 0.124178 Loss2: 1.312496
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488139 Loss1: 0.126779 Loss2: 1.361360
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.434352 Loss1: 0.125285 Loss2: 1.309067
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453501 Loss1: 0.099202 Loss2: 1.354299
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.424943 Loss1: 0.115637 Loss2: 1.309306
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435348 Loss1: 0.086121 Loss2: 1.349227
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.376990 Loss1: 0.070431 Loss2: 1.306558
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.432693 Loss1: 0.088836 Loss2: 1.343856
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.338344 Loss1: 0.047027 Loss2: 1.291317 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.231372 Loss1: 0.406321 Loss2: 1.825050 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.522626 Loss1: 0.156564 Loss2: 1.366062 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.438296 Loss1: 0.105788 Loss2: 1.332508
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.105879 Loss1: 0.287253 Loss2: 1.818626
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.594498 Loss1: 0.235684 Loss2: 1.358814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564260 Loss1: 0.171210 Loss2: 1.393050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.567993 Loss1: 0.194418 Loss2: 1.373574 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530252 Loss1: 0.166762 Loss2: 1.363490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.502390 Loss1: 0.121120 Loss2: 1.381270 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.457122 Loss1: 0.098878 Loss2: 1.358244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408920 Loss1: 0.062281 Loss2: 1.346639 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.327308 Loss1: 0.425634 Loss2: 1.901674 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594834 Loss1: 0.197448 Loss2: 1.397386 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.213159 Loss1: 0.357808 Loss2: 1.855350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.576698 Loss1: 0.219615 Loss2: 1.357083 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.511305 Loss1: 0.147448 Loss2: 1.363857 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.465773 Loss1: 0.105812 Loss2: 1.359962 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.425660 Loss1: 0.085220 Loss2: 1.340440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.401694 Loss1: 0.064243 Loss2: 1.337451 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382841 Loss1: 0.044523 Loss2: 1.338318 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.344441 Loss1: 0.023160 Loss2: 1.321281 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.593696 Loss1: 0.229231 Loss2: 1.364465 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.448141 Loss1: 0.086432 Loss2: 1.361709 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.211671 Loss1: 0.381426 Loss2: 1.830245
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.427811 Loss1: 0.081002 Loss2: 1.346809
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.541585 Loss1: 0.211727 Loss2: 1.329858
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462377 Loss1: 0.111575 Loss2: 1.350802
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.499321 Loss1: 0.152683 Loss2: 1.346638
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450342 Loss1: 0.097657 Loss2: 1.352685
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.423212 Loss1: 0.092172 Loss2: 1.331040
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435798 Loss1: 0.074256 Loss2: 1.361542
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.405013 Loss1: 0.083732 Loss2: 1.321281
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.414796 Loss1: 0.071925 Loss2: 1.342872
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.397028 Loss1: 0.078621 Loss2: 1.318407
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433444 Loss1: 0.086260 Loss2: 1.347184
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.344844 Loss1: 0.037148 Loss2: 1.307697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.347192 Loss1: 0.047476 Loss2: 1.299717 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.540038 Loss1: 0.203120 Loss2: 1.336918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490103 Loss1: 0.142304 Loss2: 1.347799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476369 Loss1: 0.132811 Loss2: 1.343558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.432140 Loss1: 0.091557 Loss2: 1.340583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409476 Loss1: 0.076678 Loss2: 1.332798 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400009 Loss1: 0.071599 Loss2: 1.328410 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384219 Loss1: 0.054909 Loss2: 1.329310 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.388851 Loss1: 0.065898 Loss2: 1.322954 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.448208 Loss1: 0.082223 Loss2: 1.365986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421266 Loss1: 0.066981 Loss2: 1.354285 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.615236 Loss1: 0.260712 Loss2: 1.354523 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551289 Loss1: 0.186093 Loss2: 1.365196 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.293168 Loss1: 0.431748 Loss2: 1.861420
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506393 Loss1: 0.157528 Loss2: 1.348865
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.621968 Loss1: 0.258815 Loss2: 1.363154
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477467 Loss1: 0.128002 Loss2: 1.349466
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.553743 Loss1: 0.178374 Loss2: 1.375370
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444327 Loss1: 0.092721 Loss2: 1.351605
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.518922 Loss1: 0.153670 Loss2: 1.365251
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.419866 Loss1: 0.076721 Loss2: 1.343145
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509135 Loss1: 0.150510 Loss2: 1.358625
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.389626 Loss1: 0.052676 Loss2: 1.336950
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.508106 Loss1: 0.138152 Loss2: 1.369954
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355270 Loss1: 0.031490 Loss2: 1.323780
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440654 Loss1: 0.088333 Loss2: 1.352321 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415816 Loss1: 0.072428 Loss2: 1.343387 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.170423 Loss1: 0.279565 Loss2: 1.890858
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.666196 Loss1: 0.246510 Loss2: 1.419686
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.608314 Loss1: 0.173481 Loss2: 1.434832
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.578757 Loss1: 0.161981 Loss2: 1.416776
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.258893 Loss1: 0.411349 Loss2: 1.847543
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.691960 Loss1: 0.345911 Loss2: 1.346050
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.562637 Loss1: 0.131705 Loss2: 1.430932
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.701391 Loss1: 0.293794 Loss2: 1.407596
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.560143 Loss1: 0.128431 Loss2: 1.431712
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.578195 Loss1: 0.212223 Loss2: 1.365972
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.505483 Loss1: 0.080716 Loss2: 1.424767
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.493334 Loss1: 0.083737 Loss2: 1.409598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.497361 Loss1: 0.085694 Loss2: 1.411667 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.496730 Loss1: 0.085486 Loss2: 1.411244 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985294
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424120 Loss1: 0.077512 Loss2: 1.346608 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.198112 Loss1: 0.388534 Loss2: 1.809578
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.628774 Loss1: 0.343902 Loss2: 1.284872
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.507575 Loss1: 0.187888 Loss2: 1.319687
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.443873 Loss1: 0.141253 Loss2: 1.302621
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.074983 Loss1: 0.304329 Loss2: 1.770654
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.512274 Loss1: 0.188245 Loss2: 1.324029 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.527780 Loss1: 0.175084 Loss2: 1.352696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.352892 Loss1: 0.067669 Loss2: 1.285223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.345667 Loss1: 0.060312 Loss2: 1.285356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.318787 Loss1: 0.039809 Loss2: 1.278977 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.393082 Loss1: 0.075839 Loss2: 1.317244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373876 Loss1: 0.059923 Loss2: 1.313953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.360088 Loss1: 0.050805 Loss2: 1.309284
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.190700 Loss1: 0.311484 Loss2: 1.879216
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.566785 Loss1: 0.216036 Loss2: 1.350749
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.492555 Loss1: 0.135832 Loss2: 1.356722
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.522028 Loss1: 0.168713 Loss2: 1.353315
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.532931 Loss1: 0.168411 Loss2: 1.364520
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.141863 Loss1: 0.366737 Loss2: 1.775126
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491724 Loss1: 0.137370 Loss2: 1.354355
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.540823 Loss1: 0.248996 Loss2: 1.291827
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.496354 Loss1: 0.145656 Loss2: 1.350698
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.496441 Loss1: 0.183367 Loss2: 1.313074
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438751 Loss1: 0.083310 Loss2: 1.355440
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.428271 Loss1: 0.133390 Loss2: 1.294882
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409989 Loss1: 0.064086 Loss2: 1.345903
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.405343 Loss1: 0.121477 Loss2: 1.283866
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417307 Loss1: 0.075504 Loss2: 1.341803
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.338835 Loss1: 0.067060 Loss2: 1.271775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.335904 Loss1: 0.065571 Loss2: 1.270333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.321529 Loss1: 0.057415 Loss2: 1.264114
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.121378 Loss1: 0.358646 Loss2: 1.762732
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.493620 Loss1: 0.192767 Loss2: 1.300854
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.452000 Loss1: 0.148224 Loss2: 1.303776
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.375414 Loss1: 0.077532 Loss2: 1.297882
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.363801 Loss1: 0.083314 Loss2: 1.280487
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.351586 Loss1: 0.070612 Loss2: 1.280975
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.243118 Loss1: 0.392439 Loss2: 1.850678
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.325145 Loss1: 0.051653 Loss2: 1.273492
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.710439 Loss1: 0.329593 Loss2: 1.380845
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.312600 Loss1: 0.039835 Loss2: 1.272765
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.588969 Loss1: 0.172181 Loss2: 1.416788
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.311755 Loss1: 0.046178 Loss2: 1.265576
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.500445 Loss1: 0.135581 Loss2: 1.364863
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.299444 Loss1: 0.034709 Loss2: 1.264736
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476390 Loss1: 0.114923 Loss2: 1.361467
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460927 Loss1: 0.104697 Loss2: 1.356230
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.440277 Loss1: 0.087398 Loss2: 1.352880
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446238 Loss1: 0.099280 Loss2: 1.346959
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425120 Loss1: 0.082456 Loss2: 1.342664
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.201283 Loss1: 0.351210 Loss2: 1.850073
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415895 Loss1: 0.074143 Loss2: 1.341752
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.569279 Loss1: 0.169831 Loss2: 1.399448 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.464861 Loss1: 0.112030 Loss2: 1.352831 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.422056 Loss1: 0.068507 Loss2: 1.353549
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.164822 Loss1: 0.318070 Loss2: 1.846752
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.587688 Loss1: 0.238480 Loss2: 1.349208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.539891 Loss1: 0.179810 Loss2: 1.360081 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.484432 Loss1: 0.126403 Loss2: 1.358029 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.347646 Loss1: 0.016147 Loss2: 1.331498
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.461327 Loss1: 0.113283 Loss2: 1.348044
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441504 Loss1: 0.096091 Loss2: 1.345413
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404990 Loss1: 0.064751 Loss2: 1.340239
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.392811 Loss1: 0.054945 Loss2: 1.337866
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.374244 Loss1: 0.046098 Loss2: 1.328146
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.206773 Loss1: 0.331481 Loss2: 1.875292
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352735 Loss1: 0.027252 Loss2: 1.325483
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.662186 Loss1: 0.241377 Loss2: 1.420808 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.500829 Loss1: 0.125430 Loss2: 1.375399 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.467261 Loss1: 0.093087 Loss2: 1.374174
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.232515 Loss1: 0.415155 Loss2: 1.817360
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.477004 Loss1: 0.108403 Loss2: 1.368600
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.578478 Loss1: 0.257342 Loss2: 1.321136
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460390 Loss1: 0.094371 Loss2: 1.366019
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.476089 Loss1: 0.132680 Loss2: 1.343409
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407263 Loss1: 0.040522 Loss2: 1.366742
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.446421 Loss1: 0.121765 Loss2: 1.324657
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.440417 Loss1: 0.081039 Loss2: 1.359378
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.395525 Loss1: 0.085416 Loss2: 1.310110
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.370558 Loss1: 0.060711 Loss2: 1.309847
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.356417 Loss1: 0.054935 Loss2: 1.301481
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.359102 Loss1: 0.056406 Loss2: 1.302696
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369840 Loss1: 0.065529 Loss2: 1.304311
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.383494 Loss1: 0.451340 Loss2: 1.932154
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.367618 Loss1: 0.066186 Loss2: 1.301432
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.589389 Loss1: 0.204926 Loss2: 1.384463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478862 Loss1: 0.121592 Loss2: 1.357270 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.250274 Loss1: 0.420487 Loss2: 1.829787 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.399377 Loss1: 0.049597 Loss2: 1.349780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405780 Loss1: 0.061477 Loss2: 1.344303 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392292 Loss1: 0.053763 Loss2: 1.338529 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.426255 Loss1: 0.095835 Loss2: 1.330420 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409810 Loss1: 0.094976 Loss2: 1.314834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410685 Loss1: 0.088497 Loss2: 1.322188 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414463 Loss1: 0.097199 Loss2: 1.317263 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.430907 Loss1: 0.091530 Loss2: 1.339377 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.374625 Loss1: 0.047834 Loss2: 1.326790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.360368 Loss1: 0.047592 Loss2: 1.312776
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.174351 Loss1: 0.310632 Loss2: 1.863719
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.511612 Loss1: 0.156972 Loss2: 1.354640 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.507023 Loss1: 0.148071 Loss2: 1.358952 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.311498 Loss1: 0.011577 Loss2: 1.299920
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.499916 Loss1: 0.138301 Loss2: 1.361615
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.466028 Loss1: 0.121569 Loss2: 1.344459
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.467674 Loss1: 0.113006 Loss2: 1.354668
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438405 Loss1: 0.085816 Loss2: 1.352588
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420498 Loss1: 0.077559 Loss2: 1.342939
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.284785 Loss1: 0.414306 Loss2: 1.870479
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394378 Loss1: 0.055286 Loss2: 1.339092
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356503 Loss1: 0.029487 Loss2: 1.327017 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.477497 Loss1: 0.114578 Loss2: 1.362920 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449428 Loss1: 0.090716 Loss2: 1.358712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416887 Loss1: 0.064343 Loss2: 1.352544
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.205203 Loss1: 0.351090 Loss2: 1.854113
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.601360 Loss1: 0.254795 Loss2: 1.346565 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.580387 Loss1: 0.195250 Loss2: 1.385137 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408040 Loss1: 0.062572 Loss2: 1.345468
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556052 Loss1: 0.176419 Loss2: 1.379633
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500759 Loss1: 0.145751 Loss2: 1.355009
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480169 Loss1: 0.121618 Loss2: 1.358551
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491941 Loss1: 0.135525 Loss2: 1.356416
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419315 Loss1: 0.069601 Loss2: 1.349714
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.189759 Loss1: 0.379762 Loss2: 1.809997
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.601865 Loss1: 0.244934 Loss2: 1.356931 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.546860 Loss1: 0.171961 Loss2: 1.374899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.452794 Loss1: 0.107885 Loss2: 1.344909 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.392388 Loss1: 0.056230 Loss2: 1.336158 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.375537 Loss1: 0.046329 Loss2: 1.329208 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.441186 Loss1: 0.115392 Loss2: 1.325794 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.438069 Loss1: 0.110434 Loss2: 1.327635 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.361754 Loss1: 0.054394 Loss2: 1.307360 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.342497 Loss1: 0.044990 Loss2: 1.297508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.324735 Loss1: 0.033453 Loss2: 1.291282
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.301313 Loss1: 0.405139 Loss2: 1.896174
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.311112 Loss1: 0.023564 Loss2: 1.287549
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.621688 Loss1: 0.223256 Loss2: 1.398432
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599284 Loss1: 0.191597 Loss2: 1.407687
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.548994 Loss1: 0.140466 Loss2: 1.408528
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503253 Loss1: 0.116871 Loss2: 1.386382
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460725 Loss1: 0.077041 Loss2: 1.383683
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.226108 Loss1: 0.358288 Loss2: 1.867820
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.476225 Loss1: 0.092302 Loss2: 1.383923
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.640709 Loss1: 0.261609 Loss2: 1.379100
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459823 Loss1: 0.070800 Loss2: 1.389023
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444216 Loss1: 0.068859 Loss2: 1.375357
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.591443 Loss1: 0.178779 Loss2: 1.412664
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438712 Loss1: 0.061395 Loss2: 1.377317
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521151 Loss1: 0.133400 Loss2: 1.387751
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.519798 Loss1: 0.138957 Loss2: 1.380841
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.498572 Loss1: 0.115103 Loss2: 1.383469
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.480786 Loss1: 0.099947 Loss2: 1.380839
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460737 Loss1: 0.083470 Loss2: 1.377267
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.053062 Loss1: 0.287968 Loss2: 1.765094
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.478175 Loss1: 0.101002 Loss2: 1.377173
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.494480 Loss1: 0.185004 Loss2: 1.309476
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.469931 Loss1: 0.101784 Loss2: 1.368147
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500427 Loss1: 0.174672 Loss2: 1.325756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470042 Loss1: 0.142757 Loss2: 1.327285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444674 Loss1: 0.129198 Loss2: 1.315476
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.089627 Loss1: 0.264812 Loss2: 1.824815
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424011 Loss1: 0.112619 Loss2: 1.311392
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.621944 Loss1: 0.261635 Loss2: 1.360309
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.410089 Loss1: 0.095962 Loss2: 1.314127
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.628977 Loss1: 0.225310 Loss2: 1.403667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.394872 Loss1: 0.080430 Loss2: 1.314443
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.576984 Loss1: 0.202453 Loss2: 1.374531
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518705 Loss1: 0.150326 Loss2: 1.368379
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.517738 Loss1: 0.141129 Loss2: 1.376609
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.495733 Loss1: 0.129140 Loss2: 1.366593
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469557 Loss1: 0.092044 Loss2: 1.377514
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.318073 Loss1: 0.400629 Loss2: 1.917444
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399692 Loss1: 0.043243 Loss2: 1.356449
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388039 Loss1: 0.037983 Loss2: 1.350056 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556773 Loss1: 0.156487 Loss2: 1.400286 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 12:23:10,789 | server.py:236 | fit_round 188 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.500725 Loss1: 0.129479 Loss2: 1.371246 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436017 Loss1: 0.063911 Loss2: 1.372107 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423246 Loss1: 0.063034 Loss2: 1.360212 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.443604 Loss1: 0.082866 Loss2: 1.360738 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995536
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.368636 Loss1: 0.074205 Loss2: 1.294431 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.351218 Loss1: 0.055937 Loss2: 1.295281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.378445 Loss1: 0.452513 Loss2: 1.925932
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.304333 Loss1: 0.022001 Loss2: 1.282332
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.636033 Loss1: 0.273104 Loss2: 1.362929
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.326193 Loss1: 0.050184 Loss2: 1.276009
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.634874 Loss1: 0.250121 Loss2: 1.384754
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.307658 Loss1: 0.036699 Loss2: 1.270959
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534880 Loss1: 0.179704 Loss2: 1.355176 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472838 Loss1: 0.113937 Loss2: 1.358901 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.209381 Loss1: 0.344820 Loss2: 1.864561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.586713 Loss1: 0.214645 Loss2: 1.372068 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.555376 Loss1: 0.173803 Loss2: 1.381573 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.525899 Loss1: 0.144125 Loss2: 1.381775 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455025 Loss1: 0.085081 Loss2: 1.369944
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.277716 Loss1: 0.350109 Loss2: 1.927608
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.657453 Loss1: 0.262812 Loss2: 1.394641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.631642 Loss1: 0.186403 Loss2: 1.445238 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.424272 Loss1: 0.073407 Loss2: 1.350865
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.529691 Loss1: 0.124457 Loss2: 1.405234
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480119 Loss1: 0.090525 Loss2: 1.389595
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519013 Loss1: 0.129116 Loss2: 1.389898
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485444 Loss1: 0.084630 Loss2: 1.400814
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469816 Loss1: 0.080670 Loss2: 1.389146
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.157760 Loss1: 0.324641 Loss2: 1.833119
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.465601 Loss1: 0.084966 Loss2: 1.380635
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.545872 Loss1: 0.196226 Loss2: 1.349647
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426167 Loss1: 0.049253 Loss2: 1.376914
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492829 Loss1: 0.136986 Loss2: 1.355843 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485325 Loss1: 0.124489 Loss2: 1.360837 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.461301 Loss1: 0.103052 Loss2: 1.358249 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.417720 Loss1: 0.071118 Loss2: 1.346602 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 12:23:10,789][flwr][DEBUG] - fit_round 188 received 50 results and 0 failures
+INFO flwr 2023-10-13 12:23:53,127 | server.py:125 | fit progress: (188, 2.3212159514046324, {'accuracy': 0.6113}, 433940.905263817)
+>> Test accuracy: 0.611300
+[2023-10-13 12:23:53,127][flwr][INFO] - fit progress: (188, 2.3212159514046324, {'accuracy': 0.6113}, 433940.905263817)
+DEBUG flwr 2023-10-13 12:23:53,127 | server.py:173 | evaluate_round 188: strategy sampled 50 clients (out of 50)
+[2023-10-13 12:23:53,127][flwr][DEBUG] - evaluate_round 188: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 12:32:56,723 | server.py:187 | evaluate_round 188 received 50 results and 0 failures
+[2023-10-13 12:32:56,723][flwr][DEBUG] - evaluate_round 188 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 12:32:56,724 | server.py:222 | fit_round 189: strategy sampled 50 clients (out of 50)
+[2023-10-13 12:32:56,724][flwr][DEBUG] - fit_round 189: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.178606 Loss1: 0.347552 Loss2: 1.831054
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.534171 Loss1: 0.169032 Loss2: 1.365140
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.538087 Loss1: 0.168735 Loss2: 1.369352
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.458064 Loss1: 0.473793 Loss2: 1.984271
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.477282 Loss1: 0.111005 Loss2: 1.366277
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.450689 Loss1: 0.097786 Loss2: 1.352902 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529108 Loss1: 0.148855 Loss2: 1.380253 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408535 Loss1: 0.063777 Loss2: 1.344758 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391582 Loss1: 0.050516 Loss2: 1.341066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412976 Loss1: 0.065536 Loss2: 1.347440 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398934 Loss1: 0.063060 Loss2: 1.335873 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.242359 Loss1: 0.391972 Loss2: 1.850387 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526036 Loss1: 0.150548 Loss2: 1.375488 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499101 Loss1: 0.127208 Loss2: 1.371893
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.169040 Loss1: 0.293901 Loss2: 1.875139
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.565458 Loss1: 0.174546 Loss2: 1.390912 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.528092 Loss1: 0.118350 Loss2: 1.409742 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.491993 Loss1: 0.098291 Loss2: 1.393703 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.494759 Loss1: 0.105385 Loss2: 1.389375 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.418740 Loss1: 0.070120 Loss2: 1.348620 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445735 Loss1: 0.072539 Loss2: 1.373196 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.420515 Loss1: 0.045640 Loss2: 1.374875 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991728
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.723064 Loss1: 0.397192 Loss2: 1.325871 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.483285 Loss1: 0.143929 Loss2: 1.339356 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.422052 Loss1: 0.079106 Loss2: 1.342945
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.254154 Loss1: 0.365855 Loss2: 1.888300
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.607481 Loss1: 0.225602 Loss2: 1.381879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.557054 Loss1: 0.154548 Loss2: 1.402506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.515352 Loss1: 0.127223 Loss2: 1.388129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524382 Loss1: 0.138737 Loss2: 1.385645 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.519294 Loss1: 0.124296 Loss2: 1.394998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485002 Loss1: 0.103932 Loss2: 1.381070 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439304 Loss1: 0.070601 Loss2: 1.368703 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.589097 Loss1: 0.257847 Loss2: 1.331250 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518693 Loss1: 0.171334 Loss2: 1.347359 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491047 Loss1: 0.146689 Loss2: 1.344358
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.215679 Loss1: 0.364161 Loss2: 1.851518
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.595647 Loss1: 0.234920 Loss2: 1.360727 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.397246 Loss1: 0.061864 Loss2: 1.335382
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.542903 Loss1: 0.161154 Loss2: 1.381749
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393034 Loss1: 0.060803 Loss2: 1.332231
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478569 Loss1: 0.113489 Loss2: 1.365079
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.397672 Loss1: 0.070502 Loss2: 1.327170
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502891 Loss1: 0.155410 Loss2: 1.347481
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.493029 Loss1: 0.127169 Loss2: 1.365860
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369499 Loss1: 0.046039 Loss2: 1.323460
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.391366 Loss1: 0.050400 Loss2: 1.340966 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.360036 Loss1: 0.029748 Loss2: 1.330288 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.539289 Loss1: 0.220747 Loss2: 1.318542 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.421859 Loss1: 0.097741 Loss2: 1.324118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.216878 Loss1: 0.366231 Loss2: 1.850647
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.390908 Loss1: 0.078161 Loss2: 1.312747
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.615219 Loss1: 0.262499 Loss2: 1.352720
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.384839 Loss1: 0.077450 Loss2: 1.307390
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.516410 Loss1: 0.138625 Loss2: 1.377785
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.347306 Loss1: 0.043281 Loss2: 1.304025
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519464 Loss1: 0.165740 Loss2: 1.353724
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.321308 Loss1: 0.020848 Loss2: 1.300460
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.489929 Loss1: 0.133910 Loss2: 1.356019
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.317948 Loss1: 0.024618 Loss2: 1.293330
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.591674 Loss1: 0.217945 Loss2: 1.373729
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.314078 Loss1: 0.026344 Loss2: 1.287734
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454304 Loss1: 0.105043 Loss2: 1.349261 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.415154 Loss1: 0.067562 Loss2: 1.347593 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.519544 Loss1: 0.176869 Loss2: 1.342676 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.423911 Loss1: 0.082988 Loss2: 1.340924 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.395473 Loss1: 0.066705 Loss2: 1.328768
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.082600 Loss1: 0.303590 Loss2: 1.779010
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.405309 Loss1: 0.078953 Loss2: 1.326356
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.501566 Loss1: 0.176297 Loss2: 1.325270
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390989 Loss1: 0.064489 Loss2: 1.326500
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.477143 Loss1: 0.147561 Loss2: 1.329582
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.375223 Loss1: 0.054404 Loss2: 1.320819
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487732 Loss1: 0.162561 Loss2: 1.325171
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.353043 Loss1: 0.036960 Loss2: 1.316083
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.454523 Loss1: 0.132393 Loss2: 1.322129
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.343762 Loss1: 0.034088 Loss2: 1.309675
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423059 Loss1: 0.101682 Loss2: 1.321377
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406958 Loss1: 0.096998 Loss2: 1.309961
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.381285 Loss1: 0.069049 Loss2: 1.312236
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369986 Loss1: 0.062456 Loss2: 1.307530
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.345125 Loss1: 0.042408 Loss2: 1.302717
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.154043 Loss1: 0.310161 Loss2: 1.843882
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.513228 Loss1: 0.155937 Loss2: 1.357291
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.463094 Loss1: 0.115060 Loss2: 1.348034
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.404937 Loss1: 0.054774 Loss2: 1.350163
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.392019 Loss1: 0.058651 Loss2: 1.333368
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.155333 Loss1: 0.331711 Loss2: 1.823622
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.588366 Loss1: 0.241022 Loss2: 1.347345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556011 Loss1: 0.195802 Loss2: 1.360210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389853 Loss1: 0.062103 Loss2: 1.327750
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.493218 Loss1: 0.130718 Loss2: 1.362499
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372380 Loss1: 0.044818 Loss2: 1.327562
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.416419 Loss1: 0.076091 Loss2: 1.340328
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386961 Loss1: 0.062509 Loss2: 1.324452
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.428435 Loss1: 0.087908 Loss2: 1.340527
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.396382 Loss1: 0.061074 Loss2: 1.335308
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382917 Loss1: 0.053290 Loss2: 1.329627
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383037 Loss1: 0.057939 Loss2: 1.325097
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.360497 Loss1: 0.036964 Loss2: 1.323533
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.165979 Loss1: 0.361891 Loss2: 1.804088
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.543798 Loss1: 0.225863 Loss2: 1.317935
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.470163 Loss1: 0.135827 Loss2: 1.334337
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.441304 Loss1: 0.115885 Loss2: 1.325419
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.430222 Loss1: 0.114733 Loss2: 1.315489
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.349737 Loss1: 0.467786 Loss2: 1.881951
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.621608 Loss1: 0.270074 Loss2: 1.351533
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460383 Loss1: 0.139566 Loss2: 1.320816
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.536487 Loss1: 0.170354 Loss2: 1.366133
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448471 Loss1: 0.129920 Loss2: 1.318551
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.525710 Loss1: 0.169929 Loss2: 1.355781
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.402815 Loss1: 0.076275 Loss2: 1.326540
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398546 Loss1: 0.080934 Loss2: 1.317612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380784 Loss1: 0.070263 Loss2: 1.310522 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.385058 Loss1: 0.047795 Loss2: 1.337263 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388734 Loss1: 0.060535 Loss2: 1.328198 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.290393 Loss1: 0.366511 Loss2: 1.923882
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.675804 Loss1: 0.262350 Loss2: 1.413453
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.618587 Loss1: 0.171312 Loss2: 1.447275
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561913 Loss1: 0.139246 Loss2: 1.422667
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.327963 Loss1: 0.473587 Loss2: 1.854377
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.653950 Loss1: 0.300522 Loss2: 1.353428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.541403 Loss1: 0.157536 Loss2: 1.383867 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536718 Loss1: 0.177687 Loss2: 1.359031 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.511086 Loss1: 0.150473 Loss2: 1.360613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487869 Loss1: 0.132043 Loss2: 1.355826 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436306 Loss1: 0.039435 Loss2: 1.396871
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456106 Loss1: 0.106023 Loss2: 1.350083
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413510 Loss1: 0.071472 Loss2: 1.342039
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399929 Loss1: 0.064518 Loss2: 1.335411
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397750 Loss1: 0.064497 Loss2: 1.333253
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.186888 Loss1: 0.371402 Loss2: 1.815487
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.603909 Loss1: 0.271005 Loss2: 1.332905
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.501591 Loss1: 0.150774 Loss2: 1.350817
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.445982 Loss1: 0.113267 Loss2: 1.332715
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.241619 Loss1: 0.343867 Loss2: 1.897752
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.560945 Loss1: 0.179140 Loss2: 1.381804 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.523594 Loss1: 0.133607 Loss2: 1.389987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.470504 Loss1: 0.082694 Loss2: 1.387810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.436080 Loss1: 0.067716 Loss2: 1.368364 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465019 Loss1: 0.094684 Loss2: 1.370335 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355110 Loss1: 0.050828 Loss2: 1.304282
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460075 Loss1: 0.090193 Loss2: 1.369882
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436709 Loss1: 0.069106 Loss2: 1.367603
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.426324 Loss1: 0.064663 Loss2: 1.361661
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.447613 Loss1: 0.084346 Loss2: 1.363267
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.230521 Loss1: 0.358303 Loss2: 1.872219
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.640420 Loss1: 0.272794 Loss2: 1.367626
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.637102 Loss1: 0.232656 Loss2: 1.404445
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.631423 Loss1: 0.213225 Loss2: 1.418199
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.189610 Loss1: 0.337755 Loss2: 1.851855
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.528058 Loss1: 0.152061 Loss2: 1.375996
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.634377 Loss1: 0.275391 Loss2: 1.358986
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448996 Loss1: 0.068714 Loss2: 1.380282
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.631237 Loss1: 0.227740 Loss2: 1.403497
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.444184 Loss1: 0.073323 Loss2: 1.370861
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553948 Loss1: 0.177051 Loss2: 1.376897
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434309 Loss1: 0.063110 Loss2: 1.371199
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.574179 Loss1: 0.200752 Loss2: 1.373427
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413672 Loss1: 0.051588 Loss2: 1.362084
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.524298 Loss1: 0.142575 Loss2: 1.381723
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405963 Loss1: 0.047229 Loss2: 1.358734
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494016 Loss1: 0.121754 Loss2: 1.372261
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.465536 Loss1: 0.107456 Loss2: 1.358079
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451416 Loss1: 0.097064 Loss2: 1.354352
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.429166 Loss1: 0.077916 Loss2: 1.351249
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.270435 Loss1: 0.375731 Loss2: 1.894704
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.583162 Loss1: 0.207387 Loss2: 1.375775
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.575379 Loss1: 0.183954 Loss2: 1.391424
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536814 Loss1: 0.155841 Loss2: 1.380973
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.094557 Loss1: 0.279206 Loss2: 1.815350
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.604817 Loss1: 0.245827 Loss2: 1.358990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.563867 Loss1: 0.158206 Loss2: 1.405661 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528730 Loss1: 0.152175 Loss2: 1.376555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.506029 Loss1: 0.137615 Loss2: 1.368414 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.483897 Loss1: 0.105293 Loss2: 1.378604 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463013 Loss1: 0.095131 Loss2: 1.367883 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.451497 Loss1: 0.078653 Loss2: 1.372844 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.207589 Loss1: 0.342871 Loss2: 1.864718 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582249 Loss1: 0.174094 Loss2: 1.408155 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.138971 Loss1: 0.291446 Loss2: 1.847524 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.577718 Loss1: 0.234343 Loss2: 1.343375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.523117 Loss1: 0.168562 Loss2: 1.354555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487959 Loss1: 0.129908 Loss2: 1.358051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527026 Loss1: 0.186740 Loss2: 1.340285 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.513144 Loss1: 0.159306 Loss2: 1.353838 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.436345 Loss1: 0.065705 Loss2: 1.370639
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427436 Loss1: 0.079679 Loss2: 1.347757
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.438011 Loss1: 0.095349 Loss2: 1.342662
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402461 Loss1: 0.061656 Loss2: 1.340805
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434001 Loss1: 0.097697 Loss2: 1.336304
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.248101 Loss1: 0.360758 Loss2: 1.887343
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.604853 Loss1: 0.213076 Loss2: 1.391777
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.593276 Loss1: 0.197604 Loss2: 1.395671
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.559337 Loss1: 0.161722 Loss2: 1.397615
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.187925 Loss1: 0.384281 Loss2: 1.803643
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.650304 Loss1: 0.303379 Loss2: 1.346925 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.528877 Loss1: 0.147014 Loss2: 1.381864 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.476704 Loss1: 0.129913 Loss2: 1.346791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.464067 Loss1: 0.115735 Loss2: 1.348332 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.443540 Loss1: 0.100010 Loss2: 1.343530 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.386516 Loss1: 0.055294 Loss2: 1.331222 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.347783 Loss1: 0.029147 Loss2: 1.318637 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.252185 Loss1: 0.383849 Loss2: 1.868336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.609767 Loss1: 0.206189 Loss2: 1.403578 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.187339 Loss1: 0.331375 Loss2: 1.855964 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.620420 Loss1: 0.267559 Loss2: 1.352860 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.534386 Loss1: 0.156307 Loss2: 1.378078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.488793 Loss1: 0.135581 Loss2: 1.353212 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.481617 Loss1: 0.134371 Loss2: 1.347246 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.470760 Loss1: 0.122240 Loss2: 1.348520 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.426706 Loss1: 0.089924 Loss2: 1.336782 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372922 Loss1: 0.043963 Loss2: 1.328958 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.676828 Loss1: 0.306728 Loss2: 1.370100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.531644 Loss1: 0.143297 Loss2: 1.388346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.501370 Loss1: 0.136523 Loss2: 1.364847
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.300027 Loss1: 0.387239 Loss2: 1.912789
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.490378 Loss1: 0.118020 Loss2: 1.372358
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.592048 Loss1: 0.207749 Loss2: 1.384299
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.578539 Loss1: 0.181601 Loss2: 1.396938 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574801 Loss1: 0.165688 Loss2: 1.409112 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.563229 Loss1: 0.171196 Loss2: 1.392033 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987723
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516898 Loss1: 0.115025 Loss2: 1.401873 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488093 Loss1: 0.111779 Loss2: 1.376314 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445502 Loss1: 0.063804 Loss2: 1.381698 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.613742 Loss1: 0.277231 Loss2: 1.336511 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.466998 Loss1: 0.124228 Loss2: 1.342770 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.296869 Loss1: 0.412716 Loss2: 1.884153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.564769 Loss1: 0.190373 Loss2: 1.374395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.536008 Loss1: 0.161160 Loss2: 1.374848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540638 Loss1: 0.168420 Loss2: 1.372218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.530079 Loss1: 0.157014 Loss2: 1.373065 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.450044 Loss1: 0.086866 Loss2: 1.363178 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450856 Loss1: 0.098389 Loss2: 1.352467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439912 Loss1: 0.088073 Loss2: 1.351839
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.337447 Loss1: 0.469144 Loss2: 1.868303
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.640440 Loss1: 0.291481 Loss2: 1.348959
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.580216 Loss1: 0.213383 Loss2: 1.366832
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488459 Loss1: 0.146583 Loss2: 1.341876
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.419927 Loss1: 0.089784 Loss2: 1.330142
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.158184 Loss1: 0.373868 Loss2: 1.784316
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440105 Loss1: 0.112120 Loss2: 1.327985
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.579535 Loss1: 0.253878 Loss2: 1.325657
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.401273 Loss1: 0.080849 Loss2: 1.320424
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.584770 Loss1: 0.221923 Loss2: 1.362847
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.363919 Loss1: 0.047792 Loss2: 1.316127
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.359467 Loss1: 0.045064 Loss2: 1.314403
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.502061 Loss1: 0.165640 Loss2: 1.336421
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.332687 Loss1: 0.032538 Loss2: 1.300149
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485512 Loss1: 0.151461 Loss2: 1.334051
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.465027 Loss1: 0.133128 Loss2: 1.331898
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.399986 Loss1: 0.080053 Loss2: 1.319933
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372613 Loss1: 0.053928 Loss2: 1.318685
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.341282 Loss1: 0.030928 Loss2: 1.310354
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.233868 Loss1: 0.350069 Loss2: 1.883799
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.325491 Loss1: 0.024287 Loss2: 1.301204
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.578062 Loss1: 0.184871 Loss2: 1.393191 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.469381 Loss1: 0.090918 Loss2: 1.378463 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.162405 Loss1: 0.340739 Loss2: 1.821665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.584316 Loss1: 0.257210 Loss2: 1.327106 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.550734 Loss1: 0.199976 Loss2: 1.350758 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.482828 Loss1: 0.139177 Loss2: 1.343650 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.461933 Loss1: 0.139814 Loss2: 1.322119 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439944 Loss1: 0.101456 Loss2: 1.338488 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390009 Loss1: 0.064062 Loss2: 1.325947 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372164 Loss1: 0.051710 Loss2: 1.320453 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.480564 Loss1: 0.159594 Loss2: 1.320970 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.371283 Loss1: 0.082194 Loss2: 1.289089 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.237496 Loss1: 0.391233 Loss2: 1.846264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.675118 Loss1: 0.323311 Loss2: 1.351808 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.316063 Loss1: 0.039326 Loss2: 1.276738 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.492439 Loss1: 0.136169 Loss2: 1.356269 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.417510 Loss1: 0.074790 Loss2: 1.342721 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.218343 Loss1: 0.405982 Loss2: 1.812361
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.404022 Loss1: 0.059860 Loss2: 1.344162
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.377242 Loss1: 0.044619 Loss2: 1.332623
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.531927 Loss1: 0.186097 Loss2: 1.345830
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362259 Loss1: 0.036698 Loss2: 1.325561
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.483435 Loss1: 0.139621 Loss2: 1.343814
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.437816 Loss1: 0.098080 Loss2: 1.339736
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.452726 Loss1: 0.121082 Loss2: 1.331644
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.415583 Loss1: 0.086998 Loss2: 1.328584
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.396710 Loss1: 0.072910 Loss2: 1.323800
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.358855 Loss1: 0.454945 Loss2: 1.903910
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.642295 Loss1: 0.279527 Loss2: 1.362768 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.610639 Loss1: 0.221885 Loss2: 1.388754 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370159 Loss1: 0.050872 Loss2: 1.319287
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.553945 Loss1: 0.170303 Loss2: 1.383643
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.443514 Loss1: 0.080745 Loss2: 1.362770
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.445466 Loss1: 0.090832 Loss2: 1.354634
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.401212 Loss1: 0.047209 Loss2: 1.354003
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.377873 Loss1: 0.039065 Loss2: 1.338808
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370827 Loss1: 0.034823 Loss2: 1.336003
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.234909 Loss1: 0.362111 Loss2: 1.872799
+(DefaultActor pid=3764) >> Training accuracy: 0.997768
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364476 Loss1: 0.034606 Loss2: 1.329869
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.648489 Loss1: 0.290934 Loss2: 1.357555
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.595583 Loss1: 0.174150 Loss2: 1.421432
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506954 Loss1: 0.145192 Loss2: 1.361762
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551686 Loss1: 0.190414 Loss2: 1.361272
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451718 Loss1: 0.086216 Loss2: 1.365502
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.214447 Loss1: 0.359768 Loss2: 1.854680
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.462267 Loss1: 0.103331 Loss2: 1.358936
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426653 Loss1: 0.071599 Loss2: 1.355054 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.404221 Loss1: 0.057414 Loss2: 1.346807 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410273 Loss1: 0.070038 Loss2: 1.340235 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482566 Loss1: 0.108859 Loss2: 1.373707 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.477311 Loss1: 0.110528 Loss2: 1.366783 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433728 Loss1: 0.067883 Loss2: 1.365845
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.188465 Loss1: 0.331708 Loss2: 1.856757
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414672 Loss1: 0.053463 Loss2: 1.361209
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.579873 Loss1: 0.238210 Loss2: 1.341663
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.525602 Loss1: 0.167283 Loss2: 1.358319
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.499319 Loss1: 0.145034 Loss2: 1.354285
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478347 Loss1: 0.132952 Loss2: 1.345394
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.434219 Loss1: 0.088406 Loss2: 1.345812
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.467014 Loss1: 0.125252 Loss2: 1.341762
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.196146 Loss1: 0.363266 Loss2: 1.832880
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.615674 Loss1: 0.294876 Loss2: 1.320798
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435950 Loss1: 0.093722 Loss2: 1.342227
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425063 Loss1: 0.087965 Loss2: 1.337098
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.545698 Loss1: 0.181216 Loss2: 1.364482
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423781 Loss1: 0.084544 Loss2: 1.339237
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496857 Loss1: 0.154625 Loss2: 1.342232
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.544080 Loss1: 0.197480 Loss2: 1.346600
+DEBUG flwr 2023-10-13 13:01:34,022 | server.py:236 | fit_round 189 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.494828 Loss1: 0.134963 Loss2: 1.359865
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.519516 Loss1: 0.173629 Loss2: 1.345888
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.446971 Loss1: 0.096332 Loss2: 1.350640
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.421740 Loss1: 0.080130 Loss2: 1.341609
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.106522 Loss1: 0.282686 Loss2: 1.823835
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388833 Loss1: 0.056966 Loss2: 1.331868
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.565513 Loss1: 0.228922 Loss2: 1.336591
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.534065 Loss1: 0.179418 Loss2: 1.354647
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506555 Loss1: 0.148508 Loss2: 1.358047
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.429268 Loss1: 0.094449 Loss2: 1.334819
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.450673 Loss1: 0.115377 Loss2: 1.335296
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.156320 Loss1: 0.311793 Loss2: 1.844528
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.407561 Loss1: 0.075476 Loss2: 1.332085
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.516889 Loss1: 0.166177 Loss2: 1.350713
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.387537 Loss1: 0.060576 Loss2: 1.326960
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.529575 Loss1: 0.175870 Loss2: 1.353705
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.401392 Loss1: 0.073018 Loss2: 1.328374
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.477211 Loss1: 0.124297 Loss2: 1.352914
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390440 Loss1: 0.061480 Loss2: 1.328961
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472165 Loss1: 0.130349 Loss2: 1.341816 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413008 Loss1: 0.073350 Loss2: 1.339658 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449887 Loss1: 0.114145 Loss2: 1.335743
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.133641 Loss1: 0.301669 Loss2: 1.831972
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438380 Loss1: 0.091886 Loss2: 1.346494
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.541895 Loss1: 0.216788 Loss2: 1.325107
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.486816 Loss1: 0.143641 Loss2: 1.343175
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.456710 Loss1: 0.116433 Loss2: 1.340277
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.426793 Loss1: 0.101748 Loss2: 1.325045
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.403551 Loss1: 0.080154 Loss2: 1.323397
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.394867 Loss1: 0.496907 Loss2: 1.897960
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409414 Loss1: 0.089673 Loss2: 1.319741
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.671022 Loss1: 0.318706 Loss2: 1.352316
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.414618 Loss1: 0.089180 Loss2: 1.325438
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399043 Loss1: 0.072227 Loss2: 1.326815 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.381061 Loss1: 0.063924 Loss2: 1.317136 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.430061 Loss1: 0.079274 Loss2: 1.350787 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.375498 Loss1: 0.038912 Loss2: 1.336586 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 13:01:34,022][flwr][DEBUG] - fit_round 189 received 50 results and 0 failures
+INFO flwr 2023-10-13 13:02:16,048 | server.py:125 | fit progress: (189, 2.321961476779974, {'accuracy': 0.6123}, 436243.826841095)
+>> Test accuracy: 0.612300
+[2023-10-13 13:02:16,048][flwr][INFO] - fit progress: (189, 2.321961476779974, {'accuracy': 0.6123}, 436243.826841095)
+DEBUG flwr 2023-10-13 13:02:16,049 | server.py:173 | evaluate_round 189: strategy sampled 50 clients (out of 50)
+[2023-10-13 13:02:16,049][flwr][DEBUG] - evaluate_round 189: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 13:11:21,709 | server.py:187 | evaluate_round 189 received 50 results and 0 failures
+[2023-10-13 13:11:21,709][flwr][DEBUG] - evaluate_round 189 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 13:11:21,709 | server.py:222 | fit_round 190: strategy sampled 50 clients (out of 50)
+[2023-10-13 13:11:21,709][flwr][DEBUG] - fit_round 190: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.212667 Loss1: 0.387677 Loss2: 1.824990 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.521473 Loss1: 0.144069 Loss2: 1.377405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.479129 Loss1: 0.138915 Loss2: 1.340214
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.073393 Loss1: 0.286060 Loss2: 1.787333
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461185 Loss1: 0.119730 Loss2: 1.341455
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.569911 Loss1: 0.238411 Loss2: 1.331501
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.415991 Loss1: 0.073517 Loss2: 1.342474
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.503800 Loss1: 0.149889 Loss2: 1.353911
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416113 Loss1: 0.077183 Loss2: 1.338929
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468801 Loss1: 0.135820 Loss2: 1.332981
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.386429 Loss1: 0.055826 Loss2: 1.330603
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.570090 Loss1: 0.231348 Loss2: 1.338742
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377909 Loss1: 0.052115 Loss2: 1.325794
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.677601 Loss1: 0.267384 Loss2: 1.410218
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.367177 Loss1: 0.043865 Loss2: 1.323312
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.503253 Loss1: 0.155426 Loss2: 1.347827
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.529739 Loss1: 0.163654 Loss2: 1.366085
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.475513 Loss1: 0.124548 Loss2: 1.350965
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482615 Loss1: 0.138277 Loss2: 1.344338
+(DefaultActor pid=3764) >> Training accuracy: 0.982422
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.387586 Loss1: 0.453570 Loss2: 1.934016
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.618232 Loss1: 0.249031 Loss2: 1.369201
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.608209 Loss1: 0.233553 Loss2: 1.374657
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511366 Loss1: 0.112964 Loss2: 1.398403
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.191022 Loss1: 0.320896 Loss2: 1.870127
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460887 Loss1: 0.110603 Loss2: 1.350285 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426543 Loss1: 0.069889 Loss2: 1.356655 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415799 Loss1: 0.067018 Loss2: 1.348781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386690 Loss1: 0.042887 Loss2: 1.343803 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374418 Loss1: 0.033596 Loss2: 1.340823 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996394
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.485888 Loss1: 0.113798 Loss2: 1.372090 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.446022 Loss1: 0.075990 Loss2: 1.370032 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423668 Loss1: 0.063875 Loss2: 1.359793
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.182664 Loss1: 0.329784 Loss2: 1.852880
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.599539 Loss1: 0.222718 Loss2: 1.376821
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591826 Loss1: 0.194748 Loss2: 1.397079
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573962 Loss1: 0.190985 Loss2: 1.382977
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487465 Loss1: 0.109762 Loss2: 1.377703
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.175379 Loss1: 0.329420 Loss2: 1.845958
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.462510 Loss1: 0.078653 Loss2: 1.383857
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.532568 Loss1: 0.202259 Loss2: 1.330309
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.471412 Loss1: 0.131633 Loss2: 1.339779
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441640 Loss1: 0.071719 Loss2: 1.369922
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.457998 Loss1: 0.117471 Loss2: 1.340527
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422139 Loss1: 0.062777 Loss2: 1.359363
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.424137 Loss1: 0.098642 Loss2: 1.325495
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409473 Loss1: 0.051616 Loss2: 1.357857
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.415626 Loss1: 0.089721 Loss2: 1.325905
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413273 Loss1: 0.065067 Loss2: 1.348206
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.347635 Loss1: 0.036657 Loss2: 1.310977 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.337136 Loss1: 0.032140 Loss2: 1.304996 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.120732 Loss1: 0.300954 Loss2: 1.819779
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.574981 Loss1: 0.222814 Loss2: 1.352167
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551979 Loss1: 0.157740 Loss2: 1.394240
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.460316 Loss1: 0.101108 Loss2: 1.359207
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.291531 Loss1: 0.459011 Loss2: 1.832520
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.575700 Loss1: 0.238603 Loss2: 1.337097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.500258 Loss1: 0.135068 Loss2: 1.365190 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426341 Loss1: 0.082281 Loss2: 1.344060
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.433445 Loss1: 0.103121 Loss2: 1.330324
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398150 Loss1: 0.055668 Loss2: 1.342482
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.400702 Loss1: 0.078868 Loss2: 1.321834
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408801 Loss1: 0.072856 Loss2: 1.335944
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.433623 Loss1: 0.105106 Loss2: 1.328517
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.402915 Loss1: 0.079040 Loss2: 1.323875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.394837 Loss1: 0.058969 Loss2: 1.335869
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.341478 Loss1: 0.031351 Loss2: 1.310128 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.242088 Loss1: 0.370043 Loss2: 1.872045 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.567085 Loss1: 0.178121 Loss2: 1.388964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527490 Loss1: 0.150840 Loss2: 1.376650
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.259067 Loss1: 0.373545 Loss2: 1.885522
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.481810 Loss1: 0.118237 Loss2: 1.363573
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.729068 Loss1: 0.360876 Loss2: 1.368192
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461662 Loss1: 0.098331 Loss2: 1.363331
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.617078 Loss1: 0.190196 Loss2: 1.426882
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.436456 Loss1: 0.074690 Loss2: 1.361765
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.592521 Loss1: 0.210249 Loss2: 1.382272
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.429124 Loss1: 0.072898 Loss2: 1.356226
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.576929 Loss1: 0.184648 Loss2: 1.392281
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.434934 Loss1: 0.081151 Loss2: 1.353783
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463590 Loss1: 0.086521 Loss2: 1.377068
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.416840 Loss1: 0.064529 Loss2: 1.352311
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.445681 Loss1: 0.081820 Loss2: 1.363861
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.443750 Loss1: 0.081196 Loss2: 1.362554
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443538 Loss1: 0.082517 Loss2: 1.361022
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400096 Loss1: 0.043921 Loss2: 1.356175
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.292083 Loss1: 0.387287 Loss2: 1.904796
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.569666 Loss1: 0.189012 Loss2: 1.380654
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551431 Loss1: 0.168485 Loss2: 1.382946
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.514873 Loss1: 0.129353 Loss2: 1.385520
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.228004 Loss1: 0.374360 Loss2: 1.853644
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.610924 Loss1: 0.264679 Loss2: 1.346245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.596012 Loss1: 0.214656 Loss2: 1.381356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540751 Loss1: 0.171330 Loss2: 1.369421 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.473225 Loss1: 0.114551 Loss2: 1.358674 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441051 Loss1: 0.087608 Loss2: 1.353442 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.376896 Loss1: 0.038017 Loss2: 1.338879
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.402104 Loss1: 0.049730 Loss2: 1.352374
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.393008 Loss1: 0.052274 Loss2: 1.340734
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363298 Loss1: 0.028775 Loss2: 1.334523
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357449 Loss1: 0.032605 Loss2: 1.324844
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.222949 Loss1: 0.345363 Loss2: 1.877586
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.642340 Loss1: 0.255114 Loss2: 1.387226
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582147 Loss1: 0.159239 Loss2: 1.422908
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.234090 Loss1: 0.353669 Loss2: 1.880421
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534562 Loss1: 0.132713 Loss2: 1.401848
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.599867 Loss1: 0.230221 Loss2: 1.369646
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.508463 Loss1: 0.120123 Loss2: 1.388341
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.574937 Loss1: 0.180563 Loss2: 1.394374
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478489 Loss1: 0.090648 Loss2: 1.387841
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.541084 Loss1: 0.157290 Loss2: 1.383794
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.522226 Loss1: 0.133798 Loss2: 1.388428
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483022 Loss1: 0.093592 Loss2: 1.389429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.476281 Loss1: 0.090155 Loss2: 1.386126 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.476520 Loss1: 0.093454 Loss2: 1.383066 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454771 Loss1: 0.081039 Loss2: 1.373731 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.108236 Loss1: 0.261370 Loss2: 1.846866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.494805 Loss1: 0.104781 Loss2: 1.390024 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.148379 Loss1: 0.361416 Loss2: 1.786963
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.501967 Loss1: 0.119347 Loss2: 1.382620
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.566123 Loss1: 0.241865 Loss2: 1.324258
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470782 Loss1: 0.093868 Loss2: 1.376913
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.596398 Loss1: 0.237052 Loss2: 1.359347
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.512665 Loss1: 0.140424 Loss2: 1.372241
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.445783 Loss1: 0.105213 Loss2: 1.340570
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.528044 Loss1: 0.138508 Loss2: 1.389536
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.434216 Loss1: 0.112292 Loss2: 1.321925
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.522228 Loss1: 0.134357 Loss2: 1.387871
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.430606 Loss1: 0.106470 Loss2: 1.324136
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.477581 Loss1: 0.093807 Loss2: 1.383774
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.442308 Loss1: 0.123087 Loss2: 1.319221
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.458183 Loss1: 0.079192 Loss2: 1.378991
+(DefaultActor pid=3765) >> Training accuracy: 0.989258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370900 Loss1: 0.054572 Loss2: 1.316328 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.242497 Loss1: 0.424980 Loss2: 1.817517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.582577 Loss1: 0.231149 Loss2: 1.351428 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.291313 Loss1: 0.338669 Loss2: 1.952644
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534864 Loss1: 0.191162 Loss2: 1.343702
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.447964 Loss1: 0.108629 Loss2: 1.339335
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.656487 Loss1: 0.244587 Loss2: 1.411900
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.401368 Loss1: 0.078468 Loss2: 1.322899
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.580783 Loss1: 0.144838 Loss2: 1.435945
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.380913 Loss1: 0.069092 Loss2: 1.311821
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574728 Loss1: 0.157261 Loss2: 1.417467
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384692 Loss1: 0.073150 Loss2: 1.311542
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.540134 Loss1: 0.129522 Loss2: 1.410612
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374708 Loss1: 0.067563 Loss2: 1.307146
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528418 Loss1: 0.114677 Loss2: 1.413741
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371083 Loss1: 0.065505 Loss2: 1.305578
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.494159 Loss1: 0.087942 Loss2: 1.406218
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.482541 Loss1: 0.080165 Loss2: 1.402376
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479053 Loss1: 0.079764 Loss2: 1.399289
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.528438 Loss1: 0.128025 Loss2: 1.400413
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.201201 Loss1: 0.365734 Loss2: 1.835468
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.570782 Loss1: 0.224085 Loss2: 1.346697
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.497273 Loss1: 0.144966 Loss2: 1.352306
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.463982 Loss1: 0.118174 Loss2: 1.345808
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.359937 Loss1: 0.458283 Loss2: 1.901654
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.705988 Loss1: 0.311178 Loss2: 1.394811 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.678797 Loss1: 0.242486 Loss2: 1.436311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416285 Loss1: 0.081586 Loss2: 1.334698
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.570853 Loss1: 0.179268 Loss2: 1.391585
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.558040 Loss1: 0.159067 Loss2: 1.398973 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.546854 Loss1: 0.156801 Loss2: 1.390053 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390003 Loss1: 0.059745 Loss2: 1.330258
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474285 Loss1: 0.095330 Loss2: 1.378955
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449114 Loss1: 0.072970 Loss2: 1.376144
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454641 Loss1: 0.079993 Loss2: 1.374648
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427798 Loss1: 0.060309 Loss2: 1.367488
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.150076 Loss1: 0.317711 Loss2: 1.832365
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.527741 Loss1: 0.195228 Loss2: 1.332513
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.517182 Loss1: 0.179623 Loss2: 1.337559
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.480017 Loss1: 0.131365 Loss2: 1.348652
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.194202 Loss1: 0.332285 Loss2: 1.861917
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.539077 Loss1: 0.192886 Loss2: 1.346191 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.485185 Loss1: 0.127640 Loss2: 1.357546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.448127 Loss1: 0.092066 Loss2: 1.356062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450999 Loss1: 0.107835 Loss2: 1.343165 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.428052 Loss1: 0.079048 Loss2: 1.349004 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.357747 Loss1: 0.053597 Loss2: 1.304150
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.415016 Loss1: 0.067148 Loss2: 1.347868
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411097 Loss1: 0.069050 Loss2: 1.342047
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391338 Loss1: 0.054286 Loss2: 1.337052
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380139 Loss1: 0.044133 Loss2: 1.336006
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.176311 Loss1: 0.327510 Loss2: 1.848801
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.613438 Loss1: 0.274458 Loss2: 1.338980
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.571552 Loss1: 0.198288 Loss2: 1.373264
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500962 Loss1: 0.158829 Loss2: 1.342133
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.233060 Loss1: 0.357603 Loss2: 1.875456
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.623257 Loss1: 0.247743 Loss2: 1.375514 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.567476 Loss1: 0.174694 Loss2: 1.392783 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.562054 Loss1: 0.178263 Loss2: 1.383791 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.467464 Loss1: 0.091046 Loss2: 1.376418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444481 Loss1: 0.076167 Loss2: 1.368314 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373311 Loss1: 0.047721 Loss2: 1.325590
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455082 Loss1: 0.091570 Loss2: 1.363512
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435020 Loss1: 0.070413 Loss2: 1.364607
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445607 Loss1: 0.078313 Loss2: 1.367294
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.490517 Loss1: 0.127672 Loss2: 1.362845
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.221403 Loss1: 0.349570 Loss2: 1.871833
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.634087 Loss1: 0.264009 Loss2: 1.370078
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599286 Loss1: 0.182302 Loss2: 1.416984
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.537795 Loss1: 0.155702 Loss2: 1.382093
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.365969 Loss1: 0.418958 Loss2: 1.947010
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.637447 Loss1: 0.290822 Loss2: 1.346625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.536049 Loss1: 0.181690 Loss2: 1.354359 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.500240 Loss1: 0.125809 Loss2: 1.374431
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456968 Loss1: 0.095239 Loss2: 1.361729
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.466005 Loss1: 0.123014 Loss2: 1.342990
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440496 Loss1: 0.075069 Loss2: 1.365427
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.425800 Loss1: 0.068572 Loss2: 1.357228 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405943 Loss1: 0.051023 Loss2: 1.354919 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380867 Loss1: 0.051254 Loss2: 1.329612 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.185895 Loss1: 0.296738 Loss2: 1.889157
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.633312 Loss1: 0.247806 Loss2: 1.385506
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.576009 Loss1: 0.163507 Loss2: 1.412502
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.485887 Loss1: 0.105664 Loss2: 1.380222
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.128633 Loss1: 0.317722 Loss2: 1.810911
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.525118 Loss1: 0.145131 Loss2: 1.379988
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.533585 Loss1: 0.216512 Loss2: 1.317073
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473750 Loss1: 0.085485 Loss2: 1.388264
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.472829 Loss1: 0.145035 Loss2: 1.327794
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.434285 Loss1: 0.058519 Loss2: 1.375766
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.445941 Loss1: 0.117150 Loss2: 1.328790
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403341 Loss1: 0.037028 Loss2: 1.366313
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.431949 Loss1: 0.127537 Loss2: 1.304412
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408125 Loss1: 0.046837 Loss2: 1.361288
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.402287 Loss1: 0.097121 Loss2: 1.305166
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403936 Loss1: 0.050238 Loss2: 1.353698
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.382067 Loss1: 0.076858 Loss2: 1.305209
+(DefaultActor pid=3765) >> Training accuracy: 0.998958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.356999 Loss1: 0.058925 Loss2: 1.298073
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.341344 Loss1: 0.046064 Loss2: 1.295280
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.343272 Loss1: 0.052334 Loss2: 1.290939
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.190645 Loss1: 0.354841 Loss2: 1.835804
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.590111 Loss1: 0.261146 Loss2: 1.328965
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.514268 Loss1: 0.169103 Loss2: 1.345165
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.456263 Loss1: 0.107528 Loss2: 1.348734
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.208273 Loss1: 0.348168 Loss2: 1.860105
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.604209 Loss1: 0.209334 Loss2: 1.394875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.602195 Loss1: 0.192771 Loss2: 1.409424 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498950 Loss1: 0.105008 Loss2: 1.393942 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483935 Loss1: 0.095939 Loss2: 1.387995 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480039 Loss1: 0.093634 Loss2: 1.386404 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.468252 Loss1: 0.088418 Loss2: 1.379834 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.429657 Loss1: 0.052132 Loss2: 1.377525 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.183439 Loss1: 0.379020 Loss2: 1.804419 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.507927 Loss1: 0.162161 Loss2: 1.345766 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.316535 Loss1: 0.316534 Loss2: 2.000001 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.725656 Loss1: 0.269789 Loss2: 1.455867 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.676918 Loss1: 0.176426 Loss2: 1.500493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.641716 Loss1: 0.157170 Loss2: 1.484546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.596415 Loss1: 0.124124 Loss2: 1.472291 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.600200 Loss1: 0.124085 Loss2: 1.476115 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.537924 Loss1: 0.069358 Loss2: 1.468566 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.541699 Loss1: 0.078605 Loss2: 1.463094 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.511510 Loss1: 0.207530 Loss2: 1.303980 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.424808 Loss1: 0.116875 Loss2: 1.307933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.302146 Loss1: 0.335729 Loss2: 1.966417
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.403055 Loss1: 0.111206 Loss2: 1.291849
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.686548 Loss1: 0.262740 Loss2: 1.423808
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.380276 Loss1: 0.078049 Loss2: 1.302227
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.609913 Loss1: 0.167044 Loss2: 1.442869
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.341262 Loss1: 0.054238 Loss2: 1.287024
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557557 Loss1: 0.128746 Loss2: 1.428811
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.310512 Loss1: 0.031677 Loss2: 1.278835
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514566 Loss1: 0.101157 Loss2: 1.413409
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.301274 Loss1: 0.028093 Loss2: 1.273181
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477850 Loss1: 0.066685 Loss2: 1.411166
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.294684 Loss1: 0.024017 Loss2: 1.270667
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449622 Loss1: 0.049139 Loss2: 1.400484 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.456623 Loss1: 0.060461 Loss2: 1.396162 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.670261 Loss1: 0.259899 Loss2: 1.410362 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.586524 Loss1: 0.151278 Loss2: 1.435246 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.560522 Loss1: 0.149639 Loss2: 1.410884
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.281983 Loss1: 0.413645 Loss2: 1.868338
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.603073 Loss1: 0.277288 Loss2: 1.325786 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556710 Loss1: 0.197404 Loss2: 1.359306 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.457353 Loss1: 0.108649 Loss2: 1.348704 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.420058 Loss1: 0.093178 Loss2: 1.326880 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986607
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.422114 Loss1: 0.088602 Loss2: 1.333513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382607 Loss1: 0.058109 Loss2: 1.324498 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354342 Loss1: 0.041238 Loss2: 1.313104 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.474468 Loss1: 0.152052 Loss2: 1.322415 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.447050 Loss1: 0.129555 Loss2: 1.317495 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.172453 Loss1: 0.341034 Loss2: 1.831418 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.475631 Loss1: 0.152910 Loss2: 1.322721 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.494650 Loss1: 0.170743 Loss2: 1.323907 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478263 Loss1: 0.137353 Loss2: 1.340909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441367 Loss1: 0.112362 Loss2: 1.329005 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.373119 Loss1: 0.055158 Loss2: 1.317961 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.360787 Loss1: 0.054708 Loss2: 1.306078 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.327451 Loss1: 0.029328 Loss2: 1.298122
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.202800 Loss1: 0.364042 Loss2: 1.838758
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.530994 Loss1: 0.190558 Loss2: 1.340435
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.555174 Loss1: 0.194223 Loss2: 1.360951
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.502817 Loss1: 0.139186 Loss2: 1.363631
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.484914 Loss1: 0.141130 Loss2: 1.343784
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.259331 Loss1: 0.419589 Loss2: 1.839742
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.546538 Loss1: 0.180526 Loss2: 1.366012
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.549768 Loss1: 0.192675 Loss2: 1.357093
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495841 Loss1: 0.138268 Loss2: 1.357573
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.510030 Loss1: 0.153391 Loss2: 1.356639
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470371 Loss1: 0.129641 Loss2: 1.340730
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.503352 Loss1: 0.144372 Loss2: 1.358980
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.427708 Loss1: 0.077325 Loss2: 1.350383
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.463271 Loss1: 0.115514 Loss2: 1.347757
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423130 Loss1: 0.085631 Loss2: 1.337499
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.436568 Loss1: 0.084609 Loss2: 1.351959 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443987 Loss1: 0.095367 Loss2: 1.348620 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.440665 Loss1: 0.092189 Loss2: 1.348476
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.243257 Loss1: 0.390905 Loss2: 1.852352
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.518624 Loss1: 0.189070 Loss2: 1.329554
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.486036 Loss1: 0.141590 Loss2: 1.344446
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.443055 Loss1: 0.099768 Loss2: 1.343286
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.384770 Loss1: 0.056044 Loss2: 1.328725
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.460322 Loss1: 0.511631 Loss2: 1.948691
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.397903 Loss1: 0.073893 Loss2: 1.324010
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.618966 Loss1: 0.204282 Loss2: 1.414684
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.400728 Loss1: 0.080635 Loss2: 1.320093
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.606461 Loss1: 0.172032 Loss2: 1.434429
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.372630 Loss1: 0.050067 Loss2: 1.322564
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.564741 Loss1: 0.150177 Loss2: 1.414564
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409099 Loss1: 0.087695 Loss2: 1.321404
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.499478 Loss1: 0.101155 Loss2: 1.398323
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386007 Loss1: 0.062661 Loss2: 1.323347
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472863 Loss1: 0.079700 Loss2: 1.393163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.439306 Loss1: 0.051676 Loss2: 1.387630 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406531 Loss1: 0.032374 Loss2: 1.374158
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.240300 Loss1: 0.364245 Loss2: 1.876055
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.646890 Loss1: 0.285466 Loss2: 1.361424
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.688651 Loss1: 0.243344 Loss2: 1.445307
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591157 Loss1: 0.219965 Loss2: 1.371192
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.555128 Loss1: 0.173010 Loss2: 1.382119
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.120563 Loss1: 0.269771 Loss2: 1.850792
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465772 Loss1: 0.085007 Loss2: 1.380765
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.558844 Loss1: 0.208860 Loss2: 1.349984
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456833 Loss1: 0.094917 Loss2: 1.361917
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.560841 Loss1: 0.204141 Loss2: 1.356701
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467786 Loss1: 0.107877 Loss2: 1.359909
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498064 Loss1: 0.146166 Loss2: 1.351898
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459021 Loss1: 0.105597 Loss2: 1.353424
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.446381 Loss1: 0.105431 Loss2: 1.340950
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423156 Loss1: 0.069672 Loss2: 1.353484
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+DEBUG flwr 2023-10-13 13:40:19,952 | server.py:236 | fit_round 190 received 50 results and 0 failures
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.413672 Loss1: 0.076138 Loss2: 1.337534 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372529 Loss1: 0.049119 Loss2: 1.323410 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361855 Loss1: 0.043573 Loss2: 1.318282
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.307544 Loss1: 0.394128 Loss2: 1.913417
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.641432 Loss1: 0.286740 Loss2: 1.354691
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.593140 Loss1: 0.204000 Loss2: 1.389141
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527237 Loss1: 0.139744 Loss2: 1.387492
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.454912 Loss1: 0.100863 Loss2: 1.354049
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.411264 Loss1: 0.418539 Loss2: 1.992725
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.419640 Loss1: 0.069479 Loss2: 1.350161
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.394417 Loss1: 0.050859 Loss2: 1.343558 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380377 Loss1: 0.041801 Loss2: 1.338576 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374892 Loss1: 0.044742 Loss2: 1.330150 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373046 Loss1: 0.041866 Loss2: 1.331180 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411937 Loss1: 0.056981 Loss2: 1.354955 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387138 Loss1: 0.045140 Loss2: 1.341998 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.109207 Loss1: 0.245202 Loss2: 1.864005
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.616298 Loss1: 0.229478 Loss2: 1.386820
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594330 Loss1: 0.175895 Loss2: 1.418435
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535777 Loss1: 0.132127 Loss2: 1.403650
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.254784 Loss1: 0.410734 Loss2: 1.844051
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.609204 Loss1: 0.265331 Loss2: 1.343873
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.508479 Loss1: 0.113209 Loss2: 1.395270
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.555117 Loss1: 0.182782 Loss2: 1.372335
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.470780 Loss1: 0.074194 Loss2: 1.396586
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497392 Loss1: 0.152741 Loss2: 1.344651
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449637 Loss1: 0.060931 Loss2: 1.388706
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.434742 Loss1: 0.089847 Loss2: 1.344895
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436242 Loss1: 0.049368 Loss2: 1.386874
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.416856 Loss1: 0.040841 Loss2: 1.376015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.427624 Loss1: 0.058592 Loss2: 1.369031 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397243 Loss1: 0.069398 Loss2: 1.327845 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.266470 Loss1: 0.389658 Loss2: 1.876812 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.504189 Loss1: 0.157765 Loss2: 1.346424 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476218 Loss1: 0.130666 Loss2: 1.345553
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.058747 Loss1: 0.249259 Loss2: 1.809488
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.509419 Loss1: 0.174084 Loss2: 1.335335 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422562 Loss1: 0.090851 Loss2: 1.331710 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.420213 Loss1: 0.091027 Loss2: 1.329186 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384786 Loss1: 0.063632 Loss2: 1.321153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371830 Loss1: 0.053885 Loss2: 1.317945 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412741 Loss1: 0.081363 Loss2: 1.331378 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392053 Loss1: 0.061666 Loss2: 1.330387 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992647
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 13:40:19,952][flwr][DEBUG] - fit_round 190 received 50 results and 0 failures
+INFO flwr 2023-10-13 13:41:00,479 | server.py:125 | fit progress: (190, 2.3261600407167746, {'accuracy': 0.6111}, 438568.257645679)
+>> Test accuracy: 0.611100
+[2023-10-13 13:41:00,479][flwr][INFO] - fit progress: (190, 2.3261600407167746, {'accuracy': 0.6111}, 438568.257645679)
+DEBUG flwr 2023-10-13 13:41:00,479 | server.py:173 | evaluate_round 190: strategy sampled 50 clients (out of 50)
+[2023-10-13 13:41:00,479][flwr][DEBUG] - evaluate_round 190: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 13:50:04,393 | server.py:187 | evaluate_round 190 received 50 results and 0 failures
+[2023-10-13 13:50:04,393][flwr][DEBUG] - evaluate_round 190 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 13:50:04,394 | server.py:222 | fit_round 191: strategy sampled 50 clients (out of 50)
+[2023-10-13 13:50:04,394][flwr][DEBUG] - fit_round 191: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.185731 Loss1: 0.343656 Loss2: 1.842075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.506183 Loss1: 0.141428 Loss2: 1.364755 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526790 Loss1: 0.167687 Loss2: 1.359103
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.343354 Loss1: 0.420383 Loss2: 1.922971
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.496067 Loss1: 0.137933 Loss2: 1.358134
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.671872 Loss1: 0.290893 Loss2: 1.380979
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.481811 Loss1: 0.127176 Loss2: 1.354635
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.589247 Loss1: 0.177295 Loss2: 1.411952
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.588108 Loss1: 0.194631 Loss2: 1.393477
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456393 Loss1: 0.108888 Loss2: 1.347505
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545766 Loss1: 0.164876 Loss2: 1.380890
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.424139 Loss1: 0.082858 Loss2: 1.341280
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.523786 Loss1: 0.139607 Loss2: 1.384179
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398699 Loss1: 0.062403 Loss2: 1.336296
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369698 Loss1: 0.040243 Loss2: 1.329455 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427786 Loss1: 0.068946 Loss2: 1.358840 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.155278 Loss1: 0.358626 Loss2: 1.796653 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.483249 Loss1: 0.157867 Loss2: 1.325381 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496726 Loss1: 0.161334 Loss2: 1.335392
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.209604 Loss1: 0.394263 Loss2: 1.815341
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.447021 Loss1: 0.124325 Loss2: 1.322695
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.642866 Loss1: 0.317613 Loss2: 1.325253
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.439957 Loss1: 0.107320 Loss2: 1.332636
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.522256 Loss1: 0.167857 Loss2: 1.354399
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.431740 Loss1: 0.107205 Loss2: 1.324536
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.541759 Loss1: 0.205091 Loss2: 1.336668
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426173 Loss1: 0.107929 Loss2: 1.318244
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.491167 Loss1: 0.150950 Loss2: 1.340216
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400218 Loss1: 0.087359 Loss2: 1.312859
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.455020 Loss1: 0.117566 Loss2: 1.337454
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377493 Loss1: 0.065334 Loss2: 1.312159
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446647 Loss1: 0.115661 Loss2: 1.330985
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.400602 Loss1: 0.075063 Loss2: 1.325539
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395504 Loss1: 0.076972 Loss2: 1.318533
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373551 Loss1: 0.056936 Loss2: 1.316615
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.243449 Loss1: 0.367943 Loss2: 1.875505
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.618274 Loss1: 0.246875 Loss2: 1.371399
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.528773 Loss1: 0.138015 Loss2: 1.390759
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.471835 Loss1: 0.100627 Loss2: 1.371208
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.217408 Loss1: 0.375394 Loss2: 1.842014
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.568657 Loss1: 0.226433 Loss2: 1.342224 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.482912 Loss1: 0.116785 Loss2: 1.366128 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.511905 Loss1: 0.164953 Loss2: 1.346952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485660 Loss1: 0.129771 Loss2: 1.355888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.456403 Loss1: 0.105556 Loss2: 1.350847 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383159 Loss1: 0.036700 Loss2: 1.346459
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.453974 Loss1: 0.108986 Loss2: 1.344987
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.456727 Loss1: 0.104161 Loss2: 1.352565
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.449918 Loss1: 0.104602 Loss2: 1.345315
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.491639 Loss1: 0.141064 Loss2: 1.350576
+(DefaultActor pid=3764) >> Training accuracy: 0.973958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.164159 Loss1: 0.303922 Loss2: 1.860237
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.653510 Loss1: 0.283526 Loss2: 1.369985
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.586558 Loss1: 0.180866 Loss2: 1.405693
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496215 Loss1: 0.119512 Loss2: 1.376702
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.142777 Loss1: 0.290779 Loss2: 1.851998
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.571433 Loss1: 0.222435 Loss2: 1.348998 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.555614 Loss1: 0.195101 Loss2: 1.360513 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.542672 Loss1: 0.182654 Loss2: 1.360018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.473164 Loss1: 0.126420 Loss2: 1.346745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.437942 Loss1: 0.094127 Loss2: 1.343815 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370962 Loss1: 0.030666 Loss2: 1.340295
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439966 Loss1: 0.097654 Loss2: 1.342312
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397209 Loss1: 0.060900 Loss2: 1.336309
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.388050 Loss1: 0.054423 Loss2: 1.333627
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376267 Loss1: 0.052184 Loss2: 1.324084
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.232607 Loss1: 0.343528 Loss2: 1.889079
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.637475 Loss1: 0.253854 Loss2: 1.383621
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.552873 Loss1: 0.145094 Loss2: 1.407779
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.471965 Loss1: 0.080924 Loss2: 1.391040
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.329185 Loss1: 0.381328 Loss2: 1.947857
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.454505 Loss1: 0.076521 Loss2: 1.377984
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.671924 Loss1: 0.228066 Loss2: 1.443858
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.414652 Loss1: 0.038356 Loss2: 1.376296
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.625627 Loss1: 0.178403 Loss2: 1.447224
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.412444 Loss1: 0.044788 Loss2: 1.367656
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.598711 Loss1: 0.153274 Loss2: 1.445438
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434600 Loss1: 0.076826 Loss2: 1.357774
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.546720 Loss1: 0.115735 Loss2: 1.430985
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413684 Loss1: 0.050914 Loss2: 1.362770
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.537741 Loss1: 0.111007 Loss2: 1.426734
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.413698 Loss1: 0.048914 Loss2: 1.364784
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.504183 Loss1: 0.079911 Loss2: 1.424273
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.492774 Loss1: 0.081373 Loss2: 1.411402
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.481837 Loss1: 0.074449 Loss2: 1.407387
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.476820 Loss1: 0.068289 Loss2: 1.408532
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.353324 Loss1: 0.490901 Loss2: 1.862423
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.642022 Loss1: 0.301701 Loss2: 1.340321
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.564750 Loss1: 0.205458 Loss2: 1.359292
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.484206 Loss1: 0.141064 Loss2: 1.343142
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.345673 Loss1: 0.394332 Loss2: 1.951341
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.764197 Loss1: 0.325659 Loss2: 1.438538 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.728655 Loss1: 0.202790 Loss2: 1.525865 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.605805 Loss1: 0.166052 Loss2: 1.439753 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.600584 Loss1: 0.153637 Loss2: 1.446948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.572090 Loss1: 0.123211 Loss2: 1.448879 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.512508 Loss1: 0.083000 Loss2: 1.429508 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454747 Loss1: 0.037061 Loss2: 1.417686 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.600584 Loss1: 0.253481 Loss2: 1.347103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.473253 Loss1: 0.118409 Loss2: 1.354844 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.250370 Loss1: 0.380760 Loss2: 1.869610
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.423914 Loss1: 0.084629 Loss2: 1.339285
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.578313 Loss1: 0.222402 Loss2: 1.355911
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416067 Loss1: 0.072036 Loss2: 1.344031
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.519653 Loss1: 0.153591 Loss2: 1.366062
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.402298 Loss1: 0.066939 Loss2: 1.335359
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487372 Loss1: 0.122598 Loss2: 1.364774
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410653 Loss1: 0.075460 Loss2: 1.335193
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483019 Loss1: 0.132577 Loss2: 1.350442
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374218 Loss1: 0.041282 Loss2: 1.332936
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484371 Loss1: 0.127516 Loss2: 1.356855
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371606 Loss1: 0.046514 Loss2: 1.325091
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445144 Loss1: 0.097839 Loss2: 1.347305 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408916 Loss1: 0.067272 Loss2: 1.341643 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.672022 Loss1: 0.279366 Loss2: 1.392656 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.552790 Loss1: 0.150450 Loss2: 1.402340 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.226336 Loss1: 0.401327 Loss2: 1.825009
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.496783 Loss1: 0.097166 Loss2: 1.399617
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.522605 Loss1: 0.192546 Loss2: 1.330059
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.464559 Loss1: 0.076299 Loss2: 1.388260
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.523930 Loss1: 0.192403 Loss2: 1.331527
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.474941 Loss1: 0.095309 Loss2: 1.379632
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.475738 Loss1: 0.136553 Loss2: 1.339185
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.456554 Loss1: 0.072362 Loss2: 1.384192
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.443731 Loss1: 0.112120 Loss2: 1.331611
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431870 Loss1: 0.050802 Loss2: 1.381068
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.420753 Loss1: 0.093273 Loss2: 1.327480
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.448914 Loss1: 0.073172 Loss2: 1.375742
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.422197 Loss1: 0.097554 Loss2: 1.324643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.364049 Loss1: 0.047964 Loss2: 1.316084 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.565575 Loss1: 0.230179 Loss2: 1.335396 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.577121 Loss1: 0.227249 Loss2: 1.349872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.550382 Loss1: 0.180633 Loss2: 1.369749 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.519585 Loss1: 0.173174 Loss2: 1.346411 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.514528 Loss1: 0.150573 Loss2: 1.363955 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.472651 Loss1: 0.129280 Loss2: 1.343371 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440237 Loss1: 0.100366 Loss2: 1.339870 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421427 Loss1: 0.086151 Loss2: 1.335276 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473344 Loss1: 0.094948 Loss2: 1.378396 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.282992 Loss1: 0.444576 Loss2: 1.838417 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.598856 Loss1: 0.213983 Loss2: 1.384873 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.495941 Loss1: 0.174665 Loss2: 1.321276
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.328719 Loss1: 0.411148 Loss2: 1.917571
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.425116 Loss1: 0.103432 Loss2: 1.321684
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.540217 Loss1: 0.215753 Loss2: 1.324464
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.455299 Loss1: 0.138210 Loss2: 1.317089
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.418415 Loss1: 0.105208 Loss2: 1.313207
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.396986 Loss1: 0.083498 Loss2: 1.313488 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.392213 Loss1: 0.079276 Loss2: 1.312937 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.363380 Loss1: 0.056336 Loss2: 1.307045 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.334142 Loss1: 0.031660 Loss2: 1.302482 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352571 Loss1: 0.053811 Loss2: 1.298760 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.206917 Loss1: 0.407430 Loss2: 1.799488
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.616762 Loss1: 0.280900 Loss2: 1.335862
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.511298 Loss1: 0.144479 Loss2: 1.366819
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.210359 Loss1: 0.313716 Loss2: 1.896643
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498289 Loss1: 0.159830 Loss2: 1.338460
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.562803 Loss1: 0.190352 Loss2: 1.372451
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.477824 Loss1: 0.134024 Loss2: 1.343801
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.525595 Loss1: 0.151380 Loss2: 1.374214
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.446734 Loss1: 0.090844 Loss2: 1.355890
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.487890 Loss1: 0.104452 Loss2: 1.383438
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.399893 Loss1: 0.070013 Loss2: 1.329881
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389414 Loss1: 0.058206 Loss2: 1.331208 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372380 Loss1: 0.045709 Loss2: 1.326671 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355467 Loss1: 0.036379 Loss2: 1.319088 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.393084 Loss1: 0.035991 Loss2: 1.357093 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.348305 Loss1: 0.405924 Loss2: 1.942381 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.659050 Loss1: 0.221230 Loss2: 1.437820 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.553851 Loss1: 0.142293 Loss2: 1.411558
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.149847 Loss1: 0.315692 Loss2: 1.834155
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.639674 Loss1: 0.244337 Loss2: 1.395336
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.549320 Loss1: 0.207905 Loss2: 1.341415
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.545076 Loss1: 0.138137 Loss2: 1.406939
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.516093 Loss1: 0.170824 Loss2: 1.345269
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.512817 Loss1: 0.124822 Loss2: 1.387994
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.472606 Loss1: 0.130892 Loss2: 1.341714
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.486645 Loss1: 0.101989 Loss2: 1.384656
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.458835 Loss1: 0.123063 Loss2: 1.335772
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.483168 Loss1: 0.092861 Loss2: 1.390307
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.448542 Loss1: 0.117269 Loss2: 1.331273
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.479998 Loss1: 0.096487 Loss2: 1.383511
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.388035 Loss1: 0.061527 Loss2: 1.326508
+(DefaultActor pid=3765) >> Training accuracy: 0.968750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445677 Loss1: 0.117141 Loss2: 1.328536
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399740 Loss1: 0.073783 Loss2: 1.325957
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434411 Loss1: 0.112373 Loss2: 1.322038
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.392461 Loss1: 0.444632 Loss2: 1.947829
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.606282 Loss1: 0.281277 Loss2: 1.325005
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.580058 Loss1: 0.244003 Loss2: 1.336054
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497574 Loss1: 0.137212 Loss2: 1.360362
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.449197 Loss1: 0.104781 Loss2: 1.344416
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442713 Loss1: 0.109362 Loss2: 1.333351 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414894 Loss1: 0.078480 Loss2: 1.336414 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.478459 Loss1: 0.143232 Loss2: 1.335227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421041 Loss1: 0.083977 Loss2: 1.337064
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.423535 Loss1: 0.093145 Loss2: 1.330390
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384486 Loss1: 0.051922 Loss2: 1.332563
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.436271 Loss1: 0.117350 Loss2: 1.318921
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.405811 Loss1: 0.086296 Loss2: 1.319515
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.398422 Loss1: 0.085707 Loss2: 1.312715
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399905 Loss1: 0.084962 Loss2: 1.314944
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.372123 Loss1: 0.058475 Loss2: 1.313648
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.276064 Loss1: 0.362263 Loss2: 1.913801
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354505 Loss1: 0.046421 Loss2: 1.308085
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.603337 Loss1: 0.177384 Loss2: 1.425952 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488225 Loss1: 0.086307 Loss2: 1.401918 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489432 Loss1: 0.090242 Loss2: 1.399190
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.197751 Loss1: 0.421509 Loss2: 1.776241
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.561605 Loss1: 0.264784 Loss2: 1.296822 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.538397 Loss1: 0.190772 Loss2: 1.347625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.423429 Loss1: 0.115462 Loss2: 1.307968 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.452323 Loss1: 0.059389 Loss2: 1.392934
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.462936 Loss1: 0.165743 Loss2: 1.297194
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447207 Loss1: 0.137802 Loss2: 1.309405
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.417620 Loss1: 0.111050 Loss2: 1.306570
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368163 Loss1: 0.070542 Loss2: 1.297621
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386086 Loss1: 0.088832 Loss2: 1.297254
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.246510 Loss1: 0.370753 Loss2: 1.875757
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.359458 Loss1: 0.071591 Loss2: 1.287868
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.612247 Loss1: 0.191308 Loss2: 1.420938 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.476753 Loss1: 0.097767 Loss2: 1.378986 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502056 Loss1: 0.112902 Loss2: 1.389154
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.058582 Loss1: 0.259075 Loss2: 1.799507
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.561758 Loss1: 0.237938 Loss2: 1.323820 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.538216 Loss1: 0.178287 Loss2: 1.359929 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.467906 Loss1: 0.132655 Loss2: 1.335250 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.407172 Loss1: 0.077127 Loss2: 1.330046 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368133 Loss1: 0.053931 Loss2: 1.314202 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.359784 Loss1: 0.049511 Loss2: 1.310273
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.242909 Loss1: 0.408035 Loss2: 1.834874
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356100 Loss1: 0.041552 Loss2: 1.314548
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.647963 Loss1: 0.256960 Loss2: 1.391003
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.580678 Loss1: 0.177202 Loss2: 1.403476
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.494355 Loss1: 0.114472 Loss2: 1.379883
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.458143 Loss1: 0.080680 Loss2: 1.377463
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444875 Loss1: 0.078684 Loss2: 1.366191
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.346110 Loss1: 0.422434 Loss2: 1.923676
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.662583 Loss1: 0.289299 Loss2: 1.373284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.557479 Loss1: 0.153462 Loss2: 1.404016 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413139 Loss1: 0.059542 Loss2: 1.353598
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.483195 Loss1: 0.102529 Loss2: 1.380666
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441203 Loss1: 0.079393 Loss2: 1.361810
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423139 Loss1: 0.073521 Loss2: 1.349618
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.417233 Loss1: 0.052159 Loss2: 1.365074 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431437 Loss1: 0.084440 Loss2: 1.346997 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393205 Loss1: 0.038885 Loss2: 1.354320
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.126611 Loss1: 0.344129 Loss2: 1.782481
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.514445 Loss1: 0.209619 Loss2: 1.304826
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.467335 Loss1: 0.147317 Loss2: 1.320019
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.427007 Loss1: 0.122509 Loss2: 1.304498
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.458060 Loss1: 0.146534 Loss2: 1.311526
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.114636 Loss1: 0.321506 Loss2: 1.793130
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.601651 Loss1: 0.300803 Loss2: 1.300848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.445900 Loss1: 0.122912 Loss2: 1.322988 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.401395 Loss1: 0.097507 Loss2: 1.303888 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.370110 Loss1: 0.080172 Loss2: 1.289938 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401694 Loss1: 0.092808 Loss2: 1.308886
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.355218 Loss1: 0.062370 Loss2: 1.292847
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.351190 Loss1: 0.063207 Loss2: 1.287983
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.363315 Loss1: 0.074003 Loss2: 1.289312
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.333561 Loss1: 0.045317 Loss2: 1.288244
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.330497 Loss1: 0.051828 Loss2: 1.278669
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.285904 Loss1: 0.358251 Loss2: 1.927652
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.622191 Loss1: 0.217270 Loss2: 1.404921
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.546154 Loss1: 0.116795 Loss2: 1.429358
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.501474 Loss1: 0.089542 Loss2: 1.411932
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.467236 Loss1: 0.067902 Loss2: 1.399334
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.301769 Loss1: 0.409422 Loss2: 1.892347
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.442241 Loss1: 0.047338 Loss2: 1.394903
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.660416 Loss1: 0.276612 Loss2: 1.383804
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.437463 Loss1: 0.051205 Loss2: 1.386258
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.609270 Loss1: 0.205386 Loss2: 1.403884
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415425 Loss1: 0.038344 Loss2: 1.377081
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521301 Loss1: 0.136333 Loss2: 1.384968
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.419339 Loss1: 0.044941 Loss2: 1.374398
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.547915 Loss1: 0.159447 Loss2: 1.388467
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430042 Loss1: 0.051526 Loss2: 1.378515
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491341 Loss1: 0.111127 Loss2: 1.380214
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438628 Loss1: 0.067216 Loss2: 1.371412
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450523 Loss1: 0.081004 Loss2: 1.369519
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416250 Loss1: 0.051124 Loss2: 1.365125
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397270 Loss1: 0.041443 Loss2: 1.355827
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.206636 Loss1: 0.330213 Loss2: 1.876423
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.574341 Loss1: 0.180168 Loss2: 1.394173
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.506447 Loss1: 0.105553 Loss2: 1.400895
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.485563 Loss1: 0.101498 Loss2: 1.384065
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.029182 Loss1: 0.277849 Loss2: 1.751333
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.502230 Loss1: 0.200629 Loss2: 1.301602 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.527190 Loss1: 0.197505 Loss2: 1.329684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.452094 Loss1: 0.141241 Loss2: 1.310853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.432749 Loss1: 0.120669 Loss2: 1.312080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.462888 Loss1: 0.150471 Loss2: 1.312417 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.444993 Loss1: 0.129487 Loss2: 1.315506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.464224 Loss1: 0.139957 Loss2: 1.324267 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.201808 Loss1: 0.346115 Loss2: 1.855694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551252 Loss1: 0.178479 Loss2: 1.372773 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.215479 Loss1: 0.348402 Loss2: 1.867077 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452260 Loss1: 0.102548 Loss2: 1.349712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404240 Loss1: 0.064169 Loss2: 1.340072 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.399114 Loss1: 0.059153 Loss2: 1.339961 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405311 Loss1: 0.069972 Loss2: 1.335338 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403282 Loss1: 0.070717 Loss2: 1.332565 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413481 Loss1: 0.060369 Loss2: 1.353111 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.468921 Loss1: 0.111311 Loss2: 1.357610 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.570759 Loss1: 0.233152 Loss2: 1.337606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.439165 Loss1: 0.109231 Loss2: 1.329934 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.412929 Loss1: 0.091381 Loss2: 1.321549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440431 Loss1: 0.110226 Loss2: 1.330205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.388484 Loss1: 0.070118 Loss2: 1.318366 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.416042 Loss1: 0.098688 Loss2: 1.317354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.373923 Loss1: 0.055766 Loss2: 1.318156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.360131 Loss1: 0.046973 Loss2: 1.313158 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366645 Loss1: 0.052456 Loss2: 1.314189 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.101001 Loss1: 0.252089 Loss2: 1.848912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.489639 Loss1: 0.129902 Loss2: 1.359737 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.135269 Loss1: 0.293715 Loss2: 1.841554
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.501833 Loss1: 0.130311 Loss2: 1.371522
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.481446 Loss1: 0.157387 Loss2: 1.324059
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531081 Loss1: 0.171104 Loss2: 1.359977
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.482269 Loss1: 0.150039 Loss2: 1.332230
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.634851 Loss1: 0.235125 Loss2: 1.399726
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.458574 Loss1: 0.112712 Loss2: 1.345862
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.563951 Loss1: 0.189503 Loss2: 1.374447
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.501800 Loss1: 0.133188 Loss2: 1.368611 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.448854 Loss1: 0.085218 Loss2: 1.363636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434038 Loss1: 0.077429 Loss2: 1.356609 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.445831 Loss1: 0.102191 Loss2: 1.343640 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.135398 Loss1: 0.330123 Loss2: 1.805276 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.538032 Loss1: 0.176413 Loss2: 1.361619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.457484 Loss1: 0.116496 Loss2: 1.340988
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.329944 Loss1: 0.453767 Loss2: 1.876176
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.447553 Loss1: 0.122825 Loss2: 1.324728
+DEBUG flwr 2023-10-13 14:19:11,361 | server.py:236 | fit_round 191 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.583984 Loss1: 0.225862 Loss2: 1.358123
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.434985 Loss1: 0.102898 Loss2: 1.332088
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.549633 Loss1: 0.191636 Loss2: 1.357998
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404334 Loss1: 0.075359 Loss2: 1.328974
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.494982 Loss1: 0.141574 Loss2: 1.353408
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.396319 Loss1: 0.068201 Loss2: 1.328118
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.443239 Loss1: 0.107447 Loss2: 1.335792
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406977 Loss1: 0.083850 Loss2: 1.323128
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429403 Loss1: 0.097437 Loss2: 1.331966
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.395772 Loss1: 0.074484 Loss2: 1.321288
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.411165 Loss1: 0.080362 Loss2: 1.330803
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.397559 Loss1: 0.069108 Loss2: 1.328451
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.378156 Loss1: 0.045410 Loss2: 1.332746
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351762 Loss1: 0.031698 Loss2: 1.320064
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.037145 Loss1: 0.247587 Loss2: 1.789558
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.473547 Loss1: 0.162982 Loss2: 1.310565
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.448649 Loss1: 0.119235 Loss2: 1.329414
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.104873 Loss1: 0.263363 Loss2: 1.841509
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.409596 Loss1: 0.088989 Loss2: 1.320607
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.576749 Loss1: 0.198371 Loss2: 1.378378
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.373185 Loss1: 0.065416 Loss2: 1.307769
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.360747 Loss1: 0.061552 Loss2: 1.299195 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.349069 Loss1: 0.050800 Loss2: 1.298269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.358976 Loss1: 0.063309 Loss2: 1.295668 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.327407 Loss1: 0.035969 Loss2: 1.291438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.316557 Loss1: 0.030781 Loss2: 1.285777 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991728
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435154 Loss1: 0.069081 Loss2: 1.366073 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.253335 Loss1: 0.378090 Loss2: 1.875244 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.511269 Loss1: 0.181909 Loss2: 1.329360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.218575 Loss1: 0.396634 Loss2: 1.821941 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.439055 Loss1: 0.117796 Loss2: 1.321259 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406835 Loss1: 0.087951 Loss2: 1.318884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.390186 Loss1: 0.076004 Loss2: 1.314183 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.378266 Loss1: 0.065702 Loss2: 1.312564 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.385693 Loss1: 0.075184 Loss2: 1.310509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422131 Loss1: 0.098980 Loss2: 1.323150 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373170 Loss1: 0.055162 Loss2: 1.318009 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 14:19:11,361][flwr][DEBUG] - fit_round 191 received 50 results and 0 failures
+INFO flwr 2023-10-13 14:19:51,945 | server.py:125 | fit progress: (191, 2.3261689364719698, {'accuracy': 0.6113}, 440899.72395899397)
+>> Test accuracy: 0.611300
+[2023-10-13 14:19:51,945][flwr][INFO] - fit progress: (191, 2.3261689364719698, {'accuracy': 0.6113}, 440899.72395899397)
+DEBUG flwr 2023-10-13 14:19:51,946 | server.py:173 | evaluate_round 191: strategy sampled 50 clients (out of 50)
+[2023-10-13 14:19:51,946][flwr][DEBUG] - evaluate_round 191: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 14:28:58,152 | server.py:187 | evaluate_round 191 received 50 results and 0 failures
+[2023-10-13 14:28:58,152][flwr][DEBUG] - evaluate_round 191 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 14:28:58,152 | server.py:222 | fit_round 192: strategy sampled 50 clients (out of 50)
+[2023-10-13 14:28:58,152][flwr][DEBUG] - fit_round 192: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.133827 Loss1: 0.283427 Loss2: 1.850400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.506198 Loss1: 0.160782 Loss2: 1.345416 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.556869 Loss1: 0.193128 Loss2: 1.363741
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.239938 Loss1: 0.334456 Loss2: 1.905482
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473991 Loss1: 0.131300 Loss2: 1.342691
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.636476 Loss1: 0.256199 Loss2: 1.380277
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444761 Loss1: 0.104344 Loss2: 1.340417
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.562786 Loss1: 0.173795 Loss2: 1.388992
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416907 Loss1: 0.081746 Loss2: 1.335161
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.561019 Loss1: 0.175868 Loss2: 1.385152
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398878 Loss1: 0.064512 Loss2: 1.334366
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524190 Loss1: 0.150504 Loss2: 1.373685
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.392693 Loss1: 0.065919 Loss2: 1.326774
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460558 Loss1: 0.088303 Loss2: 1.372255
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390155 Loss1: 0.069097 Loss2: 1.321058
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437996 Loss1: 0.073042 Loss2: 1.364954
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437098 Loss1: 0.075178 Loss2: 1.361921
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410529 Loss1: 0.048643 Loss2: 1.361886
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421499 Loss1: 0.062016 Loss2: 1.359484
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.103124 Loss1: 0.268575 Loss2: 1.834548
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.571464 Loss1: 0.214140 Loss2: 1.357324
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.594260 Loss1: 0.203700 Loss2: 1.390560
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.217348 Loss1: 0.316307 Loss2: 1.901040
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.505292 Loss1: 0.130018 Loss2: 1.375274
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.620074 Loss1: 0.195486 Loss2: 1.424588
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.456059 Loss1: 0.094831 Loss2: 1.361227
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.438388 Loss1: 0.082218 Loss2: 1.356170 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.433765 Loss1: 0.072441 Loss2: 1.361324 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.437044 Loss1: 0.077943 Loss2: 1.359102 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.408659 Loss1: 0.055238 Loss2: 1.353421 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398028 Loss1: 0.053336 Loss2: 1.344692 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991728
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.497301 Loss1: 0.090009 Loss2: 1.407292 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.171909 Loss1: 0.257753 Loss2: 1.914156 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.544681 Loss1: 0.143886 Loss2: 1.400795 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555140 Loss1: 0.147777 Loss2: 1.407363
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.113823 Loss1: 0.323004 Loss2: 1.790818
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.481857 Loss1: 0.179288 Loss2: 1.302569 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.423408 Loss1: 0.117190 Loss2: 1.306218 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.393280 Loss1: 0.087045 Loss2: 1.306234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.356356 Loss1: 0.064974 Loss2: 1.291383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.348160 Loss1: 0.059862 Loss2: 1.288297 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.389919 Loss1: 0.024077 Loss2: 1.365843
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.332281 Loss1: 0.046460 Loss2: 1.285821
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.318408 Loss1: 0.035868 Loss2: 1.282540
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.312133 Loss1: 0.033778 Loss2: 1.278355
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.290609 Loss1: 0.021580 Loss2: 1.269029
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.135900 Loss1: 0.312314 Loss2: 1.823586
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.517011 Loss1: 0.207449 Loss2: 1.309562
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.502989 Loss1: 0.177981 Loss2: 1.325008
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.460752 Loss1: 0.131732 Loss2: 1.329020
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.220248 Loss1: 0.386659 Loss2: 1.833589
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.426951 Loss1: 0.112086 Loss2: 1.314865
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.502783 Loss1: 0.190713 Loss2: 1.312070
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.378692 Loss1: 0.069742 Loss2: 1.308950
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.516689 Loss1: 0.208299 Loss2: 1.308391
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.423118 Loss1: 0.103458 Loss2: 1.319660
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.343130 Loss1: 0.046146 Loss2: 1.296984
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.397014 Loss1: 0.099840 Loss2: 1.297173
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.323089 Loss1: 0.029470 Loss2: 1.293619
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.380421 Loss1: 0.091623 Loss2: 1.288798
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.333730 Loss1: 0.044804 Loss2: 1.288926
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.342966 Loss1: 0.055114 Loss2: 1.287852 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.359002 Loss1: 0.070139 Loss2: 1.288863 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.215378 Loss1: 0.366954 Loss2: 1.848424 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.536505 Loss1: 0.163604 Loss2: 1.372901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.518452 Loss1: 0.162760 Loss2: 1.355692
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.307895 Loss1: 0.425829 Loss2: 1.882066
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.652111 Loss1: 0.274448 Loss2: 1.377663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.574540 Loss1: 0.181090 Loss2: 1.393450 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540381 Loss1: 0.149819 Loss2: 1.390562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.502077 Loss1: 0.130059 Loss2: 1.372017 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.491096 Loss1: 0.123118 Loss2: 1.367978 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370645 Loss1: 0.048013 Loss2: 1.322632
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434600 Loss1: 0.065250 Loss2: 1.369350
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.476911 Loss1: 0.114206 Loss2: 1.362705
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.438866 Loss1: 0.073688 Loss2: 1.365178
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.447667 Loss1: 0.087816 Loss2: 1.359851
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.218505 Loss1: 0.391529 Loss2: 1.826976
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.668555 Loss1: 0.310701 Loss2: 1.357854
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.557824 Loss1: 0.167720 Loss2: 1.390104
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520258 Loss1: 0.166003 Loss2: 1.354255
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.097353 Loss1: 0.307483 Loss2: 1.789870
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.516271 Loss1: 0.189172 Loss2: 1.327099 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.447275 Loss1: 0.119355 Loss2: 1.327920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.457534 Loss1: 0.134439 Loss2: 1.323096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.428696 Loss1: 0.101453 Loss2: 1.327243 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.415962 Loss1: 0.092257 Loss2: 1.323705 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419178 Loss1: 0.102713 Loss2: 1.316465 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392560 Loss1: 0.079223 Loss2: 1.313337 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.629324 Loss1: 0.277231 Loss2: 1.352094 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.545900 Loss1: 0.197282 Loss2: 1.348618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.160261 Loss1: 0.336717 Loss2: 1.823544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.540252 Loss1: 0.216027 Loss2: 1.324225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.493232 Loss1: 0.159670 Loss2: 1.333562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.411273 Loss1: 0.079844 Loss2: 1.331429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.415379 Loss1: 0.095477 Loss2: 1.319901 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988839
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.387976 Loss1: 0.075636 Loss2: 1.312340 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.344106 Loss1: 0.038163 Loss2: 1.305943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.330363 Loss1: 0.032338 Loss2: 1.298025
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.330669 Loss1: 0.395390 Loss2: 1.935279
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.662919 Loss1: 0.292349 Loss2: 1.370570
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.642157 Loss1: 0.244222 Loss2: 1.397935
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.565810 Loss1: 0.162543 Loss2: 1.403267
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503860 Loss1: 0.129959 Loss2: 1.373901
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.513220 Loss1: 0.129953 Loss2: 1.383267
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.532314 Loss1: 0.500122 Loss2: 2.032193
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.711146 Loss1: 0.337727 Loss2: 1.373419
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461122 Loss1: 0.079379 Loss2: 1.381742
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.479074 Loss1: 0.111681 Loss2: 1.367394 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444125 Loss1: 0.069187 Loss2: 1.374938 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417426 Loss1: 0.053396 Loss2: 1.364029 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998884
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.432158 Loss1: 0.059084 Loss2: 1.373074 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427597 Loss1: 0.072956 Loss2: 1.354641 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996094
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.226298 Loss1: 0.407646 Loss2: 1.818652
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.592529 Loss1: 0.244503 Loss2: 1.348026
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526101 Loss1: 0.160887 Loss2: 1.365214
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524092 Loss1: 0.171061 Loss2: 1.353031
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.171577 Loss1: 0.366083 Loss2: 1.805494
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.531328 Loss1: 0.211488 Loss2: 1.319840 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.521312 Loss1: 0.176986 Loss2: 1.344326 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.458816 Loss1: 0.130369 Loss2: 1.328447 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.481418 Loss1: 0.156128 Loss2: 1.325290 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463977 Loss1: 0.123750 Loss2: 1.340227 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.443451 Loss1: 0.114197 Loss2: 1.329254 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396941 Loss1: 0.071745 Loss2: 1.325196 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.605227 Loss1: 0.371872 Loss2: 2.233355 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.872480 Loss1: 0.163146 Loss2: 1.709334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.827906 Loss1: 0.165682 Loss2: 1.662224
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.275373 Loss1: 0.389273 Loss2: 1.886100
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.594441 Loss1: 0.220740 Loss2: 1.373701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.559673 Loss1: 0.156303 Loss2: 1.403370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.501211 Loss1: 0.113913 Loss2: 1.387298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493327 Loss1: 0.114406 Loss2: 1.378921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501327 Loss1: 0.117841 Loss2: 1.383486 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.672461 Loss1: 0.040152 Loss2: 1.632309
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.491963 Loss1: 0.114492 Loss2: 1.377471
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.498993 Loss1: 0.121806 Loss2: 1.377188
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.480357 Loss1: 0.098795 Loss2: 1.381562
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.494482 Loss1: 0.108938 Loss2: 1.385544
+(DefaultActor pid=3764) >> Training accuracy: 0.964583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.222292 Loss1: 0.380587 Loss2: 1.841705
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.620708 Loss1: 0.282126 Loss2: 1.338582
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.563309 Loss1: 0.196813 Loss2: 1.366496
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.537538 Loss1: 0.178066 Loss2: 1.359473
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.205876 Loss1: 0.340850 Loss2: 1.865026
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.590049 Loss1: 0.234260 Loss2: 1.355789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.552730 Loss1: 0.180077 Loss2: 1.372653 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.482318 Loss1: 0.134272 Loss2: 1.348047 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450409 Loss1: 0.106119 Loss2: 1.344290 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.447409 Loss1: 0.100124 Loss2: 1.347285 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.423055 Loss1: 0.081593 Loss2: 1.341462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433199 Loss1: 0.095753 Loss2: 1.337446 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.260166 Loss1: 0.339806 Loss2: 1.920360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.517107 Loss1: 0.130880 Loss2: 1.386227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.130621 Loss1: 0.304383 Loss2: 1.826238 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.564738 Loss1: 0.194556 Loss2: 1.370182 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.483327 Loss1: 0.105583 Loss2: 1.377745 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.480501 Loss1: 0.100826 Loss2: 1.379675 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.433638 Loss1: 0.071338 Loss2: 1.362300 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374907 Loss1: 0.028537 Loss2: 1.346370 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389942 Loss1: 0.042154 Loss2: 1.347788 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.371300 Loss1: 0.029330 Loss2: 1.341970 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.521144 Loss1: 0.215264 Loss2: 1.305879 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.437613 Loss1: 0.119244 Loss2: 1.318369 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.393594 Loss1: 0.096935 Loss2: 1.296659
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.288089 Loss1: 0.360351 Loss2: 1.927739
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.368093 Loss1: 0.073887 Loss2: 1.294206
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.638444 Loss1: 0.203551 Loss2: 1.434893
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.380547 Loss1: 0.095113 Loss2: 1.285434
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605990 Loss1: 0.167682 Loss2: 1.438307
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.574899 Loss1: 0.151260 Loss2: 1.423639 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522431 Loss1: 0.111717 Loss2: 1.410714 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.372379 Loss1: 0.086658 Loss2: 1.285721
+(DefaultActor pid=3765) >> Training accuracy: 0.965625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.517601 Loss1: 0.102630 Loss2: 1.414970
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.542683 Loss1: 0.135226 Loss2: 1.407457
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487706 Loss1: 0.074820 Loss2: 1.412887
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485682 Loss1: 0.087876 Loss2: 1.397807
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.512452 Loss1: 0.108690 Loss2: 1.403762
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.284428 Loss1: 0.346025 Loss2: 1.938404
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.734292 Loss1: 0.298462 Loss2: 1.435830
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.577350 Loss1: 0.115680 Loss2: 1.461670
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.554485 Loss1: 0.126101 Loss2: 1.428384
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531816 Loss1: 0.108764 Loss2: 1.423052
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.276458 Loss1: 0.412574 Loss2: 1.863884
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.602290 Loss1: 0.247798 Loss2: 1.354492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.538151 Loss1: 0.165548 Loss2: 1.372603 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.469244 Loss1: 0.101760 Loss2: 1.367484 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.430074 Loss1: 0.083207 Loss2: 1.346866 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.435873 Loss1: 0.085709 Loss2: 1.350164 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.453536 Loss1: 0.107870 Loss2: 1.345666 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406776 Loss1: 0.068034 Loss2: 1.338742 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.615551 Loss1: 0.257777 Loss2: 1.357774 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.514501 Loss1: 0.138272 Loss2: 1.376229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507824 Loss1: 0.152260 Loss2: 1.355563
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.224196 Loss1: 0.393037 Loss2: 1.831159
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.597655 Loss1: 0.256636 Loss2: 1.341020 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.500050 Loss1: 0.158616 Loss2: 1.341433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.452607 Loss1: 0.098883 Loss2: 1.353724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469829 Loss1: 0.135065 Loss2: 1.334764 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485448 Loss1: 0.128782 Loss2: 1.356666
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464421 Loss1: 0.123530 Loss2: 1.340891
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438211 Loss1: 0.104221 Loss2: 1.333989
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414599 Loss1: 0.081488 Loss2: 1.333111
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.422061 Loss1: 0.088228 Loss2: 1.333833
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378985 Loss1: 0.052260 Loss2: 1.326724
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.067386 Loss1: 0.266859 Loss2: 1.800527
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.499115 Loss1: 0.195689 Loss2: 1.303427
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.431316 Loss1: 0.120736 Loss2: 1.310580
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.405915 Loss1: 0.098764 Loss2: 1.307150
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.393228 Loss1: 0.095286 Loss2: 1.297941
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.438354 Loss1: 0.431068 Loss2: 2.007286
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.662284 Loss1: 0.267945 Loss2: 1.394340
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.379279 Loss1: 0.081360 Loss2: 1.297919
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.677881 Loss1: 0.278875 Loss2: 1.399006
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.338076 Loss1: 0.047018 Loss2: 1.291057
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.328029 Loss1: 0.041547 Loss2: 1.286482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.333508 Loss1: 0.049920 Loss2: 1.283588 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.304307 Loss1: 0.023450 Loss2: 1.280856 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.424269 Loss1: 0.056750 Loss2: 1.367519 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991587
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.215571 Loss1: 0.345908 Loss2: 1.869664 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.549875 Loss1: 0.169787 Loss2: 1.380089 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.486246 Loss1: 0.107924 Loss2: 1.378322
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.067085 Loss1: 0.311703 Loss2: 1.755382
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506474 Loss1: 0.148731 Loss2: 1.357742
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.513877 Loss1: 0.220576 Loss2: 1.293301
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.479940 Loss1: 0.164908 Loss2: 1.315032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.404404 Loss1: 0.106290 Loss2: 1.298114 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.417158 Loss1: 0.125000 Loss2: 1.292158 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.410324 Loss1: 0.114045 Loss2: 1.296280 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433705 Loss1: 0.133736 Loss2: 1.299969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.359809 Loss1: 0.068551 Loss2: 1.291258 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.141116 Loss1: 0.303006 Loss2: 1.838111 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.534078 Loss1: 0.172756 Loss2: 1.361322 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.102821 Loss1: 0.308450 Loss2: 1.794370 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.536149 Loss1: 0.204963 Loss2: 1.331186 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.484664 Loss1: 0.142356 Loss2: 1.342308 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430343 Loss1: 0.089007 Loss2: 1.341336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.430259 Loss1: 0.093897 Loss2: 1.336363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.411088 Loss1: 0.081717 Loss2: 1.329371 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368095 Loss1: 0.063326 Loss2: 1.304769 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354102 Loss1: 0.051549 Loss2: 1.302553 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986328
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.543833 Loss1: 0.198436 Loss2: 1.345397 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.514752 Loss1: 0.157783 Loss2: 1.356969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.163012 Loss1: 0.333202 Loss2: 1.829810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.558328 Loss1: 0.232680 Loss2: 1.325648 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.516143 Loss1: 0.162971 Loss2: 1.353172 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.475949 Loss1: 0.142982 Loss2: 1.332967 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.434117 Loss1: 0.110338 Loss2: 1.323779 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.364893 Loss1: 0.050768 Loss2: 1.314126 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.352800 Loss1: 0.050851 Loss2: 1.301949 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.355141 Loss1: 0.052710 Loss2: 1.302430
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.150481 Loss1: 0.359328 Loss2: 1.791153
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.497392 Loss1: 0.219386 Loss2: 1.278007
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.506879 Loss1: 0.212890 Loss2: 1.293989
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.428448 Loss1: 0.125924 Loss2: 1.302524
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.375050 Loss1: 0.099086 Loss2: 1.275965
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.174831 Loss1: 0.298104 Loss2: 1.876727
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.353117 Loss1: 0.082344 Loss2: 1.270773
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.338212 Loss1: 0.063041 Loss2: 1.275171 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.324849 Loss1: 0.060287 Loss2: 1.264562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.625918 Loss1: 0.202380 Loss2: 1.423537
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.314607 Loss1: 0.060027 Loss2: 1.254580
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.585946 Loss1: 0.167085 Loss2: 1.418861
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.308112 Loss1: 0.049845 Loss2: 1.258267
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.544490 Loss1: 0.123115 Loss2: 1.421375 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.485032 Loss1: 0.085805 Loss2: 1.399226 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.471547 Loss1: 0.072894 Loss2: 1.398653 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.646879 Loss1: 0.231229 Loss2: 1.415650 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.574218 Loss1: 0.156564 Loss2: 1.417654 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.494348 Loss1: 0.088265 Loss2: 1.406084 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.492912 Loss1: 0.088933 Loss2: 1.403979 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.455894 Loss1: 0.061129 Loss2: 1.394765 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.419384 Loss1: 0.029539 Loss2: 1.389845 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993990
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.313965 Loss1: 0.072543 Loss2: 1.241422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.294067 Loss1: 0.048965 Loss2: 1.245102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.302850 Loss1: 0.061770 Loss2: 1.241080
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.168470 Loss1: 0.333193 Loss2: 1.835278
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.293421 Loss1: 0.055047 Loss2: 1.238374
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.710618 Loss1: 0.335643 Loss2: 1.374975
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.282955 Loss1: 0.047317 Loss2: 1.235638
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.617701 Loss1: 0.181885 Loss2: 1.435816
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.544812 Loss1: 0.160070 Loss2: 1.384742
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551446 Loss1: 0.155386 Loss2: 1.396060
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.541380 Loss1: 0.147868 Loss2: 1.393512
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.485016 Loss1: 0.109323 Loss2: 1.375693
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.139259 Loss1: 0.335256 Loss2: 1.804003
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.499405 Loss1: 0.113267 Loss2: 1.386138
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.486275 Loss1: 0.112842 Loss2: 1.373433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449926 Loss1: 0.072007 Loss2: 1.377919 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.395650 Loss1: 0.084212 Loss2: 1.311438 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.367744 Loss1: 0.062764 Loss2: 1.304980 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382858 Loss1: 0.077553 Loss2: 1.305306
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.183648 Loss1: 0.361844 Loss2: 1.821804
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.550502 Loss1: 0.214420 Loss2: 1.336082 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.536871 Loss1: 0.169935 Loss2: 1.366935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.547574 Loss1: 0.193798 Loss2: 1.353776 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448861 Loss1: 0.112509 Loss2: 1.336352 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401602 Loss1: 0.065368 Loss2: 1.336234 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.590425 Loss1: 0.224167 Loss2: 1.366258 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.615401 Loss1: 0.241016 Loss2: 1.374384 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463612 Loss1: 0.105534 Loss2: 1.358078 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439399 Loss1: 0.095355 Loss2: 1.344044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398026 Loss1: 0.056483 Loss2: 1.341543
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.309244 Loss1: 0.322139 Loss2: 1.987106
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368207 Loss1: 0.037301 Loss2: 1.330906
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.733840 Loss1: 0.279184 Loss2: 1.454656
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.684120 Loss1: 0.185216 Loss2: 1.498904
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.617747 Loss1: 0.157628 Loss2: 1.460120
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589608 Loss1: 0.130239 Loss2: 1.459369
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.563750 Loss1: 0.106539 Loss2: 1.457211
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.553089 Loss1: 0.099953 Loss2: 1.453136
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.273396 Loss1: 0.402173 Loss2: 1.871223
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.542186 Loss1: 0.089465 Loss2: 1.452721
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.656999 Loss1: 0.283937 Loss2: 1.373062
+DEBUG flwr 2023-10-13 14:57:27,128 | server.py:236 | fit_round 192 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.543397 Loss1: 0.094631 Loss2: 1.448766
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.618536 Loss1: 0.214689 Loss2: 1.403847
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.507511 Loss1: 0.069976 Loss2: 1.437535
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.498381 Loss1: 0.124901 Loss2: 1.373481
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.509155 Loss1: 0.140298 Loss2: 1.368858
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.464132 Loss1: 0.101737 Loss2: 1.362395
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.463600 Loss1: 0.110994 Loss2: 1.352606
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418062 Loss1: 0.065894 Loss2: 1.352168
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405036 Loss1: 0.057475 Loss2: 1.347561
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.195053 Loss1: 0.377397 Loss2: 1.817656
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405764 Loss1: 0.059566 Loss2: 1.346198
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.497278 Loss1: 0.183475 Loss2: 1.313803
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.554222 Loss1: 0.222084 Loss2: 1.332138
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489309 Loss1: 0.156680 Loss2: 1.332629
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480806 Loss1: 0.152060 Loss2: 1.328746
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.520822 Loss1: 0.192098 Loss2: 1.328724
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.253954 Loss1: 0.408552 Loss2: 1.845402
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448157 Loss1: 0.123395 Loss2: 1.324762
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.378224 Loss1: 0.056891 Loss2: 1.321333 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.376381 Loss1: 0.066951 Loss2: 1.309429 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.337897 Loss1: 0.033445 Loss2: 1.304452 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.372024 Loss1: 0.051269 Loss2: 1.320756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.342955 Loss1: 0.042792 Loss2: 1.300163 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.328348 Loss1: 0.032594 Loss2: 1.295754 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 14:57:27,128][flwr][DEBUG] - fit_round 192 received 50 results and 0 failures
+INFO flwr 2023-10-13 14:58:08,595 | server.py:125 | fit progress: (192, 2.328017218615681, {'accuracy': 0.6118}, 443196.373934433)
+>> Test accuracy: 0.611800
+[2023-10-13 14:58:08,595][flwr][INFO] - fit progress: (192, 2.328017218615681, {'accuracy': 0.6118}, 443196.373934433)
+DEBUG flwr 2023-10-13 14:58:08,596 | server.py:173 | evaluate_round 192: strategy sampled 50 clients (out of 50)
+[2023-10-13 14:58:08,596][flwr][DEBUG] - evaluate_round 192: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 15:07:17,455 | server.py:187 | evaluate_round 192 received 50 results and 0 failures
+[2023-10-13 15:07:17,455][flwr][DEBUG] - evaluate_round 192 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 15:07:17,456 | server.py:222 | fit_round 193: strategy sampled 50 clients (out of 50)
+[2023-10-13 15:07:17,456][flwr][DEBUG] - fit_round 193: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.168701 Loss1: 0.349481 Loss2: 1.819221
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.555442 Loss1: 0.227573 Loss2: 1.327869
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.534980 Loss1: 0.186579 Loss2: 1.348401
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476527 Loss1: 0.135787 Loss2: 1.340740
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.370740 Loss1: 0.427824 Loss2: 1.942916
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.422478 Loss1: 0.098110 Loss2: 1.324368
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.685590 Loss1: 0.288106 Loss2: 1.397484
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.439851 Loss1: 0.114445 Loss2: 1.325406
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.605180 Loss1: 0.173123 Loss2: 1.432056
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390735 Loss1: 0.068590 Loss2: 1.322145
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.579909 Loss1: 0.173256 Loss2: 1.406653
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542003 Loss1: 0.142393 Loss2: 1.399610
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.373255 Loss1: 0.061499 Loss2: 1.311756
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.569525 Loss1: 0.156488 Loss2: 1.413037
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.352521 Loss1: 0.044736 Loss2: 1.307785
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.502982 Loss1: 0.106180 Loss2: 1.396803
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.335976 Loss1: 0.033151 Loss2: 1.302824
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499911 Loss1: 0.111983 Loss2: 1.387928 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.188976 Loss1: 0.380185 Loss2: 1.808792 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.547799 Loss1: 0.175990 Loss2: 1.371809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.464519 Loss1: 0.125536 Loss2: 1.338982
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.283184 Loss1: 0.410044 Loss2: 1.873140
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.408266 Loss1: 0.074105 Loss2: 1.334161
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.615681 Loss1: 0.245717 Loss2: 1.369963
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.541058 Loss1: 0.148608 Loss2: 1.392450
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.394273 Loss1: 0.057563 Loss2: 1.336710
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.497051 Loss1: 0.124610 Loss2: 1.372441
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416247 Loss1: 0.087778 Loss2: 1.328469
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.461713 Loss1: 0.097323 Loss2: 1.364390
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.388757 Loss1: 0.057206 Loss2: 1.331551
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431474 Loss1: 0.068670 Loss2: 1.362804
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377487 Loss1: 0.052938 Loss2: 1.324548
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358947 Loss1: 0.036965 Loss2: 1.321983 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.399748 Loss1: 0.051418 Loss2: 1.348330 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.186099 Loss1: 0.329019 Loss2: 1.857080 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.531004 Loss1: 0.164951 Loss2: 1.366052 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.472885 Loss1: 0.110173 Loss2: 1.362712
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.295917 Loss1: 0.380478 Loss2: 1.915439
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.425065 Loss1: 0.080536 Loss2: 1.344529
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.603817 Loss1: 0.231704 Loss2: 1.372113
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.504762 Loss1: 0.138148 Loss2: 1.366614
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440813 Loss1: 0.099924 Loss2: 1.340890
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.587171 Loss1: 0.204069 Loss2: 1.383102
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460525 Loss1: 0.118789 Loss2: 1.341737
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.523957 Loss1: 0.146345 Loss2: 1.377612
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410077 Loss1: 0.067725 Loss2: 1.342352
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.386393 Loss1: 0.048062 Loss2: 1.338331 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364518 Loss1: 0.029818 Loss2: 1.334700 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.432314 Loss1: 0.080760 Loss2: 1.351555 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.195897 Loss1: 0.332593 Loss2: 1.863304 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.565792 Loss1: 0.206392 Loss2: 1.359400 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.540743 Loss1: 0.165772 Loss2: 1.374971
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.297984 Loss1: 0.397584 Loss2: 1.900400
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.534384 Loss1: 0.169594 Loss2: 1.364791
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.639819 Loss1: 0.242171 Loss2: 1.397648
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478454 Loss1: 0.110319 Loss2: 1.368136
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.567356 Loss1: 0.162042 Loss2: 1.405314
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.483934 Loss1: 0.128193 Loss2: 1.355741
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521893 Loss1: 0.115369 Loss2: 1.406525
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.467842 Loss1: 0.111001 Loss2: 1.356841
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514317 Loss1: 0.131898 Loss2: 1.382418
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440409 Loss1: 0.083080 Loss2: 1.357329
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.488502 Loss1: 0.102093 Loss2: 1.386409
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459624 Loss1: 0.109189 Loss2: 1.350436
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441721 Loss1: 0.056799 Loss2: 1.384921
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414011 Loss1: 0.046125 Loss2: 1.367886
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402439 Loss1: 0.042725 Loss2: 1.359714
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423445 Loss1: 0.060469 Loss2: 1.362976
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.159714 Loss1: 0.323132 Loss2: 1.836582
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.575769 Loss1: 0.202419 Loss2: 1.373350
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.549267 Loss1: 0.158417 Loss2: 1.390850
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.459434 Loss1: 0.095480 Loss2: 1.363954
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.046004 Loss1: 0.327161 Loss2: 1.718843
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.455614 Loss1: 0.092003 Loss2: 1.363611
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.477212 Loss1: 0.224549 Loss2: 1.252663
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433877 Loss1: 0.076631 Loss2: 1.357246
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.452195 Loss1: 0.192289 Loss2: 1.259906
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.433078 Loss1: 0.166337 Loss2: 1.266742
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.421651 Loss1: 0.067264 Loss2: 1.354387
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.330387 Loss1: 0.086941 Loss2: 1.243447
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434460 Loss1: 0.084485 Loss2: 1.349975
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.318335 Loss1: 0.079365 Loss2: 1.238970
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.433240 Loss1: 0.082065 Loss2: 1.351175
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.303652 Loss1: 0.068225 Loss2: 1.235427
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.450147 Loss1: 0.097684 Loss2: 1.352463
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.240009 Loss1: 0.018442 Loss2: 1.221567 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.138918 Loss1: 0.308786 Loss2: 1.830132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.431711 Loss1: 0.105255 Loss2: 1.326455 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.402108 Loss1: 0.086940 Loss2: 1.315168
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.161398 Loss1: 0.351477 Loss2: 1.809921
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.520032 Loss1: 0.202539 Loss2: 1.317494 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.503418 Loss1: 0.170987 Loss2: 1.332431 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.470799 Loss1: 0.134750 Loss2: 1.336048 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.452682 Loss1: 0.136650 Loss2: 1.316032 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471217 Loss1: 0.146952 Loss2: 1.324265 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392251 Loss1: 0.075189 Loss2: 1.317062
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.425555 Loss1: 0.104092 Loss2: 1.321463
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396052 Loss1: 0.076285 Loss2: 1.319768
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.358477 Loss1: 0.045294 Loss2: 1.313183
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369067 Loss1: 0.063979 Loss2: 1.305087
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.330387 Loss1: 0.379572 Loss2: 1.950815
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.707168 Loss1: 0.284439 Loss2: 1.422729
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610157 Loss1: 0.153145 Loss2: 1.457012
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.535393 Loss1: 0.112268 Loss2: 1.423125
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.213117 Loss1: 0.334228 Loss2: 1.878889
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.628946 Loss1: 0.263866 Loss2: 1.365080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.698390 Loss1: 0.281466 Loss2: 1.416924 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.516861 Loss1: 0.136632 Loss2: 1.380229 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.503371 Loss1: 0.130034 Loss2: 1.373337 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487973 Loss1: 0.112717 Loss2: 1.375256 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486344 Loss1: 0.119423 Loss2: 1.366921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.437901 Loss1: 0.071398 Loss2: 1.366503 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.198570 Loss1: 0.359935 Loss2: 1.838635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568944 Loss1: 0.184882 Loss2: 1.384062 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.269235 Loss1: 0.395614 Loss2: 1.873621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.724753 Loss1: 0.352092 Loss2: 1.372662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.639316 Loss1: 0.213309 Loss2: 1.426006 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.512136 Loss1: 0.125917 Loss2: 1.386219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.561918 Loss1: 0.189220 Loss2: 1.372698 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511473 Loss1: 0.128603 Loss2: 1.382870 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428306 Loss1: 0.062175 Loss2: 1.366131 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.373978 Loss1: 0.022229 Loss2: 1.351749 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.703999 Loss1: 0.304315 Loss2: 1.399684 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557402 Loss1: 0.155565 Loss2: 1.401837 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.494012 Loss1: 0.087797 Loss2: 1.406215 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.465889 Loss1: 0.075409 Loss2: 1.390481 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448652 Loss1: 0.068080 Loss2: 1.380572 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461084 Loss1: 0.075528 Loss2: 1.385556 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445782 Loss1: 0.068392 Loss2: 1.377390 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421038 Loss1: 0.045431 Loss2: 1.375607 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370482 Loss1: 0.054715 Loss2: 1.315768 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.175716 Loss1: 0.383022 Loss2: 1.792694 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.535337 Loss1: 0.190995 Loss2: 1.344342 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491951 Loss1: 0.158986 Loss2: 1.332965
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.143165 Loss1: 0.301797 Loss2: 1.841368
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.533780 Loss1: 0.195968 Loss2: 1.337812 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.526453 Loss1: 0.184475 Loss2: 1.341978 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.460845 Loss1: 0.115605 Loss2: 1.345240 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.476232 Loss1: 0.146096 Loss2: 1.330137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482287 Loss1: 0.149118 Loss2: 1.333170 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422143 Loss1: 0.105497 Loss2: 1.316646
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433238 Loss1: 0.090556 Loss2: 1.342682
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.401143 Loss1: 0.076918 Loss2: 1.324225
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400209 Loss1: 0.076223 Loss2: 1.323986
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390056 Loss1: 0.064002 Loss2: 1.326054
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.244400 Loss1: 0.399811 Loss2: 1.844588
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.585703 Loss1: 0.255155 Loss2: 1.330548
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.502935 Loss1: 0.160831 Loss2: 1.342105
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.431974 Loss1: 0.103932 Loss2: 1.328042
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.217231 Loss1: 0.381597 Loss2: 1.835634
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.580776 Loss1: 0.222858 Loss2: 1.357917 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.533698 Loss1: 0.161795 Loss2: 1.371903 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492306 Loss1: 0.127146 Loss2: 1.365160 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.439679 Loss1: 0.101292 Loss2: 1.338387 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.406828 Loss1: 0.068196 Loss2: 1.338632 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.410038 Loss1: 0.083598 Loss2: 1.326440 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.405398 Loss1: 0.075055 Loss2: 1.330343 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.276557 Loss1: 0.430427 Loss2: 1.846130 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551919 Loss1: 0.229730 Loss2: 1.322189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.474525 Loss1: 0.162040 Loss2: 1.312485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461092 Loss1: 0.151548 Loss2: 1.309545 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419824 Loss1: 0.103156 Loss2: 1.316668 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.381625 Loss1: 0.062952 Loss2: 1.318673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.334678 Loss1: 0.029643 Loss2: 1.305035 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365271 Loss1: 0.071447 Loss2: 1.293824 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989183
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.379213 Loss1: 0.076534 Loss2: 1.302678 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.357316 Loss1: 0.059324 Loss2: 1.297992 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.349680 Loss1: 0.053252 Loss2: 1.296428
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.256889 Loss1: 0.366532 Loss2: 1.890357
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.703115 Loss1: 0.320495 Loss2: 1.382620
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.633349 Loss1: 0.198855 Loss2: 1.434494
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574695 Loss1: 0.180318 Loss2: 1.394377
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506621 Loss1: 0.119854 Loss2: 1.386768
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.217705 Loss1: 0.351349 Loss2: 1.866356
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524290 Loss1: 0.133438 Loss2: 1.390852
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.619299 Loss1: 0.258255 Loss2: 1.361044
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459593 Loss1: 0.080055 Loss2: 1.379537
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.533035 Loss1: 0.146702 Loss2: 1.386333
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435243 Loss1: 0.059049 Loss2: 1.376194
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478101 Loss1: 0.108574 Loss2: 1.369526
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.432704 Loss1: 0.059258 Loss2: 1.373446
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.511213 Loss1: 0.148122 Loss2: 1.363092
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.430556 Loss1: 0.061065 Loss2: 1.369491
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435568 Loss1: 0.077507 Loss2: 1.358061 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397409 Loss1: 0.049296 Loss2: 1.348113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388841 Loss1: 0.046085 Loss2: 1.342756
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.140243 Loss1: 0.275843 Loss2: 1.864400
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.597448 Loss1: 0.217645 Loss2: 1.379803
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.541485 Loss1: 0.144166 Loss2: 1.397319
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.427192 Loss1: 0.056717 Loss2: 1.370475
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.404634 Loss1: 0.044658 Loss2: 1.359976
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.129741 Loss1: 0.336576 Loss2: 1.793165
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.408450 Loss1: 0.053413 Loss2: 1.355037
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.412063 Loss1: 0.057569 Loss2: 1.354494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404506 Loss1: 0.052355 Loss2: 1.352151 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435761 Loss1: 0.083797 Loss2: 1.351964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412910 Loss1: 0.051226 Loss2: 1.361684 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.373354 Loss1: 0.079592 Loss2: 1.293762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354481 Loss1: 0.059650 Loss2: 1.294831 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.355232 Loss1: 0.064224 Loss2: 1.291008
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.279744 Loss1: 0.412953 Loss2: 1.866791
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.591357 Loss1: 0.235902 Loss2: 1.355455
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.539415 Loss1: 0.150973 Loss2: 1.388442
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520964 Loss1: 0.153256 Loss2: 1.367708
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.497578 Loss1: 0.123603 Loss2: 1.373975
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.211916 Loss1: 0.360487 Loss2: 1.851429
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.556370 Loss1: 0.210654 Loss2: 1.345716 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.462136 Loss1: 0.114661 Loss2: 1.347475 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.444256 Loss1: 0.101924 Loss2: 1.342333 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.419639 Loss1: 0.085319 Loss2: 1.334320 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387944 Loss1: 0.046581 Loss2: 1.341364
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.431390 Loss1: 0.099284 Loss2: 1.332106
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412998 Loss1: 0.081365 Loss2: 1.331632
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.424965 Loss1: 0.092873 Loss2: 1.332092
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.379962 Loss1: 0.049153 Loss2: 1.330809
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369364 Loss1: 0.044640 Loss2: 1.324724
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.231429 Loss1: 0.358948 Loss2: 1.872481
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.650256 Loss1: 0.275802 Loss2: 1.374454
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.565841 Loss1: 0.149347 Loss2: 1.416493
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.549424 Loss1: 0.160685 Loss2: 1.388739
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518914 Loss1: 0.128818 Loss2: 1.390096
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.251068 Loss1: 0.357926 Loss2: 1.893142
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.593080 Loss1: 0.205211 Loss2: 1.387868 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.524710 Loss1: 0.129816 Loss2: 1.394894 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.470220 Loss1: 0.086913 Loss2: 1.383308 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.467302 Loss1: 0.093648 Loss2: 1.373655 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.454798 Loss1: 0.081567 Loss2: 1.373231 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.469068 Loss1: 0.099218 Loss2: 1.369851 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.473549 Loss1: 0.097950 Loss2: 1.375599 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.535012 Loss1: 0.166225 Loss2: 1.368787 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498466 Loss1: 0.124309 Loss2: 1.374157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462975 Loss1: 0.101435 Loss2: 1.361541
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.158273 Loss1: 0.274764 Loss2: 1.883510
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.651476 Loss1: 0.236916 Loss2: 1.414560 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.601503 Loss1: 0.143854 Loss2: 1.457649 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.522789 Loss1: 0.114478 Loss2: 1.408311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.542439 Loss1: 0.130648 Loss2: 1.411792 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501795 Loss1: 0.083665 Loss2: 1.418130 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.478897 Loss1: 0.062581 Loss2: 1.416316 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.193048 Loss1: 0.326040 Loss2: 1.867008 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.597514 Loss1: 0.220668 Loss2: 1.376847 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448086 Loss1: 0.092967 Loss2: 1.355119 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.379632 Loss1: 0.424251 Loss2: 1.955381
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.424808 Loss1: 0.075411 Loss2: 1.349396
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.401546 Loss1: 0.058548 Loss2: 1.342998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.376121 Loss1: 0.042554 Loss2: 1.333567 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.365782 Loss1: 0.032785 Loss2: 1.332997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363344 Loss1: 0.036144 Loss2: 1.327200 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441244 Loss1: 0.100797 Loss2: 1.340446 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380232 Loss1: 0.042582 Loss2: 1.337650 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.584167 Loss1: 0.232319 Loss2: 1.351848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.558159 Loss1: 0.173123 Loss2: 1.385036 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.478876 Loss1: 0.131242 Loss2: 1.347633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.418553 Loss1: 0.072641 Loss2: 1.345912 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.392424 Loss1: 0.052119 Loss2: 1.340305 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.373852 Loss1: 0.043418 Loss2: 1.330434 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.377962 Loss1: 0.049757 Loss2: 1.328204 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384111 Loss1: 0.062183 Loss2: 1.321929 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.493847 Loss1: 0.109825 Loss2: 1.384022 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.474732 Loss1: 0.080850 Loss2: 1.393882 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.508232 Loss1: 0.165993 Loss2: 1.342239 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.451416 Loss1: 0.099063 Loss2: 1.352353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.453862 Loss1: 0.126396 Loss2: 1.327466 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505568 Loss1: 0.148407 Loss2: 1.357160 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.450495 Loss1: 0.097209 Loss2: 1.353286 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.425081 Loss1: 0.087573 Loss2: 1.337508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403285 Loss1: 0.067936 Loss2: 1.335349 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392166 Loss1: 0.057024 Loss2: 1.335142 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.378274 Loss1: 0.087512 Loss2: 1.290762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374774 Loss1: 0.082774 Loss2: 1.292000 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.574472 Loss1: 0.266786 Loss2: 1.307686 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467804 Loss1: 0.131907 Loss2: 1.335896 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.446590 Loss1: 0.137646 Loss2: 1.308944
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.220066 Loss1: 0.384029 Loss2: 1.836037
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468706 Loss1: 0.159586 Loss2: 1.309120
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.568872 Loss1: 0.222107 Loss2: 1.346765
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.413361 Loss1: 0.094648 Loss2: 1.318713
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.500599 Loss1: 0.134300 Loss2: 1.366298
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.476161 Loss1: 0.126389 Loss2: 1.349772 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.437473 Loss1: 0.091761 Loss2: 1.345712 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429853 Loss1: 0.094724 Loss2: 1.335129 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.372604 Loss1: 0.043830 Loss2: 1.328774 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366159 Loss1: 0.045901 Loss2: 1.320258 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.623125 Loss1: 0.204542 Loss2: 1.418583 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.546132 Loss1: 0.113318 Loss2: 1.432814 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.531776 Loss1: 0.115290 Loss2: 1.416485
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.068692 Loss1: 0.238348 Loss2: 1.830345
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.495301 Loss1: 0.083685 Loss2: 1.411617
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.616925 Loss1: 0.255846 Loss2: 1.361079
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473721 Loss1: 0.065601 Loss2: 1.408120
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.569713 Loss1: 0.167165 Loss2: 1.402548
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459093 Loss1: 0.059040 Loss2: 1.400053
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521689 Loss1: 0.142682 Loss2: 1.379007
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445268 Loss1: 0.045338 Loss2: 1.399930
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.531373 Loss1: 0.154492 Loss2: 1.376881
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426564 Loss1: 0.028228 Loss2: 1.398336
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.543097 Loss1: 0.154924 Loss2: 1.388173
+(DefaultActor pid=3765) >> Training accuracy: 0.998047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.518293 Loss1: 0.137240 Loss2: 1.381052
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.516095 Loss1: 0.132168 Loss2: 1.383927
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435118 Loss1: 0.052686 Loss2: 1.382432
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.408608 Loss1: 0.040593 Loss2: 1.368016
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.439710 Loss1: 0.416035 Loss2: 2.023675
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.736830 Loss1: 0.266584 Loss2: 1.470246
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.625664 Loss1: 0.123439 Loss2: 1.502224
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574517 Loss1: 0.102008 Loss2: 1.472510
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.559729 Loss1: 0.097414 Loss2: 1.462315
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.089681 Loss1: 0.268256 Loss2: 1.821425
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.508524 Loss1: 0.160428 Loss2: 1.348096 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.453778 Loss1: 0.105676 Loss2: 1.348102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.411437 Loss1: 0.069042 Loss2: 1.342395 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.396845 Loss1: 0.065416 Loss2: 1.331430 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.393801 Loss1: 0.062536 Loss2: 1.331265 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.390331 Loss1: 0.057975 Loss2: 1.332356 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362117 Loss1: 0.031217 Loss2: 1.330900
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.063865 Loss1: 0.253210 Loss2: 1.810655
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.498678 Loss1: 0.151063 Loss2: 1.347615
+DEBUG flwr 2023-10-13 15:36:02,122 | server.py:236 | fit_round 193 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.505347 Loss1: 0.154257 Loss2: 1.351090
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467145 Loss1: 0.116497 Loss2: 1.350648
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.428050 Loss1: 0.087832 Loss2: 1.340218
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.173857 Loss1: 0.324898 Loss2: 1.848959
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.562091 Loss1: 0.193995 Loss2: 1.368096 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.411844 Loss1: 0.077290 Loss2: 1.334554
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.492848 Loss1: 0.128644 Loss2: 1.364204
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.379836 Loss1: 0.043629 Loss2: 1.336207
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.479274 Loss1: 0.113472 Loss2: 1.365802
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402068 Loss1: 0.066200 Loss2: 1.335868
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496720 Loss1: 0.145635 Loss2: 1.351085
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.400707 Loss1: 0.069522 Loss2: 1.331185
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531660 Loss1: 0.165573 Loss2: 1.366086
+(DefaultActor pid=3765) >> Training accuracy: 0.971507
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.517980 Loss1: 0.144434 Loss2: 1.373547
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.473754 Loss1: 0.102046 Loss2: 1.371708
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.450007 Loss1: 0.091853 Loss2: 1.358154
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.427094 Loss1: 0.068896 Loss2: 1.358198
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.447594 Loss1: 0.448816 Loss2: 1.998778
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.606925 Loss1: 0.244682 Loss2: 1.362243
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.560957 Loss1: 0.187554 Loss2: 1.373403
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.555148 Loss1: 0.160946 Loss2: 1.394202
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.503227 Loss1: 0.126463 Loss2: 1.376764
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.485387 Loss1: 0.110118 Loss2: 1.375269
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.254633 Loss1: 0.372678 Loss2: 1.881956
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.649577 Loss1: 0.283946 Loss2: 1.365631 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399843 Loss1: 0.043773 Loss2: 1.356070 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997396
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409700 Loss1: 0.054762 Loss2: 1.354938 [repeated 2x across cluster]
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.506213 Loss1: 0.124721 Loss2: 1.381492 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.429411 Loss1: 0.066778 Loss2: 1.362632 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435028 Loss1: 0.087674 Loss2: 1.347353 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 15:36:02,122][flwr][DEBUG] - fit_round 193 received 50 results and 0 failures
+INFO flwr 2023-10-13 15:36:43,700 | server.py:125 | fit progress: (193, 2.32618732669483, {'accuracy': 0.6109}, 445511.478674337)
+>> Test accuracy: 0.610900
+[2023-10-13 15:36:43,700][flwr][INFO] - fit progress: (193, 2.32618732669483, {'accuracy': 0.6109}, 445511.478674337)
+DEBUG flwr 2023-10-13 15:36:43,700 | server.py:173 | evaluate_round 193: strategy sampled 50 clients (out of 50)
+[2023-10-13 15:36:43,700][flwr][DEBUG] - evaluate_round 193: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 15:45:49,652 | server.py:187 | evaluate_round 193 received 50 results and 0 failures
+[2023-10-13 15:45:49,652][flwr][DEBUG] - evaluate_round 193 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 15:45:49,652 | server.py:222 | fit_round 194: strategy sampled 50 clients (out of 50)
+[2023-10-13 15:45:49,652][flwr][DEBUG] - fit_round 194: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.244026 Loss1: 0.376958 Loss2: 1.867067
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.594611 Loss1: 0.247402 Loss2: 1.347210
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.562384 Loss1: 0.204155 Loss2: 1.358230
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.509130 Loss1: 0.158645 Loss2: 1.350485
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.158961 Loss1: 0.305088 Loss2: 1.853873
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487935 Loss1: 0.145378 Loss2: 1.342557
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.521548 Loss1: 0.154454 Loss2: 1.367094
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.421943 Loss1: 0.089673 Loss2: 1.332271
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.547685 Loss1: 0.184445 Loss2: 1.363240
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.426379 Loss1: 0.099295 Loss2: 1.327084
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.526693 Loss1: 0.153573 Loss2: 1.373120
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.399957 Loss1: 0.070327 Loss2: 1.329630
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.485477 Loss1: 0.134801 Loss2: 1.350676
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.384173 Loss1: 0.066791 Loss2: 1.317381
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.437275 Loss1: 0.086080 Loss2: 1.351195
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355775 Loss1: 0.040199 Loss2: 1.315576
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.424664 Loss1: 0.076557 Loss2: 1.348107
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.433217 Loss1: 0.089697 Loss2: 1.343520
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.431174 Loss1: 0.073113 Loss2: 1.358061
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405260 Loss1: 0.061962 Loss2: 1.343298
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.011167 Loss1: 0.266041 Loss2: 1.745125
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.518031 Loss1: 0.208448 Loss2: 1.309583
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.444534 Loss1: 0.118918 Loss2: 1.325616
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.139644 Loss1: 0.308667 Loss2: 1.830976
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.390465 Loss1: 0.081411 Loss2: 1.309054
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.554395 Loss1: 0.228992 Loss2: 1.325404
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.370702 Loss1: 0.073616 Loss2: 1.297086
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.543825 Loss1: 0.201838 Loss2: 1.341987
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452444 Loss1: 0.146624 Loss2: 1.305821
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.506449 Loss1: 0.164280 Loss2: 1.342170
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.415559 Loss1: 0.106350 Loss2: 1.309209
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.388322 Loss1: 0.082213 Loss2: 1.306109 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388311 Loss1: 0.078126 Loss2: 1.310184 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387631 Loss1: 0.082451 Loss2: 1.305180 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.385663 Loss1: 0.079617 Loss2: 1.306046 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.342749 Loss1: 0.456357 Loss2: 1.886392 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.507048 Loss1: 0.166813 Loss2: 1.340235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.453397 Loss1: 0.118513 Loss2: 1.334884 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.430357 Loss1: 0.099362 Loss2: 1.330995 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.400332 Loss1: 0.071762 Loss2: 1.328569 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.404643 Loss1: 0.082376 Loss2: 1.322267 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.389727 Loss1: 0.071178 Loss2: 1.318549 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.341173 Loss1: 0.027128 Loss2: 1.314045 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995192
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.353571 Loss1: 0.053533 Loss2: 1.300037 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.332986 Loss1: 0.035569 Loss2: 1.297417 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.331224 Loss1: 0.038570 Loss2: 1.292655
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.216791 Loss1: 0.351785 Loss2: 1.865006
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.606684 Loss1: 0.248259 Loss2: 1.358424
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.507091 Loss1: 0.118728 Loss2: 1.388363
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.432117 Loss1: 0.086137 Loss2: 1.345980
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.427064 Loss1: 0.087383 Loss2: 1.339681
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.134957 Loss1: 0.289717 Loss2: 1.845240
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.510235 Loss1: 0.173806 Loss2: 1.336429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.517719 Loss1: 0.188659 Loss2: 1.329060 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.473345 Loss1: 0.128839 Loss2: 1.344506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.445506 Loss1: 0.122595 Loss2: 1.322912 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.402510 Loss1: 0.080848 Loss2: 1.321663 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.369793 Loss1: 0.054757 Loss2: 1.315036 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.342565 Loss1: 0.030863 Loss2: 1.311702 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.545012 Loss1: 0.220796 Loss2: 1.324216 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.526797 Loss1: 0.173449 Loss2: 1.353347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.043288 Loss1: 0.258190 Loss2: 1.785098 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.521938 Loss1: 0.189318 Loss2: 1.332620 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474756 Loss1: 0.134090 Loss2: 1.340666 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414489 Loss1: 0.075775 Loss2: 1.338714 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.442291 Loss1: 0.103673 Loss2: 1.338618 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474845 Loss1: 0.141732 Loss2: 1.333113 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436511 Loss1: 0.091591 Loss2: 1.344920
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.144582 Loss1: 0.269333 Loss2: 1.875248
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.599057 Loss1: 0.191339 Loss2: 1.407718 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414442 Loss1: 0.082454 Loss2: 1.331988
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.541700 Loss1: 0.124602 Loss2: 1.417098
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.539553 Loss1: 0.130084 Loss2: 1.409468
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518239 Loss1: 0.116136 Loss2: 1.402103
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498042 Loss1: 0.091653 Loss2: 1.406389
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468848 Loss1: 0.072981 Loss2: 1.395866
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.285711 Loss1: 0.379269 Loss2: 1.906442
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.574020 Loss1: 0.207654 Loss2: 1.366366
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.440912 Loss1: 0.050339 Loss2: 1.390573
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.541899 Loss1: 0.156681 Loss2: 1.385218
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.444497 Loss1: 0.059171 Loss2: 1.385326
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434447 Loss1: 0.045492 Loss2: 1.388955 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991728
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471574 Loss1: 0.100164 Loss2: 1.371410 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.447734 Loss1: 0.084550 Loss2: 1.363184 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441199 Loss1: 0.078991 Loss2: 1.362208
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.229066 Loss1: 0.357839 Loss2: 1.871228
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.595023 Loss1: 0.251242 Loss2: 1.343781 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.470026 Loss1: 0.116540 Loss2: 1.353485 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.467329 Loss1: 0.111380 Loss2: 1.355950 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.442182 Loss1: 0.088955 Loss2: 1.353227 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448134 Loss1: 0.096800 Loss2: 1.351334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.473663 Loss1: 0.145107 Loss2: 1.328556
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400588 Loss1: 0.049102 Loss2: 1.351486
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.474782 Loss1: 0.144591 Loss2: 1.330191
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403302 Loss1: 0.060796 Loss2: 1.342506
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.427993 Loss1: 0.098010 Loss2: 1.329983 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.369749 Loss1: 0.059767 Loss2: 1.309982 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.085819 Loss1: 0.258905 Loss2: 1.826914
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.344496 Loss1: 0.041595 Loss2: 1.302901
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.550107 Loss1: 0.231727 Loss2: 1.318380
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.347123 Loss1: 0.049190 Loss2: 1.297934
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.519418 Loss1: 0.181659 Loss2: 1.337759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457370 Loss1: 0.120595 Loss2: 1.336775 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.495090 Loss1: 0.159591 Loss2: 1.335499
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.162654 Loss1: 0.305638 Loss2: 1.857016
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.409616 Loss1: 0.071358 Loss2: 1.338257
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.610179 Loss1: 0.246094 Loss2: 1.364085
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.426605 Loss1: 0.096559 Loss2: 1.330046
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.541266 Loss1: 0.162941 Loss2: 1.378325
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393149 Loss1: 0.058602 Loss2: 1.334548
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557490 Loss1: 0.187888 Loss2: 1.369602
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.518372 Loss1: 0.150483 Loss2: 1.367889
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.539966 Loss1: 0.173879 Loss2: 1.366087
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.477191 Loss1: 0.110179 Loss2: 1.367012
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.515894 Loss1: 0.151583 Loss2: 1.364311
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.234163 Loss1: 0.393616 Loss2: 1.840546
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.490033 Loss1: 0.123007 Loss2: 1.367026
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.454370 Loss1: 0.092844 Loss2: 1.361526
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.592604 Loss1: 0.243860 Loss2: 1.348744
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.643052 Loss1: 0.261342 Loss2: 1.381710
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.573922 Loss1: 0.211048 Loss2: 1.362875
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.568269 Loss1: 0.190925 Loss2: 1.377344
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.528602 Loss1: 0.168610 Loss2: 1.359993
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479240 Loss1: 0.118063 Loss2: 1.361177
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.163139 Loss1: 0.374134 Loss2: 1.789005
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.460502 Loss1: 0.110730 Loss2: 1.349772
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.531623 Loss1: 0.230202 Loss2: 1.301421
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441804 Loss1: 0.095033 Loss2: 1.346770
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.496320 Loss1: 0.176920 Loss2: 1.319400
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.403738 Loss1: 0.065178 Loss2: 1.338560
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.422312 Loss1: 0.110379 Loss2: 1.311933
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.426884 Loss1: 0.130292 Loss2: 1.296592
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.387732 Loss1: 0.086438 Loss2: 1.301294
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392407 Loss1: 0.097267 Loss2: 1.295140
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.401018 Loss1: 0.094691 Loss2: 1.306327
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.376707 Loss1: 0.072918 Loss2: 1.303789
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.051973 Loss1: 0.280261 Loss2: 1.771712
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379446 Loss1: 0.083946 Loss2: 1.295499
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.449478 Loss1: 0.140032 Loss2: 1.309446
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.376048 Loss1: 0.072915 Loss2: 1.303133
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.370486 Loss1: 0.077535 Loss2: 1.292952
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.356732 Loss1: 0.073447 Loss2: 1.283285
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.345889 Loss1: 0.061986 Loss2: 1.283904
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.209142 Loss1: 0.341002 Loss2: 1.868140
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.328283 Loss1: 0.045240 Loss2: 1.283043
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.302555 Loss1: 0.026812 Loss2: 1.275742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.301131 Loss1: 0.030212 Loss2: 1.270919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.280892 Loss1: 0.012628 Loss2: 1.268264 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.999023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468490 Loss1: 0.102997 Loss2: 1.365493 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.460103 Loss1: 0.096177 Loss2: 1.363926 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.204091 Loss1: 0.376940 Loss2: 1.827150 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.502009 Loss1: 0.165716 Loss2: 1.336293 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.413640 Loss1: 0.087414 Loss2: 1.326226 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.382653 Loss1: 0.060395 Loss2: 1.322258
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.223299 Loss1: 0.363808 Loss2: 1.859491
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.675753 Loss1: 0.300834 Loss2: 1.374920 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.654428 Loss1: 0.239955 Loss2: 1.414473 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594068 Loss1: 0.189891 Loss2: 1.404177 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364301 Loss1: 0.059892 Loss2: 1.304408
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508476 Loss1: 0.116313 Loss2: 1.392162
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.507249 Loss1: 0.120151 Loss2: 1.387098
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.454176 Loss1: 0.075474 Loss2: 1.378702
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.450370 Loss1: 0.071792 Loss2: 1.378578
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433351 Loss1: 0.060035 Loss2: 1.373316
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.260871 Loss1: 0.438907 Loss2: 1.821964
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.445898 Loss1: 0.074209 Loss2: 1.371689
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591006 Loss1: 0.216134 Loss2: 1.374872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.492507 Loss1: 0.144391 Loss2: 1.348116 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437149 Loss1: 0.098881 Loss2: 1.338268
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.403648 Loss1: 0.413947 Loss2: 1.989702
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395364 Loss1: 0.069148 Loss2: 1.326216
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.708418 Loss1: 0.271962 Loss2: 1.436456
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382154 Loss1: 0.058020 Loss2: 1.324135
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.635555 Loss1: 0.185230 Loss2: 1.450325
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.660324 Loss1: 0.200867 Loss2: 1.459457
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372372 Loss1: 0.055206 Loss2: 1.317165
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.578938 Loss1: 0.151413 Loss2: 1.427525
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359721 Loss1: 0.045739 Loss2: 1.313982
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.533040 Loss1: 0.101690 Loss2: 1.431350 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.484648 Loss1: 0.071193 Loss2: 1.413454 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995536
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.450371 Loss1: 0.038516 Loss2: 1.411855
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.202631 Loss1: 0.312097 Loss2: 1.890535
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.616221 Loss1: 0.226754 Loss2: 1.389467
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.620720 Loss1: 0.217176 Loss2: 1.403545
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.530486 Loss1: 0.116893 Loss2: 1.413593
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527054 Loss1: 0.130148 Loss2: 1.396906
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.146094 Loss1: 0.306598 Loss2: 1.839497
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.569918 Loss1: 0.229342 Loss2: 1.340576 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.548487 Loss1: 0.193287 Loss2: 1.355200 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.528784 Loss1: 0.166771 Loss2: 1.362014 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.483348 Loss1: 0.131961 Loss2: 1.351387 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439389 Loss1: 0.095174 Loss2: 1.344215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399288 Loss1: 0.062488 Loss2: 1.336800 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406191 Loss1: 0.070650 Loss2: 1.335541 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.593192 Loss1: 0.226730 Loss2: 1.366462 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491093 Loss1: 0.110611 Loss2: 1.380482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468345 Loss1: 0.104049 Loss2: 1.364295
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.261914 Loss1: 0.377911 Loss2: 1.884003
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.695821 Loss1: 0.303601 Loss2: 1.392221 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.643035 Loss1: 0.221480 Loss2: 1.421555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.610244 Loss1: 0.208091 Loss2: 1.402153 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515854 Loss1: 0.119794 Loss2: 1.396061 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.431234 Loss1: 0.084081 Loss2: 1.347153
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.463817 Loss1: 0.080826 Loss2: 1.382991
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437800 Loss1: 0.059127 Loss2: 1.378672
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.402904 Loss1: 0.033913 Loss2: 1.368992
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396831 Loss1: 0.032099 Loss2: 1.364733
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399267 Loss1: 0.039322 Loss2: 1.359945
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.243961 Loss1: 0.338129 Loss2: 1.905833
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.630924 Loss1: 0.277862 Loss2: 1.353061
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.549921 Loss1: 0.167735 Loss2: 1.382186
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.525134 Loss1: 0.142899 Loss2: 1.382235
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480689 Loss1: 0.119792 Loss2: 1.360896
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.171193 Loss1: 0.320518 Loss2: 1.850675
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451789 Loss1: 0.085988 Loss2: 1.365801
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463965 Loss1: 0.099776 Loss2: 1.364189 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.492118 Loss1: 0.111005 Loss2: 1.381113
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.461730 Loss1: 0.103311 Loss2: 1.358418
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.447861 Loss1: 0.079526 Loss2: 1.368335
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.453967 Loss1: 0.091879 Loss2: 1.362088
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410337 Loss1: 0.057563 Loss2: 1.352774
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.432733 Loss1: 0.080339 Loss2: 1.352394
+(DefaultActor pid=3765) >> Training accuracy: 0.995536
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472615 Loss1: 0.117259 Loss2: 1.355356
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.476815 Loss1: 0.119308 Loss2: 1.357506
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.483471 Loss1: 0.118949 Loss2: 1.364522
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.502699 Loss1: 0.132408 Loss2: 1.370291
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.459694 Loss1: 0.093557 Loss2: 1.366136
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.262512 Loss1: 0.386270 Loss2: 1.876242
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.590367 Loss1: 0.210497 Loss2: 1.379870
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.593470 Loss1: 0.173483 Loss2: 1.419987
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.598422 Loss1: 0.202825 Loss2: 1.395597
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.610202 Loss1: 0.217215 Loss2: 1.392987
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.099774 Loss1: 0.319147 Loss2: 1.780627
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.546388 Loss1: 0.141743 Loss2: 1.404645
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.473939 Loss1: 0.082799 Loss2: 1.391141
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.582206 Loss1: 0.261383 Loss2: 1.320823
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.455017 Loss1: 0.076108 Loss2: 1.378909
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.575373 Loss1: 0.194216 Loss2: 1.381157
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431946 Loss1: 0.058374 Loss2: 1.373572
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.583563 Loss1: 0.249641 Loss2: 1.333922
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.434614 Loss1: 0.061790 Loss2: 1.372824
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.569981 Loss1: 0.200107 Loss2: 1.369874
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.501889 Loss1: 0.160321 Loss2: 1.341568
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.478051 Loss1: 0.139461 Loss2: 1.338590
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484362 Loss1: 0.140066 Loss2: 1.344295
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430027 Loss1: 0.094861 Loss2: 1.335166
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.221979 Loss1: 0.361281 Loss2: 1.860699
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.551918 Loss1: 0.191857 Loss2: 1.360061 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.485359 Loss1: 0.128671 Loss2: 1.356688 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433707 Loss1: 0.094289 Loss2: 1.339419 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.413594 Loss1: 0.079184 Loss2: 1.334410 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.392096 Loss1: 0.057528 Loss2: 1.334568 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.413381 Loss1: 0.081807 Loss2: 1.331574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401840 Loss1: 0.073982 Loss2: 1.327859 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476776 Loss1: 0.131371 Loss2: 1.345405 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409269 Loss1: 0.077024 Loss2: 1.332245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.392260 Loss1: 0.064088 Loss2: 1.328172
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.175629 Loss1: 0.321598 Loss2: 1.854031
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.583689 Loss1: 0.197741 Loss2: 1.385948 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.473015 Loss1: 0.082779 Loss2: 1.390235 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.447997 Loss1: 0.071480 Loss2: 1.376517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.441617 Loss1: 0.067607 Loss2: 1.374009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444098 Loss1: 0.069042 Loss2: 1.375056 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.513004 Loss1: 0.121822 Loss2: 1.391181 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.472924 Loss1: 0.094975 Loss2: 1.377948 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.441431 Loss1: 0.076412 Loss2: 1.365020 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.413139 Loss1: 0.046041 Loss2: 1.367097 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.437050 Loss1: 0.075302 Loss2: 1.361748
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.231619 Loss1: 0.347237 Loss2: 1.884382
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.660598 Loss1: 0.239650 Loss2: 1.420948
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.583236 Loss1: 0.140011 Loss2: 1.443225
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551314 Loss1: 0.144875 Loss2: 1.406439
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.520931 Loss1: 0.118759 Loss2: 1.402172
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.200129 Loss1: 0.381742 Loss2: 1.818387
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.460526 Loss1: 0.061577 Loss2: 1.398949
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447081 Loss1: 0.058542 Loss2: 1.388540 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453136 Loss1: 0.070478 Loss2: 1.382658 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.439232 Loss1: 0.051730 Loss2: 1.387502 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.386881 Loss1: 0.081787 Loss2: 1.305094 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.373926 Loss1: 0.078354 Loss2: 1.295572 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356774 Loss1: 0.067459 Loss2: 1.289315 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991071
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.033755 Loss1: 0.286523 Loss2: 1.747233
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.522821 Loss1: 0.211781 Loss2: 1.311040
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.464248 Loss1: 0.140641 Loss2: 1.323608
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.443204 Loss1: 0.124944 Loss2: 1.318260
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.086520 Loss1: 0.288289 Loss2: 1.798231
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.584844 Loss1: 0.234879 Loss2: 1.349965 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.533818 Loss1: 0.172427 Loss2: 1.361392 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.494401 Loss1: 0.141086 Loss2: 1.353315 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.447509 Loss1: 0.106391 Loss2: 1.341118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.411398 Loss1: 0.074627 Loss2: 1.336772 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.445424 Loss1: 0.117969 Loss2: 1.327454
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.400231 Loss1: 0.071524 Loss2: 1.328707
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.394023 Loss1: 0.066577 Loss2: 1.327446
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.369194 Loss1: 0.038511 Loss2: 1.330683
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352956 Loss1: 0.038758 Loss2: 1.314198
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.602672 Loss1: 0.274709 Loss2: 1.327962 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.443853 Loss1: 0.121909 Loss2: 1.321944 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.428700 Loss1: 0.109830 Loss2: 1.318870 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.411033 Loss1: 0.086056 Loss2: 1.324977 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446441 Loss1: 0.132132 Loss2: 1.314309 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.415366 Loss1: 0.101640 Loss2: 1.313726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.382666 Loss1: 0.067461 Loss2: 1.315205 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426150 Loss1: 0.108770 Loss2: 1.317380 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.492844 Loss1: 0.101158 Loss2: 1.391686 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.459904 Loss1: 0.071895 Loss2: 1.388009 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.156988 Loss1: 0.331212 Loss2: 1.825776
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572002 Loss1: 0.242287 Loss2: 1.329715
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.586779 Loss1: 0.231338 Loss2: 1.355441
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.495771 Loss1: 0.152136 Loss2: 1.343635
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.219267 Loss1: 0.370963 Loss2: 1.848304
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.572798 Loss1: 0.230255 Loss2: 1.342543 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.503726 Loss1: 0.148199 Loss2: 1.355527 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.443319 Loss1: 0.107938 Loss2: 1.335381 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.424302 Loss1: 0.089244 Loss2: 1.335058 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421603 Loss1: 0.088231 Loss2: 1.333372 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412314 Loss1: 0.082659 Loss2: 1.329655 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.356951 Loss1: 0.034120 Loss2: 1.322832 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.255978 Loss1: 0.391031 Loss2: 1.864947 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.615329 Loss1: 0.201232 Loss2: 1.414097 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.531989 Loss1: 0.153824 Loss2: 1.378165
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.152780 Loss1: 0.315290 Loss2: 1.837490
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.496921 Loss1: 0.161478 Loss2: 1.335443 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.474000 Loss1: 0.142182 Loss2: 1.331817 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.458315 Loss1: 0.116358 Loss2: 1.341958 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 16:14:28,127 | server.py:236 | fit_round 194 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.412951 Loss1: 0.077401 Loss2: 1.335549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.384954 Loss1: 0.055722 Loss2: 1.329232 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.453911 Loss1: 0.091773 Loss2: 1.362138
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.363517 Loss1: 0.043285 Loss2: 1.320232
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.355388 Loss1: 0.042566 Loss2: 1.312822
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362771 Loss1: 0.048798 Loss2: 1.313973
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369475 Loss1: 0.052091 Loss2: 1.317383
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.157513 Loss1: 0.341613 Loss2: 1.815900
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.552231 Loss1: 0.234181 Loss2: 1.318050
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.527813 Loss1: 0.180537 Loss2: 1.347276
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.442560 Loss1: 0.116745 Loss2: 1.325815
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.408301 Loss1: 0.478415 Loss2: 1.929886
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.615394 Loss1: 0.270669 Loss2: 1.344725
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.441386 Loss1: 0.120552 Loss2: 1.320835
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.509958 Loss1: 0.161247 Loss2: 1.348711
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.407261 Loss1: 0.076651 Loss2: 1.330610
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.379586 Loss1: 0.061329 Loss2: 1.318257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.340668 Loss1: 0.030216 Loss2: 1.310451 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.353874 Loss1: 0.047580 Loss2: 1.306294 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356029 Loss1: 0.051429 Loss2: 1.304600 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.332073 Loss1: 0.022949 Loss2: 1.309124 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998798
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.194974 Loss1: 0.337032 Loss2: 1.857942
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.565225 Loss1: 0.207424 Loss2: 1.357801
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.519721 Loss1: 0.152062 Loss2: 1.367659
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536291 Loss1: 0.155777 Loss2: 1.380513
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.231703 Loss1: 0.376848 Loss2: 1.854855
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.676438 Loss1: 0.317096 Loss2: 1.359343 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.663291 Loss1: 0.256066 Loss2: 1.407225 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.547093 Loss1: 0.180206 Loss2: 1.366887 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.554386 Loss1: 0.187725 Loss2: 1.366661 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480433 Loss1: 0.114404 Loss2: 1.366029 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.428616 Loss1: 0.077695 Loss2: 1.350921
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.438994 Loss1: 0.089623 Loss2: 1.349371
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431743 Loss1: 0.086471 Loss2: 1.345272
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.400823 Loss1: 0.065577 Loss2: 1.335247
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395798 Loss1: 0.063393 Loss2: 1.332405
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 16:14:28,127][flwr][DEBUG] - fit_round 194 received 50 results and 0 failures
+INFO flwr 2023-10-13 16:15:08,656 | server.py:125 | fit progress: (194, 2.3175561528998063, {'accuracy': 0.6115}, 447816.43457298796)
+>> Test accuracy: 0.611500
+[2023-10-13 16:15:08,656][flwr][INFO] - fit progress: (194, 2.3175561528998063, {'accuracy': 0.6115}, 447816.43457298796)
+DEBUG flwr 2023-10-13 16:15:08,656 | server.py:173 | evaluate_round 194: strategy sampled 50 clients (out of 50)
+[2023-10-13 16:15:08,656][flwr][DEBUG] - evaluate_round 194: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 16:24:14,592 | server.py:187 | evaluate_round 194 received 50 results and 0 failures
+[2023-10-13 16:24:14,592][flwr][DEBUG] - evaluate_round 194 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 16:24:14,592 | server.py:222 | fit_round 195: strategy sampled 50 clients (out of 50)
+[2023-10-13 16:24:14,592][flwr][DEBUG] - fit_round 195: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.106921 Loss1: 0.312252 Loss2: 1.794669
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.523154 Loss1: 0.183749 Loss2: 1.339405
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.482911 Loss1: 0.138998 Loss2: 1.343914
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.443475 Loss1: 0.111075 Loss2: 1.332400
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.060736 Loss1: 0.287928 Loss2: 1.772808
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.447916 Loss1: 0.122205 Loss2: 1.325711
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.473331 Loss1: 0.169611 Loss2: 1.303720
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.395530 Loss1: 0.065063 Loss2: 1.330467
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.418497 Loss1: 0.099905 Loss2: 1.318592
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.371130 Loss1: 0.077519 Loss2: 1.293611 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.351764 Loss1: 0.061755 Loss2: 1.290009 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.333555 Loss1: 0.046524 Loss2: 1.287031 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993164
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.326392 Loss1: 0.043779 Loss2: 1.282613 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.305166 Loss1: 0.032572 Loss2: 1.272593 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.999081
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.303241 Loss1: 0.030287 Loss2: 1.272954
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.324939 Loss1: 0.403273 Loss2: 1.921666
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.728208 Loss1: 0.311342 Loss2: 1.416866
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.678984 Loss1: 0.226379 Loss2: 1.452605
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.628726 Loss1: 0.192491 Loss2: 1.436235
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.521876 Loss1: 0.104197 Loss2: 1.417679
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.169496 Loss1: 0.379678 Loss2: 1.789818
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.512091 Loss1: 0.095865 Loss2: 1.416226
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.601158 Loss1: 0.285935 Loss2: 1.315223
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.516353 Loss1: 0.107812 Loss2: 1.408541
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.569893 Loss1: 0.212479 Loss2: 1.357414
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.469714 Loss1: 0.060148 Loss2: 1.409567
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.449565 Loss1: 0.121159 Loss2: 1.328406
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.461789 Loss1: 0.059374 Loss2: 1.402415
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.456230 Loss1: 0.133180 Loss2: 1.323050
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.438442 Loss1: 0.037960 Loss2: 1.400483
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.388110 Loss1: 0.073258 Loss2: 1.314852
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.367231 Loss1: 0.063965 Loss2: 1.303266
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374976 Loss1: 0.077148 Loss2: 1.297828
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362651 Loss1: 0.063531 Loss2: 1.299120
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348192 Loss1: 0.048864 Loss2: 1.299329
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.077616 Loss1: 0.260527 Loss2: 1.817089
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.522168 Loss1: 0.165367 Loss2: 1.356801
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.504817 Loss1: 0.136892 Loss2: 1.367925
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.466885 Loss1: 0.108292 Loss2: 1.358593
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.091103 Loss1: 0.307845 Loss2: 1.783258
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.483992 Loss1: 0.162268 Loss2: 1.321724 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.485073 Loss1: 0.159198 Loss2: 1.325875 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.492694 Loss1: 0.158051 Loss2: 1.334643 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.458030 Loss1: 0.133273 Loss2: 1.324756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452563 Loss1: 0.123358 Loss2: 1.329205 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.385258 Loss1: 0.065685 Loss2: 1.319573 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.332357 Loss1: 0.028616 Loss2: 1.303742 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.597970 Loss1: 0.237353 Loss2: 1.360617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.557690 Loss1: 0.178128 Loss2: 1.379561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.548018 Loss1: 0.181819 Loss2: 1.366199
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.222479 Loss1: 0.379318 Loss2: 1.843161
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630753 Loss1: 0.273190 Loss2: 1.357562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556089 Loss1: 0.159542 Loss2: 1.396546 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.541391 Loss1: 0.168829 Loss2: 1.372562 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450922 Loss1: 0.084559 Loss2: 1.366363 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402314 Loss1: 0.061299 Loss2: 1.341015
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429649 Loss1: 0.067281 Loss2: 1.362368
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406252 Loss1: 0.053020 Loss2: 1.353232
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406079 Loss1: 0.059012 Loss2: 1.347067
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391734 Loss1: 0.048367 Loss2: 1.343367
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.390150 Loss1: 0.045887 Loss2: 1.344263
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.126024 Loss1: 0.294319 Loss2: 1.831705
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.550503 Loss1: 0.191788 Loss2: 1.358715
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.530257 Loss1: 0.161988 Loss2: 1.368269
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506901 Loss1: 0.141035 Loss2: 1.365865
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.115982 Loss1: 0.332372 Loss2: 1.783611
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.546584 Loss1: 0.209675 Loss2: 1.336909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.434833 Loss1: 0.093301 Loss2: 1.341533 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.425653 Loss1: 0.097991 Loss2: 1.327662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.442844 Loss1: 0.126678 Loss2: 1.316167 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.425729 Loss1: 0.096376 Loss2: 1.329353 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408154 Loss1: 0.091549 Loss2: 1.316604 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399515 Loss1: 0.079305 Loss2: 1.320210 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.567657 Loss1: 0.255785 Loss2: 1.311872 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.460923 Loss1: 0.132277 Loss2: 1.328646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.102479 Loss1: 0.334522 Loss2: 1.767957 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.476426 Loss1: 0.186515 Loss2: 1.289910 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393607 Loss1: 0.094166 Loss2: 1.299442 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.361114 Loss1: 0.059116 Loss2: 1.301998 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359324 Loss1: 0.059510 Loss2: 1.299814 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981971
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.325831 Loss1: 0.052733 Loss2: 1.273098 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.292988 Loss1: 0.025092 Loss2: 1.267896 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.282687 Loss1: 0.023186 Loss2: 1.259500 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561519 Loss1: 0.155946 Loss2: 1.405574 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.505701 Loss1: 0.137916 Loss2: 1.367785 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.437123 Loss1: 0.080150 Loss2: 1.356973
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.255463 Loss1: 0.348754 Loss2: 1.906709
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.632002 Loss1: 0.242528 Loss2: 1.389474 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.575708 Loss1: 0.163850 Loss2: 1.411858 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.536546 Loss1: 0.137360 Loss2: 1.399187 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393159 Loss1: 0.053530 Loss2: 1.339628
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.545079 Loss1: 0.158304 Loss2: 1.386775
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.511005 Loss1: 0.115466 Loss2: 1.395539
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465611 Loss1: 0.083032 Loss2: 1.382579
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.487377 Loss1: 0.108959 Loss2: 1.378417
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.473205 Loss1: 0.091972 Loss2: 1.381233
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.242872 Loss1: 0.379592 Loss2: 1.863279
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.443690 Loss1: 0.069940 Loss2: 1.373751
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.561223 Loss1: 0.149319 Loss2: 1.411905 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546184 Loss1: 0.169361 Loss2: 1.376823 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.542142 Loss1: 0.154686 Loss2: 1.387456
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.155552 Loss1: 0.362991 Loss2: 1.792561
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.521846 Loss1: 0.218913 Loss2: 1.302933 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.508729 Loss1: 0.190816 Loss2: 1.317913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478048 Loss1: 0.150821 Loss2: 1.327228 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410900 Loss1: 0.040119 Loss2: 1.370781
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.426316 Loss1: 0.115562 Loss2: 1.310754
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.405046 Loss1: 0.098270 Loss2: 1.306776
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.405747 Loss1: 0.104623 Loss2: 1.301124
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409738 Loss1: 0.109721 Loss2: 1.300016
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401785 Loss1: 0.101949 Loss2: 1.299836
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.221905 Loss1: 0.362550 Loss2: 1.859355
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.401367 Loss1: 0.099741 Loss2: 1.301626
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.572391 Loss1: 0.155543 Loss2: 1.416848 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.544861 Loss1: 0.176867 Loss2: 1.367994 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498731 Loss1: 0.109666 Loss2: 1.389065
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.083905 Loss1: 0.269733 Loss2: 1.814172
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.539739 Loss1: 0.193720 Loss2: 1.346019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.530254 Loss1: 0.164051 Loss2: 1.366204 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.444196 Loss1: 0.092251 Loss2: 1.351945 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441681 Loss1: 0.101980 Loss2: 1.339701 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433269 Loss1: 0.090489 Loss2: 1.342780 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.130928 Loss1: 0.314330 Loss2: 1.816598 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.536929 Loss1: 0.212841 Loss2: 1.324089 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.447207 Loss1: 0.113450 Loss2: 1.333757 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.388560 Loss1: 0.071625 Loss2: 1.316935 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.372250 Loss1: 0.057558 Loss2: 1.314692
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.024981 Loss1: 0.278960 Loss2: 1.746022
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.350460 Loss1: 0.039902 Loss2: 1.310559
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.483865 Loss1: 0.197826 Loss2: 1.286039
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.349049 Loss1: 0.046134 Loss2: 1.302915
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.488645 Loss1: 0.187769 Loss2: 1.300876
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.337780 Loss1: 0.036664 Loss2: 1.301116
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.421100 Loss1: 0.111718 Loss2: 1.309382
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.365241 Loss1: 0.077338 Loss2: 1.287903
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.376616 Loss1: 0.093398 Loss2: 1.283218
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.362731 Loss1: 0.084495 Loss2: 1.278236
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.355740 Loss1: 0.078671 Loss2: 1.277069
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.152411 Loss1: 0.308652 Loss2: 1.843760
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.337357 Loss1: 0.069891 Loss2: 1.267466
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.530649 Loss1: 0.194625 Loss2: 1.336024
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.316692 Loss1: 0.048121 Loss2: 1.268571
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.459123 Loss1: 0.109625 Loss2: 1.349498 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.397597 Loss1: 0.073194 Loss2: 1.324403 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.377096 Loss1: 0.058084 Loss2: 1.319012
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.142232 Loss1: 0.306341 Loss2: 1.835892
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382674 Loss1: 0.066031 Loss2: 1.316643
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.550878 Loss1: 0.195591 Loss2: 1.355287
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.353018 Loss1: 0.034212 Loss2: 1.318806
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.522098 Loss1: 0.151867 Loss2: 1.370231
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.359169 Loss1: 0.044287 Loss2: 1.314882
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.493526 Loss1: 0.134624 Loss2: 1.358902
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.528559 Loss1: 0.165585 Loss2: 1.362974
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482594 Loss1: 0.128838 Loss2: 1.353756
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422266 Loss1: 0.072040 Loss2: 1.350226
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.391988 Loss1: 0.047789 Loss2: 1.344199
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.137380 Loss1: 0.327263 Loss2: 1.810117
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370751 Loss1: 0.034765 Loss2: 1.335986
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.609572 Loss1: 0.291963 Loss2: 1.317609
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.374105 Loss1: 0.044290 Loss2: 1.329815
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497535 Loss1: 0.173996 Loss2: 1.323539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.375993 Loss1: 0.067885 Loss2: 1.308108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.349629 Loss1: 0.046342 Loss2: 1.303287
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.074650 Loss1: 0.313002 Loss2: 1.761647
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.530547 Loss1: 0.220955 Loss2: 1.309592 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.490927 Loss1: 0.159866 Loss2: 1.331060 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.415778 Loss1: 0.107311 Loss2: 1.308467 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.434708 Loss1: 0.128572 Loss2: 1.306136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.373408 Loss1: 0.067985 Loss2: 1.305423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.632595 Loss1: 0.293210 Loss2: 1.339385 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.547562 Loss1: 0.181197 Loss2: 1.366365 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981445
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.425941 Loss1: 0.090232 Loss2: 1.335709 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.380581 Loss1: 0.059286 Loss2: 1.321295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.190171 Loss1: 0.327330 Loss2: 1.862841 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.583533 Loss1: 0.223092 Loss2: 1.360441 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997768
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.513016 Loss1: 0.144935 Loss2: 1.368082 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.406008 Loss1: 0.054889 Loss2: 1.351119 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.389913 Loss1: 0.044006 Loss2: 1.345906
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.205044 Loss1: 0.323647 Loss2: 1.881398
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.566349 Loss1: 0.218666 Loss2: 1.347683 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.465733 Loss1: 0.109115 Loss2: 1.356618 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.348311 Loss1: 0.016796 Loss2: 1.331514
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.473356 Loss1: 0.119125 Loss2: 1.354231
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.408371 Loss1: 0.070929 Loss2: 1.337442
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.399221 Loss1: 0.060656 Loss2: 1.338564
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406105 Loss1: 0.069407 Loss2: 1.336698
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389288 Loss1: 0.054333 Loss2: 1.334956
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.267165 Loss1: 0.387412 Loss2: 1.879753
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396823 Loss1: 0.069448 Loss2: 1.327375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392015 Loss1: 0.057914 Loss2: 1.334101 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.466784 Loss1: 0.100865 Loss2: 1.365919 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.469065 Loss1: 0.122352 Loss2: 1.346713 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.192421 Loss1: 0.371645 Loss2: 1.820777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.527094 Loss1: 0.209555 Loss2: 1.317539 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.475673 Loss1: 0.134232 Loss2: 1.341441 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.420412 Loss1: 0.103214 Loss2: 1.317198 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.443394 Loss1: 0.115246 Loss2: 1.328149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.433100 Loss1: 0.104537 Loss2: 1.328563
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.252363 Loss1: 0.342010 Loss2: 1.910353
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.610236 Loss1: 0.216228 Loss2: 1.394007 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398824 Loss1: 0.076810 Loss2: 1.322014
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.577330 Loss1: 0.168219 Loss2: 1.409111
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.543746 Loss1: 0.139137 Loss2: 1.404609
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.479588 Loss1: 0.092928 Loss2: 1.386660
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.484448 Loss1: 0.097542 Loss2: 1.386906
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.472626 Loss1: 0.086315 Loss2: 1.386311
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.492805 Loss1: 0.492829 Loss2: 1.999976
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.431590 Loss1: 0.052181 Loss2: 1.379409
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.418813 Loss1: 0.043885 Loss2: 1.374928 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422074 Loss1: 0.054524 Loss2: 1.367550 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.464537 Loss1: 0.081625 Loss2: 1.382912 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438962 Loss1: 0.069825 Loss2: 1.369137 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.103908 Loss1: 0.326755 Loss2: 1.777153 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995192
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.449824 Loss1: 0.142545 Loss2: 1.307279 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.398059 Loss1: 0.103716 Loss2: 1.294343 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.385861 Loss1: 0.087727 Loss2: 1.298134
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.279053 Loss1: 0.375908 Loss2: 1.903144
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.681914 Loss1: 0.290809 Loss2: 1.391105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.610956 Loss1: 0.189116 Loss2: 1.421840 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498410 Loss1: 0.100114 Loss2: 1.398296 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475023 Loss1: 0.088460 Loss2: 1.386563 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425858 Loss1: 0.047893 Loss2: 1.377965 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.406829 Loss1: 0.036485 Loss2: 1.370344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377873 Loss1: 0.018688 Loss2: 1.359184 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.598415 Loss1: 0.182562 Loss2: 1.415853 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.473364 Loss1: 0.079030 Loss2: 1.394334 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.487232 Loss1: 0.096450 Loss2: 1.390782
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.237080 Loss1: 0.342669 Loss2: 1.894411
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.539358 Loss1: 0.192130 Loss2: 1.347229 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.539912 Loss1: 0.181281 Loss2: 1.358631 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.561204 Loss1: 0.194303 Loss2: 1.366901 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.422701 Loss1: 0.047143 Loss2: 1.375558
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.522385 Loss1: 0.160291 Loss2: 1.362094
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457641 Loss1: 0.109186 Loss2: 1.348455
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.413076 Loss1: 0.068957 Loss2: 1.344119
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411434 Loss1: 0.072611 Loss2: 1.338823
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.363800 Loss1: 0.033200 Loss2: 1.330600
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.140871 Loss1: 0.323096 Loss2: 1.817775
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364321 Loss1: 0.040969 Loss2: 1.323352
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.515139 Loss1: 0.159189 Loss2: 1.355950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.487319 Loss1: 0.142669 Loss2: 1.344650 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.442628 Loss1: 0.097301 Loss2: 1.345326
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.199541 Loss1: 0.344769 Loss2: 1.854772
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.523368 Loss1: 0.170219 Loss2: 1.353149 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.483216 Loss1: 0.127807 Loss2: 1.355409 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.464440 Loss1: 0.108169 Loss2: 1.356271 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.358739 Loss1: 0.045065 Loss2: 1.313674
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.461780 Loss1: 0.118881 Loss2: 1.342899
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.451427 Loss1: 0.107134 Loss2: 1.344293
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.448629 Loss1: 0.100748 Loss2: 1.347881
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.444382 Loss1: 0.092987 Loss2: 1.351395
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.417572 Loss1: 0.074609 Loss2: 1.342964
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.191455 Loss1: 0.324192 Loss2: 1.867263
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393884 Loss1: 0.047792 Loss2: 1.346092
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.547857 Loss1: 0.175615 Loss2: 1.372242 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.479838 Loss1: 0.119361 Loss2: 1.360476 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.516323 Loss1: 0.143243 Loss2: 1.373079
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.449263 Loss1: 0.458454 Loss2: 1.990809
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.733330 Loss1: 0.369134 Loss2: 1.364197 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.659386 Loss1: 0.254191 Loss2: 1.405195
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449317 Loss1: 0.084107 Loss2: 1.365210
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.453701 Loss1: 0.087593 Loss2: 1.366108 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.449715 Loss1: 0.087297 Loss2: 1.362418 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.430155 Loss1: 0.061059 Loss2: 1.369096 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.405151 Loss1: 0.046393 Loss2: 1.358759 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.161716 Loss1: 0.254523 Loss2: 1.907193
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.649007 Loss1: 0.263032 Loss2: 1.385976
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.603846 Loss1: 0.183761 Loss2: 1.420086
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.550027 Loss1: 0.135417 Loss2: 1.414610
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.172768 Loss1: 0.290366 Loss2: 1.882401
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.590555 Loss1: 0.193216 Loss2: 1.397339 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.542668 Loss1: 0.131107 Loss2: 1.411561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.528989 Loss1: 0.112025 Loss2: 1.416964 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.529902 Loss1: 0.123786 Loss2: 1.406116 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515944 Loss1: 0.106765 Loss2: 1.409179 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.464701 Loss1: 0.067366 Loss2: 1.397335 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460079 Loss1: 0.066091 Loss2: 1.393988 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985352
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.596924 Loss1: 0.229274 Loss2: 1.367650 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529990 Loss1: 0.161593 Loss2: 1.368397 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.469644 Loss1: 0.125464 Loss2: 1.344180
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.160479 Loss1: 0.346244 Loss2: 1.814235
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.535387 Loss1: 0.210876 Loss2: 1.324511 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.501389 Loss1: 0.172822 Loss2: 1.328567 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482760 Loss1: 0.153950 Loss2: 1.328810 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490683 Loss1: 0.171757 Loss2: 1.318926 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380008 Loss1: 0.038326 Loss2: 1.341681
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468290 Loss1: 0.144833 Loss2: 1.323457
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432512 Loss1: 0.116758 Loss2: 1.315754
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.389548 Loss1: 0.075265 Loss2: 1.314283
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.369717 Loss1: 0.063952 Loss2: 1.305765
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364182 Loss1: 0.068132 Loss2: 1.296050
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.178522 Loss1: 0.355209 Loss2: 1.823313
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.544921 Loss1: 0.213149 Loss2: 1.331772
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.479515 Loss1: 0.146754 Loss2: 1.332761
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.485731 Loss1: 0.150324 Loss2: 1.335407
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.417559 Loss1: 0.093678 Loss2: 1.323881
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.341446 Loss1: 0.437371 Loss2: 1.904076
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.637201 Loss1: 0.291768 Loss2: 1.345433 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.410917 Loss1: 0.089136 Loss2: 1.321781
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.572403 Loss1: 0.185289 Loss2: 1.387114
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408117 Loss1: 0.094313 Loss2: 1.313803
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.520342 Loss1: 0.156106 Loss2: 1.364237
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.457945 Loss1: 0.113099 Loss2: 1.344846
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410700 Loss1: 0.094398 Loss2: 1.316301
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.411455 Loss1: 0.059453 Loss2: 1.352002
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392424 Loss1: 0.070489 Loss2: 1.321935
+(DefaultActor pid=3764) >> Training accuracy: 0.980208
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.361454 Loss1: 0.034362 Loss2: 1.327092 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.347992 Loss1: 0.033534 Loss2: 1.314458 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997768
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.104496 Loss1: 0.240410 Loss2: 1.864086
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.529713 Loss1: 0.146080 Loss2: 1.383633
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.476706 Loss1: 0.098792 Loss2: 1.377913
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.442305 Loss1: 0.067614 Loss2: 1.374691
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.236544 Loss1: 0.333803 Loss2: 1.902741
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.464902 Loss1: 0.094031 Loss2: 1.370871
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.606824 Loss1: 0.216826 Loss2: 1.389998
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.468258 Loss1: 0.089034 Loss2: 1.379224
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.576527 Loss1: 0.162976 Loss2: 1.413550
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.550384 Loss1: 0.144905 Loss2: 1.405479
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.509885 Loss1: 0.133127 Loss2: 1.376759
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.524983 Loss1: 0.132835 Loss2: 1.392147
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.437638 Loss1: 0.064987 Loss2: 1.372651
+DEBUG flwr 2023-10-13 16:52:41,829 | server.py:236 | fit_round 195 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.523726 Loss1: 0.125428 Loss2: 1.398297
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410773 Loss1: 0.047755 Loss2: 1.363019
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.479256 Loss1: 0.083451 Loss2: 1.395804
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.411466 Loss1: 0.049007 Loss2: 1.362459
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.506580 Loss1: 0.103071 Loss2: 1.403509 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.201993 Loss1: 0.385439 Loss2: 1.816554 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.553060 Loss1: 0.194993 Loss2: 1.358066 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.504666 Loss1: 0.174671 Loss2: 1.329995
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.166126 Loss1: 0.323416 Loss2: 1.842710
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.508936 Loss1: 0.170300 Loss2: 1.338635
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.491430 Loss1: 0.153142 Loss2: 1.338288
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.475450 Loss1: 0.140980 Loss2: 1.334470
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.477628 Loss1: 0.141956 Loss2: 1.335673
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427444 Loss1: 0.094848 Loss2: 1.332596
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.506551 Loss1: 0.146758 Loss2: 1.359793
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.396005 Loss1: 0.074248 Loss2: 1.321758
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.429570 Loss1: 0.086723 Loss2: 1.342847
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386042 Loss1: 0.067072 Loss2: 1.318970
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444115 Loss1: 0.112880 Loss2: 1.331235
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369461 Loss1: 0.060298 Loss2: 1.309163
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.412861 Loss1: 0.073601 Loss2: 1.339260
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.413104 Loss1: 0.081076 Loss2: 1.332028
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.380772 Loss1: 0.048309 Loss2: 1.332463
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.363838 Loss1: 0.037975 Loss2: 1.325862
+(DefaultActor pid=3765) >> Training accuracy: 1.000000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.168519 Loss1: 0.304701 Loss2: 1.863818
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.578880 Loss1: 0.228278 Loss2: 1.350602
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556041 Loss1: 0.192204 Loss2: 1.363837
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519810 Loss1: 0.148193 Loss2: 1.371617
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493636 Loss1: 0.139448 Loss2: 1.354188
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.547696 Loss1: 0.175449 Loss2: 1.372248
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.519523 Loss1: 0.152571 Loss2: 1.366952
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.484714 Loss1: 0.119798 Loss2: 1.364916
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.469227 Loss1: 0.106314 Loss2: 1.362912
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.441004 Loss1: 0.087354 Loss2: 1.353650
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 16:52:41,829][flwr][DEBUG] - fit_round 195 received 50 results and 0 failures
+INFO flwr 2023-10-13 16:53:22,720 | server.py:125 | fit progress: (195, 2.337302811420002, {'accuracy': 0.6132}, 450110.49889478396)
+>> Test accuracy: 0.613200
+[2023-10-13 16:53:22,720][flwr][INFO] - fit progress: (195, 2.337302811420002, {'accuracy': 0.6132}, 450110.49889478396)
+DEBUG flwr 2023-10-13 16:53:22,721 | server.py:173 | evaluate_round 195: strategy sampled 50 clients (out of 50)
+[2023-10-13 16:53:22,721][flwr][DEBUG] - evaluate_round 195: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 17:02:27,987 | server.py:187 | evaluate_round 195 received 50 results and 0 failures
+[2023-10-13 17:02:27,987][flwr][DEBUG] - evaluate_round 195 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 17:02:27,987 | server.py:222 | fit_round 196: strategy sampled 50 clients (out of 50)
+[2023-10-13 17:02:27,987][flwr][DEBUG] - fit_round 196: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.231675 Loss1: 0.349445 Loss2: 1.882230
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.562381 Loss1: 0.204576 Loss2: 1.357805
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.522060 Loss1: 0.153419 Loss2: 1.368641
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.481238 Loss1: 0.119053 Loss2: 1.362185
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.462663 Loss1: 0.115907 Loss2: 1.346756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444797 Loss1: 0.099033 Loss2: 1.345764 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.423120 Loss1: 0.079320 Loss2: 1.343801 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380350 Loss1: 0.044232 Loss2: 1.336118 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.383058 Loss1: 0.054192 Loss2: 1.328866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390660 Loss1: 0.059971 Loss2: 1.330689 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.356766 Loss1: 0.065703 Loss2: 1.291063 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.419612 Loss1: 0.471051 Loss2: 1.948561 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.639798 Loss1: 0.226709 Loss2: 1.413089 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.540265 Loss1: 0.148509 Loss2: 1.391756 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.498661 Loss1: 0.099902 Loss2: 1.398759 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.456879 Loss1: 0.071488 Loss2: 1.385391 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.447887 Loss1: 0.073872 Loss2: 1.374015 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.423163 Loss1: 0.052063 Loss2: 1.371100 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.404042 Loss1: 0.036648 Loss2: 1.367394 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998798
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404091 Loss1: 0.086330 Loss2: 1.317762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404485 Loss1: 0.090381 Loss2: 1.314103 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.372681 Loss1: 0.058106 Loss2: 1.314575
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.292822 Loss1: 0.352716 Loss2: 1.940106
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.638719 Loss1: 0.235973 Loss2: 1.402745
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.635357 Loss1: 0.230291 Loss2: 1.405066
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.587649 Loss1: 0.153314 Loss2: 1.434335
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.520927 Loss1: 0.115776 Loss2: 1.405151
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493101 Loss1: 0.096557 Loss2: 1.396544
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.096714 Loss1: 0.276185 Loss2: 1.820529
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.552157 Loss1: 0.210077 Loss2: 1.342080 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.528017 Loss1: 0.152521 Loss2: 1.375496 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.450030 Loss1: 0.105643 Loss2: 1.344387 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.450775 Loss1: 0.107207 Loss2: 1.343568 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406912 Loss1: 0.070640 Loss2: 1.336272 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384187 Loss1: 0.050234 Loss2: 1.333952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.358835 Loss1: 0.033849 Loss2: 1.324986 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996324
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498218 Loss1: 0.164931 Loss2: 1.333287 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463800 Loss1: 0.132456 Loss2: 1.331344 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.460837 Loss1: 0.126982 Loss2: 1.333855
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.102782 Loss1: 0.285755 Loss2: 1.817027
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.532256 Loss1: 0.197617 Loss2: 1.334639 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374880 Loss1: 0.058978 Loss2: 1.315902
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.485133 Loss1: 0.146106 Loss2: 1.339028
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380370 Loss1: 0.063824 Loss2: 1.316546
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.472702 Loss1: 0.128748 Loss2: 1.343954
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.440332 Loss1: 0.104282 Loss2: 1.336050
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.440497 Loss1: 0.105176 Loss2: 1.335321
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.435193 Loss1: 0.106765 Loss2: 1.328428
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.418577 Loss1: 0.088894 Loss2: 1.329683
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.122738 Loss1: 0.325661 Loss2: 1.797078
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.425042 Loss1: 0.094738 Loss2: 1.330304
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.504772 Loss1: 0.187559 Loss2: 1.317213
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365905 Loss1: 0.037157 Loss2: 1.328748
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.413962 Loss1: 0.116222 Loss2: 1.297740 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.395852 Loss1: 0.086360 Loss2: 1.309492 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.290584 Loss1: 0.350770 Loss2: 1.939814
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390100 Loss1: 0.082947 Loss2: 1.307154
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.674261 Loss1: 0.305050 Loss2: 1.369211
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.357836 Loss1: 0.064011 Loss2: 1.293825
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572048 Loss1: 0.167470 Loss2: 1.404578
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.349837 Loss1: 0.056464 Loss2: 1.293373
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.348034 Loss1: 0.060538 Loss2: 1.287497 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476237 Loss1: 0.099219 Loss2: 1.377018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.419786 Loss1: 0.055619 Loss2: 1.364167 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.147666 Loss1: 0.285385 Loss2: 1.862281 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526925 Loss1: 0.159281 Loss2: 1.367643 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.511010 Loss1: 0.153035 Loss2: 1.357975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.516558 Loss1: 0.152054 Loss2: 1.364504
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.170251 Loss1: 0.301788 Loss2: 1.868462
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.576129 Loss1: 0.219179 Loss2: 1.356950 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.501476 Loss1: 0.127195 Loss2: 1.374281 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.426900 Loss1: 0.077894 Loss2: 1.349006 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387375 Loss1: 0.044042 Loss2: 1.343333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.400775 Loss1: 0.065608 Loss2: 1.335168
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.398753 Loss1: 0.060634 Loss2: 1.338119
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.386852 Loss1: 0.051297 Loss2: 1.335556
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.380384 Loss1: 0.049435 Loss2: 1.330949
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.362082 Loss1: 0.034380 Loss2: 1.327702
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.131584 Loss1: 0.305166 Loss2: 1.826418
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.388097 Loss1: 0.064187 Loss2: 1.323910
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.547986 Loss1: 0.182365 Loss2: 1.365621 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.546030 Loss1: 0.198318 Loss2: 1.347712 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.457083 Loss1: 0.116953 Loss2: 1.340131
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.168734 Loss1: 0.323150 Loss2: 1.845584
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.550291 Loss1: 0.201319 Loss2: 1.348972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.556035 Loss1: 0.191139 Loss2: 1.364896 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.529790 Loss1: 0.153470 Loss2: 1.376320 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.402720 Loss1: 0.075082 Loss2: 1.327638
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497233 Loss1: 0.139696 Loss2: 1.357536
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460977 Loss1: 0.103954 Loss2: 1.357023
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431072 Loss1: 0.082329 Loss2: 1.348743
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.445282 Loss1: 0.100790 Loss2: 1.344492
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406720 Loss1: 0.063157 Loss2: 1.343563
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.155564 Loss1: 0.334187 Loss2: 1.821376
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.409343 Loss1: 0.070070 Loss2: 1.339273
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574537 Loss1: 0.184625 Loss2: 1.389913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.482334 Loss1: 0.126929 Loss2: 1.355405 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.456131 Loss1: 0.106994 Loss2: 1.349137
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.218029 Loss1: 0.330712 Loss2: 1.887317
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.599045 Loss1: 0.224072 Loss2: 1.374972 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.623064 Loss1: 0.214973 Loss2: 1.408091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.568034 Loss1: 0.176325 Loss2: 1.391709 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.970833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.408459 Loss1: 0.073046 Loss2: 1.335413
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.534076 Loss1: 0.162819 Loss2: 1.371258
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.492855 Loss1: 0.114236 Loss2: 1.378619
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455028 Loss1: 0.087824 Loss2: 1.367205
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413655 Loss1: 0.051997 Loss2: 1.361658
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395812 Loss1: 0.040168 Loss2: 1.355644
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.320230 Loss1: 0.354314 Loss2: 1.965916
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.399463 Loss1: 0.054883 Loss2: 1.344580
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.659788 Loss1: 0.201120 Loss2: 1.458668 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517179 Loss1: 0.098227 Loss2: 1.418952 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.505500 Loss1: 0.099000 Loss2: 1.406500
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.154389 Loss1: 0.306805 Loss2: 1.847584
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.546207 Loss1: 0.197012 Loss2: 1.349194 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.505455 Loss1: 0.141107 Loss2: 1.364348 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.445529 Loss1: 0.099808 Loss2: 1.345721 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.437593 Loss1: 0.102714 Loss2: 1.334879 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.407030 Loss1: 0.077814 Loss2: 1.329215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.356446 Loss1: 0.029135 Loss2: 1.327311 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375514 Loss1: 0.051587 Loss2: 1.323927 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.472625 Loss1: 0.136186 Loss2: 1.336439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.413208 Loss1: 0.088859 Loss2: 1.324348 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.402028 Loss1: 0.076070 Loss2: 1.325958
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.200270 Loss1: 0.320826 Loss2: 1.879443
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416113 Loss1: 0.099436 Loss2: 1.316677
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.521756 Loss1: 0.165680 Loss2: 1.356076
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.438779 Loss1: 0.116942 Loss2: 1.321837
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.453647 Loss1: 0.102763 Loss2: 1.350884
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.425488 Loss1: 0.074988 Loss2: 1.350501
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.372925 Loss1: 0.053161 Loss2: 1.319765
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.382047 Loss1: 0.050792 Loss2: 1.331255
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.375586 Loss1: 0.060650 Loss2: 1.314937
+(DefaultActor pid=3765) >> Training accuracy: 0.988281
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.359969 Loss1: 0.041039 Loss2: 1.318930 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.343690 Loss1: 0.026153 Loss2: 1.317537 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.338776 Loss1: 0.029809 Loss2: 1.308967
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.078153 Loss1: 0.302772 Loss2: 1.775381
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.552800 Loss1: 0.214642 Loss2: 1.338158
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.474509 Loss1: 0.127896 Loss2: 1.346613
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.444067 Loss1: 0.117208 Loss2: 1.326859
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.417145 Loss1: 0.090939 Loss2: 1.326206
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.243478 Loss1: 0.355540 Loss2: 1.887939
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.444378 Loss1: 0.124865 Loss2: 1.319513
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.412746 Loss1: 0.091977 Loss2: 1.320768 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.372004 Loss1: 0.060966 Loss2: 1.311037 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.374547 Loss1: 0.063614 Loss2: 1.310934 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.349988 Loss1: 0.044080 Loss2: 1.305907 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461133 Loss1: 0.084078 Loss2: 1.377055 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441299 Loss1: 0.075676 Loss2: 1.365623 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.492936 Loss1: 0.124618 Loss2: 1.368318
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.184982 Loss1: 0.390724 Loss2: 1.794258
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.655738 Loss1: 0.313986 Loss2: 1.341753
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.585003 Loss1: 0.205883 Loss2: 1.379120
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488080 Loss1: 0.145223 Loss2: 1.342857
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.453810 Loss1: 0.115892 Loss2: 1.337918
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.115722 Loss1: 0.285783 Loss2: 1.829939
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.476216 Loss1: 0.158964 Loss2: 1.317252 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.435943 Loss1: 0.118038 Loss2: 1.317905 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.397923 Loss1: 0.072723 Loss2: 1.325199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.390454 Loss1: 0.073588 Loss2: 1.316866 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.372364 Loss1: 0.058034 Loss2: 1.314330 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.365274 Loss1: 0.060029 Loss2: 1.305245 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.327392 Loss1: 0.025865 Loss2: 1.301527 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.509203 Loss1: 0.158922 Loss2: 1.350281 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.470215 Loss1: 0.101533 Loss2: 1.368681 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480994 Loss1: 0.125169 Loss2: 1.355825
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.153702 Loss1: 0.316536 Loss2: 1.837166
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.449143 Loss1: 0.090647 Loss2: 1.358496
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.655360 Loss1: 0.284272 Loss2: 1.371088
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.429531 Loss1: 0.075721 Loss2: 1.353810
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.616662 Loss1: 0.205814 Loss2: 1.410848
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436766 Loss1: 0.083348 Loss2: 1.353417
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539621 Loss1: 0.159463 Loss2: 1.380158
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399238 Loss1: 0.054145 Loss2: 1.345093
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.521933 Loss1: 0.142419 Loss2: 1.379515
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410807 Loss1: 0.072301 Loss2: 1.338506
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.461346 Loss1: 0.090103 Loss2: 1.371243
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461283 Loss1: 0.101680 Loss2: 1.359602
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434319 Loss1: 0.077096 Loss2: 1.357223
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.479730 Loss1: 0.124175 Loss2: 1.355555
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.435554 Loss1: 0.073265 Loss2: 1.362289
+(DefaultActor pid=3764) >> Training accuracy: 0.987305
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.265293 Loss1: 0.390665 Loss2: 1.874628
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.580088 Loss1: 0.223012 Loss2: 1.357076
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.536523 Loss1: 0.157277 Loss2: 1.379246
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.464003 Loss1: 0.107332 Loss2: 1.356672
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.440372 Loss1: 0.093674 Loss2: 1.346698
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.209080 Loss1: 0.357911 Loss2: 1.851169
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.556353 Loss1: 0.197075 Loss2: 1.359278 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.548386 Loss1: 0.157265 Loss2: 1.391121 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.530446 Loss1: 0.152182 Loss2: 1.378265 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500816 Loss1: 0.129859 Loss2: 1.370957 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.471526 Loss1: 0.094182 Loss2: 1.377343 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405827 Loss1: 0.045256 Loss2: 1.360571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.365503 Loss1: 0.023868 Loss2: 1.341635 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.708624 Loss1: 0.292341 Loss2: 1.416284 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.571538 Loss1: 0.145910 Loss2: 1.425627 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.593116 Loss1: 0.179788 Loss2: 1.413328
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.244292 Loss1: 0.333195 Loss2: 1.911097
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.574523 Loss1: 0.175597 Loss2: 1.398926 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.555451 Loss1: 0.157454 Loss2: 1.397997 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.571380 Loss1: 0.175058 Loss2: 1.396323 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.516515 Loss1: 0.123132 Loss2: 1.393382 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.531598 Loss1: 0.139809 Loss2: 1.391789 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.511923 Loss1: 0.117208 Loss2: 1.394715 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.472723 Loss1: 0.092265 Loss2: 1.380458 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.555110 Loss1: 0.216666 Loss2: 1.338444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.454631 Loss1: 0.109161 Loss2: 1.345470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.425414 Loss1: 0.087256 Loss2: 1.338158
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.136559 Loss1: 0.318815 Loss2: 1.817744
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.601045 Loss1: 0.273563 Loss2: 1.327482
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440932 Loss1: 0.105967 Loss2: 1.334966
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.581472 Loss1: 0.224764 Loss2: 1.356708
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.463471 Loss1: 0.129594 Loss2: 1.333876
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496438 Loss1: 0.136850 Loss2: 1.359588
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.445983 Loss1: 0.106712 Loss2: 1.339271
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.537718 Loss1: 0.197171 Loss2: 1.340547
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.407341 Loss1: 0.072821 Loss2: 1.334519
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392597 Loss1: 0.065995 Loss2: 1.326602 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427164 Loss1: 0.091548 Loss2: 1.335616 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397932 Loss1: 0.065923 Loss2: 1.332008 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.578345 Loss1: 0.216470 Loss2: 1.361875 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.450958 Loss1: 0.095475 Loss2: 1.355483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.125043 Loss1: 0.294555 Loss2: 1.830489
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.426657 Loss1: 0.082372 Loss2: 1.344285
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.564428 Loss1: 0.227438 Loss2: 1.336990
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.423563 Loss1: 0.076808 Loss2: 1.346755
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.477583 Loss1: 0.122878 Loss2: 1.354705
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.386676 Loss1: 0.044527 Loss2: 1.342150
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.469934 Loss1: 0.119636 Loss2: 1.350298
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.410105 Loss1: 0.073599 Loss2: 1.336506
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450032 Loss1: 0.113718 Loss2: 1.336314
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385974 Loss1: 0.051396 Loss2: 1.334578
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.436894 Loss1: 0.093747 Loss2: 1.343147
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373966 Loss1: 0.043850 Loss2: 1.330116
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.411385 Loss1: 0.079040 Loss2: 1.332345 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378086 Loss1: 0.046648 Loss2: 1.331437 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.586501 Loss1: 0.228669 Loss2: 1.357832 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497297 Loss1: 0.121934 Loss2: 1.375363 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.457139 Loss1: 0.097340 Loss2: 1.359799 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.439598 Loss1: 0.080642 Loss2: 1.358956 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.438751 Loss1: 0.079270 Loss2: 1.359481 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.426055 Loss1: 0.077535 Loss2: 1.348520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431692 Loss1: 0.079100 Loss2: 1.352592 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.420581 Loss1: 0.071581 Loss2: 1.349001 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439106 Loss1: 0.069778 Loss2: 1.369328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.446542 Loss1: 0.085407 Loss2: 1.361135 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.129116 Loss1: 0.309932 Loss2: 1.819184
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.570563 Loss1: 0.210379 Loss2: 1.360184
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.503231 Loss1: 0.132595 Loss2: 1.370636
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482384 Loss1: 0.137516 Loss2: 1.344867
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.270199 Loss1: 0.346059 Loss2: 1.924140
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.673015 Loss1: 0.269964 Loss2: 1.403051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.592478 Loss1: 0.150241 Loss2: 1.442236 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390030 Loss1: 0.058112 Loss2: 1.331919
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.516899 Loss1: 0.095648 Loss2: 1.421251
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.387946 Loss1: 0.056462 Loss2: 1.331484
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.459011 Loss1: 0.061290 Loss2: 1.397721
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.379389 Loss1: 0.047237 Loss2: 1.332151
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460286 Loss1: 0.066936 Loss2: 1.393350
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488887 Loss1: 0.093233 Loss2: 1.395655
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.365107 Loss1: 0.037611 Loss2: 1.327496
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458587 Loss1: 0.074535 Loss2: 1.384051 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.203043 Loss1: 0.340341 Loss2: 1.862702 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.485216 Loss1: 0.138054 Loss2: 1.347162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.462427 Loss1: 0.119640 Loss2: 1.342787
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.238681 Loss1: 0.356572 Loss2: 1.882109
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.480137 Loss1: 0.147682 Loss2: 1.332454
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.614955 Loss1: 0.234602 Loss2: 1.380353
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.428410 Loss1: 0.097119 Loss2: 1.331291
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.552167 Loss1: 0.137942 Loss2: 1.414225
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.428578 Loss1: 0.093814 Loss2: 1.334763
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.524378 Loss1: 0.134699 Loss2: 1.389679
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.380484 Loss1: 0.059953 Loss2: 1.320531
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.515564 Loss1: 0.126997 Loss2: 1.388567
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.376541 Loss1: 0.060653 Loss2: 1.315887
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.515214 Loss1: 0.123784 Loss2: 1.391431
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.361148 Loss1: 0.051702 Loss2: 1.309446
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461766 Loss1: 0.076969 Loss2: 1.384797
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.449551 Loss1: 0.064695 Loss2: 1.384855
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.448673 Loss1: 0.070759 Loss2: 1.377914
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.416985 Loss1: 0.043287 Loss2: 1.373697
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.313609 Loss1: 0.416037 Loss2: 1.897573
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.644910 Loss1: 0.329618 Loss2: 1.315292
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611216 Loss1: 0.226454 Loss2: 1.384762
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.551626 Loss1: 0.213040 Loss2: 1.338586
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.162749 Loss1: 0.318780 Loss2: 1.843968
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455647 Loss1: 0.107852 Loss2: 1.347796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408387 Loss1: 0.082811 Loss2: 1.325575 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.370083 Loss1: 0.058320 Loss2: 1.311763 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.360285 Loss1: 0.051424 Loss2: 1.308861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.364634 Loss1: 0.058139 Loss2: 1.306495 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990385
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.437409 Loss1: 0.095624 Loss2: 1.341785 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.397050 Loss1: 0.055502 Loss2: 1.341548 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.387635 Loss1: 0.055002 Loss2: 1.332634
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.151081 Loss1: 0.355705 Loss2: 1.795375
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.488661 Loss1: 0.186728 Loss2: 1.301933
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.440882 Loss1: 0.128057 Loss2: 1.312825
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.387251 Loss1: 0.089296 Loss2: 1.297955
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.377134 Loss1: 0.086294 Loss2: 1.290840
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.373115 Loss1: 0.435608 Loss2: 1.937507
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659526 Loss1: 0.272410 Loss2: 1.387116 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653793 Loss1: 0.234825 Loss2: 1.418968 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.556735 Loss1: 0.152336 Loss2: 1.404398 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.340113 Loss1: 0.058368 Loss2: 1.281745
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513339 Loss1: 0.124141 Loss2: 1.389198
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.369392 Loss1: 0.084581 Loss2: 1.284812
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512242 Loss1: 0.130321 Loss2: 1.381921
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.460996 Loss1: 0.081083 Loss2: 1.379913
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.455593 Loss1: 0.084668 Loss2: 1.370925
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.430147 Loss1: 0.058530 Loss2: 1.371617
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.431440 Loss1: 0.064350 Loss2: 1.367090
+(DefaultActor pid=3764) >> Training accuracy: 0.986607
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.135393 Loss1: 0.349076 Loss2: 1.786317
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.541175 Loss1: 0.198616 Loss2: 1.342558
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.508681 Loss1: 0.160723 Loss2: 1.347958
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.490513 Loss1: 0.160524 Loss2: 1.329989
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.201674 Loss1: 0.382291 Loss2: 1.819383
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.491195 Loss1: 0.158712 Loss2: 1.332483 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.475438 Loss1: 0.151753 Loss2: 1.323685 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.440631 Loss1: 0.107933 Loss2: 1.332699 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.453275 Loss1: 0.136323 Loss2: 1.316952 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.430071 Loss1: 0.104262 Loss2: 1.325809 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387983 Loss1: 0.063545 Loss2: 1.324438
+DEBUG flwr 2023-10-13 17:31:00,648 | server.py:236 | fit_round 196 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.409086 Loss1: 0.093709 Loss2: 1.315377
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.374739 Loss1: 0.061383 Loss2: 1.313355
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.404979 Loss1: 0.091529 Loss2: 1.313449
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.368413 Loss1: 0.057755 Loss2: 1.310658
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.379473 Loss1: 0.386654 Loss2: 1.992819
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.739319 Loss1: 0.285090 Loss2: 1.454229
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.641215 Loss1: 0.165813 Loss2: 1.475402
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.591179 Loss1: 0.127106 Loss2: 1.464073
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.389006 Loss1: 0.428273 Loss2: 1.960733
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.565479 Loss1: 0.227414 Loss2: 1.338065
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.559584 Loss1: 0.109065 Loss2: 1.450519
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.539817 Loss1: 0.201398 Loss2: 1.338419
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.561936 Loss1: 0.116212 Loss2: 1.445724 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.553450 Loss1: 0.111242 Loss2: 1.442207 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.483123 Loss1: 0.141183 Loss2: 1.341940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.510448 Loss1: 0.068424 Loss2: 1.442025 [repeated 3x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413322 Loss1: 0.072979 Loss2: 1.340343 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988281
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.266912 Loss1: 0.410314 Loss2: 1.856598
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.685159 Loss1: 0.332913 Loss2: 1.352247
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.599429 Loss1: 0.173571 Loss2: 1.425857
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.476552 Loss1: 0.120507 Loss2: 1.356045
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.194118 Loss1: 0.340141 Loss2: 1.853976
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.642473 Loss1: 0.270565 Loss2: 1.371909 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.540903 Loss1: 0.146697 Loss2: 1.394206 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.477737 Loss1: 0.099247 Loss2: 1.378490 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.497840 Loss1: 0.131021 Loss2: 1.366820 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460747 Loss1: 0.093615 Loss2: 1.367133 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.452740 Loss1: 0.089017 Loss2: 1.363723 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.405047 Loss1: 0.046649 Loss2: 1.358398 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995117
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 17:31:00,648][flwr][DEBUG] - fit_round 196 received 50 results and 0 failures
+INFO flwr 2023-10-13 17:31:41,751 | server.py:125 | fit progress: (196, 2.348089092646163, {'accuracy': 0.6137}, 452409.529389542)
+>> Test accuracy: 0.613700
+[2023-10-13 17:31:41,751][flwr][INFO] - fit progress: (196, 2.348089092646163, {'accuracy': 0.6137}, 452409.529389542)
+DEBUG flwr 2023-10-13 17:31:41,751 | server.py:173 | evaluate_round 196: strategy sampled 50 clients (out of 50)
+[2023-10-13 17:31:41,751][flwr][DEBUG] - evaluate_round 196: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 17:40:44,621 | server.py:187 | evaluate_round 196 received 50 results and 0 failures
+[2023-10-13 17:40:44,621][flwr][DEBUG] - evaluate_round 196 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 17:40:44,621 | server.py:222 | fit_round 197: strategy sampled 50 clients (out of 50)
+[2023-10-13 17:40:44,621][flwr][DEBUG] - fit_round 197: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.200727 Loss1: 0.355533 Loss2: 1.845194
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.558443 Loss1: 0.201419 Loss2: 1.357024
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.520045 Loss1: 0.141039 Loss2: 1.379006
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534632 Loss1: 0.158483 Loss2: 1.376149
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.277008 Loss1: 0.391141 Loss2: 1.885868
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.506503 Loss1: 0.140177 Loss2: 1.366326
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.626831 Loss1: 0.294666 Loss2: 1.332165
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.489078 Loss1: 0.116404 Loss2: 1.372674
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.540700 Loss1: 0.167486 Loss2: 1.373214
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468454 Loss1: 0.106549 Loss2: 1.361905
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.480958 Loss1: 0.145317 Loss2: 1.335641
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.471135 Loss1: 0.132841 Loss2: 1.338294
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.473847 Loss1: 0.113549 Loss2: 1.360298
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452873 Loss1: 0.110795 Loss2: 1.342078
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.459572 Loss1: 0.096733 Loss2: 1.362840
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.432388 Loss1: 0.098114 Loss2: 1.334274
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.417448 Loss1: 0.065257 Loss2: 1.352191
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384998 Loss1: 0.056402 Loss2: 1.328596 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996652
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.204791 Loss1: 0.325240 Loss2: 1.879551 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.560038 Loss1: 0.158767 Loss2: 1.401271 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.468355 Loss1: 0.096247 Loss2: 1.372108
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.119361 Loss1: 0.325085 Loss2: 1.794276
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.436088 Loss1: 0.076713 Loss2: 1.359376
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.518631 Loss1: 0.212527 Loss2: 1.306104
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.452343 Loss1: 0.089806 Loss2: 1.362537
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.434115 Loss1: 0.112091 Loss2: 1.322024
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435978 Loss1: 0.075825 Loss2: 1.360153
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.436750 Loss1: 0.121698 Loss2: 1.315051
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.403599 Loss1: 0.053595 Loss2: 1.350004
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.367711 Loss1: 0.069730 Loss2: 1.297982
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399225 Loss1: 0.051072 Loss2: 1.348154
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.356191 Loss1: 0.059393 Loss2: 1.296798
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.374803 Loss1: 0.034745 Loss2: 1.340058
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.314309 Loss1: 0.022564 Loss2: 1.291745
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.315380 Loss1: 0.030060 Loss2: 1.285320
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.314729 Loss1: 0.033432 Loss2: 1.281297
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.310243 Loss1: 0.034811 Loss2: 1.275432
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.273429 Loss1: 0.394868 Loss2: 1.878561
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.623005 Loss1: 0.265425 Loss2: 1.357580
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568058 Loss1: 0.165908 Loss2: 1.402149
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.477767 Loss1: 0.107453 Loss2: 1.370314
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.122034 Loss1: 0.305240 Loss2: 1.816793
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.460254 Loss1: 0.092603 Loss2: 1.367652
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.516021 Loss1: 0.185600 Loss2: 1.330421
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.434731 Loss1: 0.075877 Loss2: 1.358854
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.447599 Loss1: 0.117034 Loss2: 1.330565
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.406397 Loss1: 0.052547 Loss2: 1.353850
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.426264 Loss1: 0.096606 Loss2: 1.329659
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384861 Loss1: 0.037649 Loss2: 1.347212
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.452228 Loss1: 0.143051 Loss2: 1.309177
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.364611 Loss1: 0.023504 Loss2: 1.341106
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.426207 Loss1: 0.100602 Loss2: 1.325604
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.370308 Loss1: 0.037275 Loss2: 1.333033
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.409761 Loss1: 0.092625 Loss2: 1.317135
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406374 Loss1: 0.087843 Loss2: 1.318532
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.408045 Loss1: 0.092059 Loss2: 1.315986
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.378524 Loss1: 0.063203 Loss2: 1.315321
+(DefaultActor pid=3764) >> Training accuracy: 0.983333
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.208335 Loss1: 0.371927 Loss2: 1.836408
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.590392 Loss1: 0.248693 Loss2: 1.341699
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.444664 Loss1: 0.097009 Loss2: 1.347655
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.423846 Loss1: 0.084874 Loss2: 1.338972
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.031811 Loss1: 0.240133 Loss2: 1.791678
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.539632 Loss1: 0.216988 Loss2: 1.322644 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.439185 Loss1: 0.097887 Loss2: 1.341298 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.432555 Loss1: 0.114291 Loss2: 1.318264 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.398991 Loss1: 0.079039 Loss2: 1.319952 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426121 Loss1: 0.106091 Loss2: 1.320031 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.352069 Loss1: 0.045149 Loss2: 1.306920 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.352242 Loss1: 0.053831 Loss2: 1.298410 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993566
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.589631 Loss1: 0.290863 Loss2: 1.298768 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.463548 Loss1: 0.129971 Loss2: 1.333577 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.421911 Loss1: 0.110936 Loss2: 1.310975 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408961 Loss1: 0.096271 Loss2: 1.312690 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.364390 Loss1: 0.058884 Loss2: 1.305507 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.364309 Loss1: 0.059975 Loss2: 1.304334 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.347688 Loss1: 0.053178 Loss2: 1.294510 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996652
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.439396 Loss1: 0.079845 Loss2: 1.359551 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.457555 Loss1: 0.098007 Loss2: 1.359548 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.100811 Loss1: 0.241183 Loss2: 1.859628 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.533228 Loss1: 0.156886 Loss2: 1.376342 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.539485 Loss1: 0.163268 Loss2: 1.376217 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.244382 Loss1: 0.351236 Loss2: 1.893146
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.493575 Loss1: 0.114090 Loss2: 1.379485
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.683647 Loss1: 0.288894 Loss2: 1.394753
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.497819 Loss1: 0.115433 Loss2: 1.382386
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.647429 Loss1: 0.203171 Loss2: 1.444258
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.516716 Loss1: 0.138015 Loss2: 1.378701
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.571116 Loss1: 0.152511 Loss2: 1.418605
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489529 Loss1: 0.106671 Loss2: 1.382858
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.442178 Loss1: 0.067461 Loss2: 1.374717 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.523884 Loss1: 0.124718 Loss2: 1.399166 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.499704 Loss1: 0.097942 Loss2: 1.401762 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.493046 Loss1: 0.108512 Loss2: 1.384534
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.075367 Loss1: 0.253290 Loss2: 1.822078
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.529883 Loss1: 0.180246 Loss2: 1.349637
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.508187 Loss1: 0.154208 Loss2: 1.353979
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.527338 Loss1: 0.169578 Loss2: 1.357760
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.579001 Loss1: 0.219936 Loss2: 1.359066
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.160282 Loss1: 0.333246 Loss2: 1.827036
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.561991 Loss1: 0.211752 Loss2: 1.350239 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.511081 Loss1: 0.135753 Loss2: 1.375328 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.451676 Loss1: 0.094658 Loss2: 1.357018 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.477770 Loss1: 0.119764 Loss2: 1.358005 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.998047
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391043 Loss1: 0.054560 Loss2: 1.336482
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423793 Loss1: 0.073668 Loss2: 1.350124
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.429972 Loss1: 0.080566 Loss2: 1.349405
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.488634 Loss1: 0.136564 Loss2: 1.352070
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.433922 Loss1: 0.075382 Loss2: 1.358540
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397177 Loss1: 0.054799 Loss2: 1.342379
+(DefaultActor pid=3764) >> Training accuracy: 0.991211
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.053114 Loss1: 0.306650 Loss2: 1.746464
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.496762 Loss1: 0.194465 Loss2: 1.302297
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.420389 Loss1: 0.117698 Loss2: 1.302690
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.384506 Loss1: 0.087721 Loss2: 1.296786
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.363351 Loss1: 0.076197 Loss2: 1.287153
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.102640 Loss1: 0.276102 Loss2: 1.826538
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.521166 Loss1: 0.161477 Loss2: 1.359689 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.460281 Loss1: 0.101299 Loss2: 1.358982 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.449170 Loss1: 0.094845 Loss2: 1.354325 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.424182 Loss1: 0.078468 Loss2: 1.345715 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.322838 Loss1: 0.050450 Loss2: 1.272388
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.422384 Loss1: 0.077809 Loss2: 1.344575
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.416335 Loss1: 0.070637 Loss2: 1.345698
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.441930 Loss1: 0.088619 Loss2: 1.353311
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.460270 Loss1: 0.108897 Loss2: 1.351372
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.461356 Loss1: 0.106179 Loss2: 1.355177
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.382634 Loss1: 0.430922 Loss2: 1.951712
+(DefaultActor pid=3764) >> Training accuracy: 0.985352
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.626513 Loss1: 0.281569 Loss2: 1.344944
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.554406 Loss1: 0.201693 Loss2: 1.352714
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.596482 Loss1: 0.222045 Loss2: 1.374437
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.571379 Loss1: 0.212124 Loss2: 1.359254
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.491787 Loss1: 0.142764 Loss2: 1.349023
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.472396 Loss1: 0.123140 Loss2: 1.349256
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.573299 Loss1: 0.252732 Loss2: 1.320567 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.471863 Loss1: 0.139801 Loss2: 1.332062 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990885
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.417862 Loss1: 0.114500 Loss2: 1.303362 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.400596 Loss1: 0.085912 Loss2: 1.314684 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.393192 Loss1: 0.086953 Loss2: 1.306239
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.187871 Loss1: 0.299006 Loss2: 1.888865
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.365088 Loss1: 0.057351 Loss2: 1.307736
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.653479 Loss1: 0.253640 Loss2: 1.399839
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380676 Loss1: 0.078845 Loss2: 1.301831
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.601699 Loss1: 0.166218 Loss2: 1.435482
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.542117 Loss1: 0.135821 Loss2: 1.406296
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.551881 Loss1: 0.147739 Loss2: 1.404142
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.547987 Loss1: 0.133546 Loss2: 1.414441
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.562730 Loss1: 0.155695 Loss2: 1.407035
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.557727 Loss1: 0.143106 Loss2: 1.414620
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.265879 Loss1: 0.340168 Loss2: 1.925710
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.516160 Loss1: 0.099424 Loss2: 1.416736
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.569610 Loss1: 0.161425 Loss2: 1.408185
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.485961 Loss1: 0.082833 Loss2: 1.403128
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.517572 Loss1: 0.111508 Loss2: 1.406063
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.532652 Loss1: 0.114207 Loss2: 1.418445
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.514078 Loss1: 0.111059 Loss2: 1.403018
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485483 Loss1: 0.078287 Loss2: 1.407196
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.479820 Loss1: 0.081296 Loss2: 1.398525
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486991 Loss1: 0.090353 Loss2: 1.396639
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.284704 Loss1: 0.362046 Loss2: 1.922658
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463755 Loss1: 0.061252 Loss2: 1.402502
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.625874 Loss1: 0.220411 Loss2: 1.405463
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.451786 Loss1: 0.052630 Loss2: 1.399157
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.569743 Loss1: 0.148369 Loss2: 1.421373
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.517160 Loss1: 0.104508 Loss2: 1.412653
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.491608 Loss1: 0.092927 Loss2: 1.398682
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.508973 Loss1: 0.112209 Loss2: 1.396764
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461625 Loss1: 0.066405 Loss2: 1.395220
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.436365 Loss1: 0.045528 Loss2: 1.390838
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.070514 Loss1: 0.291420 Loss2: 1.779094
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.438964 Loss1: 0.056866 Loss2: 1.382097
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.507120 Loss1: 0.195522 Loss2: 1.311598
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.459496 Loss1: 0.077171 Loss2: 1.382325
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.462412 Loss1: 0.147868 Loss2: 1.314544
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.451050 Loss1: 0.129662 Loss2: 1.321389
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.419467 Loss1: 0.104136 Loss2: 1.315331
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.387970 Loss1: 0.076717 Loss2: 1.311253
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.373118 Loss1: 0.060501 Loss2: 1.312617
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.344239 Loss1: 0.043443 Loss2: 1.300795
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.330792 Loss1: 0.421274 Loss2: 1.909518
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.345803 Loss1: 0.049167 Loss2: 1.296636
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.684289 Loss1: 0.291562 Loss2: 1.392727
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.359746 Loss1: 0.068916 Loss2: 1.290830
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.574836 Loss1: 0.165101 Loss2: 1.409735
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.509910 Loss1: 0.138181 Loss2: 1.371729
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.465078 Loss1: 0.098912 Loss2: 1.366166
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.453103 Loss1: 0.087848 Loss2: 1.365254
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.435534 Loss1: 0.074699 Loss2: 1.360835
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.095379 Loss1: 0.317587 Loss2: 1.777792
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.408649 Loss1: 0.058818 Loss2: 1.349832
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.555572 Loss1: 0.256546 Loss2: 1.299026
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.398727 Loss1: 0.049503 Loss2: 1.349224
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.533116 Loss1: 0.188139 Loss2: 1.344977
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.396969 Loss1: 0.052053 Loss2: 1.344916
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.441853 Loss1: 0.128358 Loss2: 1.313495 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.433560 Loss1: 0.121289 Loss2: 1.312271 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.410575 Loss1: 0.104504 Loss2: 1.306071
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.410392 Loss1: 0.427917 Loss2: 1.982475
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.700819 Loss1: 0.325284 Loss2: 1.375535
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368318 Loss1: 0.063936 Loss2: 1.304381
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.611368 Loss1: 0.205034 Loss2: 1.406334
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.370146 Loss1: 0.075090 Loss2: 1.295056
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.487609 Loss1: 0.117504 Loss2: 1.370105 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440187 Loss1: 0.065764 Loss2: 1.374423 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.409637 Loss1: 0.053807 Loss2: 1.355830 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.391677 Loss1: 0.043057 Loss2: 1.348620 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995192
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.502744 Loss1: 0.127019 Loss2: 1.375724 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.444178 Loss1: 0.093087 Loss2: 1.351090 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.162744 Loss1: 0.323031 Loss2: 1.839713
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.434389 Loss1: 0.086265 Loss2: 1.348125
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.425497 Loss1: 0.083370 Loss2: 1.342128
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.598864 Loss1: 0.230469 Loss2: 1.368396
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.394284 Loss1: 0.054962 Loss2: 1.339321
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551012 Loss1: 0.159037 Loss2: 1.391976
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414000 Loss1: 0.077948 Loss2: 1.336052
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.545045 Loss1: 0.165728 Loss2: 1.379317
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.518614 Loss1: 0.143076 Loss2: 1.375538
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.515190 Loss1: 0.137626 Loss2: 1.377564
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.470703 Loss1: 0.099364 Loss2: 1.371338
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449639 Loss1: 0.076355 Loss2: 1.373284
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.109478 Loss1: 0.256618 Loss2: 1.852859
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.462802 Loss1: 0.102752 Loss2: 1.360050
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.421937 Loss1: 0.061664 Loss2: 1.360273 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.501028 Loss1: 0.129600 Loss2: 1.371429 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.482052 Loss1: 0.119766 Loss2: 1.362286 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.461227 Loss1: 0.101993 Loss2: 1.359234
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.216456 Loss1: 0.366193 Loss2: 1.850263
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.548597 Loss1: 0.205551 Loss2: 1.343046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.494169 Loss1: 0.133283 Loss2: 1.360886 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.461834 Loss1: 0.123364 Loss2: 1.338470 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.413393 Loss1: 0.080570 Loss2: 1.332823 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.360502 Loss1: 0.047412 Loss2: 1.313090 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388691 Loss1: 0.077832 Loss2: 1.310859 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.349934 Loss1: 0.040128 Loss2: 1.309806 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.467196 Loss1: 0.100241 Loss2: 1.366956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.434046 Loss1: 0.082582 Loss2: 1.351464 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.124767 Loss1: 0.302217 Loss2: 1.822550 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.499550 Loss1: 0.164106 Loss2: 1.335444 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.493515 Loss1: 0.162108 Loss2: 1.331407 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.437357 Loss1: 0.109958 Loss2: 1.327398 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.394531 Loss1: 0.073925 Loss2: 1.320606 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.381791 Loss1: 0.064139 Loss2: 1.317652
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.273499 Loss1: 0.405757 Loss2: 1.867742
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.550811 Loss1: 0.232484 Loss2: 1.318327
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.385963 Loss1: 0.071666 Loss2: 1.314297
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.540711 Loss1: 0.213405 Loss2: 1.327305
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371580 Loss1: 0.060193 Loss2: 1.311387
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.435147 Loss1: 0.110465 Loss2: 1.324682 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.418458 Loss1: 0.099022 Loss2: 1.319436 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381496 Loss1: 0.058713 Loss2: 1.322783 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.361047 Loss1: 0.048928 Loss2: 1.312119 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993990
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.463439 Loss1: 0.157643 Loss2: 1.305795 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.426883 Loss1: 0.114648 Loss2: 1.312234 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.437472 Loss1: 0.130354 Loss2: 1.307117 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400511 Loss1: 0.097734 Loss2: 1.302777 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.346310 Loss1: 0.046890 Loss2: 1.299420 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.337780 Loss1: 0.047796 Loss2: 1.289984 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.403227 Loss1: 0.064381 Loss2: 1.338847 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.385861 Loss1: 0.054834 Loss2: 1.331027 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.226328 Loss1: 0.344497 Loss2: 1.881831 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.600109 Loss1: 0.192528 Loss2: 1.407581 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.558618 Loss1: 0.169111 Loss2: 1.389508 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.502172 Loss1: 0.106568 Loss2: 1.395603
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.212761 Loss1: 0.335624 Loss2: 1.877137
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.565062 Loss1: 0.190115 Loss2: 1.374948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.514652 Loss1: 0.136739 Loss2: 1.377913 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.449953 Loss1: 0.072084 Loss2: 1.377869 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.977083
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.461638 Loss1: 0.112044 Loss2: 1.349594 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.417868 Loss1: 0.059515 Loss2: 1.358353 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.415060 Loss1: 0.060009 Loss2: 1.355051 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404033 Loss1: 0.052954 Loss2: 1.351079 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.510051 Loss1: 0.161128 Loss2: 1.348923 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.441324 Loss1: 0.095804 Loss2: 1.345520 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.421297 Loss1: 0.085896 Loss2: 1.335401
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.119842 Loss1: 0.296956 Loss2: 1.822887
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.625843 Loss1: 0.289767 Loss2: 1.336076 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.551240 Loss1: 0.152416 Loss2: 1.398825 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.471082 Loss1: 0.107611 Loss2: 1.363470 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.409994 Loss1: 0.079780 Loss2: 1.330214
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.461676 Loss1: 0.118933 Loss2: 1.342743
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.500107 Loss1: 0.145421 Loss2: 1.354686
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452083 Loss1: 0.097230 Loss2: 1.354853
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.435599 Loss1: 0.091889 Loss2: 1.343709
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.414523 Loss1: 0.072468 Loss2: 1.342056
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.112567 Loss1: 0.323199 Loss2: 1.789368
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379344 Loss1: 0.043195 Loss2: 1.336149
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.497968 Loss1: 0.146405 Loss2: 1.351563 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.431750 Loss1: 0.092935 Loss2: 1.338814 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.034764 Loss1: 0.284468 Loss2: 1.750296
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.448759 Loss1: 0.123619 Loss2: 1.325139
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.557530 Loss1: 0.247715 Loss2: 1.309815
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.394795 Loss1: 0.063080 Loss2: 1.331716
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.558041 Loss1: 0.211063 Loss2: 1.346978
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.386205 Loss1: 0.065157 Loss2: 1.321047
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.439641 Loss1: 0.127730 Loss2: 1.311911
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394355 Loss1: 0.075413 Loss2: 1.318942
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.419953 Loss1: 0.118721 Loss2: 1.301232
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378649 Loss1: 0.061012 Loss2: 1.317636
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.353586 Loss1: 0.047872 Loss2: 1.305714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.334235 Loss1: 0.044533 Loss2: 1.289702 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.247018 Loss1: 0.362063 Loss2: 1.884955
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.302908 Loss1: 0.022521 Loss2: 1.280387
+(DefaultActor pid=3764) >> Training accuracy: 0.999023
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.508852 Loss1: 0.165249 Loss2: 1.343602 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.428069 Loss1: 0.093623 Loss2: 1.334446 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.221298 Loss1: 0.340196 Loss2: 1.881102 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.590023 Loss1: 0.209728 Loss2: 1.380295 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.531549 Loss1: 0.148270 Loss2: 1.383279 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.475793 Loss1: 0.090666 Loss2: 1.385127 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.985491
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.428551 Loss1: 0.067586 Loss2: 1.360965 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.410156 Loss1: 0.055203 Loss2: 1.354953 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.386896 Loss1: 0.037767 Loss2: 1.349129
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.142013 Loss1: 0.334101 Loss2: 1.807912
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.392089 Loss1: 0.044925 Loss2: 1.347164
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.537289 Loss1: 0.223250 Loss2: 1.314039
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.575152 Loss1: 0.231350 Loss2: 1.343802
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.539038 Loss1: 0.195703 Loss2: 1.343335
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488148 Loss1: 0.159627 Loss2: 1.328521
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.478396 Loss1: 0.144906 Loss2: 1.333491
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.056370 Loss1: 0.281217 Loss2: 1.775153
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.419952 Loss1: 0.100048 Loss2: 1.319903
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.514291 Loss1: 0.188194 Loss2: 1.326097
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393829 Loss1: 0.076178 Loss2: 1.317651
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.402824 Loss1: 0.089658 Loss2: 1.313166
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.503350 Loss1: 0.158227 Loss2: 1.345122
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.406323 Loss1: 0.093686 Loss2: 1.312637
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.443942 Loss1: 0.118026 Loss2: 1.325917
+(DefaultActor pid=3765) >> Training accuracy: 0.985417
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.450753 Loss1: 0.129782 Loss2: 1.320971
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441828 Loss1: 0.107015 Loss2: 1.334814
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.420895 Loss1: 0.102877 Loss2: 1.318018
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.412386 Loss1: 0.095685 Loss2: 1.316701
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.217629 Loss1: 0.379116 Loss2: 1.838514
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410111 Loss1: 0.090401 Loss2: 1.319710
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.380768 Loss1: 0.065276 Loss2: 1.315493 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482330 Loss1: 0.121284 Loss2: 1.361046 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.425854 Loss1: 0.084719 Loss2: 1.341135 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 18:09:27,834 | server.py:236 | fit_round 197 received 50 results and 0 failures
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440292 Loss1: 0.095466 Loss2: 1.344826
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.211001 Loss1: 0.403126 Loss2: 1.807875
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.556557 Loss1: 0.233012 Loss2: 1.323545 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.468800 Loss1: 0.116460 Loss2: 1.352339 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.401384 Loss1: 0.090675 Loss2: 1.310710 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.404809 Loss1: 0.095093 Loss2: 1.309717 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.368908 Loss1: 0.068548 Loss2: 1.300360 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.365743 Loss1: 0.067096 Loss2: 1.298646 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.391991 Loss1: 0.092438 Loss2: 1.299553 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.979167
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533204 Loss1: 0.142928 Loss2: 1.390276 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.440689 Loss1: 0.068058 Loss2: 1.372631 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.327209 Loss1: 0.378918 Loss2: 1.948290 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.665774 Loss1: 0.244942 Loss2: 1.420832 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.612067 Loss1: 0.160597 Loss2: 1.451470 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.513047 Loss1: 0.096952 Loss2: 1.416095 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.508514 Loss1: 0.086623 Loss2: 1.421891 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.485593 Loss1: 0.064887 Loss2: 1.420706
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.192893 Loss1: 0.327492 Loss2: 1.865401
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.571717 Loss1: 0.211477 Loss2: 1.360240 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.482293 Loss1: 0.067719 Loss2: 1.414574
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.581171 Loss1: 0.203648 Loss2: 1.377523
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.564341 Loss1: 0.181085 Loss2: 1.383256
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.494921 Loss1: 0.131961 Loss2: 1.362960
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463279 Loss1: 0.091875 Loss2: 1.371403
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.416829 Loss1: 0.058270 Loss2: 1.358559
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.176213 Loss1: 0.269023 Loss2: 1.907190
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.387410 Loss1: 0.040637 Loss2: 1.346773
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.391104 Loss1: 0.050288 Loss2: 1.340816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383574 Loss1: 0.042904 Loss2: 1.340670 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.484457 Loss1: 0.105034 Loss2: 1.379422 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.446555 Loss1: 0.077634 Loss2: 1.368921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395683 Loss1: 0.037213 Loss2: 1.358470 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 18:09:27,834][flwr][DEBUG] - fit_round 197 received 50 results and 0 failures
+INFO flwr 2023-10-13 18:10:09,046 | server.py:125 | fit progress: (197, 2.338596988600283, {'accuracy': 0.6144}, 454716.824874288)
+>> Test accuracy: 0.614400
+[2023-10-13 18:10:09,046][flwr][INFO] - fit progress: (197, 2.338596988600283, {'accuracy': 0.6144}, 454716.824874288)
+DEBUG flwr 2023-10-13 18:10:09,047 | server.py:173 | evaluate_round 197: strategy sampled 50 clients (out of 50)
+[2023-10-13 18:10:09,047][flwr][DEBUG] - evaluate_round 197: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 18:19:12,332 | server.py:187 | evaluate_round 197 received 50 results and 0 failures
+[2023-10-13 18:19:12,332][flwr][DEBUG] - evaluate_round 197 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 18:19:12,333 | server.py:222 | fit_round 198: strategy sampled 50 clients (out of 50)
+[2023-10-13 18:19:12,333][flwr][DEBUG] - fit_round 198: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.132724 Loss1: 0.298493 Loss2: 1.834231 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.579569 Loss1: 0.179005 Loss2: 1.400564 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.125319 Loss1: 0.327294 Loss2: 1.798026
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.620264 Loss1: 0.234674 Loss2: 1.385590
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.555748 Loss1: 0.242826 Loss2: 1.312922
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.573020 Loss1: 0.186895 Loss2: 1.386125
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.495569 Loss1: 0.113787 Loss2: 1.381782 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458402 Loss1: 0.079521 Loss2: 1.378882 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.441214 Loss1: 0.073755 Loss2: 1.367459 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422302 Loss1: 0.057013 Loss2: 1.365289 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.414926 Loss1: 0.055040 Loss2: 1.359886 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.361954 Loss1: 0.057215 Loss2: 1.304739 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.116007 Loss1: 0.303640 Loss2: 1.812367 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.474718 Loss1: 0.138053 Loss2: 1.336664 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.204945 Loss1: 0.290746 Loss2: 1.914200
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489387 Loss1: 0.150456 Loss2: 1.338931
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.571278 Loss1: 0.183407 Loss2: 1.387872
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.455841 Loss1: 0.116706 Loss2: 1.339135
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443570 Loss1: 0.101411 Loss2: 1.342159 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.422972 Loss1: 0.093357 Loss2: 1.329615 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.386143 Loss1: 0.057715 Loss2: 1.328428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.370488 Loss1: 0.047298 Loss2: 1.323189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384867 Loss1: 0.065365 Loss2: 1.319502 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995117
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.442912 Loss1: 0.072998 Loss2: 1.369914 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.218162 Loss1: 0.325329 Loss2: 1.892833 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.585916 Loss1: 0.155942 Loss2: 1.429974 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.174420 Loss1: 0.306647 Loss2: 1.867773
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.525792 Loss1: 0.117366 Loss2: 1.408426
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.545159 Loss1: 0.185749 Loss2: 1.359410
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488908 Loss1: 0.094694 Loss2: 1.394214
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.538685 Loss1: 0.169677 Loss2: 1.369008
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.468101 Loss1: 0.068509 Loss2: 1.399592
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458368 Loss1: 0.058892 Loss2: 1.399475 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.448671 Loss1: 0.059189 Loss2: 1.389482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.489723 Loss1: 0.101123 Loss2: 1.388600 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.466089 Loss1: 0.073238 Loss2: 1.392851 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.395952 Loss1: 0.044508 Loss2: 1.351444 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.207876 Loss1: 0.325414 Loss2: 1.882462 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551334 Loss1: 0.164512 Loss2: 1.386822 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.521892 Loss1: 0.141561 Loss2: 1.380331
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.188227 Loss1: 0.354114 Loss2: 1.834113
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.575932 Loss1: 0.233182 Loss2: 1.342750 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.514180 Loss1: 0.152892 Loss2: 1.361288 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.461549 Loss1: 0.117147 Loss2: 1.344402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455591 Loss1: 0.115602 Loss2: 1.339990 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.480944 Loss1: 0.125636 Loss2: 1.355308 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398615 Loss1: 0.044325 Loss2: 1.354290
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.424556 Loss1: 0.083583 Loss2: 1.340973
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.409960 Loss1: 0.075752 Loss2: 1.334208
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.370568 Loss1: 0.038324 Loss2: 1.332244
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396936 Loss1: 0.070572 Loss2: 1.326365
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.201885 Loss1: 0.389139 Loss2: 1.812746
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.534718 Loss1: 0.217886 Loss2: 1.316832
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.526659 Loss1: 0.180672 Loss2: 1.345987
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.458456 Loss1: 0.139035 Loss2: 1.319421
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.110666 Loss1: 0.262719 Loss2: 1.847947
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.646269 Loss1: 0.269956 Loss2: 1.376312 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.583284 Loss1: 0.169105 Loss2: 1.414179 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.401937 Loss1: 0.090705 Loss2: 1.311232 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.387084 Loss1: 0.079961 Loss2: 1.307123 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358351 Loss1: 0.054688 Loss2: 1.303662 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412712 Loss1: 0.053312 Loss2: 1.359399 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.416596 Loss1: 0.061801 Loss2: 1.354795 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402757 Loss1: 0.050153 Loss2: 1.352603
+(DefaultActor pid=3764) >> Training accuracy: 0.995404
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.185164 Loss1: 0.361746 Loss2: 1.823418
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.615268 Loss1: 0.271465 Loss2: 1.343803
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.517697 Loss1: 0.163217 Loss2: 1.354480
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.472446 Loss1: 0.126887 Loss2: 1.345559
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.422158 Loss1: 0.092516 Loss2: 1.329642
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.189274 Loss1: 0.305830 Loss2: 1.883444
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.416314 Loss1: 0.087312 Loss2: 1.329002
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.634472 Loss1: 0.262512 Loss2: 1.371961
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.404292 Loss1: 0.069640 Loss2: 1.334652
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564136 Loss1: 0.163955 Loss2: 1.400181
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.387264 Loss1: 0.063899 Loss2: 1.323366
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.549452 Loss1: 0.152344 Loss2: 1.397109
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.362768 Loss1: 0.044934 Loss2: 1.317834
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.500690 Loss1: 0.120465 Loss2: 1.380225
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.343095 Loss1: 0.027976 Loss2: 1.315119
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.449827 Loss1: 0.076212 Loss2: 1.373615 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.435825 Loss1: 0.069301 Loss2: 1.366523 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.438855 Loss1: 0.076374 Loss2: 1.362480
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.142130 Loss1: 0.265782 Loss2: 1.876348
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.573827 Loss1: 0.191820 Loss2: 1.382007
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.555093 Loss1: 0.176232 Loss2: 1.378861
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.501046 Loss1: 0.111866 Loss2: 1.389180
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.473115 Loss1: 0.108037 Loss2: 1.365078
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.130489 Loss1: 0.328796 Loss2: 1.801693
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.522020 Loss1: 0.140717 Loss2: 1.381303
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.566319 Loss1: 0.254662 Loss2: 1.311657
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.488801 Loss1: 0.111366 Loss2: 1.377436
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.554374 Loss1: 0.204424 Loss2: 1.349950
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.453343 Loss1: 0.086060 Loss2: 1.367283
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.440875 Loss1: 0.122989 Loss2: 1.317886
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.437172 Loss1: 0.068954 Loss2: 1.368218
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455314 Loss1: 0.141952 Loss2: 1.313362
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.422728 Loss1: 0.061093 Loss2: 1.361635
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.360812 Loss1: 0.052056 Loss2: 1.308756 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.366440 Loss1: 0.068346 Loss2: 1.298094 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.341220 Loss1: 0.040079 Loss2: 1.301141
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.049001 Loss1: 0.284415 Loss2: 1.764585
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.479447 Loss1: 0.206096 Loss2: 1.273352
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.458959 Loss1: 0.170052 Loss2: 1.288907
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.395962 Loss1: 0.094153 Loss2: 1.301809
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.406151 Loss1: 0.120937 Loss2: 1.285215
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.415328 Loss1: 0.124123 Loss2: 1.291205
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.264408 Loss1: 0.393231 Loss2: 1.871177
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.395933 Loss1: 0.098177 Loss2: 1.297756
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.623652 Loss1: 0.254411 Loss2: 1.369241
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.379699 Loss1: 0.093387 Loss2: 1.286312
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.653532 Loss1: 0.247164 Loss2: 1.406368
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.327496 Loss1: 0.046713 Loss2: 1.280783
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521255 Loss1: 0.138769 Loss2: 1.382486
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.333152 Loss1: 0.055978 Loss2: 1.277175
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.496277 Loss1: 0.132369 Loss2: 1.363908
+(DefaultActor pid=3765) >> Training accuracy: 0.986458
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.446574 Loss1: 0.072902 Loss2: 1.373671
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.412657 Loss1: 0.054340 Loss2: 1.358317
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.413308 Loss1: 0.063058 Loss2: 1.350250
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410049 Loss1: 0.063531 Loss2: 1.346517
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.107546 Loss1: 0.303386 Loss2: 1.804160
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.402278 Loss1: 0.056041 Loss2: 1.346237
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.480122 Loss1: 0.138819 Loss2: 1.341303 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.430157 Loss1: 0.099212 Loss2: 1.330945 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.422634 Loss1: 0.090202 Loss2: 1.332433 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.407749 Loss1: 0.078409 Loss2: 1.329340 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400711 Loss1: 0.076414 Loss2: 1.324297 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.394407 Loss1: 0.071173 Loss2: 1.323234 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.412751 Loss1: 0.090941 Loss2: 1.321810 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.990234
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.391866 Loss1: 0.088587 Loss2: 1.303279 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354552 Loss1: 0.051920 Loss2: 1.302632 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.333789 Loss1: 0.043933 Loss2: 1.289856
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.267549 Loss1: 0.348492 Loss2: 1.919057
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.654532 Loss1: 0.288597 Loss2: 1.365936
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.552290 Loss1: 0.159667 Loss2: 1.392623
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.504770 Loss1: 0.119850 Loss2: 1.384920
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.516963 Loss1: 0.147530 Loss2: 1.369433
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.360965 Loss1: 0.414632 Loss2: 1.946333
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.477951 Loss1: 0.099606 Loss2: 1.378345
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.504369 Loss1: 0.124147 Loss2: 1.380223 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.452723 Loss1: 0.081619 Loss2: 1.371103 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.446504 Loss1: 0.072603 Loss2: 1.373901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.449623 Loss1: 0.079515 Loss2: 1.370108 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420576 Loss1: 0.056306 Loss2: 1.364270 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.396631 Loss1: 0.045795 Loss2: 1.350836 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.985577
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.753293 Loss1: 0.267960 Loss2: 1.485333 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.694490 Loss1: 0.196550 Loss2: 1.497939 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.602410 Loss1: 0.105057 Loss2: 1.497353 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.597925 Loss1: 0.111442 Loss2: 1.486483 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.636714 Loss1: 0.139967 Loss2: 1.496746 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.618290 Loss1: 0.106371 Loss2: 1.511919 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.621625 Loss1: 0.138992 Loss2: 1.482633 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.610257 Loss1: 0.119217 Loss2: 1.491040 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.338795 Loss1: 0.047656 Loss2: 1.291139 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.316398 Loss1: 0.031602 Loss2: 1.284796 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.569203 Loss1: 0.187169 Loss2: 1.382034 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.524464 Loss1: 0.127824 Loss2: 1.396640 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.464731 Loss1: 0.099021 Loss2: 1.365710
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.188305 Loss1: 0.308065 Loss2: 1.880240
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.500670 Loss1: 0.137643 Loss2: 1.363027
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.550107 Loss1: 0.194136 Loss2: 1.355972
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.504059 Loss1: 0.148091 Loss2: 1.355969 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.442736 Loss1: 0.086244 Loss2: 1.356491 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.410352 Loss1: 0.074954 Loss2: 1.335398 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.440775 Loss1: 0.103478 Loss2: 1.337297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.420316 Loss1: 0.079321 Loss2: 1.340994 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.395517 Loss1: 0.067560 Loss2: 1.327957 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.546595 Loss1: 0.161877 Loss2: 1.384717 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.526023 Loss1: 0.129948 Loss2: 1.396075 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.490051 Loss1: 0.106429 Loss2: 1.383623
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.184951 Loss1: 0.374075 Loss2: 1.810876
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.630826 Loss1: 0.293656 Loss2: 1.337170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.600112 Loss1: 0.225722 Loss2: 1.374389 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.481879 Loss1: 0.139053 Loss2: 1.342827 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.440611 Loss1: 0.110534 Loss2: 1.330078 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.401367 Loss1: 0.037964 Loss2: 1.363402
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.425914 Loss1: 0.096975 Loss2: 1.328939
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.456747 Loss1: 0.129215 Loss2: 1.327532
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408927 Loss1: 0.079493 Loss2: 1.329433
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396070 Loss1: 0.072872 Loss2: 1.323198
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.369819 Loss1: 0.054436 Loss2: 1.315383
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.228310 Loss1: 0.350366 Loss2: 1.877944
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.605900 Loss1: 0.254984 Loss2: 1.350916
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.521847 Loss1: 0.134875 Loss2: 1.386971
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.435544 Loss1: 0.084264 Loss2: 1.351281
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.441941 Loss1: 0.095941 Loss2: 1.346000
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.096096 Loss1: 0.321094 Loss2: 1.775002
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.566373 Loss1: 0.257077 Loss2: 1.309296 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.550806 Loss1: 0.201347 Loss2: 1.349459 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.488487 Loss1: 0.168122 Loss2: 1.320365 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.427853 Loss1: 0.117808 Loss2: 1.310044 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.380444 Loss1: 0.049938 Loss2: 1.330506
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.386983 Loss1: 0.075563 Loss2: 1.311420
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.385714 Loss1: 0.081412 Loss2: 1.304303
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.353881 Loss1: 0.056176 Loss2: 1.297705
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.343031 Loss1: 0.056785 Loss2: 1.286246
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.357527 Loss1: 0.068027 Loss2: 1.289500
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.248376 Loss1: 0.393699 Loss2: 1.854677
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.575024 Loss1: 0.218870 Loss2: 1.356154
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.565441 Loss1: 0.191505 Loss2: 1.373935
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.464164 Loss1: 0.108715 Loss2: 1.355449
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.422329 Loss1: 0.076284 Loss2: 1.346045
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.233446 Loss1: 0.364618 Loss2: 1.868828
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.578308 Loss1: 0.208680 Loss2: 1.369628 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.572129 Loss1: 0.186025 Loss2: 1.386104 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.493552 Loss1: 0.121981 Loss2: 1.371571 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.463767 Loss1: 0.102177 Loss2: 1.361590 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.452633 Loss1: 0.096565 Loss2: 1.356068 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408855 Loss1: 0.064636 Loss2: 1.344219 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.423586 Loss1: 0.072782 Loss2: 1.350804 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.987500
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.627728 Loss1: 0.268008 Loss2: 1.359720 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.478694 Loss1: 0.119639 Loss2: 1.359055 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.406979 Loss1: 0.069307 Loss2: 1.337672 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.383001 Loss1: 0.047643 Loss2: 1.335358 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.368551 Loss1: 0.035532 Loss2: 1.333018 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.342886 Loss1: 0.015368 Loss2: 1.327517 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.356452 Loss1: 0.033537 Loss2: 1.322916 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995192
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423716 Loss1: 0.093502 Loss2: 1.330214 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.439776 Loss1: 0.115313 Loss2: 1.324463 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.058885 Loss1: 0.243304 Loss2: 1.815581 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.482427 Loss1: 0.147487 Loss2: 1.334940 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.422179 Loss1: 0.093909 Loss2: 1.328270 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.388667 Loss1: 0.064559 Loss2: 1.324108 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.380315 Loss1: 0.053204 Loss2: 1.327112 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.373605 Loss1: 0.055187 Loss2: 1.318418 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.371453 Loss1: 0.050714 Loss2: 1.320739 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355087 Loss1: 0.040207 Loss2: 1.314880 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.349087 Loss1: 0.041884 Loss2: 1.307203 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.318540 Loss1: 0.027092 Loss2: 1.291448 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.332624 Loss1: 0.037487 Loss2: 1.295136 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997070
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.547432 Loss1: 0.163945 Loss2: 1.383487 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.508741 Loss1: 0.131136 Loss2: 1.377605 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.507590 Loss1: 0.129033 Loss2: 1.378557
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.193166 Loss1: 0.361654 Loss2: 1.831512
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.624240 Loss1: 0.265244 Loss2: 1.358996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.560298 Loss1: 0.157427 Loss2: 1.402871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.527462 Loss1: 0.167064 Loss2: 1.360397 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.460673 Loss1: 0.098689 Loss2: 1.361984 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.454218 Loss1: 0.095934 Loss2: 1.358284 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.436887 Loss1: 0.080708 Loss2: 1.356180
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.123454 Loss1: 0.278517 Loss2: 1.844937
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.421925 Loss1: 0.067218 Loss2: 1.354707
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.457836 Loss1: 0.130038 Loss2: 1.327798
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.413867 Loss1: 0.092447 Loss2: 1.321420
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.448951 Loss1: 0.121348 Loss2: 1.327603
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.384905 Loss1: 0.064643 Loss2: 1.320262
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.390134 Loss1: 0.076377 Loss2: 1.313757
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.359283 Loss1: 0.046438 Loss2: 1.312845
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.011434 Loss1: 0.239077 Loss2: 1.772357
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.384829 Loss1: 0.073660 Loss2: 1.311169
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.508312 Loss1: 0.185510 Loss2: 1.322802
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.368089 Loss1: 0.056115 Loss2: 1.311975
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.492934 Loss1: 0.158769 Loss2: 1.334165
+(DefaultActor pid=3765) >> Training accuracy: 0.990625
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.353874 Loss1: 0.045191 Loss2: 1.308683
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.444685 Loss1: 0.110552 Loss2: 1.334133
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.470418 Loss1: 0.148856 Loss2: 1.321562
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.481204 Loss1: 0.154306 Loss2: 1.326898
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.452552 Loss1: 0.119511 Loss2: 1.333041
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430550 Loss1: 0.101498 Loss2: 1.329052
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.173713 Loss1: 0.331817 Loss2: 1.841896
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.616318 Loss1: 0.266795 Loss2: 1.349522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400683 Loss1: 0.078964 Loss2: 1.321719
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.524068 Loss1: 0.146157 Loss2: 1.377910
+(DefaultActor pid=3764) >> Training accuracy: 0.983398
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.493500 Loss1: 0.139216 Loss2: 1.354285
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.472787 Loss1: 0.120965 Loss2: 1.351823
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.433090 Loss1: 0.087732 Loss2: 1.345358
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.375287 Loss1: 0.042589 Loss2: 1.332697
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.193980 Loss1: 0.330954 Loss2: 1.863026
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.372856 Loss1: 0.040231 Loss2: 1.332625
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.567941 Loss1: 0.237416 Loss2: 1.330525
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.344194 Loss1: 0.024578 Loss2: 1.319616
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.497133 Loss1: 0.141985 Loss2: 1.355148
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.338158 Loss1: 0.025486 Loss2: 1.312671
+(DefaultActor pid=3765) >> Training accuracy: 0.997917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.426002 Loss1: 0.092275 Loss2: 1.333728 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.404096 Loss1: 0.070590 Loss2: 1.333506 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.364007 Loss1: 0.041816 Loss2: 1.322191
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.198520 Loss1: 0.333745 Loss2: 1.864775
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354336 Loss1: 0.036691 Loss2: 1.317645
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.610115 Loss1: 0.251237 Loss2: 1.358878
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.356851 Loss1: 0.045862 Loss2: 1.310989
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.551988 Loss1: 0.175288 Loss2: 1.376699
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.483981 Loss1: 0.108325 Loss2: 1.375656
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.432969 Loss1: 0.074287 Loss2: 1.358682
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.423147 Loss1: 0.068377 Loss2: 1.354769
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.432590 Loss1: 0.083890 Loss2: 1.348700
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459064 Loss1: 0.109127 Loss2: 1.349938
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.175841 Loss1: 0.316833 Loss2: 1.859009
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.410158 Loss1: 0.058729 Loss2: 1.351429
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.533661 Loss1: 0.196899 Loss2: 1.336762
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378414 Loss1: 0.035709 Loss2: 1.342705
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.458426 Loss1: 0.125646 Loss2: 1.332780
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.413909 Loss1: 0.074039 Loss2: 1.339870
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.384278 Loss1: 0.060256 Loss2: 1.324021
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.386760 Loss1: 0.069789 Loss2: 1.316971
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.389155 Loss1: 0.075070 Loss2: 1.314085
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.369423 Loss1: 0.056948 Loss2: 1.312474
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.138619 Loss1: 0.309630 Loss2: 1.828990
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.587035 Loss1: 0.249733 Loss2: 1.337302 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.335328 Loss1: 0.032635 Loss2: 1.302694
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.545751 Loss1: 0.177030 Loss2: 1.368721
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.536149 Loss1: 0.179856 Loss2: 1.356293
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.527010 Loss1: 0.174764 Loss2: 1.352246
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.453205 Loss1: 0.103073 Loss2: 1.350132
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.451439 Loss1: 0.105900 Loss2: 1.345539
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.192633 Loss1: 0.321412 Loss2: 1.871221
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411761 Loss1: 0.071333 Loss2: 1.340427
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.652023 Loss1: 0.284653 Loss2: 1.367371
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.400919 Loss1: 0.066684 Loss2: 1.334234
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.565114 Loss1: 0.150948 Loss2: 1.414166
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398160 Loss1: 0.066195 Loss2: 1.331965
+(DefaultActor pid=3765) >> Training accuracy: 0.980208
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.452816 Loss1: 0.091415 Loss2: 1.361401 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.486058 Loss1: 0.119941 Loss2: 1.366117 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440222 Loss1: 0.072322 Loss2: 1.367901
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.226568 Loss1: 0.278353 Loss2: 1.948215
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.402440 Loss1: 0.046338 Loss2: 1.356101
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.636310 Loss1: 0.194765 Loss2: 1.441545
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375811 Loss1: 0.032423 Loss2: 1.343388
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.552032 Loss1: 0.115321 Loss2: 1.436711
+(DefaultActor pid=3764) >> Training accuracy: 0.993750
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.569282 Loss1: 0.128287 Loss2: 1.440995
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.535650 Loss1: 0.104169 Loss2: 1.431482
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.504382 Loss1: 0.077238 Loss2: 1.427143
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.523645 Loss1: 0.096263 Loss2: 1.427382
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.485333 Loss1: 0.062817 Loss2: 1.422517
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.122823 Loss1: 0.293259 Loss2: 1.829563
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.493880 Loss1: 0.075839 Loss2: 1.418041
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.519874 Loss1: 0.196134 Loss2: 1.323741
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.483784 Loss1: 0.064526 Loss2: 1.419258
+DEBUG flwr 2023-10-13 18:47:43,456 | server.py:236 | fit_round 198 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.509677 Loss1: 0.177327 Loss2: 1.332350
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.433892 Loss1: 0.101870 Loss2: 1.332022
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.409440 Loss1: 0.098355 Loss2: 1.311085
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.378752 Loss1: 0.071220 Loss2: 1.307532
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.383956 Loss1: 0.080486 Loss2: 1.303470
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.377391 Loss1: 0.494388 Loss2: 1.883003
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.354209 Loss1: 0.052030 Loss2: 1.302179
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.659518 Loss1: 0.309316 Loss2: 1.350201
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.332850 Loss1: 0.039599 Loss2: 1.293250
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.583905 Loss1: 0.191341 Loss2: 1.392564
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.330876 Loss1: 0.038044 Loss2: 1.292831
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.475606 Loss1: 0.141412 Loss2: 1.334194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.446727 Loss1: 0.111779 Loss2: 1.334948 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.409778 Loss1: 0.452163 Loss2: 1.957615 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.732197 Loss1: 0.395713 Loss2: 1.336484 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991071
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.539527 Loss1: 0.158430 Loss2: 1.381097 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.415295 Loss1: 0.073811 Loss2: 1.341484 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406226 Loss1: 0.069599 Loss2: 1.336627 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.398611 Loss1: 0.065834 Loss2: 1.332777
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.063162 Loss1: 0.239570 Loss2: 1.823593
+(DefaultActor pid=3764) >> Training accuracy: 0.990885
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379110 Loss1: 0.045266 Loss2: 1.333844
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.495389 Loss1: 0.130401 Loss2: 1.364988
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.472941 Loss1: 0.114503 Loss2: 1.358438
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.491061 Loss1: 0.125693 Loss2: 1.365369
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.455952 Loss1: 0.086726 Loss2: 1.369226
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.469994 Loss1: 0.110594 Loss2: 1.359400
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.253494 Loss1: 0.357332 Loss2: 1.896162
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.659698 Loss1: 0.261580 Loss2: 1.398118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.626064 Loss1: 0.193854 Loss2: 1.432209 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.594420 Loss1: 0.173555 Loss2: 1.420865 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384109 Loss1: 0.030810 Loss2: 1.353299
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.573808 Loss1: 0.170205 Loss2: 1.403604
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.579904 Loss1: 0.171086 Loss2: 1.408818
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.565547 Loss1: 0.159754 Loss2: 1.405793
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.564121 Loss1: 0.154232 Loss2: 1.409889
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.507845 Loss1: 0.097171 Loss2: 1.410674
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.484317 Loss1: 0.088175 Loss2: 1.396142
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 18:47:43,456][flwr][DEBUG] - fit_round 198 received 50 results and 0 failures
+INFO flwr 2023-10-13 18:48:24,455 | server.py:125 | fit progress: (198, 2.3383814542057415, {'accuracy': 0.615}, 457012.23382741597)
+>> Test accuracy: 0.615000
+[2023-10-13 18:48:24,455][flwr][INFO] - fit progress: (198, 2.3383814542057415, {'accuracy': 0.615}, 457012.23382741597)
+DEBUG flwr 2023-10-13 18:48:24,456 | server.py:173 | evaluate_round 198: strategy sampled 50 clients (out of 50)
+[2023-10-13 18:48:24,456][flwr][DEBUG] - evaluate_round 198: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 18:57:25,895 | server.py:187 | evaluate_round 198 received 50 results and 0 failures
+[2023-10-13 18:57:25,895][flwr][DEBUG] - evaluate_round 198 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 18:57:25,895 | server.py:222 | fit_round 199: strategy sampled 50 clients (out of 50)
+[2023-10-13 18:57:25,895][flwr][DEBUG] - fit_round 199: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.139244 Loss1: 0.324870 Loss2: 1.814373
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.570316 Loss1: 0.246156 Loss2: 1.324160
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.541401 Loss1: 0.183793 Loss2: 1.357608
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.533156 Loss1: 0.183479 Loss2: 1.349677
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.501558 Loss1: 0.169364 Loss2: 1.332194 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461532 Loss1: 0.112771 Loss2: 1.348761 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480818 Loss1: 0.152711 Loss2: 1.328107 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.470335 Loss1: 0.139111 Loss2: 1.331224 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.431129 Loss1: 0.100690 Loss2: 1.330439 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358882 Loss1: 0.038968 Loss2: 1.319914 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.339432 Loss1: 0.030515 Loss2: 1.308917 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.187560 Loss1: 0.372856 Loss2: 1.814704 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.517873 Loss1: 0.158680 Loss2: 1.359192 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.475323 Loss1: 0.145237 Loss2: 1.330086
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.150044 Loss1: 0.305579 Loss2: 1.844465
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.533968 Loss1: 0.176620 Loss2: 1.357347 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.485257 Loss1: 0.125336 Loss2: 1.359921 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.428137 Loss1: 0.074194 Loss2: 1.353943 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.428944 Loss1: 0.078728 Loss2: 1.350216 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.421257 Loss1: 0.077753 Loss2: 1.343503 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.410522 Loss1: 0.097392 Loss2: 1.313129
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.383280 Loss1: 0.042386 Loss2: 1.340894
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389960 Loss1: 0.055597 Loss2: 1.334362
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.401223 Loss1: 0.065796 Loss2: 1.335427
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.385172 Loss1: 0.053279 Loss2: 1.331893
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.601154 Loss1: 0.290915 Loss2: 1.310238 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576152 Loss1: 0.195925 Loss2: 1.380227 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.189324 Loss1: 0.339166 Loss2: 1.850157 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.500554 Loss1: 0.161126 Loss2: 1.339428 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.465939 Loss1: 0.128149 Loss2: 1.337790 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421783 Loss1: 0.094501 Loss2: 1.327282 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384271 Loss1: 0.060614 Loss2: 1.323658 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.405853 Loss1: 0.069914 Loss2: 1.335939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.394506 Loss1: 0.070719 Loss2: 1.323787 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.378772 Loss1: 0.054948 Loss2: 1.323824
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.299709 Loss1: 0.418843 Loss2: 1.880865
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.579356 Loss1: 0.232040 Loss2: 1.347316 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.982292
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.574562 Loss1: 0.196477 Loss2: 1.378085 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.486473 Loss1: 0.132204 Loss2: 1.354269 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.452219 Loss1: 0.103457 Loss2: 1.348762
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.114577 Loss1: 0.300302 Loss2: 1.814275
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.607746 Loss1: 0.300790 Loss2: 1.306956 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.571467 Loss1: 0.194008 Loss2: 1.377460 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995536
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.522877 Loss1: 0.165201 Loss2: 1.357676 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.431985 Loss1: 0.096523 Loss2: 1.335462 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.386613 Loss1: 0.062782 Loss2: 1.323831
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.205399 Loss1: 0.330888 Loss2: 1.874511
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572067 Loss1: 0.211954 Loss2: 1.360113 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351808 Loss1: 0.038187 Loss2: 1.313621
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.505969 Loss1: 0.146952 Loss2: 1.359017
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.494601 Loss1: 0.119278 Loss2: 1.375323
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.496234 Loss1: 0.138632 Loss2: 1.357602
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.488806 Loss1: 0.126669 Loss2: 1.362137
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.529422 Loss1: 0.161640 Loss2: 1.367782
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.090056 Loss1: 0.241085 Loss2: 1.848971
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.537857 Loss1: 0.166080 Loss2: 1.371777
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.468906 Loss1: 0.102294 Loss2: 1.366612 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.454475 Loss1: 0.089950 Loss2: 1.364525 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.534004 Loss1: 0.186194 Loss2: 1.347810 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.507211 Loss1: 0.148605 Loss2: 1.358606 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.542536 Loss1: 0.180548 Loss2: 1.361988
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.196866 Loss1: 0.338795 Loss2: 1.858070
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.568647 Loss1: 0.214666 Loss2: 1.353981 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.494232 Loss1: 0.129428 Loss2: 1.364804 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.448718 Loss1: 0.103096 Loss2: 1.345622 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.409590 Loss1: 0.064876 Loss2: 1.344714 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411022 Loss1: 0.069540 Loss2: 1.341482 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399687 Loss1: 0.062551 Loss2: 1.337136 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.499042 Loss1: 0.150322 Loss2: 1.348720
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398133 Loss1: 0.067069 Loss2: 1.331064
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460249 Loss1: 0.124547 Loss2: 1.335702 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447592 Loss1: 0.095461 Loss2: 1.352132 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.124296 Loss1: 0.279073 Loss2: 1.845223
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.463988 Loss1: 0.117260 Loss2: 1.346729
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.548545 Loss1: 0.209516 Loss2: 1.339030
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441973 Loss1: 0.102005 Loss2: 1.339967
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.516251 Loss1: 0.162801 Loss2: 1.353450
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.414922 Loss1: 0.073646 Loss2: 1.341276
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470110 Loss1: 0.128731 Loss2: 1.341379 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.429922 Loss1: 0.086562 Loss2: 1.343360 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.383656 Loss1: 0.044311 Loss2: 1.339345
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.151142 Loss1: 0.289089 Loss2: 1.862053
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.513073 Loss1: 0.153398 Loss2: 1.359675 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.345527 Loss1: 0.021449 Loss2: 1.324078
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.498974 Loss1: 0.133495 Loss2: 1.365479
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468292 Loss1: 0.101858 Loss2: 1.366433
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.458726 Loss1: 0.099782 Loss2: 1.358944
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441652 Loss1: 0.089501 Loss2: 1.352151
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.422570 Loss1: 0.071960 Loss2: 1.350609
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.216418 Loss1: 0.341717 Loss2: 1.874701
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389589 Loss1: 0.043790 Loss2: 1.345799
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.593632 Loss1: 0.221779 Loss2: 1.371853
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.396008 Loss1: 0.057057 Loss2: 1.338951
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.514556 Loss1: 0.129264 Loss2: 1.385293
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.397257 Loss1: 0.052762 Loss2: 1.344494
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.458507 Loss1: 0.095757 Loss2: 1.362750 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.459998 Loss1: 0.096420 Loss2: 1.363578 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435363 Loss1: 0.083246 Loss2: 1.352117
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.108375 Loss1: 0.291808 Loss2: 1.816567
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.627435 Loss1: 0.270852 Loss2: 1.356584 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.615661 Loss1: 0.208170 Loss2: 1.407491 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.474855 Loss1: 0.130632 Loss2: 1.344223 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.418189 Loss1: 0.072607 Loss2: 1.345582 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.404504 Loss1: 0.064764 Loss2: 1.339740 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384195 Loss1: 0.047815 Loss2: 1.336380 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.382234 Loss1: 0.055592 Loss2: 1.326642 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.461091 Loss1: 0.112307 Loss2: 1.348784 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.431116 Loss1: 0.107701 Loss2: 1.323415 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.486969 Loss1: 0.159052 Loss2: 1.327917 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423333 Loss1: 0.103789 Loss2: 1.319545 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.973958
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.448993 Loss1: 0.113964 Loss2: 1.335029 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.386091 Loss1: 0.060847 Loss2: 1.325244 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.376319 Loss1: 0.057936 Loss2: 1.318383
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.248977 Loss1: 0.386640 Loss2: 1.862337
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.609478 Loss1: 0.272871 Loss2: 1.336607 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.997596
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.550076 Loss1: 0.189504 Loss2: 1.360572 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.493430 Loss1: 0.156867 Loss2: 1.336562 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.398451 Loss1: 0.071141 Loss2: 1.327311 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391343 Loss1: 0.069459 Loss2: 1.321883 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.405832 Loss1: 0.085622 Loss2: 1.320210 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.373417 Loss1: 0.053070 Loss2: 1.320347 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982143
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.531767 Loss1: 0.190639 Loss2: 1.341128 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.474827 Loss1: 0.124811 Loss2: 1.350015 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.381843 Loss1: 0.048436 Loss2: 1.333407 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.354188 Loss1: 0.030285 Loss2: 1.323903 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996394
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.457989 Loss1: 0.138270 Loss2: 1.319719 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.411515 Loss1: 0.109022 Loss2: 1.302494 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.374907 Loss1: 0.072853 Loss2: 1.302054 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.423635 Loss1: 0.130829 Loss2: 1.292806 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.388220 Loss1: 0.097161 Loss2: 1.291058 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.349528 Loss1: 0.052713 Loss2: 1.296815 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.404052 Loss1: 0.089072 Loss2: 1.314979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.340397 Loss1: 0.034279 Loss2: 1.306118 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.325776 Loss1: 0.028149 Loss2: 1.297627
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.275590 Loss1: 0.333265 Loss2: 1.942326
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351919 Loss1: 0.057232 Loss2: 1.294688
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.608821 Loss1: 0.199658 Loss2: 1.409163
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.628081 Loss1: 0.220475 Loss2: 1.407606
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.589781 Loss1: 0.171020 Loss2: 1.418762
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.557198 Loss1: 0.151502 Loss2: 1.405696
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.474458 Loss1: 0.064347 Loss2: 1.410111
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.158863 Loss1: 0.321334 Loss2: 1.837529
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.458238 Loss1: 0.060005 Loss2: 1.398233
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.422015 Loss1: 0.032319 Loss2: 1.389696 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.403232 Loss1: 0.024615 Loss2: 1.378617 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.393471 Loss1: 0.019208 Loss2: 1.374263 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.538066 Loss1: 0.165509 Loss2: 1.372557 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.430382 Loss1: 0.073397 Loss2: 1.356986 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.458366 Loss1: 0.106299 Loss2: 1.352068
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.177025 Loss1: 0.350409 Loss2: 1.826616
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433257 Loss1: 0.075350 Loss2: 1.357907
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.500748 Loss1: 0.180488 Loss2: 1.320260
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.472928 Loss1: 0.139781 Loss2: 1.333147
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.432596 Loss1: 0.104750 Loss2: 1.327845
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.431099 Loss1: 0.108110 Loss2: 1.322989
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.399514 Loss1: 0.081765 Loss2: 1.317749
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.119449 Loss1: 0.315699 Loss2: 1.803750
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.373888 Loss1: 0.055404 Loss2: 1.318484
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.473793 Loss1: 0.151311 Loss2: 1.322482
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.362962 Loss1: 0.053062 Loss2: 1.309899
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.498888 Loss1: 0.178598 Loss2: 1.320290
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.380439 Loss1: 0.071087 Loss2: 1.309351
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.521085 Loss1: 0.191815 Loss2: 1.329271
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.379786 Loss1: 0.068912 Loss2: 1.310874
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.399497 Loss1: 0.092017 Loss2: 1.307479 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.399994 Loss1: 0.092611 Loss2: 1.307383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.391905 Loss1: 0.086764 Loss2: 1.305141
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.064719 Loss1: 0.291510 Loss2: 1.773209
+(DefaultActor pid=3764) >> Training accuracy: 0.988542
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.522369 Loss1: 0.195553 Loss2: 1.326816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.467860 Loss1: 0.132225 Loss2: 1.335636 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.375115 Loss1: 0.060038 Loss2: 1.315076 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.398154 Loss1: 0.077416 Loss2: 1.320738 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.405060 Loss1: 0.080706 Loss2: 1.324354 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.396235 Loss1: 0.075053 Loss2: 1.321182 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398009 Loss1: 0.078785 Loss2: 1.319224 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987305
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.392185 Loss1: 0.067161 Loss2: 1.325024 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.350944 Loss1: 0.028729 Loss2: 1.322215 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.341445 Loss1: 0.029442 Loss2: 1.312003
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.093620 Loss1: 0.287245 Loss2: 1.806374
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.511554 Loss1: 0.159415 Loss2: 1.352139
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.466685 Loss1: 0.116732 Loss2: 1.349953
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.479597 Loss1: 0.126667 Loss2: 1.352929
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488828 Loss1: 0.137678 Loss2: 1.351150
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.134167 Loss1: 0.330085 Loss2: 1.804082
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.567653 Loss1: 0.216603 Loss2: 1.351050 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.483835 Loss1: 0.134280 Loss2: 1.349555 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.441390 Loss1: 0.100960 Loss2: 1.340430 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.473968 Loss1: 0.139884 Loss2: 1.334084 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.999023
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.475865 Loss1: 0.129482 Loss2: 1.346383 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.406096 Loss1: 0.076641 Loss2: 1.329455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.346291 Loss1: 0.030827 Loss2: 1.315464 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.493155 Loss1: 0.129435 Loss2: 1.363720 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.416930 Loss1: 0.080163 Loss2: 1.336767 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.409080 Loss1: 0.075951 Loss2: 1.333129
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.071479 Loss1: 0.303433 Loss2: 1.768047
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.540498 Loss1: 0.236465 Loss2: 1.304033 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.524323 Loss1: 0.208545 Loss2: 1.315778 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468485 Loss1: 0.144384 Loss2: 1.324101 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993304
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.417505 Loss1: 0.105461 Loss2: 1.312044 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.406883 Loss1: 0.113022 Loss2: 1.293861 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.346392 Loss1: 0.055849 Loss2: 1.290544 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.332917 Loss1: 0.048973 Loss2: 1.283945 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.489355 Loss1: 0.120972 Loss2: 1.368383 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.463192 Loss1: 0.097335 Loss2: 1.365857 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.484812 Loss1: 0.117205 Loss2: 1.367608
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.061808 Loss1: 0.267095 Loss2: 1.794713
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.434643 Loss1: 0.070764 Loss2: 1.363880
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.508529 Loss1: 0.176662 Loss2: 1.331868
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.412715 Loss1: 0.053235 Loss2: 1.359480
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.473627 Loss1: 0.128578 Loss2: 1.345049
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398325 Loss1: 0.045335 Loss2: 1.352991
+(DefaultActor pid=3765) >> Training accuracy: 0.989583
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.435527 Loss1: 0.103030 Loss2: 1.332497
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.420469 Loss1: 0.100379 Loss2: 1.320090
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.417526 Loss1: 0.094306 Loss2: 1.323220
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.394421 Loss1: 0.067225 Loss2: 1.327196
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.378583 Loss1: 0.058024 Loss2: 1.320559
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.075368 Loss1: 0.315408 Loss2: 1.759960
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.377827 Loss1: 0.060302 Loss2: 1.317524
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.498545 Loss1: 0.186394 Loss2: 1.312151
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.376303 Loss1: 0.056337 Loss2: 1.319966
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.472746 Loss1: 0.137900 Loss2: 1.334846
+(DefaultActor pid=3764) >> Training accuracy: 0.995404
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.450143 Loss1: 0.137366 Loss2: 1.312777
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.404550 Loss1: 0.099792 Loss2: 1.304758
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.384628 Loss1: 0.073474 Loss2: 1.311154
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.390843 Loss1: 0.092082 Loss2: 1.298761
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.160863 Loss1: 0.302408 Loss2: 1.858455
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.394987 Loss1: 0.090051 Loss2: 1.304936
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.357074 Loss1: 0.053366 Loss2: 1.303708 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.358075 Loss1: 0.061177 Loss2: 1.296898 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.455644 Loss1: 0.094926 Loss2: 1.360718 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.455958 Loss1: 0.092242 Loss2: 1.363716 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.127064 Loss1: 0.279197 Loss2: 1.847866 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.538492 Loss1: 0.169087 Loss2: 1.369405 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.496943 Loss1: 0.115316 Loss2: 1.381627 [repeated 3x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.473638 Loss1: 0.103128 Loss2: 1.370511 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.224195 Loss1: 0.368194 Loss2: 1.856001
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.517149 Loss1: 0.149028 Loss2: 1.368121
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.569020 Loss1: 0.212585 Loss2: 1.356434
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.549806 Loss1: 0.159217 Loss2: 1.390589
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.545220 Loss1: 0.178299 Loss2: 1.366922
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.517210 Loss1: 0.128227 Loss2: 1.388982
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.496470 Loss1: 0.124620 Loss2: 1.371850
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.488301 Loss1: 0.106115 Loss2: 1.382187
+(DefaultActor pid=3765) >> Training accuracy: 0.986328
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.459228 Loss1: 0.099882 Loss2: 1.359346 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.486592 Loss1: 0.126867 Loss2: 1.359725 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.454492 Loss1: 0.093309 Loss2: 1.361183
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.184068 Loss1: 0.356401 Loss2: 1.827668
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.425686 Loss1: 0.069619 Loss2: 1.356067
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.546666 Loss1: 0.212368 Loss2: 1.334297
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.501889 Loss1: 0.156094 Loss2: 1.345795
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.469834 Loss1: 0.126466 Loss2: 1.343368
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.417088 Loss1: 0.084373 Loss2: 1.332715
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.392654 Loss1: 0.064110 Loss2: 1.328543
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.165320 Loss1: 0.353981 Loss2: 1.811339
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.394215 Loss1: 0.071645 Loss2: 1.322570
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.374772 Loss1: 0.059218 Loss2: 1.315554 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.380259 Loss1: 0.061398 Loss2: 1.318862 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.355136 Loss1: 0.044719 Loss2: 1.310417 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994792
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.441496 Loss1: 0.113550 Loss2: 1.327946 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.436676 Loss1: 0.113516 Loss2: 1.323161 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.443724 Loss1: 0.124787 Loss2: 1.318937
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.222804 Loss1: 0.359263 Loss2: 1.863540
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.592598 Loss1: 0.249042 Loss2: 1.343556 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.477440 Loss1: 0.133504 Loss2: 1.343936 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.429785 Loss1: 0.091378 Loss2: 1.338407 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.425851 Loss1: 0.085950 Loss2: 1.339901 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.411879 Loss1: 0.074337 Loss2: 1.337541 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.399898 Loss1: 0.063261 Loss2: 1.336637 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.383374 Loss1: 0.052984 Loss2: 1.330390 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.528870 Loss1: 0.108321 Loss2: 1.420549 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.468130 Loss1: 0.069509 Loss2: 1.398621 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.441980 Loss1: 0.047392 Loss2: 1.394588
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.236112 Loss1: 0.374126 Loss2: 1.861986
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.590163 Loss1: 0.207519 Loss2: 1.382643 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.503103 Loss1: 0.118950 Loss2: 1.384153 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.466158 Loss1: 0.092485 Loss2: 1.373673 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.429795 Loss1: 0.056561 Loss2: 1.373234 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.428768 Loss1: 0.058752 Loss2: 1.370016 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.410989 Loss1: 0.047059 Loss2: 1.363930 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.426080 Loss1: 0.058822 Loss2: 1.367259 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.387414 Loss1: 0.060565 Loss2: 1.326848 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.368509 Loss1: 0.053272 Loss2: 1.315237 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.351160 Loss1: 0.034732 Loss2: 1.316428 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994141
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.457264 Loss1: 0.114235 Loss2: 1.343029 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.411792 Loss1: 0.097337 Loss2: 1.314455 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.150085 Loss1: 0.299151 Loss2: 1.850934 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.572279 Loss1: 0.213078 Loss2: 1.359201 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.593749 Loss1: 0.222571 Loss2: 1.371178 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.552321 Loss1: 0.170991 Loss2: 1.381329 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982292
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.512335 Loss1: 0.144636 Loss2: 1.367699 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.428611 Loss1: 0.082375 Loss2: 1.346236 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.410821 Loss1: 0.072893 Loss2: 1.337928
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.144726 Loss1: 0.304642 Loss2: 1.840084
+(DefaultActor pid=3764) >> Training accuracy: 0.990625
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.394275 Loss1: 0.054148 Loss2: 1.340127
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.535824 Loss1: 0.197918 Loss2: 1.337907
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.509913 Loss1: 0.159993 Loss2: 1.349920
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.430232 Loss1: 0.080407 Loss2: 1.349825
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.427061 Loss1: 0.100434 Loss2: 1.326627
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.400802 Loss1: 0.076143 Loss2: 1.324658
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.150087 Loss1: 0.343396 Loss2: 1.806692
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.403710 Loss1: 0.077514 Loss2: 1.326196
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.398365 Loss1: 0.073105 Loss2: 1.325260 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.353303 Loss1: 0.033787 Loss2: 1.319516 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384011 Loss1: 0.064395 Loss2: 1.319616 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.424856 Loss1: 0.089865 Loss2: 1.334991 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.379223 Loss1: 0.068138 Loss2: 1.311085 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 19:25:50,588 | server.py:236 | fit_round 199 received 50 results and 0 failures
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.354996 Loss1: 0.052733 Loss2: 1.302263
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.108938 Loss1: 0.313539 Loss2: 1.795399
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.359161 Loss1: 0.063509 Loss2: 1.295652
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.568549 Loss1: 0.236933 Loss2: 1.331615
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.525294 Loss1: 0.166568 Loss2: 1.358726
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.498101 Loss1: 0.163786 Loss2: 1.334315
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.507869 Loss1: 0.180187 Loss2: 1.327682
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.524757 Loss1: 0.188137 Loss2: 1.336621
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.112436 Loss1: 0.333518 Loss2: 1.778918
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.480531 Loss1: 0.146707 Loss2: 1.333824
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.612281 Loss1: 0.288801 Loss2: 1.323481
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.449374 Loss1: 0.110918 Loss2: 1.338456
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.524249 Loss1: 0.158683 Loss2: 1.365566
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.435258 Loss1: 0.108203 Loss2: 1.327055
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.482310 Loss1: 0.151760 Loss2: 1.330551
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.398610 Loss1: 0.072282 Loss2: 1.326328
+(DefaultActor pid=3765) >> Training accuracy: 0.972917
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.485636 Loss1: 0.151505 Loss2: 1.334131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.389179 Loss1: 0.065465 Loss2: 1.323714 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.362313 Loss1: 0.054655 Loss2: 1.307658 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 19:25:50,588][flwr][DEBUG] - fit_round 199 received 50 results and 0 failures
+INFO flwr 2023-10-13 19:26:32,808 | server.py:125 | fit progress: (199, 2.329215543529096, {'accuracy': 0.6152}, 459300.586970173)
+>> Test accuracy: 0.615200
+[2023-10-13 19:26:32,808][flwr][INFO] - fit progress: (199, 2.329215543529096, {'accuracy': 0.6152}, 459300.586970173)
+DEBUG flwr 2023-10-13 19:26:32,809 | server.py:173 | evaluate_round 199: strategy sampled 50 clients (out of 50)
+[2023-10-13 19:26:32,809][flwr][DEBUG] - evaluate_round 199: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 19:35:37,050 | server.py:187 | evaluate_round 199 received 50 results and 0 failures
+[2023-10-13 19:35:37,050][flwr][DEBUG] - evaluate_round 199 received 50 results and 0 failures
+DEBUG flwr 2023-10-13 19:35:37,050 | server.py:222 | fit_round 200: strategy sampled 50 clients (out of 50)
+[2023-10-13 19:35:37,050][flwr][DEBUG] - fit_round 200: strategy sampled 50 clients (out of 50)
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.123144 Loss1: 0.284656 Loss2: 1.838488
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.572678 Loss1: 0.215347 Loss2: 1.357332
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.504437 Loss1: 0.124196 Loss2: 1.380241
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.224622 Loss1: 0.343734 Loss2: 1.880888
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.516982 Loss1: 0.152079 Loss2: 1.364903
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.604039 Loss1: 0.237957 Loss2: 1.366082
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.451865 Loss1: 0.088126 Loss2: 1.363739
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.537273 Loss1: 0.156397 Loss2: 1.380877
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454112 Loss1: 0.092276 Loss2: 1.361836
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.566884 Loss1: 0.184783 Loss2: 1.382102
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.461226 Loss1: 0.105991 Loss2: 1.355236
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.435405 Loss1: 0.079957 Loss2: 1.355448 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.428601 Loss1: 0.082321 Loss2: 1.346280 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.387229 Loss1: 0.038978 Loss2: 1.348251 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992188
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.440413 Loss1: 0.075131 Loss2: 1.365282 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.435115 Loss1: 0.420383 Loss2: 2.014731 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.591167 Loss1: 0.206626 Loss2: 1.384541 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.512870 Loss1: 0.117930 Loss2: 1.394940 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.549883 Loss1: 0.188886 Loss2: 1.360997 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.495116 Loss1: 0.125985 Loss2: 1.369131 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.445999 Loss1: 0.073043 Loss2: 1.372956 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993490
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.423262 Loss1: 0.047600 Loss2: 1.375662 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.395923 Loss1: 0.049299 Loss2: 1.346624 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.373822 Loss1: 0.037701 Loss2: 1.336122 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.077007 Loss1: 0.270504 Loss2: 1.806502
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.358416 Loss1: 0.026924 Loss2: 1.331491
+(DefaultActor pid=3764) >> Training accuracy: 1.000000
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.593755 Loss1: 0.187777 Loss2: 1.405978 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.470830 Loss1: 0.094949 Loss2: 1.375881 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.269573 Loss1: 0.398797 Loss2: 1.870776
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.443907 Loss1: 0.080815 Loss2: 1.363091
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.602148 Loss1: 0.226838 Loss2: 1.375310
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.468821 Loss1: 0.109701 Loss2: 1.359120
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.459338 Loss1: 0.097719 Loss2: 1.361619 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.421245 Loss1: 0.068511 Loss2: 1.352734 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.390343 Loss1: 0.043576 Loss2: 1.346766 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991211
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.465632 Loss1: 0.106071 Loss2: 1.359561 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409513 Loss1: 0.060336 Loss2: 1.349177 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.413065 Loss1: 0.069909 Loss2: 1.343157
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.106074 Loss1: 0.265765 Loss2: 1.840309
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.546552 Loss1: 0.189284 Loss2: 1.357267
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.529113 Loss1: 0.154882 Loss2: 1.374231
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.482836 Loss1: 0.123089 Loss2: 1.359747
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.433006 Loss1: 0.071308 Loss2: 1.361699
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.161881 Loss1: 0.288269 Loss2: 1.873613
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.455647 Loss1: 0.101230 Loss2: 1.354417
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.529152 Loss1: 0.178144 Loss2: 1.351009
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.509016 Loss1: 0.165762 Loss2: 1.343254
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.447636 Loss1: 0.085794 Loss2: 1.361842
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.483353 Loss1: 0.120100 Loss2: 1.363253
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.427429 Loss1: 0.069607 Loss2: 1.357822
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.460886 Loss1: 0.121199 Loss2: 1.339688
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.422966 Loss1: 0.077877 Loss2: 1.345089
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.429441 Loss1: 0.095548 Loss2: 1.333892
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.386132 Loss1: 0.036536 Loss2: 1.349597
+(DefaultActor pid=3765) >> Training accuracy: 0.997070
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.427106 Loss1: 0.093127 Loss2: 1.333979 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.375188 Loss1: 0.047803 Loss2: 1.327385 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.646329 Loss1: 0.285429 Loss2: 1.360900 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.474164 Loss1: 0.117523 Loss2: 1.356641 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.450519 Loss1: 0.096454 Loss2: 1.354065 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.423332 Loss1: 0.076536 Loss2: 1.346796 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.396817 Loss1: 0.055170 Loss2: 1.341647 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.393947 Loss1: 0.057713 Loss2: 1.336234 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.395078 Loss1: 0.058576 Loss2: 1.336502 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.378720 Loss1: 0.047066 Loss2: 1.331654 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.994141
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384443 Loss1: 0.070676 Loss2: 1.313767 [repeated 3x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.991667
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.023492 Loss1: 0.269253 Loss2: 1.754239 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.552560 Loss1: 0.204969 Loss2: 1.347591 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.160586 Loss1: 0.319572 Loss2: 1.841015
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.475446 Loss1: 0.150592 Loss2: 1.324854
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.561615 Loss1: 0.217756 Loss2: 1.343859
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.468850 Loss1: 0.150952 Loss2: 1.317897
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.512008 Loss1: 0.143936 Loss2: 1.368072
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.454676 Loss1: 0.136000 Loss2: 1.318676
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.438003 Loss1: 0.083840 Loss2: 1.354163
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.440704 Loss1: 0.117707 Loss2: 1.322997
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.421098 Loss1: 0.104762 Loss2: 1.316336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.474483 Loss1: 0.153486 Loss2: 1.320997 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.460256 Loss1: 0.136704 Loss2: 1.323552 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.982422
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.384907 Loss1: 0.051050 Loss2: 1.333857 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.977083
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.171106 Loss1: 0.327726 Loss2: 1.843379 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.473249 Loss1: 0.116253 Loss2: 1.356996 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.434546 Loss1: 0.090138 Loss2: 1.344408
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.192423 Loss1: 0.349492 Loss2: 1.842932
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.528712 Loss1: 0.216524 Loss2: 1.312189 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.363014 Loss1: 0.040191 Loss2: 1.322823
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.482840 Loss1: 0.162555 Loss2: 1.320285
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.430644 Loss1: 0.098795 Loss2: 1.331849 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.397028 Loss1: 0.083330 Loss2: 1.313697 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.341602 Loss1: 0.039018 Loss2: 1.302584 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.354047 Loss1: 0.046612 Loss2: 1.307435
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.339052 Loss1: 0.041978 Loss2: 1.297074
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.326414 Loss1: 0.036401 Loss2: 1.290013
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.318835 Loss1: 0.034383 Loss2: 1.284452
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.309580 Loss1: 0.025342 Loss2: 1.284238
+(DefaultActor pid=3764) >> Training accuracy: 0.998958
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.150550 Loss1: 0.320579 Loss2: 1.829971
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.569744 Loss1: 0.239625 Loss2: 1.330119
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.531307 Loss1: 0.171389 Loss2: 1.359919
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.472989 Loss1: 0.117131 Loss2: 1.355858
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.164069 Loss1: 0.362166 Loss2: 1.801903
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.604635 Loss1: 0.262583 Loss2: 1.342052 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.616954 Loss1: 0.236106 Loss2: 1.380847 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.558370 Loss1: 0.203525 Loss2: 1.354845 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.527436 Loss1: 0.164971 Loss2: 1.362465 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.472376 Loss1: 0.119652 Loss2: 1.352724 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.427598 Loss1: 0.094475 Loss2: 1.333123 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.406922 Loss1: 0.073933 Loss2: 1.332988 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.998047
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.134443 Loss1: 0.308850 Loss2: 1.825593 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.527824 Loss1: 0.139981 Loss2: 1.387843 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.481234 Loss1: 0.111635 Loss2: 1.369599
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.176156 Loss1: 0.333794 Loss2: 1.842362
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.568739 Loss1: 0.221720 Loss2: 1.347019 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.574490 Loss1: 0.197088 Loss2: 1.377402 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.537546 Loss1: 0.173373 Loss2: 1.364173 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493750 Loss1: 0.143889 Loss2: 1.349861 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.429796 Loss1: 0.073226 Loss2: 1.356570
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.476068 Loss1: 0.123580 Loss2: 1.352488
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.433669 Loss1: 0.081220 Loss2: 1.352448
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447655 Loss1: 0.091373 Loss2: 1.356281
+(DefaultActor pid=3765) >> Training accuracy: 0.980469
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.405882 Loss1: 0.060201 Loss2: 1.345681
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.409609 Loss1: 0.065443 Loss2: 1.344166
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393842 Loss1: 0.055208 Loss2: 1.338634
+(DefaultActor pid=3764) >> Training accuracy: 0.997917
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.148429 Loss1: 0.350120 Loss2: 1.798310
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.584591 Loss1: 0.260970 Loss2: 1.323621
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.603205 Loss1: 0.254799 Loss2: 1.348406
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511039 Loss1: 0.163078 Loss2: 1.347960
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.074616 Loss1: 0.231195 Loss2: 1.843422
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.521694 Loss1: 0.146817 Loss2: 1.374877 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.473021 Loss1: 0.098854 Loss2: 1.374168 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.426909 Loss1: 0.063849 Loss2: 1.363059 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.407368 Loss1: 0.050466 Loss2: 1.356902 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.399820 Loss1: 0.079569 Loss2: 1.320251 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.995833
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.416123 Loss1: 0.056795 Loss2: 1.359328 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.393208 Loss1: 0.036413 Loss2: 1.356795 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992647
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.681266 Loss1: 0.264857 Loss2: 1.416408 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.541558 Loss1: 0.122742 Loss2: 1.418816 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.589714 Loss1: 0.171831 Loss2: 1.417883
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.259542 Loss1: 0.351611 Loss2: 1.907931
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.624044 Loss1: 0.243057 Loss2: 1.380987 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.623066 Loss1: 0.187867 Loss2: 1.435199 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.571444 Loss1: 0.172704 Loss2: 1.398740 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.524308 Loss1: 0.139574 Loss2: 1.384734 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.993750
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.522054 Loss1: 0.126646 Loss2: 1.395408 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.495168 Loss1: 0.109908 Loss2: 1.385260 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.447303 Loss1: 0.076183 Loss2: 1.371119 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.986458
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.566328 Loss1: 0.195838 Loss2: 1.370490 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.511423 Loss1: 0.139919 Loss2: 1.371503 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.176848 Loss1: 0.338552 Loss2: 1.838297 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.598946 Loss1: 0.254929 Loss2: 1.344017 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.534607 Loss1: 0.178228 Loss2: 1.356380 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.468871 Loss1: 0.122536 Loss2: 1.346335 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.430706 Loss1: 0.095631 Loss2: 1.335074 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.979167
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.396879 Loss1: 0.068089 Loss2: 1.328790 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.354967 Loss1: 0.035615 Loss2: 1.319352 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.343527 Loss1: 0.031278 Loss2: 1.312249 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.995833
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.485368 Loss1: 0.171206 Loss2: 1.314162 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.420702 Loss1: 0.104543 Loss2: 1.316159 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.114750 Loss1: 0.269494 Loss2: 1.845256 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.569279 Loss1: 0.229685 Loss2: 1.339595 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.527135 Loss1: 0.158125 Loss2: 1.369010 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.545598 Loss1: 0.182507 Loss2: 1.363091 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.535854 Loss1: 0.171573 Loss2: 1.364281 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.981250
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.439435 Loss1: 0.082793 Loss2: 1.356641 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.393182 Loss1: 0.049078 Loss2: 1.344104 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.379530 Loss1: 0.043168 Loss2: 1.336362
+(DefaultActor pid=3764) >> Training accuracy: 0.984375
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.081560 Loss1: 0.322490 Loss2: 1.759070
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.514314 Loss1: 0.200341 Loss2: 1.313974
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.478262 Loss1: 0.158056 Loss2: 1.320206
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.447548 Loss1: 0.140971 Loss2: 1.306578
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.394458 Loss1: 0.083225 Loss2: 1.311233
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.153551 Loss1: 0.323979 Loss2: 1.829572
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.380684 Loss1: 0.078833 Loss2: 1.301851
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.517104 Loss1: 0.180415 Loss2: 1.336689
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.374308 Loss1: 0.079231 Loss2: 1.295077
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.524202 Loss1: 0.160868 Loss2: 1.363333
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.341214 Loss1: 0.043685 Loss2: 1.297529
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.478585 Loss1: 0.136709 Loss2: 1.341876
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.445712 Loss1: 0.113751 Loss2: 1.331961
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.355536 Loss1: 0.062284 Loss2: 1.293252
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.423286 Loss1: 0.089924 Loss2: 1.333362
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.318117 Loss1: 0.031761 Loss2: 1.286356
+(DefaultActor pid=3765) >> Training accuracy: 0.996094
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.382109 Loss1: 0.046684 Loss2: 1.335425 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.358586 Loss1: 0.034391 Loss2: 1.324195 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.996875
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.490167 Loss1: 0.182158 Loss2: 1.308009 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.431504 Loss1: 0.108726 Loss2: 1.322779 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.315268 Loss1: 0.423049 Loss2: 1.892219
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.395762 Loss1: 0.091888 Loss2: 1.303874
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.395628 Loss1: 0.087370 Loss2: 1.308258 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.408721 Loss1: 0.100648 Loss2: 1.308073 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.455823 Loss1: 0.144345 Loss2: 1.311478 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.440587 Loss1: 0.111953 Loss2: 1.328635 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.371486 Loss1: 0.063540 Loss2: 1.307946 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992708
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.471212 Loss1: 0.114449 Loss2: 1.356763 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.434603 Loss1: 0.079644 Loss2: 1.354959 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992188
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.299353 Loss1: 0.432174 Loss2: 1.867179
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.550145 Loss1: 0.227714 Loss2: 1.322431
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.535525 Loss1: 0.207270 Loss2: 1.328256
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488865 Loss1: 0.141279 Loss2: 1.347587
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.454802 Loss1: 0.129229 Loss2: 1.325573
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.458748 Loss1: 0.136870 Loss2: 1.321878 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.449778 Loss1: 0.118812 Loss2: 1.330966 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.391309 Loss1: 0.073258 Loss2: 1.318050 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.364473 Loss1: 0.050239 Loss2: 1.314233 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.384911 Loss1: 0.073441 Loss2: 1.311470 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.984375
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.488094 Loss1: 0.127339 Loss2: 1.360755 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.463009 Loss1: 0.108361 Loss2: 1.354648 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.485775 Loss1: 0.131388 Loss2: 1.354387
+(DefaultActor pid=3764) >> Training accuracy: 0.976042
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.268009 Loss1: 0.312917 Loss2: 1.955092
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.691183 Loss1: 0.256694 Loss2: 1.434489
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.605652 Loss1: 0.159744 Loss2: 1.445908
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.566272 Loss1: 0.127561 Loss2: 1.438711
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.556246 Loss1: 0.130367 Loss2: 1.425879
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.097510 Loss1: 0.253215 Loss2: 1.844295
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.525216 Loss1: 0.095879 Loss2: 1.429337
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.535823 Loss1: 0.109097 Loss2: 1.426726 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.553455 Loss1: 0.122923 Loss2: 1.430533 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.533471 Loss1: 0.096729 Loss2: 1.436742 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.489325 Loss1: 0.069131 Loss2: 1.420194 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.975000
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.378104 Loss1: 0.061582 Loss2: 1.316522 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.383124 Loss1: 0.068128 Loss2: 1.314996 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.406259 Loss1: 0.090706 Loss2: 1.315553
+(DefaultActor pid=3764) >> Training accuracy: 0.985417
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.133181 Loss1: 0.289059 Loss2: 1.844122
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.551878 Loss1: 0.210366 Loss2: 1.341512
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.528315 Loss1: 0.180284 Loss2: 1.348032
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.497400 Loss1: 0.139046 Loss2: 1.358354
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.488360 Loss1: 0.151158 Loss2: 1.337203
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.185591 Loss1: 0.326111 Loss2: 1.859479
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.533020 Loss1: 0.186893 Loss2: 1.346127 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.564151 Loss1: 0.195526 Loss2: 1.368625 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.519068 Loss1: 0.141220 Loss2: 1.377848 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.541789 Loss1: 0.175554 Loss2: 1.366235 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.971875
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.514154 Loss1: 0.143978 Loss2: 1.370175 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.414519 Loss1: 0.054349 Loss2: 1.360170 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.404784 Loss1: 0.051591 Loss2: 1.353193 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.981250
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.523760 Loss1: 0.173062 Loss2: 1.350698 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.488123 Loss1: 0.134557 Loss2: 1.353566 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.137149 Loss1: 0.271144 Loss2: 1.866005 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.504070 Loss1: 0.164132 Loss2: 1.339939 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.463835 Loss1: 0.139170 Loss2: 1.324665 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.445728 Loss1: 0.106289 Loss2: 1.339438 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.398980 Loss1: 0.069953 Loss2: 1.329027 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.345577 Loss1: 0.031026 Loss2: 1.314552 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.355005 Loss1: 0.052752 Loss2: 1.302253 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.366985 Loss1: 0.062017 Loss2: 1.304969
+(DefaultActor pid=3764) >> Training accuracy: 0.994792
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.157280 Loss1: 0.262863 Loss2: 1.894417
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.587164 Loss1: 0.174567 Loss2: 1.412597
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.568772 Loss1: 0.146186 Loss2: 1.422587
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.513904 Loss1: 0.095385 Loss2: 1.418520
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.515084 Loss1: 0.110591 Loss2: 1.404494
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.275275 Loss1: 0.337884 Loss2: 1.937391
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.631075 Loss1: 0.238010 Loss2: 1.393065 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.466761 Loss1: 0.061238 Loss2: 1.405523
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.627205 Loss1: 0.215388 Loss2: 1.411817
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.483497 Loss1: 0.086007 Loss2: 1.397490
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.540762 Loss1: 0.121538 Loss2: 1.419224
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.493089 Loss1: 0.098482 Loss2: 1.394608
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.468683 Loss1: 0.065801 Loss2: 1.402882
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.477083 Loss1: 0.084469 Loss2: 1.392614
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.461170 Loss1: 0.061324 Loss2: 1.399846
+(DefaultActor pid=3765) >> Training accuracy: 0.983398
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.481788 Loss1: 0.090657 Loss2: 1.391131 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.475143 Loss1: 0.085195 Loss2: 1.389949 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.993304
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.366797 Loss1: 0.394522 Loss2: 1.972275
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.586099 Loss1: 0.225544 Loss2: 1.360554
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.559907 Loss1: 0.206935 Loss2: 1.352971
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.534865 Loss1: 0.144601 Loss2: 1.390264
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.510690 Loss1: 0.144274 Loss2: 1.366416
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.429943 Loss1: 0.073607 Loss2: 1.356336 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.414885 Loss1: 0.056355 Loss2: 1.358529 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.400704 Loss1: 0.052805 Loss2: 1.347899 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.381362 Loss1: 0.036104 Loss2: 1.345258 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.377273 Loss1: 0.039068 Loss2: 1.338205 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.992788
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.408313 Loss1: 0.073890 Loss2: 1.334423 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.400509 Loss1: 0.079510 Loss2: 1.320999 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989955
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.562840 Loss1: 0.175927 Loss2: 1.386913 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.537138 Loss1: 0.131791 Loss2: 1.405346 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.517017 Loss1: 0.131977 Loss2: 1.385040
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.291122 Loss1: 0.365612 Loss2: 1.925510
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.530259 Loss1: 0.131955 Loss2: 1.398305
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.590019 Loss1: 0.198865 Loss2: 1.391154
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.508645 Loss1: 0.105076 Loss2: 1.403569
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.580267 Loss1: 0.183138 Loss2: 1.397128
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.508080 Loss1: 0.113412 Loss2: 1.394668
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.557648 Loss1: 0.145087 Loss2: 1.412562
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.470980 Loss1: 0.082156 Loss2: 1.388824
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.510631 Loss1: 0.122943 Loss2: 1.387689
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.499815 Loss1: 0.110068 Loss2: 1.389747
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.486846 Loss1: 0.096548 Loss2: 1.390299
+(DefaultActor pid=3765) >> Training accuracy: 0.983333
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.447535 Loss1: 0.066396 Loss2: 1.381139
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.440880 Loss1: 0.070632 Loss2: 1.370249
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.427277 Loss1: 0.061404 Loss2: 1.365873
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.439117 Loss1: 0.074488 Loss2: 1.364629
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.260142 Loss1: 0.362961 Loss2: 1.897181
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.605452 Loss1: 0.210984 Loss2: 1.394468
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.578128 Loss1: 0.172213 Loss2: 1.405915
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.576418 Loss1: 0.172813 Loss2: 1.403605
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.543764 Loss1: 0.152936 Loss2: 1.390828
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.503190 Loss1: 0.106817 Loss2: 1.396373 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.478686 Loss1: 0.085466 Loss2: 1.393220 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.450978 Loss1: 0.060540 Loss2: 1.390438 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.441577 Loss1: 0.061546 Loss2: 1.380031 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.439871 Loss1: 0.065638 Loss2: 1.374233 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.991667
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.356436 Loss1: 0.061841 Loss2: 1.294595 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.345467 Loss1: 0.060588 Loss2: 1.284879 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.595259 Loss1: 0.220677 Loss2: 1.374582 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.480765 Loss1: 0.095508 Loss2: 1.385257 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.443533 Loss1: 0.086420 Loss2: 1.357114
+(DefaultActor pid=3764) Epoch: 0 Loss: 2.177342 Loss1: 0.331154 Loss2: 1.846188
+(DefaultActor pid=3764) Epoch: 1 Loss: 1.563972 Loss1: 0.233203 Loss2: 1.330769 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 2 Loss: 1.514236 Loss1: 0.168365 Loss2: 1.345871 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 3 Loss: 1.462002 Loss1: 0.118494 Loss2: 1.343508 [repeated 2x across cluster]
+(DefaultActor pid=3764) Epoch: 4 Loss: 1.400725 Loss1: 0.080220 Loss2: 1.320505 [repeated 2x across cluster]
+DEBUG flwr 2023-10-13 20:04:17,585 | server.py:236 | fit_round 200 received 50 results and 0 failures
+(DefaultActor pid=3765) >> Training accuracy: 0.987500
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.415338 Loss1: 0.058902 Loss2: 1.356436
+(DefaultActor pid=3764) Epoch: 5 Loss: 1.378013 Loss1: 0.058293 Loss2: 1.319720
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 6 Loss: 1.372858 Loss1: 0.056827 Loss2: 1.316030
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.363239 Loss1: 0.052274 Loss2: 1.310965
+(DefaultActor pid=3764) Epoch: 8 Loss: 1.363045 Loss1: 0.058254 Loss2: 1.304791
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.331503 Loss1: 0.027893 Loss2: 1.303610
+(DefaultActor pid=3764) >> Training accuracy: 0.992708
+(DefaultActor pid=3764)  ** Training complete **
+(DefaultActor pid=3765) Epoch: 0 Loss: 2.137559 Loss1: 0.297910 Loss2: 1.839650
+(DefaultActor pid=3765) Epoch: 1 Loss: 1.615377 Loss1: 0.268106 Loss2: 1.347271
+(DefaultActor pid=3765) Epoch: 2 Loss: 1.523468 Loss1: 0.159762 Loss2: 1.363706
+(DefaultActor pid=3765) Epoch: 3 Loss: 1.500506 Loss1: 0.141497 Loss2: 1.359010
+(DefaultActor pid=3765) Epoch: 4 Loss: 1.449131 Loss1: 0.102266 Loss2: 1.346864
+(DefaultActor pid=3765) Epoch: 5 Loss: 1.446550 Loss1: 0.103510 Loss2: 1.343040 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 6 Loss: 1.437277 Loss1: 0.095959 Loss2: 1.341317 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 7 Loss: 1.382470 Loss1: 0.045135 Loss2: 1.337335 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 8 Loss: 1.376087 Loss1: 0.049066 Loss2: 1.327021 [repeated 2x across cluster]
+(DefaultActor pid=3765) Epoch: 9 Loss: 1.392091 Loss1: 0.068598 Loss2: 1.323493 [repeated 2x across cluster]
+(DefaultActor pid=3765) >> Training accuracy: 0.988542
+(DefaultActor pid=3765)  ** Training complete **
+(DefaultActor pid=3764) Epoch: 7 Loss: 1.434338 Loss1: 0.060036 Loss2: 1.374302 [repeated 3x across cluster]
+(DefaultActor pid=3764) Epoch: 9 Loss: 1.433239 Loss1: 0.061122 Loss2: 1.372116 [repeated 2x across cluster]
+(DefaultActor pid=3764) >> Training accuracy: 0.989583
+(DefaultActor pid=3764)  ** Training complete **
+[2023-10-13 20:04:17,585][flwr][DEBUG] - fit_round 200 received 50 results and 0 failures
+INFO flwr 2023-10-13 20:04:59,370 | server.py:125 | fit progress: (200, 2.3313485875297277, {'accuracy': 0.6149}, 461607.14849393995)
+>> Test accuracy: 0.614900
+[2023-10-13 20:04:59,370][flwr][INFO] - fit progress: (200, 2.3313485875297277, {'accuracy': 0.6149}, 461607.14849393995)
+DEBUG flwr 2023-10-13 20:04:59,370 | server.py:173 | evaluate_round 200: strategy sampled 50 clients (out of 50)
+[2023-10-13 20:04:59,370][flwr][DEBUG] - evaluate_round 200: strategy sampled 50 clients (out of 50)
+DEBUG flwr 2023-10-13 20:14:03,498 | server.py:187 | evaluate_round 200 received 50 results and 0 failures
+[2023-10-13 20:14:03,498][flwr][DEBUG] - evaluate_round 200 received 50 results and 0 failures
+INFO flwr 2023-10-13 20:14:03,498 | server.py:153 | FL finished in 462151.27705474297
+[2023-10-13 20:14:03,498][flwr][INFO] - FL finished in 462151.27705474297
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+[2023-10-13 20:14:04,179][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0), (101, 0.0), (102, 0.0), (103, 0.0), (104, 0.0), (105, 0.0), (106, 0.0), (107, 0.0), (108, 0.0), (109, 0.0), (110, 0.0), (111, 0.0), (112, 0.0), (113, 0.0), (114, 0.0), (115, 0.0), (116, 0.0), (117, 0.0), (118, 0.0), (119, 0.0), (120, 0.0), (121, 0.0), (122, 0.0), (123, 0.0), (124, 0.0), (125, 0.0), (126, 0.0), (127, 0.0), (128, 0.0), (129, 0.0), (130, 0.0), (131, 0.0), (132, 0.0), (133, 0.0), (134, 0.0), (135, 0.0), (136, 0.0), (137, 0.0), (138, 0.0), (139, 0.0), (140, 0.0), (141, 0.0), (142, 0.0), (143, 0.0), (144, 0.0), (145, 0.0), (146, 0.0), (147, 0.0), (148, 0.0), (149, 0.0), (150, 0.0), (151, 0.0), (152, 0.0), (153, 0.0), (154, 0.0), (155, 0.0), (156, 0.0), (157, 0.0), (158, 0.0), (159, 0.0), (160, 0.0), (161, 0.0), (162, 0.0), (163, 0.0), (164, 0.0), (165, 0.0), (166, 0.0), (167, 0.0), (168, 0.0), (169, 0.0), (170, 0.0), (171, 0.0), (172, 0.0), (173, 0.0), (174, 0.0), (175, 0.0), (176, 0.0), (177, 0.0), (178, 0.0), (179, 0.0), (180, 0.0), (181, 0.0), (182, 0.0), (183, 0.0), (184, 0.0), (185, 0.0), (186, 0.0), (187, 0.0), (188, 0.0), (189, 0.0), (190, 0.0), (191, 0.0), (192, 0.0), (193, 0.0), (194, 0.0), (195, 0.0), (196, 0.0), (197, 0.0), (198, 0.0), (199, 0.0), (200, 0.0)]
+INFO flwr 2023-10-13 20:14:04,179 | app.py:226 | app_fit: metrics_distributed_fit {}
+[2023-10-13 20:14:04,179][flwr][INFO] - app_fit: metrics_distributed_fit {}
+INFO flwr 2023-10-13 20:14:04,179 | app.py:227 | app_fit: metrics_distributed {}
+[2023-10-13 20:14:04,179][flwr][INFO] - app_fit: metrics_distributed {}
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+INFO flwr 2023-10-13 20:14:04,180 | app.py:229 | app_fit: metrics_centralized {'accuracy': [(0, 0.01), (1, 0.01), (2, 0.01), (3, 0.01), (4, 0.0117), (5, 0.0252), (6, 0.0415), (7, 0.0497), (8, 0.0537), (9, 0.0634), (10, 0.0769), (11, 0.0903), (12, 0.1017), (13, 0.119), (14, 0.1316), (15, 0.1469), (16, 0.1625), (17, 0.177), (18, 0.1934), (19, 0.208), (20, 0.2236), (21, 0.2382), (22, 0.2531), (23, 0.2637), (24, 0.2814), (25, 0.295), (26, 0.3054), (27, 0.3206), (28, 0.3281), (29, 0.3431), (30, 0.3485), (31, 0.3589), (32, 0.3679), (33, 0.3764), (34, 0.3834), (35, 0.392), (36, 0.3976), (37, 0.4045), (38, 0.412), (39, 0.4201), (40, 0.4248), (41, 0.4319), (42, 0.4394), (43, 0.4441), (44, 0.4467), (45, 0.4568), (46, 0.4587), (47, 0.4661), (48, 0.47), (49, 0.4738), (50, 0.4764), (51, 0.4815), (52, 0.4836), (53, 0.4882), (54, 0.4907), (55, 0.4939), (56, 0.4999), (57, 0.4983), (58, 0.5016), (59, 0.5068), (60, 0.5081), (61, 0.51), (62, 0.5111), (63, 0.517), (64, 0.5197), (65, 0.5214), (66, 0.5231), (67, 0.5234), (68, 0.5227), (69, 0.5269), (70, 0.5317), (71, 0.5305), (72, 0.5342), (73, 0.5364), (74, 0.541), (75, 0.5379), (76, 0.5413), (77, 0.5416), (78, 0.5425), (79, 0.546), (80, 0.5467), (81, 0.5476), (82, 0.5485), (83, 0.5508), (84, 0.55), (85, 0.552), (86, 0.5525), (87, 0.5543), (88, 0.5567), (89, 0.5568), (90, 0.5571), (91, 0.5571), (92, 0.561), (93, 0.5612), (94, 0.5643), (95, 0.5634), (96, 0.5652), (97, 0.5673), (98, 0.5667), (99, 0.5666), (100, 0.5667), (101, 0.5696), (102, 0.5702), (103, 0.5712), (104, 0.5699), (105, 0.5705), (106, 0.5705), (107, 0.5746), (108, 0.5734), (109, 0.5739), (110, 0.5761), (111, 0.5772), (112, 0.5783), (113, 0.579), (114, 0.5787), (115, 0.5787), (116, 0.5796), (117, 0.5816), (118, 0.5821), (119, 0.5794), (120, 0.5816), (121, 0.5797), (122, 0.5839), (123, 0.5825), (124, 0.5851), (125, 0.5871), (126, 0.5847), (127, 0.5875), (128, 0.5867), (129, 0.5893), (130, 0.59), (131, 0.5878), (132, 0.5901), (133, 0.5918), (134, 0.5886), (135, 0.5909), (136, 0.5923), (137, 0.5942), (138, 0.5925), (139, 0.5932), (140, 0.5928), (141, 0.5956), (142, 0.5928), (143, 0.5926), (144, 0.5939), (145, 0.596), (146, 0.5958), (147, 0.5946), (148, 0.5964), (149, 0.5952), (150, 0.5956), (151, 0.5966), (152, 0.5963), (153, 0.595), (154, 0.5961), (155, 0.5988), (156, 0.5976), (157, 0.6005), (158, 0.6015), (159, 0.6021), (160, 0.6005), (161, 0.6029), (162, 0.6036), (163, 0.6001), (164, 0.6016), (165, 0.6029), (166, 0.6028), (167, 0.6034), (168, 0.604), (169, 0.6043), (170, 0.6037), (171, 0.6067), (172, 0.6068), (173, 0.6063), (174, 0.6048), (175, 0.6093), (176, 0.6073), (177, 0.6069), (178, 0.6079), (179, 0.6098), (180, 0.609), (181, 0.6087), (182, 0.6113), (183, 0.6095), (184, 0.6104), (185, 0.6091), (186, 0.6097), (187, 0.6109), (188, 0.6113), (189, 0.6123), (190, 0.6111), (191, 0.6113), (192, 0.6118), (193, 0.6109), (194, 0.6115), (195, 0.6132), (196, 0.6137), (197, 0.6144), (198, 0.615), (199, 0.6152), (200, 0.6149)]}
+[2023-10-13 20:14:04,180][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.01), (1, 0.01), (2, 0.01), (3, 0.01), (4, 0.0117), (5, 0.0252), (6, 0.0415), (7, 0.0497), (8, 0.0537), (9, 0.0634), (10, 0.0769), (11, 0.0903), (12, 0.1017), (13, 0.119), (14, 0.1316), (15, 0.1469), (16, 0.1625), (17, 0.177), (18, 0.1934), (19, 0.208), (20, 0.2236), (21, 0.2382), (22, 0.2531), (23, 0.2637), (24, 0.2814), (25, 0.295), (26, 0.3054), (27, 0.3206), (28, 0.3281), (29, 0.3431), (30, 0.3485), (31, 0.3589), (32, 0.3679), (33, 0.3764), (34, 0.3834), (35, 0.392), (36, 0.3976), (37, 0.4045), (38, 0.412), (39, 0.4201), (40, 0.4248), (41, 0.4319), (42, 0.4394), (43, 0.4441), (44, 0.4467), (45, 0.4568), (46, 0.4587), (47, 0.4661), (48, 0.47), (49, 0.4738), (50, 0.4764), (51, 0.4815), (52, 0.4836), (53, 0.4882), (54, 0.4907), (55, 0.4939), (56, 0.4999), (57, 0.4983), (58, 0.5016), (59, 0.5068), (60, 0.5081), (61, 0.51), (62, 0.5111), (63, 0.517), (64, 0.5197), (65, 0.5214), (66, 0.5231), (67, 0.5234), (68, 0.5227), (69, 0.5269), (70, 0.5317), (71, 0.5305), (72, 0.5342), (73, 0.5364), (74, 0.541), (75, 0.5379), (76, 0.5413), (77, 0.5416), (78, 0.5425), (79, 0.546), (80, 0.5467), (81, 0.5476), (82, 0.5485), (83, 0.5508), (84, 0.55), (85, 0.552), (86, 0.5525), (87, 0.5543), (88, 0.5567), (89, 0.5568), (90, 0.5571), (91, 0.5571), (92, 0.561), (93, 0.5612), (94, 0.5643), (95, 0.5634), (96, 0.5652), (97, 0.5673), (98, 0.5667), (99, 0.5666), (100, 0.5667), (101, 0.5696), (102, 0.5702), (103, 0.5712), (104, 0.5699), (105, 0.5705), (106, 0.5705), (107, 0.5746), (108, 0.5734), (109, 0.5739), (110, 0.5761), (111, 0.5772), (112, 0.5783), (113, 0.579), (114, 0.5787), (115, 0.5787), (116, 0.5796), (117, 0.5816), (118, 0.5821), (119, 0.5794), (120, 0.5816), (121, 0.5797), (122, 0.5839), (123, 0.5825), (124, 0.5851), (125, 0.5871), (126, 0.5847), (127, 0.5875), (128, 0.5867), (129, 0.5893), (130, 0.59), (131, 0.5878), (132, 0.5901), (133, 0.5918), (134, 0.5886), (135, 0.5909), (136, 0.5923), (137, 0.5942), (138, 0.5925), (139, 0.5932), (140, 0.5928), (141, 0.5956), (142, 0.5928), (143, 0.5926), (144, 0.5939), (145, 0.596), (146, 0.5958), (147, 0.5946), (148, 0.5964), (149, 0.5952), (150, 0.5956), (151, 0.5966), (152, 0.5963), (153, 0.595), (154, 0.5961), (155, 0.5988), (156, 0.5976), (157, 0.6005), (158, 0.6015), (159, 0.6021), (160, 0.6005), (161, 0.6029), (162, 0.6036), (163, 0.6001), (164, 0.6016), (165, 0.6029), (166, 0.6028), (167, 0.6034), (168, 0.604), (169, 0.6043), (170, 0.6037), (171, 0.6067), (172, 0.6068), (173, 0.6063), (174, 0.6048), (175, 0.6093), (176, 0.6073), (177, 0.6069), (178, 0.6079), (179, 0.6098), (180, 0.609), (181, 0.6087), (182, 0.6113), (183, 0.6095), (184, 0.6104), (185, 0.6091), (186, 0.6097), (187, 0.6109), (188, 0.6113), (189, 0.6123), (190, 0.6111), (191, 0.6113), (192, 0.6118), (193, 0.6109), (194, 0.6115), (195, 0.6132), (196, 0.6137), (197, 0.6144), (198, 0.615), (199, 0.6152), (200, 0.6149)]}
+................
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+History (loss, centralized):
+	round 0: 8.480555293659052
+	round 1: 4.678643322600343
+	round 2: 4.821835554445895
+	round 3: 4.760899214698864
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+	round 12: 3.9032422269876013
+	round 13: 3.7946639914101303
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+	round 15: 3.606255607483105
+	round 16: 3.5286484503517515
+	round 17: 3.453257521120504
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+	round 19: 3.324841410969012
+	round 20: 3.2592665303629427
+	round 21: 3.1982289168019644
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+	round 23: 3.1089451796711445
+	round 24: 3.037447242691113
+	round 25: 2.9935917195420676
+	round 26: 2.974912224486232
+	round 27: 2.8897676616431043
+	round 28: 2.9036672896089644
+	round 29: 2.8099579156016388
+	round 30: 2.813382360881891
+	round 31: 2.7770677180335928
+	round 32: 2.7599665619694767
+	round 33: 2.7204779908299064
+	round 34: 2.68708546359699
+	round 35: 2.6667960539412574
+	round 36: 2.6492519972804254
+	round 37: 2.6360169702444596
+	round 38: 2.5861118205439166
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+	round 40: 2.5703621779006127
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+	round 44: 2.493049050672367
+	round 45: 2.4865793888561263
+	round 46: 2.4787738273699826
+	round 47: 2.4710606367062455
+	round 48: 2.4517499086575008
+	round 49: 2.436963511732059
+	round 50: 2.4152099930059414
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+	round 52: 2.3937867083869424
+	round 53: 2.385063742296383
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+	round 59: 2.3416105348842975
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+	round 112: 2.2133301115645385
+	round 113: 2.1971218222246383
+	round 114: 2.199429733303789
+	round 115: 2.1888895084301883
+	round 116: 2.2013852497259268
+	round 117: 2.210739805104253
+	round 118: 2.2009005855066706
+	round 119: 2.20066045934019
+	round 120: 2.2117004977247587
+	round 121: 2.2130620091106183
+	round 122: 2.2014924769584363
+	round 123: 2.200183067268457
+	round 124: 2.2044151994747856
+	round 125: 2.2017363711667897
+	round 126: 2.198208836701731
+	round 127: 2.2097703696439823
+	round 128: 2.208596081969837
+	round 129: 2.204728903480993
+	round 130: 2.2075375242355153
+	round 131: 2.212314099168625
+	round 132: 2.2114045372405373
+	round 133: 2.2147254682958315
+	round 134: 2.212735759754912
+	round 135: 2.2166092506231974
+	round 136: 2.2225120815987025
+	round 137: 2.2192010569115417
+	round 138: 2.2246322159569103
+	round 139: 2.2211843315785686
+	round 140: 2.210895585747192
+	round 141: 2.2251683026076123
+	round 142: 2.223556954068498
+	round 143: 2.2164862047369107
+	round 144: 2.2120984548958726
+	round 145: 2.2184009710059
+	round 146: 2.2189620870370836
+	round 147: 2.237486937365974
+	round 148: 2.2302481027456897
+	round 149: 2.23768986547336
+	round 150: 2.2385582904846144
+	round 151: 2.2425448860223303
+	round 152: 2.2384562981776157
+	round 153: 2.248646311485729
+	round 154: 2.24084857896494
+	round 155: 2.2388751522039834
+	round 156: 2.2486510133971804
+	round 157: 2.243841856051558
+	round 158: 2.2473408779778037
+	round 159: 2.244518861222191
+	round 160: 2.2541892732294224
+	round 161: 2.2532033196653423
+	round 162: 2.2612200133716716
+	round 163: 2.254364108125242
+	round 164: 2.246745443191772
+	round 165: 2.264890566420631
+	round 166: 2.2735640943621673
+	round 167: 2.2618117819959744
+	round 168: 2.2627456317694423
+	round 169: 2.2763475324399174
+	round 170: 2.275574233966133
+	round 171: 2.2737315053376146
+	round 172: 2.27676078781914
+	round 173: 2.277818728559695
+	round 174: 2.2803944634934203
+	round 175: 2.274364816304594
+	round 176: 2.2847310698832186
+	round 177: 2.2940372186727798
+	round 178: 2.2912714462310744
+	round 179: 2.289638937662204
+	round 180: 2.291451721145703
+	round 181: 2.3034834168589535
+	round 182: 2.2981419727063406
+	round 183: 2.3107704240293168
+	round 184: 2.313936873937186
+	round 185: 2.306797981262207
+	round 186: 2.3054927869345816
+	round 187: 2.303106297890599
+	round 188: 2.3212159514046324
+	round 189: 2.321961476779974
+	round 190: 2.3261600407167746
+	round 191: 2.3261689364719698
+	round 192: 2.328017218615681
+	round 193: 2.32618732669483
+	round 194: 2.3175561528998063
+	round 195: 2.337302811420002
+	round 196: 2.348089092646163
+	round 197: 2.338596988600283
+	round 198: 2.3383814542057415
+	round 199: 2.329215543529096
+	round 200: 2.3313485875297277
+History (metrics, centralized):
+{'accuracy': [(0, 0.01), (1, 0.01), (2, 0.01), (3, 0.01), (4, 0.0117), (5, 0.0252), (6, 0.0415), (7, 0.0497), (8, 0.0537), (9, 0.0634), (10, 0.0769), (11, 0.0903), (12, 0.1017), (13, 0.119), (14, 0.1316), (15, 0.1469), (16, 0.1625), (17, 0.177), (18, 0.1934), (19, 0.208), (20, 0.2236), (21, 0.2382), (22, 0.2531), (23, 0.2637), (24, 0.2814), (25, 0.295), (26, 0.3054), (27, 0.3206), (28, 0.3281), (29, 0.3431), (30, 0.3485), (31, 0.3589), (32, 0.3679), (33, 0.3764), (34, 0.3834), (35, 0.392), (36, 0.3976), (37, 0.4045), (38, 0.412), (39, 0.4201), (40, 0.4248), (41, 0.4319), (42, 0.4394), (43, 0.4441), (44, 0.4467), (45, 0.4568), (46, 0.4587), (47, 0.4661), (48, 0.47), (49, 0.4738), (50, 0.4764), (51, 0.4815), (52, 0.4836), (53, 0.4882), (54, 0.4907), (55, 0.4939), (56, 0.4999), (57, 0.4983), (58, 0.5016), (59, 0.5068), (60, 0.5081), (61, 0.51), (62, 0.5111), (63, 0.517), (64, 0.5197), (65, 0.5214), (66, 0.5231), (67, 0.5234), (68, 0.5227), (69, 0.5269), (70, 0.5317), (71, 0.5305), (72, 0.5342), (73, 0.5364), (74, 0.541), (75, 0.5379), (76, 0.5413), (77, 0.5416), (78, 0.5425), (79, 0.546), (80, 0.5467), (81, 0.5476), (82, 0.5485), (83, 0.5508), (84, 0.55), (85, 0.552), (86, 0.5525), (87, 0.5543), (88, 0.5567), (89, 0.5568), (90, 0.5571), (91, 0.5571), (92, 0.561), (93, 0.5612), (94, 0.5643), (95, 0.5634), (96, 0.5652), (97, 0.5673), (98, 0.5667), (99, 0.5666), (100, 0.5667), (101, 0.5696), (102, 0.5702), (103, 0.5712), (104, 0.5699), (105, 0.5705), (106, 0.5705), (107, 0.5746), (108, 0.5734), (109, 0.5739), (110, 0.5761), (111, 0.5772), (112, 0.5783), (113, 0.579), (114, 0.5787), (115, 0.5787), (116, 0.5796), (117, 0.5816), (118, 0.5821), (119, 0.5794), (120, 0.5816), (121, 0.5797), (122, 0.5839), (123, 0.5825), (124, 0.5851), (125, 0.5871), (126, 0.5847), (127, 0.5875), (128, 0.5867), (129, 0.5893), (130, 0.59), (131, 0.5878), (132, 0.5901), (133, 0.5918), (134, 0.5886), (135, 0.5909), (136, 0.5923), (137, 0.5942), (138, 0.5925), (139, 0.5932), (140, 0.5928), (141, 0.5956), (142, 0.5928), (143, 0.5926), (144, 0.5939), (145, 0.596), (146, 0.5958), (147, 0.5946), (148, 0.5964), (149, 0.5952), (150, 0.5956), (151, 0.5966), (152, 0.5963), (153, 0.595), (154, 0.5961), (155, 0.5988), (156, 0.5976), (157, 0.6005), (158, 0.6015), (159, 0.6021), (160, 0.6005), (161, 0.6029), (162, 0.6036), (163, 0.6001), (164, 0.6016), (165, 0.6029), (166, 0.6028), (167, 0.6034), (168, 0.604), (169, 0.6043), (170, 0.6037), (171, 0.6067), (172, 0.6068), (173, 0.6063), (174, 0.6048), (175, 0.6093), (176, 0.6073), (177, 0.6069), (178, 0.6079), (179, 0.6098), (180, 0.609), (181, 0.6087), (182, 0.6113), (183, 0.6095), (184, 0.6104), (185, 0.6091), (186, 0.6097), (187, 0.6109), (188, 0.6113), (189, 0.6123), (190, 0.6111), (191, 0.6113), (192, 0.6118), (193, 0.6109), (194, 0.6115), (195, 0.6132), (196, 0.6137), (197, 0.6144), (198, 0.615), (199, 0.6152), (200, 0.6149)]}
+[2023-10-13 20:14:05,330][matplotlib.legend][WARNING] - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
+/home/ubuntu/flower/baselines/moon/moon/utils.py:124: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
+  plt.show()
diff --git a/baselines/moon/_static/cifar100_fedprox_log.txt b/baselines/moon/_static/cifar100_fedprox_log.txt
new file mode 100644
index 000000000000..d820dc54ded1
--- /dev/null
+++ b/baselines/moon/_static/cifar100_fedprox_log.txt
@@ -0,0 +1,17647 @@
+num_clients: 10
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 0.001
+temperature: 0.5
+alg: moon
+seed: 0
+server_device: cpu
+num_rounds: 100
+client_resources:
+  num_cpus: 4
+  num_gpus: 1
+dataset:
+  name: cifar100
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+model:
+  name: resnet50
+  output_dim: 256
+  dir: ./models/moon/cifar100_fedprox/
+
+Files already downloaded and verified
+Files already downloaded and verified
+[2023-09-21 03:10:49,616][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=100, round_timeout=None)
+[2023-09-21 03:10:52,718][flwr][INFO] - Flower VCE: Ray initialized with resources: {'object_store_memory': 97622652518.0, 'memory': 217786189210.0, 'node:137.132.92.49': 1.0, 'CPU': 64.0, 'node:__internal_head__': 1.0, 'accelerator_type:G': 1.0, 'GPU': 1.0}
+[2023-09-21 03:10:52,719][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 1}
+[2023-09-21 03:10:52,737][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 1 actors
+[2023-09-21 03:10:52,737][flwr][INFO] - Initializing global parameters
+[2023-09-21 03:10:52,737][flwr][INFO] - Requesting initial parameters from one random client
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 03:10:58,274][flwr][INFO] - Received initial parameters from one random client
+[2023-09-21 03:10:58,275][flwr][INFO] - Evaluating initial parameters
+test acc: 0.01
+[2023-09-21 03:11:58,338][flwr][INFO] - initial parameters (loss, other metrics): 6.156129693832641, {'accuracy': 0.01}
+[2023-09-21 03:11:58,338][flwr][INFO] - FL starting
+[2023-09-21 03:11:58,339][flwr][DEBUG] - fit_round 1: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.014042721518987342
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.049003 Loss1: 4.048447 Loss2: 0.000556
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.862238 Loss1: 3.861674 Loss2: 0.000564
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.686501 Loss1: 3.685928 Loss2: 0.000573
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.561694 Loss1: 3.561111 Loss2: 0.000584
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.456870 Loss1: 3.456278 Loss2: 0.000592
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.378081 Loss1: 3.377487 Loss2: 0.000595
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.315237 Loss1: 3.314636 Loss2: 0.000602
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.250166 Loss1: 3.249565 Loss2: 0.000601
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.181074 Loss1: 3.180473 Loss2: 0.000601
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.136153 Loss1: 3.135551 Loss2: 0.000602
+(DefaultActor pid=2839578) >> Training accuracy: 0.236155
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.0
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.108069 Loss1: 4.107460 Loss2: 0.000609
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.894048 Loss1: 3.893440 Loss2: 0.000608
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.746697 Loss1: 3.746086 Loss2: 0.000611
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.570724 Loss1: 3.570102 Loss2: 0.000622
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.453746 Loss1: 3.453118 Loss2: 0.000628
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.375604 Loss1: 3.374980 Loss2: 0.000625
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.310639 Loss1: 3.310011 Loss2: 0.000627
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.226221 Loss1: 3.225583 Loss2: 0.000638
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.165215 Loss1: 3.164583 Loss2: 0.000632
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.103058 Loss1: 3.102428 Loss2: 0.000631
+(DefaultActor pid=2839578) >> Training accuracy: 0.211234
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.0006009615384615385
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.120604 Loss1: 4.120026 Loss2: 0.000578
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.908697 Loss1: 3.908105 Loss2: 0.000592
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.678700 Loss1: 3.678103 Loss2: 0.000597
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.535451 Loss1: 3.534834 Loss2: 0.000617
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.388606 Loss1: 3.387983 Loss2: 0.000624
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.318526 Loss1: 3.317911 Loss2: 0.000616
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.227636 Loss1: 3.227019 Loss2: 0.000617
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.132973 Loss1: 3.132353 Loss2: 0.000620
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.054064 Loss1: 3.053441 Loss2: 0.000623
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.015753 Loss1: 3.015131 Loss2: 0.000622
+(DefaultActor pid=2839578) >> Training accuracy: 0.294471
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.004006410256410256
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.122952 Loss1: 4.122338 Loss2: 0.000614
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.857820 Loss1: 3.857197 Loss2: 0.000623
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.680495 Loss1: 3.679874 Loss2: 0.000622
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.578917 Loss1: 3.578284 Loss2: 0.000632
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.498336 Loss1: 3.497704 Loss2: 0.000632
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.409808 Loss1: 3.409181 Loss2: 0.000627
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.335593 Loss1: 3.334967 Loss2: 0.000626
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.290330 Loss1: 3.289705 Loss2: 0.000625
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.210379 Loss1: 3.209757 Loss2: 0.000623
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.134445 Loss1: 3.133819 Loss2: 0.000626
+(DefaultActor pid=2839578) >> Training accuracy: 0.229768
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.001714939024390244
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.041751 Loss1: 4.041103 Loss2: 0.000648
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.812356 Loss1: 3.811710 Loss2: 0.000647
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.645221 Loss1: 3.644571 Loss2: 0.000649
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.510078 Loss1: 3.509421 Loss2: 0.000657
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.424783 Loss1: 3.424130 Loss2: 0.000653
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.309564 Loss1: 3.308913 Loss2: 0.000651
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.276595 Loss1: 3.275951 Loss2: 0.000644
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.169555 Loss1: 3.168903 Loss2: 0.000652
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.126639 Loss1: 3.125992 Loss2: 0.000647
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.063766 Loss1: 3.063118 Loss2: 0.000648
+(DefaultActor pid=2839578) >> Training accuracy: 0.245998
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.0021114864864864866
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.019123 Loss1: 4.018554 Loss2: 0.000569
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.771005 Loss1: 3.770423 Loss2: 0.000582
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.665702 Loss1: 3.665108 Loss2: 0.000593
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.573484 Loss1: 3.572897 Loss2: 0.000586
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.480599 Loss1: 3.480004 Loss2: 0.000596
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.412528 Loss1: 3.411919 Loss2: 0.000609
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.313641 Loss1: 3.313028 Loss2: 0.000613
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.239101 Loss1: 3.238484 Loss2: 0.000617
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.171927 Loss1: 3.171297 Loss2: 0.000630
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.115530 Loss1: 3.114911 Loss2: 0.000619
+(DefaultActor pid=2839578) >> Training accuracy: 0.243877
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.011513157894736841
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.063392 Loss1: 4.062766 Loss2: 0.000626
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.853136 Loss1: 3.852511 Loss2: 0.000625
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.776167 Loss1: 3.775535 Loss2: 0.000632
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.668716 Loss1: 3.668090 Loss2: 0.000626
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.590686 Loss1: 3.590043 Loss2: 0.000643
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.478641 Loss1: 3.477994 Loss2: 0.000647
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.383206 Loss1: 3.382564 Loss2: 0.000643
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.353055 Loss1: 3.352404 Loss2: 0.000651
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.301246 Loss1: 3.300592 Loss2: 0.000654
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.247619 Loss1: 3.246963 Loss2: 0.000656
+(DefaultActor pid=2839578) >> Training accuracy: 0.230880
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.0
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.046561 Loss1: 4.045941 Loss2: 0.000620
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.788855 Loss1: 3.788236 Loss2: 0.000619
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.685726 Loss1: 3.685103 Loss2: 0.000623
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.576464 Loss1: 3.575836 Loss2: 0.000628
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.486076 Loss1: 3.485447 Loss2: 0.000629
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.375406 Loss1: 3.374763 Loss2: 0.000643
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.286805 Loss1: 3.286156 Loss2: 0.000650
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.231012 Loss1: 3.230359 Loss2: 0.000653
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.168501 Loss1: 3.167851 Loss2: 0.000650
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.087646 Loss1: 3.086993 Loss2: 0.000653
+(DefaultActor pid=2839578) >> Training accuracy: 0.253038
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.03164556962025317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.045481 Loss1: 4.044875 Loss2: 0.000606
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.792342 Loss1: 3.791750 Loss2: 0.000592
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.629276 Loss1: 3.628680 Loss2: 0.000595
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.531024 Loss1: 3.530416 Loss2: 0.000608
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.455813 Loss1: 3.455215 Loss2: 0.000597
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.354602 Loss1: 3.353992 Loss2: 0.000610
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.291131 Loss1: 3.290518 Loss2: 0.000613
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.231288 Loss1: 3.230674 Loss2: 0.000614
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.166940 Loss1: 3.166321 Loss2: 0.000619
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.118945 Loss1: 3.118329 Loss2: 0.000616
+(DefaultActor pid=2839578) >> Training accuracy: 0.217563
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.03303006329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.030432 Loss1: 4.029803 Loss2: 0.000629
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.752761 Loss1: 3.752137 Loss2: 0.000624
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.633537 Loss1: 3.632907 Loss2: 0.000630
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.504689 Loss1: 3.504047 Loss2: 0.000642
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.426732 Loss1: 3.426088 Loss2: 0.000643
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.343557 Loss1: 3.342910 Loss2: 0.000647
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.271297 Loss1: 3.270646 Loss2: 0.000651
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.217741 Loss1: 3.217095 Loss2: 0.000646
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.140096 Loss1: 3.139444 Loss2: 0.000652
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.075658 Loss1: 3.075012 Loss2: 0.000646
+(DefaultActor pid=2839578) >> Training accuracy: 0.235562
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 03:43:24,366][flwr][DEBUG] - fit_round 1 received 10 results and 0 failures
+[2023-09-21 03:43:27,270][flwr][WARNING] - No fit_metrics_aggregation_fn provided
+test acc: 0.01
+[2023-09-21 03:44:06,379][flwr][INFO] - fit progress: (1, 4.6852914030178665, {'accuracy': 0.01}, 1928.0405755066313)
+[2023-09-21 03:44:06,380][flwr][DEBUG] - evaluate_round 1: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 03:44:44,813][flwr][DEBUG] - evaluate_round 1 received 10 results and 0 failures
+[2023-09-21 03:44:44,814][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
+[2023-09-21 03:44:44,814][flwr][DEBUG] - fit_round 2: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.008900316455696203
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.034845 Loss1: 4.034231 Loss2: 0.000614
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.600918 Loss1: 3.600247 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.472775 Loss1: 3.472079 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.401183 Loss1: 3.400483 Loss2: 0.000700
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.341920 Loss1: 3.341219 Loss2: 0.000702
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.261761 Loss1: 3.261041 Loss2: 0.000720
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.192689 Loss1: 3.191953 Loss2: 0.000736
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.127874 Loss1: 3.127146 Loss2: 0.000728
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.063087 Loss1: 3.062348 Loss2: 0.000739
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.042643 Loss1: 3.041913 Loss2: 0.000729
+(DefaultActor pid=2839578) >> Training accuracy: 0.243671
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.011242378048780487
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.006845 Loss1: 4.006151 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.526045 Loss1: 3.525288 Loss2: 0.000757
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.418526 Loss1: 3.417792 Loss2: 0.000733
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.356660 Loss1: 3.355912 Loss2: 0.000748
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.240215 Loss1: 3.239479 Loss2: 0.000735
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.157518 Loss1: 3.156767 Loss2: 0.000751
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.099843 Loss1: 3.099087 Loss2: 0.000756
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.045006 Loss1: 3.044251 Loss2: 0.000755
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.981187 Loss1: 2.980426 Loss2: 0.000761
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.943801 Loss1: 2.943038 Loss2: 0.000763
+(DefaultActor pid=2839578) >> Training accuracy: 0.259337
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005008012820512821
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.090613 Loss1: 4.090037 Loss2: 0.000576
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.635666 Loss1: 3.634994 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.507819 Loss1: 3.507149 Loss2: 0.000669
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.410626 Loss1: 3.409946 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.340432 Loss1: 3.339741 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.257841 Loss1: 3.257146 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.202210 Loss1: 3.201497 Loss2: 0.000713
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.113738 Loss1: 3.113032 Loss2: 0.000705
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.084920 Loss1: 3.084206 Loss2: 0.000714
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.011787 Loss1: 3.011068 Loss2: 0.000718
+(DefaultActor pid=2839578) >> Training accuracy: 0.246194
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.002175632911392405
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.072911 Loss1: 4.072328 Loss2: 0.000582
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.630913 Loss1: 3.630240 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.476790 Loss1: 3.476120 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.350535 Loss1: 3.349832 Loss2: 0.000703
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.257716 Loss1: 3.257002 Loss2: 0.000714
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.200790 Loss1: 3.200078 Loss2: 0.000712
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.111061 Loss1: 3.110367 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.045489 Loss1: 3.044779 Loss2: 0.000710
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.990485 Loss1: 2.989764 Loss2: 0.000722
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.939264 Loss1: 2.938527 Loss2: 0.000736
+(DefaultActor pid=2839578) >> Training accuracy: 0.252176
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005859375
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.087791 Loss1: 4.087373 Loss2: 0.000419
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.669801 Loss1: 3.669258 Loss2: 0.000542
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.488613 Loss1: 3.488062 Loss2: 0.000552
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.375456 Loss1: 3.374908 Loss2: 0.000547
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.264577 Loss1: 3.264023 Loss2: 0.000554
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.193226 Loss1: 3.192652 Loss2: 0.000574
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.144794 Loss1: 3.144211 Loss2: 0.000583
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.054862 Loss1: 3.054282 Loss2: 0.000579
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.014779 Loss1: 3.014197 Loss2: 0.000582
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.984606 Loss1: 2.984012 Loss2: 0.000594
+(DefaultActor pid=2839578) >> Training accuracy: 0.294705
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.004944620253164557
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.101180 Loss1: 4.100464 Loss2: 0.000716
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.658290 Loss1: 3.657497 Loss2: 0.000793
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.486543 Loss1: 3.485726 Loss2: 0.000817
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.406322 Loss1: 3.405507 Loss2: 0.000816
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.327568 Loss1: 3.326766 Loss2: 0.000802
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.264791 Loss1: 3.263977 Loss2: 0.000814
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.197666 Loss1: 3.196857 Loss2: 0.000809
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.162852 Loss1: 3.162047 Loss2: 0.000805
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.068547 Loss1: 3.067753 Loss2: 0.000794
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.043007 Loss1: 3.042205 Loss2: 0.000802
+(DefaultActor pid=2839578) >> Training accuracy: 0.259494
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.006610576923076923
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.077130 Loss1: 4.076656 Loss2: 0.000473
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.593987 Loss1: 3.593401 Loss2: 0.000586
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.419757 Loss1: 3.419166 Loss2: 0.000591
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.303391 Loss1: 3.302782 Loss2: 0.000610
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.225019 Loss1: 3.224406 Loss2: 0.000613
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.147130 Loss1: 3.146521 Loss2: 0.000608
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.079573 Loss1: 3.078955 Loss2: 0.000618
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.000903 Loss1: 3.000272 Loss2: 0.000631
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.915597 Loss1: 2.914979 Loss2: 0.000617
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.854371 Loss1: 2.853744 Loss2: 0.000627
+(DefaultActor pid=2839578) >> Training accuracy: 0.322115
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.023015202702702704
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.015924 Loss1: 4.015347 Loss2: 0.000577
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.570788 Loss1: 3.570074 Loss2: 0.000714
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.454587 Loss1: 3.453886 Loss2: 0.000701
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.358572 Loss1: 3.357867 Loss2: 0.000705
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.275433 Loss1: 3.274705 Loss2: 0.000728
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.210522 Loss1: 3.209788 Loss2: 0.000734
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.132102 Loss1: 3.131375 Loss2: 0.000726
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.066365 Loss1: 3.065640 Loss2: 0.000726
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.014664 Loss1: 3.013925 Loss2: 0.000740
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.949913 Loss1: 2.949176 Loss2: 0.000737
+(DefaultActor pid=2839578) >> Training accuracy: 0.292019
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.009868421052631578
+(DefaultActor pid=2839578) Epoch: 0 Loss: 4.067316 Loss1: 4.066544 Loss2: 0.000772
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.652317 Loss1: 3.651432 Loss2: 0.000885
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.506712 Loss1: 3.505853 Loss2: 0.000859
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.450474 Loss1: 3.449623 Loss2: 0.000851
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.391708 Loss1: 3.390863 Loss2: 0.000844
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.350177 Loss1: 3.349332 Loss2: 0.000845
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.285717 Loss1: 3.284876 Loss2: 0.000841
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.247498 Loss1: 3.246660 Loss2: 0.000837
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.172712 Loss1: 3.171888 Loss2: 0.000824
+(DefaultActor pid=2839578) Epoch: 9 Loss: 3.138771 Loss1: 3.137930 Loss2: 0.000841
+(DefaultActor pid=2839578) >> Training accuracy: 0.248561
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.02333860759493671
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.978601 Loss1: 3.977865 Loss2: 0.000736
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.598737 Loss1: 3.597915 Loss2: 0.000822
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.443873 Loss1: 3.443047 Loss2: 0.000826
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.340507 Loss1: 3.339685 Loss2: 0.000823
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.271314 Loss1: 3.270473 Loss2: 0.000841
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.228984 Loss1: 3.228140 Loss2: 0.000843
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.132339 Loss1: 3.131514 Loss2: 0.000825
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.077642 Loss1: 3.076820 Loss2: 0.000822
+(DefaultActor pid=2839578) Epoch: 8 Loss: 3.028018 Loss1: 3.027206 Loss2: 0.000811
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.956295 Loss1: 2.955485 Loss2: 0.000810
+(DefaultActor pid=2839578) >> Training accuracy: 0.273141
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 04:15:53,813][flwr][DEBUG] - fit_round 2 received 10 results and 0 failures
+test acc: 0.01
+[2023-09-21 04:16:44,175][flwr][INFO] - fit progress: (2, 5.889099098242129, {'accuracy': 0.01}, 3885.8362666419707)
+[2023-09-21 04:16:44,176][flwr][DEBUG] - evaluate_round 2: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 04:17:23,530][flwr][DEBUG] - evaluate_round 2 received 10 results and 0 failures
+[2023-09-21 04:17:23,532][flwr][DEBUG] - fit_round 3: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005859375
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.497664 Loss1: 3.497270 Loss2: 0.000393
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.230846 Loss1: 3.230431 Loss2: 0.000415
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.118327 Loss1: 3.117908 Loss2: 0.000419
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.026843 Loss1: 3.026438 Loss2: 0.000405
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.965072 Loss1: 2.964659 Loss2: 0.000413
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.912766 Loss1: 2.912347 Loss2: 0.000419
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.837211 Loss1: 2.836798 Loss2: 0.000413
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.791349 Loss1: 2.790936 Loss2: 0.000413
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.733879 Loss1: 2.733456 Loss2: 0.000424
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.695446 Loss1: 2.695027 Loss2: 0.000419
+(DefaultActor pid=2839578) >> Training accuracy: 0.354167
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.022804054054054054
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.473370 Loss1: 3.472964 Loss2: 0.000406
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.240647 Loss1: 3.240219 Loss2: 0.000428
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.118030 Loss1: 3.117603 Loss2: 0.000427
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.040329 Loss1: 3.039910 Loss2: 0.000419
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.945130 Loss1: 2.944708 Loss2: 0.000422
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.891144 Loss1: 2.890710 Loss2: 0.000434
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.822074 Loss1: 2.821645 Loss2: 0.000430
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.752725 Loss1: 2.752277 Loss2: 0.000448
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.693787 Loss1: 2.693343 Loss2: 0.000443
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.634574 Loss1: 2.634124 Loss2: 0.000450
+(DefaultActor pid=2839578) >> Training accuracy: 0.352618
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.02333860759493671
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.502686 Loss1: 3.502227 Loss2: 0.000458
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.254540 Loss1: 3.254071 Loss2: 0.000468
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.155766 Loss1: 3.155294 Loss2: 0.000472
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.081745 Loss1: 3.081275 Loss2: 0.000471
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.997191 Loss1: 2.996715 Loss2: 0.000476
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.938236 Loss1: 2.937753 Loss2: 0.000483
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.887327 Loss1: 2.886846 Loss2: 0.000481
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.817731 Loss1: 2.817243 Loss2: 0.000488
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.738270 Loss1: 2.737774 Loss2: 0.000495
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.683992 Loss1: 2.683503 Loss2: 0.000489
+(DefaultActor pid=2839578) >> Training accuracy: 0.346123
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.002175632911392405
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.488878 Loss1: 3.488457 Loss2: 0.000422
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.267578 Loss1: 3.267151 Loss2: 0.000428
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.134343 Loss1: 3.133918 Loss2: 0.000425
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.040148 Loss1: 3.039714 Loss2: 0.000434
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.982089 Loss1: 2.981665 Loss2: 0.000424
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.914704 Loss1: 2.914274 Loss2: 0.000430
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.861988 Loss1: 2.861555 Loss2: 0.000433
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.787332 Loss1: 2.786902 Loss2: 0.000430
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.761444 Loss1: 2.761014 Loss2: 0.000430
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.683602 Loss1: 2.683158 Loss2: 0.000444
+(DefaultActor pid=2839578) >> Training accuracy: 0.324169
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.004944620253164557
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.565644 Loss1: 3.565142 Loss2: 0.000502
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.278970 Loss1: 3.278460 Loss2: 0.000510
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.187793 Loss1: 3.187279 Loss2: 0.000515
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.109151 Loss1: 3.108649 Loss2: 0.000502
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.007663 Loss1: 3.007154 Loss2: 0.000509
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.953384 Loss1: 2.952874 Loss2: 0.000511
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.884369 Loss1: 2.883844 Loss2: 0.000525
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.837466 Loss1: 2.836956 Loss2: 0.000510
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.798176 Loss1: 2.797651 Loss2: 0.000525
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.746838 Loss1: 2.746311 Loss2: 0.000527
+(DefaultActor pid=2839578) >> Training accuracy: 0.333663
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005008012820512821
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.526725 Loss1: 3.526301 Loss2: 0.000424
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.291798 Loss1: 3.291357 Loss2: 0.000441
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.199626 Loss1: 3.199174 Loss2: 0.000452
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.097104 Loss1: 3.096654 Loss2: 0.000449
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.036548 Loss1: 3.036093 Loss2: 0.000456
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.967198 Loss1: 2.966736 Loss2: 0.000462
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.937590 Loss1: 2.937125 Loss2: 0.000466
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.861535 Loss1: 2.861068 Loss2: 0.000467
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.815555 Loss1: 2.815086 Loss2: 0.000469
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.744658 Loss1: 2.744191 Loss2: 0.000468
+(DefaultActor pid=2839578) >> Training accuracy: 0.325120
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.006610576923076923
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.503517 Loss1: 3.503171 Loss2: 0.000346
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.231768 Loss1: 3.231403 Loss2: 0.000365
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.091991 Loss1: 3.091628 Loss2: 0.000363
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.003812 Loss1: 3.003439 Loss2: 0.000373
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.935798 Loss1: 2.935424 Loss2: 0.000375
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.849083 Loss1: 2.848704 Loss2: 0.000379
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.770331 Loss1: 2.769948 Loss2: 0.000383
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.716012 Loss1: 2.715627 Loss2: 0.000385
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.647046 Loss1: 2.646652 Loss2: 0.000393
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.580274 Loss1: 2.579886 Loss2: 0.000389
+(DefaultActor pid=2839578) >> Training accuracy: 0.374199
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.009868421052631578
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.597554 Loss1: 3.597098 Loss2: 0.000456
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.361120 Loss1: 3.360637 Loss2: 0.000483
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.284197 Loss1: 3.283719 Loss2: 0.000478
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.205098 Loss1: 3.204626 Loss2: 0.000472
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.177774 Loss1: 3.177307 Loss2: 0.000466
+(DefaultActor pid=2839578) Epoch: 5 Loss: 3.105030 Loss1: 3.104551 Loss2: 0.000480
+(DefaultActor pid=2839578) Epoch: 6 Loss: 3.082643 Loss1: 3.082167 Loss2: 0.000476
+(DefaultActor pid=2839578) Epoch: 7 Loss: 3.013746 Loss1: 3.013268 Loss2: 0.000478
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.966661 Loss1: 2.966167 Loss2: 0.000494
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.913738 Loss1: 2.913248 Loss2: 0.000490
+(DefaultActor pid=2839578) >> Training accuracy: 0.269120
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.011051829268292682
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.452149 Loss1: 3.451674 Loss2: 0.000475
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.229586 Loss1: 3.229084 Loss2: 0.000502
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.147423 Loss1: 3.146943 Loss2: 0.000481
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.043248 Loss1: 3.042759 Loss2: 0.000489
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.983966 Loss1: 2.983476 Loss2: 0.000490
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.945689 Loss1: 2.945196 Loss2: 0.000493
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.880893 Loss1: 2.880399 Loss2: 0.000494
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.841460 Loss1: 2.840964 Loss2: 0.000496
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.767905 Loss1: 2.767406 Loss2: 0.000499
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.706575 Loss1: 2.706067 Loss2: 0.000508
+(DefaultActor pid=2839578) >> Training accuracy: 0.325076
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.008900316455696203
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.528368 Loss1: 3.527968 Loss2: 0.000401
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.256940 Loss1: 3.256524 Loss2: 0.000417
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.136524 Loss1: 3.136101 Loss2: 0.000424
+(DefaultActor pid=2839578) Epoch: 3 Loss: 3.100428 Loss1: 3.100007 Loss2: 0.000421
+(DefaultActor pid=2839578) Epoch: 4 Loss: 3.048198 Loss1: 3.047770 Loss2: 0.000428
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.973330 Loss1: 2.972898 Loss2: 0.000432
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.946641 Loss1: 2.946207 Loss2: 0.000433
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.876902 Loss1: 2.876460 Loss2: 0.000442
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.798364 Loss1: 2.797919 Loss2: 0.000444
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.760363 Loss1: 2.759915 Loss2: 0.000448
+(DefaultActor pid=2839578) >> Training accuracy: 0.310720
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 04:48:19,865][flwr][DEBUG] - fit_round 3 received 10 results and 0 failures
+test acc: 0.0159
+[2023-09-21 04:48:59,026][flwr][INFO] - fit progress: (3, 5.795179260424532, {'accuracy': 0.0159}, 5820.688017355744)
+[2023-09-21 04:48:59,028][flwr][DEBUG] - evaluate_round 3: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 04:49:38,619][flwr][DEBUG] - evaluate_round 3 received 10 results and 0 failures
+[2023-09-21 04:49:38,620][flwr][DEBUG] - fit_round 4: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.035799050632911396
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.217798 Loss1: 3.217414 Loss2: 0.000384
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.013310 Loss1: 3.012904 Loss2: 0.000407
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.903406 Loss1: 2.903003 Loss2: 0.000403
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.817941 Loss1: 2.817533 Loss2: 0.000408
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.726926 Loss1: 2.726519 Loss2: 0.000406
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.672959 Loss1: 2.672544 Loss2: 0.000416
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.579776 Loss1: 2.579364 Loss2: 0.000412
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.531181 Loss1: 2.530760 Loss2: 0.000421
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.476366 Loss1: 2.475951 Loss2: 0.000416
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.394170 Loss1: 2.393747 Loss2: 0.000423
+(DefaultActor pid=2839578) >> Training accuracy: 0.383307
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.01069078947368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.307423 Loss1: 3.307018 Loss2: 0.000405
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.099389 Loss1: 3.098978 Loss2: 0.000411
+(DefaultActor pid=2839578) Epoch: 2 Loss: 3.022815 Loss1: 3.022402 Loss2: 0.000412
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.940196 Loss1: 2.939770 Loss2: 0.000426
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.886811 Loss1: 2.886400 Loss2: 0.000411
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.834288 Loss1: 2.833865 Loss2: 0.000423
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.757556 Loss1: 2.757136 Loss2: 0.000420
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.697843 Loss1: 2.697422 Loss2: 0.000421
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.645784 Loss1: 2.645369 Loss2: 0.000415
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.582188 Loss1: 2.581761 Loss2: 0.000427
+(DefaultActor pid=2839578) >> Training accuracy: 0.345806
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005537974683544304
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.220522 Loss1: 3.220117 Loss2: 0.000405
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.015141 Loss1: 3.014706 Loss2: 0.000435
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.926200 Loss1: 2.925774 Loss2: 0.000426
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.835716 Loss1: 2.835286 Loss2: 0.000430
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.739549 Loss1: 2.739104 Loss2: 0.000444
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.718363 Loss1: 2.717920 Loss2: 0.000443
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.627129 Loss1: 2.626680 Loss2: 0.000449
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.574281 Loss1: 2.573836 Loss2: 0.000445
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.508525 Loss1: 2.508081 Loss2: 0.000443
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.459976 Loss1: 2.459532 Loss2: 0.000444
+(DefaultActor pid=2839578) >> Training accuracy: 0.374604
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.015202702702702704
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.180689 Loss1: 3.180329 Loss2: 0.000360
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.944385 Loss1: 2.944005 Loss2: 0.000380
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.824908 Loss1: 2.824526 Loss2: 0.000382
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.734459 Loss1: 2.734081 Loss2: 0.000378
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.690073 Loss1: 2.689687 Loss2: 0.000387
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.603358 Loss1: 2.602973 Loss2: 0.000385
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.532423 Loss1: 2.532043 Loss2: 0.000379
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.484561 Loss1: 2.484169 Loss2: 0.000392
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.469905 Loss1: 2.469515 Loss2: 0.000390
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.366994 Loss1: 2.366593 Loss2: 0.000402
+(DefaultActor pid=2839578) >> Training accuracy: 0.426098
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005809294871794872
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.119125 Loss1: 3.118820 Loss2: 0.000304
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.945342 Loss1: 2.945023 Loss2: 0.000320
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.805461 Loss1: 2.805140 Loss2: 0.000321
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.752142 Loss1: 2.751816 Loss2: 0.000326
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.680353 Loss1: 2.680030 Loss2: 0.000323
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.608047 Loss1: 2.607717 Loss2: 0.000329
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.536142 Loss1: 2.535819 Loss2: 0.000323
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.453551 Loss1: 2.453219 Loss2: 0.000332
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.401894 Loss1: 2.401563 Loss2: 0.000331
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.302079 Loss1: 2.301741 Loss2: 0.000337
+(DefaultActor pid=2839578) >> Training accuracy: 0.415665
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.006510416666666667
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.194325 Loss1: 3.193981 Loss2: 0.000344
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.952950 Loss1: 2.952584 Loss2: 0.000366
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.872465 Loss1: 2.872104 Loss2: 0.000361
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.786886 Loss1: 2.786528 Loss2: 0.000358
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.711102 Loss1: 2.710739 Loss2: 0.000364
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.627909 Loss1: 2.627545 Loss2: 0.000363
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.545390 Loss1: 2.545025 Loss2: 0.000365
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.490828 Loss1: 2.490452 Loss2: 0.000376
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.410544 Loss1: 2.410168 Loss2: 0.000376
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.359539 Loss1: 2.359161 Loss2: 0.000378
+(DefaultActor pid=2839578) >> Training accuracy: 0.420139
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.016811708860759493
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.122448 Loss1: 3.122101 Loss2: 0.000348
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.965570 Loss1: 2.965203 Loss2: 0.000367
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.874570 Loss1: 2.874209 Loss2: 0.000362
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.793978 Loss1: 2.793616 Loss2: 0.000362
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.715998 Loss1: 2.715635 Loss2: 0.000362
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.661922 Loss1: 2.661548 Loss2: 0.000374
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.584703 Loss1: 2.584331 Loss2: 0.000372
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.528646 Loss1: 2.528269 Loss2: 0.000377
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.446880 Loss1: 2.446509 Loss2: 0.000371
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.390937 Loss1: 2.390558 Loss2: 0.000379
+(DefaultActor pid=2839578) >> Training accuracy: 0.394383
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.006724683544303798
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.218198 Loss1: 3.217857 Loss2: 0.000341
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.013430 Loss1: 3.013078 Loss2: 0.000352
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.920192 Loss1: 2.919841 Loss2: 0.000351
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.857596 Loss1: 2.857241 Loss2: 0.000355
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.779833 Loss1: 2.779473 Loss2: 0.000360
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.690679 Loss1: 2.690311 Loss2: 0.000368
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.631938 Loss1: 2.631570 Loss2: 0.000368
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.592852 Loss1: 2.592473 Loss2: 0.000379
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.476063 Loss1: 2.475686 Loss2: 0.000377
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.457981 Loss1: 2.457600 Loss2: 0.000381
+(DefaultActor pid=2839578) >> Training accuracy: 0.380934
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.015815548780487805
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.152229 Loss1: 3.151814 Loss2: 0.000415
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.021255 Loss1: 3.020834 Loss2: 0.000421
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.930871 Loss1: 2.930456 Loss2: 0.000414
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.820823 Loss1: 2.820410 Loss2: 0.000413
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.748219 Loss1: 2.747806 Loss2: 0.000413
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.673662 Loss1: 2.673241 Loss2: 0.000420
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.638957 Loss1: 2.638533 Loss2: 0.000424
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.579395 Loss1: 2.578974 Loss2: 0.000421
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.489916 Loss1: 2.489484 Loss2: 0.000432
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.471207 Loss1: 2.470778 Loss2: 0.000429
+(DefaultActor pid=2839578) >> Training accuracy: 0.349466
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.007612179487179487
+(DefaultActor pid=2839578) Epoch: 0 Loss: 3.214502 Loss1: 3.214151 Loss2: 0.000350
+(DefaultActor pid=2839578) Epoch: 1 Loss: 3.055020 Loss1: 3.054647 Loss2: 0.000373
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.937553 Loss1: 2.937179 Loss2: 0.000374
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.853051 Loss1: 2.852672 Loss2: 0.000378
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.762206 Loss1: 2.761822 Loss2: 0.000384
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.716567 Loss1: 2.716181 Loss2: 0.000387
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.654350 Loss1: 2.653964 Loss2: 0.000386
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.598232 Loss1: 2.597843 Loss2: 0.000390
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.530895 Loss1: 2.530509 Loss2: 0.000386
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.498543 Loss1: 2.498145 Loss2: 0.000398
+(DefaultActor pid=2839578) >> Training accuracy: 0.345353
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 05:20:43,992][flwr][DEBUG] - fit_round 4 received 10 results and 0 failures
+test acc: 0.0942
+[2023-09-21 05:21:33,867][flwr][INFO] - fit progress: (4, 4.141716613556249, {'accuracy': 0.0942}, 7775.528302751016)
+[2023-09-21 05:21:33,867][flwr][DEBUG] - evaluate_round 4: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 05:22:11,622][flwr][DEBUG] - evaluate_round 4 received 10 results and 0 failures
+[2023-09-21 05:22:11,624][flwr][DEBUG] - fit_round 5: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.08897569444444445
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.842027 Loss1: 2.841548 Loss2: 0.000479
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.630804 Loss1: 2.630309 Loss2: 0.000495
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.530924 Loss1: 2.530427 Loss2: 0.000497
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.460785 Loss1: 2.460283 Loss2: 0.000502
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.341677 Loss1: 2.341182 Loss2: 0.000495
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.277195 Loss1: 2.276695 Loss2: 0.000500
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.203589 Loss1: 2.203092 Loss2: 0.000496
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.133839 Loss1: 2.133330 Loss2: 0.000509
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.102173 Loss1: 2.101666 Loss2: 0.000506
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.009186 Loss1: 2.008675 Loss2: 0.000511
+(DefaultActor pid=2839578) >> Training accuracy: 0.469618
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.11223323170731707
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.898178 Loss1: 2.897678 Loss2: 0.000500
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.694501 Loss1: 2.693977 Loss2: 0.000524
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.590732 Loss1: 2.590220 Loss2: 0.000512
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.472786 Loss1: 2.472277 Loss2: 0.000510
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.449536 Loss1: 2.449024 Loss2: 0.000512
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.344897 Loss1: 2.344381 Loss2: 0.000516
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.329203 Loss1: 2.328683 Loss2: 0.000520
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.231047 Loss1: 2.230533 Loss2: 0.000514
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.180540 Loss1: 2.180023 Loss2: 0.000517
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.111728 Loss1: 2.111203 Loss2: 0.000526
+(DefaultActor pid=2839578) >> Training accuracy: 0.447218
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.08018092105263158
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.989248 Loss1: 2.988752 Loss2: 0.000496
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.801355 Loss1: 2.800844 Loss2: 0.000510
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.714794 Loss1: 2.714287 Loss2: 0.000507
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.643452 Loss1: 2.642939 Loss2: 0.000513
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.567204 Loss1: 2.566701 Loss2: 0.000503
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.531174 Loss1: 2.530662 Loss2: 0.000512
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.428815 Loss1: 2.428309 Loss2: 0.000505
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.368745 Loss1: 2.368242 Loss2: 0.000503
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.309541 Loss1: 2.309036 Loss2: 0.000505
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.267966 Loss1: 2.267452 Loss2: 0.000514
+(DefaultActor pid=2839578) >> Training accuracy: 0.454770
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.09134615384615384
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.889201 Loss1: 2.888725 Loss2: 0.000476
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.718225 Loss1: 2.717739 Loss2: 0.000486
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.641575 Loss1: 2.641079 Loss2: 0.000496
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.550665 Loss1: 2.550175 Loss2: 0.000489
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.476468 Loss1: 2.475974 Loss2: 0.000494
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.420052 Loss1: 2.419552 Loss2: 0.000500
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.352334 Loss1: 2.351841 Loss2: 0.000494
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.291334 Loss1: 2.290833 Loss2: 0.000500
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.238610 Loss1: 2.238106 Loss2: 0.000503
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.198241 Loss1: 2.197743 Loss2: 0.000498
+(DefaultActor pid=2839578) >> Training accuracy: 0.417668
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.06823575949367089
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.843715 Loss1: 2.843263 Loss2: 0.000452
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.640449 Loss1: 2.639978 Loss2: 0.000471
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.547165 Loss1: 2.546692 Loss2: 0.000473
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.480480 Loss1: 2.480006 Loss2: 0.000474
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.425617 Loss1: 2.425137 Loss2: 0.000480
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.340638 Loss1: 2.340153 Loss2: 0.000485
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.280861 Loss1: 2.280383 Loss2: 0.000477
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.226317 Loss1: 2.225833 Loss2: 0.000484
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.165107 Loss1: 2.164619 Loss2: 0.000487
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.082227 Loss1: 2.081746 Loss2: 0.000482
+(DefaultActor pid=2839578) >> Training accuracy: 0.429193
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.09715544871794872
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.766770 Loss1: 2.766324 Loss2: 0.000445
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.567833 Loss1: 2.567372 Loss2: 0.000461
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.478758 Loss1: 2.478300 Loss2: 0.000458
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.450178 Loss1: 2.449716 Loss2: 0.000463
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.328200 Loss1: 2.327738 Loss2: 0.000462
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.242607 Loss1: 2.242138 Loss2: 0.000469
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.184539 Loss1: 2.184071 Loss2: 0.000469
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.141077 Loss1: 2.140611 Loss2: 0.000466
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.066708 Loss1: 2.066237 Loss2: 0.000471
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.986865 Loss1: 1.986391 Loss2: 0.000475
+(DefaultActor pid=2839578) >> Training accuracy: 0.492188
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.061708860759493674
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.900424 Loss1: 2.899983 Loss2: 0.000441
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.686310 Loss1: 2.685853 Loss2: 0.000458
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.603197 Loss1: 2.602741 Loss2: 0.000457
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.505852 Loss1: 2.505391 Loss2: 0.000460
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.445087 Loss1: 2.444620 Loss2: 0.000467
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.399947 Loss1: 2.399487 Loss2: 0.000461
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.308242 Loss1: 2.307779 Loss2: 0.000463
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.259994 Loss1: 2.259527 Loss2: 0.000468
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.203556 Loss1: 2.203080 Loss2: 0.000477
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.153657 Loss1: 2.153182 Loss2: 0.000475
+(DefaultActor pid=2839578) >> Training accuracy: 0.458070
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.10027689873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.877591 Loss1: 2.877095 Loss2: 0.000496
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.656893 Loss1: 2.656379 Loss2: 0.000514
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.587690 Loss1: 2.587176 Loss2: 0.000514
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.501088 Loss1: 2.500580 Loss2: 0.000508
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.426702 Loss1: 2.426187 Loss2: 0.000515
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.342188 Loss1: 2.341670 Loss2: 0.000518
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.255267 Loss1: 2.254756 Loss2: 0.000510
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.215915 Loss1: 2.215398 Loss2: 0.000517
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.124104 Loss1: 2.123581 Loss2: 0.000523
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.090785 Loss1: 2.090260 Loss2: 0.000525
+(DefaultActor pid=2839578) >> Training accuracy: 0.483979
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.08544303797468354
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.901068 Loss1: 2.900552 Loss2: 0.000516
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.699047 Loss1: 2.698518 Loss2: 0.000529
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.591029 Loss1: 2.590499 Loss2: 0.000530
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.523788 Loss1: 2.523258 Loss2: 0.000530
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.441983 Loss1: 2.441450 Loss2: 0.000533
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.379017 Loss1: 2.378487 Loss2: 0.000530
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.313858 Loss1: 2.313323 Loss2: 0.000535
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.271021 Loss1: 2.270478 Loss2: 0.000543
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.200958 Loss1: 2.200423 Loss2: 0.000536
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.116327 Loss1: 2.115790 Loss2: 0.000536
+(DefaultActor pid=2839578) >> Training accuracy: 0.446598
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.06482263513513513
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.845257 Loss1: 2.844808 Loss2: 0.000449
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.600918 Loss1: 2.600450 Loss2: 0.000468
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.530885 Loss1: 2.530420 Loss2: 0.000465
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.437704 Loss1: 2.437239 Loss2: 0.000465
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.359634 Loss1: 2.359164 Loss2: 0.000470
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.290623 Loss1: 2.290151 Loss2: 0.000472
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.238191 Loss1: 2.237717 Loss2: 0.000475
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.165052 Loss1: 2.164583 Loss2: 0.000469
+(DefaultActor pid=2839578) Epoch: 8 Loss: 2.122357 Loss1: 2.121881 Loss2: 0.000476
+(DefaultActor pid=2839578) Epoch: 9 Loss: 2.051197 Loss1: 2.050721 Loss2: 0.000475
+(DefaultActor pid=2839578) >> Training accuracy: 0.488809
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 05:53:05,748][flwr][DEBUG] - fit_round 5 received 10 results and 0 failures
+test acc: 0.1964
+[2023-09-21 05:53:44,442][flwr][INFO] - fit progress: (5, 3.311973663183828, {'accuracy': 0.1964}, 9706.103674005717)
+[2023-09-21 05:53:44,443][flwr][DEBUG] - evaluate_round 5: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 05:54:22,319][flwr][DEBUG] - evaluate_round 5 received 10 results and 0 failures
+[2023-09-21 05:54:22,324][flwr][DEBUG] - fit_round 6: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.17246835443037975
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.610884 Loss1: 2.610286 Loss2: 0.000599
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.362191 Loss1: 2.361584 Loss2: 0.000607
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.280921 Loss1: 2.280314 Loss2: 0.000607
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.207432 Loss1: 2.206824 Loss2: 0.000608
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.143255 Loss1: 2.142647 Loss2: 0.000608
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.055508 Loss1: 2.054899 Loss2: 0.000608
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.994889 Loss1: 1.994277 Loss2: 0.000612
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.941048 Loss1: 1.940436 Loss2: 0.000612
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.860695 Loss1: 1.860082 Loss2: 0.000613
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.787635 Loss1: 1.787020 Loss2: 0.000615
+(DefaultActor pid=2839578) >> Training accuracy: 0.490704
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.16910601265822786
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.526193 Loss1: 2.525595 Loss2: 0.000598
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.341296 Loss1: 2.340695 Loss2: 0.000601
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.233416 Loss1: 2.232811 Loss2: 0.000605
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.168674 Loss1: 2.168063 Loss2: 0.000611
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.103103 Loss1: 2.102493 Loss2: 0.000611
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.055619 Loss1: 2.055008 Loss2: 0.000610
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.956009 Loss1: 1.955397 Loss2: 0.000612
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.895043 Loss1: 1.894429 Loss2: 0.000613
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.821400 Loss1: 1.820790 Loss2: 0.000610
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.781555 Loss1: 1.780938 Loss2: 0.000617
+(DefaultActor pid=2839578) >> Training accuracy: 0.465981
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.22275641025641027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.441899 Loss1: 2.441297 Loss2: 0.000601
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.253544 Loss1: 2.252936 Loss2: 0.000609
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.195931 Loss1: 2.195321 Loss2: 0.000610
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.081599 Loss1: 2.080990 Loss2: 0.000609
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.018430 Loss1: 2.017820 Loss2: 0.000610
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.936686 Loss1: 1.936070 Loss2: 0.000616
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.903456 Loss1: 1.902840 Loss2: 0.000616
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.798772 Loss1: 1.798154 Loss2: 0.000618
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.821713 Loss1: 1.821093 Loss2: 0.000619
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.689851 Loss1: 1.689232 Loss2: 0.000620
+(DefaultActor pid=2839578) >> Training accuracy: 0.551082
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.22804588607594936
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.553433 Loss1: 2.552793 Loss2: 0.000640
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.320722 Loss1: 2.320077 Loss2: 0.000646
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.247907 Loss1: 2.247261 Loss2: 0.000646
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.150732 Loss1: 2.150086 Loss2: 0.000646
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.085651 Loss1: 2.085004 Loss2: 0.000648
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.997454 Loss1: 1.996812 Loss2: 0.000641
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.969085 Loss1: 1.968441 Loss2: 0.000644
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.884816 Loss1: 1.884175 Loss2: 0.000641
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.839633 Loss1: 1.838990 Loss2: 0.000644
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.764731 Loss1: 1.764085 Loss2: 0.000646
+(DefaultActor pid=2839578) >> Training accuracy: 0.532239
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.16858552631578946
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.703196 Loss1: 2.702568 Loss2: 0.000628
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.509497 Loss1: 2.508859 Loss2: 0.000638
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.401490 Loss1: 2.400857 Loss2: 0.000632
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.316214 Loss1: 2.315580 Loss2: 0.000634
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.269867 Loss1: 2.269238 Loss2: 0.000629
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.209784 Loss1: 2.209148 Loss2: 0.000637
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.093191 Loss1: 2.092555 Loss2: 0.000636
+(DefaultActor pid=2839578) Epoch: 7 Loss: 2.056638 Loss1: 2.056007 Loss2: 0.000631
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.972283 Loss1: 1.971647 Loss2: 0.000636
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.893097 Loss1: 1.892462 Loss2: 0.000634
+(DefaultActor pid=2839578) >> Training accuracy: 0.495477
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.1935096153846154
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.595779 Loss1: 2.595175 Loss2: 0.000604
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.429828 Loss1: 2.429217 Loss2: 0.000611
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.332352 Loss1: 2.331739 Loss2: 0.000613
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.267508 Loss1: 2.266893 Loss2: 0.000615
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.211937 Loss1: 2.211321 Loss2: 0.000616
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.120407 Loss1: 2.119791 Loss2: 0.000616
+(DefaultActor pid=2839578) Epoch: 6 Loss: 2.066087 Loss1: 2.065470 Loss2: 0.000617
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.999616 Loss1: 1.998996 Loss2: 0.000620
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.973400 Loss1: 1.972780 Loss2: 0.000620
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.887252 Loss1: 1.886633 Loss2: 0.000619
+(DefaultActor pid=2839578) >> Training accuracy: 0.482372
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.16089527027027026
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.531171 Loss1: 2.530572 Loss2: 0.000599
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.329418 Loss1: 2.328813 Loss2: 0.000604
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.195457 Loss1: 2.194850 Loss2: 0.000607
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.135030 Loss1: 2.134423 Loss2: 0.000607
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.068874 Loss1: 2.068268 Loss2: 0.000605
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.009602 Loss1: 2.008991 Loss2: 0.000610
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.962338 Loss1: 1.961727 Loss2: 0.000611
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.894721 Loss1: 1.894111 Loss2: 0.000610
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.838573 Loss1: 1.837964 Loss2: 0.000608
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.766256 Loss1: 1.765639 Loss2: 0.000617
+(DefaultActor pid=2839578) >> Training accuracy: 0.552365
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.18928006329113925
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.615363 Loss1: 2.614735 Loss2: 0.000628
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.385206 Loss1: 2.384564 Loss2: 0.000642
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.294886 Loss1: 2.294245 Loss2: 0.000641
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.202774 Loss1: 2.202133 Loss2: 0.000641
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.120620 Loss1: 2.119979 Loss2: 0.000641
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.048082 Loss1: 2.047440 Loss2: 0.000642
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.966649 Loss1: 1.966008 Loss2: 0.000642
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.972476 Loss1: 1.971836 Loss2: 0.000639
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.850451 Loss1: 1.849805 Loss2: 0.000647
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.837381 Loss1: 1.836738 Loss2: 0.000643
+(DefaultActor pid=2839578) >> Training accuracy: 0.534415
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.212890625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.558546 Loss1: 2.557935 Loss2: 0.000612
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.307139 Loss1: 2.306524 Loss2: 0.000615
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.185024 Loss1: 2.184403 Loss2: 0.000621
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.105847 Loss1: 2.105227 Loss2: 0.000620
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.050408 Loss1: 2.049786 Loss2: 0.000622
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.964686 Loss1: 1.964064 Loss2: 0.000622
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.905946 Loss1: 1.905326 Loss2: 0.000620
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.849628 Loss1: 1.849005 Loss2: 0.000623
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.779105 Loss1: 1.778482 Loss2: 0.000623
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.707864 Loss1: 1.707236 Loss2: 0.000628
+(DefaultActor pid=2839578) >> Training accuracy: 0.547526
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.21017530487804878
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.580613 Loss1: 2.579992 Loss2: 0.000621
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.360116 Loss1: 2.359486 Loss2: 0.000629
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.295912 Loss1: 2.295285 Loss2: 0.000627
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.208185 Loss1: 2.207558 Loss2: 0.000628
+(DefaultActor pid=2839578) Epoch: 4 Loss: 2.102368 Loss1: 2.101741 Loss2: 0.000627
+(DefaultActor pid=2839578) Epoch: 5 Loss: 2.032918 Loss1: 2.032291 Loss2: 0.000627
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.997955 Loss1: 1.997327 Loss2: 0.000628
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.969829 Loss1: 1.969201 Loss2: 0.000628
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.848787 Loss1: 1.848162 Loss2: 0.000625
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.787994 Loss1: 1.787365 Loss2: 0.000629
+(DefaultActor pid=2839578) >> Training accuracy: 0.557355
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 06:25:20,076][flwr][DEBUG] - fit_round 6 received 10 results and 0 failures
+test acc: 0.2787
+[2023-09-21 06:25:59,530][flwr][INFO] - fit progress: (6, 2.8995907626593835, {'accuracy': 0.2787}, 11641.19161189394)
+[2023-09-21 06:25:59,531][flwr][DEBUG] - evaluate_round 6: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 06:26:36,542][flwr][DEBUG] - evaluate_round 6 received 10 results and 0 failures
+[2023-09-21 06:26:36,542][flwr][DEBUG] - fit_round 7: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.24527138157894737
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.481484 Loss1: 2.480817 Loss2: 0.000667
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.257154 Loss1: 2.256485 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.143389 Loss1: 2.142715 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 3 Loss: 2.045223 Loss1: 2.044552 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.995319 Loss1: 1.994649 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.954306 Loss1: 1.953636 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.860659 Loss1: 1.859990 Loss2: 0.000669
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.767192 Loss1: 1.766524 Loss2: 0.000667
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.716309 Loss1: 1.715638 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.596312 Loss1: 1.595641 Loss2: 0.000671
+(DefaultActor pid=2839578) >> Training accuracy: 0.567845
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3057725694444444
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.264856 Loss1: 2.264210 Loss2: 0.000646
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.043617 Loss1: 2.042963 Loss2: 0.000653
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.956283 Loss1: 1.955626 Loss2: 0.000657
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.844295 Loss1: 1.843639 Loss2: 0.000656
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.767495 Loss1: 1.766835 Loss2: 0.000660
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.678686 Loss1: 1.678026 Loss2: 0.000661
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.606195 Loss1: 1.605537 Loss2: 0.000658
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.569569 Loss1: 1.568910 Loss2: 0.000659
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.537661 Loss1: 1.537003 Loss2: 0.000658
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.451480 Loss1: 1.450820 Loss2: 0.000661
+(DefaultActor pid=2839578) >> Training accuracy: 0.610026
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.2674050632911392
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.283801 Loss1: 2.283151 Loss2: 0.000650
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.107849 Loss1: 2.107195 Loss2: 0.000654
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.991241 Loss1: 1.990589 Loss2: 0.000653
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.915996 Loss1: 1.915342 Loss2: 0.000654
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.865527 Loss1: 1.864873 Loss2: 0.000654
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.745645 Loss1: 1.744990 Loss2: 0.000655
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.702113 Loss1: 1.701460 Loss2: 0.000653
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.641497 Loss1: 1.640841 Loss2: 0.000655
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.577109 Loss1: 1.576453 Loss2: 0.000656
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.524116 Loss1: 1.523461 Loss2: 0.000655
+(DefaultActor pid=2839578) >> Training accuracy: 0.551622
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.2644382911392405
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.350628 Loss1: 2.349962 Loss2: 0.000665
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.090583 Loss1: 2.089911 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.008386 Loss1: 2.007711 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.930967 Loss1: 1.930293 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.839796 Loss1: 1.839120 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.761925 Loss1: 1.761252 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.747650 Loss1: 1.746974 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.648780 Loss1: 1.648098 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.578672 Loss1: 1.577995 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.539751 Loss1: 1.539074 Loss2: 0.000677
+(DefaultActor pid=2839578) >> Training accuracy: 0.605419
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.24809966216216217
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.297309 Loss1: 2.296655 Loss2: 0.000653
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.092773 Loss1: 2.092114 Loss2: 0.000659
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.959073 Loss1: 1.958415 Loss2: 0.000658
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.864718 Loss1: 1.864062 Loss2: 0.000656
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.819575 Loss1: 1.818917 Loss2: 0.000658
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.754351 Loss1: 1.753691 Loss2: 0.000660
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.687187 Loss1: 1.686527 Loss2: 0.000659
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.616783 Loss1: 1.616129 Loss2: 0.000655
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.541919 Loss1: 1.541260 Loss2: 0.000658
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.498129 Loss1: 1.497470 Loss2: 0.000659
+(DefaultActor pid=2839578) >> Training accuracy: 0.551520
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.25861378205128205
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.403830 Loss1: 2.403180 Loss2: 0.000650
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.160081 Loss1: 2.159430 Loss2: 0.000651
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.097445 Loss1: 2.096793 Loss2: 0.000652
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.997780 Loss1: 1.997125 Loss2: 0.000655
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.933168 Loss1: 1.932514 Loss2: 0.000654
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.870620 Loss1: 1.869966 Loss2: 0.000654
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.778662 Loss1: 1.778009 Loss2: 0.000653
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.698167 Loss1: 1.697512 Loss2: 0.000655
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.627072 Loss1: 1.626415 Loss2: 0.000657
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.604133 Loss1: 1.603477 Loss2: 0.000656
+(DefaultActor pid=2839578) >> Training accuracy: 0.549679
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.32535601265822783
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.295589 Loss1: 2.294914 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.054226 Loss1: 2.053539 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.955566 Loss1: 1.954885 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.868946 Loss1: 1.868267 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.799395 Loss1: 1.798717 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.721622 Loss1: 1.720945 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.677850 Loss1: 1.677174 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.627688 Loss1: 1.627008 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.569686 Loss1: 1.569009 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.546089 Loss1: 1.545408 Loss2: 0.000680
+(DefaultActor pid=2839578) >> Training accuracy: 0.590388
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3311298076923077
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.209410 Loss1: 2.208758 Loss2: 0.000653
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.973377 Loss1: 1.972717 Loss2: 0.000659
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.896091 Loss1: 1.895431 Loss2: 0.000660
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.822798 Loss1: 1.822139 Loss2: 0.000659
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.732872 Loss1: 1.732213 Loss2: 0.000659
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.674470 Loss1: 1.673812 Loss2: 0.000658
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.582294 Loss1: 1.581637 Loss2: 0.000657
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.536232 Loss1: 1.535575 Loss2: 0.000657
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.487366 Loss1: 1.486710 Loss2: 0.000657
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.436601 Loss1: 1.435940 Loss2: 0.000661
+(DefaultActor pid=2839578) >> Training accuracy: 0.615785
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.24485759493670886
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.334608 Loss1: 2.333962 Loss2: 0.000646
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.130277 Loss1: 2.129622 Loss2: 0.000655
+(DefaultActor pid=2839578) Epoch: 2 Loss: 2.006359 Loss1: 2.005705 Loss2: 0.000654
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.930811 Loss1: 1.930157 Loss2: 0.000654
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.850361 Loss1: 1.849706 Loss2: 0.000655
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.773485 Loss1: 1.772830 Loss2: 0.000655
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.712855 Loss1: 1.712199 Loss2: 0.000656
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.657515 Loss1: 1.656858 Loss2: 0.000656
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.592939 Loss1: 1.592286 Loss2: 0.000653
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.522356 Loss1: 1.521697 Loss2: 0.000659
+(DefaultActor pid=2839578) >> Training accuracy: 0.578521
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.2966844512195122
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.326112 Loss1: 2.325454 Loss2: 0.000657
+(DefaultActor pid=2839578) Epoch: 1 Loss: 2.104414 Loss1: 2.103751 Loss2: 0.000663
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.994160 Loss1: 1.993500 Loss2: 0.000661
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.915289 Loss1: 1.914627 Loss2: 0.000662
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.827631 Loss1: 1.826966 Loss2: 0.000664
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.749136 Loss1: 1.748474 Loss2: 0.000662
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.696656 Loss1: 1.695995 Loss2: 0.000661
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.646930 Loss1: 1.646269 Loss2: 0.000661
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.548566 Loss1: 1.547902 Loss2: 0.000664
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.498987 Loss1: 1.498325 Loss2: 0.000662
+(DefaultActor pid=2839578) >> Training accuracy: 0.587462
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 06:57:33,156][flwr][DEBUG] - fit_round 7 received 10 results and 0 failures
+test acc: 0.3389
+[2023-09-21 06:58:11,275][flwr][INFO] - fit progress: (7, 2.6269793068639005, {'accuracy': 0.3389}, 13572.936574874911)
+[2023-09-21 06:58:11,276][flwr][DEBUG] - evaluate_round 7: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 06:58:50,318][flwr][DEBUG] - evaluate_round 7 received 10 results and 0 failures
+[2023-09-21 06:58:50,318][flwr][DEBUG] - fit_round 8: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.36032774390243905
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.090185 Loss1: 2.089509 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.896411 Loss1: 1.895732 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.766720 Loss1: 1.766040 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.649179 Loss1: 1.648500 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.575999 Loss1: 1.575318 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.515597 Loss1: 1.514918 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.456732 Loss1: 1.456053 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.418675 Loss1: 1.417995 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.345109 Loss1: 1.344431 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.303375 Loss1: 1.302696 Loss2: 0.000679
+(DefaultActor pid=2839578) >> Training accuracy: 0.667302
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3948317307692308
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.002683 Loss1: 2.002012 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.793488 Loss1: 1.792814 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.691215 Loss1: 1.690539 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.584951 Loss1: 1.584274 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.536152 Loss1: 1.535477 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.441502 Loss1: 1.440826 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.375427 Loss1: 1.374748 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.326773 Loss1: 1.326096 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.249348 Loss1: 1.248672 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.218190 Loss1: 1.217511 Loss2: 0.000680
+(DefaultActor pid=2839578) >> Training accuracy: 0.666466
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.325751582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.118851 Loss1: 2.118167 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.857130 Loss1: 1.856443 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.767257 Loss1: 1.766568 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.676372 Loss1: 1.675685 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.630380 Loss1: 1.629695 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.527384 Loss1: 1.526696 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.473233 Loss1: 1.472546 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.424833 Loss1: 1.424144 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.370489 Loss1: 1.369801 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.349584 Loss1: 1.348892 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.659612
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3346518987341772
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.085113 Loss1: 2.084443 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.861052 Loss1: 1.860380 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.784864 Loss1: 1.784192 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.656445 Loss1: 1.655776 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.622037 Loss1: 1.621365 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.537711 Loss1: 1.537038 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.479621 Loss1: 1.478947 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.405331 Loss1: 1.404657 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.366447 Loss1: 1.365772 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.285344 Loss1: 1.284670 Loss2: 0.000674
+(DefaultActor pid=2839578) >> Training accuracy: 0.648141
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3213141025641026
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.180733 Loss1: 2.180071 Loss2: 0.000661
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.951921 Loss1: 1.951259 Loss2: 0.000662
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.840673 Loss1: 1.840009 Loss2: 0.000664
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.734268 Loss1: 1.733603 Loss2: 0.000665
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.676612 Loss1: 1.675947 Loss2: 0.000665
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.608794 Loss1: 1.608128 Loss2: 0.000666
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.545406 Loss1: 1.544740 Loss2: 0.000667
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.476264 Loss1: 1.475598 Loss2: 0.000667
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.387220 Loss1: 1.386553 Loss2: 0.000667
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.355198 Loss1: 1.354532 Loss2: 0.000666
+(DefaultActor pid=2839578) >> Training accuracy: 0.637620
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.32052364864864863
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.104289 Loss1: 2.103620 Loss2: 0.000669
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.866211 Loss1: 1.865539 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.771266 Loss1: 1.770592 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.642819 Loss1: 1.642145 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.579517 Loss1: 1.578844 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.509703 Loss1: 1.509032 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.464143 Loss1: 1.463471 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.410755 Loss1: 1.410085 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.335067 Loss1: 1.334395 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.264476 Loss1: 1.263804 Loss2: 0.000672
+(DefaultActor pid=2839578) >> Training accuracy: 0.652238
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.30636867088607594
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.134058 Loss1: 2.133389 Loss2: 0.000669
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.880838 Loss1: 1.880171 Loss2: 0.000668
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.772152 Loss1: 1.771481 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.670275 Loss1: 1.669606 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.604567 Loss1: 1.603896 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.530902 Loss1: 1.530233 Loss2: 0.000668
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.473577 Loss1: 1.472907 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.431417 Loss1: 1.430748 Loss2: 0.000669
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.323989 Loss1: 1.323320 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.296235 Loss1: 1.295564 Loss2: 0.000671
+(DefaultActor pid=2839578) >> Training accuracy: 0.622231
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.30160361842105265
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.302307 Loss1: 2.301630 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.989963 Loss1: 1.989280 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.879378 Loss1: 1.878696 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.821825 Loss1: 1.821142 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.728622 Loss1: 1.727941 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.665040 Loss1: 1.664359 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.620767 Loss1: 1.620086 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.534739 Loss1: 1.534057 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.468179 Loss1: 1.467498 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.415825 Loss1: 1.415145 Loss2: 0.000680
+(DefaultActor pid=2839578) >> Training accuracy: 0.618010
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3864715189873418
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.065775 Loss1: 2.065089 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.829913 Loss1: 1.829225 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.759148 Loss1: 1.758459 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.659585 Loss1: 1.658896 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.563335 Loss1: 1.562646 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.505092 Loss1: 1.504406 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.441659 Loss1: 1.440970 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.399921 Loss1: 1.399231 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.341090 Loss1: 1.340403 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.275567 Loss1: 1.274877 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.658030
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3500434027777778
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.106233 Loss1: 2.105566 Loss2: 0.000667
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.821204 Loss1: 1.820531 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.683732 Loss1: 1.683061 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.579992 Loss1: 1.579323 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.563132 Loss1: 1.562461 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.443790 Loss1: 1.443119 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.424410 Loss1: 1.423737 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.346587 Loss1: 1.345915 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.266106 Loss1: 1.265434 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.229760 Loss1: 1.229086 Loss2: 0.000674
+(DefaultActor pid=2839578) >> Training accuracy: 0.655165
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 07:34:30,949][flwr][DEBUG] - fit_round 8 received 10 results and 0 failures
+test acc: 0.3862
+[2023-09-21 07:35:18,210][flwr][INFO] - fit progress: (8, 2.455170800510687, {'accuracy': 0.3862}, 15799.871576023754)
+[2023-09-21 07:35:18,211][flwr][DEBUG] - evaluate_round 8: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 07:35:56,692][flwr][DEBUG] - evaluate_round 8 received 10 results and 0 failures
+[2023-09-21 07:35:56,693][flwr][DEBUG] - fit_round 9: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3560126582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.927283 Loss1: 1.926610 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.702426 Loss1: 1.701751 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.602820 Loss1: 1.602145 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.499452 Loss1: 1.498780 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.396106 Loss1: 1.395433 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.306156 Loss1: 1.305480 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.251587 Loss1: 1.250913 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.220946 Loss1: 1.220270 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.160069 Loss1: 1.159394 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.102738 Loss1: 1.102060 Loss2: 0.000678
+(DefaultActor pid=2839578) >> Training accuracy: 0.692247
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4024390243902439
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.889640 Loss1: 1.888962 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.647255 Loss1: 1.646576 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.553797 Loss1: 1.553116 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.489612 Loss1: 1.488931 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.408811 Loss1: 1.408131 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.317566 Loss1: 1.316884 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.288551 Loss1: 1.287872 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.211311 Loss1: 1.210632 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.170493 Loss1: 1.169813 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.128615 Loss1: 1.127937 Loss2: 0.000678
+(DefaultActor pid=2839578) >> Training accuracy: 0.710938
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.388251582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.897985 Loss1: 1.897311 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.678349 Loss1: 1.677672 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.556290 Loss1: 1.555613 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.488502 Loss1: 1.487825 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.416247 Loss1: 1.415571 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.295097 Loss1: 1.294421 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.280184 Loss1: 1.279508 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.214096 Loss1: 1.213419 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.180546 Loss1: 1.179868 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.145428 Loss1: 1.144750 Loss2: 0.000679
+(DefaultActor pid=2839578) >> Training accuracy: 0.693829
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.379746835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.956635 Loss1: 1.955954 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.700888 Loss1: 1.700204 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.578661 Loss1: 1.577975 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.530441 Loss1: 1.529756 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.358867 Loss1: 1.358180 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.342234 Loss1: 1.341549 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.280089 Loss1: 1.279402 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.229647 Loss1: 1.228962 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.191856 Loss1: 1.191166 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.104861 Loss1: 1.104173 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.708465
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3977864583333333
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.921093 Loss1: 1.920423 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.623214 Loss1: 1.622542 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.514003 Loss1: 1.513328 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.429523 Loss1: 1.428848 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.324683 Loss1: 1.324010 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.270632 Loss1: 1.269958 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.227159 Loss1: 1.226485 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.144824 Loss1: 1.144148 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.109372 Loss1: 1.108699 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.085896 Loss1: 1.085222 Loss2: 0.000675
+(DefaultActor pid=2839578) >> Training accuracy: 0.655816
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.44125791139240506
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.902037 Loss1: 1.901349 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.647134 Loss1: 1.646443 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.536952 Loss1: 1.536260 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.452393 Loss1: 1.451703 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.385573 Loss1: 1.384884 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.324288 Loss1: 1.323595 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.239454 Loss1: 1.238764 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.238154 Loss1: 1.237463 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.171281 Loss1: 1.170593 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.092184 Loss1: 1.091494 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.721717
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.36163651315789475
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.080123 Loss1: 2.079439 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.831850 Loss1: 1.831162 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.685333 Loss1: 1.684645 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.592516 Loss1: 1.591832 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.494913 Loss1: 1.494224 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.477352 Loss1: 1.476665 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.388652 Loss1: 1.387967 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.283609 Loss1: 1.282920 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.253341 Loss1: 1.252656 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.249124 Loss1: 1.248439 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.648438
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.35853040540540543
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.947335 Loss1: 1.946660 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.666278 Loss1: 1.665601 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.574491 Loss1: 1.573813 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.477389 Loss1: 1.476714 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.396951 Loss1: 1.396275 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.320744 Loss1: 1.320068 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.220249 Loss1: 1.219575 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.171852 Loss1: 1.171177 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.104214 Loss1: 1.103538 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.077997 Loss1: 1.077321 Loss2: 0.000675
+(DefaultActor pid=2839578) >> Training accuracy: 0.720228
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4511217948717949
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.843729 Loss1: 1.843054 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.612756 Loss1: 1.612079 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.503837 Loss1: 1.503159 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.406166 Loss1: 1.405486 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.343994 Loss1: 1.343312 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.278669 Loss1: 1.277990 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.211853 Loss1: 1.211174 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.114719 Loss1: 1.114039 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.103981 Loss1: 1.103302 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.037801 Loss1: 1.037122 Loss2: 0.000680
+(DefaultActor pid=2839578) >> Training accuracy: 0.692308
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3671875
+(DefaultActor pid=2839578) Epoch: 0 Loss: 2.039653 Loss1: 2.038987 Loss2: 0.000666
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.754892 Loss1: 1.754221 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.619528 Loss1: 1.618855 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.525886 Loss1: 1.525214 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.470324 Loss1: 1.469649 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.401900 Loss1: 1.401225 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.366652 Loss1: 1.365978 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.268322 Loss1: 1.267647 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.201946 Loss1: 1.201274 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.172559 Loss1: 1.171885 Loss2: 0.000675
+(DefaultActor pid=2839578) >> Training accuracy: 0.654046
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 08:12:25,796][flwr][DEBUG] - fit_round 9 received 10 results and 0 failures
+test acc: 0.414
+[2023-09-21 08:13:11,115][flwr][INFO] - fit progress: (9, 2.338781193803294, {'accuracy': 0.414}, 18072.776909645647)
+[2023-09-21 08:13:11,116][flwr][DEBUG] - evaluate_round 9: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 08:13:48,453][flwr][DEBUG] - evaluate_round 9 received 10 results and 0 failures
+[2023-09-21 08:13:48,454][flwr][DEBUG] - fit_round 10: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4331597222222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.768822 Loss1: 1.768151 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.462659 Loss1: 1.461987 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.329996 Loss1: 1.329323 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.268251 Loss1: 1.267578 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.172739 Loss1: 1.172065 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.133824 Loss1: 1.133149 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.033244 Loss1: 1.032570 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.972348 Loss1: 0.971675 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.951715 Loss1: 0.951039 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.881031 Loss1: 0.880356 Loss2: 0.000674
+(DefaultActor pid=2839578) >> Training accuracy: 0.751953
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3918918918918919
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.797261 Loss1: 1.796590 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.506892 Loss1: 1.506215 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.341902 Loss1: 1.341226 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.288335 Loss1: 1.287660 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.252071 Loss1: 1.251393 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.123978 Loss1: 1.123303 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.073999 Loss1: 1.073323 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.036718 Loss1: 1.036042 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.014163 Loss1: 1.013485 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.923587 Loss1: 0.922910 Loss2: 0.000677
+(DefaultActor pid=2839578) >> Training accuracy: 0.738598
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.48036858974358976
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.678107 Loss1: 1.677433 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.467067 Loss1: 1.466389 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.335469 Loss1: 1.334790 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.232564 Loss1: 1.231886 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.146392 Loss1: 1.145714 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.092340 Loss1: 1.091659 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.058374 Loss1: 1.057695 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.002816 Loss1: 1.002135 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.955005 Loss1: 0.954324 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.912029 Loss1: 0.911351 Loss2: 0.000679
+(DefaultActor pid=2839578) >> Training accuracy: 0.764223
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.44397865853658536
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.752825 Loss1: 1.752148 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.499736 Loss1: 1.499059 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.358722 Loss1: 1.358043 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.347500 Loss1: 1.346823 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.226710 Loss1: 1.226030 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.171939 Loss1: 1.171261 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.119876 Loss1: 1.119198 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.053659 Loss1: 1.052980 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.985147 Loss1: 0.984468 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.957507 Loss1: 0.956830 Loss2: 0.000677
+(DefaultActor pid=2839578) >> Training accuracy: 0.721037
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.40705128205128205
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.831035 Loss1: 1.830368 Loss2: 0.000667
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.582848 Loss1: 1.582176 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.457690 Loss1: 1.457018 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.345657 Loss1: 1.344984 Loss2: 0.000673
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.284072 Loss1: 1.283401 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.162648 Loss1: 1.161976 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.148320 Loss1: 1.147647 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.090899 Loss1: 1.090228 Loss2: 0.000671
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.029172 Loss1: 1.028500 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.014691 Loss1: 1.014018 Loss2: 0.000673
+(DefaultActor pid=2839578) >> Training accuracy: 0.729167
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4019325657894737
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.919099 Loss1: 1.918416 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.684562 Loss1: 1.683875 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.530755 Loss1: 1.530068 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.412141 Loss1: 1.411457 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.343197 Loss1: 1.342511 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.296885 Loss1: 1.296203 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.200738 Loss1: 1.200052 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.125912 Loss1: 1.125227 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.094058 Loss1: 1.093377 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 9 Loss: 1.055026 Loss1: 1.054342 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.693873
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4293908227848101
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.769907 Loss1: 1.769226 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.553301 Loss1: 1.552612 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.410357 Loss1: 1.409673 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.303660 Loss1: 1.302973 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.236919 Loss1: 1.236234 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.217920 Loss1: 1.217233 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.103311 Loss1: 1.102623 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.034625 Loss1: 1.033938 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.002365 Loss1: 1.001676 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.945531 Loss1: 0.944844 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.728639
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4252373417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.746512 Loss1: 1.745836 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.553847 Loss1: 1.553167 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.391240 Loss1: 1.390563 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.317656 Loss1: 1.316978 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.236076 Loss1: 1.235398 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.153511 Loss1: 1.152832 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.114862 Loss1: 1.114183 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.040310 Loss1: 1.039632 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.008935 Loss1: 1.008255 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.951032 Loss1: 0.950352 Loss2: 0.000680
+(DefaultActor pid=2839578) >> Training accuracy: 0.730815
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.403876582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.784272 Loss1: 1.783602 Loss2: 0.000670
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.531645 Loss1: 1.530969 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.376906 Loss1: 1.376231 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.317698 Loss1: 1.317022 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.209270 Loss1: 1.208594 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.151584 Loss1: 1.150908 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.112031 Loss1: 1.111355 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.066369 Loss1: 1.065691 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 8 Loss: 1.018915 Loss1: 1.018240 Loss2: 0.000675
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.953510 Loss1: 0.952834 Loss2: 0.000675
+(DefaultActor pid=2839578) >> Training accuracy: 0.726266
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4814082278481013
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.730989 Loss1: 1.730302 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.499374 Loss1: 1.498682 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.365379 Loss1: 1.364688 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.299249 Loss1: 1.298557 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.230900 Loss1: 1.230207 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.132769 Loss1: 1.132078 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.121166 Loss1: 1.120473 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 1.031246 Loss1: 1.030555 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.964667 Loss1: 0.963977 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.941125 Loss1: 0.940437 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.757911
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 08:50:38,874][flwr][DEBUG] - fit_round 10 received 10 results and 0 failures
+test acc: 0.4484
+[2023-09-21 08:51:24,827][flwr][INFO] - fit progress: (10, 2.2332213046832585, {'accuracy': 0.4484}, 20366.488789497875)
+[2023-09-21 08:51:24,828][flwr][DEBUG] - evaluate_round 10: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 08:52:01,833][flwr][DEBUG] - evaluate_round 10 received 10 results and 0 failures
+[2023-09-21 08:52:01,834][flwr][DEBUG] - fit_round 11: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4740901898734177
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.629546 Loss1: 1.628861 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.375977 Loss1: 1.375289 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.255404 Loss1: 1.254717 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.142108 Loss1: 1.141419 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.094845 Loss1: 1.094158 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.007558 Loss1: 1.006872 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.975380 Loss1: 0.974691 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.942220 Loss1: 0.941533 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.902019 Loss1: 0.901331 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.815933 Loss1: 0.815243 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.773536
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4557291666666667
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.731246 Loss1: 1.730568 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.412883 Loss1: 1.412200 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.314994 Loss1: 1.314311 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.224059 Loss1: 1.223376 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.165273 Loss1: 1.164590 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.061898 Loss1: 1.061218 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.993720 Loss1: 0.993037 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.918988 Loss1: 0.918302 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.911651 Loss1: 0.910971 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.879353 Loss1: 0.878671 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.761619
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5041920731707317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.627778 Loss1: 1.627094 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.361173 Loss1: 1.360488 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.231485 Loss1: 1.230799 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.140573 Loss1: 1.139886 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.088595 Loss1: 1.087910 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.004723 Loss1: 1.004035 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.951726 Loss1: 0.951039 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.912833 Loss1: 0.912148 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.865669 Loss1: 0.864983 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.830272 Loss1: 0.829585 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.766578
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4715711805555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.645126 Loss1: 1.644449 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.311527 Loss1: 1.310848 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.241229 Loss1: 1.240547 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.125019 Loss1: 1.124338 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.052496 Loss1: 1.051815 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.958869 Loss1: 0.958187 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.903812 Loss1: 0.903132 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.857576 Loss1: 0.856894 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.829263 Loss1: 0.828581 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.799555 Loss1: 0.798872 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.770616
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4341216216216216
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.651177 Loss1: 1.650494 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.397449 Loss1: 1.396764 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.227983 Loss1: 1.227297 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.173176 Loss1: 1.172492 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.059811 Loss1: 1.059128 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.998535 Loss1: 0.997853 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.962478 Loss1: 0.961796 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.903385 Loss1: 0.902702 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.867992 Loss1: 0.867308 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.799688 Loss1: 0.799004 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.766258
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5019778481012658
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.609899 Loss1: 1.609205 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.372284 Loss1: 1.371587 Loss2: 0.000697
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.257488 Loss1: 1.256790 Loss2: 0.000698
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.152002 Loss1: 1.151306 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.076884 Loss1: 1.076187 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.019827 Loss1: 1.019129 Loss2: 0.000698
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.937625 Loss1: 0.936930 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.930517 Loss1: 0.929822 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.864317 Loss1: 0.863621 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.799665 Loss1: 0.798967 Loss2: 0.000698
+(DefaultActor pid=2839578) >> Training accuracy: 0.776108
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5442708333333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.537496 Loss1: 1.536816 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.335668 Loss1: 1.334985 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.209524 Loss1: 1.208840 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.115014 Loss1: 1.114332 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.020731 Loss1: 1.020049 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.968488 Loss1: 0.967804 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.885202 Loss1: 0.884518 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.852736 Loss1: 0.852054 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.789003 Loss1: 0.788318 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.782578 Loss1: 0.781895 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.788462
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4653876582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.608857 Loss1: 1.608169 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.379345 Loss1: 1.378652 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.237433 Loss1: 1.236740 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.161681 Loss1: 1.160991 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.104506 Loss1: 1.103814 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.000347 Loss1: 0.999654 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.966659 Loss1: 0.965968 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.922085 Loss1: 0.921390 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.857371 Loss1: 0.856678 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.839263 Loss1: 0.838571 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.771954
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.44551809210526316
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.788287 Loss1: 1.787590 Loss2: 0.000697
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.503190 Loss1: 1.502494 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.354582 Loss1: 1.353888 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.295410 Loss1: 1.294717 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.213724 Loss1: 1.213030 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.121306 Loss1: 1.120613 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 1.056888 Loss1: 1.056196 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.982969 Loss1: 0.982277 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.922348 Loss1: 0.921655 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.944368 Loss1: 0.943677 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.746916
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4467958860759494
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.654432 Loss1: 1.653752 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.404548 Loss1: 1.403866 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.264324 Loss1: 1.263638 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.162715 Loss1: 1.162031 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.108653 Loss1: 1.107971 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 5 Loss: 1.022951 Loss1: 1.022266 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.971305 Loss1: 0.970622 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.938535 Loss1: 0.937851 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.864571 Loss1: 0.863887 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.851177 Loss1: 0.850495 Loss2: 0.000682
+(DefaultActor pid=2839578) >> Training accuracy: 0.761472
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 09:28:22,281][flwr][DEBUG] - fit_round 11 received 10 results and 0 failures
+test acc: 0.4735
+[2023-09-21 09:29:04,934][flwr][INFO] - fit progress: (11, 2.1865856590362402, {'accuracy': 0.4735}, 22626.5953413439)
+[2023-09-21 09:29:04,934][flwr][DEBUG] - evaluate_round 11: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 09:29:40,921][flwr][DEBUG] - evaluate_round 11 received 10 results and 0 failures
+[2023-09-21 09:29:40,922][flwr][DEBUG] - fit_round 12: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4630489864864865
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.528320 Loss1: 1.527644 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.278407 Loss1: 1.277728 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.126384 Loss1: 1.125704 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.055715 Loss1: 1.055037 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.987289 Loss1: 0.986608 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.871775 Loss1: 0.871094 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.790207 Loss1: 0.789526 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.815851 Loss1: 0.815171 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.747741 Loss1: 0.747060 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.727658 Loss1: 0.726976 Loss2: 0.000682
+(DefaultActor pid=2839578) >> Training accuracy: 0.808488
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.47468354430379744
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.549113 Loss1: 1.548435 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.255203 Loss1: 1.254521 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.133198 Loss1: 1.132519 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.046259 Loss1: 1.045582 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.978001 Loss1: 0.977321 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.893616 Loss1: 0.892936 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.837677 Loss1: 0.836996 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.842093 Loss1: 0.841412 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.740360 Loss1: 0.739679 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.704260 Loss1: 0.703578 Loss2: 0.000682
+(DefaultActor pid=2839578) >> Training accuracy: 0.807358
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5332278481012658
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.498947 Loss1: 1.498261 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.239504 Loss1: 1.238813 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.097843 Loss1: 1.097149 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.044317 Loss1: 1.043624 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.949046 Loss1: 0.948353 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.868510 Loss1: 0.867815 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.830942 Loss1: 0.830249 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.775295 Loss1: 0.774600 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.725674 Loss1: 0.724981 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.714763 Loss1: 0.714069 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.777294
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.47738486842105265
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.690661 Loss1: 1.689969 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.353340 Loss1: 1.352646 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.226899 Loss1: 1.226205 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.136943 Loss1: 1.136248 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.072870 Loss1: 1.072178 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.988486 Loss1: 0.987794 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.921914 Loss1: 0.921220 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.873217 Loss1: 0.872527 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.818827 Loss1: 0.818138 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.778337 Loss1: 0.777646 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.775905
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4906684027777778
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.518150 Loss1: 1.517471 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.223734 Loss1: 1.223055 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.045218 Loss1: 1.044536 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.999969 Loss1: 0.999286 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.885450 Loss1: 0.884768 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.842167 Loss1: 0.841484 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.761405 Loss1: 0.760721 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.740901 Loss1: 0.740218 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.717582 Loss1: 0.716899 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.683080 Loss1: 0.682396 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.842231
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5310594512195121
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.491985 Loss1: 1.491303 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.194311 Loss1: 1.193629 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.077274 Loss1: 1.076591 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.026376 Loss1: 1.025690 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.984187 Loss1: 0.983506 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.912049 Loss1: 0.911362 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.875900 Loss1: 0.875216 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.806244 Loss1: 0.805562 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.719870 Loss1: 0.719187 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.731806 Loss1: 0.731123 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.812309
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.569511217948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.467515 Loss1: 1.466833 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.195044 Loss1: 1.194361 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.082558 Loss1: 1.081876 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.975696 Loss1: 0.975013 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.886436 Loss1: 0.885752 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.844075 Loss1: 0.843391 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.773278 Loss1: 0.772594 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.715304 Loss1: 0.714619 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.713455 Loss1: 0.712771 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.672192 Loss1: 0.671507 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.822917
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5176028481012658
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.539171 Loss1: 1.538490 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.273570 Loss1: 1.272887 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.179981 Loss1: 1.179298 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.032636 Loss1: 1.031951 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.944138 Loss1: 0.943455 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.937102 Loss1: 0.936418 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.866591 Loss1: 0.865906 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.812533 Loss1: 0.811849 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.804162 Loss1: 0.803477 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.717535 Loss1: 0.716851 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.770372
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.48617788461538464
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.577239 Loss1: 1.576565 Loss2: 0.000674
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.335966 Loss1: 1.335289 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.162797 Loss1: 1.162118 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.039584 Loss1: 1.038907 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 4 Loss: 1.022853 Loss1: 1.022174 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.925070 Loss1: 0.924392 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.876713 Loss1: 0.876035 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.801927 Loss1: 0.801248 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.786687 Loss1: 0.786009 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.699872 Loss1: 0.699191 Loss2: 0.000680
+(DefaultActor pid=2839578) >> Training accuracy: 0.745593
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.495253164556962
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.509940 Loss1: 1.509256 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.278487 Loss1: 1.277800 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.129119 Loss1: 1.128428 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.058697 Loss1: 1.058008 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.956509 Loss1: 0.955820 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.909721 Loss1: 0.909031 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.896175 Loss1: 0.895485 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.845213 Loss1: 0.844521 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.777606 Loss1: 0.776917 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.707496 Loss1: 0.706805 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.780261
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 10:05:47,325][flwr][DEBUG] - fit_round 12 received 10 results and 0 failures
+test acc: 0.4908
+[2023-09-21 10:06:28,442][flwr][INFO] - fit progress: (12, 2.121484987651959, {'accuracy': 0.4908}, 24870.103177347686)
+[2023-09-21 10:06:28,442][flwr][DEBUG] - evaluate_round 12: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 10:07:04,702][flwr][DEBUG] - evaluate_round 12 received 10 results and 0 failures
+[2023-09-21 10:07:04,703][flwr][DEBUG] - fit_round 13: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5360243055555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.389737 Loss1: 1.389057 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.107157 Loss1: 1.106475 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.956591 Loss1: 0.955908 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.886601 Loss1: 0.885917 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.776553 Loss1: 0.775869 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.737383 Loss1: 0.736697 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.673512 Loss1: 0.672828 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.643751 Loss1: 0.643064 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.628153 Loss1: 0.627467 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.554398 Loss1: 0.553713 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.821181
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.533623417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.438557 Loss1: 1.437869 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.140570 Loss1: 1.139875 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.046182 Loss1: 1.045490 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.956593 Loss1: 0.955898 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.850085 Loss1: 0.849389 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.800892 Loss1: 0.800198 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.715208 Loss1: 0.714516 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.733518 Loss1: 0.732824 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.682591 Loss1: 0.681896 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.645804 Loss1: 0.645112 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.827136
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5518196202531646
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.425491 Loss1: 1.424805 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.171357 Loss1: 1.170667 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.035637 Loss1: 1.034948 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.928868 Loss1: 0.928177 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.878914 Loss1: 0.878224 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.786956 Loss1: 0.786266 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.757463 Loss1: 0.756777 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.726530 Loss1: 0.725843 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.656689 Loss1: 0.655999 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.610231 Loss1: 0.609541 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.793315
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5142227564102564
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.476086 Loss1: 1.475410 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.209909 Loss1: 1.209228 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.050208 Loss1: 1.049528 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.929968 Loss1: 0.929287 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.893766 Loss1: 0.893085 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.819729 Loss1: 0.819049 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.805120 Loss1: 0.804437 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.694165 Loss1: 0.693482 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.690133 Loss1: 0.689449 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.634745 Loss1: 0.634063 Loss2: 0.000682
+(DefaultActor pid=2839578) >> Training accuracy: 0.844151
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4995888157894737
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.555603 Loss1: 1.554914 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.287022 Loss1: 1.286332 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.131055 Loss1: 1.130364 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 1.007335 Loss1: 1.006644 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.960751 Loss1: 0.960060 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.889228 Loss1: 0.888539 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.810288 Loss1: 0.809600 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.746550 Loss1: 0.745860 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.745816 Loss1: 0.745125 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.662212 Loss1: 0.661521 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.823396
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5144382911392406
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.468708 Loss1: 1.468027 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.121961 Loss1: 1.121279 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.035017 Loss1: 1.034332 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.909480 Loss1: 0.908797 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.848390 Loss1: 0.847705 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.813788 Loss1: 0.813103 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.773006 Loss1: 0.772324 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.698911 Loss1: 0.698227 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.675887 Loss1: 0.675202 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.644252 Loss1: 0.643567 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.786986
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5735759493670886
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.412579 Loss1: 1.411888 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.132433 Loss1: 1.131740 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.990019 Loss1: 0.989325 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.937732 Loss1: 0.937038 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.846446 Loss1: 0.845749 Loss2: 0.000697
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.861525 Loss1: 0.860831 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.738973 Loss1: 0.738278 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.735227 Loss1: 0.734533 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.648211 Loss1: 0.647514 Loss2: 0.000697
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.611929 Loss1: 0.611238 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.817445
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.48627533783783783
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.501301 Loss1: 1.500618 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.155668 Loss1: 1.154983 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.011567 Loss1: 1.010881 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.943288 Loss1: 0.942602 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.845835 Loss1: 0.845149 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.735159 Loss1: 0.734473 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.720567 Loss1: 0.719880 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.690761 Loss1: 0.690076 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.648303 Loss1: 0.647619 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.625450 Loss1: 0.624764 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.797297
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5975560897435898
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.345728 Loss1: 1.345044 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.084854 Loss1: 1.084167 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.960891 Loss1: 0.960203 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.884947 Loss1: 0.884260 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.771226 Loss1: 0.770538 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.738926 Loss1: 0.738236 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.648350 Loss1: 0.647660 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.623435 Loss1: 0.622746 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.623111 Loss1: 0.622422 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.557646 Loss1: 0.556956 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.851562
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5520198170731707
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.376203 Loss1: 1.375519 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.129904 Loss1: 1.129217 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.002876 Loss1: 1.002190 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.934550 Loss1: 0.933864 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.840156 Loss1: 0.839471 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.772622 Loss1: 0.771934 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.736764 Loss1: 0.736078 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.713265 Loss1: 0.712576 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.641215 Loss1: 0.640528 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.633197 Loss1: 0.632509 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.822980
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 10:37:26,019][flwr][DEBUG] - fit_round 13 received 10 results and 0 failures
+test acc: 0.5082
+[2023-09-21 10:38:05,864][flwr][INFO] - fit progress: (13, 2.1080573364949453, {'accuracy': 0.5082}, 26767.525622681715)
+[2023-09-21 10:38:05,864][flwr][DEBUG] - evaluate_round 13: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 10:38:43,046][flwr][DEBUG] - evaluate_round 13 received 10 results and 0 failures
+[2023-09-21 10:38:43,047][flwr][DEBUG] - fit_round 14: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5224095394736842
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.495148 Loss1: 1.494462 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.176908 Loss1: 1.176220 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 1.068731 Loss1: 1.068042 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.924736 Loss1: 0.924045 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.833490 Loss1: 0.832801 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.787893 Loss1: 0.787203 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.720280 Loss1: 0.719591 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.707480 Loss1: 0.706793 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.631270 Loss1: 0.630584 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.611263 Loss1: 0.610575 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.828125
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5611155063291139
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.278083 Loss1: 1.277397 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.069906 Loss1: 1.069219 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.926905 Loss1: 0.926215 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.815089 Loss1: 0.814401 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.735671 Loss1: 0.734982 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.674660 Loss1: 0.673968 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.691047 Loss1: 0.690360 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.618026 Loss1: 0.617338 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.581989 Loss1: 0.581301 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.543672 Loss1: 0.542985 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.861155
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.586629746835443
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.336793 Loss1: 1.336111 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.037993 Loss1: 1.037308 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.926571 Loss1: 0.925886 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.803778 Loss1: 0.803090 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.779462 Loss1: 0.778776 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.745153 Loss1: 0.744465 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.686424 Loss1: 0.685738 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.619777 Loss1: 0.619092 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.568122 Loss1: 0.567434 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.567563 Loss1: 0.566876 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.836036
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.53125
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.340304 Loss1: 1.339625 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.034304 Loss1: 1.033622 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.916658 Loss1: 0.915980 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.865983 Loss1: 0.865302 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.757430 Loss1: 0.756749 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.689479 Loss1: 0.688797 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.676961 Loss1: 0.676279 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.666006 Loss1: 0.665324 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.607041 Loss1: 0.606362 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.533916 Loss1: 0.533235 Loss2: 0.000681
+(DefaultActor pid=2839578) >> Training accuracy: 0.831883
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5915743670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.325193 Loss1: 1.324509 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.050996 Loss1: 1.050307 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.895319 Loss1: 0.894631 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.804089 Loss1: 0.803400 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.764930 Loss1: 0.764239 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.692729 Loss1: 0.692040 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.634687 Loss1: 0.633997 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.637053 Loss1: 0.636362 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.620606 Loss1: 0.619918 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.574051 Loss1: 0.573363 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.852255
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5347222222222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.361732 Loss1: 1.361056 Loss2: 0.000676
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.995057 Loss1: 0.994378 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.859999 Loss1: 0.859318 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.780809 Loss1: 0.780129 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.730415 Loss1: 0.729734 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.715196 Loss1: 0.714514 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.622201 Loss1: 0.621522 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.550676 Loss1: 0.549995 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.560903 Loss1: 0.560221 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.526883 Loss1: 0.526200 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.888238
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6169871794871795
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.305988 Loss1: 1.305306 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.982471 Loss1: 0.981784 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.870373 Loss1: 0.869688 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.766332 Loss1: 0.765646 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.683106 Loss1: 0.682418 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.645629 Loss1: 0.644943 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.592413 Loss1: 0.591726 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.607627 Loss1: 0.606940 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.553094 Loss1: 0.552408 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.489491 Loss1: 0.488804 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.856771
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5813643292682927
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.285275 Loss1: 1.284595 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.031407 Loss1: 1.030722 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.918699 Loss1: 0.918018 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.843592 Loss1: 0.842908 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.743427 Loss1: 0.742744 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.712606 Loss1: 0.711924 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.612100 Loss1: 0.611417 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.611763 Loss1: 0.611078 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.554286 Loss1: 0.553602 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.514363 Loss1: 0.513680 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.837271
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5137246621621622
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.373630 Loss1: 1.372951 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.066205 Loss1: 1.065523 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.939168 Loss1: 0.938485 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.820080 Loss1: 0.819396 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.717720 Loss1: 0.717036 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.716809 Loss1: 0.716125 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.670649 Loss1: 0.669967 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.603208 Loss1: 0.602526 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.605108 Loss1: 0.604426 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.537407 Loss1: 0.536724 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.830659
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5436698717948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.388350 Loss1: 1.387678 Loss2: 0.000672
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.069646 Loss1: 1.068968 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.944736 Loss1: 0.944057 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.845517 Loss1: 0.844838 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.810837 Loss1: 0.810160 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.716508 Loss1: 0.715831 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.676502 Loss1: 0.675824 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.635517 Loss1: 0.634837 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.603081 Loss1: 0.602402 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.524852 Loss1: 0.524172 Loss2: 0.000681
+(DefaultActor pid=2839578) >> Training accuracy: 0.850561
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 11:08:38,840][flwr][DEBUG] - fit_round 14 received 10 results and 0 failures
+test acc: 0.5224
+[2023-09-21 11:09:20,673][flwr][INFO] - fit progress: (14, 2.0822741444499346, {'accuracy': 0.5224}, 28642.33480043197)
+[2023-09-21 11:09:20,674][flwr][DEBUG] - evaluate_round 14: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 11:09:56,967][flwr][DEBUG] - evaluate_round 14 received 10 results and 0 failures
+[2023-09-21 11:09:56,973][flwr][DEBUG] - fit_round 15: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.585245253164557
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.248063 Loss1: 1.247380 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.955273 Loss1: 0.954585 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.806013 Loss1: 0.805324 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.733134 Loss1: 0.732445 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.686385 Loss1: 0.685695 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.626458 Loss1: 0.625767 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.602848 Loss1: 0.602161 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.539285 Loss1: 0.538596 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.517281 Loss1: 0.516591 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.497788 Loss1: 0.497098 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.887856
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5658623417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.284005 Loss1: 1.283322 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.932303 Loss1: 0.931617 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.857271 Loss1: 0.856585 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.775608 Loss1: 0.774925 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.669581 Loss1: 0.668895 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.649746 Loss1: 0.649060 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.559732 Loss1: 0.559047 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.532957 Loss1: 0.532273 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.514418 Loss1: 0.513733 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.480694 Loss1: 0.480009 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.834256
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5639022435897436
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.301764 Loss1: 1.301086 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.009381 Loss1: 1.008699 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.843909 Loss1: 0.843228 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.783347 Loss1: 0.782664 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.716620 Loss1: 0.715938 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.625069 Loss1: 0.624385 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.625520 Loss1: 0.624837 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.551339 Loss1: 0.550657 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.528151 Loss1: 0.527469 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.487723 Loss1: 0.487040 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.853365
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5988948170731707
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.227612 Loss1: 1.226928 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.958121 Loss1: 0.957437 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.814602 Loss1: 0.813918 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.740707 Loss1: 0.740023 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.687803 Loss1: 0.687119 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.619615 Loss1: 0.618930 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.567862 Loss1: 0.567175 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.516920 Loss1: 0.516233 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.501689 Loss1: 0.501002 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.513696 Loss1: 0.513010 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.857088
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6346153846153846
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.169894 Loss1: 1.169209 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.903648 Loss1: 0.902962 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.765077 Loss1: 0.764389 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.686323 Loss1: 0.685635 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.625313 Loss1: 0.624624 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.525678 Loss1: 0.524990 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.489435 Loss1: 0.488745 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.472688 Loss1: 0.472000 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.485837 Loss1: 0.485148 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.437851 Loss1: 0.437164 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.850561
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6010680379746836
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.247205 Loss1: 1.246518 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.974121 Loss1: 0.973433 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.842835 Loss1: 0.842146 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.763555 Loss1: 0.762865 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.679691 Loss1: 0.679001 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.656608 Loss1: 0.655919 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.596825 Loss1: 0.596134 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.548391 Loss1: 0.547701 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.556992 Loss1: 0.556302 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.481873 Loss1: 0.481182 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.881329
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5646701388888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.238156 Loss1: 1.237477 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.932862 Loss1: 0.932181 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.823901 Loss1: 0.823219 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.710020 Loss1: 0.709337 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.628132 Loss1: 0.627449 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.594688 Loss1: 0.594004 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.544145 Loss1: 0.543462 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.511652 Loss1: 0.510967 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.487295 Loss1: 0.486611 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.413326 Loss1: 0.412641 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.863281
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6176819620253164
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.223622 Loss1: 1.222935 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.988298 Loss1: 0.987607 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.804023 Loss1: 0.803330 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.760880 Loss1: 0.760187 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.687859 Loss1: 0.687170 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.611328 Loss1: 0.610636 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.573867 Loss1: 0.573176 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.577203 Loss1: 0.576510 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.545243 Loss1: 0.544552 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.462672 Loss1: 0.461980 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.885878
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.542652027027027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.307491 Loss1: 1.306808 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.953105 Loss1: 0.952418 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.818019 Loss1: 0.817333 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.783487 Loss1: 0.782801 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.675442 Loss1: 0.674755 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.586629 Loss1: 0.585944 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.584960 Loss1: 0.584273 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.536859 Loss1: 0.536173 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.510628 Loss1: 0.509942 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.496409 Loss1: 0.495723 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.878167
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.553453947368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.413682 Loss1: 1.412990 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 1 Loss: 1.065420 Loss1: 1.064725 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.926535 Loss1: 0.925842 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.793192 Loss1: 0.792501 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.756182 Loss1: 0.755491 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.705888 Loss1: 0.705196 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.600377 Loss1: 0.599684 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.625310 Loss1: 0.624618 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.590795 Loss1: 0.590102 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.551023 Loss1: 0.550332 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.881990
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 11:40:21,240][flwr][DEBUG] - fit_round 15 received 10 results and 0 failures
+test acc: 0.5411
+[2023-09-21 11:41:07,624][flwr][INFO] - fit progress: (15, 2.0444571261588758, {'accuracy': 0.5411}, 30549.28551045386)
+[2023-09-21 11:41:07,624][flwr][DEBUG] - evaluate_round 15: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 11:41:44,262][flwr][DEBUG] - evaluate_round 15 received 10 results and 0 failures
+[2023-09-21 11:41:44,264][flwr][DEBUG] - fit_round 16: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5965189873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.171972 Loss1: 1.171290 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.907076 Loss1: 0.906391 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.775614 Loss1: 0.774930 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.675655 Loss1: 0.674972 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.642840 Loss1: 0.642156 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.554407 Loss1: 0.553721 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.536685 Loss1: 0.536000 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.505371 Loss1: 0.504685 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.491189 Loss1: 0.490504 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.459399 Loss1: 0.458714 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.851661
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5951522435897436
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.207781 Loss1: 1.207101 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.902751 Loss1: 0.902068 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.790941 Loss1: 0.790257 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.689388 Loss1: 0.688703 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.600925 Loss1: 0.600240 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.555160 Loss1: 0.554477 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.550878 Loss1: 0.550194 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.500430 Loss1: 0.499747 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.495864 Loss1: 0.495179 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.471448 Loss1: 0.470762 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.876002
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.629746835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.168234 Loss1: 1.167543 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.840973 Loss1: 0.840279 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.770074 Loss1: 0.769380 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.660800 Loss1: 0.660105 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.613675 Loss1: 0.612978 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.562730 Loss1: 0.562035 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.501028 Loss1: 0.500331 Loss2: 0.000697
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.472789 Loss1: 0.472091 Loss2: 0.000698
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.442583 Loss1: 0.441888 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.433179 Loss1: 0.432482 Loss2: 0.000697
+(DefaultActor pid=2839578) >> Training accuracy: 0.879549
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6358612804878049
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.161597 Loss1: 1.160916 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.871993 Loss1: 0.871308 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.736943 Loss1: 0.736258 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.668092 Loss1: 0.667406 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.610641 Loss1: 0.609956 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.571901 Loss1: 0.571212 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.533509 Loss1: 0.532821 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.463236 Loss1: 0.462549 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.454813 Loss1: 0.454124 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.431337 Loss1: 0.430650 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.877858
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.558910472972973
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.217862 Loss1: 1.217179 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.906881 Loss1: 0.906195 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.767560 Loss1: 0.766873 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.679200 Loss1: 0.678514 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.610980 Loss1: 0.610294 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.558068 Loss1: 0.557381 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.496385 Loss1: 0.495698 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.505302 Loss1: 0.504616 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.458835 Loss1: 0.458149 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.427147 Loss1: 0.426461 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.898649
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5840871710526315
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.339763 Loss1: 1.339074 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.988641 Loss1: 0.987948 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.856883 Loss1: 0.856191 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.751173 Loss1: 0.750482 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.684880 Loss1: 0.684187 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.618559 Loss1: 0.617868 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.618446 Loss1: 0.617755 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.514446 Loss1: 0.513754 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.499645 Loss1: 0.498953 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.510687 Loss1: 0.509997 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.850535
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6162974683544303
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.190312 Loss1: 1.189626 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.896312 Loss1: 0.895624 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.758354 Loss1: 0.757665 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.669706 Loss1: 0.669017 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.635962 Loss1: 0.635271 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.560865 Loss1: 0.560175 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.512129 Loss1: 0.511438 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.488315 Loss1: 0.487623 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.466634 Loss1: 0.465945 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.441537 Loss1: 0.440845 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.895570
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.65625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.109734 Loss1: 1.109050 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.801456 Loss1: 0.800768 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.676199 Loss1: 0.675510 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.627229 Loss1: 0.626540 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.594434 Loss1: 0.593745 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.481132 Loss1: 0.480443 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.480768 Loss1: 0.480080 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.472192 Loss1: 0.471503 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.405903 Loss1: 0.405211 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.411521 Loss1: 0.410832 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.913862
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5863715277777778
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.188047 Loss1: 1.187364 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.849622 Loss1: 0.848937 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.713098 Loss1: 0.712413 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.636415 Loss1: 0.635728 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.546417 Loss1: 0.545731 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.532960 Loss1: 0.532273 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.483972 Loss1: 0.483286 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.463146 Loss1: 0.462459 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.400550 Loss1: 0.399862 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.378643 Loss1: 0.377956 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.891059
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6071993670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.126219 Loss1: 1.125534 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.868880 Loss1: 0.868190 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.778676 Loss1: 0.777985 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.727137 Loss1: 0.726448 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.643110 Loss1: 0.642421 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.564256 Loss1: 0.563566 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.510575 Loss1: 0.509884 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.466732 Loss1: 0.466043 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.477715 Loss1: 0.477024 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.437627 Loss1: 0.436936 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.870847
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 12:12:36,772][flwr][DEBUG] - fit_round 16 received 10 results and 0 failures
+test acc: 0.5477
+[2023-09-21 12:13:15,888][flwr][INFO] - fit progress: (16, 2.057832792163276, {'accuracy': 0.5477}, 32477.549180646893)
+[2023-09-21 12:13:15,888][flwr][DEBUG] - evaluate_round 16: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 12:13:52,326][flwr][DEBUG] - evaluate_round 16 received 10 results and 0 failures
+[2023-09-21 12:13:52,327][flwr][DEBUG] - fit_round 17: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6040348101265823
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.142720 Loss1: 1.142041 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.797202 Loss1: 0.796519 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.699464 Loss1: 0.698782 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.629968 Loss1: 0.629285 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.534819 Loss1: 0.534136 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.521913 Loss1: 0.521231 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.469277 Loss1: 0.468593 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.466053 Loss1: 0.465371 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.438188 Loss1: 0.437504 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.431174 Loss1: 0.430491 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.900712
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5941611842105263
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.226311 Loss1: 1.225624 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.929810 Loss1: 0.929122 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.749900 Loss1: 0.749210 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.681570 Loss1: 0.680882 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.616897 Loss1: 0.616207 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.543236 Loss1: 0.542547 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.537009 Loss1: 0.536319 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.532387 Loss1: 0.531699 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.454295 Loss1: 0.453608 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.429386 Loss1: 0.428699 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.872944
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6277689873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.123544 Loss1: 1.122860 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.820124 Loss1: 0.819435 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.694871 Loss1: 0.694183 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.609780 Loss1: 0.609092 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.567276 Loss1: 0.566587 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.531333 Loss1: 0.530643 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.458245 Loss1: 0.457557 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.433619 Loss1: 0.432930 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.404882 Loss1: 0.404190 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.396286 Loss1: 0.395595 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.884494
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.574535472972973
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.176506 Loss1: 1.175822 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.833712 Loss1: 0.833025 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.715611 Loss1: 0.714924 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.587471 Loss1: 0.586783 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.557997 Loss1: 0.557310 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.519834 Loss1: 0.519148 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.447374 Loss1: 0.446686 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.432000 Loss1: 0.431312 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.408963 Loss1: 0.408274 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.377571 Loss1: 0.376885 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.906461
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6694711538461539
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.044528 Loss1: 1.043845 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.780962 Loss1: 0.780275 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.636487 Loss1: 0.635799 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.592804 Loss1: 0.592116 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.482607 Loss1: 0.481918 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.441010 Loss1: 0.440321 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.440975 Loss1: 0.440287 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.375375 Loss1: 0.374685 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.352002 Loss1: 0.351314 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.321192 Loss1: 0.320503 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.918069
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6019631410256411
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.180263 Loss1: 1.179585 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.853258 Loss1: 0.852576 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.682382 Loss1: 0.681698 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.607328 Loss1: 0.606648 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.550287 Loss1: 0.549604 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.510910 Loss1: 0.510228 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.510569 Loss1: 0.509885 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.445767 Loss1: 0.445084 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.413736 Loss1: 0.413052 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.404868 Loss1: 0.404185 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.898438
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6530854430379747
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.107662 Loss1: 1.106975 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.817546 Loss1: 0.816852 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.708375 Loss1: 0.707683 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.580685 Loss1: 0.579990 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.589865 Loss1: 0.589172 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.515806 Loss1: 0.515112 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.457559 Loss1: 0.456865 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.457055 Loss1: 0.456360 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.423172 Loss1: 0.422479 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.383532 Loss1: 0.382839 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.897745
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6490091463414634
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.091658 Loss1: 1.090976 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.802920 Loss1: 0.802236 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.672022 Loss1: 0.671340 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.576981 Loss1: 0.576297 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.542945 Loss1: 0.542259 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.495907 Loss1: 0.495221 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.490823 Loss1: 0.490139 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.467997 Loss1: 0.467312 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.389936 Loss1: 0.389251 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.365524 Loss1: 0.364836 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.893483
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6426028481012658
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.147721 Loss1: 1.147036 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.820505 Loss1: 0.819816 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.725501 Loss1: 0.724812 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.625272 Loss1: 0.624580 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.536355 Loss1: 0.535666 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.517209 Loss1: 0.516520 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.484307 Loss1: 0.483617 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.481095 Loss1: 0.480404 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.384797 Loss1: 0.384107 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.385662 Loss1: 0.384972 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.916930
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6037326388888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.111239 Loss1: 1.110557 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.757280 Loss1: 0.756596 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.645653 Loss1: 0.644968 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.600523 Loss1: 0.599839 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.551401 Loss1: 0.550715 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.448230 Loss1: 0.447545 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.418582 Loss1: 0.417896 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.425059 Loss1: 0.424371 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.387687 Loss1: 0.387001 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.380974 Loss1: 0.380287 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.919705
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 12:44:43,389][flwr][DEBUG] - fit_round 17 received 10 results and 0 failures
+test acc: 0.5608
+[2023-09-21 12:45:24,585][flwr][INFO] - fit progress: (17, 2.031206720362837, {'accuracy': 0.5608}, 34406.24665048672)
+[2023-09-21 12:45:24,586][flwr][DEBUG] - evaluate_round 17: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 12:46:00,623][flwr][DEBUG] - evaluate_round 17 received 10 results and 0 failures
+[2023-09-21 12:46:00,624][flwr][DEBUG] - fit_round 18: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.596706081081081
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.094302 Loss1: 1.093617 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.772268 Loss1: 0.771580 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.646902 Loss1: 0.646215 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.584628 Loss1: 0.583940 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.489215 Loss1: 0.488528 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.435832 Loss1: 0.435146 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.367942 Loss1: 0.367254 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.376459 Loss1: 0.375771 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.351050 Loss1: 0.350364 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.339491 Loss1: 0.338803 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.918708
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6293512658227848
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.066817 Loss1: 1.066133 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.747724 Loss1: 0.747036 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.638336 Loss1: 0.637650 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.576429 Loss1: 0.575743 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.514288 Loss1: 0.513604 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.469632 Loss1: 0.468944 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.406558 Loss1: 0.405872 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.379111 Loss1: 0.378424 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.403106 Loss1: 0.402419 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.370391 Loss1: 0.369703 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.897350
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6266025641025641
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.073012 Loss1: 1.072333 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.752451 Loss1: 0.751767 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.648601 Loss1: 0.647916 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.613215 Loss1: 0.612529 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.501580 Loss1: 0.500899 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.480923 Loss1: 0.480239 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.425263 Loss1: 0.424579 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.415489 Loss1: 0.414805 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.409410 Loss1: 0.408727 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.327927 Loss1: 0.327245 Loss2: 0.000682
+(DefaultActor pid=2839578) >> Training accuracy: 0.906250
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6582278481012658
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.051163 Loss1: 1.050479 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.760670 Loss1: 0.759977 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.624285 Loss1: 0.623592 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.558168 Loss1: 0.557477 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.478668 Loss1: 0.477976 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.437583 Loss1: 0.436890 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.399258 Loss1: 0.398566 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.417357 Loss1: 0.416666 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.383603 Loss1: 0.382911 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.323092 Loss1: 0.322400 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.917524
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6293402777777778
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.079548 Loss1: 1.078864 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.730031 Loss1: 0.729346 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.589437 Loss1: 0.588752 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.547886 Loss1: 0.547201 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.492516 Loss1: 0.491829 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.425506 Loss1: 0.424819 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.332409 Loss1: 0.331721 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.360234 Loss1: 0.359546 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.340151 Loss1: 0.339465 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.274994 Loss1: 0.274306 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.943359
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6923076923076923
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.999405 Loss1: 0.998721 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.742395 Loss1: 0.741708 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.546003 Loss1: 0.545316 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.510316 Loss1: 0.509628 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.444151 Loss1: 0.443463 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.391229 Loss1: 0.390540 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.387143 Loss1: 0.386453 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.376873 Loss1: 0.376184 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.301309 Loss1: 0.300619 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.305137 Loss1: 0.304448 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.908454
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6611946202531646
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.033610 Loss1: 1.032924 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.779320 Loss1: 0.778629 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.628951 Loss1: 0.628260 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.553686 Loss1: 0.552996 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.479745 Loss1: 0.479055 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.457277 Loss1: 0.456585 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.459642 Loss1: 0.458949 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.422731 Loss1: 0.422038 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.370492 Loss1: 0.369801 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.376730 Loss1: 0.376040 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.881329
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.610608552631579
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.168973 Loss1: 1.168284 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.842909 Loss1: 0.842216 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.705051 Loss1: 0.704359 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.632374 Loss1: 0.631680 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.611334 Loss1: 0.610643 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.534202 Loss1: 0.533514 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.461604 Loss1: 0.460914 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.395185 Loss1: 0.394496 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.385466 Loss1: 0.384776 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.401138 Loss1: 0.400449 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.875206
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6686356707317073
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.040869 Loss1: 1.040187 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.730876 Loss1: 0.730191 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.629918 Loss1: 0.629233 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.566846 Loss1: 0.566162 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.490006 Loss1: 0.489321 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.427255 Loss1: 0.426569 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.452917 Loss1: 0.452230 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.383111 Loss1: 0.382423 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.334918 Loss1: 0.334231 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.366045 Loss1: 0.365358 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.893102
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6418117088607594
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.053644 Loss1: 1.052961 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.777673 Loss1: 0.776983 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.647216 Loss1: 0.646525 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.532519 Loss1: 0.531830 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.515363 Loss1: 0.514673 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.435360 Loss1: 0.434670 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.462182 Loss1: 0.461493 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.374925 Loss1: 0.374234 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.367130 Loss1: 0.366439 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.366999 Loss1: 0.366308 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.927611
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 13:16:56,476][flwr][DEBUG] - fit_round 18 received 10 results and 0 failures
+test acc: 0.5626
+[2023-09-21 13:17:37,215][flwr][INFO] - fit progress: (18, 2.0359792596996784, {'accuracy': 0.5626}, 36338.876606587786)
+[2023-09-21 13:17:37,216][flwr][DEBUG] - evaluate_round 18: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 13:18:12,370][flwr][DEBUG] - evaluate_round 18 received 10 results and 0 failures
+[2023-09-21 13:18:12,371][flwr][DEBUG] - fit_round 19: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6821598101265823
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.959848 Loss1: 0.959162 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.662881 Loss1: 0.662191 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.565824 Loss1: 0.565132 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.485351 Loss1: 0.484658 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.463298 Loss1: 0.462606 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.416257 Loss1: 0.415564 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.374890 Loss1: 0.374198 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.349709 Loss1: 0.349017 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.353259 Loss1: 0.352567 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.339978 Loss1: 0.339285 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.923457
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6408305921052632
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.126174 Loss1: 1.125487 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.799491 Loss1: 0.798799 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.630295 Loss1: 0.629606 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.577452 Loss1: 0.576762 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.505104 Loss1: 0.504412 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.459261 Loss1: 0.458571 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.432184 Loss1: 0.431493 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.403629 Loss1: 0.402938 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.392544 Loss1: 0.391855 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.375821 Loss1: 0.375131 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.898643
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7063301282051282
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.905123 Loss1: 0.904439 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.632716 Loss1: 0.632027 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.532526 Loss1: 0.531838 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.498864 Loss1: 0.498175 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.397904 Loss1: 0.397216 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.335703 Loss1: 0.335014 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.308992 Loss1: 0.308303 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.294917 Loss1: 0.294228 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.284271 Loss1: 0.283580 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.297317 Loss1: 0.296628 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.928285
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6588212025316456
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.976973 Loss1: 0.976289 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.713869 Loss1: 0.713178 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.596170 Loss1: 0.595479 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.486015 Loss1: 0.485325 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.488097 Loss1: 0.487406 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.408293 Loss1: 0.407603 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.388820 Loss1: 0.388129 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.339421 Loss1: 0.338728 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.300090 Loss1: 0.299398 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.286503 Loss1: 0.285812 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.912381
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6034628378378378
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.072035 Loss1: 1.071350 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.724948 Loss1: 0.724261 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.581759 Loss1: 0.581070 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.474492 Loss1: 0.473804 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.466861 Loss1: 0.466173 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.394737 Loss1: 0.394050 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.383053 Loss1: 0.382363 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.345827 Loss1: 0.345138 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.341975 Loss1: 0.341287 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.343478 Loss1: 0.342790 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.913429
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6414930555555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.027264 Loss1: 1.026580 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.689439 Loss1: 0.688755 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.579712 Loss1: 0.579027 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.481138 Loss1: 0.480452 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.406247 Loss1: 0.405559 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.391223 Loss1: 0.390537 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.353412 Loss1: 0.352724 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.325630 Loss1: 0.324943 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.328879 Loss1: 0.328191 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.296515 Loss1: 0.295827 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.936849
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6796875
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.953475 Loss1: 0.952795 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.670071 Loss1: 0.669385 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.562248 Loss1: 0.561561 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.494680 Loss1: 0.493991 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.432776 Loss1: 0.432088 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.409462 Loss1: 0.408774 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.396936 Loss1: 0.396248 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.337209 Loss1: 0.336521 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.362919 Loss1: 0.362230 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.359433 Loss1: 0.358745 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.881669
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6536787974683544
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.994324 Loss1: 0.993642 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.736840 Loss1: 0.736156 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.611929 Loss1: 0.611243 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.485980 Loss1: 0.485295 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.493053 Loss1: 0.492370 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.418748 Loss1: 0.418064 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.379157 Loss1: 0.378473 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.354676 Loss1: 0.353991 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.342452 Loss1: 0.341766 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.305841 Loss1: 0.305156 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.938489
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6683148734177216
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.014479 Loss1: 1.013791 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.701590 Loss1: 0.700900 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.590088 Loss1: 0.589396 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.491388 Loss1: 0.490698 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.480777 Loss1: 0.480085 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.462162 Loss1: 0.461470 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.421123 Loss1: 0.420432 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.378390 Loss1: 0.377698 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.338295 Loss1: 0.337603 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.321099 Loss1: 0.320407 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.928204
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.647636217948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.006496 Loss1: 1.005815 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.690885 Loss1: 0.690203 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.590653 Loss1: 0.589968 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.482642 Loss1: 0.481958 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.487397 Loss1: 0.486711 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.433124 Loss1: 0.432440 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.383816 Loss1: 0.383131 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.390940 Loss1: 0.390254 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.366844 Loss1: 0.366158 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.301749 Loss1: 0.301064 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.910256
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 13:49:35,955][flwr][DEBUG] - fit_round 19 received 10 results and 0 failures
+test acc: 0.5718
+[2023-09-21 13:50:34,062][flwr][INFO] - fit progress: (19, 2.0221455788460023, {'accuracy': 0.5718}, 38315.72371325875)
+[2023-09-21 13:50:34,063][flwr][DEBUG] - evaluate_round 19: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 13:51:12,471][flwr][DEBUG] - evaluate_round 19 received 10 results and 0 failures
+[2023-09-21 13:51:12,471][flwr][DEBUG] - fit_round 20: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7062895569620253
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.954811 Loss1: 0.954123 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.665533 Loss1: 0.664838 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.534502 Loss1: 0.533807 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.448819 Loss1: 0.448124 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.393489 Loss1: 0.392792 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.450359 Loss1: 0.449663 Loss2: 0.000696
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.334775 Loss1: 0.334080 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.341575 Loss1: 0.340880 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.291867 Loss1: 0.291170 Loss2: 0.000697
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.280014 Loss1: 0.279317 Loss2: 0.000697
+(DefaultActor pid=2839578) >> Training accuracy: 0.912184
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6754351265822784
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.895974 Loss1: 0.895288 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.656032 Loss1: 0.655343 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.559093 Loss1: 0.558404 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.470448 Loss1: 0.469755 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.420889 Loss1: 0.420197 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.356547 Loss1: 0.355858 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.366510 Loss1: 0.365818 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.387680 Loss1: 0.386991 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.314847 Loss1: 0.314157 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.288951 Loss1: 0.288260 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.921875
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6666666666666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.959341 Loss1: 0.958663 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.665053 Loss1: 0.664371 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.554571 Loss1: 0.553888 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.482737 Loss1: 0.482054 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.451966 Loss1: 0.451281 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.411543 Loss1: 0.410859 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.362976 Loss1: 0.362292 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.332367 Loss1: 0.331683 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.270884 Loss1: 0.270202 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.300204 Loss1: 0.299520 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.923478
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6890822784810127
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.969077 Loss1: 0.968394 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.664713 Loss1: 0.664025 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.527326 Loss1: 0.526642 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.473413 Loss1: 0.472726 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.431975 Loss1: 0.431287 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.384006 Loss1: 0.383317 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.329720 Loss1: 0.329033 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.316652 Loss1: 0.315965 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.276848 Loss1: 0.276161 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.263223 Loss1: 0.262535 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.916139
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6492598684210527
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.048296 Loss1: 1.047608 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.730649 Loss1: 0.729958 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.618650 Loss1: 0.617960 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.526269 Loss1: 0.525574 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.474201 Loss1: 0.473509 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.432615 Loss1: 0.431924 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.385725 Loss1: 0.385034 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.355371 Loss1: 0.354681 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.365749 Loss1: 0.365057 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.325013 Loss1: 0.324323 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.928865
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7271634615384616
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.880925 Loss1: 0.880241 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.577044 Loss1: 0.576357 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.501507 Loss1: 0.500818 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.403781 Loss1: 0.403092 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.366669 Loss1: 0.365980 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.330967 Loss1: 0.330278 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.333528 Loss1: 0.332841 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.297542 Loss1: 0.296852 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.266772 Loss1: 0.266081 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.268955 Loss1: 0.268265 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.921875
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6882621951219512
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.937918 Loss1: 0.937238 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.648408 Loss1: 0.647722 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.508054 Loss1: 0.507369 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.476899 Loss1: 0.476213 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.399151 Loss1: 0.398463 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.411347 Loss1: 0.410660 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.369446 Loss1: 0.368759 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.374146 Loss1: 0.373460 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.292739 Loss1: 0.292051 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.273411 Loss1: 0.272723 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.927973
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6247888513513513
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.019912 Loss1: 1.019229 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.691106 Loss1: 0.690419 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.528049 Loss1: 0.527361 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.467166 Loss1: 0.466479 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.428732 Loss1: 0.428044 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.344272 Loss1: 0.343585 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.345131 Loss1: 0.344444 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.317066 Loss1: 0.316380 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.293325 Loss1: 0.292638 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.254468 Loss1: 0.253781 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.939823
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6571180555555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.988701 Loss1: 0.988020 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.649398 Loss1: 0.648714 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.514444 Loss1: 0.513760 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.468484 Loss1: 0.467798 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.385076 Loss1: 0.384390 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.361170 Loss1: 0.360484 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.318545 Loss1: 0.317858 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.318763 Loss1: 0.318078 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.294864 Loss1: 0.294178 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.249111 Loss1: 0.248423 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.946615
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6617879746835443
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.949268 Loss1: 0.948585 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.654062 Loss1: 0.653376 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.533020 Loss1: 0.532335 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.480708 Loss1: 0.480021 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.442745 Loss1: 0.442059 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.417431 Loss1: 0.416743 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.369522 Loss1: 0.368836 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.346798 Loss1: 0.346113 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.318132 Loss1: 0.317447 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.275973 Loss1: 0.275287 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.939478
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 14:22:43,743][flwr][DEBUG] - fit_round 20 received 10 results and 0 failures
+test acc: 0.5753
+[2023-09-21 14:23:40,201][flwr][INFO] - fit progress: (20, 2.034489405421784, {'accuracy': 0.5753}, 40301.8622248359)
+[2023-09-21 14:23:40,201][flwr][DEBUG] - evaluate_round 20: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 14:24:20,889][flwr][DEBUG] - evaluate_round 20 received 10 results and 0 failures
+[2023-09-21 14:24:20,890][flwr][DEBUG] - fit_round 21: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7397836538461539
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.824833 Loss1: 0.824149 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.543139 Loss1: 0.542451 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.473494 Loss1: 0.472805 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.374403 Loss1: 0.373714 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.323439 Loss1: 0.322750 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.328259 Loss1: 0.327569 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.265237 Loss1: 0.264547 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.245049 Loss1: 0.244359 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.262205 Loss1: 0.261516 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.221613 Loss1: 0.220923 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.945112
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.65625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 1.012585 Loss1: 1.011897 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.689855 Loss1: 0.689164 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.586339 Loss1: 0.585646 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.490496 Loss1: 0.489807 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.423021 Loss1: 0.422330 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.383382 Loss1: 0.382693 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.393623 Loss1: 0.392932 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.349961 Loss1: 0.349271 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.298128 Loss1: 0.297436 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.313965 Loss1: 0.313278 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.912418
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7027294303797469
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.912616 Loss1: 0.911929 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.626376 Loss1: 0.625689 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.518194 Loss1: 0.517506 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.420392 Loss1: 0.419704 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.371976 Loss1: 0.371287 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.340598 Loss1: 0.339910 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.338659 Loss1: 0.337971 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.327412 Loss1: 0.326725 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.299027 Loss1: 0.298338 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.253208 Loss1: 0.252520 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.927215
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6932357594936709
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.882542 Loss1: 0.881860 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.602563 Loss1: 0.601875 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.477237 Loss1: 0.476551 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.440930 Loss1: 0.440242 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.404910 Loss1: 0.404222 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.371534 Loss1: 0.370847 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.322536 Loss1: 0.321848 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.291291 Loss1: 0.290602 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.302394 Loss1: 0.301705 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.250491 Loss1: 0.249801 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.929786
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6734775641025641
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.936235 Loss1: 0.935556 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.629486 Loss1: 0.628804 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.495261 Loss1: 0.494578 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.448358 Loss1: 0.447676 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.415090 Loss1: 0.414405 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.353631 Loss1: 0.352948 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.331765 Loss1: 0.331083 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.294503 Loss1: 0.293821 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.297316 Loss1: 0.296636 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.267443 Loss1: 0.266760 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.928085
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7122231012658228
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.906181 Loss1: 0.905493 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.622214 Loss1: 0.621522 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.531727 Loss1: 0.531034 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.428849 Loss1: 0.428155 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.415786 Loss1: 0.415095 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.355280 Loss1: 0.354589 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.298220 Loss1: 0.297529 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.297549 Loss1: 0.296857 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.256684 Loss1: 0.255989 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.254162 Loss1: 0.253471 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.942049
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.703125
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.880603 Loss1: 0.879925 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.581155 Loss1: 0.580472 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.468184 Loss1: 0.467500 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.380484 Loss1: 0.379798 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.392686 Loss1: 0.392003 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.368948 Loss1: 0.368261 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.338958 Loss1: 0.338274 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.298871 Loss1: 0.298185 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.298956 Loss1: 0.298268 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.262431 Loss1: 0.261745 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.934261
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6720920138888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.885867 Loss1: 0.885187 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.580765 Loss1: 0.580081 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.475862 Loss1: 0.475178 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.406948 Loss1: 0.406263 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.363567 Loss1: 0.362882 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.309969 Loss1: 0.309282 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.277164 Loss1: 0.276478 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.227772 Loss1: 0.227087 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.245619 Loss1: 0.244931 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225764 Loss1: 0.225077 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.948351
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6746439873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.919673 Loss1: 0.918993 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.578220 Loss1: 0.577537 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.518907 Loss1: 0.518223 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.409289 Loss1: 0.408605 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.390071 Loss1: 0.389389 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.364969 Loss1: 0.364286 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.309263 Loss1: 0.308580 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.289962 Loss1: 0.289278 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.260004 Loss1: 0.259320 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.288667 Loss1: 0.287982 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.927017
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6328125
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.979426 Loss1: 0.978741 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.635645 Loss1: 0.634957 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.518889 Loss1: 0.518200 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.479992 Loss1: 0.479303 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.351588 Loss1: 0.350898 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.362277 Loss1: 0.361589 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.317835 Loss1: 0.317144 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.279659 Loss1: 0.278970 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.242820 Loss1: 0.242130 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.266235 Loss1: 0.265545 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.932855
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 14:54:48,349][flwr][DEBUG] - fit_round 21 received 10 results and 0 failures
+test acc: 0.58
+[2023-09-21 14:55:45,146][flwr][INFO] - fit progress: (21, 2.0309638719970047, {'accuracy': 0.58}, 42226.8077736008)
+[2023-09-21 14:55:45,147][flwr][DEBUG] - evaluate_round 21: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 14:56:32,892][flwr][DEBUG] - evaluate_round 21 received 10 results and 0 failures
+[2023-09-21 14:56:32,894][flwr][DEBUG] - fit_round 22: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6914556962025317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.803949 Loss1: 0.803262 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.565996 Loss1: 0.565307 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.449576 Loss1: 0.448886 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.371917 Loss1: 0.371227 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.353012 Loss1: 0.352323 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.335038 Loss1: 0.334348 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.299778 Loss1: 0.299088 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.262341 Loss1: 0.261650 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.289537 Loss1: 0.288847 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.236109 Loss1: 0.235420 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.944818
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7445913461538461
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.805326 Loss1: 0.804641 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.526743 Loss1: 0.526055 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.450884 Loss1: 0.450194 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.350697 Loss1: 0.350007 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.304397 Loss1: 0.303706 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.279762 Loss1: 0.279072 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.280171 Loss1: 0.279481 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.249646 Loss1: 0.248956 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.203021 Loss1: 0.202330 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.177469 Loss1: 0.176777 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.953926
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7221123417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.820623 Loss1: 0.819936 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.582384 Loss1: 0.581692 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.479339 Loss1: 0.478646 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.356456 Loss1: 0.355763 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.336598 Loss1: 0.335904 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.317648 Loss1: 0.316955 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.325154 Loss1: 0.324462 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.281410 Loss1: 0.280718 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.303766 Loss1: 0.303073 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.283561 Loss1: 0.282868 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.934533
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6746961805555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.891036 Loss1: 0.890351 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.544994 Loss1: 0.544308 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.449309 Loss1: 0.448621 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.425047 Loss1: 0.424359 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.361126 Loss1: 0.360439 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.313393 Loss1: 0.312705 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.250910 Loss1: 0.250220 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.269423 Loss1: 0.268736 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.245849 Loss1: 0.245160 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.205879 Loss1: 0.205190 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.937500
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.714003164556962
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.851204 Loss1: 0.850517 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.592070 Loss1: 0.591380 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.494705 Loss1: 0.494015 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.405275 Loss1: 0.404584 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.382699 Loss1: 0.382008 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.350577 Loss1: 0.349887 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.316734 Loss1: 0.316042 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.289837 Loss1: 0.289146 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.233492 Loss1: 0.232801 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.245263 Loss1: 0.244572 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.947983
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6480152027027027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.912692 Loss1: 0.912006 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.567772 Loss1: 0.567084 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.456716 Loss1: 0.456029 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.378274 Loss1: 0.377584 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.375633 Loss1: 0.374943 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.308279 Loss1: 0.307591 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.293974 Loss1: 0.293284 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.271330 Loss1: 0.270640 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.258329 Loss1: 0.257641 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.293659 Loss1: 0.292970 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.924409
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6780427631578947
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.941828 Loss1: 0.941141 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.640836 Loss1: 0.640144 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.471174 Loss1: 0.470480 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.436413 Loss1: 0.435721 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.411261 Loss1: 0.410569 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.351410 Loss1: 0.350720 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.330966 Loss1: 0.330275 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.325650 Loss1: 0.324959 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.331087 Loss1: 0.330396 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.296963 Loss1: 0.296273 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.936678
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6861155063291139
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.833602 Loss1: 0.832919 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.579613 Loss1: 0.578926 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.462821 Loss1: 0.462135 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.403105 Loss1: 0.402417 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.351034 Loss1: 0.350348 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.377130 Loss1: 0.376444 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.285004 Loss1: 0.284315 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.269557 Loss1: 0.268872 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.236383 Loss1: 0.235696 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.230013 Loss1: 0.229326 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.959256
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6909054487179487
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.848580 Loss1: 0.847897 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.579539 Loss1: 0.578852 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.451849 Loss1: 0.451162 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.381812 Loss1: 0.381126 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.350630 Loss1: 0.349944 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.329147 Loss1: 0.328461 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.312975 Loss1: 0.312289 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.265735 Loss1: 0.265050 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.327254 Loss1: 0.326568 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.272125 Loss1: 0.271440 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.918269
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7244664634146342
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.843598 Loss1: 0.842914 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.552585 Loss1: 0.551898 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.419045 Loss1: 0.418355 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.366650 Loss1: 0.365961 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.342061 Loss1: 0.341372 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.308390 Loss1: 0.307701 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.321223 Loss1: 0.320533 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.253640 Loss1: 0.252950 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.241274 Loss1: 0.240582 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.250582 Loss1: 0.249891 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.923780
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 15:28:17,500][flwr][DEBUG] - fit_round 22 received 10 results and 0 failures
+test acc: 0.5794
+[2023-09-21 15:43:12,517][flwr][INFO] - fit progress: (22, 2.0352664707948604, {'accuracy': 0.5794}, 45074.17800773773)
+[2023-09-21 15:43:12,517][flwr][DEBUG] - evaluate_round 22: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 15:44:05,792][flwr][DEBUG] - evaluate_round 22 received 10 results and 0 failures
+[2023-09-21 15:44:05,793][flwr][DEBUG] - fit_round 23: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7167721518987342
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.824136 Loss1: 0.823451 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.549475 Loss1: 0.548785 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.418791 Loss1: 0.418101 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.381816 Loss1: 0.381126 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.298096 Loss1: 0.297404 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.303099 Loss1: 0.302408 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.288763 Loss1: 0.288072 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.267822 Loss1: 0.267129 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.247003 Loss1: 0.246311 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225090 Loss1: 0.224397 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.939082
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6919070512820513
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.805127 Loss1: 0.804447 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.542462 Loss1: 0.541778 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.451714 Loss1: 0.451029 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.376672 Loss1: 0.375988 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.325679 Loss1: 0.324995 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.334058 Loss1: 0.333373 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.288432 Loss1: 0.287749 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.279191 Loss1: 0.278505 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.260787 Loss1: 0.260102 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.240564 Loss1: 0.239880 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.944912
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6990131578947368
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.913341 Loss1: 0.912651 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.577036 Loss1: 0.576343 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.447269 Loss1: 0.446577 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.423009 Loss1: 0.422316 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.397099 Loss1: 0.396406 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.368308 Loss1: 0.367618 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.304916 Loss1: 0.304224 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.290928 Loss1: 0.290237 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.237614 Loss1: 0.236921 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.227034 Loss1: 0.226340 Loss2: 0.000694
+(DefaultActor pid=2839578) >> Training accuracy: 0.940789
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7062895569620253
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.819787 Loss1: 0.819105 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.540358 Loss1: 0.539672 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.389115 Loss1: 0.388429 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.391066 Loss1: 0.390379 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.351819 Loss1: 0.351133 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.299734 Loss1: 0.299048 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.290117 Loss1: 0.289430 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.244954 Loss1: 0.244267 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.250688 Loss1: 0.250001 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.237327 Loss1: 0.236640 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.935522
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6589949324324325
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.871702 Loss1: 0.871014 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.557355 Loss1: 0.556664 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.452732 Loss1: 0.452040 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.388189 Loss1: 0.387499 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.345209 Loss1: 0.344519 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.303643 Loss1: 0.302953 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.239421 Loss1: 0.238731 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.229152 Loss1: 0.228461 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.246465 Loss1: 0.245774 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.215249 Loss1: 0.214557 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.957981
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7195411392405063
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.786715 Loss1: 0.786030 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.542083 Loss1: 0.541394 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.427712 Loss1: 0.427023 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.356983 Loss1: 0.356293 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.335741 Loss1: 0.335049 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.318976 Loss1: 0.318286 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.281052 Loss1: 0.280360 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.273622 Loss1: 0.272932 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.237452 Loss1: 0.236762 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.205321 Loss1: 0.204630 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.943038
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7389240506329114
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.789821 Loss1: 0.789133 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.543761 Loss1: 0.543067 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.379188 Loss1: 0.378494 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.365347 Loss1: 0.364653 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.320259 Loss1: 0.319565 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.314512 Loss1: 0.313818 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.299502 Loss1: 0.298810 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.265154 Loss1: 0.264460 Loss2: 0.000695
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.225665 Loss1: 0.224972 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.211969 Loss1: 0.211274 Loss2: 0.000695
+(DefaultActor pid=2839578) >> Training accuracy: 0.949367
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6866319444444444
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.853024 Loss1: 0.852341 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.508157 Loss1: 0.507470 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.397796 Loss1: 0.397110 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.342883 Loss1: 0.342195 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.284955 Loss1: 0.284267 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.224961 Loss1: 0.224273 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.270997 Loss1: 0.270311 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.253158 Loss1: 0.252470 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.225463 Loss1: 0.224776 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225452 Loss1: 0.224764 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.932509
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7336128048780488
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.827469 Loss1: 0.826787 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.498889 Loss1: 0.498204 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.416946 Loss1: 0.416260 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.353177 Loss1: 0.352492 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.308768 Loss1: 0.308082 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.306896 Loss1: 0.306210 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.276188 Loss1: 0.275501 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.267651 Loss1: 0.266965 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.228126 Loss1: 0.227438 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225639 Loss1: 0.224951 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.946456
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7546073717948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.761326 Loss1: 0.760640 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.499356 Loss1: 0.498668 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.407487 Loss1: 0.406797 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.335937 Loss1: 0.335247 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.288890 Loss1: 0.288200 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.267197 Loss1: 0.266506 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237314 Loss1: 0.236623 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.193691 Loss1: 0.192999 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.201012 Loss1: 0.200322 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.222137 Loss1: 0.221447 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.958534
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 16:15:20,819][flwr][DEBUG] - fit_round 23 received 10 results and 0 failures
+test acc: 0.5859
+[2023-09-21 16:16:32,911][flwr][INFO] - fit progress: (23, 2.0376226549712233, {'accuracy': 0.5859}, 47074.572088341694)
+[2023-09-21 16:16:32,912][flwr][DEBUG] - evaluate_round 23: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 16:17:14,742][flwr][DEBUG] - evaluate_round 23 received 10 results and 0 failures
+[2023-09-21 16:17:14,743][flwr][DEBUG] - fit_round 24: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7341772151898734
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.751340 Loss1: 0.750656 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.490719 Loss1: 0.490032 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.363891 Loss1: 0.363202 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.348134 Loss1: 0.347444 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.316647 Loss1: 0.315959 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.282471 Loss1: 0.281784 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.268883 Loss1: 0.268195 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.275336 Loss1: 0.274645 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.253431 Loss1: 0.252741 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.199564 Loss1: 0.198875 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.959256
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7005208333333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.825215 Loss1: 0.824532 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.505377 Loss1: 0.504691 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.401535 Loss1: 0.400848 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.310919 Loss1: 0.310232 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.306895 Loss1: 0.306210 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.282627 Loss1: 0.281940 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.241331 Loss1: 0.240642 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.214543 Loss1: 0.213854 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.195775 Loss1: 0.195086 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.222747 Loss1: 0.222061 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.943576
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7459984756097561
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.758384 Loss1: 0.757703 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.472129 Loss1: 0.471443 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.371137 Loss1: 0.370452 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.332945 Loss1: 0.332260 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.295966 Loss1: 0.295280 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.278833 Loss1: 0.278146 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.226507 Loss1: 0.225819 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.252484 Loss1: 0.251796 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.235723 Loss1: 0.235036 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.208849 Loss1: 0.208161 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.944169
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7509889240506329
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.738613 Loss1: 0.737926 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.470530 Loss1: 0.469838 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.410247 Loss1: 0.409557 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.350161 Loss1: 0.349470 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.331707 Loss1: 0.331017 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.291249 Loss1: 0.290559 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.249274 Loss1: 0.248583 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.241145 Loss1: 0.240454 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.218389 Loss1: 0.217696 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.206936 Loss1: 0.206242 Loss2: 0.000694
+(DefaultActor pid=2839578) >> Training accuracy: 0.945016
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7087339743589743
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.757359 Loss1: 0.756680 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.506201 Loss1: 0.505518 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.403108 Loss1: 0.402424 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.340485 Loss1: 0.339802 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.290285 Loss1: 0.289601 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.285131 Loss1: 0.284446 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.283624 Loss1: 0.282940 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.278499 Loss1: 0.277816 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.210515 Loss1: 0.209830 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.221317 Loss1: 0.220634 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.955529
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7213212025316456
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.778736 Loss1: 0.778050 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.487969 Loss1: 0.487281 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.382482 Loss1: 0.381792 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.383730 Loss1: 0.383039 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.284243 Loss1: 0.283553 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.279700 Loss1: 0.279011 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.244235 Loss1: 0.243542 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.235012 Loss1: 0.234321 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.246669 Loss1: 0.245980 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225132 Loss1: 0.224442 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.961630
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7638221153846154
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.691307 Loss1: 0.690626 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.444809 Loss1: 0.444122 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.361598 Loss1: 0.360911 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.289424 Loss1: 0.288736 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.281628 Loss1: 0.280942 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.255080 Loss1: 0.254394 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.213031 Loss1: 0.212342 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.190368 Loss1: 0.189680 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.236814 Loss1: 0.236126 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.201299 Loss1: 0.200609 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.965144
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7012746710526315
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.863654 Loss1: 0.862968 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.535128 Loss1: 0.534437 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.427521 Loss1: 0.426830 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.377123 Loss1: 0.376431 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.345876 Loss1: 0.345185 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.281138 Loss1: 0.280446 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.277964 Loss1: 0.277273 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.279213 Loss1: 0.278523 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.273045 Loss1: 0.272358 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.261927 Loss1: 0.261238 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.944901
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7122231012658228
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.770449 Loss1: 0.769770 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.512532 Loss1: 0.511849 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.390693 Loss1: 0.390008 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.327412 Loss1: 0.326730 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.299071 Loss1: 0.298386 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.287903 Loss1: 0.287218 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.303481 Loss1: 0.302797 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.255980 Loss1: 0.255295 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.236285 Loss1: 0.235599 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.199653 Loss1: 0.198968 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.933742
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6587837837837838
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.832835 Loss1: 0.832151 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.523753 Loss1: 0.523064 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.399227 Loss1: 0.398538 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.354134 Loss1: 0.353443 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.335702 Loss1: 0.335012 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.290098 Loss1: 0.289409 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.249435 Loss1: 0.248746 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.234401 Loss1: 0.233712 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.224138 Loss1: 0.223449 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.222572 Loss1: 0.221883 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.947213
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 16:49:17,180][flwr][DEBUG] - fit_round 24 received 10 results and 0 failures
+test acc: 0.5911
+[2023-09-21 16:50:27,960][flwr][INFO] - fit progress: (24, 2.040369967111764, {'accuracy': 0.5911}, 49109.62099220976)
+[2023-09-21 16:50:27,960][flwr][DEBUG] - evaluate_round 24: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 16:51:08,204][flwr][DEBUG] - evaluate_round 24 received 10 results and 0 failures
+[2023-09-21 16:51:08,206][flwr][DEBUG] - fit_round 25: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.737047697368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.821320 Loss1: 0.820633 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.510944 Loss1: 0.510253 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.388813 Loss1: 0.388122 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.355849 Loss1: 0.355158 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.315487 Loss1: 0.314796 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.323875 Loss1: 0.323183 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.300329 Loss1: 0.299637 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.233861 Loss1: 0.233171 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.202202 Loss1: 0.201511 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.198254 Loss1: 0.197564 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.956003
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7219145569620253
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.753810 Loss1: 0.753123 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.432384 Loss1: 0.431692 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.351928 Loss1: 0.351237 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.306713 Loss1: 0.306022 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.250338 Loss1: 0.249648 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.269751 Loss1: 0.269059 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.250043 Loss1: 0.249352 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.235157 Loss1: 0.234464 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.213640 Loss1: 0.212950 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.186728 Loss1: 0.186039 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.967168
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7242879746835443
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.722312 Loss1: 0.721630 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.442140 Loss1: 0.441455 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.364463 Loss1: 0.363778 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.360419 Loss1: 0.359732 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.294501 Loss1: 0.293816 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.259119 Loss1: 0.258434 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.247918 Loss1: 0.247232 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.249166 Loss1: 0.248480 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.226538 Loss1: 0.225851 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.209486 Loss1: 0.208801 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.951938
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7762419871794872
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.674149 Loss1: 0.673464 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.440279 Loss1: 0.439589 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.339242 Loss1: 0.338550 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.257786 Loss1: 0.257095 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.240745 Loss1: 0.240056 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.207922 Loss1: 0.207230 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.194243 Loss1: 0.193551 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.216477 Loss1: 0.215785 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.220482 Loss1: 0.219790 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.178628 Loss1: 0.177935 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.954127
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7287660256410257
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.708085 Loss1: 0.707404 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.534795 Loss1: 0.534111 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.396736 Loss1: 0.396050 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.336620 Loss1: 0.335935 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.285264 Loss1: 0.284578 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.265288 Loss1: 0.264603 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237579 Loss1: 0.236894 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.235607 Loss1: 0.234921 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.209277 Loss1: 0.208590 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.194240 Loss1: 0.193554 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.922676
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7478243670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.699292 Loss1: 0.698604 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.438248 Loss1: 0.437557 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.367209 Loss1: 0.366517 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.295911 Loss1: 0.295220 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.257589 Loss1: 0.256898 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.244775 Loss1: 0.244083 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.276036 Loss1: 0.275345 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.237333 Loss1: 0.236641 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.229056 Loss1: 0.228364 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.246526 Loss1: 0.245835 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.930380
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6720861486486487
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.788696 Loss1: 0.788011 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.480613 Loss1: 0.479923 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.364356 Loss1: 0.363665 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.313429 Loss1: 0.312738 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.262075 Loss1: 0.261386 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.224454 Loss1: 0.223764 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.208808 Loss1: 0.208118 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.227897 Loss1: 0.227207 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.216236 Loss1: 0.215546 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.198813 Loss1: 0.198123 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.938556
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7572408536585366
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.688046 Loss1: 0.687363 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.395401 Loss1: 0.394715 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.353045 Loss1: 0.352359 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.297340 Loss1: 0.296652 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.253839 Loss1: 0.253151 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.279134 Loss1: 0.278447 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.245478 Loss1: 0.244791 Loss2: 0.000688
+(DefaultActor pid=2839578) 
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.231056 Loss1: 0.230367 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.210081 Loss1: 0.209393 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.182395 Loss1: 0.181707 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.959985
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7215711805555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.739555 Loss1: 0.738870 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.471681 Loss1: 0.470995 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.333966 Loss1: 0.333277 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.296879 Loss1: 0.296190 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.250522 Loss1: 0.249834 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.213141 Loss1: 0.212450 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.207436 Loss1: 0.206747 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.240102 Loss1: 0.239413 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.188613 Loss1: 0.187923 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185434 Loss1: 0.184746 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.951606
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.765625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.677464 Loss1: 0.676775 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.458220 Loss1: 0.457527 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.375452 Loss1: 0.374760 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.329835 Loss1: 0.329142 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.287296 Loss1: 0.286603 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.251549 Loss1: 0.250856 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.262151 Loss1: 0.261459 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.214764 Loss1: 0.214072 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.202524 Loss1: 0.201832 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.209838 Loss1: 0.209144 Loss2: 0.000694
+(DefaultActor pid=2839578) >> Training accuracy: 0.949169
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 17:21:30,256][flwr][DEBUG] - fit_round 25 received 10 results and 0 failures
+test acc: 0.5934
+[2023-09-21 17:22:32,311][flwr][INFO] - fit progress: (25, 2.0471822914604942, {'accuracy': 0.5934}, 51033.97241008561)
+[2023-09-21 17:22:32,312][flwr][DEBUG] - evaluate_round 25: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 17:23:11,711][flwr][DEBUG] - evaluate_round 25 received 10 results and 0 failures
+[2023-09-21 17:23:11,712][flwr][DEBUG] - fit_round 26: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7365506329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.669510 Loss1: 0.668828 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.449897 Loss1: 0.449211 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.346823 Loss1: 0.346136 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.294352 Loss1: 0.293665 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.277189 Loss1: 0.276500 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.235354 Loss1: 0.234667 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.243955 Loss1: 0.243268 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.230148 Loss1: 0.229461 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.211436 Loss1: 0.210747 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.182153 Loss1: 0.181464 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.964399
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7852564102564102
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.664804 Loss1: 0.664119 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.401415 Loss1: 0.400725 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.328104 Loss1: 0.327414 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.233373 Loss1: 0.232681 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.226942 Loss1: 0.226251 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.219406 Loss1: 0.218714 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.201414 Loss1: 0.200722 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.218468 Loss1: 0.217777 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.175572 Loss1: 0.174881 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.142234 Loss1: 0.141543 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.974559
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7319078947368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.770001 Loss1: 0.769314 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.490776 Loss1: 0.490086 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.371023 Loss1: 0.370333 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.302129 Loss1: 0.301436 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.351687 Loss1: 0.350996 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.286225 Loss1: 0.285534 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.281405 Loss1: 0.280714 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.236885 Loss1: 0.236194 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.211957 Loss1: 0.211267 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.195370 Loss1: 0.194679 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.957442
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7460443037974683
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.667043 Loss1: 0.666358 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.434914 Loss1: 0.434224 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.329615 Loss1: 0.328922 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.321081 Loss1: 0.320388 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.270411 Loss1: 0.269719 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.270488 Loss1: 0.269797 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237802 Loss1: 0.237111 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.203686 Loss1: 0.202994 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.184049 Loss1: 0.183356 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.162606 Loss1: 0.161913 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.959454
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7204861111111112
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.706309 Loss1: 0.705625 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.448462 Loss1: 0.447774 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.323875 Loss1: 0.323188 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.293657 Loss1: 0.292970 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.250805 Loss1: 0.250116 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.244410 Loss1: 0.243723 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237775 Loss1: 0.237087 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.201405 Loss1: 0.200718 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.178896 Loss1: 0.178207 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.186976 Loss1: 0.186288 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.970703
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7583069620253164
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.693622 Loss1: 0.692937 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.458549 Loss1: 0.457858 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.309923 Loss1: 0.309232 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.297117 Loss1: 0.296425 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.290262 Loss1: 0.289573 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.255293 Loss1: 0.254603 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237801 Loss1: 0.237111 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.242432 Loss1: 0.241741 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.201297 Loss1: 0.200606 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.171604 Loss1: 0.170911 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.961630
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7723496835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.673661 Loss1: 0.672974 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.415174 Loss1: 0.414482 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.303973 Loss1: 0.303279 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.291243 Loss1: 0.290549 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.272274 Loss1: 0.271583 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.259890 Loss1: 0.259197 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.226039 Loss1: 0.225346 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.211092 Loss1: 0.210398 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.167100 Loss1: 0.166406 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.144354 Loss1: 0.143661 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.938093
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6855996621621622
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.728826 Loss1: 0.728140 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.427256 Loss1: 0.426567 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.401747 Loss1: 0.401057 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.264634 Loss1: 0.263946 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.231522 Loss1: 0.230834 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.239431 Loss1: 0.238742 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.271579 Loss1: 0.270890 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.248929 Loss1: 0.248240 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.172702 Loss1: 0.172012 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.181553 Loss1: 0.180864 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.950802
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7629573170731707
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.643811 Loss1: 0.643130 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.419067 Loss1: 0.418382 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.338007 Loss1: 0.337321 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.294281 Loss1: 0.293594 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.285035 Loss1: 0.284347 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.224348 Loss1: 0.223661 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.197199 Loss1: 0.196513 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.181105 Loss1: 0.180417 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.196444 Loss1: 0.195757 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.236693 Loss1: 0.236006 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.939977
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7341746794871795
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.684625 Loss1: 0.683944 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.439069 Loss1: 0.438385 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.369685 Loss1: 0.368999 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.340351 Loss1: 0.339666 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.245763 Loss1: 0.245077 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.258707 Loss1: 0.258023 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.256680 Loss1: 0.255995 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.217604 Loss1: 0.216920 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.216025 Loss1: 0.215339 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.202511 Loss1: 0.201826 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.955729
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 17:54:08,148][flwr][DEBUG] - fit_round 26 received 10 results and 0 failures
+test acc: 0.5954
+[2023-09-21 17:55:14,493][flwr][INFO] - fit progress: (26, 2.0418881324533458, {'accuracy': 0.5954}, 52996.154130037874)
+[2023-09-21 17:55:14,493][flwr][DEBUG] - evaluate_round 26: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 17:55:53,704][flwr][DEBUG] - evaluate_round 26 received 10 results and 0 failures
+[2023-09-21 17:55:53,705][flwr][DEBUG] - fit_round 27: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7587025316455697
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.681782 Loss1: 0.681097 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.431009 Loss1: 0.430319 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.353590 Loss1: 0.352900 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.287417 Loss1: 0.286727 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.234019 Loss1: 0.233328 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.248372 Loss1: 0.247681 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.208655 Loss1: 0.207963 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.201118 Loss1: 0.200427 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.167107 Loss1: 0.166414 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.196697 Loss1: 0.196006 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.955894
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7375801282051282
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.649185 Loss1: 0.648504 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.443243 Loss1: 0.442559 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.348735 Loss1: 0.348051 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.288909 Loss1: 0.288225 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.242709 Loss1: 0.242025 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.234090 Loss1: 0.233407 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.210741 Loss1: 0.210056 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.177210 Loss1: 0.176526 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.166071 Loss1: 0.165385 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185508 Loss1: 0.184823 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.944712
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7751524390243902
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.601555 Loss1: 0.600874 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.398645 Loss1: 0.397959 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.299622 Loss1: 0.298937 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.232550 Loss1: 0.231863 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.273164 Loss1: 0.272477 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201610 Loss1: 0.200921 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.185934 Loss1: 0.185245 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.191952 Loss1: 0.191264 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.180501 Loss1: 0.179813 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.198592 Loss1: 0.197903 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.948361
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7936698717948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.578123 Loss1: 0.577438 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.383055 Loss1: 0.382368 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.279032 Loss1: 0.278343 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.248131 Loss1: 0.247442 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.225122 Loss1: 0.224432 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.174657 Loss1: 0.173968 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.168909 Loss1: 0.168219 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.201064 Loss1: 0.200375 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.184009 Loss1: 0.183321 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158466 Loss1: 0.157775 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.969952
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7417534722222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.635987 Loss1: 0.635305 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.388616 Loss1: 0.387931 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.322111 Loss1: 0.321425 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.252705 Loss1: 0.252017 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.240852 Loss1: 0.240164 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.217482 Loss1: 0.216793 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.193052 Loss1: 0.192363 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.207604 Loss1: 0.206916 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.155396 Loss1: 0.154709 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.155065 Loss1: 0.154375 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.957031
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7784810126582279
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.653335 Loss1: 0.652648 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.388584 Loss1: 0.387892 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.279713 Loss1: 0.279021 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.266886 Loss1: 0.266195 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.261425 Loss1: 0.260733 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201454 Loss1: 0.200763 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.186048 Loss1: 0.185357 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.201287 Loss1: 0.200596 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.205924 Loss1: 0.205231 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.170673 Loss1: 0.169979 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.963608
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7518503289473685
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.692000 Loss1: 0.691313 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.467726 Loss1: 0.467037 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.354952 Loss1: 0.354265 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.304963 Loss1: 0.304274 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.273744 Loss1: 0.273055 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.257777 Loss1: 0.257088 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.253406 Loss1: 0.252718 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.258352 Loss1: 0.257663 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.223508 Loss1: 0.222819 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.194444 Loss1: 0.193755 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.946752
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7389240506329114
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.649446 Loss1: 0.648761 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.413377 Loss1: 0.412687 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.338940 Loss1: 0.338251 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.283006 Loss1: 0.282316 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.206884 Loss1: 0.206196 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.217038 Loss1: 0.216348 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.215413 Loss1: 0.214722 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.232612 Loss1: 0.231923 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158354 Loss1: 0.157664 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.154161 Loss1: 0.153473 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.966772
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6965793918918919
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.734338 Loss1: 0.733653 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.449680 Loss1: 0.448989 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.323263 Loss1: 0.322572 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.294686 Loss1: 0.293996 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.247183 Loss1: 0.246493 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.194697 Loss1: 0.194005 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.223873 Loss1: 0.223182 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.185619 Loss1: 0.184927 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.201469 Loss1: 0.200777 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.154650 Loss1: 0.153959 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.967061
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7478243670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.663665 Loss1: 0.662982 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.440147 Loss1: 0.439461 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.335647 Loss1: 0.334960 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.244528 Loss1: 0.243842 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.189579 Loss1: 0.188893 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.225017 Loss1: 0.224330 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.236503 Loss1: 0.235818 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.214839 Loss1: 0.214151 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.200212 Loss1: 0.199525 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185843 Loss1: 0.185157 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.946005
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 18:25:30,930][flwr][DEBUG] - fit_round 27 received 10 results and 0 failures
+test acc: 0.5962
+[2023-09-21 18:26:34,274][flwr][INFO] - fit progress: (27, 2.0698537910327364, {'accuracy': 0.5962}, 54875.935479042586)
+[2023-09-21 18:26:34,275][flwr][DEBUG] - evaluate_round 27: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 18:27:12,296][flwr][DEBUG] - evaluate_round 27 received 10 results and 0 failures
+[2023-09-21 18:27:12,297][flwr][DEBUG] - fit_round 28: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7693829113924051
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.625487 Loss1: 0.624802 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.398492 Loss1: 0.397803 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.336314 Loss1: 0.335624 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.272848 Loss1: 0.272159 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.249219 Loss1: 0.248531 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.247414 Loss1: 0.246724 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.245726 Loss1: 0.245035 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.194081 Loss1: 0.193390 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.174235 Loss1: 0.173545 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.205647 Loss1: 0.204956 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.960641
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7010135135135135
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.692299 Loss1: 0.691613 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.415334 Loss1: 0.414645 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.306172 Loss1: 0.305482 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.267952 Loss1: 0.267262 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.259115 Loss1: 0.258425 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.213631 Loss1: 0.212941 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.184821 Loss1: 0.184132 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.199693 Loss1: 0.199003 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.182766 Loss1: 0.182077 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.173181 Loss1: 0.172491 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.971706
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7470332278481012
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.590567 Loss1: 0.589885 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.368775 Loss1: 0.368090 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.295755 Loss1: 0.295070 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.267856 Loss1: 0.267171 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.265669 Loss1: 0.264983 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.228933 Loss1: 0.228246 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.188371 Loss1: 0.187685 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.175727 Loss1: 0.175041 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.189948 Loss1: 0.189262 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158662 Loss1: 0.157976 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.963014
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7768673780487805
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.614544 Loss1: 0.613861 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.394620 Loss1: 0.393935 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.292628 Loss1: 0.291942 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.244826 Loss1: 0.244141 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.216590 Loss1: 0.215903 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.194729 Loss1: 0.194042 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.207465 Loss1: 0.206778 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.206340 Loss1: 0.205653 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.198740 Loss1: 0.198053 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.176457 Loss1: 0.175770 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.955030
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7852056962025317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.605125 Loss1: 0.604436 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.411302 Loss1: 0.410610 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.307075 Loss1: 0.306382 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.238172 Loss1: 0.237480 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.230838 Loss1: 0.230144 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.222069 Loss1: 0.221375 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.199689 Loss1: 0.198996 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.195274 Loss1: 0.194581 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.153970 Loss1: 0.153277 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.133404 Loss1: 0.132710 Loss2: 0.000694
+(DefaultActor pid=2839578) >> Training accuracy: 0.976068
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7495659722222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.651949 Loss1: 0.651265 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.407713 Loss1: 0.407027 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.285877 Loss1: 0.285189 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.253701 Loss1: 0.253013 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.245600 Loss1: 0.244913 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201028 Loss1: 0.200340 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.170814 Loss1: 0.170125 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.155567 Loss1: 0.154878 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161770 Loss1: 0.161081 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.173849 Loss1: 0.173160 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.955946
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7478243670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.612782 Loss1: 0.612097 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.358741 Loss1: 0.358052 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.286460 Loss1: 0.285770 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.244081 Loss1: 0.243393 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.254408 Loss1: 0.253716 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.209746 Loss1: 0.209056 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.212843 Loss1: 0.212155 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.180765 Loss1: 0.180077 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.180519 Loss1: 0.179830 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.166627 Loss1: 0.165937 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.971717
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7952724358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.555943 Loss1: 0.555258 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.349070 Loss1: 0.348381 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.299922 Loss1: 0.299234 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.218985 Loss1: 0.218295 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.205310 Loss1: 0.204620 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.211645 Loss1: 0.210957 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.161473 Loss1: 0.160782 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.187475 Loss1: 0.186783 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.180604 Loss1: 0.179911 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.134798 Loss1: 0.134107 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.977163
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7530838815789473
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.729191 Loss1: 0.728503 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.437274 Loss1: 0.436582 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.346213 Loss1: 0.345522 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.308080 Loss1: 0.307390 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.236192 Loss1: 0.235500 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.231173 Loss1: 0.230481 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.202347 Loss1: 0.201656 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.207113 Loss1: 0.206420 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.178825 Loss1: 0.178133 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.200228 Loss1: 0.199537 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.957442
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7552083333333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.644463 Loss1: 0.643783 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.419119 Loss1: 0.418435 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.312434 Loss1: 0.311750 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.245112 Loss1: 0.244426 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.225883 Loss1: 0.225197 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.203500 Loss1: 0.202815 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.210504 Loss1: 0.209820 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.234559 Loss1: 0.233874 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.182646 Loss1: 0.181961 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158714 Loss1: 0.158029 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.963942
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 18:56:41,705][flwr][DEBUG] - fit_round 28 received 10 results and 0 failures
+test acc: 0.6041
+[2023-09-21 18:57:50,305][flwr][INFO] - fit progress: (28, 2.0548813068828644, {'accuracy': 0.6041}, 56751.96644514892)
+[2023-09-21 18:57:50,306][flwr][DEBUG] - evaluate_round 28: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 18:58:30,426][flwr][DEBUG] - evaluate_round 28 received 10 results and 0 failures
+[2023-09-21 18:58:30,427][flwr][DEBUG] - fit_round 29: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7974466463414634
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.499517 Loss1: 0.498836 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.408917 Loss1: 0.408232 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.303713 Loss1: 0.303027 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.240544 Loss1: 0.239856 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.200556 Loss1: 0.199869 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.198138 Loss1: 0.197451 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.186923 Loss1: 0.186237 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.169388 Loss1: 0.168701 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.178554 Loss1: 0.177867 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185125 Loss1: 0.184439 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.963224
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.796875
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.552599 Loss1: 0.551914 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.335998 Loss1: 0.335308 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.289382 Loss1: 0.288688 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.251998 Loss1: 0.251305 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.230438 Loss1: 0.229747 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.234804 Loss1: 0.234111 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.175976 Loss1: 0.175284 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.167702 Loss1: 0.167012 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.145913 Loss1: 0.145221 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.152713 Loss1: 0.152020 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.964992
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7620192307692307
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.611225 Loss1: 0.610542 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.381223 Loss1: 0.380538 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.305218 Loss1: 0.304533 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.254719 Loss1: 0.254033 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.228027 Loss1: 0.227342 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.184217 Loss1: 0.183531 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.191470 Loss1: 0.190785 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.193570 Loss1: 0.192885 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.152795 Loss1: 0.152108 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158597 Loss1: 0.157911 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.963141
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7452256944444444
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.603446 Loss1: 0.602762 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.417684 Loss1: 0.416998 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.249738 Loss1: 0.249052 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.237182 Loss1: 0.236494 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.167919 Loss1: 0.167231 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.202112 Loss1: 0.201424 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.165302 Loss1: 0.164614 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.161969 Loss1: 0.161282 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.148964 Loss1: 0.148277 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.141120 Loss1: 0.140433 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.967231
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.764391447368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.663510 Loss1: 0.662823 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.407834 Loss1: 0.407143 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.301532 Loss1: 0.300841 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.276905 Loss1: 0.276215 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.274562 Loss1: 0.273870 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.268013 Loss1: 0.267321 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.211488 Loss1: 0.210797 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.151737 Loss1: 0.151046 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.163795 Loss1: 0.163101 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.156869 Loss1: 0.156176 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.966900
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7644382911392406
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.598541 Loss1: 0.597855 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.374814 Loss1: 0.374126 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.278221 Loss1: 0.277530 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.263356 Loss1: 0.262666 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.187177 Loss1: 0.186486 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.160904 Loss1: 0.160213 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.159049 Loss1: 0.158359 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.146854 Loss1: 0.146164 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.177581 Loss1: 0.176890 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185451 Loss1: 0.184762 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.954509
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8062900641025641
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.584494 Loss1: 0.583809 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.346588 Loss1: 0.345897 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.245786 Loss1: 0.245095 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.242348 Loss1: 0.241657 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.178784 Loss1: 0.178092 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.212423 Loss1: 0.211731 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.205808 Loss1: 0.205118 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.170764 Loss1: 0.170074 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.155943 Loss1: 0.155251 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115192 Loss1: 0.114500 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.981971
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7848101265822784
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.619163 Loss1: 0.618478 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.381133 Loss1: 0.380445 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.334213 Loss1: 0.333523 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.238292 Loss1: 0.237600 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.225693 Loss1: 0.225002 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.215145 Loss1: 0.214455 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.174892 Loss1: 0.174201 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.161453 Loss1: 0.160761 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.175724 Loss1: 0.175035 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158866 Loss1: 0.158176 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.973892
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7058699324324325
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.611132 Loss1: 0.610447 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.381443 Loss1: 0.380754 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.289101 Loss1: 0.288411 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.273836 Loss1: 0.273147 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.242553 Loss1: 0.241861 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.194221 Loss1: 0.193531 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.198482 Loss1: 0.197790 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.177771 Loss1: 0.177079 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.154609 Loss1: 0.153918 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.153905 Loss1: 0.153214 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.964316
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7630537974683544
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.600840 Loss1: 0.600159 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.379274 Loss1: 0.378589 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.254920 Loss1: 0.254236 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.229998 Loss1: 0.229312 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.212197 Loss1: 0.211512 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.182872 Loss1: 0.182186 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.196518 Loss1: 0.195832 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.144197 Loss1: 0.143510 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.141239 Loss1: 0.140552 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.186518 Loss1: 0.185832 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.963410
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 19:28:49,825][flwr][DEBUG] - fit_round 29 received 10 results and 0 failures
+test acc: 0.6056
+[2023-09-21 19:29:58,360][flwr][INFO] - fit progress: (29, 2.0496039061119764, {'accuracy': 0.6056}, 58680.02112694876)
+[2023-09-21 19:29:58,360][flwr][DEBUG] - evaluate_round 29: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 19:30:37,618][flwr][DEBUG] - evaluate_round 29 received 10 results and 0 failures
+[2023-09-21 19:30:37,622][flwr][DEBUG] - fit_round 30: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7567274305555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.619163 Loss1: 0.618483 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.354422 Loss1: 0.353737 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.250100 Loss1: 0.249415 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.210168 Loss1: 0.209484 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.237348 Loss1: 0.236661 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.183717 Loss1: 0.183033 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.134518 Loss1: 0.133831 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.130511 Loss1: 0.129825 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.141262 Loss1: 0.140574 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.171775 Loss1: 0.171088 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.963108
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.772745253164557
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.537170 Loss1: 0.536486 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.320372 Loss1: 0.319684 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.264764 Loss1: 0.264077 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.268226 Loss1: 0.267537 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.203814 Loss1: 0.203124 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.203523 Loss1: 0.202833 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.186579 Loss1: 0.185890 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.168121 Loss1: 0.167431 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.177773 Loss1: 0.177082 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.168943 Loss1: 0.168255 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.964597
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7923018292682927
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.536380 Loss1: 0.535699 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.303742 Loss1: 0.303056 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.260898 Loss1: 0.260212 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.254468 Loss1: 0.253781 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.211443 Loss1: 0.210757 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.157854 Loss1: 0.157167 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.167154 Loss1: 0.166466 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.160240 Loss1: 0.159551 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161027 Loss1: 0.160339 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.142955 Loss1: 0.142268 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.969703
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7225506756756757
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.634419 Loss1: 0.633736 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.409264 Loss1: 0.408576 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.266493 Loss1: 0.265806 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.205440 Loss1: 0.204752 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.199161 Loss1: 0.198473 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.171140 Loss1: 0.170452 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164213 Loss1: 0.163524 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.155763 Loss1: 0.155073 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.139570 Loss1: 0.138878 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.116279 Loss1: 0.115590 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.983319
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7654246794871795
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.576078 Loss1: 0.575401 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.349580 Loss1: 0.348899 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.248985 Loss1: 0.248303 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.246659 Loss1: 0.245976 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.186492 Loss1: 0.185807 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.183709 Loss1: 0.183024 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.195294 Loss1: 0.194611 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.194443 Loss1: 0.193760 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.163554 Loss1: 0.162871 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.151310 Loss1: 0.150626 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.952724
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.790743670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.588646 Loss1: 0.587962 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.362225 Loss1: 0.361536 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.271961 Loss1: 0.271273 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.202422 Loss1: 0.201731 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182037 Loss1: 0.181346 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.225709 Loss1: 0.225018 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.193616 Loss1: 0.192924 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.172368 Loss1: 0.171677 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.142571 Loss1: 0.141879 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.151974 Loss1: 0.151284 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.955301
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.758504746835443
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.598116 Loss1: 0.597434 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.358994 Loss1: 0.358310 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.258907 Loss1: 0.258221 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.227296 Loss1: 0.226611 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.179692 Loss1: 0.179006 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.208747 Loss1: 0.208061 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.204248 Loss1: 0.203562 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.212948 Loss1: 0.212263 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.184698 Loss1: 0.184011 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.161763 Loss1: 0.161076 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.961234
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.799248417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.537271 Loss1: 0.536587 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.340708 Loss1: 0.340019 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.272131 Loss1: 0.271441 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.237960 Loss1: 0.237268 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.219939 Loss1: 0.219247 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201063 Loss1: 0.200371 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.182279 Loss1: 0.181587 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.151051 Loss1: 0.150360 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.162107 Loss1: 0.161415 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.129474 Loss1: 0.128784 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.957081
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8145032051282052
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.515882 Loss1: 0.515197 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.317556 Loss1: 0.316867 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.210685 Loss1: 0.209995 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.215758 Loss1: 0.215066 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.198075 Loss1: 0.197387 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.179987 Loss1: 0.179295 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.151450 Loss1: 0.150758 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.137068 Loss1: 0.136377 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.132534 Loss1: 0.131843 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.125621 Loss1: 0.124929 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.970152
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7598684210526315
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.604328 Loss1: 0.603642 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.402449 Loss1: 0.401757 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.306376 Loss1: 0.305684 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.233510 Loss1: 0.232818 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.222988 Loss1: 0.222296 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.200421 Loss1: 0.199730 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.227939 Loss1: 0.227249 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.216987 Loss1: 0.216297 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.181167 Loss1: 0.180478 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.170629 Loss1: 0.169940 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.975329
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 20:01:11,087][flwr][DEBUG] - fit_round 30 received 10 results and 0 failures
+test acc: 0.6064
+[2023-09-21 20:02:21,205][flwr][INFO] - fit progress: (30, 2.0852254760531954, {'accuracy': 0.6064}, 60622.86622950295)
+[2023-09-21 20:02:21,206][flwr][DEBUG] - evaluate_round 30: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 20:03:00,864][flwr][DEBUG] - evaluate_round 30 received 10 results and 0 failures
+[2023-09-21 20:03:00,866][flwr][DEBUG] - fit_round 31: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7846123417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.523108 Loss1: 0.522425 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.325361 Loss1: 0.324673 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.235289 Loss1: 0.234599 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.253728 Loss1: 0.253039 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.214257 Loss1: 0.213566 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.186834 Loss1: 0.186144 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.200372 Loss1: 0.199681 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.166633 Loss1: 0.165942 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.127651 Loss1: 0.126962 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.135003 Loss1: 0.134312 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.969739
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8048780487804879
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.525691 Loss1: 0.525013 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.336854 Loss1: 0.336170 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.248647 Loss1: 0.247964 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.214737 Loss1: 0.214053 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.174918 Loss1: 0.174232 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.180904 Loss1: 0.180218 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164346 Loss1: 0.163662 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.167090 Loss1: 0.166404 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.154995 Loss1: 0.154309 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.163444 Loss1: 0.162757 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.979040
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.770371835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.517359 Loss1: 0.516679 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.313782 Loss1: 0.313098 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.244133 Loss1: 0.243448 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.265762 Loss1: 0.265076 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.186522 Loss1: 0.185835 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.195407 Loss1: 0.194722 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.160318 Loss1: 0.159631 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.146082 Loss1: 0.145395 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.148072 Loss1: 0.147385 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.126008 Loss1: 0.125319 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.980222
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7706330128205128
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.533603 Loss1: 0.532924 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.337454 Loss1: 0.336771 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.258172 Loss1: 0.257488 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.236208 Loss1: 0.235524 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.207066 Loss1: 0.206382 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.191026 Loss1: 0.190339 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.174095 Loss1: 0.173410 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.154561 Loss1: 0.153875 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.179839 Loss1: 0.179152 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.155330 Loss1: 0.154644 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.964343
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7231841216216216
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.588376 Loss1: 0.587689 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.338421 Loss1: 0.337732 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.267117 Loss1: 0.266427 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.223808 Loss1: 0.223118 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.218573 Loss1: 0.217883 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.175265 Loss1: 0.174574 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.154397 Loss1: 0.153707 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.125324 Loss1: 0.124632 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.141807 Loss1: 0.141116 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.109875 Loss1: 0.109185 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.971284
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.759765625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.574755 Loss1: 0.574074 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.301825 Loss1: 0.301140 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.240789 Loss1: 0.240102 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.226338 Loss1: 0.225651 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.168455 Loss1: 0.167767 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.168387 Loss1: 0.167700 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.145961 Loss1: 0.145274 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.153771 Loss1: 0.153084 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.153999 Loss1: 0.153313 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.123707 Loss1: 0.123019 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.968967
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7984572784810127
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.503111 Loss1: 0.502426 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.340985 Loss1: 0.340298 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.231043 Loss1: 0.230354 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.225408 Loss1: 0.224717 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.187803 Loss1: 0.187114 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.152364 Loss1: 0.151673 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.165281 Loss1: 0.164590 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.173246 Loss1: 0.172554 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161406 Loss1: 0.160715 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.148087 Loss1: 0.147397 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.967168
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7845394736842105
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.606801 Loss1: 0.606115 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.361901 Loss1: 0.361209 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.283786 Loss1: 0.283094 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.245166 Loss1: 0.244475 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.229275 Loss1: 0.228583 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201752 Loss1: 0.201061 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.200653 Loss1: 0.199962 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.193363 Loss1: 0.192671 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.156758 Loss1: 0.156066 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.147954 Loss1: 0.147265 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.961965
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8079509493670886
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.493360 Loss1: 0.492674 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.336259 Loss1: 0.335568 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.283828 Loss1: 0.283136 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.199340 Loss1: 0.198648 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.179063 Loss1: 0.178372 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.194192 Loss1: 0.193500 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.162154 Loss1: 0.161463 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.154563 Loss1: 0.153873 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.148478 Loss1: 0.147787 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.124265 Loss1: 0.123573 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.980024
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.819511217948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.479434 Loss1: 0.478748 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.354710 Loss1: 0.354019 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.239130 Loss1: 0.238439 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.200914 Loss1: 0.200222 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.160507 Loss1: 0.159816 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.171148 Loss1: 0.170455 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164997 Loss1: 0.164306 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.160128 Loss1: 0.159437 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.124878 Loss1: 0.124187 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.101679 Loss1: 0.100988 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.982772
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 20:33:46,396][flwr][DEBUG] - fit_round 31 received 10 results and 0 failures
+test acc: 0.6099
+[2023-09-21 20:34:57,564][flwr][INFO] - fit progress: (31, 2.0679767246063525, {'accuracy': 0.6099}, 62579.225837967824)
+[2023-09-21 20:34:57,565][flwr][DEBUG] - evaluate_round 31: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 20:35:37,789][flwr][DEBUG] - evaluate_round 31 received 10 results and 0 failures
+[2023-09-21 20:35:37,789][flwr][DEBUG] - fit_round 32: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7863924050632911
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.456769 Loss1: 0.456086 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.304253 Loss1: 0.303566 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.260574 Loss1: 0.259888 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.253337 Loss1: 0.252649 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.218889 Loss1: 0.218201 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.190845 Loss1: 0.190154 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.153912 Loss1: 0.153221 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.148447 Loss1: 0.147756 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158999 Loss1: 0.158309 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.150296 Loss1: 0.149606 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.963212
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7856012658227848
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.539172 Loss1: 0.538489 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.333032 Loss1: 0.332347 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.242573 Loss1: 0.241885 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.179616 Loss1: 0.178930 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.195439 Loss1: 0.194753 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.145121 Loss1: 0.144434 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.141976 Loss1: 0.141290 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.143698 Loss1: 0.143011 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.159499 Loss1: 0.158811 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.155614 Loss1: 0.154927 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.977057
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7654079861111112
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.539950 Loss1: 0.539266 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.355104 Loss1: 0.354417 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.238524 Loss1: 0.237836 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.204626 Loss1: 0.203938 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169661 Loss1: 0.168972 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.173286 Loss1: 0.172598 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.135333 Loss1: 0.134644 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.131767 Loss1: 0.131080 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.105979 Loss1: 0.105290 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.114558 Loss1: 0.113870 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.978299
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7931743421052632
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.577771 Loss1: 0.577085 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.360149 Loss1: 0.359458 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.273298 Loss1: 0.272606 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.225071 Loss1: 0.224379 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.174889 Loss1: 0.174197 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.167708 Loss1: 0.167018 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.174263 Loss1: 0.173571 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.160056 Loss1: 0.159364 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.139197 Loss1: 0.138505 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.171915 Loss1: 0.171223 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.967516
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8092606707317073
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.457395 Loss1: 0.456715 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.295522 Loss1: 0.294837 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.249686 Loss1: 0.249001 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.201236 Loss1: 0.200550 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.217139 Loss1: 0.216451 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.182533 Loss1: 0.181846 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.192310 Loss1: 0.191623 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.122003 Loss1: 0.121316 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.141275 Loss1: 0.140589 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128147 Loss1: 0.127460 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.979421
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7426097972972973
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.612818 Loss1: 0.612131 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.305374 Loss1: 0.304683 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.282880 Loss1: 0.282189 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.260701 Loss1: 0.260010 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.196549 Loss1: 0.195859 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.186041 Loss1: 0.185348 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.142509 Loss1: 0.141818 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.123152 Loss1: 0.122461 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.143999 Loss1: 0.143308 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.135691 Loss1: 0.135001 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.972551
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8214003164556962
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.464050 Loss1: 0.463367 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.264900 Loss1: 0.264212 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.207211 Loss1: 0.206524 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.212861 Loss1: 0.212171 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.193280 Loss1: 0.192591 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.175488 Loss1: 0.174797 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.153896 Loss1: 0.153204 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.172732 Loss1: 0.172041 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161750 Loss1: 0.161059 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.118550 Loss1: 0.117858 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.967761
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8117088607594937
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.478749 Loss1: 0.478063 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.343766 Loss1: 0.343077 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.251952 Loss1: 0.251262 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.217741 Loss1: 0.217051 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.206281 Loss1: 0.205591 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.189547 Loss1: 0.188857 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.152778 Loss1: 0.152088 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.146986 Loss1: 0.146295 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.139796 Loss1: 0.139104 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.157888 Loss1: 0.157197 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.964201
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8271233974358975
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.471295 Loss1: 0.470610 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.279778 Loss1: 0.279090 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.217228 Loss1: 0.216539 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.216527 Loss1: 0.215838 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169727 Loss1: 0.169037 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.136011 Loss1: 0.135322 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.148998 Loss1: 0.148308 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.140232 Loss1: 0.139542 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.129966 Loss1: 0.129275 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108679 Loss1: 0.107987 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.981971
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7938701923076923
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.490256 Loss1: 0.489577 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.320746 Loss1: 0.320063 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.260920 Loss1: 0.260236 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.219715 Loss1: 0.219029 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.195064 Loss1: 0.194379 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.196975 Loss1: 0.196290 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.159815 Loss1: 0.159131 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.163701 Loss1: 0.163017 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.167907 Loss1: 0.167223 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.143156 Loss1: 0.142472 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.966346
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 21:05:53,664][flwr][DEBUG] - fit_round 32 received 10 results and 0 failures
+test acc: 0.613
+[2023-09-21 21:06:58,207][flwr][INFO] - fit progress: (32, 2.0636839933288744, {'accuracy': 0.613}, 64499.868195127696)
+[2023-09-21 21:06:58,207][flwr][DEBUG] - evaluate_round 32: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 21:07:36,400][flwr][DEBUG] - evaluate_round 32 received 10 results and 0 failures
+[2023-09-21 21:07:36,401][flwr][DEBUG] - fit_round 33: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7402871621621622
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.542243 Loss1: 0.541556 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.328989 Loss1: 0.328299 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.242510 Loss1: 0.241820 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.178748 Loss1: 0.178057 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169804 Loss1: 0.169111 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.169070 Loss1: 0.168378 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.137944 Loss1: 0.137252 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.139542 Loss1: 0.138851 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.132137 Loss1: 0.131445 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.156518 Loss1: 0.155827 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.969595
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7923259493670886
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.504836 Loss1: 0.504151 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.266541 Loss1: 0.265852 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.231565 Loss1: 0.230877 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.241108 Loss1: 0.240420 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.223859 Loss1: 0.223170 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.166931 Loss1: 0.166242 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.139561 Loss1: 0.138872 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114380 Loss1: 0.113690 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110902 Loss1: 0.110212 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.119918 Loss1: 0.119228 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.972903
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8125
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.474601 Loss1: 0.473918 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.344254 Loss1: 0.343566 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.217006 Loss1: 0.216318 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.198807 Loss1: 0.198118 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.174061 Loss1: 0.173373 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.142375 Loss1: 0.141685 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.153248 Loss1: 0.152560 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.173363 Loss1: 0.172674 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.138275 Loss1: 0.137586 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.117487 Loss1: 0.116798 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.972310
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7979029605263158
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.544588 Loss1: 0.543899 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.323947 Loss1: 0.323254 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.279583 Loss1: 0.278891 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.229237 Loss1: 0.228546 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.198557 Loss1: 0.197867 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.181877 Loss1: 0.181185 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.206158 Loss1: 0.205467 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.189943 Loss1: 0.189251 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.178450 Loss1: 0.177756 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.130658 Loss1: 0.129965 Loss2: 0.000694
+(DefaultActor pid=2839578) >> Training accuracy: 0.982319
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8225990853658537
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.433366 Loss1: 0.432686 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.307178 Loss1: 0.306493 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.223623 Loss1: 0.222937 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.196286 Loss1: 0.195600 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169593 Loss1: 0.168906 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.153869 Loss1: 0.153182 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.158135 Loss1: 0.157448 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.141926 Loss1: 0.141240 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.137936 Loss1: 0.137249 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.136687 Loss1: 0.135999 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.975800
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8245648734177216
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.431125 Loss1: 0.430440 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.294704 Loss1: 0.294014 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.224906 Loss1: 0.224216 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.184543 Loss1: 0.183851 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.128496 Loss1: 0.127804 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.156242 Loss1: 0.155549 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.165521 Loss1: 0.164829 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114816 Loss1: 0.114124 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.140430 Loss1: 0.139737 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.141702 Loss1: 0.141010 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.973101
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8371394230769231
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.454715 Loss1: 0.454032 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.255957 Loss1: 0.255268 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.180039 Loss1: 0.179352 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.165767 Loss1: 0.165077 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.165454 Loss1: 0.164765 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.153813 Loss1: 0.153124 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164836 Loss1: 0.164147 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.164970 Loss1: 0.164280 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.135895 Loss1: 0.135206 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.105258 Loss1: 0.104568 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.983373
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8012820512820513
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.477950 Loss1: 0.477271 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.270843 Loss1: 0.270161 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.259757 Loss1: 0.259073 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.228074 Loss1: 0.227388 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.197695 Loss1: 0.197010 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.184945 Loss1: 0.184261 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.165779 Loss1: 0.165091 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.157265 Loss1: 0.156577 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.140863 Loss1: 0.140178 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.134003 Loss1: 0.133319 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.977564
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.779296875
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.495948 Loss1: 0.495266 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.280192 Loss1: 0.279507 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.223413 Loss1: 0.222727 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.224361 Loss1: 0.223675 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.177909 Loss1: 0.177222 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.176326 Loss1: 0.175639 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.148369 Loss1: 0.147682 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.143496 Loss1: 0.142809 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123530 Loss1: 0.122844 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.122483 Loss1: 0.121795 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.962891
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7946993670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.459389 Loss1: 0.458708 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.307003 Loss1: 0.306317 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.221549 Loss1: 0.220864 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.165780 Loss1: 0.165092 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.176616 Loss1: 0.175929 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.164944 Loss1: 0.164256 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.163481 Loss1: 0.162793 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.150074 Loss1: 0.149385 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.129963 Loss1: 0.129273 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.125092 Loss1: 0.124404 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.964399
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 21:38:08,490][flwr][DEBUG] - fit_round 33 received 10 results and 0 failures
+test acc: 0.6145
+[2023-09-21 21:39:18,183][flwr][INFO] - fit progress: (33, 2.0933550024946657, {'accuracy': 0.6145}, 66439.84413567372)
+[2023-09-21 21:39:18,183][flwr][DEBUG] - evaluate_round 33: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 21:39:56,884][flwr][DEBUG] - evaluate_round 33 received 10 results and 0 failures
+[2023-09-21 21:39:56,885][flwr][DEBUG] - fit_round 34: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8377403846153846
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.409916 Loss1: 0.409233 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.244411 Loss1: 0.243722 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.197967 Loss1: 0.197277 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.179189 Loss1: 0.178499 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.150673 Loss1: 0.149983 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.165807 Loss1: 0.165115 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.138972 Loss1: 0.138282 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.105856 Loss1: 0.105164 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.095269 Loss1: 0.094578 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.092845 Loss1: 0.092153 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.988782
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8067434210526315
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.530683 Loss1: 0.529994 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.297934 Loss1: 0.297245 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.258433 Loss1: 0.257740 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.246386 Loss1: 0.245693 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.190398 Loss1: 0.189706 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.165247 Loss1: 0.164556 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.150552 Loss1: 0.149859 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133404 Loss1: 0.132714 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158042 Loss1: 0.157349 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115816 Loss1: 0.115123 Loss2: 0.000694
+(DefaultActor pid=2839578) >> Training accuracy: 0.974095
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7986550632911392
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.452223 Loss1: 0.451541 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.273462 Loss1: 0.272774 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.220063 Loss1: 0.219374 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.211701 Loss1: 0.211014 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.187651 Loss1: 0.186961 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.166072 Loss1: 0.165381 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.138216 Loss1: 0.137527 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.162450 Loss1: 0.161759 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161553 Loss1: 0.160863 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108909 Loss1: 0.108219 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.977650
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8325076219512195
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.418688 Loss1: 0.418004 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.267669 Loss1: 0.266984 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.204962 Loss1: 0.204277 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177176 Loss1: 0.176492 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.175536 Loss1: 0.174849 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.156842 Loss1: 0.156154 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.158999 Loss1: 0.158312 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.122170 Loss1: 0.121482 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109638 Loss1: 0.108951 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.107303 Loss1: 0.106616 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.977706
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8138844936708861
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.457006 Loss1: 0.456323 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.276227 Loss1: 0.275539 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.202918 Loss1: 0.202228 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.217902 Loss1: 0.217212 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182491 Loss1: 0.181800 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.188659 Loss1: 0.187968 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.155490 Loss1: 0.154799 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133102 Loss1: 0.132410 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.124510 Loss1: 0.123819 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.135701 Loss1: 0.135009 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.962421
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8465189873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.445745 Loss1: 0.445060 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.251985 Loss1: 0.251293 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.187483 Loss1: 0.186790 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.155108 Loss1: 0.154414 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.166183 Loss1: 0.165490 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.161270 Loss1: 0.160577 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.169390 Loss1: 0.168698 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.141415 Loss1: 0.140723 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.154108 Loss1: 0.153417 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.124881 Loss1: 0.124187 Loss2: 0.000694
+(DefaultActor pid=2839578) >> Training accuracy: 0.977057
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7836371527777778
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.480151 Loss1: 0.479468 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.304508 Loss1: 0.303822 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.224774 Loss1: 0.224088 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.170221 Loss1: 0.169533 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.155973 Loss1: 0.155285 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.161961 Loss1: 0.161273 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.141921 Loss1: 0.141234 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121965 Loss1: 0.121278 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.155334 Loss1: 0.154646 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.133664 Loss1: 0.132976 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.977214
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7459881756756757
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.519034 Loss1: 0.518347 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.294941 Loss1: 0.294252 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.185373 Loss1: 0.184683 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.215691 Loss1: 0.215001 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.180338 Loss1: 0.179647 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.160067 Loss1: 0.159376 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.150670 Loss1: 0.149979 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.115107 Loss1: 0.114418 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.150096 Loss1: 0.149405 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.127203 Loss1: 0.126511 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.961782
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8006810897435898
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.466680 Loss1: 0.465999 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.291337 Loss1: 0.290651 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.212643 Loss1: 0.211958 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.200803 Loss1: 0.200117 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.175401 Loss1: 0.174716 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.143558 Loss1: 0.142872 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.159303 Loss1: 0.158618 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.149873 Loss1: 0.149189 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158068 Loss1: 0.157382 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.127541 Loss1: 0.126856 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.974359
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7994462025316456
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.476988 Loss1: 0.476306 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.267038 Loss1: 0.266352 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.201137 Loss1: 0.200451 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.182875 Loss1: 0.182191 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.178174 Loss1: 0.177487 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.162445 Loss1: 0.161758 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164695 Loss1: 0.164008 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.164206 Loss1: 0.163519 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158898 Loss1: 0.158212 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.140211 Loss1: 0.139525 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.972310
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 22:11:13,109][flwr][DEBUG] - fit_round 34 received 10 results and 0 failures
+test acc: 0.6151
+[2023-09-21 22:31:44,139][flwr][INFO] - fit progress: (34, 2.083816295043348, {'accuracy': 0.6151}, 69585.80032487493)
+[2023-09-21 22:31:44,139][flwr][DEBUG] - evaluate_round 34: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 22:32:23,132][flwr][DEBUG] - evaluate_round 34 received 10 results and 0 failures
+[2023-09-21 22:32:23,133][flwr][DEBUG] - fit_round 35: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8275316455696202
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.438230 Loss1: 0.437544 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.241562 Loss1: 0.240875 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.260274 Loss1: 0.259584 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.185402 Loss1: 0.184713 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169147 Loss1: 0.168457 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.164841 Loss1: 0.164150 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.144593 Loss1: 0.143903 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.115677 Loss1: 0.114986 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.102390 Loss1: 0.101698 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.130016 Loss1: 0.129325 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.966772
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8085443037974683
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.404777 Loss1: 0.404094 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.261682 Loss1: 0.260997 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.214479 Loss1: 0.213793 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.176964 Loss1: 0.176277 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182937 Loss1: 0.182250 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.157195 Loss1: 0.156507 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.138018 Loss1: 0.137330 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133634 Loss1: 0.132946 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.138450 Loss1: 0.137761 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.150715 Loss1: 0.150027 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.963212
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8047863924050633
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.450847 Loss1: 0.450164 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.264767 Loss1: 0.264080 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.217181 Loss1: 0.216493 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.176641 Loss1: 0.175954 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.173077 Loss1: 0.172387 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.177835 Loss1: 0.177146 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.116246 Loss1: 0.115556 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.128948 Loss1: 0.128258 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.134194 Loss1: 0.133506 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.141055 Loss1: 0.140365 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.967168
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7951388888888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.471486 Loss1: 0.470803 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.327426 Loss1: 0.326740 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.230257 Loss1: 0.229570 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.173076 Loss1: 0.172388 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.138595 Loss1: 0.137908 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.122002 Loss1: 0.121315 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108659 Loss1: 0.107970 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.111391 Loss1: 0.110703 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133210 Loss1: 0.132521 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115533 Loss1: 0.114844 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.972222
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8072916666666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.470692 Loss1: 0.470011 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.274626 Loss1: 0.273941 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.220483 Loss1: 0.219799 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.180690 Loss1: 0.180007 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.161003 Loss1: 0.160319 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.167979 Loss1: 0.167294 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.142323 Loss1: 0.141638 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.134915 Loss1: 0.134228 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.160491 Loss1: 0.159806 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.166348 Loss1: 0.165662 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.952524
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8057154605263158
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.495921 Loss1: 0.495235 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.299973 Loss1: 0.299282 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.235595 Loss1: 0.234904 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.204465 Loss1: 0.203775 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.167258 Loss1: 0.166566 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.150121 Loss1: 0.149429 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.147471 Loss1: 0.146780 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.111420 Loss1: 0.110728 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.127612 Loss1: 0.126922 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.114024 Loss1: 0.113333 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.976562
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8455300632911392
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.398409 Loss1: 0.397723 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.266191 Loss1: 0.265500 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190118 Loss1: 0.189427 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.181569 Loss1: 0.180877 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.160593 Loss1: 0.159901 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123160 Loss1: 0.122469 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115638 Loss1: 0.114945 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.154207 Loss1: 0.153516 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122585 Loss1: 0.121894 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.133263 Loss1: 0.132571 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.979826
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8267911585365854
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.387204 Loss1: 0.386522 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.264668 Loss1: 0.263983 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.233994 Loss1: 0.233310 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.195024 Loss1: 0.194339 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182445 Loss1: 0.181758 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.148046 Loss1: 0.147359 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.158863 Loss1: 0.158177 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.140713 Loss1: 0.140026 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.140433 Loss1: 0.139747 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.120064 Loss1: 0.119377 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.969322
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7447212837837838
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.443909 Loss1: 0.443224 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.298731 Loss1: 0.298044 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.240602 Loss1: 0.239914 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.196052 Loss1: 0.195362 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.181139 Loss1: 0.180450 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.172744 Loss1: 0.172055 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.171533 Loss1: 0.170843 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121226 Loss1: 0.120537 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.130068 Loss1: 0.129379 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.134423 Loss1: 0.133732 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.971495
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8461538461538461
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.380708 Loss1: 0.380025 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.235765 Loss1: 0.235076 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190585 Loss1: 0.189893 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.185309 Loss1: 0.184620 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.143665 Loss1: 0.142976 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.132314 Loss1: 0.131623 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115259 Loss1: 0.114568 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114924 Loss1: 0.114232 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.112534 Loss1: 0.111843 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090379 Loss1: 0.089688 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.983974
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 23:02:40,674][flwr][DEBUG] - fit_round 35 received 10 results and 0 failures
+test acc: 0.6162
+[2023-09-21 23:03:28,980][flwr][INFO] - fit progress: (35, 2.1125483806140886, {'accuracy': 0.6162}, 71490.64193736762)
+[2023-09-21 23:03:28,981][flwr][DEBUG] - evaluate_round 35: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 23:04:05,841][flwr][DEBUG] - evaluate_round 35 received 10 results and 0 failures
+[2023-09-21 23:04:05,842][flwr][DEBUG] - fit_round 36: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8539663461538461
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.363793 Loss1: 0.363109 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.258992 Loss1: 0.258304 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.182451 Loss1: 0.181760 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.141931 Loss1: 0.141241 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.161380 Loss1: 0.160689 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.121359 Loss1: 0.120670 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.109682 Loss1: 0.108992 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.112061 Loss1: 0.111369 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097658 Loss1: 0.096968 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104829 Loss1: 0.104138 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.983774
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8139391447368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.475478 Loss1: 0.474791 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.270133 Loss1: 0.269442 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.246163 Loss1: 0.245473 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.197857 Loss1: 0.197166 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.173443 Loss1: 0.172752 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.134408 Loss1: 0.133717 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.119257 Loss1: 0.118568 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.120187 Loss1: 0.119498 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122910 Loss1: 0.122219 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.131650 Loss1: 0.130960 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.977796
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8061708860759493
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.410781 Loss1: 0.410098 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.257303 Loss1: 0.256614 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.227816 Loss1: 0.227129 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.182781 Loss1: 0.182091 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182073 Loss1: 0.181385 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.120316 Loss1: 0.119628 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.144927 Loss1: 0.144237 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.123623 Loss1: 0.122933 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.116142 Loss1: 0.115453 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.122907 Loss1: 0.122216 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.979430
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7964409722222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.492339 Loss1: 0.491656 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.285524 Loss1: 0.284836 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.170453 Loss1: 0.169766 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.148610 Loss1: 0.147923 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.132779 Loss1: 0.132092 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.127587 Loss1: 0.126899 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.120834 Loss1: 0.120146 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.106523 Loss1: 0.105834 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114751 Loss1: 0.114063 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.126275 Loss1: 0.125587 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.971571
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7599239864864865
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.481852 Loss1: 0.481166 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.263919 Loss1: 0.263229 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.226016 Loss1: 0.225326 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.144747 Loss1: 0.144057 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.157827 Loss1: 0.157136 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133248 Loss1: 0.132558 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.134697 Loss1: 0.134005 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.127542 Loss1: 0.126852 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133827 Loss1: 0.133136 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108603 Loss1: 0.107912 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.983742
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8482990506329114
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.385457 Loss1: 0.384773 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.255474 Loss1: 0.254785 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.207758 Loss1: 0.207068 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.188585 Loss1: 0.187896 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.172670 Loss1: 0.171980 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.163380 Loss1: 0.162689 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.133908 Loss1: 0.133216 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.130312 Loss1: 0.129620 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.149307 Loss1: 0.148614 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.146209 Loss1: 0.145517 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.972903
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8045886075949367
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.439354 Loss1: 0.438673 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.238819 Loss1: 0.238135 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.223125 Loss1: 0.222439 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177852 Loss1: 0.177166 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.164776 Loss1: 0.164090 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.126689 Loss1: 0.126001 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.150987 Loss1: 0.150298 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.124300 Loss1: 0.123612 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099013 Loss1: 0.098325 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.122536 Loss1: 0.121848 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.980617
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8332674050632911
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.408021 Loss1: 0.407338 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.242281 Loss1: 0.241593 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.232424 Loss1: 0.231737 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.170228 Loss1: 0.169540 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.138988 Loss1: 0.138300 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133228 Loss1: 0.132540 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.151076 Loss1: 0.150388 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.138498 Loss1: 0.137810 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.126525 Loss1: 0.125836 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.132889 Loss1: 0.132202 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.968354
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8094951923076923
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.425804 Loss1: 0.425125 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.298145 Loss1: 0.297461 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.195134 Loss1: 0.194448 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177887 Loss1: 0.177204 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.160974 Loss1: 0.160290 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.177750 Loss1: 0.177066 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.127688 Loss1: 0.127004 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.153289 Loss1: 0.152604 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109428 Loss1: 0.108743 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099926 Loss1: 0.099240 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.970353
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8376524390243902
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.360360 Loss1: 0.359680 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.249043 Loss1: 0.248360 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.179275 Loss1: 0.178591 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.154955 Loss1: 0.154269 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.187095 Loss1: 0.186409 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.189220 Loss1: 0.188532 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.171427 Loss1: 0.170740 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.127075 Loss1: 0.126387 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109851 Loss1: 0.109164 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108754 Loss1: 0.108067 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.973514
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-21 23:34:36,389][flwr][DEBUG] - fit_round 36 received 10 results and 0 failures
+test acc: 0.6189
+[2023-09-21 23:35:20,838][flwr][INFO] - fit progress: (36, 2.1186307085969576, {'accuracy': 0.6189}, 73402.4992567827)
+[2023-09-21 23:35:20,838][flwr][DEBUG] - evaluate_round 36: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-21 23:35:57,777][flwr][DEBUG] - evaluate_round 36 received 10 results and 0 failures
+[2023-09-21 23:35:57,778][flwr][DEBUG] - fit_round 37: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.762668918918919
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.403460 Loss1: 0.402774 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.250829 Loss1: 0.250139 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.196067 Loss1: 0.195375 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.161435 Loss1: 0.160744 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.149829 Loss1: 0.149137 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.142420 Loss1: 0.141729 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.125025 Loss1: 0.124331 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.116738 Loss1: 0.116046 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.137752 Loss1: 0.137060 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.152379 Loss1: 0.151686 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.977196
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8161057692307693
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.405371 Loss1: 0.404691 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.252109 Loss1: 0.251422 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.200008 Loss1: 0.199322 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.164363 Loss1: 0.163676 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.190054 Loss1: 0.189367 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.138788 Loss1: 0.138102 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.119922 Loss1: 0.119235 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.146485 Loss1: 0.145797 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.125748 Loss1: 0.125060 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.119112 Loss1: 0.118424 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.964744
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8533653846153846
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.333666 Loss1: 0.332982 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.194403 Loss1: 0.193717 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.166740 Loss1: 0.166053 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.145244 Loss1: 0.144555 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.139643 Loss1: 0.138954 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.161044 Loss1: 0.160354 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.125194 Loss1: 0.124504 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.110038 Loss1: 0.109348 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.104743 Loss1: 0.104055 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094713 Loss1: 0.094024 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.985777
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8481326219512195
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.330011 Loss1: 0.329330 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.235689 Loss1: 0.235004 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.185915 Loss1: 0.185229 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.152132 Loss1: 0.151444 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.171781 Loss1: 0.171095 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.168643 Loss1: 0.167956 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.132239 Loss1: 0.131551 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121394 Loss1: 0.120706 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.134610 Loss1: 0.133923 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.148253 Loss1: 0.147564 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.979230
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8073575949367089
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.383990 Loss1: 0.383307 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.236370 Loss1: 0.235681 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.204050 Loss1: 0.203362 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.158472 Loss1: 0.157782 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.140159 Loss1: 0.139469 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.145808 Loss1: 0.145120 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.119775 Loss1: 0.119085 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.157684 Loss1: 0.156994 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.130696 Loss1: 0.130005 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108793 Loss1: 0.108106 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.978244
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8107638888888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.403733 Loss1: 0.403050 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.250743 Loss1: 0.250056 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.221052 Loss1: 0.220364 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.190868 Loss1: 0.190179 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.148011 Loss1: 0.147322 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116631 Loss1: 0.115943 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.118644 Loss1: 0.117955 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.141826 Loss1: 0.141136 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111098 Loss1: 0.110409 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108005 Loss1: 0.107316 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.983290
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8447389240506329
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.371958 Loss1: 0.371272 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.226956 Loss1: 0.226265 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.200847 Loss1: 0.200156 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.155808 Loss1: 0.155117 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.130278 Loss1: 0.129586 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116596 Loss1: 0.115904 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115152 Loss1: 0.114461 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133276 Loss1: 0.132583 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.120384 Loss1: 0.119691 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128082 Loss1: 0.127389 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.962816
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8147615131578947
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.420701 Loss1: 0.420014 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.271798 Loss1: 0.271106 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.230548 Loss1: 0.229857 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.184567 Loss1: 0.183874 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.164862 Loss1: 0.164170 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.145320 Loss1: 0.144629 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.135432 Loss1: 0.134742 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.143895 Loss1: 0.143204 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123137 Loss1: 0.122449 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128581 Loss1: 0.127890 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.971423
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8330696202531646
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.371860 Loss1: 0.371177 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.239364 Loss1: 0.238675 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.169857 Loss1: 0.169166 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.176834 Loss1: 0.176145 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133221 Loss1: 0.132532 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.146995 Loss1: 0.146304 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.157775 Loss1: 0.157086 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.141194 Loss1: 0.140502 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108031 Loss1: 0.107341 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.117334 Loss1: 0.116643 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.971915
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8140822784810127
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.380007 Loss1: 0.379325 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.220687 Loss1: 0.220001 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.159514 Loss1: 0.158827 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.138871 Loss1: 0.138183 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.152676 Loss1: 0.151989 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.183558 Loss1: 0.182872 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.207799 Loss1: 0.207112 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.129485 Loss1: 0.128796 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.121919 Loss1: 0.121232 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.130000 Loss1: 0.129313 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.979035
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 00:06:16,282][flwr][DEBUG] - fit_round 37 received 10 results and 0 failures
+test acc: 0.6191
+[2023-09-22 00:07:00,980][flwr][INFO] - fit progress: (37, 2.1180881943565586, {'accuracy': 0.6191}, 75302.64137112582)
+[2023-09-22 00:07:00,980][flwr][DEBUG] - evaluate_round 37: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 00:07:38,756][flwr][DEBUG] - evaluate_round 37 received 10 results and 0 failures
+[2023-09-22 00:07:38,757][flwr][DEBUG] - fit_round 38: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8469145569620253
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.362624 Loss1: 0.361940 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.266418 Loss1: 0.265730 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190526 Loss1: 0.189838 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.145726 Loss1: 0.145038 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.149673 Loss1: 0.148983 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.156444 Loss1: 0.155754 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.133522 Loss1: 0.132831 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099696 Loss1: 0.099007 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111632 Loss1: 0.110944 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.105704 Loss1: 0.105015 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.981804
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8515625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.298216 Loss1: 0.297535 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.226105 Loss1: 0.225417 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.160495 Loss1: 0.159805 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.141657 Loss1: 0.140968 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.119260 Loss1: 0.118570 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.128988 Loss1: 0.128298 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.135827 Loss1: 0.135137 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119492 Loss1: 0.118802 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080819 Loss1: 0.080130 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076583 Loss1: 0.075893 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.980569
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8182357594936709
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.364088 Loss1: 0.363407 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.218110 Loss1: 0.217424 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.186206 Loss1: 0.185521 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.155733 Loss1: 0.155046 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.150376 Loss1: 0.149690 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123245 Loss1: 0.122559 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164252 Loss1: 0.163566 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.131330 Loss1: 0.130643 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123399 Loss1: 0.122713 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.113498 Loss1: 0.112811 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.966772
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8559451219512195
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.327417 Loss1: 0.326738 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.198433 Loss1: 0.197748 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.196700 Loss1: 0.196014 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177455 Loss1: 0.176769 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.152362 Loss1: 0.151677 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.132870 Loss1: 0.132183 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.093618 Loss1: 0.092933 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.111564 Loss1: 0.110879 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.121112 Loss1: 0.120426 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.136026 Loss1: 0.135339 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.975038
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8577927215189873
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.358146 Loss1: 0.357462 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.233330 Loss1: 0.232641 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.154986 Loss1: 0.154294 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.141412 Loss1: 0.140722 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.137155 Loss1: 0.136466 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.151246 Loss1: 0.150556 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.123730 Loss1: 0.123039 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117918 Loss1: 0.117227 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.115058 Loss1: 0.114364 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098941 Loss1: 0.098251 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.982199
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7652027027027027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.438918 Loss1: 0.438234 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.253588 Loss1: 0.252899 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190771 Loss1: 0.190081 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.167987 Loss1: 0.167297 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.171058 Loss1: 0.170368 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133245 Loss1: 0.132556 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.111441 Loss1: 0.110752 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.107569 Loss1: 0.106877 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.130338 Loss1: 0.129647 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.147769 Loss1: 0.147079 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.976351
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8182565789473685
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.406436 Loss1: 0.405748 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.252045 Loss1: 0.251356 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.234769 Loss1: 0.234079 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.168702 Loss1: 0.168010 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.139259 Loss1: 0.138568 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.119545 Loss1: 0.118855 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.122181 Loss1: 0.121492 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.143281 Loss1: 0.142592 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.134818 Loss1: 0.134129 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128071 Loss1: 0.127380 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.980058
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8209134615384616
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.359445 Loss1: 0.358768 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.241969 Loss1: 0.241285 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.225640 Loss1: 0.224957 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.206722 Loss1: 0.206039 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.172990 Loss1: 0.172305 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.159975 Loss1: 0.159290 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.132815 Loss1: 0.132129 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133588 Loss1: 0.132902 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.153433 Loss1: 0.152748 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.130310 Loss1: 0.129624 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.973758
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8253560126582279
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.354857 Loss1: 0.354175 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.218862 Loss1: 0.218176 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.200554 Loss1: 0.199867 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.153718 Loss1: 0.153029 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.148504 Loss1: 0.147815 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.104214 Loss1: 0.103527 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.158462 Loss1: 0.157772 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.110049 Loss1: 0.109361 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122048 Loss1: 0.121360 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.124115 Loss1: 0.123426 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.981606
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8159722222222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.402409 Loss1: 0.401727 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.191485 Loss1: 0.190799 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.185433 Loss1: 0.184746 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.158884 Loss1: 0.158197 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.145018 Loss1: 0.144332 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.179814 Loss1: 0.179128 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.163537 Loss1: 0.162850 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.124577 Loss1: 0.123889 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.104503 Loss1: 0.103812 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088566 Loss1: 0.087877 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.980903
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 00:38:05,680][flwr][DEBUG] - fit_round 38 received 10 results and 0 failures
+test acc: 0.6215
+[2023-09-22 00:38:51,169][flwr][INFO] - fit progress: (38, 2.109429546248037, {'accuracy': 0.6215}, 77212.83034385275)
+[2023-09-22 00:38:51,169][flwr][DEBUG] - evaluate_round 38: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 00:39:28,795][flwr][DEBUG] - evaluate_round 38 received 10 results and 0 failures
+[2023-09-22 00:39:28,795][flwr][DEBUG] - fit_round 39: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8306962025316456
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.347828 Loss1: 0.347145 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.222045 Loss1: 0.221360 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.169032 Loss1: 0.168344 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.150711 Loss1: 0.150024 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.126677 Loss1: 0.125991 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.136074 Loss1: 0.135386 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108946 Loss1: 0.108259 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119593 Loss1: 0.118905 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122454 Loss1: 0.121765 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128670 Loss1: 0.127982 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.972508
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8326480263157895
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.395808 Loss1: 0.395119 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.267043 Loss1: 0.266352 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.208137 Loss1: 0.207445 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.161189 Loss1: 0.160497 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.161999 Loss1: 0.161308 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.157778 Loss1: 0.157085 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.125671 Loss1: 0.124980 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114642 Loss1: 0.113950 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.103106 Loss1: 0.102413 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079816 Loss1: 0.079123 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.988692
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8347355769230769
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.326374 Loss1: 0.325692 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.254130 Loss1: 0.253447 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.180193 Loss1: 0.179507 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.174561 Loss1: 0.173877 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.152548 Loss1: 0.151862 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.125128 Loss1: 0.124443 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101555 Loss1: 0.100869 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117962 Loss1: 0.117277 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114885 Loss1: 0.114200 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.111541 Loss1: 0.110855 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.973157
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8605182926829268
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.290069 Loss1: 0.289388 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.187512 Loss1: 0.186826 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.208819 Loss1: 0.208133 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.194543 Loss1: 0.193857 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.126479 Loss1: 0.125793 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.128573 Loss1: 0.127886 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106875 Loss1: 0.106188 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096335 Loss1: 0.095646 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.118879 Loss1: 0.118190 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.124774 Loss1: 0.124087 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.981326
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8094618055555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.378187 Loss1: 0.377503 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.242625 Loss1: 0.241939 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190121 Loss1: 0.189433 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.181312 Loss1: 0.180625 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.142387 Loss1: 0.141698 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098805 Loss1: 0.098116 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076446 Loss1: 0.075757 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096569 Loss1: 0.095880 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063519 Loss1: 0.062831 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075763 Loss1: 0.075075 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.985026
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7865287162162162
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.409456 Loss1: 0.408769 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.206219 Loss1: 0.205528 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.181636 Loss1: 0.180945 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.160805 Loss1: 0.160115 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.154144 Loss1: 0.153454 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.151079 Loss1: 0.150388 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.140377 Loss1: 0.139686 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.108190 Loss1: 0.107501 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098733 Loss1: 0.098040 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098732 Loss1: 0.098040 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.979941
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8619462025316456
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.310426 Loss1: 0.309741 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.194811 Loss1: 0.194122 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.180196 Loss1: 0.179504 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.122899 Loss1: 0.122206 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.129303 Loss1: 0.128609 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.127038 Loss1: 0.126346 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.134284 Loss1: 0.133593 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.134981 Loss1: 0.134289 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.118997 Loss1: 0.118304 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.103712 Loss1: 0.103019 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.981013
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8330696202531646
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.347718 Loss1: 0.347034 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.203829 Loss1: 0.203141 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.150213 Loss1: 0.149524 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.132651 Loss1: 0.131962 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.132331 Loss1: 0.131643 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.167940 Loss1: 0.167252 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.132777 Loss1: 0.132089 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.145921 Loss1: 0.145231 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133956 Loss1: 0.133266 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.127463 Loss1: 0.126773 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.971915
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8633814102564102
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.313384 Loss1: 0.312700 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.196181 Loss1: 0.195493 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.158538 Loss1: 0.157850 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.155091 Loss1: 0.154401 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.145634 Loss1: 0.144945 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116975 Loss1: 0.116285 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087770 Loss1: 0.087079 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109252 Loss1: 0.108561 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108943 Loss1: 0.108252 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.093019 Loss1: 0.092327 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.976562
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8488924050632911
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.378133 Loss1: 0.377448 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.230632 Loss1: 0.229943 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.162450 Loss1: 0.161759 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135203 Loss1: 0.134512 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.130370 Loss1: 0.129678 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.117022 Loss1: 0.116331 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.122636 Loss1: 0.121946 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.126659 Loss1: 0.125968 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123258 Loss1: 0.122568 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.103043 Loss1: 0.102350 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.979628
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 01:08:44,661][flwr][DEBUG] - fit_round 39 received 10 results and 0 failures
+test acc: 0.6223
+[2023-09-22 01:17:01,546][flwr][INFO] - fit progress: (39, 2.0989707978769614, {'accuracy': 0.6223}, 79503.20757643087)
+[2023-09-22 01:17:01,547][flwr][DEBUG] - evaluate_round 39: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 01:17:39,896][flwr][DEBUG] - evaluate_round 39 received 10 results and 0 failures
+[2023-09-22 01:17:39,897][flwr][DEBUG] - fit_round 40: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8456003289473685
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.399010 Loss1: 0.398322 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.247761 Loss1: 0.247071 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.171125 Loss1: 0.170434 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.140004 Loss1: 0.139313 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.140056 Loss1: 0.139365 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.132114 Loss1: 0.131423 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.122413 Loss1: 0.121721 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.136674 Loss1: 0.135983 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133878 Loss1: 0.133188 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.116938 Loss1: 0.116246 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.979030
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8682753164556962
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.320604 Loss1: 0.319918 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.208731 Loss1: 0.208041 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.163293 Loss1: 0.162601 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.117979 Loss1: 0.117287 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.128975 Loss1: 0.128283 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108485 Loss1: 0.107795 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.112787 Loss1: 0.112094 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133056 Loss1: 0.132363 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.107962 Loss1: 0.107270 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.126117 Loss1: 0.125424 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.975277
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8415464743589743
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.325218 Loss1: 0.324538 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.232557 Loss1: 0.231871 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.156836 Loss1: 0.156151 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135486 Loss1: 0.134802 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.128785 Loss1: 0.128097 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.115482 Loss1: 0.114796 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.124039 Loss1: 0.123352 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119637 Loss1: 0.118950 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123604 Loss1: 0.122919 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.089314 Loss1: 0.088627 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.982772
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7871621621621622
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.409984 Loss1: 0.409300 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.222190 Loss1: 0.221499 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.177997 Loss1: 0.177306 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.141233 Loss1: 0.140543 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.093546 Loss1: 0.092855 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133099 Loss1: 0.132408 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.099419 Loss1: 0.098729 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.087211 Loss1: 0.086519 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083263 Loss1: 0.082571 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.100976 Loss1: 0.100285 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.979096
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8669969512195121
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.319334 Loss1: 0.318653 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.210538 Loss1: 0.209853 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.220966 Loss1: 0.220280 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177039 Loss1: 0.176354 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.140752 Loss1: 0.140065 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.147806 Loss1: 0.147118 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.120123 Loss1: 0.119435 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117166 Loss1: 0.116477 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133381 Loss1: 0.132692 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085089 Loss1: 0.084401 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.979802
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8356408227848101
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.341316 Loss1: 0.340634 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.207714 Loss1: 0.207027 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.152932 Loss1: 0.152246 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.158487 Loss1: 0.157799 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.104407 Loss1: 0.103718 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.114275 Loss1: 0.113588 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.148719 Loss1: 0.148030 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.162282 Loss1: 0.161593 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.151693 Loss1: 0.151003 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115004 Loss1: 0.114315 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.979035
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8320806962025317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.352418 Loss1: 0.351735 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.198213 Loss1: 0.197524 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.150360 Loss1: 0.149669 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.137073 Loss1: 0.136382 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.118619 Loss1: 0.117929 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133129 Loss1: 0.132437 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.150227 Loss1: 0.149537 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119557 Loss1: 0.118867 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.101903 Loss1: 0.101212 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096753 Loss1: 0.096064 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.980815
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.867988782051282
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.283432 Loss1: 0.282749 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.183959 Loss1: 0.183270 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.151058 Loss1: 0.150368 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.119741 Loss1: 0.119052 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.120959 Loss1: 0.120266 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.104469 Loss1: 0.103777 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101928 Loss1: 0.101238 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092920 Loss1: 0.092228 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.101631 Loss1: 0.100938 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098179 Loss1: 0.097488 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.979567
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.850870253164557
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.363746 Loss1: 0.363061 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.235824 Loss1: 0.235136 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.165863 Loss1: 0.165174 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.133063 Loss1: 0.132372 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.155452 Loss1: 0.154763 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.167013 Loss1: 0.166322 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.162273 Loss1: 0.161582 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121282 Loss1: 0.120590 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.130261 Loss1: 0.129570 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.120334 Loss1: 0.119642 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.971321
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8289930555555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.362343 Loss1: 0.361660 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.210924 Loss1: 0.210238 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.178588 Loss1: 0.177900 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.157844 Loss1: 0.157156 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.145273 Loss1: 0.144583 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133708 Loss1: 0.133020 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.144706 Loss1: 0.144019 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.108456 Loss1: 0.107767 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099956 Loss1: 0.099268 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.100313 Loss1: 0.099624 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.975477
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 01:46:38,408][flwr][DEBUG] - fit_round 40 received 10 results and 0 failures
+test acc: 0.62
+[2023-09-22 01:47:24,380][flwr][INFO] - fit progress: (40, 2.123094680019842, {'accuracy': 0.62}, 81326.04166387487)
+[2023-09-22 01:47:24,381][flwr][DEBUG] - evaluate_round 40: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 01:48:01,636][flwr][DEBUG] - evaluate_round 40 received 10 results and 0 failures
+[2023-09-22 01:48:01,637][flwr][DEBUG] - fit_round 41: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8716376582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.273720 Loss1: 0.273036 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.201964 Loss1: 0.201275 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.172309 Loss1: 0.171620 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.145083 Loss1: 0.144393 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.159214 Loss1: 0.158522 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.112471 Loss1: 0.111781 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.092836 Loss1: 0.092146 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.107123 Loss1: 0.106433 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085659 Loss1: 0.084968 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104065 Loss1: 0.103372 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.978046
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8310917721518988
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.314985 Loss1: 0.314302 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.169887 Loss1: 0.169200 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.125992 Loss1: 0.125302 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.120499 Loss1: 0.119809 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.122544 Loss1: 0.121856 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.119697 Loss1: 0.119008 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.135058 Loss1: 0.134368 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095484 Loss1: 0.094795 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108805 Loss1: 0.108113 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.117026 Loss1: 0.116336 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.967366
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8477056962025317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.280621 Loss1: 0.279940 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.184776 Loss1: 0.184090 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.167141 Loss1: 0.166455 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.143440 Loss1: 0.142753 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.131867 Loss1: 0.131180 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.115832 Loss1: 0.115145 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.124777 Loss1: 0.124089 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.134444 Loss1: 0.133756 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.126647 Loss1: 0.125959 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104762 Loss1: 0.104075 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.974881
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8591844512195121
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.305974 Loss1: 0.305293 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.166080 Loss1: 0.165396 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.148628 Loss1: 0.147941 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.142095 Loss1: 0.141407 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.106123 Loss1: 0.105436 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.121671 Loss1: 0.120985 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.100568 Loss1: 0.099880 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.123846 Loss1: 0.123159 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114175 Loss1: 0.113486 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097383 Loss1: 0.096695 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.979230
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8727964743589743
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.273971 Loss1: 0.273289 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.177342 Loss1: 0.176654 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.136085 Loss1: 0.135397 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.128562 Loss1: 0.127873 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.123969 Loss1: 0.123280 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123582 Loss1: 0.122891 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.121249 Loss1: 0.120560 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.087608 Loss1: 0.086918 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098736 Loss1: 0.098046 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071833 Loss1: 0.071144 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987981
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8279079861111112
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.341015 Loss1: 0.340333 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.204182 Loss1: 0.203497 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.173621 Loss1: 0.172935 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.132385 Loss1: 0.131699 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.094044 Loss1: 0.093356 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.125651 Loss1: 0.124964 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108937 Loss1: 0.108248 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.111231 Loss1: 0.110541 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.107188 Loss1: 0.106499 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096245 Loss1: 0.095556 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987630
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8536184210526315
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.356853 Loss1: 0.356167 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.203536 Loss1: 0.202847 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.177217 Loss1: 0.176527 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.190684 Loss1: 0.189992 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.170102 Loss1: 0.169411 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.127867 Loss1: 0.127176 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108507 Loss1: 0.107817 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.170698 Loss1: 0.170007 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.154738 Loss1: 0.154046 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.114053 Loss1: 0.113362 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.979235
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8379407051282052
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.310502 Loss1: 0.309821 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.204976 Loss1: 0.204290 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.147792 Loss1: 0.147106 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.134726 Loss1: 0.134042 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169385 Loss1: 0.168699 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.127871 Loss1: 0.127185 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.147197 Loss1: 0.146511 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.135605 Loss1: 0.134921 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.117247 Loss1: 0.116561 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104083 Loss1: 0.103397 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.980369
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7827280405405406
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.367662 Loss1: 0.366977 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.188179 Loss1: 0.187489 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.147913 Loss1: 0.147224 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.140608 Loss1: 0.139918 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133212 Loss1: 0.132522 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.113231 Loss1: 0.112541 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.114269 Loss1: 0.113580 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.116705 Loss1: 0.116015 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099381 Loss1: 0.098690 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.109546 Loss1: 0.108855 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.978252
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8409810126582279
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.329060 Loss1: 0.328376 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.205773 Loss1: 0.205083 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.164063 Loss1: 0.163374 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135085 Loss1: 0.134398 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.093243 Loss1: 0.092555 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106278 Loss1: 0.105590 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.137962 Loss1: 0.137273 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117555 Loss1: 0.116867 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.112357 Loss1: 0.111669 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.126612 Loss1: 0.125924 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.970728
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 02:17:45,792][flwr][DEBUG] - fit_round 41 received 10 results and 0 failures
+test acc: 0.6206
+[2023-09-22 02:18:31,491][flwr][INFO] - fit progress: (41, 2.148773836632506, {'accuracy': 0.6206}, 83193.15214766795)
+[2023-09-22 02:18:31,491][flwr][DEBUG] - evaluate_round 41: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 02:19:09,241][flwr][DEBUG] - evaluate_round 41 received 10 results and 0 failures
+[2023-09-22 02:19:09,242][flwr][DEBUG] - fit_round 42: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8732850609756098
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.290928 Loss1: 0.290247 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.178461 Loss1: 0.177777 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.148465 Loss1: 0.147779 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.117972 Loss1: 0.117286 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.129841 Loss1: 0.129154 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.112315 Loss1: 0.111630 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.088305 Loss1: 0.087617 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097594 Loss1: 0.096908 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.094075 Loss1: 0.093390 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074705 Loss1: 0.074019 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.979992
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8395965189873418
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.298833 Loss1: 0.298148 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.179965 Loss1: 0.179276 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.144727 Loss1: 0.144039 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135559 Loss1: 0.134868 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.107883 Loss1: 0.107194 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.114070 Loss1: 0.113382 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.117473 Loss1: 0.116784 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.125687 Loss1: 0.124998 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.128200 Loss1: 0.127511 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097221 Loss1: 0.096531 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.982595
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8619462025316456
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.316569 Loss1: 0.315886 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.215535 Loss1: 0.214847 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.170026 Loss1: 0.169337 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.121976 Loss1: 0.121285 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.120415 Loss1: 0.119726 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.115703 Loss1: 0.115012 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107390 Loss1: 0.106700 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.115117 Loss1: 0.114426 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110225 Loss1: 0.109535 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.101860 Loss1: 0.101168 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.983782
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8451522435897436
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.333463 Loss1: 0.332782 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.201181 Loss1: 0.200498 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.133278 Loss1: 0.132592 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.116656 Loss1: 0.115971 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.093124 Loss1: 0.092437 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.125780 Loss1: 0.125093 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.130203 Loss1: 0.129516 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.122431 Loss1: 0.121744 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122946 Loss1: 0.122259 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098828 Loss1: 0.098141 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.983774
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8515625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.351770 Loss1: 0.351083 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.222209 Loss1: 0.221519 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.163038 Loss1: 0.162348 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.159413 Loss1: 0.158724 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.156401 Loss1: 0.155713 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.129650 Loss1: 0.128959 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.130890 Loss1: 0.130200 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.147547 Loss1: 0.146856 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093783 Loss1: 0.093092 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078412 Loss1: 0.077720 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.987048
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8415743670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.292624 Loss1: 0.291942 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.206498 Loss1: 0.205812 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.146300 Loss1: 0.145614 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.136430 Loss1: 0.135744 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.114114 Loss1: 0.113427 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098408 Loss1: 0.097722 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085565 Loss1: 0.084878 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121193 Loss1: 0.120507 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.115149 Loss1: 0.114461 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104778 Loss1: 0.104090 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.980222
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8287760416666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.314256 Loss1: 0.313574 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.195628 Loss1: 0.194943 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.178623 Loss1: 0.177936 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.164009 Loss1: 0.163323 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.150620 Loss1: 0.149931 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.158570 Loss1: 0.157882 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.123969 Loss1: 0.123280 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102670 Loss1: 0.101982 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.115638 Loss1: 0.114950 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104159 Loss1: 0.103471 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.974826
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8795490506329114
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.292456 Loss1: 0.291771 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.179950 Loss1: 0.179261 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.160349 Loss1: 0.159659 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.125019 Loss1: 0.124328 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.126012 Loss1: 0.125322 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.139559 Loss1: 0.138868 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.139431 Loss1: 0.138741 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091045 Loss1: 0.090353 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079593 Loss1: 0.078902 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099371 Loss1: 0.098680 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.979628
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8733974358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.275901 Loss1: 0.275217 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.189426 Loss1: 0.188739 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.153338 Loss1: 0.152651 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.108349 Loss1: 0.107660 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.100714 Loss1: 0.100025 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105299 Loss1: 0.104610 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091521 Loss1: 0.090832 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078779 Loss1: 0.078090 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055006 Loss1: 0.054317 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059620 Loss1: 0.058931 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987780
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7915962837837838
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.363612 Loss1: 0.362926 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.206669 Loss1: 0.205979 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.159505 Loss1: 0.158815 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.138590 Loss1: 0.137900 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.138480 Loss1: 0.137790 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098713 Loss1: 0.098021 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.088855 Loss1: 0.088164 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081933 Loss1: 0.081242 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110766 Loss1: 0.110078 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.129854 Loss1: 0.129162 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.978674
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 02:49:00,763][flwr][DEBUG] - fit_round 42 received 10 results and 0 failures
+test acc: 0.6283
+[2023-09-22 02:49:46,951][flwr][INFO] - fit progress: (42, 2.1465409287629416, {'accuracy': 0.6283}, 85068.61267694458)
+[2023-09-22 02:49:46,952][flwr][DEBUG] - evaluate_round 42: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 02:50:24,279][flwr][DEBUG] - evaluate_round 42 received 10 results and 0 failures
+[2023-09-22 02:50:24,280][flwr][DEBUG] - fit_round 43: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7934966216216216
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.307708 Loss1: 0.307024 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.211459 Loss1: 0.210772 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.155377 Loss1: 0.154689 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.150986 Loss1: 0.150297 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.118145 Loss1: 0.117455 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106234 Loss1: 0.105544 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.111250 Loss1: 0.110561 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121360 Loss1: 0.120671 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.131742 Loss1: 0.131053 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097262 Loss1: 0.096574 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.978041
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8673780487804879
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246780 Loss1: 0.246099 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.194122 Loss1: 0.193436 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.127123 Loss1: 0.126439 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.115952 Loss1: 0.115266 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133161 Loss1: 0.132475 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.132210 Loss1: 0.131524 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106554 Loss1: 0.105868 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114851 Loss1: 0.114164 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109461 Loss1: 0.108775 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.119139 Loss1: 0.118452 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.972942
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8447389240506329
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.248744 Loss1: 0.248061 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.192221 Loss1: 0.191534 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.131185 Loss1: 0.130497 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.121172 Loss1: 0.120485 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098885 Loss1: 0.098197 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.128615 Loss1: 0.127927 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.118438 Loss1: 0.117752 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102022 Loss1: 0.101334 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.127128 Loss1: 0.126438 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.129064 Loss1: 0.128376 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.973695
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8479034810126582
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.272079 Loss1: 0.271397 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.166966 Loss1: 0.166279 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.124503 Loss1: 0.123816 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.113712 Loss1: 0.113023 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.130933 Loss1: 0.130244 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.124891 Loss1: 0.124200 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.104998 Loss1: 0.104308 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.129101 Loss1: 0.128411 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083351 Loss1: 0.082661 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090861 Loss1: 0.090170 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.982002
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8670886075949367
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.290447 Loss1: 0.289763 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.169287 Loss1: 0.168599 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.138890 Loss1: 0.138201 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.127354 Loss1: 0.126665 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.116426 Loss1: 0.115738 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.109800 Loss1: 0.109111 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.117779 Loss1: 0.117090 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.101999 Loss1: 0.101308 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.104420 Loss1: 0.103731 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094504 Loss1: 0.093815 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.976859
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8465711805555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.333998 Loss1: 0.333315 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.192263 Loss1: 0.191578 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.141086 Loss1: 0.140399 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.116961 Loss1: 0.116275 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101221 Loss1: 0.100534 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.094544 Loss1: 0.093856 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.105042 Loss1: 0.104354 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081525 Loss1: 0.080836 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087497 Loss1: 0.086807 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099929 Loss1: 0.099238 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.979601
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8653371710526315
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.326613 Loss1: 0.325927 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.209373 Loss1: 0.208684 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.151291 Loss1: 0.150601 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.128430 Loss1: 0.127740 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.154902 Loss1: 0.154211 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123907 Loss1: 0.123217 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107225 Loss1: 0.106536 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.104744 Loss1: 0.104056 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080689 Loss1: 0.080000 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066669 Loss1: 0.065980 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.984375
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8850870253164557
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.262435 Loss1: 0.261751 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.165235 Loss1: 0.164545 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.151944 Loss1: 0.151255 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.121181 Loss1: 0.120492 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.127727 Loss1: 0.127036 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108562 Loss1: 0.107872 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106547 Loss1: 0.105857 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.090073 Loss1: 0.089384 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093254 Loss1: 0.092563 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075362 Loss1: 0.074671 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.984771
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8477564102564102
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.320336 Loss1: 0.319656 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.223211 Loss1: 0.222526 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.151212 Loss1: 0.150526 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.118571 Loss1: 0.117886 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.113046 Loss1: 0.112361 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098061 Loss1: 0.097376 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.093257 Loss1: 0.092571 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.131110 Loss1: 0.130425 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.136311 Loss1: 0.135625 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.110391 Loss1: 0.109705 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.978966
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8832131410256411
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.251567 Loss1: 0.250886 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.152338 Loss1: 0.151650 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.131012 Loss1: 0.130325 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.106451 Loss1: 0.105763 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101182 Loss1: 0.100492 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092959 Loss1: 0.092269 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.080235 Loss1: 0.079544 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.090851 Loss1: 0.090160 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.086626 Loss1: 0.085935 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091078 Loss1: 0.090386 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.986579
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 03:20:27,636][flwr][DEBUG] - fit_round 43 received 10 results and 0 failures
+test acc: 0.6278
+[2023-09-22 03:21:14,774][flwr][INFO] - fit progress: (43, 2.1362349244352346, {'accuracy': 0.6278}, 86956.4356766548)
+[2023-09-22 03:21:14,775][flwr][DEBUG] - evaluate_round 43: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 03:21:52,345][flwr][DEBUG] - evaluate_round 43 received 10 results and 0 failures
+[2023-09-22 03:21:52,347][flwr][DEBUG] - fit_round 44: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8065878378378378
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.314972 Loss1: 0.314287 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.161408 Loss1: 0.160719 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.128631 Loss1: 0.127940 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.122502 Loss1: 0.121810 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.132435 Loss1: 0.131745 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092712 Loss1: 0.092022 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110657 Loss1: 0.109966 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091227 Loss1: 0.090536 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.102753 Loss1: 0.102063 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.102187 Loss1: 0.101496 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.983953
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.850870253164557
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.268531 Loss1: 0.267847 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.169299 Loss1: 0.168610 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.121889 Loss1: 0.121201 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.129088 Loss1: 0.128398 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.095953 Loss1: 0.095263 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078463 Loss1: 0.077772 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082060 Loss1: 0.081370 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.087643 Loss1: 0.086953 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.104421 Loss1: 0.103731 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115935 Loss1: 0.115244 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.977255
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8848157051282052
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.266045 Loss1: 0.265360 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.165537 Loss1: 0.164846 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.153238 Loss1: 0.152547 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.113048 Loss1: 0.112359 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.090429 Loss1: 0.089740 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.103019 Loss1: 0.102329 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.093243 Loss1: 0.092553 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085122 Loss1: 0.084432 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072233 Loss1: 0.071542 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087200 Loss1: 0.086509 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.987780
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8504774305555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.311970 Loss1: 0.311286 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.193086 Loss1: 0.192399 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.128858 Loss1: 0.128171 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.111413 Loss1: 0.110725 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133823 Loss1: 0.133135 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108475 Loss1: 0.107786 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.096371 Loss1: 0.095680 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.093904 Loss1: 0.093215 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075273 Loss1: 0.074584 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082224 Loss1: 0.081533 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.990451
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8623798076923077
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.274120 Loss1: 0.273441 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.194158 Loss1: 0.193474 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.161035 Loss1: 0.160350 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.136695 Loss1: 0.136011 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.117297 Loss1: 0.116611 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.110523 Loss1: 0.109838 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110388 Loss1: 0.109700 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114042 Loss1: 0.113356 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.101043 Loss1: 0.100356 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104629 Loss1: 0.103943 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.978365
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8878560126582279
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.275048 Loss1: 0.274364 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.153385 Loss1: 0.152696 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.121303 Loss1: 0.120613 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.100138 Loss1: 0.099447 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.130260 Loss1: 0.129569 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.139997 Loss1: 0.139306 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081925 Loss1: 0.081235 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109304 Loss1: 0.108614 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098014 Loss1: 0.097324 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096674 Loss1: 0.095982 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.978441
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8661595394736842
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.332924 Loss1: 0.332239 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.207779 Loss1: 0.207090 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.149519 Loss1: 0.148829 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.131346 Loss1: 0.130657 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.118133 Loss1: 0.117441 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.118411 Loss1: 0.117719 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.138547 Loss1: 0.137856 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114165 Loss1: 0.113473 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078798 Loss1: 0.078106 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091990 Loss1: 0.091299 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.988076
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.853243670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.251018 Loss1: 0.250335 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.163357 Loss1: 0.162671 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.160832 Loss1: 0.160146 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.126222 Loss1: 0.125534 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.096539 Loss1: 0.095851 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116692 Loss1: 0.116003 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115302 Loss1: 0.114614 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.105741 Loss1: 0.105053 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.120625 Loss1: 0.119937 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.118341 Loss1: 0.117653 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.972310
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8835746951219512
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.231131 Loss1: 0.230449 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.140251 Loss1: 0.139566 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.127058 Loss1: 0.126371 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.111156 Loss1: 0.110469 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.104939 Loss1: 0.104253 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106585 Loss1: 0.105897 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.121351 Loss1: 0.120662 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.127896 Loss1: 0.127208 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084910 Loss1: 0.084221 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090213 Loss1: 0.089526 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.988948
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8738132911392406
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.279277 Loss1: 0.278593 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.166143 Loss1: 0.165455 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.134821 Loss1: 0.134132 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.119027 Loss1: 0.118338 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.146626 Loss1: 0.145936 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108401 Loss1: 0.107712 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107695 Loss1: 0.107006 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.100921 Loss1: 0.100231 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.095555 Loss1: 0.094864 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097260 Loss1: 0.096569 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.985364
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 03:51:54,485][flwr][DEBUG] - fit_round 44 received 10 results and 0 failures
+test acc: 0.6251
+[2023-09-22 03:52:41,459][flwr][INFO] - fit progress: (44, 2.1981266999777893, {'accuracy': 0.6251}, 88843.12023476185)
+[2023-09-22 03:52:41,459][flwr][DEBUG] - evaluate_round 44: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 03:53:18,692][flwr][DEBUG] - evaluate_round 44 received 10 results and 0 failures
+[2023-09-22 03:53:18,693][flwr][DEBUG] - fit_round 45: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8589743589743589
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.268189 Loss1: 0.267510 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.183437 Loss1: 0.182753 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.140724 Loss1: 0.140039 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.128834 Loss1: 0.128150 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.139195 Loss1: 0.138511 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.129692 Loss1: 0.129008 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115057 Loss1: 0.114373 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.118817 Loss1: 0.118131 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081475 Loss1: 0.080790 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.114770 Loss1: 0.114085 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.978365
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8914161392405063
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.226891 Loss1: 0.226207 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.143172 Loss1: 0.142483 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.101741 Loss1: 0.101051 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.129039 Loss1: 0.128349 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.116743 Loss1: 0.116053 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092180 Loss1: 0.091492 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103384 Loss1: 0.102696 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.118370 Loss1: 0.117681 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110943 Loss1: 0.110255 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097507 Loss1: 0.096817 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.984771
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8524525316455697
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.259259 Loss1: 0.258576 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.155619 Loss1: 0.154934 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.123316 Loss1: 0.122631 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135842 Loss1: 0.135154 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.127597 Loss1: 0.126910 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.137728 Loss1: 0.137041 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.114426 Loss1: 0.113738 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095208 Loss1: 0.094520 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082123 Loss1: 0.081435 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087277 Loss1: 0.086589 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.980024
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8676819620253164
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.252600 Loss1: 0.251917 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.181649 Loss1: 0.180960 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.157661 Loss1: 0.156974 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.113181 Loss1: 0.112492 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105802 Loss1: 0.105114 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.113608 Loss1: 0.112919 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.123513 Loss1: 0.122824 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.100560 Loss1: 0.099871 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.094366 Loss1: 0.093676 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.084507 Loss1: 0.083818 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.981606
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8651315789473685
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.305363 Loss1: 0.304677 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.181645 Loss1: 0.180955 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.177677 Loss1: 0.176986 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.137949 Loss1: 0.137259 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.111460 Loss1: 0.110770 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.110104 Loss1: 0.109413 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086824 Loss1: 0.086132 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.098339 Loss1: 0.097648 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111963 Loss1: 0.111272 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.113313 Loss1: 0.112621 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.975946
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8890224358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.249083 Loss1: 0.248398 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.176157 Loss1: 0.175469 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.139125 Loss1: 0.138436 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.107282 Loss1: 0.106591 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088648 Loss1: 0.087959 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.094320 Loss1: 0.093629 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.092926 Loss1: 0.092237 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095457 Loss1: 0.094768 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075847 Loss1: 0.075157 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091157 Loss1: 0.090465 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.979567
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8892911585365854
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.216138 Loss1: 0.215456 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.162791 Loss1: 0.162106 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.157623 Loss1: 0.156938 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.115973 Loss1: 0.115286 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.153165 Loss1: 0.152479 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.117425 Loss1: 0.116741 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110800 Loss1: 0.110115 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102185 Loss1: 0.101500 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099101 Loss1: 0.098414 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081530 Loss1: 0.080845 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.981707
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8013091216216216
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.323981 Loss1: 0.323297 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.199447 Loss1: 0.198760 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.161122 Loss1: 0.160433 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.122968 Loss1: 0.122278 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.103262 Loss1: 0.102573 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.099341 Loss1: 0.098651 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.102385 Loss1: 0.101695 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080968 Loss1: 0.080278 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.103135 Loss1: 0.102446 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087628 Loss1: 0.086940 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.982475
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8372231012658228
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.272800 Loss1: 0.272118 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.164336 Loss1: 0.163649 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.123102 Loss1: 0.122415 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.109843 Loss1: 0.109155 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.120004 Loss1: 0.119314 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.095401 Loss1: 0.094713 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.089793 Loss1: 0.089105 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074298 Loss1: 0.073610 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072582 Loss1: 0.071894 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088548 Loss1: 0.087858 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.977255
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8474392361111112
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.325543 Loss1: 0.324860 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.191559 Loss1: 0.190871 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.145475 Loss1: 0.144788 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.136371 Loss1: 0.135684 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.122543 Loss1: 0.121855 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098366 Loss1: 0.097678 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076931 Loss1: 0.076244 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095266 Loss1: 0.094578 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068231 Loss1: 0.067541 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063820 Loss1: 0.063131 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.985460
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 04:23:26,237][flwr][DEBUG] - fit_round 45 received 10 results and 0 failures
+test acc: 0.623
+[2023-09-22 04:24:19,382][flwr][INFO] - fit progress: (45, 2.1977186043041583, {'accuracy': 0.623}, 90741.043649517)
+[2023-09-22 04:24:19,383][flwr][DEBUG] - evaluate_round 45: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 04:24:57,422][flwr][DEBUG] - evaluate_round 45 received 10 results and 0 failures
+[2023-09-22 04:24:57,422][flwr][DEBUG] - fit_round 46: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8502604166666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.269647 Loss1: 0.268966 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.167797 Loss1: 0.167112 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.145172 Loss1: 0.144486 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.109053 Loss1: 0.108367 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.089019 Loss1: 0.088331 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.088520 Loss1: 0.087832 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077364 Loss1: 0.076675 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059709 Loss1: 0.059021 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061514 Loss1: 0.060826 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072940 Loss1: 0.072252 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.984592
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8540348101265823
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.269054 Loss1: 0.268372 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.136163 Loss1: 0.135478 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.140149 Loss1: 0.139463 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.134322 Loss1: 0.133634 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.113855 Loss1: 0.113166 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.102109 Loss1: 0.101420 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087256 Loss1: 0.086567 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077095 Loss1: 0.076404 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079661 Loss1: 0.078970 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091628 Loss1: 0.090940 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.985562
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8881478658536586
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.226038 Loss1: 0.225357 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.155769 Loss1: 0.155086 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088314 Loss1: 0.087630 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.099979 Loss1: 0.099295 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.109694 Loss1: 0.109011 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.088112 Loss1: 0.087426 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091861 Loss1: 0.091175 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.107300 Loss1: 0.106614 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.105041 Loss1: 0.104355 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099137 Loss1: 0.098451 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.980373
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8963607594936709
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.236624 Loss1: 0.235941 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.168346 Loss1: 0.167658 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.139331 Loss1: 0.138641 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.130788 Loss1: 0.130098 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.110875 Loss1: 0.110184 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.100843 Loss1: 0.100151 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076928 Loss1: 0.076237 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081124 Loss1: 0.080432 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063981 Loss1: 0.063289 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060857 Loss1: 0.060166 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.989122
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8105996621621622
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.319853 Loss1: 0.319168 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.209550 Loss1: 0.208862 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.139736 Loss1: 0.139047 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.126647 Loss1: 0.125959 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105605 Loss1: 0.104915 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.097296 Loss1: 0.096606 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106436 Loss1: 0.105746 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085256 Loss1: 0.084565 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108894 Loss1: 0.108203 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082022 Loss1: 0.081332 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.985642
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8860759493670886
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.231835 Loss1: 0.231153 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.179649 Loss1: 0.178962 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.144172 Loss1: 0.143484 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.130270 Loss1: 0.129583 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.140911 Loss1: 0.140222 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.124227 Loss1: 0.123538 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.126464 Loss1: 0.125775 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109113 Loss1: 0.108425 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097949 Loss1: 0.097260 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091610 Loss1: 0.090921 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.982793
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8708881578947368
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.255938 Loss1: 0.255252 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.149564 Loss1: 0.148876 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.143960 Loss1: 0.143271 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.148248 Loss1: 0.147558 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.153689 Loss1: 0.152998 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.130884 Loss1: 0.130194 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.111390 Loss1: 0.110699 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.116714 Loss1: 0.116024 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.094475 Loss1: 0.093784 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074815 Loss1: 0.074124 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.982936
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8629807692307693
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.236459 Loss1: 0.235778 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.188766 Loss1: 0.188084 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.171077 Loss1: 0.170393 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.132693 Loss1: 0.132008 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.119916 Loss1: 0.119230 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.143976 Loss1: 0.143292 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.112901 Loss1: 0.112215 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114324 Loss1: 0.113639 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087055 Loss1: 0.086370 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071887 Loss1: 0.071201 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.984575
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8894230769230769
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246482 Loss1: 0.245800 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.140240 Loss1: 0.139552 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.086661 Loss1: 0.085974 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.091771 Loss1: 0.091080 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.110297 Loss1: 0.109607 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089111 Loss1: 0.088420 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083344 Loss1: 0.082655 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.083493 Loss1: 0.082802 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082966 Loss1: 0.082277 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064031 Loss1: 0.063342 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989784
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8579905063291139
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246647 Loss1: 0.245965 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.175744 Loss1: 0.175058 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.116249 Loss1: 0.115561 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.120706 Loss1: 0.120019 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.114175 Loss1: 0.113485 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.104957 Loss1: 0.104271 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087153 Loss1: 0.086465 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.089581 Loss1: 0.088892 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091862 Loss1: 0.091172 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.105055 Loss1: 0.104367 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.984375
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 04:55:09,793][flwr][DEBUG] - fit_round 46 received 10 results and 0 failures
+test acc: 0.6263
+[2023-09-22 04:56:20,900][flwr][INFO] - fit progress: (46, 2.2119887084625782, {'accuracy': 0.6263}, 92662.56100891856)
+[2023-09-22 04:56:20,900][flwr][DEBUG] - evaluate_round 46: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 04:57:01,385][flwr][DEBUG] - evaluate_round 46 received 10 results and 0 failures
+[2023-09-22 04:57:01,386][flwr][DEBUG] - fit_round 47: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8876582278481012
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.198378 Loss1: 0.197697 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.149738 Loss1: 0.149051 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.100720 Loss1: 0.100030 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.080089 Loss1: 0.079398 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.102557 Loss1: 0.101867 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.115378 Loss1: 0.114690 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.084736 Loss1: 0.084046 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081986 Loss1: 0.081296 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083153 Loss1: 0.082462 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082725 Loss1: 0.082034 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.981210
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8635284810126582
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.221909 Loss1: 0.221228 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.148447 Loss1: 0.147761 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.113970 Loss1: 0.113285 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.098914 Loss1: 0.098228 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073522 Loss1: 0.072834 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.088341 Loss1: 0.087654 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101975 Loss1: 0.101287 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.089757 Loss1: 0.089070 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079816 Loss1: 0.079127 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.110526 Loss1: 0.109837 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.979233
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8561197916666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246656 Loss1: 0.245974 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.152264 Loss1: 0.151579 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.137290 Loss1: 0.136603 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.092037 Loss1: 0.091351 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079072 Loss1: 0.078384 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.073779 Loss1: 0.073092 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.089185 Loss1: 0.088495 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088245 Loss1: 0.087557 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078499 Loss1: 0.077811 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094061 Loss1: 0.093372 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.982422
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.893483231707317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.240052 Loss1: 0.239371 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.143438 Loss1: 0.142753 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.130261 Loss1: 0.129576 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.109011 Loss1: 0.108326 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105876 Loss1: 0.105191 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106283 Loss1: 0.105595 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.119213 Loss1: 0.118526 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119749 Loss1: 0.119061 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098207 Loss1: 0.097520 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081569 Loss1: 0.080883 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.983994
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8152449324324325
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.289389 Loss1: 0.288706 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.134702 Loss1: 0.134015 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.108773 Loss1: 0.108084 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.081753 Loss1: 0.081063 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.115465 Loss1: 0.114774 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106842 Loss1: 0.106152 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077845 Loss1: 0.077153 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.076880 Loss1: 0.076192 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078002 Loss1: 0.077314 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068498 Loss1: 0.067808 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.980997
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8980368589743589
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.207357 Loss1: 0.206677 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.147210 Loss1: 0.146524 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.122153 Loss1: 0.121466 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.092150 Loss1: 0.091461 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091075 Loss1: 0.090386 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072978 Loss1: 0.072289 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072048 Loss1: 0.071357 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060669 Loss1: 0.059979 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084547 Loss1: 0.083857 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081586 Loss1: 0.080895 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.985777
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8601661392405063
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.224724 Loss1: 0.224042 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.158248 Loss1: 0.157560 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.129869 Loss1: 0.129181 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.116100 Loss1: 0.115413 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101502 Loss1: 0.100813 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.109807 Loss1: 0.109118 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.121932 Loss1: 0.121242 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.113831 Loss1: 0.113141 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108601 Loss1: 0.107912 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086840 Loss1: 0.086151 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.984771
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.875
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.262480 Loss1: 0.261794 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.154628 Loss1: 0.153938 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.110667 Loss1: 0.109976 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.082094 Loss1: 0.081403 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.087161 Loss1: 0.086470 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.101338 Loss1: 0.100646 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.121603 Loss1: 0.120912 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.108116 Loss1: 0.107425 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097594 Loss1: 0.096904 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094093 Loss1: 0.093403 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.984375
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8854825949367089
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.221609 Loss1: 0.220926 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.158984 Loss1: 0.158297 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.112692 Loss1: 0.112004 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.089782 Loss1: 0.089094 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105944 Loss1: 0.105255 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.110839 Loss1: 0.110151 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094122 Loss1: 0.093433 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097022 Loss1: 0.096331 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071451 Loss1: 0.070760 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075629 Loss1: 0.074938 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.984177
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8754006410256411
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.250336 Loss1: 0.249657 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.157938 Loss1: 0.157256 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.140942 Loss1: 0.140258 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.131687 Loss1: 0.131002 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088160 Loss1: 0.087475 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105111 Loss1: 0.104427 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081988 Loss1: 0.081302 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097192 Loss1: 0.096506 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.105377 Loss1: 0.104693 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096202 Loss1: 0.095517 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.981370
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 05:27:07,636][flwr][DEBUG] - fit_round 47 received 10 results and 0 failures
+test acc: 0.6254
+[2023-09-22 05:28:10,853][flwr][INFO] - fit progress: (47, 2.1827334691160405, {'accuracy': 0.6254}, 94572.51411859691)
+[2023-09-22 05:28:10,853][flwr][DEBUG] - evaluate_round 47: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 05:28:49,002][flwr][DEBUG] - evaluate_round 47 received 10 results and 0 failures
+[2023-09-22 05:28:49,010][flwr][DEBUG] - fit_round 48: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8268581081081081
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.290976 Loss1: 0.290292 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.124191 Loss1: 0.123501 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.118522 Loss1: 0.117832 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.102910 Loss1: 0.102221 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098887 Loss1: 0.098197 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078760 Loss1: 0.078069 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082651 Loss1: 0.081960 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094598 Loss1: 0.093909 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079493 Loss1: 0.078803 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074979 Loss1: 0.074290 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.986486
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8740110759493671
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.214294 Loss1: 0.213612 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.118901 Loss1: 0.118214 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.120177 Loss1: 0.119489 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.102646 Loss1: 0.101959 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.100646 Loss1: 0.099958 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.136593 Loss1: 0.135906 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.102817 Loss1: 0.102129 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.067336 Loss1: 0.066646 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087047 Loss1: 0.086358 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085333 Loss1: 0.084645 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.973892
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8657041139240507
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.234327 Loss1: 0.233645 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.123857 Loss1: 0.123172 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.121470 Loss1: 0.120781 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.119810 Loss1: 0.119121 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.118193 Loss1: 0.117504 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.095954 Loss1: 0.095266 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.112043 Loss1: 0.111354 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.082286 Loss1: 0.081597 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097922 Loss1: 0.097234 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086968 Loss1: 0.086279 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.981804
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.893483231707317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.205278 Loss1: 0.204599 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.145162 Loss1: 0.144477 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.107618 Loss1: 0.106933 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.100386 Loss1: 0.099700 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.115975 Loss1: 0.115290 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116794 Loss1: 0.116106 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087363 Loss1: 0.086676 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070789 Loss1: 0.070100 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076251 Loss1: 0.075562 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082603 Loss1: 0.081916 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.984375
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8606770833333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.244024 Loss1: 0.243341 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.136704 Loss1: 0.136018 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.131053 Loss1: 0.130365 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.129716 Loss1: 0.129030 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.112870 Loss1: 0.112182 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.099328 Loss1: 0.098640 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106952 Loss1: 0.106264 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088842 Loss1: 0.088152 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081425 Loss1: 0.080735 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069974 Loss1: 0.069286 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.985677
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8982371794871795
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.207581 Loss1: 0.206899 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.111369 Loss1: 0.110682 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.138776 Loss1: 0.138087 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.107972 Loss1: 0.107283 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088725 Loss1: 0.088036 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.080382 Loss1: 0.079692 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068338 Loss1: 0.067648 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.064417 Loss1: 0.063728 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053806 Loss1: 0.053117 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058201 Loss1: 0.057511 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.993590
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.875
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.236587 Loss1: 0.235902 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.158143 Loss1: 0.157453 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.139564 Loss1: 0.138873 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.124571 Loss1: 0.123880 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.106794 Loss1: 0.106103 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.122009 Loss1: 0.121319 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.125357 Loss1: 0.124666 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.093150 Loss1: 0.092459 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.090435 Loss1: 0.089745 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087395 Loss1: 0.086703 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.987253
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9054588607594937
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.215713 Loss1: 0.215029 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.123963 Loss1: 0.123276 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083106 Loss1: 0.082418 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.082331 Loss1: 0.081642 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079123 Loss1: 0.078433 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.096134 Loss1: 0.095445 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.097818 Loss1: 0.097129 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.113458 Loss1: 0.112768 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.100474 Loss1: 0.099785 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094762 Loss1: 0.094071 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.979430
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8705929487179487
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.211255 Loss1: 0.210575 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.152850 Loss1: 0.152168 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.101688 Loss1: 0.101004 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.104093 Loss1: 0.103408 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088363 Loss1: 0.087679 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098475 Loss1: 0.097788 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.084181 Loss1: 0.083495 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.101612 Loss1: 0.100926 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111337 Loss1: 0.110651 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.106991 Loss1: 0.106304 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.975761
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8920094936708861
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.217703 Loss1: 0.217019 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.121235 Loss1: 0.120547 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088958 Loss1: 0.088269 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.111087 Loss1: 0.110396 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091981 Loss1: 0.091292 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.100155 Loss1: 0.099466 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087088 Loss1: 0.086398 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109504 Loss1: 0.108814 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084049 Loss1: 0.083357 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098049 Loss1: 0.097359 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.983584
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 05:59:12,479][flwr][DEBUG] - fit_round 48 received 10 results and 0 failures
+test acc: 0.6296
+[2023-09-22 06:00:19,674][flwr][INFO] - fit progress: (48, 2.2119309980267534, {'accuracy': 0.6296}, 96501.33515052684)
+[2023-09-22 06:00:19,675][flwr][DEBUG] - evaluate_round 48: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 06:00:59,408][flwr][DEBUG] - evaluate_round 48 received 10 results and 0 failures
+[2023-09-22 06:00:59,409][flwr][DEBUG] - fit_round 49: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8821957236842105
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.261955 Loss1: 0.261268 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.175224 Loss1: 0.174534 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.137613 Loss1: 0.136922 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.104800 Loss1: 0.104110 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.096358 Loss1: 0.095667 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.099405 Loss1: 0.098715 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103829 Loss1: 0.103139 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.110146 Loss1: 0.109454 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082258 Loss1: 0.081565 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074628 Loss1: 0.073937 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.990954
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8641493055555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.260793 Loss1: 0.260110 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.127200 Loss1: 0.126514 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.119800 Loss1: 0.119113 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.100492 Loss1: 0.099804 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.117610 Loss1: 0.116921 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092187 Loss1: 0.091498 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087552 Loss1: 0.086863 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096358 Loss1: 0.095669 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.096524 Loss1: 0.095834 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075713 Loss1: 0.075023 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.987630
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8988381410256411
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.215812 Loss1: 0.215128 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.095514 Loss1: 0.094827 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.080295 Loss1: 0.079606 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.101805 Loss1: 0.101117 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079646 Loss1: 0.078957 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.083170 Loss1: 0.082481 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081221 Loss1: 0.080531 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091620 Loss1: 0.090930 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076989 Loss1: 0.076300 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078062 Loss1: 0.077373 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.985377
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8686708860759493
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.197535 Loss1: 0.196851 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.151177 Loss1: 0.150489 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.122762 Loss1: 0.122073 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.107385 Loss1: 0.106695 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.111596 Loss1: 0.110906 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.099931 Loss1: 0.099242 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.075917 Loss1: 0.075226 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072137 Loss1: 0.071448 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065463 Loss1: 0.064772 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071896 Loss1: 0.071205 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.985957
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8975474683544303
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.209747 Loss1: 0.209064 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.161961 Loss1: 0.161273 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.135291 Loss1: 0.134602 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.098260 Loss1: 0.097570 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.083404 Loss1: 0.082716 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.095267 Loss1: 0.094578 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077792 Loss1: 0.077102 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075028 Loss1: 0.074339 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089400 Loss1: 0.088709 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083845 Loss1: 0.083155 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.982199
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8763844936708861
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.220394 Loss1: 0.219710 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.118030 Loss1: 0.117342 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.111905 Loss1: 0.111217 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.129385 Loss1: 0.128698 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.119028 Loss1: 0.118340 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116718 Loss1: 0.116032 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110028 Loss1: 0.109340 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077122 Loss1: 0.076433 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.096651 Loss1: 0.095960 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079948 Loss1: 0.079257 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.986748
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8973496835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.213781 Loss1: 0.213098 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.108607 Loss1: 0.107918 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.094685 Loss1: 0.093994 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.075335 Loss1: 0.074645 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098891 Loss1: 0.098201 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.125268 Loss1: 0.124577 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101141 Loss1: 0.100452 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.086433 Loss1: 0.085742 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080689 Loss1: 0.079996 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074779 Loss1: 0.074089 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.984375
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.816722972972973
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.264240 Loss1: 0.263556 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.179452 Loss1: 0.178761 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.136325 Loss1: 0.135634 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.099294 Loss1: 0.098605 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.080389 Loss1: 0.079698 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068731 Loss1: 0.068040 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.096018 Loss1: 0.095327 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061416 Loss1: 0.060725 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066445 Loss1: 0.065755 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050312 Loss1: 0.049623 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.987120
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8990091463414634
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.182394 Loss1: 0.181713 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.120763 Loss1: 0.120078 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.094246 Loss1: 0.093561 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.104693 Loss1: 0.104007 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.089464 Loss1: 0.088778 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123547 Loss1: 0.122861 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.093693 Loss1: 0.093007 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078489 Loss1: 0.077802 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074246 Loss1: 0.073560 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.107473 Loss1: 0.106787 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.978468
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8812099358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.227968 Loss1: 0.227289 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.135014 Loss1: 0.134330 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.107319 Loss1: 0.106634 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087508 Loss1: 0.086823 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.089860 Loss1: 0.089174 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.070965 Loss1: 0.070280 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077450 Loss1: 0.076764 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095166 Loss1: 0.094481 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108830 Loss1: 0.108143 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099631 Loss1: 0.098944 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.981370
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 06:32:04,195][flwr][DEBUG] - fit_round 49 received 10 results and 0 failures
+test acc: 0.6282
+[2023-09-22 06:33:08,740][flwr][INFO] - fit progress: (49, 2.2019196455471053, {'accuracy': 0.6282}, 98470.40143894171)
+[2023-09-22 06:33:08,741][flwr][DEBUG] - evaluate_round 49: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 06:33:46,636][flwr][DEBUG] - evaluate_round 49 received 10 results and 0 failures
+[2023-09-22 06:33:46,641][flwr][DEBUG] - fit_round 50: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9028876582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.190870 Loss1: 0.190187 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.104922 Loss1: 0.104234 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.091389 Loss1: 0.090700 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.119542 Loss1: 0.118854 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133667 Loss1: 0.132977 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.097067 Loss1: 0.096377 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110143 Loss1: 0.109451 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121940 Loss1: 0.121249 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.088854 Loss1: 0.088163 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075811 Loss1: 0.075118 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.982002
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8955696202531646
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.200101 Loss1: 0.199417 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.139387 Loss1: 0.138702 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.133929 Loss1: 0.133242 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.130086 Loss1: 0.129399 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.106531 Loss1: 0.105843 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.087350 Loss1: 0.086661 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086242 Loss1: 0.085553 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075463 Loss1: 0.074775 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085205 Loss1: 0.084516 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098720 Loss1: 0.098031 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.979233
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9024390243902439
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.175464 Loss1: 0.174783 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.124557 Loss1: 0.123872 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.118693 Loss1: 0.118008 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086300 Loss1: 0.085614 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.090180 Loss1: 0.089495 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.076443 Loss1: 0.075755 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078956 Loss1: 0.078270 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.084594 Loss1: 0.083907 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060816 Loss1: 0.060129 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076886 Loss1: 0.076198 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.981707
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8243243243243243
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246419 Loss1: 0.245734 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.144227 Loss1: 0.143538 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.102189 Loss1: 0.101502 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.103283 Loss1: 0.102593 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.111024 Loss1: 0.110334 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.112834 Loss1: 0.112143 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.130589 Loss1: 0.129899 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.112000 Loss1: 0.111308 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.070970 Loss1: 0.070278 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047936 Loss1: 0.047245 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.994088
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8908305921052632
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.201953 Loss1: 0.201266 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.133344 Loss1: 0.132654 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.113302 Loss1: 0.112612 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088094 Loss1: 0.087404 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.110447 Loss1: 0.109755 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.119621 Loss1: 0.118929 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.090458 Loss1: 0.089767 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102750 Loss1: 0.102059 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.095600 Loss1: 0.094909 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096042 Loss1: 0.095349 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.981908
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8669704861111112
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.251657 Loss1: 0.250973 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.132485 Loss1: 0.131799 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.095299 Loss1: 0.094614 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071282 Loss1: 0.070595 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060193 Loss1: 0.059506 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058463 Loss1: 0.057776 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082129 Loss1: 0.081440 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.125792 Loss1: 0.125104 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110278 Loss1: 0.109592 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.095256 Loss1: 0.094569 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.989366
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9018429487179487
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.198664 Loss1: 0.197981 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.134820 Loss1: 0.134133 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.125656 Loss1: 0.124968 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.097046 Loss1: 0.096357 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.093462 Loss1: 0.092771 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066594 Loss1: 0.065902 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051512 Loss1: 0.050822 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.065698 Loss1: 0.065007 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054362 Loss1: 0.053671 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060767 Loss1: 0.060076 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.990585
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8774038461538461
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.193253 Loss1: 0.192574 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.133331 Loss1: 0.132647 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.123458 Loss1: 0.122773 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.114672 Loss1: 0.113987 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086013 Loss1: 0.085327 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.087977 Loss1: 0.087293 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091283 Loss1: 0.090598 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096868 Loss1: 0.096180 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.101622 Loss1: 0.100936 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.089661 Loss1: 0.088975 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.982973
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8757911392405063
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.199801 Loss1: 0.199119 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.117425 Loss1: 0.116739 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.099686 Loss1: 0.098998 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.095811 Loss1: 0.095123 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101921 Loss1: 0.101233 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082496 Loss1: 0.081807 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.100109 Loss1: 0.099420 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095137 Loss1: 0.094447 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083228 Loss1: 0.082540 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085172 Loss1: 0.084481 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.983386
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.885878164556962
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.185992 Loss1: 0.185310 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.095690 Loss1: 0.095003 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.110820 Loss1: 0.110133 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.127984 Loss1: 0.127298 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.103429 Loss1: 0.102741 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.121589 Loss1: 0.120901 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094162 Loss1: 0.093474 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094342 Loss1: 0.093653 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063662 Loss1: 0.062974 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064557 Loss1: 0.063869 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.989517
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 07:05:07,821][flwr][DEBUG] - fit_round 50 received 10 results and 0 failures
+test acc: 0.6331
+[2023-09-22 07:06:37,577][flwr][INFO] - fit progress: (50, 2.2049550935864066, {'accuracy': 0.6331}, 100479.23796597496)
+[2023-09-22 07:06:37,577][flwr][DEBUG] - evaluate_round 50: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 07:07:16,665][flwr][DEBUG] - evaluate_round 50 received 10 results and 0 failures
+[2023-09-22 07:07:16,666][flwr][DEBUG] - fit_round 51: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9035823170731707
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.175415 Loss1: 0.174735 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.119686 Loss1: 0.119001 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.117820 Loss1: 0.117135 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.121226 Loss1: 0.120542 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101486 Loss1: 0.100801 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.107914 Loss1: 0.107228 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103980 Loss1: 0.103292 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095675 Loss1: 0.094988 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091955 Loss1: 0.091269 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083865 Loss1: 0.083179 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.985709
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8789556962025317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.184885 Loss1: 0.184202 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.115576 Loss1: 0.114889 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.092202 Loss1: 0.091514 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085379 Loss1: 0.084691 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.082839 Loss1: 0.082150 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.107595 Loss1: 0.106905 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091100 Loss1: 0.090413 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102462 Loss1: 0.101774 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080680 Loss1: 0.079990 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096893 Loss1: 0.096204 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.979430
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8935032894736842
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.234641 Loss1: 0.233956 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.135527 Loss1: 0.134838 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.113436 Loss1: 0.112746 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.108958 Loss1: 0.108269 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.094245 Loss1: 0.093554 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106341 Loss1: 0.105650 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094114 Loss1: 0.093424 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078392 Loss1: 0.077701 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082706 Loss1: 0.082014 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091618 Loss1: 0.090928 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.982730
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9013053797468354
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.193550 Loss1: 0.192866 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.093386 Loss1: 0.092699 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083388 Loss1: 0.082700 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086124 Loss1: 0.085435 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073697 Loss1: 0.073007 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072441 Loss1: 0.071751 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065445 Loss1: 0.064755 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091881 Loss1: 0.091191 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109561 Loss1: 0.108871 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.112923 Loss1: 0.112233 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.980222
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.877373417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.193234 Loss1: 0.192551 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.100142 Loss1: 0.099456 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.099782 Loss1: 0.099095 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.093616 Loss1: 0.092929 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.095586 Loss1: 0.094898 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.094676 Loss1: 0.093988 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094827 Loss1: 0.094139 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.090787 Loss1: 0.090098 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089252 Loss1: 0.088563 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071248 Loss1: 0.070559 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.984177
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8892227564102564
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.188684 Loss1: 0.188003 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.109382 Loss1: 0.108699 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.110488 Loss1: 0.109804 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.104757 Loss1: 0.104072 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.072381 Loss1: 0.071694 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062159 Loss1: 0.061475 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.116713 Loss1: 0.116027 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099851 Loss1: 0.099165 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.100004 Loss1: 0.099317 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078320 Loss1: 0.077633 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.989583
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8665364583333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.223648 Loss1: 0.222965 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.119915 Loss1: 0.119227 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.090970 Loss1: 0.090282 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.105773 Loss1: 0.105085 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.100287 Loss1: 0.099597 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.075048 Loss1: 0.074359 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.084898 Loss1: 0.084209 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.086505 Loss1: 0.085817 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089672 Loss1: 0.088984 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098767 Loss1: 0.098077 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.980035
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8344594594594594
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.237782 Loss1: 0.237098 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.157147 Loss1: 0.156458 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.107584 Loss1: 0.106896 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.128742 Loss1: 0.128054 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.109256 Loss1: 0.108567 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091561 Loss1: 0.090870 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.095527 Loss1: 0.094836 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077924 Loss1: 0.077232 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110906 Loss1: 0.110215 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080897 Loss1: 0.080206 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.982897
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9060496794871795
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.194710 Loss1: 0.194028 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.120753 Loss1: 0.120066 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.123114 Loss1: 0.122427 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.114967 Loss1: 0.114278 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.099848 Loss1: 0.099158 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060538 Loss1: 0.059848 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065943 Loss1: 0.065252 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071650 Loss1: 0.070960 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081947 Loss1: 0.081257 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096895 Loss1: 0.096204 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.980970
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9117879746835443
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.170802 Loss1: 0.170117 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.107753 Loss1: 0.107066 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.098381 Loss1: 0.097691 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.083459 Loss1: 0.082771 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079114 Loss1: 0.078424 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.096153 Loss1: 0.095462 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.109443 Loss1: 0.108753 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079013 Loss1: 0.078323 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078067 Loss1: 0.077376 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.095538 Loss1: 0.094848 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.981804
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 07:37:36,260][flwr][DEBUG] - fit_round 51 received 10 results and 0 failures
+test acc: 0.6347
+[2023-09-22 07:38:35,603][flwr][INFO] - fit progress: (51, 2.181678266951832, {'accuracy': 0.6347}, 102397.2644809708)
+[2023-09-22 07:38:35,603][flwr][DEBUG] - evaluate_round 51: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 07:39:14,109][flwr][DEBUG] - evaluate_round 51 received 10 results and 0 failures
+[2023-09-22 07:39:14,110][flwr][DEBUG] - fit_round 52: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8791232638888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.205996 Loss1: 0.205314 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.122995 Loss1: 0.122310 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069256 Loss1: 0.068570 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068894 Loss1: 0.068207 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079593 Loss1: 0.078907 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091908 Loss1: 0.091222 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.109386 Loss1: 0.108699 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074210 Loss1: 0.073521 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097360 Loss1: 0.096672 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.118041 Loss1: 0.117352 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.983073
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.903391768292683
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.153114 Loss1: 0.152433 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.120135 Loss1: 0.119449 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.084659 Loss1: 0.083973 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069464 Loss1: 0.068776 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.070471 Loss1: 0.069785 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078616 Loss1: 0.077928 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072062 Loss1: 0.071375 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060992 Loss1: 0.060304 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081202 Loss1: 0.080515 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078622 Loss1: 0.077934 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.986662
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9005142405063291
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.164493 Loss1: 0.163811 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.106794 Loss1: 0.106108 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.098368 Loss1: 0.097679 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.078046 Loss1: 0.077358 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084622 Loss1: 0.083935 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.079029 Loss1: 0.078339 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.088639 Loss1: 0.087950 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075594 Loss1: 0.074906 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084665 Loss1: 0.083975 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082094 Loss1: 0.081402 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.985759
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8338260135135135
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.219879 Loss1: 0.219196 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.142812 Loss1: 0.142125 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.108483 Loss1: 0.107794 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.108859 Loss1: 0.108171 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.090618 Loss1: 0.089930 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.069271 Loss1: 0.068582 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058724 Loss1: 0.058035 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061755 Loss1: 0.061066 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081414 Loss1: 0.080726 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070336 Loss1: 0.069647 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.978252
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.907051282051282
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.167577 Loss1: 0.166895 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.091378 Loss1: 0.090691 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083648 Loss1: 0.082959 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085781 Loss1: 0.085092 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.104549 Loss1: 0.103860 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.100150 Loss1: 0.099461 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078714 Loss1: 0.078025 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068670 Loss1: 0.067981 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078230 Loss1: 0.077539 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098125 Loss1: 0.097435 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.981370
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8834134615384616
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.163807 Loss1: 0.163128 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.121847 Loss1: 0.121163 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.108014 Loss1: 0.107330 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088145 Loss1: 0.087461 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101616 Loss1: 0.100931 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089094 Loss1: 0.088407 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086311 Loss1: 0.085624 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092480 Loss1: 0.091794 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111921 Loss1: 0.111236 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.100727 Loss1: 0.100041 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.982372
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8848892405063291
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.191586 Loss1: 0.190904 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.127238 Loss1: 0.126551 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.089309 Loss1: 0.088618 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.083157 Loss1: 0.082469 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084899 Loss1: 0.084209 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.087176 Loss1: 0.086488 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.079905 Loss1: 0.079215 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117316 Loss1: 0.116628 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.096848 Loss1: 0.096158 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090283 Loss1: 0.089591 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.978244
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8900082236842105
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.194407 Loss1: 0.193722 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.112877 Loss1: 0.112189 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.087067 Loss1: 0.086379 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.070891 Loss1: 0.070201 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064048 Loss1: 0.063359 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.096083 Loss1: 0.095394 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107091 Loss1: 0.106401 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094389 Loss1: 0.093699 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077923 Loss1: 0.077232 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070956 Loss1: 0.070267 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.981908
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9064477848101266
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.164921 Loss1: 0.164237 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.109088 Loss1: 0.108398 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.099824 Loss1: 0.099133 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.098408 Loss1: 0.097718 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.082364 Loss1: 0.081673 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061554 Loss1: 0.060863 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067949 Loss1: 0.067257 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.067607 Loss1: 0.066917 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075263 Loss1: 0.074571 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072354 Loss1: 0.071662 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.986155
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8886471518987342
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.168941 Loss1: 0.168258 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.105610 Loss1: 0.104923 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.086014 Loss1: 0.085328 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.091712 Loss1: 0.091025 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.077969 Loss1: 0.077280 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105455 Loss1: 0.104768 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103936 Loss1: 0.103248 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.122103 Loss1: 0.121416 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.095164 Loss1: 0.094476 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.102305 Loss1: 0.101615 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.979628
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 08:09:16,446][flwr][DEBUG] - fit_round 52 received 10 results and 0 failures
+test acc: 0.6291
+[2023-09-22 08:10:14,663][flwr][INFO] - fit progress: (52, 2.2030137499300437, {'accuracy': 0.6291}, 104296.32475985959)
+[2023-09-22 08:10:14,664][flwr][DEBUG] - evaluate_round 52: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 08:10:54,678][flwr][DEBUG] - evaluate_round 52 received 10 results and 0 failures
+[2023-09-22 08:10:54,679][flwr][DEBUG] - fit_round 53: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.908922697368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.192943 Loss1: 0.192259 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.149768 Loss1: 0.149079 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.136060 Loss1: 0.135370 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.114696 Loss1: 0.114005 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.117991 Loss1: 0.117300 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.085299 Loss1: 0.084608 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.096773 Loss1: 0.096082 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.103478 Loss1: 0.102787 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067785 Loss1: 0.067093 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087254 Loss1: 0.086565 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.984992
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8760850694444444
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.198226 Loss1: 0.197543 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.111743 Loss1: 0.111057 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.105603 Loss1: 0.104916 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.100840 Loss1: 0.100153 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088670 Loss1: 0.087981 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106346 Loss1: 0.105658 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094824 Loss1: 0.094135 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095853 Loss1: 0.095165 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.096365 Loss1: 0.095676 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.118489 Loss1: 0.117802 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.980903
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9144435975609756
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.146122 Loss1: 0.145442 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.109500 Loss1: 0.108816 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088984 Loss1: 0.088299 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.089158 Loss1: 0.088471 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084375 Loss1: 0.083688 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.075072 Loss1: 0.074384 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071345 Loss1: 0.070657 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.093406 Loss1: 0.092718 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078378 Loss1: 0.077689 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081805 Loss1: 0.081118 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.985518
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9110576923076923
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.174968 Loss1: 0.174285 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.102167 Loss1: 0.101480 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088525 Loss1: 0.087836 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088671 Loss1: 0.087983 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.080049 Loss1: 0.079359 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063688 Loss1: 0.062996 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076587 Loss1: 0.075898 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075597 Loss1: 0.074905 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065457 Loss1: 0.064764 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053476 Loss1: 0.052784 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.991186
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8914161392405063
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.160003 Loss1: 0.159320 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.094407 Loss1: 0.093720 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.091105 Loss1: 0.090417 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.078758 Loss1: 0.078071 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098816 Loss1: 0.098128 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105619 Loss1: 0.104931 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.092667 Loss1: 0.091978 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092434 Loss1: 0.091745 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093179 Loss1: 0.092490 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.093256 Loss1: 0.092566 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.979628
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9185126582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.150637 Loss1: 0.149953 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.108621 Loss1: 0.107931 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.061349 Loss1: 0.060660 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.070652 Loss1: 0.069962 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.097630 Loss1: 0.096938 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.111295 Loss1: 0.110603 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085065 Loss1: 0.084374 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080486 Loss1: 0.079794 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053999 Loss1: 0.053307 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054765 Loss1: 0.054072 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.989913
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8340371621621622
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.215685 Loss1: 0.215001 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.119588 Loss1: 0.118900 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.103636 Loss1: 0.102948 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.090694 Loss1: 0.090004 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.092406 Loss1: 0.091717 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065990 Loss1: 0.065300 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091041 Loss1: 0.090352 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099183 Loss1: 0.098493 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.090248 Loss1: 0.089558 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071728 Loss1: 0.071038 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.991765
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.890625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.176255 Loss1: 0.175575 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.081758 Loss1: 0.081074 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.070453 Loss1: 0.069767 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058730 Loss1: 0.058046 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.053200 Loss1: 0.052516 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065084 Loss1: 0.064399 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071848 Loss1: 0.071162 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088584 Loss1: 0.087899 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080100 Loss1: 0.079413 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070343 Loss1: 0.069657 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.987380
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8981408227848101
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.164600 Loss1: 0.163916 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.119904 Loss1: 0.119216 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.092789 Loss1: 0.092099 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.096477 Loss1: 0.095788 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084054 Loss1: 0.083365 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082545 Loss1: 0.081855 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.095332 Loss1: 0.094644 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088635 Loss1: 0.087946 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076929 Loss1: 0.076240 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079832 Loss1: 0.079144 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.988924
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8839003164556962
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.171708 Loss1: 0.171023 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.104557 Loss1: 0.103868 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.085277 Loss1: 0.084587 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.089494 Loss1: 0.088805 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.123242 Loss1: 0.122552 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.118757 Loss1: 0.118067 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.122607 Loss1: 0.121918 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096462 Loss1: 0.095771 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077973 Loss1: 0.077284 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057810 Loss1: 0.057120 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.990309
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 08:42:44,725][flwr][DEBUG] - fit_round 53 received 10 results and 0 failures
+test acc: 0.6303
+[2023-09-22 08:43:53,746][flwr][INFO] - fit progress: (53, 2.2342346537227447, {'accuracy': 0.6303}, 106315.40782714868)
+[2023-09-22 08:43:53,747][flwr][DEBUG] - evaluate_round 53: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 08:44:32,373][flwr][DEBUG] - evaluate_round 53 received 10 results and 0 failures
+[2023-09-22 08:44:32,373][flwr][DEBUG] - fit_round 54: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9056566455696202
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.167288 Loss1: 0.166604 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.097642 Loss1: 0.096955 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.113440 Loss1: 0.112752 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.095614 Loss1: 0.094925 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091308 Loss1: 0.090621 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.094624 Loss1: 0.093936 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.059245 Loss1: 0.058557 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085153 Loss1: 0.084463 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098708 Loss1: 0.098019 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080631 Loss1: 0.079942 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.981408
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9124599358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.171042 Loss1: 0.170358 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078465 Loss1: 0.077778 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058451 Loss1: 0.057763 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.082387 Loss1: 0.081699 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.058704 Loss1: 0.058014 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074998 Loss1: 0.074309 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108664 Loss1: 0.107975 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097550 Loss1: 0.096861 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065083 Loss1: 0.064392 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054383 Loss1: 0.053692 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.993389
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8771701388888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.209629 Loss1: 0.208947 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.118121 Loss1: 0.117436 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.082247 Loss1: 0.081561 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.062497 Loss1: 0.061811 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073050 Loss1: 0.072364 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.071373 Loss1: 0.070686 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072974 Loss1: 0.072288 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.089055 Loss1: 0.088367 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074402 Loss1: 0.073714 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091367 Loss1: 0.090678 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.985460
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8795490506329114
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.170666 Loss1: 0.169984 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068806 Loss1: 0.068119 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060023 Loss1: 0.059334 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.075624 Loss1: 0.074937 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.122835 Loss1: 0.122146 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.119355 Loss1: 0.118668 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106346 Loss1: 0.105659 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075921 Loss1: 0.075233 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059692 Loss1: 0.059005 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066991 Loss1: 0.066301 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.983386
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9148246951219512
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144921 Loss1: 0.144241 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.103099 Loss1: 0.102415 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.079172 Loss1: 0.078487 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.095861 Loss1: 0.095174 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.092426 Loss1: 0.091739 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091826 Loss1: 0.091139 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108913 Loss1: 0.108227 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088789 Loss1: 0.088101 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072294 Loss1: 0.071607 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066796 Loss1: 0.066109 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.990473
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9046052631578947
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.197543 Loss1: 0.196858 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.116165 Loss1: 0.115477 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.107419 Loss1: 0.106728 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.105602 Loss1: 0.104912 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105169 Loss1: 0.104479 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.081557 Loss1: 0.080868 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106111 Loss1: 0.105421 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.104374 Loss1: 0.103682 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080351 Loss1: 0.079660 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063836 Loss1: 0.063144 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.988076
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8924050632911392
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.160280 Loss1: 0.159597 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.097182 Loss1: 0.096497 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.081814 Loss1: 0.081127 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.081925 Loss1: 0.081239 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.065116 Loss1: 0.064429 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089481 Loss1: 0.088794 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101293 Loss1: 0.100606 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092640 Loss1: 0.091953 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079051 Loss1: 0.078362 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074186 Loss1: 0.073499 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.983386
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9113924050632911
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.160154 Loss1: 0.159471 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.105171 Loss1: 0.104485 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083866 Loss1: 0.083179 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087309 Loss1: 0.086620 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079923 Loss1: 0.079233 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078734 Loss1: 0.078045 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078486 Loss1: 0.077799 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099406 Loss1: 0.098717 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091571 Loss1: 0.090882 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080001 Loss1: 0.079313 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.991297
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8410050675675675
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.213045 Loss1: 0.212361 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.103924 Loss1: 0.103235 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.094519 Loss1: 0.093830 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.062570 Loss1: 0.061880 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.062672 Loss1: 0.061982 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.081799 Loss1: 0.081109 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081252 Loss1: 0.080562 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061039 Loss1: 0.060348 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085062 Loss1: 0.084372 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078368 Loss1: 0.077679 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.985220
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8982371794871795
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.173393 Loss1: 0.172715 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.135783 Loss1: 0.135099 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.114653 Loss1: 0.113970 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.122311 Loss1: 0.121627 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.125289 Loss1: 0.124605 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.131248 Loss1: 0.130563 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091993 Loss1: 0.091309 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094328 Loss1: 0.093642 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071915 Loss1: 0.071229 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081809 Loss1: 0.081123 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.980970
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 09:15:06,540][flwr][DEBUG] - fit_round 54 received 10 results and 0 failures
+test acc: 0.634
+[2023-09-22 09:16:08,095][flwr][INFO] - fit progress: (54, 2.22973222568774, {'accuracy': 0.634}, 108249.7566356957)
+[2023-09-22 09:16:08,096][flwr][DEBUG] - evaluate_round 54: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 09:16:46,515][flwr][DEBUG] - evaluate_round 54 received 10 results and 0 failures
+[2023-09-22 09:16:46,516][flwr][DEBUG] - fit_round 55: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9125791139240507
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.163372 Loss1: 0.162688 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.111495 Loss1: 0.110807 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.090252 Loss1: 0.089562 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.082235 Loss1: 0.081547 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.077936 Loss1: 0.077247 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.086384 Loss1: 0.085694 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068065 Loss1: 0.067376 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044276 Loss1: 0.043584 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058871 Loss1: 0.058181 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075514 Loss1: 0.074822 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.984375
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.899129746835443
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144305 Loss1: 0.143623 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.086306 Loss1: 0.085622 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069513 Loss1: 0.068826 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067782 Loss1: 0.067096 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069383 Loss1: 0.068695 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061216 Loss1: 0.060528 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.075739 Loss1: 0.075051 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078943 Loss1: 0.078255 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.090825 Loss1: 0.090137 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075421 Loss1: 0.074733 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.981804
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9014423076923077
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.159107 Loss1: 0.158428 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.097084 Loss1: 0.096401 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.074567 Loss1: 0.073884 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.076810 Loss1: 0.076125 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073154 Loss1: 0.072471 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089229 Loss1: 0.088545 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.098183 Loss1: 0.097499 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.090698 Loss1: 0.090013 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078465 Loss1: 0.077781 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049228 Loss1: 0.048542 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.987780
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9197789634146342
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.147704 Loss1: 0.147023 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.095709 Loss1: 0.095026 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.077359 Loss1: 0.076674 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087118 Loss1: 0.086432 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.083503 Loss1: 0.082816 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.069149 Loss1: 0.068462 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062257 Loss1: 0.061571 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069879 Loss1: 0.069193 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083850 Loss1: 0.083163 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.084040 Loss1: 0.083354 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.982279
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9150641025641025
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.143408 Loss1: 0.142726 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.089993 Loss1: 0.089305 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.098660 Loss1: 0.097972 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.092374 Loss1: 0.091685 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.080860 Loss1: 0.080171 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053030 Loss1: 0.052342 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073914 Loss1: 0.073225 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.065908 Loss1: 0.065219 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063004 Loss1: 0.062315 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081936 Loss1: 0.081246 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.980569
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8924050632911392
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.136082 Loss1: 0.135400 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.091117 Loss1: 0.090431 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088034 Loss1: 0.087345 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.075125 Loss1: 0.074437 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.087920 Loss1: 0.087232 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.070275 Loss1: 0.069587 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.089805 Loss1: 0.089117 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.100987 Loss1: 0.100300 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.092629 Loss1: 0.091940 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098159 Loss1: 0.097469 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.982199
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9137658227848101
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.165001 Loss1: 0.164319 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.101940 Loss1: 0.101253 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.068548 Loss1: 0.067860 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087349 Loss1: 0.086663 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.103791 Loss1: 0.103102 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105055 Loss1: 0.104368 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.118438 Loss1: 0.117749 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.107145 Loss1: 0.106456 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081288 Loss1: 0.080600 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069719 Loss1: 0.069029 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.989715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9052220394736842
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.180560 Loss1: 0.179876 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.107927 Loss1: 0.107239 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.103379 Loss1: 0.102687 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.083178 Loss1: 0.082488 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.077848 Loss1: 0.077157 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.087957 Loss1: 0.087267 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103090 Loss1: 0.102398 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095186 Loss1: 0.094496 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091498 Loss1: 0.090809 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065852 Loss1: 0.065162 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.986842
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.880859375
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.189659 Loss1: 0.188976 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.096331 Loss1: 0.095644 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.091621 Loss1: 0.090933 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071484 Loss1: 0.070795 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091237 Loss1: 0.090548 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105195 Loss1: 0.104507 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.097614 Loss1: 0.096926 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080566 Loss1: 0.079878 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072465 Loss1: 0.071777 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056796 Loss1: 0.056107 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.988498
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.847339527027027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.205544 Loss1: 0.204860 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.104730 Loss1: 0.104042 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083760 Loss1: 0.083071 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067264 Loss1: 0.066574 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.070639 Loss1: 0.069949 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074859 Loss1: 0.074170 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.059811 Loss1: 0.059120 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075702 Loss1: 0.075011 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067956 Loss1: 0.067266 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052145 Loss1: 0.051454 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.986486
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 09:46:58,527][flwr][DEBUG] - fit_round 55 received 10 results and 0 failures
+test acc: 0.6324
+[2023-09-22 09:47:56,143][flwr][INFO] - fit progress: (55, 2.254546900526784, {'accuracy': 0.6324}, 110157.80404786859)
+[2023-09-22 09:47:56,144][flwr][DEBUG] - evaluate_round 55: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 09:48:34,892][flwr][DEBUG] - evaluate_round 55 received 10 results and 0 failures
+[2023-09-22 09:48:34,893][flwr][DEBUG] - fit_round 56: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8893229166666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.176493 Loss1: 0.175809 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.099395 Loss1: 0.098709 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083752 Loss1: 0.083064 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.092986 Loss1: 0.092297 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.089814 Loss1: 0.089127 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.079300 Loss1: 0.078611 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078367 Loss1: 0.077678 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074888 Loss1: 0.074199 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.073758 Loss1: 0.073069 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076311 Loss1: 0.075622 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.982856
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9076891447368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.157224 Loss1: 0.156538 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.117247 Loss1: 0.116556 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.084953 Loss1: 0.084263 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.081797 Loss1: 0.081107 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098019 Loss1: 0.097329 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078835 Loss1: 0.078142 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.080507 Loss1: 0.079816 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097582 Loss1: 0.096890 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082997 Loss1: 0.082304 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078030 Loss1: 0.077337 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.989926
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9235899390243902
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.131041 Loss1: 0.130362 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.084688 Loss1: 0.084002 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.105627 Loss1: 0.104943 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085415 Loss1: 0.084730 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.068279 Loss1: 0.067593 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068026 Loss1: 0.067339 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071642 Loss1: 0.070954 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071487 Loss1: 0.070799 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.070820 Loss1: 0.070132 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055239 Loss1: 0.054551 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.992759
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9167325949367089
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.150346 Loss1: 0.149661 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.083677 Loss1: 0.082988 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.071807 Loss1: 0.071118 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047845 Loss1: 0.047155 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.071747 Loss1: 0.071057 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.071031 Loss1: 0.070340 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070082 Loss1: 0.069390 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048308 Loss1: 0.047619 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064545 Loss1: 0.063854 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071298 Loss1: 0.070607 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.990704
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8488175675675675
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.186883 Loss1: 0.186200 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.115384 Loss1: 0.114695 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.089395 Loss1: 0.088707 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088259 Loss1: 0.087570 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101970 Loss1: 0.101281 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.095931 Loss1: 0.095241 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082551 Loss1: 0.081862 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.067215 Loss1: 0.066525 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058822 Loss1: 0.058131 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060070 Loss1: 0.059381 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989654
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9072516025641025
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.146384 Loss1: 0.145705 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.093807 Loss1: 0.093124 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.102518 Loss1: 0.101834 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086685 Loss1: 0.085999 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.062995 Loss1: 0.062311 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.097788 Loss1: 0.097102 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107356 Loss1: 0.106670 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091649 Loss1: 0.090963 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091814 Loss1: 0.091127 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.084596 Loss1: 0.083911 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.980970
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.915743670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.133483 Loss1: 0.132801 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.087133 Loss1: 0.086445 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.086427 Loss1: 0.085738 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086106 Loss1: 0.085416 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086350 Loss1: 0.085662 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.114939 Loss1: 0.114251 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.117489 Loss1: 0.116798 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099871 Loss1: 0.099182 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064601 Loss1: 0.063910 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055310 Loss1: 0.054622 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9009098101265823
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.153825 Loss1: 0.153143 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.101965 Loss1: 0.101280 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.100819 Loss1: 0.100133 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066236 Loss1: 0.065551 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.075881 Loss1: 0.075196 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.081027 Loss1: 0.080340 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076344 Loss1: 0.075657 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075243 Loss1: 0.074555 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071710 Loss1: 0.071021 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085332 Loss1: 0.084643 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.977650
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9190705128205128
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.136276 Loss1: 0.135593 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.099828 Loss1: 0.099141 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.074669 Loss1: 0.073981 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.081215 Loss1: 0.080526 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069422 Loss1: 0.068733 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050076 Loss1: 0.049388 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036367 Loss1: 0.035678 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062592 Loss1: 0.061901 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076502 Loss1: 0.075813 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087546 Loss1: 0.086855 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.986579
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8912183544303798
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.136684 Loss1: 0.136002 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.085612 Loss1: 0.084924 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.076345 Loss1: 0.075657 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087905 Loss1: 0.087216 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.071643 Loss1: 0.070953 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058402 Loss1: 0.057712 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072365 Loss1: 0.071676 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069774 Loss1: 0.069083 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076160 Loss1: 0.075470 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.103651 Loss1: 0.102962 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.986353
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 10:19:01,505][flwr][DEBUG] - fit_round 56 received 10 results and 0 failures
+test acc: 0.6339
+[2023-09-22 10:19:57,276][flwr][INFO] - fit progress: (56, 2.251486159170778, {'accuracy': 0.6339}, 112078.93746713502)
+[2023-09-22 10:19:57,277][flwr][DEBUG] - evaluate_round 56: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 10:20:37,401][flwr][DEBUG] - evaluate_round 56 received 10 results and 0 failures
+[2023-09-22 10:20:37,402][flwr][DEBUG] - fit_round 57: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9237804878048781
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.128587 Loss1: 0.127905 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065857 Loss1: 0.065173 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.062804 Loss1: 0.062119 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066879 Loss1: 0.066193 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.083335 Loss1: 0.082648 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.064871 Loss1: 0.064183 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073453 Loss1: 0.072767 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075907 Loss1: 0.075219 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059489 Loss1: 0.058802 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083056 Loss1: 0.082368 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.984947
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9004407051282052
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.147339 Loss1: 0.146660 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.074674 Loss1: 0.073992 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.050567 Loss1: 0.049884 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046682 Loss1: 0.045998 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.056050 Loss1: 0.055366 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089697 Loss1: 0.089012 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083117 Loss1: 0.082431 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068742 Loss1: 0.068056 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067199 Loss1: 0.066513 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087766 Loss1: 0.087083 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.975962
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9153481012658228
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.143539 Loss1: 0.142857 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.099089 Loss1: 0.098403 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.092695 Loss1: 0.092008 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.079382 Loss1: 0.078694 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086745 Loss1: 0.086057 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092763 Loss1: 0.092073 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.090099 Loss1: 0.089410 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072921 Loss1: 0.072231 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066248 Loss1: 0.065559 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059581 Loss1: 0.058890 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.987342
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9230769230769231
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.131828 Loss1: 0.131147 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.075480 Loss1: 0.074793 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.063091 Loss1: 0.062404 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058533 Loss1: 0.057845 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.066521 Loss1: 0.065833 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.070447 Loss1: 0.069758 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068559 Loss1: 0.067868 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048766 Loss1: 0.048076 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063392 Loss1: 0.062701 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049320 Loss1: 0.048628 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.984375
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8823784722222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.183960 Loss1: 0.183278 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.098113 Loss1: 0.097428 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066742 Loss1: 0.066056 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066250 Loss1: 0.065563 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064434 Loss1: 0.063748 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063566 Loss1: 0.062879 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072523 Loss1: 0.071836 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.134785 Loss1: 0.134099 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111926 Loss1: 0.111238 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091203 Loss1: 0.090516 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.985894
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9027549342105263
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.190850 Loss1: 0.190165 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.102582 Loss1: 0.101893 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.080788 Loss1: 0.080099 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.074897 Loss1: 0.074207 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.078899 Loss1: 0.078207 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078530 Loss1: 0.077838 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103668 Loss1: 0.102977 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094816 Loss1: 0.094125 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081183 Loss1: 0.080490 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083965 Loss1: 0.083274 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.987048
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9268196202531646
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.147926 Loss1: 0.147242 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.080427 Loss1: 0.079737 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.070650 Loss1: 0.069960 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.074337 Loss1: 0.073645 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.083006 Loss1: 0.082312 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074695 Loss1: 0.074004 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085594 Loss1: 0.084901 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092550 Loss1: 0.091859 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074418 Loss1: 0.073726 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058935 Loss1: 0.058243 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.989320
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8526182432432432
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.179405 Loss1: 0.178721 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.110108 Loss1: 0.109420 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.085313 Loss1: 0.084623 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069102 Loss1: 0.068412 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.065628 Loss1: 0.064939 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074693 Loss1: 0.074004 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053647 Loss1: 0.052956 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.083851 Loss1: 0.083159 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084047 Loss1: 0.083356 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057357 Loss1: 0.056666 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.988387
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9060522151898734
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144210 Loss1: 0.143529 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.090538 Loss1: 0.089851 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.080905 Loss1: 0.080218 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.080363 Loss1: 0.079676 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091376 Loss1: 0.090687 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092690 Loss1: 0.092002 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.063844 Loss1: 0.063155 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070747 Loss1: 0.070060 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047177 Loss1: 0.046489 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046374 Loss1: 0.045685 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992682
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.896756329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.149916 Loss1: 0.149233 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.090775 Loss1: 0.090088 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.068567 Loss1: 0.067879 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.073252 Loss1: 0.072564 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.081493 Loss1: 0.080804 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.073677 Loss1: 0.072987 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067829 Loss1: 0.067141 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074224 Loss1: 0.073533 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082365 Loss1: 0.081675 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078057 Loss1: 0.077366 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.986353
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 10:50:48,331][flwr][DEBUG] - fit_round 57 received 10 results and 0 failures
+test acc: 0.6369
+[2023-09-22 10:51:54,632][flwr][INFO] - fit progress: (57, 2.2792362104208705, {'accuracy': 0.6369}, 113996.29401065502)
+[2023-09-22 10:51:54,633][flwr][DEBUG] - evaluate_round 57: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 10:52:34,006][flwr][DEBUG] - evaluate_round 57 received 10 results and 0 failures
+[2023-09-22 10:52:34,007][flwr][DEBUG] - fit_round 58: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9145569620253164
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.134843 Loss1: 0.134158 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.096164 Loss1: 0.095476 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.085630 Loss1: 0.084941 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067671 Loss1: 0.066980 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.075326 Loss1: 0.074636 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.101878 Loss1: 0.101189 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091884 Loss1: 0.091194 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.104193 Loss1: 0.103502 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114539 Loss1: 0.113848 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083429 Loss1: 0.082736 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.993078
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9232772435897436
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144608 Loss1: 0.143927 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.102332 Loss1: 0.101647 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.079713 Loss1: 0.079025 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069301 Loss1: 0.068613 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.058416 Loss1: 0.057728 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036473 Loss1: 0.035784 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044998 Loss1: 0.044308 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049468 Loss1: 0.048778 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066418 Loss1: 0.065728 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064360 Loss1: 0.063672 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.984776
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9199695121951219
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.132937 Loss1: 0.132257 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.077936 Loss1: 0.077252 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064687 Loss1: 0.064002 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.074834 Loss1: 0.074149 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079693 Loss1: 0.079008 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.081262 Loss1: 0.080574 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073898 Loss1: 0.073212 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052274 Loss1: 0.051588 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044925 Loss1: 0.044237 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049436 Loss1: 0.048749 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.990091
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9090189873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.145638 Loss1: 0.144958 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078035 Loss1: 0.077351 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064996 Loss1: 0.064312 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071534 Loss1: 0.070850 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.058963 Loss1: 0.058278 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.069753 Loss1: 0.069067 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082218 Loss1: 0.081531 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.083515 Loss1: 0.082829 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059950 Loss1: 0.059261 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072201 Loss1: 0.071515 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.985166
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.919202302631579
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.157386 Loss1: 0.156702 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.080713 Loss1: 0.080024 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.073363 Loss1: 0.072674 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.057504 Loss1: 0.056816 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.076195 Loss1: 0.075505 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.056178 Loss1: 0.055489 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057746 Loss1: 0.057057 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058340 Loss1: 0.057649 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077716 Loss1: 0.077025 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072807 Loss1: 0.072116 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.976151
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.896484375
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.179366 Loss1: 0.178684 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.084353 Loss1: 0.083665 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.081176 Loss1: 0.080491 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066666 Loss1: 0.065978 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063722 Loss1: 0.063035 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074520 Loss1: 0.073832 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065400 Loss1: 0.064711 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062468 Loss1: 0.061780 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052624 Loss1: 0.051935 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062044 Loss1: 0.061356 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.991536
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9145569620253164
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.130700 Loss1: 0.130017 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.088949 Loss1: 0.088263 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.087171 Loss1: 0.086483 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.089425 Loss1: 0.088737 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.099953 Loss1: 0.099264 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.096849 Loss1: 0.096160 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072393 Loss1: 0.071704 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088184 Loss1: 0.087495 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089876 Loss1: 0.089188 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.093026 Loss1: 0.092337 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.981804
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8918117088607594
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144679 Loss1: 0.143998 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.102355 Loss1: 0.101668 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.080995 Loss1: 0.080307 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086094 Loss1: 0.085403 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084498 Loss1: 0.083809 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.085917 Loss1: 0.085228 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091265 Loss1: 0.090575 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080424 Loss1: 0.079732 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064075 Loss1: 0.063384 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073829 Loss1: 0.073137 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.981606
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8526182432432432
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.181932 Loss1: 0.181247 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.100775 Loss1: 0.100087 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.102997 Loss1: 0.102308 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069132 Loss1: 0.068442 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.074030 Loss1: 0.073340 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.075515 Loss1: 0.074825 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087681 Loss1: 0.086991 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.098005 Loss1: 0.097315 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066655 Loss1: 0.065963 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078417 Loss1: 0.077725 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.990709
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9030448717948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.127790 Loss1: 0.127109 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.085841 Loss1: 0.085157 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.094675 Loss1: 0.093991 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.096008 Loss1: 0.095324 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.092625 Loss1: 0.091941 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091244 Loss1: 0.090559 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.090298 Loss1: 0.089611 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081182 Loss1: 0.080497 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085043 Loss1: 0.084357 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062991 Loss1: 0.062305 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.989583
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 11:23:41,408][flwr][DEBUG] - fit_round 58 received 10 results and 0 failures
+test acc: 0.6379
+[2023-09-22 11:24:44,869][flwr][INFO] - fit progress: (58, 2.2519221557215, {'accuracy': 0.6379}, 115966.53004605463)
+[2023-09-22 11:24:44,869][flwr][DEBUG] - evaluate_round 58: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 11:25:24,646][flwr][DEBUG] - evaluate_round 58 received 10 results and 0 failures
+[2023-09-22 11:25:24,648][flwr][DEBUG] - fit_round 59: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9193037974683544
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.130642 Loss1: 0.129959 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.086782 Loss1: 0.086094 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.102118 Loss1: 0.101429 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086572 Loss1: 0.085883 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.068652 Loss1: 0.067964 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.073175 Loss1: 0.072486 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.064657 Loss1: 0.063968 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069834 Loss1: 0.069143 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.086151 Loss1: 0.085461 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073156 Loss1: 0.072467 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.988331
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8914930555555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.167947 Loss1: 0.167264 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.096267 Loss1: 0.095580 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065512 Loss1: 0.064825 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065051 Loss1: 0.064364 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063580 Loss1: 0.062892 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061127 Loss1: 0.060439 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077292 Loss1: 0.076602 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069326 Loss1: 0.068638 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080862 Loss1: 0.080173 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074526 Loss1: 0.073837 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.984592
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9264240506329114
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.104241 Loss1: 0.103559 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.076653 Loss1: 0.075965 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.063277 Loss1: 0.062588 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.070486 Loss1: 0.069796 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.078541 Loss1: 0.077851 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.057908 Loss1: 0.057218 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062614 Loss1: 0.061923 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061478 Loss1: 0.060786 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082326 Loss1: 0.081634 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050060 Loss1: 0.049368 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.991693
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8604307432432432
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.169320 Loss1: 0.168635 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.088719 Loss1: 0.088030 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045810 Loss1: 0.045121 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049124 Loss1: 0.048435 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060302 Loss1: 0.059613 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072245 Loss1: 0.071555 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.080025 Loss1: 0.079335 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071072 Loss1: 0.070383 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.062914 Loss1: 0.062225 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070019 Loss1: 0.069327 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.986909
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9040743670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.124525 Loss1: 0.123842 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.079348 Loss1: 0.078663 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059184 Loss1: 0.058497 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045832 Loss1: 0.045146 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.049875 Loss1: 0.049188 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061634 Loss1: 0.060947 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055440 Loss1: 0.054753 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061546 Loss1: 0.060857 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078290 Loss1: 0.077602 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.092324 Loss1: 0.091635 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.979826
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9236778846153846
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.134411 Loss1: 0.133726 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.073830 Loss1: 0.073143 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065536 Loss1: 0.064847 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.056755 Loss1: 0.056064 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051932 Loss1: 0.051241 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038628 Loss1: 0.037938 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048772 Loss1: 0.048082 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052714 Loss1: 0.052022 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049826 Loss1: 0.049135 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040987 Loss1: 0.040296 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.991787
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9176682692307693
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144010 Loss1: 0.143331 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078683 Loss1: 0.078000 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.050241 Loss1: 0.049556 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068453 Loss1: 0.067768 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088272 Loss1: 0.087586 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.102390 Loss1: 0.101704 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.116279 Loss1: 0.115593 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068930 Loss1: 0.068242 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064885 Loss1: 0.064198 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053077 Loss1: 0.052390 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.980970
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8912183544303798
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.154235 Loss1: 0.153552 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.081173 Loss1: 0.080484 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.079785 Loss1: 0.079096 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071665 Loss1: 0.070976 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073199 Loss1: 0.072510 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074048 Loss1: 0.073358 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044421 Loss1: 0.043732 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068620 Loss1: 0.067928 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055651 Loss1: 0.054960 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054590 Loss1: 0.053898 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.988133
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9291158536585366
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103590 Loss1: 0.102909 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066192 Loss1: 0.065508 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045163 Loss1: 0.044479 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052207 Loss1: 0.051521 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043808 Loss1: 0.043121 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055219 Loss1: 0.054533 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103266 Loss1: 0.102579 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.066970 Loss1: 0.066283 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066607 Loss1: 0.065919 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064232 Loss1: 0.063545 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.982088
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.91796875
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.163269 Loss1: 0.162585 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.095255 Loss1: 0.094566 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060455 Loss1: 0.059766 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.080770 Loss1: 0.080080 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.081536 Loss1: 0.080847 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078154 Loss1: 0.077463 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.097301 Loss1: 0.096611 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095880 Loss1: 0.095191 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080082 Loss1: 0.079393 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075840 Loss1: 0.075149 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.980674
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 11:56:52,497][flwr][DEBUG] - fit_round 59 received 10 results and 0 failures
+test acc: 0.6354
+[2023-09-22 11:58:36,400][flwr][INFO] - fit progress: (59, 2.2759478625398093, {'accuracy': 0.6354}, 117998.06179395458)
+[2023-09-22 11:58:36,401][flwr][DEBUG] - evaluate_round 59: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 11:59:14,036][flwr][DEBUG] - evaluate_round 59 received 10 results and 0 failures
+[2023-09-22 11:59:14,037][flwr][DEBUG] - fit_round 60: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9010416666666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.140802 Loss1: 0.140119 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.092404 Loss1: 0.091718 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.078219 Loss1: 0.077532 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061041 Loss1: 0.060353 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079201 Loss1: 0.078513 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063790 Loss1: 0.063102 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070037 Loss1: 0.069349 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.066704 Loss1: 0.066017 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046973 Loss1: 0.046284 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041775 Loss1: 0.041087 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994358
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9165348101265823
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.113589 Loss1: 0.112908 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072344 Loss1: 0.071657 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055700 Loss1: 0.055011 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050256 Loss1: 0.049570 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055228 Loss1: 0.054540 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.076516 Loss1: 0.075828 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073322 Loss1: 0.072636 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047131 Loss1: 0.046443 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072581 Loss1: 0.071892 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081622 Loss1: 0.080933 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.981013
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9155016447368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.131729 Loss1: 0.131043 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.083839 Loss1: 0.083150 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.071569 Loss1: 0.070879 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071684 Loss1: 0.070995 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086252 Loss1: 0.085562 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.093499 Loss1: 0.092808 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076033 Loss1: 0.075343 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068948 Loss1: 0.068258 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078216 Loss1: 0.077527 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068168 Loss1: 0.067476 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.988076
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9122596153846154
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.130060 Loss1: 0.129379 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.071540 Loss1: 0.070855 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059835 Loss1: 0.059151 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.063453 Loss1: 0.062769 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059722 Loss1: 0.059036 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068757 Loss1: 0.068070 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.090617 Loss1: 0.089933 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095835 Loss1: 0.095149 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093368 Loss1: 0.092683 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086888 Loss1: 0.086199 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987179
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.855785472972973
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.165608 Loss1: 0.164927 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.086624 Loss1: 0.085937 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.084446 Loss1: 0.083756 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061717 Loss1: 0.061026 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073367 Loss1: 0.072677 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091584 Loss1: 0.090895 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.064014 Loss1: 0.063323 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061050 Loss1: 0.060358 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059017 Loss1: 0.058328 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054216 Loss1: 0.053526 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.989231
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.928006329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105442 Loss1: 0.104760 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.079864 Loss1: 0.079176 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.081011 Loss1: 0.080321 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.083275 Loss1: 0.082584 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073762 Loss1: 0.073072 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082608 Loss1: 0.081918 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076863 Loss1: 0.076172 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075426 Loss1: 0.074736 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052991 Loss1: 0.052300 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060123 Loss1: 0.059432 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.989320
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8981408227848101
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.139829 Loss1: 0.139146 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067444 Loss1: 0.066756 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056657 Loss1: 0.055969 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.073841 Loss1: 0.073152 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.071606 Loss1: 0.070917 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061233 Loss1: 0.060543 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076223 Loss1: 0.075535 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080104 Loss1: 0.079415 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075340 Loss1: 0.074652 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069611 Loss1: 0.068920 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.987342
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9272836538461539
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.115755 Loss1: 0.115074 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068817 Loss1: 0.068130 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056486 Loss1: 0.055799 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053189 Loss1: 0.052501 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057391 Loss1: 0.056703 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058579 Loss1: 0.057890 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077555 Loss1: 0.076864 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079177 Loss1: 0.078487 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058953 Loss1: 0.058264 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048434 Loss1: 0.047743 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.989383
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9309731012658228
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.129098 Loss1: 0.128416 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078072 Loss1: 0.077385 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.087011 Loss1: 0.086324 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085216 Loss1: 0.084527 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.071810 Loss1: 0.071122 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.057453 Loss1: 0.056764 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054078 Loss1: 0.053389 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063304 Loss1: 0.062616 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054829 Loss1: 0.054141 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067695 Loss1: 0.067006 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989320
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9233993902439024
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.131753 Loss1: 0.131073 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066099 Loss1: 0.065414 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065768 Loss1: 0.065081 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066390 Loss1: 0.065703 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073907 Loss1: 0.073220 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.075545 Loss1: 0.074856 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.066764 Loss1: 0.066077 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056409 Loss1: 0.055721 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076010 Loss1: 0.075321 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076068 Loss1: 0.075378 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987043
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 12:31:08,804][flwr][DEBUG] - fit_round 60 received 10 results and 0 failures
+test acc: 0.6394
+[2023-09-22 12:32:14,347][flwr][INFO] - fit progress: (60, 2.300492998700553, {'accuracy': 0.6394}, 120016.00814736774)
+[2023-09-22 12:32:14,347][flwr][DEBUG] - evaluate_round 60: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 12:32:52,792][flwr][DEBUG] - evaluate_round 60 received 10 results and 0 failures
+[2023-09-22 12:32:52,793][flwr][DEBUG] - fit_round 61: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9302591463414634
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103416 Loss1: 0.102736 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.069314 Loss1: 0.068630 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064702 Loss1: 0.064019 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048388 Loss1: 0.047702 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063229 Loss1: 0.062543 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058859 Loss1: 0.058171 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061518 Loss1: 0.060832 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060381 Loss1: 0.059694 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068819 Loss1: 0.068133 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088116 Loss1: 0.087429 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.979802
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.921073717948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.117948 Loss1: 0.117268 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.076936 Loss1: 0.076253 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088159 Loss1: 0.087475 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067472 Loss1: 0.066786 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.099291 Loss1: 0.098605 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098376 Loss1: 0.097692 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069047 Loss1: 0.068361 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081835 Loss1: 0.081149 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091241 Loss1: 0.090555 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.116217 Loss1: 0.115531 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.981571
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9256329113924051
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.114813 Loss1: 0.114131 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.063793 Loss1: 0.063107 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064886 Loss1: 0.064198 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053616 Loss1: 0.052927 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064398 Loss1: 0.063708 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062372 Loss1: 0.061684 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083259 Loss1: 0.082570 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069161 Loss1: 0.068472 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068197 Loss1: 0.067507 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050882 Loss1: 0.050192 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.990902
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8985363924050633
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.126361 Loss1: 0.125679 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066233 Loss1: 0.065549 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052060 Loss1: 0.051374 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049698 Loss1: 0.049011 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073109 Loss1: 0.072421 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108843 Loss1: 0.108154 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078555 Loss1: 0.077866 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072515 Loss1: 0.071825 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065546 Loss1: 0.064855 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070832 Loss1: 0.070141 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.981804
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.862964527027027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.143786 Loss1: 0.143102 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.081736 Loss1: 0.081048 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.067516 Loss1: 0.066828 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046582 Loss1: 0.045893 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046841 Loss1: 0.046149 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050659 Loss1: 0.049968 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061968 Loss1: 0.061278 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057272 Loss1: 0.056583 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068659 Loss1: 0.067970 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066694 Loss1: 0.066002 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.991343
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9235197368421053
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.160666 Loss1: 0.159981 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.098809 Loss1: 0.098120 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.093732 Loss1: 0.093042 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085002 Loss1: 0.084311 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.066655 Loss1: 0.065964 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062249 Loss1: 0.061559 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053609 Loss1: 0.052917 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059391 Loss1: 0.058701 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077120 Loss1: 0.076428 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080405 Loss1: 0.079713 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.986637
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.90625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.142670 Loss1: 0.141988 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067134 Loss1: 0.066448 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051590 Loss1: 0.050903 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.064188 Loss1: 0.063500 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054313 Loss1: 0.053626 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074083 Loss1: 0.073397 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071940 Loss1: 0.071252 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.073737 Loss1: 0.073050 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068104 Loss1: 0.067416 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073249 Loss1: 0.072560 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.988331
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9286858974358975
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116392 Loss1: 0.115710 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.056861 Loss1: 0.056173 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.075214 Loss1: 0.074527 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088098 Loss1: 0.087410 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.087003 Loss1: 0.086312 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072272 Loss1: 0.071582 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085433 Loss1: 0.084744 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077619 Loss1: 0.076928 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064574 Loss1: 0.063883 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071262 Loss1: 0.070568 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.985978
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9378955696202531
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095339 Loss1: 0.094657 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052799 Loss1: 0.052111 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.054848 Loss1: 0.054159 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059995 Loss1: 0.059304 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054415 Loss1: 0.053725 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052097 Loss1: 0.051406 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.075925 Loss1: 0.075234 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075368 Loss1: 0.074677 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084340 Loss1: 0.083647 Loss2: 0.000694
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086159 Loss1: 0.085467 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.984573
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8951822916666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.163926 Loss1: 0.163243 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.089450 Loss1: 0.088764 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066626 Loss1: 0.065939 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.080528 Loss1: 0.079841 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.076235 Loss1: 0.075548 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078904 Loss1: 0.078217 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085788 Loss1: 0.085098 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077613 Loss1: 0.076923 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059832 Loss1: 0.059143 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070974 Loss1: 0.070283 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.987413
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 13:04:11,327][flwr][DEBUG] - fit_round 61 received 10 results and 0 failures
+test acc: 0.6379
+[2023-09-22 13:05:14,904][flwr][INFO] - fit progress: (61, 2.2899934441898577, {'accuracy': 0.6379}, 121996.56573300483)
+[2023-09-22 13:05:14,905][flwr][DEBUG] - evaluate_round 61: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 13:05:53,121][flwr][DEBUG] - evaluate_round 61 received 10 results and 0 failures
+[2023-09-22 13:05:53,123][flwr][DEBUG] - fit_round 62: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9173259493670886
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.137097 Loss1: 0.136415 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078660 Loss1: 0.077974 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.057480 Loss1: 0.056793 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.060165 Loss1: 0.059479 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052913 Loss1: 0.052228 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044016 Loss1: 0.043329 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062781 Loss1: 0.062093 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052611 Loss1: 0.051922 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.048484 Loss1: 0.047796 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042104 Loss1: 0.041416 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.987144
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9076522435897436
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.120681 Loss1: 0.120002 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.077306 Loss1: 0.076624 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.078202 Loss1: 0.077519 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.064580 Loss1: 0.063896 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038939 Loss1: 0.038255 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040172 Loss1: 0.039488 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048597 Loss1: 0.047912 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052879 Loss1: 0.052193 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041188 Loss1: 0.040502 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047691 Loss1: 0.047007 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.993389
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8600084459459459
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.153959 Loss1: 0.153275 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.079259 Loss1: 0.078570 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.073999 Loss1: 0.073310 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061111 Loss1: 0.060421 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057955 Loss1: 0.057265 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065237 Loss1: 0.064547 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.066316 Loss1: 0.065626 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079557 Loss1: 0.078867 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079170 Loss1: 0.078480 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061728 Loss1: 0.061038 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.988809
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9205411585365854
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.102850 Loss1: 0.102169 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.074770 Loss1: 0.074086 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.067762 Loss1: 0.067076 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066884 Loss1: 0.066198 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.074477 Loss1: 0.073792 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058961 Loss1: 0.058275 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055550 Loss1: 0.054863 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109136 Loss1: 0.108450 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085779 Loss1: 0.085090 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074402 Loss1: 0.073714 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.987233
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9268092105263158
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.128960 Loss1: 0.128273 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078410 Loss1: 0.077721 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066368 Loss1: 0.065678 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061040 Loss1: 0.060350 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.068576 Loss1: 0.067884 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062459 Loss1: 0.061768 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067411 Loss1: 0.066719 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071813 Loss1: 0.071121 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054572 Loss1: 0.053881 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063293 Loss1: 0.062600 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.991365
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9336939102564102
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105073 Loss1: 0.104392 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.057546 Loss1: 0.056859 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060387 Loss1: 0.059699 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052464 Loss1: 0.051776 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045134 Loss1: 0.044446 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050807 Loss1: 0.050118 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055040 Loss1: 0.054350 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037978 Loss1: 0.037290 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034445 Loss1: 0.033756 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053801 Loss1: 0.053111 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.984776
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9325553797468354
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097668 Loss1: 0.096987 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.082032 Loss1: 0.081345 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049492 Loss1: 0.048805 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050125 Loss1: 0.049436 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054260 Loss1: 0.053572 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082202 Loss1: 0.081512 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081063 Loss1: 0.080374 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078751 Loss1: 0.078063 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078800 Loss1: 0.078113 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094899 Loss1: 0.094209 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.983386
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9036787974683544
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.126656 Loss1: 0.125974 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.062474 Loss1: 0.061790 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065519 Loss1: 0.064835 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059851 Loss1: 0.059166 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.070283 Loss1: 0.069596 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.077933 Loss1: 0.077246 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072135 Loss1: 0.071447 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085405 Loss1: 0.084717 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057197 Loss1: 0.056507 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074552 Loss1: 0.073862 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.983188
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9032118055555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116264 Loss1: 0.115583 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068680 Loss1: 0.067993 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.057737 Loss1: 0.057051 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.054647 Loss1: 0.053960 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063063 Loss1: 0.062376 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065182 Loss1: 0.064495 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043215 Loss1: 0.042526 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050854 Loss1: 0.050166 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060158 Loss1: 0.059468 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062303 Loss1: 0.061613 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.988715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9396756329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.106302 Loss1: 0.105619 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.082909 Loss1: 0.082223 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051721 Loss1: 0.051033 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050189 Loss1: 0.049498 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057993 Loss1: 0.057304 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063575 Loss1: 0.062885 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039857 Loss1: 0.039167 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046375 Loss1: 0.045684 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045789 Loss1: 0.045098 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056552 Loss1: 0.055862 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.987540
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 13:36:46,510][flwr][DEBUG] - fit_round 62 received 10 results and 0 failures
+test acc: 0.6368
+[2023-09-22 13:37:51,307][flwr][INFO] - fit progress: (62, 2.312452397407434, {'accuracy': 0.6368}, 123952.96846021572)
+[2023-09-22 13:37:51,308][flwr][DEBUG] - evaluate_round 62: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 13:38:30,094][flwr][DEBUG] - evaluate_round 62 received 10 results and 0 failures
+[2023-09-22 13:38:30,095][flwr][DEBUG] - fit_round 63: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9064477848101266
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116315 Loss1: 0.115632 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065312 Loss1: 0.064625 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.057208 Loss1: 0.056520 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.079473 Loss1: 0.078784 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057359 Loss1: 0.056670 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063119 Loss1: 0.062431 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057897 Loss1: 0.057205 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071158 Loss1: 0.070469 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066850 Loss1: 0.066159 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080424 Loss1: 0.079734 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.982397
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9187104430379747
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116141 Loss1: 0.115459 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065622 Loss1: 0.064934 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059657 Loss1: 0.058969 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065359 Loss1: 0.064673 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086870 Loss1: 0.086181 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.093759 Loss1: 0.093069 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086467 Loss1: 0.085777 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079177 Loss1: 0.078488 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069880 Loss1: 0.069191 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071663 Loss1: 0.070971 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.984968
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9168669871794872
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116617 Loss1: 0.115940 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067009 Loss1: 0.066327 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.054372 Loss1: 0.053689 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044581 Loss1: 0.043898 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052085 Loss1: 0.051401 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.057673 Loss1: 0.056987 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039829 Loss1: 0.039143 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061014 Loss1: 0.060327 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067893 Loss1: 0.067206 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050373 Loss1: 0.049687 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.988582
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9340945512820513
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.098806 Loss1: 0.098125 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065376 Loss1: 0.064689 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058183 Loss1: 0.057496 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.056092 Loss1: 0.055404 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059936 Loss1: 0.059250 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047325 Loss1: 0.046637 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062337 Loss1: 0.061650 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080579 Loss1: 0.079891 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072818 Loss1: 0.072131 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063556 Loss1: 0.062866 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.986178
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9001736111111112
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.140283 Loss1: 0.139602 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.080284 Loss1: 0.079598 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.075714 Loss1: 0.075027 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066866 Loss1: 0.066179 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.074044 Loss1: 0.073358 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066494 Loss1: 0.065807 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071266 Loss1: 0.070579 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060667 Loss1: 0.059978 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079607 Loss1: 0.078919 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087282 Loss1: 0.086593 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.986762
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9189082278481012
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.133779 Loss1: 0.133100 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072649 Loss1: 0.071964 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064563 Loss1: 0.063878 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048061 Loss1: 0.047375 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042426 Loss1: 0.041740 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034272 Loss1: 0.033586 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042225 Loss1: 0.041538 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.066910 Loss1: 0.066222 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076869 Loss1: 0.076181 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078459 Loss1: 0.077772 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.989517
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8684543918918919
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.126031 Loss1: 0.125347 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.079957 Loss1: 0.079271 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.075941 Loss1: 0.075253 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065499 Loss1: 0.064811 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.077014 Loss1: 0.076325 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059368 Loss1: 0.058678 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051689 Loss1: 0.050999 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059443 Loss1: 0.058751 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054721 Loss1: 0.054030 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060846 Loss1: 0.060156 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.988598
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9333465189873418
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.108949 Loss1: 0.108267 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065271 Loss1: 0.064581 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056596 Loss1: 0.055905 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043034 Loss1: 0.042346 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.062850 Loss1: 0.062162 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060924 Loss1: 0.060235 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.050015 Loss1: 0.049325 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.055382 Loss1: 0.054691 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.073124 Loss1: 0.072433 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081873 Loss1: 0.081183 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.985364
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9407393292682927
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.088696 Loss1: 0.088016 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050541 Loss1: 0.049857 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.053384 Loss1: 0.052699 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050471 Loss1: 0.049786 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.056220 Loss1: 0.055534 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060028 Loss1: 0.059343 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.066023 Loss1: 0.065337 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062357 Loss1: 0.061669 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.092716 Loss1: 0.092030 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066069 Loss1: 0.065382 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.986471
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9263980263157895
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.140000 Loss1: 0.139316 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078907 Loss1: 0.078218 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.070443 Loss1: 0.069754 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068320 Loss1: 0.067631 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.070824 Loss1: 0.070136 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048573 Loss1: 0.047886 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.045849 Loss1: 0.045161 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052646 Loss1: 0.051956 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057146 Loss1: 0.056456 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044929 Loss1: 0.044240 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.992804
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 14:09:16,395][flwr][DEBUG] - fit_round 63 received 10 results and 0 failures
+test acc: 0.6388
+[2023-09-22 14:10:18,204][flwr][INFO] - fit progress: (63, 2.3152449610896, {'accuracy': 0.6388}, 125899.86559370672)
+[2023-09-22 14:10:18,205][flwr][DEBUG] - evaluate_round 63: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 14:10:57,634][flwr][DEBUG] - evaluate_round 63 received 10 results and 0 failures
+[2023-09-22 14:10:57,638][flwr][DEBUG] - fit_round 64: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9284855769230769
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.108665 Loss1: 0.107988 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.069719 Loss1: 0.069036 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055276 Loss1: 0.054594 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047811 Loss1: 0.047126 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069430 Loss1: 0.068744 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.077163 Loss1: 0.076479 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053870 Loss1: 0.053185 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059484 Loss1: 0.058798 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059459 Loss1: 0.058773 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088827 Loss1: 0.088142 Loss2: 0.000684
+(DefaultActor pid=2839578) >> Training accuracy: 0.976963
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9274839743589743
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091589 Loss1: 0.090907 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.056080 Loss1: 0.055392 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048647 Loss1: 0.047957 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.060056 Loss1: 0.059367 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.056729 Loss1: 0.056038 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.069593 Loss1: 0.068902 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058822 Loss1: 0.058133 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060393 Loss1: 0.059703 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043765 Loss1: 0.043075 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044035 Loss1: 0.043345 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.991587
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9021267361111112
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.122738 Loss1: 0.122057 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.080240 Loss1: 0.079555 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056126 Loss1: 0.055439 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066246 Loss1: 0.065560 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052182 Loss1: 0.051496 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.057143 Loss1: 0.056456 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069314 Loss1: 0.068628 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.064356 Loss1: 0.063670 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055299 Loss1: 0.054611 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045410 Loss1: 0.044722 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.993707
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9359177215189873
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.087224 Loss1: 0.086543 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059199 Loss1: 0.058514 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052537 Loss1: 0.051851 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048232 Loss1: 0.047546 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046178 Loss1: 0.045491 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046136 Loss1: 0.045449 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041571 Loss1: 0.040883 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.055516 Loss1: 0.054828 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052648 Loss1: 0.051960 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074587 Loss1: 0.073898 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.985562
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.92578125
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.126966 Loss1: 0.126281 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.082354 Loss1: 0.081666 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.072767 Loss1: 0.072078 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.075352 Loss1: 0.074664 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.085184 Loss1: 0.084494 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082187 Loss1: 0.081497 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062029 Loss1: 0.061340 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072294 Loss1: 0.071604 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061524 Loss1: 0.060835 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062636 Loss1: 0.061944 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.985814
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9143591772151899
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.107177 Loss1: 0.106495 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072620 Loss1: 0.071932 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059038 Loss1: 0.058351 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043800 Loss1: 0.043113 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.053651 Loss1: 0.052964 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066260 Loss1: 0.065571 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083459 Loss1: 0.082770 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075372 Loss1: 0.074682 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.073378 Loss1: 0.072689 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045999 Loss1: 0.045311 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.993869
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9380933544303798
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.085541 Loss1: 0.084860 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.051132 Loss1: 0.050446 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044484 Loss1: 0.043798 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044160 Loss1: 0.043473 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.049915 Loss1: 0.049228 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052544 Loss1: 0.051853 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062307 Loss1: 0.061616 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070805 Loss1: 0.070115 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060349 Loss1: 0.059660 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064593 Loss1: 0.063903 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.987737
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9228639240506329
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.115273 Loss1: 0.114590 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.061339 Loss1: 0.060652 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.063861 Loss1: 0.063173 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058042 Loss1: 0.057356 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052284 Loss1: 0.051596 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047551 Loss1: 0.046864 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062583 Loss1: 0.061897 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080922 Loss1: 0.080234 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.102532 Loss1: 0.101843 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.077674 Loss1: 0.076986 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.986748
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9336890243902439
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.104502 Loss1: 0.103821 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.073456 Loss1: 0.072774 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064591 Loss1: 0.063908 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069368 Loss1: 0.068684 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060986 Loss1: 0.060301 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.073538 Loss1: 0.072852 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061142 Loss1: 0.060455 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052871 Loss1: 0.052186 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066067 Loss1: 0.065380 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038269 Loss1: 0.037582 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.990282
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8743665540540541
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.139828 Loss1: 0.139144 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072055 Loss1: 0.071366 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069299 Loss1: 0.068612 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066229 Loss1: 0.065538 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063657 Loss1: 0.062968 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.084434 Loss1: 0.083745 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.095232 Loss1: 0.094544 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.087044 Loss1: 0.086354 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.086698 Loss1: 0.086007 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073186 Loss1: 0.072495 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.981208
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 14:42:14,178][flwr][DEBUG] - fit_round 64 received 10 results and 0 failures
+test acc: 0.6396
+[2023-09-22 14:43:50,672][flwr][INFO] - fit progress: (64, 2.304777619556878, {'accuracy': 0.6396}, 127912.33357553184)
+[2023-09-22 14:43:50,673][flwr][DEBUG] - evaluate_round 64: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 14:44:30,178][flwr][DEBUG] - evaluate_round 64 received 10 results and 0 failures
+[2023-09-22 14:44:30,179][flwr][DEBUG] - fit_round 65: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9222861842105263
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119930 Loss1: 0.119246 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.076098 Loss1: 0.075409 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069085 Loss1: 0.068394 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066622 Loss1: 0.065932 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.065758 Loss1: 0.065066 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072351 Loss1: 0.071660 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048889 Loss1: 0.048200 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047314 Loss1: 0.046623 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039549 Loss1: 0.038856 Loss2: 0.000693
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056911 Loss1: 0.056218 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.987048
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9071180555555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119272 Loss1: 0.118592 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.073162 Loss1: 0.072476 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.067113 Loss1: 0.066427 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053314 Loss1: 0.052628 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043886 Loss1: 0.043198 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082099 Loss1: 0.081412 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086428 Loss1: 0.085742 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070561 Loss1: 0.069873 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072613 Loss1: 0.071925 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066334 Loss1: 0.065647 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.988715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9328926282051282
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.114403 Loss1: 0.113720 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059083 Loss1: 0.058395 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043186 Loss1: 0.042498 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071660 Loss1: 0.070970 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.076808 Loss1: 0.076118 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.054261 Loss1: 0.053571 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041057 Loss1: 0.040366 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057973 Loss1: 0.057281 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049759 Loss1: 0.049068 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046736 Loss1: 0.046044 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.990585
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8775337837837838
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.147328 Loss1: 0.146644 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.075351 Loss1: 0.074661 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.075138 Loss1: 0.074450 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.072519 Loss1: 0.071830 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057954 Loss1: 0.057264 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045732 Loss1: 0.045041 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046030 Loss1: 0.045338 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051496 Loss1: 0.050805 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056546 Loss1: 0.055855 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081158 Loss1: 0.080466 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.986064
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9228766025641025
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.120834 Loss1: 0.120152 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066408 Loss1: 0.065724 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069538 Loss1: 0.068854 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.055573 Loss1: 0.054888 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059971 Loss1: 0.059285 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055162 Loss1: 0.054476 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070731 Loss1: 0.070044 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.065357 Loss1: 0.064670 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058015 Loss1: 0.057328 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051393 Loss1: 0.050706 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.989383
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9113924050632911
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.100109 Loss1: 0.099426 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048091 Loss1: 0.047404 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042625 Loss1: 0.041935 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048011 Loss1: 0.047322 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063529 Loss1: 0.062840 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060557 Loss1: 0.059869 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055546 Loss1: 0.054856 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036762 Loss1: 0.036074 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044547 Loss1: 0.043858 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040909 Loss1: 0.040220 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.993078
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9339398734177216
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.102362 Loss1: 0.101679 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066340 Loss1: 0.065654 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052239 Loss1: 0.051551 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048205 Loss1: 0.047517 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061995 Loss1: 0.061306 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055398 Loss1: 0.054710 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051673 Loss1: 0.050984 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068594 Loss1: 0.067905 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059346 Loss1: 0.058655 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058982 Loss1: 0.058293 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.988726
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9392800632911392
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097921 Loss1: 0.097238 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047394 Loss1: 0.046707 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039301 Loss1: 0.038614 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041251 Loss1: 0.040563 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039049 Loss1: 0.038359 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065020 Loss1: 0.064331 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.059525 Loss1: 0.058835 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058931 Loss1: 0.058242 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.036356 Loss1: 0.035666 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040688 Loss1: 0.039995 Loss2: 0.000693
+(DefaultActor pid=2839578) >> Training accuracy: 0.992484
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9333079268292683
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.104834 Loss1: 0.104155 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066559 Loss1: 0.065874 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.053015 Loss1: 0.052331 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059711 Loss1: 0.059026 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.076500 Loss1: 0.075814 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058191 Loss1: 0.057505 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057188 Loss1: 0.056501 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056323 Loss1: 0.055635 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064675 Loss1: 0.063987 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090480 Loss1: 0.089791 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.984756
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.921875
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.127875 Loss1: 0.127192 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067132 Loss1: 0.066446 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043285 Loss1: 0.042598 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053121 Loss1: 0.052435 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.065095 Loss1: 0.064408 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.080672 Loss1: 0.079984 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056878 Loss1: 0.056188 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063679 Loss1: 0.062991 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057037 Loss1: 0.056349 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053413 Loss1: 0.052725 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992089
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 15:19:58,441][flwr][DEBUG] - fit_round 65 received 10 results and 0 failures
+test acc: 0.6413
+[2023-09-22 15:21:00,226][flwr][INFO] - fit progress: (65, 2.337633471138561, {'accuracy': 0.6413}, 130141.88727296283)
+[2023-09-22 15:21:00,226][flwr][DEBUG] - evaluate_round 65: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 15:21:40,210][flwr][DEBUG] - evaluate_round 65 received 10 results and 0 failures
+[2023-09-22 15:21:40,216][flwr][DEBUG] - fit_round 66: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9377003205128205
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091957 Loss1: 0.091277 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046404 Loss1: 0.045720 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048458 Loss1: 0.047771 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037004 Loss1: 0.036315 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052125 Loss1: 0.051437 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066221 Loss1: 0.065532 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.075248 Loss1: 0.074560 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074952 Loss1: 0.074261 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069883 Loss1: 0.069194 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061875 Loss1: 0.061185 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.989784
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9045138888888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.133654 Loss1: 0.132971 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.060481 Loss1: 0.059796 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048751 Loss1: 0.048065 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052949 Loss1: 0.052262 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047742 Loss1: 0.047056 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050045 Loss1: 0.049357 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.074323 Loss1: 0.073637 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069500 Loss1: 0.068812 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043930 Loss1: 0.043242 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048872 Loss1: 0.048186 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.991536
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9104034810126582
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.099667 Loss1: 0.098984 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050196 Loss1: 0.049509 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065869 Loss1: 0.065183 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065066 Loss1: 0.064379 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064438 Loss1: 0.063751 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053160 Loss1: 0.052471 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053093 Loss1: 0.052401 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043266 Loss1: 0.042574 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049303 Loss1: 0.048614 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042232 Loss1: 0.041541 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.990704
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9240506329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.096170 Loss1: 0.095489 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.054546 Loss1: 0.053861 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040560 Loss1: 0.039874 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037603 Loss1: 0.036918 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038915 Loss1: 0.038228 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039124 Loss1: 0.038436 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065873 Loss1: 0.065184 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057891 Loss1: 0.057204 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.088987 Loss1: 0.088301 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062648 Loss1: 0.061960 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.987144
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9353243670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091047 Loss1: 0.090365 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046246 Loss1: 0.045559 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066122 Loss1: 0.065432 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.062641 Loss1: 0.061951 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045431 Loss1: 0.044740 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044207 Loss1: 0.043517 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.052440 Loss1: 0.051749 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069535 Loss1: 0.068845 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075899 Loss1: 0.075209 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071257 Loss1: 0.070565 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.992286
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9378810975609756
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.090510 Loss1: 0.089830 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050456 Loss1: 0.049773 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031151 Loss1: 0.030466 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035822 Loss1: 0.035138 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031114 Loss1: 0.030429 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029642 Loss1: 0.028957 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033672 Loss1: 0.032986 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033532 Loss1: 0.032847 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045175 Loss1: 0.044489 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080562 Loss1: 0.079876 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.989520
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9236778846153846
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.113763 Loss1: 0.113084 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065982 Loss1: 0.065300 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064794 Loss1: 0.064111 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059702 Loss1: 0.059020 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084141 Loss1: 0.083457 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.076019 Loss1: 0.075335 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068723 Loss1: 0.068038 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.076048 Loss1: 0.075364 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114044 Loss1: 0.113359 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085503 Loss1: 0.084816 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.981170
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9298930921052632
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.122963 Loss1: 0.122277 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059455 Loss1: 0.058764 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.062485 Loss1: 0.061796 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036774 Loss1: 0.036083 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046447 Loss1: 0.045754 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052786 Loss1: 0.052095 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.052764 Loss1: 0.052073 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053250 Loss1: 0.052559 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061802 Loss1: 0.061112 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073289 Loss1: 0.072598 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.977590
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8737331081081081
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.136917 Loss1: 0.136233 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072747 Loss1: 0.072060 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058265 Loss1: 0.057578 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.073851 Loss1: 0.073164 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069547 Loss1: 0.068860 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.070909 Loss1: 0.070220 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057462 Loss1: 0.056773 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069390 Loss1: 0.068701 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053073 Loss1: 0.052383 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059538 Loss1: 0.058848 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.985431
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9359177215189873
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105423 Loss1: 0.104741 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067160 Loss1: 0.066473 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052916 Loss1: 0.052229 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039278 Loss1: 0.038591 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047697 Loss1: 0.047010 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052566 Loss1: 0.051878 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.060839 Loss1: 0.060151 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069791 Loss1: 0.069102 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.070614 Loss1: 0.069925 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080551 Loss1: 0.079861 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.990309
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 15:51:50,335][flwr][DEBUG] - fit_round 66 received 10 results and 0 failures
+test acc: 0.6402
+[2023-09-22 15:52:58,592][flwr][INFO] - fit progress: (66, 2.3337900912799774, {'accuracy': 0.6402}, 132060.2530357819)
+[2023-09-22 15:52:58,592][flwr][DEBUG] - evaluate_round 66: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 15:53:37,554][flwr][DEBUG] - evaluate_round 66 received 10 results and 0 failures
+[2023-09-22 15:53:37,556][flwr][DEBUG] - fit_round 67: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9208860759493671
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.099872 Loss1: 0.099192 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052243 Loss1: 0.051557 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.050865 Loss1: 0.050177 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047261 Loss1: 0.046574 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032189 Loss1: 0.031503 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029637 Loss1: 0.028950 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039193 Loss1: 0.038505 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049583 Loss1: 0.048895 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058911 Loss1: 0.058223 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067973 Loss1: 0.067283 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.984968
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8794341216216216
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.151987 Loss1: 0.151303 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.055340 Loss1: 0.054652 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043924 Loss1: 0.043237 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038519 Loss1: 0.037831 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044541 Loss1: 0.043851 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052724 Loss1: 0.052035 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039920 Loss1: 0.039231 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045449 Loss1: 0.044760 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040038 Loss1: 0.039348 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033911 Loss1: 0.033221 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.994299
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9214743589743589
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103499 Loss1: 0.102821 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.062750 Loss1: 0.062065 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035691 Loss1: 0.035006 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049182 Loss1: 0.048496 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054230 Loss1: 0.053544 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052999 Loss1: 0.052313 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076360 Loss1: 0.075673 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070593 Loss1: 0.069907 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074855 Loss1: 0.074168 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088529 Loss1: 0.087844 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.979768
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9181170886075949
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.107547 Loss1: 0.106864 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.086421 Loss1: 0.085734 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066897 Loss1: 0.066209 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067398 Loss1: 0.066710 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086263 Loss1: 0.085573 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074590 Loss1: 0.073900 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069333 Loss1: 0.068644 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047591 Loss1: 0.046902 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055519 Loss1: 0.054830 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046099 Loss1: 0.045409 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.993869
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9438291139240507
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097618 Loss1: 0.096936 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068328 Loss1: 0.067640 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.085150 Loss1: 0.084460 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052685 Loss1: 0.051997 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063490 Loss1: 0.062800 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049897 Loss1: 0.049208 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040530 Loss1: 0.039839 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044219 Loss1: 0.043529 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049049 Loss1: 0.048359 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050510 Loss1: 0.049820 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.991297
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9397035256410257
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.088989 Loss1: 0.088306 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.060602 Loss1: 0.059916 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.062422 Loss1: 0.061735 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044143 Loss1: 0.043455 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052969 Loss1: 0.052282 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039587 Loss1: 0.038899 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040923 Loss1: 0.040234 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048163 Loss1: 0.047472 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049367 Loss1: 0.048676 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090389 Loss1: 0.089699 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.990585
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9373094512195121
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.098259 Loss1: 0.097579 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052508 Loss1: 0.051823 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058411 Loss1: 0.057726 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.051977 Loss1: 0.051291 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042982 Loss1: 0.042296 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025821 Loss1: 0.025134 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035642 Loss1: 0.034955 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051875 Loss1: 0.051187 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054846 Loss1: 0.054157 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058402 Loss1: 0.057715 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.988567
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9114583333333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.120255 Loss1: 0.119573 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044430 Loss1: 0.043746 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056121 Loss1: 0.055437 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049127 Loss1: 0.048441 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055048 Loss1: 0.054361 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.054684 Loss1: 0.053996 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054640 Loss1: 0.053952 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056614 Loss1: 0.055925 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061613 Loss1: 0.060925 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048991 Loss1: 0.048302 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987196
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9352384868421053
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.092188 Loss1: 0.091504 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050005 Loss1: 0.049317 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040656 Loss1: 0.039967 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038832 Loss1: 0.038143 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059594 Loss1: 0.058905 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060989 Loss1: 0.060300 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083328 Loss1: 0.082637 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.083300 Loss1: 0.082612 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061904 Loss1: 0.061212 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065060 Loss1: 0.064370 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.983758
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9384889240506329
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.079959 Loss1: 0.079277 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052420 Loss1: 0.051734 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.053242 Loss1: 0.052556 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.040751 Loss1: 0.040064 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046999 Loss1: 0.046309 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049382 Loss1: 0.048694 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051650 Loss1: 0.050961 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056061 Loss1: 0.055372 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057129 Loss1: 0.056440 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069853 Loss1: 0.069165 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.989517
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 16:24:21,451][flwr][DEBUG] - fit_round 67 received 10 results and 0 failures
+test acc: 0.6406
+[2023-09-22 16:25:21,557][flwr][INFO] - fit progress: (67, 2.357942302767842, {'accuracy': 0.6406}, 134003.21802859986)
+[2023-09-22 16:25:21,557][flwr][DEBUG] - evaluate_round 67: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 16:25:59,262][flwr][DEBUG] - evaluate_round 67 received 10 results and 0 failures
+[2023-09-22 16:25:59,264][flwr][DEBUG] - fit_round 68: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9253700657894737
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.114290 Loss1: 0.113605 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.074070 Loss1: 0.073381 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.067374 Loss1: 0.066686 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049902 Loss1: 0.049213 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060133 Loss1: 0.059443 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072144 Loss1: 0.071455 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048465 Loss1: 0.047776 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050756 Loss1: 0.050068 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042438 Loss1: 0.041750 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060536 Loss1: 0.059848 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.993010
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9407393292682927
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.083629 Loss1: 0.082946 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037256 Loss1: 0.036571 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036555 Loss1: 0.035869 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069039 Loss1: 0.068353 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046803 Loss1: 0.046117 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048884 Loss1: 0.048199 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046899 Loss1: 0.046214 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058408 Loss1: 0.057721 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077336 Loss1: 0.076649 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075827 Loss1: 0.075140 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.984947
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9256810897435898
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.101482 Loss1: 0.100803 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059443 Loss1: 0.058761 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045001 Loss1: 0.044317 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052080 Loss1: 0.051395 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.050132 Loss1: 0.049449 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.051264 Loss1: 0.050579 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056338 Loss1: 0.055654 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051177 Loss1: 0.050491 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069741 Loss1: 0.069055 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061864 Loss1: 0.061179 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.983774
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9155815972222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.113344 Loss1: 0.112662 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049462 Loss1: 0.048776 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048728 Loss1: 0.048041 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.056372 Loss1: 0.055685 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.050110 Loss1: 0.049422 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047123 Loss1: 0.046434 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070766 Loss1: 0.070077 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074782 Loss1: 0.074093 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056265 Loss1: 0.055575 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046994 Loss1: 0.046305 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989583
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8762668918918919
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.112566 Loss1: 0.111882 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.063387 Loss1: 0.062699 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056571 Loss1: 0.055882 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053260 Loss1: 0.052571 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046673 Loss1: 0.045983 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063334 Loss1: 0.062646 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055781 Loss1: 0.055092 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049033 Loss1: 0.048343 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046525 Loss1: 0.045835 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055001 Loss1: 0.054310 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.990287
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9149525316455697
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119418 Loss1: 0.118734 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050163 Loss1: 0.049476 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044949 Loss1: 0.044262 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061500 Loss1: 0.060813 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069943 Loss1: 0.069256 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068072 Loss1: 0.067383 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076611 Loss1: 0.075922 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.066367 Loss1: 0.065677 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064945 Loss1: 0.064257 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047602 Loss1: 0.046913 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.990309
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9396756329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.085015 Loss1: 0.084334 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045597 Loss1: 0.044910 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035463 Loss1: 0.034776 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046436 Loss1: 0.045747 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061637 Loss1: 0.060949 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043355 Loss1: 0.042666 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038472 Loss1: 0.037782 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069360 Loss1: 0.068669 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064586 Loss1: 0.063896 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045434 Loss1: 0.044744 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.986946
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9377003205128205
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.092529 Loss1: 0.091847 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.053134 Loss1: 0.052447 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.046012 Loss1: 0.045324 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038053 Loss1: 0.037365 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040194 Loss1: 0.039505 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041622 Loss1: 0.040933 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042034 Loss1: 0.041345 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046921 Loss1: 0.046232 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033420 Loss1: 0.032729 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.039861 Loss1: 0.039172 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.993590
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9373022151898734
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.081316 Loss1: 0.080634 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.058704 Loss1: 0.058018 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.063016 Loss1: 0.062328 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049042 Loss1: 0.048354 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043528 Loss1: 0.042840 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039514 Loss1: 0.038825 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038454 Loss1: 0.037765 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052932 Loss1: 0.052243 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071655 Loss1: 0.070964 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071725 Loss1: 0.071036 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.982991
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.935126582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103578 Loss1: 0.102897 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.040463 Loss1: 0.039777 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035209 Loss1: 0.034522 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032558 Loss1: 0.031872 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034466 Loss1: 0.033779 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029250 Loss1: 0.028562 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041981 Loss1: 0.041292 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040400 Loss1: 0.039714 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039908 Loss1: 0.039220 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047516 Loss1: 0.046828 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992286
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 16:56:32,461][flwr][DEBUG] - fit_round 68 received 10 results and 0 failures
+test acc: 0.6389
+[2023-09-22 16:58:06,885][flwr][INFO] - fit progress: (68, 2.3550231862372866, {'accuracy': 0.6389}, 135968.54609194398)
+[2023-09-22 16:58:06,885][flwr][DEBUG] - evaluate_round 68: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 16:58:44,365][flwr][DEBUG] - evaluate_round 68 received 10 results and 0 failures
+[2023-09-22 16:58:44,375][flwr][DEBUG] - fit_round 69: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9340049342105263
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105940 Loss1: 0.105254 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067407 Loss1: 0.066716 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051395 Loss1: 0.050706 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039983 Loss1: 0.039292 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039415 Loss1: 0.038725 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045496 Loss1: 0.044807 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042303 Loss1: 0.041611 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035340 Loss1: 0.034649 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041010 Loss1: 0.040318 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063622 Loss1: 0.062931 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.988487
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.939873417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.074400 Loss1: 0.073718 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045805 Loss1: 0.045118 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049652 Loss1: 0.048966 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.054523 Loss1: 0.053836 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054645 Loss1: 0.053957 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.064289 Loss1: 0.063599 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.074926 Loss1: 0.074237 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080931 Loss1: 0.080243 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.129733 Loss1: 0.129043 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099158 Loss1: 0.098470 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.981210
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9234572784810127
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105012 Loss1: 0.104329 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.061761 Loss1: 0.061075 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.046643 Loss1: 0.045956 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047684 Loss1: 0.046997 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.067646 Loss1: 0.066957 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.086588 Loss1: 0.085900 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076756 Loss1: 0.076065 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068096 Loss1: 0.067406 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093114 Loss1: 0.092425 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052971 Loss1: 0.052281 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.986748
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9432357594936709
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097226 Loss1: 0.096543 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046387 Loss1: 0.045700 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036664 Loss1: 0.035975 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033291 Loss1: 0.032602 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026493 Loss1: 0.025804 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049972 Loss1: 0.049281 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.052037 Loss1: 0.051346 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044972 Loss1: 0.044282 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063530 Loss1: 0.062840 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053903 Loss1: 0.053212 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.988528
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8743665540540541
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119457 Loss1: 0.118772 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.064397 Loss1: 0.063708 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055385 Loss1: 0.054697 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.062047 Loss1: 0.061357 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047514 Loss1: 0.046825 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053090 Loss1: 0.052401 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057535 Loss1: 0.056846 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077657 Loss1: 0.076969 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.088162 Loss1: 0.087473 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054054 Loss1: 0.053363 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.994299
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9417067307692307
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.085592 Loss1: 0.084911 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043132 Loss1: 0.042444 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043686 Loss1: 0.042999 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.051617 Loss1: 0.050929 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060103 Loss1: 0.059415 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046484 Loss1: 0.045795 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057370 Loss1: 0.056680 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057078 Loss1: 0.056388 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069979 Loss1: 0.069289 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061758 Loss1: 0.061067 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.987981
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9284018987341772
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.079293 Loss1: 0.078612 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044032 Loss1: 0.043346 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.053291 Loss1: 0.052606 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050757 Loss1: 0.050072 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046938 Loss1: 0.046252 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048892 Loss1: 0.048205 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044945 Loss1: 0.044257 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053391 Loss1: 0.052704 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082963 Loss1: 0.082276 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081594 Loss1: 0.080906 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.981210
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9439786585365854
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.087808 Loss1: 0.087127 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.061243 Loss1: 0.060558 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058583 Loss1: 0.057898 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039139 Loss1: 0.038455 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045177 Loss1: 0.044493 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040498 Loss1: 0.039813 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057518 Loss1: 0.056831 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053963 Loss1: 0.053276 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049442 Loss1: 0.048754 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058714 Loss1: 0.058027 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.984566
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9395032051282052
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084573 Loss1: 0.083892 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.061404 Loss1: 0.060719 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056101 Loss1: 0.055416 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048052 Loss1: 0.047367 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043439 Loss1: 0.042754 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048118 Loss1: 0.047433 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039031 Loss1: 0.038344 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054209 Loss1: 0.053522 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082408 Loss1: 0.081723 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.092369 Loss1: 0.091683 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.983774
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9175347222222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.093153 Loss1: 0.092472 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049692 Loss1: 0.049006 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045013 Loss1: 0.044327 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050501 Loss1: 0.049814 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051680 Loss1: 0.050993 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049985 Loss1: 0.049297 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058115 Loss1: 0.057426 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045817 Loss1: 0.045130 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038699 Loss1: 0.038011 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051970 Loss1: 0.051281 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.981120
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 17:31:38,944][flwr][DEBUG] - fit_round 69 received 10 results and 0 failures
+test acc: 0.6384
+[2023-09-22 17:32:32,855][flwr][INFO] - fit progress: (69, 2.3513612215892197, {'accuracy': 0.6384}, 138034.5161338998)
+[2023-09-22 17:32:32,855][flwr][DEBUG] - evaluate_round 69: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 17:33:10,642][flwr][DEBUG] - evaluate_round 69 received 10 results and 0 failures
+[2023-09-22 17:33:10,643][flwr][DEBUG] - fit_round 70: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9140625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119463 Loss1: 0.118782 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.071342 Loss1: 0.070656 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045937 Loss1: 0.045251 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058029 Loss1: 0.057341 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.067718 Loss1: 0.067029 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039954 Loss1: 0.039266 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028651 Loss1: 0.027963 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054470 Loss1: 0.053783 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063414 Loss1: 0.062726 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061187 Loss1: 0.060500 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.991753
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9396756329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.092533 Loss1: 0.091850 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065089 Loss1: 0.064402 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058525 Loss1: 0.057835 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.063476 Loss1: 0.062788 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064584 Loss1: 0.063894 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066715 Loss1: 0.066026 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073227 Loss1: 0.072540 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058114 Loss1: 0.057426 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044040 Loss1: 0.043351 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046474 Loss1: 0.045784 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.991100
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9342948717948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080228 Loss1: 0.079549 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042727 Loss1: 0.042045 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031514 Loss1: 0.030830 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036066 Loss1: 0.035382 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045213 Loss1: 0.044528 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059285 Loss1: 0.058602 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072477 Loss1: 0.071792 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072364 Loss1: 0.071677 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079568 Loss1: 0.078882 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062542 Loss1: 0.061856 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.988782
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9344161184210527
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097717 Loss1: 0.097035 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.053892 Loss1: 0.053203 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051085 Loss1: 0.050397 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065392 Loss1: 0.064703 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052913 Loss1: 0.052225 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052340 Loss1: 0.051651 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.079654 Loss1: 0.078965 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095803 Loss1: 0.095114 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066962 Loss1: 0.066273 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062790 Loss1: 0.062101 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987459
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9501582278481012
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066120 Loss1: 0.065439 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045378 Loss1: 0.044691 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031553 Loss1: 0.030866 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.055279 Loss1: 0.054590 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039825 Loss1: 0.039136 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034229 Loss1: 0.033539 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029536 Loss1: 0.028846 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029118 Loss1: 0.028429 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034652 Loss1: 0.033964 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054739 Loss1: 0.054049 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.988133
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9285996835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.087119 Loss1: 0.086437 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041709 Loss1: 0.041024 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.054101 Loss1: 0.053415 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046652 Loss1: 0.045965 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051079 Loss1: 0.050392 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.067016 Loss1: 0.066329 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067258 Loss1: 0.066570 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063503 Loss1: 0.062815 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.112352 Loss1: 0.111666 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.168295 Loss1: 0.167607 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.980419
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8817567567567568
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.115889 Loss1: 0.115206 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.053325 Loss1: 0.052638 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056085 Loss1: 0.055396 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049988 Loss1: 0.049300 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061727 Loss1: 0.061038 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039575 Loss1: 0.038887 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031204 Loss1: 0.030514 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036407 Loss1: 0.035718 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057572 Loss1: 0.056882 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063834 Loss1: 0.063144 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.991343
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9439102564102564
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.086145 Loss1: 0.085463 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035926 Loss1: 0.035240 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044957 Loss1: 0.044270 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048356 Loss1: 0.047669 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048071 Loss1: 0.047381 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050148 Loss1: 0.049458 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033177 Loss1: 0.032488 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040904 Loss1: 0.040214 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038573 Loss1: 0.037882 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056147 Loss1: 0.055456 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.980369
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9246439873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095269 Loss1: 0.094588 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066419 Loss1: 0.065732 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049280 Loss1: 0.048593 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068894 Loss1: 0.068206 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.078010 Loss1: 0.077323 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.079495 Loss1: 0.078807 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058481 Loss1: 0.057791 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.065968 Loss1: 0.065281 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076641 Loss1: 0.075953 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081012 Loss1: 0.080322 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.982991
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9455030487804879
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075611 Loss1: 0.074929 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043024 Loss1: 0.042340 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040302 Loss1: 0.039618 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034272 Loss1: 0.033586 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039260 Loss1: 0.038574 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052758 Loss1: 0.052073 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054299 Loss1: 0.053614 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047701 Loss1: 0.047015 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047465 Loss1: 0.046779 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043503 Loss1: 0.042816 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.991425
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 18:11:22,105][flwr][DEBUG] - fit_round 70 received 10 results and 0 failures
+test acc: 0.6375
+[2023-09-22 18:12:14,273][flwr][INFO] - fit progress: (70, 2.3543379908552566, {'accuracy': 0.6375}, 140415.93445685785)
+[2023-09-22 18:12:14,273][flwr][DEBUG] - evaluate_round 70: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 18:12:52,805][flwr][DEBUG] - evaluate_round 70 received 10 results and 0 failures
+[2023-09-22 18:12:52,806][flwr][DEBUG] - fit_round 71: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9439102564102564
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.100901 Loss1: 0.100221 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041426 Loss1: 0.040742 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044486 Loss1: 0.043802 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.056973 Loss1: 0.056288 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043711 Loss1: 0.043025 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037573 Loss1: 0.036888 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037881 Loss1: 0.037195 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031156 Loss1: 0.030470 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034636 Loss1: 0.033950 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053176 Loss1: 0.052490 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.990986
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9323575949367089
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.096905 Loss1: 0.096222 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066942 Loss1: 0.066256 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055176 Loss1: 0.054489 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.051938 Loss1: 0.051251 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.050546 Loss1: 0.049859 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036310 Loss1: 0.035624 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042218 Loss1: 0.041531 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063223 Loss1: 0.062537 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054918 Loss1: 0.054231 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058190 Loss1: 0.057503 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.991693
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9416534810126582
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068654 Loss1: 0.067972 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033145 Loss1: 0.032459 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.030833 Loss1: 0.030146 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029322 Loss1: 0.028636 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.049122 Loss1: 0.048435 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050815 Loss1: 0.050127 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056274 Loss1: 0.055585 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047814 Loss1: 0.047126 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044242 Loss1: 0.043553 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032952 Loss1: 0.032263 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.989913
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9165348101265823
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091832 Loss1: 0.091151 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038767 Loss1: 0.038079 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036376 Loss1: 0.035689 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053244 Loss1: 0.052556 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044880 Loss1: 0.044192 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046286 Loss1: 0.045596 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058321 Loss1: 0.057634 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054509 Loss1: 0.053820 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053186 Loss1: 0.052495 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044872 Loss1: 0.044180 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.989715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8832347972972973
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.107463 Loss1: 0.106779 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.077896 Loss1: 0.077208 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.070480 Loss1: 0.069792 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.057980 Loss1: 0.057290 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037195 Loss1: 0.036503 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053098 Loss1: 0.052407 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043671 Loss1: 0.042981 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050590 Loss1: 0.049899 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049177 Loss1: 0.048487 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044062 Loss1: 0.043372 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.991765
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9220920138888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091303 Loss1: 0.090621 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039632 Loss1: 0.038946 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.061003 Loss1: 0.060317 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043642 Loss1: 0.042953 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036386 Loss1: 0.035698 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037711 Loss1: 0.037022 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071228 Loss1: 0.070539 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047995 Loss1: 0.047306 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065243 Loss1: 0.064556 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057919 Loss1: 0.057231 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992839
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.946004746835443
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.079050 Loss1: 0.078366 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038568 Loss1: 0.037880 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036333 Loss1: 0.035645 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035055 Loss1: 0.034365 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046138 Loss1: 0.045449 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058657 Loss1: 0.057968 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042855 Loss1: 0.042165 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043108 Loss1: 0.042420 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049631 Loss1: 0.048940 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068528 Loss1: 0.067838 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.988924
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9428453947368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105061 Loss1: 0.104377 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066327 Loss1: 0.065638 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060015 Loss1: 0.059327 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068707 Loss1: 0.068015 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088987 Loss1: 0.088298 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.071662 Loss1: 0.070972 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091969 Loss1: 0.091278 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.064646 Loss1: 0.063955 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066615 Loss1: 0.065924 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063055 Loss1: 0.062363 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.989309
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9493140243902439
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.086527 Loss1: 0.085848 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049830 Loss1: 0.049147 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051366 Loss1: 0.050682 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053292 Loss1: 0.052608 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039369 Loss1: 0.038684 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038308 Loss1: 0.037624 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044403 Loss1: 0.043717 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054274 Loss1: 0.053588 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055206 Loss1: 0.054519 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081809 Loss1: 0.081120 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.983994
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9421073717948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075651 Loss1: 0.074971 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033320 Loss1: 0.032634 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.037837 Loss1: 0.037149 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.040698 Loss1: 0.040011 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041841 Loss1: 0.041152 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.056517 Loss1: 0.055828 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065289 Loss1: 0.064601 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056909 Loss1: 0.056220 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.086667 Loss1: 0.085978 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073473 Loss1: 0.072782 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.987780
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 18:51:09,769][flwr][DEBUG] - fit_round 71 received 10 results and 0 failures
+test acc: 0.6414
+[2023-09-22 18:52:04,264][flwr][INFO] - fit progress: (71, 2.349217086935196, {'accuracy': 0.6414}, 142805.92535427166)
+[2023-09-22 18:52:04,264][flwr][DEBUG] - evaluate_round 71: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 18:52:41,300][flwr][DEBUG] - evaluate_round 71 received 10 results and 0 failures
+[2023-09-22 18:52:41,301][flwr][DEBUG] - fit_round 72: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9517911585365854
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070838 Loss1: 0.070158 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046178 Loss1: 0.045494 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042207 Loss1: 0.041522 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045027 Loss1: 0.044343 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036531 Loss1: 0.035846 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037566 Loss1: 0.036880 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034423 Loss1: 0.033737 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046455 Loss1: 0.045768 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052238 Loss1: 0.051552 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048480 Loss1: 0.047794 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.991044
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9192708333333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.099582 Loss1: 0.098900 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047940 Loss1: 0.047254 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.038968 Loss1: 0.038281 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039729 Loss1: 0.039042 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037813 Loss1: 0.037125 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041222 Loss1: 0.040533 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061761 Loss1: 0.061072 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.076198 Loss1: 0.075509 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099594 Loss1: 0.098906 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104604 Loss1: 0.103915 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.983724
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9463141025641025
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072431 Loss1: 0.071750 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.055566 Loss1: 0.054880 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.041131 Loss1: 0.040445 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036429 Loss1: 0.035742 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045075 Loss1: 0.044387 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047408 Loss1: 0.046720 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067615 Loss1: 0.066928 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053795 Loss1: 0.053106 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039316 Loss1: 0.038626 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.035358 Loss1: 0.034669 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.995393
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9394778481012658
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.077270 Loss1: 0.076591 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052007 Loss1: 0.051322 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.054345 Loss1: 0.053660 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036923 Loss1: 0.036238 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051275 Loss1: 0.050587 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050843 Loss1: 0.050155 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076364 Loss1: 0.075676 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074320 Loss1: 0.073634 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044760 Loss1: 0.044073 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068516 Loss1: 0.067827 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.981804
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9409950657894737
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.088531 Loss1: 0.087848 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.070843 Loss1: 0.070156 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035356 Loss1: 0.034666 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039388 Loss1: 0.038699 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.056776 Loss1: 0.056086 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043787 Loss1: 0.043096 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048834 Loss1: 0.048144 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051243 Loss1: 0.050552 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054558 Loss1: 0.053868 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051210 Loss1: 0.050521 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.990543
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9475870253164557
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095270 Loss1: 0.094588 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049488 Loss1: 0.048800 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040795 Loss1: 0.040106 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048797 Loss1: 0.048108 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040141 Loss1: 0.039450 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046516 Loss1: 0.045826 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058355 Loss1: 0.057666 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052081 Loss1: 0.051391 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046534 Loss1: 0.045844 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048965 Loss1: 0.048274 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.992286
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9246439873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.089757 Loss1: 0.089076 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.056648 Loss1: 0.055963 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032668 Loss1: 0.031982 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041859 Loss1: 0.041172 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025865 Loss1: 0.025178 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029082 Loss1: 0.028394 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034982 Loss1: 0.034294 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029114 Loss1: 0.028425 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039996 Loss1: 0.039309 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043163 Loss1: 0.042475 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.991693
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9338942307692307
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075199 Loss1: 0.074520 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042381 Loss1: 0.041698 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044556 Loss1: 0.043872 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058065 Loss1: 0.057380 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.066953 Loss1: 0.066267 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061578 Loss1: 0.060892 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073921 Loss1: 0.073235 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062678 Loss1: 0.061992 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081578 Loss1: 0.080891 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052691 Loss1: 0.052004 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.990986
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.885768581081081
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.123308 Loss1: 0.122624 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.063256 Loss1: 0.062567 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.068793 Loss1: 0.068103 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061266 Loss1: 0.060576 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051160 Loss1: 0.050471 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040698 Loss1: 0.040009 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039204 Loss1: 0.038515 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.055596 Loss1: 0.054907 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045399 Loss1: 0.044709 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056092 Loss1: 0.055402 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.991765
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9493670886075949
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.073836 Loss1: 0.073154 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041517 Loss1: 0.040831 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.041778 Loss1: 0.041093 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053744 Loss1: 0.053057 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.053512 Loss1: 0.052826 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078153 Loss1: 0.077465 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086999 Loss1: 0.086310 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062783 Loss1: 0.062094 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051299 Loss1: 0.050610 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050124 Loss1: 0.049435 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.991495
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 19:30:49,992][flwr][DEBUG] - fit_round 72 received 10 results and 0 failures
+test acc: 0.6415
+[2023-09-22 19:31:44,214][flwr][INFO] - fit progress: (72, 2.383222926158113, {'accuracy': 0.6415}, 145185.87564541493)
+[2023-09-22 19:31:44,215][flwr][DEBUG] - evaluate_round 72: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 19:32:21,617][flwr][DEBUG] - evaluate_round 72 received 10 results and 0 failures
+[2023-09-22 19:32:21,617][flwr][DEBUG] - fit_round 73: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8895692567567568
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.124338 Loss1: 0.123656 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066147 Loss1: 0.065460 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058437 Loss1: 0.057749 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069973 Loss1: 0.069284 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054105 Loss1: 0.053416 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063834 Loss1: 0.063145 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069524 Loss1: 0.068836 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056442 Loss1: 0.055753 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057610 Loss1: 0.056920 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036138 Loss1: 0.035448 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.991765
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9251302083333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.093671 Loss1: 0.092988 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039408 Loss1: 0.038723 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039856 Loss1: 0.039170 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029875 Loss1: 0.029190 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034263 Loss1: 0.033577 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042257 Loss1: 0.041570 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040205 Loss1: 0.039518 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039799 Loss1: 0.039112 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043385 Loss1: 0.042697 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058456 Loss1: 0.057766 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.986762
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9449013157894737
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.076861 Loss1: 0.076178 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047412 Loss1: 0.046723 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052758 Loss1: 0.052070 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048278 Loss1: 0.047590 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051861 Loss1: 0.051172 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.067715 Loss1: 0.067026 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044268 Loss1: 0.043578 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051829 Loss1: 0.051140 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075585 Loss1: 0.074895 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054088 Loss1: 0.053398 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.990543
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9533227848101266
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063400 Loss1: 0.062718 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032488 Loss1: 0.031800 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043764 Loss1: 0.043076 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.042799 Loss1: 0.042110 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047284 Loss1: 0.046593 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049854 Loss1: 0.049164 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065176 Loss1: 0.064485 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051071 Loss1: 0.050379 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060140 Loss1: 0.059450 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049004 Loss1: 0.048313 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.989913
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9419070512820513
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067351 Loss1: 0.066671 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050160 Loss1: 0.049475 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.041262 Loss1: 0.040577 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032795 Loss1: 0.032107 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029896 Loss1: 0.029208 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028174 Loss1: 0.027487 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034348 Loss1: 0.033659 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034257 Loss1: 0.033568 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032243 Loss1: 0.031555 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.034759 Loss1: 0.034069 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.994792
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9248417721518988
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.078102 Loss1: 0.077420 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041534 Loss1: 0.040848 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040259 Loss1: 0.039573 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041840 Loss1: 0.041154 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051949 Loss1: 0.051261 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059108 Loss1: 0.058420 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051577 Loss1: 0.050888 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035697 Loss1: 0.035008 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043662 Loss1: 0.042972 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061588 Loss1: 0.060898 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.988331
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9369066455696202
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.078848 Loss1: 0.078166 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048942 Loss1: 0.048257 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042299 Loss1: 0.041614 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047032 Loss1: 0.046346 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052395 Loss1: 0.051710 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047965 Loss1: 0.047278 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062340 Loss1: 0.061652 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.073844 Loss1: 0.073157 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069607 Loss1: 0.068918 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090727 Loss1: 0.090040 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.976859
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9387019230769231
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084083 Loss1: 0.083406 Loss2: 0.000677
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048697 Loss1: 0.048014 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045057 Loss1: 0.044373 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047685 Loss1: 0.046999 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.058275 Loss1: 0.057590 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.054329 Loss1: 0.053644 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041892 Loss1: 0.041205 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043487 Loss1: 0.042800 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044469 Loss1: 0.043783 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059893 Loss1: 0.059207 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.987380
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9426424050632911
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.065678 Loss1: 0.064998 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.034237 Loss1: 0.033551 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049852 Loss1: 0.049166 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.054172 Loss1: 0.053486 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052542 Loss1: 0.051856 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059020 Loss1: 0.058334 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061051 Loss1: 0.060364 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072248 Loss1: 0.071561 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087174 Loss1: 0.086486 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075371 Loss1: 0.074682 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.983188
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9512195121951219
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.065546 Loss1: 0.064867 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048165 Loss1: 0.047483 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040205 Loss1: 0.039521 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047627 Loss1: 0.046943 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045993 Loss1: 0.045309 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061996 Loss1: 0.061310 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067645 Loss1: 0.066960 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069580 Loss1: 0.068893 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087034 Loss1: 0.086349 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079627 Loss1: 0.078939 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.988948
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 20:10:37,308][flwr][DEBUG] - fit_round 73 received 10 results and 0 failures
+test acc: 0.6393
+[2023-09-22 20:11:31,937][flwr][INFO] - fit progress: (73, 2.378727243731197, {'accuracy': 0.6393}, 147573.59824875696)
+[2023-09-22 20:11:31,937][flwr][DEBUG] - evaluate_round 73: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 20:12:10,810][flwr][DEBUG] - evaluate_round 73 received 10 results and 0 failures
+[2023-09-22 20:12:10,814][flwr][DEBUG] - fit_round 74: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9452136075949367
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.076354 Loss1: 0.075671 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035143 Loss1: 0.034456 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032128 Loss1: 0.031440 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034362 Loss1: 0.033675 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028734 Loss1: 0.028047 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020491 Loss1: 0.019804 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025742 Loss1: 0.025056 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024693 Loss1: 0.024006 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031923 Loss1: 0.031235 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044936 Loss1: 0.044247 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.992880
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9503560126582279
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075064 Loss1: 0.074381 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044493 Loss1: 0.043805 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028837 Loss1: 0.028148 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033019 Loss1: 0.032329 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061686 Loss1: 0.060996 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055346 Loss1: 0.054655 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054877 Loss1: 0.054188 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054869 Loss1: 0.054179 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053093 Loss1: 0.052402 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065282 Loss1: 0.064592 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.989715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.931368670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.077098 Loss1: 0.076416 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.034495 Loss1: 0.033810 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025439 Loss1: 0.024754 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039686 Loss1: 0.039000 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051506 Loss1: 0.050819 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039541 Loss1: 0.038854 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031848 Loss1: 0.031162 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032786 Loss1: 0.032099 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.030776 Loss1: 0.030090 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030999 Loss1: 0.030311 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.995451
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9250395569620253
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.090135 Loss1: 0.089455 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041366 Loss1: 0.040681 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039461 Loss1: 0.038776 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029575 Loss1: 0.028889 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042187 Loss1: 0.041501 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038599 Loss1: 0.037913 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040054 Loss1: 0.039368 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045441 Loss1: 0.044754 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057125 Loss1: 0.056437 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062468 Loss1: 0.061781 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.985957
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9512746710526315
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.079084 Loss1: 0.078401 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.040292 Loss1: 0.039605 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035054 Loss1: 0.034365 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041172 Loss1: 0.040483 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047648 Loss1: 0.046960 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044251 Loss1: 0.043562 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.050943 Loss1: 0.050252 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061236 Loss1: 0.060547 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067332 Loss1: 0.066641 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054094 Loss1: 0.053402 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.987459
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9370993589743589
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070797 Loss1: 0.070118 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050305 Loss1: 0.049623 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033631 Loss1: 0.032947 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034342 Loss1: 0.033658 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052746 Loss1: 0.052061 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063708 Loss1: 0.063023 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054881 Loss1: 0.054195 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056632 Loss1: 0.055947 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075728 Loss1: 0.075043 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071936 Loss1: 0.071250 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.984175
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9455030487804879
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067252 Loss1: 0.066571 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046665 Loss1: 0.045980 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029187 Loss1: 0.028502 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031018 Loss1: 0.030332 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041244 Loss1: 0.040557 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091765 Loss1: 0.091081 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068569 Loss1: 0.067882 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.067815 Loss1: 0.067130 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069817 Loss1: 0.069131 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041066 Loss1: 0.040380 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.994665
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9210069444444444
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095919 Loss1: 0.095237 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059798 Loss1: 0.059112 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051873 Loss1: 0.051185 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029744 Loss1: 0.029058 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032928 Loss1: 0.032240 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028852 Loss1: 0.028165 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034741 Loss1: 0.034053 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042778 Loss1: 0.042089 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041348 Loss1: 0.040659 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068371 Loss1: 0.067682 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.991536
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8975929054054054
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.093724 Loss1: 0.093040 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046813 Loss1: 0.046125 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059631 Loss1: 0.058942 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048512 Loss1: 0.047821 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046128 Loss1: 0.045438 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042352 Loss1: 0.041660 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041560 Loss1: 0.040868 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051730 Loss1: 0.051039 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038683 Loss1: 0.037993 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030275 Loss1: 0.029585 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.995355
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9483173076923077
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059928 Loss1: 0.059246 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048492 Loss1: 0.047807 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.057384 Loss1: 0.056699 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048050 Loss1: 0.047362 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039832 Loss1: 0.039143 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060288 Loss1: 0.059599 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053442 Loss1: 0.052752 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052583 Loss1: 0.051894 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065430 Loss1: 0.064740 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053555 Loss1: 0.052865 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.990184
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 20:50:18,799][flwr][DEBUG] - fit_round 74 received 10 results and 0 failures
+test acc: 0.6429
+[2023-09-22 20:51:12,672][flwr][INFO] - fit progress: (74, 2.381040071336606, {'accuracy': 0.6429}, 149954.3338904637)
+[2023-09-22 20:51:12,673][flwr][DEBUG] - evaluate_round 74: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 20:52:07,176][flwr][DEBUG] - evaluate_round 74 received 10 results and 0 failures
+[2023-09-22 20:52:07,196][flwr][DEBUG] - fit_round 75: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9394778481012658
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080454 Loss1: 0.079772 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039940 Loss1: 0.039255 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.038687 Loss1: 0.038003 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.040625 Loss1: 0.039939 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039221 Loss1: 0.038535 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044109 Loss1: 0.043422 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067420 Loss1: 0.066734 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063118 Loss1: 0.062432 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076863 Loss1: 0.076175 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076777 Loss1: 0.076088 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.985166
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9262152777777778
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.115416 Loss1: 0.114734 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066430 Loss1: 0.065744 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055036 Loss1: 0.054349 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050305 Loss1: 0.049618 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059499 Loss1: 0.058812 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046963 Loss1: 0.046276 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041964 Loss1: 0.041276 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046374 Loss1: 0.045687 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063489 Loss1: 0.062801 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.077964 Loss1: 0.077277 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.986979
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9457236842105263
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.096508 Loss1: 0.095823 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068707 Loss1: 0.068018 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.072841 Loss1: 0.072152 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067501 Loss1: 0.066810 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055641 Loss1: 0.054951 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050812 Loss1: 0.050124 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054079 Loss1: 0.053388 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062504 Loss1: 0.061811 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060010 Loss1: 0.059320 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069441 Loss1: 0.068751 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.977385
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9285996835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103728 Loss1: 0.103049 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042471 Loss1: 0.041784 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039042 Loss1: 0.038354 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045488 Loss1: 0.044799 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044209 Loss1: 0.043521 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041134 Loss1: 0.040446 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035714 Loss1: 0.035025 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057772 Loss1: 0.057083 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069399 Loss1: 0.068709 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072056 Loss1: 0.071365 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.980222
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9508384146341463
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.056260 Loss1: 0.055581 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039814 Loss1: 0.039131 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039695 Loss1: 0.039011 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037546 Loss1: 0.036861 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045366 Loss1: 0.044681 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043884 Loss1: 0.043199 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046931 Loss1: 0.046246 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056702 Loss1: 0.056015 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042437 Loss1: 0.041751 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040475 Loss1: 0.039789 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.990282
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9433092948717948
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067522 Loss1: 0.066841 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044391 Loss1: 0.043706 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040834 Loss1: 0.040148 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031417 Loss1: 0.030730 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024772 Loss1: 0.024085 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028741 Loss1: 0.028054 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.049965 Loss1: 0.049278 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037178 Loss1: 0.036492 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038037 Loss1: 0.037350 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052282 Loss1: 0.051594 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.990385
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.944620253164557
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.073332 Loss1: 0.072651 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046922 Loss1: 0.046238 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045822 Loss1: 0.045136 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052265 Loss1: 0.051578 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.050885 Loss1: 0.050198 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049818 Loss1: 0.049131 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046680 Loss1: 0.045992 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053758 Loss1: 0.053069 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076645 Loss1: 0.075956 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075625 Loss1: 0.074935 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.983979
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9556962025316456
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050394 Loss1: 0.049712 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032923 Loss1: 0.032237 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022594 Loss1: 0.021905 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024395 Loss1: 0.023706 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025201 Loss1: 0.024513 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035310 Loss1: 0.034621 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048918 Loss1: 0.048231 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043153 Loss1: 0.042464 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031526 Loss1: 0.030837 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052025 Loss1: 0.051335 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.994066
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9429086538461539
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.071274 Loss1: 0.070595 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.051507 Loss1: 0.050825 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.050611 Loss1: 0.049925 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044747 Loss1: 0.044062 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048802 Loss1: 0.048116 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048283 Loss1: 0.047598 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036191 Loss1: 0.035505 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052569 Loss1: 0.051884 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057959 Loss1: 0.057273 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074203 Loss1: 0.073517 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.985978
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8944256756756757
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095116 Loss1: 0.094434 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046390 Loss1: 0.045705 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048384 Loss1: 0.047698 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050931 Loss1: 0.050245 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044121 Loss1: 0.043435 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037633 Loss1: 0.036945 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040310 Loss1: 0.039621 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035117 Loss1: 0.034428 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035670 Loss1: 0.034983 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030725 Loss1: 0.030036 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994510
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 21:29:44,559][flwr][DEBUG] - fit_round 75 received 10 results and 0 failures
+test acc: 0.6427
+[2023-09-22 21:30:35,685][flwr][INFO] - fit progress: (75, 2.3815381104192035, {'accuracy': 0.6427}, 152317.34610947268)
+[2023-09-22 21:30:35,685][flwr][DEBUG] - evaluate_round 75: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 21:31:13,395][flwr][DEBUG] - evaluate_round 75 received 10 results and 0 failures
+[2023-09-22 21:31:13,395][flwr][DEBUG] - fit_round 76: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9246439873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058974 Loss1: 0.058293 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033159 Loss1: 0.032474 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031119 Loss1: 0.030432 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043028 Loss1: 0.042340 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033484 Loss1: 0.032797 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047904 Loss1: 0.047215 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.049797 Loss1: 0.049108 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.082351 Loss1: 0.081662 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084541 Loss1: 0.083851 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081905 Loss1: 0.081215 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.986155
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9481169871794872
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061283 Loss1: 0.060602 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027496 Loss1: 0.026810 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021668 Loss1: 0.020981 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016020 Loss1: 0.015333 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026790 Loss1: 0.026103 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030586 Loss1: 0.029899 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035344 Loss1: 0.034655 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056884 Loss1: 0.056193 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052643 Loss1: 0.051952 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047885 Loss1: 0.047196 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989984
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.954077743902439
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061635 Loss1: 0.060955 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024944 Loss1: 0.024261 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019452 Loss1: 0.018767 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023099 Loss1: 0.022414 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022208 Loss1: 0.021524 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024915 Loss1: 0.024230 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027770 Loss1: 0.027084 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047466 Loss1: 0.046780 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075591 Loss1: 0.074905 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067997 Loss1: 0.067311 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.985137
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9513449367088608
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.071063 Loss1: 0.070381 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036146 Loss1: 0.035458 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034686 Loss1: 0.033998 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033582 Loss1: 0.032893 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046650 Loss1: 0.045960 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038124 Loss1: 0.037433 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036287 Loss1: 0.035596 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036087 Loss1: 0.035397 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047982 Loss1: 0.047292 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060277 Loss1: 0.059587 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.986946
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.955498417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069060 Loss1: 0.068379 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036593 Loss1: 0.035906 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060269 Loss1: 0.059583 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045201 Loss1: 0.044515 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036853 Loss1: 0.036166 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043692 Loss1: 0.043006 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029116 Loss1: 0.028429 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035387 Loss1: 0.034700 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040602 Loss1: 0.039915 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.039045 Loss1: 0.038356 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.990506
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9442845394736842
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.073134 Loss1: 0.072449 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045873 Loss1: 0.045183 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049097 Loss1: 0.048408 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031886 Loss1: 0.031197 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031959 Loss1: 0.031268 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042152 Loss1: 0.041461 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048759 Loss1: 0.048067 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057782 Loss1: 0.057092 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056964 Loss1: 0.056272 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043916 Loss1: 0.043224 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.993832
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8990709459459459
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.101032 Loss1: 0.100348 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042918 Loss1: 0.042232 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.047564 Loss1: 0.046876 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045946 Loss1: 0.045258 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046633 Loss1: 0.045945 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053923 Loss1: 0.053234 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058972 Loss1: 0.058282 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042097 Loss1: 0.041408 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049104 Loss1: 0.048414 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055330 Loss1: 0.054640 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.995566
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9387019230769231
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068325 Loss1: 0.067646 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044910 Loss1: 0.044228 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032926 Loss1: 0.032243 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044300 Loss1: 0.043617 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040122 Loss1: 0.039438 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035088 Loss1: 0.034403 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041439 Loss1: 0.040754 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044749 Loss1: 0.044065 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039984 Loss1: 0.039299 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059941 Loss1: 0.059256 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.989583
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9402689873417721
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055427 Loss1: 0.054747 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028479 Loss1: 0.027794 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026134 Loss1: 0.025449 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022107 Loss1: 0.021422 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017858 Loss1: 0.017173 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023655 Loss1: 0.022969 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035891 Loss1: 0.035206 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039067 Loss1: 0.038380 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035735 Loss1: 0.035047 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067840 Loss1: 0.067153 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.989122
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9292534722222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.099176 Loss1: 0.098494 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048754 Loss1: 0.048069 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039581 Loss1: 0.038895 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035030 Loss1: 0.034343 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061472 Loss1: 0.060784 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062736 Loss1: 0.062049 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062885 Loss1: 0.062196 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034912 Loss1: 0.034223 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031577 Loss1: 0.030889 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033412 Loss1: 0.032724 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.994358
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 22:07:43,019][flwr][DEBUG] - fit_round 76 received 10 results and 0 failures
+test acc: 0.6415
+[2023-09-22 22:08:31,152][flwr][INFO] - fit progress: (76, 2.4015440281968528, {'accuracy': 0.6415}, 154592.81349731563)
+[2023-09-22 22:08:31,153][flwr][DEBUG] - evaluate_round 76: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 22:09:08,895][flwr][DEBUG] - evaluate_round 76 received 10 results and 0 failures
+[2023-09-22 22:09:08,896][flwr][DEBUG] - fit_round 77: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9455180921052632
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.086566 Loss1: 0.085884 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039065 Loss1: 0.038377 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031894 Loss1: 0.031206 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032888 Loss1: 0.032200 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029340 Loss1: 0.028653 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030754 Loss1: 0.030067 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025573 Loss1: 0.024886 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033288 Loss1: 0.032600 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046868 Loss1: 0.046179 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047929 Loss1: 0.047241 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.990543
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9541139240506329
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072407 Loss1: 0.071725 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038887 Loss1: 0.038202 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042278 Loss1: 0.041591 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044217 Loss1: 0.043529 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032293 Loss1: 0.031605 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043977 Loss1: 0.043289 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042773 Loss1: 0.042084 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052204 Loss1: 0.051515 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069396 Loss1: 0.068705 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068751 Loss1: 0.068062 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.987342
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9503205128205128
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059733 Loss1: 0.059051 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043322 Loss1: 0.042637 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031407 Loss1: 0.030720 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026724 Loss1: 0.026036 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022578 Loss1: 0.021891 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014106 Loss1: 0.013418 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.012059 Loss1: 0.011371 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023968 Loss1: 0.023279 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.021295 Loss1: 0.020606 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027056 Loss1: 0.026369 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.994992
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9505537974683544
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072603 Loss1: 0.071922 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.034108 Loss1: 0.033423 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028590 Loss1: 0.027905 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027031 Loss1: 0.026346 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041107 Loss1: 0.040420 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031095 Loss1: 0.030409 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.047251 Loss1: 0.046563 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046192 Loss1: 0.045504 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072032 Loss1: 0.071342 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076024 Loss1: 0.075335 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.984771
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9472179878048781
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.056517 Loss1: 0.055834 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045833 Loss1: 0.045149 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034324 Loss1: 0.033641 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023698 Loss1: 0.023014 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022245 Loss1: 0.021559 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021835 Loss1: 0.021150 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048898 Loss1: 0.048212 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052309 Loss1: 0.051622 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053169 Loss1: 0.052483 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043225 Loss1: 0.042538 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.996380
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9009712837837838
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091287 Loss1: 0.090604 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044155 Loss1: 0.043468 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.046258 Loss1: 0.045571 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035358 Loss1: 0.034669 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038968 Loss1: 0.038280 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053722 Loss1: 0.053034 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.049612 Loss1: 0.048922 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060352 Loss1: 0.059663 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055361 Loss1: 0.054672 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078397 Loss1: 0.077708 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.985008
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9333465189873418
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068998 Loss1: 0.068318 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031351 Loss1: 0.030667 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025849 Loss1: 0.025164 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025307 Loss1: 0.024622 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020390 Loss1: 0.019704 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024465 Loss1: 0.023778 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037962 Loss1: 0.037273 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035120 Loss1: 0.034431 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039694 Loss1: 0.039006 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062021 Loss1: 0.061331 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9481169871794872
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068448 Loss1: 0.067769 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035673 Loss1: 0.034992 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036377 Loss1: 0.035695 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029772 Loss1: 0.029090 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022315 Loss1: 0.021631 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024663 Loss1: 0.023980 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040535 Loss1: 0.039850 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038363 Loss1: 0.037678 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040793 Loss1: 0.040108 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051949 Loss1: 0.051263 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.992188
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.931640625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.088241 Loss1: 0.087558 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037624 Loss1: 0.036939 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032103 Loss1: 0.031416 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041020 Loss1: 0.040333 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030191 Loss1: 0.029503 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034160 Loss1: 0.033474 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035115 Loss1: 0.034427 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031822 Loss1: 0.031135 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046274 Loss1: 0.045586 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038974 Loss1: 0.038286 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.994358
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9392800632911392
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069153 Loss1: 0.068473 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.040057 Loss1: 0.039374 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049280 Loss1: 0.048595 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038128 Loss1: 0.037444 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051850 Loss1: 0.051166 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033419 Loss1: 0.032733 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039119 Loss1: 0.038432 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058929 Loss1: 0.058244 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052200 Loss1: 0.051513 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061883 Loss1: 0.061197 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.989122
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 22:45:49,474][flwr][DEBUG] - fit_round 77 received 10 results and 0 failures
+test acc: 0.6412
+[2023-09-22 22:46:37,766][flwr][INFO] - fit progress: (77, 2.441344348767314, {'accuracy': 0.6412}, 156879.42774670897)
+[2023-09-22 22:46:37,767][flwr][DEBUG] - evaluate_round 77: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 22:47:14,787][flwr][DEBUG] - evaluate_round 77 received 10 results and 0 failures
+[2023-09-22 22:47:14,788][flwr][DEBUG] - fit_round 78: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9582698170731707
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063682 Loss1: 0.063001 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029390 Loss1: 0.028706 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031792 Loss1: 0.031106 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035781 Loss1: 0.035095 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040689 Loss1: 0.040002 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043311 Loss1: 0.042624 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046652 Loss1: 0.045964 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063257 Loss1: 0.062570 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058838 Loss1: 0.058151 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057342 Loss1: 0.056655 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.988567
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9437099358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066517 Loss1: 0.065838 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032745 Loss1: 0.032061 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045058 Loss1: 0.044374 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046781 Loss1: 0.046096 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048889 Loss1: 0.048203 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049453 Loss1: 0.048768 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055895 Loss1: 0.055210 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057213 Loss1: 0.056527 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058528 Loss1: 0.057841 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091227 Loss1: 0.090539 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.985176
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9491693037974683
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072688 Loss1: 0.072006 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043692 Loss1: 0.043007 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040359 Loss1: 0.039674 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033424 Loss1: 0.032735 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034763 Loss1: 0.034074 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040379 Loss1: 0.039690 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031801 Loss1: 0.031112 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.041630 Loss1: 0.040940 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066985 Loss1: 0.066295 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060518 Loss1: 0.059828 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.993078
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.953125
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069652 Loss1: 0.068969 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039790 Loss1: 0.039106 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039696 Loss1: 0.039010 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.030227 Loss1: 0.029540 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057810 Loss1: 0.057124 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045051 Loss1: 0.044366 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073175 Loss1: 0.072487 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060487 Loss1: 0.059798 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071220 Loss1: 0.070533 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086426 Loss1: 0.085738 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.978837
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9288194444444444
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091677 Loss1: 0.090997 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048854 Loss1: 0.048169 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043436 Loss1: 0.042751 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029394 Loss1: 0.028708 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028492 Loss1: 0.027805 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045459 Loss1: 0.044772 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031124 Loss1: 0.030438 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036027 Loss1: 0.035341 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028802 Loss1: 0.028115 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040216 Loss1: 0.039529 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.995009
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9490131578947368
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072877 Loss1: 0.072194 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033335 Loss1: 0.032646 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029172 Loss1: 0.028484 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028193 Loss1: 0.027504 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024653 Loss1: 0.023965 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020642 Loss1: 0.019955 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029284 Loss1: 0.028596 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045626 Loss1: 0.044936 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051617 Loss1: 0.050928 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052113 Loss1: 0.051423 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.984786
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.957871835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063260 Loss1: 0.062580 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044232 Loss1: 0.043544 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031965 Loss1: 0.031275 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026388 Loss1: 0.025698 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033071 Loss1: 0.032382 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028381 Loss1: 0.027690 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035516 Loss1: 0.034826 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038164 Loss1: 0.037476 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035169 Loss1: 0.034480 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043068 Loss1: 0.042379 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.993275
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9373022151898734
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.081311 Loss1: 0.080629 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032610 Loss1: 0.031924 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042469 Loss1: 0.041782 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041453 Loss1: 0.040764 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030493 Loss1: 0.029804 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036151 Loss1: 0.035465 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044168 Loss1: 0.043480 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050045 Loss1: 0.049356 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074700 Loss1: 0.074011 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078636 Loss1: 0.077949 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.982002
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9045608108108109
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080848 Loss1: 0.080166 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046563 Loss1: 0.045877 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.041552 Loss1: 0.040866 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027117 Loss1: 0.026429 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032386 Loss1: 0.031699 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035624 Loss1: 0.034935 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042785 Loss1: 0.042096 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058027 Loss1: 0.057338 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.050043 Loss1: 0.049353 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059359 Loss1: 0.058671 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989020
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9491185897435898
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060284 Loss1: 0.059604 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033944 Loss1: 0.033258 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039224 Loss1: 0.038540 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021283 Loss1: 0.020596 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020830 Loss1: 0.020143 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018047 Loss1: 0.017359 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.021207 Loss1: 0.020518 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026922 Loss1: 0.026232 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.022513 Loss1: 0.021823 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041694 Loss1: 0.041005 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.992588
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 23:23:33,045][flwr][DEBUG] - fit_round 78 received 10 results and 0 failures
+test acc: 0.6437
+[2023-09-22 23:24:22,885][flwr][INFO] - fit progress: (78, 2.4210258411904113, {'accuracy': 0.6437}, 159144.54672596185)
+[2023-09-22 23:24:22,886][flwr][DEBUG] - evaluate_round 78: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 23:25:00,105][flwr][DEBUG] - evaluate_round 78 received 10 results and 0 failures
+[2023-09-22 23:25:00,105][flwr][DEBUG] - fit_round 79: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9268663194444444
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084878 Loss1: 0.084197 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047403 Loss1: 0.046717 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031572 Loss1: 0.030887 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029794 Loss1: 0.029109 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041653 Loss1: 0.040966 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061610 Loss1: 0.060924 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071216 Loss1: 0.070529 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063028 Loss1: 0.062342 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064156 Loss1: 0.063469 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058990 Loss1: 0.058303 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.990885
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9556962025316456
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052945 Loss1: 0.052265 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024562 Loss1: 0.023878 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020501 Loss1: 0.019815 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020880 Loss1: 0.020192 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020546 Loss1: 0.019859 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031588 Loss1: 0.030899 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035676 Loss1: 0.034988 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029385 Loss1: 0.028696 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055420 Loss1: 0.054732 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056505 Loss1: 0.055817 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.989715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9428401898734177
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.071983 Loss1: 0.071300 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043077 Loss1: 0.042391 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034445 Loss1: 0.033760 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027385 Loss1: 0.026698 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027350 Loss1: 0.026664 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038745 Loss1: 0.038059 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034285 Loss1: 0.033599 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024063 Loss1: 0.023376 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025266 Loss1: 0.024579 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030105 Loss1: 0.029418 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.986946
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9337420886075949
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064782 Loss1: 0.064101 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042236 Loss1: 0.041550 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.038623 Loss1: 0.037937 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028802 Loss1: 0.028116 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022088 Loss1: 0.021400 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026804 Loss1: 0.026116 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038280 Loss1: 0.037593 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038488 Loss1: 0.037800 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045043 Loss1: 0.044355 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050027 Loss1: 0.049339 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.993078
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9515224358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.062653 Loss1: 0.061972 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026049 Loss1: 0.025363 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026714 Loss1: 0.026028 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016547 Loss1: 0.015858 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036355 Loss1: 0.035669 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033425 Loss1: 0.032737 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029971 Loss1: 0.029285 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026315 Loss1: 0.025627 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028998 Loss1: 0.028310 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023626 Loss1: 0.022938 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.995393
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9515224358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.062748 Loss1: 0.062068 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027533 Loss1: 0.026850 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036826 Loss1: 0.036143 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032611 Loss1: 0.031927 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037788 Loss1: 0.037103 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036680 Loss1: 0.035996 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038898 Loss1: 0.038212 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045670 Loss1: 0.044985 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040588 Loss1: 0.039901 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086744 Loss1: 0.086059 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.984575
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9505537974683544
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069812 Loss1: 0.069131 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044269 Loss1: 0.043584 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035682 Loss1: 0.034996 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033255 Loss1: 0.032568 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032098 Loss1: 0.031413 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039381 Loss1: 0.038694 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056275 Loss1: 0.055588 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061068 Loss1: 0.060382 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055921 Loss1: 0.055231 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055979 Loss1: 0.055291 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.985957
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9549753289473685
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072704 Loss1: 0.072020 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.040867 Loss1: 0.040178 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032756 Loss1: 0.032067 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034188 Loss1: 0.033499 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041236 Loss1: 0.040549 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040563 Loss1: 0.039874 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046290 Loss1: 0.045600 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061383 Loss1: 0.060692 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053127 Loss1: 0.052438 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053018 Loss1: 0.052327 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.991571
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9573170731707317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064043 Loss1: 0.063363 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029653 Loss1: 0.028970 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024504 Loss1: 0.023820 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027177 Loss1: 0.026491 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032432 Loss1: 0.031746 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042406 Loss1: 0.041721 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046137 Loss1: 0.045452 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037010 Loss1: 0.036325 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042207 Loss1: 0.041520 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062573 Loss1: 0.061887 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.992950
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9028716216216216
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.086226 Loss1: 0.085543 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037719 Loss1: 0.037034 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027953 Loss1: 0.027266 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028688 Loss1: 0.028000 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028605 Loss1: 0.027917 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037654 Loss1: 0.036965 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038340 Loss1: 0.037651 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038273 Loss1: 0.037584 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058956 Loss1: 0.058266 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057120 Loss1: 0.056431 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994721
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-22 23:56:39,095][flwr][DEBUG] - fit_round 79 received 10 results and 0 failures
+test acc: 0.6414
+[2023-09-22 23:57:27,164][flwr][INFO] - fit progress: (79, 2.445481828416879, {'accuracy': 0.6414}, 161128.82584654074)
+[2023-09-22 23:57:27,165][flwr][DEBUG] - evaluate_round 79: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-22 23:58:04,487][flwr][DEBUG] - evaluate_round 79 received 10 results and 0 failures
+[2023-09-22 23:58:04,488][flwr][DEBUG] - fit_round 80: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9338107638888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080463 Loss1: 0.079781 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049802 Loss1: 0.049117 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052514 Loss1: 0.051828 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043988 Loss1: 0.043300 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060689 Loss1: 0.060001 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059976 Loss1: 0.059288 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034054 Loss1: 0.033367 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040827 Loss1: 0.040139 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031308 Loss1: 0.030620 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048351 Loss1: 0.047663 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.994141
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9022381756756757
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105723 Loss1: 0.105040 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039832 Loss1: 0.039143 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033744 Loss1: 0.033054 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027963 Loss1: 0.027274 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024330 Loss1: 0.023641 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023798 Loss1: 0.023108 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035635 Loss1: 0.034945 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030901 Loss1: 0.030210 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028339 Loss1: 0.027649 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.035737 Loss1: 0.035047 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.993454
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9539161392405063
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050153 Loss1: 0.049473 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028047 Loss1: 0.027362 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021453 Loss1: 0.020767 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020854 Loss1: 0.020166 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030052 Loss1: 0.029365 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030843 Loss1: 0.030155 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030394 Loss1: 0.029706 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027698 Loss1: 0.027009 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051713 Loss1: 0.051025 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071864 Loss1: 0.071175 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.982793
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9382911392405063
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066315 Loss1: 0.065633 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043817 Loss1: 0.043130 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029332 Loss1: 0.028646 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027096 Loss1: 0.026409 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027948 Loss1: 0.027260 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031544 Loss1: 0.030856 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033924 Loss1: 0.033237 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038279 Loss1: 0.037590 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042942 Loss1: 0.042254 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057971 Loss1: 0.057282 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987935
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9473892405063291
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059108 Loss1: 0.058427 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037260 Loss1: 0.036574 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055409 Loss1: 0.054724 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044109 Loss1: 0.043423 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.066713 Loss1: 0.066026 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049905 Loss1: 0.049217 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056472 Loss1: 0.055785 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048785 Loss1: 0.048097 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054737 Loss1: 0.054050 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083058 Loss1: 0.082369 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.981408
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9543269230769231
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057835 Loss1: 0.057154 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035212 Loss1: 0.034527 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027085 Loss1: 0.026398 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038832 Loss1: 0.038145 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030989 Loss1: 0.030300 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025774 Loss1: 0.025085 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033340 Loss1: 0.032653 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033272 Loss1: 0.032584 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.036703 Loss1: 0.036013 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062499 Loss1: 0.061811 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989784
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9513449367088608
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061109 Loss1: 0.060428 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029687 Loss1: 0.029001 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029299 Loss1: 0.028612 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027110 Loss1: 0.026423 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030857 Loss1: 0.030169 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038227 Loss1: 0.037539 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039187 Loss1: 0.038499 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040575 Loss1: 0.039887 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034478 Loss1: 0.033789 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.031398 Loss1: 0.030709 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.995055
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9545641447368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063607 Loss1: 0.062924 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027447 Loss1: 0.026758 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036843 Loss1: 0.036154 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036272 Loss1: 0.035583 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038050 Loss1: 0.037360 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036724 Loss1: 0.036034 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036206 Loss1: 0.035515 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051816 Loss1: 0.051126 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060918 Loss1: 0.060227 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057215 Loss1: 0.056524 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.988898
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9607469512195121
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044306 Loss1: 0.043626 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019185 Loss1: 0.018502 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023460 Loss1: 0.022776 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022603 Loss1: 0.021918 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035080 Loss1: 0.034396 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.051815 Loss1: 0.051129 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038797 Loss1: 0.038112 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049787 Loss1: 0.049101 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046843 Loss1: 0.046156 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074826 Loss1: 0.074139 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.982088
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9527243589743589
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063854 Loss1: 0.063175 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050307 Loss1: 0.049624 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044393 Loss1: 0.043709 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038509 Loss1: 0.037824 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039449 Loss1: 0.038764 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047499 Loss1: 0.046815 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029549 Loss1: 0.028864 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036795 Loss1: 0.036110 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049494 Loss1: 0.048807 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058887 Loss1: 0.058201 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.993590
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 00:26:55,551][flwr][DEBUG] - fit_round 80 received 10 results and 0 failures
+test acc: 0.64
+[2023-09-23 00:27:41,497][flwr][INFO] - fit progress: (80, 2.4491478545597185, {'accuracy': 0.64}, 162943.15885636583)
+[2023-09-23 00:27:41,498][flwr][DEBUG] - evaluate_round 80: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 00:28:18,541][flwr][DEBUG] - evaluate_round 80 received 10 results and 0 failures
+[2023-09-23 00:28:18,542][flwr][DEBUG] - fit_round 81: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.953125
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063023 Loss1: 0.062342 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027905 Loss1: 0.027219 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020624 Loss1: 0.019937 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031199 Loss1: 0.030512 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042991 Loss1: 0.042302 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035259 Loss1: 0.034570 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028469 Loss1: 0.027779 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030078 Loss1: 0.029390 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029312 Loss1: 0.028624 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029113 Loss1: 0.028424 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994191
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9299841772151899
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070361 Loss1: 0.069678 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048075 Loss1: 0.047388 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035377 Loss1: 0.034688 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046578 Loss1: 0.045890 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032663 Loss1: 0.031976 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025846 Loss1: 0.025158 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.050461 Loss1: 0.049771 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079188 Loss1: 0.078498 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056994 Loss1: 0.056304 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057287 Loss1: 0.056598 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.990704
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9007601351351351
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.081927 Loss1: 0.081245 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.056894 Loss1: 0.056208 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045254 Loss1: 0.044568 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029624 Loss1: 0.028936 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035445 Loss1: 0.034757 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.032179 Loss1: 0.031491 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037361 Loss1: 0.036673 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062488 Loss1: 0.061799 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072382 Loss1: 0.071693 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069150 Loss1: 0.068461 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.992399
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9525240384615384
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054524 Loss1: 0.053845 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031507 Loss1: 0.030825 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025512 Loss1: 0.024828 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032530 Loss1: 0.031846 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041360 Loss1: 0.040675 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033993 Loss1: 0.033309 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027515 Loss1: 0.026830 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040419 Loss1: 0.039734 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033386 Loss1: 0.032700 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036584 Loss1: 0.035898 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.994391
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9398871527777778
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.090336 Loss1: 0.089654 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.055137 Loss1: 0.054451 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040947 Loss1: 0.040262 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038210 Loss1: 0.037523 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042268 Loss1: 0.041582 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048006 Loss1: 0.047317 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046690 Loss1: 0.046003 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040034 Loss1: 0.039345 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051701 Loss1: 0.051011 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040289 Loss1: 0.039601 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989800
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.955797697368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080935 Loss1: 0.080249 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038918 Loss1: 0.038230 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034336 Loss1: 0.033646 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024761 Loss1: 0.024072 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023399 Loss1: 0.022710 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036288 Loss1: 0.035597 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040299 Loss1: 0.039608 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054552 Loss1: 0.053863 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056873 Loss1: 0.056182 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078648 Loss1: 0.077957 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.987870
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9551028481012658
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068927 Loss1: 0.068244 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047667 Loss1: 0.046980 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035290 Loss1: 0.034603 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038431 Loss1: 0.037742 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039673 Loss1: 0.038983 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042811 Loss1: 0.042122 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.049568 Loss1: 0.048878 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057617 Loss1: 0.056925 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049650 Loss1: 0.048960 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048233 Loss1: 0.047541 Loss2: 0.000692
+(DefaultActor pid=2839578) >> Training accuracy: 0.990704
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9569359756097561
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049108 Loss1: 0.048428 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020391 Loss1: 0.019708 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017105 Loss1: 0.016420 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018865 Loss1: 0.018181 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026731 Loss1: 0.026046 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027890 Loss1: 0.027204 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.024497 Loss1: 0.023812 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033313 Loss1: 0.032627 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052270 Loss1: 0.051584 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058175 Loss1: 0.057489 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.989520
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9454113924050633
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058487 Loss1: 0.057806 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038735 Loss1: 0.038051 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024455 Loss1: 0.023769 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034514 Loss1: 0.033828 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033159 Loss1: 0.032472 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028789 Loss1: 0.028102 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046960 Loss1: 0.046273 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059139 Loss1: 0.058451 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055025 Loss1: 0.054337 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067935 Loss1: 0.067247 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.983584
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9547072784810127
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057423 Loss1: 0.056741 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035337 Loss1: 0.034652 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035244 Loss1: 0.034558 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029535 Loss1: 0.028847 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026409 Loss1: 0.025722 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037457 Loss1: 0.036770 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042167 Loss1: 0.041479 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042640 Loss1: 0.041951 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044282 Loss1: 0.043593 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080399 Loss1: 0.079710 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.981408
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 00:57:14,238][flwr][DEBUG] - fit_round 81 received 10 results and 0 failures
+test acc: 0.6414
+[2023-09-23 00:58:00,414][flwr][INFO] - fit progress: (81, 2.4155959117526824, {'accuracy': 0.6414}, 164762.07565148594)
+[2023-09-23 00:58:00,415][flwr][DEBUG] - evaluate_round 81: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 00:58:38,637][flwr][DEBUG] - evaluate_round 81 received 10 results and 0 failures
+[2023-09-23 00:58:38,638][flwr][DEBUG] - fit_round 82: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9369066455696202
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064951 Loss1: 0.064269 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048171 Loss1: 0.047486 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033067 Loss1: 0.032382 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036741 Loss1: 0.036055 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022295 Loss1: 0.021607 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021661 Loss1: 0.020973 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.047969 Loss1: 0.047283 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051828 Loss1: 0.051140 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.048797 Loss1: 0.048109 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032981 Loss1: 0.032292 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994066
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.935546875
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080732 Loss1: 0.080049 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031089 Loss1: 0.030403 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029349 Loss1: 0.028661 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028728 Loss1: 0.028041 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032930 Loss1: 0.032243 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016456 Loss1: 0.015769 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026783 Loss1: 0.026098 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038241 Loss1: 0.037552 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058475 Loss1: 0.057787 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063883 Loss1: 0.063193 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.990668
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9560032894736842
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066390 Loss1: 0.065708 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031376 Loss1: 0.030690 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025031 Loss1: 0.024343 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022391 Loss1: 0.021703 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031576 Loss1: 0.030888 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029732 Loss1: 0.029043 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029684 Loss1: 0.028995 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043483 Loss1: 0.042795 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041187 Loss1: 0.040497 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036952 Loss1: 0.036262 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.992804
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9521360759493671
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.065048 Loss1: 0.064368 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033248 Loss1: 0.032562 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035610 Loss1: 0.034925 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034444 Loss1: 0.033757 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025993 Loss1: 0.025308 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029503 Loss1: 0.028816 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048708 Loss1: 0.048021 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032681 Loss1: 0.031993 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056375 Loss1: 0.055688 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065276 Loss1: 0.064589 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.983979
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9497195512820513
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061700 Loss1: 0.061022 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031062 Loss1: 0.030380 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024575 Loss1: 0.023892 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.042257 Loss1: 0.041574 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038400 Loss1: 0.037715 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047764 Loss1: 0.047079 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053061 Loss1: 0.052374 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046923 Loss1: 0.046237 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043008 Loss1: 0.042323 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056040 Loss1: 0.055354 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.991987
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9546493902439024
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053370 Loss1: 0.052691 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022844 Loss1: 0.022161 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023343 Loss1: 0.022660 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035893 Loss1: 0.035210 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026005 Loss1: 0.025322 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033457 Loss1: 0.032773 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029149 Loss1: 0.028465 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026362 Loss1: 0.025678 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031765 Loss1: 0.031080 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.037293 Loss1: 0.036607 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.993140
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9553006329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042494 Loss1: 0.041814 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031234 Loss1: 0.030548 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031502 Loss1: 0.030815 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.040892 Loss1: 0.040205 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042503 Loss1: 0.041816 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043463 Loss1: 0.042774 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054546 Loss1: 0.053857 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039639 Loss1: 0.038949 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039921 Loss1: 0.039231 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049884 Loss1: 0.049195 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.990111
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.957871835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055141 Loss1: 0.054460 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026878 Loss1: 0.026193 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022625 Loss1: 0.021940 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015713 Loss1: 0.015028 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016303 Loss1: 0.015617 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026065 Loss1: 0.025379 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023873 Loss1: 0.023187 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034164 Loss1: 0.033477 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053672 Loss1: 0.052985 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038563 Loss1: 0.037876 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.994462
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9565304487179487
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070620 Loss1: 0.069939 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026930 Loss1: 0.026244 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.030775 Loss1: 0.030090 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047054 Loss1: 0.046367 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026659 Loss1: 0.025973 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036034 Loss1: 0.035347 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044753 Loss1: 0.044065 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057596 Loss1: 0.056907 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059108 Loss1: 0.058419 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067913 Loss1: 0.067224 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.990585
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.903293918918919
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.083241 Loss1: 0.082559 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.069119 Loss1: 0.068431 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044358 Loss1: 0.043671 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049590 Loss1: 0.048900 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048925 Loss1: 0.048236 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044944 Loss1: 0.044255 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036954 Loss1: 0.036264 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040766 Loss1: 0.040077 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043401 Loss1: 0.042713 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054339 Loss1: 0.053650 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987542
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 01:28:33,879][flwr][DEBUG] - fit_round 82 received 10 results and 0 failures
+test acc: 0.6446
+[2023-09-23 01:29:14,095][flwr][INFO] - fit progress: (82, 2.4423680827259635, {'accuracy': 0.6446}, 166635.7560589686)
+[2023-09-23 01:29:14,095][flwr][DEBUG] - evaluate_round 82: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 01:29:59,494][flwr][DEBUG] - evaluate_round 82 received 10 results and 0 failures
+[2023-09-23 01:29:59,496][flwr][DEBUG] - fit_round 83: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9535256410256411
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066798 Loss1: 0.066116 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020814 Loss1: 0.020127 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026035 Loss1: 0.025347 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029134 Loss1: 0.028446 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032174 Loss1: 0.031486 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033844 Loss1: 0.033155 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038761 Loss1: 0.038071 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027314 Loss1: 0.026625 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039561 Loss1: 0.038872 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054156 Loss1: 0.053465 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.992989
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9446614583333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060098 Loss1: 0.059416 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033443 Loss1: 0.032758 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.037392 Loss1: 0.036706 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.030248 Loss1: 0.029561 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029006 Loss1: 0.028319 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024126 Loss1: 0.023441 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017647 Loss1: 0.016959 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019338 Loss1: 0.018651 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032824 Loss1: 0.032136 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051854 Loss1: 0.051167 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.989583
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9598496835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.047663 Loss1: 0.046983 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018728 Loss1: 0.018044 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020545 Loss1: 0.019861 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019773 Loss1: 0.019087 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023955 Loss1: 0.023269 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024838 Loss1: 0.024152 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018967 Loss1: 0.018280 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028664 Loss1: 0.027977 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019669 Loss1: 0.018981 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.021382 Loss1: 0.020693 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.995253
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.959703947368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060880 Loss1: 0.060198 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036816 Loss1: 0.036130 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033738 Loss1: 0.033049 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023514 Loss1: 0.022826 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035901 Loss1: 0.035213 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040026 Loss1: 0.039337 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056898 Loss1: 0.056208 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.073229 Loss1: 0.072539 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089446 Loss1: 0.088756 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085906 Loss1: 0.085217 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.985403
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9592225609756098
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068294 Loss1: 0.067615 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030240 Loss1: 0.029555 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029775 Loss1: 0.029091 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021421 Loss1: 0.020736 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027895 Loss1: 0.027209 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037170 Loss1: 0.036484 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043428 Loss1: 0.042742 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078758 Loss1: 0.078072 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059359 Loss1: 0.058673 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053880 Loss1: 0.053193 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.991806
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.946993670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063069 Loss1: 0.062388 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029132 Loss1: 0.028447 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018054 Loss1: 0.017370 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022380 Loss1: 0.021694 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024626 Loss1: 0.023940 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021652 Loss1: 0.020965 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027487 Loss1: 0.026800 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028322 Loss1: 0.027635 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025064 Loss1: 0.024377 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.039242 Loss1: 0.038554 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992089
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9035050675675675
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.085831 Loss1: 0.085149 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044517 Loss1: 0.043831 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026050 Loss1: 0.025363 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025358 Loss1: 0.024671 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023634 Loss1: 0.022946 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026613 Loss1: 0.025924 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029272 Loss1: 0.028584 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034768 Loss1: 0.034077 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039819 Loss1: 0.039129 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036749 Loss1: 0.036059 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.987120
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9535205696202531
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067496 Loss1: 0.066814 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029960 Loss1: 0.029273 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.030786 Loss1: 0.030099 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028098 Loss1: 0.027410 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040473 Loss1: 0.039784 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062708 Loss1: 0.062021 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042223 Loss1: 0.041533 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027157 Loss1: 0.026468 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028714 Loss1: 0.028025 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036605 Loss1: 0.035916 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.992682
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9515224358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057560 Loss1: 0.056881 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028758 Loss1: 0.028075 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022797 Loss1: 0.022112 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035423 Loss1: 0.034738 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041735 Loss1: 0.041051 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050212 Loss1: 0.049527 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031915 Loss1: 0.031230 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036227 Loss1: 0.035542 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043004 Loss1: 0.042319 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041706 Loss1: 0.041020 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.993590
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9416534810126582
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067868 Loss1: 0.067186 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033472 Loss1: 0.032787 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026228 Loss1: 0.025542 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032547 Loss1: 0.031860 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040557 Loss1: 0.039870 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038429 Loss1: 0.037741 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034226 Loss1: 0.033538 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045065 Loss1: 0.044378 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040011 Loss1: 0.039323 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.039544 Loss1: 0.038857 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.993275
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 02:00:05,773][flwr][DEBUG] - fit_round 83 received 10 results and 0 failures
+test acc: 0.6436
+[2023-09-23 02:00:54,928][flwr][INFO] - fit progress: (83, 2.4571505731667953, {'accuracy': 0.6436}, 168536.58993728273)
+[2023-09-23 02:00:54,929][flwr][DEBUG] - evaluate_round 83: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 02:01:32,139][flwr][DEBUG] - evaluate_round 83 received 10 results and 0 failures
+[2023-09-23 02:01:32,141][flwr][DEBUG] - fit_round 84: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9519382911392406
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063177 Loss1: 0.062496 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033664 Loss1: 0.032978 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024772 Loss1: 0.024087 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022898 Loss1: 0.022212 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025247 Loss1: 0.024562 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035081 Loss1: 0.034397 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028536 Loss1: 0.027849 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029973 Loss1: 0.029286 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033405 Loss1: 0.032718 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048631 Loss1: 0.047944 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.991891
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9599095394736842
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.056444 Loss1: 0.055761 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035267 Loss1: 0.034580 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039057 Loss1: 0.038369 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027981 Loss1: 0.027291 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026097 Loss1: 0.025408 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029261 Loss1: 0.028571 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036818 Loss1: 0.036129 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035374 Loss1: 0.034684 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.036234 Loss1: 0.035544 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073121 Loss1: 0.072431 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.985609
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9645579268292683
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072670 Loss1: 0.071991 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046325 Loss1: 0.045641 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029569 Loss1: 0.028883 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036192 Loss1: 0.035505 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033666 Loss1: 0.032979 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048533 Loss1: 0.047847 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055182 Loss1: 0.054494 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048938 Loss1: 0.048251 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077432 Loss1: 0.076744 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080071 Loss1: 0.079383 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.987614
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.964003164556962
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058801 Loss1: 0.058119 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047359 Loss1: 0.046671 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035060 Loss1: 0.034374 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021430 Loss1: 0.020741 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024394 Loss1: 0.023706 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030818 Loss1: 0.030129 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041115 Loss1: 0.040426 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059294 Loss1: 0.058606 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063558 Loss1: 0.062869 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074486 Loss1: 0.073796 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.992286
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9361155063291139
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072807 Loss1: 0.072125 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036010 Loss1: 0.035323 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028210 Loss1: 0.027524 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027761 Loss1: 0.027074 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032009 Loss1: 0.031320 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030693 Loss1: 0.030005 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042831 Loss1: 0.042142 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043566 Loss1: 0.042878 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039768 Loss1: 0.039077 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048099 Loss1: 0.047410 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994858
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9045608108108109
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084706 Loss1: 0.084024 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041193 Loss1: 0.040505 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043768 Loss1: 0.043082 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044891 Loss1: 0.044203 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034902 Loss1: 0.034215 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042643 Loss1: 0.041956 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.032599 Loss1: 0.031911 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033652 Loss1: 0.032966 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037517 Loss1: 0.036828 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029023 Loss1: 0.028334 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.995144
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9582674050632911
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046678 Loss1: 0.045997 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022240 Loss1: 0.021556 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021389 Loss1: 0.020704 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031412 Loss1: 0.030725 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028966 Loss1: 0.028280 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027152 Loss1: 0.026466 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031127 Loss1: 0.030440 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034360 Loss1: 0.033673 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032008 Loss1: 0.031320 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055018 Loss1: 0.054329 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9615384615384616
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061207 Loss1: 0.060524 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030605 Loss1: 0.029920 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024219 Loss1: 0.023532 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026068 Loss1: 0.025380 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027033 Loss1: 0.026345 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022097 Loss1: 0.021408 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.032278 Loss1: 0.031590 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040569 Loss1: 0.039881 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026930 Loss1: 0.026243 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027943 Loss1: 0.027252 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.998397
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9555288461538461
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064313 Loss1: 0.063635 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042620 Loss1: 0.041936 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031470 Loss1: 0.030786 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032394 Loss1: 0.031709 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025123 Loss1: 0.024438 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020477 Loss1: 0.019791 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.021108 Loss1: 0.020423 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022144 Loss1: 0.021459 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040398 Loss1: 0.039711 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.077825 Loss1: 0.077139 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.989984
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9351128472222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068401 Loss1: 0.067718 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035442 Loss1: 0.034756 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049096 Loss1: 0.048410 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.054225 Loss1: 0.053539 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059284 Loss1: 0.058597 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052341 Loss1: 0.051653 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043131 Loss1: 0.042442 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037859 Loss1: 0.037170 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051217 Loss1: 0.050528 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059824 Loss1: 0.059135 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.986762
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 02:31:11,308][flwr][DEBUG] - fit_round 84 received 10 results and 0 failures
+test acc: 0.6438
+[2023-09-23 02:31:48,655][flwr][INFO] - fit progress: (84, 2.4649806056921473, {'accuracy': 0.6438}, 170390.31651637657)
+[2023-09-23 02:31:48,655][flwr][DEBUG] - evaluate_round 84: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 02:32:25,317][flwr][DEBUG] - evaluate_round 84 received 10 results and 0 failures
+[2023-09-23 02:32:25,318][flwr][DEBUG] - fit_round 85: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9426424050632911
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059814 Loss1: 0.059134 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023981 Loss1: 0.023295 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028435 Loss1: 0.027749 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025380 Loss1: 0.024693 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028160 Loss1: 0.027472 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039352 Loss1: 0.038662 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043783 Loss1: 0.043095 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039008 Loss1: 0.038318 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038866 Loss1: 0.038176 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043455 Loss1: 0.042766 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994660
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9440104166666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058635 Loss1: 0.057955 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042277 Loss1: 0.041591 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031394 Loss1: 0.030708 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032969 Loss1: 0.032282 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043335 Loss1: 0.042647 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041710 Loss1: 0.041022 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029413 Loss1: 0.028725 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026659 Loss1: 0.025971 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031624 Loss1: 0.030934 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030673 Loss1: 0.029984 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.996745
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9489182692307693
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060784 Loss1: 0.060105 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037158 Loss1: 0.036474 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034215 Loss1: 0.033531 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026018 Loss1: 0.025334 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025145 Loss1: 0.024460 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022011 Loss1: 0.021326 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029210 Loss1: 0.028526 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030211 Loss1: 0.029525 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034638 Loss1: 0.033952 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042529 Loss1: 0.041844 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.994191
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9523338607594937
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059224 Loss1: 0.058543 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029797 Loss1: 0.029114 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026281 Loss1: 0.025596 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022521 Loss1: 0.021834 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.010690 Loss1: 0.010004 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.013125 Loss1: 0.012439 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.015117 Loss1: 0.014429 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.018739 Loss1: 0.018051 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029020 Loss1: 0.028332 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023393 Loss1: 0.022706 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.998616
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9539473684210527
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058691 Loss1: 0.058008 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037289 Loss1: 0.036602 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034826 Loss1: 0.034137 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035623 Loss1: 0.034935 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032072 Loss1: 0.031383 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037345 Loss1: 0.036657 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033678 Loss1: 0.032988 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049200 Loss1: 0.048509 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032050 Loss1: 0.031359 Loss2: 0.000692
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046414 Loss1: 0.045723 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.989720
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9115287162162162
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.078671 Loss1: 0.077990 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028609 Loss1: 0.027923 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023841 Loss1: 0.023153 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023946 Loss1: 0.023258 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025250 Loss1: 0.024563 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018114 Loss1: 0.017427 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018857 Loss1: 0.018171 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022054 Loss1: 0.021365 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028414 Loss1: 0.027726 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033186 Loss1: 0.032497 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.995144
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9636075949367089
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048481 Loss1: 0.047800 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027239 Loss1: 0.026553 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018385 Loss1: 0.017697 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.012480 Loss1: 0.011791 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.013278 Loss1: 0.012588 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.008825 Loss1: 0.008135 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011110 Loss1: 0.010421 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.012547 Loss1: 0.011858 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.011822 Loss1: 0.011132 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.018274 Loss1: 0.017585 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.997429
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9541266025641025
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050596 Loss1: 0.049914 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025578 Loss1: 0.024893 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018997 Loss1: 0.018311 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038290 Loss1: 0.037603 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026535 Loss1: 0.025847 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040181 Loss1: 0.039494 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041881 Loss1: 0.041193 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039117 Loss1: 0.038428 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038702 Loss1: 0.038014 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054892 Loss1: 0.054203 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994992
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9523338607594937
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053866 Loss1: 0.053186 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022181 Loss1: 0.021495 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019480 Loss1: 0.018794 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011935 Loss1: 0.011248 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.010239 Loss1: 0.009552 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016143 Loss1: 0.015458 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017182 Loss1: 0.016496 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023343 Loss1: 0.022656 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024574 Loss1: 0.023888 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030370 Loss1: 0.029684 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.992682
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9649390243902439
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045624 Loss1: 0.044946 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028164 Loss1: 0.027480 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018708 Loss1: 0.018023 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023898 Loss1: 0.023213 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020443 Loss1: 0.019758 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021018 Loss1: 0.020333 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.014680 Loss1: 0.013995 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.011579 Loss1: 0.010895 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.011671 Loss1: 0.010986 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.014627 Loss1: 0.013941 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.997332
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 03:02:15,040][flwr][DEBUG] - fit_round 85 received 10 results and 0 failures
+test acc: 0.6483
+[2023-09-23 03:02:53,227][flwr][INFO] - fit progress: (85, 2.5026507737537544, {'accuracy': 0.6483}, 172254.88892117888)
+[2023-09-23 03:02:53,228][flwr][DEBUG] - evaluate_round 85: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 03:03:28,979][flwr][DEBUG] - evaluate_round 85 received 10 results and 0 failures
+[2023-09-23 03:03:28,980][flwr][DEBUG] - fit_round 86: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.964003164556962
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046439 Loss1: 0.045759 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024746 Loss1: 0.024061 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026984 Loss1: 0.026299 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032114 Loss1: 0.031428 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054390 Loss1: 0.053704 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058262 Loss1: 0.057576 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065606 Loss1: 0.064919 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063767 Loss1: 0.063079 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065166 Loss1: 0.064479 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064869 Loss1: 0.064181 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.980617
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9457465277777778
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061928 Loss1: 0.061247 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022826 Loss1: 0.022141 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018676 Loss1: 0.017990 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024888 Loss1: 0.024201 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035807 Loss1: 0.035121 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026729 Loss1: 0.026043 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025297 Loss1: 0.024611 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027936 Loss1: 0.027249 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025796 Loss1: 0.025108 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030787 Loss1: 0.030098 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.992405
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9136402027027027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069521 Loss1: 0.068838 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029134 Loss1: 0.028448 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025818 Loss1: 0.025131 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022739 Loss1: 0.022051 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021053 Loss1: 0.020365 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024274 Loss1: 0.023586 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026769 Loss1: 0.026080 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031085 Loss1: 0.030396 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029567 Loss1: 0.028879 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044316 Loss1: 0.043626 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.992399
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.966376582278481
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054927 Loss1: 0.054246 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047205 Loss1: 0.046519 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035048 Loss1: 0.034360 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038093 Loss1: 0.037405 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030938 Loss1: 0.030251 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035455 Loss1: 0.034766 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044747 Loss1: 0.044059 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045644 Loss1: 0.044953 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025733 Loss1: 0.025042 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.031713 Loss1: 0.031022 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.994462
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9592927631578947
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055824 Loss1: 0.055140 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030474 Loss1: 0.029786 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020996 Loss1: 0.020306 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024315 Loss1: 0.023626 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017695 Loss1: 0.017007 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031434 Loss1: 0.030744 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026819 Loss1: 0.026129 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033426 Loss1: 0.032735 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046306 Loss1: 0.045615 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083157 Loss1: 0.082467 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.986020
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9567307692307693
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054187 Loss1: 0.053506 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030987 Loss1: 0.030303 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036449 Loss1: 0.035763 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033534 Loss1: 0.032849 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022955 Loss1: 0.022271 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.013229 Loss1: 0.012543 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020143 Loss1: 0.019456 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026988 Loss1: 0.026302 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035765 Loss1: 0.035080 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043606 Loss1: 0.042920 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.993790
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9456091772151899
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052979 Loss1: 0.052297 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035237 Loss1: 0.034551 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027393 Loss1: 0.026706 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037879 Loss1: 0.037193 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051825 Loss1: 0.051138 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.056349 Loss1: 0.055662 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.052856 Loss1: 0.052167 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075094 Loss1: 0.074406 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075334 Loss1: 0.074646 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079569 Loss1: 0.078880 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.982793
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9605368589743589
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048970 Loss1: 0.048290 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028117 Loss1: 0.027433 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029937 Loss1: 0.029249 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023905 Loss1: 0.023219 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020584 Loss1: 0.019897 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025494 Loss1: 0.024807 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030295 Loss1: 0.029608 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026685 Loss1: 0.025998 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034957 Loss1: 0.034268 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049181 Loss1: 0.048492 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987179
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9655106707317073
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044310 Loss1: 0.043629 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035127 Loss1: 0.034443 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034443 Loss1: 0.033759 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035780 Loss1: 0.035095 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033982 Loss1: 0.033296 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037948 Loss1: 0.037262 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055760 Loss1: 0.055074 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.055483 Loss1: 0.054796 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049904 Loss1: 0.049218 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.037986 Loss1: 0.037298 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.993331
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9566851265822784
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053904 Loss1: 0.053221 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033702 Loss1: 0.033016 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034790 Loss1: 0.034104 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043564 Loss1: 0.042877 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036648 Loss1: 0.035961 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053389 Loss1: 0.052701 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048907 Loss1: 0.048220 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052126 Loss1: 0.051438 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074584 Loss1: 0.073896 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.093579 Loss1: 0.092891 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.980617
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 03:33:16,989][flwr][DEBUG] - fit_round 86 received 10 results and 0 failures
+test acc: 0.6444
+[2023-09-23 03:33:55,332][flwr][INFO] - fit progress: (86, 2.4607985724275485, {'accuracy': 0.6444}, 174116.99310424365)
+[2023-09-23 03:33:55,332][flwr][DEBUG] - evaluate_round 86: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 03:34:31,484][flwr][DEBUG] - evaluate_round 86 received 10 results and 0 failures
+[2023-09-23 03:34:31,485][flwr][DEBUG] - fit_round 87: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9442246835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046494 Loss1: 0.045816 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027297 Loss1: 0.026612 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013255 Loss1: 0.012569 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013866 Loss1: 0.013178 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.013514 Loss1: 0.012825 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021356 Loss1: 0.020669 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028683 Loss1: 0.027995 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026499 Loss1: 0.025812 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035720 Loss1: 0.035033 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041512 Loss1: 0.040823 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.993275
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9136402027027027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061266 Loss1: 0.060584 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035482 Loss1: 0.034796 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034681 Loss1: 0.033994 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037472 Loss1: 0.036784 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040434 Loss1: 0.039747 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046945 Loss1: 0.046256 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056124 Loss1: 0.055435 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043577 Loss1: 0.042889 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044170 Loss1: 0.043481 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044813 Loss1: 0.044124 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.987965
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9645965189873418
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.047623 Loss1: 0.046941 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029169 Loss1: 0.028484 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022252 Loss1: 0.021566 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031589 Loss1: 0.030903 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046650 Loss1: 0.045963 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035880 Loss1: 0.035193 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025783 Loss1: 0.025095 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022848 Loss1: 0.022159 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025575 Loss1: 0.024886 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025851 Loss1: 0.025162 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.994660
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9577323717948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059134 Loss1: 0.058453 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031375 Loss1: 0.030691 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031526 Loss1: 0.030840 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026395 Loss1: 0.025708 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027891 Loss1: 0.027203 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029580 Loss1: 0.028892 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030881 Loss1: 0.030194 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040182 Loss1: 0.039493 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044837 Loss1: 0.044149 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045301 Loss1: 0.044612 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.994391
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9442274305555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075459 Loss1: 0.074776 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022781 Loss1: 0.022097 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033725 Loss1: 0.033040 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025939 Loss1: 0.025253 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024830 Loss1: 0.024144 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037631 Loss1: 0.036945 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031063 Loss1: 0.030376 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042630 Loss1: 0.041942 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038259 Loss1: 0.037572 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047106 Loss1: 0.046418 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.987413
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9628164556962026
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037155 Loss1: 0.036476 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018302 Loss1: 0.017615 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021079 Loss1: 0.020391 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.014974 Loss1: 0.014285 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017089 Loss1: 0.016401 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017621 Loss1: 0.016932 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.024762 Loss1: 0.024072 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029308 Loss1: 0.028619 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027548 Loss1: 0.026858 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.034112 Loss1: 0.033422 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.995847
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9653201219512195
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049787 Loss1: 0.049108 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030026 Loss1: 0.029342 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026109 Loss1: 0.025424 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.030906 Loss1: 0.030220 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042454 Loss1: 0.041768 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034458 Loss1: 0.033772 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026677 Loss1: 0.025990 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035393 Loss1: 0.034706 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038607 Loss1: 0.037919 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040040 Loss1: 0.039352 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992759
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9603365384615384
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055119 Loss1: 0.054439 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037950 Loss1: 0.037266 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028835 Loss1: 0.028152 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022725 Loss1: 0.022041 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031438 Loss1: 0.030754 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025245 Loss1: 0.024560 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031665 Loss1: 0.030981 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040488 Loss1: 0.039804 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024695 Loss1: 0.024010 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.019548 Loss1: 0.018863 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.995593
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9613486842105263
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052370 Loss1: 0.051686 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037862 Loss1: 0.037175 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.046298 Loss1: 0.045610 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044229 Loss1: 0.043540 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048515 Loss1: 0.047826 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.056765 Loss1: 0.056077 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068196 Loss1: 0.067506 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075327 Loss1: 0.074637 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060666 Loss1: 0.059975 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061980 Loss1: 0.061291 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.986431
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9535205696202531
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049677 Loss1: 0.048996 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021795 Loss1: 0.021111 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018668 Loss1: 0.017982 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018492 Loss1: 0.017807 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032235 Loss1: 0.031550 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026071 Loss1: 0.025383 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.032258 Loss1: 0.031571 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047149 Loss1: 0.046463 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069893 Loss1: 0.069206 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066496 Loss1: 0.065808 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.987935
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 04:04:22,961][flwr][DEBUG] - fit_round 87 received 10 results and 0 failures
+test acc: 0.6441
+[2023-09-23 04:05:00,726][flwr][INFO] - fit progress: (87, 2.49972779548968, {'accuracy': 0.6441}, 175982.38766917493)
+[2023-09-23 04:05:00,727][flwr][DEBUG] - evaluate_round 87: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 04:05:36,442][flwr][DEBUG] - evaluate_round 87 received 10 results and 0 failures
+[2023-09-23 04:05:36,443][flwr][DEBUG] - fit_round 88: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9541266025641025
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053543 Loss1: 0.052864 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039175 Loss1: 0.038493 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029693 Loss1: 0.029010 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018843 Loss1: 0.018157 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022774 Loss1: 0.022090 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022139 Loss1: 0.021454 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022160 Loss1: 0.021476 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.025665 Loss1: 0.024978 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052043 Loss1: 0.051357 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048278 Loss1: 0.047592 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.990184
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.963795731707317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045580 Loss1: 0.044900 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022739 Loss1: 0.022055 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018979 Loss1: 0.018295 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020053 Loss1: 0.019367 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033292 Loss1: 0.032607 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021427 Loss1: 0.020743 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030224 Loss1: 0.029538 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.018308 Loss1: 0.017623 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026147 Loss1: 0.025462 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032590 Loss1: 0.031904 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.994474
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9375
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059923 Loss1: 0.059242 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038172 Loss1: 0.037486 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032415 Loss1: 0.031728 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036887 Loss1: 0.036199 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022523 Loss1: 0.021835 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026391 Loss1: 0.025703 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.024212 Loss1: 0.023524 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029453 Loss1: 0.028764 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040848 Loss1: 0.040160 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042590 Loss1: 0.041901 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.989517
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.903293918918919
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064029 Loss1: 0.063347 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032748 Loss1: 0.032061 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034030 Loss1: 0.033342 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024994 Loss1: 0.024307 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022479 Loss1: 0.021791 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024043 Loss1: 0.023355 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023317 Loss1: 0.022629 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023510 Loss1: 0.022821 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034321 Loss1: 0.033631 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.034892 Loss1: 0.034202 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994299
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9604430379746836
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043938 Loss1: 0.043260 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020095 Loss1: 0.019410 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.014814 Loss1: 0.014126 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.012570 Loss1: 0.011882 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020832 Loss1: 0.020145 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026942 Loss1: 0.026255 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029660 Loss1: 0.028972 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037039 Loss1: 0.036350 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035312 Loss1: 0.034624 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.034496 Loss1: 0.033808 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.994660
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9590585443037974
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.035319 Loss1: 0.034639 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023834 Loss1: 0.023150 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024426 Loss1: 0.023741 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017109 Loss1: 0.016423 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029574 Loss1: 0.028888 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041724 Loss1: 0.041038 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029240 Loss1: 0.028554 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026587 Loss1: 0.025900 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028856 Loss1: 0.028169 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033860 Loss1: 0.033173 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.992880
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9566851265822784
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055748 Loss1: 0.055067 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030604 Loss1: 0.029920 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025746 Loss1: 0.025062 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032496 Loss1: 0.031811 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029543 Loss1: 0.028858 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028019 Loss1: 0.027332 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027091 Loss1: 0.026403 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035943 Loss1: 0.035255 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044464 Loss1: 0.043776 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051306 Loss1: 0.050618 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.990309
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9627403846153846
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046058 Loss1: 0.045377 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028840 Loss1: 0.028153 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024814 Loss1: 0.024127 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018277 Loss1: 0.017590 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021757 Loss1: 0.021070 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042326 Loss1: 0.041638 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039562 Loss1: 0.038872 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050643 Loss1: 0.049954 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049337 Loss1: 0.048648 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059600 Loss1: 0.058910 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.992388
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9518229166666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060591 Loss1: 0.059908 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030769 Loss1: 0.030082 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031979 Loss1: 0.031293 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037555 Loss1: 0.036869 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035080 Loss1: 0.034393 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042913 Loss1: 0.042225 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030845 Loss1: 0.030156 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037950 Loss1: 0.037263 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.050160 Loss1: 0.049473 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043260 Loss1: 0.042571 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.995009
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9607319078947368
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044966 Loss1: 0.044284 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021270 Loss1: 0.020585 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025405 Loss1: 0.024718 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026661 Loss1: 0.025974 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040798 Loss1: 0.040109 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036463 Loss1: 0.035775 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034923 Loss1: 0.034233 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048501 Loss1: 0.047810 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041662 Loss1: 0.040973 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047068 Loss1: 0.046378 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.985197
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 04:35:24,032][flwr][DEBUG] - fit_round 88 received 10 results and 0 failures
+test acc: 0.6475
+[2023-09-23 04:36:02,272][flwr][INFO] - fit progress: (88, 2.5036359633119725, {'accuracy': 0.6475}, 177843.93344982294)
+[2023-09-23 04:36:02,272][flwr][DEBUG] - evaluate_round 88: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 04:36:37,439][flwr][DEBUG] - evaluate_round 88 received 10 results and 0 failures
+[2023-09-23 04:36:37,440][flwr][DEBUG] - fit_round 89: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9573317307692307
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049367 Loss1: 0.048688 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021140 Loss1: 0.020458 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027071 Loss1: 0.026388 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039679 Loss1: 0.038996 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036755 Loss1: 0.036071 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040840 Loss1: 0.040155 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057719 Loss1: 0.057035 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061348 Loss1: 0.060662 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045333 Loss1: 0.044648 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054706 Loss1: 0.054020 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.988181
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9503560126582279
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057917 Loss1: 0.057237 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.034338 Loss1: 0.033654 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020192 Loss1: 0.019505 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020266 Loss1: 0.019580 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020829 Loss1: 0.020142 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022925 Loss1: 0.022239 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027223 Loss1: 0.026535 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030776 Loss1: 0.030088 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033705 Loss1: 0.033016 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032704 Loss1: 0.032015 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.995055
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9604430379746836
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049887 Loss1: 0.049206 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022493 Loss1: 0.021808 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022624 Loss1: 0.021938 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025906 Loss1: 0.025221 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025139 Loss1: 0.024453 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017784 Loss1: 0.017097 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030989 Loss1: 0.030303 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023476 Loss1: 0.022788 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.021839 Loss1: 0.021149 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033314 Loss1: 0.032626 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.995649
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.911106418918919
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061845 Loss1: 0.061162 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030165 Loss1: 0.029478 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029410 Loss1: 0.028723 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018700 Loss1: 0.018011 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021590 Loss1: 0.020902 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017399 Loss1: 0.016711 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.012870 Loss1: 0.012180 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022972 Loss1: 0.022283 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046827 Loss1: 0.046137 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050638 Loss1: 0.049949 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.990921
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.959256329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050680 Loss1: 0.049998 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022299 Loss1: 0.021612 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020728 Loss1: 0.020040 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027854 Loss1: 0.027166 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032719 Loss1: 0.032030 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039232 Loss1: 0.038543 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035053 Loss1: 0.034362 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049502 Loss1: 0.048812 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.048734 Loss1: 0.048044 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046708 Loss1: 0.046017 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.991495
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9485677083333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052725 Loss1: 0.052043 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022559 Loss1: 0.021874 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019179 Loss1: 0.018495 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013342 Loss1: 0.012656 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026326 Loss1: 0.025640 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024188 Loss1: 0.023501 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020296 Loss1: 0.019609 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023508 Loss1: 0.022821 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027453 Loss1: 0.026767 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027958 Loss1: 0.027271 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992622
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9628429878048781
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.039530 Loss1: 0.038850 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020368 Loss1: 0.019685 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024035 Loss1: 0.023351 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016658 Loss1: 0.015975 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022881 Loss1: 0.022198 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034818 Loss1: 0.034132 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065099 Loss1: 0.064412 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045005 Loss1: 0.044318 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041120 Loss1: 0.040434 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046678 Loss1: 0.045991 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.991616
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9627403846153846
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054472 Loss1: 0.053791 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032977 Loss1: 0.032291 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021061 Loss1: 0.020376 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021378 Loss1: 0.020693 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019145 Loss1: 0.018459 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014852 Loss1: 0.014166 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.010774 Loss1: 0.010087 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.013405 Loss1: 0.012718 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.020769 Loss1: 0.020081 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.014531 Loss1: 0.013844 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.998397
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9600474683544303
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.041424 Loss1: 0.040744 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027899 Loss1: 0.027215 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025132 Loss1: 0.024447 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043371 Loss1: 0.042686 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048667 Loss1: 0.047980 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040678 Loss1: 0.039990 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076563 Loss1: 0.075875 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074115 Loss1: 0.073427 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054083 Loss1: 0.053395 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063584 Loss1: 0.062895 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.984573
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9650493421052632
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046344 Loss1: 0.045661 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019981 Loss1: 0.019293 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025461 Loss1: 0.024773 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028818 Loss1: 0.028130 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029690 Loss1: 0.029001 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028987 Loss1: 0.028299 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027103 Loss1: 0.026413 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038891 Loss1: 0.038202 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047295 Loss1: 0.046607 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065990 Loss1: 0.065302 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.987253
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 05:06:18,288][flwr][DEBUG] - fit_round 89 received 10 results and 0 failures
+test acc: 0.6455
+[2023-09-23 05:07:31,433][flwr][INFO] - fit progress: (89, 2.477679441340815, {'accuracy': 0.6455}, 179733.09433418186)
+[2023-09-23 05:07:31,434][flwr][DEBUG] - evaluate_round 89: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 05:08:06,611][flwr][DEBUG] - evaluate_round 89 received 10 results and 0 failures
+[2023-09-23 05:08:06,613][flwr][DEBUG] - fit_round 90: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9703947368421053
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.039063 Loss1: 0.038380 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028870 Loss1: 0.028182 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024187 Loss1: 0.023499 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020736 Loss1: 0.020048 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020105 Loss1: 0.019415 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011630 Loss1: 0.010941 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016994 Loss1: 0.016305 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.021793 Loss1: 0.021104 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033752 Loss1: 0.033062 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.035227 Loss1: 0.034536 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.991365
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9625400641025641
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049572 Loss1: 0.048892 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027922 Loss1: 0.027238 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024240 Loss1: 0.023554 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018049 Loss1: 0.017365 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032493 Loss1: 0.031807 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022771 Loss1: 0.022085 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036589 Loss1: 0.035903 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026794 Loss1: 0.026107 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032393 Loss1: 0.031705 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040428 Loss1: 0.039740 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.995192
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9464003164556962
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058719 Loss1: 0.058040 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025197 Loss1: 0.024512 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023906 Loss1: 0.023221 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019624 Loss1: 0.018938 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017256 Loss1: 0.016571 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021606 Loss1: 0.020919 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019454 Loss1: 0.018768 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026602 Loss1: 0.025916 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032324 Loss1: 0.031636 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.031850 Loss1: 0.031162 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994066
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9163851351351351
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.071346 Loss1: 0.070664 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036677 Loss1: 0.035992 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026392 Loss1: 0.025707 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020627 Loss1: 0.019941 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024582 Loss1: 0.023895 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037030 Loss1: 0.036341 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026382 Loss1: 0.025694 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037871 Loss1: 0.037183 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027423 Loss1: 0.026734 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.026770 Loss1: 0.026081 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.996199
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9553006329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.056178 Loss1: 0.055497 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026399 Loss1: 0.025715 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025441 Loss1: 0.024756 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025590 Loss1: 0.024904 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027205 Loss1: 0.026519 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035742 Loss1: 0.035056 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025878 Loss1: 0.025191 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031171 Loss1: 0.030484 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027534 Loss1: 0.026847 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033413 Loss1: 0.032726 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.993671
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.947265625
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075781 Loss1: 0.075098 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042290 Loss1: 0.041605 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034510 Loss1: 0.033824 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026917 Loss1: 0.026230 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032695 Loss1: 0.032008 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027080 Loss1: 0.026393 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026420 Loss1: 0.025732 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024571 Loss1: 0.023882 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033672 Loss1: 0.032984 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033817 Loss1: 0.033130 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.995226
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9679588607594937
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061982 Loss1: 0.061299 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028446 Loss1: 0.027759 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033572 Loss1: 0.032886 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023249 Loss1: 0.022561 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023763 Loss1: 0.023076 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031286 Loss1: 0.030600 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.064659 Loss1: 0.063972 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050060 Loss1: 0.049372 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043162 Loss1: 0.042471 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054790 Loss1: 0.054101 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.989715
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9647943037974683
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048265 Loss1: 0.047585 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017741 Loss1: 0.017056 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013264 Loss1: 0.012578 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019929 Loss1: 0.019241 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022739 Loss1: 0.022050 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044648 Loss1: 0.043960 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025053 Loss1: 0.024363 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023021 Loss1: 0.022333 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047861 Loss1: 0.047172 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049887 Loss1: 0.049200 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.990111
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9634146341463414
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043546 Loss1: 0.042866 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.016483 Loss1: 0.015801 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022010 Loss1: 0.021326 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023404 Loss1: 0.022722 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024780 Loss1: 0.024097 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021805 Loss1: 0.021121 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.012581 Loss1: 0.011895 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.017194 Loss1: 0.016509 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.020815 Loss1: 0.020131 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023017 Loss1: 0.022331 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.996951
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.960136217948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052493 Loss1: 0.051814 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030553 Loss1: 0.029871 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025001 Loss1: 0.024317 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026061 Loss1: 0.025377 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020875 Loss1: 0.020192 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025498 Loss1: 0.024813 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043803 Loss1: 0.043118 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043501 Loss1: 0.042814 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061266 Loss1: 0.060581 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058320 Loss1: 0.057635 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.992588
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 05:37:51,453][flwr][DEBUG] - fit_round 90 received 10 results and 0 failures
+test acc: 0.6505
+[2023-09-23 05:38:27,895][flwr][INFO] - fit progress: (90, 2.4975950007621472, {'accuracy': 0.6505}, 181589.55689398665)
+[2023-09-23 05:38:27,896][flwr][DEBUG] - evaluate_round 90: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 05:39:03,049][flwr][DEBUG] - evaluate_round 90 received 10 results and 0 failures
+[2023-09-23 05:39:03,061][flwr][DEBUG] - fit_round 91: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.965891768292683
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042132 Loss1: 0.041453 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028339 Loss1: 0.027657 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032647 Loss1: 0.031962 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034987 Loss1: 0.034302 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040123 Loss1: 0.039437 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045042 Loss1: 0.044357 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043266 Loss1: 0.042580 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027638 Loss1: 0.026953 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028046 Loss1: 0.027358 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027103 Loss1: 0.026415 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.995808
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9537760416666666
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.047989 Loss1: 0.047307 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042238 Loss1: 0.041554 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018954 Loss1: 0.018268 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028641 Loss1: 0.027954 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030655 Loss1: 0.029967 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039962 Loss1: 0.039276 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039488 Loss1: 0.038800 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053689 Loss1: 0.053001 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085681 Loss1: 0.084992 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098153 Loss1: 0.097465 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.981988
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.959256329113924
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053307 Loss1: 0.052626 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023749 Loss1: 0.023064 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024534 Loss1: 0.023847 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022940 Loss1: 0.022252 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020000 Loss1: 0.019314 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016594 Loss1: 0.015907 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.013537 Loss1: 0.012850 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019692 Loss1: 0.019005 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.023664 Loss1: 0.022976 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029490 Loss1: 0.028802 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.996638
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9591346153846154
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043962 Loss1: 0.043284 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033109 Loss1: 0.032427 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023487 Loss1: 0.022804 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022641 Loss1: 0.021958 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034238 Loss1: 0.033553 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025820 Loss1: 0.025134 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028588 Loss1: 0.027903 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037037 Loss1: 0.036352 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041717 Loss1: 0.041031 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.037751 Loss1: 0.037064 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.995192
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9622231012658228
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.039498 Loss1: 0.038819 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018178 Loss1: 0.017494 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017745 Loss1: 0.017058 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025331 Loss1: 0.024644 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020534 Loss1: 0.019848 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018018 Loss1: 0.017332 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019695 Loss1: 0.019007 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.021450 Loss1: 0.020762 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.023977 Loss1: 0.023290 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023060 Loss1: 0.022372 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.994462
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9625400641025641
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.062284 Loss1: 0.061605 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025276 Loss1: 0.024590 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022746 Loss1: 0.022060 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013193 Loss1: 0.012506 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019445 Loss1: 0.018759 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023698 Loss1: 0.023011 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018644 Loss1: 0.017956 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040139 Loss1: 0.039451 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034832 Loss1: 0.034144 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027080 Loss1: 0.026392 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.996995
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9618275316455697
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048733 Loss1: 0.048052 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023778 Loss1: 0.023094 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019567 Loss1: 0.018881 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019123 Loss1: 0.018439 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021326 Loss1: 0.020640 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030149 Loss1: 0.029462 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020011 Loss1: 0.019323 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016392 Loss1: 0.015705 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.021148 Loss1: 0.020461 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.018601 Loss1: 0.017913 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.997627
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9089949324324325
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050654 Loss1: 0.049970 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018236 Loss1: 0.017549 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011357 Loss1: 0.010670 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013053 Loss1: 0.012365 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011441 Loss1: 0.010755 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.012374 Loss1: 0.011686 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.007921 Loss1: 0.007234 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.008461 Loss1: 0.007773 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.007162 Loss1: 0.006475 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.010242 Loss1: 0.009555 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.997889
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9519382911392406
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049110 Loss1: 0.048429 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022163 Loss1: 0.021479 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019519 Loss1: 0.018833 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013122 Loss1: 0.012435 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.013184 Loss1: 0.012496 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019928 Loss1: 0.019239 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.021041 Loss1: 0.020353 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022098 Loss1: 0.021410 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025436 Loss1: 0.024747 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022685 Loss1: 0.021997 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.997824
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.96484375
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044294 Loss1: 0.043611 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017217 Loss1: 0.016531 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026505 Loss1: 0.025818 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015315 Loss1: 0.014627 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014695 Loss1: 0.014004 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021871 Loss1: 0.021182 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020353 Loss1: 0.019665 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030012 Loss1: 0.029324 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042255 Loss1: 0.041565 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073268 Loss1: 0.072580 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.993010
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 06:08:37,792][flwr][DEBUG] - fit_round 91 received 10 results and 0 failures
+test acc: 0.6458
+[2023-09-23 06:09:14,883][flwr][INFO] - fit progress: (91, 2.5048911708612414, {'accuracy': 0.6458}, 183436.54472790193)
+[2023-09-23 06:09:14,884][flwr][DEBUG] - evaluate_round 91: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 06:09:50,340][flwr][DEBUG] - evaluate_round 91 received 10 results and 0 failures
+[2023-09-23 06:09:50,341][flwr][DEBUG] - fit_round 92: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9703322784810127
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037865 Loss1: 0.037185 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022882 Loss1: 0.022198 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015981 Loss1: 0.015295 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017076 Loss1: 0.016389 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016242 Loss1: 0.015556 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014905 Loss1: 0.014218 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.009774 Loss1: 0.009089 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.009552 Loss1: 0.008865 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019893 Loss1: 0.019206 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025180 Loss1: 0.024492 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.996440
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9637419871794872
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043150 Loss1: 0.042469 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023678 Loss1: 0.022992 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036134 Loss1: 0.035448 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038177 Loss1: 0.037489 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029206 Loss1: 0.028518 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028267 Loss1: 0.027578 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029948 Loss1: 0.029260 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026280 Loss1: 0.025591 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024707 Loss1: 0.024019 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022082 Loss1: 0.021392 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.996795
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9601151315789473
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054580 Loss1: 0.053896 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035662 Loss1: 0.034974 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021641 Loss1: 0.020951 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019804 Loss1: 0.019114 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026162 Loss1: 0.025472 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030307 Loss1: 0.029618 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.064865 Loss1: 0.064176 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069432 Loss1: 0.068743 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052580 Loss1: 0.051890 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047205 Loss1: 0.046514 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.987664
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9630142405063291
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.035359 Loss1: 0.034679 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021940 Loss1: 0.021255 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016822 Loss1: 0.016137 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.012462 Loss1: 0.011775 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017347 Loss1: 0.016660 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020249 Loss1: 0.019563 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018791 Loss1: 0.018103 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023877 Loss1: 0.023189 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019515 Loss1: 0.018828 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025060 Loss1: 0.024372 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.996835
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9452136075949367
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049317 Loss1: 0.048635 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022739 Loss1: 0.022054 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017643 Loss1: 0.016957 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025606 Loss1: 0.024919 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019053 Loss1: 0.018366 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022250 Loss1: 0.021562 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030696 Loss1: 0.030008 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028369 Loss1: 0.027682 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031995 Loss1: 0.031308 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025964 Loss1: 0.025275 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.996440
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9598496835443038
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046930 Loss1: 0.046249 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023680 Loss1: 0.022996 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027031 Loss1: 0.026346 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036462 Loss1: 0.035776 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028195 Loss1: 0.027508 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025636 Loss1: 0.024948 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030496 Loss1: 0.029809 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029735 Loss1: 0.029048 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029749 Loss1: 0.029062 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025588 Loss1: 0.024900 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.995055
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9714176829268293
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044528 Loss1: 0.043849 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032476 Loss1: 0.031793 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027292 Loss1: 0.026608 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024137 Loss1: 0.023453 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022779 Loss1: 0.022095 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019773 Loss1: 0.019088 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023781 Loss1: 0.023096 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046481 Loss1: 0.045796 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056993 Loss1: 0.056308 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058107 Loss1: 0.057419 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992759
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9629407051282052
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045398 Loss1: 0.044719 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022989 Loss1: 0.022307 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.014525 Loss1: 0.013842 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013891 Loss1: 0.013208 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017482 Loss1: 0.016797 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029608 Loss1: 0.028923 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023602 Loss1: 0.022917 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029267 Loss1: 0.028582 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035101 Loss1: 0.034416 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040780 Loss1: 0.040095 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.992989
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9168074324324325
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070268 Loss1: 0.069585 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030677 Loss1: 0.029990 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025184 Loss1: 0.024495 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016474 Loss1: 0.015786 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.012496 Loss1: 0.011807 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018575 Loss1: 0.017887 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017610 Loss1: 0.016923 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.017964 Loss1: 0.017276 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019865 Loss1: 0.019176 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032940 Loss1: 0.032251 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.994299
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9581163194444444
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053100 Loss1: 0.052419 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022431 Loss1: 0.021746 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035177 Loss1: 0.034492 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034730 Loss1: 0.034043 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057455 Loss1: 0.056770 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039091 Loss1: 0.038404 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036290 Loss1: 0.035602 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032477 Loss1: 0.031789 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029248 Loss1: 0.028558 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038034 Loss1: 0.037345 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992839
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 06:39:29,892][flwr][DEBUG] - fit_round 92 received 10 results and 0 failures
+test acc: 0.6463
+[2023-09-23 06:40:05,933][flwr][INFO] - fit progress: (92, 2.5276976867605705, {'accuracy': 0.6463}, 185287.59483586485)
+[2023-09-23 06:40:05,934][flwr][DEBUG] - evaluate_round 92: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 06:40:42,319][flwr][DEBUG] - evaluate_round 92 received 10 results and 0 failures
+[2023-09-23 06:40:42,320][flwr][DEBUG] - fit_round 93: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9712271341463414
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.036275 Loss1: 0.035596 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035695 Loss1: 0.035013 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027542 Loss1: 0.026858 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034604 Loss1: 0.033920 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035680 Loss1: 0.034995 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052913 Loss1: 0.052228 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043832 Loss1: 0.043147 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048488 Loss1: 0.047801 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047977 Loss1: 0.047290 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071593 Loss1: 0.070906 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.990282
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9663461538461539
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037954 Loss1: 0.037276 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019291 Loss1: 0.018610 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021034 Loss1: 0.020352 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020067 Loss1: 0.019384 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022309 Loss1: 0.021626 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027965 Loss1: 0.027281 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027022 Loss1: 0.026336 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032616 Loss1: 0.031930 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037324 Loss1: 0.036639 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044185 Loss1: 0.043499 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.989183
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9539930555555556
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053676 Loss1: 0.052994 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023925 Loss1: 0.023241 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033304 Loss1: 0.032619 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032385 Loss1: 0.031697 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026987 Loss1: 0.026302 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033667 Loss1: 0.032980 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041581 Loss1: 0.040894 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028009 Loss1: 0.027321 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040878 Loss1: 0.040191 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050460 Loss1: 0.049771 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.987196
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.946004746835443
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061121 Loss1: 0.060441 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032423 Loss1: 0.031737 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024899 Loss1: 0.024214 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028195 Loss1: 0.027508 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035309 Loss1: 0.034623 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027841 Loss1: 0.027154 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037767 Loss1: 0.037080 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043503 Loss1: 0.042814 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037340 Loss1: 0.036651 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054083 Loss1: 0.053394 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.991891
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9693667763157895
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043551 Loss1: 0.042868 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039708 Loss1: 0.039022 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032321 Loss1: 0.031633 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027715 Loss1: 0.027028 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031750 Loss1: 0.031062 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021713 Loss1: 0.021024 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027630 Loss1: 0.026940 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031932 Loss1: 0.031244 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056470 Loss1: 0.055780 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056400 Loss1: 0.055712 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992804
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9630142405063291
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049708 Loss1: 0.049028 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028382 Loss1: 0.027697 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027010 Loss1: 0.026323 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036887 Loss1: 0.036202 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044751 Loss1: 0.044064 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036088 Loss1: 0.035402 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048379 Loss1: 0.047692 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063291 Loss1: 0.062603 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.062761 Loss1: 0.062072 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063002 Loss1: 0.062315 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.993473
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9639423076923077
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.035412 Loss1: 0.034733 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017372 Loss1: 0.016689 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017448 Loss1: 0.016764 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.012312 Loss1: 0.011627 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014787 Loss1: 0.014102 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024803 Loss1: 0.024117 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011938 Loss1: 0.011251 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016946 Loss1: 0.016260 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.017644 Loss1: 0.016958 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023414 Loss1: 0.022727 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.998598
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9657832278481012
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046258 Loss1: 0.045577 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032872 Loss1: 0.032185 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024003 Loss1: 0.023314 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017488 Loss1: 0.016800 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031174 Loss1: 0.030484 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022327 Loss1: 0.021638 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034879 Loss1: 0.034189 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032771 Loss1: 0.032080 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037718 Loss1: 0.037028 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045095 Loss1: 0.044406 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.991693
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9206081081081081
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084631 Loss1: 0.083949 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048848 Loss1: 0.048162 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035838 Loss1: 0.035152 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038070 Loss1: 0.037383 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037008 Loss1: 0.036320 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035937 Loss1: 0.035249 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034668 Loss1: 0.033979 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038733 Loss1: 0.038044 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.062245 Loss1: 0.061555 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.089265 Loss1: 0.088575 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.979307
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9612341772151899
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052127 Loss1: 0.051447 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038610 Loss1: 0.037927 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033398 Loss1: 0.032712 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059373 Loss1: 0.058686 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.062990 Loss1: 0.062303 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055292 Loss1: 0.054606 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056990 Loss1: 0.056302 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075462 Loss1: 0.074775 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067213 Loss1: 0.066526 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070716 Loss1: 0.070028 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.990902
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 07:10:20,845][flwr][DEBUG] - fit_round 93 received 10 results and 0 failures
+test acc: 0.6485
+[2023-09-23 07:10:57,539][flwr][INFO] - fit progress: (93, 2.4764777248659833, {'accuracy': 0.6485}, 187139.200545819)
+[2023-09-23 07:10:57,539][flwr][DEBUG] - evaluate_round 93: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 07:11:33,217][flwr][DEBUG] - evaluate_round 93 received 10 results and 0 failures
+[2023-09-23 07:11:33,218][flwr][DEBUG] - fit_round 94: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9615384615384616
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048665 Loss1: 0.047986 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043339 Loss1: 0.042653 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034747 Loss1: 0.034061 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019168 Loss1: 0.018480 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019800 Loss1: 0.019112 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017779 Loss1: 0.017091 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.012075 Loss1: 0.011388 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.010600 Loss1: 0.009912 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.009605 Loss1: 0.008917 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.016919 Loss1: 0.016230 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.997997
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9596518987341772
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042536 Loss1: 0.041855 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.013890 Loss1: 0.013208 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019819 Loss1: 0.019136 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019070 Loss1: 0.018386 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019815 Loss1: 0.019130 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019076 Loss1: 0.018391 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026609 Loss1: 0.025922 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023302 Loss1: 0.022616 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026585 Loss1: 0.025898 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.020283 Loss1: 0.019596 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.995253
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9593349358974359
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045659 Loss1: 0.044981 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024415 Loss1: 0.023733 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026583 Loss1: 0.025898 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015689 Loss1: 0.015004 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016112 Loss1: 0.015427 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017857 Loss1: 0.017174 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022093 Loss1: 0.021409 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029313 Loss1: 0.028629 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039290 Loss1: 0.038605 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.021702 Loss1: 0.021016 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.996194
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9671677215189873
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037293 Loss1: 0.036614 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021966 Loss1: 0.021282 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018188 Loss1: 0.017504 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016750 Loss1: 0.016066 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016732 Loss1: 0.016048 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015176 Loss1: 0.014490 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022625 Loss1: 0.021939 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019661 Loss1: 0.018974 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.012507 Loss1: 0.011820 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.013468 Loss1: 0.012780 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.999011
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9489715189873418
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044133 Loss1: 0.043454 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021258 Loss1: 0.020573 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015717 Loss1: 0.015032 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015532 Loss1: 0.014847 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.018824 Loss1: 0.018136 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019035 Loss1: 0.018347 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018971 Loss1: 0.018283 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.020482 Loss1: 0.019795 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.017057 Loss1: 0.016366 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.016098 Loss1: 0.015409 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.997429
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9208192567567568
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.062152 Loss1: 0.061472 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020449 Loss1: 0.019762 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019048 Loss1: 0.018361 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019411 Loss1: 0.018725 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023791 Loss1: 0.023104 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017660 Loss1: 0.016974 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031104 Loss1: 0.030416 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030820 Loss1: 0.030131 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037049 Loss1: 0.036360 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.026228 Loss1: 0.025540 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.988176
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9666539634146342
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.031292 Loss1: 0.030612 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.009969 Loss1: 0.009287 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011106 Loss1: 0.010423 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.007964 Loss1: 0.007281 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.008328 Loss1: 0.007645 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.009542 Loss1: 0.008858 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016182 Loss1: 0.015497 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.015144 Loss1: 0.014459 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.014282 Loss1: 0.013597 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.009735 Loss1: 0.009049 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.998285
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9654605263157895
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059804 Loss1: 0.059121 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029119 Loss1: 0.028430 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021419 Loss1: 0.020731 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029657 Loss1: 0.028968 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028318 Loss1: 0.027628 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024142 Loss1: 0.023451 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019595 Loss1: 0.018905 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031151 Loss1: 0.030461 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033985 Loss1: 0.033295 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041119 Loss1: 0.040429 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.992393
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9638053797468354
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040415 Loss1: 0.039734 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021616 Loss1: 0.020931 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016070 Loss1: 0.015383 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025176 Loss1: 0.024489 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025207 Loss1: 0.024519 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035267 Loss1: 0.034578 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027087 Loss1: 0.026400 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036258 Loss1: 0.035569 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040808 Loss1: 0.040120 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048852 Loss1: 0.048163 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989122
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9468315972222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055756 Loss1: 0.055075 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022388 Loss1: 0.021704 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022125 Loss1: 0.021440 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018037 Loss1: 0.017351 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011998 Loss1: 0.011313 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016525 Loss1: 0.015840 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.013969 Loss1: 0.013282 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.009472 Loss1: 0.008786 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.013453 Loss1: 0.012766 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.016239 Loss1: 0.015554 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.998481
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 07:41:13,480][flwr][DEBUG] - fit_round 94 received 10 results and 0 failures
+test acc: 0.65
+[2023-09-23 07:41:50,473][flwr][INFO] - fit progress: (94, 2.5375279696604696, {'accuracy': 0.65}, 188992.13494228758)
+[2023-09-23 07:41:50,474][flwr][DEBUG] - evaluate_round 94: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 07:42:25,775][flwr][DEBUG] - evaluate_round 94 received 10 results and 0 failures
+[2023-09-23 07:42:25,777][flwr][DEBUG] - fit_round 95: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9704649390243902
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037019 Loss1: 0.036339 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021132 Loss1: 0.020449 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022791 Loss1: 0.022106 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017327 Loss1: 0.016642 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024278 Loss1: 0.023593 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018836 Loss1: 0.018149 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028000 Loss1: 0.027313 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016292 Loss1: 0.015606 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026745 Loss1: 0.026059 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.020212 Loss1: 0.019525 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.998285
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9719551282051282
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.032404 Loss1: 0.031724 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.015465 Loss1: 0.014784 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017144 Loss1: 0.016462 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026868 Loss1: 0.026183 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055178 Loss1: 0.054494 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043370 Loss1: 0.042685 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037415 Loss1: 0.036730 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059919 Loss1: 0.059234 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043597 Loss1: 0.042910 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063085 Loss1: 0.062399 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.986178
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9681332236842105
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043869 Loss1: 0.043187 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026281 Loss1: 0.025594 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021101 Loss1: 0.020411 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018093 Loss1: 0.017405 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019563 Loss1: 0.018875 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015250 Loss1: 0.014561 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.032712 Loss1: 0.032022 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037144 Loss1: 0.036455 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045173 Loss1: 0.044483 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053530 Loss1: 0.052840 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.980263
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9677610759493671
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044987 Loss1: 0.044307 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022590 Loss1: 0.021905 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020620 Loss1: 0.019935 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026826 Loss1: 0.026139 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021663 Loss1: 0.020977 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030782 Loss1: 0.030096 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028912 Loss1: 0.028225 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037743 Loss1: 0.037056 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.050534 Loss1: 0.049846 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045643 Loss1: 0.044955 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.993473
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.925464527027027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068055 Loss1: 0.067373 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041834 Loss1: 0.041147 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.030749 Loss1: 0.030061 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039284 Loss1: 0.038595 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055755 Loss1: 0.055066 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068559 Loss1: 0.067869 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070822 Loss1: 0.070133 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085798 Loss1: 0.085108 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069460 Loss1: 0.068770 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050048 Loss1: 0.049358 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.992188
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9641426282051282
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.031592 Loss1: 0.030912 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.011532 Loss1: 0.010847 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.010753 Loss1: 0.010069 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.010217 Loss1: 0.009532 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014239 Loss1: 0.013553 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017531 Loss1: 0.016844 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018630 Loss1: 0.017943 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024553 Loss1: 0.023866 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.018259 Loss1: 0.017571 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043181 Loss1: 0.042492 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989183
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9703322784810127
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.034704 Loss1: 0.034023 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020982 Loss1: 0.020298 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015163 Loss1: 0.014477 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017592 Loss1: 0.016905 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029520 Loss1: 0.028833 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016237 Loss1: 0.015548 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027273 Loss1: 0.026586 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.025509 Loss1: 0.024821 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.021940 Loss1: 0.021252 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054766 Loss1: 0.054078 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.990704
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9691455696202531
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045957 Loss1: 0.045275 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023198 Loss1: 0.022513 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019641 Loss1: 0.018957 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016155 Loss1: 0.015469 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025159 Loss1: 0.024473 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026356 Loss1: 0.025671 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028242 Loss1: 0.027555 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028739 Loss1: 0.028053 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042896 Loss1: 0.042212 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038589 Loss1: 0.037902 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.994660
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9503560126582279
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043213 Loss1: 0.042531 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018303 Loss1: 0.017617 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024400 Loss1: 0.023713 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025228 Loss1: 0.024541 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029765 Loss1: 0.029076 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018362 Loss1: 0.017674 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017242 Loss1: 0.016554 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022706 Loss1: 0.022018 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049075 Loss1: 0.048387 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066372 Loss1: 0.065683 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.979628
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9505208333333334
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053350 Loss1: 0.052669 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028900 Loss1: 0.028215 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022214 Loss1: 0.021527 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033432 Loss1: 0.032744 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051008 Loss1: 0.050321 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033752 Loss1: 0.033064 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042195 Loss1: 0.041506 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042784 Loss1: 0.042096 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059162 Loss1: 0.058475 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048152 Loss1: 0.047464 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992405
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 08:12:09,963][flwr][DEBUG] - fit_round 95 received 10 results and 0 failures
+test acc: 0.6502
+[2023-09-23 08:12:50,860][flwr][INFO] - fit progress: (95, 2.5060083212943884, {'accuracy': 0.6502}, 190852.5212352858)
+[2023-09-23 08:12:50,860][flwr][DEBUG] - evaluate_round 95: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 08:13:25,919][flwr][DEBUG] - evaluate_round 95 received 10 results and 0 failures
+[2023-09-23 08:13:25,920][flwr][DEBUG] - fit_round 96: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9677610759493671
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.036361 Loss1: 0.035680 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.016516 Loss1: 0.015831 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013471 Loss1: 0.012784 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017758 Loss1: 0.017071 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030353 Loss1: 0.029665 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026067 Loss1: 0.025378 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022157 Loss1: 0.021469 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.020356 Loss1: 0.019668 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024191 Loss1: 0.023503 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022188 Loss1: 0.021501 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.996835
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.95703125
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066130 Loss1: 0.065448 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025653 Loss1: 0.024967 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032063 Loss1: 0.031376 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020108 Loss1: 0.019421 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024899 Loss1: 0.024212 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025403 Loss1: 0.024715 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029626 Loss1: 0.028938 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024911 Loss1: 0.024223 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039909 Loss1: 0.039222 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.037799 Loss1: 0.037109 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.993707
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9661787974683544
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057462 Loss1: 0.056781 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023218 Loss1: 0.022534 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036145 Loss1: 0.035459 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028338 Loss1: 0.027653 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023363 Loss1: 0.022678 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019579 Loss1: 0.018894 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025198 Loss1: 0.024511 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033288 Loss1: 0.032601 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026939 Loss1: 0.026251 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025183 Loss1: 0.024496 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.998418
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9651442307692307
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043898 Loss1: 0.043220 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020544 Loss1: 0.019862 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019002 Loss1: 0.018319 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029119 Loss1: 0.028435 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020942 Loss1: 0.020258 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021220 Loss1: 0.020535 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025828 Loss1: 0.025144 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039115 Loss1: 0.038431 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046947 Loss1: 0.046262 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052073 Loss1: 0.051388 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.992388
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9619391025641025
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053508 Loss1: 0.052829 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025110 Loss1: 0.024427 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023808 Loss1: 0.023123 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026615 Loss1: 0.025929 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020310 Loss1: 0.019625 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024501 Loss1: 0.023814 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026973 Loss1: 0.026286 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035290 Loss1: 0.034602 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042552 Loss1: 0.041864 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048669 Loss1: 0.047981 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992588
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9560917721518988
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.047460 Loss1: 0.046780 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018430 Loss1: 0.017745 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016699 Loss1: 0.016014 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021413 Loss1: 0.020727 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037473 Loss1: 0.036787 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026976 Loss1: 0.026290 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027201 Loss1: 0.026514 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034256 Loss1: 0.033568 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033794 Loss1: 0.033107 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029872 Loss1: 0.029184 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.994660
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9703322784810127
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040705 Loss1: 0.040025 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017206 Loss1: 0.016522 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016866 Loss1: 0.016182 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015633 Loss1: 0.014948 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022167 Loss1: 0.021481 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018314 Loss1: 0.017627 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030161 Loss1: 0.029475 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019505 Loss1: 0.018819 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.012911 Loss1: 0.012225 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022809 Loss1: 0.022121 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.995055
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9214527027027027
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064208 Loss1: 0.063526 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031647 Loss1: 0.030961 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023141 Loss1: 0.022456 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031040 Loss1: 0.030353 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.018296 Loss1: 0.017608 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026694 Loss1: 0.026006 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039199 Loss1: 0.038510 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.041766 Loss1: 0.041076 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044895 Loss1: 0.044206 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058453 Loss1: 0.057762 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.985642
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9693216463414634
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040457 Loss1: 0.039777 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019375 Loss1: 0.018692 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018020 Loss1: 0.017337 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016696 Loss1: 0.016011 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017491 Loss1: 0.016805 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028800 Loss1: 0.028114 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038127 Loss1: 0.037441 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059120 Loss1: 0.058435 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077484 Loss1: 0.076798 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049455 Loss1: 0.048769 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.993712
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9677220394736842
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054918 Loss1: 0.054237 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033991 Loss1: 0.033303 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026557 Loss1: 0.025869 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023316 Loss1: 0.022627 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032120 Loss1: 0.031432 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021980 Loss1: 0.021289 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022169 Loss1: 0.021479 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026901 Loss1: 0.026210 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024411 Loss1: 0.023721 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029547 Loss1: 0.028859 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.995477
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 08:43:04,009][flwr][DEBUG] - fit_round 96 received 10 results and 0 failures
+test acc: 0.6487
+[2023-09-23 08:43:45,343][flwr][INFO] - fit progress: (96, 2.5280895025585406, {'accuracy': 0.6487}, 192707.00428564101)
+[2023-09-23 08:43:45,343][flwr][DEBUG] - evaluate_round 96: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 08:44:20,107][flwr][DEBUG] - evaluate_round 96 received 10 results and 0 failures
+[2023-09-23 08:44:20,109][flwr][DEBUG] - fit_round 97: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9533420138888888
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049795 Loss1: 0.049113 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026970 Loss1: 0.026285 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032876 Loss1: 0.032192 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024772 Loss1: 0.024087 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026438 Loss1: 0.025751 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037465 Loss1: 0.036778 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.047362 Loss1: 0.046677 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031097 Loss1: 0.030411 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033932 Loss1: 0.033245 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.028675 Loss1: 0.027988 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.996528
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9701891447368421
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044952 Loss1: 0.044270 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019958 Loss1: 0.019271 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017747 Loss1: 0.017061 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016175 Loss1: 0.015488 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025440 Loss1: 0.024753 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018867 Loss1: 0.018180 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016904 Loss1: 0.016217 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032947 Loss1: 0.032259 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047251 Loss1: 0.046562 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038341 Loss1: 0.037652 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989926
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9665743670886076
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040351 Loss1: 0.039673 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028802 Loss1: 0.028119 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021153 Loss1: 0.020469 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029458 Loss1: 0.028773 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028080 Loss1: 0.027394 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030290 Loss1: 0.029605 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027301 Loss1: 0.026615 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031641 Loss1: 0.030955 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032422 Loss1: 0.031735 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038663 Loss1: 0.037976 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.990704
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9645432692307693
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.030422 Loss1: 0.029744 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.011471 Loss1: 0.010787 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015973 Loss1: 0.015288 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011313 Loss1: 0.010627 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020292 Loss1: 0.019606 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015804 Loss1: 0.015117 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017309 Loss1: 0.016622 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029328 Loss1: 0.028641 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029111 Loss1: 0.028424 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049342 Loss1: 0.048655 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.990385
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9659810126582279
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042424 Loss1: 0.041746 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026914 Loss1: 0.026229 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021859 Loss1: 0.021171 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018987 Loss1: 0.018298 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022736 Loss1: 0.022049 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033119 Loss1: 0.032433 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038359 Loss1: 0.037671 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047378 Loss1: 0.046690 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040939 Loss1: 0.040248 Loss2: 0.000691
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032942 Loss1: 0.032251 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.995055
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.946004746835443
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045889 Loss1: 0.045209 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019516 Loss1: 0.018833 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021331 Loss1: 0.020646 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015146 Loss1: 0.014460 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016788 Loss1: 0.016101 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025270 Loss1: 0.024585 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.021657 Loss1: 0.020972 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023588 Loss1: 0.022903 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040825 Loss1: 0.040138 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054988 Loss1: 0.054301 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.991297
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9679588607594937
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037676 Loss1: 0.036995 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.016664 Loss1: 0.015980 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.012839 Loss1: 0.012155 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.014704 Loss1: 0.014020 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014826 Loss1: 0.014142 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017723 Loss1: 0.017038 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027529 Loss1: 0.026843 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028311 Loss1: 0.027625 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026621 Loss1: 0.025936 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027417 Loss1: 0.026730 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.995451
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9729420731707317
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.035275 Loss1: 0.034597 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023043 Loss1: 0.022360 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024052 Loss1: 0.023369 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020789 Loss1: 0.020105 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.018176 Loss1: 0.017492 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025323 Loss1: 0.024640 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018830 Loss1: 0.018145 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033568 Loss1: 0.032882 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026609 Loss1: 0.025924 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.024687 Loss1: 0.024002 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.998666
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9298986486486487
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059536 Loss1: 0.058854 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023688 Loss1: 0.023000 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013974 Loss1: 0.013287 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011462 Loss1: 0.010772 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020155 Loss1: 0.019466 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028234 Loss1: 0.027546 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023540 Loss1: 0.022852 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024256 Loss1: 0.023568 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029929 Loss1: 0.029241 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032576 Loss1: 0.031888 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.993666
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9625400641025641
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049385 Loss1: 0.048707 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027258 Loss1: 0.026576 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028254 Loss1: 0.027571 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033296 Loss1: 0.032612 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025567 Loss1: 0.024882 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025437 Loss1: 0.024754 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020962 Loss1: 0.020278 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034763 Loss1: 0.034078 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.050494 Loss1: 0.049808 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038905 Loss1: 0.038222 Loss2: 0.000683
+(DefaultActor pid=2839578) >> Training accuracy: 0.991587
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 09:13:54,272][flwr][DEBUG] - fit_round 97 received 10 results and 0 failures
+test acc: 0.6478
+[2023-09-23 09:15:54,899][flwr][INFO] - fit progress: (97, 2.522580636766391, {'accuracy': 0.6478}, 194636.56068836385)
+[2023-09-23 09:15:54,902][flwr][DEBUG] - evaluate_round 97: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 09:16:32,808][flwr][DEBUG] - evaluate_round 97 received 10 results and 0 failures
+[2023-09-23 09:16:32,809][flwr][DEBUG] - fit_round 98: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9689477848101266
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043246 Loss1: 0.042568 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026034 Loss1: 0.025351 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022152 Loss1: 0.021468 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021924 Loss1: 0.021238 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016070 Loss1: 0.015385 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021802 Loss1: 0.021117 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018425 Loss1: 0.017739 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.020875 Loss1: 0.020189 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027116 Loss1: 0.026429 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044780 Loss1: 0.044094 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.991891
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9208192567567568
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040364 Loss1: 0.039684 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020816 Loss1: 0.020130 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016369 Loss1: 0.015682 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015130 Loss1: 0.014443 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011197 Loss1: 0.010509 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014875 Loss1: 0.014187 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016851 Loss1: 0.016163 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016771 Loss1: 0.016082 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028172 Loss1: 0.027483 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.019476 Loss1: 0.018786 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.997889
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9667467948717948
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.041250 Loss1: 0.040572 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024637 Loss1: 0.023954 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018166 Loss1: 0.017483 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021484 Loss1: 0.020800 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016231 Loss1: 0.015548 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.012452 Loss1: 0.011768 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011003 Loss1: 0.010319 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019999 Loss1: 0.019315 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027710 Loss1: 0.027025 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.024455 Loss1: 0.023770 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.993189
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9527294303797469
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048303 Loss1: 0.047623 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019661 Loss1: 0.018977 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021468 Loss1: 0.020782 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023310 Loss1: 0.022622 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034345 Loss1: 0.033660 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015572 Loss1: 0.014885 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035943 Loss1: 0.035256 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.041307 Loss1: 0.040619 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045036 Loss1: 0.044348 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056624 Loss1: 0.055936 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.990309
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9535590277777778
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045351 Loss1: 0.044670 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024378 Loss1: 0.023694 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017211 Loss1: 0.016526 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.030460 Loss1: 0.029773 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023297 Loss1: 0.022610 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033375 Loss1: 0.032689 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040584 Loss1: 0.039898 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024490 Loss1: 0.023802 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057119 Loss1: 0.056431 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046756 Loss1: 0.046069 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.993924
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9722450657894737
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046407 Loss1: 0.045727 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021358 Loss1: 0.020670 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016613 Loss1: 0.015927 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016036 Loss1: 0.015349 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.008649 Loss1: 0.007962 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016581 Loss1: 0.015894 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016012 Loss1: 0.015324 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.015210 Loss1: 0.014521 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.022691 Loss1: 0.022003 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032173 Loss1: 0.031484 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.989309
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9669699367088608
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.030114 Loss1: 0.029434 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.012668 Loss1: 0.011984 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011995 Loss1: 0.011311 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013889 Loss1: 0.013206 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016393 Loss1: 0.015708 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021119 Loss1: 0.020434 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016007 Loss1: 0.015322 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027229 Loss1: 0.026543 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029445 Loss1: 0.028759 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.028365 Loss1: 0.027678 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.994858
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9744857594936709
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.038378 Loss1: 0.037698 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017256 Loss1: 0.016570 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.014967 Loss1: 0.014280 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017898 Loss1: 0.017212 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.012548 Loss1: 0.011860 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.009958 Loss1: 0.009269 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.010409 Loss1: 0.009720 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.012332 Loss1: 0.011643 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019730 Loss1: 0.019041 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.018117 Loss1: 0.017427 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.997231
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9708460365853658
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043103 Loss1: 0.042424 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018204 Loss1: 0.017521 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022927 Loss1: 0.022244 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021335 Loss1: 0.020650 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.015270 Loss1: 0.014584 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018255 Loss1: 0.017569 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022159 Loss1: 0.021472 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023139 Loss1: 0.022453 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035110 Loss1: 0.034424 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030563 Loss1: 0.029878 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.991616
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9645432692307693
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.033261 Loss1: 0.032581 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.014518 Loss1: 0.013835 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.009786 Loss1: 0.009100 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019389 Loss1: 0.018703 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017234 Loss1: 0.016548 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011685 Loss1: 0.010999 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.010237 Loss1: 0.009550 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.017191 Loss1: 0.016504 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.030352 Loss1: 0.029665 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029698 Loss1: 0.029011 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.994992
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 09:46:05,996][flwr][DEBUG] - fit_round 98 received 10 results and 0 failures
+test acc: 0.6498
+[2023-09-23 09:46:47,389][flwr][INFO] - fit progress: (98, 2.5538510150802782, {'accuracy': 0.6498}, 196489.0504324357)
+[2023-09-23 09:46:47,390][flwr][DEBUG] - evaluate_round 98: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 09:47:22,495][flwr][DEBUG] - evaluate_round 98 received 10 results and 0 failures
+[2023-09-23 09:47:22,499][flwr][DEBUG] - fit_round 99: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9582674050632911
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.033692 Loss1: 0.033013 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.013624 Loss1: 0.012940 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015558 Loss1: 0.014873 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.014197 Loss1: 0.013512 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022921 Loss1: 0.022235 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.012536 Loss1: 0.011850 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011964 Loss1: 0.011277 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027978 Loss1: 0.027292 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029356 Loss1: 0.028668 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029887 Loss1: 0.029199 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.995055
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9719893292682927
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.026330 Loss1: 0.025651 Loss2: 0.000678
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.014551 Loss1: 0.013871 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013041 Loss1: 0.012360 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011547 Loss1: 0.010863 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014278 Loss1: 0.013594 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021600 Loss1: 0.020916 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030148 Loss1: 0.029464 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016472 Loss1: 0.015787 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.014619 Loss1: 0.013934 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.021440 Loss1: 0.020755 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.997142
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9701522435897436
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.033573 Loss1: 0.032893 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.011700 Loss1: 0.011018 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011772 Loss1: 0.011090 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022542 Loss1: 0.021859 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.012144 Loss1: 0.011461 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014908 Loss1: 0.014225 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020496 Loss1: 0.019813 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.020957 Loss1: 0.020274 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.030572 Loss1: 0.029888 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038901 Loss1: 0.038216 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.989583
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9633413461538461
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043515 Loss1: 0.042834 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027110 Loss1: 0.026424 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045275 Loss1: 0.044589 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033972 Loss1: 0.033284 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044516 Loss1: 0.043828 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.064806 Loss1: 0.064117 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069158 Loss1: 0.068468 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059880 Loss1: 0.059190 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076295 Loss1: 0.075604 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072384 Loss1: 0.071694 Loss2: 0.000690
+(DefaultActor pid=2839578) >> Training accuracy: 0.993189
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9683544303797469
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040033 Loss1: 0.039354 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019667 Loss1: 0.018984 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013848 Loss1: 0.013164 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018064 Loss1: 0.017381 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011047 Loss1: 0.010363 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011111 Loss1: 0.010427 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019929 Loss1: 0.019244 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035971 Loss1: 0.035286 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035950 Loss1: 0.035264 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.026304 Loss1: 0.025617 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.996835
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9671052631578947
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053684 Loss1: 0.053003 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028471 Loss1: 0.027783 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027148 Loss1: 0.026459 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018084 Loss1: 0.017396 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017295 Loss1: 0.016606 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015541 Loss1: 0.014853 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.014221 Loss1: 0.013533 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.014314 Loss1: 0.013624 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.015240 Loss1: 0.014552 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.010565 Loss1: 0.009876 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.998561
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9585503472222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052564 Loss1: 0.051883 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025274 Loss1: 0.024590 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024310 Loss1: 0.023625 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024950 Loss1: 0.024264 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031186 Loss1: 0.030500 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031246 Loss1: 0.030560 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038404 Loss1: 0.037717 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037991 Loss1: 0.037304 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047933 Loss1: 0.047248 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.028462 Loss1: 0.027775 Loss2: 0.000687
+(DefaultActor pid=2839578) >> Training accuracy: 0.995009
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9258868243243243
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055363 Loss1: 0.054680 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020148 Loss1: 0.019462 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016642 Loss1: 0.015956 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029164 Loss1: 0.028476 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016516 Loss1: 0.015829 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016456 Loss1: 0.015768 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019049 Loss1: 0.018361 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023986 Loss1: 0.023297 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.020196 Loss1: 0.019507 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032464 Loss1: 0.031776 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.993666
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9691455696202531
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.028547 Loss1: 0.027868 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020967 Loss1: 0.020283 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023397 Loss1: 0.022712 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024016 Loss1: 0.023330 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025892 Loss1: 0.025205 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026240 Loss1: 0.025554 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031003 Loss1: 0.030315 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044505 Loss1: 0.043814 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031198 Loss1: 0.030510 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.024042 Loss1: 0.023351 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.997824
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9721123417721519
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.039316 Loss1: 0.038635 Loss2: 0.000681
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017978 Loss1: 0.017293 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021450 Loss1: 0.020764 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013030 Loss1: 0.012344 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017476 Loss1: 0.016787 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023552 Loss1: 0.022864 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029610 Loss1: 0.028922 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022827 Loss1: 0.022139 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026058 Loss1: 0.025370 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.035369 Loss1: 0.034680 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.997627
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 10:17:01,823][flwr][DEBUG] - fit_round 99 received 10 results and 0 failures
+test acc: 0.652
+[2023-09-23 10:17:43,808][flwr][INFO] - fit progress: (99, 2.5449396366128525, {'accuracy': 0.652}, 198345.46924422076)
+[2023-09-23 10:17:43,808][flwr][DEBUG] - evaluate_round 99: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 10:18:18,837][flwr][DEBUG] - evaluate_round 99 received 10 results and 0 failures
+[2023-09-23 10:18:18,842][flwr][DEBUG] - fit_round 100: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 76
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9734786184210527
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044353 Loss1: 0.043670 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043950 Loss1: 0.043263 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.038873 Loss1: 0.038188 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034590 Loss1: 0.033902 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047029 Loss1: 0.046339 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068166 Loss1: 0.067479 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055826 Loss1: 0.055136 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058309 Loss1: 0.057619 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052621 Loss1: 0.051931 Loss2: 0.000690
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064928 Loss1: 0.064237 Loss2: 0.000691
+(DefaultActor pid=2839578) >> Training accuracy: 0.985609
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9655448717948718
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.034063 Loss1: 0.033382 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019465 Loss1: 0.018783 Loss2: 0.000682
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.010984 Loss1: 0.010301 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.010069 Loss1: 0.009386 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020609 Loss1: 0.019926 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023920 Loss1: 0.023238 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028601 Loss1: 0.027916 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028348 Loss1: 0.027662 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034342 Loss1: 0.033656 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041092 Loss1: 0.040407 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.994591
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 74
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9275760135135135
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.051620 Loss1: 0.050937 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028096 Loss1: 0.027409 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020132 Loss1: 0.019446 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025032 Loss1: 0.024345 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021723 Loss1: 0.021037 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011857 Loss1: 0.011170 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016878 Loss1: 0.016191 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.013672 Loss1: 0.012985 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.017395 Loss1: 0.016708 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023668 Loss1: 0.022979 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.998733
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 72
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9565972222222222
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050698 Loss1: 0.050017 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025323 Loss1: 0.024639 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018785 Loss1: 0.018100 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018741 Loss1: 0.018055 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017641 Loss1: 0.016956 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023082 Loss1: 0.022397 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018558 Loss1: 0.017873 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.012182 Loss1: 0.011495 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.014745 Loss1: 0.014059 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.017660 Loss1: 0.016974 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.997179
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 82
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9702743902439024
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042925 Loss1: 0.042247 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017910 Loss1: 0.017226 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011749 Loss1: 0.011065 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011142 Loss1: 0.010459 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.015154 Loss1: 0.014471 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.010980 Loss1: 0.010297 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.013642 Loss1: 0.012958 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022394 Loss1: 0.021709 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037644 Loss1: 0.036959 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042038 Loss1: 0.041351 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.994855
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9719145569620253
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.038335 Loss1: 0.037655 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021065 Loss1: 0.020380 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017498 Loss1: 0.016812 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023759 Loss1: 0.023073 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022440 Loss1: 0.021755 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024551 Loss1: 0.023865 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036570 Loss1: 0.035882 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045798 Loss1: 0.045111 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065955 Loss1: 0.065267 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085343 Loss1: 0.084655 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.981408
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9596518987341772
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057725 Loss1: 0.057045 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025760 Loss1: 0.025075 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016294 Loss1: 0.015608 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015911 Loss1: 0.015224 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017960 Loss1: 0.017273 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020104 Loss1: 0.019419 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020213 Loss1: 0.019526 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032840 Loss1: 0.032152 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039268 Loss1: 0.038579 Loss2: 0.000689
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043937 Loss1: 0.043249 Loss2: 0.000688
+(DefaultActor pid=2839578) >> Training accuracy: 0.992682
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9677610759493671
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.036528 Loss1: 0.035848 Loss2: 0.000680
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021069 Loss1: 0.020386 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016254 Loss1: 0.015571 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015606 Loss1: 0.014921 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019928 Loss1: 0.019244 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019712 Loss1: 0.019027 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.015299 Loss1: 0.014612 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.013719 Loss1: 0.013033 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.010660 Loss1: 0.009975 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.019541 Loss1: 0.018857 Loss2: 0.000685
+(DefaultActor pid=2839578) >> Training accuracy: 0.997231
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 78
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.969551282051282
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.030940 Loss1: 0.030260 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017179 Loss1: 0.016496 Loss2: 0.000683
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.012073 Loss1: 0.011389 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013853 Loss1: 0.013169 Loss2: 0.000684
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011877 Loss1: 0.011192 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011186 Loss1: 0.010500 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.014932 Loss1: 0.014247 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016097 Loss1: 0.015410 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.014862 Loss1: 0.014174 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022251 Loss1: 0.021565 Loss2: 0.000686
+(DefaultActor pid=2839578) >> Training accuracy: 0.995593
+(DefaultActor pid=2839578)  ** Training complete **
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) n_training: 79
+(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9689477848101266
+(DefaultActor pid=2839578) Epoch: 0 Loss: 0.027259 Loss1: 0.026580 Loss2: 0.000679
+(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017007 Loss1: 0.016321 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 2 Loss: 0.010313 Loss1: 0.009628 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 3 Loss: 0.014969 Loss1: 0.014284 Loss2: 0.000685
+(DefaultActor pid=2839578) Epoch: 4 Loss: 0.013018 Loss1: 0.012332 Loss2: 0.000686
+(DefaultActor pid=2839578) Epoch: 5 Loss: 0.013757 Loss1: 0.013070 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011565 Loss1: 0.010878 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 7 Loss: 0.010934 Loss1: 0.010246 Loss2: 0.000688
+(DefaultActor pid=2839578) Epoch: 8 Loss: 0.017679 Loss1: 0.016992 Loss2: 0.000687
+(DefaultActor pid=2839578) Epoch: 9 Loss: 0.028235 Loss1: 0.027546 Loss2: 0.000689
+(DefaultActor pid=2839578) >> Training accuracy: 0.991891
+(DefaultActor pid=2839578)  ** Training complete **
+[2023-09-23 10:47:47,269][flwr][DEBUG] - fit_round 100 received 10 results and 0 failures
+test acc: 0.6494
+[2023-09-23 10:48:28,827][flwr][INFO] - fit progress: (100, 2.5527703958197523, {'accuracy': 0.6494}, 200190.48873032397)
+[2023-09-23 10:48:28,828][flwr][DEBUG] - evaluate_round 100: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+(DefaultActor pid=2839578) device: cuda:0
+[2023-09-23 10:49:05,428][flwr][DEBUG] - evaluate_round 100 received 10 results and 0 failures
+[2023-09-23 10:49:05,429][flwr][INFO] - FL finished in 200227.09066704987
+[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0)]
+[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: metrics_distributed_fit {}
+[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: metrics_distributed {}
+[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: losses_centralized [(0, 6.156129693832641), (1, 4.6852914030178665), (2, 5.889099098242129), (3, 5.795179260424532), (4, 4.141716613556249), (5, 3.311973663183828), (6, 2.8995907626593835), (7, 2.6269793068639005), (8, 2.455170800510687), (9, 2.338781193803294), (10, 2.2332213046832585), (11, 2.1865856590362402), (12, 2.121484987651959), (13, 2.1080573364949453), (14, 2.0822741444499346), (15, 2.0444571261588758), (16, 2.057832792163276), (17, 2.031206720362837), (18, 2.0359792596996784), (19, 2.0221455788460023), (20, 2.034489405421784), (21, 2.0309638719970047), (22, 2.0352664707948604), (23, 2.0376226549712233), (24, 2.040369967111764), (25, 2.0471822914604942), (26, 2.0418881324533458), (27, 2.0698537910327364), (28, 2.0548813068828644), (29, 2.0496039061119764), (30, 2.0852254760531954), (31, 2.0679767246063525), (32, 2.0636839933288744), (33, 2.0933550024946657), (34, 2.083816295043348), (35, 2.1125483806140886), (36, 2.1186307085969576), (37, 2.1180881943565586), (38, 2.109429546248037), (39, 2.0989707978769614), (40, 2.123094680019842), (41, 2.148773836632506), (42, 2.1465409287629416), (43, 2.1362349244352346), (44, 2.1981266999777893), (45, 2.1977186043041583), (46, 2.2119887084625782), (47, 2.1827334691160405), (48, 2.2119309980267534), (49, 2.2019196455471053), (50, 2.2049550935864066), (51, 2.181678266951832), (52, 2.2030137499300437), (53, 2.2342346537227447), (54, 2.22973222568774), (55, 2.254546900526784), (56, 2.251486159170778), (57, 2.2792362104208705), (58, 2.2519221557215), (59, 2.2759478625398093), (60, 2.300492998700553), (61, 2.2899934441898577), (62, 2.312452397407434), (63, 2.3152449610896), (64, 2.304777619556878), (65, 2.337633471138561), (66, 2.3337900912799774), (67, 2.357942302767842), (68, 2.3550231862372866), (69, 2.3513612215892197), (70, 2.3543379908552566), (71, 2.349217086935196), (72, 2.383222926158113), (73, 2.378727243731197), (74, 2.381040071336606), (75, 2.3815381104192035), (76, 2.4015440281968528), (77, 2.441344348767314), (78, 2.4210258411904113), (79, 2.445481828416879), (80, 2.4491478545597185), (81, 2.4155959117526824), (82, 2.4423680827259635), (83, 2.4571505731667953), (84, 2.4649806056921473), (85, 2.5026507737537544), (86, 2.4607985724275485), (87, 2.49972779548968), (88, 2.5036359633119725), (89, 2.477679441340815), (90, 2.4975950007621472), (91, 2.5048911708612414), (92, 2.5276976867605705), (93, 2.4764777248659833), (94, 2.5375279696604696), (95, 2.5060083212943884), (96, 2.5280895025585406), (97, 2.522580636766391), (98, 2.5538510150802782), (99, 2.5449396366128525), (100, 2.5527703958197523)]
+[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.01), (1, 0.01), (2, 0.01), (3, 0.0159), (4, 0.0942), (5, 0.1964), (6, 0.2787), (7, 0.3389), (8, 0.3862), (9, 0.414), (10, 0.4484), (11, 0.4735), (12, 0.4908), (13, 0.5082), (14, 0.5224), (15, 0.5411), (16, 0.5477), (17, 0.5608), (18, 0.5626), (19, 0.5718), (20, 0.5753), (21, 0.58), (22, 0.5794), (23, 0.5859), (24, 0.5911), (25, 0.5934), (26, 0.5954), (27, 0.5962), (28, 0.6041), (29, 0.6056), (30, 0.6064), (31, 0.6099), (32, 0.613), (33, 0.6145), (34, 0.6151), (35, 0.6162), (36, 0.6189), (37, 0.6191), (38, 0.6215), (39, 0.6223), (40, 0.62), (41, 0.6206), (42, 0.6283), (43, 0.6278), (44, 0.6251), (45, 0.623), (46, 0.6263), (47, 0.6254), (48, 0.6296), (49, 0.6282), (50, 0.6331), (51, 0.6347), (52, 0.6291), (53, 0.6303), (54, 0.634), (55, 0.6324), (56, 0.6339), (57, 0.6369), (58, 0.6379), (59, 0.6354), (60, 0.6394), (61, 0.6379), (62, 0.6368), (63, 0.6388), (64, 0.6396), (65, 0.6413), (66, 0.6402), (67, 0.6406), (68, 0.6389), (69, 0.6384), (70, 0.6375), (71, 0.6414), (72, 0.6415), (73, 0.6393), (74, 0.6429), (75, 0.6427), (76, 0.6415), (77, 0.6412), (78, 0.6437), (79, 0.6414), (80, 0.64), (81, 0.6414), (82, 0.6446), (83, 0.6436), (84, 0.6438), (85, 0.6483), (86, 0.6444), (87, 0.6441), (88, 0.6475), (89, 0.6455), (90, 0.6505), (91, 0.6458), (92, 0.6463), (93, 0.6485), (94, 0.65), (95, 0.6502), (96, 0.6487), (97, 0.6478), (98, 0.6498), (99, 0.652), (100, 0.6494)]}
diff --git a/baselines/moon/_static/cifar100_moon_log.txt b/baselines/moon/_static/cifar100_moon_log.txt
new file mode 100644
index 000000000000..622d6da52391
--- /dev/null
+++ b/baselines/moon/_static/cifar100_moon_log.txt
@@ -0,0 +1,12852 @@
+num_clients: 10
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 1
+temperature: 0.5
+alg: moon
+seed: 0
+server_device: cpu
+num_rounds: 100
+client_resources:
+  num_cpus: 4
+  num_gpus: 1
+dataset:
+  name: cifar100
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+model:
+  name: resnet50
+  output_dim: 256
+  dir: ./models/moon/cifar100/
+
+Files already downloaded and verified
+Files already downloaded and verified
+[2023-09-27 06:17:51,660][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=100, round_timeout=None)
+[2023-09-27 06:17:54,778][flwr][INFO] - Flower VCE: Ray initialized with resources: {'node:137.132.92.49': 1.0, 'object_store_memory': 49246977638.0, 'node:__internal_head__': 1.0, 'memory': 104909614490.0, 'accelerator_type:G': 1.0, 'GPU': 1.0, 'CPU': 64.0}
+[2023-09-27 06:17:54,779][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 1}
+[2023-09-27 06:17:54,794][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 1 actors
+[2023-09-27 06:17:54,795][flwr][INFO] - Initializing global parameters
+[2023-09-27 06:17:54,795][flwr][INFO] - Requesting initial parameters from one random client
+[2023-09-27 06:18:00,797][flwr][INFO] - Received initial parameters from one random client
+[2023-09-27 06:18:00,797][flwr][INFO] - Evaluating initial parameters
+>> Test accuracy: 0.009000
+[2023-09-27 06:19:37,108][flwr][INFO] - initial parameters (loss, other metrics): 6.430294827531321, {'accuracy': 0.009}
+[2023-09-27 06:19:37,109][flwr][INFO] - FL starting
+[2023-09-27 06:19:37,110][flwr][DEBUG] - fit_round 1: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.369784 Loss1: 4.083765 Loss2: 0.286019
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.126744 Loss1: 3.863692 Loss2: 0.263052
+(DefaultActor pid=1838052) Epoch: 2 Loss: 4.037508 Loss1: 3.766124 Loss2: 0.271384
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.876198 Loss1: 3.598186 Loss2: 0.278012
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.764268 Loss1: 3.481796 Loss2: 0.282473
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.661503 Loss1: 3.374911 Loss2: 0.286592
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.559833 Loss1: 3.272496 Loss2: 0.287337
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.483088 Loss1: 3.196996 Loss2: 0.286092
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.444711 Loss1: 3.155687 Loss2: 0.289024
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.408479 Loss1: 3.119518 Loss2: 0.288961
+(DefaultActor pid=1838052) >> Training accuracy: 0.229628
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.346154 Loss1: 4.060577 Loss2: 0.285577
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.068530 Loss1: 3.804054 Loss2: 0.264476
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.919490 Loss1: 3.645709 Loss2: 0.273781
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.821928 Loss1: 3.545576 Loss2: 0.276352
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.758056 Loss1: 3.479816 Loss2: 0.278240
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.666878 Loss1: 3.387973 Loss2: 0.278905
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.616499 Loss1: 3.335261 Loss2: 0.281238
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.551534 Loss1: 3.269626 Loss2: 0.281908
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.505023 Loss1: 3.220934 Loss2: 0.284089
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.448401 Loss1: 3.164640 Loss2: 0.283761
+(DefaultActor pid=1838052) >> Training accuracy: 0.222508
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.362599 Loss1: 4.073072 Loss2: 0.289527
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.112317 Loss1: 3.844480 Loss2: 0.267837
+(DefaultActor pid=1838052) Epoch: 2 Loss: 4.049583 Loss1: 3.781704 Loss2: 0.267879
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.972073 Loss1: 3.702887 Loss2: 0.269187
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.885627 Loss1: 3.611692 Loss2: 0.273935
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.822126 Loss1: 3.546345 Loss2: 0.275780
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.754706 Loss1: 3.478197 Loss2: 0.276509
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.695414 Loss1: 3.417056 Loss2: 0.278358
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.641857 Loss1: 3.363491 Loss2: 0.278366
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.620797 Loss1: 3.340018 Loss2: 0.280779
+(DefaultActor pid=1838052) >> Training accuracy: 0.201686
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.339807 Loss1: 4.052010 Loss2: 0.287797
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.115066 Loss1: 3.853241 Loss2: 0.261826
+(DefaultActor pid=1838052) Epoch: 2 Loss: 4.038403 Loss1: 3.773888 Loss2: 0.264515
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.912889 Loss1: 3.643108 Loss2: 0.269782
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.793036 Loss1: 3.514153 Loss2: 0.278882
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.685227 Loss1: 3.403987 Loss2: 0.281240
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.606935 Loss1: 3.325554 Loss2: 0.281380
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.528747 Loss1: 3.246711 Loss2: 0.282036
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.488578 Loss1: 3.201779 Loss2: 0.286799
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.417189 Loss1: 3.132426 Loss2: 0.284763
+(DefaultActor pid=1838052) >> Training accuracy: 0.236090
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.427387 Loss1: 4.143911 Loss2: 0.283476
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.202346 Loss1: 3.942770 Loss2: 0.259576
+(DefaultActor pid=1838052) Epoch: 2 Loss: 4.089324 Loss1: 3.824064 Loss2: 0.265260
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.887307 Loss1: 3.607379 Loss2: 0.279927
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.790842 Loss1: 3.508758 Loss2: 0.282084
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.717089 Loss1: 3.435043 Loss2: 0.282046
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.634166 Loss1: 3.352003 Loss2: 0.282164
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.592898 Loss1: 3.310381 Loss2: 0.282517
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.522705 Loss1: 3.238769 Loss2: 0.283936
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.485529 Loss1: 3.201372 Loss2: 0.284157
+(DefaultActor pid=1838052) >> Training accuracy: 0.188101
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.329792 Loss1: 4.037371 Loss2: 0.292421
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.032470 Loss1: 3.766303 Loss2: 0.266167
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.935961 Loss1: 3.670046 Loss2: 0.265915
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.803394 Loss1: 3.528398 Loss2: 0.274996
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.736129 Loss1: 3.457929 Loss2: 0.278200
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.650748 Loss1: 3.370759 Loss2: 0.279989
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.577409 Loss1: 3.299005 Loss2: 0.278405
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.517058 Loss1: 3.233132 Loss2: 0.283926
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.459693 Loss1: 3.175883 Loss2: 0.283809
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.379744 Loss1: 3.096116 Loss2: 0.283628
+(DefaultActor pid=1838052) >> Training accuracy: 0.242089
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.301462 Loss1: 4.017734 Loss2: 0.283728
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.038520 Loss1: 3.773132 Loss2: 0.265387
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.943192 Loss1: 3.677212 Loss2: 0.265980
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.852988 Loss1: 3.579469 Loss2: 0.273519
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.755028 Loss1: 3.479172 Loss2: 0.275856
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.667556 Loss1: 3.387615 Loss2: 0.279941
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.582211 Loss1: 3.297829 Loss2: 0.284381
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.518371 Loss1: 3.229607 Loss2: 0.288764
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.431761 Loss1: 3.144861 Loss2: 0.286900
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.360612 Loss1: 3.073343 Loss2: 0.287269
+(DefaultActor pid=1838052) >> Training accuracy: 0.275549
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.422230 Loss1: 4.135496 Loss2: 0.286734
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.192760 Loss1: 3.930573 Loss2: 0.262187
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.963340 Loss1: 3.687979 Loss2: 0.275361
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.825529 Loss1: 3.543690 Loss2: 0.281840
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.717797 Loss1: 3.432226 Loss2: 0.285572
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.624084 Loss1: 3.339734 Loss2: 0.284350
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.551592 Loss1: 3.264009 Loss2: 0.287583
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.468409 Loss1: 3.182516 Loss2: 0.285894
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.405261 Loss1: 3.117090 Loss2: 0.288171
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.344626 Loss1: 3.057201 Loss2: 0.287425
+(DefaultActor pid=1838052) >> Training accuracy: 0.279848
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.337995 Loss1: 4.053333 Loss2: 0.284662
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.055274 Loss1: 3.792687 Loss2: 0.262587
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.971644 Loss1: 3.709966 Loss2: 0.261677
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.892300 Loss1: 3.626549 Loss2: 0.265751
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.776625 Loss1: 3.501122 Loss2: 0.275503
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.678157 Loss1: 3.398068 Loss2: 0.280089
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.631299 Loss1: 3.351895 Loss2: 0.279403
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.571316 Loss1: 3.290765 Loss2: 0.280551
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.509884 Loss1: 3.227711 Loss2: 0.282173
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.457262 Loss1: 3.177141 Loss2: 0.280121
+(DefaultActor pid=1838052) >> Training accuracy: 0.235562
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.333940 Loss1: 4.044128 Loss2: 0.289811
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.064459 Loss1: 3.800716 Loss2: 0.263743
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.971100 Loss1: 3.703330 Loss2: 0.267770
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.867576 Loss1: 3.596127 Loss2: 0.271449
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.799606 Loss1: 3.524901 Loss2: 0.274705
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.695966 Loss1: 3.417971 Loss2: 0.277995
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.632793 Loss1: 3.352437 Loss2: 0.280356
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.553112 Loss1: 3.271284 Loss2: 0.281829
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.489069 Loss1: 3.205962 Loss2: 0.283108
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.408333 Loss1: 3.120609 Loss2: 0.287724
+(DefaultActor pid=1838052) >> Training accuracy: 0.232205
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 06:49:48,032][flwr][DEBUG] - fit_round 1 received 10 results and 0 failures
+[2023-09-27 06:49:50,488][flwr][WARNING] - No fit_metrics_aggregation_fn provided
+>> Test accuracy: 0.010000
+[2023-09-27 06:50:32,439][flwr][INFO] - fit progress: (1, 4.861440579350383, {'accuracy': 0.01}, 1855.3291893731803)
+[2023-09-27 06:50:32,440][flwr][DEBUG] - evaluate_round 1: strategy sampled 10 clients (out of 10)
+[2023-09-27 06:51:11,688][flwr][DEBUG] - evaluate_round 1 received 10 results and 0 failures
+[2023-09-27 06:51:11,689][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
+[2023-09-27 06:51:11,689][flwr][DEBUG] - fit_round 2: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.644373 Loss1: 4.184493 Loss2: 0.459881
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.198303 Loss1: 3.733462 Loss2: 0.464840
+(DefaultActor pid=1838052) Epoch: 2 Loss: 4.013687 Loss1: 3.536389 Loss2: 0.477298
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.932287 Loss1: 3.455386 Loss2: 0.476901
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.806476 Loss1: 3.340193 Loss2: 0.466283
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.746450 Loss1: 3.286199 Loss2: 0.460251
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.638469 Loss1: 3.183025 Loss2: 0.455444
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.604254 Loss1: 3.153255 Loss2: 0.450999
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.504562 Loss1: 3.064321 Loss2: 0.440241
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.448699 Loss1: 3.015176 Loss2: 0.433522
+(DefaultActor pid=1838052) >> Training accuracy: 0.283253
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.667550 Loss1: 4.214587 Loss2: 0.452963
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.220379 Loss1: 3.757211 Loss2: 0.463168
+(DefaultActor pid=1838052) Epoch: 2 Loss: 4.076105 Loss1: 3.599009 Loss2: 0.477095
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.985424 Loss1: 3.513090 Loss2: 0.472334
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.903181 Loss1: 3.436756 Loss2: 0.466425
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.843354 Loss1: 3.387080 Loss2: 0.456274
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.750510 Loss1: 3.302325 Loss2: 0.448185
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.706948 Loss1: 3.264065 Loss2: 0.442884
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.608896 Loss1: 3.177834 Loss2: 0.431062
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.583576 Loss1: 3.161815 Loss2: 0.421761
+(DefaultActor pid=1838052) >> Training accuracy: 0.209135
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.568606 Loss1: 4.104333 Loss2: 0.464273
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.077084 Loss1: 3.611639 Loss2: 0.465444
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.981827 Loss1: 3.515113 Loss2: 0.466714
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.905688 Loss1: 3.442643 Loss2: 0.463044
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.827841 Loss1: 3.372064 Loss2: 0.455777
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.795793 Loss1: 3.345474 Loss2: 0.450319
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.753882 Loss1: 3.308774 Loss2: 0.445108
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.679453 Loss1: 3.240107 Loss2: 0.439347
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.642235 Loss1: 3.206805 Loss2: 0.435430
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.559729 Loss1: 3.133299 Loss2: 0.426430
+(DefaultActor pid=1838052) >> Training accuracy: 0.218354
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.731526 Loss1: 4.305523 Loss2: 0.426003
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.210372 Loss1: 3.770380 Loss2: 0.439992
+(DefaultActor pid=1838052) Epoch: 2 Loss: 4.034736 Loss1: 3.588960 Loss2: 0.445775
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.953219 Loss1: 3.499736 Loss2: 0.453484
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.861044 Loss1: 3.415457 Loss2: 0.445586
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.774768 Loss1: 3.334986 Loss2: 0.439782
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.711004 Loss1: 3.280312 Loss2: 0.430692
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.650057 Loss1: 3.226051 Loss2: 0.424005
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.602845 Loss1: 3.186046 Loss2: 0.416799
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.520022 Loss1: 3.113187 Loss2: 0.406835
+(DefaultActor pid=1838052) >> Training accuracy: 0.253906
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.991454 Loss1: 3.935299 Loss2: 0.056156
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.588267 Loss1: 3.536850 Loss2: 0.051418
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.488563 Loss1: 3.436851 Loss2: 0.051712
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.424850 Loss1: 3.373044 Loss2: 0.051806
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.338276 Loss1: 3.285797 Loss2: 0.052479
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.266924 Loss1: 3.214751 Loss2: 0.052173
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.198642 Loss1: 3.144631 Loss2: 0.054011
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.145674 Loss1: 3.090927 Loss2: 0.054746
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.117068 Loss1: 3.060616 Loss2: 0.056452
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.049991 Loss1: 2.993472 Loss2: 0.056519
+(DefaultActor pid=1838052) >> Training accuracy: 0.273932
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.613039 Loss1: 4.153980 Loss2: 0.459058
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.198233 Loss1: 3.753234 Loss2: 0.444999
+(DefaultActor pid=1838052) Epoch: 2 Loss: 4.030522 Loss1: 3.574830 Loss2: 0.455692
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.948132 Loss1: 3.503039 Loss2: 0.445093
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.904944 Loss1: 3.469663 Loss2: 0.435281
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.841591 Loss1: 3.414879 Loss2: 0.426711
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.776404 Loss1: 3.358964 Loss2: 0.417439
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.756225 Loss1: 3.344306 Loss2: 0.411920
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.703344 Loss1: 3.302161 Loss2: 0.401183
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.673633 Loss1: 3.276277 Loss2: 0.397356
+(DefaultActor pid=1838052) >> Training accuracy: 0.230263
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.981784 Loss1: 3.929316 Loss2: 0.052467
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.588564 Loss1: 3.538386 Loss2: 0.050178
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.433607 Loss1: 3.382880 Loss2: 0.050727
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.367239 Loss1: 3.315783 Loss2: 0.051456
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.291270 Loss1: 3.238080 Loss2: 0.053190
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.207599 Loss1: 3.153870 Loss2: 0.053729
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.175349 Loss1: 3.120854 Loss2: 0.054495
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.106231 Loss1: 3.049030 Loss2: 0.057202
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.061699 Loss1: 3.004801 Loss2: 0.056898
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.002184 Loss1: 2.943285 Loss2: 0.058898
+(DefaultActor pid=1838052) >> Training accuracy: 0.233782
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.917315 Loss1: 3.863439 Loss2: 0.053876
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.527755 Loss1: 3.476411 Loss2: 0.051344
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.443776 Loss1: 3.392791 Loss2: 0.050985
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.370201 Loss1: 3.319509 Loss2: 0.050693
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.273357 Loss1: 3.222251 Loss2: 0.051106
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.221033 Loss1: 3.168118 Loss2: 0.052914
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.140141 Loss1: 3.086229 Loss2: 0.053911
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.109909 Loss1: 3.054987 Loss2: 0.054923
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.060353 Loss1: 3.003993 Loss2: 0.056361
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.992805 Loss1: 2.936097 Loss2: 0.056708
+(DefaultActor pid=1838052) >> Training accuracy: 0.285823
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 4.493748 Loss1: 3.995604 Loss2: 0.498144
+(DefaultActor pid=1838052) Epoch: 1 Loss: 4.122479 Loss1: 3.635837 Loss2: 0.486642
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.995465 Loss1: 3.511020 Loss2: 0.484445
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.901413 Loss1: 3.418500 Loss2: 0.482913
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.831103 Loss1: 3.356049 Loss2: 0.475053
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.765394 Loss1: 3.294033 Loss2: 0.471360
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.695697 Loss1: 3.234495 Loss2: 0.461201
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.642169 Loss1: 3.188603 Loss2: 0.453565
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.574997 Loss1: 3.126612 Loss2: 0.448385
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.513407 Loss1: 3.071809 Loss2: 0.441598
+(DefaultActor pid=1838052) >> Training accuracy: 0.244462
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.997624 Loss1: 3.939611 Loss2: 0.058013
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.596121 Loss1: 3.544284 Loss2: 0.051837
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.443146 Loss1: 3.392343 Loss2: 0.050802
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.356063 Loss1: 3.303394 Loss2: 0.052669
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.276302 Loss1: 3.222434 Loss2: 0.053868
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.230555 Loss1: 3.174897 Loss2: 0.055659
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.161892 Loss1: 3.105874 Loss2: 0.056018
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.117249 Loss1: 3.060366 Loss2: 0.056883
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.029329 Loss1: 2.971722 Loss2: 0.057607
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.963145 Loss1: 2.905953 Loss2: 0.057192
+(DefaultActor pid=1838052) >> Training accuracy: 0.280828
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 07:21:07,003][flwr][DEBUG] - fit_round 2 received 10 results and 0 failures
+>> Test accuracy: 0.010000
+[2023-09-27 07:21:49,833][flwr][INFO] - fit progress: (2, 5.477163912008365, {'accuracy': 0.01}, 3732.7233330453746)
+[2023-09-27 07:21:49,835][flwr][DEBUG] - evaluate_round 2: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:22:29,717][flwr][DEBUG] - evaluate_round 2 received 10 results and 0 failures
+[2023-09-27 07:22:29,718][flwr][DEBUG] - fit_round 3: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.922555 Loss1: 3.557274 Loss2: 0.365281
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.606061 Loss1: 3.293600 Loss2: 0.312461
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.504368 Loss1: 3.205235 Loss2: 0.299133
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.428207 Loss1: 3.137463 Loss2: 0.290744
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.343418 Loss1: 3.053441 Loss2: 0.289977
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.261979 Loss1: 2.974511 Loss2: 0.287468
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.245885 Loss1: 2.958061 Loss2: 0.287823
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.149963 Loss1: 2.863760 Loss2: 0.286203
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.101458 Loss1: 2.813974 Loss2: 0.287485
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.039259 Loss1: 2.753741 Loss2: 0.285518
+(DefaultActor pid=1838052) >> Training accuracy: 0.305556
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.828347 Loss1: 3.534358 Loss2: 0.293988
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.554310 Loss1: 3.283775 Loss2: 0.270536
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.464295 Loss1: 3.198007 Loss2: 0.266288
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.406251 Loss1: 3.135592 Loss2: 0.270659
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.335067 Loss1: 3.064223 Loss2: 0.270844
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.284846 Loss1: 3.012912 Loss2: 0.271934
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.223960 Loss1: 2.954009 Loss2: 0.269951
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.172071 Loss1: 2.901174 Loss2: 0.270898
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.115965 Loss1: 2.841745 Loss2: 0.274220
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.019910 Loss1: 2.747705 Loss2: 0.272205
+(DefaultActor pid=1838052) >> Training accuracy: 0.294764
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.850639 Loss1: 3.529540 Loss2: 0.321099
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.566213 Loss1: 3.292225 Loss2: 0.273988
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.475061 Loss1: 3.208274 Loss2: 0.266787
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.390075 Loss1: 3.126561 Loss2: 0.263515
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.296098 Loss1: 3.034898 Loss2: 0.261200
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.265400 Loss1: 3.003450 Loss2: 0.261950
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.207430 Loss1: 2.944783 Loss2: 0.262647
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.139398 Loss1: 2.873270 Loss2: 0.266129
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.101593 Loss1: 2.837857 Loss2: 0.263736
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.051368 Loss1: 2.788499 Loss2: 0.262869
+(DefaultActor pid=1838052) >> Training accuracy: 0.291535
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.851295 Loss1: 3.547933 Loss2: 0.303362
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.576642 Loss1: 3.299925 Loss2: 0.276716
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.456705 Loss1: 3.181631 Loss2: 0.275075
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.424054 Loss1: 3.148893 Loss2: 0.275161
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.348423 Loss1: 3.070073 Loss2: 0.278351
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.269554 Loss1: 2.992072 Loss2: 0.277482
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.234234 Loss1: 2.952364 Loss2: 0.281870
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.166968 Loss1: 2.886840 Loss2: 0.280127
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.099678 Loss1: 2.821619 Loss2: 0.278058
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.040664 Loss1: 2.763697 Loss2: 0.276967
+(DefaultActor pid=1838052) >> Training accuracy: 0.298655
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.920883 Loss1: 3.602874 Loss2: 0.318009
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.616597 Loss1: 3.329797 Loss2: 0.286801
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.521105 Loss1: 3.239110 Loss2: 0.281994
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.448692 Loss1: 3.165734 Loss2: 0.282958
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.375298 Loss1: 3.097168 Loss2: 0.278130
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.332728 Loss1: 3.052265 Loss2: 0.280462
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.232993 Loss1: 2.955120 Loss2: 0.277873
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.249608 Loss1: 2.969839 Loss2: 0.279769
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.173826 Loss1: 2.898052 Loss2: 0.275773
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.128311 Loss1: 2.853575 Loss2: 0.274736
+(DefaultActor pid=1838052) >> Training accuracy: 0.309731
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.655502 Loss1: 3.590250 Loss2: 0.065252
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.382125 Loss1: 3.324907 Loss2: 0.057218
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.287139 Loss1: 3.230372 Loss2: 0.056767
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.214660 Loss1: 3.156809 Loss2: 0.057852
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.150348 Loss1: 3.091685 Loss2: 0.058662
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.088283 Loss1: 3.029709 Loss2: 0.058573
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.046257 Loss1: 2.986612 Loss2: 0.059645
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.989624 Loss1: 2.927651 Loss2: 0.061972
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.916961 Loss1: 2.855189 Loss2: 0.061772
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.882330 Loss1: 2.819250 Loss2: 0.063080
+(DefaultActor pid=1838052) >> Training accuracy: 0.290264
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.560737 Loss1: 3.498825 Loss2: 0.061912
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.303921 Loss1: 3.247327 Loss2: 0.056594
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.220241 Loss1: 3.163955 Loss2: 0.056286
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.143950 Loss1: 3.087456 Loss2: 0.056494
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.016872 Loss1: 2.959652 Loss2: 0.057220
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.943949 Loss1: 2.885173 Loss2: 0.058776
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.893974 Loss1: 2.834528 Loss2: 0.059446
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.821323 Loss1: 2.760880 Loss2: 0.060443
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.764922 Loss1: 2.703962 Loss2: 0.060959
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.714211 Loss1: 2.651170 Loss2: 0.063041
+(DefaultActor pid=1838052) >> Training accuracy: 0.354167
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.656666 Loss1: 3.592971 Loss2: 0.063694
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.445064 Loss1: 3.391160 Loss2: 0.053903
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.366218 Loss1: 3.313455 Loss2: 0.052763
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.322735 Loss1: 3.268185 Loss2: 0.054550
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.281451 Loss1: 3.227969 Loss2: 0.053482
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.210883 Loss1: 3.155668 Loss2: 0.055214
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.144232 Loss1: 3.087339 Loss2: 0.056893
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.122254 Loss1: 3.065861 Loss2: 0.056393
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.091077 Loss1: 3.032098 Loss2: 0.058979
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.018300 Loss1: 2.958169 Loss2: 0.060131
+(DefaultActor pid=1838052) >> Training accuracy: 0.278988
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.562108 Loss1: 3.510223 Loss2: 0.051884
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.283523 Loss1: 3.235999 Loss2: 0.047524
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.211609 Loss1: 3.164451 Loss2: 0.047158
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.125271 Loss1: 3.077918 Loss2: 0.047353
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.071585 Loss1: 3.023945 Loss2: 0.047639
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.004476 Loss1: 2.955765 Loss2: 0.048710
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.974421 Loss1: 2.924276 Loss2: 0.050145
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.940153 Loss1: 2.888216 Loss2: 0.051938
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.862987 Loss1: 2.810040 Loss2: 0.052947
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.843785 Loss1: 2.790512 Loss2: 0.053273
+(DefaultActor pid=1838052) >> Training accuracy: 0.315549
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.677349 Loss1: 3.559477 Loss2: 0.117872
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.399072 Loss1: 3.297309 Loss2: 0.101763
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.308856 Loss1: 3.209720 Loss2: 0.099136
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.226705 Loss1: 3.128742 Loss2: 0.097962
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.145654 Loss1: 3.047904 Loss2: 0.097750
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.146708 Loss1: 3.048723 Loss2: 0.097985
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.073893 Loss1: 2.975577 Loss2: 0.098316
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.008721 Loss1: 2.909682 Loss2: 0.099040
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.946285 Loss1: 2.846163 Loss2: 0.100123
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.894182 Loss1: 2.793671 Loss2: 0.100511
+(DefaultActor pid=1838052) >> Training accuracy: 0.314873
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 07:52:04,826][flwr][DEBUG] - fit_round 3 received 10 results and 0 failures
+>> Test accuracy: 0.014100
+[2023-09-27 07:52:47,706][flwr][INFO] - fit progress: (3, 5.5055647475270035, {'accuracy': 0.0141}, 5590.596718014218)
+[2023-09-27 07:52:47,708][flwr][DEBUG] - evaluate_round 3: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:53:32,486][flwr][DEBUG] - evaluate_round 3 received 10 results and 0 failures
+[2023-09-27 07:53:32,488][flwr][DEBUG] - fit_round 4: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.463621 Loss1: 3.216236 Loss2: 0.247384
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.265612 Loss1: 3.055579 Loss2: 0.210033
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.145339 Loss1: 2.944292 Loss2: 0.201047
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.089212 Loss1: 2.891579 Loss2: 0.197632
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.050139 Loss1: 2.850335 Loss2: 0.199804
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.935722 Loss1: 2.736376 Loss2: 0.199345
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.871901 Loss1: 2.671933 Loss2: 0.199968
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.814916 Loss1: 2.618288 Loss2: 0.196628
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.778902 Loss1: 2.579310 Loss2: 0.199592
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.688708 Loss1: 2.490068 Loss2: 0.198640
+(DefaultActor pid=1838052) >> Training accuracy: 0.359573
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.827630 Loss1: 3.361022 Loss2: 0.466608
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.603237 Loss1: 3.193316 Loss2: 0.409921
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.511392 Loss1: 3.112292 Loss2: 0.399099
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.442427 Loss1: 3.044361 Loss2: 0.398066
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.385098 Loss1: 2.993434 Loss2: 0.391663
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.318931 Loss1: 2.924867 Loss2: 0.394064
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.294363 Loss1: 2.897327 Loss2: 0.397037
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.223198 Loss1: 2.830592 Loss2: 0.392606
+(DefaultActor pid=1838052) Epoch: 8 Loss: 3.175710 Loss1: 2.780731 Loss2: 0.394979
+(DefaultActor pid=1838052) Epoch: 9 Loss: 3.120332 Loss1: 2.727254 Loss2: 0.393078
+(DefaultActor pid=1838052) >> Training accuracy: 0.330181
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.282682 Loss1: 3.232115 Loss2: 0.050566
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.099791 Loss1: 3.053422 Loss2: 0.046369
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.987802 Loss1: 2.942296 Loss2: 0.045507
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.916563 Loss1: 2.870740 Loss2: 0.045823
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.851462 Loss1: 2.805344 Loss2: 0.046119
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.811548 Loss1: 2.763247 Loss2: 0.048301
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.725912 Loss1: 2.677642 Loss2: 0.048269
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.694947 Loss1: 2.645510 Loss2: 0.049438
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.591619 Loss1: 2.541165 Loss2: 0.050454
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.565490 Loss1: 2.512605 Loss2: 0.052885
+(DefaultActor pid=1838052) >> Training accuracy: 0.368473
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.259794 Loss1: 3.210508 Loss2: 0.049286
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.027122 Loss1: 2.982449 Loss2: 0.044673
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.952811 Loss1: 2.908267 Loss2: 0.044544
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.849582 Loss1: 2.804490 Loss2: 0.045092
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.753046 Loss1: 2.706741 Loss2: 0.046305
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.701504 Loss1: 2.654246 Loss2: 0.047258
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.697666 Loss1: 2.648308 Loss2: 0.049358
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.586713 Loss1: 2.536130 Loss2: 0.050583
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.558675 Loss1: 2.508268 Loss2: 0.050407
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.482649 Loss1: 2.430579 Loss2: 0.052070
+(DefaultActor pid=1838052) >> Training accuracy: 0.380490
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.208056 Loss1: 3.158335 Loss2: 0.049722
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.022045 Loss1: 2.975547 Loss2: 0.046498
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.924929 Loss1: 2.878575 Loss2: 0.046354
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.829727 Loss1: 2.783190 Loss2: 0.046537
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.759261 Loss1: 2.711374 Loss2: 0.047887
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.670441 Loss1: 2.620735 Loss2: 0.049707
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.614755 Loss1: 2.564584 Loss2: 0.050171
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.559418 Loss1: 2.507530 Loss2: 0.051888
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.472070 Loss1: 2.419986 Loss2: 0.052084
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.411061 Loss1: 2.358198 Loss2: 0.052862
+(DefaultActor pid=1838052) >> Training accuracy: 0.397035
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.293607 Loss1: 3.234862 Loss2: 0.058745
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.063403 Loss1: 3.010807 Loss2: 0.052596
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.975066 Loss1: 2.924591 Loss2: 0.050475
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.878276 Loss1: 2.826151 Loss2: 0.052124
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.810593 Loss1: 2.757815 Loss2: 0.052779
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.727358 Loss1: 2.673098 Loss2: 0.054259
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.681398 Loss1: 2.625040 Loss2: 0.056357
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.595525 Loss1: 2.538674 Loss2: 0.056851
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.558311 Loss1: 2.500617 Loss2: 0.057694
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.516464 Loss1: 2.457347 Loss2: 0.059117
+(DefaultActor pid=1838052) >> Training accuracy: 0.391710
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.222874 Loss1: 3.178139 Loss2: 0.044734
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.033196 Loss1: 2.989958 Loss2: 0.043239
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.946469 Loss1: 2.903984 Loss2: 0.042485
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.874091 Loss1: 2.831174 Loss2: 0.042918
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.834365 Loss1: 2.790535 Loss2: 0.043829
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.747757 Loss1: 2.703132 Loss2: 0.044625
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.691127 Loss1: 2.644962 Loss2: 0.046165
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.632341 Loss1: 2.584584 Loss2: 0.047757
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.556889 Loss1: 2.508931 Loss2: 0.047958
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.534964 Loss1: 2.485370 Loss2: 0.049593
+(DefaultActor pid=1838052) >> Training accuracy: 0.358188
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.557245 Loss1: 3.245894 Loss2: 0.311351
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.363731 Loss1: 3.095614 Loss2: 0.268118
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.268273 Loss1: 3.009341 Loss2: 0.258932
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.186438 Loss1: 2.929719 Loss2: 0.256719
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.142631 Loss1: 2.886133 Loss2: 0.256497
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.071703 Loss1: 2.816265 Loss2: 0.255438
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.036513 Loss1: 2.779075 Loss2: 0.257439
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.985743 Loss1: 2.727855 Loss2: 0.257887
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.887329 Loss1: 2.630912 Loss2: 0.256417
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.862139 Loss1: 2.602498 Loss2: 0.259641
+(DefaultActor pid=1838052) >> Training accuracy: 0.341179
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.671132 Loss1: 3.258866 Loss2: 0.412266
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.409693 Loss1: 3.050773 Loss2: 0.358921
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.336526 Loss1: 2.987356 Loss2: 0.349170
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.259327 Loss1: 2.916673 Loss2: 0.342654
+(DefaultActor pid=1838052) Epoch: 4 Loss: 3.202082 Loss1: 2.859971 Loss2: 0.342111
+(DefaultActor pid=1838052) Epoch: 5 Loss: 3.163279 Loss1: 2.821047 Loss2: 0.342233
+(DefaultActor pid=1838052) Epoch: 6 Loss: 3.097888 Loss1: 2.752977 Loss2: 0.344911
+(DefaultActor pid=1838052) Epoch: 7 Loss: 3.036831 Loss1: 2.694183 Loss2: 0.342647
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.970583 Loss1: 2.626212 Loss2: 0.344371
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.890262 Loss1: 2.547522 Loss2: 0.342740
+(DefaultActor pid=1838052) >> Training accuracy: 0.369665
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.392899 Loss1: 3.243730 Loss2: 0.149169
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.178866 Loss1: 3.058680 Loss2: 0.120186
+(DefaultActor pid=1838052) Epoch: 2 Loss: 3.082310 Loss1: 2.967496 Loss2: 0.114814
+(DefaultActor pid=1838052) Epoch: 3 Loss: 3.016231 Loss1: 2.901184 Loss2: 0.115047
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.970972 Loss1: 2.856173 Loss2: 0.114799
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.886622 Loss1: 2.770577 Loss2: 0.116045
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.806940 Loss1: 2.689884 Loss2: 0.117056
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.770023 Loss1: 2.653150 Loss2: 0.116873
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.701240 Loss1: 2.582509 Loss2: 0.118731
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.640212 Loss1: 2.519890 Loss2: 0.120322
+(DefaultActor pid=1838052) >> Training accuracy: 0.344551
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 08:23:25,915][flwr][DEBUG] - fit_round 4 received 10 results and 0 failures
+>> Test accuracy: 0.088200
+[2023-09-27 08:24:10,741][flwr][INFO] - fit progress: (4, 4.169462466011413, {'accuracy': 0.0882}, 7473.631052463315)
+[2023-09-27 08:24:10,742][flwr][DEBUG] - evaluate_round 4: strategy sampled 10 clients (out of 10)
+[2023-09-27 08:24:50,446][flwr][DEBUG] - evaluate_round 4 received 10 results and 0 failures
+[2023-09-27 08:24:50,448][flwr][DEBUG] - fit_round 5: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.044559 Loss1: 2.934706 Loss2: 0.109853
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.769363 Loss1: 2.675678 Loss2: 0.093685
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.687641 Loss1: 2.598878 Loss2: 0.088763
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.599898 Loss1: 2.512106 Loss2: 0.087792
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.540585 Loss1: 2.453451 Loss2: 0.087134
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.468175 Loss1: 2.381435 Loss2: 0.086740
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.415930 Loss1: 2.328302 Loss2: 0.087628
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.352162 Loss1: 2.265323 Loss2: 0.086840
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.255332 Loss1: 2.167591 Loss2: 0.087741
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.220588 Loss1: 2.131302 Loss2: 0.089286
+(DefaultActor pid=1838052) >> Training accuracy: 0.450087
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.237433 Loss1: 2.956613 Loss2: 0.280820
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.010297 Loss1: 2.762902 Loss2: 0.247394
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.917174 Loss1: 2.676857 Loss2: 0.240317
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.818864 Loss1: 2.577074 Loss2: 0.241790
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.729454 Loss1: 2.488832 Loss2: 0.240622
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.697842 Loss1: 2.453622 Loss2: 0.244220
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.666894 Loss1: 2.421276 Loss2: 0.245618
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.568640 Loss1: 2.324768 Loss2: 0.243872
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.536173 Loss1: 2.290029 Loss2: 0.246144
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.454216 Loss1: 2.207339 Loss2: 0.246877
+(DefaultActor pid=1838052) >> Training accuracy: 0.425877
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.117160 Loss1: 3.058576 Loss2: 0.058585
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.923077 Loss1: 2.869900 Loss2: 0.053176
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.819140 Loss1: 2.768482 Loss2: 0.050658
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.749814 Loss1: 2.698656 Loss2: 0.051157
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.712673 Loss1: 2.660845 Loss2: 0.051828
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.648868 Loss1: 2.596063 Loss2: 0.052805
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.556787 Loss1: 2.503860 Loss2: 0.052927
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.532586 Loss1: 2.476940 Loss2: 0.055646
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.465118 Loss1: 2.408557 Loss2: 0.056561
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.425746 Loss1: 2.368631 Loss2: 0.057116
+(DefaultActor pid=1838052) >> Training accuracy: 0.403783
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.002072 Loss1: 2.947792 Loss2: 0.054280
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.826481 Loss1: 2.774957 Loss2: 0.051523
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.758476 Loss1: 2.708199 Loss2: 0.050277
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.700022 Loss1: 2.650502 Loss2: 0.049520
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.594647 Loss1: 2.545112 Loss2: 0.049535
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.561690 Loss1: 2.510262 Loss2: 0.051428
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.492958 Loss1: 2.441024 Loss2: 0.051934
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.460541 Loss1: 2.407330 Loss2: 0.053212
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.372188 Loss1: 2.318846 Loss2: 0.053341
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.323567 Loss1: 2.269347 Loss2: 0.054219
+(DefaultActor pid=1838052) >> Training accuracy: 0.411859
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.943132 Loss1: 2.896993 Loss2: 0.046140
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.743960 Loss1: 2.699422 Loss2: 0.044538
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.672550 Loss1: 2.627953 Loss2: 0.044596
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.581770 Loss1: 2.538061 Loss2: 0.043709
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.536205 Loss1: 2.490766 Loss2: 0.045438
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.472918 Loss1: 2.427032 Loss2: 0.045886
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.414953 Loss1: 2.367332 Loss2: 0.047621
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.341351 Loss1: 2.292401 Loss2: 0.048950
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.289473 Loss1: 2.239205 Loss2: 0.050268
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.229166 Loss1: 2.179152 Loss2: 0.050015
+(DefaultActor pid=1838052) >> Training accuracy: 0.428204
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.877014 Loss1: 2.828293 Loss2: 0.048721
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.673691 Loss1: 2.627127 Loss2: 0.046564
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.593290 Loss1: 2.547474 Loss2: 0.045815
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.528434 Loss1: 2.481120 Loss2: 0.047314
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.447478 Loss1: 2.400185 Loss2: 0.047293
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.360431 Loss1: 2.312344 Loss2: 0.048087
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.308135 Loss1: 2.258981 Loss2: 0.049154
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.246187 Loss1: 2.196381 Loss2: 0.049806
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.180565 Loss1: 2.129732 Loss2: 0.050833
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.146498 Loss1: 2.093765 Loss2: 0.052733
+(DefaultActor pid=1838052) >> Training accuracy: 0.489383
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.306062 Loss1: 3.003298 Loss2: 0.302764
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.057813 Loss1: 2.804032 Loss2: 0.253781
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.940257 Loss1: 2.693761 Loss2: 0.246495
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.891829 Loss1: 2.642646 Loss2: 0.249183
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.807374 Loss1: 2.561056 Loss2: 0.246318
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.773237 Loss1: 2.525510 Loss2: 0.247727
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.664517 Loss1: 2.414085 Loss2: 0.250432
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.626118 Loss1: 2.376161 Loss2: 0.249957
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.538084 Loss1: 2.288568 Loss2: 0.249516
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.490878 Loss1: 2.239237 Loss2: 0.251641
+(DefaultActor pid=1838052) >> Training accuracy: 0.409810
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.038147 Loss1: 2.911986 Loss2: 0.126161
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.805732 Loss1: 2.697179 Loss2: 0.108553
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.757872 Loss1: 2.653028 Loss2: 0.104844
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.660189 Loss1: 2.557299 Loss2: 0.102890
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.581510 Loss1: 2.478467 Loss2: 0.103042
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.501657 Loss1: 2.398222 Loss2: 0.103435
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.441542 Loss1: 2.338488 Loss2: 0.103054
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.363660 Loss1: 2.258737 Loss2: 0.104923
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.315893 Loss1: 2.209484 Loss2: 0.106408
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.222165 Loss1: 2.115981 Loss2: 0.106184
+(DefaultActor pid=1838052) >> Training accuracy: 0.459256
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.999583 Loss1: 2.949495 Loss2: 0.050088
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.776681 Loss1: 2.730141 Loss2: 0.046539
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.703977 Loss1: 2.656840 Loss2: 0.047137
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.601425 Loss1: 2.554533 Loss2: 0.046892
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.563780 Loss1: 2.517054 Loss2: 0.046725
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.488050 Loss1: 2.439933 Loss2: 0.048117
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.450278 Loss1: 2.401121 Loss2: 0.049156
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.399282 Loss1: 2.348249 Loss2: 0.051034
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.331828 Loss1: 2.281056 Loss2: 0.050772
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.283067 Loss1: 2.231295 Loss2: 0.051772
+(DefaultActor pid=1838052) >> Training accuracy: 0.430973
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.989596 Loss1: 2.938972 Loss2: 0.050624
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.717162 Loss1: 2.671275 Loss2: 0.045887
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.610293 Loss1: 2.565772 Loss2: 0.044521
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.548616 Loss1: 2.503526 Loss2: 0.045090
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.476767 Loss1: 2.431182 Loss2: 0.045585
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.435620 Loss1: 2.389084 Loss2: 0.046535
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.356208 Loss1: 2.308446 Loss2: 0.047762
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.303738 Loss1: 2.254503 Loss2: 0.049235
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.274659 Loss1: 2.225275 Loss2: 0.049384
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.187924 Loss1: 2.138006 Loss2: 0.049918
+(DefaultActor pid=1838052) >> Training accuracy: 0.471706
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 08:55:01,857][flwr][DEBUG] - fit_round 5 received 10 results and 0 failures
+>> Test accuracy: 0.185100
+[2023-09-27 08:55:45,398][flwr][INFO] - fit progress: (5, 3.436301054665075, {'accuracy': 0.1851}, 9368.288240125403)
+[2023-09-27 08:55:45,399][flwr][DEBUG] - evaluate_round 5: strategy sampled 10 clients (out of 10)
+[2023-09-27 08:56:24,856][flwr][DEBUG] - evaluate_round 5 received 10 results and 0 failures
+[2023-09-27 08:56:24,864][flwr][DEBUG] - fit_round 6: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.751836 Loss1: 2.701415 Loss2: 0.050422
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.479313 Loss1: 2.430408 Loss2: 0.048905
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.431509 Loss1: 2.382269 Loss2: 0.049240
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.311491 Loss1: 2.262098 Loss2: 0.049393
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.262790 Loss1: 2.212745 Loss2: 0.050045
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.183130 Loss1: 2.132272 Loss2: 0.050859
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.146769 Loss1: 2.093164 Loss2: 0.053604
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.070664 Loss1: 2.017400 Loss2: 0.053264
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.071346 Loss1: 2.016372 Loss2: 0.054974
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.965831 Loss1: 1.910385 Loss2: 0.055447
+(DefaultActor pid=1838052) >> Training accuracy: 0.487935
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.155894 Loss1: 2.614803 Loss2: 0.541090
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.936520 Loss1: 2.429278 Loss2: 0.507242
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.854657 Loss1: 2.363557 Loss2: 0.491100
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.758776 Loss1: 2.275993 Loss2: 0.482783
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.711209 Loss1: 2.237653 Loss2: 0.473556
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.635475 Loss1: 2.163789 Loss2: 0.471686
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.563616 Loss1: 2.094371 Loss2: 0.469245
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.476910 Loss1: 2.011973 Loss2: 0.464937
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.424913 Loss1: 1.962074 Loss2: 0.462839
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.367295 Loss1: 1.905642 Loss2: 0.461652
+(DefaultActor pid=1838052) >> Training accuracy: 0.477453
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.089129 Loss1: 2.525116 Loss2: 0.564013
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.881016 Loss1: 2.343470 Loss2: 0.537546
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.773552 Loss1: 2.252192 Loss2: 0.521360
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.749501 Loss1: 2.236806 Loss2: 0.512695
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.625130 Loss1: 2.119113 Loss2: 0.506016
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.563063 Loss1: 2.062221 Loss2: 0.500842
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.494423 Loss1: 1.995556 Loss2: 0.498867
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.431896 Loss1: 1.933219 Loss2: 0.498677
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.374054 Loss1: 1.878336 Loss2: 0.495718
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.327299 Loss1: 1.832026 Loss2: 0.495273
+(DefaultActor pid=1838052) >> Training accuracy: 0.515024
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.688836 Loss1: 2.646750 Loss2: 0.042087
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.458496 Loss1: 2.416032 Loss2: 0.042463
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.338702 Loss1: 2.296307 Loss2: 0.042395
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.275615 Loss1: 2.232534 Loss2: 0.043082
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.201506 Loss1: 2.156234 Loss2: 0.045272
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.126003 Loss1: 2.080944 Loss2: 0.045059
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.078231 Loss1: 2.031971 Loss2: 0.046260
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.973676 Loss1: 1.926965 Loss2: 0.046711
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.944341 Loss1: 1.897105 Loss2: 0.047236
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.864110 Loss1: 1.815267 Loss2: 0.048844
+(DefaultActor pid=1838052) >> Training accuracy: 0.527097
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.915674 Loss1: 2.775380 Loss2: 0.140293
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.678610 Loss1: 2.552722 Loss2: 0.125888
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.587836 Loss1: 2.469362 Loss2: 0.118473
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.504990 Loss1: 2.390158 Loss2: 0.114832
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.436021 Loss1: 2.322543 Loss2: 0.113478
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.393003 Loss1: 2.281694 Loss2: 0.111309
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.308569 Loss1: 2.197315 Loss2: 0.111255
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.219614 Loss1: 2.107673 Loss2: 0.111940
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.161694 Loss1: 2.048745 Loss2: 0.112949
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.110654 Loss1: 1.997354 Loss2: 0.113301
+(DefaultActor pid=1838052) >> Training accuracy: 0.475329
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.271192 Loss1: 2.706923 Loss2: 0.564269
+(DefaultActor pid=1838052) Epoch: 1 Loss: 3.057707 Loss1: 2.520843 Loss2: 0.536865
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.949954 Loss1: 2.427119 Loss2: 0.522836
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.865103 Loss1: 2.354588 Loss2: 0.510515
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.785164 Loss1: 2.280804 Loss2: 0.504360
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.737701 Loss1: 2.238102 Loss2: 0.499598
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.649067 Loss1: 2.152446 Loss2: 0.496620
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.610668 Loss1: 2.115667 Loss2: 0.495001
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.544845 Loss1: 2.053600 Loss2: 0.491244
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.489181 Loss1: 2.000086 Loss2: 0.489094
+(DefaultActor pid=1838052) >> Training accuracy: 0.468750
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.655289 Loss1: 2.612728 Loss2: 0.042561
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.402221 Loss1: 2.360754 Loss2: 0.041467
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.352630 Loss1: 2.310891 Loss2: 0.041739
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.286953 Loss1: 2.245093 Loss2: 0.041860
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.187729 Loss1: 2.145578 Loss2: 0.042151
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.102283 Loss1: 2.059197 Loss2: 0.043086
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.056768 Loss1: 2.013463 Loss2: 0.043304
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.011705 Loss1: 1.966885 Loss2: 0.044819
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.928871 Loss1: 1.884365 Loss2: 0.044506
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.879843 Loss1: 1.834284 Loss2: 0.045559
+(DefaultActor pid=1838052) >> Training accuracy: 0.484164
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.716554 Loss1: 2.675632 Loss2: 0.040922
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.485425 Loss1: 2.444461 Loss2: 0.040964
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.417044 Loss1: 2.376554 Loss2: 0.040490
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.310046 Loss1: 2.269425 Loss2: 0.040620
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.257078 Loss1: 2.215514 Loss2: 0.041564
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.169135 Loss1: 2.126211 Loss2: 0.042925
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.112179 Loss1: 2.068664 Loss2: 0.043515
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.037349 Loss1: 1.991942 Loss2: 0.045408
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.022126 Loss1: 1.976244 Loss2: 0.045882
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.964382 Loss1: 1.917693 Loss2: 0.046688
+(DefaultActor pid=1838052) >> Training accuracy: 0.520174
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.640774 Loss1: 2.598306 Loss2: 0.042467
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.394114 Loss1: 2.351851 Loss2: 0.042263
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.327695 Loss1: 2.285256 Loss2: 0.042439
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.233984 Loss1: 2.191664 Loss2: 0.042320
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.182790 Loss1: 2.138782 Loss2: 0.044007
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.066764 Loss1: 2.022676 Loss2: 0.044088
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.030041 Loss1: 1.984802 Loss2: 0.045239
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.945564 Loss1: 1.899846 Loss2: 0.045718
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.914925 Loss1: 1.867621 Loss2: 0.047304
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.850702 Loss1: 1.802917 Loss2: 0.047786
+(DefaultActor pid=1838052) >> Training accuracy: 0.510200
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.702155 Loss1: 2.657159 Loss2: 0.044996
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.469334 Loss1: 2.425178 Loss2: 0.044156
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.374899 Loss1: 2.330835 Loss2: 0.044064
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.311232 Loss1: 2.265749 Loss2: 0.045483
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.233758 Loss1: 2.186783 Loss2: 0.046975
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.170333 Loss1: 2.122493 Loss2: 0.047840
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.129544 Loss1: 2.080849 Loss2: 0.048694
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.047190 Loss1: 1.997600 Loss2: 0.049590
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.965713 Loss1: 1.916071 Loss2: 0.049642
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.935802 Loss1: 1.884039 Loss2: 0.051763
+(DefaultActor pid=1838052) >> Training accuracy: 0.484756
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 09:26:44,172][flwr][DEBUG] - fit_round 6 received 10 results and 0 failures
+>> Test accuracy: 0.265600
+[2023-09-27 09:27:27,904][flwr][INFO] - fit progress: (6, 2.9990923823639988, {'accuracy': 0.2656}, 11270.794376714155)
+[2023-09-27 09:27:27,905][flwr][DEBUG] - evaluate_round 6: strategy sampled 10 clients (out of 10)
+[2023-09-27 09:28:06,801][flwr][DEBUG] - evaluate_round 6 received 10 results and 0 failures
+[2023-09-27 09:28:06,802][flwr][DEBUG] - fit_round 7: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 3.038368 Loss1: 2.562169 Loss2: 0.476199
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.746011 Loss1: 2.317340 Loss2: 0.428671
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.675241 Loss1: 2.259863 Loss2: 0.415379
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.534589 Loss1: 2.128173 Loss2: 0.406416
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.485553 Loss1: 2.086853 Loss2: 0.398700
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.395840 Loss1: 1.996251 Loss2: 0.399589
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.302802 Loss1: 1.904951 Loss2: 0.397851
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.240049 Loss1: 1.845210 Loss2: 0.394839
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.199316 Loss1: 1.804891 Loss2: 0.394426
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.124580 Loss1: 1.730497 Loss2: 0.394083
+(DefaultActor pid=1838052) >> Training accuracy: 0.549753
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.888956 Loss1: 2.357722 Loss2: 0.531234
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.663728 Loss1: 2.152318 Loss2: 0.511410
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.542977 Loss1: 2.049075 Loss2: 0.493902
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.427323 Loss1: 1.940012 Loss2: 0.487311
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.337680 Loss1: 1.858943 Loss2: 0.478737
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.261514 Loss1: 1.784681 Loss2: 0.476833
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.190657 Loss1: 1.718342 Loss2: 0.472315
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.132809 Loss1: 1.664659 Loss2: 0.468150
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.052751 Loss1: 1.586620 Loss2: 0.466131
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.034692 Loss1: 1.569798 Loss2: 0.464894
+(DefaultActor pid=1838052) >> Training accuracy: 0.573134
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.437739 Loss1: 2.390755 Loss2: 0.046984
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.202863 Loss1: 2.155630 Loss2: 0.047233
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.101550 Loss1: 2.054831 Loss2: 0.046719
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.041015 Loss1: 1.993579 Loss2: 0.047435
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.975215 Loss1: 1.927540 Loss2: 0.047675
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.891276 Loss1: 1.843435 Loss2: 0.047841
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.818787 Loss1: 1.770216 Loss2: 0.048571
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.765475 Loss1: 1.715332 Loss2: 0.050143
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.701571 Loss1: 1.650092 Loss2: 0.051479
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.656374 Loss1: 1.605174 Loss2: 0.051200
+(DefaultActor pid=1838052) >> Training accuracy: 0.562896
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.945608 Loss1: 2.411046 Loss2: 0.534562
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.695073 Loss1: 2.191803 Loss2: 0.503270
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.587890 Loss1: 2.106691 Loss2: 0.481198
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.501471 Loss1: 2.031273 Loss2: 0.470198
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.414115 Loss1: 1.948835 Loss2: 0.465280
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.332738 Loss1: 1.874193 Loss2: 0.458545
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.271809 Loss1: 1.819450 Loss2: 0.452359
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.240149 Loss1: 1.788653 Loss2: 0.451496
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.149713 Loss1: 1.702144 Loss2: 0.447569
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.105232 Loss1: 1.659092 Loss2: 0.446140
+(DefaultActor pid=1838052) >> Training accuracy: 0.530854
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.928966 Loss1: 2.397249 Loss2: 0.531717
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.651359 Loss1: 2.145323 Loss2: 0.506036
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.541983 Loss1: 2.057517 Loss2: 0.484466
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.455260 Loss1: 1.982334 Loss2: 0.472926
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.377887 Loss1: 1.910885 Loss2: 0.467002
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.316031 Loss1: 1.853550 Loss2: 0.462481
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.277933 Loss1: 1.816566 Loss2: 0.461367
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.192708 Loss1: 1.737410 Loss2: 0.455299
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.125546 Loss1: 1.673634 Loss2: 0.451911
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.064328 Loss1: 1.610872 Loss2: 0.453456
+(DefaultActor pid=1838052) >> Training accuracy: 0.587416
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.507867 Loss1: 2.462027 Loss2: 0.045841
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.297519 Loss1: 2.250985 Loss2: 0.046534
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.211698 Loss1: 2.164862 Loss2: 0.046836
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.097358 Loss1: 2.050977 Loss2: 0.046381
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.084012 Loss1: 2.036266 Loss2: 0.047746
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.983402 Loss1: 1.934274 Loss2: 0.049128
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.905383 Loss1: 1.856372 Loss2: 0.049011
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.863379 Loss1: 1.813138 Loss2: 0.050241
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.780735 Loss1: 1.729572 Loss2: 0.051164
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.718726 Loss1: 1.666463 Loss2: 0.052263
+(DefaultActor pid=1838052) >> Training accuracy: 0.554688
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.901962 Loss1: 2.363081 Loss2: 0.538881
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.677309 Loss1: 2.163601 Loss2: 0.513708
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.572973 Loss1: 2.072346 Loss2: 0.500626
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.439842 Loss1: 1.951545 Loss2: 0.488297
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.378833 Loss1: 1.896198 Loss2: 0.482635
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.300539 Loss1: 1.821762 Loss2: 0.478778
+(DefaultActor pid=1838052) Epoch: 6 Loss: 2.233892 Loss1: 1.759312 Loss2: 0.474581
+(DefaultActor pid=1838052) Epoch: 7 Loss: 2.185474 Loss1: 1.710874 Loss2: 0.474600
+(DefaultActor pid=1838052) Epoch: 8 Loss: 2.128373 Loss1: 1.655531 Loss2: 0.472843
+(DefaultActor pid=1838052) Epoch: 9 Loss: 2.048312 Loss1: 1.583787 Loss2: 0.464524
+(DefaultActor pid=1838052) >> Training accuracy: 0.565665
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.320779 Loss1: 2.275763 Loss2: 0.045016
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.100775 Loss1: 2.055190 Loss2: 0.045585
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.014105 Loss1: 1.968281 Loss2: 0.045824
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.910302 Loss1: 1.864589 Loss2: 0.045712
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.851738 Loss1: 1.804991 Loss2: 0.046747
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.808684 Loss1: 1.762045 Loss2: 0.046639
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.773843 Loss1: 1.725070 Loss2: 0.048772
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.689813 Loss1: 1.640815 Loss2: 0.048997
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.668106 Loss1: 1.618305 Loss2: 0.049801
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.557219 Loss1: 1.507442 Loss2: 0.049777
+(DefaultActor pid=1838052) >> Training accuracy: 0.594151
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.496745 Loss1: 2.449957 Loss2: 0.046788
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.240896 Loss1: 2.193993 Loss2: 0.046902
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.139938 Loss1: 2.094332 Loss2: 0.045606
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.048838 Loss1: 2.002012 Loss2: 0.046826
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.011080 Loss1: 1.963032 Loss2: 0.048048
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.907839 Loss1: 1.860163 Loss2: 0.047676
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.823823 Loss1: 1.775656 Loss2: 0.048167
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.796489 Loss1: 1.746802 Loss2: 0.049687
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.736592 Loss1: 1.686067 Loss2: 0.050525
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.672962 Loss1: 1.621122 Loss2: 0.051840
+(DefaultActor pid=1838052) >> Training accuracy: 0.569027
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.426663 Loss1: 2.384081 Loss2: 0.042582
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.195642 Loss1: 2.152860 Loss2: 0.042782
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.124190 Loss1: 2.080782 Loss2: 0.043408
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.020221 Loss1: 1.976717 Loss2: 0.043504
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.967469 Loss1: 1.921816 Loss2: 0.045653
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.906630 Loss1: 1.860995 Loss2: 0.045635
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.816724 Loss1: 1.771206 Loss2: 0.045518
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.738499 Loss1: 1.691252 Loss2: 0.047246
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.703427 Loss1: 1.655645 Loss2: 0.047782
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.624083 Loss1: 1.576412 Loss2: 0.047671
+(DefaultActor pid=1838052) >> Training accuracy: 0.596037
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 10:06:13,486][flwr][DEBUG] - fit_round 7 received 10 results and 0 failures
+>> Test accuracy: 0.327600
+[2023-09-27 10:13:56,312][flwr][INFO] - fit progress: (7, 2.694728255652772, {'accuracy': 0.3276}, 14059.20233049104)
+[2023-09-27 10:13:56,313][flwr][DEBUG] - evaluate_round 7: strategy sampled 10 clients (out of 10)
+[2023-09-27 10:14:41,267][flwr][DEBUG] - evaluate_round 7 received 10 results and 0 failures
+[2023-09-27 10:14:41,268][flwr][DEBUG] - fit_round 8: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.709258 Loss1: 2.193764 Loss2: 0.515494
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.450222 Loss1: 1.985959 Loss2: 0.464263
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.308524 Loss1: 1.860867 Loss2: 0.447658
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.224968 Loss1: 1.790743 Loss2: 0.434225
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.125124 Loss1: 1.697002 Loss2: 0.428122
+(DefaultActor pid=1838052) Epoch: 5 Loss: 2.040160 Loss1: 1.615971 Loss2: 0.424189
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.955509 Loss1: 1.532119 Loss2: 0.423391
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.929111 Loss1: 1.509179 Loss2: 0.419932
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.880534 Loss1: 1.460954 Loss2: 0.419580
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.817624 Loss1: 1.396569 Loss2: 0.421056
+(DefaultActor pid=1838052) >> Training accuracy: 0.621570
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.105405 Loss1: 2.058321 Loss2: 0.047084
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.905677 Loss1: 1.858115 Loss2: 0.047562
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.784470 Loss1: 1.736776 Loss2: 0.047694
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.713247 Loss1: 1.665482 Loss2: 0.047765
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.645971 Loss1: 1.597550 Loss2: 0.048421
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.559744 Loss1: 1.511669 Loss2: 0.048075
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.501264 Loss1: 1.451665 Loss2: 0.049599
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.445245 Loss1: 1.395123 Loss2: 0.050122
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.416307 Loss1: 1.365615 Loss2: 0.050692
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.376832 Loss1: 1.324568 Loss2: 0.052264
+(DefaultActor pid=1838052) >> Training accuracy: 0.654046
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.538070 Loss1: 2.184997 Loss2: 0.353073
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.293320 Loss1: 1.991480 Loss2: 0.301840
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.198845 Loss1: 1.904147 Loss2: 0.294698
+(DefaultActor pid=1838052) Epoch: 3 Loss: 2.088673 Loss1: 1.795117 Loss2: 0.293556
+(DefaultActor pid=1838052) Epoch: 4 Loss: 2.041745 Loss1: 1.752587 Loss2: 0.289158
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.933712 Loss1: 1.643607 Loss2: 0.290105
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.857243 Loss1: 1.566921 Loss2: 0.290322
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.790275 Loss1: 1.499669 Loss2: 0.290606
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.767002 Loss1: 1.477334 Loss2: 0.289667
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.670968 Loss1: 1.382848 Loss2: 0.288120
+(DefaultActor pid=1838052) >> Training accuracy: 0.609771
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.229676 Loss1: 2.180633 Loss2: 0.049043
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.999429 Loss1: 1.950458 Loss2: 0.048971
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.879991 Loss1: 1.832707 Loss2: 0.047284
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.788700 Loss1: 1.739867 Loss2: 0.048833
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.737861 Loss1: 1.688916 Loss2: 0.048946
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.653562 Loss1: 1.603552 Loss2: 0.050010
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.594129 Loss1: 1.543742 Loss2: 0.050388
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.529029 Loss1: 1.478005 Loss2: 0.051024
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.466471 Loss1: 1.415306 Loss2: 0.051165
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.427690 Loss1: 1.374947 Loss2: 0.052743
+(DefaultActor pid=1838052) >> Training accuracy: 0.622627
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.324779 Loss1: 2.273377 Loss2: 0.051402
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.085034 Loss1: 2.034113 Loss2: 0.050921
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.960676 Loss1: 1.911133 Loss2: 0.049543
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.878204 Loss1: 1.828605 Loss2: 0.049598
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.806229 Loss1: 1.755470 Loss2: 0.050759
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.752396 Loss1: 1.701374 Loss2: 0.051023
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.641017 Loss1: 1.589424 Loss2: 0.051593
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.616418 Loss1: 1.563836 Loss2: 0.052583
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.536063 Loss1: 1.482626 Loss2: 0.053438
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.484237 Loss1: 1.429649 Loss2: 0.054588
+(DefaultActor pid=1838052) >> Training accuracy: 0.627404
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.212654 Loss1: 2.161069 Loss2: 0.051585
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.970997 Loss1: 1.921090 Loss2: 0.049906
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.848390 Loss1: 1.798299 Loss2: 0.050091
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.776417 Loss1: 1.727323 Loss2: 0.049094
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.662752 Loss1: 1.614086 Loss2: 0.048666
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.643215 Loss1: 1.592441 Loss2: 0.050774
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.562824 Loss1: 1.512461 Loss2: 0.050364
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.519930 Loss1: 1.467670 Loss2: 0.052261
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.467383 Loss1: 1.415635 Loss2: 0.051748
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.416752 Loss1: 1.364691 Loss2: 0.052061
+(DefaultActor pid=1838052) >> Training accuracy: 0.605574
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.287485 Loss1: 2.192550 Loss2: 0.094935
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.080599 Loss1: 1.992231 Loss2: 0.088367
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.927901 Loss1: 1.844398 Loss2: 0.083503
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.848616 Loss1: 1.766995 Loss2: 0.081621
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.752736 Loss1: 1.673596 Loss2: 0.079140
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.666515 Loss1: 1.587857 Loss2: 0.078659
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.639753 Loss1: 1.560818 Loss2: 0.078935
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.596466 Loss1: 1.517104 Loss2: 0.079362
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.473657 Loss1: 1.394624 Loss2: 0.079033
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.442232 Loss1: 1.362043 Loss2: 0.080189
+(DefaultActor pid=1838052) >> Training accuracy: 0.628362
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.436166 Loss1: 2.382002 Loss2: 0.054165
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.124762 Loss1: 2.071675 Loss2: 0.053087
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.021695 Loss1: 1.968917 Loss2: 0.052779
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.889169 Loss1: 1.836666 Loss2: 0.052503
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.841533 Loss1: 1.788870 Loss2: 0.052664
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.781700 Loss1: 1.728068 Loss2: 0.053631
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.711755 Loss1: 1.657249 Loss2: 0.054506
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.617402 Loss1: 1.563040 Loss2: 0.054362
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.595740 Loss1: 1.539876 Loss2: 0.055864
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.565929 Loss1: 1.509126 Loss2: 0.056802
+(DefaultActor pid=1838052) >> Training accuracy: 0.578125
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.191149 Loss1: 2.140179 Loss2: 0.050971
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.984707 Loss1: 1.932545 Loss2: 0.052162
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.852725 Loss1: 1.801735 Loss2: 0.050989
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.796879 Loss1: 1.745036 Loss2: 0.051843
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.691202 Loss1: 1.639753 Loss2: 0.051449
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.613612 Loss1: 1.561049 Loss2: 0.052562
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.614165 Loss1: 1.561582 Loss2: 0.052583
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.494153 Loss1: 1.441415 Loss2: 0.052738
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.457285 Loss1: 1.402657 Loss2: 0.054628
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.398477 Loss1: 1.344362 Loss2: 0.054115
+(DefaultActor pid=1838052) >> Training accuracy: 0.618078
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.197662 Loss1: 2.145249 Loss2: 0.052413
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.952747 Loss1: 1.899454 Loss2: 0.053292
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.823356 Loss1: 1.771653 Loss2: 0.051703
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.720134 Loss1: 1.668449 Loss2: 0.051684
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.633180 Loss1: 1.580959 Loss2: 0.052222
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.594015 Loss1: 1.542053 Loss2: 0.051962
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.546247 Loss1: 1.492758 Loss2: 0.053489
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.460070 Loss1: 1.407149 Loss2: 0.052921
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.414514 Loss1: 1.360665 Loss2: 0.053849
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.344694 Loss1: 1.290703 Loss2: 0.053991
+(DefaultActor pid=1838052) >> Training accuracy: 0.652561
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 10:44:40,445][flwr][DEBUG] - fit_round 8 received 10 results and 0 failures
+>> Test accuracy: 0.363100
+[2023-09-27 10:45:21,070][flwr][INFO] - fit progress: (8, 2.53471215883383, {'accuracy': 0.3631}, 15943.95996950008)
+[2023-09-27 10:45:21,070][flwr][DEBUG] - evaluate_round 8: strategy sampled 10 clients (out of 10)
+[2023-09-27 10:45:57,406][flwr][DEBUG] - evaluate_round 8 received 10 results and 0 failures
+[2023-09-27 10:45:57,408][flwr][DEBUG] - fit_round 9: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.483164 Loss1: 2.022059 Loss2: 0.461105
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.174246 Loss1: 1.761471 Loss2: 0.412776
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.076548 Loss1: 1.673937 Loss2: 0.402611
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.992947 Loss1: 1.595190 Loss2: 0.397757
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.920922 Loss1: 1.522376 Loss2: 0.398546
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.836413 Loss1: 1.444410 Loss2: 0.392002
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.757734 Loss1: 1.361894 Loss2: 0.395841
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.714487 Loss1: 1.321761 Loss2: 0.392726
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.692480 Loss1: 1.296318 Loss2: 0.396162
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.640328 Loss1: 1.243931 Loss2: 0.396396
+(DefaultActor pid=1838052) >> Training accuracy: 0.673457
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.291617 Loss1: 1.996569 Loss2: 0.295049
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.013461 Loss1: 1.759975 Loss2: 0.253486
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.893522 Loss1: 1.643657 Loss2: 0.249864
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.797450 Loss1: 1.547480 Loss2: 0.249970
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.730625 Loss1: 1.481469 Loss2: 0.249156
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.666812 Loss1: 1.416942 Loss2: 0.249870
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.581224 Loss1: 1.333046 Loss2: 0.248178
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.536576 Loss1: 1.286189 Loss2: 0.250387
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.498855 Loss1: 1.249098 Loss2: 0.249756
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.450144 Loss1: 1.199807 Loss2: 0.250336
+(DefaultActor pid=1838052) >> Training accuracy: 0.703125
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.032739 Loss1: 1.987099 Loss2: 0.045640
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.792446 Loss1: 1.745455 Loss2: 0.046992
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.694991 Loss1: 1.647607 Loss2: 0.047384
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.588681 Loss1: 1.541860 Loss2: 0.046821
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.514134 Loss1: 1.466740 Loss2: 0.047393
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.462381 Loss1: 1.414918 Loss2: 0.047463
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.409842 Loss1: 1.360360 Loss2: 0.049482
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.321396 Loss1: 1.272115 Loss2: 0.049281
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.265217 Loss1: 1.215398 Loss2: 0.049819
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.240747 Loss1: 1.190683 Loss2: 0.050064
+(DefaultActor pid=1838052) >> Training accuracy: 0.687302
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.073298 Loss1: 2.015616 Loss2: 0.057683
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.804739 Loss1: 1.747477 Loss2: 0.057262
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.725224 Loss1: 1.667854 Loss2: 0.057370
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.625716 Loss1: 1.568936 Loss2: 0.056780
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.579494 Loss1: 1.522323 Loss2: 0.057171
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.491105 Loss1: 1.434163 Loss2: 0.056941
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.426014 Loss1: 1.368936 Loss2: 0.057078
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.361619 Loss1: 1.304072 Loss2: 0.057547
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.303245 Loss1: 1.245014 Loss2: 0.058231
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.270276 Loss1: 1.210905 Loss2: 0.059371
+(DefaultActor pid=1838052) >> Training accuracy: 0.645174
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.505409 Loss1: 1.971813 Loss2: 0.533596
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.219221 Loss1: 1.716395 Loss2: 0.502826
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.062575 Loss1: 1.579922 Loss2: 0.482653
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.964055 Loss1: 1.492236 Loss2: 0.471818
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.905229 Loss1: 1.438894 Loss2: 0.466335
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.800004 Loss1: 1.339459 Loss2: 0.460545
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.779953 Loss1: 1.324905 Loss2: 0.455048
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.650619 Loss1: 1.198204 Loss2: 0.452415
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.674609 Loss1: 1.220895 Loss2: 0.453714
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.565232 Loss1: 1.110312 Loss2: 0.454920
+(DefaultActor pid=1838052) >> Training accuracy: 0.669705
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.044502 Loss1: 1.949320 Loss2: 0.095183
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.821164 Loss1: 1.732977 Loss2: 0.088187
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.713148 Loss1: 1.627787 Loss2: 0.085361
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.583881 Loss1: 1.501918 Loss2: 0.081963
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.563371 Loss1: 1.481926 Loss2: 0.081445
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.434184 Loss1: 1.353921 Loss2: 0.080263
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.435732 Loss1: 1.354814 Loss2: 0.080918
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.330903 Loss1: 1.250673 Loss2: 0.080230
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.273577 Loss1: 1.192867 Loss2: 0.080710
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.231068 Loss1: 1.150285 Loss2: 0.080784
+(DefaultActor pid=1838052) >> Training accuracy: 0.710047
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.207649 Loss1: 2.160165 Loss2: 0.047484
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.940629 Loss1: 1.893360 Loss2: 0.047270
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.800931 Loss1: 1.754174 Loss2: 0.046757
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.718512 Loss1: 1.671113 Loss2: 0.047400
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.630826 Loss1: 1.582458 Loss2: 0.048368
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.571100 Loss1: 1.523248 Loss2: 0.047852
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.491830 Loss1: 1.441916 Loss2: 0.049914
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.426030 Loss1: 1.376627 Loss2: 0.049403
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.380751 Loss1: 1.330678 Loss2: 0.050072
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.318328 Loss1: 1.267029 Loss2: 0.051299
+(DefaultActor pid=1838052) >> Training accuracy: 0.646587
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.017901 Loss1: 1.972448 Loss2: 0.045454
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.761279 Loss1: 1.715932 Loss2: 0.045347
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.638405 Loss1: 1.593540 Loss2: 0.044865
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.575547 Loss1: 1.529818 Loss2: 0.045729
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.501544 Loss1: 1.455872 Loss2: 0.045672
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.432503 Loss1: 1.385774 Loss2: 0.046729
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.367846 Loss1: 1.319872 Loss2: 0.047974
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.293503 Loss1: 1.245790 Loss2: 0.047713
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.260436 Loss1: 1.211115 Loss2: 0.049321
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.217975 Loss1: 1.168225 Loss2: 0.049750
+(DefaultActor pid=1838052) >> Training accuracy: 0.685600
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.935895 Loss1: 1.892041 Loss2: 0.043854
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.689612 Loss1: 1.644613 Loss2: 0.044999
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.585124 Loss1: 1.539809 Loss2: 0.045315
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.498261 Loss1: 1.453278 Loss2: 0.044983
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.438307 Loss1: 1.392096 Loss2: 0.046211
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.349123 Loss1: 1.301974 Loss2: 0.047149
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.300648 Loss1: 1.253797 Loss2: 0.046850
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.251814 Loss1: 1.203828 Loss2: 0.047986
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.180361 Loss1: 1.132411 Loss2: 0.047950
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.146891 Loss1: 1.097750 Loss2: 0.049141
+(DefaultActor pid=1838052) >> Training accuracy: 0.723558
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.112665 Loss1: 2.065152 Loss2: 0.047514
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.873670 Loss1: 1.826792 Loss2: 0.046877
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.739122 Loss1: 1.691739 Loss2: 0.047383
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.666848 Loss1: 1.618717 Loss2: 0.048130
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.558942 Loss1: 1.511299 Loss2: 0.047643
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.493940 Loss1: 1.445721 Loss2: 0.048218
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.425809 Loss1: 1.377186 Loss2: 0.048622
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.391005 Loss1: 1.341315 Loss2: 0.049690
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.319018 Loss1: 1.268520 Loss2: 0.050498
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.264777 Loss1: 1.213900 Loss2: 0.050876
+(DefaultActor pid=1838052) >> Training accuracy: 0.655048
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 11:15:44,745][flwr][DEBUG] - fit_round 9 received 10 results and 0 failures
+>> Test accuracy: 0.402800
+[2023-09-27 11:16:27,246][flwr][INFO] - fit progress: (9, 2.3920893143541133, {'accuracy': 0.4028}, 17810.13637669524)
+[2023-09-27 11:16:27,247][flwr][DEBUG] - evaluate_round 9: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:17:06,115][flwr][DEBUG] - evaluate_round 9 received 10 results and 0 failures
+[2023-09-27 11:17:06,116][flwr][DEBUG] - fit_round 10: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.862358 Loss1: 1.811308 Loss2: 0.051051
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.528035 Loss1: 1.477194 Loss2: 0.050841
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.440354 Loss1: 1.389661 Loss2: 0.050694
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.341792 Loss1: 1.291691 Loss2: 0.050102
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.243090 Loss1: 1.193546 Loss2: 0.049543
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.248569 Loss1: 1.197097 Loss2: 0.051472
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.125267 Loss1: 1.074977 Loss2: 0.050290
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.122117 Loss1: 1.070431 Loss2: 0.051685
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.043366 Loss1: 0.991470 Loss2: 0.051896
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.992677 Loss1: 0.940407 Loss2: 0.052271
+(DefaultActor pid=1838052) >> Training accuracy: 0.747613
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.435301 Loss1: 1.877486 Loss2: 0.557815
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.133092 Loss1: 1.596880 Loss2: 0.536212
+(DefaultActor pid=1838052) Epoch: 2 Loss: 2.011163 Loss1: 1.492553 Loss2: 0.518610
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.893599 Loss1: 1.384484 Loss2: 0.509115
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.823359 Loss1: 1.321671 Loss2: 0.501688
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.721987 Loss1: 1.226574 Loss2: 0.495413
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.658681 Loss1: 1.162960 Loss2: 0.495721
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.612958 Loss1: 1.119015 Loss2: 0.493943
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.580621 Loss1: 1.087977 Loss2: 0.492644
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.490550 Loss1: 1.001240 Loss2: 0.489311
+(DefaultActor pid=1838052) >> Training accuracy: 0.728463
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.283596 Loss1: 1.721090 Loss2: 0.562506
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.059127 Loss1: 1.508027 Loss2: 0.551100
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.985261 Loss1: 1.446468 Loss2: 0.538793
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.875150 Loss1: 1.346869 Loss2: 0.528281
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.777953 Loss1: 1.254683 Loss2: 0.523271
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.708697 Loss1: 1.192789 Loss2: 0.515908
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.630432 Loss1: 1.117521 Loss2: 0.512911
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.608310 Loss1: 1.096894 Loss2: 0.511416
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.560256 Loss1: 1.052741 Loss2: 0.507514
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.485672 Loss1: 0.981775 Loss2: 0.503898
+(DefaultActor pid=1838052) >> Training accuracy: 0.761018
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.827917 Loss1: 1.782943 Loss2: 0.044974
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.598348 Loss1: 1.552477 Loss2: 0.045871
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.501987 Loss1: 1.456168 Loss2: 0.045819
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.386020 Loss1: 1.339919 Loss2: 0.046101
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.353128 Loss1: 1.305820 Loss2: 0.047308
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.260229 Loss1: 1.212748 Loss2: 0.047481
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.213464 Loss1: 1.165445 Loss2: 0.048019
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.130631 Loss1: 1.081682 Loss2: 0.048949
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.120580 Loss1: 1.070213 Loss2: 0.050366
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.031097 Loss1: 0.981006 Loss2: 0.050091
+(DefaultActor pid=1838052) >> Training accuracy: 0.729230
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.964355 Loss1: 1.918646 Loss2: 0.045709
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.680616 Loss1: 1.634708 Loss2: 0.045908
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.574703 Loss1: 1.528838 Loss2: 0.045865
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.476901 Loss1: 1.430997 Loss2: 0.045905
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.378372 Loss1: 1.331878 Loss2: 0.046494
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.320519 Loss1: 1.274074 Loss2: 0.046445
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.235180 Loss1: 1.187672 Loss2: 0.047509
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.199375 Loss1: 1.151468 Loss2: 0.047906
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.130082 Loss1: 1.081482 Loss2: 0.048600
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.051851 Loss1: 1.003203 Loss2: 0.048648
+(DefaultActor pid=1838052) >> Training accuracy: 0.691506
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.102657 Loss1: 1.986094 Loss2: 0.116563
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.802432 Loss1: 1.693205 Loss2: 0.109227
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.693528 Loss1: 1.589958 Loss2: 0.103570
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.586806 Loss1: 1.486075 Loss2: 0.100731
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.511076 Loss1: 1.411874 Loss2: 0.099202
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.435536 Loss1: 1.339157 Loss2: 0.096379
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.336721 Loss1: 1.240369 Loss2: 0.096352
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.322896 Loss1: 1.227519 Loss2: 0.095377
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.280009 Loss1: 1.184508 Loss2: 0.095501
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.224980 Loss1: 1.128843 Loss2: 0.096138
+(DefaultActor pid=1838052) >> Training accuracy: 0.689556
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.874148 Loss1: 1.827384 Loss2: 0.046764
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.653649 Loss1: 1.606344 Loss2: 0.047305
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.519549 Loss1: 1.473262 Loss2: 0.046288
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.439325 Loss1: 1.391900 Loss2: 0.047424
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.321988 Loss1: 1.274736 Loss2: 0.047252
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.295450 Loss1: 1.247660 Loss2: 0.047791
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.207968 Loss1: 1.160306 Loss2: 0.047662
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.182043 Loss1: 1.132560 Loss2: 0.049483
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.118976 Loss1: 1.068770 Loss2: 0.050206
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.064214 Loss1: 1.013434 Loss2: 0.050780
+(DefaultActor pid=1838052) >> Training accuracy: 0.723695
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.868656 Loss1: 1.826229 Loss2: 0.042426
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.609726 Loss1: 1.566389 Loss2: 0.043337
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.491913 Loss1: 1.448195 Loss2: 0.043718
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.437801 Loss1: 1.393220 Loss2: 0.044581
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.328415 Loss1: 1.284275 Loss2: 0.044139
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.257426 Loss1: 1.212318 Loss2: 0.045108
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.225654 Loss1: 1.180080 Loss2: 0.045574
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.154266 Loss1: 1.108592 Loss2: 0.045673
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.104742 Loss1: 1.058727 Loss2: 0.046016
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.065420 Loss1: 1.018403 Loss2: 0.047016
+(DefaultActor pid=1838052) >> Training accuracy: 0.692445
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.921002 Loss1: 1.874309 Loss2: 0.046693
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.617948 Loss1: 1.570759 Loss2: 0.047189
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.524450 Loss1: 1.477341 Loss2: 0.047108
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.436319 Loss1: 1.389338 Loss2: 0.046980
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.341560 Loss1: 1.294709 Loss2: 0.046851
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.262671 Loss1: 1.214963 Loss2: 0.047708
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.208369 Loss1: 1.159713 Loss2: 0.048656
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.147788 Loss1: 1.099275 Loss2: 0.048512
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.096264 Loss1: 1.047530 Loss2: 0.048734
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.112723 Loss1: 1.061931 Loss2: 0.050792
+(DefaultActor pid=1838052) >> Training accuracy: 0.721519
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.831443 Loss1: 1.785154 Loss2: 0.046289
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.618005 Loss1: 1.570813 Loss2: 0.047193
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.467992 Loss1: 1.420834 Loss2: 0.047157
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.412969 Loss1: 1.365451 Loss2: 0.047518
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.322851 Loss1: 1.275421 Loss2: 0.047430
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.244911 Loss1: 1.196595 Loss2: 0.048316
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.206775 Loss1: 1.157975 Loss2: 0.048800
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.148915 Loss1: 1.099588 Loss2: 0.049327
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.076589 Loss1: 1.027516 Loss2: 0.049073
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.013728 Loss1: 0.964659 Loss2: 0.049069
+(DefaultActor pid=1838052) >> Training accuracy: 0.703916
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 11:46:55,258][flwr][DEBUG] - fit_round 10 received 10 results and 0 failures
+>> Test accuracy: 0.432500
+[2023-09-27 11:47:38,282][flwr][INFO] - fit progress: (10, 2.306128243287912, {'accuracy': 0.4325}, 19681.17216801131)
+[2023-09-27 11:47:38,282][flwr][DEBUG] - evaluate_round 10: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:48:15,010][flwr][DEBUG] - evaluate_round 10 received 10 results and 0 failures
+[2023-09-27 11:48:15,011][flwr][DEBUG] - fit_round 11: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.228839 Loss1: 1.690956 Loss2: 0.537883
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.972339 Loss1: 1.449610 Loss2: 0.522729
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.827159 Loss1: 1.322654 Loss2: 0.504505
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.732215 Loss1: 1.235593 Loss2: 0.496622
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.638652 Loss1: 1.147168 Loss2: 0.491484
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.610436 Loss1: 1.126918 Loss2: 0.483518
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.554755 Loss1: 1.071154 Loss2: 0.483601
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.469402 Loss1: 0.990560 Loss2: 0.478842
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.403971 Loss1: 0.927886 Loss2: 0.476085
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.435490 Loss1: 0.960198 Loss2: 0.475292
+(DefaultActor pid=1838052) >> Training accuracy: 0.773932
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.287210 Loss1: 1.733411 Loss2: 0.553799
+(DefaultActor pid=1838052) Epoch: 1 Loss: 2.069189 Loss1: 1.524927 Loss2: 0.544262
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.907554 Loss1: 1.375822 Loss2: 0.531732
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.830269 Loss1: 1.304631 Loss2: 0.525638
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.727497 Loss1: 1.212253 Loss2: 0.515244
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.635794 Loss1: 1.122507 Loss2: 0.513287
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.603246 Loss1: 1.093685 Loss2: 0.509561
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.507037 Loss1: 0.999390 Loss2: 0.507647
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.473524 Loss1: 0.970642 Loss2: 0.502882
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.449859 Loss1: 0.949968 Loss2: 0.499891
+(DefaultActor pid=1838052) >> Training accuracy: 0.729567
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.783118 Loss1: 1.675418 Loss2: 0.107700
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.536141 Loss1: 1.434422 Loss2: 0.101719
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.404730 Loss1: 1.309178 Loss2: 0.095552
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.290162 Loss1: 1.198426 Loss2: 0.091735
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.231024 Loss1: 1.140290 Loss2: 0.090734
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.151973 Loss1: 1.062849 Loss2: 0.089123
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.109323 Loss1: 1.020535 Loss2: 0.088789
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.065294 Loss1: 0.977294 Loss2: 0.088000
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.013230 Loss1: 0.925636 Loss2: 0.087595
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.893710 Loss1: 0.807978 Loss2: 0.085732
+(DefaultActor pid=1838052) >> Training accuracy: 0.783155
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.248558 Loss1: 1.686949 Loss2: 0.561609
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.968134 Loss1: 1.425322 Loss2: 0.542812
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.814263 Loss1: 1.290757 Loss2: 0.523507
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.677921 Loss1: 1.167483 Loss2: 0.510438
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.598105 Loss1: 1.097519 Loss2: 0.500586
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.539657 Loss1: 1.040890 Loss2: 0.498767
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.473407 Loss1: 0.981500 Loss2: 0.491907
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.425616 Loss1: 0.936394 Loss2: 0.489222
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.380020 Loss1: 0.893017 Loss2: 0.487003
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.333665 Loss1: 0.847062 Loss2: 0.486603
+(DefaultActor pid=1838052) >> Training accuracy: 0.766710
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.737535 Loss1: 1.687103 Loss2: 0.050432
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.457572 Loss1: 1.407846 Loss2: 0.049726
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.343201 Loss1: 1.294328 Loss2: 0.048873
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.279532 Loss1: 1.228860 Loss2: 0.050672
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.189242 Loss1: 1.138898 Loss2: 0.050345
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.110622 Loss1: 1.060680 Loss2: 0.049942
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.078308 Loss1: 1.027218 Loss2: 0.051090
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.977282 Loss1: 0.926006 Loss2: 0.051276
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.963781 Loss1: 0.912071 Loss2: 0.051710
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.890512 Loss1: 0.838414 Loss2: 0.052097
+(DefaultActor pid=1838052) >> Training accuracy: 0.762880
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.197270 Loss1: 1.659346 Loss2: 0.537924
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.966318 Loss1: 1.440425 Loss2: 0.525893
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.826330 Loss1: 1.311974 Loss2: 0.514356
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.717596 Loss1: 1.215235 Loss2: 0.502362
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.666870 Loss1: 1.168286 Loss2: 0.498584
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.602386 Loss1: 1.107464 Loss2: 0.494922
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.485532 Loss1: 0.994780 Loss2: 0.490752
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.496632 Loss1: 1.009584 Loss2: 0.487048
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.400894 Loss1: 0.915545 Loss2: 0.485349
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.346589 Loss1: 0.862341 Loss2: 0.484248
+(DefaultActor pid=1838052) >> Training accuracy: 0.746044
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.694693 Loss1: 1.609166 Loss2: 0.085527
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.410883 Loss1: 1.331592 Loss2: 0.079291
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.290418 Loss1: 1.215641 Loss2: 0.074777
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.233516 Loss1: 1.158229 Loss2: 0.075287
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.158469 Loss1: 1.084715 Loss2: 0.073754
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.070362 Loss1: 0.996491 Loss2: 0.073871
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.057596 Loss1: 0.983701 Loss2: 0.073895
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.968375 Loss1: 0.894020 Loss2: 0.074354
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.936037 Loss1: 0.861505 Loss2: 0.074532
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.882501 Loss1: 0.807091 Loss2: 0.075410
+(DefaultActor pid=1838052) >> Training accuracy: 0.783253
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.730202 Loss1: 1.683644 Loss2: 0.046559
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.459240 Loss1: 1.412478 Loss2: 0.046762
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.387769 Loss1: 1.340624 Loss2: 0.047145
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.257527 Loss1: 1.210388 Loss2: 0.047139
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.183747 Loss1: 1.136280 Loss2: 0.047467
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.104424 Loss1: 1.056289 Loss2: 0.048135
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.057739 Loss1: 1.010206 Loss2: 0.047533
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.007800 Loss1: 0.959688 Loss2: 0.048112
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.989570 Loss1: 0.941049 Loss2: 0.048521
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.956839 Loss1: 0.906850 Loss2: 0.049989
+(DefaultActor pid=1838052) >> Training accuracy: 0.722310
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.911975 Loss1: 1.865288 Loss2: 0.046687
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.601775 Loss1: 1.554788 Loss2: 0.046986
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.473948 Loss1: 1.427843 Loss2: 0.046105
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.375845 Loss1: 1.329082 Loss2: 0.046763
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.290734 Loss1: 1.243504 Loss2: 0.047230
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.229256 Loss1: 1.181716 Loss2: 0.047540
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.138686 Loss1: 1.090596 Loss2: 0.048090
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.117314 Loss1: 1.068909 Loss2: 0.048405
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.076808 Loss1: 1.027740 Loss2: 0.049068
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.039699 Loss1: 0.989777 Loss2: 0.049923
+(DefaultActor pid=1838052) >> Training accuracy: 0.709498
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.759646 Loss1: 1.716644 Loss2: 0.043003
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.466085 Loss1: 1.421826 Loss2: 0.044258
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.347210 Loss1: 1.303412 Loss2: 0.043798
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.301649 Loss1: 1.256793 Loss2: 0.044856
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.162983 Loss1: 1.118485 Loss2: 0.044498
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.138937 Loss1: 1.093364 Loss2: 0.045573
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.081846 Loss1: 1.035627 Loss2: 0.046219
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.994340 Loss1: 0.947921 Loss2: 0.046419
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.983943 Loss1: 0.936798 Loss2: 0.047145
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.884830 Loss1: 0.837533 Loss2: 0.047298
+(DefaultActor pid=1838052) >> Training accuracy: 0.734968
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 12:18:06,366][flwr][DEBUG] - fit_round 11 received 10 results and 0 failures
+>> Test accuracy: 0.455400
+[2023-09-27 12:29:06,096][flwr][INFO] - fit progress: (11, 2.207922473883096, {'accuracy': 0.4554}, 22168.98662959831)
+[2023-09-27 12:29:06,097][flwr][DEBUG] - evaluate_round 11: strategy sampled 10 clients (out of 10)
+[2023-09-27 12:29:46,033][flwr][DEBUG] - evaluate_round 11 received 10 results and 0 failures
+[2023-09-27 12:29:46,034][flwr][DEBUG] - fit_round 12: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.696502 Loss1: 1.604173 Loss2: 0.092329
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.382078 Loss1: 1.296494 Loss2: 0.085584
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.268286 Loss1: 1.186455 Loss2: 0.081831
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.145832 Loss1: 1.067608 Loss2: 0.078224
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.084431 Loss1: 1.006865 Loss2: 0.077566
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.025428 Loss1: 0.947773 Loss2: 0.077656
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.936653 Loss1: 0.860779 Loss2: 0.075874
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.885741 Loss1: 0.810211 Loss2: 0.075530
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.873348 Loss1: 0.796690 Loss2: 0.076658
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.779120 Loss1: 0.704078 Loss2: 0.075042
+(DefaultActor pid=1838052) >> Training accuracy: 0.767103
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.139194 Loss1: 1.600817 Loss2: 0.538377
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.850734 Loss1: 1.323443 Loss2: 0.527291
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.730281 Loss1: 1.219105 Loss2: 0.511176
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.598443 Loss1: 1.095692 Loss2: 0.502751
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.543539 Loss1: 1.045003 Loss2: 0.498536
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.470764 Loss1: 0.975742 Loss2: 0.495022
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.452400 Loss1: 0.959250 Loss2: 0.493150
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.373787 Loss1: 0.886306 Loss2: 0.487480
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.330127 Loss1: 0.843642 Loss2: 0.486485
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.286330 Loss1: 0.801427 Loss2: 0.484903
+(DefaultActor pid=1838052) >> Training accuracy: 0.785799
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.604226 Loss1: 1.558196 Loss2: 0.046030
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.329578 Loss1: 1.282138 Loss2: 0.047440
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.228815 Loss1: 1.180986 Loss2: 0.047829
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.122086 Loss1: 1.074898 Loss2: 0.047188
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.046588 Loss1: 0.998788 Loss2: 0.047800
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.974297 Loss1: 0.926425 Loss2: 0.047872
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.915838 Loss1: 0.867125 Loss2: 0.048713
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.865167 Loss1: 0.816418 Loss2: 0.048749
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.873625 Loss1: 0.823902 Loss2: 0.049723
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.801558 Loss1: 0.751349 Loss2: 0.050209
+(DefaultActor pid=1838052) >> Training accuracy: 0.801028
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.269507 Loss1: 1.737487 Loss2: 0.532019
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.946939 Loss1: 1.427895 Loss2: 0.519044
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.818711 Loss1: 1.311530 Loss2: 0.507181
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.734302 Loss1: 1.236181 Loss2: 0.498121
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.613647 Loss1: 1.123944 Loss2: 0.489703
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.555663 Loss1: 1.070264 Loss2: 0.485399
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.476205 Loss1: 0.991962 Loss2: 0.484244
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.438511 Loss1: 0.955854 Loss2: 0.482657
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.370841 Loss1: 0.894179 Loss2: 0.476662
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.357362 Loss1: 0.882079 Loss2: 0.475284
+(DefaultActor pid=1838052) >> Training accuracy: 0.735609
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.619455 Loss1: 1.567618 Loss2: 0.051837
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.296341 Loss1: 1.244463 Loss2: 0.051877
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.163482 Loss1: 1.113277 Loss2: 0.050206
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.084031 Loss1: 1.033791 Loss2: 0.050240
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.981475 Loss1: 0.930854 Loss2: 0.050621
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.931489 Loss1: 0.880837 Loss2: 0.050652
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.887600 Loss1: 0.837607 Loss2: 0.049993
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.860959 Loss1: 0.810067 Loss2: 0.050892
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.797950 Loss1: 0.747796 Loss2: 0.050154
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.781181 Loss1: 0.730698 Loss2: 0.050483
+(DefaultActor pid=1838052) >> Training accuracy: 0.801649
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.580993 Loss1: 1.534829 Loss2: 0.046164
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.346125 Loss1: 1.298024 Loss2: 0.048101
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.170532 Loss1: 1.123020 Loss2: 0.047512
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.114665 Loss1: 1.066870 Loss2: 0.047794
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.017029 Loss1: 0.968992 Loss2: 0.048038
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.998879 Loss1: 0.950715 Loss2: 0.048164
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.935546 Loss1: 0.886967 Loss2: 0.048579
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.876100 Loss1: 0.827174 Loss2: 0.048925
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.852450 Loss1: 0.803249 Loss2: 0.049202
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.815558 Loss1: 0.765513 Loss2: 0.050045
+(DefaultActor pid=1838052) >> Training accuracy: 0.793636
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.983399 Loss1: 1.503712 Loss2: 0.479687
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.716486 Loss1: 1.286209 Loss2: 0.430277
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.565044 Loss1: 1.149048 Loss2: 0.415996
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.459187 Loss1: 1.050836 Loss2: 0.408351
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.374790 Loss1: 0.971740 Loss2: 0.403050
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.294766 Loss1: 0.895499 Loss2: 0.399267
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.271600 Loss1: 0.871334 Loss2: 0.400267
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.239451 Loss1: 0.839476 Loss2: 0.399976
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.170682 Loss1: 0.771547 Loss2: 0.399135
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.104936 Loss1: 0.713346 Loss2: 0.391590
+(DefaultActor pid=1838052) >> Training accuracy: 0.788662
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.648810 Loss1: 1.548754 Loss2: 0.100057
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.435098 Loss1: 1.342491 Loss2: 0.092606
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.263582 Loss1: 1.173651 Loss2: 0.089931
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.177296 Loss1: 1.090206 Loss2: 0.087089
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.095152 Loss1: 1.008439 Loss2: 0.086713
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.002073 Loss1: 0.916437 Loss2: 0.085637
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.950258 Loss1: 0.864610 Loss2: 0.085648
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.927875 Loss1: 0.842948 Loss2: 0.084927
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.900996 Loss1: 0.815524 Loss2: 0.085471
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.848823 Loss1: 0.763075 Loss2: 0.085747
+(DefaultActor pid=1838052) >> Training accuracy: 0.804589
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.685175 Loss1: 1.635260 Loss2: 0.049915
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.368898 Loss1: 1.320185 Loss2: 0.048713
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.247768 Loss1: 1.198963 Loss2: 0.048805
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.164373 Loss1: 1.115239 Loss2: 0.049135
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.080091 Loss1: 1.031298 Loss2: 0.048792
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.003993 Loss1: 0.955844 Loss2: 0.048149
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.989895 Loss1: 0.939535 Loss2: 0.050360
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.928191 Loss1: 0.878188 Loss2: 0.050004
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.915477 Loss1: 0.864241 Loss2: 0.051236
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.846737 Loss1: 0.795714 Loss2: 0.051023
+(DefaultActor pid=1838052) >> Training accuracy: 0.761218
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.613427 Loss1: 1.567732 Loss2: 0.045695
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.324408 Loss1: 1.277897 Loss2: 0.046511
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.214042 Loss1: 1.167681 Loss2: 0.046360
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.144975 Loss1: 1.098396 Loss2: 0.046579
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.060169 Loss1: 1.012972 Loss2: 0.047197
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.970030 Loss1: 0.922585 Loss2: 0.047444
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.960799 Loss1: 0.912538 Loss2: 0.048261
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.877419 Loss1: 0.828849 Loss2: 0.048570
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.827678 Loss1: 0.779264 Loss2: 0.048414
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.821562 Loss1: 0.772071 Loss2: 0.049490
+(DefaultActor pid=1838052) >> Training accuracy: 0.787184
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 12:59:34,357][flwr][DEBUG] - fit_round 12 received 10 results and 0 failures
+>> Test accuracy: 0.476600
+[2023-09-27 13:00:16,314][flwr][INFO] - fit progress: (12, 2.172221914647867, {'accuracy': 0.4766}, 24039.204636499286)
+[2023-09-27 13:00:16,315][flwr][DEBUG] - evaluate_round 12: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:00:53,655][flwr][DEBUG] - evaluate_round 12 received 10 results and 0 failures
+[2023-09-27 13:00:53,656][flwr][DEBUG] - fit_round 13: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.554835 Loss1: 1.454155 Loss2: 0.100680
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.263591 Loss1: 1.171754 Loss2: 0.091837
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.091764 Loss1: 1.005758 Loss2: 0.086006
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.983549 Loss1: 0.899556 Loss2: 0.083993
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.906307 Loss1: 0.824280 Loss2: 0.082026
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.826566 Loss1: 0.744631 Loss2: 0.081935
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.795618 Loss1: 0.714581 Loss2: 0.081037
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.800221 Loss1: 0.717713 Loss2: 0.082508
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.749293 Loss1: 0.667136 Loss2: 0.082157
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.721261 Loss1: 0.639743 Loss2: 0.081518
+(DefaultActor pid=1838052) >> Training accuracy: 0.838108
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 2.004140 Loss1: 1.449903 Loss2: 0.554237
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.750747 Loss1: 1.209867 Loss2: 0.540881
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.638205 Loss1: 1.115335 Loss2: 0.522870
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.516286 Loss1: 1.000660 Loss2: 0.515626
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.425229 Loss1: 0.919964 Loss2: 0.505265
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.408799 Loss1: 0.906878 Loss2: 0.501921
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.320876 Loss1: 0.824557 Loss2: 0.496319
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.274672 Loss1: 0.781712 Loss2: 0.492960
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.262220 Loss1: 0.770368 Loss2: 0.491851
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.150150 Loss1: 0.665414 Loss2: 0.484735
+(DefaultActor pid=1838052) >> Training accuracy: 0.836630
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.920536 Loss1: 1.454712 Loss2: 0.465824
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.620226 Loss1: 1.199250 Loss2: 0.420976
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.498174 Loss1: 1.099452 Loss2: 0.398722
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.440593 Loss1: 1.042596 Loss2: 0.397997
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.312793 Loss1: 0.921517 Loss2: 0.391275
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.268877 Loss1: 0.881397 Loss2: 0.387480
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.186551 Loss1: 0.803247 Loss2: 0.383305
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.142036 Loss1: 0.756402 Loss2: 0.385634
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.101028 Loss1: 0.717452 Loss2: 0.383576
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.076308 Loss1: 0.694860 Loss2: 0.381448
+(DefaultActor pid=1838052) >> Training accuracy: 0.834059
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.567896 Loss1: 1.521016 Loss2: 0.046880
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.276825 Loss1: 1.228428 Loss2: 0.048397
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.142931 Loss1: 1.095957 Loss2: 0.046975
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.053065 Loss1: 1.005751 Loss2: 0.047314
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.982628 Loss1: 0.935532 Loss2: 0.047097
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.893231 Loss1: 0.845122 Loss2: 0.048109
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.842195 Loss1: 0.794813 Loss2: 0.047381
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.799314 Loss1: 0.750605 Loss2: 0.048709
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.763987 Loss1: 0.714904 Loss2: 0.049083
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.759205 Loss1: 0.709371 Loss2: 0.049834
+(DefaultActor pid=1838052) >> Training accuracy: 0.803085
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.665501 Loss1: 1.614046 Loss2: 0.051455
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.375214 Loss1: 1.322926 Loss2: 0.052288
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.246519 Loss1: 1.195564 Loss2: 0.050955
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.152787 Loss1: 1.101485 Loss2: 0.051301
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.069978 Loss1: 1.018989 Loss2: 0.050988
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.975712 Loss1: 0.924633 Loss2: 0.051080
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.924454 Loss1: 0.872859 Loss2: 0.051595
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.927438 Loss1: 0.875117 Loss2: 0.052321
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.873256 Loss1: 0.821236 Loss2: 0.052021
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.795077 Loss1: 0.742721 Loss2: 0.052357
+(DefaultActor pid=1838052) >> Training accuracy: 0.788240
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.895489 Loss1: 1.479517 Loss2: 0.415972
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.595703 Loss1: 1.230900 Loss2: 0.364803
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.474178 Loss1: 1.122575 Loss2: 0.351602
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.346187 Loss1: 1.003396 Loss2: 0.342791
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.256289 Loss1: 0.912047 Loss2: 0.344242
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.181732 Loss1: 0.843056 Loss2: 0.338676
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.150029 Loss1: 0.813942 Loss2: 0.336087
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.115359 Loss1: 0.777712 Loss2: 0.337646
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.084740 Loss1: 0.745850 Loss2: 0.338889
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.038159 Loss1: 0.702005 Loss2: 0.336154
+(DefaultActor pid=1838052) >> Training accuracy: 0.803402
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.470621 Loss1: 1.425996 Loss2: 0.044625
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.220362 Loss1: 1.174972 Loss2: 0.045389
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.073288 Loss1: 1.027647 Loss2: 0.045640
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.009783 Loss1: 0.963091 Loss2: 0.046692
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.935358 Loss1: 0.888386 Loss2: 0.046973
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.909304 Loss1: 0.861883 Loss2: 0.047421
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.841449 Loss1: 0.794086 Loss2: 0.047363
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.791900 Loss1: 0.743663 Loss2: 0.048238
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.744319 Loss1: 0.695903 Loss2: 0.048416
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.700492 Loss1: 0.652331 Loss2: 0.048161
+(DefaultActor pid=1838052) >> Training accuracy: 0.822389
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.571147 Loss1: 1.472625 Loss2: 0.098522
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.301049 Loss1: 1.208132 Loss2: 0.092917
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.142392 Loss1: 1.053642 Loss2: 0.088750
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.083360 Loss1: 0.995785 Loss2: 0.087575
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.004304 Loss1: 0.917974 Loss2: 0.086330
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.905985 Loss1: 0.821219 Loss2: 0.084765
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.866658 Loss1: 0.781908 Loss2: 0.084749
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.809350 Loss1: 0.724998 Loss2: 0.084352
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.779905 Loss1: 0.696061 Loss2: 0.083844
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.732342 Loss1: 0.648623 Loss2: 0.083719
+(DefaultActor pid=1838052) >> Training accuracy: 0.812078
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.498577 Loss1: 1.401504 Loss2: 0.097073
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.199662 Loss1: 1.113427 Loss2: 0.086235
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.063247 Loss1: 0.981113 Loss2: 0.082134
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.018538 Loss1: 0.938055 Loss2: 0.080483
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.967311 Loss1: 0.887273 Loss2: 0.080038
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.863235 Loss1: 0.783897 Loss2: 0.079338
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.811326 Loss1: 0.732853 Loss2: 0.078473
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.740894 Loss1: 0.663834 Loss2: 0.077060
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.717681 Loss1: 0.639783 Loss2: 0.077898
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.715556 Loss1: 0.635895 Loss2: 0.079661
+(DefaultActor pid=1838052) >> Training accuracy: 0.809495
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.482855 Loss1: 1.437660 Loss2: 0.045195
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.234490 Loss1: 1.187932 Loss2: 0.046558
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.082912 Loss1: 1.037670 Loss2: 0.045241
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.978647 Loss1: 0.933599 Loss2: 0.045048
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.913985 Loss1: 0.868135 Loss2: 0.045849
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.883662 Loss1: 0.837119 Loss2: 0.046543
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.818779 Loss1: 0.772541 Loss2: 0.046238
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.767398 Loss1: 0.720443 Loss2: 0.046955
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.718579 Loss1: 0.671454 Loss2: 0.047126
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.764798 Loss1: 0.716289 Loss2: 0.048509
+(DefaultActor pid=1838052) >> Training accuracy: 0.807355
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 13:30:49,534][flwr][DEBUG] - fit_round 13 received 10 results and 0 failures
+>> Test accuracy: 0.503800
+[2023-09-27 13:31:30,256][flwr][INFO] - fit progress: (13, 2.099844414205216, {'accuracy': 0.5038}, 25913.14676936716)
+[2023-09-27 13:31:30,257][flwr][DEBUG] - evaluate_round 13: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:32:07,980][flwr][DEBUG] - evaluate_round 13 received 10 results and 0 failures
+[2023-09-27 13:32:07,990][flwr][DEBUG] - fit_round 14: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.557152 Loss1: 1.511130 Loss2: 0.046022
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.259024 Loss1: 1.212024 Loss2: 0.047000
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.122818 Loss1: 1.076116 Loss2: 0.046702
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.013356 Loss1: 0.966772 Loss2: 0.046584
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.937772 Loss1: 0.890585 Loss2: 0.047187
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.889109 Loss1: 0.842068 Loss2: 0.047041
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.818426 Loss1: 0.770466 Loss2: 0.047960
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.786141 Loss1: 0.738146 Loss2: 0.047995
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.726220 Loss1: 0.678479 Loss2: 0.047741
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.676681 Loss1: 0.628145 Loss2: 0.048536
+(DefaultActor pid=1838052) >> Training accuracy: 0.787829
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.453810 Loss1: 1.401582 Loss2: 0.052228
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.141189 Loss1: 1.089325 Loss2: 0.051863
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.984282 Loss1: 0.933786 Loss2: 0.050497
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.941054 Loss1: 0.889980 Loss2: 0.051074
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.860273 Loss1: 0.809583 Loss2: 0.050690
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.792863 Loss1: 0.742287 Loss2: 0.050576
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.759813 Loss1: 0.709023 Loss2: 0.050790
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.712590 Loss1: 0.660877 Loss2: 0.051714
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.642647 Loss1: 0.592295 Loss2: 0.050352
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.647938 Loss1: 0.596175 Loss2: 0.051763
+(DefaultActor pid=1838052) >> Training accuracy: 0.832476
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.416352 Loss1: 1.366951 Loss2: 0.049401
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.188801 Loss1: 1.139160 Loss2: 0.049640
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.990698 Loss1: 0.942223 Loss2: 0.048475
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.892987 Loss1: 0.844968 Loss2: 0.048019
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.848866 Loss1: 0.799971 Loss2: 0.048895
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.796335 Loss1: 0.747873 Loss2: 0.048462
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.781299 Loss1: 0.732501 Loss2: 0.048798
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.718373 Loss1: 0.669898 Loss2: 0.048476
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.691530 Loss1: 0.642592 Loss2: 0.048938
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.668425 Loss1: 0.619779 Loss2: 0.048646
+(DefaultActor pid=1838052) >> Training accuracy: 0.866297
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.403894 Loss1: 1.355331 Loss2: 0.048563
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.148994 Loss1: 1.099598 Loss2: 0.049396
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.988017 Loss1: 0.938865 Loss2: 0.049152
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.916696 Loss1: 0.867460 Loss2: 0.049236
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.864692 Loss1: 0.814977 Loss2: 0.049715
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.808746 Loss1: 0.759185 Loss2: 0.049560
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.714593 Loss1: 0.665119 Loss2: 0.049474
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.720373 Loss1: 0.670219 Loss2: 0.050154
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.685206 Loss1: 0.633750 Loss2: 0.051456
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.622513 Loss1: 0.571220 Loss2: 0.051293
+(DefaultActor pid=1838052) >> Training accuracy: 0.847112
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.375961 Loss1: 1.333037 Loss2: 0.042924
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.108578 Loss1: 1.063932 Loss2: 0.044646
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.009323 Loss1: 0.964388 Loss2: 0.044936
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.894059 Loss1: 0.849501 Loss2: 0.044557
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.838799 Loss1: 0.793374 Loss2: 0.045425
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.777399 Loss1: 0.732365 Loss2: 0.045034
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.707488 Loss1: 0.661873 Loss2: 0.045615
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.677475 Loss1: 0.631436 Loss2: 0.046040
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.671097 Loss1: 0.624670 Loss2: 0.046427
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.602229 Loss1: 0.555628 Loss2: 0.046601
+(DefaultActor pid=1838052) >> Training accuracy: 0.844937
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.396977 Loss1: 1.350401 Loss2: 0.046577
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.097820 Loss1: 1.049547 Loss2: 0.048273
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.985922 Loss1: 0.939401 Loss2: 0.046521
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.848148 Loss1: 0.801753 Loss2: 0.046395
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.777936 Loss1: 0.730802 Loss2: 0.047134
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.711903 Loss1: 0.664628 Loss2: 0.047276
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.717066 Loss1: 0.669050 Loss2: 0.048016
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627829 Loss1: 0.580129 Loss2: 0.047700
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.612711 Loss1: 0.564503 Loss2: 0.048208
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.563070 Loss1: 0.514047 Loss2: 0.049022
+(DefaultActor pid=1838052) >> Training accuracy: 0.839844
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.790708 Loss1: 1.318562 Loss2: 0.472146
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.481774 Loss1: 1.058937 Loss2: 0.422837
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.351668 Loss1: 0.944196 Loss2: 0.407472
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.243171 Loss1: 0.839179 Loss2: 0.403993
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.171931 Loss1: 0.776857 Loss2: 0.395074
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.120945 Loss1: 0.725885 Loss2: 0.395060
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.084169 Loss1: 0.692995 Loss2: 0.391173
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.047033 Loss1: 0.656470 Loss2: 0.390562
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.980545 Loss1: 0.592056 Loss2: 0.388490
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.945049 Loss1: 0.558628 Loss2: 0.386421
+(DefaultActor pid=1838052) >> Training accuracy: 0.844551
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.927190 Loss1: 1.374670 Loss2: 0.552521
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.634197 Loss1: 1.091279 Loss2: 0.542919
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.491793 Loss1: 0.960572 Loss2: 0.531220
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.443121 Loss1: 0.918524 Loss2: 0.524597
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.360523 Loss1: 0.848840 Loss2: 0.511683
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.292534 Loss1: 0.788095 Loss2: 0.504440
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.204907 Loss1: 0.706245 Loss2: 0.498662
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.167800 Loss1: 0.675282 Loss2: 0.492517
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.168541 Loss1: 0.676807 Loss2: 0.491734
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.073724 Loss1: 0.585894 Loss2: 0.487830
+(DefaultActor pid=1838052) >> Training accuracy: 0.796684
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.901270 Loss1: 1.409981 Loss2: 0.491289
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.561869 Loss1: 1.115767 Loss2: 0.446102
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.402262 Loss1: 0.974089 Loss2: 0.428173
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.325875 Loss1: 0.904090 Loss2: 0.421785
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.221278 Loss1: 0.805318 Loss2: 0.415960
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.162254 Loss1: 0.750492 Loss2: 0.411761
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.122975 Loss1: 0.712856 Loss2: 0.410119
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.083349 Loss1: 0.674138 Loss2: 0.409212
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.041990 Loss1: 0.633488 Loss2: 0.408502
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.987648 Loss1: 0.581224 Loss2: 0.406424
+(DefaultActor pid=1838052) >> Training accuracy: 0.828125
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.541563 Loss1: 1.442738 Loss2: 0.098825
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.245615 Loss1: 1.151689 Loss2: 0.093926
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.085352 Loss1: 0.998063 Loss2: 0.087290
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.965421 Loss1: 0.881662 Loss2: 0.083759
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.897522 Loss1: 0.813997 Loss2: 0.083525
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.893132 Loss1: 0.810288 Loss2: 0.082844
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.802376 Loss1: 0.721566 Loss2: 0.080810
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.751052 Loss1: 0.670483 Loss2: 0.080569
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.734165 Loss1: 0.653686 Loss2: 0.080479
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.698602 Loss1: 0.618290 Loss2: 0.080311
+(DefaultActor pid=1838052) >> Training accuracy: 0.846354
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 14:02:00,761][flwr][DEBUG] - fit_round 14 received 10 results and 0 failures
+>> Test accuracy: 0.515100
+[2023-09-27 14:02:42,089][flwr][INFO] - fit progress: (14, 2.0913502991009065, {'accuracy': 0.5151}, 27784.97900109645)
+[2023-09-27 14:02:42,089][flwr][DEBUG] - evaluate_round 14: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:03:18,877][flwr][DEBUG] - evaluate_round 14 received 10 results and 0 failures
+[2023-09-27 14:03:18,878][flwr][DEBUG] - fit_round 15: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.869129 Loss1: 1.308595 Loss2: 0.560535
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.573622 Loss1: 1.021704 Loss2: 0.551918
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.447462 Loss1: 0.906873 Loss2: 0.540589
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.354771 Loss1: 0.822637 Loss2: 0.532134
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.265965 Loss1: 0.741893 Loss2: 0.524072
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.181272 Loss1: 0.666060 Loss2: 0.515212
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.199235 Loss1: 0.685760 Loss2: 0.513474
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.124943 Loss1: 0.615068 Loss2: 0.509875
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.109834 Loss1: 0.602950 Loss2: 0.506884
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.067237 Loss1: 0.565200 Loss2: 0.502038
+(DefaultActor pid=1838052) >> Training accuracy: 0.822191
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.828038 Loss1: 1.273057 Loss2: 0.554981
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.593089 Loss1: 1.052854 Loss2: 0.540234
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.429582 Loss1: 0.908420 Loss2: 0.521162
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.353168 Loss1: 0.838462 Loss2: 0.514706
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.301650 Loss1: 0.797783 Loss2: 0.503867
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.205751 Loss1: 0.706573 Loss2: 0.499178
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.176490 Loss1: 0.681927 Loss2: 0.494563
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.099609 Loss1: 0.609922 Loss2: 0.489687
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.028665 Loss1: 0.542186 Loss2: 0.486479
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.049175 Loss1: 0.562645 Loss2: 0.486530
+(DefaultActor pid=1838052) >> Training accuracy: 0.839597
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.870054 Loss1: 1.361167 Loss2: 0.508887
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.491865 Loss1: 1.009895 Loss2: 0.481970
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.401711 Loss1: 0.930827 Loss2: 0.470884
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.282198 Loss1: 0.817848 Loss2: 0.464349
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.243128 Loss1: 0.782798 Loss2: 0.460330
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.151812 Loss1: 0.693782 Loss2: 0.458029
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.140058 Loss1: 0.684226 Loss2: 0.455832
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.081519 Loss1: 0.628365 Loss2: 0.453154
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.038572 Loss1: 0.584713 Loss2: 0.453859
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.982956 Loss1: 0.532422 Loss2: 0.450534
+(DefaultActor pid=1838052) >> Training accuracy: 0.857171
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.623706 Loss1: 1.245031 Loss2: 0.378674
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.322409 Loss1: 0.993271 Loss2: 0.329138
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.165327 Loss1: 0.852323 Loss2: 0.313004
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.116715 Loss1: 0.807186 Loss2: 0.309529
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.038606 Loss1: 0.733570 Loss2: 0.305036
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.035618 Loss1: 0.732039 Loss2: 0.303578
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.936315 Loss1: 0.636771 Loss2: 0.299544
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.876060 Loss1: 0.576444 Loss2: 0.299616
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.840033 Loss1: 0.541692 Loss2: 0.298340
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.800036 Loss1: 0.502964 Loss2: 0.297072
+(DefaultActor pid=1838052) >> Training accuracy: 0.859947
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.256632 Loss1: 1.212641 Loss2: 0.043991
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.991994 Loss1: 0.945800 Loss2: 0.046195
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.868010 Loss1: 0.823083 Loss2: 0.044928
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.768437 Loss1: 0.724393 Loss2: 0.044044
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.747501 Loss1: 0.702641 Loss2: 0.044860
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.643532 Loss1: 0.598627 Loss2: 0.044905
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.604150 Loss1: 0.559140 Loss2: 0.045010
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.598801 Loss1: 0.553932 Loss2: 0.044869
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.527113 Loss1: 0.482246 Loss2: 0.044867
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.521045 Loss1: 0.475291 Loss2: 0.045754
+(DefaultActor pid=1838052) >> Training accuracy: 0.874199
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.336072 Loss1: 1.291875 Loss2: 0.044198
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.056133 Loss1: 1.010235 Loss2: 0.045897
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.910034 Loss1: 0.864552 Loss2: 0.045482
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.821806 Loss1: 0.776930 Loss2: 0.044876
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.756741 Loss1: 0.710725 Loss2: 0.046016
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.734409 Loss1: 0.688249 Loss2: 0.046161
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.646828 Loss1: 0.600766 Loss2: 0.046062
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.644152 Loss1: 0.597489 Loss2: 0.046663
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.577532 Loss1: 0.531558 Loss2: 0.045974
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.580927 Loss1: 0.533505 Loss2: 0.047422
+(DefaultActor pid=1838052) >> Training accuracy: 0.847508
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.320253 Loss1: 1.274614 Loss2: 0.045639
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.992112 Loss1: 0.945725 Loss2: 0.046387
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.864470 Loss1: 0.818449 Loss2: 0.046021
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.791462 Loss1: 0.745302 Loss2: 0.046160
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.728706 Loss1: 0.682335 Loss2: 0.046371
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.666581 Loss1: 0.620047 Loss2: 0.046534
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.586274 Loss1: 0.539887 Loss2: 0.046387
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.601000 Loss1: 0.553364 Loss2: 0.047636
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.518533 Loss1: 0.471377 Loss2: 0.047156
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.518376 Loss1: 0.470743 Loss2: 0.047633
+(DefaultActor pid=1838052) >> Training accuracy: 0.823351
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.300836 Loss1: 1.258486 Loss2: 0.042350
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.030871 Loss1: 0.986440 Loss2: 0.044431
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.908857 Loss1: 0.864670 Loss2: 0.044187
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.813293 Loss1: 0.769064 Loss2: 0.044229
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.733936 Loss1: 0.689634 Loss2: 0.044302
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.690252 Loss1: 0.645341 Loss2: 0.044911
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.692036 Loss1: 0.646373 Loss2: 0.045662
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.624007 Loss1: 0.578629 Loss2: 0.045378
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.586680 Loss1: 0.540861 Loss2: 0.045819
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.552512 Loss1: 0.505963 Loss2: 0.046549
+(DefaultActor pid=1838052) >> Training accuracy: 0.813884
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.440221 Loss1: 1.361511 Loss2: 0.078710
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.063102 Loss1: 0.987606 Loss2: 0.075497
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.975592 Loss1: 0.903563 Loss2: 0.072029
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.903377 Loss1: 0.832173 Loss2: 0.071204
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.775594 Loss1: 0.704977 Loss2: 0.070616
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.710341 Loss1: 0.641682 Loss2: 0.068659
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.689901 Loss1: 0.621679 Loss2: 0.068222
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627053 Loss1: 0.559046 Loss2: 0.068006
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.588798 Loss1: 0.519809 Loss2: 0.068988
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.555248 Loss1: 0.487502 Loss2: 0.067746
+(DefaultActor pid=1838052) >> Training accuracy: 0.869299
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.471926 Loss1: 1.427650 Loss2: 0.044276
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.154139 Loss1: 1.108520 Loss2: 0.045619
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.006635 Loss1: 0.961235 Loss2: 0.045400
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.952977 Loss1: 0.906991 Loss2: 0.045986
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.874394 Loss1: 0.828489 Loss2: 0.045905
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.810183 Loss1: 0.763152 Loss2: 0.047031
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.718065 Loss1: 0.671644 Loss2: 0.046421
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.670572 Loss1: 0.623228 Loss2: 0.047344
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.615017 Loss1: 0.567657 Loss2: 0.047360
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.627492 Loss1: 0.578667 Loss2: 0.048824
+(DefaultActor pid=1838052) >> Training accuracy: 0.847245
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 14:33:09,578][flwr][DEBUG] - fit_round 15 received 10 results and 0 failures
+>> Test accuracy: 0.533200
+[2023-09-27 14:33:51,388][flwr][INFO] - fit progress: (15, 2.0594057168442603, {'accuracy': 0.5332}, 29654.278763433453)
+[2023-09-27 14:33:51,389][flwr][DEBUG] - evaluate_round 15: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:34:30,081][flwr][DEBUG] - evaluate_round 15 received 10 results and 0 failures
+[2023-09-27 14:34:30,082][flwr][DEBUG] - fit_round 16: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.319365 Loss1: 1.231179 Loss2: 0.088186
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.989450 Loss1: 0.908675 Loss2: 0.080775
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.888398 Loss1: 0.809992 Loss2: 0.078406
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.791136 Loss1: 0.715338 Loss2: 0.075797
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.725526 Loss1: 0.649637 Loss2: 0.075889
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.625476 Loss1: 0.551067 Loss2: 0.074410
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.634477 Loss1: 0.560165 Loss2: 0.074312
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.577461 Loss1: 0.503432 Loss2: 0.074029
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.519255 Loss1: 0.445199 Loss2: 0.074055
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.534348 Loss1: 0.459257 Loss2: 0.075092
+(DefaultActor pid=1838052) >> Training accuracy: 0.879747
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.665154 Loss1: 1.259666 Loss2: 0.405489
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.322873 Loss1: 0.989865 Loss2: 0.333008
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.167477 Loss1: 0.850643 Loss2: 0.316834
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.081441 Loss1: 0.769983 Loss2: 0.311458
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.976954 Loss1: 0.670694 Loss2: 0.306260
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.977265 Loss1: 0.668300 Loss2: 0.308965
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.868333 Loss1: 0.563778 Loss2: 0.304555
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.846894 Loss1: 0.542077 Loss2: 0.304816
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.837206 Loss1: 0.533814 Loss2: 0.303392
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.804672 Loss1: 0.499836 Loss2: 0.304836
+(DefaultActor pid=1838052) >> Training accuracy: 0.848157
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.222760 Loss1: 1.180292 Loss2: 0.042468
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.934930 Loss1: 0.890929 Loss2: 0.044001
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.821343 Loss1: 0.777787 Loss2: 0.043556
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.743289 Loss1: 0.699175 Loss2: 0.044114
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.704035 Loss1: 0.659915 Loss2: 0.044120
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.644134 Loss1: 0.599523 Loss2: 0.044610
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.596153 Loss1: 0.551381 Loss2: 0.044772
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.549509 Loss1: 0.504436 Loss2: 0.045073
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.523912 Loss1: 0.478274 Loss2: 0.045638
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.526950 Loss1: 0.481351 Loss2: 0.045600
+(DefaultActor pid=1838052) >> Training accuracy: 0.835839
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.240767 Loss1: 1.194333 Loss2: 0.046434
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.950228 Loss1: 0.902954 Loss2: 0.047274
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.853440 Loss1: 0.806351 Loss2: 0.047089
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.744117 Loss1: 0.697283 Loss2: 0.046834
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.669267 Loss1: 0.622003 Loss2: 0.047264
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.626842 Loss1: 0.579548 Loss2: 0.047294
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.566617 Loss1: 0.519363 Loss2: 0.047254
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.588784 Loss1: 0.540459 Loss2: 0.048324
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.518987 Loss1: 0.470823 Loss2: 0.048163
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.485265 Loss1: 0.436593 Loss2: 0.048672
+(DefaultActor pid=1838052) >> Training accuracy: 0.908537
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.821027 Loss1: 1.274033 Loss2: 0.546994
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.461127 Loss1: 0.936447 Loss2: 0.524680
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.297903 Loss1: 0.796373 Loss2: 0.501530
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.195251 Loss1: 0.701814 Loss2: 0.493438
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.170281 Loss1: 0.684465 Loss2: 0.485816
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.129800 Loss1: 0.650146 Loss2: 0.479654
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.023941 Loss1: 0.549766 Loss2: 0.474175
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.027316 Loss1: 0.555367 Loss2: 0.471949
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.009425 Loss1: 0.540247 Loss2: 0.469178
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.935867 Loss1: 0.467048 Loss2: 0.468819
+(DefaultActor pid=1838052) >> Training accuracy: 0.871833
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.424465 Loss1: 1.321884 Loss2: 0.102580
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.109187 Loss1: 1.011768 Loss2: 0.097419
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.968349 Loss1: 0.874718 Loss2: 0.093631
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.887493 Loss1: 0.797273 Loss2: 0.090220
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.798571 Loss1: 0.711186 Loss2: 0.087386
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.771472 Loss1: 0.683475 Loss2: 0.087997
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.710740 Loss1: 0.624208 Loss2: 0.086532
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.673604 Loss1: 0.587967 Loss2: 0.085637
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.614862 Loss1: 0.531050 Loss2: 0.083813
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.596927 Loss1: 0.512820 Loss2: 0.084108
+(DefaultActor pid=1838052) >> Training accuracy: 0.863487
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.243623 Loss1: 1.198888 Loss2: 0.044735
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.961667 Loss1: 0.915511 Loss2: 0.046155
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.850438 Loss1: 0.804972 Loss2: 0.045466
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.732224 Loss1: 0.686608 Loss2: 0.045616
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.674944 Loss1: 0.629683 Loss2: 0.045261
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.642545 Loss1: 0.596580 Loss2: 0.045964
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.610690 Loss1: 0.563999 Loss2: 0.046692
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.570927 Loss1: 0.524009 Loss2: 0.046919
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.551139 Loss1: 0.504300 Loss2: 0.046839
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.539553 Loss1: 0.491748 Loss2: 0.047805
+(DefaultActor pid=1838052) >> Training accuracy: 0.872429
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.182955 Loss1: 1.141674 Loss2: 0.041281
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.907693 Loss1: 0.864174 Loss2: 0.043519
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.815081 Loss1: 0.771494 Loss2: 0.043587
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.667460 Loss1: 0.624303 Loss2: 0.043158
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.618589 Loss1: 0.575520 Loss2: 0.043069
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.599503 Loss1: 0.555854 Loss2: 0.043650
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.563147 Loss1: 0.518972 Loss2: 0.044175
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.519071 Loss1: 0.473719 Loss2: 0.045352
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.479138 Loss1: 0.433944 Loss2: 0.045194
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.479000 Loss1: 0.433847 Loss2: 0.045153
+(DefaultActor pid=1838052) >> Training accuracy: 0.870393
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.265032 Loss1: 1.220601 Loss2: 0.044431
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.919550 Loss1: 0.873368 Loss2: 0.046182
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.793637 Loss1: 0.748834 Loss2: 0.044803
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.686991 Loss1: 0.642594 Loss2: 0.044397
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.642008 Loss1: 0.596989 Loss2: 0.045019
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.608282 Loss1: 0.562880 Loss2: 0.045402
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.554434 Loss1: 0.509358 Loss2: 0.045076
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.532932 Loss1: 0.486894 Loss2: 0.046039
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.493670 Loss1: 0.448155 Loss2: 0.045515
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.452236 Loss1: 0.406581 Loss2: 0.045654
+(DefaultActor pid=1838052) >> Training accuracy: 0.856337
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.279682 Loss1: 1.191143 Loss2: 0.088540
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.018267 Loss1: 0.935634 Loss2: 0.082633
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.885615 Loss1: 0.807029 Loss2: 0.078586
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.771969 Loss1: 0.696472 Loss2: 0.075497
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.695094 Loss1: 0.620698 Loss2: 0.074396
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.642214 Loss1: 0.569360 Loss2: 0.072854
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.619980 Loss1: 0.546553 Loss2: 0.073427
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.639584 Loss1: 0.566370 Loss2: 0.073214
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.585676 Loss1: 0.512339 Loss2: 0.073337
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.523151 Loss1: 0.451738 Loss2: 0.071413
+(DefaultActor pid=1838052) >> Training accuracy: 0.856606
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 15:04:25,755][flwr][DEBUG] - fit_round 16 received 10 results and 0 failures
+>> Test accuracy: 0.541200
+[2023-09-27 15:05:07,094][flwr][INFO] - fit progress: (16, 2.043369851554164, {'accuracy': 0.5412}, 31529.984162893146)
+[2023-09-27 15:05:07,094][flwr][DEBUG] - evaluate_round 16: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:05:43,531][flwr][DEBUG] - evaluate_round 16 received 10 results and 0 failures
+[2023-09-27 15:05:43,535][flwr][DEBUG] - fit_round 17: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.724071 Loss1: 1.192031 Loss2: 0.532040
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.397202 Loss1: 0.905425 Loss2: 0.491777
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.248782 Loss1: 0.780901 Loss2: 0.467882
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.143744 Loss1: 0.689073 Loss2: 0.454671
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.061379 Loss1: 0.616862 Loss2: 0.444517
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.993349 Loss1: 0.550992 Loss2: 0.442358
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.975412 Loss1: 0.534316 Loss2: 0.441096
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.922566 Loss1: 0.488946 Loss2: 0.433620
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.881468 Loss1: 0.449421 Loss2: 0.432046
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.932681 Loss1: 0.497816 Loss2: 0.434866
+(DefaultActor pid=1838052) >> Training accuracy: 0.867880
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.814572 Loss1: 1.276490 Loss2: 0.538082
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.488853 Loss1: 0.968639 Loss2: 0.520214
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.320097 Loss1: 0.813617 Loss2: 0.506480
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.219865 Loss1: 0.721865 Loss2: 0.498000
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.190685 Loss1: 0.699883 Loss2: 0.490802
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.133862 Loss1: 0.647056 Loss2: 0.486806
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.042862 Loss1: 0.560342 Loss2: 0.482519
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.040098 Loss1: 0.557847 Loss2: 0.482251
+(DefaultActor pid=1838052) Epoch: 8 Loss: 1.004696 Loss1: 0.524774 Loss2: 0.479921
+(DefaultActor pid=1838052) Epoch: 9 Loss: 1.002923 Loss1: 0.525671 Loss2: 0.477252
+(DefaultActor pid=1838052) >> Training accuracy: 0.846423
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.203019 Loss1: 1.131675 Loss2: 0.071343
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.908636 Loss1: 0.841907 Loss2: 0.066729
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.792948 Loss1: 0.729141 Loss2: 0.063807
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.683123 Loss1: 0.621723 Loss2: 0.061400
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.647688 Loss1: 0.586367 Loss2: 0.061321
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.613439 Loss1: 0.553321 Loss2: 0.060118
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.555569 Loss1: 0.496033 Loss2: 0.059536
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.561862 Loss1: 0.502249 Loss2: 0.059613
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.533264 Loss1: 0.472994 Loss2: 0.060270
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.441068 Loss1: 0.381871 Loss2: 0.059197
+(DefaultActor pid=1838052) >> Training accuracy: 0.905261
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.593260 Loss1: 1.220183 Loss2: 0.373076
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.209884 Loss1: 0.899372 Loss2: 0.310512
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.078203 Loss1: 0.779001 Loss2: 0.299202
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.964305 Loss1: 0.673296 Loss2: 0.291010
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.900407 Loss1: 0.608347 Loss2: 0.292060
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.823449 Loss1: 0.538349 Loss2: 0.285099
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.813651 Loss1: 0.527237 Loss2: 0.286414
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.705440 Loss1: 0.423188 Loss2: 0.282252
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.764947 Loss1: 0.478261 Loss2: 0.286686
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.705319 Loss1: 0.422625 Loss2: 0.282694
+(DefaultActor pid=1838052) >> Training accuracy: 0.875845
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.619478 Loss1: 1.061922 Loss2: 0.557556
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.359962 Loss1: 0.803257 Loss2: 0.556705
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.225204 Loss1: 0.681625 Loss2: 0.543579
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.171590 Loss1: 0.635735 Loss2: 0.535854
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.076883 Loss1: 0.547675 Loss2: 0.529208
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.013207 Loss1: 0.491951 Loss2: 0.521256
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.970228 Loss1: 0.452400 Loss2: 0.517828
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.964139 Loss1: 0.453762 Loss2: 0.510377
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.937465 Loss1: 0.428026 Loss2: 0.509439
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.869677 Loss1: 0.364503 Loss2: 0.505174
+(DefaultActor pid=1838052) >> Training accuracy: 0.915665
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.281250 Loss1: 1.190858 Loss2: 0.090392
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.938171 Loss1: 0.854814 Loss2: 0.083357
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.850935 Loss1: 0.770949 Loss2: 0.079986
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.732652 Loss1: 0.655639 Loss2: 0.077013
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.654519 Loss1: 0.579889 Loss2: 0.074629
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.644013 Loss1: 0.570031 Loss2: 0.073982
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.579281 Loss1: 0.507002 Loss2: 0.072279
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.539600 Loss1: 0.467647 Loss2: 0.071953
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.494389 Loss1: 0.423889 Loss2: 0.070500
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.500732 Loss1: 0.429311 Loss2: 0.071421
+(DefaultActor pid=1838052) >> Training accuracy: 0.893429
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.178007 Loss1: 1.134182 Loss2: 0.043825
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.875088 Loss1: 0.829974 Loss2: 0.045114
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.741451 Loss1: 0.697497 Loss2: 0.043954
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.694167 Loss1: 0.649733 Loss2: 0.044435
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.644145 Loss1: 0.598985 Loss2: 0.045160
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.587464 Loss1: 0.542296 Loss2: 0.045169
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.542717 Loss1: 0.497174 Loss2: 0.045543
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.475920 Loss1: 0.430484 Loss2: 0.045436
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.470198 Loss1: 0.424211 Loss2: 0.045987
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.452873 Loss1: 0.406996 Loss2: 0.045877
+(DefaultActor pid=1838052) >> Training accuracy: 0.890427
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.681233 Loss1: 1.106129 Loss2: 0.575104
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.423035 Loss1: 0.851118 Loss2: 0.571917
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.306713 Loss1: 0.747175 Loss2: 0.559538
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.192403 Loss1: 0.643979 Loss2: 0.548424
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.126465 Loss1: 0.588898 Loss2: 0.537567
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.113644 Loss1: 0.583666 Loss2: 0.529978
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.041499 Loss1: 0.516176 Loss2: 0.525322
+(DefaultActor pid=1838052) Epoch: 7 Loss: 1.012730 Loss1: 0.492442 Loss2: 0.520288
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.993970 Loss1: 0.477058 Loss2: 0.516912
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.955380 Loss1: 0.442304 Loss2: 0.513076
+(DefaultActor pid=1838052) >> Training accuracy: 0.884337
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.174437 Loss1: 1.130578 Loss2: 0.043859
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.892509 Loss1: 0.846867 Loss2: 0.045642
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.749081 Loss1: 0.703812 Loss2: 0.045269
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.688468 Loss1: 0.643613 Loss2: 0.044855
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.637087 Loss1: 0.591454 Loss2: 0.045633
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.621744 Loss1: 0.575384 Loss2: 0.046360
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.513209 Loss1: 0.468082 Loss2: 0.045127
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.479677 Loss1: 0.434227 Loss2: 0.045450
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.521849 Loss1: 0.474908 Loss2: 0.046941
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.473112 Loss1: 0.426953 Loss2: 0.046159
+(DefaultActor pid=1838052) >> Training accuracy: 0.902888
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.220185 Loss1: 1.109585 Loss2: 0.110600
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.959343 Loss1: 0.855129 Loss2: 0.104213
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.794161 Loss1: 0.695801 Loss2: 0.098360
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.679846 Loss1: 0.585103 Loss2: 0.094743
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.637958 Loss1: 0.545511 Loss2: 0.092447
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.565120 Loss1: 0.475683 Loss2: 0.089437
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.574675 Loss1: 0.486980 Loss2: 0.087695
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.515102 Loss1: 0.428117 Loss2: 0.086985
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.520519 Loss1: 0.433546 Loss2: 0.086973
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.483020 Loss1: 0.397309 Loss2: 0.085711
+(DefaultActor pid=1838052) >> Training accuracy: 0.907769
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 15:35:38,006][flwr][DEBUG] - fit_round 17 received 10 results and 0 failures
+>> Test accuracy: 0.555200
+[2023-09-27 15:36:19,112][flwr][INFO] - fit progress: (17, 2.0114177884385227, {'accuracy': 0.5552}, 33402.00278136041)
+[2023-09-27 15:36:19,113][flwr][DEBUG] - evaluate_round 17: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:36:55,813][flwr][DEBUG] - evaluate_round 17 received 10 results and 0 failures
+[2023-09-27 15:36:55,814][flwr][DEBUG] - fit_round 18: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.151850 Loss1: 1.102207 Loss2: 0.049643
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.882989 Loss1: 0.832441 Loss2: 0.050548
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.689155 Loss1: 0.639842 Loss2: 0.049314
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.631845 Loss1: 0.583206 Loss2: 0.048639
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.566820 Loss1: 0.517939 Loss2: 0.048881
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.551032 Loss1: 0.501674 Loss2: 0.049358
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.545742 Loss1: 0.495955 Loss2: 0.049787
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.455554 Loss1: 0.406643 Loss2: 0.048912
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.410234 Loss1: 0.361451 Loss2: 0.048784
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.360835 Loss1: 0.312199 Loss2: 0.048636
+(DefaultActor pid=1838052) >> Training accuracy: 0.899282
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.147591 Loss1: 1.095451 Loss2: 0.052140
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.905347 Loss1: 0.851917 Loss2: 0.053430
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.727907 Loss1: 0.676317 Loss2: 0.051590
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.639653 Loss1: 0.589274 Loss2: 0.050379
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.602735 Loss1: 0.552357 Loss2: 0.050378
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.525467 Loss1: 0.475621 Loss2: 0.049846
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.502656 Loss1: 0.452750 Loss2: 0.049905
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.486071 Loss1: 0.436450 Loss2: 0.049621
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.475068 Loss1: 0.424594 Loss2: 0.050474
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.484269 Loss1: 0.433953 Loss2: 0.050316
+(DefaultActor pid=1838052) >> Training accuracy: 0.906646
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.616979 Loss1: 1.129082 Loss2: 0.487896
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.294852 Loss1: 0.853851 Loss2: 0.441001
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.121358 Loss1: 0.693252 Loss2: 0.428106
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.088324 Loss1: 0.671312 Loss2: 0.417012
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.988800 Loss1: 0.571813 Loss2: 0.416988
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.916039 Loss1: 0.502736 Loss2: 0.413303
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.930943 Loss1: 0.520122 Loss2: 0.410821
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.890067 Loss1: 0.483481 Loss2: 0.406586
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.808451 Loss1: 0.404837 Loss2: 0.403613
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.821626 Loss1: 0.414988 Loss2: 0.406638
+(DefaultActor pid=1838052) >> Training accuracy: 0.891026
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.604111 Loss1: 1.026361 Loss2: 0.577750
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.379711 Loss1: 0.800240 Loss2: 0.579471
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.242236 Loss1: 0.671332 Loss2: 0.570903
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.134007 Loss1: 0.572132 Loss2: 0.561875
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.103937 Loss1: 0.550762 Loss2: 0.553175
+(DefaultActor pid=1838052) Epoch: 5 Loss: 1.058524 Loss1: 0.509558 Loss2: 0.548967
+(DefaultActor pid=1838052) Epoch: 6 Loss: 1.007588 Loss1: 0.464244 Loss2: 0.543344
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.978135 Loss1: 0.439950 Loss2: 0.538185
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.980892 Loss1: 0.446465 Loss2: 0.534427
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.942437 Loss1: 0.412905 Loss2: 0.529532
+(DefaultActor pid=1838052) >> Training accuracy: 0.889043
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.584543 Loss1: 1.062201 Loss2: 0.522342
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.264573 Loss1: 0.769338 Loss2: 0.495235
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.136152 Loss1: 0.663482 Loss2: 0.472670
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.050714 Loss1: 0.583485 Loss2: 0.467229
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.004701 Loss1: 0.547634 Loss2: 0.457067
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.951836 Loss1: 0.495918 Loss2: 0.455918
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.853237 Loss1: 0.406415 Loss2: 0.446822
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.835672 Loss1: 0.395297 Loss2: 0.440375
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.764212 Loss1: 0.326737 Loss2: 0.437475
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.786782 Loss1: 0.350071 Loss2: 0.436711
+(DefaultActor pid=1838052) >> Training accuracy: 0.903429
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.074072 Loss1: 1.027718 Loss2: 0.046354
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.784241 Loss1: 0.736678 Loss2: 0.047563
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.637449 Loss1: 0.591155 Loss2: 0.046295
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.554658 Loss1: 0.508476 Loss2: 0.046182
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.543558 Loss1: 0.496824 Loss2: 0.046734
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.506882 Loss1: 0.459398 Loss2: 0.047484
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.447064 Loss1: 0.400058 Loss2: 0.047006
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.409010 Loss1: 0.361930 Loss2: 0.047079
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.376404 Loss1: 0.329408 Loss2: 0.046997
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.414653 Loss1: 0.366102 Loss2: 0.048551
+(DefaultActor pid=1838052) >> Training accuracy: 0.919671
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.117711 Loss1: 1.073847 Loss2: 0.043865
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.845978 Loss1: 0.800484 Loss2: 0.045494
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.725233 Loss1: 0.679895 Loss2: 0.045338
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.626525 Loss1: 0.581438 Loss2: 0.045087
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.589257 Loss1: 0.543124 Loss2: 0.046133
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.511490 Loss1: 0.466347 Loss2: 0.045143
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.499907 Loss1: 0.453784 Loss2: 0.046123
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.462077 Loss1: 0.415951 Loss2: 0.046126
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.437042 Loss1: 0.390851 Loss2: 0.046190
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.412851 Loss1: 0.366421 Loss2: 0.046430
+(DefaultActor pid=1838052) >> Training accuracy: 0.885878
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.288395 Loss1: 1.185564 Loss2: 0.102831
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.993716 Loss1: 0.901586 Loss2: 0.092130
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.835738 Loss1: 0.749405 Loss2: 0.086333
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.762372 Loss1: 0.679679 Loss2: 0.082693
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.704111 Loss1: 0.622991 Loss2: 0.081119
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.637661 Loss1: 0.557796 Loss2: 0.079864
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.609971 Loss1: 0.531625 Loss2: 0.078347
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.564713 Loss1: 0.486830 Loss2: 0.077882
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.516841 Loss1: 0.439786 Loss2: 0.077055
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.499605 Loss1: 0.422977 Loss2: 0.076629
+(DefaultActor pid=1838052) >> Training accuracy: 0.883635
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.093677 Loss1: 1.045198 Loss2: 0.048479
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.799330 Loss1: 0.749327 Loss2: 0.050002
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.690984 Loss1: 0.641503 Loss2: 0.049482
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.617008 Loss1: 0.567725 Loss2: 0.049283
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.576823 Loss1: 0.528085 Loss2: 0.048738
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.551630 Loss1: 0.503076 Loss2: 0.048554
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.452142 Loss1: 0.403730 Loss2: 0.048412
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.435855 Loss1: 0.388393 Loss2: 0.047462
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.401437 Loss1: 0.352876 Loss2: 0.048561
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.444832 Loss1: 0.395207 Loss2: 0.049625
+(DefaultActor pid=1838052) >> Training accuracy: 0.850610
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.098568 Loss1: 1.051298 Loss2: 0.047270
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.846442 Loss1: 0.798179 Loss2: 0.048263
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.704741 Loss1: 0.656632 Loss2: 0.048108
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.633382 Loss1: 0.586161 Loss2: 0.047221
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.553920 Loss1: 0.506687 Loss2: 0.047233
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.520138 Loss1: 0.473042 Loss2: 0.047096
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.495444 Loss1: 0.448481 Loss2: 0.046963
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.477814 Loss1: 0.430215 Loss2: 0.047598
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.434395 Loss1: 0.386232 Loss2: 0.048162
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.431528 Loss1: 0.383876 Loss2: 0.047651
+(DefaultActor pid=1838052) >> Training accuracy: 0.902888
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 16:06:45,012][flwr][DEBUG] - fit_round 18 received 10 results and 0 failures
+>> Test accuracy: 0.559500
+[2023-09-27 16:07:25,503][flwr][INFO] - fit progress: (18, 2.017150927846805, {'accuracy': 0.5595}, 35268.393114050385)
+[2023-09-27 16:07:25,503][flwr][DEBUG] - evaluate_round 18: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:08:03,143][flwr][DEBUG] - evaluate_round 18 received 10 results and 0 failures
+[2023-09-27 16:08:03,144][flwr][DEBUG] - fit_round 19: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.072423 Loss1: 1.025426 Loss2: 0.046998
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.724178 Loss1: 0.676316 Loss2: 0.047862
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.638816 Loss1: 0.590574 Loss2: 0.048242
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.541173 Loss1: 0.494070 Loss2: 0.047103
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.536851 Loss1: 0.488933 Loss2: 0.047917
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.471999 Loss1: 0.424226 Loss2: 0.047773
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.455944 Loss1: 0.407527 Loss2: 0.048417
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.437839 Loss1: 0.389738 Loss2: 0.048101
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.413090 Loss1: 0.364927 Loss2: 0.048163
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.384046 Loss1: 0.336231 Loss2: 0.047815
+(DefaultActor pid=1838052) >> Training accuracy: 0.913172
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.647601 Loss1: 1.170356 Loss2: 0.477245
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.276275 Loss1: 0.846552 Loss2: 0.429723
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.139066 Loss1: 0.726082 Loss2: 0.412985
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.072279 Loss1: 0.668411 Loss2: 0.403868
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.950794 Loss1: 0.552784 Loss2: 0.398009
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.899799 Loss1: 0.504604 Loss2: 0.395195
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.874083 Loss1: 0.479904 Loss2: 0.394179
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.858376 Loss1: 0.464795 Loss2: 0.393581
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.810812 Loss1: 0.420533 Loss2: 0.390279
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.784761 Loss1: 0.395265 Loss2: 0.389496
+(DefaultActor pid=1838052) >> Training accuracy: 0.914679
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.006817 Loss1: 0.963756 Loss2: 0.043061
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.740130 Loss1: 0.695481 Loss2: 0.044649
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.609965 Loss1: 0.566379 Loss2: 0.043586
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.513614 Loss1: 0.469943 Loss2: 0.043670
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.481199 Loss1: 0.436849 Loss2: 0.044349
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.431784 Loss1: 0.387609 Loss2: 0.044175
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.416802 Loss1: 0.371981 Loss2: 0.044821
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.383177 Loss1: 0.338348 Loss2: 0.044829
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.346540 Loss1: 0.301674 Loss2: 0.044866
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.342785 Loss1: 0.297885 Loss2: 0.044900
+(DefaultActor pid=1838052) >> Training accuracy: 0.910657
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.062043 Loss1: 1.018211 Loss2: 0.043832
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.768667 Loss1: 0.722160 Loss2: 0.046506
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.602303 Loss1: 0.557545 Loss2: 0.044758
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.573391 Loss1: 0.527849 Loss2: 0.045542
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.556267 Loss1: 0.510164 Loss2: 0.046103
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.509954 Loss1: 0.462856 Loss2: 0.047098
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.456527 Loss1: 0.410131 Loss2: 0.046396
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.404051 Loss1: 0.357903 Loss2: 0.046148
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.362776 Loss1: 0.317228 Loss2: 0.045548
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.369069 Loss1: 0.323157 Loss2: 0.045911
+(DefaultActor pid=1838052) >> Training accuracy: 0.917722
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.133527 Loss1: 1.086964 Loss2: 0.046563
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.771041 Loss1: 0.723159 Loss2: 0.047882
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.647489 Loss1: 0.601376 Loss2: 0.046113
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.552546 Loss1: 0.506683 Loss2: 0.045863
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.556132 Loss1: 0.508557 Loss2: 0.047575
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.504572 Loss1: 0.457466 Loss2: 0.047106
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.434810 Loss1: 0.388068 Loss2: 0.046742
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.448467 Loss1: 0.401154 Loss2: 0.047313
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.391151 Loss1: 0.344130 Loss2: 0.047021
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.368441 Loss1: 0.320417 Loss2: 0.048025
+(DefaultActor pid=1838052) >> Training accuracy: 0.926943
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.020442 Loss1: 0.974636 Loss2: 0.045807
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.732875 Loss1: 0.685751 Loss2: 0.047124
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.627098 Loss1: 0.579875 Loss2: 0.047223
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.540052 Loss1: 0.492797 Loss2: 0.047256
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.494960 Loss1: 0.448496 Loss2: 0.046464
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.493236 Loss1: 0.446072 Loss2: 0.047164
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.417185 Loss1: 0.370479 Loss2: 0.046706
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.404850 Loss1: 0.358624 Loss2: 0.046227
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.347062 Loss1: 0.301978 Loss2: 0.045085
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.316555 Loss1: 0.271186 Loss2: 0.045369
+(DefaultActor pid=1838052) >> Training accuracy: 0.924913
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.049166 Loss1: 1.006273 Loss2: 0.042892
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.760058 Loss1: 0.714711 Loss2: 0.045348
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.648274 Loss1: 0.603691 Loss2: 0.044583
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.579731 Loss1: 0.534787 Loss2: 0.044944
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.551903 Loss1: 0.505815 Loss2: 0.046087
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.467522 Loss1: 0.422879 Loss2: 0.044644
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.455439 Loss1: 0.410061 Loss2: 0.045378
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.418916 Loss1: 0.373362 Loss2: 0.045555
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.393119 Loss1: 0.348159 Loss2: 0.044960
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.415593 Loss1: 0.369383 Loss2: 0.046209
+(DefaultActor pid=1838052) >> Training accuracy: 0.909489
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.122089 Loss1: 1.016830 Loss2: 0.105260
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.828840 Loss1: 0.729226 Loss2: 0.099613
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.704360 Loss1: 0.609469 Loss2: 0.094891
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.625981 Loss1: 0.534246 Loss2: 0.091736
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.601096 Loss1: 0.510603 Loss2: 0.090494
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.545421 Loss1: 0.456452 Loss2: 0.088969
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.498218 Loss1: 0.411243 Loss2: 0.086975
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.469274 Loss1: 0.384182 Loss2: 0.085092
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.470382 Loss1: 0.384529 Loss2: 0.085853
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.401793 Loss1: 0.318421 Loss2: 0.083373
+(DefaultActor pid=1838052) >> Training accuracy: 0.879945
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.570058 Loss1: 1.010276 Loss2: 0.559782
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.311304 Loss1: 0.755484 Loss2: 0.555820
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.191218 Loss1: 0.653780 Loss2: 0.537438
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.092964 Loss1: 0.567382 Loss2: 0.525582
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.073860 Loss1: 0.557575 Loss2: 0.516285
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.973117 Loss1: 0.463550 Loss2: 0.509568
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.937814 Loss1: 0.434690 Loss2: 0.503123
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.902070 Loss1: 0.408125 Loss2: 0.493945
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.858425 Loss1: 0.365613 Loss2: 0.492812
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.861992 Loss1: 0.373332 Loss2: 0.488660
+(DefaultActor pid=1838052) >> Training accuracy: 0.911392
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.146627 Loss1: 1.054895 Loss2: 0.091732
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.830245 Loss1: 0.744791 Loss2: 0.085454
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.699353 Loss1: 0.620052 Loss2: 0.079301
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.642160 Loss1: 0.563889 Loss2: 0.078271
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.585266 Loss1: 0.509939 Loss2: 0.075327
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.548909 Loss1: 0.474029 Loss2: 0.074879
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.487629 Loss1: 0.414460 Loss2: 0.073169
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.455683 Loss1: 0.382777 Loss2: 0.072906
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.453079 Loss1: 0.380573 Loss2: 0.072506
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.384286 Loss1: 0.312532 Loss2: 0.071753
+(DefaultActor pid=1838052) >> Training accuracy: 0.917067
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 16:37:49,400][flwr][DEBUG] - fit_round 19 received 10 results and 0 failures
+>> Test accuracy: 0.568500
+[2023-09-27 16:38:32,280][flwr][INFO] - fit progress: (19, 2.010581853100286, {'accuracy': 0.5685}, 37135.17036648141)
+[2023-09-27 16:38:32,280][flwr][DEBUG] - evaluate_round 19: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:39:09,510][flwr][DEBUG] - evaluate_round 19 received 10 results and 0 failures
+[2023-09-27 16:39:09,511][flwr][DEBUG] - fit_round 20: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.557567 Loss1: 0.981923 Loss2: 0.575644
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.276597 Loss1: 0.696957 Loss2: 0.579640
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.143573 Loss1: 0.575979 Loss2: 0.567594
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.078683 Loss1: 0.519164 Loss2: 0.559518
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.992975 Loss1: 0.440549 Loss2: 0.552426
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.963377 Loss1: 0.421037 Loss2: 0.542340
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.936405 Loss1: 0.399138 Loss2: 0.537267
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.927093 Loss1: 0.393949 Loss2: 0.533144
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.877520 Loss1: 0.351057 Loss2: 0.526463
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.851879 Loss1: 0.329258 Loss2: 0.522621
+(DefaultActor pid=1838052) >> Training accuracy: 0.922271
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.555893 Loss1: 0.969948 Loss2: 0.585945
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.299866 Loss1: 0.715770 Loss2: 0.584095
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.175296 Loss1: 0.603976 Loss2: 0.571320
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.065792 Loss1: 0.507682 Loss2: 0.558110
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.016069 Loss1: 0.469441 Loss2: 0.546628
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.995524 Loss1: 0.457796 Loss2: 0.537728
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.935638 Loss1: 0.405149 Loss2: 0.530488
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.883340 Loss1: 0.359196 Loss2: 0.524144
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.879462 Loss1: 0.359216 Loss2: 0.520247
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.870339 Loss1: 0.353070 Loss2: 0.517268
+(DefaultActor pid=1838052) >> Training accuracy: 0.894778
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.535467 Loss1: 1.039561 Loss2: 0.495906
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.170456 Loss1: 0.727478 Loss2: 0.442977
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.024480 Loss1: 0.603928 Loss2: 0.420552
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.953348 Loss1: 0.535009 Loss2: 0.418339
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.931060 Loss1: 0.520416 Loss2: 0.410644
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.834455 Loss1: 0.429233 Loss2: 0.405221
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.832354 Loss1: 0.429019 Loss2: 0.403335
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.787421 Loss1: 0.386765 Loss2: 0.400656
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.792282 Loss1: 0.391054 Loss2: 0.401229
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.749314 Loss1: 0.351064 Loss2: 0.398250
+(DefaultActor pid=1838052) >> Training accuracy: 0.926482
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.378333 Loss1: 0.982160 Loss2: 0.396173
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.022265 Loss1: 0.689239 Loss2: 0.333026
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.917970 Loss1: 0.596456 Loss2: 0.321513
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.823823 Loss1: 0.504976 Loss2: 0.318847
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.756528 Loss1: 0.444056 Loss2: 0.312472
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.745703 Loss1: 0.435626 Loss2: 0.310078
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.712403 Loss1: 0.405192 Loss2: 0.307212
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.694511 Loss1: 0.386857 Loss2: 0.307653
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.662818 Loss1: 0.358684 Loss2: 0.304134
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.674153 Loss1: 0.367485 Loss2: 0.306668
+(DefaultActor pid=1838052) >> Training accuracy: 0.875791
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.162589 Loss1: 1.112539 Loss2: 0.050049
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.814500 Loss1: 0.763900 Loss2: 0.050599
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.662577 Loss1: 0.613629 Loss2: 0.048948
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.611336 Loss1: 0.561951 Loss2: 0.049385
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.531084 Loss1: 0.481680 Loss2: 0.049404
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.519723 Loss1: 0.470308 Loss2: 0.049415
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.498210 Loss1: 0.449128 Loss2: 0.049082
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.429970 Loss1: 0.380877 Loss2: 0.049093
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.437739 Loss1: 0.388109 Loss2: 0.049629
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.386747 Loss1: 0.336892 Loss2: 0.049855
+(DefaultActor pid=1838052) >> Training accuracy: 0.919819
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.943452 Loss1: 0.901955 Loss2: 0.041497
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.692220 Loss1: 0.647867 Loss2: 0.044352
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.532901 Loss1: 0.489467 Loss2: 0.043434
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.481723 Loss1: 0.438085 Loss2: 0.043638
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.431016 Loss1: 0.387912 Loss2: 0.043104
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.401392 Loss1: 0.357521 Loss2: 0.043871
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.374015 Loss1: 0.330119 Loss2: 0.043896
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.344715 Loss1: 0.300534 Loss2: 0.044180
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.324748 Loss1: 0.280795 Loss2: 0.043954
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.285352 Loss1: 0.241212 Loss2: 0.044140
+(DefaultActor pid=1838052) >> Training accuracy: 0.951723
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.497004 Loss1: 0.923439 Loss2: 0.573564
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.242268 Loss1: 0.674792 Loss2: 0.567476
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.141855 Loss1: 0.587375 Loss2: 0.554480
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.062666 Loss1: 0.520272 Loss2: 0.542393
+(DefaultActor pid=1838052) Epoch: 4 Loss: 1.013499 Loss1: 0.483532 Loss2: 0.529968
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.960152 Loss1: 0.439046 Loss2: 0.521105
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.949741 Loss1: 0.435501 Loss2: 0.514240
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.867497 Loss1: 0.360525 Loss2: 0.506972
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.881732 Loss1: 0.378075 Loss2: 0.503657
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.808992 Loss1: 0.309981 Loss2: 0.499010
+(DefaultActor pid=1838052) >> Training accuracy: 0.918636
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.071353 Loss1: 1.026061 Loss2: 0.045292
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.740544 Loss1: 0.694702 Loss2: 0.045841
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.598450 Loss1: 0.553362 Loss2: 0.045088
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.543369 Loss1: 0.498021 Loss2: 0.045348
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.491014 Loss1: 0.445601 Loss2: 0.045413
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.432636 Loss1: 0.386774 Loss2: 0.045861
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.384475 Loss1: 0.339304 Loss2: 0.045171
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.372290 Loss1: 0.326946 Loss2: 0.045345
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.346189 Loss1: 0.300735 Loss2: 0.045454
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.341397 Loss1: 0.296020 Loss2: 0.045377
+(DefaultActor pid=1838052) >> Training accuracy: 0.947424
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.006887 Loss1: 0.963247 Loss2: 0.043640
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.703091 Loss1: 0.657852 Loss2: 0.045239
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.579959 Loss1: 0.535249 Loss2: 0.044710
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.500123 Loss1: 0.455449 Loss2: 0.044673
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.474804 Loss1: 0.429448 Loss2: 0.045356
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.383119 Loss1: 0.338756 Loss2: 0.044363
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.365224 Loss1: 0.320371 Loss2: 0.044853
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.395885 Loss1: 0.350060 Loss2: 0.045825
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.369360 Loss1: 0.323700 Loss2: 0.045660
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.334130 Loss1: 0.288798 Loss2: 0.045332
+(DefaultActor pid=1838052) >> Training accuracy: 0.931641
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.031388 Loss1: 0.985435 Loss2: 0.045953
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.702099 Loss1: 0.654730 Loss2: 0.047369
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.602965 Loss1: 0.556070 Loss2: 0.046895
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.555393 Loss1: 0.508033 Loss2: 0.047360
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.520120 Loss1: 0.472078 Loss2: 0.048042
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.445848 Loss1: 0.399014 Loss2: 0.046834
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.410376 Loss1: 0.363109 Loss2: 0.047267
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.379320 Loss1: 0.332344 Loss2: 0.046976
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.399758 Loss1: 0.351975 Loss2: 0.047783
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.346040 Loss1: 0.298618 Loss2: 0.047422
+(DefaultActor pid=1838052) >> Training accuracy: 0.929786
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 17:09:06,770][flwr][DEBUG] - fit_round 20 received 10 results and 0 failures
+>> Test accuracy: 0.578400
+[2023-09-27 17:09:50,073][flwr][INFO] - fit progress: (20, 1.9967298349633384, {'accuracy': 0.5784}, 39012.96377548808)
+[2023-09-27 17:09:50,074][flwr][DEBUG] - evaluate_round 20: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:10:28,741][flwr][DEBUG] - evaluate_round 20 received 10 results and 0 failures
+[2023-09-27 17:10:28,742][flwr][DEBUG] - fit_round 21: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.441074 Loss1: 0.859878 Loss2: 0.581196
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.199901 Loss1: 0.618381 Loss2: 0.581520
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.056999 Loss1: 0.488822 Loss2: 0.568177
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.976549 Loss1: 0.420051 Loss2: 0.556498
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.960750 Loss1: 0.409162 Loss2: 0.551588
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.878549 Loss1: 0.336997 Loss2: 0.541552
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.887394 Loss1: 0.351022 Loss2: 0.536372
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.822501 Loss1: 0.291778 Loss2: 0.530723
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.848029 Loss1: 0.319236 Loss2: 0.528793
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.772479 Loss1: 0.250474 Loss2: 0.522005
+(DefaultActor pid=1838052) >> Training accuracy: 0.940505
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.045304 Loss1: 1.000116 Loss2: 0.045188
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.713351 Loss1: 0.667202 Loss2: 0.046149
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.604490 Loss1: 0.558919 Loss2: 0.045571
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.547714 Loss1: 0.501507 Loss2: 0.046207
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.494390 Loss1: 0.447834 Loss2: 0.046556
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.484695 Loss1: 0.437551 Loss2: 0.047144
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.403437 Loss1: 0.356885 Loss2: 0.046552
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.422633 Loss1: 0.375701 Loss2: 0.046932
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.415666 Loss1: 0.368436 Loss2: 0.047231
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.355755 Loss1: 0.308305 Loss2: 0.047451
+(DefaultActor pid=1838052) >> Training accuracy: 0.925781
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.952721 Loss1: 0.904454 Loss2: 0.048266
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.677717 Loss1: 0.628052 Loss2: 0.049665
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.570709 Loss1: 0.522028 Loss2: 0.048681
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.499929 Loss1: 0.450646 Loss2: 0.049283
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.481601 Loss1: 0.432837 Loss2: 0.048764
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.427915 Loss1: 0.378968 Loss2: 0.048947
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.352349 Loss1: 0.304148 Loss2: 0.048201
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.379245 Loss1: 0.330256 Loss2: 0.048990
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.332342 Loss1: 0.284393 Loss2: 0.047949
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.312073 Loss1: 0.264045 Loss2: 0.048028
+(DefaultActor pid=1838052) >> Training accuracy: 0.910601
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.327963 Loss1: 0.914075 Loss2: 0.413889
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.987661 Loss1: 0.628477 Loss2: 0.359184
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.887983 Loss1: 0.544178 Loss2: 0.343805
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.834299 Loss1: 0.497696 Loss2: 0.336603
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.737909 Loss1: 0.404606 Loss2: 0.333303
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.714861 Loss1: 0.384924 Loss2: 0.329937
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.686495 Loss1: 0.360132 Loss2: 0.326363
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.640504 Loss1: 0.315885 Loss2: 0.324618
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.651182 Loss1: 0.324467 Loss2: 0.326715
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.608174 Loss1: 0.287499 Loss2: 0.320675
+(DefaultActor pid=1838052) >> Training accuracy: 0.934335
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.967278 Loss1: 0.917821 Loss2: 0.049456
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.689100 Loss1: 0.637787 Loss2: 0.051313
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.575058 Loss1: 0.525446 Loss2: 0.049612
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.515126 Loss1: 0.465500 Loss2: 0.049627
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.453983 Loss1: 0.405027 Loss2: 0.048957
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.439727 Loss1: 0.391352 Loss2: 0.048376
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.392861 Loss1: 0.344768 Loss2: 0.048093
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.398073 Loss1: 0.349548 Loss2: 0.048525
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.357302 Loss1: 0.309088 Loss2: 0.048214
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.361836 Loss1: 0.313243 Loss2: 0.048593
+(DefaultActor pid=1838052) >> Training accuracy: 0.914663
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.959772 Loss1: 0.912866 Loss2: 0.046906
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.706844 Loss1: 0.657386 Loss2: 0.049459
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.547176 Loss1: 0.499532 Loss2: 0.047644
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.481747 Loss1: 0.434211 Loss2: 0.047536
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.471999 Loss1: 0.424831 Loss2: 0.047168
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.414049 Loss1: 0.365776 Loss2: 0.048273
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.386935 Loss1: 0.339662 Loss2: 0.047274
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.352964 Loss1: 0.305911 Loss2: 0.047053
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.361224 Loss1: 0.313369 Loss2: 0.047855
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.320947 Loss1: 0.273593 Loss2: 0.047354
+(DefaultActor pid=1838052) >> Training accuracy: 0.918908
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.913349 Loss1: 0.868458 Loss2: 0.044891
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.676273 Loss1: 0.629097 Loss2: 0.047176
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.566736 Loss1: 0.519984 Loss2: 0.046752
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.486017 Loss1: 0.439703 Loss2: 0.046314
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.438555 Loss1: 0.391941 Loss2: 0.046615
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.399655 Loss1: 0.352631 Loss2: 0.047023
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.354819 Loss1: 0.308622 Loss2: 0.046198
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.380022 Loss1: 0.333457 Loss2: 0.046565
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.320814 Loss1: 0.274735 Loss2: 0.046079
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.312016 Loss1: 0.265779 Loss2: 0.046237
+(DefaultActor pid=1838052) >> Training accuracy: 0.904345
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.983647 Loss1: 0.939547 Loss2: 0.044099
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.649935 Loss1: 0.604044 Loss2: 0.045891
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.540792 Loss1: 0.495479 Loss2: 0.045312
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.448605 Loss1: 0.403888 Loss2: 0.044717
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.432135 Loss1: 0.387196 Loss2: 0.044939
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.364650 Loss1: 0.320168 Loss2: 0.044481
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.369096 Loss1: 0.324701 Loss2: 0.044396
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.332601 Loss1: 0.287835 Loss2: 0.044766
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.309000 Loss1: 0.264531 Loss2: 0.044468
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.261675 Loss1: 0.217272 Loss2: 0.044402
+(DefaultActor pid=1838052) >> Training accuracy: 0.938585
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.965494 Loss1: 0.921074 Loss2: 0.044421
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.661705 Loss1: 0.615895 Loss2: 0.045810
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.524004 Loss1: 0.478808 Loss2: 0.045196
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.502442 Loss1: 0.456562 Loss2: 0.045880
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.469690 Loss1: 0.423445 Loss2: 0.046245
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.384042 Loss1: 0.338435 Loss2: 0.045608
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.399315 Loss1: 0.352755 Loss2: 0.046561
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.341316 Loss1: 0.295108 Loss2: 0.046208
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.306559 Loss1: 0.260701 Loss2: 0.045858
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.324713 Loss1: 0.278167 Loss2: 0.046546
+(DefaultActor pid=1838052) >> Training accuracy: 0.938093
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.056950 Loss1: 0.951987 Loss2: 0.104963
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.734951 Loss1: 0.635487 Loss2: 0.099464
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.618416 Loss1: 0.522625 Loss2: 0.095791
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.541144 Loss1: 0.448410 Loss2: 0.092734
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.462529 Loss1: 0.373575 Loss2: 0.088954
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.437146 Loss1: 0.350089 Loss2: 0.087057
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.435166 Loss1: 0.348518 Loss2: 0.086648
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.379911 Loss1: 0.296075 Loss2: 0.083836
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.368705 Loss1: 0.284690 Loss2: 0.084015
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.346089 Loss1: 0.262899 Loss2: 0.083189
+(DefaultActor pid=1838052) >> Training accuracy: 0.933488
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 17:40:22,056][flwr][DEBUG] - fit_round 21 received 10 results and 0 failures
+>> Test accuracy: 0.582800
+[2023-09-27 17:41:04,561][flwr][INFO] - fit progress: (21, 2.0065166573174085, {'accuracy': 0.5828}, 40887.45143873524)
+[2023-09-27 17:41:04,561][flwr][DEBUG] - evaluate_round 21: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:41:42,490][flwr][DEBUG] - evaluate_round 21 received 10 results and 0 failures
+[2023-09-27 17:41:42,491][flwr][DEBUG] - fit_round 22: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.941718 Loss1: 0.865424 Loss2: 0.076293
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.696892 Loss1: 0.621861 Loss2: 0.075030
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.561636 Loss1: 0.489644 Loss2: 0.071992
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.484527 Loss1: 0.414949 Loss2: 0.069578
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.414811 Loss1: 0.347628 Loss2: 0.067184
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.406092 Loss1: 0.338522 Loss2: 0.067570
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.367119 Loss1: 0.301095 Loss2: 0.066024
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.326634 Loss1: 0.261858 Loss2: 0.064776
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.362481 Loss1: 0.295838 Loss2: 0.066643
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.348904 Loss1: 0.283527 Loss2: 0.065377
+(DefaultActor pid=1838052) >> Training accuracy: 0.928204
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.885240 Loss1: 0.803654 Loss2: 0.081586
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.634237 Loss1: 0.553165 Loss2: 0.081072
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.508780 Loss1: 0.431880 Loss2: 0.076900
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.464929 Loss1: 0.389477 Loss2: 0.075451
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.405151 Loss1: 0.331214 Loss2: 0.073937
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.357989 Loss1: 0.286904 Loss2: 0.071086
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.377463 Loss1: 0.305869 Loss2: 0.071594
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.342598 Loss1: 0.272293 Loss2: 0.070305
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.315901 Loss1: 0.246300 Loss2: 0.069601
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.298775 Loss1: 0.229496 Loss2: 0.069280
+(DefaultActor pid=1838052) >> Training accuracy: 0.955329
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.421984 Loss1: 0.844938 Loss2: 0.577047
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.171000 Loss1: 0.594710 Loss2: 0.576290
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.034807 Loss1: 0.469479 Loss2: 0.565329
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.009238 Loss1: 0.453666 Loss2: 0.555572
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.935657 Loss1: 0.388004 Loss2: 0.547654
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.888108 Loss1: 0.351023 Loss2: 0.537085
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.860570 Loss1: 0.328567 Loss2: 0.532003
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.844411 Loss1: 0.319333 Loss2: 0.525078
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.840301 Loss1: 0.319082 Loss2: 0.521219
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.774654 Loss1: 0.258259 Loss2: 0.516395
+(DefaultActor pid=1838052) >> Training accuracy: 0.931962
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.465225 Loss1: 0.874702 Loss2: 0.590523
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.151426 Loss1: 0.560967 Loss2: 0.590459
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.070212 Loss1: 0.492032 Loss2: 0.578180
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.975420 Loss1: 0.409807 Loss2: 0.565613
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.933141 Loss1: 0.377815 Loss2: 0.555326
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.905085 Loss1: 0.357038 Loss2: 0.548046
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.831857 Loss1: 0.294995 Loss2: 0.536862
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.848960 Loss1: 0.318074 Loss2: 0.530886
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.839222 Loss1: 0.313344 Loss2: 0.525878
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.780549 Loss1: 0.261909 Loss2: 0.518640
+(DefaultActor pid=1838052) >> Training accuracy: 0.950738
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.451825 Loss1: 0.885333 Loss2: 0.566492
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.145621 Loss1: 0.582361 Loss2: 0.563259
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.052582 Loss1: 0.510052 Loss2: 0.542530
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.934325 Loss1: 0.406857 Loss2: 0.527468
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.926565 Loss1: 0.408536 Loss2: 0.518028
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.880250 Loss1: 0.370399 Loss2: 0.509851
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.845695 Loss1: 0.342828 Loss2: 0.502866
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.810349 Loss1: 0.314635 Loss2: 0.495714
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.804705 Loss1: 0.312464 Loss2: 0.492241
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.776538 Loss1: 0.288647 Loss2: 0.487891
+(DefaultActor pid=1838052) >> Training accuracy: 0.924644
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.483488 Loss1: 0.953191 Loss2: 0.530296
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.114051 Loss1: 0.606851 Loss2: 0.507199
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.973157 Loss1: 0.488574 Loss2: 0.484584
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.846898 Loss1: 0.375816 Loss2: 0.471082
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.841865 Loss1: 0.379892 Loss2: 0.461973
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.822580 Loss1: 0.366168 Loss2: 0.456411
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.807519 Loss1: 0.350467 Loss2: 0.457052
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.773651 Loss1: 0.320141 Loss2: 0.453510
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.776132 Loss1: 0.327328 Loss2: 0.448803
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.738742 Loss1: 0.288053 Loss2: 0.450689
+(DefaultActor pid=1838052) >> Training accuracy: 0.940667
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.981012 Loss1: 0.938015 Loss2: 0.042997
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.752022 Loss1: 0.707032 Loss2: 0.044991
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.588469 Loss1: 0.543059 Loss2: 0.045410
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.520652 Loss1: 0.475320 Loss2: 0.045332
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.448172 Loss1: 0.403459 Loss2: 0.044714
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.438653 Loss1: 0.392293 Loss2: 0.046360
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.397857 Loss1: 0.352811 Loss2: 0.045046
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.357486 Loss1: 0.312429 Loss2: 0.045057
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.364831 Loss1: 0.318827 Loss2: 0.046003
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.338922 Loss1: 0.293680 Loss2: 0.045242
+(DefaultActor pid=1838052) >> Training accuracy: 0.936061
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.427771 Loss1: 0.844918 Loss2: 0.582853
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.163618 Loss1: 0.575676 Loss2: 0.587942
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.085144 Loss1: 0.506600 Loss2: 0.578544
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.040379 Loss1: 0.469790 Loss2: 0.570589
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.945710 Loss1: 0.381638 Loss2: 0.564072
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.907066 Loss1: 0.350437 Loss2: 0.556629
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.939953 Loss1: 0.388294 Loss2: 0.551659
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.874640 Loss1: 0.329594 Loss2: 0.545046
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.836881 Loss1: 0.297318 Loss2: 0.539563
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.843252 Loss1: 0.308878 Loss2: 0.534374
+(DefaultActor pid=1838052) >> Training accuracy: 0.922468
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.940165 Loss1: 0.897418 Loss2: 0.042747
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.635666 Loss1: 0.591524 Loss2: 0.044142
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.536437 Loss1: 0.492373 Loss2: 0.044064
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.477844 Loss1: 0.433476 Loss2: 0.044369
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.423757 Loss1: 0.380108 Loss2: 0.043649
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.391274 Loss1: 0.347670 Loss2: 0.043604
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.359430 Loss1: 0.315772 Loss2: 0.043658
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.351545 Loss1: 0.307039 Loss2: 0.044506
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.306453 Loss1: 0.262495 Loss2: 0.043958
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.291937 Loss1: 0.247541 Loss2: 0.044396
+(DefaultActor pid=1838052) >> Training accuracy: 0.933494
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.883845 Loss1: 0.841526 Loss2: 0.042319
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.608033 Loss1: 0.563382 Loss2: 0.044651
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.491709 Loss1: 0.447866 Loss2: 0.043843
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.410151 Loss1: 0.366840 Loss2: 0.043311
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.409852 Loss1: 0.365871 Loss2: 0.043981
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.387459 Loss1: 0.343453 Loss2: 0.044005
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.379443 Loss1: 0.334925 Loss2: 0.044518
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.347798 Loss1: 0.302837 Loss2: 0.044961
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.312934 Loss1: 0.268691 Loss2: 0.044242
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.300063 Loss1: 0.255966 Loss2: 0.044097
+(DefaultActor pid=1838052) >> Training accuracy: 0.917683
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 18:11:35,463][flwr][DEBUG] - fit_round 22 received 10 results and 0 failures
+>> Test accuracy: 0.589800
+[2023-09-27 18:12:16,304][flwr][INFO] - fit progress: (22, 1.9742871688577694, {'accuracy': 0.5898}, 42759.194697763305)
+[2023-09-27 18:12:16,305][flwr][DEBUG] - evaluate_round 22: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:12:53,899][flwr][DEBUG] - evaluate_round 22 received 10 results and 0 failures
+[2023-09-27 18:12:53,901][flwr][DEBUG] - fit_round 23: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.897760 Loss1: 0.853816 Loss2: 0.043944
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.604854 Loss1: 0.559184 Loss2: 0.045670
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.472775 Loss1: 0.427964 Loss2: 0.044811
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.401707 Loss1: 0.357052 Loss2: 0.044655
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.378779 Loss1: 0.334109 Loss2: 0.044669
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.394755 Loss1: 0.349103 Loss2: 0.045652
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.348546 Loss1: 0.303126 Loss2: 0.045420
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.310794 Loss1: 0.266043 Loss2: 0.044751
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.297991 Loss1: 0.252554 Loss2: 0.045437
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.290053 Loss1: 0.244538 Loss2: 0.045515
+(DefaultActor pid=1838052) >> Training accuracy: 0.948576
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.832921 Loss1: 0.791847 Loss2: 0.041074
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.593942 Loss1: 0.550451 Loss2: 0.043491
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.508246 Loss1: 0.464336 Loss2: 0.043909
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.392121 Loss1: 0.349331 Loss2: 0.042789
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.385467 Loss1: 0.342090 Loss2: 0.043378
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.367000 Loss1: 0.323835 Loss2: 0.043165
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.344153 Loss1: 0.300110 Loss2: 0.044043
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.315541 Loss1: 0.271735 Loss2: 0.043805
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.319785 Loss1: 0.276011 Loss2: 0.043774
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.284982 Loss1: 0.241442 Loss2: 0.043539
+(DefaultActor pid=1838052) >> Training accuracy: 0.946314
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.482602 Loss1: 0.907027 Loss2: 0.575575
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.207923 Loss1: 0.630182 Loss2: 0.577740
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.067583 Loss1: 0.501542 Loss2: 0.566041
+(DefaultActor pid=1838052) Epoch: 3 Loss: 1.004722 Loss1: 0.451139 Loss2: 0.553583
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.927729 Loss1: 0.383389 Loss2: 0.544340
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.907322 Loss1: 0.371932 Loss2: 0.535390
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.898847 Loss1: 0.370326 Loss2: 0.528521
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.855222 Loss1: 0.330604 Loss2: 0.524617
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.832190 Loss1: 0.311103 Loss2: 0.521087
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.812630 Loss1: 0.299143 Loss2: 0.513487
+(DefaultActor pid=1838052) >> Training accuracy: 0.909334
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.848869 Loss1: 0.804794 Loss2: 0.044076
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.593681 Loss1: 0.546680 Loss2: 0.047001
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.471065 Loss1: 0.425092 Loss2: 0.045972
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.453508 Loss1: 0.406421 Loss2: 0.047087
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.388486 Loss1: 0.342365 Loss2: 0.046121
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.352411 Loss1: 0.307167 Loss2: 0.045245
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.309301 Loss1: 0.264170 Loss2: 0.045131
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.295137 Loss1: 0.249810 Loss2: 0.045327
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.309300 Loss1: 0.263729 Loss2: 0.045571
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.275973 Loss1: 0.230811 Loss2: 0.045162
+(DefaultActor pid=1838052) >> Training accuracy: 0.941851
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.937725 Loss1: 0.891583 Loss2: 0.046142
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.603266 Loss1: 0.555953 Loss2: 0.047313
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.518921 Loss1: 0.472649 Loss2: 0.046271
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.436992 Loss1: 0.391205 Loss2: 0.045786
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.416439 Loss1: 0.370635 Loss2: 0.045803
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.353077 Loss1: 0.307042 Loss2: 0.046035
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.305383 Loss1: 0.260458 Loss2: 0.044924
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.310008 Loss1: 0.264782 Loss2: 0.045226
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.311976 Loss1: 0.266445 Loss2: 0.045531
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.288780 Loss1: 0.242930 Loss2: 0.045849
+(DefaultActor pid=1838052) >> Training accuracy: 0.947213
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.354231 Loss1: 0.816539 Loss2: 0.537692
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.087861 Loss1: 0.594086 Loss2: 0.493775
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.968870 Loss1: 0.499127 Loss2: 0.469743
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.892358 Loss1: 0.431604 Loss2: 0.460753
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.855238 Loss1: 0.403312 Loss2: 0.451926
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.775091 Loss1: 0.334337 Loss2: 0.440754
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.776764 Loss1: 0.337149 Loss2: 0.439615
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.714903 Loss1: 0.275481 Loss2: 0.439422
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.696339 Loss1: 0.263274 Loss2: 0.433065
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.721158 Loss1: 0.287909 Loss2: 0.433249
+(DefaultActor pid=1838052) >> Training accuracy: 0.942840
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.879163 Loss1: 0.834244 Loss2: 0.044919
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.612132 Loss1: 0.565633 Loss2: 0.046498
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.513995 Loss1: 0.467981 Loss2: 0.046015
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.420509 Loss1: 0.375547 Loss2: 0.044962
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.415696 Loss1: 0.370325 Loss2: 0.045371
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.326305 Loss1: 0.282147 Loss2: 0.044159
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.296134 Loss1: 0.251415 Loss2: 0.044719
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.309509 Loss1: 0.264494 Loss2: 0.045015
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.297027 Loss1: 0.252582 Loss2: 0.044445
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.278421 Loss1: 0.233806 Loss2: 0.044614
+(DefaultActor pid=1838052) >> Training accuracy: 0.951147
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.903548 Loss1: 0.820889 Loss2: 0.082658
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.579367 Loss1: 0.500709 Loss2: 0.078658
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.508653 Loss1: 0.434270 Loss2: 0.074382
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.445606 Loss1: 0.373158 Loss2: 0.072448
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.423586 Loss1: 0.352290 Loss2: 0.071296
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.340060 Loss1: 0.271922 Loss2: 0.068137
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.322795 Loss1: 0.255081 Loss2: 0.067715
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.333363 Loss1: 0.264658 Loss2: 0.068705
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.287051 Loss1: 0.220343 Loss2: 0.066709
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.269206 Loss1: 0.203312 Loss2: 0.065894
+(DefaultActor pid=1838052) >> Training accuracy: 0.964627
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.379302 Loss1: 0.807354 Loss2: 0.571948
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.124667 Loss1: 0.558584 Loss2: 0.566083
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.028679 Loss1: 0.479081 Loss2: 0.549598
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.926977 Loss1: 0.390628 Loss2: 0.536349
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.878971 Loss1: 0.356331 Loss2: 0.522640
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.859091 Loss1: 0.345992 Loss2: 0.513099
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.814484 Loss1: 0.310347 Loss2: 0.504137
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.826766 Loss1: 0.325325 Loss2: 0.501441
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.773020 Loss1: 0.277156 Loss2: 0.495864
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.757246 Loss1: 0.267514 Loss2: 0.489732
+(DefaultActor pid=1838052) >> Training accuracy: 0.928163
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.830002 Loss1: 0.789474 Loss2: 0.040528
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.545267 Loss1: 0.502546 Loss2: 0.042721
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.460087 Loss1: 0.417907 Loss2: 0.042180
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.397142 Loss1: 0.354301 Loss2: 0.042840
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.359462 Loss1: 0.316694 Loss2: 0.042768
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.315923 Loss1: 0.272895 Loss2: 0.043027
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.312817 Loss1: 0.269844 Loss2: 0.042973
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.288614 Loss1: 0.245959 Loss2: 0.042655
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.285487 Loss1: 0.242437 Loss2: 0.043050
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.228473 Loss1: 0.185811 Loss2: 0.042661
+(DefaultActor pid=1838052) >> Training accuracy: 0.961138
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 18:42:30,195][flwr][DEBUG] - fit_round 23 received 10 results and 0 failures
+>> Test accuracy: 0.593400
+[2023-09-27 18:43:10,608][flwr][INFO] - fit progress: (23, 1.9792633229932084, {'accuracy': 0.5934}, 44613.49830036238)
+[2023-09-27 18:43:10,608][flwr][DEBUG] - evaluate_round 23: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:43:47,608][flwr][DEBUG] - evaluate_round 23 received 10 results and 0 failures
+[2023-09-27 18:43:47,609][flwr][DEBUG] - fit_round 24: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.809671 Loss1: 0.767432 Loss2: 0.042239
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.554770 Loss1: 0.509987 Loss2: 0.044783
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.491414 Loss1: 0.446746 Loss2: 0.044668
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.391403 Loss1: 0.347328 Loss2: 0.044075
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.391900 Loss1: 0.347654 Loss2: 0.044246
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.365596 Loss1: 0.321002 Loss2: 0.044594
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.296963 Loss1: 0.252662 Loss2: 0.044301
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.281741 Loss1: 0.237689 Loss2: 0.044052
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.286024 Loss1: 0.241630 Loss2: 0.044394
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.229328 Loss1: 0.185938 Loss2: 0.043390
+(DefaultActor pid=1838052) >> Training accuracy: 0.957278
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.335275 Loss1: 0.803665 Loss2: 0.531610
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.031538 Loss1: 0.544039 Loss2: 0.487499
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.921955 Loss1: 0.470787 Loss2: 0.451167
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.820945 Loss1: 0.387759 Loss2: 0.433186
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.774924 Loss1: 0.350948 Loss2: 0.423976
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.714086 Loss1: 0.299265 Loss2: 0.414821
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.641442 Loss1: 0.235724 Loss2: 0.405718
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.641401 Loss1: 0.238626 Loss2: 0.402775
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.640612 Loss1: 0.235056 Loss2: 0.405556
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.607392 Loss1: 0.208483 Loss2: 0.398909
+(DefaultActor pid=1838052) >> Training accuracy: 0.955512
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.169631 Loss1: 0.757580 Loss2: 0.412051
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.893505 Loss1: 0.544042 Loss2: 0.349462
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.806313 Loss1: 0.473007 Loss2: 0.333307
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.674513 Loss1: 0.355356 Loss2: 0.319156
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.611235 Loss1: 0.300586 Loss2: 0.310649
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.581628 Loss1: 0.274044 Loss2: 0.307583
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.592211 Loss1: 0.282807 Loss2: 0.309403
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.598014 Loss1: 0.291208 Loss2: 0.306806
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.571527 Loss1: 0.267148 Loss2: 0.304380
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.552564 Loss1: 0.247574 Loss2: 0.304990
+(DefaultActor pid=1838052) >> Training accuracy: 0.947790
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.775766 Loss1: 0.735807 Loss2: 0.039959
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.550558 Loss1: 0.507518 Loss2: 0.043040
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.481144 Loss1: 0.438147 Loss2: 0.042997
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.378559 Loss1: 0.336400 Loss2: 0.042158
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.359676 Loss1: 0.317511 Loss2: 0.042165
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.330441 Loss1: 0.287966 Loss2: 0.042474
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.273435 Loss1: 0.231868 Loss2: 0.041566
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.281946 Loss1: 0.240010 Loss2: 0.041936
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.311953 Loss1: 0.269245 Loss2: 0.042708
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.276991 Loss1: 0.234055 Loss2: 0.042936
+(DefaultActor pid=1838052) >> Training accuracy: 0.946005
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.405272 Loss1: 0.817210 Loss2: 0.588063
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.145517 Loss1: 0.563906 Loss2: 0.581611
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.006487 Loss1: 0.436867 Loss2: 0.569619
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.958589 Loss1: 0.398926 Loss2: 0.559663
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.895410 Loss1: 0.344969 Loss2: 0.550440
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.875142 Loss1: 0.333187 Loss2: 0.541955
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.851563 Loss1: 0.315570 Loss2: 0.535993
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.880980 Loss1: 0.348838 Loss2: 0.532142
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.795961 Loss1: 0.269256 Loss2: 0.526705
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.757297 Loss1: 0.235669 Loss2: 0.521628
+(DefaultActor pid=1838052) >> Training accuracy: 0.958734
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.835096 Loss1: 0.756379 Loss2: 0.078716
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.575227 Loss1: 0.500019 Loss2: 0.075208
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.496603 Loss1: 0.425786 Loss2: 0.070816
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.430763 Loss1: 0.362188 Loss2: 0.068575
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.403293 Loss1: 0.335560 Loss2: 0.067733
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.353969 Loss1: 0.287625 Loss2: 0.066344
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.348013 Loss1: 0.282697 Loss2: 0.065317
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.286386 Loss1: 0.221949 Loss2: 0.064436
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.316084 Loss1: 0.250965 Loss2: 0.065119
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.289504 Loss1: 0.225389 Loss2: 0.064114
+(DefaultActor pid=1838052) >> Training accuracy: 0.931962
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.773026 Loss1: 0.734111 Loss2: 0.038915
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.488842 Loss1: 0.447579 Loss2: 0.041263
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.419913 Loss1: 0.378964 Loss2: 0.040949
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.339868 Loss1: 0.299392 Loss2: 0.040476
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.300203 Loss1: 0.259697 Loss2: 0.040506
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.307518 Loss1: 0.266035 Loss2: 0.041484
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.273466 Loss1: 0.231876 Loss2: 0.041590
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.252018 Loss1: 0.210434 Loss2: 0.041584
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.243432 Loss1: 0.201768 Loss2: 0.041664
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.234819 Loss1: 0.193425 Loss2: 0.041394
+(DefaultActor pid=1838052) >> Training accuracy: 0.958734
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.926591 Loss1: 0.844133 Loss2: 0.082458
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.622943 Loss1: 0.544533 Loss2: 0.078410
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.539840 Loss1: 0.465063 Loss2: 0.074777
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.532233 Loss1: 0.459447 Loss2: 0.072786
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.441220 Loss1: 0.370842 Loss2: 0.070377
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.361286 Loss1: 0.292582 Loss2: 0.068704
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.328175 Loss1: 0.259930 Loss2: 0.068245
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.347943 Loss1: 0.280649 Loss2: 0.067294
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.313023 Loss1: 0.246508 Loss2: 0.066515
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.303495 Loss1: 0.237389 Loss2: 0.066106
+(DefaultActor pid=1838052) >> Training accuracy: 0.935033
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.357395 Loss1: 0.784028 Loss2: 0.573367
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.069473 Loss1: 0.497718 Loss2: 0.571754
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.000071 Loss1: 0.439079 Loss2: 0.560992
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.947387 Loss1: 0.398598 Loss2: 0.548789
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.881775 Loss1: 0.341615 Loss2: 0.540161
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.857513 Loss1: 0.321647 Loss2: 0.535867
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.811181 Loss1: 0.284508 Loss2: 0.526673
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.811145 Loss1: 0.291165 Loss2: 0.519980
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.801091 Loss1: 0.286774 Loss2: 0.514317
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.737694 Loss1: 0.227459 Loss2: 0.510235
+(DefaultActor pid=1838052) >> Training accuracy: 0.955301
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.849381 Loss1: 0.808420 Loss2: 0.040961
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.570889 Loss1: 0.528026 Loss2: 0.042863
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.458986 Loss1: 0.415984 Loss2: 0.043002
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.358058 Loss1: 0.315957 Loss2: 0.042101
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.351924 Loss1: 0.310259 Loss2: 0.041665
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.332808 Loss1: 0.290141 Loss2: 0.042668
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.288577 Loss1: 0.246681 Loss2: 0.041896
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.285486 Loss1: 0.242406 Loss2: 0.043080
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.260894 Loss1: 0.217639 Loss2: 0.043255
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.247928 Loss1: 0.205018 Loss2: 0.042911
+(DefaultActor pid=1838052) >> Training accuracy: 0.948691
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 19:13:22,422][flwr][DEBUG] - fit_round 24 received 10 results and 0 failures
+>> Test accuracy: 0.592300
+[2023-09-27 19:14:02,714][flwr][INFO] - fit progress: (24, 2.0303315805931823, {'accuracy': 0.5923}, 46465.60474496428)
+[2023-09-27 19:14:02,715][flwr][DEBUG] - evaluate_round 24: strategy sampled 10 clients (out of 10)
+[2023-09-27 19:14:40,522][flwr][DEBUG] - evaluate_round 24 received 10 results and 0 failures
+[2023-09-27 19:14:40,526][flwr][DEBUG] - fit_round 25: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.329997 Loss1: 0.809417 Loss2: 0.520580
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.038079 Loss1: 0.566591 Loss2: 0.471489
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.928189 Loss1: 0.483159 Loss2: 0.445030
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.860489 Loss1: 0.426273 Loss2: 0.434216
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.807056 Loss1: 0.381910 Loss2: 0.425146
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.743316 Loss1: 0.325851 Loss2: 0.417466
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.724209 Loss1: 0.310999 Loss2: 0.413210
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.691067 Loss1: 0.282567 Loss2: 0.408500
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.648827 Loss1: 0.246798 Loss2: 0.402028
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.706358 Loss1: 0.300824 Loss2: 0.405533
+(DefaultActor pid=1838052) >> Training accuracy: 0.899671
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.256178 Loss1: 0.764300 Loss2: 0.491878
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.952706 Loss1: 0.518371 Loss2: 0.434335
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.867101 Loss1: 0.452533 Loss2: 0.414569
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.779830 Loss1: 0.374151 Loss2: 0.405678
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.726078 Loss1: 0.328193 Loss2: 0.397885
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.705921 Loss1: 0.307748 Loss2: 0.398173
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.669683 Loss1: 0.279284 Loss2: 0.390398
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.651646 Loss1: 0.264214 Loss2: 0.387432
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.635348 Loss1: 0.250480 Loss2: 0.384868
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.618906 Loss1: 0.234758 Loss2: 0.384148
+(DefaultActor pid=1838052) >> Training accuracy: 0.934335
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.778346 Loss1: 0.735770 Loss2: 0.042576
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.515475 Loss1: 0.470531 Loss2: 0.044944
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.462843 Loss1: 0.417798 Loss2: 0.045045
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.365726 Loss1: 0.321141 Loss2: 0.044585
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.325845 Loss1: 0.281654 Loss2: 0.044190
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.347942 Loss1: 0.302778 Loss2: 0.045164
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.304242 Loss1: 0.259390 Loss2: 0.044852
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.258155 Loss1: 0.213793 Loss2: 0.044362
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.273992 Loss1: 0.229184 Loss2: 0.044808
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.267303 Loss1: 0.221804 Loss2: 0.045499
+(DefaultActor pid=1838052) >> Training accuracy: 0.951345
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.312456 Loss1: 0.712473 Loss2: 0.599983
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.057236 Loss1: 0.448906 Loss2: 0.608331
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.962449 Loss1: 0.364300 Loss2: 0.598149
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.918843 Loss1: 0.327828 Loss2: 0.591015
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.845136 Loss1: 0.264435 Loss2: 0.580702
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.814914 Loss1: 0.243952 Loss2: 0.570962
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.817702 Loss1: 0.250645 Loss2: 0.567058
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.809295 Loss1: 0.247987 Loss2: 0.561307
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.784566 Loss1: 0.228535 Loss2: 0.556031
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.712430 Loss1: 0.162854 Loss2: 0.549576
+(DefaultActor pid=1838052) >> Training accuracy: 0.968950
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.790465 Loss1: 0.745323 Loss2: 0.045142
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.520170 Loss1: 0.473148 Loss2: 0.047022
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.440760 Loss1: 0.394355 Loss2: 0.046406
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.408869 Loss1: 0.362846 Loss2: 0.046023
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.370658 Loss1: 0.324982 Loss2: 0.045676
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.318662 Loss1: 0.273805 Loss2: 0.044857
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.302257 Loss1: 0.256891 Loss2: 0.045366
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.298903 Loss1: 0.253518 Loss2: 0.045385
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.299818 Loss1: 0.254821 Loss2: 0.044997
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.240192 Loss1: 0.195716 Loss2: 0.044476
+(DefaultActor pid=1838052) >> Training accuracy: 0.948718
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.782021 Loss1: 0.740751 Loss2: 0.041270
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.534417 Loss1: 0.490867 Loss2: 0.043550
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.417567 Loss1: 0.374426 Loss2: 0.043141
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.375231 Loss1: 0.332057 Loss2: 0.043174
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.330891 Loss1: 0.287669 Loss2: 0.043222
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.319109 Loss1: 0.275819 Loss2: 0.043290
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.303572 Loss1: 0.260073 Loss2: 0.043499
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.287586 Loss1: 0.243874 Loss2: 0.043712
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.246327 Loss1: 0.203842 Loss2: 0.042485
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.279693 Loss1: 0.236235 Loss2: 0.043459
+(DefaultActor pid=1838052) >> Training accuracy: 0.932753
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.426305 Loss1: 0.842161 Loss2: 0.584143
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.088509 Loss1: 0.501216 Loss2: 0.587292
+(DefaultActor pid=1838052) Epoch: 2 Loss: 1.006018 Loss1: 0.435116 Loss2: 0.570902
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.941382 Loss1: 0.384386 Loss2: 0.556996
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.879953 Loss1: 0.329938 Loss2: 0.550015
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.846112 Loss1: 0.308679 Loss2: 0.537432
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.824629 Loss1: 0.293451 Loss2: 0.531178
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.793173 Loss1: 0.269141 Loss2: 0.524032
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.751007 Loss1: 0.234943 Loss2: 0.516064
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.711479 Loss1: 0.202238 Loss2: 0.509241
+(DefaultActor pid=1838052) >> Training accuracy: 0.948057
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.736281 Loss1: 0.691854 Loss2: 0.044426
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.480219 Loss1: 0.433988 Loss2: 0.046231
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.417790 Loss1: 0.372172 Loss2: 0.045618
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.388054 Loss1: 0.342259 Loss2: 0.045794
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.343320 Loss1: 0.298035 Loss2: 0.045285
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.276188 Loss1: 0.231426 Loss2: 0.044762
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.261829 Loss1: 0.216741 Loss2: 0.045088
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.302489 Loss1: 0.256886 Loss2: 0.045604
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.256206 Loss1: 0.210527 Loss2: 0.045679
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.242100 Loss1: 0.197070 Loss2: 0.045029
+(DefaultActor pid=1838052) >> Training accuracy: 0.956364
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.822499 Loss1: 0.774482 Loss2: 0.048017
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.535868 Loss1: 0.485756 Loss2: 0.050112
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.451289 Loss1: 0.402874 Loss2: 0.048415
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.374764 Loss1: 0.327667 Loss2: 0.047097
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.313717 Loss1: 0.266879 Loss2: 0.046838
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.267606 Loss1: 0.221725 Loss2: 0.045881
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.309855 Loss1: 0.263296 Loss2: 0.046559
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.269192 Loss1: 0.222515 Loss2: 0.046676
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.243778 Loss1: 0.198089 Loss2: 0.045689
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.229755 Loss1: 0.183835 Loss2: 0.045919
+(DefaultActor pid=1838052) >> Training accuracy: 0.951172
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.759659 Loss1: 0.719646 Loss2: 0.040012
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.474219 Loss1: 0.432075 Loss2: 0.042144
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.392891 Loss1: 0.351218 Loss2: 0.041673
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.352903 Loss1: 0.311112 Loss2: 0.041791
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.355364 Loss1: 0.313029 Loss2: 0.042335
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.282094 Loss1: 0.240547 Loss2: 0.041547
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.303291 Loss1: 0.260490 Loss2: 0.042801
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.308169 Loss1: 0.265280 Loss2: 0.042889
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.256916 Loss1: 0.214957 Loss2: 0.041959
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.238460 Loss1: 0.196458 Loss2: 0.042002
+(DefaultActor pid=1838052) >> Training accuracy: 0.967959
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 19:44:23,473][flwr][DEBUG] - fit_round 25 received 10 results and 0 failures
+>> Test accuracy: 0.600600
+[2023-09-27 19:45:04,991][flwr][INFO] - fit progress: (25, 1.9995412224778732, {'accuracy': 0.6006}, 48327.880902301054)
+[2023-09-27 19:45:04,991][flwr][DEBUG] - evaluate_round 25: strategy sampled 10 clients (out of 10)
+[2023-09-27 19:45:42,323][flwr][DEBUG] - evaluate_round 25 received 10 results and 0 failures
+[2023-09-27 19:45:42,324][flwr][DEBUG] - fit_round 26: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.307609 Loss1: 0.736728 Loss2: 0.570881
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.063060 Loss1: 0.498692 Loss2: 0.564369
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.939616 Loss1: 0.390017 Loss2: 0.549599
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.850015 Loss1: 0.321918 Loss2: 0.528097
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.872049 Loss1: 0.349522 Loss2: 0.522527
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.825248 Loss1: 0.310281 Loss2: 0.514967
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.806860 Loss1: 0.296424 Loss2: 0.510435
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.734933 Loss1: 0.232399 Loss2: 0.502534
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.714305 Loss1: 0.216596 Loss2: 0.497709
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.720780 Loss1: 0.223876 Loss2: 0.496904
+(DefaultActor pid=1838052) >> Training accuracy: 0.948378
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.708218 Loss1: 0.666537 Loss2: 0.041680
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.443802 Loss1: 0.400048 Loss2: 0.043754
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.357236 Loss1: 0.314299 Loss2: 0.042937
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.310887 Loss1: 0.268357 Loss2: 0.042530
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.271997 Loss1: 0.229497 Loss2: 0.042501
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.244033 Loss1: 0.201567 Loss2: 0.042466
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.261948 Loss1: 0.219346 Loss2: 0.042602
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.199135 Loss1: 0.157392 Loss2: 0.041744
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.185488 Loss1: 0.143832 Loss2: 0.041655
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.199730 Loss1: 0.157406 Loss2: 0.042325
+(DefaultActor pid=1838052) >> Training accuracy: 0.953125
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.858796 Loss1: 0.810445 Loss2: 0.048351
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.573353 Loss1: 0.523456 Loss2: 0.049897
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.454274 Loss1: 0.406157 Loss2: 0.048117
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.372983 Loss1: 0.325553 Loss2: 0.047430
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.370592 Loss1: 0.322694 Loss2: 0.047898
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.348996 Loss1: 0.301763 Loss2: 0.047232
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.310236 Loss1: 0.263016 Loss2: 0.047220
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.280667 Loss1: 0.233432 Loss2: 0.047235
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.286905 Loss1: 0.240425 Loss2: 0.046480
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.302074 Loss1: 0.254421 Loss2: 0.047653
+(DefaultActor pid=1838052) >> Training accuracy: 0.933183
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.749445 Loss1: 0.702145 Loss2: 0.047300
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.513036 Loss1: 0.464738 Loss2: 0.048298
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.377657 Loss1: 0.331102 Loss2: 0.046555
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.327487 Loss1: 0.282172 Loss2: 0.045315
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.294247 Loss1: 0.248933 Loss2: 0.045314
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.309151 Loss1: 0.263982 Loss2: 0.045168
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.310645 Loss1: 0.264854 Loss2: 0.045791
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.269481 Loss1: 0.224453 Loss2: 0.045028
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.250297 Loss1: 0.206408 Loss2: 0.043889
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.242481 Loss1: 0.197844 Loss2: 0.044637
+(DefaultActor pid=1838052) >> Training accuracy: 0.964201
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.739217 Loss1: 0.697954 Loss2: 0.041262
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.460103 Loss1: 0.415727 Loss2: 0.044376
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.413208 Loss1: 0.369592 Loss2: 0.043616
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.345216 Loss1: 0.301352 Loss2: 0.043863
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.319468 Loss1: 0.276097 Loss2: 0.043372
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.269692 Loss1: 0.226086 Loss2: 0.043607
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.277958 Loss1: 0.234574 Loss2: 0.043384
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.240899 Loss1: 0.198079 Loss2: 0.042820
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.230003 Loss1: 0.186654 Loss2: 0.043348
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.215585 Loss1: 0.172640 Loss2: 0.042945
+(DefaultActor pid=1838052) >> Training accuracy: 0.965278
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.803408 Loss1: 0.711184 Loss2: 0.092224
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.539614 Loss1: 0.449625 Loss2: 0.089989
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.453037 Loss1: 0.367429 Loss2: 0.085608
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.364378 Loss1: 0.281564 Loss2: 0.082814
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.388432 Loss1: 0.307434 Loss2: 0.080998
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.388608 Loss1: 0.307669 Loss2: 0.080938
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.330087 Loss1: 0.251514 Loss2: 0.078573
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.282251 Loss1: 0.204598 Loss2: 0.077652
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.304125 Loss1: 0.227660 Loss2: 0.076466
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.280841 Loss1: 0.204271 Loss2: 0.076569
+(DefaultActor pid=1838052) >> Training accuracy: 0.946005
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.275490 Loss1: 0.684764 Loss2: 0.590726
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.015861 Loss1: 0.432333 Loss2: 0.583528
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.934169 Loss1: 0.363726 Loss2: 0.570443
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.911339 Loss1: 0.352716 Loss2: 0.558623
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.814563 Loss1: 0.267862 Loss2: 0.546701
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.850898 Loss1: 0.310980 Loss2: 0.539919
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.791972 Loss1: 0.256382 Loss2: 0.535590
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.789446 Loss1: 0.256866 Loss2: 0.532579
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.749567 Loss1: 0.224281 Loss2: 0.525286
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.706731 Loss1: 0.186564 Loss2: 0.520168
+(DefaultActor pid=1838052) >> Training accuracy: 0.957674
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.823103 Loss1: 0.777706 Loss2: 0.045397
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.531531 Loss1: 0.483836 Loss2: 0.047695
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.410695 Loss1: 0.363961 Loss2: 0.046734
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.358452 Loss1: 0.312262 Loss2: 0.046190
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.318534 Loss1: 0.272690 Loss2: 0.045844
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.289983 Loss1: 0.243936 Loss2: 0.046047
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.286393 Loss1: 0.240019 Loss2: 0.046373
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.226492 Loss1: 0.180647 Loss2: 0.045845
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.226848 Loss1: 0.182400 Loss2: 0.044448
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.231644 Loss1: 0.186726 Loss2: 0.044919
+(DefaultActor pid=1838052) >> Training accuracy: 0.942356
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.707280 Loss1: 0.666059 Loss2: 0.041222
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.460701 Loss1: 0.417215 Loss2: 0.043487
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.391393 Loss1: 0.348482 Loss2: 0.042912
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.342186 Loss1: 0.299330 Loss2: 0.042857
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.303638 Loss1: 0.261163 Loss2: 0.042475
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.267833 Loss1: 0.225248 Loss2: 0.042585
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.263346 Loss1: 0.220522 Loss2: 0.042824
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.260324 Loss1: 0.217184 Loss2: 0.043139
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.261633 Loss1: 0.218306 Loss2: 0.043327
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.242152 Loss1: 0.199277 Loss2: 0.042875
+(DefaultActor pid=1838052) >> Training accuracy: 0.955793
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.791956 Loss1: 0.704503 Loss2: 0.087453
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.546324 Loss1: 0.463850 Loss2: 0.082474
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.445188 Loss1: 0.367720 Loss2: 0.077468
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.381346 Loss1: 0.307673 Loss2: 0.073673
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.362376 Loss1: 0.290364 Loss2: 0.072013
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.329719 Loss1: 0.258371 Loss2: 0.071348
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.332203 Loss1: 0.261551 Loss2: 0.070653
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.332045 Loss1: 0.262244 Loss2: 0.069801
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.282110 Loss1: 0.212550 Loss2: 0.069560
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.252673 Loss1: 0.184938 Loss2: 0.067735
+(DefaultActor pid=1838052) >> Training accuracy: 0.962941
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 20:15:21,337][flwr][DEBUG] - fit_round 26 received 10 results and 0 failures
+>> Test accuracy: 0.600900
+[2023-09-27 20:16:03,496][flwr][INFO] - fit progress: (26, 2.022627312535295, {'accuracy': 0.6009}, 50186.38644901337)
+[2023-09-27 20:16:03,496][flwr][DEBUG] - evaluate_round 26: strategy sampled 10 clients (out of 10)
+[2023-09-27 20:16:40,760][flwr][DEBUG] - evaluate_round 26 received 10 results and 0 failures
+[2023-09-27 20:16:40,768][flwr][DEBUG] - fit_round 27: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.716829 Loss1: 0.675524 Loss2: 0.041305
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.490902 Loss1: 0.446680 Loss2: 0.044222
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.368907 Loss1: 0.325645 Loss2: 0.043262
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.349402 Loss1: 0.305222 Loss2: 0.044180
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.298198 Loss1: 0.254888 Loss2: 0.043309
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.298342 Loss1: 0.254968 Loss2: 0.043374
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.249775 Loss1: 0.205971 Loss2: 0.043805
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.245906 Loss1: 0.203141 Loss2: 0.042766
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.268300 Loss1: 0.224518 Loss2: 0.043782
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.219230 Loss1: 0.176128 Loss2: 0.043102
+(DefaultActor pid=1838052) >> Training accuracy: 0.965783
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.717385 Loss1: 0.657095 Loss2: 0.060290
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.480629 Loss1: 0.421247 Loss2: 0.059381
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.395177 Loss1: 0.339883 Loss2: 0.055294
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.399366 Loss1: 0.344697 Loss2: 0.054669
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.312214 Loss1: 0.259410 Loss2: 0.052804
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.303250 Loss1: 0.251261 Loss2: 0.051989
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.289556 Loss1: 0.238906 Loss2: 0.050650
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.273054 Loss1: 0.222267 Loss2: 0.050787
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.259198 Loss1: 0.208929 Loss2: 0.050269
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.200483 Loss1: 0.151784 Loss2: 0.048699
+(DefaultActor pid=1838052) >> Training accuracy: 0.960938
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.236836 Loss1: 0.643126 Loss2: 0.593710
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.009365 Loss1: 0.413419 Loss2: 0.595946
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.927049 Loss1: 0.348469 Loss2: 0.578579
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.860755 Loss1: 0.293641 Loss2: 0.567114
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.835735 Loss1: 0.278047 Loss2: 0.557688
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.833953 Loss1: 0.285483 Loss2: 0.548471
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.779594 Loss1: 0.236050 Loss2: 0.543544
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.787101 Loss1: 0.249168 Loss2: 0.537933
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.751625 Loss1: 0.219536 Loss2: 0.532089
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.766342 Loss1: 0.237862 Loss2: 0.528479
+(DefaultActor pid=1838052) >> Training accuracy: 0.950648
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.679282 Loss1: 0.641185 Loss2: 0.038097
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.417522 Loss1: 0.377009 Loss2: 0.040513
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.333061 Loss1: 0.293008 Loss2: 0.040053
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.298908 Loss1: 0.258525 Loss2: 0.040383
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.286204 Loss1: 0.245257 Loss2: 0.040947
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.252820 Loss1: 0.211704 Loss2: 0.041116
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.230131 Loss1: 0.189316 Loss2: 0.040815
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.192185 Loss1: 0.152068 Loss2: 0.040116
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.213965 Loss1: 0.173337 Loss2: 0.040628
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.230187 Loss1: 0.189021 Loss2: 0.041166
+(DefaultActor pid=1838052) >> Training accuracy: 0.952324
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.298190 Loss1: 0.696432 Loss2: 0.601758
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.032305 Loss1: 0.433411 Loss2: 0.598894
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.912406 Loss1: 0.332215 Loss2: 0.580190
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.929515 Loss1: 0.359647 Loss2: 0.569869
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.841935 Loss1: 0.282316 Loss2: 0.559619
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.795458 Loss1: 0.245011 Loss2: 0.550446
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.773979 Loss1: 0.234177 Loss2: 0.539802
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.740368 Loss1: 0.206261 Loss2: 0.534107
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.727348 Loss1: 0.198459 Loss2: 0.528889
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.744922 Loss1: 0.218948 Loss2: 0.525974
+(DefaultActor pid=1838052) >> Training accuracy: 0.941623
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.725778 Loss1: 0.681765 Loss2: 0.044014
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.447611 Loss1: 0.402621 Loss2: 0.044990
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.363807 Loss1: 0.319400 Loss2: 0.044407
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.315280 Loss1: 0.271143 Loss2: 0.044137
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.319019 Loss1: 0.274760 Loss2: 0.044259
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.264092 Loss1: 0.220276 Loss2: 0.043816
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.252676 Loss1: 0.208778 Loss2: 0.043898
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.257009 Loss1: 0.213152 Loss2: 0.043856
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.228965 Loss1: 0.185850 Loss2: 0.043115
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.257806 Loss1: 0.213964 Loss2: 0.043842
+(DefaultActor pid=1838052) >> Training accuracy: 0.945016
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.757254 Loss1: 0.715112 Loss2: 0.042142
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.542875 Loss1: 0.497684 Loss2: 0.045191
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.430824 Loss1: 0.386217 Loss2: 0.044607
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.357960 Loss1: 0.313278 Loss2: 0.044682
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.328626 Loss1: 0.284560 Loss2: 0.044067
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.288506 Loss1: 0.243563 Loss2: 0.044942
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.264788 Loss1: 0.221060 Loss2: 0.043728
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.287106 Loss1: 0.242978 Loss2: 0.044128
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.286793 Loss1: 0.241560 Loss2: 0.045233
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.258187 Loss1: 0.213464 Loss2: 0.044723
+(DefaultActor pid=1838052) >> Training accuracy: 0.960732
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.691833 Loss1: 0.650921 Loss2: 0.040912
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.493886 Loss1: 0.449778 Loss2: 0.044109
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.340478 Loss1: 0.297366 Loss2: 0.043112
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.310208 Loss1: 0.267723 Loss2: 0.042485
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.314442 Loss1: 0.271369 Loss2: 0.043073
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.267343 Loss1: 0.224131 Loss2: 0.043212
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.278235 Loss1: 0.234406 Loss2: 0.043829
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.230124 Loss1: 0.187056 Loss2: 0.043068
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.234309 Loss1: 0.191550 Loss2: 0.042758
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.238355 Loss1: 0.195019 Loss2: 0.043336
+(DefaultActor pid=1838052) >> Training accuracy: 0.955301
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.769019 Loss1: 0.727786 Loss2: 0.041234
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.464093 Loss1: 0.420724 Loss2: 0.043368
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.363980 Loss1: 0.321986 Loss2: 0.041994
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.354912 Loss1: 0.312125 Loss2: 0.042787
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.303808 Loss1: 0.260812 Loss2: 0.042996
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.256324 Loss1: 0.214323 Loss2: 0.042001
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.245323 Loss1: 0.202433 Loss2: 0.042890
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.229945 Loss1: 0.187007 Loss2: 0.042938
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.235672 Loss1: 0.192590 Loss2: 0.043082
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.196757 Loss1: 0.154161 Loss2: 0.042595
+(DefaultActor pid=1838052) >> Training accuracy: 0.964738
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.781613 Loss1: 0.694398 Loss2: 0.087215
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.489808 Loss1: 0.408833 Loss2: 0.080975
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.399242 Loss1: 0.323572 Loss2: 0.075671
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.369989 Loss1: 0.295731 Loss2: 0.074258
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.334783 Loss1: 0.262468 Loss2: 0.072315
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.288946 Loss1: 0.219001 Loss2: 0.069945
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.273322 Loss1: 0.203766 Loss2: 0.069557
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.249249 Loss1: 0.181009 Loss2: 0.068240
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.244970 Loss1: 0.177835 Loss2: 0.067135
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.267737 Loss1: 0.200274 Loss2: 0.067463
+(DefaultActor pid=1838052) >> Training accuracy: 0.955301
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 20:46:23,403][flwr][DEBUG] - fit_round 27 received 10 results and 0 failures
+>> Test accuracy: 0.607200
+[2023-09-27 20:47:17,348][flwr][INFO] - fit progress: (27, 1.9937346629060495, {'accuracy': 0.6072}, 52060.238440748304)
+[2023-09-27 20:47:17,349][flwr][DEBUG] - evaluate_round 27: strategy sampled 10 clients (out of 10)
+[2023-09-27 20:47:54,443][flwr][DEBUG] - evaluate_round 27 received 10 results and 0 failures
+[2023-09-27 20:47:54,445][flwr][DEBUG] - fit_round 28: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.177499 Loss1: 0.587720 Loss2: 0.589778
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.997574 Loss1: 0.407888 Loss2: 0.589686
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.914400 Loss1: 0.336873 Loss2: 0.577528
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.860932 Loss1: 0.292887 Loss2: 0.568044
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.882401 Loss1: 0.322464 Loss2: 0.559937
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.836113 Loss1: 0.281923 Loss2: 0.554190
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.780129 Loss1: 0.234181 Loss2: 0.545947
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.758080 Loss1: 0.220503 Loss2: 0.537577
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.755611 Loss1: 0.218884 Loss2: 0.536727
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.740003 Loss1: 0.208614 Loss2: 0.531389
+(DefaultActor pid=1838052) >> Training accuracy: 0.952927
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.280394 Loss1: 0.683823 Loss2: 0.596571
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.042676 Loss1: 0.436843 Loss2: 0.605832
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.958412 Loss1: 0.363102 Loss2: 0.595310
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.868183 Loss1: 0.289814 Loss2: 0.578369
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.833320 Loss1: 0.261704 Loss2: 0.571616
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.813932 Loss1: 0.249905 Loss2: 0.564027
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.814511 Loss1: 0.258781 Loss2: 0.555730
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.763751 Loss1: 0.214377 Loss2: 0.549374
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.754031 Loss1: 0.211335 Loss2: 0.542696
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.737770 Loss1: 0.197981 Loss2: 0.539790
+(DefaultActor pid=1838052) >> Training accuracy: 0.960093
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.121187 Loss1: 0.654946 Loss2: 0.466241
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.852466 Loss1: 0.436757 Loss2: 0.415709
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.754742 Loss1: 0.357456 Loss2: 0.397286
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.721636 Loss1: 0.329777 Loss2: 0.391860
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.675751 Loss1: 0.287343 Loss2: 0.388409
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.631540 Loss1: 0.249050 Loss2: 0.382490
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.603074 Loss1: 0.224895 Loss2: 0.378178
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.620134 Loss1: 0.240548 Loss2: 0.379586
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.560094 Loss1: 0.186140 Loss2: 0.373954
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.591902 Loss1: 0.219117 Loss2: 0.372785
+(DefaultActor pid=1838052) >> Training accuracy: 0.942445
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.641224 Loss1: 0.601713 Loss2: 0.039512
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.400158 Loss1: 0.358245 Loss2: 0.041913
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.355149 Loss1: 0.313395 Loss2: 0.041754
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.312157 Loss1: 0.270722 Loss2: 0.041435
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.307111 Loss1: 0.265116 Loss2: 0.041995
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.247563 Loss1: 0.205720 Loss2: 0.041844
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.232229 Loss1: 0.190890 Loss2: 0.041339
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.246193 Loss1: 0.204057 Loss2: 0.042136
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.226924 Loss1: 0.184765 Loss2: 0.042159
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.237727 Loss1: 0.195503 Loss2: 0.042224
+(DefaultActor pid=1838052) >> Training accuracy: 0.958270
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.645695 Loss1: 0.607548 Loss2: 0.038146
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.414433 Loss1: 0.374205 Loss2: 0.040228
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.365731 Loss1: 0.325263 Loss2: 0.040468
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.304798 Loss1: 0.264357 Loss2: 0.040441
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.288989 Loss1: 0.248294 Loss2: 0.040695
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.294973 Loss1: 0.252866 Loss2: 0.042107
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.269186 Loss1: 0.227791 Loss2: 0.041395
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.237118 Loss1: 0.196994 Loss2: 0.040124
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.257407 Loss1: 0.215507 Loss2: 0.041900
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.228797 Loss1: 0.187380 Loss2: 0.041417
+(DefaultActor pid=1838052) >> Training accuracy: 0.964003
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.702489 Loss1: 0.658110 Loss2: 0.044379
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.448254 Loss1: 0.402296 Loss2: 0.045958
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.344919 Loss1: 0.300397 Loss2: 0.044523
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.303985 Loss1: 0.259816 Loss2: 0.044169
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.309023 Loss1: 0.264674 Loss2: 0.044349
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.244956 Loss1: 0.201206 Loss2: 0.043750
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.216750 Loss1: 0.174343 Loss2: 0.042407
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.216073 Loss1: 0.173873 Loss2: 0.042200
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.216481 Loss1: 0.173423 Loss2: 0.043059
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.208570 Loss1: 0.165991 Loss2: 0.042579
+(DefaultActor pid=1838052) >> Training accuracy: 0.967665
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.727481 Loss1: 0.638008 Loss2: 0.089473
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.494464 Loss1: 0.408062 Loss2: 0.086402
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.406864 Loss1: 0.324648 Loss2: 0.082216
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.364106 Loss1: 0.285396 Loss2: 0.078710
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.312121 Loss1: 0.235078 Loss2: 0.077042
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.295697 Loss1: 0.221805 Loss2: 0.073892
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.293448 Loss1: 0.218643 Loss2: 0.074804
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.304306 Loss1: 0.230408 Loss2: 0.073898
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.252374 Loss1: 0.180240 Loss2: 0.072133
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.233606 Loss1: 0.163471 Loss2: 0.070135
+(DefaultActor pid=1838052) >> Training accuracy: 0.973299
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.613084 Loss1: 0.576290 Loss2: 0.036794
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.396140 Loss1: 0.356770 Loss2: 0.039370
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.305053 Loss1: 0.265649 Loss2: 0.039404
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.259517 Loss1: 0.220334 Loss2: 0.039183
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.247862 Loss1: 0.208343 Loss2: 0.039519
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.254341 Loss1: 0.214421 Loss2: 0.039920
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.246924 Loss1: 0.206685 Loss2: 0.040239
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.225944 Loss1: 0.185983 Loss2: 0.039961
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.179295 Loss1: 0.139808 Loss2: 0.039487
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166240 Loss1: 0.126612 Loss2: 0.039628
+(DefaultActor pid=1838052) >> Training accuracy: 0.968950
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.745247 Loss1: 0.704614 Loss2: 0.040633
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.477103 Loss1: 0.434564 Loss2: 0.042539
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.383070 Loss1: 0.340844 Loss2: 0.042225
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.328652 Loss1: 0.286451 Loss2: 0.042201
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.300784 Loss1: 0.258217 Loss2: 0.042567
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.288436 Loss1: 0.244919 Loss2: 0.043517
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.264212 Loss1: 0.221609 Loss2: 0.042603
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.248111 Loss1: 0.204903 Loss2: 0.043207
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.242711 Loss1: 0.199806 Loss2: 0.042906
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.266911 Loss1: 0.222853 Loss2: 0.044058
+(DefaultActor pid=1838052) >> Training accuracy: 0.943462
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.678786 Loss1: 0.637983 Loss2: 0.040803
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.471213 Loss1: 0.427199 Loss2: 0.044014
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.386896 Loss1: 0.343218 Loss2: 0.043678
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.352327 Loss1: 0.308400 Loss2: 0.043926
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.324248 Loss1: 0.280130 Loss2: 0.044118
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.291913 Loss1: 0.248312 Loss2: 0.043600
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.229252 Loss1: 0.186709 Loss2: 0.042543
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.236890 Loss1: 0.194043 Loss2: 0.042847
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.229988 Loss1: 0.187529 Loss2: 0.042459
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.229906 Loss1: 0.187175 Loss2: 0.042731
+(DefaultActor pid=1838052) >> Training accuracy: 0.958534
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 21:17:39,383][flwr][DEBUG] - fit_round 28 received 10 results and 0 failures
+>> Test accuracy: 0.608100
+[2023-09-27 21:18:19,931][flwr][INFO] - fit progress: (28, 2.021510124206543, {'accuracy': 0.6081}, 53922.82100760145)
+[2023-09-27 21:18:19,931][flwr][DEBUG] - evaluate_round 28: strategy sampled 10 clients (out of 10)
+[2023-09-27 21:18:56,020][flwr][DEBUG] - evaluate_round 28 received 10 results and 0 failures
+[2023-09-27 21:18:56,023][flwr][DEBUG] - fit_round 29: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.609343 Loss1: 0.569689 Loss2: 0.039654
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.384192 Loss1: 0.342446 Loss2: 0.041746
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.308441 Loss1: 0.267434 Loss2: 0.041007
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.311209 Loss1: 0.268946 Loss2: 0.042263
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.273300 Loss1: 0.231416 Loss2: 0.041884
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.221078 Loss1: 0.179840 Loss2: 0.041238
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.219603 Loss1: 0.178329 Loss2: 0.041274
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.213994 Loss1: 0.172220 Loss2: 0.041774
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.183487 Loss1: 0.142291 Loss2: 0.041196
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.203540 Loss1: 0.162281 Loss2: 0.041260
+(DefaultActor pid=1838052) >> Training accuracy: 0.958460
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.167830 Loss1: 0.573188 Loss2: 0.594641
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.972766 Loss1: 0.374860 Loss2: 0.597906
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.872644 Loss1: 0.288810 Loss2: 0.583834
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.853867 Loss1: 0.281314 Loss2: 0.572553
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.831808 Loss1: 0.267523 Loss2: 0.564285
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.784296 Loss1: 0.228438 Loss2: 0.555858
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.785732 Loss1: 0.233632 Loss2: 0.552099
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.759950 Loss1: 0.213973 Loss2: 0.545976
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.730845 Loss1: 0.190183 Loss2: 0.540662
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.739384 Loss1: 0.201537 Loss2: 0.537848
+(DefaultActor pid=1838052) >> Training accuracy: 0.950554
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.635625 Loss1: 0.596136 Loss2: 0.039489
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.412654 Loss1: 0.371131 Loss2: 0.041523
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.353941 Loss1: 0.312029 Loss2: 0.041913
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.281941 Loss1: 0.241066 Loss2: 0.040875
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.285294 Loss1: 0.243241 Loss2: 0.042053
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.286111 Loss1: 0.243231 Loss2: 0.042880
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.277236 Loss1: 0.235021 Loss2: 0.042215
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.253191 Loss1: 0.210772 Loss2: 0.042420
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.217026 Loss1: 0.175335 Loss2: 0.041691
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.206248 Loss1: 0.165261 Loss2: 0.040988
+(DefaultActor pid=1838052) >> Training accuracy: 0.960136
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.226984 Loss1: 0.618265 Loss2: 0.608719
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.995025 Loss1: 0.379163 Loss2: 0.615863
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.897348 Loss1: 0.292678 Loss2: 0.604670
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.882489 Loss1: 0.285508 Loss2: 0.596981
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.860895 Loss1: 0.274788 Loss2: 0.586107
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.816783 Loss1: 0.234258 Loss2: 0.582525
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.782721 Loss1: 0.209984 Loss2: 0.572737
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.750611 Loss1: 0.183057 Loss2: 0.567554
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.757368 Loss1: 0.196490 Loss2: 0.560878
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.702494 Loss1: 0.145876 Loss2: 0.556618
+(DefaultActor pid=1838052) >> Training accuracy: 0.962023
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.721243 Loss1: 0.680995 Loss2: 0.040248
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.475314 Loss1: 0.432782 Loss2: 0.042533
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.384264 Loss1: 0.341708 Loss2: 0.042556
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.304846 Loss1: 0.262417 Loss2: 0.042429
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.279489 Loss1: 0.237825 Loss2: 0.041665
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.242468 Loss1: 0.201186 Loss2: 0.041282
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.239861 Loss1: 0.198775 Loss2: 0.041086
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.219473 Loss1: 0.178015 Loss2: 0.041458
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.235116 Loss1: 0.193465 Loss2: 0.041652
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.236709 Loss1: 0.194275 Loss2: 0.042434
+(DefaultActor pid=1838052) >> Training accuracy: 0.956414
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.147349 Loss1: 0.614236 Loss2: 0.533113
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.893210 Loss1: 0.399053 Loss2: 0.494157
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.837763 Loss1: 0.355695 Loss2: 0.482068
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.765410 Loss1: 0.294237 Loss2: 0.471173
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.734894 Loss1: 0.265958 Loss2: 0.468936
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.720206 Loss1: 0.255044 Loss2: 0.465162
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.671209 Loss1: 0.212200 Loss2: 0.459009
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.655429 Loss1: 0.197627 Loss2: 0.457802
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.650467 Loss1: 0.196233 Loss2: 0.454234
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.642643 Loss1: 0.190718 Loss2: 0.451925
+(DefaultActor pid=1838052) >> Training accuracy: 0.963212
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.149229 Loss1: 0.563162 Loss2: 0.586067
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.948661 Loss1: 0.357722 Loss2: 0.590939
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.823357 Loss1: 0.255749 Loss2: 0.567607
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.790932 Loss1: 0.231437 Loss2: 0.559494
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.789250 Loss1: 0.239456 Loss2: 0.549794
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.770460 Loss1: 0.227281 Loss2: 0.543179
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.758205 Loss1: 0.218504 Loss2: 0.539701
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.716766 Loss1: 0.188212 Loss2: 0.528554
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.696864 Loss1: 0.171569 Loss2: 0.525295
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.673486 Loss1: 0.156062 Loss2: 0.517424
+(DefaultActor pid=1838052) >> Training accuracy: 0.963341
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.678957 Loss1: 0.603156 Loss2: 0.075801
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.449492 Loss1: 0.375799 Loss2: 0.073693
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.379342 Loss1: 0.308845 Loss2: 0.070497
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.327709 Loss1: 0.257764 Loss2: 0.069944
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.303658 Loss1: 0.235983 Loss2: 0.067676
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.244616 Loss1: 0.178586 Loss2: 0.066030
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.212771 Loss1: 0.148628 Loss2: 0.064142
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.238458 Loss1: 0.174575 Loss2: 0.063883
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.218952 Loss1: 0.155028 Loss2: 0.063925
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.236997 Loss1: 0.172274 Loss2: 0.064723
+(DefaultActor pid=1838052) >> Training accuracy: 0.963608
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.748009 Loss1: 0.672297 Loss2: 0.075712
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.482392 Loss1: 0.407664 Loss2: 0.074728
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.375665 Loss1: 0.306668 Loss2: 0.068997
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.313917 Loss1: 0.246745 Loss2: 0.067172
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.262749 Loss1: 0.197428 Loss2: 0.065321
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.252916 Loss1: 0.189239 Loss2: 0.063677
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.227047 Loss1: 0.164002 Loss2: 0.063045
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.240649 Loss1: 0.177635 Loss2: 0.063014
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.240205 Loss1: 0.177488 Loss2: 0.062717
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.232059 Loss1: 0.169484 Loss2: 0.062576
+(DefaultActor pid=1838052) >> Training accuracy: 0.961149
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.657874 Loss1: 0.576924 Loss2: 0.080950
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.453362 Loss1: 0.376315 Loss2: 0.077048
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.354366 Loss1: 0.281917 Loss2: 0.072448
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.299531 Loss1: 0.229745 Loss2: 0.069786
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.284920 Loss1: 0.217802 Loss2: 0.067119
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.281298 Loss1: 0.214522 Loss2: 0.066776
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.236804 Loss1: 0.171236 Loss2: 0.065568
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.197984 Loss1: 0.134497 Loss2: 0.063487
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.225552 Loss1: 0.160845 Loss2: 0.064707
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.244948 Loss1: 0.180164 Loss2: 0.064784
+(DefaultActor pid=1838052) >> Training accuracy: 0.946994
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 21:48:32,756][flwr][DEBUG] - fit_round 29 received 10 results and 0 failures
+>> Test accuracy: 0.613600
+[2023-09-27 21:49:13,084][flwr][INFO] - fit progress: (29, 2.0112326653620687, {'accuracy': 0.6136}, 55775.973916619085)
+[2023-09-27 21:49:13,084][flwr][DEBUG] - evaluate_round 29: strategy sampled 10 clients (out of 10)
+[2023-09-27 21:49:50,074][flwr][DEBUG] - evaluate_round 29 received 10 results and 0 failures
+[2023-09-27 21:49:50,084][flwr][DEBUG] - fit_round 30: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.624713 Loss1: 0.583612 Loss2: 0.041101
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.371815 Loss1: 0.327672 Loss2: 0.044144
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.326926 Loss1: 0.283500 Loss2: 0.043426
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.257823 Loss1: 0.215917 Loss2: 0.041906
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.229837 Loss1: 0.187765 Loss2: 0.042072
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.250343 Loss1: 0.207143 Loss2: 0.043200
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.252589 Loss1: 0.209742 Loss2: 0.042847
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.193767 Loss1: 0.151453 Loss2: 0.042314
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.221844 Loss1: 0.179983 Loss2: 0.041861
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.211218 Loss1: 0.168521 Loss2: 0.042697
+(DefaultActor pid=1838052) >> Training accuracy: 0.962891
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.611579 Loss1: 0.569620 Loss2: 0.041959
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.381968 Loss1: 0.338363 Loss2: 0.043605
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.334093 Loss1: 0.289867 Loss2: 0.044227
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.297800 Loss1: 0.254134 Loss2: 0.043666
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.257089 Loss1: 0.213895 Loss2: 0.043195
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.242959 Loss1: 0.199802 Loss2: 0.043157
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.222784 Loss1: 0.179572 Loss2: 0.043212
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.212993 Loss1: 0.170071 Loss2: 0.042922
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.219501 Loss1: 0.176038 Loss2: 0.043463
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.210239 Loss1: 0.167343 Loss2: 0.042896
+(DefaultActor pid=1838052) >> Training accuracy: 0.956290
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.539029 Loss1: 0.501446 Loss2: 0.037583
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.390278 Loss1: 0.350068 Loss2: 0.040210
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.295013 Loss1: 0.255239 Loss2: 0.039773
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.250562 Loss1: 0.210572 Loss2: 0.039990
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.237424 Loss1: 0.196656 Loss2: 0.040768
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.225065 Loss1: 0.184921 Loss2: 0.040145
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.222692 Loss1: 0.182174 Loss2: 0.040518
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.208484 Loss1: 0.168295 Loss2: 0.040189
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.192171 Loss1: 0.151839 Loss2: 0.040333
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.170491 Loss1: 0.130105 Loss2: 0.040386
+(DefaultActor pid=1838052) >> Training accuracy: 0.969703
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.151093 Loss1: 0.630034 Loss2: 0.521058
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.890118 Loss1: 0.415282 Loss2: 0.474836
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.736240 Loss1: 0.290739 Loss2: 0.445501
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.741215 Loss1: 0.302946 Loss2: 0.438269
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.707304 Loss1: 0.278793 Loss2: 0.428511
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.625389 Loss1: 0.203296 Loss2: 0.422093
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.663295 Loss1: 0.239767 Loss2: 0.423527
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.605965 Loss1: 0.192113 Loss2: 0.413852
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.592576 Loss1: 0.182260 Loss2: 0.410316
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.588795 Loss1: 0.174767 Loss2: 0.414027
+(DefaultActor pid=1838052) >> Training accuracy: 0.963894
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.181091 Loss1: 0.581691 Loss2: 0.599400
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.962498 Loss1: 0.355950 Loss2: 0.606548
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.895521 Loss1: 0.298311 Loss2: 0.597210
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.845417 Loss1: 0.258436 Loss2: 0.586981
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.798640 Loss1: 0.220894 Loss2: 0.577745
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.808439 Loss1: 0.236100 Loss2: 0.572339
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.823353 Loss1: 0.252429 Loss2: 0.570924
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.813892 Loss1: 0.248958 Loss2: 0.564933
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.757118 Loss1: 0.195711 Loss2: 0.561407
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.728902 Loss1: 0.175798 Loss2: 0.553103
+(DefaultActor pid=1838052) >> Training accuracy: 0.952123
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.583465 Loss1: 0.543655 Loss2: 0.039810
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.410598 Loss1: 0.367157 Loss2: 0.043441
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.328883 Loss1: 0.285642 Loss2: 0.043241
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.284070 Loss1: 0.241463 Loss2: 0.042607
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.270479 Loss1: 0.228150 Loss2: 0.042329
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.266857 Loss1: 0.223746 Loss2: 0.043111
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.220983 Loss1: 0.178536 Loss2: 0.042447
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.240032 Loss1: 0.197216 Loss2: 0.042816
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.197071 Loss1: 0.154885 Loss2: 0.042186
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.189636 Loss1: 0.147482 Loss2: 0.042154
+(DefaultActor pid=1838052) >> Training accuracy: 0.958070
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.061179 Loss1: 0.594748 Loss2: 0.466431
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.816578 Loss1: 0.402703 Loss2: 0.413875
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.727632 Loss1: 0.323853 Loss2: 0.403780
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.692014 Loss1: 0.300215 Loss2: 0.391799
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.640419 Loss1: 0.254008 Loss2: 0.386410
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.620816 Loss1: 0.236238 Loss2: 0.384577
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.594845 Loss1: 0.214773 Loss2: 0.380072
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.533945 Loss1: 0.160313 Loss2: 0.373632
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.565998 Loss1: 0.188468 Loss2: 0.377530
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.584388 Loss1: 0.207761 Loss2: 0.376627
+(DefaultActor pid=1838052) >> Training accuracy: 0.956883
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.642753 Loss1: 0.561934 Loss2: 0.080819
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.411801 Loss1: 0.335183 Loss2: 0.076619
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.322232 Loss1: 0.250782 Loss2: 0.071450
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.288031 Loss1: 0.219001 Loss2: 0.069030
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.267718 Loss1: 0.199920 Loss2: 0.067798
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.284620 Loss1: 0.217322 Loss2: 0.067297
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.271186 Loss1: 0.203976 Loss2: 0.067210
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.215731 Loss1: 0.150541 Loss2: 0.065190
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.238301 Loss1: 0.172974 Loss2: 0.065327
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.175744 Loss1: 0.112573 Loss2: 0.063171
+(DefaultActor pid=1838052) >> Training accuracy: 0.973892
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.561107 Loss1: 0.521005 Loss2: 0.040101
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.379943 Loss1: 0.337011 Loss2: 0.042933
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.302756 Loss1: 0.260599 Loss2: 0.042157
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.239388 Loss1: 0.198166 Loss2: 0.041222
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.207714 Loss1: 0.166879 Loss2: 0.040835
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.210695 Loss1: 0.168852 Loss2: 0.041843
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.194991 Loss1: 0.153470 Loss2: 0.041521
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.210504 Loss1: 0.168920 Loss2: 0.041585
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.192397 Loss1: 0.151314 Loss2: 0.041084
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.175819 Loss1: 0.134688 Loss2: 0.041130
+(DefaultActor pid=1838052) >> Training accuracy: 0.969952
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.236232 Loss1: 0.639819 Loss2: 0.596413
+(DefaultActor pid=1838052) Epoch: 1 Loss: 1.001039 Loss1: 0.396086 Loss2: 0.604953
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.903614 Loss1: 0.310821 Loss2: 0.592793
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.852472 Loss1: 0.268292 Loss2: 0.584181
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.815309 Loss1: 0.242300 Loss2: 0.573009
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.799533 Loss1: 0.236774 Loss2: 0.562760
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.774075 Loss1: 0.215839 Loss2: 0.558236
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.776684 Loss1: 0.224087 Loss2: 0.552597
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.770680 Loss1: 0.222491 Loss2: 0.548190
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.778668 Loss1: 0.235601 Loss2: 0.543067
+(DefaultActor pid=1838052) >> Training accuracy: 0.948602
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 22:19:27,048][flwr][DEBUG] - fit_round 30 received 10 results and 0 failures
+>> Test accuracy: 0.613300
+[2023-09-27 22:20:07,654][flwr][INFO] - fit progress: (30, 2.0224276781082153, {'accuracy': 0.6133}, 57630.5447032582)
+[2023-09-27 22:20:07,655][flwr][DEBUG] - evaluate_round 30: strategy sampled 10 clients (out of 10)
+[2023-09-27 22:20:43,581][flwr][DEBUG] - evaluate_round 30 received 10 results and 0 failures
+[2023-09-27 22:20:43,587][flwr][DEBUG] - fit_round 31: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.542811 Loss1: 0.503471 Loss2: 0.039340
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.348682 Loss1: 0.306207 Loss2: 0.042476
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.317740 Loss1: 0.274736 Loss2: 0.043004
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.306664 Loss1: 0.263193 Loss2: 0.043471
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.230721 Loss1: 0.188776 Loss2: 0.041945
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.229566 Loss1: 0.187075 Loss2: 0.042491
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.214551 Loss1: 0.172369 Loss2: 0.042182
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.267446 Loss1: 0.223608 Loss2: 0.043838
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.258311 Loss1: 0.214109 Loss2: 0.044202
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.209247 Loss1: 0.166136 Loss2: 0.043111
+(DefaultActor pid=1838052) >> Training accuracy: 0.953916
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.526774 Loss1: 0.488778 Loss2: 0.037996
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.362124 Loss1: 0.321414 Loss2: 0.040709
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.306700 Loss1: 0.266653 Loss2: 0.040047
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.260542 Loss1: 0.220252 Loss2: 0.040289
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.236668 Loss1: 0.196618 Loss2: 0.040050
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.234770 Loss1: 0.193979 Loss2: 0.040790
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.203769 Loss1: 0.163228 Loss2: 0.040542
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.187119 Loss1: 0.146868 Loss2: 0.040251
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.166274 Loss1: 0.126210 Loss2: 0.040064
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.177694 Loss1: 0.137843 Loss2: 0.039851
+(DefaultActor pid=1838052) >> Training accuracy: 0.970465
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.872140 Loss1: 0.551539 Loss2: 0.320601
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.580989 Loss1: 0.327464 Loss2: 0.253525
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.519508 Loss1: 0.288421 Loss2: 0.231086
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.477374 Loss1: 0.252042 Loss2: 0.225332
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.440154 Loss1: 0.219887 Loss2: 0.220267
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.404187 Loss1: 0.185485 Loss2: 0.218701
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.397163 Loss1: 0.179884 Loss2: 0.217280
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.367259 Loss1: 0.152581 Loss2: 0.214678
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.379468 Loss1: 0.163574 Loss2: 0.215894
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.400454 Loss1: 0.184127 Loss2: 0.216326
+(DefaultActor pid=1838052) >> Training accuracy: 0.962421
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.583732 Loss1: 0.542629 Loss2: 0.041103
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.401465 Loss1: 0.356972 Loss2: 0.044493
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.312095 Loss1: 0.268307 Loss2: 0.043789
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.279607 Loss1: 0.236258 Loss2: 0.043349
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.239690 Loss1: 0.197323 Loss2: 0.042367
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.207612 Loss1: 0.164535 Loss2: 0.043077
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.194273 Loss1: 0.152346 Loss2: 0.041927
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.222839 Loss1: 0.180386 Loss2: 0.042453
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.200021 Loss1: 0.157479 Loss2: 0.042542
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.205539 Loss1: 0.163119 Loss2: 0.042420
+(DefaultActor pid=1838052) >> Training accuracy: 0.962139
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.646631 Loss1: 0.601819 Loss2: 0.044813
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.406513 Loss1: 0.359871 Loss2: 0.046642
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.322334 Loss1: 0.276869 Loss2: 0.045465
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.266368 Loss1: 0.221210 Loss2: 0.045158
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.295741 Loss1: 0.250208 Loss2: 0.045533
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.260588 Loss1: 0.215408 Loss2: 0.045180
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.266344 Loss1: 0.221237 Loss2: 0.045107
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.204396 Loss1: 0.159819 Loss2: 0.044578
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.154845 Loss1: 0.111555 Loss2: 0.043290
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.143092 Loss1: 0.101181 Loss2: 0.041911
+(DefaultActor pid=1838052) >> Training accuracy: 0.980997
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.609556 Loss1: 0.570044 Loss2: 0.039512
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.358535 Loss1: 0.316469 Loss2: 0.042066
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.287761 Loss1: 0.246567 Loss2: 0.041194
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.269137 Loss1: 0.228108 Loss2: 0.041028
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.253593 Loss1: 0.212118 Loss2: 0.041475
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.206246 Loss1: 0.165324 Loss2: 0.040922
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197727 Loss1: 0.157298 Loss2: 0.040429
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.198943 Loss1: 0.157862 Loss2: 0.041081
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.167709 Loss1: 0.126819 Loss2: 0.040890
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.164736 Loss1: 0.125097 Loss2: 0.039639
+(DefaultActor pid=1838052) >> Training accuracy: 0.975694
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.090813 Loss1: 0.507798 Loss2: 0.583015
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.913406 Loss1: 0.336587 Loss2: 0.576819
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.852258 Loss1: 0.294208 Loss2: 0.558050
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.824889 Loss1: 0.279507 Loss2: 0.545382
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.775578 Loss1: 0.239810 Loss2: 0.535768
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.724813 Loss1: 0.199476 Loss2: 0.525337
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.750865 Loss1: 0.227969 Loss2: 0.522897
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.697224 Loss1: 0.179899 Loss2: 0.517325
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.723800 Loss1: 0.210182 Loss2: 0.513618
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.688681 Loss1: 0.178816 Loss2: 0.509865
+(DefaultActor pid=1838052) >> Training accuracy: 0.956487
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.642603 Loss1: 0.564438 Loss2: 0.078165
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.449798 Loss1: 0.374076 Loss2: 0.075722
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.383380 Loss1: 0.312825 Loss2: 0.070555
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.325726 Loss1: 0.255855 Loss2: 0.069871
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.327188 Loss1: 0.259634 Loss2: 0.067554
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.260639 Loss1: 0.195199 Loss2: 0.065440
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.250584 Loss1: 0.186177 Loss2: 0.064407
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.274176 Loss1: 0.209443 Loss2: 0.064733
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.257072 Loss1: 0.192522 Loss2: 0.064551
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.208913 Loss1: 0.146138 Loss2: 0.062775
+(DefaultActor pid=1838052) >> Training accuracy: 0.967722
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.010193 Loss1: 0.551193 Loss2: 0.459000
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.772256 Loss1: 0.364258 Loss2: 0.407998
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.721445 Loss1: 0.328681 Loss2: 0.392765
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.652749 Loss1: 0.268531 Loss2: 0.384218
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613130 Loss1: 0.235376 Loss2: 0.377754
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.613734 Loss1: 0.237336 Loss2: 0.376397
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.601616 Loss1: 0.230316 Loss2: 0.371300
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.577861 Loss1: 0.205889 Loss2: 0.371971
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.530369 Loss1: 0.162171 Loss2: 0.368199
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.518571 Loss1: 0.154678 Loss2: 0.363892
+(DefaultActor pid=1838052) >> Training accuracy: 0.956290
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.548453 Loss1: 0.511459 Loss2: 0.036995
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.335441 Loss1: 0.295003 Loss2: 0.040438
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.273459 Loss1: 0.233219 Loss2: 0.040241
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.228162 Loss1: 0.189019 Loss2: 0.039143
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.219203 Loss1: 0.179503 Loss2: 0.039700
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.197192 Loss1: 0.157450 Loss2: 0.039742
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.174300 Loss1: 0.134998 Loss2: 0.039302
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.175769 Loss1: 0.136502 Loss2: 0.039267
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.180381 Loss1: 0.140610 Loss2: 0.039771
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.167129 Loss1: 0.127391 Loss2: 0.039738
+(DefaultActor pid=1838052) >> Training accuracy: 0.973157
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 22:50:01,733][flwr][DEBUG] - fit_round 31 received 10 results and 0 failures
+>> Test accuracy: 0.618400
+[2023-09-27 22:50:42,237][flwr][INFO] - fit progress: (31, 2.050073419706509, {'accuracy': 0.6184}, 59465.12725453032)
+[2023-09-27 22:50:42,237][flwr][DEBUG] - evaluate_round 31: strategy sampled 10 clients (out of 10)
+[2023-09-27 22:51:18,717][flwr][DEBUG] - evaluate_round 31 received 10 results and 0 failures
+[2023-09-27 22:51:18,719][flwr][DEBUG] - fit_round 32: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.536587 Loss1: 0.497775 Loss2: 0.038812
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.360771 Loss1: 0.318940 Loss2: 0.041831
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.309850 Loss1: 0.268158 Loss2: 0.041692
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.253670 Loss1: 0.212143 Loss2: 0.041526
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.233662 Loss1: 0.192484 Loss2: 0.041178
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.190820 Loss1: 0.149959 Loss2: 0.040860
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.191075 Loss1: 0.150232 Loss2: 0.040843
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.177017 Loss1: 0.136279 Loss2: 0.040739
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.134248 Loss1: 0.094787 Loss2: 0.039461
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166040 Loss1: 0.126170 Loss2: 0.039869
+(DefaultActor pid=1838052) >> Training accuracy: 0.969739
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.550920 Loss1: 0.507856 Loss2: 0.043063
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.337583 Loss1: 0.292669 Loss2: 0.044914
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.303331 Loss1: 0.258569 Loss2: 0.044761
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.253884 Loss1: 0.209714 Loss2: 0.044170
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.228291 Loss1: 0.183977 Loss2: 0.044314
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.210484 Loss1: 0.166160 Loss2: 0.044324
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.191880 Loss1: 0.147996 Loss2: 0.043884
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.201165 Loss1: 0.157001 Loss2: 0.044164
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.190577 Loss1: 0.146577 Loss2: 0.044000
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.164904 Loss1: 0.121156 Loss2: 0.043749
+(DefaultActor pid=1838052) >> Training accuracy: 0.977650
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.166470 Loss1: 0.556362 Loss2: 0.610108
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.974741 Loss1: 0.353287 Loss2: 0.621454
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.854441 Loss1: 0.250079 Loss2: 0.604362
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.818817 Loss1: 0.226995 Loss2: 0.591821
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.802034 Loss1: 0.220669 Loss2: 0.581365
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.764549 Loss1: 0.191687 Loss2: 0.572863
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.764540 Loss1: 0.199930 Loss2: 0.564611
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.732172 Loss1: 0.177418 Loss2: 0.554754
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.752028 Loss1: 0.202027 Loss2: 0.550001
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.691193 Loss1: 0.146729 Loss2: 0.544464
+(DefaultActor pid=1838052) >> Training accuracy: 0.970920
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.112048 Loss1: 0.571827 Loss2: 0.540221
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.867117 Loss1: 0.360008 Loss2: 0.507109
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.783486 Loss1: 0.309916 Loss2: 0.473570
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.780830 Loss1: 0.322608 Loss2: 0.458222
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.728898 Loss1: 0.282441 Loss2: 0.446457
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.707764 Loss1: 0.271509 Loss2: 0.436255
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.649368 Loss1: 0.218577 Loss2: 0.430791
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.626353 Loss1: 0.204741 Loss2: 0.421612
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.596505 Loss1: 0.176256 Loss2: 0.420249
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.577360 Loss1: 0.161457 Loss2: 0.415903
+(DefaultActor pid=1838052) >> Training accuracy: 0.967516
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.094270 Loss1: 0.494531 Loss2: 0.599739
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.922611 Loss1: 0.317724 Loss2: 0.604887
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.867142 Loss1: 0.278349 Loss2: 0.588793
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.798692 Loss1: 0.223435 Loss2: 0.575257
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.789518 Loss1: 0.223103 Loss2: 0.566415
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.766258 Loss1: 0.210585 Loss2: 0.555674
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.732159 Loss1: 0.187299 Loss2: 0.544860
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.719440 Loss1: 0.177053 Loss2: 0.542388
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.700046 Loss1: 0.167097 Loss2: 0.532949
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.703518 Loss1: 0.174192 Loss2: 0.529325
+(DefaultActor pid=1838052) >> Training accuracy: 0.953887
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.609161 Loss1: 0.569645 Loss2: 0.039516
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.351188 Loss1: 0.309368 Loss2: 0.041820
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.282915 Loss1: 0.241856 Loss2: 0.041059
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.257201 Loss1: 0.215887 Loss2: 0.041314
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.231890 Loss1: 0.191220 Loss2: 0.040670
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.222289 Loss1: 0.181062 Loss2: 0.041228
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.206924 Loss1: 0.165580 Loss2: 0.041344
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.162623 Loss1: 0.122276 Loss2: 0.040346
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.170512 Loss1: 0.130312 Loss2: 0.040200
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.158871 Loss1: 0.118471 Loss2: 0.040400
+(DefaultActor pid=1838052) >> Training accuracy: 0.981208
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.809992 Loss1: 0.473596 Loss2: 0.336396
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.575195 Loss1: 0.317806 Loss2: 0.257390
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.520550 Loss1: 0.276588 Loss2: 0.243962
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.490347 Loss1: 0.253582 Loss2: 0.236764
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.424109 Loss1: 0.189517 Loss2: 0.234592
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.433677 Loss1: 0.200438 Loss2: 0.233238
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.403350 Loss1: 0.172916 Loss2: 0.230434
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.425388 Loss1: 0.193507 Loss2: 0.231881
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.421485 Loss1: 0.189359 Loss2: 0.232127
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.390347 Loss1: 0.160662 Loss2: 0.229685
+(DefaultActor pid=1838052) >> Training accuracy: 0.968157
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.542264 Loss1: 0.500784 Loss2: 0.041480
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.396876 Loss1: 0.351900 Loss2: 0.044976
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.319428 Loss1: 0.274870 Loss2: 0.044557
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.270113 Loss1: 0.226208 Loss2: 0.043906
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.230319 Loss1: 0.187018 Loss2: 0.043301
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.223836 Loss1: 0.180872 Loss2: 0.042965
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.219336 Loss1: 0.176068 Loss2: 0.043268
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.188449 Loss1: 0.145551 Loss2: 0.042897
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.166326 Loss1: 0.124630 Loss2: 0.041696
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163949 Loss1: 0.122289 Loss2: 0.041660
+(DefaultActor pid=1838052) >> Training accuracy: 0.970728
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.101020 Loss1: 0.500479 Loss2: 0.600541
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.892132 Loss1: 0.289279 Loss2: 0.602854
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.826895 Loss1: 0.236642 Loss2: 0.590252
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.826528 Loss1: 0.245984 Loss2: 0.580544
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.777244 Loss1: 0.208082 Loss2: 0.569162
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.764032 Loss1: 0.202602 Loss2: 0.561430
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.750622 Loss1: 0.198058 Loss2: 0.552564
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.734349 Loss1: 0.186303 Loss2: 0.548046
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.712161 Loss1: 0.172401 Loss2: 0.539759
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.673598 Loss1: 0.140213 Loss2: 0.533385
+(DefaultActor pid=1838052) >> Training accuracy: 0.968950
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.560746 Loss1: 0.520331 Loss2: 0.040415
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.386143 Loss1: 0.343115 Loss2: 0.043028
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.320650 Loss1: 0.277888 Loss2: 0.042763
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.269298 Loss1: 0.226781 Loss2: 0.042517
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.241543 Loss1: 0.199359 Loss2: 0.042184
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.232838 Loss1: 0.190580 Loss2: 0.042258
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197023 Loss1: 0.155540 Loss2: 0.041483
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.214894 Loss1: 0.172620 Loss2: 0.042274
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.186956 Loss1: 0.145389 Loss2: 0.041567
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.188300 Loss1: 0.146267 Loss2: 0.042034
+(DefaultActor pid=1838052) >> Training accuracy: 0.965144
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 23:20:44,388][flwr][DEBUG] - fit_round 32 received 10 results and 0 failures
+>> Test accuracy: 0.619800
+[2023-09-27 23:21:24,660][flwr][INFO] - fit progress: (32, 2.048540641515019, {'accuracy': 0.6198}, 61307.55033142725)
+[2023-09-27 23:21:24,660][flwr][DEBUG] - evaluate_round 32: strategy sampled 10 clients (out of 10)
+[2023-09-27 23:22:02,969][flwr][DEBUG] - evaluate_round 32 received 10 results and 0 failures
+[2023-09-27 23:22:02,970][flwr][DEBUG] - fit_round 33: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.133193 Loss1: 0.536348 Loss2: 0.596845
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.941873 Loss1: 0.347818 Loss2: 0.594055
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.882994 Loss1: 0.310424 Loss2: 0.572571
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.809549 Loss1: 0.254939 Loss2: 0.554610
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.725978 Loss1: 0.188166 Loss2: 0.537812
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.733334 Loss1: 0.203863 Loss2: 0.529471
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.745452 Loss1: 0.215207 Loss2: 0.530245
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.721618 Loss1: 0.199076 Loss2: 0.522542
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.693973 Loss1: 0.174918 Loss2: 0.519055
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.704063 Loss1: 0.190802 Loss2: 0.513262
+(DefaultActor pid=1838052) >> Training accuracy: 0.962204
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.050017 Loss1: 0.457848 Loss2: 0.592169
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.894274 Loss1: 0.308711 Loss2: 0.585563
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.804050 Loss1: 0.240517 Loss2: 0.563533
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.790197 Loss1: 0.242794 Loss2: 0.547403
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.748458 Loss1: 0.210287 Loss2: 0.538171
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.771665 Loss1: 0.236549 Loss2: 0.535116
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.742235 Loss1: 0.215551 Loss2: 0.526683
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.680855 Loss1: 0.162937 Loss2: 0.517918
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.695080 Loss1: 0.180766 Loss2: 0.514314
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.675440 Loss1: 0.165756 Loss2: 0.509683
+(DefaultActor pid=1838052) >> Training accuracy: 0.966574
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.093594 Loss1: 0.503535 Loss2: 0.590059
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.903160 Loss1: 0.321787 Loss2: 0.581373
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.816497 Loss1: 0.257277 Loss2: 0.559220
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.806905 Loss1: 0.261082 Loss2: 0.545823
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.771880 Loss1: 0.235519 Loss2: 0.536361
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.735923 Loss1: 0.209668 Loss2: 0.526255
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.717031 Loss1: 0.195360 Loss2: 0.521671
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.720609 Loss1: 0.202913 Loss2: 0.517696
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.684922 Loss1: 0.168529 Loss2: 0.516393
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.647857 Loss1: 0.142263 Loss2: 0.505594
+(DefaultActor pid=1838052) >> Training accuracy: 0.972508
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.570801 Loss1: 0.528248 Loss2: 0.042553
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.349988 Loss1: 0.304826 Loss2: 0.045163
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.305277 Loss1: 0.261308 Loss2: 0.043969
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.283909 Loss1: 0.238669 Loss2: 0.045241
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.257624 Loss1: 0.212954 Loss2: 0.044670
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.207553 Loss1: 0.163951 Loss2: 0.043602
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.218424 Loss1: 0.174716 Loss2: 0.043708
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.210539 Loss1: 0.167077 Loss2: 0.043462
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.241151 Loss1: 0.196903 Loss2: 0.044249
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.204872 Loss1: 0.161557 Loss2: 0.043315
+(DefaultActor pid=1838052) >> Training accuracy: 0.968133
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.864245 Loss1: 0.447914 Loss2: 0.416332
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.664635 Loss1: 0.305953 Loss2: 0.358682
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.579162 Loss1: 0.249498 Loss2: 0.329664
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.547282 Loss1: 0.220724 Loss2: 0.326558
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.511747 Loss1: 0.192763 Loss2: 0.318984
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.485716 Loss1: 0.170435 Loss2: 0.315282
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.486268 Loss1: 0.171660 Loss2: 0.314608
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.496851 Loss1: 0.185283 Loss2: 0.311568
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.507988 Loss1: 0.193600 Loss2: 0.314388
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.450663 Loss1: 0.141109 Loss2: 0.309554
+(DefaultActor pid=1838052) >> Training accuracy: 0.968559
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.805456 Loss1: 0.514687 Loss2: 0.290769
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.578728 Loss1: 0.337664 Loss2: 0.241063
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.495694 Loss1: 0.266741 Loss2: 0.228953
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.445774 Loss1: 0.223967 Loss2: 0.221808
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.448817 Loss1: 0.226253 Loss2: 0.222564
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.409995 Loss1: 0.192296 Loss2: 0.217699
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.386264 Loss1: 0.170617 Loss2: 0.215646
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.364939 Loss1: 0.150403 Loss2: 0.214535
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.341609 Loss1: 0.132059 Loss2: 0.209549
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.368031 Loss1: 0.152627 Loss2: 0.215404
+(DefaultActor pid=1838052) >> Training accuracy: 0.972903
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.484217 Loss1: 0.446835 Loss2: 0.037382
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.329790 Loss1: 0.289171 Loss2: 0.040619
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.262454 Loss1: 0.222143 Loss2: 0.040311
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.228854 Loss1: 0.188928 Loss2: 0.039925
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.200577 Loss1: 0.159899 Loss2: 0.040678
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.165097 Loss1: 0.124809 Loss2: 0.040287
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.176630 Loss1: 0.136454 Loss2: 0.040176
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.194849 Loss1: 0.154486 Loss2: 0.040363
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.153122 Loss1: 0.113373 Loss2: 0.039749
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.145422 Loss1: 0.106092 Loss2: 0.039330
+(DefaultActor pid=1838052) >> Training accuracy: 0.979167
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.062219 Loss1: 0.474158 Loss2: 0.588061
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.920565 Loss1: 0.335937 Loss2: 0.584628
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.830214 Loss1: 0.269317 Loss2: 0.560897
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.809771 Loss1: 0.259475 Loss2: 0.550296
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.747882 Loss1: 0.208566 Loss2: 0.539317
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.762109 Loss1: 0.231108 Loss2: 0.531001
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.780106 Loss1: 0.252485 Loss2: 0.527622
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.755211 Loss1: 0.226376 Loss2: 0.528835
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.701976 Loss1: 0.184335 Loss2: 0.517641
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.706899 Loss1: 0.193698 Loss2: 0.513200
+(DefaultActor pid=1838052) >> Training accuracy: 0.944912
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.581569 Loss1: 0.500718 Loss2: 0.080852
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.391947 Loss1: 0.312857 Loss2: 0.079090
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.331267 Loss1: 0.256537 Loss2: 0.074729
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.259750 Loss1: 0.188306 Loss2: 0.071445
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.257864 Loss1: 0.187782 Loss2: 0.070082
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.260655 Loss1: 0.191931 Loss2: 0.068724
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.226730 Loss1: 0.159486 Loss2: 0.067244
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.207541 Loss1: 0.141379 Loss2: 0.066162
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.211372 Loss1: 0.144816 Loss2: 0.066555
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.210871 Loss1: 0.144156 Loss2: 0.066716
+(DefaultActor pid=1838052) >> Training accuracy: 0.976562
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.493139 Loss1: 0.455137 Loss2: 0.038001
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.327287 Loss1: 0.286079 Loss2: 0.041208
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.253732 Loss1: 0.213277 Loss2: 0.040454
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.244925 Loss1: 0.204048 Loss2: 0.040878
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.233245 Loss1: 0.192584 Loss2: 0.040661
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.204357 Loss1: 0.163164 Loss2: 0.041192
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.187279 Loss1: 0.146079 Loss2: 0.041200
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.158541 Loss1: 0.118384 Loss2: 0.040157
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.169959 Loss1: 0.129500 Loss2: 0.040459
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.152114 Loss1: 0.111393 Loss2: 0.040722
+(DefaultActor pid=1838052) >> Training accuracy: 0.967366
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-27 23:51:44,497][flwr][DEBUG] - fit_round 33 received 10 results and 0 failures
+>> Test accuracy: 0.623000
+[2023-09-27 23:52:24,960][flwr][INFO] - fit progress: (33, 2.033169127881717, {'accuracy': 0.623}, 63167.85070159007)
+[2023-09-27 23:52:24,961][flwr][DEBUG] - evaluate_round 33: strategy sampled 10 clients (out of 10)
+[2023-09-27 23:53:01,869][flwr][DEBUG] - evaluate_round 33 received 10 results and 0 failures
+[2023-09-27 23:53:01,870][flwr][DEBUG] - fit_round 34: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.033783 Loss1: 0.450828 Loss2: 0.582955
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.852281 Loss1: 0.285015 Loss2: 0.567266
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.750209 Loss1: 0.206568 Loss2: 0.543641
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.724705 Loss1: 0.191985 Loss2: 0.532719
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.695212 Loss1: 0.170763 Loss2: 0.524449
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.718836 Loss1: 0.195270 Loss2: 0.523565
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.663196 Loss1: 0.147611 Loss2: 0.515585
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.653662 Loss1: 0.144184 Loss2: 0.509478
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.630743 Loss1: 0.124893 Loss2: 0.505850
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.644606 Loss1: 0.140279 Loss2: 0.504328
+(DefaultActor pid=1838052) >> Training accuracy: 0.982171
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.153076 Loss1: 0.557757 Loss2: 0.595318
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.957679 Loss1: 0.362115 Loss2: 0.595564
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.853342 Loss1: 0.269631 Loss2: 0.583711
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.802652 Loss1: 0.229733 Loss2: 0.572919
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.760242 Loss1: 0.201127 Loss2: 0.559115
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.734511 Loss1: 0.181603 Loss2: 0.552908
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.742028 Loss1: 0.192090 Loss2: 0.549938
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.747434 Loss1: 0.203012 Loss2: 0.544422
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.697952 Loss1: 0.156901 Loss2: 0.541050
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.710527 Loss1: 0.170070 Loss2: 0.540457
+(DefaultActor pid=1838052) >> Training accuracy: 0.967928
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.872657 Loss1: 0.460356 Loss2: 0.412301
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.670256 Loss1: 0.327720 Loss2: 0.342537
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.559326 Loss1: 0.244811 Loss2: 0.314515
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.523884 Loss1: 0.216160 Loss2: 0.307724
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.484698 Loss1: 0.182804 Loss2: 0.301895
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.466621 Loss1: 0.167603 Loss2: 0.299019
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.499292 Loss1: 0.195300 Loss2: 0.303992
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.454507 Loss1: 0.154558 Loss2: 0.299949
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.433313 Loss1: 0.139287 Loss2: 0.294026
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.423247 Loss1: 0.128395 Loss2: 0.294852
+(DefaultActor pid=1838052) >> Training accuracy: 0.967761
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.500527 Loss1: 0.457349 Loss2: 0.043178
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.314269 Loss1: 0.268559 Loss2: 0.045710
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.277822 Loss1: 0.233265 Loss2: 0.044557
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.275667 Loss1: 0.230932 Loss2: 0.044735
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.194530 Loss1: 0.150609 Loss2: 0.043921
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.193385 Loss1: 0.149493 Loss2: 0.043892
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.175828 Loss1: 0.132291 Loss2: 0.043537
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.170243 Loss1: 0.127300 Loss2: 0.042943
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.182269 Loss1: 0.138379 Loss2: 0.043890
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.165931 Loss1: 0.122174 Loss2: 0.043756
+(DefaultActor pid=1838052) >> Training accuracy: 0.981136
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.551112 Loss1: 0.476132 Loss2: 0.074980
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.385077 Loss1: 0.315383 Loss2: 0.069694
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.285819 Loss1: 0.219891 Loss2: 0.065928
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.275000 Loss1: 0.211621 Loss2: 0.063378
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.224239 Loss1: 0.162348 Loss2: 0.061891
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.239403 Loss1: 0.177772 Loss2: 0.061631
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.214142 Loss1: 0.152833 Loss2: 0.061309
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.210013 Loss1: 0.150119 Loss2: 0.059894
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.185056 Loss1: 0.125646 Loss2: 0.059410
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.177410 Loss1: 0.118415 Loss2: 0.058995
+(DefaultActor pid=1838052) >> Training accuracy: 0.975475
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.702658 Loss1: 0.441077 Loss2: 0.261581
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.477231 Loss1: 0.259880 Loss2: 0.217352
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.426483 Loss1: 0.214756 Loss2: 0.211727
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.429319 Loss1: 0.222379 Loss2: 0.206940
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.438939 Loss1: 0.227288 Loss2: 0.211651
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.378943 Loss1: 0.175132 Loss2: 0.203811
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.385334 Loss1: 0.178839 Loss2: 0.206494
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.389345 Loss1: 0.185156 Loss2: 0.204188
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.357754 Loss1: 0.153481 Loss2: 0.204273
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.337218 Loss1: 0.135774 Loss2: 0.201444
+(DefaultActor pid=1838052) >> Training accuracy: 0.970530
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.509580 Loss1: 0.469496 Loss2: 0.040084
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.313158 Loss1: 0.269755 Loss2: 0.043403
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.248491 Loss1: 0.206076 Loss2: 0.042415
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.221865 Loss1: 0.179359 Loss2: 0.042505
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.222380 Loss1: 0.179880 Loss2: 0.042500
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.191976 Loss1: 0.149401 Loss2: 0.042575
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.185717 Loss1: 0.143961 Loss2: 0.041757
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.177929 Loss1: 0.135902 Loss2: 0.042027
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.181871 Loss1: 0.139380 Loss2: 0.042490
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.142581 Loss1: 0.101281 Loss2: 0.041300
+(DefaultActor pid=1838052) >> Training accuracy: 0.982205
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.594359 Loss1: 0.552362 Loss2: 0.041998
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.348723 Loss1: 0.304296 Loss2: 0.044428
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.263012 Loss1: 0.220008 Loss2: 0.043004
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.273833 Loss1: 0.230466 Loss2: 0.043367
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.213875 Loss1: 0.170913 Loss2: 0.042962
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.193901 Loss1: 0.152038 Loss2: 0.041864
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.156248 Loss1: 0.115122 Loss2: 0.041127
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.186242 Loss1: 0.144995 Loss2: 0.041247
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.165645 Loss1: 0.124638 Loss2: 0.041006
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.174256 Loss1: 0.132878 Loss2: 0.041377
+(DefaultActor pid=1838052) >> Training accuracy: 0.977196
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.554435 Loss1: 0.471124 Loss2: 0.083311
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.348131 Loss1: 0.271784 Loss2: 0.076347
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.281211 Loss1: 0.208050 Loss2: 0.073161
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.289044 Loss1: 0.219507 Loss2: 0.069536
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.270999 Loss1: 0.201340 Loss2: 0.069659
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.253373 Loss1: 0.185583 Loss2: 0.067790
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.227021 Loss1: 0.160417 Loss2: 0.066604
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.227990 Loss1: 0.161239 Loss2: 0.066751
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.218643 Loss1: 0.152879 Loss2: 0.065765
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.188273 Loss1: 0.123198 Loss2: 0.065075
+(DefaultActor pid=1838052) >> Training accuracy: 0.976162
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.065766 Loss1: 0.477379 Loss2: 0.588387
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.866357 Loss1: 0.284431 Loss2: 0.581926
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.793329 Loss1: 0.234178 Loss2: 0.559151
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.787153 Loss1: 0.238629 Loss2: 0.548525
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.742902 Loss1: 0.204094 Loss2: 0.538808
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.717744 Loss1: 0.186071 Loss2: 0.531674
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.682854 Loss1: 0.159868 Loss2: 0.522986
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.690007 Loss1: 0.172432 Loss2: 0.517575
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.684791 Loss1: 0.166803 Loss2: 0.517988
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.656727 Loss1: 0.142949 Loss2: 0.513778
+(DefaultActor pid=1838052) >> Training accuracy: 0.976661
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 00:22:26,663][flwr][DEBUG] - fit_round 34 received 10 results and 0 failures
+>> Test accuracy: 0.619400
+[2023-09-28 00:23:06,527][flwr][INFO] - fit progress: (34, 2.0350445369942882, {'accuracy': 0.6194}, 65009.41777968733)
+[2023-09-28 00:23:06,528][flwr][DEBUG] - evaluate_round 34: strategy sampled 10 clients (out of 10)
+[2023-09-28 00:23:43,163][flwr][DEBUG] - evaluate_round 34 received 10 results and 0 failures
+[2023-09-28 00:23:43,164][flwr][DEBUG] - fit_round 35: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.881965 Loss1: 0.456196 Loss2: 0.425769
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.732760 Loss1: 0.352555 Loss2: 0.380206
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.651459 Loss1: 0.282561 Loss2: 0.368898
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.577396 Loss1: 0.218995 Loss2: 0.358401
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.548025 Loss1: 0.197821 Loss2: 0.350204
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.543956 Loss1: 0.191210 Loss2: 0.352746
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.547633 Loss1: 0.192350 Loss2: 0.355284
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.512846 Loss1: 0.163037 Loss2: 0.349809
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.504983 Loss1: 0.156855 Loss2: 0.348128
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.489318 Loss1: 0.144545 Loss2: 0.344773
+(DefaultActor pid=1838052) >> Training accuracy: 0.955498
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.774092 Loss1: 0.412578 Loss2: 0.361514
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.591957 Loss1: 0.295418 Loss2: 0.296539
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.518265 Loss1: 0.241322 Loss2: 0.276943
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.495252 Loss1: 0.221559 Loss2: 0.273693
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.468616 Loss1: 0.198977 Loss2: 0.269640
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.445323 Loss1: 0.178724 Loss2: 0.266599
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.434252 Loss1: 0.171044 Loss2: 0.263208
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.435938 Loss1: 0.172418 Loss2: 0.263520
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.404837 Loss1: 0.143959 Loss2: 0.260878
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.423229 Loss1: 0.159392 Loss2: 0.263837
+(DefaultActor pid=1838052) >> Training accuracy: 0.974288
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.451612 Loss1: 0.408680 Loss2: 0.042932
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.293270 Loss1: 0.248079 Loss2: 0.045191
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.273564 Loss1: 0.228413 Loss2: 0.045150
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.250691 Loss1: 0.205134 Loss2: 0.045557
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.201395 Loss1: 0.156635 Loss2: 0.044760
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.166014 Loss1: 0.121913 Loss2: 0.044101
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.166397 Loss1: 0.122948 Loss2: 0.043449
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.162507 Loss1: 0.118848 Loss2: 0.043659
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160766 Loss1: 0.116754 Loss2: 0.044011
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.173217 Loss1: 0.129153 Loss2: 0.044064
+(DefaultActor pid=1838052) >> Training accuracy: 0.972706
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.051301 Loss1: 0.462961 Loss2: 0.588340
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.881437 Loss1: 0.297879 Loss2: 0.583557
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.787924 Loss1: 0.223775 Loss2: 0.564149
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.766272 Loss1: 0.216061 Loss2: 0.550211
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.726596 Loss1: 0.184598 Loss2: 0.541998
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.722047 Loss1: 0.184755 Loss2: 0.537292
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.697717 Loss1: 0.167785 Loss2: 0.529932
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.671787 Loss1: 0.147333 Loss2: 0.524453
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.640108 Loss1: 0.120682 Loss2: 0.519425
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.648877 Loss1: 0.134074 Loss2: 0.514803
+(DefaultActor pid=1838052) >> Training accuracy: 0.979818
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.008200 Loss1: 0.480630 Loss2: 0.527571
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.755938 Loss1: 0.287490 Loss2: 0.468448
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.733531 Loss1: 0.283376 Loss2: 0.450156
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.697026 Loss1: 0.253898 Loss2: 0.443128
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.641498 Loss1: 0.208883 Loss2: 0.432615
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.622536 Loss1: 0.196509 Loss2: 0.426028
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.615587 Loss1: 0.191656 Loss2: 0.423931
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.589394 Loss1: 0.169435 Loss2: 0.419960
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.580495 Loss1: 0.161769 Loss2: 0.418726
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.585534 Loss1: 0.167277 Loss2: 0.418257
+(DefaultActor pid=1838052) >> Training accuracy: 0.967548
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.561240 Loss1: 0.518389 Loss2: 0.042852
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.314516 Loss1: 0.269489 Loss2: 0.045027
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.241856 Loss1: 0.198992 Loss2: 0.042864
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.243758 Loss1: 0.200659 Loss2: 0.043099
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.206952 Loss1: 0.164689 Loss2: 0.042264
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.188019 Loss1: 0.145716 Loss2: 0.042303
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.241004 Loss1: 0.198330 Loss2: 0.042674
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.222825 Loss1: 0.179121 Loss2: 0.043704
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.198052 Loss1: 0.154933 Loss2: 0.043119
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.189439 Loss1: 0.146324 Loss2: 0.043115
+(DefaultActor pid=1838052) >> Training accuracy: 0.973684
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.500768 Loss1: 0.452001 Loss2: 0.048767
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.293858 Loss1: 0.246002 Loss2: 0.047856
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.223353 Loss1: 0.177282 Loss2: 0.046071
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.232571 Loss1: 0.184740 Loss2: 0.047832
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.214981 Loss1: 0.168349 Loss2: 0.046632
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.193339 Loss1: 0.147398 Loss2: 0.045941
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.181212 Loss1: 0.135680 Loss2: 0.045532
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.176537 Loss1: 0.130803 Loss2: 0.045734
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160348 Loss1: 0.115366 Loss2: 0.044981
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163699 Loss1: 0.118523 Loss2: 0.045176
+(DefaultActor pid=1838052) >> Training accuracy: 0.973892
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.441091 Loss1: 0.404010 Loss2: 0.037081
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.305314 Loss1: 0.264947 Loss2: 0.040367
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.261066 Loss1: 0.221188 Loss2: 0.039878
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.220977 Loss1: 0.180557 Loss2: 0.040419
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.199112 Loss1: 0.158952 Loss2: 0.040160
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.184796 Loss1: 0.144306 Loss2: 0.040489
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.177134 Loss1: 0.137761 Loss2: 0.039372
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.148755 Loss1: 0.109331 Loss2: 0.039424
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.155173 Loss1: 0.115386 Loss2: 0.039787
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.159042 Loss1: 0.119253 Loss2: 0.039790
+(DefaultActor pid=1838052) >> Training accuracy: 0.975991
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.604015 Loss1: 0.514748 Loss2: 0.089267
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.358291 Loss1: 0.270769 Loss2: 0.087523
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.310712 Loss1: 0.228035 Loss2: 0.082677
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.283604 Loss1: 0.203606 Loss2: 0.079998
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.253687 Loss1: 0.176773 Loss2: 0.076914
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.250676 Loss1: 0.174426 Loss2: 0.076250
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.242168 Loss1: 0.167325 Loss2: 0.074844
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.202716 Loss1: 0.130070 Loss2: 0.072645
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.207279 Loss1: 0.135272 Loss2: 0.072007
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.204517 Loss1: 0.133297 Loss2: 0.071220
+(DefaultActor pid=1838052) >> Training accuracy: 0.970439
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.440377 Loss1: 0.400558 Loss2: 0.039819
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.287125 Loss1: 0.244661 Loss2: 0.042464
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.252477 Loss1: 0.210558 Loss2: 0.041919
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.212548 Loss1: 0.171748 Loss2: 0.040800
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.184031 Loss1: 0.144167 Loss2: 0.039863
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.201869 Loss1: 0.161251 Loss2: 0.040618
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.202986 Loss1: 0.161117 Loss2: 0.041869
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.170125 Loss1: 0.129266 Loss2: 0.040859
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130420 Loss1: 0.090366 Loss2: 0.040053
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.129454 Loss1: 0.090075 Loss2: 0.039379
+(DefaultActor pid=1838052) >> Training accuracy: 0.969151
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 00:52:45,639][flwr][DEBUG] - fit_round 35 received 10 results and 0 failures
+>> Test accuracy: 0.624700
+[2023-09-28 00:53:25,630][flwr][INFO] - fit progress: (35, 2.080257884039285, {'accuracy': 0.6247}, 66828.52071374701)
+[2023-09-28 00:53:25,631][flwr][DEBUG] - evaluate_round 35: strategy sampled 10 clients (out of 10)
+[2023-09-28 00:54:03,176][flwr][DEBUG] - evaluate_round 35 received 10 results and 0 failures
+[2023-09-28 00:54:03,177][flwr][DEBUG] - fit_round 36: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.004384 Loss1: 0.426333 Loss2: 0.578051
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.834268 Loss1: 0.261511 Loss2: 0.572756
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.744293 Loss1: 0.194714 Loss2: 0.549578
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.708946 Loss1: 0.170061 Loss2: 0.538885
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.715020 Loss1: 0.182942 Loss2: 0.532078
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.694159 Loss1: 0.166993 Loss2: 0.527165
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.668154 Loss1: 0.145636 Loss2: 0.522517
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.623612 Loss1: 0.108279 Loss2: 0.515333
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.612601 Loss1: 0.101562 Loss2: 0.511039
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.639298 Loss1: 0.130060 Loss2: 0.509238
+(DefaultActor pid=1838052) >> Training accuracy: 0.975561
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.035667 Loss1: 0.441950 Loss2: 0.593716
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.916822 Loss1: 0.321006 Loss2: 0.595816
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.827702 Loss1: 0.242358 Loss2: 0.585344
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.796618 Loss1: 0.223543 Loss2: 0.573076
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.768766 Loss1: 0.205838 Loss2: 0.562928
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.764374 Loss1: 0.210838 Loss2: 0.553536
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.766590 Loss1: 0.213938 Loss2: 0.552652
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.694140 Loss1: 0.150599 Loss2: 0.543541
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.699779 Loss1: 0.163463 Loss2: 0.536315
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.685114 Loss1: 0.155306 Loss2: 0.529808
+(DefaultActor pid=1838052) >> Training accuracy: 0.967928
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.448415 Loss1: 0.410281 Loss2: 0.038134
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.294767 Loss1: 0.253232 Loss2: 0.041535
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.246580 Loss1: 0.205361 Loss2: 0.041219
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.218614 Loss1: 0.177387 Loss2: 0.041227
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.195923 Loss1: 0.155260 Loss2: 0.040663
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.173704 Loss1: 0.133524 Loss2: 0.040180
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.177505 Loss1: 0.136655 Loss2: 0.040849
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.172077 Loss1: 0.131120 Loss2: 0.040957
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.150894 Loss1: 0.110661 Loss2: 0.040233
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.140986 Loss1: 0.100973 Loss2: 0.040013
+(DefaultActor pid=1838052) >> Training accuracy: 0.977255
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.475448 Loss1: 0.432289 Loss2: 0.043159
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.311390 Loss1: 0.264920 Loss2: 0.046470
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.307159 Loss1: 0.260762 Loss2: 0.046397
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.254904 Loss1: 0.209177 Loss2: 0.045727
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.198646 Loss1: 0.154617 Loss2: 0.044029
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.217051 Loss1: 0.173619 Loss2: 0.043432
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.196110 Loss1: 0.152361 Loss2: 0.043749
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.164707 Loss1: 0.121559 Loss2: 0.043148
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.149451 Loss1: 0.106931 Loss2: 0.042521
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.137956 Loss1: 0.096060 Loss2: 0.041896
+(DefaultActor pid=1838052) >> Training accuracy: 0.988064
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.004667 Loss1: 0.517676 Loss2: 0.486991
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.787042 Loss1: 0.342650 Loss2: 0.444391
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.682995 Loss1: 0.255840 Loss2: 0.427155
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.606668 Loss1: 0.196234 Loss2: 0.410434
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.593237 Loss1: 0.187490 Loss2: 0.405748
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.551658 Loss1: 0.152780 Loss2: 0.398878
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.559701 Loss1: 0.161798 Loss2: 0.397903
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.543919 Loss1: 0.150044 Loss2: 0.393875
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.512970 Loss1: 0.124253 Loss2: 0.388717
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.526106 Loss1: 0.132486 Loss2: 0.393620
+(DefaultActor pid=1838052) >> Training accuracy: 0.962838
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.467354 Loss1: 0.406619 Loss2: 0.060735
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.329361 Loss1: 0.269889 Loss2: 0.059473
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.249494 Loss1: 0.192400 Loss2: 0.057094
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.229654 Loss1: 0.174783 Loss2: 0.054871
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.213665 Loss1: 0.158439 Loss2: 0.055226
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.193187 Loss1: 0.139989 Loss2: 0.053198
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.210155 Loss1: 0.156308 Loss2: 0.053847
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.170748 Loss1: 0.119040 Loss2: 0.051708
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.191630 Loss1: 0.139132 Loss2: 0.052499
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.164328 Loss1: 0.112488 Loss2: 0.051841
+(DefaultActor pid=1838052) >> Training accuracy: 0.976464
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.445319 Loss1: 0.401135 Loss2: 0.044184
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.293550 Loss1: 0.247561 Loss2: 0.045989
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.213340 Loss1: 0.168804 Loss2: 0.044537
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.208307 Loss1: 0.163375 Loss2: 0.044932
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.201170 Loss1: 0.156149 Loss2: 0.045020
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.206998 Loss1: 0.162001 Loss2: 0.044997
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.165663 Loss1: 0.121473 Loss2: 0.044190
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.164476 Loss1: 0.120165 Loss2: 0.044311
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.165124 Loss1: 0.120775 Loss2: 0.044349
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.181228 Loss1: 0.136677 Loss2: 0.044551
+(DefaultActor pid=1838052) >> Training accuracy: 0.976464
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.450606 Loss1: 0.409279 Loss2: 0.041327
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.319035 Loss1: 0.274983 Loss2: 0.044052
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.255444 Loss1: 0.212576 Loss2: 0.042867
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.222652 Loss1: 0.180227 Loss2: 0.042425
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.209508 Loss1: 0.167220 Loss2: 0.042288
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.192106 Loss1: 0.151109 Loss2: 0.040997
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.184480 Loss1: 0.143231 Loss2: 0.041249
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.190162 Loss1: 0.148013 Loss2: 0.042149
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.182308 Loss1: 0.140774 Loss2: 0.041534
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.178621 Loss1: 0.136756 Loss2: 0.041865
+(DefaultActor pid=1838052) >> Training accuracy: 0.963410
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.435427 Loss1: 0.391637 Loss2: 0.043790
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.310643 Loss1: 0.264078 Loss2: 0.046565
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.239881 Loss1: 0.195119 Loss2: 0.044762
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.195046 Loss1: 0.151180 Loss2: 0.043866
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.222468 Loss1: 0.178569 Loss2: 0.043899
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.221185 Loss1: 0.176075 Loss2: 0.045109
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.171564 Loss1: 0.128735 Loss2: 0.042829
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.209668 Loss1: 0.166391 Loss2: 0.043277
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.197066 Loss1: 0.153165 Loss2: 0.043901
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.186030 Loss1: 0.142300 Loss2: 0.043731
+(DefaultActor pid=1838052) >> Training accuracy: 0.971955
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.431079 Loss1: 0.394451 Loss2: 0.036628
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.260713 Loss1: 0.221070 Loss2: 0.039643
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.236473 Loss1: 0.197392 Loss2: 0.039080
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.207589 Loss1: 0.168401 Loss2: 0.039188
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.187411 Loss1: 0.147889 Loss2: 0.039522
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.174918 Loss1: 0.136001 Loss2: 0.038916
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.168021 Loss1: 0.128809 Loss2: 0.039212
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.170653 Loss1: 0.130965 Loss2: 0.039689
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.156114 Loss1: 0.116347 Loss2: 0.039767
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.153759 Loss1: 0.114468 Loss2: 0.039291
+(DefaultActor pid=1838052) >> Training accuracy: 0.971799
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 01:23:00,304][flwr][DEBUG] - fit_round 36 received 10 results and 0 failures
+>> Test accuracy: 0.627200
+[2023-09-28 01:23:40,441][flwr][INFO] - fit progress: (36, 2.0718342880852307, {'accuracy': 0.6272}, 68643.33109073108)
+[2023-09-28 01:23:40,441][flwr][DEBUG] - evaluate_round 36: strategy sampled 10 clients (out of 10)
+[2023-09-28 01:24:17,438][flwr][DEBUG] - evaluate_round 36 received 10 results and 0 failures
+[2023-09-28 01:24:17,438][flwr][DEBUG] - fit_round 37: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.810445 Loss1: 0.471249 Loss2: 0.339197
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.564282 Loss1: 0.292775 Loss2: 0.271507
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.460486 Loss1: 0.207309 Loss2: 0.253177
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.427260 Loss1: 0.181970 Loss2: 0.245290
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.419362 Loss1: 0.175408 Loss2: 0.243953
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.394846 Loss1: 0.151811 Loss2: 0.243035
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.370462 Loss1: 0.131531 Loss2: 0.238931
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.384271 Loss1: 0.146787 Loss2: 0.237484
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.380276 Loss1: 0.140025 Loss2: 0.240251
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.348957 Loss1: 0.113767 Loss2: 0.235190
+(DefaultActor pid=1838052) >> Training accuracy: 0.975507
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.965778 Loss1: 0.383375 Loss2: 0.582403
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.832350 Loss1: 0.255994 Loss2: 0.576356
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.799497 Loss1: 0.238697 Loss2: 0.560800
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.740749 Loss1: 0.192285 Loss2: 0.548464
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.718680 Loss1: 0.181131 Loss2: 0.537549
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.724454 Loss1: 0.192907 Loss2: 0.531548
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.690983 Loss1: 0.163403 Loss2: 0.527580
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.674683 Loss1: 0.153969 Loss2: 0.520714
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.645818 Loss1: 0.128254 Loss2: 0.517564
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.634768 Loss1: 0.123150 Loss2: 0.511618
+(DefaultActor pid=1838052) >> Training accuracy: 0.970553
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.401076 Loss1: 0.364839 Loss2: 0.036237
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.247880 Loss1: 0.208623 Loss2: 0.039257
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.223916 Loss1: 0.184252 Loss2: 0.039664
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.184548 Loss1: 0.145469 Loss2: 0.039079
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.162397 Loss1: 0.123427 Loss2: 0.038970
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.145824 Loss1: 0.106996 Loss2: 0.038828
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.136844 Loss1: 0.098610 Loss2: 0.038234
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.150703 Loss1: 0.111642 Loss2: 0.039060
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.145500 Loss1: 0.106543 Loss2: 0.038957
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.137395 Loss1: 0.098652 Loss2: 0.038743
+(DefaultActor pid=1838052) >> Training accuracy: 0.985176
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.969862 Loss1: 0.364683 Loss2: 0.605178
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.851277 Loss1: 0.242028 Loss2: 0.609249
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.797040 Loss1: 0.202748 Loss2: 0.594292
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.806942 Loss1: 0.221685 Loss2: 0.585258
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.760809 Loss1: 0.187012 Loss2: 0.573797
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.711695 Loss1: 0.151830 Loss2: 0.559864
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.703279 Loss1: 0.147686 Loss2: 0.555594
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.695986 Loss1: 0.146934 Loss2: 0.549053
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.670449 Loss1: 0.125563 Loss2: 0.544886
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.707857 Loss1: 0.168197 Loss2: 0.539660
+(DefaultActor pid=1838052) >> Training accuracy: 0.958079
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.377437 Loss1: 0.341308 Loss2: 0.036129
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.270031 Loss1: 0.230887 Loss2: 0.039143
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.250155 Loss1: 0.210417 Loss2: 0.039738
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.211890 Loss1: 0.172367 Loss2: 0.039523
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.178826 Loss1: 0.139163 Loss2: 0.039663
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.192750 Loss1: 0.153039 Loss2: 0.039711
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.176109 Loss1: 0.136231 Loss2: 0.039878
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.180502 Loss1: 0.140356 Loss2: 0.040145
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130092 Loss1: 0.090996 Loss2: 0.039096
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121654 Loss1: 0.083288 Loss2: 0.038366
+(DefaultActor pid=1838052) >> Training accuracy: 0.981013
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.452071 Loss1: 0.414766 Loss2: 0.037305
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.290111 Loss1: 0.249850 Loss2: 0.040261
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.230780 Loss1: 0.191009 Loss2: 0.039771
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.192630 Loss1: 0.152700 Loss2: 0.039930
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.199678 Loss1: 0.159419 Loss2: 0.040258
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.196609 Loss1: 0.156353 Loss2: 0.040256
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.170675 Loss1: 0.130459 Loss2: 0.040217
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.143848 Loss1: 0.103957 Loss2: 0.039891
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.159888 Loss1: 0.119834 Loss2: 0.040055
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.150512 Loss1: 0.110520 Loss2: 0.039992
+(DefaultActor pid=1838052) >> Training accuracy: 0.982205
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.435299 Loss1: 0.369485 Loss2: 0.065814
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.289655 Loss1: 0.229028 Loss2: 0.060627
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.267689 Loss1: 0.208688 Loss2: 0.059001
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.209366 Loss1: 0.153482 Loss2: 0.055884
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.203564 Loss1: 0.148883 Loss2: 0.054681
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.182769 Loss1: 0.128161 Loss2: 0.054608
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.174617 Loss1: 0.121091 Loss2: 0.053526
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.159125 Loss1: 0.106280 Loss2: 0.052845
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.172193 Loss1: 0.119267 Loss2: 0.052926
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.157035 Loss1: 0.104320 Loss2: 0.052715
+(DefaultActor pid=1838052) >> Training accuracy: 0.977848
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.508836 Loss1: 0.427532 Loss2: 0.081305
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.331576 Loss1: 0.249831 Loss2: 0.081744
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.287708 Loss1: 0.210121 Loss2: 0.077587
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.275275 Loss1: 0.199117 Loss2: 0.076158
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.263615 Loss1: 0.188778 Loss2: 0.074837
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.249887 Loss1: 0.175915 Loss2: 0.073972
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.247570 Loss1: 0.174491 Loss2: 0.073079
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.214315 Loss1: 0.143469 Loss2: 0.070846
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.191275 Loss1: 0.122367 Loss2: 0.068908
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.178610 Loss1: 0.109737 Loss2: 0.068873
+(DefaultActor pid=1838052) >> Training accuracy: 0.979030
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.422888 Loss1: 0.385750 Loss2: 0.037138
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.275621 Loss1: 0.235310 Loss2: 0.040311
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.232703 Loss1: 0.192602 Loss2: 0.040102
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.207591 Loss1: 0.167724 Loss2: 0.039867
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.181623 Loss1: 0.141836 Loss2: 0.039786
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.187898 Loss1: 0.147220 Loss2: 0.040678
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.198104 Loss1: 0.157263 Loss2: 0.040841
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.183048 Loss1: 0.142453 Loss2: 0.040595
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.153646 Loss1: 0.113569 Loss2: 0.040077
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163460 Loss1: 0.122976 Loss2: 0.040484
+(DefaultActor pid=1838052) >> Training accuracy: 0.967959
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.418127 Loss1: 0.381082 Loss2: 0.037045
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.294499 Loss1: 0.254179 Loss2: 0.040320
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.220567 Loss1: 0.180139 Loss2: 0.040428
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.196536 Loss1: 0.156745 Loss2: 0.039792
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.182663 Loss1: 0.142716 Loss2: 0.039948
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.173034 Loss1: 0.133222 Loss2: 0.039812
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.153829 Loss1: 0.113595 Loss2: 0.040235
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.156088 Loss1: 0.116533 Loss2: 0.039555
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.167497 Loss1: 0.126682 Loss2: 0.040815
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.175991 Loss1: 0.135116 Loss2: 0.040874
+(DefaultActor pid=1838052) >> Training accuracy: 0.969739
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 01:53:18,857][flwr][DEBUG] - fit_round 37 received 10 results and 0 failures
+>> Test accuracy: 0.628500
+[2023-09-28 01:53:59,072][flwr][INFO] - fit progress: (37, 2.0764910361637323, {'accuracy': 0.6285}, 70461.9626628072)
+[2023-09-28 01:53:59,074][flwr][DEBUG] - evaluate_round 37: strategy sampled 10 clients (out of 10)
+[2023-09-28 01:54:36,391][flwr][DEBUG] - evaluate_round 37 received 10 results and 0 failures
+[2023-09-28 01:54:36,392][flwr][DEBUG] - fit_round 38: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.947822 Loss1: 0.374450 Loss2: 0.573373
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.848486 Loss1: 0.277471 Loss2: 0.571015
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.777773 Loss1: 0.224904 Loss2: 0.552869
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.752050 Loss1: 0.210657 Loss2: 0.541392
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.708309 Loss1: 0.175708 Loss2: 0.532601
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.662429 Loss1: 0.139916 Loss2: 0.522513
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.665203 Loss1: 0.146384 Loss2: 0.518819
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.663120 Loss1: 0.148298 Loss2: 0.514822
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.651463 Loss1: 0.141839 Loss2: 0.509624
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.658726 Loss1: 0.149726 Loss2: 0.509000
+(DefaultActor pid=1838052) >> Training accuracy: 0.972706
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.424511 Loss1: 0.350841 Loss2: 0.073670
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.289621 Loss1: 0.214440 Loss2: 0.075180
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.239398 Loss1: 0.168562 Loss2: 0.070836
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.191914 Loss1: 0.123757 Loss2: 0.068158
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.180414 Loss1: 0.114490 Loss2: 0.065924
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.171252 Loss1: 0.107022 Loss2: 0.064230
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.169429 Loss1: 0.105778 Loss2: 0.063651
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.184517 Loss1: 0.120550 Loss2: 0.063967
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.179749 Loss1: 0.116773 Loss2: 0.062977
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163132 Loss1: 0.100751 Loss2: 0.062381
+(DefaultActor pid=1838052) >> Training accuracy: 0.982372
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.986121 Loss1: 0.397978 Loss2: 0.588143
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.860657 Loss1: 0.275372 Loss2: 0.585286
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.777476 Loss1: 0.211531 Loss2: 0.565945
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.746671 Loss1: 0.188813 Loss2: 0.557858
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.699729 Loss1: 0.153610 Loss2: 0.546119
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.697623 Loss1: 0.158519 Loss2: 0.539104
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.661519 Loss1: 0.132408 Loss2: 0.529110
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.658148 Loss1: 0.133780 Loss2: 0.524368
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.664719 Loss1: 0.146350 Loss2: 0.518369
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.651168 Loss1: 0.136638 Loss2: 0.514530
+(DefaultActor pid=1838052) >> Training accuracy: 0.969343
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.361544 Loss1: 0.322763 Loss2: 0.038781
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.243783 Loss1: 0.201674 Loss2: 0.042109
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.204140 Loss1: 0.162170 Loss2: 0.041970
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.196888 Loss1: 0.155141 Loss2: 0.041747
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.191800 Loss1: 0.150225 Loss2: 0.041575
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.171654 Loss1: 0.130041 Loss2: 0.041612
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.186243 Loss1: 0.144255 Loss2: 0.041987
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.147774 Loss1: 0.107076 Loss2: 0.040697
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.167422 Loss1: 0.126134 Loss2: 0.041288
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.145339 Loss1: 0.104135 Loss2: 0.041204
+(DefaultActor pid=1838052) >> Training accuracy: 0.978468
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.831277 Loss1: 0.354209 Loss2: 0.477068
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.663230 Loss1: 0.248407 Loss2: 0.414823
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.618408 Loss1: 0.217446 Loss2: 0.400962
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.587431 Loss1: 0.190108 Loss2: 0.397323
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.619908 Loss1: 0.225034 Loss2: 0.394875
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.586103 Loss1: 0.193573 Loss2: 0.392530
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.526128 Loss1: 0.142016 Loss2: 0.384112
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.538322 Loss1: 0.155955 Loss2: 0.382367
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.550350 Loss1: 0.167412 Loss2: 0.382938
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.538203 Loss1: 0.155536 Loss2: 0.382667
+(DefaultActor pid=1838052) >> Training accuracy: 0.972903
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.495553 Loss1: 0.419142 Loss2: 0.076411
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.329229 Loss1: 0.255518 Loss2: 0.073711
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.255036 Loss1: 0.187344 Loss2: 0.067692
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.239961 Loss1: 0.174302 Loss2: 0.065659
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.231891 Loss1: 0.168089 Loss2: 0.063802
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.208998 Loss1: 0.146952 Loss2: 0.062045
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197066 Loss1: 0.136135 Loss2: 0.060931
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.173787 Loss1: 0.113702 Loss2: 0.060085
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.164605 Loss1: 0.105283 Loss2: 0.059322
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166683 Loss1: 0.108213 Loss2: 0.058471
+(DefaultActor pid=1838052) >> Training accuracy: 0.973606
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.012162 Loss1: 0.436682 Loss2: 0.575481
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.799008 Loss1: 0.235905 Loss2: 0.563103
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.756858 Loss1: 0.222804 Loss2: 0.534054
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.733322 Loss1: 0.207774 Loss2: 0.525548
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.702424 Loss1: 0.189256 Loss2: 0.513168
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.705594 Loss1: 0.197283 Loss2: 0.508311
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.719106 Loss1: 0.211684 Loss2: 0.507423
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.685717 Loss1: 0.182639 Loss2: 0.503077
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.644746 Loss1: 0.148841 Loss2: 0.495905
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.622592 Loss1: 0.128666 Loss2: 0.493926
+(DefaultActor pid=1838052) >> Training accuracy: 0.976562
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.431880 Loss1: 0.388548 Loss2: 0.043332
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.276171 Loss1: 0.230499 Loss2: 0.045672
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.218978 Loss1: 0.174298 Loss2: 0.044680
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.201014 Loss1: 0.156810 Loss2: 0.044205
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.187500 Loss1: 0.143486 Loss2: 0.044014
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.182115 Loss1: 0.138580 Loss2: 0.043535
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.175891 Loss1: 0.131818 Loss2: 0.044072
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.169518 Loss1: 0.126151 Loss2: 0.043367
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.161924 Loss1: 0.119234 Loss2: 0.042690
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.179014 Loss1: 0.135679 Loss2: 0.043335
+(DefaultActor pid=1838052) >> Training accuracy: 0.965745
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.429497 Loss1: 0.391638 Loss2: 0.037859
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.261756 Loss1: 0.221767 Loss2: 0.039989
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.185411 Loss1: 0.146013 Loss2: 0.039398
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.198675 Loss1: 0.158774 Loss2: 0.039901
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.200744 Loss1: 0.159850 Loss2: 0.040893
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.179659 Loss1: 0.139394 Loss2: 0.040265
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.169840 Loss1: 0.129769 Loss2: 0.040071
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.151742 Loss1: 0.111105 Loss2: 0.040637
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.138337 Loss1: 0.098767 Loss2: 0.039570
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.138291 Loss1: 0.098277 Loss2: 0.040014
+(DefaultActor pid=1838052) >> Training accuracy: 0.976661
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.440684 Loss1: 0.403122 Loss2: 0.037562
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.288016 Loss1: 0.246606 Loss2: 0.041410
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.252886 Loss1: 0.212527 Loss2: 0.040360
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.185070 Loss1: 0.145241 Loss2: 0.039830
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.176021 Loss1: 0.135887 Loss2: 0.040134
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.161470 Loss1: 0.121480 Loss2: 0.039990
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.157327 Loss1: 0.117544 Loss2: 0.039783
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.149039 Loss1: 0.109396 Loss2: 0.039643
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.142299 Loss1: 0.102509 Loss2: 0.039790
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.158509 Loss1: 0.118205 Loss2: 0.040304
+(DefaultActor pid=1838052) >> Training accuracy: 0.985243
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 02:23:30,222][flwr][DEBUG] - fit_round 38 received 10 results and 0 failures
+>> Test accuracy: 0.628300
+[2023-09-28 02:24:10,207][flwr][INFO] - fit progress: (38, 2.065860210897062, {'accuracy': 0.6283}, 72273.0968576204)
+[2023-09-28 02:24:10,207][flwr][DEBUG] - evaluate_round 38: strategy sampled 10 clients (out of 10)
+[2023-09-28 02:24:47,513][flwr][DEBUG] - evaluate_round 38 received 10 results and 0 failures
+[2023-09-28 02:24:47,514][flwr][DEBUG] - fit_round 39: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.384961 Loss1: 0.344681 Loss2: 0.040280
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.260230 Loss1: 0.216422 Loss2: 0.043808
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.233905 Loss1: 0.190407 Loss2: 0.043497
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.206171 Loss1: 0.163037 Loss2: 0.043134
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.175923 Loss1: 0.133205 Loss2: 0.042717
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.172038 Loss1: 0.129571 Loss2: 0.042466
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197703 Loss1: 0.154523 Loss2: 0.043181
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.162750 Loss1: 0.120047 Loss2: 0.042702
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.167713 Loss1: 0.124752 Loss2: 0.042961
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163586 Loss1: 0.121087 Loss2: 0.042499
+(DefaultActor pid=1838052) >> Training accuracy: 0.978244
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.419245 Loss1: 0.377613 Loss2: 0.041632
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.279324 Loss1: 0.233723 Loss2: 0.045601
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.247699 Loss1: 0.203414 Loss2: 0.044285
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.214926 Loss1: 0.170675 Loss2: 0.044251
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.191573 Loss1: 0.147695 Loss2: 0.043878
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.212024 Loss1: 0.168384 Loss2: 0.043639
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.154553 Loss1: 0.111660 Loss2: 0.042893
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.150527 Loss1: 0.107758 Loss2: 0.042770
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.155705 Loss1: 0.112207 Loss2: 0.043498
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166108 Loss1: 0.122595 Loss2: 0.043513
+(DefaultActor pid=1838052) >> Training accuracy: 0.975946
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.950546 Loss1: 0.364580 Loss2: 0.585967
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.790651 Loss1: 0.212926 Loss2: 0.577726
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.763584 Loss1: 0.202202 Loss2: 0.561381
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.735404 Loss1: 0.188785 Loss2: 0.546619
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.716583 Loss1: 0.176884 Loss2: 0.539699
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.735777 Loss1: 0.202108 Loss2: 0.533669
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.698596 Loss1: 0.171457 Loss2: 0.527138
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.701719 Loss1: 0.179092 Loss2: 0.522627
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.636034 Loss1: 0.120701 Loss2: 0.515333
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.626950 Loss1: 0.117925 Loss2: 0.509025
+(DefaultActor pid=1838052) >> Training accuracy: 0.969752
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.348304 Loss1: 0.312684 Loss2: 0.035619
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.226025 Loss1: 0.187666 Loss2: 0.038358
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.219118 Loss1: 0.180018 Loss2: 0.039100
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.180326 Loss1: 0.141487 Loss2: 0.038839
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.187125 Loss1: 0.147872 Loss2: 0.039253
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.190643 Loss1: 0.151182 Loss2: 0.039461
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.147020 Loss1: 0.107963 Loss2: 0.039056
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.135119 Loss1: 0.096145 Loss2: 0.038974
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.133582 Loss1: 0.095137 Loss2: 0.038445
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.145749 Loss1: 0.106441 Loss2: 0.039308
+(DefaultActor pid=1838052) >> Training accuracy: 0.970274
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 1.005744 Loss1: 0.411087 Loss2: 0.594657
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.825049 Loss1: 0.228833 Loss2: 0.596216
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.772448 Loss1: 0.192247 Loss2: 0.580201
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.776096 Loss1: 0.204248 Loss2: 0.571848
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.756268 Loss1: 0.193302 Loss2: 0.562966
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.699358 Loss1: 0.145462 Loss2: 0.553896
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.684253 Loss1: 0.136019 Loss2: 0.548234
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.691315 Loss1: 0.147532 Loss2: 0.543783
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.675440 Loss1: 0.136525 Loss2: 0.538914
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.639372 Loss1: 0.106977 Loss2: 0.532395
+(DefaultActor pid=1838052) >> Training accuracy: 0.975694
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.880657 Loss1: 0.420931 Loss2: 0.459726
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.699179 Loss1: 0.278237 Loss2: 0.420942
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.586774 Loss1: 0.188452 Loss2: 0.398322
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615108 Loss1: 0.223527 Loss2: 0.391581
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.573486 Loss1: 0.185209 Loss2: 0.388277
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.546234 Loss1: 0.162117 Loss2: 0.384117
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.502468 Loss1: 0.126442 Loss2: 0.376026
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.501679 Loss1: 0.124646 Loss2: 0.377034
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.523940 Loss1: 0.144373 Loss2: 0.379567
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.494666 Loss1: 0.119756 Loss2: 0.374909
+(DefaultActor pid=1838052) >> Training accuracy: 0.975084
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.357959 Loss1: 0.317613 Loss2: 0.040347
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.252431 Loss1: 0.208905 Loss2: 0.043526
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.182230 Loss1: 0.140668 Loss2: 0.041562
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.206335 Loss1: 0.163465 Loss2: 0.042870
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.191784 Loss1: 0.149030 Loss2: 0.042754
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.194444 Loss1: 0.151649 Loss2: 0.042795
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.169078 Loss1: 0.126995 Loss2: 0.042083
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.173534 Loss1: 0.131383 Loss2: 0.042150
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.163550 Loss1: 0.121244 Loss2: 0.042306
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.150125 Loss1: 0.108769 Loss2: 0.041356
+(DefaultActor pid=1838052) >> Training accuracy: 0.978837
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.387201 Loss1: 0.351239 Loss2: 0.035962
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.246278 Loss1: 0.207492 Loss2: 0.038786
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.208893 Loss1: 0.170108 Loss2: 0.038785
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.174972 Loss1: 0.136711 Loss2: 0.038260
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.161681 Loss1: 0.123439 Loss2: 0.038242
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.154717 Loss1: 0.116237 Loss2: 0.038479
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.166681 Loss1: 0.128114 Loss2: 0.038567
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.155058 Loss1: 0.116301 Loss2: 0.038757
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.148972 Loss1: 0.110280 Loss2: 0.038692
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.157001 Loss1: 0.117789 Loss2: 0.039212
+(DefaultActor pid=1838052) >> Training accuracy: 0.970134
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.348883 Loss1: 0.312887 Loss2: 0.035996
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.237904 Loss1: 0.198361 Loss2: 0.039542
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.193108 Loss1: 0.153567 Loss2: 0.039541
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.183219 Loss1: 0.143437 Loss2: 0.039783
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.161211 Loss1: 0.122015 Loss2: 0.039196
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.164748 Loss1: 0.125633 Loss2: 0.039116
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129833 Loss1: 0.090883 Loss2: 0.038949
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.131490 Loss1: 0.092257 Loss2: 0.039232
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.138833 Loss1: 0.099562 Loss2: 0.039271
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.135714 Loss1: 0.096327 Loss2: 0.039388
+(DefaultActor pid=1838052) >> Training accuracy: 0.978766
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.405173 Loss1: 0.341129 Loss2: 0.064044
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.305308 Loss1: 0.244066 Loss2: 0.061243
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.250796 Loss1: 0.191348 Loss2: 0.059448
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.227043 Loss1: 0.168130 Loss2: 0.058913
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.202156 Loss1: 0.145157 Loss2: 0.056999
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.190824 Loss1: 0.134405 Loss2: 0.056419
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.193735 Loss1: 0.136758 Loss2: 0.056977
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.181427 Loss1: 0.124822 Loss2: 0.056605
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160186 Loss1: 0.104372 Loss2: 0.055814
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.139883 Loss1: 0.085171 Loss2: 0.054713
+(DefaultActor pid=1838052) >> Training accuracy: 0.979233
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 02:53:44,476][flwr][DEBUG] - fit_round 39 received 10 results and 0 failures
+>> Test accuracy: 0.632100
+[2023-09-28 02:54:24,943][flwr][INFO] - fit progress: (39, 2.087371099490327, {'accuracy': 0.6321}, 74087.83291663835)
+[2023-09-28 02:54:24,943][flwr][DEBUG] - evaluate_round 39: strategy sampled 10 clients (out of 10)
+[2023-09-28 02:55:01,197][flwr][DEBUG] - evaluate_round 39 received 10 results and 0 failures
+[2023-09-28 02:55:01,198][flwr][DEBUG] - fit_round 40: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.408748 Loss1: 0.371365 Loss2: 0.037383
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.256682 Loss1: 0.216206 Loss2: 0.040476
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.199833 Loss1: 0.159833 Loss2: 0.040000
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.186427 Loss1: 0.146193 Loss2: 0.040234
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.161897 Loss1: 0.122046 Loss2: 0.039851
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.153733 Loss1: 0.114602 Loss2: 0.039131
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.134971 Loss1: 0.095389 Loss2: 0.039582
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.142230 Loss1: 0.103251 Loss2: 0.038979
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160427 Loss1: 0.120315 Loss2: 0.040112
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.154296 Loss1: 0.113859 Loss2: 0.040437
+(DefaultActor pid=1838052) >> Training accuracy: 0.982319
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.330824 Loss1: 0.294385 Loss2: 0.036439
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.233003 Loss1: 0.193123 Loss2: 0.039880
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.201093 Loss1: 0.161182 Loss2: 0.039911
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.190235 Loss1: 0.150459 Loss2: 0.039776
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.192865 Loss1: 0.152882 Loss2: 0.039983
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.161443 Loss1: 0.121004 Loss2: 0.040439
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.153238 Loss1: 0.113122 Loss2: 0.040116
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.129261 Loss1: 0.089713 Loss2: 0.039548
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.127062 Loss1: 0.088358 Loss2: 0.038704
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.130641 Loss1: 0.091483 Loss2: 0.039158
+(DefaultActor pid=1838052) >> Training accuracy: 0.983386
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.407933 Loss1: 0.367325 Loss2: 0.040608
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.278527 Loss1: 0.233740 Loss2: 0.044788
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.214077 Loss1: 0.170411 Loss2: 0.043666
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.198942 Loss1: 0.156016 Loss2: 0.042925
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.188335 Loss1: 0.145823 Loss2: 0.042513
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.190338 Loss1: 0.147250 Loss2: 0.043088
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.168581 Loss1: 0.126148 Loss2: 0.042433
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.129846 Loss1: 0.087646 Loss2: 0.042200
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.133897 Loss1: 0.092052 Loss2: 0.041845
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.161388 Loss1: 0.119436 Loss2: 0.041952
+(DefaultActor pid=1838052) >> Training accuracy: 0.976362
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.682889 Loss1: 0.392449 Loss2: 0.290440
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.508769 Loss1: 0.271475 Loss2: 0.237294
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.423154 Loss1: 0.198237 Loss2: 0.224917
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.414200 Loss1: 0.194476 Loss2: 0.219724
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.399932 Loss1: 0.184020 Loss2: 0.215912
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.335244 Loss1: 0.123115 Loss2: 0.212129
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.353691 Loss1: 0.143417 Loss2: 0.210274
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.352336 Loss1: 0.140922 Loss2: 0.211414
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.335024 Loss1: 0.126958 Loss2: 0.208067
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.339112 Loss1: 0.130933 Loss2: 0.208179
+(DefaultActor pid=1838052) >> Training accuracy: 0.979941
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.873990 Loss1: 0.290741 Loss2: 0.583249
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.786760 Loss1: 0.209383 Loss2: 0.577378
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.753608 Loss1: 0.193459 Loss2: 0.560149
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.741402 Loss1: 0.192711 Loss2: 0.548691
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.726168 Loss1: 0.185820 Loss2: 0.540349
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.707480 Loss1: 0.170327 Loss2: 0.537153
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.700496 Loss1: 0.170853 Loss2: 0.529642
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.666595 Loss1: 0.142121 Loss2: 0.524474
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.636955 Loss1: 0.118994 Loss2: 0.517961
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.660299 Loss1: 0.142924 Loss2: 0.517375
+(DefaultActor pid=1838052) >> Training accuracy: 0.953125
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.416937 Loss1: 0.345542 Loss2: 0.071395
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.298435 Loss1: 0.224267 Loss2: 0.074168
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.254772 Loss1: 0.182867 Loss2: 0.071905
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.234351 Loss1: 0.164021 Loss2: 0.070330
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.208594 Loss1: 0.141584 Loss2: 0.067010
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.186025 Loss1: 0.121010 Loss2: 0.065015
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.186290 Loss1: 0.121658 Loss2: 0.064631
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.166847 Loss1: 0.102611 Loss2: 0.064236
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.193944 Loss1: 0.129933 Loss2: 0.064012
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.206284 Loss1: 0.141703 Loss2: 0.064581
+(DefaultActor pid=1838052) >> Training accuracy: 0.978244
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.387497 Loss1: 0.351502 Loss2: 0.035995
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.255647 Loss1: 0.216250 Loss2: 0.039397
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.194337 Loss1: 0.155517 Loss2: 0.038821
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.177022 Loss1: 0.138489 Loss2: 0.038533
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.173612 Loss1: 0.134330 Loss2: 0.039282
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.163418 Loss1: 0.123791 Loss2: 0.039627
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.159709 Loss1: 0.119983 Loss2: 0.039727
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.147168 Loss1: 0.108354 Loss2: 0.038814
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.184937 Loss1: 0.144985 Loss2: 0.039952
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.152178 Loss1: 0.112575 Loss2: 0.039603
+(DefaultActor pid=1838052) >> Training accuracy: 0.980419
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.326927 Loss1: 0.292648 Loss2: 0.034279
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.218566 Loss1: 0.181296 Loss2: 0.037270
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.173206 Loss1: 0.135855 Loss2: 0.037352
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.196992 Loss1: 0.158581 Loss2: 0.038411
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.184802 Loss1: 0.146134 Loss2: 0.038668
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.137278 Loss1: 0.099655 Loss2: 0.037624
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.140152 Loss1: 0.102617 Loss2: 0.037535
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.117930 Loss1: 0.080638 Loss2: 0.037291
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.103696 Loss1: 0.066361 Loss2: 0.037334
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095788 Loss1: 0.059174 Loss2: 0.036614
+(DefaultActor pid=1838052) >> Training accuracy: 0.990585
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.771447 Loss1: 0.365754 Loss2: 0.405693
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.630135 Loss1: 0.251205 Loss2: 0.378930
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.571623 Loss1: 0.209364 Loss2: 0.362259
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.596883 Loss1: 0.229713 Loss2: 0.367171
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.538459 Loss1: 0.180682 Loss2: 0.357778
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.516317 Loss1: 0.163355 Loss2: 0.352961
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.511856 Loss1: 0.159579 Loss2: 0.352277
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.516610 Loss1: 0.165799 Loss2: 0.350810
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.477944 Loss1: 0.131002 Loss2: 0.346942
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.485606 Loss1: 0.138995 Loss2: 0.346611
+(DefaultActor pid=1838052) >> Training accuracy: 0.964201
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.440309 Loss1: 0.366806 Loss2: 0.073503
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.272921 Loss1: 0.204990 Loss2: 0.067931
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.220161 Loss1: 0.155084 Loss2: 0.065078
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.218061 Loss1: 0.155425 Loss2: 0.062636
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.200922 Loss1: 0.138717 Loss2: 0.062204
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.184539 Loss1: 0.123208 Loss2: 0.061331
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.164389 Loss1: 0.104189 Loss2: 0.060200
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.174791 Loss1: 0.114803 Loss2: 0.059988
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.158840 Loss1: 0.099994 Loss2: 0.058845
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.165501 Loss1: 0.106472 Loss2: 0.059029
+(DefaultActor pid=1838052) >> Training accuracy: 0.977431
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 03:24:09,304][flwr][DEBUG] - fit_round 40 received 10 results and 0 failures
+>> Test accuracy: 0.632600
+[2023-09-28 03:24:50,377][flwr][INFO] - fit progress: (40, 2.067515920335873, {'accuracy': 0.6326}, 75913.26707566809)
+[2023-09-28 03:24:50,377][flwr][DEBUG] - evaluate_round 40: strategy sampled 10 clients (out of 10)
+[2023-09-28 03:25:27,001][flwr][DEBUG] - evaluate_round 40 received 10 results and 0 failures
+[2023-09-28 03:25:27,002][flwr][DEBUG] - fit_round 41: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.365663 Loss1: 0.330417 Loss2: 0.035247
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.228918 Loss1: 0.190550 Loss2: 0.038369
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.179874 Loss1: 0.142129 Loss2: 0.037745
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.160471 Loss1: 0.122666 Loss2: 0.037806
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.145423 Loss1: 0.107171 Loss2: 0.038252
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.134378 Loss1: 0.096479 Loss2: 0.037899
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.141110 Loss1: 0.103334 Loss2: 0.037776
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.139664 Loss1: 0.101590 Loss2: 0.038074
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.145802 Loss1: 0.107697 Loss2: 0.038106
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.161847 Loss1: 0.122948 Loss2: 0.038899
+(DefaultActor pid=1838052) >> Training accuracy: 0.973695
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.931210 Loss1: 0.331647 Loss2: 0.599563
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.823036 Loss1: 0.225797 Loss2: 0.597239
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.744993 Loss1: 0.164794 Loss2: 0.580199
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.724070 Loss1: 0.160383 Loss2: 0.563687
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.718951 Loss1: 0.164273 Loss2: 0.554678
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.712391 Loss1: 0.164017 Loss2: 0.548374
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.700716 Loss1: 0.158603 Loss2: 0.542113
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.659692 Loss1: 0.126746 Loss2: 0.532946
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.648997 Loss1: 0.120491 Loss2: 0.528506
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.656761 Loss1: 0.134202 Loss2: 0.522559
+(DefaultActor pid=1838052) >> Training accuracy: 0.978244
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.853531 Loss1: 0.316967 Loss2: 0.536565
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.720408 Loss1: 0.204684 Loss2: 0.515724
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.682960 Loss1: 0.179036 Loss2: 0.503924
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.657902 Loss1: 0.163568 Loss2: 0.494334
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.649053 Loss1: 0.159953 Loss2: 0.489100
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.645362 Loss1: 0.157363 Loss2: 0.487999
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.589850 Loss1: 0.110590 Loss2: 0.479260
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.602257 Loss1: 0.121052 Loss2: 0.481205
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.591025 Loss1: 0.113572 Loss2: 0.477454
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.609282 Loss1: 0.132575 Loss2: 0.476707
+(DefaultActor pid=1838052) >> Training accuracy: 0.962619
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.336995 Loss1: 0.297144 Loss2: 0.039850
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.247467 Loss1: 0.204440 Loss2: 0.043028
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.171000 Loss1: 0.128535 Loss2: 0.042465
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136347 Loss1: 0.095475 Loss2: 0.040872
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.153248 Loss1: 0.112323 Loss2: 0.040925
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.165721 Loss1: 0.124008 Loss2: 0.041713
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.165849 Loss1: 0.122950 Loss2: 0.042899
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.156489 Loss1: 0.114811 Loss2: 0.041678
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160582 Loss1: 0.118926 Loss2: 0.041655
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.159132 Loss1: 0.116929 Loss2: 0.042203
+(DefaultActor pid=1838052) >> Training accuracy: 0.977706
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.342911 Loss1: 0.307969 Loss2: 0.034942
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.234825 Loss1: 0.196638 Loss2: 0.038187
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.171980 Loss1: 0.133949 Loss2: 0.038031
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.155417 Loss1: 0.117580 Loss2: 0.037837
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.143503 Loss1: 0.105742 Loss2: 0.037760
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.153051 Loss1: 0.115299 Loss2: 0.037751
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.139322 Loss1: 0.101189 Loss2: 0.038133
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.133509 Loss1: 0.095521 Loss2: 0.037988
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112395 Loss1: 0.075142 Loss2: 0.037253
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118172 Loss1: 0.080728 Loss2: 0.037444
+(DefaultActor pid=1838052) >> Training accuracy: 0.987380
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.799106 Loss1: 0.348458 Loss2: 0.450648
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.674629 Loss1: 0.258734 Loss2: 0.415895
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.650436 Loss1: 0.246229 Loss2: 0.404207
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.559243 Loss1: 0.168224 Loss2: 0.391019
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.561473 Loss1: 0.175454 Loss2: 0.386019
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.512757 Loss1: 0.131254 Loss2: 0.381502
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.519132 Loss1: 0.141093 Loss2: 0.378039
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.542210 Loss1: 0.157917 Loss2: 0.384292
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.516640 Loss1: 0.140560 Loss2: 0.376080
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.485644 Loss1: 0.115314 Loss2: 0.370330
+(DefaultActor pid=1838052) >> Training accuracy: 0.974826
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.391346 Loss1: 0.354144 Loss2: 0.037202
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.270883 Loss1: 0.230388 Loss2: 0.040495
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.225941 Loss1: 0.185563 Loss2: 0.040378
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.192887 Loss1: 0.152032 Loss2: 0.040854
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.162173 Loss1: 0.121727 Loss2: 0.040446
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178743 Loss1: 0.137967 Loss2: 0.040776
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.179264 Loss1: 0.137918 Loss2: 0.041347
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.157555 Loss1: 0.117260 Loss2: 0.040296
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.164613 Loss1: 0.123855 Loss2: 0.040758
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.135625 Loss1: 0.095420 Loss2: 0.040205
+(DefaultActor pid=1838052) >> Training accuracy: 0.968339
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.354935 Loss1: 0.318966 Loss2: 0.035969
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.210173 Loss1: 0.171611 Loss2: 0.038562
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.186113 Loss1: 0.147160 Loss2: 0.038953
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.162910 Loss1: 0.124103 Loss2: 0.038807
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.199243 Loss1: 0.159341 Loss2: 0.039903
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.207098 Loss1: 0.166585 Loss2: 0.040513
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.177047 Loss1: 0.136647 Loss2: 0.040400
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.177057 Loss1: 0.136605 Loss2: 0.040452
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.150514 Loss1: 0.110885 Loss2: 0.039629
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.158607 Loss1: 0.118235 Loss2: 0.040371
+(DefaultActor pid=1838052) >> Training accuracy: 0.978165
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.435481 Loss1: 0.367341 Loss2: 0.068140
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.282816 Loss1: 0.216163 Loss2: 0.066653
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.230712 Loss1: 0.168692 Loss2: 0.062020
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.232974 Loss1: 0.171560 Loss2: 0.061413
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.215682 Loss1: 0.155480 Loss2: 0.060202
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.187192 Loss1: 0.128747 Loss2: 0.058445
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.193881 Loss1: 0.135563 Loss2: 0.058318
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.180464 Loss1: 0.122625 Loss2: 0.057839
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.154857 Loss1: 0.098097 Loss2: 0.056760
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.171681 Loss1: 0.114913 Loss2: 0.056768
+(DefaultActor pid=1838052) >> Training accuracy: 0.979519
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.374787 Loss1: 0.312822 Loss2: 0.061965
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.260902 Loss1: 0.202977 Loss2: 0.057926
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.223140 Loss1: 0.166527 Loss2: 0.056613
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.203431 Loss1: 0.147003 Loss2: 0.056428
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.200187 Loss1: 0.144343 Loss2: 0.055845
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.187361 Loss1: 0.131560 Loss2: 0.055801
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.191250 Loss1: 0.135888 Loss2: 0.055362
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.169950 Loss1: 0.114638 Loss2: 0.055312
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.184799 Loss1: 0.128627 Loss2: 0.056172
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.153772 Loss1: 0.099232 Loss2: 0.054541
+(DefaultActor pid=1838052) >> Training accuracy: 0.983979
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 03:54:28,045][flwr][DEBUG] - fit_round 41 received 10 results and 0 failures
+>> Test accuracy: 0.634100
+[2023-09-28 03:55:08,509][flwr][INFO] - fit progress: (41, 2.094820894753209, {'accuracy': 0.6341}, 77731.39968940802)
+[2023-09-28 03:55:08,510][flwr][DEBUG] - evaluate_round 41: strategy sampled 10 clients (out of 10)
+[2023-09-28 03:55:45,284][flwr][DEBUG] - evaluate_round 41 received 10 results and 0 failures
+[2023-09-28 03:55:45,285][flwr][DEBUG] - fit_round 42: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.282961 Loss1: 0.247893 Loss2: 0.035068
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.198014 Loss1: 0.159779 Loss2: 0.038234
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.184060 Loss1: 0.145359 Loss2: 0.038701
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.156623 Loss1: 0.117933 Loss2: 0.038689
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.141080 Loss1: 0.103140 Loss2: 0.037940
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.162968 Loss1: 0.124399 Loss2: 0.038569
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.208433 Loss1: 0.168083 Loss2: 0.040350
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.151405 Loss1: 0.112030 Loss2: 0.039375
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.133598 Loss1: 0.094682 Loss2: 0.038916
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115680 Loss1: 0.077597 Loss2: 0.038083
+(DefaultActor pid=1838052) >> Training accuracy: 0.983994
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.343418 Loss1: 0.302792 Loss2: 0.040626
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.238469 Loss1: 0.195201 Loss2: 0.043268
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.190821 Loss1: 0.148473 Loss2: 0.042348
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.177271 Loss1: 0.135086 Loss2: 0.042185
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.163452 Loss1: 0.121658 Loss2: 0.041794
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.165660 Loss1: 0.124301 Loss2: 0.041359
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.141909 Loss1: 0.100319 Loss2: 0.041589
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.146433 Loss1: 0.105611 Loss2: 0.040822
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.163767 Loss1: 0.122136 Loss2: 0.041630
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.123815 Loss1: 0.082807 Loss2: 0.041008
+(DefaultActor pid=1838052) >> Training accuracy: 0.977057
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.667660 Loss1: 0.317764 Loss2: 0.349896
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.626047 Loss1: 0.286947 Loss2: 0.339101
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.556331 Loss1: 0.226211 Loss2: 0.330120
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.494485 Loss1: 0.177126 Loss2: 0.317359
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.499835 Loss1: 0.179844 Loss2: 0.319991
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.456447 Loss1: 0.145975 Loss2: 0.310473
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.475145 Loss1: 0.160171 Loss2: 0.314974
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.464884 Loss1: 0.153477 Loss2: 0.311406
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.446647 Loss1: 0.135771 Loss2: 0.310875
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.428262 Loss1: 0.119478 Loss2: 0.308784
+(DefaultActor pid=1838052) >> Training accuracy: 0.973101
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.921521 Loss1: 0.346193 Loss2: 0.575328
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.790455 Loss1: 0.217924 Loss2: 0.572531
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.736875 Loss1: 0.176813 Loss2: 0.560062
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.683661 Loss1: 0.136360 Loss2: 0.547300
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.679269 Loss1: 0.140169 Loss2: 0.539100
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.685928 Loss1: 0.150338 Loss2: 0.535590
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.689668 Loss1: 0.157337 Loss2: 0.532331
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.690349 Loss1: 0.163135 Loss2: 0.527214
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.687016 Loss1: 0.161105 Loss2: 0.525910
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.644956 Loss1: 0.121956 Loss2: 0.523001
+(DefaultActor pid=1838052) >> Training accuracy: 0.975160
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.349861 Loss1: 0.313053 Loss2: 0.036808
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.249057 Loss1: 0.208817 Loss2: 0.040239
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.207112 Loss1: 0.167218 Loss2: 0.039894
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.219699 Loss1: 0.178674 Loss2: 0.041025
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.190301 Loss1: 0.149370 Loss2: 0.040931
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178170 Loss1: 0.137478 Loss2: 0.040693
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.189778 Loss1: 0.148625 Loss2: 0.041153
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.163671 Loss1: 0.122896 Loss2: 0.040775
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140772 Loss1: 0.100118 Loss2: 0.040654
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.141555 Loss1: 0.101302 Loss2: 0.040253
+(DefaultActor pid=1838052) >> Training accuracy: 0.982525
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.705869 Loss1: 0.374161 Loss2: 0.331708
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.504914 Loss1: 0.225456 Loss2: 0.279458
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.432206 Loss1: 0.170160 Loss2: 0.262046
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.408075 Loss1: 0.151973 Loss2: 0.256102
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.418689 Loss1: 0.162836 Loss2: 0.255853
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.427930 Loss1: 0.170999 Loss2: 0.256931
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.387531 Loss1: 0.136020 Loss2: 0.251511
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.359555 Loss1: 0.111299 Loss2: 0.248256
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.377214 Loss1: 0.126171 Loss2: 0.251043
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.371156 Loss1: 0.123189 Loss2: 0.247968
+(DefaultActor pid=1838052) >> Training accuracy: 0.974090
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.391325 Loss1: 0.351154 Loss2: 0.040171
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.224268 Loss1: 0.181907 Loss2: 0.042361
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.223382 Loss1: 0.181233 Loss2: 0.042149
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.172724 Loss1: 0.131553 Loss2: 0.041171
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.141090 Loss1: 0.100974 Loss2: 0.040117
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.138989 Loss1: 0.098605 Loss2: 0.040383
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129905 Loss1: 0.090279 Loss2: 0.039626
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.147625 Loss1: 0.107419 Loss2: 0.040206
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.116048 Loss1: 0.077064 Loss2: 0.038984
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.110495 Loss1: 0.071319 Loss2: 0.039176
+(DefaultActor pid=1838052) >> Training accuracy: 0.981337
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.310546 Loss1: 0.274975 Loss2: 0.035570
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.227447 Loss1: 0.188508 Loss2: 0.038939
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.194796 Loss1: 0.155867 Loss2: 0.038929
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.177591 Loss1: 0.138462 Loss2: 0.039129
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.143985 Loss1: 0.105381 Loss2: 0.038604
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.135813 Loss1: 0.097171 Loss2: 0.038642
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.161289 Loss1: 0.122369 Loss2: 0.038920
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.163918 Loss1: 0.124671 Loss2: 0.039247
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.139108 Loss1: 0.099543 Loss2: 0.039565
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.151961 Loss1: 0.112641 Loss2: 0.039320
+(DefaultActor pid=1838052) >> Training accuracy: 0.973101
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.348037 Loss1: 0.279274 Loss2: 0.068764
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.245691 Loss1: 0.174621 Loss2: 0.071069
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.203232 Loss1: 0.135433 Loss2: 0.067799
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.184965 Loss1: 0.120127 Loss2: 0.064838
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.195637 Loss1: 0.130544 Loss2: 0.065093
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178709 Loss1: 0.115010 Loss2: 0.063699
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.165109 Loss1: 0.102430 Loss2: 0.062680
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.145173 Loss1: 0.084489 Loss2: 0.060684
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.142507 Loss1: 0.082294 Loss2: 0.060212
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.172323 Loss1: 0.111828 Loss2: 0.060495
+(DefaultActor pid=1838052) >> Training accuracy: 0.982171
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.840658 Loss1: 0.331950 Loss2: 0.508708
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.683527 Loss1: 0.223477 Loss2: 0.460050
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.647043 Loss1: 0.205072 Loss2: 0.441970
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.621233 Loss1: 0.190779 Loss2: 0.430454
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.596415 Loss1: 0.172472 Loss2: 0.423943
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.599424 Loss1: 0.176818 Loss2: 0.422607
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.564358 Loss1: 0.147170 Loss2: 0.417188
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.526255 Loss1: 0.114892 Loss2: 0.411363
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.514644 Loss1: 0.107009 Loss2: 0.407635
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.515364 Loss1: 0.111466 Loss2: 0.403898
+(DefaultActor pid=1838052) >> Training accuracy: 0.971284
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 04:25:23,403][flwr][DEBUG] - fit_round 42 received 10 results and 0 failures
+>> Test accuracy: 0.636900
+[2023-09-28 04:26:05,111][flwr][INFO] - fit progress: (42, 2.0926969356049363, {'accuracy': 0.6369}, 79588.00169806927)
+[2023-09-28 04:26:05,112][flwr][DEBUG] - evaluate_round 42: strategy sampled 10 clients (out of 10)
+[2023-09-28 04:26:41,630][flwr][DEBUG] - evaluate_round 42 received 10 results and 0 failures
+[2023-09-28 04:26:41,632][flwr][DEBUG] - fit_round 43: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.622454 Loss1: 0.334983 Loss2: 0.287472
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.460811 Loss1: 0.224719 Loss2: 0.236092
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.404370 Loss1: 0.186312 Loss2: 0.218058
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.426915 Loss1: 0.210120 Loss2: 0.216795
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.390577 Loss1: 0.178378 Loss2: 0.212199
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.352321 Loss1: 0.143994 Loss2: 0.208328
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.332623 Loss1: 0.125436 Loss2: 0.207187
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.314753 Loss1: 0.110039 Loss2: 0.204715
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.292947 Loss1: 0.090591 Loss2: 0.202356
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.312968 Loss1: 0.108573 Loss2: 0.204396
+(DefaultActor pid=1838052) >> Training accuracy: 0.971073
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.277103 Loss1: 0.242914 Loss2: 0.034190
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.202641 Loss1: 0.165083 Loss2: 0.037558
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.165324 Loss1: 0.127421 Loss2: 0.037903
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.151122 Loss1: 0.113767 Loss2: 0.037355
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.163657 Loss1: 0.125336 Loss2: 0.038321
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.148759 Loss1: 0.110548 Loss2: 0.038211
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.138187 Loss1: 0.100298 Loss2: 0.037889
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.129537 Loss1: 0.091197 Loss2: 0.038340
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.127048 Loss1: 0.089250 Loss2: 0.037799
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.131357 Loss1: 0.093383 Loss2: 0.037974
+(DefaultActor pid=1838052) >> Training accuracy: 0.984756
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.551241 Loss1: 0.314662 Loss2: 0.236580
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.389170 Loss1: 0.192214 Loss2: 0.196956
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.364679 Loss1: 0.176759 Loss2: 0.187920
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.344949 Loss1: 0.160685 Loss2: 0.184263
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.323784 Loss1: 0.141348 Loss2: 0.182436
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.324854 Loss1: 0.144682 Loss2: 0.180172
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.354143 Loss1: 0.172713 Loss2: 0.181430
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.320019 Loss1: 0.141244 Loss2: 0.178775
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.291362 Loss1: 0.114669 Loss2: 0.176694
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.318065 Loss1: 0.136417 Loss2: 0.181648
+(DefaultActor pid=1838052) >> Training accuracy: 0.957081
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.345857 Loss1: 0.309490 Loss2: 0.036367
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.231980 Loss1: 0.193347 Loss2: 0.038633
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.195384 Loss1: 0.156432 Loss2: 0.038952
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.174435 Loss1: 0.135845 Loss2: 0.038590
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.190379 Loss1: 0.150994 Loss2: 0.039385
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.152666 Loss1: 0.113921 Loss2: 0.038745
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130272 Loss1: 0.092181 Loss2: 0.038091
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.133521 Loss1: 0.095190 Loss2: 0.038331
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120534 Loss1: 0.082588 Loss2: 0.037945
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.113473 Loss1: 0.076162 Loss2: 0.037311
+(DefaultActor pid=1838052) >> Training accuracy: 0.987540
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.312291 Loss1: 0.274862 Loss2: 0.037429
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.211997 Loss1: 0.171833 Loss2: 0.040164
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.172954 Loss1: 0.132881 Loss2: 0.040073
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.186943 Loss1: 0.146893 Loss2: 0.040049
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.174381 Loss1: 0.133920 Loss2: 0.040460
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.183817 Loss1: 0.143304 Loss2: 0.040513
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.155665 Loss1: 0.115521 Loss2: 0.040144
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.157943 Loss1: 0.118221 Loss2: 0.039722
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.136332 Loss1: 0.096591 Loss2: 0.039741
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116564 Loss1: 0.077551 Loss2: 0.039013
+(DefaultActor pid=1838052) >> Training accuracy: 0.984573
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.333276 Loss1: 0.298383 Loss2: 0.034893
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.217971 Loss1: 0.179511 Loss2: 0.038460
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.192641 Loss1: 0.153969 Loss2: 0.038673
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.182213 Loss1: 0.143692 Loss2: 0.038521
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.178010 Loss1: 0.139232 Loss2: 0.038778
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.179962 Loss1: 0.140685 Loss2: 0.039277
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.143453 Loss1: 0.104435 Loss2: 0.039018
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.138911 Loss1: 0.100512 Loss2: 0.038399
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.137207 Loss1: 0.098276 Loss2: 0.038931
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.105014 Loss1: 0.067202 Loss2: 0.037811
+(DefaultActor pid=1838052) >> Training accuracy: 0.989800
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.318975 Loss1: 0.282861 Loss2: 0.036114
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.226722 Loss1: 0.187669 Loss2: 0.039053
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.197444 Loss1: 0.158010 Loss2: 0.039434
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.154976 Loss1: 0.115749 Loss2: 0.039227
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.137773 Loss1: 0.098997 Loss2: 0.038776
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.170771 Loss1: 0.131585 Loss2: 0.039186
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.177031 Loss1: 0.136970 Loss2: 0.040061
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.169341 Loss1: 0.128799 Loss2: 0.040542
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.150091 Loss1: 0.109739 Loss2: 0.040352
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.131410 Loss1: 0.091581 Loss2: 0.039829
+(DefaultActor pid=1838052) >> Training accuracy: 0.985403
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.303265 Loss1: 0.268084 Loss2: 0.035180
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.195352 Loss1: 0.157304 Loss2: 0.038048
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.156414 Loss1: 0.118118 Loss2: 0.038296
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.170626 Loss1: 0.131955 Loss2: 0.038671
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.140194 Loss1: 0.101907 Loss2: 0.038287
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.153874 Loss1: 0.115353 Loss2: 0.038521
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.120149 Loss1: 0.081940 Loss2: 0.038209
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125134 Loss1: 0.087332 Loss2: 0.037802
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120537 Loss1: 0.082501 Loss2: 0.038036
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114448 Loss1: 0.076515 Loss2: 0.037933
+(DefaultActor pid=1838052) >> Training accuracy: 0.985957
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.318507 Loss1: 0.279583 Loss2: 0.038924
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.201696 Loss1: 0.160295 Loss2: 0.041401
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.165498 Loss1: 0.124511 Loss2: 0.040987
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.151609 Loss1: 0.110836 Loss2: 0.040773
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.151873 Loss1: 0.111333 Loss2: 0.040540
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.177589 Loss1: 0.136080 Loss2: 0.041510
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.178600 Loss1: 0.137028 Loss2: 0.041572
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.153412 Loss1: 0.112567 Loss2: 0.040845
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.142766 Loss1: 0.102032 Loss2: 0.040734
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.110102 Loss1: 0.070100 Loss2: 0.040002
+(DefaultActor pid=1838052) >> Training accuracy: 0.976963
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.827188 Loss1: 0.281634 Loss2: 0.545554
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.691270 Loss1: 0.165272 Loss2: 0.525998
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.623977 Loss1: 0.117932 Loss2: 0.506045
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.637940 Loss1: 0.134702 Loss2: 0.503238
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.679641 Loss1: 0.175277 Loss2: 0.504364
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.637436 Loss1: 0.140403 Loss2: 0.497033
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.598166 Loss1: 0.106050 Loss2: 0.492116
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.583214 Loss1: 0.095952 Loss2: 0.487262
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.594512 Loss1: 0.107120 Loss2: 0.487393
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.589025 Loss1: 0.103759 Loss2: 0.485266
+(DefaultActor pid=1838052) >> Training accuracy: 0.981370
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 04:56:23,506][flwr][DEBUG] - fit_round 43 received 10 results and 0 failures
+>> Test accuracy: 0.635300
+[2023-09-28 04:57:04,439][flwr][INFO] - fit progress: (43, 2.1266209250821855, {'accuracy': 0.6353}, 81447.3296973533)
+[2023-09-28 04:57:04,440][flwr][DEBUG] - evaluate_round 43: strategy sampled 10 clients (out of 10)
+[2023-09-28 04:57:41,768][flwr][DEBUG] - evaluate_round 43 received 10 results and 0 failures
+[2023-09-28 04:57:41,769][flwr][DEBUG] - fit_round 44: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.387533 Loss1: 0.346942 Loss2: 0.040591
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.209495 Loss1: 0.167296 Loss2: 0.042198
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.184008 Loss1: 0.142733 Loss2: 0.041276
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.188618 Loss1: 0.147181 Loss2: 0.041437
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.146726 Loss1: 0.106148 Loss2: 0.040578
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.139971 Loss1: 0.099541 Loss2: 0.040430
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.154447 Loss1: 0.113913 Loss2: 0.040534
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.141856 Loss1: 0.100993 Loss2: 0.040862
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.114645 Loss1: 0.074961 Loss2: 0.039685
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127267 Loss1: 0.087459 Loss2: 0.039808
+(DefaultActor pid=1838052) >> Training accuracy: 0.979307
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.862240 Loss1: 0.286437 Loss2: 0.575803
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.755235 Loss1: 0.191662 Loss2: 0.563574
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.738925 Loss1: 0.183534 Loss2: 0.555392
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.716703 Loss1: 0.169719 Loss2: 0.546984
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.723830 Loss1: 0.177770 Loss2: 0.546060
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.693369 Loss1: 0.157645 Loss2: 0.535724
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.668064 Loss1: 0.139362 Loss2: 0.528702
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.648697 Loss1: 0.123244 Loss2: 0.525454
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.630081 Loss1: 0.107431 Loss2: 0.522651
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.634798 Loss1: 0.116332 Loss2: 0.518466
+(DefaultActor pid=1838052) >> Training accuracy: 0.982595
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.614427 Loss1: 0.319129 Loss2: 0.295299
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.450638 Loss1: 0.205423 Loss2: 0.245215
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.384596 Loss1: 0.158690 Loss2: 0.225906
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.387231 Loss1: 0.160728 Loss2: 0.226503
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.352342 Loss1: 0.132348 Loss2: 0.219994
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.361304 Loss1: 0.143110 Loss2: 0.218194
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.353245 Loss1: 0.133408 Loss2: 0.219837
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.343845 Loss1: 0.126226 Loss2: 0.217619
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.310026 Loss1: 0.097021 Loss2: 0.213005
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.285808 Loss1: 0.076902 Loss2: 0.208906
+(DefaultActor pid=1838052) >> Training accuracy: 0.987380
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.347964 Loss1: 0.313405 Loss2: 0.034559
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.213423 Loss1: 0.176397 Loss2: 0.037026
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.170107 Loss1: 0.132628 Loss2: 0.037479
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.138185 Loss1: 0.101779 Loss2: 0.036406
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.137981 Loss1: 0.101112 Loss2: 0.036869
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.136111 Loss1: 0.098948 Loss2: 0.037163
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.144079 Loss1: 0.107033 Loss2: 0.037046
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137191 Loss1: 0.099720 Loss2: 0.037472
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120727 Loss1: 0.083499 Loss2: 0.037228
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.131239 Loss1: 0.094089 Loss2: 0.037150
+(DefaultActor pid=1838052) >> Training accuracy: 0.981120
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.888154 Loss1: 0.282231 Loss2: 0.605923
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.778603 Loss1: 0.165931 Loss2: 0.612672
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.745413 Loss1: 0.145877 Loss2: 0.599537
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.783196 Loss1: 0.187291 Loss2: 0.595905
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.746624 Loss1: 0.153325 Loss2: 0.593299
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.736725 Loss1: 0.152470 Loss2: 0.584254
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.704185 Loss1: 0.127500 Loss2: 0.576685
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.742210 Loss1: 0.169364 Loss2: 0.572846
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.693950 Loss1: 0.126407 Loss2: 0.567543
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.661564 Loss1: 0.099625 Loss2: 0.561939
+(DefaultActor pid=1838052) >> Training accuracy: 0.977965
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.861854 Loss1: 0.271823 Loss2: 0.590032
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.778888 Loss1: 0.191630 Loss2: 0.587258
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.738646 Loss1: 0.165692 Loss2: 0.572953
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.692825 Loss1: 0.134218 Loss2: 0.558607
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.662459 Loss1: 0.110094 Loss2: 0.552365
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.667975 Loss1: 0.120855 Loss2: 0.547120
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.654712 Loss1: 0.110662 Loss2: 0.544050
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.669289 Loss1: 0.129668 Loss2: 0.539620
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.668415 Loss1: 0.129263 Loss2: 0.539152
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.651973 Loss1: 0.116077 Loss2: 0.535896
+(DefaultActor pid=1838052) >> Training accuracy: 0.983584
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.362656 Loss1: 0.326293 Loss2: 0.036363
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.273253 Loss1: 0.233908 Loss2: 0.039346
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.194030 Loss1: 0.155183 Loss2: 0.038847
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.178384 Loss1: 0.138811 Loss2: 0.039573
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.177862 Loss1: 0.138525 Loss2: 0.039336
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.149984 Loss1: 0.111535 Loss2: 0.038449
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.159844 Loss1: 0.121483 Loss2: 0.038361
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.159869 Loss1: 0.121057 Loss2: 0.038812
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.150881 Loss1: 0.111804 Loss2: 0.039077
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.136011 Loss1: 0.097154 Loss2: 0.038857
+(DefaultActor pid=1838052) >> Training accuracy: 0.984169
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.339182 Loss1: 0.262603 Loss2: 0.076580
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.213333 Loss1: 0.140377 Loss2: 0.072955
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.200058 Loss1: 0.132029 Loss2: 0.068030
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.200491 Loss1: 0.134855 Loss2: 0.065635
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.171949 Loss1: 0.108255 Loss2: 0.063693
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.208690 Loss1: 0.144811 Loss2: 0.063878
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.187412 Loss1: 0.124194 Loss2: 0.063219
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.177690 Loss1: 0.115653 Loss2: 0.062038
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.182263 Loss1: 0.120605 Loss2: 0.061658
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.156499 Loss1: 0.095973 Loss2: 0.060526
+(DefaultActor pid=1838052) >> Training accuracy: 0.981013
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.275111 Loss1: 0.242409 Loss2: 0.032702
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.181755 Loss1: 0.145795 Loss2: 0.035960
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.151643 Loss1: 0.115747 Loss2: 0.035897
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.157621 Loss1: 0.120639 Loss2: 0.036983
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.151281 Loss1: 0.114453 Loss2: 0.036828
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.133573 Loss1: 0.097270 Loss2: 0.036303
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.141143 Loss1: 0.104130 Loss2: 0.037013
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.131283 Loss1: 0.094072 Loss2: 0.037211
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.144935 Loss1: 0.107167 Loss2: 0.037768
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.136587 Loss1: 0.099254 Loss2: 0.037333
+(DefaultActor pid=1838052) >> Training accuracy: 0.974657
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.292809 Loss1: 0.258582 Loss2: 0.034227
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.210566 Loss1: 0.172722 Loss2: 0.037844
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.177470 Loss1: 0.140277 Loss2: 0.037193
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.163791 Loss1: 0.126341 Loss2: 0.037451
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.166139 Loss1: 0.127971 Loss2: 0.038167
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.195773 Loss1: 0.156794 Loss2: 0.038979
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.157543 Loss1: 0.118907 Loss2: 0.038636
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.136986 Loss1: 0.098942 Loss2: 0.038044
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.126202 Loss1: 0.088863 Loss2: 0.037339
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121048 Loss1: 0.083696 Loss2: 0.037352
+(DefaultActor pid=1838052) >> Training accuracy: 0.983979
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 05:27:13,441][flwr][DEBUG] - fit_round 44 received 10 results and 0 failures
+>> Test accuracy: 0.639900
+[2023-09-28 05:27:54,176][flwr][INFO] - fit progress: (44, 2.132381713047576, {'accuracy': 0.6399}, 83297.06599513115)
+[2023-09-28 05:27:54,176][flwr][DEBUG] - evaluate_round 44: strategy sampled 10 clients (out of 10)
+[2023-09-28 05:28:31,533][flwr][DEBUG] - evaluate_round 44 received 10 results and 0 failures
+[2023-09-28 05:28:31,533][flwr][DEBUG] - fit_round 45: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.694145 Loss1: 0.282507 Loss2: 0.411639
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.614729 Loss1: 0.238218 Loss2: 0.376512
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.552899 Loss1: 0.195011 Loss2: 0.357888
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.508408 Loss1: 0.163700 Loss2: 0.344708
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.528889 Loss1: 0.186647 Loss2: 0.342242
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.538339 Loss1: 0.190846 Loss2: 0.347494
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.470620 Loss1: 0.133183 Loss2: 0.337438
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.439023 Loss1: 0.108614 Loss2: 0.330409
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.436418 Loss1: 0.107259 Loss2: 0.329158
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.424909 Loss1: 0.099916 Loss2: 0.324993
+(DefaultActor pid=1838052) >> Training accuracy: 0.976562
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.275192 Loss1: 0.241824 Loss2: 0.033368
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.202433 Loss1: 0.165061 Loss2: 0.037372
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.173176 Loss1: 0.135542 Loss2: 0.037635
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.164091 Loss1: 0.126809 Loss2: 0.037282
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.154477 Loss1: 0.117036 Loss2: 0.037441
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.136687 Loss1: 0.099535 Loss2: 0.037152
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.145230 Loss1: 0.108134 Loss2: 0.037096
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.129871 Loss1: 0.092498 Loss2: 0.037372
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.114875 Loss1: 0.078053 Loss2: 0.036822
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121990 Loss1: 0.084904 Loss2: 0.037086
+(DefaultActor pid=1838052) >> Training accuracy: 0.981804
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.667328 Loss1: 0.267837 Loss2: 0.399491
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.557072 Loss1: 0.202973 Loss2: 0.354099
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.553410 Loss1: 0.209670 Loss2: 0.343740
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.511822 Loss1: 0.174126 Loss2: 0.337696
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.463738 Loss1: 0.134718 Loss2: 0.329019
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.477067 Loss1: 0.148459 Loss2: 0.328608
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.444798 Loss1: 0.118039 Loss2: 0.326759
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.442297 Loss1: 0.116169 Loss2: 0.326127
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.445527 Loss1: 0.117500 Loss2: 0.328027
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.441979 Loss1: 0.117789 Loss2: 0.324190
+(DefaultActor pid=1838052) >> Training accuracy: 0.973101
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.876477 Loss1: 0.269410 Loss2: 0.607066
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.767157 Loss1: 0.163952 Loss2: 0.603205
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.761011 Loss1: 0.168608 Loss2: 0.592402
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.748511 Loss1: 0.163517 Loss2: 0.584994
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.719597 Loss1: 0.144902 Loss2: 0.574695
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.690769 Loss1: 0.121715 Loss2: 0.569053
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.684726 Loss1: 0.121797 Loss2: 0.562929
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.685218 Loss1: 0.128862 Loss2: 0.556355
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.675132 Loss1: 0.120600 Loss2: 0.554532
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.687241 Loss1: 0.137180 Loss2: 0.550061
+(DefaultActor pid=1838052) >> Training accuracy: 0.982397
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.307098 Loss1: 0.271716 Loss2: 0.035382
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.203708 Loss1: 0.166013 Loss2: 0.037695
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.176355 Loss1: 0.138474 Loss2: 0.037880
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.195096 Loss1: 0.156322 Loss2: 0.038774
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.173590 Loss1: 0.134492 Loss2: 0.039098
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.159439 Loss1: 0.120930 Loss2: 0.038509
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.162531 Loss1: 0.123505 Loss2: 0.039026
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.171742 Loss1: 0.132114 Loss2: 0.039628
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.142288 Loss1: 0.103034 Loss2: 0.039254
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.133059 Loss1: 0.094299 Loss2: 0.038759
+(DefaultActor pid=1838052) >> Training accuracy: 0.986020
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.300428 Loss1: 0.230797 Loss2: 0.069632
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.204254 Loss1: 0.138243 Loss2: 0.066011
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.193114 Loss1: 0.129060 Loss2: 0.064053
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.185847 Loss1: 0.122450 Loss2: 0.063396
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.161676 Loss1: 0.100239 Loss2: 0.061437
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.153279 Loss1: 0.091455 Loss2: 0.061824
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.163237 Loss1: 0.101550 Loss2: 0.061687
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123087 Loss1: 0.062324 Loss2: 0.060763
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.133392 Loss1: 0.073467 Loss2: 0.059925
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.154869 Loss1: 0.094029 Loss2: 0.060840
+(DefaultActor pid=1838052) >> Training accuracy: 0.979367
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.802437 Loss1: 0.261405 Loss2: 0.541033
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.703578 Loss1: 0.176709 Loss2: 0.526869
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.685384 Loss1: 0.174070 Loss2: 0.511314
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.684110 Loss1: 0.174921 Loss2: 0.509190
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.669401 Loss1: 0.165634 Loss2: 0.503768
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.623916 Loss1: 0.129022 Loss2: 0.494894
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.625147 Loss1: 0.128987 Loss2: 0.496160
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.623809 Loss1: 0.133765 Loss2: 0.490044
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.595621 Loss1: 0.109700 Loss2: 0.485920
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.608744 Loss1: 0.121270 Loss2: 0.487474
+(DefaultActor pid=1838052) >> Training accuracy: 0.983613
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.389837 Loss1: 0.302419 Loss2: 0.087418
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.252667 Loss1: 0.166550 Loss2: 0.086117
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.237164 Loss1: 0.155447 Loss2: 0.081718
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.220709 Loss1: 0.140645 Loss2: 0.080064
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.215519 Loss1: 0.136703 Loss2: 0.078816
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.175011 Loss1: 0.099589 Loss2: 0.075422
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.168935 Loss1: 0.095187 Loss2: 0.073748
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.171050 Loss1: 0.097288 Loss2: 0.073762
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.180124 Loss1: 0.106729 Loss2: 0.073395
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.190813 Loss1: 0.117432 Loss2: 0.073381
+(DefaultActor pid=1838052) >> Training accuracy: 0.980785
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.350054 Loss1: 0.310751 Loss2: 0.039303
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.208492 Loss1: 0.169185 Loss2: 0.039307
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.160491 Loss1: 0.122145 Loss2: 0.038346
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.151848 Loss1: 0.113563 Loss2: 0.038285
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.168821 Loss1: 0.130435 Loss2: 0.038386
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.151719 Loss1: 0.113197 Loss2: 0.038521
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.138338 Loss1: 0.100224 Loss2: 0.038114
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.127121 Loss1: 0.089321 Loss2: 0.037800
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.131555 Loss1: 0.094433 Loss2: 0.037122
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127171 Loss1: 0.089681 Loss2: 0.037490
+(DefaultActor pid=1838052) >> Training accuracy: 0.987342
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.323396 Loss1: 0.288707 Loss2: 0.034689
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.196987 Loss1: 0.160058 Loss2: 0.036929
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.174197 Loss1: 0.137207 Loss2: 0.036990
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.169050 Loss1: 0.131846 Loss2: 0.037205
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.139165 Loss1: 0.101712 Loss2: 0.037454
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.117286 Loss1: 0.079887 Loss2: 0.037399
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104113 Loss1: 0.067810 Loss2: 0.036303
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137924 Loss1: 0.100608 Loss2: 0.037316
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.143607 Loss1: 0.105937 Loss2: 0.037669
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.139370 Loss1: 0.101688 Loss2: 0.037682
+(DefaultActor pid=1838052) >> Training accuracy: 0.976128
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 05:58:07,652][flwr][DEBUG] - fit_round 45 received 10 results and 0 failures
+>> Test accuracy: 0.641000
+[2023-09-28 06:00:22,375][flwr][INFO] - fit progress: (45, 2.1219718056364942, {'accuracy': 0.641}, 85245.26535388315)
+[2023-09-28 06:00:22,376][flwr][DEBUG] - evaluate_round 45: strategy sampled 10 clients (out of 10)
+[2023-09-28 06:00:59,471][flwr][DEBUG] - evaluate_round 45 received 10 results and 0 failures
+[2023-09-28 06:00:59,472][flwr][DEBUG] - fit_round 46: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.848461 Loss1: 0.264681 Loss2: 0.583780
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.788239 Loss1: 0.206051 Loss2: 0.582189
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.742917 Loss1: 0.175010 Loss2: 0.567907
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.694170 Loss1: 0.143787 Loss2: 0.550383
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.690724 Loss1: 0.143492 Loss2: 0.547232
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.659334 Loss1: 0.124818 Loss2: 0.534515
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.668685 Loss1: 0.135850 Loss2: 0.532835
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.678549 Loss1: 0.150061 Loss2: 0.528488
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.643480 Loss1: 0.117386 Loss2: 0.526094
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.602135 Loss1: 0.083892 Loss2: 0.518243
+(DefaultActor pid=1838052) >> Training accuracy: 0.984375
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.862052 Loss1: 0.243395 Loss2: 0.618657
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.776351 Loss1: 0.159908 Loss2: 0.616444
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.763344 Loss1: 0.155515 Loss2: 0.607828
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.757852 Loss1: 0.158222 Loss2: 0.599629
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.743615 Loss1: 0.150490 Loss2: 0.593125
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.732730 Loss1: 0.146038 Loss2: 0.586693
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.695341 Loss1: 0.117196 Loss2: 0.578145
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.673431 Loss1: 0.101184 Loss2: 0.572247
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.688424 Loss1: 0.118772 Loss2: 0.569652
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.654489 Loss1: 0.089141 Loss2: 0.565348
+(DefaultActor pid=1838052) >> Training accuracy: 0.980617
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.610135 Loss1: 0.239830 Loss2: 0.370305
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.513963 Loss1: 0.191855 Loss2: 0.322109
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.457236 Loss1: 0.146234 Loss2: 0.311002
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.464621 Loss1: 0.154738 Loss2: 0.309883
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.437215 Loss1: 0.130953 Loss2: 0.306262
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.428542 Loss1: 0.125567 Loss2: 0.302976
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.444156 Loss1: 0.137332 Loss2: 0.306825
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.420540 Loss1: 0.119004 Loss2: 0.301536
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.417245 Loss1: 0.116377 Loss2: 0.300869
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.393874 Loss1: 0.096342 Loss2: 0.297531
+(DefaultActor pid=1838052) >> Training accuracy: 0.981707
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.283858 Loss1: 0.249572 Loss2: 0.034286
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.208449 Loss1: 0.171456 Loss2: 0.036993
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.169710 Loss1: 0.132479 Loss2: 0.037231
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.145026 Loss1: 0.108198 Loss2: 0.036828
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.154962 Loss1: 0.117845 Loss2: 0.037117
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111089 Loss1: 0.074557 Loss2: 0.036532
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.117350 Loss1: 0.081058 Loss2: 0.036292
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110664 Loss1: 0.074594 Loss2: 0.036070
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.131536 Loss1: 0.094839 Loss2: 0.036697
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104676 Loss1: 0.068068 Loss2: 0.036608
+(DefaultActor pid=1838052) >> Training accuracy: 0.984375
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.342949 Loss1: 0.302269 Loss2: 0.040680
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.237851 Loss1: 0.194417 Loss2: 0.043434
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.205350 Loss1: 0.162466 Loss2: 0.042884
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.173088 Loss1: 0.130687 Loss2: 0.042402
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.139015 Loss1: 0.097887 Loss2: 0.041127
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.137494 Loss1: 0.096259 Loss2: 0.041235
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.128183 Loss1: 0.087332 Loss2: 0.040851
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.130188 Loss1: 0.088612 Loss2: 0.041576
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120243 Loss1: 0.079445 Loss2: 0.040798
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.106191 Loss1: 0.065861 Loss2: 0.040330
+(DefaultActor pid=1838052) >> Training accuracy: 0.985220
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.273775 Loss1: 0.237738 Loss2: 0.036037
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.195542 Loss1: 0.156508 Loss2: 0.039034
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.174747 Loss1: 0.136117 Loss2: 0.038630
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.146146 Loss1: 0.107817 Loss2: 0.038329
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.136748 Loss1: 0.099228 Loss2: 0.037520
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.180948 Loss1: 0.141743 Loss2: 0.039205
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.151197 Loss1: 0.112603 Loss2: 0.038594
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.114325 Loss1: 0.076983 Loss2: 0.037342
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.116101 Loss1: 0.078605 Loss2: 0.037495
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.122317 Loss1: 0.085320 Loss2: 0.036998
+(DefaultActor pid=1838052) >> Training accuracy: 0.978639
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.871202 Loss1: 0.266720 Loss2: 0.604482
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.790816 Loss1: 0.188830 Loss2: 0.601986
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.754011 Loss1: 0.165597 Loss2: 0.588414
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.767475 Loss1: 0.186716 Loss2: 0.580760
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.720211 Loss1: 0.146219 Loss2: 0.573992
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.710112 Loss1: 0.143476 Loss2: 0.566636
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.726080 Loss1: 0.162462 Loss2: 0.563618
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.730727 Loss1: 0.169599 Loss2: 0.561128
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.678655 Loss1: 0.123838 Loss2: 0.554817
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.671736 Loss1: 0.122570 Loss2: 0.549167
+(DefaultActor pid=1838052) >> Training accuracy: 0.982936
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.310331 Loss1: 0.230354 Loss2: 0.079978
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.216229 Loss1: 0.140083 Loss2: 0.076146
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.173376 Loss1: 0.101124 Loss2: 0.072252
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.186817 Loss1: 0.116595 Loss2: 0.070222
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.177515 Loss1: 0.108422 Loss2: 0.069093
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.166103 Loss1: 0.098147 Loss2: 0.067956
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.183675 Loss1: 0.116641 Loss2: 0.067033
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.225218 Loss1: 0.156767 Loss2: 0.068450
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.161302 Loss1: 0.092811 Loss2: 0.068491
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.135570 Loss1: 0.070487 Loss2: 0.065083
+(DefaultActor pid=1838052) >> Training accuracy: 0.983173
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.261168 Loss1: 0.225255 Loss2: 0.035914
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.163083 Loss1: 0.124911 Loss2: 0.038173
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.132043 Loss1: 0.094028 Loss2: 0.038015
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.130161 Loss1: 0.092217 Loss2: 0.037945
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.138869 Loss1: 0.100432 Loss2: 0.038437
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.133730 Loss1: 0.094818 Loss2: 0.038912
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.119449 Loss1: 0.080830 Loss2: 0.038618
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.105398 Loss1: 0.067491 Loss2: 0.037907
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.080825 Loss1: 0.043512 Loss2: 0.037313
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083325 Loss1: 0.046669 Loss2: 0.036656
+(DefaultActor pid=1838052) >> Training accuracy: 0.992788
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.257814 Loss1: 0.222057 Loss2: 0.035757
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.213831 Loss1: 0.174874 Loss2: 0.038957
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.204794 Loss1: 0.165859 Loss2: 0.038935
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.152337 Loss1: 0.114416 Loss2: 0.037920
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.127611 Loss1: 0.090751 Loss2: 0.036860
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.129386 Loss1: 0.092681 Loss2: 0.036706
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.111996 Loss1: 0.075578 Loss2: 0.036418
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.108422 Loss1: 0.072145 Loss2: 0.036277
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.126583 Loss1: 0.090568 Loss2: 0.036015
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.133889 Loss1: 0.096799 Loss2: 0.037090
+(DefaultActor pid=1838052) >> Training accuracy: 0.974684
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 06:30:30,150][flwr][DEBUG] - fit_round 46 received 10 results and 0 failures
+>> Test accuracy: 0.642000
+[2023-09-28 06:31:10,847][flwr][INFO] - fit progress: (46, 2.146202865500039, {'accuracy': 0.642}, 87093.73735058215)
+[2023-09-28 06:31:10,848][flwr][DEBUG] - evaluate_round 46: strategy sampled 10 clients (out of 10)
+[2023-09-28 06:31:47,499][flwr][DEBUG] - evaluate_round 46 received 10 results and 0 failures
+[2023-09-28 06:31:47,503][flwr][DEBUG] - fit_round 47: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.826519 Loss1: 0.246986 Loss2: 0.579533
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.746512 Loss1: 0.175532 Loss2: 0.570980
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.682461 Loss1: 0.127524 Loss2: 0.554937
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.692002 Loss1: 0.146680 Loss2: 0.545322
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.681200 Loss1: 0.142896 Loss2: 0.538304
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.668204 Loss1: 0.135436 Loss2: 0.532768
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.632000 Loss1: 0.105026 Loss2: 0.526974
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.634739 Loss1: 0.111943 Loss2: 0.522797
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.623241 Loss1: 0.103227 Loss2: 0.520014
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.600112 Loss1: 0.084930 Loss2: 0.515182
+(DefaultActor pid=1838052) >> Training accuracy: 0.975672
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.754997 Loss1: 0.262774 Loss2: 0.492223
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.639769 Loss1: 0.176214 Loss2: 0.463555
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.578036 Loss1: 0.128873 Loss2: 0.449162
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.576761 Loss1: 0.134069 Loss2: 0.442692
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.560940 Loss1: 0.119660 Loss2: 0.441280
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.572139 Loss1: 0.135029 Loss2: 0.437109
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.566585 Loss1: 0.126113 Loss2: 0.440472
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.554006 Loss1: 0.119124 Loss2: 0.434882
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.540399 Loss1: 0.108085 Loss2: 0.432314
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.519639 Loss1: 0.089883 Loss2: 0.429755
+(DefaultActor pid=1838052) >> Training accuracy: 0.984573
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.293364 Loss1: 0.256640 Loss2: 0.036724
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.183397 Loss1: 0.143580 Loss2: 0.039817
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.142610 Loss1: 0.103847 Loss2: 0.038763
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.143210 Loss1: 0.104317 Loss2: 0.038893
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.126876 Loss1: 0.088171 Loss2: 0.038705
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.127572 Loss1: 0.088824 Loss2: 0.038748
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130539 Loss1: 0.091704 Loss2: 0.038835
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.118130 Loss1: 0.079573 Loss2: 0.038558
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.102604 Loss1: 0.064157 Loss2: 0.038447
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102383 Loss1: 0.064324 Loss2: 0.038059
+(DefaultActor pid=1838052) >> Training accuracy: 0.989583
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.234818 Loss1: 0.197355 Loss2: 0.037463
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.171210 Loss1: 0.130874 Loss2: 0.040336
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.163565 Loss1: 0.123192 Loss2: 0.040373
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.144254 Loss1: 0.104149 Loss2: 0.040105
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.127849 Loss1: 0.087980 Loss2: 0.039869
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.119997 Loss1: 0.079835 Loss2: 0.040163
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122741 Loss1: 0.082491 Loss2: 0.040249
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137312 Loss1: 0.096725 Loss2: 0.040586
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.164109 Loss1: 0.122871 Loss2: 0.041238
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.111260 Loss1: 0.070666 Loss2: 0.040594
+(DefaultActor pid=1838052) >> Training accuracy: 0.987233
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.884729 Loss1: 0.273550 Loss2: 0.611179
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.785469 Loss1: 0.176231 Loss2: 0.609238
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.732611 Loss1: 0.136143 Loss2: 0.596468
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.734576 Loss1: 0.147852 Loss2: 0.586724
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.686977 Loss1: 0.108979 Loss2: 0.577998
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.691269 Loss1: 0.121387 Loss2: 0.569882
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.686273 Loss1: 0.121189 Loss2: 0.565084
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.690263 Loss1: 0.129588 Loss2: 0.560675
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.697102 Loss1: 0.137766 Loss2: 0.559336
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.708139 Loss1: 0.152513 Loss2: 0.555626
+(DefaultActor pid=1838052) >> Training accuracy: 0.971495
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.818430 Loss1: 0.208513 Loss2: 0.609917
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.752321 Loss1: 0.143291 Loss2: 0.609030
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.755222 Loss1: 0.156614 Loss2: 0.598608
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.731815 Loss1: 0.143534 Loss2: 0.588282
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.717784 Loss1: 0.137515 Loss2: 0.580269
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.695648 Loss1: 0.123444 Loss2: 0.572205
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.676405 Loss1: 0.110015 Loss2: 0.566390
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.671684 Loss1: 0.110745 Loss2: 0.560938
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.647722 Loss1: 0.089282 Loss2: 0.558440
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.646258 Loss1: 0.093485 Loss2: 0.552773
+(DefaultActor pid=1838052) >> Training accuracy: 0.980168
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.270065 Loss1: 0.233474 Loss2: 0.036591
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.199851 Loss1: 0.160259 Loss2: 0.039592
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.174402 Loss1: 0.135148 Loss2: 0.039254
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.170691 Loss1: 0.130886 Loss2: 0.039805
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.152940 Loss1: 0.113856 Loss2: 0.039084
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.173180 Loss1: 0.133590 Loss2: 0.039590
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.134658 Loss1: 0.095189 Loss2: 0.039469
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110684 Loss1: 0.072387 Loss2: 0.038297
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.117008 Loss1: 0.078776 Loss2: 0.038232
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.111146 Loss1: 0.072311 Loss2: 0.038835
+(DefaultActor pid=1838052) >> Training accuracy: 0.986946
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.307561 Loss1: 0.270047 Loss2: 0.037514
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.181948 Loss1: 0.141734 Loss2: 0.040214
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.183103 Loss1: 0.142720 Loss2: 0.040383
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.191854 Loss1: 0.151215 Loss2: 0.040639
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.173827 Loss1: 0.132775 Loss2: 0.041052
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.158566 Loss1: 0.118015 Loss2: 0.040551
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.120985 Loss1: 0.081734 Loss2: 0.039251
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.127988 Loss1: 0.089236 Loss2: 0.038752
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.110851 Loss1: 0.071834 Loss2: 0.039017
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.139183 Loss1: 0.098842 Loss2: 0.040340
+(DefaultActor pid=1838052) >> Training accuracy: 0.981086
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.296703 Loss1: 0.226276 Loss2: 0.070427
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.197244 Loss1: 0.127861 Loss2: 0.069384
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.202944 Loss1: 0.135660 Loss2: 0.067284
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.198740 Loss1: 0.131345 Loss2: 0.067395
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.207827 Loss1: 0.140752 Loss2: 0.067075
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.184041 Loss1: 0.117666 Loss2: 0.066375
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.195945 Loss1: 0.129418 Loss2: 0.066527
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.164611 Loss1: 0.099591 Loss2: 0.065019
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.162838 Loss1: 0.098741 Loss2: 0.064097
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166438 Loss1: 0.102473 Loss2: 0.063964
+(DefaultActor pid=1838052) >> Training accuracy: 0.984968
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.695491 Loss1: 0.242566 Loss2: 0.452924
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.630754 Loss1: 0.208238 Loss2: 0.422516
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.567405 Loss1: 0.162931 Loss2: 0.404475
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.539920 Loss1: 0.139801 Loss2: 0.400119
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.557226 Loss1: 0.160958 Loss2: 0.396268
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.525294 Loss1: 0.133701 Loss2: 0.391593
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.514169 Loss1: 0.125475 Loss2: 0.388694
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.502046 Loss1: 0.115714 Loss2: 0.386333
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.495029 Loss1: 0.111092 Loss2: 0.383938
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.496593 Loss1: 0.111865 Loss2: 0.384728
+(DefaultActor pid=1838052) >> Training accuracy: 0.972155
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 07:01:27,356][flwr][DEBUG] - fit_round 47 received 10 results and 0 failures
+>> Test accuracy: 0.644900
+[2023-09-28 07:02:07,032][flwr][INFO] - fit progress: (47, 2.1143142310575174, {'accuracy': 0.6449}, 88949.92235814035)
+[2023-09-28 07:02:07,032][flwr][DEBUG] - evaluate_round 47: strategy sampled 10 clients (out of 10)
+[2023-09-28 07:02:43,465][flwr][DEBUG] - evaluate_round 47 received 10 results and 0 failures
+[2023-09-28 07:02:43,466][flwr][DEBUG] - fit_round 48: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.727833 Loss1: 0.278922 Loss2: 0.448911
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.547202 Loss1: 0.167729 Loss2: 0.379473
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.515864 Loss1: 0.158427 Loss2: 0.357438
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.487436 Loss1: 0.140862 Loss2: 0.346575
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.475624 Loss1: 0.129411 Loss2: 0.346213
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.448883 Loss1: 0.109095 Loss2: 0.339788
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.448099 Loss1: 0.109717 Loss2: 0.338382
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.439536 Loss1: 0.104155 Loss2: 0.335381
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.423729 Loss1: 0.089546 Loss2: 0.334183
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.423829 Loss1: 0.092041 Loss2: 0.331788
+(DefaultActor pid=1838052) >> Training accuracy: 0.983953
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.286425 Loss1: 0.229642 Loss2: 0.056784
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.211989 Loss1: 0.156159 Loss2: 0.055830
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.160044 Loss1: 0.106592 Loss2: 0.053452
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.162199 Loss1: 0.108761 Loss2: 0.053438
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.160813 Loss1: 0.108324 Loss2: 0.052489
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.148362 Loss1: 0.094858 Loss2: 0.053504
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131680 Loss1: 0.079353 Loss2: 0.052328
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.124515 Loss1: 0.072753 Loss2: 0.051763
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.169393 Loss1: 0.116404 Loss2: 0.052989
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.160758 Loss1: 0.107665 Loss2: 0.053093
+(DefaultActor pid=1838052) >> Training accuracy: 0.975277
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.746180 Loss1: 0.230774 Loss2: 0.515406
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.664873 Loss1: 0.160591 Loss2: 0.504282
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.644329 Loss1: 0.158118 Loss2: 0.486211
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.671457 Loss1: 0.183834 Loss2: 0.487622
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.614013 Loss1: 0.137242 Loss2: 0.476771
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.610826 Loss1: 0.137114 Loss2: 0.473712
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.602774 Loss1: 0.133873 Loss2: 0.468901
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.575182 Loss1: 0.111295 Loss2: 0.463887
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.596087 Loss1: 0.132990 Loss2: 0.463097
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.584741 Loss1: 0.122054 Loss2: 0.462687
+(DefaultActor pid=1838052) >> Training accuracy: 0.962025
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.217883 Loss1: 0.183429 Loss2: 0.034454
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.154075 Loss1: 0.117210 Loss2: 0.036865
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.144030 Loss1: 0.106734 Loss2: 0.037295
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.170348 Loss1: 0.132323 Loss2: 0.038024
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.138654 Loss1: 0.100226 Loss2: 0.038428
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.155939 Loss1: 0.116896 Loss2: 0.039044
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.123208 Loss1: 0.084723 Loss2: 0.038485
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.138917 Loss1: 0.100443 Loss2: 0.038473
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.129138 Loss1: 0.090389 Loss2: 0.038748
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.156871 Loss1: 0.117442 Loss2: 0.039429
+(DefaultActor pid=1838052) >> Training accuracy: 0.973704
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.789646 Loss1: 0.305331 Loss2: 0.484315
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.651313 Loss1: 0.184159 Loss2: 0.467154
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.629400 Loss1: 0.174960 Loss2: 0.454440
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.579172 Loss1: 0.134624 Loss2: 0.444548
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.549163 Loss1: 0.109020 Loss2: 0.440143
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.591700 Loss1: 0.150224 Loss2: 0.441476
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.559915 Loss1: 0.122428 Loss2: 0.437486
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.543989 Loss1: 0.107551 Loss2: 0.436438
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.536890 Loss1: 0.103134 Loss2: 0.433756
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.576015 Loss1: 0.139044 Loss2: 0.436970
+(DefaultActor pid=1838052) >> Training accuracy: 0.978299
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.250774 Loss1: 0.214780 Loss2: 0.035995
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.169270 Loss1: 0.129888 Loss2: 0.039382
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.144053 Loss1: 0.105509 Loss2: 0.038543
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.123957 Loss1: 0.085155 Loss2: 0.038802
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.121573 Loss1: 0.083010 Loss2: 0.038563
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.122617 Loss1: 0.084060 Loss2: 0.038557
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.111982 Loss1: 0.073865 Loss2: 0.038117
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.134627 Loss1: 0.096008 Loss2: 0.038619
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092891 Loss1: 0.054843 Loss2: 0.038049
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080483 Loss1: 0.043163 Loss2: 0.037321
+(DefaultActor pid=1838052) >> Training accuracy: 0.995393
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.259602 Loss1: 0.223912 Loss2: 0.035690
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.179866 Loss1: 0.141457 Loss2: 0.038409
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.161913 Loss1: 0.123177 Loss2: 0.038736
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.163654 Loss1: 0.124801 Loss2: 0.038853
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.157792 Loss1: 0.118816 Loss2: 0.038976
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.145221 Loss1: 0.106249 Loss2: 0.038973
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.137531 Loss1: 0.098146 Loss2: 0.039385
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.128791 Loss1: 0.090140 Loss2: 0.038651
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140113 Loss1: 0.100873 Loss2: 0.039239
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114801 Loss1: 0.075802 Loss2: 0.038999
+(DefaultActor pid=1838052) >> Training accuracy: 0.979441
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.249448 Loss1: 0.210898 Loss2: 0.038549
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.145104 Loss1: 0.104256 Loss2: 0.040848
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.137770 Loss1: 0.097518 Loss2: 0.040252
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.147649 Loss1: 0.107281 Loss2: 0.040369
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.131308 Loss1: 0.090839 Loss2: 0.040469
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.134800 Loss1: 0.094607 Loss2: 0.040194
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.150757 Loss1: 0.110370 Loss2: 0.040387
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.156451 Loss1: 0.115528 Loss2: 0.040923
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.132047 Loss1: 0.090969 Loss2: 0.041077
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114589 Loss1: 0.074398 Loss2: 0.040192
+(DefaultActor pid=1838052) >> Training accuracy: 0.975277
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.271715 Loss1: 0.232701 Loss2: 0.039014
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.188261 Loss1: 0.147135 Loss2: 0.041126
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.173655 Loss1: 0.132582 Loss2: 0.041073
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.127953 Loss1: 0.087420 Loss2: 0.040533
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.123004 Loss1: 0.082864 Loss2: 0.040140
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.127873 Loss1: 0.087787 Loss2: 0.040086
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131478 Loss1: 0.091193 Loss2: 0.040284
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.134727 Loss1: 0.094465 Loss2: 0.040261
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.128698 Loss1: 0.088009 Loss2: 0.040689
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.150162 Loss1: 0.108962 Loss2: 0.041199
+(DefaultActor pid=1838052) >> Training accuracy: 0.980168
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.693942 Loss1: 0.212595 Loss2: 0.481347
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.637007 Loss1: 0.163897 Loss2: 0.473110
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.616321 Loss1: 0.155035 Loss2: 0.461286
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.612769 Loss1: 0.151301 Loss2: 0.461468
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.552201 Loss1: 0.099944 Loss2: 0.452257
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.561007 Loss1: 0.112468 Loss2: 0.448540
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.588871 Loss1: 0.137574 Loss2: 0.451296
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.596754 Loss1: 0.145972 Loss2: 0.450783
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.559795 Loss1: 0.117142 Loss2: 0.442653
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.550189 Loss1: 0.108176 Loss2: 0.442012
+(DefaultActor pid=1838052) >> Training accuracy: 0.979628
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 07:32:20,414][flwr][DEBUG] - fit_round 48 received 10 results and 0 failures
+>> Test accuracy: 0.644800
+[2023-09-28 07:33:00,998][flwr][INFO] - fit progress: (48, 2.1224685852139142, {'accuracy': 0.6448}, 90803.88817573013)
+[2023-09-28 07:33:00,998][flwr][DEBUG] - evaluate_round 48: strategy sampled 10 clients (out of 10)
+[2023-09-28 07:33:38,966][flwr][DEBUG] - evaluate_round 48 received 10 results and 0 failures
+[2023-09-28 07:33:38,967][flwr][DEBUG] - fit_round 49: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.800283 Loss1: 0.235867 Loss2: 0.564417
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.706499 Loss1: 0.153670 Loss2: 0.552829
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.670413 Loss1: 0.130323 Loss2: 0.540090
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.676188 Loss1: 0.144582 Loss2: 0.531606
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.668697 Loss1: 0.141998 Loss2: 0.526699
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.661675 Loss1: 0.135826 Loss2: 0.525849
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.649152 Loss1: 0.130247 Loss2: 0.518905
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.619118 Loss1: 0.104389 Loss2: 0.514729
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.607040 Loss1: 0.095288 Loss2: 0.511752
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.603873 Loss1: 0.095465 Loss2: 0.508408
+(DefaultActor pid=1838052) >> Training accuracy: 0.971217
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.260689 Loss1: 0.222543 Loss2: 0.038146
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.207535 Loss1: 0.165967 Loss2: 0.041568
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.203266 Loss1: 0.161251 Loss2: 0.042015
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.154076 Loss1: 0.112317 Loss2: 0.041758
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116052 Loss1: 0.075964 Loss2: 0.040089
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.110625 Loss1: 0.070975 Loss2: 0.039650
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.110625 Loss1: 0.071271 Loss2: 0.039354
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123693 Loss1: 0.083633 Loss2: 0.040060
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.119365 Loss1: 0.078453 Loss2: 0.040912
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112407 Loss1: 0.072468 Loss2: 0.039940
+(DefaultActor pid=1838052) >> Training accuracy: 0.991536
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.775402 Loss1: 0.225349 Loss2: 0.550054
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.688478 Loss1: 0.149645 Loss2: 0.538833
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.659594 Loss1: 0.131331 Loss2: 0.528263
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.623413 Loss1: 0.107100 Loss2: 0.516313
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.621473 Loss1: 0.107989 Loss2: 0.513485
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.647367 Loss1: 0.137248 Loss2: 0.510119
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.616397 Loss1: 0.105859 Loss2: 0.510538
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.607964 Loss1: 0.102598 Loss2: 0.505366
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.615918 Loss1: 0.111319 Loss2: 0.504599
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.612285 Loss1: 0.108382 Loss2: 0.503903
+(DefaultActor pid=1838052) >> Training accuracy: 0.986178
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.263045 Loss1: 0.222493 Loss2: 0.040552
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.178789 Loss1: 0.135662 Loss2: 0.043128
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.182321 Loss1: 0.139121 Loss2: 0.043200
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.168419 Loss1: 0.126118 Loss2: 0.042301
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.151001 Loss1: 0.108297 Loss2: 0.042705
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.165619 Loss1: 0.123631 Loss2: 0.041988
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130839 Loss1: 0.089206 Loss2: 0.041633
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.148221 Loss1: 0.106291 Loss2: 0.041930
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115141 Loss1: 0.073863 Loss2: 0.041278
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104098 Loss1: 0.063420 Loss2: 0.040678
+(DefaultActor pid=1838052) >> Training accuracy: 0.986946
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.461516 Loss1: 0.229614 Loss2: 0.231902
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.353949 Loss1: 0.150797 Loss2: 0.203152
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.351573 Loss1: 0.153141 Loss2: 0.198431
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.342210 Loss1: 0.143929 Loss2: 0.198281
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.360933 Loss1: 0.166062 Loss2: 0.194871
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.338493 Loss1: 0.143764 Loss2: 0.194729
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.307357 Loss1: 0.115126 Loss2: 0.192231
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.283312 Loss1: 0.093755 Loss2: 0.189556
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.267675 Loss1: 0.079356 Loss2: 0.188319
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.287598 Loss1: 0.099722 Loss2: 0.187876
+(DefaultActor pid=1838052) >> Training accuracy: 0.973892
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.249883 Loss1: 0.214250 Loss2: 0.035633
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.173586 Loss1: 0.134440 Loss2: 0.039146
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.119259 Loss1: 0.081119 Loss2: 0.038140
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.115610 Loss1: 0.077582 Loss2: 0.038028
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.122172 Loss1: 0.084039 Loss2: 0.038134
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108381 Loss1: 0.069624 Loss2: 0.038757
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131242 Loss1: 0.092572 Loss2: 0.038670
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137579 Loss1: 0.098005 Loss2: 0.039574
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.121424 Loss1: 0.082589 Loss2: 0.038835
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097894 Loss1: 0.059396 Loss2: 0.038498
+(DefaultActor pid=1838052) >> Training accuracy: 0.990704
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.801597 Loss1: 0.211495 Loss2: 0.590102
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.744493 Loss1: 0.158720 Loss2: 0.585773
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.726586 Loss1: 0.152879 Loss2: 0.573708
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.679242 Loss1: 0.119215 Loss2: 0.560027
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.684024 Loss1: 0.131668 Loss2: 0.552356
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.654164 Loss1: 0.107292 Loss2: 0.546872
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.660328 Loss1: 0.119914 Loss2: 0.540414
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.640434 Loss1: 0.101579 Loss2: 0.538855
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.645844 Loss1: 0.113561 Loss2: 0.532283
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.617444 Loss1: 0.086353 Loss2: 0.531091
+(DefaultActor pid=1838052) >> Training accuracy: 0.986946
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.285496 Loss1: 0.211652 Loss2: 0.073843
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.201770 Loss1: 0.131058 Loss2: 0.070712
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.184576 Loss1: 0.118144 Loss2: 0.066433
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.205506 Loss1: 0.141840 Loss2: 0.063666
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.174666 Loss1: 0.112929 Loss2: 0.061737
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.158089 Loss1: 0.098670 Loss2: 0.059419
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130196 Loss1: 0.072526 Loss2: 0.057671
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.136555 Loss1: 0.080045 Loss2: 0.056511
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.129871 Loss1: 0.073990 Loss2: 0.055882
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115067 Loss1: 0.059215 Loss2: 0.055852
+(DefaultActor pid=1838052) >> Training accuracy: 0.990076
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.206424 Loss1: 0.172822 Loss2: 0.033603
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.143090 Loss1: 0.106841 Loss2: 0.036249
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.135107 Loss1: 0.098263 Loss2: 0.036844
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.165496 Loss1: 0.127891 Loss2: 0.037605
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.121091 Loss1: 0.083405 Loss2: 0.037686
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123729 Loss1: 0.085839 Loss2: 0.037890
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.150633 Loss1: 0.112730 Loss2: 0.037903
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.127385 Loss1: 0.089024 Loss2: 0.038361
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096919 Loss1: 0.059232 Loss2: 0.037687
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121104 Loss1: 0.083005 Loss2: 0.038099
+(DefaultActor pid=1838052) >> Training accuracy: 0.985518
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.238868 Loss1: 0.204776 Loss2: 0.034092
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.192598 Loss1: 0.154961 Loss2: 0.037637
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.157438 Loss1: 0.119965 Loss2: 0.037473
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.175664 Loss1: 0.137015 Loss2: 0.038649
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.159847 Loss1: 0.120734 Loss2: 0.039113
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.135716 Loss1: 0.097406 Loss2: 0.038310
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.145672 Loss1: 0.106872 Loss2: 0.038800
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.152308 Loss1: 0.112920 Loss2: 0.039388
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.147572 Loss1: 0.108360 Loss2: 0.039212
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.137137 Loss1: 0.098422 Loss2: 0.038715
+(DefaultActor pid=1838052) >> Training accuracy: 0.977163
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 08:03:04,483][flwr][DEBUG] - fit_round 49 received 10 results and 0 failures
+>> Test accuracy: 0.645500
+[2023-09-28 08:03:45,715][flwr][INFO] - fit progress: (49, 2.117806362458311, {'accuracy': 0.6455}, 92648.6054644552)
+[2023-09-28 08:03:45,716][flwr][DEBUG] - evaluate_round 49: strategy sampled 10 clients (out of 10)
+[2023-09-28 08:04:37,237][flwr][DEBUG] - evaluate_round 49 received 10 results and 0 failures
+[2023-09-28 08:04:37,237][flwr][DEBUG] - fit_round 50: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.457734 Loss1: 0.205913 Loss2: 0.251822
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.380481 Loss1: 0.156246 Loss2: 0.224236
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.355945 Loss1: 0.137161 Loss2: 0.218784
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.362627 Loss1: 0.144658 Loss2: 0.217969
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.331825 Loss1: 0.120099 Loss2: 0.211727
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.317311 Loss1: 0.105102 Loss2: 0.212208
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.324519 Loss1: 0.113263 Loss2: 0.211256
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.295451 Loss1: 0.088140 Loss2: 0.207311
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.310271 Loss1: 0.102181 Loss2: 0.208090
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.314135 Loss1: 0.105296 Loss2: 0.208839
+(DefaultActor pid=1838052) >> Training accuracy: 0.977453
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.221488 Loss1: 0.186279 Loss2: 0.035209
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.141165 Loss1: 0.103216 Loss2: 0.037949
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.130700 Loss1: 0.093343 Loss2: 0.037358
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.126159 Loss1: 0.088506 Loss2: 0.037653
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.119110 Loss1: 0.081530 Loss2: 0.037580
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.128196 Loss1: 0.090498 Loss2: 0.037698
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.137597 Loss1: 0.099508 Loss2: 0.038089
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.141456 Loss1: 0.102521 Loss2: 0.038934
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.123703 Loss1: 0.084977 Loss2: 0.038726
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118957 Loss1: 0.080489 Loss2: 0.038469
+(DefaultActor pid=1838052) >> Training accuracy: 0.984375
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.704669 Loss1: 0.192003 Loss2: 0.512666
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.657960 Loss1: 0.156845 Loss2: 0.501115
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.629961 Loss1: 0.139524 Loss2: 0.490437
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.643681 Loss1: 0.157373 Loss2: 0.486308
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.630713 Loss1: 0.148655 Loss2: 0.482058
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.599413 Loss1: 0.121895 Loss2: 0.477518
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.615635 Loss1: 0.139848 Loss2: 0.475787
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.611761 Loss1: 0.135872 Loss2: 0.475890
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.563042 Loss1: 0.095021 Loss2: 0.468021
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.582975 Loss1: 0.112476 Loss2: 0.470499
+(DefaultActor pid=1838052) >> Training accuracy: 0.969131
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.647117 Loss1: 0.274487 Loss2: 0.372630
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.483496 Loss1: 0.159219 Loss2: 0.324277
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.465954 Loss1: 0.150692 Loss2: 0.315262
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.462479 Loss1: 0.151867 Loss2: 0.310612
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.459287 Loss1: 0.146134 Loss2: 0.313153
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.437864 Loss1: 0.129334 Loss2: 0.308530
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.438143 Loss1: 0.131966 Loss2: 0.306176
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.402026 Loss1: 0.099147 Loss2: 0.302880
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.392727 Loss1: 0.090350 Loss2: 0.302378
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.419554 Loss1: 0.118235 Loss2: 0.301318
+(DefaultActor pid=1838052) >> Training accuracy: 0.972762
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.259399 Loss1: 0.222550 Loss2: 0.036849
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.182168 Loss1: 0.142731 Loss2: 0.039437
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.154777 Loss1: 0.115559 Loss2: 0.039218
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.141386 Loss1: 0.102205 Loss2: 0.039181
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.125012 Loss1: 0.086357 Loss2: 0.038655
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123779 Loss1: 0.085211 Loss2: 0.038568
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.136009 Loss1: 0.096874 Loss2: 0.039135
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.132676 Loss1: 0.094232 Loss2: 0.038444
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.171731 Loss1: 0.132074 Loss2: 0.039657
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.129421 Loss1: 0.090115 Loss2: 0.039306
+(DefaultActor pid=1838052) >> Training accuracy: 0.981908
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.246653 Loss1: 0.212370 Loss2: 0.034284
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.198191 Loss1: 0.160176 Loss2: 0.038015
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.155949 Loss1: 0.117837 Loss2: 0.038112
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.154647 Loss1: 0.115835 Loss2: 0.038813
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.138247 Loss1: 0.099470 Loss2: 0.038777
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.130950 Loss1: 0.092056 Loss2: 0.038895
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129848 Loss1: 0.091525 Loss2: 0.038323
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.118971 Loss1: 0.080526 Loss2: 0.038444
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.124823 Loss1: 0.085755 Loss2: 0.039068
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.111211 Loss1: 0.073216 Loss2: 0.037995
+(DefaultActor pid=1838052) >> Training accuracy: 0.990017
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.224596 Loss1: 0.190406 Loss2: 0.034190
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.144448 Loss1: 0.107461 Loss2: 0.036987
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.141178 Loss1: 0.104156 Loss2: 0.037023
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113409 Loss1: 0.076769 Loss2: 0.036639
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.114424 Loss1: 0.078083 Loss2: 0.036341
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.121248 Loss1: 0.084824 Loss2: 0.036425
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.123133 Loss1: 0.086295 Loss2: 0.036839
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.136495 Loss1: 0.099060 Loss2: 0.037435
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115815 Loss1: 0.078795 Loss2: 0.037020
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083860 Loss1: 0.047771 Loss2: 0.036090
+(DefaultActor pid=1838052) >> Training accuracy: 0.994191
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.272904 Loss1: 0.236451 Loss2: 0.036453
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.175755 Loss1: 0.136156 Loss2: 0.039599
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.140564 Loss1: 0.101775 Loss2: 0.038789
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.135949 Loss1: 0.096934 Loss2: 0.039014
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.135631 Loss1: 0.096733 Loss2: 0.038898
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.100073 Loss1: 0.062308 Loss2: 0.037765
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.102037 Loss1: 0.064019 Loss2: 0.038018
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110003 Loss1: 0.071402 Loss2: 0.038600
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112297 Loss1: 0.074184 Loss2: 0.038113
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.126564 Loss1: 0.087445 Loss2: 0.039119
+(DefaultActor pid=1838052) >> Training accuracy: 0.980769
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.225578 Loss1: 0.190930 Loss2: 0.034648
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.157522 Loss1: 0.119512 Loss2: 0.038010
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.143751 Loss1: 0.105687 Loss2: 0.038064
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.138982 Loss1: 0.101027 Loss2: 0.037956
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.124248 Loss1: 0.086214 Loss2: 0.038034
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.116804 Loss1: 0.078818 Loss2: 0.037985
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.113504 Loss1: 0.075649 Loss2: 0.037855
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.114159 Loss1: 0.076476 Loss2: 0.037683
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.111359 Loss1: 0.073182 Loss2: 0.038178
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116378 Loss1: 0.078189 Loss2: 0.038189
+(DefaultActor pid=1838052) >> Training accuracy: 0.984573
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.242084 Loss1: 0.206702 Loss2: 0.035382
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.166276 Loss1: 0.127862 Loss2: 0.038414
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.150098 Loss1: 0.111468 Loss2: 0.038630
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.122171 Loss1: 0.084220 Loss2: 0.037951
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110487 Loss1: 0.072252 Loss2: 0.038235
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.101929 Loss1: 0.063560 Loss2: 0.038369
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.121583 Loss1: 0.082590 Loss2: 0.038992
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.111044 Loss1: 0.071887 Loss2: 0.039157
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.103266 Loss1: 0.064945 Loss2: 0.038321
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.130210 Loss1: 0.090877 Loss2: 0.039333
+(DefaultActor pid=1838052) >> Training accuracy: 0.976266
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 08:33:56,504][flwr][DEBUG] - fit_round 50 received 10 results and 0 failures
+>> Test accuracy: 0.643300
+[2023-09-28 08:34:36,467][flwr][INFO] - fit progress: (50, 2.142119585134732, {'accuracy': 0.6433}, 94499.35791312112)
+[2023-09-28 08:34:36,468][flwr][DEBUG] - evaluate_round 50: strategy sampled 10 clients (out of 10)
+[2023-09-28 08:35:13,180][flwr][DEBUG] - evaluate_round 50 received 10 results and 0 failures
+[2023-09-28 08:35:13,181][flwr][DEBUG] - fit_round 51: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.181487 Loss1: 0.148718 Loss2: 0.032768
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.107925 Loss1: 0.073830 Loss2: 0.034094
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.121426 Loss1: 0.086758 Loss2: 0.034669
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.158157 Loss1: 0.122031 Loss2: 0.036126
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.149733 Loss1: 0.113353 Loss2: 0.036380
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.164926 Loss1: 0.127911 Loss2: 0.037015
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.161250 Loss1: 0.124223 Loss2: 0.037027
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.138708 Loss1: 0.101653 Loss2: 0.037055
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.143373 Loss1: 0.106565 Loss2: 0.036808
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118444 Loss1: 0.082194 Loss2: 0.036249
+(DefaultActor pid=1838052) >> Training accuracy: 0.982660
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.270857 Loss1: 0.194077 Loss2: 0.076780
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.203219 Loss1: 0.124421 Loss2: 0.078798
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.164609 Loss1: 0.089741 Loss2: 0.074869
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.177767 Loss1: 0.103794 Loss2: 0.073973
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.190469 Loss1: 0.117454 Loss2: 0.073015
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.171507 Loss1: 0.099640 Loss2: 0.071868
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.154060 Loss1: 0.083339 Loss2: 0.070720
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.142557 Loss1: 0.073331 Loss2: 0.069225
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.149875 Loss1: 0.081347 Loss2: 0.068529
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.168748 Loss1: 0.100659 Loss2: 0.068090
+(DefaultActor pid=1838052) >> Training accuracy: 0.978837
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.813573 Loss1: 0.226774 Loss2: 0.586799
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.735238 Loss1: 0.153799 Loss2: 0.581439
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.697594 Loss1: 0.129181 Loss2: 0.568413
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.707821 Loss1: 0.144335 Loss2: 0.563486
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.704090 Loss1: 0.145462 Loss2: 0.558628
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.682774 Loss1: 0.127736 Loss2: 0.555039
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.674277 Loss1: 0.124096 Loss2: 0.550182
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.662181 Loss1: 0.118187 Loss2: 0.543993
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.649346 Loss1: 0.105790 Loss2: 0.543556
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.640203 Loss1: 0.103415 Loss2: 0.536788
+(DefaultActor pid=1838052) >> Training accuracy: 0.983553
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.215419 Loss1: 0.181200 Loss2: 0.034219
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.146605 Loss1: 0.109088 Loss2: 0.037517
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.166358 Loss1: 0.127716 Loss2: 0.038642
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.155105 Loss1: 0.116130 Loss2: 0.038976
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.152750 Loss1: 0.113407 Loss2: 0.039343
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.148395 Loss1: 0.108840 Loss2: 0.039555
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131333 Loss1: 0.092418 Loss2: 0.038916
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.113846 Loss1: 0.075342 Loss2: 0.038504
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.121290 Loss1: 0.082271 Loss2: 0.039019
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.125286 Loss1: 0.086368 Loss2: 0.038918
+(DefaultActor pid=1838052) >> Training accuracy: 0.978639
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.690874 Loss1: 0.212457 Loss2: 0.478417
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.635475 Loss1: 0.167072 Loss2: 0.468404
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.610756 Loss1: 0.155941 Loss2: 0.454815
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.582099 Loss1: 0.134551 Loss2: 0.447548
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.565325 Loss1: 0.124478 Loss2: 0.440847
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.557868 Loss1: 0.114745 Loss2: 0.443123
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.554997 Loss1: 0.113199 Loss2: 0.441798
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.585071 Loss1: 0.143322 Loss2: 0.441749
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.579197 Loss1: 0.135086 Loss2: 0.444111
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.537705 Loss1: 0.100189 Loss2: 0.437516
+(DefaultActor pid=1838052) >> Training accuracy: 0.980419
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.724328 Loss1: 0.205427 Loss2: 0.518900
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.645562 Loss1: 0.141635 Loss2: 0.503927
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.614026 Loss1: 0.120963 Loss2: 0.493064
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.607597 Loss1: 0.122487 Loss2: 0.485110
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.648116 Loss1: 0.163024 Loss2: 0.485092
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.656785 Loss1: 0.170533 Loss2: 0.486251
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.598964 Loss1: 0.122138 Loss2: 0.476826
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.625258 Loss1: 0.147797 Loss2: 0.477461
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.597226 Loss1: 0.124316 Loss2: 0.472910
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.583954 Loss1: 0.114254 Loss2: 0.469700
+(DefaultActor pid=1838052) >> Training accuracy: 0.972556
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.231887 Loss1: 0.197663 Loss2: 0.034224
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.192640 Loss1: 0.154701 Loss2: 0.037939
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.137638 Loss1: 0.100360 Loss2: 0.037277
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.122720 Loss1: 0.085216 Loss2: 0.037504
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.137258 Loss1: 0.099188 Loss2: 0.038070
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.130036 Loss1: 0.091834 Loss2: 0.038202
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.170992 Loss1: 0.131884 Loss2: 0.039107
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.130539 Loss1: 0.091626 Loss2: 0.038912
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.121011 Loss1: 0.082765 Loss2: 0.038246
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.126219 Loss1: 0.087645 Loss2: 0.038573
+(DefaultActor pid=1838052) >> Training accuracy: 0.985894
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.293581 Loss1: 0.256068 Loss2: 0.037513
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.175708 Loss1: 0.136829 Loss2: 0.038880
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.148066 Loss1: 0.110503 Loss2: 0.037563
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.111335 Loss1: 0.074055 Loss2: 0.037280
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.090379 Loss1: 0.053934 Loss2: 0.036446
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.104565 Loss1: 0.068076 Loss2: 0.036489
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.106649 Loss1: 0.070037 Loss2: 0.036612
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.095515 Loss1: 0.059022 Loss2: 0.036493
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.093688 Loss1: 0.057510 Loss2: 0.036178
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.103971 Loss1: 0.067940 Loss2: 0.036030
+(DefaultActor pid=1838052) >> Training accuracy: 0.986064
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.190078 Loss1: 0.157987 Loss2: 0.032091
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.127788 Loss1: 0.093067 Loss2: 0.034721
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.145164 Loss1: 0.109760 Loss2: 0.035405
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.133183 Loss1: 0.097519 Loss2: 0.035664
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.124934 Loss1: 0.088839 Loss2: 0.036095
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111510 Loss1: 0.075966 Loss2: 0.035544
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129809 Loss1: 0.093203 Loss2: 0.036606
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.108198 Loss1: 0.071841 Loss2: 0.036356
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101529 Loss1: 0.066063 Loss2: 0.035465
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.087608 Loss1: 0.052194 Loss2: 0.035414
+(DefaultActor pid=1838052) >> Training accuracy: 0.989583
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.256689 Loss1: 0.174100 Loss2: 0.082589
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.197408 Loss1: 0.117739 Loss2: 0.079669
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.184519 Loss1: 0.109261 Loss2: 0.075258
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.171440 Loss1: 0.098470 Loss2: 0.072969
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.182038 Loss1: 0.110677 Loss2: 0.071361
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178472 Loss1: 0.108123 Loss2: 0.070349
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.179728 Loss1: 0.108736 Loss2: 0.070992
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.168042 Loss1: 0.097720 Loss2: 0.070322
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.161427 Loss1: 0.091619 Loss2: 0.069807
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.165307 Loss1: 0.096843 Loss2: 0.068464
+(DefaultActor pid=1838052) >> Training accuracy: 0.982002
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 09:04:26,741][flwr][DEBUG] - fit_round 51 received 10 results and 0 failures
+>> Test accuracy: 0.645700
+[2023-09-28 09:05:09,088][flwr][INFO] - fit progress: (51, 2.135459794404027, {'accuracy': 0.6457}, 96331.97838461725)
+[2023-09-28 09:05:09,088][flwr][DEBUG] - evaluate_round 51: strategy sampled 10 clients (out of 10)
+[2023-09-28 09:05:45,528][flwr][DEBUG] - evaluate_round 51 received 10 results and 0 failures
+[2023-09-28 09:05:45,529][flwr][DEBUG] - fit_round 52: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.756197 Loss1: 0.214852 Loss2: 0.541345
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.693283 Loss1: 0.154754 Loss2: 0.538529
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.692817 Loss1: 0.165807 Loss2: 0.527010
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.654689 Loss1: 0.143108 Loss2: 0.511581
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.635468 Loss1: 0.124345 Loss2: 0.511123
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.631203 Loss1: 0.125736 Loss2: 0.505466
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.623747 Loss1: 0.121335 Loss2: 0.502413
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.607221 Loss1: 0.108508 Loss2: 0.498713
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.601027 Loss1: 0.108429 Loss2: 0.492598
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.579998 Loss1: 0.091135 Loss2: 0.488863
+(DefaultActor pid=1838052) >> Training accuracy: 0.976780
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.189986 Loss1: 0.157608 Loss2: 0.032378
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.115845 Loss1: 0.081945 Loss2: 0.033900
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.110463 Loss1: 0.077251 Loss2: 0.033212
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.101246 Loss1: 0.067603 Loss2: 0.033643
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116610 Loss1: 0.082881 Loss2: 0.033729
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108667 Loss1: 0.074621 Loss2: 0.034046
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.088769 Loss1: 0.055169 Loss2: 0.033600
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.101782 Loss1: 0.067567 Loss2: 0.034215
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.099429 Loss1: 0.064942 Loss2: 0.034486
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.096631 Loss1: 0.062240 Loss2: 0.034391
+(DefaultActor pid=1838052) >> Training accuracy: 0.989901
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.776350 Loss1: 0.188007 Loss2: 0.588342
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.725609 Loss1: 0.139489 Loss2: 0.586120
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.725707 Loss1: 0.151253 Loss2: 0.574454
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.708753 Loss1: 0.144284 Loss2: 0.564469
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.673343 Loss1: 0.114586 Loss2: 0.558757
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.672116 Loss1: 0.118870 Loss2: 0.553246
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.646087 Loss1: 0.097794 Loss2: 0.548293
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.639955 Loss1: 0.095317 Loss2: 0.544637
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.628291 Loss1: 0.091072 Loss2: 0.537219
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.619683 Loss1: 0.086868 Loss2: 0.532815
+(DefaultActor pid=1838052) >> Training accuracy: 0.974881
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.823337 Loss1: 0.209947 Loss2: 0.613390
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.745215 Loss1: 0.136214 Loss2: 0.609000
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.696324 Loss1: 0.105661 Loss2: 0.590663
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.689577 Loss1: 0.111095 Loss2: 0.578482
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.699551 Loss1: 0.130541 Loss2: 0.569010
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.692823 Loss1: 0.131519 Loss2: 0.561304
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.662141 Loss1: 0.106808 Loss2: 0.555333
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.642739 Loss1: 0.096159 Loss2: 0.546581
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.647917 Loss1: 0.102385 Loss2: 0.545532
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.645947 Loss1: 0.104596 Loss2: 0.541351
+(DefaultActor pid=1838052) >> Training accuracy: 0.984164
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.242587 Loss1: 0.174474 Loss2: 0.068113
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.192377 Loss1: 0.122156 Loss2: 0.070221
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.166112 Loss1: 0.097289 Loss2: 0.068823
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.176376 Loss1: 0.108543 Loss2: 0.067833
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.179613 Loss1: 0.110915 Loss2: 0.068698
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.158915 Loss1: 0.091663 Loss2: 0.067252
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.125228 Loss1: 0.061470 Loss2: 0.063758
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.112320 Loss1: 0.049711 Loss2: 0.062609
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.123225 Loss1: 0.061059 Loss2: 0.062166
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112926 Loss1: 0.051877 Loss2: 0.061050
+(DefaultActor pid=1838052) >> Training accuracy: 0.991386
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.214881 Loss1: 0.179908 Loss2: 0.034973
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.144975 Loss1: 0.108824 Loss2: 0.036151
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.138126 Loss1: 0.102165 Loss2: 0.035961
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.131821 Loss1: 0.095644 Loss2: 0.036177
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.125141 Loss1: 0.089370 Loss2: 0.035770
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.119865 Loss1: 0.083848 Loss2: 0.036017
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.109315 Loss1: 0.073860 Loss2: 0.035456
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.111432 Loss1: 0.076283 Loss2: 0.035149
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.099649 Loss1: 0.064170 Loss2: 0.035479
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.103873 Loss1: 0.068948 Loss2: 0.034925
+(DefaultActor pid=1838052) >> Training accuracy: 0.982171
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.205148 Loss1: 0.170449 Loss2: 0.034699
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.137100 Loss1: 0.100053 Loss2: 0.037046
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.127334 Loss1: 0.089376 Loss2: 0.037958
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113812 Loss1: 0.076840 Loss2: 0.036972
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.118140 Loss1: 0.080745 Loss2: 0.037395
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.133459 Loss1: 0.094990 Loss2: 0.038469
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.137863 Loss1: 0.098934 Loss2: 0.038929
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123172 Loss1: 0.084290 Loss2: 0.038881
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.110611 Loss1: 0.072410 Loss2: 0.038201
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121041 Loss1: 0.082674 Loss2: 0.038367
+(DefaultActor pid=1838052) >> Training accuracy: 0.986353
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.235159 Loss1: 0.200091 Loss2: 0.035069
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.149182 Loss1: 0.112440 Loss2: 0.036742
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.139669 Loss1: 0.103264 Loss2: 0.036405
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.121448 Loss1: 0.085296 Loss2: 0.036152
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.135758 Loss1: 0.099053 Loss2: 0.036705
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.151579 Loss1: 0.114657 Loss2: 0.036922
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.160198 Loss1: 0.122279 Loss2: 0.037919
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125145 Loss1: 0.087564 Loss2: 0.037581
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.129268 Loss1: 0.092337 Loss2: 0.036930
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.108174 Loss1: 0.071576 Loss2: 0.036599
+(DefaultActor pid=1838052) >> Training accuracy: 0.981702
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.585557 Loss1: 0.194603 Loss2: 0.390955
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.514901 Loss1: 0.145923 Loss2: 0.368979
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.479095 Loss1: 0.124461 Loss2: 0.354634
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.476630 Loss1: 0.123072 Loss2: 0.353558
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.455177 Loss1: 0.107658 Loss2: 0.347519
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.473603 Loss1: 0.122775 Loss2: 0.350828
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.441905 Loss1: 0.098450 Loss2: 0.343455
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.474381 Loss1: 0.125397 Loss2: 0.348984
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.488132 Loss1: 0.135021 Loss2: 0.353111
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.505391 Loss1: 0.151465 Loss2: 0.353925
+(DefaultActor pid=1838052) >> Training accuracy: 0.973101
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.203402 Loss1: 0.169748 Loss2: 0.033654
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.166116 Loss1: 0.130925 Loss2: 0.035191
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.124180 Loss1: 0.089247 Loss2: 0.034933
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.109394 Loss1: 0.074631 Loss2: 0.034763
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116291 Loss1: 0.081436 Loss2: 0.034854
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.134818 Loss1: 0.099097 Loss2: 0.035721
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.097230 Loss1: 0.062531 Loss2: 0.034700
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100248 Loss1: 0.065854 Loss2: 0.034394
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.107072 Loss1: 0.072341 Loss2: 0.034731
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118100 Loss1: 0.082650 Loss2: 0.035450
+(DefaultActor pid=1838052) >> Training accuracy: 0.978046
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 09:34:48,206][flwr][DEBUG] - fit_round 52 received 10 results and 0 failures
+>> Test accuracy: 0.646500
+[2023-09-28 09:35:30,596][flwr][INFO] - fit progress: (52, 2.1817148396382318, {'accuracy': 0.6465}, 98153.48656181712)
+[2023-09-28 09:35:30,597][flwr][DEBUG] - evaluate_round 52: strategy sampled 10 clients (out of 10)
+[2023-09-28 09:36:07,690][flwr][DEBUG] - evaluate_round 52 received 10 results and 0 failures
+[2023-09-28 09:36:07,691][flwr][DEBUG] - fit_round 53: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.785909 Loss1: 0.205183 Loss2: 0.580726
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.713460 Loss1: 0.143256 Loss2: 0.570204
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.691062 Loss1: 0.134219 Loss2: 0.556843
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.686015 Loss1: 0.132635 Loss2: 0.553380
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.667316 Loss1: 0.122301 Loss2: 0.545015
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646188 Loss1: 0.105339 Loss2: 0.540850
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.646962 Loss1: 0.109392 Loss2: 0.537569
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.643486 Loss1: 0.110881 Loss2: 0.532605
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.635133 Loss1: 0.102379 Loss2: 0.532754
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.624094 Loss1: 0.095864 Loss2: 0.528231
+(DefaultActor pid=1838052) >> Training accuracy: 0.978824
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.594565 Loss1: 0.193689 Loss2: 0.400876
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.488682 Loss1: 0.131459 Loss2: 0.357223
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.473077 Loss1: 0.138048 Loss2: 0.335030
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.410063 Loss1: 0.088469 Loss2: 0.321594
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.404997 Loss1: 0.090999 Loss2: 0.313998
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.380555 Loss1: 0.068825 Loss2: 0.311730
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.368171 Loss1: 0.059769 Loss2: 0.308403
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.394628 Loss1: 0.086258 Loss2: 0.308370
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.375409 Loss1: 0.067619 Loss2: 0.307791
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.396913 Loss1: 0.087849 Loss2: 0.309065
+(DefaultActor pid=1838052) >> Training accuracy: 0.978299
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.196169 Loss1: 0.163713 Loss2: 0.032457
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.123632 Loss1: 0.089180 Loss2: 0.034451
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.115925 Loss1: 0.081373 Loss2: 0.034553
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.132805 Loss1: 0.098240 Loss2: 0.034565
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.112175 Loss1: 0.076796 Loss2: 0.035379
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123331 Loss1: 0.088005 Loss2: 0.035325
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122996 Loss1: 0.087335 Loss2: 0.035661
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123696 Loss1: 0.087791 Loss2: 0.035905
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101946 Loss1: 0.066419 Loss2: 0.035527
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097290 Loss1: 0.061985 Loss2: 0.035306
+(DefaultActor pid=1838052) >> Training accuracy: 0.985518
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.687835 Loss1: 0.183848 Loss2: 0.503987
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.638064 Loss1: 0.148281 Loss2: 0.489783
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.619479 Loss1: 0.140005 Loss2: 0.479475
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.580644 Loss1: 0.110291 Loss2: 0.470353
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.586776 Loss1: 0.119592 Loss2: 0.467184
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.553178 Loss1: 0.091140 Loss2: 0.462038
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.577211 Loss1: 0.114262 Loss2: 0.462949
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.562757 Loss1: 0.102748 Loss2: 0.460009
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.539645 Loss1: 0.083061 Loss2: 0.456584
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.549722 Loss1: 0.093901 Loss2: 0.455821
+(DefaultActor pid=1838052) >> Training accuracy: 0.985377
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.188690 Loss1: 0.156643 Loss2: 0.032047
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.117524 Loss1: 0.083202 Loss2: 0.034322
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.104054 Loss1: 0.070364 Loss2: 0.033690
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085207 Loss1: 0.051681 Loss2: 0.033526
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092568 Loss1: 0.059100 Loss2: 0.033468
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.099905 Loss1: 0.065991 Loss2: 0.033914
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130229 Loss1: 0.094555 Loss2: 0.035674
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.124643 Loss1: 0.089469 Loss2: 0.035173
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.145695 Loss1: 0.109769 Loss2: 0.035926
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127988 Loss1: 0.092199 Loss2: 0.035790
+(DefaultActor pid=1838052) >> Training accuracy: 0.985759
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.540069 Loss1: 0.157874 Loss2: 0.382194
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.438008 Loss1: 0.108731 Loss2: 0.329277
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.384962 Loss1: 0.089836 Loss2: 0.295126
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.445128 Loss1: 0.152949 Loss2: 0.292179
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.420591 Loss1: 0.131015 Loss2: 0.289576
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.412996 Loss1: 0.125577 Loss2: 0.287419
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.368398 Loss1: 0.084865 Loss2: 0.283532
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.365283 Loss1: 0.084307 Loss2: 0.280976
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.377866 Loss1: 0.096457 Loss2: 0.281409
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.382351 Loss1: 0.100940 Loss2: 0.281411
+(DefaultActor pid=1838052) >> Training accuracy: 0.981210
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.231614 Loss1: 0.196132 Loss2: 0.035482
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.139687 Loss1: 0.103584 Loss2: 0.036103
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.128435 Loss1: 0.092591 Loss2: 0.035844
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136101 Loss1: 0.099495 Loss2: 0.036606
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.115435 Loss1: 0.079209 Loss2: 0.036225
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096064 Loss1: 0.060614 Loss2: 0.035451
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.086670 Loss1: 0.051635 Loss2: 0.035035
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.081080 Loss1: 0.046136 Loss2: 0.034944
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101316 Loss1: 0.066131 Loss2: 0.035186
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104095 Loss1: 0.067969 Loss2: 0.036126
+(DefaultActor pid=1838052) >> Training accuracy: 0.989020
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.193302 Loss1: 0.159801 Loss2: 0.033501
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.131964 Loss1: 0.096918 Loss2: 0.035046
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.136597 Loss1: 0.101040 Loss2: 0.035557
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.123699 Loss1: 0.087964 Loss2: 0.035734
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.117441 Loss1: 0.081752 Loss2: 0.035690
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111029 Loss1: 0.075156 Loss2: 0.035873
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.110762 Loss1: 0.075227 Loss2: 0.035535
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.116619 Loss1: 0.080209 Loss2: 0.036409
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.135181 Loss1: 0.098116 Loss2: 0.037065
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.138863 Loss1: 0.101578 Loss2: 0.037285
+(DefaultActor pid=1838052) >> Training accuracy: 0.978365
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.241973 Loss1: 0.154011 Loss2: 0.087962
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.206534 Loss1: 0.120371 Loss2: 0.086163
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.204327 Loss1: 0.118790 Loss2: 0.085537
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.163651 Loss1: 0.080947 Loss2: 0.082704
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.157991 Loss1: 0.077559 Loss2: 0.080432
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178191 Loss1: 0.097177 Loss2: 0.081015
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.173931 Loss1: 0.093324 Loss2: 0.080607
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.148825 Loss1: 0.069440 Loss2: 0.079385
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140472 Loss1: 0.062310 Loss2: 0.078162
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.132638 Loss1: 0.055639 Loss2: 0.076999
+(DefaultActor pid=1838052) >> Training accuracy: 0.990506
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.202804 Loss1: 0.167822 Loss2: 0.034982
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.142767 Loss1: 0.106446 Loss2: 0.036321
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.143885 Loss1: 0.107294 Loss2: 0.036591
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.112779 Loss1: 0.076322 Loss2: 0.036457
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110090 Loss1: 0.073669 Loss2: 0.036421
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.107590 Loss1: 0.071134 Loss2: 0.036456
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.115976 Loss1: 0.079162 Loss2: 0.036814
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.105005 Loss1: 0.068128 Loss2: 0.036878
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.105621 Loss1: 0.068754 Loss2: 0.036867
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114251 Loss1: 0.077168 Loss2: 0.037083
+(DefaultActor pid=1838052) >> Training accuracy: 0.990902
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 10:05:35,615][flwr][DEBUG] - fit_round 53 received 10 results and 0 failures
+>> Test accuracy: 0.645700
+[2023-09-28 10:06:16,280][flwr][INFO] - fit progress: (53, 2.183458357382887, {'accuracy': 0.6457}, 99999.17066497216)
+[2023-09-28 10:06:16,281][flwr][DEBUG] - evaluate_round 53: strategy sampled 10 clients (out of 10)
+[2023-09-28 10:06:52,930][flwr][DEBUG] - evaluate_round 53 received 10 results and 0 failures
+[2023-09-28 10:06:52,932][flwr][DEBUG] - fit_round 54: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.622136 Loss1: 0.154174 Loss2: 0.467963
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.569218 Loss1: 0.137967 Loss2: 0.431251
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.575470 Loss1: 0.152849 Loss2: 0.422621
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.573431 Loss1: 0.158843 Loss2: 0.414587
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.562856 Loss1: 0.158140 Loss2: 0.404716
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.506398 Loss1: 0.108552 Loss2: 0.397847
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.497419 Loss1: 0.105152 Loss2: 0.392267
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.503289 Loss1: 0.107830 Loss2: 0.395459
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.502117 Loss1: 0.108815 Loss2: 0.393302
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.506279 Loss1: 0.112386 Loss2: 0.393893
+(DefaultActor pid=1838052) >> Training accuracy: 0.975870
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.180767 Loss1: 0.148063 Loss2: 0.032704
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.126421 Loss1: 0.091417 Loss2: 0.035004
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.141848 Loss1: 0.106017 Loss2: 0.035831
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136410 Loss1: 0.099675 Loss2: 0.036735
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.099563 Loss1: 0.063524 Loss2: 0.036039
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.118136 Loss1: 0.081980 Loss2: 0.036155
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.125191 Loss1: 0.088410 Loss2: 0.036781
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.092475 Loss1: 0.056595 Loss2: 0.035880
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.107760 Loss1: 0.071335 Loss2: 0.036425
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.105110 Loss1: 0.068568 Loss2: 0.036541
+(DefaultActor pid=1838052) >> Training accuracy: 0.985978
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.230630 Loss1: 0.194305 Loss2: 0.036325
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.161563 Loss1: 0.122558 Loss2: 0.039005
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.127220 Loss1: 0.087931 Loss2: 0.039290
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136200 Loss1: 0.096764 Loss2: 0.039437
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.118723 Loss1: 0.079306 Loss2: 0.039418
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.129795 Loss1: 0.090109 Loss2: 0.039686
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.125486 Loss1: 0.085560 Loss2: 0.039926
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123181 Loss1: 0.082874 Loss2: 0.040307
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081868 Loss1: 0.042757 Loss2: 0.039111
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083551 Loss1: 0.045462 Loss2: 0.038089
+(DefaultActor pid=1838052) >> Training accuracy: 0.991970
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.200038 Loss1: 0.167201 Loss2: 0.032838
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.152761 Loss1: 0.116529 Loss2: 0.036231
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.141273 Loss1: 0.104489 Loss2: 0.036784
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.138773 Loss1: 0.101916 Loss2: 0.036857
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.121501 Loss1: 0.084781 Loss2: 0.036719
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.110344 Loss1: 0.073392 Loss2: 0.036952
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.093005 Loss1: 0.056615 Loss2: 0.036391
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.104131 Loss1: 0.067697 Loss2: 0.036434
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.123408 Loss1: 0.086259 Loss2: 0.037149
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.145425 Loss1: 0.107747 Loss2: 0.037677
+(DefaultActor pid=1838052) >> Training accuracy: 0.983782
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.195537 Loss1: 0.163126 Loss2: 0.032411
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.138573 Loss1: 0.103339 Loss2: 0.035234
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.090075 Loss1: 0.055221 Loss2: 0.034854
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.091540 Loss1: 0.057504 Loss2: 0.034035
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.094832 Loss1: 0.060270 Loss2: 0.034562
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.112205 Loss1: 0.077027 Loss2: 0.035178
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122674 Loss1: 0.086297 Loss2: 0.036377
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.112849 Loss1: 0.076805 Loss2: 0.036044
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.126293 Loss1: 0.089979 Loss2: 0.036314
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127105 Loss1: 0.090114 Loss2: 0.036991
+(DefaultActor pid=1838052) >> Training accuracy: 0.981326
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.230388 Loss1: 0.196235 Loss2: 0.034153
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.164832 Loss1: 0.128574 Loss2: 0.036258
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.123411 Loss1: 0.087192 Loss2: 0.036219
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.115689 Loss1: 0.079673 Loss2: 0.036016
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.127602 Loss1: 0.090908 Loss2: 0.036695
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.141038 Loss1: 0.103870 Loss2: 0.037169
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.148759 Loss1: 0.110389 Loss2: 0.038370
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.114486 Loss1: 0.077101 Loss2: 0.037386
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.104428 Loss1: 0.067599 Loss2: 0.036829
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091997 Loss1: 0.055231 Loss2: 0.036767
+(DefaultActor pid=1838052) >> Training accuracy: 0.982730
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.205695 Loss1: 0.172571 Loss2: 0.033124
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.124160 Loss1: 0.088157 Loss2: 0.036004
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.124109 Loss1: 0.087829 Loss2: 0.036280
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.126048 Loss1: 0.088994 Loss2: 0.037054
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.108591 Loss1: 0.072476 Loss2: 0.036115
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.114148 Loss1: 0.077882 Loss2: 0.036266
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122066 Loss1: 0.085164 Loss2: 0.036902
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123094 Loss1: 0.085698 Loss2: 0.037396
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120192 Loss1: 0.082755 Loss2: 0.037437
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.146598 Loss1: 0.108099 Loss2: 0.038499
+(DefaultActor pid=1838052) >> Training accuracy: 0.977255
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.409763 Loss1: 0.153484 Loss2: 0.256279
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.351551 Loss1: 0.129191 Loss2: 0.222360
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.313635 Loss1: 0.105180 Loss2: 0.208455
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.316109 Loss1: 0.111753 Loss2: 0.204356
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.314940 Loss1: 0.109583 Loss2: 0.205357
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.319605 Loss1: 0.114523 Loss2: 0.205082
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.335283 Loss1: 0.132100 Loss2: 0.203184
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.363245 Loss1: 0.156264 Loss2: 0.206981
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.291851 Loss1: 0.092542 Loss2: 0.199309
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.308711 Loss1: 0.108949 Loss2: 0.199762
+(DefaultActor pid=1838052) >> Training accuracy: 0.980024
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.279613 Loss1: 0.234386 Loss2: 0.045227
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.156388 Loss1: 0.112065 Loss2: 0.044323
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.143478 Loss1: 0.100560 Loss2: 0.042918
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.132019 Loss1: 0.088975 Loss2: 0.043044
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.136943 Loss1: 0.094208 Loss2: 0.042735
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.109744 Loss1: 0.068139 Loss2: 0.041605
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.100752 Loss1: 0.059691 Loss2: 0.041061
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.116631 Loss1: 0.074956 Loss2: 0.041675
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.105426 Loss1: 0.063764 Loss2: 0.041662
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.106936 Loss1: 0.066025 Loss2: 0.040910
+(DefaultActor pid=1838052) >> Training accuracy: 0.987753
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.192457 Loss1: 0.159655 Loss2: 0.032802
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.152515 Loss1: 0.116800 Loss2: 0.035715
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.143007 Loss1: 0.106848 Loss2: 0.036160
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.118045 Loss1: 0.081521 Loss2: 0.036524
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.096065 Loss1: 0.060511 Loss2: 0.035554
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077471 Loss1: 0.042620 Loss2: 0.034851
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104392 Loss1: 0.069349 Loss2: 0.035043
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.151025 Loss1: 0.113969 Loss2: 0.037056
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140034 Loss1: 0.102981 Loss2: 0.037052
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.147951 Loss1: 0.110101 Loss2: 0.037850
+(DefaultActor pid=1838052) >> Training accuracy: 0.971154
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 10:36:35,856][flwr][DEBUG] - fit_round 54 received 10 results and 0 failures
+>> Test accuracy: 0.647400
+[2023-09-28 10:37:19,325][flwr][INFO] - fit progress: (54, 2.1794180731042125, {'accuracy': 0.6474}, 101862.21575574903)
+[2023-09-28 10:37:19,326][flwr][DEBUG] - evaluate_round 54: strategy sampled 10 clients (out of 10)
+[2023-09-28 10:37:56,797][flwr][DEBUG] - evaluate_round 54 received 10 results and 0 failures
+[2023-09-28 10:37:56,798][flwr][DEBUG] - fit_round 55: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.509107 Loss1: 0.139607 Loss2: 0.369500
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.391602 Loss1: 0.107168 Loss2: 0.284434
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.354173 Loss1: 0.087316 Loss2: 0.266857
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.374317 Loss1: 0.109795 Loss2: 0.264522
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.391970 Loss1: 0.124658 Loss2: 0.267312
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.431046 Loss1: 0.162478 Loss2: 0.268568
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.424645 Loss1: 0.156158 Loss2: 0.268487
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.367781 Loss1: 0.106355 Loss2: 0.261426
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.360489 Loss1: 0.099400 Loss2: 0.261089
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.357288 Loss1: 0.099691 Loss2: 0.257597
+(DefaultActor pid=1838052) >> Training accuracy: 0.981013
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.728560 Loss1: 0.181768 Loss2: 0.546792
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.665100 Loss1: 0.126072 Loss2: 0.539028
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.638633 Loss1: 0.107014 Loss2: 0.531619
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.634475 Loss1: 0.111062 Loss2: 0.523413
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.651574 Loss1: 0.130198 Loss2: 0.521376
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.620937 Loss1: 0.105771 Loss2: 0.515167
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.640100 Loss1: 0.126973 Loss2: 0.513127
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.642876 Loss1: 0.128065 Loss2: 0.514811
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.617850 Loss1: 0.108544 Loss2: 0.509306
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.624843 Loss1: 0.118169 Loss2: 0.506674
+(DefaultActor pid=1838052) >> Training accuracy: 0.983584
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.737243 Loss1: 0.177329 Loss2: 0.559914
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.669951 Loss1: 0.119622 Loss2: 0.550329
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.643896 Loss1: 0.106643 Loss2: 0.537252
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.655991 Loss1: 0.127084 Loss2: 0.528908
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.643710 Loss1: 0.117745 Loss2: 0.525965
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.679772 Loss1: 0.152641 Loss2: 0.527131
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.645790 Loss1: 0.126922 Loss2: 0.518867
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.639748 Loss1: 0.121880 Loss2: 0.517868
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.643863 Loss1: 0.130430 Loss2: 0.513433
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.603106 Loss1: 0.095421 Loss2: 0.507685
+(DefaultActor pid=1838052) >> Training accuracy: 0.985176
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.741672 Loss1: 0.149283 Loss2: 0.592389
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.737297 Loss1: 0.148724 Loss2: 0.588573
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.690914 Loss1: 0.116848 Loss2: 0.574066
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.644237 Loss1: 0.082112 Loss2: 0.562125
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.670881 Loss1: 0.112733 Loss2: 0.558148
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.657615 Loss1: 0.104420 Loss2: 0.553194
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.634579 Loss1: 0.087046 Loss2: 0.547533
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.639683 Loss1: 0.096185 Loss2: 0.543498
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.631597 Loss1: 0.091130 Loss2: 0.540467
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.641202 Loss1: 0.101451 Loss2: 0.539751
+(DefaultActor pid=1838052) >> Training accuracy: 0.982470
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.219434 Loss1: 0.147122 Loss2: 0.072313
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.155270 Loss1: 0.081840 Loss2: 0.073430
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.139659 Loss1: 0.069118 Loss2: 0.070541
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.173246 Loss1: 0.103464 Loss2: 0.069782
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.155793 Loss1: 0.085574 Loss2: 0.070219
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.149930 Loss1: 0.082565 Loss2: 0.067365
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.145283 Loss1: 0.077662 Loss2: 0.067621
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.132349 Loss1: 0.065489 Loss2: 0.066860
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.119049 Loss1: 0.052954 Loss2: 0.066096
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.136354 Loss1: 0.071514 Loss2: 0.064840
+(DefaultActor pid=1838052) >> Training accuracy: 0.987179
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.193966 Loss1: 0.160798 Loss2: 0.033168
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.125068 Loss1: 0.089561 Loss2: 0.035507
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.108657 Loss1: 0.073441 Loss2: 0.035215
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.117500 Loss1: 0.081755 Loss2: 0.035745
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110119 Loss1: 0.074182 Loss2: 0.035937
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.114053 Loss1: 0.077793 Loss2: 0.036259
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.081104 Loss1: 0.045143 Loss2: 0.035961
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.083663 Loss1: 0.048273 Loss2: 0.035390
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.083033 Loss1: 0.048109 Loss2: 0.034924
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089912 Loss1: 0.054380 Loss2: 0.035532
+(DefaultActor pid=1838052) >> Training accuracy: 0.987935
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.186867 Loss1: 0.151491 Loss2: 0.035375
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.133544 Loss1: 0.096096 Loss2: 0.037448
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.121823 Loss1: 0.084776 Loss2: 0.037047
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.111273 Loss1: 0.074171 Loss2: 0.037102
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.135255 Loss1: 0.098158 Loss2: 0.037097
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.127709 Loss1: 0.090205 Loss2: 0.037504
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104897 Loss1: 0.067449 Loss2: 0.037447
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110731 Loss1: 0.073745 Loss2: 0.036985
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112040 Loss1: 0.074573 Loss2: 0.037466
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.108008 Loss1: 0.071421 Loss2: 0.036587
+(DefaultActor pid=1838052) >> Training accuracy: 0.986155
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.805964 Loss1: 0.211358 Loss2: 0.594606
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.706638 Loss1: 0.123289 Loss2: 0.583350
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.700220 Loss1: 0.127327 Loss2: 0.572893
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.678621 Loss1: 0.114865 Loss2: 0.563757
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.693927 Loss1: 0.136865 Loss2: 0.557062
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.677190 Loss1: 0.123776 Loss2: 0.553414
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.634181 Loss1: 0.087688 Loss2: 0.546492
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.642666 Loss1: 0.102565 Loss2: 0.540102
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.643664 Loss1: 0.104839 Loss2: 0.538825
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.619228 Loss1: 0.083172 Loss2: 0.536057
+(DefaultActor pid=1838052) >> Training accuracy: 0.983758
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.216300 Loss1: 0.160449 Loss2: 0.055851
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.163901 Loss1: 0.107993 Loss2: 0.055908
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.163288 Loss1: 0.108135 Loss2: 0.055152
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136151 Loss1: 0.082778 Loss2: 0.053374
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.152339 Loss1: 0.100592 Loss2: 0.051748
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.143100 Loss1: 0.090733 Loss2: 0.052367
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.146868 Loss1: 0.095544 Loss2: 0.051325
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.131349 Loss1: 0.080359 Loss2: 0.050990
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.132510 Loss1: 0.082124 Loss2: 0.050386
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.125518 Loss1: 0.075180 Loss2: 0.050338
+(DefaultActor pid=1838052) >> Training accuracy: 0.980903
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.236540 Loss1: 0.203229 Loss2: 0.033312
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.147496 Loss1: 0.112321 Loss2: 0.035175
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.123307 Loss1: 0.088721 Loss2: 0.034586
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.117232 Loss1: 0.082250 Loss2: 0.034982
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.134691 Loss1: 0.098697 Loss2: 0.035994
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108842 Loss1: 0.073523 Loss2: 0.035319
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.114903 Loss1: 0.079297 Loss2: 0.035606
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.093586 Loss1: 0.058267 Loss2: 0.035319
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101489 Loss1: 0.066643 Loss2: 0.034846
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.122730 Loss1: 0.087150 Loss2: 0.035580
+(DefaultActor pid=1838052) >> Training accuracy: 0.982052
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 11:07:26,968][flwr][DEBUG] - fit_round 55 received 10 results and 0 failures
+>> Test accuracy: 0.648500
+[2023-09-28 11:08:07,543][flwr][INFO] - fit progress: (55, 2.1586610794829104, {'accuracy': 0.6485}, 103710.43312141532)
+[2023-09-28 11:08:07,543][flwr][DEBUG] - evaluate_round 55: strategy sampled 10 clients (out of 10)
+[2023-09-28 11:08:44,912][flwr][DEBUG] - evaluate_round 55 received 10 results and 0 failures
+[2023-09-28 11:08:44,913][flwr][DEBUG] - fit_round 56: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.727927 Loss1: 0.151545 Loss2: 0.576382
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.668980 Loss1: 0.106308 Loss2: 0.562671
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.654360 Loss1: 0.105175 Loss2: 0.549186
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.640883 Loss1: 0.104826 Loss2: 0.536057
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.628374 Loss1: 0.099566 Loss2: 0.528807
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.623337 Loss1: 0.102038 Loss2: 0.521299
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.619590 Loss1: 0.100232 Loss2: 0.519358
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.624106 Loss1: 0.106243 Loss2: 0.517863
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.610624 Loss1: 0.098335 Loss2: 0.512289
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.576537 Loss1: 0.067120 Loss2: 0.509416
+(DefaultActor pid=1838052) >> Training accuracy: 0.986111
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.537136 Loss1: 0.169005 Loss2: 0.368132
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.450082 Loss1: 0.137723 Loss2: 0.312360
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.400576 Loss1: 0.104170 Loss2: 0.296407
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.371847 Loss1: 0.081856 Loss2: 0.289991
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.379443 Loss1: 0.093832 Loss2: 0.285611
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.390227 Loss1: 0.102142 Loss2: 0.288085
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.383465 Loss1: 0.098755 Loss2: 0.284711
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.407772 Loss1: 0.121314 Loss2: 0.286458
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.382628 Loss1: 0.097965 Loss2: 0.284663
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.385287 Loss1: 0.101852 Loss2: 0.283435
+(DefaultActor pid=1838052) >> Training accuracy: 0.976357
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.159987 Loss1: 0.128010 Loss2: 0.031977
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.099831 Loss1: 0.065629 Loss2: 0.034202
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082502 Loss1: 0.049178 Loss2: 0.033324
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.086562 Loss1: 0.052876 Loss2: 0.033685
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092037 Loss1: 0.058212 Loss2: 0.033825
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.115565 Loss1: 0.081241 Loss2: 0.034324
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131305 Loss1: 0.096259 Loss2: 0.035047
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125715 Loss1: 0.090216 Loss2: 0.035499
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.131941 Loss1: 0.096155 Loss2: 0.035786
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.117578 Loss1: 0.082577 Loss2: 0.035001
+(DefaultActor pid=1838052) >> Training accuracy: 0.986662
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.154054 Loss1: 0.115815 Loss2: 0.038238
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104346 Loss1: 0.064637 Loss2: 0.039709
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.118724 Loss1: 0.078461 Loss2: 0.040263
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.100779 Loss1: 0.060140 Loss2: 0.040639
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.123187 Loss1: 0.082709 Loss2: 0.040478
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.119476 Loss1: 0.077889 Loss2: 0.041586
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.110313 Loss1: 0.068974 Loss2: 0.041339
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110189 Loss1: 0.068995 Loss2: 0.041194
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.104789 Loss1: 0.064095 Loss2: 0.040694
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095657 Loss1: 0.055326 Loss2: 0.040332
+(DefaultActor pid=1838052) >> Training accuracy: 0.990309
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.780855 Loss1: 0.176837 Loss2: 0.604018
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.710486 Loss1: 0.111909 Loss2: 0.598577
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.718505 Loss1: 0.133631 Loss2: 0.584874
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.689565 Loss1: 0.112940 Loss2: 0.576625
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.665915 Loss1: 0.100332 Loss2: 0.565583
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.682013 Loss1: 0.122729 Loss2: 0.559284
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.662582 Loss1: 0.109458 Loss2: 0.553124
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.653371 Loss1: 0.105543 Loss2: 0.547828
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.623914 Loss1: 0.082041 Loss2: 0.541873
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.626898 Loss1: 0.088845 Loss2: 0.538053
+(DefaultActor pid=1838052) >> Training accuracy: 0.989443
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.223465 Loss1: 0.155426 Loss2: 0.068038
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.168936 Loss1: 0.101319 Loss2: 0.067618
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.162329 Loss1: 0.096148 Loss2: 0.066181
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.134258 Loss1: 0.068986 Loss2: 0.065271
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116840 Loss1: 0.053351 Loss2: 0.063488
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.144447 Loss1: 0.080872 Loss2: 0.063575
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197688 Loss1: 0.131322 Loss2: 0.066366
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.188073 Loss1: 0.121150 Loss2: 0.066924
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.144763 Loss1: 0.078614 Loss2: 0.066148
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.138316 Loss1: 0.073393 Loss2: 0.064923
+(DefaultActor pid=1838052) >> Training accuracy: 0.985577
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.157841 Loss1: 0.126011 Loss2: 0.031830
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.127801 Loss1: 0.093433 Loss2: 0.034368
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.106549 Loss1: 0.071870 Loss2: 0.034679
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.096656 Loss1: 0.061872 Loss2: 0.034784
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.103501 Loss1: 0.068949 Loss2: 0.034552
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.110564 Loss1: 0.075314 Loss2: 0.035250
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.124421 Loss1: 0.088641 Loss2: 0.035780
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125161 Loss1: 0.089355 Loss2: 0.035806
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.117617 Loss1: 0.081615 Loss2: 0.036002
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116800 Loss1: 0.081025 Loss2: 0.035775
+(DefaultActor pid=1838052) >> Training accuracy: 0.976859
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.206014 Loss1: 0.135141 Loss2: 0.070874
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.157345 Loss1: 0.087309 Loss2: 0.070036
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.140422 Loss1: 0.072356 Loss2: 0.068067
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.125993 Loss1: 0.061009 Loss2: 0.064984
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116148 Loss1: 0.051215 Loss2: 0.064933
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.120048 Loss1: 0.055735 Loss2: 0.064312
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129189 Loss1: 0.064914 Loss2: 0.064275
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125577 Loss1: 0.061014 Loss2: 0.064562
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.124191 Loss1: 0.059107 Loss2: 0.065084
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.119287 Loss1: 0.054317 Loss2: 0.064971
+(DefaultActor pid=1838052) >> Training accuracy: 0.984573
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.616891 Loss1: 0.166891 Loss2: 0.449999
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.548977 Loss1: 0.120219 Loss2: 0.428758
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.552758 Loss1: 0.125282 Loss2: 0.427476
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.558730 Loss1: 0.135606 Loss2: 0.423124
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.526450 Loss1: 0.110427 Loss2: 0.416023
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.511199 Loss1: 0.099162 Loss2: 0.412037
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.522820 Loss1: 0.110490 Loss2: 0.412330
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.491541 Loss1: 0.085275 Loss2: 0.406266
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.500004 Loss1: 0.092952 Loss2: 0.407051
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.493455 Loss1: 0.088163 Loss2: 0.405292
+(DefaultActor pid=1838052) >> Training accuracy: 0.988381
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.749918 Loss1: 0.176920 Loss2: 0.572999
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.669471 Loss1: 0.106581 Loss2: 0.562890
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.668420 Loss1: 0.119977 Loss2: 0.548443
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.671859 Loss1: 0.127550 Loss2: 0.544308
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.663311 Loss1: 0.125230 Loss2: 0.538081
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.629387 Loss1: 0.098818 Loss2: 0.530570
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.633850 Loss1: 0.107973 Loss2: 0.525877
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.628228 Loss1: 0.103050 Loss2: 0.525179
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.604798 Loss1: 0.085778 Loss2: 0.519020
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.612686 Loss1: 0.095127 Loss2: 0.517559
+(DefaultActor pid=1838052) >> Training accuracy: 0.976464
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 11:37:46,851][flwr][DEBUG] - fit_round 56 received 10 results and 0 failures
+>> Test accuracy: 0.649400
+[2023-09-28 11:38:28,114][flwr][INFO] - fit progress: (56, 2.188702217115762, {'accuracy': 0.6494}, 105531.00491379201)
+[2023-09-28 11:38:28,115][flwr][DEBUG] - evaluate_round 56: strategy sampled 10 clients (out of 10)
+[2023-09-28 11:39:04,806][flwr][DEBUG] - evaluate_round 56 received 10 results and 0 failures
+[2023-09-28 11:39:04,807][flwr][DEBUG] - fit_round 57: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.159412 Loss1: 0.127576 Loss2: 0.031836
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.111505 Loss1: 0.077653 Loss2: 0.033852
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.096708 Loss1: 0.062394 Loss2: 0.034314
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.102927 Loss1: 0.068151 Loss2: 0.034776
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101493 Loss1: 0.066624 Loss2: 0.034870
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.095581 Loss1: 0.060473 Loss2: 0.035108
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.098483 Loss1: 0.062753 Loss2: 0.035729
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.112339 Loss1: 0.076345 Loss2: 0.035994
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.097793 Loss1: 0.061838 Loss2: 0.035955
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095055 Loss1: 0.059455 Loss2: 0.035601
+(DefaultActor pid=1838052) >> Training accuracy: 0.988377
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.654340 Loss1: 0.181008 Loss2: 0.473331
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.608649 Loss1: 0.159380 Loss2: 0.449269
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.566031 Loss1: 0.137019 Loss2: 0.429013
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.557758 Loss1: 0.136072 Loss2: 0.421685
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.515284 Loss1: 0.103467 Loss2: 0.411816
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.554279 Loss1: 0.140778 Loss2: 0.413501
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.515892 Loss1: 0.109788 Loss2: 0.406104
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.487456 Loss1: 0.086242 Loss2: 0.401213
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.480572 Loss1: 0.083434 Loss2: 0.397138
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.496667 Loss1: 0.099540 Loss2: 0.397128
+(DefaultActor pid=1838052) >> Training accuracy: 0.977163
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.715136 Loss1: 0.141886 Loss2: 0.573250
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.654387 Loss1: 0.100358 Loss2: 0.554030
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.632873 Loss1: 0.096072 Loss2: 0.536801
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.683262 Loss1: 0.147172 Loss2: 0.536090
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.647558 Loss1: 0.117567 Loss2: 0.529992
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.633394 Loss1: 0.111344 Loss2: 0.522050
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.661515 Loss1: 0.142375 Loss2: 0.519140
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.618256 Loss1: 0.102735 Loss2: 0.515521
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.617323 Loss1: 0.102929 Loss2: 0.514394
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.583372 Loss1: 0.074712 Loss2: 0.508660
+(DefaultActor pid=1838052) >> Training accuracy: 0.986353
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.385604 Loss1: 0.141929 Loss2: 0.243675
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.295075 Loss1: 0.104314 Loss2: 0.190761
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.273646 Loss1: 0.088892 Loss2: 0.184754
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.264531 Loss1: 0.082015 Loss2: 0.182517
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.262769 Loss1: 0.080669 Loss2: 0.182099
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.307022 Loss1: 0.122173 Loss2: 0.184848
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.296163 Loss1: 0.112681 Loss2: 0.183482
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.269282 Loss1: 0.087350 Loss2: 0.181932
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.253429 Loss1: 0.074806 Loss2: 0.178624
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.279642 Loss1: 0.098979 Loss2: 0.180663
+(DefaultActor pid=1838052) >> Training accuracy: 0.979367
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.185895 Loss1: 0.150937 Loss2: 0.034958
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.118102 Loss1: 0.081130 Loss2: 0.036972
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.095875 Loss1: 0.059081 Loss2: 0.036794
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.104535 Loss1: 0.067393 Loss2: 0.037142
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.108296 Loss1: 0.070291 Loss2: 0.038005
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.098406 Loss1: 0.061118 Loss2: 0.037287
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.102844 Loss1: 0.065647 Loss2: 0.037197
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.112077 Loss1: 0.074731 Loss2: 0.037347
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.116638 Loss1: 0.078688 Loss2: 0.037951
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112812 Loss1: 0.074695 Loss2: 0.038117
+(DefaultActor pid=1838052) >> Training accuracy: 0.986111
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.182736 Loss1: 0.146785 Loss2: 0.035951
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.131441 Loss1: 0.092782 Loss2: 0.038659
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.121543 Loss1: 0.082969 Loss2: 0.038574
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.100595 Loss1: 0.062073 Loss2: 0.038522
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.093788 Loss1: 0.055465 Loss2: 0.038324
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077691 Loss1: 0.040050 Loss2: 0.037641
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077059 Loss1: 0.040114 Loss2: 0.036945
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.111015 Loss1: 0.072657 Loss2: 0.038359
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112440 Loss1: 0.073292 Loss2: 0.039148
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114192 Loss1: 0.074667 Loss2: 0.039525
+(DefaultActor pid=1838052) >> Training accuracy: 0.988281
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.726076 Loss1: 0.126683 Loss2: 0.599393
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.680191 Loss1: 0.091661 Loss2: 0.588530
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.649898 Loss1: 0.083753 Loss2: 0.566144
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.641738 Loss1: 0.096627 Loss2: 0.545111
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.659660 Loss1: 0.117707 Loss2: 0.541953
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.616219 Loss1: 0.083791 Loss2: 0.532428
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.617898 Loss1: 0.090153 Loss2: 0.527745
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.616052 Loss1: 0.092467 Loss2: 0.523585
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.626489 Loss1: 0.102532 Loss2: 0.523957
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.614516 Loss1: 0.095230 Loss2: 0.519286
+(DefaultActor pid=1838052) >> Training accuracy: 0.986353
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.215743 Loss1: 0.179985 Loss2: 0.035759
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.138404 Loss1: 0.100404 Loss2: 0.038000
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.129299 Loss1: 0.090970 Loss2: 0.038329
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.122226 Loss1: 0.083535 Loss2: 0.038691
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.118416 Loss1: 0.080214 Loss2: 0.038202
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.113142 Loss1: 0.075164 Loss2: 0.037978
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.088402 Loss1: 0.051232 Loss2: 0.037170
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.088475 Loss1: 0.051600 Loss2: 0.036876
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092215 Loss1: 0.055680 Loss2: 0.036535
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095231 Loss1: 0.058321 Loss2: 0.036909
+(DefaultActor pid=1838052) >> Training accuracy: 0.992188
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.643341 Loss1: 0.200770 Loss2: 0.442571
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.561641 Loss1: 0.146401 Loss2: 0.415241
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.530616 Loss1: 0.131595 Loss2: 0.399021
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.517252 Loss1: 0.126751 Loss2: 0.390501
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.506054 Loss1: 0.119253 Loss2: 0.386801
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.477254 Loss1: 0.093293 Loss2: 0.383961
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.481116 Loss1: 0.102352 Loss2: 0.378764
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.512012 Loss1: 0.131244 Loss2: 0.380768
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.483105 Loss1: 0.103043 Loss2: 0.380063
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.462344 Loss1: 0.090528 Loss2: 0.371817
+(DefaultActor pid=1838052) >> Training accuracy: 0.980222
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.224140 Loss1: 0.155389 Loss2: 0.068751
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.172266 Loss1: 0.108930 Loss2: 0.063336
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.159034 Loss1: 0.096701 Loss2: 0.062333
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.144568 Loss1: 0.084533 Loss2: 0.060035
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.137390 Loss1: 0.077300 Loss2: 0.060090
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.128858 Loss1: 0.069550 Loss2: 0.059307
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.157239 Loss1: 0.096981 Loss2: 0.060259
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.135407 Loss1: 0.075841 Loss2: 0.059566
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115173 Loss1: 0.056191 Loss2: 0.058981
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114776 Loss1: 0.056364 Loss2: 0.058412
+(DefaultActor pid=1838052) >> Training accuracy: 0.990902
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 12:08:09,301][flwr][DEBUG] - fit_round 57 received 10 results and 0 failures
+>> Test accuracy: 0.649000
+[2023-09-28 12:08:49,309][flwr][INFO] - fit progress: (57, 2.171707748224179, {'accuracy': 0.649}, 107352.19973800331)
+[2023-09-28 12:08:49,310][flwr][DEBUG] - evaluate_round 57: strategy sampled 10 clients (out of 10)
+[2023-09-28 12:09:26,119][flwr][DEBUG] - evaluate_round 57 received 10 results and 0 failures
+[2023-09-28 12:09:26,120][flwr][DEBUG] - fit_round 58: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.147945 Loss1: 0.111275 Loss2: 0.036670
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.112951 Loss1: 0.073892 Loss2: 0.039059
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.108352 Loss1: 0.069250 Loss2: 0.039102
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.097100 Loss1: 0.058187 Loss2: 0.038913
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.103745 Loss1: 0.064421 Loss2: 0.039324
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.099903 Loss1: 0.060657 Loss2: 0.039247
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.098456 Loss1: 0.058963 Loss2: 0.039494
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091128 Loss1: 0.052167 Loss2: 0.038961
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.091893 Loss1: 0.052927 Loss2: 0.038966
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098782 Loss1: 0.059483 Loss2: 0.039299
+(DefaultActor pid=1838052) >> Training accuracy: 0.991495
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.399444 Loss1: 0.153394 Loss2: 0.246050
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.342823 Loss1: 0.115139 Loss2: 0.227684
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.347885 Loss1: 0.122592 Loss2: 0.225294
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.321751 Loss1: 0.101174 Loss2: 0.220578
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.353968 Loss1: 0.129047 Loss2: 0.224921
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.330284 Loss1: 0.108709 Loss2: 0.221575
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.309639 Loss1: 0.092961 Loss2: 0.216678
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.282380 Loss1: 0.068092 Loss2: 0.214288
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.273853 Loss1: 0.061243 Loss2: 0.212610
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.286239 Loss1: 0.072773 Loss2: 0.213466
+(DefaultActor pid=1838052) >> Training accuracy: 0.983774
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.146454 Loss1: 0.114584 Loss2: 0.031870
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.135294 Loss1: 0.099943 Loss2: 0.035351
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.133512 Loss1: 0.096633 Loss2: 0.036879
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.102933 Loss1: 0.066629 Loss2: 0.036304
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.104468 Loss1: 0.068256 Loss2: 0.036212
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108812 Loss1: 0.072145 Loss2: 0.036667
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.099903 Loss1: 0.063179 Loss2: 0.036725
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.121946 Loss1: 0.084610 Loss2: 0.037337
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108276 Loss1: 0.070769 Loss2: 0.037507
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102496 Loss1: 0.064921 Loss2: 0.037575
+(DefaultActor pid=1838052) >> Training accuracy: 0.987805
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.372480 Loss1: 0.162994 Loss2: 0.209486
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.289361 Loss1: 0.108573 Loss2: 0.180788
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.266111 Loss1: 0.091865 Loss2: 0.174246
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.246521 Loss1: 0.075409 Loss2: 0.171112
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.248948 Loss1: 0.078991 Loss2: 0.169956
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.241948 Loss1: 0.072071 Loss2: 0.169877
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.255750 Loss1: 0.084756 Loss2: 0.170994
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.230603 Loss1: 0.060963 Loss2: 0.169640
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.228720 Loss1: 0.061114 Loss2: 0.167606
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.241194 Loss1: 0.071449 Loss2: 0.169745
+(DefaultActor pid=1838052) >> Training accuracy: 0.988528
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.187765 Loss1: 0.153198 Loss2: 0.034566
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.145260 Loss1: 0.107630 Loss2: 0.037630
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.117815 Loss1: 0.079658 Loss2: 0.038157
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.110414 Loss1: 0.072606 Loss2: 0.037808
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.127068 Loss1: 0.088935 Loss2: 0.038133
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.116233 Loss1: 0.077997 Loss2: 0.038236
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.103598 Loss1: 0.065384 Loss2: 0.038215
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.107716 Loss1: 0.069418 Loss2: 0.038298
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096825 Loss1: 0.058586 Loss2: 0.038240
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.090470 Loss1: 0.052614 Loss2: 0.037856
+(DefaultActor pid=1838052) >> Training accuracy: 0.988898
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.756585 Loss1: 0.162869 Loss2: 0.593716
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.726951 Loss1: 0.135710 Loss2: 0.591241
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.663453 Loss1: 0.092921 Loss2: 0.570531
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.657172 Loss1: 0.098783 Loss2: 0.558388
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.679877 Loss1: 0.127839 Loss2: 0.552038
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.658477 Loss1: 0.111548 Loss2: 0.546928
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.656905 Loss1: 0.117788 Loss2: 0.539117
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.643800 Loss1: 0.108751 Loss2: 0.535049
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.630536 Loss1: 0.098550 Loss2: 0.531986
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.643369 Loss1: 0.111169 Loss2: 0.532200
+(DefaultActor pid=1838052) >> Training accuracy: 0.978733
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.170237 Loss1: 0.131843 Loss2: 0.038394
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.107843 Loss1: 0.067098 Loss2: 0.040745
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.092017 Loss1: 0.051586 Loss2: 0.040430
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.097059 Loss1: 0.057047 Loss2: 0.040013
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.087190 Loss1: 0.047541 Loss2: 0.039649
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.097085 Loss1: 0.056877 Loss2: 0.040208
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.116938 Loss1: 0.076521 Loss2: 0.040417
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.141012 Loss1: 0.098385 Loss2: 0.042628
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.129578 Loss1: 0.086923 Loss2: 0.042655
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115640 Loss1: 0.072983 Loss2: 0.042657
+(DefaultActor pid=1838052) >> Training accuracy: 0.982002
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.570413 Loss1: 0.167244 Loss2: 0.403168
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.512570 Loss1: 0.130824 Loss2: 0.381746
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.528651 Loss1: 0.147983 Loss2: 0.380668
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.542733 Loss1: 0.163940 Loss2: 0.378792
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.493703 Loss1: 0.124773 Loss2: 0.368930
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.483122 Loss1: 0.118604 Loss2: 0.364518
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.464605 Loss1: 0.104856 Loss2: 0.359749
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.475765 Loss1: 0.113216 Loss2: 0.362549
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.459841 Loss1: 0.100882 Loss2: 0.358959
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.439759 Loss1: 0.084364 Loss2: 0.355394
+(DefaultActor pid=1838052) >> Training accuracy: 0.985759
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.185958 Loss1: 0.151364 Loss2: 0.034593
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.127864 Loss1: 0.090942 Loss2: 0.036922
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.097208 Loss1: 0.059825 Loss2: 0.037383
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.103684 Loss1: 0.066673 Loss2: 0.037011
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101618 Loss1: 0.063868 Loss2: 0.037750
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.116352 Loss1: 0.077908 Loss2: 0.038444
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.142148 Loss1: 0.102117 Loss2: 0.040031
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.132275 Loss1: 0.092133 Loss2: 0.040143
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.116635 Loss1: 0.077183 Loss2: 0.039452
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.111513 Loss1: 0.072632 Loss2: 0.038881
+(DefaultActor pid=1838052) >> Training accuracy: 0.984375
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.177659 Loss1: 0.139832 Loss2: 0.037827
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.123577 Loss1: 0.083665 Loss2: 0.039911
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.090999 Loss1: 0.052117 Loss2: 0.038882
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113299 Loss1: 0.074469 Loss2: 0.038830
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.113466 Loss1: 0.073697 Loss2: 0.039769
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.121984 Loss1: 0.082355 Loss2: 0.039630
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104431 Loss1: 0.065044 Loss2: 0.039387
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.094564 Loss1: 0.055599 Loss2: 0.038965
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092780 Loss1: 0.053955 Loss2: 0.038825
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.120653 Loss1: 0.081063 Loss2: 0.039590
+(DefaultActor pid=1838052) >> Training accuracy: 0.985377
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 12:38:19,010][flwr][DEBUG] - fit_round 58 received 10 results and 0 failures
+>> Test accuracy: 0.649900
+[2023-09-28 12:39:00,197][flwr][INFO] - fit progress: (58, 2.195324074726897, {'accuracy': 0.6499}, 109163.08724016929)
+[2023-09-28 12:39:00,197][flwr][DEBUG] - evaluate_round 58: strategy sampled 10 clients (out of 10)
+[2023-09-28 12:39:36,655][flwr][DEBUG] - evaluate_round 58 received 10 results and 0 failures
+[2023-09-28 12:39:36,656][flwr][DEBUG] - fit_round 59: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.653095 Loss1: 0.140832 Loss2: 0.512263
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.606218 Loss1: 0.098673 Loss2: 0.507545
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.586061 Loss1: 0.087415 Loss2: 0.498646
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.592401 Loss1: 0.098309 Loss2: 0.494092
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.594689 Loss1: 0.102060 Loss2: 0.492628
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.576589 Loss1: 0.089144 Loss2: 0.487445
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.562617 Loss1: 0.080613 Loss2: 0.482004
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.568812 Loss1: 0.086782 Loss2: 0.482030
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.556661 Loss1: 0.078670 Loss2: 0.477991
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.553333 Loss1: 0.077364 Loss2: 0.475969
+(DefaultActor pid=1838052) >> Training accuracy: 0.981804
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.184157 Loss1: 0.145639 Loss2: 0.038518
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.124897 Loss1: 0.083406 Loss2: 0.041491
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.134244 Loss1: 0.093030 Loss2: 0.041214
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.114920 Loss1: 0.074005 Loss2: 0.040914
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.123072 Loss1: 0.082212 Loss2: 0.040860
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.125438 Loss1: 0.083750 Loss2: 0.041688
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.153332 Loss1: 0.110595 Loss2: 0.042737
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.127817 Loss1: 0.085845 Loss2: 0.041972
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140450 Loss1: 0.097830 Loss2: 0.042620
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.130074 Loss1: 0.087689 Loss2: 0.042385
+(DefaultActor pid=1838052) >> Training accuracy: 0.988281
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.149234 Loss1: 0.116420 Loss2: 0.032815
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104293 Loss1: 0.069407 Loss2: 0.034886
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.107612 Loss1: 0.072071 Loss2: 0.035541
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.108612 Loss1: 0.072672 Loss2: 0.035940
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.105247 Loss1: 0.069082 Loss2: 0.036164
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.101100 Loss1: 0.065256 Loss2: 0.035844
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101475 Loss1: 0.065106 Loss2: 0.036369
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.097806 Loss1: 0.061609 Loss2: 0.036197
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.107162 Loss1: 0.070622 Loss2: 0.036540
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098746 Loss1: 0.061433 Loss2: 0.037313
+(DefaultActor pid=1838052) >> Training accuracy: 0.989517
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.187738 Loss1: 0.154319 Loss2: 0.033419
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.129019 Loss1: 0.092595 Loss2: 0.036424
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.119049 Loss1: 0.082071 Loss2: 0.036978
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.115069 Loss1: 0.078422 Loss2: 0.036647
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.139890 Loss1: 0.102217 Loss2: 0.037673
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.124683 Loss1: 0.086434 Loss2: 0.038249
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101359 Loss1: 0.063691 Loss2: 0.037669
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.099210 Loss1: 0.061547 Loss2: 0.037663
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092146 Loss1: 0.054826 Loss2: 0.037320
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.069114 Loss1: 0.032843 Loss2: 0.036271
+(DefaultActor pid=1838052) >> Training accuracy: 0.993243
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.176875 Loss1: 0.119960 Loss2: 0.056915
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.118469 Loss1: 0.063024 Loss2: 0.055445
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.116921 Loss1: 0.063021 Loss2: 0.053900
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.103955 Loss1: 0.050088 Loss2: 0.053868
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.097100 Loss1: 0.044309 Loss2: 0.052792
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.105119 Loss1: 0.051736 Loss2: 0.053383
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.105521 Loss1: 0.052342 Loss2: 0.053179
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125895 Loss1: 0.071644 Loss2: 0.054251
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108975 Loss1: 0.054526 Loss2: 0.054449
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121688 Loss1: 0.067216 Loss2: 0.054472
+(DefaultActor pid=1838052) >> Training accuracy: 0.985759
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.205910 Loss1: 0.140980 Loss2: 0.064930
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.140703 Loss1: 0.079438 Loss2: 0.061266
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.125620 Loss1: 0.065540 Loss2: 0.060081
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113243 Loss1: 0.055858 Loss2: 0.057386
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.132916 Loss1: 0.075341 Loss2: 0.057575
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.137875 Loss1: 0.079129 Loss2: 0.058746
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.144593 Loss1: 0.085284 Loss2: 0.059309
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137234 Loss1: 0.078200 Loss2: 0.059035
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.125504 Loss1: 0.066772 Loss2: 0.058732
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115427 Loss1: 0.058178 Loss2: 0.057250
+(DefaultActor pid=1838052) >> Training accuracy: 0.988381
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.699866 Loss1: 0.136791 Loss2: 0.563075
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.652117 Loss1: 0.105541 Loss2: 0.546576
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.649402 Loss1: 0.112588 Loss2: 0.536814
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615120 Loss1: 0.085926 Loss2: 0.529194
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.618060 Loss1: 0.092448 Loss2: 0.525612
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646008 Loss1: 0.119965 Loss2: 0.526043
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.627227 Loss1: 0.107043 Loss2: 0.520184
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.646585 Loss1: 0.126096 Loss2: 0.520490
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.592714 Loss1: 0.078797 Loss2: 0.513917
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.579348 Loss1: 0.069590 Loss2: 0.509758
+(DefaultActor pid=1838052) >> Training accuracy: 0.988782
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.162598 Loss1: 0.125945 Loss2: 0.036653
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.113153 Loss1: 0.074789 Loss2: 0.038365
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.104121 Loss1: 0.065212 Loss2: 0.038909
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.101958 Loss1: 0.063653 Loss2: 0.038305
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.108612 Loss1: 0.070132 Loss2: 0.038480
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.113301 Loss1: 0.074857 Loss2: 0.038444
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.121263 Loss1: 0.081846 Loss2: 0.039417
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.107200 Loss1: 0.068371 Loss2: 0.038828
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115840 Loss1: 0.076288 Loss2: 0.039552
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102656 Loss1: 0.063284 Loss2: 0.039372
+(DefaultActor pid=1838052) >> Training accuracy: 0.987935
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.144898 Loss1: 0.112793 Loss2: 0.032105
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.096932 Loss1: 0.062308 Loss2: 0.034624
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.099578 Loss1: 0.064651 Loss2: 0.034927
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085400 Loss1: 0.050153 Loss2: 0.035247
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.080777 Loss1: 0.045469 Loss2: 0.035309
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096529 Loss1: 0.060788 Loss2: 0.035741
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104686 Loss1: 0.068223 Loss2: 0.036464
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.102462 Loss1: 0.065809 Loss2: 0.036653
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.119382 Loss1: 0.082198 Loss2: 0.037184
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.086069 Loss1: 0.049261 Loss2: 0.036807
+(DefaultActor pid=1838052) >> Training accuracy: 0.987995
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.158378 Loss1: 0.125507 Loss2: 0.032871
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.121435 Loss1: 0.084913 Loss2: 0.036522
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.105456 Loss1: 0.068902 Loss2: 0.036554
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113813 Loss1: 0.076853 Loss2: 0.036960
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.138597 Loss1: 0.100987 Loss2: 0.037609
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.156129 Loss1: 0.117267 Loss2: 0.038862
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.114743 Loss1: 0.076456 Loss2: 0.038287
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.128988 Loss1: 0.090955 Loss2: 0.038033
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.132117 Loss1: 0.092912 Loss2: 0.039205
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112736 Loss1: 0.073646 Loss2: 0.039091
+(DefaultActor pid=1838052) >> Training accuracy: 0.989104
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 13:08:29,317][flwr][DEBUG] - fit_round 59 received 10 results and 0 failures
+>> Test accuracy: 0.650500
+[2023-09-28 13:09:09,776][flwr][INFO] - fit progress: (59, 2.2019545649187253, {'accuracy': 0.6505}, 110972.66598086525)
+[2023-09-28 13:09:09,776][flwr][DEBUG] - evaluate_round 59: strategy sampled 10 clients (out of 10)
+[2023-09-28 13:09:46,393][flwr][DEBUG] - evaluate_round 59 received 10 results and 0 failures
+[2023-09-28 13:09:46,394][flwr][DEBUG] - fit_round 60: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.751069 Loss1: 0.151690 Loss2: 0.599378
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.701111 Loss1: 0.104400 Loss2: 0.596711
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.693096 Loss1: 0.106510 Loss2: 0.586586
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.681731 Loss1: 0.104970 Loss2: 0.576761
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.685732 Loss1: 0.117083 Loss2: 0.568650
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.671654 Loss1: 0.105583 Loss2: 0.566070
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.678010 Loss1: 0.117617 Loss2: 0.560394
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.636156 Loss1: 0.082512 Loss2: 0.553645
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.643292 Loss1: 0.095417 Loss2: 0.547876
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.639488 Loss1: 0.094310 Loss2: 0.545179
+(DefaultActor pid=1838052) >> Training accuracy: 0.980903
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.142712 Loss1: 0.109831 Loss2: 0.032881
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.084959 Loss1: 0.050008 Loss2: 0.034950
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.080724 Loss1: 0.045459 Loss2: 0.035266
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.095768 Loss1: 0.059836 Loss2: 0.035932
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.090865 Loss1: 0.054445 Loss2: 0.036420
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.088822 Loss1: 0.052165 Loss2: 0.036657
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101130 Loss1: 0.064114 Loss2: 0.037016
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100227 Loss1: 0.062874 Loss2: 0.037352
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.128180 Loss1: 0.090213 Loss2: 0.037967
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.142661 Loss1: 0.104231 Loss2: 0.038430
+(DefaultActor pid=1838052) >> Training accuracy: 0.978639
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.740027 Loss1: 0.167236 Loss2: 0.572791
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.652332 Loss1: 0.096570 Loss2: 0.555762
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.620631 Loss1: 0.081368 Loss2: 0.539263
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.627828 Loss1: 0.097872 Loss2: 0.529957
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.653539 Loss1: 0.125812 Loss2: 0.527727
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646119 Loss1: 0.121239 Loss2: 0.524881
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.623819 Loss1: 0.104872 Loss2: 0.518947
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.631050 Loss1: 0.113660 Loss2: 0.517390
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.607183 Loss1: 0.095232 Loss2: 0.511951
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.603305 Loss1: 0.095238 Loss2: 0.508067
+(DefaultActor pid=1838052) >> Training accuracy: 0.983347
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.171303 Loss1: 0.137473 Loss2: 0.033831
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.119225 Loss1: 0.083045 Loss2: 0.036179
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.090841 Loss1: 0.054224 Loss2: 0.036617
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.082015 Loss1: 0.045911 Loss2: 0.036104
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.099232 Loss1: 0.062279 Loss2: 0.036953
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.097228 Loss1: 0.059963 Loss2: 0.037265
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.115113 Loss1: 0.077174 Loss2: 0.037939
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.138358 Loss1: 0.099714 Loss2: 0.038644
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.124717 Loss1: 0.085977 Loss2: 0.038740
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114395 Loss1: 0.075898 Loss2: 0.038497
+(DefaultActor pid=1838052) >> Training accuracy: 0.982171
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.184530 Loss1: 0.151846 Loss2: 0.032684
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.126511 Loss1: 0.091017 Loss2: 0.035494
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.138256 Loss1: 0.101935 Loss2: 0.036321
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.118666 Loss1: 0.082137 Loss2: 0.036529
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.103594 Loss1: 0.066955 Loss2: 0.036639
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.074906 Loss1: 0.038983 Loss2: 0.035923
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.093259 Loss1: 0.057258 Loss2: 0.036001
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.084672 Loss1: 0.049298 Loss2: 0.035374
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.103158 Loss1: 0.067014 Loss2: 0.036144
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089134 Loss1: 0.052838 Loss2: 0.036295
+(DefaultActor pid=1838052) >> Training accuracy: 0.992188
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.694724 Loss1: 0.114901 Loss2: 0.579823
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.665133 Loss1: 0.095390 Loss2: 0.569743
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.675493 Loss1: 0.115889 Loss2: 0.559604
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.671360 Loss1: 0.115847 Loss2: 0.555513
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.643444 Loss1: 0.096497 Loss2: 0.546948
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.636226 Loss1: 0.094309 Loss2: 0.541917
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.670256 Loss1: 0.129916 Loss2: 0.540340
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.654326 Loss1: 0.116539 Loss2: 0.537786
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.658664 Loss1: 0.126082 Loss2: 0.532582
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.652680 Loss1: 0.122124 Loss2: 0.530557
+(DefaultActor pid=1838052) >> Training accuracy: 0.984375
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.162319 Loss1: 0.128887 Loss2: 0.033433
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.111079 Loss1: 0.075342 Loss2: 0.035737
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.106915 Loss1: 0.070976 Loss2: 0.035939
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107729 Loss1: 0.071400 Loss2: 0.036328
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086707 Loss1: 0.050236 Loss2: 0.036471
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.095095 Loss1: 0.058605 Loss2: 0.036490
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.089393 Loss1: 0.052873 Loss2: 0.036520
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.093167 Loss1: 0.056476 Loss2: 0.036690
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.109524 Loss1: 0.072323 Loss2: 0.037202
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.117482 Loss1: 0.079504 Loss2: 0.037978
+(DefaultActor pid=1838052) >> Training accuracy: 0.979826
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.154453 Loss1: 0.121755 Loss2: 0.032699
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104130 Loss1: 0.069552 Loss2: 0.034578
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.084324 Loss1: 0.049941 Loss2: 0.034382
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.073996 Loss1: 0.039681 Loss2: 0.034316
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.066741 Loss1: 0.032857 Loss2: 0.033884
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077275 Loss1: 0.043352 Loss2: 0.033923
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.069361 Loss1: 0.035100 Loss2: 0.034260
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100181 Loss1: 0.065035 Loss2: 0.035145
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.088846 Loss1: 0.053464 Loss2: 0.035383
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.092508 Loss1: 0.056627 Loss2: 0.035881
+(DefaultActor pid=1838052) >> Training accuracy: 0.988782
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.141494 Loss1: 0.104050 Loss2: 0.037444
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.106230 Loss1: 0.066460 Loss2: 0.039770
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.107260 Loss1: 0.067122 Loss2: 0.040138
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.108513 Loss1: 0.068242 Loss2: 0.040272
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.096926 Loss1: 0.056426 Loss2: 0.040500
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.098720 Loss1: 0.058246 Loss2: 0.040475
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.092002 Loss1: 0.052159 Loss2: 0.039843
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.111896 Loss1: 0.070867 Loss2: 0.041030
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.139531 Loss1: 0.097271 Loss2: 0.042260
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.132750 Loss1: 0.090053 Loss2: 0.042697
+(DefaultActor pid=1838052) >> Training accuracy: 0.981804
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.123642 Loss1: 0.091817 Loss2: 0.031825
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.092192 Loss1: 0.058508 Loss2: 0.033684
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.117623 Loss1: 0.083101 Loss2: 0.034521
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.128793 Loss1: 0.092451 Loss2: 0.036342
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110780 Loss1: 0.074024 Loss2: 0.036756
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.093124 Loss1: 0.056410 Loss2: 0.036714
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.095942 Loss1: 0.059540 Loss2: 0.036402
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.088939 Loss1: 0.052721 Loss2: 0.036218
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092372 Loss1: 0.055797 Loss2: 0.036575
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099736 Loss1: 0.063334 Loss2: 0.036402
+(DefaultActor pid=1838052) >> Training accuracy: 0.992188
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 13:38:41,244][flwr][DEBUG] - fit_round 60 received 10 results and 0 failures
+>> Test accuracy: 0.650000
+[2023-09-28 13:39:21,703][flwr][INFO] - fit progress: (60, 2.1900063330373065, {'accuracy': 0.65}, 112784.59380016942)
+[2023-09-28 13:39:21,704][flwr][DEBUG] - evaluate_round 60: strategy sampled 10 clients (out of 10)
+[2023-09-28 13:39:58,629][flwr][DEBUG] - evaluate_round 60 received 10 results and 0 failures
+[2023-09-28 13:39:58,630][flwr][DEBUG] - fit_round 61: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.708451 Loss1: 0.111271 Loss2: 0.597179
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.680266 Loss1: 0.090237 Loss2: 0.590030
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.662436 Loss1: 0.087404 Loss2: 0.575033
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.647867 Loss1: 0.083162 Loss2: 0.564704
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.651275 Loss1: 0.090218 Loss2: 0.561058
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.657386 Loss1: 0.101817 Loss2: 0.555568
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.669883 Loss1: 0.117627 Loss2: 0.552256
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.660866 Loss1: 0.111149 Loss2: 0.549717
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.663261 Loss1: 0.116581 Loss2: 0.546680
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.640661 Loss1: 0.099569 Loss2: 0.541092
+(DefaultActor pid=1838052) >> Training accuracy: 0.977896
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.140845 Loss1: 0.108397 Loss2: 0.032448
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.100331 Loss1: 0.066292 Loss2: 0.034039
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.104372 Loss1: 0.069617 Loss2: 0.034755
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.097903 Loss1: 0.062426 Loss2: 0.035477
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.094600 Loss1: 0.058860 Loss2: 0.035740
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.072253 Loss1: 0.036842 Loss2: 0.035412
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.067760 Loss1: 0.032694 Loss2: 0.035067
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073460 Loss1: 0.038663 Loss2: 0.034797
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.078212 Loss1: 0.042819 Loss2: 0.035393
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.086291 Loss1: 0.050916 Loss2: 0.035375
+(DefaultActor pid=1838052) >> Training accuracy: 0.990585
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.150930 Loss1: 0.102451 Loss2: 0.048479
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.105339 Loss1: 0.058493 Loss2: 0.046846
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.100174 Loss1: 0.054461 Loss2: 0.045713
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.095474 Loss1: 0.050370 Loss2: 0.045104
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.126259 Loss1: 0.079790 Loss2: 0.046469
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.118289 Loss1: 0.071524 Loss2: 0.046765
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.113317 Loss1: 0.066866 Loss2: 0.046452
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.108587 Loss1: 0.062395 Loss2: 0.046193
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.094598 Loss1: 0.049046 Loss2: 0.045553
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115553 Loss1: 0.068956 Loss2: 0.046597
+(DefaultActor pid=1838052) >> Training accuracy: 0.987144
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.699011 Loss1: 0.124786 Loss2: 0.574225
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.644589 Loss1: 0.088705 Loss2: 0.555884
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.651779 Loss1: 0.108161 Loss2: 0.543618
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.651853 Loss1: 0.110912 Loss2: 0.540941
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.625602 Loss1: 0.092637 Loss2: 0.532965
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.596942 Loss1: 0.074117 Loss2: 0.522825
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.594180 Loss1: 0.074803 Loss2: 0.519377
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.589607 Loss1: 0.073445 Loss2: 0.516163
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.602258 Loss1: 0.088603 Loss2: 0.513655
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.610448 Loss1: 0.098796 Loss2: 0.511652
+(DefaultActor pid=1838052) >> Training accuracy: 0.981210
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.749375 Loss1: 0.153775 Loss2: 0.595600
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.702559 Loss1: 0.111876 Loss2: 0.590684
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.671072 Loss1: 0.094092 Loss2: 0.576981
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.663024 Loss1: 0.092936 Loss2: 0.570088
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.679122 Loss1: 0.114105 Loss2: 0.565017
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.662453 Loss1: 0.099268 Loss2: 0.563185
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.662273 Loss1: 0.101312 Loss2: 0.560961
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.641428 Loss1: 0.086006 Loss2: 0.555422
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.653304 Loss1: 0.101641 Loss2: 0.551663
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.649219 Loss1: 0.097954 Loss2: 0.551265
+(DefaultActor pid=1838052) >> Training accuracy: 0.980574
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.171309 Loss1: 0.115105 Loss2: 0.056204
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.133219 Loss1: 0.079876 Loss2: 0.053343
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.120097 Loss1: 0.066330 Loss2: 0.053767
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107116 Loss1: 0.054342 Loss2: 0.052774
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.109337 Loss1: 0.056835 Loss2: 0.052502
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.110910 Loss1: 0.058544 Loss2: 0.052365
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.113087 Loss1: 0.060175 Loss2: 0.052913
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110739 Loss1: 0.058297 Loss2: 0.052442
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.102373 Loss1: 0.050141 Loss2: 0.052232
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.101863 Loss1: 0.050218 Loss2: 0.051645
+(DefaultActor pid=1838052) >> Training accuracy: 0.984169
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.707236 Loss1: 0.112525 Loss2: 0.594711
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.684146 Loss1: 0.098644 Loss2: 0.585503
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.677544 Loss1: 0.102870 Loss2: 0.574674
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.667324 Loss1: 0.098460 Loss2: 0.568864
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.638750 Loss1: 0.080610 Loss2: 0.558139
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.653583 Loss1: 0.102851 Loss2: 0.550732
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.640305 Loss1: 0.095133 Loss2: 0.545173
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.628427 Loss1: 0.085889 Loss2: 0.542538
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.629651 Loss1: 0.093232 Loss2: 0.536418
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.619996 Loss1: 0.088939 Loss2: 0.531057
+(DefaultActor pid=1838052) >> Training accuracy: 0.983584
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136121 Loss1: 0.105823 Loss2: 0.030298
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.108512 Loss1: 0.075440 Loss2: 0.033073
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.094577 Loss1: 0.060664 Loss2: 0.033913
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.105908 Loss1: 0.071405 Loss2: 0.034504
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.091261 Loss1: 0.057078 Loss2: 0.034182
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096809 Loss1: 0.062125 Loss2: 0.034684
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104387 Loss1: 0.068718 Loss2: 0.035669
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.103310 Loss1: 0.067804 Loss2: 0.035506
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085209 Loss1: 0.050299 Loss2: 0.034911
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.074145 Loss1: 0.039607 Loss2: 0.034538
+(DefaultActor pid=1838052) >> Training accuracy: 0.994792
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.163163 Loss1: 0.102603 Loss2: 0.060560
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.121270 Loss1: 0.063145 Loss2: 0.058126
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.126588 Loss1: 0.069740 Loss2: 0.056847
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.118671 Loss1: 0.063003 Loss2: 0.055668
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.131230 Loss1: 0.075859 Loss2: 0.055372
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111423 Loss1: 0.056915 Loss2: 0.054508
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.115012 Loss1: 0.060087 Loss2: 0.054924
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.102965 Loss1: 0.049019 Loss2: 0.053946
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.104747 Loss1: 0.050648 Loss2: 0.054099
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116404 Loss1: 0.062845 Loss2: 0.053559
+(DefaultActor pid=1838052) >> Training accuracy: 0.987935
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.208515 Loss1: 0.149132 Loss2: 0.059383
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.156770 Loss1: 0.100324 Loss2: 0.056446
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.148279 Loss1: 0.092262 Loss2: 0.056017
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.120779 Loss1: 0.066843 Loss2: 0.053935
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.109965 Loss1: 0.057769 Loss2: 0.052196
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.113252 Loss1: 0.061441 Loss2: 0.051811
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129103 Loss1: 0.076954 Loss2: 0.052149
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.113142 Loss1: 0.061029 Loss2: 0.052113
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108926 Loss1: 0.057472 Loss2: 0.051454
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115022 Loss1: 0.063616 Loss2: 0.051405
+(DefaultActor pid=1838052) >> Training accuracy: 0.988932
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 14:09:08,320][flwr][DEBUG] - fit_round 61 received 10 results and 0 failures
+>> Test accuracy: 0.651200
+[2023-09-28 14:09:49,848][flwr][INFO] - fit progress: (61, 2.20785686230888, {'accuracy': 0.6512}, 114612.7381554232)
+[2023-09-28 14:09:49,848][flwr][DEBUG] - evaluate_round 61: strategy sampled 10 clients (out of 10)
+[2023-09-28 14:10:26,896][flwr][DEBUG] - evaluate_round 61 received 10 results and 0 failures
+[2023-09-28 14:10:26,898][flwr][DEBUG] - fit_round 62: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.449134 Loss1: 0.129271 Loss2: 0.319863
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.380067 Loss1: 0.093343 Loss2: 0.286724
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.375470 Loss1: 0.094202 Loss2: 0.281268
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.388596 Loss1: 0.110578 Loss2: 0.278018
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.381240 Loss1: 0.105335 Loss2: 0.275905
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.360677 Loss1: 0.087120 Loss2: 0.273557
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.365775 Loss1: 0.092334 Loss2: 0.273441
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.358685 Loss1: 0.085914 Loss2: 0.272771
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.346486 Loss1: 0.078835 Loss2: 0.267651
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.352327 Loss1: 0.082494 Loss2: 0.269833
+(DefaultActor pid=1838052) >> Training accuracy: 0.983188
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.675487 Loss1: 0.113259 Loss2: 0.562228
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.615851 Loss1: 0.072053 Loss2: 0.543798
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.617263 Loss1: 0.084157 Loss2: 0.533106
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.624560 Loss1: 0.089777 Loss2: 0.534783
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.614558 Loss1: 0.088169 Loss2: 0.526389
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646171 Loss1: 0.118979 Loss2: 0.527192
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.676914 Loss1: 0.146802 Loss2: 0.530111
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.628422 Loss1: 0.108848 Loss2: 0.519575
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.617618 Loss1: 0.100014 Loss2: 0.517604
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.599605 Loss1: 0.085358 Loss2: 0.514246
+(DefaultActor pid=1838052) >> Training accuracy: 0.982372
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.175652 Loss1: 0.141292 Loss2: 0.034360
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.120340 Loss1: 0.083944 Loss2: 0.036396
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.113403 Loss1: 0.075879 Loss2: 0.037524
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.095602 Loss1: 0.059090 Loss2: 0.036513
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.111543 Loss1: 0.074499 Loss2: 0.037044
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123502 Loss1: 0.086030 Loss2: 0.037472
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.112705 Loss1: 0.074997 Loss2: 0.037708
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.085782 Loss1: 0.049296 Loss2: 0.036486
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.082599 Loss1: 0.046666 Loss2: 0.035933
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.081234 Loss1: 0.045493 Loss2: 0.035740
+(DefaultActor pid=1838052) >> Training accuracy: 0.989865
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.135998 Loss1: 0.103107 Loss2: 0.032891
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.095150 Loss1: 0.059706 Loss2: 0.035444
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.086584 Loss1: 0.051304 Loss2: 0.035280
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.083266 Loss1: 0.047802 Loss2: 0.035464
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.084220 Loss1: 0.048457 Loss2: 0.035763
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083142 Loss1: 0.047630 Loss2: 0.035512
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.107685 Loss1: 0.072165 Loss2: 0.035520
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.136728 Loss1: 0.099369 Loss2: 0.037359
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130872 Loss1: 0.092659 Loss2: 0.038214
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114882 Loss1: 0.077643 Loss2: 0.037239
+(DefaultActor pid=1838052) >> Training accuracy: 0.980945
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.517744 Loss1: 0.166388 Loss2: 0.351356
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.448140 Loss1: 0.127522 Loss2: 0.320617
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.446152 Loss1: 0.135891 Loss2: 0.310261
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.436608 Loss1: 0.128695 Loss2: 0.307913
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.432190 Loss1: 0.127196 Loss2: 0.304994
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.436457 Loss1: 0.132963 Loss2: 0.303494
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.413621 Loss1: 0.117374 Loss2: 0.296246
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.409229 Loss1: 0.111688 Loss2: 0.297540
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.385048 Loss1: 0.092803 Loss2: 0.292244
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.386810 Loss1: 0.093718 Loss2: 0.293092
+(DefaultActor pid=1838052) >> Training accuracy: 0.978618
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.727502 Loss1: 0.123812 Loss2: 0.603690
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.671853 Loss1: 0.074913 Loss2: 0.596939
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.669790 Loss1: 0.081497 Loss2: 0.588293
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.664338 Loss1: 0.083886 Loss2: 0.580452
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.680255 Loss1: 0.103455 Loss2: 0.576801
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.691680 Loss1: 0.118532 Loss2: 0.573148
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.688788 Loss1: 0.118170 Loss2: 0.570618
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.671464 Loss1: 0.105196 Loss2: 0.566268
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.646562 Loss1: 0.084374 Loss2: 0.562188
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.621076 Loss1: 0.066417 Loss2: 0.554660
+(DefaultActor pid=1838052) >> Training accuracy: 0.990184
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.186288 Loss1: 0.118102 Loss2: 0.068187
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.136452 Loss1: 0.070246 Loss2: 0.066206
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.121155 Loss1: 0.055566 Loss2: 0.065590
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.127957 Loss1: 0.062433 Loss2: 0.065524
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.146322 Loss1: 0.080728 Loss2: 0.065594
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.142236 Loss1: 0.076006 Loss2: 0.066230
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.164359 Loss1: 0.097358 Loss2: 0.067001
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.133881 Loss1: 0.067991 Loss2: 0.065889
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130089 Loss1: 0.064500 Loss2: 0.065589
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.135148 Loss1: 0.069690 Loss2: 0.065458
+(DefaultActor pid=1838052) >> Training accuracy: 0.986946
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.150578 Loss1: 0.117391 Loss2: 0.033187
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.109658 Loss1: 0.074146 Loss2: 0.035512
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.105490 Loss1: 0.069562 Loss2: 0.035928
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.106721 Loss1: 0.070112 Loss2: 0.036609
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.117056 Loss1: 0.080213 Loss2: 0.036842
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.122173 Loss1: 0.084068 Loss2: 0.038105
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.141848 Loss1: 0.103130 Loss2: 0.038718
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.119964 Loss1: 0.081255 Loss2: 0.038709
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.123265 Loss1: 0.084745 Loss2: 0.038521
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.100937 Loss1: 0.063332 Loss2: 0.037605
+(DefaultActor pid=1838052) >> Training accuracy: 0.990704
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.644779 Loss1: 0.124909 Loss2: 0.519870
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.597199 Loss1: 0.115355 Loss2: 0.481845
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.563411 Loss1: 0.105547 Loss2: 0.457864
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.560848 Loss1: 0.115163 Loss2: 0.445685
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.546898 Loss1: 0.109724 Loss2: 0.437173
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.523513 Loss1: 0.095808 Loss2: 0.427705
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.536174 Loss1: 0.111649 Loss2: 0.424525
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.540602 Loss1: 0.112873 Loss2: 0.427729
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.524885 Loss1: 0.101478 Loss2: 0.423407
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.513735 Loss1: 0.094437 Loss2: 0.419298
+(DefaultActor pid=1838052) >> Training accuracy: 0.983507
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.118838 Loss1: 0.086092 Loss2: 0.032745
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.092394 Loss1: 0.057533 Loss2: 0.034862
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.086579 Loss1: 0.051473 Loss2: 0.035107
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081212 Loss1: 0.045701 Loss2: 0.035512
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.074770 Loss1: 0.039370 Loss2: 0.035401
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069609 Loss1: 0.034256 Loss2: 0.035352
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.087262 Loss1: 0.051911 Loss2: 0.035351
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068552 Loss1: 0.032864 Loss2: 0.035688
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077896 Loss1: 0.042149 Loss2: 0.035747
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.078640 Loss1: 0.042539 Loss2: 0.036100
+(DefaultActor pid=1838052) >> Training accuracy: 0.994660
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 14:39:29,182][flwr][DEBUG] - fit_round 62 received 10 results and 0 failures
+>> Test accuracy: 0.653500
+[2023-09-28 14:40:10,049][flwr][INFO] - fit progress: (62, 2.1942758756323744, {'accuracy': 0.6535}, 116432.9392821202)
+[2023-09-28 14:40:10,049][flwr][DEBUG] - evaluate_round 62: strategy sampled 10 clients (out of 10)
+[2023-09-28 14:40:47,627][flwr][DEBUG] - evaluate_round 62 received 10 results and 0 failures
+[2023-09-28 14:40:47,629][flwr][DEBUG] - fit_round 63: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.657650 Loss1: 0.120610 Loss2: 0.537039
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.630092 Loss1: 0.108825 Loss2: 0.521267
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.588210 Loss1: 0.081034 Loss2: 0.507177
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.586595 Loss1: 0.087704 Loss2: 0.498891
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.602582 Loss1: 0.104608 Loss2: 0.497974
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.598731 Loss1: 0.102818 Loss2: 0.495913
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.562558 Loss1: 0.072516 Loss2: 0.490042
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.580376 Loss1: 0.091352 Loss2: 0.489024
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.587975 Loss1: 0.099302 Loss2: 0.488672
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.606956 Loss1: 0.119110 Loss2: 0.487846
+(DefaultActor pid=1838052) >> Training accuracy: 0.981013
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.415176 Loss1: 0.135584 Loss2: 0.279591
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.377940 Loss1: 0.110182 Loss2: 0.267758
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.376363 Loss1: 0.112532 Loss2: 0.263832
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.383371 Loss1: 0.117254 Loss2: 0.266117
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.371986 Loss1: 0.109499 Loss2: 0.262488
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.394755 Loss1: 0.132984 Loss2: 0.261771
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.408671 Loss1: 0.142601 Loss2: 0.266069
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.383928 Loss1: 0.121823 Loss2: 0.262105
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.359165 Loss1: 0.103691 Loss2: 0.255474
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.365025 Loss1: 0.106319 Loss2: 0.258705
+(DefaultActor pid=1838052) >> Training accuracy: 0.976661
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.148439 Loss1: 0.114579 Loss2: 0.033861
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.109939 Loss1: 0.074195 Loss2: 0.035745
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.098420 Loss1: 0.062911 Loss2: 0.035510
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.092353 Loss1: 0.056457 Loss2: 0.035896
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.095133 Loss1: 0.059938 Loss2: 0.035194
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.094704 Loss1: 0.059867 Loss2: 0.034836
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.071054 Loss1: 0.036293 Loss2: 0.034761
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.076405 Loss1: 0.041658 Loss2: 0.034747
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077542 Loss1: 0.043015 Loss2: 0.034527
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.078518 Loss1: 0.043992 Loss2: 0.034526
+(DefaultActor pid=1838052) >> Training accuracy: 0.989183
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.132120 Loss1: 0.100475 Loss2: 0.031645
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.094125 Loss1: 0.060037 Loss2: 0.034088
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.075500 Loss1: 0.041870 Loss2: 0.033630
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.082414 Loss1: 0.048595 Loss2: 0.033819
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.084483 Loss1: 0.050414 Loss2: 0.034069
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079457 Loss1: 0.045326 Loss2: 0.034131
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073725 Loss1: 0.039954 Loss2: 0.033772
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.062841 Loss1: 0.029498 Loss2: 0.033342
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.079904 Loss1: 0.045513 Loss2: 0.034391
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.086031 Loss1: 0.051954 Loss2: 0.034077
+(DefaultActor pid=1838052) >> Training accuracy: 0.987179
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.379791 Loss1: 0.155338 Loss2: 0.224454
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.297067 Loss1: 0.109315 Loss2: 0.187752
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.301045 Loss1: 0.118547 Loss2: 0.182498
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.281140 Loss1: 0.104895 Loss2: 0.176245
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.258016 Loss1: 0.085885 Loss2: 0.172131
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.242882 Loss1: 0.073445 Loss2: 0.169437
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.282904 Loss1: 0.109712 Loss2: 0.173192
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.272903 Loss1: 0.099443 Loss2: 0.173460
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.303985 Loss1: 0.128255 Loss2: 0.175730
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.277318 Loss1: 0.103778 Loss2: 0.173540
+(DefaultActor pid=1838052) >> Training accuracy: 0.980469
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.134136 Loss1: 0.102246 Loss2: 0.031890
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.076494 Loss1: 0.043251 Loss2: 0.033243
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066454 Loss1: 0.033756 Loss2: 0.032698
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.061142 Loss1: 0.028699 Loss2: 0.032443
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.071996 Loss1: 0.039062 Loss2: 0.032934
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.067057 Loss1: 0.033923 Loss2: 0.033133
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054223 Loss1: 0.021773 Loss2: 0.032450
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065887 Loss1: 0.033343 Loss2: 0.032544
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070118 Loss1: 0.037068 Loss2: 0.033050
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.071432 Loss1: 0.038453 Loss2: 0.032980
+(DefaultActor pid=1838052) >> Training accuracy: 0.994858
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.164402 Loss1: 0.132291 Loss2: 0.032112
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.113969 Loss1: 0.078976 Loss2: 0.034993
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.124206 Loss1: 0.088964 Loss2: 0.035241
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107451 Loss1: 0.071861 Loss2: 0.035590
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.098842 Loss1: 0.063456 Loss2: 0.035387
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.092259 Loss1: 0.057136 Loss2: 0.035124
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.089031 Loss1: 0.053505 Loss2: 0.035526
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082746 Loss1: 0.047009 Loss2: 0.035737
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101844 Loss1: 0.066477 Loss2: 0.035367
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098690 Loss1: 0.063002 Loss2: 0.035688
+(DefaultActor pid=1838052) >> Training accuracy: 0.989020
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.627562 Loss1: 0.120537 Loss2: 0.507025
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.602393 Loss1: 0.114329 Loss2: 0.488063
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.575494 Loss1: 0.097201 Loss2: 0.478293
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.590531 Loss1: 0.112548 Loss2: 0.477983
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.561345 Loss1: 0.094632 Loss2: 0.466713
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.557713 Loss1: 0.090582 Loss2: 0.467130
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.557728 Loss1: 0.093899 Loss2: 0.463829
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.571193 Loss1: 0.106586 Loss2: 0.464607
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.572074 Loss1: 0.107562 Loss2: 0.464511
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.534868 Loss1: 0.076133 Loss2: 0.458736
+(DefaultActor pid=1838052) >> Training accuracy: 0.981408
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.147709 Loss1: 0.082032 Loss2: 0.065677
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.111522 Loss1: 0.046316 Loss2: 0.065206
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.106664 Loss1: 0.043666 Loss2: 0.062998
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.102567 Loss1: 0.040967 Loss2: 0.061600
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.095852 Loss1: 0.036184 Loss2: 0.059668
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111061 Loss1: 0.050907 Loss2: 0.060154
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.099343 Loss1: 0.041525 Loss2: 0.057818
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.118753 Loss1: 0.059314 Loss2: 0.059439
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.124178 Loss1: 0.064518 Loss2: 0.059660
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114729 Loss1: 0.055522 Loss2: 0.059208
+(DefaultActor pid=1838052) >> Training accuracy: 0.986090
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.164241 Loss1: 0.096008 Loss2: 0.068233
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.114148 Loss1: 0.050413 Loss2: 0.063735
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.099730 Loss1: 0.036961 Loss2: 0.062769
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.104169 Loss1: 0.041946 Loss2: 0.062223
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.111008 Loss1: 0.048984 Loss2: 0.062024
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.121584 Loss1: 0.058231 Loss2: 0.063354
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.119534 Loss1: 0.055854 Loss2: 0.063680
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.130246 Loss1: 0.066042 Loss2: 0.064204
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120709 Loss1: 0.055979 Loss2: 0.064730
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.117142 Loss1: 0.053659 Loss2: 0.063482
+(DefaultActor pid=1838052) >> Training accuracy: 0.985403
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 15:09:53,062][flwr][DEBUG] - fit_round 63 received 10 results and 0 failures
+>> Test accuracy: 0.653700
+[2023-09-28 15:10:32,662][flwr][INFO] - fit progress: (63, 2.2481949498859075, {'accuracy': 0.6537}, 118255.55269732606)
+[2023-09-28 15:10:32,663][flwr][DEBUG] - evaluate_round 63: strategy sampled 10 clients (out of 10)
+[2023-09-28 15:11:08,888][flwr][DEBUG] - evaluate_round 63 received 10 results and 0 failures
+[2023-09-28 15:11:08,889][flwr][DEBUG] - fit_round 64: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.709447 Loss1: 0.117464 Loss2: 0.591983
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.700546 Loss1: 0.113869 Loss2: 0.586677
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.669588 Loss1: 0.094780 Loss2: 0.574808
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.667779 Loss1: 0.102425 Loss2: 0.565354
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.678560 Loss1: 0.114023 Loss2: 0.564537
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.655814 Loss1: 0.095015 Loss2: 0.560800
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.635753 Loss1: 0.080159 Loss2: 0.555593
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.647856 Loss1: 0.093199 Loss2: 0.554657
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.638380 Loss1: 0.087793 Loss2: 0.550587
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.640686 Loss1: 0.094307 Loss2: 0.546379
+(DefaultActor pid=1838052) >> Training accuracy: 0.984976
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.667078 Loss1: 0.101403 Loss2: 0.565675
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.626178 Loss1: 0.073986 Loss2: 0.552192
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.634152 Loss1: 0.088128 Loss2: 0.546024
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.637685 Loss1: 0.092445 Loss2: 0.545240
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.631804 Loss1: 0.091105 Loss2: 0.540699
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.647677 Loss1: 0.106263 Loss2: 0.541413
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.609943 Loss1: 0.075696 Loss2: 0.534247
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.632207 Loss1: 0.098894 Loss2: 0.533314
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.633270 Loss1: 0.101398 Loss2: 0.531872
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.605310 Loss1: 0.076961 Loss2: 0.528349
+(DefaultActor pid=1838052) >> Training accuracy: 0.979167
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.529243 Loss1: 0.129694 Loss2: 0.399548
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.428318 Loss1: 0.098185 Loss2: 0.330133
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.413530 Loss1: 0.109906 Loss2: 0.303624
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.381099 Loss1: 0.087622 Loss2: 0.293477
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.378831 Loss1: 0.093522 Loss2: 0.285309
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.382816 Loss1: 0.097516 Loss2: 0.285299
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.379751 Loss1: 0.095819 Loss2: 0.283931
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.362102 Loss1: 0.081709 Loss2: 0.280394
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.378220 Loss1: 0.097966 Loss2: 0.280254
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.350933 Loss1: 0.072013 Loss2: 0.278921
+(DefaultActor pid=1838052) >> Training accuracy: 0.979167
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136386 Loss1: 0.106803 Loss2: 0.029583
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.094912 Loss1: 0.063571 Loss2: 0.031341
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.097730 Loss1: 0.065869 Loss2: 0.031861
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081732 Loss1: 0.050042 Loss2: 0.031689
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.079337 Loss1: 0.047981 Loss2: 0.031356
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079755 Loss1: 0.048417 Loss2: 0.031338
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.091367 Loss1: 0.059522 Loss2: 0.031845
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082365 Loss1: 0.050636 Loss2: 0.031729
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.095742 Loss1: 0.063782 Loss2: 0.031960
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099242 Loss1: 0.066931 Loss2: 0.032312
+(DefaultActor pid=1838052) >> Training accuracy: 0.981804
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.452683 Loss1: 0.157062 Loss2: 0.295622
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.397059 Loss1: 0.131810 Loss2: 0.265249
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.371570 Loss1: 0.116883 Loss2: 0.254687
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.391486 Loss1: 0.131299 Loss2: 0.260187
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.363112 Loss1: 0.113277 Loss2: 0.249835
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.386170 Loss1: 0.135112 Loss2: 0.251058
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.422380 Loss1: 0.166239 Loss2: 0.256142
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.377539 Loss1: 0.126742 Loss2: 0.250797
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.380907 Loss1: 0.130742 Loss2: 0.250165
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.400473 Loss1: 0.144700 Loss2: 0.255773
+(DefaultActor pid=1838052) >> Training accuracy: 0.980674
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.159482 Loss1: 0.105032 Loss2: 0.054450
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104208 Loss1: 0.052461 Loss2: 0.051747
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.110832 Loss1: 0.060238 Loss2: 0.050594
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107088 Loss1: 0.057550 Loss2: 0.049538
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.108731 Loss1: 0.060302 Loss2: 0.048430
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.107856 Loss1: 0.058842 Loss2: 0.049014
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.098192 Loss1: 0.050878 Loss2: 0.047314
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.113721 Loss1: 0.066585 Loss2: 0.047135
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140807 Loss1: 0.092782 Loss2: 0.048025
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.152239 Loss1: 0.102381 Loss2: 0.049859
+(DefaultActor pid=1838052) >> Training accuracy: 0.982199
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.137212 Loss1: 0.106113 Loss2: 0.031100
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.102759 Loss1: 0.070171 Loss2: 0.032587
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.091058 Loss1: 0.058572 Loss2: 0.032486
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081038 Loss1: 0.048837 Loss2: 0.032202
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077755 Loss1: 0.045986 Loss2: 0.031769
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070167 Loss1: 0.038518 Loss2: 0.031649
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075124 Loss1: 0.044088 Loss2: 0.031036
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100403 Loss1: 0.068287 Loss2: 0.032116
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.084927 Loss1: 0.052594 Loss2: 0.032334
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.075823 Loss1: 0.043956 Loss2: 0.031867
+(DefaultActor pid=1838052) >> Training accuracy: 0.993275
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136263 Loss1: 0.106650 Loss2: 0.029612
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.081998 Loss1: 0.050344 Loss2: 0.031654
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067237 Loss1: 0.035793 Loss2: 0.031444
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.056976 Loss1: 0.026124 Loss2: 0.030852
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065236 Loss1: 0.033943 Loss2: 0.031292
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.062663 Loss1: 0.031222 Loss2: 0.031441
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.066351 Loss1: 0.034693 Loss2: 0.031658
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.093339 Loss1: 0.061047 Loss2: 0.032292
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096674 Loss1: 0.063522 Loss2: 0.033153
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.088109 Loss1: 0.054559 Loss2: 0.033550
+(DefaultActor pid=1838052) >> Training accuracy: 0.991495
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.619337 Loss1: 0.128296 Loss2: 0.491041
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.571407 Loss1: 0.102047 Loss2: 0.469360
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.594900 Loss1: 0.128071 Loss2: 0.466830
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.590155 Loss1: 0.127119 Loss2: 0.463036
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.579132 Loss1: 0.126071 Loss2: 0.453061
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.572402 Loss1: 0.117336 Loss2: 0.455066
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.573663 Loss1: 0.121514 Loss2: 0.452149
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.580977 Loss1: 0.127453 Loss2: 0.453524
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.528622 Loss1: 0.087810 Loss2: 0.440812
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.537969 Loss1: 0.096271 Loss2: 0.441698
+(DefaultActor pid=1838052) >> Training accuracy: 0.983041
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.153823 Loss1: 0.121816 Loss2: 0.032007
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.101274 Loss1: 0.067743 Loss2: 0.033530
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.103263 Loss1: 0.069307 Loss2: 0.033956
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.101900 Loss1: 0.066966 Loss2: 0.034934
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.109041 Loss1: 0.073829 Loss2: 0.035212
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.097528 Loss1: 0.062335 Loss2: 0.035193
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.091047 Loss1: 0.056116 Loss2: 0.034931
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.096329 Loss1: 0.061312 Loss2: 0.035017
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.105145 Loss1: 0.069420 Loss2: 0.035725
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.081426 Loss1: 0.046235 Loss2: 0.035191
+(DefaultActor pid=1838052) >> Training accuracy: 0.990921
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 15:40:08,743][flwr][DEBUG] - fit_round 64 received 10 results and 0 failures
+>> Test accuracy: 0.654500
+[2023-09-28 15:40:48,740][flwr][INFO] - fit progress: (64, 2.2214675100085834, {'accuracy': 0.6545}, 120071.6302740383)
+[2023-09-28 15:40:48,740][flwr][DEBUG] - evaluate_round 64: strategy sampled 10 clients (out of 10)
+[2023-09-28 15:41:25,463][flwr][DEBUG] - evaluate_round 64 received 10 results and 0 failures
+[2023-09-28 15:41:25,464][flwr][DEBUG] - fit_round 65: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.491537 Loss1: 0.126625 Loss2: 0.364912
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.372112 Loss1: 0.054484 Loss2: 0.317628
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.372575 Loss1: 0.061709 Loss2: 0.310866
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.361990 Loss1: 0.053323 Loss2: 0.308667
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.350470 Loss1: 0.043432 Loss2: 0.307038
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.374422 Loss1: 0.067900 Loss2: 0.306522
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.362035 Loss1: 0.055712 Loss2: 0.306322
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.372731 Loss1: 0.065435 Loss2: 0.307296
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.400838 Loss1: 0.092472 Loss2: 0.308366
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.376981 Loss1: 0.068482 Loss2: 0.308499
+(DefaultActor pid=1838052) >> Training accuracy: 0.986842
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.151660 Loss1: 0.109606 Loss2: 0.042054
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.088826 Loss1: 0.047267 Loss2: 0.041559
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.079629 Loss1: 0.038916 Loss2: 0.040713
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.090216 Loss1: 0.049377 Loss2: 0.040839
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.083707 Loss1: 0.043184 Loss2: 0.040522
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.082246 Loss1: 0.041757 Loss2: 0.040489
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.074914 Loss1: 0.035647 Loss2: 0.039267
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.084049 Loss1: 0.044045 Loss2: 0.040004
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.100116 Loss1: 0.059804 Loss2: 0.040312
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121081 Loss1: 0.079039 Loss2: 0.042042
+(DefaultActor pid=1838052) >> Training accuracy: 0.987847
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.187377 Loss1: 0.096938 Loss2: 0.090439
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.135771 Loss1: 0.052621 Loss2: 0.083150
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.119219 Loss1: 0.038587 Loss2: 0.080631
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.108355 Loss1: 0.030315 Loss2: 0.078041
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.117892 Loss1: 0.040161 Loss2: 0.077731
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.120905 Loss1: 0.043725 Loss2: 0.077180
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122229 Loss1: 0.044903 Loss2: 0.077326
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.119012 Loss1: 0.041008 Loss2: 0.078005
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115447 Loss1: 0.037895 Loss2: 0.077552
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118607 Loss1: 0.041810 Loss2: 0.076797
+(DefaultActor pid=1838052) >> Training accuracy: 0.994591
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.212659 Loss1: 0.135288 Loss2: 0.077372
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.154261 Loss1: 0.079429 Loss2: 0.074832
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.148654 Loss1: 0.079654 Loss2: 0.069001
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.146168 Loss1: 0.079334 Loss2: 0.066834
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.132632 Loss1: 0.066900 Loss2: 0.065733
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.117874 Loss1: 0.054276 Loss2: 0.063598
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122375 Loss1: 0.059881 Loss2: 0.062494
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.126805 Loss1: 0.063952 Loss2: 0.062852
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.107757 Loss1: 0.046632 Loss2: 0.061125
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098670 Loss1: 0.039258 Loss2: 0.059412
+(DefaultActor pid=1838052) >> Training accuracy: 0.993877
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.139913 Loss1: 0.108288 Loss2: 0.031625
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091350 Loss1: 0.058937 Loss2: 0.032413
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077688 Loss1: 0.045703 Loss2: 0.031985
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081254 Loss1: 0.049446 Loss2: 0.031808
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075019 Loss1: 0.043233 Loss2: 0.031787
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.095895 Loss1: 0.063940 Loss2: 0.031955
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.099040 Loss1: 0.066343 Loss2: 0.032697
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.096442 Loss1: 0.064011 Loss2: 0.032431
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.074916 Loss1: 0.042771 Loss2: 0.032144
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.088136 Loss1: 0.055745 Loss2: 0.032392
+(DefaultActor pid=1838052) >> Training accuracy: 0.985377
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.121626 Loss1: 0.090768 Loss2: 0.030858
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091318 Loss1: 0.058993 Loss2: 0.032325
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.086679 Loss1: 0.054610 Loss2: 0.032069
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.092199 Loss1: 0.059554 Loss2: 0.032645
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.081023 Loss1: 0.048253 Loss2: 0.032769
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108664 Loss1: 0.074908 Loss2: 0.033756
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.110886 Loss1: 0.076990 Loss2: 0.033896
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.089261 Loss1: 0.055479 Loss2: 0.033782
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.093229 Loss1: 0.060193 Loss2: 0.033036
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093428 Loss1: 0.060370 Loss2: 0.033058
+(DefaultActor pid=1838052) >> Training accuracy: 0.993078
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.103266 Loss1: 0.073756 Loss2: 0.029511
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.088320 Loss1: 0.057327 Loss2: 0.030994
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.097814 Loss1: 0.065752 Loss2: 0.032062
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.089636 Loss1: 0.057344 Loss2: 0.032293
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.098269 Loss1: 0.066040 Loss2: 0.032229
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.093992 Loss1: 0.061347 Loss2: 0.032645
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.082080 Loss1: 0.049326 Loss2: 0.032754
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075449 Loss1: 0.043535 Loss2: 0.031914
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081920 Loss1: 0.049976 Loss2: 0.031944
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.071128 Loss1: 0.039024 Loss2: 0.032103
+(DefaultActor pid=1838052) >> Training accuracy: 0.994066
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.133891 Loss1: 0.103975 Loss2: 0.029917
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.087301 Loss1: 0.056390 Loss2: 0.030911
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.072157 Loss1: 0.041367 Loss2: 0.030791
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.069328 Loss1: 0.038902 Loss2: 0.030426
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.087862 Loss1: 0.056112 Loss2: 0.031751
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108097 Loss1: 0.075744 Loss2: 0.032352
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.109433 Loss1: 0.076456 Loss2: 0.032977
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.102952 Loss1: 0.070218 Loss2: 0.032735
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.099263 Loss1: 0.066228 Loss2: 0.033035
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.069053 Loss1: 0.036648 Loss2: 0.032405
+(DefaultActor pid=1838052) >> Training accuracy: 0.994066
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.479039 Loss1: 0.087113 Loss2: 0.391926
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.388362 Loss1: 0.056758 Loss2: 0.331604
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.412242 Loss1: 0.095255 Loss2: 0.316987
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.394112 Loss1: 0.084785 Loss2: 0.309327
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.379982 Loss1: 0.078258 Loss2: 0.301725
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.349858 Loss1: 0.053476 Loss2: 0.296383
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.357286 Loss1: 0.064589 Loss2: 0.292697
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.372418 Loss1: 0.077408 Loss2: 0.295009
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.410878 Loss1: 0.114548 Loss2: 0.296330
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.406804 Loss1: 0.109321 Loss2: 0.297483
+(DefaultActor pid=1838052) >> Training accuracy: 0.978659
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.122206 Loss1: 0.093049 Loss2: 0.029157
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.089760 Loss1: 0.058474 Loss2: 0.031286
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.076903 Loss1: 0.045289 Loss2: 0.031614
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068789 Loss1: 0.037541 Loss2: 0.031247
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077564 Loss1: 0.045935 Loss2: 0.031629
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075252 Loss1: 0.043512 Loss2: 0.031739
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.085769 Loss1: 0.053803 Loss2: 0.031966
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100278 Loss1: 0.067014 Loss2: 0.033264
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085922 Loss1: 0.052749 Loss2: 0.033173
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099525 Loss1: 0.066353 Loss2: 0.033173
+(DefaultActor pid=1838052) >> Training accuracy: 0.988726
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 16:10:15,517][flwr][DEBUG] - fit_round 65 received 10 results and 0 failures
+>> Test accuracy: 0.654300
+[2023-09-28 16:10:57,965][flwr][INFO] - fit progress: (65, 2.2834309385226557, {'accuracy': 0.6543}, 121880.8550542253)
+[2023-09-28 16:10:57,965][flwr][DEBUG] - evaluate_round 65: strategy sampled 10 clients (out of 10)
+[2023-09-28 16:11:34,611][flwr][DEBUG] - evaluate_round 65 received 10 results and 0 failures
+[2023-09-28 16:11:34,612][flwr][DEBUG] - fit_round 66: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.609281 Loss1: 0.107658 Loss2: 0.501623
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.538693 Loss1: 0.072233 Loss2: 0.466460
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.545741 Loss1: 0.092775 Loss2: 0.452965
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.518550 Loss1: 0.078608 Loss2: 0.439942
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.526302 Loss1: 0.087554 Loss2: 0.438748
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.576302 Loss1: 0.136522 Loss2: 0.439780
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.542465 Loss1: 0.108631 Loss2: 0.433834
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.511823 Loss1: 0.083085 Loss2: 0.428737
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.489802 Loss1: 0.067782 Loss2: 0.422020
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.502745 Loss1: 0.079575 Loss2: 0.423170
+(DefaultActor pid=1838052) >> Training accuracy: 0.977965
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.152642 Loss1: 0.120920 Loss2: 0.031722
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.102516 Loss1: 0.068371 Loss2: 0.034146
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.089198 Loss1: 0.055013 Loss2: 0.034185
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.072435 Loss1: 0.038270 Loss2: 0.034165
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.071446 Loss1: 0.037823 Loss2: 0.033623
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070744 Loss1: 0.037002 Loss2: 0.033742
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079586 Loss1: 0.045640 Loss2: 0.033946
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.087464 Loss1: 0.052952 Loss2: 0.034512
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.100517 Loss1: 0.065746 Loss2: 0.034771
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091235 Loss1: 0.055771 Loss2: 0.035464
+(DefaultActor pid=1838052) >> Training accuracy: 0.990234
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.542297 Loss1: 0.124145 Loss2: 0.418152
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.504217 Loss1: 0.099105 Loss2: 0.405112
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.493792 Loss1: 0.095024 Loss2: 0.398768
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.480129 Loss1: 0.088449 Loss2: 0.391679
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.486150 Loss1: 0.092624 Loss2: 0.393526
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.500265 Loss1: 0.107743 Loss2: 0.392523
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.520217 Loss1: 0.124111 Loss2: 0.396106
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.503341 Loss1: 0.109925 Loss2: 0.393416
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.488204 Loss1: 0.097629 Loss2: 0.390575
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.492204 Loss1: 0.100468 Loss2: 0.391736
+(DefaultActor pid=1838052) >> Training accuracy: 0.982991
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.599444 Loss1: 0.115652 Loss2: 0.483792
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.549445 Loss1: 0.079867 Loss2: 0.469577
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.567592 Loss1: 0.101848 Loss2: 0.465744
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.554085 Loss1: 0.091254 Loss2: 0.462832
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.544290 Loss1: 0.086435 Loss2: 0.457854
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.526856 Loss1: 0.072888 Loss2: 0.453968
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.526981 Loss1: 0.075638 Loss2: 0.451343
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.516399 Loss1: 0.066282 Loss2: 0.450117
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.531068 Loss1: 0.082826 Loss2: 0.448243
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.545823 Loss1: 0.097306 Loss2: 0.448517
+(DefaultActor pid=1838052) >> Training accuracy: 0.978046
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.554038 Loss1: 0.111325 Loss2: 0.442714
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.528141 Loss1: 0.099493 Loss2: 0.428648
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.503303 Loss1: 0.080711 Loss2: 0.422592
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.507569 Loss1: 0.090223 Loss2: 0.417346
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.522225 Loss1: 0.101806 Loss2: 0.420419
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.516935 Loss1: 0.098392 Loss2: 0.418543
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.532025 Loss1: 0.112503 Loss2: 0.419522
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.529500 Loss1: 0.112486 Loss2: 0.417014
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.508774 Loss1: 0.092825 Loss2: 0.415949
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.485572 Loss1: 0.075617 Loss2: 0.409955
+(DefaultActor pid=1838052) >> Training accuracy: 0.985759
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.115332 Loss1: 0.079195 Loss2: 0.036137
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.098775 Loss1: 0.060893 Loss2: 0.037882
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.096833 Loss1: 0.058222 Loss2: 0.038611
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.091760 Loss1: 0.052874 Loss2: 0.038886
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075082 Loss1: 0.036783 Loss2: 0.038299
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081381 Loss1: 0.042966 Loss2: 0.038415
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.084531 Loss1: 0.045965 Loss2: 0.038566
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.097721 Loss1: 0.058317 Loss2: 0.039404
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092131 Loss1: 0.052885 Loss2: 0.039246
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097073 Loss1: 0.058028 Loss2: 0.039046
+(DefaultActor pid=1838052) >> Training accuracy: 0.990473
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.584882 Loss1: 0.137811 Loss2: 0.447072
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.520853 Loss1: 0.093218 Loss2: 0.427636
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.524077 Loss1: 0.100078 Loss2: 0.423999
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.526856 Loss1: 0.103819 Loss2: 0.423037
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.555784 Loss1: 0.131692 Loss2: 0.424092
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.535973 Loss1: 0.114513 Loss2: 0.421460
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.517775 Loss1: 0.101583 Loss2: 0.416192
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.487519 Loss1: 0.074169 Loss2: 0.413350
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.480131 Loss1: 0.067780 Loss2: 0.412351
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.494106 Loss1: 0.083957 Loss2: 0.410149
+(DefaultActor pid=1838052) >> Training accuracy: 0.980569
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.171131 Loss1: 0.105740 Loss2: 0.065391
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.131542 Loss1: 0.068779 Loss2: 0.062763
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.104967 Loss1: 0.045708 Loss2: 0.059260
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.116102 Loss1: 0.058936 Loss2: 0.057166
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.107920 Loss1: 0.052086 Loss2: 0.055834
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.134106 Loss1: 0.078386 Loss2: 0.055721
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.137103 Loss1: 0.081651 Loss2: 0.055451
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.115307 Loss1: 0.059881 Loss2: 0.055426
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101605 Loss1: 0.048334 Loss2: 0.053272
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104002 Loss1: 0.051168 Loss2: 0.052834
+(DefaultActor pid=1838052) >> Training accuracy: 0.985197
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.153199 Loss1: 0.119641 Loss2: 0.033558
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.093894 Loss1: 0.058032 Loss2: 0.035863
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.099825 Loss1: 0.064054 Loss2: 0.035770
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.082274 Loss1: 0.046183 Loss2: 0.036091
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086267 Loss1: 0.050589 Loss2: 0.035678
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079816 Loss1: 0.044382 Loss2: 0.035434
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.107897 Loss1: 0.071430 Loss2: 0.036467
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.144813 Loss1: 0.106495 Loss2: 0.038318
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.114678 Loss1: 0.076077 Loss2: 0.038601
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093660 Loss1: 0.055937 Loss2: 0.037724
+(DefaultActor pid=1838052) >> Training accuracy: 0.986698
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.566553 Loss1: 0.110588 Loss2: 0.455965
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.521766 Loss1: 0.079565 Loss2: 0.442200
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.502349 Loss1: 0.067125 Loss2: 0.435224
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.509342 Loss1: 0.075454 Loss2: 0.433889
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.501762 Loss1: 0.071327 Loss2: 0.430435
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.510315 Loss1: 0.078857 Loss2: 0.431457
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.551352 Loss1: 0.114932 Loss2: 0.436420
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.536451 Loss1: 0.103768 Loss2: 0.432683
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.555214 Loss1: 0.122310 Loss2: 0.432904
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.540710 Loss1: 0.108278 Loss2: 0.432432
+(DefaultActor pid=1838052) >> Training accuracy: 0.978046
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 16:40:25,647][flwr][DEBUG] - fit_round 66 received 10 results and 0 failures
+>> Test accuracy: 0.656800
+[2023-09-28 16:41:04,576][flwr][INFO] - fit progress: (66, 2.208361754592615, {'accuracy': 0.6568}, 123687.46669059945)
+[2023-09-28 16:41:04,577][flwr][DEBUG] - evaluate_round 66: strategy sampled 10 clients (out of 10)
+[2023-09-28 16:41:41,097][flwr][DEBUG] - evaluate_round 66 received 10 results and 0 failures
+[2023-09-28 16:41:41,099][flwr][DEBUG] - fit_round 67: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.144103 Loss1: 0.106281 Loss2: 0.037822
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104395 Loss1: 0.064562 Loss2: 0.039834
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.102272 Loss1: 0.062049 Loss2: 0.040223
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081783 Loss1: 0.041702 Loss2: 0.040081
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.078866 Loss1: 0.038919 Loss2: 0.039947
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077160 Loss1: 0.037605 Loss2: 0.039555
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063891 Loss1: 0.024743 Loss2: 0.039147
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.074213 Loss1: 0.034938 Loss2: 0.039275
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085233 Loss1: 0.045624 Loss2: 0.039609
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099049 Loss1: 0.058675 Loss2: 0.040374
+(DefaultActor pid=1838052) >> Training accuracy: 0.990111
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.486114 Loss1: 0.149260 Loss2: 0.336853
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.429218 Loss1: 0.109031 Loss2: 0.320187
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.455352 Loss1: 0.131107 Loss2: 0.324245
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.432401 Loss1: 0.116793 Loss2: 0.315608
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.417203 Loss1: 0.108137 Loss2: 0.309066
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.432747 Loss1: 0.114039 Loss2: 0.318708
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.418835 Loss1: 0.106756 Loss2: 0.312079
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.424477 Loss1: 0.113405 Loss2: 0.311072
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.408894 Loss1: 0.098593 Loss2: 0.310301
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.390443 Loss1: 0.083911 Loss2: 0.306533
+(DefaultActor pid=1838052) >> Training accuracy: 0.979730
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.138999 Loss1: 0.099703 Loss2: 0.039296
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.112015 Loss1: 0.070446 Loss2: 0.041569
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082773 Loss1: 0.042034 Loss2: 0.040738
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085183 Loss1: 0.044321 Loss2: 0.040861
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101536 Loss1: 0.059687 Loss2: 0.041849
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.114248 Loss1: 0.071266 Loss2: 0.042982
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.103870 Loss1: 0.060731 Loss2: 0.043139
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.097441 Loss1: 0.054053 Loss2: 0.043388
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.078714 Loss1: 0.036539 Loss2: 0.042176
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.085655 Loss1: 0.043220 Loss2: 0.042435
+(DefaultActor pid=1838052) >> Training accuracy: 0.993389
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.126707 Loss1: 0.094732 Loss2: 0.031976
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.085050 Loss1: 0.051374 Loss2: 0.033676
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.071016 Loss1: 0.037843 Loss2: 0.033173
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.071298 Loss1: 0.038377 Loss2: 0.032921
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073372 Loss1: 0.040295 Loss2: 0.033077
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081242 Loss1: 0.047616 Loss2: 0.033626
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073700 Loss1: 0.040236 Loss2: 0.033464
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.070871 Loss1: 0.037866 Loss2: 0.033006
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064488 Loss1: 0.031406 Loss2: 0.033081
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073481 Loss1: 0.039989 Loss2: 0.033492
+(DefaultActor pid=1838052) >> Training accuracy: 0.991693
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.118071 Loss1: 0.080274 Loss2: 0.037796
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.085923 Loss1: 0.046431 Loss2: 0.039492
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.074427 Loss1: 0.034963 Loss2: 0.039464
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063173 Loss1: 0.023729 Loss2: 0.039444
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061693 Loss1: 0.022926 Loss2: 0.038766
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.071602 Loss1: 0.032415 Loss2: 0.039187
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.091506 Loss1: 0.051264 Loss2: 0.040242
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.102013 Loss1: 0.060526 Loss2: 0.041488
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112318 Loss1: 0.070267 Loss2: 0.042051
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.096936 Loss1: 0.054655 Loss2: 0.042281
+(DefaultActor pid=1838052) >> Training accuracy: 0.988528
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.135533 Loss1: 0.084684 Loss2: 0.050850
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.099246 Loss1: 0.050337 Loss2: 0.048909
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082653 Loss1: 0.035241 Loss2: 0.047411
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.073040 Loss1: 0.027464 Loss2: 0.045577
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.081682 Loss1: 0.037308 Loss2: 0.044374
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081369 Loss1: 0.037264 Loss2: 0.044104
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.076467 Loss1: 0.032184 Loss2: 0.044283
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073048 Loss1: 0.029390 Loss2: 0.043658
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081225 Loss1: 0.037840 Loss2: 0.043385
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.085049 Loss1: 0.041449 Loss2: 0.043600
+(DefaultActor pid=1838052) >> Training accuracy: 0.990585
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.521051 Loss1: 0.083417 Loss2: 0.437634
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.478469 Loss1: 0.063468 Loss2: 0.415000
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.495580 Loss1: 0.078826 Loss2: 0.416753
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.500096 Loss1: 0.084482 Loss2: 0.415613
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.508261 Loss1: 0.089120 Loss2: 0.419141
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.507315 Loss1: 0.091103 Loss2: 0.416213
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.528410 Loss1: 0.109096 Loss2: 0.419313
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.548101 Loss1: 0.124962 Loss2: 0.423139
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.528286 Loss1: 0.107963 Loss2: 0.420322
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.521246 Loss1: 0.108122 Loss2: 0.413125
+(DefaultActor pid=1838052) >> Training accuracy: 0.981707
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.127234 Loss1: 0.090148 Loss2: 0.037086
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079451 Loss1: 0.040716 Loss2: 0.038735
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.081414 Loss1: 0.042571 Loss2: 0.038844
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.077269 Loss1: 0.038176 Loss2: 0.039093
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060321 Loss1: 0.021716 Loss2: 0.038606
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060387 Loss1: 0.021888 Loss2: 0.038498
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.066474 Loss1: 0.027726 Loss2: 0.038748
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067434 Loss1: 0.028901 Loss2: 0.038533
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068671 Loss1: 0.030271 Loss2: 0.038401
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080309 Loss1: 0.040639 Loss2: 0.039671
+(DefaultActor pid=1838052) >> Training accuracy: 0.991536
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.368974 Loss1: 0.132962 Loss2: 0.236012
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.320482 Loss1: 0.093755 Loss2: 0.226727
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.339613 Loss1: 0.112773 Loss2: 0.226840
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.342240 Loss1: 0.116645 Loss2: 0.225594
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.370397 Loss1: 0.141207 Loss2: 0.229189
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.355442 Loss1: 0.129229 Loss2: 0.226213
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.328436 Loss1: 0.107103 Loss2: 0.221334
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.321069 Loss1: 0.101032 Loss2: 0.220037
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.303634 Loss1: 0.085497 Loss2: 0.218137
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.305157 Loss1: 0.087997 Loss2: 0.217160
+(DefaultActor pid=1838052) >> Training accuracy: 0.980469
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.109196 Loss1: 0.078425 Loss2: 0.030771
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.082973 Loss1: 0.050469 Loss2: 0.032504
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.080775 Loss1: 0.048016 Loss2: 0.032759
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074082 Loss1: 0.040551 Loss2: 0.033531
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.068635 Loss1: 0.035535 Loss2: 0.033100
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.071843 Loss1: 0.039049 Loss2: 0.032794
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072889 Loss1: 0.039905 Loss2: 0.032983
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.077716 Loss1: 0.044580 Loss2: 0.033136
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077618 Loss1: 0.044799 Loss2: 0.032819
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099708 Loss1: 0.065559 Loss2: 0.034149
+(DefaultActor pid=1838052) >> Training accuracy: 0.989122
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 17:10:51,169][flwr][DEBUG] - fit_round 67 received 10 results and 0 failures
+>> Test accuracy: 0.656200
+[2023-09-28 17:11:30,350][flwr][INFO] - fit progress: (67, 2.293970344736934, {'accuracy': 0.6562}, 125513.24056398915)
+[2023-09-28 17:11:30,351][flwr][DEBUG] - evaluate_round 67: strategy sampled 10 clients (out of 10)
+[2023-09-28 17:12:07,618][flwr][DEBUG] - evaluate_round 67 received 10 results and 0 failures
+[2023-09-28 17:12:07,620][flwr][DEBUG] - fit_round 68: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.457432 Loss1: 0.110428 Loss2: 0.347004
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.386061 Loss1: 0.071660 Loss2: 0.314401
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.351715 Loss1: 0.043828 Loss2: 0.307887
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.367665 Loss1: 0.063756 Loss2: 0.303909
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.364930 Loss1: 0.061683 Loss2: 0.303247
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.353188 Loss1: 0.052864 Loss2: 0.300324
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.366536 Loss1: 0.065584 Loss2: 0.300952
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.378014 Loss1: 0.076026 Loss2: 0.301988
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.376218 Loss1: 0.073473 Loss2: 0.302745
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.376597 Loss1: 0.075606 Loss2: 0.300991
+(DefaultActor pid=1838052) >> Training accuracy: 0.988076
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.107460 Loss1: 0.068304 Loss2: 0.039155
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.099088 Loss1: 0.057914 Loss2: 0.041174
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.084251 Loss1: 0.042893 Loss2: 0.041359
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080260 Loss1: 0.039042 Loss2: 0.041219
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077608 Loss1: 0.036372 Loss2: 0.041236
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075996 Loss1: 0.034347 Loss2: 0.041649
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.087761 Loss1: 0.045648 Loss2: 0.042113
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091441 Loss1: 0.048766 Loss2: 0.042675
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.089484 Loss1: 0.046708 Loss2: 0.042776
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116126 Loss1: 0.072445 Loss2: 0.043681
+(DefaultActor pid=1838052) >> Training accuracy: 0.987424
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.543858 Loss1: 0.127631 Loss2: 0.416227
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.532805 Loss1: 0.124001 Loss2: 0.408804
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.554013 Loss1: 0.143662 Loss2: 0.410351
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.540712 Loss1: 0.136215 Loss2: 0.404496
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.565044 Loss1: 0.159601 Loss2: 0.405443
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.540314 Loss1: 0.139713 Loss2: 0.400602
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.529025 Loss1: 0.128707 Loss2: 0.400318
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.498909 Loss1: 0.106733 Loss2: 0.392176
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.490284 Loss1: 0.099951 Loss2: 0.390333
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.522316 Loss1: 0.131414 Loss2: 0.390902
+(DefaultActor pid=1838052) >> Training accuracy: 0.973758
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.541812 Loss1: 0.140365 Loss2: 0.401448
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.518432 Loss1: 0.129041 Loss2: 0.389391
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.510413 Loss1: 0.123586 Loss2: 0.386827
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.526939 Loss1: 0.143590 Loss2: 0.383349
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.571913 Loss1: 0.187322 Loss2: 0.384591
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.517304 Loss1: 0.140078 Loss2: 0.377225
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.510538 Loss1: 0.136298 Loss2: 0.374240
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.502059 Loss1: 0.130077 Loss2: 0.371982
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.487606 Loss1: 0.119035 Loss2: 0.368571
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.437129 Loss1: 0.081657 Loss2: 0.355472
+(DefaultActor pid=1838052) >> Training accuracy: 0.986545
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.143564 Loss1: 0.112425 Loss2: 0.031139
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072578 Loss1: 0.041043 Loss2: 0.031535
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.078750 Loss1: 0.047127 Loss2: 0.031623
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.070137 Loss1: 0.038909 Loss2: 0.031228
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073456 Loss1: 0.041948 Loss2: 0.031508
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.067097 Loss1: 0.035592 Loss2: 0.031505
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.074530 Loss1: 0.043047 Loss2: 0.031484
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.090661 Loss1: 0.058220 Loss2: 0.032441
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.083866 Loss1: 0.051569 Loss2: 0.032297
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.122232 Loss1: 0.088880 Loss2: 0.033352
+(DefaultActor pid=1838052) >> Training accuracy: 0.985853
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.128777 Loss1: 0.094100 Loss2: 0.034677
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.084636 Loss1: 0.048400 Loss2: 0.036236
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.092204 Loss1: 0.055404 Loss2: 0.036799
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080585 Loss1: 0.043752 Loss2: 0.036833
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072141 Loss1: 0.035895 Loss2: 0.036246
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.074976 Loss1: 0.038772 Loss2: 0.036203
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.083089 Loss1: 0.046199 Loss2: 0.036890
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.103295 Loss1: 0.065526 Loss2: 0.037768
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.118002 Loss1: 0.079327 Loss2: 0.038675
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104051 Loss1: 0.065059 Loss2: 0.038993
+(DefaultActor pid=1838052) >> Training accuracy: 0.985957
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.127244 Loss1: 0.082039 Loss2: 0.045205
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091223 Loss1: 0.045755 Loss2: 0.045468
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.108544 Loss1: 0.063350 Loss2: 0.045194
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.099470 Loss1: 0.055049 Loss2: 0.044421
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101836 Loss1: 0.057714 Loss2: 0.044122
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.099944 Loss1: 0.055841 Loss2: 0.044103
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101323 Loss1: 0.058543 Loss2: 0.042780
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.135291 Loss1: 0.090915 Loss2: 0.044375
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092068 Loss1: 0.049571 Loss2: 0.042497
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.094039 Loss1: 0.052210 Loss2: 0.041829
+(DefaultActor pid=1838052) >> Training accuracy: 0.982002
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.658077 Loss1: 0.114001 Loss2: 0.544077
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.567152 Loss1: 0.077684 Loss2: 0.489468
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.535023 Loss1: 0.075271 Loss2: 0.459751
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.515785 Loss1: 0.072036 Loss2: 0.443749
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.547286 Loss1: 0.106127 Loss2: 0.441159
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.555352 Loss1: 0.114117 Loss2: 0.441235
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.541236 Loss1: 0.108045 Loss2: 0.433192
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.537121 Loss1: 0.102847 Loss2: 0.434274
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.519419 Loss1: 0.091093 Loss2: 0.428326
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.533166 Loss1: 0.107272 Loss2: 0.425894
+(DefaultActor pid=1838052) >> Training accuracy: 0.976562
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.665524 Loss1: 0.109146 Loss2: 0.556378
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.626066 Loss1: 0.078824 Loss2: 0.547242
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.626696 Loss1: 0.083973 Loss2: 0.542723
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.604763 Loss1: 0.066696 Loss2: 0.538067
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.597665 Loss1: 0.066208 Loss2: 0.531457
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.632417 Loss1: 0.096289 Loss2: 0.536128
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.619979 Loss1: 0.089515 Loss2: 0.530463
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.618692 Loss1: 0.086908 Loss2: 0.531784
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.600653 Loss1: 0.074261 Loss2: 0.526392
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.616513 Loss1: 0.088922 Loss2: 0.527590
+(DefaultActor pid=1838052) >> Training accuracy: 0.978639
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.130401 Loss1: 0.084558 Loss2: 0.045843
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.089246 Loss1: 0.043187 Loss2: 0.046059
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.083449 Loss1: 0.038977 Loss2: 0.044472
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.094212 Loss1: 0.049954 Loss2: 0.044257
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.088600 Loss1: 0.044362 Loss2: 0.044238
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.100342 Loss1: 0.056934 Loss2: 0.043408
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.105788 Loss1: 0.062292 Loss2: 0.043497
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.119396 Loss1: 0.075104 Loss2: 0.044292
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.104034 Loss1: 0.060377 Loss2: 0.043657
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.108598 Loss1: 0.065555 Loss2: 0.043043
+(DefaultActor pid=1838052) >> Training accuracy: 0.983188
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 17:41:11,575][flwr][DEBUG] - fit_round 68 received 10 results and 0 failures
+>> Test accuracy: 0.656100
+[2023-09-28 17:44:23,129][flwr][INFO] - fit progress: (68, 2.207248735922975, {'accuracy': 0.6561}, 127486.01882739831)
+[2023-09-28 17:44:23,129][flwr][DEBUG] - evaluate_round 68: strategy sampled 10 clients (out of 10)
+[2023-09-28 17:45:00,558][flwr][DEBUG] - evaluate_round 68 received 10 results and 0 failures
+[2023-09-28 17:45:00,560][flwr][DEBUG] - fit_round 69: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.268587 Loss1: 0.121042 Loss2: 0.147545
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.219065 Loss1: 0.082442 Loss2: 0.136623
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.249787 Loss1: 0.108027 Loss2: 0.141760
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.247801 Loss1: 0.108066 Loss2: 0.139735
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.258516 Loss1: 0.118559 Loss2: 0.139956
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.238055 Loss1: 0.100527 Loss2: 0.137528
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.253793 Loss1: 0.116411 Loss2: 0.137382
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.268497 Loss1: 0.127226 Loss2: 0.141270
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.234380 Loss1: 0.097992 Loss2: 0.136388
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.225595 Loss1: 0.089466 Loss2: 0.136129
+(DefaultActor pid=1838052) >> Training accuracy: 0.975329
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.463904 Loss1: 0.082376 Loss2: 0.381528
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.411145 Loss1: 0.057931 Loss2: 0.353214
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.394757 Loss1: 0.048046 Loss2: 0.346711
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.405531 Loss1: 0.064897 Loss2: 0.340634
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.388763 Loss1: 0.050462 Loss2: 0.338301
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.374992 Loss1: 0.039926 Loss2: 0.335066
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.380532 Loss1: 0.047249 Loss2: 0.333283
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.380234 Loss1: 0.047932 Loss2: 0.332302
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.402401 Loss1: 0.068211 Loss2: 0.334190
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.400747 Loss1: 0.066767 Loss2: 0.333981
+(DefaultActor pid=1838052) >> Training accuracy: 0.981804
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.133629 Loss1: 0.100309 Loss2: 0.033320
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074403 Loss1: 0.040519 Loss2: 0.033884
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.076822 Loss1: 0.042507 Loss2: 0.034315
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080130 Loss1: 0.045457 Loss2: 0.034673
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069039 Loss1: 0.034484 Loss2: 0.034556
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.085547 Loss1: 0.050466 Loss2: 0.035081
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.094971 Loss1: 0.059310 Loss2: 0.035661
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.092185 Loss1: 0.056197 Loss2: 0.035988
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108536 Loss1: 0.072014 Loss2: 0.036522
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097218 Loss1: 0.060495 Loss2: 0.036723
+(DefaultActor pid=1838052) >> Training accuracy: 0.984968
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.627363 Loss1: 0.082523 Loss2: 0.544839
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.570130 Loss1: 0.054612 Loss2: 0.515518
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.541853 Loss1: 0.045596 Loss2: 0.496256
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.535243 Loss1: 0.047421 Loss2: 0.487822
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.543661 Loss1: 0.057734 Loss2: 0.485927
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.551119 Loss1: 0.067321 Loss2: 0.483798
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.540577 Loss1: 0.059918 Loss2: 0.480659
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.534073 Loss1: 0.055524 Loss2: 0.478550
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.533005 Loss1: 0.054591 Loss2: 0.478415
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.561753 Loss1: 0.082681 Loss2: 0.479072
+(DefaultActor pid=1838052) >> Training accuracy: 0.986946
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.718674 Loss1: 0.134736 Loss2: 0.583939
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.658627 Loss1: 0.088423 Loss2: 0.570205
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.635746 Loss1: 0.077747 Loss2: 0.558000
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.607413 Loss1: 0.060348 Loss2: 0.547065
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.605724 Loss1: 0.063200 Loss2: 0.542523
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.618902 Loss1: 0.079637 Loss2: 0.539266
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.625040 Loss1: 0.085806 Loss2: 0.539234
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627850 Loss1: 0.088755 Loss2: 0.539095
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.651810 Loss1: 0.112739 Loss2: 0.539071
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.613640 Loss1: 0.080258 Loss2: 0.533382
+(DefaultActor pid=1838052) >> Training accuracy: 0.984164
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.109721 Loss1: 0.077056 Loss2: 0.032665
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.081446 Loss1: 0.047094 Loss2: 0.034352
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082478 Loss1: 0.048274 Loss2: 0.034204
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068238 Loss1: 0.034323 Loss2: 0.033915
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.067689 Loss1: 0.034157 Loss2: 0.033531
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069160 Loss1: 0.035499 Loss2: 0.033661
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062251 Loss1: 0.028765 Loss2: 0.033486
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.062480 Loss1: 0.029419 Loss2: 0.033061
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064992 Loss1: 0.031639 Loss2: 0.033354
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073475 Loss1: 0.039927 Loss2: 0.033548
+(DefaultActor pid=1838052) >> Training accuracy: 0.980769
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.641556 Loss1: 0.091717 Loss2: 0.549839
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.592728 Loss1: 0.068669 Loss2: 0.524058
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.593242 Loss1: 0.085646 Loss2: 0.507596
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.560708 Loss1: 0.061365 Loss2: 0.499343
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.574020 Loss1: 0.079876 Loss2: 0.494144
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.573035 Loss1: 0.077043 Loss2: 0.495993
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.552019 Loss1: 0.062292 Loss2: 0.489727
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.536126 Loss1: 0.048702 Loss2: 0.487423
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.526953 Loss1: 0.043507 Loss2: 0.483446
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.513671 Loss1: 0.034382 Loss2: 0.479289
+(DefaultActor pid=1838052) >> Training accuracy: 0.995847
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.114460 Loss1: 0.081604 Loss2: 0.032856
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.093827 Loss1: 0.058701 Loss2: 0.035125
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.089313 Loss1: 0.053614 Loss2: 0.035699
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.071600 Loss1: 0.035977 Loss2: 0.035623
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.071175 Loss1: 0.036010 Loss2: 0.035165
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.068353 Loss1: 0.032871 Loss2: 0.035482
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072887 Loss1: 0.037550 Loss2: 0.035336
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061738 Loss1: 0.026748 Loss2: 0.034990
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.074849 Loss1: 0.039232 Loss2: 0.035616
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070607 Loss1: 0.034720 Loss2: 0.035887
+(DefaultActor pid=1838052) >> Training accuracy: 0.995236
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.133089 Loss1: 0.091298 Loss2: 0.041791
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.083437 Loss1: 0.041342 Loss2: 0.042095
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.087703 Loss1: 0.046627 Loss2: 0.041076
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.101200 Loss1: 0.060254 Loss2: 0.040947
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.097899 Loss1: 0.057231 Loss2: 0.040668
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.082610 Loss1: 0.042676 Loss2: 0.039934
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.086775 Loss1: 0.047452 Loss2: 0.039323
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.109761 Loss1: 0.069125 Loss2: 0.040637
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081347 Loss1: 0.041631 Loss2: 0.039716
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104671 Loss1: 0.065025 Loss2: 0.039646
+(DefaultActor pid=1838052) >> Training accuracy: 0.989383
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.159791 Loss1: 0.095969 Loss2: 0.063822
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.106470 Loss1: 0.044681 Loss2: 0.061789
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.099718 Loss1: 0.039773 Loss2: 0.059945
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.086197 Loss1: 0.026949 Loss2: 0.059247
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.082037 Loss1: 0.023696 Loss2: 0.058341
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081657 Loss1: 0.024254 Loss2: 0.057403
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075088 Loss1: 0.017820 Loss2: 0.057268
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.078734 Loss1: 0.022237 Loss2: 0.056498
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.095230 Loss1: 0.038036 Loss2: 0.057194
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.109066 Loss1: 0.050702 Loss2: 0.058364
+(DefaultActor pid=1838052) >> Training accuracy: 0.990668
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 18:14:31,543][flwr][DEBUG] - fit_round 69 received 10 results and 0 failures
+>> Test accuracy: 0.660300
+[2023-09-28 18:15:11,639][flwr][INFO] - fit progress: (69, 2.268605324026114, {'accuracy': 0.6603}, 129334.52930339333)
+[2023-09-28 18:15:11,639][flwr][DEBUG] - evaluate_round 69: strategy sampled 10 clients (out of 10)
+[2023-09-28 18:15:49,178][flwr][DEBUG] - evaluate_round 69 received 10 results and 0 failures
+[2023-09-28 18:15:49,179][flwr][DEBUG] - fit_round 70: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.646806 Loss1: 0.099552 Loss2: 0.547254
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.577458 Loss1: 0.078660 Loss2: 0.498799
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.554041 Loss1: 0.084670 Loss2: 0.469371
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.545974 Loss1: 0.093966 Loss2: 0.452008
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.539311 Loss1: 0.092881 Loss2: 0.446430
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.533393 Loss1: 0.098560 Loss2: 0.434833
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.539089 Loss1: 0.103285 Loss2: 0.435804
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.556081 Loss1: 0.123898 Loss2: 0.432182
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.579610 Loss1: 0.148077 Loss2: 0.431533
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.545674 Loss1: 0.117004 Loss2: 0.428671
+(DefaultActor pid=1838052) >> Training accuracy: 0.979167
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.343501 Loss1: 0.112835 Loss2: 0.230666
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.307415 Loss1: 0.094452 Loss2: 0.212963
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.297129 Loss1: 0.094881 Loss2: 0.202248
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.303820 Loss1: 0.105282 Loss2: 0.198538
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.288536 Loss1: 0.094006 Loss2: 0.194530
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.312870 Loss1: 0.117492 Loss2: 0.195377
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.300906 Loss1: 0.105359 Loss2: 0.195547
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.274632 Loss1: 0.084936 Loss2: 0.189696
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.273972 Loss1: 0.085357 Loss2: 0.188614
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.265560 Loss1: 0.078532 Loss2: 0.187028
+(DefaultActor pid=1838052) >> Training accuracy: 0.983979
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.440135 Loss1: 0.081261 Loss2: 0.358874
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.411626 Loss1: 0.065571 Loss2: 0.346055
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.419928 Loss1: 0.074900 Loss2: 0.345028
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.414011 Loss1: 0.069928 Loss2: 0.344082
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.412520 Loss1: 0.071554 Loss2: 0.340966
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.415291 Loss1: 0.073139 Loss2: 0.342153
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.442672 Loss1: 0.097528 Loss2: 0.345144
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.405962 Loss1: 0.064831 Loss2: 0.341131
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.436424 Loss1: 0.090777 Loss2: 0.345647
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.421530 Loss1: 0.078928 Loss2: 0.342602
+(DefaultActor pid=1838052) >> Training accuracy: 0.987179
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136586 Loss1: 0.064532 Loss2: 0.072055
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.109094 Loss1: 0.039261 Loss2: 0.069833
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.108734 Loss1: 0.042626 Loss2: 0.066108
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.112493 Loss1: 0.047596 Loss2: 0.064897
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101821 Loss1: 0.038261 Loss2: 0.063559
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.091834 Loss1: 0.030177 Loss2: 0.061657
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.092505 Loss1: 0.032026 Loss2: 0.060479
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091349 Loss1: 0.031462 Loss2: 0.059887
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101777 Loss1: 0.042011 Loss2: 0.059766
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098070 Loss1: 0.038295 Loss2: 0.059775
+(DefaultActor pid=1838052) >> Training accuracy: 0.990748
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.112007 Loss1: 0.079815 Loss2: 0.032192
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.090061 Loss1: 0.055321 Loss2: 0.034739
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.088951 Loss1: 0.053996 Loss2: 0.034954
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.065665 Loss1: 0.030883 Loss2: 0.034782
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063674 Loss1: 0.029638 Loss2: 0.034037
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070014 Loss1: 0.035530 Loss2: 0.034483
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.081776 Loss1: 0.046463 Loss2: 0.035313
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.080273 Loss1: 0.044882 Loss2: 0.035391
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.111226 Loss1: 0.075195 Loss2: 0.036032
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127576 Loss1: 0.090493 Loss2: 0.037083
+(DefaultActor pid=1838052) >> Training accuracy: 0.981013
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.127003 Loss1: 0.093643 Loss2: 0.033360
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079852 Loss1: 0.044640 Loss2: 0.035212
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.088045 Loss1: 0.052257 Loss2: 0.035788
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.083588 Loss1: 0.047489 Loss2: 0.036099
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.085742 Loss1: 0.049224 Loss2: 0.036518
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.067577 Loss1: 0.032034 Loss2: 0.035542
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077637 Loss1: 0.042006 Loss2: 0.035631
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075219 Loss1: 0.039093 Loss2: 0.036126
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092659 Loss1: 0.056261 Loss2: 0.036398
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.074339 Loss1: 0.038547 Loss2: 0.035791
+(DefaultActor pid=1838052) >> Training accuracy: 0.989913
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.147278 Loss1: 0.111368 Loss2: 0.035911
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.103074 Loss1: 0.065648 Loss2: 0.037426
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.097085 Loss1: 0.059190 Loss2: 0.037895
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.119185 Loss1: 0.080125 Loss2: 0.039061
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.102637 Loss1: 0.064203 Loss2: 0.038434
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077938 Loss1: 0.040406 Loss2: 0.037532
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.081058 Loss1: 0.043891 Loss2: 0.037167
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082165 Loss1: 0.044667 Loss2: 0.037498
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085876 Loss1: 0.047671 Loss2: 0.038205
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093055 Loss1: 0.055085 Loss2: 0.037970
+(DefaultActor pid=1838052) >> Training accuracy: 0.992188
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.099023 Loss1: 0.069583 Loss2: 0.029440
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.076317 Loss1: 0.045304 Loss2: 0.031014
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061240 Loss1: 0.029729 Loss2: 0.031510
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.054378 Loss1: 0.023093 Loss2: 0.031285
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050060 Loss1: 0.018968 Loss2: 0.031092
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047871 Loss1: 0.016947 Loss2: 0.030924
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.045186 Loss1: 0.014300 Loss2: 0.030886
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050256 Loss1: 0.019046 Loss2: 0.031210
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051590 Loss1: 0.020402 Loss2: 0.031189
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062751 Loss1: 0.030929 Loss2: 0.031822
+(DefaultActor pid=1838052) >> Training accuracy: 0.991386
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110367 Loss1: 0.078851 Loss2: 0.031516
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075344 Loss1: 0.042155 Loss2: 0.033189
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068468 Loss1: 0.035449 Loss2: 0.033019
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.072361 Loss1: 0.038738 Loss2: 0.033623
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069667 Loss1: 0.035820 Loss2: 0.033847
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083387 Loss1: 0.049237 Loss2: 0.034150
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079663 Loss1: 0.045275 Loss2: 0.034388
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068890 Loss1: 0.034541 Loss2: 0.034348
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076702 Loss1: 0.042193 Loss2: 0.034509
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.088159 Loss1: 0.053054 Loss2: 0.035105
+(DefaultActor pid=1838052) >> Training accuracy: 0.986946
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.140039 Loss1: 0.089912 Loss2: 0.050127
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.111543 Loss1: 0.062228 Loss2: 0.049314
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.093282 Loss1: 0.044038 Loss2: 0.049244
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074970 Loss1: 0.027372 Loss2: 0.047598
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.078369 Loss1: 0.031432 Loss2: 0.046937
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.099884 Loss1: 0.052206 Loss2: 0.047678
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.108809 Loss1: 0.059888 Loss2: 0.048921
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.109442 Loss1: 0.060518 Loss2: 0.048924
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130704 Loss1: 0.080857 Loss2: 0.049848
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.119713 Loss1: 0.069845 Loss2: 0.049868
+(DefaultActor pid=1838052) >> Training accuracy: 0.985518
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 18:45:21,920][flwr][DEBUG] - fit_round 70 received 10 results and 0 failures
+>> Test accuracy: 0.659100
+[2023-09-28 18:46:01,617][flwr][INFO] - fit progress: (70, 2.256014349171148, {'accuracy': 0.6591}, 131184.50726347417)
+[2023-09-28 18:46:01,618][flwr][DEBUG] - evaluate_round 70: strategy sampled 10 clients (out of 10)
+[2023-09-28 18:46:38,942][flwr][DEBUG] - evaluate_round 70 received 10 results and 0 failures
+[2023-09-28 18:46:38,944][flwr][DEBUG] - fit_round 71: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.115043 Loss1: 0.077548 Loss2: 0.037495
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080576 Loss1: 0.041318 Loss2: 0.039258
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067165 Loss1: 0.028006 Loss2: 0.039159
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068792 Loss1: 0.029834 Loss2: 0.038958
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086887 Loss1: 0.047157 Loss2: 0.039729
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.084277 Loss1: 0.044157 Loss2: 0.040119
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.080795 Loss1: 0.040485 Loss2: 0.040309
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.106616 Loss1: 0.065518 Loss2: 0.041098
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.118332 Loss1: 0.075751 Loss2: 0.042581
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.113944 Loss1: 0.071281 Loss2: 0.042662
+(DefaultActor pid=1838052) >> Training accuracy: 0.989984
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.685140 Loss1: 0.084760 Loss2: 0.600380
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.634056 Loss1: 0.051543 Loss2: 0.582513
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.621005 Loss1: 0.056900 Loss2: 0.564105
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.605215 Loss1: 0.054013 Loss2: 0.551203
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.606625 Loss1: 0.065987 Loss2: 0.540638
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.633599 Loss1: 0.096900 Loss2: 0.536699
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.613320 Loss1: 0.078812 Loss2: 0.534509
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.633197 Loss1: 0.101367 Loss2: 0.531830
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.623499 Loss1: 0.093301 Loss2: 0.530198
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.605620 Loss1: 0.081616 Loss2: 0.524004
+(DefaultActor pid=1838052) >> Training accuracy: 0.986155
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.137515 Loss1: 0.076528 Loss2: 0.060987
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.100703 Loss1: 0.039886 Loss2: 0.060817
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.094067 Loss1: 0.034358 Loss2: 0.059709
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.089674 Loss1: 0.030717 Loss2: 0.058957
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.100787 Loss1: 0.041265 Loss2: 0.059521
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.107454 Loss1: 0.046715 Loss2: 0.060739
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104928 Loss1: 0.044578 Loss2: 0.060350
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.095746 Loss1: 0.035357 Loss2: 0.060389
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.102357 Loss1: 0.041579 Loss2: 0.060778
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.119717 Loss1: 0.058181 Loss2: 0.061536
+(DefaultActor pid=1838052) >> Training accuracy: 0.991891
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.694692 Loss1: 0.100225 Loss2: 0.594467
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.627829 Loss1: 0.054463 Loss2: 0.573366
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.615180 Loss1: 0.055977 Loss2: 0.559204
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615965 Loss1: 0.065249 Loss2: 0.550717
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.609485 Loss1: 0.063140 Loss2: 0.546344
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.611348 Loss1: 0.068233 Loss2: 0.543115
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.632162 Loss1: 0.091486 Loss2: 0.540676
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.648222 Loss1: 0.107233 Loss2: 0.540989
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.645472 Loss1: 0.107046 Loss2: 0.538426
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.651161 Loss1: 0.112840 Loss2: 0.538321
+(DefaultActor pid=1838052) >> Training accuracy: 0.978441
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.648801 Loss1: 0.108837 Loss2: 0.539963
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.610026 Loss1: 0.078616 Loss2: 0.531410
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.615952 Loss1: 0.088405 Loss2: 0.527548
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615273 Loss1: 0.089574 Loss2: 0.525699
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.605574 Loss1: 0.085219 Loss2: 0.520355
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.582381 Loss1: 0.065590 Loss2: 0.516791
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.590598 Loss1: 0.078713 Loss2: 0.511885
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.619171 Loss1: 0.107734 Loss2: 0.511436
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.618853 Loss1: 0.108405 Loss2: 0.510448
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.622856 Loss1: 0.111080 Loss2: 0.511776
+(DefaultActor pid=1838052) >> Training accuracy: 0.986064
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110402 Loss1: 0.076295 Loss2: 0.034108
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.062757 Loss1: 0.027339 Loss2: 0.035419
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066469 Loss1: 0.031354 Loss2: 0.035116
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.072852 Loss1: 0.037586 Loss2: 0.035267
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075520 Loss1: 0.040157 Loss2: 0.035362
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.071826 Loss1: 0.036465 Loss2: 0.035361
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.066920 Loss1: 0.031999 Loss2: 0.034921
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.077239 Loss1: 0.041774 Loss2: 0.035465
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086388 Loss1: 0.050612 Loss2: 0.035776
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091087 Loss1: 0.054867 Loss2: 0.036221
+(DefaultActor pid=1838052) >> Training accuracy: 0.990885
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.672433 Loss1: 0.082608 Loss2: 0.589825
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.645533 Loss1: 0.065327 Loss2: 0.580206
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.637003 Loss1: 0.067948 Loss2: 0.569055
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.613918 Loss1: 0.054048 Loss2: 0.559869
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.620495 Loss1: 0.069221 Loss2: 0.551275
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.629582 Loss1: 0.081538 Loss2: 0.548044
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.603718 Loss1: 0.062210 Loss2: 0.541508
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.598205 Loss1: 0.061323 Loss2: 0.536882
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.604055 Loss1: 0.069106 Loss2: 0.534950
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.619580 Loss1: 0.087347 Loss2: 0.532233
+(DefaultActor pid=1838052) >> Training accuracy: 0.985562
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.109757 Loss1: 0.076193 Loss2: 0.033565
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079678 Loss1: 0.044107 Loss2: 0.035571
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067103 Loss1: 0.032056 Loss2: 0.035047
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074643 Loss1: 0.039004 Loss2: 0.035639
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.062913 Loss1: 0.027505 Loss2: 0.035408
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.061967 Loss1: 0.026893 Loss2: 0.035074
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065744 Loss1: 0.030531 Loss2: 0.035213
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067426 Loss1: 0.031929 Loss2: 0.035496
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086725 Loss1: 0.050158 Loss2: 0.036567
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.100391 Loss1: 0.063060 Loss2: 0.037331
+(DefaultActor pid=1838052) >> Training accuracy: 0.988692
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.473609 Loss1: 0.071246 Loss2: 0.402363
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.437430 Loss1: 0.053432 Loss2: 0.383998
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.446285 Loss1: 0.065308 Loss2: 0.380977
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.456000 Loss1: 0.075193 Loss2: 0.380807
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.450837 Loss1: 0.071238 Loss2: 0.379599
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.485873 Loss1: 0.103310 Loss2: 0.382563
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.484078 Loss1: 0.101832 Loss2: 0.382246
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.478338 Loss1: 0.094144 Loss2: 0.384195
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.444641 Loss1: 0.066606 Loss2: 0.378034
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.462776 Loss1: 0.085085 Loss2: 0.377692
+(DefaultActor pid=1838052) >> Training accuracy: 0.981517
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.097183 Loss1: 0.067574 Loss2: 0.029609
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.065240 Loss1: 0.034242 Loss2: 0.030998
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054308 Loss1: 0.023118 Loss2: 0.031190
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068394 Loss1: 0.036797 Loss2: 0.031596
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059101 Loss1: 0.027268 Loss2: 0.031833
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060467 Loss1: 0.028697 Loss2: 0.031769
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052685 Loss1: 0.021050 Loss2: 0.031635
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068028 Loss1: 0.035912 Loss2: 0.032116
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064845 Loss1: 0.032403 Loss2: 0.032442
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060485 Loss1: 0.028155 Loss2: 0.032330
+(DefaultActor pid=1838052) >> Training accuracy: 0.996194
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 19:16:04,756][flwr][DEBUG] - fit_round 71 received 10 results and 0 failures
+>> Test accuracy: 0.658900
+[2023-09-28 19:19:03,949][flwr][INFO] - fit progress: (71, 2.247258449610049, {'accuracy': 0.6589}, 133166.83895978006)
+[2023-09-28 19:19:03,949][flwr][DEBUG] - evaluate_round 71: strategy sampled 10 clients (out of 10)
+[2023-09-28 19:19:40,418][flwr][DEBUG] - evaluate_round 71 received 10 results and 0 failures
+[2023-09-28 19:19:40,420][flwr][DEBUG] - fit_round 72: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.334006 Loss1: 0.071820 Loss2: 0.262185
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.293129 Loss1: 0.047685 Loss2: 0.245443
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.287226 Loss1: 0.046376 Loss2: 0.240851
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.279684 Loss1: 0.041344 Loss2: 0.238339
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.284995 Loss1: 0.047455 Loss2: 0.237540
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.278258 Loss1: 0.041550 Loss2: 0.236708
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.276271 Loss1: 0.040255 Loss2: 0.236016
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.272093 Loss1: 0.036380 Loss2: 0.235713
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.280662 Loss1: 0.043993 Loss2: 0.236669
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.293949 Loss1: 0.057071 Loss2: 0.236878
+(DefaultActor pid=1838052) >> Training accuracy: 0.988758
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.479851 Loss1: 0.106654 Loss2: 0.373197
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.412590 Loss1: 0.061989 Loss2: 0.350602
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.414077 Loss1: 0.068037 Loss2: 0.346039
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.418062 Loss1: 0.071172 Loss2: 0.346891
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.439831 Loss1: 0.090352 Loss2: 0.349479
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.458024 Loss1: 0.107813 Loss2: 0.350211
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.445916 Loss1: 0.097475 Loss2: 0.348442
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.416853 Loss1: 0.070546 Loss2: 0.346307
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.410498 Loss1: 0.066795 Loss2: 0.343704
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.417528 Loss1: 0.075143 Loss2: 0.342384
+(DefaultActor pid=1838052) >> Training accuracy: 0.989800
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.513457 Loss1: 0.107679 Loss2: 0.405778
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.477036 Loss1: 0.082281 Loss2: 0.394755
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.463054 Loss1: 0.073979 Loss2: 0.389075
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.452064 Loss1: 0.063262 Loss2: 0.388802
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.456399 Loss1: 0.069403 Loss2: 0.386996
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.481028 Loss1: 0.094961 Loss2: 0.386067
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.490946 Loss1: 0.102357 Loss2: 0.388589
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.473918 Loss1: 0.086257 Loss2: 0.387661
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.482137 Loss1: 0.094005 Loss2: 0.388132
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.482464 Loss1: 0.094539 Loss2: 0.387925
+(DefaultActor pid=1838052) >> Training accuracy: 0.973357
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.120752 Loss1: 0.079541 Loss2: 0.041211
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091950 Loss1: 0.049508 Loss2: 0.042443
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.083782 Loss1: 0.040954 Loss2: 0.042828
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074213 Loss1: 0.032148 Loss2: 0.042065
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.076725 Loss1: 0.035460 Loss2: 0.041264
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.080512 Loss1: 0.038619 Loss2: 0.041893
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.080688 Loss1: 0.038934 Loss2: 0.041755
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.096697 Loss1: 0.053971 Loss2: 0.042727
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081297 Loss1: 0.039327 Loss2: 0.041970
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080084 Loss1: 0.037570 Loss2: 0.042514
+(DefaultActor pid=1838052) >> Training accuracy: 0.993078
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.531238 Loss1: 0.118742 Loss2: 0.412496
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.512335 Loss1: 0.104622 Loss2: 0.407713
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.493497 Loss1: 0.092481 Loss2: 0.401016
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.522391 Loss1: 0.120563 Loss2: 0.401828
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.506143 Loss1: 0.106448 Loss2: 0.399695
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.524430 Loss1: 0.126602 Loss2: 0.397828
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.511061 Loss1: 0.114021 Loss2: 0.397040
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.461670 Loss1: 0.075242 Loss2: 0.386428
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.471400 Loss1: 0.081932 Loss2: 0.389468
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.452972 Loss1: 0.067832 Loss2: 0.385140
+(DefaultActor pid=1838052) >> Training accuracy: 0.985197
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092911 Loss1: 0.058672 Loss2: 0.034239
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069047 Loss1: 0.032958 Loss2: 0.036089
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066787 Loss1: 0.030402 Loss2: 0.036386
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.057206 Loss1: 0.021280 Loss2: 0.035926
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055699 Loss1: 0.020192 Loss2: 0.035507
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060013 Loss1: 0.024455 Loss2: 0.035558
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.061951 Loss1: 0.025597 Loss2: 0.036354
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.069018 Loss1: 0.032800 Loss2: 0.036218
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.071801 Loss1: 0.035471 Loss2: 0.036330
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070199 Loss1: 0.033657 Loss2: 0.036541
+(DefaultActor pid=1838052) >> Training accuracy: 0.992880
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110574 Loss1: 0.075103 Loss2: 0.035472
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079897 Loss1: 0.042734 Loss2: 0.037164
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077841 Loss1: 0.040029 Loss2: 0.037813
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062986 Loss1: 0.025772 Loss2: 0.037214
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070970 Loss1: 0.034054 Loss2: 0.036916
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.080646 Loss1: 0.043469 Loss2: 0.037177
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075353 Loss1: 0.037486 Loss2: 0.037868
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071902 Loss1: 0.034303 Loss2: 0.037600
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076319 Loss1: 0.038607 Loss2: 0.037712
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097233 Loss1: 0.058246 Loss2: 0.038987
+(DefaultActor pid=1838052) >> Training accuracy: 0.987737
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.636792 Loss1: 0.082685 Loss2: 0.554107
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.599127 Loss1: 0.056549 Loss2: 0.542578
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.598873 Loss1: 0.064804 Loss2: 0.534070
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.577811 Loss1: 0.051737 Loss2: 0.526074
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.566156 Loss1: 0.046475 Loss2: 0.519681
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.558254 Loss1: 0.044284 Loss2: 0.513970
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.574858 Loss1: 0.060899 Loss2: 0.513958
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.597451 Loss1: 0.082501 Loss2: 0.514950
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.616385 Loss1: 0.097945 Loss2: 0.518440
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.607316 Loss1: 0.091676 Loss2: 0.515641
+(DefaultActor pid=1838052) >> Training accuracy: 0.984976
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.131483 Loss1: 0.098034 Loss2: 0.033449
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079606 Loss1: 0.044622 Loss2: 0.034984
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082109 Loss1: 0.047056 Loss2: 0.035052
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.075446 Loss1: 0.039955 Loss2: 0.035491
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.078943 Loss1: 0.043730 Loss2: 0.035213
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.068295 Loss1: 0.033194 Loss2: 0.035101
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.061638 Loss1: 0.026773 Loss2: 0.034866
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058294 Loss1: 0.023954 Loss2: 0.034339
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057168 Loss1: 0.023013 Loss2: 0.034155
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056410 Loss1: 0.022299 Loss2: 0.034112
+(DefaultActor pid=1838052) >> Training accuracy: 0.997466
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.097986 Loss1: 0.066894 Loss2: 0.031092
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074842 Loss1: 0.042094 Loss2: 0.032748
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066767 Loss1: 0.033720 Loss2: 0.033047
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.065897 Loss1: 0.032520 Loss2: 0.033378
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.056125 Loss1: 0.022931 Loss2: 0.033194
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.073999 Loss1: 0.040452 Loss2: 0.033548
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073208 Loss1: 0.038968 Loss2: 0.034239
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.080991 Loss1: 0.046097 Loss2: 0.034894
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.082998 Loss1: 0.048075 Loss2: 0.034923
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.088763 Loss1: 0.053191 Loss2: 0.035572
+(DefaultActor pid=1838052) >> Training accuracy: 0.989320
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 19:49:13,324][flwr][DEBUG] - fit_round 72 received 10 results and 0 failures
+>> Test accuracy: 0.660600
+[2023-09-28 19:49:53,759][flwr][INFO] - fit progress: (72, 2.2466823984258855, {'accuracy': 0.6606}, 135016.6493149884)
+[2023-09-28 19:49:53,759][flwr][DEBUG] - evaluate_round 72: strategy sampled 10 clients (out of 10)
+[2023-09-28 19:50:29,577][flwr][DEBUG] - evaluate_round 72 received 10 results and 0 failures
+[2023-09-28 19:50:29,578][flwr][DEBUG] - fit_round 73: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.673638 Loss1: 0.108258 Loss2: 0.565380
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.633620 Loss1: 0.075549 Loss2: 0.558071
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.622391 Loss1: 0.074737 Loss2: 0.547653
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.625924 Loss1: 0.080615 Loss2: 0.545309
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.623605 Loss1: 0.078361 Loss2: 0.545244
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.623706 Loss1: 0.082353 Loss2: 0.541353
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.608523 Loss1: 0.070351 Loss2: 0.538172
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.594561 Loss1: 0.061707 Loss2: 0.532854
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.597341 Loss1: 0.064333 Loss2: 0.533009
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.633998 Loss1: 0.098491 Loss2: 0.535507
+(DefaultActor pid=1838052) >> Training accuracy: 0.981419
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.129152 Loss1: 0.098975 Loss2: 0.030177
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077955 Loss1: 0.046095 Loss2: 0.031859
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.084502 Loss1: 0.052430 Loss2: 0.032072
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.077832 Loss1: 0.045351 Loss2: 0.032481
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070781 Loss1: 0.038489 Loss2: 0.032292
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.059450 Loss1: 0.027727 Loss2: 0.031723
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.067981 Loss1: 0.036261 Loss2: 0.031721
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.070035 Loss1: 0.037557 Loss2: 0.032478
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068169 Loss1: 0.035598 Loss2: 0.032571
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055655 Loss1: 0.023594 Loss2: 0.032061
+(DefaultActor pid=1838052) >> Training accuracy: 0.996311
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.101370 Loss1: 0.071751 Loss2: 0.029619
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.065424 Loss1: 0.033880 Loss2: 0.031544
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060725 Loss1: 0.029431 Loss2: 0.031294
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.077161 Loss1: 0.045417 Loss2: 0.031744
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.090884 Loss1: 0.058174 Loss2: 0.032710
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.089796 Loss1: 0.056618 Loss2: 0.033178
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.082803 Loss1: 0.049882 Loss2: 0.032922
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.063326 Loss1: 0.030929 Loss2: 0.032396
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070572 Loss1: 0.037797 Loss2: 0.032775
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082457 Loss1: 0.049368 Loss2: 0.033089
+(DefaultActor pid=1838052) >> Training accuracy: 0.989720
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.538087 Loss1: 0.094301 Loss2: 0.443787
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.533680 Loss1: 0.091047 Loss2: 0.442632
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.500283 Loss1: 0.068120 Loss2: 0.432164
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.544778 Loss1: 0.112838 Loss2: 0.431940
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.569025 Loss1: 0.126417 Loss2: 0.442607
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.547477 Loss1: 0.113405 Loss2: 0.434072
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.534011 Loss1: 0.101425 Loss2: 0.432586
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.531248 Loss1: 0.099104 Loss2: 0.432143
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.549392 Loss1: 0.117979 Loss2: 0.431413
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.560925 Loss1: 0.126749 Loss2: 0.434175
+(DefaultActor pid=1838052) >> Training accuracy: 0.980419
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.336145 Loss1: 0.068925 Loss2: 0.267221
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.300541 Loss1: 0.051033 Loss2: 0.249508
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.279600 Loss1: 0.032579 Loss2: 0.247021
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.276894 Loss1: 0.031359 Loss2: 0.245535
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.291737 Loss1: 0.044615 Loss2: 0.247123
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.298416 Loss1: 0.051002 Loss2: 0.247415
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.283314 Loss1: 0.036133 Loss2: 0.247182
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.292676 Loss1: 0.044963 Loss2: 0.247713
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.301104 Loss1: 0.052937 Loss2: 0.248167
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.303304 Loss1: 0.054134 Loss2: 0.249170
+(DefaultActor pid=1838052) >> Training accuracy: 0.988982
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110477 Loss1: 0.078358 Loss2: 0.032119
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079401 Loss1: 0.045449 Loss2: 0.033952
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.092323 Loss1: 0.058018 Loss2: 0.034305
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.077091 Loss1: 0.042326 Loss2: 0.034765
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.074355 Loss1: 0.039666 Loss2: 0.034690
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.073698 Loss1: 0.038915 Loss2: 0.034783
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063901 Loss1: 0.029215 Loss2: 0.034685
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057051 Loss1: 0.023009 Loss2: 0.034043
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.060899 Loss1: 0.027083 Loss2: 0.033816
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062822 Loss1: 0.028278 Loss2: 0.034543
+(DefaultActor pid=1838052) >> Training accuracy: 0.995055
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092523 Loss1: 0.055047 Loss2: 0.037476
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072095 Loss1: 0.035770 Loss2: 0.036325
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068521 Loss1: 0.032609 Loss2: 0.035911
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.069830 Loss1: 0.033456 Loss2: 0.036373
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.068225 Loss1: 0.032033 Loss2: 0.036192
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.072579 Loss1: 0.036098 Loss2: 0.036481
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104435 Loss1: 0.066799 Loss2: 0.037636
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.119332 Loss1: 0.080363 Loss2: 0.038969
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120641 Loss1: 0.080805 Loss2: 0.039835
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.110755 Loss1: 0.071412 Loss2: 0.039343
+(DefaultActor pid=1838052) >> Training accuracy: 0.986155
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.096809 Loss1: 0.065676 Loss2: 0.031133
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074710 Loss1: 0.042281 Loss2: 0.032428
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067395 Loss1: 0.034999 Loss2: 0.032397
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060701 Loss1: 0.028483 Loss2: 0.032218
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060758 Loss1: 0.028451 Loss2: 0.032307
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058991 Loss1: 0.027065 Loss2: 0.031926
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.059636 Loss1: 0.027489 Loss2: 0.032147
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071412 Loss1: 0.038704 Loss2: 0.032708
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.071387 Loss1: 0.038098 Loss2: 0.033290
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070413 Loss1: 0.037115 Loss2: 0.033299
+(DefaultActor pid=1838052) >> Training accuracy: 0.991987
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.094807 Loss1: 0.065381 Loss2: 0.029426
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077399 Loss1: 0.045285 Loss2: 0.032114
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.089002 Loss1: 0.056321 Loss2: 0.032681
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.099601 Loss1: 0.066244 Loss2: 0.033358
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.106334 Loss1: 0.071603 Loss2: 0.034731
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.126074 Loss1: 0.090663 Loss2: 0.035411
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.118062 Loss1: 0.082748 Loss2: 0.035314
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.098797 Loss1: 0.063379 Loss2: 0.035418
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101565 Loss1: 0.066549 Loss2: 0.035016
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116716 Loss1: 0.081358 Loss2: 0.035358
+(DefaultActor pid=1838052) >> Training accuracy: 0.988133
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.114681 Loss1: 0.073248 Loss2: 0.041433
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.099172 Loss1: 0.055927 Loss2: 0.043246
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.106820 Loss1: 0.062141 Loss2: 0.044678
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.097524 Loss1: 0.052410 Loss2: 0.045114
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.083398 Loss1: 0.038435 Loss2: 0.044963
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.091689 Loss1: 0.046443 Loss2: 0.045246
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079409 Loss1: 0.034445 Loss2: 0.044964
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075943 Loss1: 0.031604 Loss2: 0.044339
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064228 Loss1: 0.020331 Loss2: 0.043897
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082329 Loss1: 0.038450 Loss2: 0.043879
+(DefaultActor pid=1838052) >> Training accuracy: 0.990854
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 20:19:53,958][flwr][DEBUG] - fit_round 73 received 10 results and 0 failures
+>> Test accuracy: 0.661600
+[2023-09-28 20:20:34,203][flwr][INFO] - fit progress: (73, 2.2818810541789754, {'accuracy': 0.6616}, 136857.0931070121)
+[2023-09-28 20:20:34,203][flwr][DEBUG] - evaluate_round 73: strategy sampled 10 clients (out of 10)
+[2023-09-28 20:21:11,246][flwr][DEBUG] - evaluate_round 73 received 10 results and 0 failures
+[2023-09-28 20:21:11,252][flwr][DEBUG] - fit_round 74: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.662811 Loss1: 0.060218 Loss2: 0.602593
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.644562 Loss1: 0.047703 Loss2: 0.596859
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.662146 Loss1: 0.070087 Loss2: 0.592059
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.654467 Loss1: 0.068479 Loss2: 0.585988
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.666121 Loss1: 0.084771 Loss2: 0.581349
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646969 Loss1: 0.069925 Loss2: 0.577044
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.627960 Loss1: 0.057820 Loss2: 0.570139
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.625864 Loss1: 0.060307 Loss2: 0.565557
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.640015 Loss1: 0.076381 Loss2: 0.563634
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.669658 Loss1: 0.103755 Loss2: 0.565904
+(DefaultActor pid=1838052) >> Training accuracy: 0.979233
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.091611 Loss1: 0.062414 Loss2: 0.029197
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069992 Loss1: 0.039330 Loss2: 0.030662
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056005 Loss1: 0.025673 Loss2: 0.030332
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062142 Loss1: 0.031355 Loss2: 0.030787
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059007 Loss1: 0.028390 Loss2: 0.030617
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.067739 Loss1: 0.036946 Loss2: 0.030793
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064407 Loss1: 0.033417 Loss2: 0.030991
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.074244 Loss1: 0.042876 Loss2: 0.031368
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067637 Loss1: 0.036559 Loss2: 0.031078
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082022 Loss1: 0.050128 Loss2: 0.031894
+(DefaultActor pid=1838052) >> Training accuracy: 0.989122
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.098536 Loss1: 0.069854 Loss2: 0.028681
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.076614 Loss1: 0.046335 Loss2: 0.030279
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068054 Loss1: 0.037250 Loss2: 0.030804
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060521 Loss1: 0.029808 Loss2: 0.030713
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.062661 Loss1: 0.031867 Loss2: 0.030794
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.062169 Loss1: 0.031186 Loss2: 0.030983
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.085040 Loss1: 0.053377 Loss2: 0.031664
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.076921 Loss1: 0.044467 Loss2: 0.032454
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.062378 Loss1: 0.030397 Loss2: 0.031982
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073518 Loss1: 0.041228 Loss2: 0.032289
+(DefaultActor pid=1838052) >> Training accuracy: 0.992682
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087522 Loss1: 0.057859 Loss2: 0.029663
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072561 Loss1: 0.040962 Loss2: 0.031599
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061290 Loss1: 0.029665 Loss2: 0.031626
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.073856 Loss1: 0.041715 Loss2: 0.032141
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060595 Loss1: 0.028602 Loss2: 0.031993
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070586 Loss1: 0.038157 Loss2: 0.032428
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064689 Loss1: 0.031982 Loss2: 0.032707
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.078051 Loss1: 0.045112 Loss2: 0.032938
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077190 Loss1: 0.044116 Loss2: 0.033073
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095002 Loss1: 0.061274 Loss2: 0.033728
+(DefaultActor pid=1838052) >> Training accuracy: 0.986551
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.664619 Loss1: 0.074971 Loss2: 0.589648
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.627937 Loss1: 0.055767 Loss2: 0.572170
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.626250 Loss1: 0.061056 Loss2: 0.565194
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.624752 Loss1: 0.063988 Loss2: 0.560764
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.607934 Loss1: 0.052565 Loss2: 0.555369
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.613670 Loss1: 0.063656 Loss2: 0.550014
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.621288 Loss1: 0.074004 Loss2: 0.547284
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.633284 Loss1: 0.086433 Loss2: 0.546851
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.632014 Loss1: 0.085726 Loss2: 0.546287
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.629510 Loss1: 0.085179 Loss2: 0.544331
+(DefaultActor pid=1838052) >> Training accuracy: 0.980880
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.108521 Loss1: 0.078985 Loss2: 0.029536
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074599 Loss1: 0.043333 Loss2: 0.031266
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067773 Loss1: 0.036211 Loss2: 0.031562
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.066130 Loss1: 0.034709 Loss2: 0.031421
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.067987 Loss1: 0.036163 Loss2: 0.031824
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.091757 Loss1: 0.059033 Loss2: 0.032723
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.082074 Loss1: 0.049085 Loss2: 0.032989
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.081519 Loss1: 0.048207 Loss2: 0.033312
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.094912 Loss1: 0.061697 Loss2: 0.033215
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.094011 Loss1: 0.060625 Loss2: 0.033386
+(DefaultActor pid=1838052) >> Training accuracy: 0.991987
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102214 Loss1: 0.071542 Loss2: 0.030671
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.067833 Loss1: 0.035714 Loss2: 0.032119
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067619 Loss1: 0.035090 Loss2: 0.032529
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068597 Loss1: 0.035600 Loss2: 0.032997
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077607 Loss1: 0.043985 Loss2: 0.033621
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.074456 Loss1: 0.040703 Loss2: 0.033754
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072962 Loss1: 0.039494 Loss2: 0.033468
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073162 Loss1: 0.038953 Loss2: 0.034209
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.073964 Loss1: 0.039189 Loss2: 0.034775
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082256 Loss1: 0.047633 Loss2: 0.034623
+(DefaultActor pid=1838052) >> Training accuracy: 0.992569
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136106 Loss1: 0.071801 Loss2: 0.064305
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.098531 Loss1: 0.034228 Loss2: 0.064303
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.089280 Loss1: 0.027232 Loss2: 0.062048
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107391 Loss1: 0.045306 Loss2: 0.062085
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110611 Loss1: 0.047980 Loss2: 0.062630
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123781 Loss1: 0.060770 Loss2: 0.063011
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.113712 Loss1: 0.050657 Loss2: 0.063054
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.106327 Loss1: 0.044604 Loss2: 0.061724
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112005 Loss1: 0.051273 Loss2: 0.060732
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.134000 Loss1: 0.071088 Loss2: 0.062912
+(DefaultActor pid=1838052) >> Training accuracy: 0.987847
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.116106 Loss1: 0.085451 Loss2: 0.030654
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.071673 Loss1: 0.039829 Loss2: 0.031844
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066850 Loss1: 0.035165 Loss2: 0.031685
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080442 Loss1: 0.047963 Loss2: 0.032479
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.067440 Loss1: 0.035327 Loss2: 0.032113
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.092456 Loss1: 0.059116 Loss2: 0.033340
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077594 Loss1: 0.044276 Loss2: 0.033319
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075301 Loss1: 0.042344 Loss2: 0.032957
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065022 Loss1: 0.032354 Loss2: 0.032667
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082146 Loss1: 0.049332 Loss2: 0.032814
+(DefaultActor pid=1838052) >> Training accuracy: 0.987331
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.105318 Loss1: 0.069342 Loss2: 0.035976
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.081928 Loss1: 0.044764 Loss2: 0.037164
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.081635 Loss1: 0.043635 Loss2: 0.038000
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.082892 Loss1: 0.043885 Loss2: 0.039007
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.080792 Loss1: 0.041621 Loss2: 0.039171
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075706 Loss1: 0.036601 Loss2: 0.039105
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065661 Loss1: 0.026649 Loss2: 0.039013
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055703 Loss1: 0.017051 Loss2: 0.038652
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.060982 Loss1: 0.022780 Loss2: 0.038202
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055909 Loss1: 0.017786 Loss2: 0.038124
+(DefaultActor pid=1838052) >> Training accuracy: 0.997196
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 20:50:41,720][flwr][DEBUG] - fit_round 74 received 10 results and 0 failures
+>> Test accuracy: 0.659500
+[2023-09-28 20:51:20,535][flwr][INFO] - fit progress: (74, 2.2942246132003614, {'accuracy': 0.6595}, 138703.42533513112)
+[2023-09-28 20:51:20,535][flwr][DEBUG] - evaluate_round 74: strategy sampled 10 clients (out of 10)
+[2023-09-28 20:51:56,054][flwr][DEBUG] - evaluate_round 74 received 10 results and 0 failures
+[2023-09-28 20:51:56,055][flwr][DEBUG] - fit_round 75: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.100961 Loss1: 0.071570 Loss2: 0.029391
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.071864 Loss1: 0.040889 Loss2: 0.030975
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.063991 Loss1: 0.032526 Loss2: 0.031465
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.054525 Loss1: 0.023278 Loss2: 0.031247
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070200 Loss1: 0.038408 Loss2: 0.031793
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083295 Loss1: 0.051000 Loss2: 0.032295
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.091976 Loss1: 0.058426 Loss2: 0.033550
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.094828 Loss1: 0.060931 Loss2: 0.033897
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085104 Loss1: 0.050948 Loss2: 0.034157
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.087167 Loss1: 0.053681 Loss2: 0.033486
+(DefaultActor pid=1838052) >> Training accuracy: 0.988726
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.585234 Loss1: 0.084618 Loss2: 0.500616
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.540224 Loss1: 0.058160 Loss2: 0.482064
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.526077 Loss1: 0.049158 Loss2: 0.476920
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.530687 Loss1: 0.059385 Loss2: 0.471302
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.539215 Loss1: 0.065975 Loss2: 0.473240
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.533129 Loss1: 0.062815 Loss2: 0.470314
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.526202 Loss1: 0.060700 Loss2: 0.465502
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.551335 Loss1: 0.079796 Loss2: 0.471539
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.531990 Loss1: 0.064049 Loss2: 0.467941
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.560655 Loss1: 0.091694 Loss2: 0.468961
+(DefaultActor pid=1838052) >> Training accuracy: 0.991319
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102854 Loss1: 0.068907 Loss2: 0.033947
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.081489 Loss1: 0.044799 Loss2: 0.036690
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069469 Loss1: 0.033243 Loss2: 0.036226
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081516 Loss1: 0.045273 Loss2: 0.036242
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.082026 Loss1: 0.045216 Loss2: 0.036810
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.082577 Loss1: 0.045315 Loss2: 0.037262
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.076896 Loss1: 0.039447 Loss2: 0.037449
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.084641 Loss1: 0.047031 Loss2: 0.037610
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076642 Loss1: 0.039135 Loss2: 0.037507
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.067755 Loss1: 0.030196 Loss2: 0.037559
+(DefaultActor pid=1838052) >> Training accuracy: 0.998150
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.668555 Loss1: 0.081862 Loss2: 0.586694
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.639188 Loss1: 0.067752 Loss2: 0.571436
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.635683 Loss1: 0.071805 Loss2: 0.563878
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.622801 Loss1: 0.063000 Loss2: 0.559801
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.635413 Loss1: 0.080364 Loss2: 0.555049
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.631632 Loss1: 0.081016 Loss2: 0.550616
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.627095 Loss1: 0.079605 Loss2: 0.547490
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.618720 Loss1: 0.075704 Loss2: 0.543016
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.614972 Loss1: 0.075155 Loss2: 0.539817
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.627704 Loss1: 0.089986 Loss2: 0.537717
+(DefaultActor pid=1838052) >> Training accuracy: 0.985562
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.676030 Loss1: 0.063420 Loss2: 0.612610
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.664312 Loss1: 0.057112 Loss2: 0.607200
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.656573 Loss1: 0.053309 Loss2: 0.603264
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.644560 Loss1: 0.047814 Loss2: 0.596746
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.640101 Loss1: 0.053527 Loss2: 0.586574
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.656079 Loss1: 0.070769 Loss2: 0.585310
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.665353 Loss1: 0.079226 Loss2: 0.586127
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.668701 Loss1: 0.083833 Loss2: 0.584868
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.683217 Loss1: 0.100438 Loss2: 0.582779
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.682284 Loss1: 0.097867 Loss2: 0.584417
+(DefaultActor pid=1838052) >> Training accuracy: 0.986280
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.098711 Loss1: 0.069722 Loss2: 0.028989
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.061549 Loss1: 0.031013 Loss2: 0.030536
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.064640 Loss1: 0.033910 Loss2: 0.030730
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.056075 Loss1: 0.024962 Loss2: 0.031114
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054562 Loss1: 0.023331 Loss2: 0.031231
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051793 Loss1: 0.020928 Loss2: 0.030866
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.051821 Loss1: 0.020885 Loss2: 0.030936
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.053366 Loss1: 0.022093 Loss2: 0.031273
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.066980 Loss1: 0.035329 Loss2: 0.031650
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056260 Loss1: 0.024425 Loss2: 0.031835
+(DefaultActor pid=1838052) >> Training accuracy: 0.996795
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.514734 Loss1: 0.066974 Loss2: 0.447760
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.448740 Loss1: 0.048314 Loss2: 0.400426
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.432620 Loss1: 0.048649 Loss2: 0.383971
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.416950 Loss1: 0.040516 Loss2: 0.376434
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.420857 Loss1: 0.050784 Loss2: 0.370073
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.434074 Loss1: 0.066863 Loss2: 0.367211
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.461760 Loss1: 0.092839 Loss2: 0.368921
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.469688 Loss1: 0.098899 Loss2: 0.370789
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.458105 Loss1: 0.090900 Loss2: 0.367206
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.431319 Loss1: 0.065425 Loss2: 0.365893
+(DefaultActor pid=1838052) >> Training accuracy: 0.987935
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.079157 Loss1: 0.050941 Loss2: 0.028216
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.063251 Loss1: 0.033296 Loss2: 0.029955
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052138 Loss1: 0.021886 Loss2: 0.030252
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049388 Loss1: 0.019342 Loss2: 0.030046
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.052835 Loss1: 0.022726 Loss2: 0.030109
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055632 Loss1: 0.025359 Loss2: 0.030273
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050536 Loss1: 0.020187 Loss2: 0.030349
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058775 Loss1: 0.028010 Loss2: 0.030765
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.052309 Loss1: 0.021667 Loss2: 0.030642
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060332 Loss1: 0.029167 Loss2: 0.031165
+(DefaultActor pid=1838052) >> Training accuracy: 0.998418
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.095856 Loss1: 0.065338 Loss2: 0.030518
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058537 Loss1: 0.027021 Loss2: 0.031516
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057261 Loss1: 0.025834 Loss2: 0.031427
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058642 Loss1: 0.027144 Loss2: 0.031498
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.062246 Loss1: 0.030412 Loss2: 0.031834
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075241 Loss1: 0.042541 Loss2: 0.032699
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065909 Loss1: 0.032744 Loss2: 0.033165
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065675 Loss1: 0.032851 Loss2: 0.032824
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067400 Loss1: 0.034303 Loss2: 0.033096
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070451 Loss1: 0.036950 Loss2: 0.033500
+(DefaultActor pid=1838052) >> Training accuracy: 0.994191
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.133075 Loss1: 0.101967 Loss2: 0.031108
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075233 Loss1: 0.043227 Loss2: 0.032006
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.071783 Loss1: 0.039616 Loss2: 0.032167
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085448 Loss1: 0.052499 Loss2: 0.032949
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075417 Loss1: 0.042157 Loss2: 0.033261
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.084133 Loss1: 0.050532 Loss2: 0.033600
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.082056 Loss1: 0.048375 Loss2: 0.033682
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075076 Loss1: 0.041629 Loss2: 0.033448
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.083107 Loss1: 0.049111 Loss2: 0.033996
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080842 Loss1: 0.046809 Loss2: 0.034033
+(DefaultActor pid=1838052) >> Training accuracy: 0.994299
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 21:21:30,541][flwr][DEBUG] - fit_round 75 received 10 results and 0 failures
+>> Test accuracy: 0.657800
+[2023-09-28 21:22:09,784][flwr][INFO] - fit progress: (75, 2.3269932089141383, {'accuracy': 0.6578}, 140552.6741445833)
+[2023-09-28 21:22:09,784][flwr][DEBUG] - evaluate_round 75: strategy sampled 10 clients (out of 10)
+[2023-09-28 21:22:45,506][flwr][DEBUG] - evaluate_round 75 received 10 results and 0 failures
+[2023-09-28 21:22:45,507][flwr][DEBUG] - fit_round 76: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.399900 Loss1: 0.102663 Loss2: 0.297236
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.374364 Loss1: 0.095677 Loss2: 0.278687
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.352855 Loss1: 0.080865 Loss2: 0.271990
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.354487 Loss1: 0.086840 Loss2: 0.267647
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.361324 Loss1: 0.091707 Loss2: 0.269617
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.345665 Loss1: 0.079899 Loss2: 0.265766
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.349906 Loss1: 0.085081 Loss2: 0.264824
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.347095 Loss1: 0.083651 Loss2: 0.263444
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.372121 Loss1: 0.102452 Loss2: 0.269670
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.349123 Loss1: 0.084244 Loss2: 0.264879
+(DefaultActor pid=1838052) >> Training accuracy: 0.982595
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.696535 Loss1: 0.071182 Loss2: 0.625353
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.678641 Loss1: 0.058537 Loss2: 0.620103
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.679190 Loss1: 0.066184 Loss2: 0.613007
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.658352 Loss1: 0.051042 Loss2: 0.607310
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.644844 Loss1: 0.048043 Loss2: 0.596802
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.634954 Loss1: 0.045486 Loss2: 0.589468
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.675581 Loss1: 0.085443 Loss2: 0.590138
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.672799 Loss1: 0.083243 Loss2: 0.589556
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.653171 Loss1: 0.066971 Loss2: 0.586200
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.671320 Loss1: 0.084675 Loss2: 0.586645
+(DefaultActor pid=1838052) >> Training accuracy: 0.981170
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.119381 Loss1: 0.064081 Loss2: 0.055300
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091074 Loss1: 0.036028 Loss2: 0.055047
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.083848 Loss1: 0.030424 Loss2: 0.053424
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.075703 Loss1: 0.024196 Loss2: 0.051506
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065573 Loss1: 0.016157 Loss2: 0.049416
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065389 Loss1: 0.017040 Loss2: 0.048349
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.071336 Loss1: 0.022808 Loss2: 0.048527
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073862 Loss1: 0.024965 Loss2: 0.048896
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.095595 Loss1: 0.045775 Loss2: 0.049820
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.105788 Loss1: 0.055316 Loss2: 0.050472
+(DefaultActor pid=1838052) >> Training accuracy: 0.994093
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.625743 Loss1: 0.086412 Loss2: 0.539332
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.574517 Loss1: 0.049814 Loss2: 0.524703
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.595255 Loss1: 0.075762 Loss2: 0.519493
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.580232 Loss1: 0.063584 Loss2: 0.516648
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.566289 Loss1: 0.057292 Loss2: 0.508997
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.603420 Loss1: 0.094781 Loss2: 0.508639
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.617816 Loss1: 0.108345 Loss2: 0.509471
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.587952 Loss1: 0.082611 Loss2: 0.505341
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.579439 Loss1: 0.076812 Loss2: 0.502627
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.594713 Loss1: 0.088942 Loss2: 0.505771
+(DefaultActor pid=1838052) >> Training accuracy: 0.981606
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092705 Loss1: 0.058310 Loss2: 0.034396
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069664 Loss1: 0.034776 Loss2: 0.034888
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.074005 Loss1: 0.038663 Loss2: 0.035342
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067915 Loss1: 0.032066 Loss2: 0.035849
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072133 Loss1: 0.036108 Loss2: 0.036025
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083154 Loss1: 0.046855 Loss2: 0.036299
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.090030 Loss1: 0.053013 Loss2: 0.037018
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082173 Loss1: 0.044493 Loss2: 0.037679
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.066829 Loss1: 0.029950 Loss2: 0.036879
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.066364 Loss1: 0.029906 Loss2: 0.036458
+(DefaultActor pid=1838052) >> Training accuracy: 0.994264
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087835 Loss1: 0.057896 Loss2: 0.029939
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052184 Loss1: 0.021160 Loss2: 0.031023
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056343 Loss1: 0.025431 Loss2: 0.030912
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064756 Loss1: 0.033393 Loss2: 0.031363
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.068428 Loss1: 0.036074 Loss2: 0.032354
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.056363 Loss1: 0.023708 Loss2: 0.032655
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052947 Loss1: 0.021040 Loss2: 0.031907
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051079 Loss1: 0.019237 Loss2: 0.031842
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053274 Loss1: 0.021408 Loss2: 0.031866
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.050790 Loss1: 0.018793 Loss2: 0.031997
+(DefaultActor pid=1838052) >> Training accuracy: 0.997533
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.103851 Loss1: 0.074418 Loss2: 0.029433
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080445 Loss1: 0.048997 Loss2: 0.031448
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.071611 Loss1: 0.039823 Loss2: 0.031788
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068966 Loss1: 0.037027 Loss2: 0.031939
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.066391 Loss1: 0.034406 Loss2: 0.031985
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077420 Loss1: 0.044832 Loss2: 0.032588
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.068112 Loss1: 0.035650 Loss2: 0.032462
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.088299 Loss1: 0.055243 Loss2: 0.033057
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075243 Loss1: 0.041664 Loss2: 0.033579
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104118 Loss1: 0.070093 Loss2: 0.034026
+(DefaultActor pid=1838052) >> Training accuracy: 0.982897
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.099038 Loss1: 0.069523 Loss2: 0.029514
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077232 Loss1: 0.046077 Loss2: 0.031156
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069978 Loss1: 0.038319 Loss2: 0.031659
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.061146 Loss1: 0.029429 Loss2: 0.031716
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073974 Loss1: 0.042347 Loss2: 0.031627
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.080008 Loss1: 0.047633 Loss2: 0.032375
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.088683 Loss1: 0.055802 Loss2: 0.032882
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082435 Loss1: 0.049539 Loss2: 0.032896
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070851 Loss1: 0.037506 Loss2: 0.033345
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060619 Loss1: 0.027880 Loss2: 0.032739
+(DefaultActor pid=1838052) >> Training accuracy: 0.994191
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102080 Loss1: 0.072855 Loss2: 0.029226
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073629 Loss1: 0.042948 Loss2: 0.030681
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060107 Loss1: 0.029234 Loss2: 0.030873
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.059618 Loss1: 0.028541 Loss2: 0.031077
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.058735 Loss1: 0.027576 Loss2: 0.031158
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058436 Loss1: 0.027176 Loss2: 0.031260
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054376 Loss1: 0.023194 Loss2: 0.031182
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058051 Loss1: 0.026649 Loss2: 0.031402
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.061430 Loss1: 0.029453 Loss2: 0.031977
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070756 Loss1: 0.038589 Loss2: 0.032167
+(DefaultActor pid=1838052) >> Training accuracy: 0.992880
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.366481 Loss1: 0.091601 Loss2: 0.274881
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.299065 Loss1: 0.057926 Loss2: 0.241138
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.294784 Loss1: 0.061693 Loss2: 0.233091
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.298557 Loss1: 0.066685 Loss2: 0.231872
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.289508 Loss1: 0.061978 Loss2: 0.227530
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.316124 Loss1: 0.086633 Loss2: 0.229491
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.306495 Loss1: 0.077933 Loss2: 0.228562
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.293173 Loss1: 0.067702 Loss2: 0.225470
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.289981 Loss1: 0.065469 Loss2: 0.224512
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.281442 Loss1: 0.057991 Loss2: 0.223450
+(DefaultActor pid=1838052) >> Training accuracy: 0.991102
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 21:52:14,046][flwr][DEBUG] - fit_round 76 received 10 results and 0 failures
+>> Test accuracy: 0.660600
+[2023-09-28 21:55:28,725][flwr][INFO] - fit progress: (76, 2.30433221423207, {'accuracy': 0.6606}, 142551.61542777205)
+[2023-09-28 21:55:28,726][flwr][DEBUG] - evaluate_round 76: strategy sampled 10 clients (out of 10)
+[2023-09-28 21:56:05,392][flwr][DEBUG] - evaluate_round 76 received 10 results and 0 failures
+[2023-09-28 21:56:05,393][flwr][DEBUG] - fit_round 77: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.597592 Loss1: 0.076634 Loss2: 0.520957
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.571603 Loss1: 0.059696 Loss2: 0.511907
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.557060 Loss1: 0.051033 Loss2: 0.506027
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.564381 Loss1: 0.062669 Loss2: 0.501712
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.556905 Loss1: 0.059306 Loss2: 0.497599
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.596124 Loss1: 0.097005 Loss2: 0.499119
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.584668 Loss1: 0.084570 Loss2: 0.500098
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.572677 Loss1: 0.078637 Loss2: 0.494041
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.588078 Loss1: 0.091992 Loss2: 0.496086
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.602399 Loss1: 0.106125 Loss2: 0.496274
+(DefaultActor pid=1838052) >> Training accuracy: 0.975946
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.094644 Loss1: 0.062216 Loss2: 0.032428
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053801 Loss1: 0.019953 Loss2: 0.033848
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.048810 Loss1: 0.015609 Loss2: 0.033201
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062887 Loss1: 0.029451 Loss2: 0.033436
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073223 Loss1: 0.038744 Loss2: 0.034480
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069574 Loss1: 0.034159 Loss2: 0.035415
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063609 Loss1: 0.028518 Loss2: 0.035091
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065102 Loss1: 0.030239 Loss2: 0.034863
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070694 Loss1: 0.035430 Loss2: 0.035264
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084454 Loss1: 0.048349 Loss2: 0.036106
+(DefaultActor pid=1838052) >> Training accuracy: 0.994462
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083777 Loss1: 0.053931 Loss2: 0.029845
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066622 Loss1: 0.035091 Loss2: 0.031532
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.059287 Loss1: 0.027415 Loss2: 0.031872
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067308 Loss1: 0.034965 Loss2: 0.032343
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054042 Loss1: 0.021809 Loss2: 0.032233
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075287 Loss1: 0.042612 Loss2: 0.032675
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072555 Loss1: 0.039173 Loss2: 0.033382
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058749 Loss1: 0.025416 Loss2: 0.033333
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.069571 Loss1: 0.035673 Loss2: 0.033898
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093707 Loss1: 0.059427 Loss2: 0.034280
+(DefaultActor pid=1838052) >> Training accuracy: 0.990785
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.676406 Loss1: 0.066580 Loss2: 0.609826
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.643161 Loss1: 0.044948 Loss2: 0.598213
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.632348 Loss1: 0.048714 Loss2: 0.583634
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.630539 Loss1: 0.055922 Loss2: 0.574617
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.642474 Loss1: 0.069688 Loss2: 0.572786
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.649234 Loss1: 0.081319 Loss2: 0.567915
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.643952 Loss1: 0.079815 Loss2: 0.564138
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.647614 Loss1: 0.083328 Loss2: 0.564286
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.647905 Loss1: 0.086661 Loss2: 0.561244
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.670576 Loss1: 0.113809 Loss2: 0.556768
+(DefaultActor pid=1838052) >> Training accuracy: 0.970926
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.613912 Loss1: 0.063271 Loss2: 0.550641
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.560748 Loss1: 0.047868 Loss2: 0.512880
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.562227 Loss1: 0.071101 Loss2: 0.491126
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.559659 Loss1: 0.082297 Loss2: 0.477362
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.550821 Loss1: 0.083435 Loss2: 0.467386
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.588974 Loss1: 0.123334 Loss2: 0.465640
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.558806 Loss1: 0.099727 Loss2: 0.459080
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.546088 Loss1: 0.092343 Loss2: 0.453745
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.561632 Loss1: 0.109580 Loss2: 0.452053
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.544499 Loss1: 0.095439 Loss2: 0.449060
+(DefaultActor pid=1838052) >> Training accuracy: 0.973323
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.112056 Loss1: 0.081646 Loss2: 0.030410
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072510 Loss1: 0.040337 Loss2: 0.032173
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069612 Loss1: 0.036840 Loss2: 0.032771
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.078847 Loss1: 0.045368 Loss2: 0.033479
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092569 Loss1: 0.058474 Loss2: 0.034095
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096612 Loss1: 0.061655 Loss2: 0.034957
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.106195 Loss1: 0.070434 Loss2: 0.035761
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.078158 Loss1: 0.042758 Loss2: 0.035401
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.079439 Loss1: 0.044316 Loss2: 0.035123
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.081057 Loss1: 0.046205 Loss2: 0.034853
+(DefaultActor pid=1838052) >> Training accuracy: 0.995355
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092459 Loss1: 0.059058 Loss2: 0.033401
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.068806 Loss1: 0.034275 Loss2: 0.034530
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067119 Loss1: 0.032246 Loss2: 0.034873
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.070883 Loss1: 0.035932 Loss2: 0.034952
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.066434 Loss1: 0.031064 Loss2: 0.035370
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.068068 Loss1: 0.032732 Loss2: 0.035335
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.081524 Loss1: 0.045140 Loss2: 0.036384
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091305 Loss1: 0.053969 Loss2: 0.037337
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108100 Loss1: 0.070538 Loss2: 0.037562
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102360 Loss1: 0.064385 Loss2: 0.037975
+(DefaultActor pid=1838052) >> Training accuracy: 0.984771
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.097371 Loss1: 0.067350 Loss2: 0.030021
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.071794 Loss1: 0.039913 Loss2: 0.031881
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068408 Loss1: 0.036349 Loss2: 0.032059
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.078930 Loss1: 0.046362 Loss2: 0.032568
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072690 Loss1: 0.039680 Loss2: 0.033010
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065792 Loss1: 0.032553 Loss2: 0.033239
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062131 Loss1: 0.029457 Loss2: 0.032674
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067841 Loss1: 0.034644 Loss2: 0.033197
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.074115 Loss1: 0.040522 Loss2: 0.033593
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.079415 Loss1: 0.045891 Loss2: 0.033523
+(DefaultActor pid=1838052) >> Training accuracy: 0.983974
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.116270 Loss1: 0.082166 Loss2: 0.034104
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075795 Loss1: 0.040538 Loss2: 0.035258
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069994 Loss1: 0.034336 Loss2: 0.035659
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063215 Loss1: 0.027536 Loss2: 0.035680
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.056590 Loss1: 0.021256 Loss2: 0.035334
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.066524 Loss1: 0.031371 Loss2: 0.035154
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056687 Loss1: 0.021328 Loss2: 0.035359
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061177 Loss1: 0.026218 Loss2: 0.034959
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057940 Loss1: 0.022818 Loss2: 0.035122
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073775 Loss1: 0.038555 Loss2: 0.035220
+(DefaultActor pid=1838052) >> Training accuracy: 0.988281
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.104396 Loss1: 0.073863 Loss2: 0.030534
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058799 Loss1: 0.026884 Loss2: 0.031914
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052753 Loss1: 0.021073 Loss2: 0.031680
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.057159 Loss1: 0.025194 Loss2: 0.031965
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.079635 Loss1: 0.046387 Loss2: 0.033249
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081566 Loss1: 0.048165 Loss2: 0.033401
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075514 Loss1: 0.041535 Loss2: 0.033978
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067435 Loss1: 0.033559 Loss2: 0.033876
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.071727 Loss1: 0.037944 Loss2: 0.033782
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083080 Loss1: 0.048374 Loss2: 0.034706
+(DefaultActor pid=1838052) >> Training accuracy: 0.991100
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 22:25:18,205][flwr][DEBUG] - fit_round 77 received 10 results and 0 failures
+>> Test accuracy: 0.656300
+[2023-09-28 22:25:56,771][flwr][INFO] - fit progress: (77, 2.305668424303158, {'accuracy': 0.6563}, 144379.66140946606)
+[2023-09-28 22:25:56,771][flwr][DEBUG] - evaluate_round 77: strategy sampled 10 clients (out of 10)
+[2023-09-28 22:26:32,767][flwr][DEBUG] - evaluate_round 77 received 10 results and 0 failures
+[2023-09-28 22:26:32,768][flwr][DEBUG] - fit_round 78: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083075 Loss1: 0.051744 Loss2: 0.031331
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053950 Loss1: 0.021764 Loss2: 0.032187
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057015 Loss1: 0.024583 Loss2: 0.032432
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049274 Loss1: 0.016748 Loss2: 0.032527
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050862 Loss1: 0.018140 Loss2: 0.032722
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050724 Loss1: 0.017746 Loss2: 0.032977
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048264 Loss1: 0.015505 Loss2: 0.032759
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052376 Loss1: 0.019291 Loss2: 0.033085
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051750 Loss1: 0.018144 Loss2: 0.033606
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048041 Loss1: 0.015072 Loss2: 0.032968
+(DefaultActor pid=1838052) >> Training accuracy: 0.998095
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.595603 Loss1: 0.081339 Loss2: 0.514264
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.576154 Loss1: 0.071192 Loss2: 0.504962
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.575004 Loss1: 0.077685 Loss2: 0.497319
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.604136 Loss1: 0.106119 Loss2: 0.498018
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.580180 Loss1: 0.088119 Loss2: 0.492061
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.582607 Loss1: 0.094328 Loss2: 0.488278
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.580554 Loss1: 0.094833 Loss2: 0.485721
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.566255 Loss1: 0.085370 Loss2: 0.480885
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.559725 Loss1: 0.079679 Loss2: 0.480046
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.538916 Loss1: 0.062728 Loss2: 0.476188
+(DefaultActor pid=1838052) >> Training accuracy: 0.985777
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.076009 Loss1: 0.044700 Loss2: 0.031310
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052441 Loss1: 0.019989 Loss2: 0.032451
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.055676 Loss1: 0.023075 Loss2: 0.032601
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050056 Loss1: 0.017255 Loss2: 0.032801
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049178 Loss1: 0.016813 Loss2: 0.032366
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.049797 Loss1: 0.017547 Loss2: 0.032249
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043002 Loss1: 0.010840 Loss2: 0.032162
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046446 Loss1: 0.014020 Loss2: 0.032426
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.049287 Loss1: 0.016842 Loss2: 0.032444
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.057488 Loss1: 0.024833 Loss2: 0.032655
+(DefaultActor pid=1838052) >> Training accuracy: 0.997231
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.586939 Loss1: 0.072923 Loss2: 0.514016
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.546554 Loss1: 0.043326 Loss2: 0.503228
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.535822 Loss1: 0.042161 Loss2: 0.493661
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.540816 Loss1: 0.051927 Loss2: 0.488889
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.542749 Loss1: 0.056009 Loss2: 0.486740
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.556821 Loss1: 0.072961 Loss2: 0.483860
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.557422 Loss1: 0.073784 Loss2: 0.483638
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.571881 Loss1: 0.090374 Loss2: 0.481507
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.575873 Loss1: 0.094631 Loss2: 0.481242
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.554214 Loss1: 0.074231 Loss2: 0.479982
+(DefaultActor pid=1838052) >> Training accuracy: 0.988331
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.091189 Loss1: 0.060970 Loss2: 0.030219
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072022 Loss1: 0.040234 Loss2: 0.031787
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.062831 Loss1: 0.030206 Loss2: 0.032626
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058024 Loss1: 0.025500 Loss2: 0.032524
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061602 Loss1: 0.028833 Loss2: 0.032769
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.064033 Loss1: 0.031018 Loss2: 0.033015
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.069740 Loss1: 0.035846 Loss2: 0.033894
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073363 Loss1: 0.039215 Loss2: 0.034148
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.121869 Loss1: 0.086054 Loss2: 0.035816
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084808 Loss1: 0.049360 Loss2: 0.035448
+(DefaultActor pid=1838052) >> Training accuracy: 0.984592
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102884 Loss1: 0.068372 Loss2: 0.034513
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.078258 Loss1: 0.041540 Loss2: 0.036718
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068586 Loss1: 0.032327 Loss2: 0.036259
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068863 Loss1: 0.032495 Loss2: 0.036368
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.067506 Loss1: 0.031297 Loss2: 0.036209
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079790 Loss1: 0.043304 Loss2: 0.036486
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.078176 Loss1: 0.041161 Loss2: 0.037015
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.086292 Loss1: 0.049071 Loss2: 0.037221
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.095331 Loss1: 0.056669 Loss2: 0.038662
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127224 Loss1: 0.087364 Loss2: 0.039860
+(DefaultActor pid=1838052) >> Training accuracy: 0.974095
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.084291 Loss1: 0.054354 Loss2: 0.029937
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.054611 Loss1: 0.023584 Loss2: 0.031026
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.051195 Loss1: 0.020267 Loss2: 0.030929
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055886 Loss1: 0.024655 Loss2: 0.031231
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054803 Loss1: 0.023010 Loss2: 0.031794
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.061706 Loss1: 0.029603 Loss2: 0.032102
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072652 Loss1: 0.039959 Loss2: 0.032693
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065282 Loss1: 0.031994 Loss2: 0.033288
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.079120 Loss1: 0.045150 Loss2: 0.033970
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080576 Loss1: 0.045907 Loss2: 0.034670
+(DefaultActor pid=1838052) >> Training accuracy: 0.995253
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.122064 Loss1: 0.063328 Loss2: 0.058736
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.087950 Loss1: 0.031135 Loss2: 0.056815
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082478 Loss1: 0.028210 Loss2: 0.054268
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.084752 Loss1: 0.031523 Loss2: 0.053228
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.085940 Loss1: 0.032036 Loss2: 0.053904
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077569 Loss1: 0.024438 Loss2: 0.053131
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075111 Loss1: 0.023247 Loss2: 0.051864
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073674 Loss1: 0.022365 Loss2: 0.051309
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075663 Loss1: 0.024443 Loss2: 0.051221
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089700 Loss1: 0.038469 Loss2: 0.051231
+(DefaultActor pid=1838052) >> Training accuracy: 0.995055
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.618706 Loss1: 0.095667 Loss2: 0.523039
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.576135 Loss1: 0.067431 Loss2: 0.508704
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.562875 Loss1: 0.062791 Loss2: 0.500084
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.558842 Loss1: 0.063153 Loss2: 0.495688
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.541389 Loss1: 0.050092 Loss2: 0.491297
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.568880 Loss1: 0.079445 Loss2: 0.489435
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.621667 Loss1: 0.128281 Loss2: 0.493386
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.571306 Loss1: 0.084185 Loss2: 0.487121
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.548173 Loss1: 0.066027 Loss2: 0.482146
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.574584 Loss1: 0.090725 Loss2: 0.483859
+(DefaultActor pid=1838052) >> Training accuracy: 0.984586
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.132806 Loss1: 0.075939 Loss2: 0.056867
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.101232 Loss1: 0.042916 Loss2: 0.058315
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.096377 Loss1: 0.039147 Loss2: 0.057229
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.094908 Loss1: 0.038059 Loss2: 0.056849
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092162 Loss1: 0.036586 Loss2: 0.055577
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.087863 Loss1: 0.034053 Loss2: 0.053810
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.080659 Loss1: 0.026798 Loss2: 0.053860
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.093753 Loss1: 0.040667 Loss2: 0.053086
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092050 Loss1: 0.038608 Loss2: 0.053442
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089870 Loss1: 0.036958 Loss2: 0.052912
+(DefaultActor pid=1838052) >> Training accuracy: 0.980769
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 22:55:51,267][flwr][DEBUG] - fit_round 78 received 10 results and 0 failures
+>> Test accuracy: 0.657500
+[2023-09-28 22:56:28,163][flwr][INFO] - fit progress: (78, 2.337304946332694, {'accuracy': 0.6575}, 146211.0534162484)
+[2023-09-28 22:56:28,164][flwr][DEBUG] - evaluate_round 78: strategy sampled 10 clients (out of 10)
+[2023-09-28 22:57:04,445][flwr][DEBUG] - evaluate_round 78 received 10 results and 0 failures
+[2023-09-28 22:57:04,446][flwr][DEBUG] - fit_round 79: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.116836 Loss1: 0.082713 Loss2: 0.034123
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.078077 Loss1: 0.042284 Loss2: 0.035793
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.065937 Loss1: 0.030129 Loss2: 0.035808
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052446 Loss1: 0.017322 Loss2: 0.035125
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.051558 Loss1: 0.016586 Loss2: 0.034972
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.057345 Loss1: 0.022255 Loss2: 0.035090
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.051199 Loss1: 0.016086 Loss2: 0.035113
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051893 Loss1: 0.016741 Loss2: 0.035152
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053798 Loss1: 0.018748 Loss2: 0.035050
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055066 Loss1: 0.020098 Loss2: 0.034968
+(DefaultActor pid=1838052) >> Training accuracy: 0.995877
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.540314 Loss1: 0.081126 Loss2: 0.459188
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.523515 Loss1: 0.073024 Loss2: 0.450491
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.507723 Loss1: 0.064710 Loss2: 0.443014
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.530223 Loss1: 0.085689 Loss2: 0.444534
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.521980 Loss1: 0.080112 Loss2: 0.441868
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.569400 Loss1: 0.121992 Loss2: 0.447407
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.553960 Loss1: 0.113808 Loss2: 0.440151
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.570797 Loss1: 0.126655 Loss2: 0.444143
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.563982 Loss1: 0.120288 Loss2: 0.443694
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.523427 Loss1: 0.086116 Loss2: 0.437311
+(DefaultActor pid=1838052) >> Training accuracy: 0.977848
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092314 Loss1: 0.060404 Loss2: 0.031910
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058920 Loss1: 0.026211 Loss2: 0.032708
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060548 Loss1: 0.027785 Loss2: 0.032762
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050702 Loss1: 0.018107 Loss2: 0.032595
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055005 Loss1: 0.022464 Loss2: 0.032541
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051479 Loss1: 0.019155 Loss2: 0.032324
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.059182 Loss1: 0.026354 Loss2: 0.032828
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.063120 Loss1: 0.030043 Loss2: 0.033078
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.074833 Loss1: 0.041129 Loss2: 0.033704
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080170 Loss1: 0.045281 Loss2: 0.034889
+(DefaultActor pid=1838052) >> Training accuracy: 0.990704
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.663209 Loss1: 0.066803 Loss2: 0.596406
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.623014 Loss1: 0.046108 Loss2: 0.576906
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.607138 Loss1: 0.046419 Loss2: 0.560719
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.599095 Loss1: 0.049024 Loss2: 0.550071
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.591613 Loss1: 0.048053 Loss2: 0.543560
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.601400 Loss1: 0.062368 Loss2: 0.539032
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.606856 Loss1: 0.070412 Loss2: 0.536443
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.608452 Loss1: 0.070199 Loss2: 0.538253
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.583472 Loss1: 0.049461 Loss2: 0.534011
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.582071 Loss1: 0.052543 Loss2: 0.529528
+(DefaultActor pid=1838052) >> Training accuracy: 0.992089
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.510906 Loss1: 0.072879 Loss2: 0.438027
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.509500 Loss1: 0.080947 Loss2: 0.428554
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.499588 Loss1: 0.073597 Loss2: 0.425991
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.477514 Loss1: 0.057354 Loss2: 0.420160
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.472995 Loss1: 0.055014 Loss2: 0.417981
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.497627 Loss1: 0.078586 Loss2: 0.419042
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.525124 Loss1: 0.100674 Loss2: 0.424450
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.504942 Loss1: 0.085480 Loss2: 0.419461
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.503924 Loss1: 0.085821 Loss2: 0.418103
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.498209 Loss1: 0.079779 Loss2: 0.418430
+(DefaultActor pid=1838052) >> Training accuracy: 0.987981
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081562 Loss1: 0.049969 Loss2: 0.031593
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.060849 Loss1: 0.027900 Loss2: 0.032949
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058394 Loss1: 0.025172 Loss2: 0.033222
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052329 Loss1: 0.019887 Loss2: 0.032442
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055730 Loss1: 0.023287 Loss2: 0.032442
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.057857 Loss1: 0.025283 Loss2: 0.032574
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063084 Loss1: 0.030548 Loss2: 0.032537
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.070521 Loss1: 0.037230 Loss2: 0.033290
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065790 Loss1: 0.032429 Loss2: 0.033361
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.072347 Loss1: 0.038287 Loss2: 0.034060
+(DefaultActor pid=1838052) >> Training accuracy: 0.995593
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102724 Loss1: 0.069370 Loss2: 0.033354
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.063946 Loss1: 0.029121 Loss2: 0.034826
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.062589 Loss1: 0.027774 Loss2: 0.034815
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058169 Loss1: 0.022986 Loss2: 0.035183
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059620 Loss1: 0.024496 Loss2: 0.035124
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053685 Loss1: 0.018811 Loss2: 0.034874
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064694 Loss1: 0.029604 Loss2: 0.035090
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.079230 Loss1: 0.043069 Loss2: 0.036161
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076774 Loss1: 0.040107 Loss2: 0.036667
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084202 Loss1: 0.047287 Loss2: 0.036915
+(DefaultActor pid=1838052) >> Training accuracy: 0.991100
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.099033 Loss1: 0.067388 Loss2: 0.031644
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079275 Loss1: 0.045207 Loss2: 0.034068
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.075600 Loss1: 0.040843 Loss2: 0.034757
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.066058 Loss1: 0.031228 Loss2: 0.034830
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069085 Loss1: 0.034332 Loss2: 0.034754
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077793 Loss1: 0.042794 Loss2: 0.034999
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.070580 Loss1: 0.035370 Loss2: 0.035210
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066324 Loss1: 0.031312 Loss2: 0.035012
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064416 Loss1: 0.029416 Loss2: 0.034999
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060377 Loss1: 0.025056 Loss2: 0.035320
+(DefaultActor pid=1838052) >> Training accuracy: 0.996505
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.090622 Loss1: 0.058285 Loss2: 0.032337
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075170 Loss1: 0.040964 Loss2: 0.034206
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077142 Loss1: 0.042102 Loss2: 0.035041
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.088198 Loss1: 0.052210 Loss2: 0.035988
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.088315 Loss1: 0.051627 Loss2: 0.036687
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.088429 Loss1: 0.051168 Loss2: 0.037262
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077179 Loss1: 0.039908 Loss2: 0.037271
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.108426 Loss1: 0.070101 Loss2: 0.038325
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.094707 Loss1: 0.055839 Loss2: 0.038868
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082099 Loss1: 0.043642 Loss2: 0.038457
+(DefaultActor pid=1838052) >> Training accuracy: 0.993521
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.117421 Loss1: 0.084595 Loss2: 0.032826
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075321 Loss1: 0.041531 Loss2: 0.033790
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.072326 Loss1: 0.038090 Loss2: 0.034236
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.073807 Loss1: 0.039784 Loss2: 0.034024
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073644 Loss1: 0.039408 Loss2: 0.034236
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.061155 Loss1: 0.026970 Loss2: 0.034185
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.055788 Loss1: 0.021959 Loss2: 0.033829
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055499 Loss1: 0.022001 Loss2: 0.033498
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.055984 Loss1: 0.022776 Loss2: 0.033208
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.071300 Loss1: 0.037738 Loss2: 0.033563
+(DefaultActor pid=1838052) >> Training accuracy: 0.996622
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 23:26:27,445][flwr][DEBUG] - fit_round 79 received 10 results and 0 failures
+>> Test accuracy: 0.659500
+[2023-09-28 23:27:02,232][flwr][INFO] - fit progress: (79, 2.348709229844066, {'accuracy': 0.6595}, 148045.12243541144)
+[2023-09-28 23:27:02,233][flwr][DEBUG] - evaluate_round 79: strategy sampled 10 clients (out of 10)
+[2023-09-28 23:27:38,359][flwr][DEBUG] - evaluate_round 79 received 10 results and 0 failures
+[2023-09-28 23:27:38,360][flwr][DEBUG] - fit_round 80: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.100009 Loss1: 0.067586 Loss2: 0.032423
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074269 Loss1: 0.040333 Loss2: 0.033936
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.073600 Loss1: 0.039081 Loss2: 0.034519
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.066790 Loss1: 0.032285 Loss2: 0.034505
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054930 Loss1: 0.021025 Loss2: 0.033905
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053360 Loss1: 0.019617 Loss2: 0.033743
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.053232 Loss1: 0.019407 Loss2: 0.033825
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052460 Loss1: 0.018835 Loss2: 0.033625
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053508 Loss1: 0.020120 Loss2: 0.033388
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060306 Loss1: 0.026485 Loss2: 0.033821
+(DefaultActor pid=1838052) >> Training accuracy: 0.995877
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.709541 Loss1: 0.101496 Loss2: 0.608046
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.656735 Loss1: 0.059760 Loss2: 0.596975
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.657840 Loss1: 0.068090 Loss2: 0.589749
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.634617 Loss1: 0.052640 Loss2: 0.581977
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.619091 Loss1: 0.044384 Loss2: 0.574706
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.613078 Loss1: 0.043732 Loss2: 0.569346
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.612925 Loss1: 0.048325 Loss2: 0.564600
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.633442 Loss1: 0.067851 Loss2: 0.565592
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.636400 Loss1: 0.071667 Loss2: 0.564733
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.628630 Loss1: 0.066847 Loss2: 0.561783
+(DefaultActor pid=1838052) >> Training accuracy: 0.990921
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.085088 Loss1: 0.053504 Loss2: 0.031583
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073968 Loss1: 0.040870 Loss2: 0.033098
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.070461 Loss1: 0.036805 Loss2: 0.033656
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060082 Loss1: 0.026386 Loss2: 0.033695
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063947 Loss1: 0.030293 Loss2: 0.033654
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.076683 Loss1: 0.042451 Loss2: 0.034232
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065382 Loss1: 0.031190 Loss2: 0.034192
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057381 Loss1: 0.023287 Loss2: 0.034094
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.061563 Loss1: 0.027530 Loss2: 0.034033
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068177 Loss1: 0.033742 Loss2: 0.034435
+(DefaultActor pid=1838052) >> Training accuracy: 0.992880
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.089742 Loss1: 0.056923 Loss2: 0.032819
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073252 Loss1: 0.039029 Loss2: 0.034224
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061301 Loss1: 0.026587 Loss2: 0.034714
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055495 Loss1: 0.021744 Loss2: 0.033751
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054296 Loss1: 0.020511 Loss2: 0.033785
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053862 Loss1: 0.019937 Loss2: 0.033926
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049890 Loss1: 0.016415 Loss2: 0.033476
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046179 Loss1: 0.012690 Loss2: 0.033489
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.045865 Loss1: 0.012732 Loss2: 0.033133
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.061276 Loss1: 0.027622 Loss2: 0.033654
+(DefaultActor pid=1838052) >> Training accuracy: 0.997033
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.640713 Loss1: 0.063208 Loss2: 0.577506
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.621925 Loss1: 0.051060 Loss2: 0.570865
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.612004 Loss1: 0.047625 Loss2: 0.564378
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.592334 Loss1: 0.034832 Loss2: 0.557502
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.589422 Loss1: 0.038502 Loss2: 0.550920
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.593388 Loss1: 0.045729 Loss2: 0.547659
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.595747 Loss1: 0.049229 Loss2: 0.546518
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.597769 Loss1: 0.053477 Loss2: 0.544293
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.598820 Loss1: 0.053140 Loss2: 0.545680
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.617416 Loss1: 0.072013 Loss2: 0.545403
+(DefaultActor pid=1838052) >> Training accuracy: 0.985759
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078980 Loss1: 0.051571 Loss2: 0.027408
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053762 Loss1: 0.025146 Loss2: 0.028616
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.065714 Loss1: 0.036566 Loss2: 0.029148
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047236 Loss1: 0.018078 Loss2: 0.029158
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050175 Loss1: 0.021034 Loss2: 0.029141
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.043883 Loss1: 0.015112 Loss2: 0.028771
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050974 Loss1: 0.021949 Loss2: 0.029025
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050479 Loss1: 0.021136 Loss2: 0.029343
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.055393 Loss1: 0.025989 Loss2: 0.029404
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048298 Loss1: 0.019225 Loss2: 0.029072
+(DefaultActor pid=1838052) >> Training accuracy: 0.995192
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.088124 Loss1: 0.049328 Loss2: 0.038796
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.067073 Loss1: 0.027946 Loss2: 0.039127
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.059729 Loss1: 0.021508 Loss2: 0.038222
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067918 Loss1: 0.029730 Loss2: 0.038189
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063169 Loss1: 0.025277 Loss2: 0.037893
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065964 Loss1: 0.028538 Loss2: 0.037426
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056363 Loss1: 0.019451 Loss2: 0.036912
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057417 Loss1: 0.020753 Loss2: 0.036664
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.073220 Loss1: 0.036080 Loss2: 0.037140
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073025 Loss1: 0.035259 Loss2: 0.037766
+(DefaultActor pid=1838052) >> Training accuracy: 0.994462
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.101728 Loss1: 0.070010 Loss2: 0.031719
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069636 Loss1: 0.036139 Loss2: 0.033497
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058840 Loss1: 0.025508 Loss2: 0.033332
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058599 Loss1: 0.025459 Loss2: 0.033140
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063797 Loss1: 0.030687 Loss2: 0.033110
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070487 Loss1: 0.036536 Loss2: 0.033951
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.067109 Loss1: 0.032905 Loss2: 0.034204
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.080440 Loss1: 0.045560 Loss2: 0.034880
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.078726 Loss1: 0.043651 Loss2: 0.035074
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.074870 Loss1: 0.039825 Loss2: 0.035044
+(DefaultActor pid=1838052) >> Training accuracy: 0.992188
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.093041 Loss1: 0.062252 Loss2: 0.030789
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.065807 Loss1: 0.033181 Loss2: 0.032626
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057094 Loss1: 0.024727 Loss2: 0.032368
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.057320 Loss1: 0.024323 Loss2: 0.032998
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069547 Loss1: 0.036697 Loss2: 0.032850
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.063417 Loss1: 0.030144 Loss2: 0.033273
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065899 Loss1: 0.032478 Loss2: 0.033422
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066149 Loss1: 0.032261 Loss2: 0.033889
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068831 Loss1: 0.034530 Loss2: 0.034301
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.077950 Loss1: 0.043702 Loss2: 0.034247
+(DefaultActor pid=1838052) >> Training accuracy: 0.991425
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.140475 Loss1: 0.065257 Loss2: 0.075217
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104977 Loss1: 0.032915 Loss2: 0.072062
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.107929 Loss1: 0.037584 Loss2: 0.070345
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.129382 Loss1: 0.057820 Loss2: 0.071562
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.103715 Loss1: 0.032730 Loss2: 0.070985
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096207 Loss1: 0.027038 Loss2: 0.069169
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101244 Loss1: 0.032322 Loss2: 0.068922
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100334 Loss1: 0.031320 Loss2: 0.069015
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.128117 Loss1: 0.057827 Loss2: 0.070290
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118358 Loss1: 0.047909 Loss2: 0.070449
+(DefaultActor pid=1838052) >> Training accuracy: 0.992188
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-28 23:57:04,005][flwr][DEBUG] - fit_round 80 received 10 results and 0 failures
+>> Test accuracy: 0.660200
+[2023-09-28 23:57:40,852][flwr][INFO] - fit progress: (80, 2.37383095201212, {'accuracy': 0.6602}, 149883.74229009543)
+[2023-09-28 23:57:40,852][flwr][DEBUG] - evaluate_round 80: strategy sampled 10 clients (out of 10)
+[2023-09-28 23:58:17,165][flwr][DEBUG] - evaluate_round 80 received 10 results and 0 failures
+[2023-09-28 23:58:17,166][flwr][DEBUG] - fit_round 81: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.650115 Loss1: 0.059922 Loss2: 0.590192
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.609214 Loss1: 0.043426 Loss2: 0.565789
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.632361 Loss1: 0.070158 Loss2: 0.562203
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.606748 Loss1: 0.048576 Loss2: 0.558172
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.605377 Loss1: 0.053175 Loss2: 0.552201
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.605140 Loss1: 0.057401 Loss2: 0.547740
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.629063 Loss1: 0.081307 Loss2: 0.547755
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.599318 Loss1: 0.056089 Loss2: 0.543229
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.585232 Loss1: 0.045582 Loss2: 0.539650
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.589485 Loss1: 0.053723 Loss2: 0.535763
+(DefaultActor pid=1838052) >> Training accuracy: 0.987580
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.685330 Loss1: 0.075823 Loss2: 0.609506
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.642180 Loss1: 0.052032 Loss2: 0.590148
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.629133 Loss1: 0.054281 Loss2: 0.574852
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.647898 Loss1: 0.081706 Loss2: 0.566192
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.660330 Loss1: 0.097444 Loss2: 0.562886
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.650097 Loss1: 0.091888 Loss2: 0.558209
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.630429 Loss1: 0.079051 Loss2: 0.551378
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.656826 Loss1: 0.109643 Loss2: 0.547184
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.626867 Loss1: 0.084252 Loss2: 0.542615
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.632447 Loss1: 0.090888 Loss2: 0.541559
+(DefaultActor pid=1838052) >> Training accuracy: 0.985759
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.111174 Loss1: 0.080093 Loss2: 0.031082
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.068824 Loss1: 0.037101 Loss2: 0.031722
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.086077 Loss1: 0.053577 Loss2: 0.032500
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.084126 Loss1: 0.051107 Loss2: 0.033019
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065068 Loss1: 0.032158 Loss2: 0.032910
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.078637 Loss1: 0.045622 Loss2: 0.033015
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079222 Loss1: 0.046197 Loss2: 0.033026
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068654 Loss1: 0.035773 Loss2: 0.032881
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075832 Loss1: 0.042736 Loss2: 0.033096
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.074260 Loss1: 0.040688 Loss2: 0.033572
+(DefaultActor pid=1838052) >> Training accuracy: 0.991132
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.412938 Loss1: 0.088582 Loss2: 0.324356
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.393784 Loss1: 0.080933 Loss2: 0.312850
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.411551 Loss1: 0.104296 Loss2: 0.307255
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.421979 Loss1: 0.116710 Loss2: 0.305269
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.451120 Loss1: 0.140263 Loss2: 0.310856
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.461345 Loss1: 0.153417 Loss2: 0.307928
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.441644 Loss1: 0.141541 Loss2: 0.300103
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.409425 Loss1: 0.112465 Loss2: 0.296960
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.407464 Loss1: 0.110091 Loss2: 0.297374
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.415583 Loss1: 0.119029 Loss2: 0.296554
+(DefaultActor pid=1838052) >> Training accuracy: 0.962340
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083949 Loss1: 0.054536 Loss2: 0.029412
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073261 Loss1: 0.041843 Loss2: 0.031418
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.079477 Loss1: 0.047359 Loss2: 0.032118
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067868 Loss1: 0.035233 Loss2: 0.032634
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065301 Loss1: 0.032674 Loss2: 0.032628
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083015 Loss1: 0.050076 Loss2: 0.032938
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079738 Loss1: 0.046023 Loss2: 0.033714
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.098486 Loss1: 0.063853 Loss2: 0.034633
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086821 Loss1: 0.052440 Loss2: 0.034382
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070358 Loss1: 0.036102 Loss2: 0.034257
+(DefaultActor pid=1838052) >> Training accuracy: 0.994792
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.096208 Loss1: 0.066572 Loss2: 0.029636
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072643 Loss1: 0.040762 Loss2: 0.031881
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.071773 Loss1: 0.039683 Loss2: 0.032090
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068587 Loss1: 0.035642 Loss2: 0.032946
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.074962 Loss1: 0.041888 Loss2: 0.033074
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.059242 Loss1: 0.026622 Loss2: 0.032620
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064571 Loss1: 0.031759 Loss2: 0.032812
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061340 Loss1: 0.028926 Loss2: 0.032414
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057428 Loss1: 0.024958 Loss2: 0.032470
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.075618 Loss1: 0.042404 Loss2: 0.033215
+(DefaultActor pid=1838052) >> Training accuracy: 0.991571
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.507350 Loss1: 0.047867 Loss2: 0.459482
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.469050 Loss1: 0.040804 Loss2: 0.428246
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.451553 Loss1: 0.027339 Loss2: 0.424213
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.447852 Loss1: 0.028515 Loss2: 0.419337
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.450468 Loss1: 0.031753 Loss2: 0.418715
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.461636 Loss1: 0.042675 Loss2: 0.418961
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.505051 Loss1: 0.081081 Loss2: 0.423970
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.498217 Loss1: 0.074067 Loss2: 0.424150
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.492316 Loss1: 0.070302 Loss2: 0.422013
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.526047 Loss1: 0.099369 Loss2: 0.426678
+(DefaultActor pid=1838052) >> Training accuracy: 0.971123
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.632165 Loss1: 0.063826 Loss2: 0.568340
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.625716 Loss1: 0.064756 Loss2: 0.560960
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.623001 Loss1: 0.068739 Loss2: 0.554261
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.634539 Loss1: 0.085599 Loss2: 0.548940
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.631543 Loss1: 0.085788 Loss2: 0.545755
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.635841 Loss1: 0.090986 Loss2: 0.544855
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.615024 Loss1: 0.076247 Loss2: 0.538778
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.615434 Loss1: 0.077993 Loss2: 0.537442
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.633434 Loss1: 0.096926 Loss2: 0.536508
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.648356 Loss1: 0.110089 Loss2: 0.538266
+(DefaultActor pid=1838052) >> Training accuracy: 0.984375
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.085361 Loss1: 0.055569 Loss2: 0.029792
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.059269 Loss1: 0.028249 Loss2: 0.031020
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058912 Loss1: 0.027800 Loss2: 0.031112
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060659 Loss1: 0.029189 Loss2: 0.031470
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055653 Loss1: 0.023984 Loss2: 0.031670
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.061281 Loss1: 0.029578 Loss2: 0.031702
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.068360 Loss1: 0.036137 Loss2: 0.032223
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065458 Loss1: 0.032711 Loss2: 0.032747
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.094737 Loss1: 0.060759 Loss2: 0.033977
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112745 Loss1: 0.077679 Loss2: 0.035066
+(DefaultActor pid=1838052) >> Training accuracy: 0.980617
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.080547 Loss1: 0.051153 Loss2: 0.029394
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.056733 Loss1: 0.026428 Loss2: 0.030305
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057763 Loss1: 0.027085 Loss2: 0.030679
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050274 Loss1: 0.019315 Loss2: 0.030959
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064655 Loss1: 0.033214 Loss2: 0.031441
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055759 Loss1: 0.023941 Loss2: 0.031818
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065616 Loss1: 0.033739 Loss2: 0.031876
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057874 Loss1: 0.025861 Loss2: 0.032013
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.063671 Loss1: 0.031765 Loss2: 0.031905
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084382 Loss1: 0.051600 Loss2: 0.032782
+(DefaultActor pid=1838052) >> Training accuracy: 0.988726
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 00:26:46,927][flwr][DEBUG] - fit_round 81 received 10 results and 0 failures
+>> Test accuracy: 0.661900
+[2023-09-29 00:27:22,528][flwr][INFO] - fit progress: (81, 2.300124180012237, {'accuracy': 0.6619}, 151665.4180758763)
+[2023-09-29 00:27:22,528][flwr][DEBUG] - evaluate_round 81: strategy sampled 10 clients (out of 10)
+[2023-09-29 00:27:57,875][flwr][DEBUG] - evaluate_round 81 received 10 results and 0 failures
+[2023-09-29 00:27:57,877][flwr][DEBUG] - fit_round 82: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087299 Loss1: 0.054185 Loss2: 0.033114
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.060703 Loss1: 0.026792 Loss2: 0.033911
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.053937 Loss1: 0.020360 Loss2: 0.033577
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051056 Loss1: 0.017983 Loss2: 0.033073
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055856 Loss1: 0.023043 Loss2: 0.032813
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.059866 Loss1: 0.026568 Loss2: 0.033297
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.060673 Loss1: 0.027191 Loss2: 0.033482
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.077418 Loss1: 0.042801 Loss2: 0.034617
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076519 Loss1: 0.042023 Loss2: 0.034496
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070885 Loss1: 0.036248 Loss2: 0.034637
+(DefaultActor pid=1838052) >> Training accuracy: 0.996044
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.682720 Loss1: 0.070605 Loss2: 0.612115
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.640273 Loss1: 0.037372 Loss2: 0.602902
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.623532 Loss1: 0.037785 Loss2: 0.585747
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.623010 Loss1: 0.047402 Loss2: 0.575608
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.623198 Loss1: 0.050493 Loss2: 0.572705
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.624922 Loss1: 0.059206 Loss2: 0.565717
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.609980 Loss1: 0.047577 Loss2: 0.562403
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.609400 Loss1: 0.053276 Loss2: 0.556124
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.618151 Loss1: 0.062195 Loss2: 0.555957
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.612446 Loss1: 0.061202 Loss2: 0.551244
+(DefaultActor pid=1838052) >> Training accuracy: 0.987196
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.672335 Loss1: 0.061776 Loss2: 0.610558
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.647458 Loss1: 0.049813 Loss2: 0.597645
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.635979 Loss1: 0.051667 Loss2: 0.584312
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.637866 Loss1: 0.055098 Loss2: 0.582768
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.633748 Loss1: 0.059202 Loss2: 0.574546
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.654132 Loss1: 0.079167 Loss2: 0.574965
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.656527 Loss1: 0.081148 Loss2: 0.575379
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.657765 Loss1: 0.089624 Loss2: 0.568141
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.644110 Loss1: 0.076497 Loss2: 0.567614
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.650452 Loss1: 0.086204 Loss2: 0.564249
+(DefaultActor pid=1838052) >> Training accuracy: 0.987253
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.644143 Loss1: 0.056603 Loss2: 0.587540
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.621297 Loss1: 0.049061 Loss2: 0.572236
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.609537 Loss1: 0.046330 Loss2: 0.563206
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.606262 Loss1: 0.054569 Loss2: 0.551692
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.589564 Loss1: 0.042820 Loss2: 0.546744
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.583139 Loss1: 0.044061 Loss2: 0.539078
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.588742 Loss1: 0.051846 Loss2: 0.536896
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.600833 Loss1: 0.065062 Loss2: 0.535771
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.602868 Loss1: 0.069150 Loss2: 0.533719
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.582602 Loss1: 0.051540 Loss2: 0.531062
+(DefaultActor pid=1838052) >> Training accuracy: 0.983782
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074816 Loss1: 0.043138 Loss2: 0.031678
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051042 Loss1: 0.018420 Loss2: 0.032622
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049053 Loss1: 0.016294 Loss2: 0.032759
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050653 Loss1: 0.018203 Loss2: 0.032450
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.045136 Loss1: 0.012512 Loss2: 0.032624
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046479 Loss1: 0.013944 Loss2: 0.032535
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.041666 Loss1: 0.009463 Loss2: 0.032203
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046929 Loss1: 0.014651 Loss2: 0.032278
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.041534 Loss1: 0.009216 Loss2: 0.032318
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040728 Loss1: 0.008806 Loss2: 0.031921
+(DefaultActor pid=1838052) >> Training accuracy: 0.999399
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.076821 Loss1: 0.046419 Loss2: 0.030402
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.046300 Loss1: 0.014956 Loss2: 0.031343
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.040427 Loss1: 0.009764 Loss2: 0.030663
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039016 Loss1: 0.008734 Loss2: 0.030282
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041677 Loss1: 0.011544 Loss2: 0.030133
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040800 Loss1: 0.010586 Loss2: 0.030215
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.036113 Loss1: 0.006274 Loss2: 0.029839
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.039009 Loss1: 0.009254 Loss2: 0.029755
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.037856 Loss1: 0.007726 Loss2: 0.030130
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.036718 Loss1: 0.006832 Loss2: 0.029886
+(DefaultActor pid=1838052) >> Training accuracy: 0.999428
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081558 Loss1: 0.052770 Loss2: 0.028788
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052226 Loss1: 0.022253 Loss2: 0.029973
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.048206 Loss1: 0.018430 Loss2: 0.029776
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052648 Loss1: 0.022695 Loss2: 0.029953
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061335 Loss1: 0.030679 Loss2: 0.030656
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055811 Loss1: 0.024712 Loss2: 0.031098
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.046948 Loss1: 0.016025 Loss2: 0.030922
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.054972 Loss1: 0.024167 Loss2: 0.030805
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.059571 Loss1: 0.028368 Loss2: 0.031204
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.059312 Loss1: 0.027551 Loss2: 0.031762
+(DefaultActor pid=1838052) >> Training accuracy: 0.995451
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.126804 Loss1: 0.060236 Loss2: 0.066567
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.089380 Loss1: 0.024109 Loss2: 0.065271
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.090925 Loss1: 0.026551 Loss2: 0.064374
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.090556 Loss1: 0.026293 Loss2: 0.064262
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.089017 Loss1: 0.025901 Loss2: 0.063116
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.082122 Loss1: 0.020443 Loss2: 0.061679
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.083408 Loss1: 0.022616 Loss2: 0.060792
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.089321 Loss1: 0.028593 Loss2: 0.060728
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096172 Loss1: 0.034752 Loss2: 0.061420
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.101354 Loss1: 0.038338 Loss2: 0.063016
+(DefaultActor pid=1838052) >> Training accuracy: 0.994660
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110154 Loss1: 0.046662 Loss2: 0.063491
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079471 Loss1: 0.017277 Loss2: 0.062194
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.075121 Loss1: 0.013997 Loss2: 0.061124
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074263 Loss1: 0.013801 Loss2: 0.060462
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.078540 Loss1: 0.018050 Loss2: 0.060490
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.085424 Loss1: 0.024529 Loss2: 0.060895
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.103606 Loss1: 0.041049 Loss2: 0.062557
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.090467 Loss1: 0.028214 Loss2: 0.062253
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.082395 Loss1: 0.020390 Loss2: 0.062005
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093039 Loss1: 0.030804 Loss2: 0.062235
+(DefaultActor pid=1838052) >> Training accuracy: 0.995593
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.634412 Loss1: 0.080763 Loss2: 0.553649
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.606634 Loss1: 0.060216 Loss2: 0.546418
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.603575 Loss1: 0.061998 Loss2: 0.541576
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.584562 Loss1: 0.047953 Loss2: 0.536609
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.569311 Loss1: 0.041578 Loss2: 0.527732
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.581209 Loss1: 0.055066 Loss2: 0.526142
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.593361 Loss1: 0.066286 Loss2: 0.527075
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.580433 Loss1: 0.055778 Loss2: 0.524654
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.562449 Loss1: 0.039802 Loss2: 0.522647
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.565679 Loss1: 0.047562 Loss2: 0.518118
+(DefaultActor pid=1838052) >> Training accuracy: 0.979730
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 00:56:34,096][flwr][DEBUG] - fit_round 82 received 10 results and 0 failures
+>> Test accuracy: 0.663200
+[2023-09-29 00:57:10,567][flwr][INFO] - fit progress: (82, 2.3621472944847692, {'accuracy': 0.6632}, 153453.45776463626)
+[2023-09-29 00:57:10,568][flwr][DEBUG] - evaluate_round 82: strategy sampled 10 clients (out of 10)
+[2023-09-29 00:57:46,371][flwr][DEBUG] - evaluate_round 82 received 10 results and 0 failures
+[2023-09-29 00:57:46,372][flwr][DEBUG] - fit_round 83: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.560626 Loss1: 0.065522 Loss2: 0.495105
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.534453 Loss1: 0.049963 Loss2: 0.484490
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.522993 Loss1: 0.044667 Loss2: 0.478325
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.522862 Loss1: 0.046264 Loss2: 0.476597
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.524931 Loss1: 0.052779 Loss2: 0.472152
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.520828 Loss1: 0.049326 Loss2: 0.471502
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.529461 Loss1: 0.056765 Loss2: 0.472696
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.534423 Loss1: 0.062418 Loss2: 0.472004
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.549236 Loss1: 0.076607 Loss2: 0.472630
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.532523 Loss1: 0.060981 Loss2: 0.471542
+(DefaultActor pid=1838052) >> Training accuracy: 0.986178
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.084626 Loss1: 0.051665 Loss2: 0.032961
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058278 Loss1: 0.023774 Loss2: 0.034504
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.065124 Loss1: 0.030724 Loss2: 0.034400
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067144 Loss1: 0.031630 Loss2: 0.035513
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060622 Loss1: 0.025483 Loss2: 0.035139
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058093 Loss1: 0.023188 Loss2: 0.034905
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065562 Loss1: 0.030544 Loss2: 0.035017
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.060957 Loss1: 0.025299 Loss2: 0.035658
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.066910 Loss1: 0.031094 Loss2: 0.035816
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068515 Loss1: 0.032392 Loss2: 0.036123
+(DefaultActor pid=1838052) >> Training accuracy: 0.996745
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.422990 Loss1: 0.054669 Loss2: 0.368321
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.354482 Loss1: 0.029642 Loss2: 0.324840
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.339252 Loss1: 0.034769 Loss2: 0.304483
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.334324 Loss1: 0.036183 Loss2: 0.298141
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.338688 Loss1: 0.042619 Loss2: 0.296069
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.344737 Loss1: 0.049635 Loss2: 0.295103
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.345852 Loss1: 0.052054 Loss2: 0.293798
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.390916 Loss1: 0.094168 Loss2: 0.296748
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.413102 Loss1: 0.113011 Loss2: 0.300091
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.402107 Loss1: 0.099392 Loss2: 0.302715
+(DefaultActor pid=1838052) >> Training accuracy: 0.984177
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081212 Loss1: 0.049824 Loss2: 0.031389
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058839 Loss1: 0.025867 Loss2: 0.032972
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049905 Loss1: 0.017253 Loss2: 0.032652
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049802 Loss1: 0.016797 Loss2: 0.033005
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049694 Loss1: 0.016941 Loss2: 0.032753
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050261 Loss1: 0.017428 Loss2: 0.032832
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049292 Loss1: 0.016471 Loss2: 0.032821
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.048675 Loss1: 0.015813 Loss2: 0.032862
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.049463 Loss1: 0.016568 Loss2: 0.032895
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.044703 Loss1: 0.011808 Loss2: 0.032895
+(DefaultActor pid=1838052) >> Training accuracy: 0.996505
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071511 Loss1: 0.039791 Loss2: 0.031720
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052760 Loss1: 0.020461 Loss2: 0.032299
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052611 Loss1: 0.020156 Loss2: 0.032455
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.053571 Loss1: 0.020642 Loss2: 0.032929
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050849 Loss1: 0.017725 Loss2: 0.033123
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050736 Loss1: 0.017720 Loss2: 0.033016
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043657 Loss1: 0.010726 Loss2: 0.032931
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046082 Loss1: 0.013241 Loss2: 0.032840
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.050776 Loss1: 0.018104 Loss2: 0.032671
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.052767 Loss1: 0.019632 Loss2: 0.033136
+(DefaultActor pid=1838052) >> Training accuracy: 0.996761
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.095487 Loss1: 0.064731 Loss2: 0.030756
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057370 Loss1: 0.025305 Loss2: 0.032065
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052298 Loss1: 0.020369 Loss2: 0.031929
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051685 Loss1: 0.019858 Loss2: 0.031827
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061595 Loss1: 0.029078 Loss2: 0.032516
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.063783 Loss1: 0.030779 Loss2: 0.033004
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064145 Loss1: 0.030903 Loss2: 0.033243
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.069104 Loss1: 0.035647 Loss2: 0.033457
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.099534 Loss1: 0.064902 Loss2: 0.034632
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084546 Loss1: 0.049864 Loss2: 0.034681
+(DefaultActor pid=1838052) >> Training accuracy: 0.989913
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.125994 Loss1: 0.066143 Loss2: 0.059851
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.090098 Loss1: 0.032534 Loss2: 0.057564
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.085786 Loss1: 0.031042 Loss2: 0.054743
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.090646 Loss1: 0.036985 Loss2: 0.053661
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.083683 Loss1: 0.031376 Loss2: 0.052307
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079680 Loss1: 0.027698 Loss2: 0.051982
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075054 Loss1: 0.023895 Loss2: 0.051159
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.070402 Loss1: 0.020323 Loss2: 0.050078
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077002 Loss1: 0.027669 Loss2: 0.049333
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098980 Loss1: 0.048238 Loss2: 0.050741
+(DefaultActor pid=1838052) >> Training accuracy: 0.989654
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.476288 Loss1: 0.065004 Loss2: 0.411284
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.452704 Loss1: 0.053346 Loss2: 0.399358
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.483497 Loss1: 0.078498 Loss2: 0.404999
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.506350 Loss1: 0.101119 Loss2: 0.405231
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.491187 Loss1: 0.092530 Loss2: 0.398658
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.489430 Loss1: 0.090782 Loss2: 0.398648
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.467677 Loss1: 0.070543 Loss2: 0.397134
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.503094 Loss1: 0.099770 Loss2: 0.403323
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.502942 Loss1: 0.101409 Loss2: 0.401533
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.501221 Loss1: 0.097930 Loss2: 0.403290
+(DefaultActor pid=1838052) >> Training accuracy: 0.982199
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.084942 Loss1: 0.041165 Loss2: 0.043777
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.062798 Loss1: 0.019461 Loss2: 0.043336
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054233 Loss1: 0.011380 Loss2: 0.042852
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.053387 Loss1: 0.011304 Loss2: 0.042082
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.053790 Loss1: 0.011989 Loss2: 0.041801
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060872 Loss1: 0.018747 Loss2: 0.042125
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.061512 Loss1: 0.019222 Loss2: 0.042290
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.064517 Loss1: 0.021905 Loss2: 0.042612
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.093697 Loss1: 0.050073 Loss2: 0.043624
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104607 Loss1: 0.058965 Loss2: 0.045642
+(DefaultActor pid=1838052) >> Training accuracy: 0.993790
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.108246 Loss1: 0.059243 Loss2: 0.049004
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080237 Loss1: 0.032553 Loss2: 0.047684
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.076748 Loss1: 0.029584 Loss2: 0.047164
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.084934 Loss1: 0.037710 Loss2: 0.047224
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086625 Loss1: 0.039144 Loss2: 0.047481
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.074429 Loss1: 0.027318 Loss2: 0.047111
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077938 Loss1: 0.030690 Loss2: 0.047248
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071275 Loss1: 0.024334 Loss2: 0.046941
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075142 Loss1: 0.028241 Loss2: 0.046901
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080727 Loss1: 0.033198 Loss2: 0.047529
+(DefaultActor pid=1838052) >> Training accuracy: 0.995055
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 01:26:37,128][flwr][DEBUG] - fit_round 83 received 10 results and 0 failures
+>> Test accuracy: 0.662700
+[2023-09-29 01:27:15,957][flwr][INFO] - fit progress: (83, 2.3891840624733094, {'accuracy': 0.6627}, 155258.84766899934)
+[2023-09-29 01:27:15,958][flwr][DEBUG] - evaluate_round 83: strategy sampled 10 clients (out of 10)
+[2023-09-29 01:27:51,609][flwr][DEBUG] - evaluate_round 83 received 10 results and 0 failures
+[2023-09-29 01:27:51,611][flwr][DEBUG] - fit_round 84: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.646308 Loss1: 0.057634 Loss2: 0.588674
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.617129 Loss1: 0.039464 Loss2: 0.577665
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.598269 Loss1: 0.033606 Loss2: 0.564663
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.606034 Loss1: 0.047600 Loss2: 0.558434
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.629266 Loss1: 0.071344 Loss2: 0.557922
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.641275 Loss1: 0.085096 Loss2: 0.556179
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.645134 Loss1: 0.087613 Loss2: 0.557521
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.643378 Loss1: 0.089636 Loss2: 0.553742
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.652173 Loss1: 0.100143 Loss2: 0.552030
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.665602 Loss1: 0.113378 Loss2: 0.552224
+(DefaultActor pid=1838052) >> Training accuracy: 0.976661
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.674546 Loss1: 0.064134 Loss2: 0.610411
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.646093 Loss1: 0.044742 Loss2: 0.601351
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.644085 Loss1: 0.048214 Loss2: 0.595870
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.642042 Loss1: 0.053671 Loss2: 0.588372
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.643980 Loss1: 0.059044 Loss2: 0.584935
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.645235 Loss1: 0.065713 Loss2: 0.579522
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.655888 Loss1: 0.079380 Loss2: 0.576508
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.632928 Loss1: 0.058495 Loss2: 0.574433
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.619829 Loss1: 0.051252 Loss2: 0.568577
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.641138 Loss1: 0.073325 Loss2: 0.567813
+(DefaultActor pid=1838052) >> Training accuracy: 0.985609
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071223 Loss1: 0.042425 Loss2: 0.028798
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.062819 Loss1: 0.032551 Loss2: 0.030267
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060055 Loss1: 0.029268 Loss2: 0.030787
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.069358 Loss1: 0.037881 Loss2: 0.031477
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060109 Loss1: 0.028229 Loss2: 0.031879
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053077 Loss1: 0.021484 Loss2: 0.031592
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.068203 Loss1: 0.036216 Loss2: 0.031987
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066700 Loss1: 0.034313 Loss2: 0.032386
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075012 Loss1: 0.042121 Loss2: 0.032891
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091448 Loss1: 0.057555 Loss2: 0.033893
+(DefaultActor pid=1838052) >> Training accuracy: 0.993331
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075941 Loss1: 0.046637 Loss2: 0.029304
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049421 Loss1: 0.019119 Loss2: 0.030302
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047607 Loss1: 0.017328 Loss2: 0.030279
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039889 Loss1: 0.009891 Loss2: 0.029998
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.040518 Loss1: 0.010363 Loss2: 0.030155
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038429 Loss1: 0.008680 Loss2: 0.029749
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.039956 Loss1: 0.010219 Loss2: 0.029738
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.037960 Loss1: 0.007993 Loss2: 0.029967
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.035958 Loss1: 0.006364 Loss2: 0.029594
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.042039 Loss1: 0.012103 Loss2: 0.029936
+(DefaultActor pid=1838052) >> Training accuracy: 0.999011
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.437679 Loss1: 0.076745 Loss2: 0.360934
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.415603 Loss1: 0.066189 Loss2: 0.349414
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.408454 Loss1: 0.058792 Loss2: 0.349662
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.406693 Loss1: 0.059172 Loss2: 0.347520
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.417608 Loss1: 0.070832 Loss2: 0.346776
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.435268 Loss1: 0.085835 Loss2: 0.349433
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.471314 Loss1: 0.116844 Loss2: 0.354470
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.445338 Loss1: 0.093422 Loss2: 0.351916
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.423798 Loss1: 0.075245 Loss2: 0.348553
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.410109 Loss1: 0.063976 Loss2: 0.346133
+(DefaultActor pid=1838052) >> Training accuracy: 0.989913
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.625479 Loss1: 0.081350 Loss2: 0.544129
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.559343 Loss1: 0.062836 Loss2: 0.496507
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.534733 Loss1: 0.068449 Loss2: 0.466284
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.511667 Loss1: 0.064322 Loss2: 0.447345
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.534439 Loss1: 0.097387 Loss2: 0.437052
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.544034 Loss1: 0.109997 Loss2: 0.434038
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.506247 Loss1: 0.078479 Loss2: 0.427768
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.531434 Loss1: 0.107913 Loss2: 0.423521
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.505557 Loss1: 0.085670 Loss2: 0.419887
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.515328 Loss1: 0.096650 Loss2: 0.418678
+(DefaultActor pid=1838052) >> Training accuracy: 0.976562
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074221 Loss1: 0.041831 Loss2: 0.032390
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.050897 Loss1: 0.018199 Loss2: 0.032698
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058037 Loss1: 0.025008 Loss2: 0.033029
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049048 Loss1: 0.016082 Loss2: 0.032966
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.045394 Loss1: 0.012697 Loss2: 0.032697
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042129 Loss1: 0.009837 Loss2: 0.032292
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052179 Loss1: 0.019538 Loss2: 0.032641
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052878 Loss1: 0.019957 Loss2: 0.032921
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.049492 Loss1: 0.016455 Loss2: 0.033038
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.050795 Loss1: 0.017681 Loss2: 0.033114
+(DefaultActor pid=1838052) >> Training accuracy: 0.997033
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075746 Loss1: 0.044934 Loss2: 0.030812
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058536 Loss1: 0.026738 Loss2: 0.031799
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.059402 Loss1: 0.026963 Loss2: 0.032440
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049213 Loss1: 0.016626 Loss2: 0.032587
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046463 Loss1: 0.014107 Loss2: 0.032356
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046871 Loss1: 0.014962 Loss2: 0.031909
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043159 Loss1: 0.011335 Loss2: 0.031825
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.047404 Loss1: 0.015375 Loss2: 0.032029
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.044449 Loss1: 0.012432 Loss2: 0.032017
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.045203 Loss1: 0.013259 Loss2: 0.031944
+(DefaultActor pid=1838052) >> Training accuracy: 0.997396
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.500466 Loss1: 0.065533 Loss2: 0.434933
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.461166 Loss1: 0.039483 Loss2: 0.421683
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.445385 Loss1: 0.026766 Loss2: 0.418618
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.457424 Loss1: 0.042901 Loss2: 0.414523
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.459675 Loss1: 0.046145 Loss2: 0.413530
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.458926 Loss1: 0.047195 Loss2: 0.411731
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.475668 Loss1: 0.061366 Loss2: 0.414302
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.465493 Loss1: 0.053474 Loss2: 0.412018
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.469351 Loss1: 0.058110 Loss2: 0.411240
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.478606 Loss1: 0.063698 Loss2: 0.414907
+(DefaultActor pid=1838052) >> Training accuracy: 0.992989
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083804 Loss1: 0.055040 Loss2: 0.028764
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066048 Loss1: 0.035930 Loss2: 0.030119
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.063106 Loss1: 0.032086 Loss2: 0.031020
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.065944 Loss1: 0.034708 Loss2: 0.031236
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064190 Loss1: 0.032887 Loss2: 0.031303
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.078181 Loss1: 0.045989 Loss2: 0.032193
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.076054 Loss1: 0.043241 Loss2: 0.032813
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.095161 Loss1: 0.061852 Loss2: 0.033310
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081490 Loss1: 0.047510 Loss2: 0.033980
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080811 Loss1: 0.047099 Loss2: 0.033712
+(DefaultActor pid=1838052) >> Training accuracy: 0.987196
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 01:56:32,781][flwr][DEBUG] - fit_round 84 received 10 results and 0 failures
+>> Test accuracy: 0.662100
+[2023-09-29 01:57:15,538][flwr][INFO] - fit progress: (84, 2.332587601468205, {'accuracy': 0.6621}, 157058.4282416273)
+[2023-09-29 01:57:15,539][flwr][DEBUG] - evaluate_round 84: strategy sampled 10 clients (out of 10)
+[2023-09-29 01:57:52,996][flwr][DEBUG] - evaluate_round 84 received 10 results and 0 failures
+[2023-09-29 01:57:52,998][flwr][DEBUG] - fit_round 85: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.322791 Loss1: 0.055232 Loss2: 0.267559
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.285244 Loss1: 0.037820 Loss2: 0.247425
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.287282 Loss1: 0.040523 Loss2: 0.246759
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.287731 Loss1: 0.041694 Loss2: 0.246037
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.282650 Loss1: 0.037407 Loss2: 0.245243
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.284860 Loss1: 0.039288 Loss2: 0.245573
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.278924 Loss1: 0.033249 Loss2: 0.245675
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.292698 Loss1: 0.045641 Loss2: 0.247058
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.294988 Loss1: 0.047255 Loss2: 0.247733
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.302236 Loss1: 0.053264 Loss2: 0.248972
+(DefaultActor pid=1838052) >> Training accuracy: 0.994462
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.128180 Loss1: 0.054703 Loss2: 0.073477
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.103659 Loss1: 0.032380 Loss2: 0.071279
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.088555 Loss1: 0.021216 Loss2: 0.067339
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085394 Loss1: 0.019153 Loss2: 0.066242
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086712 Loss1: 0.021835 Loss2: 0.064877
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.080585 Loss1: 0.017160 Loss2: 0.063426
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.085738 Loss1: 0.023051 Loss2: 0.062687
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.086766 Loss1: 0.024399 Loss2: 0.062366
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.083463 Loss1: 0.021919 Loss2: 0.061544
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093506 Loss1: 0.031238 Loss2: 0.062268
+(DefaultActor pid=1838052) >> Training accuracy: 0.993056
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.089341 Loss1: 0.053175 Loss2: 0.036167
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066808 Loss1: 0.029781 Loss2: 0.037027
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066669 Loss1: 0.029142 Loss2: 0.037527
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055482 Loss1: 0.018375 Loss2: 0.037107
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.057496 Loss1: 0.020404 Loss2: 0.037092
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051860 Loss1: 0.015067 Loss2: 0.036793
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049964 Loss1: 0.013343 Loss2: 0.036621
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051173 Loss1: 0.014531 Loss2: 0.036642
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.054691 Loss1: 0.017911 Loss2: 0.036780
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.059921 Loss1: 0.022524 Loss2: 0.037397
+(DefaultActor pid=1838052) >> Training accuracy: 0.997196
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.551919 Loss1: 0.064467 Loss2: 0.487453
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.540917 Loss1: 0.064491 Loss2: 0.476426
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.516836 Loss1: 0.051321 Loss2: 0.465515
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.527295 Loss1: 0.063401 Loss2: 0.463894
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.548783 Loss1: 0.082819 Loss2: 0.465963
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.554401 Loss1: 0.090741 Loss2: 0.463660
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.552947 Loss1: 0.091659 Loss2: 0.461288
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.539787 Loss1: 0.081648 Loss2: 0.458138
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.516523 Loss1: 0.063488 Loss2: 0.453035
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.551925 Loss1: 0.093691 Loss2: 0.458234
+(DefaultActor pid=1838052) >> Training accuracy: 0.980419
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.094666 Loss1: 0.052301 Loss2: 0.042365
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080976 Loss1: 0.039818 Loss2: 0.041158
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067109 Loss1: 0.026241 Loss2: 0.040868
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064954 Loss1: 0.024471 Loss2: 0.040482
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060815 Loss1: 0.020814 Loss2: 0.040001
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055470 Loss1: 0.015899 Loss2: 0.039571
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064535 Loss1: 0.025242 Loss2: 0.039293
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.076058 Loss1: 0.035757 Loss2: 0.040301
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.069590 Loss1: 0.028983 Loss2: 0.040606
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068264 Loss1: 0.027805 Loss2: 0.040459
+(DefaultActor pid=1838052) >> Training accuracy: 0.994655
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.090421 Loss1: 0.054133 Loss2: 0.036288
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051305 Loss1: 0.014886 Loss2: 0.036418
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.050250 Loss1: 0.014828 Loss2: 0.035422
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052742 Loss1: 0.017108 Loss2: 0.035634
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050410 Loss1: 0.014987 Loss2: 0.035423
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053429 Loss1: 0.017814 Loss2: 0.035615
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.058174 Loss1: 0.022278 Loss2: 0.035895
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051891 Loss1: 0.016225 Loss2: 0.035666
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057437 Loss1: 0.021543 Loss2: 0.035894
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.069653 Loss1: 0.033375 Loss2: 0.036278
+(DefaultActor pid=1838052) >> Training accuracy: 0.990076
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078763 Loss1: 0.046251 Loss2: 0.032512
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073281 Loss1: 0.039314 Loss2: 0.033967
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060912 Loss1: 0.026061 Loss2: 0.034851
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055138 Loss1: 0.020250 Loss2: 0.034888
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.053296 Loss1: 0.018342 Loss2: 0.034954
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051810 Loss1: 0.016750 Loss2: 0.035060
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.055589 Loss1: 0.020464 Loss2: 0.035125
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.056311 Loss1: 0.020828 Loss2: 0.035483
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065645 Loss1: 0.029478 Loss2: 0.036167
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083232 Loss1: 0.045958 Loss2: 0.037274
+(DefaultActor pid=1838052) >> Training accuracy: 0.996638
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074140 Loss1: 0.043730 Loss2: 0.030410
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.061949 Loss1: 0.029508 Loss2: 0.032442
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069943 Loss1: 0.036884 Loss2: 0.033059
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051314 Loss1: 0.017946 Loss2: 0.033368
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055318 Loss1: 0.022054 Loss2: 0.033264
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065848 Loss1: 0.032504 Loss2: 0.033344
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.074375 Loss1: 0.039802 Loss2: 0.034573
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071447 Loss1: 0.036078 Loss2: 0.035369
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058252 Loss1: 0.023352 Loss2: 0.034900
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.065797 Loss1: 0.030838 Loss2: 0.034959
+(DefaultActor pid=1838052) >> Training accuracy: 0.994792
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.089037 Loss1: 0.056887 Loss2: 0.032150
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.065852 Loss1: 0.031480 Loss2: 0.034372
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.064095 Loss1: 0.029824 Loss2: 0.034271
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055816 Loss1: 0.021945 Loss2: 0.033871
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.056452 Loss1: 0.022586 Loss2: 0.033866
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050701 Loss1: 0.017145 Loss2: 0.033556
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.053752 Loss1: 0.020458 Loss2: 0.033294
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.053546 Loss1: 0.020016 Loss2: 0.033530
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057578 Loss1: 0.023638 Loss2: 0.033940
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.045569 Loss1: 0.012137 Loss2: 0.033432
+(DefaultActor pid=1838052) >> Training accuracy: 0.996440
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071776 Loss1: 0.039710 Loss2: 0.032067
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049684 Loss1: 0.016913 Loss2: 0.032770
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047343 Loss1: 0.014601 Loss2: 0.032743
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052429 Loss1: 0.019510 Loss2: 0.032919
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048307 Loss1: 0.014956 Loss2: 0.033352
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.044668 Loss1: 0.011529 Loss2: 0.033139
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047570 Loss1: 0.014352 Loss2: 0.033218
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055156 Loss1: 0.021346 Loss2: 0.033810
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.073598 Loss1: 0.039112 Loss2: 0.034486
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.067629 Loss1: 0.032357 Loss2: 0.035272
+(DefaultActor pid=1838052) >> Training accuracy: 0.995808
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 02:26:29,461][flwr][DEBUG] - fit_round 85 received 10 results and 0 failures
+>> Test accuracy: 0.664700
+[2023-09-29 02:27:06,906][flwr][INFO] - fit progress: (85, 2.3793240404738403, {'accuracy': 0.6647}, 158849.7960057431)
+[2023-09-29 02:27:06,906][flwr][DEBUG] - evaluate_round 85: strategy sampled 10 clients (out of 10)
+[2023-09-29 02:27:43,055][flwr][DEBUG] - evaluate_round 85 received 10 results and 0 failures
+[2023-09-29 02:27:43,056][flwr][DEBUG] - fit_round 86: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.098312 Loss1: 0.051866 Loss2: 0.046446
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066325 Loss1: 0.021151 Loss2: 0.045174
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.063597 Loss1: 0.018819 Loss2: 0.044777
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058487 Loss1: 0.013831 Loss2: 0.044655
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054385 Loss1: 0.010293 Loss2: 0.044092
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058329 Loss1: 0.014156 Loss2: 0.044173
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.057388 Loss1: 0.013335 Loss2: 0.044053
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052976 Loss1: 0.009328 Loss2: 0.043648
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.062442 Loss1: 0.018632 Loss2: 0.043810
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062994 Loss1: 0.018489 Loss2: 0.044506
+(DefaultActor pid=1838052) >> Training accuracy: 0.997429
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.565815 Loss1: 0.066851 Loss2: 0.498964
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.511467 Loss1: 0.038389 Loss2: 0.473077
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.519245 Loss1: 0.057495 Loss2: 0.461750
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.521738 Loss1: 0.063405 Loss2: 0.458333
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.540733 Loss1: 0.084511 Loss2: 0.456222
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.546560 Loss1: 0.091270 Loss2: 0.455290
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.528773 Loss1: 0.077718 Loss2: 0.451055
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.510483 Loss1: 0.062616 Loss2: 0.447867
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.507306 Loss1: 0.060989 Loss2: 0.446317
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.493429 Loss1: 0.047965 Loss2: 0.445464
+(DefaultActor pid=1838052) >> Training accuracy: 0.993056
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.629554 Loss1: 0.076253 Loss2: 0.553301
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.569992 Loss1: 0.036956 Loss2: 0.533035
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.562682 Loss1: 0.041875 Loss2: 0.520807
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.567938 Loss1: 0.056825 Loss2: 0.511113
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.554777 Loss1: 0.047668 Loss2: 0.507109
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.566167 Loss1: 0.059678 Loss2: 0.506488
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.549748 Loss1: 0.045936 Loss2: 0.503811
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.544724 Loss1: 0.046451 Loss2: 0.498273
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.554891 Loss1: 0.059661 Loss2: 0.495230
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.583938 Loss1: 0.084249 Loss2: 0.499690
+(DefaultActor pid=1838052) >> Training accuracy: 0.985431
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.062920 Loss1: 0.035192 Loss2: 0.027728
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049286 Loss1: 0.020179 Loss2: 0.029107
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046895 Loss1: 0.017626 Loss2: 0.029269
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038727 Loss1: 0.009443 Loss2: 0.029285
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.042893 Loss1: 0.013678 Loss2: 0.029215
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048849 Loss1: 0.019102 Loss2: 0.029747
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.045990 Loss1: 0.016091 Loss2: 0.029899
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.040355 Loss1: 0.010603 Loss2: 0.029751
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.044719 Loss1: 0.014793 Loss2: 0.029926
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040910 Loss1: 0.010844 Loss2: 0.030065
+(DefaultActor pid=1838052) >> Training accuracy: 0.998022
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102356 Loss1: 0.070962 Loss2: 0.031394
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077930 Loss1: 0.044071 Loss2: 0.033859
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068169 Loss1: 0.034233 Loss2: 0.033936
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.087022 Loss1: 0.052519 Loss2: 0.034503
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.091100 Loss1: 0.055530 Loss2: 0.035570
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.087556 Loss1: 0.051725 Loss2: 0.035832
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073193 Loss1: 0.037357 Loss2: 0.035835
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.069888 Loss1: 0.034641 Loss2: 0.035248
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.059791 Loss1: 0.024790 Loss2: 0.035002
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.059247 Loss1: 0.024804 Loss2: 0.034443
+(DefaultActor pid=1838052) >> Training accuracy: 0.995888
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.643846 Loss1: 0.051226 Loss2: 0.592619
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.635689 Loss1: 0.050054 Loss2: 0.585635
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.618762 Loss1: 0.041063 Loss2: 0.577699
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.612185 Loss1: 0.040198 Loss2: 0.571987
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.608022 Loss1: 0.041139 Loss2: 0.566883
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.619746 Loss1: 0.056459 Loss2: 0.563286
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.653737 Loss1: 0.089562 Loss2: 0.564175
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627689 Loss1: 0.066875 Loss2: 0.560814
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.623295 Loss1: 0.062652 Loss2: 0.560643
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.638409 Loss1: 0.079997 Loss2: 0.558412
+(DefaultActor pid=1838052) >> Training accuracy: 0.987981
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.114075 Loss1: 0.063345 Loss2: 0.050730
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075356 Loss1: 0.025559 Loss2: 0.049797
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.064383 Loss1: 0.015967 Loss2: 0.048416
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068885 Loss1: 0.020872 Loss2: 0.048013
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073395 Loss1: 0.024735 Loss2: 0.048659
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070446 Loss1: 0.022071 Loss2: 0.048375
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.067364 Loss1: 0.018918 Loss2: 0.048445
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066228 Loss1: 0.018160 Loss2: 0.048068
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.069515 Loss1: 0.021336 Loss2: 0.048179
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.066055 Loss1: 0.018213 Loss2: 0.047842
+(DefaultActor pid=1838052) >> Training accuracy: 0.998220
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075731 Loss1: 0.047413 Loss2: 0.028318
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.056773 Loss1: 0.027328 Loss2: 0.029445
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061285 Loss1: 0.031108 Loss2: 0.030177
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058013 Loss1: 0.027323 Loss2: 0.030690
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054120 Loss1: 0.023175 Loss2: 0.030945
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.059778 Loss1: 0.028648 Loss2: 0.031130
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047247 Loss1: 0.016547 Loss2: 0.030700
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.047142 Loss1: 0.016357 Loss2: 0.030785
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057631 Loss1: 0.026043 Loss2: 0.031589
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070088 Loss1: 0.038027 Loss2: 0.032062
+(DefaultActor pid=1838052) >> Training accuracy: 0.995192
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071032 Loss1: 0.042002 Loss2: 0.029030
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.048883 Loss1: 0.018559 Loss2: 0.030324
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.050976 Loss1: 0.020605 Loss2: 0.030371
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.053120 Loss1: 0.022116 Loss2: 0.031004
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.051190 Loss1: 0.020081 Loss2: 0.031109
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058446 Loss1: 0.026684 Loss2: 0.031762
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.069430 Loss1: 0.037346 Loss2: 0.032083
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.060756 Loss1: 0.028246 Loss2: 0.032510
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.059492 Loss1: 0.026565 Loss2: 0.032928
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.072500 Loss1: 0.039511 Loss2: 0.032989
+(DefaultActor pid=1838052) >> Training accuracy: 0.990854
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.068005 Loss1: 0.039228 Loss2: 0.028777
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.048408 Loss1: 0.018570 Loss2: 0.029838
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046657 Loss1: 0.016318 Loss2: 0.030339
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044512 Loss1: 0.014230 Loss2: 0.030283
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046111 Loss1: 0.015718 Loss2: 0.030393
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.052239 Loss1: 0.021514 Loss2: 0.030725
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048297 Loss1: 0.017267 Loss2: 0.031030
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.059264 Loss1: 0.028014 Loss2: 0.031251
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064141 Loss1: 0.032030 Loss2: 0.032112
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.057936 Loss1: 0.025680 Loss2: 0.032256
+(DefaultActor pid=1838052) >> Training accuracy: 0.991891
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 02:56:25,003][flwr][DEBUG] - fit_round 86 received 10 results and 0 failures
+>> Test accuracy: 0.661700
+[2023-09-29 02:57:01,817][flwr][INFO] - fit progress: (86, 2.393653464393494, {'accuracy': 0.6617}, 160644.70745004108)
+[2023-09-29 02:57:01,818][flwr][DEBUG] - evaluate_round 86: strategy sampled 10 clients (out of 10)
+[2023-09-29 02:57:38,812][flwr][DEBUG] - evaluate_round 86 received 10 results and 0 failures
+[2023-09-29 02:57:38,813][flwr][DEBUG] - fit_round 87: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.441977 Loss1: 0.057166 Loss2: 0.384812
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.425802 Loss1: 0.054459 Loss2: 0.371344
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.412960 Loss1: 0.041865 Loss2: 0.371095
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.434099 Loss1: 0.064123 Loss2: 0.369977
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.437840 Loss1: 0.067717 Loss2: 0.370123
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.433845 Loss1: 0.062260 Loss2: 0.371585
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.463559 Loss1: 0.090712 Loss2: 0.372847
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.446612 Loss1: 0.075132 Loss2: 0.371480
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.466711 Loss1: 0.095313 Loss2: 0.371397
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.434169 Loss1: 0.063411 Loss2: 0.370757
+(DefaultActor pid=1838052) >> Training accuracy: 0.989122
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087774 Loss1: 0.056365 Loss2: 0.031409
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052053 Loss1: 0.019727 Loss2: 0.032326
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052429 Loss1: 0.020095 Loss2: 0.032334
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047850 Loss1: 0.016008 Loss2: 0.031842
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059816 Loss1: 0.027721 Loss2: 0.032095
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048522 Loss1: 0.016039 Loss2: 0.032483
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.045304 Loss1: 0.013364 Loss2: 0.031940
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.054348 Loss1: 0.021890 Loss2: 0.032458
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056932 Loss1: 0.024124 Loss2: 0.032809
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062324 Loss1: 0.028982 Loss2: 0.033342
+(DefaultActor pid=1838052) >> Training accuracy: 0.994721
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081804 Loss1: 0.051177 Loss2: 0.030627
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057129 Loss1: 0.025027 Loss2: 0.032102
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.050434 Loss1: 0.018483 Loss2: 0.031951
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.054252 Loss1: 0.022166 Loss2: 0.032086
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054452 Loss1: 0.022250 Loss2: 0.032202
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045141 Loss1: 0.012915 Loss2: 0.032226
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052367 Loss1: 0.020379 Loss2: 0.031988
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050487 Loss1: 0.017901 Loss2: 0.032587
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.048313 Loss1: 0.016015 Loss2: 0.032298
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056088 Loss1: 0.023270 Loss2: 0.032819
+(DefaultActor pid=1838052) >> Training accuracy: 0.999407
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.636684 Loss1: 0.056778 Loss2: 0.579906
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.611740 Loss1: 0.041284 Loss2: 0.570456
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.603837 Loss1: 0.042064 Loss2: 0.561772
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615140 Loss1: 0.057017 Loss2: 0.558123
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.627169 Loss1: 0.074493 Loss2: 0.552676
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.603516 Loss1: 0.054001 Loss2: 0.549515
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.599270 Loss1: 0.055654 Loss2: 0.543616
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.584678 Loss1: 0.045363 Loss2: 0.539315
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.607078 Loss1: 0.069583 Loss2: 0.537495
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.616286 Loss1: 0.077487 Loss2: 0.538798
+(DefaultActor pid=1838052) >> Training accuracy: 0.984976
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.121922 Loss1: 0.060088 Loss2: 0.061834
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.094360 Loss1: 0.034200 Loss2: 0.060161
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077968 Loss1: 0.021446 Loss2: 0.056522
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085332 Loss1: 0.030111 Loss2: 0.055221
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075581 Loss1: 0.021793 Loss2: 0.053788
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069972 Loss1: 0.017692 Loss2: 0.052280
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.066736 Loss1: 0.015359 Loss2: 0.051377
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066577 Loss1: 0.015968 Loss2: 0.050609
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070718 Loss1: 0.020805 Loss2: 0.049913
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089688 Loss1: 0.038623 Loss2: 0.051065
+(DefaultActor pid=1838052) >> Training accuracy: 0.994141
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.621822 Loss1: 0.055375 Loss2: 0.566447
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.595726 Loss1: 0.036942 Loss2: 0.558784
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.609728 Loss1: 0.055731 Loss2: 0.553997
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.589667 Loss1: 0.041511 Loss2: 0.548157
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.579453 Loss1: 0.038379 Loss2: 0.541075
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.586256 Loss1: 0.045967 Loss2: 0.540289
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.593709 Loss1: 0.055820 Loss2: 0.537889
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.575513 Loss1: 0.043548 Loss2: 0.531966
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.589252 Loss1: 0.057516 Loss2: 0.531735
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.598552 Loss1: 0.064957 Loss2: 0.533594
+(DefaultActor pid=1838052) >> Training accuracy: 0.988924
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.612545 Loss1: 0.047554 Loss2: 0.564990
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.583133 Loss1: 0.032256 Loss2: 0.550877
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.568848 Loss1: 0.026381 Loss2: 0.542467
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.573152 Loss1: 0.036581 Loss2: 0.536570
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.576809 Loss1: 0.041431 Loss2: 0.535378
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.585830 Loss1: 0.052412 Loss2: 0.533418
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.604535 Loss1: 0.072513 Loss2: 0.532022
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.613082 Loss1: 0.079389 Loss2: 0.533692
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.629492 Loss1: 0.096548 Loss2: 0.532944
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.614324 Loss1: 0.084391 Loss2: 0.529933
+(DefaultActor pid=1838052) >> Training accuracy: 0.987233
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.090831 Loss1: 0.038226 Loss2: 0.052606
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069818 Loss1: 0.019478 Loss2: 0.050340
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.075557 Loss1: 0.025874 Loss2: 0.049683
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062373 Loss1: 0.013056 Loss2: 0.049317
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064953 Loss1: 0.015992 Loss2: 0.048962
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.064154 Loss1: 0.014796 Loss2: 0.049358
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062411 Loss1: 0.013603 Loss2: 0.048808
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.069233 Loss1: 0.020252 Loss2: 0.048982
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086228 Loss1: 0.036396 Loss2: 0.049832
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.079028 Loss1: 0.029102 Loss2: 0.049926
+(DefaultActor pid=1838052) >> Training accuracy: 0.995393
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.080350 Loss1: 0.050719 Loss2: 0.029632
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058630 Loss1: 0.027738 Loss2: 0.030892
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.055597 Loss1: 0.024467 Loss2: 0.031130
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062280 Loss1: 0.030733 Loss2: 0.031547
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.051160 Loss1: 0.019538 Loss2: 0.031622
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050861 Loss1: 0.019419 Loss2: 0.031442
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.044055 Loss1: 0.012528 Loss2: 0.031528
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.049248 Loss1: 0.017745 Loss2: 0.031503
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076753 Loss1: 0.044457 Loss2: 0.032296
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.063160 Loss1: 0.030113 Loss2: 0.033048
+(DefaultActor pid=1838052) >> Training accuracy: 0.996916
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.079168 Loss1: 0.049752 Loss2: 0.029416
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053204 Loss1: 0.022319 Loss2: 0.030885
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045356 Loss1: 0.014763 Loss2: 0.030593
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044521 Loss1: 0.013821 Loss2: 0.030700
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048645 Loss1: 0.017830 Loss2: 0.030815
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048187 Loss1: 0.017324 Loss2: 0.030863
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063522 Loss1: 0.031869 Loss2: 0.031652
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075333 Loss1: 0.042817 Loss2: 0.032515
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070241 Loss1: 0.036981 Loss2: 0.033259
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084268 Loss1: 0.050504 Loss2: 0.033764
+(DefaultActor pid=1838052) >> Training accuracy: 0.992880
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 03:26:11,144][flwr][DEBUG] - fit_round 87 received 10 results and 0 failures
+>> Test accuracy: 0.663500
+[2023-09-29 03:26:46,229][flwr][INFO] - fit progress: (87, 2.3520108950785557, {'accuracy': 0.6635}, 162429.119870577)
+[2023-09-29 03:26:46,230][flwr][DEBUG] - evaluate_round 87: strategy sampled 10 clients (out of 10)
+[2023-09-29 03:27:21,550][flwr][DEBUG] - evaluate_round 87 received 10 results and 0 failures
+[2023-09-29 03:27:21,551][flwr][DEBUG] - fit_round 88: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.406120 Loss1: 0.055217 Loss2: 0.350903
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.394525 Loss1: 0.056053 Loss2: 0.338472
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.385928 Loss1: 0.052123 Loss2: 0.333805
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.404042 Loss1: 0.070612 Loss2: 0.333430
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.429045 Loss1: 0.096107 Loss2: 0.332938
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.410508 Loss1: 0.080114 Loss2: 0.330394
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.440353 Loss1: 0.109120 Loss2: 0.331233
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.428399 Loss1: 0.097356 Loss2: 0.331043
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.426665 Loss1: 0.095485 Loss2: 0.331180
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.419585 Loss1: 0.090529 Loss2: 0.329056
+(DefaultActor pid=1838052) >> Training accuracy: 0.983974
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.283785 Loss1: 0.043908 Loss2: 0.239877
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.261972 Loss1: 0.037606 Loss2: 0.224365
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.245820 Loss1: 0.026854 Loss2: 0.218966
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.249719 Loss1: 0.032139 Loss2: 0.217580
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.260903 Loss1: 0.041169 Loss2: 0.219734
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.249009 Loss1: 0.031811 Loss2: 0.217198
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.248649 Loss1: 0.032344 Loss2: 0.216305
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.264230 Loss1: 0.046876 Loss2: 0.217354
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.272846 Loss1: 0.050699 Loss2: 0.222147
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.276432 Loss1: 0.057447 Loss2: 0.218985
+(DefaultActor pid=1838052) >> Training accuracy: 0.991806
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.086839 Loss1: 0.052414 Loss2: 0.034424
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052031 Loss1: 0.016808 Loss2: 0.035223
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056386 Loss1: 0.021098 Loss2: 0.035288
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063880 Loss1: 0.028309 Loss2: 0.035571
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054052 Loss1: 0.018579 Loss2: 0.035472
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055271 Loss1: 0.019759 Loss2: 0.035512
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.051675 Loss1: 0.016443 Loss2: 0.035233
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.049606 Loss1: 0.014752 Loss2: 0.034854
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058029 Loss1: 0.023066 Loss2: 0.034963
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.075530 Loss1: 0.039412 Loss2: 0.036118
+(DefaultActor pid=1838052) >> Training accuracy: 0.993869
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.091475 Loss1: 0.060975 Loss2: 0.030500
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.059554 Loss1: 0.027771 Loss2: 0.031783
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057402 Loss1: 0.025458 Loss2: 0.031944
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.061707 Loss1: 0.029268 Loss2: 0.032439
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063180 Loss1: 0.030305 Loss2: 0.032875
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.054745 Loss1: 0.021763 Loss2: 0.032982
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065746 Loss1: 0.032468 Loss2: 0.033278
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055243 Loss1: 0.021791 Loss2: 0.033452
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056812 Loss1: 0.023616 Loss2: 0.033196
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046542 Loss1: 0.014129 Loss2: 0.032413
+(DefaultActor pid=1838052) >> Training accuracy: 0.998944
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.251050 Loss1: 0.046935 Loss2: 0.204114
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.231294 Loss1: 0.039398 Loss2: 0.191896
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.229309 Loss1: 0.039683 Loss2: 0.189626
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.232342 Loss1: 0.043669 Loss2: 0.188672
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.239476 Loss1: 0.049881 Loss2: 0.189595
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.273283 Loss1: 0.081205 Loss2: 0.192079
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.264752 Loss1: 0.073799 Loss2: 0.190953
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.251732 Loss1: 0.060698 Loss2: 0.191035
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.247140 Loss1: 0.057498 Loss2: 0.189642
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.250640 Loss1: 0.061172 Loss2: 0.189468
+(DefaultActor pid=1838052) >> Training accuracy: 0.989913
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.062562 Loss1: 0.033727 Loss2: 0.028835
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.045783 Loss1: 0.015937 Loss2: 0.029846
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046619 Loss1: 0.016475 Loss2: 0.030144
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.041514 Loss1: 0.011360 Loss2: 0.030153
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.038226 Loss1: 0.008424 Loss2: 0.029802
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042710 Loss1: 0.012720 Loss2: 0.029990
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.040594 Loss1: 0.010387 Loss2: 0.030207
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.038089 Loss1: 0.007824 Loss2: 0.030265
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.041158 Loss1: 0.010990 Loss2: 0.030168
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.049498 Loss1: 0.018839 Loss2: 0.030659
+(DefaultActor pid=1838052) >> Training accuracy: 0.997231
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.069653 Loss1: 0.040023 Loss2: 0.029630
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049292 Loss1: 0.018712 Loss2: 0.030580
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047944 Loss1: 0.017048 Loss2: 0.030896
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047370 Loss1: 0.016371 Loss2: 0.030999
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.044873 Loss1: 0.013897 Loss2: 0.030976
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.044612 Loss1: 0.013685 Loss2: 0.030927
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043278 Loss1: 0.012095 Loss2: 0.031183
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046025 Loss1: 0.014387 Loss2: 0.031638
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.043346 Loss1: 0.012141 Loss2: 0.031205
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046710 Loss1: 0.015375 Loss2: 0.031335
+(DefaultActor pid=1838052) >> Training accuracy: 0.998220
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083065 Loss1: 0.050961 Loss2: 0.032104
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.062582 Loss1: 0.029127 Loss2: 0.033455
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.053545 Loss1: 0.020274 Loss2: 0.033271
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044876 Loss1: 0.012253 Loss2: 0.032624
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041910 Loss1: 0.009825 Loss2: 0.032084
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046827 Loss1: 0.014544 Loss2: 0.032282
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.044942 Loss1: 0.012618 Loss2: 0.032324
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051114 Loss1: 0.018452 Loss2: 0.032662
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053820 Loss1: 0.020822 Loss2: 0.032998
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060700 Loss1: 0.027631 Loss2: 0.033070
+(DefaultActor pid=1838052) >> Training accuracy: 0.995192
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.496139 Loss1: 0.060992 Loss2: 0.435147
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.446380 Loss1: 0.039522 Loss2: 0.406858
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.429557 Loss1: 0.035882 Loss2: 0.393675
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.432941 Loss1: 0.045940 Loss2: 0.387002
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.455643 Loss1: 0.069865 Loss2: 0.385778
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.453702 Loss1: 0.067119 Loss2: 0.386583
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.438078 Loss1: 0.056070 Loss2: 0.382008
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.446686 Loss1: 0.064971 Loss2: 0.381715
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.434574 Loss1: 0.053283 Loss2: 0.381292
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.448151 Loss1: 0.068566 Loss2: 0.379585
+(DefaultActor pid=1838052) >> Training accuracy: 0.983290
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.657218 Loss1: 0.050631 Loss2: 0.606587
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.627372 Loss1: 0.036014 Loss2: 0.591358
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.628207 Loss1: 0.046057 Loss2: 0.582151
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.619112 Loss1: 0.044577 Loss2: 0.574535
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.614398 Loss1: 0.045649 Loss2: 0.568748
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.606649 Loss1: 0.043863 Loss2: 0.562785
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.617395 Loss1: 0.056473 Loss2: 0.560923
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.656455 Loss1: 0.092393 Loss2: 0.564061
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.646316 Loss1: 0.083202 Loss2: 0.563114
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.669114 Loss1: 0.108516 Loss2: 0.560598
+(DefaultActor pid=1838052) >> Training accuracy: 0.976357
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 03:55:57,566][flwr][DEBUG] - fit_round 88 received 10 results and 0 failures
+>> Test accuracy: 0.664800
+[2023-09-29 03:56:34,010][flwr][INFO] - fit progress: (88, 2.374164987867252, {'accuracy': 0.6648}, 164216.90060469508)
+[2023-09-29 03:56:34,011][flwr][DEBUG] - evaluate_round 88: strategy sampled 10 clients (out of 10)
+[2023-09-29 03:57:09,085][flwr][DEBUG] - evaluate_round 88 received 10 results and 0 failures
+[2023-09-29 03:57:09,086][flwr][DEBUG] - fit_round 89: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071644 Loss1: 0.037986 Loss2: 0.033658
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.054522 Loss1: 0.019637 Loss2: 0.034885
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054448 Loss1: 0.019171 Loss2: 0.035277
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051616 Loss1: 0.016458 Loss2: 0.035159
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049181 Loss1: 0.014406 Loss2: 0.034775
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.043774 Loss1: 0.009494 Loss2: 0.034280
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050579 Loss1: 0.015988 Loss2: 0.034591
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051464 Loss1: 0.016947 Loss2: 0.034518
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058776 Loss1: 0.024002 Loss2: 0.034774
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.067732 Loss1: 0.032148 Loss2: 0.035585
+(DefaultActor pid=1838052) >> Training accuracy: 0.996595
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.067589 Loss1: 0.038952 Loss2: 0.028637
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051780 Loss1: 0.021794 Loss2: 0.029985
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.038815 Loss1: 0.009042 Loss2: 0.029773
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038165 Loss1: 0.009023 Loss2: 0.029141
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.038011 Loss1: 0.008925 Loss2: 0.029086
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038225 Loss1: 0.009159 Loss2: 0.029066
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.038938 Loss1: 0.009714 Loss2: 0.029223
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.037943 Loss1: 0.008843 Loss2: 0.029100
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.039983 Loss1: 0.010648 Loss2: 0.029335
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.038570 Loss1: 0.009081 Loss2: 0.029489
+(DefaultActor pid=1838052) >> Training accuracy: 0.997231
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.686534 Loss1: 0.074371 Loss2: 0.612163
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.657312 Loss1: 0.054380 Loss2: 0.602932
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.673211 Loss1: 0.078687 Loss2: 0.594524
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.675816 Loss1: 0.084847 Loss2: 0.590969
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.675911 Loss1: 0.088687 Loss2: 0.587225
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.690383 Loss1: 0.104403 Loss2: 0.585980
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.641577 Loss1: 0.062766 Loss2: 0.578811
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.652875 Loss1: 0.078783 Loss2: 0.574093
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.649147 Loss1: 0.075334 Loss2: 0.573812
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.631461 Loss1: 0.059510 Loss2: 0.571951
+(DefaultActor pid=1838052) >> Training accuracy: 0.990902
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065218 Loss1: 0.036382 Loss2: 0.028837
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057600 Loss1: 0.027163 Loss2: 0.030438
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049763 Loss1: 0.018832 Loss2: 0.030931
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.043959 Loss1: 0.013536 Loss2: 0.030423
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048707 Loss1: 0.018073 Loss2: 0.030634
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045641 Loss1: 0.014881 Loss2: 0.030760
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.042749 Loss1: 0.012041 Loss2: 0.030708
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.053388 Loss1: 0.021917 Loss2: 0.031471
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.044603 Loss1: 0.012851 Loss2: 0.031752
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048225 Loss1: 0.016652 Loss2: 0.031573
+(DefaultActor pid=1838052) >> Training accuracy: 0.997255
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065614 Loss1: 0.035335 Loss2: 0.030279
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.043474 Loss1: 0.012917 Loss2: 0.030557
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046575 Loss1: 0.015530 Loss2: 0.031045
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046565 Loss1: 0.015338 Loss2: 0.031227
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.042827 Loss1: 0.011592 Loss2: 0.031235
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046022 Loss1: 0.014661 Loss2: 0.031361
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049548 Loss1: 0.017764 Loss2: 0.031783
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.049545 Loss1: 0.017461 Loss2: 0.032083
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053863 Loss1: 0.021655 Loss2: 0.032208
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048566 Loss1: 0.015900 Loss2: 0.032666
+(DefaultActor pid=1838052) >> Training accuracy: 0.997429
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.077659 Loss1: 0.048260 Loss2: 0.029400
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.050785 Loss1: 0.020109 Loss2: 0.030675
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049893 Loss1: 0.019366 Loss2: 0.030526
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048054 Loss1: 0.017444 Loss2: 0.030611
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043917 Loss1: 0.013451 Loss2: 0.030466
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047613 Loss1: 0.017170 Loss2: 0.030443
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.051153 Loss1: 0.020324 Loss2: 0.030829
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.059689 Loss1: 0.028226 Loss2: 0.031464
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064105 Loss1: 0.032101 Loss2: 0.032004
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.061645 Loss1: 0.029003 Loss2: 0.032643
+(DefaultActor pid=1838052) >> Training accuracy: 0.991102
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092564 Loss1: 0.038759 Loss2: 0.053805
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.078068 Loss1: 0.024756 Loss2: 0.053312
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.079522 Loss1: 0.026320 Loss2: 0.053202
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.075898 Loss1: 0.023610 Loss2: 0.052288
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073370 Loss1: 0.021671 Loss2: 0.051699
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069883 Loss1: 0.018434 Loss2: 0.051449
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073775 Loss1: 0.022813 Loss2: 0.050962
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.074915 Loss1: 0.023716 Loss2: 0.051199
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070930 Loss1: 0.019377 Loss2: 0.051553
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091581 Loss1: 0.038770 Loss2: 0.052812
+(DefaultActor pid=1838052) >> Training accuracy: 0.991425
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.068098 Loss1: 0.039297 Loss2: 0.028800
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049934 Loss1: 0.019999 Loss2: 0.029935
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.044835 Loss1: 0.014822 Loss2: 0.030013
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.043770 Loss1: 0.013530 Loss2: 0.030240
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046480 Loss1: 0.016265 Loss2: 0.030215
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045519 Loss1: 0.015518 Loss2: 0.030001
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063246 Loss1: 0.032089 Loss2: 0.031157
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065597 Loss1: 0.033771 Loss2: 0.031826
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058311 Loss1: 0.026012 Loss2: 0.032300
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.063633 Loss1: 0.031042 Loss2: 0.032591
+(DefaultActor pid=1838052) >> Training accuracy: 0.994591
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.679083 Loss1: 0.056348 Loss2: 0.622735
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.662612 Loss1: 0.049964 Loss2: 0.612648
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.653118 Loss1: 0.051631 Loss2: 0.601487
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.633148 Loss1: 0.040020 Loss2: 0.593128
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.628647 Loss1: 0.042555 Loss2: 0.586092
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.645763 Loss1: 0.062115 Loss2: 0.583648
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.656558 Loss1: 0.072043 Loss2: 0.584515
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.644685 Loss1: 0.061009 Loss2: 0.583676
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.616666 Loss1: 0.043180 Loss2: 0.573486
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.628940 Loss1: 0.057480 Loss2: 0.571460
+(DefaultActor pid=1838052) >> Training accuracy: 0.987540
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066951 Loss1: 0.036722 Loss2: 0.030229
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049626 Loss1: 0.018328 Loss2: 0.031297
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045928 Loss1: 0.014585 Loss2: 0.031343
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.042695 Loss1: 0.011547 Loss2: 0.031148
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.038229 Loss1: 0.007239 Loss2: 0.030990
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.037012 Loss1: 0.006728 Loss2: 0.030283
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.037716 Loss1: 0.007597 Loss2: 0.030120
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.037339 Loss1: 0.006971 Loss2: 0.030367
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.037460 Loss1: 0.007272 Loss2: 0.030188
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046239 Loss1: 0.015746 Loss2: 0.030493
+(DefaultActor pid=1838052) >> Training accuracy: 0.997738
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 04:25:40,513][flwr][DEBUG] - fit_round 89 received 10 results and 0 failures
+>> Test accuracy: 0.659700
+[2023-09-29 04:26:18,142][flwr][INFO] - fit progress: (89, 2.4286907109589624, {'accuracy': 0.6597}, 166001.0325497142)
+[2023-09-29 04:26:18,143][flwr][DEBUG] - evaluate_round 89: strategy sampled 10 clients (out of 10)
+[2023-09-29 04:26:54,281][flwr][DEBUG] - evaluate_round 89 received 10 results and 0 failures
+[2023-09-29 04:26:54,282][flwr][DEBUG] - fit_round 90: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.099711 Loss1: 0.040346 Loss2: 0.059365
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.082719 Loss1: 0.026215 Loss2: 0.056504
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.072163 Loss1: 0.017921 Loss2: 0.054242
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074771 Loss1: 0.021458 Loss2: 0.053314
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.071455 Loss1: 0.018992 Loss2: 0.052464
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.072924 Loss1: 0.021364 Loss2: 0.051560
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073150 Loss1: 0.021409 Loss2: 0.051741
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.077892 Loss1: 0.026444 Loss2: 0.051447
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070154 Loss1: 0.019211 Loss2: 0.050943
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.076582 Loss1: 0.026003 Loss2: 0.050579
+(DefaultActor pid=1838052) >> Training accuracy: 0.996299
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.655961 Loss1: 0.053607 Loss2: 0.602354
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.619723 Loss1: 0.037230 Loss2: 0.582492
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.614388 Loss1: 0.040233 Loss2: 0.574155
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.611986 Loss1: 0.043370 Loss2: 0.568617
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.594116 Loss1: 0.033595 Loss2: 0.560521
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.597528 Loss1: 0.041945 Loss2: 0.555583
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.619191 Loss1: 0.064829 Loss2: 0.554362
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.661083 Loss1: 0.100180 Loss2: 0.560903
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.639353 Loss1: 0.078275 Loss2: 0.561078
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.682824 Loss1: 0.120663 Loss2: 0.562161
+(DefaultActor pid=1838052) >> Training accuracy: 0.974760
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.659938 Loss1: 0.052746 Loss2: 0.607192
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.645730 Loss1: 0.056142 Loss2: 0.589588
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.640932 Loss1: 0.054897 Loss2: 0.586035
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.639587 Loss1: 0.057396 Loss2: 0.582191
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.636991 Loss1: 0.057324 Loss2: 0.579668
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.628252 Loss1: 0.057844 Loss2: 0.570408
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.648447 Loss1: 0.080644 Loss2: 0.567803
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.648938 Loss1: 0.079797 Loss2: 0.569141
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.667933 Loss1: 0.097857 Loss2: 0.570076
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.644379 Loss1: 0.078274 Loss2: 0.566104
+(DefaultActor pid=1838052) >> Training accuracy: 0.990506
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.711292 Loss1: 0.085927 Loss2: 0.625365
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.654889 Loss1: 0.045887 Loss2: 0.609002
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.656258 Loss1: 0.058611 Loss2: 0.597647
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.634479 Loss1: 0.044404 Loss2: 0.590076
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.653466 Loss1: 0.068631 Loss2: 0.584835
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.665276 Loss1: 0.079236 Loss2: 0.586040
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.652544 Loss1: 0.069815 Loss2: 0.582729
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.666323 Loss1: 0.085839 Loss2: 0.580484
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.667447 Loss1: 0.088625 Loss2: 0.578822
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.654704 Loss1: 0.079026 Loss2: 0.575678
+(DefaultActor pid=1838052) >> Training accuracy: 0.979096
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074800 Loss1: 0.044452 Loss2: 0.030348
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051950 Loss1: 0.020235 Loss2: 0.031714
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054670 Loss1: 0.022627 Loss2: 0.032044
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051177 Loss1: 0.018928 Loss2: 0.032249
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.056524 Loss1: 0.023920 Loss2: 0.032604
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.054999 Loss1: 0.022551 Loss2: 0.032448
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054704 Loss1: 0.021533 Loss2: 0.033171
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066533 Loss1: 0.033235 Loss2: 0.033297
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065803 Loss1: 0.031931 Loss2: 0.033871
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068262 Loss1: 0.034207 Loss2: 0.034055
+(DefaultActor pid=1838052) >> Training accuracy: 0.995055
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110945 Loss1: 0.052890 Loss2: 0.058055
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074827 Loss1: 0.018626 Loss2: 0.056201
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.074685 Loss1: 0.021105 Loss2: 0.053580
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.071167 Loss1: 0.018386 Loss2: 0.052781
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077753 Loss1: 0.025540 Loss2: 0.052213
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075235 Loss1: 0.023079 Loss2: 0.052156
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.083197 Loss1: 0.031209 Loss2: 0.051988
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.080750 Loss1: 0.029190 Loss2: 0.051561
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.072863 Loss1: 0.021286 Loss2: 0.051577
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.092515 Loss1: 0.040320 Loss2: 0.052195
+(DefaultActor pid=1838052) >> Training accuracy: 0.995009
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.459780 Loss1: 0.038036 Loss2: 0.421744
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.396971 Loss1: 0.035462 Loss2: 0.361510
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.388238 Loss1: 0.049946 Loss2: 0.338292
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.378118 Loss1: 0.051788 Loss2: 0.326329
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.365736 Loss1: 0.045025 Loss2: 0.320711
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.365241 Loss1: 0.048247 Loss2: 0.316994
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.387522 Loss1: 0.070606 Loss2: 0.316915
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.403280 Loss1: 0.083416 Loss2: 0.319864
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.376396 Loss1: 0.060637 Loss2: 0.315759
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.380419 Loss1: 0.065759 Loss2: 0.314659
+(DefaultActor pid=1838052) >> Training accuracy: 0.985364
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065606 Loss1: 0.037100 Loss2: 0.028506
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.043986 Loss1: 0.014832 Loss2: 0.029153
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046294 Loss1: 0.016839 Loss2: 0.029455
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.042382 Loss1: 0.013062 Loss2: 0.029320
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041054 Loss1: 0.011641 Loss2: 0.029413
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040659 Loss1: 0.011275 Loss2: 0.029385
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.041054 Loss1: 0.011601 Loss2: 0.029454
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041195 Loss1: 0.011699 Loss2: 0.029496
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.039134 Loss1: 0.009712 Loss2: 0.029422
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056027 Loss1: 0.026056 Loss2: 0.029971
+(DefaultActor pid=1838052) >> Training accuracy: 0.996044
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.398437 Loss1: 0.052001 Loss2: 0.346436
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.400188 Loss1: 0.062059 Loss2: 0.338128
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.385492 Loss1: 0.050303 Loss2: 0.335189
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.402294 Loss1: 0.069198 Loss2: 0.333096
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.434845 Loss1: 0.092613 Loss2: 0.342233
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.461109 Loss1: 0.113087 Loss2: 0.348022
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.431369 Loss1: 0.092947 Loss2: 0.338422
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.446240 Loss1: 0.104343 Loss2: 0.341897
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.441490 Loss1: 0.102412 Loss2: 0.339078
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.433401 Loss1: 0.094050 Loss2: 0.339351
+(DefaultActor pid=1838052) >> Training accuracy: 0.978277
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.070456 Loss1: 0.041161 Loss2: 0.029295
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053858 Loss1: 0.023229 Loss2: 0.030629
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056189 Loss1: 0.025228 Loss2: 0.030961
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.057119 Loss1: 0.025606 Loss2: 0.031514
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070508 Loss1: 0.038564 Loss2: 0.031944
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.063213 Loss1: 0.030894 Loss2: 0.032319
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.086509 Loss1: 0.053156 Loss2: 0.033353
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067965 Loss1: 0.034153 Loss2: 0.033813
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077385 Loss1: 0.043782 Loss2: 0.033603
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070597 Loss1: 0.036688 Loss2: 0.033909
+(DefaultActor pid=1838052) >> Training accuracy: 0.992788
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 04:55:35,824][flwr][DEBUG] - fit_round 90 received 10 results and 0 failures
+>> Test accuracy: 0.661100
+[2023-09-29 04:56:12,044][flwr][INFO] - fit progress: (90, 2.328940248908326, {'accuracy': 0.6611}, 167794.9346008231)
+[2023-09-29 04:56:12,045][flwr][DEBUG] - evaluate_round 90: strategy sampled 10 clients (out of 10)
+[2023-09-29 04:56:48,333][flwr][DEBUG] - evaluate_round 90 received 10 results and 0 failures
+[2023-09-29 04:56:48,334][flwr][DEBUG] - fit_round 91: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.278048 Loss1: 0.043487 Loss2: 0.234562
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.247817 Loss1: 0.028685 Loss2: 0.219131
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.248073 Loss1: 0.031345 Loss2: 0.216728
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.228627 Loss1: 0.013844 Loss2: 0.214783
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.237728 Loss1: 0.024063 Loss2: 0.213664
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.249886 Loss1: 0.035238 Loss2: 0.214647
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.253118 Loss1: 0.038162 Loss2: 0.214956
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.247070 Loss1: 0.031204 Loss2: 0.215865
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.247702 Loss1: 0.032687 Loss2: 0.215015
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.236886 Loss1: 0.022983 Loss2: 0.213903
+(DefaultActor pid=1838052) >> Training accuracy: 0.992950
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.085616 Loss1: 0.054907 Loss2: 0.030710
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058067 Loss1: 0.025834 Loss2: 0.032232
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045259 Loss1: 0.013395 Loss2: 0.031864
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048369 Loss1: 0.016932 Loss2: 0.031437
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049111 Loss1: 0.017230 Loss2: 0.031880
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.049908 Loss1: 0.018213 Loss2: 0.031695
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.042413 Loss1: 0.010766 Loss2: 0.031647
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.042048 Loss1: 0.010466 Loss2: 0.031582
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.045001 Loss1: 0.013453 Loss2: 0.031548
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.044398 Loss1: 0.012595 Loss2: 0.031803
+(DefaultActor pid=1838052) >> Training accuracy: 0.998047
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.645264 Loss1: 0.053895 Loss2: 0.591369
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.602606 Loss1: 0.021256 Loss2: 0.581350
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.598862 Loss1: 0.032383 Loss2: 0.566479
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.605079 Loss1: 0.041911 Loss2: 0.563168
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.609426 Loss1: 0.048423 Loss2: 0.561003
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.619658 Loss1: 0.058072 Loss2: 0.561587
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.618967 Loss1: 0.059063 Loss2: 0.559904
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.624178 Loss1: 0.066305 Loss2: 0.557873
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.603563 Loss1: 0.047533 Loss2: 0.556030
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.605525 Loss1: 0.051868 Loss2: 0.553657
+(DefaultActor pid=1838052) >> Training accuracy: 0.988726
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.626324 Loss1: 0.045146 Loss2: 0.581178
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.602448 Loss1: 0.037590 Loss2: 0.564859
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.582966 Loss1: 0.028281 Loss2: 0.554685
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.586348 Loss1: 0.038429 Loss2: 0.547919
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.587687 Loss1: 0.039263 Loss2: 0.548424
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.604981 Loss1: 0.057496 Loss2: 0.547486
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.604710 Loss1: 0.057606 Loss2: 0.547104
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.632001 Loss1: 0.085415 Loss2: 0.546586
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.646796 Loss1: 0.095731 Loss2: 0.551065
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.653394 Loss1: 0.097146 Loss2: 0.556248
+(DefaultActor pid=1838052) >> Training accuracy: 0.978165
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074778 Loss1: 0.046447 Loss2: 0.028332
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.055425 Loss1: 0.026101 Loss2: 0.029324
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052908 Loss1: 0.023377 Loss2: 0.029531
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044509 Loss1: 0.014790 Loss2: 0.029719
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046156 Loss1: 0.016593 Loss2: 0.029563
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046655 Loss1: 0.017003 Loss2: 0.029652
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.044128 Loss1: 0.014222 Loss2: 0.029905
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055137 Loss1: 0.024904 Loss2: 0.030233
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057377 Loss1: 0.026559 Loss2: 0.030818
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.058928 Loss1: 0.027724 Loss2: 0.031204
+(DefaultActor pid=1838052) >> Training accuracy: 0.993869
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066342 Loss1: 0.037334 Loss2: 0.029008
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.056524 Loss1: 0.026076 Loss2: 0.030448
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.048242 Loss1: 0.017536 Loss2: 0.030706
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.037796 Loss1: 0.007709 Loss2: 0.030087
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041330 Loss1: 0.011694 Loss2: 0.029636
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047287 Loss1: 0.017374 Loss2: 0.029913
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050999 Loss1: 0.020801 Loss2: 0.030198
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.053376 Loss1: 0.022682 Loss2: 0.030694
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056326 Loss1: 0.025188 Loss2: 0.031138
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.072278 Loss1: 0.039818 Loss2: 0.032460
+(DefaultActor pid=1838052) >> Training accuracy: 0.987580
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.304536 Loss1: 0.049048 Loss2: 0.255489
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.248568 Loss1: 0.043886 Loss2: 0.204683
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.241333 Loss1: 0.045335 Loss2: 0.195998
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.228504 Loss1: 0.036162 Loss2: 0.192342
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.235135 Loss1: 0.046558 Loss2: 0.188577
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.249628 Loss1: 0.060813 Loss2: 0.188815
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.270827 Loss1: 0.080172 Loss2: 0.190655
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.272455 Loss1: 0.080544 Loss2: 0.191911
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.303920 Loss1: 0.107821 Loss2: 0.196099
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.322621 Loss1: 0.122255 Loss2: 0.200366
+(DefaultActor pid=1838052) >> Training accuracy: 0.978837
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.119445 Loss1: 0.051789 Loss2: 0.067656
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.083153 Loss1: 0.018482 Loss2: 0.064671
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077751 Loss1: 0.015082 Loss2: 0.062669
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.071829 Loss1: 0.011163 Loss2: 0.060666
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064264 Loss1: 0.005492 Loss2: 0.058772
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.063262 Loss1: 0.006034 Loss2: 0.057228
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062756 Loss1: 0.005667 Loss2: 0.057089
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.063206 Loss1: 0.006842 Loss2: 0.056363
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065016 Loss1: 0.008635 Loss2: 0.056382
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.064530 Loss1: 0.008119 Loss2: 0.056411
+(DefaultActor pid=1838052) >> Training accuracy: 0.999155
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.082620 Loss1: 0.046441 Loss2: 0.036178
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058601 Loss1: 0.021345 Loss2: 0.037257
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.055978 Loss1: 0.018687 Loss2: 0.037291
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046008 Loss1: 0.009573 Loss2: 0.036435
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048691 Loss1: 0.012962 Loss2: 0.035729
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.052509 Loss1: 0.016164 Loss2: 0.036345
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054421 Loss1: 0.018097 Loss2: 0.036324
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052984 Loss1: 0.016676 Loss2: 0.036308
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053165 Loss1: 0.016854 Loss2: 0.036311
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048445 Loss1: 0.011942 Loss2: 0.036503
+(DefaultActor pid=1838052) >> Training accuracy: 0.998418
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.079656 Loss1: 0.049047 Loss2: 0.030609
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.063654 Loss1: 0.031437 Loss2: 0.032217
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061876 Loss1: 0.029031 Loss2: 0.032845
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064860 Loss1: 0.031735 Loss2: 0.033125
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061875 Loss1: 0.028694 Loss2: 0.033180
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051209 Loss1: 0.018172 Loss2: 0.033037
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.059833 Loss1: 0.026593 Loss2: 0.033240
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055677 Loss1: 0.022103 Loss2: 0.033573
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058216 Loss1: 0.024796 Loss2: 0.033420
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053941 Loss1: 0.020253 Loss2: 0.033688
+(DefaultActor pid=1838052) >> Training accuracy: 0.996711
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 05:25:31,916][flwr][DEBUG] - fit_round 91 received 10 results and 0 failures
+>> Test accuracy: 0.661900
+[2023-09-29 05:26:08,211][flwr][INFO] - fit progress: (91, 2.403618623273441, {'accuracy': 0.6619}, 169591.10123086534)
+[2023-09-29 05:26:08,211][flwr][DEBUG] - evaluate_round 91: strategy sampled 10 clients (out of 10)
+[2023-09-29 05:26:43,417][flwr][DEBUG] - evaluate_round 91 received 10 results and 0 failures
+[2023-09-29 05:26:43,418][flwr][DEBUG] - fit_round 92: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066839 Loss1: 0.034493 Loss2: 0.032346
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.050711 Loss1: 0.018081 Loss2: 0.032630
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046551 Loss1: 0.014137 Loss2: 0.032414
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048531 Loss1: 0.016900 Loss2: 0.031631
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048574 Loss1: 0.016299 Loss2: 0.032274
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045525 Loss1: 0.013412 Loss2: 0.032112
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049398 Loss1: 0.017311 Loss2: 0.032087
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058428 Loss1: 0.025977 Loss2: 0.032451
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.055433 Loss1: 0.022345 Loss2: 0.033089
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.077289 Loss1: 0.043251 Loss2: 0.034038
+(DefaultActor pid=1838052) >> Training accuracy: 0.988726
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.486372 Loss1: 0.047523 Loss2: 0.438849
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.471386 Loss1: 0.043961 Loss2: 0.427425
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.473173 Loss1: 0.049041 Loss2: 0.424132
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.495037 Loss1: 0.063946 Loss2: 0.431091
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.487301 Loss1: 0.060001 Loss2: 0.427300
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.534881 Loss1: 0.102083 Loss2: 0.432798
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.512794 Loss1: 0.082029 Loss2: 0.430766
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.485613 Loss1: 0.061011 Loss2: 0.424603
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.510489 Loss1: 0.085504 Loss2: 0.424984
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.525292 Loss1: 0.096709 Loss2: 0.428583
+(DefaultActor pid=1838052) >> Training accuracy: 0.983974
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.519259 Loss1: 0.051738 Loss2: 0.467521
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.497392 Loss1: 0.043794 Loss2: 0.453598
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.502559 Loss1: 0.053793 Loss2: 0.448767
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.498130 Loss1: 0.054180 Loss2: 0.443950
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.540610 Loss1: 0.091050 Loss2: 0.449560
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.536041 Loss1: 0.087576 Loss2: 0.448465
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.570638 Loss1: 0.118646 Loss2: 0.451992
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.539059 Loss1: 0.089925 Loss2: 0.449133
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.520386 Loss1: 0.075362 Loss2: 0.445025
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.506215 Loss1: 0.065885 Loss2: 0.440329
+(DefaultActor pid=1838052) >> Training accuracy: 0.982730
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071982 Loss1: 0.043314 Loss2: 0.028668
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.046466 Loss1: 0.016782 Loss2: 0.029684
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047904 Loss1: 0.018110 Loss2: 0.029794
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044018 Loss1: 0.014531 Loss2: 0.029487
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043433 Loss1: 0.014025 Loss2: 0.029408
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.056511 Loss1: 0.026424 Loss2: 0.030086
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049459 Loss1: 0.018996 Loss2: 0.030463
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041792 Loss1: 0.011327 Loss2: 0.030465
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.042709 Loss1: 0.012323 Loss2: 0.030386
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.043468 Loss1: 0.013291 Loss2: 0.030177
+(DefaultActor pid=1838052) >> Training accuracy: 0.998813
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074285 Loss1: 0.045570 Loss2: 0.028716
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.055638 Loss1: 0.025400 Loss2: 0.030238
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047103 Loss1: 0.016760 Loss2: 0.030343
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.045501 Loss1: 0.015671 Loss2: 0.029829
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.040759 Loss1: 0.011072 Loss2: 0.029687
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045841 Loss1: 0.016005 Loss2: 0.029836
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049399 Loss1: 0.019183 Loss2: 0.030216
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051655 Loss1: 0.021206 Loss2: 0.030448
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.048287 Loss1: 0.017566 Loss2: 0.030721
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.058442 Loss1: 0.027656 Loss2: 0.030786
+(DefaultActor pid=1838052) >> Training accuracy: 0.992286
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.093899 Loss1: 0.032514 Loss2: 0.061385
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077668 Loss1: 0.019541 Loss2: 0.058127
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068053 Loss1: 0.012561 Loss2: 0.055492
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064018 Loss1: 0.011311 Loss2: 0.052707
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069503 Loss1: 0.017885 Loss2: 0.051618
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.066001 Loss1: 0.014375 Loss2: 0.051626
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.070774 Loss1: 0.019384 Loss2: 0.051390
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082145 Loss1: 0.030225 Loss2: 0.051919
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.093730 Loss1: 0.040668 Loss2: 0.053062
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082492 Loss1: 0.030072 Loss2: 0.052420
+(DefaultActor pid=1838052) >> Training accuracy: 0.996835
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075631 Loss1: 0.045635 Loss2: 0.029996
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047018 Loss1: 0.016308 Loss2: 0.030710
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046234 Loss1: 0.015733 Loss2: 0.030500
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050188 Loss1: 0.019415 Loss2: 0.030772
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064549 Loss1: 0.033255 Loss2: 0.031294
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.056729 Loss1: 0.025137 Loss2: 0.031593
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048362 Loss1: 0.016581 Loss2: 0.031781
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050450 Loss1: 0.018814 Loss2: 0.031636
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.048231 Loss1: 0.016694 Loss2: 0.031537
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053126 Loss1: 0.021166 Loss2: 0.031960
+(DefaultActor pid=1838052) >> Training accuracy: 0.994855
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087518 Loss1: 0.040373 Loss2: 0.047145
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066096 Loss1: 0.021539 Loss2: 0.044557
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.063151 Loss1: 0.019263 Loss2: 0.043888
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060773 Loss1: 0.017433 Loss2: 0.043339
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065943 Loss1: 0.022434 Loss2: 0.043509
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053967 Loss1: 0.010789 Loss2: 0.043178
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052611 Loss1: 0.010274 Loss2: 0.042336
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.054604 Loss1: 0.012300 Loss2: 0.042303
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.050196 Loss1: 0.007973 Loss2: 0.042222
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055404 Loss1: 0.013385 Loss2: 0.042019
+(DefaultActor pid=1838052) >> Training accuracy: 0.998798
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.604502 Loss1: 0.068105 Loss2: 0.536397
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.568311 Loss1: 0.050922 Loss2: 0.517389
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.541038 Loss1: 0.034757 Loss2: 0.506282
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.531904 Loss1: 0.032554 Loss2: 0.499350
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.551042 Loss1: 0.052621 Loss2: 0.498421
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.563028 Loss1: 0.066790 Loss2: 0.496238
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.567526 Loss1: 0.070968 Loss2: 0.496559
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.564123 Loss1: 0.066032 Loss2: 0.498091
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.565659 Loss1: 0.070240 Loss2: 0.495419
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.589754 Loss1: 0.092261 Loss2: 0.497493
+(DefaultActor pid=1838052) >> Training accuracy: 0.968328
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078716 Loss1: 0.050033 Loss2: 0.028683
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049204 Loss1: 0.019304 Loss2: 0.029900
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.053939 Loss1: 0.023782 Loss2: 0.030157
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048861 Loss1: 0.018404 Loss2: 0.030458
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.047265 Loss1: 0.016910 Loss2: 0.030355
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.056918 Loss1: 0.026175 Loss2: 0.030743
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054364 Loss1: 0.022993 Loss2: 0.031371
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.062183 Loss1: 0.030426 Loss2: 0.031757
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057481 Loss1: 0.025803 Loss2: 0.031678
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060523 Loss1: 0.028548 Loss2: 0.031975
+(DefaultActor pid=1838052) >> Training accuracy: 0.998264
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 05:55:12,283][flwr][DEBUG] - fit_round 92 received 10 results and 0 failures
+>> Test accuracy: 0.663600
+[2023-09-29 05:55:50,087][flwr][INFO] - fit progress: (92, 2.4000819242609954, {'accuracy': 0.6636}, 171372.97783041233)
+[2023-09-29 05:55:50,088][flwr][DEBUG] - evaluate_round 92: strategy sampled 10 clients (out of 10)
+[2023-09-29 05:56:34,742][flwr][DEBUG] - evaluate_round 92 received 10 results and 0 failures
+[2023-09-29 05:56:34,743][flwr][DEBUG] - fit_round 93: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.653048 Loss1: 0.038956 Loss2: 0.614091
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.633762 Loss1: 0.033599 Loss2: 0.600162
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.637122 Loss1: 0.043031 Loss2: 0.594091
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.642095 Loss1: 0.051017 Loss2: 0.591078
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.638575 Loss1: 0.048681 Loss2: 0.589894
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.651951 Loss1: 0.062859 Loss2: 0.589092
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.656996 Loss1: 0.071069 Loss2: 0.585927
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.649762 Loss1: 0.066458 Loss2: 0.583304
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.655445 Loss1: 0.071662 Loss2: 0.583783
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.629852 Loss1: 0.051996 Loss2: 0.577856
+(DefaultActor pid=1838052) >> Training accuracy: 0.989139
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.522463 Loss1: 0.043584 Loss2: 0.478879
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.487804 Loss1: 0.037474 Loss2: 0.450330
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.492792 Loss1: 0.048766 Loss2: 0.444027
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.491697 Loss1: 0.052414 Loss2: 0.439283
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.495183 Loss1: 0.056223 Loss2: 0.438960
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.493880 Loss1: 0.054377 Loss2: 0.439503
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.533694 Loss1: 0.095665 Loss2: 0.438029
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.532861 Loss1: 0.090942 Loss2: 0.441920
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.501269 Loss1: 0.064613 Loss2: 0.436656
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.515259 Loss1: 0.078211 Loss2: 0.437048
+(DefaultActor pid=1838052) >> Training accuracy: 0.986178
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.578006 Loss1: 0.080094 Loss2: 0.497912
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.554748 Loss1: 0.062015 Loss2: 0.492734
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.545735 Loss1: 0.059965 Loss2: 0.485770
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.556583 Loss1: 0.072234 Loss2: 0.484349
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.572087 Loss1: 0.090286 Loss2: 0.481801
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.591907 Loss1: 0.108219 Loss2: 0.483687
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.617382 Loss1: 0.133684 Loss2: 0.483698
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.581354 Loss1: 0.102310 Loss2: 0.479044
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.554778 Loss1: 0.079653 Loss2: 0.475125
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.545187 Loss1: 0.073836 Loss2: 0.471351
+(DefaultActor pid=1838052) >> Training accuracy: 0.988064
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.612852 Loss1: 0.055534 Loss2: 0.557318
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.596783 Loss1: 0.048208 Loss2: 0.548575
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.585753 Loss1: 0.045534 Loss2: 0.540220
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.585347 Loss1: 0.048931 Loss2: 0.536416
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.595189 Loss1: 0.061432 Loss2: 0.533757
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.589526 Loss1: 0.059194 Loss2: 0.530332
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.598925 Loss1: 0.068320 Loss2: 0.530605
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.608990 Loss1: 0.079532 Loss2: 0.529458
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.590332 Loss1: 0.064468 Loss2: 0.525864
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.621915 Loss1: 0.091943 Loss2: 0.529972
+(DefaultActor pid=1838052) >> Training accuracy: 0.982991
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.121291 Loss1: 0.049527 Loss2: 0.071764
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.095413 Loss1: 0.026031 Loss2: 0.069383
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.094711 Loss1: 0.025777 Loss2: 0.068934
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.088054 Loss1: 0.019677 Loss2: 0.068377
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.079763 Loss1: 0.012245 Loss2: 0.067519
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.087945 Loss1: 0.020589 Loss2: 0.067355
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.100577 Loss1: 0.032301 Loss2: 0.068275
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.094817 Loss1: 0.026284 Loss2: 0.068534
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.102180 Loss1: 0.033523 Loss2: 0.068657
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102909 Loss1: 0.033296 Loss2: 0.069614
+(DefaultActor pid=1838052) >> Training accuracy: 0.997122
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.514676 Loss1: 0.051195 Loss2: 0.463481
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.502860 Loss1: 0.055146 Loss2: 0.447714
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.495472 Loss1: 0.054906 Loss2: 0.440565
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.499863 Loss1: 0.065524 Loss2: 0.434339
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.495651 Loss1: 0.061609 Loss2: 0.434042
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.487733 Loss1: 0.058435 Loss2: 0.429298
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.505981 Loss1: 0.069998 Loss2: 0.435984
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.510141 Loss1: 0.075711 Loss2: 0.434430
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.525442 Loss1: 0.090563 Loss2: 0.434880
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.541394 Loss1: 0.103026 Loss2: 0.438368
+(DefaultActor pid=1838052) >> Training accuracy: 0.975475
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.061495 Loss1: 0.033823 Loss2: 0.027673
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.043595 Loss1: 0.015381 Loss2: 0.028214
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.035117 Loss1: 0.007124 Loss2: 0.027993
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.031894 Loss1: 0.004151 Loss2: 0.027743
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.032339 Loss1: 0.004826 Loss2: 0.027512
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.034908 Loss1: 0.007371 Loss2: 0.027537
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.036448 Loss1: 0.008722 Loss2: 0.027726
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.034844 Loss1: 0.007095 Loss2: 0.027749
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.034356 Loss1: 0.006567 Loss2: 0.027789
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.032581 Loss1: 0.005034 Loss2: 0.027547
+(DefaultActor pid=1838052) >> Training accuracy: 1.000000
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.580873 Loss1: 0.061184 Loss2: 0.519688
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.561393 Loss1: 0.050899 Loss2: 0.510494
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.595318 Loss1: 0.081433 Loss2: 0.513885
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.573135 Loss1: 0.069372 Loss2: 0.503763
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.589975 Loss1: 0.090190 Loss2: 0.499785
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.581884 Loss1: 0.081454 Loss2: 0.500429
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.600547 Loss1: 0.103347 Loss2: 0.497200
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.609911 Loss1: 0.111924 Loss2: 0.497987
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.576925 Loss1: 0.085424 Loss2: 0.491500
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.565529 Loss1: 0.072991 Loss2: 0.492537
+(DefaultActor pid=1838052) >> Training accuracy: 0.980024
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081193 Loss1: 0.051266 Loss2: 0.029927
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058883 Loss1: 0.027832 Loss2: 0.031051
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047028 Loss1: 0.016487 Loss2: 0.030540
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046485 Loss1: 0.016016 Loss2: 0.030470
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046896 Loss1: 0.016253 Loss2: 0.030643
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045589 Loss1: 0.015356 Loss2: 0.030234
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048510 Loss1: 0.017702 Loss2: 0.030808
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.056103 Loss1: 0.025020 Loss2: 0.031083
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.050658 Loss1: 0.019106 Loss2: 0.031552
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073398 Loss1: 0.041332 Loss2: 0.032066
+(DefaultActor pid=1838052) >> Training accuracy: 0.994721
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074266 Loss1: 0.043600 Loss2: 0.030666
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.048159 Loss1: 0.016366 Loss2: 0.031793
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043844 Loss1: 0.012092 Loss2: 0.031752
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048190 Loss1: 0.016415 Loss2: 0.031774
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.052921 Loss1: 0.020521 Loss2: 0.032401
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047399 Loss1: 0.015030 Loss2: 0.032369
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052533 Loss1: 0.019771 Loss2: 0.032761
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.060908 Loss1: 0.027226 Loss2: 0.033682
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067250 Loss1: 0.033351 Loss2: 0.033899
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.081979 Loss1: 0.047634 Loss2: 0.034345
+(DefaultActor pid=1838052) >> Training accuracy: 0.991693
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 06:24:59,930][flwr][DEBUG] - fit_round 93 received 10 results and 0 failures
+>> Test accuracy: 0.664800
+[2023-09-29 06:25:35,305][flwr][INFO] - fit progress: (93, 2.330398244598803, {'accuracy': 0.6648}, 173158.19589267345)
+[2023-09-29 06:25:35,306][flwr][DEBUG] - evaluate_round 93: strategy sampled 10 clients (out of 10)
+[2023-09-29 06:26:10,427][flwr][DEBUG] - evaluate_round 93 received 10 results and 0 failures
+[2023-09-29 06:26:10,428][flwr][DEBUG] - fit_round 94: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110466 Loss1: 0.037187 Loss2: 0.073279
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.084070 Loss1: 0.014305 Loss2: 0.069765
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.080423 Loss1: 0.011326 Loss2: 0.069097
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080557 Loss1: 0.011341 Loss2: 0.069215
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072644 Loss1: 0.004509 Loss2: 0.068135
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075593 Loss1: 0.007769 Loss2: 0.067824
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075085 Loss1: 0.007696 Loss2: 0.067389
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075566 Loss1: 0.008201 Loss2: 0.067364
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086404 Loss1: 0.018381 Loss2: 0.068023
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.077473 Loss1: 0.009193 Loss2: 0.068280
+(DefaultActor pid=1838052) >> Training accuracy: 0.999399
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.539367 Loss1: 0.044702 Loss2: 0.494665
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.518679 Loss1: 0.035253 Loss2: 0.483425
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.525908 Loss1: 0.049530 Loss2: 0.476378
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.534598 Loss1: 0.059113 Loss2: 0.475484
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.528559 Loss1: 0.059044 Loss2: 0.469515
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.527778 Loss1: 0.058273 Loss2: 0.469504
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.545428 Loss1: 0.075728 Loss2: 0.469700
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.559491 Loss1: 0.089147 Loss2: 0.470344
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.556594 Loss1: 0.084055 Loss2: 0.472539
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.545373 Loss1: 0.077072 Loss2: 0.468301
+(DefaultActor pid=1838052) >> Training accuracy: 0.983188
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.082356 Loss1: 0.040446 Loss2: 0.041910
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.059505 Loss1: 0.017612 Loss2: 0.041893
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056417 Loss1: 0.015382 Loss2: 0.041036
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.054102 Loss1: 0.013174 Loss2: 0.040929
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.058146 Loss1: 0.016976 Loss2: 0.041170
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.054487 Loss1: 0.013559 Loss2: 0.040929
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047388 Loss1: 0.007363 Loss2: 0.040026
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.047438 Loss1: 0.007583 Loss2: 0.039855
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.060456 Loss1: 0.020476 Loss2: 0.039980
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053489 Loss1: 0.012547 Loss2: 0.040942
+(DefaultActor pid=1838052) >> Training accuracy: 0.997596
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.057365 Loss1: 0.029190 Loss2: 0.028175
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.038585 Loss1: 0.010045 Loss2: 0.028540
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.038119 Loss1: 0.009607 Loss2: 0.028512
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.034691 Loss1: 0.006268 Loss2: 0.028423
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.037409 Loss1: 0.008847 Loss2: 0.028562
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.035451 Loss1: 0.006902 Loss2: 0.028549
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.031994 Loss1: 0.003631 Loss2: 0.028364
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.035473 Loss1: 0.007327 Loss2: 0.028146
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.032847 Loss1: 0.004526 Loss2: 0.028321
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.033471 Loss1: 0.005506 Loss2: 0.027965
+(DefaultActor pid=1838052) >> Training accuracy: 0.999604
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.069533 Loss1: 0.039347 Loss2: 0.030187
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.054217 Loss1: 0.023325 Loss2: 0.030892
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.051313 Loss1: 0.020047 Loss2: 0.031266
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046351 Loss1: 0.015106 Loss2: 0.031245
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043210 Loss1: 0.011960 Loss2: 0.031251
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048492 Loss1: 0.017376 Loss2: 0.031116
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050771 Loss1: 0.019146 Loss2: 0.031625
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.047249 Loss1: 0.015233 Loss2: 0.032015
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.042554 Loss1: 0.011245 Loss2: 0.031309
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.050523 Loss1: 0.018801 Loss2: 0.031723
+(DefaultActor pid=1838052) >> Training accuracy: 0.997033
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.080586 Loss1: 0.049815 Loss2: 0.030771
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049742 Loss1: 0.019389 Loss2: 0.030353
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.042376 Loss1: 0.012455 Loss2: 0.029921
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038378 Loss1: 0.008679 Loss2: 0.029699
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041654 Loss1: 0.012044 Loss2: 0.029610
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045943 Loss1: 0.015849 Loss2: 0.030094
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047259 Loss1: 0.016727 Loss2: 0.030531
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041804 Loss1: 0.011442 Loss2: 0.030363
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.039506 Loss1: 0.009459 Loss2: 0.030047
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.039729 Loss1: 0.009469 Loss2: 0.030260
+(DefaultActor pid=1838052) >> Training accuracy: 0.999155
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.058072 Loss1: 0.028313 Loss2: 0.029758
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047329 Loss1: 0.017114 Loss2: 0.030216
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045122 Loss1: 0.014904 Loss2: 0.030218
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.041075 Loss1: 0.011114 Loss2: 0.029961
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046530 Loss1: 0.016100 Loss2: 0.030430
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046487 Loss1: 0.015802 Loss2: 0.030685
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047831 Loss1: 0.017084 Loss2: 0.030747
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.048839 Loss1: 0.017357 Loss2: 0.031482
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.049066 Loss1: 0.017854 Loss2: 0.031212
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056523 Loss1: 0.024816 Loss2: 0.031707
+(DefaultActor pid=1838052) >> Training accuracy: 0.997904
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.293355 Loss1: 0.047727 Loss2: 0.245627
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.277894 Loss1: 0.041445 Loss2: 0.236449
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.293092 Loss1: 0.058348 Loss2: 0.234744
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.332158 Loss1: 0.091979 Loss2: 0.240179
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.341653 Loss1: 0.098441 Loss2: 0.243212
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.340289 Loss1: 0.101590 Loss2: 0.238699
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.365897 Loss1: 0.123295 Loss2: 0.242603
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.350486 Loss1: 0.107353 Loss2: 0.243134
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.359660 Loss1: 0.115577 Loss2: 0.244082
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.377701 Loss1: 0.133488 Loss2: 0.244213
+(DefaultActor pid=1838052) >> Training accuracy: 0.976562
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.122366 Loss1: 0.036535 Loss2: 0.085831
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.095511 Loss1: 0.014093 Loss2: 0.081418
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.088762 Loss1: 0.007852 Loss2: 0.080910
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.087355 Loss1: 0.006895 Loss2: 0.080460
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092784 Loss1: 0.011999 Loss2: 0.080785
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.093917 Loss1: 0.012926 Loss2: 0.080991
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.098371 Loss1: 0.017149 Loss2: 0.081223
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091655 Loss1: 0.010199 Loss2: 0.081456
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.091236 Loss1: 0.010059 Loss2: 0.081176
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091141 Loss1: 0.009791 Loss2: 0.081350
+(DefaultActor pid=1838052) >> Training accuracy: 0.996242
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.311708 Loss1: 0.050380 Loss2: 0.261328
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.260853 Loss1: 0.019363 Loss2: 0.241489
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.257494 Loss1: 0.019530 Loss2: 0.237964
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.252537 Loss1: 0.017164 Loss2: 0.235373
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.259718 Loss1: 0.024401 Loss2: 0.235317
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.255187 Loss1: 0.020024 Loss2: 0.235164
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.251642 Loss1: 0.017524 Loss2: 0.234118
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.246466 Loss1: 0.013908 Loss2: 0.232558
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.258874 Loss1: 0.025401 Loss2: 0.233473
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.247594 Loss1: 0.013961 Loss2: 0.233633
+(DefaultActor pid=1838052) >> Training accuracy: 0.998915
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 06:54:46,133][flwr][DEBUG] - fit_round 94 received 10 results and 0 failures
+>> Test accuracy: 0.666500
+[2023-09-29 06:55:23,773][flwr][INFO] - fit progress: (94, 2.432824038849852, {'accuracy': 0.6665}, 174946.66300495435)
+[2023-09-29 06:55:23,773][flwr][DEBUG] - evaluate_round 94: strategy sampled 10 clients (out of 10)
+[2023-09-29 06:55:58,588][flwr][DEBUG] - evaluate_round 94 received 10 results and 0 failures
+[2023-09-29 06:55:58,589][flwr][DEBUG] - fit_round 95: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.548781 Loss1: 0.046104 Loss2: 0.502677
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.510661 Loss1: 0.023152 Loss2: 0.487508
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.520165 Loss1: 0.035032 Loss2: 0.485133
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.528778 Loss1: 0.044354 Loss2: 0.484424
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.532262 Loss1: 0.049960 Loss2: 0.482302
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.538332 Loss1: 0.055550 Loss2: 0.482782
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.552523 Loss1: 0.068361 Loss2: 0.484161
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.557828 Loss1: 0.075605 Loss2: 0.482223
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.556086 Loss1: 0.073278 Loss2: 0.482809
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.540596 Loss1: 0.060924 Loss2: 0.479672
+(DefaultActor pid=1838052) >> Training accuracy: 0.988948
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.656795 Loss1: 0.043361 Loss2: 0.613434
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.635113 Loss1: 0.042885 Loss2: 0.592228
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.614583 Loss1: 0.043989 Loss2: 0.570594
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.603436 Loss1: 0.046760 Loss2: 0.556676
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.614414 Loss1: 0.060461 Loss2: 0.553953
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.607194 Loss1: 0.053821 Loss2: 0.553373
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.611967 Loss1: 0.062693 Loss2: 0.549274
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.603677 Loss1: 0.055121 Loss2: 0.548556
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.619788 Loss1: 0.071683 Loss2: 0.548104
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.610820 Loss1: 0.062930 Loss2: 0.547890
+(DefaultActor pid=1838052) >> Training accuracy: 0.988582
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.062750 Loss1: 0.032107 Loss2: 0.030643
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053580 Loss1: 0.021554 Loss2: 0.032026
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049123 Loss1: 0.017326 Loss2: 0.031798
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044107 Loss1: 0.012237 Loss2: 0.031870
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.047437 Loss1: 0.015487 Loss2: 0.031950
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046549 Loss1: 0.014713 Loss2: 0.031835
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043280 Loss1: 0.011467 Loss2: 0.031814
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.062050 Loss1: 0.029514 Loss2: 0.032536
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.059366 Loss1: 0.026242 Loss2: 0.033124
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.058463 Loss1: 0.024762 Loss2: 0.033701
+(DefaultActor pid=1838052) >> Training accuracy: 0.995066
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.058094 Loss1: 0.029736 Loss2: 0.028358
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.038943 Loss1: 0.009556 Loss2: 0.029386
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.037454 Loss1: 0.008319 Loss2: 0.029135
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.040218 Loss1: 0.011036 Loss2: 0.029182
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.034829 Loss1: 0.005809 Loss2: 0.029020
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.036113 Loss1: 0.007183 Loss2: 0.028930
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.035847 Loss1: 0.006871 Loss2: 0.028976
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041231 Loss1: 0.012174 Loss2: 0.029057
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051941 Loss1: 0.021798 Loss2: 0.030143
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053211 Loss1: 0.022544 Loss2: 0.030667
+(DefaultActor pid=1838052) >> Training accuracy: 0.996638
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.084105 Loss1: 0.054194 Loss2: 0.029911
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051820 Loss1: 0.020611 Loss2: 0.031209
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057332 Loss1: 0.025780 Loss2: 0.031552
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044786 Loss1: 0.013672 Loss2: 0.031114
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.045549 Loss1: 0.014553 Loss2: 0.030997
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042915 Loss1: 0.011967 Loss2: 0.030948
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047287 Loss1: 0.016332 Loss2: 0.030955
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051627 Loss1: 0.020167 Loss2: 0.031460
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068786 Loss1: 0.036547 Loss2: 0.032239
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.067214 Loss1: 0.034116 Loss2: 0.033097
+(DefaultActor pid=1838052) >> Training accuracy: 0.988387
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065643 Loss1: 0.035393 Loss2: 0.030250
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052707 Loss1: 0.021388 Loss2: 0.031319
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046568 Loss1: 0.015011 Loss2: 0.031557
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046123 Loss1: 0.014323 Loss2: 0.031799
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.044752 Loss1: 0.013184 Loss2: 0.031568
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042386 Loss1: 0.010865 Loss2: 0.031522
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056096 Loss1: 0.024512 Loss2: 0.031584
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066697 Loss1: 0.034062 Loss2: 0.032635
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067985 Loss1: 0.034582 Loss2: 0.033403
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068810 Loss1: 0.034585 Loss2: 0.034225
+(DefaultActor pid=1838052) >> Training accuracy: 0.996394
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.405841 Loss1: 0.049492 Loss2: 0.356349
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.388848 Loss1: 0.042845 Loss2: 0.346003
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.404767 Loss1: 0.061538 Loss2: 0.343228
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.428909 Loss1: 0.084414 Loss2: 0.344494
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.446586 Loss1: 0.096738 Loss2: 0.349849
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.448414 Loss1: 0.100036 Loss2: 0.348378
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.454726 Loss1: 0.105576 Loss2: 0.349151
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.438072 Loss1: 0.094007 Loss2: 0.344065
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.444214 Loss1: 0.098949 Loss2: 0.345265
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.439681 Loss1: 0.096403 Loss2: 0.343278
+(DefaultActor pid=1838052) >> Training accuracy: 0.984573
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.064985 Loss1: 0.033991 Loss2: 0.030994
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.050529 Loss1: 0.018462 Loss2: 0.032067
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047581 Loss1: 0.015370 Loss2: 0.032211
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047756 Loss1: 0.015387 Loss2: 0.032370
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.047028 Loss1: 0.014648 Loss2: 0.032380
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047866 Loss1: 0.015538 Loss2: 0.032328
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056711 Loss1: 0.023613 Loss2: 0.033098
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.045970 Loss1: 0.012980 Loss2: 0.032990
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051945 Loss1: 0.018882 Loss2: 0.033063
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.061848 Loss1: 0.027859 Loss2: 0.033989
+(DefaultActor pid=1838052) >> Training accuracy: 0.997033
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.593286 Loss1: 0.050753 Loss2: 0.542534
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.561686 Loss1: 0.033492 Loss2: 0.528193
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.570427 Loss1: 0.047984 Loss2: 0.522443
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.563443 Loss1: 0.043882 Loss2: 0.519561
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.568680 Loss1: 0.053131 Loss2: 0.515549
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.585074 Loss1: 0.069202 Loss2: 0.515872
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.616136 Loss1: 0.099058 Loss2: 0.517077
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.654474 Loss1: 0.133592 Loss2: 0.520881
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.620880 Loss1: 0.103116 Loss2: 0.517764
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.608952 Loss1: 0.094297 Loss2: 0.514655
+(DefaultActor pid=1838052) >> Training accuracy: 0.986155
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078255 Loss1: 0.044389 Loss2: 0.033866
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.045782 Loss1: 0.011853 Loss2: 0.033928
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047826 Loss1: 0.013691 Loss2: 0.034135
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.043581 Loss1: 0.009613 Loss2: 0.033968
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048812 Loss1: 0.014806 Loss2: 0.034007
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.043700 Loss1: 0.009758 Loss2: 0.033942
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043775 Loss1: 0.009868 Loss2: 0.033906
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.042608 Loss1: 0.008829 Loss2: 0.033779
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.042374 Loss1: 0.008419 Loss2: 0.033955
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.042471 Loss1: 0.008339 Loss2: 0.034132
+(DefaultActor pid=1838052) >> Training accuracy: 0.999349
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 07:24:52,482][flwr][DEBUG] - fit_round 95 received 10 results and 0 failures
+>> Test accuracy: 0.667000
+[2023-09-29 07:25:30,114][flwr][INFO] - fit progress: (95, 2.362161700337078, {'accuracy': 0.667}, 176753.00478482526)
+[2023-09-29 07:25:30,115][flwr][DEBUG] - evaluate_round 95: strategy sampled 10 clients (out of 10)
+[2023-09-29 07:26:06,249][flwr][DEBUG] - evaluate_round 95 received 10 results and 0 failures
+[2023-09-29 07:26:06,250][flwr][DEBUG] - fit_round 96: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.060340 Loss1: 0.030559 Loss2: 0.029781
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049432 Loss1: 0.019117 Loss2: 0.030315
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.037841 Loss1: 0.007385 Loss2: 0.030456
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.036836 Loss1: 0.006613 Loss2: 0.030223
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.036979 Loss1: 0.006717 Loss2: 0.030263
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.035406 Loss1: 0.005311 Loss2: 0.030095
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.039030 Loss1: 0.008767 Loss2: 0.030263
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.040398 Loss1: 0.009674 Loss2: 0.030724
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.046717 Loss1: 0.015544 Loss2: 0.031173
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040527 Loss1: 0.009163 Loss2: 0.031364
+(DefaultActor pid=1838052) >> Training accuracy: 0.998616
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.623382 Loss1: 0.051037 Loss2: 0.572345
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.614379 Loss1: 0.051221 Loss2: 0.563157
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.603091 Loss1: 0.049289 Loss2: 0.553803
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.609932 Loss1: 0.061844 Loss2: 0.548088
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613025 Loss1: 0.068017 Loss2: 0.545007
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.600232 Loss1: 0.058569 Loss2: 0.541662
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.588822 Loss1: 0.049830 Loss2: 0.538992
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.574998 Loss1: 0.041718 Loss2: 0.533280
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.596328 Loss1: 0.065572 Loss2: 0.530755
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.622831 Loss1: 0.088661 Loss2: 0.534170
+(DefaultActor pid=1838052) >> Training accuracy: 0.978516
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.512524 Loss1: 0.046882 Loss2: 0.465641
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.501720 Loss1: 0.048880 Loss2: 0.452840
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.506365 Loss1: 0.056842 Loss2: 0.449524
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.490490 Loss1: 0.045535 Loss2: 0.444955
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.516481 Loss1: 0.071637 Loss2: 0.444844
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.519268 Loss1: 0.072506 Loss2: 0.446762
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.493083 Loss1: 0.050676 Loss2: 0.442406
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.492261 Loss1: 0.053369 Loss2: 0.438891
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.515195 Loss1: 0.074288 Loss2: 0.440907
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.538769 Loss1: 0.095002 Loss2: 0.443767
+(DefaultActor pid=1838052) >> Training accuracy: 0.968750
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.064732 Loss1: 0.035217 Loss2: 0.029515
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.040580 Loss1: 0.010175 Loss2: 0.030406
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.041445 Loss1: 0.011170 Loss2: 0.030276
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039524 Loss1: 0.009116 Loss2: 0.030409
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043641 Loss1: 0.013110 Loss2: 0.030531
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.049363 Loss1: 0.018658 Loss2: 0.030704
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.053109 Loss1: 0.022256 Loss2: 0.030852
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050274 Loss1: 0.018962 Loss2: 0.031312
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.047855 Loss1: 0.016522 Loss2: 0.031333
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.041157 Loss1: 0.009957 Loss2: 0.031200
+(DefaultActor pid=1838052) >> Training accuracy: 0.999199
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075094 Loss1: 0.046130 Loss2: 0.028964
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.044847 Loss1: 0.015281 Loss2: 0.029565
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045376 Loss1: 0.015603 Loss2: 0.029773
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.041503 Loss1: 0.011598 Loss2: 0.029905
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.040974 Loss1: 0.011429 Loss2: 0.029545
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.052354 Loss1: 0.022152 Loss2: 0.030202
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.057439 Loss1: 0.026540 Loss2: 0.030899
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071672 Loss1: 0.039823 Loss2: 0.031848
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056706 Loss1: 0.025255 Loss2: 0.031451
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083940 Loss1: 0.051395 Loss2: 0.032545
+(DefaultActor pid=1838052) >> Training accuracy: 0.992188
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065331 Loss1: 0.031605 Loss2: 0.033726
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.048756 Loss1: 0.014647 Loss2: 0.034109
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047412 Loss1: 0.013197 Loss2: 0.034214
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047453 Loss1: 0.013241 Loss2: 0.034213
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.042436 Loss1: 0.008800 Loss2: 0.033635
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038424 Loss1: 0.004790 Loss2: 0.033634
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.039830 Loss1: 0.006700 Loss2: 0.033130
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041534 Loss1: 0.008239 Loss2: 0.033295
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.041286 Loss1: 0.008091 Loss2: 0.033195
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.047830 Loss1: 0.014192 Loss2: 0.033638
+(DefaultActor pid=1838052) >> Training accuracy: 0.996440
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.056651 Loss1: 0.028744 Loss2: 0.027907
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.045854 Loss1: 0.016768 Loss2: 0.029087
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.037951 Loss1: 0.008564 Loss2: 0.029387
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.037614 Loss1: 0.008540 Loss2: 0.029073
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.040430 Loss1: 0.011339 Loss2: 0.029092
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.044693 Loss1: 0.014434 Loss2: 0.030259
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050328 Loss1: 0.019740 Loss2: 0.030589
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057468 Loss1: 0.026093 Loss2: 0.031376
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057385 Loss1: 0.025755 Loss2: 0.031630
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055521 Loss1: 0.023553 Loss2: 0.031968
+(DefaultActor pid=1838052) >> Training accuracy: 0.996044
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083450 Loss1: 0.054272 Loss2: 0.029177
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057452 Loss1: 0.026879 Loss2: 0.030573
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052028 Loss1: 0.021380 Loss2: 0.030647
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.043875 Loss1: 0.013398 Loss2: 0.030477
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043299 Loss1: 0.012940 Loss2: 0.030359
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042412 Loss1: 0.012065 Loss2: 0.030347
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049777 Loss1: 0.019074 Loss2: 0.030703
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061648 Loss1: 0.030382 Loss2: 0.031267
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056023 Loss1: 0.024368 Loss2: 0.031655
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.049466 Loss1: 0.017674 Loss2: 0.031791
+(DefaultActor pid=1838052) >> Training accuracy: 0.998522
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.063258 Loss1: 0.029279 Loss2: 0.033979
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.043540 Loss1: 0.009752 Loss2: 0.033788
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046370 Loss1: 0.012488 Loss2: 0.033881
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047796 Loss1: 0.013564 Loss2: 0.034233
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049889 Loss1: 0.015545 Loss2: 0.034344
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047356 Loss1: 0.013055 Loss2: 0.034301
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.044150 Loss1: 0.009915 Loss2: 0.034235
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046422 Loss1: 0.012095 Loss2: 0.034327
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.047747 Loss1: 0.012895 Loss2: 0.034852
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053839 Loss1: 0.018898 Loss2: 0.034941
+(DefaultActor pid=1838052) >> Training accuracy: 0.995998
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.064461 Loss1: 0.035806 Loss2: 0.028655
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049076 Loss1: 0.018939 Loss2: 0.030137
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043187 Loss1: 0.013175 Loss2: 0.030013
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.042490 Loss1: 0.012644 Loss2: 0.029846
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041436 Loss1: 0.011364 Loss2: 0.030071
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.039949 Loss1: 0.010021 Loss2: 0.029928
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.041656 Loss1: 0.011503 Loss2: 0.030154
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.043643 Loss1: 0.013289 Loss2: 0.030354
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.038146 Loss1: 0.007936 Loss2: 0.030210
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.038103 Loss1: 0.008037 Loss2: 0.030067
+(DefaultActor pid=1838052) >> Training accuracy: 0.999589
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 07:55:01,121][flwr][DEBUG] - fit_round 96 received 10 results and 0 failures
+>> Test accuracy: 0.661600
+[2023-09-29 07:55:36,685][flwr][INFO] - fit progress: (96, 2.4384049723704404, {'accuracy': 0.6616}, 178559.57587395515)
+[2023-09-29 07:55:36,686][flwr][DEBUG] - evaluate_round 96: strategy sampled 10 clients (out of 10)
+[2023-09-29 07:56:11,644][flwr][DEBUG] - evaluate_round 96 received 10 results and 0 failures
+[2023-09-29 07:56:11,645][flwr][DEBUG] - fit_round 97: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.079201 Loss1: 0.044307 Loss2: 0.034894
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051630 Loss1: 0.016083 Loss2: 0.035547
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058143 Loss1: 0.022472 Loss2: 0.035672
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048878 Loss1: 0.013551 Loss2: 0.035327
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048773 Loss1: 0.013478 Loss2: 0.035295
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045660 Loss1: 0.010540 Loss2: 0.035119
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048224 Loss1: 0.013231 Loss2: 0.034993
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.059652 Loss1: 0.024064 Loss2: 0.035588
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.061979 Loss1: 0.025861 Loss2: 0.036118
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062316 Loss1: 0.025888 Loss2: 0.036428
+(DefaultActor pid=1838052) >> Training accuracy: 0.997613
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.613362 Loss1: 0.041162 Loss2: 0.572199
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.602336 Loss1: 0.043445 Loss2: 0.558891
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.634721 Loss1: 0.074418 Loss2: 0.560303
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.646811 Loss1: 0.089407 Loss2: 0.557404
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613747 Loss1: 0.062880 Loss2: 0.550867
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.606836 Loss1: 0.059779 Loss2: 0.547057
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.629734 Loss1: 0.079527 Loss2: 0.550207
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.645443 Loss1: 0.094997 Loss2: 0.550446
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.660590 Loss1: 0.107696 Loss2: 0.552894
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.640517 Loss1: 0.092468 Loss2: 0.548049
+(DefaultActor pid=1838052) >> Training accuracy: 0.982936
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.052777 Loss1: 0.025819 Loss2: 0.026958
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.036519 Loss1: 0.008874 Loss2: 0.027644
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043347 Loss1: 0.015231 Loss2: 0.028116
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048600 Loss1: 0.020040 Loss2: 0.028561
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.045275 Loss1: 0.016237 Loss2: 0.029037
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040131 Loss1: 0.011429 Loss2: 0.028702
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.036546 Loss1: 0.007969 Loss2: 0.028577
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.045030 Loss1: 0.016672 Loss2: 0.028358
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056171 Loss1: 0.026744 Loss2: 0.029427
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056962 Loss1: 0.026638 Loss2: 0.030324
+(DefaultActor pid=1838052) >> Training accuracy: 0.996638
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.628083 Loss1: 0.034002 Loss2: 0.594081
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.608314 Loss1: 0.027965 Loss2: 0.580349
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.615309 Loss1: 0.039854 Loss2: 0.575455
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.613916 Loss1: 0.041234 Loss2: 0.572682
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613677 Loss1: 0.044763 Loss2: 0.568913
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.603527 Loss1: 0.039002 Loss2: 0.564524
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.600781 Loss1: 0.040526 Loss2: 0.560255
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.617002 Loss1: 0.058709 Loss2: 0.558293
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.632651 Loss1: 0.068477 Loss2: 0.564175
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.628182 Loss1: 0.067241 Loss2: 0.560941
+(DefaultActor pid=1838052) >> Training accuracy: 0.989383
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.676872 Loss1: 0.044786 Loss2: 0.632087
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.656034 Loss1: 0.034128 Loss2: 0.621906
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.646525 Loss1: 0.031786 Loss2: 0.614739
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.646006 Loss1: 0.035973 Loss2: 0.610032
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.663049 Loss1: 0.055582 Loss2: 0.607467
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.659951 Loss1: 0.055409 Loss2: 0.604542
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.662044 Loss1: 0.060314 Loss2: 0.601730
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.673822 Loss1: 0.073299 Loss2: 0.600523
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.674618 Loss1: 0.075371 Loss2: 0.599248
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.666939 Loss1: 0.071995 Loss2: 0.594944
+(DefaultActor pid=1838052) >> Training accuracy: 0.977848
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.651725 Loss1: 0.051222 Loss2: 0.600503
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.630316 Loss1: 0.037295 Loss2: 0.593021
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.621509 Loss1: 0.038870 Loss2: 0.582639
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.619217 Loss1: 0.036743 Loss2: 0.582474
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.621586 Loss1: 0.044857 Loss2: 0.576729
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.637770 Loss1: 0.063554 Loss2: 0.574216
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.644034 Loss1: 0.069280 Loss2: 0.574754
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.640288 Loss1: 0.068926 Loss2: 0.571362
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.657318 Loss1: 0.085771 Loss2: 0.571547
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.651679 Loss1: 0.081227 Loss2: 0.570453
+(DefaultActor pid=1838052) >> Training accuracy: 0.978441
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092739 Loss1: 0.037250 Loss2: 0.055489
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.070755 Loss1: 0.018018 Loss2: 0.052737
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068109 Loss1: 0.015535 Loss2: 0.052574
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063818 Loss1: 0.011243 Loss2: 0.052575
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064558 Loss1: 0.012594 Loss2: 0.051964
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060489 Loss1: 0.008800 Loss2: 0.051688
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072672 Loss1: 0.019967 Loss2: 0.052705
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073645 Loss1: 0.020675 Loss2: 0.052970
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068419 Loss1: 0.015763 Loss2: 0.052656
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.066765 Loss1: 0.014162 Loss2: 0.052603
+(DefaultActor pid=1838052) >> Training accuracy: 0.998220
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.581819 Loss1: 0.044835 Loss2: 0.536984
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.558548 Loss1: 0.032912 Loss2: 0.525636
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.553959 Loss1: 0.033114 Loss2: 0.520845
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.549609 Loss1: 0.034162 Loss2: 0.515447
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.549386 Loss1: 0.034103 Loss2: 0.515283
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.556947 Loss1: 0.043391 Loss2: 0.513556
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.566227 Loss1: 0.051572 Loss2: 0.514655
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.571161 Loss1: 0.057243 Loss2: 0.513918
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.569418 Loss1: 0.054606 Loss2: 0.514812
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.579376 Loss1: 0.063368 Loss2: 0.516008
+(DefaultActor pid=1838052) >> Training accuracy: 0.989329
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.067762 Loss1: 0.039682 Loss2: 0.028079
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047411 Loss1: 0.018534 Loss2: 0.028877
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.044362 Loss1: 0.015531 Loss2: 0.028831
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.042811 Loss1: 0.013756 Loss2: 0.029055
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.044947 Loss1: 0.015662 Loss2: 0.029284
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.043699 Loss1: 0.014184 Loss2: 0.029515
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.040352 Loss1: 0.011035 Loss2: 0.029318
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.039108 Loss1: 0.009739 Loss2: 0.029369
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.043160 Loss1: 0.013463 Loss2: 0.029697
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.037832 Loss1: 0.008469 Loss2: 0.029363
+(DefaultActor pid=1838052) >> Training accuracy: 0.997255
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078080 Loss1: 0.034478 Loss2: 0.043602
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.060356 Loss1: 0.018719 Loss2: 0.041637
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054633 Loss1: 0.014571 Loss2: 0.040063
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.056015 Loss1: 0.016592 Loss2: 0.039423
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046490 Loss1: 0.007593 Loss2: 0.038896
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050656 Loss1: 0.012393 Loss2: 0.038263
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.053522 Loss1: 0.014756 Loss2: 0.038765
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.060399 Loss1: 0.021147 Loss2: 0.039252
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.060786 Loss1: 0.021442 Loss2: 0.039344
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.058868 Loss1: 0.019547 Loss2: 0.039320
+(DefaultActor pid=1838052) >> Training accuracy: 0.997596
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 08:24:41,483][flwr][DEBUG] - fit_round 97 received 10 results and 0 failures
+>> Test accuracy: 0.665400
+[2023-09-29 08:25:17,679][flwr][INFO] - fit progress: (97, 2.410859399329359, {'accuracy': 0.6654}, 180340.56950572738)
+[2023-09-29 08:25:17,680][flwr][DEBUG] - evaluate_round 97: strategy sampled 10 clients (out of 10)
+[2023-09-29 08:25:53,375][flwr][DEBUG] - evaluate_round 97 received 10 results and 0 failures
+[2023-09-29 08:25:53,395][flwr][DEBUG] - fit_round 98: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.549777 Loss1: 0.043757 Loss2: 0.506020
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.533998 Loss1: 0.035112 Loss2: 0.498886
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.539338 Loss1: 0.042695 Loss2: 0.496644
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.533252 Loss1: 0.044032 Loss2: 0.489220
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.525870 Loss1: 0.039986 Loss2: 0.485883
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.537527 Loss1: 0.054712 Loss2: 0.482815
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.532382 Loss1: 0.049909 Loss2: 0.482473
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.557447 Loss1: 0.073527 Loss2: 0.483920
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.598190 Loss1: 0.108657 Loss2: 0.489533
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.604176 Loss1: 0.115428 Loss2: 0.488748
+(DefaultActor pid=1838052) >> Training accuracy: 0.984968
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.586126 Loss1: 0.057872 Loss2: 0.528254
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.560101 Loss1: 0.043648 Loss2: 0.516453
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.563934 Loss1: 0.047748 Loss2: 0.516186
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.555805 Loss1: 0.044376 Loss2: 0.511428
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.574949 Loss1: 0.065538 Loss2: 0.509411
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.572481 Loss1: 0.065818 Loss2: 0.506663
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.567877 Loss1: 0.061644 Loss2: 0.506233
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.546704 Loss1: 0.045630 Loss2: 0.501074
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.564503 Loss1: 0.062443 Loss2: 0.502060
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.555968 Loss1: 0.056911 Loss2: 0.499057
+(DefaultActor pid=1838052) >> Training accuracy: 0.986486
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.590116 Loss1: 0.043552 Loss2: 0.546564
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.516645 Loss1: 0.038507 Loss2: 0.478139
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.517280 Loss1: 0.055420 Loss2: 0.461861
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.515714 Loss1: 0.057608 Loss2: 0.458106
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.521289 Loss1: 0.064280 Loss2: 0.457009
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.566376 Loss1: 0.105529 Loss2: 0.460847
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.573215 Loss1: 0.111553 Loss2: 0.461663
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.550571 Loss1: 0.094488 Loss2: 0.456082
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.548599 Loss1: 0.095250 Loss2: 0.453349
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.547884 Loss1: 0.094037 Loss2: 0.453847
+(DefaultActor pid=1838052) >> Training accuracy: 0.977564
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.067766 Loss1: 0.037395 Loss2: 0.030371
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047285 Loss1: 0.016018 Loss2: 0.031267
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.044344 Loss1: 0.012981 Loss2: 0.031363
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038346 Loss1: 0.007416 Loss2: 0.030931
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.036410 Loss1: 0.005644 Loss2: 0.030766
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038429 Loss1: 0.008041 Loss2: 0.030389
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.037872 Loss1: 0.007634 Loss2: 0.030239
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.038358 Loss1: 0.008115 Loss2: 0.030243
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.039163 Loss1: 0.009079 Loss2: 0.030084
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.039657 Loss1: 0.009274 Loss2: 0.030383
+(DefaultActor pid=1838052) >> Training accuracy: 0.998616
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.090059 Loss1: 0.033480 Loss2: 0.056579
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080192 Loss1: 0.026249 Loss2: 0.053943
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.079071 Loss1: 0.025447 Loss2: 0.053624
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.084066 Loss1: 0.030342 Loss2: 0.053724
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077223 Loss1: 0.023740 Loss2: 0.053483
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.072529 Loss1: 0.019496 Loss2: 0.053034
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.078954 Loss1: 0.026482 Loss2: 0.052472
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.084011 Loss1: 0.030998 Loss2: 0.053013
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096512 Loss1: 0.042889 Loss2: 0.053623
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.100532 Loss1: 0.045552 Loss2: 0.054980
+(DefaultActor pid=1838052) >> Training accuracy: 0.988715
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078757 Loss1: 0.041823 Loss2: 0.036934
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057158 Loss1: 0.019653 Loss2: 0.037505
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056250 Loss1: 0.018631 Loss2: 0.037619
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.061552 Loss1: 0.023596 Loss2: 0.037957
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059991 Loss1: 0.022248 Loss2: 0.037743
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.057174 Loss1: 0.019012 Loss2: 0.038162
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052241 Loss1: 0.014950 Loss2: 0.037291
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.054873 Loss1: 0.017266 Loss2: 0.037607
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.055658 Loss1: 0.017738 Loss2: 0.037920
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.051158 Loss1: 0.013499 Loss2: 0.037658
+(DefaultActor pid=1838052) >> Training accuracy: 0.997533
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.060092 Loss1: 0.030627 Loss2: 0.029465
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.042577 Loss1: 0.012876 Loss2: 0.029700
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046277 Loss1: 0.016380 Loss2: 0.029897
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.041854 Loss1: 0.011636 Loss2: 0.030218
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.039437 Loss1: 0.009516 Loss2: 0.029921
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040861 Loss1: 0.010740 Loss2: 0.030121
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.040881 Loss1: 0.010936 Loss2: 0.029945
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.039393 Loss1: 0.009473 Loss2: 0.029920
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.035240 Loss1: 0.005221 Loss2: 0.030020
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040414 Loss1: 0.010637 Loss2: 0.029777
+(DefaultActor pid=1838052) >> Training accuracy: 0.998220
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078735 Loss1: 0.027144 Loss2: 0.051590
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.064853 Loss1: 0.015020 Loss2: 0.049833
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061920 Loss1: 0.012209 Loss2: 0.049711
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.059924 Loss1: 0.010502 Loss2: 0.049421
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.057479 Loss1: 0.008267 Loss2: 0.049212
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.057075 Loss1: 0.008080 Loss2: 0.048996
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056646 Loss1: 0.008031 Loss2: 0.048615
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051844 Loss1: 0.003231 Loss2: 0.048613
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051384 Loss1: 0.003341 Loss2: 0.048043
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055235 Loss1: 0.007448 Loss2: 0.047787
+(DefaultActor pid=1838052) >> Training accuracy: 1.000000
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.250230 Loss1: 0.040699 Loss2: 0.209530
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.225524 Loss1: 0.026124 Loss2: 0.199401
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.220431 Loss1: 0.024229 Loss2: 0.196203
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.212984 Loss1: 0.017082 Loss2: 0.195902
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.206861 Loss1: 0.014108 Loss2: 0.192752
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.206119 Loss1: 0.014018 Loss2: 0.192101
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.226799 Loss1: 0.033210 Loss2: 0.193589
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.296702 Loss1: 0.093519 Loss2: 0.203182
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.252407 Loss1: 0.052149 Loss2: 0.200258
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.236775 Loss1: 0.038658 Loss2: 0.198117
+(DefaultActor pid=1838052) >> Training accuracy: 0.995808
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.085674 Loss1: 0.033271 Loss2: 0.052403
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066818 Loss1: 0.016953 Loss2: 0.049864
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058875 Loss1: 0.009403 Loss2: 0.049471
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068729 Loss1: 0.019642 Loss2: 0.049087
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070481 Loss1: 0.020923 Loss2: 0.049558
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.066848 Loss1: 0.016730 Loss2: 0.050119
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062186 Loss1: 0.012442 Loss2: 0.049744
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061445 Loss1: 0.011959 Loss2: 0.049485
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.061224 Loss1: 0.011802 Loss2: 0.049422
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060591 Loss1: 0.011275 Loss2: 0.049315
+(DefaultActor pid=1838052) >> Training accuracy: 0.997596
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 08:54:22,744][flwr][DEBUG] - fit_round 98 received 10 results and 0 failures
+>> Test accuracy: 0.665100
+[2023-09-29 08:54:58,949][flwr][INFO] - fit progress: (98, 2.4004773120529737, {'accuracy': 0.6651}, 182121.83961292123)
+[2023-09-29 08:54:58,950][flwr][DEBUG] - evaluate_round 98: strategy sampled 10 clients (out of 10)
+[2023-09-29 08:55:34,687][flwr][DEBUG] - evaluate_round 98 received 10 results and 0 failures
+[2023-09-29 08:55:34,687][flwr][DEBUG] - fit_round 99: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.091405 Loss1: 0.048241 Loss2: 0.043164
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069584 Loss1: 0.027362 Loss2: 0.042222
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.064826 Loss1: 0.022848 Loss2: 0.041977
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064725 Loss1: 0.022653 Loss2: 0.042072
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.062853 Loss1: 0.021004 Loss2: 0.041849
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.073984 Loss1: 0.031241 Loss2: 0.042743
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.078138 Loss1: 0.035332 Loss2: 0.042805
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068190 Loss1: 0.025580 Loss2: 0.042610
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067457 Loss1: 0.024652 Loss2: 0.042805
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.069374 Loss1: 0.026923 Loss2: 0.042451
+(DefaultActor pid=1838052) >> Training accuracy: 0.995253
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.052547 Loss1: 0.024230 Loss2: 0.028318
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.037621 Loss1: 0.008835 Loss2: 0.028786
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.037036 Loss1: 0.008433 Loss2: 0.028603
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.037986 Loss1: 0.009429 Loss2: 0.028557
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.034258 Loss1: 0.006005 Loss2: 0.028253
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.035206 Loss1: 0.007043 Loss2: 0.028163
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.036100 Loss1: 0.007946 Loss2: 0.028155
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.032335 Loss1: 0.004322 Loss2: 0.028012
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.033711 Loss1: 0.005984 Loss2: 0.027726
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.031973 Loss1: 0.004002 Loss2: 0.027971
+(DefaultActor pid=1838052) >> Training accuracy: 1.000000
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066475 Loss1: 0.034365 Loss2: 0.032110
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053745 Loss1: 0.021121 Loss2: 0.032623
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047927 Loss1: 0.014906 Loss2: 0.033020
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049496 Loss1: 0.016957 Loss2: 0.032538
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049878 Loss1: 0.017380 Loss2: 0.032497
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046233 Loss1: 0.014142 Loss2: 0.032090
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.045089 Loss1: 0.013204 Loss2: 0.031885
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.043781 Loss1: 0.011740 Loss2: 0.032041
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.048554 Loss1: 0.016643 Loss2: 0.031911
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.052603 Loss1: 0.019838 Loss2: 0.032765
+(DefaultActor pid=1838052) >> Training accuracy: 0.992588
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.445488 Loss1: 0.034367 Loss2: 0.411120
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.427664 Loss1: 0.037176 Loss2: 0.390488
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.439320 Loss1: 0.051871 Loss2: 0.387449
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.425915 Loss1: 0.041308 Loss2: 0.384607
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.414247 Loss1: 0.030455 Loss2: 0.383792
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.428394 Loss1: 0.046970 Loss2: 0.381424
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.460247 Loss1: 0.073402 Loss2: 0.386844
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.458096 Loss1: 0.072418 Loss2: 0.385677
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.450445 Loss1: 0.066240 Loss2: 0.384205
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.510458 Loss1: 0.121829 Loss2: 0.388629
+(DefaultActor pid=1838052) >> Training accuracy: 0.985777
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.633143 Loss1: 0.041697 Loss2: 0.591446
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.617976 Loss1: 0.037288 Loss2: 0.580688
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.617360 Loss1: 0.045875 Loss2: 0.571485
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.608476 Loss1: 0.039195 Loss2: 0.569281
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613147 Loss1: 0.050556 Loss2: 0.562591
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.619712 Loss1: 0.057202 Loss2: 0.562510
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.611205 Loss1: 0.053241 Loss2: 0.557964
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.615260 Loss1: 0.059919 Loss2: 0.555341
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.631103 Loss1: 0.076967 Loss2: 0.554136
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.659488 Loss1: 0.098410 Loss2: 0.561078
+(DefaultActor pid=1838052) >> Training accuracy: 0.983386
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.062918 Loss1: 0.032763 Loss2: 0.030156
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.045068 Loss1: 0.014004 Loss2: 0.031063
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.050368 Loss1: 0.019171 Loss2: 0.031197
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039185 Loss1: 0.007998 Loss2: 0.031188
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041897 Loss1: 0.010807 Loss2: 0.031090
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040737 Loss1: 0.009418 Loss2: 0.031318
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.039845 Loss1: 0.008672 Loss2: 0.031174
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.039724 Loss1: 0.008396 Loss2: 0.031329
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.037339 Loss1: 0.006380 Loss2: 0.030960
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046528 Loss1: 0.015346 Loss2: 0.031182
+(DefaultActor pid=1838052) >> Training accuracy: 0.998972
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.068213 Loss1: 0.038528 Loss2: 0.029685
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.044380 Loss1: 0.014492 Loss2: 0.029888
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.038089 Loss1: 0.008528 Loss2: 0.029561
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038544 Loss1: 0.009269 Loss2: 0.029275
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.037195 Loss1: 0.008015 Loss2: 0.029181
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.033440 Loss1: 0.004350 Loss2: 0.029090
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.032494 Loss1: 0.003690 Loss2: 0.028803
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.035732 Loss1: 0.006899 Loss2: 0.028833
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.036087 Loss1: 0.007021 Loss2: 0.029066
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.034972 Loss1: 0.005814 Loss2: 0.029157
+(DefaultActor pid=1838052) >> Training accuracy: 1.000000
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.109486 Loss1: 0.054376 Loss2: 0.055110
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073680 Loss1: 0.022969 Loss2: 0.050711
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068716 Loss1: 0.019366 Loss2: 0.049351
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068770 Loss1: 0.020379 Loss2: 0.048391
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072104 Loss1: 0.023777 Loss2: 0.048327
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065356 Loss1: 0.017112 Loss2: 0.048244
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065114 Loss1: 0.017873 Loss2: 0.047241
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061497 Loss1: 0.014405 Loss2: 0.047093
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075357 Loss1: 0.028068 Loss2: 0.047289
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082637 Loss1: 0.034325 Loss2: 0.048312
+(DefaultActor pid=1838052) >> Training accuracy: 0.995566
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.458671 Loss1: 0.031645 Loss2: 0.427026
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.439331 Loss1: 0.029116 Loss2: 0.410215
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.468083 Loss1: 0.056617 Loss2: 0.411466
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.462329 Loss1: 0.048776 Loss2: 0.413553
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.466376 Loss1: 0.055224 Loss2: 0.411153
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.457259 Loss1: 0.043735 Loss2: 0.413523
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.473618 Loss1: 0.061420 Loss2: 0.412197
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.455332 Loss1: 0.044716 Loss2: 0.410616
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.463711 Loss1: 0.051457 Loss2: 0.412255
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.505182 Loss1: 0.087670 Loss2: 0.417512
+(DefaultActor pid=1838052) >> Training accuracy: 0.985166
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078853 Loss1: 0.027445 Loss2: 0.051408
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057425 Loss1: 0.010058 Loss2: 0.047367
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.051580 Loss1: 0.005846 Loss2: 0.045734
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049698 Loss1: 0.005078 Loss2: 0.044620
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.047146 Loss1: 0.003213 Loss2: 0.043933
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048095 Loss1: 0.004686 Loss2: 0.043408
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.046344 Loss1: 0.003210 Loss2: 0.043134
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050643 Loss1: 0.007985 Loss2: 0.042658
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053782 Loss1: 0.010515 Loss2: 0.043267
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055183 Loss1: 0.011663 Loss2: 0.043520
+(DefaultActor pid=1838052) >> Training accuracy: 0.998418
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 09:24:15,597][flwr][DEBUG] - fit_round 99 received 10 results and 0 failures
+>> Test accuracy: 0.664000
+[2023-09-29 09:24:52,549][flwr][INFO] - fit progress: (99, 2.4279736249972457, {'accuracy': 0.664}, 183915.4392115213)
+[2023-09-29 09:24:52,549][flwr][DEBUG] - evaluate_round 99: strategy sampled 10 clients (out of 10)
+[2023-09-29 09:25:28,085][flwr][DEBUG] - evaluate_round 99 received 10 results and 0 failures
+[2023-09-29 09:25:28,086][flwr][DEBUG] - fit_round 100: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.633721 Loss1: 0.042953 Loss2: 0.590768
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.611071 Loss1: 0.029493 Loss2: 0.581578
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.628639 Loss1: 0.051905 Loss2: 0.576734
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.610027 Loss1: 0.036689 Loss2: 0.573338
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.615064 Loss1: 0.047613 Loss2: 0.567451
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.619173 Loss1: 0.051979 Loss2: 0.567193
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.614499 Loss1: 0.052522 Loss2: 0.561977
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627429 Loss1: 0.062283 Loss2: 0.565146
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.637309 Loss1: 0.073219 Loss2: 0.564090
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.632829 Loss1: 0.072466 Loss2: 0.560363
+(DefaultActor pid=1838052) >> Training accuracy: 0.985197
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.619186 Loss1: 0.037708 Loss2: 0.581478
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.573218 Loss1: 0.031769 Loss2: 0.541448
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.566535 Loss1: 0.034625 Loss2: 0.531910
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.567666 Loss1: 0.038374 Loss2: 0.529291
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.563711 Loss1: 0.034891 Loss2: 0.528821
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.560821 Loss1: 0.035298 Loss2: 0.525523
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.559427 Loss1: 0.034536 Loss2: 0.524892
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.556643 Loss1: 0.035079 Loss2: 0.521564
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.575358 Loss1: 0.052009 Loss2: 0.523349
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.589059 Loss1: 0.063919 Loss2: 0.525140
+(DefaultActor pid=1838052) >> Training accuracy: 0.989183
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.421478 Loss1: 0.054516 Loss2: 0.366961
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.369016 Loss1: 0.036506 Loss2: 0.332509
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.365587 Loss1: 0.044795 Loss2: 0.320792
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.358666 Loss1: 0.042269 Loss2: 0.316397
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.384042 Loss1: 0.065753 Loss2: 0.318289
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.381686 Loss1: 0.062266 Loss2: 0.319421
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.412713 Loss1: 0.092310 Loss2: 0.320403
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.410705 Loss1: 0.087939 Loss2: 0.322766
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.431225 Loss1: 0.107201 Loss2: 0.324025
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.431145 Loss1: 0.107388 Loss2: 0.323757
+(DefaultActor pid=1838052) >> Training accuracy: 0.969172
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.506859 Loss1: 0.043866 Loss2: 0.462993
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.490824 Loss1: 0.043318 Loss2: 0.447506
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.476251 Loss1: 0.033295 Loss2: 0.442956
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.477720 Loss1: 0.037749 Loss2: 0.439971
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.505571 Loss1: 0.062868 Loss2: 0.442704
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.506398 Loss1: 0.064462 Loss2: 0.441936
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.509670 Loss1: 0.067286 Loss2: 0.442383
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.522342 Loss1: 0.079608 Loss2: 0.442733
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.520771 Loss1: 0.078097 Loss2: 0.442674
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.506931 Loss1: 0.069365 Loss2: 0.437566
+(DefaultActor pid=1838052) >> Training accuracy: 0.983724
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.056152 Loss1: 0.028145 Loss2: 0.028007
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.038266 Loss1: 0.009795 Loss2: 0.028472
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.034966 Loss1: 0.006531 Loss2: 0.028435
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.036343 Loss1: 0.007854 Loss2: 0.028489
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.035347 Loss1: 0.006803 Loss2: 0.028544
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.034385 Loss1: 0.005901 Loss2: 0.028484
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.033212 Loss1: 0.004810 Loss2: 0.028402
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.031462 Loss1: 0.003270 Loss2: 0.028192
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.030965 Loss1: 0.002861 Loss2: 0.028104
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.033267 Loss1: 0.005174 Loss2: 0.028093
+(DefaultActor pid=1838052) >> Training accuracy: 0.999047
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.414799 Loss1: 0.036181 Loss2: 0.378618
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.372005 Loss1: 0.026293 Loss2: 0.345712
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.375939 Loss1: 0.038718 Loss2: 0.337221
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.373756 Loss1: 0.040258 Loss2: 0.333498
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.383736 Loss1: 0.052527 Loss2: 0.331209
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.395758 Loss1: 0.064488 Loss2: 0.331270
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.442589 Loss1: 0.107571 Loss2: 0.335019
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.418919 Loss1: 0.082914 Loss2: 0.336005
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.420480 Loss1: 0.085369 Loss2: 0.335111
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.407298 Loss1: 0.074801 Loss2: 0.332498
+(DefaultActor pid=1838052) >> Training accuracy: 0.983386
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.073496 Loss1: 0.041620 Loss2: 0.031877
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047048 Loss1: 0.015015 Loss2: 0.032033
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043382 Loss1: 0.011544 Loss2: 0.031838
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.045780 Loss1: 0.013962 Loss2: 0.031818
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043644 Loss1: 0.011758 Loss2: 0.031886
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038814 Loss1: 0.007252 Loss2: 0.031562
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.042853 Loss1: 0.011127 Loss2: 0.031726
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.040428 Loss1: 0.008698 Loss2: 0.031731
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.038025 Loss1: 0.006288 Loss2: 0.031736
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040730 Loss1: 0.008930 Loss2: 0.031800
+(DefaultActor pid=1838052) >> Training accuracy: 0.999209
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071357 Loss1: 0.037867 Loss2: 0.033490
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053780 Loss1: 0.019348 Loss2: 0.034432
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052485 Loss1: 0.018421 Loss2: 0.034064
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044673 Loss1: 0.010844 Loss2: 0.033829
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.042454 Loss1: 0.009210 Loss2: 0.033244
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042526 Loss1: 0.009397 Loss2: 0.033129
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043839 Loss1: 0.010529 Loss2: 0.033310
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.044904 Loss1: 0.011533 Loss2: 0.033371
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.047014 Loss1: 0.013463 Loss2: 0.033551
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.049394 Loss1: 0.015597 Loss2: 0.033797
+(DefaultActor pid=1838052) >> Training accuracy: 0.997627
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081581 Loss1: 0.027750 Loss2: 0.053831
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.063456 Loss1: 0.013656 Loss2: 0.049800
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.062776 Loss1: 0.013436 Loss2: 0.049341
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063601 Loss1: 0.014275 Loss2: 0.049326
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070511 Loss1: 0.020976 Loss2: 0.049535
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065924 Loss1: 0.016261 Loss2: 0.049664
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073745 Loss1: 0.023804 Loss2: 0.049941
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066668 Loss1: 0.016710 Loss2: 0.049958
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068941 Loss1: 0.018549 Loss2: 0.050391
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.078619 Loss1: 0.028174 Loss2: 0.050445
+(DefaultActor pid=1838052) >> Training accuracy: 0.997796
+(DefaultActor pid=1838052)  ** Training complete **
+(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066184 Loss1: 0.036712 Loss2: 0.029472
+(DefaultActor pid=1838052) Epoch: 1 Loss: 0.042205 Loss1: 0.011927 Loss2: 0.030277
+(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043972 Loss1: 0.013629 Loss2: 0.030343
+(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039843 Loss1: 0.009638 Loss2: 0.030206
+(DefaultActor pid=1838052) Epoch: 4 Loss: 0.037968 Loss1: 0.007669 Loss2: 0.030300
+(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038546 Loss1: 0.008416 Loss2: 0.030130
+(DefaultActor pid=1838052) Epoch: 6 Loss: 0.046121 Loss1: 0.015874 Loss2: 0.030247
+(DefaultActor pid=1838052) Epoch: 7 Loss: 0.038334 Loss1: 0.008077 Loss2: 0.030257
+(DefaultActor pid=1838052) Epoch: 8 Loss: 0.036362 Loss1: 0.006121 Loss2: 0.030240
+(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046879 Loss1: 0.016407 Loss2: 0.030472
+(DefaultActor pid=1838052) >> Training accuracy: 0.998616
+(DefaultActor pid=1838052)  ** Training complete **
+[2023-09-29 09:54:11,154][flwr][DEBUG] - fit_round 100 received 10 results and 0 failures
+>> Test accuracy: 0.663500
+[2023-09-29 09:54:47,613][flwr][INFO] - fit progress: (100, 2.423790014970798, {'accuracy': 0.6635}, 185710.50300032226)
+[2023-09-29 09:54:47,613][flwr][DEBUG] - evaluate_round 100: strategy sampled 10 clients (out of 10)
+[2023-09-29 09:55:23,487][flwr][DEBUG] - evaluate_round 100 received 10 results and 0 failures
+[2023-09-29 09:55:23,488][flwr][INFO] - FL finished in 185746.37847093912
+[2023-09-29 09:55:23,512][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0)]
+[2023-09-29 09:55:23,512][flwr][INFO] - app_fit: metrics_distributed_fit {}
+[2023-09-29 09:55:23,513][flwr][INFO] - app_fit: metrics_distributed {}
+[2023-09-29 09:55:23,513][flwr][INFO] - app_fit: losses_centralized [(0, 6.430294827531321), (1, 4.861440579350383), (2, 5.477163912008365), (3, 5.5055647475270035), (4, 4.169462466011413), (5, 3.436301054665075), (6, 2.9990923823639988), (7, 2.694728255652772), (8, 2.53471215883383), (9, 2.3920893143541133), (10, 2.306128243287912), (11, 2.207922473883096), (12, 2.172221914647867), (13, 2.099844414205216), (14, 2.0913502991009065), (15, 2.0594057168442603), (16, 2.043369851554164), (17, 2.0114177884385227), (18, 2.017150927846805), (19, 2.010581853100286), (20, 1.9967298349633384), (21, 2.0065166573174085), (22, 1.9742871688577694), (23, 1.9792633229932084), (24, 2.0303315805931823), (25, 1.9995412224778732), (26, 2.022627312535295), (27, 1.9937346629060495), (28, 2.021510124206543), (29, 2.0112326653620687), (30, 2.0224276781082153), (31, 2.050073419706509), (32, 2.048540641515019), (33, 2.033169127881717), (34, 2.0350445369942882), (35, 2.080257884039285), (36, 2.0718342880852307), (37, 2.0764910361637323), (38, 2.065860210897062), (39, 2.087371099490327), (40, 2.067515920335873), (41, 2.094820894753209), (42, 2.0926969356049363), (43, 2.1266209250821855), (44, 2.132381713047576), (45, 2.1219718056364942), (46, 2.146202865500039), (47, 2.1143142310575174), (48, 2.1224685852139142), (49, 2.117806362458311), (50, 2.142119585134732), (51, 2.135459794404027), (52, 2.1817148396382318), (53, 2.183458357382887), (54, 2.1794180731042125), (55, 2.1586610794829104), (56, 2.188702217115762), (57, 2.171707748224179), (58, 2.195324074726897), (59, 2.2019545649187253), (60, 2.1900063330373065), (61, 2.20785686230888), (62, 2.1942758756323744), (63, 2.2481949498859075), (64, 2.2214675100085834), (65, 2.2834309385226557), (66, 2.208361754592615), (67, 2.293970344736934), (68, 2.207248735922975), (69, 2.268605324026114), (70, 2.256014349171148), (71, 2.247258449610049), (72, 2.2466823984258855), (73, 2.2818810541789754), (74, 2.2942246132003614), (75, 2.3269932089141383), (76, 2.30433221423207), (77, 2.305668424303158), (78, 2.337304946332694), (79, 2.348709229844066), (80, 2.37383095201212), (81, 2.300124180012237), (82, 2.3621472944847692), (83, 2.3891840624733094), (84, 2.332587601468205), (85, 2.3793240404738403), (86, 2.393653464393494), (87, 2.3520108950785557), (88, 2.374164987867252), (89, 2.4286907109589624), (90, 2.328940248908326), (91, 2.403618623273441), (92, 2.4000819242609954), (93, 2.330398244598803), (94, 2.432824038849852), (95, 2.362161700337078), (96, 2.4384049723704404), (97, 2.410859399329359), (98, 2.4004773120529737), (99, 2.4279736249972457), (100, 2.423790014970798)]
+[2023-09-29 09:55:23,513][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.009), (1, 0.01), (2, 0.01), (3, 0.0141), (4, 0.0882), (5, 0.1851), (6, 0.2656), (7, 0.3276), (8, 0.3631), (9, 0.4028), (10, 0.4325), (11, 0.4554), (12, 0.4766), (13, 0.5038), (14, 0.5151), (15, 0.5332), (16, 0.5412), (17, 0.5552), (18, 0.5595), (19, 0.5685), (20, 0.5784), (21, 0.5828), (22, 0.5898), (23, 0.5934), (24, 0.5923), (25, 0.6006), (26, 0.6009), (27, 0.6072), (28, 0.6081), (29, 0.6136), (30, 0.6133), (31, 0.6184), (32, 0.6198), (33, 0.623), (34, 0.6194), (35, 0.6247), (36, 0.6272), (37, 0.6285), (38, 0.6283), (39, 0.6321), (40, 0.6326), (41, 0.6341), (42, 0.6369), (43, 0.6353), (44, 0.6399), (45, 0.641), (46, 0.642), (47, 0.6449), (48, 0.6448), (49, 0.6455), (50, 0.6433), (51, 0.6457), (52, 0.6465), (53, 0.6457), (54, 0.6474), (55, 0.6485), (56, 0.6494), (57, 0.649), (58, 0.6499), (59, 0.6505), (60, 0.65), (61, 0.6512), (62, 0.6535), (63, 0.6537), (64, 0.6545), (65, 0.6543), (66, 0.6568), (67, 0.6562), (68, 0.6561), (69, 0.6603), (70, 0.6591), (71, 0.6589), (72, 0.6606), (73, 0.6616), (74, 0.6595), (75, 0.6578), (76, 0.6606), (77, 0.6563), (78, 0.6575), (79, 0.6595), (80, 0.6602), (81, 0.6619), (82, 0.6632), (83, 0.6627), (84, 0.6621), (85, 0.6647), (86, 0.6617), (87, 0.6635), (88, 0.6648), (89, 0.6597), (90, 0.6611), (91, 0.6619), (92, 0.6636), (93, 0.6648), (94, 0.6665), (95, 0.667), (96, 0.6616), (97, 0.6654), (98, 0.6651), (99, 0.664), (100, 0.6635)]}
+................
+History (loss, distributed):
+	round 1: 0.0
+	round 2: 0.0
+	round 3: 0.0
+	round 4: 0.0
+	round 5: 0.0
+	round 6: 0.0
+	round 7: 0.0
+	round 8: 0.0
+	round 9: 0.0
+	round 10: 0.0
+	round 11: 0.0
+	round 12: 0.0
+	round 13: 0.0
+	round 14: 0.0
+	round 15: 0.0
+	round 16: 0.0
+	round 17: 0.0
+	round 18: 0.0
+	round 19: 0.0
+	round 20: 0.0
+	round 21: 0.0
+	round 22: 0.0
+	round 23: 0.0
+	round 24: 0.0
+	round 25: 0.0
+	round 26: 0.0
+	round 27: 0.0
+	round 28: 0.0
+	round 29: 0.0
+	round 30: 0.0
+	round 31: 0.0
+	round 32: 0.0
+	round 33: 0.0
+	round 34: 0.0
+	round 35: 0.0
+	round 36: 0.0
+	round 37: 0.0
+	round 38: 0.0
+	round 39: 0.0
+	round 40: 0.0
+	round 41: 0.0
+	round 42: 0.0
+	round 43: 0.0
+	round 44: 0.0
+	round 45: 0.0
+	round 46: 0.0
+	round 47: 0.0
+	round 48: 0.0
+	round 49: 0.0
+	round 50: 0.0
+	round 51: 0.0
+	round 52: 0.0
+	round 53: 0.0
+	round 54: 0.0
+	round 55: 0.0
+	round 56: 0.0
+	round 57: 0.0
+	round 58: 0.0
+	round 59: 0.0
+	round 60: 0.0
+	round 61: 0.0
+	round 62: 0.0
+	round 63: 0.0
+	round 64: 0.0
+	round 65: 0.0
+	round 66: 0.0
+	round 67: 0.0
+	round 68: 0.0
+	round 69: 0.0
+	round 70: 0.0
+	round 71: 0.0
+	round 72: 0.0
+	round 73: 0.0
+	round 74: 0.0
+	round 75: 0.0
+	round 76: 0.0
+	round 77: 0.0
+	round 78: 0.0
+	round 79: 0.0
+	round 80: 0.0
+	round 81: 0.0
+	round 82: 0.0
+	round 83: 0.0
+	round 84: 0.0
+	round 85: 0.0
+	round 86: 0.0
+	round 87: 0.0
+	round 88: 0.0
+	round 89: 0.0
+	round 90: 0.0
+	round 91: 0.0
+	round 92: 0.0
+	round 93: 0.0
+	round 94: 0.0
+	round 95: 0.0
+	round 96: 0.0
+	round 97: 0.0
+	round 98: 0.0
+	round 99: 0.0
+	round 100: 0.0
+History (loss, centralized):
+	round 0: 6.430294827531321
+	round 1: 4.861440579350383
+	round 2: 5.477163912008365
+	round 3: 5.5055647475270035
+	round 4: 4.169462466011413
+	round 5: 3.436301054665075
+	round 6: 2.9990923823639988
+	round 7: 2.694728255652772
+	round 8: 2.53471215883383
+	round 9: 2.3920893143541133
+	round 10: 2.306128243287912
+	round 11: 2.207922473883096
+	round 12: 2.172221914647867
+	round 13: 2.099844414205216
+	round 14: 2.0913502991009065
+	round 15: 2.0594057168442603
+	round 16: 2.043369851554164
+	round 17: 2.0114177884385227
+	round 18: 2.017150927846805
+	round 19: 2.010581853100286
+	round 20: 1.9967298349633384
+	round 21: 2.0065166573174085
+	round 22: 1.9742871688577694
+	round 23: 1.9792633229932084
+	round 24: 2.0303315805931823
+	round 25: 1.9995412224778732
+	round 26: 2.022627312535295
+	round 27: 1.9937346629060495
+	round 28: 2.021510124206543
+	round 29: 2.0112326653620687
+	round 30: 2.0224276781082153
+	round 31: 2.050073419706509
+	round 32: 2.048540641515019
+	round 33: 2.033169127881717
+	round 34: 2.0350445369942882
+	round 35: 2.080257884039285
+	round 36: 2.0718342880852307
+	round 37: 2.0764910361637323
+	round 38: 2.065860210897062
+	round 39: 2.087371099490327
+	round 40: 2.067515920335873
+	round 41: 2.094820894753209
+	round 42: 2.0926969356049363
+	round 43: 2.1266209250821855
+	round 44: 2.132381713047576
+	round 45: 2.1219718056364942
+	round 46: 2.146202865500039
+	round 47: 2.1143142310575174
+	round 48: 2.1224685852139142
+	round 49: 2.117806362458311
+	round 50: 2.142119585134732
+	round 51: 2.135459794404027
+	round 52: 2.1817148396382318
+	round 53: 2.183458357382887
+	round 54: 2.1794180731042125
+	round 55: 2.1586610794829104
+	round 56: 2.188702217115762
+	round 57: 2.171707748224179
+	round 58: 2.195324074726897
+	round 59: 2.2019545649187253
+	round 60: 2.1900063330373065
+	round 61: 2.20785686230888
+	round 62: 2.1942758756323744
+	round 63: 2.2481949498859075
+	round 64: 2.2214675100085834
+	round 65: 2.2834309385226557
+	round 66: 2.208361754592615
+	round 67: 2.293970344736934
+	round 68: 2.207248735922975
+	round 69: 2.268605324026114
+	round 70: 2.256014349171148
+	round 71: 2.247258449610049
+	round 72: 2.2466823984258855
+	round 73: 2.2818810541789754
+	round 74: 2.2942246132003614
+	round 75: 2.3269932089141383
+	round 76: 2.30433221423207
+	round 77: 2.305668424303158
+	round 78: 2.337304946332694
+	round 79: 2.348709229844066
+	round 80: 2.37383095201212
+	round 81: 2.300124180012237
+	round 82: 2.3621472944847692
+	round 83: 2.3891840624733094
+	round 84: 2.332587601468205
+	round 85: 2.3793240404738403
+	round 86: 2.393653464393494
+	round 87: 2.3520108950785557
+	round 88: 2.374164987867252
+	round 89: 2.4286907109589624
+	round 90: 2.328940248908326
+	round 91: 2.403618623273441
+	round 92: 2.4000819242609954
+	round 93: 2.330398244598803
+	round 94: 2.432824038849852
+	round 95: 2.362161700337078
+	round 96: 2.4384049723704404
+	round 97: 2.410859399329359
+	round 98: 2.4004773120529737
+	round 99: 2.4279736249972457
+	round 100: 2.423790014970798
+History (metrics, centralized):
+{'accuracy': [(0, 0.009), (1, 0.01), (2, 0.01), (3, 0.0141), (4, 0.0882), (5, 0.1851), (6, 0.2656), (7, 0.3276), (8, 0.3631), (9, 0.4028), (10, 0.4325), (11, 0.4554), (12, 0.4766), (13, 0.5038), (14, 0.5151), (15, 0.5332), (16, 0.5412), (17, 0.5552), (18, 0.5595), (19, 0.5685), (20, 0.5784), (21, 0.5828), (22, 0.5898), (23, 0.5934), (24, 0.5923), (25, 0.6006), (26, 0.6009), (27, 0.6072), (28, 0.6081), (29, 0.6136), (30, 0.6133), (31, 0.6184), (32, 0.6198), (33, 0.623), (34, 0.6194), (35, 0.6247), (36, 0.6272), (37, 0.6285), (38, 0.6283), (39, 0.6321), (40, 0.6326), (41, 0.6341), (42, 0.6369), (43, 0.6353), (44, 0.6399), (45, 0.641), (46, 0.642), (47, 0.6449), (48, 0.6448), (49, 0.6455), (50, 0.6433), (51, 0.6457), (52, 0.6465), (53, 0.6457), (54, 0.6474), (55, 0.6485), (56, 0.6494), (57, 0.649), (58, 0.6499), (59, 0.6505), (60, 0.65), (61, 0.6512), (62, 0.6535), (63, 0.6537), (64, 0.6545), (65, 0.6543), (66, 0.6568), (67, 0.6562), (68, 0.6561), (69, 0.6603), (70, 0.6591), (71, 0.6589), (72, 0.6606), (73, 0.6616), (74, 0.6595), (75, 0.6578), (76, 0.6606), (77, 0.6563), (78, 0.6575), (79, 0.6595), (80, 0.6602), (81, 0.6619), (82, 0.6632), (83, 0.6627), (84, 0.6621), (85, 0.6647), (86, 0.6617), (87, 0.6635), (88, 0.6648), (89, 0.6597), (90, 0.6611), (91, 0.6619), (92, 0.6636), (93, 0.6648), (94, 0.6665), (95, 0.667), (96, 0.6616), (97, 0.6654), (98, 0.6651), (99, 0.664), (100, 0.6635)]}
+[2023-09-29 09:55:23,972][matplotlib.legend][WARNING] - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
diff --git a/baselines/moon/_static/cifar10_fedprox_log.txt b/baselines/moon/_static/cifar10_fedprox_log.txt
new file mode 100644
index 000000000000..318a94f03fdd
--- /dev/null
+++ b/baselines/moon/_static/cifar10_fedprox_log.txt
@@ -0,0 +1,6852 @@
+num_clients: 10
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 0.01
+temperature: 0.5
+alg: fedprox
+seed: 0
+server_device: cpu
+num_rounds: 100
+client_resources:
+  num_cpus: 4
+  num_gpus: 1
+dataset:
+  name: cifar10
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+model:
+  name: simple-cnn
+  output_dim: 256
+  dir: ./models/moon/cifar10_fedprox/
+
+Files already downloaded and verified
+Files already downloaded and verified
+[2023-09-21 03:09:22,298][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=100, round_timeout=None)
+[2023-09-21 03:09:25,352][flwr][INFO] - Flower VCE: Ray initialized with resources: {'node:137.132.92.49': 1.0, 'node:__internal_head__': 1.0, 'CPU': 64.0, 'memory': 222860751872.0, 'object_store_memory': 99797465088.0, 'GPU': 1.0, 'accelerator_type:G': 1.0}
+[2023-09-21 03:09:25,352][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 1}
+[2023-09-21 03:09:25,361][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 1 actors
+[2023-09-21 03:09:25,361][flwr][INFO] - Initializing global parameters
+[2023-09-21 03:09:25,361][flwr][INFO] - Requesting initial parameters from one random client
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 03:09:30,151][flwr][INFO] - Received initial parameters from one random client
+[2023-09-21 03:09:30,152][flwr][INFO] - Evaluating initial parameters
+test acc: 0.1
+[2023-09-21 03:09:31,539][flwr][INFO] - initial parameters (loss, other metrics): 2.304941604693477, {'accuracy': 0.1}
+[2023-09-21 03:09:31,540][flwr][INFO] - FL starting
+[2023-09-21 03:09:31,540][flwr][DEBUG] - fit_round 1: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.0
+(DefaultActor pid=2820544) >> Training accuracy: 0.712577
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.0
+(DefaultActor pid=2820544) >> Training accuracy: 0.637386
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.011408730158730158
+(DefaultActor pid=2820544) >> Training accuracy: 0.658854
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.14342350746268656
+(DefaultActor pid=2820544) >> Training accuracy: 0.553871
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.00026483050847457627
+(DefaultActor pid=2820544) >> Training accuracy: 0.650424
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.01782852564102564
+(DefaultActor pid=2820544) >> Training accuracy: 0.628806
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.36709104938271603
+(DefaultActor pid=2820544) >> Training accuracy: 0.694059
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.026721014492753624
+(DefaultActor pid=2820544) >> Training accuracy: 0.561141
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.0
+(DefaultActor pid=2820544) >> Training accuracy: 0.586075
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4395559210526316
+[2023-09-21 03:17:27,442][flwr][DEBUG] - fit_round 1 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.649671
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 03:17:27,482][flwr][WARNING] - No fit_metrics_aggregation_fn provided
+test acc: 0.1148
+[2023-09-21 03:17:29,253][flwr][INFO] - fit progress: (1, 2.2892096804353756, {'accuracy': 0.1148}, 477.7133622728288)
+[2023-09-21 03:17:29,253][flwr][DEBUG] - evaluate_round 1: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 03:18:01,534][flwr][DEBUG] - evaluate_round 1 received 10 results and 0 failures
+[2023-09-21 03:18:01,535][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
+[2023-09-21 03:18:01,535][flwr][DEBUG] - fit_round 2: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.09046052631578948
+(DefaultActor pid=2820544) >> Training accuracy: 0.652138
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3736758474576271
+(DefaultActor pid=2820544) >> Training accuracy: 0.655985
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.022154850746268658
+(DefaultActor pid=2820544) >> Training accuracy: 0.576259
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.1169969512195122
+(DefaultActor pid=2820544) >> Training accuracy: 0.675686
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.18410326086956522
+(DefaultActor pid=2820544) >> Training accuracy: 0.600091
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.05343364197530864
+(DefaultActor pid=2820544) >> Training accuracy: 0.735918
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.09623015873015874
+(DefaultActor pid=2820544) >> Training accuracy: 0.678943
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.15384615384615385
+(DefaultActor pid=2820544) >> Training accuracy: 0.664062
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.08449074074074074
+(DefaultActor pid=2820544) >> Training accuracy: 0.733218
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.01953125
+[2023-09-21 03:25:11,194][flwr][DEBUG] - fit_round 2 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.696957
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.2752
+[2023-09-21 03:25:12,670][flwr][INFO] - fit progress: (2, 1.9268630602108403, {'accuracy': 0.2752}, 941.1305831400678)
+[2023-09-21 03:25:12,671][flwr][DEBUG] - evaluate_round 2: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 03:25:43,833][flwr][DEBUG] - evaluate_round 2 received 10 results and 0 failures
+[2023-09-21 03:25:43,834][flwr][DEBUG] - fit_round 3: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.15692934782608695
+(DefaultActor pid=2820544) >> Training accuracy: 0.616621
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3135016025641026
+(DefaultActor pid=2820544) >> Training accuracy: 0.696715
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.10587993421052631
+(DefaultActor pid=2820544) >> Training accuracy: 0.724918
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.23342225609756098
+(DefaultActor pid=2820544) >> Training accuracy: 0.704459
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4245756172839506
+(DefaultActor pid=2820544) >> Training accuracy: 0.759452
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.26236007462686567
+(DefaultActor pid=2820544) >> Training accuracy: 0.593983
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.23387896825396826
+(DefaultActor pid=2820544) >> Training accuracy: 0.671875
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.29012345679012347
+(DefaultActor pid=2820544) >> Training accuracy: 0.766590
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3495762711864407
+(DefaultActor pid=2820544) >> Training accuracy: 0.707892
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.21957236842105263
+[2023-09-21 03:33:09,175][flwr][DEBUG] - fit_round 3 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.678454
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.3791
+[2023-09-21 03:33:10,642][flwr][INFO] - fit progress: (3, 1.6586600408767358, {'accuracy': 0.3791}, 1419.102149719838)
+[2023-09-21 03:33:10,642][flwr][DEBUG] - evaluate_round 3: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 03:33:42,112][flwr][DEBUG] - evaluate_round 3 received 10 results and 0 failures
+[2023-09-21 03:33:42,112][flwr][DEBUG] - fit_round 4: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.17393092105263158
+(DefaultActor pid=2820544) >> Training accuracy: 0.730469
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3125
+(DefaultActor pid=2820544) >> Training accuracy: 0.788194
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5727237654320988
+(DefaultActor pid=2820544) >> Training accuracy: 0.777199
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47896634615384615
+(DefaultActor pid=2820544) >> Training accuracy: 0.712139
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.27455357142857145
+(DefaultActor pid=2820544) >> Training accuracy: 0.715774
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3342391304347826
+(DefaultActor pid=2820544) >> Training accuracy: 0.644022
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3195503048780488
+(DefaultActor pid=2820544) >> Training accuracy: 0.717797
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3393640350877193
+(DefaultActor pid=2820544) >> Training accuracy: 0.680373
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.517478813559322
+(DefaultActor pid=2820544) >> Training accuracy: 0.732256
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.2943097014925373
+[2023-09-21 03:40:51,343][flwr][DEBUG] - fit_round 4 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.608675
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.4339
+[2023-09-21 03:40:52,829][flwr][INFO] - fit progress: (4, 1.5162620251171124, {'accuracy': 0.4339}, 1881.2891843491234)
+[2023-09-21 03:40:52,829][flwr][DEBUG] - evaluate_round 4: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 03:41:39,499][flwr][DEBUG] - evaluate_round 4 received 10 results and 0 failures
+[2023-09-21 03:41:39,500][flwr][DEBUG] - fit_round 5: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3761322463768116
+(DefaultActor pid=2820544) >> Training accuracy: 0.663270
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5558792372881356
+(DefaultActor pid=2820544) >> Training accuracy: 0.747881
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3636188271604938
+(DefaultActor pid=2820544) >> Training accuracy: 0.790123
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.35774253731343286
+(DefaultActor pid=2820544) >> Training accuracy: 0.656250
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.39634146341463417
+(DefaultActor pid=2820544) >> Training accuracy: 0.726753
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3304811507936508
+(DefaultActor pid=2820544) >> Training accuracy: 0.709821
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3432017543859649
+(DefaultActor pid=2820544) >> Training accuracy: 0.689693
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.2450657894736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.755345
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5923996913580247
+(DefaultActor pid=2820544) >> Training accuracy: 0.732253
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.539863782051282
+(DefaultActor pid=2820544) >> Training accuracy: 0.722957
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 03:48:56,490][flwr][DEBUG] - fit_round 5 received 10 results and 0 failures
+test acc: 0.4926
+[2023-09-21 03:48:58,100][flwr][INFO] - fit progress: (5, 1.3799412298126343, {'accuracy': 0.4926}, 2366.5606768671423)
+[2023-09-21 03:48:58,101][flwr][DEBUG] - evaluate_round 5: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 03:49:28,835][flwr][DEBUG] - evaluate_round 5 received 10 results and 0 failures
+[2023-09-21 03:49:28,836][flwr][DEBUG] - fit_round 6: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4095394736842105
+(DefaultActor pid=2820544) >> Training accuracy: 0.697094
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4529344512195122
+(DefaultActor pid=2820544) >> Training accuracy: 0.722942
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3699156746031746
+(DefaultActor pid=2820544) >> Training accuracy: 0.730407
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3548519736842105
+(DefaultActor pid=2820544) >> Training accuracy: 0.741776
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4369212962962963
+(DefaultActor pid=2820544) >> Training accuracy: 0.798418
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.40928171641791045
+(DefaultActor pid=2820544) >> Training accuracy: 0.662080
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5941506410256411
+(DefaultActor pid=2820544) >> Training accuracy: 0.738381
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6554783950617284
+(DefaultActor pid=2820544) >> Training accuracy: 0.788966
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4470108695652174
+(DefaultActor pid=2820544) >> Training accuracy: 0.663949
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5434322033898306
+[2023-09-21 03:56:35,432][flwr][DEBUG] - fit_round 6 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.740466
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.5244
+[2023-09-21 03:56:37,232][flwr][INFO] - fit progress: (6, 1.3085029963106394, {'accuracy': 0.5244}, 2825.6921509918757)
+[2023-09-21 03:56:37,233][flwr][DEBUG] - evaluate_round 6: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 03:57:08,634][flwr][DEBUG] - evaluate_round 6 received 10 results and 0 failures
+[2023-09-21 03:57:08,635][flwr][DEBUG] - fit_round 7: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4810956790123457
+(DefaultActor pid=2820544) >> Training accuracy: 0.804205
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4798460144927536
+(DefaultActor pid=2820544) >> Training accuracy: 0.674139
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.43940548780487804
+(DefaultActor pid=2820544) >> Training accuracy: 0.751905
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6456404320987654
+(DefaultActor pid=2820544) >> Training accuracy: 0.793596
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6338141025641025
+(DefaultActor pid=2820544) >> Training accuracy: 0.748998
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.42723880597014924
+(DefaultActor pid=2820544) >> Training accuracy: 0.670243
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.40316611842105265
+(DefaultActor pid=2820544) >> Training accuracy: 0.762747
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3991815476190476
+(DefaultActor pid=2820544) >> Training accuracy: 0.745660
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.42077850877192985
+(DefaultActor pid=2820544) >> Training accuracy: 0.684211
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.602489406779661
+[2023-09-21 04:04:12,947][flwr][DEBUG] - fit_round 7 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.735434
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.5421
+[2023-09-21 04:04:14,292][flwr][INFO] - fit progress: (7, 1.270832797208914, {'accuracy': 0.5421}, 3282.7528554419987)
+[2023-09-21 04:04:14,293][flwr][DEBUG] - evaluate_round 7: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 04:04:44,141][flwr][DEBUG] - evaluate_round 7 received 10 results and 0 failures
+[2023-09-21 04:04:44,142][flwr][DEBUG] - fit_round 8: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6310911016949152
+(DefaultActor pid=2820544) >> Training accuracy: 0.760858
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4243551587301587
+(DefaultActor pid=2820544) >> Training accuracy: 0.743304
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.660108024691358
+(DefaultActor pid=2820544) >> Training accuracy: 0.798804
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4647484756097561
+(DefaultActor pid=2820544) >> Training accuracy: 0.743331
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.44029850746268656
+(DefaultActor pid=2820544) >> Training accuracy: 0.674674
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6386217948717948
+(DefaultActor pid=2820544) >> Training accuracy: 0.747997
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4237938596491228
+(DefaultActor pid=2820544) >> Training accuracy: 0.721765
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4903549382716049
+(DefaultActor pid=2820544) >> Training accuracy: 0.808449
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4120065789473684
+(DefaultActor pid=2820544) >> Training accuracy: 0.765008
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4941123188405797
+[2023-09-21 04:11:54,971][flwr][DEBUG] - fit_round 8 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.687274
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.5669
+[2023-09-21 04:11:56,421][flwr][INFO] - fit progress: (8, 1.2019853355785528, {'accuracy': 0.5669}, 3744.8811060180888)
+[2023-09-21 04:11:56,421][flwr][DEBUG] - evaluate_round 8: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 04:12:37,451][flwr][DEBUG] - evaluate_round 8 received 10 results and 0 failures
+[2023-09-21 04:12:37,452][flwr][DEBUG] - fit_round 9: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.46902412280701755
+(DefaultActor pid=2820544) >> Training accuracy: 0.706689
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6313559322033898
+(DefaultActor pid=2820544) >> Training accuracy: 0.783633
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47865853658536583
+(DefaultActor pid=2820544) >> Training accuracy: 0.747332
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6905864197530864
+(DefaultActor pid=2820544) >> Training accuracy: 0.792438
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5187952898550725
+(DefaultActor pid=2820544) >> Training accuracy: 0.698370
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.45785361842105265
+(DefaultActor pid=2820544) >> Training accuracy: 0.773643
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4967206790123457
+(DefaultActor pid=2820544) >> Training accuracy: 0.812500
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6368189102564102
+(DefaultActor pid=2820544) >> Training accuracy: 0.762220
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4820188492063492
+(DefaultActor pid=2820544) >> Training accuracy: 0.731895
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5013992537313433
+[2023-09-21 04:20:00,046][flwr][DEBUG] - fit_round 9 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.682603
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.5791
+[2023-09-21 04:20:01,448][flwr][INFO] - fit progress: (9, 1.1783232848865155, {'accuracy': 0.5791}, 4229.908353412058)
+[2023-09-21 04:20:01,449][flwr][DEBUG] - evaluate_round 9: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 04:20:37,215][flwr][DEBUG] - evaluate_round 9 received 10 results and 0 failures
+[2023-09-21 04:20:37,216][flwr][DEBUG] - fit_round 10: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5253623188405797
+(DefaultActor pid=2820544) >> Training accuracy: 0.692935
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6706730769230769
+(DefaultActor pid=2820544) >> Training accuracy: 0.771635
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47371031746031744
+(DefaultActor pid=2820544) >> Training accuracy: 0.752976
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6191737288135594
+(DefaultActor pid=2820544) >> Training accuracy: 0.788136
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5072294776119403
+(DefaultActor pid=2820544) >> Training accuracy: 0.690532
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5709876543209876
+(DefaultActor pid=2820544) >> Training accuracy: 0.824267
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.689429012345679
+(DefaultActor pid=2820544) >> Training accuracy: 0.818480
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4712271341463415
+(DefaultActor pid=2820544) >> Training accuracy: 0.759718
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47231359649122806
+(DefaultActor pid=2820544) >> Training accuracy: 0.722314
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4993832236842105
+[2023-09-21 04:27:37,375][flwr][DEBUG] - fit_round 10 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.778577
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.586
+[2023-09-21 04:27:38,723][flwr][INFO] - fit progress: (10, 1.1620713434280299, {'accuracy': 0.586}, 4687.183470572811)
+[2023-09-21 04:27:38,723][flwr][DEBUG] - evaluate_round 10: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 04:28:09,105][flwr][DEBUG] - evaluate_round 10 received 10 results and 0 failures
+[2023-09-21 04:28:09,106][flwr][DEBUG] - fit_round 11: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5274390243902439
+(DefaultActor pid=2820544) >> Training accuracy: 0.760480
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.503731343283582
+(DefaultActor pid=2820544) >> Training accuracy: 0.704991
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6559851694915254
+(DefaultActor pid=2820544) >> Training accuracy: 0.790254
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5400815217391305
+(DefaultActor pid=2820544) >> Training accuracy: 0.698596
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6760817307692307
+(DefaultActor pid=2820544) >> Training accuracy: 0.765425
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47265625
+(DefaultActor pid=2820544) >> Training accuracy: 0.789679
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5001240079365079
+(DefaultActor pid=2820544) >> Training accuracy: 0.762773
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6844135802469136
+(DefaultActor pid=2820544) >> Training accuracy: 0.822338
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5229552469135802
+(DefaultActor pid=2820544) >> Training accuracy: 0.821566
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.46847587719298245
+[2023-09-21 04:35:06,696][flwr][DEBUG] - fit_round 11 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.723958
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.5979
+[2023-09-21 04:35:08,041][flwr][INFO] - fit progress: (11, 1.1295021063984392, {'accuracy': 0.5979}, 5136.501239712816)
+[2023-09-21 04:35:08,041][flwr][DEBUG] - evaluate_round 11: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 04:35:38,353][flwr][DEBUG] - evaluate_round 11 received 10 results and 0 failures
+[2023-09-21 04:35:38,354][flwr][DEBUG] - fit_round 12: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6792868589743589
+(DefaultActor pid=2820544) >> Training accuracy: 0.792268
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5021929824561403
+(DefaultActor pid=2820544) >> Training accuracy: 0.732182
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5318667763157895
+(DefaultActor pid=2820544) >> Training accuracy: 0.790090
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5883487654320988
+(DefaultActor pid=2820544) >> Training accuracy: 0.832176
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5593297101449275
+(DefaultActor pid=2820544) >> Training accuracy: 0.698822
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6096398305084746
+(DefaultActor pid=2820544) >> Training accuracy: 0.788400
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5159970238095238
+(DefaultActor pid=2820544) >> Training accuracy: 0.759177
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.49352134146341464
+(DefaultActor pid=2820544) >> Training accuracy: 0.773247
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5340485074626866
+(DefaultActor pid=2820544) >> Training accuracy: 0.697295
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7019675925925926
+[2023-09-21 04:42:32,819][flwr][DEBUG] - fit_round 12 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.819059
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6048
+[2023-09-21 04:42:34,278][flwr][INFO] - fit progress: (12, 1.1191708752141594, {'accuracy': 0.6048}, 5582.738002989907)
+[2023-09-21 04:42:34,278][flwr][DEBUG] - evaluate_round 12: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 04:43:04,165][flwr][DEBUG] - evaluate_round 12 received 10 results and 0 failures
+[2023-09-21 04:43:04,166][flwr][DEBUG] - fit_round 13: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5679347826086957
+(DefaultActor pid=2820544) >> Training accuracy: 0.713542
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7087191358024691
+(DefaultActor pid=2820544) >> Training accuracy: 0.819252
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5211509146341463
+(DefaultActor pid=2820544) >> Training accuracy: 0.771341
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5135261194029851
+(DefaultActor pid=2820544) >> Training accuracy: 0.712687
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5333719135802469
+(DefaultActor pid=2820544) >> Training accuracy: 0.827353
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5019188596491229
+(DefaultActor pid=2820544) >> Training accuracy: 0.725877
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.48725328947368424
+(DefaultActor pid=2820544) >> Training accuracy: 0.792969
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.694511217948718
+(DefaultActor pid=2820544) >> Training accuracy: 0.786258
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5372023809523809
+(DefaultActor pid=2820544) >> Training accuracy: 0.768601
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6607521186440678
+[2023-09-21 04:50:25,654][flwr][DEBUG] - fit_round 13 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.790254
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6034
+[2023-09-21 04:50:27,003][flwr][INFO] - fit progress: (13, 1.106805816054725, {'accuracy': 0.6034}, 6055.463005594909)
+[2023-09-21 04:50:27,003][flwr][DEBUG] - evaluate_round 13: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 04:50:57,831][flwr][DEBUG] - evaluate_round 13 received 10 results and 0 failures
+[2023-09-21 04:50:57,831][flwr][DEBUG] - fit_round 14: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5655864197530864
+(DefaultActor pid=2820544) >> Training accuracy: 0.831211
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7085262345679012
+(DefaultActor pid=2820544) >> Training accuracy: 0.825810
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5264862804878049
+(DefaultActor pid=2820544) >> Training accuracy: 0.775534
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5117872807017544
+(DefaultActor pid=2820544) >> Training accuracy: 0.739857
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5145970394736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.795436
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5711050724637681
+(DefaultActor pid=2820544) >> Training accuracy: 0.717391
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5369543650793651
+(DefaultActor pid=2820544) >> Training accuracy: 0.764013
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.653072033898305
+(DefaultActor pid=2820544) >> Training accuracy: 0.808792
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6794871794871795
+(DefaultActor pid=2820544) >> Training accuracy: 0.781050
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5412779850746269
+[2023-09-21 04:57:52,031][flwr][DEBUG] - fit_round 14 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.693330
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6153
+[2023-09-21 04:57:53,629][flwr][INFO] - fit progress: (14, 1.0845167545464853, {'accuracy': 0.6153}, 6502.089797993191)
+[2023-09-21 04:57:53,630][flwr][DEBUG] - evaluate_round 14: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 04:58:24,056][flwr][DEBUG] - evaluate_round 14 received 10 results and 0 failures
+[2023-09-21 04:58:24,057][flwr][DEBUG] - fit_round 15: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7139274691358025
+(DefaultActor pid=2820544) >> Training accuracy: 0.823302
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5082236842105263
+(DefaultActor pid=2820544) >> Training accuracy: 0.763432
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5536380597014925
+(DefaultActor pid=2820544) >> Training accuracy: 0.684701
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.649364406779661
+(DefaultActor pid=2820544) >> Training accuracy: 0.790784
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5596478174603174
+(DefaultActor pid=2820544) >> Training accuracy: 0.769717
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5449695121951219
+(DefaultActor pid=2820544) >> Training accuracy: 0.772675
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5729166666666666
+(DefaultActor pid=2820544) >> Training accuracy: 0.705163
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5244654605263158
+(DefaultActor pid=2820544) >> Training accuracy: 0.793174
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7009214743589743
+(DefaultActor pid=2820544) >> Training accuracy: 0.777043
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5723379629629629
+[2023-09-21 05:05:22,373][flwr][DEBUG] - fit_round 15 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.830826
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6155
+[2023-09-21 05:05:23,771][flwr][INFO] - fit progress: (15, 1.0962572912819468, {'accuracy': 0.6155}, 6952.231259225868)
+[2023-09-21 05:05:23,771][flwr][DEBUG] - evaluate_round 15: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 05:05:53,410][flwr][DEBUG] - evaluate_round 15 received 10 results and 0 failures
+[2023-09-21 05:05:53,411][flwr][DEBUG] - fit_round 16: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4994517543859649
+(DefaultActor pid=2820544) >> Training accuracy: 0.740954
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5501399253731343
+(DefaultActor pid=2820544) >> Training accuracy: 0.722715
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5088404605263158
+(DefaultActor pid=2820544) >> Training accuracy: 0.801604
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6490995762711864
+(DefaultActor pid=2820544) >> Training accuracy: 0.806674
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6852964743589743
+(DefaultActor pid=2820544) >> Training accuracy: 0.783654
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5848765432098766
+(DefaultActor pid=2820544) >> Training accuracy: 0.839892
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5289634146341463
+(DefaultActor pid=2820544) >> Training accuracy: 0.762195
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5780009920634921
+(DefaultActor pid=2820544) >> Training accuracy: 0.778894
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.563858695652174
+(DefaultActor pid=2820544) >> Training accuracy: 0.715353
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6815200617283951
+[2023-09-21 05:13:05,054][flwr][DEBUG] - fit_round 16 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.829090
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6256
+[2023-09-21 05:13:06,472][flwr][INFO] - fit progress: (16, 1.0545658187363476, {'accuracy': 0.6256}, 7414.931961627211)
+[2023-09-21 05:13:06,472][flwr][DEBUG] - evaluate_round 16: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 05:13:37,043][flwr][DEBUG] - evaluate_round 16 received 10 results and 0 failures
+[2023-09-21 05:13:37,044][flwr][DEBUG] - fit_round 17: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5191885964912281
+(DefaultActor pid=2820544) >> Training accuracy: 0.748629
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.595679012345679
+(DefaultActor pid=2820544) >> Training accuracy: 0.845486
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7096836419753086
+(DefaultActor pid=2820544) >> Training accuracy: 0.828318
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7201522435897436
+(DefaultActor pid=2820544) >> Training accuracy: 0.798678
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5512152777777778
+(DefaultActor pid=2820544) >> Training accuracy: 0.770709
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.538945895522388
+(DefaultActor pid=2820544) >> Training accuracy: 0.709655
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5544819078947368
+(DefaultActor pid=2820544) >> Training accuracy: 0.795230
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6665783898305084
+(DefaultActor pid=2820544) >> Training accuracy: 0.785487
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5491615853658537
+(DefaultActor pid=2820544) >> Training accuracy: 0.779916
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6009963768115942
+[2023-09-21 05:21:20,281][flwr][DEBUG] - fit_round 17 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.716486
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6281
+[2023-09-21 05:21:29,598][flwr][INFO] - fit progress: (17, 1.061014118857277, {'accuracy': 0.6281}, 7918.058736578096)
+[2023-09-21 05:21:29,599][flwr][DEBUG] - evaluate_round 17: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 05:22:01,501][flwr][DEBUG] - evaluate_round 17 received 10 results and 0 failures
+[2023-09-21 05:22:01,502][flwr][DEBUG] - fit_round 18: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.694511217948718
+(DefaultActor pid=2820544) >> Training accuracy: 0.790465
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5331688596491229
+(DefaultActor pid=2820544) >> Training accuracy: 0.756031
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5461753731343284
+(DefaultActor pid=2820544) >> Training accuracy: 0.695896
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5341282894736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.800781
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5842391304347826
+(DefaultActor pid=2820544) >> Training accuracy: 0.705389
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5559275793650794
+(DefaultActor pid=2820544) >> Training accuracy: 0.758805
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5567835365853658
+(DefaultActor pid=2820544) >> Training accuracy: 0.771723
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5974151234567902
+(DefaultActor pid=2820544) >> Training accuracy: 0.843943
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.652542372881356
+(DefaultActor pid=2820544) >> Training accuracy: 0.800318
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7278163580246914
+[2023-09-21 05:29:01,699][flwr][DEBUG] - fit_round 18 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.832562
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6203
+[2023-09-21 05:29:03,103][flwr][INFO] - fit progress: (18, 1.083283578435453, {'accuracy': 0.6203}, 8371.562929124106)
+[2023-09-21 05:29:03,103][flwr][DEBUG] - evaluate_round 18: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 05:29:34,393][flwr][DEBUG] - evaluate_round 18 received 10 results and 0 failures
+[2023-09-21 05:29:34,394][flwr][DEBUG] - fit_round 19: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5164473684210527
+(DefaultActor pid=2820544) >> Training accuracy: 0.796053
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5565200617283951
+(DefaultActor pid=2820544) >> Training accuracy: 0.817515
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5530753968253969
+(DefaultActor pid=2820544) >> Training accuracy: 0.782490
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.714891975308642
+(DefaultActor pid=2820544) >> Training accuracy: 0.821373
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7111378205128205
+(DefaultActor pid=2820544) >> Training accuracy: 0.795873
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6046195652173914
+(DefaultActor pid=2820544) >> Training accuracy: 0.720788
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.668697033898305
+(DefaultActor pid=2820544) >> Training accuracy: 0.810117
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5345394736842105
+(DefaultActor pid=2820544) >> Training accuracy: 0.761239
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5623094512195121
+(DefaultActor pid=2820544) >> Training accuracy: 0.778963
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5408115671641791
+[2023-09-21 05:36:35,288][flwr][DEBUG] - fit_round 19 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.729011
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6361
+[2023-09-21 05:36:36,683][flwr][INFO] - fit progress: (19, 1.024500151411794, {'accuracy': 0.6361}, 8825.14358962886)
+[2023-09-21 05:36:36,684][flwr][DEBUG] - evaluate_round 19: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 05:37:07,686][flwr][DEBUG] - evaluate_round 19 received 10 results and 0 failures
+[2023-09-21 05:37:07,688][flwr][DEBUG] - fit_round 20: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5838815789473685
+(DefaultActor pid=2820544) >> Training accuracy: 0.797286
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7158564814814815
+(DefaultActor pid=2820544) >> Training accuracy: 0.816165
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5886194029850746
+(DefaultActor pid=2820544) >> Training accuracy: 0.720616
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5544969512195121
+(DefaultActor pid=2820544) >> Training accuracy: 0.790396
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.609375
+(DefaultActor pid=2820544) >> Training accuracy: 0.810764
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5997023809523809
+(DefaultActor pid=2820544) >> Training accuracy: 0.788938
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6694915254237288
+(DefaultActor pid=2820544) >> Training accuracy: 0.814883
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7081330128205128
+(DefaultActor pid=2820544) >> Training accuracy: 0.787460
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5921648550724637
+(DefaultActor pid=2820544) >> Training accuracy: 0.726676
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5139802631578947
+[2023-09-21 05:44:10,490][flwr][DEBUG] - fit_round 20 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.760143
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6331
+[2023-09-21 05:44:12,098][flwr][INFO] - fit progress: (20, 1.0367834657525863, {'accuracy': 0.6331}, 9280.558003693819)
+[2023-09-21 05:44:12,098][flwr][DEBUG] - evaluate_round 20: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 05:44:42,550][flwr][DEBUG] - evaluate_round 20 received 10 results and 0 failures
+[2023-09-21 05:44:42,551][flwr][DEBUG] - fit_round 21: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6028025793650794
+(DefaultActor pid=2820544) >> Training accuracy: 0.792163
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5877700617283951
+(DefaultActor pid=2820544) >> Training accuracy: 0.853588
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5697408536585366
+(DefaultActor pid=2820544) >> Training accuracy: 0.773438
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7010030864197531
+(DefaultActor pid=2820544) >> Training accuracy: 0.839313
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5837220149253731
+(DefaultActor pid=2820544) >> Training accuracy: 0.745336
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.557360197368421
+(DefaultActor pid=2820544) >> Training accuracy: 0.805510
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6591631355932204
+(DefaultActor pid=2820544) >> Training accuracy: 0.805614
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6105072463768116
+(DefaultActor pid=2820544) >> Training accuracy: 0.721920
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5328947368421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.767818
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7151442307692307
+[2023-09-21 05:51:58,643][flwr][DEBUG] - fit_round 21 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.805489
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6446
+[2023-09-21 05:52:00,414][flwr][INFO] - fit progress: (21, 1.0136565257566044, {'accuracy': 0.6446}, 9748.874145396054)
+[2023-09-21 05:52:00,414][flwr][DEBUG] - evaluate_round 21: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 05:52:30,601][flwr][DEBUG] - evaluate_round 21 received 10 results and 0 failures
+[2023-09-21 05:52:30,602][flwr][DEBUG] - fit_round 22: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7079475308641975
+(DefaultActor pid=2820544) >> Training accuracy: 0.833719
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6258680555555556
+(DefaultActor pid=2820544) >> Training accuracy: 0.794023
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5610608552631579
+(DefaultActor pid=2820544) >> Training accuracy: 0.806538
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6231884057971014
+(DefaultActor pid=2820544) >> Training accuracy: 0.739130
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5642149390243902
+(DefaultActor pid=2820544) >> Training accuracy: 0.790587
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7175480769230769
+(DefaultActor pid=2820544) >> Training accuracy: 0.806290
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6005015432098766
+(DefaultActor pid=2820544) >> Training accuracy: 0.837963
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5460526315789473
+(DefaultActor pid=2820544) >> Training accuracy: 0.738213
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5993470149253731
+(DefaultActor pid=2820544) >> Training accuracy: 0.749767
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.664989406779661
+[2023-09-21 05:59:42,140][flwr][DEBUG] - fit_round 22 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.808263
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6354
+[2023-09-21 05:59:43,525][flwr][INFO] - fit progress: (22, 1.0296277775170324, {'accuracy': 0.6354}, 10211.985237995163)
+[2023-09-21 05:59:43,525][flwr][DEBUG] - evaluate_round 22: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 06:00:14,922][flwr][DEBUG] - evaluate_round 22 received 10 results and 0 failures
+[2023-09-21 06:00:14,924][flwr][DEBUG] - fit_round 23: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5554496951219512
+(DefaultActor pid=2820544) >> Training accuracy: 0.788681
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5879197761194029
+(DefaultActor pid=2820544) >> Training accuracy: 0.723881
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6022376543209876
+(DefaultActor pid=2820544) >> Training accuracy: 0.836227
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5276864035087719
+(DefaultActor pid=2820544) >> Training accuracy: 0.758498
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7169471153846154
+(DefaultActor pid=2820544) >> Training accuracy: 0.798277
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7183641975308642
+(DefaultActor pid=2820544) >> Training accuracy: 0.826003
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.563733552631579
+(DefaultActor pid=2820544) >> Training accuracy: 0.791118
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5991847826086957
+(DefaultActor pid=2820544) >> Training accuracy: 0.734149
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6758474576271186
+(DefaultActor pid=2820544) >> Training accuracy: 0.819650
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5873015873015873
+[2023-09-21 06:07:23,946][flwr][DEBUG] - fit_round 23 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.776166
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6427
+[2023-09-21 06:07:25,352][flwr][INFO] - fit progress: (23, 1.0151812633196005, {'accuracy': 0.6427}, 10673.812659171876)
+[2023-09-21 06:07:25,353][flwr][DEBUG] - evaluate_round 23: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 06:07:56,172][flwr][DEBUG] - evaluate_round 23 received 10 results and 0 failures
+[2023-09-21 06:07:56,173][flwr][DEBUG] - fit_round 24: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5701219512195121
+(DefaultActor pid=2820544) >> Training accuracy: 0.787348
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6302083333333334
+(DefaultActor pid=2820544) >> Training accuracy: 0.721467
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6784957627118644
+(DefaultActor pid=2820544) >> Training accuracy: 0.804555
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.57421875
+(DefaultActor pid=2820544) >> Training accuracy: 0.803248
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.581856343283582
+(DefaultActor pid=2820544) >> Training accuracy: 0.738340
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7397762345679012
+(DefaultActor pid=2820544) >> Training accuracy: 0.839892
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5949900793650794
+(DefaultActor pid=2820544) >> Training accuracy: 0.779762
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6070601851851852
+(DefaultActor pid=2820544) >> Training accuracy: 0.848187
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5485197368421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.774671
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7309695512820513
+[2023-09-21 06:14:55,728][flwr][DEBUG] - fit_round 24 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.804087
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6476
+[2023-09-21 06:14:57,438][flwr][INFO] - fit progress: (24, 1.0049766376376532, {'accuracy': 0.6476}, 11125.898730413988)
+[2023-09-21 06:14:57,439][flwr][DEBUG] - evaluate_round 24: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 06:15:28,311][flwr][DEBUG] - evaluate_round 24 received 10 results and 0 failures
+[2023-09-21 06:15:28,312][flwr][DEBUG] - fit_round 25: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6182484567901234
+(DefaultActor pid=2820544) >> Training accuracy: 0.844715
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.71875
+(DefaultActor pid=2820544) >> Training accuracy: 0.832562
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5345394736842105
+(DefaultActor pid=2820544) >> Training accuracy: 0.753015
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6024305555555556
+(DefaultActor pid=2820544) >> Training accuracy: 0.779390
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5874533582089553
+(DefaultActor pid=2820544) >> Training accuracy: 0.720149
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5733612804878049
+(DefaultActor pid=2820544) >> Training accuracy: 0.798590
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7275641025641025
+(DefaultActor pid=2820544) >> Training accuracy: 0.805689
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6840572033898306
+(DefaultActor pid=2820544) >> Training accuracy: 0.824153
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.615036231884058
+(DefaultActor pid=2820544) >> Training accuracy: 0.733922
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5692845394736842
+[2023-09-21 06:22:38,068][flwr][DEBUG] - fit_round 25 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.810650
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6517
+[2023-09-21 06:22:39,477][flwr][INFO] - fit progress: (25, 1.000602862705438, {'accuracy': 0.6517}, 11587.93703436805)
+[2023-09-21 06:22:39,477][flwr][DEBUG] - evaluate_round 25: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 06:23:11,323][flwr][DEBUG] - evaluate_round 25 received 10 results and 0 failures
+[2023-09-21 06:23:11,324][flwr][DEBUG] - fit_round 26: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5608552631578947
+(DefaultActor pid=2820544) >> Training accuracy: 0.747259
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.591765873015873
+(DefaultActor pid=2820544) >> Training accuracy: 0.793775
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6248070987654321
+(DefaultActor pid=2820544) >> Training accuracy: 0.843557
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7195216049382716
+(DefaultActor pid=2820544) >> Training accuracy: 0.831790
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6340579710144928
+(DefaultActor pid=2820544) >> Training accuracy: 0.734601
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5625
+(DefaultActor pid=2820544) >> Training accuracy: 0.791921
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5982730263157895
+(DefaultActor pid=2820544) >> Training accuracy: 0.811472
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7183493589743589
+(DefaultActor pid=2820544) >> Training accuracy: 0.807692
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6618114406779662
+(DefaultActor pid=2820544) >> Training accuracy: 0.800053
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.590018656716418
+[2023-09-21 06:30:30,276][flwr][DEBUG] - fit_round 26 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.726679
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6437
+[2023-09-21 06:30:31,556][flwr][INFO] - fit progress: (26, 1.0190478839432469, {'accuracy': 0.6437}, 12060.016203787178)
+[2023-09-21 06:30:31,556][flwr][DEBUG] - evaluate_round 26: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 06:31:03,470][flwr][DEBUG] - evaluate_round 26 received 10 results and 0 failures
+[2023-09-21 06:31:03,471][flwr][DEBUG] - fit_round 27: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.578125
+(DefaultActor pid=2820544) >> Training accuracy: 0.805831
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5706623134328358
+(DefaultActor pid=2820544) >> Training accuracy: 0.729944
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6729343220338984
+(DefaultActor pid=2820544) >> Training accuracy: 0.819915
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5881696428571429
+(DefaultActor pid=2820544) >> Training accuracy: 0.788814
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6213768115942029
+(DefaultActor pid=2820544) >> Training accuracy: 0.736187
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5466694078947368
+(DefaultActor pid=2820544) >> Training accuracy: 0.828331
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5839120370370371
+(DefaultActor pid=2820544) >> Training accuracy: 0.842978
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7328317901234568
+(DefaultActor pid=2820544) >> Training accuracy: 0.836420
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7255608974358975
+(DefaultActor pid=2820544) >> Training accuracy: 0.815905
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5474232456140351
+[2023-09-21 06:38:02,695][flwr][DEBUG] - fit_round 27 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.771107
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6539
+[2023-09-21 06:38:04,097][flwr][INFO] - fit progress: (27, 0.9883538024684492, {'accuracy': 0.6539}, 12512.557022120804)
+[2023-09-21 06:38:04,097][flwr][DEBUG] - evaluate_round 27: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 06:38:35,068][flwr][DEBUG] - evaluate_round 27 received 10 results and 0 failures
+[2023-09-21 06:38:35,068][flwr][DEBUG] - fit_round 28: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5950838414634146
+(DefaultActor pid=2820544) >> Training accuracy: 0.794779
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7277644230769231
+(DefaultActor pid=2820544) >> Training accuracy: 0.807492
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5485197368421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.761787
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6811440677966102
+(DefaultActor pid=2820544) >> Training accuracy: 0.818326
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5758634868421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.799753
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6252264492753623
+(DefaultActor pid=2820544) >> Training accuracy: 0.736639
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7349537037037037
+(DefaultActor pid=2820544) >> Training accuracy: 0.853202
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6032986111111112
+(DefaultActor pid=2820544) >> Training accuracy: 0.780630
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6101466049382716
+(DefaultActor pid=2820544) >> Training accuracy: 0.851466
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5841884328358209
+[2023-09-21 06:45:30,072][flwr][DEBUG] - fit_round 28 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.754664
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6491
+[2023-09-21 06:45:31,486][flwr][INFO] - fit progress: (28, 0.9920400703867404, {'accuracy': 0.6491}, 12959.946306405123)
+[2023-09-21 06:45:31,486][flwr][DEBUG] - evaluate_round 28: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 06:46:03,421][flwr][DEBUG] - evaluate_round 28 received 10 results and 0 failures
+[2023-09-21 06:46:03,422][flwr][DEBUG] - fit_round 29: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6814088983050848
+(DefaultActor pid=2820544) >> Training accuracy: 0.821769
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5974506578947368
+(DefaultActor pid=2820544) >> Training accuracy: 0.820312
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5916511194029851
+(DefaultActor pid=2820544) >> Training accuracy: 0.741604
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6279438405797102
+(DefaultActor pid=2820544) >> Training accuracy: 0.735281
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6369598765432098
+(DefaultActor pid=2820544) >> Training accuracy: 0.849923
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7386188271604939
+(DefaultActor pid=2820544) >> Training accuracy: 0.849537
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6067708333333334
+(DefaultActor pid=2820544) >> Training accuracy: 0.789807
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5876524390243902
+(DefaultActor pid=2820544) >> Training accuracy: 0.798209
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7339743589743589
+(DefaultActor pid=2820544) >> Training accuracy: 0.801482
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5526315789473685
+[2023-09-21 06:53:15,962][flwr][DEBUG] - fit_round 29 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.770833
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6535
+[2023-09-21 06:53:17,393][flwr][INFO] - fit progress: (29, 0.9918740025153175, {'accuracy': 0.6535}, 13425.852999129798)
+[2023-09-21 06:53:17,393][flwr][DEBUG] - evaluate_round 29: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 06:53:55,270][flwr][DEBUG] - evaluate_round 29 received 10 results and 0 failures
+[2023-09-21 06:53:55,271][flwr][DEBUG] - fit_round 30: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6154891304347826
+(DefaultActor pid=2820544) >> Training accuracy: 0.752717
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7220293209876543
+(DefaultActor pid=2820544) >> Training accuracy: 0.842014
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6702860169491526
+(DefaultActor pid=2820544) >> Training accuracy: 0.802436
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7161458333333334
+(DefaultActor pid=2820544) >> Training accuracy: 0.805288
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6121735074626866
+(DefaultActor pid=2820544) >> Training accuracy: 0.738806
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.583079268292683
+(DefaultActor pid=2820544) >> Training accuracy: 0.794588
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5635964912280702
+(DefaultActor pid=2820544) >> Training accuracy: 0.752467
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5758634868421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.823191
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6294642857142857
+(DefaultActor pid=2820544) >> Training accuracy: 0.790427
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6213348765432098
+[2023-09-21 07:01:05,752][flwr][DEBUG] - fit_round 30 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.860147
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6504
+[2023-09-21 07:01:07,287][flwr][INFO] - fit progress: (30, 0.999864658418174, {'accuracy': 0.6504}, 13895.747061056085)
+[2023-09-21 07:01:07,287][flwr][DEBUG] - evaluate_round 30: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 07:01:38,500][flwr][DEBUG] - evaluate_round 30 received 10 results and 0 failures
+[2023-09-21 07:01:38,501][flwr][DEBUG] - fit_round 31: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7422839506172839
+(DefaultActor pid=2820544) >> Training accuracy: 0.838349
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6777012711864406
+(DefaultActor pid=2820544) >> Training accuracy: 0.824417
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5731907894736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.775493
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5848880597014925
+(DefaultActor pid=2820544) >> Training accuracy: 0.747435
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7307692307692307
+(DefaultActor pid=2820544) >> Training accuracy: 0.818710
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.620697463768116
+(DefaultActor pid=2820544) >> Training accuracy: 0.744565
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5838414634146342
+(DefaultActor pid=2820544) >> Training accuracy: 0.798209
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6180555555555556
+(DefaultActor pid=2820544) >> Training accuracy: 0.869020
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5629111842105263
+(DefaultActor pid=2820544) >> Training accuracy: 0.824836
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5952380952380952
+[2023-09-21 07:08:49,812][flwr][DEBUG] - fit_round 31 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.800595
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6604
+[2023-09-21 07:08:51,306][flwr][INFO] - fit progress: (31, 0.9666112412850316, {'accuracy': 0.6604}, 14359.766338087153)
+[2023-09-21 07:08:51,306][flwr][DEBUG] - evaluate_round 31: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 07:09:24,231][flwr][DEBUG] - evaluate_round 31 received 10 results and 0 failures
+[2023-09-21 07:09:24,233][flwr][DEBUG] - fit_round 32: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7417052469135802
+(DefaultActor pid=2820544) >> Training accuracy: 0.840471
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5679824561403509
+(DefaultActor pid=2820544) >> Training accuracy: 0.746711
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6274909420289855
+(DefaultActor pid=2820544) >> Training accuracy: 0.752944
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6209490740740741
+(DefaultActor pid=2820544) >> Training accuracy: 0.860532
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7070974576271186
+(DefaultActor pid=2820544) >> Training accuracy: 0.830244
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7323717948717948
+(DefaultActor pid=2820544) >> Training accuracy: 0.819311
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5836759868421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.823191
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5863185975609756
+(DefaultActor pid=2820544) >> Training accuracy: 0.804688
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6383928571428571
+(DefaultActor pid=2820544) >> Training accuracy: 0.792411
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6142723880597015
+[2023-09-21 07:16:55,930][flwr][DEBUG] - fit_round 32 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.725280
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6638
+[2023-09-21 07:17:04,344][flwr][INFO] - fit progress: (32, 0.9633961186622279, {'accuracy': 0.6638}, 14852.804559036158)
+[2023-09-21 07:17:04,345][flwr][DEBUG] - evaluate_round 32: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 07:17:51,379][flwr][DEBUG] - evaluate_round 32 received 10 results and 0 failures
+[2023-09-21 07:17:51,379][flwr][DEBUG] - fit_round 33: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7445913461538461
+(DefaultActor pid=2820544) >> Training accuracy: 0.810497
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7511574074074074
+(DefaultActor pid=2820544) >> Training accuracy: 0.851080
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.586890243902439
+(DefaultActor pid=2820544) >> Training accuracy: 0.799352
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6294367283950617
+(DefaultActor pid=2820544) >> Training accuracy: 0.861111
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7007415254237288
+(DefaultActor pid=2820544) >> Training accuracy: 0.828655
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6219161184210527
+(DefaultActor pid=2820544) >> Training accuracy: 0.823396
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6202876984126984
+(DefaultActor pid=2820544) >> Training accuracy: 0.785962
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6005130597014925
+(DefaultActor pid=2820544) >> Training accuracy: 0.758629
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6433423913043478
+(DefaultActor pid=2820544) >> Training accuracy: 0.755661
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5496162280701754
+[2023-09-21 07:25:20,205][flwr][DEBUG] - fit_round 33 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.767818
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6482
+[2023-09-21 07:25:22,088][flwr][INFO] - fit progress: (33, 1.003742164411484, {'accuracy': 0.6482}, 15350.548849062063)
+[2023-09-21 07:25:22,089][flwr][DEBUG] - evaluate_round 33: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 07:25:53,785][flwr][DEBUG] - evaluate_round 33 received 10 results and 0 failures
+[2023-09-21 07:25:53,786][flwr][DEBUG] - fit_round 34: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6200396825396826
+(DefaultActor pid=2820544) >> Training accuracy: 0.796751
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6035879629629629
+(DefaultActor pid=2820544) >> Training accuracy: 0.853974
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7415123456790124
+(DefaultActor pid=2820544) >> Training accuracy: 0.844329
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.675052966101695
+(DefaultActor pid=2820544) >> Training accuracy: 0.831568
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5859375
+(DefaultActor pid=2820544) >> Training accuracy: 0.810404
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5810032894736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.818462
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6272644927536232
+(DefaultActor pid=2820544) >> Training accuracy: 0.752264
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.742988782051282
+(DefaultActor pid=2820544) >> Training accuracy: 0.820112
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6000466417910447
+(DefaultActor pid=2820544) >> Training accuracy: 0.760261
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5482456140350878
+[2023-09-21 07:33:22,730][flwr][DEBUG] - fit_round 34 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.766996
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6594
+[2023-09-21 07:33:24,306][flwr][INFO] - fit progress: (34, 0.9889103397012899, {'accuracy': 0.6594}, 15832.766589079052)
+[2023-09-21 07:33:24,307][flwr][DEBUG] - evaluate_round 34: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 07:33:56,320][flwr][DEBUG] - evaluate_round 34 received 10 results and 0 failures
+[2023-09-21 07:33:56,321][flwr][DEBUG] - fit_round 35: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.588795731707317
+(DefaultActor pid=2820544) >> Training accuracy: 0.793064
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5627741228070176
+(DefaultActor pid=2820544) >> Training accuracy: 0.786732
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7438271604938271
+(DefaultActor pid=2820544) >> Training accuracy: 0.854745
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6324728260869565
+(DefaultActor pid=2820544) >> Training accuracy: 0.741395
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5965485074626866
+(DefaultActor pid=2820544) >> Training accuracy: 0.762360
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6255787037037037
+(DefaultActor pid=2820544) >> Training accuracy: 0.858410
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6005345394736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.816201
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6909427966101694
+(DefaultActor pid=2820544) >> Training accuracy: 0.824947
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7323717948717948
+(DefaultActor pid=2820544) >> Training accuracy: 0.802484
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6023065476190477
+(DefaultActor pid=2820544) >> Training accuracy: 0.794023
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 07:41:46,221][flwr][DEBUG] - fit_round 35 received 10 results and 0 failures
+test acc: 0.6561
+[2023-09-21 07:41:47,815][flwr][INFO] - fit progress: (35, 0.9822426000342201, {'accuracy': 0.6561}, 16336.27490788186)
+[2023-09-21 07:41:47,815][flwr][DEBUG] - evaluate_round 35: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 07:42:20,167][flwr][DEBUG] - evaluate_round 35 received 10 results and 0 failures
+[2023-09-21 07:42:20,169][flwr][DEBUG] - fit_round 36: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6333085317460317
+(DefaultActor pid=2820544) >> Training accuracy: 0.801835
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5906635802469136
+(DefaultActor pid=2820544) >> Training accuracy: 0.853009
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7322530864197531
+(DefaultActor pid=2820544) >> Training accuracy: 0.855903
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6374547101449275
+(DefaultActor pid=2820544) >> Training accuracy: 0.761775
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7439903846153846
+(DefaultActor pid=2820544) >> Training accuracy: 0.807091
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5746299342105263
+(DefaultActor pid=2820544) >> Training accuracy: 0.811061
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5597587719298246
+(DefaultActor pid=2820544) >> Training accuracy: 0.776316
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.609375
+(DefaultActor pid=2820544) >> Training accuracy: 0.804497
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6044776119402985
+(DefaultActor pid=2820544) >> Training accuracy: 0.735075
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6972987288135594
+[2023-09-21 07:50:16,332][flwr][DEBUG] - fit_round 36 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.824682
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6629
+[2023-09-21 07:50:18,132][flwr][INFO] - fit progress: (36, 0.962386382559237, {'accuracy': 0.6629}, 16846.592430986)
+[2023-09-21 07:50:18,132][flwr][DEBUG] - evaluate_round 36: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 07:50:49,770][flwr][DEBUG] - evaluate_round 36 received 10 results and 0 failures
+[2023-09-21 07:50:49,771][flwr][DEBUG] - fit_round 37: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.733573717948718
+(DefaultActor pid=2820544) >> Training accuracy: 0.821114
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6245335820895522
+(DefaultActor pid=2820544) >> Training accuracy: 0.741604
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6422371031746031
+(DefaultActor pid=2820544) >> Training accuracy: 0.793403
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6933262711864406
+(DefaultActor pid=2820544) >> Training accuracy: 0.813559
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7380401234567902
+(DefaultActor pid=2820544) >> Training accuracy: 0.860532
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6399456521739131
+(DefaultActor pid=2820544) >> Training accuracy: 0.752038
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6021792763157895
+(DefaultActor pid=2820544) >> Training accuracy: 0.817640
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6130401234567902
+(DefaultActor pid=2820544) >> Training accuracy: 0.849344
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6141387195121951
+(DefaultActor pid=2820544) >> Training accuracy: 0.803925
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.578125
+[2023-09-21 07:58:20,828][flwr][DEBUG] - fit_round 37 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.768914
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6614
+[2023-09-21 07:58:22,599][flwr][INFO] - fit progress: (37, 0.9726330711247441, {'accuracy': 0.6614}, 17331.05953423679)
+[2023-09-21 07:58:22,600][flwr][DEBUG] - evaluate_round 37: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 07:58:54,099][flwr][DEBUG] - evaluate_round 37 received 10 results and 0 failures
+[2023-09-21 07:58:54,100][flwr][DEBUG] - fit_round 38: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5981326219512195
+(DefaultActor pid=2820544) >> Training accuracy: 0.811928
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6390128968253969
+(DefaultActor pid=2820544) >> Training accuracy: 0.801835
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5529057017543859
+(DefaultActor pid=2820544) >> Training accuracy: 0.774945
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6694915254237288
+(DefaultActor pid=2820544) >> Training accuracy: 0.840042
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5999177631578947
+(DefaultActor pid=2820544) >> Training accuracy: 0.829975
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7453926282051282
+(DefaultActor pid=2820544) >> Training accuracy: 0.802885
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.626929012345679
+(DefaultActor pid=2820544) >> Training accuracy: 0.858218
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6229011194029851
+(DefaultActor pid=2820544) >> Training accuracy: 0.762593
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7370756172839507
+(DefaultActor pid=2820544) >> Training accuracy: 0.838542
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6283967391304348
+[2023-09-21 08:06:20,812][flwr][DEBUG] - fit_round 38 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.764719
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6656
+[2023-09-21 08:06:22,365][flwr][INFO] - fit progress: (38, 0.965197785498616, {'accuracy': 0.6656}, 17810.825569720007)
+[2023-09-21 08:06:22,366][flwr][DEBUG] - evaluate_round 38: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 08:06:54,159][flwr][DEBUG] - evaluate_round 38 received 10 results and 0 failures
+[2023-09-21 08:06:54,160][flwr][DEBUG] - fit_round 39: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5553728070175439
+(DefaultActor pid=2820544) >> Training accuracy: 0.773849
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6417824074074074
+(DefaultActor pid=2820544) >> Training accuracy: 0.866127
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6103078358208955
+(DefaultActor pid=2820544) >> Training accuracy: 0.762593
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6970338983050848
+(DefaultActor pid=2820544) >> Training accuracy: 0.826006
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6426630434782609
+(DefaultActor pid=2820544) >> Training accuracy: 0.755435
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7542067307692307
+(DefaultActor pid=2820544) >> Training accuracy: 0.820312
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6052631578947368
+(DefaultActor pid=2820544) >> Training accuracy: 0.822163
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7272376543209876
+(DefaultActor pid=2820544) >> Training accuracy: 0.852238
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6233878968253969
+(DefaultActor pid=2820544) >> Training accuracy: 0.801711
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6048018292682927
+[2023-09-21 08:14:38,416][flwr][DEBUG] - fit_round 39 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.814405
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.666
+[2023-09-21 08:14:39,999][flwr][INFO] - fit progress: (39, 0.9574256779286808, {'accuracy': 0.666}, 18308.459737964906)
+[2023-09-21 08:14:40,000][flwr][DEBUG] - evaluate_round 39: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 08:15:11,361][flwr][DEBUG] - evaluate_round 39 received 10 results and 0 failures
+[2023-09-21 08:15:11,361][flwr][DEBUG] - fit_round 40: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6251929012345679
+(DefaultActor pid=2820544) >> Training accuracy: 0.869213
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6101973684210527
+(DefaultActor pid=2820544) >> Training accuracy: 0.829975
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6128731343283582
+(DefaultActor pid=2820544) >> Training accuracy: 0.758629
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7467948717948718
+(DefaultActor pid=2820544) >> Training accuracy: 0.820312
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6885593220338984
+(DefaultActor pid=2820544) >> Training accuracy: 0.836600
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5641447368421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.777961
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7361111111111112
+(DefaultActor pid=2820544) >> Training accuracy: 0.838542
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6382688492063492
+(DefaultActor pid=2820544) >> Training accuracy: 0.809152
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6027057926829268
+(DefaultActor pid=2820544) >> Training accuracy: 0.767912
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.639266304347826
+[2023-09-21 08:23:00,325][flwr][DEBUG] - fit_round 40 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.751812
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6597
+[2023-09-21 08:23:01,939][flwr][INFO] - fit progress: (40, 0.9920141804522981, {'accuracy': 0.6597}, 18810.39952501189)
+[2023-09-21 08:23:01,940][flwr][DEBUG] - evaluate_round 40: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 08:23:33,599][flwr][DEBUG] - evaluate_round 40 received 10 results and 0 failures
+[2023-09-21 08:23:33,600][flwr][DEBUG] - fit_round 41: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6124588815789473
+(DefaultActor pid=2820544) >> Training accuracy: 0.824013
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7347608024691358
+(DefaultActor pid=2820544) >> Training accuracy: 0.856481
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5441337719298246
+(DefaultActor pid=2820544) >> Training accuracy: 0.788925
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7113347457627118
+(DefaultActor pid=2820544) >> Training accuracy: 0.845339
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6123511904761905
+(DefaultActor pid=2820544) >> Training accuracy: 0.795015
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6220561594202898
+(DefaultActor pid=2820544) >> Training accuracy: 0.747509
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6516203703703703
+(DefaultActor pid=2820544) >> Training accuracy: 0.878279
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6058768656716418
+(DefaultActor pid=2820544) >> Training accuracy: 0.749300
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7407852564102564
+(DefaultActor pid=2820544) >> Training accuracy: 0.823117
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5663109756097561
+[2023-09-21 08:31:07,100][flwr][DEBUG] - fit_round 41 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.813834
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6662
+[2023-09-21 08:31:09,115][flwr][INFO] - fit progress: (41, 0.9609055894251448, {'accuracy': 0.6662}, 19297.57527715387)
+[2023-09-21 08:31:09,116][flwr][DEBUG] - evaluate_round 41: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 08:31:40,554][flwr][DEBUG] - evaluate_round 41 received 10 results and 0 failures
+[2023-09-21 08:31:40,554][flwr][DEBUG] - fit_round 42: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6859110169491526
+(DefaultActor pid=2820544) >> Training accuracy: 0.844809
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7361111111111112
+(DefaultActor pid=2820544) >> Training accuracy: 0.858218
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6267149390243902
+(DefaultActor pid=2820544) >> Training accuracy: 0.816502
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6208022388059702
+(DefaultActor pid=2820544) >> Training accuracy: 0.749534
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6271219135802469
+(DefaultActor pid=2820544) >> Training accuracy: 0.871914
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5753837719298246
+(DefaultActor pid=2820544) >> Training accuracy: 0.763706
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6397192028985508
+(DefaultActor pid=2820544) >> Training accuracy: 0.762455
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6030016447368421
+(DefaultActor pid=2820544) >> Training accuracy: 0.830181
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6423611111111112
+(DefaultActor pid=2820544) >> Training accuracy: 0.800843
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7405849358974359
+[2023-09-21 08:39:32,059][flwr][DEBUG] - fit_round 42 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.815304
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6485
+[2023-09-21 08:40:04,596][flwr][INFO] - fit progress: (42, 0.9998491283613272, {'accuracy': 0.6485}, 19833.056645926088)
+[2023-09-21 08:40:04,597][flwr][DEBUG] - evaluate_round 42: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 08:40:40,242][flwr][DEBUG] - evaluate_round 42 received 10 results and 0 failures
+[2023-09-21 08:40:40,243][flwr][DEBUG] - fit_round 43: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7415865384615384
+(DefaultActor pid=2820544) >> Training accuracy: 0.824519
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6914724576271186
+(DefaultActor pid=2820544) >> Training accuracy: 0.840042
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5556469298245614
+(DefaultActor pid=2820544) >> Training accuracy: 0.771930
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7586805555555556
+(DefaultActor pid=2820544) >> Training accuracy: 0.856289
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6068978658536586
+(DefaultActor pid=2820544) >> Training accuracy: 0.815549
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6286231884057971
+(DefaultActor pid=2820544) >> Training accuracy: 0.757473
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6273148148148148
+(DefaultActor pid=2820544) >> Training accuracy: 0.867477
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5927220394736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.834704
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5907182835820896
+(DefaultActor pid=2820544) >> Training accuracy: 0.761660
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5901537698412699
+(DefaultActor pid=2820544) >> Training accuracy: 0.810764
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 08:48:02,839][flwr][DEBUG] - fit_round 43 received 10 results and 0 failures
+test acc: 0.6638
+[2023-09-21 08:48:04,306][flwr][INFO] - fit progress: (43, 0.970430683404112, {'accuracy': 0.6638}, 20312.766624896787)
+[2023-09-21 08:48:04,307][flwr][DEBUG] - evaluate_round 43: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 08:48:34,869][flwr][DEBUG] - evaluate_round 43 received 10 results and 0 failures
+[2023-09-21 08:48:34,870][flwr][DEBUG] - fit_round 44: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7457932692307693
+(DefaultActor pid=2820544) >> Training accuracy: 0.811298
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7567515432098766
+(DefaultActor pid=2820544) >> Training accuracy: 0.860532
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6312003968253969
+(DefaultActor pid=2820544) >> Training accuracy: 0.802207
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6526268115942029
+(DefaultActor pid=2820544) >> Training accuracy: 0.766078
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.609375
+(DefaultActor pid=2820544) >> Training accuracy: 0.755364
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5904605263157895
+(DefaultActor pid=2820544) >> Training accuracy: 0.834910
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6224922839506173
+(DefaultActor pid=2820544) >> Training accuracy: 0.855710
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5685307017543859
+(DefaultActor pid=2820544) >> Training accuracy: 0.782072
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6845868644067796
+(DefaultActor pid=2820544) >> Training accuracy: 0.829979
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6068978658536586
+[2023-09-21 08:56:26,493][flwr][DEBUG] - fit_round 44 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.810785
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6673
+[2023-09-21 08:56:27,857][flwr][INFO] - fit progress: (44, 0.9538876035342962, {'accuracy': 0.6673}, 20816.31762043899)
+[2023-09-21 08:56:27,858][flwr][DEBUG] - evaluate_round 44: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 08:56:58,951][flwr][DEBUG] - evaluate_round 44 received 10 results and 0 failures
+[2023-09-21 08:56:58,952][flwr][DEBUG] - fit_round 45: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6145055970149254
+(DefaultActor pid=2820544) >> Training accuracy: 0.749767
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6165707236842105
+(DefaultActor pid=2820544) >> Training accuracy: 0.828742
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.555921052631579
+(DefaultActor pid=2820544) >> Training accuracy: 0.788103
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6175685975609756
+(DefaultActor pid=2820544) >> Training accuracy: 0.815739
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6477623456790124
+(DefaultActor pid=2820544) >> Training accuracy: 0.864969
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6431051587301587
+(DefaultActor pid=2820544) >> Training accuracy: 0.791667
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6909427966101694
+(DefaultActor pid=2820544) >> Training accuracy: 0.835540
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7566105769230769
+(DefaultActor pid=2820544) >> Training accuracy: 0.811899
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7326388888888888
+(DefaultActor pid=2820544) >> Training accuracy: 0.842785
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644927536231884
+[2023-09-21 09:04:18,260][flwr][DEBUG] - fit_round 45 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.744339
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6649
+[2023-09-21 09:04:19,680][flwr][INFO] - fit progress: (45, 0.9652769756964601, {'accuracy': 0.6649}, 21288.140167111065)
+[2023-09-21 09:04:19,680][flwr][DEBUG] - evaluate_round 45: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 09:04:51,440][flwr][DEBUG] - evaluate_round 45 received 10 results and 0 failures
+[2023-09-21 09:04:51,441][flwr][DEBUG] - fit_round 46: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6297554347826086
+(DefaultActor pid=2820544) >> Training accuracy: 0.753397
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7457561728395061
+(DefaultActor pid=2820544) >> Training accuracy: 0.857639
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6957097457627118
+(DefaultActor pid=2820544) >> Training accuracy: 0.834746
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6023848684210527
+(DefaultActor pid=2820544) >> Training accuracy: 0.835526
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.749198717948718
+(DefaultActor pid=2820544) >> Training accuracy: 0.825721
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5931783536585366
+(DefaultActor pid=2820544) >> Training accuracy: 0.802973
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6392746913580247
+(DefaultActor pid=2820544) >> Training accuracy: 0.865741
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.613106343283582
+(DefaultActor pid=2820544) >> Training accuracy: 0.766091
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6470734126984127
+(DefaultActor pid=2820544) >> Training accuracy: 0.808904
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5570175438596491
+[2023-09-21 09:12:10,903][flwr][DEBUG] - fit_round 46 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.781250
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6647
+[2023-09-21 09:12:12,527][flwr][INFO] - fit progress: (46, 0.9712253968936567, {'accuracy': 0.6647}, 21760.987880504224)
+[2023-09-21 09:12:12,528][flwr][DEBUG] - evaluate_round 46: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 09:12:43,146][flwr][DEBUG] - evaluate_round 46 received 10 results and 0 failures
+[2023-09-21 09:12:43,147][flwr][DEBUG] - fit_round 47: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5902549342105263
+(DefaultActor pid=2820544) >> Training accuracy: 0.828947
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.553453947368421
+(DefaultActor pid=2820544) >> Training accuracy: 0.799616
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.636322463768116
+(DefaultActor pid=2820544) >> Training accuracy: 0.776268
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7097457627118644
+(DefaultActor pid=2820544) >> Training accuracy: 0.800847
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7455929487179487
+(DefaultActor pid=2820544) >> Training accuracy: 0.818510
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.638640873015873
+(DefaultActor pid=2820544) >> Training accuracy: 0.811880
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7449845679012346
+(DefaultActor pid=2820544) >> Training accuracy: 0.853395
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6203703703703703
+(DefaultActor pid=2820544) >> Training accuracy: 0.863619
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6133765243902439
+(DefaultActor pid=2820544) >> Training accuracy: 0.812691
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6177705223880597
+[2023-09-21 09:19:56,576][flwr][DEBUG] - fit_round 47 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.762826
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6737
+[2023-09-21 09:20:13,742][flwr][INFO] - fit progress: (47, 0.9433850042355327, {'accuracy': 0.6737}, 22242.20204451913)
+[2023-09-21 09:20:13,743][flwr][DEBUG] - evaluate_round 47: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 09:20:48,906][flwr][DEBUG] - evaluate_round 47 received 10 results and 0 failures
+[2023-09-21 09:20:48,907][flwr][DEBUG] - fit_round 48: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7544070512820513
+(DefaultActor pid=2820544) >> Training accuracy: 0.825321
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5709978070175439
+(DefaultActor pid=2820544) >> Training accuracy: 0.777686
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7440200617283951
+(DefaultActor pid=2820544) >> Training accuracy: 0.858218
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6951800847457628
+(DefaultActor pid=2820544) >> Training accuracy: 0.829979
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6517210144927537
+(DefaultActor pid=2820544) >> Training accuracy: 0.774457
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6556299603174603
+(DefaultActor pid=2820544) >> Training accuracy: 0.808036
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6429398148148148
+(DefaultActor pid=2820544) >> Training accuracy: 0.870177
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6221217105263158
+(DefaultActor pid=2820544) >> Training accuracy: 0.825041
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6140391791044776
+(DefaultActor pid=2820544) >> Training accuracy: 0.744403
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6251905487804879
+[2023-09-21 09:27:58,065][flwr][DEBUG] - fit_round 48 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.829078
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6741
+[2023-09-21 09:27:59,833][flwr][INFO] - fit progress: (48, 0.9481769482167764, {'accuracy': 0.6741}, 22708.29312482383)
+[2023-09-21 09:27:59,833][flwr][DEBUG] - evaluate_round 48: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 09:28:34,244][flwr][DEBUG] - evaluate_round 48 received 10 results and 0 failures
+[2023-09-21 09:28:34,245][flwr][DEBUG] - fit_round 49: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6412037037037037
+(DefaultActor pid=2820544) >> Training accuracy: 0.864776
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6641757246376812
+(DefaultActor pid=2820544) >> Training accuracy: 0.769475
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6439732142857143
+(DefaultActor pid=2820544) >> Training accuracy: 0.788070
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7467206790123457
+(DefaultActor pid=2820544) >> Training accuracy: 0.850116
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6168064024390244
+(DefaultActor pid=2820544) >> Training accuracy: 0.825648
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5581140350877193
+(DefaultActor pid=2820544) >> Training accuracy: 0.782346
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.622327302631579
+(DefaultActor pid=2820544) >> Training accuracy: 0.832648
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7658253205128205
+(DefaultActor pid=2820544) >> Training accuracy: 0.816506
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6885593220338984
+(DefaultActor pid=2820544) >> Training accuracy: 0.826006
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6187033582089553
+(DefaultActor pid=2820544) >> Training accuracy: 0.745336
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 09:36:02,552][flwr][DEBUG] - fit_round 49 received 10 results and 0 failures
+test acc: 0.6726
+[2023-09-21 09:36:04,282][flwr][INFO] - fit progress: (49, 0.9416967020057642, {'accuracy': 0.6726}, 23192.74199562706)
+[2023-09-21 09:36:04,282][flwr][DEBUG] - evaluate_round 49: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 09:36:34,398][flwr][DEBUG] - evaluate_round 49 received 10 results and 0 failures
+[2023-09-21 09:36:34,399][flwr][DEBUG] - fit_round 50: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5960115131578947
+(DefaultActor pid=2820544) >> Training accuracy: 0.842105
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6410060975609756
+(DefaultActor pid=2820544) >> Training accuracy: 0.818216
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7203389830508474
+(DefaultActor pid=2820544) >> Training accuracy: 0.837924
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7481971153846154
+(DefaultActor pid=2820544) >> Training accuracy: 0.829127
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6122685185185185
+(DefaultActor pid=2820544) >> Training accuracy: 0.861304
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6533061594202898
+(DefaultActor pid=2820544) >> Training accuracy: 0.774004
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6631944444444444
+(DefaultActor pid=2820544) >> Training accuracy: 0.816344
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6236007462686567
+(DefaultActor pid=2820544) >> Training accuracy: 0.723647
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7494212962962963
+(DefaultActor pid=2820544) >> Training accuracy: 0.852623
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5526315789473685
+(DefaultActor pid=2820544) >> Training accuracy: 0.778509
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 09:43:45,752][flwr][DEBUG] - fit_round 50 received 10 results and 0 failures
+test acc: 0.672
+[2023-09-21 09:43:47,117][flwr][INFO] - fit progress: (50, 0.9449833774338134, {'accuracy': 0.672}, 23655.57692045998)
+[2023-09-21 09:43:47,117][flwr][DEBUG] - evaluate_round 50: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 09:44:17,397][flwr][DEBUG] - evaluate_round 50 received 10 results and 0 failures
+[2023-09-21 09:44:17,398][flwr][DEBUG] - fit_round 51: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6986228813559322
+(DefaultActor pid=2820544) >> Training accuracy: 0.818591
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7472993827160493
+(DefaultActor pid=2820544) >> Training accuracy: 0.862076
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6348379629629629
+(DefaultActor pid=2820544) >> Training accuracy: 0.869599
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6284298780487805
+(DefaultActor pid=2820544) >> Training accuracy: 0.813453
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6011513157894737
+(DefaultActor pid=2820544) >> Training accuracy: 0.793311
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6296641791044776
+(DefaultActor pid=2820544) >> Training accuracy: 0.754198
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6639492753623188
+(DefaultActor pid=2820544) >> Training accuracy: 0.774230
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7371794871794872
+(DefaultActor pid=2820544) >> Training accuracy: 0.824119
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6485615079365079
+(DefaultActor pid=2820544) >> Training accuracy: 0.810888
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6079358552631579
+[2023-09-21 09:51:36,440][flwr][DEBUG] - fit_round 51 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.831209
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6596
+[2023-09-21 09:51:37,863][flwr][INFO] - fit progress: (51, 0.977153589931159, {'accuracy': 0.6596}, 24126.323618939146)
+[2023-09-21 09:51:37,864][flwr][DEBUG] - evaluate_round 51: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 09:52:08,503][flwr][DEBUG] - evaluate_round 51 received 10 results and 0 failures
+[2023-09-21 09:52:08,503][flwr][DEBUG] - fit_round 52: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644927536231884
+(DefaultActor pid=2820544) >> Training accuracy: 0.761322
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.698093220338983
+(DefaultActor pid=2820544) >> Training accuracy: 0.839513
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6166158536585366
+(DefaultActor pid=2820544) >> Training accuracy: 0.810213
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7407852564102564
+(DefaultActor pid=2820544) >> Training accuracy: 0.836338
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6158234126984127
+(DefaultActor pid=2820544) >> Training accuracy: 0.813864
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6077425373134329
+(DefaultActor pid=2820544) >> Training accuracy: 0.765159
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7488425925925926
+(DefaultActor pid=2820544) >> Training accuracy: 0.861497
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6350308641975309
+(DefaultActor pid=2820544) >> Training accuracy: 0.859568
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6089638157894737
+(DefaultActor pid=2820544) >> Training accuracy: 0.825863
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.584703947368421
+(DefaultActor pid=2820544) >> Training accuracy: 0.784539
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 09:59:28,650][flwr][DEBUG] - fit_round 52 received 10 results and 0 failures
+test acc: 0.6647
+[2023-09-21 09:59:30,545][flwr][INFO] - fit progress: (52, 0.9636962722284725, {'accuracy': 0.6647}, 24599.005290116183)
+[2023-09-21 09:59:30,546][flwr][DEBUG] - evaluate_round 52: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 10:00:01,755][flwr][DEBUG] - evaluate_round 52 received 10 results and 0 failures
+[2023-09-21 10:00:01,756][flwr][DEBUG] - fit_round 53: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.623070987654321
+(DefaultActor pid=2820544) >> Training accuracy: 0.864776
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6551177536231884
+(DefaultActor pid=2820544) >> Training accuracy: 0.758605
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7055084745762712
+(DefaultActor pid=2820544) >> Training accuracy: 0.838983
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644469246031746
+(DefaultActor pid=2820544) >> Training accuracy: 0.815476
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5849780701754386
+(DefaultActor pid=2820544) >> Training accuracy: 0.782072
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6089638157894737
+(DefaultActor pid=2820544) >> Training accuracy: 0.837788
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7405849358974359
+(DefaultActor pid=2820544) >> Training accuracy: 0.825521
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6312966417910447
+(DefaultActor pid=2820544) >> Training accuracy: 0.761894
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6219512195121951
+(DefaultActor pid=2820544) >> Training accuracy: 0.825648
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7393904320987654
+[2023-09-21 10:07:34,641][flwr][DEBUG] - fit_round 53 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.863040
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6687
+[2023-09-21 10:07:35,995][flwr][INFO] - fit progress: (53, 0.9485500901461409, {'accuracy': 0.6687}, 25084.455141921062)
+[2023-09-21 10:07:35,995][flwr][DEBUG] - evaluate_round 53: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 10:08:06,753][flwr][DEBUG] - evaluate_round 53 received 10 results and 0 failures
+[2023-09-21 10:08:06,753][flwr][DEBUG] - fit_round 54: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6366234756097561
+(DefaultActor pid=2820544) >> Training accuracy: 0.814787
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6407490079365079
+(DefaultActor pid=2820544) >> Training accuracy: 0.803323
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6512681159420289
+(DefaultActor pid=2820544) >> Training accuracy: 0.766304
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7588734567901234
+(DefaultActor pid=2820544) >> Training accuracy: 0.865934
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7020656779661016
+(DefaultActor pid=2820544) >> Training accuracy: 0.848517
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6398533950617284
+(DefaultActor pid=2820544) >> Training accuracy: 0.862847
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5797697368421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.772752
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.635485197368421
+(DefaultActor pid=2820544) >> Training accuracy: 0.840461
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7568108974358975
+(DefaultActor pid=2820544) >> Training accuracy: 0.800481
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6238339552238806
+[2023-09-21 10:15:20,795][flwr][DEBUG] - fit_round 54 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.756297
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6674
+[2023-09-21 10:15:22,179][flwr][INFO] - fit progress: (54, 0.9527737906756112, {'accuracy': 0.6674}, 25550.639077940024)
+[2023-09-21 10:15:22,179][flwr][DEBUG] - evaluate_round 54: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 10:15:53,033][flwr][DEBUG] - evaluate_round 54 received 10 results and 0 failures
+[2023-09-21 10:15:53,034][flwr][DEBUG] - fit_round 55: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.61328125
+(DefaultActor pid=2820544) >> Training accuracy: 0.822368
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5778508771929824
+(DefaultActor pid=2820544) >> Training accuracy: 0.792489
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6198694029850746
+(DefaultActor pid=2820544) >> Training accuracy: 0.752799
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6949152542372882
+(DefaultActor pid=2820544) >> Training accuracy: 0.847722
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6607142857142857
+(DefaultActor pid=2820544) >> Training accuracy: 0.816716
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7530864197530864
+(DefaultActor pid=2820544) >> Training accuracy: 0.856481
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6269054878048781
+(DefaultActor pid=2820544) >> Training accuracy: 0.815549
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6363811728395061
+(DefaultActor pid=2820544) >> Training accuracy: 0.876736
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6521739130434783
+(DefaultActor pid=2820544) >> Training accuracy: 0.770380
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7425881410256411
+[2023-09-21 10:23:26,976][flwr][DEBUG] - fit_round 55 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.821114
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6701
+[2023-09-21 10:23:58,108][flwr][INFO] - fit progress: (55, 0.9540609658335726, {'accuracy': 0.6701}, 26066.56836123299)
+[2023-09-21 10:23:58,109][flwr][DEBUG] - evaluate_round 55: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 10:24:37,793][flwr][DEBUG] - evaluate_round 55 received 10 results and 0 failures
+[2023-09-21 10:24:37,794][flwr][DEBUG] - fit_round 56: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6578351449275363
+(DefaultActor pid=2820544) >> Training accuracy: 0.768795
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6309799382716049
+(DefaultActor pid=2820544) >> Training accuracy: 0.859954
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.701271186440678
+(DefaultActor pid=2820544) >> Training accuracy: 0.839778
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6056743421052632
+(DefaultActor pid=2820544) >> Training accuracy: 0.845189
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7526041666666666
+(DefaultActor pid=2820544) >> Training accuracy: 0.828726
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6175373134328358
+(DefaultActor pid=2820544) >> Training accuracy: 0.773554
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6118521341463414
+(DefaultActor pid=2820544) >> Training accuracy: 0.819931
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6033442982456141
+(DefaultActor pid=2820544) >> Training accuracy: 0.789474
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6548859126984127
+(DefaultActor pid=2820544) >> Training accuracy: 0.820685
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7401620370370371
+[2023-09-21 10:31:44,945][flwr][DEBUG] - fit_round 56 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.869213
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6709
+[2023-09-21 10:31:46,745][flwr][INFO] - fit progress: (56, 0.9453530991420197, {'accuracy': 0.6709}, 26535.205561616924)
+[2023-09-21 10:31:46,746][flwr][DEBUG] - evaluate_round 56: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 10:32:17,198][flwr][DEBUG] - evaluate_round 56 received 10 results and 0 failures
+[2023-09-21 10:32:17,199][flwr][DEBUG] - fit_round 57: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6972987288135594
+(DefaultActor pid=2820544) >> Training accuracy: 0.819650
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6336287313432836
+(DefaultActor pid=2820544) >> Training accuracy: 0.778685
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.625
+(DefaultActor pid=2820544) >> Training accuracy: 0.799352
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6531498015873016
+(DefaultActor pid=2820544) >> Training accuracy: 0.819320
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6551177536231884
+(DefaultActor pid=2820544) >> Training accuracy: 0.768116
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.638695987654321
+(DefaultActor pid=2820544) >> Training accuracy: 0.873457
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6241776315789473
+(DefaultActor pid=2820544) >> Training accuracy: 0.817845
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7441907051282052
+(DefaultActor pid=2820544) >> Training accuracy: 0.835337
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5970394736842105
+(DefaultActor pid=2820544) >> Training accuracy: 0.781798
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7511574074074074
+[2023-09-21 10:39:36,000][flwr][DEBUG] - fit_round 57 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.853781
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6732
+[2023-09-21 10:39:37,745][flwr][INFO] - fit progress: (57, 0.9411518906061642, {'accuracy': 0.6732}, 27006.205031685065)
+[2023-09-21 10:39:37,745][flwr][DEBUG] - evaluate_round 57: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 10:40:08,681][flwr][DEBUG] - evaluate_round 57 received 10 results and 0 failures
+[2023-09-21 10:40:08,682][flwr][DEBUG] - fit_round 58: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6231496710526315
+(DefaultActor pid=2820544) >> Training accuracy: 0.834498
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6568700396825397
+(DefaultActor pid=2820544) >> Training accuracy: 0.817460
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.706302966101695
+(DefaultActor pid=2820544) >> Training accuracy: 0.846928
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5638706140350878
+(DefaultActor pid=2820544) >> Training accuracy: 0.797149
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6574074074074074
+(DefaultActor pid=2820544) >> Training accuracy: 0.867670
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6496829710144928
+(DefaultActor pid=2820544) >> Training accuracy: 0.774004
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6324314024390244
+(DefaultActor pid=2820544) >> Training accuracy: 0.810785
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7426697530864198
+(DefaultActor pid=2820544) >> Training accuracy: 0.866127
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7526041666666666
+(DefaultActor pid=2820544) >> Training accuracy: 0.832131
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.632695895522388
+[2023-09-21 10:47:16,783][flwr][DEBUG] - fit_round 58 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.763993
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6721
+[2023-09-21 10:47:18,303][flwr][INFO] - fit progress: (58, 0.9347897329079077, {'accuracy': 0.6721}, 27466.763446252793)
+[2023-09-21 10:47:18,303][flwr][DEBUG] - evaluate_round 58: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 10:47:49,450][flwr][DEBUG] - evaluate_round 58 received 10 results and 0 failures
+[2023-09-21 10:47:49,450][flwr][DEBUG] - fit_round 59: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6370045731707317
+(DefaultActor pid=2820544) >> Training accuracy: 0.818788
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.650588768115942
+(DefaultActor pid=2820544) >> Training accuracy: 0.767663
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6110197368421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.834087
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7520032051282052
+(DefaultActor pid=2820544) >> Training accuracy: 0.822716
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5770285087719298
+(DefaultActor pid=2820544) >> Training accuracy: 0.791118
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6770833333333334
+(DefaultActor pid=2820544) >> Training accuracy: 0.805928
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6424906716417911
+(DefaultActor pid=2820544) >> Training accuracy: 0.764459
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7386188271604939
+(DefaultActor pid=2820544) >> Training accuracy: 0.854552
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7166313559322034
+(DefaultActor pid=2820544) >> Training accuracy: 0.855932
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6410108024691358
+[2023-09-21 10:55:05,114][flwr][DEBUG] - fit_round 59 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.869985
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6748
+[2023-09-21 10:55:06,654][flwr][INFO] - fit progress: (59, 0.9279640743526788, {'accuracy': 0.6748}, 27935.114211172797)
+[2023-09-21 10:55:06,654][flwr][DEBUG] - evaluate_round 59: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 10:55:36,941][flwr][DEBUG] - evaluate_round 59 received 10 results and 0 failures
+[2023-09-21 10:55:36,942][flwr][DEBUG] - fit_round 60: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6490036231884058
+(DefaultActor pid=2820544) >> Training accuracy: 0.782156
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.581140350877193
+(DefaultActor pid=2820544) >> Training accuracy: 0.784265
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.642554012345679
+(DefaultActor pid=2820544) >> Training accuracy: 0.867670
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6506529850746269
+(DefaultActor pid=2820544) >> Training accuracy: 0.777985
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7449919871794872
+(DefaultActor pid=2820544) >> Training accuracy: 0.830729
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6206825657894737
+(DefaultActor pid=2820544) >> Training accuracy: 0.848684
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7523148148148148
+(DefaultActor pid=2820544) >> Training accuracy: 0.866705
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6794394841269841
+(DefaultActor pid=2820544) >> Training accuracy: 0.814732
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6196646341463414
+(DefaultActor pid=2820544) >> Training accuracy: 0.821646
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7097457627118644
+[2023-09-21 11:02:46,617][flwr][DEBUG] - fit_round 60 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.841631
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6675
+[2023-09-21 11:02:48,093][flwr][INFO] - fit progress: (60, 0.950613231990284, {'accuracy': 0.6675}, 28396.553373389877)
+[2023-09-21 11:02:48,093][flwr][DEBUG] - evaluate_round 60: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 11:03:18,883][flwr][DEBUG] - evaluate_round 60 received 10 results and 0 failures
+[2023-09-21 11:03:18,884][flwr][DEBUG] - fit_round 61: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6867055084745762
+(DefaultActor pid=2820544) >> Training accuracy: 0.815943
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6310634328358209
+(DefaultActor pid=2820544) >> Training accuracy: 0.774720
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6229039634146342
+(DefaultActor pid=2820544) >> Training accuracy: 0.826791
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7478780864197531
+(DefaultActor pid=2820544) >> Training accuracy: 0.857832
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7437900641025641
+(DefaultActor pid=2820544) >> Training accuracy: 0.821715
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6483410493827161
+(DefaultActor pid=2820544) >> Training accuracy: 0.875386
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5879934210526315
+(DefaultActor pid=2820544) >> Training accuracy: 0.792489
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6496775793650794
+(DefaultActor pid=2820544) >> Training accuracy: 0.811384
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6143092105263158
+(DefaultActor pid=2820544) >> Training accuracy: 0.833265
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6490036231884058
+[2023-09-21 11:10:36,104][flwr][DEBUG] - fit_round 61 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.778986
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6723
+[2023-09-21 11:10:37,335][flwr][INFO] - fit progress: (61, 0.9391038782489948, {'accuracy': 0.6723}, 28865.795383579098)
+[2023-09-21 11:10:37,335][flwr][DEBUG] - evaluate_round 61: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 11:11:07,493][flwr][DEBUG] - evaluate_round 61 received 10 results and 0 failures
+[2023-09-21 11:11:07,494][flwr][DEBUG] - fit_round 62: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5893640350877193
+(DefaultActor pid=2820544) >> Training accuracy: 0.781250
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6275652985074627
+(DefaultActor pid=2820544) >> Training accuracy: 0.779851
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7534054487179487
+(DefaultActor pid=2820544) >> Training accuracy: 0.802083
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6967690677966102
+(DefaultActor pid=2820544) >> Training accuracy: 0.846663
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6385030864197531
+(DefaultActor pid=2820544) >> Training accuracy: 0.871914
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6459573412698413
+(DefaultActor pid=2820544) >> Training accuracy: 0.817832
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6234756097560976
+(DefaultActor pid=2820544) >> Training accuracy: 0.812881
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7608024691358025
+(DefaultActor pid=2820544) >> Training accuracy: 0.861111
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6639492753623188
+(DefaultActor pid=2820544) >> Training accuracy: 0.780571
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6221217105263158
+[2023-09-21 11:18:15,242][flwr][DEBUG] - fit_round 62 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.819901
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6629
+[2023-09-21 11:18:16,600][flwr][INFO] - fit progress: (62, 0.9639180962460491, {'accuracy': 0.6629}, 29325.060337544885)
+[2023-09-21 11:18:16,600][flwr][DEBUG] - evaluate_round 62: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 11:18:46,804][flwr][DEBUG] - evaluate_round 62 received 10 results and 0 failures
+[2023-09-21 11:18:46,804][flwr][DEBUG] - fit_round 63: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7453703703703703
+(DefaultActor pid=2820544) >> Training accuracy: 0.869213
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6175685975609756
+(DefaultActor pid=2820544) >> Training accuracy: 0.809642
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6380597014925373
+(DefaultActor pid=2820544) >> Training accuracy: 0.767957
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6516617063492064
+(DefaultActor pid=2820544) >> Training accuracy: 0.820809
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644927536231884
+(DefaultActor pid=2820544) >> Training accuracy: 0.772871
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5866228070175439
+(DefaultActor pid=2820544) >> Training accuracy: 0.791941
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7347756410256411
+(DefaultActor pid=2820544) >> Training accuracy: 0.834335
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5972450657894737
+(DefaultActor pid=2820544) >> Training accuracy: 0.826891
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715572033898305
+(DefaultActor pid=2820544) >> Training accuracy: 0.845869
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6296296296296297
+[2023-09-21 11:26:00,102][flwr][DEBUG] - fit_round 63 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.867284
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6689
+[2023-09-21 11:26:05,098][flwr][INFO] - fit progress: (63, 0.9449828718416988, {'accuracy': 0.6689}, 29793.558222265914)
+[2023-09-21 11:26:05,098][flwr][DEBUG] - evaluate_round 63: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 11:26:35,007][flwr][DEBUG] - evaluate_round 63 received 10 results and 0 failures
+[2023-09-21 11:26:35,008][flwr][DEBUG] - fit_round 64: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6229011194029851
+(DefaultActor pid=2820544) >> Training accuracy: 0.773321
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6412450396825397
+(DefaultActor pid=2820544) >> Training accuracy: 0.822173
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6578351449275363
+(DefaultActor pid=2820544) >> Training accuracy: 0.762908
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6310975609756098
+(DefaultActor pid=2820544) >> Training accuracy: 0.822218
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6481481481481481
+(DefaultActor pid=2820544) >> Training accuracy: 0.865355
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7563657407407407
+(DefaultActor pid=2820544) >> Training accuracy: 0.867863
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6204769736842105
+(DefaultActor pid=2820544) >> Training accuracy: 0.836143
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5731907894736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.804550
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.710010593220339
+(DefaultActor pid=2820544) >> Training accuracy: 0.836335
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7646233974358975
+[2023-09-21 11:33:37,054][flwr][DEBUG] - fit_round 64 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.835136
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6708
+[2023-09-21 11:33:38,496][flwr][INFO] - fit progress: (64, 0.945188293537012, {'accuracy': 0.6708}, 30246.95655811485)
+[2023-09-21 11:33:38,497][flwr][DEBUG] - evaluate_round 64: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 11:34:09,158][flwr][DEBUG] - evaluate_round 64 received 10 results and 0 failures
+[2023-09-21 11:34:09,159][flwr][DEBUG] - fit_round 65: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6585648148148148
+(DefaultActor pid=2820544) >> Training accuracy: 0.880787
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6435688405797102
+(DefaultActor pid=2820544) >> Training accuracy: 0.777174
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6467013888888888
+(DefaultActor pid=2820544) >> Training accuracy: 0.807168
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7497996794871795
+(DefaultActor pid=2820544) >> Training accuracy: 0.818309
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.636660447761194
+(DefaultActor pid=2820544) >> Training accuracy: 0.780784
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7474922839506173
+(DefaultActor pid=2820544) >> Training accuracy: 0.863812
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.616234756097561
+(DefaultActor pid=2820544) >> Training accuracy: 0.822790
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6247944078947368
+(DefaultActor pid=2820544) >> Training accuracy: 0.835732
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7153072033898306
+(DefaultActor pid=2820544) >> Training accuracy: 0.850371
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5893640350877193
+[2023-09-21 11:41:31,948][flwr][DEBUG] - fit_round 65 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.787829
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.668
+[2023-09-21 11:41:33,356][flwr][INFO] - fit progress: (65, 0.9480573036038457, {'accuracy': 0.668}, 30721.81667397404)
+[2023-09-21 11:41:33,357][flwr][DEBUG] - evaluate_round 65: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 11:42:03,845][flwr][DEBUG] - evaluate_round 65 received 10 results and 0 failures
+[2023-09-21 11:42:03,846][flwr][DEBUG] - fit_round 66: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6392609126984127
+(DefaultActor pid=2820544) >> Training accuracy: 0.817956
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6567028985507246
+(DefaultActor pid=2820544) >> Training accuracy: 0.777174
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7598379629629629
+(DefaultActor pid=2820544) >> Training accuracy: 0.856674
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5836074561403509
+(DefaultActor pid=2820544) >> Training accuracy: 0.788925
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6101973684210527
+(DefaultActor pid=2820544) >> Training accuracy: 0.837171
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.706832627118644
+(DefaultActor pid=2820544) >> Training accuracy: 0.857786
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6291977611940298
+(DefaultActor pid=2820544) >> Training accuracy: 0.768657
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6485339506172839
+(DefaultActor pid=2820544) >> Training accuracy: 0.874614
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7483974358974359
+(DefaultActor pid=2820544) >> Training accuracy: 0.823518
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6213795731707317
+[2023-09-21 11:49:04,794][flwr][DEBUG] - fit_round 66 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.831745
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6687
+[2023-09-21 11:49:06,376][flwr][INFO] - fit progress: (66, 0.942290931178358, {'accuracy': 0.6687}, 31174.83631586982)
+[2023-09-21 11:49:06,376][flwr][DEBUG] - evaluate_round 66: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 11:49:37,075][flwr][DEBUG] - evaluate_round 66 received 10 results and 0 failures
+[2023-09-21 11:49:37,077][flwr][DEBUG] - fit_round 67: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5822368421052632
+(DefaultActor pid=2820544) >> Training accuracy: 0.779057
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7610176282051282
+(DefaultActor pid=2820544) >> Training accuracy: 0.829127
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6261660447761194
+(DefaultActor pid=2820544) >> Training accuracy: 0.756297
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7619598765432098
+(DefaultActor pid=2820544) >> Training accuracy: 0.862076
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6079358552631579
+(DefaultActor pid=2820544) >> Training accuracy: 0.838816
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6557539682539683
+(DefaultActor pid=2820544) >> Training accuracy: 0.818824
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.713718220338983
+(DefaultActor pid=2820544) >> Training accuracy: 0.847193
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6537590579710145
+(DefaultActor pid=2820544) >> Training accuracy: 0.771966
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6369598765432098
+(DefaultActor pid=2820544) >> Training accuracy: 0.868248
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6331935975609756
+[2023-09-21 11:56:48,318][flwr][DEBUG] - fit_round 67 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.815358
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6764
+[2023-09-21 11:56:49,655][flwr][INFO] - fit progress: (67, 0.92270217916836, {'accuracy': 0.6764}, 31638.11498604808)
+[2023-09-21 11:56:49,655][flwr][DEBUG] - evaluate_round 67: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 11:57:19,102][flwr][DEBUG] - evaluate_round 67 received 10 results and 0 failures
+[2023-09-21 11:57:19,103][flwr][DEBUG] - fit_round 68: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6400462962962963
+(DefaultActor pid=2820544) >> Training accuracy: 0.865548
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715042372881356
+(DefaultActor pid=2820544) >> Training accuracy: 0.833422
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6378264925373134
+(DefaultActor pid=2820544) >> Training accuracy: 0.774021
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6677989130434783
+(DefaultActor pid=2820544) >> Training accuracy: 0.772871
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7399839743589743
+(DefaultActor pid=2820544) >> Training accuracy: 0.840545
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7434413580246914
+(DefaultActor pid=2820544) >> Training accuracy: 0.868248
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6215049342105263
+(DefaultActor pid=2820544) >> Training accuracy: 0.835526
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6609623015873016
+(DefaultActor pid=2820544) >> Training accuracy: 0.825025
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6349085365853658
+(DefaultActor pid=2820544) >> Training accuracy: 0.822790
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5794956140350878
+[2023-09-21 12:04:20,577][flwr][DEBUG] - fit_round 68 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.789748
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6705
+[2023-09-21 12:04:21,982][flwr][INFO] - fit progress: (68, 0.9357810918325052, {'accuracy': 0.6705}, 32090.44285089709)
+[2023-09-21 12:04:21,983][flwr][DEBUG] - evaluate_round 68: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 12:04:51,756][flwr][DEBUG] - evaluate_round 68 received 10 results and 0 failures
+[2023-09-21 12:04:51,757][flwr][DEBUG] - fit_round 69: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6585648148148148
+(DefaultActor pid=2820544) >> Training accuracy: 0.869792
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6343368902439024
+(DefaultActor pid=2820544) >> Training accuracy: 0.827172
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7509645061728395
+(DefaultActor pid=2820544) >> Training accuracy: 0.867091
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6324013157894737
+(DefaultActor pid=2820544) >> Training accuracy: 0.842105
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7461939102564102
+(DefaultActor pid=2820544) >> Training accuracy: 0.826122
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6586061507936508
+(DefaultActor pid=2820544) >> Training accuracy: 0.815352
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5953947368421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.795504
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7052436440677966
+(DefaultActor pid=2820544) >> Training accuracy: 0.840572
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6466884328358209
+(DefaultActor pid=2820544) >> Training accuracy: 0.768424
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6664402173913043
+(DefaultActor pid=2820544) >> Training accuracy: 0.780797
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 12:12:02,139][flwr][DEBUG] - fit_round 69 received 10 results and 0 failures
+test acc: 0.6761
+[2023-09-21 12:12:04,106][flwr][INFO] - fit progress: (69, 0.9341149679578531, {'accuracy': 0.6761}, 32552.566110807005)
+[2023-09-21 12:12:04,106][flwr][DEBUG] - evaluate_round 69: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 12:12:34,752][flwr][DEBUG] - evaluate_round 69 received 10 results and 0 failures
+[2023-09-21 12:12:34,753][flwr][DEBUG] - fit_round 70: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.657608695652174
+(DefaultActor pid=2820544) >> Training accuracy: 0.773551
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6312881097560976
+(DefaultActor pid=2820544) >> Training accuracy: 0.815739
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6282649253731343
+(DefaultActor pid=2820544) >> Training accuracy: 0.776119
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6527777777777778
+(DefaultActor pid=2820544) >> Training accuracy: 0.878279
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6321957236842105
+(DefaultActor pid=2820544) >> Training accuracy: 0.836143
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6066337719298246
+(DefaultActor pid=2820544) >> Training accuracy: 0.802083
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7588141025641025
+(DefaultActor pid=2820544) >> Training accuracy: 0.842949
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6553819444444444
+(DefaultActor pid=2820544) >> Training accuracy: 0.814112
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7530864197530864
+(DefaultActor pid=2820544) >> Training accuracy: 0.870177
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7004766949152542
+[2023-09-21 12:19:48,750][flwr][DEBUG] - fit_round 70 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.843485
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6728
+[2023-09-21 12:19:50,541][flwr][INFO] - fit progress: (70, 0.9335093384924026, {'accuracy': 0.6728}, 33019.001658937894)
+[2023-09-21 12:19:50,542][flwr][DEBUG] - evaluate_round 70: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 12:20:20,736][flwr][DEBUG] - evaluate_round 70 received 10 results and 0 failures
+[2023-09-21 12:20:20,737][flwr][DEBUG] - fit_round 71: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6254664179104478
+(DefaultActor pid=2820544) >> Training accuracy: 0.756996
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5699013157894737
+(DefaultActor pid=2820544) >> Training accuracy: 0.807018
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.627858231707317
+(DefaultActor pid=2820544) >> Training accuracy: 0.817645
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7515432098765432
+(DefaultActor pid=2820544) >> Training accuracy: 0.866705
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7592147435897436
+(DefaultActor pid=2820544) >> Training accuracy: 0.794671
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6612318840579711
+(DefaultActor pid=2820544) >> Training accuracy: 0.795969
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6377467105263158
+(DefaultActor pid=2820544) >> Training accuracy: 0.839433
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.658179012345679
+(DefaultActor pid=2820544) >> Training accuracy: 0.885417
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7142478813559322
+(DefaultActor pid=2820544) >> Training accuracy: 0.853549
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6521577380952381
+(DefaultActor pid=2820544) >> Training accuracy: 0.821553
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 12:27:32,822][flwr][DEBUG] - fit_round 71 received 10 results and 0 failures
+test acc: 0.6729
+[2023-09-21 12:27:34,063][flwr][INFO] - fit progress: (71, 0.9386814056684415, {'accuracy': 0.6729}, 33482.52336926106)
+[2023-09-21 12:27:34,063][flwr][DEBUG] - evaluate_round 71: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 12:28:03,947][flwr][DEBUG] - evaluate_round 71 received 10 results and 0 failures
+[2023-09-21 12:28:03,948][flwr][DEBUG] - fit_round 72: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7213983050847458
+(DefaultActor pid=2820544) >> Training accuracy: 0.846663
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6657608695652174
+(DefaultActor pid=2820544) >> Training accuracy: 0.771966
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6672867063492064
+(DefaultActor pid=2820544) >> Training accuracy: 0.816096
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5803179824561403
+(DefaultActor pid=2820544) >> Training accuracy: 0.807566
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6379243827160493
+(DefaultActor pid=2820544) >> Training accuracy: 0.872685
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.602796052631579
+(DefaultActor pid=2820544) >> Training accuracy: 0.847039
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7638888888888888
+(DefaultActor pid=2820544) >> Training accuracy: 0.858603
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6317630597014925
+(DefaultActor pid=2820544) >> Training accuracy: 0.769823
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7566105769230769
+(DefaultActor pid=2820544) >> Training accuracy: 0.835537
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6230945121951219
+[2023-09-21 12:35:07,103][flwr][DEBUG] - fit_round 72 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.821265
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6683
+[2023-09-21 12:35:08,462][flwr][INFO] - fit progress: (72, 0.950544156300755, {'accuracy': 0.6683}, 33936.92198925698)
+[2023-09-21 12:35:08,462][flwr][DEBUG] - evaluate_round 72: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 12:35:38,635][flwr][DEBUG] - evaluate_round 72 received 10 results and 0 failures
+[2023-09-21 12:35:38,636][flwr][DEBUG] - fit_round 73: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7590144230769231
+(DefaultActor pid=2820544) >> Training accuracy: 0.830329
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6480978260869565
+(DefaultActor pid=2820544) >> Training accuracy: 0.787364
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6224922839506173
+(DefaultActor pid=2820544) >> Training accuracy: 0.864005
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5997121710526315
+(DefaultActor pid=2820544) >> Training accuracy: 0.842105
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6521577380952381
+(DefaultActor pid=2820544) >> Training accuracy: 0.828125
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7702546296296297
+(DefaultActor pid=2820544) >> Training accuracy: 0.855710
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5674342105263158
+(DefaultActor pid=2820544) >> Training accuracy: 0.792763
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6233675373134329
+(DefaultActor pid=2820544) >> Training accuracy: 0.772854
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6480564024390244
+(DefaultActor pid=2820544) >> Training accuracy: 0.826220
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.722457627118644
+[2023-09-21 12:42:41,330][flwr][DEBUG] - fit_round 73 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.849841
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6788
+[2023-09-21 12:42:42,688][flwr][INFO] - fit progress: (73, 0.9230360760094639, {'accuracy': 0.6788}, 34391.147913408)
+[2023-09-21 12:42:42,688][flwr][DEBUG] - evaluate_round 73: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 12:43:13,193][flwr][DEBUG] - evaluate_round 73 received 10 results and 0 failures
+[2023-09-21 12:43:13,195][flwr][DEBUG] - fit_round 74: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6490091463414634
+(DefaultActor pid=2820544) >> Training accuracy: 0.828316
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6414473684210527
+(DefaultActor pid=2820544) >> Training accuracy: 0.828536
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5890899122807017
+(DefaultActor pid=2820544) >> Training accuracy: 0.808388
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7594521604938271
+(DefaultActor pid=2820544) >> Training accuracy: 0.844136
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6587577160493827
+(DefaultActor pid=2820544) >> Training accuracy: 0.872299
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6308302238805971
+(DefaultActor pid=2820544) >> Training accuracy: 0.766091
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7129237288135594
+(DefaultActor pid=2820544) >> Training accuracy: 0.845074
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6655344202898551
+(DefaultActor pid=2820544) >> Training accuracy: 0.786685
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7568108974358975
+(DefaultActor pid=2820544) >> Training accuracy: 0.841346
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6526537698412699
+[2023-09-21 12:50:26,338][flwr][DEBUG] - fit_round 74 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.822049
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6734
+[2023-09-21 12:50:27,922][flwr][INFO] - fit progress: (74, 0.9372135468374807, {'accuracy': 0.6734}, 34856.38190944586)
+[2023-09-21 12:50:27,922][flwr][DEBUG] - evaluate_round 74: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 12:50:58,128][flwr][DEBUG] - evaluate_round 74 received 10 results and 0 failures
+[2023-09-21 12:50:58,129][flwr][DEBUG] - fit_round 75: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5923793859649122
+(DefaultActor pid=2820544) >> Training accuracy: 0.782346
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6598731884057971
+(DefaultActor pid=2820544) >> Training accuracy: 0.788043
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6552854938271605
+(DefaultActor pid=2820544) >> Training accuracy: 0.875386
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7521219135802469
+(DefaultActor pid=2820544) >> Training accuracy: 0.867670
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7057733050847458
+(DefaultActor pid=2820544) >> Training accuracy: 0.839778
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.654265873015873
+(DefaultActor pid=2820544) >> Training accuracy: 0.825521
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6217606707317073
+(DefaultActor pid=2820544) >> Training accuracy: 0.837462
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6173930921052632
+(DefaultActor pid=2820544) >> Training accuracy: 0.844367
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6354944029850746
+(DefaultActor pid=2820544) >> Training accuracy: 0.775886
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7485977564102564
+[2023-09-21 12:58:23,212][flwr][DEBUG] - fit_round 75 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.833133
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.671
+[2023-09-21 12:58:24,904][flwr][INFO] - fit progress: (75, 0.9488034000792823, {'accuracy': 0.671}, 35333.36447527679)
+[2023-09-21 12:58:24,904][flwr][DEBUG] - evaluate_round 75: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 12:58:56,235][flwr][DEBUG] - evaluate_round 75 received 10 results and 0 failures
+[2023-09-21 12:58:56,236][flwr][DEBUG] - fit_round 76: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7629243827160493
+(DefaultActor pid=2820544) >> Training accuracy: 0.867091
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6608382936507936
+(DefaultActor pid=2820544) >> Training accuracy: 0.812624
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7110699152542372
+(DefaultActor pid=2820544) >> Training accuracy: 0.853814
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6149259868421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.839227
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6097560975609756
+(DefaultActor pid=2820544) >> Training accuracy: 0.824314
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6512345679012346
+(DefaultActor pid=2820544) >> Training accuracy: 0.876543
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7600160256410257
+(DefaultActor pid=2820544) >> Training accuracy: 0.833133
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6308302238805971
+(DefaultActor pid=2820544) >> Training accuracy: 0.744170
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5819627192982456
+(DefaultActor pid=2820544) >> Training accuracy: 0.793037
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6621376811594203
+[2023-09-21 13:05:55,298][flwr][DEBUG] - fit_round 76 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.791440
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6748
+[2023-09-21 13:05:56,738][flwr][INFO] - fit progress: (76, 0.933324966758204, {'accuracy': 0.6748}, 35785.19834174914)
+[2023-09-21 13:05:56,738][flwr][DEBUG] - evaluate_round 76: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 13:06:27,449][flwr][DEBUG] - evaluate_round 76 received 10 results and 0 failures
+[2023-09-21 13:06:27,450][flwr][DEBUG] - fit_round 77: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6658950617283951
+(DefaultActor pid=2820544) >> Training accuracy: 0.870949
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6284950657894737
+(DefaultActor pid=2820544) >> Training accuracy: 0.832854
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6608382936507936
+(DefaultActor pid=2820544) >> Training accuracy: 0.818824
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6126143292682927
+(DefaultActor pid=2820544) >> Training accuracy: 0.814405
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7121292372881356
+(DefaultActor pid=2820544) >> Training accuracy: 0.835805
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7698317307692307
+(DefaultActor pid=2820544) >> Training accuracy: 0.827123
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7716049382716049
+(DefaultActor pid=2820544) >> Training accuracy: 0.866705
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6259328358208955
+(DefaultActor pid=2820544) >> Training accuracy: 0.770522
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6625905797101449
+(DefaultActor pid=2820544) >> Training accuracy: 0.778306
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.587171052631579
+[2023-09-21 13:13:30,708][flwr][DEBUG] - fit_round 77 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.790296
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6749
+[2023-09-21 13:13:32,087][flwr][INFO] - fit progress: (77, 0.9317882342841297, {'accuracy': 0.6749}, 36240.546968831215)
+[2023-09-21 13:13:32,087][flwr][DEBUG] - evaluate_round 77: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 13:14:02,493][flwr][DEBUG] - evaluate_round 77 received 10 results and 0 failures
+[2023-09-21 13:14:02,494][flwr][DEBUG] - fit_round 78: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7094809322033898
+(DefaultActor pid=2820544) >> Training accuracy: 0.846133
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6364272388059702
+(DefaultActor pid=2820544) >> Training accuracy: 0.778451
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6459603658536586
+(DefaultActor pid=2820544) >> Training accuracy: 0.829459
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5926535087719298
+(DefaultActor pid=2820544) >> Training accuracy: 0.802906
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6800271739130435
+(DefaultActor pid=2820544) >> Training accuracy: 0.771286
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6427469135802469
+(DefaultActor pid=2820544) >> Training accuracy: 0.870563
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6278782894736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.845806
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7640817901234568
+(DefaultActor pid=2820544) >> Training accuracy: 0.842785
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7700320512820513
+(DefaultActor pid=2820544) >> Training accuracy: 0.826522
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6634424603174603
+[2023-09-21 13:21:21,070][flwr][DEBUG] - fit_round 78 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.824777
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6794
+[2023-09-21 13:21:22,864][flwr][INFO] - fit progress: (78, 0.9255952789379766, {'accuracy': 0.6794}, 36711.32486301195)
+[2023-09-21 13:21:22,865][flwr][DEBUG] - evaluate_round 78: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 13:21:52,936][flwr][DEBUG] - evaluate_round 78 received 10 results and 0 failures
+[2023-09-21 13:21:52,937][flwr][DEBUG] - fit_round 79: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.671875
+(DefaultActor pid=2820544) >> Training accuracy: 0.782609
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6373355263157895
+(DefaultActor pid=2820544) >> Training accuracy: 0.842516
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5874451754385965
+(DefaultActor pid=2820544) >> Training accuracy: 0.799068
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7453703703703703
+(DefaultActor pid=2820544) >> Training accuracy: 0.854167
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6677827380952381
+(DefaultActor pid=2820544) >> Training accuracy: 0.831101
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6394589552238806
+(DefaultActor pid=2820544) >> Training accuracy: 0.774021
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.627858231707317
+(DefaultActor pid=2820544) >> Training accuracy: 0.829840
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6608796296296297
+(DefaultActor pid=2820544) >> Training accuracy: 0.877122
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715572033898305
+(DefaultActor pid=2820544) >> Training accuracy: 0.816737
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7604166666666666
+[2023-09-21 13:29:27,851][flwr][DEBUG] - fit_round 79 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.845954
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6773
+[2023-09-21 13:29:29,239][flwr][INFO] - fit progress: (79, 0.9406886980556451, {'accuracy': 0.6773}, 37197.69971890794)
+[2023-09-21 13:29:29,240][flwr][DEBUG] - evaluate_round 79: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 13:30:01,963][flwr][DEBUG] - evaluate_round 79 received 10 results and 0 failures
+[2023-09-21 13:30:01,963][flwr][DEBUG] - fit_round 80: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6582880434782609
+(DefaultActor pid=2820544) >> Training accuracy: 0.790987
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7554086538461539
+(DefaultActor pid=2820544) >> Training accuracy: 0.840545
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6272865853658537
+(DefaultActor pid=2820544) >> Training accuracy: 0.832127
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7463348765432098
+(DefaultActor pid=2820544) >> Training accuracy: 0.869792
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5918311403508771
+(DefaultActor pid=2820544) >> Training accuracy: 0.797149
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6716269841269841
+(DefaultActor pid=2820544) >> Training accuracy: 0.819320
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6178042763157895
+(DefaultActor pid=2820544) >> Training accuracy: 0.828536
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6520061728395061
+(DefaultActor pid=2820544) >> Training accuracy: 0.873843
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7094809322033898
+(DefaultActor pid=2820544) >> Training accuracy: 0.851165
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6315298507462687
+[2023-09-21 13:37:36,693][flwr][DEBUG] - fit_round 80 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.775886
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6798
+[2023-09-21 13:37:38,599][flwr][INFO] - fit progress: (80, 0.9281163449866322, {'accuracy': 0.6798}, 37687.05938832415)
+[2023-09-21 13:37:38,599][flwr][DEBUG] - evaluate_round 80: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 13:38:11,543][flwr][DEBUG] - evaluate_round 80 received 10 results and 0 failures
+[2023-09-21 13:38:11,545][flwr][DEBUG] - fit_round 81: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6639384920634921
+(DefaultActor pid=2820544) >> Training accuracy: 0.822669
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7741126543209876
+(DefaultActor pid=2820544) >> Training accuracy: 0.869599
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7538060897435898
+(DefaultActor pid=2820544) >> Training accuracy: 0.827524
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6408582089552238
+(DefaultActor pid=2820544) >> Training accuracy: 0.775653
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6677989130434783
+(DefaultActor pid=2820544) >> Training accuracy: 0.786458
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6398533950617284
+(DefaultActor pid=2820544) >> Training accuracy: 0.875193
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6091694078947368
+(DefaultActor pid=2820544) >> Training accuracy: 0.840461
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.725635593220339
+(DefaultActor pid=2820544) >> Training accuracy: 0.841367
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5896381578947368
+(DefaultActor pid=2820544) >> Training accuracy: 0.790844
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6257621951219512
+(DefaultActor pid=2820544) >> Training accuracy: 0.830602
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 13:45:59,871][flwr][DEBUG] - fit_round 81 received 10 results and 0 failures
+test acc: 0.6754
+[2023-09-21 13:46:01,500][flwr][INFO] - fit progress: (81, 0.9390814332916333, {'accuracy': 0.6754}, 38189.96000787895)
+[2023-09-21 13:46:01,500][flwr][DEBUG] - evaluate_round 81: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 13:46:34,062][flwr][DEBUG] - evaluate_round 81 received 10 results and 0 failures
+[2023-09-21 13:46:34,063][flwr][DEBUG] - fit_round 82: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7604166666666666
+(DefaultActor pid=2820544) >> Training accuracy: 0.873843
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6707427536231884
+(DefaultActor pid=2820544) >> Training accuracy: 0.784194
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6614583333333334
+(DefaultActor pid=2820544) >> Training accuracy: 0.875579
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6145198170731707
+(DefaultActor pid=2820544) >> Training accuracy: 0.828125
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6392257462686567
+(DefaultActor pid=2820544) >> Training accuracy: 0.777285
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7025953389830508
+(DefaultActor pid=2820544) >> Training accuracy: 0.838718
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6317845394736842
+(DefaultActor pid=2820544) >> Training accuracy: 0.831003
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6140350877192983
+(DefaultActor pid=2820544) >> Training accuracy: 0.782621
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6555059523809523
+(DefaultActor pid=2820544) >> Training accuracy: 0.832093
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7520032051282052
+[2023-09-21 13:55:28,943][flwr][DEBUG] - fit_round 82 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.827123
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6798
+[2023-09-21 13:55:31,512][flwr][INFO] - fit progress: (82, 0.928261538473562, {'accuracy': 0.6798}, 38759.97228321992)
+[2023-09-21 13:55:31,512][flwr][DEBUG] - evaluate_round 82: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 13:56:03,830][flwr][DEBUG] - evaluate_round 82 received 10 results and 0 failures
+[2023-09-21 13:56:03,831][flwr][DEBUG] - fit_round 83: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6878720238095238
+(DefaultActor pid=2820544) >> Training accuracy: 0.817832
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6630434782608695
+(DefaultActor pid=2820544) >> Training accuracy: 0.790534
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6411966463414634
+(DefaultActor pid=2820544) >> Training accuracy: 0.818026
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6531635802469136
+(DefaultActor pid=2820544) >> Training accuracy: 0.869406
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7163665254237288
+(DefaultActor pid=2820544) >> Training accuracy: 0.845604
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5693530701754386
+(DefaultActor pid=2820544) >> Training accuracy: 0.796875
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7780448717948718
+(DefaultActor pid=2820544) >> Training accuracy: 0.813902
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6350740131578947
+(DefaultActor pid=2820544) >> Training accuracy: 0.829564
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6494869402985075
+(DefaultActor pid=2820544) >> Training accuracy: 0.782183
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7413194444444444
+(DefaultActor pid=2820544) >> Training accuracy: 0.869599
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 14:03:58,129][flwr][DEBUG] - fit_round 83 received 10 results and 0 failures
+test acc: 0.6803
+[2023-09-21 14:03:59,983][flwr][INFO] - fit progress: (83, 0.9337312611528098, {'accuracy': 0.6803}, 39268.443283197936)
+[2023-09-21 14:03:59,984][flwr][DEBUG] - evaluate_round 83: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 14:04:31,937][flwr][DEBUG] - evaluate_round 83 received 10 results and 0 failures
+[2023-09-21 14:04:31,938][flwr][DEBUG] - fit_round 84: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5984100877192983
+(DefaultActor pid=2820544) >> Training accuracy: 0.796327
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644483024691358
+(DefaultActor pid=2820544) >> Training accuracy: 0.866705
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7198093220338984
+(DefaultActor pid=2820544) >> Training accuracy: 0.845869
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6235608552631579
+(DefaultActor pid=2820544) >> Training accuracy: 0.843544
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7654320987654321
+(DefaultActor pid=2820544) >> Training accuracy: 0.874035
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7598157051282052
+(DefaultActor pid=2820544) >> Training accuracy: 0.833333
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6282393292682927
+(DefaultActor pid=2820544) >> Training accuracy: 0.826220
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6622023809523809
+(DefaultActor pid=2820544) >> Training accuracy: 0.824157
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6464552238805971
+(DefaultActor pid=2820544) >> Training accuracy: 0.780317
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6524003623188406
+[2023-09-21 14:12:23,958][flwr][DEBUG] - fit_round 84 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.787817
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6744
+[2023-09-21 14:12:25,817][flwr][INFO] - fit progress: (84, 0.9405634570807314, {'accuracy': 0.6744}, 39774.27761795977)
+[2023-09-21 14:12:25,818][flwr][DEBUG] - evaluate_round 84: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 14:12:57,696][flwr][DEBUG] - evaluate_round 84 received 10 results and 0 failures
+[2023-09-21 14:12:57,697][flwr][DEBUG] - fit_round 85: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7559799382716049
+(DefaultActor pid=2820544) >> Training accuracy: 0.869599
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6709692028985508
+(DefaultActor pid=2820544) >> Training accuracy: 0.764040
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6394589552238806
+(DefaultActor pid=2820544) >> Training accuracy: 0.773787
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6179496951219512
+(DefaultActor pid=2820544) >> Training accuracy: 0.837652
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6537422839506173
+(DefaultActor pid=2820544) >> Training accuracy: 0.872106
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7580128205128205
+(DefaultActor pid=2820544) >> Training accuracy: 0.833934
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6208881578947368
+(DefaultActor pid=2820544) >> Training accuracy: 0.837788
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6657986111111112
+(DefaultActor pid=2820544) >> Training accuracy: 0.826017
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5904605263157895
+(DefaultActor pid=2820544) >> Training accuracy: 0.808114
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7123940677966102
+(DefaultActor pid=2820544) >> Training accuracy: 0.856992
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 14:20:52,387][flwr][DEBUG] - fit_round 85 received 10 results and 0 failures
+test acc: 0.6786
+[2023-09-21 14:20:54,448][flwr][INFO] - fit progress: (85, 0.9334670937480256, {'accuracy': 0.6786}, 40282.90827135788)
+[2023-09-21 14:20:54,449][flwr][DEBUG] - evaluate_round 85: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 14:21:26,613][flwr][DEBUG] - evaluate_round 85 received 10 results and 0 failures
+[2023-09-21 14:21:26,614][flwr][DEBUG] - fit_round 86: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6194740853658537
+(DefaultActor pid=2820544) >> Training accuracy: 0.827363
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.666213768115942
+(DefaultActor pid=2820544) >> Training accuracy: 0.788496
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7566105769230769
+(DefaultActor pid=2820544) >> Training accuracy: 0.839343
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.622327302631579
+(DefaultActor pid=2820544) >> Training accuracy: 0.849095
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6473765432098766
+(DefaultActor pid=2820544) >> Training accuracy: 0.887539
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6455223880597015
+(DefaultActor pid=2820544) >> Training accuracy: 0.780317
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7494212962962963
+(DefaultActor pid=2820544) >> Training accuracy: 0.869792
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6809275793650794
+(DefaultActor pid=2820544) >> Training accuracy: 0.815972
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7251059322033898
+(DefaultActor pid=2820544) >> Training accuracy: 0.849841
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5825109649122807
+(DefaultActor pid=2820544) >> Training accuracy: 0.805921
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 14:29:56,854][flwr][DEBUG] - fit_round 86 received 10 results and 0 failures
+test acc: 0.6738
+[2023-09-21 14:29:58,704][flwr][INFO] - fit progress: (86, 0.9398519292045325, {'accuracy': 0.6738}, 40827.163921646774)
+[2023-09-21 14:29:58,704][flwr][DEBUG] - evaluate_round 86: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 14:30:32,641][flwr][DEBUG] - evaluate_round 86 received 10 results and 0 failures
+[2023-09-21 14:30:32,642][flwr][DEBUG] - fit_round 87: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7665895061728395
+(DefaultActor pid=2820544) >> Training accuracy: 0.869792
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7646233974358975
+(DefaultActor pid=2820544) >> Training accuracy: 0.825921
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6347179878048781
+(DefaultActor pid=2820544) >> Training accuracy: 0.833270
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6586061507936508
+(DefaultActor pid=2820544) >> Training accuracy: 0.813244
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6603260869565217
+(DefaultActor pid=2820544) >> Training accuracy: 0.785326
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6171875
+(DefaultActor pid=2820544) >> Training accuracy: 0.841077
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7134533898305084
+(DefaultActor pid=2820544) >> Training accuracy: 0.856197
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6343283582089553
+(DefaultActor pid=2820544) >> Training accuracy: 0.771922
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6466049382716049
+(DefaultActor pid=2820544) >> Training accuracy: 0.858603
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5679824561403509
+[2023-09-21 14:38:26,670][flwr][DEBUG] - fit_round 87 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.805921
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6793
+[2023-09-21 14:38:29,223][flwr][INFO] - fit progress: (87, 0.9277755803764819, {'accuracy': 0.6793}, 41337.683894667774)
+[2023-09-21 14:38:29,224][flwr][DEBUG] - evaluate_round 87: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 14:39:02,040][flwr][DEBUG] - evaluate_round 87 received 10 results and 0 failures
+[2023-09-21 14:39:02,041][flwr][DEBUG] - fit_round 88: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6424906716417911
+(DefaultActor pid=2820544) >> Training accuracy: 0.751866
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7192796610169492
+(DefaultActor pid=2820544) >> Training accuracy: 0.850371
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.75
+(DefaultActor pid=2820544) >> Training accuracy: 0.875965
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7528044871794872
+(DefaultActor pid=2820544) >> Training accuracy: 0.846554
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6291118421052632
+(DefaultActor pid=2820544) >> Training accuracy: 0.850740
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6006030701754386
+(DefaultActor pid=2820544) >> Training accuracy: 0.803728
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6368140243902439
+(DefaultActor pid=2820544) >> Training accuracy: 0.807736
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6608382936507936
+(DefaultActor pid=2820544) >> Training accuracy: 0.826017
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6730072463768116
+(DefaultActor pid=2820544) >> Training accuracy: 0.788270
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6616512345679012
+[2023-09-21 14:47:18,159][flwr][DEBUG] - fit_round 88 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.875579
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6815
+[2023-09-21 14:47:19,819][flwr][INFO] - fit progress: (88, 0.9132904114243322, {'accuracy': 0.6815}, 41868.27986165322)
+[2023-09-21 14:47:19,820][flwr][DEBUG] - evaluate_round 88: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 14:47:53,236][flwr][DEBUG] - evaluate_round 88 received 10 results and 0 failures
+[2023-09-21 14:47:53,237][flwr][DEBUG] - fit_round 89: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6466884328358209
+(DefaultActor pid=2820544) >> Training accuracy: 0.785215
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7700617283950617
+(DefaultActor pid=2820544) >> Training accuracy: 0.865741
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6049890350877193
+(DefaultActor pid=2820544) >> Training accuracy: 0.804276
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7610176282051282
+(DefaultActor pid=2820544) >> Training accuracy: 0.826522
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6408305921052632
+(DefaultActor pid=2820544) >> Training accuracy: 0.840255
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6770833333333334
+(DefaultActor pid=2820544) >> Training accuracy: 0.777174
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7182203389830508
+(DefaultActor pid=2820544) >> Training accuracy: 0.846398
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6759672619047619
+(DefaultActor pid=2820544) >> Training accuracy: 0.809028
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6387195121951219
+(DefaultActor pid=2820544) >> Training accuracy: 0.834604
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6585648148148148
+[2023-09-21 14:56:06,676][flwr][DEBUG] - fit_round 89 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.884645
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6773
+[2023-09-21 14:56:08,958][flwr][INFO] - fit progress: (89, 0.9310566076455405, {'accuracy': 0.6773}, 42397.418632068206)
+[2023-09-21 14:56:08,959][flwr][DEBUG] - evaluate_round 89: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 14:56:53,894][flwr][DEBUG] - evaluate_round 89 received 10 results and 0 failures
+[2023-09-21 14:56:53,894][flwr][DEBUG] - fit_round 90: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6550925925925926
+(DefaultActor pid=2820544) >> Training accuracy: 0.874807
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6688988095238095
+(DefaultActor pid=2820544) >> Training accuracy: 0.817088
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7648533950617284
+(DefaultActor pid=2820544) >> Training accuracy: 0.865934
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7437900641025641
+(DefaultActor pid=2820544) >> Training accuracy: 0.847356
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6107456140350878
+(DefaultActor pid=2820544) >> Training accuracy: 0.804825
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6621376811594203
+(DefaultActor pid=2820544) >> Training accuracy: 0.784873
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.637766768292683
+(DefaultActor pid=2820544) >> Training accuracy: 0.839367
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6206825657894737
+(DefaultActor pid=2820544) >> Training accuracy: 0.841900
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6988877118644068
+(DefaultActor pid=2820544) >> Training accuracy: 0.846663
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6450559701492538
+[2023-09-21 15:04:59,853][flwr][DEBUG] - fit_round 90 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.781250
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6795
+[2023-09-21 15:05:02,323][flwr][INFO] - fit progress: (90, 0.928617595769346, {'accuracy': 0.6795}, 42930.78378260601)
+[2023-09-21 15:05:02,324][flwr][DEBUG] - evaluate_round 90: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 15:05:36,104][flwr][DEBUG] - evaluate_round 90 received 10 results and 0 failures
+[2023-09-21 15:05:36,104][flwr][DEBUG] - fit_round 91: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7139830508474576
+(DefaultActor pid=2820544) >> Training accuracy: 0.842956
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6677989130434783
+(DefaultActor pid=2820544) >> Training accuracy: 0.790082
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5973135964912281
+(DefaultActor pid=2820544) >> Training accuracy: 0.805647
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6392257462686567
+(DefaultActor pid=2820544) >> Training accuracy: 0.773321
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6266447368421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.841283
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6588541666666666
+(DefaultActor pid=2820544) >> Training accuracy: 0.807044
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6402439024390244
+(DefaultActor pid=2820544) >> Training accuracy: 0.820122
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7514022435897436
+(DefaultActor pid=2820544) >> Training accuracy: 0.824920
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7667824074074074
+(DefaultActor pid=2820544) >> Training accuracy: 0.869792
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6489197530864198
+[2023-09-21 15:13:27,923][flwr][DEBUG] - fit_round 91 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.879051
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6796
+[2023-09-21 15:13:29,826][flwr][INFO] - fit progress: (91, 0.9291766289704905, {'accuracy': 0.6796}, 43438.28658555681)
+[2023-09-21 15:13:29,827][flwr][DEBUG] - evaluate_round 91: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 15:14:04,413][flwr][DEBUG] - evaluate_round 91 received 10 results and 0 failures
+[2023-09-21 15:14:04,414][flwr][DEBUG] - fit_round 92: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6423399390243902
+(DefaultActor pid=2820544) >> Training accuracy: 0.818026
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6765873015873016
+(DefaultActor pid=2820544) >> Training accuracy: 0.829489
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6361882716049383
+(DefaultActor pid=2820544) >> Training accuracy: 0.880787
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.615953947368421
+(DefaultActor pid=2820544) >> Training accuracy: 0.837171
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7542438271604939
+(DefaultActor pid=2820544) >> Training accuracy: 0.870370
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6134868421052632
+(DefaultActor pid=2820544) >> Training accuracy: 0.789474
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7134533898305084
+(DefaultActor pid=2820544) >> Training accuracy: 0.852754
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6441231343283582
+(DefaultActor pid=2820544) >> Training accuracy: 0.785215
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7411858974358975
+(DefaultActor pid=2820544) >> Training accuracy: 0.840144
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6487771739130435
+[2023-09-21 15:22:00,247][flwr][DEBUG] - fit_round 92 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.795516
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6799
+[2023-09-21 15:22:01,831][flwr][INFO] - fit progress: (92, 0.929426233894147, {'accuracy': 0.6799}, 43950.29138105223)
+[2023-09-21 15:22:01,832][flwr][DEBUG] - evaluate_round 92: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 15:22:37,183][flwr][DEBUG] - evaluate_round 92 received 10 results and 0 failures
+[2023-09-21 15:22:37,183][flwr][DEBUG] - fit_round 93: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715042372881356
+(DefaultActor pid=2820544) >> Training accuracy: 0.865731
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6527518656716418
+(DefaultActor pid=2820544) >> Training accuracy: 0.772854
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6723278985507246
+(DefaultActor pid=2820544) >> Training accuracy: 0.781476
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7594521604938271
+(DefaultActor pid=2820544) >> Training accuracy: 0.872106
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6496913580246914
+(DefaultActor pid=2820544) >> Training accuracy: 0.862269
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6463414634146342
+(DefaultActor pid=2820544) >> Training accuracy: 0.833270
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6588541666666666
+(DefaultActor pid=2820544) >> Training accuracy: 0.828125
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6346628289473685
+(DefaultActor pid=2820544) >> Training accuracy: 0.838816
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7614182692307693
+(DefaultActor pid=2820544) >> Training accuracy: 0.844151
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5978618421052632
+(DefaultActor pid=2820544) >> Training accuracy: 0.808662
+(DefaultActor pid=2820544)  ** Training complete **
+[2023-09-21 15:42:01,343][flwr][DEBUG] - fit_round 93 received 10 results and 0 failures
+test acc: 0.6791
+[2023-09-21 15:43:00,516][flwr][INFO] - fit progress: (93, 0.9336311500102948, {'accuracy': 0.6791}, 45208.97631138982)
+[2023-09-21 15:43:00,518][flwr][DEBUG] - evaluate_round 93: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 15:43:44,788][flwr][DEBUG] - evaluate_round 93 received 10 results and 0 failures
+[2023-09-21 15:43:44,789][flwr][DEBUG] - fit_round 94: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6697668650793651
+(DefaultActor pid=2820544) >> Training accuracy: 0.812252
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6137609649122807
+(DefaultActor pid=2820544) >> Training accuracy: 0.802632
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6413246268656716
+(DefaultActor pid=2820544) >> Training accuracy: 0.764459
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6331935975609756
+(DefaultActor pid=2820544) >> Training accuracy: 0.836890
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7754629629629629
+(DefaultActor pid=2820544) >> Training accuracy: 0.857832
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.749198717948718
+(DefaultActor pid=2820544) >> Training accuracy: 0.845353
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.713718220338983
+(DefaultActor pid=2820544) >> Training accuracy: 0.857256
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6377314814814815
+(DefaultActor pid=2820544) >> Training accuracy: 0.843557
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.609375
+(DefaultActor pid=2820544) >> Training accuracy: 0.841077
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6600996376811594
+[2023-09-21 15:51:49,403][flwr][DEBUG] - fit_round 94 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.780344
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6835
+[2023-09-21 15:51:51,810][flwr][INFO] - fit progress: (94, 0.9300604007495478, {'accuracy': 0.6835}, 45740.27047397988)
+[2023-09-21 15:51:51,811][flwr][DEBUG] - evaluate_round 94: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 15:52:24,781][flwr][DEBUG] - evaluate_round 94 received 10 results and 0 failures
+[2023-09-21 15:52:24,782][flwr][DEBUG] - fit_round 95: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7243114406779662
+(DefaultActor pid=2820544) >> Training accuracy: 0.846398
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6322294776119403
+(DefaultActor pid=2820544) >> Training accuracy: 0.766791
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6437114197530864
+(DefaultActor pid=2820544) >> Training accuracy: 0.875193
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.631859756097561
+(DefaultActor pid=2820544) >> Training accuracy: 0.832698
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7616185897435898
+(DefaultActor pid=2820544) >> Training accuracy: 0.816306
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6706349206349206
+(DefaultActor pid=2820544) >> Training accuracy: 0.825893
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6194490131578947
+(DefaultActor pid=2820544) >> Training accuracy: 0.831826
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5860745614035088
+(DefaultActor pid=2820544) >> Training accuracy: 0.796875
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7513503086419753
+(DefaultActor pid=2820544) >> Training accuracy: 0.859182
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6655344202898551
+[2023-09-21 16:00:16,329][flwr][DEBUG] - fit_round 95 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.792120
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6783
+[2023-09-21 16:00:18,806][flwr][INFO] - fit progress: (95, 0.9361367561756232, {'accuracy': 0.6783}, 46247.26676111622)
+[2023-09-21 16:00:18,807][flwr][DEBUG] - evaluate_round 95: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 16:00:51,717][flwr][DEBUG] - evaluate_round 95 received 10 results and 0 failures
+[2023-09-21 16:00:51,717][flwr][DEBUG] - fit_round 96: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6025904605263158
+(DefaultActor pid=2820544) >> Training accuracy: 0.853413
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.661911231884058
+(DefaultActor pid=2820544) >> Training accuracy: 0.779665
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6110197368421053
+(DefaultActor pid=2820544) >> Training accuracy: 0.792215
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6529850746268657
+(DefaultActor pid=2820544) >> Training accuracy: 0.776119
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6767113095238095
+(DefaultActor pid=2820544) >> Training accuracy: 0.838294
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7554012345679012
+(DefaultActor pid=2820544) >> Training accuracy: 0.860532
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6326219512195121
+(DefaultActor pid=2820544) >> Training accuracy: 0.821265
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7572115384615384
+(DefaultActor pid=2820544) >> Training accuracy: 0.840345
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7243114406779662
+(DefaultActor pid=2820544) >> Training accuracy: 0.843220
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6346450617283951
+[2023-09-21 16:09:05,277][flwr][DEBUG] - fit_round 96 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.867863
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6745
+[2023-09-21 16:09:08,397][flwr][INFO] - fit progress: (96, 0.9574828951503522, {'accuracy': 0.6745}, 46776.85781038599)
+[2023-09-21 16:09:08,398][flwr][DEBUG] - evaluate_round 96: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 16:09:43,234][flwr][DEBUG] - evaluate_round 96 received 10 results and 0 failures
+[2023-09-21 16:09:43,235][flwr][DEBUG] - fit_round 97: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6653079710144928
+(DefaultActor pid=2820544) >> Training accuracy: 0.781024
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6431327160493827
+(DefaultActor pid=2820544) >> Training accuracy: 0.865162
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6129954268292683
+(DefaultActor pid=2820544) >> Training accuracy: 0.827172
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6555059523809523
+(DefaultActor pid=2820544) >> Training accuracy: 0.828621
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.602796052631579
+(DefaultActor pid=2820544) >> Training accuracy: 0.844778
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7644675925925926
+(DefaultActor pid=2820544) >> Training accuracy: 0.870177
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6099232456140351
+(DefaultActor pid=2820544) >> Training accuracy: 0.814145
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7259004237288136
+(DefaultActor pid=2820544) >> Training accuracy: 0.859110
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.758613782051282
+(DefaultActor pid=2820544) >> Training accuracy: 0.850962
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6436567164179104
+[2023-09-21 16:18:28,589][flwr][DEBUG] - fit_round 97 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.777519
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6807
+[2023-09-21 16:18:31,190][flwr][INFO] - fit progress: (97, 0.9375480963780095, {'accuracy': 0.6807}, 47339.65043702116)
+[2023-09-21 16:18:31,191][flwr][DEBUG] - evaluate_round 97: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 16:19:04,799][flwr][DEBUG] - evaluate_round 97 received 10 results and 0 failures
+[2023-09-21 16:19:04,799][flwr][DEBUG] - fit_round 98: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6371951219512195
+(DefaultActor pid=2820544) >> Training accuracy: 0.843369
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7644230769230769
+(DefaultActor pid=2820544) >> Training accuracy: 0.813902
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6387593283582089
+(DefaultActor pid=2820544) >> Training accuracy: 0.771222
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7654320987654321
+(DefaultActor pid=2820544) >> Training accuracy: 0.879823
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6750452898550725
+(DefaultActor pid=2820544) >> Training accuracy: 0.782382
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6400462962962963
+(DefaultActor pid=2820544) >> Training accuracy: 0.874421
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6016995614035088
+(DefaultActor pid=2820544) >> Training accuracy: 0.799068
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6085526315789473
+(DefaultActor pid=2820544) >> Training accuracy: 0.835321
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715572033898305
+(DefaultActor pid=2820544) >> Training accuracy: 0.862818
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6638144841269841
+[2023-09-21 16:26:57,735][flwr][DEBUG] - fit_round 98 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.828993
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6782
+[2023-09-21 16:27:00,900][flwr][INFO] - fit progress: (98, 0.9493495685795245, {'accuracy': 0.6782}, 47849.360782171134)
+[2023-09-21 16:27:00,901][flwr][DEBUG] - evaluate_round 98: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 16:27:35,216][flwr][DEBUG] - evaluate_round 98 received 10 results and 0 failures
+[2023-09-21 16:27:35,216][flwr][DEBUG] - fit_round 99: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7631172839506173
+(DefaultActor pid=2820544) >> Training accuracy: 0.876736
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7229872881355932
+(DefaultActor pid=2820544) >> Training accuracy: 0.858581
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6422574626865671
+(DefaultActor pid=2820544) >> Training accuracy: 0.776586
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6684027777777778
+(DefaultActor pid=2820544) >> Training accuracy: 0.832217
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5932017543859649
+(DefaultActor pid=2820544) >> Training accuracy: 0.816612
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6282793209876543
+(DefaultActor pid=2820544) >> Training accuracy: 0.885224
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6689311594202898
+(DefaultActor pid=2820544) >> Training accuracy: 0.781250
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7694310897435898
+(DefaultActor pid=2820544) >> Training accuracy: 0.846154
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6009457236842105
+(DefaultActor pid=2820544) >> Training accuracy: 0.840872
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6400533536585366
+[2023-09-21 16:35:31,701][flwr][DEBUG] - fit_round 99 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.824123
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6787
+[2023-09-21 16:35:34,030][flwr][INFO] - fit progress: (99, 0.9315846239606412, {'accuracy': 0.6787}, 48362.49043702893)
+[2023-09-21 16:35:34,031][flwr][DEBUG] - evaluate_round 99: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 16:36:07,745][flwr][DEBUG] - evaluate_round 99 received 10 results and 0 failures
+[2023-09-21 16:36:07,746][flwr][DEBUG] - fit_round 100: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6442901234567902
+(DefaultActor pid=2820544) >> Training accuracy: 0.874035
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 67
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6350279850746269
+(DefaultActor pid=2820544) >> Training accuracy: 0.766558
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 78
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7646233974358975
+(DefaultActor pid=2820544) >> Training accuracy: 0.838742
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 69
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6716485507246377
+(DefaultActor pid=2820544) >> Training accuracy: 0.788270
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 59
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7195444915254238
+(DefaultActor pid=2820544) >> Training accuracy: 0.862288
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 82
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6465320121951219
+(DefaultActor pid=2820544) >> Training accuracy: 0.831936
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 81
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7644675925925926
+(DefaultActor pid=2820544) >> Training accuracy: 0.864583
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 57
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5964912280701754
+(DefaultActor pid=2820544) >> Training accuracy: 0.810033
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 126
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6626984126984127
+(DefaultActor pid=2820544) >> Training accuracy: 0.831225
+(DefaultActor pid=2820544)  ** Training complete **
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) n_training: 76
+(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.614514802631579
+[2023-09-21 16:44:47,430][flwr][DEBUG] - fit_round 100 received 10 results and 0 failures
+(DefaultActor pid=2820544) >> Training accuracy: 0.851768
+(DefaultActor pid=2820544)  ** Training complete **
+test acc: 0.6852
+[2023-09-21 16:44:49,813][flwr][INFO] - fit progress: (100, 0.9221907237086433, {'accuracy': 0.6852}, 48918.27312586922)
+[2023-09-21 16:44:49,813][flwr][DEBUG] - evaluate_round 100: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 16:45:23,548][flwr][DEBUG] - evaluate_round 100 received 10 results and 0 failures
+(DefaultActor pid=2820544) device: cuda:0
+[2023-09-21 16:45:23,549][flwr][INFO] - FL finished in 48952.00935825519
+[2023-09-21 16:45:23,566][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0)]
+[2023-09-21 16:45:23,566][flwr][INFO] - app_fit: metrics_distributed_fit {}
+[2023-09-21 16:45:23,566][flwr][INFO] - app_fit: metrics_distributed {}
+[2023-09-21 16:45:23,567][flwr][INFO] - app_fit: losses_centralized [(0, 2.304941604693477), (1, 2.2892096804353756), (2, 1.9268630602108403), (3, 1.6586600408767358), (4, 1.5162620251171124), (5, 1.3799412298126343), (6, 1.3085029963106394), (7, 1.270832797208914), (8, 1.2019853355785528), (9, 1.1783232848865155), (10, 1.1620713434280299), (11, 1.1295021063984392), (12, 1.1191708752141594), (13, 1.106805816054725), (14, 1.0845167545464853), (15, 1.0962572912819468), (16, 1.0545658187363476), (17, 1.061014118857277), (18, 1.083283578435453), (19, 1.024500151411794), (20, 1.0367834657525863), (21, 1.0136565257566044), (22, 1.0296277775170324), (23, 1.0151812633196005), (24, 1.0049766376376532), (25, 1.000602862705438), (26, 1.0190478839432469), (27, 0.9883538024684492), (28, 0.9920400703867404), (29, 0.9918740025153175), (30, 0.999864658418174), (31, 0.9666112412850316), (32, 0.9633961186622279), (33, 1.003742164411484), (34, 0.9889103397012899), (35, 0.9822426000342201), (36, 0.962386382559237), (37, 0.9726330711247441), (38, 0.965197785498616), (39, 0.9574256779286808), (40, 0.9920141804522981), (41, 0.9609055894251448), (42, 0.9998491283613272), (43, 0.970430683404112), (44, 0.9538876035342962), (45, 0.9652769756964601), (46, 0.9712253968936567), (47, 0.9433850042355327), (48, 0.9481769482167764), (49, 0.9416967020057642), (50, 0.9449833774338134), (51, 0.977153589931159), (52, 0.9636962722284725), (53, 0.9485500901461409), (54, 0.9527737906756112), (55, 0.9540609658335726), (56, 0.9453530991420197), (57, 0.9411518906061642), (58, 0.9347897329079077), (59, 0.9279640743526788), (60, 0.950613231990284), (61, 0.9391038782489948), (62, 0.9639180962460491), (63, 0.9449828718416988), (64, 0.945188293537012), (65, 0.9480573036038457), (66, 0.942290931178358), (67, 0.92270217916836), (68, 0.9357810918325052), (69, 0.9341149679578531), (70, 0.9335093384924026), (71, 0.9386814056684415), (72, 0.950544156300755), (73, 0.9230360760094639), (74, 0.9372135468374807), (75, 0.9488034000792823), (76, 0.933324966758204), (77, 0.9317882342841297), (78, 0.9255952789379766), (79, 0.9406886980556451), (80, 0.9281163449866322), (81, 0.9390814332916333), (82, 0.928261538473562), (83, 0.9337312611528098), (84, 0.9405634570807314), (85, 0.9334670937480256), (86, 0.9398519292045325), (87, 0.9277755803764819), (88, 0.9132904114243322), (89, 0.9310566076455405), (90, 0.928617595769346), (91, 0.9291766289704905), (92, 0.929426233894147), (93, 0.9336311500102948), (94, 0.9300604007495478), (95, 0.9361367561756232), (96, 0.9574828951503522), (97, 0.9375480963780095), (98, 0.9493495685795245), (99, 0.9315846239606412), (100, 0.9221907237086433)]
+[2023-09-21 16:45:23,567][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.1), (1, 0.1148), (2, 0.2752), (3, 0.3791), (4, 0.4339), (5, 0.4926), (6, 0.5244), (7, 0.5421), (8, 0.5669), (9, 0.5791), (10, 0.586), (11, 0.5979), (12, 0.6048), (13, 0.6034), (14, 0.6153), (15, 0.6155), (16, 0.6256), (17, 0.6281), (18, 0.6203), (19, 0.6361), (20, 0.6331), (21, 0.6446), (22, 0.6354), (23, 0.6427), (24, 0.6476), (25, 0.6517), (26, 0.6437), (27, 0.6539), (28, 0.6491), (29, 0.6535), (30, 0.6504), (31, 0.6604), (32, 0.6638), (33, 0.6482), (34, 0.6594), (35, 0.6561), (36, 0.6629), (37, 0.6614), (38, 0.6656), (39, 0.666), (40, 0.6597), (41, 0.6662), (42, 0.6485), (43, 0.6638), (44, 0.6673), (45, 0.6649), (46, 0.6647), (47, 0.6737), (48, 0.6741), (49, 0.6726), (50, 0.672), (51, 0.6596), (52, 0.6647), (53, 0.6687), (54, 0.6674), (55, 0.6701), (56, 0.6709), (57, 0.6732), (58, 0.6721), (59, 0.6748), (60, 0.6675), (61, 0.6723), (62, 0.6629), (63, 0.6689), (64, 0.6708), (65, 0.668), (66, 0.6687), (67, 0.6764), (68, 0.6705), (69, 0.6761), (70, 0.6728), (71, 0.6729), (72, 0.6683), (73, 0.6788), (74, 0.6734), (75, 0.671), (76, 0.6748), (77, 0.6749), (78, 0.6794), (79, 0.6773), (80, 0.6798), (81, 0.6754), (82, 0.6798), (83, 0.6803), (84, 0.6744), (85, 0.6786), (86, 0.6738), (87, 0.6793), (88, 0.6815), (89, 0.6773), (90, 0.6795), (91, 0.6796), (92, 0.6799), (93, 0.6791), (94, 0.6835), (95, 0.6783), (96, 0.6745), (97, 0.6807), (98, 0.6782), (99, 0.6787), (100, 0.6852)]}
+................
+History (loss, distributed):
+	round 1: 0.0
+	round 2: 0.0
+	round 3: 0.0
+	round 4: 0.0
+	round 5: 0.0
+	round 6: 0.0
+	round 7: 0.0
+	round 8: 0.0
+	round 9: 0.0
+	round 10: 0.0
+	round 11: 0.0
+	round 12: 0.0
+	round 13: 0.0
+	round 14: 0.0
+	round 15: 0.0
+	round 16: 0.0
+	round 17: 0.0
+	round 18: 0.0
+	round 19: 0.0
+	round 20: 0.0
+	round 21: 0.0
+	round 22: 0.0
+	round 23: 0.0
+	round 24: 0.0
+	round 25: 0.0
+	round 26: 0.0
+	round 27: 0.0
+	round 28: 0.0
+	round 29: 0.0
+	round 30: 0.0
+	round 31: 0.0
+	round 32: 0.0
+	round 33: 0.0
+	round 34: 0.0
+	round 35: 0.0
+	round 36: 0.0
+	round 37: 0.0
+	round 38: 0.0
+	round 39: 0.0
+	round 40: 0.0
+	round 41: 0.0
+	round 42: 0.0
+	round 43: 0.0
+	round 44: 0.0
+	round 45: 0.0
+	round 46: 0.0
+	round 47: 0.0
+	round 48: 0.0
+	round 49: 0.0
+	round 50: 0.0
+	round 51: 0.0
+	round 52: 0.0
+	round 53: 0.0
+	round 54: 0.0
+	round 55: 0.0
+	round 56: 0.0
+	round 57: 0.0
+	round 58: 0.0
+	round 59: 0.0
+	round 60: 0.0
+	round 61: 0.0
+	round 62: 0.0
+	round 63: 0.0
+	round 64: 0.0
+	round 65: 0.0
+	round 66: 0.0
+	round 67: 0.0
+	round 68: 0.0
+	round 69: 0.0
+	round 70: 0.0
+	round 71: 0.0
+	round 72: 0.0
+	round 73: 0.0
+	round 74: 0.0
+	round 75: 0.0
+	round 76: 0.0
+	round 77: 0.0
+	round 78: 0.0
+	round 79: 0.0
+	round 80: 0.0
+	round 81: 0.0
+	round 82: 0.0
+	round 83: 0.0
+	round 84: 0.0
+	round 85: 0.0
+	round 86: 0.0
+	round 87: 0.0
+	round 88: 0.0
+	round 89: 0.0
+	round 90: 0.0
+	round 91: 0.0
+	round 92: 0.0
+	round 93: 0.0
+	round 94: 0.0
+	round 95: 0.0
+	round 96: 0.0
+	round 97: 0.0
+	round 98: 0.0
+	round 99: 0.0
+	round 100: 0.0
+History (loss, centralized):
+	round 0: 2.304941604693477
+	round 1: 2.2892096804353756
+	round 2: 1.9268630602108403
+	round 3: 1.6586600408767358
+	round 4: 1.5162620251171124
+	round 5: 1.3799412298126343
+	round 6: 1.3085029963106394
+	round 7: 1.270832797208914
+	round 8: 1.2019853355785528
+	round 9: 1.1783232848865155
+	round 10: 1.1620713434280299
+	round 11: 1.1295021063984392
+	round 12: 1.1191708752141594
+	round 13: 1.106805816054725
+	round 14: 1.0845167545464853
+	round 15: 1.0962572912819468
+	round 16: 1.0545658187363476
+	round 17: 1.061014118857277
+	round 18: 1.083283578435453
+	round 19: 1.024500151411794
+	round 20: 1.0367834657525863
+	round 21: 1.0136565257566044
+	round 22: 1.0296277775170324
+	round 23: 1.0151812633196005
+	round 24: 1.0049766376376532
+	round 25: 1.000602862705438
+	round 26: 1.0190478839432469
+	round 27: 0.9883538024684492
+	round 28: 0.9920400703867404
+	round 29: 0.9918740025153175
+	round 30: 0.999864658418174
+	round 31: 0.9666112412850316
+	round 32: 0.9633961186622279
+	round 33: 1.003742164411484
+	round 34: 0.9889103397012899
+	round 35: 0.9822426000342201
+	round 36: 0.962386382559237
+	round 37: 0.9726330711247441
+	round 38: 0.965197785498616
+	round 39: 0.9574256779286808
+	round 40: 0.9920141804522981
+	round 41: 0.9609055894251448
+	round 42: 0.9998491283613272
+	round 43: 0.970430683404112
+	round 44: 0.9538876035342962
+	round 45: 0.9652769756964601
+	round 46: 0.9712253968936567
+	round 47: 0.9433850042355327
+	round 48: 0.9481769482167764
+	round 49: 0.9416967020057642
+	round 50: 0.9449833774338134
+	round 51: 0.977153589931159
+	round 52: 0.9636962722284725
+	round 53: 0.9485500901461409
+	round 54: 0.9527737906756112
+	round 55: 0.9540609658335726
+	round 56: 0.9453530991420197
+	round 57: 0.9411518906061642
+	round 58: 0.9347897329079077
+	round 59: 0.9279640743526788
+	round 60: 0.950613231990284
+	round 61: 0.9391038782489948
+	round 62: 0.9639180962460491
+	round 63: 0.9449828718416988
+	round 64: 0.945188293537012
+	round 65: 0.9480573036038457
+	round 66: 0.942290931178358
+	round 67: 0.92270217916836
+	round 68: 0.9357810918325052
+	round 69: 0.9341149679578531
+	round 70: 0.9335093384924026
+	round 71: 0.9386814056684415
+	round 72: 0.950544156300755
+	round 73: 0.9230360760094639
+	round 74: 0.9372135468374807
+	round 75: 0.9488034000792823
+	round 76: 0.933324966758204
+	round 77: 0.9317882342841297
+	round 78: 0.9255952789379766
+	round 79: 0.9406886980556451
+	round 80: 0.9281163449866322
+	round 81: 0.9390814332916333
+	round 82: 0.928261538473562
+	round 83: 0.9337312611528098
+	round 84: 0.9405634570807314
+	round 85: 0.9334670937480256
+	round 86: 0.9398519292045325
+	round 87: 0.9277755803764819
+	round 88: 0.9132904114243322
+	round 89: 0.9310566076455405
+	round 90: 0.928617595769346
+	round 91: 0.9291766289704905
+	round 92: 0.929426233894147
+	round 93: 0.9336311500102948
+	round 94: 0.9300604007495478
+	round 95: 0.9361367561756232
+	round 96: 0.9574828951503522
+	round 97: 0.9375480963780095
+	round 98: 0.9493495685795245
+	round 99: 0.9315846239606412
+	round 100: 0.9221907237086433
+History (metrics, centralized):
+{'accuracy': [(0, 0.1), (1, 0.1148), (2, 0.2752), (3, 0.3791), (4, 0.4339), (5, 0.4926), (6, 0.5244), (7, 0.5421), (8, 0.5669), (9, 0.5791), (10, 0.586), (11, 0.5979), (12, 0.6048), (13, 0.6034), (14, 0.6153), (15, 0.6155), (16, 0.6256), (17, 0.6281), (18, 0.6203), (19, 0.6361), (20, 0.6331), (21, 0.6446), (22, 0.6354), (23, 0.6427), (24, 0.6476), (25, 0.6517), (26, 0.6437), (27, 0.6539), (28, 0.6491), (29, 0.6535), (30, 0.6504), (31, 0.6604), (32, 0.6638), (33, 0.6482), (34, 0.6594), (35, 0.6561), (36, 0.6629), (37, 0.6614), (38, 0.6656), (39, 0.666), (40, 0.6597), (41, 0.6662), (42, 0.6485), (43, 0.6638), (44, 0.6673), (45, 0.6649), (46, 0.6647), (47, 0.6737), (48, 0.6741), (49, 0.6726), (50, 0.672), (51, 0.6596), (52, 0.6647), (53, 0.6687), (54, 0.6674), (55, 0.6701), (56, 0.6709), (57, 0.6732), (58, 0.6721), (59, 0.6748), (60, 0.6675), (61, 0.6723), (62, 0.6629), (63, 0.6689), (64, 0.6708), (65, 0.668), (66, 0.6687), (67, 0.6764), (68, 0.6705), (69, 0.6761), (70, 0.6728), (71, 0.6729), (72, 0.6683), (73, 0.6788), (74, 0.6734), (75, 0.671), (76, 0.6748), (77, 0.6749), (78, 0.6794), (79, 0.6773), (80, 0.6798), (81, 0.6754), (82, 0.6798), (83, 0.6803), (84, 0.6744), (85, 0.6786), (86, 0.6738), (87, 0.6793), (88, 0.6815), (89, 0.6773), (90, 0.6795), (91, 0.6796), (92, 0.6799), (93, 0.6791), (94, 0.6835), (95, 0.6783), (96, 0.6745), (97, 0.6807), (98, 0.6782), (99, 0.6787), (100, 0.6852)]}
diff --git a/baselines/moon/_static/cifar10_moon_log.txt b/baselines/moon/_static/cifar10_moon_log.txt
new file mode 100644
index 000000000000..9125bcf6bdc2
--- /dev/null
+++ b/baselines/moon/_static/cifar10_moon_log.txt
@@ -0,0 +1,12852 @@
+num_clients: 10
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 5
+temperature: 0.5
+alg: moon
+seed: 0
+server_device: cpu
+num_rounds: 100
+client_resources:
+  num_cpus: 4
+  num_gpus: 1
+dataset:
+  name: cifar10
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+model:
+  name: simple-cnn
+  output_dim: 256
+  dir: ./models/moon/cifar10/
+
+Files already downloaded and verified
+Files already downloaded and verified
+[2023-09-27 06:17:37,469][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=100, round_timeout=None)
+[2023-09-27 06:17:40,589][flwr][INFO] - Flower VCE: Ray initialized with resources: {'node:137.132.92.49': 1.0, 'memory': 108447056896.0, 'node:__internal_head__': 1.0, 'CPU': 64.0, 'object_store_memory': 50763024384.0, 'GPU': 1.0, 'accelerator_type:G': 1.0}
+[2023-09-27 06:17:40,590][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 1}
+[2023-09-27 06:17:40,602][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 1 actors
+[2023-09-27 06:17:40,602][flwr][INFO] - Initializing global parameters
+[2023-09-27 06:17:40,602][flwr][INFO] - Requesting initial parameters from one random client
+[2023-09-27 06:17:45,852][flwr][INFO] - Received initial parameters from one random client
+[2023-09-27 06:17:45,852][flwr][INFO] - Evaluating initial parameters
+>> Test accuracy: 0.100000
+[2023-09-27 06:17:47,163][flwr][INFO] - initial parameters (loss, other metrics): 2.3034089754183835, {'accuracy': 0.1}
+[2023-09-27 06:17:47,164][flwr][INFO] - FL starting
+[2023-09-27 06:17:47,164][flwr][DEBUG] - fit_round 1: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.158569 Loss1: 1.692832 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 4.896290 Loss1: 1.430553 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 4.739466 Loss1: 1.273728 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.667786 Loss1: 1.202048 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.633436 Loss1: 1.167699 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.567831 Loss1: 1.102093 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.526783 Loss1: 1.061046 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.494906 Loss1: 1.029169 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.480337 Loss1: 1.014599 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.474858 Loss1: 1.009121 Loss2: 3.465737
+(DefaultActor pid=1831567) >> Training accuracy: 0.651486
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.206951 Loss1: 1.741214 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 4.917848 Loss1: 1.452111 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 4.891381 Loss1: 1.425644 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.869079 Loss1: 1.403341 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.848765 Loss1: 1.383028 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.821104 Loss1: 1.355366 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.774738 Loss1: 1.309001 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.732943 Loss1: 1.267206 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.735277 Loss1: 1.269539 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.685486 Loss1: 1.219748 Loss2: 3.465737
+(DefaultActor pid=1831567) >> Training accuracy: 0.514529
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.113451 Loss1: 1.647714 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 4.689537 Loss1: 1.223800 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 4.516952 Loss1: 1.051215 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.468171 Loss1: 1.002433 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.399912 Loss1: 0.934175 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.363108 Loss1: 0.897370 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.359947 Loss1: 0.894210 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.317211 Loss1: 0.851474 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.317719 Loss1: 0.851982 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.280708 Loss1: 0.814970 Loss2: 3.465737
+(DefaultActor pid=1831567) >> Training accuracy: 0.725116
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.197811 Loss1: 1.732074 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 4.769177 Loss1: 1.303440 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 4.658409 Loss1: 1.192672 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.636492 Loss1: 1.170755 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.616583 Loss1: 1.150845 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.595197 Loss1: 1.129460 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.585674 Loss1: 1.119936 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.550918 Loss1: 1.085181 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.525555 Loss1: 1.059818 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.522698 Loss1: 1.056960 Loss2: 3.465737
+(DefaultActor pid=1831567) >> Training accuracy: 0.649364
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.481436 Loss1: 2.015699 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 5.241791 Loss1: 1.776053 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 5.046539 Loss1: 1.580802 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.911101 Loss1: 1.445364 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.850332 Loss1: 1.384595 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.794764 Loss1: 1.329027 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.796497 Loss1: 1.330759 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.714140 Loss1: 1.248403 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.667242 Loss1: 1.201504 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.698191 Loss1: 1.232454 Loss2: 3.465737
+(DefaultActor pid=1831567) >> Training accuracy: 0.569030
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.331566 Loss1: 1.865829 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 5.088427 Loss1: 1.622690 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 4.842888 Loss1: 1.377151 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.774306 Loss1: 1.308568 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.709764 Loss1: 1.244027 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.665516 Loss1: 1.199779 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.601637 Loss1: 1.135900 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.572209 Loss1: 1.106471 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.556724 Loss1: 1.090987 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.496975 Loss1: 1.031237 Loss2: 3.465737
+(DefaultActor pid=1831567) >> Training accuracy: 0.668174
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.450127 Loss1: 1.984389 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 5.171041 Loss1: 1.705303 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 4.996551 Loss1: 1.530813 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.886612 Loss1: 1.420875 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.790521 Loss1: 1.324784 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.699627 Loss1: 1.233889 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.659423 Loss1: 1.193686 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.625899 Loss1: 1.160162 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.587107 Loss1: 1.121369 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.591118 Loss1: 1.125381 Loss2: 3.465737
+(DefaultActor pid=1831567) >> Training accuracy: 0.653045
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.163323 Loss1: 1.697586 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 4.805612 Loss1: 1.339875 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 4.685387 Loss1: 1.219650 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.598157 Loss1: 1.132419 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.560990 Loss1: 1.095253 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.511063 Loss1: 1.045325 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.491972 Loss1: 1.026235 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.493953 Loss1: 1.028216 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.473142 Loss1: 1.007405 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.446181 Loss1: 0.980444 Loss2: 3.465737
+(DefaultActor pid=1831567) >> Training accuracy: 0.665799
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.032352 Loss1: 1.566614 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 4.630193 Loss1: 1.164456 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 4.512794 Loss1: 1.047056 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.449821 Loss1: 0.984083 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.378381 Loss1: 0.912643 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.333268 Loss1: 0.867531 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.301698 Loss1: 0.835961 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.266389 Loss1: 0.800651 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.268311 Loss1: 0.802573 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.253135 Loss1: 0.787397 Loss2: 3.465737
+(DefaultActor pid=1831567) >> Training accuracy: 0.720872
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 5.467179 Loss1: 2.001442 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 1 Loss: 5.238392 Loss1: 1.772655 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 5.084332 Loss1: 1.618595 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 4.978634 Loss1: 1.512896 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 4 Loss: 4.940774 Loss1: 1.475037 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 5 Loss: 4.867042 Loss1: 1.401304 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 6 Loss: 4.824176 Loss1: 1.358439 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 7 Loss: 4.797907 Loss1: 1.332169 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 4.768697 Loss1: 1.302959 Loss2: 3.465737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 4.729307 Loss1: 1.263569 Loss2: 3.465737
+[2023-09-27 06:25:59,433][flwr][DEBUG] - fit_round 1 received 10 results and 0 failures
+[2023-09-27 06:25:59,475][flwr][WARNING] - No fit_metrics_aggregation_fn provided
+(DefaultActor pid=1831567) >> Training accuracy: 0.571784
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.110800
+[2023-09-27 06:26:01,348][flwr][INFO] - fit progress: (1, 2.249783382629053, {'accuracy': 0.1108}, 494.1848308178596)
+[2023-09-27 06:26:01,349][flwr][DEBUG] - evaluate_round 1: strategy sampled 10 clients (out of 10)
+[2023-09-27 06:26:32,464][flwr][DEBUG] - evaluate_round 1 received 10 results and 0 failures
+[2023-09-27 06:26:32,464][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
+[2023-09-27 06:26:32,465][flwr][DEBUG] - fit_round 2: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.489323 Loss1: 1.316230 Loss2: 1.173093
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.128980 Loss1: 1.115258 Loss2: 1.013722
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.037592 Loss1: 1.055536 Loss2: 0.982056
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.972622 Loss1: 1.008581 Loss2: 0.964042
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.951247 Loss1: 0.994555 Loss2: 0.956692
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.932614 Loss1: 0.976732 Loss2: 0.955882
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.931079 Loss1: 0.982240 Loss2: 0.948839
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.899865 Loss1: 0.951152 Loss2: 0.948713
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.875930 Loss1: 0.934466 Loss2: 0.941465
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.861577 Loss1: 0.918292 Loss2: 0.943285
+(DefaultActor pid=1831567) >> Training accuracy: 0.687376
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 3.124021 Loss1: 1.691626 Loss2: 1.432395
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.548971 Loss1: 1.337176 Loss2: 1.211795
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.411907 Loss1: 1.243433 Loss2: 1.168474
+(DefaultActor pid=1831567) Epoch: 3 Loss: 2.354926 Loss1: 1.209341 Loss2: 1.145585
+(DefaultActor pid=1831567) Epoch: 4 Loss: 2.311437 Loss1: 1.182279 Loss2: 1.129158
+(DefaultActor pid=1831567) Epoch: 5 Loss: 2.264077 Loss1: 1.143350 Loss2: 1.120726
+(DefaultActor pid=1831567) Epoch: 6 Loss: 2.240020 Loss1: 1.131831 Loss2: 1.108189
+(DefaultActor pid=1831567) Epoch: 7 Loss: 2.213008 Loss1: 1.108307 Loss2: 1.104701
+(DefaultActor pid=1831567) Epoch: 8 Loss: 2.186627 Loss1: 1.084042 Loss2: 1.102585
+(DefaultActor pid=1831567) Epoch: 9 Loss: 2.222774 Loss1: 1.113324 Loss2: 1.109450
+(DefaultActor pid=1831567) >> Training accuracy: 0.604244
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.920703 Loss1: 1.506812 Loss2: 1.413891
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.458384 Loss1: 1.257722 Loss2: 1.200662
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.382522 Loss1: 1.212991 Loss2: 1.169531
+(DefaultActor pid=1831567) Epoch: 3 Loss: 2.301260 Loss1: 1.154995 Loss2: 1.146265
+(DefaultActor pid=1831567) Epoch: 4 Loss: 2.248873 Loss1: 1.115564 Loss2: 1.133309
+(DefaultActor pid=1831567) Epoch: 5 Loss: 2.219878 Loss1: 1.102111 Loss2: 1.117768
+(DefaultActor pid=1831567) Epoch: 6 Loss: 2.187055 Loss1: 1.080567 Loss2: 1.106488
+(DefaultActor pid=1831567) Epoch: 7 Loss: 2.163706 Loss1: 1.060792 Loss2: 1.102914
+(DefaultActor pid=1831567) Epoch: 8 Loss: 2.153145 Loss1: 1.057809 Loss2: 1.095336
+(DefaultActor pid=1831567) Epoch: 9 Loss: 2.114643 Loss1: 1.020472 Loss2: 1.094171
+(DefaultActor pid=1831567) >> Training accuracy: 0.655428
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.992441 Loss1: 1.714720 Loss2: 1.277721
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.537592 Loss1: 1.413801 Loss2: 1.123791
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.454944 Loss1: 1.357857 Loss2: 1.097088
+(DefaultActor pid=1831567) Epoch: 3 Loss: 2.383839 Loss1: 1.307906 Loss2: 1.075933
+(DefaultActor pid=1831567) Epoch: 4 Loss: 2.323788 Loss1: 1.267844 Loss2: 1.055944
+(DefaultActor pid=1831567) Epoch: 5 Loss: 2.250789 Loss1: 1.221746 Loss2: 1.029043
+(DefaultActor pid=1831567) Epoch: 6 Loss: 2.212965 Loss1: 1.199516 Loss2: 1.013450
+(DefaultActor pid=1831567) Epoch: 7 Loss: 2.180420 Loss1: 1.179093 Loss2: 1.001327
+(DefaultActor pid=1831567) Epoch: 8 Loss: 2.180193 Loss1: 1.182074 Loss2: 0.998118
+(DefaultActor pid=1831567) Epoch: 9 Loss: 2.149488 Loss1: 1.160679 Loss2: 0.988808
+(DefaultActor pid=1831567) >> Training accuracy: 0.593524
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.253620 Loss1: 1.033009 Loss2: 1.220610
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.840969 Loss1: 0.835251 Loss2: 1.005718
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.795802 Loss1: 0.813215 Loss2: 0.982587
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.749537 Loss1: 0.776824 Loss2: 0.972713
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.708988 Loss1: 0.745287 Loss2: 0.963701
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.741212 Loss1: 0.773054 Loss2: 0.968158
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.700433 Loss1: 0.739704 Loss2: 0.960730
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.680513 Loss1: 0.721711 Loss2: 0.958802
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.665503 Loss1: 0.709136 Loss2: 0.956367
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.651594 Loss1: 0.694869 Loss2: 0.956724
+(DefaultActor pid=1831567) >> Training accuracy: 0.759259
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.681585 Loss1: 1.210303 Loss2: 1.471283
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.142403 Loss1: 0.894329 Loss2: 1.248075
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.060230 Loss1: 0.854530 Loss2: 1.205700
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.999412 Loss1: 0.820088 Loss2: 1.179323
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.947119 Loss1: 0.785363 Loss2: 1.161756
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.932777 Loss1: 0.775603 Loss2: 1.157174
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.892730 Loss1: 0.743485 Loss2: 1.149246
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.848938 Loss1: 0.711241 Loss2: 1.137697
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.869207 Loss1: 0.726130 Loss2: 1.143077
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.842067 Loss1: 0.709037 Loss2: 1.133031
+(DefaultActor pid=1831567) >> Training accuracy: 0.756752
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.863051 Loss1: 1.466288 Loss2: 1.396763
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.332419 Loss1: 1.106958 Loss2: 1.225461
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.256490 Loss1: 1.052797 Loss2: 1.203693
+(DefaultActor pid=1831567) Epoch: 3 Loss: 2.223645 Loss1: 1.027398 Loss2: 1.196247
+(DefaultActor pid=1831567) Epoch: 4 Loss: 2.192343 Loss1: 1.006122 Loss2: 1.186221
+(DefaultActor pid=1831567) Epoch: 5 Loss: 2.140795 Loss1: 0.966229 Loss2: 1.174565
+(DefaultActor pid=1831567) Epoch: 6 Loss: 2.115790 Loss1: 0.945553 Loss2: 1.170237
+(DefaultActor pid=1831567) Epoch: 7 Loss: 2.091640 Loss1: 0.930235 Loss2: 1.161405
+(DefaultActor pid=1831567) Epoch: 8 Loss: 2.093366 Loss1: 0.929592 Loss2: 1.163774
+(DefaultActor pid=1831567) Epoch: 9 Loss: 2.077639 Loss1: 0.922619 Loss2: 1.155020
+(DefaultActor pid=1831567) >> Training accuracy: 0.688834
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.879141 Loss1: 1.274636 Loss2: 1.604504
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.380485 Loss1: 1.047008 Loss2: 1.333477
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.337278 Loss1: 1.031792 Loss2: 1.305486
+(DefaultActor pid=1831567) Epoch: 3 Loss: 2.306475 Loss1: 1.012002 Loss2: 1.294474
+(DefaultActor pid=1831567) Epoch: 4 Loss: 2.267435 Loss1: 0.980702 Loss2: 1.286733
+(DefaultActor pid=1831567) Epoch: 5 Loss: 2.262260 Loss1: 0.978342 Loss2: 1.283918
+(DefaultActor pid=1831567) Epoch: 6 Loss: 2.236680 Loss1: 0.955554 Loss2: 1.281127
+(DefaultActor pid=1831567) Epoch: 7 Loss: 2.223227 Loss1: 0.944070 Loss2: 1.279157
+(DefaultActor pid=1831567) Epoch: 8 Loss: 2.204390 Loss1: 0.926889 Loss2: 1.277502
+(DefaultActor pid=1831567) Epoch: 9 Loss: 2.225336 Loss1: 0.936644 Loss2: 1.288692
+(DefaultActor pid=1831567) >> Training accuracy: 0.682468
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.730791 Loss1: 1.555200 Loss2: 1.175591
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.226825 Loss1: 1.211303 Loss2: 1.015522
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.096587 Loss1: 1.125094 Loss2: 0.971493
+(DefaultActor pid=1831567) Epoch: 3 Loss: 2.053540 Loss1: 1.106777 Loss2: 0.946763
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.996769 Loss1: 1.067671 Loss2: 0.929098
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.927768 Loss1: 1.011452 Loss2: 0.916316
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.898969 Loss1: 0.992453 Loss2: 0.906516
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.859915 Loss1: 0.959470 Loss2: 0.900446
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.862439 Loss1: 0.962096 Loss2: 0.900344
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.839648 Loss1: 0.940929 Loss2: 0.898718
+(DefaultActor pid=1831567) >> Training accuracy: 0.694079
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 3.128362 Loss1: 1.555960 Loss2: 1.572402
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.599911 Loss1: 1.250455 Loss2: 1.349457
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.516453 Loss1: 1.222700 Loss2: 1.293753
+(DefaultActor pid=1831567) Epoch: 3 Loss: 2.375933 Loss1: 1.136756 Loss2: 1.239177
+(DefaultActor pid=1831567) Epoch: 4 Loss: 2.346102 Loss1: 1.125003 Loss2: 1.221099
+(DefaultActor pid=1831567) Epoch: 5 Loss: 2.324877 Loss1: 1.122106 Loss2: 1.202771
+(DefaultActor pid=1831567) Epoch: 6 Loss: 2.254508 Loss1: 1.070175 Loss2: 1.184333
+(DefaultActor pid=1831567) Epoch: 7 Loss: 2.206692 Loss1: 1.030726 Loss2: 1.175967
+(DefaultActor pid=1831567) Epoch: 8 Loss: 2.216142 Loss1: 1.047160 Loss2: 1.168981
+(DefaultActor pid=1831567) Epoch: 9 Loss: 2.211113 Loss1: 1.041155 Loss2: 1.169958
+[2023-09-27 06:33:26,467][flwr][DEBUG] - fit_round 2 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.681691
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.172300
+[2023-09-27 06:33:28,046][flwr][INFO] - fit progress: (2, 2.1481322312888245, {'accuracy': 0.1723}, 940.8822364257649)
+[2023-09-27 06:33:28,046][flwr][DEBUG] - evaluate_round 2: strategy sampled 10 clients (out of 10)
+[2023-09-27 06:33:59,059][flwr][DEBUG] - evaluate_round 2 received 10 results and 0 failures
+[2023-09-27 06:33:59,060][flwr][DEBUG] - fit_round 3: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.415017 Loss1: 1.340264 Loss2: 1.074752
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.172792 Loss1: 1.221425 Loss2: 0.951367
+(DefaultActor pid=1831567) Epoch: 2 Loss: 2.101085 Loss1: 1.166882 Loss2: 0.934204
+(DefaultActor pid=1831567) Epoch: 3 Loss: 2.080422 Loss1: 1.151924 Loss2: 0.928498
+(DefaultActor pid=1831567) Epoch: 4 Loss: 2.090270 Loss1: 1.156074 Loss2: 0.934196
+(DefaultActor pid=1831567) Epoch: 5 Loss: 2.042260 Loss1: 1.112338 Loss2: 0.929922
+(DefaultActor pid=1831567) Epoch: 6 Loss: 2.036564 Loss1: 1.105297 Loss2: 0.931267
+(DefaultActor pid=1831567) Epoch: 7 Loss: 2.032168 Loss1: 1.100159 Loss2: 0.932009
+(DefaultActor pid=1831567) Epoch: 8 Loss: 2.018011 Loss1: 1.089233 Loss2: 0.928778
+(DefaultActor pid=1831567) Epoch: 9 Loss: 2.010427 Loss1: 1.080171 Loss2: 0.930256
+(DefaultActor pid=1831567) >> Training accuracy: 0.633605
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.093357 Loss1: 1.172810 Loss2: 0.920547
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.843151 Loss1: 1.047722 Loss2: 0.795429
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.826291 Loss1: 1.034690 Loss2: 0.791601
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.781128 Loss1: 0.993119 Loss2: 0.788009
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.785663 Loss1: 0.994848 Loss2: 0.790815
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.754628 Loss1: 0.964852 Loss2: 0.789776
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.732483 Loss1: 0.937501 Loss2: 0.794982
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.717012 Loss1: 0.925001 Loss2: 0.792012
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.702077 Loss1: 0.912627 Loss2: 0.789450
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.705002 Loss1: 0.913456 Loss2: 0.791547
+(DefaultActor pid=1831567) >> Training accuracy: 0.710737
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.190994 Loss1: 1.182659 Loss2: 1.008334
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.880774 Loss1: 0.996359 Loss2: 0.884416
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.873274 Loss1: 0.992147 Loss2: 0.881127
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.802021 Loss1: 0.925518 Loss2: 0.876503
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.799720 Loss1: 0.920502 Loss2: 0.879218
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.769061 Loss1: 0.894752 Loss2: 0.874309
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.772411 Loss1: 0.893572 Loss2: 0.878839
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.779955 Loss1: 0.901409 Loss2: 0.878546
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.720463 Loss1: 0.848836 Loss2: 0.871627
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.743792 Loss1: 0.868272 Loss2: 0.875520
+(DefaultActor pid=1831567) >> Training accuracy: 0.724095
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.011623 Loss1: 1.122678 Loss2: 0.888945
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.759060 Loss1: 0.964328 Loss2: 0.794732
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.703840 Loss1: 0.915880 Loss2: 0.787960
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.698495 Loss1: 0.910532 Loss2: 0.787963
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.683558 Loss1: 0.898623 Loss2: 0.784935
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.678711 Loss1: 0.891963 Loss2: 0.786748
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.647680 Loss1: 0.865040 Loss2: 0.782640
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.650382 Loss1: 0.865970 Loss2: 0.784412
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.628939 Loss1: 0.850231 Loss2: 0.778708
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.611600 Loss1: 0.827350 Loss2: 0.784250
+(DefaultActor pid=1831567) >> Training accuracy: 0.696456
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.711629 Loss1: 0.887350 Loss2: 0.824279
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.483841 Loss1: 0.751461 Loss2: 0.732380
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.451170 Loss1: 0.723947 Loss2: 0.727223
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.423101 Loss1: 0.697866 Loss2: 0.725236
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.412495 Loss1: 0.687028 Loss2: 0.725467
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.409845 Loss1: 0.685068 Loss2: 0.724777
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.403730 Loss1: 0.677839 Loss2: 0.725891
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.378036 Loss1: 0.655308 Loss2: 0.722728
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.383578 Loss1: 0.660803 Loss2: 0.722774
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.367885 Loss1: 0.642426 Loss2: 0.725459
+(DefaultActor pid=1831567) >> Training accuracy: 0.773148
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.270632 Loss1: 1.308304 Loss2: 0.962328
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.964598 Loss1: 1.128164 Loss2: 0.836434
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.950813 Loss1: 1.116982 Loss2: 0.833831
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.906136 Loss1: 1.083079 Loss2: 0.823057
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.885191 Loss1: 1.061671 Loss2: 0.823519
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.879146 Loss1: 1.054564 Loss2: 0.824581
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.852256 Loss1: 1.028222 Loss2: 0.824035
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.869318 Loss1: 1.036677 Loss2: 0.832641
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.849916 Loss1: 1.021882 Loss2: 0.828034
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.825829 Loss1: 1.000298 Loss2: 0.825530
+(DefaultActor pid=1831567) >> Training accuracy: 0.641791
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.949525 Loss1: 1.101430 Loss2: 0.848096
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.777640 Loss1: 1.000434 Loss2: 0.777206
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.700591 Loss1: 0.945236 Loss2: 0.755355
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.670217 Loss1: 0.921027 Loss2: 0.749190
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.667481 Loss1: 0.915101 Loss2: 0.752380
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.643583 Loss1: 0.891951 Loss2: 0.751633
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.646424 Loss1: 0.891818 Loss2: 0.754606
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.639266 Loss1: 0.885003 Loss2: 0.754263
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.626275 Loss1: 0.874671 Loss2: 0.751604
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.609771 Loss1: 0.855592 Loss2: 0.754179
+(DefaultActor pid=1831567) >> Training accuracy: 0.697545
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.781840 Loss1: 0.901620 Loss2: 0.880220
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.544827 Loss1: 0.770414 Loss2: 0.774413
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.488349 Loss1: 0.726030 Loss2: 0.762319
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.487448 Loss1: 0.725119 Loss2: 0.762330
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.471013 Loss1: 0.710808 Loss2: 0.760205
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.458193 Loss1: 0.698750 Loss2: 0.759443
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.426932 Loss1: 0.670192 Loss2: 0.756740
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.433413 Loss1: 0.675099 Loss2: 0.758314
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.422528 Loss1: 0.662213 Loss2: 0.760315
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.391620 Loss1: 0.636294 Loss2: 0.755326
+(DefaultActor pid=1831567) >> Training accuracy: 0.780864
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.824429 Loss1: 1.094397 Loss2: 0.730032
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.619105 Loss1: 0.985364 Loss2: 0.633741
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.572357 Loss1: 0.946040 Loss2: 0.626316
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.573492 Loss1: 0.948926 Loss2: 0.624566
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.555508 Loss1: 0.931599 Loss2: 0.623910
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.505799 Loss1: 0.883942 Loss2: 0.621857
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.518284 Loss1: 0.893849 Loss2: 0.624435
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.501452 Loss1: 0.877521 Loss2: 0.623931
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.486402 Loss1: 0.862998 Loss2: 0.623404
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.495106 Loss1: 0.870252 Loss2: 0.624854
+(DefaultActor pid=1831567) >> Training accuracy: 0.690678
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.347707 Loss1: 1.278294 Loss2: 1.069413
+(DefaultActor pid=1831567) Epoch: 1 Loss: 2.039271 Loss1: 1.118373 Loss2: 0.920898
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.966311 Loss1: 1.069537 Loss2: 0.896774
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.906505 Loss1: 1.028925 Loss2: 0.877580
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.920017 Loss1: 1.043727 Loss2: 0.876290
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.863755 Loss1: 0.997565 Loss2: 0.866190
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.872020 Loss1: 1.003879 Loss2: 0.868140
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.842994 Loss1: 0.982384 Loss2: 0.860610
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.824816 Loss1: 0.964970 Loss2: 0.859846
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.827546 Loss1: 0.969686 Loss2: 0.857860
+[2023-09-27 06:41:23,136][flwr][DEBUG] - fit_round 3 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.669956
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.385000
+[2023-09-27 06:41:24,960][flwr][INFO] - fit progress: (3, 1.647436479029183, {'accuracy': 0.385}, 1417.7966564488597)
+[2023-09-27 06:41:24,961][flwr][DEBUG] - evaluate_round 3: strategy sampled 10 clients (out of 10)
+[2023-09-27 06:41:57,573][flwr][DEBUG] - evaluate_round 3 received 10 results and 0 failures
+[2023-09-27 06:41:57,574][flwr][DEBUG] - fit_round 4: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.959768 Loss1: 1.095686 Loss2: 0.864081
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.654144 Loss1: 0.923206 Loss2: 0.730938
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.616120 Loss1: 0.902758 Loss2: 0.713362
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.588044 Loss1: 0.878248 Loss2: 0.709796
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.574441 Loss1: 0.863692 Loss2: 0.710748
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.545140 Loss1: 0.837102 Loss2: 0.708038
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.543230 Loss1: 0.835479 Loss2: 0.707751
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.538823 Loss1: 0.831749 Loss2: 0.707074
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.559673 Loss1: 0.850185 Loss2: 0.709487
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.523678 Loss1: 0.817619 Loss2: 0.706059
+(DefaultActor pid=1831567) >> Training accuracy: 0.729030
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.761747 Loss1: 0.839047 Loss2: 0.922700
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.506742 Loss1: 0.721557 Loss2: 0.785185
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.469778 Loss1: 0.694414 Loss2: 0.775364
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.446657 Loss1: 0.674826 Loss2: 0.771831
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.431892 Loss1: 0.661955 Loss2: 0.769937
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.406065 Loss1: 0.634406 Loss2: 0.771659
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.419861 Loss1: 0.648368 Loss2: 0.771494
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.409758 Loss1: 0.643129 Loss2: 0.766629
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.365714 Loss1: 0.601492 Loss2: 0.764223
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.382863 Loss1: 0.614867 Loss2: 0.767996
+(DefaultActor pid=1831567) >> Training accuracy: 0.793596
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.642644 Loss1: 0.803485 Loss2: 0.839159
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.433953 Loss1: 0.708844 Loss2: 0.725109
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.391559 Loss1: 0.674852 Loss2: 0.716707
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.363835 Loss1: 0.649616 Loss2: 0.714219
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.370542 Loss1: 0.654846 Loss2: 0.715696
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.342658 Loss1: 0.629414 Loss2: 0.713244
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.334084 Loss1: 0.619713 Loss2: 0.714372
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.339267 Loss1: 0.624595 Loss2: 0.714672
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.338185 Loss1: 0.621479 Loss2: 0.716706
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.313594 Loss1: 0.601295 Loss2: 0.712299
+(DefaultActor pid=1831567) >> Training accuracy: 0.768326
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.190779 Loss1: 1.082939 Loss2: 1.107840
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.948287 Loss1: 1.002792 Loss2: 0.945496
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.918061 Loss1: 0.990976 Loss2: 0.927085
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.863494 Loss1: 0.945495 Loss2: 0.918000
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.854082 Loss1: 0.940077 Loss2: 0.914004
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.857931 Loss1: 0.940210 Loss2: 0.917721
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.813290 Loss1: 0.903588 Loss2: 0.909702
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.829213 Loss1: 0.913456 Loss2: 0.915757
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.807834 Loss1: 0.896602 Loss2: 0.911232
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.795018 Loss1: 0.883855 Loss2: 0.911163
+(DefaultActor pid=1831567) >> Training accuracy: 0.708534
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.849038 Loss1: 1.026859 Loss2: 0.822179
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.651148 Loss1: 0.916622 Loss2: 0.734526
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.626542 Loss1: 0.893981 Loss2: 0.732561
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.638858 Loss1: 0.901524 Loss2: 0.737334
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.601716 Loss1: 0.872871 Loss2: 0.728845
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.597795 Loss1: 0.868252 Loss2: 0.729543
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.583544 Loss1: 0.851136 Loss2: 0.732407
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.580667 Loss1: 0.847451 Loss2: 0.733217
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.565120 Loss1: 0.832593 Loss2: 0.732527
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.549264 Loss1: 0.819774 Loss2: 0.729489
+(DefaultActor pid=1831567) >> Training accuracy: 0.721354
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.177091 Loss1: 1.285170 Loss2: 0.891921
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.915349 Loss1: 1.153907 Loss2: 0.761442
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.865854 Loss1: 1.116816 Loss2: 0.749038
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.852904 Loss1: 1.110707 Loss2: 0.742197
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.829410 Loss1: 1.092330 Loss2: 0.737080
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.822381 Loss1: 1.086184 Loss2: 0.736196
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.809970 Loss1: 1.072961 Loss2: 0.737008
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.796816 Loss1: 1.059436 Loss2: 0.737380
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.759374 Loss1: 1.029298 Loss2: 0.730076
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.751741 Loss1: 1.020766 Loss2: 0.730975
+(DefaultActor pid=1831567) >> Training accuracy: 0.645154
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.883651 Loss1: 1.055108 Loss2: 0.828543
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.598716 Loss1: 0.899386 Loss2: 0.699330
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.578926 Loss1: 0.888044 Loss2: 0.690882
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.550933 Loss1: 0.865345 Loss2: 0.685588
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.557859 Loss1: 0.870223 Loss2: 0.687635
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.521566 Loss1: 0.836734 Loss2: 0.684832
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.524148 Loss1: 0.836299 Loss2: 0.687849
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.526126 Loss1: 0.834731 Loss2: 0.691395
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.507760 Loss1: 0.819961 Loss2: 0.687799
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.499967 Loss1: 0.814594 Loss2: 0.685373
+(DefaultActor pid=1831567) >> Training accuracy: 0.724466
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.079600 Loss1: 1.168584 Loss2: 0.911016
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.772841 Loss1: 1.030948 Loss2: 0.741894
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.706571 Loss1: 0.988061 Loss2: 0.718511
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.697365 Loss1: 0.984650 Loss2: 0.712715
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.663224 Loss1: 0.958731 Loss2: 0.704493
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.661957 Loss1: 0.956330 Loss2: 0.705627
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.661999 Loss1: 0.955479 Loss2: 0.706519
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.642397 Loss1: 0.937431 Loss2: 0.704966
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.634093 Loss1: 0.928832 Loss2: 0.705261
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.601517 Loss1: 0.899996 Loss2: 0.701522
+(DefaultActor pid=1831567) >> Training accuracy: 0.686952
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.980264 Loss1: 1.007347 Loss2: 0.972917
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.713912 Loss1: 0.898850 Loss2: 0.815062
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.681733 Loss1: 0.878808 Loss2: 0.802925
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.650499 Loss1: 0.844604 Loss2: 0.805895
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.640387 Loss1: 0.833196 Loss2: 0.807191
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.615824 Loss1: 0.818313 Loss2: 0.797511
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.630029 Loss1: 0.824238 Loss2: 0.805791
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.625690 Loss1: 0.819255 Loss2: 0.806435
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.607854 Loss1: 0.799895 Loss2: 0.807959
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.621111 Loss1: 0.811001 Loss2: 0.810111
+(DefaultActor pid=1831567) >> Training accuracy: 0.709216
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.144534 Loss1: 1.216463 Loss2: 0.928071
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.820308 Loss1: 1.055431 Loss2: 0.764877
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.788406 Loss1: 1.034002 Loss2: 0.754403
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.774354 Loss1: 1.018354 Loss2: 0.756000
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.766350 Loss1: 1.017112 Loss2: 0.749237
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.741835 Loss1: 0.987054 Loss2: 0.754781
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.750159 Loss1: 0.998012 Loss2: 0.752147
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.727953 Loss1: 0.979115 Loss2: 0.748839
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.713808 Loss1: 0.961341 Loss2: 0.752467
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.718466 Loss1: 0.970034 Loss2: 0.748432
+[2023-09-27 06:48:45,728][flwr][DEBUG] - fit_round 4 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.642724
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.444800
+[2023-09-27 06:48:47,367][flwr][INFO] - fit progress: (4, 1.4994674932461578, {'accuracy': 0.4448}, 1860.2031150087714)
+[2023-09-27 06:48:47,367][flwr][DEBUG] - evaluate_round 4: strategy sampled 10 clients (out of 10)
+[2023-09-27 06:49:20,039][flwr][DEBUG] - evaluate_round 4 received 10 results and 0 failures
+[2023-09-27 06:49:20,040][flwr][DEBUG] - fit_round 5: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 2.014243 Loss1: 1.188244 Loss2: 0.825999
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.818569 Loss1: 1.085272 Loss2: 0.733297
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.771165 Loss1: 1.046833 Loss2: 0.724332
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.782243 Loss1: 1.052951 Loss2: 0.729292
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.763231 Loss1: 1.035690 Loss2: 0.727541
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.764852 Loss1: 1.031938 Loss2: 0.732914
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.757314 Loss1: 1.028570 Loss2: 0.728744
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.732338 Loss1: 1.002636 Loss2: 0.729702
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.701840 Loss1: 0.977936 Loss2: 0.723904
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.706227 Loss1: 0.980068 Loss2: 0.726159
+(DefaultActor pid=1831567) >> Training accuracy: 0.653533
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.692891 Loss1: 0.959691 Loss2: 0.733200
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.506459 Loss1: 0.866903 Loss2: 0.639556
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.466208 Loss1: 0.831412 Loss2: 0.634795
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.441681 Loss1: 0.807731 Loss2: 0.633950
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.420730 Loss1: 0.786706 Loss2: 0.634024
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.434917 Loss1: 0.799899 Loss2: 0.635018
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.401121 Loss1: 0.766696 Loss2: 0.634425
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.391031 Loss1: 0.757915 Loss2: 0.633117
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.404584 Loss1: 0.766761 Loss2: 0.637823
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.406269 Loss1: 0.768637 Loss2: 0.637632
+(DefaultActor pid=1831567) >> Training accuracy: 0.732786
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.529666 Loss1: 0.795142 Loss2: 0.734524
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.325898 Loss1: 0.669581 Loss2: 0.656317
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.299674 Loss1: 0.645223 Loss2: 0.654450
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.263410 Loss1: 0.613045 Loss2: 0.650364
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.281851 Loss1: 0.629112 Loss2: 0.652738
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.272507 Loss1: 0.616743 Loss2: 0.655764
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258849 Loss1: 0.603788 Loss2: 0.655061
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.237609 Loss1: 0.582127 Loss2: 0.655482
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244634 Loss1: 0.588563 Loss2: 0.656070
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.252852 Loss1: 0.595281 Loss2: 0.657572
+(DefaultActor pid=1831567) >> Training accuracy: 0.795332
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.883753 Loss1: 1.104919 Loss2: 0.778834
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.712403 Loss1: 1.026433 Loss2: 0.685970
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.686781 Loss1: 1.006981 Loss2: 0.679801
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.672725 Loss1: 0.991034 Loss2: 0.681692
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.626696 Loss1: 0.951325 Loss2: 0.675371
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.636485 Loss1: 0.953849 Loss2: 0.682636
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.638114 Loss1: 0.956022 Loss2: 0.682092
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.602415 Loss1: 0.924855 Loss2: 0.677561
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.612828 Loss1: 0.931136 Loss2: 0.681692
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.595127 Loss1: 0.914920 Loss2: 0.680207
+(DefaultActor pid=1831567) >> Training accuracy: 0.646922
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.718595 Loss1: 0.959638 Loss2: 0.758958
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.544794 Loss1: 0.858285 Loss2: 0.686509
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.520121 Loss1: 0.838044 Loss2: 0.682077
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.519642 Loss1: 0.834129 Loss2: 0.685513
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.494727 Loss1: 0.815452 Loss2: 0.679276
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.495673 Loss1: 0.811537 Loss2: 0.684137
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.483132 Loss1: 0.799975 Loss2: 0.683157
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.467450 Loss1: 0.783536 Loss2: 0.683914
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.463590 Loss1: 0.779140 Loss2: 0.684450
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.439845 Loss1: 0.754819 Loss2: 0.685026
+(DefaultActor pid=1831567) >> Training accuracy: 0.740663
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.801618 Loss1: 0.957439 Loss2: 0.844179
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.656688 Loss1: 0.872454 Loss2: 0.784234
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.636137 Loss1: 0.855533 Loss2: 0.780604
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.630167 Loss1: 0.849935 Loss2: 0.780232
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.620729 Loss1: 0.838806 Loss2: 0.781924
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.610510 Loss1: 0.828549 Loss2: 0.781961
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.610964 Loss1: 0.825643 Loss2: 0.785321
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.585073 Loss1: 0.807889 Loss2: 0.777184
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.571801 Loss1: 0.790916 Loss2: 0.780884
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.579860 Loss1: 0.794278 Loss2: 0.785582
+(DefaultActor pid=1831567) >> Training accuracy: 0.732763
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.909832 Loss1: 1.091551 Loss2: 0.818282
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.681050 Loss1: 0.974363 Loss2: 0.706687
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.649299 Loss1: 0.950373 Loss2: 0.698926
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.655506 Loss1: 0.958529 Loss2: 0.696976
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.601037 Loss1: 0.911543 Loss2: 0.689494
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.568086 Loss1: 0.879798 Loss2: 0.688288
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.576533 Loss1: 0.888174 Loss2: 0.688359
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.585644 Loss1: 0.898440 Loss2: 0.687204
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.554587 Loss1: 0.863171 Loss2: 0.691416
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.552208 Loss1: 0.864154 Loss2: 0.688054
+(DefaultActor pid=1831567) >> Training accuracy: 0.702303
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.750890 Loss1: 0.952034 Loss2: 0.798856
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.566870 Loss1: 0.852061 Loss2: 0.714809
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.538249 Loss1: 0.829282 Loss2: 0.708967
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.554682 Loss1: 0.839347 Loss2: 0.715335
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.534906 Loss1: 0.824781 Loss2: 0.710126
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.521418 Loss1: 0.810705 Loss2: 0.710714
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.492046 Loss1: 0.783252 Loss2: 0.708795
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.494058 Loss1: 0.781443 Loss2: 0.712616
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.473160 Loss1: 0.762181 Loss2: 0.710979
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.485544 Loss1: 0.774253 Loss2: 0.711290
+(DefaultActor pid=1831567) >> Training accuracy: 0.743010
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.430940 Loss1: 0.728699 Loss2: 0.702241
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.289461 Loss1: 0.659202 Loss2: 0.630259
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260535 Loss1: 0.635518 Loss2: 0.625017
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.253021 Loss1: 0.626774 Loss2: 0.626247
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.250595 Loss1: 0.625559 Loss2: 0.625036
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.242292 Loss1: 0.613702 Loss2: 0.628589
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220224 Loss1: 0.594907 Loss2: 0.625318
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.219791 Loss1: 0.596135 Loss2: 0.623656
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.234372 Loss1: 0.607136 Loss2: 0.627236
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229269 Loss1: 0.600677 Loss2: 0.628592
+(DefaultActor pid=1831567) >> Training accuracy: 0.776042
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.738075 Loss1: 0.985871 Loss2: 0.752204
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.607921 Loss1: 0.935295 Loss2: 0.672627
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.595121 Loss1: 0.922947 Loss2: 0.672174
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.554395 Loss1: 0.887373 Loss2: 0.667022
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.550219 Loss1: 0.882243 Loss2: 0.667976
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.535800 Loss1: 0.862845 Loss2: 0.672955
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.529615 Loss1: 0.858552 Loss2: 0.671064
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.518144 Loss1: 0.846215 Loss2: 0.671929
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.505655 Loss1: 0.833487 Loss2: 0.672168
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.509816 Loss1: 0.837040 Loss2: 0.672776
+[2023-09-27 06:56:48,343][flwr][DEBUG] - fit_round 5 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.696514
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.489400
+[2023-09-27 06:56:50,049][flwr][INFO] - fit progress: (5, 1.3875796671111744, {'accuracy': 0.4894}, 2342.885829200037)
+[2023-09-27 06:56:50,050][flwr][DEBUG] - evaluate_round 5: strategy sampled 10 clients (out of 10)
+[2023-09-27 06:57:34,777][flwr][DEBUG] - evaluate_round 5 received 10 results and 0 failures
+[2023-09-27 06:57:34,778][flwr][DEBUG] - fit_round 6: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.949481 Loss1: 1.057654 Loss2: 0.891827
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.664165 Loss1: 0.932542 Loss2: 0.731623
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.634032 Loss1: 0.910815 Loss2: 0.723218
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.598717 Loss1: 0.882653 Loss2: 0.716064
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.600445 Loss1: 0.881961 Loss2: 0.718484
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.581901 Loss1: 0.863670 Loss2: 0.718231
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.592011 Loss1: 0.875328 Loss2: 0.716683
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.572243 Loss1: 0.853427 Loss2: 0.718816
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.553528 Loss1: 0.836697 Loss2: 0.716831
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.543531 Loss1: 0.825607 Loss2: 0.717925
+(DefaultActor pid=1831567) >> Training accuracy: 0.706414
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.781243 Loss1: 0.950038 Loss2: 0.831205
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.524293 Loss1: 0.830335 Loss2: 0.693957
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.488881 Loss1: 0.796452 Loss2: 0.692428
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.489036 Loss1: 0.797133 Loss2: 0.691903
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.497525 Loss1: 0.804665 Loss2: 0.692860
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.457374 Loss1: 0.769296 Loss2: 0.688078
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.456766 Loss1: 0.766715 Loss2: 0.690050
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.456312 Loss1: 0.766672 Loss2: 0.689641
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.437612 Loss1: 0.745680 Loss2: 0.691932
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.452189 Loss1: 0.757865 Loss2: 0.694325
+(DefaultActor pid=1831567) >> Training accuracy: 0.738377
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.685009 Loss1: 0.914753 Loss2: 0.770256
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.533744 Loss1: 0.841567 Loss2: 0.692177
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.522414 Loss1: 0.830460 Loss2: 0.691953
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.520060 Loss1: 0.828377 Loss2: 0.691682
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.519292 Loss1: 0.823110 Loss2: 0.696182
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.517640 Loss1: 0.823539 Loss2: 0.694101
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.477393 Loss1: 0.786311 Loss2: 0.691082
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.466187 Loss1: 0.775382 Loss2: 0.690806
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.469411 Loss1: 0.774163 Loss2: 0.695247
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.476252 Loss1: 0.779557 Loss2: 0.696695
+(DefaultActor pid=1831567) >> Training accuracy: 0.727307
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.714899 Loss1: 0.914384 Loss2: 0.800515
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.522630 Loss1: 0.837226 Loss2: 0.685403
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.481397 Loss1: 0.802115 Loss2: 0.679282
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.488365 Loss1: 0.808946 Loss2: 0.679420
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.471416 Loss1: 0.790751 Loss2: 0.680665
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.454509 Loss1: 0.775775 Loss2: 0.678734
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.449837 Loss1: 0.769987 Loss2: 0.679849
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.440428 Loss1: 0.758750 Loss2: 0.681678
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.437842 Loss1: 0.755136 Loss2: 0.682706
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.428721 Loss1: 0.747492 Loss2: 0.681229
+(DefaultActor pid=1831567) >> Training accuracy: 0.774054
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.570600 Loss1: 0.717890 Loss2: 0.852710
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.372785 Loss1: 0.644828 Loss2: 0.727957
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354526 Loss1: 0.631671 Loss2: 0.722855
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.326202 Loss1: 0.608120 Loss2: 0.718082
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333935 Loss1: 0.614410 Loss2: 0.719525
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.307776 Loss1: 0.587609 Loss2: 0.720168
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295713 Loss1: 0.575356 Loss2: 0.720356
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.304860 Loss1: 0.584288 Loss2: 0.720572
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.289421 Loss1: 0.567478 Loss2: 0.721943
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.282068 Loss1: 0.561936 Loss2: 0.720133
+(DefaultActor pid=1831567) >> Training accuracy: 0.801890
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.912906 Loss1: 1.078457 Loss2: 0.834449
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.662744 Loss1: 0.968059 Loss2: 0.694685
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.643141 Loss1: 0.952241 Loss2: 0.690900
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.635528 Loss1: 0.946002 Loss2: 0.689526
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.614003 Loss1: 0.926269 Loss2: 0.687734
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.610983 Loss1: 0.918155 Loss2: 0.692828
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.607673 Loss1: 0.923298 Loss2: 0.684375
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.602167 Loss1: 0.913330 Loss2: 0.688837
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.598345 Loss1: 0.902702 Loss2: 0.695643
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.561684 Loss1: 0.873343 Loss2: 0.688341
+(DefaultActor pid=1831567) >> Training accuracy: 0.676073
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.864469 Loss1: 0.983174 Loss2: 0.881295
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.646952 Loss1: 0.892650 Loss2: 0.754301
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.610051 Loss1: 0.865930 Loss2: 0.744121
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.614231 Loss1: 0.870160 Loss2: 0.744071
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.581443 Loss1: 0.843542 Loss2: 0.737901
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.587021 Loss1: 0.846743 Loss2: 0.740279
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.536245 Loss1: 0.801643 Loss2: 0.734602
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.531117 Loss1: 0.791209 Loss2: 0.739908
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.555219 Loss1: 0.814352 Loss2: 0.740866
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.510869 Loss1: 0.775725 Loss2: 0.735144
+(DefaultActor pid=1831567) >> Training accuracy: 0.733574
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.534970 Loss1: 0.699719 Loss2: 0.835251
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.354766 Loss1: 0.630411 Loss2: 0.724355
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.323006 Loss1: 0.604877 Loss2: 0.718129
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325520 Loss1: 0.607876 Loss2: 0.717644
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.319769 Loss1: 0.601734 Loss2: 0.718036
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.297679 Loss1: 0.580817 Loss2: 0.716862
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.284857 Loss1: 0.570394 Loss2: 0.714463
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.293728 Loss1: 0.577702 Loss2: 0.716026
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.278424 Loss1: 0.561386 Loss2: 0.717037
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.263424 Loss1: 0.546727 Loss2: 0.716698
+(DefaultActor pid=1831567) >> Training accuracy: 0.804012
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.959389 Loss1: 1.135186 Loss2: 0.824203
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.741100 Loss1: 1.045928 Loss2: 0.695172
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.742160 Loss1: 1.054101 Loss2: 0.688059
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.713174 Loss1: 1.028878 Loss2: 0.684295
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.690718 Loss1: 1.006516 Loss2: 0.684202
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.663452 Loss1: 0.982956 Loss2: 0.680496
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.669116 Loss1: 0.985029 Loss2: 0.684087
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.663717 Loss1: 0.978592 Loss2: 0.685125
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.641271 Loss1: 0.957595 Loss2: 0.683676
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.616372 Loss1: 0.931420 Loss2: 0.684952
+(DefaultActor pid=1831567) >> Training accuracy: 0.663043
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.863680 Loss1: 0.931315 Loss2: 0.932366
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.590819 Loss1: 0.806492 Loss2: 0.784327
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.572245 Loss1: 0.797498 Loss2: 0.774747
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.550774 Loss1: 0.775769 Loss2: 0.775006
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.537187 Loss1: 0.764298 Loss2: 0.772889
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.513759 Loss1: 0.740009 Loss2: 0.773750
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.502633 Loss1: 0.726434 Loss2: 0.776199
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.511660 Loss1: 0.736871 Loss2: 0.774790
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.518611 Loss1: 0.741715 Loss2: 0.776896
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.492375 Loss1: 0.719407 Loss2: 0.772968
+[2023-09-27 07:04:28,784][flwr][DEBUG] - fit_round 6 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.750000
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.541900
+[2023-09-27 07:04:30,382][flwr][INFO] - fit progress: (6, 1.274572859556911, {'accuracy': 0.5419}, 2803.2188604199328)
+[2023-09-27 07:04:30,383][flwr][DEBUG] - evaluate_round 6: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:05:02,430][flwr][DEBUG] - evaluate_round 6 received 10 results and 0 failures
+[2023-09-27 07:05:02,431][flwr][DEBUG] - fit_round 7: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.435357 Loss1: 0.701093 Loss2: 0.734264
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.271574 Loss1: 0.612838 Loss2: 0.658736
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.270210 Loss1: 0.614429 Loss2: 0.655781
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.245928 Loss1: 0.588767 Loss2: 0.657161
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.225781 Loss1: 0.574590 Loss2: 0.651190
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.213438 Loss1: 0.561002 Loss2: 0.652436
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220872 Loss1: 0.567949 Loss2: 0.652923
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.228524 Loss1: 0.574701 Loss2: 0.653823
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211820 Loss1: 0.559020 Loss2: 0.652799
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193645 Loss1: 0.540066 Loss2: 0.653579
+(DefaultActor pid=1831567) >> Training accuracy: 0.829090
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.901637 Loss1: 1.080371 Loss2: 0.821265
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.745633 Loss1: 1.010138 Loss2: 0.735495
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.719805 Loss1: 0.987385 Loss2: 0.732420
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.702559 Loss1: 0.971044 Loss2: 0.731515
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.698351 Loss1: 0.966140 Loss2: 0.732211
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.678714 Loss1: 0.946500 Loss2: 0.732214
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.671743 Loss1: 0.937814 Loss2: 0.733929
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.648845 Loss1: 0.912542 Loss2: 0.736303
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.662639 Loss1: 0.924705 Loss2: 0.737935
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.673463 Loss1: 0.935931 Loss2: 0.737532
+(DefaultActor pid=1831567) >> Training accuracy: 0.685688
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.661436 Loss1: 0.890856 Loss2: 0.770580
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.493395 Loss1: 0.799773 Loss2: 0.693622
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.453340 Loss1: 0.764514 Loss2: 0.688826
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.466542 Loss1: 0.777008 Loss2: 0.689534
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.454732 Loss1: 0.763514 Loss2: 0.691218
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.440306 Loss1: 0.749435 Loss2: 0.690871
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.429030 Loss1: 0.738533 Loss2: 0.690497
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.432540 Loss1: 0.739575 Loss2: 0.692965
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.448988 Loss1: 0.751452 Loss2: 0.697536
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.416622 Loss1: 0.721873 Loss2: 0.694748
+(DefaultActor pid=1831567) >> Training accuracy: 0.754954
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386384 Loss1: 0.652351 Loss2: 0.734032
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.276321 Loss1: 0.613222 Loss2: 0.663099
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262983 Loss1: 0.603429 Loss2: 0.659554
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.240873 Loss1: 0.581801 Loss2: 0.659073
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.243162 Loss1: 0.582909 Loss2: 0.660254
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211481 Loss1: 0.552956 Loss2: 0.658525
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.209546 Loss1: 0.550977 Loss2: 0.658569
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202302 Loss1: 0.544093 Loss2: 0.658208
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205841 Loss1: 0.547620 Loss2: 0.658221
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189089 Loss1: 0.530188 Loss2: 0.658900
+(DefaultActor pid=1831567) >> Training accuracy: 0.809221
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.650251 Loss1: 0.893308 Loss2: 0.756944
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.521360 Loss1: 0.845774 Loss2: 0.675586
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.517851 Loss1: 0.841608 Loss2: 0.676244
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.493973 Loss1: 0.816693 Loss2: 0.677281
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.485495 Loss1: 0.802130 Loss2: 0.683366
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.468271 Loss1: 0.787280 Loss2: 0.680991
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.478236 Loss1: 0.795216 Loss2: 0.683020
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.471842 Loss1: 0.788707 Loss2: 0.683135
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.462458 Loss1: 0.780196 Loss2: 0.682261
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.417471 Loss1: 0.740012 Loss2: 0.677459
+(DefaultActor pid=1831567) >> Training accuracy: 0.758413
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.814517 Loss1: 1.037505 Loss2: 0.777011
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.636276 Loss1: 0.953649 Loss2: 0.682627
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.627110 Loss1: 0.949248 Loss2: 0.677862
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.599820 Loss1: 0.923121 Loss2: 0.676699
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.579760 Loss1: 0.909241 Loss2: 0.670519
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.542626 Loss1: 0.871414 Loss2: 0.671211
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.561024 Loss1: 0.884639 Loss2: 0.676385
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.554627 Loss1: 0.875202 Loss2: 0.679424
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.554946 Loss1: 0.875982 Loss2: 0.678964
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.526332 Loss1: 0.850930 Loss2: 0.675402
+(DefaultActor pid=1831567) >> Training accuracy: 0.671409
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.635260 Loss1: 0.869212 Loss2: 0.766048
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.480182 Loss1: 0.799306 Loss2: 0.680876
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.452572 Loss1: 0.777021 Loss2: 0.675551
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.484670 Loss1: 0.802922 Loss2: 0.681747
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.438803 Loss1: 0.762365 Loss2: 0.676438
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.442346 Loss1: 0.764025 Loss2: 0.678321
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.432606 Loss1: 0.751242 Loss2: 0.681364
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.415513 Loss1: 0.736573 Loss2: 0.678941
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.418011 Loss1: 0.738729 Loss2: 0.679283
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.397196 Loss1: 0.717054 Loss2: 0.680142
+(DefaultActor pid=1831567) >> Training accuracy: 0.769531
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.711397 Loss1: 0.897639 Loss2: 0.813758
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.558160 Loss1: 0.814727 Loss2: 0.743432
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.561732 Loss1: 0.815823 Loss2: 0.745909
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.542268 Loss1: 0.796264 Loss2: 0.746004
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.530906 Loss1: 0.784081 Loss2: 0.746825
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.513561 Loss1: 0.769874 Loss2: 0.743687
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.515595 Loss1: 0.768147 Loss2: 0.747448
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.513422 Loss1: 0.769002 Loss2: 0.744420
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.518798 Loss1: 0.771551 Loss2: 0.747247
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.492441 Loss1: 0.745859 Loss2: 0.746582
+(DefaultActor pid=1831567) >> Training accuracy: 0.742808
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.778866 Loss1: 0.977694 Loss2: 0.801172
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.607632 Loss1: 0.916037 Loss2: 0.691595
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.565199 Loss1: 0.885379 Loss2: 0.679820
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.583809 Loss1: 0.901504 Loss2: 0.682305
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.547217 Loss1: 0.865505 Loss2: 0.681712
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.513597 Loss1: 0.833575 Loss2: 0.680022
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.508989 Loss1: 0.832459 Loss2: 0.676530
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.516859 Loss1: 0.838394 Loss2: 0.678465
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.501376 Loss1: 0.821828 Loss2: 0.679548
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.559230 Loss1: 0.874286 Loss2: 0.684944
+(DefaultActor pid=1831567) >> Training accuracy: 0.722039
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.571614 Loss1: 0.862365 Loss2: 0.709249
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.415244 Loss1: 0.795467 Loss2: 0.619778
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.364745 Loss1: 0.752305 Loss2: 0.612440
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.373379 Loss1: 0.762340 Loss2: 0.611038
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.344513 Loss1: 0.734419 Loss2: 0.610094
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.330328 Loss1: 0.716853 Loss2: 0.613475
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.348404 Loss1: 0.733027 Loss2: 0.615378
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.307706 Loss1: 0.694162 Loss2: 0.613544
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.280494 Loss1: 0.665255 Loss2: 0.615239
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.296710 Loss1: 0.682354 Loss2: 0.614356
+[2023-09-27 07:11:58,454][flwr][DEBUG] - fit_round 7 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.765890
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.552100
+[2023-09-27 07:11:59,883][flwr][INFO] - fit progress: (7, 1.238289627785119, {'accuracy': 0.5521}, 3252.718985403888)
+[2023-09-27 07:11:59,883][flwr][DEBUG] - evaluate_round 7: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:12:32,066][flwr][DEBUG] - evaluate_round 7 received 10 results and 0 failures
+[2023-09-27 07:12:32,067][flwr][DEBUG] - fit_round 8: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.682072 Loss1: 0.811707 Loss2: 0.870364
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.520389 Loss1: 0.770071 Loss2: 0.750318
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.462655 Loss1: 0.727571 Loss2: 0.735085
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.447234 Loss1: 0.712887 Loss2: 0.734347
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.436632 Loss1: 0.702306 Loss2: 0.734326
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.437953 Loss1: 0.699939 Loss2: 0.738013
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.430649 Loss1: 0.693252 Loss2: 0.737398
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.423962 Loss1: 0.685660 Loss2: 0.738302
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.407612 Loss1: 0.667534 Loss2: 0.740078
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.404828 Loss1: 0.659479 Loss2: 0.745349
+(DefaultActor pid=1831567) >> Training accuracy: 0.768803
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.636150 Loss1: 0.847176 Loss2: 0.788974
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.485820 Loss1: 0.773230 Loss2: 0.712590
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.497597 Loss1: 0.782785 Loss2: 0.714812
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.472706 Loss1: 0.756444 Loss2: 0.716262
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.475003 Loss1: 0.759464 Loss2: 0.715539
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.452171 Loss1: 0.738188 Loss2: 0.713983
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.444683 Loss1: 0.729882 Loss2: 0.714801
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.458533 Loss1: 0.742746 Loss2: 0.715787
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.465312 Loss1: 0.745772 Loss2: 0.719540
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.446197 Loss1: 0.731413 Loss2: 0.714784
+(DefaultActor pid=1831567) >> Training accuracy: 0.751488
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.500320 Loss1: 0.676306 Loss2: 0.824014
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.315050 Loss1: 0.601702 Loss2: 0.713348
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.275389 Loss1: 0.569898 Loss2: 0.705491
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261817 Loss1: 0.558701 Loss2: 0.703117
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.260959 Loss1: 0.556146 Loss2: 0.704812
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.257460 Loss1: 0.557293 Loss2: 0.700167
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.251971 Loss1: 0.547799 Loss2: 0.704172
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.257791 Loss1: 0.550827 Loss2: 0.706964
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.241905 Loss1: 0.536175 Loss2: 0.705730
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.222244 Loss1: 0.518498 Loss2: 0.703746
+(DefaultActor pid=1831567) >> Training accuracy: 0.816744
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.684813 Loss1: 0.872279 Loss2: 0.812534
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.476752 Loss1: 0.786270 Loss2: 0.690482
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.439966 Loss1: 0.756122 Loss2: 0.683844
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.441683 Loss1: 0.758542 Loss2: 0.683140
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.421568 Loss1: 0.742133 Loss2: 0.679435
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.420592 Loss1: 0.737779 Loss2: 0.682813
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.400930 Loss1: 0.718572 Loss2: 0.682358
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.411389 Loss1: 0.727822 Loss2: 0.683567
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.384770 Loss1: 0.699433 Loss2: 0.685337
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.388786 Loss1: 0.702527 Loss2: 0.686259
+(DefaultActor pid=1831567) >> Training accuracy: 0.738758
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.868116 Loss1: 1.010244 Loss2: 0.857873
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.632811 Loss1: 0.916836 Loss2: 0.715976
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.629890 Loss1: 0.918655 Loss2: 0.711235
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.602153 Loss1: 0.895759 Loss2: 0.706394
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.585578 Loss1: 0.877208 Loss2: 0.708370
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.568751 Loss1: 0.860097 Loss2: 0.708654
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.549774 Loss1: 0.842258 Loss2: 0.707516
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.580390 Loss1: 0.867635 Loss2: 0.712755
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.569928 Loss1: 0.859313 Loss2: 0.710615
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.549115 Loss1: 0.837251 Loss2: 0.711864
+(DefaultActor pid=1831567) >> Training accuracy: 0.693563
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.702762 Loss1: 0.876985 Loss2: 0.825777
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.532387 Loss1: 0.821003 Loss2: 0.711384
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.527182 Loss1: 0.818863 Loss2: 0.708319
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.496480 Loss1: 0.791985 Loss2: 0.704495
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.498896 Loss1: 0.791425 Loss2: 0.707470
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.494125 Loss1: 0.788244 Loss2: 0.705880
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.441542 Loss1: 0.736152 Loss2: 0.705390
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.457928 Loss1: 0.753082 Loss2: 0.704846
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.466015 Loss1: 0.759737 Loss2: 0.706278
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.435423 Loss1: 0.730287 Loss2: 0.705136
+(DefaultActor pid=1831567) >> Training accuracy: 0.760216
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.814310 Loss1: 0.961312 Loss2: 0.852998
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.592058 Loss1: 0.890154 Loss2: 0.701904
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.555251 Loss1: 0.859059 Loss2: 0.696192
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.559418 Loss1: 0.865639 Loss2: 0.693779
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.507137 Loss1: 0.818503 Loss2: 0.688634
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.525827 Loss1: 0.831868 Loss2: 0.693960
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.530041 Loss1: 0.835894 Loss2: 0.694147
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.498407 Loss1: 0.807515 Loss2: 0.690892
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.487322 Loss1: 0.796554 Loss2: 0.690768
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.482338 Loss1: 0.791043 Loss2: 0.691294
+(DefaultActor pid=1831567) >> Training accuracy: 0.712993
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.507601 Loss1: 0.697145 Loss2: 0.810456
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.271276 Loss1: 0.588720 Loss2: 0.682556
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.272344 Loss1: 0.591921 Loss2: 0.680423
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231021 Loss1: 0.554408 Loss2: 0.676613
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.271260 Loss1: 0.592121 Loss2: 0.679138
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.215031 Loss1: 0.535014 Loss2: 0.680018
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.211846 Loss1: 0.533333 Loss2: 0.678513
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207193 Loss1: 0.528694 Loss2: 0.678499
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211935 Loss1: 0.532695 Loss2: 0.679240
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.207777 Loss1: 0.530075 Loss2: 0.677702
+(DefaultActor pid=1831567) >> Training accuracy: 0.820795
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.634720 Loss1: 0.851593 Loss2: 0.783127
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.450865 Loss1: 0.777118 Loss2: 0.673747
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.424778 Loss1: 0.755693 Loss2: 0.669085
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.438706 Loss1: 0.766788 Loss2: 0.671918
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.392925 Loss1: 0.724328 Loss2: 0.668598
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.408994 Loss1: 0.738156 Loss2: 0.670838
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.411233 Loss1: 0.739364 Loss2: 0.671869
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.400411 Loss1: 0.725070 Loss2: 0.675341
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.367422 Loss1: 0.695461 Loss2: 0.671962
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.358954 Loss1: 0.688854 Loss2: 0.670101
+(DefaultActor pid=1831567) >> Training accuracy: 0.773849
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.838495 Loss1: 1.035475 Loss2: 0.803021
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.686672 Loss1: 1.000554 Loss2: 0.686119
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.642826 Loss1: 0.962633 Loss2: 0.680193
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.630633 Loss1: 0.950282 Loss2: 0.680351
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.636780 Loss1: 0.953487 Loss2: 0.683293
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.615341 Loss1: 0.933494 Loss2: 0.681847
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.592805 Loss1: 0.911235 Loss2: 0.681571
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.604849 Loss1: 0.919874 Loss2: 0.684974
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.584868 Loss1: 0.899313 Loss2: 0.685555
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.590336 Loss1: 0.906307 Loss2: 0.684029
+[2023-09-27 07:19:56,785][flwr][DEBUG] - fit_round 8 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.694067
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.587200
+[2023-09-27 07:19:58,773][flwr][INFO] - fit progress: (8, 1.1646041104587883, {'accuracy': 0.5872}, 3731.609143916052)
+[2023-09-27 07:19:58,773][flwr][DEBUG] - evaluate_round 8: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:20:32,157][flwr][DEBUG] - evaluate_round 8 received 10 results and 0 failures
+[2023-09-27 07:20:32,158][flwr][DEBUG] - fit_round 9: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.767282 Loss1: 0.963873 Loss2: 0.803410
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.556373 Loss1: 0.866648 Loss2: 0.689726
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.526153 Loss1: 0.838665 Loss2: 0.687488
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.515749 Loss1: 0.828779 Loss2: 0.686970
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.489376 Loss1: 0.800452 Loss2: 0.688924
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.515557 Loss1: 0.829296 Loss2: 0.686260
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.480050 Loss1: 0.795593 Loss2: 0.684457
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.453588 Loss1: 0.770481 Loss2: 0.683107
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.470970 Loss1: 0.781356 Loss2: 0.689614
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.433652 Loss1: 0.746402 Loss2: 0.687251
+(DefaultActor pid=1831567) >> Training accuracy: 0.712171
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.550982 Loss1: 0.819620 Loss2: 0.731362
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370935 Loss1: 0.735542 Loss2: 0.635392
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354556 Loss1: 0.724084 Loss2: 0.630471
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347261 Loss1: 0.714055 Loss2: 0.633206
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.342652 Loss1: 0.709036 Loss2: 0.633615
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.324781 Loss1: 0.687702 Loss2: 0.637079
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309977 Loss1: 0.675268 Loss2: 0.634709
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.316721 Loss1: 0.685143 Loss2: 0.631578
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.297810 Loss1: 0.664965 Loss2: 0.632845
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.283077 Loss1: 0.648773 Loss2: 0.634304
+(DefaultActor pid=1831567) >> Training accuracy: 0.767479
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.555717 Loss1: 0.836286 Loss2: 0.719432
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.407840 Loss1: 0.752557 Loss2: 0.655283
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.404346 Loss1: 0.746881 Loss2: 0.657465
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.384621 Loss1: 0.731089 Loss2: 0.653532
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.375291 Loss1: 0.718986 Loss2: 0.656305
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.391921 Loss1: 0.732660 Loss2: 0.659262
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.364259 Loss1: 0.704538 Loss2: 0.659721
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.357560 Loss1: 0.698481 Loss2: 0.659079
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.354468 Loss1: 0.694350 Loss2: 0.660118
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.338396 Loss1: 0.679380 Loss2: 0.659015
+(DefaultActor pid=1831567) >> Training accuracy: 0.758003
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.368403 Loss1: 0.620856 Loss2: 0.747547
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.246010 Loss1: 0.573449 Loss2: 0.672561
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.253389 Loss1: 0.582575 Loss2: 0.670814
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.244390 Loss1: 0.574049 Loss2: 0.670342
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.225271 Loss1: 0.555834 Loss2: 0.669436
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.215358 Loss1: 0.544428 Loss2: 0.670930
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197070 Loss1: 0.528094 Loss2: 0.668976
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185178 Loss1: 0.517247 Loss2: 0.667931
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.191626 Loss1: 0.520115 Loss2: 0.671511
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193648 Loss1: 0.522101 Loss2: 0.671547
+(DefaultActor pid=1831567) >> Training accuracy: 0.808063
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.777046 Loss1: 0.992993 Loss2: 0.784052
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.674895 Loss1: 0.971955 Loss2: 0.702941
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.647737 Loss1: 0.944167 Loss2: 0.703570
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.634876 Loss1: 0.927783 Loss2: 0.707093
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.623571 Loss1: 0.919684 Loss2: 0.703887
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.609314 Loss1: 0.905741 Loss2: 0.703573
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.601217 Loss1: 0.897688 Loss2: 0.703529
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.577935 Loss1: 0.871736 Loss2: 0.706199
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.590916 Loss1: 0.881814 Loss2: 0.709102
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.591979 Loss1: 0.880545 Loss2: 0.711434
+(DefaultActor pid=1831567) >> Training accuracy: 0.680480
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.562885 Loss1: 0.799018 Loss2: 0.763867
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.448291 Loss1: 0.765638 Loss2: 0.682653
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.410554 Loss1: 0.730680 Loss2: 0.679874
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.445275 Loss1: 0.761781 Loss2: 0.683494
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.398899 Loss1: 0.717315 Loss2: 0.681584
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.407695 Loss1: 0.724762 Loss2: 0.682933
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.414380 Loss1: 0.731791 Loss2: 0.682589
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.390353 Loss1: 0.705806 Loss2: 0.684546
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.369140 Loss1: 0.685658 Loss2: 0.683483
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.382612 Loss1: 0.695342 Loss2: 0.687270
+(DefaultActor pid=1831567) >> Training accuracy: 0.783100
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.387370 Loss1: 0.653918 Loss2: 0.733451
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243797 Loss1: 0.588296 Loss2: 0.655500
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218522 Loss1: 0.567125 Loss2: 0.651397
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210604 Loss1: 0.558838 Loss2: 0.651766
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.212335 Loss1: 0.560158 Loss2: 0.652177
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.198708 Loss1: 0.544851 Loss2: 0.653857
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.183338 Loss1: 0.530219 Loss2: 0.653119
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.172903 Loss1: 0.519848 Loss2: 0.653055
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165200 Loss1: 0.511362 Loss2: 0.653838
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172382 Loss1: 0.518499 Loss2: 0.653882
+(DefaultActor pid=1831567) >> Training accuracy: 0.823110
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.629405 Loss1: 0.851086 Loss2: 0.778319
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.489852 Loss1: 0.792948 Loss2: 0.696904
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.474484 Loss1: 0.779979 Loss2: 0.694504
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.450436 Loss1: 0.757155 Loss2: 0.693281
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.457664 Loss1: 0.759300 Loss2: 0.698365
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.444785 Loss1: 0.745912 Loss2: 0.698873
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.423247 Loss1: 0.724367 Loss2: 0.698879
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.442809 Loss1: 0.741324 Loss2: 0.701485
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.404128 Loss1: 0.703771 Loss2: 0.700356
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.445211 Loss1: 0.739935 Loss2: 0.705275
+(DefaultActor pid=1831567) >> Training accuracy: 0.739984
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.553776 Loss1: 0.835918 Loss2: 0.717858
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.433910 Loss1: 0.773012 Loss2: 0.660897
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.413715 Loss1: 0.751614 Loss2: 0.662101
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.414150 Loss1: 0.752723 Loss2: 0.661427
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.400037 Loss1: 0.737468 Loss2: 0.662569
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.395023 Loss1: 0.732583 Loss2: 0.662439
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.383715 Loss1: 0.722674 Loss2: 0.661041
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.389653 Loss1: 0.728063 Loss2: 0.661590
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.376249 Loss1: 0.713708 Loss2: 0.662540
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.383339 Loss1: 0.721227 Loss2: 0.662112
+(DefaultActor pid=1831567) >> Training accuracy: 0.756820
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.732463 Loss1: 0.964378 Loss2: 0.768086
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.574690 Loss1: 0.901147 Loss2: 0.673543
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.538939 Loss1: 0.870904 Loss2: 0.668036
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.540208 Loss1: 0.868575 Loss2: 0.671634
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.510269 Loss1: 0.843574 Loss2: 0.666696
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.513781 Loss1: 0.842776 Loss2: 0.671005
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.514410 Loss1: 0.844745 Loss2: 0.669665
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.510831 Loss1: 0.839799 Loss2: 0.671032
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.476011 Loss1: 0.808263 Loss2: 0.667749
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.490790 Loss1: 0.822013 Loss2: 0.668777
+[2023-09-27 07:27:40,836][flwr][DEBUG] - fit_round 9 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.670942
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.579100
+[2023-09-27 07:27:42,444][flwr][INFO] - fit progress: (9, 1.1750041659647665, {'accuracy': 0.5791}, 4195.280460507143)
+[2023-09-27 07:27:42,444][flwr][DEBUG] - evaluate_round 9: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:28:14,012][flwr][DEBUG] - evaluate_round 9 received 10 results and 0 failures
+[2023-09-27 07:28:14,012][flwr][DEBUG] - fit_round 10: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.815599 Loss1: 1.012174 Loss2: 0.803425
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.627114 Loss1: 0.937628 Loss2: 0.689486
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.628370 Loss1: 0.937943 Loss2: 0.690427
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.589757 Loss1: 0.902892 Loss2: 0.686865
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.598415 Loss1: 0.907724 Loss2: 0.690691
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.594773 Loss1: 0.902901 Loss2: 0.691873
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.573926 Loss1: 0.884511 Loss2: 0.689415
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.542708 Loss1: 0.853537 Loss2: 0.689171
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.558470 Loss1: 0.864553 Loss2: 0.693917
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.544092 Loss1: 0.851461 Loss2: 0.692632
+(DefaultActor pid=1831567) >> Training accuracy: 0.710598
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.612394 Loss1: 0.851570 Loss2: 0.760824
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.447102 Loss1: 0.790546 Loss2: 0.656556
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.440679 Loss1: 0.787215 Loss2: 0.653464
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.427512 Loss1: 0.774620 Loss2: 0.652892
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.394972 Loss1: 0.744030 Loss2: 0.650942
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.395143 Loss1: 0.745263 Loss2: 0.649880
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.358024 Loss1: 0.707940 Loss2: 0.650084
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.354622 Loss1: 0.706946 Loss2: 0.647676
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.361081 Loss1: 0.710356 Loss2: 0.650725
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.373208 Loss1: 0.718682 Loss2: 0.654525
+(DefaultActor pid=1831567) >> Training accuracy: 0.772636
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.577577 Loss1: 0.789356 Loss2: 0.788221
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.469519 Loss1: 0.752697 Loss2: 0.716822
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.459752 Loss1: 0.742093 Loss2: 0.717659
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.456028 Loss1: 0.740459 Loss2: 0.715569
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.447290 Loss1: 0.728944 Loss2: 0.718346
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.440171 Loss1: 0.722429 Loss2: 0.717742
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.434871 Loss1: 0.716208 Loss2: 0.718663
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.424500 Loss1: 0.704038 Loss2: 0.720462
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.424456 Loss1: 0.704341 Loss2: 0.720116
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.403840 Loss1: 0.685772 Loss2: 0.718067
+(DefaultActor pid=1831567) >> Training accuracy: 0.750372
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.603985 Loss1: 0.774449 Loss2: 0.829536
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.418736 Loss1: 0.707712 Loss2: 0.711024
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.382571 Loss1: 0.676302 Loss2: 0.706269
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.402931 Loss1: 0.696496 Loss2: 0.706434
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.394770 Loss1: 0.688370 Loss2: 0.706400
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.342543 Loss1: 0.637872 Loss2: 0.704671
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.346455 Loss1: 0.641350 Loss2: 0.705105
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.348748 Loss1: 0.641029 Loss2: 0.707720
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.375281 Loss1: 0.663269 Loss2: 0.712012
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.340315 Loss1: 0.630444 Loss2: 0.709872
+(DefaultActor pid=1831567) >> Training accuracy: 0.789725
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.893280 Loss1: 0.974201 Loss2: 0.919079
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.659243 Loss1: 0.880718 Loss2: 0.778525
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.657245 Loss1: 0.882756 Loss2: 0.774489
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.630225 Loss1: 0.860158 Loss2: 0.770067
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.590538 Loss1: 0.824885 Loss2: 0.765653
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.619691 Loss1: 0.848464 Loss2: 0.771227
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.624928 Loss1: 0.851318 Loss2: 0.773610
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.588176 Loss1: 0.818277 Loss2: 0.769898
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.580922 Loss1: 0.805782 Loss2: 0.775139
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.568940 Loss1: 0.799353 Loss2: 0.769587
+(DefaultActor pid=1831567) >> Training accuracy: 0.690765
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488231 Loss1: 0.669017 Loss2: 0.819214
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.278396 Loss1: 0.579735 Loss2: 0.698661
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.243164 Loss1: 0.547770 Loss2: 0.695394
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.227678 Loss1: 0.534646 Loss2: 0.693032
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.220596 Loss1: 0.526460 Loss2: 0.694136
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232642 Loss1: 0.536260 Loss2: 0.696382
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.208526 Loss1: 0.515715 Loss2: 0.692811
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229012 Loss1: 0.530883 Loss2: 0.698129
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.194903 Loss1: 0.501364 Loss2: 0.693538
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201546 Loss1: 0.506781 Loss2: 0.694765
+(DefaultActor pid=1831567) >> Training accuracy: 0.790509
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.458632 Loss1: 0.638149 Loss2: 0.820482
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.294972 Loss1: 0.580683 Loss2: 0.714289
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262323 Loss1: 0.558810 Loss2: 0.703513
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.248409 Loss1: 0.544596 Loss2: 0.703814
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.238214 Loss1: 0.536469 Loss2: 0.701746
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231400 Loss1: 0.530637 Loss2: 0.700764
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.214380 Loss1: 0.511745 Loss2: 0.702635
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222912 Loss1: 0.517069 Loss2: 0.705843
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.231532 Loss1: 0.528483 Loss2: 0.703049
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214245 Loss1: 0.507804 Loss2: 0.706442
+(DefaultActor pid=1831567) >> Training accuracy: 0.810571
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.630765 Loss1: 0.820427 Loss2: 0.810338
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.436797 Loss1: 0.747638 Loss2: 0.689159
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.409270 Loss1: 0.728725 Loss2: 0.680545
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.389757 Loss1: 0.707515 Loss2: 0.682241
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.404856 Loss1: 0.723527 Loss2: 0.681329
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.369030 Loss1: 0.686498 Loss2: 0.682532
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.364501 Loss1: 0.682721 Loss2: 0.681781
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.371983 Loss1: 0.691776 Loss2: 0.680207
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.356249 Loss1: 0.673448 Loss2: 0.682801
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.367241 Loss1: 0.682338 Loss2: 0.684902
+(DefaultActor pid=1831567) >> Training accuracy: 0.766387
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.772556 Loss1: 0.930209 Loss2: 0.842347
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.573863 Loss1: 0.862785 Loss2: 0.711078
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.523109 Loss1: 0.819599 Loss2: 0.703509
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.498672 Loss1: 0.797400 Loss2: 0.701271
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.536996 Loss1: 0.833562 Loss2: 0.703434
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.503271 Loss1: 0.799313 Loss2: 0.703959
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.510294 Loss1: 0.805138 Loss2: 0.705156
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.481032 Loss1: 0.775325 Loss2: 0.705707
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.485884 Loss1: 0.779005 Loss2: 0.706879
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.469104 Loss1: 0.763691 Loss2: 0.705413
+(DefaultActor pid=1831567) >> Training accuracy: 0.731086
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.614144 Loss1: 0.852872 Loss2: 0.761273
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.393520 Loss1: 0.734116 Loss2: 0.659404
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.392818 Loss1: 0.732513 Loss2: 0.660305
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.392237 Loss1: 0.731855 Loss2: 0.660382
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.391193 Loss1: 0.729649 Loss2: 0.661544
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.355547 Loss1: 0.694652 Loss2: 0.660894
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352225 Loss1: 0.693210 Loss2: 0.659015
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.369125 Loss1: 0.708619 Loss2: 0.660506
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.328146 Loss1: 0.668769 Loss2: 0.659377
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329398 Loss1: 0.668360 Loss2: 0.661037
+[2023-09-27 07:35:34,969][flwr][DEBUG] - fit_round 10 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.780428
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.600800
+[2023-09-27 07:35:36,460][flwr][INFO] - fit progress: (10, 1.128453626800269, {'accuracy': 0.6008}, 4669.296330975834)
+[2023-09-27 07:35:36,461][flwr][DEBUG] - evaluate_round 10: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:36:07,726][flwr][DEBUG] - evaluate_round 10 received 10 results and 0 failures
+[2023-09-27 07:36:07,727][flwr][DEBUG] - fit_round 11: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.533785 Loss1: 0.797140 Loss2: 0.736645
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.402241 Loss1: 0.729774 Loss2: 0.672468
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.378221 Loss1: 0.709039 Loss2: 0.669182
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.378378 Loss1: 0.708024 Loss2: 0.670354
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.364618 Loss1: 0.692593 Loss2: 0.672024
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.355564 Loss1: 0.682952 Loss2: 0.672612
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.361993 Loss1: 0.684666 Loss2: 0.677327
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347081 Loss1: 0.672100 Loss2: 0.674981
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.357602 Loss1: 0.681353 Loss2: 0.676250
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.337621 Loss1: 0.660798 Loss2: 0.676823
+(DefaultActor pid=1831567) >> Training accuracy: 0.783918
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.679884 Loss1: 0.926363 Loss2: 0.753521
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.528575 Loss1: 0.868938 Loss2: 0.659637
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.516750 Loss1: 0.859409 Loss2: 0.657340
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.528537 Loss1: 0.873242 Loss2: 0.655295
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.506278 Loss1: 0.850723 Loss2: 0.655555
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.462943 Loss1: 0.809983 Loss2: 0.652960
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.470518 Loss1: 0.813514 Loss2: 0.657004
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.476389 Loss1: 0.818866 Loss2: 0.657524
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.476091 Loss1: 0.816944 Loss2: 0.659146
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.478762 Loss1: 0.820048 Loss2: 0.658714
+(DefaultActor pid=1831567) >> Training accuracy: 0.701259
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.552934 Loss1: 0.793166 Loss2: 0.759769
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.410739 Loss1: 0.755692 Loss2: 0.655047
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363047 Loss1: 0.710858 Loss2: 0.652189
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.355318 Loss1: 0.703677 Loss2: 0.651641
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.309705 Loss1: 0.656545 Loss2: 0.653160
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.278812 Loss1: 0.629880 Loss2: 0.648932
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295662 Loss1: 0.642231 Loss2: 0.653431
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289132 Loss1: 0.635929 Loss2: 0.653203
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.276769 Loss1: 0.626963 Loss2: 0.649806
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.277223 Loss1: 0.623794 Loss2: 0.653429
+(DefaultActor pid=1831567) >> Training accuracy: 0.773040
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.780977 Loss1: 0.985803 Loss2: 0.795173
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.635304 Loss1: 0.922934 Loss2: 0.712370
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.626476 Loss1: 0.912382 Loss2: 0.714094
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.606571 Loss1: 0.891289 Loss2: 0.715282
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.588978 Loss1: 0.874679 Loss2: 0.714299
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.588099 Loss1: 0.872926 Loss2: 0.715173
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.567798 Loss1: 0.853443 Loss2: 0.714354
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.594739 Loss1: 0.873223 Loss2: 0.721517
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.578093 Loss1: 0.857344 Loss2: 0.720749
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.558957 Loss1: 0.840045 Loss2: 0.718913
+(DefaultActor pid=1831567) >> Training accuracy: 0.701766
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.617676 Loss1: 0.813111 Loss2: 0.804565
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.468541 Loss1: 0.749242 Loss2: 0.719298
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.468599 Loss1: 0.749154 Loss2: 0.719445
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.436253 Loss1: 0.720590 Loss2: 0.715663
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.429364 Loss1: 0.710381 Loss2: 0.718983
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.445850 Loss1: 0.721367 Loss2: 0.724483
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.417447 Loss1: 0.694709 Loss2: 0.722738
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.457138 Loss1: 0.732254 Loss2: 0.724884
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.413713 Loss1: 0.692159 Loss2: 0.721554
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.401992 Loss1: 0.677226 Loss2: 0.724765
+(DefaultActor pid=1831567) >> Training accuracy: 0.773037
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.531234 Loss1: 0.766907 Loss2: 0.764326
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.419242 Loss1: 0.736564 Loss2: 0.682677
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.377321 Loss1: 0.701734 Loss2: 0.675587
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.357220 Loss1: 0.680718 Loss2: 0.676502
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.364312 Loss1: 0.687887 Loss2: 0.676424
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341366 Loss1: 0.663260 Loss2: 0.678107
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.361421 Loss1: 0.680698 Loss2: 0.680723
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.357961 Loss1: 0.677994 Loss2: 0.679967
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.340704 Loss1: 0.658543 Loss2: 0.682160
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.343161 Loss1: 0.659754 Loss2: 0.683407
+(DefaultActor pid=1831567) >> Training accuracy: 0.784951
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.555648 Loss1: 0.781224 Loss2: 0.774424
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.457894 Loss1: 0.743054 Loss2: 0.714840
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.435514 Loss1: 0.721943 Loss2: 0.713571
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.433552 Loss1: 0.720266 Loss2: 0.713286
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.415315 Loss1: 0.703652 Loss2: 0.711664
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.436546 Loss1: 0.717354 Loss2: 0.719192
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.403619 Loss1: 0.688933 Loss2: 0.714686
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.417846 Loss1: 0.700225 Loss2: 0.717621
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.405390 Loss1: 0.688833 Loss2: 0.716557
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.396222 Loss1: 0.678921 Loss2: 0.717301
+(DefaultActor pid=1831567) >> Training accuracy: 0.768229
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348304 Loss1: 0.618043 Loss2: 0.730260
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.231708 Loss1: 0.573988 Loss2: 0.657720
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195189 Loss1: 0.543899 Loss2: 0.651290
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175077 Loss1: 0.525287 Loss2: 0.649790
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163480 Loss1: 0.513460 Loss2: 0.650020
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.166208 Loss1: 0.513266 Loss2: 0.652942
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161944 Loss1: 0.509201 Loss2: 0.652743
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162438 Loss1: 0.507985 Loss2: 0.654453
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161619 Loss1: 0.508648 Loss2: 0.652971
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136332 Loss1: 0.483603 Loss2: 0.652729
+(DefaultActor pid=1831567) >> Training accuracy: 0.816165
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.347200 Loss1: 0.630041 Loss2: 0.717159
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202751 Loss1: 0.558152 Loss2: 0.644599
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173849 Loss1: 0.531575 Loss2: 0.642274
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.182334 Loss1: 0.535654 Loss2: 0.646680
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.164860 Loss1: 0.518634 Loss2: 0.646226
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.155684 Loss1: 0.510339 Loss2: 0.645345
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.137568 Loss1: 0.489980 Loss2: 0.647589
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163274 Loss1: 0.514293 Loss2: 0.648982
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140543 Loss1: 0.490210 Loss2: 0.650333
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.138334 Loss1: 0.488841 Loss2: 0.649493
+(DefaultActor pid=1831567) >> Training accuracy: 0.834298
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.680971 Loss1: 0.903186 Loss2: 0.777785
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.499720 Loss1: 0.826270 Loss2: 0.673450
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.481330 Loss1: 0.811912 Loss2: 0.669417
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.498143 Loss1: 0.830680 Loss2: 0.667463
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.441093 Loss1: 0.776281 Loss2: 0.664813
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.460735 Loss1: 0.791239 Loss2: 0.669495
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.459340 Loss1: 0.788289 Loss2: 0.671051
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.469777 Loss1: 0.797680 Loss2: 0.672096
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.428577 Loss1: 0.752570 Loss2: 0.676008
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.416014 Loss1: 0.745213 Loss2: 0.670801
+[2023-09-27 07:43:04,575][flwr][DEBUG] - fit_round 11 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.746162
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.597800
+[2023-09-27 07:43:06,596][flwr][INFO] - fit progress: (11, 1.1231593939062126, {'accuracy': 0.5978}, 5119.431959697045)
+[2023-09-27 07:43:06,597][flwr][DEBUG] - evaluate_round 11: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:43:38,780][flwr][DEBUG] - evaluate_round 11 received 10 results and 0 failures
+[2023-09-27 07:43:38,781][flwr][DEBUG] - fit_round 12: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.560323 Loss1: 0.798809 Loss2: 0.761514
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.424971 Loss1: 0.754416 Loss2: 0.670555
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.422624 Loss1: 0.757047 Loss2: 0.665577
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.365493 Loss1: 0.703241 Loss2: 0.662252
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.373474 Loss1: 0.709200 Loss2: 0.664274
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362990 Loss1: 0.697882 Loss2: 0.665108
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.349536 Loss1: 0.683769 Loss2: 0.665767
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.364160 Loss1: 0.698090 Loss2: 0.666069
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.359105 Loss1: 0.688556 Loss2: 0.670549
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.358418 Loss1: 0.687509 Loss2: 0.670909
+(DefaultActor pid=1831567) >> Training accuracy: 0.769431
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.765948 Loss1: 0.916799 Loss2: 0.849150
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.536700 Loss1: 0.827548 Loss2: 0.709152
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.525938 Loss1: 0.819496 Loss2: 0.706442
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.489261 Loss1: 0.782236 Loss2: 0.707025
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.495004 Loss1: 0.789078 Loss2: 0.705926
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.460037 Loss1: 0.755036 Loss2: 0.705001
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.474926 Loss1: 0.768486 Loss2: 0.706440
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.452758 Loss1: 0.750684 Loss2: 0.702074
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.439569 Loss1: 0.731186 Loss2: 0.708383
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.452487 Loss1: 0.743884 Loss2: 0.708603
+(DefaultActor pid=1831567) >> Training accuracy: 0.748081
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.518506 Loss1: 0.754237 Loss2: 0.764270
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.374456 Loss1: 0.699560 Loss2: 0.674896
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.374428 Loss1: 0.701081 Loss2: 0.673347
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364044 Loss1: 0.690054 Loss2: 0.673991
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.334347 Loss1: 0.659737 Loss2: 0.674610
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.350395 Loss1: 0.676687 Loss2: 0.673708
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.332741 Loss1: 0.657140 Loss2: 0.675601
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.317960 Loss1: 0.642643 Loss2: 0.675317
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.338629 Loss1: 0.662017 Loss2: 0.676612
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.325917 Loss1: 0.648431 Loss2: 0.677487
+(DefaultActor pid=1831567) >> Training accuracy: 0.795641
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.409219 Loss1: 0.598002 Loss2: 0.811217
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236592 Loss1: 0.537354 Loss2: 0.699238
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226524 Loss1: 0.532725 Loss2: 0.693799
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202579 Loss1: 0.505976 Loss2: 0.696602
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201397 Loss1: 0.507463 Loss2: 0.693935
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191179 Loss1: 0.496941 Loss2: 0.694238
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.186290 Loss1: 0.487731 Loss2: 0.698558
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.183493 Loss1: 0.488124 Loss2: 0.695369
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.177810 Loss1: 0.483808 Loss2: 0.694003
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.182589 Loss1: 0.484876 Loss2: 0.697714
+(DefaultActor pid=1831567) >> Training accuracy: 0.837191
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.760507 Loss1: 0.970174 Loss2: 0.790333
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.595966 Loss1: 0.914477 Loss2: 0.681489
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.606599 Loss1: 0.923381 Loss2: 0.683218
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.571381 Loss1: 0.891533 Loss2: 0.679848
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.551802 Loss1: 0.870529 Loss2: 0.681273
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.559432 Loss1: 0.877836 Loss2: 0.681596
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.541575 Loss1: 0.861114 Loss2: 0.680462
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.521583 Loss1: 0.841282 Loss2: 0.680301
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.503648 Loss1: 0.823852 Loss2: 0.679796
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.535044 Loss1: 0.852418 Loss2: 0.682626
+(DefaultActor pid=1831567) >> Training accuracy: 0.711051
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.586741 Loss1: 0.762426 Loss2: 0.824315
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.403363 Loss1: 0.696723 Loss2: 0.706640
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.375464 Loss1: 0.672496 Loss2: 0.702968
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.362980 Loss1: 0.657039 Loss2: 0.705941
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.366239 Loss1: 0.662760 Loss2: 0.703480
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.336160 Loss1: 0.629010 Loss2: 0.707150
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.339857 Loss1: 0.634776 Loss2: 0.705081
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.328460 Loss1: 0.620929 Loss2: 0.707531
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306822 Loss1: 0.600272 Loss2: 0.706550
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.319197 Loss1: 0.611518 Loss2: 0.707679
+(DefaultActor pid=1831567) >> Training accuracy: 0.792903
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.575427 Loss1: 0.784921 Loss2: 0.790507
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.438122 Loss1: 0.714344 Loss2: 0.723779
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.421260 Loss1: 0.700394 Loss2: 0.720866
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.424372 Loss1: 0.701936 Loss2: 0.722436
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.436689 Loss1: 0.711501 Loss2: 0.725187
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.407652 Loss1: 0.683392 Loss2: 0.724260
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.408762 Loss1: 0.684407 Loss2: 0.724355
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.408258 Loss1: 0.683536 Loss2: 0.724722
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.409017 Loss1: 0.683464 Loss2: 0.725553
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.402606 Loss1: 0.676303 Loss2: 0.726303
+(DefaultActor pid=1831567) >> Training accuracy: 0.773562
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.599258 Loss1: 0.804549 Loss2: 0.794709
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.424250 Loss1: 0.743235 Loss2: 0.681014
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.375731 Loss1: 0.702497 Loss2: 0.673234
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.381837 Loss1: 0.707335 Loss2: 0.674502
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.351053 Loss1: 0.677990 Loss2: 0.673063
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.359974 Loss1: 0.686369 Loss2: 0.673604
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.335291 Loss1: 0.663661 Loss2: 0.671629
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.346949 Loss1: 0.673001 Loss2: 0.673948
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.321656 Loss1: 0.647823 Loss2: 0.673833
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.327283 Loss1: 0.652546 Loss2: 0.674737
+(DefaultActor pid=1831567) >> Training accuracy: 0.782965
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.784347 Loss1: 0.940359 Loss2: 0.843988
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.574727 Loss1: 0.859676 Loss2: 0.715051
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.555978 Loss1: 0.849322 Loss2: 0.706656
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.544357 Loss1: 0.836248 Loss2: 0.708109
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.570737 Loss1: 0.859714 Loss2: 0.711023
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.508001 Loss1: 0.799999 Loss2: 0.708002
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.518251 Loss1: 0.808246 Loss2: 0.710005
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.520710 Loss1: 0.808047 Loss2: 0.712663
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.503142 Loss1: 0.791628 Loss2: 0.711514
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.490204 Loss1: 0.778082 Loss2: 0.712121
+(DefaultActor pid=1831567) >> Training accuracy: 0.703358
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.441816 Loss1: 0.618548 Loss2: 0.823268
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.273062 Loss1: 0.554410 Loss2: 0.718652
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.246438 Loss1: 0.533174 Loss2: 0.713264
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237757 Loss1: 0.527019 Loss2: 0.710738
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.206606 Loss1: 0.496767 Loss2: 0.709839
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.209470 Loss1: 0.500079 Loss2: 0.709391
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.203409 Loss1: 0.493276 Loss2: 0.710133
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.188493 Loss1: 0.478090 Loss2: 0.710403
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183159 Loss1: 0.473446 Loss2: 0.709713
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.219679 Loss1: 0.505808 Loss2: 0.713870
+(DefaultActor pid=1831567) >> Training accuracy: 0.815972
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 07:50:36,714][flwr][DEBUG] - fit_round 12 received 10 results and 0 failures
+>> Test accuracy: 0.612200
+[2023-09-27 07:50:38,307][flwr][INFO] - fit progress: (12, 1.0893270417143361, {'accuracy': 0.6122}, 5571.143073525745)
+[2023-09-27 07:50:38,307][flwr][DEBUG] - evaluate_round 12: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:51:10,006][flwr][DEBUG] - evaluate_round 12 received 10 results and 0 failures
+[2023-09-27 07:51:10,007][flwr][DEBUG] - fit_round 13: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.725892 Loss1: 0.934786 Loss2: 0.791106
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.587016 Loss1: 0.877035 Loss2: 0.709981
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.590304 Loss1: 0.878714 Loss2: 0.711590
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.574611 Loss1: 0.863718 Loss2: 0.710893
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.583946 Loss1: 0.872142 Loss2: 0.711803
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.580666 Loss1: 0.866145 Loss2: 0.714522
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.546354 Loss1: 0.836429 Loss2: 0.709925
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.555712 Loss1: 0.841188 Loss2: 0.714525
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.554583 Loss1: 0.836809 Loss2: 0.717774
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.545713 Loss1: 0.830436 Loss2: 0.715277
+(DefaultActor pid=1831567) >> Training accuracy: 0.686821
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.323828 Loss1: 0.610688 Loss2: 0.713139
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.160142 Loss1: 0.522676 Loss2: 0.637466
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.141188 Loss1: 0.508331 Loss2: 0.632857
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.142863 Loss1: 0.509955 Loss2: 0.632908
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.121871 Loss1: 0.487687 Loss2: 0.634184
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.141098 Loss1: 0.505057 Loss2: 0.636041
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.126344 Loss1: 0.492324 Loss2: 0.634020
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.118672 Loss1: 0.484499 Loss2: 0.634173
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.114078 Loss1: 0.480182 Loss2: 0.633896
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107437 Loss1: 0.472479 Loss2: 0.634958
+(DefaultActor pid=1831567) >> Training accuracy: 0.828897
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.512097 Loss1: 0.793155 Loss2: 0.718942
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.362093 Loss1: 0.706694 Loss2: 0.655398
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.343615 Loss1: 0.687345 Loss2: 0.656270
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339117 Loss1: 0.683525 Loss2: 0.655592
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.328873 Loss1: 0.673185 Loss2: 0.655688
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.314614 Loss1: 0.657937 Loss2: 0.656677
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.311882 Loss1: 0.654168 Loss2: 0.657714
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.317371 Loss1: 0.658308 Loss2: 0.659063
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.333581 Loss1: 0.673245 Loss2: 0.660337
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290425 Loss1: 0.628057 Loss2: 0.662367
+(DefaultActor pid=1831567) >> Training accuracy: 0.790968
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.717419 Loss1: 0.921159 Loss2: 0.796260
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.552100 Loss1: 0.855224 Loss2: 0.696876
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.529100 Loss1: 0.836191 Loss2: 0.692909
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.512662 Loss1: 0.820619 Loss2: 0.692043
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.481028 Loss1: 0.791659 Loss2: 0.689369
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.494022 Loss1: 0.801426 Loss2: 0.692597
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.502410 Loss1: 0.809678 Loss2: 0.692732
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.469498 Loss1: 0.779570 Loss2: 0.689928
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.470051 Loss1: 0.777379 Loss2: 0.692671
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.469253 Loss1: 0.774199 Loss2: 0.695054
+(DefaultActor pid=1831567) >> Training accuracy: 0.705924
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.334560 Loss1: 0.613507 Loss2: 0.721053
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.177061 Loss1: 0.532787 Loss2: 0.644274
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.168143 Loss1: 0.518661 Loss2: 0.649482
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.192708 Loss1: 0.543814 Loss2: 0.648894
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.173531 Loss1: 0.525472 Loss2: 0.648059
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.151228 Loss1: 0.503098 Loss2: 0.648130
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.160397 Loss1: 0.509334 Loss2: 0.651063
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133843 Loss1: 0.484947 Loss2: 0.648896
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.129902 Loss1: 0.479275 Loss2: 0.650627
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.135880 Loss1: 0.485394 Loss2: 0.650486
+(DefaultActor pid=1831567) >> Training accuracy: 0.833140
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.601572 Loss1: 0.868832 Loss2: 0.732740
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.460044 Loss1: 0.823405 Loss2: 0.636639
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.426078 Loss1: 0.794205 Loss2: 0.631873
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.408145 Loss1: 0.776295 Loss2: 0.631850
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.419276 Loss1: 0.787548 Loss2: 0.631727
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.409805 Loss1: 0.772503 Loss2: 0.637303
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386134 Loss1: 0.754279 Loss2: 0.631855
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.375408 Loss1: 0.741667 Loss2: 0.633741
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.381031 Loss1: 0.743636 Loss2: 0.637394
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.377067 Loss1: 0.740010 Loss2: 0.637057
+(DefaultActor pid=1831567) >> Training accuracy: 0.748904
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.473511 Loss1: 0.741311 Loss2: 0.732200
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.356619 Loss1: 0.706822 Loss2: 0.649797
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.332346 Loss1: 0.684819 Loss2: 0.647528
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.336388 Loss1: 0.685307 Loss2: 0.651080
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.339418 Loss1: 0.686912 Loss2: 0.652506
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.317759 Loss1: 0.667716 Loss2: 0.650043
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295051 Loss1: 0.643663 Loss2: 0.651388
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.302067 Loss1: 0.649211 Loss2: 0.652855
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.300369 Loss1: 0.644231 Loss2: 0.656138
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.293104 Loss1: 0.638017 Loss2: 0.655087
+(DefaultActor pid=1831567) >> Training accuracy: 0.799137
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.521691 Loss1: 0.761666 Loss2: 0.760024
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.427226 Loss1: 0.748119 Loss2: 0.679107
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.412431 Loss1: 0.734347 Loss2: 0.678084
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.391719 Loss1: 0.717154 Loss2: 0.674565
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.348796 Loss1: 0.669329 Loss2: 0.679467
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.360333 Loss1: 0.684079 Loss2: 0.676254
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.333519 Loss1: 0.657161 Loss2: 0.676357
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.345494 Loss1: 0.666681 Loss2: 0.678813
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.362947 Loss1: 0.681521 Loss2: 0.681426
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.331116 Loss1: 0.653252 Loss2: 0.677864
+(DefaultActor pid=1831567) >> Training accuracy: 0.791667
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493409 Loss1: 0.766468 Loss2: 0.726940
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382557 Loss1: 0.711428 Loss2: 0.671130
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.366924 Loss1: 0.699590 Loss2: 0.667334
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.351930 Loss1: 0.686857 Loss2: 0.665073
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.353773 Loss1: 0.687032 Loss2: 0.666740
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.332104 Loss1: 0.664379 Loss2: 0.667725
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350464 Loss1: 0.678807 Loss2: 0.671657
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347634 Loss1: 0.674540 Loss2: 0.673094
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.340173 Loss1: 0.667713 Loss2: 0.672461
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.348835 Loss1: 0.673174 Loss2: 0.675662
+(DefaultActor pid=1831567) >> Training accuracy: 0.773065
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.522577 Loss1: 0.781571 Loss2: 0.741006
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329258 Loss1: 0.693669 Loss2: 0.635589
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.289134 Loss1: 0.656745 Loss2: 0.632389
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.278786 Loss1: 0.644248 Loss2: 0.634538
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282477 Loss1: 0.648909 Loss2: 0.633569
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.254545 Loss1: 0.620421 Loss2: 0.634124
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.256020 Loss1: 0.621802 Loss2: 0.634217
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.252712 Loss1: 0.621521 Loss2: 0.631191
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232755 Loss1: 0.599330 Loss2: 0.633425
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.228804 Loss1: 0.593300 Loss2: 0.635504
+[2023-09-27 07:58:32,416][flwr][DEBUG] - fit_round 13 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.807203
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.616700
+[2023-09-27 07:58:34,101][flwr][INFO] - fit progress: (13, 1.0760116641894697, {'accuracy': 0.6167}, 6046.937410460785)
+[2023-09-27 07:58:34,102][flwr][DEBUG] - evaluate_round 13: strategy sampled 10 clients (out of 10)
+[2023-09-27 07:59:05,669][flwr][DEBUG] - evaluate_round 13 received 10 results and 0 failures
+[2023-09-27 07:59:05,670][flwr][DEBUG] - fit_round 14: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.404020 Loss1: 0.599873 Loss2: 0.804147
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.251751 Loss1: 0.553303 Loss2: 0.698448
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213655 Loss1: 0.518210 Loss2: 0.695444
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.201935 Loss1: 0.507946 Loss2: 0.693989
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203698 Loss1: 0.508318 Loss2: 0.695380
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183848 Loss1: 0.488824 Loss2: 0.695024
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.203135 Loss1: 0.506579 Loss2: 0.696556
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168492 Loss1: 0.472521 Loss2: 0.695971
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161366 Loss1: 0.463730 Loss2: 0.697636
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.143075 Loss1: 0.444768 Loss2: 0.698307
+(DefaultActor pid=1831567) >> Training accuracy: 0.826775
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.397180 Loss1: 0.604698 Loss2: 0.792482
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227775 Loss1: 0.531255 Loss2: 0.696520
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212031 Loss1: 0.520683 Loss2: 0.691348
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184616 Loss1: 0.495373 Loss2: 0.689244
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.174565 Loss1: 0.485503 Loss2: 0.689062
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184082 Loss1: 0.493562 Loss2: 0.690520
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165532 Loss1: 0.476401 Loss2: 0.689131
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.165962 Loss1: 0.476303 Loss2: 0.689659
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.167192 Loss1: 0.475846 Loss2: 0.691345
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.160406 Loss1: 0.468304 Loss2: 0.692102
+(DefaultActor pid=1831567) >> Training accuracy: 0.835841
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568945 Loss1: 0.767593 Loss2: 0.801352
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.420097 Loss1: 0.729891 Loss2: 0.690206
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.362511 Loss1: 0.681955 Loss2: 0.680556
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353467 Loss1: 0.672002 Loss2: 0.681465
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.342689 Loss1: 0.663275 Loss2: 0.679414
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328490 Loss1: 0.651243 Loss2: 0.677246
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.344880 Loss1: 0.662986 Loss2: 0.681894
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320875 Loss1: 0.638918 Loss2: 0.681957
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.307766 Loss1: 0.627048 Loss2: 0.680718
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.322690 Loss1: 0.637332 Loss2: 0.685358
+(DefaultActor pid=1831567) >> Training accuracy: 0.758575
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.739323 Loss1: 0.883367 Loss2: 0.855956
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.540058 Loss1: 0.813863 Loss2: 0.726195
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.503454 Loss1: 0.779681 Loss2: 0.723773
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.497687 Loss1: 0.778942 Loss2: 0.718745
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.494281 Loss1: 0.775995 Loss2: 0.718287
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.468234 Loss1: 0.743908 Loss2: 0.724326
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.482502 Loss1: 0.760430 Loss2: 0.722073
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.432490 Loss1: 0.709980 Loss2: 0.722510
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.454774 Loss1: 0.734012 Loss2: 0.720762
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.427800 Loss1: 0.703362 Loss2: 0.724438
+(DefaultActor pid=1831567) >> Training accuracy: 0.760143
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.537227 Loss1: 0.772702 Loss2: 0.764524
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.368339 Loss1: 0.694645 Loss2: 0.673694
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.339108 Loss1: 0.666835 Loss2: 0.672273
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.330296 Loss1: 0.657345 Loss2: 0.672952
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.334406 Loss1: 0.659497 Loss2: 0.674910
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316307 Loss1: 0.643574 Loss2: 0.672733
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.316153 Loss1: 0.640153 Loss2: 0.676000
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334452 Loss1: 0.655839 Loss2: 0.678613
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.298713 Loss1: 0.620638 Loss2: 0.678076
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.305099 Loss1: 0.626609 Loss2: 0.678490
+(DefaultActor pid=1831567) >> Training accuracy: 0.794819
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.741865 Loss1: 0.948532 Loss2: 0.793334
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.583736 Loss1: 0.900835 Loss2: 0.682901
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.542111 Loss1: 0.861600 Loss2: 0.680510
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.535744 Loss1: 0.854269 Loss2: 0.681476
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.513819 Loss1: 0.831268 Loss2: 0.682551
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.532953 Loss1: 0.849850 Loss2: 0.683103
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.513073 Loss1: 0.830398 Loss2: 0.682675
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.514231 Loss1: 0.827981 Loss2: 0.686250
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.516169 Loss1: 0.830897 Loss2: 0.685272
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.478276 Loss1: 0.791754 Loss2: 0.686522
+(DefaultActor pid=1831567) >> Training accuracy: 0.707428
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.507283 Loss1: 0.740491 Loss2: 0.766792
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.414350 Loss1: 0.708520 Loss2: 0.705830
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.400144 Loss1: 0.695155 Loss2: 0.704989
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.388718 Loss1: 0.684930 Loss2: 0.703788
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.382341 Loss1: 0.677957 Loss2: 0.704384
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.374709 Loss1: 0.668047 Loss2: 0.706662
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.359265 Loss1: 0.653751 Loss2: 0.705514
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.374998 Loss1: 0.667456 Loss2: 0.707542
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.372159 Loss1: 0.660479 Loss2: 0.711680
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.357991 Loss1: 0.650076 Loss2: 0.707916
+(DefaultActor pid=1831567) >> Training accuracy: 0.766865
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.598217 Loss1: 0.761112 Loss2: 0.837105
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394979 Loss1: 0.677304 Loss2: 0.717674
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.352117 Loss1: 0.637421 Loss2: 0.714695
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.359349 Loss1: 0.641678 Loss2: 0.717671
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317109 Loss1: 0.602337 Loss2: 0.714771
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.321617 Loss1: 0.607232 Loss2: 0.714385
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.341147 Loss1: 0.623181 Loss2: 0.717965
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329031 Loss1: 0.610898 Loss2: 0.718132
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.319175 Loss1: 0.600415 Loss2: 0.718760
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304420 Loss1: 0.585130 Loss2: 0.719291
+(DefaultActor pid=1831567) >> Training accuracy: 0.812235
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.529621 Loss1: 0.772814 Loss2: 0.756807
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382500 Loss1: 0.714579 Loss2: 0.667922
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.374563 Loss1: 0.708333 Loss2: 0.666230
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.378192 Loss1: 0.713334 Loss2: 0.664857
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.356143 Loss1: 0.690342 Loss2: 0.665800
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337139 Loss1: 0.673318 Loss2: 0.663821
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.337127 Loss1: 0.668923 Loss2: 0.668204
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.327199 Loss1: 0.660318 Loss2: 0.666881
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.329809 Loss1: 0.661817 Loss2: 0.667991
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.339422 Loss1: 0.669465 Loss2: 0.669957
+(DefaultActor pid=1831567) >> Training accuracy: 0.779647
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.717452 Loss1: 0.896624 Loss2: 0.820828
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.578020 Loss1: 0.876032 Loss2: 0.701988
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.515344 Loss1: 0.819546 Loss2: 0.695798
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.494783 Loss1: 0.799478 Loss2: 0.695305
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.488629 Loss1: 0.794073 Loss2: 0.694557
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.496212 Loss1: 0.798746 Loss2: 0.697466
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.488922 Loss1: 0.791145 Loss2: 0.697777
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.433228 Loss1: 0.735471 Loss2: 0.697757
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.451149 Loss1: 0.754588 Loss2: 0.696561
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.439627 Loss1: 0.742842 Loss2: 0.696785
+[2023-09-27 08:06:10,664][flwr][DEBUG] - fit_round 14 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.692164
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.632100
+[2023-09-27 08:06:12,553][flwr][INFO] - fit progress: (14, 1.0299229365758622, {'accuracy': 0.6321}, 6505.389118962921)
+[2023-09-27 08:06:12,554][flwr][DEBUG] - evaluate_round 14: strategy sampled 10 clients (out of 10)
+[2023-09-27 08:06:44,514][flwr][DEBUG] - evaluate_round 14 received 10 results and 0 failures
+[2023-09-27 08:06:44,515][flwr][DEBUG] - fit_round 15: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.318274 Loss1: 0.578763 Loss2: 0.739511
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.188297 Loss1: 0.524843 Loss2: 0.663454
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.174460 Loss1: 0.515031 Loss2: 0.659429
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.151525 Loss1: 0.493344 Loss2: 0.658181
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154321 Loss1: 0.496840 Loss2: 0.657480
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131162 Loss1: 0.474486 Loss2: 0.656676
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.151904 Loss1: 0.493647 Loss2: 0.658257
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.128384 Loss1: 0.470369 Loss2: 0.658015
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134306 Loss1: 0.472512 Loss2: 0.661794
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.112116 Loss1: 0.452291 Loss2: 0.659825
+(DefaultActor pid=1831567) >> Training accuracy: 0.842400
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.634437 Loss1: 0.866398 Loss2: 0.768039
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.473740 Loss1: 0.811159 Loss2: 0.662581
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.417314 Loss1: 0.761858 Loss2: 0.655456
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.424200 Loss1: 0.768739 Loss2: 0.655461
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.425411 Loss1: 0.765250 Loss2: 0.660161
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.413614 Loss1: 0.754931 Loss2: 0.658683
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.394052 Loss1: 0.733688 Loss2: 0.660364
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.379506 Loss1: 0.718253 Loss2: 0.661252
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.377109 Loss1: 0.717010 Loss2: 0.660099
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.383837 Loss1: 0.722488 Loss2: 0.661349
+(DefaultActor pid=1831567) >> Training accuracy: 0.746162
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.649027 Loss1: 0.851455 Loss2: 0.797572
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.519058 Loss1: 0.821664 Loss2: 0.697394
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.507567 Loss1: 0.810715 Loss2: 0.696852
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.520169 Loss1: 0.820612 Loss2: 0.699557
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.488859 Loss1: 0.790108 Loss2: 0.698752
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.497016 Loss1: 0.797458 Loss2: 0.699559
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.471984 Loss1: 0.775325 Loss2: 0.696659
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.459293 Loss1: 0.762600 Loss2: 0.696694
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.435714 Loss1: 0.739179 Loss2: 0.696535
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.444690 Loss1: 0.746173 Loss2: 0.698516
+(DefaultActor pid=1831567) >> Training accuracy: 0.739972
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485746 Loss1: 0.738720 Loss2: 0.747025
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304453 Loss1: 0.661628 Loss2: 0.642825
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.274540 Loss1: 0.636358 Loss2: 0.638182
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.298535 Loss1: 0.656770 Loss2: 0.641766
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.241287 Loss1: 0.601358 Loss2: 0.639928
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.246407 Loss1: 0.606669 Loss2: 0.639738
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243657 Loss1: 0.604333 Loss2: 0.639325
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.239341 Loss1: 0.597032 Loss2: 0.642309
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.203484 Loss1: 0.562275 Loss2: 0.641208
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.247621 Loss1: 0.599034 Loss2: 0.648586
+(DefaultActor pid=1831567) >> Training accuracy: 0.789989
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459273 Loss1: 0.730783 Loss2: 0.728490
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.360275 Loss1: 0.684454 Loss2: 0.675822
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.352184 Loss1: 0.674871 Loss2: 0.677313
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.342144 Loss1: 0.665283 Loss2: 0.676861
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.350099 Loss1: 0.672423 Loss2: 0.677676
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.346626 Loss1: 0.666596 Loss2: 0.680030
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.346859 Loss1: 0.665236 Loss2: 0.681623
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.351183 Loss1: 0.670503 Loss2: 0.680681
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.317445 Loss1: 0.638158 Loss2: 0.679288
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.324071 Loss1: 0.643905 Loss2: 0.680166
+(DefaultActor pid=1831567) >> Training accuracy: 0.782242
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.512058 Loss1: 0.758228 Loss2: 0.753829
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367199 Loss1: 0.682605 Loss2: 0.684594
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.333657 Loss1: 0.651108 Loss2: 0.682549
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.373510 Loss1: 0.686735 Loss2: 0.686775
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.324833 Loss1: 0.640880 Loss2: 0.683953
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.320383 Loss1: 0.633824 Loss2: 0.686560
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.324016 Loss1: 0.636108 Loss2: 0.687908
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.321285 Loss1: 0.633291 Loss2: 0.687994
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.304356 Loss1: 0.616832 Loss2: 0.687524
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307717 Loss1: 0.619022 Loss2: 0.688695
+(DefaultActor pid=1831567) >> Training accuracy: 0.772294
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.671069 Loss1: 0.927964 Loss2: 0.743106
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.538919 Loss1: 0.873850 Loss2: 0.665069
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.515793 Loss1: 0.848919 Loss2: 0.666874
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.501310 Loss1: 0.834527 Loss2: 0.666782
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.497779 Loss1: 0.832535 Loss2: 0.665243
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.502838 Loss1: 0.835603 Loss2: 0.667236
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.497056 Loss1: 0.826444 Loss2: 0.670612
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.500920 Loss1: 0.830690 Loss2: 0.670230
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.492077 Loss1: 0.823204 Loss2: 0.668874
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.468825 Loss1: 0.797736 Loss2: 0.671089
+(DefaultActor pid=1831567) >> Training accuracy: 0.694520
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481080 Loss1: 0.721209 Loss2: 0.759872
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367179 Loss1: 0.686680 Loss2: 0.680498
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.340255 Loss1: 0.662057 Loss2: 0.678198
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347622 Loss1: 0.671529 Loss2: 0.676093
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.291219 Loss1: 0.612249 Loss2: 0.678970
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.309928 Loss1: 0.630421 Loss2: 0.679507
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.307826 Loss1: 0.628640 Loss2: 0.679186
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.302722 Loss1: 0.621346 Loss2: 0.681376
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.308212 Loss1: 0.627367 Loss2: 0.680846
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.297546 Loss1: 0.616054 Loss2: 0.681492
+(DefaultActor pid=1831567) >> Training accuracy: 0.805921
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.506695 Loss1: 0.728798 Loss2: 0.777897
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.423015 Loss1: 0.723521 Loss2: 0.699494
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.379241 Loss1: 0.687310 Loss2: 0.691932
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.376259 Loss1: 0.679058 Loss2: 0.697201
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.357088 Loss1: 0.659761 Loss2: 0.697327
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.344303 Loss1: 0.647355 Loss2: 0.696948
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.351449 Loss1: 0.651099 Loss2: 0.700351
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.365707 Loss1: 0.668268 Loss2: 0.697439
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.313922 Loss1: 0.615592 Loss2: 0.698330
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329115 Loss1: 0.630841 Loss2: 0.698275
+(DefaultActor pid=1831567) >> Training accuracy: 0.797075
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.303284 Loss1: 0.578314 Loss2: 0.724970
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.178052 Loss1: 0.530455 Loss2: 0.647597
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.170699 Loss1: 0.522154 Loss2: 0.648545
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.143719 Loss1: 0.498636 Loss2: 0.645082
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.157930 Loss1: 0.509427 Loss2: 0.648502
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.125417 Loss1: 0.479466 Loss2: 0.645951
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122363 Loss1: 0.476899 Loss2: 0.645464
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.135759 Loss1: 0.487154 Loss2: 0.648605
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.131177 Loss1: 0.484722 Loss2: 0.646455
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107997 Loss1: 0.459113 Loss2: 0.648885
+[2023-09-27 08:14:07,039][flwr][DEBUG] - fit_round 15 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.843171
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.635800
+[2023-09-27 08:14:08,587][flwr][INFO] - fit progress: (15, 1.022295751796363, {'accuracy': 0.6358}, 6981.423728279769)
+[2023-09-27 08:14:08,588][flwr][DEBUG] - evaluate_round 15: strategy sampled 10 clients (out of 10)
+[2023-09-27 08:14:39,673][flwr][DEBUG] - evaluate_round 15 received 10 results and 0 failures
+[2023-09-27 08:14:39,673][flwr][DEBUG] - fit_round 16: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.679300 Loss1: 0.870646 Loss2: 0.808654
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.476782 Loss1: 0.789699 Loss2: 0.687083
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.460519 Loss1: 0.777455 Loss2: 0.683064
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.423578 Loss1: 0.742671 Loss2: 0.680907
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.436440 Loss1: 0.755765 Loss2: 0.680675
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.418655 Loss1: 0.735685 Loss2: 0.682970
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.417971 Loss1: 0.733385 Loss2: 0.684586
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.404048 Loss1: 0.719611 Loss2: 0.684436
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.383729 Loss1: 0.699428 Loss2: 0.684300
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.406373 Loss1: 0.719506 Loss2: 0.686867
+(DefaultActor pid=1831567) >> Training accuracy: 0.755208
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.675010 Loss1: 0.879926 Loss2: 0.795085
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.501250 Loss1: 0.821465 Loss2: 0.679785
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.490082 Loss1: 0.809959 Loss2: 0.680123
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.460390 Loss1: 0.783580 Loss2: 0.676810
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.485269 Loss1: 0.806837 Loss2: 0.678432
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.473281 Loss1: 0.792890 Loss2: 0.680391
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.452693 Loss1: 0.773374 Loss2: 0.679319
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.430596 Loss1: 0.751591 Loss2: 0.679005
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.455989 Loss1: 0.774810 Loss2: 0.681179
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.421739 Loss1: 0.742066 Loss2: 0.679674
+(DefaultActor pid=1831567) >> Training accuracy: 0.715019
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.474118 Loss1: 0.714576 Loss2: 0.759542
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.344134 Loss1: 0.678558 Loss2: 0.665576
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.320278 Loss1: 0.657115 Loss2: 0.663163
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.313361 Loss1: 0.649087 Loss2: 0.664275
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.301806 Loss1: 0.636089 Loss2: 0.665717
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303273 Loss1: 0.635803 Loss2: 0.667470
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.266526 Loss1: 0.600329 Loss2: 0.666197
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.286588 Loss1: 0.620047 Loss2: 0.666542
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.265914 Loss1: 0.596253 Loss2: 0.669661
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284088 Loss1: 0.614155 Loss2: 0.669933
+(DefaultActor pid=1831567) >> Training accuracy: 0.806127
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.503335 Loss1: 0.699654 Loss2: 0.803681
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347137 Loss1: 0.650245 Loss2: 0.696892
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.324682 Loss1: 0.633289 Loss2: 0.691393
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.295266 Loss1: 0.604412 Loss2: 0.690853
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.312995 Loss1: 0.620185 Loss2: 0.692810
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.305567 Loss1: 0.610004 Loss2: 0.695563
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298382 Loss1: 0.604399 Loss2: 0.693983
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.272986 Loss1: 0.578610 Loss2: 0.694376
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.277843 Loss1: 0.581392 Loss2: 0.696450
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.278657 Loss1: 0.581970 Loss2: 0.696687
+(DefaultActor pid=1831567) >> Training accuracy: 0.806939
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.506062 Loss1: 0.746045 Loss2: 0.760017
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.385900 Loss1: 0.708708 Loss2: 0.677192
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.372595 Loss1: 0.696498 Loss2: 0.676097
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.365435 Loss1: 0.689082 Loss2: 0.676352
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.347490 Loss1: 0.672396 Loss2: 0.675094
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.321883 Loss1: 0.644288 Loss2: 0.677595
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.304668 Loss1: 0.626217 Loss2: 0.678451
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334637 Loss1: 0.656455 Loss2: 0.678182
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.330959 Loss1: 0.648990 Loss2: 0.681969
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.306636 Loss1: 0.626346 Loss2: 0.680290
+(DefaultActor pid=1831567) >> Training accuracy: 0.798478
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.366328 Loss1: 0.582416 Loss2: 0.783912
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216539 Loss1: 0.532543 Loss2: 0.683997
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.177819 Loss1: 0.502110 Loss2: 0.675709
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.179255 Loss1: 0.501662 Loss2: 0.677594
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158795 Loss1: 0.484028 Loss2: 0.674767
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.155557 Loss1: 0.479851 Loss2: 0.675706
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.144943 Loss1: 0.467996 Loss2: 0.676947
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140414 Loss1: 0.461573 Loss2: 0.678841
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.146416 Loss1: 0.467862 Loss2: 0.678554
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.131136 Loss1: 0.452019 Loss2: 0.679117
+(DefaultActor pid=1831567) >> Training accuracy: 0.844136
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.482374 Loss1: 0.742112 Loss2: 0.740263
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.333709 Loss1: 0.699006 Loss2: 0.634703
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.289127 Loss1: 0.658140 Loss2: 0.630987
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.285301 Loss1: 0.653592 Loss2: 0.631709
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.277492 Loss1: 0.648477 Loss2: 0.629015
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.273321 Loss1: 0.641984 Loss2: 0.631336
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.265789 Loss1: 0.634307 Loss2: 0.631482
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273173 Loss1: 0.641437 Loss2: 0.631736
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239072 Loss1: 0.607578 Loss2: 0.631495
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.265827 Loss1: 0.632670 Loss2: 0.633156
+(DefaultActor pid=1831567) >> Training accuracy: 0.789444
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481886 Loss1: 0.728465 Loss2: 0.753420
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387081 Loss1: 0.690024 Loss2: 0.697058
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363992 Loss1: 0.668120 Loss2: 0.695872
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.359406 Loss1: 0.664785 Loss2: 0.694621
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.343113 Loss1: 0.649593 Loss2: 0.693520
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.351872 Loss1: 0.655729 Loss2: 0.696143
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.355378 Loss1: 0.657794 Loss2: 0.697584
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.336852 Loss1: 0.639654 Loss2: 0.697198
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.343467 Loss1: 0.645090 Loss2: 0.698376
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.335633 Loss1: 0.634385 Loss2: 0.701248
+(DefaultActor pid=1831567) >> Training accuracy: 0.780754
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.727581 Loss1: 0.910705 Loss2: 0.816876
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.591995 Loss1: 0.881009 Loss2: 0.710986
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.541183 Loss1: 0.833164 Loss2: 0.708019
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.543076 Loss1: 0.832549 Loss2: 0.710527
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.525178 Loss1: 0.813727 Loss2: 0.711450
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.548125 Loss1: 0.830855 Loss2: 0.717270
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.531694 Loss1: 0.818604 Loss2: 0.713091
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.525901 Loss1: 0.811439 Loss2: 0.714462
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.492925 Loss1: 0.781132 Loss2: 0.711794
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.514526 Loss1: 0.799138 Loss2: 0.715388
+(DefaultActor pid=1831567) >> Training accuracy: 0.717844
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.351156 Loss1: 0.575299 Loss2: 0.775857
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200833 Loss1: 0.518616 Loss2: 0.682218
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.164630 Loss1: 0.488147 Loss2: 0.676484
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165934 Loss1: 0.486553 Loss2: 0.679381
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167519 Loss1: 0.489993 Loss2: 0.677526
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.142958 Loss1: 0.464458 Loss2: 0.678500
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148333 Loss1: 0.471065 Loss2: 0.677268
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.141742 Loss1: 0.462617 Loss2: 0.679125
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.164072 Loss1: 0.483210 Loss2: 0.680862
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.117318 Loss1: 0.437872 Loss2: 0.679446
+(DefaultActor pid=1831567) >> Training accuracy: 0.838735
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 08:21:35,561][flwr][DEBUG] - fit_round 16 received 10 results and 0 failures
+>> Test accuracy: 0.640900
+[2023-09-27 08:21:36,986][flwr][INFO] - fit progress: (16, 1.0062863030753577, {'accuracy': 0.6409}, 7429.822125886101)
+[2023-09-27 08:21:36,986][flwr][DEBUG] - evaluate_round 16: strategy sampled 10 clients (out of 10)
+[2023-09-27 08:22:09,039][flwr][DEBUG] - evaluate_round 16 received 10 results and 0 failures
+[2023-09-27 08:22:09,039][flwr][DEBUG] - fit_round 17: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.602871 Loss1: 0.846304 Loss2: 0.756567
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.473130 Loss1: 0.819852 Loss2: 0.653278
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.415443 Loss1: 0.763989 Loss2: 0.651454
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.405471 Loss1: 0.754470 Loss2: 0.651001
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.389351 Loss1: 0.739753 Loss2: 0.649598
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.382027 Loss1: 0.733686 Loss2: 0.648341
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.413846 Loss1: 0.761120 Loss2: 0.652726
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.355947 Loss1: 0.702086 Loss2: 0.653861
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.356272 Loss1: 0.704646 Loss2: 0.651626
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.371418 Loss1: 0.718447 Loss2: 0.652971
+(DefaultActor pid=1831567) >> Training accuracy: 0.763706
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.328250 Loss1: 0.570238 Loss2: 0.758012
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187072 Loss1: 0.510571 Loss2: 0.676501
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.180068 Loss1: 0.502769 Loss2: 0.677299
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165866 Loss1: 0.488759 Loss2: 0.677107
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.149205 Loss1: 0.471546 Loss2: 0.677659
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162787 Loss1: 0.485592 Loss2: 0.677195
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.143947 Loss1: 0.465983 Loss2: 0.677963
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.137620 Loss1: 0.460581 Loss2: 0.677039
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.144958 Loss1: 0.464258 Loss2: 0.680700
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.117051 Loss1: 0.437716 Loss2: 0.679335
+(DefaultActor pid=1831567) >> Training accuracy: 0.856096
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289048 Loss1: 0.557847 Loss2: 0.731201
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.160928 Loss1: 0.501013 Loss2: 0.659915
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.154063 Loss1: 0.496991 Loss2: 0.657072
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.164212 Loss1: 0.503326 Loss2: 0.660887
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.135842 Loss1: 0.482029 Loss2: 0.653813
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.118217 Loss1: 0.460396 Loss2: 0.657820
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.125671 Loss1: 0.468583 Loss2: 0.657088
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.129971 Loss1: 0.474291 Loss2: 0.655680
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.131991 Loss1: 0.472841 Loss2: 0.659149
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.118333 Loss1: 0.462938 Loss2: 0.655396
+(DefaultActor pid=1831567) >> Training accuracy: 0.813657
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495839 Loss1: 0.738307 Loss2: 0.757532
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347937 Loss1: 0.674049 Loss2: 0.673888
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318235 Loss1: 0.644805 Loss2: 0.673430
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.336807 Loss1: 0.661931 Loss2: 0.674876
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345347 Loss1: 0.669575 Loss2: 0.675771
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.315317 Loss1: 0.635160 Loss2: 0.680158
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.330035 Loss1: 0.650533 Loss2: 0.679502
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.308053 Loss1: 0.631286 Loss2: 0.676767
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.316624 Loss1: 0.640318 Loss2: 0.676306
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.295729 Loss1: 0.615132 Loss2: 0.680597
+(DefaultActor pid=1831567) >> Training accuracy: 0.802284
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.390078 Loss1: 0.708281 Loss2: 0.681797
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.311005 Loss1: 0.678565 Loss2: 0.632440
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.286772 Loss1: 0.654333 Loss2: 0.632439
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.292880 Loss1: 0.659090 Loss2: 0.633790
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.281745 Loss1: 0.646796 Loss2: 0.634949
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.286572 Loss1: 0.654517 Loss2: 0.632055
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.288557 Loss1: 0.654113 Loss2: 0.634444
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.285097 Loss1: 0.649628 Loss2: 0.635469
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.278100 Loss1: 0.640642 Loss2: 0.637459
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279626 Loss1: 0.639307 Loss2: 0.640318
+(DefaultActor pid=1831567) >> Training accuracy: 0.786582
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.630215 Loss1: 0.855710 Loss2: 0.774505
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.512468 Loss1: 0.832825 Loss2: 0.679643
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.470245 Loss1: 0.793739 Loss2: 0.676506
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.453196 Loss1: 0.778471 Loss2: 0.674725
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.466882 Loss1: 0.792012 Loss2: 0.674871
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.435821 Loss1: 0.757554 Loss2: 0.678268
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.418832 Loss1: 0.743141 Loss2: 0.675691
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.429565 Loss1: 0.751865 Loss2: 0.677700
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.419340 Loss1: 0.738714 Loss2: 0.680626
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.411394 Loss1: 0.732933 Loss2: 0.678461
+(DefaultActor pid=1831567) >> Training accuracy: 0.736940
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.463794 Loss1: 0.722100 Loss2: 0.741695
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332807 Loss1: 0.672097 Loss2: 0.660710
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.320879 Loss1: 0.659760 Loss2: 0.661119
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.288493 Loss1: 0.625961 Loss2: 0.662532
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.297089 Loss1: 0.631605 Loss2: 0.665484
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.280411 Loss1: 0.615663 Loss2: 0.664748
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.285947 Loss1: 0.620125 Loss2: 0.665822
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289646 Loss1: 0.622947 Loss2: 0.666699
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.287960 Loss1: 0.619192 Loss2: 0.668769
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.252946 Loss1: 0.586533 Loss2: 0.666413
+(DefaultActor pid=1831567) >> Training accuracy: 0.815378
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.474167 Loss1: 0.717688 Loss2: 0.756479
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.302376 Loss1: 0.647996 Loss2: 0.654380
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.274947 Loss1: 0.621852 Loss2: 0.653096
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.297508 Loss1: 0.644374 Loss2: 0.653133
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.269013 Loss1: 0.616888 Loss2: 0.652125
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.267916 Loss1: 0.611825 Loss2: 0.656091
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.246518 Loss1: 0.590489 Loss2: 0.656029
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212040 Loss1: 0.562038 Loss2: 0.650003
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.238960 Loss1: 0.583156 Loss2: 0.655803
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229736 Loss1: 0.571373 Loss2: 0.658364
+(DefaultActor pid=1831567) >> Training accuracy: 0.804025
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517329 Loss1: 0.741636 Loss2: 0.775693
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.392263 Loss1: 0.685922 Loss2: 0.706342
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.396239 Loss1: 0.688214 Loss2: 0.708025
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.359762 Loss1: 0.653764 Loss2: 0.705998
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.342426 Loss1: 0.634847 Loss2: 0.707578
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.338909 Loss1: 0.631570 Loss2: 0.707339
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.327554 Loss1: 0.618958 Loss2: 0.708596
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.315391 Loss1: 0.608085 Loss2: 0.707306
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.314083 Loss1: 0.603700 Loss2: 0.710382
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.311512 Loss1: 0.600576 Loss2: 0.710936
+(DefaultActor pid=1831567) >> Training accuracy: 0.788681
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.662025 Loss1: 0.903206 Loss2: 0.758820
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.534640 Loss1: 0.858949 Loss2: 0.675690
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.519827 Loss1: 0.841637 Loss2: 0.678190
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.522024 Loss1: 0.844181 Loss2: 0.677843
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.480295 Loss1: 0.803909 Loss2: 0.676385
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.486443 Loss1: 0.807269 Loss2: 0.679174
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.477285 Loss1: 0.799672 Loss2: 0.677613
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.460142 Loss1: 0.782700 Loss2: 0.677441
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.488527 Loss1: 0.806249 Loss2: 0.682278
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.458280 Loss1: 0.779159 Loss2: 0.679121
+[2023-09-27 08:29:27,233][flwr][DEBUG] - fit_round 17 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.697237
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.637900
+[2023-09-27 08:29:28,909][flwr][INFO] - fit progress: (17, 1.0163589238930053, {'accuracy': 0.6379}, 7901.745227077976)
+[2023-09-27 08:29:28,909][flwr][DEBUG] - evaluate_round 17: strategy sampled 10 clients (out of 10)
+[2023-09-27 08:30:01,490][flwr][DEBUG] - evaluate_round 17 received 10 results and 0 failures
+[2023-09-27 08:30:01,491][flwr][DEBUG] - fit_round 18: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.490662 Loss1: 0.730779 Loss2: 0.759882
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.348507 Loss1: 0.674206 Loss2: 0.674301
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.325600 Loss1: 0.654589 Loss2: 0.671011
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339310 Loss1: 0.661674 Loss2: 0.677637
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.305527 Loss1: 0.632610 Loss2: 0.672917
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.338161 Loss1: 0.659299 Loss2: 0.678862
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.313923 Loss1: 0.638229 Loss2: 0.675694
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.309916 Loss1: 0.633993 Loss2: 0.675923
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299823 Loss1: 0.623635 Loss2: 0.676188
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281868 Loss1: 0.605517 Loss2: 0.676351
+(DefaultActor pid=1831567) >> Training accuracy: 0.798678
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.660648 Loss1: 0.837632 Loss2: 0.823016
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.454964 Loss1: 0.756945 Loss2: 0.698019
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.432062 Loss1: 0.740191 Loss2: 0.691871
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.450487 Loss1: 0.753346 Loss2: 0.697141
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.442939 Loss1: 0.745275 Loss2: 0.697663
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.405409 Loss1: 0.713163 Loss2: 0.692246
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.415683 Loss1: 0.719498 Loss2: 0.696185
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.392359 Loss1: 0.693967 Loss2: 0.698392
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.397315 Loss1: 0.696532 Loss2: 0.700783
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.392206 Loss1: 0.689842 Loss2: 0.702364
+(DefaultActor pid=1831567) >> Training accuracy: 0.767544
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.640886 Loss1: 0.855296 Loss2: 0.785590
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.461817 Loss1: 0.787278 Loss2: 0.674539
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.454200 Loss1: 0.781606 Loss2: 0.672594
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.431244 Loss1: 0.760112 Loss2: 0.671131
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.438423 Loss1: 0.765857 Loss2: 0.672566
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.416566 Loss1: 0.744379 Loss2: 0.672187
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.423859 Loss1: 0.747573 Loss2: 0.676286
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.426227 Loss1: 0.748598 Loss2: 0.677630
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.415863 Loss1: 0.740014 Loss2: 0.675849
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.433754 Loss1: 0.757505 Loss2: 0.676248
+(DefaultActor pid=1831567) >> Training accuracy: 0.735541
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485117 Loss1: 0.725170 Loss2: 0.759948
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.313295 Loss1: 0.648392 Loss2: 0.664903
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.292939 Loss1: 0.630022 Loss2: 0.662916
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.288303 Loss1: 0.623664 Loss2: 0.664639
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.297463 Loss1: 0.632964 Loss2: 0.664498
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.308015 Loss1: 0.641040 Loss2: 0.666974
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.282158 Loss1: 0.613516 Loss2: 0.668642
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.295707 Loss1: 0.624751 Loss2: 0.670956
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257023 Loss1: 0.587492 Loss2: 0.669532
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.261797 Loss1: 0.590425 Loss2: 0.671372
+(DefaultActor pid=1831567) >> Training accuracy: 0.810855
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.700803 Loss1: 0.912897 Loss2: 0.787905
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.537293 Loss1: 0.854019 Loss2: 0.683273
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.505722 Loss1: 0.824960 Loss2: 0.680761
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.515926 Loss1: 0.835358 Loss2: 0.680568
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.485915 Loss1: 0.803965 Loss2: 0.681950
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.474077 Loss1: 0.792083 Loss2: 0.681994
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.460124 Loss1: 0.777241 Loss2: 0.682883
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.466878 Loss1: 0.783629 Loss2: 0.683249
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.468315 Loss1: 0.782952 Loss2: 0.685363
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.472552 Loss1: 0.785676 Loss2: 0.686877
+(DefaultActor pid=1831567) >> Training accuracy: 0.742074
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.465585 Loss1: 0.693322 Loss2: 0.772263
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.403285 Loss1: 0.684616 Loss2: 0.718669
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.373976 Loss1: 0.656280 Loss2: 0.717696
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.358104 Loss1: 0.641360 Loss2: 0.716744
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.363668 Loss1: 0.644388 Loss2: 0.719281
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362215 Loss1: 0.645202 Loss2: 0.717013
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.341331 Loss1: 0.623163 Loss2: 0.718169
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347990 Loss1: 0.627738 Loss2: 0.720253
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.353386 Loss1: 0.633358 Loss2: 0.720027
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.354316 Loss1: 0.633821 Loss2: 0.720495
+(DefaultActor pid=1831567) >> Training accuracy: 0.797495
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.478018 Loss1: 0.718571 Loss2: 0.759447
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.357720 Loss1: 0.694680 Loss2: 0.663040
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334312 Loss1: 0.676662 Loss2: 0.657650
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.286466 Loss1: 0.633591 Loss2: 0.652875
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.280485 Loss1: 0.626326 Loss2: 0.654159
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.283401 Loss1: 0.628311 Loss2: 0.655090
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.267164 Loss1: 0.613256 Loss2: 0.653908
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273329 Loss1: 0.616928 Loss2: 0.656401
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.256371 Loss1: 0.602023 Loss2: 0.654348
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.267762 Loss1: 0.609365 Loss2: 0.658398
+(DefaultActor pid=1831567) >> Training accuracy: 0.782393
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360928 Loss1: 0.571273 Loss2: 0.789655
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.193475 Loss1: 0.507807 Loss2: 0.685668
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183329 Loss1: 0.498893 Loss2: 0.684437
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.166732 Loss1: 0.480644 Loss2: 0.686088
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.157856 Loss1: 0.477021 Loss2: 0.680834
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.130804 Loss1: 0.447563 Loss2: 0.683241
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.127931 Loss1: 0.443124 Loss2: 0.684808
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154397 Loss1: 0.469563 Loss2: 0.684834
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.142583 Loss1: 0.454490 Loss2: 0.688092
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.127077 Loss1: 0.441939 Loss2: 0.685138
+(DefaultActor pid=1831567) >> Training accuracy: 0.856674
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.526807 Loss1: 0.696622 Loss2: 0.830186
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.321581 Loss1: 0.609247 Loss2: 0.712334
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.326288 Loss1: 0.613274 Loss2: 0.713014
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316966 Loss1: 0.606982 Loss2: 0.709984
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.301117 Loss1: 0.593547 Loss2: 0.707570
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.295480 Loss1: 0.584095 Loss2: 0.711385
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.291882 Loss1: 0.583105 Loss2: 0.708777
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.275009 Loss1: 0.563587 Loss2: 0.711422
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.271725 Loss1: 0.558853 Loss2: 0.712872
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.243688 Loss1: 0.532628 Loss2: 0.711060
+(DefaultActor pid=1831567) >> Training accuracy: 0.815413
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.287726 Loss1: 0.543158 Loss2: 0.744568
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.179410 Loss1: 0.517956 Loss2: 0.661454
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.134587 Loss1: 0.480033 Loss2: 0.654554
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.138706 Loss1: 0.486930 Loss2: 0.651775
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.122237 Loss1: 0.470926 Loss2: 0.651312
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.109997 Loss1: 0.458613 Loss2: 0.651384
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.099017 Loss1: 0.446339 Loss2: 0.652678
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.101098 Loss1: 0.448405 Loss2: 0.652693
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.094473 Loss1: 0.441671 Loss2: 0.652802
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.120724 Loss1: 0.463026 Loss2: 0.657698
+(DefaultActor pid=1831567) >> Training accuracy: 0.845100
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 08:37:08,247][flwr][DEBUG] - fit_round 18 received 10 results and 0 failures
+>> Test accuracy: 0.645500
+[2023-09-27 08:37:09,722][flwr][INFO] - fit progress: (18, 0.9984818176149179, {'accuracy': 0.6455}, 8362.55843326496)
+[2023-09-27 08:37:09,722][flwr][DEBUG] - evaluate_round 18: strategy sampled 10 clients (out of 10)
+[2023-09-27 08:37:41,408][flwr][DEBUG] - evaluate_round 18 received 10 results and 0 failures
+[2023-09-27 08:37:41,409][flwr][DEBUG] - fit_round 19: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.447308 Loss1: 0.706907 Loss2: 0.740401
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.317943 Loss1: 0.657888 Loss2: 0.660055
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.310639 Loss1: 0.649833 Loss2: 0.660806
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.284068 Loss1: 0.625043 Loss2: 0.659025
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.294914 Loss1: 0.634652 Loss2: 0.660262
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.264590 Loss1: 0.606447 Loss2: 0.658143
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.262209 Loss1: 0.598638 Loss2: 0.663571
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.260502 Loss1: 0.598272 Loss2: 0.662229
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230508 Loss1: 0.567969 Loss2: 0.662539
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.258453 Loss1: 0.596465 Loss2: 0.661988
+(DefaultActor pid=1831567) >> Training accuracy: 0.813939
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326442 Loss1: 0.574461 Loss2: 0.751981
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.163242 Loss1: 0.497494 Loss2: 0.665748
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.124650 Loss1: 0.458949 Loss2: 0.665701
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.133593 Loss1: 0.468188 Loss2: 0.665405
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.122937 Loss1: 0.456914 Loss2: 0.666023
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.136470 Loss1: 0.467835 Loss2: 0.668635
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123320 Loss1: 0.456278 Loss2: 0.667041
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.123156 Loss1: 0.453604 Loss2: 0.669552
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.119037 Loss1: 0.451288 Loss2: 0.667749
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.086199 Loss1: 0.415051 Loss2: 0.671147
+(DefaultActor pid=1831567) >> Training accuracy: 0.861111
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.399242 Loss1: 0.691203 Loss2: 0.708039
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.318754 Loss1: 0.663555 Loss2: 0.655199
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.306266 Loss1: 0.650487 Loss2: 0.655780
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.278725 Loss1: 0.625370 Loss2: 0.653355
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293244 Loss1: 0.638976 Loss2: 0.654268
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.281363 Loss1: 0.624942 Loss2: 0.656421
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.287762 Loss1: 0.629804 Loss2: 0.657958
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.272129 Loss1: 0.615569 Loss2: 0.656560
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.286089 Loss1: 0.625098 Loss2: 0.660991
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.278309 Loss1: 0.619426 Loss2: 0.658883
+(DefaultActor pid=1831567) >> Training accuracy: 0.793899
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.294768 Loss1: 0.543600 Loss2: 0.751168
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.174185 Loss1: 0.499060 Loss2: 0.675125
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.144574 Loss1: 0.475951 Loss2: 0.668623
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144923 Loss1: 0.475189 Loss2: 0.669733
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.138160 Loss1: 0.467899 Loss2: 0.670261
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.114237 Loss1: 0.445738 Loss2: 0.668499
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122970 Loss1: 0.454542 Loss2: 0.668428
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.128622 Loss1: 0.456912 Loss2: 0.671710
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.118420 Loss1: 0.447918 Loss2: 0.670502
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.102246 Loss1: 0.430340 Loss2: 0.671906
+(DefaultActor pid=1831567) >> Training accuracy: 0.846836
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.453915 Loss1: 0.686066 Loss2: 0.767849
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346406 Loss1: 0.658568 Loss2: 0.687838
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356750 Loss1: 0.665224 Loss2: 0.691526
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339694 Loss1: 0.648894 Loss2: 0.690800
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.321220 Loss1: 0.631087 Loss2: 0.690133
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.327552 Loss1: 0.636686 Loss2: 0.690866
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.326879 Loss1: 0.632074 Loss2: 0.694804
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.299315 Loss1: 0.607090 Loss2: 0.692224
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.289947 Loss1: 0.596084 Loss2: 0.693863
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.300922 Loss1: 0.606967 Loss2: 0.693954
+(DefaultActor pid=1831567) >> Training accuracy: 0.808093
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.623506 Loss1: 0.862874 Loss2: 0.760632
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.535536 Loss1: 0.855057 Loss2: 0.680479
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.532056 Loss1: 0.852763 Loss2: 0.679293
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.492094 Loss1: 0.814142 Loss2: 0.677952
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.497565 Loss1: 0.818015 Loss2: 0.679550
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.487896 Loss1: 0.804792 Loss2: 0.683104
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.492773 Loss1: 0.809416 Loss2: 0.683357
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.444985 Loss1: 0.764181 Loss2: 0.680805
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.483467 Loss1: 0.797339 Loss2: 0.686128
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.463083 Loss1: 0.779404 Loss2: 0.683679
+(DefaultActor pid=1831567) >> Training accuracy: 0.732111
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.444237 Loss1: 0.699368 Loss2: 0.744869
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.269748 Loss1: 0.630093 Loss2: 0.639655
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251043 Loss1: 0.612364 Loss2: 0.638679
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.233567 Loss1: 0.595816 Loss2: 0.637751
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.217482 Loss1: 0.580007 Loss2: 0.637475
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226735 Loss1: 0.587145 Loss2: 0.639590
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.246018 Loss1: 0.602053 Loss2: 0.643964
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.217220 Loss1: 0.576671 Loss2: 0.640549
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196875 Loss1: 0.556891 Loss2: 0.639983
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.195424 Loss1: 0.552841 Loss2: 0.642583
+(DefaultActor pid=1831567) >> Training accuracy: 0.812500
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.636434 Loss1: 0.854367 Loss2: 0.782067
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.421876 Loss1: 0.750704 Loss2: 0.671172
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.423106 Loss1: 0.751620 Loss2: 0.671486
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.443349 Loss1: 0.768752 Loss2: 0.674597
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.380581 Loss1: 0.709079 Loss2: 0.671501
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.373890 Loss1: 0.698838 Loss2: 0.675052
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.373786 Loss1: 0.700208 Loss2: 0.673578
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.372446 Loss1: 0.698373 Loss2: 0.674072
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.376551 Loss1: 0.702150 Loss2: 0.674401
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.401844 Loss1: 0.723802 Loss2: 0.678042
+(DefaultActor pid=1831567) >> Training accuracy: 0.754112
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459279 Loss1: 0.716750 Loss2: 0.742529
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332403 Loss1: 0.659466 Loss2: 0.672937
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.315148 Loss1: 0.641674 Loss2: 0.673475
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291254 Loss1: 0.618323 Loss2: 0.672931
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.311324 Loss1: 0.635065 Loss2: 0.676259
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.294753 Loss1: 0.618616 Loss2: 0.676137
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.300353 Loss1: 0.621492 Loss2: 0.678861
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.291479 Loss1: 0.615327 Loss2: 0.676152
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.269529 Loss1: 0.591039 Loss2: 0.678489
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.271777 Loss1: 0.592158 Loss2: 0.679619
+(DefaultActor pid=1831567) >> Training accuracy: 0.789253
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.628972 Loss1: 0.869221 Loss2: 0.759751
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.477548 Loss1: 0.813932 Loss2: 0.663617
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.429877 Loss1: 0.768675 Loss2: 0.661203
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.419497 Loss1: 0.758834 Loss2: 0.660663
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.397369 Loss1: 0.736081 Loss2: 0.661288
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.435392 Loss1: 0.770308 Loss2: 0.665084
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.411979 Loss1: 0.747169 Loss2: 0.664810
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.378119 Loss1: 0.715275 Loss2: 0.662844
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.391234 Loss1: 0.726463 Loss2: 0.664771
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.402201 Loss1: 0.735888 Loss2: 0.666313
+[2023-09-27 08:44:36,522][flwr][DEBUG] - fit_round 19 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.732976
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.647400
+[2023-09-27 08:44:38,422][flwr][INFO] - fit progress: (19, 1.0004730579761651, {'accuracy': 0.6474}, 8811.258530317806)
+[2023-09-27 08:44:38,423][flwr][DEBUG] - evaluate_round 19: strategy sampled 10 clients (out of 10)
+[2023-09-27 08:45:15,962][flwr][DEBUG] - evaluate_round 19 received 10 results and 0 failures
+[2023-09-27 08:45:15,963][flwr][DEBUG] - fit_round 20: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475558 Loss1: 0.714390 Loss2: 0.761168
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.321916 Loss1: 0.646287 Loss2: 0.675630
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.300586 Loss1: 0.627348 Loss2: 0.673238
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.304026 Loss1: 0.626040 Loss2: 0.677986
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282089 Loss1: 0.607012 Loss2: 0.675076
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.273291 Loss1: 0.597064 Loss2: 0.676227
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.283280 Loss1: 0.605801 Loss2: 0.677478
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253943 Loss1: 0.578319 Loss2: 0.675624
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.274910 Loss1: 0.595535 Loss2: 0.679375
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259253 Loss1: 0.581962 Loss2: 0.677291
+(DefaultActor pid=1831567) >> Training accuracy: 0.820724
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.317436 Loss1: 0.552934 Loss2: 0.764502
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180813 Loss1: 0.500938 Loss2: 0.679875
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.145237 Loss1: 0.471003 Loss2: 0.674233
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.154307 Loss1: 0.482637 Loss2: 0.671670
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.133470 Loss1: 0.461388 Loss2: 0.672083
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.108877 Loss1: 0.436004 Loss2: 0.672873
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.117315 Loss1: 0.446132 Loss2: 0.671184
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.100614 Loss1: 0.427205 Loss2: 0.673409
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.111074 Loss1: 0.438244 Loss2: 0.672829
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.114337 Loss1: 0.437237 Loss2: 0.677099
+(DefaultActor pid=1831567) >> Training accuracy: 0.852623
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.617051 Loss1: 0.846284 Loss2: 0.770767
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.454355 Loss1: 0.790653 Loss2: 0.663702
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.437650 Loss1: 0.775915 Loss2: 0.661735
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.449419 Loss1: 0.788065 Loss2: 0.661354
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.427814 Loss1: 0.767218 Loss2: 0.660596
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.405306 Loss1: 0.744129 Loss2: 0.661177
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.410511 Loss1: 0.746641 Loss2: 0.663870
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.437831 Loss1: 0.773538 Loss2: 0.664293
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.368969 Loss1: 0.705038 Loss2: 0.663931
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.398739 Loss1: 0.733408 Loss2: 0.665331
+(DefaultActor pid=1831567) >> Training accuracy: 0.744869
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493420 Loss1: 0.749245 Loss2: 0.744174
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.323423 Loss1: 0.685102 Loss2: 0.638321
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.261660 Loss1: 0.629320 Loss2: 0.632340
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.263530 Loss1: 0.631639 Loss2: 0.631891
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.271083 Loss1: 0.640220 Loss2: 0.630863
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.253888 Loss1: 0.623802 Loss2: 0.630086
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.227512 Loss1: 0.595335 Loss2: 0.632177
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225991 Loss1: 0.592486 Loss2: 0.633505
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.234916 Loss1: 0.600736 Loss2: 0.634180
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.230125 Loss1: 0.594411 Loss2: 0.635714
+(DefaultActor pid=1831567) >> Training accuracy: 0.805640
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.322487 Loss1: 0.549265 Loss2: 0.773222
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.186087 Loss1: 0.509583 Loss2: 0.676503
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.146879 Loss1: 0.472542 Loss2: 0.674337
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.129235 Loss1: 0.455324 Loss2: 0.673912
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.146508 Loss1: 0.472264 Loss2: 0.674245
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.153488 Loss1: 0.479960 Loss2: 0.673528
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.128491 Loss1: 0.456385 Loss2: 0.672105
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.121813 Loss1: 0.449746 Loss2: 0.672067
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.102745 Loss1: 0.427490 Loss2: 0.675255
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.104846 Loss1: 0.430248 Loss2: 0.674598
+(DefaultActor pid=1831567) >> Training accuracy: 0.855131
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.455290 Loss1: 0.680659 Loss2: 0.774631
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.364396 Loss1: 0.644320 Loss2: 0.720076
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.360581 Loss1: 0.642460 Loss2: 0.718121
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364534 Loss1: 0.644964 Loss2: 0.719570
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.352131 Loss1: 0.631881 Loss2: 0.720249
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.339303 Loss1: 0.619717 Loss2: 0.719586
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338692 Loss1: 0.616257 Loss2: 0.722435
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.346869 Loss1: 0.624827 Loss2: 0.722042
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.335061 Loss1: 0.611372 Loss2: 0.723690
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.328024 Loss1: 0.605836 Loss2: 0.722187
+(DefaultActor pid=1831567) >> Training accuracy: 0.804688
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.523219 Loss1: 0.676625 Loss2: 0.846595
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.349708 Loss1: 0.617992 Loss2: 0.731716
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.344933 Loss1: 0.618839 Loss2: 0.726095
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.320620 Loss1: 0.592252 Loss2: 0.728367
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.311998 Loss1: 0.582785 Loss2: 0.729213
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289162 Loss1: 0.563281 Loss2: 0.725882
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295283 Loss1: 0.567283 Loss2: 0.728000
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.290304 Loss1: 0.561248 Loss2: 0.729056
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.319784 Loss1: 0.586281 Loss2: 0.733503
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.292894 Loss1: 0.561853 Loss2: 0.731041
+(DefaultActor pid=1831567) >> Training accuracy: 0.805085
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.483556 Loss1: 0.715243 Loss2: 0.768313
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.342139 Loss1: 0.660573 Loss2: 0.681565
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.333776 Loss1: 0.652385 Loss2: 0.681391
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.332876 Loss1: 0.652171 Loss2: 0.680705
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.336677 Loss1: 0.655185 Loss2: 0.681491
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316247 Loss1: 0.634036 Loss2: 0.682211
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.286934 Loss1: 0.604707 Loss2: 0.682226
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.296937 Loss1: 0.614655 Loss2: 0.682282
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.293419 Loss1: 0.608893 Loss2: 0.684526
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.263578 Loss1: 0.580923 Loss2: 0.682654
+(DefaultActor pid=1831567) >> Training accuracy: 0.809095
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.620449 Loss1: 0.854440 Loss2: 0.766009
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.501821 Loss1: 0.832867 Loss2: 0.668954
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.477826 Loss1: 0.807765 Loss2: 0.670061
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.474054 Loss1: 0.805338 Loss2: 0.668715
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.474957 Loss1: 0.804547 Loss2: 0.670410
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.426158 Loss1: 0.759032 Loss2: 0.667125
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.458155 Loss1: 0.783775 Loss2: 0.674380
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.415378 Loss1: 0.745348 Loss2: 0.670030
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.453044 Loss1: 0.779311 Loss2: 0.673733
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.467334 Loss1: 0.790254 Loss2: 0.677080
+(DefaultActor pid=1831567) >> Training accuracy: 0.728261
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.625509 Loss1: 0.840579 Loss2: 0.784930
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.441576 Loss1: 0.766230 Loss2: 0.675347
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.419406 Loss1: 0.746482 Loss2: 0.672924
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.393639 Loss1: 0.719990 Loss2: 0.673649
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.393570 Loss1: 0.717661 Loss2: 0.675909
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.391018 Loss1: 0.712867 Loss2: 0.678151
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.390787 Loss1: 0.714887 Loss2: 0.675901
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.369164 Loss1: 0.690275 Loss2: 0.678889
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.376331 Loss1: 0.698832 Loss2: 0.677500
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.361776 Loss1: 0.681073 Loss2: 0.680703
+[2023-09-27 08:54:24,219][flwr][DEBUG] - fit_round 20 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.744243
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.655300
+[2023-09-27 08:54:25,959][flwr][INFO] - fit progress: (20, 0.9751454913578095, {'accuracy': 0.6553}, 9398.79502367787)
+[2023-09-27 08:54:25,959][flwr][DEBUG] - evaluate_round 20: strategy sampled 10 clients (out of 10)
+[2023-09-27 08:54:57,962][flwr][DEBUG] - evaluate_round 20 received 10 results and 0 failures
+[2023-09-27 08:54:57,963][flwr][DEBUG] - fit_round 21: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382565 Loss1: 0.679574 Loss2: 0.702991
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.286234 Loss1: 0.633651 Loss2: 0.652582
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.305601 Loss1: 0.653372 Loss2: 0.652229
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.284846 Loss1: 0.632735 Loss2: 0.652111
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.279790 Loss1: 0.627712 Loss2: 0.652078
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.281296 Loss1: 0.627335 Loss2: 0.653961
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.269737 Loss1: 0.615599 Loss2: 0.654138
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.264872 Loss1: 0.607804 Loss2: 0.657069
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.279820 Loss1: 0.623710 Loss2: 0.656110
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.264892 Loss1: 0.609386 Loss2: 0.655506
+(DefaultActor pid=1831567) >> Training accuracy: 0.780506
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.273524 Loss1: 0.545656 Loss2: 0.727868
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.126820 Loss1: 0.478000 Loss2: 0.648820
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.145095 Loss1: 0.496738 Loss2: 0.648357
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.119783 Loss1: 0.467852 Loss2: 0.651931
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.125708 Loss1: 0.472034 Loss2: 0.653674
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.102569 Loss1: 0.450289 Loss2: 0.652281
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.089994 Loss1: 0.436586 Loss2: 0.653408
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.098476 Loss1: 0.443129 Loss2: 0.655348
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.085444 Loss1: 0.431174 Loss2: 0.654270
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.070908 Loss1: 0.418826 Loss2: 0.652082
+(DefaultActor pid=1831567) >> Training accuracy: 0.858025
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.467955 Loss1: 0.707741 Loss2: 0.760214
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.353785 Loss1: 0.655240 Loss2: 0.698546
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.316765 Loss1: 0.619747 Loss2: 0.697018
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.330263 Loss1: 0.634742 Loss2: 0.695521
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.310456 Loss1: 0.611869 Loss2: 0.698587
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.304883 Loss1: 0.606703 Loss2: 0.698180
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.310401 Loss1: 0.611272 Loss2: 0.699129
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.301266 Loss1: 0.599771 Loss2: 0.701495
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299247 Loss1: 0.596787 Loss2: 0.702460
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281133 Loss1: 0.579460 Loss2: 0.701674
+(DefaultActor pid=1831567) >> Training accuracy: 0.787538
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.269926 Loss1: 0.548169 Loss2: 0.721757
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.124904 Loss1: 0.475627 Loss2: 0.649276
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.122672 Loss1: 0.476590 Loss2: 0.646082
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.112199 Loss1: 0.465094 Loss2: 0.647105
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.103010 Loss1: 0.458611 Loss2: 0.644399
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.101328 Loss1: 0.453507 Loss2: 0.647821
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.089281 Loss1: 0.441319 Loss2: 0.647961
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.091210 Loss1: 0.444451 Loss2: 0.646759
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.083601 Loss1: 0.435663 Loss2: 0.647938
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.089940 Loss1: 0.441726 Loss2: 0.648214
+(DefaultActor pid=1831567) >> Training accuracy: 0.848765
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.620086 Loss1: 0.836674 Loss2: 0.783412
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.457771 Loss1: 0.773724 Loss2: 0.684046
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.458576 Loss1: 0.773777 Loss2: 0.684799
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.429625 Loss1: 0.745448 Loss2: 0.684177
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.441306 Loss1: 0.755522 Loss2: 0.685784
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.389357 Loss1: 0.704971 Loss2: 0.684386
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.429139 Loss1: 0.743399 Loss2: 0.685740
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.405216 Loss1: 0.719332 Loss2: 0.685884
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.387869 Loss1: 0.698407 Loss2: 0.689462
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.406337 Loss1: 0.717125 Loss2: 0.689212
+(DefaultActor pid=1831567) >> Training accuracy: 0.737873
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.398209 Loss1: 0.675966 Loss2: 0.722242
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.285977 Loss1: 0.642005 Loss2: 0.643972
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.276184 Loss1: 0.631793 Loss2: 0.644391
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.249570 Loss1: 0.604733 Loss2: 0.644837
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.261189 Loss1: 0.614636 Loss2: 0.646554
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232557 Loss1: 0.584799 Loss2: 0.647758
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224113 Loss1: 0.577650 Loss2: 0.646463
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222113 Loss1: 0.573275 Loss2: 0.648838
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.226921 Loss1: 0.575943 Loss2: 0.650978
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.215758 Loss1: 0.566216 Loss2: 0.649542
+(DefaultActor pid=1831567) >> Training accuracy: 0.822985
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.390686 Loss1: 0.676123 Loss2: 0.714562
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250812 Loss1: 0.634612 Loss2: 0.616201
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205009 Loss1: 0.592853 Loss2: 0.612156
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202391 Loss1: 0.587462 Loss2: 0.614929
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192140 Loss1: 0.575866 Loss2: 0.616274
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180336 Loss1: 0.564191 Loss2: 0.616144
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.189182 Loss1: 0.572895 Loss2: 0.616287
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.183448 Loss1: 0.567574 Loss2: 0.615874
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183818 Loss1: 0.564847 Loss2: 0.618971
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.170347 Loss1: 0.552946 Loss2: 0.617401
+(DefaultActor pid=1831567) >> Training accuracy: 0.812235
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.604783 Loss1: 0.853066 Loss2: 0.751717
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.502989 Loss1: 0.829274 Loss2: 0.673715
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.491108 Loss1: 0.815669 Loss2: 0.675439
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.470218 Loss1: 0.794664 Loss2: 0.675554
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.461099 Loss1: 0.782221 Loss2: 0.678878
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.428303 Loss1: 0.751887 Loss2: 0.676416
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.471648 Loss1: 0.792728 Loss2: 0.678920
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.453930 Loss1: 0.773895 Loss2: 0.680036
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.431963 Loss1: 0.753042 Loss2: 0.678921
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.423678 Loss1: 0.744500 Loss2: 0.679177
+(DefaultActor pid=1831567) >> Training accuracy: 0.726676
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.585205 Loss1: 0.812090 Loss2: 0.773115
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.410618 Loss1: 0.745442 Loss2: 0.665176
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.400270 Loss1: 0.739350 Loss2: 0.660920
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.391987 Loss1: 0.733144 Loss2: 0.658844
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.362120 Loss1: 0.702163 Loss2: 0.659957
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.363539 Loss1: 0.702174 Loss2: 0.661365
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.354037 Loss1: 0.690791 Loss2: 0.663246
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.375429 Loss1: 0.710451 Loss2: 0.664978
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.332803 Loss1: 0.667962 Loss2: 0.664842
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.344110 Loss1: 0.677908 Loss2: 0.666202
+(DefaultActor pid=1831567) >> Training accuracy: 0.771930
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.395932 Loss1: 0.669449 Loss2: 0.726483
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.327267 Loss1: 0.675388 Loss2: 0.651880
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.303336 Loss1: 0.650259 Loss2: 0.653077
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.299434 Loss1: 0.649157 Loss2: 0.650277
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.257500 Loss1: 0.607814 Loss2: 0.649685
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.294210 Loss1: 0.639640 Loss2: 0.654571
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.281178 Loss1: 0.623739 Loss2: 0.657439
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.286144 Loss1: 0.630450 Loss2: 0.655694
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.242663 Loss1: 0.588327 Loss2: 0.654336
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.270470 Loss1: 0.614638 Loss2: 0.655832
+(DefaultActor pid=1831567) >> Training accuracy: 0.819912
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 09:02:07,936][flwr][DEBUG] - fit_round 21 received 10 results and 0 failures
+>> Test accuracy: 0.658000
+[2023-09-27 09:02:09,249][flwr][INFO] - fit progress: (21, 0.9615000673947623, {'accuracy': 0.658}, 9862.085126371123)
+[2023-09-27 09:02:09,249][flwr][DEBUG] - evaluate_round 21: strategy sampled 10 clients (out of 10)
+[2023-09-27 09:02:41,833][flwr][DEBUG] - evaluate_round 21 received 10 results and 0 failures
+[2023-09-27 09:02:41,834][flwr][DEBUG] - fit_round 22: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.304684 Loss1: 0.569267 Loss2: 0.735416
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.141615 Loss1: 0.484821 Loss2: 0.656795
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.127340 Loss1: 0.474538 Loss2: 0.652802
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.110002 Loss1: 0.458768 Loss2: 0.651235
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.102411 Loss1: 0.453178 Loss2: 0.649233
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.112564 Loss1: 0.463801 Loss2: 0.648763
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.108355 Loss1: 0.459117 Loss2: 0.649238
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.098207 Loss1: 0.445500 Loss2: 0.652707
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.058130 Loss1: 0.409228 Loss2: 0.648902
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.087851 Loss1: 0.434326 Loss2: 0.653525
+(DefaultActor pid=1831567) >> Training accuracy: 0.852045
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.444036 Loss1: 0.682885 Loss2: 0.761151
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.344976 Loss1: 0.636430 Loss2: 0.708546
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.328771 Loss1: 0.625235 Loss2: 0.703536
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331523 Loss1: 0.626431 Loss2: 0.705093
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317135 Loss1: 0.611704 Loss2: 0.705430
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.309719 Loss1: 0.600512 Loss2: 0.709208
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.324201 Loss1: 0.613959 Loss2: 0.710243
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.311558 Loss1: 0.600952 Loss2: 0.710606
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.318152 Loss1: 0.606733 Loss2: 0.711419
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.296720 Loss1: 0.589556 Loss2: 0.707164
+(DefaultActor pid=1831567) >> Training accuracy: 0.784970
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.433290 Loss1: 0.687870 Loss2: 0.745421
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.311491 Loss1: 0.650701 Loss2: 0.660790
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.257606 Loss1: 0.599079 Loss2: 0.658527
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.269667 Loss1: 0.610019 Loss2: 0.659648
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.259221 Loss1: 0.597956 Loss2: 0.661265
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.235624 Loss1: 0.573948 Loss2: 0.661675
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.265285 Loss1: 0.602286 Loss2: 0.662999
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.244493 Loss1: 0.580970 Loss2: 0.663523
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230959 Loss1: 0.568654 Loss2: 0.662304
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.224016 Loss1: 0.560166 Loss2: 0.663850
+(DefaultActor pid=1831567) >> Training accuracy: 0.820107
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.652388 Loss1: 0.871809 Loss2: 0.780579
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.490418 Loss1: 0.809320 Loss2: 0.681098
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.470041 Loss1: 0.788804 Loss2: 0.681238
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.473239 Loss1: 0.791023 Loss2: 0.682216
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.452847 Loss1: 0.771404 Loss2: 0.681443
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.471884 Loss1: 0.787782 Loss2: 0.684102
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.434727 Loss1: 0.753278 Loss2: 0.681449
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.436968 Loss1: 0.755003 Loss2: 0.681965
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.420875 Loss1: 0.738163 Loss2: 0.682711
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.442072 Loss1: 0.755011 Loss2: 0.687061
+(DefaultActor pid=1831567) >> Training accuracy: 0.746830
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.427636 Loss1: 0.712241 Loss2: 0.715394
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.284975 Loss1: 0.663817 Loss2: 0.621157
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.248494 Loss1: 0.634158 Loss2: 0.614336
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.250529 Loss1: 0.635477 Loss2: 0.615052
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.240366 Loss1: 0.626437 Loss2: 0.613928
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220273 Loss1: 0.606933 Loss2: 0.613340
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.217637 Loss1: 0.601080 Loss2: 0.616557
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.192991 Loss1: 0.578891 Loss2: 0.614101
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199235 Loss1: 0.584297 Loss2: 0.614938
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184007 Loss1: 0.568774 Loss2: 0.615233
+(DefaultActor pid=1831567) >> Training accuracy: 0.805259
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.455510 Loss1: 0.682026 Loss2: 0.773483
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.358602 Loss1: 0.667155 Loss2: 0.691447
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.342452 Loss1: 0.650669 Loss2: 0.691783
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316722 Loss1: 0.623029 Loss2: 0.693692
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.309369 Loss1: 0.618123 Loss2: 0.691246
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.298757 Loss1: 0.607152 Loss2: 0.691604
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.297630 Loss1: 0.604506 Loss2: 0.693124
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.282516 Loss1: 0.587788 Loss2: 0.694728
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305953 Loss1: 0.608668 Loss2: 0.697284
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308094 Loss1: 0.609656 Loss2: 0.698438
+(DefaultActor pid=1831567) >> Training accuracy: 0.799880
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327160 Loss1: 0.552122 Loss2: 0.775038
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172751 Loss1: 0.495045 Loss2: 0.677705
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.146569 Loss1: 0.469712 Loss2: 0.676857
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.118620 Loss1: 0.442940 Loss2: 0.675679
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.123851 Loss1: 0.444208 Loss2: 0.679643
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.134380 Loss1: 0.454934 Loss2: 0.679447
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.112995 Loss1: 0.433674 Loss2: 0.679321
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.113952 Loss1: 0.433295 Loss2: 0.680657
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.104336 Loss1: 0.425225 Loss2: 0.679111
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.100652 Loss1: 0.420907 Loss2: 0.679746
+(DefaultActor pid=1831567) >> Training accuracy: 0.853974
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.624781 Loss1: 0.830773 Loss2: 0.794008
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.448631 Loss1: 0.767829 Loss2: 0.680803
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.436013 Loss1: 0.752829 Loss2: 0.683183
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.386369 Loss1: 0.710556 Loss2: 0.675814
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.400776 Loss1: 0.719713 Loss2: 0.681062
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.379735 Loss1: 0.700918 Loss2: 0.678817
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.396632 Loss1: 0.711408 Loss2: 0.685224
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.360377 Loss1: 0.676275 Loss2: 0.684102
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.368206 Loss1: 0.680460 Loss2: 0.687746
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.319983 Loss1: 0.635765 Loss2: 0.684219
+(DefaultActor pid=1831567) >> Training accuracy: 0.768092
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.630742 Loss1: 0.837592 Loss2: 0.793150
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.491228 Loss1: 0.803105 Loss2: 0.688123
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.445726 Loss1: 0.762416 Loss2: 0.683310
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.420936 Loss1: 0.740006 Loss2: 0.680930
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.401144 Loss1: 0.721161 Loss2: 0.679983
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.431374 Loss1: 0.749100 Loss2: 0.682274
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.405549 Loss1: 0.723866 Loss2: 0.681683
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.390657 Loss1: 0.705794 Loss2: 0.684863
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.410603 Loss1: 0.724681 Loss2: 0.685923
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.393122 Loss1: 0.704450 Loss2: 0.688672
+(DefaultActor pid=1831567) >> Training accuracy: 0.744869
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.496330 Loss1: 0.661094 Loss2: 0.835236
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.334288 Loss1: 0.613990 Loss2: 0.720298
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.308284 Loss1: 0.587335 Loss2: 0.720949
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339974 Loss1: 0.615666 Loss2: 0.724308
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.276629 Loss1: 0.556373 Loss2: 0.720255
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.283874 Loss1: 0.560942 Loss2: 0.722932
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.262001 Loss1: 0.538904 Loss2: 0.723097
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268303 Loss1: 0.543864 Loss2: 0.724439
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.297200 Loss1: 0.567344 Loss2: 0.729856
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.270218 Loss1: 0.541812 Loss2: 0.728406
+[2023-09-27 09:09:36,223][flwr][DEBUG] - fit_round 22 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.831568
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.662200
+[2023-09-27 09:09:38,011][flwr][INFO] - fit progress: (22, 0.9505386705787037, {'accuracy': 0.6622}, 10310.847262899857)
+[2023-09-27 09:09:38,011][flwr][DEBUG] - evaluate_round 22: strategy sampled 10 clients (out of 10)
+[2023-09-27 09:10:13,522][flwr][DEBUG] - evaluate_round 22 received 10 results and 0 failures
+[2023-09-27 09:10:13,524][flwr][DEBUG] - fit_round 23: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.468812 Loss1: 0.698979 Loss2: 0.769833
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.344240 Loss1: 0.641695 Loss2: 0.702545
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.331760 Loss1: 0.629602 Loss2: 0.702158
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.318049 Loss1: 0.615234 Loss2: 0.702815
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293438 Loss1: 0.592562 Loss2: 0.700875
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289138 Loss1: 0.586097 Loss2: 0.703041
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.316909 Loss1: 0.612168 Loss2: 0.704741
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.283244 Loss1: 0.578192 Loss2: 0.705051
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.296414 Loss1: 0.591695 Loss2: 0.704719
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.285332 Loss1: 0.577772 Loss2: 0.707560
+(DefaultActor pid=1831567) >> Training accuracy: 0.802782
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.572051 Loss1: 0.821143 Loss2: 0.750908
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.440161 Loss1: 0.785657 Loss2: 0.654504
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.395702 Loss1: 0.744257 Loss2: 0.651446
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.406681 Loss1: 0.755970 Loss2: 0.650711
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.397699 Loss1: 0.744061 Loss2: 0.653639
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.385006 Loss1: 0.734114 Loss2: 0.650892
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.354033 Loss1: 0.701974 Loss2: 0.652058
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.336683 Loss1: 0.682097 Loss2: 0.654586
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.366586 Loss1: 0.711158 Loss2: 0.655428
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.366881 Loss1: 0.709015 Loss2: 0.657866
+(DefaultActor pid=1831567) >> Training accuracy: 0.739272
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.288392 Loss1: 0.537270 Loss2: 0.751121
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.170947 Loss1: 0.500160 Loss2: 0.670787
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.147124 Loss1: 0.478336 Loss2: 0.668788
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.121837 Loss1: 0.454834 Loss2: 0.667003
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.102029 Loss1: 0.433805 Loss2: 0.668224
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.112459 Loss1: 0.441948 Loss2: 0.670511
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.096986 Loss1: 0.426047 Loss2: 0.670939
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.109402 Loss1: 0.438522 Loss2: 0.670880
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.097111 Loss1: 0.423939 Loss2: 0.673172
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.097192 Loss1: 0.424373 Loss2: 0.672819
+(DefaultActor pid=1831567) >> Training accuracy: 0.867863
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.597257 Loss1: 0.808820 Loss2: 0.788437
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.423027 Loss1: 0.744493 Loss2: 0.678535
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.398212 Loss1: 0.721847 Loss2: 0.676365
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.401325 Loss1: 0.724146 Loss2: 0.677179
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.379283 Loss1: 0.701888 Loss2: 0.677395
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.384467 Loss1: 0.704419 Loss2: 0.680048
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.358902 Loss1: 0.674977 Loss2: 0.683925
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.367556 Loss1: 0.686232 Loss2: 0.681324
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.342858 Loss1: 0.661052 Loss2: 0.681806
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.360587 Loss1: 0.676688 Loss2: 0.683899
+(DefaultActor pid=1831567) >> Training accuracy: 0.768366
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.428432 Loss1: 0.687496 Loss2: 0.740935
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.313393 Loss1: 0.647671 Loss2: 0.665722
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.300294 Loss1: 0.638025 Loss2: 0.662269
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281637 Loss1: 0.618428 Loss2: 0.663210
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.279676 Loss1: 0.616077 Loss2: 0.663598
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289080 Loss1: 0.624154 Loss2: 0.664926
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.273831 Loss1: 0.606128 Loss2: 0.667703
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.256142 Loss1: 0.587432 Loss2: 0.668710
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.247503 Loss1: 0.581433 Loss2: 0.666070
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.246453 Loss1: 0.578119 Loss2: 0.668334
+(DefaultActor pid=1831567) >> Training accuracy: 0.823718
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.265150 Loss1: 0.529697 Loss2: 0.735453
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.167189 Loss1: 0.504967 Loss2: 0.662222
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.132634 Loss1: 0.477185 Loss2: 0.655449
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.115517 Loss1: 0.461392 Loss2: 0.654125
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.104731 Loss1: 0.450939 Loss2: 0.653792
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.090789 Loss1: 0.438238 Loss2: 0.652551
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.105477 Loss1: 0.449660 Loss2: 0.655818
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.081778 Loss1: 0.427637 Loss2: 0.654141
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.087530 Loss1: 0.431850 Loss2: 0.655680
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.075599 Loss1: 0.419914 Loss2: 0.655685
+(DefaultActor pid=1831567) >> Training accuracy: 0.840471
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.442020 Loss1: 0.684245 Loss2: 0.757774
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.290614 Loss1: 0.615379 Loss2: 0.675235
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.283440 Loss1: 0.607194 Loss2: 0.676246
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.279625 Loss1: 0.603613 Loss2: 0.676012
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.266080 Loss1: 0.587629 Loss2: 0.678451
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.263299 Loss1: 0.582931 Loss2: 0.680367
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.297882 Loss1: 0.613081 Loss2: 0.684801
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.260067 Loss1: 0.576776 Loss2: 0.683291
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257963 Loss1: 0.576382 Loss2: 0.681581
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.251181 Loss1: 0.567422 Loss2: 0.683759
+(DefaultActor pid=1831567) >> Training accuracy: 0.808388
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.608826 Loss1: 0.846855 Loss2: 0.761971
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.501592 Loss1: 0.821855 Loss2: 0.679737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.477842 Loss1: 0.796479 Loss2: 0.681363
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.449068 Loss1: 0.767603 Loss2: 0.681465
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.450676 Loss1: 0.768555 Loss2: 0.682121
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.431079 Loss1: 0.748785 Loss2: 0.682294
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.459467 Loss1: 0.777355 Loss2: 0.682111
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.451120 Loss1: 0.766002 Loss2: 0.685118
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.449702 Loss1: 0.765147 Loss2: 0.684555
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.435665 Loss1: 0.748333 Loss2: 0.687332
+(DefaultActor pid=1831567) >> Training accuracy: 0.743886
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.447104 Loss1: 0.697902 Loss2: 0.749202
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250192 Loss1: 0.608480 Loss2: 0.641712
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.239218 Loss1: 0.599073 Loss2: 0.640144
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.209416 Loss1: 0.568619 Loss2: 0.640796
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221406 Loss1: 0.580916 Loss2: 0.640490
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.223934 Loss1: 0.580617 Loss2: 0.643318
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200313 Loss1: 0.557508 Loss2: 0.642805
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207592 Loss1: 0.565727 Loss2: 0.641866
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180679 Loss1: 0.538091 Loss2: 0.642588
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.185295 Loss1: 0.542114 Loss2: 0.643180
+(DefaultActor pid=1831567) >> Training accuracy: 0.821504
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.380921 Loss1: 0.691242 Loss2: 0.689679
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.278230 Loss1: 0.635028 Loss2: 0.643203
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.279391 Loss1: 0.636691 Loss2: 0.642700
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.256569 Loss1: 0.616060 Loss2: 0.640509
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.265177 Loss1: 0.621017 Loss2: 0.644160
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.255761 Loss1: 0.609072 Loss2: 0.646690
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.247983 Loss1: 0.604386 Loss2: 0.643597
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.245796 Loss1: 0.601249 Loss2: 0.644547
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232108 Loss1: 0.589331 Loss2: 0.642777
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229978 Loss1: 0.583936 Loss2: 0.646042
+[2023-09-27 09:17:21,927][flwr][DEBUG] - fit_round 23 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.796131
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.666700
+[2023-09-27 09:17:23,375][flwr][INFO] - fit progress: (23, 0.9466991268407804, {'accuracy': 0.6667}, 10776.211382081732)
+[2023-09-27 09:17:23,376][flwr][DEBUG] - evaluate_round 23: strategy sampled 10 clients (out of 10)
+[2023-09-27 09:17:55,594][flwr][DEBUG] - evaluate_round 23 received 10 results and 0 failures
+[2023-09-27 09:17:55,595][flwr][DEBUG] - fit_round 24: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.442585 Loss1: 0.696537 Loss2: 0.746048
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.287944 Loss1: 0.632086 Loss2: 0.655858
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260650 Loss1: 0.607534 Loss2: 0.653116
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.265549 Loss1: 0.614910 Loss2: 0.650639
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.256463 Loss1: 0.604914 Loss2: 0.651549
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.264265 Loss1: 0.611488 Loss2: 0.652777
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.232195 Loss1: 0.580208 Loss2: 0.651988
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.234776 Loss1: 0.582677 Loss2: 0.652098
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.222000 Loss1: 0.568326 Loss2: 0.653674
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.223261 Loss1: 0.569499 Loss2: 0.653762
+(DefaultActor pid=1831567) >> Training accuracy: 0.786966
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.611949 Loss1: 0.836405 Loss2: 0.775544
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.516094 Loss1: 0.833549 Loss2: 0.682545
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.504144 Loss1: 0.824844 Loss2: 0.679300
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.458776 Loss1: 0.780132 Loss2: 0.678643
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.443208 Loss1: 0.765878 Loss2: 0.677330
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.442916 Loss1: 0.763962 Loss2: 0.678954
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.456964 Loss1: 0.775383 Loss2: 0.681581
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.430724 Loss1: 0.753145 Loss2: 0.677579
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.423485 Loss1: 0.742665 Loss2: 0.680820
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.406220 Loss1: 0.724844 Loss2: 0.681376
+(DefaultActor pid=1831567) >> Training accuracy: 0.743886
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.506055 Loss1: 0.657923 Loss2: 0.848132
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.337300 Loss1: 0.606138 Loss2: 0.731161
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.311947 Loss1: 0.586314 Loss2: 0.725633
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291171 Loss1: 0.565533 Loss2: 0.725638
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.305692 Loss1: 0.576049 Loss2: 0.729643
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.267456 Loss1: 0.539071 Loss2: 0.728386
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.282163 Loss1: 0.551173 Loss2: 0.730990
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289985 Loss1: 0.560338 Loss2: 0.729647
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.270409 Loss1: 0.537560 Loss2: 0.732849
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.248267 Loss1: 0.517354 Loss2: 0.730913
+(DefaultActor pid=1831567) >> Training accuracy: 0.833686
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.400963 Loss1: 0.670244 Loss2: 0.730719
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.294876 Loss1: 0.645512 Loss2: 0.649363
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.256280 Loss1: 0.605290 Loss2: 0.650990
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.243827 Loss1: 0.592801 Loss2: 0.651026
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.249524 Loss1: 0.596894 Loss2: 0.652630
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.247849 Loss1: 0.594655 Loss2: 0.653193
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.231193 Loss1: 0.578372 Loss2: 0.652821
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.221434 Loss1: 0.569845 Loss2: 0.651588
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.223261 Loss1: 0.566541 Loss2: 0.656719
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.205130 Loss1: 0.550873 Loss2: 0.654257
+(DefaultActor pid=1831567) >> Training accuracy: 0.807977
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.589442 Loss1: 0.824839 Loss2: 0.764603
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.433182 Loss1: 0.768295 Loss2: 0.664887
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.402562 Loss1: 0.741583 Loss2: 0.660979
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.387515 Loss1: 0.727527 Loss2: 0.659988
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.375725 Loss1: 0.715425 Loss2: 0.660300
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.388543 Loss1: 0.726197 Loss2: 0.662345
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.382612 Loss1: 0.722035 Loss2: 0.660577
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.344169 Loss1: 0.682310 Loss2: 0.661860
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.362639 Loss1: 0.699073 Loss2: 0.663566
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.340342 Loss1: 0.675105 Loss2: 0.665237
+(DefaultActor pid=1831567) >> Training accuracy: 0.753032
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.277194 Loss1: 0.540282 Loss2: 0.736912
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.129066 Loss1: 0.469851 Loss2: 0.659215
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.131327 Loss1: 0.477052 Loss2: 0.654275
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.101582 Loss1: 0.451179 Loss2: 0.650403
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.092248 Loss1: 0.441539 Loss2: 0.650709
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.117023 Loss1: 0.460657 Loss2: 0.656365
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.100613 Loss1: 0.446930 Loss2: 0.653683
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.087969 Loss1: 0.433744 Loss2: 0.654224
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.087819 Loss1: 0.432577 Loss2: 0.655242
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.072699 Loss1: 0.416770 Loss2: 0.655929
+(DefaultActor pid=1831567) >> Training accuracy: 0.854167
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.452005 Loss1: 0.665145 Loss2: 0.786861
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341335 Loss1: 0.614097 Loss2: 0.727238
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.339971 Loss1: 0.612550 Loss2: 0.727421
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.346923 Loss1: 0.614950 Loss2: 0.731972
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.326639 Loss1: 0.598125 Loss2: 0.728514
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.330303 Loss1: 0.602203 Loss2: 0.728100
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.331367 Loss1: 0.603890 Loss2: 0.727477
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.318702 Loss1: 0.589627 Loss2: 0.729075
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.329242 Loss1: 0.594719 Loss2: 0.734523
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.323982 Loss1: 0.589316 Loss2: 0.734666
+(DefaultActor pid=1831567) >> Training accuracy: 0.783854
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289518 Loss1: 0.528777 Loss2: 0.760741
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.150867 Loss1: 0.486177 Loss2: 0.664691
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.132503 Loss1: 0.465289 Loss2: 0.667214
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.114794 Loss1: 0.449958 Loss2: 0.664837
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.102086 Loss1: 0.438184 Loss2: 0.663902
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.133347 Loss1: 0.466684 Loss2: 0.666664
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.098338 Loss1: 0.433601 Loss2: 0.664738
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.116851 Loss1: 0.452951 Loss2: 0.663900
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.096390 Loss1: 0.429531 Loss2: 0.666859
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.085259 Loss1: 0.419530 Loss2: 0.665728
+(DefaultActor pid=1831567) >> Training accuracy: 0.862076
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.590187 Loss1: 0.816137 Loss2: 0.774051
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.407339 Loss1: 0.744876 Loss2: 0.662462
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.388933 Loss1: 0.728200 Loss2: 0.660733
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.379559 Loss1: 0.716746 Loss2: 0.662813
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.365034 Loss1: 0.703619 Loss2: 0.661414
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.358104 Loss1: 0.691825 Loss2: 0.666279
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.336700 Loss1: 0.670735 Loss2: 0.665965
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.349781 Loss1: 0.686691 Loss2: 0.663090
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.345652 Loss1: 0.675487 Loss2: 0.670165
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.350464 Loss1: 0.685416 Loss2: 0.665048
+(DefaultActor pid=1831567) >> Training accuracy: 0.770011
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462043 Loss1: 0.699976 Loss2: 0.762067
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346701 Loss1: 0.662361 Loss2: 0.684340
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.305083 Loss1: 0.623848 Loss2: 0.681236
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.310227 Loss1: 0.627174 Loss2: 0.683054
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.304995 Loss1: 0.619295 Loss2: 0.685700
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.311936 Loss1: 0.623459 Loss2: 0.688477
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.262375 Loss1: 0.577371 Loss2: 0.685004
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.285511 Loss1: 0.598022 Loss2: 0.687488
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.256919 Loss1: 0.572078 Loss2: 0.684841
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.262832 Loss1: 0.576323 Loss2: 0.686509
+[2023-09-27 09:24:50,901][flwr][DEBUG] - fit_round 24 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.821314
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.669900
+[2023-09-27 09:24:52,443][flwr][INFO] - fit progress: (24, 0.9378842182052783, {'accuracy': 0.6699}, 11225.279507511761)
+[2023-09-27 09:24:52,444][flwr][DEBUG] - evaluate_round 24: strategy sampled 10 clients (out of 10)
+[2023-09-27 09:25:24,111][flwr][DEBUG] - evaluate_round 24 received 10 results and 0 failures
+[2023-09-27 09:25:24,113][flwr][DEBUG] - fit_round 25: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291298 Loss1: 0.532985 Loss2: 0.758313
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.131819 Loss1: 0.461659 Loss2: 0.670160
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.141225 Loss1: 0.470075 Loss2: 0.671150
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.107678 Loss1: 0.436257 Loss2: 0.671421
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.106007 Loss1: 0.436567 Loss2: 0.669440
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.107363 Loss1: 0.434615 Loss2: 0.672748
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.102492 Loss1: 0.431918 Loss2: 0.670574
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.107574 Loss1: 0.434896 Loss2: 0.672678
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.084462 Loss1: 0.408173 Loss2: 0.676288
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.087364 Loss1: 0.413978 Loss2: 0.673386
+(DefaultActor pid=1831567) >> Training accuracy: 0.861883
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297911 Loss1: 0.513162 Loss2: 0.784750
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.163240 Loss1: 0.461377 Loss2: 0.701862
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.153529 Loss1: 0.458818 Loss2: 0.694711
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.124529 Loss1: 0.430932 Loss2: 0.693597
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.133000 Loss1: 0.439967 Loss2: 0.693032
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.119512 Loss1: 0.426214 Loss2: 0.693297
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.131649 Loss1: 0.437142 Loss2: 0.694507
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.108978 Loss1: 0.414884 Loss2: 0.694094
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.107696 Loss1: 0.413930 Loss2: 0.693766
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.105525 Loss1: 0.413505 Loss2: 0.692020
+(DefaultActor pid=1831567) >> Training accuracy: 0.853009
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.559663 Loss1: 0.786652 Loss2: 0.773011
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.396127 Loss1: 0.735033 Loss2: 0.661094
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.353533 Loss1: 0.696391 Loss2: 0.657142
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.357409 Loss1: 0.702681 Loss2: 0.654727
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.347556 Loss1: 0.692423 Loss2: 0.655133
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.343808 Loss1: 0.686236 Loss2: 0.657572
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.332582 Loss1: 0.672757 Loss2: 0.659825
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.323876 Loss1: 0.665043 Loss2: 0.658832
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.339127 Loss1: 0.678283 Loss2: 0.660844
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304752 Loss1: 0.645690 Loss2: 0.659062
+(DefaultActor pid=1831567) >> Training accuracy: 0.776042
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.347441 Loss1: 0.670608 Loss2: 0.676832
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.248378 Loss1: 0.617231 Loss2: 0.631147
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.256618 Loss1: 0.623606 Loss2: 0.633012
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.255551 Loss1: 0.623081 Loss2: 0.632470
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.225479 Loss1: 0.592018 Loss2: 0.633462
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.243790 Loss1: 0.610043 Loss2: 0.633747
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243294 Loss1: 0.609663 Loss2: 0.633631
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.216637 Loss1: 0.583240 Loss2: 0.633397
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.237761 Loss1: 0.600579 Loss2: 0.637182
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.233289 Loss1: 0.598867 Loss2: 0.634422
+(DefaultActor pid=1831567) >> Training accuracy: 0.805804
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.603617 Loss1: 0.830971 Loss2: 0.772646
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.442133 Loss1: 0.768705 Loss2: 0.673428
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.432666 Loss1: 0.763812 Loss2: 0.668854
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.423279 Loss1: 0.754392 Loss2: 0.668887
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.392107 Loss1: 0.721716 Loss2: 0.670391
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.424996 Loss1: 0.753468 Loss2: 0.671528
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.363118 Loss1: 0.692246 Loss2: 0.670872
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.392274 Loss1: 0.720560 Loss2: 0.671715
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.393743 Loss1: 0.717690 Loss2: 0.676053
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.393328 Loss1: 0.719999 Loss2: 0.673329
+(DefaultActor pid=1831567) >> Training accuracy: 0.736940
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.439437 Loss1: 0.687025 Loss2: 0.752413
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.313614 Loss1: 0.629478 Loss2: 0.684136
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.300109 Loss1: 0.618150 Loss2: 0.681959
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.280730 Loss1: 0.598353 Loss2: 0.682377
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302838 Loss1: 0.614916 Loss2: 0.687922
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.271411 Loss1: 0.586943 Loss2: 0.684468
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.271443 Loss1: 0.586872 Loss2: 0.684572
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.254209 Loss1: 0.567318 Loss2: 0.686891
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.242223 Loss1: 0.557742 Loss2: 0.684481
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.256396 Loss1: 0.569394 Loss2: 0.687002
+(DefaultActor pid=1831567) >> Training accuracy: 0.810976
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.380416 Loss1: 0.660370 Loss2: 0.720046
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.302112 Loss1: 0.654655 Loss2: 0.647457
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.272347 Loss1: 0.627405 Loss2: 0.644943
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.265572 Loss1: 0.616843 Loss2: 0.648729
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.263064 Loss1: 0.612045 Loss2: 0.651019
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232622 Loss1: 0.585685 Loss2: 0.646937
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.248488 Loss1: 0.597570 Loss2: 0.650918
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.231512 Loss1: 0.583667 Loss2: 0.647845
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.237681 Loss1: 0.583760 Loss2: 0.653920
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.258152 Loss1: 0.603976 Loss2: 0.654175
+(DefaultActor pid=1831567) >> Training accuracy: 0.793269
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.412691 Loss1: 0.677170 Loss2: 0.735521
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236814 Loss1: 0.600152 Loss2: 0.636662
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222843 Loss1: 0.589647 Loss2: 0.633196
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199805 Loss1: 0.566025 Loss2: 0.633780
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.202263 Loss1: 0.569327 Loss2: 0.632936
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.177151 Loss1: 0.543150 Loss2: 0.634001
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192694 Loss1: 0.557798 Loss2: 0.634896
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158053 Loss1: 0.524251 Loss2: 0.633802
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182180 Loss1: 0.547044 Loss2: 0.635136
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172206 Loss1: 0.537278 Loss2: 0.634928
+(DefaultActor pid=1831567) >> Training accuracy: 0.832097
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.596245 Loss1: 0.833172 Loss2: 0.763072
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.480003 Loss1: 0.795339 Loss2: 0.684665
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.466028 Loss1: 0.781772 Loss2: 0.684257
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.447583 Loss1: 0.759336 Loss2: 0.688247
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.424588 Loss1: 0.741306 Loss2: 0.683282
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.444529 Loss1: 0.756129 Loss2: 0.688400
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.427014 Loss1: 0.737842 Loss2: 0.689172
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.434003 Loss1: 0.745304 Loss2: 0.688699
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.442339 Loss1: 0.749924 Loss2: 0.692415
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.430417 Loss1: 0.737635 Loss2: 0.692782
+(DefaultActor pid=1831567) >> Training accuracy: 0.755888
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.364737 Loss1: 0.647355 Loss2: 0.717382
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.252550 Loss1: 0.610841 Loss2: 0.641709
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236448 Loss1: 0.594998 Loss2: 0.641450
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.240521 Loss1: 0.596934 Loss2: 0.643587
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.214728 Loss1: 0.569747 Loss2: 0.644981
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.205929 Loss1: 0.561922 Loss2: 0.644008
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.225666 Loss1: 0.578972 Loss2: 0.646695
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.216546 Loss1: 0.568983 Loss2: 0.647563
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.187083 Loss1: 0.540598 Loss2: 0.646485
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193139 Loss1: 0.543273 Loss2: 0.649866
+[2023-09-27 09:51:06,009][flwr][DEBUG] - fit_round 25 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.820518
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.661900
+[2023-09-27 09:51:07,723][flwr][INFO] - fit progress: (25, 0.9540704172640182, {'accuracy': 0.6619}, 12800.559811873827)
+[2023-09-27 09:51:07,724][flwr][DEBUG] - evaluate_round 25: strategy sampled 10 clients (out of 10)
+[2023-09-27 09:51:39,117][flwr][DEBUG] - evaluate_round 25 received 10 results and 0 failures
+[2023-09-27 09:51:39,118][flwr][DEBUG] - fit_round 26: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.579937 Loss1: 0.788341 Loss2: 0.791596
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.400771 Loss1: 0.725262 Loss2: 0.675509
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393012 Loss1: 0.715690 Loss2: 0.677322
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.358456 Loss1: 0.684957 Loss2: 0.673499
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.371962 Loss1: 0.693885 Loss2: 0.678077
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.363979 Loss1: 0.683321 Loss2: 0.680657
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.354206 Loss1: 0.672180 Loss2: 0.682025
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.357099 Loss1: 0.677719 Loss2: 0.679380
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.362558 Loss1: 0.680811 Loss2: 0.681747
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.335656 Loss1: 0.652158 Loss2: 0.683498
+(DefaultActor pid=1831567) >> Training accuracy: 0.757401
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.449867 Loss1: 0.652957 Loss2: 0.796910
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382986 Loss1: 0.638685 Loss2: 0.744300
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.352945 Loss1: 0.610763 Loss2: 0.742182
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.344095 Loss1: 0.602170 Loss2: 0.741925
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345198 Loss1: 0.599742 Loss2: 0.745456
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.358466 Loss1: 0.611384 Loss2: 0.747082
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322897 Loss1: 0.577306 Loss2: 0.745590
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.330038 Loss1: 0.583062 Loss2: 0.746975
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.323383 Loss1: 0.575720 Loss2: 0.747663
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.330938 Loss1: 0.580277 Loss2: 0.750660
+(DefaultActor pid=1831567) >> Training accuracy: 0.810640
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.278662 Loss1: 0.529956 Loss2: 0.748705
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.127076 Loss1: 0.472016 Loss2: 0.655060
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.099400 Loss1: 0.448599 Loss2: 0.650801
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.099539 Loss1: 0.447419 Loss2: 0.652121
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.089999 Loss1: 0.437392 Loss2: 0.652608
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.074432 Loss1: 0.420066 Loss2: 0.654366
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.073378 Loss1: 0.416858 Loss2: 0.656520
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.083157 Loss1: 0.427954 Loss2: 0.655203
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.081000 Loss1: 0.423217 Loss2: 0.657783
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.076021 Loss1: 0.419653 Loss2: 0.656368
+(DefaultActor pid=1831567) >> Training accuracy: 0.858410
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.256905 Loss1: 0.507756 Loss2: 0.749149
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.156353 Loss1: 0.482242 Loss2: 0.674110
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.129385 Loss1: 0.457267 Loss2: 0.672118
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.116327 Loss1: 0.447331 Loss2: 0.668996
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.116679 Loss1: 0.447464 Loss2: 0.669215
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.097869 Loss1: 0.427128 Loss2: 0.670741
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.094620 Loss1: 0.422649 Loss2: 0.671971
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.087470 Loss1: 0.416920 Loss2: 0.670550
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.088435 Loss1: 0.417269 Loss2: 0.671166
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.088999 Loss1: 0.417628 Loss2: 0.671370
+(DefaultActor pid=1831567) >> Training accuracy: 0.860532
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.575258 Loss1: 0.840257 Loss2: 0.735001
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.423118 Loss1: 0.774373 Loss2: 0.648745
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.439829 Loss1: 0.790934 Loss2: 0.648895
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.427558 Loss1: 0.778342 Loss2: 0.649215
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.422870 Loss1: 0.771653 Loss2: 0.651216
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.414211 Loss1: 0.764718 Loss2: 0.649493
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386701 Loss1: 0.736232 Loss2: 0.650469
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.384865 Loss1: 0.733927 Loss2: 0.650938
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.387554 Loss1: 0.735866 Loss2: 0.651687
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.367781 Loss1: 0.716147 Loss2: 0.651634
+(DefaultActor pid=1831567) >> Training accuracy: 0.759511
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.426826 Loss1: 0.689926 Loss2: 0.736900
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.273715 Loss1: 0.628046 Loss2: 0.645669
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.249497 Loss1: 0.608553 Loss2: 0.640944
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.247141 Loss1: 0.604232 Loss2: 0.642909
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.223931 Loss1: 0.579743 Loss2: 0.644187
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225793 Loss1: 0.585519 Loss2: 0.640275
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.231523 Loss1: 0.587130 Loss2: 0.644393
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225234 Loss1: 0.581247 Loss2: 0.643986
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.229293 Loss1: 0.582420 Loss2: 0.646873
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.206049 Loss1: 0.560457 Loss2: 0.645592
+(DefaultActor pid=1831567) >> Training accuracy: 0.809832
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.393214 Loss1: 0.658750 Loss2: 0.734464
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.275248 Loss1: 0.625547 Loss2: 0.649701
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238389 Loss1: 0.591130 Loss2: 0.647259
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.230892 Loss1: 0.580775 Loss2: 0.650117
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.247179 Loss1: 0.595497 Loss2: 0.651682
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218580 Loss1: 0.564239 Loss2: 0.654341
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.221888 Loss1: 0.568569 Loss2: 0.653319
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204439 Loss1: 0.550079 Loss2: 0.654360
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.219265 Loss1: 0.562102 Loss2: 0.657163
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.234365 Loss1: 0.577786 Loss2: 0.656579
+(DefaultActor pid=1831567) >> Training accuracy: 0.822780
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.424447 Loss1: 0.661867 Loss2: 0.762580
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.309849 Loss1: 0.626357 Loss2: 0.683492
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.322890 Loss1: 0.637699 Loss2: 0.685191
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.308170 Loss1: 0.623773 Loss2: 0.684397
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282029 Loss1: 0.595792 Loss2: 0.686237
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.284751 Loss1: 0.599392 Loss2: 0.685360
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.251167 Loss1: 0.563948 Loss2: 0.687219
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.299734 Loss1: 0.609962 Loss2: 0.689771
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.281578 Loss1: 0.589368 Loss2: 0.692210
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.242978 Loss1: 0.553357 Loss2: 0.689621
+(DefaultActor pid=1831567) >> Training accuracy: 0.806290
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517193 Loss1: 0.678666 Loss2: 0.838527
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.307189 Loss1: 0.585330 Loss2: 0.721859
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.310832 Loss1: 0.586600 Loss2: 0.724233
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.268351 Loss1: 0.548302 Loss2: 0.720050
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.275366 Loss1: 0.551415 Loss2: 0.723951
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.274670 Loss1: 0.549893 Loss2: 0.724776
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.260397 Loss1: 0.539858 Loss2: 0.720538
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.283776 Loss1: 0.553563 Loss2: 0.730213
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.270436 Loss1: 0.544095 Loss2: 0.726340
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.248022 Loss1: 0.518548 Loss2: 0.729474
+(DefaultActor pid=1831567) >> Training accuracy: 0.840042
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.581076 Loss1: 0.801565 Loss2: 0.779511
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.434514 Loss1: 0.753070 Loss2: 0.681444
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.430657 Loss1: 0.755300 Loss2: 0.675357
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.398434 Loss1: 0.721442 Loss2: 0.676992
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.385873 Loss1: 0.707093 Loss2: 0.678780
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.395713 Loss1: 0.717280 Loss2: 0.678433
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.372354 Loss1: 0.693839 Loss2: 0.678516
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.373755 Loss1: 0.693159 Loss2: 0.680596
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.378777 Loss1: 0.698205 Loss2: 0.680572
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.358462 Loss1: 0.679644 Loss2: 0.678818
+[2023-09-27 09:58:31,674][flwr][DEBUG] - fit_round 26 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.763993
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.666400
+[2023-09-27 09:58:33,024][flwr][INFO] - fit progress: (26, 0.9397244629578088, {'accuracy': 0.6664}, 13245.86027173698)
+[2023-09-27 09:58:33,024][flwr][DEBUG] - evaluate_round 26: strategy sampled 10 clients (out of 10)
+[2023-09-27 09:59:04,818][flwr][DEBUG] - evaluate_round 26 received 10 results and 0 failures
+[2023-09-27 09:59:04,819][flwr][DEBUG] - fit_round 27: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.449583 Loss1: 0.690530 Loss2: 0.759053
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.296770 Loss1: 0.609999 Loss2: 0.686771
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.301178 Loss1: 0.612058 Loss2: 0.689121
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.284295 Loss1: 0.596648 Loss2: 0.687648
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.266546 Loss1: 0.581180 Loss2: 0.685366
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.267941 Loss1: 0.579791 Loss2: 0.688151
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258347 Loss1: 0.568537 Loss2: 0.689810
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268895 Loss1: 0.580373 Loss2: 0.688522
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.264487 Loss1: 0.574231 Loss2: 0.690256
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.266871 Loss1: 0.574517 Loss2: 0.692353
+(DefaultActor pid=1831567) >> Training accuracy: 0.821456
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.553716 Loss1: 0.808962 Loss2: 0.744754
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.393486 Loss1: 0.742398 Loss2: 0.651088
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.394969 Loss1: 0.746892 Loss2: 0.648078
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.411690 Loss1: 0.760596 Loss2: 0.651094
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.378727 Loss1: 0.730115 Loss2: 0.648612
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341018 Loss1: 0.692348 Loss2: 0.648671
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.364923 Loss1: 0.714833 Loss2: 0.650090
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.381115 Loss1: 0.726566 Loss2: 0.654550
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.374915 Loss1: 0.720598 Loss2: 0.654317
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.325245 Loss1: 0.671102 Loss2: 0.654144
+(DefaultActor pid=1831567) >> Training accuracy: 0.773088
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.363789 Loss1: 0.644337 Loss2: 0.719452
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201952 Loss1: 0.578580 Loss2: 0.623372
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.206212 Loss1: 0.583278 Loss2: 0.622934
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210962 Loss1: 0.588102 Loss2: 0.622859
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192423 Loss1: 0.567312 Loss2: 0.625111
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.153705 Loss1: 0.528875 Loss2: 0.624830
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.185644 Loss1: 0.557967 Loss2: 0.627677
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.148644 Loss1: 0.523978 Loss2: 0.624666
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.170491 Loss1: 0.544268 Loss2: 0.626223
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.145878 Loss1: 0.517445 Loss2: 0.628433
+(DefaultActor pid=1831567) >> Training accuracy: 0.812235
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358632 Loss1: 0.651874 Loss2: 0.706758
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.267711 Loss1: 0.605041 Loss2: 0.662670
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236873 Loss1: 0.580189 Loss2: 0.656684
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261908 Loss1: 0.603533 Loss2: 0.658375
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.265325 Loss1: 0.604271 Loss2: 0.661053
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249532 Loss1: 0.588019 Loss2: 0.661513
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.251672 Loss1: 0.588851 Loss2: 0.662822
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.239603 Loss1: 0.577299 Loss2: 0.662304
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.237296 Loss1: 0.574499 Loss2: 0.662797
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.241677 Loss1: 0.576387 Loss2: 0.665290
+(DefaultActor pid=1831567) >> Training accuracy: 0.815228
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.616389 Loss1: 0.848396 Loss2: 0.767993
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.488945 Loss1: 0.805598 Loss2: 0.683347
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.465658 Loss1: 0.778338 Loss2: 0.687320
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.460593 Loss1: 0.774837 Loss2: 0.685755
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.462736 Loss1: 0.773943 Loss2: 0.688794
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.430867 Loss1: 0.742141 Loss2: 0.688726
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.423608 Loss1: 0.734616 Loss2: 0.688992
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.421408 Loss1: 0.731139 Loss2: 0.690269
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.427985 Loss1: 0.739306 Loss2: 0.688679
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.411158 Loss1: 0.723440 Loss2: 0.687718
+(DefaultActor pid=1831567) >> Training accuracy: 0.757246
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.444652 Loss1: 0.681700 Loss2: 0.762952
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.290312 Loss1: 0.610782 Loss2: 0.679530
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.270683 Loss1: 0.592385 Loss2: 0.678298
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281492 Loss1: 0.599640 Loss2: 0.681852
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.250848 Loss1: 0.571253 Loss2: 0.679594
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.258113 Loss1: 0.575518 Loss2: 0.682595
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.242819 Loss1: 0.561299 Loss2: 0.681520
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229820 Loss1: 0.544917 Loss2: 0.684904
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230244 Loss1: 0.547959 Loss2: 0.682285
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240552 Loss1: 0.555453 Loss2: 0.685099
+(DefaultActor pid=1831567) >> Training accuracy: 0.818462
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.295270 Loss1: 0.523966 Loss2: 0.771304
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.168281 Loss1: 0.478028 Loss2: 0.690253
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.141702 Loss1: 0.455448 Loss2: 0.686254
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.118096 Loss1: 0.430919 Loss2: 0.687177
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.111567 Loss1: 0.424975 Loss2: 0.686592
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.134685 Loss1: 0.443841 Loss2: 0.690844
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.127537 Loss1: 0.438621 Loss2: 0.688916
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.111485 Loss1: 0.419299 Loss2: 0.692186
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.100949 Loss1: 0.410271 Loss2: 0.690677
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.093137 Loss1: 0.399657 Loss2: 0.693480
+(DefaultActor pid=1831567) >> Training accuracy: 0.865162
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.252538 Loss1: 0.524924 Loss2: 0.727614
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.117815 Loss1: 0.464849 Loss2: 0.652966
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.101431 Loss1: 0.454336 Loss2: 0.647094
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.095650 Loss1: 0.446353 Loss2: 0.649297
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.067999 Loss1: 0.421154 Loss2: 0.646844
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.072220 Loss1: 0.426395 Loss2: 0.645825
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.053222 Loss1: 0.405447 Loss2: 0.647776
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.085172 Loss1: 0.436083 Loss2: 0.649089
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.075878 Loss1: 0.425026 Loss2: 0.650852
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.067523 Loss1: 0.417062 Loss2: 0.650461
+(DefaultActor pid=1831567) >> Training accuracy: 0.856674
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389390 Loss1: 0.665034 Loss2: 0.724355
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304669 Loss1: 0.653222 Loss2: 0.651447
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.270689 Loss1: 0.621811 Loss2: 0.648879
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.234148 Loss1: 0.587116 Loss2: 0.647032
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.251013 Loss1: 0.600719 Loss2: 0.650294
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.277636 Loss1: 0.622558 Loss2: 0.655079
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.217177 Loss1: 0.566579 Loss2: 0.650598
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202146 Loss1: 0.550539 Loss2: 0.651607
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.222114 Loss1: 0.567161 Loss2: 0.654952
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.212037 Loss1: 0.556720 Loss2: 0.655317
+(DefaultActor pid=1831567) >> Training accuracy: 0.810897
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.604415 Loss1: 0.812420 Loss2: 0.791995
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.427729 Loss1: 0.749371 Loss2: 0.678358
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.383589 Loss1: 0.712978 Loss2: 0.670611
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353658 Loss1: 0.679043 Loss2: 0.674615
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.364780 Loss1: 0.690445 Loss2: 0.674335
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362384 Loss1: 0.687861 Loss2: 0.674523
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352810 Loss1: 0.675408 Loss2: 0.677402
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.337719 Loss1: 0.662399 Loss2: 0.675320
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.333320 Loss1: 0.656117 Loss2: 0.677204
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.326474 Loss1: 0.653764 Loss2: 0.672710
+[2023-09-27 10:05:58,557][flwr][DEBUG] - fit_round 27 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.784265
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.664700
+[2023-09-27 10:06:00,240][flwr][INFO] - fit progress: (27, 0.9426276777118159, {'accuracy': 0.6647}, 13693.076540732756)
+[2023-09-27 10:06:00,241][flwr][DEBUG] - evaluate_round 27: strategy sampled 10 clients (out of 10)
+[2023-09-27 10:06:38,122][flwr][DEBUG] - evaluate_round 27 received 10 results and 0 failures
+[2023-09-27 10:06:38,123][flwr][DEBUG] - fit_round 28: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.418768 Loss1: 0.686215 Loss2: 0.732554
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.297841 Loss1: 0.654486 Loss2: 0.643356
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.256738 Loss1: 0.619040 Loss2: 0.637697
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.235252 Loss1: 0.600433 Loss2: 0.634819
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.228107 Loss1: 0.590599 Loss2: 0.637508
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232736 Loss1: 0.595422 Loss2: 0.637314
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212010 Loss1: 0.573910 Loss2: 0.638099
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.194359 Loss1: 0.558259 Loss2: 0.636099
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.209921 Loss1: 0.572485 Loss2: 0.637436
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198044 Loss1: 0.558994 Loss2: 0.639050
+(DefaultActor pid=1831567) >> Training accuracy: 0.799162
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.433249 Loss1: 0.668183 Loss2: 0.765066
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.315007 Loss1: 0.629281 Loss2: 0.685727
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.277019 Loss1: 0.594631 Loss2: 0.682388
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291903 Loss1: 0.610321 Loss2: 0.681582
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302241 Loss1: 0.616474 Loss2: 0.685767
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.273708 Loss1: 0.587307 Loss2: 0.686401
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.274596 Loss1: 0.584567 Loss2: 0.690030
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.263132 Loss1: 0.575076 Loss2: 0.688056
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.242392 Loss1: 0.553507 Loss2: 0.688885
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.223973 Loss1: 0.535816 Loss2: 0.688156
+(DefaultActor pid=1831567) >> Training accuracy: 0.818510
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.550636 Loss1: 0.770576 Loss2: 0.780060
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.404205 Loss1: 0.737243 Loss2: 0.666961
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.374638 Loss1: 0.708243 Loss2: 0.666395
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.357123 Loss1: 0.690724 Loss2: 0.666399
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.336926 Loss1: 0.670080 Loss2: 0.666846
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.344860 Loss1: 0.678120 Loss2: 0.666740
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338643 Loss1: 0.670485 Loss2: 0.668158
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.332172 Loss1: 0.661554 Loss2: 0.670618
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.323821 Loss1: 0.653988 Loss2: 0.669833
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308221 Loss1: 0.638238 Loss2: 0.669983
+(DefaultActor pid=1831567) >> Training accuracy: 0.766721
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.455305 Loss1: 0.636382 Loss2: 0.818923
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.291476 Loss1: 0.585002 Loss2: 0.706474
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.293225 Loss1: 0.588143 Loss2: 0.705082
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.266649 Loss1: 0.558072 Loss2: 0.708577
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.254135 Loss1: 0.547963 Loss2: 0.706172
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.247226 Loss1: 0.538264 Loss2: 0.708962
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.237383 Loss1: 0.529307 Loss2: 0.708076
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229799 Loss1: 0.521264 Loss2: 0.708535
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.209138 Loss1: 0.498008 Loss2: 0.711130
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.211328 Loss1: 0.502127 Loss2: 0.709200
+(DefaultActor pid=1831567) >> Training accuracy: 0.820180
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.401725 Loss1: 0.657920 Loss2: 0.743805
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.265344 Loss1: 0.606006 Loss2: 0.659338
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251176 Loss1: 0.589000 Loss2: 0.662176
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.234312 Loss1: 0.573394 Loss2: 0.660918
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221983 Loss1: 0.559297 Loss2: 0.662687
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249051 Loss1: 0.584100 Loss2: 0.664950
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.222472 Loss1: 0.557912 Loss2: 0.664560
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220432 Loss1: 0.560078 Loss2: 0.660354
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213997 Loss1: 0.549719 Loss2: 0.664279
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240863 Loss1: 0.574954 Loss2: 0.665910
+(DefaultActor pid=1831567) >> Training accuracy: 0.816406
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568702 Loss1: 0.824548 Loss2: 0.744154
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.441129 Loss1: 0.788478 Loss2: 0.652652
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.426707 Loss1: 0.774806 Loss2: 0.651901
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.405568 Loss1: 0.755009 Loss2: 0.650559
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.409905 Loss1: 0.759398 Loss2: 0.650507
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.402611 Loss1: 0.748745 Loss2: 0.653866
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.392475 Loss1: 0.737982 Loss2: 0.654492
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.378369 Loss1: 0.724729 Loss2: 0.653640
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.379661 Loss1: 0.722017 Loss2: 0.657645
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.372371 Loss1: 0.716533 Loss2: 0.655839
+(DefaultActor pid=1831567) >> Training accuracy: 0.763587
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.245802 Loss1: 0.524239 Loss2: 0.721563
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.106318 Loss1: 0.461317 Loss2: 0.645001
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.098987 Loss1: 0.454362 Loss2: 0.644625
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.077071 Loss1: 0.435293 Loss2: 0.641778
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.084853 Loss1: 0.442075 Loss2: 0.642778
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.074157 Loss1: 0.432492 Loss2: 0.641665
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.065995 Loss1: 0.424209 Loss2: 0.641786
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.059374 Loss1: 0.417833 Loss2: 0.641542
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.065397 Loss1: 0.419789 Loss2: 0.645609
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.047830 Loss1: 0.403059 Loss2: 0.644771
+(DefaultActor pid=1831567) >> Training accuracy: 0.850116
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.392082 Loss1: 0.645682 Loss2: 0.746399
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.311556 Loss1: 0.615339 Loss2: 0.696217
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.291585 Loss1: 0.593636 Loss2: 0.697949
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.302202 Loss1: 0.604944 Loss2: 0.697257
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.281444 Loss1: 0.582316 Loss2: 0.699128
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.290823 Loss1: 0.591659 Loss2: 0.699164
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.292169 Loss1: 0.592937 Loss2: 0.699232
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.294687 Loss1: 0.594369 Loss2: 0.700318
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.278720 Loss1: 0.580873 Loss2: 0.697847
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259913 Loss1: 0.559329 Loss2: 0.700584
+(DefaultActor pid=1831567) >> Training accuracy: 0.798611
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.259746 Loss1: 0.508897 Loss2: 0.750849
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.131362 Loss1: 0.467543 Loss2: 0.663820
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.123275 Loss1: 0.462183 Loss2: 0.661092
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.120915 Loss1: 0.461125 Loss2: 0.659791
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.086349 Loss1: 0.428965 Loss2: 0.657384
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.084815 Loss1: 0.425144 Loss2: 0.659670
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.076643 Loss1: 0.415860 Loss2: 0.660784
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.064891 Loss1: 0.405176 Loss2: 0.659715
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.064812 Loss1: 0.403675 Loss2: 0.661137
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.049509 Loss1: 0.387751 Loss2: 0.661758
+(DefaultActor pid=1831567) >> Training accuracy: 0.860725
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.573778 Loss1: 0.789138 Loss2: 0.784640
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.466705 Loss1: 0.778649 Loss2: 0.688056
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.447096 Loss1: 0.760558 Loss2: 0.686538
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.423077 Loss1: 0.736934 Loss2: 0.686144
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.405165 Loss1: 0.719516 Loss2: 0.685649
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.385724 Loss1: 0.702465 Loss2: 0.683259
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.397575 Loss1: 0.712839 Loss2: 0.684736
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.386951 Loss1: 0.699629 Loss2: 0.687322
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.369247 Loss1: 0.684339 Loss2: 0.684908
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.384623 Loss1: 0.697205 Loss2: 0.687418
+[2023-09-27 10:19:54,162][flwr][DEBUG] - fit_round 28 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.733442
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.678000
+[2023-09-27 10:19:55,522][flwr][INFO] - fit progress: (28, 0.9165207516080656, {'accuracy': 0.678}, 14528.358099716716)
+[2023-09-27 10:19:55,522][flwr][DEBUG] - evaluate_round 28: strategy sampled 10 clients (out of 10)
+[2023-09-27 10:20:27,050][flwr][DEBUG] - evaluate_round 28 received 10 results and 0 failures
+[2023-09-27 10:20:27,051][flwr][DEBUG] - fit_round 29: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370029 Loss1: 0.631503 Loss2: 0.738526
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.226544 Loss1: 0.596735 Loss2: 0.629808
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.206555 Loss1: 0.575237 Loss2: 0.631317
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199460 Loss1: 0.569987 Loss2: 0.629473
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169510 Loss1: 0.539560 Loss2: 0.629949
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.187580 Loss1: 0.557282 Loss2: 0.630298
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168789 Loss1: 0.536684 Loss2: 0.632105
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.150701 Loss1: 0.517597 Loss2: 0.633104
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.154675 Loss1: 0.521047 Loss2: 0.633628
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140726 Loss1: 0.505695 Loss2: 0.635031
+(DefaultActor pid=1831567) >> Training accuracy: 0.836600
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.390817 Loss1: 0.633910 Loss2: 0.756907
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.287583 Loss1: 0.611897 Loss2: 0.675686
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.259390 Loss1: 0.583481 Loss2: 0.675909
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.262353 Loss1: 0.586387 Loss2: 0.675967
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.243120 Loss1: 0.565592 Loss2: 0.677528
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.250752 Loss1: 0.573260 Loss2: 0.677493
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.264689 Loss1: 0.586816 Loss2: 0.677873
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229545 Loss1: 0.550821 Loss2: 0.678724
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.233407 Loss1: 0.554371 Loss2: 0.679036
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.232845 Loss1: 0.553213 Loss2: 0.679632
+(DefaultActor pid=1831567) >> Training accuracy: 0.806538
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.585579 Loss1: 0.810008 Loss2: 0.775571
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.429684 Loss1: 0.754251 Loss2: 0.675433
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.411040 Loss1: 0.735261 Loss2: 0.675779
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.416790 Loss1: 0.739172 Loss2: 0.677618
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.380690 Loss1: 0.704945 Loss2: 0.675745
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.377753 Loss1: 0.700152 Loss2: 0.677600
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.374199 Loss1: 0.697348 Loss2: 0.676850
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.367466 Loss1: 0.687880 Loss2: 0.679585
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.377289 Loss1: 0.698104 Loss2: 0.679185
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.339278 Loss1: 0.660231 Loss2: 0.679048
+(DefaultActor pid=1831567) >> Training accuracy: 0.751866
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.611101 Loss1: 0.830996 Loss2: 0.780105
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.468160 Loss1: 0.773560 Loss2: 0.694600
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.494242 Loss1: 0.798755 Loss2: 0.695487
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.446940 Loss1: 0.753391 Loss2: 0.693548
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.465876 Loss1: 0.769483 Loss2: 0.696393
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.421482 Loss1: 0.726651 Loss2: 0.694831
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.444376 Loss1: 0.745228 Loss2: 0.699148
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.421725 Loss1: 0.725207 Loss2: 0.696518
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.421947 Loss1: 0.724504 Loss2: 0.697443
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.418528 Loss1: 0.716543 Loss2: 0.701985
+(DefaultActor pid=1831567) >> Training accuracy: 0.725317
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.274756 Loss1: 0.530785 Loss2: 0.743971
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.144970 Loss1: 0.480711 Loss2: 0.664259
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.104926 Loss1: 0.439425 Loss2: 0.665501
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.095962 Loss1: 0.433343 Loss2: 0.662619
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.097205 Loss1: 0.434741 Loss2: 0.662464
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.093557 Loss1: 0.427506 Loss2: 0.666050
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.069530 Loss1: 0.406484 Loss2: 0.663046
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.059522 Loss1: 0.394130 Loss2: 0.665393
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.101034 Loss1: 0.433776 Loss2: 0.667258
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.073603 Loss1: 0.406521 Loss2: 0.667082
+(DefaultActor pid=1831567) >> Training accuracy: 0.867091
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.255015 Loss1: 0.483934 Loss2: 0.771080
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.154187 Loss1: 0.462894 Loss2: 0.691293
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155095 Loss1: 0.470317 Loss2: 0.684777
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.133934 Loss1: 0.446505 Loss2: 0.687429
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.100514 Loss1: 0.417344 Loss2: 0.683170
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.114542 Loss1: 0.431890 Loss2: 0.682652
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.092493 Loss1: 0.410354 Loss2: 0.682139
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.082487 Loss1: 0.400856 Loss2: 0.681631
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.112725 Loss1: 0.426506 Loss2: 0.686219
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.090143 Loss1: 0.404309 Loss2: 0.685834
+(DefaultActor pid=1831567) >> Training accuracy: 0.860918
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.371168 Loss1: 0.637341 Loss2: 0.733827
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.309142 Loss1: 0.622190 Loss2: 0.686952
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.281147 Loss1: 0.597276 Loss2: 0.683870
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.278825 Loss1: 0.596327 Loss2: 0.682498
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.268777 Loss1: 0.584700 Loss2: 0.684077
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249542 Loss1: 0.566618 Loss2: 0.682924
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.270567 Loss1: 0.584703 Loss2: 0.685864
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273444 Loss1: 0.586418 Loss2: 0.687025
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.265126 Loss1: 0.576587 Loss2: 0.688539
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.270341 Loss1: 0.582286 Loss2: 0.688055
+(DefaultActor pid=1831567) >> Training accuracy: 0.809028
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.432216 Loss1: 0.668746 Loss2: 0.763470
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.321014 Loss1: 0.627252 Loss2: 0.693762
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.274246 Loss1: 0.584233 Loss2: 0.690014
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.288295 Loss1: 0.594718 Loss2: 0.693578
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.276742 Loss1: 0.585401 Loss2: 0.691340
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.268025 Loss1: 0.573129 Loss2: 0.694896
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.270715 Loss1: 0.578046 Loss2: 0.692669
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273003 Loss1: 0.578904 Loss2: 0.694098
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239917 Loss1: 0.545208 Loss2: 0.694710
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.269890 Loss1: 0.571299 Loss2: 0.698591
+(DefaultActor pid=1831567) >> Training accuracy: 0.822790
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381699 Loss1: 0.649553 Loss2: 0.732146
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.262403 Loss1: 0.609453 Loss2: 0.652950
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.250059 Loss1: 0.599715 Loss2: 0.650344
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225126 Loss1: 0.573626 Loss2: 0.651499
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.249635 Loss1: 0.592861 Loss2: 0.656774
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222812 Loss1: 0.567778 Loss2: 0.655034
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.222320 Loss1: 0.566053 Loss2: 0.656266
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268306 Loss1: 0.607701 Loss2: 0.660605
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.233410 Loss1: 0.575375 Loss2: 0.658035
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201376 Loss1: 0.545252 Loss2: 0.656124
+(DefaultActor pid=1831567) >> Training accuracy: 0.825721
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.594404 Loss1: 0.792304 Loss2: 0.802101
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.407948 Loss1: 0.716385 Loss2: 0.691563
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.401503 Loss1: 0.710420 Loss2: 0.691083
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.360940 Loss1: 0.671525 Loss2: 0.689416
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.355550 Loss1: 0.661906 Loss2: 0.693644
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.378702 Loss1: 0.685507 Loss2: 0.693195
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.335081 Loss1: 0.643218 Loss2: 0.691863
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.343584 Loss1: 0.650090 Loss2: 0.693494
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.324952 Loss1: 0.630504 Loss2: 0.694448
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.337312 Loss1: 0.641988 Loss2: 0.695323
+[2023-09-27 10:27:26,438][flwr][DEBUG] - fit_round 29 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.780976
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.674000
+[2023-09-27 10:27:27,727][flwr][INFO] - fit progress: (29, 0.93534632089039, {'accuracy': 0.674}, 14980.56385146873)
+[2023-09-27 10:27:27,728][flwr][DEBUG] - evaluate_round 29: strategy sampled 10 clients (out of 10)
+[2023-09-27 10:28:02,214][flwr][DEBUG] - evaluate_round 29 received 10 results and 0 failures
+[2023-09-27 10:28:02,214][flwr][DEBUG] - fit_round 30: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.558626 Loss1: 0.817066 Loss2: 0.741560
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.413558 Loss1: 0.766425 Loss2: 0.647133
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.400337 Loss1: 0.754652 Loss2: 0.645686
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.394529 Loss1: 0.749216 Loss2: 0.645314
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.395406 Loss1: 0.745560 Loss2: 0.649846
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.426118 Loss1: 0.772786 Loss2: 0.653332
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.375800 Loss1: 0.724928 Loss2: 0.650872
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.378639 Loss1: 0.726266 Loss2: 0.652373
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.373143 Loss1: 0.718696 Loss2: 0.654447
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.368305 Loss1: 0.716517 Loss2: 0.651787
+(DefaultActor pid=1831567) >> Training accuracy: 0.749774
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.260257 Loss1: 0.501170 Loss2: 0.759087
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.140659 Loss1: 0.462086 Loss2: 0.678573
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.112292 Loss1: 0.438559 Loss2: 0.673733
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.114388 Loss1: 0.440584 Loss2: 0.673804
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.105189 Loss1: 0.432603 Loss2: 0.672586
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.099388 Loss1: 0.426862 Loss2: 0.672526
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.108283 Loss1: 0.432789 Loss2: 0.675495
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.087846 Loss1: 0.414836 Loss2: 0.673010
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.076672 Loss1: 0.402778 Loss2: 0.673893
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.083153 Loss1: 0.406479 Loss2: 0.676674
+(DefaultActor pid=1831567) >> Training accuracy: 0.843364
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.436802 Loss1: 0.626263 Loss2: 0.810539
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.269520 Loss1: 0.571260 Loss2: 0.698260
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.254141 Loss1: 0.557352 Loss2: 0.696789
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261991 Loss1: 0.565440 Loss2: 0.696551
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.278638 Loss1: 0.574527 Loss2: 0.704111
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.265103 Loss1: 0.564920 Loss2: 0.700183
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212292 Loss1: 0.513843 Loss2: 0.698450
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222560 Loss1: 0.521835 Loss2: 0.700725
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.208476 Loss1: 0.507235 Loss2: 0.701241
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.232460 Loss1: 0.528061 Loss2: 0.704399
+(DefaultActor pid=1831567) >> Training accuracy: 0.835275
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.404904 Loss1: 0.654320 Loss2: 0.750584
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.295220 Loss1: 0.621118 Loss2: 0.674102
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.289150 Loss1: 0.613698 Loss2: 0.675451
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.292535 Loss1: 0.616216 Loss2: 0.676319
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.248166 Loss1: 0.572596 Loss2: 0.675571
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.259981 Loss1: 0.581523 Loss2: 0.678458
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.235138 Loss1: 0.556705 Loss2: 0.678433
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.247438 Loss1: 0.567640 Loss2: 0.679799
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.224590 Loss1: 0.545108 Loss2: 0.679482
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.238383 Loss1: 0.560560 Loss2: 0.677823
+(DefaultActor pid=1831567) >> Training accuracy: 0.813101
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.549941 Loss1: 0.779077 Loss2: 0.770864
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.461824 Loss1: 0.783822 Loss2: 0.678002
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.424424 Loss1: 0.754189 Loss2: 0.670235
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.370290 Loss1: 0.698999 Loss2: 0.671291
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.425855 Loss1: 0.752385 Loss2: 0.673470
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.373312 Loss1: 0.701452 Loss2: 0.671860
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.368848 Loss1: 0.696468 Loss2: 0.672380
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.377855 Loss1: 0.701377 Loss2: 0.676478
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.371340 Loss1: 0.695974 Loss2: 0.675366
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.351777 Loss1: 0.679653 Loss2: 0.672124
+(DefaultActor pid=1831567) >> Training accuracy: 0.748601
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389986 Loss1: 0.668196 Loss2: 0.721790
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.264228 Loss1: 0.627347 Loss2: 0.636881
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.237283 Loss1: 0.605493 Loss2: 0.631791
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205321 Loss1: 0.572864 Loss2: 0.632457
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210440 Loss1: 0.578637 Loss2: 0.631803
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.198124 Loss1: 0.565521 Loss2: 0.632603
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.206264 Loss1: 0.572125 Loss2: 0.634138
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.193234 Loss1: 0.558451 Loss2: 0.634783
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.202801 Loss1: 0.565422 Loss2: 0.637379
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.191967 Loss1: 0.558019 Loss2: 0.633948
+(DefaultActor pid=1831567) >> Training accuracy: 0.814024
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.547663 Loss1: 0.783505 Loss2: 0.764158
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.357851 Loss1: 0.705407 Loss2: 0.652444
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.367947 Loss1: 0.712333 Loss2: 0.655614
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335306 Loss1: 0.684015 Loss2: 0.651290
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.307043 Loss1: 0.655365 Loss2: 0.651678
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.339054 Loss1: 0.684167 Loss2: 0.654887
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.319173 Loss1: 0.660908 Loss2: 0.658264
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300238 Loss1: 0.645922 Loss2: 0.654316
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.301182 Loss1: 0.645565 Loss2: 0.655617
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.305780 Loss1: 0.643004 Loss2: 0.662776
+(DefaultActor pid=1831567) >> Training accuracy: 0.795230
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.375261 Loss1: 0.640911 Loss2: 0.734350
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.246504 Loss1: 0.597998 Loss2: 0.648506
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.259804 Loss1: 0.610064 Loss2: 0.649740
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223819 Loss1: 0.575085 Loss2: 0.648734
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.232002 Loss1: 0.582823 Loss2: 0.649179
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218637 Loss1: 0.569355 Loss2: 0.649282
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.194277 Loss1: 0.543152 Loss2: 0.651125
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212205 Loss1: 0.559040 Loss2: 0.653165
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180128 Loss1: 0.528536 Loss2: 0.651592
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198392 Loss1: 0.545072 Loss2: 0.653319
+(DefaultActor pid=1831567) >> Training accuracy: 0.807566
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.400264 Loss1: 0.637098 Loss2: 0.763166
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.317031 Loss1: 0.603671 Loss2: 0.713360
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.311202 Loss1: 0.596910 Loss2: 0.714293
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.300785 Loss1: 0.585410 Loss2: 0.715375
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.288633 Loss1: 0.576285 Loss2: 0.712348
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.291267 Loss1: 0.573350 Loss2: 0.717917
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295193 Loss1: 0.575447 Loss2: 0.719746
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.285321 Loss1: 0.567441 Loss2: 0.717880
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.289892 Loss1: 0.571756 Loss2: 0.718136
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.264984 Loss1: 0.549373 Loss2: 0.715611
+(DefaultActor pid=1831567) >> Training accuracy: 0.812748
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.261714 Loss1: 0.518648 Loss2: 0.743065
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.110453 Loss1: 0.457037 Loss2: 0.653416
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.087576 Loss1: 0.439000 Loss2: 0.648575
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.114602 Loss1: 0.467021 Loss2: 0.647581
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.087260 Loss1: 0.436172 Loss2: 0.651088
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.070131 Loss1: 0.422431 Loss2: 0.647699
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.057946 Loss1: 0.407818 Loss2: 0.650129
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.050529 Loss1: 0.401546 Loss2: 0.648983
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.046195 Loss1: 0.395995 Loss2: 0.650200
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.051978 Loss1: 0.398700 Loss2: 0.653278
+[2023-09-27 10:35:04,289][flwr][DEBUG] - fit_round 30 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.857060
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.671100
+[2023-09-27 10:35:05,982][flwr][INFO] - fit progress: (30, 0.9313092758289923, {'accuracy': 0.6711}, 15438.818260560744)
+[2023-09-27 10:35:05,982][flwr][DEBUG] - evaluate_round 30: strategy sampled 10 clients (out of 10)
+[2023-09-27 10:35:36,618][flwr][DEBUG] - evaluate_round 30 received 10 results and 0 failures
+[2023-09-27 10:35:36,619][flwr][DEBUG] - fit_round 31: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.318481 Loss1: 0.524676 Loss2: 0.793805
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172348 Loss1: 0.467429 Loss2: 0.704919
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.148309 Loss1: 0.450472 Loss2: 0.697837
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.135866 Loss1: 0.439225 Loss2: 0.696641
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.117671 Loss1: 0.420694 Loss2: 0.696977
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.112772 Loss1: 0.415330 Loss2: 0.697442
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.117213 Loss1: 0.418585 Loss2: 0.698628
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.112376 Loss1: 0.413609 Loss2: 0.698767
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.099943 Loss1: 0.398749 Loss2: 0.701194
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107395 Loss1: 0.408351 Loss2: 0.699044
+(DefaultActor pid=1831567) >> Training accuracy: 0.868827
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381897 Loss1: 0.626073 Loss2: 0.755824
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.246133 Loss1: 0.594569 Loss2: 0.651564
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213192 Loss1: 0.559767 Loss2: 0.653424
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217011 Loss1: 0.563342 Loss2: 0.653669
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221570 Loss1: 0.563055 Loss2: 0.658514
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184986 Loss1: 0.527920 Loss2: 0.657066
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.184445 Loss1: 0.531058 Loss2: 0.653386
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.188682 Loss1: 0.528865 Loss2: 0.659817
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165120 Loss1: 0.506863 Loss2: 0.658257
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.151379 Loss1: 0.491876 Loss2: 0.659504
+(DefaultActor pid=1831567) >> Training accuracy: 0.834746
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.556718 Loss1: 0.759221 Loss2: 0.797498
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.414273 Loss1: 0.723142 Loss2: 0.691131
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.396355 Loss1: 0.709236 Loss2: 0.687119
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.372544 Loss1: 0.687609 Loss2: 0.684935
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.367964 Loss1: 0.681690 Loss2: 0.686274
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.381332 Loss1: 0.692552 Loss2: 0.688779
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352522 Loss1: 0.666001 Loss2: 0.686521
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.330141 Loss1: 0.641856 Loss2: 0.688285
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.322310 Loss1: 0.631865 Loss2: 0.690446
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.314573 Loss1: 0.620856 Loss2: 0.693717
+(DefaultActor pid=1831567) >> Training accuracy: 0.770833
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568077 Loss1: 0.802130 Loss2: 0.765947
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.440122 Loss1: 0.765017 Loss2: 0.675105
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.389755 Loss1: 0.719540 Loss2: 0.670214
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.394776 Loss1: 0.721660 Loss2: 0.673116
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.370134 Loss1: 0.699684 Loss2: 0.670449
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.390300 Loss1: 0.713919 Loss2: 0.676382
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.374032 Loss1: 0.699712 Loss2: 0.674320
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.359830 Loss1: 0.684656 Loss2: 0.675175
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.365339 Loss1: 0.689086 Loss2: 0.676253
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.346665 Loss1: 0.668452 Loss2: 0.678213
+(DefaultActor pid=1831567) >> Training accuracy: 0.744636
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389446 Loss1: 0.651919 Loss2: 0.737527
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.256708 Loss1: 0.596062 Loss2: 0.660646
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.248834 Loss1: 0.587579 Loss2: 0.661255
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.250901 Loss1: 0.590708 Loss2: 0.660193
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.244757 Loss1: 0.582184 Loss2: 0.662572
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.233933 Loss1: 0.570383 Loss2: 0.663550
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212883 Loss1: 0.550092 Loss2: 0.662791
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202128 Loss1: 0.539471 Loss2: 0.662657
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193710 Loss1: 0.529646 Loss2: 0.664064
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229367 Loss1: 0.565310 Loss2: 0.664057
+(DefaultActor pid=1831567) >> Training accuracy: 0.798878
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568768 Loss1: 0.810714 Loss2: 0.758054
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.453431 Loss1: 0.782540 Loss2: 0.670891
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.433000 Loss1: 0.761375 Loss2: 0.671625
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.412114 Loss1: 0.738572 Loss2: 0.673542
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.419463 Loss1: 0.743573 Loss2: 0.675890
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.407767 Loss1: 0.733066 Loss2: 0.674701
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.385884 Loss1: 0.709902 Loss2: 0.675982
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.388036 Loss1: 0.711226 Loss2: 0.676810
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.383085 Loss1: 0.700846 Loss2: 0.682239
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.378320 Loss1: 0.698095 Loss2: 0.680226
+(DefaultActor pid=1831567) >> Training accuracy: 0.757699
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.414641 Loss1: 0.659552 Loss2: 0.755089
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304909 Loss1: 0.615488 Loss2: 0.689421
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260009 Loss1: 0.572576 Loss2: 0.687433
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.274827 Loss1: 0.583199 Loss2: 0.691629
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.257452 Loss1: 0.567403 Loss2: 0.690049
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.262718 Loss1: 0.574698 Loss2: 0.688020
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.253865 Loss1: 0.564301 Loss2: 0.689564
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253584 Loss1: 0.559025 Loss2: 0.694559
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.260163 Loss1: 0.565931 Loss2: 0.694232
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.237485 Loss1: 0.546293 Loss2: 0.691191
+(DefaultActor pid=1831567) >> Training accuracy: 0.815739
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.265647 Loss1: 0.515405 Loss2: 0.750242
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.148273 Loss1: 0.477683 Loss2: 0.670590
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.102515 Loss1: 0.434877 Loss2: 0.667639
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.101623 Loss1: 0.431943 Loss2: 0.669680
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.080640 Loss1: 0.411121 Loss2: 0.669519
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.092136 Loss1: 0.419470 Loss2: 0.672666
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.086206 Loss1: 0.413277 Loss2: 0.672929
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.074010 Loss1: 0.401805 Loss2: 0.672205
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.093885 Loss1: 0.420124 Loss2: 0.673760
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.086745 Loss1: 0.410857 Loss2: 0.675888
+(DefaultActor pid=1831567) >> Training accuracy: 0.865355
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370387 Loss1: 0.642303 Loss2: 0.728084
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.252343 Loss1: 0.600848 Loss2: 0.651495
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.225648 Loss1: 0.572770 Loss2: 0.652878
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237439 Loss1: 0.584109 Loss2: 0.653330
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221949 Loss1: 0.566885 Loss2: 0.655064
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210578 Loss1: 0.555931 Loss2: 0.654647
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.203393 Loss1: 0.549504 Loss2: 0.653889
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.195181 Loss1: 0.541379 Loss2: 0.653802
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211408 Loss1: 0.554740 Loss2: 0.656669
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.202276 Loss1: 0.544649 Loss2: 0.657627
+(DefaultActor pid=1831567) >> Training accuracy: 0.819285
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339168 Loss1: 0.631968 Loss2: 0.707200
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.257929 Loss1: 0.599360 Loss2: 0.658570
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.244631 Loss1: 0.587868 Loss2: 0.656763
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.258766 Loss1: 0.599637 Loss2: 0.659129
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224585 Loss1: 0.566992 Loss2: 0.657592
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226515 Loss1: 0.565182 Loss2: 0.661333
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.236504 Loss1: 0.574970 Loss2: 0.661533
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.232089 Loss1: 0.573164 Loss2: 0.658924
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.214778 Loss1: 0.555741 Loss2: 0.659036
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.239289 Loss1: 0.576625 Loss2: 0.662664
+[2023-09-27 10:42:13,465][flwr][DEBUG] - fit_round 31 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.797991
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.667200
+[2023-09-27 10:42:14,811][flwr][INFO] - fit progress: (31, 0.950761117874243, {'accuracy': 0.6672}, 15867.64734768914)
+[2023-09-27 10:42:14,811][flwr][DEBUG] - evaluate_round 31: strategy sampled 10 clients (out of 10)
+[2023-09-27 10:42:45,511][flwr][DEBUG] - evaluate_round 31 received 10 results and 0 failures
+[2023-09-27 10:42:45,512][flwr][DEBUG] - fit_round 32: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.219102 Loss1: 0.496179 Loss2: 0.722924
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.116710 Loss1: 0.467179 Loss2: 0.649531
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.089487 Loss1: 0.441603 Loss2: 0.647885
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.064095 Loss1: 0.420517 Loss2: 0.643578
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.059225 Loss1: 0.413688 Loss2: 0.645536
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.067248 Loss1: 0.422453 Loss2: 0.644795
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.056614 Loss1: 0.410646 Loss2: 0.645967
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.044186 Loss1: 0.398396 Loss2: 0.645790
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.055185 Loss1: 0.406964 Loss2: 0.648221
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.046289 Loss1: 0.398926 Loss2: 0.647363
+(DefaultActor pid=1831567) >> Training accuracy: 0.842785
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.556820 Loss1: 0.782392 Loss2: 0.774428
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387926 Loss1: 0.717873 Loss2: 0.670053
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.372649 Loss1: 0.708212 Loss2: 0.664437
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335313 Loss1: 0.669520 Loss2: 0.665792
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.335238 Loss1: 0.669276 Loss2: 0.665962
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.335229 Loss1: 0.668545 Loss2: 0.666684
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.354810 Loss1: 0.684078 Loss2: 0.670731
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.340156 Loss1: 0.670673 Loss2: 0.669483
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.317825 Loss1: 0.647909 Loss2: 0.669916
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.288242 Loss1: 0.621178 Loss2: 0.667063
+(DefaultActor pid=1831567) >> Training accuracy: 0.783991
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.565338 Loss1: 0.817728 Loss2: 0.747610
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.464383 Loss1: 0.805782 Loss2: 0.658601
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.434822 Loss1: 0.776132 Loss2: 0.658689
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.405516 Loss1: 0.748178 Loss2: 0.657338
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.391029 Loss1: 0.735166 Loss2: 0.655863
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.390619 Loss1: 0.734760 Loss2: 0.655859
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386164 Loss1: 0.723952 Loss2: 0.662213
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.370069 Loss1: 0.709727 Loss2: 0.660341
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.357719 Loss1: 0.697410 Loss2: 0.660309
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.362144 Loss1: 0.697073 Loss2: 0.665070
+(DefaultActor pid=1831567) >> Training accuracy: 0.759737
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.254695 Loss1: 0.501699 Loss2: 0.752997
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.114124 Loss1: 0.450709 Loss2: 0.663414
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.101788 Loss1: 0.439228 Loss2: 0.662560
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.097778 Loss1: 0.436597 Loss2: 0.661180
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.083729 Loss1: 0.423001 Loss2: 0.660728
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.072088 Loss1: 0.408736 Loss2: 0.663352
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.067371 Loss1: 0.405534 Loss2: 0.661838
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.073227 Loss1: 0.408795 Loss2: 0.664432
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.072764 Loss1: 0.407384 Loss2: 0.665380
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.044675 Loss1: 0.380833 Loss2: 0.663842
+(DefaultActor pid=1831567) >> Training accuracy: 0.873071
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.411665 Loss1: 0.614747 Loss2: 0.796919
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.260302 Loss1: 0.565939 Loss2: 0.694363
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260779 Loss1: 0.565326 Loss2: 0.695453
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.246143 Loss1: 0.554117 Loss2: 0.692026
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211749 Loss1: 0.517765 Loss2: 0.693984
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.209946 Loss1: 0.515813 Loss2: 0.694133
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.213994 Loss1: 0.518900 Loss2: 0.695094
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212657 Loss1: 0.516355 Loss2: 0.696303
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.195912 Loss1: 0.499336 Loss2: 0.696576
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200235 Loss1: 0.506994 Loss2: 0.693241
+(DefaultActor pid=1831567) >> Training accuracy: 0.842426
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384404 Loss1: 0.643240 Loss2: 0.741164
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.267781 Loss1: 0.601291 Loss2: 0.666490
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.268303 Loss1: 0.600889 Loss2: 0.667414
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261032 Loss1: 0.590682 Loss2: 0.670350
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.243756 Loss1: 0.572748 Loss2: 0.671009
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.247519 Loss1: 0.578891 Loss2: 0.668628
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.230443 Loss1: 0.561862 Loss2: 0.668581
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.224945 Loss1: 0.554450 Loss2: 0.670494
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.226907 Loss1: 0.555753 Loss2: 0.671154
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229373 Loss1: 0.554106 Loss2: 0.675267
+(DefaultActor pid=1831567) >> Training accuracy: 0.819712
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.393278 Loss1: 0.635024 Loss2: 0.758254
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.284211 Loss1: 0.611999 Loss2: 0.672212
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.230400 Loss1: 0.559353 Loss2: 0.671047
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225985 Loss1: 0.554711 Loss2: 0.671273
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.217098 Loss1: 0.544388 Loss2: 0.672710
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226741 Loss1: 0.551213 Loss2: 0.675528
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.214185 Loss1: 0.543904 Loss2: 0.670281
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202325 Loss1: 0.528911 Loss2: 0.673414
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.216133 Loss1: 0.541267 Loss2: 0.674866
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193411 Loss1: 0.518714 Loss2: 0.674697
+(DefaultActor pid=1831567) >> Training accuracy: 0.823808
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370198 Loss1: 0.660590 Loss2: 0.709608
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.224096 Loss1: 0.599133 Loss2: 0.624963
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223966 Loss1: 0.603487 Loss2: 0.620479
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.201685 Loss1: 0.579327 Loss2: 0.622358
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.193420 Loss1: 0.571522 Loss2: 0.621898
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.188620 Loss1: 0.567968 Loss2: 0.620652
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.190054 Loss1: 0.568768 Loss2: 0.621286
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.184383 Loss1: 0.561887 Loss2: 0.622495
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175966 Loss1: 0.551829 Loss2: 0.624137
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.177887 Loss1: 0.553308 Loss2: 0.624579
+(DefaultActor pid=1831567) >> Training accuracy: 0.815168
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.411151 Loss1: 0.635296 Loss2: 0.775855
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.318345 Loss1: 0.591417 Loss2: 0.726928
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.315307 Loss1: 0.590673 Loss2: 0.724633
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.309690 Loss1: 0.583322 Loss2: 0.726369
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293438 Loss1: 0.567998 Loss2: 0.725440
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.288607 Loss1: 0.562863 Loss2: 0.725744
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309332 Loss1: 0.580953 Loss2: 0.728379
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.287193 Loss1: 0.561194 Loss2: 0.725999
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.280090 Loss1: 0.551444 Loss2: 0.728645
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.292674 Loss1: 0.564018 Loss2: 0.728656
+(DefaultActor pid=1831567) >> Training accuracy: 0.815972
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.554617 Loss1: 0.776580 Loss2: 0.778037
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.427736 Loss1: 0.745925 Loss2: 0.681811
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.429623 Loss1: 0.750332 Loss2: 0.679290
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.398889 Loss1: 0.718647 Loss2: 0.680242
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.376433 Loss1: 0.695796 Loss2: 0.680637
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.356132 Loss1: 0.676528 Loss2: 0.679604
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350100 Loss1: 0.667986 Loss2: 0.682114
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.354838 Loss1: 0.671459 Loss2: 0.683378
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.344060 Loss1: 0.660762 Loss2: 0.683298
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.353935 Loss1: 0.669574 Loss2: 0.684361
+[2023-09-27 10:49:36,782][flwr][DEBUG] - fit_round 32 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.758862
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.682900
+[2023-09-27 10:49:38,444][flwr][INFO] - fit progress: (32, 0.9076737036910681, {'accuracy': 0.6829}, 16311.280521166045)
+[2023-09-27 10:49:38,445][flwr][DEBUG] - evaluate_round 32: strategy sampled 10 clients (out of 10)
+[2023-09-27 10:50:09,857][flwr][DEBUG] - evaluate_round 32 received 10 results and 0 failures
+[2023-09-27 10:50:09,858][flwr][DEBUG] - fit_round 33: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384447 Loss1: 0.650191 Loss2: 0.734255
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258980 Loss1: 0.596251 Loss2: 0.662728
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251010 Loss1: 0.588198 Loss2: 0.662813
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.259525 Loss1: 0.594804 Loss2: 0.664721
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.272169 Loss1: 0.604377 Loss2: 0.667793
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.235765 Loss1: 0.569407 Loss2: 0.666358
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.210795 Loss1: 0.545710 Loss2: 0.665085
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.245027 Loss1: 0.575342 Loss2: 0.669685
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211726 Loss1: 0.542790 Loss2: 0.668936
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.220186 Loss1: 0.549887 Loss2: 0.670299
+(DefaultActor pid=1831567) >> Training accuracy: 0.825521
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.265495 Loss1: 0.511322 Loss2: 0.754173
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.127196 Loss1: 0.458161 Loss2: 0.669034
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.092651 Loss1: 0.430307 Loss2: 0.662343
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.093286 Loss1: 0.430985 Loss2: 0.662301
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.081962 Loss1: 0.418243 Loss2: 0.663719
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.076276 Loss1: 0.413053 Loss2: 0.663223
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.057289 Loss1: 0.393068 Loss2: 0.664222
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.076456 Loss1: 0.410825 Loss2: 0.665631
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.066189 Loss1: 0.403990 Loss2: 0.662199
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.066320 Loss1: 0.401038 Loss2: 0.665282
+(DefaultActor pid=1831567) >> Training accuracy: 0.850887
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.426434 Loss1: 0.648700 Loss2: 0.777734
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301711 Loss1: 0.593021 Loss2: 0.708690
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.282818 Loss1: 0.574997 Loss2: 0.707822
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.294671 Loss1: 0.584576 Loss2: 0.710095
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.283370 Loss1: 0.575162 Loss2: 0.708208
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.258721 Loss1: 0.550779 Loss2: 0.707942
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.255795 Loss1: 0.548102 Loss2: 0.707693
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.264079 Loss1: 0.555433 Loss2: 0.708646
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.246059 Loss1: 0.536434 Loss2: 0.709626
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.251750 Loss1: 0.537849 Loss2: 0.713901
+(DefaultActor pid=1831567) >> Training accuracy: 0.815168
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.255130 Loss1: 0.499268 Loss2: 0.755862
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.121540 Loss1: 0.447230 Loss2: 0.674310
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.110182 Loss1: 0.434118 Loss2: 0.676063
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.102790 Loss1: 0.426606 Loss2: 0.676183
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.089251 Loss1: 0.414063 Loss2: 0.675188
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073827 Loss1: 0.398519 Loss2: 0.675309
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.080708 Loss1: 0.404229 Loss2: 0.676480
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.088510 Loss1: 0.410947 Loss2: 0.677563
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.087178 Loss1: 0.408657 Loss2: 0.678521
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.074868 Loss1: 0.397316 Loss2: 0.677552
+(DefaultActor pid=1831567) >> Training accuracy: 0.861690
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.361457 Loss1: 0.623938 Loss2: 0.737519
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217346 Loss1: 0.578572 Loss2: 0.638775
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231183 Loss1: 0.592452 Loss2: 0.638731
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202983 Loss1: 0.566582 Loss2: 0.636401
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169496 Loss1: 0.529241 Loss2: 0.640256
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183071 Loss1: 0.544443 Loss2: 0.638628
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161426 Loss1: 0.525017 Loss2: 0.636409
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168391 Loss1: 0.529195 Loss2: 0.639196
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147507 Loss1: 0.508492 Loss2: 0.639015
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.138784 Loss1: 0.497418 Loss2: 0.641366
+(DefaultActor pid=1831567) >> Training accuracy: 0.823358
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.378798 Loss1: 0.648755 Loss2: 0.730043
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225786 Loss1: 0.573907 Loss2: 0.651879
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.220600 Loss1: 0.567683 Loss2: 0.652917
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.224488 Loss1: 0.570152 Loss2: 0.654335
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203382 Loss1: 0.550573 Loss2: 0.652809
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218113 Loss1: 0.562373 Loss2: 0.655740
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.207066 Loss1: 0.549129 Loss2: 0.657938
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190318 Loss1: 0.535404 Loss2: 0.654914
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.190155 Loss1: 0.531068 Loss2: 0.659087
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.211844 Loss1: 0.553469 Loss2: 0.658375
+(DefaultActor pid=1831567) >> Training accuracy: 0.830387
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341997 Loss1: 0.636561 Loss2: 0.705437
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.249810 Loss1: 0.586515 Loss2: 0.663295
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.254278 Loss1: 0.595750 Loss2: 0.658528
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231291 Loss1: 0.572974 Loss2: 0.658317
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.238060 Loss1: 0.575979 Loss2: 0.662080
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.237757 Loss1: 0.575831 Loss2: 0.661926
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.229946 Loss1: 0.568167 Loss2: 0.661779
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222174 Loss1: 0.562311 Loss2: 0.659863
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.227281 Loss1: 0.565225 Loss2: 0.662056
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.233201 Loss1: 0.568096 Loss2: 0.665105
+(DefaultActor pid=1831567) >> Training accuracy: 0.807664
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.594224 Loss1: 0.815967 Loss2: 0.778256
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.412860 Loss1: 0.735195 Loss2: 0.677665
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.403821 Loss1: 0.722719 Loss2: 0.681102
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.416456 Loss1: 0.736913 Loss2: 0.679543
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.380465 Loss1: 0.698373 Loss2: 0.682092
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.400679 Loss1: 0.718919 Loss2: 0.681760
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.356271 Loss1: 0.677291 Loss2: 0.678980
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.372839 Loss1: 0.690568 Loss2: 0.682272
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.340338 Loss1: 0.658452 Loss2: 0.681886
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.322825 Loss1: 0.639318 Loss2: 0.683507
+(DefaultActor pid=1831567) >> Training accuracy: 0.743004
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.564665 Loss1: 0.811934 Loss2: 0.752730
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.428956 Loss1: 0.759793 Loss2: 0.669163
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.400642 Loss1: 0.731659 Loss2: 0.668983
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.418535 Loss1: 0.750684 Loss2: 0.667852
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.413732 Loss1: 0.741934 Loss2: 0.671797
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.399114 Loss1: 0.724799 Loss2: 0.674315
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.400988 Loss1: 0.722601 Loss2: 0.678387
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.417294 Loss1: 0.740545 Loss2: 0.676748
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.368559 Loss1: 0.690758 Loss2: 0.677800
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.389428 Loss1: 0.713856 Loss2: 0.675573
+(DefaultActor pid=1831567) >> Training accuracy: 0.751359
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.531808 Loss1: 0.763448 Loss2: 0.768360
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.375101 Loss1: 0.709509 Loss2: 0.665593
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.348480 Loss1: 0.684443 Loss2: 0.664037
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.334976 Loss1: 0.670137 Loss2: 0.664839
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.337352 Loss1: 0.669817 Loss2: 0.667535
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306019 Loss1: 0.642383 Loss2: 0.663636
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.308911 Loss1: 0.641655 Loss2: 0.667256
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320185 Loss1: 0.653150 Loss2: 0.667035
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299893 Loss1: 0.633155 Loss2: 0.666739
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284668 Loss1: 0.615381 Loss2: 0.669287
+[2023-09-27 10:56:45,449][flwr][DEBUG] - fit_round 33 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.772752
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.682900
+[2023-09-27 10:56:47,281][flwr][INFO] - fit progress: (33, 0.908714735469879, {'accuracy': 0.6829}, 16740.117826285772)
+[2023-09-27 10:56:47,282][flwr][DEBUG] - evaluate_round 33: strategy sampled 10 clients (out of 10)
+[2023-09-27 10:57:18,579][flwr][DEBUG] - evaluate_round 33 received 10 results and 0 failures
+[2023-09-27 10:57:18,581][flwr][DEBUG] - fit_round 34: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475017 Loss1: 0.633700 Loss2: 0.841317
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.379035 Loss1: 0.594066 Loss2: 0.784969
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.379857 Loss1: 0.591406 Loss2: 0.788451
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.354553 Loss1: 0.568668 Loss2: 0.785885
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.353994 Loss1: 0.566431 Loss2: 0.787562
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.355304 Loss1: 0.568587 Loss2: 0.786717
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.339747 Loss1: 0.551052 Loss2: 0.788695
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.338278 Loss1: 0.549548 Loss2: 0.788729
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.363598 Loss1: 0.569157 Loss2: 0.794441
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.349777 Loss1: 0.556532 Loss2: 0.793246
+(DefaultActor pid=1831567) >> Training accuracy: 0.811136
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.239694 Loss1: 0.486988 Loss2: 0.752707
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.122264 Loss1: 0.457270 Loss2: 0.664993
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.095314 Loss1: 0.431496 Loss2: 0.663818
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.098679 Loss1: 0.435437 Loss2: 0.663243
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.075976 Loss1: 0.413044 Loss2: 0.662932
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.066267 Loss1: 0.403459 Loss2: 0.662808
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.090017 Loss1: 0.423201 Loss2: 0.666816
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.069447 Loss1: 0.404332 Loss2: 0.665116
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.062167 Loss1: 0.394915 Loss2: 0.667252
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.049129 Loss1: 0.382096 Loss2: 0.667034
+(DefaultActor pid=1831567) >> Training accuracy: 0.864583
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.252673 Loss1: 0.519799 Loss2: 0.732873
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.097341 Loss1: 0.442043 Loss2: 0.655298
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.093679 Loss1: 0.441285 Loss2: 0.652394
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.077316 Loss1: 0.429712 Loss2: 0.647604
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.068362 Loss1: 0.420616 Loss2: 0.647746
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.061788 Loss1: 0.412561 Loss2: 0.649228
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.063109 Loss1: 0.412738 Loss2: 0.650371
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.055122 Loss1: 0.404789 Loss2: 0.650332
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.056020 Loss1: 0.400592 Loss2: 0.655428
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.066587 Loss1: 0.412737 Loss2: 0.653849
+(DefaultActor pid=1831567) >> Training accuracy: 0.864198
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.424691 Loss1: 0.610894 Loss2: 0.813797
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.273467 Loss1: 0.574051 Loss2: 0.699416
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238297 Loss1: 0.540203 Loss2: 0.698095
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.245212 Loss1: 0.548649 Loss2: 0.696562
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234180 Loss1: 0.538169 Loss2: 0.696011
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225633 Loss1: 0.527005 Loss2: 0.698629
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196999 Loss1: 0.497917 Loss2: 0.699082
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.205749 Loss1: 0.509418 Loss2: 0.696331
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199982 Loss1: 0.498454 Loss2: 0.701527
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.166910 Loss1: 0.463805 Loss2: 0.703105
+(DefaultActor pid=1831567) >> Training accuracy: 0.841102
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386680 Loss1: 0.675369 Loss2: 0.711311
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.229557 Loss1: 0.598662 Loss2: 0.630895
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.217211 Loss1: 0.588027 Loss2: 0.629184
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.219590 Loss1: 0.592156 Loss2: 0.627434
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.205894 Loss1: 0.577543 Loss2: 0.628352
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191928 Loss1: 0.562599 Loss2: 0.629329
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179949 Loss1: 0.551027 Loss2: 0.628923
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169134 Loss1: 0.538692 Loss2: 0.630442
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.181324 Loss1: 0.549900 Loss2: 0.631424
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175284 Loss1: 0.543845 Loss2: 0.631439
+(DefaultActor pid=1831567) >> Training accuracy: 0.821456
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.398307 Loss1: 0.632853 Loss2: 0.765454
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.264394 Loss1: 0.585010 Loss2: 0.679384
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.254308 Loss1: 0.574451 Loss2: 0.679858
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.253563 Loss1: 0.575505 Loss2: 0.678058
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246062 Loss1: 0.563818 Loss2: 0.682244
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225206 Loss1: 0.547906 Loss2: 0.677300
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.222156 Loss1: 0.542002 Loss2: 0.680153
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207801 Loss1: 0.524881 Loss2: 0.682920
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.228696 Loss1: 0.544810 Loss2: 0.683885
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.213657 Loss1: 0.529395 Loss2: 0.684262
+(DefaultActor pid=1831567) >> Training accuracy: 0.841283
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.558379 Loss1: 0.805226 Loss2: 0.753153
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.431122 Loss1: 0.769990 Loss2: 0.661133
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.421131 Loss1: 0.763690 Loss2: 0.657441
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.389120 Loss1: 0.728422 Loss2: 0.660698
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.404049 Loss1: 0.740757 Loss2: 0.663292
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.381727 Loss1: 0.718302 Loss2: 0.663425
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.392166 Loss1: 0.726716 Loss2: 0.665450
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.368953 Loss1: 0.703824 Loss2: 0.665129
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.363911 Loss1: 0.697862 Loss2: 0.666048
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.332924 Loss1: 0.666688 Loss2: 0.666236
+(DefaultActor pid=1831567) >> Training accuracy: 0.752038
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.431423 Loss1: 0.627414 Loss2: 0.804009
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.328244 Loss1: 0.611440 Loss2: 0.716803
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.286193 Loss1: 0.570023 Loss2: 0.716170
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.297165 Loss1: 0.577086 Loss2: 0.720080
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.271972 Loss1: 0.551330 Loss2: 0.720642
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.276008 Loss1: 0.557192 Loss2: 0.718816
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.279315 Loss1: 0.559699 Loss2: 0.719615
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.269747 Loss1: 0.548033 Loss2: 0.721714
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.268322 Loss1: 0.547266 Loss2: 0.721056
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.235370 Loss1: 0.513323 Loss2: 0.722047
+(DefaultActor pid=1831567) >> Training accuracy: 0.824319
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.541979 Loss1: 0.786196 Loss2: 0.755783
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.391877 Loss1: 0.729492 Loss2: 0.662385
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.387285 Loss1: 0.723226 Loss2: 0.664059
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.362913 Loss1: 0.702732 Loss2: 0.660181
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.365882 Loss1: 0.701809 Loss2: 0.664073
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.339527 Loss1: 0.675377 Loss2: 0.664151
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.368029 Loss1: 0.703118 Loss2: 0.664911
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.374544 Loss1: 0.706783 Loss2: 0.667761
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331300 Loss1: 0.663424 Loss2: 0.667875
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.324130 Loss1: 0.656359 Loss2: 0.667770
+(DefaultActor pid=1831567) >> Training accuracy: 0.753032
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.562511 Loss1: 0.783677 Loss2: 0.778834
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.390501 Loss1: 0.717437 Loss2: 0.673064
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.369927 Loss1: 0.702149 Loss2: 0.667778
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335635 Loss1: 0.664647 Loss2: 0.670988
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.328444 Loss1: 0.656940 Loss2: 0.671504
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.347048 Loss1: 0.669645 Loss2: 0.677403
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.329111 Loss1: 0.654001 Loss2: 0.675110
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.312552 Loss1: 0.636825 Loss2: 0.675727
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.324556 Loss1: 0.649652 Loss2: 0.674904
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309247 Loss1: 0.630633 Loss2: 0.678614
+[2023-09-27 11:03:57,763][flwr][DEBUG] - fit_round 34 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.785910
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.684800
+[2023-09-27 11:03:59,362][flwr][INFO] - fit progress: (34, 0.8978847988878196, {'accuracy': 0.6848}, 17172.19873186201)
+[2023-09-27 11:03:59,363][flwr][DEBUG] - evaluate_round 34: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:04:31,584][flwr][DEBUG] - evaluate_round 34 received 10 results and 0 failures
+[2023-09-27 11:04:31,585][flwr][DEBUG] - fit_round 35: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.390210 Loss1: 0.642241 Loss2: 0.747969
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.278092 Loss1: 0.599312 Loss2: 0.678780
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262059 Loss1: 0.582444 Loss2: 0.679615
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.254097 Loss1: 0.573727 Loss2: 0.680370
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.244221 Loss1: 0.565445 Loss2: 0.678776
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.248959 Loss1: 0.569841 Loss2: 0.679118
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.230369 Loss1: 0.548705 Loss2: 0.681664
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220266 Loss1: 0.537827 Loss2: 0.682439
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213232 Loss1: 0.531522 Loss2: 0.681710
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.231077 Loss1: 0.546122 Loss2: 0.684956
+(DefaultActor pid=1831567) >> Training accuracy: 0.825648
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.532664 Loss1: 0.776279 Loss2: 0.756384
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.364373 Loss1: 0.715570 Loss2: 0.648803
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356376 Loss1: 0.702793 Loss2: 0.653583
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316278 Loss1: 0.665665 Loss2: 0.650613
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322679 Loss1: 0.672790 Loss2: 0.649889
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.300067 Loss1: 0.647063 Loss2: 0.653004
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.296383 Loss1: 0.641564 Loss2: 0.654819
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.283433 Loss1: 0.630643 Loss2: 0.652791
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.303772 Loss1: 0.647070 Loss2: 0.656702
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289017 Loss1: 0.633105 Loss2: 0.655912
+(DefaultActor pid=1831567) >> Training accuracy: 0.776316
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.299481 Loss1: 0.504577 Loss2: 0.794904
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.175022 Loss1: 0.468387 Loss2: 0.706635
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.135895 Loss1: 0.438528 Loss2: 0.697367
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.122444 Loss1: 0.427535 Loss2: 0.694909
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.114991 Loss1: 0.420576 Loss2: 0.694416
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.103506 Loss1: 0.407288 Loss2: 0.696218
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.106786 Loss1: 0.411241 Loss2: 0.695545
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.112447 Loss1: 0.414152 Loss2: 0.698295
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.106173 Loss1: 0.407903 Loss2: 0.698270
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.081215 Loss1: 0.382320 Loss2: 0.698895
+(DefaultActor pid=1831567) >> Training accuracy: 0.857253
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.544851 Loss1: 0.788498 Loss2: 0.756353
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.444979 Loss1: 0.768285 Loss2: 0.676694
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.440160 Loss1: 0.761097 Loss2: 0.679063
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.415233 Loss1: 0.737260 Loss2: 0.677973
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.383951 Loss1: 0.708409 Loss2: 0.675542
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.431077 Loss1: 0.749665 Loss2: 0.681411
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.388212 Loss1: 0.709792 Loss2: 0.678420
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.379347 Loss1: 0.698952 Loss2: 0.680395
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.378520 Loss1: 0.694073 Loss2: 0.684448
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.375880 Loss1: 0.694850 Loss2: 0.681031
+(DefaultActor pid=1831567) >> Training accuracy: 0.782382
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.555381 Loss1: 0.797564 Loss2: 0.757817
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.398338 Loss1: 0.731944 Loss2: 0.666394
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.395034 Loss1: 0.729616 Loss2: 0.665418
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.372497 Loss1: 0.703380 Loss2: 0.669118
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.364591 Loss1: 0.696700 Loss2: 0.667891
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362444 Loss1: 0.693190 Loss2: 0.669254
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.364459 Loss1: 0.694370 Loss2: 0.670089
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.351047 Loss1: 0.678397 Loss2: 0.672650
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.343448 Loss1: 0.669278 Loss2: 0.674170
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.365640 Loss1: 0.691362 Loss2: 0.674278
+(DefaultActor pid=1831567) >> Training accuracy: 0.760494
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.251233 Loss1: 0.502805 Loss2: 0.748428
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.129077 Loss1: 0.452560 Loss2: 0.676517
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.096307 Loss1: 0.427947 Loss2: 0.668360
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074935 Loss1: 0.406936 Loss2: 0.667998
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.092050 Loss1: 0.421427 Loss2: 0.670623
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.077240 Loss1: 0.404447 Loss2: 0.672794
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.066090 Loss1: 0.395255 Loss2: 0.670835
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.076338 Loss1: 0.404829 Loss2: 0.671509
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.088996 Loss1: 0.414027 Loss2: 0.674969
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.070604 Loss1: 0.397217 Loss2: 0.673387
+(DefaultActor pid=1831567) >> Training accuracy: 0.873071
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.366899 Loss1: 0.624854 Loss2: 0.742045
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.256077 Loss1: 0.595511 Loss2: 0.660566
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.228683 Loss1: 0.568906 Loss2: 0.659777
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225139 Loss1: 0.562270 Loss2: 0.662869
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211653 Loss1: 0.547844 Loss2: 0.663808
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206654 Loss1: 0.540480 Loss2: 0.666175
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204758 Loss1: 0.539974 Loss2: 0.664784
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.214991 Loss1: 0.545906 Loss2: 0.669085
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199832 Loss1: 0.531201 Loss2: 0.668631
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201108 Loss1: 0.531159 Loss2: 0.669949
+(DefaultActor pid=1831567) >> Training accuracy: 0.833470
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.401977 Loss1: 0.651244 Loss2: 0.750733
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206619 Loss1: 0.560637 Loss2: 0.645982
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198715 Loss1: 0.555527 Loss2: 0.643188
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205283 Loss1: 0.562423 Loss2: 0.642860
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196711 Loss1: 0.551850 Loss2: 0.644861
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.151224 Loss1: 0.507891 Loss2: 0.643333
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.162265 Loss1: 0.518712 Loss2: 0.643553
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161595 Loss1: 0.515314 Loss2: 0.646281
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.152900 Loss1: 0.504289 Loss2: 0.648610
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150007 Loss1: 0.501065 Loss2: 0.648942
+(DefaultActor pid=1831567) >> Training accuracy: 0.836070
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.351274 Loss1: 0.628353 Loss2: 0.722920
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243711 Loss1: 0.592945 Loss2: 0.650766
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.258755 Loss1: 0.606268 Loss2: 0.652487
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.221774 Loss1: 0.570163 Loss2: 0.651611
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.226613 Loss1: 0.573981 Loss2: 0.652631
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220626 Loss1: 0.568550 Loss2: 0.652076
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.216403 Loss1: 0.559945 Loss2: 0.656458
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.196424 Loss1: 0.541705 Loss2: 0.654719
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193325 Loss1: 0.538160 Loss2: 0.655165
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.219117 Loss1: 0.560545 Loss2: 0.658572
+(DefaultActor pid=1831567) >> Training accuracy: 0.831931
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.323305 Loss1: 0.604157 Loss2: 0.719147
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259338 Loss1: 0.591419 Loss2: 0.667919
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.233652 Loss1: 0.566311 Loss2: 0.667341
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.248800 Loss1: 0.580641 Loss2: 0.668159
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234628 Loss1: 0.566141 Loss2: 0.668487
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231585 Loss1: 0.564185 Loss2: 0.667400
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.239291 Loss1: 0.568216 Loss2: 0.671075
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.218905 Loss1: 0.548414 Loss2: 0.670491
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.210384 Loss1: 0.540575 Loss2: 0.669809
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.209167 Loss1: 0.538911 Loss2: 0.670256
+(DefaultActor pid=1831567) >> Training accuracy: 0.818948
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 11:11:44,114][flwr][DEBUG] - fit_round 35 received 10 results and 0 failures
+>> Test accuracy: 0.682700
+[2023-09-27 11:11:45,679][flwr][INFO] - fit progress: (35, 0.9095158785486374, {'accuracy': 0.6827}, 17638.51521933591)
+[2023-09-27 11:11:45,679][flwr][DEBUG] - evaluate_round 35: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:12:17,059][flwr][DEBUG] - evaluate_round 35 received 10 results and 0 failures
+[2023-09-27 11:12:17,060][flwr][DEBUG] - fit_round 36: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.383397 Loss1: 0.606824 Loss2: 0.776574
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.314283 Loss1: 0.583446 Loss2: 0.730837
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.298306 Loss1: 0.568811 Loss2: 0.729496
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.286757 Loss1: 0.558560 Loss2: 0.728197
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.298195 Loss1: 0.566029 Loss2: 0.732166
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.276647 Loss1: 0.546496 Loss2: 0.730151
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.274698 Loss1: 0.543753 Loss2: 0.730945
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273347 Loss1: 0.542291 Loss2: 0.731056
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.270192 Loss1: 0.536165 Loss2: 0.734027
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279044 Loss1: 0.546284 Loss2: 0.732760
+(DefaultActor pid=1831567) >> Training accuracy: 0.813616
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.233945 Loss1: 0.508357 Loss2: 0.725588
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092571 Loss1: 0.447691 Loss2: 0.644879
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.075411 Loss1: 0.433510 Loss2: 0.641902
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.079343 Loss1: 0.437253 Loss2: 0.642090
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.030520 Loss1: 0.390402 Loss2: 0.640117
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.050940 Loss1: 0.408725 Loss2: 0.642215
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051226 Loss1: 0.408277 Loss2: 0.642949
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.029197 Loss1: 0.386887 Loss2: 0.642310
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.025145 Loss1: 0.380993 Loss2: 0.644152
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.047941 Loss1: 0.399978 Loss2: 0.647963
+(DefaultActor pid=1831567) >> Training accuracy: 0.870563
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.184246 Loss1: 0.474458 Loss2: 0.709788
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.081247 Loss1: 0.447782 Loss2: 0.633465
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.064890 Loss1: 0.433040 Loss2: 0.631850
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.044086 Loss1: 0.414092 Loss2: 0.629994
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.058439 Loss1: 0.424406 Loss2: 0.634033
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.050779 Loss1: 0.417734 Loss2: 0.633045
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.045932 Loss1: 0.412805 Loss2: 0.633127
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030708 Loss1: 0.396648 Loss2: 0.634060
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.040893 Loss1: 0.405891 Loss2: 0.635002
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028094 Loss1: 0.393282 Loss2: 0.634812
+(DefaultActor pid=1831567) >> Training accuracy: 0.871914
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568145 Loss1: 0.807623 Loss2: 0.760522
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.418200 Loss1: 0.749892 Loss2: 0.668308
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.429597 Loss1: 0.760372 Loss2: 0.669225
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.387558 Loss1: 0.719310 Loss2: 0.668248
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.396536 Loss1: 0.729373 Loss2: 0.667164
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.360599 Loss1: 0.689723 Loss2: 0.670875
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386071 Loss1: 0.717339 Loss2: 0.668732
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.382315 Loss1: 0.711005 Loss2: 0.671309
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.359620 Loss1: 0.687875 Loss2: 0.671745
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.341683 Loss1: 0.667787 Loss2: 0.673896
+(DefaultActor pid=1831567) >> Training accuracy: 0.770833
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.399399 Loss1: 0.642255 Loss2: 0.757145
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.274158 Loss1: 0.591437 Loss2: 0.682721
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.283996 Loss1: 0.601357 Loss2: 0.682639
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225926 Loss1: 0.545452 Loss2: 0.680474
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.239194 Loss1: 0.559014 Loss2: 0.680180
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.250772 Loss1: 0.566207 Loss2: 0.684565
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.241540 Loss1: 0.560914 Loss2: 0.680626
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.227764 Loss1: 0.543695 Loss2: 0.684069
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.223503 Loss1: 0.537202 Loss2: 0.686301
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200008 Loss1: 0.516313 Loss2: 0.683696
+(DefaultActor pid=1831567) >> Training accuracy: 0.817308
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.371809 Loss1: 0.607393 Loss2: 0.764416
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.249992 Loss1: 0.566656 Loss2: 0.683336
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.249456 Loss1: 0.569110 Loss2: 0.680345
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.243327 Loss1: 0.562596 Loss2: 0.680731
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221797 Loss1: 0.537224 Loss2: 0.684572
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.235401 Loss1: 0.553424 Loss2: 0.681977
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212449 Loss1: 0.528269 Loss2: 0.684180
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220939 Loss1: 0.536045 Loss2: 0.684893
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.204087 Loss1: 0.521217 Loss2: 0.682871
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.205834 Loss1: 0.519269 Loss2: 0.686565
+(DefaultActor pid=1831567) >> Training accuracy: 0.831003
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.536928 Loss1: 0.774324 Loss2: 0.762604
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394230 Loss1: 0.735886 Loss2: 0.658343
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356743 Loss1: 0.701514 Loss2: 0.655229
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.337404 Loss1: 0.678244 Loss2: 0.659160
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.307386 Loss1: 0.649024 Loss2: 0.658362
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.301628 Loss1: 0.642160 Loss2: 0.659468
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294270 Loss1: 0.636096 Loss2: 0.658174
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276010 Loss1: 0.616822 Loss2: 0.659189
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.276233 Loss1: 0.615452 Loss2: 0.660781
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.288924 Loss1: 0.629506 Loss2: 0.659418
+(DefaultActor pid=1831567) >> Training accuracy: 0.790022
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.367574 Loss1: 0.653970 Loss2: 0.713604
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236591 Loss1: 0.601235 Loss2: 0.635356
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.202091 Loss1: 0.570758 Loss2: 0.631333
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205355 Loss1: 0.573512 Loss2: 0.631842
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.199387 Loss1: 0.566891 Loss2: 0.632496
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.178302 Loss1: 0.546684 Loss2: 0.631618
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179693 Loss1: 0.546419 Loss2: 0.633273
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.186934 Loss1: 0.552938 Loss2: 0.633996
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.174617 Loss1: 0.536826 Loss2: 0.637791
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174913 Loss1: 0.538006 Loss2: 0.636907
+(DefaultActor pid=1831567) >> Training accuracy: 0.825457
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.539336 Loss1: 0.767164 Loss2: 0.772172
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.438389 Loss1: 0.756948 Loss2: 0.681441
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.402772 Loss1: 0.726928 Loss2: 0.675844
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.366110 Loss1: 0.690691 Loss2: 0.675419
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.348022 Loss1: 0.673214 Loss2: 0.674808
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.325518 Loss1: 0.649887 Loss2: 0.675631
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338548 Loss1: 0.662949 Loss2: 0.675599
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.337191 Loss1: 0.658117 Loss2: 0.679075
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.363622 Loss1: 0.681451 Loss2: 0.682171
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.316784 Loss1: 0.637116 Loss2: 0.679668
+(DefaultActor pid=1831567) >> Training accuracy: 0.771455
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.409619 Loss1: 0.636841 Loss2: 0.772777
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.254682 Loss1: 0.588228 Loss2: 0.666454
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.221690 Loss1: 0.561508 Loss2: 0.660182
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187933 Loss1: 0.527754 Loss2: 0.660180
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.177965 Loss1: 0.517053 Loss2: 0.660911
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206680 Loss1: 0.541280 Loss2: 0.665400
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.155394 Loss1: 0.493966 Loss2: 0.661427
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173381 Loss1: 0.507135 Loss2: 0.666246
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.168289 Loss1: 0.499681 Loss2: 0.668608
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176538 Loss1: 0.505568 Loss2: 0.670970
+(DefaultActor pid=1831567) >> Training accuracy: 0.828919
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 11:19:15,336][flwr][DEBUG] - fit_round 36 received 10 results and 0 failures
+>> Test accuracy: 0.680400
+[2023-09-27 11:19:17,177][flwr][INFO] - fit progress: (36, 0.9122387365030404, {'accuracy': 0.6804}, 18090.013447194826)
+[2023-09-27 11:19:17,177][flwr][DEBUG] - evaluate_round 36: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:19:48,165][flwr][DEBUG] - evaluate_round 36 received 10 results and 0 failures
+[2023-09-27 11:19:48,166][flwr][DEBUG] - fit_round 37: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.319233 Loss1: 0.603299 Loss2: 0.715934
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227616 Loss1: 0.586480 Loss2: 0.641136
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215966 Loss1: 0.573170 Loss2: 0.642796
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200604 Loss1: 0.557570 Loss2: 0.643034
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191656 Loss1: 0.546564 Loss2: 0.645092
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225052 Loss1: 0.575145 Loss2: 0.649907
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192341 Loss1: 0.543111 Loss2: 0.649230
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190647 Loss1: 0.541712 Loss2: 0.648935
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.209962 Loss1: 0.561298 Loss2: 0.648665
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.190961 Loss1: 0.542186 Loss2: 0.648776
+(DefaultActor pid=1831567) >> Training accuracy: 0.818710
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.585184 Loss1: 0.793920 Loss2: 0.791264
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.454871 Loss1: 0.760101 Loss2: 0.694770
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.384791 Loss1: 0.694020 Loss2: 0.690770
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.378452 Loss1: 0.685596 Loss2: 0.692856
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.381580 Loss1: 0.689617 Loss2: 0.691963
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.372818 Loss1: 0.680421 Loss2: 0.692398
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.363875 Loss1: 0.666044 Loss2: 0.697830
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.362735 Loss1: 0.666137 Loss2: 0.696598
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.375477 Loss1: 0.676522 Loss2: 0.698954
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.371967 Loss1: 0.675847 Loss2: 0.696120
+(DefaultActor pid=1831567) >> Training accuracy: 0.750700
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.350585 Loss1: 0.628479 Loss2: 0.722106
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.255138 Loss1: 0.579474 Loss2: 0.675664
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.263238 Loss1: 0.588316 Loss2: 0.674922
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226910 Loss1: 0.556084 Loss2: 0.670826
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234403 Loss1: 0.559307 Loss2: 0.675096
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.229672 Loss1: 0.556098 Loss2: 0.673573
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224332 Loss1: 0.548598 Loss2: 0.675734
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.244946 Loss1: 0.567347 Loss2: 0.677600
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.227913 Loss1: 0.550891 Loss2: 0.677022
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.233896 Loss1: 0.555295 Loss2: 0.678601
+(DefaultActor pid=1831567) >> Training accuracy: 0.819320
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.377110 Loss1: 0.611828 Loss2: 0.765281
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.235703 Loss1: 0.569995 Loss2: 0.665708
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219834 Loss1: 0.558426 Loss2: 0.661408
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199773 Loss1: 0.539554 Loss2: 0.660219
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.181706 Loss1: 0.520879 Loss2: 0.660827
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167769 Loss1: 0.508537 Loss2: 0.659232
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187368 Loss1: 0.523161 Loss2: 0.664207
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173617 Loss1: 0.509193 Loss2: 0.664424
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134429 Loss1: 0.473774 Loss2: 0.660655
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.159387 Loss1: 0.493918 Loss2: 0.665469
+(DefaultActor pid=1831567) >> Training accuracy: 0.837129
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.296580 Loss1: 0.494273 Loss2: 0.802307
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.161230 Loss1: 0.442690 Loss2: 0.718540
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.128817 Loss1: 0.416341 Loss2: 0.712477
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.134163 Loss1: 0.425114 Loss2: 0.709050
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.120117 Loss1: 0.407146 Loss2: 0.712971
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.122403 Loss1: 0.408204 Loss2: 0.714199
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.115137 Loss1: 0.405347 Loss2: 0.709790
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.087925 Loss1: 0.377470 Loss2: 0.710455
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.108816 Loss1: 0.395335 Loss2: 0.713481
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.095654 Loss1: 0.381773 Loss2: 0.713882
+(DefaultActor pid=1831567) >> Training accuracy: 0.860147
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.557518 Loss1: 0.792415 Loss2: 0.765103
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.449832 Loss1: 0.766533 Loss2: 0.683298
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.437499 Loss1: 0.752264 Loss2: 0.685235
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.425691 Loss1: 0.741188 Loss2: 0.684504
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.395194 Loss1: 0.710438 Loss2: 0.684757
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.386820 Loss1: 0.699025 Loss2: 0.687795
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.384561 Loss1: 0.698069 Loss2: 0.686492
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.404385 Loss1: 0.712352 Loss2: 0.692032
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.396742 Loss1: 0.703567 Loss2: 0.693175
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.381311 Loss1: 0.691068 Loss2: 0.690244
+(DefaultActor pid=1831567) >> Training accuracy: 0.755888
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.388952 Loss1: 0.636919 Loss2: 0.752032
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236677 Loss1: 0.563239 Loss2: 0.673438
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.250906 Loss1: 0.577864 Loss2: 0.673041
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.221810 Loss1: 0.547710 Loss2: 0.674100
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.213258 Loss1: 0.540143 Loss2: 0.673114
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.229987 Loss1: 0.551632 Loss2: 0.678355
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.218580 Loss1: 0.541952 Loss2: 0.676627
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.193421 Loss1: 0.515120 Loss2: 0.678301
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.215550 Loss1: 0.538485 Loss2: 0.677065
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200092 Loss1: 0.522059 Loss2: 0.678033
+(DefaultActor pid=1831567) >> Training accuracy: 0.823602
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.276100 Loss1: 0.509299 Loss2: 0.766802
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.133558 Loss1: 0.440836 Loss2: 0.692722
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.125217 Loss1: 0.437541 Loss2: 0.687675
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.093261 Loss1: 0.406550 Loss2: 0.686711
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.095356 Loss1: 0.409606 Loss2: 0.685750
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.105725 Loss1: 0.416673 Loss2: 0.689052
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.088740 Loss1: 0.398834 Loss2: 0.689906
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.071193 Loss1: 0.381276 Loss2: 0.689917
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.073447 Loss1: 0.385350 Loss2: 0.688097
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.095836 Loss1: 0.403730 Loss2: 0.692106
+(DefaultActor pid=1831567) >> Training accuracy: 0.870563
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.411959 Loss1: 0.644734 Loss2: 0.767225
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.288458 Loss1: 0.590947 Loss2: 0.697511
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.284965 Loss1: 0.585766 Loss2: 0.699199
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.282864 Loss1: 0.582004 Loss2: 0.700860
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.254399 Loss1: 0.556521 Loss2: 0.697878
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249018 Loss1: 0.551157 Loss2: 0.697861
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.249782 Loss1: 0.550838 Loss2: 0.698945
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.242212 Loss1: 0.544648 Loss2: 0.697564
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232321 Loss1: 0.527975 Loss2: 0.704346
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.241878 Loss1: 0.538825 Loss2: 0.703053
+(DefaultActor pid=1831567) >> Training accuracy: 0.792492
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.559444 Loss1: 0.777973 Loss2: 0.781471
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.372298 Loss1: 0.698462 Loss2: 0.673837
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.357250 Loss1: 0.683380 Loss2: 0.673870
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335564 Loss1: 0.663920 Loss2: 0.671644
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345237 Loss1: 0.671214 Loss2: 0.674024
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.297774 Loss1: 0.626248 Loss2: 0.671526
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.318794 Loss1: 0.640793 Loss2: 0.678002
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.310843 Loss1: 0.634925 Loss2: 0.675918
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.288904 Loss1: 0.614189 Loss2: 0.674715
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279715 Loss1: 0.603732 Loss2: 0.675983
+[2023-09-27 11:26:32,759][flwr][DEBUG] - fit_round 37 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.765899
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.685100
+[2023-09-27 11:26:34,046][flwr][INFO] - fit progress: (37, 0.9061162161370055, {'accuracy': 0.6851}, 18526.882115995977)
+[2023-09-27 11:26:34,046][flwr][DEBUG] - evaluate_round 37: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:27:05,260][flwr][DEBUG] - evaluate_round 37 received 10 results and 0 failures
+[2023-09-27 11:27:05,261][flwr][DEBUG] - fit_round 38: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.357352 Loss1: 0.653402 Loss2: 0.703950
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.241971 Loss1: 0.607456 Loss2: 0.634514
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.220117 Loss1: 0.589572 Loss2: 0.630545
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190067 Loss1: 0.559410 Loss2: 0.630656
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211126 Loss1: 0.579375 Loss2: 0.631751
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193097 Loss1: 0.559101 Loss2: 0.633996
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175464 Loss1: 0.543360 Loss2: 0.632104
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.184492 Loss1: 0.552277 Loss2: 0.632216
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211846 Loss1: 0.576866 Loss2: 0.634980
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174794 Loss1: 0.539711 Loss2: 0.635082
+(DefaultActor pid=1831567) >> Training accuracy: 0.813453
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.398627 Loss1: 0.619241 Loss2: 0.779386
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.326149 Loss1: 0.593277 Loss2: 0.732872
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.296804 Loss1: 0.566718 Loss2: 0.730086
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.286250 Loss1: 0.558537 Loss2: 0.727713
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282109 Loss1: 0.550724 Loss2: 0.731385
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.280376 Loss1: 0.553000 Loss2: 0.727377
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.291293 Loss1: 0.562074 Loss2: 0.729219
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.274207 Loss1: 0.542975 Loss2: 0.731232
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.271548 Loss1: 0.537219 Loss2: 0.734329
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.282832 Loss1: 0.549288 Loss2: 0.733544
+(DefaultActor pid=1831567) >> Training accuracy: 0.819072
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.535791 Loss1: 0.772063 Loss2: 0.763728
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.363420 Loss1: 0.703860 Loss2: 0.659560
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.326551 Loss1: 0.667439 Loss2: 0.659112
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.312689 Loss1: 0.650701 Loss2: 0.661988
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.321756 Loss1: 0.657356 Loss2: 0.664400
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.313094 Loss1: 0.651256 Loss2: 0.661838
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.287719 Loss1: 0.626296 Loss2: 0.661423
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.267114 Loss1: 0.602875 Loss2: 0.664239
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306025 Loss1: 0.638073 Loss2: 0.667952
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.296240 Loss1: 0.631649 Loss2: 0.664591
+(DefaultActor pid=1831567) >> Training accuracy: 0.778509
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.415445 Loss1: 0.617497 Loss2: 0.797948
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.256768 Loss1: 0.567939 Loss2: 0.688829
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.230440 Loss1: 0.542943 Loss2: 0.687497
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223920 Loss1: 0.537274 Loss2: 0.686646
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196650 Loss1: 0.508357 Loss2: 0.688293
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.214170 Loss1: 0.521258 Loss2: 0.692912
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.190515 Loss1: 0.499033 Loss2: 0.691481
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.211131 Loss1: 0.516524 Loss2: 0.694607
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.171709 Loss1: 0.477834 Loss2: 0.693875
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175704 Loss1: 0.481156 Loss2: 0.694547
+(DefaultActor pid=1831567) >> Training accuracy: 0.843485
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.374802 Loss1: 0.639641 Loss2: 0.735161
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.239107 Loss1: 0.584567 Loss2: 0.654541
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208092 Loss1: 0.558764 Loss2: 0.649328
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187316 Loss1: 0.536272 Loss2: 0.651044
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191405 Loss1: 0.540348 Loss2: 0.651056
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.202489 Loss1: 0.549206 Loss2: 0.653283
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191418 Loss1: 0.534946 Loss2: 0.656472
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191512 Loss1: 0.536834 Loss2: 0.654678
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173654 Loss1: 0.519134 Loss2: 0.654520
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165176 Loss1: 0.509098 Loss2: 0.656078
+(DefaultActor pid=1831567) >> Training accuracy: 0.834087
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386690 Loss1: 0.623147 Loss2: 0.763543
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.289795 Loss1: 0.605893 Loss2: 0.683902
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251273 Loss1: 0.571983 Loss2: 0.679290
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.239560 Loss1: 0.557992 Loss2: 0.681568
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.245465 Loss1: 0.559299 Loss2: 0.686166
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.245344 Loss1: 0.557937 Loss2: 0.687407
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.247373 Loss1: 0.559873 Loss2: 0.687500
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229383 Loss1: 0.540214 Loss2: 0.689169
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.234133 Loss1: 0.545554 Loss2: 0.688579
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.217023 Loss1: 0.527354 Loss2: 0.689670
+(DefaultActor pid=1831567) >> Training accuracy: 0.811298
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.247659 Loss1: 0.492927 Loss2: 0.754731
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.118702 Loss1: 0.447733 Loss2: 0.670968
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.100463 Loss1: 0.430060 Loss2: 0.670403
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.087616 Loss1: 0.421657 Loss2: 0.665959
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.084302 Loss1: 0.414465 Loss2: 0.669837
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.079408 Loss1: 0.408517 Loss2: 0.670892
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.061586 Loss1: 0.391417 Loss2: 0.670169
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.077262 Loss1: 0.405389 Loss2: 0.671874
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.056351 Loss1: 0.384069 Loss2: 0.672282
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.062684 Loss1: 0.391201 Loss2: 0.671483
+(DefaultActor pid=1831567) >> Training accuracy: 0.872492
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.529249 Loss1: 0.761182 Loss2: 0.768068
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.437166 Loss1: 0.758191 Loss2: 0.678975
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.361330 Loss1: 0.690889 Loss2: 0.670441
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.360778 Loss1: 0.690226 Loss2: 0.670553
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.359911 Loss1: 0.683685 Loss2: 0.676226
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.376890 Loss1: 0.699768 Loss2: 0.677122
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.371297 Loss1: 0.692144 Loss2: 0.679153
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.336698 Loss1: 0.659196 Loss2: 0.677502
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.328461 Loss1: 0.649731 Loss2: 0.678730
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.341277 Loss1: 0.660849 Loss2: 0.680428
+(DefaultActor pid=1831567) >> Training accuracy: 0.757463
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.197499 Loss1: 0.488826 Loss2: 0.708672
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.098852 Loss1: 0.464329 Loss2: 0.634523
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.043535 Loss1: 0.409990 Loss2: 0.633546
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.053478 Loss1: 0.418166 Loss2: 0.635312
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.040013 Loss1: 0.406763 Loss2: 0.633250
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.050255 Loss1: 0.414422 Loss2: 0.635833
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.043880 Loss1: 0.409038 Loss2: 0.634842
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026629 Loss1: 0.389506 Loss2: 0.637122
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.029176 Loss1: 0.392816 Loss2: 0.636359
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.031169 Loss1: 0.390816 Loss2: 0.640353
+(DefaultActor pid=1831567) >> Training accuracy: 0.859182
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517237 Loss1: 0.795688 Loss2: 0.721549
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.393460 Loss1: 0.759157 Loss2: 0.634303
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.382975 Loss1: 0.749122 Loss2: 0.633852
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.361914 Loss1: 0.728013 Loss2: 0.633902
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.354625 Loss1: 0.718643 Loss2: 0.635982
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.364925 Loss1: 0.729546 Loss2: 0.635379
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.357019 Loss1: 0.719696 Loss2: 0.637323
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329931 Loss1: 0.691921 Loss2: 0.638010
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.324871 Loss1: 0.687474 Loss2: 0.637398
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.330100 Loss1: 0.692268 Loss2: 0.637832
+[2023-09-27 11:33:50,110][flwr][DEBUG] - fit_round 38 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.756114
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.688400
+[2023-09-27 11:33:51,801][flwr][INFO] - fit progress: (38, 0.8966945318368297, {'accuracy': 0.6884}, 18964.637040854897)
+[2023-09-27 11:33:51,801][flwr][DEBUG] - evaluate_round 38: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:34:22,935][flwr][DEBUG] - evaluate_round 38 received 10 results and 0 failures
+[2023-09-27 11:34:22,936][flwr][DEBUG] - fit_round 39: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.554307 Loss1: 0.787088 Loss2: 0.767219
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.371278 Loss1: 0.707448 Loss2: 0.663831
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336321 Loss1: 0.676177 Loss2: 0.660144
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.320814 Loss1: 0.660017 Loss2: 0.660797
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.326962 Loss1: 0.662436 Loss2: 0.664526
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.284836 Loss1: 0.621307 Loss2: 0.663529
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.290939 Loss1: 0.627115 Loss2: 0.663824
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.299901 Loss1: 0.635106 Loss2: 0.664795
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.261316 Loss1: 0.594872 Loss2: 0.666444
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290028 Loss1: 0.623444 Loss2: 0.666583
+(DefaultActor pid=1831567) >> Training accuracy: 0.773575
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.261773 Loss1: 0.493180 Loss2: 0.768594
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.114879 Loss1: 0.432242 Loss2: 0.682637
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.104399 Loss1: 0.426902 Loss2: 0.677497
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.112260 Loss1: 0.432534 Loss2: 0.679726
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.116061 Loss1: 0.434456 Loss2: 0.681605
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.082451 Loss1: 0.400317 Loss2: 0.682134
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.077627 Loss1: 0.399274 Loss2: 0.678354
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.075518 Loss1: 0.394913 Loss2: 0.680605
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.059709 Loss1: 0.376833 Loss2: 0.682876
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.063986 Loss1: 0.377555 Loss2: 0.686432
+(DefaultActor pid=1831567) >> Training accuracy: 0.861304
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.511542 Loss1: 0.780250 Loss2: 0.731292
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370132 Loss1: 0.726564 Loss2: 0.643568
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.360838 Loss1: 0.719568 Loss2: 0.641270
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331924 Loss1: 0.689597 Loss2: 0.642327
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.338457 Loss1: 0.696391 Loss2: 0.642066
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.338045 Loss1: 0.691858 Loss2: 0.646186
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.304153 Loss1: 0.655402 Loss2: 0.648751
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.306844 Loss1: 0.661725 Loss2: 0.645119
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.285599 Loss1: 0.641063 Loss2: 0.644535
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284982 Loss1: 0.638488 Loss2: 0.646494
+(DefaultActor pid=1831567) >> Training accuracy: 0.767491
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369842 Loss1: 0.605307 Loss2: 0.764534
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.230520 Loss1: 0.565070 Loss2: 0.665450
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208905 Loss1: 0.545113 Loss2: 0.663792
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208641 Loss1: 0.545673 Loss2: 0.662968
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.217596 Loss1: 0.550066 Loss2: 0.667530
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.196711 Loss1: 0.529934 Loss2: 0.666776
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.185355 Loss1: 0.518525 Loss2: 0.666829
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.153389 Loss1: 0.485839 Loss2: 0.667550
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161365 Loss1: 0.496106 Loss2: 0.665259
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154443 Loss1: 0.487983 Loss2: 0.666460
+(DefaultActor pid=1831567) >> Training accuracy: 0.823093
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.563585 Loss1: 0.789510 Loss2: 0.774075
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.439852 Loss1: 0.751222 Loss2: 0.688630
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.444716 Loss1: 0.753556 Loss2: 0.691159
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.440678 Loss1: 0.747265 Loss2: 0.693413
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.407841 Loss1: 0.718721 Loss2: 0.689121
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.396749 Loss1: 0.705322 Loss2: 0.691427
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.383521 Loss1: 0.688322 Loss2: 0.695199
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.400241 Loss1: 0.706824 Loss2: 0.693417
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.396418 Loss1: 0.700066 Loss2: 0.696351
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.395641 Loss1: 0.699185 Loss2: 0.696456
+(DefaultActor pid=1831567) >> Training accuracy: 0.763134
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.357377 Loss1: 0.616771 Loss2: 0.740606
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.247526 Loss1: 0.576776 Loss2: 0.670750
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.273280 Loss1: 0.603832 Loss2: 0.669449
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.242437 Loss1: 0.572325 Loss2: 0.670112
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.235109 Loss1: 0.563863 Loss2: 0.671245
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.242876 Loss1: 0.571229 Loss2: 0.671647
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224582 Loss1: 0.552258 Loss2: 0.672324
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.230843 Loss1: 0.559420 Loss2: 0.671423
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.221327 Loss1: 0.546756 Loss2: 0.674570
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.195018 Loss1: 0.523037 Loss2: 0.671980
+(DefaultActor pid=1831567) >> Training accuracy: 0.822316
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.396162 Loss1: 0.630285 Loss2: 0.765877
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.279431 Loss1: 0.594951 Loss2: 0.684480
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.243780 Loss1: 0.561163 Loss2: 0.682616
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.239644 Loss1: 0.556224 Loss2: 0.683421
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.240648 Loss1: 0.554456 Loss2: 0.686193
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.233874 Loss1: 0.550376 Loss2: 0.683498
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.207716 Loss1: 0.522440 Loss2: 0.685277
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220935 Loss1: 0.532408 Loss2: 0.688527
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193491 Loss1: 0.503809 Loss2: 0.689682
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.213768 Loss1: 0.522158 Loss2: 0.691610
+(DefaultActor pid=1831567) >> Training accuracy: 0.835732
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297886 Loss1: 0.495698 Loss2: 0.802187
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.173412 Loss1: 0.453982 Loss2: 0.719430
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.142445 Loss1: 0.432133 Loss2: 0.710312
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.119618 Loss1: 0.408475 Loss2: 0.711143
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.121476 Loss1: 0.410744 Loss2: 0.710732
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131538 Loss1: 0.418800 Loss2: 0.712738
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.111379 Loss1: 0.398430 Loss2: 0.712949
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.103688 Loss1: 0.391403 Loss2: 0.712285
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.098441 Loss1: 0.386367 Loss2: 0.712074
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107334 Loss1: 0.392965 Loss2: 0.714370
+(DefaultActor pid=1831567) >> Training accuracy: 0.864390
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326586 Loss1: 0.604602 Loss2: 0.721984
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.251506 Loss1: 0.572864 Loss2: 0.678642
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.242116 Loss1: 0.567527 Loss2: 0.674589
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.228294 Loss1: 0.554985 Loss2: 0.673309
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.240094 Loss1: 0.563449 Loss2: 0.676645
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.233397 Loss1: 0.558676 Loss2: 0.674721
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.239469 Loss1: 0.562656 Loss2: 0.676813
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.221566 Loss1: 0.545603 Loss2: 0.675964
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.206633 Loss1: 0.530340 Loss2: 0.676294
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229473 Loss1: 0.548812 Loss2: 0.680661
+(DefaultActor pid=1831567) >> Training accuracy: 0.812996
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.395186 Loss1: 0.644253 Loss2: 0.750933
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.270456 Loss1: 0.586397 Loss2: 0.684059
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.252313 Loss1: 0.568964 Loss2: 0.683348
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.260389 Loss1: 0.579862 Loss2: 0.680528
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.227028 Loss1: 0.543647 Loss2: 0.683380
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232485 Loss1: 0.546958 Loss2: 0.685527
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.234371 Loss1: 0.552103 Loss2: 0.682268
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225981 Loss1: 0.541659 Loss2: 0.684322
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.214592 Loss1: 0.527873 Loss2: 0.686720
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214070 Loss1: 0.527154 Loss2: 0.686916
+[2023-09-27 11:41:04,608][flwr][DEBUG] - fit_round 39 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.819169
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.683200
+[2023-09-27 11:41:05,990][flwr][INFO] - fit progress: (39, 0.9046378726966846, {'accuracy': 0.6832}, 19398.826610946096)
+[2023-09-27 11:41:05,991][flwr][DEBUG] - evaluate_round 39: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:41:43,281][flwr][DEBUG] - evaluate_round 39 received 10 results and 0 failures
+[2023-09-27 11:41:43,282][flwr][DEBUG] - fit_round 40: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.257980 Loss1: 0.506809 Loss2: 0.751170
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.088687 Loss1: 0.426436 Loss2: 0.662251
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.083518 Loss1: 0.423395 Loss2: 0.660123
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.071372 Loss1: 0.412241 Loss2: 0.659131
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.069970 Loss1: 0.407586 Loss2: 0.662384
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.064381 Loss1: 0.402067 Loss2: 0.662313
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.062979 Loss1: 0.402074 Loss2: 0.660905
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.049072 Loss1: 0.386241 Loss2: 0.662831
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.051168 Loss1: 0.385224 Loss2: 0.665944
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.032984 Loss1: 0.369535 Loss2: 0.663449
+(DefaultActor pid=1831567) >> Training accuracy: 0.861883
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353879 Loss1: 0.612086 Loss2: 0.741793
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.244073 Loss1: 0.585674 Loss2: 0.658399
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.197955 Loss1: 0.541323 Loss2: 0.656632
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210326 Loss1: 0.552278 Loss2: 0.658048
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218505 Loss1: 0.555821 Loss2: 0.662684
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180952 Loss1: 0.522929 Loss2: 0.658023
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193039 Loss1: 0.532778 Loss2: 0.660261
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.192272 Loss1: 0.530153 Loss2: 0.662118
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.186081 Loss1: 0.524101 Loss2: 0.661980
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.177725 Loss1: 0.518026 Loss2: 0.659699
+(DefaultActor pid=1831567) >> Training accuracy: 0.822574
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.557811 Loss1: 0.769171 Loss2: 0.788639
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.421844 Loss1: 0.730514 Loss2: 0.691330
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.389192 Loss1: 0.701069 Loss2: 0.688123
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.357735 Loss1: 0.675175 Loss2: 0.682561
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.372177 Loss1: 0.682764 Loss2: 0.689413
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.385484 Loss1: 0.694042 Loss2: 0.691442
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.359965 Loss1: 0.667791 Loss2: 0.692174
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.358296 Loss1: 0.664466 Loss2: 0.693830
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.344580 Loss1: 0.650471 Loss2: 0.694109
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.354642 Loss1: 0.659948 Loss2: 0.694694
+(DefaultActor pid=1831567) >> Training accuracy: 0.764226
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.344923 Loss1: 0.606622 Loss2: 0.738301
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.252972 Loss1: 0.591157 Loss2: 0.661815
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.252261 Loss1: 0.588604 Loss2: 0.663657
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199078 Loss1: 0.537905 Loss2: 0.661173
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.242167 Loss1: 0.575294 Loss2: 0.666873
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.219224 Loss1: 0.552092 Loss2: 0.667132
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224907 Loss1: 0.556150 Loss2: 0.668757
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.183507 Loss1: 0.516682 Loss2: 0.666825
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196920 Loss1: 0.529203 Loss2: 0.667716
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.206793 Loss1: 0.536728 Loss2: 0.670065
+(DefaultActor pid=1831567) >> Training accuracy: 0.813301
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381083 Loss1: 0.610133 Loss2: 0.770950
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206842 Loss1: 0.546694 Loss2: 0.660148
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194779 Loss1: 0.538453 Loss2: 0.656326
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.172872 Loss1: 0.518116 Loss2: 0.654756
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.173906 Loss1: 0.515545 Loss2: 0.658361
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.177190 Loss1: 0.519738 Loss2: 0.657452
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.178834 Loss1: 0.517738 Loss2: 0.661097
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.156450 Loss1: 0.497836 Loss2: 0.658614
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.160452 Loss1: 0.502042 Loss2: 0.658410
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148788 Loss1: 0.490884 Loss2: 0.657904
+(DefaultActor pid=1831567) >> Training accuracy: 0.845339
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.543509 Loss1: 0.781604 Loss2: 0.761905
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.373146 Loss1: 0.714763 Loss2: 0.658383
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.342847 Loss1: 0.684679 Loss2: 0.658168
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331479 Loss1: 0.670191 Loss2: 0.661288
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333387 Loss1: 0.669418 Loss2: 0.663968
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.326043 Loss1: 0.657670 Loss2: 0.668372
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294489 Loss1: 0.629890 Loss2: 0.664598
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.262679 Loss1: 0.597842 Loss2: 0.664837
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.290208 Loss1: 0.624528 Loss2: 0.665680
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279731 Loss1: 0.612740 Loss2: 0.666991
+(DefaultActor pid=1831567) >> Training accuracy: 0.787281
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.200364 Loss1: 0.492162 Loss2: 0.708202
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.066194 Loss1: 0.429940 Loss2: 0.636254
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.061912 Loss1: 0.426200 Loss2: 0.635712
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.052927 Loss1: 0.418876 Loss2: 0.634051
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.047297 Loss1: 0.413961 Loss2: 0.633336
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.043074 Loss1: 0.406560 Loss2: 0.636514
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.023041 Loss1: 0.389473 Loss2: 0.633568
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.065331 Loss1: 0.427782 Loss2: 0.637549
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.037835 Loss1: 0.399753 Loss2: 0.638082
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.031311 Loss1: 0.390748 Loss2: 0.640563
+(DefaultActor pid=1831567) >> Training accuracy: 0.861883
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382681 Loss1: 0.607292 Loss2: 0.775389
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.278428 Loss1: 0.557204 Loss2: 0.721224
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.281754 Loss1: 0.562880 Loss2: 0.718875
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.271283 Loss1: 0.551129 Loss2: 0.720154
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.257656 Loss1: 0.534294 Loss2: 0.723363
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.288304 Loss1: 0.561531 Loss2: 0.726772
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.257801 Loss1: 0.533116 Loss2: 0.724685
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268709 Loss1: 0.541925 Loss2: 0.726784
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.263918 Loss1: 0.536420 Loss2: 0.727498
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259120 Loss1: 0.531042 Loss2: 0.728078
+(DefaultActor pid=1831567) >> Training accuracy: 0.804812
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370434 Loss1: 0.650962 Loss2: 0.719472
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.238554 Loss1: 0.593317 Loss2: 0.645236
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.235767 Loss1: 0.591838 Loss2: 0.643929
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210106 Loss1: 0.568477 Loss2: 0.641629
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.188730 Loss1: 0.545544 Loss2: 0.643186
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.196510 Loss1: 0.551333 Loss2: 0.645178
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.188894 Loss1: 0.545286 Loss2: 0.643609
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.167962 Loss1: 0.522637 Loss2: 0.645325
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.198005 Loss1: 0.552705 Loss2: 0.645300
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.170997 Loss1: 0.524294 Loss2: 0.646703
+(DefaultActor pid=1831567) >> Training accuracy: 0.826601
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.526834 Loss1: 0.783310 Loss2: 0.743524
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.426421 Loss1: 0.769760 Loss2: 0.656661
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.398998 Loss1: 0.741329 Loss2: 0.657669
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.389652 Loss1: 0.733711 Loss2: 0.655941
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.361844 Loss1: 0.704729 Loss2: 0.657115
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.345729 Loss1: 0.687670 Loss2: 0.658059
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.369481 Loss1: 0.708065 Loss2: 0.661415
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.369034 Loss1: 0.709193 Loss2: 0.659842
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.352951 Loss1: 0.691476 Loss2: 0.661476
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.352285 Loss1: 0.689545 Loss2: 0.662740
+[2023-09-27 11:48:50,104][flwr][DEBUG] - fit_round 40 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.768569
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.679500
+[2023-09-27 11:48:51,532][flwr][INFO] - fit progress: (40, 0.9118956097017843, {'accuracy': 0.6795}, 19864.368800325785)
+[2023-09-27 11:48:51,533][flwr][DEBUG] - evaluate_round 40: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:49:23,488][flwr][DEBUG] - evaluate_round 40 received 10 results and 0 failures
+[2023-09-27 11:49:23,489][flwr][DEBUG] - fit_round 41: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.376407 Loss1: 0.619553 Loss2: 0.756854
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243427 Loss1: 0.566204 Loss2: 0.677223
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222532 Loss1: 0.549301 Loss2: 0.673231
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.213968 Loss1: 0.536578 Loss2: 0.677389
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.214146 Loss1: 0.536022 Loss2: 0.678124
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220728 Loss1: 0.541209 Loss2: 0.679518
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.206207 Loss1: 0.525074 Loss2: 0.681133
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204738 Loss1: 0.526478 Loss2: 0.678260
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178710 Loss1: 0.498880 Loss2: 0.679830
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.194124 Loss1: 0.510540 Loss2: 0.683584
+(DefaultActor pid=1831567) >> Training accuracy: 0.832237
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.287963 Loss1: 0.492734 Loss2: 0.795228
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.147870 Loss1: 0.436526 Loss2: 0.711344
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.140502 Loss1: 0.431421 Loss2: 0.709081
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144228 Loss1: 0.436314 Loss2: 0.707914
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.123869 Loss1: 0.415275 Loss2: 0.708595
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.103055 Loss1: 0.396813 Loss2: 0.706242
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.130262 Loss1: 0.418793 Loss2: 0.711469
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.100477 Loss1: 0.392754 Loss2: 0.707724
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.090206 Loss1: 0.383804 Loss2: 0.706402
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.091915 Loss1: 0.383129 Loss2: 0.708787
+(DefaultActor pid=1831567) >> Training accuracy: 0.861883
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.516518 Loss1: 0.744964 Loss2: 0.771554
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.372687 Loss1: 0.707443 Loss2: 0.665245
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.319154 Loss1: 0.657794 Loss2: 0.661360
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325080 Loss1: 0.661849 Loss2: 0.663231
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.329491 Loss1: 0.664612 Loss2: 0.664879
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.313604 Loss1: 0.649448 Loss2: 0.664156
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298542 Loss1: 0.632828 Loss2: 0.665715
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.298636 Loss1: 0.631276 Loss2: 0.667360
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.279631 Loss1: 0.612400 Loss2: 0.667232
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.287818 Loss1: 0.616759 Loss2: 0.671059
+(DefaultActor pid=1831567) >> Training accuracy: 0.791393
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.365785 Loss1: 0.611657 Loss2: 0.754128
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.221641 Loss1: 0.570172 Loss2: 0.651469
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208968 Loss1: 0.559349 Loss2: 0.649619
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.167859 Loss1: 0.517158 Loss2: 0.650701
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187681 Loss1: 0.537061 Loss2: 0.650620
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181050 Loss1: 0.533160 Loss2: 0.647891
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164370 Loss1: 0.513559 Loss2: 0.650810
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.129008 Loss1: 0.474661 Loss2: 0.654347
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.149378 Loss1: 0.497207 Loss2: 0.652171
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.127316 Loss1: 0.473884 Loss2: 0.653432
+(DefaultActor pid=1831567) >> Training accuracy: 0.847987
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340646 Loss1: 0.602314 Loss2: 0.738332
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.262707 Loss1: 0.571763 Loss2: 0.690944
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.254432 Loss1: 0.561460 Loss2: 0.692973
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.255567 Loss1: 0.561387 Loss2: 0.694181
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.248310 Loss1: 0.554881 Loss2: 0.693429
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.240960 Loss1: 0.547467 Loss2: 0.693494
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243251 Loss1: 0.548034 Loss2: 0.695217
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.252921 Loss1: 0.559345 Loss2: 0.693576
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.235438 Loss1: 0.538979 Loss2: 0.696459
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.213892 Loss1: 0.523196 Loss2: 0.690696
+(DefaultActor pid=1831567) >> Training accuracy: 0.812128
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.567676 Loss1: 0.793427 Loss2: 0.774248
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.455764 Loss1: 0.764081 Loss2: 0.691683
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.415178 Loss1: 0.729805 Loss2: 0.685373
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.413100 Loss1: 0.724345 Loss2: 0.688755
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.383355 Loss1: 0.693909 Loss2: 0.689446
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.387973 Loss1: 0.701037 Loss2: 0.686935
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.380574 Loss1: 0.687757 Loss2: 0.692818
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.368252 Loss1: 0.674560 Loss2: 0.693692
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.383209 Loss1: 0.690003 Loss2: 0.693206
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.365431 Loss1: 0.672769 Loss2: 0.692663
+(DefaultActor pid=1831567) >> Training accuracy: 0.776042
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.248701 Loss1: 0.496487 Loss2: 0.752214
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.110753 Loss1: 0.443500 Loss2: 0.667253
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.100764 Loss1: 0.433367 Loss2: 0.667396
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.090442 Loss1: 0.424998 Loss2: 0.665443
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.103193 Loss1: 0.431622 Loss2: 0.671570
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.065875 Loss1: 0.400600 Loss2: 0.665275
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.044218 Loss1: 0.375736 Loss2: 0.668482
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.059196 Loss1: 0.390542 Loss2: 0.668654
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.073570 Loss1: 0.403424 Loss2: 0.670146
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.045877 Loss1: 0.376693 Loss2: 0.669184
+(DefaultActor pid=1831567) >> Training accuracy: 0.867863
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.522217 Loss1: 0.770657 Loss2: 0.751560
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.399520 Loss1: 0.736843 Loss2: 0.662677
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.358212 Loss1: 0.695361 Loss2: 0.662851
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.350804 Loss1: 0.686513 Loss2: 0.664292
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.332878 Loss1: 0.666326 Loss2: 0.666553
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.339834 Loss1: 0.676487 Loss2: 0.663347
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.335552 Loss1: 0.667740 Loss2: 0.667812
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.332558 Loss1: 0.666783 Loss2: 0.665775
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.302575 Loss1: 0.636764 Loss2: 0.665811
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.299396 Loss1: 0.630077 Loss2: 0.669319
+(DefaultActor pid=1831567) >> Training accuracy: 0.756530
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.388003 Loss1: 0.642591 Loss2: 0.745412
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.256987 Loss1: 0.587417 Loss2: 0.669570
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.246513 Loss1: 0.579644 Loss2: 0.666868
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.247146 Loss1: 0.576699 Loss2: 0.670447
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.236310 Loss1: 0.566122 Loss2: 0.670188
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231619 Loss1: 0.559893 Loss2: 0.671727
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197853 Loss1: 0.527875 Loss2: 0.669978
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.217567 Loss1: 0.544998 Loss2: 0.672569
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.200916 Loss1: 0.529436 Loss2: 0.671479
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.197876 Loss1: 0.526946 Loss2: 0.670929
+(DefaultActor pid=1831567) >> Training accuracy: 0.825921
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.399071 Loss1: 0.652978 Loss2: 0.746093
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.257290 Loss1: 0.581346 Loss2: 0.675944
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223922 Loss1: 0.551169 Loss2: 0.672754
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.220860 Loss1: 0.546367 Loss2: 0.674493
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215777 Loss1: 0.541021 Loss2: 0.674757
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.217190 Loss1: 0.540204 Loss2: 0.676987
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.208949 Loss1: 0.533478 Loss2: 0.675472
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.221172 Loss1: 0.542989 Loss2: 0.678184
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199732 Loss1: 0.521592 Loss2: 0.678140
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.202723 Loss1: 0.525360 Loss2: 0.677363
+[2023-09-27 11:56:21,623][flwr][DEBUG] - fit_round 41 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.821646
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.691200
+[2023-09-27 11:56:23,203][flwr][INFO] - fit progress: (41, 0.8914938339600548, {'accuracy': 0.6912}, 20316.0390293058)
+[2023-09-27 11:56:23,204][flwr][DEBUG] - evaluate_round 41: strategy sampled 10 clients (out of 10)
+[2023-09-27 11:56:53,769][flwr][DEBUG] - evaluate_round 41 received 10 results and 0 failures
+[2023-09-27 11:56:53,770][flwr][DEBUG] - fit_round 42: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.424741 Loss1: 0.622516 Loss2: 0.802225
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.267781 Loss1: 0.581000 Loss2: 0.686780
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213061 Loss1: 0.532386 Loss2: 0.680675
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212231 Loss1: 0.528400 Loss2: 0.683831
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218670 Loss1: 0.535190 Loss2: 0.683480
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.214486 Loss1: 0.529014 Loss2: 0.685472
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164629 Loss1: 0.480160 Loss2: 0.684469
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154115 Loss1: 0.470578 Loss2: 0.683537
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.157672 Loss1: 0.473017 Loss2: 0.684654
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184818 Loss1: 0.496410 Loss2: 0.688409
+(DefaultActor pid=1831567) >> Training accuracy: 0.844280
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.215526 Loss1: 0.492215 Loss2: 0.723311
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.088936 Loss1: 0.440206 Loss2: 0.648730
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.056314 Loss1: 0.411450 Loss2: 0.644865
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074883 Loss1: 0.427299 Loss2: 0.647584
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066890 Loss1: 0.419796 Loss2: 0.647094
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.045756 Loss1: 0.398283 Loss2: 0.647473
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050583 Loss1: 0.405713 Loss2: 0.644869
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.034028 Loss1: 0.388249 Loss2: 0.645779
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028114 Loss1: 0.380549 Loss2: 0.647565
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.035081 Loss1: 0.386788 Loss2: 0.648292
+(DefaultActor pid=1831567) >> Training accuracy: 0.865741
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381300 Loss1: 0.650777 Loss2: 0.730522
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.244802 Loss1: 0.584880 Loss2: 0.659922
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238008 Loss1: 0.581718 Loss2: 0.656290
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.213890 Loss1: 0.561035 Loss2: 0.652855
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210178 Loss1: 0.554921 Loss2: 0.655257
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.204183 Loss1: 0.547830 Loss2: 0.656353
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196964 Loss1: 0.542264 Loss2: 0.654700
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.176982 Loss1: 0.521112 Loss2: 0.655870
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178596 Loss1: 0.520733 Loss2: 0.657864
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.182101 Loss1: 0.522437 Loss2: 0.659664
+(DefaultActor pid=1831567) >> Training accuracy: 0.822790
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.542440 Loss1: 0.767725 Loss2: 0.774715
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394420 Loss1: 0.719568 Loss2: 0.674852
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.372342 Loss1: 0.699737 Loss2: 0.672605
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.371302 Loss1: 0.696546 Loss2: 0.674756
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.379592 Loss1: 0.705050 Loss2: 0.674542
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.363315 Loss1: 0.689801 Loss2: 0.673514
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338685 Loss1: 0.659310 Loss2: 0.679375
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.330920 Loss1: 0.651391 Loss2: 0.679529
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.326348 Loss1: 0.647902 Loss2: 0.678445
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.332995 Loss1: 0.652562 Loss2: 0.680433
+(DefaultActor pid=1831567) >> Training accuracy: 0.767257
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.245516 Loss1: 0.492238 Loss2: 0.753277
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.086641 Loss1: 0.421109 Loss2: 0.665532
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.083036 Loss1: 0.420356 Loss2: 0.662680
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.080544 Loss1: 0.414313 Loss2: 0.666231
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.045255 Loss1: 0.381141 Loss2: 0.664114
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.047193 Loss1: 0.383292 Loss2: 0.663900
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.055083 Loss1: 0.387209 Loss2: 0.667874
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.046395 Loss1: 0.379363 Loss2: 0.667032
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.074728 Loss1: 0.404528 Loss2: 0.670200
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.030588 Loss1: 0.365412 Loss2: 0.665175
+(DefaultActor pid=1831567) >> Training accuracy: 0.857446
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.533228 Loss1: 0.756607 Loss2: 0.776621
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.377307 Loss1: 0.708298 Loss2: 0.669009
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337987 Loss1: 0.671443 Loss2: 0.666544
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.341406 Loss1: 0.675314 Loss2: 0.666092
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.291911 Loss1: 0.623348 Loss2: 0.668564
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.320487 Loss1: 0.651006 Loss2: 0.669481
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.296564 Loss1: 0.625391 Loss2: 0.671173
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300339 Loss1: 0.629325 Loss2: 0.671014
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299384 Loss1: 0.626350 Loss2: 0.673034
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.295474 Loss1: 0.621842 Loss2: 0.673632
+(DefaultActor pid=1831567) >> Training accuracy: 0.783991
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517114 Loss1: 0.772895 Loss2: 0.744219
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.398582 Loss1: 0.742560 Loss2: 0.656021
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.375209 Loss1: 0.721110 Loss2: 0.654099
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.365511 Loss1: 0.710825 Loss2: 0.654685
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.360379 Loss1: 0.705638 Loss2: 0.654741
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.343325 Loss1: 0.687790 Loss2: 0.655536
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338853 Loss1: 0.679266 Loss2: 0.659587
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.356552 Loss1: 0.694129 Loss2: 0.662423
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.339934 Loss1: 0.680213 Loss2: 0.659721
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.338509 Loss1: 0.676575 Loss2: 0.661934
+(DefaultActor pid=1831567) >> Training accuracy: 0.770833
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.364364 Loss1: 0.620791 Loss2: 0.743573
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.254249 Loss1: 0.586944 Loss2: 0.667305
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208033 Loss1: 0.544371 Loss2: 0.663662
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217304 Loss1: 0.551601 Loss2: 0.665703
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.186088 Loss1: 0.523291 Loss2: 0.662797
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210861 Loss1: 0.544158 Loss2: 0.666703
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.201819 Loss1: 0.535725 Loss2: 0.666094
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.209552 Loss1: 0.541786 Loss2: 0.667766
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182218 Loss1: 0.514510 Loss2: 0.667708
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.188624 Loss1: 0.518675 Loss2: 0.669949
+(DefaultActor pid=1831567) >> Training accuracy: 0.822574
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381148 Loss1: 0.605354 Loss2: 0.775793
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.285658 Loss1: 0.564166 Loss2: 0.721492
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.278621 Loss1: 0.558219 Loss2: 0.720402
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.268944 Loss1: 0.549011 Loss2: 0.719933
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.278635 Loss1: 0.553783 Loss2: 0.724852
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.271900 Loss1: 0.546238 Loss2: 0.725662
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.255037 Loss1: 0.531483 Loss2: 0.723554
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.281364 Loss1: 0.552786 Loss2: 0.728578
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267605 Loss1: 0.542787 Loss2: 0.724818
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.256727 Loss1: 0.531742 Loss2: 0.724985
+(DefaultActor pid=1831567) >> Training accuracy: 0.817584
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.373961 Loss1: 0.612551 Loss2: 0.761410
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.273541 Loss1: 0.587282 Loss2: 0.686259
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262731 Loss1: 0.576560 Loss2: 0.686171
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.220553 Loss1: 0.535741 Loss2: 0.684812
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224784 Loss1: 0.535752 Loss2: 0.689031
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226509 Loss1: 0.540937 Loss2: 0.685573
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220009 Loss1: 0.533183 Loss2: 0.686826
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.235305 Loss1: 0.545673 Loss2: 0.689633
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217751 Loss1: 0.524719 Loss2: 0.693032
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.237468 Loss1: 0.542930 Loss2: 0.694538
+[2023-09-27 12:03:37,273][flwr][DEBUG] - fit_round 42 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.828125
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.688800
+[2023-09-27 12:03:38,844][flwr][INFO] - fit progress: (42, 0.8886514548866894, {'accuracy': 0.6888}, 20751.680621833075)
+[2023-09-27 12:03:38,845][flwr][DEBUG] - evaluate_round 42: strategy sampled 10 clients (out of 10)
+[2023-09-27 12:04:09,944][flwr][DEBUG] - evaluate_round 42 received 10 results and 0 failures
+[2023-09-27 12:04:09,945][flwr][DEBUG] - fit_round 43: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.356694 Loss1: 0.618696 Loss2: 0.737998
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225755 Loss1: 0.565196 Loss2: 0.660558
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.242388 Loss1: 0.581801 Loss2: 0.660587
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.236985 Loss1: 0.573084 Loss2: 0.663901
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.213797 Loss1: 0.550970 Loss2: 0.662828
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222756 Loss1: 0.559375 Loss2: 0.663382
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.199399 Loss1: 0.532805 Loss2: 0.666594
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180752 Loss1: 0.515227 Loss2: 0.665525
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.190897 Loss1: 0.528043 Loss2: 0.662854
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.191332 Loss1: 0.525779 Loss2: 0.665553
+(DefaultActor pid=1831567) >> Training accuracy: 0.789864
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386452 Loss1: 0.618806 Loss2: 0.767646
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.213943 Loss1: 0.552617 Loss2: 0.661325
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188244 Loss1: 0.523276 Loss2: 0.664968
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189316 Loss1: 0.522900 Loss2: 0.666417
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.190635 Loss1: 0.522683 Loss2: 0.667952
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.182663 Loss1: 0.514885 Loss2: 0.667777
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.199213 Loss1: 0.528462 Loss2: 0.670750
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158958 Loss1: 0.491322 Loss2: 0.667636
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.154509 Loss1: 0.484391 Loss2: 0.670118
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136105 Loss1: 0.466507 Loss2: 0.669598
+(DefaultActor pid=1831567) >> Training accuracy: 0.841896
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.524434 Loss1: 0.749240 Loss2: 0.775195
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340306 Loss1: 0.670607 Loss2: 0.669699
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336495 Loss1: 0.667401 Loss2: 0.669094
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339967 Loss1: 0.671075 Loss2: 0.668892
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.323305 Loss1: 0.651728 Loss2: 0.671577
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.329731 Loss1: 0.656892 Loss2: 0.672839
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.332238 Loss1: 0.658299 Loss2: 0.673939
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.306453 Loss1: 0.631625 Loss2: 0.674828
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.255377 Loss1: 0.582824 Loss2: 0.672553
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309504 Loss1: 0.633321 Loss2: 0.676183
+(DefaultActor pid=1831567) >> Training accuracy: 0.793586
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327403 Loss1: 0.480040 Loss2: 0.847363
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.185415 Loss1: 0.434982 Loss2: 0.750432
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.146305 Loss1: 0.402456 Loss2: 0.743849
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165009 Loss1: 0.420266 Loss2: 0.744743
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.139546 Loss1: 0.396942 Loss2: 0.742605
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.129066 Loss1: 0.386271 Loss2: 0.742794
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.120700 Loss1: 0.378653 Loss2: 0.742047
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.135028 Loss1: 0.389427 Loss2: 0.745601
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.143643 Loss1: 0.392779 Loss2: 0.750864
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136093 Loss1: 0.386609 Loss2: 0.749484
+(DefaultActor pid=1831567) >> Training accuracy: 0.857060
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.379224 Loss1: 0.644364 Loss2: 0.734859
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243190 Loss1: 0.577596 Loss2: 0.665594
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222699 Loss1: 0.559696 Loss2: 0.663003
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.219089 Loss1: 0.559918 Loss2: 0.659171
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201418 Loss1: 0.537450 Loss2: 0.663968
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206730 Loss1: 0.544263 Loss2: 0.662467
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197535 Loss1: 0.534164 Loss2: 0.663371
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.211915 Loss1: 0.547377 Loss2: 0.664538
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193848 Loss1: 0.528589 Loss2: 0.665259
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.203817 Loss1: 0.532739 Loss2: 0.671078
+(DefaultActor pid=1831567) >> Training accuracy: 0.833079
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.548647 Loss1: 0.791457 Loss2: 0.757190
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.429926 Loss1: 0.755292 Loss2: 0.674635
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393001 Loss1: 0.718302 Loss2: 0.674699
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.393178 Loss1: 0.717512 Loss2: 0.675667
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.379242 Loss1: 0.704882 Loss2: 0.674360
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.378497 Loss1: 0.699814 Loss2: 0.678682
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.404002 Loss1: 0.724596 Loss2: 0.679406
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.361171 Loss1: 0.682339 Loss2: 0.678832
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.342484 Loss1: 0.665351 Loss2: 0.677132
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.357808 Loss1: 0.677323 Loss2: 0.680485
+(DefaultActor pid=1831567) >> Training accuracy: 0.774230
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.233074 Loss1: 0.477540 Loss2: 0.755534
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.144451 Loss1: 0.466630 Loss2: 0.677820
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.109160 Loss1: 0.434967 Loss2: 0.674193
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.089546 Loss1: 0.413710 Loss2: 0.675836
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.082447 Loss1: 0.408091 Loss2: 0.674356
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073672 Loss1: 0.400842 Loss2: 0.672830
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.067704 Loss1: 0.390693 Loss2: 0.677011
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.065486 Loss1: 0.390454 Loss2: 0.675032
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.035947 Loss1: 0.363258 Loss2: 0.672689
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.068228 Loss1: 0.392466 Loss2: 0.675762
+(DefaultActor pid=1831567) >> Training accuracy: 0.867477
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362477 Loss1: 0.599438 Loss2: 0.763039
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258638 Loss1: 0.572501 Loss2: 0.686137
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223688 Loss1: 0.539024 Loss2: 0.684665
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225656 Loss1: 0.541591 Loss2: 0.684065
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.229507 Loss1: 0.542402 Loss2: 0.687106
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.204123 Loss1: 0.516499 Loss2: 0.687624
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.177305 Loss1: 0.491109 Loss2: 0.686196
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222192 Loss1: 0.533866 Loss2: 0.688327
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.207599 Loss1: 0.517822 Loss2: 0.689777
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200023 Loss1: 0.508088 Loss2: 0.691935
+(DefaultActor pid=1831567) >> Training accuracy: 0.830798
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.525663 Loss1: 0.756010 Loss2: 0.769653
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.417677 Loss1: 0.735012 Loss2: 0.682665
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.362736 Loss1: 0.683259 Loss2: 0.679477
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.368039 Loss1: 0.684197 Loss2: 0.683842
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.353899 Loss1: 0.672211 Loss2: 0.681688
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.347096 Loss1: 0.664986 Loss2: 0.682110
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.356586 Loss1: 0.671362 Loss2: 0.685224
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347425 Loss1: 0.662475 Loss2: 0.684950
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.348145 Loss1: 0.659709 Loss2: 0.688436
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.345772 Loss1: 0.658316 Loss2: 0.687456
+(DefaultActor pid=1831567) >> Training accuracy: 0.776353
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.331618 Loss1: 0.595966 Loss2: 0.735652
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.254519 Loss1: 0.564451 Loss2: 0.690068
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.233375 Loss1: 0.545459 Loss2: 0.687916
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.249050 Loss1: 0.558135 Loss2: 0.690916
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.249769 Loss1: 0.560000 Loss2: 0.689768
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.221412 Loss1: 0.535949 Loss2: 0.685463
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.237552 Loss1: 0.548687 Loss2: 0.688865
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.236073 Loss1: 0.544516 Loss2: 0.691557
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.246166 Loss1: 0.551697 Loss2: 0.694469
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.235750 Loss1: 0.543900 Loss2: 0.691850
+[2023-09-27 12:10:54,145][flwr][DEBUG] - fit_round 43 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.797247
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.693200
+[2023-09-27 12:10:55,971][flwr][INFO] - fit progress: (43, 0.8876500287756752, {'accuracy': 0.6932}, 21188.807384195738)
+[2023-09-27 12:10:55,972][flwr][DEBUG] - evaluate_round 43: strategy sampled 10 clients (out of 10)
+[2023-09-27 12:11:27,410][flwr][DEBUG] - evaluate_round 43 received 10 results and 0 failures
+[2023-09-27 12:11:27,411][flwr][DEBUG] - fit_round 44: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.379226 Loss1: 0.635263 Loss2: 0.743963
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.238906 Loss1: 0.571269 Loss2: 0.667638
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.250384 Loss1: 0.585894 Loss2: 0.664490
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.247543 Loss1: 0.581216 Loss2: 0.666328
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.230841 Loss1: 0.564382 Loss2: 0.666458
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193202 Loss1: 0.525371 Loss2: 0.667830
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204858 Loss1: 0.537621 Loss2: 0.667237
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191051 Loss1: 0.524164 Loss2: 0.666887
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.204976 Loss1: 0.532862 Loss2: 0.672114
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179300 Loss1: 0.508952 Loss2: 0.670348
+(DefaultActor pid=1831567) >> Training accuracy: 0.843750
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.204735 Loss1: 0.489710 Loss2: 0.715025
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.062048 Loss1: 0.422620 Loss2: 0.639428
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.060948 Loss1: 0.422593 Loss2: 0.638356
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.035464 Loss1: 0.401393 Loss2: 0.634070
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.055824 Loss1: 0.417440 Loss2: 0.638384
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.031942 Loss1: 0.393395 Loss2: 0.638547
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019894 Loss1: 0.378374 Loss2: 0.641520
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030693 Loss1: 0.391097 Loss2: 0.639596
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.034344 Loss1: 0.391649 Loss2: 0.642694
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.006119 Loss1: 0.365380 Loss2: 0.640739
+(DefaultActor pid=1831567) >> Training accuracy: 0.859761
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389665 Loss1: 0.581837 Loss2: 0.807828
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.323512 Loss1: 0.566961 Loss2: 0.756552
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.319457 Loss1: 0.563094 Loss2: 0.756363
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316199 Loss1: 0.559180 Loss2: 0.757019
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.313958 Loss1: 0.553855 Loss2: 0.760103
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.307890 Loss1: 0.548633 Loss2: 0.759257
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305430 Loss1: 0.545646 Loss2: 0.759784
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.292457 Loss1: 0.535388 Loss2: 0.757069
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.276833 Loss1: 0.521042 Loss2: 0.755791
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.287689 Loss1: 0.525843 Loss2: 0.761846
+(DefaultActor pid=1831567) >> Training accuracy: 0.811756
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.508866 Loss1: 0.764544 Loss2: 0.744323
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.371126 Loss1: 0.715040 Loss2: 0.656086
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.372522 Loss1: 0.716343 Loss2: 0.656179
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.388818 Loss1: 0.726505 Loss2: 0.662313
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.347988 Loss1: 0.688073 Loss2: 0.659915
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362443 Loss1: 0.703202 Loss2: 0.659242
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.377674 Loss1: 0.714576 Loss2: 0.663098
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.352069 Loss1: 0.689579 Loss2: 0.662490
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.353436 Loss1: 0.688892 Loss2: 0.664544
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.342448 Loss1: 0.676406 Loss2: 0.666042
+(DefaultActor pid=1831567) >> Training accuracy: 0.757473
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.525279 Loss1: 0.744128 Loss2: 0.781151
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.390036 Loss1: 0.705340 Loss2: 0.684696
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.397514 Loss1: 0.715276 Loss2: 0.682238
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.368793 Loss1: 0.685967 Loss2: 0.682826
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.386320 Loss1: 0.703586 Loss2: 0.682734
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.358623 Loss1: 0.671915 Loss2: 0.686708
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.331440 Loss1: 0.648093 Loss2: 0.683347
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.352731 Loss1: 0.669892 Loss2: 0.682839
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.337650 Loss1: 0.652281 Loss2: 0.685369
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.320569 Loss1: 0.635530 Loss2: 0.685038
+(DefaultActor pid=1831567) >> Training accuracy: 0.774254
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.349911 Loss1: 0.601862 Loss2: 0.748049
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.244792 Loss1: 0.575617 Loss2: 0.669175
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.228794 Loss1: 0.562093 Loss2: 0.666701
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.222719 Loss1: 0.551756 Loss2: 0.670963
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218550 Loss1: 0.545575 Loss2: 0.672974
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.217023 Loss1: 0.542746 Loss2: 0.674276
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.211359 Loss1: 0.539349 Loss2: 0.672011
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.211067 Loss1: 0.534872 Loss2: 0.676195
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196954 Loss1: 0.521673 Loss2: 0.675281
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193335 Loss1: 0.519793 Loss2: 0.673542
+(DefaultActor pid=1831567) >> Training accuracy: 0.831209
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.239219 Loss1: 0.478654 Loss2: 0.760565
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.105586 Loss1: 0.431928 Loss2: 0.673658
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.105641 Loss1: 0.430878 Loss2: 0.674762
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.089399 Loss1: 0.415559 Loss2: 0.673841
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085533 Loss1: 0.413036 Loss2: 0.672497
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.061133 Loss1: 0.388789 Loss2: 0.672344
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.064830 Loss1: 0.389143 Loss2: 0.675687
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.055497 Loss1: 0.379848 Loss2: 0.675649
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.059802 Loss1: 0.384676 Loss2: 0.675127
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.045076 Loss1: 0.367545 Loss2: 0.677532
+(DefaultActor pid=1831567) >> Training accuracy: 0.865355
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.490684 Loss1: 0.724729 Loss2: 0.765955
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.361970 Loss1: 0.694516 Loss2: 0.667454
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337429 Loss1: 0.672573 Loss2: 0.664856
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.322292 Loss1: 0.652702 Loss2: 0.669590
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.319199 Loss1: 0.649030 Loss2: 0.670169
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.305330 Loss1: 0.634609 Loss2: 0.670722
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295628 Loss1: 0.625312 Loss2: 0.670317
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.279888 Loss1: 0.606862 Loss2: 0.673026
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.273036 Loss1: 0.599284 Loss2: 0.673751
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.286792 Loss1: 0.613728 Loss2: 0.673064
+(DefaultActor pid=1831567) >> Training accuracy: 0.791667
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362190 Loss1: 0.603522 Loss2: 0.758669
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206981 Loss1: 0.557713 Loss2: 0.649268
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.179263 Loss1: 0.530491 Loss2: 0.648772
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.195216 Loss1: 0.545260 Loss2: 0.649956
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170458 Loss1: 0.520480 Loss2: 0.649978
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157299 Loss1: 0.505477 Loss2: 0.651822
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.150330 Loss1: 0.495449 Loss2: 0.654880
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.179526 Loss1: 0.524076 Loss2: 0.655450
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.151557 Loss1: 0.499479 Loss2: 0.652079
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.119880 Loss1: 0.464697 Loss2: 0.655184
+(DefaultActor pid=1831567) >> Training accuracy: 0.830508
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353582 Loss1: 0.619189 Loss2: 0.734392
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245153 Loss1: 0.580618 Loss2: 0.664535
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251420 Loss1: 0.588639 Loss2: 0.662780
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.241866 Loss1: 0.580499 Loss2: 0.661367
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.212292 Loss1: 0.551700 Loss2: 0.660592
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.208979 Loss1: 0.545254 Loss2: 0.663724
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.229008 Loss1: 0.563675 Loss2: 0.665333
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.199629 Loss1: 0.535291 Loss2: 0.664339
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175580 Loss1: 0.511959 Loss2: 0.663621
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.187356 Loss1: 0.521760 Loss2: 0.665596
+[2023-09-27 12:18:11,550][flwr][DEBUG] - fit_round 44 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.828887
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.688200
+[2023-09-27 12:19:08,031][flwr][INFO] - fit progress: (44, 0.8899790141910029, {'accuracy': 0.6882}, 21680.867300234735)
+[2023-09-27 12:19:08,032][flwr][DEBUG] - evaluate_round 44: strategy sampled 10 clients (out of 10)
+[2023-09-27 12:19:52,431][flwr][DEBUG] - evaluate_round 44 received 10 results and 0 failures
+[2023-09-27 12:19:52,432][flwr][DEBUG] - fit_round 45: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.540662 Loss1: 0.766665 Loss2: 0.773996
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.388599 Loss1: 0.708834 Loss2: 0.679764
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.389373 Loss1: 0.710460 Loss2: 0.678913
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.352199 Loss1: 0.674390 Loss2: 0.677809
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.378257 Loss1: 0.693156 Loss2: 0.685101
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.335052 Loss1: 0.654737 Loss2: 0.680315
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.345262 Loss1: 0.662370 Loss2: 0.682892
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.341220 Loss1: 0.656379 Loss2: 0.684840
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.335511 Loss1: 0.650784 Loss2: 0.684727
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.354078 Loss1: 0.667607 Loss2: 0.686471
+(DefaultActor pid=1831567) >> Training accuracy: 0.753731
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.396841 Loss1: 0.628455 Loss2: 0.768385
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.262142 Loss1: 0.581955 Loss2: 0.680187
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.225278 Loss1: 0.546578 Loss2: 0.678700
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223267 Loss1: 0.543521 Loss2: 0.679746
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.212318 Loss1: 0.533081 Loss2: 0.679237
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193569 Loss1: 0.514401 Loss2: 0.679167
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192670 Loss1: 0.512659 Loss2: 0.680011
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191915 Loss1: 0.511175 Loss2: 0.680739
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188025 Loss1: 0.506817 Loss2: 0.681207
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189407 Loss1: 0.505589 Loss2: 0.683818
+(DefaultActor pid=1831567) >> Training accuracy: 0.836965
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.530607 Loss1: 0.750426 Loss2: 0.780181
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347522 Loss1: 0.679951 Loss2: 0.667571
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334192 Loss1: 0.663128 Loss2: 0.671064
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.327108 Loss1: 0.655335 Loss2: 0.671773
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.288815 Loss1: 0.619792 Loss2: 0.669022
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.333040 Loss1: 0.657487 Loss2: 0.675553
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309377 Loss1: 0.634038 Loss2: 0.675339
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300262 Loss1: 0.629134 Loss2: 0.671127
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.278544 Loss1: 0.604739 Loss2: 0.673805
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.270571 Loss1: 0.594894 Loss2: 0.675677
+(DefaultActor pid=1831567) >> Training accuracy: 0.787007
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.371460 Loss1: 0.635608 Loss2: 0.735852
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.241619 Loss1: 0.576746 Loss2: 0.664873
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.233715 Loss1: 0.570902 Loss2: 0.662813
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199053 Loss1: 0.536031 Loss2: 0.663022
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.214607 Loss1: 0.550523 Loss2: 0.664084
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.190943 Loss1: 0.528228 Loss2: 0.662715
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182015 Loss1: 0.515897 Loss2: 0.666117
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202690 Loss1: 0.538567 Loss2: 0.664123
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185481 Loss1: 0.519197 Loss2: 0.666285
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180859 Loss1: 0.513444 Loss2: 0.667416
+(DefaultActor pid=1831567) >> Training accuracy: 0.827172
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.219198 Loss1: 0.485625 Loss2: 0.733573
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.089277 Loss1: 0.433301 Loss2: 0.655976
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.083246 Loss1: 0.429113 Loss2: 0.654133
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.071485 Loss1: 0.416817 Loss2: 0.654668
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.082756 Loss1: 0.426540 Loss2: 0.656216
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.033818 Loss1: 0.379865 Loss2: 0.653953
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.038560 Loss1: 0.383238 Loss2: 0.655322
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.038598 Loss1: 0.382349 Loss2: 0.656249
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.035807 Loss1: 0.378567 Loss2: 0.657240
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.038037 Loss1: 0.380601 Loss2: 0.657435
+(DefaultActor pid=1831567) >> Training accuracy: 0.882137
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.345236 Loss1: 0.595153 Loss2: 0.750082
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.269448 Loss1: 0.568871 Loss2: 0.700577
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260760 Loss1: 0.561397 Loss2: 0.699363
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.245752 Loss1: 0.547979 Loss2: 0.697773
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.251896 Loss1: 0.552040 Loss2: 0.699856
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.242196 Loss1: 0.540327 Loss2: 0.701869
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.245368 Loss1: 0.545177 Loss2: 0.700192
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.236898 Loss1: 0.538537 Loss2: 0.698361
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239375 Loss1: 0.535016 Loss2: 0.704359
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.236435 Loss1: 0.533760 Loss2: 0.702676
+(DefaultActor pid=1831567) >> Training accuracy: 0.824033
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.415570 Loss1: 0.626142 Loss2: 0.789429
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225684 Loss1: 0.540442 Loss2: 0.685242
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.227072 Loss1: 0.541040 Loss2: 0.686032
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.195730 Loss1: 0.512288 Loss2: 0.683442
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210866 Loss1: 0.524881 Loss2: 0.685985
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.213600 Loss1: 0.525740 Loss2: 0.687860
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.185362 Loss1: 0.496698 Loss2: 0.688664
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.171893 Loss1: 0.486507 Loss2: 0.685386
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161885 Loss1: 0.473174 Loss2: 0.688711
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174778 Loss1: 0.486245 Loss2: 0.688533
+(DefaultActor pid=1831567) >> Training accuracy: 0.833951
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341027 Loss1: 0.580952 Loss2: 0.760075
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259609 Loss1: 0.578007 Loss2: 0.681602
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.271452 Loss1: 0.586594 Loss2: 0.684858
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.252278 Loss1: 0.570252 Loss2: 0.682026
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.231330 Loss1: 0.551461 Loss2: 0.679869
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222371 Loss1: 0.539899 Loss2: 0.682472
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.241424 Loss1: 0.557535 Loss2: 0.683888
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.209941 Loss1: 0.525951 Loss2: 0.683990
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.218129 Loss1: 0.533416 Loss2: 0.684713
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214609 Loss1: 0.527983 Loss2: 0.686626
+(DefaultActor pid=1831567) >> Training accuracy: 0.822115
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.323592 Loss1: 0.507285 Loss2: 0.816307
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.138096 Loss1: 0.417069 Loss2: 0.721027
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.132181 Loss1: 0.420797 Loss2: 0.711384
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.127307 Loss1: 0.414334 Loss2: 0.712972
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.118867 Loss1: 0.402282 Loss2: 0.716584
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.103603 Loss1: 0.392847 Loss2: 0.710756
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.095179 Loss1: 0.382803 Loss2: 0.712376
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.105662 Loss1: 0.390032 Loss2: 0.715629
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.090869 Loss1: 0.373630 Loss2: 0.717240
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.096051 Loss1: 0.378974 Loss2: 0.717077
+(DefaultActor pid=1831567) >> Training accuracy: 0.874614
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.539240 Loss1: 0.773452 Loss2: 0.765789
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.433056 Loss1: 0.750285 Loss2: 0.682771
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.397252 Loss1: 0.712060 Loss2: 0.685192
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.399261 Loss1: 0.716638 Loss2: 0.682623
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.387986 Loss1: 0.703749 Loss2: 0.684237
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.382628 Loss1: 0.696595 Loss2: 0.686033
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.383337 Loss1: 0.693777 Loss2: 0.689560
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.393373 Loss1: 0.701217 Loss2: 0.692157
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.366409 Loss1: 0.675414 Loss2: 0.690996
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.357304 Loss1: 0.665207 Loss2: 0.692097
+[2023-09-27 12:34:19,391][flwr][DEBUG] - fit_round 45 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.774004
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.690600
+[2023-09-27 12:34:21,047][flwr][INFO] - fit progress: (45, 0.8930932635697313, {'accuracy': 0.6906}, 22593.883164820727)
+[2023-09-27 12:34:21,047][flwr][DEBUG] - evaluate_round 45: strategy sampled 10 clients (out of 10)
+[2023-09-27 12:34:51,964][flwr][DEBUG] - evaluate_round 45 received 10 results and 0 failures
+[2023-09-27 12:34:51,965][flwr][DEBUG] - fit_round 46: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.519174 Loss1: 0.774902 Loss2: 0.744272
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.400126 Loss1: 0.742099 Loss2: 0.658027
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.431027 Loss1: 0.769044 Loss2: 0.661982
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.381556 Loss1: 0.722664 Loss2: 0.658892
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.365551 Loss1: 0.707898 Loss2: 0.657653
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.371698 Loss1: 0.708155 Loss2: 0.663543
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.347465 Loss1: 0.686076 Loss2: 0.661389
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.332567 Loss1: 0.669776 Loss2: 0.662792
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.330893 Loss1: 0.670076 Loss2: 0.660817
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309459 Loss1: 0.647001 Loss2: 0.662457
+(DefaultActor pid=1831567) >> Training accuracy: 0.783967
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.260663 Loss1: 0.512964 Loss2: 0.747700
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099822 Loss1: 0.437587 Loss2: 0.662236
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.076170 Loss1: 0.414168 Loss2: 0.662002
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.092802 Loss1: 0.430571 Loss2: 0.662231
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.060970 Loss1: 0.401718 Loss2: 0.659251
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.071738 Loss1: 0.409101 Loss2: 0.662637
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.065166 Loss1: 0.403180 Loss2: 0.661986
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.033784 Loss1: 0.371340 Loss2: 0.662444
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028840 Loss1: 0.367701 Loss2: 0.661139
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.072237 Loss1: 0.404997 Loss2: 0.667239
+(DefaultActor pid=1831567) >> Training accuracy: 0.866898
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389219 Loss1: 0.612979 Loss2: 0.776240
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211821 Loss1: 0.543833 Loss2: 0.667988
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203491 Loss1: 0.539457 Loss2: 0.664034
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184746 Loss1: 0.521667 Loss2: 0.663079
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.155653 Loss1: 0.490296 Loss2: 0.665358
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162977 Loss1: 0.496150 Loss2: 0.666827
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.160500 Loss1: 0.490183 Loss2: 0.670317
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158081 Loss1: 0.488701 Loss2: 0.669380
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.145686 Loss1: 0.475495 Loss2: 0.670192
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.139441 Loss1: 0.469494 Loss2: 0.669947
+(DefaultActor pid=1831567) >> Training accuracy: 0.838453
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359411 Loss1: 0.615811 Loss2: 0.743600
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258452 Loss1: 0.589259 Loss2: 0.669194
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231762 Loss1: 0.565948 Loss2: 0.665814
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184424 Loss1: 0.518908 Loss2: 0.665515
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201148 Loss1: 0.534082 Loss2: 0.667066
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.197665 Loss1: 0.525448 Loss2: 0.672217
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171917 Loss1: 0.501082 Loss2: 0.670836
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169320 Loss1: 0.496711 Loss2: 0.672609
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193441 Loss1: 0.521840 Loss2: 0.671601
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.173736 Loss1: 0.501473 Loss2: 0.672263
+(DefaultActor pid=1831567) >> Training accuracy: 0.839227
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359361 Loss1: 0.609979 Loss2: 0.749381
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.233192 Loss1: 0.559281 Loss2: 0.673911
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231312 Loss1: 0.560300 Loss2: 0.671012
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.246056 Loss1: 0.573384 Loss2: 0.672671
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.212402 Loss1: 0.539147 Loss2: 0.673255
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191013 Loss1: 0.517436 Loss2: 0.673577
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.222504 Loss1: 0.543775 Loss2: 0.678728
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191320 Loss1: 0.516214 Loss2: 0.675106
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.200384 Loss1: 0.522521 Loss2: 0.677863
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189134 Loss1: 0.510812 Loss2: 0.678322
+(DefaultActor pid=1831567) >> Training accuracy: 0.840144
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.373018 Loss1: 0.627958 Loss2: 0.745060
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.246678 Loss1: 0.573146 Loss2: 0.673532
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231086 Loss1: 0.562501 Loss2: 0.668585
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223838 Loss1: 0.552527 Loss2: 0.671311
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204021 Loss1: 0.531714 Loss2: 0.672307
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.221638 Loss1: 0.549716 Loss2: 0.671923
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.201495 Loss1: 0.530530 Loss2: 0.670965
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200365 Loss1: 0.527735 Loss2: 0.672630
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.195594 Loss1: 0.522781 Loss2: 0.672813
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.187842 Loss1: 0.514576 Loss2: 0.673266
+(DefaultActor pid=1831567) >> Training accuracy: 0.818598
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.240206 Loss1: 0.479301 Loss2: 0.760905
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.100128 Loss1: 0.428951 Loss2: 0.671177
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.081063 Loss1: 0.411598 Loss2: 0.669465
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074939 Loss1: 0.404252 Loss2: 0.670687
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.061138 Loss1: 0.394088 Loss2: 0.667049
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.047271 Loss1: 0.377164 Loss2: 0.670107
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050661 Loss1: 0.379122 Loss2: 0.671539
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.047839 Loss1: 0.377821 Loss2: 0.670018
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.038265 Loss1: 0.367055 Loss2: 0.671209
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.040316 Loss1: 0.367383 Loss2: 0.672933
+(DefaultActor pid=1831567) >> Training accuracy: 0.874807
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.538479 Loss1: 0.761922 Loss2: 0.776557
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.386540 Loss1: 0.705572 Loss2: 0.680968
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.366569 Loss1: 0.687731 Loss2: 0.678838
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.376170 Loss1: 0.698747 Loss2: 0.677423
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.366040 Loss1: 0.682276 Loss2: 0.683764
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.352730 Loss1: 0.670123 Loss2: 0.682606
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.362248 Loss1: 0.679057 Loss2: 0.683191
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347620 Loss1: 0.664202 Loss2: 0.683418
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.342364 Loss1: 0.657592 Loss2: 0.684772
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.300903 Loss1: 0.620276 Loss2: 0.680627
+(DefaultActor pid=1831567) >> Training accuracy: 0.768190
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360037 Loss1: 0.584552 Loss2: 0.775485
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.291671 Loss1: 0.568758 Loss2: 0.722913
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.278329 Loss1: 0.556018 Loss2: 0.722312
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.265293 Loss1: 0.542868 Loss2: 0.722426
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246025 Loss1: 0.523246 Loss2: 0.722779
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.282761 Loss1: 0.555708 Loss2: 0.727054
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258317 Loss1: 0.530418 Loss2: 0.727898
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.259266 Loss1: 0.532805 Loss2: 0.726461
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.243714 Loss1: 0.517646 Loss2: 0.726068
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.232983 Loss1: 0.507193 Loss2: 0.725790
+(DefaultActor pid=1831567) >> Training accuracy: 0.821181
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.500969 Loss1: 0.735485 Loss2: 0.765484
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.365670 Loss1: 0.704062 Loss2: 0.661608
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.320685 Loss1: 0.656183 Loss2: 0.664502
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.305818 Loss1: 0.641446 Loss2: 0.664372
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.315027 Loss1: 0.649673 Loss2: 0.665354
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.309656 Loss1: 0.638381 Loss2: 0.671275
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298077 Loss1: 0.630287 Loss2: 0.667790
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276976 Loss1: 0.608901 Loss2: 0.668074
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.273642 Loss1: 0.606643 Loss2: 0.666998
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259826 Loss1: 0.588602 Loss2: 0.671224
+[2023-09-27 12:41:49,315][flwr][DEBUG] - fit_round 46 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.812500
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.688100
+[2023-09-27 12:41:50,634][flwr][INFO] - fit progress: (46, 0.896309151150548, {'accuracy': 0.6881}, 23043.470007136)
+[2023-09-27 12:41:50,634][flwr][DEBUG] - evaluate_round 46: strategy sampled 10 clients (out of 10)
+[2023-09-27 12:42:21,692][flwr][DEBUG] - evaluate_round 46 received 10 results and 0 failures
+[2023-09-27 12:42:21,693][flwr][DEBUG] - fit_round 47: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384295 Loss1: 0.607016 Loss2: 0.777279
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.276272 Loss1: 0.583376 Loss2: 0.692896
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236611 Loss1: 0.545478 Loss2: 0.691132
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.224907 Loss1: 0.535143 Loss2: 0.689764
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.226826 Loss1: 0.536168 Loss2: 0.690658
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.202867 Loss1: 0.513156 Loss2: 0.689711
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.227808 Loss1: 0.534605 Loss2: 0.693204
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.206042 Loss1: 0.511145 Loss2: 0.694897
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.201413 Loss1: 0.507216 Loss2: 0.694197
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.215941 Loss1: 0.518073 Loss2: 0.697867
+(DefaultActor pid=1831567) >> Training accuracy: 0.833676
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.487276 Loss1: 0.730478 Loss2: 0.756798
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.371065 Loss1: 0.711278 Loss2: 0.659787
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337291 Loss1: 0.678366 Loss2: 0.658925
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.294634 Loss1: 0.637075 Loss2: 0.657559
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.288455 Loss1: 0.629864 Loss2: 0.658591
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.285126 Loss1: 0.625333 Loss2: 0.659793
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.278102 Loss1: 0.619189 Loss2: 0.658913
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.296242 Loss1: 0.633690 Loss2: 0.662552
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.285275 Loss1: 0.623576 Loss2: 0.661699
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.257414 Loss1: 0.593378 Loss2: 0.664036
+(DefaultActor pid=1831567) >> Training accuracy: 0.772204
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.548712 Loss1: 0.776832 Loss2: 0.771880
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.432132 Loss1: 0.746481 Loss2: 0.685651
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.408598 Loss1: 0.723436 Loss2: 0.685163
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.398733 Loss1: 0.710109 Loss2: 0.688624
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.384542 Loss1: 0.696232 Loss2: 0.688310
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.402486 Loss1: 0.710397 Loss2: 0.692089
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.363751 Loss1: 0.675999 Loss2: 0.687752
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.396467 Loss1: 0.703564 Loss2: 0.692902
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.344741 Loss1: 0.655316 Loss2: 0.689425
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.342790 Loss1: 0.651785 Loss2: 0.691006
+(DefaultActor pid=1831567) >> Training accuracy: 0.783741
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360427 Loss1: 0.602068 Loss2: 0.758359
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.193507 Loss1: 0.538991 Loss2: 0.654516
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.227278 Loss1: 0.571734 Loss2: 0.655544
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177819 Loss1: 0.528541 Loss2: 0.649278
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170937 Loss1: 0.518509 Loss2: 0.652428
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157021 Loss1: 0.504325 Loss2: 0.652696
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.157342 Loss1: 0.502975 Loss2: 0.654366
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.147184 Loss1: 0.489976 Loss2: 0.657208
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.116643 Loss1: 0.462239 Loss2: 0.654404
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.132321 Loss1: 0.475815 Loss2: 0.656506
+(DefaultActor pid=1831567) >> Training accuracy: 0.827595
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339867 Loss1: 0.604926 Loss2: 0.734941
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225687 Loss1: 0.563915 Loss2: 0.661772
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212759 Loss1: 0.549012 Loss2: 0.663748
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212350 Loss1: 0.552455 Loss2: 0.659895
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187653 Loss1: 0.525770 Loss2: 0.661882
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.223646 Loss1: 0.558160 Loss2: 0.665486
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.201068 Loss1: 0.535620 Loss2: 0.665448
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.184951 Loss1: 0.517343 Loss2: 0.667607
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.215626 Loss1: 0.545267 Loss2: 0.670359
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.168145 Loss1: 0.498219 Loss2: 0.669926
+(DefaultActor pid=1831567) >> Training accuracy: 0.835737
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.312320 Loss1: 0.584557 Loss2: 0.727763
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245620 Loss1: 0.560183 Loss2: 0.685437
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.220902 Loss1: 0.540965 Loss2: 0.679936
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.247869 Loss1: 0.560169 Loss2: 0.687700
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218779 Loss1: 0.533453 Loss2: 0.685326
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.230759 Loss1: 0.543237 Loss2: 0.687522
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.236369 Loss1: 0.547459 Loss2: 0.688910
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.237779 Loss1: 0.548806 Loss2: 0.688973
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211841 Loss1: 0.526177 Loss2: 0.685664
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.228884 Loss1: 0.538582 Loss2: 0.690302
+(DefaultActor pid=1831567) >> Training accuracy: 0.829737
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.273799 Loss1: 0.501700 Loss2: 0.772099
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.108187 Loss1: 0.429030 Loss2: 0.679157
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086024 Loss1: 0.409148 Loss2: 0.676877
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.101473 Loss1: 0.424002 Loss2: 0.677471
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.082294 Loss1: 0.404859 Loss2: 0.677435
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.072927 Loss1: 0.397748 Loss2: 0.675179
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.065087 Loss1: 0.387785 Loss2: 0.677303
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.058873 Loss1: 0.377618 Loss2: 0.681254
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042787 Loss1: 0.364538 Loss2: 0.678250
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.063303 Loss1: 0.382388 Loss2: 0.680915
+(DefaultActor pid=1831567) >> Training accuracy: 0.871142
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.230503 Loss1: 0.477037 Loss2: 0.753466
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.097851 Loss1: 0.425837 Loss2: 0.672014
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.068180 Loss1: 0.398314 Loss2: 0.669866
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.090348 Loss1: 0.418064 Loss2: 0.672284
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.065906 Loss1: 0.397609 Loss2: 0.668297
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.072482 Loss1: 0.399875 Loss2: 0.672607
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.040333 Loss1: 0.368301 Loss2: 0.672031
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.072307 Loss1: 0.399421 Loss2: 0.672886
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.049362 Loss1: 0.377029 Loss2: 0.672333
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.043317 Loss1: 0.371580 Loss2: 0.671737
+(DefaultActor pid=1831567) >> Training accuracy: 0.868441
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339192 Loss1: 0.624902 Loss2: 0.714291
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212612 Loss1: 0.564364 Loss2: 0.648248
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.197193 Loss1: 0.554338 Loss2: 0.642855
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.201603 Loss1: 0.557223 Loss2: 0.644380
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.202087 Loss1: 0.552934 Loss2: 0.649153
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181033 Loss1: 0.534887 Loss2: 0.646146
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175362 Loss1: 0.526994 Loss2: 0.648368
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.165377 Loss1: 0.516926 Loss2: 0.648451
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.158411 Loss1: 0.513205 Loss2: 0.645206
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150663 Loss1: 0.504427 Loss2: 0.646236
+(DefaultActor pid=1831567) >> Training accuracy: 0.829078
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.509377 Loss1: 0.735399 Loss2: 0.773978
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.390224 Loss1: 0.713422 Loss2: 0.676802
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.365220 Loss1: 0.691335 Loss2: 0.673885
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.390660 Loss1: 0.713902 Loss2: 0.676758
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.352261 Loss1: 0.675085 Loss2: 0.677175
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.340058 Loss1: 0.663138 Loss2: 0.676919
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.328016 Loss1: 0.649673 Loss2: 0.678343
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.324667 Loss1: 0.644580 Loss2: 0.680087
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.316094 Loss1: 0.635028 Loss2: 0.681066
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.315193 Loss1: 0.631019 Loss2: 0.684174
+(DefaultActor pid=1831567) >> Training accuracy: 0.756996
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 12:49:03,919][flwr][DEBUG] - fit_round 47 received 10 results and 0 failures
+>> Test accuracy: 0.695000
+[2023-09-27 12:49:05,426][flwr][INFO] - fit progress: (47, 0.8794247569938818, {'accuracy': 0.695}, 23478.262214047834)
+[2023-09-27 12:49:05,426][flwr][DEBUG] - evaluate_round 47: strategy sampled 10 clients (out of 10)
+[2023-09-27 12:49:36,775][flwr][DEBUG] - evaluate_round 47 received 10 results and 0 failures
+[2023-09-27 12:49:36,775][flwr][DEBUG] - fit_round 48: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.333916 Loss1: 0.587240 Loss2: 0.746676
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.247359 Loss1: 0.571602 Loss2: 0.675758
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238878 Loss1: 0.566366 Loss2: 0.672513
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.249689 Loss1: 0.571757 Loss2: 0.677931
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215672 Loss1: 0.543565 Loss2: 0.672107
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.216042 Loss1: 0.536988 Loss2: 0.679054
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.208428 Loss1: 0.528471 Loss2: 0.679957
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.216201 Loss1: 0.536138 Loss2: 0.680063
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178854 Loss1: 0.502305 Loss2: 0.676550
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.192472 Loss1: 0.512855 Loss2: 0.679617
+(DefaultActor pid=1831567) >> Training accuracy: 0.819912
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.528928 Loss1: 0.757035 Loss2: 0.771893
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.343505 Loss1: 0.670498 Loss2: 0.673007
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.350779 Loss1: 0.679158 Loss2: 0.671620
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.306773 Loss1: 0.637025 Loss2: 0.669748
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.309107 Loss1: 0.637871 Loss2: 0.671236
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.294317 Loss1: 0.618603 Loss2: 0.675714
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.302349 Loss1: 0.627284 Loss2: 0.675065
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.291708 Loss1: 0.614647 Loss2: 0.677061
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.319332 Loss1: 0.641353 Loss2: 0.677979
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.272179 Loss1: 0.594260 Loss2: 0.677919
+(DefaultActor pid=1831567) >> Training accuracy: 0.794956
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.247262 Loss1: 0.494544 Loss2: 0.752718
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.124499 Loss1: 0.444209 Loss2: 0.680290
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.114144 Loss1: 0.434558 Loss2: 0.679586
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.086074 Loss1: 0.407951 Loss2: 0.678123
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.082534 Loss1: 0.406061 Loss2: 0.676473
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.065492 Loss1: 0.388642 Loss2: 0.676849
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.074536 Loss1: 0.396481 Loss2: 0.678054
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.062223 Loss1: 0.384206 Loss2: 0.678018
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.072424 Loss1: 0.395530 Loss2: 0.676894
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.058028 Loss1: 0.381149 Loss2: 0.676879
+(DefaultActor pid=1831567) >> Training accuracy: 0.871721
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.404530 Loss1: 0.606017 Loss2: 0.798513
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.241847 Loss1: 0.548318 Loss2: 0.693529
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214050 Loss1: 0.523287 Loss2: 0.690763
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212708 Loss1: 0.522048 Loss2: 0.690661
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195004 Loss1: 0.502398 Loss2: 0.692606
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211168 Loss1: 0.516876 Loss2: 0.694292
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182983 Loss1: 0.484397 Loss2: 0.698586
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.178084 Loss1: 0.482187 Loss2: 0.695897
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178520 Loss1: 0.481317 Loss2: 0.697203
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200482 Loss1: 0.500929 Loss2: 0.699552
+(DefaultActor pid=1831567) >> Training accuracy: 0.843485
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.514834 Loss1: 0.772101 Loss2: 0.742733
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.389011 Loss1: 0.735454 Loss2: 0.653557
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.351930 Loss1: 0.697902 Loss2: 0.654027
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.344360 Loss1: 0.688990 Loss2: 0.655371
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.360047 Loss1: 0.703492 Loss2: 0.656555
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.342285 Loss1: 0.681891 Loss2: 0.660393
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.311529 Loss1: 0.653902 Loss2: 0.657628
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334732 Loss1: 0.677209 Loss2: 0.657523
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.332793 Loss1: 0.674072 Loss2: 0.658721
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.346838 Loss1: 0.684227 Loss2: 0.662611
+(DefaultActor pid=1831567) >> Training accuracy: 0.769248
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.361066 Loss1: 0.579990 Loss2: 0.781076
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.302655 Loss1: 0.566947 Loss2: 0.735707
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.282802 Loss1: 0.549858 Loss2: 0.732944
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.274998 Loss1: 0.543133 Loss2: 0.731865
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.258763 Loss1: 0.527591 Loss2: 0.731173
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.280331 Loss1: 0.546713 Loss2: 0.733619
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.277452 Loss1: 0.545069 Loss2: 0.732383
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.251023 Loss1: 0.518762 Loss2: 0.732261
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.266558 Loss1: 0.531170 Loss2: 0.735389
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.254253 Loss1: 0.519187 Loss2: 0.735066
+(DefaultActor pid=1831567) >> Training accuracy: 0.829241
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.205386 Loss1: 0.461198 Loss2: 0.744188
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.078932 Loss1: 0.420410 Loss2: 0.658522
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.067040 Loss1: 0.410279 Loss2: 0.656760
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.073094 Loss1: 0.413641 Loss2: 0.659452
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.053354 Loss1: 0.396938 Loss2: 0.656416
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.032002 Loss1: 0.376341 Loss2: 0.655660
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.044299 Loss1: 0.385338 Loss2: 0.658962
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.045986 Loss1: 0.386248 Loss2: 0.659737
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.047094 Loss1: 0.384948 Loss2: 0.662145
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.029627 Loss1: 0.369067 Loss2: 0.660561
+(DefaultActor pid=1831567) >> Training accuracy: 0.866898
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.308347 Loss1: 0.596310 Loss2: 0.712038
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.183224 Loss1: 0.542981 Loss2: 0.640243
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191324 Loss1: 0.551454 Loss2: 0.639870
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190049 Loss1: 0.549459 Loss2: 0.640590
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150015 Loss1: 0.506982 Loss2: 0.643033
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168075 Loss1: 0.522905 Loss2: 0.645169
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.143183 Loss1: 0.498250 Loss2: 0.644933
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.152376 Loss1: 0.505206 Loss2: 0.647170
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183341 Loss1: 0.534828 Loss2: 0.648513
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.122925 Loss1: 0.475684 Loss2: 0.647241
+(DefaultActor pid=1831567) >> Training accuracy: 0.834910
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.518945 Loss1: 0.768758 Loss2: 0.750188
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.392639 Loss1: 0.731993 Loss2: 0.660646
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.345411 Loss1: 0.687412 Loss2: 0.658000
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331650 Loss1: 0.674969 Loss2: 0.656681
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.319966 Loss1: 0.663243 Loss2: 0.656722
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.298611 Loss1: 0.641174 Loss2: 0.657437
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.303569 Loss1: 0.642780 Loss2: 0.660789
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.307967 Loss1: 0.646424 Loss2: 0.661543
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.311285 Loss1: 0.645507 Loss2: 0.665778
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304477 Loss1: 0.643216 Loss2: 0.661261
+(DefaultActor pid=1831567) >> Training accuracy: 0.769823
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382953 Loss1: 0.629597 Loss2: 0.753356
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245300 Loss1: 0.562753 Loss2: 0.682547
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262615 Loss1: 0.581906 Loss2: 0.680709
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226302 Loss1: 0.544830 Loss2: 0.681471
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.227932 Loss1: 0.546613 Loss2: 0.681319
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218532 Loss1: 0.537676 Loss2: 0.680856
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192419 Loss1: 0.509418 Loss2: 0.683001
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204432 Loss1: 0.521829 Loss2: 0.682603
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.222712 Loss1: 0.536010 Loss2: 0.686702
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.203882 Loss1: 0.518391 Loss2: 0.685490
+[2023-09-27 12:56:20,878][flwr][DEBUG] - fit_round 48 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.827934
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.694200
+[2023-09-27 12:56:22,569][flwr][INFO] - fit progress: (48, 0.8850831008566835, {'accuracy': 0.6942}, 23915.405494552106)
+[2023-09-27 12:56:22,570][flwr][DEBUG] - evaluate_round 48: strategy sampled 10 clients (out of 10)
+[2023-09-27 12:56:53,863][flwr][DEBUG] - evaluate_round 48 received 10 results and 0 failures
+[2023-09-27 12:56:53,864][flwr][DEBUG] - fit_round 49: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.205831 Loss1: 0.461481 Loss2: 0.744350
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.103200 Loss1: 0.438027 Loss2: 0.665172
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086822 Loss1: 0.420793 Loss2: 0.666029
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.075835 Loss1: 0.413296 Loss2: 0.662540
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.040837 Loss1: 0.377994 Loss2: 0.662843
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.040867 Loss1: 0.377129 Loss2: 0.663738
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.034103 Loss1: 0.368695 Loss2: 0.665408
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.034367 Loss1: 0.368031 Loss2: 0.666336
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.051984 Loss1: 0.383545 Loss2: 0.668439
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.044407 Loss1: 0.377266 Loss2: 0.667141
+(DefaultActor pid=1831567) >> Training accuracy: 0.859375
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.530797 Loss1: 0.770342 Loss2: 0.760454
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.402810 Loss1: 0.726040 Loss2: 0.676770
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.392216 Loss1: 0.716201 Loss2: 0.676015
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.376797 Loss1: 0.697660 Loss2: 0.679137
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.383840 Loss1: 0.702259 Loss2: 0.681581
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.378496 Loss1: 0.699156 Loss2: 0.679340
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.345480 Loss1: 0.663654 Loss2: 0.681826
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.369513 Loss1: 0.687914 Loss2: 0.681600
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.360079 Loss1: 0.675511 Loss2: 0.684569
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.357917 Loss1: 0.674021 Loss2: 0.683896
+(DefaultActor pid=1831567) >> Training accuracy: 0.774457
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.343907 Loss1: 0.586201 Loss2: 0.757705
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.262711 Loss1: 0.555317 Loss2: 0.707394
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.246324 Loss1: 0.541249 Loss2: 0.705075
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237878 Loss1: 0.535584 Loss2: 0.702294
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246817 Loss1: 0.539479 Loss2: 0.707339
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.236719 Loss1: 0.528661 Loss2: 0.708058
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.240025 Loss1: 0.532846 Loss2: 0.707179
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.233214 Loss1: 0.526048 Loss2: 0.707166
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232768 Loss1: 0.523901 Loss2: 0.708866
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.222679 Loss1: 0.512900 Loss2: 0.709779
+(DefaultActor pid=1831567) >> Training accuracy: 0.811508
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.257069 Loss1: 0.486958 Loss2: 0.770111
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.109189 Loss1: 0.427586 Loss2: 0.681603
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.085223 Loss1: 0.407812 Loss2: 0.677411
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.091179 Loss1: 0.409501 Loss2: 0.681678
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085487 Loss1: 0.403685 Loss2: 0.681802
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073977 Loss1: 0.391803 Loss2: 0.682173
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.078001 Loss1: 0.393495 Loss2: 0.684506
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.046892 Loss1: 0.364973 Loss2: 0.681919
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.048755 Loss1: 0.370508 Loss2: 0.678248
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.047053 Loss1: 0.366280 Loss2: 0.680773
+(DefaultActor pid=1831567) >> Training accuracy: 0.854552
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339006 Loss1: 0.630861 Loss2: 0.708146
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.214745 Loss1: 0.580586 Loss2: 0.634159
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193097 Loss1: 0.556118 Loss2: 0.636980
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.179641 Loss1: 0.546193 Loss2: 0.633448
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.179527 Loss1: 0.542860 Loss2: 0.636667
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180666 Loss1: 0.539376 Loss2: 0.641290
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161487 Loss1: 0.524529 Loss2: 0.636959
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151112 Loss1: 0.514684 Loss2: 0.636428
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.167279 Loss1: 0.527984 Loss2: 0.639295
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150279 Loss1: 0.510193 Loss2: 0.640085
+(DefaultActor pid=1831567) >> Training accuracy: 0.820884
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481290 Loss1: 0.739544 Loss2: 0.741747
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332603 Loss1: 0.686557 Loss2: 0.646047
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.317385 Loss1: 0.669253 Loss2: 0.648132
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.267769 Loss1: 0.620170 Loss2: 0.647599
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.274014 Loss1: 0.624070 Loss2: 0.649944
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.274661 Loss1: 0.626243 Loss2: 0.648418
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.270431 Loss1: 0.620648 Loss2: 0.649783
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.245305 Loss1: 0.593139 Loss2: 0.652166
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257319 Loss1: 0.607028 Loss2: 0.650290
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.269420 Loss1: 0.614278 Loss2: 0.655142
+(DefaultActor pid=1831567) >> Training accuracy: 0.793037
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.396318 Loss1: 0.619322 Loss2: 0.776996
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259682 Loss1: 0.569542 Loss2: 0.690140
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.232743 Loss1: 0.542639 Loss2: 0.690104
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202909 Loss1: 0.511152 Loss2: 0.691757
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224806 Loss1: 0.534991 Loss2: 0.689815
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.203079 Loss1: 0.511994 Loss2: 0.691085
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195233 Loss1: 0.505999 Loss2: 0.689234
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.201108 Loss1: 0.507711 Loss2: 0.693397
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.198189 Loss1: 0.503421 Loss2: 0.694768
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.208878 Loss1: 0.511988 Loss2: 0.696890
+(DefaultActor pid=1831567) >> Training accuracy: 0.835732
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.352101 Loss1: 0.612069 Loss2: 0.740032
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.220541 Loss1: 0.555826 Loss2: 0.664714
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212216 Loss1: 0.547771 Loss2: 0.664445
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231896 Loss1: 0.561024 Loss2: 0.670872
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.208795 Loss1: 0.538765 Loss2: 0.670030
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.197519 Loss1: 0.527229 Loss2: 0.670290
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.218589 Loss1: 0.545518 Loss2: 0.673071
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.203567 Loss1: 0.532452 Loss2: 0.671115
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.162831 Loss1: 0.491283 Loss2: 0.671548
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189999 Loss1: 0.513829 Loss2: 0.676171
+(DefaultActor pid=1831567) >> Training accuracy: 0.834535
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360562 Loss1: 0.611936 Loss2: 0.748626
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.209313 Loss1: 0.559421 Loss2: 0.649892
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173976 Loss1: 0.526970 Loss2: 0.647006
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.154367 Loss1: 0.506906 Loss2: 0.647460
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.147000 Loss1: 0.498657 Loss2: 0.648344
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.173448 Loss1: 0.520713 Loss2: 0.652735
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.150142 Loss1: 0.500380 Loss2: 0.649761
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.118740 Loss1: 0.469440 Loss2: 0.649301
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140291 Loss1: 0.489554 Loss2: 0.650737
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.119383 Loss1: 0.468674 Loss2: 0.650709
+(DefaultActor pid=1831567) >> Training accuracy: 0.850900
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.523930 Loss1: 0.737683 Loss2: 0.786247
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.391903 Loss1: 0.703971 Loss2: 0.687932
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.364666 Loss1: 0.680847 Loss2: 0.683819
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.363851 Loss1: 0.674647 Loss2: 0.689205
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.359019 Loss1: 0.669284 Loss2: 0.689736
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.367115 Loss1: 0.677336 Loss2: 0.689779
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352974 Loss1: 0.660336 Loss2: 0.692638
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.345760 Loss1: 0.649153 Loss2: 0.696607
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.333655 Loss1: 0.639827 Loss2: 0.693828
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.314111 Loss1: 0.621277 Loss2: 0.692834
+[2023-09-27 13:03:50,781][flwr][DEBUG] - fit_round 49 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.764925
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.697900
+[2023-09-27 13:03:52,107][flwr][INFO] - fit progress: (49, 0.8720772449200908, {'accuracy': 0.6979}, 24364.94322586106)
+[2023-09-27 13:03:52,107][flwr][DEBUG] - evaluate_round 49: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:04:23,238][flwr][DEBUG] - evaluate_round 49 received 10 results and 0 failures
+[2023-09-27 13:04:23,239][flwr][DEBUG] - fit_round 50: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329488 Loss1: 0.574049 Loss2: 0.755438
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234892 Loss1: 0.562977 Loss2: 0.671915
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215256 Loss1: 0.540728 Loss2: 0.674528
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212468 Loss1: 0.533005 Loss2: 0.679463
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.186633 Loss1: 0.513553 Loss2: 0.673080
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193858 Loss1: 0.518507 Loss2: 0.675351
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174424 Loss1: 0.497138 Loss2: 0.677286
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173264 Loss1: 0.495847 Loss2: 0.677416
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.174019 Loss1: 0.494337 Loss2: 0.679682
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184802 Loss1: 0.503815 Loss2: 0.680986
+(DefaultActor pid=1831567) >> Training accuracy: 0.841488
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.367244 Loss1: 0.617600 Loss2: 0.749643
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.257916 Loss1: 0.578054 Loss2: 0.679861
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.243926 Loss1: 0.568045 Loss2: 0.675880
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.206622 Loss1: 0.531440 Loss2: 0.675182
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.213104 Loss1: 0.537932 Loss2: 0.675172
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211871 Loss1: 0.533577 Loss2: 0.678294
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200658 Loss1: 0.521886 Loss2: 0.678773
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190284 Loss1: 0.511790 Loss2: 0.678494
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180457 Loss1: 0.500631 Loss2: 0.679826
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176787 Loss1: 0.493000 Loss2: 0.683787
+(DefaultActor pid=1831567) >> Training accuracy: 0.822980
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.375870 Loss1: 0.595551 Loss2: 0.780319
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225479 Loss1: 0.548515 Loss2: 0.676964
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191062 Loss1: 0.516010 Loss2: 0.675053
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225912 Loss1: 0.544525 Loss2: 0.681386
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211210 Loss1: 0.525736 Loss2: 0.685475
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185369 Loss1: 0.503725 Loss2: 0.681644
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161132 Loss1: 0.480189 Loss2: 0.680943
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.170386 Loss1: 0.485740 Loss2: 0.684646
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.172963 Loss1: 0.490115 Loss2: 0.682848
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.156410 Loss1: 0.473070 Loss2: 0.683340
+(DefaultActor pid=1831567) >> Training accuracy: 0.850900
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326530 Loss1: 0.592627 Loss2: 0.733904
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.226738 Loss1: 0.564109 Loss2: 0.662629
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223401 Loss1: 0.557933 Loss2: 0.665467
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188539 Loss1: 0.522143 Loss2: 0.666396
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204558 Loss1: 0.536276 Loss2: 0.668282
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.201179 Loss1: 0.532333 Loss2: 0.668846
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195807 Loss1: 0.525340 Loss2: 0.670467
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.176122 Loss1: 0.508904 Loss2: 0.667218
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188129 Loss1: 0.516386 Loss2: 0.671742
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165711 Loss1: 0.495966 Loss2: 0.669745
+(DefaultActor pid=1831567) >> Training accuracy: 0.828926
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.219449 Loss1: 0.469354 Loss2: 0.750095
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.085495 Loss1: 0.423622 Loss2: 0.661873
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.070928 Loss1: 0.409425 Loss2: 0.661503
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.058661 Loss1: 0.395346 Loss2: 0.663315
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.074556 Loss1: 0.411237 Loss2: 0.663319
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.045669 Loss1: 0.384222 Loss2: 0.661447
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.040691 Loss1: 0.379531 Loss2: 0.661160
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.018287 Loss1: 0.355471 Loss2: 0.662816
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042613 Loss1: 0.378778 Loss2: 0.663835
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019421 Loss1: 0.353851 Loss2: 0.665570
+(DefaultActor pid=1831567) >> Training accuracy: 0.878472
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.508920 Loss1: 0.761210 Loss2: 0.747709
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387345 Loss1: 0.729720 Loss2: 0.657625
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.395457 Loss1: 0.734305 Loss2: 0.661152
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.367563 Loss1: 0.705018 Loss2: 0.662545
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.367144 Loss1: 0.708664 Loss2: 0.658480
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.373119 Loss1: 0.705528 Loss2: 0.667591
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352669 Loss1: 0.684653 Loss2: 0.668017
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.316307 Loss1: 0.653289 Loss2: 0.663018
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331580 Loss1: 0.667685 Loss2: 0.663895
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.360940 Loss1: 0.690783 Loss2: 0.670157
+(DefaultActor pid=1831567) >> Training accuracy: 0.754982
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.368013 Loss1: 0.586852 Loss2: 0.781161
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.280686 Loss1: 0.551347 Loss2: 0.729339
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.271317 Loss1: 0.539871 Loss2: 0.731445
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.263253 Loss1: 0.533283 Loss2: 0.729970
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.260648 Loss1: 0.530967 Loss2: 0.729681
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.251908 Loss1: 0.520344 Loss2: 0.731565
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.263162 Loss1: 0.527629 Loss2: 0.735533
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.267522 Loss1: 0.531181 Loss2: 0.736341
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.253012 Loss1: 0.521863 Loss2: 0.731149
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250916 Loss1: 0.513227 Loss2: 0.737689
+(DefaultActor pid=1831567) >> Training accuracy: 0.822421
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.531702 Loss1: 0.767664 Loss2: 0.764038
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382971 Loss1: 0.714639 Loss2: 0.668332
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.352177 Loss1: 0.687447 Loss2: 0.664730
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331850 Loss1: 0.665801 Loss2: 0.666049
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.326441 Loss1: 0.659424 Loss2: 0.667017
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.329816 Loss1: 0.659326 Loss2: 0.670490
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.340003 Loss1: 0.670382 Loss2: 0.669620
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.322073 Loss1: 0.652807 Loss2: 0.669266
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305205 Loss1: 0.634911 Loss2: 0.670294
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307307 Loss1: 0.636915 Loss2: 0.670392
+(DefaultActor pid=1831567) >> Training accuracy: 0.777285
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.192522 Loss1: 0.463894 Loss2: 0.728627
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.085103 Loss1: 0.429725 Loss2: 0.655378
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.057078 Loss1: 0.402751 Loss2: 0.654327
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.070370 Loss1: 0.414405 Loss2: 0.655965
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.069916 Loss1: 0.411377 Loss2: 0.658539
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.062000 Loss1: 0.401953 Loss2: 0.660048
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.044360 Loss1: 0.388810 Loss2: 0.655550
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.046142 Loss1: 0.385850 Loss2: 0.660292
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.031329 Loss1: 0.374949 Loss2: 0.656380
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.033869 Loss1: 0.373285 Loss2: 0.660585
+(DefaultActor pid=1831567) >> Training accuracy: 0.871528
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.542379 Loss1: 0.762388 Loss2: 0.779991
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.357444 Loss1: 0.679413 Loss2: 0.678031
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.350013 Loss1: 0.675012 Loss2: 0.675001
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.318135 Loss1: 0.644297 Loss2: 0.673837
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.310695 Loss1: 0.633250 Loss2: 0.677445
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.300877 Loss1: 0.622548 Loss2: 0.678329
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299567 Loss1: 0.619326 Loss2: 0.680241
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.301052 Loss1: 0.617887 Loss2: 0.683165
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.272314 Loss1: 0.590687 Loss2: 0.681626
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259797 Loss1: 0.580198 Loss2: 0.679599
+[2023-09-27 13:11:19,527][flwr][DEBUG] - fit_round 50 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.799068
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.691600
+[2023-09-27 13:11:20,922][flwr][INFO] - fit progress: (50, 0.8935196316851595, {'accuracy': 0.6916}, 24813.75863668602)
+[2023-09-27 13:11:20,923][flwr][DEBUG] - evaluate_round 50: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:11:51,765][flwr][DEBUG] - evaluate_round 50 received 10 results and 0 failures
+[2023-09-27 13:11:51,765][flwr][DEBUG] - fit_round 51: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369676 Loss1: 0.599857 Loss2: 0.769819
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.228444 Loss1: 0.561079 Loss2: 0.667365
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198833 Loss1: 0.533108 Loss2: 0.665725
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189212 Loss1: 0.523711 Loss2: 0.665502
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170465 Loss1: 0.501303 Loss2: 0.669162
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.155324 Loss1: 0.489232 Loss2: 0.666092
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148286 Loss1: 0.479311 Loss2: 0.668975
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.175643 Loss1: 0.507430 Loss2: 0.668212
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.159907 Loss1: 0.488308 Loss2: 0.671598
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.129929 Loss1: 0.462695 Loss2: 0.667234
+(DefaultActor pid=1831567) >> Training accuracy: 0.836864
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.252698 Loss1: 0.474150 Loss2: 0.778548
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.134749 Loss1: 0.444886 Loss2: 0.689863
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.089313 Loss1: 0.405423 Loss2: 0.683890
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.090509 Loss1: 0.406052 Loss2: 0.684457
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066572 Loss1: 0.385428 Loss2: 0.681144
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073644 Loss1: 0.392746 Loss2: 0.680899
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.061099 Loss1: 0.375473 Loss2: 0.685626
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.063514 Loss1: 0.379305 Loss2: 0.684209
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.065510 Loss1: 0.379986 Loss2: 0.685523
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.065989 Loss1: 0.376326 Loss2: 0.689663
+(DefaultActor pid=1831567) >> Training accuracy: 0.869599
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.209627 Loss1: 0.485330 Loss2: 0.724297
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.062609 Loss1: 0.417637 Loss2: 0.644972
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.061135 Loss1: 0.416571 Loss2: 0.644565
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.043968 Loss1: 0.399670 Loss2: 0.644298
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.013707 Loss1: 0.373562 Loss2: 0.640145
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.030321 Loss1: 0.387559 Loss2: 0.642762
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.006787 Loss1: 0.364807 Loss2: 0.641980
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.028197 Loss1: 0.385259 Loss2: 0.642938
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.010431 Loss1: 0.366140 Loss2: 0.644291
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.017947 Loss1: 0.373090 Loss2: 0.644857
+(DefaultActor pid=1831567) >> Training accuracy: 0.870563
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.311317 Loss1: 0.602673 Loss2: 0.708645
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200054 Loss1: 0.558530 Loss2: 0.641524
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205118 Loss1: 0.562051 Loss2: 0.643067
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191366 Loss1: 0.551155 Loss2: 0.640212
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185421 Loss1: 0.543436 Loss2: 0.641985
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.174266 Loss1: 0.533482 Loss2: 0.640783
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.167133 Loss1: 0.523831 Loss2: 0.643302
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.147575 Loss1: 0.504882 Loss2: 0.642693
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.152650 Loss1: 0.506080 Loss2: 0.646569
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.139968 Loss1: 0.493321 Loss2: 0.646647
+(DefaultActor pid=1831567) >> Training accuracy: 0.802591
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.502769 Loss1: 0.746020 Loss2: 0.756748
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332198 Loss1: 0.676619 Loss2: 0.655580
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.329595 Loss1: 0.672764 Loss2: 0.656832
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291170 Loss1: 0.632581 Loss2: 0.658588
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282455 Loss1: 0.624114 Loss2: 0.658341
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.285502 Loss1: 0.625739 Loss2: 0.659763
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.280680 Loss1: 0.620175 Loss2: 0.660505
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.277251 Loss1: 0.612468 Loss2: 0.664783
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267469 Loss1: 0.605335 Loss2: 0.662135
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.246542 Loss1: 0.584307 Loss2: 0.662235
+(DefaultActor pid=1831567) >> Training accuracy: 0.789200
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.534354 Loss1: 0.748475 Loss2: 0.785879
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.426062 Loss1: 0.733830 Loss2: 0.692232
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.386556 Loss1: 0.696161 Loss2: 0.690395
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.370162 Loss1: 0.679832 Loss2: 0.690330
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.357703 Loss1: 0.667251 Loss2: 0.690452
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341027 Loss1: 0.651162 Loss2: 0.689865
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.348202 Loss1: 0.652556 Loss2: 0.695646
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.339945 Loss1: 0.649336 Loss2: 0.690609
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.336431 Loss1: 0.643770 Loss2: 0.692661
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.337868 Loss1: 0.643739 Loss2: 0.694129
+(DefaultActor pid=1831567) >> Training accuracy: 0.773554
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.538965 Loss1: 0.784271 Loss2: 0.754695
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.384056 Loss1: 0.713201 Loss2: 0.670855
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.397616 Loss1: 0.723814 Loss2: 0.673802
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.361425 Loss1: 0.688131 Loss2: 0.673295
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.372172 Loss1: 0.698998 Loss2: 0.673174
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.365348 Loss1: 0.692467 Loss2: 0.672881
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.344183 Loss1: 0.670404 Loss2: 0.673779
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.309176 Loss1: 0.633927 Loss2: 0.675250
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.340131 Loss1: 0.660067 Loss2: 0.680065
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329782 Loss1: 0.650914 Loss2: 0.678868
+(DefaultActor pid=1831567) >> Training accuracy: 0.783514
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.318571 Loss1: 0.597921 Loss2: 0.720650
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212457 Loss1: 0.562649 Loss2: 0.649808
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.210490 Loss1: 0.558296 Loss2: 0.652194
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202500 Loss1: 0.552307 Loss2: 0.650192
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.179083 Loss1: 0.530578 Loss2: 0.648505
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.177744 Loss1: 0.529305 Loss2: 0.648440
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.177296 Loss1: 0.523454 Loss2: 0.653841
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158734 Loss1: 0.507762 Loss2: 0.650972
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163359 Loss1: 0.509076 Loss2: 0.654283
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165555 Loss1: 0.513595 Loss2: 0.651960
+(DefaultActor pid=1831567) >> Training accuracy: 0.823918
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.328914 Loss1: 0.580654 Loss2: 0.748260
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259594 Loss1: 0.554585 Loss2: 0.705010
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.255605 Loss1: 0.551772 Loss2: 0.703833
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237273 Loss1: 0.536272 Loss2: 0.701001
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.254118 Loss1: 0.548185 Loss2: 0.705933
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232574 Loss1: 0.529367 Loss2: 0.703207
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.226360 Loss1: 0.519860 Loss2: 0.706500
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.219783 Loss1: 0.515663 Loss2: 0.704120
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217178 Loss1: 0.510942 Loss2: 0.706236
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.242955 Loss1: 0.536196 Loss2: 0.706759
+(DefaultActor pid=1831567) >> Training accuracy: 0.826141
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369767 Loss1: 0.611898 Loss2: 0.757869
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211972 Loss1: 0.540702 Loss2: 0.671271
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199474 Loss1: 0.527048 Loss2: 0.672426
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205365 Loss1: 0.531195 Loss2: 0.674171
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201150 Loss1: 0.523688 Loss2: 0.677462
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168433 Loss1: 0.497778 Loss2: 0.670656
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.188743 Loss1: 0.510713 Loss2: 0.678030
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.179115 Loss1: 0.503056 Loss2: 0.676059
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185269 Loss1: 0.508704 Loss2: 0.676565
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.167728 Loss1: 0.489583 Loss2: 0.678145
+[2023-09-27 13:18:50,366][flwr][DEBUG] - fit_round 51 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.838199
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.686400
+[2023-09-27 13:18:51,699][flwr][INFO] - fit progress: (51, 0.8929546851510057, {'accuracy': 0.6864}, 25264.535360032693)
+[2023-09-27 13:18:51,699][flwr][DEBUG] - evaluate_round 51: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:19:22,603][flwr][DEBUG] - evaluate_round 51 received 10 results and 0 failures
+[2023-09-27 13:19:22,604][flwr][DEBUG] - fit_round 52: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.524771 Loss1: 0.769963 Loss2: 0.754807
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.437541 Loss1: 0.766104 Loss2: 0.671437
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.396995 Loss1: 0.729561 Loss2: 0.667433
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.377354 Loss1: 0.708528 Loss2: 0.668826
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.360514 Loss1: 0.692201 Loss2: 0.668313
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341243 Loss1: 0.671018 Loss2: 0.670225
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.335678 Loss1: 0.666717 Loss2: 0.668961
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.337643 Loss1: 0.666662 Loss2: 0.670981
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.337028 Loss1: 0.662607 Loss2: 0.674420
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.348530 Loss1: 0.674542 Loss2: 0.673988
+(DefaultActor pid=1831567) >> Training accuracy: 0.772871
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.352897 Loss1: 0.580754 Loss2: 0.772143
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.196349 Loss1: 0.529161 Loss2: 0.667188
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194956 Loss1: 0.524900 Loss2: 0.670056
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165803 Loss1: 0.498155 Loss2: 0.667649
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.182541 Loss1: 0.511405 Loss2: 0.671136
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162586 Loss1: 0.492887 Loss2: 0.669698
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.152586 Loss1: 0.480544 Loss2: 0.672042
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.142528 Loss1: 0.472754 Loss2: 0.669774
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140630 Loss1: 0.471058 Loss2: 0.669572
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.151861 Loss1: 0.478917 Loss2: 0.672943
+(DefaultActor pid=1831567) >> Training accuracy: 0.849576
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.367350 Loss1: 0.631076 Loss2: 0.736274
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245103 Loss1: 0.574806 Loss2: 0.670297
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213947 Loss1: 0.550343 Loss2: 0.663604
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208758 Loss1: 0.541125 Loss2: 0.667633
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200215 Loss1: 0.532646 Loss2: 0.667568
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199071 Loss1: 0.529666 Loss2: 0.669405
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174407 Loss1: 0.508772 Loss2: 0.665635
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180312 Loss1: 0.509053 Loss2: 0.671259
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180074 Loss1: 0.513505 Loss2: 0.666569
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.168472 Loss1: 0.498498 Loss2: 0.669974
+(DefaultActor pid=1831567) >> Training accuracy: 0.822790
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.379579 Loss1: 0.620168 Loss2: 0.759412
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.255731 Loss1: 0.571207 Loss2: 0.684524
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.241625 Loss1: 0.555548 Loss2: 0.686078
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217380 Loss1: 0.534277 Loss2: 0.683104
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.189201 Loss1: 0.504386 Loss2: 0.684814
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.203011 Loss1: 0.517287 Loss2: 0.685724
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.215004 Loss1: 0.525645 Loss2: 0.689360
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.218173 Loss1: 0.529238 Loss2: 0.688934
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213548 Loss1: 0.523508 Loss2: 0.690040
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179349 Loss1: 0.489516 Loss2: 0.689833
+(DefaultActor pid=1831567) >> Training accuracy: 0.792268
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.365033 Loss1: 0.587731 Loss2: 0.777302
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.288043 Loss1: 0.562142 Loss2: 0.725902
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.252190 Loss1: 0.528784 Loss2: 0.723406
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.253414 Loss1: 0.531115 Loss2: 0.722299
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.253823 Loss1: 0.529783 Loss2: 0.724041
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.260111 Loss1: 0.534174 Loss2: 0.725938
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.248290 Loss1: 0.520506 Loss2: 0.727784
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.248324 Loss1: 0.520136 Loss2: 0.728188
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257834 Loss1: 0.526261 Loss2: 0.731574
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.227476 Loss1: 0.501095 Loss2: 0.726381
+(DefaultActor pid=1831567) >> Training accuracy: 0.805308
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.519840 Loss1: 0.763031 Loss2: 0.756810
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370047 Loss1: 0.706222 Loss2: 0.663825
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.361147 Loss1: 0.696479 Loss2: 0.664668
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.345445 Loss1: 0.682928 Loss2: 0.662517
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.305160 Loss1: 0.644769 Loss2: 0.660391
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.322354 Loss1: 0.659721 Loss2: 0.662633
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314781 Loss1: 0.648246 Loss2: 0.666535
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.314845 Loss1: 0.648197 Loss2: 0.666648
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.295039 Loss1: 0.626521 Loss2: 0.668519
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.301688 Loss1: 0.632699 Loss2: 0.668989
+(DefaultActor pid=1831567) >> Training accuracy: 0.768424
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.192172 Loss1: 0.464828 Loss2: 0.727345
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092068 Loss1: 0.438421 Loss2: 0.653647
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.052479 Loss1: 0.398153 Loss2: 0.654326
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.047981 Loss1: 0.395664 Loss2: 0.652318
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.052077 Loss1: 0.398168 Loss2: 0.653909
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.047259 Loss1: 0.390558 Loss2: 0.656701
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.030456 Loss1: 0.373678 Loss2: 0.656778
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.036238 Loss1: 0.379984 Loss2: 0.656254
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.024813 Loss1: 0.367482 Loss2: 0.657332
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.025695 Loss1: 0.368447 Loss2: 0.657248
+(DefaultActor pid=1831567) >> Training accuracy: 0.867670
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226332 Loss1: 0.476302 Loss2: 0.750031
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.093950 Loss1: 0.427426 Loss2: 0.666525
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.070362 Loss1: 0.403934 Loss2: 0.666428
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.061101 Loss1: 0.396861 Loss2: 0.664240
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.072756 Loss1: 0.408617 Loss2: 0.664139
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.053465 Loss1: 0.389004 Loss2: 0.664462
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.036440 Loss1: 0.370429 Loss2: 0.666011
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.025584 Loss1: 0.360722 Loss2: 0.664861
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.029650 Loss1: 0.362231 Loss2: 0.667419
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.027996 Loss1: 0.361199 Loss2: 0.666798
+(DefaultActor pid=1831567) >> Training accuracy: 0.869792
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.315011 Loss1: 0.596857 Loss2: 0.718154
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.189520 Loss1: 0.545140 Loss2: 0.644379
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.178131 Loss1: 0.532094 Loss2: 0.646037
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.173067 Loss1: 0.524525 Loss2: 0.648542
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.175328 Loss1: 0.526291 Loss2: 0.649036
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.158769 Loss1: 0.508524 Loss2: 0.650246
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.156795 Loss1: 0.505932 Loss2: 0.650863
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.164972 Loss1: 0.514317 Loss2: 0.650654
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.145418 Loss1: 0.493951 Loss2: 0.651467
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154207 Loss1: 0.500691 Loss2: 0.653516
+(DefaultActor pid=1831567) >> Training accuracy: 0.834498
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488409 Loss1: 0.707600 Loss2: 0.780809
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.354448 Loss1: 0.672980 Loss2: 0.681468
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334730 Loss1: 0.656665 Loss2: 0.678065
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.328182 Loss1: 0.646134 Loss2: 0.682048
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.306471 Loss1: 0.621321 Loss2: 0.685151
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.298457 Loss1: 0.613881 Loss2: 0.684575
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.307919 Loss1: 0.621249 Loss2: 0.686670
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.272565 Loss1: 0.586228 Loss2: 0.686337
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267132 Loss1: 0.582349 Loss2: 0.684783
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289894 Loss1: 0.599773 Loss2: 0.690121
+[2023-09-27 13:26:05,181][flwr][DEBUG] - fit_round 52 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.793860
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.695000
+[2023-09-27 13:26:06,692][flwr][INFO] - fit progress: (52, 0.8784053408490202, {'accuracy': 0.695}, 25699.52871695673)
+[2023-09-27 13:26:06,693][flwr][DEBUG] - evaluate_round 52: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:26:37,453][flwr][DEBUG] - evaluate_round 52 received 10 results and 0 failures
+[2023-09-27 13:26:37,454][flwr][DEBUG] - fit_round 53: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.247715 Loss1: 0.484390 Loss2: 0.763325
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.109400 Loss1: 0.427463 Loss2: 0.681936
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.081966 Loss1: 0.403707 Loss2: 0.678259
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.066915 Loss1: 0.386437 Loss2: 0.680477
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.071969 Loss1: 0.392096 Loss2: 0.679873
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073082 Loss1: 0.395279 Loss2: 0.677803
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.046966 Loss1: 0.365563 Loss2: 0.681404
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.055015 Loss1: 0.374976 Loss2: 0.680039
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.043052 Loss1: 0.360242 Loss2: 0.682810
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.037408 Loss1: 0.353858 Loss2: 0.683550
+(DefaultActor pid=1831567) >> Training accuracy: 0.876543
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.483210 Loss1: 0.741269 Loss2: 0.741941
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382283 Loss1: 0.720816 Loss2: 0.661467
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363572 Loss1: 0.703434 Loss2: 0.660139
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.365129 Loss1: 0.704057 Loss2: 0.661073
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.351205 Loss1: 0.687190 Loss2: 0.664014
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.348922 Loss1: 0.684432 Loss2: 0.664491
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350524 Loss1: 0.683931 Loss2: 0.666593
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.324444 Loss1: 0.656307 Loss2: 0.668137
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.316868 Loss1: 0.648106 Loss2: 0.668763
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.341060 Loss1: 0.669861 Loss2: 0.671199
+(DefaultActor pid=1831567) >> Training accuracy: 0.759737
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382548 Loss1: 0.616507 Loss2: 0.766041
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.215006 Loss1: 0.556071 Loss2: 0.658935
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208225 Loss1: 0.548379 Loss2: 0.659846
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.185878 Loss1: 0.527084 Loss2: 0.658795
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.139408 Loss1: 0.481014 Loss2: 0.658394
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.140269 Loss1: 0.479020 Loss2: 0.661249
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.160450 Loss1: 0.497927 Loss2: 0.662523
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.131257 Loss1: 0.467868 Loss2: 0.663389
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120205 Loss1: 0.457591 Loss2: 0.662614
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.114676 Loss1: 0.452478 Loss2: 0.662198
+(DefaultActor pid=1831567) >> Training accuracy: 0.851430
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291978 Loss1: 0.562872 Loss2: 0.729106
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.233074 Loss1: 0.551895 Loss2: 0.681179
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218907 Loss1: 0.536106 Loss2: 0.682801
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.215831 Loss1: 0.530489 Loss2: 0.685342
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221061 Loss1: 0.535114 Loss2: 0.685947
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210711 Loss1: 0.527490 Loss2: 0.683221
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197493 Loss1: 0.511869 Loss2: 0.685624
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.205659 Loss1: 0.519024 Loss2: 0.686635
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199233 Loss1: 0.511593 Loss2: 0.687640
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.204707 Loss1: 0.516687 Loss2: 0.688019
+(DefaultActor pid=1831567) >> Training accuracy: 0.831349
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.497412 Loss1: 0.744785 Loss2: 0.752628
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.351285 Loss1: 0.699289 Loss2: 0.651996
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318724 Loss1: 0.669402 Loss2: 0.649322
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.304114 Loss1: 0.652278 Loss2: 0.651837
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.294742 Loss1: 0.642394 Loss2: 0.652348
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.277135 Loss1: 0.625100 Loss2: 0.652034
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.260145 Loss1: 0.607375 Loss2: 0.652769
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.267181 Loss1: 0.609315 Loss2: 0.657866
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.245916 Loss1: 0.590298 Loss2: 0.655618
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.258656 Loss1: 0.602523 Loss2: 0.656133
+(DefaultActor pid=1831567) >> Training accuracy: 0.788103
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348422 Loss1: 0.586652 Loss2: 0.761770
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.222996 Loss1: 0.543363 Loss2: 0.679634
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201713 Loss1: 0.524850 Loss2: 0.676863
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191989 Loss1: 0.514936 Loss2: 0.677053
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.186240 Loss1: 0.508369 Loss2: 0.677871
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191848 Loss1: 0.513581 Loss2: 0.678268
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.173851 Loss1: 0.494466 Loss2: 0.679385
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.183878 Loss1: 0.501187 Loss2: 0.682691
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173026 Loss1: 0.488346 Loss2: 0.684680
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176497 Loss1: 0.493589 Loss2: 0.682907
+(DefaultActor pid=1831567) >> Training accuracy: 0.833470
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327788 Loss1: 0.595910 Loss2: 0.731877
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225735 Loss1: 0.563675 Loss2: 0.662060
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.210461 Loss1: 0.548068 Loss2: 0.662393
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194621 Loss1: 0.531331 Loss2: 0.663291
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191610 Loss1: 0.526936 Loss2: 0.664674
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.207348 Loss1: 0.537442 Loss2: 0.669906
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.166883 Loss1: 0.501415 Loss2: 0.665468
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182010 Loss1: 0.516137 Loss2: 0.665873
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178205 Loss1: 0.510931 Loss2: 0.667274
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175195 Loss1: 0.507675 Loss2: 0.667520
+(DefaultActor pid=1831567) >> Training accuracy: 0.845954
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493809 Loss1: 0.714529 Loss2: 0.779280
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.401196 Loss1: 0.719542 Loss2: 0.681654
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.370101 Loss1: 0.686492 Loss2: 0.683609
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.359652 Loss1: 0.678253 Loss2: 0.681399
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.369696 Loss1: 0.683836 Loss2: 0.685859
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.351792 Loss1: 0.663225 Loss2: 0.688567
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.367079 Loss1: 0.676127 Loss2: 0.690952
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.342085 Loss1: 0.654948 Loss2: 0.687137
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.310607 Loss1: 0.625908 Loss2: 0.684699
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.340250 Loss1: 0.651145 Loss2: 0.689105
+(DefaultActor pid=1831567) >> Training accuracy: 0.770289
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348740 Loss1: 0.615751 Loss2: 0.732989
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236878 Loss1: 0.575607 Loss2: 0.661271
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219551 Loss1: 0.556354 Loss2: 0.663197
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.213054 Loss1: 0.551391 Loss2: 0.661664
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201391 Loss1: 0.540645 Loss2: 0.660746
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184956 Loss1: 0.523852 Loss2: 0.661104
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.180188 Loss1: 0.517516 Loss2: 0.662672
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202408 Loss1: 0.536795 Loss2: 0.665613
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173551 Loss1: 0.509561 Loss2: 0.663990
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.153932 Loss1: 0.488912 Loss2: 0.665020
+(DefaultActor pid=1831567) >> Training accuracy: 0.829268
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.280389 Loss1: 0.476103 Loss2: 0.804286
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.141747 Loss1: 0.423798 Loss2: 0.717949
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.120216 Loss1: 0.408588 Loss2: 0.711628
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.114581 Loss1: 0.404514 Loss2: 0.710068
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.105167 Loss1: 0.394410 Loss2: 0.710757
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.092021 Loss1: 0.380307 Loss2: 0.711714
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.103213 Loss1: 0.390512 Loss2: 0.712701
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.065282 Loss1: 0.353987 Loss2: 0.711295
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.079711 Loss1: 0.368236 Loss2: 0.711475
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.082995 Loss1: 0.367358 Loss2: 0.715637
+[2023-09-27 13:33:37,964][flwr][DEBUG] - fit_round 53 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.871528
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.695700
+[2023-09-27 13:33:39,904][flwr][INFO] - fit progress: (53, 0.8694329051354441, {'accuracy': 0.6957}, 26152.74077124009)
+[2023-09-27 13:33:39,905][flwr][DEBUG] - evaluate_round 53: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:34:10,406][flwr][DEBUG] - evaluate_round 53 received 10 results and 0 failures
+[2023-09-27 13:34:10,407][flwr][DEBUG] - fit_round 54: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362001 Loss1: 0.633961 Loss2: 0.728041
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.249663 Loss1: 0.585434 Loss2: 0.664229
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215409 Loss1: 0.557034 Loss2: 0.658375
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183037 Loss1: 0.525314 Loss2: 0.657723
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.208118 Loss1: 0.546147 Loss2: 0.661971
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191514 Loss1: 0.531472 Loss2: 0.660042
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.181157 Loss1: 0.520613 Loss2: 0.660544
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.176699 Loss1: 0.512104 Loss2: 0.664595
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163123 Loss1: 0.498932 Loss2: 0.664191
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.163273 Loss1: 0.497887 Loss2: 0.665385
+(DefaultActor pid=1831567) >> Training accuracy: 0.833651
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.345068 Loss1: 0.583856 Loss2: 0.761212
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.252150 Loss1: 0.543384 Loss2: 0.708766
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.235417 Loss1: 0.528200 Loss2: 0.707217
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.246408 Loss1: 0.538127 Loss2: 0.708281
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234829 Loss1: 0.526151 Loss2: 0.708679
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222793 Loss1: 0.515385 Loss2: 0.707407
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.234438 Loss1: 0.521957 Loss2: 0.712481
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220465 Loss1: 0.510807 Loss2: 0.709658
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239711 Loss1: 0.525875 Loss2: 0.713836
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.227176 Loss1: 0.515554 Loss2: 0.711622
+(DefaultActor pid=1831567) >> Training accuracy: 0.825645
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.497680 Loss1: 0.759641 Loss2: 0.738039
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.375377 Loss1: 0.717991 Loss2: 0.657386
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.362163 Loss1: 0.706649 Loss2: 0.655514
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.354674 Loss1: 0.698820 Loss2: 0.655854
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.338994 Loss1: 0.679684 Loss2: 0.659310
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337021 Loss1: 0.678427 Loss2: 0.658594
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.340257 Loss1: 0.681870 Loss2: 0.658388
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347651 Loss1: 0.687226 Loss2: 0.660425
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.338822 Loss1: 0.673279 Loss2: 0.665542
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.343187 Loss1: 0.678104 Loss2: 0.665083
+(DefaultActor pid=1831567) >> Training accuracy: 0.776947
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.171316 Loss1: 0.486828 Loss2: 0.684488
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.042427 Loss1: 0.423750 Loss2: 0.618677
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.037536 Loss1: 0.421537 Loss2: 0.615999
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.014967 Loss1: 0.398717 Loss2: 0.616250
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.000424 Loss1: 0.384899 Loss2: 0.615525
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.001818 Loss1: 0.382649 Loss2: 0.619169
+(DefaultActor pid=1831567) Epoch: 6 Loss: 0.984814 Loss1: 0.367643 Loss2: 0.617171
+(DefaultActor pid=1831567) Epoch: 7 Loss: 0.994382 Loss1: 0.375418 Loss2: 0.618964
+(DefaultActor pid=1831567) Epoch: 8 Loss: 0.996229 Loss1: 0.375597 Loss2: 0.620632
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.985759 Loss1: 0.363194 Loss2: 0.622564
+(DefaultActor pid=1831567) >> Training accuracy: 0.874807
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.373517 Loss1: 0.593225 Loss2: 0.780292
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.233321 Loss1: 0.555269 Loss2: 0.678052
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.175395 Loss1: 0.497823 Loss2: 0.677572
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190448 Loss1: 0.513359 Loss2: 0.677089
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167698 Loss1: 0.492792 Loss2: 0.674906
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.171162 Loss1: 0.490436 Loss2: 0.680726
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.178391 Loss1: 0.495100 Loss2: 0.683291
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.159085 Loss1: 0.476173 Loss2: 0.682912
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134973 Loss1: 0.453883 Loss2: 0.681091
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154721 Loss1: 0.472110 Loss2: 0.682611
+(DefaultActor pid=1831567) >> Training accuracy: 0.847193
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.223557 Loss1: 0.478856 Loss2: 0.744702
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092645 Loss1: 0.434314 Loss2: 0.658331
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048860 Loss1: 0.393926 Loss2: 0.654934
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.031393 Loss1: 0.380164 Loss2: 0.651230
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.043801 Loss1: 0.386166 Loss2: 0.657634
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.042580 Loss1: 0.385221 Loss2: 0.657360
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.059586 Loss1: 0.401863 Loss2: 0.657723
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030000 Loss1: 0.372258 Loss2: 0.657742
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.018259 Loss1: 0.360409 Loss2: 0.657850
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.025003 Loss1: 0.366568 Loss2: 0.658435
+(DefaultActor pid=1831567) >> Training accuracy: 0.869020
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.538590 Loss1: 0.751949 Loss2: 0.786641
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.331578 Loss1: 0.641120 Loss2: 0.690458
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.342827 Loss1: 0.655768 Loss2: 0.687059
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.330478 Loss1: 0.638345 Loss2: 0.692133
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.343470 Loss1: 0.648667 Loss2: 0.694802
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.323379 Loss1: 0.628490 Loss2: 0.694889
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.310277 Loss1: 0.621976 Loss2: 0.688301
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276107 Loss1: 0.582599 Loss2: 0.693507
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.304031 Loss1: 0.608093 Loss2: 0.695939
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.302375 Loss1: 0.606582 Loss2: 0.695794
+(DefaultActor pid=1831567) >> Training accuracy: 0.800987
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.333758 Loss1: 0.592220 Loss2: 0.741538
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.213435 Loss1: 0.547510 Loss2: 0.665925
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199287 Loss1: 0.533527 Loss2: 0.665760
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177523 Loss1: 0.514790 Loss2: 0.662734
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184366 Loss1: 0.517247 Loss2: 0.667119
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180756 Loss1: 0.511514 Loss2: 0.669242
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.169854 Loss1: 0.500171 Loss2: 0.669683
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.164272 Loss1: 0.493142 Loss2: 0.671130
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147194 Loss1: 0.478406 Loss2: 0.668788
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.169697 Loss1: 0.496685 Loss2: 0.673013
+(DefaultActor pid=1831567) >> Training accuracy: 0.842722
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.352130 Loss1: 0.605413 Loss2: 0.746717
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.255865 Loss1: 0.580964 Loss2: 0.674902
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208314 Loss1: 0.534667 Loss2: 0.673647
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.209395 Loss1: 0.534881 Loss2: 0.674514
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.220647 Loss1: 0.543750 Loss2: 0.676897
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.201971 Loss1: 0.525439 Loss2: 0.676532
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193236 Loss1: 0.516627 Loss2: 0.676609
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200158 Loss1: 0.519986 Loss2: 0.680172
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.223173 Loss1: 0.539067 Loss2: 0.684106
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.194929 Loss1: 0.512975 Loss2: 0.681954
+(DefaultActor pid=1831567) >> Training accuracy: 0.838742
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.490632 Loss1: 0.725746 Loss2: 0.764886
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.386952 Loss1: 0.708861 Loss2: 0.678090
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.371194 Loss1: 0.694567 Loss2: 0.676627
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325565 Loss1: 0.650084 Loss2: 0.675481
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.373198 Loss1: 0.694656 Loss2: 0.678542
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.331869 Loss1: 0.654552 Loss2: 0.677316
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.330080 Loss1: 0.651699 Loss2: 0.678382
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.323758 Loss1: 0.643123 Loss2: 0.680636
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.324449 Loss1: 0.642821 Loss2: 0.681628
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290754 Loss1: 0.612609 Loss2: 0.678144
+[2023-09-27 13:40:58,659][flwr][DEBUG] - fit_round 54 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.764925
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.697900
+[2023-09-27 13:40:59,981][flwr][INFO] - fit progress: (54, 0.8700203883190887, {'accuracy': 0.6979}, 26592.817508330103)
+[2023-09-27 13:40:59,981][flwr][DEBUG] - evaluate_round 54: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:41:34,985][flwr][DEBUG] - evaluate_round 54 received 10 results and 0 failures
+[2023-09-27 13:41:34,986][flwr][DEBUG] - fit_round 55: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326257 Loss1: 0.576070 Loss2: 0.750187
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.242953 Loss1: 0.571023 Loss2: 0.671930
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213013 Loss1: 0.546022 Loss2: 0.666992
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.198382 Loss1: 0.526287 Loss2: 0.672095
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.164294 Loss1: 0.492841 Loss2: 0.671453
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193778 Loss1: 0.523820 Loss2: 0.669959
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174500 Loss1: 0.502002 Loss2: 0.672498
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168380 Loss1: 0.496736 Loss2: 0.671644
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175149 Loss1: 0.500396 Loss2: 0.674753
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.164259 Loss1: 0.491449 Loss2: 0.672810
+(DefaultActor pid=1831567) >> Training accuracy: 0.826275
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.499274 Loss1: 0.758392 Loss2: 0.740882
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.286091 Loss1: 0.646272 Loss2: 0.639819
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.288737 Loss1: 0.648348 Loss2: 0.640389
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.282485 Loss1: 0.640600 Loss2: 0.641885
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.253065 Loss1: 0.611623 Loss2: 0.641441
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249175 Loss1: 0.602783 Loss2: 0.646392
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.275367 Loss1: 0.631054 Loss2: 0.644313
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.271504 Loss1: 0.624798 Loss2: 0.646706
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.218826 Loss1: 0.571794 Loss2: 0.647031
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.258956 Loss1: 0.608815 Loss2: 0.650141
+(DefaultActor pid=1831567) >> Training accuracy: 0.780428
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.494342 Loss1: 0.720317 Loss2: 0.774025
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.366267 Loss1: 0.685749 Loss2: 0.680518
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.351119 Loss1: 0.669544 Loss2: 0.681574
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.361675 Loss1: 0.679023 Loss2: 0.682652
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.348671 Loss1: 0.666813 Loss2: 0.681858
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.340961 Loss1: 0.658301 Loss2: 0.682660
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.342277 Loss1: 0.657332 Loss2: 0.684945
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320231 Loss1: 0.633604 Loss2: 0.686626
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305770 Loss1: 0.617971 Loss2: 0.687799
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.323931 Loss1: 0.633187 Loss2: 0.690744
+(DefaultActor pid=1831567) >> Training accuracy: 0.756996
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370088 Loss1: 0.593129 Loss2: 0.776959
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.238569 Loss1: 0.565367 Loss2: 0.673202
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208216 Loss1: 0.541472 Loss2: 0.666744
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.182914 Loss1: 0.514559 Loss2: 0.668355
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.165835 Loss1: 0.497329 Loss2: 0.668507
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.156847 Loss1: 0.487968 Loss2: 0.668880
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.149130 Loss1: 0.476695 Loss2: 0.672436
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.143711 Loss1: 0.472813 Loss2: 0.670899
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136332 Loss1: 0.466036 Loss2: 0.670296
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.120818 Loss1: 0.448642 Loss2: 0.672176
+(DefaultActor pid=1831567) >> Training accuracy: 0.833951
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.310844 Loss1: 0.578766 Loss2: 0.732078
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234164 Loss1: 0.546138 Loss2: 0.688026
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238034 Loss1: 0.548633 Loss2: 0.689401
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217668 Loss1: 0.527669 Loss2: 0.690000
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198614 Loss1: 0.513256 Loss2: 0.685358
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.236214 Loss1: 0.543804 Loss2: 0.692409
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.215277 Loss1: 0.523292 Loss2: 0.691985
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.206877 Loss1: 0.518243 Loss2: 0.688634
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.204212 Loss1: 0.514749 Loss2: 0.689463
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.202626 Loss1: 0.507728 Loss2: 0.694898
+(DefaultActor pid=1831567) >> Training accuracy: 0.834201
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.305924 Loss1: 0.487922 Loss2: 0.818001
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.145136 Loss1: 0.422717 Loss2: 0.722419
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.130176 Loss1: 0.406553 Loss2: 0.723623
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.112758 Loss1: 0.393912 Loss2: 0.718846
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.115495 Loss1: 0.390105 Loss2: 0.725390
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.086187 Loss1: 0.365375 Loss2: 0.720811
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.120973 Loss1: 0.391992 Loss2: 0.728981
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.085838 Loss1: 0.360989 Loss2: 0.724849
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.113746 Loss1: 0.391684 Loss2: 0.722062
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.109828 Loss1: 0.380502 Loss2: 0.729326
+(DefaultActor pid=1831567) >> Training accuracy: 0.863812
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340350 Loss1: 0.613029 Loss2: 0.727321
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227310 Loss1: 0.569291 Loss2: 0.658018
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.221539 Loss1: 0.564515 Loss2: 0.657023
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187517 Loss1: 0.528573 Loss2: 0.658945
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192426 Loss1: 0.534711 Loss2: 0.657715
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.153790 Loss1: 0.499285 Loss2: 0.654505
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.158838 Loss1: 0.503309 Loss2: 0.655529
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185985 Loss1: 0.525076 Loss2: 0.660909
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.159795 Loss1: 0.501990 Loss2: 0.657805
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146988 Loss1: 0.489223 Loss2: 0.657765
+(DefaultActor pid=1831567) >> Training accuracy: 0.833841
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.232255 Loss1: 0.486683 Loss2: 0.745572
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.104400 Loss1: 0.439791 Loss2: 0.664608
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.090004 Loss1: 0.423123 Loss2: 0.666880
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.041556 Loss1: 0.379733 Loss2: 0.661823
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.047803 Loss1: 0.384710 Loss2: 0.663093
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.053465 Loss1: 0.387593 Loss2: 0.665872
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.042090 Loss1: 0.373808 Loss2: 0.668283
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.062236 Loss1: 0.393246 Loss2: 0.668990
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.038912 Loss1: 0.368483 Loss2: 0.670429
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019888 Loss1: 0.352725 Loss2: 0.667162
+(DefaultActor pid=1831567) >> Training accuracy: 0.880594
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517841 Loss1: 0.773281 Loss2: 0.744560
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.386019 Loss1: 0.720090 Loss2: 0.665929
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.364515 Loss1: 0.698879 Loss2: 0.665636
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.363198 Loss1: 0.694144 Loss2: 0.669053
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.346480 Loss1: 0.677754 Loss2: 0.668726
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.349011 Loss1: 0.681118 Loss2: 0.667893
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.334240 Loss1: 0.664090 Loss2: 0.670150
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.348112 Loss1: 0.677295 Loss2: 0.670818
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.301946 Loss1: 0.628856 Loss2: 0.673090
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.323579 Loss1: 0.651323 Loss2: 0.672256
+(DefaultActor pid=1831567) >> Training accuracy: 0.771060
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.312927 Loss1: 0.596236 Loss2: 0.716691
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216526 Loss1: 0.570812 Loss2: 0.645714
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192467 Loss1: 0.546340 Loss2: 0.646127
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.214608 Loss1: 0.564432 Loss2: 0.650176
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169207 Loss1: 0.521643 Loss2: 0.647565
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184782 Loss1: 0.535076 Loss2: 0.649706
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163469 Loss1: 0.513725 Loss2: 0.649744
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180547 Loss1: 0.526875 Loss2: 0.653672
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.144114 Loss1: 0.491872 Loss2: 0.652242
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.168253 Loss1: 0.513529 Loss2: 0.654724
+[2023-09-27 13:48:34,336][flwr][DEBUG] - fit_round 55 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.832131
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.702800
+[2023-09-27 13:48:35,822][flwr][INFO] - fit progress: (55, 0.8612132831312977, {'accuracy': 0.7028}, 27048.658763975836)
+[2023-09-27 13:48:35,823][flwr][DEBUG] - evaluate_round 55: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:49:06,456][flwr][DEBUG] - evaluate_round 55 received 10 results and 0 failures
+[2023-09-27 13:49:06,457][flwr][DEBUG] - fit_round 56: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.521974 Loss1: 0.761758 Loss2: 0.760216
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.388698 Loss1: 0.715260 Loss2: 0.673438
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.389518 Loss1: 0.711472 Loss2: 0.678046
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.375567 Loss1: 0.699418 Loss2: 0.676149
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.356923 Loss1: 0.678205 Loss2: 0.678718
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.343104 Loss1: 0.666888 Loss2: 0.676217
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.351482 Loss1: 0.674153 Loss2: 0.677329
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.366400 Loss1: 0.685615 Loss2: 0.680785
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.350989 Loss1: 0.667406 Loss2: 0.683583
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.324810 Loss1: 0.643460 Loss2: 0.681350
+(DefaultActor pid=1831567) >> Training accuracy: 0.783741
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.202950 Loss1: 0.484341 Loss2: 0.718609
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.043935 Loss1: 0.407972 Loss2: 0.635964
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.066111 Loss1: 0.430409 Loss2: 0.635702
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019180 Loss1: 0.384869 Loss2: 0.634311
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.011656 Loss1: 0.376273 Loss2: 0.635383
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.028323 Loss1: 0.392982 Loss2: 0.635342
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.025466 Loss1: 0.388186 Loss2: 0.637280
+(DefaultActor pid=1831567) Epoch: 7 Loss: 0.992641 Loss1: 0.354810 Loss2: 0.637832
+(DefaultActor pid=1831567) Epoch: 8 Loss: 0.998410 Loss1: 0.360874 Loss2: 0.637537
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.009581 Loss1: 0.372235 Loss2: 0.637346
+(DefaultActor pid=1831567) >> Training accuracy: 0.878472
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.338790 Loss1: 0.585962 Loss2: 0.752828
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187752 Loss1: 0.534523 Loss2: 0.653230
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.161958 Loss1: 0.513010 Loss2: 0.648949
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.133166 Loss1: 0.487492 Loss2: 0.645674
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154501 Loss1: 0.502735 Loss2: 0.651766
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.141295 Loss1: 0.488429 Loss2: 0.652866
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.110485 Loss1: 0.456541 Loss2: 0.653943
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.150346 Loss1: 0.494253 Loss2: 0.656093
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.121474 Loss1: 0.467455 Loss2: 0.654019
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.103903 Loss1: 0.448955 Loss2: 0.654949
+(DefaultActor pid=1831567) >> Training accuracy: 0.843485
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.376918 Loss1: 0.629300 Loss2: 0.747618
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.239395 Loss1: 0.574486 Loss2: 0.664909
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192204 Loss1: 0.530750 Loss2: 0.661454
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205937 Loss1: 0.539814 Loss2: 0.666123
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185689 Loss1: 0.518198 Loss2: 0.667491
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186690 Loss1: 0.519477 Loss2: 0.667214
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163594 Loss1: 0.494390 Loss2: 0.669204
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161235 Loss1: 0.493287 Loss2: 0.667947
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184405 Loss1: 0.514316 Loss2: 0.670089
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172468 Loss1: 0.502057 Loss2: 0.670412
+(DefaultActor pid=1831567) >> Training accuracy: 0.838816
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369468 Loss1: 0.604311 Loss2: 0.765157
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.247643 Loss1: 0.558355 Loss2: 0.689288
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236079 Loss1: 0.548372 Loss2: 0.687707
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.224967 Loss1: 0.534924 Loss2: 0.690043
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203860 Loss1: 0.516592 Loss2: 0.687267
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211234 Loss1: 0.522089 Loss2: 0.689145
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.223435 Loss1: 0.532598 Loss2: 0.690837
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.226178 Loss1: 0.531318 Loss2: 0.694860
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.215550 Loss1: 0.518701 Loss2: 0.696848
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184872 Loss1: 0.491514 Loss2: 0.693358
+(DefaultActor pid=1831567) >> Training accuracy: 0.836939
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.476066 Loss1: 0.733313 Loss2: 0.742753
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.353571 Loss1: 0.697059 Loss2: 0.656511
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.329620 Loss1: 0.675190 Loss2: 0.654430
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.307466 Loss1: 0.649602 Loss2: 0.657865
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.339999 Loss1: 0.678897 Loss2: 0.661102
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328404 Loss1: 0.670066 Loss2: 0.658337
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.329058 Loss1: 0.668017 Loss2: 0.661042
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288036 Loss1: 0.628973 Loss2: 0.659063
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.290377 Loss1: 0.629554 Loss2: 0.660823
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308199 Loss1: 0.644858 Loss2: 0.663341
+(DefaultActor pid=1831567) >> Training accuracy: 0.765858
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384337 Loss1: 0.637792 Loss2: 0.746545
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234595 Loss1: 0.552350 Loss2: 0.682246
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218924 Loss1: 0.541215 Loss2: 0.677709
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.228275 Loss1: 0.546283 Loss2: 0.681992
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196895 Loss1: 0.520279 Loss2: 0.676615
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220729 Loss1: 0.538566 Loss2: 0.682163
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197170 Loss1: 0.513707 Loss2: 0.683462
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.177271 Loss1: 0.495701 Loss2: 0.681570
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183333 Loss1: 0.499320 Loss2: 0.684013
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.208038 Loss1: 0.520540 Loss2: 0.687497
+(DefaultActor pid=1831567) >> Training accuracy: 0.843369
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.564877 Loss1: 0.739735 Loss2: 0.825142
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.388682 Loss1: 0.672963 Loss2: 0.715719
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.380320 Loss1: 0.665891 Loss2: 0.714429
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.330005 Loss1: 0.613044 Loss2: 0.716961
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.321650 Loss1: 0.606975 Loss2: 0.714675
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328845 Loss1: 0.616297 Loss2: 0.712548
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.329981 Loss1: 0.613877 Loss2: 0.716104
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.332752 Loss1: 0.614391 Loss2: 0.718361
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.325792 Loss1: 0.607031 Loss2: 0.718762
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.316995 Loss1: 0.596541 Loss2: 0.720454
+(DefaultActor pid=1831567) >> Training accuracy: 0.808114
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329089 Loss1: 0.571206 Loss2: 0.757883
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245349 Loss1: 0.540071 Loss2: 0.705278
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236246 Loss1: 0.534470 Loss2: 0.701776
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237850 Loss1: 0.533258 Loss2: 0.704592
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.231049 Loss1: 0.525075 Loss2: 0.705974
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220566 Loss1: 0.513164 Loss2: 0.707403
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258197 Loss1: 0.546880 Loss2: 0.711317
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225660 Loss1: 0.519102 Loss2: 0.706557
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.225280 Loss1: 0.515115 Loss2: 0.710165
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.232372 Loss1: 0.521541 Loss2: 0.710831
+(DefaultActor pid=1831567) >> Training accuracy: 0.828621
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.154468 Loss1: 0.463140 Loss2: 0.691328
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.030206 Loss1: 0.404587 Loss2: 0.625619
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.038038 Loss1: 0.412716 Loss2: 0.625322
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019304 Loss1: 0.393652 Loss2: 0.625652
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.002812 Loss1: 0.379776 Loss2: 0.623036
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.012174 Loss1: 0.386604 Loss2: 0.625570
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.001269 Loss1: 0.376264 Loss2: 0.625005
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.007877 Loss1: 0.380856 Loss2: 0.627021
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.007006 Loss1: 0.377708 Loss2: 0.629297
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.983878 Loss1: 0.353319 Loss2: 0.630559
+[2023-09-27 13:55:52,024][flwr][DEBUG] - fit_round 56 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.876157
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.695100
+[2023-09-27 13:55:53,658][flwr][INFO] - fit progress: (56, 0.8703011946556286, {'accuracy': 0.6951}, 27486.493984027766)
+[2023-09-27 13:55:53,658][flwr][DEBUG] - evaluate_round 56: strategy sampled 10 clients (out of 10)
+[2023-09-27 13:56:25,431][flwr][DEBUG] - evaluate_round 56 received 10 results and 0 failures
+[2023-09-27 13:56:25,432][flwr][DEBUG] - fit_round 57: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382444 Loss1: 0.591541 Loss2: 0.790903
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223973 Loss1: 0.542060 Loss2: 0.681913
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199853 Loss1: 0.517805 Loss2: 0.682048
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191658 Loss1: 0.510469 Loss2: 0.681189
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.168365 Loss1: 0.489598 Loss2: 0.678767
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191864 Loss1: 0.508567 Loss2: 0.683297
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.142463 Loss1: 0.460846 Loss2: 0.681617
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140906 Loss1: 0.460653 Loss2: 0.680253
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.142776 Loss1: 0.460728 Loss2: 0.682048
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.157794 Loss1: 0.473836 Loss2: 0.683958
+(DefaultActor pid=1831567) >> Training accuracy: 0.849576
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495419 Loss1: 0.728565 Loss2: 0.766854
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.369880 Loss1: 0.689877 Loss2: 0.680003
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.368960 Loss1: 0.691816 Loss2: 0.677144
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.342911 Loss1: 0.666090 Loss2: 0.676822
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317326 Loss1: 0.641041 Loss2: 0.676285
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.324800 Loss1: 0.645743 Loss2: 0.679057
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305976 Loss1: 0.627756 Loss2: 0.678220
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.316854 Loss1: 0.636133 Loss2: 0.680721
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.327175 Loss1: 0.645421 Loss2: 0.681754
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309089 Loss1: 0.625557 Loss2: 0.683532
+(DefaultActor pid=1831567) >> Training accuracy: 0.787547
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.354494 Loss1: 0.616445 Loss2: 0.738049
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.224329 Loss1: 0.561508 Loss2: 0.662822
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.202689 Loss1: 0.540413 Loss2: 0.662276
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181075 Loss1: 0.520998 Loss2: 0.660077
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198205 Loss1: 0.537237 Loss2: 0.660968
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.172437 Loss1: 0.509686 Loss2: 0.662751
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179162 Loss1: 0.516092 Loss2: 0.663070
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191265 Loss1: 0.529911 Loss2: 0.661354
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182088 Loss1: 0.514279 Loss2: 0.667809
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.167100 Loss1: 0.502684 Loss2: 0.664416
+(DefaultActor pid=1831567) >> Training accuracy: 0.832508
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.279624 Loss1: 0.559270 Loss2: 0.720354
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.222820 Loss1: 0.547274 Loss2: 0.675546
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.224467 Loss1: 0.544533 Loss2: 0.679934
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.206739 Loss1: 0.529719 Loss2: 0.677020
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.207430 Loss1: 0.529020 Loss2: 0.678410
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.213733 Loss1: 0.533705 Loss2: 0.680028
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.199570 Loss1: 0.515452 Loss2: 0.684118
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.205572 Loss1: 0.524231 Loss2: 0.681341
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.190161 Loss1: 0.509743 Loss2: 0.680418
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184205 Loss1: 0.502656 Loss2: 0.681549
+(DefaultActor pid=1831567) >> Training accuracy: 0.836310
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.468304 Loss1: 0.731398 Loss2: 0.736906
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.368430 Loss1: 0.714158 Loss2: 0.654272
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.361851 Loss1: 0.707606 Loss2: 0.654245
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353223 Loss1: 0.697832 Loss2: 0.655391
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.328896 Loss1: 0.670570 Loss2: 0.658326
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.333734 Loss1: 0.675862 Loss2: 0.657871
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.343425 Loss1: 0.681623 Loss2: 0.661802
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.331006 Loss1: 0.669969 Loss2: 0.661038
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299843 Loss1: 0.640893 Loss2: 0.658950
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.336065 Loss1: 0.673097 Loss2: 0.662968
+(DefaultActor pid=1831567) >> Training accuracy: 0.772418
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.262517 Loss1: 0.477013 Loss2: 0.785504
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.132536 Loss1: 0.432188 Loss2: 0.700348
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.099735 Loss1: 0.404045 Loss2: 0.695690
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.097428 Loss1: 0.402497 Loss2: 0.694930
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085557 Loss1: 0.390099 Loss2: 0.695457
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.075295 Loss1: 0.377391 Loss2: 0.697904
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.074548 Loss1: 0.375151 Loss2: 0.699397
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.071116 Loss1: 0.372998 Loss2: 0.698118
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.071245 Loss1: 0.372997 Loss2: 0.698247
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.061594 Loss1: 0.364517 Loss2: 0.697077
+(DefaultActor pid=1831567) >> Training accuracy: 0.870563
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348240 Loss1: 0.588697 Loss2: 0.759544
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.214477 Loss1: 0.537738 Loss2: 0.676738
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226577 Loss1: 0.548871 Loss2: 0.677707
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.193584 Loss1: 0.517523 Loss2: 0.676061
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203965 Loss1: 0.525339 Loss2: 0.678626
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.166164 Loss1: 0.488595 Loss2: 0.677569
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182830 Loss1: 0.502652 Loss2: 0.680177
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212804 Loss1: 0.528285 Loss2: 0.684519
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205760 Loss1: 0.522182 Loss2: 0.683578
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.178495 Loss1: 0.496305 Loss2: 0.682190
+(DefaultActor pid=1831567) >> Training accuracy: 0.827919
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341696 Loss1: 0.613517 Loss2: 0.728179
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.204684 Loss1: 0.546570 Loss2: 0.658114
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.210961 Loss1: 0.556702 Loss2: 0.654260
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.196320 Loss1: 0.536861 Loss2: 0.659459
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196673 Loss1: 0.536421 Loss2: 0.660252
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.189845 Loss1: 0.528018 Loss2: 0.661826
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168119 Loss1: 0.508003 Loss2: 0.660116
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202122 Loss1: 0.538287 Loss2: 0.663835
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.168200 Loss1: 0.505684 Loss2: 0.662515
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.160455 Loss1: 0.497208 Loss2: 0.663248
+(DefaultActor pid=1831567) >> Training accuracy: 0.829728
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485326 Loss1: 0.731698 Loss2: 0.753628
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304922 Loss1: 0.647740 Loss2: 0.657182
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.319242 Loss1: 0.659464 Loss2: 0.659778
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281561 Loss1: 0.624653 Loss2: 0.656908
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282377 Loss1: 0.623814 Loss2: 0.658562
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.287511 Loss1: 0.625527 Loss2: 0.661984
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.282633 Loss1: 0.621632 Loss2: 0.661001
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.270237 Loss1: 0.609373 Loss2: 0.660864
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.266173 Loss1: 0.599464 Loss2: 0.666710
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.244149 Loss1: 0.579529 Loss2: 0.664620
+(DefaultActor pid=1831567) >> Training accuracy: 0.804002
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.258780 Loss1: 0.466259 Loss2: 0.792520
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.122900 Loss1: 0.414808 Loss2: 0.708092
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.101581 Loss1: 0.400063 Loss2: 0.701518
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.102560 Loss1: 0.400069 Loss2: 0.702491
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.090204 Loss1: 0.388253 Loss2: 0.701951
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.085824 Loss1: 0.382052 Loss2: 0.703772
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.080462 Loss1: 0.376926 Loss2: 0.703536
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.069502 Loss1: 0.363005 Loss2: 0.706497
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.089892 Loss1: 0.385080 Loss2: 0.704812
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.061822 Loss1: 0.355121 Loss2: 0.706701
+[2023-09-27 14:03:38,970][flwr][DEBUG] - fit_round 57 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.873457
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.697300
+[2023-09-27 14:03:40,742][flwr][INFO] - fit progress: (57, 0.8694903847698967, {'accuracy': 0.6973}, 27953.578594708815)
+[2023-09-27 14:03:40,743][flwr][DEBUG] - evaluate_round 57: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:04:11,294][flwr][DEBUG] - evaluate_round 57 received 10 results and 0 failures
+[2023-09-27 14:04:11,295][flwr][DEBUG] - fit_round 58: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362219 Loss1: 0.592152 Loss2: 0.770066
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227314 Loss1: 0.540047 Loss2: 0.687268
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.220008 Loss1: 0.532216 Loss2: 0.687792
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.197700 Loss1: 0.510479 Loss2: 0.687221
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195502 Loss1: 0.505342 Loss2: 0.690159
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.209849 Loss1: 0.517852 Loss2: 0.691996
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.194334 Loss1: 0.502461 Loss2: 0.691873
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200955 Loss1: 0.506696 Loss2: 0.694259
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183803 Loss1: 0.488220 Loss2: 0.695583
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.173535 Loss1: 0.478593 Loss2: 0.694942
+(DefaultActor pid=1831567) >> Training accuracy: 0.841283
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370857 Loss1: 0.568611 Loss2: 0.802246
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301047 Loss1: 0.549659 Loss2: 0.751387
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.271623 Loss1: 0.525241 Loss2: 0.746382
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.279020 Loss1: 0.531022 Loss2: 0.747998
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.280974 Loss1: 0.532872 Loss2: 0.748102
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.256962 Loss1: 0.508081 Loss2: 0.748882
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.255481 Loss1: 0.510923 Loss2: 0.744559
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.277813 Loss1: 0.525686 Loss2: 0.752127
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.264653 Loss1: 0.515438 Loss2: 0.749216
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250171 Loss1: 0.502302 Loss2: 0.747870
+(DefaultActor pid=1831567) >> Training accuracy: 0.833333
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.344302 Loss1: 0.604707 Loss2: 0.739594
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.177673 Loss1: 0.529423 Loss2: 0.648250
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.160180 Loss1: 0.514676 Loss2: 0.645504
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.130209 Loss1: 0.487684 Loss2: 0.642525
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.135252 Loss1: 0.487961 Loss2: 0.647290
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.120168 Loss1: 0.471969 Loss2: 0.648198
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.137457 Loss1: 0.492357 Loss2: 0.645100
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.113678 Loss1: 0.468015 Loss2: 0.645663
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.102611 Loss1: 0.456501 Loss2: 0.646111
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146184 Loss1: 0.494613 Loss2: 0.651571
+(DefaultActor pid=1831567) >> Training accuracy: 0.847987
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.542613 Loss1: 0.732746 Loss2: 0.809867
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.366507 Loss1: 0.669717 Loss2: 0.696790
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.343393 Loss1: 0.649346 Loss2: 0.694047
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325546 Loss1: 0.626613 Loss2: 0.698933
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322167 Loss1: 0.626347 Loss2: 0.695821
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.325615 Loss1: 0.628778 Loss2: 0.696838
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.304698 Loss1: 0.605716 Loss2: 0.698982
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.305734 Loss1: 0.604647 Loss2: 0.701088
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.294631 Loss1: 0.594564 Loss2: 0.700067
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.282434 Loss1: 0.583770 Loss2: 0.698664
+(DefaultActor pid=1831567) >> Training accuracy: 0.798520
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.217143 Loss1: 0.474505 Loss2: 0.742639
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.070647 Loss1: 0.413974 Loss2: 0.656673
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.062811 Loss1: 0.407301 Loss2: 0.655510
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.049701 Loss1: 0.393503 Loss2: 0.656198
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.055754 Loss1: 0.397994 Loss2: 0.657760
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.050714 Loss1: 0.393039 Loss2: 0.657675
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.022228 Loss1: 0.364792 Loss2: 0.657435
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.039350 Loss1: 0.379458 Loss2: 0.659892
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.033409 Loss1: 0.372424 Loss2: 0.660985
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.006485 Loss1: 0.346462 Loss2: 0.660022
+(DefaultActor pid=1831567) >> Training accuracy: 0.875386
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.512532 Loss1: 0.765472 Loss2: 0.747060
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.375124 Loss1: 0.716653 Loss2: 0.658471
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.345172 Loss1: 0.684963 Loss2: 0.660208
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.360707 Loss1: 0.696220 Loss2: 0.664487
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.350324 Loss1: 0.687561 Loss2: 0.662762
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.334220 Loss1: 0.668373 Loss2: 0.665847
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.340940 Loss1: 0.674097 Loss2: 0.666843
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.326970 Loss1: 0.658491 Loss2: 0.668479
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.301865 Loss1: 0.631266 Loss2: 0.670598
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.333858 Loss1: 0.660807 Loss2: 0.673052
+(DefaultActor pid=1831567) >> Training accuracy: 0.778306
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.385162 Loss1: 0.610276 Loss2: 0.774887
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.260794 Loss1: 0.556435 Loss2: 0.704359
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262439 Loss1: 0.559153 Loss2: 0.703286
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.214198 Loss1: 0.514653 Loss2: 0.699545
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246846 Loss1: 0.541576 Loss2: 0.705270
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.237645 Loss1: 0.533538 Loss2: 0.704107
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197714 Loss1: 0.497882 Loss2: 0.699832
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.217066 Loss1: 0.516042 Loss2: 0.701024
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.200700 Loss1: 0.496199 Loss2: 0.704500
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.202336 Loss1: 0.496335 Loss2: 0.706001
+(DefaultActor pid=1831567) >> Training accuracy: 0.839939
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.173668 Loss1: 0.474135 Loss2: 0.699534
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.070918 Loss1: 0.434506 Loss2: 0.636412
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.039834 Loss1: 0.408815 Loss2: 0.631019
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019584 Loss1: 0.387621 Loss2: 0.631963
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.033219 Loss1: 0.397759 Loss2: 0.635459
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.000468 Loss1: 0.367334 Loss2: 0.633134
+(DefaultActor pid=1831567) Epoch: 6 Loss: 0.998884 Loss1: 0.364387 Loss2: 0.634497
+(DefaultActor pid=1831567) Epoch: 7 Loss: 0.993859 Loss1: 0.359407 Loss2: 0.634452
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.018727 Loss1: 0.379847 Loss2: 0.638880
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.998591 Loss1: 0.364089 Loss2: 0.634502
+(DefaultActor pid=1831567) >> Training accuracy: 0.868827
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386434 Loss1: 0.613558 Loss2: 0.772875
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.241774 Loss1: 0.550251 Loss2: 0.691523
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231956 Loss1: 0.540595 Loss2: 0.691360
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.259430 Loss1: 0.562282 Loss2: 0.697148
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.209930 Loss1: 0.516477 Loss2: 0.693453
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.217835 Loss1: 0.521350 Loss2: 0.696485
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.218236 Loss1: 0.522020 Loss2: 0.696216
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200203 Loss1: 0.502805 Loss2: 0.697398
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.201799 Loss1: 0.503028 Loss2: 0.698771
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189914 Loss1: 0.489281 Loss2: 0.700633
+(DefaultActor pid=1831567) >> Training accuracy: 0.822115
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475258 Loss1: 0.712274 Loss2: 0.762984
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370794 Loss1: 0.697793 Loss2: 0.673002
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356677 Loss1: 0.680536 Loss2: 0.676142
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353942 Loss1: 0.683370 Loss2: 0.670572
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.331249 Loss1: 0.658877 Loss2: 0.672372
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328212 Loss1: 0.656639 Loss2: 0.671573
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.288406 Loss1: 0.613534 Loss2: 0.674871
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.310470 Loss1: 0.633153 Loss2: 0.677317
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.309677 Loss1: 0.633542 Loss2: 0.676135
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.334650 Loss1: 0.655448 Loss2: 0.679202
+[2023-09-27 14:10:55,808][flwr][DEBUG] - fit_round 58 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.770289
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.696200
+[2023-09-27 14:10:57,489][flwr][INFO] - fit progress: (58, 0.8769776419328805, {'accuracy': 0.6962}, 28390.325752868783)
+[2023-09-27 14:10:57,490][flwr][DEBUG] - evaluate_round 58: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:11:28,672][flwr][DEBUG] - evaluate_round 58 received 10 results and 0 failures
+[2023-09-27 14:11:28,673][flwr][DEBUG] - fit_round 59: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329928 Loss1: 0.611223 Loss2: 0.718705
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225512 Loss1: 0.573324 Loss2: 0.652188
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.207174 Loss1: 0.559078 Loss2: 0.648096
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183047 Loss1: 0.538757 Loss2: 0.644290
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.176648 Loss1: 0.528854 Loss2: 0.647794
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.172469 Loss1: 0.527828 Loss2: 0.644641
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.162533 Loss1: 0.515471 Loss2: 0.647062
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161918 Loss1: 0.513835 Loss2: 0.648083
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.156319 Loss1: 0.506630 Loss2: 0.649689
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148608 Loss1: 0.499402 Loss2: 0.649206
+(DefaultActor pid=1831567) >> Training accuracy: 0.818979
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.503620 Loss1: 0.763119 Loss2: 0.740501
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.377187 Loss1: 0.719117 Loss2: 0.658070
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.370827 Loss1: 0.710818 Loss2: 0.660010
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.349174 Loss1: 0.687166 Loss2: 0.662008
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345450 Loss1: 0.683364 Loss2: 0.662086
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.333582 Loss1: 0.670027 Loss2: 0.663555
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.336145 Loss1: 0.673329 Loss2: 0.662816
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.326280 Loss1: 0.662171 Loss2: 0.664108
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.334467 Loss1: 0.667395 Loss2: 0.667073
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.326074 Loss1: 0.656445 Loss2: 0.669628
+(DefaultActor pid=1831567) >> Training accuracy: 0.775362
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348568 Loss1: 0.603917 Loss2: 0.744652
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.210051 Loss1: 0.548670 Loss2: 0.661381
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.175652 Loss1: 0.517893 Loss2: 0.657760
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184210 Loss1: 0.526507 Loss2: 0.657703
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.174719 Loss1: 0.513909 Loss2: 0.660810
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.160111 Loss1: 0.499032 Loss2: 0.661079
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163530 Loss1: 0.501290 Loss2: 0.662241
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.150454 Loss1: 0.486430 Loss2: 0.664024
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.150506 Loss1: 0.487282 Loss2: 0.663224
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.135696 Loss1: 0.471303 Loss2: 0.664393
+(DefaultActor pid=1831567) >> Training accuracy: 0.846834
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.306028 Loss1: 0.589684 Loss2: 0.716344
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201386 Loss1: 0.550162 Loss2: 0.651224
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.207560 Loss1: 0.553234 Loss2: 0.654327
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194195 Loss1: 0.539419 Loss2: 0.654776
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170663 Loss1: 0.518831 Loss2: 0.651832
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.170459 Loss1: 0.516849 Loss2: 0.653610
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168473 Loss1: 0.515425 Loss2: 0.653048
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163691 Loss1: 0.507790 Loss2: 0.655901
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.172597 Loss1: 0.514197 Loss2: 0.658399
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.159077 Loss1: 0.498496 Loss2: 0.660581
+(DefaultActor pid=1831567) >> Training accuracy: 0.837941
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462127 Loss1: 0.732617 Loss2: 0.729509
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.318311 Loss1: 0.683606 Loss2: 0.634705
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.281504 Loss1: 0.648028 Loss2: 0.633476
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.280935 Loss1: 0.646369 Loss2: 0.634566
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.247977 Loss1: 0.610659 Loss2: 0.637318
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.255042 Loss1: 0.615945 Loss2: 0.639097
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.238694 Loss1: 0.601909 Loss2: 0.636785
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.247621 Loss1: 0.611351 Loss2: 0.636270
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232252 Loss1: 0.594367 Loss2: 0.637885
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.209504 Loss1: 0.574327 Loss2: 0.635176
+(DefaultActor pid=1831567) >> Training accuracy: 0.802357
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.334009 Loss1: 0.573014 Loss2: 0.760995
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.253526 Loss1: 0.539548 Loss2: 0.713977
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251058 Loss1: 0.537188 Loss2: 0.713870
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.230532 Loss1: 0.519392 Loss2: 0.711140
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.241025 Loss1: 0.526598 Loss2: 0.714427
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226554 Loss1: 0.512176 Loss2: 0.714378
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.249007 Loss1: 0.533694 Loss2: 0.715312
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.230061 Loss1: 0.514220 Loss2: 0.715841
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232714 Loss1: 0.517720 Loss2: 0.714994
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.212955 Loss1: 0.500796 Loss2: 0.712159
+(DefaultActor pid=1831567) >> Training accuracy: 0.835938
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.566436 Loss1: 0.754765 Loss2: 0.811671
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.400606 Loss1: 0.694367 Loss2: 0.706239
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393994 Loss1: 0.687373 Loss2: 0.706621
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.367226 Loss1: 0.661768 Loss2: 0.705458
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.391618 Loss1: 0.680575 Loss2: 0.711043
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.346028 Loss1: 0.638490 Loss2: 0.707538
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386915 Loss1: 0.672781 Loss2: 0.714134
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.344558 Loss1: 0.633705 Loss2: 0.710853
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.335837 Loss1: 0.621841 Loss2: 0.713996
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.340631 Loss1: 0.629768 Loss2: 0.710863
+(DefaultActor pid=1831567) >> Training accuracy: 0.792211
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.249695 Loss1: 0.464351 Loss2: 0.785344
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.117896 Loss1: 0.421055 Loss2: 0.696841
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.103740 Loss1: 0.406743 Loss2: 0.696998
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.101055 Loss1: 0.405224 Loss2: 0.695831
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.062880 Loss1: 0.366163 Loss2: 0.696717
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.076957 Loss1: 0.379709 Loss2: 0.697249
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.067617 Loss1: 0.367956 Loss2: 0.699661
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.078176 Loss1: 0.377252 Loss2: 0.700924
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.062052 Loss1: 0.367049 Loss2: 0.695003
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.051767 Loss1: 0.353099 Loss2: 0.698668
+(DefaultActor pid=1831567) >> Training accuracy: 0.872299
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.383086 Loss1: 0.590812 Loss2: 0.792274
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227859 Loss1: 0.533276 Loss2: 0.694583
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222058 Loss1: 0.532107 Loss2: 0.689951
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181909 Loss1: 0.493580 Loss2: 0.688329
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.182112 Loss1: 0.491993 Loss2: 0.690119
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.161984 Loss1: 0.472330 Loss2: 0.689654
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.147265 Loss1: 0.459679 Loss2: 0.687586
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168300 Loss1: 0.474110 Loss2: 0.694190
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.154999 Loss1: 0.461267 Loss2: 0.693732
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154603 Loss1: 0.459350 Loss2: 0.695252
+(DefaultActor pid=1831567) >> Training accuracy: 0.841631
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.237189 Loss1: 0.472048 Loss2: 0.765141
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.113985 Loss1: 0.430118 Loss2: 0.683866
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.107151 Loss1: 0.422870 Loss2: 0.684281
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.061145 Loss1: 0.379852 Loss2: 0.681293
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.050102 Loss1: 0.369025 Loss2: 0.681077
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.063404 Loss1: 0.380347 Loss2: 0.683057
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.053786 Loss1: 0.368923 Loss2: 0.684863
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.053042 Loss1: 0.369200 Loss2: 0.683842
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.073313 Loss1: 0.387803 Loss2: 0.685510
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.060096 Loss1: 0.376460 Loss2: 0.683636
+[2023-09-27 14:18:12,279][flwr][DEBUG] - fit_round 59 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.877894
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.703200
+[2023-09-27 14:18:14,157][flwr][INFO] - fit progress: (59, 0.8658733374584978, {'accuracy': 0.7032}, 28826.99361290969)
+[2023-09-27 14:18:14,158][flwr][DEBUG] - evaluate_round 59: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:18:44,626][flwr][DEBUG] - evaluate_round 59 received 10 results and 0 failures
+[2023-09-27 14:18:44,627][flwr][DEBUG] - fit_round 60: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.522455 Loss1: 0.762286 Loss2: 0.760168
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.378740 Loss1: 0.707269 Loss2: 0.671470
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.373832 Loss1: 0.697831 Loss2: 0.676001
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.362338 Loss1: 0.689826 Loss2: 0.672512
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.352582 Loss1: 0.680443 Loss2: 0.672139
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.333344 Loss1: 0.659366 Loss2: 0.673978
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.333651 Loss1: 0.657007 Loss2: 0.676644
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.343738 Loss1: 0.664853 Loss2: 0.678885
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331197 Loss1: 0.650599 Loss2: 0.680598
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.318345 Loss1: 0.637836 Loss2: 0.680509
+(DefaultActor pid=1831567) >> Training accuracy: 0.781703
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.531023 Loss1: 0.740747 Loss2: 0.790276
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340137 Loss1: 0.653682 Loss2: 0.686454
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.349762 Loss1: 0.663680 Loss2: 0.686082
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.315836 Loss1: 0.633888 Loss2: 0.681948
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293101 Loss1: 0.610349 Loss2: 0.682752
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.290177 Loss1: 0.605478 Loss2: 0.684698
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294729 Loss1: 0.608208 Loss2: 0.686521
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.287072 Loss1: 0.601629 Loss2: 0.685443
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.280046 Loss1: 0.591969 Loss2: 0.688078
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.283918 Loss1: 0.596812 Loss2: 0.687105
+(DefaultActor pid=1831567) >> Training accuracy: 0.782621
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.203166 Loss1: 0.469278 Loss2: 0.733889
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.064799 Loss1: 0.413783 Loss2: 0.651015
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.042575 Loss1: 0.397410 Loss2: 0.645165
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.044282 Loss1: 0.396907 Loss2: 0.647375
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.046915 Loss1: 0.399676 Loss2: 0.647238
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.029162 Loss1: 0.382074 Loss2: 0.647088
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019351 Loss1: 0.369560 Loss2: 0.649791
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.003105 Loss1: 0.353489 Loss2: 0.649616
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.025436 Loss1: 0.373871 Loss2: 0.651565
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.027528 Loss1: 0.374986 Loss2: 0.652542
+(DefaultActor pid=1831567) >> Training accuracy: 0.883681
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.504058 Loss1: 0.751220 Loss2: 0.752838
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367567 Loss1: 0.702733 Loss2: 0.664834
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336442 Loss1: 0.675659 Loss2: 0.660783
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331778 Loss1: 0.668138 Loss2: 0.663639
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.351899 Loss1: 0.683510 Loss2: 0.668389
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306023 Loss1: 0.642511 Loss2: 0.663512
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.302522 Loss1: 0.638786 Loss2: 0.663736
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.309908 Loss1: 0.640948 Loss2: 0.668960
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.295272 Loss1: 0.628078 Loss2: 0.667194
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.296369 Loss1: 0.628715 Loss2: 0.667653
+(DefaultActor pid=1831567) >> Training accuracy: 0.789179
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.350557 Loss1: 0.594104 Loss2: 0.756453
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.244441 Loss1: 0.565355 Loss2: 0.679086
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222966 Loss1: 0.538709 Loss2: 0.684257
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223316 Loss1: 0.541034 Loss2: 0.682282
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.190147 Loss1: 0.510285 Loss2: 0.679862
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210575 Loss1: 0.527337 Loss2: 0.683238
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196177 Loss1: 0.514366 Loss2: 0.681810
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.216329 Loss1: 0.533297 Loss2: 0.683032
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.207266 Loss1: 0.520951 Loss2: 0.686314
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.209946 Loss1: 0.523129 Loss2: 0.686818
+(DefaultActor pid=1831567) >> Training accuracy: 0.835136
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329516 Loss1: 0.590243 Loss2: 0.739272
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211511 Loss1: 0.554974 Loss2: 0.656537
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.180673 Loss1: 0.524338 Loss2: 0.656335
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.178839 Loss1: 0.521097 Loss2: 0.657742
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.171298 Loss1: 0.513656 Loss2: 0.657642
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.169363 Loss1: 0.508492 Loss2: 0.660871
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.158528 Loss1: 0.497873 Loss2: 0.660655
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163694 Loss1: 0.501604 Loss2: 0.662090
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.150523 Loss1: 0.491624 Loss2: 0.658899
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.139250 Loss1: 0.477130 Loss2: 0.662121
+(DefaultActor pid=1831567) >> Training accuracy: 0.831003
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.184126 Loss1: 0.465604 Loss2: 0.718522
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.079608 Loss1: 0.423463 Loss2: 0.656145
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.051986 Loss1: 0.404234 Loss2: 0.647752
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.034591 Loss1: 0.386901 Loss2: 0.647690
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.029269 Loss1: 0.379525 Loss2: 0.649744
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.028227 Loss1: 0.374845 Loss2: 0.653382
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019984 Loss1: 0.369423 Loss2: 0.650561
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.037100 Loss1: 0.385608 Loss2: 0.651491
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.009401 Loss1: 0.356406 Loss2: 0.652995
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.010419 Loss1: 0.358603 Loss2: 0.651816
+(DefaultActor pid=1831567) >> Training accuracy: 0.873843
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.332807 Loss1: 0.556011 Loss2: 0.776796
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.260772 Loss1: 0.532623 Loss2: 0.728149
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.244416 Loss1: 0.521077 Loss2: 0.723339
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.262450 Loss1: 0.536712 Loss2: 0.725738
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.247870 Loss1: 0.520294 Loss2: 0.727576
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.253872 Loss1: 0.524866 Loss2: 0.729006
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.239849 Loss1: 0.511634 Loss2: 0.728216
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.246569 Loss1: 0.515781 Loss2: 0.730788
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.254486 Loss1: 0.519398 Loss2: 0.735087
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.222016 Loss1: 0.491949 Loss2: 0.730068
+(DefaultActor pid=1831567) >> Training accuracy: 0.830605
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384972 Loss1: 0.613068 Loss2: 0.771904
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250663 Loss1: 0.552971 Loss2: 0.697692
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.241323 Loss1: 0.544325 Loss2: 0.696999
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.238076 Loss1: 0.539942 Loss2: 0.698133
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.244866 Loss1: 0.546798 Loss2: 0.698068
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.251045 Loss1: 0.551098 Loss2: 0.699946
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204507 Loss1: 0.508467 Loss2: 0.696040
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207571 Loss1: 0.507129 Loss2: 0.700442
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.187385 Loss1: 0.491588 Loss2: 0.695797
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.219355 Loss1: 0.520172 Loss2: 0.699182
+(DefaultActor pid=1831567) >> Training accuracy: 0.821456
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.379876 Loss1: 0.602918 Loss2: 0.776958
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202026 Loss1: 0.528213 Loss2: 0.673812
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.207688 Loss1: 0.532522 Loss2: 0.675166
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183791 Loss1: 0.508914 Loss2: 0.674877
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.226159 Loss1: 0.547541 Loss2: 0.678618
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.161430 Loss1: 0.486873 Loss2: 0.674557
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.151547 Loss1: 0.475619 Loss2: 0.675928
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.152089 Loss1: 0.474746 Loss2: 0.677344
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.126261 Loss1: 0.446014 Loss2: 0.680246
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.145105 Loss1: 0.467740 Loss2: 0.677365
+[2023-09-27 14:25:42,560][flwr][DEBUG] - fit_round 60 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.822034
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.700000
+[2023-09-27 14:25:43,898][flwr][INFO] - fit progress: (60, 0.8708832763825742, {'accuracy': 0.7}, 29276.734741013963)
+[2023-09-27 14:25:43,899][flwr][DEBUG] - evaluate_round 60: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:26:14,460][flwr][DEBUG] - evaluate_round 60 received 10 results and 0 failures
+[2023-09-27 14:26:14,461][flwr][DEBUG] - fit_round 61: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326675 Loss1: 0.586113 Loss2: 0.740562
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171048 Loss1: 0.527742 Loss2: 0.643306
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155933 Loss1: 0.513194 Loss2: 0.642738
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.129527 Loss1: 0.487805 Loss2: 0.641722
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.103373 Loss1: 0.464390 Loss2: 0.638983
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.144919 Loss1: 0.500198 Loss2: 0.644721
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122246 Loss1: 0.477053 Loss2: 0.645193
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.127260 Loss1: 0.480967 Loss2: 0.646293
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.128916 Loss1: 0.478331 Loss2: 0.650585
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.119934 Loss1: 0.471438 Loss2: 0.648496
+(DefaultActor pid=1831567) >> Training accuracy: 0.840572
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.512263 Loss1: 0.720696 Loss2: 0.791568
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.373909 Loss1: 0.684901 Loss2: 0.689008
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356014 Loss1: 0.667483 Loss2: 0.688531
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347293 Loss1: 0.656539 Loss2: 0.690754
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322818 Loss1: 0.631324 Loss2: 0.691493
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.310772 Loss1: 0.622201 Loss2: 0.688571
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350056 Loss1: 0.657094 Loss2: 0.692962
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320188 Loss1: 0.623923 Loss2: 0.696265
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.346804 Loss1: 0.648557 Loss2: 0.698247
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307713 Loss1: 0.612253 Loss2: 0.695460
+(DefaultActor pid=1831567) >> Training accuracy: 0.776819
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.304942 Loss1: 0.601054 Loss2: 0.703888
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.168042 Loss1: 0.536537 Loss2: 0.631505
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195444 Loss1: 0.563956 Loss2: 0.631488
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165582 Loss1: 0.532816 Loss2: 0.632765
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140631 Loss1: 0.510554 Loss2: 0.630077
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.143186 Loss1: 0.511005 Loss2: 0.632182
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.153428 Loss1: 0.518841 Loss2: 0.634587
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168327 Loss1: 0.534017 Loss2: 0.634310
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138791 Loss1: 0.504384 Loss2: 0.634407
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.130521 Loss1: 0.494919 Loss2: 0.635601
+(DefaultActor pid=1831567) >> Training accuracy: 0.838986
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.223580 Loss1: 0.463147 Loss2: 0.760433
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.116158 Loss1: 0.440342 Loss2: 0.675816
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.060734 Loss1: 0.393718 Loss2: 0.667016
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.065373 Loss1: 0.396064 Loss2: 0.669309
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.060688 Loss1: 0.391728 Loss2: 0.668960
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.049201 Loss1: 0.380413 Loss2: 0.668788
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.039199 Loss1: 0.369985 Loss2: 0.669214
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.024170 Loss1: 0.358392 Loss2: 0.665778
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.032074 Loss1: 0.358817 Loss2: 0.673257
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.043997 Loss1: 0.370889 Loss2: 0.673108
+(DefaultActor pid=1831567) >> Training accuracy: 0.864776
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320712 Loss1: 0.581110 Loss2: 0.739601
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.221973 Loss1: 0.550207 Loss2: 0.671766
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208937 Loss1: 0.537187 Loss2: 0.671750
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.209296 Loss1: 0.537663 Loss2: 0.671633
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210649 Loss1: 0.534881 Loss2: 0.675768
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185648 Loss1: 0.510586 Loss2: 0.675062
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.203287 Loss1: 0.527603 Loss2: 0.675683
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.178404 Loss1: 0.502720 Loss2: 0.675684
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185459 Loss1: 0.507376 Loss2: 0.678084
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180984 Loss1: 0.503115 Loss2: 0.677869
+(DefaultActor pid=1831567) >> Training accuracy: 0.836939
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226226 Loss1: 0.463359 Loss2: 0.762867
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.083575 Loss1: 0.402705 Loss2: 0.680870
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.098719 Loss1: 0.420740 Loss2: 0.677978
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.060769 Loss1: 0.382157 Loss2: 0.678612
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.057776 Loss1: 0.376141 Loss2: 0.681636
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.074879 Loss1: 0.395737 Loss2: 0.679142
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.057444 Loss1: 0.376823 Loss2: 0.680621
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030635 Loss1: 0.350577 Loss2: 0.680058
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.070820 Loss1: 0.388215 Loss2: 0.682605
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.038893 Loss1: 0.358097 Loss2: 0.680797
+(DefaultActor pid=1831567) >> Training accuracy: 0.881752
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.445912 Loss1: 0.721061 Loss2: 0.724852
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301384 Loss1: 0.672282 Loss2: 0.629102
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.300373 Loss1: 0.670024 Loss2: 0.630349
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.266794 Loss1: 0.634554 Loss2: 0.632239
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.252384 Loss1: 0.618778 Loss2: 0.633606
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.260924 Loss1: 0.631436 Loss2: 0.629488
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.228790 Loss1: 0.596196 Loss2: 0.632594
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.227653 Loss1: 0.594380 Loss2: 0.633274
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211889 Loss1: 0.580158 Loss2: 0.631731
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.234693 Loss1: 0.599922 Loss2: 0.634771
+(DefaultActor pid=1831567) >> Training accuracy: 0.790844
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.343631 Loss1: 0.574220 Loss2: 0.769411
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.257289 Loss1: 0.537156 Loss2: 0.720133
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.252166 Loss1: 0.532201 Loss2: 0.719966
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226176 Loss1: 0.508227 Loss2: 0.717949
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.251014 Loss1: 0.529842 Loss2: 0.721173
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.242765 Loss1: 0.522259 Loss2: 0.720505
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.230411 Loss1: 0.508391 Loss2: 0.722020
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.224135 Loss1: 0.504523 Loss2: 0.719612
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239271 Loss1: 0.513379 Loss2: 0.725892
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.230073 Loss1: 0.508547 Loss2: 0.721526
+(DefaultActor pid=1831567) >> Training accuracy: 0.829613
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.349797 Loss1: 0.594158 Loss2: 0.755639
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217188 Loss1: 0.545983 Loss2: 0.671205
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.206539 Loss1: 0.534886 Loss2: 0.671653
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200808 Loss1: 0.526806 Loss2: 0.674001
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.182749 Loss1: 0.508409 Loss2: 0.674340
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163592 Loss1: 0.488860 Loss2: 0.674731
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.167280 Loss1: 0.489923 Loss2: 0.677357
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151241 Loss1: 0.477195 Loss2: 0.674046
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.155215 Loss1: 0.478530 Loss2: 0.676685
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152720 Loss1: 0.477374 Loss2: 0.675346
+(DefaultActor pid=1831567) >> Training accuracy: 0.842722
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.492221 Loss1: 0.740646 Loss2: 0.751576
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.368763 Loss1: 0.701530 Loss2: 0.667233
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363037 Loss1: 0.694703 Loss2: 0.668335
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.341035 Loss1: 0.677538 Loss2: 0.663497
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.366742 Loss1: 0.696868 Loss2: 0.669874
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341182 Loss1: 0.672693 Loss2: 0.668489
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.330835 Loss1: 0.661225 Loss2: 0.669610
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334119 Loss1: 0.662203 Loss2: 0.671916
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305017 Loss1: 0.630681 Loss2: 0.674336
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.312275 Loss1: 0.639207 Loss2: 0.673068
+(DefaultActor pid=1831567) >> Training accuracy: 0.776042
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 14:32:57,547][flwr][DEBUG] - fit_round 61 received 10 results and 0 failures
+>> Test accuracy: 0.695300
+[2023-09-27 14:32:59,190][flwr][INFO] - fit progress: (61, 0.8809174903855918, {'accuracy': 0.6953}, 29712.026695901062)
+[2023-09-27 14:32:59,191][flwr][DEBUG] - evaluate_round 61: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:33:38,989][flwr][DEBUG] - evaluate_round 61 received 10 results and 0 failures
+[2023-09-27 14:33:38,990][flwr][DEBUG] - fit_round 62: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.515077 Loss1: 0.705244 Loss2: 0.809833
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382614 Loss1: 0.675866 Loss2: 0.706748
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.378682 Loss1: 0.670697 Loss2: 0.707985
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.321875 Loss1: 0.616494 Loss2: 0.705381
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.339706 Loss1: 0.636170 Loss2: 0.703537
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.315958 Loss1: 0.607961 Loss2: 0.707998
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.300139 Loss1: 0.592913 Loss2: 0.707226
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288297 Loss1: 0.579139 Loss2: 0.709158
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299504 Loss1: 0.586625 Loss2: 0.712879
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.273917 Loss1: 0.563871 Loss2: 0.710046
+(DefaultActor pid=1831567) >> Training accuracy: 0.802632
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.478822 Loss1: 0.730280 Loss2: 0.748543
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340293 Loss1: 0.686988 Loss2: 0.653305
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.332048 Loss1: 0.677169 Loss2: 0.654879
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.337180 Loss1: 0.678483 Loss2: 0.658696
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317538 Loss1: 0.657624 Loss2: 0.659914
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.297543 Loss1: 0.637583 Loss2: 0.659960
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309719 Loss1: 0.651748 Loss2: 0.657971
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288905 Loss1: 0.630578 Loss2: 0.658327
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306724 Loss1: 0.647154 Loss2: 0.659570
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.277348 Loss1: 0.616263 Loss2: 0.661085
+(DefaultActor pid=1831567) >> Training accuracy: 0.776353
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.349216 Loss1: 0.599338 Loss2: 0.749878
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212419 Loss1: 0.539766 Loss2: 0.672654
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195049 Loss1: 0.526058 Loss2: 0.668990
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.224972 Loss1: 0.551381 Loss2: 0.673592
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187843 Loss1: 0.510842 Loss2: 0.677001
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218565 Loss1: 0.542344 Loss2: 0.676222
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.214663 Loss1: 0.536524 Loss2: 0.678139
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173666 Loss1: 0.498109 Loss2: 0.675557
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.168897 Loss1: 0.491096 Loss2: 0.677800
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175125 Loss1: 0.496972 Loss2: 0.678153
+(DefaultActor pid=1831567) >> Training accuracy: 0.838542
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.345392 Loss1: 0.557428 Loss2: 0.787963
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225435 Loss1: 0.537486 Loss2: 0.687949
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212264 Loss1: 0.523341 Loss2: 0.688924
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200309 Loss1: 0.509638 Loss2: 0.690671
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198414 Loss1: 0.509108 Loss2: 0.689306
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157888 Loss1: 0.469593 Loss2: 0.688295
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165263 Loss1: 0.477525 Loss2: 0.687738
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.149231 Loss1: 0.456983 Loss2: 0.692247
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.171233 Loss1: 0.478060 Loss2: 0.693173
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.155512 Loss1: 0.464876 Loss2: 0.690636
+(DefaultActor pid=1831567) >> Training accuracy: 0.845074
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.217826 Loss1: 0.481216 Loss2: 0.736610
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.060610 Loss1: 0.406504 Loss2: 0.654106
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.055238 Loss1: 0.401947 Loss2: 0.653292
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.057087 Loss1: 0.406948 Loss2: 0.650139
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.041089 Loss1: 0.390057 Loss2: 0.651032
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.030372 Loss1: 0.376248 Loss2: 0.654124
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.042924 Loss1: 0.388352 Loss2: 0.654572
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.033391 Loss1: 0.379357 Loss2: 0.654034
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.041063 Loss1: 0.385796 Loss2: 0.655267
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.005051 Loss1: 0.350481 Loss2: 0.654570
+(DefaultActor pid=1831567) >> Training accuracy: 0.877894
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.311420 Loss1: 0.569441 Loss2: 0.741980
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225054 Loss1: 0.528523 Loss2: 0.696531
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215983 Loss1: 0.518774 Loss2: 0.697209
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.220200 Loss1: 0.523234 Loss2: 0.696967
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215170 Loss1: 0.517475 Loss2: 0.697695
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.237090 Loss1: 0.533873 Loss2: 0.703216
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.205291 Loss1: 0.507447 Loss2: 0.697844
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204364 Loss1: 0.503466 Loss2: 0.700898
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213313 Loss1: 0.510639 Loss2: 0.702674
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176584 Loss1: 0.479810 Loss2: 0.696774
+(DefaultActor pid=1831567) >> Training accuracy: 0.830481
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.397408 Loss1: 0.624917 Loss2: 0.772491
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.251157 Loss1: 0.550621 Loss2: 0.700536
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.224139 Loss1: 0.528204 Loss2: 0.695935
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.239343 Loss1: 0.538702 Loss2: 0.700641
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216114 Loss1: 0.514627 Loss2: 0.701488
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.203075 Loss1: 0.498268 Loss2: 0.704807
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.225847 Loss1: 0.522072 Loss2: 0.703774
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.197847 Loss1: 0.494999 Loss2: 0.702848
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.218003 Loss1: 0.514596 Loss2: 0.703407
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.187671 Loss1: 0.484174 Loss2: 0.703497
+(DefaultActor pid=1831567) >> Training accuracy: 0.820122
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.258195 Loss1: 0.459880 Loss2: 0.798315
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.129754 Loss1: 0.408654 Loss2: 0.721100
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.102059 Loss1: 0.382905 Loss2: 0.719154
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.127615 Loss1: 0.409511 Loss2: 0.718104
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.115623 Loss1: 0.392995 Loss2: 0.722628
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.095292 Loss1: 0.373557 Loss2: 0.721734
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.094366 Loss1: 0.371930 Loss2: 0.722436
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.110347 Loss1: 0.386015 Loss2: 0.724332
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.088301 Loss1: 0.367535 Loss2: 0.720766
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.082422 Loss1: 0.359112 Loss2: 0.723310
+(DefaultActor pid=1831567) >> Training accuracy: 0.869020
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.520529 Loss1: 0.762255 Loss2: 0.758274
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.391834 Loss1: 0.719607 Loss2: 0.672228
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.387400 Loss1: 0.713868 Loss2: 0.673533
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.360994 Loss1: 0.688600 Loss2: 0.672394
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.348885 Loss1: 0.674626 Loss2: 0.674259
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.356135 Loss1: 0.682714 Loss2: 0.673421
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.343615 Loss1: 0.666146 Loss2: 0.677469
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.330153 Loss1: 0.654297 Loss2: 0.675855
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.337656 Loss1: 0.660978 Loss2: 0.676678
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.330285 Loss1: 0.653480 Loss2: 0.676805
+(DefaultActor pid=1831567) >> Training accuracy: 0.758379
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348198 Loss1: 0.600474 Loss2: 0.747723
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200599 Loss1: 0.535921 Loss2: 0.664678
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219120 Loss1: 0.553215 Loss2: 0.665905
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187394 Loss1: 0.524851 Loss2: 0.662543
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.188790 Loss1: 0.523456 Loss2: 0.665334
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.149214 Loss1: 0.486686 Loss2: 0.662528
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.162575 Loss1: 0.495464 Loss2: 0.667111
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163362 Loss1: 0.494790 Loss2: 0.668572
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.154197 Loss1: 0.485220 Loss2: 0.668977
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140106 Loss1: 0.470354 Loss2: 0.669753
+[2023-09-27 14:40:43,586][flwr][DEBUG] - fit_round 62 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.833265
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.690700
+[2023-09-27 14:40:44,860][flwr][INFO] - fit progress: (62, 0.8817045592461912, {'accuracy': 0.6907}, 30177.696814930066)
+[2023-09-27 14:40:44,861][flwr][DEBUG] - evaluate_round 62: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:41:15,632][flwr][DEBUG] - evaluate_round 62 received 10 results and 0 failures
+[2023-09-27 14:41:15,633][flwr][DEBUG] - fit_round 63: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226843 Loss1: 0.447229 Loss2: 0.779614
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.123498 Loss1: 0.424485 Loss2: 0.699013
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.097748 Loss1: 0.399982 Loss2: 0.697766
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.076791 Loss1: 0.383229 Loss2: 0.693562
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.088134 Loss1: 0.393642 Loss2: 0.694491
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.078291 Loss1: 0.386579 Loss2: 0.691712
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.056119 Loss1: 0.365015 Loss2: 0.691104
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.072717 Loss1: 0.378537 Loss2: 0.694180
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.061771 Loss1: 0.368584 Loss2: 0.693186
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.060516 Loss1: 0.364542 Loss2: 0.695974
+(DefaultActor pid=1831567) >> Training accuracy: 0.881559
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.300211 Loss1: 0.587473 Loss2: 0.712738
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.199432 Loss1: 0.557283 Loss2: 0.642148
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.171365 Loss1: 0.532251 Loss2: 0.639114
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175490 Loss1: 0.540192 Loss2: 0.635298
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.164516 Loss1: 0.529138 Loss2: 0.635378
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167524 Loss1: 0.528380 Loss2: 0.639144
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148919 Loss1: 0.510844 Loss2: 0.638075
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151114 Loss1: 0.512004 Loss2: 0.639110
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.143363 Loss1: 0.501638 Loss2: 0.641725
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134767 Loss1: 0.495479 Loss2: 0.639288
+(DefaultActor pid=1831567) >> Training accuracy: 0.833841
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.497253 Loss1: 0.727041 Loss2: 0.770213
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.355612 Loss1: 0.674700 Loss2: 0.680912
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.369486 Loss1: 0.689530 Loss2: 0.679956
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.362141 Loss1: 0.678712 Loss2: 0.683429
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322146 Loss1: 0.643444 Loss2: 0.678702
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337990 Loss1: 0.652923 Loss2: 0.685068
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322909 Loss1: 0.638807 Loss2: 0.684102
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.313395 Loss1: 0.627850 Loss2: 0.685545
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.308851 Loss1: 0.622100 Loss2: 0.686752
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.306366 Loss1: 0.620218 Loss2: 0.686148
+(DefaultActor pid=1831567) >> Training accuracy: 0.777752
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321727 Loss1: 0.563622 Loss2: 0.758105
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.235225 Loss1: 0.530906 Loss2: 0.704319
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.243605 Loss1: 0.539304 Loss2: 0.704302
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.238641 Loss1: 0.530940 Loss2: 0.707701
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.223415 Loss1: 0.516822 Loss2: 0.706593
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.234895 Loss1: 0.525528 Loss2: 0.709367
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200224 Loss1: 0.492601 Loss2: 0.707623
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.210157 Loss1: 0.501186 Loss2: 0.708972
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188507 Loss1: 0.481417 Loss2: 0.707091
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.220000 Loss1: 0.510315 Loss2: 0.709686
+(DefaultActor pid=1831567) >> Training accuracy: 0.828621
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.519055 Loss1: 0.758830 Loss2: 0.760226
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.404027 Loss1: 0.725143 Loss2: 0.678884
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.366808 Loss1: 0.694178 Loss2: 0.672630
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364398 Loss1: 0.686283 Loss2: 0.678115
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.369368 Loss1: 0.688934 Loss2: 0.680433
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.359227 Loss1: 0.680044 Loss2: 0.679184
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.323842 Loss1: 0.645099 Loss2: 0.678742
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.371408 Loss1: 0.686490 Loss2: 0.684918
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.320955 Loss1: 0.639516 Loss2: 0.681439
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.332290 Loss1: 0.651131 Loss2: 0.681160
+(DefaultActor pid=1831567) >> Training accuracy: 0.776947
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.483242 Loss1: 0.726110 Loss2: 0.757132
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.335080 Loss1: 0.677128 Loss2: 0.657952
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.308584 Loss1: 0.648877 Loss2: 0.659707
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.292785 Loss1: 0.632724 Loss2: 0.660061
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.285252 Loss1: 0.625042 Loss2: 0.660210
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.275011 Loss1: 0.614412 Loss2: 0.660599
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.265410 Loss1: 0.602963 Loss2: 0.662447
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268015 Loss1: 0.602994 Loss2: 0.665021
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.258789 Loss1: 0.594463 Loss2: 0.664326
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.255676 Loss1: 0.590317 Loss2: 0.665359
+(DefaultActor pid=1831567) >> Training accuracy: 0.797149
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.374430 Loss1: 0.597466 Loss2: 0.776964
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250120 Loss1: 0.546629 Loss2: 0.703492
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.248402 Loss1: 0.544627 Loss2: 0.703775
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.249140 Loss1: 0.542073 Loss2: 0.707067
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224527 Loss1: 0.516774 Loss2: 0.707752
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.234051 Loss1: 0.522616 Loss2: 0.711435
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224892 Loss1: 0.514170 Loss2: 0.710722
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.203762 Loss1: 0.495708 Loss2: 0.708054
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.220095 Loss1: 0.507321 Loss2: 0.712774
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198902 Loss1: 0.487312 Loss2: 0.711590
+(DefaultActor pid=1831567) >> Training accuracy: 0.813301
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341269 Loss1: 0.575729 Loss2: 0.765540
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.229904 Loss1: 0.546410 Loss2: 0.683494
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.189457 Loss1: 0.508986 Loss2: 0.680470
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.203888 Loss1: 0.523398 Loss2: 0.680490
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.208298 Loss1: 0.524845 Loss2: 0.683453
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186039 Loss1: 0.505311 Loss2: 0.680728
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187630 Loss1: 0.498368 Loss2: 0.689263
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.178493 Loss1: 0.491584 Loss2: 0.686908
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175043 Loss1: 0.489657 Loss2: 0.685386
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165438 Loss1: 0.477487 Loss2: 0.687951
+(DefaultActor pid=1831567) >> Training accuracy: 0.844161
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.364723 Loss1: 0.579033 Loss2: 0.785690
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250337 Loss1: 0.570505 Loss2: 0.679832
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201217 Loss1: 0.522536 Loss2: 0.678681
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191798 Loss1: 0.513035 Loss2: 0.678764
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154916 Loss1: 0.477440 Loss2: 0.677476
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.126674 Loss1: 0.451394 Loss2: 0.675280
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.154523 Loss1: 0.475207 Loss2: 0.679316
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155978 Loss1: 0.474343 Loss2: 0.681635
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.133315 Loss1: 0.454404 Loss2: 0.678911
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.153601 Loss1: 0.472868 Loss2: 0.680733
+(DefaultActor pid=1831567) >> Training accuracy: 0.845604
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.207631 Loss1: 0.442698 Loss2: 0.764933
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092862 Loss1: 0.408410 Loss2: 0.684452
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.116319 Loss1: 0.433298 Loss2: 0.683021
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.060464 Loss1: 0.378038 Loss2: 0.682426
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.069804 Loss1: 0.388845 Loss2: 0.680959
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.052355 Loss1: 0.370651 Loss2: 0.681703
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.054814 Loss1: 0.372502 Loss2: 0.682311
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.045106 Loss1: 0.360860 Loss2: 0.684246
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.051653 Loss1: 0.367633 Loss2: 0.684020
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.024956 Loss1: 0.340431 Loss2: 0.684525
+[2023-09-27 14:48:03,660][flwr][DEBUG] - fit_round 63 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.865741
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.700800
+[2023-09-27 14:48:05,273][flwr][INFO] - fit progress: (63, 0.8681536297828626, {'accuracy': 0.7008}, 30618.10951640876)
+[2023-09-27 14:48:05,274][flwr][DEBUG] - evaluate_round 63: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:48:37,051][flwr][DEBUG] - evaluate_round 63 received 10 results and 0 failures
+[2023-09-27 14:48:37,051][flwr][DEBUG] - fit_round 64: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462730 Loss1: 0.727581 Loss2: 0.735149
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340665 Loss1: 0.690840 Loss2: 0.649825
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334233 Loss1: 0.683406 Loss2: 0.650827
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.312743 Loss1: 0.660758 Loss2: 0.651985
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.292086 Loss1: 0.642122 Loss2: 0.649963
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.307211 Loss1: 0.655502 Loss2: 0.651709
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298864 Loss1: 0.639975 Loss2: 0.658888
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.284432 Loss1: 0.631827 Loss2: 0.652604
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.282537 Loss1: 0.630138 Loss2: 0.652398
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.267936 Loss1: 0.611715 Loss2: 0.656221
+(DefaultActor pid=1831567) >> Training accuracy: 0.783815
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340451 Loss1: 0.555263 Loss2: 0.785188
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258675 Loss1: 0.528608 Loss2: 0.730066
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.278325 Loss1: 0.544433 Loss2: 0.733892
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.263432 Loss1: 0.528968 Loss2: 0.734464
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.253636 Loss1: 0.520821 Loss2: 0.732814
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249463 Loss1: 0.515537 Loss2: 0.733926
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.240179 Loss1: 0.507139 Loss2: 0.733040
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.232492 Loss1: 0.500384 Loss2: 0.732108
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.235685 Loss1: 0.501710 Loss2: 0.733976
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240152 Loss1: 0.504016 Loss2: 0.736136
+(DefaultActor pid=1831567) >> Training accuracy: 0.838542
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.489630 Loss1: 0.745296 Loss2: 0.744334
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394515 Loss1: 0.735581 Loss2: 0.658933
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.350947 Loss1: 0.695256 Loss2: 0.655691
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.334373 Loss1: 0.673456 Loss2: 0.660917
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.335003 Loss1: 0.673931 Loss2: 0.661072
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.327984 Loss1: 0.666188 Loss2: 0.661795
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.315706 Loss1: 0.655885 Loss2: 0.659822
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.324045 Loss1: 0.662286 Loss2: 0.661760
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.313953 Loss1: 0.651240 Loss2: 0.662713
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.314386 Loss1: 0.647870 Loss2: 0.666516
+(DefaultActor pid=1831567) >> Training accuracy: 0.784194
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.399496 Loss1: 0.611060 Loss2: 0.788437
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258414 Loss1: 0.542933 Loss2: 0.715481
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.256036 Loss1: 0.542251 Loss2: 0.713786
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.245409 Loss1: 0.531328 Loss2: 0.714081
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.219432 Loss1: 0.506244 Loss2: 0.713188
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.221484 Loss1: 0.505643 Loss2: 0.715841
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.241084 Loss1: 0.521421 Loss2: 0.719663
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.235368 Loss1: 0.517858 Loss2: 0.717509
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217206 Loss1: 0.496045 Loss2: 0.721162
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.206466 Loss1: 0.487169 Loss2: 0.719297
+(DefaultActor pid=1831567) >> Training accuracy: 0.836128
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.175564 Loss1: 0.445561 Loss2: 0.730003
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.049593 Loss1: 0.400521 Loss2: 0.649072
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.070014 Loss1: 0.420614 Loss2: 0.649400
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.036553 Loss1: 0.387703 Loss2: 0.648850
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.027433 Loss1: 0.380765 Loss2: 0.646667
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.030092 Loss1: 0.379138 Loss2: 0.650954
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.008932 Loss1: 0.358183 Loss2: 0.650750
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.012894 Loss1: 0.360868 Loss2: 0.652026
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042608 Loss1: 0.388062 Loss2: 0.654546
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.001309 Loss1: 0.350433 Loss2: 0.650876
+(DefaultActor pid=1831567) >> Training accuracy: 0.883681
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.169928 Loss1: 0.463094 Loss2: 0.706834
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.041522 Loss1: 0.400164 Loss2: 0.641358
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.026008 Loss1: 0.384078 Loss2: 0.641931
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.034367 Loss1: 0.392500 Loss2: 0.641867
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.014188 Loss1: 0.372892 Loss2: 0.641296
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.018014 Loss1: 0.377105 Loss2: 0.640909
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.006873 Loss1: 0.367820 Loss2: 0.639053
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.010586 Loss1: 0.364711 Loss2: 0.645875
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.012302 Loss1: 0.365459 Loss2: 0.646843
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028604 Loss1: 0.381551 Loss2: 0.647053
+(DefaultActor pid=1831567) >> Training accuracy: 0.877315
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339453 Loss1: 0.583892 Loss2: 0.755561
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212505 Loss1: 0.539010 Loss2: 0.673495
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198972 Loss1: 0.527749 Loss2: 0.671224
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.198444 Loss1: 0.522764 Loss2: 0.675681
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.161938 Loss1: 0.488278 Loss2: 0.673660
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.166027 Loss1: 0.489818 Loss2: 0.676208
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.181071 Loss1: 0.502525 Loss2: 0.678546
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.174202 Loss1: 0.494434 Loss2: 0.679768
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165939 Loss1: 0.486223 Loss2: 0.679716
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140544 Loss1: 0.462378 Loss2: 0.678165
+(DefaultActor pid=1831567) >> Training accuracy: 0.842722
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.505932 Loss1: 0.711266 Loss2: 0.794666
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346657 Loss1: 0.660898 Loss2: 0.685759
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.312651 Loss1: 0.629361 Loss2: 0.683290
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.306625 Loss1: 0.619631 Loss2: 0.686994
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.321894 Loss1: 0.631526 Loss2: 0.690368
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.302433 Loss1: 0.610537 Loss2: 0.691896
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.297075 Loss1: 0.605474 Loss2: 0.691601
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.293479 Loss1: 0.600675 Loss2: 0.692803
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.295090 Loss1: 0.595839 Loss2: 0.699251
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290331 Loss1: 0.592970 Loss2: 0.697361
+(DefaultActor pid=1831567) >> Training accuracy: 0.795504
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.354779 Loss1: 0.578571 Loss2: 0.776208
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.208587 Loss1: 0.532334 Loss2: 0.676253
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193248 Loss1: 0.517957 Loss2: 0.675291
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165130 Loss1: 0.489251 Loss2: 0.675880
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.161601 Loss1: 0.485775 Loss2: 0.675826
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.156122 Loss1: 0.480572 Loss2: 0.675551
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.132965 Loss1: 0.457182 Loss2: 0.675783
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.132009 Loss1: 0.451864 Loss2: 0.680145
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138004 Loss1: 0.456428 Loss2: 0.681576
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.129263 Loss1: 0.447331 Loss2: 0.681932
+(DefaultActor pid=1831567) >> Training accuracy: 0.807998
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.299247 Loss1: 0.584624 Loss2: 0.714624
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.185035 Loss1: 0.543908 Loss2: 0.641127
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195437 Loss1: 0.554468 Loss2: 0.640969
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.161360 Loss1: 0.517894 Loss2: 0.643466
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.160834 Loss1: 0.517437 Loss2: 0.643397
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.152825 Loss1: 0.512109 Loss2: 0.640716
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170487 Loss1: 0.524337 Loss2: 0.646149
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.159103 Loss1: 0.513268 Loss2: 0.645835
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.146806 Loss1: 0.500530 Loss2: 0.646276
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146908 Loss1: 0.501169 Loss2: 0.645739
+[2023-09-27 14:55:23,352][flwr][DEBUG] - fit_round 64 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.838942
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.695000
+[2023-09-27 14:55:24,899][flwr][INFO] - fit progress: (64, 0.8740351014434339, {'accuracy': 0.695}, 31057.73582381988)
+[2023-09-27 14:55:24,900][flwr][DEBUG] - evaluate_round 64: strategy sampled 10 clients (out of 10)
+[2023-09-27 14:55:56,851][flwr][DEBUG] - evaluate_round 64 received 10 results and 0 failures
+[2023-09-27 14:55:56,852][flwr][DEBUG] - fit_round 65: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.207375 Loss1: 0.447012 Loss2: 0.760363
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099528 Loss1: 0.422736 Loss2: 0.676792
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.076674 Loss1: 0.400434 Loss2: 0.676241
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.043602 Loss1: 0.366687 Loss2: 0.676915
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.053771 Loss1: 0.377058 Loss2: 0.676713
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.049069 Loss1: 0.372029 Loss2: 0.677040
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.038666 Loss1: 0.361560 Loss2: 0.677106
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.033600 Loss1: 0.356060 Loss2: 0.677540
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042125 Loss1: 0.362494 Loss2: 0.679631
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.042600 Loss1: 0.361343 Loss2: 0.681257
+(DefaultActor pid=1831567) >> Training accuracy: 0.882330
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.515185 Loss1: 0.740192 Loss2: 0.774993
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.361249 Loss1: 0.678534 Loss2: 0.682715
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.376112 Loss1: 0.684847 Loss2: 0.691265
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.382798 Loss1: 0.692025 Loss2: 0.690773
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.379424 Loss1: 0.687565 Loss2: 0.691859
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.349952 Loss1: 0.661144 Loss2: 0.688808
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.375819 Loss1: 0.681146 Loss2: 0.694673
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.336607 Loss1: 0.641342 Loss2: 0.695265
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.336012 Loss1: 0.642657 Loss2: 0.693355
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.339486 Loss1: 0.643512 Loss2: 0.695974
+(DefaultActor pid=1831567) >> Training accuracy: 0.767437
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.312861 Loss1: 0.560726 Loss2: 0.752134
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.242303 Loss1: 0.531703 Loss2: 0.710600
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226373 Loss1: 0.519685 Loss2: 0.706688
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.232507 Loss1: 0.521377 Loss2: 0.711130
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.232685 Loss1: 0.522315 Loss2: 0.710370
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.215794 Loss1: 0.502958 Loss2: 0.712836
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.198916 Loss1: 0.489260 Loss2: 0.709656
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.221066 Loss1: 0.510762 Loss2: 0.710304
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.222944 Loss1: 0.507841 Loss2: 0.715103
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.207292 Loss1: 0.493992 Loss2: 0.713300
+(DefaultActor pid=1831567) >> Training accuracy: 0.832589
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.316522 Loss1: 0.571148 Loss2: 0.745374
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.260666 Loss1: 0.578910 Loss2: 0.681756
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.224280 Loss1: 0.544827 Loss2: 0.679453
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.207829 Loss1: 0.528263 Loss2: 0.679566
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191228 Loss1: 0.514200 Loss2: 0.677028
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.194057 Loss1: 0.514577 Loss2: 0.679480
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.194499 Loss1: 0.510625 Loss2: 0.683874
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.193228 Loss1: 0.510382 Loss2: 0.682845
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.177941 Loss1: 0.493139 Loss2: 0.684802
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180268 Loss1: 0.494923 Loss2: 0.685345
+(DefaultActor pid=1831567) >> Training accuracy: 0.838542
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.492752 Loss1: 0.720467 Loss2: 0.772285
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.349707 Loss1: 0.667209 Loss2: 0.682499
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.373813 Loss1: 0.690959 Loss2: 0.682854
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347384 Loss1: 0.661941 Loss2: 0.685442
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.354493 Loss1: 0.669655 Loss2: 0.684838
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337615 Loss1: 0.650772 Loss2: 0.686842
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309858 Loss1: 0.622971 Loss2: 0.686887
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.303439 Loss1: 0.613670 Loss2: 0.689770
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.297019 Loss1: 0.610502 Loss2: 0.686517
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.311448 Loss1: 0.622483 Loss2: 0.688965
+(DefaultActor pid=1831567) >> Training accuracy: 0.753498
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.239908 Loss1: 0.455092 Loss2: 0.784816
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.102348 Loss1: 0.397705 Loss2: 0.704643
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.112180 Loss1: 0.409105 Loss2: 0.703075
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.085666 Loss1: 0.384653 Loss2: 0.701013
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.105391 Loss1: 0.398290 Loss2: 0.707101
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.087925 Loss1: 0.383089 Loss2: 0.704835
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.097966 Loss1: 0.390569 Loss2: 0.707397
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.070290 Loss1: 0.365952 Loss2: 0.704339
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.055476 Loss1: 0.354127 Loss2: 0.701349
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.076319 Loss1: 0.372626 Loss2: 0.703693
+(DefaultActor pid=1831567) >> Training accuracy: 0.877894
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.294918 Loss1: 0.604212 Loss2: 0.690706
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.177588 Loss1: 0.556040 Loss2: 0.621548
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.154169 Loss1: 0.536157 Loss2: 0.618012
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.145975 Loss1: 0.527108 Loss2: 0.618867
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.128596 Loss1: 0.510104 Loss2: 0.618492
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.147436 Loss1: 0.530094 Loss2: 0.617342
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.118716 Loss1: 0.500354 Loss2: 0.618362
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.124762 Loss1: 0.506673 Loss2: 0.618089
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.105961 Loss1: 0.486767 Loss2: 0.619194
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.108944 Loss1: 0.488826 Loss2: 0.620119
+(DefaultActor pid=1831567) >> Training accuracy: 0.835175
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340422 Loss1: 0.590586 Loss2: 0.749837
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.222969 Loss1: 0.553612 Loss2: 0.669356
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199148 Loss1: 0.532361 Loss2: 0.666787
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181992 Loss1: 0.514387 Loss2: 0.667605
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.182503 Loss1: 0.510388 Loss2: 0.672114
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.165641 Loss1: 0.494642 Loss2: 0.671000
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.151327 Loss1: 0.479230 Loss2: 0.672097
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151696 Loss1: 0.480783 Loss2: 0.670913
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.164138 Loss1: 0.492378 Loss2: 0.671760
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.159164 Loss1: 0.487265 Loss2: 0.671899
+(DefaultActor pid=1831567) >> Training accuracy: 0.839638
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358534 Loss1: 0.583398 Loss2: 0.775135
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.221122 Loss1: 0.553252 Loss2: 0.667870
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194882 Loss1: 0.528287 Loss2: 0.666595
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.168131 Loss1: 0.502021 Loss2: 0.666110
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184780 Loss1: 0.517065 Loss2: 0.667715
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.147048 Loss1: 0.477287 Loss2: 0.669761
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.154805 Loss1: 0.488070 Loss2: 0.666735
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140136 Loss1: 0.473094 Loss2: 0.667042
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.132122 Loss1: 0.461925 Loss2: 0.670197
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.156467 Loss1: 0.487555 Loss2: 0.668912
+(DefaultActor pid=1831567) >> Training accuracy: 0.845074
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477047 Loss1: 0.726062 Loss2: 0.750985
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.284871 Loss1: 0.637985 Loss2: 0.646886
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.279929 Loss1: 0.630424 Loss2: 0.649506
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281116 Loss1: 0.629427 Loss2: 0.651689
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.284758 Loss1: 0.631359 Loss2: 0.653398
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.258842 Loss1: 0.607752 Loss2: 0.651090
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.289525 Loss1: 0.633423 Loss2: 0.656101
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253601 Loss1: 0.593747 Loss2: 0.659854
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.253531 Loss1: 0.593779 Loss2: 0.659752
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.251962 Loss1: 0.592415 Loss2: 0.659548
+[2023-09-27 15:02:52,509][flwr][DEBUG] - fit_round 65 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.805921
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.697600
+[2023-09-27 15:02:53,877][flwr][INFO] - fit progress: (65, 0.8715874363248721, {'accuracy': 0.6976}, 31506.713589180727)
+[2023-09-27 15:02:53,878][flwr][DEBUG] - evaluate_round 65: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:03:25,153][flwr][DEBUG] - evaluate_round 65 received 10 results and 0 failures
+[2023-09-27 15:03:25,154][flwr][DEBUG] - fit_round 66: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324580 Loss1: 0.579214 Loss2: 0.745366
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.221395 Loss1: 0.527187 Loss2: 0.694209
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.234917 Loss1: 0.536418 Loss2: 0.698499
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.239570 Loss1: 0.541908 Loss2: 0.697662
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216113 Loss1: 0.520929 Loss2: 0.695184
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.208219 Loss1: 0.513202 Loss2: 0.695018
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.199438 Loss1: 0.502841 Loss2: 0.696597
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190995 Loss1: 0.494246 Loss2: 0.696749
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193010 Loss1: 0.494696 Loss2: 0.698314
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201187 Loss1: 0.503612 Loss2: 0.697575
+(DefaultActor pid=1831567) >> Training accuracy: 0.826761
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.476845 Loss1: 0.740963 Loss2: 0.735882
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340792 Loss1: 0.690511 Loss2: 0.650281
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336483 Loss1: 0.685433 Loss2: 0.651051
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333696 Loss1: 0.679620 Loss2: 0.654076
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317663 Loss1: 0.665835 Loss2: 0.651829
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.360265 Loss1: 0.704195 Loss2: 0.656071
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.331417 Loss1: 0.678492 Loss2: 0.652925
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.294654 Loss1: 0.639948 Loss2: 0.654707
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.284581 Loss1: 0.628864 Loss2: 0.655716
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.272518 Loss1: 0.615498 Loss2: 0.657020
+(DefaultActor pid=1831567) >> Training accuracy: 0.775362
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.229757 Loss1: 0.466181 Loss2: 0.763576
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.095326 Loss1: 0.406474 Loss2: 0.688852
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086796 Loss1: 0.395250 Loss2: 0.691545
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.088028 Loss1: 0.396502 Loss2: 0.691526
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.083553 Loss1: 0.392880 Loss2: 0.690673
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.069885 Loss1: 0.377775 Loss2: 0.692110
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.064911 Loss1: 0.373674 Loss2: 0.691236
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.054366 Loss1: 0.361243 Loss2: 0.693122
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.064557 Loss1: 0.372941 Loss2: 0.691617
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.038392 Loss1: 0.349260 Loss2: 0.689133
+(DefaultActor pid=1831567) >> Training accuracy: 0.874421
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475308 Loss1: 0.711790 Loss2: 0.763517
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.307360 Loss1: 0.649707 Loss2: 0.657653
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307078 Loss1: 0.648617 Loss2: 0.658461
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291675 Loss1: 0.627265 Loss2: 0.664410
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.284796 Loss1: 0.624027 Loss2: 0.660769
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.276481 Loss1: 0.614622 Loss2: 0.661859
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.239224 Loss1: 0.577258 Loss2: 0.661966
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.246407 Loss1: 0.582331 Loss2: 0.664075
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.269585 Loss1: 0.604402 Loss2: 0.665183
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.237662 Loss1: 0.574316 Loss2: 0.663346
+(DefaultActor pid=1831567) >> Training accuracy: 0.800164
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321621 Loss1: 0.577607 Loss2: 0.744014
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201801 Loss1: 0.538461 Loss2: 0.663340
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199502 Loss1: 0.535523 Loss2: 0.663979
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189504 Loss1: 0.522562 Loss2: 0.666942
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.177123 Loss1: 0.509212 Loss2: 0.667911
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164551 Loss1: 0.497621 Loss2: 0.666930
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179468 Loss1: 0.510520 Loss2: 0.668948
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.148415 Loss1: 0.478923 Loss2: 0.669492
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.118326 Loss1: 0.451072 Loss2: 0.667254
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.141467 Loss1: 0.472273 Loss2: 0.669193
+(DefaultActor pid=1831567) >> Training accuracy: 0.841900
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353940 Loss1: 0.597815 Loss2: 0.756125
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180156 Loss1: 0.522212 Loss2: 0.657944
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.166096 Loss1: 0.508951 Loss2: 0.657145
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162591 Loss1: 0.504667 Loss2: 0.657923
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158976 Loss1: 0.499324 Loss2: 0.659652
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.128344 Loss1: 0.468236 Loss2: 0.660108
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123985 Loss1: 0.461659 Loss2: 0.662326
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.127268 Loss1: 0.465302 Loss2: 0.661966
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.101952 Loss1: 0.439711 Loss2: 0.662242
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.089118 Loss1: 0.430331 Loss2: 0.658788
+(DefaultActor pid=1831567) >> Training accuracy: 0.859640
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.498746 Loss1: 0.745817 Loss2: 0.752929
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.376295 Loss1: 0.711740 Loss2: 0.664554
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.339839 Loss1: 0.676623 Loss2: 0.663215
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.346557 Loss1: 0.683643 Loss2: 0.662913
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.314766 Loss1: 0.653285 Loss2: 0.661480
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.295718 Loss1: 0.632078 Loss2: 0.663640
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.302646 Loss1: 0.638429 Loss2: 0.664217
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.292246 Loss1: 0.629251 Loss2: 0.662995
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.292343 Loss1: 0.626339 Loss2: 0.666004
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304420 Loss1: 0.635741 Loss2: 0.668679
+(DefaultActor pid=1831567) >> Training accuracy: 0.791278
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.200191 Loss1: 0.454593 Loss2: 0.745599
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.077804 Loss1: 0.413368 Loss2: 0.664437
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.060196 Loss1: 0.397051 Loss2: 0.663145
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.060160 Loss1: 0.395172 Loss2: 0.664988
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.036566 Loss1: 0.376336 Loss2: 0.660230
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.033694 Loss1: 0.370182 Loss2: 0.663512
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.048713 Loss1: 0.384112 Loss2: 0.664601
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.014180 Loss1: 0.347999 Loss2: 0.666181
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.027484 Loss1: 0.359666 Loss2: 0.667817
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.044227 Loss1: 0.375419 Loss2: 0.668808
+(DefaultActor pid=1831567) >> Training accuracy: 0.878279
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.333283 Loss1: 0.600590 Loss2: 0.732693
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216747 Loss1: 0.556008 Loss2: 0.660739
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.200480 Loss1: 0.541320 Loss2: 0.659160
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184451 Loss1: 0.520701 Loss2: 0.663750
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.179479 Loss1: 0.516522 Loss2: 0.662957
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.178353 Loss1: 0.515782 Loss2: 0.662570
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163678 Loss1: 0.500867 Loss2: 0.662811
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.197176 Loss1: 0.529711 Loss2: 0.667465
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.167647 Loss1: 0.501929 Loss2: 0.665718
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175172 Loss1: 0.507252 Loss2: 0.667920
+(DefaultActor pid=1831567) >> Training accuracy: 0.839944
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359117 Loss1: 0.585248 Loss2: 0.773869
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.266894 Loss1: 0.560551 Loss2: 0.706343
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.248093 Loss1: 0.544937 Loss2: 0.703156
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.234971 Loss1: 0.532741 Loss2: 0.702230
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.222008 Loss1: 0.518362 Loss2: 0.703645
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211284 Loss1: 0.504727 Loss2: 0.706557
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.190040 Loss1: 0.488265 Loss2: 0.701775
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.189740 Loss1: 0.485830 Loss2: 0.703910
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188283 Loss1: 0.480208 Loss2: 0.708075
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193635 Loss1: 0.484527 Loss2: 0.709108
+(DefaultActor pid=1831567) >> Training accuracy: 0.832889
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 15:10:24,111][flwr][DEBUG] - fit_round 66 received 10 results and 0 failures
+>> Test accuracy: 0.699200
+[2023-09-27 15:10:25,830][flwr][INFO] - fit progress: (66, 0.8717131253819876, {'accuracy': 0.6992}, 31958.666429997887)
+[2023-09-27 15:10:25,831][flwr][DEBUG] - evaluate_round 66: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:10:56,958][flwr][DEBUG] - evaluate_round 66 received 10 results and 0 failures
+[2023-09-27 15:10:56,959][flwr][DEBUG] - fit_round 67: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.469715 Loss1: 0.718741 Loss2: 0.750974
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.316742 Loss1: 0.661560 Loss2: 0.655182
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.305060 Loss1: 0.650921 Loss2: 0.654139
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.278406 Loss1: 0.623822 Loss2: 0.654584
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.278099 Loss1: 0.622094 Loss2: 0.656005
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.266994 Loss1: 0.606715 Loss2: 0.660279
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.257253 Loss1: 0.599745 Loss2: 0.657507
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.240874 Loss1: 0.583605 Loss2: 0.657269
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244358 Loss1: 0.583427 Loss2: 0.660931
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.235278 Loss1: 0.574967 Loss2: 0.660311
+(DefaultActor pid=1831567) >> Training accuracy: 0.790844
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.355516 Loss1: 0.583898 Loss2: 0.771618
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258328 Loss1: 0.557525 Loss2: 0.700803
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.239538 Loss1: 0.539370 Loss2: 0.700168
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223034 Loss1: 0.520454 Loss2: 0.702580
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.229398 Loss1: 0.529757 Loss2: 0.699641
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.207454 Loss1: 0.503135 Loss2: 0.704318
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.225024 Loss1: 0.519945 Loss2: 0.705079
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207399 Loss1: 0.502971 Loss2: 0.704428
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.202843 Loss1: 0.497609 Loss2: 0.705233
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184758 Loss1: 0.477511 Loss2: 0.707247
+(DefaultActor pid=1831567) >> Training accuracy: 0.831530
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.497941 Loss1: 0.727073 Loss2: 0.770869
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346958 Loss1: 0.669434 Loss2: 0.677525
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337405 Loss1: 0.660417 Loss2: 0.676988
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.337373 Loss1: 0.658180 Loss2: 0.679193
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.341524 Loss1: 0.662184 Loss2: 0.679340
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.315899 Loss1: 0.633725 Loss2: 0.682174
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.317345 Loss1: 0.633876 Loss2: 0.683469
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.304792 Loss1: 0.621203 Loss2: 0.683589
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.320871 Loss1: 0.633592 Loss2: 0.687278
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304663 Loss1: 0.620241 Loss2: 0.684423
+(DefaultActor pid=1831567) >> Training accuracy: 0.782416
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.217328 Loss1: 0.468127 Loss2: 0.749201
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.081478 Loss1: 0.414459 Loss2: 0.667019
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.062623 Loss1: 0.399000 Loss2: 0.663623
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.062132 Loss1: 0.397476 Loss2: 0.664656
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.043142 Loss1: 0.376358 Loss2: 0.666784
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.039785 Loss1: 0.374114 Loss2: 0.665671
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.038138 Loss1: 0.371857 Loss2: 0.666282
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.037605 Loss1: 0.371833 Loss2: 0.665772
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.038853 Loss1: 0.370018 Loss2: 0.668836
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.033169 Loss1: 0.363896 Loss2: 0.669273
+(DefaultActor pid=1831567) >> Training accuracy: 0.872878
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341986 Loss1: 0.582007 Loss2: 0.759979
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217837 Loss1: 0.546653 Loss2: 0.671184
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.196040 Loss1: 0.520998 Loss2: 0.675041
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194215 Loss1: 0.521486 Loss2: 0.672728
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185449 Loss1: 0.512024 Loss2: 0.673426
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.151455 Loss1: 0.476120 Loss2: 0.675335
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168672 Loss1: 0.490176 Loss2: 0.678496
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173523 Loss1: 0.492036 Loss2: 0.681487
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.148473 Loss1: 0.468324 Loss2: 0.680149
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.132146 Loss1: 0.454653 Loss2: 0.677493
+(DefaultActor pid=1831567) >> Training accuracy: 0.839021
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.319064 Loss1: 0.562713 Loss2: 0.756351
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234961 Loss1: 0.527147 Loss2: 0.707814
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218561 Loss1: 0.511103 Loss2: 0.707458
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.230382 Loss1: 0.520057 Loss2: 0.710326
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.239128 Loss1: 0.528194 Loss2: 0.710934
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.228948 Loss1: 0.517093 Loss2: 0.711855
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.215982 Loss1: 0.505369 Loss2: 0.710613
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.208194 Loss1: 0.496267 Loss2: 0.711927
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.216259 Loss1: 0.502190 Loss2: 0.714069
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.224912 Loss1: 0.513385 Loss2: 0.711527
+(DefaultActor pid=1831567) >> Training accuracy: 0.838666
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.354522 Loss1: 0.583538 Loss2: 0.770985
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.208124 Loss1: 0.539063 Loss2: 0.669061
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205317 Loss1: 0.537763 Loss2: 0.667554
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191349 Loss1: 0.519543 Loss2: 0.671806
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.155120 Loss1: 0.492247 Loss2: 0.662873
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.152435 Loss1: 0.483687 Loss2: 0.668747
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.134018 Loss1: 0.467246 Loss2: 0.666772
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140414 Loss1: 0.470106 Loss2: 0.670308
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.119548 Loss1: 0.447168 Loss2: 0.672380
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.141009 Loss1: 0.470674 Loss2: 0.670335
+(DefaultActor pid=1831567) >> Training accuracy: 0.849047
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.498620 Loss1: 0.732167 Loss2: 0.766453
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394749 Loss1: 0.711771 Loss2: 0.682978
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.361064 Loss1: 0.679327 Loss2: 0.681737
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.361856 Loss1: 0.676711 Loss2: 0.685145
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.366269 Loss1: 0.681607 Loss2: 0.684662
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.353257 Loss1: 0.666655 Loss2: 0.686602
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.362695 Loss1: 0.672572 Loss2: 0.690124
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.351697 Loss1: 0.659118 Loss2: 0.692579
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.360369 Loss1: 0.667452 Loss2: 0.692918
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.328717 Loss1: 0.638099 Loss2: 0.690618
+(DefaultActor pid=1831567) >> Training accuracy: 0.771286
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.222339 Loss1: 0.457808 Loss2: 0.764532
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.093748 Loss1: 0.411114 Loss2: 0.682635
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.071809 Loss1: 0.391829 Loss2: 0.679979
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.059070 Loss1: 0.379406 Loss2: 0.679664
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.056031 Loss1: 0.376254 Loss2: 0.679777
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.046910 Loss1: 0.367463 Loss2: 0.679448
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.053208 Loss1: 0.369660 Loss2: 0.683547
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.039738 Loss1: 0.357286 Loss2: 0.682452
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.037773 Loss1: 0.352633 Loss2: 0.685140
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028848 Loss1: 0.343795 Loss2: 0.685053
+(DefaultActor pid=1831567) >> Training accuracy: 0.878858
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.292541 Loss1: 0.602283 Loss2: 0.690258
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.190664 Loss1: 0.564594 Loss2: 0.626070
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.145342 Loss1: 0.522815 Loss2: 0.622527
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162237 Loss1: 0.536716 Loss2: 0.625522
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150775 Loss1: 0.524585 Loss2: 0.626190
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.135085 Loss1: 0.509731 Loss2: 0.625354
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.154984 Loss1: 0.530562 Loss2: 0.624422
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.122515 Loss1: 0.499949 Loss2: 0.622567
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.128759 Loss1: 0.499414 Loss2: 0.629346
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.109307 Loss1: 0.481010 Loss2: 0.628297
+(DefaultActor pid=1831567) >> Training accuracy: 0.832698
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 15:17:38,596][flwr][DEBUG] - fit_round 67 received 10 results and 0 failures
+>> Test accuracy: 0.697100
+[2023-09-27 15:17:40,233][flwr][INFO] - fit progress: (67, 0.8726276551572659, {'accuracy': 0.6971}, 32393.0690530031)
+[2023-09-27 15:17:40,233][flwr][DEBUG] - evaluate_round 67: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:18:11,781][flwr][DEBUG] - evaluate_round 67 received 10 results and 0 failures
+[2023-09-27 15:18:11,782][flwr][DEBUG] - fit_round 68: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.205122 Loss1: 0.478130 Loss2: 0.726993
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.039065 Loss1: 0.391876 Loss2: 0.647190
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.068473 Loss1: 0.420981 Loss2: 0.647493
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.042930 Loss1: 0.397086 Loss2: 0.645845
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.029401 Loss1: 0.383887 Loss2: 0.645514
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.033554 Loss1: 0.385090 Loss2: 0.648464
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.011791 Loss1: 0.363737 Loss2: 0.648054
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.017600 Loss1: 0.371125 Loss2: 0.646475
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.006600 Loss1: 0.359718 Loss2: 0.646883
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.987520 Loss1: 0.340452 Loss2: 0.647068
+(DefaultActor pid=1831567) >> Training accuracy: 0.880594
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348801 Loss1: 0.576460 Loss2: 0.772340
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.204376 Loss1: 0.528374 Loss2: 0.676002
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194581 Loss1: 0.519450 Loss2: 0.675131
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177953 Loss1: 0.504771 Loss2: 0.673182
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.141922 Loss1: 0.468883 Loss2: 0.673039
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167613 Loss1: 0.492622 Loss2: 0.674991
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148280 Loss1: 0.475168 Loss2: 0.673113
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.124487 Loss1: 0.447681 Loss2: 0.676806
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.159613 Loss1: 0.479192 Loss2: 0.680421
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152348 Loss1: 0.473291 Loss2: 0.679057
+(DefaultActor pid=1831567) >> Training accuracy: 0.834481
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.491634 Loss1: 0.729809 Loss2: 0.761825
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367456 Loss1: 0.696983 Loss2: 0.670473
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363809 Loss1: 0.690428 Loss2: 0.673381
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353391 Loss1: 0.682042 Loss2: 0.671349
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.298181 Loss1: 0.629619 Loss2: 0.668562
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.313122 Loss1: 0.641716 Loss2: 0.671405
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.302440 Loss1: 0.633082 Loss2: 0.669358
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.314106 Loss1: 0.640751 Loss2: 0.673355
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.266658 Loss1: 0.597539 Loss2: 0.669118
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.274577 Loss1: 0.598971 Loss2: 0.675606
+(DefaultActor pid=1831567) >> Training accuracy: 0.778685
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.484356 Loss1: 0.722878 Loss2: 0.761477
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370471 Loss1: 0.692881 Loss2: 0.677590
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.387640 Loss1: 0.707470 Loss2: 0.680170
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.369453 Loss1: 0.691590 Loss2: 0.677863
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.361990 Loss1: 0.680278 Loss2: 0.681713
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.334908 Loss1: 0.655272 Loss2: 0.679636
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.341384 Loss1: 0.660433 Loss2: 0.680951
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.350182 Loss1: 0.667380 Loss2: 0.682802
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.346301 Loss1: 0.661757 Loss2: 0.684544
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308283 Loss1: 0.625141 Loss2: 0.683142
+(DefaultActor pid=1831567) >> Training accuracy: 0.777174
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321736 Loss1: 0.601547 Loss2: 0.720189
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.191921 Loss1: 0.546189 Loss2: 0.645732
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183512 Loss1: 0.540652 Loss2: 0.642859
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208899 Loss1: 0.559043 Loss2: 0.649856
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.172929 Loss1: 0.525632 Loss2: 0.647297
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.170729 Loss1: 0.519403 Loss2: 0.651326
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.185862 Loss1: 0.533258 Loss2: 0.652604
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.160960 Loss1: 0.509421 Loss2: 0.651539
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.150595 Loss1: 0.499289 Loss2: 0.651306
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.138745 Loss1: 0.486267 Loss2: 0.652478
+(DefaultActor pid=1831567) >> Training accuracy: 0.844551
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.246719 Loss1: 0.475308 Loss2: 0.771411
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.108469 Loss1: 0.416084 Loss2: 0.692386
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.077635 Loss1: 0.384667 Loss2: 0.692967
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.089646 Loss1: 0.396419 Loss2: 0.693227
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.062572 Loss1: 0.374239 Loss2: 0.688333
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.040659 Loss1: 0.354273 Loss2: 0.686386
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050832 Loss1: 0.358374 Loss2: 0.692458
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.060901 Loss1: 0.369867 Loss2: 0.691034
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.054416 Loss1: 0.361474 Loss2: 0.692942
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.053776 Loss1: 0.364695 Loss2: 0.689082
+(DefaultActor pid=1831567) >> Training accuracy: 0.876736
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.300825 Loss1: 0.570855 Loss2: 0.729970
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180070 Loss1: 0.529306 Loss2: 0.650763
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.186318 Loss1: 0.533423 Loss2: 0.652895
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199491 Loss1: 0.542784 Loss2: 0.656707
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.159562 Loss1: 0.507756 Loss2: 0.651806
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157771 Loss1: 0.502573 Loss2: 0.655199
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.173871 Loss1: 0.515303 Loss2: 0.658568
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.156083 Loss1: 0.495682 Loss2: 0.660401
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147053 Loss1: 0.488628 Loss2: 0.658425
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148954 Loss1: 0.490242 Loss2: 0.658712
+(DefaultActor pid=1831567) >> Training accuracy: 0.839638
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320182 Loss1: 0.556745 Loss2: 0.763437
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259852 Loss1: 0.541007 Loss2: 0.718845
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.237912 Loss1: 0.525682 Loss2: 0.712231
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.216524 Loss1: 0.503535 Loss2: 0.712988
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234390 Loss1: 0.516878 Loss2: 0.717511
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.219124 Loss1: 0.503461 Loss2: 0.715663
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212641 Loss1: 0.495887 Loss2: 0.716754
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.192911 Loss1: 0.477483 Loss2: 0.715428
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.243801 Loss1: 0.522549 Loss2: 0.721251
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.213379 Loss1: 0.493718 Loss2: 0.719661
+(DefaultActor pid=1831567) >> Training accuracy: 0.821677
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.418927 Loss1: 0.612525 Loss2: 0.806402
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.287809 Loss1: 0.555693 Loss2: 0.732116
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.242502 Loss1: 0.515733 Loss2: 0.726769
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.257708 Loss1: 0.530252 Loss2: 0.727456
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.245050 Loss1: 0.514275 Loss2: 0.730774
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222742 Loss1: 0.495079 Loss2: 0.727663
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.242589 Loss1: 0.509807 Loss2: 0.732781
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229372 Loss1: 0.500981 Loss2: 0.728391
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.218140 Loss1: 0.484859 Loss2: 0.733281
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214098 Loss1: 0.481503 Loss2: 0.732595
+(DefaultActor pid=1831567) >> Training accuracy: 0.829459
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.527542 Loss1: 0.734032 Loss2: 0.793510
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340239 Loss1: 0.655287 Loss2: 0.684952
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.319218 Loss1: 0.634707 Loss2: 0.684510
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.307086 Loss1: 0.619910 Loss2: 0.687176
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.324607 Loss1: 0.639066 Loss2: 0.685541
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303088 Loss1: 0.616547 Loss2: 0.686541
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.271768 Loss1: 0.581670 Loss2: 0.690098
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.260635 Loss1: 0.571758 Loss2: 0.688877
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.275516 Loss1: 0.585505 Loss2: 0.690011
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281424 Loss1: 0.592353 Loss2: 0.689071
+[2023-09-27 15:25:11,538][flwr][DEBUG] - fit_round 68 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.809211
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.696600
+[2023-09-27 15:25:12,869][flwr][INFO] - fit progress: (68, 0.8746880760398535, {'accuracy': 0.6966}, 32845.704982507974)
+[2023-09-27 15:25:12,869][flwr][DEBUG] - evaluate_round 68: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:25:44,072][flwr][DEBUG] - evaluate_round 68 received 10 results and 0 failures
+[2023-09-27 15:25:44,073][flwr][DEBUG] - fit_round 69: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187205 Loss1: 0.429962 Loss2: 0.757243
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.094303 Loss1: 0.419557 Loss2: 0.674746
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.059656 Loss1: 0.388051 Loss2: 0.671605
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.059840 Loss1: 0.386298 Loss2: 0.673542
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.053661 Loss1: 0.380752 Loss2: 0.672909
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.040436 Loss1: 0.367601 Loss2: 0.672835
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.039572 Loss1: 0.364289 Loss2: 0.675283
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.042065 Loss1: 0.364522 Loss2: 0.677543
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.023682 Loss1: 0.345504 Loss2: 0.678179
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.031063 Loss1: 0.353691 Loss2: 0.677372
+(DefaultActor pid=1831567) >> Training accuracy: 0.863040
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.314545 Loss1: 0.612154 Loss2: 0.702391
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.176407 Loss1: 0.543437 Loss2: 0.632971
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.180679 Loss1: 0.546668 Loss2: 0.634011
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.169818 Loss1: 0.537819 Loss2: 0.631999
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.171686 Loss1: 0.538444 Loss2: 0.633242
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.148360 Loss1: 0.514336 Loss2: 0.634023
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.143693 Loss1: 0.509247 Loss2: 0.634446
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.125408 Loss1: 0.491464 Loss2: 0.633943
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.121319 Loss1: 0.489445 Loss2: 0.631874
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.122004 Loss1: 0.488951 Loss2: 0.633054
+(DefaultActor pid=1831567) >> Training accuracy: 0.834604
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.250749 Loss1: 0.460149 Loss2: 0.790600
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.109730 Loss1: 0.405830 Loss2: 0.703900
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.105792 Loss1: 0.402948 Loss2: 0.702844
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.089547 Loss1: 0.385118 Loss2: 0.704429
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.095039 Loss1: 0.387393 Loss2: 0.707647
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.080770 Loss1: 0.375531 Loss2: 0.705239
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.075400 Loss1: 0.369809 Loss2: 0.705591
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.063039 Loss1: 0.358005 Loss2: 0.705033
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.058584 Loss1: 0.350707 Loss2: 0.707877
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.067648 Loss1: 0.362453 Loss2: 0.705194
+(DefaultActor pid=1831567) >> Training accuracy: 0.866127
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.331819 Loss1: 0.572834 Loss2: 0.758985
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.218971 Loss1: 0.545731 Loss2: 0.673240
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193338 Loss1: 0.518787 Loss2: 0.674551
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183960 Loss1: 0.511152 Loss2: 0.672809
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198451 Loss1: 0.521603 Loss2: 0.676848
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163399 Loss1: 0.487508 Loss2: 0.675890
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.172615 Loss1: 0.495565 Loss2: 0.677049
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162474 Loss1: 0.484685 Loss2: 0.677789
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.146818 Loss1: 0.467552 Loss2: 0.679267
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.163156 Loss1: 0.485871 Loss2: 0.677285
+(DefaultActor pid=1831567) >> Training accuracy: 0.842105
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.342918 Loss1: 0.587914 Loss2: 0.755004
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.231685 Loss1: 0.544100 Loss2: 0.687585
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226098 Loss1: 0.536823 Loss2: 0.689275
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.219406 Loss1: 0.528210 Loss2: 0.691196
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.222810 Loss1: 0.530115 Loss2: 0.692695
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.188125 Loss1: 0.494831 Loss2: 0.693294
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.178000 Loss1: 0.484027 Loss2: 0.693973
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.214960 Loss1: 0.518759 Loss2: 0.696201
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.208177 Loss1: 0.513483 Loss2: 0.694695
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172051 Loss1: 0.477291 Loss2: 0.694760
+(DefaultActor pid=1831567) >> Training accuracy: 0.849960
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.269498 Loss1: 0.545146 Loss2: 0.724353
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.224011 Loss1: 0.538585 Loss2: 0.685426
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203467 Loss1: 0.518405 Loss2: 0.685062
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205169 Loss1: 0.520085 Loss2: 0.685084
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191794 Loss1: 0.505920 Loss2: 0.685874
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.195002 Loss1: 0.508490 Loss2: 0.686513
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170980 Loss1: 0.488846 Loss2: 0.682134
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180228 Loss1: 0.492419 Loss2: 0.687809
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180023 Loss1: 0.491779 Loss2: 0.688244
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172682 Loss1: 0.489276 Loss2: 0.683406
+(DefaultActor pid=1831567) >> Training accuracy: 0.835565
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.458025 Loss1: 0.727180 Loss2: 0.730844
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301342 Loss1: 0.656307 Loss2: 0.645035
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.299237 Loss1: 0.658454 Loss2: 0.640783
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.268467 Loss1: 0.624621 Loss2: 0.643846
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246515 Loss1: 0.604150 Loss2: 0.642365
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.251522 Loss1: 0.606183 Loss2: 0.645338
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.221314 Loss1: 0.579819 Loss2: 0.641495
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.243572 Loss1: 0.597511 Loss2: 0.646061
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.226139 Loss1: 0.577612 Loss2: 0.648528
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.236435 Loss1: 0.586427 Loss2: 0.650007
+(DefaultActor pid=1831567) >> Training accuracy: 0.811404
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324342 Loss1: 0.582088 Loss2: 0.742254
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.182271 Loss1: 0.536726 Loss2: 0.645545
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.150145 Loss1: 0.504727 Loss2: 0.645418
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.148318 Loss1: 0.503157 Loss2: 0.645160
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.142549 Loss1: 0.500541 Loss2: 0.642008
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.141492 Loss1: 0.495429 Loss2: 0.646063
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.121696 Loss1: 0.473558 Loss2: 0.648138
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.099439 Loss1: 0.450822 Loss2: 0.648618
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.099553 Loss1: 0.449702 Loss2: 0.649850
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.118841 Loss1: 0.471313 Loss2: 0.647528
+(DefaultActor pid=1831567) >> Training accuracy: 0.839513
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.508980 Loss1: 0.737851 Loss2: 0.771129
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.373491 Loss1: 0.688614 Loss2: 0.684877
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.360817 Loss1: 0.676714 Loss2: 0.684103
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.313857 Loss1: 0.633253 Loss2: 0.680604
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.335389 Loss1: 0.653649 Loss2: 0.681740
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.318044 Loss1: 0.636219 Loss2: 0.681825
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.315374 Loss1: 0.630800 Loss2: 0.684574
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.327494 Loss1: 0.641732 Loss2: 0.685762
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.317271 Loss1: 0.632846 Loss2: 0.684425
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307618 Loss1: 0.620972 Loss2: 0.686646
+(DefaultActor pid=1831567) >> Training accuracy: 0.787080
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.466594 Loss1: 0.710285 Loss2: 0.756309
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.415244 Loss1: 0.740308 Loss2: 0.674936
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.392771 Loss1: 0.719691 Loss2: 0.673080
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.385194 Loss1: 0.712794 Loss2: 0.672400
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333331 Loss1: 0.660250 Loss2: 0.673081
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.317061 Loss1: 0.645104 Loss2: 0.671957
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314585 Loss1: 0.640212 Loss2: 0.674372
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.315870 Loss1: 0.639403 Loss2: 0.676467
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.302825 Loss1: 0.625707 Loss2: 0.677118
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.297976 Loss1: 0.619549 Loss2: 0.678427
+(DefaultActor pid=1831567) >> Training accuracy: 0.775589
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 15:32:26,256][flwr][DEBUG] - fit_round 69 received 10 results and 0 failures
+>> Test accuracy: 0.699900
+[2023-09-27 15:32:27,985][flwr][INFO] - fit progress: (69, 0.8618371694232709, {'accuracy': 0.6999}, 33280.821781103965)
+[2023-09-27 15:32:27,986][flwr][DEBUG] - evaluate_round 69: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:32:58,258][flwr][DEBUG] - evaluate_round 69 received 10 results and 0 failures
+[2023-09-27 15:32:58,259][flwr][DEBUG] - fit_round 70: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493993 Loss1: 0.747787 Loss2: 0.746206
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.361313 Loss1: 0.703321 Loss2: 0.657992
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.376423 Loss1: 0.714148 Loss2: 0.662275
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353793 Loss1: 0.689079 Loss2: 0.664714
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.326336 Loss1: 0.662470 Loss2: 0.663867
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.312079 Loss1: 0.649174 Loss2: 0.662905
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.319986 Loss1: 0.654825 Loss2: 0.665161
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.315922 Loss1: 0.648441 Loss2: 0.667480
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.311368 Loss1: 0.647183 Loss2: 0.664185
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289370 Loss1: 0.623476 Loss2: 0.665894
+(DefaultActor pid=1831567) >> Training accuracy: 0.794158
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353382 Loss1: 0.586784 Loss2: 0.766598
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.255952 Loss1: 0.558028 Loss2: 0.697924
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238305 Loss1: 0.540145 Loss2: 0.698160
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205374 Loss1: 0.507285 Loss2: 0.698089
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204686 Loss1: 0.503653 Loss2: 0.701033
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.195847 Loss1: 0.492221 Loss2: 0.703627
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.202945 Loss1: 0.499497 Loss2: 0.703447
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200529 Loss1: 0.495921 Loss2: 0.704608
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.187148 Loss1: 0.482242 Loss2: 0.704906
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179840 Loss1: 0.474027 Loss2: 0.705813
+(DefaultActor pid=1831567) >> Training accuracy: 0.834223
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488112 Loss1: 0.721507 Loss2: 0.766605
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.366333 Loss1: 0.688324 Loss2: 0.678009
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.368786 Loss1: 0.690202 Loss2: 0.678585
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353599 Loss1: 0.675228 Loss2: 0.678371
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.323849 Loss1: 0.647767 Loss2: 0.676082
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.329771 Loss1: 0.653160 Loss2: 0.676612
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.319857 Loss1: 0.642425 Loss2: 0.677432
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.319065 Loss1: 0.639572 Loss2: 0.679493
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306841 Loss1: 0.627086 Loss2: 0.679754
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.291320 Loss1: 0.610617 Loss2: 0.680703
+(DefaultActor pid=1831567) >> Training accuracy: 0.791045
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.209483 Loss1: 0.489088 Loss2: 0.720395
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.031709 Loss1: 0.392545 Loss2: 0.639165
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.047009 Loss1: 0.410442 Loss2: 0.636567
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.024305 Loss1: 0.386521 Loss2: 0.637783
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.003441 Loss1: 0.363370 Loss2: 0.640071
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.012551 Loss1: 0.375449 Loss2: 0.637103
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.008384 Loss1: 0.366513 Loss2: 0.641870
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.017504 Loss1: 0.375639 Loss2: 0.641865
+(DefaultActor pid=1831567) Epoch: 8 Loss: 0.989203 Loss1: 0.349271 Loss2: 0.639933
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.999849 Loss1: 0.359266 Loss2: 0.640583
+(DefaultActor pid=1831567) >> Training accuracy: 0.869985
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297138 Loss1: 0.556606 Loss2: 0.740531
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.188052 Loss1: 0.526560 Loss2: 0.661491
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.170896 Loss1: 0.509223 Loss2: 0.661673
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181371 Loss1: 0.517695 Loss2: 0.663676
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.193683 Loss1: 0.528860 Loss2: 0.664823
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.182521 Loss1: 0.517034 Loss2: 0.665487
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.145584 Loss1: 0.483669 Loss2: 0.661915
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.157764 Loss1: 0.490569 Loss2: 0.667194
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.122032 Loss1: 0.455154 Loss2: 0.666878
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152289 Loss1: 0.483917 Loss2: 0.668372
+(DefaultActor pid=1831567) >> Training accuracy: 0.836965
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.484259 Loss1: 0.700955 Loss2: 0.783304
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.334666 Loss1: 0.656738 Loss2: 0.677928
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.283993 Loss1: 0.608463 Loss2: 0.675530
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.303651 Loss1: 0.627326 Loss2: 0.676324
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317336 Loss1: 0.642303 Loss2: 0.675033
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.287606 Loss1: 0.609260 Loss2: 0.678346
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.283019 Loss1: 0.603970 Loss2: 0.679048
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.275782 Loss1: 0.596652 Loss2: 0.679130
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.284478 Loss1: 0.604860 Loss2: 0.679618
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.237155 Loss1: 0.557914 Loss2: 0.679241
+(DefaultActor pid=1831567) >> Training accuracy: 0.804825
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.361047 Loss1: 0.597405 Loss2: 0.763642
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.237672 Loss1: 0.552715 Loss2: 0.684957
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.235150 Loss1: 0.550877 Loss2: 0.684273
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.201025 Loss1: 0.511729 Loss2: 0.689297
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195534 Loss1: 0.506288 Loss2: 0.689246
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.179664 Loss1: 0.493456 Loss2: 0.686208
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191758 Loss1: 0.507592 Loss2: 0.684166
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190807 Loss1: 0.502147 Loss2: 0.688660
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.200824 Loss1: 0.512390 Loss2: 0.688434
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198943 Loss1: 0.507548 Loss2: 0.691395
+(DefaultActor pid=1831567) >> Training accuracy: 0.844952
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320884 Loss1: 0.566351 Loss2: 0.754532
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.247665 Loss1: 0.538607 Loss2: 0.709057
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.242032 Loss1: 0.534372 Loss2: 0.707659
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208429 Loss1: 0.503974 Loss2: 0.704455
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210692 Loss1: 0.503731 Loss2: 0.706961
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.219642 Loss1: 0.510296 Loss2: 0.709347
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.219648 Loss1: 0.510841 Loss2: 0.708807
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212378 Loss1: 0.501070 Loss2: 0.711308
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213210 Loss1: 0.501968 Loss2: 0.711241
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214207 Loss1: 0.503043 Loss2: 0.711164
+(DefaultActor pid=1831567) >> Training accuracy: 0.834697
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.231432 Loss1: 0.462417 Loss2: 0.769015
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.093953 Loss1: 0.402645 Loss2: 0.691308
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.081724 Loss1: 0.394148 Loss2: 0.687576
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074943 Loss1: 0.384736 Loss2: 0.690207
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.071562 Loss1: 0.380205 Loss2: 0.691357
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.071631 Loss1: 0.378005 Loss2: 0.693626
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.069039 Loss1: 0.374527 Loss2: 0.694512
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.069688 Loss1: 0.375430 Loss2: 0.694258
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.059300 Loss1: 0.363374 Loss2: 0.695926
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.057981 Loss1: 0.366186 Loss2: 0.691795
+(DefaultActor pid=1831567) >> Training accuracy: 0.871914
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360314 Loss1: 0.576333 Loss2: 0.783981
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206654 Loss1: 0.523216 Loss2: 0.683438
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183590 Loss1: 0.499376 Loss2: 0.684213
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.176158 Loss1: 0.490610 Loss2: 0.685549
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192296 Loss1: 0.501264 Loss2: 0.691031
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164320 Loss1: 0.475297 Loss2: 0.689023
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165409 Loss1: 0.476799 Loss2: 0.688610
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.149126 Loss1: 0.458571 Loss2: 0.690555
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.139301 Loss1: 0.448844 Loss2: 0.690457
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134761 Loss1: 0.442556 Loss2: 0.692205
+[2023-09-27 15:40:08,733][flwr][DEBUG] - fit_round 70 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.854608
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.701300
+[2023-09-27 15:40:10,370][flwr][INFO] - fit progress: (70, 0.8689377218389663, {'accuracy': 0.7013}, 33743.20618806314)
+[2023-09-27 15:40:10,370][flwr][DEBUG] - evaluate_round 70: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:40:40,686][flwr][DEBUG] - evaluate_round 70 received 10 results and 0 failures
+[2023-09-27 15:40:40,687][flwr][DEBUG] - fit_round 71: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.514160 Loss1: 0.736470 Loss2: 0.777690
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367250 Loss1: 0.680143 Loss2: 0.687106
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.341041 Loss1: 0.660198 Loss2: 0.680842
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333397 Loss1: 0.649890 Loss2: 0.683507
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.334300 Loss1: 0.649606 Loss2: 0.684693
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.331900 Loss1: 0.649031 Loss2: 0.682869
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.306782 Loss1: 0.619799 Loss2: 0.686983
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.337787 Loss1: 0.647131 Loss2: 0.690656
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.319849 Loss1: 0.623004 Loss2: 0.696845
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.335501 Loss1: 0.641388 Loss2: 0.694113
+(DefaultActor pid=1831567) >> Training accuracy: 0.789646
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.467585 Loss1: 0.706581 Loss2: 0.761004
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329991 Loss1: 0.659959 Loss2: 0.670032
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.302627 Loss1: 0.632478 Loss2: 0.670149
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.292220 Loss1: 0.620221 Loss2: 0.671999
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.279501 Loss1: 0.606280 Loss2: 0.673221
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.293307 Loss1: 0.617080 Loss2: 0.676227
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.264369 Loss1: 0.590279 Loss2: 0.674090
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.303390 Loss1: 0.624261 Loss2: 0.679128
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.262773 Loss1: 0.584593 Loss2: 0.678181
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.246745 Loss1: 0.568907 Loss2: 0.677839
+(DefaultActor pid=1831567) >> Training accuracy: 0.814419
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.296418 Loss1: 0.589665 Loss2: 0.706752
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.174463 Loss1: 0.536784 Loss2: 0.637679
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.187433 Loss1: 0.552229 Loss2: 0.635204
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177672 Loss1: 0.539767 Loss2: 0.637906
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154052 Loss1: 0.520607 Loss2: 0.633445
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.142724 Loss1: 0.506555 Loss2: 0.636169
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.135451 Loss1: 0.499217 Loss2: 0.636234
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154435 Loss1: 0.514852 Loss2: 0.639584
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134915 Loss1: 0.494794 Loss2: 0.640120
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.119853 Loss1: 0.481597 Loss2: 0.638256
+(DefaultActor pid=1831567) >> Training accuracy: 0.826410
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.149072 Loss1: 0.443256 Loss2: 0.705816
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.046541 Loss1: 0.409861 Loss2: 0.636681
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.035375 Loss1: 0.404116 Loss2: 0.631260
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.015038 Loss1: 0.382235 Loss2: 0.632803
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.012296 Loss1: 0.380987 Loss2: 0.631310
+(DefaultActor pid=1831567) Epoch: 5 Loss: 0.989841 Loss1: 0.359149 Loss2: 0.630691
+(DefaultActor pid=1831567) Epoch: 6 Loss: 0.992291 Loss1: 0.362226 Loss2: 0.630065
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.004523 Loss1: 0.372567 Loss2: 0.631956
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.003859 Loss1: 0.372861 Loss2: 0.630998
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.991287 Loss1: 0.357151 Loss2: 0.634135
+(DefaultActor pid=1831567) >> Training accuracy: 0.867477
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.311559 Loss1: 0.575923 Loss2: 0.735636
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202813 Loss1: 0.532888 Loss2: 0.669925
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195561 Loss1: 0.526732 Loss2: 0.668829
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.220087 Loss1: 0.545253 Loss2: 0.674834
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.171340 Loss1: 0.500028 Loss2: 0.671312
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199236 Loss1: 0.522632 Loss2: 0.676604
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170589 Loss1: 0.495295 Loss2: 0.675294
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.160706 Loss1: 0.485036 Loss2: 0.675670
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.160512 Loss1: 0.484508 Loss2: 0.676004
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.185563 Loss1: 0.503722 Loss2: 0.681841
+(DefaultActor pid=1831567) >> Training accuracy: 0.845353
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488137 Loss1: 0.726327 Loss2: 0.761810
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.395277 Loss1: 0.712392 Loss2: 0.682885
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.385406 Loss1: 0.700575 Loss2: 0.684831
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.348354 Loss1: 0.664413 Loss2: 0.683941
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.344082 Loss1: 0.661644 Loss2: 0.682438
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.349710 Loss1: 0.667249 Loss2: 0.682461
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.324077 Loss1: 0.638248 Loss2: 0.685829
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334923 Loss1: 0.650011 Loss2: 0.684912
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.361780 Loss1: 0.674118 Loss2: 0.687662
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.326573 Loss1: 0.634226 Loss2: 0.692347
+(DefaultActor pid=1831567) >> Training accuracy: 0.786458
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341206 Loss1: 0.584915 Loss2: 0.756291
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217007 Loss1: 0.541349 Loss2: 0.675658
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.179415 Loss1: 0.506065 Loss2: 0.673350
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.211554 Loss1: 0.533675 Loss2: 0.677879
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154285 Loss1: 0.479635 Loss2: 0.674650
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164791 Loss1: 0.489573 Loss2: 0.675219
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165380 Loss1: 0.487857 Loss2: 0.677524
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.170546 Loss1: 0.491930 Loss2: 0.678616
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.135664 Loss1: 0.457553 Loss2: 0.678112
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.141720 Loss1: 0.462516 Loss2: 0.679204
+(DefaultActor pid=1831567) >> Training accuracy: 0.843956
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.203871 Loss1: 0.442046 Loss2: 0.761825
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092592 Loss1: 0.412812 Loss2: 0.679780
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.070500 Loss1: 0.393814 Loss2: 0.676687
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.069893 Loss1: 0.393014 Loss2: 0.676879
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.039407 Loss1: 0.365411 Loss2: 0.673997
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.043348 Loss1: 0.362641 Loss2: 0.680708
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.042123 Loss1: 0.362811 Loss2: 0.679312
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.037503 Loss1: 0.358754 Loss2: 0.678749
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.035484 Loss1: 0.357277 Loss2: 0.678207
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.054302 Loss1: 0.372059 Loss2: 0.682244
+(DefaultActor pid=1831567) >> Training accuracy: 0.870177
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.338447 Loss1: 0.572901 Loss2: 0.765546
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.163775 Loss1: 0.503687 Loss2: 0.660088
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195008 Loss1: 0.533825 Loss2: 0.661182
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.125849 Loss1: 0.465757 Loss2: 0.660092
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.131975 Loss1: 0.474601 Loss2: 0.657374
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.152817 Loss1: 0.490825 Loss2: 0.661993
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.139700 Loss1: 0.477387 Loss2: 0.662313
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133332 Loss1: 0.469225 Loss2: 0.664106
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.148682 Loss1: 0.484180 Loss2: 0.664502
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.118784 Loss1: 0.452878 Loss2: 0.665905
+(DefaultActor pid=1831567) >> Training accuracy: 0.847987
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.277582 Loss1: 0.551481 Loss2: 0.726101
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200031 Loss1: 0.519371 Loss2: 0.680660
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.202424 Loss1: 0.519838 Loss2: 0.682586
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.197650 Loss1: 0.515810 Loss2: 0.681840
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.206942 Loss1: 0.516321 Loss2: 0.690621
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184926 Loss1: 0.495966 Loss2: 0.688960
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179255 Loss1: 0.493719 Loss2: 0.685536
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.194243 Loss1: 0.507175 Loss2: 0.687068
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182642 Loss1: 0.496603 Loss2: 0.686039
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.195516 Loss1: 0.507390 Loss2: 0.688126
+[2023-09-27 15:47:22,549][flwr][DEBUG] - fit_round 71 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.842262
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.704200
+[2023-09-27 15:47:24,011][flwr][INFO] - fit progress: (71, 0.8598026045785544, {'accuracy': 0.7042}, 34176.8469454227)
+[2023-09-27 15:47:24,012][flwr][DEBUG] - evaluate_round 71: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:47:54,613][flwr][DEBUG] - evaluate_round 71 received 10 results and 0 failures
+[2023-09-27 15:47:54,614][flwr][DEBUG] - fit_round 72: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.336425 Loss1: 0.564673 Loss2: 0.771752
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.186469 Loss1: 0.515297 Loss2: 0.671172
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.171589 Loss1: 0.499942 Loss2: 0.671646
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.156270 Loss1: 0.487576 Loss2: 0.668694
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163632 Loss1: 0.492814 Loss2: 0.670818
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164688 Loss1: 0.489886 Loss2: 0.674802
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.113033 Loss1: 0.438414 Loss2: 0.674620
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.128438 Loss1: 0.450376 Loss2: 0.678063
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147661 Loss1: 0.468983 Loss2: 0.678678
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146797 Loss1: 0.468915 Loss2: 0.677882
+(DefaultActor pid=1831567) >> Training accuracy: 0.843485
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.451305 Loss1: 0.707160 Loss2: 0.744145
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387557 Loss1: 0.725558 Loss2: 0.661999
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.330910 Loss1: 0.671036 Loss2: 0.659873
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.323897 Loss1: 0.664442 Loss2: 0.659454
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345581 Loss1: 0.682962 Loss2: 0.662620
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.347205 Loss1: 0.679733 Loss2: 0.667472
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314600 Loss1: 0.646708 Loss2: 0.667893
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.323786 Loss1: 0.657914 Loss2: 0.665871
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.313730 Loss1: 0.645689 Loss2: 0.668041
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.327825 Loss1: 0.656825 Loss2: 0.671001
+(DefaultActor pid=1831567) >> Training accuracy: 0.778759
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.330791 Loss1: 0.555146 Loss2: 0.775645
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.242805 Loss1: 0.515727 Loss2: 0.727078
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.237749 Loss1: 0.509697 Loss2: 0.728052
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.255584 Loss1: 0.526715 Loss2: 0.728868
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234015 Loss1: 0.503489 Loss2: 0.730526
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.235609 Loss1: 0.508271 Loss2: 0.727338
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243311 Loss1: 0.511725 Loss2: 0.731587
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.244342 Loss1: 0.509396 Loss2: 0.734946
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.224621 Loss1: 0.493765 Loss2: 0.730857
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.215770 Loss1: 0.483266 Loss2: 0.732504
+(DefaultActor pid=1831567) >> Training accuracy: 0.819692
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485176 Loss1: 0.716361 Loss2: 0.768814
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.331040 Loss1: 0.665171 Loss2: 0.665869
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307138 Loss1: 0.639825 Loss2: 0.667313
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.305615 Loss1: 0.640229 Loss2: 0.665386
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.269716 Loss1: 0.605081 Loss2: 0.664635
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.261888 Loss1: 0.600493 Loss2: 0.661395
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.260611 Loss1: 0.593319 Loss2: 0.667292
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.243367 Loss1: 0.576435 Loss2: 0.666932
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.277572 Loss1: 0.607945 Loss2: 0.669628
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.249654 Loss1: 0.581862 Loss2: 0.667791
+(DefaultActor pid=1831567) >> Training accuracy: 0.813596
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.196431 Loss1: 0.474940 Loss2: 0.721491
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.050413 Loss1: 0.412705 Loss2: 0.637708
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.025201 Loss1: 0.387721 Loss2: 0.637480
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.014504 Loss1: 0.377039 Loss2: 0.637465
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.024563 Loss1: 0.385896 Loss2: 0.638667
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.006084 Loss1: 0.368532 Loss2: 0.637553
+(DefaultActor pid=1831567) Epoch: 6 Loss: 0.997144 Loss1: 0.357444 Loss2: 0.639699
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.012011 Loss1: 0.373440 Loss2: 0.638571
+(DefaultActor pid=1831567) Epoch: 8 Loss: 0.988331 Loss1: 0.350404 Loss2: 0.637927
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.016751 Loss1: 0.374149 Loss2: 0.642602
+(DefaultActor pid=1831567) >> Training accuracy: 0.871335
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.338885 Loss1: 0.595092 Loss2: 0.743793
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202131 Loss1: 0.541054 Loss2: 0.661076
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.189219 Loss1: 0.529123 Loss2: 0.660096
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162577 Loss1: 0.501753 Loss2: 0.660824
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.171755 Loss1: 0.509975 Loss2: 0.661780
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167798 Loss1: 0.502876 Loss2: 0.664922
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.147203 Loss1: 0.481893 Loss2: 0.665310
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151308 Loss1: 0.486670 Loss2: 0.664638
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.158843 Loss1: 0.492885 Loss2: 0.665958
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.139016 Loss1: 0.473522 Loss2: 0.665494
+(DefaultActor pid=1831567) >> Training accuracy: 0.844161
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.259160 Loss1: 0.476859 Loss2: 0.782301
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.123202 Loss1: 0.403774 Loss2: 0.719428
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.104798 Loss1: 0.393173 Loss2: 0.711625
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.096962 Loss1: 0.385925 Loss2: 0.711038
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.089291 Loss1: 0.381869 Loss2: 0.707422
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.065985 Loss1: 0.357285 Loss2: 0.708701
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.085268 Loss1: 0.369651 Loss2: 0.715617
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.066297 Loss1: 0.352917 Loss2: 0.713381
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.074104 Loss1: 0.361837 Loss2: 0.712267
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.066836 Loss1: 0.349119 Loss2: 0.717717
+(DefaultActor pid=1831567) >> Training accuracy: 0.880787
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462069 Loss1: 0.714208 Loss2: 0.747860
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.353502 Loss1: 0.691824 Loss2: 0.661679
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.351199 Loss1: 0.687643 Loss2: 0.663555
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.315941 Loss1: 0.650420 Loss2: 0.665521
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.294875 Loss1: 0.634399 Loss2: 0.660475
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.293148 Loss1: 0.629174 Loss2: 0.663974
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305874 Loss1: 0.637822 Loss2: 0.668052
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.292717 Loss1: 0.627034 Loss2: 0.665682
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.263877 Loss1: 0.598831 Loss2: 0.665046
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.294166 Loss1: 0.625478 Loss2: 0.668689
+(DefaultActor pid=1831567) >> Training accuracy: 0.770056
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289469 Loss1: 0.555893 Loss2: 0.733576
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223837 Loss1: 0.558659 Loss2: 0.665178
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194883 Loss1: 0.532647 Loss2: 0.662235
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.185817 Loss1: 0.521994 Loss2: 0.663823
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.178100 Loss1: 0.513529 Loss2: 0.664571
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181001 Loss1: 0.515735 Loss2: 0.665266
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191828 Loss1: 0.527267 Loss2: 0.664561
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162150 Loss1: 0.497268 Loss2: 0.664881
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.150779 Loss1: 0.487534 Loss2: 0.663245
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148445 Loss1: 0.484553 Loss2: 0.663892
+(DefaultActor pid=1831567) >> Training accuracy: 0.847756
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.342409 Loss1: 0.587398 Loss2: 0.755011
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216083 Loss1: 0.533672 Loss2: 0.682411
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215177 Loss1: 0.532418 Loss2: 0.682759
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188954 Loss1: 0.505386 Loss2: 0.683568
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203603 Loss1: 0.516774 Loss2: 0.686829
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.200192 Loss1: 0.513877 Loss2: 0.686315
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175795 Loss1: 0.486762 Loss2: 0.689033
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.192052 Loss1: 0.504121 Loss2: 0.687931
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.176323 Loss1: 0.486184 Loss2: 0.690138
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174844 Loss1: 0.486031 Loss2: 0.688813
+[2023-09-27 15:54:36,757][flwr][DEBUG] - fit_round 72 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.838034
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.700200
+[2023-09-27 15:54:38,104][flwr][INFO] - fit progress: (72, 0.8614236178299108, {'accuracy': 0.7002}, 34610.94003685191)
+[2023-09-27 15:54:38,104][flwr][DEBUG] - evaluate_round 72: strategy sampled 10 clients (out of 10)
+[2023-09-27 15:55:08,304][flwr][DEBUG] - evaluate_round 72 received 10 results and 0 failures
+[2023-09-27 15:55:08,305][flwr][DEBUG] - fit_round 73: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340173 Loss1: 0.578090 Loss2: 0.762083
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.271073 Loss1: 0.571719 Loss2: 0.699355
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223428 Loss1: 0.531177 Loss2: 0.692251
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208997 Loss1: 0.512973 Loss2: 0.696023
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200849 Loss1: 0.507222 Loss2: 0.693627
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.215254 Loss1: 0.518443 Loss2: 0.696811
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191118 Loss1: 0.491652 Loss2: 0.699467
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.181624 Loss1: 0.482536 Loss2: 0.699088
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183441 Loss1: 0.484871 Loss2: 0.698570
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198953 Loss1: 0.497329 Loss2: 0.701624
+(DefaultActor pid=1831567) >> Training accuracy: 0.825721
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.511806 Loss1: 0.739789 Loss2: 0.772018
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394516 Loss1: 0.704731 Loss2: 0.689785
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393777 Loss1: 0.704116 Loss2: 0.689661
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.368046 Loss1: 0.679846 Loss2: 0.688200
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.339370 Loss1: 0.648567 Loss2: 0.690803
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.356738 Loss1: 0.665197 Loss2: 0.691541
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338111 Loss1: 0.643082 Loss2: 0.695029
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.344463 Loss1: 0.650378 Loss2: 0.694085
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.360928 Loss1: 0.664982 Loss2: 0.695946
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308758 Loss1: 0.614728 Loss2: 0.694031
+(DefaultActor pid=1831567) >> Training accuracy: 0.783741
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.216823 Loss1: 0.448976 Loss2: 0.767847
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.091402 Loss1: 0.404878 Loss2: 0.686524
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.083542 Loss1: 0.397458 Loss2: 0.686084
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.069089 Loss1: 0.380520 Loss2: 0.688569
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066429 Loss1: 0.378131 Loss2: 0.688297
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.051365 Loss1: 0.363381 Loss2: 0.687984
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.040094 Loss1: 0.353934 Loss2: 0.686161
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.057268 Loss1: 0.367930 Loss2: 0.689338
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.047272 Loss1: 0.359976 Loss2: 0.687296
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.064678 Loss1: 0.372513 Loss2: 0.692166
+(DefaultActor pid=1831567) >> Training accuracy: 0.883488
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.345006 Loss1: 0.595545 Loss2: 0.749460
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.196359 Loss1: 0.525203 Loss2: 0.671156
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.187573 Loss1: 0.515817 Loss2: 0.671756
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.166838 Loss1: 0.496536 Loss2: 0.670301
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.173728 Loss1: 0.500614 Loss2: 0.673114
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163559 Loss1: 0.493465 Loss2: 0.670094
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.178657 Loss1: 0.497797 Loss2: 0.680860
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.153194 Loss1: 0.475030 Loss2: 0.678164
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134683 Loss1: 0.457412 Loss2: 0.677272
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.170479 Loss1: 0.486148 Loss2: 0.684331
+(DefaultActor pid=1831567) >> Training accuracy: 0.846834
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.293301 Loss1: 0.564006 Loss2: 0.729295
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194760 Loss1: 0.507894 Loss2: 0.686867
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198936 Loss1: 0.511192 Loss2: 0.687745
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190061 Loss1: 0.503966 Loss2: 0.686096
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.199843 Loss1: 0.512906 Loss2: 0.686936
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.182960 Loss1: 0.494111 Loss2: 0.688849
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187015 Loss1: 0.497133 Loss2: 0.689882
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.195108 Loss1: 0.502512 Loss2: 0.692596
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178795 Loss1: 0.484888 Loss2: 0.693907
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.177779 Loss1: 0.486692 Loss2: 0.691088
+(DefaultActor pid=1831567) >> Training accuracy: 0.836806
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.230230 Loss1: 0.456883 Loss2: 0.773348
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099805 Loss1: 0.409041 Loss2: 0.690764
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.077108 Loss1: 0.390925 Loss2: 0.686183
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.058917 Loss1: 0.378595 Loss2: 0.680322
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085336 Loss1: 0.395284 Loss2: 0.690052
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.061655 Loss1: 0.377346 Loss2: 0.684309
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050742 Loss1: 0.367633 Loss2: 0.683109
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.041725 Loss1: 0.357859 Loss2: 0.683867
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.039937 Loss1: 0.352290 Loss2: 0.687647
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.016720 Loss1: 0.330901 Loss2: 0.685820
+(DefaultActor pid=1831567) >> Training accuracy: 0.874228
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.508416 Loss1: 0.722858 Loss2: 0.785558
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.339187 Loss1: 0.645844 Loss2: 0.693343
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.346755 Loss1: 0.653854 Loss2: 0.692901
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.301726 Loss1: 0.610159 Loss2: 0.691567
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.296414 Loss1: 0.602584 Loss2: 0.693830
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303014 Loss1: 0.613220 Loss2: 0.689794
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.285535 Loss1: 0.591030 Loss2: 0.694505
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.295980 Loss1: 0.601693 Loss2: 0.694287
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.302282 Loss1: 0.604890 Loss2: 0.697391
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.274996 Loss1: 0.578029 Loss2: 0.696967
+(DefaultActor pid=1831567) >> Training accuracy: 0.810307
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488969 Loss1: 0.718254 Loss2: 0.770715
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.356428 Loss1: 0.676600 Loss2: 0.679828
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.353246 Loss1: 0.675173 Loss2: 0.678073
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.336501 Loss1: 0.657201 Loss2: 0.679300
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.320631 Loss1: 0.640465 Loss2: 0.680167
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.327437 Loss1: 0.644609 Loss2: 0.682828
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.326048 Loss1: 0.639542 Loss2: 0.686506
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.309938 Loss1: 0.627604 Loss2: 0.682334
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.310836 Loss1: 0.626800 Loss2: 0.684037
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.293762 Loss1: 0.610060 Loss2: 0.683701
+(DefaultActor pid=1831567) >> Training accuracy: 0.776119
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291961 Loss1: 0.595821 Loss2: 0.696139
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171482 Loss1: 0.542376 Loss2: 0.629106
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.158523 Loss1: 0.534168 Loss2: 0.624355
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.158470 Loss1: 0.534761 Loss2: 0.623709
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150436 Loss1: 0.523863 Loss2: 0.626573
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.136320 Loss1: 0.510976 Loss2: 0.625344
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.124970 Loss1: 0.499009 Loss2: 0.625961
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.112571 Loss1: 0.486571 Loss2: 0.625999
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.130798 Loss1: 0.500780 Loss2: 0.630018
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.116941 Loss1: 0.489125 Loss2: 0.627816
+(DefaultActor pid=1831567) >> Training accuracy: 0.843369
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369154 Loss1: 0.590637 Loss2: 0.778516
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.192586 Loss1: 0.521045 Loss2: 0.671541
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.164102 Loss1: 0.495451 Loss2: 0.668651
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.197955 Loss1: 0.524360 Loss2: 0.673595
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.137361 Loss1: 0.469023 Loss2: 0.668339
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146145 Loss1: 0.476017 Loss2: 0.670128
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.149640 Loss1: 0.478328 Loss2: 0.671312
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.167322 Loss1: 0.492525 Loss2: 0.674797
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134768 Loss1: 0.459325 Loss2: 0.675443
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.100868 Loss1: 0.426402 Loss2: 0.674466
+[2023-09-27 16:01:54,172][flwr][DEBUG] - fit_round 73 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.858845
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.701900
+[2023-09-27 16:01:55,606][flwr][INFO] - fit progress: (73, 0.8532162613381212, {'accuracy': 0.7019}, 35048.44225243013)
+[2023-09-27 16:01:55,607][flwr][DEBUG] - evaluate_round 73: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:02:26,970][flwr][DEBUG] - evaluate_round 73 received 10 results and 0 failures
+[2023-09-27 16:02:26,971][flwr][DEBUG] - fit_round 74: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.344600 Loss1: 0.580651 Loss2: 0.763949
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.228773 Loss1: 0.535982 Loss2: 0.692791
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214914 Loss1: 0.523744 Loss2: 0.691170
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.222157 Loss1: 0.530014 Loss2: 0.692143
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200632 Loss1: 0.505013 Loss2: 0.695619
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.192252 Loss1: 0.494716 Loss2: 0.697536
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.201779 Loss1: 0.509577 Loss2: 0.692202
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.187945 Loss1: 0.491474 Loss2: 0.696471
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.192705 Loss1: 0.496778 Loss2: 0.695926
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.169177 Loss1: 0.473582 Loss2: 0.695596
+(DefaultActor pid=1831567) >> Training accuracy: 0.828316
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.331301 Loss1: 0.574748 Loss2: 0.756553
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187882 Loss1: 0.515766 Loss2: 0.672116
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.200985 Loss1: 0.526085 Loss2: 0.674900
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.193234 Loss1: 0.516450 Loss2: 0.676784
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167899 Loss1: 0.491051 Loss2: 0.676848
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.177020 Loss1: 0.499784 Loss2: 0.677236
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.166838 Loss1: 0.487859 Loss2: 0.678978
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.166071 Loss1: 0.486589 Loss2: 0.679482
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173112 Loss1: 0.493501 Loss2: 0.679612
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.142091 Loss1: 0.462295 Loss2: 0.679796
+(DefaultActor pid=1831567) >> Training accuracy: 0.822163
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.451986 Loss1: 0.684760 Loss2: 0.767227
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.327229 Loss1: 0.663180 Loss2: 0.664049
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.291629 Loss1: 0.632128 Loss2: 0.659501
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.282351 Loss1: 0.620990 Loss2: 0.661361
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.273754 Loss1: 0.612869 Loss2: 0.660885
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.274513 Loss1: 0.608154 Loss2: 0.666358
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.250923 Loss1: 0.585349 Loss2: 0.665574
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.232189 Loss1: 0.568194 Loss2: 0.663994
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.249269 Loss1: 0.584292 Loss2: 0.664978
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.264051 Loss1: 0.594918 Loss2: 0.669132
+(DefaultActor pid=1831567) >> Training accuracy: 0.810581
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.224970 Loss1: 0.459444 Loss2: 0.765527
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.127509 Loss1: 0.430640 Loss2: 0.696869
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.095937 Loss1: 0.400554 Loss2: 0.695383
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.065018 Loss1: 0.370024 Loss2: 0.694994
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.064013 Loss1: 0.368813 Loss2: 0.695199
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.055646 Loss1: 0.363493 Loss2: 0.692154
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.062347 Loss1: 0.366912 Loss2: 0.695435
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.076480 Loss1: 0.381414 Loss2: 0.695066
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.057384 Loss1: 0.358893 Loss2: 0.698491
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.060966 Loss1: 0.358973 Loss2: 0.701993
+(DefaultActor pid=1831567) >> Training accuracy: 0.879630
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.204543 Loss1: 0.464813 Loss2: 0.739729
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.055880 Loss1: 0.402034 Loss2: 0.653846
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.051711 Loss1: 0.399004 Loss2: 0.652707
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019424 Loss1: 0.369401 Loss2: 0.650023
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.029337 Loss1: 0.377938 Loss2: 0.651400
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.042409 Loss1: 0.387674 Loss2: 0.654735
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.023730 Loss1: 0.367335 Loss2: 0.656395
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.018659 Loss1: 0.364322 Loss2: 0.654337
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.031413 Loss1: 0.374578 Loss2: 0.656834
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.999847 Loss1: 0.347473 Loss2: 0.652375
+(DefaultActor pid=1831567) >> Training accuracy: 0.888310
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.465357 Loss1: 0.719948 Loss2: 0.745409
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.333890 Loss1: 0.669082 Loss2: 0.664808
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.327856 Loss1: 0.664692 Loss2: 0.663164
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.332504 Loss1: 0.670114 Loss2: 0.662390
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.291643 Loss1: 0.625761 Loss2: 0.665883
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.318113 Loss1: 0.649480 Loss2: 0.668633
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.313051 Loss1: 0.643736 Loss2: 0.669315
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.321064 Loss1: 0.651646 Loss2: 0.669418
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.288623 Loss1: 0.621203 Loss2: 0.667420
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.319290 Loss1: 0.643963 Loss2: 0.675327
+(DefaultActor pid=1831567) >> Training accuracy: 0.768890
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348266 Loss1: 0.571811 Loss2: 0.776456
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.218643 Loss1: 0.542929 Loss2: 0.675714
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.190151 Loss1: 0.518906 Loss2: 0.671245
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.145594 Loss1: 0.476543 Loss2: 0.669052
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.162825 Loss1: 0.489515 Loss2: 0.673309
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.117843 Loss1: 0.447640 Loss2: 0.670203
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.126025 Loss1: 0.452015 Loss2: 0.674010
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.125467 Loss1: 0.452140 Loss2: 0.673327
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.126849 Loss1: 0.454223 Loss2: 0.672626
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134821 Loss1: 0.455813 Loss2: 0.679009
+(DefaultActor pid=1831567) >> Training accuracy: 0.854343
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.478894 Loss1: 0.724788 Loss2: 0.754107
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.400770 Loss1: 0.725458 Loss2: 0.675313
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.374656 Loss1: 0.697536 Loss2: 0.677120
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.345210 Loss1: 0.670413 Loss2: 0.674797
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.344420 Loss1: 0.668691 Loss2: 0.675729
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.323637 Loss1: 0.648293 Loss2: 0.675344
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.332824 Loss1: 0.652130 Loss2: 0.680694
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.344103 Loss1: 0.663461 Loss2: 0.680643
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.321787 Loss1: 0.642124 Loss2: 0.679663
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.316958 Loss1: 0.635384 Loss2: 0.681574
+(DefaultActor pid=1831567) >> Training accuracy: 0.788496
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297056 Loss1: 0.573653 Loss2: 0.723403
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194730 Loss1: 0.541421 Loss2: 0.653309
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198437 Loss1: 0.544831 Loss2: 0.653605
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.154076 Loss1: 0.503434 Loss2: 0.650642
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.165408 Loss1: 0.507925 Loss2: 0.657483
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162534 Loss1: 0.506791 Loss2: 0.655742
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.177748 Loss1: 0.518207 Loss2: 0.659541
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.170181 Loss1: 0.509775 Loss2: 0.660406
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147395 Loss1: 0.488552 Loss2: 0.658843
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134027 Loss1: 0.475833 Loss2: 0.658194
+(DefaultActor pid=1831567) >> Training accuracy: 0.843750
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.284563 Loss1: 0.547660 Loss2: 0.736902
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212707 Loss1: 0.515293 Loss2: 0.697413
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219901 Loss1: 0.525904 Loss2: 0.693997
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.227688 Loss1: 0.529976 Loss2: 0.697712
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.205453 Loss1: 0.511279 Loss2: 0.694174
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.205583 Loss1: 0.507244 Loss2: 0.698339
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.205412 Loss1: 0.509162 Loss2: 0.696250
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.198814 Loss1: 0.501600 Loss2: 0.697214
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.192913 Loss1: 0.494993 Loss2: 0.697921
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.182734 Loss1: 0.483972 Loss2: 0.698762
+[2023-09-27 16:09:43,162][flwr][DEBUG] - fit_round 74 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.833829
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.707000
+[2023-09-27 16:09:44,477][flwr][INFO] - fit progress: (74, 0.8540887534618378, {'accuracy': 0.707}, 35517.31335649779)
+[2023-09-27 16:09:44,477][flwr][DEBUG] - evaluate_round 74: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:10:15,092][flwr][DEBUG] - evaluate_round 74 received 10 results and 0 failures
+[2023-09-27 16:10:15,093][flwr][DEBUG] - fit_round 75: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.502115 Loss1: 0.719985 Loss2: 0.782130
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.330057 Loss1: 0.651349 Loss2: 0.678708
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.316951 Loss1: 0.631628 Loss2: 0.685322
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.302792 Loss1: 0.617423 Loss2: 0.685369
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302635 Loss1: 0.615714 Loss2: 0.686921
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.273219 Loss1: 0.588987 Loss2: 0.684232
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.281725 Loss1: 0.594362 Loss2: 0.687363
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.317820 Loss1: 0.627679 Loss2: 0.690141
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.282871 Loss1: 0.590203 Loss2: 0.692669
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290932 Loss1: 0.596556 Loss2: 0.694377
+(DefaultActor pid=1831567) >> Training accuracy: 0.810855
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.479868 Loss1: 0.720336 Loss2: 0.759532
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.385353 Loss1: 0.703987 Loss2: 0.681367
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.369321 Loss1: 0.689484 Loss2: 0.679837
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.366558 Loss1: 0.688819 Loss2: 0.677740
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345893 Loss1: 0.663756 Loss2: 0.682136
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341157 Loss1: 0.660201 Loss2: 0.680957
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.325764 Loss1: 0.644899 Loss2: 0.680865
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.314603 Loss1: 0.628574 Loss2: 0.686029
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.322057 Loss1: 0.638271 Loss2: 0.683787
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307278 Loss1: 0.623536 Loss2: 0.683742
+(DefaultActor pid=1831567) >> Training accuracy: 0.785100
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.214522 Loss1: 0.455660 Loss2: 0.758862
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.094062 Loss1: 0.411210 Loss2: 0.682852
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.049965 Loss1: 0.373132 Loss2: 0.676833
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.044883 Loss1: 0.365853 Loss2: 0.679030
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.061798 Loss1: 0.381472 Loss2: 0.680326
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.042378 Loss1: 0.362997 Loss2: 0.679381
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.038579 Loss1: 0.358917 Loss2: 0.679662
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.028544 Loss1: 0.349153 Loss2: 0.679391
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.020073 Loss1: 0.341928 Loss2: 0.678145
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.017182 Loss1: 0.337323 Loss2: 0.679859
+(DefaultActor pid=1831567) >> Training accuracy: 0.880401
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.240953 Loss1: 0.468632 Loss2: 0.772320
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099270 Loss1: 0.408311 Loss2: 0.690959
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.078647 Loss1: 0.389884 Loss2: 0.688763
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.085908 Loss1: 0.396036 Loss2: 0.689872
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.065214 Loss1: 0.375442 Loss2: 0.689772
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.062346 Loss1: 0.371994 Loss2: 0.690352
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051676 Loss1: 0.358650 Loss2: 0.693026
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.047084 Loss1: 0.355982 Loss2: 0.691102
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042395 Loss1: 0.351721 Loss2: 0.690674
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.057126 Loss1: 0.365192 Loss2: 0.691934
+(DefaultActor pid=1831567) >> Training accuracy: 0.875000
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321836 Loss1: 0.576552 Loss2: 0.745284
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171384 Loss1: 0.524889 Loss2: 0.646495
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155391 Loss1: 0.510283 Loss2: 0.645108
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.143672 Loss1: 0.497436 Loss2: 0.646236
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.127625 Loss1: 0.479523 Loss2: 0.648103
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.112800 Loss1: 0.466597 Loss2: 0.646203
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.109990 Loss1: 0.463317 Loss2: 0.646673
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.110118 Loss1: 0.457770 Loss2: 0.652348
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.070017 Loss1: 0.420652 Loss2: 0.649365
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.089201 Loss1: 0.436810 Loss2: 0.652391
+(DefaultActor pid=1831567) >> Training accuracy: 0.846398
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.299968 Loss1: 0.549596 Loss2: 0.750372
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.233675 Loss1: 0.524013 Loss2: 0.709662
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.221865 Loss1: 0.519126 Loss2: 0.702740
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199441 Loss1: 0.489653 Loss2: 0.709788
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216038 Loss1: 0.510401 Loss2: 0.705637
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199478 Loss1: 0.494784 Loss2: 0.704694
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204857 Loss1: 0.497543 Loss2: 0.707314
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202234 Loss1: 0.490900 Loss2: 0.711334
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213125 Loss1: 0.501179 Loss2: 0.711946
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214239 Loss1: 0.502535 Loss2: 0.711704
+(DefaultActor pid=1831567) >> Training accuracy: 0.823041
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.330214 Loss1: 0.621633 Loss2: 0.708581
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.179871 Loss1: 0.545539 Loss2: 0.634332
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.162210 Loss1: 0.531599 Loss2: 0.630611
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175085 Loss1: 0.543097 Loss2: 0.631989
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.151452 Loss1: 0.517180 Loss2: 0.634272
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.139405 Loss1: 0.506090 Loss2: 0.633315
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.133520 Loss1: 0.499955 Loss2: 0.633565
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.153985 Loss1: 0.516318 Loss2: 0.637667
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.135914 Loss1: 0.501375 Loss2: 0.634539
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.122434 Loss1: 0.484461 Loss2: 0.637973
+(DefaultActor pid=1831567) >> Training accuracy: 0.834223
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358781 Loss1: 0.589394 Loss2: 0.769387
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.205560 Loss1: 0.520599 Loss2: 0.684961
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199228 Loss1: 0.514248 Loss2: 0.684980
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191348 Loss1: 0.500152 Loss2: 0.691196
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.180358 Loss1: 0.493717 Loss2: 0.686641
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186058 Loss1: 0.497422 Loss2: 0.688636
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.190092 Loss1: 0.500064 Loss2: 0.690029
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173598 Loss1: 0.485194 Loss2: 0.688405
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161815 Loss1: 0.472023 Loss2: 0.689792
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.160992 Loss1: 0.468880 Loss2: 0.692111
+(DefaultActor pid=1831567) >> Training accuracy: 0.847656
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.483087 Loss1: 0.714751 Loss2: 0.768336
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.355205 Loss1: 0.678597 Loss2: 0.676608
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.366748 Loss1: 0.688472 Loss2: 0.678276
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.349401 Loss1: 0.669569 Loss2: 0.679831
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333295 Loss1: 0.651672 Loss2: 0.681623
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303148 Loss1: 0.622241 Loss2: 0.680907
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305182 Loss1: 0.623816 Loss2: 0.681365
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.328102 Loss1: 0.645764 Loss2: 0.682338
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.320889 Loss1: 0.638661 Loss2: 0.682228
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284087 Loss1: 0.604088 Loss2: 0.679999
+(DefaultActor pid=1831567) >> Training accuracy: 0.779384
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321583 Loss1: 0.573715 Loss2: 0.747869
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227081 Loss1: 0.541040 Loss2: 0.686042
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205794 Loss1: 0.520034 Loss2: 0.685760
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.182863 Loss1: 0.501928 Loss2: 0.680935
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.209174 Loss1: 0.523582 Loss2: 0.685592
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.194279 Loss1: 0.505867 Loss2: 0.688412
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193025 Loss1: 0.502345 Loss2: 0.690680
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185773 Loss1: 0.497874 Loss2: 0.687899
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.166123 Loss1: 0.482616 Loss2: 0.683507
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.164496 Loss1: 0.475870 Loss2: 0.688627
+[2023-09-27 16:17:04,733][flwr][DEBUG] - fit_round 75 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.832732
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.699300
+[2023-09-27 16:17:06,491][flwr][INFO] - fit progress: (75, 0.8667597494567164, {'accuracy': 0.6993}, 35959.32748264214)
+[2023-09-27 16:17:06,492][flwr][DEBUG] - evaluate_round 75: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:17:37,747][flwr][DEBUG] - evaluate_round 75 received 10 results and 0 failures
+[2023-09-27 16:17:37,748][flwr][DEBUG] - fit_round 76: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226004 Loss1: 0.458585 Loss2: 0.767419
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.107330 Loss1: 0.410310 Loss2: 0.697019
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.092540 Loss1: 0.400449 Loss2: 0.692091
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.081804 Loss1: 0.390856 Loss2: 0.690948
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.078567 Loss1: 0.382121 Loss2: 0.696445
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.052464 Loss1: 0.360447 Loss2: 0.692016
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.055926 Loss1: 0.362926 Loss2: 0.693000
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.045158 Loss1: 0.354051 Loss2: 0.691107
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.049630 Loss1: 0.357080 Loss2: 0.692550
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.059644 Loss1: 0.361937 Loss2: 0.697707
+(DefaultActor pid=1831567) >> Training accuracy: 0.868441
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.293394 Loss1: 0.544260 Loss2: 0.749134
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223622 Loss1: 0.519098 Loss2: 0.704525
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201087 Loss1: 0.500222 Loss2: 0.700865
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.206916 Loss1: 0.503527 Loss2: 0.703388
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.199996 Loss1: 0.496720 Loss2: 0.703276
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.209446 Loss1: 0.501394 Loss2: 0.708052
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.213602 Loss1: 0.505498 Loss2: 0.708104
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.196643 Loss1: 0.490548 Loss2: 0.706095
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217511 Loss1: 0.508410 Loss2: 0.709100
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200966 Loss1: 0.490279 Loss2: 0.710687
+(DefaultActor pid=1831567) >> Training accuracy: 0.835938
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353523 Loss1: 0.576142 Loss2: 0.777381
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171577 Loss1: 0.498993 Loss2: 0.672584
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.176833 Loss1: 0.505726 Loss2: 0.671107
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.152724 Loss1: 0.478161 Loss2: 0.674563
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.135177 Loss1: 0.461607 Loss2: 0.673570
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131690 Loss1: 0.455682 Loss2: 0.676008
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123595 Loss1: 0.447043 Loss2: 0.676552
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140369 Loss1: 0.460158 Loss2: 0.680211
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.119849 Loss1: 0.438177 Loss2: 0.681671
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.120149 Loss1: 0.442064 Loss2: 0.678085
+(DefaultActor pid=1831567) >> Training accuracy: 0.854343
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.311809 Loss1: 0.589801 Loss2: 0.722008
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202174 Loss1: 0.565027 Loss2: 0.637147
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.144537 Loss1: 0.510103 Loss2: 0.634434
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144244 Loss1: 0.506237 Loss2: 0.638006
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.143088 Loss1: 0.502120 Loss2: 0.640968
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.127538 Loss1: 0.487532 Loss2: 0.640006
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.138449 Loss1: 0.497536 Loss2: 0.640913
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.125271 Loss1: 0.484279 Loss2: 0.640992
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136590 Loss1: 0.491039 Loss2: 0.645551
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.105161 Loss1: 0.462974 Loss2: 0.642188
+(DefaultActor pid=1831567) >> Training accuracy: 0.838610
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381557 Loss1: 0.578484 Loss2: 0.803073
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259417 Loss1: 0.534147 Loss2: 0.725270
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.245684 Loss1: 0.521635 Loss2: 0.724048
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.250634 Loss1: 0.522868 Loss2: 0.727767
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.231255 Loss1: 0.502601 Loss2: 0.728654
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225750 Loss1: 0.496970 Loss2: 0.728780
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.229749 Loss1: 0.500275 Loss2: 0.729474
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.223212 Loss1: 0.495545 Loss2: 0.727667
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.214489 Loss1: 0.481962 Loss2: 0.732527
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.212198 Loss1: 0.480003 Loss2: 0.732195
+(DefaultActor pid=1831567) >> Training accuracy: 0.835747
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.186526 Loss1: 0.467716 Loss2: 0.718810
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.056653 Loss1: 0.419503 Loss2: 0.637150
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.031771 Loss1: 0.395091 Loss2: 0.636681
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.015167 Loss1: 0.380631 Loss2: 0.634536
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.013900 Loss1: 0.376037 Loss2: 0.637862
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.028508 Loss1: 0.391881 Loss2: 0.636627
+(DefaultActor pid=1831567) Epoch: 6 Loss: 0.984662 Loss1: 0.350514 Loss2: 0.634148
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.005505 Loss1: 0.367835 Loss2: 0.637669
+(DefaultActor pid=1831567) Epoch: 8 Loss: 0.989317 Loss1: 0.351103 Loss2: 0.638214
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.989186 Loss1: 0.351299 Loss2: 0.637887
+(DefaultActor pid=1831567) >> Training accuracy: 0.875965
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.283676 Loss1: 0.570229 Loss2: 0.713447
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.190909 Loss1: 0.546263 Loss2: 0.644647
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183837 Loss1: 0.541959 Loss2: 0.641878
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.146117 Loss1: 0.503408 Loss2: 0.642708
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.144665 Loss1: 0.500822 Loss2: 0.643843
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.126606 Loss1: 0.481420 Loss2: 0.645186
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.155799 Loss1: 0.506598 Loss2: 0.649201
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.136956 Loss1: 0.491317 Loss2: 0.645640
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.145576 Loss1: 0.495821 Loss2: 0.649755
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146839 Loss1: 0.499427 Loss2: 0.647412
+(DefaultActor pid=1831567) >> Training accuracy: 0.831931
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485142 Loss1: 0.723236 Loss2: 0.761906
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.350851 Loss1: 0.681168 Loss2: 0.669683
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.323805 Loss1: 0.654937 Loss2: 0.668868
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.309694 Loss1: 0.639716 Loss2: 0.669978
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.287386 Loss1: 0.620650 Loss2: 0.666736
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.292739 Loss1: 0.620596 Loss2: 0.672143
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.337438 Loss1: 0.661098 Loss2: 0.676340
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.281530 Loss1: 0.610230 Loss2: 0.671300
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305291 Loss1: 0.629737 Loss2: 0.675554
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289251 Loss1: 0.610930 Loss2: 0.678321
+(DefaultActor pid=1831567) >> Training accuracy: 0.784049
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.482032 Loss1: 0.709749 Loss2: 0.772283
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.323624 Loss1: 0.656858 Loss2: 0.666766
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.282381 Loss1: 0.618812 Loss2: 0.663569
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.246366 Loss1: 0.581216 Loss2: 0.665150
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.278155 Loss1: 0.610673 Loss2: 0.667483
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.278304 Loss1: 0.610930 Loss2: 0.667374
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.269031 Loss1: 0.596919 Loss2: 0.672112
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.286952 Loss1: 0.617080 Loss2: 0.669872
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.270615 Loss1: 0.600170 Loss2: 0.670444
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.246471 Loss1: 0.572406 Loss2: 0.674065
+(DefaultActor pid=1831567) >> Training accuracy: 0.796327
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.470974 Loss1: 0.734741 Loss2: 0.736232
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.378506 Loss1: 0.721270 Loss2: 0.657236
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.341881 Loss1: 0.686922 Loss2: 0.654958
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.334215 Loss1: 0.679748 Loss2: 0.654467
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.292327 Loss1: 0.636119 Loss2: 0.656208
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316074 Loss1: 0.660589 Loss2: 0.655485
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322862 Loss1: 0.662745 Loss2: 0.660117
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.322756 Loss1: 0.662163 Loss2: 0.660593
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.313001 Loss1: 0.652161 Loss2: 0.660840
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.261875 Loss1: 0.604238 Loss2: 0.657637
+[2023-09-27 16:24:10,946][flwr][DEBUG] - fit_round 76 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.783967
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.696300
+[2023-09-27 16:24:12,446][flwr][INFO] - fit progress: (76, 0.8745543113150916, {'accuracy': 0.6963}, 36385.282472547144)
+[2023-09-27 16:24:12,447][flwr][DEBUG] - evaluate_round 76: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:24:43,195][flwr][DEBUG] - evaluate_round 76 received 10 results and 0 failures
+[2023-09-27 16:24:43,195][flwr][DEBUG] - fit_round 77: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.213556 Loss1: 0.448184 Loss2: 0.765371
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.095992 Loss1: 0.407451 Loss2: 0.688541
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086714 Loss1: 0.398183 Loss2: 0.688531
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.055346 Loss1: 0.370480 Loss2: 0.684866
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.049464 Loss1: 0.364235 Loss2: 0.685229
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.065399 Loss1: 0.377501 Loss2: 0.687898
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.065562 Loss1: 0.376526 Loss2: 0.689037
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.080210 Loss1: 0.385975 Loss2: 0.694234
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.043833 Loss1: 0.353873 Loss2: 0.689960
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.038701 Loss1: 0.349232 Loss2: 0.689470
+(DefaultActor pid=1831567) >> Training accuracy: 0.879630
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348746 Loss1: 0.577289 Loss2: 0.771457
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211800 Loss1: 0.520267 Loss2: 0.691533
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195518 Loss1: 0.505790 Loss2: 0.689728
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194018 Loss1: 0.498965 Loss2: 0.695053
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.197448 Loss1: 0.501449 Loss2: 0.695999
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186966 Loss1: 0.490302 Loss2: 0.696664
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.189115 Loss1: 0.492359 Loss2: 0.696756
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162907 Loss1: 0.469329 Loss2: 0.693578
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.164616 Loss1: 0.470132 Loss2: 0.694484
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180351 Loss1: 0.479344 Loss2: 0.701007
+(DefaultActor pid=1831567) >> Training accuracy: 0.841900
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289243 Loss1: 0.541033 Loss2: 0.748210
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.215328 Loss1: 0.511809 Loss2: 0.703519
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.234231 Loss1: 0.524290 Loss2: 0.709941
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212587 Loss1: 0.504456 Loss2: 0.708131
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216008 Loss1: 0.508149 Loss2: 0.707859
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.202222 Loss1: 0.495510 Loss2: 0.706711
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.189268 Loss1: 0.484225 Loss2: 0.705043
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.209346 Loss1: 0.500517 Loss2: 0.708829
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205078 Loss1: 0.495305 Loss2: 0.709774
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.205632 Loss1: 0.495131 Loss2: 0.710501
+(DefaultActor pid=1831567) >> Training accuracy: 0.839782
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.307384 Loss1: 0.587292 Loss2: 0.720092
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.178572 Loss1: 0.532379 Loss2: 0.646193
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.166027 Loss1: 0.523693 Loss2: 0.642334
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.147704 Loss1: 0.507555 Loss2: 0.640150
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.160195 Loss1: 0.518714 Loss2: 0.641481
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.158831 Loss1: 0.515034 Loss2: 0.643796
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.131630 Loss1: 0.489643 Loss2: 0.641988
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.142870 Loss1: 0.496376 Loss2: 0.646494
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.117543 Loss1: 0.472445 Loss2: 0.645098
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.121112 Loss1: 0.474011 Loss2: 0.647101
+(DefaultActor pid=1831567) >> Training accuracy: 0.838605
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.350635 Loss1: 0.595556 Loss2: 0.755079
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.162725 Loss1: 0.511560 Loss2: 0.651165
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.160459 Loss1: 0.506392 Loss2: 0.654068
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.137562 Loss1: 0.486040 Loss2: 0.651522
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.131131 Loss1: 0.481705 Loss2: 0.649426
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.130149 Loss1: 0.476259 Loss2: 0.653890
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.129501 Loss1: 0.473114 Loss2: 0.656388
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133140 Loss1: 0.478900 Loss2: 0.654240
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.103801 Loss1: 0.446286 Loss2: 0.657515
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.100772 Loss1: 0.445554 Loss2: 0.655218
+(DefaultActor pid=1831567) >> Training accuracy: 0.843485
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340539 Loss1: 0.581058 Loss2: 0.759481
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236578 Loss1: 0.545454 Loss2: 0.691124
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226125 Loss1: 0.532627 Loss2: 0.693498
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226987 Loss1: 0.532337 Loss2: 0.694649
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216085 Loss1: 0.520224 Loss2: 0.695861
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185831 Loss1: 0.491562 Loss2: 0.694270
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187582 Loss1: 0.490793 Loss2: 0.696789
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.187859 Loss1: 0.491075 Loss2: 0.696784
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178884 Loss1: 0.481775 Loss2: 0.697110
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.185457 Loss1: 0.484004 Loss2: 0.701453
+(DefaultActor pid=1831567) >> Training accuracy: 0.842147
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.217665 Loss1: 0.483030 Loss2: 0.734635
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.055532 Loss1: 0.401890 Loss2: 0.653642
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.038699 Loss1: 0.390071 Loss2: 0.648628
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.027711 Loss1: 0.382020 Loss2: 0.645691
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.037703 Loss1: 0.389364 Loss2: 0.648339
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.032627 Loss1: 0.379192 Loss2: 0.653435
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.023711 Loss1: 0.371270 Loss2: 0.652441
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.021331 Loss1: 0.369670 Loss2: 0.651661
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.009719 Loss1: 0.358401 Loss2: 0.651317
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.994343 Loss1: 0.341885 Loss2: 0.652458
+(DefaultActor pid=1831567) >> Training accuracy: 0.867863
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459797 Loss1: 0.704602 Loss2: 0.755194
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.327741 Loss1: 0.664998 Loss2: 0.662744
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318677 Loss1: 0.655601 Loss2: 0.663076
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.338414 Loss1: 0.671142 Loss2: 0.667272
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.311649 Loss1: 0.643325 Loss2: 0.668324
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.312683 Loss1: 0.645514 Loss2: 0.667169
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.306126 Loss1: 0.636684 Loss2: 0.669442
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.311093 Loss1: 0.639155 Loss2: 0.671939
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.272422 Loss1: 0.602821 Loss2: 0.669600
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290152 Loss1: 0.620200 Loss2: 0.669952
+(DefaultActor pid=1831567) >> Training accuracy: 0.775187
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493945 Loss1: 0.721836 Loss2: 0.772108
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387447 Loss1: 0.702515 Loss2: 0.684932
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.348697 Loss1: 0.664913 Loss2: 0.683784
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364860 Loss1: 0.679166 Loss2: 0.685693
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.354275 Loss1: 0.666162 Loss2: 0.688113
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341691 Loss1: 0.651173 Loss2: 0.690518
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322065 Loss1: 0.634637 Loss2: 0.687429
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.360281 Loss1: 0.666021 Loss2: 0.694260
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331961 Loss1: 0.639373 Loss2: 0.692587
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.326884 Loss1: 0.630845 Loss2: 0.696039
+(DefaultActor pid=1831567) >> Training accuracy: 0.795516
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.439665 Loss1: 0.699077 Loss2: 0.740588
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301818 Loss1: 0.648632 Loss2: 0.653185
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.277183 Loss1: 0.623547 Loss2: 0.653636
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.266859 Loss1: 0.617375 Loss2: 0.649484
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.277380 Loss1: 0.621713 Loss2: 0.655668
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.251029 Loss1: 0.595742 Loss2: 0.655287
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243204 Loss1: 0.588311 Loss2: 0.654893
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.260481 Loss1: 0.602568 Loss2: 0.657913
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244246 Loss1: 0.583421 Loss2: 0.660825
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.255936 Loss1: 0.592188 Loss2: 0.663748
+[2023-09-27 16:31:33,440][flwr][DEBUG] - fit_round 77 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.797697
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.699700
+[2023-09-27 16:31:35,148][flwr][INFO] - fit progress: (77, 0.8666860393632334, {'accuracy': 0.6997}, 36827.984321075026)
+[2023-09-27 16:31:35,149][flwr][DEBUG] - evaluate_round 77: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:32:05,859][flwr][DEBUG] - evaluate_round 77 received 10 results and 0 failures
+[2023-09-27 16:32:05,860][flwr][DEBUG] - fit_round 78: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.374009 Loss1: 0.589121 Loss2: 0.784888
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.215234 Loss1: 0.532749 Loss2: 0.682485
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.196449 Loss1: 0.515066 Loss2: 0.681383
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.169540 Loss1: 0.488417 Loss2: 0.681122
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.175567 Loss1: 0.489796 Loss2: 0.685772
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.138523 Loss1: 0.454405 Loss2: 0.684118
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.143649 Loss1: 0.460875 Loss2: 0.682774
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.138348 Loss1: 0.453685 Loss2: 0.684663
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.121375 Loss1: 0.436629 Loss2: 0.684746
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.144044 Loss1: 0.457999 Loss2: 0.686045
+(DefaultActor pid=1831567) >> Training accuracy: 0.846663
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.479313 Loss1: 0.722783 Loss2: 0.756530
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.352197 Loss1: 0.684368 Loss2: 0.667829
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.333746 Loss1: 0.666683 Loss2: 0.667063
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.326882 Loss1: 0.656749 Loss2: 0.670132
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.296928 Loss1: 0.626361 Loss2: 0.670566
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.334401 Loss1: 0.663618 Loss2: 0.670784
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.300875 Loss1: 0.629992 Loss2: 0.670883
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289261 Loss1: 0.617023 Loss2: 0.672238
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.283579 Loss1: 0.609926 Loss2: 0.673653
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.294849 Loss1: 0.621759 Loss2: 0.673090
+(DefaultActor pid=1831567) >> Training accuracy: 0.784049
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348658 Loss1: 0.592667 Loss2: 0.755991
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.222373 Loss1: 0.535930 Loss2: 0.686443
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215028 Loss1: 0.526824 Loss2: 0.688204
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.186045 Loss1: 0.501838 Loss2: 0.684207
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185415 Loss1: 0.498893 Loss2: 0.686521
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.195085 Loss1: 0.507280 Loss2: 0.687804
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174554 Loss1: 0.486721 Loss2: 0.687833
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185074 Loss1: 0.496803 Loss2: 0.688271
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163518 Loss1: 0.474826 Loss2: 0.688692
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.161351 Loss1: 0.470563 Loss2: 0.690788
+(DefaultActor pid=1831567) >> Training accuracy: 0.840130
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495368 Loss1: 0.714571 Loss2: 0.780797
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.323360 Loss1: 0.651311 Loss2: 0.672048
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.304320 Loss1: 0.634349 Loss2: 0.669971
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.279751 Loss1: 0.608184 Loss2: 0.671566
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.275793 Loss1: 0.604925 Loss2: 0.670868
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.275197 Loss1: 0.603321 Loss2: 0.671876
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.264126 Loss1: 0.591395 Loss2: 0.672731
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253112 Loss1: 0.575144 Loss2: 0.677968
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.256098 Loss1: 0.581087 Loss2: 0.675010
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240149 Loss1: 0.565273 Loss2: 0.674877
+(DefaultActor pid=1831567) >> Training accuracy: 0.790570
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493426 Loss1: 0.749714 Loss2: 0.743712
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.362381 Loss1: 0.700417 Loss2: 0.661964
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354591 Loss1: 0.694560 Loss2: 0.660031
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333777 Loss1: 0.672008 Loss2: 0.661770
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.340689 Loss1: 0.677724 Loss2: 0.662965
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.317825 Loss1: 0.657645 Loss2: 0.660180
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299239 Loss1: 0.634651 Loss2: 0.664588
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.298907 Loss1: 0.634754 Loss2: 0.664153
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.286487 Loss1: 0.623998 Loss2: 0.662489
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.305315 Loss1: 0.637589 Loss2: 0.667726
+(DefaultActor pid=1831567) >> Training accuracy: 0.792799
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.206330 Loss1: 0.465463 Loss2: 0.740867
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.049569 Loss1: 0.391773 Loss2: 0.657796
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.026041 Loss1: 0.368387 Loss2: 0.657654
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.050688 Loss1: 0.392791 Loss2: 0.657897
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.032422 Loss1: 0.373041 Loss2: 0.659380
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.021058 Loss1: 0.364303 Loss2: 0.656755
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019856 Loss1: 0.360569 Loss2: 0.659287
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.013852 Loss1: 0.355680 Loss2: 0.658171
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.002921 Loss1: 0.342389 Loss2: 0.660532
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.011757 Loss1: 0.352119 Loss2: 0.659639
+(DefaultActor pid=1831567) >> Training accuracy: 0.887924
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291649 Loss1: 0.565359 Loss2: 0.726290
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172886 Loss1: 0.525365 Loss2: 0.647522
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.174225 Loss1: 0.522472 Loss2: 0.651753
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.152999 Loss1: 0.502351 Loss2: 0.650647
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150679 Loss1: 0.497896 Loss2: 0.652783
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.143681 Loss1: 0.490112 Loss2: 0.653569
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.145483 Loss1: 0.493054 Loss2: 0.652429
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.114156 Loss1: 0.458856 Loss2: 0.655300
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136134 Loss1: 0.480356 Loss2: 0.655778
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.128779 Loss1: 0.472561 Loss2: 0.656218
+(DefaultActor pid=1831567) >> Training accuracy: 0.846012
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.255727 Loss1: 0.452376 Loss2: 0.803351
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.122764 Loss1: 0.392769 Loss2: 0.729995
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.133290 Loss1: 0.405996 Loss2: 0.727294
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.131115 Loss1: 0.400548 Loss2: 0.730567
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.095392 Loss1: 0.368422 Loss2: 0.726970
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.103974 Loss1: 0.374435 Loss2: 0.729539
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.089656 Loss1: 0.362872 Loss2: 0.726784
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.079411 Loss1: 0.350004 Loss2: 0.729407
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.063497 Loss1: 0.337688 Loss2: 0.725808
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.096777 Loss1: 0.366574 Loss2: 0.730203
+(DefaultActor pid=1831567) >> Training accuracy: 0.877508
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.313227 Loss1: 0.582140 Loss2: 0.731087
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.189986 Loss1: 0.529135 Loss2: 0.660851
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192311 Loss1: 0.533174 Loss2: 0.659137
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210086 Loss1: 0.546054 Loss2: 0.664031
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204102 Loss1: 0.541048 Loss2: 0.663053
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.175997 Loss1: 0.513701 Loss2: 0.662295
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187869 Loss1: 0.524694 Loss2: 0.663175
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154544 Loss1: 0.490852 Loss2: 0.663691
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.141473 Loss1: 0.479175 Loss2: 0.662298
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140492 Loss1: 0.477531 Loss2: 0.662961
+(DefaultActor pid=1831567) >> Training accuracy: 0.851362
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.308545 Loss1: 0.546334 Loss2: 0.762211
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.235599 Loss1: 0.512505 Loss2: 0.723094
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222549 Loss1: 0.502690 Loss2: 0.719859
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.221329 Loss1: 0.498714 Loss2: 0.722615
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218855 Loss1: 0.496288 Loss2: 0.722567
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231990 Loss1: 0.510736 Loss2: 0.721254
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220709 Loss1: 0.499798 Loss2: 0.720911
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229528 Loss1: 0.505456 Loss2: 0.724072
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.212370 Loss1: 0.486261 Loss2: 0.726109
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229951 Loss1: 0.504289 Loss2: 0.725662
+[2023-09-27 16:39:01,891][flwr][DEBUG] - fit_round 78 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.835565
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.700200
+[2023-09-27 16:39:03,407][flwr][INFO] - fit progress: (78, 0.8683196400491574, {'accuracy': 0.7002}, 37276.24380477099)
+[2023-09-27 16:39:03,408][flwr][DEBUG] - evaluate_round 78: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:39:34,188][flwr][DEBUG] - evaluate_round 78 received 10 results and 0 failures
+[2023-09-27 16:39:34,189][flwr][DEBUG] - fit_round 79: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.489681 Loss1: 0.727224 Loss2: 0.762457
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.383131 Loss1: 0.705417 Loss2: 0.677715
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.348042 Loss1: 0.668252 Loss2: 0.679791
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.346453 Loss1: 0.666310 Loss2: 0.680144
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.336539 Loss1: 0.654414 Loss2: 0.682125
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.344074 Loss1: 0.659196 Loss2: 0.684878
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.324119 Loss1: 0.637318 Loss2: 0.686801
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.306630 Loss1: 0.618376 Loss2: 0.688254
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.352125 Loss1: 0.660356 Loss2: 0.691770
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.320029 Loss1: 0.628390 Loss2: 0.691639
+(DefaultActor pid=1831567) >> Training accuracy: 0.787591
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360518 Loss1: 0.575321 Loss2: 0.785197
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.248956 Loss1: 0.546778 Loss2: 0.702178
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.210532 Loss1: 0.512002 Loss2: 0.698530
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210935 Loss1: 0.505981 Loss2: 0.704954
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216805 Loss1: 0.511255 Loss2: 0.705550
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.190501 Loss1: 0.485374 Loss2: 0.705127
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200200 Loss1: 0.493245 Loss2: 0.706955
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.164120 Loss1: 0.458391 Loss2: 0.705730
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.166339 Loss1: 0.460693 Loss2: 0.705646
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189312 Loss1: 0.477605 Loss2: 0.711707
+(DefaultActor pid=1831567) >> Training accuracy: 0.854646
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495876 Loss1: 0.724985 Loss2: 0.770891
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.314112 Loss1: 0.643827 Loss2: 0.670284
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.326094 Loss1: 0.655655 Loss2: 0.670439
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.296382 Loss1: 0.622307 Loss2: 0.674075
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.275470 Loss1: 0.603335 Loss2: 0.672135
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.279871 Loss1: 0.604248 Loss2: 0.675623
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.256204 Loss1: 0.585886 Loss2: 0.670318
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253652 Loss1: 0.578914 Loss2: 0.674738
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.281052 Loss1: 0.600979 Loss2: 0.680073
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250059 Loss1: 0.572523 Loss2: 0.677536
+(DefaultActor pid=1831567) >> Training accuracy: 0.805647
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.184380 Loss1: 0.438733 Loss2: 0.745647
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.083660 Loss1: 0.415062 Loss2: 0.668599
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.077668 Loss1: 0.419510 Loss2: 0.658157
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.028396 Loss1: 0.368085 Loss2: 0.660312
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.031409 Loss1: 0.370374 Loss2: 0.661035
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.026018 Loss1: 0.363775 Loss2: 0.662243
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.020482 Loss1: 0.357879 Loss2: 0.662604
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.025906 Loss1: 0.367783 Loss2: 0.658123
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.013335 Loss1: 0.353317 Loss2: 0.660018
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.013566 Loss1: 0.348777 Loss2: 0.664788
+(DefaultActor pid=1831567) >> Training accuracy: 0.850502
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.281430 Loss1: 0.542710 Loss2: 0.738720
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.208496 Loss1: 0.511369 Loss2: 0.697127
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208448 Loss1: 0.511693 Loss2: 0.696755
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.207889 Loss1: 0.511083 Loss2: 0.696806
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215982 Loss1: 0.517900 Loss2: 0.698082
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199179 Loss1: 0.501121 Loss2: 0.698058
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196513 Loss1: 0.498598 Loss2: 0.697915
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.188006 Loss1: 0.489388 Loss2: 0.698618
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.192434 Loss1: 0.491164 Loss2: 0.701270
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.196435 Loss1: 0.495597 Loss2: 0.700838
+(DefaultActor pid=1831567) >> Training accuracy: 0.840278
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481537 Loss1: 0.724912 Loss2: 0.756625
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347002 Loss1: 0.681248 Loss2: 0.665754
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334949 Loss1: 0.666013 Loss2: 0.668935
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.320480 Loss1: 0.652806 Loss2: 0.667675
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.314398 Loss1: 0.646674 Loss2: 0.667724
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316226 Loss1: 0.646884 Loss2: 0.669342
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.311239 Loss1: 0.641263 Loss2: 0.669975
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.284108 Loss1: 0.611345 Loss2: 0.672763
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.314525 Loss1: 0.639999 Loss2: 0.674526
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.293639 Loss1: 0.621826 Loss2: 0.671813
+(DefaultActor pid=1831567) >> Training accuracy: 0.788713
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327462 Loss1: 0.606930 Loss2: 0.720531
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.195008 Loss1: 0.546712 Loss2: 0.648296
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.162201 Loss1: 0.516293 Loss2: 0.645907
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.158942 Loss1: 0.515698 Loss2: 0.643244
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170684 Loss1: 0.522754 Loss2: 0.647929
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.134766 Loss1: 0.485816 Loss2: 0.648949
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.132603 Loss1: 0.485482 Loss2: 0.647120
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.150677 Loss1: 0.502316 Loss2: 0.648360
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136352 Loss1: 0.490269 Loss2: 0.646084
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.102434 Loss1: 0.453732 Loss2: 0.648702
+(DefaultActor pid=1831567) >> Training accuracy: 0.834794
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.218258 Loss1: 0.459014 Loss2: 0.759244
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.089135 Loss1: 0.408481 Loss2: 0.680654
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.058281 Loss1: 0.381046 Loss2: 0.677235
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.058548 Loss1: 0.381754 Loss2: 0.676794
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.041523 Loss1: 0.364552 Loss2: 0.676971
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.049728 Loss1: 0.373045 Loss2: 0.676682
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.045667 Loss1: 0.365474 Loss2: 0.680193
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.021993 Loss1: 0.344049 Loss2: 0.677944
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.011483 Loss1: 0.333384 Loss2: 0.678100
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.029786 Loss1: 0.349982 Loss2: 0.679804
+(DefaultActor pid=1831567) >> Training accuracy: 0.889660
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324411 Loss1: 0.579175 Loss2: 0.745236
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.167526 Loss1: 0.521566 Loss2: 0.645960
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.153990 Loss1: 0.504877 Loss2: 0.649113
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.145543 Loss1: 0.497577 Loss2: 0.647966
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.119814 Loss1: 0.473530 Loss2: 0.646284
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.114120 Loss1: 0.464528 Loss2: 0.649592
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.102373 Loss1: 0.450470 Loss2: 0.651902
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140993 Loss1: 0.487162 Loss2: 0.653831
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.115831 Loss1: 0.463786 Loss2: 0.652045
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.091007 Loss1: 0.439457 Loss2: 0.651550
+(DefaultActor pid=1831567) >> Training accuracy: 0.857521
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.306864 Loss1: 0.573517 Loss2: 0.733347
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.219735 Loss1: 0.547251 Loss2: 0.672484
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203239 Loss1: 0.530915 Loss2: 0.672324
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181774 Loss1: 0.511264 Loss2: 0.670511
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195833 Loss1: 0.520701 Loss2: 0.675132
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.197259 Loss1: 0.521380 Loss2: 0.675879
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171717 Loss1: 0.497523 Loss2: 0.674195
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169910 Loss1: 0.492287 Loss2: 0.677623
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.128973 Loss1: 0.453897 Loss2: 0.675076
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.137801 Loss1: 0.462459 Loss2: 0.675343
+(DefaultActor pid=1831567) >> Training accuracy: 0.839343
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 16:46:36,918][flwr][DEBUG] - fit_round 79 received 10 results and 0 failures
+>> Test accuracy: 0.699400
+[2023-09-27 16:46:51,400][flwr][INFO] - fit progress: (79, 0.8694912555118719, {'accuracy': 0.6994}, 37744.23628887907)
+[2023-09-27 16:46:51,400][flwr][DEBUG] - evaluate_round 79: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:47:30,322][flwr][DEBUG] - evaluate_round 79 received 10 results and 0 failures
+[2023-09-27 16:47:30,323][flwr][DEBUG] - fit_round 80: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.454493 Loss1: 0.723664 Loss2: 0.730829
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.339708 Loss1: 0.687802 Loss2: 0.651906
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.312909 Loss1: 0.663822 Loss2: 0.649087
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.326385 Loss1: 0.675338 Loss2: 0.651048
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.314774 Loss1: 0.662518 Loss2: 0.652256
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.310456 Loss1: 0.655531 Loss2: 0.654925
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298010 Loss1: 0.641606 Loss2: 0.656404
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268482 Loss1: 0.615733 Loss2: 0.652748
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.320107 Loss1: 0.662105 Loss2: 0.658002
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.283731 Loss1: 0.625588 Loss2: 0.658144
+(DefaultActor pid=1831567) >> Training accuracy: 0.783967
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.328291 Loss1: 0.565311 Loss2: 0.762981
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.195891 Loss1: 0.509624 Loss2: 0.686268
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231968 Loss1: 0.543275 Loss2: 0.688693
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226243 Loss1: 0.538262 Loss2: 0.687982
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185108 Loss1: 0.497793 Loss2: 0.687315
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206626 Loss1: 0.515994 Loss2: 0.690632
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196014 Loss1: 0.501950 Loss2: 0.694065
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.195426 Loss1: 0.505985 Loss2: 0.689441
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180321 Loss1: 0.487959 Loss2: 0.692361
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.170034 Loss1: 0.476739 Loss2: 0.693295
+(DefaultActor pid=1831567) >> Training accuracy: 0.841146
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.365451 Loss1: 0.595498 Loss2: 0.769953
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236919 Loss1: 0.542041 Loss2: 0.694878
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191502 Loss1: 0.502629 Loss2: 0.688873
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.211844 Loss1: 0.513758 Loss2: 0.698085
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184251 Loss1: 0.493998 Loss2: 0.690253
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.202649 Loss1: 0.508044 Loss2: 0.694605
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.180795 Loss1: 0.485625 Loss2: 0.695170
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.177056 Loss1: 0.478426 Loss2: 0.698630
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.186964 Loss1: 0.490092 Loss2: 0.696872
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.162090 Loss1: 0.461919 Loss2: 0.700172
+(DefaultActor pid=1831567) >> Training accuracy: 0.840511
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.254640 Loss1: 0.463941 Loss2: 0.790699
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.121312 Loss1: 0.408946 Loss2: 0.712366
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.110466 Loss1: 0.401493 Loss2: 0.708973
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.084075 Loss1: 0.374209 Loss2: 0.709866
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085935 Loss1: 0.376579 Loss2: 0.709356
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.097952 Loss1: 0.386425 Loss2: 0.711527
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.071768 Loss1: 0.358037 Loss2: 0.713731
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.072209 Loss1: 0.360187 Loss2: 0.712022
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.059443 Loss1: 0.346472 Loss2: 0.712971
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.065948 Loss1: 0.354313 Loss2: 0.711635
+(DefaultActor pid=1831567) >> Training accuracy: 0.866898
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459694 Loss1: 0.696204 Loss2: 0.763490
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.306950 Loss1: 0.652029 Loss2: 0.654921
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.310914 Loss1: 0.654534 Loss2: 0.656380
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.284436 Loss1: 0.625903 Loss2: 0.658533
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.227217 Loss1: 0.576183 Loss2: 0.651034
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.263319 Loss1: 0.611006 Loss2: 0.652313
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.233488 Loss1: 0.579877 Loss2: 0.653611
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.251796 Loss1: 0.593255 Loss2: 0.658541
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.250598 Loss1: 0.593881 Loss2: 0.656717
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289120 Loss1: 0.627479 Loss2: 0.661641
+(DefaultActor pid=1831567) >> Training accuracy: 0.799616
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.269004 Loss1: 0.540504 Loss2: 0.728500
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234204 Loss1: 0.543390 Loss2: 0.690814
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.204314 Loss1: 0.515942 Loss2: 0.688372
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.196022 Loss1: 0.505734 Loss2: 0.690288
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184733 Loss1: 0.495071 Loss2: 0.689662
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.194362 Loss1: 0.503303 Loss2: 0.691059
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.184447 Loss1: 0.491237 Loss2: 0.693210
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.172962 Loss1: 0.482379 Loss2: 0.690583
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.203060 Loss1: 0.507610 Loss2: 0.695450
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179909 Loss1: 0.485599 Loss2: 0.694310
+(DefaultActor pid=1831567) >> Training accuracy: 0.830481
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297454 Loss1: 0.568408 Loss2: 0.729046
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180181 Loss1: 0.532017 Loss2: 0.648164
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.160266 Loss1: 0.512059 Loss2: 0.648207
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.170723 Loss1: 0.519121 Loss2: 0.651602
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.146723 Loss1: 0.493858 Loss2: 0.652865
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146239 Loss1: 0.492483 Loss2: 0.653757
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.139678 Loss1: 0.485026 Loss2: 0.654652
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133895 Loss1: 0.480571 Loss2: 0.653324
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.127217 Loss1: 0.473554 Loss2: 0.653663
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.106672 Loss1: 0.453622 Loss2: 0.653050
+(DefaultActor pid=1831567) >> Training accuracy: 0.840461
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.209776 Loss1: 0.459287 Loss2: 0.750490
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.111342 Loss1: 0.439064 Loss2: 0.672278
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.067302 Loss1: 0.395541 Loss2: 0.671761
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.040194 Loss1: 0.371852 Loss2: 0.668342
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.052917 Loss1: 0.384846 Loss2: 0.668071
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.033326 Loss1: 0.364983 Loss2: 0.668342
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.034612 Loss1: 0.364301 Loss2: 0.670311
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.034158 Loss1: 0.360371 Loss2: 0.673787
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.017835 Loss1: 0.347053 Loss2: 0.670782
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.016462 Loss1: 0.344951 Loss2: 0.671510
+(DefaultActor pid=1831567) >> Training accuracy: 0.863040
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.354327 Loss1: 0.571872 Loss2: 0.782454
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201609 Loss1: 0.519288 Loss2: 0.682321
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.178111 Loss1: 0.497327 Loss2: 0.680785
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.166653 Loss1: 0.483096 Loss2: 0.683556
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167567 Loss1: 0.481819 Loss2: 0.685748
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.148876 Loss1: 0.466829 Loss2: 0.682047
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.147886 Loss1: 0.460418 Loss2: 0.687469
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133137 Loss1: 0.447643 Loss2: 0.685494
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.158036 Loss1: 0.468022 Loss2: 0.690014
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.142751 Loss1: 0.452918 Loss2: 0.689832
+(DefaultActor pid=1831567) >> Training accuracy: 0.838189
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.537864 Loss1: 0.741343 Loss2: 0.796521
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.404987 Loss1: 0.699644 Loss2: 0.705344
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.380391 Loss1: 0.676326 Loss2: 0.704065
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.369740 Loss1: 0.664977 Loss2: 0.704762
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.343714 Loss1: 0.639463 Loss2: 0.704251
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.348802 Loss1: 0.641958 Loss2: 0.706844
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.328083 Loss1: 0.623463 Loss2: 0.704620
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.328473 Loss1: 0.623006 Loss2: 0.705467
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.329175 Loss1: 0.620607 Loss2: 0.708568
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.328157 Loss1: 0.615535 Loss2: 0.712623
+[2023-09-27 16:54:12,710][flwr][DEBUG] - fit_round 80 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.779151
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.692700
+[2023-09-27 16:54:14,354][flwr][INFO] - fit progress: (80, 0.8883003936217616, {'accuracy': 0.6927}, 38187.19075930398)
+[2023-09-27 16:54:14,355][flwr][DEBUG] - evaluate_round 80: strategy sampled 10 clients (out of 10)
+[2023-09-27 16:54:45,994][flwr][DEBUG] - evaluate_round 80 received 10 results and 0 failures
+[2023-09-27 16:54:45,995][flwr][DEBUG] - fit_round 81: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.300540 Loss1: 0.546124 Loss2: 0.754416
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.215495 Loss1: 0.502057 Loss2: 0.713438
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203739 Loss1: 0.494000 Loss2: 0.709739
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.209664 Loss1: 0.500396 Loss2: 0.709267
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.219611 Loss1: 0.506395 Loss2: 0.713216
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211257 Loss1: 0.496688 Loss2: 0.714569
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212045 Loss1: 0.496708 Loss2: 0.715337
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.213101 Loss1: 0.497642 Loss2: 0.715459
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.207396 Loss1: 0.493332 Loss2: 0.714063
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.209689 Loss1: 0.491359 Loss2: 0.718330
+(DefaultActor pid=1831567) >> Training accuracy: 0.833457
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187680 Loss1: 0.447050 Loss2: 0.740630
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.061610 Loss1: 0.402402 Loss2: 0.659208
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.044217 Loss1: 0.384807 Loss2: 0.659409
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.039955 Loss1: 0.380685 Loss2: 0.659271
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.045495 Loss1: 0.385480 Loss2: 0.660015
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.017948 Loss1: 0.360314 Loss2: 0.657634
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.028419 Loss1: 0.366645 Loss2: 0.661774
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026755 Loss1: 0.363917 Loss2: 0.662838
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.033666 Loss1: 0.371787 Loss2: 0.661879
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.003370 Loss1: 0.343162 Loss2: 0.660209
+(DefaultActor pid=1831567) >> Training accuracy: 0.858410
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324305 Loss1: 0.584975 Loss2: 0.739330
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.191577 Loss1: 0.518751 Loss2: 0.672826
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.184092 Loss1: 0.515493 Loss2: 0.668600
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194349 Loss1: 0.518621 Loss2: 0.675727
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.168716 Loss1: 0.495332 Loss2: 0.673384
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183847 Loss1: 0.511194 Loss2: 0.672653
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.176124 Loss1: 0.502404 Loss2: 0.673720
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180820 Loss1: 0.503281 Loss2: 0.677539
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147135 Loss1: 0.470189 Loss2: 0.676945
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.178842 Loss1: 0.497939 Loss2: 0.680902
+(DefaultActor pid=1831567) >> Training accuracy: 0.845353
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.453269 Loss1: 0.707223 Loss2: 0.746047
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.335721 Loss1: 0.680495 Loss2: 0.655226
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.335820 Loss1: 0.678961 Loss2: 0.656860
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.302291 Loss1: 0.645425 Loss2: 0.656866
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.297271 Loss1: 0.640139 Loss2: 0.657132
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.288180 Loss1: 0.627032 Loss2: 0.661148
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.293450 Loss1: 0.632152 Loss2: 0.661298
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.294177 Loss1: 0.632655 Loss2: 0.661522
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.276674 Loss1: 0.613893 Loss2: 0.662781
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.295908 Loss1: 0.630861 Loss2: 0.665047
+(DefaultActor pid=1831567) >> Training accuracy: 0.778685
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.518419 Loss1: 0.722636 Loss2: 0.795784
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.406849 Loss1: 0.701113 Loss2: 0.705737
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393866 Loss1: 0.685118 Loss2: 0.708749
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.391511 Loss1: 0.680939 Loss2: 0.710572
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.369378 Loss1: 0.661238 Loss2: 0.708140
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362421 Loss1: 0.650714 Loss2: 0.711706
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338002 Loss1: 0.627718 Loss2: 0.710284
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.331386 Loss1: 0.617772 Loss2: 0.713614
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.358380 Loss1: 0.644738 Loss2: 0.713643
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.352306 Loss1: 0.639416 Loss2: 0.712890
+(DefaultActor pid=1831567) >> Training accuracy: 0.778759
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226462 Loss1: 0.461796 Loss2: 0.764665
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.085327 Loss1: 0.402304 Loss2: 0.683022
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.080871 Loss1: 0.398117 Loss2: 0.682754
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.080696 Loss1: 0.396185 Loss2: 0.684512
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066125 Loss1: 0.380581 Loss2: 0.685544
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.068261 Loss1: 0.383840 Loss2: 0.684421
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.041232 Loss1: 0.354763 Loss2: 0.686469
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.049936 Loss1: 0.364873 Loss2: 0.685063
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.032306 Loss1: 0.345683 Loss2: 0.686623
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.030793 Loss1: 0.343582 Loss2: 0.687212
+(DefaultActor pid=1831567) >> Training accuracy: 0.893711
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.355668 Loss1: 0.577239 Loss2: 0.778429
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217776 Loss1: 0.521577 Loss2: 0.696199
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214805 Loss1: 0.517901 Loss2: 0.696904
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200087 Loss1: 0.503026 Loss2: 0.697061
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187831 Loss1: 0.491580 Loss2: 0.696251
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.188989 Loss1: 0.492543 Loss2: 0.696446
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182921 Loss1: 0.482080 Loss2: 0.700841
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.181522 Loss1: 0.479544 Loss2: 0.701978
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.174566 Loss1: 0.469970 Loss2: 0.704595
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176864 Loss1: 0.474183 Loss2: 0.702682
+(DefaultActor pid=1831567) >> Training accuracy: 0.843133
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327246 Loss1: 0.578864 Loss2: 0.748381
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.164927 Loss1: 0.517694 Loss2: 0.647233
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.142656 Loss1: 0.501796 Loss2: 0.640861
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.131374 Loss1: 0.487031 Loss2: 0.644343
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.121453 Loss1: 0.479665 Loss2: 0.641788
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.139153 Loss1: 0.492593 Loss2: 0.646560
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.104358 Loss1: 0.456990 Loss2: 0.647368
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.121340 Loss1: 0.474532 Loss2: 0.646808
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.092212 Loss1: 0.444599 Loss2: 0.647613
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.102187 Loss1: 0.454107 Loss2: 0.648080
+(DefaultActor pid=1831567) >> Training accuracy: 0.854078
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.469989 Loss1: 0.703244 Loss2: 0.766745
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.361671 Loss1: 0.686308 Loss2: 0.675363
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.310838 Loss1: 0.638611 Loss2: 0.672227
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.301971 Loss1: 0.626805 Loss2: 0.675166
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.290725 Loss1: 0.616052 Loss2: 0.674673
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.279431 Loss1: 0.603771 Loss2: 0.675660
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.276540 Loss1: 0.598715 Loss2: 0.677825
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.269534 Loss1: 0.589600 Loss2: 0.679934
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.261710 Loss1: 0.580629 Loss2: 0.681081
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.256590 Loss1: 0.573644 Loss2: 0.682947
+(DefaultActor pid=1831567) >> Training accuracy: 0.783991
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.323510 Loss1: 0.597050 Loss2: 0.726460
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.181453 Loss1: 0.535667 Loss2: 0.645786
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.164301 Loss1: 0.519993 Loss2: 0.644308
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.154907 Loss1: 0.511565 Loss2: 0.643342
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158417 Loss1: 0.513735 Loss2: 0.644681
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163088 Loss1: 0.515852 Loss2: 0.647236
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.144812 Loss1: 0.497199 Loss2: 0.647613
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155809 Loss1: 0.509354 Loss2: 0.646456
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120312 Loss1: 0.474323 Loss2: 0.645989
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134396 Loss1: 0.486348 Loss2: 0.648048
+[2023-09-27 17:01:26,837][flwr][DEBUG] - fit_round 81 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.832127
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.701700
+[2023-09-27 17:01:28,342][flwr][INFO] - fit progress: (81, 0.8609136937144465, {'accuracy': 0.7017}, 38621.17888039211)
+[2023-09-27 17:01:28,343][flwr][DEBUG] - evaluate_round 81: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:01:59,473][flwr][DEBUG] - evaluate_round 81 received 10 results and 0 failures
+[2023-09-27 17:01:59,474][flwr][DEBUG] - fit_round 82: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.225876 Loss1: 0.459700 Loss2: 0.766177
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.102310 Loss1: 0.406331 Loss2: 0.695979
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.079561 Loss1: 0.389791 Loss2: 0.689770
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074747 Loss1: 0.382154 Loss2: 0.692594
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.068374 Loss1: 0.373046 Loss2: 0.695328
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.057631 Loss1: 0.365959 Loss2: 0.691672
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051816 Loss1: 0.358996 Loss2: 0.692820
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.050105 Loss1: 0.356746 Loss2: 0.693359
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.055659 Loss1: 0.357930 Loss2: 0.697729
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.035115 Loss1: 0.337155 Loss2: 0.697960
+(DefaultActor pid=1831567) >> Training accuracy: 0.873650
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.478549 Loss1: 0.754050 Loss2: 0.724498
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341815 Loss1: 0.700035 Loss2: 0.641780
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.326330 Loss1: 0.686113 Loss2: 0.640217
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.319127 Loss1: 0.675639 Loss2: 0.643488
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.320766 Loss1: 0.678518 Loss2: 0.642247
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316427 Loss1: 0.671002 Loss2: 0.645424
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.306544 Loss1: 0.660470 Loss2: 0.646073
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.278041 Loss1: 0.633493 Loss2: 0.644548
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299236 Loss1: 0.652151 Loss2: 0.647085
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281247 Loss1: 0.633193 Loss2: 0.648054
+(DefaultActor pid=1831567) >> Training accuracy: 0.793705
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187097 Loss1: 0.450672 Loss2: 0.736425
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.059204 Loss1: 0.400660 Loss2: 0.658544
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.066863 Loss1: 0.406648 Loss2: 0.660215
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.037349 Loss1: 0.379068 Loss2: 0.658281
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.036073 Loss1: 0.377496 Loss2: 0.658577
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.026326 Loss1: 0.365932 Loss2: 0.660393
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.013307 Loss1: 0.355435 Loss2: 0.657872
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.009402 Loss1: 0.351155 Loss2: 0.658248
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.006964 Loss1: 0.345660 Loss2: 0.661303
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.010783 Loss1: 0.349893 Loss2: 0.660891
+(DefaultActor pid=1831567) >> Training accuracy: 0.884259
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362159 Loss1: 0.608009 Loss2: 0.754150
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217056 Loss1: 0.528950 Loss2: 0.688106
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198089 Loss1: 0.510476 Loss2: 0.687613
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189345 Loss1: 0.504348 Loss2: 0.684997
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.209595 Loss1: 0.519426 Loss2: 0.690169
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.173807 Loss1: 0.486358 Loss2: 0.687449
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195002 Loss1: 0.505296 Loss2: 0.689705
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.187773 Loss1: 0.497466 Loss2: 0.690307
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.174982 Loss1: 0.485423 Loss2: 0.689559
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.181688 Loss1: 0.489080 Loss2: 0.692607
+(DefaultActor pid=1831567) >> Training accuracy: 0.835938
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.461120 Loss1: 0.696088 Loss2: 0.765032
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341877 Loss1: 0.659731 Loss2: 0.682146
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.348368 Loss1: 0.662036 Loss2: 0.686332
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364534 Loss1: 0.673649 Loss2: 0.690884
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.332826 Loss1: 0.647652 Loss2: 0.685174
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.305762 Loss1: 0.619896 Loss2: 0.685866
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299146 Loss1: 0.614193 Loss2: 0.684953
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320626 Loss1: 0.633404 Loss2: 0.687221
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306998 Loss1: 0.616918 Loss2: 0.690080
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.260878 Loss1: 0.570644 Loss2: 0.690234
+(DefaultActor pid=1831567) >> Training accuracy: 0.787547
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.331205 Loss1: 0.559605 Loss2: 0.771600
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.182568 Loss1: 0.510097 Loss2: 0.672471
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.181649 Loss1: 0.509024 Loss2: 0.672625
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.173257 Loss1: 0.498579 Loss2: 0.674678
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.148088 Loss1: 0.471666 Loss2: 0.676422
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.150377 Loss1: 0.472769 Loss2: 0.677608
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.131895 Loss1: 0.454303 Loss2: 0.677592
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.136146 Loss1: 0.456524 Loss2: 0.679622
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.125933 Loss1: 0.443957 Loss2: 0.681977
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.102352 Loss1: 0.424265 Loss2: 0.678087
+(DefaultActor pid=1831567) >> Training accuracy: 0.850371
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.304025 Loss1: 0.581984 Loss2: 0.722041
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.175310 Loss1: 0.532924 Loss2: 0.642386
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183051 Loss1: 0.536709 Loss2: 0.646342
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.140776 Loss1: 0.496954 Loss2: 0.643823
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.131045 Loss1: 0.488541 Loss2: 0.642504
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.123096 Loss1: 0.480013 Loss2: 0.643083
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.132190 Loss1: 0.483286 Loss2: 0.648904
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.123279 Loss1: 0.477473 Loss2: 0.645807
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.124815 Loss1: 0.473885 Loss2: 0.650930
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.117974 Loss1: 0.468031 Loss2: 0.649943
+(DefaultActor pid=1831567) >> Training accuracy: 0.848890
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.464372 Loss1: 0.728034 Loss2: 0.736338
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.274654 Loss1: 0.642626 Loss2: 0.632028
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.278112 Loss1: 0.643639 Loss2: 0.634472
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.242153 Loss1: 0.613317 Loss2: 0.628836
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.236202 Loss1: 0.601388 Loss2: 0.634814
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.236666 Loss1: 0.603316 Loss2: 0.633350
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.215005 Loss1: 0.579945 Loss2: 0.635060
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.236204 Loss1: 0.600520 Loss2: 0.635684
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199749 Loss1: 0.564576 Loss2: 0.635172
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189876 Loss1: 0.555664 Loss2: 0.634212
+(DefaultActor pid=1831567) >> Training accuracy: 0.807292
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.252011 Loss1: 0.533859 Loss2: 0.718152
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.196107 Loss1: 0.514239 Loss2: 0.681868
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193181 Loss1: 0.515873 Loss2: 0.677308
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190092 Loss1: 0.509964 Loss2: 0.680128
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.180031 Loss1: 0.499533 Loss2: 0.680498
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.179383 Loss1: 0.499448 Loss2: 0.679935
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163178 Loss1: 0.485575 Loss2: 0.677603
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.178003 Loss1: 0.495305 Loss2: 0.682698
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180236 Loss1: 0.496272 Loss2: 0.683964
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.162133 Loss1: 0.484266 Loss2: 0.677866
+(DefaultActor pid=1831567) >> Training accuracy: 0.834945
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.347016 Loss1: 0.580076 Loss2: 0.766940
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212751 Loss1: 0.525317 Loss2: 0.687434
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.237546 Loss1: 0.545027 Loss2: 0.692519
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231030 Loss1: 0.538090 Loss2: 0.692940
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195393 Loss1: 0.504312 Loss2: 0.691081
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.187265 Loss1: 0.497527 Loss2: 0.689738
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195500 Loss1: 0.503040 Loss2: 0.692460
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185035 Loss1: 0.490835 Loss2: 0.694200
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.176327 Loss1: 0.480496 Loss2: 0.695832
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.188059 Loss1: 0.493385 Loss2: 0.694673
+[2023-09-27 17:08:42,475][flwr][DEBUG] - fit_round 82 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.833534
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.695700
+[2023-09-27 17:08:43,977][flwr][INFO] - fit progress: (82, 0.874075241743947, {'accuracy': 0.6957}, 39056.813858431764)
+[2023-09-27 17:08:43,978][flwr][DEBUG] - evaluate_round 82: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:09:19,184][flwr][DEBUG] - evaluate_round 82 received 10 results and 0 failures
+[2023-09-27 17:09:19,185][flwr][DEBUG] - fit_round 83: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.302863 Loss1: 0.558000 Loss2: 0.744863
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.229562 Loss1: 0.526489 Loss2: 0.703073
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.216321 Loss1: 0.513711 Loss2: 0.702610
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202443 Loss1: 0.502556 Loss2: 0.699887
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196037 Loss1: 0.494868 Loss2: 0.701169
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199328 Loss1: 0.493659 Loss2: 0.705669
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191297 Loss1: 0.487447 Loss2: 0.703850
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.177889 Loss1: 0.475652 Loss2: 0.702237
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.181066 Loss1: 0.477087 Loss2: 0.703978
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198783 Loss1: 0.494595 Loss2: 0.704189
+(DefaultActor pid=1831567) >> Training accuracy: 0.829613
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.521168 Loss1: 0.725138 Loss2: 0.796031
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.389501 Loss1: 0.683016 Loss2: 0.706485
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.375343 Loss1: 0.667924 Loss2: 0.707419
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.371667 Loss1: 0.663850 Loss2: 0.707817
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.356949 Loss1: 0.647938 Loss2: 0.709011
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.352264 Loss1: 0.644742 Loss2: 0.707522
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.374415 Loss1: 0.661857 Loss2: 0.712558
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.345334 Loss1: 0.632427 Loss2: 0.712907
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.342216 Loss1: 0.628971 Loss2: 0.713245
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329653 Loss1: 0.616644 Loss2: 0.713009
+(DefaultActor pid=1831567) >> Training accuracy: 0.791667
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353717 Loss1: 0.597056 Loss2: 0.756661
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194164 Loss1: 0.521044 Loss2: 0.673120
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.204645 Loss1: 0.531414 Loss2: 0.673231
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188113 Loss1: 0.519557 Loss2: 0.668555
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.179617 Loss1: 0.506214 Loss2: 0.673403
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.172761 Loss1: 0.502201 Loss2: 0.670560
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164486 Loss1: 0.492547 Loss2: 0.671940
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154586 Loss1: 0.478079 Loss2: 0.676508
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.157216 Loss1: 0.483414 Loss2: 0.673802
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.155398 Loss1: 0.481830 Loss2: 0.673568
+(DefaultActor pid=1831567) >> Training accuracy: 0.841082
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.199093 Loss1: 0.443312 Loss2: 0.755781
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.094829 Loss1: 0.419068 Loss2: 0.675761
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.050883 Loss1: 0.376262 Loss2: 0.674621
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.055012 Loss1: 0.377787 Loss2: 0.677225
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.051650 Loss1: 0.376031 Loss2: 0.675619
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.036899 Loss1: 0.362411 Loss2: 0.674488
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.027037 Loss1: 0.349722 Loss2: 0.677315
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.037622 Loss1: 0.362304 Loss2: 0.675318
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028121 Loss1: 0.352230 Loss2: 0.675891
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019191 Loss1: 0.340139 Loss2: 0.679052
+(DefaultActor pid=1831567) >> Training accuracy: 0.884645
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.301001 Loss1: 0.538386 Loss2: 0.762615
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.183999 Loss1: 0.522973 Loss2: 0.661026
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.160129 Loss1: 0.503322 Loss2: 0.656807
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175228 Loss1: 0.514732 Loss2: 0.660495
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.143664 Loss1: 0.481100 Loss2: 0.662564
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.117056 Loss1: 0.455274 Loss2: 0.661782
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171384 Loss1: 0.507385 Loss2: 0.663999
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.112225 Loss1: 0.451424 Loss2: 0.660801
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.115973 Loss1: 0.453613 Loss2: 0.662360
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.096143 Loss1: 0.432459 Loss2: 0.663684
+(DefaultActor pid=1831567) >> Training accuracy: 0.858581
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.501064 Loss1: 0.722270 Loss2: 0.778794
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332527 Loss1: 0.648550 Loss2: 0.683977
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.321813 Loss1: 0.637834 Loss2: 0.683979
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.273402 Loss1: 0.591213 Loss2: 0.682189
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.301487 Loss1: 0.619598 Loss2: 0.681889
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.278418 Loss1: 0.590420 Loss2: 0.687999
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.297957 Loss1: 0.609346 Loss2: 0.688612
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.252867 Loss1: 0.568012 Loss2: 0.684854
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.266297 Loss1: 0.578498 Loss2: 0.687799
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.262122 Loss1: 0.573044 Loss2: 0.689079
+(DefaultActor pid=1831567) >> Training accuracy: 0.796053
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.302774 Loss1: 0.564176 Loss2: 0.738599
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.189899 Loss1: 0.518799 Loss2: 0.671100
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.182887 Loss1: 0.510823 Loss2: 0.672063
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.180236 Loss1: 0.508055 Loss2: 0.672181
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191515 Loss1: 0.513948 Loss2: 0.677567
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.161628 Loss1: 0.485697 Loss2: 0.675931
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187716 Loss1: 0.510473 Loss2: 0.677243
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163040 Loss1: 0.482543 Loss2: 0.680497
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185930 Loss1: 0.504539 Loss2: 0.681391
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.151684 Loss1: 0.471194 Loss2: 0.680491
+(DefaultActor pid=1831567) >> Training accuracy: 0.849359
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321015 Loss1: 0.564107 Loss2: 0.756909
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.193873 Loss1: 0.517826 Loss2: 0.676047
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193025 Loss1: 0.518312 Loss2: 0.674713
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188700 Loss1: 0.511190 Loss2: 0.677510
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187875 Loss1: 0.504033 Loss2: 0.683843
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163188 Loss1: 0.482684 Loss2: 0.680504
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.180822 Loss1: 0.499981 Loss2: 0.680841
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.153541 Loss1: 0.473894 Loss2: 0.679647
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.155197 Loss1: 0.472857 Loss2: 0.682340
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.163529 Loss1: 0.483963 Loss2: 0.679566
+(DefaultActor pid=1831567) >> Training accuracy: 0.848067
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493233 Loss1: 0.736185 Loss2: 0.757048
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.355788 Loss1: 0.694075 Loss2: 0.661713
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.284866 Loss1: 0.627067 Loss2: 0.657799
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.318868 Loss1: 0.658848 Loss2: 0.660019
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293667 Loss1: 0.633287 Loss2: 0.660379
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.291577 Loss1: 0.632259 Loss2: 0.659318
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.282500 Loss1: 0.621221 Loss2: 0.661279
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.285835 Loss1: 0.626831 Loss2: 0.659004
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.273515 Loss1: 0.606602 Loss2: 0.666913
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290968 Loss1: 0.626486 Loss2: 0.664482
+(DefaultActor pid=1831567) >> Training accuracy: 0.763526
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.183256 Loss1: 0.443283 Loss2: 0.739973
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.055212 Loss1: 0.394707 Loss2: 0.660505
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048904 Loss1: 0.389723 Loss2: 0.659181
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.035898 Loss1: 0.375126 Loss2: 0.660772
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.038818 Loss1: 0.381313 Loss2: 0.657505
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.039003 Loss1: 0.380314 Loss2: 0.658689
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.042613 Loss1: 0.380687 Loss2: 0.661926
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.028748 Loss1: 0.367317 Loss2: 0.661431
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.025529 Loss1: 0.364591 Loss2: 0.660938
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.025992 Loss1: 0.365217 Loss2: 0.660775
+[2023-09-27 17:16:15,659][flwr][DEBUG] - fit_round 83 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.874807
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.694500
+[2023-09-27 17:16:17,231][flwr][INFO] - fit progress: (83, 0.8777990603980165, {'accuracy': 0.6945}, 39510.06702556601)
+[2023-09-27 17:16:17,231][flwr][DEBUG] - evaluate_round 83: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:16:47,942][flwr][DEBUG] - evaluate_round 83 received 10 results and 0 failures
+[2023-09-27 17:16:47,943][flwr][DEBUG] - fit_round 84: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.511909 Loss1: 0.748393 Loss2: 0.763516
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.306131 Loss1: 0.648063 Loss2: 0.658068
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.276425 Loss1: 0.616415 Loss2: 0.660010
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281843 Loss1: 0.622920 Loss2: 0.658923
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.262386 Loss1: 0.603416 Loss2: 0.658970
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.254404 Loss1: 0.592910 Loss2: 0.661494
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.251656 Loss1: 0.588401 Loss2: 0.663255
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.259139 Loss1: 0.594753 Loss2: 0.664386
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.247935 Loss1: 0.584515 Loss2: 0.663420
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240786 Loss1: 0.577216 Loss2: 0.663570
+(DefaultActor pid=1831567) >> Training accuracy: 0.787829
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.184028 Loss1: 0.455350 Loss2: 0.728678
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.067037 Loss1: 0.417673 Loss2: 0.649364
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.035302 Loss1: 0.389534 Loss2: 0.645768
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019697 Loss1: 0.371475 Loss2: 0.648222
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.024274 Loss1: 0.379043 Loss2: 0.645232
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.002072 Loss1: 0.354566 Loss2: 0.647506
+(DefaultActor pid=1831567) Epoch: 6 Loss: 0.999567 Loss1: 0.351994 Loss2: 0.647573
+(DefaultActor pid=1831567) Epoch: 7 Loss: 0.997191 Loss1: 0.349373 Loss2: 0.647818
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.015637 Loss1: 0.365776 Loss2: 0.649862
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.984840 Loss1: 0.334446 Loss2: 0.650394
+(DefaultActor pid=1831567) >> Training accuracy: 0.879437
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.304335 Loss1: 0.553626 Loss2: 0.750709
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.167857 Loss1: 0.516815 Loss2: 0.651042
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.167665 Loss1: 0.514051 Loss2: 0.653614
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.132824 Loss1: 0.477408 Loss2: 0.655415
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.138154 Loss1: 0.481919 Loss2: 0.656235
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.129181 Loss1: 0.471896 Loss2: 0.657285
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.111151 Loss1: 0.456446 Loss2: 0.654705
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.117285 Loss1: 0.457952 Loss2: 0.659333
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.125185 Loss1: 0.465218 Loss2: 0.659968
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.104483 Loss1: 0.446060 Loss2: 0.658423
+(DefaultActor pid=1831567) >> Training accuracy: 0.860699
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.279080 Loss1: 0.558179 Loss2: 0.720902
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.164000 Loss1: 0.525789 Loss2: 0.638212
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.140375 Loss1: 0.499769 Loss2: 0.640606
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.146081 Loss1: 0.505231 Loss2: 0.640850
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.126785 Loss1: 0.484901 Loss2: 0.641884
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.115231 Loss1: 0.471233 Loss2: 0.643997
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.109766 Loss1: 0.467207 Loss2: 0.642558
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.119686 Loss1: 0.474354 Loss2: 0.645332
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.111459 Loss1: 0.466691 Loss2: 0.644767
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.153473 Loss1: 0.504805 Loss2: 0.648668
+(DefaultActor pid=1831567) >> Training accuracy: 0.847039
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.242915 Loss1: 0.450491 Loss2: 0.792424
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.111014 Loss1: 0.396338 Loss2: 0.714676
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.093148 Loss1: 0.380185 Loss2: 0.712962
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.087021 Loss1: 0.375632 Loss2: 0.711389
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.105451 Loss1: 0.392375 Loss2: 0.713076
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.091174 Loss1: 0.377574 Loss2: 0.713599
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.073757 Loss1: 0.359771 Loss2: 0.713986
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.062586 Loss1: 0.349977 Loss2: 0.712609
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.090144 Loss1: 0.369366 Loss2: 0.720778
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.057589 Loss1: 0.343899 Loss2: 0.713690
+(DefaultActor pid=1831567) >> Training accuracy: 0.877894
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.290740 Loss1: 0.544058 Loss2: 0.746682
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.196518 Loss1: 0.525171 Loss2: 0.671347
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.196757 Loss1: 0.523999 Loss2: 0.672758
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.197675 Loss1: 0.523953 Loss2: 0.673721
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.175683 Loss1: 0.503722 Loss2: 0.671961
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168597 Loss1: 0.495248 Loss2: 0.673349
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193261 Loss1: 0.518184 Loss2: 0.675077
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.181404 Loss1: 0.503997 Loss2: 0.677406
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.167498 Loss1: 0.491333 Loss2: 0.676165
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.145591 Loss1: 0.470144 Loss2: 0.675448
+(DefaultActor pid=1831567) >> Training accuracy: 0.844952
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.336098 Loss1: 0.578863 Loss2: 0.757235
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243505 Loss1: 0.551497 Loss2: 0.692009
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205652 Loss1: 0.511414 Loss2: 0.694238
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208691 Loss1: 0.514798 Loss2: 0.693893
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200079 Loss1: 0.505226 Loss2: 0.694853
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186198 Loss1: 0.495519 Loss2: 0.690680
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193322 Loss1: 0.496973 Loss2: 0.696349
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.175833 Loss1: 0.479102 Loss2: 0.696731
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.172246 Loss1: 0.477133 Loss2: 0.695113
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154825 Loss1: 0.458806 Loss2: 0.696020
+(DefaultActor pid=1831567) >> Training accuracy: 0.833460
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.286909 Loss1: 0.544129 Loss2: 0.742780
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.220157 Loss1: 0.522833 Loss2: 0.697324
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.182329 Loss1: 0.486484 Loss2: 0.695846
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199298 Loss1: 0.498317 Loss2: 0.700981
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201486 Loss1: 0.500701 Loss2: 0.700785
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.189287 Loss1: 0.486290 Loss2: 0.702997
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.198554 Loss1: 0.494571 Loss2: 0.703984
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.197461 Loss1: 0.490516 Loss2: 0.706946
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185523 Loss1: 0.481665 Loss2: 0.703858
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.187013 Loss1: 0.481986 Loss2: 0.705027
+(DefaultActor pid=1831567) >> Training accuracy: 0.842758
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.465796 Loss1: 0.702315 Loss2: 0.763481
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.373122 Loss1: 0.691994 Loss2: 0.681128
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336274 Loss1: 0.658786 Loss2: 0.677488
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333983 Loss1: 0.652930 Loss2: 0.681053
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.315414 Loss1: 0.633408 Loss2: 0.682006
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.299174 Loss1: 0.618318 Loss2: 0.680855
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.313772 Loss1: 0.633575 Loss2: 0.680197
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320372 Loss1: 0.638994 Loss2: 0.681378
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299203 Loss1: 0.615951 Loss2: 0.683253
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.291353 Loss1: 0.606160 Loss2: 0.685193
+(DefaultActor pid=1831567) >> Training accuracy: 0.775653
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.448403 Loss1: 0.715355 Loss2: 0.733048
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.342255 Loss1: 0.694559 Loss2: 0.647696
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.325408 Loss1: 0.673492 Loss2: 0.651916
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333408 Loss1: 0.681928 Loss2: 0.651480
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.313137 Loss1: 0.662541 Loss2: 0.650596
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.302836 Loss1: 0.652306 Loss2: 0.650530
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294985 Loss1: 0.642527 Loss2: 0.652458
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300004 Loss1: 0.647365 Loss2: 0.652639
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.275509 Loss1: 0.618361 Loss2: 0.657148
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.291177 Loss1: 0.634584 Loss2: 0.656593
+[2023-09-27 17:23:40,172][flwr][DEBUG] - fit_round 84 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.786005
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.704200
+[2023-09-27 17:23:54,237][flwr][INFO] - fit progress: (84, 0.8604000634469163, {'accuracy': 0.7042}, 39967.073669589125)
+[2023-09-27 17:23:54,238][flwr][DEBUG] - evaluate_round 84: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:24:29,969][flwr][DEBUG] - evaluate_round 84 received 10 results and 0 failures
+[2023-09-27 17:24:29,970][flwr][DEBUG] - fit_round 85: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.160340 Loss1: 0.450565 Loss2: 0.709775
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.052789 Loss1: 0.416908 Loss2: 0.635881
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048348 Loss1: 0.411766 Loss2: 0.636583
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.012145 Loss1: 0.378601 Loss2: 0.633544
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.012006 Loss1: 0.375012 Loss2: 0.636994
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.012042 Loss1: 0.376060 Loss2: 0.635983
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.020037 Loss1: 0.384886 Loss2: 0.635151
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.004418 Loss1: 0.366581 Loss2: 0.637836
+(DefaultActor pid=1831567) Epoch: 8 Loss: 0.995722 Loss1: 0.358187 Loss2: 0.637534
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.006914 Loss1: 0.367057 Loss2: 0.639857
+(DefaultActor pid=1831567) >> Training accuracy: 0.886381
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477253 Loss1: 0.725951 Loss2: 0.751302
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.360460 Loss1: 0.686412 Loss2: 0.674048
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.365709 Loss1: 0.689773 Loss2: 0.675936
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339781 Loss1: 0.663907 Loss2: 0.675874
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.353661 Loss1: 0.674501 Loss2: 0.679160
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.325687 Loss1: 0.646949 Loss2: 0.678738
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295778 Loss1: 0.619518 Loss2: 0.676260
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.315358 Loss1: 0.637391 Loss2: 0.677967
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.297099 Loss1: 0.617509 Loss2: 0.679591
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309207 Loss1: 0.626139 Loss2: 0.683068
+(DefaultActor pid=1831567) >> Training accuracy: 0.769475
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477415 Loss1: 0.733194 Loss2: 0.744221
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.321785 Loss1: 0.666469 Loss2: 0.655317
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307781 Loss1: 0.649267 Loss2: 0.658514
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.317912 Loss1: 0.661619 Loss2: 0.656293
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.300332 Loss1: 0.641158 Loss2: 0.659174
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.301529 Loss1: 0.640404 Loss2: 0.661125
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.275357 Loss1: 0.614089 Loss2: 0.661268
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.295697 Loss1: 0.632951 Loss2: 0.662746
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.256720 Loss1: 0.594717 Loss2: 0.662004
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281671 Loss1: 0.618467 Loss2: 0.663203
+(DefaultActor pid=1831567) >> Training accuracy: 0.775653
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.293255 Loss1: 0.580838 Loss2: 0.712417
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.185930 Loss1: 0.548371 Loss2: 0.637559
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.148577 Loss1: 0.513739 Loss2: 0.634839
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.149550 Loss1: 0.512291 Loss2: 0.637259
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140327 Loss1: 0.503474 Loss2: 0.636853
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.145479 Loss1: 0.510679 Loss2: 0.634800
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.137767 Loss1: 0.499839 Loss2: 0.637928
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.125535 Loss1: 0.490953 Loss2: 0.634582
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.108608 Loss1: 0.474914 Loss2: 0.633694
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.112302 Loss1: 0.473605 Loss2: 0.638698
+(DefaultActor pid=1831567) >> Training accuracy: 0.840320
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187136 Loss1: 0.446767 Loss2: 0.740369
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.041990 Loss1: 0.377508 Loss2: 0.664482
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.052184 Loss1: 0.389076 Loss2: 0.663108
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.036773 Loss1: 0.372221 Loss2: 0.664552
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.036315 Loss1: 0.371610 Loss2: 0.664705
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.036649 Loss1: 0.370391 Loss2: 0.666258
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.020316 Loss1: 0.356734 Loss2: 0.663582
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.017098 Loss1: 0.350302 Loss2: 0.666796
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.021524 Loss1: 0.356243 Loss2: 0.665281
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.016540 Loss1: 0.349253 Loss2: 0.667287
+(DefaultActor pid=1831567) >> Training accuracy: 0.857446
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297794 Loss1: 0.561182 Loss2: 0.736612
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223167 Loss1: 0.549715 Loss2: 0.673452
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.171950 Loss1: 0.499806 Loss2: 0.672144
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217295 Loss1: 0.539805 Loss2: 0.677490
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211295 Loss1: 0.534907 Loss2: 0.676388
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163929 Loss1: 0.487970 Loss2: 0.675959
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.172629 Loss1: 0.498440 Loss2: 0.674189
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.186112 Loss1: 0.504329 Loss2: 0.681783
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184383 Loss1: 0.501537 Loss2: 0.682846
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172241 Loss1: 0.491433 Loss2: 0.680808
+(DefaultActor pid=1831567) >> Training accuracy: 0.835537
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.373373 Loss1: 0.578050 Loss2: 0.795323
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.218361 Loss1: 0.508862 Loss2: 0.709499
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214792 Loss1: 0.505042 Loss2: 0.709750
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.228219 Loss1: 0.516499 Loss2: 0.711720
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198076 Loss1: 0.487207 Loss2: 0.710869
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.195631 Loss1: 0.482154 Loss2: 0.713477
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204262 Loss1: 0.492218 Loss2: 0.712044
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.176083 Loss1: 0.464509 Loss2: 0.711574
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.189108 Loss1: 0.476887 Loss2: 0.712221
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180192 Loss1: 0.465650 Loss2: 0.714543
+(DefaultActor pid=1831567) >> Training accuracy: 0.847656
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.294847 Loss1: 0.547763 Loss2: 0.747084
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.214251 Loss1: 0.511814 Loss2: 0.702438
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219682 Loss1: 0.516983 Loss2: 0.702699
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189519 Loss1: 0.486346 Loss2: 0.703173
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200952 Loss1: 0.500028 Loss2: 0.700924
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186632 Loss1: 0.483038 Loss2: 0.703593
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195770 Loss1: 0.492572 Loss2: 0.703198
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.201433 Loss1: 0.496100 Loss2: 0.705333
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196742 Loss1: 0.488844 Loss2: 0.707898
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200205 Loss1: 0.493016 Loss2: 0.707189
+(DefaultActor pid=1831567) >> Training accuracy: 0.828001
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481746 Loss1: 0.711044 Loss2: 0.770702
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.310266 Loss1: 0.646617 Loss2: 0.663648
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.292209 Loss1: 0.625788 Loss2: 0.666421
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.283623 Loss1: 0.617919 Loss2: 0.665704
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.283731 Loss1: 0.615132 Loss2: 0.668599
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249304 Loss1: 0.581357 Loss2: 0.667947
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.252732 Loss1: 0.580602 Loss2: 0.672130
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.250952 Loss1: 0.578051 Loss2: 0.672901
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.245483 Loss1: 0.571088 Loss2: 0.674395
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250520 Loss1: 0.572722 Loss2: 0.677798
+(DefaultActor pid=1831567) >> Training accuracy: 0.804276
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.356034 Loss1: 0.584095 Loss2: 0.771939
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.176992 Loss1: 0.508895 Loss2: 0.668097
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193763 Loss1: 0.526787 Loss2: 0.666976
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.146080 Loss1: 0.475481 Loss2: 0.670598
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.147305 Loss1: 0.475649 Loss2: 0.671655
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.135773 Loss1: 0.463828 Loss2: 0.671945
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122502 Loss1: 0.451973 Loss2: 0.670529
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.122670 Loss1: 0.451619 Loss2: 0.671052
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.121276 Loss1: 0.443490 Loss2: 0.677786
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.103704 Loss1: 0.434117 Loss2: 0.669586
+(DefaultActor pid=1831567) >> Training accuracy: 0.863347
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 17:31:28,587][flwr][DEBUG] - fit_round 85 received 10 results and 0 failures
+>> Test accuracy: 0.704300
+[2023-09-27 17:31:30,159][flwr][INFO] - fit progress: (85, 0.8605043698614017, {'accuracy': 0.7043}, 40422.99546373589)
+[2023-09-27 17:31:30,160][flwr][DEBUG] - evaluate_round 85: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:32:01,178][flwr][DEBUG] - evaluate_round 85 received 10 results and 0 failures
+[2023-09-27 17:32:01,179][flwr][DEBUG] - fit_round 86: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359345 Loss1: 0.606351 Loss2: 0.752994
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.203693 Loss1: 0.521575 Loss2: 0.682118
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201479 Loss1: 0.521484 Loss2: 0.679994
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200524 Loss1: 0.519196 Loss2: 0.681328
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198766 Loss1: 0.512428 Loss2: 0.686339
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181249 Loss1: 0.495183 Loss2: 0.686066
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.152408 Loss1: 0.465567 Loss2: 0.686841
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161698 Loss1: 0.476988 Loss2: 0.684710
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173334 Loss1: 0.487385 Loss2: 0.685949
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179672 Loss1: 0.493657 Loss2: 0.686015
+(DefaultActor pid=1831567) >> Training accuracy: 0.843750
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477050 Loss1: 0.734247 Loss2: 0.742803
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347213 Loss1: 0.687358 Loss2: 0.659855
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.333732 Loss1: 0.674332 Loss2: 0.659400
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.314433 Loss1: 0.653336 Loss2: 0.661097
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.318225 Loss1: 0.659153 Loss2: 0.659072
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.293807 Loss1: 0.633525 Loss2: 0.660282
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298165 Loss1: 0.638663 Loss2: 0.659502
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300376 Loss1: 0.636412 Loss2: 0.663964
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.309555 Loss1: 0.645504 Loss2: 0.664051
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.277064 Loss1: 0.612957 Loss2: 0.664107
+(DefaultActor pid=1831567) >> Training accuracy: 0.795516
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324563 Loss1: 0.562687 Loss2: 0.761876
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.208274 Loss1: 0.527189 Loss2: 0.681085
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173865 Loss1: 0.496547 Loss2: 0.677318
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184841 Loss1: 0.503033 Loss2: 0.681809
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198149 Loss1: 0.515095 Loss2: 0.683053
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183530 Loss1: 0.499757 Loss2: 0.683773
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.173924 Loss1: 0.488517 Loss2: 0.685407
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169248 Loss1: 0.482869 Loss2: 0.686378
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173774 Loss1: 0.486286 Loss2: 0.687488
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174884 Loss1: 0.485361 Loss2: 0.689523
+(DefaultActor pid=1831567) >> Training accuracy: 0.844752
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.283958 Loss1: 0.553808 Loss2: 0.730150
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171366 Loss1: 0.523505 Loss2: 0.647861
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.166175 Loss1: 0.517619 Loss2: 0.648555
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.163863 Loss1: 0.513155 Loss2: 0.650708
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.151326 Loss1: 0.498446 Loss2: 0.652880
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.118918 Loss1: 0.467104 Loss2: 0.651814
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.153945 Loss1: 0.496503 Loss2: 0.657442
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.136785 Loss1: 0.480268 Loss2: 0.656517
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.129705 Loss1: 0.472436 Loss2: 0.657269
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.113162 Loss1: 0.461206 Loss2: 0.651956
+(DefaultActor pid=1831567) >> Training accuracy: 0.856497
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.211306 Loss1: 0.442984 Loss2: 0.768322
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.086067 Loss1: 0.401933 Loss2: 0.684133
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.056998 Loss1: 0.375972 Loss2: 0.681026
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.034764 Loss1: 0.356506 Loss2: 0.678258
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.061188 Loss1: 0.379131 Loss2: 0.682057
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.031814 Loss1: 0.351060 Loss2: 0.680755
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.056553 Loss1: 0.375073 Loss2: 0.681480
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.032874 Loss1: 0.347962 Loss2: 0.684913
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.054129 Loss1: 0.370511 Loss2: 0.683618
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028980 Loss1: 0.344518 Loss2: 0.684462
+(DefaultActor pid=1831567) >> Training accuracy: 0.890432
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477639 Loss1: 0.683112 Loss2: 0.794527
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.368458 Loss1: 0.665862 Loss2: 0.702596
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.377903 Loss1: 0.674809 Loss2: 0.703094
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.379525 Loss1: 0.671944 Loss2: 0.707581
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333171 Loss1: 0.629963 Loss2: 0.703208
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.340103 Loss1: 0.635074 Loss2: 0.705029
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.343396 Loss1: 0.636296 Loss2: 0.707100
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.333581 Loss1: 0.624011 Loss2: 0.709569
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.303505 Loss1: 0.598638 Loss2: 0.704867
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.306341 Loss1: 0.597615 Loss2: 0.708726
+(DefaultActor pid=1831567) >> Training accuracy: 0.785215
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.247883 Loss1: 0.459232 Loss2: 0.788651
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.120874 Loss1: 0.406444 Loss2: 0.714430
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.106560 Loss1: 0.396665 Loss2: 0.709894
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.083283 Loss1: 0.367731 Loss2: 0.715551
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.104850 Loss1: 0.387797 Loss2: 0.717053
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.078205 Loss1: 0.366630 Loss2: 0.711575
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.070225 Loss1: 0.357942 Loss2: 0.712283
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.072836 Loss1: 0.360414 Loss2: 0.712423
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.064615 Loss1: 0.349562 Loss2: 0.715053
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.065910 Loss1: 0.348294 Loss2: 0.717616
+(DefaultActor pid=1831567) >> Training accuracy: 0.879630
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.242443 Loss1: 0.531965 Loss2: 0.710478
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172917 Loss1: 0.501454 Loss2: 0.671463
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.171926 Loss1: 0.500509 Loss2: 0.671417
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175920 Loss1: 0.504292 Loss2: 0.671628
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.165067 Loss1: 0.489082 Loss2: 0.675985
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.160811 Loss1: 0.486977 Loss2: 0.673834
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.180050 Loss1: 0.505648 Loss2: 0.674402
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169040 Loss1: 0.492025 Loss2: 0.677015
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165634 Loss1: 0.487204 Loss2: 0.678430
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201633 Loss1: 0.519976 Loss2: 0.681656
+(DefaultActor pid=1831567) >> Training accuracy: 0.821429
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.344840 Loss1: 0.577810 Loss2: 0.767030
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172130 Loss1: 0.513238 Loss2: 0.658892
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155893 Loss1: 0.492616 Loss2: 0.663277
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144785 Loss1: 0.482231 Loss2: 0.662554
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.117232 Loss1: 0.451731 Loss2: 0.665500
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.125389 Loss1: 0.460046 Loss2: 0.665343
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123653 Loss1: 0.456409 Loss2: 0.667245
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.132489 Loss1: 0.465209 Loss2: 0.667280
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.099558 Loss1: 0.432235 Loss2: 0.667322
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.113593 Loss1: 0.442957 Loss2: 0.670636
+(DefaultActor pid=1831567) >> Training accuracy: 0.824947
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.471939 Loss1: 0.694220 Loss2: 0.777719
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.325193 Loss1: 0.658658 Loss2: 0.666535
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.290463 Loss1: 0.627366 Loss2: 0.663096
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.307049 Loss1: 0.641149 Loss2: 0.665900
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.271640 Loss1: 0.604207 Loss2: 0.667433
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.259730 Loss1: 0.591870 Loss2: 0.667859
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.263458 Loss1: 0.596534 Loss2: 0.666924
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.248134 Loss1: 0.576444 Loss2: 0.671690
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.254429 Loss1: 0.584432 Loss2: 0.669997
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240560 Loss1: 0.568057 Loss2: 0.672503
+[2023-09-27 17:38:39,577][flwr][DEBUG] - fit_round 86 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.793311
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.705100
+[2023-09-27 17:38:41,245][flwr][INFO] - fit progress: (86, 0.8559781925175518, {'accuracy': 0.7051}, 40854.081239733845)
+[2023-09-27 17:38:41,245][flwr][DEBUG] - evaluate_round 86: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:39:12,925][flwr][DEBUG] - evaluate_round 86 received 10 results and 0 failures
+[2023-09-27 17:39:12,926][flwr][DEBUG] - fit_round 87: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.212844 Loss1: 0.459272 Loss2: 0.753572
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099947 Loss1: 0.420493 Loss2: 0.679454
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.065534 Loss1: 0.391962 Loss2: 0.673572
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.065012 Loss1: 0.389745 Loss2: 0.675267
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.048358 Loss1: 0.374550 Loss2: 0.673808
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.037587 Loss1: 0.365669 Loss2: 0.671918
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.025979 Loss1: 0.350454 Loss2: 0.675525
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.032480 Loss1: 0.355991 Loss2: 0.676489
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028530 Loss1: 0.349800 Loss2: 0.678731
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028513 Loss1: 0.351308 Loss2: 0.677205
+(DefaultActor pid=1831567) >> Training accuracy: 0.868441
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326975 Loss1: 0.576469 Loss2: 0.750505
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.189623 Loss1: 0.510943 Loss2: 0.678680
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.202465 Loss1: 0.524097 Loss2: 0.678368
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.186211 Loss1: 0.505256 Loss2: 0.680955
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201551 Loss1: 0.517600 Loss2: 0.683951
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.190209 Loss1: 0.504179 Loss2: 0.686030
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174712 Loss1: 0.489347 Loss2: 0.685365
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180301 Loss1: 0.493938 Loss2: 0.686363
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.159619 Loss1: 0.473494 Loss2: 0.686125
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.156252 Loss1: 0.470681 Loss2: 0.685571
+(DefaultActor pid=1831567) >> Training accuracy: 0.834535
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.306147 Loss1: 0.566444 Loss2: 0.739703
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.199767 Loss1: 0.535454 Loss2: 0.664313
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.167367 Loss1: 0.505792 Loss2: 0.661575
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.169264 Loss1: 0.508166 Loss2: 0.661098
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.166125 Loss1: 0.506712 Loss2: 0.659413
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146998 Loss1: 0.483551 Loss2: 0.663446
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161635 Loss1: 0.498424 Loss2: 0.663210
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.167304 Loss1: 0.502853 Loss2: 0.664451
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.146333 Loss1: 0.479222 Loss2: 0.667111
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136104 Loss1: 0.474022 Loss2: 0.662082
+(DefaultActor pid=1831567) >> Training accuracy: 0.842226
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.315746 Loss1: 0.543785 Loss2: 0.771961
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.249234 Loss1: 0.523134 Loss2: 0.726100
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218247 Loss1: 0.491393 Loss2: 0.726854
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.215265 Loss1: 0.489837 Loss2: 0.725429
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224916 Loss1: 0.496089 Loss2: 0.728827
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.230328 Loss1: 0.500462 Loss2: 0.729867
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.230058 Loss1: 0.501214 Loss2: 0.728844
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212903 Loss1: 0.483111 Loss2: 0.729792
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217099 Loss1: 0.485469 Loss2: 0.731630
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229469 Loss1: 0.498552 Loss2: 0.730918
+(DefaultActor pid=1831567) >> Training accuracy: 0.840030
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.467405 Loss1: 0.716095 Loss2: 0.751310
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341810 Loss1: 0.672168 Loss2: 0.669641
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.345875 Loss1: 0.677810 Loss2: 0.668065
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.340935 Loss1: 0.672768 Loss2: 0.668167
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.337178 Loss1: 0.668836 Loss2: 0.668342
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.330788 Loss1: 0.655794 Loss2: 0.674995
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314629 Loss1: 0.641568 Loss2: 0.673061
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288973 Loss1: 0.615714 Loss2: 0.673259
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.286328 Loss1: 0.614329 Loss2: 0.671998
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289640 Loss1: 0.613719 Loss2: 0.675921
+(DefaultActor pid=1831567) >> Training accuracy: 0.788270
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341352 Loss1: 0.572081 Loss2: 0.769271
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.214798 Loss1: 0.529993 Loss2: 0.684805
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.180327 Loss1: 0.495210 Loss2: 0.685116
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.173049 Loss1: 0.488257 Loss2: 0.684792
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.166772 Loss1: 0.481867 Loss2: 0.684905
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.155285 Loss1: 0.470068 Loss2: 0.685217
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.145191 Loss1: 0.459242 Loss2: 0.685950
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.174089 Loss1: 0.484652 Loss2: 0.689437
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183487 Loss1: 0.490627 Loss2: 0.692861
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.149560 Loss1: 0.456440 Loss2: 0.693119
+(DefaultActor pid=1831567) >> Training accuracy: 0.849095
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.336307 Loss1: 0.584579 Loss2: 0.751728
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.166299 Loss1: 0.518260 Loss2: 0.648039
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.140218 Loss1: 0.493721 Loss2: 0.646497
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.131254 Loss1: 0.486480 Loss2: 0.644774
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.101042 Loss1: 0.455461 Loss2: 0.645581
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.126969 Loss1: 0.480393 Loss2: 0.646577
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.128723 Loss1: 0.479223 Loss2: 0.649500
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.114548 Loss1: 0.462392 Loss2: 0.652155
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.096796 Loss1: 0.448230 Loss2: 0.648566
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.113309 Loss1: 0.462303 Loss2: 0.651006
+(DefaultActor pid=1831567) >> Training accuracy: 0.836335
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.452722 Loss1: 0.695038 Loss2: 0.757684
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.362993 Loss1: 0.693831 Loss2: 0.669162
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.321902 Loss1: 0.651141 Loss2: 0.670761
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.343692 Loss1: 0.670966 Loss2: 0.672726
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.318813 Loss1: 0.647413 Loss2: 0.671400
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.302612 Loss1: 0.633022 Loss2: 0.669589
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299352 Loss1: 0.626836 Loss2: 0.672517
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.282935 Loss1: 0.609785 Loss2: 0.673150
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.303050 Loss1: 0.625345 Loss2: 0.677705
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.265307 Loss1: 0.591289 Loss2: 0.674018
+(DefaultActor pid=1831567) >> Training accuracy: 0.772155
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.230634 Loss1: 0.472436 Loss2: 0.758198
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.058699 Loss1: 0.382249 Loss2: 0.676451
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.068595 Loss1: 0.391312 Loss2: 0.677283
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.042831 Loss1: 0.367588 Loss2: 0.675244
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.033409 Loss1: 0.358210 Loss2: 0.675199
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.036338 Loss1: 0.358679 Loss2: 0.677658
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.033882 Loss1: 0.356291 Loss2: 0.677591
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.029456 Loss1: 0.348450 Loss2: 0.681006
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.013775 Loss1: 0.334378 Loss2: 0.679397
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.039898 Loss1: 0.359406 Loss2: 0.680492
+(DefaultActor pid=1831567) >> Training accuracy: 0.870949
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.463191 Loss1: 0.709005 Loss2: 0.754185
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.298160 Loss1: 0.646077 Loss2: 0.652084
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.270958 Loss1: 0.617013 Loss2: 0.653945
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.262086 Loss1: 0.608870 Loss2: 0.653216
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246622 Loss1: 0.591891 Loss2: 0.654731
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.271897 Loss1: 0.613514 Loss2: 0.658383
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.278423 Loss1: 0.616663 Loss2: 0.661761
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.245395 Loss1: 0.585188 Loss2: 0.660207
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.248369 Loss1: 0.587752 Loss2: 0.660617
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250175 Loss1: 0.586231 Loss2: 0.663944
+[2023-09-27 17:46:11,513][flwr][DEBUG] - fit_round 87 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.815241
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.698700
+[2023-09-27 17:46:13,159][flwr][INFO] - fit progress: (87, 0.8728009527102827, {'accuracy': 0.6987}, 41305.99577134708)
+[2023-09-27 17:46:13,160][flwr][DEBUG] - evaluate_round 87: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:46:45,188][flwr][DEBUG] - evaluate_round 87 received 10 results and 0 failures
+[2023-09-27 17:46:45,189][flwr][DEBUG] - fit_round 88: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.507696 Loss1: 0.709089 Loss2: 0.798607
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387023 Loss1: 0.676297 Loss2: 0.710726
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.357412 Loss1: 0.651522 Loss2: 0.705889
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.345929 Loss1: 0.634469 Loss2: 0.711460
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.362956 Loss1: 0.648897 Loss2: 0.714059
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.330503 Loss1: 0.621274 Loss2: 0.709229
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.310002 Loss1: 0.601561 Loss2: 0.708441
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.350400 Loss1: 0.637343 Loss2: 0.713056
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.334327 Loss1: 0.621262 Loss2: 0.713065
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.330045 Loss1: 0.615202 Loss2: 0.714843
+(DefaultActor pid=1831567) >> Training accuracy: 0.772388
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359104 Loss1: 0.570685 Loss2: 0.788419
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.193210 Loss1: 0.507838 Loss2: 0.685371
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.177976 Loss1: 0.494145 Loss2: 0.683832
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187695 Loss1: 0.503486 Loss2: 0.684210
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185553 Loss1: 0.498892 Loss2: 0.686661
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.145772 Loss1: 0.458610 Loss2: 0.687162
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171713 Loss1: 0.479698 Loss2: 0.692015
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.172531 Loss1: 0.481374 Loss2: 0.691157
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.132534 Loss1: 0.438657 Loss2: 0.693877
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148068 Loss1: 0.454983 Loss2: 0.693085
+(DefaultActor pid=1831567) >> Training accuracy: 0.847193
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.208443 Loss1: 0.429822 Loss2: 0.778621
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.104958 Loss1: 0.401076 Loss2: 0.703883
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.099087 Loss1: 0.394571 Loss2: 0.704516
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.079020 Loss1: 0.376602 Loss2: 0.702418
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.060163 Loss1: 0.359267 Loss2: 0.700895
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.069035 Loss1: 0.362111 Loss2: 0.706924
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.078325 Loss1: 0.371693 Loss2: 0.706631
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.063704 Loss1: 0.358920 Loss2: 0.704784
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.044958 Loss1: 0.339818 Loss2: 0.705140
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.049494 Loss1: 0.342667 Loss2: 0.706827
+(DefaultActor pid=1831567) >> Training accuracy: 0.870563
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.332638 Loss1: 0.567895 Loss2: 0.764743
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200776 Loss1: 0.517807 Loss2: 0.682970
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198664 Loss1: 0.516387 Loss2: 0.682277
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181389 Loss1: 0.497813 Loss2: 0.683576
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169682 Loss1: 0.486459 Loss2: 0.683223
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.172825 Loss1: 0.487354 Loss2: 0.685471
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175502 Loss1: 0.487868 Loss2: 0.687634
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.164361 Loss1: 0.477662 Loss2: 0.686699
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185053 Loss1: 0.496850 Loss2: 0.688204
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176714 Loss1: 0.487104 Loss2: 0.689610
+(DefaultActor pid=1831567) >> Training accuracy: 0.833534
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.276563 Loss1: 0.559325 Loss2: 0.717239
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.167086 Loss1: 0.528872 Loss2: 0.638214
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.136623 Loss1: 0.502317 Loss2: 0.634305
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.147576 Loss1: 0.506191 Loss2: 0.641386
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.126460 Loss1: 0.490300 Loss2: 0.636160
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.123122 Loss1: 0.486061 Loss2: 0.637061
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123249 Loss1: 0.483670 Loss2: 0.639579
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.138241 Loss1: 0.494568 Loss2: 0.643673
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.116994 Loss1: 0.473059 Loss2: 0.643936
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.122993 Loss1: 0.481765 Loss2: 0.641229
+(DefaultActor pid=1831567) >> Training accuracy: 0.854441
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.492965 Loss1: 0.708319 Loss2: 0.784646
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.328627 Loss1: 0.649596 Loss2: 0.679031
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.323273 Loss1: 0.646267 Loss2: 0.677006
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.287120 Loss1: 0.610106 Loss2: 0.677014
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.284346 Loss1: 0.604022 Loss2: 0.680324
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289594 Loss1: 0.608433 Loss2: 0.681161
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294931 Loss1: 0.616283 Loss2: 0.678648
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.250975 Loss1: 0.570843 Loss2: 0.680132
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.253681 Loss1: 0.571321 Loss2: 0.682360
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279920 Loss1: 0.595082 Loss2: 0.684838
+(DefaultActor pid=1831567) >> Training accuracy: 0.789200
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.349843 Loss1: 0.596748 Loss2: 0.753096
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.203796 Loss1: 0.519480 Loss2: 0.684316
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194858 Loss1: 0.512883 Loss2: 0.681974
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202151 Loss1: 0.519542 Loss2: 0.682609
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187764 Loss1: 0.503629 Loss2: 0.684135
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163181 Loss1: 0.480975 Loss2: 0.682205
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.167219 Loss1: 0.484701 Loss2: 0.682517
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.159322 Loss1: 0.474632 Loss2: 0.684690
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.177151 Loss1: 0.488382 Loss2: 0.688769
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.169023 Loss1: 0.481480 Loss2: 0.687543
+(DefaultActor pid=1831567) >> Training accuracy: 0.846608
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289917 Loss1: 0.529333 Loss2: 0.760584
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206155 Loss1: 0.491296 Loss2: 0.714859
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219486 Loss1: 0.503103 Loss2: 0.716383
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.233054 Loss1: 0.513557 Loss2: 0.719497
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.227749 Loss1: 0.506306 Loss2: 0.721443
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220545 Loss1: 0.503683 Loss2: 0.716861
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191197 Loss1: 0.471091 Loss2: 0.720106
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207861 Loss1: 0.487059 Loss2: 0.720802
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205797 Loss1: 0.485649 Loss2: 0.720148
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.183696 Loss1: 0.466759 Loss2: 0.716938
+(DefaultActor pid=1831567) >> Training accuracy: 0.833705
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.496859 Loss1: 0.742783 Loss2: 0.754076
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370987 Loss1: 0.704410 Loss2: 0.666578
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.353889 Loss1: 0.687871 Loss2: 0.666017
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.349900 Loss1: 0.681503 Loss2: 0.668397
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.331821 Loss1: 0.663050 Loss2: 0.668771
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.321716 Loss1: 0.651898 Loss2: 0.669818
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299527 Loss1: 0.629927 Loss2: 0.669600
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.314935 Loss1: 0.642438 Loss2: 0.672497
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.294102 Loss1: 0.625447 Loss2: 0.668656
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308984 Loss1: 0.635914 Loss2: 0.673070
+(DefaultActor pid=1831567) >> Training accuracy: 0.789629
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.220996 Loss1: 0.461679 Loss2: 0.759317
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.082360 Loss1: 0.409727 Loss2: 0.672633
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048222 Loss1: 0.377019 Loss2: 0.671203
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.038329 Loss1: 0.370163 Loss2: 0.668166
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.055247 Loss1: 0.385241 Loss2: 0.670005
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.030428 Loss1: 0.359434 Loss2: 0.670995
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.008357 Loss1: 0.337125 Loss2: 0.671231
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.049904 Loss1: 0.378715 Loss2: 0.671189
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.019051 Loss1: 0.348804 Loss2: 0.670247
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.021424 Loss1: 0.349997 Loss2: 0.671427
+[2023-09-27 17:53:25,458][flwr][DEBUG] - fit_round 88 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.882330
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.705700
+[2023-09-27 17:53:26,908][flwr][INFO] - fit progress: (88, 0.8516317514565807, {'accuracy': 0.7057}, 41739.744232431985)
+[2023-09-27 17:53:26,908][flwr][DEBUG] - evaluate_round 88: strategy sampled 10 clients (out of 10)
+[2023-09-27 17:53:58,203][flwr][DEBUG] - evaluate_round 88 received 10 results and 0 failures
+[2023-09-27 17:53:58,204][flwr][DEBUG] - fit_round 89: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.452386 Loss1: 0.693835 Loss2: 0.758551
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.357453 Loss1: 0.687537 Loss2: 0.669916
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337236 Loss1: 0.671361 Loss2: 0.665876
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.314879 Loss1: 0.646546 Loss2: 0.668333
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.297221 Loss1: 0.628683 Loss2: 0.668538
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.310280 Loss1: 0.639767 Loss2: 0.670514
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299484 Loss1: 0.624642 Loss2: 0.674842
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.274881 Loss1: 0.601612 Loss2: 0.673269
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.294358 Loss1: 0.619767 Loss2: 0.674592
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.272395 Loss1: 0.596455 Loss2: 0.675940
+(DefaultActor pid=1831567) >> Training accuracy: 0.774254
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.181574 Loss1: 0.445350 Loss2: 0.736225
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.058674 Loss1: 0.395081 Loss2: 0.663593
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.051431 Loss1: 0.389730 Loss2: 0.661701
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.031158 Loss1: 0.370897 Loss2: 0.660260
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.037268 Loss1: 0.374907 Loss2: 0.662361
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.038831 Loss1: 0.376114 Loss2: 0.662717
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.046306 Loss1: 0.382121 Loss2: 0.664185
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.021971 Loss1: 0.357502 Loss2: 0.664470
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.010978 Loss1: 0.347886 Loss2: 0.663092
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.023760 Loss1: 0.356510 Loss2: 0.667251
+(DefaultActor pid=1831567) >> Training accuracy: 0.873457
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.514023 Loss1: 0.718774 Loss2: 0.795249
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.331784 Loss1: 0.645518 Loss2: 0.686266
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.309540 Loss1: 0.624276 Loss2: 0.685264
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.303571 Loss1: 0.614226 Loss2: 0.689344
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.300127 Loss1: 0.608100 Loss2: 0.692027
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.288028 Loss1: 0.592691 Loss2: 0.695337
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.287911 Loss1: 0.594403 Loss2: 0.693508
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.271804 Loss1: 0.575280 Loss2: 0.696524
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.287946 Loss1: 0.587771 Loss2: 0.700175
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.247065 Loss1: 0.550962 Loss2: 0.696102
+(DefaultActor pid=1831567) >> Training accuracy: 0.805099
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.281318 Loss1: 0.559530 Loss2: 0.721788
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.192242 Loss1: 0.535556 Loss2: 0.656686
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173294 Loss1: 0.519226 Loss2: 0.654068
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.178973 Loss1: 0.521325 Loss2: 0.657648
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.180165 Loss1: 0.520875 Loss2: 0.659290
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162578 Loss1: 0.503912 Loss2: 0.658666
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.145789 Loss1: 0.488991 Loss2: 0.656798
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.149394 Loss1: 0.489282 Loss2: 0.660112
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.118080 Loss1: 0.456507 Loss2: 0.661573
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.142555 Loss1: 0.479314 Loss2: 0.663241
+(DefaultActor pid=1831567) >> Training accuracy: 0.854968
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.333484 Loss1: 0.529336 Loss2: 0.804148
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236371 Loss1: 0.516041 Loss2: 0.720330
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223614 Loss1: 0.504772 Loss2: 0.718842
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202688 Loss1: 0.482105 Loss2: 0.720583
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.207822 Loss1: 0.485379 Loss2: 0.722443
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.203013 Loss1: 0.479661 Loss2: 0.723352
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.186613 Loss1: 0.466090 Loss2: 0.720523
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204616 Loss1: 0.478132 Loss2: 0.726484
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.181779 Loss1: 0.460093 Loss2: 0.721686
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172772 Loss1: 0.446961 Loss2: 0.725811
+(DefaultActor pid=1831567) >> Training accuracy: 0.853618
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475638 Loss1: 0.722122 Loss2: 0.753515
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341297 Loss1: 0.670470 Loss2: 0.670826
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.349809 Loss1: 0.676017 Loss2: 0.673793
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.345737 Loss1: 0.671914 Loss2: 0.673823
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.365876 Loss1: 0.686791 Loss2: 0.679086
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.326483 Loss1: 0.649642 Loss2: 0.676841
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314135 Loss1: 0.638001 Loss2: 0.676134
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.299855 Loss1: 0.621455 Loss2: 0.678401
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331602 Loss1: 0.649710 Loss2: 0.681893
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.334676 Loss1: 0.649689 Loss2: 0.684987
+(DefaultActor pid=1831567) >> Training accuracy: 0.770380
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.332744 Loss1: 0.584889 Loss2: 0.747855
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.159260 Loss1: 0.509702 Loss2: 0.649558
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.149457 Loss1: 0.498705 Loss2: 0.650752
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.142427 Loss1: 0.492229 Loss2: 0.650198
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150700 Loss1: 0.498734 Loss2: 0.651967
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.123166 Loss1: 0.471818 Loss2: 0.651348
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.131629 Loss1: 0.480161 Loss2: 0.651468
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.085854 Loss1: 0.433084 Loss2: 0.652770
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.101304 Loss1: 0.450984 Loss2: 0.650320
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.098123 Loss1: 0.444352 Loss2: 0.653770
+(DefaultActor pid=1831567) >> Training accuracy: 0.848517
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.261070 Loss1: 0.527813 Loss2: 0.733257
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194124 Loss1: 0.503330 Loss2: 0.690794
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.204267 Loss1: 0.513225 Loss2: 0.691042
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199023 Loss1: 0.504795 Loss2: 0.694227
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.175123 Loss1: 0.482196 Loss2: 0.692927
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185242 Loss1: 0.491085 Loss2: 0.694157
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.169619 Loss1: 0.474313 Loss2: 0.695307
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.198778 Loss1: 0.501530 Loss2: 0.697247
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184473 Loss1: 0.492145 Loss2: 0.692328
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.185007 Loss1: 0.490712 Loss2: 0.694295
+(DefaultActor pid=1831567) >> Training accuracy: 0.849330
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.282585 Loss1: 0.550141 Loss2: 0.732444
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201534 Loss1: 0.543432 Loss2: 0.658102
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.179518 Loss1: 0.523651 Loss2: 0.655867
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.158126 Loss1: 0.503242 Loss2: 0.654884
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.188389 Loss1: 0.530328 Loss2: 0.658061
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.148145 Loss1: 0.493280 Loss2: 0.654865
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.138794 Loss1: 0.483080 Loss2: 0.655714
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.142856 Loss1: 0.482327 Loss2: 0.660528
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138812 Loss1: 0.480708 Loss2: 0.658104
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148346 Loss1: 0.489521 Loss2: 0.658825
+(DefaultActor pid=1831567) >> Training accuracy: 0.841463
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.210049 Loss1: 0.458839 Loss2: 0.751210
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.062024 Loss1: 0.394565 Loss2: 0.667459
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.034032 Loss1: 0.371846 Loss2: 0.662186
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.051450 Loss1: 0.385698 Loss2: 0.665752
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.035752 Loss1: 0.371081 Loss2: 0.664672
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.011511 Loss1: 0.347388 Loss2: 0.664123
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.005968 Loss1: 0.340251 Loss2: 0.665717
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.013078 Loss1: 0.345954 Loss2: 0.667124
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.010020 Loss1: 0.342597 Loss2: 0.667423
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.013268 Loss1: 0.345716 Loss2: 0.667552
+[2023-09-27 18:00:32,959][flwr][DEBUG] - fit_round 89 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.879630
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.703800
+[2023-09-27 18:00:34,342][flwr][INFO] - fit progress: (89, 0.8544797211790237, {'accuracy': 0.7038}, 42167.1785627068)
+[2023-09-27 18:00:34,343][flwr][DEBUG] - evaluate_round 89: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:01:04,805][flwr][DEBUG] - evaluate_round 89 received 10 results and 0 failures
+[2023-09-27 18:01:04,805][flwr][DEBUG] - fit_round 90: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.189360 Loss1: 0.456074 Loss2: 0.733286
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.042452 Loss1: 0.391218 Loss2: 0.651234
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.049769 Loss1: 0.396813 Loss2: 0.652956
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.016271 Loss1: 0.365395 Loss2: 0.650876
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.033044 Loss1: 0.381477 Loss2: 0.651567
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.018037 Loss1: 0.365510 Loss2: 0.652528
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.016064 Loss1: 0.363898 Loss2: 0.652166
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.012260 Loss1: 0.358922 Loss2: 0.653338
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.009925 Loss1: 0.356042 Loss2: 0.653883
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.991634 Loss1: 0.337703 Loss2: 0.653931
+(DefaultActor pid=1831567) >> Training accuracy: 0.873264
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.234589 Loss1: 0.526749 Loss2: 0.707840
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187658 Loss1: 0.517869 Loss2: 0.669789
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188182 Loss1: 0.517551 Loss2: 0.670631
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.164242 Loss1: 0.495755 Loss2: 0.668487
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.174373 Loss1: 0.498871 Loss2: 0.675503
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.154103 Loss1: 0.481411 Loss2: 0.672692
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170796 Loss1: 0.497946 Loss2: 0.672850
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158529 Loss1: 0.485932 Loss2: 0.672598
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.152844 Loss1: 0.478981 Loss2: 0.673863
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.157527 Loss1: 0.484101 Loss2: 0.673426
+(DefaultActor pid=1831567) >> Training accuracy: 0.837302
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.206051 Loss1: 0.448987 Loss2: 0.757063
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.065366 Loss1: 0.382917 Loss2: 0.682449
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.074031 Loss1: 0.393411 Loss2: 0.680621
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.045905 Loss1: 0.363903 Loss2: 0.682002
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.062472 Loss1: 0.377905 Loss2: 0.684567
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.045865 Loss1: 0.364285 Loss2: 0.681579
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051254 Loss1: 0.368724 Loss2: 0.682530
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.033756 Loss1: 0.351388 Loss2: 0.682367
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.038641 Loss1: 0.356027 Loss2: 0.682614
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.033596 Loss1: 0.350917 Loss2: 0.682679
+(DefaultActor pid=1831567) >> Training accuracy: 0.879051
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.343663 Loss1: 0.571863 Loss2: 0.771800
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.213291 Loss1: 0.517522 Loss2: 0.695769
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222679 Loss1: 0.528656 Loss2: 0.694024
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.211040 Loss1: 0.512906 Loss2: 0.698133
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.180337 Loss1: 0.482840 Loss2: 0.697497
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181217 Loss1: 0.486530 Loss2: 0.694687
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192197 Loss1: 0.493913 Loss2: 0.698284
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.181084 Loss1: 0.482580 Loss2: 0.698504
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184070 Loss1: 0.481412 Loss2: 0.702658
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165431 Loss1: 0.465150 Loss2: 0.700281
+(DefaultActor pid=1831567) >> Training accuracy: 0.833333
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.474439 Loss1: 0.719106 Loss2: 0.755333
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.302590 Loss1: 0.653387 Loss2: 0.649203
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.273491 Loss1: 0.623948 Loss2: 0.649543
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.269132 Loss1: 0.616871 Loss2: 0.652262
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.238496 Loss1: 0.584726 Loss2: 0.653769
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.259003 Loss1: 0.603300 Loss2: 0.655703
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.246966 Loss1: 0.592169 Loss2: 0.654797
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.233184 Loss1: 0.576782 Loss2: 0.656402
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.241995 Loss1: 0.586250 Loss2: 0.655745
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.217479 Loss1: 0.561856 Loss2: 0.655623
+(DefaultActor pid=1831567) >> Training accuracy: 0.804002
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462536 Loss1: 0.712483 Loss2: 0.750053
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347465 Loss1: 0.683730 Loss2: 0.663734
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.344140 Loss1: 0.676052 Loss2: 0.668088
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.326454 Loss1: 0.659523 Loss2: 0.666931
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322738 Loss1: 0.652626 Loss2: 0.670112
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306891 Loss1: 0.636237 Loss2: 0.670653
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.312336 Loss1: 0.639618 Loss2: 0.672718
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329874 Loss1: 0.656171 Loss2: 0.673703
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.335400 Loss1: 0.657385 Loss2: 0.678015
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.287976 Loss1: 0.612411 Loss2: 0.675565
+(DefaultActor pid=1831567) >> Training accuracy: 0.788496
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.337981 Loss1: 0.580833 Loss2: 0.757149
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225143 Loss1: 0.539147 Loss2: 0.685996
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.209822 Loss1: 0.528529 Loss2: 0.681293
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.193855 Loss1: 0.508738 Loss2: 0.685116
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.186846 Loss1: 0.501477 Loss2: 0.685369
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.187799 Loss1: 0.499974 Loss2: 0.687826
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.181962 Loss1: 0.494842 Loss2: 0.687119
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162221 Loss1: 0.473803 Loss2: 0.688418
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.162253 Loss1: 0.473352 Loss2: 0.688901
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.151189 Loss1: 0.464028 Loss2: 0.687161
+(DefaultActor pid=1831567) >> Training accuracy: 0.842607
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.301743 Loss1: 0.587541 Loss2: 0.714202
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.157302 Loss1: 0.518303 Loss2: 0.638998
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.146565 Loss1: 0.505322 Loss2: 0.641242
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.128767 Loss1: 0.489313 Loss2: 0.639454
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.146081 Loss1: 0.505722 Loss2: 0.640358
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.126216 Loss1: 0.484663 Loss2: 0.641553
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.113824 Loss1: 0.471306 Loss2: 0.642519
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.116703 Loss1: 0.474980 Loss2: 0.641723
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.115442 Loss1: 0.470788 Loss2: 0.644653
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.095804 Loss1: 0.453626 Loss2: 0.642178
+(DefaultActor pid=1831567) >> Training accuracy: 0.835526
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.377279 Loss1: 0.594101 Loss2: 0.783178
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.198322 Loss1: 0.511916 Loss2: 0.686406
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173333 Loss1: 0.495554 Loss2: 0.677779
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.173129 Loss1: 0.488962 Loss2: 0.684167
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170702 Loss1: 0.486122 Loss2: 0.684579
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146742 Loss1: 0.465695 Loss2: 0.681046
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.130422 Loss1: 0.445812 Loss2: 0.684610
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140535 Loss1: 0.455527 Loss2: 0.685008
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163487 Loss1: 0.474109 Loss2: 0.689377
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148919 Loss1: 0.461548 Loss2: 0.687370
+(DefaultActor pid=1831567) >> Training accuracy: 0.857256
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.492912 Loss1: 0.703841 Loss2: 0.789071
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.364643 Loss1: 0.666982 Loss2: 0.697661
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.357271 Loss1: 0.661572 Loss2: 0.695698
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347292 Loss1: 0.651212 Loss2: 0.696080
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.355154 Loss1: 0.656572 Loss2: 0.698582
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.350352 Loss1: 0.646372 Loss2: 0.703979
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322612 Loss1: 0.623740 Loss2: 0.698872
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.302210 Loss1: 0.604206 Loss2: 0.698004
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.310726 Loss1: 0.609371 Loss2: 0.701355
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290251 Loss1: 0.589053 Loss2: 0.701198
+(DefaultActor pid=1831567) >> Training accuracy: 0.784515
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 18:08:01,063][flwr][DEBUG] - fit_round 90 received 10 results and 0 failures
+>> Test accuracy: 0.707800
+[2023-09-27 18:08:02,593][flwr][INFO] - fit progress: (90, 0.8642163840345681, {'accuracy': 0.7078}, 42615.42914600996)
+[2023-09-27 18:08:02,593][flwr][DEBUG] - evaluate_round 90: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:08:33,394][flwr][DEBUG] - evaluate_round 90 received 10 results and 0 failures
+[2023-09-27 18:08:33,395][flwr][DEBUG] - fit_round 91: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.294979 Loss1: 0.556368 Loss2: 0.738611
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.162098 Loss1: 0.522108 Loss2: 0.639990
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155986 Loss1: 0.518373 Loss2: 0.637613
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.128554 Loss1: 0.491833 Loss2: 0.636721
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.111824 Loss1: 0.474510 Loss2: 0.637314
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.110636 Loss1: 0.471091 Loss2: 0.639545
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.098417 Loss1: 0.457262 Loss2: 0.641155
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.066887 Loss1: 0.428834 Loss2: 0.638053
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.091870 Loss1: 0.448358 Loss2: 0.643512
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.062314 Loss1: 0.421832 Loss2: 0.640482
+(DefaultActor pid=1831567) >> Training accuracy: 0.845339
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493497 Loss1: 0.730413 Loss2: 0.763084
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370013 Loss1: 0.690305 Loss2: 0.679708
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363701 Loss1: 0.684713 Loss2: 0.678989
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.363809 Loss1: 0.681712 Loss2: 0.682097
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.324046 Loss1: 0.645950 Loss2: 0.678097
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.349229 Loss1: 0.667585 Loss2: 0.681644
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.308214 Loss1: 0.624818 Loss2: 0.683396
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.317602 Loss1: 0.631997 Loss2: 0.685604
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.318911 Loss1: 0.634010 Loss2: 0.684901
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.310421 Loss1: 0.623896 Loss2: 0.686525
+(DefaultActor pid=1831567) >> Training accuracy: 0.785779
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517917 Loss1: 0.710012 Loss2: 0.807905
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329462 Loss1: 0.627631 Loss2: 0.701831
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318209 Loss1: 0.616289 Loss2: 0.701920
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.300614 Loss1: 0.598626 Loss2: 0.701988
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322061 Loss1: 0.616791 Loss2: 0.705270
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.318034 Loss1: 0.610770 Loss2: 0.707264
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.303179 Loss1: 0.595885 Loss2: 0.707294
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.292641 Loss1: 0.585433 Loss2: 0.707208
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.269458 Loss1: 0.562348 Loss2: 0.707110
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.263640 Loss1: 0.552000 Loss2: 0.711639
+(DefaultActor pid=1831567) >> Training accuracy: 0.816338
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.432917 Loss1: 0.702612 Loss2: 0.730305
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304803 Loss1: 0.658064 Loss2: 0.646739
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307836 Loss1: 0.662376 Loss2: 0.645460
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.319741 Loss1: 0.669472 Loss2: 0.650269
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.269302 Loss1: 0.622324 Loss2: 0.646979
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.271815 Loss1: 0.624838 Loss2: 0.646977
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.246114 Loss1: 0.597448 Loss2: 0.648666
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.265835 Loss1: 0.616166 Loss2: 0.649670
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.263890 Loss1: 0.616116 Loss2: 0.647774
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.227660 Loss1: 0.579879 Loss2: 0.647781
+(DefaultActor pid=1831567) >> Training accuracy: 0.801306
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358521 Loss1: 0.562808 Loss2: 0.795713
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227293 Loss1: 0.518631 Loss2: 0.708662
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212402 Loss1: 0.504053 Loss2: 0.708349
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.203046 Loss1: 0.491615 Loss2: 0.711431
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192293 Loss1: 0.480715 Loss2: 0.711578
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180329 Loss1: 0.470618 Loss2: 0.709711
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174071 Loss1: 0.464474 Loss2: 0.709597
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173945 Loss1: 0.460343 Loss2: 0.713602
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182034 Loss1: 0.465099 Loss2: 0.716935
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.147392 Loss1: 0.435798 Loss2: 0.711595
+(DefaultActor pid=1831567) >> Training accuracy: 0.856086
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.307380 Loss1: 0.536494 Loss2: 0.770885
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216238 Loss1: 0.494038 Loss2: 0.722200
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.225971 Loss1: 0.502515 Loss2: 0.723456
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.207055 Loss1: 0.483219 Loss2: 0.723836
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215577 Loss1: 0.492835 Loss2: 0.722742
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.230746 Loss1: 0.503986 Loss2: 0.726759
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200397 Loss1: 0.473729 Loss2: 0.726667
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.227474 Loss1: 0.496640 Loss2: 0.730834
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230160 Loss1: 0.501758 Loss2: 0.728402
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.217404 Loss1: 0.487501 Loss2: 0.729903
+(DefaultActor pid=1831567) >> Training accuracy: 0.834449
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.337852 Loss1: 0.602597 Loss2: 0.735255
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200805 Loss1: 0.537873 Loss2: 0.662932
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199264 Loss1: 0.539885 Loss2: 0.659379
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.160408 Loss1: 0.504864 Loss2: 0.655544
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163692 Loss1: 0.507403 Loss2: 0.656289
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157329 Loss1: 0.499938 Loss2: 0.657392
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.158848 Loss1: 0.503091 Loss2: 0.655756
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.145980 Loss1: 0.486383 Loss2: 0.659597
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.141462 Loss1: 0.483447 Loss2: 0.658015
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.161944 Loss1: 0.500553 Loss2: 0.661392
+(DefaultActor pid=1831567) >> Training accuracy: 0.842797
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.319831 Loss1: 0.573926 Loss2: 0.745905
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.197501 Loss1: 0.528371 Loss2: 0.669130
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191130 Loss1: 0.518606 Loss2: 0.672524
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183073 Loss1: 0.512701 Loss2: 0.670371
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.151648 Loss1: 0.478066 Loss2: 0.673582
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167461 Loss1: 0.492573 Loss2: 0.674888
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163458 Loss1: 0.488279 Loss2: 0.675179
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168624 Loss1: 0.492040 Loss2: 0.676583
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.169069 Loss1: 0.490129 Loss2: 0.678940
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.130059 Loss1: 0.454723 Loss2: 0.675337
+(DefaultActor pid=1831567) >> Training accuracy: 0.857372
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.194107 Loss1: 0.451307 Loss2: 0.742799
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.059528 Loss1: 0.398308 Loss2: 0.661220
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.044508 Loss1: 0.384862 Loss2: 0.659645
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.022587 Loss1: 0.360838 Loss2: 0.661748
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.028337 Loss1: 0.368630 Loss2: 0.659707
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.041214 Loss1: 0.378977 Loss2: 0.662237
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.027167 Loss1: 0.366105 Loss2: 0.661062
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.027200 Loss1: 0.363810 Loss2: 0.663390
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.002653 Loss1: 0.339337 Loss2: 0.663316
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.023565 Loss1: 0.360593 Loss2: 0.662972
+(DefaultActor pid=1831567) >> Training accuracy: 0.885802
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.196679 Loss1: 0.446161 Loss2: 0.750518
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.045282 Loss1: 0.377927 Loss2: 0.667355
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048063 Loss1: 0.382531 Loss2: 0.665533
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.027698 Loss1: 0.361793 Loss2: 0.665905
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.032380 Loss1: 0.367531 Loss2: 0.664849
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.029629 Loss1: 0.363490 Loss2: 0.666139
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.000711 Loss1: 0.334718 Loss2: 0.665993
+(DefaultActor pid=1831567) Epoch: 7 Loss: 0.993695 Loss1: 0.327346 Loss2: 0.666349
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028469 Loss1: 0.360459 Loss2: 0.668010
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.001857 Loss1: 0.335765 Loss2: 0.666092
+[2023-09-27 18:15:50,451][flwr][DEBUG] - fit_round 91 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.889853
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.706800
+[2023-09-27 18:15:51,867][flwr][INFO] - fit progress: (91, 0.8547620579076651, {'accuracy': 0.7068}, 43084.703277640045)
+[2023-09-27 18:15:51,867][flwr][DEBUG] - evaluate_round 91: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:16:23,352][flwr][DEBUG] - evaluate_round 91 received 10 results and 0 failures
+[2023-09-27 18:16:23,353][flwr][DEBUG] - fit_round 92: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.351543 Loss1: 0.578846 Loss2: 0.772697
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243998 Loss1: 0.541146 Loss2: 0.702852
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222491 Loss1: 0.522831 Loss2: 0.699659
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199841 Loss1: 0.504622 Loss2: 0.695219
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210201 Loss1: 0.505858 Loss2: 0.704343
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.204536 Loss1: 0.500909 Loss2: 0.703627
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.189801 Loss1: 0.485901 Loss2: 0.703900
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.197366 Loss1: 0.493599 Loss2: 0.703768
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182151 Loss1: 0.478116 Loss2: 0.704036
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180293 Loss1: 0.476894 Loss2: 0.703399
+(DefaultActor pid=1831567) >> Training accuracy: 0.844703
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.251869 Loss1: 0.534211 Loss2: 0.717659
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.175667 Loss1: 0.501590 Loss2: 0.674077
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188722 Loss1: 0.513730 Loss2: 0.674991
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.169337 Loss1: 0.493255 Loss2: 0.676081
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163984 Loss1: 0.487986 Loss2: 0.675998
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.174638 Loss1: 0.495261 Loss2: 0.679377
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164467 Loss1: 0.485972 Loss2: 0.678495
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168049 Loss1: 0.486853 Loss2: 0.681197
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163368 Loss1: 0.481280 Loss2: 0.682088
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.160771 Loss1: 0.482318 Loss2: 0.678453
+(DefaultActor pid=1831567) >> Training accuracy: 0.838790
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.172887 Loss1: 0.441809 Loss2: 0.731078
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.084074 Loss1: 0.426978 Loss2: 0.657096
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.029409 Loss1: 0.376360 Loss2: 0.653049
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.032970 Loss1: 0.377559 Loss2: 0.655411
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.015917 Loss1: 0.361354 Loss2: 0.654563
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.009810 Loss1: 0.354008 Loss2: 0.655802
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.015264 Loss1: 0.357263 Loss2: 0.658001
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026928 Loss1: 0.366495 Loss2: 0.660432
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.015967 Loss1: 0.356020 Loss2: 0.659947
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.997532 Loss1: 0.337055 Loss2: 0.660477
+(DefaultActor pid=1831567) >> Training accuracy: 0.887539
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.287935 Loss1: 0.570554 Loss2: 0.717381
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.144843 Loss1: 0.507838 Loss2: 0.637005
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.135667 Loss1: 0.499830 Loss2: 0.635838
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.143380 Loss1: 0.506462 Loss2: 0.636918
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.107132 Loss1: 0.469850 Loss2: 0.637283
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131515 Loss1: 0.490799 Loss2: 0.640716
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.106118 Loss1: 0.467513 Loss2: 0.638605
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.116198 Loss1: 0.476441 Loss2: 0.639757
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120011 Loss1: 0.477753 Loss2: 0.642258
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.118780 Loss1: 0.473423 Loss2: 0.645357
+(DefaultActor pid=1831567) >> Training accuracy: 0.860814
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.178923 Loss1: 0.436106 Loss2: 0.742817
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.078437 Loss1: 0.403982 Loss2: 0.674455
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.067005 Loss1: 0.394671 Loss2: 0.672334
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.051285 Loss1: 0.378344 Loss2: 0.672941
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.039626 Loss1: 0.369180 Loss2: 0.670446
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.021414 Loss1: 0.350442 Loss2: 0.670972
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051188 Loss1: 0.377768 Loss2: 0.673420
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.029950 Loss1: 0.354403 Loss2: 0.675547
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.014912 Loss1: 0.342021 Loss2: 0.672892
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.018811 Loss1: 0.344981 Loss2: 0.673829
+(DefaultActor pid=1831567) >> Training accuracy: 0.878086
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462531 Loss1: 0.702067 Loss2: 0.760464
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.294381 Loss1: 0.642764 Loss2: 0.651617
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.281311 Loss1: 0.632306 Loss2: 0.649005
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.277225 Loss1: 0.622776 Loss2: 0.654449
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.266947 Loss1: 0.614359 Loss2: 0.652589
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231863 Loss1: 0.578445 Loss2: 0.653419
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.264977 Loss1: 0.610300 Loss2: 0.654677
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.244049 Loss1: 0.589301 Loss2: 0.654748
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230472 Loss1: 0.573826 Loss2: 0.656645
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.224607 Loss1: 0.569687 Loss2: 0.654920
+(DefaultActor pid=1831567) >> Training accuracy: 0.793860
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358662 Loss1: 0.569663 Loss2: 0.789000
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.207384 Loss1: 0.526834 Loss2: 0.680551
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.156308 Loss1: 0.476574 Loss2: 0.679734
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188480 Loss1: 0.505617 Loss2: 0.682863
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158037 Loss1: 0.474731 Loss2: 0.683306
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.121957 Loss1: 0.440553 Loss2: 0.681405
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.124763 Loss1: 0.440323 Loss2: 0.684440
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140078 Loss1: 0.454957 Loss2: 0.685121
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136283 Loss1: 0.450308 Loss2: 0.685975
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.128613 Loss1: 0.437972 Loss2: 0.690642
+(DefaultActor pid=1831567) >> Training accuracy: 0.835805
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.526140 Loss1: 0.725320 Loss2: 0.800821
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.376417 Loss1: 0.670078 Loss2: 0.706339
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354365 Loss1: 0.647216 Loss2: 0.707149
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.358819 Loss1: 0.651866 Loss2: 0.706953
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.355480 Loss1: 0.644841 Loss2: 0.710639
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337490 Loss1: 0.630012 Loss2: 0.707477
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.323005 Loss1: 0.616342 Loss2: 0.706663
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.325215 Loss1: 0.615539 Loss2: 0.709677
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.327249 Loss1: 0.615864 Loss2: 0.711385
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.306910 Loss1: 0.595276 Loss2: 0.711634
+(DefaultActor pid=1831567) >> Training accuracy: 0.778685
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.337870 Loss1: 0.566669 Loss2: 0.771201
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.226108 Loss1: 0.534820 Loss2: 0.691289
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213716 Loss1: 0.523421 Loss2: 0.690295
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.213647 Loss1: 0.520545 Loss2: 0.693101
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.189085 Loss1: 0.498545 Loss2: 0.690539
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.179577 Loss1: 0.484876 Loss2: 0.694701
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182556 Loss1: 0.489988 Loss2: 0.692567
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155918 Loss1: 0.461616 Loss2: 0.694303
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184532 Loss1: 0.490362 Loss2: 0.694171
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.166559 Loss1: 0.471545 Loss2: 0.695014
+(DefaultActor pid=1831567) >> Training accuracy: 0.837340
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.460598 Loss1: 0.729050 Loss2: 0.731547
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.333445 Loss1: 0.688571 Loss2: 0.644873
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.338793 Loss1: 0.692664 Loss2: 0.646129
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.300545 Loss1: 0.652508 Loss2: 0.648037
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302119 Loss1: 0.654644 Loss2: 0.647475
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.291726 Loss1: 0.643242 Loss2: 0.648484
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.312348 Loss1: 0.660165 Loss2: 0.652183
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.283779 Loss1: 0.631234 Loss2: 0.652544
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267355 Loss1: 0.615903 Loss2: 0.651452
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.243184 Loss1: 0.590469 Loss2: 0.652715
+[2023-09-27 18:23:04,059][flwr][DEBUG] - fit_round 92 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.784873
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.704600
+[2023-09-27 18:23:05,693][flwr][INFO] - fit progress: (92, 0.8611169435536138, {'accuracy': 0.7046}, 43518.529675052036)
+[2023-09-27 18:23:05,694][flwr][DEBUG] - evaluate_round 92: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:23:38,189][flwr][DEBUG] - evaluate_round 92 received 10 results and 0 failures
+[2023-09-27 18:23:38,190][flwr][DEBUG] - fit_round 93: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.325426 Loss1: 0.575375 Loss2: 0.750051
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.156793 Loss1: 0.511973 Loss2: 0.644821
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.132132 Loss1: 0.487476 Loss2: 0.644656
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.111729 Loss1: 0.466460 Loss2: 0.645269
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.114760 Loss1: 0.466222 Loss2: 0.648538
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.113449 Loss1: 0.467182 Loss2: 0.646267
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.100095 Loss1: 0.447896 Loss2: 0.652199
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.096133 Loss1: 0.447784 Loss2: 0.648349
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.094667 Loss1: 0.446190 Loss2: 0.648477
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.085448 Loss1: 0.437037 Loss2: 0.648411
+(DefaultActor pid=1831567) >> Training accuracy: 0.855403
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.463395 Loss1: 0.724386 Loss2: 0.739009
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.325966 Loss1: 0.668234 Loss2: 0.657732
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.304147 Loss1: 0.647735 Loss2: 0.656411
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325142 Loss1: 0.667558 Loss2: 0.657584
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.292579 Loss1: 0.634925 Loss2: 0.657654
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289136 Loss1: 0.632459 Loss2: 0.656677
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.292192 Loss1: 0.635907 Loss2: 0.656285
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276686 Loss1: 0.614482 Loss2: 0.662205
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.269726 Loss1: 0.611497 Loss2: 0.658229
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.263570 Loss1: 0.603388 Loss2: 0.660183
+(DefaultActor pid=1831567) >> Training accuracy: 0.770289
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.464784 Loss1: 0.712405 Loss2: 0.752379
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.365528 Loss1: 0.694593 Loss2: 0.670935
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354858 Loss1: 0.681156 Loss2: 0.673702
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.344296 Loss1: 0.669940 Loss2: 0.674356
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317381 Loss1: 0.644267 Loss2: 0.673115
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328224 Loss1: 0.654782 Loss2: 0.673442
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.326740 Loss1: 0.649627 Loss2: 0.677113
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.321813 Loss1: 0.643943 Loss2: 0.677871
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.300997 Loss1: 0.623078 Loss2: 0.677919
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.327048 Loss1: 0.646776 Loss2: 0.680272
+(DefaultActor pid=1831567) >> Training accuracy: 0.791893
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.193993 Loss1: 0.449521 Loss2: 0.744472
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.074319 Loss1: 0.403644 Loss2: 0.670674
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.074835 Loss1: 0.405537 Loss2: 0.669298
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.042998 Loss1: 0.376236 Loss2: 0.666763
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.038369 Loss1: 0.372127 Loss2: 0.666243
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.051072 Loss1: 0.385972 Loss2: 0.665100
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019304 Loss1: 0.353373 Loss2: 0.665931
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.024217 Loss1: 0.357541 Loss2: 0.666676
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.025361 Loss1: 0.358038 Loss2: 0.667323
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.013142 Loss1: 0.343471 Loss2: 0.669671
+(DefaultActor pid=1831567) >> Training accuracy: 0.880015
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.231381 Loss1: 0.452354 Loss2: 0.779026
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.087691 Loss1: 0.391983 Loss2: 0.695708
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.096610 Loss1: 0.403890 Loss2: 0.692719
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.063825 Loss1: 0.369256 Loss2: 0.694569
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.070408 Loss1: 0.376095 Loss2: 0.694313
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.055987 Loss1: 0.360212 Loss2: 0.695775
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.030272 Loss1: 0.333391 Loss2: 0.696881
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.044749 Loss1: 0.344850 Loss2: 0.699899
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.044953 Loss1: 0.349695 Loss2: 0.695258
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.052648 Loss1: 0.355191 Loss2: 0.697458
+(DefaultActor pid=1831567) >> Training accuracy: 0.893133
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.301782 Loss1: 0.579410 Loss2: 0.722372
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194094 Loss1: 0.545876 Loss2: 0.648218
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.151485 Loss1: 0.506404 Loss2: 0.645081
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.149181 Loss1: 0.506922 Loss2: 0.642259
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140851 Loss1: 0.499185 Loss2: 0.641667
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.121224 Loss1: 0.478471 Loss2: 0.642753
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.113021 Loss1: 0.470083 Loss2: 0.642938
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.139641 Loss1: 0.494492 Loss2: 0.645148
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120894 Loss1: 0.477873 Loss2: 0.643022
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140672 Loss1: 0.494499 Loss2: 0.646174
+(DefaultActor pid=1831567) >> Training accuracy: 0.833079
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.298304 Loss1: 0.530855 Loss2: 0.767449
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223839 Loss1: 0.502678 Loss2: 0.721161
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.224144 Loss1: 0.501080 Loss2: 0.723063
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231501 Loss1: 0.508884 Loss2: 0.722617
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216354 Loss1: 0.491568 Loss2: 0.724786
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.233464 Loss1: 0.509450 Loss2: 0.724014
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.225305 Loss1: 0.498979 Loss2: 0.726326
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191735 Loss1: 0.465923 Loss2: 0.725811
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.225156 Loss1: 0.495800 Loss2: 0.729356
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.220930 Loss1: 0.492516 Loss2: 0.728414
+(DefaultActor pid=1831567) >> Training accuracy: 0.832093
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.351336 Loss1: 0.564150 Loss2: 0.787186
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.213609 Loss1: 0.513801 Loss2: 0.699808
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199672 Loss1: 0.503045 Loss2: 0.696627
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.198488 Loss1: 0.500754 Loss2: 0.697734
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184695 Loss1: 0.487244 Loss2: 0.697452
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199644 Loss1: 0.499999 Loss2: 0.699645
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174135 Loss1: 0.474337 Loss2: 0.699798
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168871 Loss1: 0.467736 Loss2: 0.701135
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.164158 Loss1: 0.465639 Loss2: 0.698519
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.153929 Loss1: 0.448802 Loss2: 0.705127
+(DefaultActor pid=1831567) >> Training accuracy: 0.833470
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.322577 Loss1: 0.573174 Loss2: 0.749403
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.186984 Loss1: 0.511238 Loss2: 0.675746
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.204359 Loss1: 0.524852 Loss2: 0.679507
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177219 Loss1: 0.500605 Loss2: 0.676614
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163514 Loss1: 0.488260 Loss2: 0.675255
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.179300 Loss1: 0.501152 Loss2: 0.678147
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161264 Loss1: 0.480482 Loss2: 0.680783
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.146652 Loss1: 0.466107 Loss2: 0.680545
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165313 Loss1: 0.481820 Loss2: 0.683493
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.162573 Loss1: 0.479772 Loss2: 0.682802
+(DefaultActor pid=1831567) >> Training accuracy: 0.849159
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517826 Loss1: 0.721557 Loss2: 0.796269
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.318928 Loss1: 0.636076 Loss2: 0.682852
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.298937 Loss1: 0.617749 Loss2: 0.681187
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.283541 Loss1: 0.596455 Loss2: 0.687086
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.287802 Loss1: 0.601870 Loss2: 0.685932
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249674 Loss1: 0.564548 Loss2: 0.685127
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.248086 Loss1: 0.560766 Loss2: 0.687320
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276135 Loss1: 0.587420 Loss2: 0.688715
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257769 Loss1: 0.567340 Loss2: 0.690428
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.260884 Loss1: 0.567150 Loss2: 0.693734
+[2023-09-27 18:30:16,379][flwr][DEBUG] - fit_round 93 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.802632
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.701000
+[2023-09-27 18:30:18,020][flwr][INFO] - fit progress: (93, 0.863727054847315, {'accuracy': 0.701}, 43950.856044563)
+[2023-09-27 18:30:18,020][flwr][DEBUG] - evaluate_round 93: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:30:49,439][flwr][DEBUG] - evaluate_round 93 received 10 results and 0 failures
+[2023-09-27 18:30:49,440][flwr][DEBUG] - fit_round 94: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.284334 Loss1: 0.541783 Loss2: 0.742551
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201367 Loss1: 0.503209 Loss2: 0.698158
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213209 Loss1: 0.514012 Loss2: 0.699198
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217906 Loss1: 0.517147 Loss2: 0.700759
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.202073 Loss1: 0.500899 Loss2: 0.701174
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.192157 Loss1: 0.493714 Loss2: 0.698443
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195092 Loss1: 0.492737 Loss2: 0.702355
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190636 Loss1: 0.491573 Loss2: 0.699063
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175294 Loss1: 0.474175 Loss2: 0.701120
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.177087 Loss1: 0.477381 Loss2: 0.699706
+(DefaultActor pid=1831567) >> Training accuracy: 0.834697
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.441438 Loss1: 0.671319 Loss2: 0.770120
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.310803 Loss1: 0.649771 Loss2: 0.661031
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307069 Loss1: 0.641457 Loss2: 0.665611
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.269858 Loss1: 0.606673 Loss2: 0.663184
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.257730 Loss1: 0.589791 Loss2: 0.667939
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289883 Loss1: 0.621899 Loss2: 0.667984
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.262278 Loss1: 0.592255 Loss2: 0.670023
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.240381 Loss1: 0.569781 Loss2: 0.670601
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230591 Loss1: 0.557554 Loss2: 0.673038
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.243533 Loss1: 0.569869 Loss2: 0.673664
+(DefaultActor pid=1831567) >> Training accuracy: 0.794682
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493836 Loss1: 0.699601 Loss2: 0.794235
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370966 Loss1: 0.670091 Loss2: 0.700875
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.349535 Loss1: 0.648649 Loss2: 0.700886
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.367288 Loss1: 0.663899 Loss2: 0.703389
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.337395 Loss1: 0.633779 Loss2: 0.703616
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.323327 Loss1: 0.621362 Loss2: 0.701965
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.329066 Loss1: 0.622700 Loss2: 0.706366
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.310036 Loss1: 0.606028 Loss2: 0.704008
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305618 Loss1: 0.597332 Loss2: 0.708286
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329407 Loss1: 0.619508 Loss2: 0.709900
+(DefaultActor pid=1831567) >> Training accuracy: 0.779618
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.352572 Loss1: 0.586365 Loss2: 0.766206
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.203686 Loss1: 0.510058 Loss2: 0.693628
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214102 Loss1: 0.525922 Loss2: 0.688180
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.204074 Loss1: 0.515192 Loss2: 0.688882
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167185 Loss1: 0.478086 Loss2: 0.689099
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183637 Loss1: 0.491924 Loss2: 0.691713
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171360 Loss1: 0.477898 Loss2: 0.693461
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161998 Loss1: 0.468296 Loss2: 0.693702
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183975 Loss1: 0.488018 Loss2: 0.695957
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.162270 Loss1: 0.468762 Loss2: 0.693508
+(DefaultActor pid=1831567) >> Training accuracy: 0.837843
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.204173 Loss1: 0.444924 Loss2: 0.759249
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.082537 Loss1: 0.397211 Loss2: 0.685326
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.073158 Loss1: 0.389569 Loss2: 0.683589
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.076363 Loss1: 0.389245 Loss2: 0.687118
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.050854 Loss1: 0.367652 Loss2: 0.683202
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.029922 Loss1: 0.346297 Loss2: 0.683625
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.049366 Loss1: 0.366992 Loss2: 0.682374
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.040104 Loss1: 0.354667 Loss2: 0.685437
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.026651 Loss1: 0.340179 Loss2: 0.686472
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.036104 Loss1: 0.351641 Loss2: 0.684463
+(DefaultActor pid=1831567) >> Training accuracy: 0.882137
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.301704 Loss1: 0.558812 Loss2: 0.742892
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.237614 Loss1: 0.565717 Loss2: 0.671897
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203275 Loss1: 0.532228 Loss2: 0.671047
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.232280 Loss1: 0.556995 Loss2: 0.675284
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163986 Loss1: 0.490989 Loss2: 0.672997
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.175691 Loss1: 0.501185 Loss2: 0.674507
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.176839 Loss1: 0.500432 Loss2: 0.676407
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155433 Loss1: 0.479466 Loss2: 0.675967
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.123040 Loss1: 0.447939 Loss2: 0.675101
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148414 Loss1: 0.471561 Loss2: 0.676853
+(DefaultActor pid=1831567) >> Training accuracy: 0.830128
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369436 Loss1: 0.573036 Loss2: 0.796400
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200074 Loss1: 0.506652 Loss2: 0.693423
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188791 Loss1: 0.492121 Loss2: 0.696671
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.179861 Loss1: 0.481935 Loss2: 0.697926
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.177674 Loss1: 0.480588 Loss2: 0.697086
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.166069 Loss1: 0.466342 Loss2: 0.699728
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.144000 Loss1: 0.442944 Loss2: 0.701055
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.147022 Loss1: 0.447897 Loss2: 0.699125
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138534 Loss1: 0.437387 Loss2: 0.701147
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154773 Loss1: 0.452995 Loss2: 0.701778
+(DefaultActor pid=1831567) >> Training accuracy: 0.851430
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.213504 Loss1: 0.461886 Loss2: 0.751618
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.046841 Loss1: 0.377288 Loss2: 0.669553
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.047122 Loss1: 0.379367 Loss2: 0.667755
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.034462 Loss1: 0.367118 Loss2: 0.667345
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.031365 Loss1: 0.364404 Loss2: 0.666961
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.036852 Loss1: 0.368860 Loss2: 0.667993
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.033271 Loss1: 0.360068 Loss2: 0.673203
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.007271 Loss1: 0.338347 Loss2: 0.668924
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.008925 Loss1: 0.340505 Loss2: 0.668420
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.029345 Loss1: 0.356519 Loss2: 0.672826
+(DefaultActor pid=1831567) >> Training accuracy: 0.866898
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.264431 Loss1: 0.546285 Loss2: 0.718145
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.168259 Loss1: 0.522943 Loss2: 0.645316
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.147093 Loss1: 0.501937 Loss2: 0.645157
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.161478 Loss1: 0.515780 Loss2: 0.645698
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.131088 Loss1: 0.486227 Loss2: 0.644861
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.119966 Loss1: 0.476295 Loss2: 0.643671
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.136016 Loss1: 0.490464 Loss2: 0.645552
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.102041 Loss1: 0.455758 Loss2: 0.646283
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.127955 Loss1: 0.482095 Loss2: 0.645860
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.088252 Loss1: 0.441785 Loss2: 0.646467
+(DefaultActor pid=1831567) >> Training accuracy: 0.851974
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.448148 Loss1: 0.715612 Loss2: 0.732536
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346284 Loss1: 0.701961 Loss2: 0.644323
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.331316 Loss1: 0.682599 Loss2: 0.648718
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316897 Loss1: 0.669054 Loss2: 0.647843
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.307082 Loss1: 0.658963 Loss2: 0.648119
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.290226 Loss1: 0.639495 Loss2: 0.650731
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.286541 Loss1: 0.635356 Loss2: 0.651184
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.290383 Loss1: 0.637239 Loss2: 0.653144
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.322632 Loss1: 0.667043 Loss2: 0.655589
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.282846 Loss1: 0.630161 Loss2: 0.652685
+[2023-09-27 18:37:21,786][flwr][DEBUG] - fit_round 94 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.798007
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.702800
+[2023-09-27 18:37:23,188][flwr][INFO] - fit progress: (94, 0.8653616505309035, {'accuracy': 0.7028}, 44376.02431618376)
+[2023-09-27 18:37:23,188][flwr][DEBUG] - evaluate_round 94: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:37:54,653][flwr][DEBUG] - evaluate_round 94 received 10 results and 0 failures
+[2023-09-27 18:37:54,654][flwr][DEBUG] - fit_round 95: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.316856 Loss1: 0.571226 Loss2: 0.745630
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.155570 Loss1: 0.508550 Loss2: 0.647020
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.139890 Loss1: 0.496123 Loss2: 0.643767
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.122866 Loss1: 0.480756 Loss2: 0.642110
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.117961 Loss1: 0.473134 Loss2: 0.644827
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146275 Loss1: 0.500143 Loss2: 0.646132
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.111790 Loss1: 0.462332 Loss2: 0.649458
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.103812 Loss1: 0.458314 Loss2: 0.645498
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.090381 Loss1: 0.439838 Loss2: 0.650543
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.080745 Loss1: 0.434032 Loss2: 0.646713
+(DefaultActor pid=1831567) >> Training accuracy: 0.851165
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.487706 Loss1: 0.739298 Loss2: 0.748407
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.362299 Loss1: 0.699581 Loss2: 0.662718
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.302995 Loss1: 0.643947 Loss2: 0.659048
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.310271 Loss1: 0.646475 Loss2: 0.663796
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.289273 Loss1: 0.625730 Loss2: 0.663543
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.302476 Loss1: 0.638628 Loss2: 0.663848
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.280980 Loss1: 0.619076 Loss2: 0.661904
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289295 Loss1: 0.623049 Loss2: 0.666246
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.301853 Loss1: 0.633611 Loss2: 0.668242
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.262533 Loss1: 0.598032 Loss2: 0.664500
+(DefaultActor pid=1831567) >> Training accuracy: 0.759562
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.237018 Loss1: 0.463876 Loss2: 0.773142
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.103305 Loss1: 0.412817 Loss2: 0.690488
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086297 Loss1: 0.394972 Loss2: 0.691325
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.069363 Loss1: 0.381145 Loss2: 0.688217
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.034136 Loss1: 0.348533 Loss2: 0.685603
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.044830 Loss1: 0.358319 Loss2: 0.686512
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.040177 Loss1: 0.353230 Loss2: 0.686947
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030902 Loss1: 0.341689 Loss2: 0.689213
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.026209 Loss1: 0.336138 Loss2: 0.690071
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.020302 Loss1: 0.328255 Loss2: 0.692047
+(DefaultActor pid=1831567) >> Training accuracy: 0.887539
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.298527 Loss1: 0.563281 Loss2: 0.735246
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.199376 Loss1: 0.533650 Loss2: 0.665727
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.181749 Loss1: 0.518691 Loss2: 0.663058
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.176622 Loss1: 0.511005 Loss2: 0.665617
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169133 Loss1: 0.504940 Loss2: 0.664193
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.138229 Loss1: 0.475606 Loss2: 0.662624
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.159360 Loss1: 0.496568 Loss2: 0.662792
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.141627 Loss1: 0.479510 Loss2: 0.662116
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140942 Loss1: 0.476722 Loss2: 0.664219
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.143873 Loss1: 0.479359 Loss2: 0.664513
+(DefaultActor pid=1831567) >> Training accuracy: 0.837081
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324517 Loss1: 0.578401 Loss2: 0.746115
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187351 Loss1: 0.512985 Loss2: 0.674365
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191372 Loss1: 0.517379 Loss2: 0.673992
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194812 Loss1: 0.516612 Loss2: 0.678200
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158879 Loss1: 0.481923 Loss2: 0.676956
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.190141 Loss1: 0.513073 Loss2: 0.677069
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171741 Loss1: 0.490934 Loss2: 0.680808
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.144107 Loss1: 0.464636 Loss2: 0.679471
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188015 Loss1: 0.507643 Loss2: 0.680372
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136205 Loss1: 0.454416 Loss2: 0.681790
+(DefaultActor pid=1831567) >> Training accuracy: 0.833333
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.282082 Loss1: 0.540317 Loss2: 0.741765
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.205360 Loss1: 0.509383 Loss2: 0.695978
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.196343 Loss1: 0.497577 Loss2: 0.698766
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.178458 Loss1: 0.480210 Loss2: 0.698247
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187210 Loss1: 0.489266 Loss2: 0.697945
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186918 Loss1: 0.484520 Loss2: 0.702398
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.186107 Loss1: 0.485508 Loss2: 0.700599
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182623 Loss1: 0.481712 Loss2: 0.700911
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.187831 Loss1: 0.483649 Loss2: 0.704182
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.173323 Loss1: 0.470130 Loss2: 0.703194
+(DefaultActor pid=1831567) >> Training accuracy: 0.837178
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320297 Loss1: 0.550211 Loss2: 0.770086
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.184722 Loss1: 0.503197 Loss2: 0.681525
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.178988 Loss1: 0.497446 Loss2: 0.681542
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.182458 Loss1: 0.499382 Loss2: 0.683077
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.168378 Loss1: 0.484381 Loss2: 0.683997
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.188880 Loss1: 0.500844 Loss2: 0.688036
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.154772 Loss1: 0.469608 Loss2: 0.685163
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155044 Loss1: 0.467918 Loss2: 0.687126
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.135461 Loss1: 0.448298 Loss2: 0.687162
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.145028 Loss1: 0.456880 Loss2: 0.688149
+(DefaultActor pid=1831567) >> Training accuracy: 0.852590
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495990 Loss1: 0.710876 Loss2: 0.785114
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341345 Loss1: 0.660309 Loss2: 0.681036
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.292733 Loss1: 0.609185 Loss2: 0.683548
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.297011 Loss1: 0.613572 Loss2: 0.683439
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.269372 Loss1: 0.583180 Loss2: 0.686192
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.274426 Loss1: 0.586240 Loss2: 0.688186
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.293868 Loss1: 0.602524 Loss2: 0.691344
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.287838 Loss1: 0.597690 Loss2: 0.690148
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.259306 Loss1: 0.568871 Loss2: 0.690435
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.251407 Loss1: 0.563095 Loss2: 0.688312
+(DefaultActor pid=1831567) >> Training accuracy: 0.810855
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187186 Loss1: 0.443795 Loss2: 0.743391
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.074813 Loss1: 0.401128 Loss2: 0.673685
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048879 Loss1: 0.378320 Loss2: 0.670559
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.046702 Loss1: 0.377557 Loss2: 0.669144
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.026352 Loss1: 0.358174 Loss2: 0.668178
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.040384 Loss1: 0.369319 Loss2: 0.671065
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.024406 Loss1: 0.355106 Loss2: 0.669300
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.012009 Loss1: 0.342021 Loss2: 0.669988
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.022820 Loss1: 0.352900 Loss2: 0.669919
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.029622 Loss1: 0.359393 Loss2: 0.670228
+(DefaultActor pid=1831567) >> Training accuracy: 0.876543
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.503427 Loss1: 0.720445 Loss2: 0.782981
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.397137 Loss1: 0.698479 Loss2: 0.698657
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.359998 Loss1: 0.661072 Loss2: 0.698925
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335523 Loss1: 0.638765 Loss2: 0.696758
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.338304 Loss1: 0.640188 Loss2: 0.698116
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.345705 Loss1: 0.643139 Loss2: 0.702566
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.359666 Loss1: 0.654302 Loss2: 0.705363
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.354000 Loss1: 0.646598 Loss2: 0.707403
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.326801 Loss1: 0.618195 Loss2: 0.708606
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.333106 Loss1: 0.624610 Loss2: 0.708496
+(DefaultActor pid=1831567) >> Training accuracy: 0.788496
+(DefaultActor pid=1831567)  ** Training complete **
+[2023-09-27 18:44:59,614][flwr][DEBUG] - fit_round 95 received 10 results and 0 failures
+>> Test accuracy: 0.700100
+[2023-09-27 18:45:01,048][flwr][INFO] - fit progress: (95, 0.8646818039516291, {'accuracy': 0.7001}, 44833.88447048701)
+[2023-09-27 18:45:01,049][flwr][DEBUG] - evaluate_round 95: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:45:30,939][flwr][DEBUG] - evaluate_round 95 received 10 results and 0 failures
+[2023-09-27 18:45:30,940][flwr][DEBUG] - fit_round 96: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.288744 Loss1: 0.560342 Loss2: 0.728402
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.159504 Loss1: 0.509641 Loss2: 0.649862
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.140537 Loss1: 0.489457 Loss2: 0.651080
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.167064 Loss1: 0.511377 Loss2: 0.655688
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.138387 Loss1: 0.482603 Loss2: 0.655784
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.140277 Loss1: 0.484831 Loss2: 0.655446
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.116925 Loss1: 0.460514 Loss2: 0.656412
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.143003 Loss1: 0.481793 Loss2: 0.661210
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.139304 Loss1: 0.481123 Loss2: 0.658181
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.124868 Loss1: 0.465988 Loss2: 0.658880
+(DefaultActor pid=1831567) >> Training accuracy: 0.841488
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.428814 Loss1: 0.698598 Loss2: 0.730215
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.334131 Loss1: 0.685155 Loss2: 0.648976
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.325535 Loss1: 0.677307 Loss2: 0.648227
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.323937 Loss1: 0.671848 Loss2: 0.652089
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302027 Loss1: 0.650828 Loss2: 0.651199
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303485 Loss1: 0.650651 Loss2: 0.652835
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.283947 Loss1: 0.634075 Loss2: 0.649873
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.277392 Loss1: 0.623505 Loss2: 0.653887
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.274906 Loss1: 0.622631 Loss2: 0.652274
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.257234 Loss1: 0.601792 Loss2: 0.655442
+(DefaultActor pid=1831567) >> Training accuracy: 0.792572
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.473494 Loss1: 0.710747 Loss2: 0.762747
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.291565 Loss1: 0.638894 Loss2: 0.652670
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.282740 Loss1: 0.630835 Loss2: 0.651905
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.277852 Loss1: 0.622076 Loss2: 0.655776
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.274873 Loss1: 0.621905 Loss2: 0.652967
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.250611 Loss1: 0.593183 Loss2: 0.657428
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.261898 Loss1: 0.601413 Loss2: 0.660485
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.248181 Loss1: 0.591291 Loss2: 0.656890
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.238720 Loss1: 0.577922 Loss2: 0.660798
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.215073 Loss1: 0.556728 Loss2: 0.658345
+(DefaultActor pid=1831567) >> Training accuracy: 0.784265
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.467212 Loss1: 0.676933 Loss2: 0.790279
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.354591 Loss1: 0.657622 Loss2: 0.696969
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.351915 Loss1: 0.654534 Loss2: 0.697382
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.351898 Loss1: 0.650860 Loss2: 0.701038
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.330436 Loss1: 0.635493 Loss2: 0.694944
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.309470 Loss1: 0.613470 Loss2: 0.696000
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.334594 Loss1: 0.637471 Loss2: 0.697122
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.316240 Loss1: 0.619222 Loss2: 0.697019
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.314703 Loss1: 0.611402 Loss2: 0.703301
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.293362 Loss1: 0.590205 Loss2: 0.703157
+(DefaultActor pid=1831567) >> Training accuracy: 0.788479
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291462 Loss1: 0.541927 Loss2: 0.749536
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.210765 Loss1: 0.502617 Loss2: 0.708148
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198882 Loss1: 0.493469 Loss2: 0.705413
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.207849 Loss1: 0.497879 Loss2: 0.709969
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204472 Loss1: 0.494490 Loss2: 0.709982
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186488 Loss1: 0.478672 Loss2: 0.707816
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192465 Loss1: 0.482060 Loss2: 0.710405
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.194145 Loss1: 0.482648 Loss2: 0.711497
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188969 Loss1: 0.478142 Loss2: 0.710827
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.190271 Loss1: 0.478907 Loss2: 0.711364
+(DefaultActor pid=1831567) >> Training accuracy: 0.842014
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.194833 Loss1: 0.441395 Loss2: 0.753438
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.091309 Loss1: 0.407771 Loss2: 0.683538
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.069153 Loss1: 0.386394 Loss2: 0.682759
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.068251 Loss1: 0.385100 Loss2: 0.683151
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.046245 Loss1: 0.365392 Loss2: 0.680853
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.057654 Loss1: 0.376274 Loss2: 0.681380
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050373 Loss1: 0.367011 Loss2: 0.683362
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030496 Loss1: 0.344615 Loss2: 0.685881
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.048540 Loss1: 0.363626 Loss2: 0.684915
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.048037 Loss1: 0.360398 Loss2: 0.687639
+(DefaultActor pid=1831567) >> Training accuracy: 0.885995
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.314268 Loss1: 0.577496 Loss2: 0.736772
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211993 Loss1: 0.539921 Loss2: 0.672072
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.187048 Loss1: 0.517789 Loss2: 0.669259
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.160192 Loss1: 0.492722 Loss2: 0.667470
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.178088 Loss1: 0.506376 Loss2: 0.671712
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.159631 Loss1: 0.490426 Loss2: 0.669205
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148493 Loss1: 0.478736 Loss2: 0.669757
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.156976 Loss1: 0.485330 Loss2: 0.671646
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163938 Loss1: 0.488990 Loss2: 0.674948
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.132456 Loss1: 0.458752 Loss2: 0.673704
+(DefaultActor pid=1831567) >> Training accuracy: 0.842416
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340894 Loss1: 0.557626 Loss2: 0.783268
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245302 Loss1: 0.543755 Loss2: 0.701547
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.206622 Loss1: 0.502862 Loss2: 0.703760
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205806 Loss1: 0.501850 Loss2: 0.703956
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.193825 Loss1: 0.490193 Loss2: 0.703633
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206163 Loss1: 0.497161 Loss2: 0.709002
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.211008 Loss1: 0.497886 Loss2: 0.713121
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.184585 Loss1: 0.472049 Loss2: 0.712536
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.203050 Loss1: 0.490636 Loss2: 0.712414
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.178666 Loss1: 0.468942 Loss2: 0.709724
+(DefaultActor pid=1831567) >> Training accuracy: 0.849359
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329475 Loss1: 0.556760 Loss2: 0.772716
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180745 Loss1: 0.508451 Loss2: 0.672293
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.217876 Loss1: 0.540870 Loss2: 0.677006
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162911 Loss1: 0.488819 Loss2: 0.674091
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140895 Loss1: 0.464988 Loss2: 0.675907
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.152158 Loss1: 0.475428 Loss2: 0.676731
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122531 Loss1: 0.446423 Loss2: 0.676107
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.135906 Loss1: 0.456941 Loss2: 0.678965
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.110301 Loss1: 0.432059 Loss2: 0.678243
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152469 Loss1: 0.469805 Loss2: 0.682664
+(DefaultActor pid=1831567) >> Training accuracy: 0.860964
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.193263 Loss1: 0.444518 Loss2: 0.748745
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.069663 Loss1: 0.404769 Loss2: 0.664894
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.039570 Loss1: 0.378314 Loss2: 0.661255
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019393 Loss1: 0.361638 Loss2: 0.657755
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.020042 Loss1: 0.355208 Loss2: 0.664834
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.027609 Loss1: 0.363943 Loss2: 0.663666
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.026107 Loss1: 0.361008 Loss2: 0.665099
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026726 Loss1: 0.360788 Loss2: 0.665938
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.033963 Loss1: 0.366608 Loss2: 0.667354
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.010579 Loss1: 0.345125 Loss2: 0.665454
+[2023-09-27 18:52:14,917][flwr][DEBUG] - fit_round 96 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.895255
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.706400
+[2023-09-27 18:52:24,051][flwr][INFO] - fit progress: (96, 0.8532181625929884, {'accuracy': 0.7064}, 45276.887487936765)
+[2023-09-27 18:52:24,052][flwr][DEBUG] - evaluate_round 96: strategy sampled 10 clients (out of 10)
+[2023-09-27 18:53:01,650][flwr][DEBUG] - evaluate_round 96 received 10 results and 0 failures
+[2023-09-27 18:53:01,651][flwr][DEBUG] - fit_round 97: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.457590 Loss1: 0.702180 Loss2: 0.755410
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.356703 Loss1: 0.680357 Loss2: 0.676346
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.341841 Loss1: 0.665529 Loss2: 0.676312
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316971 Loss1: 0.640023 Loss2: 0.676949
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.312898 Loss1: 0.633377 Loss2: 0.679521
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.338020 Loss1: 0.657155 Loss2: 0.680865
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.327908 Loss1: 0.645670 Loss2: 0.682239
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329162 Loss1: 0.648483 Loss2: 0.680679
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.300097 Loss1: 0.617658 Loss2: 0.682439
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.283042 Loss1: 0.600664 Loss2: 0.682378
+(DefaultActor pid=1831567) >> Training accuracy: 0.795516
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.213683 Loss1: 0.452483 Loss2: 0.761200
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.068170 Loss1: 0.392939 Loss2: 0.675231
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.060927 Loss1: 0.386080 Loss2: 0.674847
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.018747 Loss1: 0.345161 Loss2: 0.673586
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.038639 Loss1: 0.363253 Loss2: 0.675385
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.022878 Loss1: 0.349847 Loss2: 0.673031
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.032260 Loss1: 0.354359 Loss2: 0.677901
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026103 Loss1: 0.350443 Loss2: 0.675660
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.022278 Loss1: 0.347389 Loss2: 0.674889
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.022364 Loss1: 0.345581 Loss2: 0.676783
+(DefaultActor pid=1831567) >> Training accuracy: 0.861690
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.283780 Loss1: 0.572852 Loss2: 0.710927
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.174768 Loss1: 0.537456 Loss2: 0.637312
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.151877 Loss1: 0.519539 Loss2: 0.632337
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144127 Loss1: 0.505746 Loss2: 0.638382
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.120727 Loss1: 0.483485 Loss2: 0.637243
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.127400 Loss1: 0.486454 Loss2: 0.640946
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.115845 Loss1: 0.474545 Loss2: 0.641299
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.123749 Loss1: 0.484609 Loss2: 0.639140
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.126372 Loss1: 0.487473 Loss2: 0.638898
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.089254 Loss1: 0.447748 Loss2: 0.641506
+(DefaultActor pid=1831567) >> Training accuracy: 0.841654
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.292639 Loss1: 0.534990 Loss2: 0.757649
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225268 Loss1: 0.508129 Loss2: 0.717139
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.209869 Loss1: 0.495838 Loss2: 0.714030
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.218568 Loss1: 0.498577 Loss2: 0.719992
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.193902 Loss1: 0.475837 Loss2: 0.718065
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210156 Loss1: 0.491381 Loss2: 0.718775
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.214581 Loss1: 0.492736 Loss2: 0.721845
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182756 Loss1: 0.467129 Loss2: 0.715627
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213842 Loss1: 0.490886 Loss2: 0.722955
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201447 Loss1: 0.477891 Loss2: 0.723556
+(DefaultActor pid=1831567) >> Training accuracy: 0.839286
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.318824 Loss1: 0.545167 Loss2: 0.773657
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.203498 Loss1: 0.512905 Loss2: 0.690593
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.185574 Loss1: 0.496584 Loss2: 0.688990
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189562 Loss1: 0.502379 Loss2: 0.687183
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.208434 Loss1: 0.512820 Loss2: 0.695614
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168675 Loss1: 0.476093 Loss2: 0.692583
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168864 Loss1: 0.473532 Loss2: 0.695331
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182015 Loss1: 0.488552 Loss2: 0.693463
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.144859 Loss1: 0.451672 Loss2: 0.693187
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.141699 Loss1: 0.451198 Loss2: 0.690502
+(DefaultActor pid=1831567) >> Training accuracy: 0.836143
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.171188 Loss1: 0.445680 Loss2: 0.725508
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.089351 Loss1: 0.436189 Loss2: 0.653162
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.041324 Loss1: 0.391976 Loss2: 0.649347
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.017775 Loss1: 0.369533 Loss2: 0.648242
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.021445 Loss1: 0.369220 Loss2: 0.652225
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.009838 Loss1: 0.361353 Loss2: 0.648485
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019587 Loss1: 0.366519 Loss2: 0.653068
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.013729 Loss1: 0.360007 Loss2: 0.653721
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.019606 Loss1: 0.368731 Loss2: 0.650875
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019582 Loss1: 0.367152 Loss2: 0.652430
+(DefaultActor pid=1831567) >> Training accuracy: 0.880208
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.491278 Loss1: 0.702040 Loss2: 0.789239
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.345583 Loss1: 0.665182 Loss2: 0.680402
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.291087 Loss1: 0.608133 Loss2: 0.682954
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291043 Loss1: 0.607514 Loss2: 0.683529
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.290201 Loss1: 0.604547 Loss2: 0.685654
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.286333 Loss1: 0.597891 Loss2: 0.688443
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.260539 Loss1: 0.574716 Loss2: 0.685823
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.258497 Loss1: 0.568455 Loss2: 0.690042
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.275961 Loss1: 0.585499 Loss2: 0.690462
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284669 Loss1: 0.594387 Loss2: 0.690282
+(DefaultActor pid=1831567) >> Training accuracy: 0.797149
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.357632 Loss1: 0.580104 Loss2: 0.777528
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.175590 Loss1: 0.507595 Loss2: 0.667995
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.147009 Loss1: 0.481688 Loss2: 0.665320
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.145014 Loss1: 0.475669 Loss2: 0.669344
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.130571 Loss1: 0.461142 Loss2: 0.669429
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.138414 Loss1: 0.466395 Loss2: 0.672019
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.155574 Loss1: 0.484294 Loss2: 0.671280
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.120315 Loss1: 0.449537 Loss2: 0.670778
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.104806 Loss1: 0.433246 Loss2: 0.671560
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.114966 Loss1: 0.440151 Loss2: 0.674814
+(DefaultActor pid=1831567) >> Training accuracy: 0.841102
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297051 Loss1: 0.558942 Loss2: 0.738109
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202843 Loss1: 0.533015 Loss2: 0.669828
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.186473 Loss1: 0.517995 Loss2: 0.668479
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189819 Loss1: 0.520481 Loss2: 0.669338
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.181632 Loss1: 0.512335 Loss2: 0.669297
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164162 Loss1: 0.492424 Loss2: 0.671738
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.167453 Loss1: 0.493898 Loss2: 0.673555
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155807 Loss1: 0.481367 Loss2: 0.674439
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147445 Loss1: 0.472076 Loss2: 0.675369
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.126730 Loss1: 0.452144 Loss2: 0.674586
+(DefaultActor pid=1831567) >> Training accuracy: 0.850761
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.438487 Loss1: 0.688212 Loss2: 0.750275
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.335217 Loss1: 0.672873 Loss2: 0.662345
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336285 Loss1: 0.674308 Loss2: 0.661976
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.303626 Loss1: 0.641813 Loss2: 0.661813
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317460 Loss1: 0.652235 Loss2: 0.665225
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306189 Loss1: 0.642377 Loss2: 0.663812
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.290045 Loss1: 0.624661 Loss2: 0.665383
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.282455 Loss1: 0.610597 Loss2: 0.671859
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.282146 Loss1: 0.611351 Loss2: 0.670796
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.286279 Loss1: 0.616739 Loss2: 0.669540
+[2023-09-27 18:59:42,034][flwr][DEBUG] - fit_round 97 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.751399
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.700800
+[2023-09-27 18:59:43,571][flwr][INFO] - fit progress: (97, 0.8687959163904951, {'accuracy': 0.7008}, 45716.40724924812)
+[2023-09-27 18:59:43,571][flwr][DEBUG] - evaluate_round 97: strategy sampled 10 clients (out of 10)
+[2023-09-27 19:00:13,690][flwr][DEBUG] - evaluate_round 97 received 10 results and 0 failures
+[2023-09-27 19:00:13,691][flwr][DEBUG] - fit_round 98: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340778 Loss1: 0.578274 Loss2: 0.762504
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217503 Loss1: 0.525593 Loss2: 0.691910
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188921 Loss1: 0.498884 Loss2: 0.690037
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.192616 Loss1: 0.503187 Loss2: 0.689430
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167441 Loss1: 0.480594 Loss2: 0.686847
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181009 Loss1: 0.491707 Loss2: 0.689303
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187761 Loss1: 0.493647 Loss2: 0.694114
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.156396 Loss1: 0.461697 Loss2: 0.694699
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196454 Loss1: 0.501302 Loss2: 0.695152
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150453 Loss1: 0.456620 Loss2: 0.693833
+(DefaultActor pid=1831567) >> Training accuracy: 0.847561
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.334039 Loss1: 0.569615 Loss2: 0.764425
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211468 Loss1: 0.526037 Loss2: 0.685431
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.189189 Loss1: 0.506787 Loss2: 0.682402
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190688 Loss1: 0.504082 Loss2: 0.686606
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.183153 Loss1: 0.497236 Loss2: 0.685917
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.159037 Loss1: 0.472996 Loss2: 0.686041
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191776 Loss1: 0.502463 Loss2: 0.689313
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182074 Loss1: 0.493778 Loss2: 0.688297
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.151472 Loss1: 0.459110 Loss2: 0.692362
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.161699 Loss1: 0.472848 Loss2: 0.688851
+(DefaultActor pid=1831567) >> Training accuracy: 0.840345
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.516448 Loss1: 0.726828 Loss2: 0.789620
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.359716 Loss1: 0.665007 Loss2: 0.694710
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318312 Loss1: 0.622245 Loss2: 0.696067
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.366114 Loss1: 0.668739 Loss2: 0.697375
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.307320 Loss1: 0.608819 Loss2: 0.698501
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.326793 Loss1: 0.629980 Loss2: 0.696812
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.296732 Loss1: 0.599405 Loss2: 0.697326
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329807 Loss1: 0.628644 Loss2: 0.701163
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.330613 Loss1: 0.626087 Loss2: 0.704526
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304473 Loss1: 0.601777 Loss2: 0.702697
+(DefaultActor pid=1831567) >> Training accuracy: 0.766558
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.212875 Loss1: 0.433981 Loss2: 0.778894
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.121500 Loss1: 0.413957 Loss2: 0.707543
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.075628 Loss1: 0.372668 Loss2: 0.702960
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.080289 Loss1: 0.378796 Loss2: 0.701493
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066194 Loss1: 0.363546 Loss2: 0.702648
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.068614 Loss1: 0.365850 Loss2: 0.702765
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.078752 Loss1: 0.372679 Loss2: 0.706073
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.066208 Loss1: 0.358167 Loss2: 0.708042
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.053666 Loss1: 0.346697 Loss2: 0.706969
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.049440 Loss1: 0.343968 Loss2: 0.705472
+(DefaultActor pid=1831567) >> Training accuracy: 0.874228
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.472642 Loss1: 0.732679 Loss2: 0.739963
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329749 Loss1: 0.676406 Loss2: 0.653343
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.330769 Loss1: 0.678224 Loss2: 0.652545
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.315542 Loss1: 0.662665 Loss2: 0.652877
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.313586 Loss1: 0.658395 Loss2: 0.655192
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.296021 Loss1: 0.638439 Loss2: 0.657582
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.301093 Loss1: 0.644666 Loss2: 0.656426
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288354 Loss1: 0.630816 Loss2: 0.657538
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.286993 Loss1: 0.627537 Loss2: 0.659456
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.291139 Loss1: 0.630316 Loss2: 0.660822
+(DefaultActor pid=1831567) >> Training accuracy: 0.779212
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.220410 Loss1: 0.478165 Loss2: 0.742245
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.053255 Loss1: 0.392492 Loss2: 0.660763
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.024705 Loss1: 0.365900 Loss2: 0.658804
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.025169 Loss1: 0.370392 Loss2: 0.654777
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.009374 Loss1: 0.356163 Loss2: 0.653211
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.023851 Loss1: 0.368569 Loss2: 0.655282
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.000706 Loss1: 0.342766 Loss2: 0.657940
+(DefaultActor pid=1831567) Epoch: 7 Loss: 0.995205 Loss1: 0.340750 Loss2: 0.654455
+(DefaultActor pid=1831567) Epoch: 8 Loss: 0.996985 Loss1: 0.338515 Loss2: 0.658470
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.006919 Loss1: 0.347615 Loss2: 0.659304
+(DefaultActor pid=1831567) >> Training accuracy: 0.892554
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.471849 Loss1: 0.712952 Loss2: 0.758898
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.293735 Loss1: 0.637650 Loss2: 0.656085
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.304341 Loss1: 0.643622 Loss2: 0.660719
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.275161 Loss1: 0.619078 Loss2: 0.656083
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.265082 Loss1: 0.606998 Loss2: 0.658084
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.234615 Loss1: 0.577565 Loss2: 0.657050
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.248457 Loss1: 0.591460 Loss2: 0.656997
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.249269 Loss1: 0.587749 Loss2: 0.661520
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244943 Loss1: 0.584278 Loss2: 0.660665
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.233966 Loss1: 0.573224 Loss2: 0.660742
+(DefaultActor pid=1831567) >> Training accuracy: 0.811129
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.296514 Loss1: 0.569608 Loss2: 0.726906
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.166274 Loss1: 0.516972 Loss2: 0.649303
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.163934 Loss1: 0.510833 Loss2: 0.653101
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.170285 Loss1: 0.513186 Loss2: 0.657100
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.148328 Loss1: 0.493126 Loss2: 0.655201
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.125511 Loss1: 0.467494 Loss2: 0.658018
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.138427 Loss1: 0.481398 Loss2: 0.657029
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.126706 Loss1: 0.469617 Loss2: 0.657089
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136797 Loss1: 0.477691 Loss2: 0.659106
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.110737 Loss1: 0.453488 Loss2: 0.657249
+(DefaultActor pid=1831567) >> Training accuracy: 0.847451
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.375727 Loss1: 0.594384 Loss2: 0.781343
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.169987 Loss1: 0.493643 Loss2: 0.676344
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.162873 Loss1: 0.486416 Loss2: 0.676457
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162291 Loss1: 0.485268 Loss2: 0.677022
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.148723 Loss1: 0.470465 Loss2: 0.678258
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.117542 Loss1: 0.439935 Loss2: 0.677607
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.159691 Loss1: 0.480352 Loss2: 0.679339
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.104509 Loss1: 0.426286 Loss2: 0.678223
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140061 Loss1: 0.458261 Loss2: 0.681801
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107715 Loss1: 0.428076 Loss2: 0.679639
+(DefaultActor pid=1831567) >> Training accuracy: 0.868114
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.278558 Loss1: 0.530530 Loss2: 0.748028
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201024 Loss1: 0.496145 Loss2: 0.704879
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.207920 Loss1: 0.500060 Loss2: 0.707860
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189060 Loss1: 0.483168 Loss2: 0.705892
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192593 Loss1: 0.487171 Loss2: 0.705422
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.214368 Loss1: 0.506101 Loss2: 0.708267
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.184584 Loss1: 0.474435 Loss2: 0.710148
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.201394 Loss1: 0.490466 Loss2: 0.710928
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.209376 Loss1: 0.495708 Loss2: 0.713668
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.190802 Loss1: 0.479628 Loss2: 0.711174
+[2023-09-27 19:06:49,009][flwr][DEBUG] - fit_round 98 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.843006
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.706400
+[2023-09-27 19:06:50,363][flwr][INFO] - fit progress: (98, 0.8529771449276433, {'accuracy': 0.7064}, 46143.19974304596)
+[2023-09-27 19:06:50,364][flwr][DEBUG] - evaluate_round 98: strategy sampled 10 clients (out of 10)
+[2023-09-27 19:07:20,600][flwr][DEBUG] - evaluate_round 98 received 10 results and 0 failures
+[2023-09-27 19:07:20,601][flwr][DEBUG] - fit_round 99: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.164623 Loss1: 0.445211 Loss2: 0.719412
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.048527 Loss1: 0.402063 Loss2: 0.646464
+(DefaultActor pid=1831567) Epoch: 2 Loss: 0.998323 Loss1: 0.357478 Loss2: 0.640845
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.021874 Loss1: 0.376715 Loss2: 0.645159
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.014261 Loss1: 0.374058 Loss2: 0.640203
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.013677 Loss1: 0.369069 Loss2: 0.644609
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.007988 Loss1: 0.362983 Loss2: 0.645005
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.004943 Loss1: 0.359774 Loss2: 0.645169
+(DefaultActor pid=1831567) Epoch: 8 Loss: 0.993500 Loss1: 0.347044 Loss2: 0.646456
+(DefaultActor pid=1831567) Epoch: 9 Loss: 0.991017 Loss1: 0.343741 Loss2: 0.647276
+(DefaultActor pid=1831567) >> Training accuracy: 0.881366
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.299637 Loss1: 0.539278 Loss2: 0.760359
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.143325 Loss1: 0.488973 Loss2: 0.654352
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.174077 Loss1: 0.516414 Loss2: 0.657664
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.128350 Loss1: 0.473661 Loss2: 0.654689
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.123968 Loss1: 0.466989 Loss2: 0.656979
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131668 Loss1: 0.472689 Loss2: 0.658980
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.137678 Loss1: 0.477352 Loss2: 0.660326
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.130493 Loss1: 0.473549 Loss2: 0.656944
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.100390 Loss1: 0.441545 Loss2: 0.658846
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.064859 Loss1: 0.405819 Loss2: 0.659040
+(DefaultActor pid=1831567) >> Training accuracy: 0.865466
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.450386 Loss1: 0.702282 Loss2: 0.748103
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329910 Loss1: 0.667420 Loss2: 0.662489
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336599 Loss1: 0.671811 Loss2: 0.664788
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291464 Loss1: 0.628291 Loss2: 0.663173
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.285995 Loss1: 0.623004 Loss2: 0.662991
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306405 Loss1: 0.639902 Loss2: 0.666503
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.278717 Loss1: 0.612445 Loss2: 0.666272
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.264067 Loss1: 0.597467 Loss2: 0.666601
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267597 Loss1: 0.600834 Loss2: 0.666763
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.287706 Loss1: 0.620472 Loss2: 0.667235
+(DefaultActor pid=1831567) >> Training accuracy: 0.791744
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.250568 Loss1: 0.516503 Loss2: 0.734065
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.210341 Loss1: 0.513909 Loss2: 0.696432
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192100 Loss1: 0.494179 Loss2: 0.697921
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.192256 Loss1: 0.495781 Loss2: 0.696476
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.176562 Loss1: 0.479718 Loss2: 0.696844
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185815 Loss1: 0.485001 Loss2: 0.700814
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.188467 Loss1: 0.489019 Loss2: 0.699448
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185235 Loss1: 0.479053 Loss2: 0.706182
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205322 Loss1: 0.503167 Loss2: 0.702155
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165443 Loss1: 0.463038 Loss2: 0.702405
+(DefaultActor pid=1831567) >> Training accuracy: 0.840278
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475794 Loss1: 0.698289 Loss2: 0.777505
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.331082 Loss1: 0.659091 Loss2: 0.671992
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.320618 Loss1: 0.641205 Loss2: 0.679413
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261531 Loss1: 0.587621 Loss2: 0.673909
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.310595 Loss1: 0.629424 Loss2: 0.681171
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.284640 Loss1: 0.605688 Loss2: 0.678951
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258328 Loss1: 0.579615 Loss2: 0.678712
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.249605 Loss1: 0.569061 Loss2: 0.680544
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244968 Loss1: 0.561187 Loss2: 0.683781
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.265018 Loss1: 0.583168 Loss2: 0.681850
+(DefaultActor pid=1831567) >> Training accuracy: 0.791667
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187514 Loss1: 0.438562 Loss2: 0.748952
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.052822 Loss1: 0.382883 Loss2: 0.669939
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.042007 Loss1: 0.376476 Loss2: 0.665532
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.013801 Loss1: 0.348379 Loss2: 0.665421
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.021087 Loss1: 0.354356 Loss2: 0.666731
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.016216 Loss1: 0.349176 Loss2: 0.667040
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.023239 Loss1: 0.353417 Loss2: 0.669823
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.003867 Loss1: 0.332973 Loss2: 0.670894
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.013841 Loss1: 0.342780 Loss2: 0.671061
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.008050 Loss1: 0.337970 Loss2: 0.670081
+(DefaultActor pid=1831567) >> Training accuracy: 0.893519
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.480497 Loss1: 0.732158 Loss2: 0.748338
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.342843 Loss1: 0.678047 Loss2: 0.664796
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.339697 Loss1: 0.674175 Loss2: 0.665522
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325708 Loss1: 0.659722 Loss2: 0.665986
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317717 Loss1: 0.649137 Loss2: 0.668580
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.314103 Loss1: 0.645051 Loss2: 0.669053
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350054 Loss1: 0.677634 Loss2: 0.672420
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.306497 Loss1: 0.635392 Loss2: 0.671104
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.302386 Loss1: 0.630899 Loss2: 0.671487
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.302250 Loss1: 0.628331 Loss2: 0.673919
+(DefaultActor pid=1831567) >> Training accuracy: 0.779891
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320256 Loss1: 0.556884 Loss2: 0.763372
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211613 Loss1: 0.516663 Loss2: 0.694950
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208962 Loss1: 0.515800 Loss2: 0.693161
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194747 Loss1: 0.497351 Loss2: 0.697396
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.199814 Loss1: 0.505053 Loss2: 0.694760
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183912 Loss1: 0.488795 Loss2: 0.695118
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165819 Loss1: 0.469369 Loss2: 0.696450
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.172995 Loss1: 0.473736 Loss2: 0.699258
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.203491 Loss1: 0.502811 Loss2: 0.700680
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184346 Loss1: 0.485527 Loss2: 0.698819
+(DefaultActor pid=1831567) >> Training accuracy: 0.852764
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.314450 Loss1: 0.546293 Loss2: 0.768157
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.198277 Loss1: 0.515580 Loss2: 0.682696
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201336 Loss1: 0.516567 Loss2: 0.684769
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.172246 Loss1: 0.487869 Loss2: 0.684377
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.144172 Loss1: 0.459224 Loss2: 0.684948
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168924 Loss1: 0.481233 Loss2: 0.687691
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175815 Loss1: 0.489488 Loss2: 0.686327
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155720 Loss1: 0.468937 Loss2: 0.686783
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.170158 Loss1: 0.481686 Loss2: 0.688472
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152306 Loss1: 0.464561 Loss2: 0.687745
+(DefaultActor pid=1831567) >> Training accuracy: 0.844161
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.325654 Loss1: 0.596603 Loss2: 0.729051
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.197408 Loss1: 0.541432 Loss2: 0.655976
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192692 Loss1: 0.540041 Loss2: 0.652651
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.146838 Loss1: 0.493521 Loss2: 0.653317
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140368 Loss1: 0.490018 Loss2: 0.650351
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.130523 Loss1: 0.479261 Loss2: 0.651262
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.147788 Loss1: 0.494155 Loss2: 0.653633
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.143442 Loss1: 0.490436 Loss2: 0.653006
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138952 Loss1: 0.487763 Loss2: 0.651188
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.124620 Loss1: 0.471595 Loss2: 0.653025
+[2023-09-27 19:14:06,284][flwr][DEBUG] - fit_round 99 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.845846
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.705700
+[2023-09-27 19:14:07,675][flwr][INFO] - fit progress: (99, 0.8568882018613359, {'accuracy': 0.7057}, 46580.51172851771)
+[2023-09-27 19:14:07,676][flwr][DEBUG] - evaluate_round 99: strategy sampled 10 clients (out of 10)
+[2023-09-27 19:14:38,070][flwr][DEBUG] - evaluate_round 99 received 10 results and 0 failures
+[2023-09-27 19:14:38,071][flwr][DEBUG] - fit_round 100: strategy sampled 10 clients (out of 10)
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.186312 Loss1: 0.439108 Loss2: 0.747204
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.059389 Loss1: 0.394870 Loss2: 0.664519
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.034510 Loss1: 0.366840 Loss2: 0.667670
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.043886 Loss1: 0.378863 Loss2: 0.665023
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.048814 Loss1: 0.379796 Loss2: 0.669018
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.041255 Loss1: 0.374520 Loss2: 0.666735
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.014572 Loss1: 0.349016 Loss2: 0.665556
+(DefaultActor pid=1831567) Epoch: 7 Loss: 0.999291 Loss1: 0.331946 Loss2: 0.667345
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.027996 Loss1: 0.357560 Loss2: 0.670437
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.013616 Loss1: 0.345285 Loss2: 0.668331
+(DefaultActor pid=1831567) >> Training accuracy: 0.891975
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.489291 Loss1: 0.699721 Loss2: 0.789569
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.369014 Loss1: 0.670312 Loss2: 0.698703
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.345978 Loss1: 0.647902 Loss2: 0.698076
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325854 Loss1: 0.626532 Loss2: 0.699322
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.355271 Loss1: 0.654259 Loss2: 0.701012
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.318410 Loss1: 0.618734 Loss2: 0.699677
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305149 Loss1: 0.603749 Loss2: 0.701400
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.313890 Loss1: 0.611219 Loss2: 0.702671
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.314912 Loss1: 0.610003 Loss2: 0.704909
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.319659 Loss1: 0.612447 Loss2: 0.707211
+(DefaultActor pid=1831567) >> Training accuracy: 0.749534
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.310976 Loss1: 0.571327 Loss2: 0.739648
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.210961 Loss1: 0.546513 Loss2: 0.664447
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.165625 Loss1: 0.506521 Loss2: 0.659104
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.166968 Loss1: 0.502704 Loss2: 0.664265
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158452 Loss1: 0.491865 Loss2: 0.666587
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.174907 Loss1: 0.504750 Loss2: 0.670157
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164945 Loss1: 0.495783 Loss2: 0.669163
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151184 Loss1: 0.483295 Loss2: 0.667889
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.133278 Loss1: 0.466447 Loss2: 0.666831
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.143732 Loss1: 0.473862 Loss2: 0.669870
+(DefaultActor pid=1831567) >> Training accuracy: 0.846955
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.464313 Loss1: 0.707903 Loss2: 0.756410
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.376465 Loss1: 0.699296 Loss2: 0.677168
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.346178 Loss1: 0.667975 Loss2: 0.678203
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.334925 Loss1: 0.658813 Loss2: 0.676112
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333571 Loss1: 0.655151 Loss2: 0.678419
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337280 Loss1: 0.657483 Loss2: 0.679797
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.325959 Loss1: 0.643266 Loss2: 0.682693
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.295503 Loss1: 0.614013 Loss2: 0.681490
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.334531 Loss1: 0.651543 Loss2: 0.682988
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.315943 Loss1: 0.632955 Loss2: 0.682988
+(DefaultActor pid=1831567) >> Training accuracy: 0.782382
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.303401 Loss1: 0.548972 Loss2: 0.754429
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.160303 Loss1: 0.501955 Loss2: 0.658348
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.156430 Loss1: 0.496788 Loss2: 0.659642
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.142267 Loss1: 0.482168 Loss2: 0.660099
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.123734 Loss1: 0.462111 Loss2: 0.661622
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.114698 Loss1: 0.454316 Loss2: 0.660382
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.129943 Loss1: 0.465322 Loss2: 0.664622
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.118602 Loss1: 0.452474 Loss2: 0.666128
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120383 Loss1: 0.454934 Loss2: 0.665448
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.094397 Loss1: 0.429316 Loss2: 0.665081
+(DefaultActor pid=1831567) >> Training accuracy: 0.860434
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.319415 Loss1: 0.570902 Loss2: 0.748513
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.209982 Loss1: 0.531365 Loss2: 0.678617
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.175753 Loss1: 0.499539 Loss2: 0.676215
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162175 Loss1: 0.489621 Loss2: 0.672553
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.165618 Loss1: 0.490827 Loss2: 0.674792
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.170681 Loss1: 0.493302 Loss2: 0.677379
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170429 Loss1: 0.492096 Loss2: 0.678334
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.149901 Loss1: 0.473123 Loss2: 0.676779
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134043 Loss1: 0.455546 Loss2: 0.678497
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150426 Loss1: 0.470493 Loss2: 0.679933
+(DefaultActor pid=1831567) >> Training accuracy: 0.835175
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.198478 Loss1: 0.446652 Loss2: 0.751826
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.073016 Loss1: 0.393536 Loss2: 0.679479
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.076403 Loss1: 0.396213 Loss2: 0.680189
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.064346 Loss1: 0.382484 Loss2: 0.681862
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.037385 Loss1: 0.356044 Loss2: 0.681341
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.038743 Loss1: 0.357641 Loss2: 0.681101
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.024350 Loss1: 0.343749 Loss2: 0.680601
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.050445 Loss1: 0.368122 Loss2: 0.682323
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.032503 Loss1: 0.348422 Loss2: 0.684081
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019709 Loss1: 0.337958 Loss2: 0.681752
+(DefaultActor pid=1831567) >> Training accuracy: 0.871335
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459724 Loss1: 0.694398 Loss2: 0.765326
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.281041 Loss1: 0.622245 Loss2: 0.658796
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.293258 Loss1: 0.631570 Loss2: 0.661688
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.286642 Loss1: 0.621989 Loss2: 0.664654
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.273285 Loss1: 0.612967 Loss2: 0.660318
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.264030 Loss1: 0.601495 Loss2: 0.662535
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.272734 Loss1: 0.602941 Loss2: 0.669793
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225071 Loss1: 0.560418 Loss2: 0.664653
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.243618 Loss1: 0.577967 Loss2: 0.665651
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.228452 Loss1: 0.561613 Loss2: 0.666839
+(DefaultActor pid=1831567) >> Training accuracy: 0.805099
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.309363 Loss1: 0.535557 Loss2: 0.773806
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.219919 Loss1: 0.492848 Loss2: 0.727071
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231249 Loss1: 0.499739 Loss2: 0.731510
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.232671 Loss1: 0.503736 Loss2: 0.728935
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.238966 Loss1: 0.506470 Loss2: 0.732496
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.216264 Loss1: 0.489373 Loss2: 0.726891
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220475 Loss1: 0.488938 Loss2: 0.731536
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204671 Loss1: 0.473770 Loss2: 0.730901
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213265 Loss1: 0.482392 Loss2: 0.730873
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.219283 Loss1: 0.483676 Loss2: 0.735607
+(DefaultActor pid=1831567) >> Training accuracy: 0.807168
+(DefaultActor pid=1831567)  ** Training complete **
+(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289845 Loss1: 0.564746 Loss2: 0.725099
+(DefaultActor pid=1831567) Epoch: 1 Loss: 1.146777 Loss1: 0.498783 Loss2: 0.647994
+(DefaultActor pid=1831567) Epoch: 2 Loss: 1.145596 Loss1: 0.497244 Loss2: 0.648352
+(DefaultActor pid=1831567) Epoch: 3 Loss: 1.147653 Loss1: 0.494979 Loss2: 0.652675
+(DefaultActor pid=1831567) Epoch: 4 Loss: 1.147044 Loss1: 0.492462 Loss2: 0.654582
+(DefaultActor pid=1831567) Epoch: 5 Loss: 1.129521 Loss1: 0.474271 Loss2: 0.655250
+(DefaultActor pid=1831567) Epoch: 6 Loss: 1.120133 Loss1: 0.467975 Loss2: 0.652158
+(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158980 Loss1: 0.503951 Loss2: 0.655029
+(DefaultActor pid=1831567) Epoch: 8 Loss: 1.126423 Loss1: 0.471174 Loss2: 0.655249
+(DefaultActor pid=1831567) Epoch: 9 Loss: 1.100029 Loss1: 0.442179 Loss2: 0.657851
+[2023-09-27 19:21:27,388][flwr][DEBUG] - fit_round 100 received 10 results and 0 failures
+(DefaultActor pid=1831567) >> Training accuracy: 0.855058
+(DefaultActor pid=1831567)  ** Training complete **
+>> Test accuracy: 0.707100
+[2023-09-27 19:21:29,430][flwr][INFO] - fit progress: (100, 0.8504751595064474, {'accuracy': 0.7071}, 47022.266861764714)
+[2023-09-27 19:21:29,431][flwr][DEBUG] - evaluate_round 100: strategy sampled 10 clients (out of 10)
+[2023-09-27 19:22:00,469][flwr][DEBUG] - evaluate_round 100 received 10 results and 0 failures
+[2023-09-27 19:22:00,470][flwr][INFO] - FL finished in 47053.30649761995
+[2023-09-27 19:22:00,487][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0)]
+[2023-09-27 19:22:00,488][flwr][INFO] - app_fit: metrics_distributed_fit {}
+[2023-09-27 19:22:00,488][flwr][INFO] - app_fit: metrics_distributed {}
+[2023-09-27 19:22:00,488][flwr][INFO] - app_fit: losses_centralized [(0, 2.3034089754183835), (1, 2.249783382629053), (2, 2.1481322312888245), (3, 1.647436479029183), (4, 1.4994674932461578), (5, 1.3875796671111744), (6, 1.274572859556911), (7, 1.238289627785119), (8, 1.1646041104587883), (9, 1.1750041659647665), (10, 1.128453626800269), (11, 1.1231593939062126), (12, 1.0893270417143361), (13, 1.0760116641894697), (14, 1.0299229365758622), (15, 1.022295751796363), (16, 1.0062863030753577), (17, 1.0163589238930053), (18, 0.9984818176149179), (19, 1.0004730579761651), (20, 0.9751454913578095), (21, 0.9615000673947623), (22, 0.9505386705787037), (23, 0.9466991268407804), (24, 0.9378842182052783), (25, 0.9540704172640182), (26, 0.9397244629578088), (27, 0.9426276777118159), (28, 0.9165207516080656), (29, 0.93534632089039), (30, 0.9313092758289923), (31, 0.950761117874243), (32, 0.9076737036910681), (33, 0.908714735469879), (34, 0.8978847988878196), (35, 0.9095158785486374), (36, 0.9122387365030404), (37, 0.9061162161370055), (38, 0.8966945318368297), (39, 0.9046378726966846), (40, 0.9118956097017843), (41, 0.8914938339600548), (42, 0.8886514548866894), (43, 0.8876500287756752), (44, 0.8899790141910029), (45, 0.8930932635697313), (46, 0.896309151150548), (47, 0.8794247569938818), (48, 0.8850831008566835), (49, 0.8720772449200908), (50, 0.8935196316851595), (51, 0.8929546851510057), (52, 0.8784053408490202), (53, 0.8694329051354441), (54, 0.8700203883190887), (55, 0.8612132831312977), (56, 0.8703011946556286), (57, 0.8694903847698967), (58, 0.8769776419328805), (59, 0.8658733374584978), (60, 0.8708832763825742), (61, 0.8809174903855918), (62, 0.8817045592461912), (63, 0.8681536297828626), (64, 0.8740351014434339), (65, 0.8715874363248721), (66, 0.8717131253819876), (67, 0.8726276551572659), (68, 0.8746880760398535), (69, 0.8618371694232709), (70, 0.8689377218389663), (71, 0.8598026045785544), (72, 0.8614236178299108), (73, 0.8532162613381212), (74, 0.8540887534618378), (75, 0.8667597494567164), (76, 0.8745543113150916), (77, 0.8666860393632334), (78, 0.8683196400491574), (79, 0.8694912555118719), (80, 0.8883003936217616), (81, 0.8609136937144465), (82, 0.874075241743947), (83, 0.8777990603980165), (84, 0.8604000634469163), (85, 0.8605043698614017), (86, 0.8559781925175518), (87, 0.8728009527102827), (88, 0.8516317514565807), (89, 0.8544797211790237), (90, 0.8642163840345681), (91, 0.8547620579076651), (92, 0.8611169435536138), (93, 0.863727054847315), (94, 0.8653616505309035), (95, 0.8646818039516291), (96, 0.8532181625929884), (97, 0.8687959163904951), (98, 0.8529771449276433), (99, 0.8568882018613359), (100, 0.8504751595064474)]
+[2023-09-27 19:22:00,489][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.1), (1, 0.1108), (2, 0.1723), (3, 0.385), (4, 0.4448), (5, 0.4894), (6, 0.5419), (7, 0.5521), (8, 0.5872), (9, 0.5791), (10, 0.6008), (11, 0.5978), (12, 0.6122), (13, 0.6167), (14, 0.6321), (15, 0.6358), (16, 0.6409), (17, 0.6379), (18, 0.6455), (19, 0.6474), (20, 0.6553), (21, 0.658), (22, 0.6622), (23, 0.6667), (24, 0.6699), (25, 0.6619), (26, 0.6664), (27, 0.6647), (28, 0.678), (29, 0.674), (30, 0.6711), (31, 0.6672), (32, 0.6829), (33, 0.6829), (34, 0.6848), (35, 0.6827), (36, 0.6804), (37, 0.6851), (38, 0.6884), (39, 0.6832), (40, 0.6795), (41, 0.6912), (42, 0.6888), (43, 0.6932), (44, 0.6882), (45, 0.6906), (46, 0.6881), (47, 0.695), (48, 0.6942), (49, 0.6979), (50, 0.6916), (51, 0.6864), (52, 0.695), (53, 0.6957), (54, 0.6979), (55, 0.7028), (56, 0.6951), (57, 0.6973), (58, 0.6962), (59, 0.7032), (60, 0.7), (61, 0.6953), (62, 0.6907), (63, 0.7008), (64, 0.695), (65, 0.6976), (66, 0.6992), (67, 0.6971), (68, 0.6966), (69, 0.6999), (70, 0.7013), (71, 0.7042), (72, 0.7002), (73, 0.7019), (74, 0.707), (75, 0.6993), (76, 0.6963), (77, 0.6997), (78, 0.7002), (79, 0.6994), (80, 0.6927), (81, 0.7017), (82, 0.6957), (83, 0.6945), (84, 0.7042), (85, 0.7043), (86, 0.7051), (87, 0.6987), (88, 0.7057), (89, 0.7038), (90, 0.7078), (91, 0.7068), (92, 0.7046), (93, 0.701), (94, 0.7028), (95, 0.7001), (96, 0.7064), (97, 0.7008), (98, 0.7064), (99, 0.7057), (100, 0.7071)]}
+................
+History (loss, distributed):
+	round 1: 0.0
+	round 2: 0.0
+	round 3: 0.0
+	round 4: 0.0
+	round 5: 0.0
+	round 6: 0.0
+	round 7: 0.0
+	round 8: 0.0
+	round 9: 0.0
+	round 10: 0.0
+	round 11: 0.0
+	round 12: 0.0
+	round 13: 0.0
+	round 14: 0.0
+	round 15: 0.0
+	round 16: 0.0
+	round 17: 0.0
+	round 18: 0.0
+	round 19: 0.0
+	round 20: 0.0
+	round 21: 0.0
+	round 22: 0.0
+	round 23: 0.0
+	round 24: 0.0
+	round 25: 0.0
+	round 26: 0.0
+	round 27: 0.0
+	round 28: 0.0
+	round 29: 0.0
+	round 30: 0.0
+	round 31: 0.0
+	round 32: 0.0
+	round 33: 0.0
+	round 34: 0.0
+	round 35: 0.0
+	round 36: 0.0
+	round 37: 0.0
+	round 38: 0.0
+	round 39: 0.0
+	round 40: 0.0
+	round 41: 0.0
+	round 42: 0.0
+	round 43: 0.0
+	round 44: 0.0
+	round 45: 0.0
+	round 46: 0.0
+	round 47: 0.0
+	round 48: 0.0
+	round 49: 0.0
+	round 50: 0.0
+	round 51: 0.0
+	round 52: 0.0
+	round 53: 0.0
+	round 54: 0.0
+	round 55: 0.0
+	round 56: 0.0
+	round 57: 0.0
+	round 58: 0.0
+	round 59: 0.0
+	round 60: 0.0
+	round 61: 0.0
+	round 62: 0.0
+	round 63: 0.0
+	round 64: 0.0
+	round 65: 0.0
+	round 66: 0.0
+	round 67: 0.0
+	round 68: 0.0
+	round 69: 0.0
+	round 70: 0.0
+	round 71: 0.0
+	round 72: 0.0
+	round 73: 0.0
+	round 74: 0.0
+	round 75: 0.0
+	round 76: 0.0
+	round 77: 0.0
+	round 78: 0.0
+	round 79: 0.0
+	round 80: 0.0
+	round 81: 0.0
+	round 82: 0.0
+	round 83: 0.0
+	round 84: 0.0
+	round 85: 0.0
+	round 86: 0.0
+	round 87: 0.0
+	round 88: 0.0
+	round 89: 0.0
+	round 90: 0.0
+	round 91: 0.0
+	round 92: 0.0
+	round 93: 0.0
+	round 94: 0.0
+	round 95: 0.0
+	round 96: 0.0
+	round 97: 0.0
+	round 98: 0.0
+	round 99: 0.0
+	round 100: 0.0
+History (loss, centralized):
+	round 0: 2.3034089754183835
+	round 1: 2.249783382629053
+	round 2: 2.1481322312888245
+	round 3: 1.647436479029183
+	round 4: 1.4994674932461578
+	round 5: 1.3875796671111744
+	round 6: 1.274572859556911
+	round 7: 1.238289627785119
+	round 8: 1.1646041104587883
+	round 9: 1.1750041659647665
+	round 10: 1.128453626800269
+	round 11: 1.1231593939062126
+	round 12: 1.0893270417143361
+	round 13: 1.0760116641894697
+	round 14: 1.0299229365758622
+	round 15: 1.022295751796363
+	round 16: 1.0062863030753577
+	round 17: 1.0163589238930053
+	round 18: 0.9984818176149179
+	round 19: 1.0004730579761651
+	round 20: 0.9751454913578095
+	round 21: 0.9615000673947623
+	round 22: 0.9505386705787037
+	round 23: 0.9466991268407804
+	round 24: 0.9378842182052783
+	round 25: 0.9540704172640182
+	round 26: 0.9397244629578088
+	round 27: 0.9426276777118159
+	round 28: 0.9165207516080656
+	round 29: 0.93534632089039
+	round 30: 0.9313092758289923
+	round 31: 0.950761117874243
+	round 32: 0.9076737036910681
+	round 33: 0.908714735469879
+	round 34: 0.8978847988878196
+	round 35: 0.9095158785486374
+	round 36: 0.9122387365030404
+	round 37: 0.9061162161370055
+	round 38: 0.8966945318368297
+	round 39: 0.9046378726966846
+	round 40: 0.9118956097017843
+	round 41: 0.8914938339600548
+	round 42: 0.8886514548866894
+	round 43: 0.8876500287756752
+	round 44: 0.8899790141910029
+	round 45: 0.8930932635697313
+	round 46: 0.896309151150548
+	round 47: 0.8794247569938818
+	round 48: 0.8850831008566835
+	round 49: 0.8720772449200908
+	round 50: 0.8935196316851595
+	round 51: 0.8929546851510057
+	round 52: 0.8784053408490202
+	round 53: 0.8694329051354441
+	round 54: 0.8700203883190887
+	round 55: 0.8612132831312977
+	round 56: 0.8703011946556286
+	round 57: 0.8694903847698967
+	round 58: 0.8769776419328805
+	round 59: 0.8658733374584978
+	round 60: 0.8708832763825742
+	round 61: 0.8809174903855918
+	round 62: 0.8817045592461912
+	round 63: 0.8681536297828626
+	round 64: 0.8740351014434339
+	round 65: 0.8715874363248721
+	round 66: 0.8717131253819876
+	round 67: 0.8726276551572659
+	round 68: 0.8746880760398535
+	round 69: 0.8618371694232709
+	round 70: 0.8689377218389663
+	round 71: 0.8598026045785544
+	round 72: 0.8614236178299108
+	round 73: 0.8532162613381212
+	round 74: 0.8540887534618378
+	round 75: 0.8667597494567164
+	round 76: 0.8745543113150916
+	round 77: 0.8666860393632334
+	round 78: 0.8683196400491574
+	round 79: 0.8694912555118719
+	round 80: 0.8883003936217616
+	round 81: 0.8609136937144465
+	round 82: 0.874075241743947
+	round 83: 0.8777990603980165
+	round 84: 0.8604000634469163
+	round 85: 0.8605043698614017
+	round 86: 0.8559781925175518
+	round 87: 0.8728009527102827
+	round 88: 0.8516317514565807
+	round 89: 0.8544797211790237
+	round 90: 0.8642163840345681
+	round 91: 0.8547620579076651
+	round 92: 0.8611169435536138
+	round 93: 0.863727054847315
+	round 94: 0.8653616505309035
+	round 95: 0.8646818039516291
+	round 96: 0.8532181625929884
+	round 97: 0.8687959163904951
+	round 98: 0.8529771449276433
+	round 99: 0.8568882018613359
+	round 100: 0.8504751595064474
+History (metrics, centralized):
+{'accuracy': [(0, 0.1), (1, 0.1108), (2, 0.1723), (3, 0.385), (4, 0.4448), (5, 0.4894), (6, 0.5419), (7, 0.5521), (8, 0.5872), (9, 0.5791), (10, 0.6008), (11, 0.5978), (12, 0.6122), (13, 0.6167), (14, 0.6321), (15, 0.6358), (16, 0.6409), (17, 0.6379), (18, 0.6455), (19, 0.6474), (20, 0.6553), (21, 0.658), (22, 0.6622), (23, 0.6667), (24, 0.6699), (25, 0.6619), (26, 0.6664), (27, 0.6647), (28, 0.678), (29, 0.674), (30, 0.6711), (31, 0.6672), (32, 0.6829), (33, 0.6829), (34, 0.6848), (35, 0.6827), (36, 0.6804), (37, 0.6851), (38, 0.6884), (39, 0.6832), (40, 0.6795), (41, 0.6912), (42, 0.6888), (43, 0.6932), (44, 0.6882), (45, 0.6906), (46, 0.6881), (47, 0.695), (48, 0.6942), (49, 0.6979), (50, 0.6916), (51, 0.6864), (52, 0.695), (53, 0.6957), (54, 0.6979), (55, 0.7028), (56, 0.6951), (57, 0.6973), (58, 0.6962), (59, 0.7032), (60, 0.7), (61, 0.6953), (62, 0.6907), (63, 0.7008), (64, 0.695), (65, 0.6976), (66, 0.6992), (67, 0.6971), (68, 0.6966), (69, 0.6999), (70, 0.7013), (71, 0.7042), (72, 0.7002), (73, 0.7019), (74, 0.707), (75, 0.6993), (76, 0.6963), (77, 0.6997), (78, 0.7002), (79, 0.6994), (80, 0.6927), (81, 0.7017), (82, 0.6957), (83, 0.6945), (84, 0.7042), (85, 0.7043), (86, 0.7051), (87, 0.6987), (88, 0.7057), (89, 0.7038), (90, 0.7078), (91, 0.7068), (92, 0.7046), (93, 0.701), (94, 0.7028), (95, 0.7001), (96, 0.7064), (97, 0.7008), (98, 0.7064), (99, 0.7057), (100, 0.7071)]}
+[2023-09-27 19:22:00,636][matplotlib.legend][WARNING] - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

From 323a94d8bb777c1a6630f1af2f86a951446b541a Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 18 Oct 2023 09:28:03 +0800
Subject: [PATCH 41/51] update README

---
 baselines/moon/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index ef5cfce41694..b986e83672b0 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -106,9 +106,9 @@ python -m moon.main --config-name cifar100_fedprox
 
 ## Expected Results
 
-You can find the output log in `_static` directory. After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071 (see `_static/cifar10_moon.log`). 
+You can find the output log in `_static` directory. After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071 (see `_static/cifar10_moon_log.txt`). 
 
-For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see `_static/cifar10_fedprox.log`). For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6636 (see`_static/cifar100_moon.log`). For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
+For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see `_static/cifar10_fedprox_log.txt`). For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6636 (see`_static/cifar100_moon_log.txt`). For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
 
 
 |  | CIFAR-10 | CIFAR-100 |

From a008f0d92799f8d46817033dedb371d1facf5038 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Wed, 18 Oct 2023 09:28:24 +0800
Subject: [PATCH 42/51] add conf

---
 .../moon/conf/cifar100_50clients_fedprox.yaml | 33 +++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 baselines/moon/moon/conf/cifar100_50clients_fedprox.yaml

diff --git a/baselines/moon/moon/conf/cifar100_50clients_fedprox.yaml b/baselines/moon/moon/conf/cifar100_50clients_fedprox.yaml
new file mode 100644
index 000000000000..69691021438a
--- /dev/null
+++ b/baselines/moon/moon/conf/cifar100_50clients_fedprox.yaml
@@ -0,0 +1,33 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+num_clients: 50
+num_epochs: 10
+fraction_fit: 1.0
+batch_size: 64
+learning_rate: 0.01
+mu: 0.001
+temperature: 0.5
+alg: fedprox
+seed: 0
+server_device: cpu
+num_rounds: 200
+
+client_resources:
+  num_cpus: 4
+  num_gpus: 0.5
+
+dataset:
+  # dataset config
+  name: cifar100
+  dir: ./data/moon/
+  partition: noniid
+  beta: 0.5
+
+model:
+  # model config
+  name: resnet50
+  output_dim: 256
+  dir: ./client_states/fedprox/cifar100_50clients/
\ No newline at end of file

From eb48726a11885dde4f824dfe0f3ab4c4466efb58 Mon Sep 17 00:00:00 2001
From: jafermarq <jafermarq@gmail.com>
Date: Wed, 18 Oct 2023 21:29:31 +0000
Subject: [PATCH 43/51] minor formatting

---
 baselines/moon/README.md      | 32 ++++++++++++++++----------------
 baselines/moon/moon/client.py |  2 --
 baselines/moon/moon/main.py   | 18 ------------------
 baselines/moon/moon/models.py |  2 +-
 baselines/moon/moon/server.py |  2 +-
 baselines/moon/pyproject.toml |  7 +++++--
 6 files changed, 23 insertions(+), 40 deletions(-)

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index e775c76d1b93..495aecee2c89 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -10,35 +10,35 @@ dataset: [CIFAR-10, CIFAR-100]
 > Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
 
 
-****Paper:**** [arxiv.org/abs/2103.16257](https://arxiv.org/abs/2103.16257)
+**Paper:** [arxiv.org/abs/2103.16257](https://arxiv.org/abs/2103.16257)
 
-****Authors:**** Qinbin Li, Bingsheng He, Dawn Song
+**Authors:** Qinbin Li, Bingsheng He, Dawn Song
 
-****Abstract:**** Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: modelcontrastive federated learning. MOON is a simple and effective federated learning framework. The key idea of MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. Our extensive experiments show that MOON significantly outperforms the other state-of-the-art federated learning algorithms on various image classification tasks.
+**Abstract:** Federated learning enables multiple parties to collaboratively train a machine learning model without communicating their local data. A key challenge in federated learning is to handle the heterogeneity of local data distribution across parties. Although many studies have been proposed to address this challenge, we find that they fail to achieve high performance in image datasets with deep learning models. In this paper, we propose MOON: modelcontrastive federated learning. MOON is a simple and effective federated learning framework. The key idea of MOON is to utilize the similarity between model representations to correct the local training of individual parties, i.e., conducting contrastive learning in model-level. Our extensive experiments show that MOON significantly outperforms the other state-of-the-art federated learning algorithms on various image classification tasks.
 
 
 
 ## About this baseline
 
-****What’s implemented:**** The code in this directory replicates the experiments in *Model-Contrastive Federated Learning* (Li et al., 2021), which proposed the MOON algorithm. Concretely ,it replicates the results of MOON for CIFAR-10 and CIFAR-100 in Table 1.
+**What’s implemented:** The code in this directory replicates the experiments in *Model-Contrastive Federated Learning* (Li et al., 2021), which proposed the MOON algorithm. Concretely ,it replicates the results of MOON for CIFAR-10 and CIFAR-100 in Table 1.
 
-****Datasets:**** CIFAR-10 and CIFAR-100
+**Datasets:** CIFAR-10 and CIFAR-100
 
-****Hardware Setup:**** The experiments are run on a server with 4x Intel Xeon Gold 6226R and 8x Nvidia GeForce RTX 3090. A machine with at least 1x 16GB GPU should be able to run the experiments in a reasonable time.
+**Hardware Setup:** The experiments are run on a server with 4x Intel Xeon Gold 6226R and 8x Nvidia GeForce RTX 3090. A machine with at least 1x 16GB GPU should be able to run the experiments in a reasonable time.
 
-****Contributors:**** Qinbin Li
+**Contributors:** Qinbin Li
 
-****Description:**** MOON requires to compute the model-contrastive loss in local training, which requires access to the local model of the previous round (Lines 14-17 of Algorithm 1 of the paper). Since currently `FlowerClient` does not preserve the states when starting a new round, we store the local models into the specified `model.dir` in local training indexed by the client ID, which will be loaded to the corresponding client in the next round.
+**Description:** MOON requires to compute the model-contrastive loss in local training, which requires access to the local model of the previous round (Lines 14-17 of Algorithm 1 of the paper). Since currently `FlowerClient` does not preserve the states when starting a new round, we store the local models into the specified `model.dir` in local training indexed by the client ID, which will be loaded to the corresponding client in the next round.
 
 ## Experimental Setup
 
-****Task:**** Image classification.
+**Task:** Image classification.
 
-****Model:**** This directory implements two models as same as the paper:
+**Model:** This directory implements two models as same as the paper:
 * A simple-CNN with a projection head for CIFAR-10
 * A ResNet-50 with a projection head for CIFAR-100.
   
-****Dataset:**** This directory includes CIFAR-10 and CIFAR-100. They are partitioned in the same way as the paper. The settings are as follow:
+**Dataset:** This directory includes CIFAR-10 and CIFAR-100. They are partitioned in the same way as the paper. The settings are as follow:
 
 | Dataset | partitioning method |
 | :------ | :---: |
@@ -46,7 +46,7 @@ dataset: [CIFAR-10, CIFAR-100]
 | CIFAR-100 | Dirichlet with beta 0.5 |
 
 
-****Training Hyperparameters:****
+**Training Hyperparameters:**
 
 | Description | Default Value |
 | ----------- | ----- |
@@ -68,14 +68,14 @@ dataset: [CIFAR-10, CIFAR-100]
 To construct the Python environment follow these steps:
 
 ```bash
-# set local python version via pyenv
+# Set local python version via pyenv
 pyenv local 3.10.6
-# then fix that for poetry
+# Then fix that for poetry
 poetry env use 3.10.6
-# then install poetry env
+# Then install poetry env
 poetry install
 
-# activate the environment
+# Activate the environment
 poetry shell
 ```
 
diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index e1edd123d539..7281375aabf1 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -17,8 +17,6 @@
 
 from moon.models import init_net, train_fedprox, train_moon
 
-# pylint: disable=E1101
-
 
 # pylint: disable=too-many-instance-attributes
 class FlowerClient(fl.client.NumPyClient):
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 221ab2bf0e7a..41b7f7a38543 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -34,12 +34,6 @@ def main(cfg: DictConfig) -> None:
     # 1. Print parsed config
     print(OmegaConf.to_yaml(cfg))
     # 2. Prepare your dataset
-    # here you should call a function in datasets.py that returns whatever is needed to:
-    # (1) ensure the server can access the dataset used to evaluate your model after
-    # aggregation
-    # (2) tell each client what dataset partitions they should use (e.g. a this could
-    # be a location in the file system, a list of dataloader, a list of ids to extract
-    # from a dataset, it's up to you)
     np.random.seed(cfg.seed)
     torch.manual_seed(cfg.seed)
     if torch.cuda.is_available():
@@ -90,9 +84,6 @@ def main(cfg: DictConfig) -> None:
     evaluate_fn = server.gen_evaluate_fn(test_global_dl, device=device, cfg=cfg)
 
     # 4. Define your strategy
-    # pass all relevant argument (including the global dataset used after aggregation,
-    # if needed by your method.)
-    # strategy = instantiate(cfg.strategy, <additional arguments if desired>)
     strategy = fl.server.strategy.FedAvg(
         fraction_fit=cfg.fraction_fit, evaluate_fn=evaluate_fn
     )
@@ -113,15 +104,6 @@ def main(cfg: DictConfig) -> None:
         shutil.rmtree(cfg.model.dir)
 
     # 6. Save your results
-    # Here you can save the `history` returned by the simulation and include
-    # also other buffers, statistics, info needed to be saved in order to later
-    # on generate the plots you provide in the README.md. You can for instance
-    # access elements that belong to the strategy for example:
-    # data = strategy.get_my_custom_data() -- assuming you have such method defined.
-    # Hydra will generate for you a directory each time you run the code. You
-    # can retrieve the path to that directory with this:
-    # save_path = HydraConfig.get().runtime.output_dir
-
     # Experiment completed. Now we save the results and
     # generate plots using the `history`
     print("................")
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
index 6b34a0b5cb27..d241c5448773 100644
--- a/baselines/moon/moon/models.py
+++ b/baselines/moon/moon/models.py
@@ -258,7 +258,7 @@ def _forward_impl(self, x):
         x = self.layer4(x)
 
         x = self.avgpool(x)
-        x = torch.flatten(x, 1)  # pylint: disable=E1101
+        x = torch.flatten(x, 1)
         x = self.fc(x)
 
         return x
diff --git a/baselines/moon/moon/server.py b/baselines/moon/moon/server.py
index cf33c2664a37..0cf812b88666 100644
--- a/baselines/moon/moon/server.py
+++ b/baselines/moon/moon/server.py
@@ -17,7 +17,7 @@
 
 def gen_evaluate_fn(
     testloader: DataLoader,
-    device: torch.device,  # pylint: disable=E1101
+    device: torch.device,
     cfg: DictConfig,
 ) -> Callable[
     [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]
diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index 01155eca250e..e9f826abb2ea 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -5,7 +5,7 @@ build-backend = "poetry.masonry.api"
 [tool.poetry]
 name = "moon" # <----- Ensure it matches the name of your baseline directory containing all the source code
 version = "1.0.0"
-description = "Flower Baselines - Model-Contrastive Federated Learning"
+description = "Model-Contrastive Federated Learning"
 license = "Apache-2.0"
 authors = ["The Flower Authors <hello@flower.dev>", "Qinbin Li <liqinbin1998@gmail.com>"]
 readme = "README.md"
@@ -82,7 +82,7 @@ strict = false
 plugins = "numpy.typing.mypy_plugin"
 
 [tool.pylint."MESSAGES CONTROL"]
-disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias,E1101"
+disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y,K,N,X_train,X_test,fc,l1,l2,l3,h,lr,mu"
 max-args = 10
 max-attributes = 15
@@ -91,6 +91,9 @@ max-branches = 20
 max-statements = 55
 signature-mutators="hydra.main.main"
 
+[tool.pylint.typecheck]
+generated-members="numpy.*, torch.*, tensorflow.*"
+
 [[tool.mypy.overrides]]
 module = [
     "importlib.metadata.*",

From 1d18dab761b53854af2dd817136fc519f3dbcecf Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 20 Oct 2023 09:15:31 +0800
Subject: [PATCH 44/51] Update baselines/moon/moon/main.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/moon/main.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 41b7f7a38543..73a367572167 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -80,7 +80,7 @@ def main(cfg: DictConfig) -> None:
 
     # get function that will executed by the strategy's evaluate() method
     # Set server's device
-    device = cfg.server_device
+    device = torch.device("cuda:0") if torch.cuda.is_available() and cfg.server_device=="cuda" else "cpu"
     evaluate_fn = server.gen_evaluate_fn(test_global_dl, device=device, cfg=cfg)
 
     # 4. Define your strategy

From 8d1253731e8ee2dbca1c3a7430d2660d44aac747 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 20 Oct 2023 09:15:55 +0800
Subject: [PATCH 45/51] Update baselines/moon/moon/main.py

Co-authored-by: Javier <jafermarq@users.noreply.github.com>
---
 baselines/moon/moon/main.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 73a367572167..a05dba8637e4 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -85,7 +85,8 @@ def main(cfg: DictConfig) -> None:
 
     # 4. Define your strategy
     strategy = fl.server.strategy.FedAvg(
-        fraction_fit=cfg.fraction_fit, evaluate_fn=evaluate_fn
+        # Clients in MOON do not perform federated evaluation (see the client's evaluate())
+        fraction_fit=cfg.fraction_fit, fraction_evaluate=0.0, evaluate_fn=evaluate_fn
     )
     # 5. Start Simulation
     # history = fl.simulation.start_simulation(<arguments for simulation>)

From 873c3cb7661eebf8a2485506cf626fd29c800168 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 20 Oct 2023 09:36:50 +0800
Subject: [PATCH 46/51] update _static

---
 .../cifar100_50clients_moon_fedprox.png       |   Bin 0 -> 47422 bytes
 .../_static/cifar100_50clients_moon_log.txt   | 93907 ----------------
 .../moon/_static/cifar100_fedprox_log.txt     | 17647 ---
 baselines/moon/_static/cifar100_moon_log.txt  | 12852 ---
 .../moon/_static/cifar10_fedprox_log.txt      |  6852 --
 baselines/moon/_static/cifar10_moon_log.txt   | 12852 ---
 6 files changed, 144110 deletions(-)
 create mode 100644 baselines/moon/_static/cifar100_50clients_moon_fedprox.png
 delete mode 100644 baselines/moon/_static/cifar100_50clients_moon_log.txt
 delete mode 100644 baselines/moon/_static/cifar100_fedprox_log.txt
 delete mode 100644 baselines/moon/_static/cifar100_moon_log.txt
 delete mode 100644 baselines/moon/_static/cifar10_fedprox_log.txt
 delete mode 100644 baselines/moon/_static/cifar10_moon_log.txt

diff --git a/baselines/moon/_static/cifar100_50clients_moon_fedprox.png b/baselines/moon/_static/cifar100_50clients_moon_fedprox.png
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diff --git a/baselines/moon/_static/cifar100_50clients_moon_log.txt b/baselines/moon/_static/cifar100_50clients_moon_log.txt
deleted file mode 100644
index bb5684a67359..000000000000
--- a/baselines/moon/_static/cifar100_50clients_moon_log.txt
+++ /dev/null
@@ -1,93907 +0,0 @@
-INFO flwr 2023-10-08 11:50:04,801 | app.py:175 | Starting Flower simulation, config: ServerConfig(num_rounds=200, round_timeout=None)
-num_clients: 50
-num_epochs: 10
-fraction_fit: 1.0
-batch_size: 64
-learning_rate: 0.01
-mu: 10
-temperature: 0.5
-alg: moon
-seed: 0
-server_device: cpu
-num_rounds: 200
-client_resources:
-  num_cpus: 4
-  num_gpus: 0.5
-dataset:
-  name: cifar100
-  dir: ./data/moon/
-  partition: noniid
-  beta: 0.5
-model:
-  name: resnet50
-  output_dim: 256
-  dir: ./client_states/moon/cifar100_50clients/
-
-Files already downloaded and verified
-Files already downloaded and verified
-[2023-10-08 11:50:04,801][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=200, round_timeout=None)
-2023-10-08 11:50:18,357	INFO worker.py:1621 -- Started a local Ray instance.
-INFO flwr 2023-10-08 11:50:19,193 | app.py:210 | Flower VCE: Ray initialized with resources: {'memory': 81556637492.0, 'node:__internal_head__': 1.0, 'node:172.31.26.157': 1.0, 'object_store_memory': 39238558924.0, 'accelerator_type:A10G': 1.0, 'CPU': 32.0, 'GPU': 1.0}
-[2023-10-08 11:50:19,193][flwr][INFO] - Flower VCE: Ray initialized with resources: {'memory': 81556637492.0, 'node:__internal_head__': 1.0, 'node:172.31.26.157': 1.0, 'object_store_memory': 39238558924.0, 'accelerator_type:A10G': 1.0, 'CPU': 32.0, 'GPU': 1.0}
-INFO flwr 2023-10-08 11:50:19,193 | app.py:224 | Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 0.5}
-[2023-10-08 11:50:19,193][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 0.5}
-INFO flwr 2023-10-08 11:50:19,204 | app.py:270 | Flower VCE: Creating VirtualClientEngineActorPool with 2 actors
-[2023-10-08 11:50:19,204][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 2 actors
-INFO flwr 2023-10-08 11:50:19,205 | server.py:89 | Initializing global parameters
-[2023-10-08 11:50:19,205][flwr][INFO] - Initializing global parameters
-INFO flwr 2023-10-08 11:50:19,205 | server.py:276 | Requesting initial parameters from one random client
-[2023-10-08 11:50:19,205][flwr][INFO] - Requesting initial parameters from one random client
-INFO flwr 2023-10-08 11:50:35,071 | server.py:280 | Received initial parameters from one random client
-[2023-10-08 11:50:35,071][flwr][INFO] - Received initial parameters from one random client
-INFO flwr 2023-10-08 11:50:35,071 | server.py:91 | Evaluating initial parameters
-[2023-10-08 11:50:35,071][flwr][INFO] - Evaluating initial parameters
-INFO flwr 2023-10-08 11:51:32,221 | server.py:94 | initial parameters (loss, other metrics): 8.480555293659052, {'accuracy': 0.01}
->> Test accuracy: 0.010000
-[2023-10-08 11:51:32,221][flwr][INFO] - initial parameters (loss, other metrics): 8.480555293659052, {'accuracy': 0.01}
-INFO flwr 2023-10-08 11:51:32,221 | server.py:104 | FL starting
-[2023-10-08 11:51:32,221][flwr][INFO] - FL starting
-DEBUG flwr 2023-10-08 11:51:32,222 | server.py:222 | fit_round 1: strategy sampled 50 clients (out of 50)
-[2023-10-08 11:51:32,222][flwr][DEBUG] - fit_round 1: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.141429 Loss1: 4.623704 Loss2: 2.517726
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.815246 Loss1: 4.377664 Loss2: 2.437582 [repeated 2x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/ray-logging.html#log-deduplication for more options.)
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.374610 Loss1: 4.066654 Loss2: 2.307956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.241068 Loss1: 3.962804 Loss2: 2.278263 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.166380 Loss1: 3.903815 Loss2: 2.262565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.178163 Loss1: 3.900820 Loss2: 2.277343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.104259 Loss1: 3.844198 Loss2: 2.260060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.088216 Loss1: 3.827865 Loss2: 2.260351 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 6.078566 Loss1: 3.824166 Loss2: 2.254400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 6.061410 Loss1: 3.801478 Loss2: 2.259932 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.066667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.998799 Loss1: 4.461400 Loss2: 2.537398 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.123958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.011871 Loss1: 3.718402 Loss2: 2.293469 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.956115 Loss1: 3.669647 Loss2: 2.286468
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.181386 Loss1: 4.618802 Loss2: 2.562584
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.918989 Loss1: 3.647250 Loss2: 2.271739
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.842120 Loss1: 4.361955 Loss2: 2.480164
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.840175 Loss1: 3.571401 Loss2: 2.268774
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.376729 Loss1: 4.042464 Loss2: 2.334265
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.816871 Loss1: 3.543732 Loss2: 2.273139
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.195627 Loss1: 3.891672 Loss2: 2.303955
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.087691 Loss1: 3.799478 Loss2: 2.288213
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.803647 Loss1: 3.532195 Loss2: 2.271452
-(DefaultActor pid=3764) Epoch: 5 Loss: 6.080831 Loss1: 3.788156 Loss2: 2.292675
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.779242 Loss1: 3.500759 Loss2: 2.278483
-(DefaultActor pid=3764) Epoch: 6 Loss: 6.061819 Loss1: 3.781776 Loss2: 2.280043
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.756836 Loss1: 3.483867 Loss2: 2.272970
-(DefaultActor pid=3765) >> Training accuracy: 0.127930
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 6.033707 Loss1: 3.753887 Loss2: 2.279820 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.112500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.068958 Loss1: 4.511810 Loss2: 2.557148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.362267 Loss1: 3.947855 Loss2: 2.414412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.128006 Loss1: 3.790947 Loss2: 2.337059
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.435804 Loss1: 4.878289 Loss2: 2.557515
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.608788 Loss1: 4.109474 Loss2: 2.499314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.094079 Loss1: 3.772397 Loss2: 2.321682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.994196 Loss1: 3.713923 Loss2: 2.280273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.007387 Loss1: 3.731402 Loss2: 2.275984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.920667 Loss1: 3.655903 Loss2: 2.264764 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.142708
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.904706 Loss1: 3.569312 Loss2: 2.335394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.931455 Loss1: 3.660315 Loss2: 2.271140
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.929248 Loss1: 3.664463 Loss2: 2.264785
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.866408 Loss1: 3.607654 Loss2: 2.258754
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.924855 Loss1: 3.665475 Loss2: 2.259380
-(DefaultActor pid=3764) >> Training accuracy: 0.151042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.026988 Loss1: 4.495027 Loss2: 2.531961
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.383704 Loss1: 3.998182 Loss2: 2.385522
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.127318 Loss1: 3.837085 Loss2: 2.290233
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.027926 Loss1: 3.748938 Loss2: 2.278988
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.322096 Loss1: 4.777526 Loss2: 2.544570
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.986878 Loss1: 3.704436 Loss2: 2.282442
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.791483 Loss1: 4.334203 Loss2: 2.457280
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.984870 Loss1: 3.723202 Loss2: 2.261669
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.353883 Loss1: 4.023972 Loss2: 2.329911
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.280449 Loss1: 3.991762 Loss2: 2.288688
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.931523 Loss1: 3.659399 Loss2: 2.272124
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.231285 Loss1: 3.950874 Loss2: 2.280410
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.896951 Loss1: 3.632989 Loss2: 2.263962
-(DefaultActor pid=3764) Epoch: 5 Loss: 6.169997 Loss1: 3.900239 Loss2: 2.269759
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.910534 Loss1: 3.634108 Loss2: 2.276426
-(DefaultActor pid=3764) Epoch: 6 Loss: 6.139892 Loss1: 3.865301 Loss2: 2.274591
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.853818 Loss1: 3.583443 Loss2: 2.270374
-(DefaultActor pid=3765) >> Training accuracy: 0.123047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 6.097143 Loss1: 3.818687 Loss2: 2.278456 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.097917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.064795 Loss1: 4.524777 Loss2: 2.540019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.211571 Loss1: 3.885131 Loss2: 2.326441 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.083697 Loss1: 3.802457 Loss2: 2.281240
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.191528 Loss1: 4.638426 Loss2: 2.553101
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.033659 Loss1: 3.769624 Loss2: 2.264035
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.570753 Loss1: 4.101366 Loss2: 2.469386
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.993022 Loss1: 3.724712 Loss2: 2.268310
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.215262 Loss1: 3.883995 Loss2: 2.331266
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.982977 Loss1: 3.722326 Loss2: 2.260651
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.122611 Loss1: 3.804615 Loss2: 2.317996
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.957207 Loss1: 3.691987 Loss2: 2.265220
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.093045 Loss1: 3.801351 Loss2: 2.291694
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.972124 Loss1: 3.710627 Loss2: 2.261497
-(DefaultActor pid=3764) Epoch: 5 Loss: 6.037806 Loss1: 3.764050 Loss2: 2.273756
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.986772 Loss1: 3.719188 Loss2: 2.267583
-(DefaultActor pid=3764) Epoch: 6 Loss: 6.057003 Loss1: 3.766012 Loss2: 2.290990
-(DefaultActor pid=3765) >> Training accuracy: 0.103125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 6.033271 Loss1: 3.750150 Loss2: 2.283120
-(DefaultActor pid=3764) Epoch: 8 Loss: 6.016183 Loss1: 3.735457 Loss2: 2.280726
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.967865 Loss1: 3.693606 Loss2: 2.274260
-(DefaultActor pid=3764) >> Training accuracy: 0.093750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.128295 Loss1: 4.605429 Loss2: 2.522866
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.619682 Loss1: 4.190636 Loss2: 2.429046
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.287781 Loss1: 3.987398 Loss2: 2.300383
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.127619 Loss1: 4.577561 Loss2: 2.550058
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.212747 Loss1: 3.932031 Loss2: 2.280716
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.510792 Loss1: 4.070234 Loss2: 2.440558
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.190337 Loss1: 3.911305 Loss2: 2.279032
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.141527 Loss1: 3.816872 Loss2: 2.324654
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.185869 Loss1: 3.899968 Loss2: 2.285900
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.180899 Loss1: 3.896198 Loss2: 2.284701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.140523 Loss1: 3.868726 Loss2: 2.271797 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 6.131425 Loss1: 3.858042 Loss2: 2.273383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 6.111321 Loss1: 3.834449 Loss2: 2.276872 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.086914
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.860326 Loss1: 3.542153 Loss2: 2.318173 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.155208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.038699 Loss1: 4.460744 Loss2: 2.577954 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.233620 Loss1: 3.851945 Loss2: 2.381675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.117404 Loss1: 4.564858 Loss2: 2.552547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.737299 Loss1: 4.298208 Loss2: 2.439091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.295244 Loss1: 3.939232 Loss2: 2.356012 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.913371 Loss1: 3.635229 Loss2: 2.278142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.869057 Loss1: 3.595674 Loss2: 2.273383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.862464 Loss1: 3.576904 Loss2: 2.285560 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.151442
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 6.053141 Loss1: 3.776185 Loss2: 2.276957 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 6.024238 Loss1: 3.745498 Loss2: 2.278740 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.094866
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.506909 Loss1: 4.091000 Loss2: 2.415909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.151743 Loss1: 3.832490 Loss2: 2.319253 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.199954 Loss1: 4.610970 Loss2: 2.588984
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.077528 Loss1: 3.763050 Loss2: 2.314478
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.350672 Loss1: 3.863934 Loss2: 2.486738
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.056630 Loss1: 3.753362 Loss2: 2.303268
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.903112 Loss1: 3.575800 Loss2: 2.327312
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.013280 Loss1: 3.710236 Loss2: 2.303044
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.891206 Loss1: 3.571019 Loss2: 2.320186
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.006853 Loss1: 3.701072 Loss2: 2.305780
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.816187 Loss1: 3.504008 Loss2: 2.312180
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.991969 Loss1: 3.695174 Loss2: 2.296794
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.741192 Loss1: 3.443818 Loss2: 2.297374
-(DefaultActor pid=3765) Epoch: 9 Loss: 6.020029 Loss1: 3.715516 Loss2: 2.304514
-(DefaultActor pid=3765) >> Training accuracy: 0.115625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.752878 Loss1: 3.460613 Loss2: 2.292265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.686469 Loss1: 3.386332 Loss2: 2.300137 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.261458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.627912 Loss1: 4.215508 Loss2: 2.412404 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.195109 Loss1: 3.896981 Loss2: 2.298128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.155177 Loss1: 3.880020 Loss2: 2.275157
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.072679 Loss1: 4.504504 Loss2: 2.568176
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.125374 Loss1: 3.845791 Loss2: 2.279583
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.174388 Loss1: 3.692269 Loss2: 2.482119
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.063761 Loss1: 3.786196 Loss2: 2.277565
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.857945 Loss1: 3.521350 Loss2: 2.336595
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.072601 Loss1: 3.798733 Loss2: 2.273868
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.700555 Loss1: 3.384597 Loss2: 2.315958
-(DefaultActor pid=3765) Epoch: 8 Loss: 6.053261 Loss1: 3.783617 Loss2: 2.269644
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.632288 Loss1: 3.341463 Loss2: 2.290825
-(DefaultActor pid=3765) Epoch: 9 Loss: 6.021415 Loss1: 3.752326 Loss2: 2.269089
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.641399 Loss1: 3.352493 Loss2: 2.288906
-(DefaultActor pid=3765) >> Training accuracy: 0.120833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.627193 Loss1: 3.333685 Loss2: 2.293508
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.639312 Loss1: 3.350642 Loss2: 2.288670
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.612912 Loss1: 3.319420 Loss2: 2.293492
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.578630 Loss1: 3.292686 Loss2: 2.285944
-(DefaultActor pid=3764) >> Training accuracy: 0.162500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.084108 Loss1: 4.538016 Loss2: 2.546092
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.432670 Loss1: 4.026887 Loss2: 2.405783
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.157578 Loss1: 3.833854 Loss2: 2.323724
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.060689 Loss1: 3.779562 Loss2: 2.281127
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.011738 Loss1: 3.744229 Loss2: 2.267509
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.993636 Loss1: 3.719531 Loss2: 2.274105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.988533 Loss1: 3.717606 Loss2: 2.270927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.951626 Loss1: 3.673982 Loss2: 2.277644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.916644 Loss1: 3.648319 Loss2: 2.268324 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.872494 Loss1: 3.588714 Loss2: 2.283780 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.146875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.935379 Loss1: 3.649701 Loss2: 2.285678 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.871895 Loss1: 3.580260 Loss2: 2.291635 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.131250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.663894 Loss1: 4.227140 Loss2: 2.436755 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.155752 Loss1: 3.870981 Loss2: 2.284770 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.089448 Loss1: 3.814068 Loss2: 2.275380
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.240052 Loss1: 4.693091 Loss2: 2.546962
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.030305 Loss1: 3.769179 Loss2: 2.261127
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.772390 Loss1: 4.322044 Loss2: 2.450345
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.033571 Loss1: 3.753089 Loss2: 2.280482
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.354126 Loss1: 4.043020 Loss2: 2.311106
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.011510 Loss1: 3.742050 Loss2: 2.269460
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.243465 Loss1: 3.954798 Loss2: 2.288666
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.991652 Loss1: 3.722581 Loss2: 2.269072
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.190981 Loss1: 3.918658 Loss2: 2.272324
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.964920 Loss1: 3.691332 Loss2: 2.273588
-(DefaultActor pid=3764) Epoch: 5 Loss: 6.152439 Loss1: 3.876621 Loss2: 2.275818
-(DefaultActor pid=3765) >> Training accuracy: 0.143750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 6.128333 Loss1: 3.856822 Loss2: 2.271511
-(DefaultActor pid=3764) Epoch: 7 Loss: 6.077478 Loss1: 3.805771 Loss2: 2.271708
-(DefaultActor pid=3764) Epoch: 8 Loss: 6.014235 Loss1: 3.733060 Loss2: 2.281175
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.996519 Loss1: 3.702227 Loss2: 2.294292
-(DefaultActor pid=3764) >> Training accuracy: 0.107292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.171661 Loss1: 4.635104 Loss2: 2.536557
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.552720 Loss1: 4.164377 Loss2: 2.388342
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.309671 Loss1: 4.002199 Loss2: 2.307472
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.206646 Loss1: 3.933991 Loss2: 2.272655
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.161701 Loss1: 3.902683 Loss2: 2.259019
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.055893 Loss1: 4.510388 Loss2: 2.545505
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.125587 Loss1: 3.868824 Loss2: 2.256763
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.411029 Loss1: 4.004062 Loss2: 2.406967
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.109979 Loss1: 3.846207 Loss2: 2.263772
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.086884 Loss1: 3.762529 Loss2: 2.324356
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.073465 Loss1: 3.812326 Loss2: 2.261139
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.997496 Loss1: 3.722405 Loss2: 2.275091
-(DefaultActor pid=3765) Epoch: 8 Loss: 6.084823 Loss1: 3.806532 Loss2: 2.278291
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.945211 Loss1: 3.683070 Loss2: 2.262142
-(DefaultActor pid=3765) Epoch: 9 Loss: 6.057984 Loss1: 3.784317 Loss2: 2.273667
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.908533 Loss1: 3.641167 Loss2: 2.267366
-(DefaultActor pid=3765) >> Training accuracy: 0.094792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.854538 Loss1: 3.595176 Loss2: 2.259362
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.794705 Loss1: 3.520395 Loss2: 2.274310
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.794794 Loss1: 3.498505 Loss2: 2.296289
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.717706 Loss1: 3.440580 Loss2: 2.277126
-(DefaultActor pid=3764) >> Training accuracy: 0.178125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.213911 Loss1: 4.680363 Loss2: 2.533548
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.774469 Loss1: 4.291164 Loss2: 2.483305
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.270601 Loss1: 3.988687 Loss2: 2.281915
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.180628 Loss1: 3.902276 Loss2: 2.278352
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.967719 Loss1: 4.443473 Loss2: 2.524246
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.514890 Loss1: 4.103145 Loss2: 2.411746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.163852 Loss1: 3.838869 Loss2: 2.324983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.065022 Loss1: 3.777764 Loss2: 2.287258 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.995721 Loss1: 3.725123 Loss2: 2.270598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 6.014970 Loss1: 3.730334 Loss2: 2.284636 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.100586
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 6.007014 Loss1: 3.730481 Loss2: 2.276533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.962610 Loss1: 3.683919 Loss2: 2.278691 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.092773
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.989625 Loss1: 4.492349 Loss2: 2.497276 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.120593 Loss1: 3.843326 Loss2: 2.277266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.079006 Loss1: 3.804085 Loss2: 2.274921
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.181620 Loss1: 4.648129 Loss2: 2.533491
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.006323 Loss1: 3.747408 Loss2: 2.258915
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.685569 Loss1: 4.231674 Loss2: 2.453894
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.977309 Loss1: 3.708946 Loss2: 2.268362
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.207360 Loss1: 3.903498 Loss2: 2.303863
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.137814 Loss1: 3.855406 Loss2: 2.282408
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.964624 Loss1: 3.705809 Loss2: 2.258814
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.083649 Loss1: 3.826849 Loss2: 2.256800
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.972669 Loss1: 3.711862 Loss2: 2.260807
-(DefaultActor pid=3764) Epoch: 5 Loss: 6.083215 Loss1: 3.815381 Loss2: 2.267834
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.952184 Loss1: 3.689802 Loss2: 2.262382
-(DefaultActor pid=3764) Epoch: 6 Loss: 6.070061 Loss1: 3.808608 Loss2: 2.261453
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.942261 Loss1: 3.671258 Loss2: 2.271003
-(DefaultActor pid=3765) >> Training accuracy: 0.159007
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 6.012077 Loss1: 3.738924 Loss2: 2.273153 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.118164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.145576 Loss1: 4.591568 Loss2: 2.554008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.059524 Loss1: 3.756231 Loss2: 2.303292 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.983273 Loss1: 3.697950 Loss2: 2.285323
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.180025 Loss1: 4.658500 Loss2: 2.521525
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.950432 Loss1: 3.681686 Loss2: 2.268747
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.592387 Loss1: 4.125542 Loss2: 2.466845
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.914484 Loss1: 3.655438 Loss2: 2.259046
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.148547 Loss1: 3.842844 Loss2: 2.305702
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.892381 Loss1: 3.625403 Loss2: 2.266978
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.052101 Loss1: 3.771008 Loss2: 2.281093
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.878439 Loss1: 3.616365 Loss2: 2.262073
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.005809 Loss1: 3.733280 Loss2: 2.272530
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.865431 Loss1: 3.599052 Loss2: 2.266380
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.968922 Loss1: 3.712544 Loss2: 2.256379
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.832757 Loss1: 3.564582 Loss2: 2.268176
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.958870 Loss1: 3.703643 Loss2: 2.255226
-(DefaultActor pid=3765) >> Training accuracy: 0.120833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.944243 Loss1: 3.675237 Loss2: 2.269006
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.935714 Loss1: 3.680455 Loss2: 2.255259
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.932854 Loss1: 3.666784 Loss2: 2.266070
-(DefaultActor pid=3764) >> Training accuracy: 0.135417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.152187 Loss1: 4.625960 Loss2: 2.526227
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.581698 Loss1: 4.154398 Loss2: 2.427300
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.156885 Loss1: 3.866930 Loss2: 2.289955
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.041232 Loss1: 3.783649 Loss2: 2.257583
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.382555 Loss1: 4.821339 Loss2: 2.561216
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.938643 Loss1: 4.405163 Loss2: 2.533480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.493779 Loss1: 4.165896 Loss2: 2.327884 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.279132 Loss1: 3.983918 Loss2: 2.295214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.253925 Loss1: 3.971184 Loss2: 2.282741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 6.212219 Loss1: 3.928256 Loss2: 2.283963 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.136458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.936466 Loss1: 3.690321 Loss2: 2.246145
-(DefaultActor pid=3764) Epoch: 6 Loss: 6.139661 Loss1: 3.864607 Loss2: 2.275054
-(DefaultActor pid=3764) Epoch: 7 Loss: 6.145165 Loss1: 3.859347 Loss2: 2.285818
-(DefaultActor pid=3764) Epoch: 8 Loss: 6.092483 Loss1: 3.809030 Loss2: 2.283453
-(DefaultActor pid=3764) Epoch: 9 Loss: 6.064030 Loss1: 3.766958 Loss2: 2.297072
-(DefaultActor pid=3764) >> Training accuracy: 0.104167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.214621 Loss1: 4.676474 Loss2: 2.538147
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.549240 Loss1: 4.119652 Loss2: 2.429588
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.212656 Loss1: 3.892913 Loss2: 2.319743
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.113772 Loss1: 3.831893 Loss2: 2.281880
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.896751 Loss1: 4.340740 Loss2: 2.556011
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.268480 Loss1: 3.866587 Loss2: 2.401893 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.948745 Loss1: 3.593974 Loss2: 2.354771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.846087 Loss1: 3.509974 Loss2: 2.336114 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.812407 Loss1: 3.495653 Loss2: 2.316755 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.780797 Loss1: 3.456718 Loss2: 2.324079 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.089583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.723133 Loss1: 3.406896 Loss2: 2.316236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.764972 Loss1: 3.441460 Loss2: 2.323512 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.156250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.225350 Loss1: 4.689721 Loss2: 2.535629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.378551 Loss1: 4.061458 Loss2: 2.317093 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.188882 Loss1: 3.917229 Loss2: 2.271653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.141548 Loss1: 3.866832 Loss2: 2.274715 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.138974 Loss1: 3.877311 Loss2: 2.261663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.115868 Loss1: 3.851701 Loss2: 2.264167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 6.117192 Loss1: 3.849115 Loss2: 2.268077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 6.105393 Loss1: 3.838330 Loss2: 2.267063 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.101562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.950829 Loss1: 3.684106 Loss2: 2.266723 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.979712 Loss1: 3.706565 Loss2: 2.273147 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.104911
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.190710 Loss1: 4.656746 Loss2: 2.533965
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.489152 Loss1: 4.074285 Loss2: 2.414866
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.201815 Loss1: 3.896238 Loss2: 2.305577
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.163559 Loss1: 3.874082 Loss2: 2.289477
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.114285 Loss1: 4.554744 Loss2: 2.559541
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.506265 Loss1: 4.028638 Loss2: 2.477627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.085327 Loss1: 3.743407 Loss2: 2.341920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.079062 Loss1: 3.804695 Loss2: 2.274367
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.942504 Loss1: 3.630262 Loss2: 2.312242
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.062537 Loss1: 3.786662 Loss2: 2.275874
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.909682 Loss1: 3.591185 Loss2: 2.318497
-(DefaultActor pid=3765) Epoch: 8 Loss: 6.039089 Loss1: 3.769985 Loss2: 2.269104
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.849954 Loss1: 3.525251 Loss2: 2.324703
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.843701 Loss1: 3.522789 Loss2: 2.320912
-(DefaultActor pid=3765) Epoch: 9 Loss: 6.014301 Loss1: 3.747548 Loss2: 2.266754
-(DefaultActor pid=3765) >> Training accuracy: 0.125000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.796873 Loss1: 3.477319 Loss2: 2.319553 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.119792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.275325 Loss1: 4.714535 Loss2: 2.560790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.339616 Loss1: 4.032198 Loss2: 2.307418 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.127757 Loss1: 3.839748 Loss2: 2.288010
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.145596 Loss1: 4.594524 Loss2: 2.551072
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.080274 Loss1: 3.804215 Loss2: 2.276059
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.642611 Loss1: 4.182493 Loss2: 2.460118
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.015004 Loss1: 3.748530 Loss2: 2.266474
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.326457 Loss1: 3.990239 Loss2: 2.336218
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.011532 Loss1: 3.727954 Loss2: 2.283578
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.194300 Loss1: 3.893483 Loss2: 2.300817
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.969237 Loss1: 3.680405 Loss2: 2.288832
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.116862 Loss1: 3.822944 Loss2: 2.293918
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.923430 Loss1: 3.625415 Loss2: 2.298015
-(DefaultActor pid=3764) Epoch: 5 Loss: 6.106173 Loss1: 3.826514 Loss2: 2.279659
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.932329 Loss1: 3.645882 Loss2: 2.286447
-(DefaultActor pid=3764) Epoch: 6 Loss: 6.052308 Loss1: 3.766482 Loss2: 2.285826
-(DefaultActor pid=3765) >> Training accuracy: 0.133333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 6.019671 Loss1: 3.730621 Loss2: 2.289050
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.984038 Loss1: 3.691334 Loss2: 2.292704
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.937605 Loss1: 3.639196 Loss2: 2.298409
-(DefaultActor pid=3764) >> Training accuracy: 0.144792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.136243 Loss1: 4.614826 Loss2: 2.521418
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.635895 Loss1: 4.218840 Loss2: 2.417055
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.353589 Loss1: 4.042289 Loss2: 2.311300
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.019168 Loss1: 4.493465 Loss2: 2.525703
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.241870 Loss1: 3.970135 Loss2: 2.271735
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.383961 Loss1: 4.000970 Loss2: 2.382990
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.208028 Loss1: 3.936542 Loss2: 2.271486
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.139002 Loss1: 3.811059 Loss2: 2.327944
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.226305 Loss1: 3.948728 Loss2: 2.277577
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.022970 Loss1: 3.747508 Loss2: 2.275462
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.192274 Loss1: 3.917312 Loss2: 2.274962
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.171634 Loss1: 3.897760 Loss2: 2.273874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 6.159508 Loss1: 3.883811 Loss2: 2.275696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 6.142049 Loss1: 3.856993 Loss2: 2.285056 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.133789
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.937371 Loss1: 3.672004 Loss2: 2.265367 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.154167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.196278 Loss1: 4.628571 Loss2: 2.567707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.392679 Loss1: 4.002028 Loss2: 2.390651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.202050 Loss1: 3.921360 Loss2: 2.280690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.169333 Loss1: 3.887614 Loss2: 2.281719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 6.167543 Loss1: 3.891992 Loss2: 2.275551 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 6.117102 Loss1: 3.843067 Loss2: 2.274035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 6.094176 Loss1: 3.826054 Loss2: 2.268122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 6.089311 Loss1: 3.830210 Loss2: 2.259101
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.924723 Loss1: 3.660154 Loss2: 2.264568
-(DefaultActor pid=3765) >> Training accuracy: 0.102865
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.931353 Loss1: 3.646852 Loss2: 2.284501
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.859534 Loss1: 3.579844 Loss2: 2.279690
-DEBUG flwr 2023-10-08 12:23:44,163 | server.py:236 | fit_round 1 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.852454 Loss1: 3.583233 Loss2: 2.269220
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.819475 Loss1: 3.538484 Loss2: 2.280991
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.015870 Loss1: 4.487405 Loss2: 2.528464
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.791314 Loss1: 3.507455 Loss2: 2.283859
-(DefaultActor pid=3764) >> Training accuracy: 0.167708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.064575 Loss1: 3.746441 Loss2: 2.318134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.917632 Loss1: 3.626283 Loss2: 2.291349 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.178667 Loss1: 4.624337 Loss2: 2.554330
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.924207 Loss1: 3.635186 Loss2: 2.289021
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.591751 Loss1: 4.134132 Loss2: 2.457620
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.910531 Loss1: 3.616131 Loss2: 2.294400
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.148717 Loss1: 3.795228 Loss2: 2.353489
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.856889 Loss1: 3.559833 Loss2: 2.297056
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.877022 Loss1: 3.581382 Loss2: 2.295640 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.851040 Loss1: 3.560739 Loss2: 2.290301 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.125977
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.974001 Loss1: 3.655436 Loss2: 2.318564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.940438 Loss1: 3.629637 Loss2: 2.310801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.930029 Loss1: 3.618528 Loss2: 2.311501
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.184542 Loss1: 4.603412 Loss2: 2.581130
-(DefaultActor pid=3764) >> Training accuracy: 0.089583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 7.117036 Loss1: 4.613428 Loss2: 2.503608
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.479479 Loss1: 4.107953 Loss2: 2.371526
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.294121 Loss1: 3.966963 Loss2: 2.327159
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.209696 Loss1: 3.903325 Loss2: 2.306371
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.161352 Loss1: 3.864282 Loss2: 2.297070
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.033862 Loss1: 4.487883 Loss2: 2.545979
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.527542 Loss1: 4.055054 Loss2: 2.472488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.992077 Loss1: 3.678016 Loss2: 2.314061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.889023 Loss1: 3.604848 Loss2: 2.284174 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.097356
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.807133 Loss1: 3.541518 Loss2: 2.265615 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.773297 Loss1: 3.505598 Loss2: 2.267699 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.166290 Loss1: 4.609559 Loss2: 2.556731
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.751809 Loss1: 3.471298 Loss2: 2.280510
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.482598 Loss1: 4.050295 Loss2: 2.432302
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.685450 Loss1: 3.407820 Loss2: 2.277630
-(DefaultActor pid=3764) >> Training accuracy: 0.188542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.046612 Loss1: 3.766048 Loss2: 2.280564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.000233 Loss1: 3.738040 Loss2: 2.262193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.998445 Loss1: 3.733374 Loss2: 2.265071
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.181176 Loss1: 4.629001 Loss2: 2.552175
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.987026 Loss1: 3.724103 Loss2: 2.262924
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.556262 Loss1: 4.127262 Loss2: 2.429000
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.952155 Loss1: 3.683132 Loss2: 2.269023
-(DefaultActor pid=3764) Epoch: 2 Loss: 6.192904 Loss1: 3.877822 Loss2: 2.315081
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.959764 Loss1: 3.689728 Loss2: 2.270036
-(DefaultActor pid=3764) Epoch: 3 Loss: 6.124613 Loss1: 3.813027 Loss2: 2.311585
-(DefaultActor pid=3765) >> Training accuracy: 0.109375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 6.024185 Loss1: 3.722135 Loss2: 2.302050
-(DefaultActor pid=3764) Epoch: 5 Loss: 6.011222 Loss1: 3.717666 Loss2: 2.293556
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.999209 Loss1: 3.688202 Loss2: 2.311007
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.972166 Loss1: 3.674102 Loss2: 2.298063
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.981482 Loss1: 3.669964 Loss2: 2.311518
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.946748 Loss1: 3.647218 Loss2: 2.299530
-(DefaultActor pid=3764) >> Training accuracy: 0.162500
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 12:23:44,163][flwr][DEBUG] - fit_round 1 received 50 results and 0 failures
-WARNING flwr 2023-10-08 12:23:48,551 | fedavg.py:242 | No fit_metrics_aggregation_fn provided
-[2023-10-08 12:23:48,551][flwr][WARNING] - No fit_metrics_aggregation_fn provided
-INFO flwr 2023-10-08 12:24:27,083 | server.py:125 | fit progress: (1, 4.678643322600343, {'accuracy': 0.01}, 1974.861092187)
->> Test accuracy: 0.010000
-[2023-10-08 12:24:27,083][flwr][INFO] - fit progress: (1, 4.678643322600343, {'accuracy': 0.01}, 1974.861092187)
-DEBUG flwr 2023-10-08 12:24:27,083 | server.py:173 | evaluate_round 1: strategy sampled 50 clients (out of 50)
-[2023-10-08 12:24:27,083][flwr][DEBUG] - evaluate_round 1: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 12:33:31,389 | server.py:187 | evaluate_round 1 received 50 results and 0 failures
-[2023-10-08 12:33:31,389][flwr][DEBUG] - evaluate_round 1 received 50 results and 0 failures
-WARNING flwr 2023-10-08 12:33:31,389 | fedavg.py:273 | No evaluate_metrics_aggregation_fn provided
-[2023-10-08 12:33:31,389][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
-DEBUG flwr 2023-10-08 12:33:31,389 | server.py:222 | fit_round 2: strategy sampled 50 clients (out of 50)
-[2023-10-08 12:33:31,389][flwr][DEBUG] - fit_round 2: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.637836 Loss1: 4.190580 Loss2: 6.447256
-(DefaultActor pid=3765) Epoch: 1 Loss: 9.976554 Loss1: 3.921571 Loss2: 6.054984
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.692835 Loss1: 3.727470 Loss2: 5.965365
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.640974 Loss1: 3.700444 Loss2: 5.940530
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.594960 Loss1: 4.241553 Loss2: 6.353407
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.594271 Loss1: 3.694578 Loss2: 5.899693
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.841622 Loss1: 3.874930 Loss2: 5.966692
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.507963 Loss1: 3.643234 Loss2: 5.864729
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.613104 Loss1: 3.770304 Loss2: 5.842801
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.459440 Loss1: 3.612508 Loss2: 5.846931
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.488919 Loss1: 3.701728 Loss2: 5.787192
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.453676 Loss1: 3.607583 Loss2: 5.846092
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.427125 Loss1: 3.663602 Loss2: 5.763523
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.425889 Loss1: 3.600830 Loss2: 5.825059
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.340038 Loss1: 3.626946 Loss2: 5.713092
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.439987 Loss1: 3.615669 Loss2: 5.824318
-(DefaultActor pid=3765) >> Training accuracy: 0.115625
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.319818 Loss1: 3.606650 Loss2: 5.713168
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.324199 Loss1: 3.620062 Loss2: 5.704137
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.314916 Loss1: 3.616407 Loss2: 5.698508
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.207206 Loss1: 3.564857 Loss2: 5.642349
-(DefaultActor pid=3764) >> Training accuracy: 0.175000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.771675 Loss1: 4.280236 Loss2: 6.491439
-(DefaultActor pid=3765) Epoch: 1 Loss: 10.018878 Loss1: 3.975184 Loss2: 6.043695
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.807585 Loss1: 3.922243 Loss2: 5.885342
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.824159 Loss1: 4.297789 Loss2: 6.526370
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.717546 Loss1: 3.855670 Loss2: 5.861875
-(DefaultActor pid=3764) Epoch: 1 Loss: 10.015044 Loss1: 3.921217 Loss2: 6.093827
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.673834 Loss1: 3.831498 Loss2: 5.842335
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.816785 Loss1: 3.864733 Loss2: 5.952052
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.649830 Loss1: 3.823077 Loss2: 5.826753
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.621138 Loss1: 3.810802 Loss2: 5.810336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.638162 Loss1: 3.806122 Loss2: 5.832040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.578147 Loss1: 3.773955 Loss2: 5.804191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.536077 Loss1: 3.748276 Loss2: 5.787801 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.119792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.474311 Loss1: 3.605538 Loss2: 5.868773 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.137277
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.652685 Loss1: 4.252413 Loss2: 6.400272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.763214 Loss1: 3.864845 Loss2: 5.898369 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.654279 Loss1: 3.793074 Loss2: 5.861205
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.641576 Loss1: 4.212208 Loss2: 6.429368
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.967749 Loss1: 3.908536 Loss2: 6.059214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.691604 Loss1: 3.758919 Loss2: 5.932685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.647544 Loss1: 3.754553 Loss2: 5.892991 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.558958 Loss1: 3.697666 Loss2: 5.861293 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.538385 Loss1: 3.676304 Loss2: 5.862081 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.130208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.487256 Loss1: 3.657618 Loss2: 5.829637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.407890 Loss1: 3.589601 Loss2: 5.818289 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.152344
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.601140 Loss1: 4.224583 Loss2: 6.376557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.736439 Loss1: 3.946441 Loss2: 5.789997 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.719372 Loss1: 4.297521 Loss2: 6.421851 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 10.000176 Loss1: 3.953815 Loss2: 6.046362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.770465 Loss1: 3.821641 Loss2: 5.948824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.700196 Loss1: 3.795949 Loss2: 5.904247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.619483 Loss1: 3.741422 Loss2: 5.878061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.580794 Loss1: 3.726423 Loss2: 5.854370 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.101042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.525874 Loss1: 3.706135 Loss2: 5.819739 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.477863 Loss1: 3.662309 Loss2: 5.815554 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.125000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 10.107184 Loss1: 4.061199 Loss2: 6.045985 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.882278 Loss1: 3.970554 Loss2: 5.911724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.713988 Loss1: 4.202564 Loss2: 6.511424
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.826803 Loss1: 3.959741 Loss2: 5.867061
-(DefaultActor pid=3764) Epoch: 1 Loss: 10.117976 Loss1: 3.992430 Loss2: 6.125546
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.792426 Loss1: 3.941595 Loss2: 5.850831
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.934952 Loss1: 3.892701 Loss2: 6.042251
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.797875 Loss1: 3.931617 Loss2: 5.866258
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.850462 Loss1: 3.853579 Loss2: 5.996883
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.775853 Loss1: 3.933323 Loss2: 5.842530
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.750509 Loss1: 3.818110 Loss2: 5.932399
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.762758 Loss1: 3.920850 Loss2: 5.841909
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.746376 Loss1: 3.799555 Loss2: 5.946821
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.734156 Loss1: 3.904936 Loss2: 5.829220
-(DefaultActor pid=3765) >> Training accuracy: 0.115234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.672107 Loss1: 3.743145 Loss2: 5.928962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.644898 Loss1: 3.728153 Loss2: 5.916744 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.099609
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 9.795480 Loss1: 3.750212 Loss2: 6.045268 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.488928 Loss1: 3.632664 Loss2: 5.856264 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.425572 Loss1: 3.598649 Loss2: 5.826923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.398169 Loss1: 3.590263 Loss2: 5.807906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.340383 Loss1: 3.536039 Loss2: 5.804344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.326616 Loss1: 3.537983 Loss2: 5.788633 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.550909 Loss1: 3.859723 Loss2: 5.691185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.492956 Loss1: 3.809476 Loss2: 5.683480 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.113281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.416769 Loss1: 3.764166 Loss2: 5.652603 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.090402
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.833566 Loss1: 4.327127 Loss2: 6.506439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.946128 Loss1: 3.860144 Loss2: 6.085984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.839081 Loss1: 4.220955 Loss2: 6.618126
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.859521 Loss1: 3.821896 Loss2: 6.037624
-(DefaultActor pid=3764) Epoch: 1 Loss: 10.117047 Loss1: 3.900791 Loss2: 6.216255
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.740665 Loss1: 3.722306 Loss2: 6.018360
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.893418 Loss1: 3.846798 Loss2: 6.046620
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.759525 Loss1: 3.730355 Loss2: 6.029170
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.815371 Loss1: 3.830314 Loss2: 5.985056
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.698248 Loss1: 3.690235 Loss2: 6.008013
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.653507 Loss1: 3.651367 Loss2: 6.002140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.629687 Loss1: 3.615256 Loss2: 6.014431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.587763 Loss1: 3.589767 Loss2: 5.997996 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.147461
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.598274 Loss1: 3.710448 Loss2: 5.887827 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.098958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.714870 Loss1: 4.268578 Loss2: 6.446293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.824622 Loss1: 3.931455 Loss2: 5.893168 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.720104 Loss1: 3.864763 Loss2: 5.855341
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.858602 Loss1: 4.382486 Loss2: 6.476115
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.669850 Loss1: 3.865755 Loss2: 5.804095
-(DefaultActor pid=3764) Epoch: 1 Loss: 10.101550 Loss1: 3.990484 Loss2: 6.111066
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.631948 Loss1: 3.838572 Loss2: 5.793375
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.893639 Loss1: 3.913948 Loss2: 5.979691
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.613233 Loss1: 3.832063 Loss2: 5.781170
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.810846 Loss1: 3.846251 Loss2: 5.964595
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.719163 Loss1: 3.800570 Loss2: 5.918593
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.554400 Loss1: 3.810565 Loss2: 5.743835
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.701065 Loss1: 3.802588 Loss2: 5.898477
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.552926 Loss1: 3.784079 Loss2: 5.768847
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.674050 Loss1: 3.796071 Loss2: 5.877979
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.477926 Loss1: 3.733268 Loss2: 5.744657
-(DefaultActor pid=3765) >> Training accuracy: 0.104167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.645398 Loss1: 3.796198 Loss2: 5.849200 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.103795
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.554327 Loss1: 4.122124 Loss2: 6.432204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.413669 Loss1: 3.507592 Loss2: 5.906077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.383995 Loss1: 3.541977 Loss2: 5.842017
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.459539 Loss1: 4.115703 Loss2: 6.343837
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.712369 Loss1: 3.787863 Loss2: 5.924506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.459639 Loss1: 3.653027 Loss2: 5.806611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.377694 Loss1: 3.605024 Loss2: 5.772671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.325220 Loss1: 3.616160 Loss2: 5.709060 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.294610 Loss1: 3.593041 Loss2: 5.701569 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.248958
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.120221 Loss1: 3.361153 Loss2: 5.759068
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.260141 Loss1: 3.565868 Loss2: 5.694273
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.225864 Loss1: 3.549637 Loss2: 5.676227
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.156384 Loss1: 3.497253 Loss2: 5.659131
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.152884 Loss1: 3.491014 Loss2: 5.661870
-(DefaultActor pid=3764) >> Training accuracy: 0.111458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.541196 Loss1: 4.225392 Loss2: 6.315804
-(DefaultActor pid=3765) Epoch: 1 Loss: 9.777527 Loss1: 3.935185 Loss2: 5.842342
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.563549 Loss1: 3.828953 Loss2: 5.734595
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.442931 Loss1: 3.752719 Loss2: 5.690212
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.793967 Loss1: 4.285539 Loss2: 6.508428
-(DefaultActor pid=3764) Epoch: 1 Loss: 10.103335 Loss1: 4.007536 Loss2: 6.095799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.353892 Loss1: 3.719582 Loss2: 5.634310
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.866446 Loss1: 3.931696 Loss2: 5.934750
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.741218 Loss1: 3.877523 Loss2: 5.863695
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.313694 Loss1: 3.713614 Loss2: 5.600080
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.302611 Loss1: 3.709807 Loss2: 5.592804 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.318756 Loss1: 3.692372 Loss2: 5.626384 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.261945 Loss1: 3.675049 Loss2: 5.586896 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.106250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.530101 Loss1: 3.750192 Loss2: 5.779909 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.106971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.744404 Loss1: 4.296521 Loss2: 6.447883
-(DefaultActor pid=3765) Epoch: 1 Loss: 9.937258 Loss1: 3.930883 Loss2: 6.006375
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.737718 Loss1: 3.785188 Loss2: 5.952531
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.627638 Loss1: 3.731983 Loss2: 5.895654
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.682749 Loss1: 4.142052 Loss2: 6.540697
-(DefaultActor pid=3764) Epoch: 1 Loss: 10.009344 Loss1: 3.843287 Loss2: 6.166057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.788435 Loss1: 3.742180 Loss2: 6.046254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.734535 Loss1: 3.736097 Loss2: 5.998438 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.659610 Loss1: 3.694191 Loss2: 5.965419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.588574 Loss1: 3.666707 Loss2: 5.921867 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.138542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.445799 Loss1: 3.632870 Loss2: 5.812929
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.569317 Loss1: 3.660271 Loss2: 5.909046
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.525640 Loss1: 3.615687 Loss2: 5.909954
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.493718 Loss1: 3.603991 Loss2: 5.889727
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.453791 Loss1: 3.570200 Loss2: 5.883591
-(DefaultActor pid=3764) >> Training accuracy: 0.119792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.657896 Loss1: 4.177572 Loss2: 6.480324
-(DefaultActor pid=3765) Epoch: 1 Loss: 10.003504 Loss1: 3.919831 Loss2: 6.083673
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.748192 Loss1: 3.780401 Loss2: 5.967791
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.664308 Loss1: 3.757226 Loss2: 5.907081
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.356075 Loss1: 4.164318 Loss2: 6.191757
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.614167 Loss1: 3.718640 Loss2: 5.895527
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.538110 Loss1: 3.789763 Loss2: 5.748347
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.536380 Loss1: 3.690014 Loss2: 5.846366
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.367953 Loss1: 3.703727 Loss2: 5.664226
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.541355 Loss1: 3.670259 Loss2: 5.871095
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.272350 Loss1: 3.676055 Loss2: 5.596296
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.465765 Loss1: 3.641314 Loss2: 5.824452
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.213708 Loss1: 3.641877 Loss2: 5.571830
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.441137 Loss1: 3.585224 Loss2: 5.855913
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.129308 Loss1: 3.578903 Loss2: 5.550405
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.391586 Loss1: 3.548695 Loss2: 5.842891
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.094977 Loss1: 3.549146 Loss2: 5.545831
-(DefaultActor pid=3765) >> Training accuracy: 0.137500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.064834 Loss1: 3.511552 Loss2: 5.553283
-(DefaultActor pid=3764) Epoch: 8 Loss: 8.999669 Loss1: 3.472173 Loss2: 5.527496
-(DefaultActor pid=3764) Epoch: 9 Loss: 8.946095 Loss1: 3.431198 Loss2: 5.514897
-(DefaultActor pid=3764) >> Training accuracy: 0.153125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.575111 Loss1: 4.096109 Loss2: 6.479002
-(DefaultActor pid=3765) Epoch: 1 Loss: 9.911172 Loss1: 3.847541 Loss2: 6.063631
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.718339 Loss1: 3.729885 Loss2: 5.988455
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.618442 Loss1: 3.691965 Loss2: 5.926476
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.836223 Loss1: 4.347470 Loss2: 6.488752
-(DefaultActor pid=3764) Epoch: 1 Loss: 10.052698 Loss1: 3.993670 Loss2: 6.059028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.872723 Loss1: 3.929094 Loss2: 5.943629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.727139 Loss1: 3.845502 Loss2: 5.881636 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.715427 Loss1: 3.885239 Loss2: 5.830188 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.647742 Loss1: 3.799690 Loss2: 5.848052 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.166667
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.107588 Loss1: 3.480518 Loss2: 5.627070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.649327 Loss1: 3.801738 Loss2: 5.847589
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.597159 Loss1: 3.786555 Loss2: 5.810604
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.516748 Loss1: 3.708655 Loss2: 5.808094
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.530191 Loss1: 3.734705 Loss2: 5.795485
-(DefaultActor pid=3764) >> Training accuracy: 0.115625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.686062 Loss1: 4.196250 Loss2: 6.489812
-(DefaultActor pid=3765) Epoch: 1 Loss: 9.989050 Loss1: 3.928703 Loss2: 6.060347
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.728920 Loss1: 3.821730 Loss2: 5.907190
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.687483 Loss1: 3.805944 Loss2: 5.881539
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.503141 Loss1: 4.138347 Loss2: 6.364794
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.831063 Loss1: 3.874199 Loss2: 5.956864 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.561377 Loss1: 3.722831 Loss2: 5.838545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.495142 Loss1: 3.716147 Loss2: 5.778995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.468325 Loss1: 3.679229 Loss2: 5.789096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.441754 Loss1: 3.656899 Loss2: 5.784855 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.115625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.319609 Loss1: 3.605931 Loss2: 5.713678 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.285955 Loss1: 3.578991 Loss2: 5.706963 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.136719
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 9.785292 Loss1: 4.004951 Loss2: 5.780341 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.454764 Loss1: 3.838589 Loss2: 5.616174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 11.029210 Loss1: 4.420167 Loss2: 6.609043
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.382492 Loss1: 3.785829 Loss2: 5.596663
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.287707 Loss1: 3.721356 Loss2: 5.566351 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.282151 Loss1: 3.721080 Loss2: 5.561071 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.270940 Loss1: 3.730857 Loss2: 5.540083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.815740 Loss1: 3.849322 Loss2: 5.966418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.775648 Loss1: 3.834996 Loss2: 5.940652 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.145833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.727230 Loss1: 3.774140 Loss2: 5.953091 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.102865
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.681232 Loss1: 4.270233 Loss2: 6.410999 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.726148 Loss1: 3.857509 Loss2: 5.868639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.614128 Loss1: 3.807013 Loss2: 5.807115
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.534492 Loss1: 4.114267 Loss2: 6.420225
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.855433 Loss1: 3.796730 Loss2: 6.058703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.624794 Loss1: 3.689479 Loss2: 5.935316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.548176 Loss1: 3.650949 Loss2: 5.897227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.446170 Loss1: 3.572997 Loss2: 5.873172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.421490 Loss1: 3.566178 Loss2: 5.855312 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.109375
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.345133 Loss1: 3.654656 Loss2: 5.690477
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.382988 Loss1: 3.518498 Loss2: 5.864490
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.372723 Loss1: 3.482304 Loss2: 5.890419
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.347202 Loss1: 3.459721 Loss2: 5.887481
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.322032 Loss1: 3.435241 Loss2: 5.886791
-(DefaultActor pid=3764) >> Training accuracy: 0.179167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.331028 Loss1: 4.164000 Loss2: 6.167028
-(DefaultActor pid=3765) Epoch: 1 Loss: 9.695290 Loss1: 3.895446 Loss2: 5.799843
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.430650 Loss1: 3.751587 Loss2: 5.679062
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.310185 Loss1: 3.703583 Loss2: 5.606602
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.669753 Loss1: 4.189651 Loss2: 6.480102
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.959448 Loss1: 3.896816 Loss2: 6.062632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.726233 Loss1: 3.762142 Loss2: 5.964091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.605897 Loss1: 3.729162 Loss2: 5.876735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.563627 Loss1: 3.670131 Loss2: 5.893496 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.534119 Loss1: 3.684701 Loss2: 5.849417 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.153125
-(DefaultActor pid=3765) Epoch: 9 Loss: 8.732340 Loss1: 3.445579 Loss2: 5.286761
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.491700 Loss1: 3.639489 Loss2: 5.852211
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.518808 Loss1: 3.650798 Loss2: 5.868010
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.435658 Loss1: 3.589059 Loss2: 5.846600
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.434950 Loss1: 3.592908 Loss2: 5.842043
-(DefaultActor pid=3764) >> Training accuracy: 0.157292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.848761 Loss1: 4.324665 Loss2: 6.524096
-(DefaultActor pid=3765) Epoch: 1 Loss: 10.072184 Loss1: 3.884131 Loss2: 6.188053
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.821466 Loss1: 3.761192 Loss2: 6.060273
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.776668 Loss1: 3.784546 Loss2: 5.992122
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.688833 Loss1: 3.726467 Loss2: 5.962366
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.621974 Loss1: 3.684707 Loss2: 5.937267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.558368 Loss1: 3.645302 Loss2: 5.913067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.552690 Loss1: 3.635356 Loss2: 5.917335 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.476609 Loss1: 3.561661 Loss2: 5.914947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.263857 Loss1: 3.766380 Loss2: 5.497477
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.475863 Loss1: 3.564449 Loss2: 5.911414
-(DefaultActor pid=3765) >> Training accuracy: 0.138221
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.178989 Loss1: 3.744039 Loss2: 5.434949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.184733 Loss1: 3.749408 Loss2: 5.435325 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.772070 Loss1: 4.302489 Loss2: 6.469581
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.173345 Loss1: 3.721502 Loss2: 5.451843
-(DefaultActor pid=3764) >> Training accuracy: 0.108398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.875539 Loss1: 3.954466 Loss2: 5.921073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.686421 Loss1: 3.848566 Loss2: 5.837855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.620740 Loss1: 3.796890 Loss2: 5.823850
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.718717 Loss1: 4.255756 Loss2: 6.462961
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.904809 Loss1: 3.954151 Loss2: 5.950658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.654817 Loss1: 3.810745 Loss2: 5.844072 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.590017 Loss1: 3.780618 Loss2: 5.809399 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.108333
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.497835 Loss1: 3.663922 Loss2: 5.833913
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.528583 Loss1: 3.754888 Loss2: 5.773695
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.488371 Loss1: 3.740890 Loss2: 5.747481
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.448910 Loss1: 3.706955 Loss2: 5.741955
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.457279 Loss1: 3.712904 Loss2: 5.744375
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.461905 Loss1: 3.704660 Loss2: 5.757245
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.589808 Loss1: 4.269219 Loss2: 6.320589
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.476294 Loss1: 3.696930 Loss2: 5.779364
-(DefaultActor pid=3764) >> Training accuracy: 0.129167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.686617 Loss1: 3.882598 Loss2: 5.804019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.531678 Loss1: 3.817210 Loss2: 5.714468 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.413258 Loss1: 3.731065 Loss2: 5.682193
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.573467 Loss1: 4.215427 Loss2: 6.358040
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.830434 Loss1: 3.878489 Loss2: 5.951945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.598990 Loss1: 3.808078 Loss2: 5.790912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.525582 Loss1: 3.753918 Loss2: 5.771664 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.080208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.465058 Loss1: 3.719705 Loss2: 5.745353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.331377 Loss1: 3.630269 Loss2: 5.701109 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.222510 Loss1: 3.568976 Loss2: 5.653534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.235193 Loss1: 3.603144 Loss2: 5.632049 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.138542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.740749 Loss1: 3.842407 Loss2: 5.898342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.588993 Loss1: 3.755523 Loss2: 5.833471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.557939 Loss1: 3.744185 Loss2: 5.813753
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.604908 Loss1: 4.151583 Loss2: 6.453326
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.824718 Loss1: 3.828602 Loss2: 5.996116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.611880 Loss1: 3.724438 Loss2: 5.887442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.508505 Loss1: 3.682727 Loss2: 5.825778 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.136458
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.436936 Loss1: 3.608846 Loss2: 5.828090
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.527967 Loss1: 3.699391 Loss2: 5.828576
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.450804 Loss1: 3.642496 Loss2: 5.808309
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.395119 Loss1: 3.611091 Loss2: 5.784029
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.405193 Loss1: 3.615323 Loss2: 5.789870
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.347845 Loss1: 3.574632 Loss2: 5.773213
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.577079 Loss1: 4.189072 Loss2: 6.388008
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.326152 Loss1: 3.575595 Loss2: 5.750557
-(DefaultActor pid=3764) >> Training accuracy: 0.145833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.711740 Loss1: 3.780396 Loss2: 5.931343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.587536 Loss1: 3.743034 Loss2: 5.844502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.406955 Loss1: 4.075046 Loss2: 6.331909
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.517713 Loss1: 3.700468 Loss2: 5.817244
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.595434 Loss1: 3.707498 Loss2: 5.887936
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.492055 Loss1: 3.716743 Loss2: 5.775312
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.408548 Loss1: 3.618060 Loss2: 5.790488
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.466571 Loss1: 3.680417 Loss2: 5.786154
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.290920 Loss1: 3.579918 Loss2: 5.711002
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.436718 Loss1: 3.660235 Loss2: 5.776483
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.420137 Loss1: 3.644818 Loss2: 5.775319 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.119141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.174120 Loss1: 3.510401 Loss2: 5.663719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.138565 Loss1: 3.454635 Loss2: 5.683930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.054536 Loss1: 3.417136 Loss2: 5.637400
-(DefaultActor pid=3764) >> Training accuracy: 0.193750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.782680 Loss1: 4.165881 Loss2: 6.616799
-(DefaultActor pid=3765) Epoch: 1 Loss: 10.090299 Loss1: 3.825660 Loss2: 6.264639
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.937680 Loss1: 3.821735 Loss2: 6.115944
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.843707 Loss1: 3.785454 Loss2: 6.058253
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.830243 Loss1: 3.790780 Loss2: 6.039462
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.319664 Loss1: 4.140807 Loss2: 6.178857
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.746831 Loss1: 3.719699 Loss2: 6.027133
-(DefaultActor pid=3765) Epoch: 6 Loss: 9.702945 Loss1: 3.691170 Loss2: 6.011775 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 9.679168 Loss1: 3.676987 Loss2: 6.002181 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 9.649411 Loss1: 3.666260 Loss2: 5.983150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.634332 Loss1: 3.659953 Loss2: 5.974379 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.132292
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-08 13:02:33,601 | server.py:236 | fit_round 2 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.015283 Loss1: 3.514796 Loss2: 5.500487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 8.939481 Loss1: 3.451104 Loss2: 5.488377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 8.937252 Loss1: 3.410933 Loss2: 5.526319
-(DefaultActor pid=3764) >> Training accuracy: 0.188542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.551498 Loss1: 4.195489 Loss2: 6.356010
-(DefaultActor pid=3765) Epoch: 1 Loss: 9.807654 Loss1: 3.888830 Loss2: 5.918824
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.591351 Loss1: 3.790048 Loss2: 5.801303
-(DefaultActor pid=3765) Epoch: 3 Loss: 9.427212 Loss1: 3.693550 Loss2: 5.733662
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.367227 Loss1: 3.681224 Loss2: 5.686003
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.645859 Loss1: 4.267877 Loss2: 6.377981
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.831931 Loss1: 3.871746 Loss2: 5.960185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.629921 Loss1: 3.760460 Loss2: 5.869461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.565801 Loss1: 3.736602 Loss2: 5.829199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.564130 Loss1: 3.739203 Loss2: 5.824927 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.126953
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.516901 Loss1: 3.702281 Loss2: 5.814620 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.474562 Loss1: 3.674293 Loss2: 5.800269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 10.639294 Loss1: 4.150609 Loss2: 6.488685 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.106618
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 9.703857 Loss1: 3.746675 Loss2: 5.957182 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 9.583931 Loss1: 3.689402 Loss2: 5.894529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 9.565596 Loss1: 3.658388 Loss2: 5.907209
-(DefaultActor pid=3764) Epoch: 0 Loss: 10.382244 Loss1: 3.952499 Loss2: 6.429745
-(DefaultActor pid=3764) Epoch: 1 Loss: 9.695833 Loss1: 3.584588 Loss2: 6.111245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 9.446024 Loss1: 3.507743 Loss2: 5.938281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 9.310786 Loss1: 3.422200 Loss2: 5.888586 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.114583
-(DefaultActor pid=3765) Epoch: 9 Loss: 9.477358 Loss1: 3.599002 Loss2: 5.878356
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 9.240992 Loss1: 3.403801 Loss2: 5.837191
-(DefaultActor pid=3764) Epoch: 5 Loss: 9.207359 Loss1: 3.411104 Loss2: 5.796255
-(DefaultActor pid=3764) Epoch: 6 Loss: 9.168722 Loss1: 3.373243 Loss2: 5.795479
-(DefaultActor pid=3764) Epoch: 7 Loss: 9.120609 Loss1: 3.347399 Loss2: 5.773210
-(DefaultActor pid=3764) Epoch: 8 Loss: 9.085291 Loss1: 3.335649 Loss2: 5.749642
-(DefaultActor pid=3764) Epoch: 9 Loss: 9.104629 Loss1: 3.329757 Loss2: 5.774872
-(DefaultActor pid=3764) >> Training accuracy: 0.159375
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 13:02:33,601][flwr][DEBUG] - fit_round 2 received 50 results and 0 failures
-INFO flwr 2023-10-08 13:03:15,667 | server.py:125 | fit progress: (2, 4.821835554445895, {'accuracy': 0.01}, 4303.445578031)
->> Test accuracy: 0.010000
-[2023-10-08 13:03:15,667][flwr][INFO] - fit progress: (2, 4.821835554445895, {'accuracy': 0.01}, 4303.445578031)
-DEBUG flwr 2023-10-08 13:03:15,667 | server.py:173 | evaluate_round 2: strategy sampled 50 clients (out of 50)
-[2023-10-08 13:03:15,667][flwr][DEBUG] - evaluate_round 2: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 13:12:22,946 | server.py:187 | evaluate_round 2 received 50 results and 0 failures
-[2023-10-08 13:12:22,946][flwr][DEBUG] - evaluate_round 2 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 13:12:22,946 | server.py:222 | fit_round 3: strategy sampled 50 clients (out of 50)
-[2023-10-08 13:12:22,946][flwr][DEBUG] - fit_round 3: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.509499 Loss1: 4.291107 Loss2: 2.218391
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.782715 Loss1: 3.970325 Loss2: 1.812390
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.608737 Loss1: 3.888419 Loss2: 1.720317
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.553848 Loss1: 3.857506 Loss2: 1.696343
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.473258 Loss1: 3.770481 Loss2: 1.702777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.440834 Loss1: 3.777198 Loss2: 1.663636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.442849 Loss1: 3.783813 Loss2: 1.659036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.369352 Loss1: 3.702770 Loss2: 1.666583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.406806 Loss1: 3.736604 Loss2: 1.670203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.388356 Loss1: 3.725838 Loss2: 1.662518 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.115625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.558604 Loss1: 3.449669 Loss2: 2.108935 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.192708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 7.128228 Loss1: 4.188956 Loss2: 2.939271 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 6.130828 Loss1: 3.639144 Loss2: 2.491685 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.037510 Loss1: 3.609220 Loss2: 2.428290
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.839636 Loss1: 4.293334 Loss2: 2.546302
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.900643 Loss1: 3.756669 Loss2: 2.143975 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.728970 Loss1: 3.624116 Loss2: 2.104854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.672135 Loss1: 3.578416 Loss2: 2.093719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.575873 Loss1: 3.520588 Loss2: 2.055285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.519092 Loss1: 3.465894 Loss2: 2.053199 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.154167
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.886233 Loss1: 3.518525 Loss2: 2.367708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.454450 Loss1: 3.429420 Loss2: 2.025031
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.447165 Loss1: 3.422253 Loss2: 2.024912
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.494466 Loss1: 3.436932 Loss2: 2.057534
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.409122 Loss1: 3.400326 Loss2: 2.008796
-(DefaultActor pid=3764) >> Training accuracy: 0.187500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.118442 Loss1: 4.318853 Loss2: 1.799589
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.366549 Loss1: 3.922341 Loss2: 1.444207
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.184735 Loss1: 3.776878 Loss2: 1.407857
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.104487 Loss1: 3.703748 Loss2: 1.400739
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.720279 Loss1: 4.255459 Loss2: 2.464820
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.828644 Loss1: 3.751709 Loss2: 2.076936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.658790 Loss1: 3.640234 Loss2: 2.018556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.560093 Loss1: 3.580053 Loss2: 1.980041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.568995 Loss1: 3.561187 Loss2: 2.007808 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.553685 Loss1: 3.562483 Loss2: 1.991202 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.119792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.495091 Loss1: 3.530910 Loss2: 1.964181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.383349 Loss1: 3.447998 Loss2: 1.935351 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.164583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.870425 Loss1: 4.356579 Loss2: 2.513846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.867490 Loss1: 3.805292 Loss2: 2.062198 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.694060 Loss1: 3.707523 Loss2: 1.986537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.685203 Loss1: 3.706526 Loss2: 1.978677 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.659501 Loss1: 3.684159 Loss2: 1.975342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.620306 Loss1: 3.663343 Loss2: 1.956963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.607583 Loss1: 3.659464 Loss2: 1.948119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.559990 Loss1: 3.632932 Loss2: 1.927058 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.132812
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.724758 Loss1: 3.668533 Loss2: 2.056225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.675342 Loss1: 3.606726 Loss2: 2.068616 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.105208
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.676518 Loss1: 3.608096 Loss2: 2.068421
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.637448 Loss1: 4.292729 Loss2: 2.344719
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.673161 Loss1: 3.737708 Loss2: 1.935453
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.458543 Loss1: 3.607386 Loss2: 1.851157
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.345862 Loss1: 3.566317 Loss2: 1.779545
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.316035 Loss1: 3.530180 Loss2: 1.785855
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.540706 Loss1: 4.354466 Loss2: 2.186240
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.279355 Loss1: 3.538543 Loss2: 1.740812
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.690161 Loss1: 3.958630 Loss2: 1.731531
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.468895 Loss1: 3.821352 Loss2: 1.647543
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.272803 Loss1: 3.527242 Loss2: 1.745561
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.362704 Loss1: 3.732241 Loss2: 1.630462
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.274422 Loss1: 3.526969 Loss2: 1.747453
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.263406 Loss1: 3.488579 Loss2: 1.774826 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.201451 Loss1: 3.433850 Loss2: 1.767601 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.140625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.253331 Loss1: 3.684080 Loss2: 1.569252 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.099760
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.636875 Loss1: 4.244285 Loss2: 2.392590 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.338592 Loss1: 3.511187 Loss2: 1.827405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.151580 Loss1: 3.419614 Loss2: 1.731966
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.378741 Loss1: 4.315368 Loss2: 2.063373
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.596110 Loss1: 3.872143 Loss2: 1.723966 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.385690 Loss1: 3.755969 Loss2: 1.629721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.301792 Loss1: 3.681638 Loss2: 1.620154 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.264299 Loss1: 3.686232 Loss2: 1.578067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.254095 Loss1: 3.660621 Loss2: 1.593474 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.170833
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.939447 Loss1: 3.284833 Loss2: 1.654614
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.218619 Loss1: 3.628859 Loss2: 1.589759
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.213140 Loss1: 3.619688 Loss2: 1.593452
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.186658 Loss1: 3.600146 Loss2: 1.586511
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.178976 Loss1: 3.605775 Loss2: 1.573201
-(DefaultActor pid=3764) >> Training accuracy: 0.140625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.441238 Loss1: 4.167719 Loss2: 2.273519
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.543196 Loss1: 3.629273 Loss2: 1.913922
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.377174 Loss1: 3.521849 Loss2: 1.855324
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.272371 Loss1: 3.448905 Loss2: 1.823467
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.179101 Loss1: 4.339722 Loss2: 1.839380
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.547049 Loss1: 3.958807 Loss2: 1.588243 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.366866 Loss1: 3.882616 Loss2: 1.484250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.283809 Loss1: 3.807469 Loss2: 1.476340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.251715 Loss1: 3.784272 Loss2: 1.467443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.232827 Loss1: 3.784192 Loss2: 1.448636 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.192708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.830456 Loss1: 3.207497 Loss2: 1.622959
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.214114 Loss1: 3.766779 Loss2: 1.447335
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.152651 Loss1: 3.716606 Loss2: 1.436045
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.125604 Loss1: 3.698681 Loss2: 1.426923
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.152112 Loss1: 3.703309 Loss2: 1.448803
-(DefaultActor pid=3764) >> Training accuracy: 0.091667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.227456 Loss1: 4.140491 Loss2: 2.086964
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.477879 Loss1: 3.724529 Loss2: 1.753350
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.205857 Loss1: 3.547070 Loss2: 1.658787
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.157387 Loss1: 3.517769 Loss2: 1.639618
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.200932 Loss1: 4.292203 Loss2: 1.908728
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.115675 Loss1: 3.488301 Loss2: 1.627374
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.483138 Loss1: 3.928472 Loss2: 1.554666
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.077896 Loss1: 3.455822 Loss2: 1.622074
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.321397 Loss1: 3.828254 Loss2: 1.493143
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.268517 Loss1: 3.802015 Loss2: 1.466501
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.037838 Loss1: 3.425193 Loss2: 1.612644
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.217051 Loss1: 3.748135 Loss2: 1.468916
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.043260 Loss1: 3.404107 Loss2: 1.639153
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.254624 Loss1: 3.790932 Loss2: 1.463692
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.015454 Loss1: 3.390243 Loss2: 1.625211
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.256500 Loss1: 3.789494 Loss2: 1.467006
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.040310 Loss1: 3.375351 Loss2: 1.664958
-(DefaultActor pid=3765) >> Training accuracy: 0.146484
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.207428 Loss1: 3.760213 Loss2: 1.447216 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.107292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.648278 Loss1: 4.277422 Loss2: 2.370855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.611748 Loss1: 3.678749 Loss2: 1.932998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.709167 Loss1: 4.240226 Loss2: 2.468941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.868630 Loss1: 3.754162 Loss2: 2.114468 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.348988 Loss1: 3.523137 Loss2: 1.825852 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.262994 Loss1: 3.455916 Loss2: 1.807078 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.218221 Loss1: 3.404802 Loss2: 1.813420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.150533 Loss1: 3.375025 Loss2: 1.775508 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.177885
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.445896 Loss1: 3.513781 Loss2: 1.932115 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.338440 Loss1: 3.438521 Loss2: 1.899919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.329170 Loss1: 3.444045 Loss2: 1.885125
-(DefaultActor pid=3764) >> Training accuracy: 0.159375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.738125 Loss1: 4.224071 Loss2: 2.514054
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.748603 Loss1: 3.652374 Loss2: 2.096229
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.589993 Loss1: 3.559067 Loss2: 2.030926
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.434966 Loss1: 3.474177 Loss2: 1.960788
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.410427 Loss1: 3.460586 Loss2: 1.949841
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.700931 Loss1: 4.313076 Loss2: 2.387855
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.375773 Loss1: 3.417338 Loss2: 1.958435
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.307643 Loss1: 3.375863 Loss2: 1.931781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.298883 Loss1: 3.350369 Loss2: 1.948514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.229314 Loss1: 3.332626 Loss2: 1.896688 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.264847 Loss1: 3.332724 Loss2: 1.932123 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.200000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.475168 Loss1: 3.577481 Loss2: 1.897688 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.432457 Loss1: 3.558734 Loss2: 1.873723 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.133929
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.119756 Loss1: 3.861742 Loss2: 2.258015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.845227 Loss1: 3.691676 Loss2: 2.153551 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.847890 Loss1: 3.697695 Loss2: 2.150195
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.310255 Loss1: 4.284118 Loss2: 2.026137
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.820598 Loss1: 3.686020 Loss2: 2.134578
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.412409 Loss1: 3.711430 Loss2: 1.700979
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.754983 Loss1: 3.626014 Loss2: 2.128969
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.274426 Loss1: 3.623027 Loss2: 1.651398
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.826242 Loss1: 3.688282 Loss2: 2.137960
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.176818 Loss1: 3.553246 Loss2: 1.623572
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.751460 Loss1: 3.629709 Loss2: 2.121751
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.097551 Loss1: 3.492310 Loss2: 1.605241
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.773168 Loss1: 3.630421 Loss2: 2.142747
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.103396 Loss1: 3.525095 Loss2: 1.578300
-(DefaultActor pid=3765) >> Training accuracy: 0.128125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.030870 Loss1: 3.468246 Loss2: 1.562624
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.024772 Loss1: 3.458450 Loss2: 1.566323
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.928369 Loss1: 3.367201 Loss2: 1.561168
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.823274 Loss1: 3.290642 Loss2: 1.532632
-(DefaultActor pid=3764) >> Training accuracy: 0.208333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.668985 Loss1: 4.426165 Loss2: 2.242820
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.911075 Loss1: 4.025209 Loss2: 1.885866
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.741209 Loss1: 3.889287 Loss2: 1.851922
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.638609 Loss1: 3.859427 Loss2: 1.779182
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.647425 Loss1: 3.840076 Loss2: 1.807350
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.572669 Loss1: 3.792617 Loss2: 1.780052 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.568381 Loss1: 3.802917 Loss2: 1.765463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.463024 Loss1: 3.877448 Loss2: 1.585576
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.541440 Loss1: 3.774713 Loss2: 1.766727
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.514571 Loss1: 3.752424 Loss2: 1.762146
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.433111 Loss1: 3.863991 Loss2: 1.569120
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.545861 Loss1: 3.776311 Loss2: 1.769549
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.415099 Loss1: 3.854392 Loss2: 1.560707
-(DefaultActor pid=3765) >> Training accuracy: 0.084821
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.406793 Loss1: 3.853584 Loss2: 1.553208
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.352568 Loss1: 3.802358 Loss2: 1.550211
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.340669 Loss1: 3.793463 Loss2: 1.547207
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.351124 Loss1: 3.806072 Loss2: 1.545053
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.938561 Loss1: 4.274014 Loss2: 2.664547
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.326867 Loss1: 3.787764 Loss2: 1.539103
-(DefaultActor pid=3764) >> Training accuracy: 0.123047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.933189 Loss1: 3.781034 Loss2: 2.152155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.824275 Loss1: 3.716818 Loss2: 2.107457 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.796566 Loss1: 3.692860 Loss2: 2.103707
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.348866 Loss1: 4.269794 Loss2: 2.079072
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.437487 Loss1: 3.736722 Loss2: 1.700765 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.402978 Loss1: 3.732933 Loss2: 1.670045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.307423 Loss1: 3.661779 Loss2: 1.645644 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.094792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.237968 Loss1: 3.613423 Loss2: 1.624545 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.206759 Loss1: 3.597171 Loss2: 1.609588 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.216167 Loss1: 3.602919 Loss2: 1.613248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.198789 Loss1: 3.576024 Loss2: 1.622764 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.142463
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.527649 Loss1: 3.637852 Loss2: 1.889797 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.434810 Loss1: 3.600605 Loss2: 1.834205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.300026 Loss1: 3.529970 Loss2: 1.770056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.251029 Loss1: 3.499068 Loss2: 1.751961 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.167585 Loss1: 3.439355 Loss2: 1.728230 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.160417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.299968 Loss1: 3.721409 Loss2: 1.578559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.257002 Loss1: 3.694298 Loss2: 1.562704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.215166 Loss1: 3.654680 Loss2: 1.560486
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.573303 Loss1: 4.257578 Loss2: 2.315725
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.750493 Loss1: 3.841529 Loss2: 1.908964 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.120833
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.188747 Loss1: 3.625065 Loss2: 1.563682
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.523194 Loss1: 3.708078 Loss2: 1.815116
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.445943 Loss1: 3.651641 Loss2: 1.794302
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.443331 Loss1: 3.658676 Loss2: 1.784655
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.430508 Loss1: 3.644905 Loss2: 1.785602
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.392459 Loss1: 3.615992 Loss2: 1.776467
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.863149 Loss1: 4.383745 Loss2: 2.479404
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.394233 Loss1: 3.620371 Loss2: 1.773861
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.396060 Loss1: 3.614506 Loss2: 1.781554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.766683 Loss1: 3.822181 Loss2: 1.944502 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.132292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.667250 Loss1: 3.761311 Loss2: 1.905939 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.596326 Loss1: 3.707634 Loss2: 1.888691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.592643 Loss1: 3.727317 Loss2: 1.865326 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.105469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.890499 Loss1: 3.776319 Loss2: 2.114180 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.690188 Loss1: 3.654875 Loss2: 2.035313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.649350 Loss1: 3.623629 Loss2: 2.025721
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.583308 Loss1: 4.268462 Loss2: 2.314847
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.698276 Loss1: 3.757323 Loss2: 1.940954 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.534160 Loss1: 3.651824 Loss2: 1.882336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.488287 Loss1: 3.624619 Loss2: 1.863668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.402980 Loss1: 3.550019 Loss2: 1.852962 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.135417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.337267 Loss1: 3.503699 Loss2: 1.833568 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.229219 Loss1: 3.436992 Loss2: 1.792227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.553301 Loss1: 4.260844 Loss2: 2.292457
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.159898 Loss1: 3.384278 Loss2: 1.775619
-(DefaultActor pid=3764) >> Training accuracy: 0.178711
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.490527 Loss1: 3.681302 Loss2: 1.809225 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.467865 Loss1: 3.664389 Loss2: 1.803476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.459998 Loss1: 3.658836 Loss2: 1.801162
-(DefaultActor pid=3764) Epoch: 0 Loss: 7.202234 Loss1: 4.365919 Loss2: 2.836316
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.109084 Loss1: 3.783174 Loss2: 2.325909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.992916 Loss1: 3.722439 Loss2: 2.270477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.910675 Loss1: 3.644982 Loss2: 2.265693 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.156250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.879080 Loss1: 3.646835 Loss2: 2.232245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.809757 Loss1: 3.597236 Loss2: 2.212521 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.776847 Loss1: 3.572643 Loss2: 2.204204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.497892 Loss1: 4.359822 Loss2: 2.138070
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.744608 Loss1: 3.525905 Loss2: 2.218703
-(DefaultActor pid=3764) >> Training accuracy: 0.109375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.425192 Loss1: 3.721792 Loss2: 1.703400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.398973 Loss1: 3.679215 Loss2: 1.719758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.781611 Loss1: 4.325584 Loss2: 2.456027
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.376683 Loss1: 3.665006 Loss2: 1.711677
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.888939 Loss1: 3.826974 Loss2: 2.061965
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.331333 Loss1: 3.649171 Loss2: 1.682162
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.652126 Loss1: 3.665258 Loss2: 1.986868
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.336973 Loss1: 3.687385 Loss2: 1.649588
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.231027 Loss1: 3.599498 Loss2: 1.631529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.267945 Loss1: 3.595534 Loss2: 1.672411 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.111328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.318373 Loss1: 3.466116 Loss2: 1.852257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.200200 Loss1: 3.399547 Loss2: 1.800653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.153771 Loss1: 3.359763 Loss2: 1.794008
-(DefaultActor pid=3764) >> Training accuracy: 0.170833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.902587 Loss1: 4.236542 Loss2: 2.666045
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.045047 Loss1: 3.775249 Loss2: 2.269798
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.815690 Loss1: 3.614953 Loss2: 2.200737
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.727063 Loss1: 3.563436 Loss2: 2.163627
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.694475 Loss1: 3.544533 Loss2: 2.149942
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.450687 Loss1: 4.214126 Loss2: 2.236560
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.742648 Loss1: 3.852119 Loss2: 1.890529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.505725 Loss1: 3.662183 Loss2: 1.843542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.434321 Loss1: 3.625126 Loss2: 1.809195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.371062 Loss1: 3.598039 Loss2: 1.773023 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.131836
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.598423 Loss1: 3.477808 Loss2: 2.120615
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.329597 Loss1: 3.568354 Loss2: 1.761243
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.256723 Loss1: 3.535310 Loss2: 1.721414
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.217144 Loss1: 3.542045 Loss2: 1.675099
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.174930 Loss1: 3.488323 Loss2: 1.686608
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.142006 Loss1: 3.484467 Loss2: 1.657539
-(DefaultActor pid=3764) >> Training accuracy: 0.106445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.285078 Loss1: 4.015767 Loss2: 2.269310
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.417541 Loss1: 3.586205 Loss2: 1.831336
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.210712 Loss1: 3.448782 Loss2: 1.761930
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.135158 Loss1: 3.403987 Loss2: 1.731171
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.067918 Loss1: 3.351372 Loss2: 1.716546
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.979857 Loss1: 4.396101 Loss2: 2.583756
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.049196 Loss1: 3.334401 Loss2: 1.714795
-(DefaultActor pid=3764) Epoch: 1 Loss: 6.060441 Loss1: 3.864132 Loss2: 2.196309
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.018751 Loss1: 3.323872 Loss2: 1.694878
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.661571 Loss1: 3.700789 Loss2: 1.960782
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.995537 Loss1: 3.298363 Loss2: 1.697174
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.044254 Loss1: 3.341787 Loss2: 1.702468
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.423207 Loss1: 3.582910 Loss2: 1.840297
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.970595 Loss1: 3.291227 Loss2: 1.679367
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.307689 Loss1: 3.546158 Loss2: 1.761530
-(DefaultActor pid=3765) >> Training accuracy: 0.269792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.250746 Loss1: 3.516769 Loss2: 1.733977
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.219923 Loss1: 3.518770 Loss2: 1.701153
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.165997 Loss1: 3.472164 Loss2: 1.693833
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.102147 Loss1: 3.442149 Loss2: 1.659998
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.755708 Loss1: 4.241524 Loss2: 2.514184
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.117368 Loss1: 3.448877 Loss2: 1.668491
-(DefaultActor pid=3764) >> Training accuracy: 0.138672
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.721055 Loss1: 3.666276 Loss2: 2.054779 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.641993 Loss1: 3.586458 Loss2: 2.055535 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.632628 Loss1: 3.594145 Loss2: 2.038483
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.666313 Loss1: 4.313137 Loss2: 2.353176
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.912234 Loss1: 3.886761 Loss2: 2.025472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.738351 Loss1: 3.768423 Loss2: 1.969928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.703192 Loss1: 3.745427 Loss2: 1.957765 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.150000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.485243 Loss1: 3.504643 Loss2: 1.980600
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.593531 Loss1: 3.685970 Loss2: 1.907561
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.614480 Loss1: 3.676200 Loss2: 1.938280
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.555312 Loss1: 3.620263 Loss2: 1.935050
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.542142 Loss1: 3.617121 Loss2: 1.925021
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.461168 Loss1: 3.576626 Loss2: 1.884542
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.490227 Loss1: 4.273312 Loss2: 2.216915
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.465781 Loss1: 3.562895 Loss2: 1.902886
-(DefaultActor pid=3764) >> Training accuracy: 0.135417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.623223 Loss1: 3.803966 Loss2: 1.819257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.496081 Loss1: 3.738313 Loss2: 1.757768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.509066 Loss1: 4.273288 Loss2: 2.235778
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.461298 Loss1: 3.691830 Loss2: 1.769467
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.393234 Loss1: 3.654195 Loss2: 1.739039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.379604 Loss1: 3.630687 Loss2: 1.748917 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.327989 Loss1: 3.600242 Loss2: 1.727747 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.286108 Loss1: 3.566857 Loss2: 1.719252 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.142578
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.280581 Loss1: 3.589303 Loss2: 1.691278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.252442 Loss1: 3.561602 Loss2: 1.690840 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.144792
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.213292 Loss1: 3.511872 Loss2: 1.701420
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.746030 Loss1: 4.423893 Loss2: 2.322137
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.946753 Loss1: 3.953257 Loss2: 1.993497
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.767600 Loss1: 3.851511 Loss2: 1.916090
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.725563 Loss1: 3.813681 Loss2: 1.911883
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.657981 Loss1: 3.781198 Loss2: 1.876784
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.730249 Loss1: 4.065112 Loss2: 2.665137
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.597556 Loss1: 3.739025 Loss2: 1.858531
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.769182 Loss1: 3.449020 Loss2: 2.320162
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.584744 Loss1: 3.698117 Loss2: 1.886626
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.599129 Loss1: 3.380095 Loss2: 2.219034
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.507457 Loss1: 3.306899 Loss2: 2.200558
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.497208 Loss1: 3.654501 Loss2: 1.842706
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.397430 Loss1: 3.246061 Loss2: 2.151368
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.423074 Loss1: 3.608405 Loss2: 1.814670
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.345630 Loss1: 3.223569 Loss2: 2.122061
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.471888 Loss1: 3.618592 Loss2: 1.853296
-(DefaultActor pid=3765) >> Training accuracy: 0.111328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.271960 Loss1: 3.195892 Loss2: 2.076069 [repeated 2x across cluster]
-DEBUG flwr 2023-10-08 13:41:01,600 | server.py:236 | fit_round 3 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.206812 Loss1: 3.150896 Loss2: 2.055917 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.142708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 6.276676 Loss1: 3.574323 Loss2: 2.702353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 6.132503 Loss1: 3.469489 Loss2: 2.663014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.398414 Loss1: 4.250052 Loss2: 2.148362
-(DefaultActor pid=3765) Epoch: 4 Loss: 6.003278 Loss1: 3.393034 Loss2: 2.610243
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.628065 Loss1: 3.827264 Loss2: 1.800801
-(DefaultActor pid=3765) Epoch: 5 Loss: 6.000932 Loss1: 3.405060 Loss2: 2.595872
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.393520 Loss1: 3.683033 Loss2: 1.710487
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.881842 Loss1: 3.320844 Loss2: 2.560999
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.318447 Loss1: 3.633140 Loss2: 1.685307
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.856018 Loss1: 3.298348 Loss2: 2.557669
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.334231 Loss1: 3.644618 Loss2: 1.689613
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.795250 Loss1: 3.252646 Loss2: 2.542605
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.253840 Loss1: 3.587477 Loss2: 1.666363
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.808585 Loss1: 3.263955 Loss2: 2.544630
-(DefaultActor pid=3765) >> Training accuracy: 0.195833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.244376 Loss1: 3.582774 Loss2: 1.661602 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.127149 Loss1: 3.495455 Loss2: 1.631694 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.141667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.564099 Loss1: 3.832230 Loss2: 1.731869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.008653 Loss1: 3.477129 Loss2: 1.531524 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.811212 Loss1: 4.307578 Loss2: 2.503634
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.871952 Loss1: 3.411036 Loss2: 1.460917
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.932590 Loss1: 3.882903 Loss2: 2.049687
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.767583 Loss1: 3.360200 Loss2: 1.407383
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.668679 Loss1: 3.698201 Loss2: 1.970478
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.689080 Loss1: 3.307840 Loss2: 1.381240
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.594124 Loss1: 3.649628 Loss2: 1.944496
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.631038 Loss1: 3.261961 Loss2: 1.369077
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.551496 Loss1: 3.609912 Loss2: 1.941584
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.636485 Loss1: 3.256297 Loss2: 1.380188
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.542715 Loss1: 3.604177 Loss2: 1.938538
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.573450 Loss1: 3.211592 Loss2: 1.361858
-(DefaultActor pid=3765) >> Training accuracy: 0.192708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.521203 Loss1: 3.588348 Loss2: 1.932855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.459185 Loss1: 3.531509 Loss2: 1.927675 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.121875
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 13:41:01,600][flwr][DEBUG] - fit_round 3 received 50 results and 0 failures
-INFO flwr 2023-10-08 13:41:43,485 | server.py:125 | fit progress: (3, 4.760899214698864, {'accuracy': 0.01}, 6611.263448652)
->> Test accuracy: 0.010000
-[2023-10-08 13:41:43,485][flwr][INFO] - fit progress: (3, 4.760899214698864, {'accuracy': 0.01}, 6611.263448652)
-DEBUG flwr 2023-10-08 13:41:43,485 | server.py:173 | evaluate_round 3: strategy sampled 50 clients (out of 50)
-[2023-10-08 13:41:43,485][flwr][DEBUG] - evaluate_round 3: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 13:50:48,071 | server.py:187 | evaluate_round 3 received 50 results and 0 failures
-[2023-10-08 13:50:48,071][flwr][DEBUG] - evaluate_round 3 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 13:50:48,072 | server.py:222 | fit_round 4: strategy sampled 50 clients (out of 50)
-[2023-10-08 13:50:48,072][flwr][DEBUG] - fit_round 4: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.844691 Loss1: 4.278797 Loss2: 1.565894
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.080191 Loss1: 3.768652 Loss2: 1.311539
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.920249 Loss1: 3.649276 Loss2: 1.270973
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.134528 Loss1: 4.409609 Loss2: 1.724919
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.368229 Loss1: 3.913881 Loss2: 1.454348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.112838 Loss1: 3.722394 Loss2: 1.390444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.047701 Loss1: 3.667627 Loss2: 1.380075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.769319 Loss1: 3.539148 Loss2: 1.230171
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.947771 Loss1: 3.580849 Loss2: 1.366922
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.703801 Loss1: 3.477871 Loss2: 1.225930
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.915109 Loss1: 3.546890 Loss2: 1.368219
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.907364 Loss1: 3.538434 Loss2: 1.368930
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.707812 Loss1: 3.476274 Loss2: 1.231538
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.848440 Loss1: 3.484444 Loss2: 1.363995
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.695703 Loss1: 3.466603 Loss2: 1.229100
-(DefaultActor pid=3765) >> Training accuracy: 0.159926
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.845906 Loss1: 3.482581 Loss2: 1.363325 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.127083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.321091 Loss1: 4.139068 Loss2: 2.182023
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.326005 Loss1: 3.472348 Loss2: 1.853657
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.083334 Loss1: 3.332510 Loss2: 1.750824
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.019605 Loss1: 3.287279 Loss2: 1.732327
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.104270 Loss1: 4.429928 Loss2: 1.674342
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.021011 Loss1: 3.309015 Loss2: 1.711995
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.398769 Loss1: 4.006677 Loss2: 1.392092
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.939359 Loss1: 3.247996 Loss2: 1.691363
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.097217 Loss1: 3.759848 Loss2: 1.337369
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.964931 Loss1: 3.237538 Loss2: 1.727393
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.008381 Loss1: 3.699625 Loss2: 1.308756
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.925406 Loss1: 3.214171 Loss2: 1.711234
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.963797 Loss1: 3.664055 Loss2: 1.299741
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.910108 Loss1: 3.218019 Loss2: 1.692089
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.902445 Loss1: 3.618707 Loss2: 1.283738
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.807803 Loss1: 3.157842 Loss2: 1.649962
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.923825 Loss1: 3.622673 Loss2: 1.301152
-(DefaultActor pid=3765) >> Training accuracy: 0.172917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.913186 Loss1: 3.614046 Loss2: 1.299140
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.824933 Loss1: 3.538606 Loss2: 1.286327
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.797131 Loss1: 3.503533 Loss2: 1.293598
-(DefaultActor pid=3764) >> Training accuracy: 0.141667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.145307 Loss1: 4.337297 Loss2: 1.808010
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.357828 Loss1: 3.852297 Loss2: 1.505531
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.167208 Loss1: 3.668811 Loss2: 1.498397
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.035082 Loss1: 3.620491 Loss2: 1.414590
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.803567 Loss1: 4.295406 Loss2: 1.508160
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.978763 Loss1: 3.580445 Loss2: 1.398318
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.061271 Loss1: 3.774611 Loss2: 1.286660
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.965755 Loss1: 3.567565 Loss2: 1.398190
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.834399 Loss1: 3.630298 Loss2: 1.204101
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.936459 Loss1: 3.541227 Loss2: 1.395231
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.791394 Loss1: 3.597514 Loss2: 1.193880
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.945916 Loss1: 3.539010 Loss2: 1.406906
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.755868 Loss1: 3.585717 Loss2: 1.170151
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.886392 Loss1: 3.494043 Loss2: 1.392349
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.736972 Loss1: 3.553199 Loss2: 1.183772
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.854318 Loss1: 3.444978 Loss2: 1.409340
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.727726 Loss1: 3.535695 Loss2: 1.192031
-(DefaultActor pid=3765) >> Training accuracy: 0.137500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.691622 Loss1: 3.524446 Loss2: 1.167176
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.630140 Loss1: 3.460924 Loss2: 1.169216
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.601058 Loss1: 3.403299 Loss2: 1.197759
-(DefaultActor pid=3764) >> Training accuracy: 0.165625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.932342 Loss1: 4.384148 Loss2: 1.548195
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.127146 Loss1: 3.821315 Loss2: 1.305830
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.882576 Loss1: 3.646340 Loss2: 1.236236
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.837084 Loss1: 3.613046 Loss2: 1.224038
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.459057 Loss1: 4.263563 Loss2: 2.195494
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.739089 Loss1: 3.529132 Loss2: 1.209957
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.587187 Loss1: 3.675679 Loss2: 1.911507
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.350633 Loss1: 3.578013 Loss2: 1.772620 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.227666 Loss1: 3.490536 Loss2: 1.737131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.185766 Loss1: 3.461212 Loss2: 1.724553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.096364 Loss1: 3.417117 Loss2: 1.679247 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.142708
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.655743 Loss1: 3.432484 Loss2: 1.223259
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.009110 Loss1: 3.354543 Loss2: 1.654567
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.932518 Loss1: 3.342567 Loss2: 1.589951
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.813094 Loss1: 3.262709 Loss2: 1.550385
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.713664 Loss1: 3.205247 Loss2: 1.508417
-(DefaultActor pid=3764) >> Training accuracy: 0.193750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.425271 Loss1: 4.374965 Loss2: 2.050306
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.565929 Loss1: 3.848974 Loss2: 1.716955
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.344169 Loss1: 3.684735 Loss2: 1.659433
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.239172 Loss1: 3.617215 Loss2: 1.621957
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.025611 Loss1: 4.310849 Loss2: 1.714762
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.314745 Loss1: 3.824660 Loss2: 1.490085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.070875 Loss1: 3.652709 Loss2: 1.418166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.004500 Loss1: 3.597905 Loss2: 1.406595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.977521 Loss1: 3.555078 Loss2: 1.422443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.933316 Loss1: 3.525930 Loss2: 1.407385 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.146875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.905517 Loss1: 3.508758 Loss2: 1.396759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.845104 Loss1: 3.455062 Loss2: 1.390042 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.152083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.982456 Loss1: 4.291523 Loss2: 1.690933 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.952970 Loss1: 3.634888 Loss2: 1.318082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.862345 Loss1: 3.561360 Loss2: 1.300985
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.467949 Loss1: 4.412515 Loss2: 2.055434
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.715646 Loss1: 3.936963 Loss2: 1.778683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.467784 Loss1: 3.793313 Loss2: 1.674472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.330522 Loss1: 3.688815 Loss2: 1.641707 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.306795 Loss1: 3.695163 Loss2: 1.611632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.290725 Loss1: 3.695542 Loss2: 1.595183 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.128125
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.778292 Loss1: 3.504912 Loss2: 1.273380
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.265249 Loss1: 3.668298 Loss2: 1.596951
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.223015 Loss1: 3.622178 Loss2: 1.600837
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.201135 Loss1: 3.579084 Loss2: 1.622051
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.101452 Loss1: 3.544143 Loss2: 1.557309
-(DefaultActor pid=3764) >> Training accuracy: 0.136458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.016205 Loss1: 4.386092 Loss2: 1.630114
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.293610 Loss1: 3.860582 Loss2: 1.433028
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.075467 Loss1: 3.681402 Loss2: 1.394065
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.893750 Loss1: 3.566531 Loss2: 1.327219
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.459579 Loss1: 4.444006 Loss2: 2.015573
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.690144 Loss1: 3.963400 Loss2: 1.726744 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.523372 Loss1: 3.834162 Loss2: 1.689210 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.403177 Loss1: 3.757471 Loss2: 1.645706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.385986 Loss1: 3.754303 Loss2: 1.631683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.342846 Loss1: 3.713406 Loss2: 1.629440 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.164583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.285377 Loss1: 3.679809 Loss2: 1.605568 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.259119 Loss1: 3.679570 Loss2: 1.579549 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.122070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.434370 Loss1: 3.776455 Loss2: 1.657915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.190957 Loss1: 3.616205 Loss2: 1.574752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.159294 Loss1: 3.582117 Loss2: 1.577177
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.087707 Loss1: 4.292841 Loss2: 1.794866
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.146968 Loss1: 3.588334 Loss2: 1.558634
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.201386 Loss1: 3.723196 Loss2: 1.478190
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.118019 Loss1: 3.584533 Loss2: 1.533486
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.018721 Loss1: 3.602373 Loss2: 1.416348
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.944611 Loss1: 3.550432 Loss2: 1.394179 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.912232 Loss1: 3.509691 Loss2: 1.402541 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.135417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.889521 Loss1: 3.465564 Loss2: 1.423957 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.859373 Loss1: 3.444588 Loss2: 1.414785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.783815 Loss1: 3.373955 Loss2: 1.409861 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.155273
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.085178 Loss1: 3.597289 Loss2: 1.487888 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.886924 Loss1: 3.432477 Loss2: 1.454447 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.861033 Loss1: 3.421842 Loss2: 1.439191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.835253 Loss1: 3.408244 Loss2: 1.427009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.801702 Loss1: 3.393648 Loss2: 1.408055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.762097 Loss1: 3.345761 Loss2: 1.416335 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.733652 Loss1: 3.316520 Loss2: 1.417132 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.197917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.099166 Loss1: 3.633428 Loss2: 1.465738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.080691 Loss1: 3.604497 Loss2: 1.476193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.117188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.022845 Loss1: 4.301452 Loss2: 1.721393 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.990974 Loss1: 3.648794 Loss2: 1.342180 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.836868 Loss1: 3.540798 Loss2: 1.296070 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.820153 Loss1: 3.529836 Loss2: 1.290316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.812571 Loss1: 3.526847 Loss2: 1.285724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.763952 Loss1: 3.470612 Loss2: 1.293341 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.766718 Loss1: 3.458367 Loss2: 1.308351 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.724314 Loss1: 3.420329 Loss2: 1.303984 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.212500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.888086 Loss1: 3.515453 Loss2: 1.372634 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.792005 Loss1: 3.437495 Loss2: 1.354510 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.126042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.331278 Loss1: 3.697644 Loss2: 1.633634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.070746 Loss1: 3.528177 Loss2: 1.542569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.029602 Loss1: 3.523749 Loss2: 1.505853
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.937587 Loss1: 4.399371 Loss2: 1.538216
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.219694 Loss1: 3.847863 Loss2: 1.371831 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.012477 Loss1: 3.721732 Loss2: 1.290745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.882817 Loss1: 3.642822 Loss2: 1.239995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.839223 Loss1: 3.574858 Loss2: 1.264365 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.188542
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.828552 Loss1: 3.306262 Loss2: 1.522290
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.811923 Loss1: 3.528266 Loss2: 1.283657
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.753648 Loss1: 3.496584 Loss2: 1.257064
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.721086 Loss1: 3.474196 Loss2: 1.246890
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.604016 Loss1: 3.405654 Loss2: 1.198362
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.571364 Loss1: 3.394488 Loss2: 1.176876
-(DefaultActor pid=3764) >> Training accuracy: 0.170833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.000725 Loss1: 4.299792 Loss2: 1.700933
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.218339 Loss1: 3.738250 Loss2: 1.480089
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.980838 Loss1: 3.607363 Loss2: 1.373475
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.869777 Loss1: 3.523686 Loss2: 1.346091
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.813766 Loss1: 3.492331 Loss2: 1.321435
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.119655 Loss1: 4.399344 Loss2: 1.720311
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.449465 Loss1: 4.005602 Loss2: 1.443863 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.286088 Loss1: 3.890777 Loss2: 1.395311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.214712 Loss1: 3.840348 Loss2: 1.374364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.170902 Loss1: 3.810980 Loss2: 1.359922 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.152083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.174476 Loss1: 3.810741 Loss2: 1.363736 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.111647 Loss1: 3.779678 Loss2: 1.331970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.072481 Loss1: 3.746862 Loss2: 1.325619 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.126953
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.222474 Loss1: 3.716274 Loss2: 1.506201 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.129758 Loss1: 3.679290 Loss2: 1.450467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.021300 Loss1: 4.373652 Loss2: 1.647648 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.298789 Loss1: 3.909124 Loss2: 1.389666 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.030596 Loss1: 3.541169 Loss2: 1.489427 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.138021
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.905743 Loss1: 3.626357 Loss2: 1.279385 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.899095 Loss1: 3.602519 Loss2: 1.296576 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.156646 Loss1: 4.420819 Loss2: 1.735827
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.907970 Loss1: 3.607944 Loss2: 1.300026
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.379586 Loss1: 3.951435 Loss2: 1.428152
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.867426 Loss1: 3.572975 Loss2: 1.294451
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.161027 Loss1: 3.789738 Loss2: 1.371289
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.835458 Loss1: 3.551211 Loss2: 1.284247
-(DefaultActor pid=3764) >> Training accuracy: 0.131250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.098986 Loss1: 3.739820 Loss2: 1.359166 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.039136 Loss1: 3.695258 Loss2: 1.343878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.038328 Loss1: 3.690714 Loss2: 1.347613
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.177197 Loss1: 4.282611 Loss2: 1.894585
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.985398 Loss1: 3.630321 Loss2: 1.355077
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.315193 Loss1: 3.690918 Loss2: 1.624276
-(DefaultActor pid=3765) >> Training accuracy: 0.121875
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.975105 Loss1: 3.621978 Loss2: 1.353126
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.074265 Loss1: 3.532563 Loss2: 1.541702
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.037125 Loss1: 3.501069 Loss2: 1.536056
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.996598 Loss1: 3.457142 Loss2: 1.539456
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.983211 Loss1: 3.470782 Loss2: 1.512430
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.908168 Loss1: 3.411795 Loss2: 1.496373
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.136380 Loss1: 4.141817 Loss2: 1.994563
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.162734 Loss1: 3.517898 Loss2: 1.644836 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.034732 Loss1: 3.436933 Loss2: 1.597799 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.152344
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.908198 Loss1: 3.404501 Loss2: 1.503697
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.935772 Loss1: 3.350354 Loss2: 1.585419
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.949319 Loss1: 3.345329 Loss2: 1.603990
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.850578 Loss1: 3.271782 Loss2: 1.578795
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.867491 Loss1: 3.303113 Loss2: 1.564378
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.820437 Loss1: 3.210602 Loss2: 1.609835
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.014271 Loss1: 4.251495 Loss2: 1.762777
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.796405 Loss1: 3.201211 Loss2: 1.595194
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.757965 Loss1: 3.197197 Loss2: 1.560768 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.191667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.900474 Loss1: 3.518912 Loss2: 1.381561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.789549 Loss1: 3.403132 Loss2: 1.386416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.816477 Loss1: 3.423682 Loss2: 1.392795
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.033089 Loss1: 4.297840 Loss2: 1.735249
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.250787 Loss1: 3.791057 Loss2: 1.459730 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.947172 Loss1: 3.575974 Loss2: 1.371198 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.898812 Loss1: 3.516844 Loss2: 1.381968
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.760956 Loss1: 3.374892 Loss2: 1.386064
-(DefaultActor pid=3764) >> Training accuracy: 0.191667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.767052 Loss1: 3.404382 Loss2: 1.362671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.668153 Loss1: 3.330751 Loss2: 1.337402 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.659927 Loss1: 3.347230 Loss2: 1.312698 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.185096
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.148606 Loss1: 3.704379 Loss2: 1.444227 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.000164 Loss1: 3.581232 Loss2: 1.418932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.931105 Loss1: 3.551057 Loss2: 1.380048 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.864508 Loss1: 3.489953 Loss2: 1.374555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.864999 Loss1: 3.520592 Loss2: 1.344407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.836242 Loss1: 3.460938 Loss2: 1.375304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.793462 Loss1: 3.424979 Loss2: 1.368483 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.150391
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.980441 Loss1: 3.634676 Loss2: 1.345765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.874070 Loss1: 3.566117 Loss2: 1.307953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.851202 Loss1: 3.536299 Loss2: 1.314903
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.790894 Loss1: 4.369197 Loss2: 1.421698
-(DefaultActor pid=3765) >> Training accuracy: 0.128906
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.002728 Loss1: 3.758812 Loss2: 1.243916
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.776688 Loss1: 3.636894 Loss2: 1.139794
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.678358 Loss1: 3.542966 Loss2: 1.135392
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.658652 Loss1: 3.534558 Loss2: 1.124094
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.002225 Loss1: 4.413736 Loss2: 1.588489
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.657446 Loss1: 3.527312 Loss2: 1.130134
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.543684 Loss1: 3.443924 Loss2: 1.099759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.530206 Loss1: 3.420809 Loss2: 1.109396 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.927819 Loss1: 3.688461 Loss2: 1.239358 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.899357 Loss1: 3.695326 Loss2: 1.204031 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.164062
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.876089 Loss1: 3.651915 Loss2: 1.224174 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.758633 Loss1: 3.565444 Loss2: 1.193189 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.108173
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.146806 Loss1: 4.467016 Loss2: 1.679790
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.307363 Loss1: 3.911381 Loss2: 1.395982
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.053593 Loss1: 3.770443 Loss2: 1.283150
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.970016 Loss1: 3.696887 Loss2: 1.273129
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.522144 Loss1: 4.450119 Loss2: 2.072025
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.696340 Loss1: 3.998142 Loss2: 1.698199 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.420270 Loss1: 3.821018 Loss2: 1.599252 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.329094 Loss1: 3.765953 Loss2: 1.563141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.332515 Loss1: 3.732023 Loss2: 1.600492 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.296210 Loss1: 3.684343 Loss2: 1.611867 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.103125
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.837653 Loss1: 3.592419 Loss2: 1.245234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.280915 Loss1: 3.671797 Loss2: 1.609118
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.208472 Loss1: 3.622351 Loss2: 1.586121
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.194609 Loss1: 3.611390 Loss2: 1.583220
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.172710 Loss1: 3.578960 Loss2: 1.593751
-(DefaultActor pid=3765) >> Training accuracy: 0.118750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.402382 Loss1: 4.323937 Loss2: 2.078445
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.612452 Loss1: 3.809243 Loss2: 1.803210
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.412907 Loss1: 3.710613 Loss2: 1.702293
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.319114 Loss1: 3.659403 Loss2: 1.659711
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.114987 Loss1: 4.321159 Loss2: 1.793828
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.411817 Loss1: 3.882987 Loss2: 1.528830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.204815 Loss1: 3.612872 Loss2: 1.591942
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.119776 Loss1: 3.670232 Loss2: 1.449545
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.046038 Loss1: 3.535337 Loss2: 1.510701
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.006657 Loss1: 3.604206 Loss2: 1.402451
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.985320 Loss1: 3.506605 Loss2: 1.478715
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.997809 Loss1: 3.589743 Loss2: 1.408067
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.946395 Loss1: 3.527970 Loss2: 1.418425
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.973900 Loss1: 3.505980 Loss2: 1.467919
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.913771 Loss1: 3.525242 Loss2: 1.388529
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.939408 Loss1: 3.459829 Loss2: 1.479579
-(DefaultActor pid=3764) >> Training accuracy: 0.128906
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.925597 Loss1: 3.507691 Loss2: 1.417906 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.155208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.141739 Loss1: 4.377557 Loss2: 1.764181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.157374 Loss1: 3.717684 Loss2: 1.439690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.082452 Loss1: 3.682276 Loss2: 1.400176
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.189450 Loss1: 4.387096 Loss2: 1.802355
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.334341 Loss1: 3.823895 Loss2: 1.510446 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.111303 Loss1: 3.684281 Loss2: 1.427023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.042259 Loss1: 3.628937 Loss2: 1.413323 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.927879 Loss1: 3.540995 Loss2: 1.386884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.876912 Loss1: 3.506911 Loss2: 1.370001 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.127083
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.942963 Loss1: 3.584045 Loss2: 1.358918
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.816944 Loss1: 3.482169 Loss2: 1.334775
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.811980 Loss1: 3.443299 Loss2: 1.368681
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.780915 Loss1: 3.422989 Loss2: 1.357926
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.730913 Loss1: 3.381368 Loss2: 1.349545
-(DefaultActor pid=3765) >> Training accuracy: 0.189583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.657172 Loss1: 4.422149 Loss2: 2.235023
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.715478 Loss1: 3.828318 Loss2: 1.887160
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.527238 Loss1: 3.714598 Loss2: 1.812640
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.446415 Loss1: 3.657413 Loss2: 1.789002
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.977610 Loss1: 4.295782 Loss2: 1.681828
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.104732 Loss1: 3.691032 Loss2: 1.413700
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.388377 Loss1: 3.591562 Loss2: 1.796815
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.765672 Loss1: 3.450720 Loss2: 1.314952
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.376724 Loss1: 3.590170 Loss2: 1.786554
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.687902 Loss1: 3.392504 Loss2: 1.295397
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.324730 Loss1: 3.538793 Loss2: 1.785936
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.660190 Loss1: 3.365150 Loss2: 1.295041
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.329393 Loss1: 3.529941 Loss2: 1.799451
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.553788 Loss1: 3.276850 Loss2: 1.276938
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.302363 Loss1: 3.502657 Loss2: 1.799706
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.284703 Loss1: 3.488412 Loss2: 1.796291 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.153320
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.531618 Loss1: 3.261703 Loss2: 1.269915 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.275000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.980387 Loss1: 4.404908 Loss2: 2.575479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.830568 Loss1: 3.760052 Loss2: 2.070516 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.651596 Loss1: 3.631489 Loss2: 2.020107
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.249212 Loss1: 4.451821 Loss2: 1.797390
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.576951 Loss1: 4.017952 Loss2: 1.558999
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.589877 Loss1: 3.633106 Loss2: 1.956771
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.311196 Loss1: 3.833775 Loss2: 1.477421
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.607117 Loss1: 3.640604 Loss2: 1.966512
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.185576 Loss1: 3.746640 Loss2: 1.438935
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.526922 Loss1: 3.599381 Loss2: 1.927541
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.119826 Loss1: 3.699128 Loss2: 1.420698
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.470339 Loss1: 3.549065 Loss2: 1.921274
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.483116 Loss1: 3.560017 Loss2: 1.923099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.499488 Loss1: 3.566647 Loss2: 1.932841 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.132292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.052352 Loss1: 3.614526 Loss2: 1.437826 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.117188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.037470 Loss1: 4.283843 Loss2: 1.753627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.000373 Loss1: 3.534168 Loss2: 1.466205 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.881819 Loss1: 3.464629 Loss2: 1.417190
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.059982 Loss1: 4.471628 Loss2: 1.588354
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.841020 Loss1: 3.431747 Loss2: 1.409273
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.319042 Loss1: 3.993307 Loss2: 1.325735
-DEBUG flwr 2023-10-08 14:19:48,676 | server.py:236 | fit_round 4 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.761805 Loss1: 3.377317 Loss2: 1.384488
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.100400 Loss1: 3.819093 Loss2: 1.281307
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.790762 Loss1: 3.381699 Loss2: 1.409063
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.044012 Loss1: 3.791536 Loss2: 1.252476
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.701863 Loss1: 3.326918 Loss2: 1.374945
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.992100 Loss1: 3.744666 Loss2: 1.247434
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.702039 Loss1: 3.326954 Loss2: 1.375084
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.979109 Loss1: 3.733936 Loss2: 1.245173
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.659274 Loss1: 3.319977 Loss2: 1.339298
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.955080 Loss1: 3.704076 Loss2: 1.251004
-(DefaultActor pid=3764) >> Training accuracy: 0.186458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.935631 Loss1: 3.686009 Loss2: 1.249621
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.934930 Loss1: 3.683681 Loss2: 1.251249
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.955066 Loss1: 3.680863 Loss2: 1.274203
-(DefaultActor pid=3765) >> Training accuracy: 0.117708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.936339 Loss1: 4.431189 Loss2: 1.505149
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.279033 Loss1: 3.994380 Loss2: 1.284653
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.107112 Loss1: 3.860553 Loss2: 1.246558
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.042828 Loss1: 3.815475 Loss2: 1.227353
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.198831 Loss1: 4.376030 Loss2: 1.822801
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.352185 Loss1: 3.846339 Loss2: 1.505846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.108207 Loss1: 3.668402 Loss2: 1.439805 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.056090 Loss1: 3.639738 Loss2: 1.416352 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.992052 Loss1: 3.561158 Loss2: 1.430894 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.968873 Loss1: 3.565364 Loss2: 1.403510 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.125000
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.867059 Loss1: 3.656841 Loss2: 1.210217
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.942275 Loss1: 3.540120 Loss2: 1.402154
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.871355 Loss1: 3.478864 Loss2: 1.392491
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.890644 Loss1: 3.494892 Loss2: 1.395753
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.892013 Loss1: 3.496340 Loss2: 1.395673
-(DefaultActor pid=3765) >> Training accuracy: 0.152083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.253345 Loss1: 4.419525 Loss2: 1.833820
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.289994 Loss1: 3.779649 Loss2: 1.510345
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.034729 Loss1: 3.642034 Loss2: 1.392695
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.000304 Loss1: 3.599775 Loss2: 1.400530
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.959750 Loss1: 3.557150 Loss2: 1.402601
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.930915 Loss1: 3.542151 Loss2: 1.388764
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.864145 Loss1: 3.493682 Loss2: 1.370463
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.885165 Loss1: 3.510075 Loss2: 1.375090
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.896219 Loss1: 3.490527 Loss2: 1.405692
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.860034 Loss1: 3.459637 Loss2: 1.400396
-(DefaultActor pid=3764) >> Training accuracy: 0.143973
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 14:19:48,676][flwr][DEBUG] - fit_round 4 received 50 results and 0 failures
-INFO flwr 2023-10-08 14:20:31,003 | server.py:125 | fit progress: (4, 4.635519420757842, {'accuracy': 0.0117}, 8938.781652659)
->> Test accuracy: 0.011700
-[2023-10-08 14:20:31,003][flwr][INFO] - fit progress: (4, 4.635519420757842, {'accuracy': 0.0117}, 8938.781652659)
-DEBUG flwr 2023-10-08 14:20:31,003 | server.py:173 | evaluate_round 4: strategy sampled 50 clients (out of 50)
-[2023-10-08 14:20:31,003][flwr][DEBUG] - evaluate_round 4: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 14:29:34,905 | server.py:187 | evaluate_round 4 received 50 results and 0 failures
-[2023-10-08 14:29:34,905][flwr][DEBUG] - evaluate_round 4 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 14:29:34,905 | server.py:222 | fit_round 5: strategy sampled 50 clients (out of 50)
-[2023-10-08 14:29:34,905][flwr][DEBUG] - fit_round 5: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.248227 Loss1: 4.396324 Loss2: 1.851903
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.437683 Loss1: 3.940437 Loss2: 1.497247
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.135633 Loss1: 3.704142 Loss2: 1.431491
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.080403 Loss1: 3.685175 Loss2: 1.395228
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.029415 Loss1: 3.626819 Loss2: 1.402596
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.275757 Loss1: 4.395536 Loss2: 1.880221
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.938820 Loss1: 3.546676 Loss2: 1.392145
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.163097 Loss1: 3.631471 Loss2: 1.531626 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.890016 Loss1: 3.502588 Loss2: 1.387428 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.152344
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.876185 Loss1: 3.489396 Loss2: 1.386788 [repeated 2x across cluster]
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.999109 Loss1: 3.496803 Loss2: 1.502307 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.936079 Loss1: 3.425255 Loss2: 1.510824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.937275 Loss1: 3.420856 Loss2: 1.516420 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.169792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.209014 Loss1: 3.549661 Loss2: 1.659354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.094582 Loss1: 3.442069 Loss2: 1.652512 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.082614 Loss1: 3.431744 Loss2: 1.650870
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.170768 Loss1: 4.320873 Loss2: 1.849896
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.068074 Loss1: 3.423430 Loss2: 1.644644
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.340757 Loss1: 3.783302 Loss2: 1.557454
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.022859 Loss1: 3.393030 Loss2: 1.629829
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.148643 Loss1: 3.672436 Loss2: 1.476207
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.004888 Loss1: 3.371407 Loss2: 1.633481
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.071111 Loss1: 3.619124 Loss2: 1.451988
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.002692 Loss1: 3.554666 Loss2: 1.448025
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.009919 Loss1: 3.366019 Loss2: 1.643900
-(DefaultActor pid=3765) >> Training accuracy: 0.152344
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.963398 Loss1: 3.528541 Loss2: 1.434857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.936816 Loss1: 3.511676 Loss2: 1.425140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.928544 Loss1: 3.496450 Loss2: 1.432093
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.897887 Loss1: 4.313379 Loss2: 1.584508
-(DefaultActor pid=3764) >> Training accuracy: 0.145833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.101164 Loss1: 3.805344 Loss2: 1.295820
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.884700 Loss1: 3.652114 Loss2: 1.232586
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.806347 Loss1: 3.587775 Loss2: 1.218572
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.774664 Loss1: 3.558422 Loss2: 1.216243
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.051109 Loss1: 4.201374 Loss2: 1.849734
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.213894 Loss1: 3.633573 Loss2: 1.580321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.924420 Loss1: 3.436781 Loss2: 1.487639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.874754 Loss1: 3.408929 Loss2: 1.465825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.784650 Loss1: 3.322793 Loss2: 1.461857 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.664679 Loss1: 3.454271 Loss2: 1.210408
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.734873 Loss1: 3.291652 Loss2: 1.443222
-(DefaultActor pid=3765) >> Training accuracy: 0.140625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.746703 Loss1: 3.293628 Loss2: 1.453074
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.678842 Loss1: 3.226787 Loss2: 1.452055
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.714074 Loss1: 3.274148 Loss2: 1.439926
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.684555 Loss1: 3.248963 Loss2: 1.435592
-(DefaultActor pid=3764) >> Training accuracy: 0.205208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.176817 Loss1: 4.347385 Loss2: 1.829433
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.347177 Loss1: 3.848861 Loss2: 1.498316
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.118245 Loss1: 3.685415 Loss2: 1.432830
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.047235 Loss1: 3.621655 Loss2: 1.425580
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.233440 Loss1: 4.248360 Loss2: 1.985080
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.022778 Loss1: 3.610098 Loss2: 1.412680
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.287085 Loss1: 3.653982 Loss2: 1.633104
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.036688 Loss1: 3.626031 Loss2: 1.410657
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.022395 Loss1: 3.605789 Loss2: 1.416606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.001181 Loss1: 3.587896 Loss2: 1.413286 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.946688 Loss1: 3.528826 Loss2: 1.417862 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.931571 Loss1: 3.519768 Loss2: 1.411803 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.145833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.707641 Loss1: 3.256998 Loss2: 1.450643 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.188702
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.936291 Loss1: 4.176821 Loss2: 1.759469 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.867047 Loss1: 3.467207 Loss2: 1.399839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.763824 Loss1: 3.355619 Loss2: 1.408206
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.996476 Loss1: 4.262129 Loss2: 1.734347
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.106373 Loss1: 3.667289 Loss2: 1.439084
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.743772 Loss1: 3.349176 Loss2: 1.394596
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.877481 Loss1: 3.515296 Loss2: 1.362185
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.704488 Loss1: 3.311117 Loss2: 1.393371
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.873707 Loss1: 3.507744 Loss2: 1.365963
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.689974 Loss1: 3.298988 Loss2: 1.390987
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.829263 Loss1: 3.461350 Loss2: 1.367914
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.677427 Loss1: 3.256553 Loss2: 1.420874
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.696993 Loss1: 3.289210 Loss2: 1.407783 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.646649 Loss1: 3.239275 Loss2: 1.407374 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.161133
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.767833 Loss1: 3.409063 Loss2: 1.358770 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.160417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.033716 Loss1: 4.229845 Loss2: 1.803871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.992559 Loss1: 3.567420 Loss2: 1.425139 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.892283 Loss1: 3.493009 Loss2: 1.399274
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.485543 Loss1: 4.458547 Loss2: 2.026996
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.724026 Loss1: 4.027135 Loss2: 1.696890
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.946388 Loss1: 3.522031 Loss2: 1.424356
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.427771 Loss1: 3.793197 Loss2: 1.634573
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.875647 Loss1: 3.476208 Loss2: 1.399439
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.382699 Loss1: 3.758434 Loss2: 1.624265
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.855267 Loss1: 3.438740 Loss2: 1.416527
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.837242 Loss1: 3.435909 Loss2: 1.401333 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.844639 Loss1: 3.435986 Loss2: 1.408652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.801041 Loss1: 3.406739 Loss2: 1.394302 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.169792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.252579 Loss1: 3.656683 Loss2: 1.595896 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.117188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.014563 Loss1: 4.089986 Loss2: 1.924577 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.877215 Loss1: 3.369084 Loss2: 1.508131 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.843180 Loss1: 3.358614 Loss2: 1.484567
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.095216 Loss1: 4.340099 Loss2: 1.755118
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.290531 Loss1: 3.840277 Loss2: 1.450254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.108811 Loss1: 3.700719 Loss2: 1.408091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.042723 Loss1: 3.653357 Loss2: 1.389365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.989940 Loss1: 3.603667 Loss2: 1.386273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.044553 Loss1: 3.636714 Loss2: 1.407839 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.267708
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.573172 Loss1: 3.115901 Loss2: 1.457271
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.974860 Loss1: 3.586772 Loss2: 1.388088
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.974766 Loss1: 3.581476 Loss2: 1.393290
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.004089 Loss1: 3.609915 Loss2: 1.394174
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.929292 Loss1: 3.544703 Loss2: 1.384588
-(DefaultActor pid=3764) >> Training accuracy: 0.133333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.275703 Loss1: 4.361426 Loss2: 1.914277
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.433223 Loss1: 3.852173 Loss2: 1.581050
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.263215 Loss1: 3.727729 Loss2: 1.535486
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.182521 Loss1: 3.654693 Loss2: 1.527828
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.966209 Loss1: 4.249379 Loss2: 1.716830
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.162322 Loss1: 3.626975 Loss2: 1.535347
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.090662 Loss1: 3.674542 Loss2: 1.416120
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.137459 Loss1: 3.623739 Loss2: 1.513721
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.877579 Loss1: 3.489159 Loss2: 1.388420
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.882158 Loss1: 3.495637 Loss2: 1.386522
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.112182 Loss1: 3.596541 Loss2: 1.515641
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.784684 Loss1: 3.416334 Loss2: 1.368350
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.103623 Loss1: 3.577765 Loss2: 1.525858
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.766490 Loss1: 3.412103 Loss2: 1.354387
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.117812 Loss1: 3.598554 Loss2: 1.519258
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.708643 Loss1: 3.349897 Loss2: 1.358745
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.084374 Loss1: 3.564294 Loss2: 1.520080
-(DefaultActor pid=3765) >> Training accuracy: 0.102539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.689035 Loss1: 3.321248 Loss2: 1.367787 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.189583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.037046 Loss1: 4.395273 Loss2: 1.641774 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.124501 Loss1: 3.823172 Loss2: 1.301329 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.067388 Loss1: 3.791907 Loss2: 1.275481
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.123525 Loss1: 4.280477 Loss2: 1.843048
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.006750 Loss1: 3.740342 Loss2: 1.266408
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.226769 Loss1: 3.716358 Loss2: 1.510411
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.027659 Loss1: 3.734084 Loss2: 1.293575
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.066754 Loss1: 3.604297 Loss2: 1.462458
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.986648 Loss1: 3.697460 Loss2: 1.289189
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.978138 Loss1: 3.520193 Loss2: 1.457945
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.004017 Loss1: 3.711715 Loss2: 1.292302
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.931963 Loss1: 3.489566 Loss2: 1.442397
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.942760 Loss1: 3.668104 Loss2: 1.274656
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.905329 Loss1: 3.454457 Loss2: 1.450872
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.940113 Loss1: 3.644934 Loss2: 1.295179
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.939594 Loss1: 3.471929 Loss2: 1.467666
-(DefaultActor pid=3765) >> Training accuracy: 0.108333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.903750 Loss1: 3.443640 Loss2: 1.460110
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.887494 Loss1: 3.414445 Loss2: 1.473049
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.871818 Loss1: 3.425493 Loss2: 1.446325
-(DefaultActor pid=3764) >> Training accuracy: 0.170833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.844048 Loss1: 4.073249 Loss2: 1.770799
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.880139 Loss1: 3.384256 Loss2: 1.495884
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.693494 Loss1: 3.273870 Loss2: 1.419624
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.628786 Loss1: 3.228191 Loss2: 1.400595
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.991105 Loss1: 4.207611 Loss2: 1.783494
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.561520 Loss1: 3.186256 Loss2: 1.375263
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.189485 Loss1: 3.724101 Loss2: 1.465384
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.464151 Loss1: 3.105676 Loss2: 1.358475
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.034788 Loss1: 3.596951 Loss2: 1.437837
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.495109 Loss1: 3.143578 Loss2: 1.351531
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.958946 Loss1: 3.561812 Loss2: 1.397134
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.411705 Loss1: 3.059224 Loss2: 1.352481
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.900460 Loss1: 3.477978 Loss2: 1.422482
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.401866 Loss1: 3.050941 Loss2: 1.350924
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.824949 Loss1: 3.422143 Loss2: 1.402806
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.411535 Loss1: 3.064841 Loss2: 1.346695
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.827405 Loss1: 3.416798 Loss2: 1.410606
-(DefaultActor pid=3765) >> Training accuracy: 0.208333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.861161 Loss1: 3.409067 Loss2: 1.452095
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.813267 Loss1: 3.408139 Loss2: 1.405128
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.746246 Loss1: 3.354181 Loss2: 1.392065
-(DefaultActor pid=3764) >> Training accuracy: 0.170833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.874426 Loss1: 4.191581 Loss2: 1.682845
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.062355 Loss1: 3.686215 Loss2: 1.376140
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.902113 Loss1: 3.565889 Loss2: 1.336224
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.861385 Loss1: 3.542317 Loss2: 1.319069
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.988082 Loss1: 4.258927 Loss2: 1.729155
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.129481 Loss1: 3.728837 Loss2: 1.400644 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.944524 Loss1: 3.590279 Loss2: 1.354245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.908145 Loss1: 3.547454 Loss2: 1.360691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.851616 Loss1: 3.519531 Loss2: 1.332085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.786244 Loss1: 3.451364 Loss2: 1.334880 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.185417
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.695717 Loss1: 3.363672 Loss2: 1.332044
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.773929 Loss1: 3.440882 Loss2: 1.333047
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.780695 Loss1: 3.449910 Loss2: 1.330784
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.759300 Loss1: 3.437248 Loss2: 1.322052
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.817690 Loss1: 3.481751 Loss2: 1.335938
-(DefaultActor pid=3764) >> Training accuracy: 0.120833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.310742 Loss1: 4.329351 Loss2: 1.981391
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.466906 Loss1: 3.787037 Loss2: 1.679869
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.265440 Loss1: 3.655424 Loss2: 1.610016
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.184478 Loss1: 3.597900 Loss2: 1.586578
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.114390 Loss1: 4.330266 Loss2: 1.784124
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.128939 Loss1: 3.533588 Loss2: 1.595350
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.241712 Loss1: 3.803063 Loss2: 1.438649
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.034592 Loss1: 3.653464 Loss2: 1.381128
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.055486 Loss1: 3.484360 Loss2: 1.571126
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.977578 Loss1: 3.624401 Loss2: 1.353177
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.031647 Loss1: 3.467525 Loss2: 1.564122
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.936401 Loss1: 3.588098 Loss2: 1.348304
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.035346 Loss1: 3.448346 Loss2: 1.587000
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.071075 Loss1: 3.483138 Loss2: 1.587937 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.010749 Loss1: 3.422961 Loss2: 1.587788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.156250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.866613 Loss1: 3.514177 Loss2: 1.352436 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.139509
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.193827 Loss1: 4.212172 Loss2: 1.981654 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.116516 Loss1: 3.565942 Loss2: 1.550574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.066870 Loss1: 3.517170 Loss2: 1.549700
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.966674 Loss1: 4.243962 Loss2: 1.722712
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.096607 Loss1: 3.536973 Loss2: 1.559633
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.120398 Loss1: 3.714333 Loss2: 1.406064
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.002222 Loss1: 3.458877 Loss2: 1.543345
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.930463 Loss1: 3.569493 Loss2: 1.360969
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.980090 Loss1: 3.425353 Loss2: 1.554737
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.919087 Loss1: 3.547888 Loss2: 1.371199
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.973177 Loss1: 3.426381 Loss2: 1.546796
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.896906 Loss1: 3.527421 Loss2: 1.369485
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.910350 Loss1: 3.380716 Loss2: 1.529634
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.833732 Loss1: 3.479708 Loss2: 1.354024
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.970606 Loss1: 3.436000 Loss2: 1.534606
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.798624 Loss1: 3.433832 Loss2: 1.364792
-(DefaultActor pid=3765) >> Training accuracy: 0.160417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.787188 Loss1: 3.428345 Loss2: 1.358844
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.747484 Loss1: 3.390720 Loss2: 1.356764
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.758271 Loss1: 3.407769 Loss2: 1.350503
-(DefaultActor pid=3764) >> Training accuracy: 0.144792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.223810 Loss1: 4.351334 Loss2: 1.872476
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.274381 Loss1: 3.703205 Loss2: 1.571175
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.012777 Loss1: 3.530215 Loss2: 1.482562
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.947204 Loss1: 3.474327 Loss2: 1.472878
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.848904 Loss1: 4.121425 Loss2: 1.727479
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.994584 Loss1: 3.541449 Loss2: 1.453135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.756469 Loss1: 3.391157 Loss2: 1.365312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.606575 Loss1: 3.228916 Loss2: 1.377659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.647834 Loss1: 3.282440 Loss2: 1.365393 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.583512 Loss1: 3.235576 Loss2: 1.347936 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.144792
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.853919 Loss1: 3.402141 Loss2: 1.451778
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.538981 Loss1: 3.176256 Loss2: 1.362725
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.519399 Loss1: 3.169229 Loss2: 1.350170
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.535126 Loss1: 3.167554 Loss2: 1.367571
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.484691 Loss1: 3.122071 Loss2: 1.362620
-(DefaultActor pid=3764) >> Training accuracy: 0.210417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.458938 Loss1: 4.357315 Loss2: 2.101622
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.527286 Loss1: 3.728953 Loss2: 1.798333
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.231243 Loss1: 3.526716 Loss2: 1.704528
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.107083 Loss1: 3.453794 Loss2: 1.653288
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.266578 Loss1: 4.258020 Loss2: 2.008558
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.436154 Loss1: 3.738599 Loss2: 1.697555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.248469 Loss1: 3.600257 Loss2: 1.648212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.157104 Loss1: 3.554015 Loss2: 1.603088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.098155 Loss1: 3.547212 Loss2: 1.550943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.030888 Loss1: 3.505714 Loss2: 1.525174 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.179167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.989312 Loss1: 3.473508 Loss2: 1.515804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.909078 Loss1: 3.407057 Loss2: 1.502021 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.156250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.202071 Loss1: 4.415309 Loss2: 1.786762 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.126114 Loss1: 3.745287 Loss2: 1.380827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.056205 Loss1: 3.695010 Loss2: 1.361195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.032873 Loss1: 3.670146 Loss2: 1.362727 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.952590 Loss1: 3.607761 Loss2: 1.344829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.940806 Loss1: 3.602065 Loss2: 1.338741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.918009 Loss1: 3.568245 Loss2: 1.349764
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.176924 Loss1: 3.744330 Loss2: 1.432594
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.911287 Loss1: 3.584703 Loss2: 1.326584
-(DefaultActor pid=3765) >> Training accuracy: 0.123798
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.125448 Loss1: 3.691320 Loss2: 1.434128
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.110269 Loss1: 3.673906 Loss2: 1.436362
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.111414 Loss1: 3.663177 Loss2: 1.448237
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.091220 Loss1: 3.626910 Loss2: 1.464310
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.087340 Loss1: 3.645285 Loss2: 1.442055
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.136401 Loss1: 4.282081 Loss2: 1.854320
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.023902 Loss1: 3.571834 Loss2: 1.452068
-(DefaultActor pid=3764) >> Training accuracy: 0.117708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.059288 Loss1: 3.589754 Loss2: 1.469534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.942432 Loss1: 3.499689 Loss2: 1.442743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.970481 Loss1: 4.244631 Loss2: 1.725851 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.066617 Loss1: 3.662277 Loss2: 1.404340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.903230 Loss1: 3.562155 Loss2: 1.341075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.802703 Loss1: 3.378252 Loss2: 1.424451 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.152902
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.792861 Loss1: 3.448662 Loss2: 1.344199 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.703588 Loss1: 3.372432 Loss2: 1.331155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.656076 Loss1: 3.313738 Loss2: 1.342337
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.062823 Loss1: 4.332961 Loss2: 1.729863
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.697212 Loss1: 3.342597 Loss2: 1.354615
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.167326 Loss1: 3.749741 Loss2: 1.417584
-(DefaultActor pid=3764) >> Training accuracy: 0.182292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.994416 Loss1: 3.616642 Loss2: 1.377774
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.935058 Loss1: 3.573152 Loss2: 1.361905
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.863803 Loss1: 3.508332 Loss2: 1.355472
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.883169 Loss1: 3.506356 Loss2: 1.376813
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.812017 Loss1: 3.452371 Loss2: 1.359646
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.514833 Loss1: 4.405469 Loss2: 2.109364
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.808978 Loss1: 3.448397 Loss2: 1.360581
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.558289 Loss1: 3.819908 Loss2: 1.738381
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.829557 Loss1: 3.445816 Loss2: 1.383741
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.320909 Loss1: 3.652682 Loss2: 1.668227
-(DefaultActor pid=3765) >> Training accuracy: 0.144792
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.832551 Loss1: 3.457470 Loss2: 1.375081
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.200123 Loss1: 3.597568 Loss2: 1.602555
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.120202 Loss1: 3.514878 Loss2: 1.605324
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.124148 Loss1: 3.526049 Loss2: 1.598099
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.070772 Loss1: 3.502750 Loss2: 1.568021
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.057203 Loss1: 3.487625 Loss2: 1.569578
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.915707 Loss1: 4.308788 Loss2: 1.606919
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.182079 Loss1: 3.858659 Loss2: 1.323420 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.125977
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.071507 Loss1: 3.472212 Loss2: 1.599294
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.028622 Loss1: 3.764951 Loss2: 1.263671
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.973244 Loss1: 3.716870 Loss2: 1.256374
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.915452 Loss1: 3.669137 Loss2: 1.246315
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.912814 Loss1: 3.666364 Loss2: 1.246451
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.849370 Loss1: 3.610924 Loss2: 1.238445
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.984219 Loss1: 4.273950 Loss2: 1.710269
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.859123 Loss1: 3.606709 Loss2: 1.252414
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.825590 Loss1: 3.578293 Loss2: 1.247297 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.851396 Loss1: 3.598878 Loss2: 1.252518 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.137500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.717858 Loss1: 3.344199 Loss2: 1.373659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.613629 Loss1: 3.251746 Loss2: 1.361883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.633906 Loss1: 3.270237 Loss2: 1.363669
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.303412 Loss1: 4.440942 Loss2: 1.862469
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.537331 Loss1: 3.982646 Loss2: 1.554686 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.219792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.331561 Loss1: 3.813058 Loss2: 1.518503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.250189 Loss1: 3.756704 Loss2: 1.493485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.205583 Loss1: 3.710532 Loss2: 1.495051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.179644 Loss1: 3.690856 Loss2: 1.488788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.209982 Loss1: 3.698870 Loss2: 1.511112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.129807 Loss1: 3.647090 Loss2: 1.482717 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.171875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.736772 Loss1: 3.270849 Loss2: 1.465923 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.716012 Loss1: 3.267961 Loss2: 1.448051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.656000 Loss1: 3.212285 Loss2: 1.443715
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.127029 Loss1: 4.279263 Loss2: 1.847765
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.620254 Loss1: 3.154267 Loss2: 1.465987
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.268786 Loss1: 3.734849 Loss2: 1.533937
-(DefaultActor pid=3764) >> Training accuracy: 0.206250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.010444 Loss1: 3.531623 Loss2: 1.478821
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.981697 Loss1: 3.525613 Loss2: 1.456084
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.964194 Loss1: 3.513674 Loss2: 1.450520
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.957538 Loss1: 3.492545 Loss2: 1.464993
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.149681 Loss1: 4.295852 Loss2: 1.853829
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.878979 Loss1: 3.431632 Loss2: 1.447347
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.381590 Loss1: 3.856818 Loss2: 1.524772
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.839280 Loss1: 3.380714 Loss2: 1.458565
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.182480 Loss1: 3.695604 Loss2: 1.486877
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.829252 Loss1: 3.365752 Loss2: 1.463500
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.115884 Loss1: 3.650051 Loss2: 1.465833
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.810015 Loss1: 3.345449 Loss2: 1.464566
-(DefaultActor pid=3765) >> Training accuracy: 0.148958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.133920 Loss1: 3.641389 Loss2: 1.492531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.031794 Loss1: 3.553700 Loss2: 1.478094 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.031757 Loss1: 3.557031 Loss2: 1.474726
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.577476 Loss1: 4.293759 Loss2: 2.283718
-(DefaultActor pid=3764) >> Training accuracy: 0.115625
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.985081 Loss1: 3.529562 Loss2: 1.455519
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.389064 Loss1: 3.591048 Loss2: 1.798016
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.109888 Loss1: 3.431988 Loss2: 1.677900
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.001347 Loss1: 3.347924 Loss2: 1.653422
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.954816 Loss1: 3.328564 Loss2: 1.626251
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.949210 Loss1: 3.321456 Loss2: 1.627753
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.016297 Loss1: 4.290928 Loss2: 1.725369
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.884270 Loss1: 3.259957 Loss2: 1.624313
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.836367 Loss1: 3.243576 Loss2: 1.592791 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.846810 Loss1: 3.247223 Loss2: 1.599587 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.800766 Loss1: 3.225079 Loss2: 1.575688 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.215625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.958115 Loss1: 3.594476 Loss2: 1.363639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.895042 Loss1: 3.541516 Loss2: 1.353527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.843781 Loss1: 3.497213 Loss2: 1.346568
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.298930 Loss1: 4.462518 Loss2: 1.836412
-(DefaultActor pid=3764) >> Training accuracy: 0.128125
-DEBUG flwr 2023-10-08 14:58:18,604 | server.py:236 | fit_round 5 received 50 results and 0 failures
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.348853 Loss1: 3.795747 Loss2: 1.553107 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.013264 Loss1: 3.536259 Loss2: 1.477006 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.914841 Loss1: 3.463826 Loss2: 1.451015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.933671 Loss1: 3.480856 Loss2: 1.452815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.915397 Loss1: 3.451782 Loss2: 1.463615 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.284101 Loss1: 3.726254 Loss2: 1.557847
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.894823 Loss1: 3.440153 Loss2: 1.454670
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.237134 Loss1: 3.682134 Loss2: 1.555001
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.867351 Loss1: 3.389054 Loss2: 1.478297
-(DefaultActor pid=3765) >> Training accuracy: 0.146875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.193575 Loss1: 3.639431 Loss2: 1.554144 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.074186 Loss1: 3.545534 Loss2: 1.528652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.036974 Loss1: 4.201509 Loss2: 1.835466
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.060800 Loss1: 3.539042 Loss2: 1.521758
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.230979 Loss1: 3.725925 Loss2: 1.505054
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.035204 Loss1: 3.507255 Loss2: 1.527949
-(DefaultActor pid=3764) >> Training accuracy: 0.140625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.016281 Loss1: 3.582462 Loss2: 1.433818 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.946687 Loss1: 3.506771 Loss2: 1.439916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.916209 Loss1: 3.481599 Loss2: 1.434610
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.361835 Loss1: 4.425004 Loss2: 1.936831
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.506723 Loss1: 3.880847 Loss2: 1.625876 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.262552 Loss1: 3.705358 Loss2: 1.557194 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.119792
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.943290 Loss1: 3.494575 Loss2: 1.448715
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.198644 Loss1: 3.657667 Loss2: 1.540977
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.121425 Loss1: 3.585423 Loss2: 1.536003
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.157029 Loss1: 3.621476 Loss2: 1.535552
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.076208 Loss1: 3.546539 Loss2: 1.529669
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.049770 Loss1: 3.516577 Loss2: 1.533192
-(DefaultActor pid=3764) Epoch: 8 Loss: 5.025371 Loss1: 3.515226 Loss2: 1.510145
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.109057 Loss1: 4.273945 Loss2: 1.835111
-(DefaultActor pid=3764) Epoch: 9 Loss: 5.036649 Loss1: 3.509686 Loss2: 1.526963
-(DefaultActor pid=3764) >> Training accuracy: 0.132292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.202959 Loss1: 3.691898 Loss2: 1.511062
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.059697 Loss1: 3.580099 Loss2: 1.479598
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.018920 Loss1: 3.532723 Loss2: 1.486196
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.981694 Loss1: 3.503720 Loss2: 1.477975
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.961469 Loss1: 3.505772 Loss2: 1.455697
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.773865 Loss1: 4.178549 Loss2: 1.595316
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.945578 Loss1: 3.483688 Loss2: 1.461890
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.933481 Loss1: 3.621221 Loss2: 1.312260
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.873480 Loss1: 3.432373 Loss2: 1.441107
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.748705 Loss1: 3.501107 Loss2: 1.247599
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.856634 Loss1: 3.419680 Loss2: 1.436954
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.736013 Loss1: 3.492485 Loss2: 1.243528
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.662867 Loss1: 3.408507 Loss2: 1.254359
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.854236 Loss1: 3.395561 Loss2: 1.458675
-(DefaultActor pid=3765) >> Training accuracy: 0.165441
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.625659 Loss1: 3.370930 Loss2: 1.254728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.569144 Loss1: 3.323865 Loss2: 1.245280 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.189453
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 14:58:18,604][flwr][DEBUG] - fit_round 5 received 50 results and 0 failures
-INFO flwr 2023-10-08 14:59:00,061 | server.py:125 | fit progress: (5, 4.483498603772051, {'accuracy': 0.0252}, 11247.839639639002)
->> Test accuracy: 0.025200
-[2023-10-08 14:59:00,061][flwr][INFO] - fit progress: (5, 4.483498603772051, {'accuracy': 0.0252}, 11247.839639639002)
-DEBUG flwr 2023-10-08 14:59:00,061 | server.py:173 | evaluate_round 5: strategy sampled 50 clients (out of 50)
-[2023-10-08 14:59:00,061][flwr][DEBUG] - evaluate_round 5: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 15:08:04,825 | server.py:187 | evaluate_round 5 received 50 results and 0 failures
-[2023-10-08 15:08:04,825][flwr][DEBUG] - evaluate_round 5 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 15:08:04,825 | server.py:222 | fit_round 6: strategy sampled 50 clients (out of 50)
-[2023-10-08 15:08:04,825][flwr][DEBUG] - fit_round 6: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.900495 Loss1: 4.190622 Loss2: 1.709874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.865359 Loss1: 3.540431 Loss2: 1.324928 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.779275 Loss1: 3.445119 Loss2: 1.334156
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.001278 Loss1: 4.130771 Loss2: 1.870508
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.768931 Loss1: 3.462789 Loss2: 1.306142
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.994466 Loss1: 3.474292 Loss2: 1.520175
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.784043 Loss1: 3.462651 Loss2: 1.321392
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.802204 Loss1: 3.352487 Loss2: 1.449717
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.716503 Loss1: 3.400823 Loss2: 1.315680
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.695584 Loss1: 3.273607 Loss2: 1.421977
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.708305 Loss1: 3.386377 Loss2: 1.321928
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.693200 Loss1: 3.279491 Loss2: 1.413709
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.723875 Loss1: 3.400935 Loss2: 1.322939
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.642843 Loss1: 3.227508 Loss2: 1.415335
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.639668 Loss1: 3.318440 Loss2: 1.321228
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.641895 Loss1: 3.225845 Loss2: 1.416051
-(DefaultActor pid=3765) >> Training accuracy: 0.157292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.620603 Loss1: 3.203999 Loss2: 1.416604
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.535747 Loss1: 3.118311 Loss2: 1.417436
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.574317 Loss1: 3.147155 Loss2: 1.427162
-(DefaultActor pid=3764) >> Training accuracy: 0.226042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.017918 Loss1: 4.162757 Loss2: 1.855161
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.160018 Loss1: 3.684721 Loss2: 1.475298
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.017787 Loss1: 3.590470 Loss2: 1.427317
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.887454 Loss1: 4.133293 Loss2: 1.754162
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.929651 Loss1: 3.506397 Loss2: 1.423254
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.113038 Loss1: 3.704368 Loss2: 1.408670
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.871113 Loss1: 3.457027 Loss2: 1.414086
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.912863 Loss1: 3.579697 Loss2: 1.333166
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.883475 Loss1: 3.468271 Loss2: 1.415205
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.866570 Loss1: 3.435923 Loss2: 1.430647 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.825548 Loss1: 3.394127 Loss2: 1.431421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.850552 Loss1: 3.427612 Loss2: 1.422939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.785982 Loss1: 3.360210 Loss2: 1.425772 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.164062
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.714159 Loss1: 3.382138 Loss2: 1.332021 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.167708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.116914 Loss1: 4.322986 Loss2: 1.793928 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.100621 Loss1: 3.699335 Loss2: 1.401286 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.093172 Loss1: 4.288610 Loss2: 1.804562
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.024551 Loss1: 3.627579 Loss2: 1.396973
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.199244 Loss1: 3.743526 Loss2: 1.455719
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.968036 Loss1: 3.575035 Loss2: 1.393001
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.002848 Loss1: 3.596365 Loss2: 1.406483
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.948707 Loss1: 3.557294 Loss2: 1.391413
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.954697 Loss1: 3.547844 Loss2: 1.406853
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.957041 Loss1: 3.558582 Loss2: 1.398459
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.913095 Loss1: 3.510335 Loss2: 1.402760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.893018 Loss1: 3.477491 Loss2: 1.415527 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.903140 Loss1: 3.492807 Loss2: 1.410332 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.161133
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.881218 Loss1: 3.478768 Loss2: 1.402450 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.138542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.042389 Loss1: 4.253516 Loss2: 1.788873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.996743 Loss1: 3.606897 Loss2: 1.389846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.986165 Loss1: 3.603759 Loss2: 1.382406
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.764183 Loss1: 3.953877 Loss2: 1.810307
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.933302 Loss1: 3.561912 Loss2: 1.371390
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.817218 Loss1: 3.326884 Loss2: 1.490334
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.867694 Loss1: 3.500260 Loss2: 1.367434
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.574329 Loss1: 3.168850 Loss2: 1.405478
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.853813 Loss1: 3.487550 Loss2: 1.366263
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.501027 Loss1: 3.113116 Loss2: 1.387911
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.882283 Loss1: 3.501856 Loss2: 1.380427
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.469000 Loss1: 3.101293 Loss2: 1.367707
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.818039 Loss1: 3.441287 Loss2: 1.376752
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.484371 Loss1: 3.112909 Loss2: 1.371462
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.847353 Loss1: 3.460899 Loss2: 1.386454
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.430749 Loss1: 3.056573 Loss2: 1.374176
-(DefaultActor pid=3765) >> Training accuracy: 0.172917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.419612 Loss1: 3.037464 Loss2: 1.382148
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.380751 Loss1: 2.996945 Loss2: 1.383807
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.418750 Loss1: 3.033888 Loss2: 1.384862
-(DefaultActor pid=3764) >> Training accuracy: 0.197917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.912304 Loss1: 4.215448 Loss2: 1.696855
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.988488 Loss1: 3.623635 Loss2: 1.364853
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.818206 Loss1: 3.508465 Loss2: 1.309742
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.780328 Loss1: 3.495998 Loss2: 1.284330
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.083412 Loss1: 4.157688 Loss2: 1.925724
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.177584 Loss1: 3.642300 Loss2: 1.535284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.997444 Loss1: 3.525554 Loss2: 1.471890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.925260 Loss1: 3.464175 Loss2: 1.461086 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.846005 Loss1: 3.386912 Loss2: 1.459092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.884293 Loss1: 3.414852 Loss2: 1.469441 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.190848
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.797090 Loss1: 3.327232 Loss2: 1.469859 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.838164 Loss1: 3.360513 Loss2: 1.477650 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.200000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.504930 Loss1: 3.887771 Loss2: 1.617159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.253801 Loss1: 3.693406 Loss2: 1.560394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.035985 Loss1: 4.229272 Loss2: 1.806713
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.230895 Loss1: 3.672288 Loss2: 1.558608
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.128233 Loss1: 3.669396 Loss2: 1.458837
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.183580 Loss1: 3.634563 Loss2: 1.549017
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.914528 Loss1: 3.508706 Loss2: 1.405822
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.153188 Loss1: 3.595882 Loss2: 1.557306
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.908757 Loss1: 3.514628 Loss2: 1.394129
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.185896 Loss1: 3.628005 Loss2: 1.557892
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.844971 Loss1: 3.437727 Loss2: 1.407243
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.157240 Loss1: 3.588160 Loss2: 1.569080
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.799697 Loss1: 3.406186 Loss2: 1.393512
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.147233 Loss1: 3.580865 Loss2: 1.566368
-(DefaultActor pid=3765) >> Training accuracy: 0.115625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.799571 Loss1: 3.417449 Loss2: 1.382122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.755260 Loss1: 3.366211 Loss2: 1.389049 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.194792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.170287 Loss1: 3.624128 Loss2: 1.546159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.821129 Loss1: 3.368615 Loss2: 1.452514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.815090 Loss1: 3.368572 Loss2: 1.446518
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.306738 Loss1: 4.260717 Loss2: 2.046020
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.491572 Loss1: 3.892842 Loss2: 1.598731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.245380 Loss1: 3.689671 Loss2: 1.555709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.145076 Loss1: 3.599199 Loss2: 1.545877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.085076 Loss1: 3.561955 Loss2: 1.523121 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.187500
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.629272 Loss1: 3.191520 Loss2: 1.437752
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.054762 Loss1: 3.523465 Loss2: 1.531297
-(DefaultActor pid=3764) Epoch: 6 Loss: 5.061757 Loss1: 3.537703 Loss2: 1.524054
-(DefaultActor pid=3764) Epoch: 7 Loss: 5.043039 Loss1: 3.508419 Loss2: 1.534620
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.968924 Loss1: 3.450937 Loss2: 1.517988
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.969599 Loss1: 3.434942 Loss2: 1.534657
-(DefaultActor pid=3764) >> Training accuracy: 0.132212
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.949450 Loss1: 4.034739 Loss2: 1.914711
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.989085 Loss1: 3.461128 Loss2: 1.527957
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.842441 Loss1: 3.401521 Loss2: 1.440920
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.705468 Loss1: 3.301997 Loss2: 1.403471
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.755518 Loss1: 3.969894 Loss2: 1.785624
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.847053 Loss1: 3.376386 Loss2: 1.470668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.697880 Loss1: 3.272468 Loss2: 1.425411 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.628244 Loss1: 3.229339 Loss2: 1.398905 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.564061 Loss1: 3.164602 Loss2: 1.399459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.562999 Loss1: 3.163247 Loss2: 1.399752 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.239583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.517033 Loss1: 3.113955 Loss2: 1.403077 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.457467 Loss1: 3.061669 Loss2: 1.395798 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.211458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.090448 Loss1: 3.620023 Loss2: 1.470425 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.864920 Loss1: 3.452369 Loss2: 1.412551 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.173282 Loss1: 4.142182 Loss2: 2.031101
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.819239 Loss1: 3.413786 Loss2: 1.405452
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.174537 Loss1: 3.558094 Loss2: 1.616443
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.769577 Loss1: 3.371006 Loss2: 1.398571
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.013973 Loss1: 3.448444 Loss2: 1.565529
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.837401 Loss1: 3.426828 Loss2: 1.410573
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.951961 Loss1: 3.402062 Loss2: 1.549899
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.749455 Loss1: 3.356050 Loss2: 1.393405
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.872127 Loss1: 3.337023 Loss2: 1.535104
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.754837 Loss1: 3.337635 Loss2: 1.417202
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.895206 Loss1: 3.341175 Loss2: 1.554031
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.716931 Loss1: 3.304541 Loss2: 1.412391
-(DefaultActor pid=3765) >> Training accuracy: 0.191667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.874123 Loss1: 3.331623 Loss2: 1.542499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.812605 Loss1: 3.265287 Loss2: 1.547317 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.182292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.288959 Loss1: 3.789329 Loss2: 1.499629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.084346 Loss1: 3.616719 Loss2: 1.467627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.907100 Loss1: 4.146392 Loss2: 1.760709
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.033474 Loss1: 3.577135 Loss2: 1.456339
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.074194 Loss1: 3.639661 Loss2: 1.434533
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.978576 Loss1: 3.531641 Loss2: 1.446936
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.912390 Loss1: 3.539961 Loss2: 1.372428
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.979457 Loss1: 3.526134 Loss2: 1.453323
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.801068 Loss1: 3.423091 Loss2: 1.377978
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.973787 Loss1: 3.514077 Loss2: 1.459710
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.816428 Loss1: 3.448115 Loss2: 1.368313
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.937902 Loss1: 3.476426 Loss2: 1.461476
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.794434 Loss1: 3.422371 Loss2: 1.372063
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.924125 Loss1: 3.449554 Loss2: 1.474572
-(DefaultActor pid=3765) >> Training accuracy: 0.139583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.711958 Loss1: 3.332775 Loss2: 1.379183 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.653985 Loss1: 3.287806 Loss2: 1.366179 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.180208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.106849 Loss1: 3.647388 Loss2: 1.459460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.761543 Loss1: 3.371061 Loss2: 1.390482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.809561 Loss1: 3.421881 Loss2: 1.387681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.749323 Loss1: 3.355412 Loss2: 1.393911 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.720212 Loss1: 3.355411 Loss2: 1.364801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.700033 Loss1: 3.308286 Loss2: 1.391747 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.677193 Loss1: 3.295032 Loss2: 1.382161 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.716810 Loss1: 3.307753 Loss2: 1.409057 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.157292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.759330 Loss1: 3.392793 Loss2: 1.366537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.722877 Loss1: 3.359832 Loss2: 1.363045 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.161458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.088366 Loss1: 3.653181 Loss2: 1.435185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.875359 Loss1: 3.484424 Loss2: 1.390936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.154654 Loss1: 4.308972 Loss2: 1.845682
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.820303 Loss1: 3.442054 Loss2: 1.378249
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.794756 Loss1: 3.413323 Loss2: 1.381433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.821599 Loss1: 3.435956 Loss2: 1.385643 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.772473 Loss1: 3.383898 Loss2: 1.388575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.715563 Loss1: 3.322806 Loss2: 1.392757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.713150 Loss1: 3.321113 Loss2: 1.392036 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.182292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.970504 Loss1: 3.572242 Loss2: 1.398262 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.910600 Loss1: 3.504137 Loss2: 1.406463 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.137277
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.887755 Loss1: 4.116374 Loss2: 1.771381
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.911408 Loss1: 3.503385 Loss2: 1.408024
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.692416 Loss1: 3.340216 Loss2: 1.352200
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.645603 Loss1: 3.307734 Loss2: 1.337869
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.112074 Loss1: 4.234205 Loss2: 1.877869
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.549956 Loss1: 3.204361 Loss2: 1.345595
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.198334 Loss1: 3.699264 Loss2: 1.499069
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.010109 Loss1: 3.567791 Loss2: 1.442318 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.958405 Loss1: 3.521415 Loss2: 1.436991 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.930761 Loss1: 3.511956 Loss2: 1.418804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.935011 Loss1: 3.519267 Loss2: 1.415745 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.286458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.871100 Loss1: 3.443579 Loss2: 1.427520 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.870384 Loss1: 3.431652 Loss2: 1.438732 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.154297
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.145118 Loss1: 4.307555 Loss2: 1.837562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.039475 Loss1: 3.623639 Loss2: 1.415836 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.003579 Loss1: 4.140804 Loss2: 1.862775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.129599 Loss1: 3.653289 Loss2: 1.476310 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.954580 Loss1: 3.529028 Loss2: 1.425552 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.902599 Loss1: 3.486025 Loss2: 1.416575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.874943 Loss1: 3.455481 Loss2: 1.419462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.795775 Loss1: 3.392416 Loss2: 1.403359 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.180208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.766301 Loss1: 3.365693 Loss2: 1.400608 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.723397 Loss1: 3.309710 Loss2: 1.413687 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.158333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.959039 Loss1: 3.594554 Loss2: 1.364485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.781290 Loss1: 3.491584 Loss2: 1.289706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.171215 Loss1: 4.245531 Loss2: 1.925684
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.743430 Loss1: 3.449541 Loss2: 1.293889
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.287514 Loss1: 3.759159 Loss2: 1.528355
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.722643 Loss1: 3.427930 Loss2: 1.294712
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.063794 Loss1: 3.593779 Loss2: 1.470015
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.664392 Loss1: 3.366356 Loss2: 1.298035
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.010719 Loss1: 3.542091 Loss2: 1.468628
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.685854 Loss1: 3.382105 Loss2: 1.303749
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.974262 Loss1: 3.516078 Loss2: 1.458185
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.717534 Loss1: 3.420514 Loss2: 1.297019
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.956004 Loss1: 3.492770 Loss2: 1.463233
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.673639 Loss1: 3.365646 Loss2: 1.307994
-(DefaultActor pid=3765) >> Training accuracy: 0.171875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.905698 Loss1: 3.426512 Loss2: 1.479186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.902738 Loss1: 3.439476 Loss2: 1.463263 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.138542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.139108 Loss1: 3.634856 Loss2: 1.504251 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.928181 Loss1: 3.511480 Loss2: 1.416701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.848770 Loss1: 3.457666 Loss2: 1.391105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.873812 Loss1: 3.478449 Loss2: 1.395363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.830602 Loss1: 3.437365 Loss2: 1.393237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.767233 Loss1: 3.384145 Loss2: 1.383087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.743262 Loss1: 3.349278 Loss2: 1.393984 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.711056 Loss1: 3.322242 Loss2: 1.388815 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.164062
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.713176 Loss1: 3.246326 Loss2: 1.466850 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.202083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.102814 Loss1: 4.257854 Loss2: 1.844961 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.033513 Loss1: 3.648315 Loss2: 1.385198 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.061708 Loss1: 3.956660 Loss2: 2.105047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.187903 Loss1: 3.551522 Loss2: 1.636381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.939535 Loss1: 3.360026 Loss2: 1.579509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.883019 Loss1: 3.332928 Loss2: 1.550090 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.864056 Loss1: 3.302192 Loss2: 1.561864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.839549 Loss1: 3.467564 Loss2: 1.371985 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.137277
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.799998 Loss1: 3.240765 Loss2: 1.559233 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.831819 Loss1: 3.246471 Loss2: 1.585348 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.217708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.093101 Loss1: 3.644579 Loss2: 1.448522 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.828982 Loss1: 3.447756 Loss2: 1.381226 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.790535 Loss1: 3.412505 Loss2: 1.378031
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.955093 Loss1: 4.140942 Loss2: 1.814151
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.795711 Loss1: 3.407630 Loss2: 1.388082
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.061069 Loss1: 3.615036 Loss2: 1.446033
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.797533 Loss1: 3.410275 Loss2: 1.387258
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.897374 Loss1: 3.490711 Loss2: 1.406662
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.737346 Loss1: 3.358606 Loss2: 1.378740
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.867033 Loss1: 3.481235 Loss2: 1.385799
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.792440 Loss1: 3.387177 Loss2: 1.405263
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.813183 Loss1: 3.417042 Loss2: 1.396141
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.783186 Loss1: 3.380184 Loss2: 1.403003
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.762241 Loss1: 3.373296 Loss2: 1.388945
-(DefaultActor pid=3765) >> Training accuracy: 0.170833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.733892 Loss1: 3.351853 Loss2: 1.382039
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.708665 Loss1: 3.317689 Loss2: 1.390975
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.682162 Loss1: 3.291540 Loss2: 1.390622
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.720846 Loss1: 3.314270 Loss2: 1.406575
-(DefaultActor pid=3764) >> Training accuracy: 0.147917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.004221 Loss1: 4.070209 Loss2: 1.934012
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.139836 Loss1: 3.582363 Loss2: 1.557473
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.954651 Loss1: 3.477057 Loss2: 1.477594
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.897405 Loss1: 3.410963 Loss2: 1.486442
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.070187 Loss1: 4.137527 Loss2: 1.932660
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.834641 Loss1: 3.364026 Loss2: 1.470615
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.122775 Loss1: 3.551768 Loss2: 1.571007
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.812565 Loss1: 3.326925 Loss2: 1.485640
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.796167 Loss1: 3.319472 Loss2: 1.476695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.736452 Loss1: 3.244031 Loss2: 1.492421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.749728 Loss1: 3.250148 Loss2: 1.499580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.721893 Loss1: 3.228841 Loss2: 1.493051 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.182617
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.748466 Loss1: 3.224877 Loss2: 1.523589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.660783 Loss1: 3.133765 Loss2: 1.527018 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.206250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.861306 Loss1: 4.118803 Loss2: 1.742503
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.914680 Loss1: 3.495785 Loss2: 1.418895
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.764870 Loss1: 3.419926 Loss2: 1.344944
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.707296 Loss1: 3.360585 Loss2: 1.346711
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.194680 Loss1: 4.263971 Loss2: 1.930709
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.671610 Loss1: 3.329105 Loss2: 1.342506
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.362858 Loss1: 3.833443 Loss2: 1.529415
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.677735 Loss1: 3.321791 Loss2: 1.355944
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.177871 Loss1: 3.681223 Loss2: 1.496648
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.684950 Loss1: 3.316000 Loss2: 1.368950
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.127153 Loss1: 3.639321 Loss2: 1.487832
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.633258 Loss1: 3.271786 Loss2: 1.361472
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.056634 Loss1: 3.579429 Loss2: 1.477205
-(DefaultActor pid=3764) Epoch: 5 Loss: 5.016617 Loss1: 3.530343 Loss2: 1.486274
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.654396 Loss1: 3.278349 Loss2: 1.376047
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.981990 Loss1: 3.495665 Loss2: 1.486325
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.560900 Loss1: 3.188841 Loss2: 1.372060
-(DefaultActor pid=3765) >> Training accuracy: 0.177734
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.937392 Loss1: 3.441221 Loss2: 1.496171 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.181250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.040676 Loss1: 4.199370 Loss2: 1.841306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.081421 Loss1: 3.641873 Loss2: 1.439548 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.026292 Loss1: 3.595884 Loss2: 1.430408
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.018125 Loss1: 4.269140 Loss2: 1.748985
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.247547 Loss1: 3.853748 Loss2: 1.393799
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.981043 Loss1: 3.552052 Loss2: 1.428991
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.980262 Loss1: 3.642676 Loss2: 1.337586
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.961691 Loss1: 3.534210 Loss2: 1.427481
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.905241 Loss1: 3.582345 Loss2: 1.322896
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.926727 Loss1: 3.495520 Loss2: 1.431207
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.865127 Loss1: 3.558102 Loss2: 1.307025
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.912124 Loss1: 3.476661 Loss2: 1.435463
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.931462 Loss1: 3.490275 Loss2: 1.441187 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.944127 Loss1: 3.492136 Loss2: 1.451991 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.143555
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.791220 Loss1: 3.463364 Loss2: 1.327856 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.126042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.113930 Loss1: 4.235195 Loss2: 1.878735 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.036779 Loss1: 3.656704 Loss2: 1.380075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.882803 Loss1: 3.527293 Loss2: 1.355510 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.093565 Loss1: 3.590762 Loss2: 1.502803 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.873465 Loss1: 3.432956 Loss2: 1.440509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.755460 Loss1: 3.396954 Loss2: 1.358507 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.194010
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.769506 Loss1: 3.399049 Loss2: 1.370457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.698421 Loss1: 3.284605 Loss2: 1.413816 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.675345 Loss1: 3.256050 Loss2: 1.419294 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.602403 Loss1: 3.194816 Loss2: 1.407587 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.176042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.915057 Loss1: 3.470351 Loss2: 1.444706 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.798841 Loss1: 3.349311 Loss2: 1.449530 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.780125 Loss1: 3.332587 Loss2: 1.447537
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.976750 Loss1: 4.283407 Loss2: 1.693343
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.779923 Loss1: 3.319507 Loss2: 1.460416
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.248631 Loss1: 3.883528 Loss2: 1.365103
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.743389 Loss1: 3.271524 Loss2: 1.471865
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.114146 Loss1: 3.783651 Loss2: 1.330495
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.708039 Loss1: 3.248012 Loss2: 1.460027
-(DefaultActor pid=3764) Epoch: 3 Loss: 5.044365 Loss1: 3.719691 Loss2: 1.324674
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.713047 Loss1: 3.237591 Loss2: 1.475456
-(DefaultActor pid=3764) Epoch: 4 Loss: 5.009922 Loss1: 3.684106 Loss2: 1.325817
-(DefaultActor pid=3765) >> Training accuracy: 0.209961
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.996174 Loss1: 3.666032 Loss2: 1.330142
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.996227 Loss1: 3.662542 Loss2: 1.333685
-DEBUG flwr 2023-10-08 15:36:46,038 | server.py:236 | fit_round 6 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.971051 Loss1: 3.632509 Loss2: 1.338541
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.951487 Loss1: 3.615491 Loss2: 1.335997
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.922954 Loss1: 4.156120 Loss2: 1.766834
-(DefaultActor pid=3764) >> Training accuracy: 0.149414
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.099912 Loss1: 3.656556 Loss2: 1.443355 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.862869 Loss1: 3.502635 Loss2: 1.360233 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.766301 Loss1: 3.410951 Loss2: 1.355349 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.728923 Loss1: 3.368720 Loss2: 1.360203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.741903 Loss1: 3.378148 Loss2: 1.363756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.824529 Loss1: 3.481812 Loss2: 1.342717
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.742281 Loss1: 3.375051 Loss2: 1.367230
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.703484 Loss1: 3.329820 Loss2: 1.373664
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.802292 Loss1: 3.475934 Loss2: 1.326358
-(DefaultActor pid=3765) >> Training accuracy: 0.159375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.753922 Loss1: 3.417251 Loss2: 1.336672
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.721835 Loss1: 3.407196 Loss2: 1.314639
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.724094 Loss1: 3.407470 Loss2: 1.316624
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.687509 Loss1: 3.352246 Loss2: 1.335264
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.282365 Loss1: 4.244117 Loss2: 2.038249
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.418847 Loss1: 3.797866 Loss2: 1.620980 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.176471
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.686946 Loss1: 3.352942 Loss2: 1.334004
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.278955 Loss1: 3.695839 Loss2: 1.583116
-(DefaultActor pid=3765) Epoch: 3 Loss: 5.264377 Loss1: 3.663128 Loss2: 1.601250
-(DefaultActor pid=3765) Epoch: 4 Loss: 5.210522 Loss1: 3.615818 Loss2: 1.594703
-(DefaultActor pid=3765) Epoch: 5 Loss: 5.149733 Loss1: 3.574951 Loss2: 1.574783
-(DefaultActor pid=3765) Epoch: 6 Loss: 5.134275 Loss1: 3.553866 Loss2: 1.580410
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.807259 Loss1: 4.192339 Loss2: 1.614920
-(DefaultActor pid=3765) Epoch: 7 Loss: 5.118162 Loss1: 3.533702 Loss2: 1.584459
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.997415 Loss1: 3.661630 Loss2: 1.335785
-(DefaultActor pid=3765) Epoch: 8 Loss: 5.072049 Loss1: 3.473844 Loss2: 1.598204
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.773219 Loss1: 3.499507 Loss2: 1.273712
-(DefaultActor pid=3765) Epoch: 9 Loss: 5.022581 Loss1: 3.424430 Loss2: 1.598150
-(DefaultActor pid=3765) >> Training accuracy: 0.136458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.651687 Loss1: 3.404164 Loss2: 1.247523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.606433 Loss1: 3.359651 Loss2: 1.246782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.572852 Loss1: 3.327156 Loss2: 1.245696 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.194792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 15:36:46,038][flwr][DEBUG] - fit_round 6 received 50 results and 0 failures
-INFO flwr 2023-10-08 15:37:27,327 | server.py:125 | fit progress: (6, 4.310671744636073, {'accuracy': 0.0415}, 13555.105305370998)
->> Test accuracy: 0.041500
-[2023-10-08 15:37:27,327][flwr][INFO] - fit progress: (6, 4.310671744636073, {'accuracy': 0.0415}, 13555.105305370998)
-DEBUG flwr 2023-10-08 15:37:27,327 | server.py:173 | evaluate_round 6: strategy sampled 50 clients (out of 50)
-[2023-10-08 15:37:27,327][flwr][DEBUG] - evaluate_round 6: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 15:46:33,816 | server.py:187 | evaluate_round 6 received 50 results and 0 failures
-[2023-10-08 15:46:33,816][flwr][DEBUG] - evaluate_round 6 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 15:46:33,816 | server.py:222 | fit_round 7: strategy sampled 50 clients (out of 50)
-[2023-10-08 15:46:33,816][flwr][DEBUG] - fit_round 7: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.765522 Loss1: 3.878056 Loss2: 1.887467 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.710307 Loss1: 3.271797 Loss2: 1.438509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.693316 Loss1: 3.268632 Loss2: 1.424684
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.078043 Loss1: 4.216658 Loss2: 1.861384
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.605845 Loss1: 3.185500 Loss2: 1.420344
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.054804 Loss1: 3.586362 Loss2: 1.468443
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.493393 Loss1: 3.082758 Loss2: 1.410635
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.872199 Loss1: 3.456689 Loss2: 1.415510
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.495708 Loss1: 3.063748 Loss2: 1.431961
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.844780 Loss1: 3.434999 Loss2: 1.409781
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.502387 Loss1: 3.084897 Loss2: 1.417490
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.802559 Loss1: 3.373953 Loss2: 1.428606
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.443765 Loss1: 3.027313 Loss2: 1.416451
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.755748 Loss1: 3.346167 Loss2: 1.409581
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.451998 Loss1: 3.020045 Loss2: 1.431953
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.694337 Loss1: 3.297671 Loss2: 1.396666
-(DefaultActor pid=3765) >> Training accuracy: 0.290625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.688641 Loss1: 3.283071 Loss2: 1.405571
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.669550 Loss1: 3.254969 Loss2: 1.414581
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.626493 Loss1: 3.206832 Loss2: 1.419661
-(DefaultActor pid=3764) >> Training accuracy: 0.171875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.031734 Loss1: 4.130672 Loss2: 1.901061
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.160477 Loss1: 3.697156 Loss2: 1.463320
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.974419 Loss1: 3.565532 Loss2: 1.408887
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.901216 Loss1: 3.502155 Loss2: 1.399061
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.940227 Loss1: 4.207410 Loss2: 1.732817
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.183246 Loss1: 3.807387 Loss2: 1.375859 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.011132 Loss1: 3.676096 Loss2: 1.335036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.933195 Loss1: 3.612709 Loss2: 1.320486 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.896582 Loss1: 3.570027 Loss2: 1.326556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.902611 Loss1: 3.578310 Loss2: 1.324301 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.166295
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.895185 Loss1: 3.558117 Loss2: 1.337068 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.808270 Loss1: 3.469482 Loss2: 1.338788 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.156250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.976738 Loss1: 3.396877 Loss2: 1.579861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.580909 Loss1: 3.090620 Loss2: 1.490289 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.498582 Loss1: 3.014327 Loss2: 1.484255
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.953140 Loss1: 4.226318 Loss2: 1.726823
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.487269 Loss1: 2.992405 Loss2: 1.494863
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.148478 Loss1: 3.757320 Loss2: 1.391158
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.485171 Loss1: 2.991943 Loss2: 1.493228
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.049447 Loss1: 3.690109 Loss2: 1.359338
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.989268 Loss1: 3.621503 Loss2: 1.367766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.948962 Loss1: 3.586596 Loss2: 1.362366 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.227083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.890918 Loss1: 3.529130 Loss2: 1.361788 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.844452 Loss1: 3.461747 Loss2: 1.382705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.835670 Loss1: 3.447820 Loss2: 1.387850 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.197266
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.985327 Loss1: 3.541985 Loss2: 1.443341 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.915458 Loss1: 3.471690 Loss2: 1.443768 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.875964 Loss1: 3.435466 Loss2: 1.440497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.817450 Loss1: 3.367993 Loss2: 1.449458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.802672 Loss1: 3.352373 Loss2: 1.450298 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.789484 Loss1: 3.328934 Loss2: 1.460550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.778589 Loss1: 3.320841 Loss2: 1.457748 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.141667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.768111 Loss1: 3.387505 Loss2: 1.380606 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.767327 Loss1: 3.384816 Loss2: 1.382511 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.173077
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.966586 Loss1: 4.151413 Loss2: 1.815173
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.005542 Loss1: 3.603673 Loss2: 1.401869
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.846401 Loss1: 3.463010 Loss2: 1.383391
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.779155 Loss1: 3.413657 Loss2: 1.365498
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.951396 Loss1: 4.131328 Loss2: 1.820068
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.041213 Loss1: 3.637880 Loss2: 1.403333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.908441 Loss1: 3.546745 Loss2: 1.361696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.846603 Loss1: 3.488497 Loss2: 1.358106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.806935 Loss1: 3.441564 Loss2: 1.365371 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.793242 Loss1: 3.429326 Loss2: 1.363916 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.173958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.742105 Loss1: 3.382335 Loss2: 1.359771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.723696 Loss1: 3.355044 Loss2: 1.368652 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.178125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.918618 Loss1: 4.058139 Loss2: 1.860479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.906554 Loss1: 3.465118 Loss2: 1.441436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.860744 Loss1: 4.061261 Loss2: 1.799483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.024759 Loss1: 3.601649 Loss2: 1.423110 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.841288 Loss1: 3.474323 Loss2: 1.366965 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.744061 Loss1: 3.380781 Loss2: 1.363280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.736666 Loss1: 3.374570 Loss2: 1.362095 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.671337 Loss1: 3.293089 Loss2: 1.378248 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.189583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.680529 Loss1: 3.305703 Loss2: 1.374827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.676805 Loss1: 3.289222 Loss2: 1.387583 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.204167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.980671 Loss1: 4.072246 Loss2: 1.908425 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.854593 Loss1: 3.400391 Loss2: 1.454202 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.866885 Loss1: 4.124938 Loss2: 1.741947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.743117 Loss1: 3.309021 Loss2: 1.434096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.685871 Loss1: 3.242415 Loss2: 1.443456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.708609 Loss1: 3.259450 Loss2: 1.449159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.715694 Loss1: 3.261265 Loss2: 1.454429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.650982 Loss1: 3.189805 Loss2: 1.461177 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.191964
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.759896 Loss1: 3.383975 Loss2: 1.375920 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.742977 Loss1: 3.361769 Loss2: 1.381208 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.184375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.099248 Loss1: 3.540193 Loss2: 1.559055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.852996 Loss1: 3.368176 Loss2: 1.484820 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.869326 Loss1: 3.388120 Loss2: 1.481206
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.691860 Loss1: 3.915558 Loss2: 1.776302
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.893999 Loss1: 3.491739 Loss2: 1.402260 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.685797 Loss1: 3.317494 Loss2: 1.368303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.688504 Loss1: 3.325453 Loss2: 1.363051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.578559 Loss1: 3.209682 Loss2: 1.368877 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.177083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.571236 Loss1: 3.189254 Loss2: 1.381982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.553369 Loss1: 3.166951 Loss2: 1.386418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.530425 Loss1: 3.133869 Loss2: 1.396556 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.209961
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.636871 Loss1: 3.299933 Loss2: 1.336938 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.494812 Loss1: 3.180056 Loss2: 1.314756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.921184 Loss1: 4.067470 Loss2: 1.853714
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.533286 Loss1: 3.211014 Loss2: 1.322272
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.075478 Loss1: 3.622444 Loss2: 1.453034
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.465007 Loss1: 3.142697 Loss2: 1.322309
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.813908 Loss1: 3.424736 Loss2: 1.389171
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.476875 Loss1: 3.150891 Loss2: 1.325984
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.807403 Loss1: 3.427884 Loss2: 1.379519
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.499944 Loss1: 3.166853 Loss2: 1.333091
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.754844 Loss1: 3.364780 Loss2: 1.390063
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.419280 Loss1: 3.089635 Loss2: 1.329646
-(DefaultActor pid=3765) >> Training accuracy: 0.213542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.683333 Loss1: 3.287867 Loss2: 1.395466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.668056 Loss1: 3.280246 Loss2: 1.387810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.610353 Loss1: 3.216328 Loss2: 1.394025
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.051537 Loss1: 4.190912 Loss2: 1.860626
-(DefaultActor pid=3764) >> Training accuracy: 0.193750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.116455 Loss1: 3.646526 Loss2: 1.469929
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.994476 Loss1: 3.551939 Loss2: 1.442538
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.875236 Loss1: 3.459339 Loss2: 1.415897
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.857166 Loss1: 3.435189 Loss2: 1.421978
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.311000 Loss1: 4.361141 Loss2: 1.949859
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.813073 Loss1: 3.404545 Loss2: 1.408528
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.812696 Loss1: 3.394264 Loss2: 1.418432 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.758459 Loss1: 3.328876 Loss2: 1.429583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.801055 Loss1: 3.362680 Loss2: 1.438376 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.717031 Loss1: 3.286759 Loss2: 1.430271 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.196875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.896202 Loss1: 3.446753 Loss2: 1.449449 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.942290 Loss1: 3.476609 Loss2: 1.465681 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.148438
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.825514 Loss1: 3.368598 Loss2: 1.456916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.567259 Loss1: 3.155689 Loss2: 1.411570 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.573493 Loss1: 3.162981 Loss2: 1.410512
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.942528 Loss1: 4.008909 Loss2: 1.933620
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.513639 Loss1: 3.118245 Loss2: 1.395394
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.063349 Loss1: 3.544235 Loss2: 1.519114
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.458058 Loss1: 3.058927 Loss2: 1.399131
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.937773 Loss1: 3.453664 Loss2: 1.484109
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.393611 Loss1: 2.984714 Loss2: 1.408898
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.814662 Loss1: 3.324101 Loss2: 1.490561
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.426897 Loss1: 3.021286 Loss2: 1.405611
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.791511 Loss1: 3.300390 Loss2: 1.491121
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.426860 Loss1: 3.022721 Loss2: 1.404139
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.734654 Loss1: 3.247197 Loss2: 1.487457
-(DefaultActor pid=3765) >> Training accuracy: 0.253125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.772195 Loss1: 3.276688 Loss2: 1.495507
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.716148 Loss1: 3.213471 Loss2: 1.502676
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.657377 Loss1: 3.154089 Loss2: 1.503288
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.700728 Loss1: 3.188406 Loss2: 1.512323
-(DefaultActor pid=3764) >> Training accuracy: 0.196875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.842840 Loss1: 4.067979 Loss2: 1.774861
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.967930 Loss1: 3.576777 Loss2: 1.391153
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.727372 Loss1: 3.387901 Loss2: 1.339471
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.692167 Loss1: 3.363693 Loss2: 1.328473
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.627109 Loss1: 3.286565 Loss2: 1.340544
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.664244 Loss1: 3.323247 Loss2: 1.340997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.621992 Loss1: 3.273979 Loss2: 1.348013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.577510 Loss1: 3.221077 Loss2: 1.356434 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.577569 Loss1: 3.217171 Loss2: 1.360398 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.556039 Loss1: 3.192538 Loss2: 1.363501 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.200000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.610083 Loss1: 3.187652 Loss2: 1.422431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.550461 Loss1: 3.140686 Loss2: 1.409775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.520248 Loss1: 3.101343 Loss2: 1.418905
-(DefaultActor pid=3764) >> Training accuracy: 0.247070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.800726 Loss1: 4.040547 Loss2: 1.760179
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.039594 Loss1: 3.660698 Loss2: 1.378896
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.864738 Loss1: 3.518649 Loss2: 1.346090
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.803732 Loss1: 3.463542 Loss2: 1.340189
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.783877 Loss1: 3.444086 Loss2: 1.339791
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.935534 Loss1: 4.148066 Loss2: 1.787468
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.784640 Loss1: 3.426171 Loss2: 1.358469
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.734799 Loss1: 3.387772 Loss2: 1.347027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.731115 Loss1: 3.371363 Loss2: 1.359752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.691772 Loss1: 3.336319 Loss2: 1.355453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.690272 Loss1: 3.326758 Loss2: 1.363514 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.180208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.735011 Loss1: 3.381898 Loss2: 1.353113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.744087 Loss1: 3.388455 Loss2: 1.355632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.695206 Loss1: 3.332778 Loss2: 1.362428
-(DefaultActor pid=3764) >> Training accuracy: 0.175000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.785653 Loss1: 3.985734 Loss2: 1.799919
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.957932 Loss1: 3.545876 Loss2: 1.412056
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.834531 Loss1: 3.449989 Loss2: 1.384543
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.760249 Loss1: 3.378187 Loss2: 1.382063
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.713488 Loss1: 3.331451 Loss2: 1.382037
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.817401 Loss1: 3.969547 Loss2: 1.847854
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.705491 Loss1: 3.313308 Loss2: 1.392183
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.959122 Loss1: 3.521701 Loss2: 1.437421
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.673869 Loss1: 3.288367 Loss2: 1.385503
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.781402 Loss1: 3.392738 Loss2: 1.388664
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.625015 Loss1: 3.231371 Loss2: 1.393644
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.714223 Loss1: 3.330091 Loss2: 1.384132
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.616474 Loss1: 3.201511 Loss2: 1.414963
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.629167 Loss1: 3.258338 Loss2: 1.370829
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.630540 Loss1: 3.216617 Loss2: 1.413923
-(DefaultActor pid=3765) >> Training accuracy: 0.173958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.574790 Loss1: 3.185625 Loss2: 1.389164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.523142 Loss1: 3.133329 Loss2: 1.389813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.567172 Loss1: 3.164210 Loss2: 1.402962
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.714686 Loss1: 3.865424 Loss2: 1.849262
-(DefaultActor pid=3764) >> Training accuracy: 0.208333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.874650 Loss1: 3.421805 Loss2: 1.452845
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.658646 Loss1: 3.272831 Loss2: 1.385815
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.553121 Loss1: 3.178746 Loss2: 1.374375
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.509130 Loss1: 3.129435 Loss2: 1.379695
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.908279 Loss1: 4.170110 Loss2: 1.738169
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.540933 Loss1: 3.144252 Loss2: 1.396681
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.058474 Loss1: 3.690743 Loss2: 1.367731
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.500387 Loss1: 3.111550 Loss2: 1.388837
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.440182 Loss1: 3.049378 Loss2: 1.390804
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.890563 Loss1: 3.560225 Loss2: 1.330338
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.395765 Loss1: 3.003295 Loss2: 1.392470
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.851247 Loss1: 3.519491 Loss2: 1.331755
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.371797 Loss1: 2.976317 Loss2: 1.395481
-(DefaultActor pid=3765) >> Training accuracy: 0.240625
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.860014 Loss1: 3.521869 Loss2: 1.338145
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.868676 Loss1: 3.535723 Loss2: 1.332953
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.800294 Loss1: 3.454740 Loss2: 1.345554
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.796871 Loss1: 3.456011 Loss2: 1.340860
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.732751 Loss1: 3.391190 Loss2: 1.341561
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.004228 Loss1: 4.082413 Loss2: 1.921815
-(DefaultActor pid=3764) >> Training accuracy: 0.142578
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.079705 Loss1: 3.568352 Loss2: 1.511354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.886727 Loss1: 3.417140 Loss2: 1.469587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.737961 Loss1: 4.017025 Loss2: 1.720936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.775930 Loss1: 3.409368 Loss2: 1.366562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.647571 Loss1: 3.310725 Loss2: 1.336846 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.580684 Loss1: 3.258442 Loss2: 1.322242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.564810 Loss1: 3.234272 Loss2: 1.330537 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.190257
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.506893 Loss1: 3.176679 Loss2: 1.330214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.463221 Loss1: 3.127281 Loss2: 1.335941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.421931 Loss1: 3.072639 Loss2: 1.349292 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.208984
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.913330 Loss1: 3.425939 Loss2: 1.487392 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.787828 Loss1: 3.307841 Loss2: 1.479987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.835910 Loss1: 4.007331 Loss2: 1.828579
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.804729 Loss1: 3.320914 Loss2: 1.483814
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.970318 Loss1: 3.560412 Loss2: 1.409907
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.786392 Loss1: 3.307968 Loss2: 1.478423
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.812779 Loss1: 3.436155 Loss2: 1.376623
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.749194 Loss1: 3.266529 Loss2: 1.482665
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.753797 Loss1: 3.379664 Loss2: 1.374134
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.752034 Loss1: 3.258514 Loss2: 1.493520
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.689928 Loss1: 3.317634 Loss2: 1.372294
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.721657 Loss1: 3.224806 Loss2: 1.496851
-(DefaultActor pid=3765) >> Training accuracy: 0.183333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.659178 Loss1: 3.276134 Loss2: 1.383044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.687347 Loss1: 3.294362 Loss2: 1.392985 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.645054 Loss1: 3.259079 Loss2: 1.385975
-(DefaultActor pid=3764) >> Training accuracy: 0.208333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.984502 Loss1: 4.135516 Loss2: 1.848986
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.167237 Loss1: 3.689921 Loss2: 1.477316
-(DefaultActor pid=3765) Epoch: 2 Loss: 5.019366 Loss1: 3.606566 Loss2: 1.412800
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.963334 Loss1: 3.541706 Loss2: 1.421628
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.903580 Loss1: 3.483024 Loss2: 1.420556
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.900696 Loss1: 4.048868 Loss2: 1.851829
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.015458 Loss1: 3.556897 Loss2: 1.458560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.795166 Loss1: 3.370083 Loss2: 1.425083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.730918 Loss1: 3.336500 Loss2: 1.394418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.727822 Loss1: 3.329095 Loss2: 1.398727 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.811575 Loss1: 3.372752 Loss2: 1.438824
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.716740 Loss1: 3.311779 Loss2: 1.404961
-(DefaultActor pid=3765) >> Training accuracy: 0.164062
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.650094 Loss1: 3.258073 Loss2: 1.392022
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.660122 Loss1: 3.265852 Loss2: 1.394270
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.647533 Loss1: 3.248833 Loss2: 1.398700
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.588265 Loss1: 3.194235 Loss2: 1.394030
-(DefaultActor pid=3764) >> Training accuracy: 0.178125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.009679 Loss1: 4.052616 Loss2: 1.957062
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.010831 Loss1: 3.530729 Loss2: 1.480101
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.761121 Loss1: 3.339173 Loss2: 1.421949
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.663968 Loss1: 3.254828 Loss2: 1.409140
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.659124 Loss1: 3.249025 Loss2: 1.410099
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.661671 Loss1: 3.241739 Loss2: 1.419932
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.013005 Loss1: 4.107547 Loss2: 1.905458
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.166743 Loss1: 3.676427 Loss2: 1.490316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.963398 Loss1: 3.511800 Loss2: 1.451598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.935404 Loss1: 3.493812 Loss2: 1.441592 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.215144
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.876692 Loss1: 3.434095 Loss2: 1.442597 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.873781 Loss1: 3.416758 Loss2: 1.457023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.865072 Loss1: 4.106703 Loss2: 1.758369
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.805690 Loss1: 3.355089 Loss2: 1.450602
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.147706 Loss1: 3.735916 Loss2: 1.411791
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.803078 Loss1: 3.335742 Loss2: 1.467335
-(DefaultActor pid=3764) >> Training accuracy: 0.185547
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.966798 Loss1: 3.608805 Loss2: 1.357993 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.817144 Loss1: 3.461057 Loss2: 1.356087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.807279 Loss1: 3.434224 Loss2: 1.373055
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.082740 Loss1: 4.153858 Loss2: 1.928881
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.801611 Loss1: 3.423310 Loss2: 1.378301
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.104655 Loss1: 3.570529 Loss2: 1.534126
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.786272 Loss1: 3.396399 Loss2: 1.389873
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.903832 Loss1: 3.432683 Loss2: 1.471149
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.705628 Loss1: 3.324259 Loss2: 1.381369
-(DefaultActor pid=3765) >> Training accuracy: 0.161458
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.836885 Loss1: 3.375518 Loss2: 1.461367
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.783799 Loss1: 3.315096 Loss2: 1.468703
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.803865 Loss1: 3.317832 Loss2: 1.486033
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.713900 Loss1: 3.232833 Loss2: 1.481067
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.696299 Loss1: 3.215379 Loss2: 1.480919
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.627573 Loss1: 3.146482 Loss2: 1.481091
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.912611 Loss1: 4.104510 Loss2: 1.808101
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.607869 Loss1: 3.125394 Loss2: 1.482475
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.028396 Loss1: 3.617588 Loss2: 1.410808
-(DefaultActor pid=3764) >> Training accuracy: 0.203125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.877849 Loss1: 3.484606 Loss2: 1.393243
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.847284 Loss1: 3.471752 Loss2: 1.375532
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.818860 Loss1: 3.450862 Loss2: 1.367999
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.767224 Loss1: 3.384400 Loss2: 1.382825
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.731488 Loss1: 3.997179 Loss2: 1.734309
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.746577 Loss1: 3.356829 Loss2: 1.389749
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.700153 Loss1: 3.321650 Loss2: 1.378503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.650083 Loss1: 3.258488 Loss2: 1.391595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.660645 Loss1: 3.268833 Loss2: 1.391812 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.194336
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.569484 Loss1: 3.254441 Loss2: 1.315042 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.441464 Loss1: 3.136019 Loss2: 1.305445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.494505 Loss1: 3.184552 Loss2: 1.309953
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.647471 Loss1: 3.908748 Loss2: 1.738724
-(DefaultActor pid=3764) >> Training accuracy: 0.254167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.853522 Loss1: 3.482275 Loss2: 1.371247 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.554965 Loss1: 3.247881 Loss2: 1.307084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.425602 Loss1: 3.128048 Loss2: 1.297554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.401871 Loss1: 3.094285 Loss2: 1.307586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.399461 Loss1: 3.081362 Loss2: 1.318100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.912490 Loss1: 3.478165 Loss2: 1.434325 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.852118 Loss1: 3.428794 Loss2: 1.423323 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.259375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.806991 Loss1: 3.366976 Loss2: 1.440016 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.773474 Loss1: 3.326088 Loss2: 1.447386 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.192708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.858489 Loss1: 3.970440 Loss2: 1.888049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.849661 Loss1: 3.426260 Loss2: 1.423401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.973313 Loss1: 4.086156 Loss2: 1.887157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.141702 Loss1: 3.662750 Loss2: 1.478952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 5.014499 Loss1: 3.568582 Loss2: 1.445917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.946375 Loss1: 3.509799 Loss2: 1.436576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.882738 Loss1: 3.435288 Loss2: 1.447451 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.864486 Loss1: 3.406801 Loss2: 1.457685 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.161458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.838354 Loss1: 3.375960 Loss2: 1.462393 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.767691 Loss1: 3.290695 Loss2: 1.476996 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.190625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.079730 Loss1: 3.570363 Loss2: 1.509368 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.824476 Loss1: 3.343894 Loss2: 1.480582 [repeated 2x across cluster]
-DEBUG flwr 2023-10-08 16:15:13,625 | server.py:236 | fit_round 7 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.783453 Loss1: 3.302113 Loss2: 1.481340 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.768858 Loss1: 3.284133 Loss2: 1.484724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.797041 Loss1: 3.307468 Loss2: 1.489573 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.703858 Loss1: 3.208962 Loss2: 1.494897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.720214 Loss1: 3.217509 Loss2: 1.502705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.691067 Loss1: 3.179479 Loss2: 1.511588 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.172917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.490961 Loss1: 3.100768 Loss2: 1.390194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.440509 Loss1: 3.044649 Loss2: 1.395861 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.234375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.048418 Loss1: 3.587933 Loss2: 1.460485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.816245 Loss1: 3.411020 Loss2: 1.405225 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.827086 Loss1: 3.410607 Loss2: 1.416479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.784714 Loss1: 3.364431 Loss2: 1.420283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.754191 Loss1: 3.349100 Loss2: 1.405091 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.745663 Loss1: 3.324458 Loss2: 1.421205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.704642 Loss1: 3.279416 Loss2: 1.425226 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.699322 Loss1: 3.263530 Loss2: 1.435793 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.174805
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.773272 Loss1: 3.277173 Loss2: 1.496100 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.196875
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 16:15:13,625][flwr][DEBUG] - fit_round 7 received 50 results and 0 failures
-INFO flwr 2023-10-08 16:15:54,592 | server.py:125 | fit progress: (7, 4.30479558816733, {'accuracy': 0.0497}, 15862.370257114002)
->> Test accuracy: 0.049700
-[2023-10-08 16:15:54,592][flwr][INFO] - fit progress: (7, 4.30479558816733, {'accuracy': 0.0497}, 15862.370257114002)
-DEBUG flwr 2023-10-08 16:15:54,592 | server.py:173 | evaluate_round 7: strategy sampled 50 clients (out of 50)
-[2023-10-08 16:15:54,592][flwr][DEBUG] - evaluate_round 7: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 16:25:00,961 | server.py:187 | evaluate_round 7 received 50 results and 0 failures
-[2023-10-08 16:25:00,961][flwr][DEBUG] - evaluate_round 7 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 16:25:00,961 | server.py:222 | fit_round 8: strategy sampled 50 clients (out of 50)
-[2023-10-08 16:25:00,961][flwr][DEBUG] - fit_round 8: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.598194 Loss1: 3.801508 Loss2: 1.796686 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.670448 Loss1: 3.300732 Loss2: 1.369716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.545829 Loss1: 3.186581 Loss2: 1.359248
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.898422 Loss1: 3.910995 Loss2: 1.987428
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.026556 Loss1: 3.453524 Loss2: 1.573032
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.559240 Loss1: 3.201887 Loss2: 1.357353
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.764776 Loss1: 3.264322 Loss2: 1.500454
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.435083 Loss1: 3.091858 Loss2: 1.343225
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.671630 Loss1: 3.194996 Loss2: 1.476634
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.470167 Loss1: 3.103637 Loss2: 1.366530
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.614289 Loss1: 3.138440 Loss2: 1.475849
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.414784 Loss1: 3.046238 Loss2: 1.368545
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.331379 Loss1: 2.960608 Loss2: 1.370771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.324274 Loss1: 2.947200 Loss2: 1.377074 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.247070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.451031 Loss1: 2.964839 Loss2: 1.486192 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.259375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.844169 Loss1: 3.904169 Loss2: 1.940001 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.637368 Loss1: 3.183440 Loss2: 1.453927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.636300 Loss1: 3.190630 Loss2: 1.445670
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.712364 Loss1: 4.013050 Loss2: 1.699314
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.905418 Loss1: 3.560286 Loss2: 1.345132 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.753472 Loss1: 3.446803 Loss2: 1.306670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.703250 Loss1: 3.404604 Loss2: 1.298646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.709824 Loss1: 3.406147 Loss2: 1.303677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.697756 Loss1: 3.382929 Loss2: 1.314827 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.235417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.615040 Loss1: 3.306340 Loss2: 1.308700 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.564416 Loss1: 3.245714 Loss2: 1.318701 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.175781
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.556870 Loss1: 3.705114 Loss2: 1.851756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.537643 Loss1: 3.164716 Loss2: 1.372926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.590648 Loss1: 3.773791 Loss2: 1.816857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.568447 Loss1: 3.160906 Loss2: 1.407541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.394303 Loss1: 3.033607 Loss2: 1.360695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.315118 Loss1: 2.965438 Loss2: 1.349680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.278117 Loss1: 2.929740 Loss2: 1.348377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.229322 Loss1: 2.892652 Loss2: 1.336671 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.267708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.204345 Loss1: 2.859031 Loss2: 1.345314 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.185927 Loss1: 2.839051 Loss2: 1.346876 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.226042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.637509 Loss1: 3.269088 Loss2: 1.368421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.355572 Loss1: 3.028518 Loss2: 1.327053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.715957 Loss1: 3.887944 Loss2: 1.828012
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.320428 Loss1: 2.974642 Loss2: 1.345786
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.888116 Loss1: 3.448276 Loss2: 1.439839
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.288116 Loss1: 2.956323 Loss2: 1.331794
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.685319 Loss1: 3.296019 Loss2: 1.389300
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.295373 Loss1: 2.947413 Loss2: 1.347960
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.616561 Loss1: 3.242202 Loss2: 1.374359
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.267360 Loss1: 2.922687 Loss2: 1.344673
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.571036 Loss1: 3.192571 Loss2: 1.378465
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.250874 Loss1: 2.914286 Loss2: 1.336588
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.557252 Loss1: 3.170169 Loss2: 1.387084
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.180932 Loss1: 2.845117 Loss2: 1.335815
-(DefaultActor pid=3765) >> Training accuracy: 0.284375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.465531 Loss1: 3.080740 Loss2: 1.384791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.430866 Loss1: 3.039130 Loss2: 1.391736 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.234375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.717838 Loss1: 3.869272 Loss2: 1.848565
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.876863 Loss1: 3.423305 Loss2: 1.453558
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.696934 Loss1: 3.291459 Loss2: 1.405475
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.598680 Loss1: 3.187835 Loss2: 1.410845
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.824100 Loss1: 4.034732 Loss2: 1.789368
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.882943 Loss1: 3.524159 Loss2: 1.358784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.691977 Loss1: 3.365675 Loss2: 1.326302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.499435 Loss1: 3.086275 Loss2: 1.413160
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.632939 Loss1: 3.309445 Loss2: 1.323494
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.496953 Loss1: 3.087917 Loss2: 1.409035
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.549170 Loss1: 3.224474 Loss2: 1.324696
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.451445 Loss1: 3.029659 Loss2: 1.421786
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.532880 Loss1: 3.205948 Loss2: 1.326933
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.484867 Loss1: 3.061125 Loss2: 1.423742
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.508654 Loss1: 3.187140 Loss2: 1.321514
-(DefaultActor pid=3765) >> Training accuracy: 0.247070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.460658 Loss1: 3.124124 Loss2: 1.336535
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.492179 Loss1: 3.145539 Loss2: 1.346640
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.465521 Loss1: 3.124606 Loss2: 1.340915
-(DefaultActor pid=3764) >> Training accuracy: 0.204167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.738029 Loss1: 3.957879 Loss2: 1.780150
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.854367 Loss1: 3.467434 Loss2: 1.386933
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.670518 Loss1: 3.336448 Loss2: 1.334069
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.568709 Loss1: 3.241754 Loss2: 1.326955
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.771067 Loss1: 4.002056 Loss2: 1.769010
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.981811 Loss1: 3.619525 Loss2: 1.362286 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.750862 Loss1: 3.424998 Loss2: 1.325864 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.698463 Loss1: 3.374110 Loss2: 1.324353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.693446 Loss1: 3.369965 Loss2: 1.323482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.583561 Loss1: 3.268067 Loss2: 1.315495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.222917
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.402239 Loss1: 3.029520 Loss2: 1.372719
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.562243 Loss1: 3.246403 Loss2: 1.315840
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.581497 Loss1: 3.232962 Loss2: 1.348535
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.627604 Loss1: 3.282703 Loss2: 1.344900
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.536925 Loss1: 3.199084 Loss2: 1.337841
-(DefaultActor pid=3764) >> Training accuracy: 0.205208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.795355 Loss1: 4.025287 Loss2: 1.770068
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.083944 Loss1: 3.677656 Loss2: 1.406288
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.945394 Loss1: 3.572615 Loss2: 1.372779
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.777453 Loss1: 3.977005 Loss2: 1.800449
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.863818 Loss1: 3.486066 Loss2: 1.377752
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.888963 Loss1: 3.489646 Loss2: 1.399318
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.883246 Loss1: 3.488770 Loss2: 1.394477
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.748099 Loss1: 3.382284 Loss2: 1.365815
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.823335 Loss1: 3.436482 Loss2: 1.386853
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.840783 Loss1: 3.438934 Loss2: 1.401849 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.785643 Loss1: 3.396743 Loss2: 1.388899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.759696 Loss1: 3.359549 Loss2: 1.400147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.714686 Loss1: 3.307322 Loss2: 1.407364 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.200195
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.415353 Loss1: 3.038086 Loss2: 1.377266 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.222917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.931275 Loss1: 4.054254 Loss2: 1.877021 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.865052 Loss1: 3.442877 Loss2: 1.422175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.797275 Loss1: 3.377430 Loss2: 1.419845
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.688281 Loss1: 3.923647 Loss2: 1.764634
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.756711 Loss1: 3.347485 Loss2: 1.409226
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.870664 Loss1: 3.472995 Loss2: 1.397668
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.716368 Loss1: 3.299845 Loss2: 1.416523
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.705154 Loss1: 3.355846 Loss2: 1.349307
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.700035 Loss1: 3.278191 Loss2: 1.421844
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.627779 Loss1: 3.282549 Loss2: 1.345230
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.679291 Loss1: 3.237443 Loss2: 1.441848
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.629156 Loss1: 3.280321 Loss2: 1.348835
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.675219 Loss1: 3.244498 Loss2: 1.430721
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.571930 Loss1: 3.221172 Loss2: 1.350758
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.620481 Loss1: 3.184551 Loss2: 1.435931
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.568786 Loss1: 3.218923 Loss2: 1.349863
-(DefaultActor pid=3765) >> Training accuracy: 0.191667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.544887 Loss1: 3.190011 Loss2: 1.354876
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.532632 Loss1: 3.164685 Loss2: 1.367947
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.495631 Loss1: 3.138049 Loss2: 1.357583
-(DefaultActor pid=3764) >> Training accuracy: 0.208333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.728536 Loss1: 3.874015 Loss2: 1.854522
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.873837 Loss1: 3.425233 Loss2: 1.448604
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.625989 Loss1: 3.229133 Loss2: 1.396857
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.597479 Loss1: 3.195718 Loss2: 1.401761
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.626297 Loss1: 3.760236 Loss2: 1.866061
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.540309 Loss1: 3.128881 Loss2: 1.411428
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.742966 Loss1: 3.313111 Loss2: 1.429855
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.566670 Loss1: 3.158867 Loss2: 1.407803
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.517950 Loss1: 3.142241 Loss2: 1.375709
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.532787 Loss1: 3.115059 Loss2: 1.417728
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.418088 Loss1: 3.031094 Loss2: 1.386994
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.530453 Loss1: 3.124266 Loss2: 1.406187
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.435474 Loss1: 3.066141 Loss2: 1.369333
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.486250 Loss1: 3.067461 Loss2: 1.418789
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.415612 Loss1: 3.026511 Loss2: 1.389101
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.448624 Loss1: 3.029834 Loss2: 1.418790
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.399685 Loss1: 3.009553 Loss2: 1.390132
-(DefaultActor pid=3765) >> Training accuracy: 0.221875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.385974 Loss1: 2.994547 Loss2: 1.391427
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.373578 Loss1: 2.978311 Loss2: 1.395267
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.357461 Loss1: 2.940462 Loss2: 1.416998
-(DefaultActor pid=3764) >> Training accuracy: 0.239583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.815493 Loss1: 4.007737 Loss2: 1.807757
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.953453 Loss1: 3.559306 Loss2: 1.394147
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.776447 Loss1: 3.409439 Loss2: 1.367007
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.669699 Loss1: 3.313966 Loss2: 1.355733
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.953243 Loss1: 4.055139 Loss2: 1.898104
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.705499 Loss1: 3.353097 Loss2: 1.352402
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.091744 Loss1: 3.648985 Loss2: 1.442759
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.902275 Loss1: 3.490105 Loss2: 1.412170
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.638346 Loss1: 3.281446 Loss2: 1.356901
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.670014 Loss1: 3.298972 Loss2: 1.371042 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.613822 Loss1: 3.245502 Loss2: 1.368321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.611585 Loss1: 3.238724 Loss2: 1.372861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.594342 Loss1: 3.210868 Loss2: 1.383474 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.216667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.641101 Loss1: 3.230744 Loss2: 1.410357 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.206731
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.940000 Loss1: 4.069937 Loss2: 1.870064
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.080808 Loss1: 3.606177 Loss2: 1.474630
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.928162 Loss1: 3.500295 Loss2: 1.427867
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.742032 Loss1: 3.963257 Loss2: 1.778775
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.871794 Loss1: 3.436762 Loss2: 1.435032
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.882069 Loss1: 3.479100 Loss2: 1.402968
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.822637 Loss1: 3.392264 Loss2: 1.430373
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.696905 Loss1: 3.349302 Loss2: 1.347603
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.766848 Loss1: 3.340228 Loss2: 1.426620
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.693239 Loss1: 3.341671 Loss2: 1.351569
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.781102 Loss1: 3.350940 Loss2: 1.430162
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.604780 Loss1: 3.251241 Loss2: 1.353538
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.721566 Loss1: 3.280234 Loss2: 1.441332
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.579120 Loss1: 3.216796 Loss2: 1.362324
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.700296 Loss1: 3.251748 Loss2: 1.448549
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.548684 Loss1: 3.190628 Loss2: 1.358056
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.675613 Loss1: 3.228994 Loss2: 1.446620
-(DefaultActor pid=3765) >> Training accuracy: 0.202148
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.494034 Loss1: 3.104354 Loss2: 1.389680 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.232422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.757329 Loss1: 3.883418 Loss2: 1.873910 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.642391 Loss1: 3.273687 Loss2: 1.368704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.998033 Loss1: 4.133665 Loss2: 1.864368 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.497302 Loss1: 3.129629 Loss2: 1.367673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.413982 Loss1: 3.050773 Loss2: 1.363210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.399322 Loss1: 3.025042 Loss2: 1.374280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.330265 Loss1: 2.956101 Loss2: 1.374164 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.324398 Loss1: 2.955068 Loss2: 1.369331 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.242788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.903145 Loss1: 3.473466 Loss2: 1.429678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.787520 Loss1: 3.354841 Loss2: 1.432679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.780087 Loss1: 3.328323 Loss2: 1.451764
-(DefaultActor pid=3764) >> Training accuracy: 0.181250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.712178 Loss1: 4.043608 Loss2: 1.668571
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.802044 Loss1: 3.477012 Loss2: 1.325032
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.650677 Loss1: 3.362605 Loss2: 1.288072
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.631770 Loss1: 3.342905 Loss2: 1.288865
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.648744 Loss1: 3.929681 Loss2: 1.719063
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.616113 Loss1: 3.321576 Loss2: 1.294537
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.837964 Loss1: 3.485593 Loss2: 1.352371
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.571580 Loss1: 3.282262 Loss2: 1.289319
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.520131 Loss1: 3.221566 Loss2: 1.298565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.509240 Loss1: 3.202945 Loss2: 1.306295 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.442452 Loss1: 3.138292 Loss2: 1.304160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.437244 Loss1: 3.124806 Loss2: 1.312438 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.201287
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.453144 Loss1: 3.131272 Loss2: 1.321872 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.182292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.096672 Loss1: 4.175593 Loss2: 1.921078 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.917638 Loss1: 3.497304 Loss2: 1.420334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.930730 Loss1: 4.076249 Loss2: 1.854481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.084402 Loss1: 3.617459 Loss2: 1.466943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.895930 Loss1: 3.471989 Loss2: 1.423941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.820768 Loss1: 3.407650 Loss2: 1.413118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.781056 Loss1: 3.360896 Loss2: 1.420160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.738415 Loss1: 3.321111 Loss2: 1.417304 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.184152
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.648116 Loss1: 3.231165 Loss2: 1.416952 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.698072 Loss1: 3.253363 Loss2: 1.444709 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.161458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.964474 Loss1: 3.502647 Loss2: 1.461827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.689903 Loss1: 3.292774 Loss2: 1.397129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.772421 Loss1: 3.945021 Loss2: 1.827401
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.627491 Loss1: 3.237185 Loss2: 1.390305
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.912718 Loss1: 3.484239 Loss2: 1.428479
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.586114 Loss1: 3.197574 Loss2: 1.388541
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.647029 Loss1: 3.268448 Loss2: 1.378581
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.523071 Loss1: 3.125031 Loss2: 1.398040
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.593690 Loss1: 3.217047 Loss2: 1.376643
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.528973 Loss1: 3.128397 Loss2: 1.400576
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.573309 Loss1: 3.189414 Loss2: 1.383896
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.525541 Loss1: 3.130072 Loss2: 1.395469
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.527809 Loss1: 3.152135 Loss2: 1.375674
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.505181 Loss1: 3.089392 Loss2: 1.415788
-(DefaultActor pid=3765) >> Training accuracy: 0.217708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.542681 Loss1: 3.153947 Loss2: 1.388734 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.505937 Loss1: 3.087829 Loss2: 1.418108 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.237500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.728120 Loss1: 3.336003 Loss2: 1.392117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.511459 Loss1: 3.176721 Loss2: 1.334738 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.506765 Loss1: 3.154854 Loss2: 1.351911 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.468131 Loss1: 3.109537 Loss2: 1.358595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.470986 Loss1: 3.108611 Loss2: 1.362374 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.408607 Loss1: 3.065102 Loss2: 1.343505 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.387886 Loss1: 3.025269 Loss2: 1.362617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.414251 Loss1: 3.050827 Loss2: 1.363424 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.201172
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.486096 Loss1: 3.110884 Loss2: 1.375211 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.207292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.661441 Loss1: 3.874842 Loss2: 1.786598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.667636 Loss1: 3.303108 Loss2: 1.364528 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.559824 Loss1: 3.200682 Loss2: 1.359142
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.767191 Loss1: 3.928330 Loss2: 1.838861
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.497264 Loss1: 3.142888 Loss2: 1.354375
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.908450 Loss1: 3.480121 Loss2: 1.428329
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.555179 Loss1: 3.187154 Loss2: 1.368024
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.706757 Loss1: 3.318073 Loss2: 1.388684
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.478233 Loss1: 3.109450 Loss2: 1.368783
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.655970 Loss1: 3.276158 Loss2: 1.379811
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.459065 Loss1: 3.090015 Loss2: 1.369050
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.639046 Loss1: 3.268214 Loss2: 1.370832
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.395022 Loss1: 3.028121 Loss2: 1.366900
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.531715 Loss1: 3.140001 Loss2: 1.391714
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.383020 Loss1: 3.006827 Loss2: 1.376193
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.551010 Loss1: 3.154070 Loss2: 1.396940
-(DefaultActor pid=3765) >> Training accuracy: 0.243750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.537999 Loss1: 3.138270 Loss2: 1.399729
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.469170 Loss1: 3.062163 Loss2: 1.407007
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.401499 Loss1: 2.997893 Loss2: 1.403607
-(DefaultActor pid=3764) >> Training accuracy: 0.240625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.820160 Loss1: 3.988503 Loss2: 1.831657
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.849737 Loss1: 3.425838 Loss2: 1.423899
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.747079 Loss1: 3.346763 Loss2: 1.400317
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.668457 Loss1: 3.268183 Loss2: 1.400274
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.643474 Loss1: 3.840915 Loss2: 1.802559
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.623233 Loss1: 3.216955 Loss2: 1.406279
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.890307 Loss1: 3.475550 Loss2: 1.414756
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.555877 Loss1: 3.160733 Loss2: 1.395144
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.722506 Loss1: 3.342072 Loss2: 1.380434
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.603838 Loss1: 3.193210 Loss2: 1.410628
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.591882 Loss1: 3.224026 Loss2: 1.367855
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.486573 Loss1: 3.081842 Loss2: 1.404731
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.589592 Loss1: 3.228012 Loss2: 1.361580
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.451554 Loss1: 3.038099 Loss2: 1.413455
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.560570 Loss1: 3.199733 Loss2: 1.360836
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.418881 Loss1: 2.998473 Loss2: 1.420408
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.504264 Loss1: 3.133137 Loss2: 1.371126
-(DefaultActor pid=3765) >> Training accuracy: 0.256250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.522445 Loss1: 3.145015 Loss2: 1.377429
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.468861 Loss1: 3.078847 Loss2: 1.390015
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.422754 Loss1: 3.049249 Loss2: 1.373504
-(DefaultActor pid=3764) >> Training accuracy: 0.223958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.852764 Loss1: 3.950390 Loss2: 1.902374
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.993531 Loss1: 3.489059 Loss2: 1.504472
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.831100 Loss1: 3.361360 Loss2: 1.469741
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.786409 Loss1: 3.934690 Loss2: 1.851719
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.749935 Loss1: 3.289705 Loss2: 1.460230
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.694951 Loss1: 3.236326 Loss2: 1.458625 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.700332 Loss1: 3.231293 Loss2: 1.469040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.683817 Loss1: 3.210110 Loss2: 1.473707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.685944 Loss1: 3.208844 Loss2: 1.477100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.597820 Loss1: 3.120193 Loss2: 1.477627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.484482 Loss1: 3.117309 Loss2: 1.367174 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.203125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.449090 Loss1: 3.065872 Loss2: 1.383218 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.232143
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.733132 Loss1: 3.961504 Loss2: 1.771628
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.857214 Loss1: 3.478915 Loss2: 1.378299
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.714834 Loss1: 3.378789 Loss2: 1.336046
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.630817 Loss1: 3.297441 Loss2: 1.333376
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.774149 Loss1: 3.868448 Loss2: 1.905701
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.811634 Loss1: 3.342485 Loss2: 1.469149 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.595548 Loss1: 3.176057 Loss2: 1.419490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.451477 Loss1: 3.026118 Loss2: 1.425359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.451061 Loss1: 3.021089 Loss2: 1.429972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.386132 Loss1: 2.972618 Loss2: 1.413514 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.215625
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.497651 Loss1: 3.147874 Loss2: 1.349777
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.366043 Loss1: 2.939404 Loss2: 1.426638
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.273754 Loss1: 2.836582 Loss2: 1.437173
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.268298 Loss1: 2.815137 Loss2: 1.453161
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.247085 Loss1: 2.797419 Loss2: 1.449666
-(DefaultActor pid=3764) >> Training accuracy: 0.348958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.958805 Loss1: 4.084408 Loss2: 1.874398
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.045172 Loss1: 3.608491 Loss2: 1.436682
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.842490 Loss1: 3.464717 Loss2: 1.377773
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.776972 Loss1: 3.396654 Loss2: 1.380319
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.620730 Loss1: 3.788964 Loss2: 1.831766
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.747126 Loss1: 3.305649 Loss2: 1.441477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.561835 Loss1: 3.170548 Loss2: 1.391287 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.513357 Loss1: 3.113513 Loss2: 1.399844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.436297 Loss1: 3.054222 Loss2: 1.382075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.403909 Loss1: 3.015162 Loss2: 1.388746 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.190625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.368425 Loss1: 2.980249 Loss2: 1.388176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.363174 Loss1: 2.974053 Loss2: 1.389121 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.263542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.872035 Loss1: 4.010292 Loss2: 1.861743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.847047 Loss1: 3.425304 Loss2: 1.421743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.797937 Loss1: 3.371046 Loss2: 1.426890
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.722508 Loss1: 3.943267 Loss2: 1.779241
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.799628 Loss1: 3.408039 Loss2: 1.391590 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.717964 Loss1: 3.370175 Loss2: 1.347789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.586656 Loss1: 3.246556 Loss2: 1.340100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.669102 Loss1: 3.223063 Loss2: 1.446039
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.550552 Loss1: 3.195314 Loss2: 1.355238
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.472076 Loss1: 3.135949 Loss2: 1.336127 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.226042
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.641129 Loss1: 3.183702 Loss2: 1.457426
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.513927 Loss1: 3.161731 Loss2: 1.352196
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.520655 Loss1: 3.155241 Loss2: 1.365414
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.414212 Loss1: 3.047562 Loss2: 1.366650
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.432325 Loss1: 3.064755 Loss2: 1.367571
-(DefaultActor pid=3764) >> Training accuracy: 0.221875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.921916 Loss1: 4.022102 Loss2: 1.899814
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.010016 Loss1: 3.589897 Loss2: 1.420120
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.759962 Loss1: 3.401536 Loss2: 1.358426
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.665759 Loss1: 3.322816 Loss2: 1.342944
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.621219 Loss1: 3.273683 Loss2: 1.347536
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.566390 Loss1: 3.213488 Loss2: 1.352902 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.613171 Loss1: 3.250661 Loss2: 1.362509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.561683 Loss1: 3.185939 Loss2: 1.375743 [repeated 2x across cluster]
-DEBUG flwr 2023-10-08 16:54:08,040 | server.py:236 | fit_round 8 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.549646 Loss1: 3.168704 Loss2: 1.380942
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.619256 Loss1: 3.278140 Loss2: 1.341116
-(DefaultActor pid=3765) >> Training accuracy: 0.229167
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.516659 Loss1: 3.139677 Loss2: 1.376982
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.585836 Loss1: 3.241877 Loss2: 1.343959
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.529704 Loss1: 3.180816 Loss2: 1.348888
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.543818 Loss1: 3.191245 Loss2: 1.352573
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.523758 Loss1: 3.165668 Loss2: 1.358089
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.499898 Loss1: 3.138786 Loss2: 1.361112
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.838026 Loss1: 4.043191 Loss2: 1.794835
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.418179 Loss1: 3.066265 Loss2: 1.351914
-(DefaultActor pid=3764) >> Training accuracy: 0.216667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.823950 Loss1: 3.436042 Loss2: 1.387909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.735656 Loss1: 3.355443 Loss2: 1.380213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.701402 Loss1: 3.325447 Loss2: 1.375955
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.864302 Loss1: 4.010571 Loss2: 1.853732
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.098195 Loss1: 3.653788 Loss2: 1.444407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.871621 Loss1: 3.447442 Loss2: 1.424179 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.860027 Loss1: 3.441261 Loss2: 1.418766 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.179167
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.572575 Loss1: 3.182581 Loss2: 1.389994
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.760167 Loss1: 3.343243 Loss2: 1.416924
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.759782 Loss1: 3.336250 Loss2: 1.423532
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.689790 Loss1: 3.265807 Loss2: 1.423983
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.735505 Loss1: 3.296395 Loss2: 1.439109
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.658213 Loss1: 3.218133 Loss2: 1.440080
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.915537 Loss1: 4.118868 Loss2: 1.796669
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.643863 Loss1: 3.204822 Loss2: 1.439041
-(DefaultActor pid=3764) >> Training accuracy: 0.196875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.943322 Loss1: 3.574087 Loss2: 1.369235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.764574 Loss1: 3.413220 Loss2: 1.351354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.821279 Loss1: 4.113729 Loss2: 1.707550 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.917879 Loss1: 3.586689 Loss2: 1.331190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.621247 Loss1: 3.257353 Loss2: 1.363893 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.650853 Loss1: 3.273169 Loss2: 1.377684 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.176339
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.572204 Loss1: 3.263601 Loss2: 1.308603 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.547916 Loss1: 3.233820 Loss2: 1.314096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.518986 Loss1: 3.185468 Loss2: 1.333519 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.214844
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 16:54:08,040][flwr][DEBUG] - fit_round 8 received 50 results and 0 failures
-INFO flwr 2023-10-08 16:54:49,722 | server.py:125 | fit progress: (8, 4.350954430552717, {'accuracy': 0.0537}, 18197.50099603)
->> Test accuracy: 0.053700
-[2023-10-08 16:54:49,722][flwr][INFO] - fit progress: (8, 4.350954430552717, {'accuracy': 0.0537}, 18197.50099603)
-DEBUG flwr 2023-10-08 16:54:49,723 | server.py:173 | evaluate_round 8: strategy sampled 50 clients (out of 50)
-[2023-10-08 16:54:49,723][flwr][DEBUG] - evaluate_round 8: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 17:03:52,702 | server.py:187 | evaluate_round 8 received 50 results and 0 failures
-[2023-10-08 17:03:52,702][flwr][DEBUG] - evaluate_round 8 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 17:03:52,702 | server.py:222 | fit_round 9: strategy sampled 50 clients (out of 50)
-[2023-10-08 17:03:52,702][flwr][DEBUG] - fit_round 9: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.565408 Loss1: 3.772120 Loss2: 1.793288
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.744628 Loss1: 3.346770 Loss2: 1.397858
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.536483 Loss1: 3.169311 Loss2: 1.367173
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.827645 Loss1: 3.895229 Loss2: 1.932416
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.463214 Loss1: 3.094219 Loss2: 1.368995
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.864692 Loss1: 3.390317 Loss2: 1.474376
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.424830 Loss1: 3.060264 Loss2: 1.364566
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.750479 Loss1: 3.292933 Loss2: 1.457546
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.379152 Loss1: 3.015094 Loss2: 1.364058
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.720466 Loss1: 3.267483 Loss2: 1.452983
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.373637 Loss1: 3.001676 Loss2: 1.371961
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.338513 Loss1: 2.970817 Loss2: 1.367696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.346071 Loss1: 2.960761 Loss2: 1.385310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.299399 Loss1: 2.906787 Loss2: 1.392612 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.230469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.562975 Loss1: 3.104404 Loss2: 1.458571 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.255208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.682514 Loss1: 3.843689 Loss2: 1.838825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.528577 Loss1: 3.148107 Loss2: 1.380470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.491326 Loss1: 3.112698 Loss2: 1.378628
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.580949 Loss1: 3.681861 Loss2: 1.899088
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.651312 Loss1: 3.210970 Loss2: 1.440342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.464848 Loss1: 3.073569 Loss2: 1.391279 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.367950 Loss1: 2.975450 Loss2: 1.392501 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.294402 Loss1: 2.907828 Loss2: 1.386574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.329495 Loss1: 2.931109 Loss2: 1.398386 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.271875
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.260864 Loss1: 2.870536 Loss2: 1.390328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.245025 Loss1: 2.850402 Loss2: 1.394623
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.278446 Loss1: 2.871684 Loss2: 1.406763
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.202046 Loss1: 2.800409 Loss2: 1.401638
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.223056 Loss1: 2.807615 Loss2: 1.415441
-(DefaultActor pid=3764) >> Training accuracy: 0.301042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.822111 Loss1: 3.922429 Loss2: 1.899682
-(DefaultActor pid=3765) Epoch: 1 Loss: 5.080897 Loss1: 3.599439 Loss2: 1.481458
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.849555 Loss1: 3.383324 Loss2: 1.466231
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.845445 Loss1: 3.379847 Loss2: 1.465597
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.696833 Loss1: 3.777013 Loss2: 1.919820
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.796788 Loss1: 3.312633 Loss2: 1.484155
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.785634 Loss1: 3.311194 Loss2: 1.474440
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.690747 Loss1: 3.245247 Loss2: 1.445500
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.742181 Loss1: 3.268869 Loss2: 1.473312
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.693772 Loss1: 3.257587 Loss2: 1.436186
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.689795 Loss1: 3.208774 Loss2: 1.481022
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.613422 Loss1: 3.175682 Loss2: 1.437740
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.684020 Loss1: 3.198210 Loss2: 1.485811
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.648094 Loss1: 3.159010 Loss2: 1.489084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.614618 Loss1: 3.123970 Loss2: 1.490648 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.215820
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.501397 Loss1: 3.044013 Loss2: 1.457383 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.260417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.704258 Loss1: 3.838409 Loss2: 1.865850 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.709926 Loss1: 3.320893 Loss2: 1.389033 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.677468 Loss1: 3.280015 Loss2: 1.397453
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.655943 Loss1: 3.808042 Loss2: 1.847902
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.851392 Loss1: 3.428963 Loss2: 1.422429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.650994 Loss1: 3.276569 Loss2: 1.374425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.622542 Loss1: 3.246588 Loss2: 1.375954 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.573405 Loss1: 3.193818 Loss2: 1.379587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.514322 Loss1: 3.134994 Loss2: 1.379328 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.231250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.485651 Loss1: 3.089896 Loss2: 1.395755 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.436415 Loss1: 3.035150 Loss2: 1.401265 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.223633
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.755438 Loss1: 3.800353 Loss2: 1.955085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.547193 Loss1: 3.115706 Loss2: 1.431487 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.442252 Loss1: 3.022076 Loss2: 1.420176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.296507 Loss1: 2.883066 Loss2: 1.413441 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.870200 Loss1: 3.483518 Loss2: 1.386682
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.256021 Loss1: 2.830125 Loss2: 1.425896
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.251213 Loss1: 2.810130 Loss2: 1.441083
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.708331 Loss1: 3.353780 Loss2: 1.354551
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.648129 Loss1: 3.298087 Loss2: 1.350042 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.292067
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.645556 Loss1: 3.273206 Loss2: 1.372350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.549932 Loss1: 3.188782 Loss2: 1.361151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.482692 Loss1: 3.103483 Loss2: 1.379209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.473654 Loss1: 3.095126 Loss2: 1.378527 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.218750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.819749 Loss1: 3.405120 Loss2: 1.414629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.665773 Loss1: 3.245815 Loss2: 1.419958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.648575 Loss1: 3.223807 Loss2: 1.424768
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.889294 Loss1: 3.909280 Loss2: 1.980015
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.884836 Loss1: 3.367283 Loss2: 1.517553
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.629163 Loss1: 3.200437 Loss2: 1.428726
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.694903 Loss1: 3.218996 Loss2: 1.475907
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.609920 Loss1: 3.180591 Loss2: 1.429329
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.695157 Loss1: 3.218954 Loss2: 1.476203
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.625117 Loss1: 3.176975 Loss2: 1.448142
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.605247 Loss1: 3.128893 Loss2: 1.476354
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.568764 Loss1: 3.127855 Loss2: 1.440909
-(DefaultActor pid=3765) >> Training accuracy: 0.201172
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.607153 Loss1: 3.122176 Loss2: 1.484977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.543550 Loss1: 3.052861 Loss2: 1.490689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.490153 Loss1: 2.996462 Loss2: 1.493691
-(DefaultActor pid=3764) >> Training accuracy: 0.246875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.746030 Loss1: 3.883533 Loss2: 1.862497
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.821553 Loss1: 3.369870 Loss2: 1.451683
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.671197 Loss1: 3.261579 Loss2: 1.409618
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.581196 Loss1: 3.168857 Loss2: 1.412339
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.528360 Loss1: 3.129345 Loss2: 1.399015
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.513094 Loss1: 3.672528 Loss2: 1.840565
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.504506 Loss1: 3.082582 Loss2: 1.421924
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.642558 Loss1: 3.224698 Loss2: 1.417860
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.454210 Loss1: 3.040046 Loss2: 1.414164
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.433886 Loss1: 3.067873 Loss2: 1.366014
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.435995 Loss1: 3.027325 Loss2: 1.408670
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.255988 Loss1: 2.902157 Loss2: 1.353831
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.439377 Loss1: 3.011835 Loss2: 1.427543
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.194458 Loss1: 2.846763 Loss2: 1.347695
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.431396 Loss1: 3.008183 Loss2: 1.423213
-(DefaultActor pid=3765) >> Training accuracy: 0.207292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.118274 Loss1: 2.773391 Loss2: 1.344884 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.101319 Loss1: 2.743741 Loss2: 1.357578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.032909 Loss1: 2.674891 Loss2: 1.358018
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.613171 Loss1: 3.775716 Loss2: 1.837456
-(DefaultActor pid=3764) >> Training accuracy: 0.258333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.871121 Loss1: 3.454734 Loss2: 1.416387
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.694574 Loss1: 3.293444 Loss2: 1.401130
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.620914 Loss1: 3.226656 Loss2: 1.394258
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.587400 Loss1: 3.202438 Loss2: 1.384962
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.664068 Loss1: 3.728651 Loss2: 1.935417
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.524859 Loss1: 3.121891 Loss2: 1.402968
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.878846 Loss1: 3.384440 Loss2: 1.494407
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.514233 Loss1: 3.115149 Loss2: 1.399084
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.807009 Loss1: 3.353110 Loss2: 1.453899
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.497203 Loss1: 3.088622 Loss2: 1.408580
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.597705 Loss1: 3.138903 Loss2: 1.458802
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.478720 Loss1: 3.056077 Loss2: 1.422642
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.605596 Loss1: 3.158857 Loss2: 1.446739
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.469746 Loss1: 3.067379 Loss2: 1.402367
-(DefaultActor pid=3765) >> Training accuracy: 0.241667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.520463 Loss1: 3.054620 Loss2: 1.465843 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.490321 Loss1: 3.021599 Loss2: 1.468723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.451478 Loss1: 2.996386 Loss2: 1.455091
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.706498 Loss1: 3.770821 Loss2: 1.935677
-(DefaultActor pid=3764) >> Training accuracy: 0.239583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.865225 Loss1: 3.362527 Loss2: 1.502698
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.711587 Loss1: 3.256866 Loss2: 1.454721
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.589644 Loss1: 3.138454 Loss2: 1.451190
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.543666 Loss1: 3.092626 Loss2: 1.451040
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.762352 Loss1: 3.826716 Loss2: 1.935636
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.529295 Loss1: 3.082845 Loss2: 1.446450
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.458466 Loss1: 3.006085 Loss2: 1.452381 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.484174 Loss1: 3.024471 Loss2: 1.459703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.473680 Loss1: 3.006969 Loss2: 1.466710 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.434759 Loss1: 2.959225 Loss2: 1.475534 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.257292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.528949 Loss1: 3.086239 Loss2: 1.442710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.435204 Loss1: 2.980026 Loss2: 1.455177 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.271205
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.538268 Loss1: 3.676051 Loss2: 1.862217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.421621 Loss1: 3.021235 Loss2: 1.400386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.262751 Loss1: 2.874913 Loss2: 1.387838 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.225079 Loss1: 2.823409 Loss2: 1.401670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.273425 Loss1: 2.875447 Loss2: 1.397978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.180629 Loss1: 2.783557 Loss2: 1.397072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.140666 Loss1: 2.733493 Loss2: 1.407172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.155685 Loss1: 2.747199 Loss2: 1.408486 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.305208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.271387 Loss1: 2.867633 Loss2: 1.403753 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.219377 Loss1: 2.802560 Loss2: 1.416817 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.295833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.869369 Loss1: 3.412948 Loss2: 1.456420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.543988 Loss1: 3.118399 Loss2: 1.425589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.796354 Loss1: 3.992439 Loss2: 1.803914
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.498637 Loss1: 3.090957 Loss2: 1.407681
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.976073 Loss1: 3.585143 Loss2: 1.390929
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.454618 Loss1: 3.026123 Loss2: 1.428495
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.829482 Loss1: 3.473277 Loss2: 1.356205
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.452806 Loss1: 3.014118 Loss2: 1.438688
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.725812 Loss1: 3.360828 Loss2: 1.364983
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.377618 Loss1: 2.940493 Loss2: 1.437125
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.629195 Loss1: 3.261908 Loss2: 1.367287
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.356891 Loss1: 2.924464 Loss2: 1.432427
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.628560 Loss1: 3.252511 Loss2: 1.376048
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.375839 Loss1: 2.927493 Loss2: 1.448346
-(DefaultActor pid=3765) >> Training accuracy: 0.277083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.574491 Loss1: 3.195089 Loss2: 1.379401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.509960 Loss1: 3.116123 Loss2: 1.393837 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.202083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.832544 Loss1: 3.477054 Loss2: 1.355490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.612687 Loss1: 3.287701 Loss2: 1.324986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.619294 Loss1: 3.805962 Loss2: 1.813332
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.565662 Loss1: 3.232767 Loss2: 1.332895
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.818676 Loss1: 3.416664 Loss2: 1.402012
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.513460 Loss1: 3.190492 Loss2: 1.322968
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.724651 Loss1: 3.349842 Loss2: 1.374808
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.419761 Loss1: 3.093626 Loss2: 1.326136
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.626659 Loss1: 3.254459 Loss2: 1.372200
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.392299 Loss1: 3.064166 Loss2: 1.328133
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.633211 Loss1: 3.244921 Loss2: 1.388290
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.466729 Loss1: 3.119852 Loss2: 1.346877
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.568694 Loss1: 3.174908 Loss2: 1.393786
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.404157 Loss1: 3.063694 Loss2: 1.340463
-(DefaultActor pid=3765) >> Training accuracy: 0.207292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.437785 Loss1: 3.041134 Loss2: 1.396651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.431079 Loss1: 3.029474 Loss2: 1.401606 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.247917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.803914 Loss1: 3.343524 Loss2: 1.460390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.540571 Loss1: 3.123722 Loss2: 1.416849 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.799002 Loss1: 3.898858 Loss2: 1.900144
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.532910 Loss1: 3.112693 Loss2: 1.420217
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.965723 Loss1: 3.482540 Loss2: 1.483183
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.520101 Loss1: 3.089551 Loss2: 1.430550
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.851643 Loss1: 3.406748 Loss2: 1.444895
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.441466 Loss1: 3.007486 Loss2: 1.433979
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.392720 Loss1: 2.967383 Loss2: 1.425337
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.823015 Loss1: 3.382821 Loss2: 1.440194
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.380924 Loss1: 2.943353 Loss2: 1.437571
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.739335 Loss1: 3.296254 Loss2: 1.443081
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.365826 Loss1: 2.923711 Loss2: 1.442115
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.721882 Loss1: 3.267616 Loss2: 1.454266
-(DefaultActor pid=3765) >> Training accuracy: 0.279167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.641705 Loss1: 3.189193 Loss2: 1.452512
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.647877 Loss1: 3.194853 Loss2: 1.453023
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.627187 Loss1: 3.157535 Loss2: 1.469652
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.554425 Loss1: 3.087156 Loss2: 1.467269
-(DefaultActor pid=3764) >> Training accuracy: 0.191406
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.792248 Loss1: 3.834586 Loss2: 1.957662
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.833264 Loss1: 3.316145 Loss2: 1.517119
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.629601 Loss1: 3.175944 Loss2: 1.453657
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.545500 Loss1: 3.090963 Loss2: 1.454538
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.518751 Loss1: 3.059809 Loss2: 1.458942
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.715845 Loss1: 3.846219 Loss2: 1.869626
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.858112 Loss1: 3.398590 Loss2: 1.459523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.682075 Loss1: 3.247294 Loss2: 1.434781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.571170 Loss1: 3.144140 Loss2: 1.427030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.521305 Loss1: 3.087362 Loss2: 1.433942 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.277083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.462917 Loss1: 3.033903 Loss2: 1.429014 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.454722 Loss1: 3.001407 Loss2: 1.453315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.343489 Loss1: 2.913375 Loss2: 1.430114
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.466782 Loss1: 3.594367 Loss2: 1.872415
-(DefaultActor pid=3764) >> Training accuracy: 0.237305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.626089 Loss1: 3.185152 Loss2: 1.440937
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.474759 Loss1: 3.068206 Loss2: 1.406552
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.373352 Loss1: 2.975274 Loss2: 1.398078
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.353856 Loss1: 2.951772 Loss2: 1.402084
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.344277 Loss1: 2.927115 Loss2: 1.417162
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.669243 Loss1: 3.831637 Loss2: 1.837606
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.293172 Loss1: 2.883530 Loss2: 1.409642
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.696298 Loss1: 3.294257 Loss2: 1.402040
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.219552 Loss1: 2.809139 Loss2: 1.410413
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.492148 Loss1: 3.125997 Loss2: 1.366152
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.141250 Loss1: 2.724643 Loss2: 1.416607
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.426830 Loss1: 3.063352 Loss2: 1.363478
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.161165 Loss1: 2.736645 Loss2: 1.424520
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.418015 Loss1: 3.054781 Loss2: 1.363235
-(DefaultActor pid=3765) >> Training accuracy: 0.304167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.344968 Loss1: 2.984460 Loss2: 1.360508
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.334191 Loss1: 2.961829 Loss2: 1.372362
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.325692 Loss1: 2.940788 Loss2: 1.384904
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.244368 Loss1: 2.869933 Loss2: 1.374435
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.275624 Loss1: 2.891040 Loss2: 1.384584
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.818058 Loss1: 3.954138 Loss2: 1.863919
-(DefaultActor pid=3764) >> Training accuracy: 0.244792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.958701 Loss1: 3.523218 Loss2: 1.435483
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.705214 Loss1: 3.316462 Loss2: 1.388752
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.647645 Loss1: 3.243600 Loss2: 1.404046
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.630533 Loss1: 3.221552 Loss2: 1.408981
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.515475 Loss1: 3.654797 Loss2: 1.860678
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.614178 Loss1: 3.199718 Loss2: 1.414460
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.674562 Loss1: 3.246019 Loss2: 1.428543
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.604287 Loss1: 3.186978 Loss2: 1.417310
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.511426 Loss1: 3.114689 Loss2: 1.396737
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.549386 Loss1: 3.122474 Loss2: 1.426912
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.440160 Loss1: 3.039818 Loss2: 1.400342
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.536157 Loss1: 3.113671 Loss2: 1.422487
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.356895 Loss1: 2.947274 Loss2: 1.409620
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.497007 Loss1: 3.074065 Loss2: 1.422942
-(DefaultActor pid=3765) >> Training accuracy: 0.247070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.329553 Loss1: 2.915053 Loss2: 1.414499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.230072 Loss1: 2.812364 Loss2: 1.417708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.585869 Loss1: 3.786868 Loss2: 1.799001
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.314266 Loss1: 2.877860 Loss2: 1.436405
-(DefaultActor pid=3764) >> Training accuracy: 0.274414
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.579854 Loss1: 3.228451 Loss2: 1.351403 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.432701 Loss1: 3.080350 Loss2: 1.352351 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.372616 Loss1: 3.023912 Loss2: 1.348704
-(DefaultActor pid=3764) Epoch: 0 Loss: 6.038271 Loss1: 4.087950 Loss2: 1.950321
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.340961 Loss1: 2.976039 Loss2: 1.364923
-(DefaultActor pid=3764) Epoch: 1 Loss: 5.071873 Loss1: 3.567594 Loss2: 1.504279
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.881033 Loss1: 3.434323 Loss2: 1.446710
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.313389 Loss1: 2.938634 Loss2: 1.374755
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.812080 Loss1: 3.371885 Loss2: 1.440195
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.279340 Loss1: 2.914024 Loss2: 1.365316
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.763638 Loss1: 3.329016 Loss2: 1.434622
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.303798 Loss1: 2.929505 Loss2: 1.374294
-(DefaultActor pid=3765) >> Training accuracy: 0.276042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.686506 Loss1: 3.240618 Loss2: 1.445888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.662005 Loss1: 3.217695 Loss2: 1.444310 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.171875
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.646308 Loss1: 3.191260 Loss2: 1.455048
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.702707 Loss1: 3.845522 Loss2: 1.857186
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.864442 Loss1: 3.420413 Loss2: 1.444029
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.723404 Loss1: 3.320896 Loss2: 1.402508
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.622400 Loss1: 3.221593 Loss2: 1.400807
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.584124 Loss1: 3.173120 Loss2: 1.411004
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.938826 Loss1: 4.035775 Loss2: 1.903051
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.992138 Loss1: 3.546472 Loss2: 1.445666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.795912 Loss1: 3.389181 Loss2: 1.406731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.743668 Loss1: 3.333938 Loss2: 1.409731 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.492217 Loss1: 3.070274 Loss2: 1.421943
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.694131 Loss1: 3.298926 Loss2: 1.395205
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.479435 Loss1: 3.042729 Loss2: 1.436706
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.687590 Loss1: 3.267153 Loss2: 1.420437
-(DefaultActor pid=3765) >> Training accuracy: 0.232292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.631794 Loss1: 3.209426 Loss2: 1.422368
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.602979 Loss1: 3.181215 Loss2: 1.421764
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.591458 Loss1: 3.182894 Loss2: 1.408564
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.532870 Loss1: 3.112174 Loss2: 1.420696
-(DefaultActor pid=3764) >> Training accuracy: 0.209821
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.605850 Loss1: 3.636264 Loss2: 1.969586
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.749958 Loss1: 3.213896 Loss2: 1.536062
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.606008 Loss1: 3.115720 Loss2: 1.490288
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.492655 Loss1: 3.009213 Loss2: 1.483442
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.562552 Loss1: 3.676678 Loss2: 1.885874
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.786354 Loss1: 3.328827 Loss2: 1.457527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.623459 Loss1: 3.205244 Loss2: 1.418215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.561851 Loss1: 3.149291 Loss2: 1.412560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.498579 Loss1: 3.073057 Loss2: 1.425522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.467007 Loss1: 3.047755 Loss2: 1.419253 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.292708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.363515 Loss1: 2.946533 Loss2: 1.416982 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.393588 Loss1: 2.954937 Loss2: 1.438651 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.251042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.825044 Loss1: 3.364662 Loss2: 1.460383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.594516 Loss1: 3.155055 Loss2: 1.439461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.508840 Loss1: 3.759040 Loss2: 1.749800
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.514773 Loss1: 3.085820 Loss2: 1.428953
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.569184 Loss1: 3.222283 Loss2: 1.346901
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.482753 Loss1: 3.059217 Loss2: 1.423535
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.360701 Loss1: 3.043787 Loss2: 1.316914
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.428678 Loss1: 2.996480 Loss2: 1.432198
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.278668 Loss1: 2.986773 Loss2: 1.291894
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.444057 Loss1: 3.005900 Loss2: 1.438157
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.163987 Loss1: 2.850745 Loss2: 1.313242
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.404150 Loss1: 2.955079 Loss2: 1.449070
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.135027 Loss1: 2.822040 Loss2: 1.312987
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.413553 Loss1: 2.959940 Loss2: 1.453613
-(DefaultActor pid=3765) >> Training accuracy: 0.238542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.126244 Loss1: 2.815073 Loss2: 1.311171 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.095190 Loss1: 2.780331 Loss2: 1.314859 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.325000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.686657 Loss1: 3.256096 Loss2: 1.430561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.453576 Loss1: 3.051737 Loss2: 1.401839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.417503 Loss1: 3.009651 Loss2: 1.407852 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.350440 Loss1: 2.937176 Loss2: 1.413264 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.356138 Loss1: 2.939497 Loss2: 1.416641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.298514 Loss1: 2.874891 Loss2: 1.423624 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.284297 Loss1: 2.860956 Loss2: 1.423341 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.249014 Loss1: 2.813871 Loss2: 1.435143 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.270508
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.436481 Loss1: 3.008926 Loss2: 1.427554 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.253125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.891856 Loss1: 3.902026 Loss2: 1.989830 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.727788 Loss1: 3.249999 Loss2: 1.477788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.650740 Loss1: 3.184869 Loss2: 1.465871
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.858932 Loss1: 3.927098 Loss2: 1.931834
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.898477 Loss1: 3.365251 Loss2: 1.533226 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.704418 Loss1: 3.209817 Loss2: 1.494602 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.682114 Loss1: 3.180231 Loss2: 1.501883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.650393 Loss1: 3.152574 Loss2: 1.497819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.428584 Loss1: 2.948081 Loss2: 1.480503 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.262500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.544496 Loss1: 3.034512 Loss2: 1.509984 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.470134 Loss1: 2.959471 Loss2: 1.510663 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.258272
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.806262 Loss1: 3.397705 Loss2: 1.408557 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.595042 Loss1: 3.195580 Loss2: 1.399461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.590252 Loss1: 3.173923 Loss2: 1.416329
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.964526 Loss1: 3.958540 Loss2: 2.005986
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.996893 Loss1: 3.534340 Loss2: 1.462553 [repeated 2x across cluster]
-DEBUG flwr 2023-10-08 17:32:29,490 | server.py:236 | fit_round 9 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.725282 Loss1: 3.288695 Loss2: 1.436587 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.546178 Loss1: 3.132751 Loss2: 1.413427
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.613428 Loss1: 3.199292 Loss2: 1.414136
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.579973 Loss1: 3.159427 Loss2: 1.420546
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.416337 Loss1: 3.008864 Loss2: 1.407474
-(DefaultActor pid=3765) >> Training accuracy: 0.233173
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.482042 Loss1: 3.047623 Loss2: 1.434419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.421677 Loss1: 2.981141 Loss2: 1.440536 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.266927
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.632924 Loss1: 3.791371 Loss2: 1.841553 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.564889 Loss1: 3.179630 Loss2: 1.385259 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.473056 Loss1: 3.086487 Loss2: 1.386569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.418707 Loss1: 3.032709 Loss2: 1.385999 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.402035 Loss1: 3.012971 Loss2: 1.389065 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.402192 Loss1: 3.001658 Loss2: 1.400534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.367096 Loss1: 2.962856 Loss2: 1.404240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.355400 Loss1: 2.965861 Loss2: 1.389540 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.241667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.330692 Loss1: 2.966116 Loss2: 1.364576 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.355749 Loss1: 2.963886 Loss2: 1.391863 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.264583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.885193 Loss1: 3.459017 Loss2: 1.426176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.607108 Loss1: 3.217585 Loss2: 1.389523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.586711 Loss1: 3.193312 Loss2: 1.393399
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.657619 Loss1: 3.798494 Loss2: 1.859124
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.525522 Loss1: 3.129424 Loss2: 1.396099
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.770299 Loss1: 3.347837 Loss2: 1.422462
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.551641 Loss1: 3.155655 Loss2: 1.395986
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.604227 Loss1: 3.203908 Loss2: 1.400319
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.501517 Loss1: 3.104254 Loss2: 1.397263
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.541602 Loss1: 3.140371 Loss2: 1.401230
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.444893 Loss1: 3.047870 Loss2: 1.397023
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.497455 Loss1: 3.098284 Loss2: 1.399171
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.473847 Loss1: 3.064856 Loss2: 1.408991
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.511160 Loss1: 3.102533 Loss2: 1.408626
-(DefaultActor pid=3765) >> Training accuracy: 0.226042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.487462 Loss1: 3.073603 Loss2: 1.413859
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.435634 Loss1: 3.030099 Loss2: 1.405535
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.314151 Loss1: 2.907370 Loss2: 1.406780
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.402080 Loss1: 2.979253 Loss2: 1.422827
-(DefaultActor pid=3764) >> Training accuracy: 0.268750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 17:32:29,490][flwr][DEBUG] - fit_round 9 received 50 results and 0 failures
-INFO flwr 2023-10-08 17:33:10,854 | server.py:125 | fit progress: (9, 4.2343795116717065, {'accuracy': 0.0634}, 20498.632143409002)
->> Test accuracy: 0.063400
-[2023-10-08 17:33:10,854][flwr][INFO] - fit progress: (9, 4.2343795116717065, {'accuracy': 0.0634}, 20498.632143409002)
-DEBUG flwr 2023-10-08 17:33:10,854 | server.py:173 | evaluate_round 9: strategy sampled 50 clients (out of 50)
-[2023-10-08 17:33:10,854][flwr][DEBUG] - evaluate_round 9: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 17:42:12,578 | server.py:187 | evaluate_round 9 received 50 results and 0 failures
-[2023-10-08 17:42:12,578][flwr][DEBUG] - evaluate_round 9 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 17:42:12,578 | server.py:222 | fit_round 10: strategy sampled 50 clients (out of 50)
-[2023-10-08 17:42:12,578][flwr][DEBUG] - fit_round 10: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.490547 Loss1: 3.657879 Loss2: 1.832668
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.608509 Loss1: 3.174912 Loss2: 1.433597
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.468246 Loss1: 3.073031 Loss2: 1.395215
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.719433 Loss1: 3.736987 Loss2: 1.982446
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.412262 Loss1: 3.004707 Loss2: 1.407555
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.372880 Loss1: 2.969746 Loss2: 1.403134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.265336 Loss1: 2.862289 Loss2: 1.403046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.554800 Loss1: 3.095774 Loss2: 1.459026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.509914 Loss1: 3.050159 Loss2: 1.459754 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.511482 Loss1: 3.048255 Loss2: 1.463227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.479134 Loss1: 2.997247 Loss2: 1.481887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.395708 Loss1: 2.920767 Loss2: 1.474940 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.290039
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.376722 Loss1: 3.461251 Loss2: 1.915471 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.262019
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.373269 Loss1: 2.970722 Loss2: 1.402547 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.319090 Loss1: 2.942010 Loss2: 1.377080
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.445089 Loss1: 3.663626 Loss2: 1.781462
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.559748 Loss1: 3.173599 Loss2: 1.386149 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.395149 Loss1: 3.049083 Loss2: 1.346066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.323018 Loss1: 2.990325 Loss2: 1.332693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.330251 Loss1: 2.990885 Loss2: 1.339366 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.282664 Loss1: 2.930126 Loss2: 1.352538 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.289583
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.126491 Loss1: 2.725589 Loss2: 1.400902
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.194973 Loss1: 2.840512 Loss2: 1.354462
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.211344 Loss1: 2.851307 Loss2: 1.360036
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.137764 Loss1: 2.775106 Loss2: 1.362658
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.095132 Loss1: 2.714591 Loss2: 1.380541
-(DefaultActor pid=3764) >> Training accuracy: 0.297917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.720532 Loss1: 3.904563 Loss2: 1.815969
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.696883 Loss1: 3.320260 Loss2: 1.376623
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.512905 Loss1: 3.170187 Loss2: 1.342718
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.433279 Loss1: 3.104389 Loss2: 1.328890
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.450482 Loss1: 3.491134 Loss2: 1.959348
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.375839 Loss1: 3.041575 Loss2: 1.334263
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.605928 Loss1: 3.114339 Loss2: 1.491589
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.357902 Loss1: 3.022562 Loss2: 1.335341
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.373571 Loss1: 2.913128 Loss2: 1.460442
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.311475 Loss1: 2.972261 Loss2: 1.339214
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.321956 Loss1: 2.886254 Loss2: 1.435702
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.287191 Loss1: 2.938836 Loss2: 1.348356
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.256100 Loss1: 2.804515 Loss2: 1.451585
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.241738 Loss1: 2.887090 Loss2: 1.354648
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.208129 Loss1: 2.754756 Loss2: 1.453373
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.222899 Loss1: 2.881664 Loss2: 1.341234
-(DefaultActor pid=3765) >> Training accuracy: 0.270833
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.201161 Loss1: 2.752310 Loss2: 1.448851
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.187039 Loss1: 2.721831 Loss2: 1.465208
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.116930 Loss1: 2.653271 Loss2: 1.463659
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.111304 Loss1: 2.644543 Loss2: 1.466761
-(DefaultActor pid=3764) >> Training accuracy: 0.343750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.679110 Loss1: 3.823044 Loss2: 1.856066
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.800754 Loss1: 3.378435 Loss2: 1.422319
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.603693 Loss1: 3.210681 Loss2: 1.393013
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.544942 Loss1: 3.157418 Loss2: 1.387524
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.662154 Loss1: 3.795543 Loss2: 1.866611
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.495993 Loss1: 3.115059 Loss2: 1.380934
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.777170 Loss1: 3.320551 Loss2: 1.456619
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.635183 Loss1: 3.213589 Loss2: 1.421594
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.428538 Loss1: 3.040631 Loss2: 1.387907
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.617737 Loss1: 3.196991 Loss2: 1.420746
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.427921 Loss1: 3.015843 Loss2: 1.412078
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.531672 Loss1: 3.116436 Loss2: 1.415236
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.481294 Loss1: 3.065999 Loss2: 1.415296
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.534800 Loss1: 3.111497 Loss2: 1.423303
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.352168 Loss1: 2.940303 Loss2: 1.411865
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.360133 Loss1: 2.942154 Loss2: 1.417978 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.281250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.354233 Loss1: 2.921004 Loss2: 1.433229 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.259375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.682875 Loss1: 3.778213 Loss2: 1.904662 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.444787 Loss1: 3.009043 Loss2: 1.435744 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.378877 Loss1: 2.951990 Loss2: 1.426887
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.676869 Loss1: 3.805412 Loss2: 1.871457
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.285868 Loss1: 2.873739 Loss2: 1.412128
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.830969 Loss1: 3.384892 Loss2: 1.446077
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.354435 Loss1: 2.927096 Loss2: 1.427339
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.595805 Loss1: 3.173652 Loss2: 1.422154
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.357990 Loss1: 2.932614 Loss2: 1.425376
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.512498 Loss1: 3.102306 Loss2: 1.410193
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.263162 Loss1: 2.831720 Loss2: 1.431442
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.546719 Loss1: 3.113997 Loss2: 1.432722
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.244560 Loss1: 2.810900 Loss2: 1.433660
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.481684 Loss1: 3.065448 Loss2: 1.416236
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.231805 Loss1: 2.791980 Loss2: 1.439824
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.435882 Loss1: 3.015230 Loss2: 1.420653
-(DefaultActor pid=3765) >> Training accuracy: 0.266667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.364706 Loss1: 2.943616 Loss2: 1.421090
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.352230 Loss1: 2.910357 Loss2: 1.441873
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.360139 Loss1: 2.916837 Loss2: 1.443302
-(DefaultActor pid=3764) >> Training accuracy: 0.244792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.576628 Loss1: 3.706997 Loss2: 1.869631
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.825664 Loss1: 3.393864 Loss2: 1.431799
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.634858 Loss1: 3.224633 Loss2: 1.410225
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.533348 Loss1: 3.126704 Loss2: 1.406645
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.682081 Loss1: 3.770246 Loss2: 1.911836
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.790707 Loss1: 3.328547 Loss2: 1.462160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.632509 Loss1: 3.203358 Loss2: 1.429151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.575401 Loss1: 3.155569 Loss2: 1.419832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.504187 Loss1: 3.077000 Loss2: 1.427186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.470175 Loss1: 3.048020 Loss2: 1.422155 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.267708
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.340447 Loss1: 2.893636 Loss2: 1.446811
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.470180 Loss1: 3.043820 Loss2: 1.426359
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.390114 Loss1: 2.950068 Loss2: 1.440046
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.336272 Loss1: 2.901190 Loss2: 1.435082
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.361699 Loss1: 2.914046 Loss2: 1.447653
-(DefaultActor pid=3764) >> Training accuracy: 0.252083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.610469 Loss1: 3.833945 Loss2: 1.776524
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.829599 Loss1: 3.439581 Loss2: 1.390018
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.665699 Loss1: 3.303617 Loss2: 1.362082
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.570547 Loss1: 3.209324 Loss2: 1.361223
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.622267 Loss1: 3.865443 Loss2: 1.756824
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.569313 Loss1: 3.192800 Loss2: 1.376512
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.764374 Loss1: 3.397590 Loss2: 1.366784
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.537144 Loss1: 3.153360 Loss2: 1.383784
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.610563 Loss1: 3.273047 Loss2: 1.337516
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.484051 Loss1: 3.105212 Loss2: 1.378839
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.535223 Loss1: 3.195959 Loss2: 1.339264
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.518482 Loss1: 3.126181 Loss2: 1.392301
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.519395 Loss1: 3.176951 Loss2: 1.342444
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.436014 Loss1: 3.035196 Loss2: 1.400817
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.474287 Loss1: 3.124804 Loss2: 1.349483
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.417834 Loss1: 3.026392 Loss2: 1.391442
-(DefaultActor pid=3765) >> Training accuracy: 0.246094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.446855 Loss1: 3.101883 Loss2: 1.344972
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.423910 Loss1: 3.060786 Loss2: 1.363124
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.443498 Loss1: 3.083905 Loss2: 1.359593
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.400028 Loss1: 3.029763 Loss2: 1.370265
-(DefaultActor pid=3764) >> Training accuracy: 0.232422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.601104 Loss1: 3.753640 Loss2: 1.847463
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.694757 Loss1: 3.268076 Loss2: 1.426681
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.509627 Loss1: 3.116014 Loss2: 1.393613
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.600015 Loss1: 3.694911 Loss2: 1.905104
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.506221 Loss1: 3.095271 Loss2: 1.410950
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.672834 Loss1: 3.225341 Loss2: 1.447492
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.462820 Loss1: 3.052183 Loss2: 1.410637
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.507884 Loss1: 3.095220 Loss2: 1.412664
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.398135 Loss1: 3.001474 Loss2: 1.396661
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.361195 Loss1: 2.962287 Loss2: 1.398908 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.413483 Loss1: 3.003010 Loss2: 1.410474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.268127 Loss1: 2.869436 Loss2: 1.398691 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.222946 Loss1: 2.807436 Loss2: 1.415511 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.273897
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.277135 Loss1: 2.848659 Loss2: 1.428476 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.289583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.794312 Loss1: 3.866240 Loss2: 1.928072
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.972043 Loss1: 3.495656 Loss2: 1.476387
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.809533 Loss1: 3.376406 Loss2: 1.433127
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.688100 Loss1: 3.254974 Loss2: 1.433126
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.714361 Loss1: 3.877039 Loss2: 1.837322
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.875133 Loss1: 3.439106 Loss2: 1.436027 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.656325 Loss1: 3.266835 Loss2: 1.389490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.549607 Loss1: 3.107633 Loss2: 1.441974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.494377 Loss1: 3.053722 Loss2: 1.440655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.437605 Loss1: 2.993233 Loss2: 1.444371 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.237723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.471484 Loss1: 3.096049 Loss2: 1.375435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.386112 Loss1: 2.991649 Loss2: 1.394462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.323507 Loss1: 2.928168 Loss2: 1.395339
-(DefaultActor pid=3764) >> Training accuracy: 0.266602
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.527264 Loss1: 3.583177 Loss2: 1.944087
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.737954 Loss1: 3.232187 Loss2: 1.505767
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.555113 Loss1: 3.096192 Loss2: 1.458921
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.421675 Loss1: 2.979838 Loss2: 1.441836
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.431565 Loss1: 2.978530 Loss2: 1.453036
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.472136 Loss1: 3.520656 Loss2: 1.951480
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.350358 Loss1: 2.901430 Loss2: 1.448928
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.367445 Loss1: 2.902338 Loss2: 1.465107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.473551 Loss1: 3.032581 Loss2: 1.440970
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.371972 Loss1: 2.913053 Loss2: 1.458918
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.340190 Loss1: 2.891306 Loss2: 1.448885
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.286316 Loss1: 2.824334 Loss2: 1.461982
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.270115 Loss1: 2.826071 Loss2: 1.444044
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.230959 Loss1: 2.776473 Loss2: 1.454486
-(DefaultActor pid=3765) >> Training accuracy: 0.251042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.286012 Loss1: 2.830060 Loss2: 1.455951 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.180282 Loss1: 2.727398 Loss2: 1.452884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.779049 Loss1: 3.751105 Loss2: 2.027944
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.124553 Loss1: 2.669446 Loss2: 1.455107
-(DefaultActor pid=3764) >> Training accuracy: 0.299805
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.745658 Loss1: 3.258182 Loss2: 1.487476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.600650 Loss1: 3.110266 Loss2: 1.490384 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.595360 Loss1: 3.096040 Loss2: 1.499321
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.488850 Loss1: 3.583361 Loss2: 1.905489
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.526355 Loss1: 3.036418 Loss2: 1.489937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.349161 Loss1: 2.908895 Loss2: 1.440266 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.257906 Loss1: 2.838853 Loss2: 1.419053 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.246875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.474013 Loss1: 2.956296 Loss2: 1.517717
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.175074 Loss1: 2.756038 Loss2: 1.419036
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.187963 Loss1: 2.769778 Loss2: 1.418184
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.130349 Loss1: 2.712624 Loss2: 1.417725
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.140697 Loss1: 2.718200 Loss2: 1.422497
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.119688 Loss1: 2.697581 Loss2: 1.422107
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.595066 Loss1: 3.644091 Loss2: 1.950975
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.046250 Loss1: 2.616421 Loss2: 1.429829
-(DefaultActor pid=3764) >> Training accuracy: 0.302083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.408505 Loss1: 2.983119 Loss2: 1.425386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.272555 Loss1: 2.866654 Loss2: 1.405900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.218716 Loss1: 2.807117 Loss2: 1.411599 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.142196 Loss1: 2.713848 Loss2: 1.428349 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.171341 Loss1: 2.751390 Loss2: 1.419951 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.072093 Loss1: 2.651140 Loss2: 1.420953 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.324519
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.327206 Loss1: 2.975527 Loss2: 1.351680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.361723 Loss1: 3.001241 Loss2: 1.360482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.284547 Loss1: 2.912543 Loss2: 1.372004
-(DefaultActor pid=3765) Epoch: 0 Loss: 6.043284 Loss1: 3.994438 Loss2: 2.048846
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.978825 Loss1: 3.429846 Loss2: 1.548980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.233025 Loss1: 2.848030 Loss2: 1.384995
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.786470 Loss1: 3.291998 Loss2: 1.494472
-(DefaultActor pid=3764) >> Training accuracy: 0.280208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.684866 Loss1: 3.195074 Loss2: 1.489792
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.667822 Loss1: 3.170108 Loss2: 1.497715
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.636948 Loss1: 3.147558 Loss2: 1.489390
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.612948 Loss1: 3.116791 Loss2: 1.496157
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.570458 Loss1: 3.074732 Loss2: 1.495726
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.483323 Loss1: 3.664341 Loss2: 1.818982
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.595671 Loss1: 3.170762 Loss2: 1.424909 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.229911
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.531421 Loss1: 3.025454 Loss2: 1.505967
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.421492 Loss1: 3.038975 Loss2: 1.382517
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.351489 Loss1: 2.978796 Loss2: 1.372693
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.299247 Loss1: 2.938647 Loss2: 1.360600
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.247848 Loss1: 2.878182 Loss2: 1.369665
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.217575 Loss1: 2.833275 Loss2: 1.384300
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.474878 Loss1: 3.700997 Loss2: 1.773880
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.187149 Loss1: 2.803456 Loss2: 1.383693
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.574611 Loss1: 3.164531 Loss2: 1.410080
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.179975 Loss1: 2.784556 Loss2: 1.395418
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.126130 Loss1: 2.747820 Loss2: 1.378310
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.398833 Loss1: 3.014412 Loss2: 1.384421
-(DefaultActor pid=3764) >> Training accuracy: 0.281250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.350179 Loss1: 2.963128 Loss2: 1.387050
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.303776 Loss1: 2.910647 Loss2: 1.393130
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.307796 Loss1: 2.926048 Loss2: 1.381748
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.219083 Loss1: 2.823602 Loss2: 1.395481
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.724944 Loss1: 3.751462 Loss2: 1.973483
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.190671 Loss1: 2.785446 Loss2: 1.405225
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.184901 Loss1: 2.776329 Loss2: 1.408572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.474774 Loss1: 3.085414 Loss2: 1.389360 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.267578
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.379953 Loss1: 2.988473 Loss2: 1.391480 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.237030 Loss1: 2.836337 Loss2: 1.400693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.278543 Loss1: 2.860532 Loss2: 1.418011 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.278646
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.808355 Loss1: 3.286367 Loss2: 1.521988 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.554811 Loss1: 3.077945 Loss2: 1.476866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.581118 Loss1: 3.721138 Loss2: 1.859980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.722557 Loss1: 3.310184 Loss2: 1.412373 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.584901 Loss1: 3.200723 Loss2: 1.384178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.514988 Loss1: 3.139173 Loss2: 1.375815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.427795 Loss1: 3.047863 Loss2: 1.379932 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.265625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.400813 Loss1: 3.019390 Loss2: 1.381423 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.361741 Loss1: 2.966433 Loss2: 1.395308 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.291184 Loss1: 2.896704 Loss2: 1.394480 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.243750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.594219 Loss1: 3.160668 Loss2: 1.433551 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.384807 Loss1: 2.985157 Loss2: 1.399650 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.328914 Loss1: 2.922232 Loss2: 1.406682
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.510330 Loss1: 3.625880 Loss2: 1.884450
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.684885 Loss1: 3.248463 Loss2: 1.436422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.487235 Loss1: 3.104277 Loss2: 1.382958 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.374179 Loss1: 3.002234 Loss2: 1.371945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.382237 Loss1: 2.997138 Loss2: 1.385099 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.288542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.327339 Loss1: 2.938163 Loss2: 1.389177 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.312689 Loss1: 2.910183 Loss2: 1.402506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.166412 Loss1: 2.777234 Loss2: 1.389178 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.300000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.367435 Loss1: 2.921909 Loss2: 1.445526 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.289506 Loss1: 2.842557 Loss2: 1.446949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.728558 Loss1: 3.706765 Loss2: 2.021793
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.187613 Loss1: 2.736950 Loss2: 1.450663
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.640068 Loss1: 3.059842 Loss2: 1.580226
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.145102 Loss1: 2.696437 Loss2: 1.448665
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.431615 Loss1: 2.906043 Loss2: 1.525571
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.117932 Loss1: 2.655756 Loss2: 1.462176
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.316672 Loss1: 2.793249 Loss2: 1.523423
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.175292 Loss1: 2.698750 Loss2: 1.476542
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.323122 Loss1: 2.789977 Loss2: 1.533145
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.079264 Loss1: 2.620170 Loss2: 1.459094
-(DefaultActor pid=3765) >> Training accuracy: 0.283333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.231300 Loss1: 2.710566 Loss2: 1.520734 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.191656 Loss1: 2.656539 Loss2: 1.535117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.128143 Loss1: 2.612235 Loss2: 1.515908
-(DefaultActor pid=3764) >> Training accuracy: 0.378125
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.632347 Loss1: 3.749747 Loss2: 1.882600
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.777262 Loss1: 3.327409 Loss2: 1.449853
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.624656 Loss1: 3.212395 Loss2: 1.412261
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.527811 Loss1: 3.132504 Loss2: 1.395306
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.463888 Loss1: 3.059957 Loss2: 1.403931
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.630234 Loss1: 3.725014 Loss2: 1.905220
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.465559 Loss1: 3.051511 Loss2: 1.414048
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.705034 Loss1: 3.238883 Loss2: 1.466151
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.463427 Loss1: 3.045448 Loss2: 1.417979
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.525963 Loss1: 3.104564 Loss2: 1.421399
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.371816 Loss1: 2.954611 Loss2: 1.417205
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.467722 Loss1: 3.039654 Loss2: 1.428069
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.396507 Loss1: 2.971884 Loss2: 1.424623
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.401042 Loss1: 2.981337 Loss2: 1.419705
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.391933 Loss1: 2.959854 Loss2: 1.432079
-(DefaultActor pid=3765) >> Training accuracy: 0.222917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.308957 Loss1: 2.864427 Loss2: 1.444530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.264896 Loss1: 2.825066 Loss2: 1.439830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.233537 Loss1: 2.806410 Loss2: 1.427126
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.588558 Loss1: 3.781320 Loss2: 1.807238
-(DefaultActor pid=3764) >> Training accuracy: 0.272917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.717557 Loss1: 3.310348 Loss2: 1.407209
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.566462 Loss1: 3.206496 Loss2: 1.359966
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.469648 Loss1: 3.114319 Loss2: 1.355329
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.409802 Loss1: 3.047271 Loss2: 1.362531
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.810967 Loss1: 3.834629 Loss2: 1.976339
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.368056 Loss1: 3.000248 Loss2: 1.367808
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.311918 Loss1: 2.945523 Loss2: 1.366395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.326640 Loss1: 2.948348 Loss2: 1.378293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.320698 Loss1: 2.949448 Loss2: 1.371250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.293254 Loss1: 2.915414 Loss2: 1.377839 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.263542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.416780 Loss1: 2.936227 Loss2: 1.480553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.336502 Loss1: 2.856586 Loss2: 1.479916 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.291295
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.499451 Loss1: 3.701365 Loss2: 1.798086 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.449455 Loss1: 3.108238 Loss2: 1.341217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.359309 Loss1: 3.014971 Loss2: 1.344338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.322606 Loss1: 2.972239 Loss2: 1.350367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.384581 Loss1: 3.027657 Loss2: 1.356925 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.284936 Loss1: 2.940483 Loss2: 1.344454 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.205835 Loss1: 2.845522 Loss2: 1.360313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.203110 Loss1: 2.847220 Loss2: 1.355890 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.262500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.513354 Loss1: 3.090446 Loss2: 1.422908 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.381876 Loss1: 2.939240 Loss2: 1.442636 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.250000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.796063 Loss1: 3.261778 Loss2: 1.534284 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.537174 Loss1: 3.069003 Loss2: 1.468171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.454329 Loss1: 2.982304 Loss2: 1.472025
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.727298 Loss1: 3.907898 Loss2: 1.819401
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.818438 Loss1: 3.422047 Loss2: 1.396391 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.699435 Loss1: 3.322513 Loss2: 1.376922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.612242 Loss1: 3.242848 Loss2: 1.369394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.595289 Loss1: 3.202352 Loss2: 1.392937 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.258333
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.362882 Loss1: 2.875558 Loss2: 1.487324
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.563731 Loss1: 3.175652 Loss2: 1.388080
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.538595 Loss1: 3.149453 Loss2: 1.389142
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.526759 Loss1: 3.121500 Loss2: 1.405260
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.475269 Loss1: 3.070336 Loss2: 1.404934
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.411118 Loss1: 3.008847 Loss2: 1.402271
-(DefaultActor pid=3764) >> Training accuracy: 0.201042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.594228 Loss1: 3.682998 Loss2: 1.911230
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.707294 Loss1: 3.247546 Loss2: 1.459747
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.520578 Loss1: 3.103023 Loss2: 1.417554
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.448639 Loss1: 3.041606 Loss2: 1.407033
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.394179 Loss1: 2.982253 Loss2: 1.411925
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.478075 Loss1: 3.655541 Loss2: 1.822534
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.708983 Loss1: 3.275788 Loss2: 1.433195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.534465 Loss1: 3.143586 Loss2: 1.390879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.462973 Loss1: 3.079770 Loss2: 1.383203 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.339290 Loss1: 2.962001 Loss2: 1.377288 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.279167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.411428 Loss1: 3.028583 Loss2: 1.382845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.242863 Loss1: 2.851386 Loss2: 1.391477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.250783 Loss1: 2.842087 Loss2: 1.408696 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.278320
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.429972 Loss1: 2.966957 Loss2: 1.463016 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.292302 Loss1: 2.832961 Loss2: 1.459341 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.295521 Loss1: 2.842206 Loss2: 1.453315
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.394688 Loss1: 3.562679 Loss2: 1.832010
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.179949 Loss1: 2.728115 Loss2: 1.451833
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.560098 Loss1: 3.160656 Loss2: 1.399442
-DEBUG flwr 2023-10-08 18:10:48,233 | server.py:236 | fit_round 10 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.188297 Loss1: 2.735032 Loss2: 1.453265
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.316348 Loss1: 2.942408 Loss2: 1.373940
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.175935 Loss1: 2.719123 Loss2: 1.456812
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.274779 Loss1: 2.906863 Loss2: 1.367916
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.156534 Loss1: 2.697511 Loss2: 1.459023
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.233258 Loss1: 2.862655 Loss2: 1.370603
-(DefaultActor pid=3765) >> Training accuracy: 0.315625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.127497 Loss1: 2.761128 Loss2: 1.366369
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.122948 Loss1: 2.759441 Loss2: 1.363506
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.077047 Loss1: 2.700835 Loss2: 1.376212
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.088078 Loss1: 2.708499 Loss2: 1.379579
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.039217 Loss1: 2.672773 Loss2: 1.366444
-(DefaultActor pid=3764) >> Training accuracy: 0.330208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.394720 Loss1: 3.543457 Loss2: 1.851262
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.680211 Loss1: 3.267463 Loss2: 1.412748
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.455865 Loss1: 3.068214 Loss2: 1.387651
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.385407 Loss1: 2.979100 Loss2: 1.406307
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.378750 Loss1: 2.996437 Loss2: 1.382313
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.698202 Loss1: 3.781303 Loss2: 1.916899
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.303235 Loss1: 2.909454 Loss2: 1.393781
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.813492 Loss1: 3.299170 Loss2: 1.514322
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.203189 Loss1: 2.821061 Loss2: 1.382127
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.643188 Loss1: 3.168713 Loss2: 1.474475
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.295624 Loss1: 2.884248 Loss2: 1.411376
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.578566 Loss1: 3.111164 Loss2: 1.467402
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.116480 Loss1: 2.713074 Loss2: 1.403407
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.173383 Loss1: 2.771285 Loss2: 1.402097
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.533325 Loss1: 3.059615 Loss2: 1.473710
-(DefaultActor pid=3765) >> Training accuracy: 0.298958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.547041 Loss1: 3.061703 Loss2: 1.485338
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.505237 Loss1: 3.021145 Loss2: 1.484091
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.444359 Loss1: 2.964666 Loss2: 1.479693
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.408370 Loss1: 2.927613 Loss2: 1.480757
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.622188 Loss1: 3.684749 Loss2: 1.937439
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.365241 Loss1: 2.881613 Loss2: 1.483629
-(DefaultActor pid=3764) >> Training accuracy: 0.250977
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.679543 Loss1: 3.208461 Loss2: 1.471082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.525289 Loss1: 3.060094 Loss2: 1.465195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.460247 Loss1: 2.988795 Loss2: 1.471452
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.584363 Loss1: 3.665743 Loss2: 1.918619
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.678379 Loss1: 3.222017 Loss2: 1.456362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.513811 Loss1: 3.082691 Loss2: 1.431120 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.454077 Loss1: 3.022949 Loss2: 1.431129 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.281250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.335101 Loss1: 2.844803 Loss2: 1.490299
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.480319 Loss1: 3.036630 Loss2: 1.443689
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.363343 Loss1: 2.926205 Loss2: 1.437137
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.287945 Loss1: 2.847384 Loss2: 1.440561
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.283769 Loss1: 2.833984 Loss2: 1.449785
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.291862 Loss1: 2.846277 Loss2: 1.445585
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.263851 Loss1: 2.810779 Loss2: 1.453073
-(DefaultActor pid=3764) >> Training accuracy: 0.281250
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 18:10:48,233][flwr][DEBUG] - fit_round 10 received 50 results and 0 failures
-INFO flwr 2023-10-08 18:11:29,549 | server.py:125 | fit progress: (10, 4.114291173581498, {'accuracy': 0.0769}, 22797.328031038)
->> Test accuracy: 0.076900
-[2023-10-08 18:11:29,549][flwr][INFO] - fit progress: (10, 4.114291173581498, {'accuracy': 0.0769}, 22797.328031038)
-DEBUG flwr 2023-10-08 18:11:29,550 | server.py:173 | evaluate_round 10: strategy sampled 50 clients (out of 50)
-[2023-10-08 18:11:29,550][flwr][DEBUG] - evaluate_round 10: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 18:20:34,870 | server.py:187 | evaluate_round 10 received 50 results and 0 failures
-[2023-10-08 18:20:34,870][flwr][DEBUG] - evaluate_round 10 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 18:20:34,871 | server.py:222 | fit_round 11: strategy sampled 50 clients (out of 50)
-[2023-10-08 18:20:34,871][flwr][DEBUG] - fit_round 11: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.447740 Loss1: 3.525053 Loss2: 1.922687
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.434265 Loss1: 2.966626 Loss2: 1.467639
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.244946 Loss1: 2.825804 Loss2: 1.419143
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.128324 Loss1: 2.710900 Loss2: 1.417424
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.598630 Loss1: 3.072165 Loss2: 1.526465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.303037 Loss1: 2.850390 Loss2: 1.452647 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.187674 Loss1: 2.730020 Loss2: 1.457653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.144572 Loss1: 2.690031 Loss2: 1.454540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.123314 Loss1: 2.648075 Loss2: 1.475239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.077006 Loss1: 2.616945 Loss2: 1.460061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.921319 Loss1: 2.471353 Loss2: 1.449965
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.046273 Loss1: 2.558448 Loss2: 1.487825
-(DefaultActor pid=3765) >> Training accuracy: 0.308333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.022440 Loss1: 2.539124 Loss2: 1.483317
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.057734 Loss1: 2.572590 Loss2: 1.485143
-(DefaultActor pid=3764) >> Training accuracy: 0.370192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.712925 Loss1: 3.776170 Loss2: 1.936756
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.836736 Loss1: 3.351638 Loss2: 1.485098
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.624766 Loss1: 3.159064 Loss2: 1.465702
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.439217 Loss1: 3.560886 Loss2: 1.878331
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.675273 Loss1: 3.248524 Loss2: 1.426749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.533511 Loss1: 3.126423 Loss2: 1.407088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.455148 Loss1: 3.067598 Loss2: 1.387550 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.419909 Loss1: 3.026692 Loss2: 1.393217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.318385 Loss1: 2.922499 Loss2: 1.395887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.332007 Loss1: 2.917216 Loss2: 1.414791 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.274414
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.318371 Loss1: 2.893369 Loss2: 1.425002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.269468 Loss1: 2.850907 Loss2: 1.418561 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.269792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.315198 Loss1: 3.380105 Loss2: 1.935093
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.377056 Loss1: 2.903469 Loss2: 1.473587
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.203190 Loss1: 2.759918 Loss2: 1.443272
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.147798 Loss1: 2.706427 Loss2: 1.441370
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.421306 Loss1: 3.511200 Loss2: 1.910106
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.625484 Loss1: 3.151107 Loss2: 1.474377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.392339 Loss1: 2.949259 Loss2: 1.443079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.361902 Loss1: 2.924406 Loss2: 1.437496 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.347016 Loss1: 2.890498 Loss2: 1.456518 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.295376 Loss1: 2.843763 Loss2: 1.451613 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.309375
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.951254 Loss1: 2.496803 Loss2: 1.454451
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.260694 Loss1: 2.794671 Loss2: 1.466023
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.219997 Loss1: 2.762726 Loss2: 1.457271
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.207523 Loss1: 2.744474 Loss2: 1.463049
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.236142 Loss1: 2.759720 Loss2: 1.476422
-(DefaultActor pid=3764) >> Training accuracy: 0.292708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.499980 Loss1: 3.583586 Loss2: 1.916395
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.699924 Loss1: 3.236773 Loss2: 1.463152
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.479326 Loss1: 3.054654 Loss2: 1.424672
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.910708 Loss1: 3.904617 Loss2: 2.006091
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.400429 Loss1: 2.978248 Loss2: 1.422180
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.922684 Loss1: 3.400343 Loss2: 1.522341
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.349337 Loss1: 2.929187 Loss2: 1.420149
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.774353 Loss1: 3.303428 Loss2: 1.470925
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.274876 Loss1: 2.850771 Loss2: 1.424105
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.279895 Loss1: 2.844226 Loss2: 1.435669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.233184 Loss1: 2.798407 Loss2: 1.434777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.236229 Loss1: 2.801671 Loss2: 1.434558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.187722 Loss1: 2.747082 Loss2: 1.440641 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.288542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.390624 Loss1: 2.912571 Loss2: 1.478053 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.267857
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.480075 Loss1: 3.560720 Loss2: 1.919354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.433094 Loss1: 2.993674 Loss2: 1.439420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.376754 Loss1: 2.946039 Loss2: 1.430715
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.749433 Loss1: 3.677922 Loss2: 2.071512
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.787152 Loss1: 3.261177 Loss2: 1.525975
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.286099 Loss1: 2.847660 Loss2: 1.438440
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.607651 Loss1: 3.105618 Loss2: 1.502033
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.181427 Loss1: 2.734134 Loss2: 1.447293
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.192835 Loss1: 2.749676 Loss2: 1.443159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.153747 Loss1: 2.702310 Loss2: 1.451437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.126781 Loss1: 2.675929 Loss2: 1.450852 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.151889 Loss1: 2.674390 Loss2: 1.477499 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.320833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.289273 Loss1: 2.782240 Loss2: 1.507033 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.312500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.690743 Loss1: 3.706613 Loss2: 1.984130
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.822636 Loss1: 3.314879 Loss2: 1.507757
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.497700 Loss1: 3.028357 Loss2: 1.469343
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.501848 Loss1: 3.041780 Loss2: 1.460067
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.565705 Loss1: 3.644022 Loss2: 1.921683
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.668073 Loss1: 3.199153 Loss2: 1.468920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.523692 Loss1: 3.079619 Loss2: 1.444073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.449715 Loss1: 3.010943 Loss2: 1.438772 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.415860 Loss1: 2.981932 Loss2: 1.433928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.331532 Loss1: 2.903386 Loss2: 1.428146 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.275000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.378170 Loss1: 2.933189 Loss2: 1.444982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.307249 Loss1: 2.865122 Loss2: 1.442126 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.277344
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.497467 Loss1: 3.712789 Loss2: 1.784678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.266638 Loss1: 2.941812 Loss2: 1.324826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.464146 Loss1: 3.558785 Loss2: 1.905361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.610208 Loss1: 3.134616 Loss2: 1.475592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.416720 Loss1: 2.980009 Loss2: 1.436711 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.328027 Loss1: 2.882695 Loss2: 1.445332 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.272340 Loss1: 2.826777 Loss2: 1.445562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.241576 Loss1: 2.778983 Loss2: 1.462594 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.318750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.178871 Loss1: 2.720068 Loss2: 1.458803 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.116510 Loss1: 2.643711 Loss2: 1.472799 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.330208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.705118 Loss1: 3.234506 Loss2: 1.470612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.469481 Loss1: 3.038787 Loss2: 1.430694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.412238 Loss1: 2.970078 Loss2: 1.442160
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.600072 Loss1: 3.708742 Loss2: 1.891330
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.470330 Loss1: 3.012039 Loss2: 1.458292
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.701201 Loss1: 3.219822 Loss2: 1.481379
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.437895 Loss1: 3.007038 Loss2: 1.430857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.398152 Loss1: 2.964418 Loss2: 1.433734 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.368983 Loss1: 2.938825 Loss2: 1.430159 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.252083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.335777 Loss1: 2.884974 Loss2: 1.450803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.241644 Loss1: 2.795011 Loss2: 1.446633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.389226 Loss1: 3.548069 Loss2: 1.841157 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.295956
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.142749 Loss1: 2.780308 Loss2: 1.362441 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.044974 Loss1: 2.670566 Loss2: 1.374408 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.023060 Loss1: 2.668429 Loss2: 1.354631
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.547384 Loss1: 3.654024 Loss2: 1.893360
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.573609 Loss1: 3.118768 Loss2: 1.454841 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.946688 Loss1: 2.577955 Loss2: 1.368734
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.376770 Loss1: 2.988253 Loss2: 1.388517
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.933004 Loss1: 2.559775 Loss2: 1.373229
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.307257 Loss1: 2.896102 Loss2: 1.411155
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.943135 Loss1: 2.560015 Loss2: 1.383120
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.261130 Loss1: 2.856315 Loss2: 1.404815
-(DefaultActor pid=3765) >> Training accuracy: 0.404167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.249936 Loss1: 2.834865 Loss2: 1.415071
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.247072 Loss1: 2.824527 Loss2: 1.422545
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.220437 Loss1: 2.791651 Loss2: 1.428786
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.124281 Loss1: 2.702401 Loss2: 1.421880
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.127057 Loss1: 2.695649 Loss2: 1.431407
-(DefaultActor pid=3764) >> Training accuracy: 0.301339
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.538120 Loss1: 3.592758 Loss2: 1.945362
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.670517 Loss1: 3.207242 Loss2: 1.463275
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.504119 Loss1: 3.068643 Loss2: 1.435476
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.368859 Loss1: 2.955412 Loss2: 1.413447
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.336106 Loss1: 2.911320 Loss2: 1.424787
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.607795 Loss1: 3.682204 Loss2: 1.925591
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.623582 Loss1: 3.147238 Loss2: 1.476344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.458855 Loss1: 3.024876 Loss2: 1.433979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.437721 Loss1: 3.005537 Loss2: 1.432184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.322668 Loss1: 2.904574 Loss2: 1.418093 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.308333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.348041 Loss1: 2.910064 Loss2: 1.437977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.183505 Loss1: 2.733140 Loss2: 1.450365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.237853 Loss1: 2.776458 Loss2: 1.461395 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.258333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.585150 Loss1: 3.065580 Loss2: 1.519571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.360175 Loss1: 2.875547 Loss2: 1.484628 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.296064 Loss1: 2.820456 Loss2: 1.475608
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.394305 Loss1: 3.549390 Loss2: 1.844914
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.599826 Loss1: 3.185667 Loss2: 1.414159 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.482656 Loss1: 3.098620 Loss2: 1.384036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.364378 Loss1: 2.992096 Loss2: 1.372283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.318561 Loss1: 2.935804 Loss2: 1.382757 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.337500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.254412 Loss1: 2.877628 Loss2: 1.376784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.216809 Loss1: 2.824366 Loss2: 1.392443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.139720 Loss1: 2.725467 Loss2: 1.414253 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.297917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.771036 Loss1: 3.313158 Loss2: 1.457877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.550525 Loss1: 3.132045 Loss2: 1.418480 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.489597 Loss1: 3.058683 Loss2: 1.430914
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.451880 Loss1: 3.582980 Loss2: 1.868900
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.487782 Loss1: 3.053631 Loss2: 1.434151
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.589962 Loss1: 3.167700 Loss2: 1.422261
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.441319 Loss1: 3.007667 Loss2: 1.433652
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.409169 Loss1: 3.027596 Loss2: 1.381573
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.314608 Loss1: 2.930440 Loss2: 1.384168
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.410990 Loss1: 2.966509 Loss2: 1.444480
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.227019 Loss1: 2.845935 Loss2: 1.381084
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.334062 Loss1: 2.885670 Loss2: 1.448391
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.238332 Loss1: 2.851465 Loss2: 1.386867
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.318629 Loss1: 2.869012 Loss2: 1.449618
-(DefaultActor pid=3765) >> Training accuracy: 0.286133
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.185716 Loss1: 2.791581 Loss2: 1.394135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.114031 Loss1: 2.733216 Loss2: 1.380815 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.280208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.747399 Loss1: 3.229156 Loss2: 1.518242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.466734 Loss1: 2.997120 Loss2: 1.469613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.346489 Loss1: 2.870234 Loss2: 1.476254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.335008 Loss1: 2.855188 Loss2: 1.479820 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.646674 Loss1: 3.189913 Loss2: 1.456761
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.340925 Loss1: 2.862438 Loss2: 1.478487
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.566999 Loss1: 3.118343 Loss2: 1.448656
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.339747 Loss1: 2.838953 Loss2: 1.500793
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.497096 Loss1: 3.043301 Loss2: 1.453795
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.320940 Loss1: 2.817194 Loss2: 1.503747
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.270761 Loss1: 2.767235 Loss2: 1.503526
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.514418 Loss1: 3.049945 Loss2: 1.464473
-(DefaultActor pid=3765) >> Training accuracy: 0.314583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.478579 Loss1: 2.999823 Loss2: 1.478755
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.454969 Loss1: 2.981027 Loss2: 1.473942
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.432456 Loss1: 2.957320 Loss2: 1.475136
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.387717 Loss1: 2.903810 Loss2: 1.483907
-(DefaultActor pid=3764) >> Training accuracy: 0.259766
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.600529 Loss1: 3.671233 Loss2: 1.929296
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.717769 Loss1: 3.254019 Loss2: 1.463750
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.522911 Loss1: 3.086267 Loss2: 1.436644
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.488928 Loss1: 3.051120 Loss2: 1.437808
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.385575 Loss1: 2.949055 Loss2: 1.436520
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.378149 Loss1: 3.621019 Loss2: 1.757129
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.474078 Loss1: 3.093303 Loss2: 1.380775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.278658 Loss1: 2.924556 Loss2: 1.354102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.193679 Loss1: 2.859576 Loss2: 1.334103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.104124 Loss1: 2.766469 Loss2: 1.337655 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.271875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.094557 Loss1: 2.745415 Loss2: 1.349143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.047386 Loss1: 2.689409 Loss2: 1.357978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.408479 Loss1: 3.544830 Loss2: 1.863648 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.308594
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.291177 Loss1: 2.930734 Loss2: 1.360443 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.116712 Loss1: 2.768926 Loss2: 1.347786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.034391 Loss1: 2.684859 Loss2: 1.349532
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.488457 Loss1: 3.604444 Loss2: 1.884013
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.963010 Loss1: 2.615897 Loss2: 1.347113
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.696435 Loss1: 3.269225 Loss2: 1.427210
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.923125 Loss1: 2.561319 Loss2: 1.361806
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.566436 Loss1: 3.169331 Loss2: 1.397105
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.889968 Loss1: 2.516329 Loss2: 1.373640
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.437175 Loss1: 3.037204 Loss2: 1.399972
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.938844 Loss1: 2.566229 Loss2: 1.372614
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.378418 Loss1: 2.980460 Loss2: 1.397958
-(DefaultActor pid=3765) >> Training accuracy: 0.275000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.423880 Loss1: 3.011930 Loss2: 1.411949
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.370438 Loss1: 2.974677 Loss2: 1.395761
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.340102 Loss1: 2.922061 Loss2: 1.418042
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.299956 Loss1: 2.874890 Loss2: 1.425066
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.776750 Loss1: 3.631357 Loss2: 2.145392
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.287297 Loss1: 2.857329 Loss2: 1.429968
-(DefaultActor pid=3764) >> Training accuracy: 0.266667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.612610 Loss1: 3.098737 Loss2: 1.513873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.409652 Loss1: 2.911294 Loss2: 1.498358 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.331855 Loss1: 2.830794 Loss2: 1.501060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.275008 Loss1: 2.774596 Loss2: 1.500412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.287242 Loss1: 2.783525 Loss2: 1.503717
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.748852 Loss1: 3.146223 Loss2: 1.602629
-(DefaultActor pid=3765) >> Training accuracy: 0.291667
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.307215 Loss1: 2.793444 Loss2: 1.513771
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.615801 Loss1: 3.046759 Loss2: 1.569042
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.557665 Loss1: 3.000821 Loss2: 1.556844
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.446246 Loss1: 2.885985 Loss2: 1.560262
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.464840 Loss1: 2.894205 Loss2: 1.570635
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.358507 Loss1: 2.804145 Loss2: 1.554362
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.678832 Loss1: 3.648826 Loss2: 2.030006
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.320439 Loss1: 2.754913 Loss2: 1.565526
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.336639 Loss1: 2.763800 Loss2: 1.572839
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.776859 Loss1: 3.208845 Loss2: 1.568013
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.313207 Loss1: 2.733143 Loss2: 1.580064
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.601306 Loss1: 3.067913 Loss2: 1.533393
-(DefaultActor pid=3764) >> Training accuracy: 0.319792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.498689 Loss1: 2.978600 Loss2: 1.520089
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.470575 Loss1: 2.946593 Loss2: 1.523982
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.338550 Loss1: 2.813806 Loss2: 1.524744
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.357847 Loss1: 2.825214 Loss2: 1.532633
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.766458 Loss1: 3.786601 Loss2: 1.979857
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.333951 Loss1: 2.804213 Loss2: 1.529738
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.326661 Loss1: 2.799973 Loss2: 1.526688 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.293561 Loss1: 2.757167 Loss2: 1.536394 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.263672
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.607025 Loss1: 3.116479 Loss2: 1.490546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.519524 Loss1: 3.023940 Loss2: 1.495584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.448101 Loss1: 2.951709 Loss2: 1.496392
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.505255 Loss1: 3.656946 Loss2: 1.848309
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.514765 Loss1: 3.109305 Loss2: 1.405460 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.214583
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.446017 Loss1: 2.934694 Loss2: 1.511323
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.348570 Loss1: 2.985642 Loss2: 1.362927
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.230299 Loss1: 2.876194 Loss2: 1.354105
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.235148 Loss1: 2.867854 Loss2: 1.367294
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.143851 Loss1: 2.777645 Loss2: 1.366207
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.131769 Loss1: 2.763724 Loss2: 1.368045
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.786756 Loss1: 3.868781 Loss2: 1.917975
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.113097 Loss1: 2.737116 Loss2: 1.375981
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.081647 Loss1: 2.702571 Loss2: 1.379076 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.069822 Loss1: 2.696967 Loss2: 1.372855 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.279167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.403716 Loss1: 2.990845 Loss2: 1.412870 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.325185 Loss1: 2.899424 Loss2: 1.425761 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.324080 Loss1: 2.887738 Loss2: 1.436342
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.324846 Loss1: 3.468452 Loss2: 1.856394
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.523241 Loss1: 3.100859 Loss2: 1.422382 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.263542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.372643 Loss1: 2.986030 Loss2: 1.386613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.295006 Loss1: 2.902654 Loss2: 1.392352 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.199771 Loss1: 2.801035 Loss2: 1.398736 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.164216 Loss1: 2.744917 Loss2: 1.419299 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.128600 Loss1: 2.715332 Loss2: 1.413268 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.079355 Loss1: 2.669022 Loss2: 1.410333 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.294792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.384972 Loss1: 3.024871 Loss2: 1.360101 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.331326 Loss1: 2.969714 Loss2: 1.361612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.259243 Loss1: 2.882739 Loss2: 1.376504
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.391775 Loss1: 3.561786 Loss2: 1.829988
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.278817 Loss1: 2.900602 Loss2: 1.378215
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.470418 Loss1: 3.076054 Loss2: 1.394364
-(DefaultActor pid=3764) >> Training accuracy: 0.226562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.330041 Loss1: 2.971685 Loss2: 1.358356
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.227728 Loss1: 2.875849 Loss2: 1.351878
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.208067 Loss1: 2.842820 Loss2: 1.365247
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.187324 Loss1: 2.815015 Loss2: 1.372309
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.624397 Loss1: 3.748587 Loss2: 1.875810
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.139239 Loss1: 2.764715 Loss2: 1.374524
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.764210 Loss1: 3.311664 Loss2: 1.452546
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.056442 Loss1: 2.694554 Loss2: 1.361889
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.617339 Loss1: 3.187673 Loss2: 1.429665
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.074921 Loss1: 2.703826 Loss2: 1.371095
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.554812 Loss1: 3.143212 Loss2: 1.411600
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.094911 Loss1: 2.718262 Loss2: 1.376649
-(DefaultActor pid=3765) >> Training accuracy: 0.298828
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.525144 Loss1: 3.089542 Loss2: 1.435602 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.445652 Loss1: 3.000346 Loss2: 1.445306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.471860 Loss1: 3.465953 Loss2: 2.005907
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.431895 Loss1: 2.977435 Loss2: 1.454460
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.470252 Loss1: 2.967762 Loss2: 1.502491
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.420106 Loss1: 2.957121 Loss2: 1.462985
-(DefaultActor pid=3764) >> Training accuracy: 0.263672
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.256058 Loss1: 2.792511 Loss2: 1.463546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.152548 Loss1: 2.693562 Loss2: 1.458986 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.138468 Loss1: 2.664735 Loss2: 1.473732
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.366966 Loss1: 3.466219 Loss2: 1.900747
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.497577 Loss1: 3.058420 Loss2: 1.439157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.287217 Loss1: 2.893294 Loss2: 1.393923 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.328125
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.046538 Loss1: 2.573761 Loss2: 1.472777
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.223399 Loss1: 2.823559 Loss2: 1.399840
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.119445 Loss1: 2.716911 Loss2: 1.402534
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.114447 Loss1: 2.709519 Loss2: 1.404928
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.122442 Loss1: 2.720882 Loss2: 1.401560
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.064574 Loss1: 2.653710 Loss2: 1.410864
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.730715 Loss1: 3.722483 Loss2: 2.008231
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.983389 Loss1: 2.574455 Loss2: 1.408934
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.769823 Loss1: 3.259524 Loss2: 1.510298
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.032246 Loss1: 2.602746 Loss2: 1.429501
-(DefaultActor pid=3764) >> Training accuracy: 0.358333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.402914 Loss1: 2.938078 Loss2: 1.464835 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.347436 Loss1: 2.880983 Loss2: 1.466453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.332430 Loss1: 2.849407 Loss2: 1.483024
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.332414 Loss1: 3.437447 Loss2: 1.894967
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.489232 Loss1: 3.046409 Loss2: 1.442823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.290252 Loss1: 2.887415 Loss2: 1.402837 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.301042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.161372 Loss1: 2.759987 Loss2: 1.401385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.136898 Loss1: 2.717886 Loss2: 1.419012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.071945 Loss1: 2.662566 Loss2: 1.409379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.010282 Loss1: 2.585937 Loss2: 1.424345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.984842 Loss1: 2.568272 Loss2: 1.416571 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.330078
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.338242 Loss1: 2.991140 Loss2: 1.347102 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.250203 Loss1: 2.890710 Loss2: 1.359493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.430447 Loss1: 3.433301 Loss2: 1.997146
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.329785 Loss1: 2.957580 Loss2: 1.372205
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.555141 Loss1: 3.044270 Loss2: 1.510871
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.257464 Loss1: 2.880050 Loss2: 1.377414
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.315167 Loss1: 2.845200 Loss2: 1.469967
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.264860 Loss1: 2.886094 Loss2: 1.378766
-(DefaultActor pid=3765) >> Training accuracy: 0.304167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.233346 Loss1: 2.766161 Loss2: 1.467185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.236512 Loss1: 2.764887 Loss2: 1.471625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.216882 Loss1: 2.728621 Loss2: 1.488261
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.518362 Loss1: 3.590420 Loss2: 1.927941
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.137514 Loss1: 2.653724 Loss2: 1.483791
-DEBUG flwr 2023-10-08 18:49:19,704 | server.py:236 | fit_round 11 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.644777 Loss1: 3.172139 Loss2: 1.472638
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.041456 Loss1: 2.562732 Loss2: 1.478723
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.407437 Loss1: 2.962528 Loss2: 1.444909
-(DefaultActor pid=3764) >> Training accuracy: 0.357292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.377740 Loss1: 2.939399 Loss2: 1.438340
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.353962 Loss1: 2.910822 Loss2: 1.443140
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.262464 Loss1: 2.821808 Loss2: 1.440656
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.240542 Loss1: 2.780519 Loss2: 1.460023
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.479660 Loss1: 3.559673 Loss2: 1.919987
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.216669 Loss1: 2.765665 Loss2: 1.451004
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.563823 Loss1: 3.117070 Loss2: 1.446753
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.208601 Loss1: 2.753693 Loss2: 1.454908
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.369440 Loss1: 2.954241 Loss2: 1.415199
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.161868 Loss1: 2.700563 Loss2: 1.461306
-(DefaultActor pid=3765) >> Training accuracy: 0.284375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.268790 Loss1: 2.837998 Loss2: 1.430792 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.309809 Loss1: 2.878739 Loss2: 1.431070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.205480 Loss1: 2.769081 Loss2: 1.436400
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.594874 Loss1: 3.625276 Loss2: 1.969597
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.131494 Loss1: 2.708950 Loss2: 1.422544
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.716796 Loss1: 3.216508 Loss2: 1.500288
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.123229 Loss1: 2.691331 Loss2: 1.431899
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.615500 Loss1: 3.136149 Loss2: 1.479352
-(DefaultActor pid=3764) >> Training accuracy: 0.341667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.508441 Loss1: 3.025273 Loss2: 1.483169
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.534764 Loss1: 3.048294 Loss2: 1.486470
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.432513 Loss1: 2.955774 Loss2: 1.476739
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.418451 Loss1: 2.935604 Loss2: 1.482847
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.597166 Loss1: 3.573975 Loss2: 2.023191
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.384843 Loss1: 2.894402 Loss2: 1.490441
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.648384 Loss1: 3.108873 Loss2: 1.539510
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.404547 Loss1: 2.896290 Loss2: 1.508257
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.536076 Loss1: 3.011270 Loss2: 1.524806
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.333698 Loss1: 2.826319 Loss2: 1.507379
-(DefaultActor pid=3765) >> Training accuracy: 0.295833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.399715 Loss1: 2.884842 Loss2: 1.514873 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.346972 Loss1: 2.816295 Loss2: 1.530677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.269078 Loss1: 2.730646 Loss2: 1.538431 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.329167
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 18:49:19,704][flwr][DEBUG] - fit_round 11 received 50 results and 0 failures
-INFO flwr 2023-10-08 18:50:00,263 | server.py:125 | fit progress: (11, 4.006379742972767, {'accuracy': 0.0903}, 25108.041343163)
->> Test accuracy: 0.090300
-[2023-10-08 18:50:00,263][flwr][INFO] - fit progress: (11, 4.006379742972767, {'accuracy': 0.0903}, 25108.041343163)
-DEBUG flwr 2023-10-08 18:50:00,263 | server.py:173 | evaluate_round 11: strategy sampled 50 clients (out of 50)
-[2023-10-08 18:50:00,263][flwr][DEBUG] - evaluate_round 11: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 18:59:05,242 | server.py:187 | evaluate_round 11 received 50 results and 0 failures
-[2023-10-08 18:59:05,242][flwr][DEBUG] - evaluate_round 11 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 18:59:05,242 | server.py:222 | fit_round 12: strategy sampled 50 clients (out of 50)
-[2023-10-08 18:59:05,242][flwr][DEBUG] - fit_round 12: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.410837 Loss1: 3.520975 Loss2: 1.889862 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.341465 Loss1: 2.928652 Loss2: 1.412813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.210141 Loss1: 2.787406 Loss2: 1.422735
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.642732 Loss1: 3.667675 Loss2: 1.975057
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.729386 Loss1: 3.220852 Loss2: 1.508534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.545397 Loss1: 3.084955 Loss2: 1.460443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.494853 Loss1: 3.037123 Loss2: 1.457730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.398857 Loss1: 2.945858 Loss2: 1.452999 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.292060 Loss1: 2.834785 Loss2: 1.457275 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.335417
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.026035 Loss1: 2.582041 Loss2: 1.443995
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.262347 Loss1: 2.803703 Loss2: 1.458644
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.394903 Loss1: 2.905238 Loss2: 1.489665
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.347983 Loss1: 2.865840 Loss2: 1.482142
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.324770 Loss1: 2.850961 Loss2: 1.473809
-(DefaultActor pid=3764) >> Training accuracy: 0.261458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.643559 Loss1: 3.757672 Loss2: 1.885886
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.742463 Loss1: 3.310681 Loss2: 1.431782
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.577351 Loss1: 3.185801 Loss2: 1.391550
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.497440 Loss1: 3.113677 Loss2: 1.383763
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.420718 Loss1: 3.454349 Loss2: 1.966369
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.501680 Loss1: 3.016828 Loss2: 1.484852 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.211140 Loss1: 2.789963 Loss2: 1.421176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.093454 Loss1: 2.691234 Loss2: 1.402219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.031222 Loss1: 2.627209 Loss2: 1.404013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.215007 Loss1: 2.811994 Loss2: 1.403013
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.048223 Loss1: 2.632400 Loss2: 1.415823
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.218360 Loss1: 2.808903 Loss2: 1.409457
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.913922 Loss1: 2.488234 Loss2: 1.425687
-(DefaultActor pid=3765) >> Training accuracy: 0.292411
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.890278 Loss1: 2.474112 Loss2: 1.416166
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.867090 Loss1: 2.448288 Loss2: 1.418802
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.838153 Loss1: 2.422113 Loss2: 1.416040
-(DefaultActor pid=3764) >> Training accuracy: 0.375000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.362514 Loss1: 3.467669 Loss2: 1.894845
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.555531 Loss1: 3.134711 Loss2: 1.420819
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.340444 Loss1: 2.958520 Loss2: 1.381924
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.220510 Loss1: 2.835006 Loss2: 1.385504
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.571934 Loss1: 3.526945 Loss2: 2.044990
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.210292 Loss1: 2.818879 Loss2: 1.391413
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.675492 Loss1: 3.103125 Loss2: 1.572366
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.161378 Loss1: 2.768565 Loss2: 1.392813
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.489033 Loss1: 2.960743 Loss2: 1.528289
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.121290 Loss1: 2.734961 Loss2: 1.386329
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.389477 Loss1: 2.874645 Loss2: 1.514832
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.060719 Loss1: 2.671643 Loss2: 1.389075
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.387763 Loss1: 2.864214 Loss2: 1.523549
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.029307 Loss1: 2.642653 Loss2: 1.386653
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.277090 Loss1: 2.736190 Loss2: 1.540901
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.072286 Loss1: 2.665919 Loss2: 1.406367
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.208234 Loss1: 2.692792 Loss2: 1.515442
-(DefaultActor pid=3765) >> Training accuracy: 0.317708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.194952 Loss1: 2.667451 Loss2: 1.527501
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.141703 Loss1: 2.606052 Loss2: 1.535651
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.117590 Loss1: 2.578458 Loss2: 1.539132
-(DefaultActor pid=3764) >> Training accuracy: 0.320833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.329261 Loss1: 3.486057 Loss2: 1.843204
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.615774 Loss1: 3.203652 Loss2: 1.412121
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.482720 Loss1: 3.091613 Loss2: 1.391107
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.362221 Loss1: 2.986006 Loss2: 1.376215
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.338705 Loss1: 3.477142 Loss2: 1.861562
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.461372 Loss1: 3.060861 Loss2: 1.400512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.248952 Loss1: 2.884319 Loss2: 1.364633 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.197379 Loss1: 2.835866 Loss2: 1.361513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.146433 Loss1: 2.771248 Loss2: 1.375185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.112739 Loss1: 2.735409 Loss2: 1.377330 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.270833
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.162401 Loss1: 2.755908 Loss2: 1.406493
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.061343 Loss1: 2.691322 Loss2: 1.370021
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.011930 Loss1: 2.632380 Loss2: 1.379550
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.025625 Loss1: 2.640756 Loss2: 1.384870
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.949446 Loss1: 2.563334 Loss2: 1.386112
-(DefaultActor pid=3764) >> Training accuracy: 0.323958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.497854 Loss1: 3.712194 Loss2: 1.785660
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.619612 Loss1: 3.251968 Loss2: 1.367644
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.452520 Loss1: 3.112198 Loss2: 1.340323
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.350740 Loss1: 3.026026 Loss2: 1.324714
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.265574 Loss1: 3.336510 Loss2: 1.929064
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.457354 Loss1: 2.996538 Loss2: 1.460817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.251703 Loss1: 2.810952 Loss2: 1.440751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.178116 Loss1: 2.747555 Loss2: 1.430561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.175482 Loss1: 2.722490 Loss2: 1.452993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.126932 Loss1: 2.686000 Loss2: 1.440932 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.251042
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.233795 Loss1: 2.864016 Loss2: 1.369780
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.058000 Loss1: 2.613227 Loss2: 1.444773
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.029077 Loss1: 2.571124 Loss2: 1.457953
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.992992 Loss1: 2.534029 Loss2: 1.458963
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.980200 Loss1: 2.523273 Loss2: 1.456927
-(DefaultActor pid=3764) >> Training accuracy: 0.352083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.370407 Loss1: 3.435310 Loss2: 1.935097
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.527445 Loss1: 3.051867 Loss2: 1.475578
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.405456 Loss1: 2.939042 Loss2: 1.466414
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.267028 Loss1: 2.816129 Loss2: 1.450899
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.329820 Loss1: 3.496996 Loss2: 1.832824
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.524917 Loss1: 3.059202 Loss2: 1.465715 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.272860 Loss1: 2.855545 Loss2: 1.417315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.167322 Loss1: 2.757104 Loss2: 1.410218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.119619 Loss1: 2.708756 Loss2: 1.410862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.068970 Loss1: 2.648902 Loss2: 1.420068 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.346875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.078682 Loss1: 2.652122 Loss2: 1.426560 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.939208 Loss1: 2.504718 Loss2: 1.434490 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.327148
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.542552 Loss1: 3.062974 Loss2: 1.479578 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.204451 Loss1: 2.773963 Loss2: 1.430488 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.201475 Loss1: 2.773344 Loss2: 1.428131
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.455081 Loss1: 3.515901 Loss2: 1.939180
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.118519 Loss1: 2.684253 Loss2: 1.434266
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.524278 Loss1: 3.035230 Loss2: 1.489048
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.090103 Loss1: 2.655395 Loss2: 1.434708
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.365481 Loss1: 2.920732 Loss2: 1.444749
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.077060 Loss1: 2.630175 Loss2: 1.446885
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.323043 Loss1: 2.876514 Loss2: 1.446530
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.250605 Loss1: 2.799634 Loss2: 1.450971 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.328125
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.006833 Loss1: 2.555307 Loss2: 1.451526
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.239973 Loss1: 2.769965 Loss2: 1.470008
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.207703 Loss1: 2.751419 Loss2: 1.456284
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.104747 Loss1: 2.642906 Loss2: 1.461841
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.139827 Loss1: 2.672280 Loss2: 1.467547
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.113257 Loss1: 2.645315 Loss2: 1.467942
-(DefaultActor pid=3764) >> Training accuracy: 0.322266
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.679287 Loss1: 3.736273 Loss2: 1.943014
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.768916 Loss1: 3.259148 Loss2: 1.509768
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.587425 Loss1: 3.134381 Loss2: 1.453045
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.480604 Loss1: 3.036254 Loss2: 1.444350
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.453092 Loss1: 2.996907 Loss2: 1.456185
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.423947 Loss1: 3.639868 Loss2: 1.784079
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.634392 Loss1: 3.253568 Loss2: 1.380824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.526386 Loss1: 3.160090 Loss2: 1.366296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.405283 Loss1: 3.040996 Loss2: 1.364287 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.328468 Loss1: 2.964655 Loss2: 1.363813 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.303711
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.377547 Loss1: 3.011008 Loss2: 1.366540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.267249 Loss1: 2.873079 Loss2: 1.394170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.257259 Loss1: 2.867370 Loss2: 1.389889 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.278320
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.361234 Loss1: 2.949309 Loss2: 1.411925 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.190181 Loss1: 2.783075 Loss2: 1.407106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.606540 Loss1: 3.713196 Loss2: 1.893345
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.127050 Loss1: 2.708467 Loss2: 1.418583
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.563739 Loss1: 3.143424 Loss2: 1.420315
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.167712 Loss1: 2.749677 Loss2: 1.418034
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.353152 Loss1: 2.985076 Loss2: 1.368077
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.035504 Loss1: 2.623437 Loss2: 1.412068
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.287486 Loss1: 2.922224 Loss2: 1.365262
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.970405 Loss1: 2.536847 Loss2: 1.433558
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.260267 Loss1: 2.887310 Loss2: 1.372957
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.982213 Loss1: 2.548828 Loss2: 1.433385
-(DefaultActor pid=3765) >> Training accuracy: 0.339583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.189006 Loss1: 2.821097 Loss2: 1.367908 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.099150 Loss1: 2.697000 Loss2: 1.402151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.062228 Loss1: 2.667327 Loss2: 1.394901
-(DefaultActor pid=3764) >> Training accuracy: 0.266667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.372348 Loss1: 3.386925 Loss2: 1.985423
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.487434 Loss1: 2.963566 Loss2: 1.523867
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.286967 Loss1: 2.809498 Loss2: 1.477469
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.134552 Loss1: 2.679592 Loss2: 1.454960
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.123046 Loss1: 2.660317 Loss2: 1.462728
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.495721 Loss1: 3.419380 Loss2: 2.076341
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.086629 Loss1: 2.615423 Loss2: 1.471206
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.108097 Loss1: 2.636983 Loss2: 1.471115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.997443 Loss1: 2.528387 Loss2: 1.469057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.378748 Loss1: 2.840065 Loss2: 1.538684
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.014195 Loss1: 2.531766 Loss2: 1.482429
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.228880 Loss1: 2.701609 Loss2: 1.527271
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.892206 Loss1: 2.410165 Loss2: 1.482041
-(DefaultActor pid=3765) >> Training accuracy: 0.353125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.220625 Loss1: 2.664940 Loss2: 1.555685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.205327 Loss1: 2.642409 Loss2: 1.562918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.117879 Loss1: 2.560434 Loss2: 1.557445
-(DefaultActor pid=3764) >> Training accuracy: 0.339583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.660444 Loss1: 3.708933 Loss2: 1.951511
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.736850 Loss1: 3.283369 Loss2: 1.453482
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.504853 Loss1: 3.065876 Loss2: 1.438977
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.440932 Loss1: 3.001924 Loss2: 1.439008
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.374605 Loss1: 2.932612 Loss2: 1.441994
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.479106 Loss1: 3.483874 Loss2: 1.995232
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.442705 Loss1: 2.905110 Loss2: 1.537595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.309296 Loss1: 2.814231 Loss2: 1.495065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.157626 Loss1: 2.668325 Loss2: 1.489301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.165696 Loss1: 2.686039 Loss2: 1.479657
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.089434 Loss1: 2.605893 Loss2: 1.483541
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.201131 Loss1: 2.728319 Loss2: 1.472812
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.055542 Loss1: 2.565075 Loss2: 1.490467
-(DefaultActor pid=3765) >> Training accuracy: 0.300781
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.023578 Loss1: 2.542342 Loss2: 1.481237
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.958904 Loss1: 2.477250 Loss2: 1.481654
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.962906 Loss1: 2.461901 Loss2: 1.501005
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.911880 Loss1: 2.415071 Loss2: 1.496809
-(DefaultActor pid=3764) >> Training accuracy: 0.428125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.517319 Loss1: 3.663372 Loss2: 1.853947
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.707114 Loss1: 3.274071 Loss2: 1.433044
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.521364 Loss1: 3.102667 Loss2: 1.418697
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.467989 Loss1: 3.047038 Loss2: 1.420951
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.337223 Loss1: 3.493683 Loss2: 1.843540
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.516273 Loss1: 3.107242 Loss2: 1.409030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.348135 Loss1: 2.989226 Loss2: 1.358909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.240659 Loss1: 2.876049 Loss2: 1.364611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.205829 Loss1: 2.831399 Loss2: 1.374430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.152682 Loss1: 2.789832 Loss2: 1.362850 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.272461
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.237582 Loss1: 2.801606 Loss2: 1.435976
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.122596 Loss1: 2.732461 Loss2: 1.390135
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.079440 Loss1: 2.700733 Loss2: 1.378707
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.002787 Loss1: 2.611364 Loss2: 1.391423
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.010634 Loss1: 2.617335 Loss2: 1.393298
-(DefaultActor pid=3764) >> Training accuracy: 0.332292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.687851 Loss1: 3.676700 Loss2: 2.011151
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.621574 Loss1: 3.085425 Loss2: 1.536149
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.396427 Loss1: 2.908260 Loss2: 1.488168
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.325783 Loss1: 2.838064 Loss2: 1.487719
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.356433 Loss1: 3.407482 Loss2: 1.948951
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.600087 Loss1: 3.099263 Loss2: 1.500824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.439803 Loss1: 2.960818 Loss2: 1.478985 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.379293 Loss1: 2.904388 Loss2: 1.474905 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.260988 Loss1: 2.777505 Loss2: 1.483483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.156036 Loss1: 2.682799 Loss2: 1.473236 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.316667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.169092 Loss1: 2.692781 Loss2: 1.476311 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.087158 Loss1: 2.603229 Loss2: 1.483930 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.330078
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.593559 Loss1: 3.120977 Loss2: 1.472582 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.329996 Loss1: 2.903830 Loss2: 1.426167 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.472355 Loss1: 3.528671 Loss2: 1.943684
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.248709 Loss1: 2.822585 Loss2: 1.426124
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.583600 Loss1: 3.081578 Loss2: 1.502022
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.322925 Loss1: 2.880073 Loss2: 1.442851
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.387326 Loss1: 2.913226 Loss2: 1.474100
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.210354 Loss1: 2.771186 Loss2: 1.439168
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.287180 Loss1: 2.834161 Loss2: 1.453019
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.150819 Loss1: 2.711143 Loss2: 1.439675
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.270284 Loss1: 2.794181 Loss2: 1.476103
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.138452 Loss1: 2.694151 Loss2: 1.444301
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.141866 Loss1: 2.660884 Loss2: 1.480982
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.066304 Loss1: 2.607947 Loss2: 1.458357
-(DefaultActor pid=3765) >> Training accuracy: 0.273958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.124578 Loss1: 2.648596 Loss2: 1.475982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.050949 Loss1: 2.538436 Loss2: 1.512513 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.337500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.440255 Loss1: 2.928165 Loss2: 1.512089 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.121469 Loss1: 2.649906 Loss2: 1.471563 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.118146 Loss1: 2.636881 Loss2: 1.481264 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.064080 Loss1: 2.566358 Loss2: 1.497723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.305832 Loss1: 2.924539 Loss2: 1.381293 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.246175 Loss1: 2.856002 Loss2: 1.390172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.275799 Loss1: 2.891297 Loss2: 1.384502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.187684 Loss1: 2.801570 Loss2: 1.386114 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.120544 Loss1: 2.735306 Loss2: 1.385238 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.377930
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.131382 Loss1: 2.714359 Loss2: 1.417023 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.338942
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.404452 Loss1: 3.496361 Loss2: 1.908091
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.381919 Loss1: 2.955043 Loss2: 1.426876
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.122422 Loss1: 2.716712 Loss2: 1.405710
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.049246 Loss1: 2.660975 Loss2: 1.388271
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.605341 Loss1: 3.565104 Loss2: 2.040237
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.009307 Loss1: 2.612148 Loss2: 1.397159
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.705904 Loss1: 3.166594 Loss2: 1.539311
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.955970 Loss1: 2.567385 Loss2: 1.388585
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.571672 Loss1: 3.070293 Loss2: 1.501379
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.937452 Loss1: 2.537036 Loss2: 1.400416
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.469611 Loss1: 2.981792 Loss2: 1.487819
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.874222 Loss1: 2.466507 Loss2: 1.407716
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.417548 Loss1: 2.929845 Loss2: 1.487703
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.796439 Loss1: 2.398396 Loss2: 1.398042
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.441462 Loss1: 2.942767 Loss2: 1.498695
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.742630 Loss1: 2.336245 Loss2: 1.406385
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.302061 Loss1: 2.802976 Loss2: 1.499085
-(DefaultActor pid=3765) >> Training accuracy: 0.326042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.302414 Loss1: 2.808061 Loss2: 1.494353
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.354063 Loss1: 2.831085 Loss2: 1.522978
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.294660 Loss1: 2.774173 Loss2: 1.520487
-(DefaultActor pid=3764) >> Training accuracy: 0.291667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.607250 Loss1: 3.552418 Loss2: 2.054832
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.589143 Loss1: 3.105586 Loss2: 1.483557
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.449895 Loss1: 3.012004 Loss2: 1.437891
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.258312 Loss1: 2.824082 Loss2: 1.434229
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.198569 Loss1: 2.755503 Loss2: 1.443065
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.223537 Loss1: 2.789020 Loss2: 1.434517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.144494 Loss1: 2.709268 Loss2: 1.435226 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.036390 Loss1: 2.593377 Loss2: 1.443013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.120729 Loss1: 2.668094 Loss2: 1.452635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.142224 Loss1: 2.682209 Loss2: 1.460014 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.325521
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.011212 Loss1: 2.621383 Loss2: 1.389828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.031168 Loss1: 2.624892 Loss2: 1.406276 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.951451 Loss1: 2.538508 Loss2: 1.412943
-(DefaultActor pid=3764) >> Training accuracy: 0.334821
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.833239 Loss1: 3.837714 Loss2: 1.995525
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.802821 Loss1: 3.287133 Loss2: 1.515688
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.568191 Loss1: 3.096776 Loss2: 1.471416
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.533400 Loss1: 3.068107 Loss2: 1.465293
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.493452 Loss1: 3.032330 Loss2: 1.461121
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.344088 Loss1: 2.877919 Loss2: 1.466169
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.476538 Loss1: 3.581547 Loss2: 1.894991
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.324783 Loss1: 2.850721 Loss2: 1.474062
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.573095 Loss1: 3.110186 Loss2: 1.462909
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.437585 Loss1: 3.013266 Loss2: 1.424319 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.332707 Loss1: 2.913574 Loss2: 1.419133 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.242188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.283984 Loss1: 2.866145 Loss2: 1.417839 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.183047 Loss1: 2.754533 Loss2: 1.428514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.598793 Loss1: 3.630121 Loss2: 1.968672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.670407 Loss1: 3.185123 Loss2: 1.485285 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.302734
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.403551 Loss1: 2.957885 Loss2: 1.445666 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.334514 Loss1: 2.883406 Loss2: 1.451108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.301904 Loss1: 2.847503 Loss2: 1.454401
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.293300 Loss1: 3.353897 Loss2: 1.939404
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.273411 Loss1: 2.801660 Loss2: 1.471751
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.394025 Loss1: 2.944177 Loss2: 1.449848
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.306031 Loss1: 2.840687 Loss2: 1.465344
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.134473 Loss1: 2.717214 Loss2: 1.417259
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.159808 Loss1: 2.692446 Loss2: 1.467362
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.128903 Loss1: 2.705575 Loss2: 1.423328
-(DefaultActor pid=3765) >> Training accuracy: 0.307292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.101704 Loss1: 2.680675 Loss2: 1.421028
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.036140 Loss1: 2.613332 Loss2: 1.422808
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.001814 Loss1: 2.587712 Loss2: 1.414102
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.988085 Loss1: 2.556348 Loss2: 1.431738
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.412854 Loss1: 3.473701 Loss2: 1.939153
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.875783 Loss1: 2.441724 Loss2: 1.434059
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.532794 Loss1: 3.080089 Loss2: 1.452705
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.853956 Loss1: 2.426223 Loss2: 1.427733
-(DefaultActor pid=3764) >> Training accuracy: 0.356250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.205843 Loss1: 2.792888 Loss2: 1.412955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.132221 Loss1: 2.719197 Loss2: 1.413024 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.117817 Loss1: 2.686121 Loss2: 1.431696
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.321253 Loss1: 3.446312 Loss2: 1.874941
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.004359 Loss1: 2.591983 Loss2: 1.412375
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.589490 Loss1: 3.136125 Loss2: 1.453365
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.066663 Loss1: 2.643411 Loss2: 1.423252
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.348678 Loss1: 2.934678 Loss2: 1.414000
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.084662 Loss1: 2.642983 Loss2: 1.441679
-(DefaultActor pid=3765) >> Training accuracy: 0.297917
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.339478 Loss1: 2.928294 Loss2: 1.411184
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.297265 Loss1: 2.883829 Loss2: 1.413436
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.238028 Loss1: 2.811119 Loss2: 1.426909
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.196919 Loss1: 2.778094 Loss2: 1.418826
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.143430 Loss1: 2.720087 Loss2: 1.423344
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.113477 Loss1: 2.705089 Loss2: 1.408388
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.543605 Loss1: 3.619670 Loss2: 1.923935
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.096836 Loss1: 2.658582 Loss2: 1.438254
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.558009 Loss1: 3.099600 Loss2: 1.458409
-(DefaultActor pid=3764) >> Training accuracy: 0.325000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.401659 Loss1: 2.966686 Loss2: 1.434973
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.321776 Loss1: 2.880106 Loss2: 1.441670
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.245406 Loss1: 2.790797 Loss2: 1.454609
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.231766 Loss1: 2.789628 Loss2: 1.442137
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.212980 Loss1: 3.307665 Loss2: 1.905315
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.203355 Loss1: 2.751874 Loss2: 1.451481
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.300713 Loss1: 2.846314 Loss2: 1.454398
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.151378 Loss1: 2.704021 Loss2: 1.447357
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.047971 Loss1: 2.651897 Loss2: 1.396074
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.098638 Loss1: 2.696679 Loss2: 1.401959
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.132693 Loss1: 2.674570 Loss2: 1.458123
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.019869 Loss1: 2.613763 Loss2: 1.406106
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.107590 Loss1: 2.651389 Loss2: 1.456201
-(DefaultActor pid=3765) >> Training accuracy: 0.341912
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.913162 Loss1: 2.511888 Loss2: 1.401273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.919109 Loss1: 2.492599 Loss2: 1.426511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.867710 Loss1: 2.450785 Loss2: 1.416925
-(DefaultActor pid=3764) >> Training accuracy: 0.394792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.508099 Loss1: 3.600644 Loss2: 1.907454
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.593299 Loss1: 3.140874 Loss2: 1.452426
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.371104 Loss1: 2.957690 Loss2: 1.413414
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.300791 Loss1: 2.899806 Loss2: 1.400985
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.262831 Loss1: 2.855900 Loss2: 1.406931
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.499739 Loss1: 3.557675 Loss2: 1.942063
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.160435 Loss1: 2.760655 Loss2: 1.399780
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.137087 Loss1: 2.720692 Loss2: 1.416395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.135879 Loss1: 2.722033 Loss2: 1.413846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.125718 Loss1: 2.710676 Loss2: 1.415042 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.095683 Loss1: 2.680649 Loss2: 1.415034 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.311458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.208582 Loss1: 2.754108 Loss2: 1.454474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.182624 Loss1: 2.712981 Loss2: 1.469643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.104896 Loss1: 2.637301 Loss2: 1.467595
-DEBUG flwr 2023-10-08 19:27:52,814 | server.py:236 | fit_round 12 received 50 results and 0 failures
-(DefaultActor pid=3764) >> Training accuracy: 0.326042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.786204 Loss1: 3.775515 Loss2: 2.010689
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.804978 Loss1: 3.289705 Loss2: 1.515272
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.544593 Loss1: 3.086038 Loss2: 1.458556
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.460979 Loss1: 3.004627 Loss2: 1.456352
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.434062 Loss1: 2.966292 Loss2: 1.467770
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.337413 Loss1: 3.404670 Loss2: 1.932742
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.392584 Loss1: 2.922329 Loss2: 1.470254
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.284934 Loss1: 2.810638 Loss2: 1.474296 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.381503 Loss1: 2.883656 Loss2: 1.497847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.254799 Loss1: 2.781703 Loss2: 1.473095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.229605 Loss1: 2.739876 Loss2: 1.489729 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.296875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.042441 Loss1: 2.638978 Loss2: 1.403463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.947152 Loss1: 2.548366 Loss2: 1.398786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.933794 Loss1: 2.522447 Loss2: 1.411347
-(DefaultActor pid=3764) >> Training accuracy: 0.365625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.414107 Loss1: 3.510519 Loss2: 1.903587
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.597112 Loss1: 3.175640 Loss2: 1.421472
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.378472 Loss1: 2.988921 Loss2: 1.389551
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.328606 Loss1: 2.932787 Loss2: 1.395819
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.279336 Loss1: 2.886777 Loss2: 1.392559
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.206782 Loss1: 3.350803 Loss2: 1.855979
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.319135 Loss1: 2.880670 Loss2: 1.438465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.097673 Loss1: 2.688581 Loss2: 1.409092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.989168 Loss1: 2.577796 Loss2: 1.411372 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.939142 Loss1: 2.536453 Loss2: 1.402689 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.268750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.974656 Loss1: 2.553832 Loss2: 1.420824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.875100 Loss1: 2.441895 Loss2: 1.433205 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.808619 Loss1: 2.377071 Loss2: 1.431547 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.381250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.466470 Loss1: 3.095840 Loss2: 1.370630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.265795 Loss1: 2.934401 Loss2: 1.331395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.166424 Loss1: 2.835333 Loss2: 1.331091
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.571924 Loss1: 3.661048 Loss2: 1.910877
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.729516 Loss1: 3.268596 Loss2: 1.460920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.449066 Loss1: 3.015299 Loss2: 1.433767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.417996 Loss1: 3.000176 Loss2: 1.417819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.259048 Loss1: 2.837808 Loss2: 1.421240 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.334375
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.050870 Loss1: 2.692380 Loss2: 1.358489
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.253757 Loss1: 2.818313 Loss2: 1.435444
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.317760 Loss1: 2.863746 Loss2: 1.454013
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.189300 Loss1: 2.743850 Loss2: 1.445450
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.149534 Loss1: 2.700391 Loss2: 1.449143
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.106765 Loss1: 2.659438 Loss2: 1.447327
-(DefaultActor pid=3764) >> Training accuracy: 0.279167
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 19:27:52,814][flwr][DEBUG] - fit_round 12 received 50 results and 0 failures
-INFO flwr 2023-10-08 19:28:33,828 | server.py:125 | fit progress: (12, 3.9032422269876013, {'accuracy': 0.1017}, 27421.606692538)
->> Test accuracy: 0.101700
-[2023-10-08 19:28:33,828][flwr][INFO] - fit progress: (12, 3.9032422269876013, {'accuracy': 0.1017}, 27421.606692538)
-DEBUG flwr 2023-10-08 19:28:33,828 | server.py:173 | evaluate_round 12: strategy sampled 50 clients (out of 50)
-[2023-10-08 19:28:33,828][flwr][DEBUG] - evaluate_round 12: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 19:37:36,836 | server.py:187 | evaluate_round 12 received 50 results and 0 failures
-[2023-10-08 19:37:36,836][flwr][DEBUG] - evaluate_round 12 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 19:37:36,837 | server.py:222 | fit_round 13: strategy sampled 50 clients (out of 50)
-[2023-10-08 19:37:36,837][flwr][DEBUG] - fit_round 13: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.377749 Loss1: 3.460418 Loss2: 1.917331
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.640888 Loss1: 3.160430 Loss2: 1.480457
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.402727 Loss1: 2.961238 Loss2: 1.441489
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.307202 Loss1: 2.866631 Loss2: 1.440572
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.530783 Loss1: 3.545818 Loss2: 1.984965
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.715094 Loss1: 3.205001 Loss2: 1.510093 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.520742 Loss1: 3.042618 Loss2: 1.478124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.450010 Loss1: 2.969685 Loss2: 1.480325 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.381247 Loss1: 2.889376 Loss2: 1.491871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.390372 Loss1: 2.891341 Loss2: 1.499031 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.336458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.348545 Loss1: 2.840679 Loss2: 1.507866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.247070 Loss1: 2.733971 Loss2: 1.513099 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.298828
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.308724 Loss1: 3.414457 Loss2: 1.894267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.172117 Loss1: 2.788537 Loss2: 1.383580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.086061 Loss1: 2.700296 Loss2: 1.385764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.012200 Loss1: 2.623565 Loss2: 1.388635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.953364 Loss1: 2.549131 Loss2: 1.404233 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.954901 Loss1: 2.536852 Loss2: 1.418049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.829685 Loss1: 2.413240 Loss2: 1.416445 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.873692 Loss1: 2.455838 Loss2: 1.417854 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.332292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.971672 Loss1: 2.532976 Loss2: 1.438696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.878587 Loss1: 2.426669 Loss2: 1.451918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.879225 Loss1: 2.418277 Loss2: 1.460947
-(DefaultActor pid=3764) >> Training accuracy: 0.333008
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.357183 Loss1: 3.433857 Loss2: 1.923325
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.484369 Loss1: 3.010061 Loss2: 1.474309
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.313030 Loss1: 2.887616 Loss2: 1.425414
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.194395 Loss1: 2.770958 Loss2: 1.423437
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.150758 Loss1: 2.730824 Loss2: 1.419934
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.450850 Loss1: 3.478205 Loss2: 1.972646
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.092135 Loss1: 2.672806 Loss2: 1.419329
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.518682 Loss1: 2.990854 Loss2: 1.527828
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.282842 Loss1: 2.805254 Loss2: 1.477587
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.017849 Loss1: 2.596706 Loss2: 1.421142
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.193944 Loss1: 2.737309 Loss2: 1.456635
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.039743 Loss1: 2.603191 Loss2: 1.436552
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.003142 Loss1: 2.577899 Loss2: 1.425243 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.984187 Loss1: 2.550405 Loss2: 1.433782 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.349609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.016209 Loss1: 2.532289 Loss2: 1.483919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.953864 Loss1: 2.472306 Loss2: 1.481558 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.377232
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.330394 Loss1: 3.428100 Loss2: 1.902294
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.498446 Loss1: 3.044238 Loss2: 1.454208
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.347444 Loss1: 2.902265 Loss2: 1.445179
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.308582 Loss1: 3.439815 Loss2: 1.868767
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.123041 Loss1: 2.695206 Loss2: 1.427835
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.437297 Loss1: 3.007174 Loss2: 1.430123
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.094069 Loss1: 2.674249 Loss2: 1.419820
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.260903 Loss1: 2.864821 Loss2: 1.396081
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.014443 Loss1: 2.592577 Loss2: 1.421866
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.035457 Loss1: 2.655476 Loss2: 1.379981
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.133711 Loss1: 2.683397 Loss2: 1.450314
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.002590 Loss1: 2.564128 Loss2: 1.438462 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.974484 Loss1: 2.532082 Loss2: 1.442402 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.939959 Loss1: 2.493151 Loss2: 1.446808 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.384766
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.909223 Loss1: 2.492772 Loss2: 1.416451 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.348958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.487700 Loss1: 3.551976 Loss2: 1.935724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.341685 Loss1: 2.907199 Loss2: 1.434486 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.272760 Loss1: 2.831445 Loss2: 1.441315
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.353933 Loss1: 3.425492 Loss2: 1.928441
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.283466 Loss1: 2.847756 Loss2: 1.435710
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.419527 Loss1: 2.976986 Loss2: 1.442541
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.249102 Loss1: 2.808308 Loss2: 1.440794
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.262590 Loss1: 2.851235 Loss2: 1.411355
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.145491 Loss1: 2.710712 Loss2: 1.434779
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.208849 Loss1: 2.800637 Loss2: 1.408212
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.084259 Loss1: 2.630482 Loss2: 1.453777
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.131393 Loss1: 2.728601 Loss2: 1.402791
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.055208 Loss1: 2.605455 Loss2: 1.449753
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.103610 Loss1: 2.680531 Loss2: 1.423079
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.024758 Loss1: 2.560670 Loss2: 1.464088
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.003536 Loss1: 2.583908 Loss2: 1.419628
-(DefaultActor pid=3765) >> Training accuracy: 0.358333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.931376 Loss1: 2.519904 Loss2: 1.411472
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.905755 Loss1: 2.490408 Loss2: 1.415347
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.998757 Loss1: 2.562786 Loss2: 1.435970
-(DefaultActor pid=3764) >> Training accuracy: 0.318750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.394792 Loss1: 3.405028 Loss2: 1.989764
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.408124 Loss1: 2.874176 Loss2: 1.533948
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.190838 Loss1: 2.704757 Loss2: 1.486081
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.055940 Loss1: 2.589550 Loss2: 1.466390
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.326077 Loss1: 3.361973 Loss2: 1.964103
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.465139 Loss1: 2.965138 Loss2: 1.500001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.266892 Loss1: 2.793595 Loss2: 1.473297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.185897 Loss1: 2.708180 Loss2: 1.477717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.133347 Loss1: 2.649067 Loss2: 1.484280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.176373 Loss1: 2.685234 Loss2: 1.491139 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.397917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.725856 Loss1: 2.229556 Loss2: 1.496300
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.067019 Loss1: 2.575506 Loss2: 1.491512
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.047810 Loss1: 2.558904 Loss2: 1.488907
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.006863 Loss1: 2.514888 Loss2: 1.491975
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.971564 Loss1: 2.467679 Loss2: 1.503885
-(DefaultActor pid=3764) >> Training accuracy: 0.355208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.385894 Loss1: 3.403877 Loss2: 1.982016
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.342336 Loss1: 2.864191 Loss2: 1.478145
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.090756 Loss1: 2.649180 Loss2: 1.441576
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.018921 Loss1: 2.565154 Loss2: 1.453767
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.554394 Loss1: 3.611453 Loss2: 1.942940
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.480940 Loss1: 3.005086 Loss2: 1.475854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.279741 Loss1: 2.847933 Loss2: 1.431808 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.160908 Loss1: 2.729555 Loss2: 1.431353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.145442 Loss1: 2.692127 Loss2: 1.453314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.094216 Loss1: 2.635812 Loss2: 1.458404 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.332292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.036960 Loss1: 2.580966 Loss2: 1.455994 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.983935 Loss1: 2.521237 Loss2: 1.462698 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.357292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.060460 Loss1: 3.206066 Loss2: 1.854394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.132077 Loss1: 2.754113 Loss2: 1.377963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.007655 Loss1: 2.637797 Loss2: 1.369857
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.374660 Loss1: 3.492541 Loss2: 1.882118
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.468128 Loss1: 3.035245 Loss2: 1.432883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.340358 Loss1: 2.942959 Loss2: 1.397399 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.240538 Loss1: 2.853949 Loss2: 1.386589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.210496 Loss1: 2.807014 Loss2: 1.403482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.152119 Loss1: 2.746038 Loss2: 1.406081 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.408333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.072540 Loss1: 2.665528 Loss2: 1.407012 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.035077 Loss1: 2.616781 Loss2: 1.418296 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.341797
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.520935 Loss1: 3.023013 Loss2: 1.497921 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.269912 Loss1: 2.810058 Loss2: 1.459854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.228462 Loss1: 2.758935 Loss2: 1.469527
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.185041 Loss1: 3.341919 Loss2: 1.843122
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.341363 Loss1: 2.932520 Loss2: 1.408843 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.130898 Loss1: 2.751787 Loss2: 1.379110 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.075795 Loss1: 2.705608 Loss2: 1.370187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.089553 Loss1: 2.707069 Loss2: 1.382484 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.341667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.983940 Loss1: 2.486195 Loss2: 1.497745
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.947274 Loss1: 2.571936 Loss2: 1.375338
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.978920 Loss1: 2.599632 Loss2: 1.379288
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.876301 Loss1: 2.494017 Loss2: 1.382284
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.831082 Loss1: 2.458784 Loss2: 1.372297
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.873743 Loss1: 2.485201 Loss2: 1.388543
-(DefaultActor pid=3764) >> Training accuracy: 0.338542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.336586 Loss1: 3.310404 Loss2: 2.026182
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.406324 Loss1: 2.889903 Loss2: 1.516421
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.271390 Loss1: 2.787496 Loss2: 1.483894
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.065813 Loss1: 2.588330 Loss2: 1.477482
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.050759 Loss1: 2.561899 Loss2: 1.488860
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.679357 Loss1: 3.673675 Loss2: 2.005682
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.023449 Loss1: 2.531734 Loss2: 1.491714
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.769876 Loss1: 3.260178 Loss2: 1.509698
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.999649 Loss1: 2.507014 Loss2: 1.492635
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.527558 Loss1: 3.063951 Loss2: 1.463607
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.969912 Loss1: 2.465820 Loss2: 1.504093
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.406911 Loss1: 2.944295 Loss2: 1.462616
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.366090 Loss1: 2.908044 Loss2: 1.458046
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.854932 Loss1: 2.357690 Loss2: 1.497242
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.319858 Loss1: 2.837327 Loss2: 1.482531
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.883799 Loss1: 2.371897 Loss2: 1.511902
-(DefaultActor pid=3765) >> Training accuracy: 0.360417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.186865 Loss1: 2.716469 Loss2: 1.470396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.099518 Loss1: 2.620283 Loss2: 1.479235 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.313616
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.500455 Loss1: 2.994595 Loss2: 1.505860 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.289300 Loss1: 2.820691 Loss2: 1.468609 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.120524 Loss1: 2.641561 Loss2: 1.478963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.130433 Loss1: 2.644813 Loss2: 1.485620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.072049 Loss1: 2.585219 Loss2: 1.486829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.008436 Loss1: 2.513193 Loss2: 1.495244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.026439 Loss1: 2.512984 Loss2: 1.513455 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.999870 Loss1: 2.474663 Loss2: 1.525207 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.353125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.115422 Loss1: 2.678398 Loss2: 1.437024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.029958 Loss1: 2.590012 Loss2: 1.439946 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.335417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.232629 Loss1: 3.244761 Loss2: 1.987867
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.302683 Loss1: 2.788731 Loss2: 1.513952
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.110314 Loss1: 2.632266 Loss2: 1.478048
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.033219 Loss1: 2.551919 Loss2: 1.481300
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.313668 Loss1: 3.373578 Loss2: 1.940090
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.488075 Loss1: 3.028389 Loss2: 1.459686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.281215 Loss1: 2.836151 Loss2: 1.445064 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.106119 Loss1: 2.677894 Loss2: 1.428225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.078378 Loss1: 2.639597 Loss2: 1.438781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.020708 Loss1: 2.605812 Loss2: 1.414895 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.428125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.962818 Loss1: 2.525256 Loss2: 1.437562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.920412 Loss1: 2.469965 Loss2: 1.450446 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.356250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.348260 Loss1: 3.466729 Loss2: 1.881531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.378787 Loss1: 2.972612 Loss2: 1.406175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.307848 Loss1: 2.921484 Loss2: 1.386364
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.452768 Loss1: 3.460755 Loss2: 1.992013
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.664228 Loss1: 3.176655 Loss2: 1.487574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.463387 Loss1: 3.008874 Loss2: 1.454513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.337515 Loss1: 2.885514 Loss2: 1.452001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.233257 Loss1: 2.772061 Loss2: 1.461196 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.244619 Loss1: 2.773895 Loss2: 1.470725 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.308333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.214236 Loss1: 2.735281 Loss2: 1.478955 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.133211 Loss1: 2.643612 Loss2: 1.489599 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.355208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.338750 Loss1: 3.365329 Loss2: 1.973422 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.350121 Loss1: 2.896077 Loss2: 1.454044 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.235016 Loss1: 2.779692 Loss2: 1.455324
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.466162 Loss1: 3.459998 Loss2: 2.006164
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.536776 Loss1: 3.020064 Loss2: 1.516712 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.308742 Loss1: 2.813046 Loss2: 1.495696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.247259 Loss1: 2.766233 Loss2: 1.481026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.179943 Loss1: 2.679812 Loss2: 1.500131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.174157 Loss1: 2.677054 Loss2: 1.497103 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.347917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.109830 Loss1: 2.615557 Loss2: 1.494273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.062707 Loss1: 2.551858 Loss2: 1.510849 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.347917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.429872 Loss1: 3.534455 Loss2: 1.895418 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.310728 Loss1: 2.875224 Loss2: 1.435504 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.557493 Loss1: 3.624861 Loss2: 1.932632
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.224798 Loss1: 2.777687 Loss2: 1.447112
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.580085 Loss1: 3.101035 Loss2: 1.479049
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.163237 Loss1: 2.693382 Loss2: 1.469855
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.405577 Loss1: 2.961102 Loss2: 1.444475
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.132015 Loss1: 2.671024 Loss2: 1.460990
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.305242 Loss1: 2.866103 Loss2: 1.439139
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.093393 Loss1: 2.626305 Loss2: 1.467088
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.272793 Loss1: 2.815601 Loss2: 1.457192
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.028222 Loss1: 2.564345 Loss2: 1.463876
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.000909 Loss1: 2.530859 Loss2: 1.470050 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.988550 Loss1: 2.516731 Loss2: 1.471819 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.326287
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.099772 Loss1: 2.645495 Loss2: 1.454277 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.291016
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.536008 Loss1: 3.465272 Loss2: 2.070736 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.383391 Loss1: 2.882577 Loss2: 1.500814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.194798 Loss1: 2.713444 Loss2: 1.481354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.162427 Loss1: 2.676354 Loss2: 1.486073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.124245 Loss1: 2.631894 Loss2: 1.492351 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.084108 Loss1: 2.576861 Loss2: 1.507247 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.043783 Loss1: 2.558629 Loss2: 1.485153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.043007 Loss1: 2.542352 Loss2: 1.500655 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.356971
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.062673 Loss1: 2.554284 Loss2: 1.508388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.078904 Loss1: 2.568887 Loss2: 1.510016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.980648 Loss1: 2.465563 Loss2: 1.515085
-(DefaultActor pid=3764) >> Training accuracy: 0.378125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.618766 Loss1: 3.609449 Loss2: 2.009318
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.744254 Loss1: 3.209699 Loss2: 1.534555
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.610008 Loss1: 3.111946 Loss2: 1.498063
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.446907 Loss1: 2.935193 Loss2: 1.511714
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.405658 Loss1: 2.895597 Loss2: 1.510061
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.581841 Loss1: 3.450255 Loss2: 2.131586
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.382612 Loss1: 2.876285 Loss2: 1.506327
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.308007 Loss1: 2.782085 Loss2: 1.525922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.220423 Loss1: 2.733482 Loss2: 1.486941 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.217879 Loss1: 2.683518 Loss2: 1.534360 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.211023 Loss1: 2.686098 Loss2: 1.524925 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.306250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.010153 Loss1: 2.512094 Loss2: 1.498059 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.365885
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.530095 Loss1: 3.554373 Loss2: 1.975722 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.374021 Loss1: 2.894739 Loss2: 1.479283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.276054 Loss1: 2.797323 Loss2: 1.478732
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.515643 Loss1: 3.531603 Loss2: 1.984040
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.653147 Loss1: 3.171056 Loss2: 1.482092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.335653 Loss1: 2.882554 Loss2: 1.453099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.232774 Loss1: 2.785417 Loss2: 1.447358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.193705 Loss1: 2.737677 Loss2: 1.456028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.090204 Loss1: 2.631475 Loss2: 1.458729 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.295833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.022799 Loss1: 2.519044 Loss2: 1.503755
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.064651 Loss1: 2.600160 Loss2: 1.464492
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.121655 Loss1: 2.646447 Loss2: 1.475208
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.039837 Loss1: 2.576993 Loss2: 1.462845
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.010309 Loss1: 2.530411 Loss2: 1.479898
-(DefaultActor pid=3764) >> Training accuracy: 0.332292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.397949 Loss1: 3.506947 Loss2: 1.891002
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.587787 Loss1: 3.137587 Loss2: 1.450200
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.340806 Loss1: 2.915548 Loss2: 1.425257
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.222793 Loss1: 2.803526 Loss2: 1.419267
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.205407 Loss1: 3.280435 Loss2: 1.924972
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.331743 Loss1: 2.877317 Loss2: 1.454426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.093716 Loss1: 2.673341 Loss2: 1.420375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.028219 Loss1: 2.600349 Loss2: 1.427869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.001794 Loss1: 2.564554 Loss2: 1.437240 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.960878 Loss1: 2.521405 Loss2: 1.439473 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.277083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.925290 Loss1: 2.480042 Loss2: 1.445249 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.813549 Loss1: 2.377187 Loss2: 1.436362 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.361328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.430409 Loss1: 3.417700 Loss2: 2.012709 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.195803 Loss1: 2.710005 Loss2: 1.485797 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.013648 Loss1: 2.568245 Loss2: 1.445403 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.920448 Loss1: 2.463523 Loss2: 1.456925 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.932687 Loss1: 2.471220 Loss2: 1.461467
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.665801 Loss1: 3.155667 Loss2: 1.510135
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.833823 Loss1: 2.377640 Loss2: 1.456184
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.566857 Loss1: 3.069022 Loss2: 1.497836
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.415395 Loss1: 2.920745 Loss2: 1.494651 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.393029
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.473900 Loss1: 2.977021 Loss2: 1.496879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.327897 Loss1: 2.818148 Loss2: 1.509748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.299201 Loss1: 2.776917 Loss2: 1.522285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.219856 Loss1: 2.690240 Loss2: 1.529616 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.291016
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.418712 Loss1: 2.965216 Loss2: 1.453496 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.251738 Loss1: 2.783850 Loss2: 1.467889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.188719 Loss1: 3.307284 Loss2: 1.881435
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.200255 Loss1: 2.729216 Loss2: 1.471039
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.227772 Loss1: 2.800733 Loss2: 1.427039
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.191761 Loss1: 2.707810 Loss2: 1.483952
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.033985 Loss1: 2.639537 Loss2: 1.394448
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.160923 Loss1: 2.665931 Loss2: 1.494992
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.973424 Loss1: 2.587490 Loss2: 1.385934
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.090595 Loss1: 2.603132 Loss2: 1.487463
-(DefaultActor pid=3765) >> Training accuracy: 0.320833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.821244 Loss1: 2.429784 Loss2: 1.391460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.750794 Loss1: 2.350084 Loss2: 1.400709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.785160 Loss1: 2.374968 Loss2: 1.410192
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.554639 Loss1: 3.551373 Loss2: 2.003266
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.799973 Loss1: 2.387950 Loss2: 1.412024
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.650762 Loss1: 3.138716 Loss2: 1.512046
-(DefaultActor pid=3764) >> Training accuracy: 0.440625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.410634 Loss1: 2.918386 Loss2: 1.492248
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.318744 Loss1: 2.843931 Loss2: 1.474813
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.240572 Loss1: 2.755338 Loss2: 1.485234
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.196392 Loss1: 2.716710 Loss2: 1.479682
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.541352 Loss1: 3.544138 Loss2: 1.997214
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.225955 Loss1: 2.732038 Loss2: 1.493917
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.500367 Loss1: 3.005598 Loss2: 1.494769
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.166934 Loss1: 2.668726 Loss2: 1.498208
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.360869 Loss1: 2.889708 Loss2: 1.471161
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.149695 Loss1: 2.642745 Loss2: 1.506950
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.265518 Loss1: 2.813309 Loss2: 1.452209
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.059216 Loss1: 2.530608 Loss2: 1.528609
-(DefaultActor pid=3765) >> Training accuracy: 0.351042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.122992 Loss1: 2.662633 Loss2: 1.460359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.034804 Loss1: 2.567519 Loss2: 1.467285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.059986 Loss1: 2.592823 Loss2: 1.467163
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.362145 Loss1: 3.457913 Loss2: 1.904232
-(DefaultActor pid=3764) >> Training accuracy: 0.318750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.457457 Loss1: 3.013922 Loss2: 1.443535 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.102756 Loss1: 2.689095 Loss2: 1.413661 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.012405 Loss1: 2.582249 Loss2: 1.430156 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.494716 Loss1: 3.559329 Loss2: 1.935387
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.029242 Loss1: 2.597959 Loss2: 1.431283
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.604507 Loss1: 3.128906 Loss2: 1.475601
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.032268 Loss1: 2.584965 Loss2: 1.447303
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.489289 Loss1: 3.052249 Loss2: 1.437040
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.980623 Loss1: 2.537752 Loss2: 1.442871
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.356641 Loss1: 2.923419 Loss2: 1.433222
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.930247 Loss1: 2.482582 Loss2: 1.447664
-(DefaultActor pid=3765) >> Training accuracy: 0.359375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.200963 Loss1: 2.752018 Loss2: 1.448945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.161469 Loss1: 2.703783 Loss2: 1.457686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.170486 Loss1: 2.718561 Loss2: 1.451925
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.275775 Loss1: 3.242250 Loss2: 2.033525
-(DefaultActor pid=3764) >> Training accuracy: 0.317708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.450766 Loss1: 2.944450 Loss2: 1.506316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.167090 Loss1: 2.683068 Loss2: 1.484022 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.050898 Loss1: 2.574363 Loss2: 1.476535 [repeated 2x across cluster]
-DEBUG flwr 2023-10-08 20:06:07,198 | server.py:236 | fit_round 13 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.009029 Loss1: 2.510114 Loss2: 1.498915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.655114 Loss1: 3.195857 Loss2: 1.459257
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.005406 Loss1: 2.512340 Loss2: 1.493066
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.482451 Loss1: 3.055059 Loss2: 1.427392
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.891993 Loss1: 2.383492 Loss2: 1.508501
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.364092 Loss1: 2.940424 Loss2: 1.423668
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.875340 Loss1: 2.383562 Loss2: 1.491778
-(DefaultActor pid=3765) >> Training accuracy: 0.367708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.302227 Loss1: 2.875097 Loss2: 1.427130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.182934 Loss1: 2.730038 Loss2: 1.452896 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.217387 Loss1: 3.291394 Loss2: 1.925993
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.171139 Loss1: 2.723210 Loss2: 1.447928
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.086349 Loss1: 2.651123 Loss2: 1.435226 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.320312
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.048400 Loss1: 2.645037 Loss2: 1.403363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.915023 Loss1: 2.527172 Loss2: 1.387851 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.830818 Loss1: 2.429598 Loss2: 1.401220
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.837804 Loss1: 3.709448 Loss2: 2.128356
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.826199 Loss1: 2.420684 Loss2: 1.405516
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.824022 Loss1: 3.241432 Loss2: 1.582590
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.766714 Loss1: 2.358665 Loss2: 1.408048
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.588733 Loss1: 3.043895 Loss2: 1.544838
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.732179 Loss1: 2.319283 Loss2: 1.412896
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.457303 Loss1: 2.917654 Loss2: 1.539649
-(DefaultActor pid=3765) >> Training accuracy: 0.362500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.452275 Loss1: 2.896098 Loss2: 1.556177
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.389587 Loss1: 2.832711 Loss2: 1.556876
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.339756 Loss1: 2.773407 Loss2: 1.566349
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.299803 Loss1: 2.741410 Loss2: 1.558394
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.294272 Loss1: 2.717617 Loss2: 1.576656
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.249973 Loss1: 2.676419 Loss2: 1.573554
-(DefaultActor pid=3764) >> Training accuracy: 0.292411
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 20:06:07,198][flwr][DEBUG] - fit_round 13 received 50 results and 0 failures
-INFO flwr 2023-10-08 20:06:48,260 | server.py:125 | fit progress: (13, 3.7946639914101303, {'accuracy': 0.119}, 29716.038441409)
->> Test accuracy: 0.119000
-[2023-10-08 20:06:48,260][flwr][INFO] - fit progress: (13, 3.7946639914101303, {'accuracy': 0.119}, 29716.038441409)
-DEBUG flwr 2023-10-08 20:06:48,260 | server.py:173 | evaluate_round 13: strategy sampled 50 clients (out of 50)
-[2023-10-08 20:06:48,260][flwr][DEBUG] - evaluate_round 13: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 20:15:52,509 | server.py:187 | evaluate_round 13 received 50 results and 0 failures
-[2023-10-08 20:15:52,509][flwr][DEBUG] - evaluate_round 13 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 20:15:52,509 | server.py:222 | fit_round 14: strategy sampled 50 clients (out of 50)
-[2023-10-08 20:15:52,509][flwr][DEBUG] - fit_round 14: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.218975 Loss1: 3.297913 Loss2: 1.921062
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.374268 Loss1: 2.911961 Loss2: 1.462307
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.123111 Loss1: 2.701898 Loss2: 1.421212
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.984862 Loss1: 2.551388 Loss2: 1.433473
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.321269 Loss1: 3.352669 Loss2: 1.968599
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.009016 Loss1: 2.561161 Loss2: 1.447855
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.466407 Loss1: 2.963215 Loss2: 1.503192
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.209139 Loss1: 2.736703 Loss2: 1.472436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.143210 Loss1: 2.659766 Loss2: 1.483444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.093480 Loss1: 2.607238 Loss2: 1.486243 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.974586 Loss1: 2.495788 Loss2: 1.478798 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.400000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.022538 Loss1: 2.531050 Loss2: 1.491487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.962940 Loss1: 2.458943 Loss2: 1.503996 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.355469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.235302 Loss1: 3.227597 Loss2: 2.007705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.219193 Loss1: 2.708461 Loss2: 1.510732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.400670 Loss1: 3.460910 Loss2: 1.939761 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.449889 Loss1: 2.997765 Loss2: 1.452124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.304068 Loss1: 2.882316 Loss2: 1.421751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.204740 Loss1: 2.773837 Loss2: 1.430903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.236517 Loss1: 2.803295 Loss2: 1.433222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.145690 Loss1: 2.704207 Loss2: 1.441483 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.389583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.078971 Loss1: 2.647015 Loss2: 1.431956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.978110 Loss1: 2.519007 Loss2: 1.459104 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.327083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.452575 Loss1: 3.460377 Loss2: 1.992198 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.299641 Loss1: 2.837219 Loss2: 1.462422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.351422 Loss1: 3.450519 Loss2: 1.900903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.505441 Loss1: 3.015042 Loss2: 1.490399 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.367363 Loss1: 2.915929 Loss2: 1.451434 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.216438 Loss1: 2.771789 Loss2: 1.444649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.069648 Loss1: 2.629352 Loss2: 1.440296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.040850 Loss1: 2.591764 Loss2: 1.449086 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.377083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.981550 Loss1: 2.505448 Loss2: 1.476101 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.929024 Loss1: 2.452785 Loss2: 1.476240 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.322266
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.472484 Loss1: 2.934371 Loss2: 1.538113 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.181036 Loss1: 2.696212 Loss2: 1.484824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.066330 Loss1: 2.573561 Loss2: 1.492768
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.565942 Loss1: 3.572950 Loss2: 1.992993
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.011676 Loss1: 2.525108 Loss2: 1.486568
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.604392 Loss1: 3.114427 Loss2: 1.489965
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.434537 Loss1: 2.968766 Loss2: 1.465771
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.915862 Loss1: 2.422976 Loss2: 1.492885
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.321534 Loss1: 2.872527 Loss2: 1.449007
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.958911 Loss1: 2.459289 Loss2: 1.499622
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.232525 Loss1: 2.758637 Loss2: 1.473888
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.908220 Loss1: 2.410841 Loss2: 1.497379
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.866100 Loss1: 2.360068 Loss2: 1.506033 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.407292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.091252 Loss1: 2.601780 Loss2: 1.489472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.104823 Loss1: 2.623619 Loss2: 1.481203 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.290179
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.372816 Loss1: 3.499970 Loss2: 1.872845
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.544901 Loss1: 3.135776 Loss2: 1.409125
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.338983 Loss1: 2.961499 Loss2: 1.377484
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.201671 Loss1: 2.815801 Loss2: 1.385870
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.379556 Loss1: 3.398511 Loss2: 1.981045
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.438835 Loss1: 2.918711 Loss2: 1.520124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.298307 Loss1: 2.833558 Loss2: 1.464749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.092435 Loss1: 2.628295 Loss2: 1.464139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.086576 Loss1: 2.620979 Loss2: 1.465597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.015577 Loss1: 2.584849 Loss2: 1.430729 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.339583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.865714 Loss1: 2.383447 Loss2: 1.482268 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.906354 Loss1: 2.420161 Loss2: 1.486194 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.384766
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.596184 Loss1: 3.113297 Loss2: 1.482887 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.237592 Loss1: 2.794550 Loss2: 1.443042 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.225461 Loss1: 2.769453 Loss2: 1.456008
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.382177 Loss1: 3.406845 Loss2: 1.975332
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.093485 Loss1: 2.648359 Loss2: 1.445126
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.442503 Loss1: 2.949943 Loss2: 1.492560
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.038040 Loss1: 2.581045 Loss2: 1.456994
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.305713 Loss1: 2.826259 Loss2: 1.479453
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.959245 Loss1: 2.495095 Loss2: 1.464150
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.083204 Loss1: 2.611620 Loss2: 1.471584
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.208834 Loss1: 2.736774 Loss2: 1.472060 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.355469
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.982547 Loss1: 2.497186 Loss2: 1.485361
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.122305 Loss1: 2.629706 Loss2: 1.492599
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.063993 Loss1: 2.585226 Loss2: 1.478767
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.976348 Loss1: 2.489013 Loss2: 1.487334
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.979615 Loss1: 2.486517 Loss2: 1.493098
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.956934 Loss1: 2.451471 Loss2: 1.505463
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.387165 Loss1: 3.471146 Loss2: 1.916019
-(DefaultActor pid=3764) >> Training accuracy: 0.388787
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.517656 Loss1: 3.053902 Loss2: 1.463754
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.239991 Loss1: 2.805808 Loss2: 1.434183
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.124605 Loss1: 2.702780 Loss2: 1.421825
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.087422 Loss1: 2.667086 Loss2: 1.420336
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.348487 Loss1: 3.330512 Loss2: 2.017975
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.099995 Loss1: 2.657385 Loss2: 1.442610
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.403844 Loss1: 2.895133 Loss2: 1.508710
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.994144 Loss1: 2.556538 Loss2: 1.437606
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.209992 Loss1: 2.714276 Loss2: 1.495717
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.957466 Loss1: 2.516053 Loss2: 1.441412
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.128024 Loss1: 2.639948 Loss2: 1.488076
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.942828 Loss1: 2.492639 Loss2: 1.450189
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.137882 Loss1: 2.646791 Loss2: 1.491091
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.852827 Loss1: 2.388962 Loss2: 1.463865
-(DefaultActor pid=3765) >> Training accuracy: 0.337500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.999206 Loss1: 2.505238 Loss2: 1.493968 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.960989 Loss1: 2.447605 Loss2: 1.513383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.949914 Loss1: 2.430693 Loss2: 1.519221
-(DefaultActor pid=3764) >> Training accuracy: 0.363542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.449357 Loss1: 3.388207 Loss2: 2.061151
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.565997 Loss1: 3.003477 Loss2: 1.562520
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.378792 Loss1: 2.869616 Loss2: 1.509177
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.241791 Loss1: 2.735238 Loss2: 1.506553
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.248911 Loss1: 2.743658 Loss2: 1.505253
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.350720 Loss1: 3.433465 Loss2: 1.917255
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.194589 Loss1: 2.667302 Loss2: 1.527287
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.535585 Loss1: 3.040908 Loss2: 1.494677
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.137950 Loss1: 2.616544 Loss2: 1.521406
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.233684 Loss1: 2.788396 Loss2: 1.445287
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.098503 Loss1: 2.571438 Loss2: 1.527066
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.066278 Loss1: 2.631825 Loss2: 1.434453
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.062904 Loss1: 2.530803 Loss2: 1.532101
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.032795 Loss1: 2.600080 Loss2: 1.432715
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.048057 Loss1: 2.527625 Loss2: 1.520432
-(DefaultActor pid=3765) >> Training accuracy: 0.330208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.916823 Loss1: 2.467285 Loss2: 1.449538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.893296 Loss1: 2.429371 Loss2: 1.463925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.883152 Loss1: 2.408143 Loss2: 1.475010
-(DefaultActor pid=3764) >> Training accuracy: 0.362500
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.659058 Loss1: 3.599910 Loss2: 2.059148
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.573735 Loss1: 3.023690 Loss2: 1.550046
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.281686 Loss1: 2.788349 Loss2: 1.493337
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.271400 Loss1: 2.776119 Loss2: 1.495281
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.215690 Loss1: 2.722203 Loss2: 1.493487
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.282655 Loss1: 3.324413 Loss2: 1.958242
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.115176 Loss1: 2.617041 Loss2: 1.498135
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.521885 Loss1: 3.035385 Loss2: 1.486500
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.083637 Loss1: 2.580803 Loss2: 1.502834
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.335987 Loss1: 2.885778 Loss2: 1.450209
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.014241 Loss1: 2.505202 Loss2: 1.509039
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.282211 Loss1: 2.831324 Loss2: 1.450886
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.972887 Loss1: 2.470848 Loss2: 1.502039
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.189787 Loss1: 2.730322 Loss2: 1.459465
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.893926 Loss1: 2.385053 Loss2: 1.508873
-(DefaultActor pid=3765) >> Training accuracy: 0.359375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.140971 Loss1: 2.659696 Loss2: 1.481275 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.013394 Loss1: 2.545565 Loss2: 1.467829 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.991369 Loss1: 2.510806 Loss2: 1.480562
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.332425 Loss1: 3.306840 Loss2: 2.025585
-(DefaultActor pid=3764) >> Training accuracy: 0.368750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.298776 Loss1: 2.760516 Loss2: 1.538260
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.064912 Loss1: 2.581909 Loss2: 1.483004
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.966149 Loss1: 2.502143 Loss2: 1.464006
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.970895 Loss1: 2.505341 Loss2: 1.465553
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.808604 Loss1: 2.344387 Loss2: 1.464217
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.215499 Loss1: 3.303982 Loss2: 1.911516
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.348720 Loss1: 2.888047 Loss2: 1.460674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.814820 Loss1: 2.328048 Loss2: 1.486772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.706241 Loss1: 2.229493 Loss2: 1.476749 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.411058
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.938906 Loss1: 2.508726 Loss2: 1.430180 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.868387 Loss1: 2.440947 Loss2: 1.427440 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.339286 Loss1: 3.441003 Loss2: 1.898283
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.904323 Loss1: 2.443423 Loss2: 1.460900
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.498779 Loss1: 3.060857 Loss2: 1.437922
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.810389 Loss1: 2.373632 Loss2: 1.436757
-(DefaultActor pid=3764) >> Training accuracy: 0.386458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.232474 Loss1: 2.821883 Loss2: 1.410591 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.086851 Loss1: 2.665213 Loss2: 1.421639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.009915 Loss1: 2.584428 Loss2: 1.425487
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.114815 Loss1: 3.196586 Loss2: 1.918229
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.956652 Loss1: 2.536264 Loss2: 1.420388
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.244769 Loss1: 2.759386 Loss2: 1.485384
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.957290 Loss1: 2.516259 Loss2: 1.441032
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.992529 Loss1: 2.530588 Loss2: 1.461940
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.887635 Loss1: 2.439631 Loss2: 1.448004
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.937165 Loss1: 2.490588 Loss2: 1.446577
-(DefaultActor pid=3765) >> Training accuracy: 0.353125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.876458 Loss1: 2.417468 Loss2: 1.458991
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.775829 Loss1: 2.327631 Loss2: 1.448198
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.724680 Loss1: 2.277306 Loss2: 1.447374
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.658073 Loss1: 2.194346 Loss2: 1.463727
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.138178 Loss1: 3.215599 Loss2: 1.922579
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.649293 Loss1: 2.176882 Loss2: 1.472411
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.257311 Loss1: 2.818944 Loss2: 1.438367
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.636301 Loss1: 2.157670 Loss2: 1.478631
-(DefaultActor pid=3764) >> Training accuracy: 0.434375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.955450 Loss1: 2.534022 Loss2: 1.421428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.844306 Loss1: 2.416223 Loss2: 1.428083 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.829337 Loss1: 2.401447 Loss2: 1.427890
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.447963 Loss1: 3.509114 Loss2: 1.938850
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.859165 Loss1: 2.416748 Loss2: 1.442417
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.599299 Loss1: 3.116782 Loss2: 1.482517
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.724018 Loss1: 2.282409 Loss2: 1.441609
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.467291 Loss1: 2.995379 Loss2: 1.471912
-(DefaultActor pid=3765) >> Training accuracy: 0.386458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.638388 Loss1: 2.193938 Loss2: 1.444449
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.365743 Loss1: 2.893089 Loss2: 1.472654
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.267442 Loss1: 2.794825 Loss2: 1.472617
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.269825 Loss1: 2.796674 Loss2: 1.473151
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.284169 Loss1: 2.801586 Loss2: 1.482583
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.150092 Loss1: 2.659343 Loss2: 1.490749
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.253760 Loss1: 3.300546 Loss2: 1.953213
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.395568 Loss1: 2.893367 Loss2: 1.502200 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.369141
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.136590 Loss1: 2.633801 Loss2: 1.502789
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.275453 Loss1: 2.805722 Loss2: 1.469731
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.122793 Loss1: 2.661767 Loss2: 1.461026
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.113898 Loss1: 2.644936 Loss2: 1.468961
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.016589 Loss1: 2.554130 Loss2: 1.462459
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.973734 Loss1: 2.498421 Loss2: 1.475313
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.733555 Loss1: 3.654997 Loss2: 2.078558
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.005385 Loss1: 2.529879 Loss2: 1.475505
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.053710 Loss1: 2.569278 Loss2: 1.484433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.892659 Loss1: 2.426602 Loss2: 1.466057 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.385417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.230579 Loss1: 2.738238 Loss2: 1.492341 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.160560 Loss1: 2.642661 Loss2: 1.517899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.206887 Loss1: 2.683386 Loss2: 1.523501 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.127014 Loss1: 2.595021 Loss2: 1.531993 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.324777
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.988641 Loss1: 2.502991 Loss2: 1.485650 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.878530 Loss1: 2.388752 Loss2: 1.489778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.405884 Loss1: 3.360203 Loss2: 2.045682
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.765870 Loss1: 2.268899 Loss2: 1.496971
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.741398 Loss1: 2.237685 Loss2: 1.503713 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.675389 Loss1: 2.180635 Loss2: 1.494754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.748068 Loss1: 2.219749 Loss2: 1.528318 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.378125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.032568 Loss1: 2.546574 Loss2: 1.485995 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.879729 Loss1: 2.396762 Loss2: 1.482966 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.350962
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.842378 Loss1: 2.336366 Loss2: 1.506012
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.590078 Loss1: 3.603738 Loss2: 1.986339
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.585914 Loss1: 3.107786 Loss2: 1.478127
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.350337 Loss1: 2.908367 Loss2: 1.441970
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.268581 Loss1: 2.825742 Loss2: 1.442839
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.143643 Loss1: 2.694027 Loss2: 1.449616
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.479384 Loss1: 3.345597 Loss2: 2.133787
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.520217 Loss1: 3.011678 Loss2: 1.508539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.305598 Loss1: 2.843566 Loss2: 1.462032
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.060340 Loss1: 2.601566 Loss2: 1.458774
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.062948 Loss1: 2.595509 Loss2: 1.467438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.054283 Loss1: 2.581007 Loss2: 1.473276 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.012954 Loss1: 2.529966 Loss2: 1.482988 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.340625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.945671 Loss1: 2.464418 Loss2: 1.481253 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.385417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.253174 Loss1: 3.271968 Loss2: 1.981206 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.245318 Loss1: 2.733677 Loss2: 1.511642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.071774 Loss1: 2.607782 Loss2: 1.463992
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.151221 Loss1: 3.144712 Loss2: 2.006510
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.004787 Loss1: 2.537556 Loss2: 1.467230
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.273336 Loss1: 2.758160 Loss2: 1.515176
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.107905 Loss1: 2.615102 Loss2: 1.492803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.010825 Loss1: 2.533137 Loss2: 1.477688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.990039 Loss1: 2.497323 Loss2: 1.492716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.859064 Loss1: 2.374815 Loss2: 1.484250 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.357292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.805914 Loss1: 2.319692 Loss2: 1.486222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.750417 Loss1: 2.237600 Loss2: 1.512818 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.415039
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.329609 Loss1: 3.357710 Loss2: 1.971900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.066872 Loss1: 2.595255 Loss2: 1.471617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.348462 Loss1: 3.323076 Loss2: 2.025386 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.449730 Loss1: 2.933371 Loss2: 1.516359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.252880 Loss1: 2.776652 Loss2: 1.476227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.063017 Loss1: 2.588943 Loss2: 1.474074 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.039961 Loss1: 2.579358 Loss2: 1.460602 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.992462 Loss1: 2.523516 Loss2: 1.468946 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.439583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.029950 Loss1: 2.548530 Loss2: 1.481420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.935673 Loss1: 2.432219 Loss2: 1.503454 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.401786
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.333657 Loss1: 3.321056 Loss2: 2.012601 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.080097 Loss1: 2.610839 Loss2: 1.469258 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.958793 Loss1: 2.499296 Loss2: 1.459497
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.556978 Loss1: 3.530268 Loss2: 2.026711
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.552781 Loss1: 3.025953 Loss2: 1.526828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.341405 Loss1: 2.842271 Loss2: 1.499134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.286149 Loss1: 2.793658 Loss2: 1.492491 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.214698 Loss1: 2.712277 Loss2: 1.502421 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.175346 Loss1: 2.672671 Loss2: 1.502675 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.388542
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.820050 Loss1: 2.333867 Loss2: 1.486183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.176627 Loss1: 2.653653 Loss2: 1.522974
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.104985 Loss1: 2.591613 Loss2: 1.513372
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.038298 Loss1: 2.519300 Loss2: 1.518998
-(DefaultActor pid=3764) Epoch: 9 Loss: 4.024096 Loss1: 2.498817 Loss2: 1.525280
-(DefaultActor pid=3764) >> Training accuracy: 0.328125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.268582 Loss1: 3.304436 Loss2: 1.964145
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.243090 Loss1: 2.782795 Loss2: 1.460295
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.973233 Loss1: 2.534434 Loss2: 1.438800
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.877586 Loss1: 2.451846 Loss2: 1.425740
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.166445 Loss1: 3.311749 Loss2: 1.854695
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.868369 Loss1: 2.440206 Loss2: 1.428163
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.323649 Loss1: 2.852569 Loss2: 1.471080
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.739393 Loss1: 2.305882 Loss2: 1.433511
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.118314 Loss1: 2.685429 Loss2: 1.432886
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.026241 Loss1: 2.595436 Loss2: 1.430805 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.007221 Loss1: 2.571761 Loss2: 1.435460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.915031 Loss1: 2.464969 Loss2: 1.450062 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.381250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.897303 Loss1: 2.443785 Loss2: 1.453517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.759079 Loss1: 2.297668 Loss2: 1.461411 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.380859
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.503627 Loss1: 3.548924 Loss2: 1.954703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.415424 Loss1: 2.966398 Loss2: 1.449026 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.379973 Loss1: 2.921169 Loss2: 1.458804
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.243489 Loss1: 3.336919 Loss2: 1.906570
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.437825 Loss1: 2.982066 Loss2: 1.455759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.218652 Loss1: 2.796031 Loss2: 1.422621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.143333 Loss1: 2.716776 Loss2: 1.426557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.121191 Loss1: 2.681742 Loss2: 1.439450 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.075377 Loss1: 2.602860 Loss2: 1.472517
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.078381 Loss1: 2.636771 Loss2: 1.441609
-(DefaultActor pid=3765) Epoch: 9 Loss: 4.004289 Loss1: 2.532668 Loss2: 1.471621
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.026907 Loss1: 2.576910 Loss2: 1.449997
-(DefaultActor pid=3765) >> Training accuracy: 0.343750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.981832 Loss1: 2.539768 Loss2: 1.442064
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.008627 Loss1: 2.558053 Loss2: 1.450573
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.856273 Loss1: 2.409462 Loss2: 1.446811
-(DefaultActor pid=3764) >> Training accuracy: 0.386458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.367664 Loss1: 3.377251 Loss2: 1.990413
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.500898 Loss1: 2.954961 Loss2: 1.545937
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.191266 Loss1: 2.701897 Loss2: 1.489369
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.127386 Loss1: 2.631882 Loss2: 1.495504
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.380781 Loss1: 3.331965 Loss2: 2.048817
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.087211 Loss1: 2.590858 Loss2: 1.496353
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.490001 Loss1: 2.937248 Loss2: 1.552753
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.056870 Loss1: 2.560254 Loss2: 1.496617
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.272333 Loss1: 2.751424 Loss2: 1.520909
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.975160 Loss1: 2.484798 Loss2: 1.490363
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.141141 Loss1: 2.613493 Loss2: 1.527648
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.981451 Loss1: 2.470401 Loss2: 1.511050
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.154469 Loss1: 2.623050 Loss2: 1.531419
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.914869 Loss1: 2.415170 Loss2: 1.499699
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.123149 Loss1: 2.577325 Loss2: 1.545824
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.804851 Loss1: 2.304000 Loss2: 1.500851
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.049681 Loss1: 2.507618 Loss2: 1.542063
-(DefaultActor pid=3765) >> Training accuracy: 0.378125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.999964 Loss1: 2.451998 Loss2: 1.547966
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.941061 Loss1: 2.395144 Loss2: 1.545917
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.940777 Loss1: 2.383409 Loss2: 1.557368
-(DefaultActor pid=3764) >> Training accuracy: 0.378125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.298840 Loss1: 3.360110 Loss2: 1.938730
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.390366 Loss1: 2.919792 Loss2: 1.470574
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.197941 Loss1: 2.745894 Loss2: 1.452047
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.110495 Loss1: 2.671149 Loss2: 1.439346
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.312244 Loss1: 3.241455 Loss2: 2.070789
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.301094 Loss1: 2.714547 Loss2: 1.586547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.172316 Loss1: 2.635907 Loss2: 1.536409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.029860 Loss1: 2.496012 Loss2: 1.533848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.997352 Loss1: 2.458586 Loss2: 1.538766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.953669 Loss1: 2.423385 Loss2: 1.530284 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.398958
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.785350 Loss1: 2.332011 Loss2: 1.453339
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.879664 Loss1: 2.330390 Loss2: 1.549275
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.823515 Loss1: 2.286671 Loss2: 1.536844
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.761688 Loss1: 2.210268 Loss2: 1.551420
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.710774 Loss1: 2.156834 Loss2: 1.553940
-(DefaultActor pid=3764) >> Training accuracy: 0.417708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.392356 Loss1: 3.474351 Loss2: 1.918005
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.390922 Loss1: 2.960472 Loss2: 1.430450
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.250108 Loss1: 2.851048 Loss2: 1.399060
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.155328 Loss1: 2.773300 Loss2: 1.382028
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.236581 Loss1: 3.313253 Loss2: 1.923327
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.505037 Loss1: 3.041815 Loss2: 1.463222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.206353 Loss1: 2.792531 Loss2: 1.413823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.076578 Loss1: 2.667360 Loss2: 1.409218 [repeated 2x across cluster]
-DEBUG flwr 2023-10-08 20:44:42,081 | server.py:236 | fit_round 14 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.974785 Loss1: 2.555707 Loss2: 1.419077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.946234 Loss1: 2.520259 Loss2: 1.425975 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.384375
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.835282 Loss1: 2.410015 Loss2: 1.425267
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.853742 Loss1: 2.424056 Loss2: 1.429686
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.896136 Loss1: 2.456617 Loss2: 1.439519
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.857308 Loss1: 2.413817 Loss2: 1.443491
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.823028 Loss1: 2.382625 Loss2: 1.440403
-(DefaultActor pid=3764) >> Training accuracy: 0.416667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.437683 Loss1: 3.458297 Loss2: 1.979386
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.616248 Loss1: 3.104179 Loss2: 1.512068
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.397432 Loss1: 2.913504 Loss2: 1.483928
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.246935 Loss1: 2.788239 Loss2: 1.458696
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.291682 Loss1: 3.235639 Loss2: 2.056043
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.347411 Loss1: 2.829718 Loss2: 1.517693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.114264 Loss1: 2.632089 Loss2: 1.482175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.017986 Loss1: 2.556583 Loss2: 1.461403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.962692 Loss1: 2.492597 Loss2: 1.470095 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.915401 Loss1: 2.449887 Loss2: 1.465515 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.342708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.846669 Loss1: 2.369976 Loss2: 1.476693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.870327 Loss1: 2.374729 Loss2: 1.495598 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.365625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.373791 Loss1: 3.495273 Loss2: 1.878518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.359940 Loss1: 2.947126 Loss2: 1.412814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.294998 Loss1: 2.880432 Loss2: 1.414566
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.389983 Loss1: 3.474328 Loss2: 1.915655
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.391852 Loss1: 2.941911 Loss2: 1.449941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.178444 Loss1: 2.770258 Loss2: 1.408187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.007076 Loss1: 2.617177 Loss2: 1.389899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.107600 Loss1: 2.672131 Loss2: 1.435468
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.018130 Loss1: 2.622340 Loss2: 1.395790
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.990381 Loss1: 2.545748 Loss2: 1.444632
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.930825 Loss1: 2.537107 Loss2: 1.393718
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.994259 Loss1: 2.543509 Loss2: 1.450749
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.897653 Loss1: 2.496723 Loss2: 1.400931
-(DefaultActor pid=3765) >> Training accuracy: 0.360352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.837082 Loss1: 2.432379 Loss2: 1.404703
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.793531 Loss1: 2.375483 Loss2: 1.418048
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.776108 Loss1: 2.367715 Loss2: 1.408393
-(DefaultActor pid=3764) >> Training accuracy: 0.369792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 20:44:42,081][flwr][DEBUG] - fit_round 14 received 50 results and 0 failures
-INFO flwr 2023-10-08 20:45:23,822 | server.py:125 | fit progress: (14, 3.694523496749683, {'accuracy': 0.1316}, 32031.600813974997)
->> Test accuracy: 0.131600
-[2023-10-08 20:45:23,822][flwr][INFO] - fit progress: (14, 3.694523496749683, {'accuracy': 0.1316}, 32031.600813974997)
-DEBUG flwr 2023-10-08 20:45:23,823 | server.py:173 | evaluate_round 14: strategy sampled 50 clients (out of 50)
-[2023-10-08 20:45:23,823][flwr][DEBUG] - evaluate_round 14: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 20:54:30,870 | server.py:187 | evaluate_round 14 received 50 results and 0 failures
-[2023-10-08 20:54:30,870][flwr][DEBUG] - evaluate_round 14 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 20:54:30,871 | server.py:222 | fit_round 15: strategy sampled 50 clients (out of 50)
-[2023-10-08 20:54:30,871][flwr][DEBUG] - fit_round 15: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.332531 Loss1: 3.395482 Loss2: 1.937049
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.515927 Loss1: 3.040229 Loss2: 1.475698
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.277959 Loss1: 2.849290 Loss2: 1.428669
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.142993 Loss1: 2.705938 Loss2: 1.437056
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.141977 Loss1: 3.131861 Loss2: 2.010115
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.068556 Loss1: 2.639447 Loss2: 1.429109
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.296860 Loss1: 2.797893 Loss2: 1.498967
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.906008 Loss1: 2.472763 Loss2: 1.433246
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.035630 Loss1: 2.572822 Loss2: 1.462808
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.946380 Loss1: 2.492709 Loss2: 1.453670
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.900146 Loss1: 2.455720 Loss2: 1.444426
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.941273 Loss1: 2.486460 Loss2: 1.454812
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.829113 Loss1: 2.374357 Loss2: 1.454756
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.966295 Loss1: 2.502582 Loss2: 1.463713
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.803541 Loss1: 2.345890 Loss2: 1.457650
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.932843 Loss1: 2.458672 Loss2: 1.474171
-(DefaultActor pid=3765) >> Training accuracy: 0.344792
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.792812 Loss1: 2.334256 Loss2: 1.458556
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.706124 Loss1: 2.248535 Loss2: 1.457589
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.720101 Loss1: 2.254707 Loss2: 1.465394
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.636786 Loss1: 2.167554 Loss2: 1.469232
-(DefaultActor pid=3764) >> Training accuracy: 0.435417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.195003 Loss1: 3.260360 Loss2: 1.934643
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.346742 Loss1: 2.892650 Loss2: 1.454093
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.114602 Loss1: 2.665339 Loss2: 1.449263
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.973213 Loss1: 2.545911 Loss2: 1.427301
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.148642 Loss1: 3.238893 Loss2: 1.909749
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.294889 Loss1: 2.855832 Loss2: 1.439057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.076593 Loss1: 2.668394 Loss2: 1.408199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.961216 Loss1: 2.551396 Loss2: 1.409820 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.890235 Loss1: 2.482024 Loss2: 1.408211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.872102 Loss1: 2.456667 Loss2: 1.415434 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.409375
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.713751 Loss1: 2.239053 Loss2: 1.474698
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.835968 Loss1: 2.401429 Loss2: 1.434539
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.867407 Loss1: 2.431847 Loss2: 1.435559
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.782709 Loss1: 2.349064 Loss2: 1.433645
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.748081 Loss1: 2.298644 Loss2: 1.449438
-(DefaultActor pid=3764) >> Training accuracy: 0.396875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.226228 Loss1: 3.298784 Loss2: 1.927445
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.290773 Loss1: 2.848188 Loss2: 1.442585
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.104517 Loss1: 2.693189 Loss2: 1.411328
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.988373 Loss1: 2.584697 Loss2: 1.403675
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.472599 Loss1: 3.320378 Loss2: 2.152221
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.465917 Loss1: 2.934386 Loss2: 1.531531
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.941837 Loss1: 2.527786 Loss2: 1.414050
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.868513 Loss1: 2.462555 Loss2: 1.405958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.830277 Loss1: 2.419653 Loss2: 1.410624 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.790199 Loss1: 2.362918 Loss2: 1.427282 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.792789 Loss1: 2.303465 Loss2: 1.489324 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.864810 Loss1: 2.373493 Loss2: 1.491317 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.401042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.716819 Loss1: 2.204181 Loss2: 1.512638 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.440104
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.267665 Loss1: 3.230600 Loss2: 2.037066
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.190132 Loss1: 2.647112 Loss2: 1.543019
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.966296 Loss1: 2.467290 Loss2: 1.499007
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.817482 Loss1: 2.328301 Loss2: 1.489181
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.440281 Loss1: 3.374136 Loss2: 2.066146
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.494524 Loss1: 2.950957 Loss2: 1.543567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.248921 Loss1: 2.726095 Loss2: 1.522826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.121283 Loss1: 2.618003 Loss2: 1.503280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.086268 Loss1: 2.574411 Loss2: 1.511857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.026721 Loss1: 2.520826 Loss2: 1.505895 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.428125
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.584403 Loss1: 2.074238 Loss2: 1.510164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.977728 Loss1: 2.449868 Loss2: 1.527860
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.975525 Loss1: 2.452410 Loss2: 1.523114
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.860634 Loss1: 2.338265 Loss2: 1.522369
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.841161 Loss1: 2.299805 Loss2: 1.541356
-(DefaultActor pid=3764) >> Training accuracy: 0.386458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.353877 Loss1: 3.317719 Loss2: 2.036158
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.477921 Loss1: 2.969465 Loss2: 1.508456
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.272947 Loss1: 2.783802 Loss2: 1.489145
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.160458 Loss1: 2.675678 Loss2: 1.484780
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.569891 Loss1: 3.549888 Loss2: 2.020003
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.553440 Loss1: 3.060539 Loss2: 1.492901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.347787 Loss1: 2.890981 Loss2: 1.456806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.023646 Loss1: 2.520427 Loss2: 1.503218
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.187505 Loss1: 2.735739 Loss2: 1.451766
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.969634 Loss1: 2.468743 Loss2: 1.500892
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.118494 Loss1: 2.662383 Loss2: 1.456111
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.897016 Loss1: 2.386809 Loss2: 1.510208
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.140608 Loss1: 2.647115 Loss2: 1.493493
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.035020 Loss1: 2.552067 Loss2: 1.482954
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.849114 Loss1: 2.330599 Loss2: 1.518515
-(DefaultActor pid=3765) >> Training accuracy: 0.365625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.914701 Loss1: 2.420262 Loss2: 1.494439 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.372768
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.081463 Loss1: 3.171251 Loss2: 1.910211 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.030826 Loss1: 2.626968 Loss2: 1.403858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.923758 Loss1: 2.519737 Loss2: 1.404021
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.389831 Loss1: 3.343828 Loss2: 2.046004
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.862823 Loss1: 2.437782 Loss2: 1.425042
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.456632 Loss1: 2.935989 Loss2: 1.520643
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.914828 Loss1: 2.487850 Loss2: 1.426977
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.234389 Loss1: 2.735639 Loss2: 1.498750
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.869144 Loss1: 2.447161 Loss2: 1.421982
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.128266 Loss1: 2.631999 Loss2: 1.496267
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.681658 Loss1: 2.254923 Loss2: 1.426735
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.060857 Loss1: 2.556213 Loss2: 1.504644
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.703804 Loss1: 2.277837 Loss2: 1.425967
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.024848 Loss1: 2.518689 Loss2: 1.506160
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.588415 Loss1: 2.155497 Loss2: 1.432918
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.974153 Loss1: 2.462086 Loss2: 1.512066
-(DefaultActor pid=3765) >> Training accuracy: 0.419792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.955772 Loss1: 2.433468 Loss2: 1.522304
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.899696 Loss1: 2.366990 Loss2: 1.532707
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.887822 Loss1: 2.350835 Loss2: 1.536988
-(DefaultActor pid=3764) >> Training accuracy: 0.368750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.612262 Loss1: 3.541078 Loss2: 2.071184
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.599471 Loss1: 3.061353 Loss2: 1.538118
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.457392 Loss1: 2.942410 Loss2: 1.514983
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.316879 Loss1: 2.808819 Loss2: 1.508060
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.181022 Loss1: 3.232354 Loss2: 1.948668
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.324613 Loss1: 2.861462 Loss2: 1.463151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.182315 Loss1: 2.745857 Loss2: 1.436457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.098275 Loss1: 2.667363 Loss2: 1.430912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.022476 Loss1: 2.589195 Loss2: 1.433281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.934842 Loss1: 2.492183 Loss2: 1.442659 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.340402
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.811367 Loss1: 2.365817 Loss2: 1.445550 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.775967 Loss1: 2.312801 Loss2: 1.463166 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.382292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.263904 Loss1: 2.748940 Loss2: 1.514965 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.967542 Loss1: 2.489538 Loss2: 1.478004 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.862933 Loss1: 2.374101 Loss2: 1.488832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.055012 Loss1: 2.508645 Loss2: 1.546367 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.985681 Loss1: 2.456528 Loss2: 1.529153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.821795 Loss1: 2.294376 Loss2: 1.527420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.817223 Loss1: 2.281537 Loss2: 1.535685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.801956 Loss1: 2.263998 Loss2: 1.537958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.686382 Loss1: 2.175883 Loss2: 1.510499
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.695013 Loss1: 2.157133 Loss2: 1.537880
-(DefaultActor pid=3765) >> Training accuracy: 0.386719
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.647796 Loss1: 2.108128 Loss2: 1.539668
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.574187 Loss1: 2.017205 Loss2: 1.556982
-(DefaultActor pid=3764) >> Training accuracy: 0.436298
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.347582 Loss1: 3.282245 Loss2: 2.065337
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.371563 Loss1: 2.809689 Loss2: 1.561874
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.112410 Loss1: 2.585268 Loss2: 1.527142
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.033364 Loss1: 2.521352 Loss2: 1.512012
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.053573 Loss1: 2.541438 Loss2: 1.512136 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.958301 Loss1: 2.431235 Loss2: 1.527066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.896830 Loss1: 2.362520 Loss2: 1.534310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.869404 Loss1: 2.342840 Loss2: 1.526564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.790061 Loss1: 2.258330 Loss2: 1.531730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.981583 Loss1: 2.562610 Loss2: 1.418973
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.785275 Loss1: 2.236497 Loss2: 1.548778
-(DefaultActor pid=3765) >> Training accuracy: 0.435268
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.963668 Loss1: 2.529757 Loss2: 1.433910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.863985 Loss1: 2.425809 Loss2: 1.438175 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.366667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.340187 Loss1: 2.776756 Loss2: 1.563430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.057123 Loss1: 2.506010 Loss2: 1.551114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.876148 Loss1: 2.340704 Loss2: 1.535444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.866076 Loss1: 2.317676 Loss2: 1.548399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.822647 Loss1: 2.272859 Loss2: 1.549788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.784142 Loss1: 2.228466 Loss2: 1.555675 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.761812 Loss1: 2.189462 Loss2: 1.572351 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.697308 Loss1: 2.137147 Loss2: 1.560161 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.419792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.908963 Loss1: 2.433484 Loss2: 1.475479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.816237 Loss1: 2.331105 Loss2: 1.485132 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.391667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.119493 Loss1: 3.282285 Loss2: 1.837209
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.230348 Loss1: 2.834874 Loss2: 1.395473
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.025008 Loss1: 2.649370 Loss2: 1.375638
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.916683 Loss1: 2.555121 Loss2: 1.361561
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.347308 Loss1: 3.349542 Loss2: 1.997766
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.426601 Loss1: 2.907656 Loss2: 1.518945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.226147 Loss1: 2.732440 Loss2: 1.493707 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.093730 Loss1: 2.618151 Loss2: 1.475579 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.001958 Loss1: 2.522540 Loss2: 1.479418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.948476 Loss1: 2.450547 Loss2: 1.497929 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.396484
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.939633 Loss1: 2.440457 Loss2: 1.499175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.903048 Loss1: 2.404303 Loss2: 1.498745 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.379883
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.310540 Loss1: 3.330612 Loss2: 1.979928 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.243604 Loss1: 2.762186 Loss2: 1.481419 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.188517 Loss1: 2.716744 Loss2: 1.471773
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.237429 Loss1: 3.270517 Loss2: 1.966912
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.298796 Loss1: 2.824382 Loss2: 1.474415 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.100180 Loss1: 2.653163 Loss2: 1.447018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.010634 Loss1: 2.566618 Loss2: 1.444016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.944154 Loss1: 2.491093 Loss2: 1.453061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.825010 Loss1: 2.316951 Loss2: 1.508059
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.867440 Loss1: 2.416678 Loss2: 1.450762
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.829739 Loss1: 2.305829 Loss2: 1.523909
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.872145 Loss1: 2.405686 Loss2: 1.466458
-(DefaultActor pid=3765) >> Training accuracy: 0.387695
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.831876 Loss1: 2.363875 Loss2: 1.468001
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.714740 Loss1: 2.245619 Loss2: 1.469121
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.655439 Loss1: 2.181768 Loss2: 1.473671
-(DefaultActor pid=3764) >> Training accuracy: 0.391667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.404466 Loss1: 3.399908 Loss2: 2.004558
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.606980 Loss1: 3.086835 Loss2: 1.520145
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.427197 Loss1: 2.913328 Loss2: 1.513869
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.386419 Loss1: 3.397363 Loss2: 1.989056
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.309566 Loss1: 2.795488 Loss2: 1.514079
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.504309 Loss1: 3.007384 Loss2: 1.496925
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.229585 Loss1: 2.713350 Loss2: 1.516236
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.343116 Loss1: 2.868680 Loss2: 1.474436
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.222063 Loss1: 2.698181 Loss2: 1.523882
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.204592 Loss1: 2.732768 Loss2: 1.471825
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.214345 Loss1: 2.663073 Loss2: 1.551272
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.215860 Loss1: 2.729811 Loss2: 1.486048
-(DefaultActor pid=3765) Epoch: 7 Loss: 4.167460 Loss1: 2.630650 Loss2: 1.536811
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.067883 Loss1: 2.584708 Loss2: 1.483175
-(DefaultActor pid=3765) Epoch: 8 Loss: 4.089576 Loss1: 2.551676 Loss2: 1.537900
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.012532 Loss1: 2.518755 Loss2: 1.493777
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.996384 Loss1: 2.461837 Loss2: 1.534546
-(DefaultActor pid=3765) >> Training accuracy: 0.325195
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 4.052939 Loss1: 2.548917 Loss2: 1.504023 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.342773
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.412603 Loss1: 3.449291 Loss2: 1.963312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.138442 Loss1: 2.692382 Loss2: 1.446060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.016430 Loss1: 2.573467 Loss2: 1.442963
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.325592 Loss1: 3.363364 Loss2: 1.962228
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.930655 Loss1: 2.480394 Loss2: 1.450262
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.456190 Loss1: 2.957311 Loss2: 1.498879
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.842055 Loss1: 2.381939 Loss2: 1.460116
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.240613 Loss1: 2.782668 Loss2: 1.457946
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.845629 Loss1: 2.389417 Loss2: 1.456212
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.077876 Loss1: 2.621414 Loss2: 1.456462
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.794621 Loss1: 2.325574 Loss2: 1.469047
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.020142 Loss1: 2.557670 Loss2: 1.462472
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.687461 Loss1: 2.216619 Loss2: 1.470842
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.962225 Loss1: 2.494086 Loss2: 1.468138
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.691753 Loss1: 2.195465 Loss2: 1.496287
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.900475 Loss1: 2.433499 Loss2: 1.466976
-(DefaultActor pid=3765) >> Training accuracy: 0.417708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.921774 Loss1: 2.451244 Loss2: 1.470531
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.822681 Loss1: 2.342246 Loss2: 1.480435
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.861020 Loss1: 2.374909 Loss2: 1.486111
-(DefaultActor pid=3764) >> Training accuracy: 0.360417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.160991 Loss1: 3.274547 Loss2: 1.886445
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.173961 Loss1: 2.749190 Loss2: 1.424771
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.024072 Loss1: 2.629495 Loss2: 1.394577
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.906185 Loss1: 2.515885 Loss2: 1.390300
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.265044 Loss1: 3.298498 Loss2: 1.966546
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.816827 Loss1: 2.421137 Loss2: 1.395690
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.411728 Loss1: 2.919543 Loss2: 1.492185
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.765383 Loss1: 2.365315 Loss2: 1.400068
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.168062 Loss1: 2.715275 Loss2: 1.452787
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.775915 Loss1: 2.353223 Loss2: 1.422692
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.055048 Loss1: 2.597526 Loss2: 1.457522
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.743001 Loss1: 2.317699 Loss2: 1.425302
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.961018 Loss1: 2.500332 Loss2: 1.460686
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.617504 Loss1: 2.191248 Loss2: 1.426257
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.957287 Loss1: 2.496413 Loss2: 1.460874
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.626953 Loss1: 2.202021 Loss2: 1.424932
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.919896 Loss1: 2.451959 Loss2: 1.467937
-(DefaultActor pid=3765) >> Training accuracy: 0.454167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.888145 Loss1: 2.401593 Loss2: 1.486552
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.886034 Loss1: 2.394123 Loss2: 1.491911
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.782775 Loss1: 2.306241 Loss2: 1.476534
-(DefaultActor pid=3764) >> Training accuracy: 0.394792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.136901 Loss1: 3.242877 Loss2: 1.894024
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.054006 Loss1: 2.643141 Loss2: 1.410865
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.828380 Loss1: 2.447662 Loss2: 1.380719
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.792489 Loss1: 2.402688 Loss2: 1.389800
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.263560 Loss1: 3.318403 Loss2: 1.945157
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.717434 Loss1: 2.319881 Loss2: 1.397553
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.414610 Loss1: 2.941188 Loss2: 1.473422
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.666126 Loss1: 2.272832 Loss2: 1.393294
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.244223 Loss1: 2.793383 Loss2: 1.450840
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.670075 Loss1: 2.262609 Loss2: 1.407465
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.125079 Loss1: 2.675464 Loss2: 1.449615
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.577906 Loss1: 2.174429 Loss2: 1.403477
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.035091 Loss1: 2.587134 Loss2: 1.447957
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.550676 Loss1: 2.127837 Loss2: 1.422839
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.062766 Loss1: 2.599258 Loss2: 1.463508
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.533740 Loss1: 2.106869 Loss2: 1.426871
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.972912 Loss1: 2.507485 Loss2: 1.465427
-(DefaultActor pid=3765) >> Training accuracy: 0.418750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.895595 Loss1: 2.436905 Loss2: 1.458690
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.949902 Loss1: 2.475400 Loss2: 1.474502
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.850547 Loss1: 2.382443 Loss2: 1.468104
-(DefaultActor pid=3764) >> Training accuracy: 0.395833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.188350 Loss1: 3.243060 Loss2: 1.945290
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.346755 Loss1: 2.871256 Loss2: 1.475498
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.100989 Loss1: 2.651947 Loss2: 1.449042
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.942062 Loss1: 2.504592 Loss2: 1.437470
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.228406 Loss1: 3.244127 Loss2: 1.984279
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.940038 Loss1: 2.500145 Loss2: 1.439893
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.407892 Loss1: 2.934433 Loss2: 1.473459
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.852401 Loss1: 2.393978 Loss2: 1.458422
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.179729 Loss1: 2.722571 Loss2: 1.457159
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.914542 Loss1: 2.443712 Loss2: 1.470830
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.042653 Loss1: 2.593422 Loss2: 1.449231
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.850377 Loss1: 2.378775 Loss2: 1.471603
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.045085 Loss1: 2.585391 Loss2: 1.459694
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.781234 Loss1: 2.302460 Loss2: 1.478774
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.868267 Loss1: 2.424339 Loss2: 1.443928
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.753415 Loss1: 2.266629 Loss2: 1.486787
-(DefaultActor pid=3765) >> Training accuracy: 0.451042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.816344 Loss1: 2.340202 Loss2: 1.476142
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.782069 Loss1: 2.306309 Loss2: 1.475761
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.780718 Loss1: 2.296894 Loss2: 1.483823
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.784842 Loss1: 2.301446 Loss2: 1.483396
-(DefaultActor pid=3764) >> Training accuracy: 0.417708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.304189 Loss1: 3.322428 Loss2: 1.981761
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.341544 Loss1: 2.903729 Loss2: 1.437815
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.166713 Loss1: 2.744473 Loss2: 1.422241
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.049673 Loss1: 2.642768 Loss2: 1.406905
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.189463 Loss1: 3.205032 Loss2: 1.984431
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.135117 Loss1: 2.652705 Loss2: 1.482412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.859252 Loss1: 2.417510 Loss2: 1.441742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.821688 Loss1: 2.392948 Loss2: 1.428740 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.751074 Loss1: 2.306960 Loss2: 1.444114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.796896 Loss1: 2.354842 Loss2: 1.442054 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.387019
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.688476 Loss1: 2.211814 Loss2: 1.476662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.639010 Loss1: 2.162113 Loss2: 1.476897 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.447917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.710062 Loss1: 2.205883 Loss2: 1.504180
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.012079 Loss1: 3.050673 Loss2: 1.961405
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.108827 Loss1: 2.634779 Loss2: 1.474048
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.897591 Loss1: 2.461005 Loss2: 1.436586
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.873953 Loss1: 2.451445 Loss2: 1.422508
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.745778 Loss1: 2.312394 Loss2: 1.433384
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.379466 Loss1: 3.415865 Loss2: 1.963601
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.491808 Loss1: 3.017900 Loss2: 1.473908 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.351915 Loss1: 2.905568 Loss2: 1.446348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.197195 Loss1: 2.750452 Loss2: 1.446743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.129378 Loss1: 2.677176 Loss2: 1.452202 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.430208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.130805 Loss1: 2.669569 Loss2: 1.461236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.061822 Loss1: 2.587078 Loss2: 1.474744 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.976563 Loss1: 2.483316 Loss2: 1.493247 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.342708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.190376 Loss1: 2.703097 Loss2: 1.487279 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.900286 Loss1: 2.448029 Loss2: 1.452257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.846434 Loss1: 2.385233 Loss2: 1.461201
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.166511 Loss1: 3.155253 Loss2: 2.011258
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.360726 Loss1: 2.829933 Loss2: 1.530793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.750745 Loss1: 2.288811 Loss2: 1.461934
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.144107 Loss1: 2.643304 Loss2: 1.500803
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.626729 Loss1: 2.154486 Loss2: 1.472243
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.026279 Loss1: 2.520379 Loss2: 1.505901
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.614289 Loss1: 2.135383 Loss2: 1.478906
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.026959 Loss1: 2.516283 Loss2: 1.510676
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.937010 Loss1: 2.436243 Loss2: 1.500766
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.598007 Loss1: 2.116972 Loss2: 1.481035
-(DefaultActor pid=3765) >> Training accuracy: 0.443359
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.892123 Loss1: 2.355630 Loss2: 1.536492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.780261 Loss1: 2.256330 Loss2: 1.523931 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.377083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.467248 Loss1: 2.987914 Loss2: 1.479334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.033659 Loss1: 2.596914 Loss2: 1.436745 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.981110 Loss1: 2.531017 Loss2: 1.450094
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.248870 Loss1: 3.172794 Loss2: 2.076076
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.971665 Loss1: 2.509023 Loss2: 1.462641
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.243293 Loss1: 2.703819 Loss2: 1.539474
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.915754 Loss1: 2.447311 Loss2: 1.468443
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.033893 Loss1: 2.536558 Loss2: 1.497334
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.827353 Loss1: 2.357672 Loss2: 1.469682
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.964334 Loss1: 2.471512 Loss2: 1.492822
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.765828 Loss1: 2.304401 Loss2: 1.461428
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.867692 Loss1: 2.369927 Loss2: 1.497765
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.780868 Loss1: 2.294242 Loss2: 1.486626
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.812120 Loss1: 2.305681 Loss2: 1.506439
-(DefaultActor pid=3765) >> Training accuracy: 0.400000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.800287 Loss1: 2.287480 Loss2: 1.512807
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.819807 Loss1: 2.302504 Loss2: 1.517304
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.799092 Loss1: 2.281380 Loss2: 1.517711
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.717519 Loss1: 2.199971 Loss2: 1.517548
-(DefaultActor pid=3764) >> Training accuracy: 0.435417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.553380 Loss1: 3.528462 Loss2: 2.024918
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.538916 Loss1: 3.003147 Loss2: 1.535769
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.371720 Loss1: 2.868600 Loss2: 1.503120
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.194902 Loss1: 2.702367 Loss2: 1.492535
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.228300 Loss1: 2.699581 Loss2: 1.528719
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.261996 Loss1: 3.378879 Loss2: 1.883117
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.396598 Loss1: 2.968346 Loss2: 1.428252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.236097 Loss1: 2.812708 Loss2: 1.423389 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.124264 Loss1: 2.703086 Loss2: 1.421178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.002423 Loss1: 2.572747 Loss2: 1.429676 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.405208
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.963067 Loss1: 2.428518 Loss2: 1.534548
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-08 21:23:35,803 | server.py:236 | fit_round 15 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.986424 Loss1: 2.548728 Loss2: 1.437697
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.967346 Loss1: 2.519278 Loss2: 1.448068
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.977714 Loss1: 2.524790 Loss2: 1.452924
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.946559 Loss1: 2.487100 Loss2: 1.459459
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.878918 Loss1: 2.418292 Loss2: 1.460626
-(DefaultActor pid=3764) >> Training accuracy: 0.383333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.078732 Loss1: 3.116748 Loss2: 1.961985
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.158943 Loss1: 2.681449 Loss2: 1.477494
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.958710 Loss1: 2.504565 Loss2: 1.454146
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.804463 Loss1: 2.364228 Loss2: 1.440236
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.783919 Loss1: 2.327440 Loss2: 1.456480
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.384942 Loss1: 3.433440 Loss2: 1.951502
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.517136 Loss1: 3.031913 Loss2: 1.485222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.313382 Loss1: 2.867591 Loss2: 1.445791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.235708 Loss1: 2.777601 Loss2: 1.458108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.153192 Loss1: 2.693195 Loss2: 1.459997 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.405208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.064535 Loss1: 2.614061 Loss2: 1.450474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 4.015063 Loss1: 2.541820 Loss2: 1.473243 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.877820 Loss1: 2.391629 Loss2: 1.486191 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.332031
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.444838 Loss1: 2.919762 Loss2: 1.525077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.081691 Loss1: 2.577804 Loss2: 1.503887 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.009714 Loss1: 2.499116 Loss2: 1.510598
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.223780 Loss1: 3.166488 Loss2: 2.057292
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.014257 Loss1: 2.499218 Loss2: 1.515039
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.369595 Loss1: 2.828178 Loss2: 1.541417
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.153545 Loss1: 2.627231 Loss2: 1.526314
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.936418 Loss1: 2.406434 Loss2: 1.529984
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.057430 Loss1: 2.532420 Loss2: 1.525010
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.893539 Loss1: 2.367926 Loss2: 1.525613
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.873328 Loss1: 2.348530 Loss2: 1.524798 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.825495 Loss1: 2.300309 Loss2: 1.525186 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.403493
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.903878 Loss1: 2.342502 Loss2: 1.561376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.755262 Loss1: 2.201878 Loss2: 1.553383 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.410417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.449481 Loss1: 3.526274 Loss2: 1.923207
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.437615 Loss1: 2.961255 Loss2: 1.476360
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.206920 Loss1: 2.767907 Loss2: 1.439013
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.157767 Loss1: 2.725804 Loss2: 1.431963
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.375661 Loss1: 3.370970 Loss2: 2.004691
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.400005 Loss1: 2.880017 Loss2: 1.519988 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.022685 Loss1: 2.571504 Loss2: 1.451181
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.230140 Loss1: 2.750374 Loss2: 1.479766
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.965191 Loss1: 2.510388 Loss2: 1.454802
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.088025 Loss1: 2.617854 Loss2: 1.470170
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.881807 Loss1: 2.418497 Loss2: 1.463311
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.102647 Loss1: 2.613063 Loss2: 1.489584
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.017916 Loss1: 2.522647 Loss2: 1.495269
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.837146 Loss1: 2.366204 Loss2: 1.470941
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.988104 Loss1: 2.489461 Loss2: 1.498643
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.900182 Loss1: 2.424519 Loss2: 1.475662
-(DefaultActor pid=3765) >> Training accuracy: 0.410156
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.937659 Loss1: 2.409587 Loss2: 1.528073 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.342708
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 21:23:35,803][flwr][DEBUG] - fit_round 15 received 50 results and 0 failures
-INFO flwr 2023-10-08 21:24:16,658 | server.py:125 | fit progress: (15, 3.606255607483105, {'accuracy': 0.1469}, 34364.437004246)
->> Test accuracy: 0.146900
-[2023-10-08 21:24:16,658][flwr][INFO] - fit progress: (15, 3.606255607483105, {'accuracy': 0.1469}, 34364.437004246)
-DEBUG flwr 2023-10-08 21:24:16,659 | server.py:173 | evaluate_round 15: strategy sampled 50 clients (out of 50)
-[2023-10-08 21:24:16,659][flwr][DEBUG] - evaluate_round 15: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 21:33:17,637 | server.py:187 | evaluate_round 15 received 50 results and 0 failures
-[2023-10-08 21:33:17,637][flwr][DEBUG] - evaluate_round 15 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 21:33:17,637 | server.py:222 | fit_round 16: strategy sampled 50 clients (out of 50)
-[2023-10-08 21:33:17,637][flwr][DEBUG] - fit_round 16: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.262776 Loss1: 3.236910 Loss2: 2.025866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.097632 Loss1: 2.587082 Loss2: 1.510550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.994579 Loss1: 2.495561 Loss2: 1.499018
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.022405 Loss1: 3.130563 Loss2: 1.891842
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.048157 Loss1: 2.624573 Loss2: 1.423584
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.912171 Loss1: 2.409911 Loss2: 1.502260
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.826442 Loss1: 2.449197 Loss2: 1.377245
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.830994 Loss1: 2.309676 Loss2: 1.521318
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.820008 Loss1: 2.283608 Loss2: 1.536400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.707270 Loss1: 2.186594 Loss2: 1.520676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.694082 Loss1: 2.157879 Loss2: 1.536203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.778284 Loss1: 2.224630 Loss2: 1.553653 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.455208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.314564 Loss1: 1.908254 Loss2: 1.406309 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.484375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.188935 Loss1: 3.179953 Loss2: 2.008982
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.222028 Loss1: 2.750287 Loss2: 1.471741
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.022886 Loss1: 2.575868 Loss2: 1.447018
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.906828 Loss1: 2.474105 Loss2: 1.432724
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.097709 Loss1: 3.038766 Loss2: 2.058943
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.170564 Loss1: 2.608036 Loss2: 1.562528 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.985346 Loss1: 2.455650 Loss2: 1.529696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.806549 Loss1: 2.332043 Loss2: 1.474506 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.639505 Loss1: 2.180375 Loss2: 1.459130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.604936 Loss1: 2.139882 Loss2: 1.465054 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.401786
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.768441 Loss1: 2.221958 Loss2: 1.546483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.588420 Loss1: 2.040724 Loss2: 1.547696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.459761 Loss1: 1.918750 Loss2: 1.541012
-(DefaultActor pid=3764) >> Training accuracy: 0.487305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.115712 Loss1: 3.168729 Loss2: 1.946983
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.338239 Loss1: 2.838995 Loss2: 1.499244
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.110972 Loss1: 2.633709 Loss2: 1.477263
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.008589 Loss1: 2.539311 Loss2: 1.469278
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.877150 Loss1: 2.407138 Loss2: 1.470012
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.175311 Loss1: 3.202042 Loss2: 1.973269
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.299817 Loss1: 2.798070 Loss2: 1.501747 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.127190 Loss1: 2.652360 Loss2: 1.474830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.951832 Loss1: 2.483782 Loss2: 1.468050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.874043 Loss1: 2.388470 Loss2: 1.485573 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.428125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.782671 Loss1: 2.310432 Loss2: 1.472239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.706764 Loss1: 2.205771 Loss2: 1.500993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.659862 Loss1: 2.150835 Loss2: 1.509027 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.416667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.474672 Loss1: 2.977870 Loss2: 1.496802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.155078 Loss1: 2.696398 Loss2: 1.458680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.114682 Loss1: 2.646191 Loss2: 1.468491
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.341729 Loss1: 3.353589 Loss2: 1.988140
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.451334 Loss1: 2.949067 Loss2: 1.502266 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.227510 Loss1: 2.752046 Loss2: 1.475464 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.862201 Loss1: 2.383553 Loss2: 1.478648
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.175793 Loss1: 2.690802 Loss2: 1.484990
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.810868 Loss1: 2.318049 Loss2: 1.492819
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.068574 Loss1: 2.577178 Loss2: 1.491396
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.834997 Loss1: 2.333659 Loss2: 1.501338
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.057460 Loss1: 2.557947 Loss2: 1.499513
-(DefaultActor pid=3765) >> Training accuracy: 0.400391
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.042672 Loss1: 2.528841 Loss2: 1.513831
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.958121 Loss1: 2.452793 Loss2: 1.505328
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.957580 Loss1: 2.445128 Loss2: 1.512452
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.909606 Loss1: 2.383246 Loss2: 1.526360
-(DefaultActor pid=3764) >> Training accuracy: 0.354167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.137123 Loss1: 3.099210 Loss2: 2.037912
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.237758 Loss1: 2.689985 Loss2: 1.547773
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.098115 Loss1: 2.569541 Loss2: 1.528574
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.961480 Loss1: 2.449070 Loss2: 1.512410
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.159061 Loss1: 3.161979 Loss2: 1.997083
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.269545 Loss1: 2.753893 Loss2: 1.515652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.073976 Loss1: 2.588341 Loss2: 1.485635 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.882678 Loss1: 2.408835 Loss2: 1.473842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.862937 Loss1: 2.381673 Loss2: 1.481264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.820318 Loss1: 2.325236 Loss2: 1.495082 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.431250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.654521 Loss1: 2.159007 Loss2: 1.495514 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.679269 Loss1: 2.149868 Loss2: 1.529400 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.430208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.200308 Loss1: 2.728466 Loss2: 1.471842 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.788585 Loss1: 2.350093 Loss2: 1.438492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.361894 Loss1: 3.391775 Loss2: 1.970119
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.778594 Loss1: 2.340884 Loss2: 1.437709
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.458238 Loss1: 2.952916 Loss2: 1.505321
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.632379 Loss1: 2.200291 Loss2: 1.432089
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.618773 Loss1: 2.176407 Loss2: 1.442365
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.264581 Loss1: 2.783425 Loss2: 1.481156
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.518427 Loss1: 2.068054 Loss2: 1.450372
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.149427 Loss1: 2.666592 Loss2: 1.482835
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.601704 Loss1: 2.141921 Loss2: 1.459783
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.056528 Loss1: 2.567693 Loss2: 1.488836
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.557782 Loss1: 2.105422 Loss2: 1.452360
-(DefaultActor pid=3765) >> Training accuracy: 0.457292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.031480 Loss1: 2.540746 Loss2: 1.490734
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.988334 Loss1: 2.498076 Loss2: 1.490258
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.885222 Loss1: 2.384303 Loss2: 1.500919
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.921762 Loss1: 2.405605 Loss2: 1.516157
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.838848 Loss1: 2.310106 Loss2: 1.528742
-(DefaultActor pid=3764) >> Training accuracy: 0.388672
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.287879 Loss1: 3.301108 Loss2: 1.986771
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.355565 Loss1: 2.845472 Loss2: 1.510093
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.147597 Loss1: 2.670681 Loss2: 1.476916
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.026292 Loss1: 2.545286 Loss2: 1.481006
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.984409 Loss1: 2.494396 Loss2: 1.490014
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.108943 Loss1: 3.166898 Loss2: 1.942045
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.289868 Loss1: 2.788109 Loss2: 1.501759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.062031 Loss1: 2.619608 Loss2: 1.442423 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.936742 Loss1: 2.491684 Loss2: 1.445057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.841780 Loss1: 2.382003 Loss2: 1.459778 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.450000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.776838 Loss1: 2.327232 Loss2: 1.449606 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.683286 Loss1: 2.210498 Loss2: 1.472788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.157245 Loss1: 3.177703 Loss2: 1.979542
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.615173 Loss1: 2.143691 Loss2: 1.471481
-(DefaultActor pid=3764) >> Training accuracy: 0.437500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.955879 Loss1: 2.498332 Loss2: 1.457548 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.857034 Loss1: 2.379264 Loss2: 1.477770 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.835179 Loss1: 2.352263 Loss2: 1.482917
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.170973 Loss1: 3.134119 Loss2: 2.036854
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.306007 Loss1: 2.761659 Loss2: 1.544348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.082874 Loss1: 2.571775 Loss2: 1.511099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.886027 Loss1: 2.377239 Loss2: 1.508788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.416667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.844815 Loss1: 2.339164 Loss2: 1.505651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.736126 Loss1: 2.202087 Loss2: 1.534039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.678254 Loss1: 2.141263 Loss2: 1.536991 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.646282 Loss1: 2.099490 Loss2: 1.546792 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.426042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.873498 Loss1: 2.447085 Loss2: 1.426414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.601567 Loss1: 2.188023 Loss2: 1.413544 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.528105 Loss1: 2.103971 Loss2: 1.424134
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.535009 Loss1: 3.473225 Loss2: 2.061783
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.581278 Loss1: 3.049237 Loss2: 1.532041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.308637 Loss1: 2.821896 Loss2: 1.486741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.106037 Loss1: 2.627678 Loss2: 1.478360 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.493750
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.453070 Loss1: 2.015550 Loss2: 1.437520
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.031878 Loss1: 2.539673 Loss2: 1.492205
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.063159 Loss1: 2.560951 Loss2: 1.502208
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.037526 Loss1: 2.533607 Loss2: 1.503919
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.934791 Loss1: 2.414526 Loss2: 1.520266
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.827114 Loss1: 2.305301 Loss2: 1.521813
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.815607 Loss1: 2.286938 Loss2: 1.528669
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.331084 Loss1: 3.304396 Loss2: 2.026688
-(DefaultActor pid=3764) >> Training accuracy: 0.385045
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.415464 Loss1: 2.861206 Loss2: 1.554257
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.098713 Loss1: 2.570483 Loss2: 1.528231
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.968938 Loss1: 2.466912 Loss2: 1.502026
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.001955 Loss1: 2.484624 Loss2: 1.517331
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.315318 Loss1: 3.186873 Loss2: 2.128444
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.836799 Loss1: 2.319309 Loss2: 1.517490
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.788210 Loss1: 2.274912 Loss2: 1.513298 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.739939 Loss1: 2.207595 Loss2: 1.532343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.707580 Loss1: 2.171650 Loss2: 1.535931 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.675055 Loss1: 2.142107 Loss2: 1.532948 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.417708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.799452 Loss1: 2.214640 Loss2: 1.584812 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.757572 Loss1: 2.181742 Loss2: 1.575830 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.411058
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.184347 Loss1: 2.622625 Loss2: 1.561722 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.863670 Loss1: 2.362574 Loss2: 1.501096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.815326 Loss1: 2.315444 Loss2: 1.499882 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.109266 Loss1: 2.696926 Loss2: 1.412340
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.787838 Loss1: 2.264051 Loss2: 1.523788
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.892140 Loss1: 2.509390 Loss2: 1.382750
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.739279 Loss1: 2.219741 Loss2: 1.519538
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.773005 Loss1: 2.401516 Loss2: 1.371489
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.649670 Loss1: 2.130872 Loss2: 1.518798
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.604372 Loss1: 2.087236 Loss2: 1.517136
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.731608 Loss1: 2.348731 Loss2: 1.382878
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.567451 Loss1: 2.040346 Loss2: 1.527106
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.615143 Loss1: 2.229107 Loss2: 1.386035
-(DefaultActor pid=3765) >> Training accuracy: 0.436458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.654705 Loss1: 2.265391 Loss2: 1.389313
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.626026 Loss1: 2.225561 Loss2: 1.400465
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.595687 Loss1: 2.186532 Loss2: 1.409155
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.491667 Loss1: 2.083055 Loss2: 1.408613
-(DefaultActor pid=3764) >> Training accuracy: 0.432617
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.405519 Loss1: 3.313698 Loss2: 2.091820
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.508036 Loss1: 2.930015 Loss2: 1.578021
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.305339 Loss1: 2.756626 Loss2: 1.548714
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.226906 Loss1: 2.671154 Loss2: 1.555752
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.192765 Loss1: 2.632696 Loss2: 1.560069
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.397818 Loss1: 3.382960 Loss2: 2.014858
-(DefaultActor pid=3765) Epoch: 5 Loss: 4.127967 Loss1: 2.556497 Loss2: 1.571471
-(DefaultActor pid=3765) Epoch: 6 Loss: 4.034217 Loss1: 2.469820 Loss2: 1.564397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.975956 Loss1: 2.395431 Loss2: 1.580525 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.195649 Loss1: 2.674749 Loss2: 1.520901
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.912817 Loss1: 2.335511 Loss2: 1.577307
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.079079 Loss1: 2.555857 Loss2: 1.523222
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.950976 Loss1: 2.379902 Loss2: 1.571074
-(DefaultActor pid=3765) >> Training accuracy: 0.370833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.936080 Loss1: 2.403655 Loss2: 1.532425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.873561 Loss1: 2.333340 Loss2: 1.540221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.854069 Loss1: 2.310801 Loss2: 1.543267 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.417969
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.026047 Loss1: 2.539228 Loss2: 1.486818 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.911963 Loss1: 2.402745 Loss2: 1.509218 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.770976 Loss1: 2.234786 Loss2: 1.536190 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.297798 Loss1: 2.738138 Loss2: 1.559660 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.453125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.125257 Loss1: 2.612854 Loss2: 1.512402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.886140 Loss1: 2.361379 Loss2: 1.524761 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.821386 Loss1: 2.284293 Loss2: 1.537093 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.771922 Loss1: 2.220707 Loss2: 1.551215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.717703 Loss1: 2.170550 Loss2: 1.547153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.636295 Loss1: 2.076055 Loss2: 1.560239 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.469792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.130600 Loss1: 2.611844 Loss2: 1.518756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.941011 Loss1: 2.411975 Loss2: 1.529036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.880479 Loss1: 2.345629 Loss2: 1.534850
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.149729 Loss1: 3.150496 Loss2: 1.999233
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.115665 Loss1: 2.630304 Loss2: 1.485362 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.354167
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.927301 Loss1: 2.391881 Loss2: 1.535421
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.906248 Loss1: 2.451960 Loss2: 1.454288
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.712462 Loss1: 2.260037 Loss2: 1.452425
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.622721 Loss1: 2.162283 Loss2: 1.460438
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.552445 Loss1: 2.095595 Loss2: 1.456850
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.544545 Loss1: 2.079956 Loss2: 1.464590
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.092514 Loss1: 3.086513 Loss2: 2.006001
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.533917 Loss1: 2.068956 Loss2: 1.464961
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.222050 Loss1: 2.725457 Loss2: 1.496593
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.503661 Loss1: 2.022658 Loss2: 1.481003
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.990829 Loss1: 2.520660 Loss2: 1.470169
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.407444 Loss1: 1.926580 Loss2: 1.480864
-(DefaultActor pid=3764) >> Training accuracy: 0.450000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.825296 Loss1: 2.342201 Loss2: 1.483094 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.684514 Loss1: 2.207261 Loss2: 1.477253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.639741 Loss1: 2.144398 Loss2: 1.495343
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.390399 Loss1: 3.341868 Loss2: 2.048531
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.387017 Loss1: 2.831992 Loss2: 1.555025 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.435417
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.574622 Loss1: 2.061924 Loss2: 1.512698
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.086478 Loss1: 2.574470 Loss2: 1.512008
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.960081 Loss1: 2.459373 Loss2: 1.500708
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.897514 Loss1: 2.391506 Loss2: 1.506008
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.779213 Loss1: 2.276821 Loss2: 1.502392
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.848618 Loss1: 2.314005 Loss2: 1.534613
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.170814 Loss1: 3.302174 Loss2: 1.868640
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.743265 Loss1: 2.210233 Loss2: 1.533032
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.405486 Loss1: 2.982141 Loss2: 1.423345
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.707624 Loss1: 2.184526 Loss2: 1.523098
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.656392 Loss1: 2.120711 Loss2: 1.535682
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.228943 Loss1: 2.817048 Loss2: 1.411895
-(DefaultActor pid=3764) >> Training accuracy: 0.414583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.124542 Loss1: 2.710516 Loss2: 1.414026
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.037502 Loss1: 2.615092 Loss2: 1.422410
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.963757 Loss1: 2.533497 Loss2: 1.430259
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.986562 Loss1: 2.556541 Loss2: 1.430021
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.243751 Loss1: 3.258208 Loss2: 1.985543
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.879083 Loss1: 2.444779 Loss2: 1.434304
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.284928 Loss1: 2.764873 Loss2: 1.520055
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.880668 Loss1: 2.429920 Loss2: 1.450749
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.784699 Loss1: 2.341583 Loss2: 1.443116 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.370117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.839215 Loss1: 2.360153 Loss2: 1.479061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.795555 Loss1: 2.308301 Loss2: 1.487254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.779364 Loss1: 2.284008 Loss2: 1.495356
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.148727 Loss1: 3.251061 Loss2: 1.897665
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.323116 Loss1: 2.896810 Loss2: 1.426305 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.391667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.087024 Loss1: 2.689409 Loss2: 1.397615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.876659 Loss1: 2.465922 Loss2: 1.410737 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.818145 Loss1: 2.392155 Loss2: 1.425990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.804367 Loss1: 2.378841 Loss2: 1.425526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.778065 Loss1: 2.348630 Loss2: 1.429435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.657639 Loss1: 2.233172 Loss2: 1.424467 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.418750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.938304 Loss1: 2.392490 Loss2: 1.545814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.865511 Loss1: 2.299231 Loss2: 1.566280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.788000 Loss1: 2.218659 Loss2: 1.569341
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.880004 Loss1: 2.947474 Loss2: 1.932530
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.035265 Loss1: 2.591843 Loss2: 1.443422 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.425000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.910592 Loss1: 2.469695 Loss2: 1.440898 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.693579 Loss1: 2.256329 Loss2: 1.437250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.515824 Loss1: 2.067382 Loss2: 1.448442 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.459973 Loss1: 2.012573 Loss2: 1.447400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.515620 Loss1: 2.051575 Loss2: 1.464046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.435534 Loss1: 1.980534 Loss2: 1.455000 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.451042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.708852 Loss1: 2.187744 Loss2: 1.521108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.664547 Loss1: 2.131573 Loss2: 1.532975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.245310 Loss1: 3.299682 Loss2: 1.945628 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.383651 Loss1: 2.940023 Loss2: 1.443628 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.498958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.152978 Loss1: 2.730295 Loss2: 1.422684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.933593 Loss1: 2.483874 Loss2: 1.449719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.904689 Loss1: 2.432650 Loss2: 1.472039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.876110 Loss1: 2.412388 Loss2: 1.463722 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.803833 Loss1: 2.337775 Loss2: 1.466058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.798792 Loss1: 2.318581 Loss2: 1.480211 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.388542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.148791 Loss1: 2.571353 Loss2: 1.577438 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 4.076174 Loss1: 2.481013 Loss2: 1.595161 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.167849 Loss1: 3.234952 Loss2: 1.932897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.279565 Loss1: 2.828213 Loss2: 1.451352 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.396875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.035226 Loss1: 2.620491 Loss2: 1.414735 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.812149 Loss1: 2.387304 Loss2: 1.424845 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.881604 Loss1: 2.438148 Loss2: 1.443457
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.412463 Loss1: 3.443588 Loss2: 1.968875
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.334447 Loss1: 2.866684 Loss2: 1.467763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.076934 Loss1: 2.646324 Loss2: 1.430610 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.417708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.004311 Loss1: 2.564806 Loss2: 1.439505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.821414 Loss1: 2.378567 Loss2: 1.442847 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.743303 Loss1: 2.287034 Loss2: 1.456269 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.697369 Loss1: 2.245715 Loss2: 1.451654 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.702727 Loss1: 2.238328 Loss2: 1.464399 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.402083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.074758 Loss1: 2.629067 Loss2: 1.445692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.935617 Loss1: 2.485611 Loss2: 1.450007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.822852 Loss1: 2.350311 Loss2: 1.472541 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.795839 Loss1: 2.300880 Loss2: 1.494959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.781270 Loss1: 2.299028 Loss2: 1.482243 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.407366
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.899880 Loss1: 2.470083 Loss2: 1.429797 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.790242 Loss1: 2.359838 Loss2: 1.430404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.742709 Loss1: 2.296541 Loss2: 1.446167
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.318235 Loss1: 3.321047 Loss2: 1.997188
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.371653 Loss1: 2.861737 Loss2: 1.509916
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.672536 Loss1: 2.215305 Loss2: 1.457231
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.177058 Loss1: 2.702409 Loss2: 1.474649
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.726167 Loss1: 2.263621 Loss2: 1.462547
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.041593 Loss1: 2.556297 Loss2: 1.485296
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.615455 Loss1: 2.146046 Loss2: 1.469410
-(DefaultActor pid=3764) >> Training accuracy: 0.430147
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.962192 Loss1: 2.453008 Loss2: 1.509184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.848146 Loss1: 2.335650 Loss2: 1.512496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.792164 Loss1: 2.277516 Loss2: 1.514648
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.956624 Loss1: 3.003016 Loss2: 1.953608
-(DefaultActor pid=3765) >> Training accuracy: 0.438542
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.717777 Loss1: 2.195021 Loss2: 1.522756
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.126200 Loss1: 2.614995 Loss2: 1.511206
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.946527 Loss1: 2.475127 Loss2: 1.471401
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.787554 Loss1: 2.330287 Loss2: 1.457267
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.715700 Loss1: 2.264648 Loss2: 1.451052
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.571846 Loss1: 2.119572 Loss2: 1.452274
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.130162 Loss1: 3.115109 Loss2: 2.015053
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.492935 Loss1: 2.035118 Loss2: 1.457817
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.468346 Loss1: 1.999049 Loss2: 1.469297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.571594 Loss1: 2.100592 Loss2: 1.471001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.465481 Loss1: 2.005557 Loss2: 1.459924 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.456250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.748560 Loss1: 2.276476 Loss2: 1.472084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.605749 Loss1: 2.117398 Loss2: 1.488351 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.785584 Loss1: 2.278097 Loss2: 1.507488
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.151516 Loss1: 3.206426 Loss2: 1.945089
-(DefaultActor pid=3765) >> Training accuracy: 0.423958
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.660386 Loss1: 2.165655 Loss2: 1.494731
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.355132 Loss1: 2.891760 Loss2: 1.463371
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.096868 Loss1: 2.659269 Loss2: 1.437599
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.041850 Loss1: 2.598062 Loss2: 1.443788
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.009840 Loss1: 2.568650 Loss2: 1.441190
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.976042 Loss1: 2.534355 Loss2: 1.441687
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.259264 Loss1: 3.309028 Loss2: 1.950236
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.808731 Loss1: 2.359266 Loss2: 1.449465
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.394809 Loss1: 2.905155 Loss2: 1.489654
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.877662 Loss1: 2.417861 Loss2: 1.459801
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.169752 Loss1: 2.708689 Loss2: 1.461063
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.756657 Loss1: 2.288320 Loss2: 1.468337
-DEBUG flwr 2023-10-08 22:01:52,537 | server.py:236 | fit_round 16 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.067492 Loss1: 2.606226 Loss2: 1.461266
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.771047 Loss1: 2.301015 Loss2: 1.470032
-(DefaultActor pid=3764) >> Training accuracy: 0.392708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.913687 Loss1: 2.428476 Loss2: 1.485211 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.768974 Loss1: 2.286827 Loss2: 1.482147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.680693 Loss1: 2.185413 Loss2: 1.495280
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.361032 Loss1: 3.298405 Loss2: 2.062627
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.648225 Loss1: 2.152644 Loss2: 1.495581
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.410745 Loss1: 2.867782 Loss2: 1.542962
-(DefaultActor pid=3765) >> Training accuracy: 0.434375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.118900 Loss1: 2.610147 Loss2: 1.508752
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.980439 Loss1: 2.473917 Loss2: 1.506522
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.960027 Loss1: 2.445183 Loss2: 1.514844
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.977794 Loss1: 2.442073 Loss2: 1.535721
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.893446 Loss1: 2.371281 Loss2: 1.522165
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.137937 Loss1: 3.128884 Loss2: 2.009053
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.240623 Loss1: 2.704971 Loss2: 1.535651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.084462 Loss1: 2.571443 Loss2: 1.513018 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.419792
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.752456 Loss1: 2.205439 Loss2: 1.547017
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.111526 Loss1: 2.592284 Loss2: 1.519243
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.998894 Loss1: 2.477976 Loss2: 1.520918
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.908859 Loss1: 2.388368 Loss2: 1.520491
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.849810 Loss1: 2.316541 Loss2: 1.533269
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.839083 Loss1: 2.302348 Loss2: 1.536735
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.152687 Loss1: 3.256494 Loss2: 1.896194
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.707102 Loss1: 2.176209 Loss2: 1.530893
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.333844 Loss1: 2.880105 Loss2: 1.453739
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.714130 Loss1: 2.175465 Loss2: 1.538664
-(DefaultActor pid=3765) >> Training accuracy: 0.431641
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.076243 Loss1: 2.622746 Loss2: 1.453496
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.971513 Loss1: 2.536620 Loss2: 1.434893
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.963279 Loss1: 2.516525 Loss2: 1.446754
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.812515 Loss1: 2.359082 Loss2: 1.453433
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.819361 Loss1: 2.360691 Loss2: 1.458671
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.799412 Loss1: 2.337152 Loss2: 1.462261
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.751435 Loss1: 2.279406 Loss2: 1.472029
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.602928 Loss1: 2.126620 Loss2: 1.476308
-(DefaultActor pid=3764) >> Training accuracy: 0.430664
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 22:01:52,537][flwr][DEBUG] - fit_round 16 received 50 results and 0 failures
-INFO flwr 2023-10-08 22:02:34,375 | server.py:125 | fit progress: (16, 3.5286484503517515, {'accuracy': 0.1625}, 36662.153458993)
->> Test accuracy: 0.162500
-[2023-10-08 22:02:34,375][flwr][INFO] - fit progress: (16, 3.5286484503517515, {'accuracy': 0.1625}, 36662.153458993)
-DEBUG flwr 2023-10-08 22:02:34,375 | server.py:173 | evaluate_round 16: strategy sampled 50 clients (out of 50)
-[2023-10-08 22:02:34,375][flwr][DEBUG] - evaluate_round 16: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 22:11:38,186 | server.py:187 | evaluate_round 16 received 50 results and 0 failures
-[2023-10-08 22:11:38,186][flwr][DEBUG] - evaluate_round 16 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 22:11:38,186 | server.py:222 | fit_round 17: strategy sampled 50 clients (out of 50)
-[2023-10-08 22:11:38,186][flwr][DEBUG] - fit_round 17: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.134847 Loss1: 3.144107 Loss2: 1.990740
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.293025 Loss1: 2.759237 Loss2: 1.533788
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.015880 Loss1: 2.537912 Loss2: 1.477968
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.449250 Loss1: 3.175766 Loss2: 2.273484
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.986560 Loss1: 2.504592 Loss2: 1.481968
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.861494 Loss1: 2.391727 Loss2: 1.469767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.739726 Loss1: 2.275082 Loss2: 1.464645 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.862827 Loss1: 2.309249 Loss2: 1.553578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.807669 Loss1: 2.222128 Loss2: 1.585541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.725876 Loss1: 2.144568 Loss2: 1.581308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.599156 Loss1: 2.103922 Loss2: 1.495233
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.593449 Loss1: 2.014369 Loss2: 1.579080
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.567572 Loss1: 1.989213 Loss2: 1.578359
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.534545 Loss1: 2.034743 Loss2: 1.499802
-(DefaultActor pid=3765) >> Training accuracy: 0.429688
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.157190 Loss1: 3.160573 Loss2: 1.996618 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.490885
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.181703 Loss1: 2.687134 Loss2: 1.494570 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.968767 Loss1: 2.476224 Loss2: 1.492543
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.065940 Loss1: 3.043198 Loss2: 2.022742
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.197074 Loss1: 2.675419 Loss2: 1.521655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.946938 Loss1: 2.467037 Loss2: 1.479901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.841186 Loss1: 2.355936 Loss2: 1.485250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.721791 Loss1: 2.235923 Loss2: 1.485869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.629734 Loss1: 2.138978 Loss2: 1.490756 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.433333
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.678090 Loss1: 2.140526 Loss2: 1.537564
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.654763 Loss1: 2.156154 Loss2: 1.498609
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.642774 Loss1: 2.138721 Loss2: 1.504052
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.539225 Loss1: 2.038519 Loss2: 1.500706
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.575316 Loss1: 2.065105 Loss2: 1.510212
-(DefaultActor pid=3764) >> Training accuracy: 0.418750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.278222 Loss1: 3.268329 Loss2: 2.009892
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.333789 Loss1: 2.816755 Loss2: 1.517035
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.149120 Loss1: 2.663277 Loss2: 1.485844
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.983890 Loss1: 2.505595 Loss2: 1.478295
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.147700 Loss1: 3.102962 Loss2: 2.044739
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.135692 Loss1: 2.638062 Loss2: 1.497630 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.921202 Loss1: 2.458171 Loss2: 1.463031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.808595 Loss1: 2.344420 Loss2: 1.464175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.788460 Loss1: 2.314227 Loss2: 1.474233 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.701107 Loss1: 2.223473 Loss2: 1.477634 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.412500
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.675928 Loss1: 2.172022 Loss2: 1.503906
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.587067 Loss1: 2.110651 Loss2: 1.476416
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.554797 Loss1: 2.063549 Loss2: 1.491248
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.583891 Loss1: 2.082561 Loss2: 1.501330
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.521947 Loss1: 2.009571 Loss2: 1.512376
-(DefaultActor pid=3764) >> Training accuracy: 0.464583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.429219 Loss1: 3.472476 Loss2: 1.956743
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.286585 Loss1: 2.800064 Loss2: 1.486521
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.102362 Loss1: 2.653130 Loss2: 1.449232
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.017416 Loss1: 2.576359 Loss2: 1.441057
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.008440 Loss1: 3.023049 Loss2: 1.985392
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.270022 Loss1: 2.767352 Loss2: 1.502671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.070245 Loss1: 2.596741 Loss2: 1.473504 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.976782 Loss1: 2.496569 Loss2: 1.480213 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.857212 Loss1: 2.386892 Loss2: 1.470320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.845049 Loss1: 2.376527 Loss2: 1.468522 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.439583
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.552261 Loss1: 2.069645 Loss2: 1.482617
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.735950 Loss1: 2.250739 Loss2: 1.485211
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.637658 Loss1: 2.146095 Loss2: 1.491563
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.633337 Loss1: 2.133046 Loss2: 1.500291
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.605336 Loss1: 2.099100 Loss2: 1.506236
-(DefaultActor pid=3764) >> Training accuracy: 0.466667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.889649 Loss1: 2.899184 Loss2: 1.990465
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.998966 Loss1: 2.508923 Loss2: 1.490043
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.815653 Loss1: 2.360807 Loss2: 1.454846
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.740528 Loss1: 2.270378 Loss2: 1.470150
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.993010 Loss1: 3.043376 Loss2: 1.949634
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.908268 Loss1: 2.458297 Loss2: 1.449971
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.723553 Loss1: 2.243061 Loss2: 1.480492
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.717715 Loss1: 2.283548 Loss2: 1.434168
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.574743 Loss1: 2.099819 Loss2: 1.474924
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.666199 Loss1: 2.232085 Loss2: 1.434113
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.484999 Loss1: 2.017155 Loss2: 1.467845
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.557918 Loss1: 2.108955 Loss2: 1.448962
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.460807 Loss1: 1.981695 Loss2: 1.479112
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.442571 Loss1: 1.974638 Loss2: 1.467932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.416241 Loss1: 1.927928 Loss2: 1.488313 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.488281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.396188 Loss1: 1.924882 Loss2: 1.471306 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.475000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.065637 Loss1: 3.034733 Loss2: 2.030904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.961333 Loss1: 2.464561 Loss2: 1.496772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.825951 Loss1: 2.338276 Loss2: 1.487675
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.093987 Loss1: 3.153622 Loss2: 1.940364
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.149728 Loss1: 2.701600 Loss2: 1.448128 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.004337 Loss1: 2.581040 Loss2: 1.423297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.870297 Loss1: 2.445148 Loss2: 1.425149 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.711436 Loss1: 2.298285 Loss2: 1.413150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.709451 Loss1: 2.276668 Loss2: 1.432783 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.456473
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.655654 Loss1: 2.196448 Loss2: 1.459207 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.506692 Loss1: 2.052315 Loss2: 1.454377 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.441667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.247743 Loss1: 2.652987 Loss2: 1.594756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.764069 Loss1: 2.213692 Loss2: 1.550376 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.659132 Loss1: 2.113809 Loss2: 1.545324
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.104496 Loss1: 3.082697 Loss2: 2.021800
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.267547 Loss1: 2.741761 Loss2: 1.525786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.088472 Loss1: 2.559015 Loss2: 1.529457 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.438387 Loss1: 1.875043 Loss2: 1.563344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.478591 Loss1: 1.895119 Loss2: 1.583472 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.479567
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.899759 Loss1: 2.357475 Loss2: 1.542284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.760058 Loss1: 2.221752 Loss2: 1.538305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.721180 Loss1: 2.161231 Loss2: 1.559949
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.190115 Loss1: 3.060088 Loss2: 2.130026
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.334249 Loss1: 2.803860 Loss2: 1.530390 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.472656
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.960590 Loss1: 2.445324 Loss2: 1.515266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.746310 Loss1: 2.237919 Loss2: 1.508391 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.100988 Loss1: 3.074570 Loss2: 2.026418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.193452 Loss1: 2.684127 Loss2: 1.509325 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.559472 Loss1: 2.038321 Loss2: 1.521151 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.481971
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.716727 Loss1: 2.219625 Loss2: 1.497103 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.683302 Loss1: 2.187028 Loss2: 1.496274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.629258 Loss1: 2.126446 Loss2: 1.502813
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.382518 Loss1: 3.262355 Loss2: 2.120164
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.629362 Loss1: 2.111135 Loss2: 1.518226
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.510498 Loss1: 2.900056 Loss2: 1.610442
-(DefaultActor pid=3764) >> Training accuracy: 0.436458
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.485980 Loss1: 1.961067 Loss2: 1.524912
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.296692 Loss1: 2.717007 Loss2: 1.579685
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.247090 Loss1: 2.671256 Loss2: 1.575834
-(DefaultActor pid=3765) Epoch: 4 Loss: 4.126950 Loss1: 2.546923 Loss2: 1.580027
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.994305 Loss1: 2.419700 Loss2: 1.574604
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.906839 Loss1: 2.320877 Loss2: 1.585963
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.092221 Loss1: 3.202494 Loss2: 1.889728
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.155588 Loss1: 2.752035 Loss2: 1.403553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.944880 Loss1: 2.569028 Loss2: 1.375852 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.435547
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.705022 Loss1: 2.100035 Loss2: 1.604987
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.852158 Loss1: 2.482180 Loss2: 1.369978
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.749536 Loss1: 2.361536 Loss2: 1.388000
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.614129 Loss1: 2.219195 Loss2: 1.394934
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.567137 Loss1: 2.158860 Loss2: 1.408278
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.555780 Loss1: 2.157757 Loss2: 1.398022
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.045798 Loss1: 3.116668 Loss2: 1.929129
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.517036 Loss1: 2.116088 Loss2: 1.400947
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.190876 Loss1: 2.737818 Loss2: 1.453058
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.528490 Loss1: 2.104076 Loss2: 1.424414
-(DefaultActor pid=3764) >> Training accuracy: 0.413542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.779750 Loss1: 2.353437 Loss2: 1.426313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.683769 Loss1: 2.257491 Loss2: 1.426279 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.626309 Loss1: 2.181371 Loss2: 1.444938 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.649073 Loss1: 2.189357 Loss2: 1.459716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.539395 Loss1: 2.077033 Loss2: 1.462362 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.534016 Loss1: 2.059544 Loss2: 1.474472 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.458008
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.641130 Loss1: 2.154051 Loss2: 1.487079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.643464 Loss1: 2.152536 Loss2: 1.490927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.395889 Loss1: 3.368073 Loss2: 2.027815 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.472917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.131501 Loss1: 2.650083 Loss2: 1.481418 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.945644 Loss1: 2.455007 Loss2: 1.490637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.311236 Loss1: 3.258989 Loss2: 2.052247 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.825034 Loss1: 2.315910 Loss2: 1.509125 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.750084 Loss1: 2.223242 Loss2: 1.526842 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.629301 Loss1: 2.112619 Loss2: 1.516682 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.416295
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.047032 Loss1: 2.485801 Loss2: 1.561231 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.869549 Loss1: 2.303105 Loss2: 1.566444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.862620 Loss1: 2.303301 Loss2: 1.559319
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.043623 Loss1: 2.981539 Loss2: 2.062084
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.097664 Loss1: 2.534774 Loss2: 1.562891 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.427734
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.693646 Loss1: 2.101539 Loss2: 1.592107
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.853149 Loss1: 2.313671 Loss2: 1.539479
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.806353 Loss1: 2.277948 Loss2: 1.528405
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.738745 Loss1: 2.202490 Loss2: 1.536254
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.636397 Loss1: 2.104102 Loss2: 1.532295
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.563400 Loss1: 2.018400 Loss2: 1.544999
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.006398 Loss1: 2.999631 Loss2: 2.006767
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.448757 Loss1: 1.910264 Loss2: 1.538493
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.447711 Loss1: 1.903712 Loss2: 1.543999 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.413604 Loss1: 1.853416 Loss2: 1.560188 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.489583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.714464 Loss1: 2.225706 Loss2: 1.488758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.563633 Loss1: 2.081626 Loss2: 1.482007 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.523549 Loss1: 2.023304 Loss2: 1.500245
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.284673 Loss1: 3.281603 Loss2: 2.003071
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.304087 Loss1: 2.790214 Loss2: 1.513874 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.461458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.086836 Loss1: 2.601153 Loss2: 1.485683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.863981 Loss1: 2.378273 Loss2: 1.485708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.851072 Loss1: 2.336694 Loss2: 1.514379 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.752068 Loss1: 2.246976 Loss2: 1.505092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.693036 Loss1: 2.177090 Loss2: 1.515946 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.667984 Loss1: 2.152573 Loss2: 1.515411 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.439453
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.519543 Loss1: 2.103131 Loss2: 1.416412 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.498181 Loss1: 2.050334 Loss2: 1.447847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.186843 Loss1: 3.249977 Loss2: 1.936866
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.378720 Loss1: 1.938748 Loss2: 1.439972
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.229714 Loss1: 1.792146 Loss2: 1.437568
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.405493 Loss1: 2.926493 Loss2: 1.479000
-(DefaultActor pid=3764) >> Training accuracy: 0.520833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.212973 Loss1: 2.749155 Loss2: 1.463818
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.108797 Loss1: 2.649101 Loss2: 1.459696
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.961469 Loss1: 2.497200 Loss2: 1.464268
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.940425 Loss1: 2.478874 Loss2: 1.461551
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.256394 Loss1: 3.190360 Loss2: 2.066034
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.957684 Loss1: 2.466996 Loss2: 1.490688
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.306236 Loss1: 2.764843 Loss2: 1.541393
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.886215 Loss1: 2.401020 Loss2: 1.485195
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.788185 Loss1: 2.301274 Loss2: 1.486912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.706045 Loss1: 2.218858 Loss2: 1.487186 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.437500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.815001 Loss1: 2.291813 Loss2: 1.523187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.767127 Loss1: 2.244934 Loss2: 1.522193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.726402 Loss1: 2.196048 Loss2: 1.530354
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.992670 Loss1: 3.034397 Loss2: 1.958274
-(DefaultActor pid=3764) >> Training accuracy: 0.434375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.099743 Loss1: 2.641431 Loss2: 1.458312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.756055 Loss1: 2.322549 Loss2: 1.433506 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.534100 Loss1: 2.088019 Loss2: 1.446081 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.579655 Loss1: 2.133761 Loss2: 1.445894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.147289 Loss1: 2.600808 Loss2: 1.546481
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.546301 Loss1: 2.077290 Loss2: 1.469011
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.890249 Loss1: 2.375402 Loss2: 1.514847
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.437054 Loss1: 1.965198 Loss2: 1.471856
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.745866 Loss1: 2.232820 Loss2: 1.513046
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.507837 Loss1: 2.033649 Loss2: 1.474188
-(DefaultActor pid=3765) >> Training accuracy: 0.435417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.692104 Loss1: 2.165575 Loss2: 1.526529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.642536 Loss1: 2.091826 Loss2: 1.550710 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.110546 Loss1: 3.116981 Loss2: 1.993565
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.558067 Loss1: 2.017138 Loss2: 1.540929
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.241899 Loss1: 2.717181 Loss2: 1.524718
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.432913 Loss1: 1.889566 Loss2: 1.543346
-(DefaultActor pid=3764) >> Training accuracy: 0.470703
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.862273 Loss1: 2.387152 Loss2: 1.475121 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.710932 Loss1: 2.214911 Loss2: 1.496021 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.652868 Loss1: 2.153676 Loss2: 1.499192
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.227131 Loss1: 3.249134 Loss2: 1.977997
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.156346 Loss1: 2.676564 Loss2: 1.479782 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.579417 Loss1: 2.067974 Loss2: 1.511443
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.947643 Loss1: 2.518136 Loss2: 1.429506
-(DefaultActor pid=3765) >> Training accuracy: 0.487500
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.499839 Loss1: 1.992685 Loss2: 1.507154
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.829861 Loss1: 2.401219 Loss2: 1.428641
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.729938 Loss1: 2.301729 Loss2: 1.428209
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.648954 Loss1: 2.210172 Loss2: 1.438782
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.615178 Loss1: 2.155412 Loss2: 1.459766
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.708569 Loss1: 2.252350 Loss2: 1.456219
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.292249 Loss1: 3.256464 Loss2: 2.035785
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.547436 Loss1: 2.086448 Loss2: 1.460988
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.223573 Loss1: 2.699160 Loss2: 1.524413
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.548055 Loss1: 2.070089 Loss2: 1.477966
-(DefaultActor pid=3764) >> Training accuracy: 0.413542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.923497 Loss1: 2.424381 Loss2: 1.499116 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.848862 Loss1: 2.319302 Loss2: 1.529560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.826247 Loss1: 2.314228 Loss2: 1.512020
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.161209 Loss1: 3.164372 Loss2: 1.996837
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.730176 Loss1: 2.204929 Loss2: 1.525247
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.346789 Loss1: 2.827877 Loss2: 1.518912
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.718614 Loss1: 2.189760 Loss2: 1.528854
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.151354 Loss1: 2.652103 Loss2: 1.499251
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.688151 Loss1: 2.146807 Loss2: 1.541344
-(DefaultActor pid=3765) >> Training accuracy: 0.441667
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.988611 Loss1: 2.495913 Loss2: 1.492698
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.952402 Loss1: 2.446721 Loss2: 1.505681
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.881634 Loss1: 2.375140 Loss2: 1.506495
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.829683 Loss1: 2.314530 Loss2: 1.515154
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.747265 Loss1: 2.217828 Loss2: 1.529438
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.205224 Loss1: 3.217572 Loss2: 1.987652
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.706491 Loss1: 2.183365 Loss2: 1.523127
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.313116 Loss1: 2.810887 Loss2: 1.502229
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.700258 Loss1: 2.161544 Loss2: 1.538714
-(DefaultActor pid=3764) >> Training accuracy: 0.440625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.988237 Loss1: 2.495145 Loss2: 1.493092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.848884 Loss1: 2.338485 Loss2: 1.510399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.876299 Loss1: 2.359757 Loss2: 1.516541
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.183969 Loss1: 3.093826 Loss2: 2.090143
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.820942 Loss1: 2.302922 Loss2: 1.518020
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.275564 Loss1: 2.720138 Loss2: 1.555426
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.758875 Loss1: 2.223614 Loss2: 1.535261
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.976252 Loss1: 2.436030 Loss2: 1.540221
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.740985 Loss1: 2.219838 Loss2: 1.521147
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.884951 Loss1: 2.339418 Loss2: 1.545533
-(DefaultActor pid=3765) >> Training accuracy: 0.384375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.957051 Loss1: 2.398721 Loss2: 1.558330
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.911887 Loss1: 2.340368 Loss2: 1.571519
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.779970 Loss1: 2.209824 Loss2: 1.570146
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.717788 Loss1: 2.148098 Loss2: 1.569690
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.909962 Loss1: 2.917664 Loss2: 1.992298
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.589626 Loss1: 1.999660 Loss2: 1.589967
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.198479 Loss1: 2.701873 Loss2: 1.496605
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.573989 Loss1: 2.001055 Loss2: 1.572934
-(DefaultActor pid=3764) >> Training accuracy: 0.471875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.690284 Loss1: 2.205835 Loss2: 1.484449 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.570991 Loss1: 2.097236 Loss2: 1.473755 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.536012 Loss1: 2.052773 Loss2: 1.483240
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.242693 Loss1: 3.214388 Loss2: 2.028305
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.504381 Loss1: 2.014124 Loss2: 1.490257
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.441236 Loss1: 2.903425 Loss2: 1.537811
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.366165 Loss1: 1.884224 Loss2: 1.481941
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.203738 Loss1: 2.686626 Loss2: 1.517112
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.463073 Loss1: 1.973336 Loss2: 1.489736
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.147760 Loss1: 2.631704 Loss2: 1.516056
-(DefaultActor pid=3765) >> Training accuracy: 0.440625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.968800 Loss1: 2.460584 Loss2: 1.508217
-(DefaultActor pid=3764) Epoch: 5 Loss: 4.012980 Loss1: 2.481291 Loss2: 1.531689
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.910814 Loss1: 2.376252 Loss2: 1.534562
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.814518 Loss1: 2.280883 Loss2: 1.533635
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.444588 Loss1: 3.346004 Loss2: 2.098584
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.831128 Loss1: 2.284028 Loss2: 1.547100
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.808854 Loss1: 2.256606 Loss2: 1.552248 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.409375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.091913 Loss1: 2.558601 Loss2: 1.533312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.975964 Loss1: 2.429849 Loss2: 1.546115 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.174518 Loss1: 3.035845 Loss2: 2.138672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.211014 Loss1: 2.586392 Loss2: 1.624623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.670358 Loss1: 2.094906 Loss2: 1.575453 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.446429
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.671639 Loss1: 2.117931 Loss2: 1.553708 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.569510 Loss1: 1.992035 Loss2: 1.577474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.395099 Loss1: 1.820692 Loss2: 1.574407
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.358487 Loss1: 3.254277 Loss2: 2.104209
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.426627 Loss1: 1.855382 Loss2: 1.571245
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.373459 Loss1: 2.793284 Loss2: 1.580176
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.437589 Loss1: 1.848394 Loss2: 1.589195
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.156422 Loss1: 2.614844 Loss2: 1.541578
-(DefaultActor pid=3764) >> Training accuracy: 0.514583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 4.007383 Loss1: 2.451469 Loss2: 1.555914
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.935263 Loss1: 2.392507 Loss2: 1.542756
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.913031 Loss1: 2.351186 Loss2: 1.561845
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.849076 Loss1: 2.281509 Loss2: 1.567567
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.794091 Loss1: 2.221286 Loss2: 1.572805
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.036055 Loss1: 3.102839 Loss2: 1.933216
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.725054 Loss1: 2.152536 Loss2: 1.572518
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.145929 Loss1: 2.704882 Loss2: 1.441047
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.721020 Loss1: 2.125638 Loss2: 1.595382
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.998387 Loss1: 2.562134 Loss2: 1.436253
-(DefaultActor pid=3765) >> Training accuracy: 0.394792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.847084 Loss1: 2.425449 Loss2: 1.421635
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.759110 Loss1: 2.337424 Loss2: 1.421686
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.664710 Loss1: 2.230675 Loss2: 1.434036
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.611984 Loss1: 2.173984 Loss2: 1.438000
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.636967 Loss1: 2.196614 Loss2: 1.440352
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.098783 Loss1: 3.045274 Loss2: 2.053508
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.612993 Loss1: 2.170920 Loss2: 1.442072
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.120877 Loss1: 2.600023 Loss2: 1.520854
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.576208 Loss1: 2.110555 Loss2: 1.465654
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.917171 Loss1: 2.433736 Loss2: 1.483435
-(DefaultActor pid=3764) >> Training accuracy: 0.431250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.796545 Loss1: 2.312751 Loss2: 1.483794
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.725068 Loss1: 2.243977 Loss2: 1.481090
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.615338 Loss1: 2.133221 Loss2: 1.482117
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.506893 Loss1: 2.021990 Loss2: 1.484902
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.497716 Loss1: 2.004321 Loss2: 1.493395
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.072381 Loss1: 3.057726 Loss2: 2.014655
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.481233 Loss1: 1.978205 Loss2: 1.503027
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.119953 Loss1: 2.622601 Loss2: 1.497352
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.522705 Loss1: 2.004288 Loss2: 1.518417
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.886175 Loss1: 2.419734 Loss2: 1.466442
-(DefaultActor pid=3765) >> Training accuracy: 0.476042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.762833 Loss1: 2.300734 Loss2: 1.462099
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.685907 Loss1: 2.214863 Loss2: 1.471043
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.558585 Loss1: 2.097441 Loss2: 1.461144
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.551032 Loss1: 2.076079 Loss2: 1.474953
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.042608 Loss1: 3.088410 Loss2: 1.954198
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.579728 Loss1: 2.097597 Loss2: 1.482131
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.181392 Loss1: 2.704890 Loss2: 1.476502
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.535667 Loss1: 2.036658 Loss2: 1.499009
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.978478 Loss1: 2.526640 Loss2: 1.451839
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.473704 Loss1: 1.982901 Loss2: 1.490803
-(DefaultActor pid=3764) >> Training accuracy: 0.470833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.761816 Loss1: 2.311466 Loss2: 1.450350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.569887 Loss1: 2.114711 Loss2: 1.455176 [repeated 2x across cluster]
-DEBUG flwr 2023-10-08 22:40:24,829 | server.py:236 | fit_round 17 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.639442 Loss1: 2.169914 Loss2: 1.469528
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.154678 Loss1: 3.211535 Loss2: 1.943142
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.594998 Loss1: 2.136366 Loss2: 1.458632
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.194095 Loss1: 2.729342 Loss2: 1.464753
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.476258 Loss1: 1.999840 Loss2: 1.476418
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.127178 Loss1: 2.674701 Loss2: 1.452477
-(DefaultActor pid=3765) >> Training accuracy: 0.500000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.888936 Loss1: 2.449047 Loss2: 1.439889
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.815183 Loss1: 2.371818 Loss2: 1.443365
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.778069 Loss1: 2.325728 Loss2: 1.452341
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.682486 Loss1: 2.227258 Loss2: 1.455228
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.014866 Loss1: 3.027522 Loss2: 1.987344
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.718556 Loss1: 2.258237 Loss2: 1.460319
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.067814 Loss1: 2.581766 Loss2: 1.486048
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.669812 Loss1: 2.189227 Loss2: 1.480585
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.815400 Loss1: 2.352085 Loss2: 1.463316
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.579868 Loss1: 2.106626 Loss2: 1.473241
-(DefaultActor pid=3764) >> Training accuracy: 0.407292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.654822 Loss1: 2.183617 Loss2: 1.471204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.523941 Loss1: 2.033886 Loss2: 1.490055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.594250 Loss1: 2.087038 Loss2: 1.507212
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.244058 Loss1: 3.192288 Loss2: 2.051770
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.506502 Loss1: 2.015703 Loss2: 1.490799
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.304546 Loss1: 2.767828 Loss2: 1.536718
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.547488 Loss1: 2.036185 Loss2: 1.511303
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.111373 Loss1: 2.602236 Loss2: 1.509137
-(DefaultActor pid=3765) >> Training accuracy: 0.477083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.970151 Loss1: 2.462044 Loss2: 1.508107
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.924894 Loss1: 2.413082 Loss2: 1.511812
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.943880 Loss1: 2.420718 Loss2: 1.523163
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.808321 Loss1: 2.257115 Loss2: 1.551206
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.294998 Loss1: 3.324915 Loss2: 1.970084
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.707533 Loss1: 2.163469 Loss2: 1.544064
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.299321 Loss1: 2.850791 Loss2: 1.448530
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.673149 Loss1: 2.129042 Loss2: 1.544107
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.101271 Loss1: 2.668946 Loss2: 1.432325
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.588566 Loss1: 2.037909 Loss2: 1.550657
-(DefaultActor pid=3764) >> Training accuracy: 0.443750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.899267 Loss1: 2.465458 Loss2: 1.433809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.729068 Loss1: 2.280067 Loss2: 1.449001 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.737645 Loss1: 2.286394 Loss2: 1.451251
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.969348 Loss1: 3.106342 Loss2: 1.863007
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.123208 Loss1: 2.699982 Loss2: 1.423226 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.439583
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.620855 Loss1: 2.148351 Loss2: 1.472504
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.879321 Loss1: 2.467776 Loss2: 1.411545
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.840819 Loss1: 2.426964 Loss2: 1.413856
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.732987 Loss1: 2.307626 Loss2: 1.425361
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.672554 Loss1: 2.253782 Loss2: 1.418773
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.773309 Loss1: 2.326047 Loss2: 1.447263
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.673546 Loss1: 2.239659 Loss2: 1.433887
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.582616 Loss1: 2.137355 Loss2: 1.445261
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.581756 Loss1: 2.135336 Loss2: 1.446421
-(DefaultActor pid=3764) >> Training accuracy: 0.454044
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 22:40:24,829][flwr][DEBUG] - fit_round 17 received 50 results and 0 failures
-INFO flwr 2023-10-08 22:41:06,375 | server.py:125 | fit progress: (17, 3.453257521120504, {'accuracy': 0.177}, 38974.153807173)
->> Test accuracy: 0.177000
-[2023-10-08 22:41:06,375][flwr][INFO] - fit progress: (17, 3.453257521120504, {'accuracy': 0.177}, 38974.153807173)
-DEBUG flwr 2023-10-08 22:41:06,376 | server.py:173 | evaluate_round 17: strategy sampled 50 clients (out of 50)
-[2023-10-08 22:41:06,376][flwr][DEBUG] - evaluate_round 17: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 22:50:13,891 | server.py:187 | evaluate_round 17 received 50 results and 0 failures
-[2023-10-08 22:50:13,891][flwr][DEBUG] - evaluate_round 17 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 22:50:13,892 | server.py:222 | fit_round 18: strategy sampled 50 clients (out of 50)
-[2023-10-08 22:50:13,892][flwr][DEBUG] - fit_round 18: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.136857 Loss1: 3.140472 Loss2: 1.996385
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.228466 Loss1: 2.718414 Loss2: 1.510052
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.023183 Loss1: 2.544073 Loss2: 1.479110
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.922538 Loss1: 2.438141 Loss2: 1.484397
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.873132 Loss1: 2.855610 Loss2: 2.017522
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.857537 Loss1: 2.373934 Loss2: 1.483603
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.026071 Loss1: 2.494871 Loss2: 1.531199
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.762636 Loss1: 2.262105 Loss2: 1.500531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.609611 Loss1: 2.118757 Loss2: 1.490854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.584407 Loss1: 2.071478 Loss2: 1.512929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.445993 Loss1: 1.954765 Loss2: 1.491228 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.409375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.406152 Loss1: 1.898818 Loss2: 1.507335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.341649 Loss1: 1.810192 Loss2: 1.531457 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.525391
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.892033 Loss1: 3.026592 Loss2: 1.865441 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.765162 Loss1: 2.352000 Loss2: 1.413162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.620161 Loss1: 2.217396 Loss2: 1.402765
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.167570 Loss1: 3.196818 Loss2: 1.970751
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.364502 Loss1: 2.876802 Loss2: 1.487699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.111766 Loss1: 2.635910 Loss2: 1.475856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.401349 Loss1: 1.979432 Loss2: 1.421916
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.922685 Loss1: 2.453939 Loss2: 1.468746
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.378768 Loss1: 1.947744 Loss2: 1.431024
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.845754 Loss1: 2.381169 Loss2: 1.464585
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.367800 Loss1: 1.932775 Loss2: 1.435025
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.775102 Loss1: 2.306053 Loss2: 1.469049
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.677647 Loss1: 2.188639 Loss2: 1.489009
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.292364 Loss1: 1.834490 Loss2: 1.457874
-(DefaultActor pid=3765) >> Training accuracy: 0.498047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.664521 Loss1: 2.160348 Loss2: 1.504173 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.439583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.989953 Loss1: 3.019784 Loss2: 1.970169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.684422 Loss1: 2.268359 Loss2: 1.416063 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.992588 Loss1: 2.978023 Loss2: 2.014565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.473776 Loss1: 2.034412 Loss2: 1.439363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.462472 Loss1: 2.035281 Loss2: 1.427191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.386785 Loss1: 1.942451 Loss2: 1.444334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.249171 Loss1: 1.819041 Loss2: 1.430129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.204916 Loss1: 1.763739 Loss2: 1.441177 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.516827
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.594002 Loss1: 2.057863 Loss2: 1.536139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.436020 Loss1: 1.903785 Loss2: 1.532235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.282197 Loss1: 1.741382 Loss2: 1.540815
-(DefaultActor pid=3764) >> Training accuracy: 0.526042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.103454 Loss1: 3.089332 Loss2: 2.014122
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.164985 Loss1: 2.603914 Loss2: 1.561071
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.949426 Loss1: 2.448265 Loss2: 1.501160
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.826870 Loss1: 2.319902 Loss2: 1.506967
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.797896 Loss1: 2.298459 Loss2: 1.499437
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.064793 Loss1: 3.062428 Loss2: 2.002366
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.153704 Loss1: 2.622728 Loss2: 1.530976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.976632 Loss1: 2.460171 Loss2: 1.516461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.866215 Loss1: 2.352014 Loss2: 1.514201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.761251 Loss1: 2.238314 Loss2: 1.522936 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.430664
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.552870 Loss1: 2.008083 Loss2: 1.544787
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.664376 Loss1: 2.142818 Loss2: 1.521558
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.601964 Loss1: 2.063864 Loss2: 1.538100
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.546453 Loss1: 2.005873 Loss2: 1.540580
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.429206 Loss1: 1.882954 Loss2: 1.546252
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.424694 Loss1: 1.869653 Loss2: 1.555041
-(DefaultActor pid=3764) >> Training accuracy: 0.468750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.069230 Loss1: 3.047811 Loss2: 2.021418
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.141062 Loss1: 2.605958 Loss2: 1.535104
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.879763 Loss1: 2.381155 Loss2: 1.498608
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.741405 Loss1: 2.239363 Loss2: 1.502042
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.684994 Loss1: 2.182724 Loss2: 1.502270
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.043820 Loss1: 2.958250 Loss2: 2.085570
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.688361 Loss1: 2.169502 Loss2: 1.518859
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.582165 Loss1: 2.043994 Loss2: 1.538170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.575648 Loss1: 2.043309 Loss2: 1.532339 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.742944 Loss1: 2.230734 Loss2: 1.512210 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.707794 Loss1: 2.206238 Loss2: 1.501556 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.501042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.477023 Loss1: 1.956385 Loss2: 1.520638 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.395898 Loss1: 1.867736 Loss2: 1.528162 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.456731
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.903450 Loss1: 2.858301 Loss2: 2.045149
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.971832 Loss1: 2.444302 Loss2: 1.527530
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.727639 Loss1: 2.245395 Loss2: 1.482244
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.625122 Loss1: 2.135027 Loss2: 1.490095
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.169075 Loss1: 3.187548 Loss2: 1.981527
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.211943 Loss1: 2.714667 Loss2: 1.497276 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.026342 Loss1: 2.558240 Loss2: 1.468102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.920508 Loss1: 2.441635 Loss2: 1.478873 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.816808 Loss1: 2.345090 Loss2: 1.471718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.737671 Loss1: 2.257639 Loss2: 1.480032 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.537500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.726513 Loss1: 2.237203 Loss2: 1.489311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.590603 Loss1: 2.085853 Loss2: 1.504749 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.480469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.342172 Loss1: 3.212704 Loss2: 2.129468 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.035687 Loss1: 2.498410 Loss2: 1.537277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.015082 Loss1: 2.979133 Loss2: 2.035949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.040547 Loss1: 2.545013 Loss2: 1.495534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.811555 Loss1: 2.350347 Loss2: 1.461209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.685713 Loss1: 2.222568 Loss2: 1.463146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.672194 Loss1: 2.202422 Loss2: 1.469772 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.580227 Loss1: 2.101003 Loss2: 1.479224 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.465625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.347346 Loss1: 1.882237 Loss2: 1.465109 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.350867 Loss1: 1.848596 Loss2: 1.502271 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.497917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.064620 Loss1: 3.035146 Loss2: 2.029475
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.203400 Loss1: 2.669160 Loss2: 1.534240
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.975891 Loss1: 2.460292 Loss2: 1.515599
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.725892 Loss1: 2.214667 Loss2: 1.511225
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.348292 Loss1: 3.311788 Loss2: 2.036504
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.754263 Loss1: 2.230060 Loss2: 1.524202
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.327909 Loss1: 2.792004 Loss2: 1.535905
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.758425 Loss1: 2.223623 Loss2: 1.534801
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.045135 Loss1: 2.560593 Loss2: 1.484542
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.989900 Loss1: 2.499978 Loss2: 1.489921
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.602157 Loss1: 2.060404 Loss2: 1.541753
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.892243 Loss1: 2.391415 Loss2: 1.500828
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.640272 Loss1: 2.094384 Loss2: 1.545888
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.794003 Loss1: 2.298710 Loss2: 1.495293
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.662736 Loss1: 2.085624 Loss2: 1.577112
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.536419 Loss1: 1.975467 Loss2: 1.560952 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.470588
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.578106 Loss1: 2.071716 Loss2: 1.506390 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.465625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.145815 Loss1: 3.198774 Loss2: 1.947040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.127567 Loss1: 2.645168 Loss2: 1.482399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.972456 Loss1: 2.475946 Loss2: 1.496510
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.723014 Loss1: 2.834152 Loss2: 1.888862
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.943391 Loss1: 2.447306 Loss2: 1.496085
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.964330 Loss1: 2.510117 Loss2: 1.454213
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.642351 Loss1: 2.211695 Loss2: 1.430656
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.933039 Loss1: 2.432049 Loss2: 1.500990
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.487414 Loss1: 2.066214 Loss2: 1.421200
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.792663 Loss1: 2.280344 Loss2: 1.512318
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.410813 Loss1: 1.984408 Loss2: 1.426405
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.781826 Loss1: 2.257652 Loss2: 1.524175
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.445152 Loss1: 2.010223 Loss2: 1.434929
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.717398 Loss1: 2.190719 Loss2: 1.526679
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.704640 Loss1: 2.167500 Loss2: 1.537139 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.408203
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.262903 Loss1: 1.823208 Loss2: 1.439696 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.520833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.025727 Loss1: 3.036086 Loss2: 1.989641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.843790 Loss1: 2.387549 Loss2: 1.456242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.784551 Loss1: 2.334372 Loss2: 1.450179
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.424323 Loss1: 3.334541 Loss2: 2.089782
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.674097 Loss1: 2.213049 Loss2: 1.461047
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.368145 Loss1: 2.834369 Loss2: 1.533776
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.578540 Loss1: 2.112984 Loss2: 1.465556
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.121531 Loss1: 2.627670 Loss2: 1.493861
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.035580 Loss1: 2.541624 Loss2: 1.493955
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.581543 Loss1: 2.112220 Loss2: 1.469323
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.893403 Loss1: 2.386820 Loss2: 1.506583
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.487015 Loss1: 2.003838 Loss2: 1.483176
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.877181 Loss1: 2.366824 Loss2: 1.510357
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.413895 Loss1: 1.928843 Loss2: 1.485051
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.461693 Loss1: 1.967186 Loss2: 1.494507 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.414583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.605720 Loss1: 2.086282 Loss2: 1.519438 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.439732
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.091544 Loss1: 3.203201 Loss2: 1.888343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.986058 Loss1: 2.589545 Loss2: 1.396513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.107671 Loss1: 3.163756 Loss2: 1.943915
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.916972 Loss1: 2.494431 Loss2: 1.422541
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.141039 Loss1: 2.674817 Loss2: 1.466222
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.839056 Loss1: 2.417291 Loss2: 1.421764
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.990064 Loss1: 2.543089 Loss2: 1.446975
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.713749 Loss1: 2.284326 Loss2: 1.429424
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.826848 Loss1: 2.386842 Loss2: 1.440007
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.621271 Loss1: 2.187033 Loss2: 1.434239
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.682741 Loss1: 2.235324 Loss2: 1.447417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.605288 Loss1: 2.148528 Loss2: 1.456760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.527465 Loss1: 2.077744 Loss2: 1.449721 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.478516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.504496 Loss1: 2.051189 Loss2: 1.453307 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.446875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.034540 Loss1: 2.954860 Loss2: 2.079680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.992308 Loss1: 2.473217 Loss2: 1.519091 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.856452 Loss1: 2.351427 Loss2: 1.505026
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.123329 Loss1: 3.102525 Loss2: 2.020804
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.112024 Loss1: 2.577816 Loss2: 1.534209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.002348 Loss1: 2.483305 Loss2: 1.519043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.866624 Loss1: 2.360702 Loss2: 1.505923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.719173 Loss1: 2.212149 Loss2: 1.507024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.714405 Loss1: 2.199677 Loss2: 1.514728 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.469792
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.459076 Loss1: 1.908583 Loss2: 1.550493
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.709587 Loss1: 2.190146 Loss2: 1.519441
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.541225 Loss1: 2.024201 Loss2: 1.517024
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.476162 Loss1: 1.948619 Loss2: 1.527543
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.400798 Loss1: 1.876410 Loss2: 1.524388
-(DefaultActor pid=3764) >> Training accuracy: 0.507292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.097794 Loss1: 3.113074 Loss2: 1.984720
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.252139 Loss1: 2.768504 Loss2: 1.483635
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.973524 Loss1: 2.512721 Loss2: 1.460803
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.849772 Loss1: 2.384767 Loss2: 1.465004
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.012365 Loss1: 2.950857 Loss2: 2.061508
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.070839 Loss1: 2.533646 Loss2: 1.537193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.843883 Loss1: 2.342898 Loss2: 1.500986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.726759 Loss1: 2.233618 Loss2: 1.493141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.673106 Loss1: 2.196229 Loss2: 1.476876 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.537881 Loss1: 2.047233 Loss2: 1.490648 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.472917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.450776 Loss1: 1.957958 Loss2: 1.492818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.442553 Loss1: 1.925186 Loss2: 1.517368 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.539583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.075341 Loss1: 3.045691 Loss2: 2.029650 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.901708 Loss1: 2.384401 Loss2: 1.517307 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.003312 Loss1: 2.969862 Loss2: 2.033450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.175782 Loss1: 2.617694 Loss2: 1.558088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.916917 Loss1: 2.389819 Loss2: 1.527098 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.799294 Loss1: 2.277692 Loss2: 1.521601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.636779 Loss1: 2.107200 Loss2: 1.529579 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.565044 Loss1: 2.041422 Loss2: 1.523622 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.472917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.626963 Loss1: 2.081231 Loss2: 1.545732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.515536 Loss1: 1.963458 Loss2: 1.552078 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.488542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.075425 Loss1: 3.046418 Loss2: 2.029007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.732601 Loss1: 2.269164 Loss2: 1.463437 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.025945 Loss1: 3.036373 Loss2: 1.989573 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.105590 Loss1: 2.600028 Loss2: 1.505563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.871448 Loss1: 2.395607 Loss2: 1.475841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.726250 Loss1: 2.240676 Loss2: 1.485574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.587235 Loss1: 2.114081 Loss2: 1.473153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.602007 Loss1: 2.115349 Loss2: 1.486658 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.553125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.550414 Loss1: 2.046696 Loss2: 1.503719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.371633 Loss1: 1.880415 Loss2: 1.491219 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.485417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.998755 Loss1: 3.034883 Loss2: 1.963872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.898037 Loss1: 2.447613 Loss2: 1.450424 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.714400 Loss1: 2.254080 Loss2: 1.460320
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.241281 Loss1: 3.242595 Loss2: 1.998686
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.315682 Loss1: 2.852326 Loss2: 1.463356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.085498 Loss1: 2.624589 Loss2: 1.460909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.972056 Loss1: 2.512713 Loss2: 1.459343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.502855 Loss1: 2.031488 Loss2: 1.471367
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.866344 Loss1: 2.405977 Loss2: 1.460367
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.482371 Loss1: 1.981391 Loss2: 1.500980
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.916344 Loss1: 2.444389 Loss2: 1.471955
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.468255 Loss1: 1.982601 Loss2: 1.485654
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.750614 Loss1: 2.263537 Loss2: 1.487076
-(DefaultActor pid=3765) >> Training accuracy: 0.520833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.691835 Loss1: 2.196427 Loss2: 1.495408
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.572349 Loss1: 2.086155 Loss2: 1.486194
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.542712 Loss1: 2.042966 Loss2: 1.499745
-(DefaultActor pid=3764) >> Training accuracy: 0.448661
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.127391 Loss1: 3.130291 Loss2: 1.997099
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.192497 Loss1: 2.661050 Loss2: 1.531447
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.995484 Loss1: 2.487517 Loss2: 1.507967
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.867549 Loss1: 2.385513 Loss2: 1.482036
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.258976 Loss1: 3.189566 Loss2: 2.069411
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.396623 Loss1: 2.844891 Loss2: 1.551732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.228615 Loss1: 2.665580 Loss2: 1.563034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.044827 Loss1: 2.507626 Loss2: 1.537201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 4.035102 Loss1: 2.489722 Loss2: 1.545380 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.877158 Loss1: 2.325798 Loss2: 1.551360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.485655 Loss1: 1.959385 Loss2: 1.526269
-(DefaultActor pid=3765) >> Training accuracy: 0.485417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.775944 Loss1: 2.224883 Loss2: 1.551061
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.673990 Loss1: 2.111534 Loss2: 1.562456
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.655873 Loss1: 2.086996 Loss2: 1.568877
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.550161 Loss1: 1.970050 Loss2: 1.580111
-(DefaultActor pid=3764) >> Training accuracy: 0.463542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.198133 Loss1: 3.092431 Loss2: 2.105701
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.123361 Loss1: 2.650983 Loss2: 1.472379
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.832096 Loss1: 2.409306 Loss2: 1.422790
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.701225 Loss1: 2.273242 Loss2: 1.427982
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.630001 Loss1: 2.200158 Loss2: 1.429843
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.573044 Loss1: 2.106498 Loss2: 1.466546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.503626 Loss1: 2.054071 Loss2: 1.449555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.440542 Loss1: 1.985562 Loss2: 1.454980
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.000702 Loss1: 2.525415 Loss2: 1.475287
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.880687 Loss1: 2.398068 Loss2: 1.482619 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.447917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.759302 Loss1: 2.269827 Loss2: 1.489474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.730388 Loss1: 2.230071 Loss2: 1.500317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.505397 Loss1: 2.008840 Loss2: 1.496557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.530874 Loss1: 2.024314 Loss2: 1.506560 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.444792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.875475 Loss1: 2.406811 Loss2: 1.468664 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.575464 Loss1: 2.115630 Loss2: 1.459834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.136703 Loss1: 3.172098 Loss2: 1.964605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.281000 Loss1: 2.786262 Loss2: 1.494738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.058994 Loss1: 2.595074 Loss2: 1.463920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.971870 Loss1: 2.503355 Loss2: 1.468515 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.485417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.778384 Loss1: 2.302738 Loss2: 1.475646 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.643145 Loss1: 2.145874 Loss2: 1.497271 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.596417 Loss1: 2.090685 Loss2: 1.505732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.554506 Loss1: 2.035088 Loss2: 1.519418 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.384766
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.901377 Loss1: 2.421582 Loss2: 1.479794 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.767505 Loss1: 2.291974 Loss2: 1.475531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.643018 Loss1: 2.156585 Loss2: 1.486433
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.858801 Loss1: 2.880962 Loss2: 1.977839
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.971848 Loss1: 2.480022 Loss2: 1.491826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.670298 Loss1: 2.209589 Loss2: 1.460708 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.430208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.605696 Loss1: 2.139946 Loss2: 1.465749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.487289 Loss1: 2.001588 Loss2: 1.485701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.376135 Loss1: 1.888128 Loss2: 1.488008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.422233 Loss1: 1.908688 Loss2: 1.513545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.296432 Loss1: 1.794720 Loss2: 1.501712 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.456250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.808500 Loss1: 2.398917 Loss2: 1.409583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.679736 Loss1: 2.264393 Loss2: 1.415343 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.001831 Loss1: 3.057885 Loss2: 1.943946 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.164283 Loss1: 2.673362 Loss2: 1.490921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.903135 Loss1: 2.435096 Loss2: 1.468039 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.491667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.802453 Loss1: 2.318238 Loss2: 1.484215 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.626839 Loss1: 2.131655 Loss2: 1.495184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.194524 Loss1: 3.168151 Loss2: 2.026373
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.613106 Loss1: 2.105421 Loss2: 1.507685
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.528531 Loss1: 2.025502 Loss2: 1.503029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.427780 Loss1: 1.920379 Loss2: 1.507401 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.523438
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.808811 Loss1: 2.298587 Loss2: 1.510223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.631047 Loss1: 2.125147 Loss2: 1.505900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.551929 Loss1: 2.032010 Loss2: 1.519919
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.970894 Loss1: 2.979744 Loss2: 1.991150
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.958130 Loss1: 2.461423 Loss2: 1.496707 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.427083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.700985 Loss1: 2.238834 Loss2: 1.462151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.482674 Loss1: 2.026143 Loss2: 1.456531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.339024 Loss1: 1.861952 Loss2: 1.477072 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.297998 Loss1: 1.820956 Loss2: 1.477042 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.268558 Loss1: 1.781059 Loss2: 1.487499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.194554 Loss1: 1.700070 Loss2: 1.494484 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.511458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.763030 Loss1: 2.238118 Loss2: 1.524911 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.680691 Loss1: 2.132960 Loss2: 1.547731 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.570827 Loss1: 2.031043 Loss2: 1.539785
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.047855 Loss1: 3.129664 Loss2: 1.918191
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.180180 Loss1: 2.734321 Loss2: 1.445860 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.487500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.950427 Loss1: 2.521507 Loss2: 1.428920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.855448 Loss1: 2.411966 Loss2: 1.443482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.687226 Loss1: 2.231550 Loss2: 1.455676 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.640407 Loss1: 2.197043 Loss2: 1.443364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.526356 Loss1: 2.054329 Loss2: 1.472027 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.547500 Loss1: 2.070129 Loss2: 1.477371 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.433333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.706876 Loss1: 2.158816 Loss2: 1.548059 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.510163 Loss1: 1.962809 Loss2: 1.547354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.402958 Loss1: 1.833950 Loss2: 1.569008
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.242576 Loss1: 3.199058 Loss2: 2.043518
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.320773 Loss1: 2.789723 Loss2: 1.531051 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.506250
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.345365 Loss1: 1.766079 Loss2: 1.579286
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.123133 Loss1: 2.610609 Loss2: 1.512524
-DEBUG flwr 2023-10-08 23:18:56,950 | server.py:236 | fit_round 18 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 3 Loss: 4.003972 Loss1: 2.500763 Loss2: 1.503209
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.965033 Loss1: 2.431108 Loss2: 1.533925
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.891870 Loss1: 2.350512 Loss2: 1.541357
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.898049 Loss1: 2.350176 Loss2: 1.547874
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.010351 Loss1: 2.946829 Loss2: 2.063522
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.788175 Loss1: 2.238156 Loss2: 1.550020
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.768595 Loss1: 2.205190 Loss2: 1.563404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.726301 Loss1: 2.170041 Loss2: 1.556260 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.468750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.653268 Loss1: 2.119437 Loss2: 1.533831 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.594810 Loss1: 2.054821 Loss2: 1.539990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.499322 Loss1: 1.933876 Loss2: 1.565446
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.116009 Loss1: 3.025553 Loss2: 2.090456
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.122729 Loss1: 2.584518 Loss2: 1.538211 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.453125
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.384165 Loss1: 1.826298 Loss2: 1.557867
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.898850 Loss1: 2.389616 Loss2: 1.509234
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.883273 Loss1: 2.378252 Loss2: 1.505021
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.682668 Loss1: 2.170777 Loss2: 1.511890
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.561650 Loss1: 2.053251 Loss2: 1.508399
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.501749 Loss1: 1.977951 Loss2: 1.523798
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.501401 Loss1: 1.975127 Loss2: 1.526274
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.499304 Loss1: 1.959909 Loss2: 1.539395
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.565146 Loss1: 2.023971 Loss2: 1.541175
-(DefaultActor pid=3764) >> Training accuracy: 0.506696
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 23:18:56,950][flwr][DEBUG] - fit_round 18 received 50 results and 0 failures
-INFO flwr 2023-10-08 23:19:38,794 | server.py:125 | fit progress: (18, 3.4021275843294285, {'accuracy': 0.1934}, 41286.572079595004)
->> Test accuracy: 0.193400
-[2023-10-08 23:19:38,794][flwr][INFO] - fit progress: (18, 3.4021275843294285, {'accuracy': 0.1934}, 41286.572079595004)
-DEBUG flwr 2023-10-08 23:19:38,794 | server.py:173 | evaluate_round 18: strategy sampled 50 clients (out of 50)
-[2023-10-08 23:19:38,794][flwr][DEBUG] - evaluate_round 18: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-08 23:28:44,755 | server.py:187 | evaluate_round 18 received 50 results and 0 failures
-[2023-10-08 23:28:44,755][flwr][DEBUG] - evaluate_round 18 received 50 results and 0 failures
-DEBUG flwr 2023-10-08 23:28:44,755 | server.py:222 | fit_round 19: strategy sampled 50 clients (out of 50)
-[2023-10-08 23:28:44,755][flwr][DEBUG] - fit_round 19: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.089913 Loss1: 3.076141 Loss2: 2.013773
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.176945 Loss1: 2.706927 Loss2: 1.470018
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.951002 Loss1: 2.498280 Loss2: 1.452723
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.797290 Loss1: 2.345836 Loss2: 1.451455
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.209688 Loss1: 3.127394 Loss2: 2.082294
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.291119 Loss1: 2.730826 Loss2: 1.560292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.072488 Loss1: 2.513165 Loss2: 1.559323 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.945890 Loss1: 2.399739 Loss2: 1.546151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.764027 Loss1: 2.216241 Loss2: 1.547786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.753012 Loss1: 2.188893 Loss2: 1.564118 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.412500
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.395006 Loss1: 1.927366 Loss2: 1.467639
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.686006 Loss1: 2.123265 Loss2: 1.562741
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.574511 Loss1: 2.005052 Loss2: 1.569458
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.576838 Loss1: 1.997628 Loss2: 1.579210
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.548590 Loss1: 1.965880 Loss2: 1.582711
-(DefaultActor pid=3764) >> Training accuracy: 0.489583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.096405 Loss1: 3.008433 Loss2: 2.087972
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.117826 Loss1: 2.606875 Loss2: 1.510951
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.768026 Loss1: 2.273596 Loss2: 1.494430
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.738310 Loss1: 2.251553 Loss2: 1.486758
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.999451 Loss1: 3.027936 Loss2: 1.971515
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.579213 Loss1: 2.062156 Loss2: 1.517057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.483785 Loss1: 1.961061 Loss2: 1.522724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.381782 Loss1: 1.856715 Loss2: 1.525067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.475067 Loss1: 1.935663 Loss2: 1.539404 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.317636 Loss1: 1.777365 Loss2: 1.540271 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.520433
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.615137 Loss1: 2.125198 Loss2: 1.489939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.412083 Loss1: 1.916840 Loss2: 1.495243 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.501042
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.321498 Loss1: 1.831151 Loss2: 1.490348
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.922782 Loss1: 2.962954 Loss2: 1.959828
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.934246 Loss1: 2.486180 Loss2: 1.448066
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.692704 Loss1: 2.273170 Loss2: 1.419534
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.615671 Loss1: 2.195205 Loss2: 1.420465
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.546161 Loss1: 2.115677 Loss2: 1.430484
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.219511 Loss1: 3.064341 Loss2: 2.155170
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.229197 Loss1: 2.621347 Loss2: 1.607850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 4.002032 Loss1: 2.416285 Loss2: 1.585748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.826224 Loss1: 2.240317 Loss2: 1.585907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.765190 Loss1: 2.175487 Loss2: 1.589702 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.555208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.685772 Loss1: 2.084004 Loss2: 1.601769 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.584340 Loss1: 1.969944 Loss2: 1.614396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.456462 Loss1: 1.837057 Loss2: 1.619404 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.547917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.033860 Loss1: 2.502881 Loss2: 1.530979 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.613492 Loss1: 2.105588 Loss2: 1.507904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.513431 Loss1: 2.003961 Loss2: 1.509470
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.844160 Loss1: 2.899974 Loss2: 1.944186
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.999211 Loss1: 2.526557 Loss2: 1.472654 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.780445 Loss1: 2.327614 Loss2: 1.452831 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.618886 Loss1: 2.162168 Loss2: 1.456718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.575032 Loss1: 2.123905 Loss2: 1.451127 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.591667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.501502 Loss1: 2.027064 Loss2: 1.474438 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.324530 Loss1: 1.862713 Loss2: 1.461817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.273708 Loss1: 1.780889 Loss2: 1.492820 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.486458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.816901 Loss1: 2.285856 Loss2: 1.531045 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.448285 Loss1: 1.969048 Loss2: 1.479237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.400039 Loss1: 1.917298 Loss2: 1.482741
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.139302 Loss1: 3.122867 Loss2: 2.016435
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.118099 Loss1: 2.627565 Loss2: 1.490535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.930096 Loss1: 2.463692 Loss2: 1.466404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.793535 Loss1: 2.305554 Loss2: 1.487980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.671428 Loss1: 2.187213 Loss2: 1.484216 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.529167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.565879 Loss1: 2.089625 Loss2: 1.476254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.507821 Loss1: 2.000415 Loss2: 1.507407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.531028 Loss1: 2.008578 Loss2: 1.522451 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.469792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.323590 Loss1: 2.839481 Loss2: 1.484110 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.853518 Loss1: 2.392834 Loss2: 1.460684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.958836 Loss1: 2.999041 Loss2: 1.959795 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.647281 Loss1: 2.148312 Loss2: 1.498969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.567484 Loss1: 2.070825 Loss2: 1.496659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.544781 Loss1: 2.024120 Loss2: 1.520661 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.440080 Loss1: 1.931585 Loss2: 1.508496 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.426339
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.560383 Loss1: 2.098014 Loss2: 1.462368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.447260 Loss1: 1.959282 Loss2: 1.487978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.361900 Loss1: 1.880909 Loss2: 1.480991
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.050568 Loss1: 2.951685 Loss2: 2.098884
-(DefaultActor pid=3764) >> Training accuracy: 0.485352
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.363150 Loss1: 1.854086 Loss2: 1.509064
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.073209 Loss1: 2.505143 Loss2: 1.568067
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.964173 Loss1: 2.411022 Loss2: 1.553151
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.783686 Loss1: 2.233718 Loss2: 1.549969
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.630552 Loss1: 2.087797 Loss2: 1.542755
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.532711 Loss1: 1.980396 Loss2: 1.552315
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.016294 Loss1: 2.947727 Loss2: 2.068567
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.058034 Loss1: 2.521256 Loss2: 1.536778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.781580 Loss1: 2.280471 Loss2: 1.501109 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.626379 Loss1: 2.113918 Loss2: 1.512460 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.523958
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.391578 Loss1: 1.809623 Loss2: 1.581955
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.601928 Loss1: 2.085730 Loss2: 1.516198
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.527153 Loss1: 2.011109 Loss2: 1.516044
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.445193 Loss1: 1.923521 Loss2: 1.521672
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.556871 Loss1: 2.005438 Loss2: 1.551434
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.308936 Loss1: 1.767031 Loss2: 1.541905
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.409373 Loss1: 1.857183 Loss2: 1.552190
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.919472 Loss1: 2.969889 Loss2: 1.949582
-(DefaultActor pid=3764) >> Training accuracy: 0.441964
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.086334 Loss1: 2.614330 Loss2: 1.472004
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.815677 Loss1: 2.371289 Loss2: 1.444388
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.667137 Loss1: 2.241101 Loss2: 1.426036
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.559048 Loss1: 2.110953 Loss2: 1.448095
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.941390 Loss1: 2.917656 Loss2: 2.023734
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.467936 Loss1: 2.018594 Loss2: 1.449341
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.441897 Loss1: 1.977705 Loss2: 1.464192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.429968 Loss1: 1.955616 Loss2: 1.474352 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.343428 Loss1: 1.861191 Loss2: 1.482237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.318019 Loss1: 1.819444 Loss2: 1.498575 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.469792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.303172 Loss1: 1.829954 Loss2: 1.473218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.268389 Loss1: 1.764499 Loss2: 1.503890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.193060 Loss1: 1.690116 Loss2: 1.502944
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.977144 Loss1: 2.918306 Loss2: 2.058838
-(DefaultActor pid=3764) >> Training accuracy: 0.535417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.953075 Loss1: 2.421759 Loss2: 1.531316
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.715598 Loss1: 2.211410 Loss2: 1.504188
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.670228 Loss1: 2.179644 Loss2: 1.490584
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.586490 Loss1: 2.087386 Loss2: 1.499104
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.057611 Loss1: 3.129134 Loss2: 1.928478
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.497210 Loss1: 2.000436 Loss2: 1.496774
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.182592 Loss1: 2.749189 Loss2: 1.433403
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.445421 Loss1: 1.943024 Loss2: 1.502396
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.993801 Loss1: 2.568874 Loss2: 1.424927
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.435087 Loss1: 1.905949 Loss2: 1.529138
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.304090 Loss1: 1.782660 Loss2: 1.521430
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.856704 Loss1: 2.438021 Loss2: 1.418683
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.271402 Loss1: 1.750026 Loss2: 1.521376
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.745292 Loss1: 2.304547 Loss2: 1.440744
-(DefaultActor pid=3765) >> Training accuracy: 0.530208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.724574 Loss1: 2.275543 Loss2: 1.449032
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.795014 Loss1: 2.342680 Loss2: 1.452334
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.653133 Loss1: 2.194904 Loss2: 1.458228
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.622327 Loss1: 2.152324 Loss2: 1.470003
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.132833 Loss1: 3.067871 Loss2: 2.064962
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.454674 Loss1: 1.982266 Loss2: 1.472408
-(DefaultActor pid=3764) >> Training accuracy: 0.453125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.855770 Loss1: 2.342111 Loss2: 1.513659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.730199 Loss1: 2.187277 Loss2: 1.542922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.763025 Loss1: 2.196699 Loss2: 1.566325
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.056782 Loss1: 2.822443 Loss2: 2.234339
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.126935 Loss1: 2.459749 Loss2: 1.667186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.807699 Loss1: 2.185434 Loss2: 1.622265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.589832 Loss1: 1.981744 Loss2: 1.608088 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.520833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.493808 Loss1: 1.883939 Loss2: 1.609870 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.436588 Loss1: 1.807838 Loss2: 1.628751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.374860 Loss1: 1.731141 Loss2: 1.643718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.317084 Loss1: 1.658438 Loss2: 1.658646 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.559375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.937007 Loss1: 2.452897 Loss2: 1.484110 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.693736 Loss1: 2.218124 Loss2: 1.475611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.921881 Loss1: 2.934937 Loss2: 1.986944
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.666012 Loss1: 2.168257 Loss2: 1.497755
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.033067 Loss1: 2.550783 Loss2: 1.482284
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.553601 Loss1: 2.058888 Loss2: 1.494713
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.775623 Loss1: 2.303619 Loss2: 1.472004
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.532857 Loss1: 2.030999 Loss2: 1.501858
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.704175 Loss1: 2.237674 Loss2: 1.466501
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.487656 Loss1: 1.963505 Loss2: 1.524150
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.586191 Loss1: 2.072595 Loss2: 1.513596 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.491211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.461766 Loss1: 1.975268 Loss2: 1.486498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.405667 Loss1: 1.917930 Loss2: 1.487737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.252554 Loss1: 1.753155 Loss2: 1.499399
-(DefaultActor pid=3764) >> Training accuracy: 0.550000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.914835 Loss1: 2.967542 Loss2: 1.947293
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.975795 Loss1: 2.528458 Loss2: 1.447336
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.808115 Loss1: 2.374488 Loss2: 1.433627
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.684810 Loss1: 2.250420 Loss2: 1.434390
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.518664 Loss1: 2.075319 Loss2: 1.443345
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.079766 Loss1: 3.058990 Loss2: 2.020776
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.550821 Loss1: 2.109310 Loss2: 1.441511
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.492068 Loss1: 2.022960 Loss2: 1.469108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.379027 Loss1: 1.908036 Loss2: 1.470991 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.358095 Loss1: 1.879946 Loss2: 1.478149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.247407 Loss1: 1.774037 Loss2: 1.473371 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.529167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.576019 Loss1: 2.058808 Loss2: 1.517212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.448316 Loss1: 1.918651 Loss2: 1.529665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.519099 Loss1: 1.981065 Loss2: 1.538035
-(DefaultActor pid=3764) >> Training accuracy: 0.507292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.913638 Loss1: 2.890271 Loss2: 2.023367
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.988202 Loss1: 2.489276 Loss2: 1.498926
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.737506 Loss1: 2.251480 Loss2: 1.486026
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.609152 Loss1: 2.121536 Loss2: 1.487616
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.553862 Loss1: 2.054349 Loss2: 1.499513
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.028243 Loss1: 3.058635 Loss2: 1.969607
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.087957 Loss1: 2.608833 Loss2: 1.479124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.868774 Loss1: 2.421043 Loss2: 1.447732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.712837 Loss1: 2.262879 Loss2: 1.449959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.644205 Loss1: 2.198738 Loss2: 1.445468 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.489583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.561960 Loss1: 2.106620 Loss2: 1.455340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.449828 Loss1: 1.987983 Loss2: 1.461845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.349421 Loss1: 1.869906 Loss2: 1.479516 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.448958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.960479 Loss1: 2.497082 Loss2: 1.463397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.603888 Loss1: 2.164807 Loss2: 1.439081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.939550 Loss1: 2.961392 Loss2: 1.978157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.102744 Loss1: 2.635816 Loss2: 1.466928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.870405 Loss1: 2.425212 Loss2: 1.445193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.673594 Loss1: 2.226823 Loss2: 1.446770 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.583479 Loss1: 2.127477 Loss2: 1.456003 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.510417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.453250 Loss1: 1.982673 Loss2: 1.470578 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.409958 Loss1: 1.915099 Loss2: 1.494859 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.201320 Loss1: 1.719579 Loss2: 1.481741
-(DefaultActor pid=3764) >> Training accuracy: 0.492708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.140947 Loss1: 3.172425 Loss2: 1.968522
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.169630 Loss1: 2.679426 Loss2: 1.490204
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.961882 Loss1: 2.491438 Loss2: 1.470444
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.870900 Loss1: 2.415680 Loss2: 1.455220
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.736898 Loss1: 2.267933 Loss2: 1.468965
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.874324 Loss1: 2.843837 Loss2: 2.030487
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.738673 Loss1: 2.260790 Loss2: 1.477882
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.905379 Loss1: 2.368523 Loss2: 1.536856
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.607628 Loss1: 2.135383 Loss2: 1.472244
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.724343 Loss1: 2.226791 Loss2: 1.497552
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.581919 Loss1: 2.108522 Loss2: 1.473397
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.579969 Loss1: 2.066058 Loss2: 1.513911
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.488036 Loss1: 1.993973 Loss2: 1.494063
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.481074 Loss1: 1.990750 Loss2: 1.490324
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.505494 Loss1: 1.987544 Loss2: 1.517950
-(DefaultActor pid=3765) >> Training accuracy: 0.447917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.350760 Loss1: 1.826496 Loss2: 1.524265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.221038 Loss1: 1.691380 Loss2: 1.529658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.122945 Loss1: 1.597716 Loss2: 1.525228
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.304473 Loss1: 3.035558 Loss2: 2.268915
-(DefaultActor pid=3764) >> Training accuracy: 0.617708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.194320 Loss1: 2.604977 Loss2: 1.589344
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.935812 Loss1: 2.383433 Loss2: 1.552379
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.724684 Loss1: 2.165851 Loss2: 1.558833
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.618225 Loss1: 2.067650 Loss2: 1.550575
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.556053 Loss1: 1.999824 Loss2: 1.556228
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.483925 Loss1: 1.923417 Loss2: 1.560508
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.539653 Loss1: 1.962136 Loss2: 1.577516 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.468937 Loss1: 1.884088 Loss2: 1.584849 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.368440 Loss1: 1.789579 Loss2: 1.578861 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.490885
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.772098 Loss1: 2.238371 Loss2: 1.533727 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.684207 Loss1: 2.130789 Loss2: 1.553418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.565151 Loss1: 2.013225 Loss2: 1.551925
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.015288 Loss1: 2.911719 Loss2: 2.103570
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.527899 Loss1: 1.966305 Loss2: 1.561594
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.100549 Loss1: 2.531909 Loss2: 1.568639
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.435094 Loss1: 1.872114 Loss2: 1.562980
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.877933 Loss1: 2.335977 Loss2: 1.541957
-(DefaultActor pid=3764) >> Training accuracy: 0.492708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.731203 Loss1: 2.182469 Loss2: 1.548734
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.688275 Loss1: 2.127323 Loss2: 1.560951
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.525563 Loss1: 1.960562 Loss2: 1.565001
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.514877 Loss1: 1.937535 Loss2: 1.577342
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.166128 Loss1: 2.942385 Loss2: 2.223742
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.455309 Loss1: 1.879134 Loss2: 1.576175
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.460086 Loss1: 1.879651 Loss2: 1.580435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.454809 Loss1: 1.851491 Loss2: 1.603318 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.516667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.494758 Loss1: 1.877884 Loss2: 1.616874 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.487662 Loss1: 1.865316 Loss2: 1.622346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.854349 Loss1: 3.010912 Loss2: 1.843437 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.522837
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.876128 Loss1: 2.476713 Loss2: 1.399416 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.528221 Loss1: 2.150749 Loss2: 1.377472 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.385198 Loss1: 1.995978 Loss2: 1.389220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.400030 Loss1: 2.010475 Loss2: 1.389555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.331451 Loss1: 1.925257 Loss2: 1.406193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.284245 Loss1: 1.868372 Loss2: 1.415873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.204495 Loss1: 1.798417 Loss2: 1.406078 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.526367
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.710342 Loss1: 2.157372 Loss2: 1.552970 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.570139 Loss1: 2.012194 Loss2: 1.557945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.546324 Loss1: 1.971897 Loss2: 1.574427 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.472656
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.972527 Loss1: 2.506260 Loss2: 1.466267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.724342 Loss1: 2.266625 Loss2: 1.457717 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.769084 Loss1: 2.282966 Loss2: 1.486118
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.116138 Loss1: 3.113293 Loss2: 2.002845
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.615203 Loss1: 2.127599 Loss2: 1.487604
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.118346 Loss1: 2.637427 Loss2: 1.480919
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.524224 Loss1: 2.028773 Loss2: 1.495451
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.839969 Loss1: 2.357648 Loss2: 1.482320
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.497755 Loss1: 2.002440 Loss2: 1.495315
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.675157 Loss1: 2.210203 Loss2: 1.464954
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.640969 Loss1: 2.162500 Loss2: 1.478469
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.398847 Loss1: 1.900249 Loss2: 1.498598
-(DefaultActor pid=3765) >> Training accuracy: 0.496094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.525723 Loss1: 2.032051 Loss2: 1.493672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.402450 Loss1: 1.888865 Loss2: 1.513585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.311307 Loss1: 3.176785 Loss2: 2.134522
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.439530 Loss1: 1.927147 Loss2: 1.512383
-(DefaultActor pid=3764) >> Training accuracy: 0.502083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 4.132410 Loss1: 2.576794 Loss2: 1.555615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.862407 Loss1: 2.295183 Loss2: 1.567224 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.804395 Loss1: 2.227232 Loss2: 1.577163
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.878956 Loss1: 2.932483 Loss2: 1.946473
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.007979 Loss1: 2.511170 Loss2: 1.496809 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.807728 Loss1: 2.332913 Loss2: 1.474815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.658728 Loss1: 2.193966 Loss2: 1.464762 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.497768
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.438619 Loss1: 1.956458 Loss2: 1.482161 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.339382 Loss1: 1.852507 Loss2: 1.486875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.344948 Loss1: 1.839551 Loss2: 1.505397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.201285 Loss1: 1.700915 Loss2: 1.500370 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.527344
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.500111 Loss1: 2.031632 Loss2: 1.468479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.357345 Loss1: 1.873400 Loss2: 1.483945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.180358 Loss1: 1.698996 Loss2: 1.481362
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.111529 Loss1: 3.050317 Loss2: 2.061212
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.104945 Loss1: 2.567307 Loss2: 1.537638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.915143 Loss1: 2.399395 Loss2: 1.515748 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.600000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.082053 Loss1: 1.569360 Loss2: 1.512693
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.851045 Loss1: 2.334248 Loss2: 1.516797
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.702307 Loss1: 2.170160 Loss2: 1.532147
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.731394 Loss1: 2.195406 Loss2: 1.535987
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.571294 Loss1: 2.039360 Loss2: 1.531934
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.472252 Loss1: 1.930624 Loss2: 1.541629
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.832310 Loss1: 2.962175 Loss2: 1.870136
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.448401 Loss1: 1.891856 Loss2: 1.556545
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.070898 Loss1: 2.646673 Loss2: 1.424225
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.435476 Loss1: 1.874556 Loss2: 1.560920
-(DefaultActor pid=3764) >> Training accuracy: 0.504167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.629636 Loss1: 2.247706 Loss2: 1.381929 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.446900 Loss1: 2.045553 Loss2: 1.401347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.135490 Loss1: 3.123315 Loss2: 2.012175
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.477880 Loss1: 2.049094 Loss2: 1.428786
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.340214 Loss1: 1.926719 Loss2: 1.413495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.262639 Loss1: 1.854252 Loss2: 1.408388 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.189290 Loss1: 1.778749 Loss2: 1.410541 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.505859
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.668515 Loss1: 2.149114 Loss2: 1.519401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.519199 Loss1: 1.979485 Loss2: 1.539714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.466669 Loss1: 1.941681 Loss2: 1.524988
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.656154 Loss1: 2.699399 Loss2: 1.956755
-(DefaultActor pid=3764) >> Training accuracy: 0.418750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.907081 Loss1: 2.455993 Loss2: 1.451087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.556960 Loss1: 2.106141 Loss2: 1.450819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.399911 Loss1: 1.948302 Loss2: 1.451609 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.417896 Loss1: 1.961641 Loss2: 1.456255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.236740 Loss1: 1.772059 Loss2: 1.464680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.133340 Loss1: 1.689164 Loss2: 1.444176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.088934 Loss1: 1.621312 Loss2: 1.467622 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.512500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.405998 Loss1: 1.958817 Loss2: 1.447181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.351403 Loss1: 1.891347 Loss2: 1.460057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.297691 Loss1: 1.828730 Loss2: 1.468961
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.081005 Loss1: 3.129293 Loss2: 1.951711
-(DefaultActor pid=3764) >> Training accuracy: 0.504167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.345571 Loss1: 1.865181 Loss2: 1.480389
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.154626 Loss1: 2.721826 Loss2: 1.432799
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.896181 Loss1: 2.471694 Loss2: 1.424487
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.850838 Loss1: 2.423028 Loss2: 1.427810
-DEBUG flwr 2023-10-08 23:57:00,444 | server.py:236 | fit_round 19 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.698078 Loss1: 2.261565 Loss2: 1.436513
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.645545 Loss1: 2.207908 Loss2: 1.437638
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.158892 Loss1: 3.050023 Loss2: 2.108869
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.558062 Loss1: 2.103245 Loss2: 1.454817
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.568903 Loss1: 2.103930 Loss2: 1.464973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.427979 Loss1: 1.954778 Loss2: 1.473200 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.379789 Loss1: 1.904794 Loss2: 1.474995 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.525000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.766914 Loss1: 2.198827 Loss2: 1.568087 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.686368 Loss1: 2.103141 Loss2: 1.583228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.643393 Loss1: 2.055399 Loss2: 1.587995
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.857039 Loss1: 2.944999 Loss2: 1.912040
-(DefaultActor pid=3764) >> Training accuracy: 0.444792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.030730 Loss1: 2.564030 Loss2: 1.466701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.665960 Loss1: 2.222721 Loss2: 1.443239 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.474075 Loss1: 2.023171 Loss2: 1.450904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.972554 Loss1: 2.437543 Loss2: 1.535011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.702438 Loss1: 2.196032 Loss2: 1.506407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.549171 Loss1: 2.044448 Loss2: 1.504723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.518444 Loss1: 2.008851 Loss2: 1.509594 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.522978
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.386611 Loss1: 1.861772 Loss2: 1.524839 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.217485 Loss1: 1.681367 Loss2: 1.536118 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.549805
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-08 23:57:00,444][flwr][DEBUG] - fit_round 19 received 50 results and 0 failures
-INFO flwr 2023-10-08 23:57:42,206 | server.py:125 | fit progress: (19, 3.324841410969012, {'accuracy': 0.208}, 43569.985048101)
->> Test accuracy: 0.208000
-[2023-10-08 23:57:42,206][flwr][INFO] - fit progress: (19, 3.324841410969012, {'accuracy': 0.208}, 43569.985048101)
-DEBUG flwr 2023-10-08 23:57:42,207 | server.py:173 | evaluate_round 19: strategy sampled 50 clients (out of 50)
-[2023-10-08 23:57:42,207][flwr][DEBUG] - evaluate_round 19: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 00:06:44,806 | server.py:187 | evaluate_round 19 received 50 results and 0 failures
-[2023-10-09 00:06:44,806][flwr][DEBUG] - evaluate_round 19 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 00:06:44,806 | server.py:222 | fit_round 20: strategy sampled 50 clients (out of 50)
-[2023-10-09 00:06:44,806][flwr][DEBUG] - fit_round 20: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.977883 Loss1: 2.903936 Loss2: 2.073947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.701241 Loss1: 2.208853 Loss2: 1.492387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.528964 Loss1: 2.050114 Loss2: 1.478850
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.896231 Loss1: 2.965932 Loss2: 1.930299
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.941887 Loss1: 2.490072 Loss2: 1.451816 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.709979 Loss1: 2.281983 Loss2: 1.427995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.577378 Loss1: 2.142024 Loss2: 1.435354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.555845 Loss1: 2.108341 Loss2: 1.447504 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.473184 Loss1: 2.019735 Loss2: 1.453449 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.583333
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.120472 Loss1: 1.598982 Loss2: 1.521489
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.338299 Loss1: 1.871062 Loss2: 1.467237
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.394014 Loss1: 1.926036 Loss2: 1.467978
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.343309 Loss1: 1.866243 Loss2: 1.477066
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.214856 Loss1: 1.740474 Loss2: 1.474381
-(DefaultActor pid=3764) >> Training accuracy: 0.568750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.914293 Loss1: 2.950825 Loss2: 1.963468
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.982598 Loss1: 2.512120 Loss2: 1.470478
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.744240 Loss1: 2.306291 Loss2: 1.437949
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.624397 Loss1: 2.182130 Loss2: 1.442266
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.906375 Loss1: 2.890385 Loss2: 2.015991
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.947323 Loss1: 2.425548 Loss2: 1.521775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.701953 Loss1: 2.193568 Loss2: 1.508384 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.412068 Loss1: 1.937852 Loss2: 1.474216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.315069 Loss1: 1.842195 Loss2: 1.472874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.274044 Loss1: 1.793684 Loss2: 1.480360 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.522917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.402466 Loss1: 1.868143 Loss2: 1.534323 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.367403 Loss1: 1.818072 Loss2: 1.549332 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.369251 Loss1: 1.815966 Loss2: 1.553285
-(DefaultActor pid=3764) >> Training accuracy: 0.539522
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.842456 Loss1: 2.863302 Loss2: 1.979154
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.004423 Loss1: 2.501596 Loss2: 1.502827
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.836335 Loss1: 2.359949 Loss2: 1.476385
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.666124 Loss1: 2.184937 Loss2: 1.481186
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.661493 Loss1: 2.183693 Loss2: 1.477800
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.069759 Loss1: 2.966932 Loss2: 2.102827
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.569461 Loss1: 2.054536 Loss2: 1.514925
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.177565 Loss1: 2.604991 Loss2: 1.572574
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.447916 Loss1: 1.959114 Loss2: 1.488802
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.986336 Loss1: 2.422569 Loss2: 1.563767
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.326948 Loss1: 1.836515 Loss2: 1.490432
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.798910 Loss1: 2.236805 Loss2: 1.562105
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.282666 Loss1: 1.779265 Loss2: 1.503402
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.676579 Loss1: 2.103261 Loss2: 1.573319
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.404330 Loss1: 1.892031 Loss2: 1.512299
-(DefaultActor pid=3765) >> Training accuracy: 0.507292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.550487 Loss1: 1.964778 Loss2: 1.585708 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.475745 Loss1: 1.881988 Loss2: 1.593756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.371070 Loss1: 1.771587 Loss2: 1.599484
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.954745 Loss1: 2.855772 Loss2: 2.098973
-(DefaultActor pid=3764) >> Training accuracy: 0.498958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.013773 Loss1: 2.431053 Loss2: 1.582719
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.787108 Loss1: 2.232302 Loss2: 1.554806
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.649454 Loss1: 2.099881 Loss2: 1.549573
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.627890 Loss1: 2.065061 Loss2: 1.562829
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.644526 Loss1: 2.723969 Loss2: 1.920558
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.630831 Loss1: 2.047175 Loss2: 1.583655
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.686778 Loss1: 2.248843 Loss2: 1.437935
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.528557 Loss1: 1.962381 Loss2: 1.566176
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.435751 Loss1: 2.022217 Loss2: 1.413534
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.426056 Loss1: 1.836940 Loss2: 1.589116
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.272405 Loss1: 1.849121 Loss2: 1.423284
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.345811 Loss1: 1.756175 Loss2: 1.589636
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.287741 Loss1: 1.860771 Loss2: 1.426970
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.305289 Loss1: 1.703368 Loss2: 1.601921
-(DefaultActor pid=3765) >> Training accuracy: 0.562500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.152109 Loss1: 1.713032 Loss2: 1.439077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.010516 Loss1: 1.569957 Loss2: 1.440560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.980643 Loss1: 1.535103 Loss2: 1.445540
-(DefaultActor pid=3764) >> Training accuracy: 0.589583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.845133 Loss1: 2.889283 Loss2: 1.955850
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.017186 Loss1: 2.533860 Loss2: 1.483326
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.750551 Loss1: 2.270295 Loss2: 1.480256
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.612670 Loss1: 2.128593 Loss2: 1.484077
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.580313 Loss1: 2.099525 Loss2: 1.480788
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.076365 Loss1: 2.991660 Loss2: 2.084704
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.203816 Loss1: 2.625816 Loss2: 1.578001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.937338 Loss1: 2.380709 Loss2: 1.556630 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.811938 Loss1: 2.237813 Loss2: 1.574124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.671336 Loss1: 2.099233 Loss2: 1.572103 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.485352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.595959 Loss1: 2.022441 Loss2: 1.573518 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.560859 Loss1: 1.951158 Loss2: 1.609701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.378862 Loss1: 1.779286 Loss2: 1.599576 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.486328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.713196 Loss1: 2.253233 Loss2: 1.459962 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.495164 Loss1: 2.034551 Loss2: 1.460612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.165381 Loss1: 3.237120 Loss2: 1.928261
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.537758 Loss1: 2.065329 Loss2: 1.472429
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.161623 Loss1: 2.715940 Loss2: 1.445683
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.345578 Loss1: 1.854342 Loss2: 1.491236
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.850806 Loss1: 2.442252 Loss2: 1.408554
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.321644 Loss1: 1.847523 Loss2: 1.474120
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.222934 Loss1: 1.732452 Loss2: 1.490482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.144960 Loss1: 1.653037 Loss2: 1.491922 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.483333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.419583 Loss1: 1.992619 Loss2: 1.426964 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.367548 Loss1: 1.934767 Loss2: 1.432781 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.502232
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.321294 Loss1: 1.883165 Loss2: 1.438129
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.732676 Loss1: 2.803532 Loss2: 1.929144
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.804281 Loss1: 2.356787 Loss2: 1.447495
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.564970 Loss1: 2.132295 Loss2: 1.432675
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.489564 Loss1: 2.058293 Loss2: 1.431271
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.458632 Loss1: 2.010806 Loss2: 1.447826
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.096803 Loss1: 2.880175 Loss2: 2.216628
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.839017 Loss1: 2.331975 Loss2: 1.507042 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.267238 Loss1: 1.800184 Loss2: 1.467054 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.236932 Loss1: 1.781949 Loss2: 1.454983 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.144677 Loss1: 1.674573 Loss2: 1.470104 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.516667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.203641 Loss1: 1.671251 Loss2: 1.532390 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.524740
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.094457 Loss1: 3.096110 Loss2: 1.998347 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.884122 Loss1: 2.381271 Loss2: 1.502851 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.767147 Loss1: 2.253792 Loss2: 1.513356
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.820773 Loss1: 2.867251 Loss2: 1.953521
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.684498 Loss1: 2.179631 Loss2: 1.504867
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.936750 Loss1: 2.477679 Loss2: 1.459070
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.713531 Loss1: 2.266527 Loss2: 1.447004
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.613955 Loss1: 2.098364 Loss2: 1.515591
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.544144 Loss1: 2.094727 Loss2: 1.449417
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.523136 Loss1: 1.997353 Loss2: 1.525784
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.383590 Loss1: 1.929205 Loss2: 1.454386
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.454878 Loss1: 1.925962 Loss2: 1.528916
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.380637 Loss1: 1.930215 Loss2: 1.450422
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.446646 Loss1: 1.906557 Loss2: 1.540088
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.457671 Loss1: 1.914087 Loss2: 1.543584 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.487305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.306457 Loss1: 1.820173 Loss2: 1.486284 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.584375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.010294 Loss1: 3.051531 Loss2: 1.958763 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.823953 Loss1: 2.373686 Loss2: 1.450267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.710586 Loss1: 2.278439 Loss2: 1.432146
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.124790 Loss1: 2.926192 Loss2: 2.198598
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.156829 Loss1: 2.460269 Loss2: 1.696560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.909907 Loss1: 2.267655 Loss2: 1.642253 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.746921 Loss1: 2.091538 Loss2: 1.655383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.716147 Loss1: 2.058457 Loss2: 1.657690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.669602 Loss1: 2.000996 Loss2: 1.668605 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.504167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.489644 Loss1: 1.819877 Loss2: 1.669767 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.412624 Loss1: 1.727952 Loss2: 1.684672 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.466797
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.749315 Loss1: 2.306442 Loss2: 1.442873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.246929 Loss1: 1.825842 Loss2: 1.421087 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.035904 Loss1: 2.985119 Loss2: 2.050785
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.243200 Loss1: 1.822986 Loss2: 1.420214
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.104644 Loss1: 2.575364 Loss2: 1.529281
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.182665 Loss1: 1.745817 Loss2: 1.436848
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.811682 Loss1: 2.308276 Loss2: 1.503406
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.179121 Loss1: 1.743562 Loss2: 1.435559
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.687497 Loss1: 2.192315 Loss2: 1.495183
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.143305 Loss1: 1.698272 Loss2: 1.445033
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.583127 Loss1: 2.067471 Loss2: 1.515656
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.113783 Loss1: 1.664994 Loss2: 1.448789
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.507925 Loss1: 1.985987 Loss2: 1.521938
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.010671 Loss1: 1.552589 Loss2: 1.458082
-(DefaultActor pid=3765) >> Training accuracy: 0.530208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.444659 Loss1: 1.901866 Loss2: 1.542793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.316589 Loss1: 1.763295 Loss2: 1.553294 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.493750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.224615 Loss1: 2.656655 Loss2: 1.567960 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.864132 Loss1: 2.338506 Loss2: 1.525626 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.856169 Loss1: 3.028429 Loss2: 1.827740
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.758803 Loss1: 2.236038 Loss2: 1.522765
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.882443 Loss1: 2.512329 Loss2: 1.370114
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.637112 Loss1: 2.102302 Loss2: 1.534810
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.771128 Loss1: 2.403091 Loss2: 1.368036
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.580207 Loss1: 2.037345 Loss2: 1.542862
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.603519 Loss1: 2.233673 Loss2: 1.369846
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.446686 Loss1: 1.908201 Loss2: 1.538484
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.495227 Loss1: 2.131396 Loss2: 1.363831
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.473874 Loss1: 1.914107 Loss2: 1.559767
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.445961 Loss1: 2.064630 Loss2: 1.381331
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.342167 Loss1: 1.781123 Loss2: 1.561045
-(DefaultActor pid=3765) >> Training accuracy: 0.538542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.334505 Loss1: 1.944401 Loss2: 1.390105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.175972 Loss1: 1.775482 Loss2: 1.400490 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.483333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.058250 Loss1: 2.487054 Loss2: 1.571197 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.706658 Loss1: 2.185451 Loss2: 1.521207 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.517399 Loss1: 1.985136 Loss2: 1.532262 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.401521 Loss1: 1.871689 Loss2: 1.529832 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.350386 Loss1: 1.817068 Loss2: 1.533318 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.259804 Loss1: 1.703808 Loss2: 1.555996 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.225366 Loss1: 1.681747 Loss2: 1.543619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.054799 Loss1: 1.507951 Loss2: 1.546849 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.615625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.473952 Loss1: 1.957306 Loss2: 1.516646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.283992 Loss1: 1.764808 Loss2: 1.519184 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.547917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.887992 Loss1: 2.324445 Loss2: 1.563546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.479539 Loss1: 1.951621 Loss2: 1.527918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.930998 Loss1: 2.932883 Loss2: 1.998115
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.398721 Loss1: 1.870040 Loss2: 1.528681
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.072435 Loss1: 2.520210 Loss2: 1.552225
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.411569 Loss1: 1.860522 Loss2: 1.551047
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.827100 Loss1: 2.327024 Loss2: 1.500076
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.326344 Loss1: 1.775578 Loss2: 1.550767
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.200294 Loss1: 1.645185 Loss2: 1.555109
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.747923 Loss1: 2.230971 Loss2: 1.516952
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.138064 Loss1: 1.592707 Loss2: 1.545356
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.585974 Loss1: 2.058910 Loss2: 1.527063
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.124816 Loss1: 1.561008 Loss2: 1.563808
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.498967 Loss1: 1.982620 Loss2: 1.516347
-(DefaultActor pid=3765) >> Training accuracy: 0.607292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.403253 Loss1: 1.886470 Loss2: 1.516783
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.365104 Loss1: 1.821449 Loss2: 1.543655
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.323442 Loss1: 1.783476 Loss2: 1.539967
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.356339 Loss1: 1.811027 Loss2: 1.545312
-(DefaultActor pid=3764) >> Training accuracy: 0.512695
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.736074 Loss1: 2.722464 Loss2: 2.013611
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.797442 Loss1: 2.281052 Loss2: 1.516390
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.587648 Loss1: 2.089843 Loss2: 1.497805
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.438101 Loss1: 1.950783 Loss2: 1.487317
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.007884 Loss1: 2.994990 Loss2: 2.012894
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.336516 Loss1: 1.853404 Loss2: 1.483113
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.004447 Loss1: 2.477135 Loss2: 1.527312
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.353403 Loss1: 1.851734 Loss2: 1.501669
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.709061 Loss1: 2.222025 Loss2: 1.487036
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.311592 Loss1: 1.807006 Loss2: 1.504586
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.563609 Loss1: 2.084051 Loss2: 1.479558
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.194232 Loss1: 1.696649 Loss2: 1.497583
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.211545 Loss1: 1.694423 Loss2: 1.517122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.121033 Loss1: 1.610776 Loss2: 1.510257 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.573242
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.316702 Loss1: 1.803982 Loss2: 1.512720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.163946 Loss1: 1.647361 Loss2: 1.516585 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.565625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.941213 Loss1: 2.905854 Loss2: 2.035359
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.019379 Loss1: 2.497752 Loss2: 1.521627
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.771418 Loss1: 2.255704 Loss2: 1.515714
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.613491 Loss1: 2.097720 Loss2: 1.515771
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.077635 Loss1: 3.079509 Loss2: 1.998126
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.127621 Loss1: 2.633481 Loss2: 1.494140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.865539 Loss1: 2.405155 Loss2: 1.460384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.704824 Loss1: 2.229724 Loss2: 1.475100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.635956 Loss1: 2.153456 Loss2: 1.482500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.628564 Loss1: 2.141164 Loss2: 1.487400 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.603125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.552978 Loss1: 2.058176 Loss2: 1.494802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.345828 Loss1: 1.840463 Loss2: 1.505365 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.501042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.083073 Loss1: 3.096941 Loss2: 1.986132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.833638 Loss1: 2.389657 Loss2: 1.443981 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.056935 Loss1: 3.114067 Loss2: 1.942868 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.109673 Loss1: 2.624350 Loss2: 1.485323 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.876284 Loss1: 2.407937 Loss2: 1.468347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.841135 Loss1: 2.362534 Loss2: 1.478601 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.396904 Loss1: 1.903474 Loss2: 1.493430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.338221 Loss1: 1.828142 Loss2: 1.510079 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.435417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.501454 Loss1: 2.001207 Loss2: 1.500247 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.404872 Loss1: 1.889921 Loss2: 1.514951 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.486328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.858987 Loss1: 2.329334 Loss2: 1.529653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.498256 Loss1: 2.007144 Loss2: 1.491111 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.432149 Loss1: 1.945075 Loss2: 1.487074
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.876186 Loss1: 2.725207 Loss2: 2.150979
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.999996 Loss1: 2.385992 Loss2: 1.614005 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.673571 Loss1: 2.099594 Loss2: 1.573977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.486388 Loss1: 1.922861 Loss2: 1.563527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.409241 Loss1: 1.833594 Loss2: 1.575647 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.110036 Loss1: 1.579238 Loss2: 1.530798
-(DefaultActor pid=3765) >> Training accuracy: 0.594792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.404730 Loss1: 1.811052 Loss2: 1.593679
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.327731 Loss1: 1.738351 Loss2: 1.589380
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.241177 Loss1: 1.659286 Loss2: 1.581891
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.191989 Loss1: 1.595000 Loss2: 1.596989
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.145124 Loss1: 1.550017 Loss2: 1.595107
-(DefaultActor pid=3764) >> Training accuracy: 0.557292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.831788 Loss1: 2.771568 Loss2: 2.060220
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.911953 Loss1: 2.354341 Loss2: 1.557613
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.725032 Loss1: 2.204089 Loss2: 1.520943
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.540422 Loss1: 2.020847 Loss2: 1.519575
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.443200 Loss1: 1.912935 Loss2: 1.530264
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.899117 Loss1: 2.811801 Loss2: 2.087317
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.343274 Loss1: 1.821766 Loss2: 1.521508
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.871451 Loss1: 2.317428 Loss2: 1.554023
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.337654 Loss1: 1.803515 Loss2: 1.534139
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.666893 Loss1: 2.123045 Loss2: 1.543848
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.172436 Loss1: 1.650886 Loss2: 1.521550
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.531532 Loss1: 1.982332 Loss2: 1.549199
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.158804 Loss1: 1.622851 Loss2: 1.535954
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.572593 Loss1: 2.016080 Loss2: 1.556513
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.080918 Loss1: 1.549585 Loss2: 1.531334
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.468680 Loss1: 1.895047 Loss2: 1.573633
-(DefaultActor pid=3765) >> Training accuracy: 0.575000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.382409 Loss1: 1.808457 Loss2: 1.573952
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.259586 Loss1: 1.675781 Loss2: 1.583805
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.255114 Loss1: 1.671978 Loss2: 1.583135
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.194260 Loss1: 1.599067 Loss2: 1.595193
-(DefaultActor pid=3764) >> Training accuracy: 0.503125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.046278 Loss1: 2.877017 Loss2: 2.169261
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.087590 Loss1: 2.454794 Loss2: 1.632796
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.782018 Loss1: 2.164803 Loss2: 1.617215
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.666676 Loss1: 2.062929 Loss2: 1.603746
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.582205 Loss1: 1.964131 Loss2: 1.618073
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.531312 Loss1: 1.902893 Loss2: 1.628419 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.539774 Loss1: 1.890498 Loss2: 1.649275 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.454652 Loss1: 1.806615 Loss2: 1.648037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.416704 Loss1: 1.782093 Loss2: 1.634610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.418871 Loss1: 1.761774 Loss2: 1.657098 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.522917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.435059 Loss1: 1.940249 Loss2: 1.494810 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.376015 Loss1: 1.871263 Loss2: 1.504752 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.510417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.984752 Loss1: 2.508353 Loss2: 1.476399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.575538 Loss1: 2.136900 Loss2: 1.438638 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.431408 Loss1: 1.987766 Loss2: 1.443642
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.035122 Loss1: 3.056020 Loss2: 1.979102
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.433548 Loss1: 1.980662 Loss2: 1.452885
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.253407 Loss1: 2.754517 Loss2: 1.498891
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.345129 Loss1: 1.892508 Loss2: 1.452621
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.973829 Loss1: 2.460718 Loss2: 1.513111
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.296314 Loss1: 1.833244 Loss2: 1.463070
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.821230 Loss1: 2.312588 Loss2: 1.508642
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.273059 Loss1: 1.798949 Loss2: 1.474110
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.735767 Loss1: 2.218781 Loss2: 1.516987
-(DefaultActor pid=3765) >> Training accuracy: 0.500000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.217066 Loss1: 1.750936 Loss2: 1.466130
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.697555 Loss1: 2.168681 Loss2: 1.528874
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.628340 Loss1: 2.091381 Loss2: 1.536958
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.559097 Loss1: 2.012090 Loss2: 1.547008
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.517915 Loss1: 1.970051 Loss2: 1.547864
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.398090 Loss1: 1.848807 Loss2: 1.549283
-(DefaultActor pid=3764) >> Training accuracy: 0.518555
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.779616 Loss1: 2.881438 Loss2: 1.898178
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.896373 Loss1: 2.420820 Loss2: 1.475552
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.623706 Loss1: 2.188959 Loss2: 1.434748
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.453552 Loss1: 2.016997 Loss2: 1.436555
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.462897 Loss1: 2.010746 Loss2: 1.452151
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.904825 Loss1: 2.942296 Loss2: 1.962529
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.369334 Loss1: 1.926696 Loss2: 1.442637
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.992953 Loss1: 2.538555 Loss2: 1.454397
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.276100 Loss1: 1.821703 Loss2: 1.454397
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.797005 Loss1: 2.360065 Loss2: 1.436939
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.240310 Loss1: 1.772604 Loss2: 1.467706
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.650341 Loss1: 2.213905 Loss2: 1.436436
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.606681 Loss1: 2.167638 Loss2: 1.439044
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.165499 Loss1: 1.690960 Loss2: 1.474539
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.426985 Loss1: 1.990406 Loss2: 1.436579
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.191314 Loss1: 1.715365 Loss2: 1.475949
-(DefaultActor pid=3765) >> Training accuracy: 0.541016
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.318625 Loss1: 1.861050 Loss2: 1.457575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.219278 Loss1: 1.742078 Loss2: 1.477200 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.557292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.752754 Loss1: 2.310569 Loss2: 1.442186 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.369673 Loss1: 1.968703 Loss2: 1.400969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.917774 Loss1: 2.882296 Loss2: 2.035477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.920828 Loss1: 2.406686 Loss2: 1.514142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.042829 Loss1: 1.613742 Loss2: 1.429088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.998230 Loss1: 1.572478 Loss2: 1.425752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.065344 Loss1: 1.621730 Loss2: 1.443614 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.556490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.497321 Loss1: 1.987204 Loss2: 1.510117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.289013 Loss1: 1.762185 Loss2: 1.526828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.310012 Loss1: 1.795714 Loss2: 1.514298
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.089954 Loss1: 3.118711 Loss2: 1.971243
-(DefaultActor pid=3764) >> Training accuracy: 0.529167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.144606 Loss1: 2.681674 Loss2: 1.462933 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.825460 Loss1: 2.374542 Loss2: 1.450918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.540867 Loss1: 2.079349 Loss2: 1.461518 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.956552 Loss1: 2.921935 Loss2: 2.034616
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.548845 Loss1: 2.074387 Loss2: 1.474458
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.450367 Loss1: 1.969428 Loss2: 1.480939
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.012646 Loss1: 2.489372 Loss2: 1.523274
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.384649 Loss1: 1.911576 Loss2: 1.473072
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.776149 Loss1: 2.285545 Loss2: 1.490604
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.421138 Loss1: 1.924896 Loss2: 1.496243
-DEBUG flwr 2023-10-09 00:35:20,188 | server.py:236 | fit_round 20 received 50 results and 0 failures
-(DefaultActor pid=3765) >> Training accuracy: 0.445312
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.631434 Loss1: 2.129455 Loss2: 1.501979
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.594404 Loss1: 2.093780 Loss2: 1.500625
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.458145 Loss1: 1.943853 Loss2: 1.514292
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.453825 Loss1: 1.945532 Loss2: 1.508293
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.322349 Loss1: 1.800724 Loss2: 1.521626
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.024960 Loss1: 2.917579 Loss2: 2.107380
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.237480 Loss1: 1.711055 Loss2: 1.526425
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.255825 Loss1: 1.719113 Loss2: 1.536712 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.506250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.607342 Loss1: 2.088195 Loss2: 1.519148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.406214 Loss1: 1.870852 Loss2: 1.535362 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.391318 Loss1: 1.834289 Loss2: 1.557029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.402757 Loss1: 1.826453 Loss2: 1.576304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.383473 Loss1: 1.811386 Loss2: 1.572087 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.472356
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.548676 Loss1: 2.069668 Loss2: 1.479008 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.395852 Loss1: 1.902370 Loss2: 1.493482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.226075 Loss1: 1.735714 Loss2: 1.490361
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.079024 Loss1: 3.056403 Loss2: 2.022620
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.125598 Loss1: 2.614865 Loss2: 1.510733 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.550223
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.883597 Loss1: 2.381676 Loss2: 1.501921 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.700838 Loss1: 2.189863 Loss2: 1.510975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.511088 Loss1: 1.976098 Loss2: 1.534990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.509660 Loss1: 1.975985 Loss2: 1.533676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.462186 Loss1: 1.906285 Loss2: 1.555901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.396478 Loss1: 1.851142 Loss2: 1.545336 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.523958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.527536 Loss1: 2.061856 Loss2: 1.465679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.241312 Loss1: 1.789072 Loss2: 1.452240 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.201318 Loss1: 1.717982 Loss2: 1.483336 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.536458
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 00:35:20,188][flwr][DEBUG] - fit_round 20 received 50 results and 0 failures
-INFO flwr 2023-10-09 00:36:02,123 | server.py:125 | fit progress: (20, 3.2592665303629427, {'accuracy': 0.2236}, 45869.901820325)
->> Test accuracy: 0.223600
-[2023-10-09 00:36:02,123][flwr][INFO] - fit progress: (20, 3.2592665303629427, {'accuracy': 0.2236}, 45869.901820325)
-DEBUG flwr 2023-10-09 00:36:02,124 | server.py:173 | evaluate_round 20: strategy sampled 50 clients (out of 50)
-[2023-10-09 00:36:02,124][flwr][DEBUG] - evaluate_round 20: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 00:45:07,574 | server.py:187 | evaluate_round 20 received 50 results and 0 failures
-[2023-10-09 00:45:07,574][flwr][DEBUG] - evaluate_round 20 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 00:45:07,575 | server.py:222 | fit_round 21: strategy sampled 50 clients (out of 50)
-[2023-10-09 00:45:07,575][flwr][DEBUG] - fit_round 21: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.980237 Loss1: 2.887763 Loss2: 2.092474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.809417 Loss1: 2.278761 Loss2: 1.530656 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.604696 Loss1: 2.077398 Loss2: 1.527298
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.977505 Loss1: 2.983080 Loss2: 1.994425
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.167466 Loss1: 2.678797 Loss2: 1.488670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.837671 Loss1: 2.358943 Loss2: 1.478728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.756292 Loss1: 2.278421 Loss2: 1.477871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.562187 Loss1: 2.078906 Loss2: 1.483281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.517710 Loss1: 2.030138 Loss2: 1.487573 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.572917
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.195448 Loss1: 1.624119 Loss2: 1.571330
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.478674 Loss1: 1.979040 Loss2: 1.499633
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.317724 Loss1: 1.819485 Loss2: 1.498240
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.358057 Loss1: 1.827947 Loss2: 1.530111
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.259865 Loss1: 1.733500 Loss2: 1.526365
-(DefaultActor pid=3764) >> Training accuracy: 0.511458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.924045 Loss1: 2.900949 Loss2: 2.023096
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.982428 Loss1: 2.475801 Loss2: 1.506627
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.634698 Loss1: 2.161568 Loss2: 1.473130
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.584817 Loss1: 2.102953 Loss2: 1.481864
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.878973 Loss1: 2.802859 Loss2: 2.076115
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.891057 Loss1: 2.355768 Loss2: 1.535289 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.607511 Loss1: 2.096546 Loss2: 1.510965 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.499107 Loss1: 1.986520 Loss2: 1.512586 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.439626 Loss1: 1.917449 Loss2: 1.522177 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.331139 Loss1: 1.796723 Loss2: 1.534416 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.531250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.263435 Loss1: 1.716348 Loss2: 1.547087 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.190727 Loss1: 1.631538 Loss2: 1.559189 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.605208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.811267 Loss1: 2.841441 Loss2: 1.969826 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.677334 Loss1: 2.200193 Loss2: 1.477140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.051830 Loss1: 2.954013 Loss2: 2.097817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.944655 Loss1: 2.399208 Loss2: 1.545448 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.707184 Loss1: 2.181715 Loss2: 1.525469 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.514335 Loss1: 1.984648 Loss2: 1.529687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.449912 Loss1: 1.923102 Loss2: 1.526810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.371843 Loss1: 1.822067 Loss2: 1.549776 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.544792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.283136 Loss1: 1.738120 Loss2: 1.545016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.254666 Loss1: 1.692508 Loss2: 1.562157 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.581250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.830647 Loss1: 2.828667 Loss2: 2.001981 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.680549 Loss1: 2.181493 Loss2: 1.499056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.490783 Loss1: 1.984382 Loss2: 1.506401
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.875508 Loss1: 2.874183 Loss2: 2.001326
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.963751 Loss1: 2.478695 Loss2: 1.485056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.757259 Loss1: 2.288726 Loss2: 1.468533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.217433 Loss1: 1.694273 Loss2: 1.523160
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.654341 Loss1: 2.190443 Loss2: 1.463898
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.166474 Loss1: 1.642046 Loss2: 1.524428
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.568148 Loss1: 2.086714 Loss2: 1.481434
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.162883 Loss1: 1.620014 Loss2: 1.542869
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.418109 Loss1: 1.931779 Loss2: 1.486329
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.385767 Loss1: 1.905542 Loss2: 1.480225
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.140322 Loss1: 1.573273 Loss2: 1.567049
-(DefaultActor pid=3765) >> Training accuracy: 0.599609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.184481 Loss1: 1.682966 Loss2: 1.501515 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.519792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.855185 Loss1: 2.755798 Loss2: 2.099387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.605318 Loss1: 2.078239 Loss2: 1.527079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.476403 Loss1: 1.975413 Loss2: 1.500990
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.947983 Loss1: 2.954666 Loss2: 1.993317
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.014855 Loss1: 2.496758 Loss2: 1.518097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.701314 Loss1: 2.220900 Loss2: 1.480414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.503950 Loss1: 2.041527 Loss2: 1.462422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.471175 Loss1: 1.993334 Loss2: 1.477840 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.181955 Loss1: 1.628073 Loss2: 1.553881
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.405469 Loss1: 1.923185 Loss2: 1.482284
-(DefaultActor pid=3765) >> Training accuracy: 0.573958
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.123595 Loss1: 1.559791 Loss2: 1.563804
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.302106 Loss1: 1.802597 Loss2: 1.499509
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.388548 Loss1: 1.890405 Loss2: 1.498143
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.263858 Loss1: 1.745428 Loss2: 1.518430
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.099776 Loss1: 1.601699 Loss2: 1.498077
-(DefaultActor pid=3764) >> Training accuracy: 0.564583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.856276 Loss1: 2.786319 Loss2: 2.069957
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.836739 Loss1: 2.310278 Loss2: 1.526461
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.560922 Loss1: 2.061467 Loss2: 1.499455
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.401848 Loss1: 1.900256 Loss2: 1.501592
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.359053 Loss1: 3.114650 Loss2: 2.244403
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.325497 Loss1: 1.813540 Loss2: 1.511957
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.259976 Loss1: 2.585018 Loss2: 1.674957
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.386467 Loss1: 1.855972 Loss2: 1.530495
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.985347 Loss1: 2.343111 Loss2: 1.642236
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.220893 Loss1: 1.691282 Loss2: 1.529611
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.853641 Loss1: 2.187808 Loss2: 1.665833
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.755559 Loss1: 2.105348 Loss2: 1.650211
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.163846 Loss1: 1.643177 Loss2: 1.520669
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.664339 Loss1: 1.997547 Loss2: 1.666792
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.119099 Loss1: 1.592385 Loss2: 1.526713
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.634697 Loss1: 1.950820 Loss2: 1.683877
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.123107 Loss1: 1.572952 Loss2: 1.550155
-(DefaultActor pid=3765) >> Training accuracy: 0.497917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.481880 Loss1: 1.786354 Loss2: 1.695526 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.476562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.253926 Loss1: 3.156827 Loss2: 2.097099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.895085 Loss1: 2.374807 Loss2: 1.520278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.767843 Loss1: 2.671945 Loss2: 2.095898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.896499 Loss1: 2.305422 Loss2: 1.591077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.468084 Loss1: 1.915269 Loss2: 1.552815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.487736 Loss1: 1.918883 Loss2: 1.568853 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.387051 Loss1: 1.831000 Loss2: 1.556051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.353748 Loss1: 1.773967 Loss2: 1.579781 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.496652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.222833 Loss1: 1.647581 Loss2: 1.575252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.196079 Loss1: 1.605123 Loss2: 1.590956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.052865 Loss1: 1.484932 Loss2: 1.567933
-(DefaultActor pid=3764) >> Training accuracy: 0.560547
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.779875 Loss1: 2.752650 Loss2: 2.027225
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.757135 Loss1: 2.260645 Loss2: 1.496490
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.529979 Loss1: 2.038521 Loss2: 1.491458
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.461443 Loss1: 1.972886 Loss2: 1.488557
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.391480 Loss1: 1.894279 Loss2: 1.497201
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.638018 Loss1: 2.597697 Loss2: 2.040321
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.243701 Loss1: 1.725104 Loss2: 1.518598
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.717646 Loss1: 2.172116 Loss2: 1.545530
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.156792 Loss1: 1.634100 Loss2: 1.522692
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.577290 Loss1: 2.080655 Loss2: 1.496635
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.226473 Loss1: 1.693731 Loss2: 1.532743
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.422516 Loss1: 1.921959 Loss2: 1.500557
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.099324 Loss1: 1.558272 Loss2: 1.541052
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.265530 Loss1: 1.777141 Loss2: 1.488389
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.042735 Loss1: 1.504094 Loss2: 1.538641
-(DefaultActor pid=3765) >> Training accuracy: 0.625000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.066832 Loss1: 1.589277 Loss2: 1.477554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.980235 Loss1: 1.489571 Loss2: 1.490665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.960723 Loss1: 1.468755 Loss2: 1.491969
-(DefaultActor pid=3764) >> Training accuracy: 0.611458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.874925 Loss1: 2.884772 Loss2: 1.990153
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.956578 Loss1: 2.431067 Loss2: 1.525511
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.659118 Loss1: 2.171091 Loss2: 1.488027
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.530328 Loss1: 2.021888 Loss2: 1.508440
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.525797 Loss1: 2.013608 Loss2: 1.512189
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.716410 Loss1: 2.808067 Loss2: 1.908343
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.922994 Loss1: 2.480800 Loss2: 1.442195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.658967 Loss1: 2.230429 Loss2: 1.428538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.502621 Loss1: 2.070124 Loss2: 1.432497 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.396167 Loss1: 1.958563 Loss2: 1.437604 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.537109
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.300185 Loss1: 1.853277 Loss2: 1.446909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.283610 Loss1: 1.821944 Loss2: 1.461666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.011762 Loss1: 1.566631 Loss2: 1.445131 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.549805
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.851710 Loss1: 2.368308 Loss2: 1.483402 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.627553 Loss1: 2.094569 Loss2: 1.532984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.732983 Loss1: 2.670678 Loss2: 2.062305
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.561146 Loss1: 2.027987 Loss2: 1.533159
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.846108 Loss1: 2.299819 Loss2: 1.546288
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.405524 Loss1: 1.887238 Loss2: 1.518286
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.642175 Loss1: 2.135008 Loss2: 1.507166
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.288397 Loss1: 1.751493 Loss2: 1.536904
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.386209 Loss1: 1.873540 Loss2: 1.512668
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.287745 Loss1: 1.744455 Loss2: 1.543290
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.318407 Loss1: 1.808466 Loss2: 1.509940
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.261129 Loss1: 1.689325 Loss2: 1.571804
-(DefaultActor pid=3765) >> Training accuracy: 0.543750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.126121 Loss1: 1.605248 Loss2: 1.520873 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.081578 Loss1: 1.552380 Loss2: 1.529199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.071356 Loss1: 1.520227 Loss2: 1.551128
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.777130 Loss1: 2.823147 Loss2: 1.953983
-(DefaultActor pid=3764) >> Training accuracy: 0.575000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.726967 Loss1: 2.276355 Loss2: 1.450613
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.591187 Loss1: 2.168209 Loss2: 1.422978
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.401142 Loss1: 1.983393 Loss2: 1.417749
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.280343 Loss1: 1.864608 Loss2: 1.415736
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.208455 Loss1: 1.779828 Loss2: 1.428627
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.985447 Loss1: 3.030869 Loss2: 1.954577
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.106218 Loss1: 1.664170 Loss2: 1.442048
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.085713 Loss1: 2.622359 Loss2: 1.463353
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.150780 Loss1: 1.692046 Loss2: 1.458734
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.869311 Loss1: 2.410527 Loss2: 1.458784
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.692532 Loss1: 2.230120 Loss2: 1.462412 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.559375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.042521 Loss1: 1.559098 Loss2: 1.483422
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.631846 Loss1: 2.153601 Loss2: 1.478245
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.596927 Loss1: 2.107402 Loss2: 1.489525
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.446075 Loss1: 1.952402 Loss2: 1.493673
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.384221 Loss1: 1.885843 Loss2: 1.498377
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.357960 Loss1: 1.844052 Loss2: 1.513907
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.056495 Loss1: 2.957832 Loss2: 2.098663
-(DefaultActor pid=3764) >> Training accuracy: 0.491211
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.409060 Loss1: 1.894829 Loss2: 1.514230
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.068947 Loss1: 2.488964 Loss2: 1.579983
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.815249 Loss1: 2.239833 Loss2: 1.575416
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.781742 Loss1: 2.215160 Loss2: 1.566582
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.633410 Loss1: 2.059547 Loss2: 1.573863
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.478250 Loss1: 1.893701 Loss2: 1.584550
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.119567 Loss1: 3.004083 Loss2: 2.115484
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.460753 Loss1: 1.863837 Loss2: 1.596917
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.124384 Loss1: 2.532785 Loss2: 1.591598
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.419466 Loss1: 1.822461 Loss2: 1.597006
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.805053 Loss1: 2.251590 Loss2: 1.553463
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.356577 Loss1: 1.757703 Loss2: 1.598874
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.663000 Loss1: 2.108267 Loss2: 1.554733
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.267861 Loss1: 1.656514 Loss2: 1.611347
-(DefaultActor pid=3765) >> Training accuracy: 0.502083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.541461 Loss1: 1.957983 Loss2: 1.583478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.449937 Loss1: 1.858967 Loss2: 1.590969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.431081 Loss1: 1.829062 Loss2: 1.602019
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.760608 Loss1: 2.838097 Loss2: 1.922511
-(DefaultActor pid=3764) >> Training accuracy: 0.535417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.341053 Loss1: 1.726684 Loss2: 1.614369
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.817957 Loss1: 2.382645 Loss2: 1.435312
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.627321 Loss1: 2.198369 Loss2: 1.428952
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.489912 Loss1: 2.040022 Loss2: 1.449890
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.397478 Loss1: 1.963493 Loss2: 1.433985
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.354842 Loss1: 1.904297 Loss2: 1.450545
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.807905 Loss1: 2.779845 Loss2: 2.028060
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.353618 Loss1: 1.906963 Loss2: 1.446654
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.846465 Loss1: 2.348891 Loss2: 1.497574
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.654349 Loss1: 2.194036 Loss2: 1.460313
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.207379 Loss1: 1.749398 Loss2: 1.457981
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.447164 Loss1: 1.977949 Loss2: 1.469215
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.207712 Loss1: 1.742170 Loss2: 1.465541
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.137753 Loss1: 1.651177 Loss2: 1.486576 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.578125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.202839 Loss1: 1.715159 Loss2: 1.487679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.162965 Loss1: 1.652877 Loss2: 1.510088 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.587054
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.983579 Loss1: 1.490869 Loss2: 1.492710
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.005561 Loss1: 2.885524 Loss2: 2.120037
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.003518 Loss1: 2.423353 Loss2: 1.580165
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.867655 Loss1: 2.292577 Loss2: 1.575079
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.689968 Loss1: 2.136263 Loss2: 1.553705
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.536687 Loss1: 1.976419 Loss2: 1.560268
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.866973 Loss1: 2.855077 Loss2: 2.011896
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.915577 Loss1: 2.437571 Loss2: 1.478006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.642798 Loss1: 2.173784 Loss2: 1.469014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.506377 Loss1: 2.036081 Loss2: 1.470296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.436001 Loss1: 1.945443 Loss2: 1.490558 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.495833
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.260685 Loss1: 1.658282 Loss2: 1.602404
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.328940 Loss1: 1.838348 Loss2: 1.490592
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.246805 Loss1: 1.755655 Loss2: 1.491150
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.267134 Loss1: 1.752166 Loss2: 1.514969
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.189942 Loss1: 1.681716 Loss2: 1.508225
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.210786 Loss1: 1.696756 Loss2: 1.514030
-(DefaultActor pid=3764) >> Training accuracy: 0.525000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.925626 Loss1: 2.868827 Loss2: 2.056800
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.938668 Loss1: 2.454099 Loss2: 1.484570
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.655367 Loss1: 2.196926 Loss2: 1.458441
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.510582 Loss1: 2.038416 Loss2: 1.472166
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.380050 Loss1: 1.907106 Loss2: 1.472943
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.332907 Loss1: 1.850452 Loss2: 1.482455
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.629289 Loss1: 2.696884 Loss2: 1.932404
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.785130 Loss1: 2.341711 Loss2: 1.443418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.497320 Loss1: 2.086087 Loss2: 1.411233 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.354710 Loss1: 1.929333 Loss2: 1.425377 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.546875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.194525 Loss1: 1.751725 Loss2: 1.442800 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.133895 Loss1: 1.670873 Loss2: 1.463022 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.125638 Loss1: 2.919677 Loss2: 2.205961
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.051262 Loss1: 1.585820 Loss2: 1.465442
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.991456 Loss1: 1.537156 Loss2: 1.454299 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.594792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.406682 Loss1: 1.904005 Loss2: 1.502677 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.189481 Loss1: 1.677730 Loss2: 1.511751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.972617 Loss1: 2.896739 Loss2: 2.075878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.150589 Loss1: 1.608693 Loss2: 1.541896 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.546875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.617769 Loss1: 2.106612 Loss2: 1.511157 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.486440 Loss1: 1.986649 Loss2: 1.499791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.387302 Loss1: 1.866904 Loss2: 1.520398
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.828127 Loss1: 2.787272 Loss2: 2.040856
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.295489 Loss1: 1.767912 Loss2: 1.527577
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.861556 Loss1: 2.349889 Loss2: 1.511667
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.349566 Loss1: 1.815235 Loss2: 1.534331
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.730703 Loss1: 2.228987 Loss2: 1.501716
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.283641 Loss1: 1.741039 Loss2: 1.542601
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.517821 Loss1: 2.006459 Loss2: 1.511362
-(DefaultActor pid=3764) >> Training accuracy: 0.539583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.451466 Loss1: 1.936021 Loss2: 1.515446
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.351019 Loss1: 1.829856 Loss2: 1.521163
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.265863 Loss1: 1.733102 Loss2: 1.532762
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.122515 Loss1: 1.581897 Loss2: 1.540618
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.238225 Loss1: 1.701388 Loss2: 1.536838
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.060532 Loss1: 3.047835 Loss2: 2.012697
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.130882 Loss1: 1.585474 Loss2: 1.545408
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.122513 Loss1: 2.621286 Loss2: 1.501227
-(DefaultActor pid=3765) >> Training accuracy: 0.539583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.796026 Loss1: 2.317235 Loss2: 1.478791
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.706479 Loss1: 2.215721 Loss2: 1.490758
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.514733 Loss1: 2.017596 Loss2: 1.497137
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.481317 Loss1: 1.983344 Loss2: 1.497973
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.350317 Loss1: 1.839444 Loss2: 1.510873
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.938753 Loss1: 2.814127 Loss2: 2.124626
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.375968 Loss1: 1.853921 Loss2: 1.522047
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.942977 Loss1: 2.384060 Loss2: 1.558917
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.323567 Loss1: 1.800986 Loss2: 1.522581
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.762286 Loss1: 2.226522 Loss2: 1.535764
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.265019 Loss1: 1.728589 Loss2: 1.536430
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.625168 Loss1: 2.087094 Loss2: 1.538074
-(DefaultActor pid=3764) >> Training accuracy: 0.495833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.367686 Loss1: 1.841933 Loss2: 1.525754
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.368056 Loss1: 1.851240 Loss2: 1.516816
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.356761 Loss1: 1.814210 Loss2: 1.542551
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.245644 Loss1: 1.694688 Loss2: 1.550956
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.867792 Loss1: 2.776085 Loss2: 2.091708
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.150961 Loss1: 1.608137 Loss2: 1.542824
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.685702 Loss1: 2.169151 Loss2: 1.516551
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.169487 Loss1: 1.615046 Loss2: 1.554440
-(DefaultActor pid=3765) >> Training accuracy: 0.568750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.341485 Loss1: 1.862375 Loss2: 1.479110 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.211501 Loss1: 1.693036 Loss2: 1.518465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.192560 Loss1: 1.676278 Loss2: 1.516282
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.912572 Loss1: 2.739337 Loss2: 2.173235
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.786937 Loss1: 2.183246 Loss2: 1.603691 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.608803 Loss1: 2.043440 Loss2: 1.565364 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.584375
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.036809 Loss1: 1.507389 Loss2: 1.529420
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.336534 Loss1: 1.777612 Loss2: 1.558921
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.190888 Loss1: 1.638160 Loss2: 1.552728
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.224021 Loss1: 1.669923 Loss2: 1.554098
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.093720 Loss1: 1.525106 Loss2: 1.568614
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.101275 Loss1: 1.523584 Loss2: 1.577691
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.061312 Loss1: 1.478643 Loss2: 1.582669
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.913014 Loss1: 2.846032 Loss2: 2.066982
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.928983 Loss1: 2.394079 Loss2: 1.534904
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.014327 Loss1: 1.411984 Loss2: 1.602343
-(DefaultActor pid=3765) >> Training accuracy: 0.625000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.647651 Loss1: 2.135893 Loss2: 1.511757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.418868 Loss1: 1.899378 Loss2: 1.519490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.344430 Loss1: 1.811784 Loss2: 1.532646
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.573093 Loss1: 2.581144 Loss2: 1.991949
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.263653 Loss1: 1.721415 Loss2: 1.542238
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.731615 Loss1: 2.262627 Loss2: 1.468989
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.277648 Loss1: 1.730020 Loss2: 1.547627
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.487106 Loss1: 2.024763 Loss2: 1.462343
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.228467 Loss1: 1.662364 Loss2: 1.566104
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.355256 Loss1: 1.897743 Loss2: 1.457513
-(DefaultActor pid=3764) >> Training accuracy: 0.498958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.334638 Loss1: 1.852089 Loss2: 1.482550
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.214410 Loss1: 1.748821 Loss2: 1.465589
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.087823 Loss1: 1.626532 Loss2: 1.461291
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.103564 Loss1: 1.605921 Loss2: 1.497643
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.021929 Loss1: 1.521063 Loss2: 1.500865
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.156756 Loss1: 3.031122 Loss2: 2.125634
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.025729 Loss1: 1.529176 Loss2: 1.496553
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.114829 Loss1: 2.542909 Loss2: 1.571920
-(DefaultActor pid=3765) >> Training accuracy: 0.527083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.872104 Loss1: 2.329562 Loss2: 1.542542
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.708442 Loss1: 2.150911 Loss2: 1.557532
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.586649 Loss1: 2.022606 Loss2: 1.564042
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.477926 Loss1: 1.921717 Loss2: 1.556210
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.356376 Loss1: 1.789043 Loss2: 1.567333
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.711123 Loss1: 2.769356 Loss2: 1.941767
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.843115 Loss1: 2.390597 Loss2: 1.452517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.600195 Loss1: 2.142378 Loss2: 1.457817 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.541667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.498477 Loss1: 2.038710 Loss2: 1.459767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.330428 Loss1: 1.861877 Loss2: 1.468551 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.051287 Loss1: 2.834386 Loss2: 2.216901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.982455 Loss1: 2.357110 Loss2: 1.625345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.681034 Loss1: 2.077821 Loss2: 1.603213 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.500351 Loss1: 1.905074 Loss2: 1.595277
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.055267 Loss1: 1.560130 Loss2: 1.495137
-(DefaultActor pid=3765) >> Training accuracy: 0.591912
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.307577 Loss1: 1.699670 Loss2: 1.607907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.110929 Loss1: 1.498256 Loss2: 1.612673 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.139636 Loss1: 1.517464 Loss2: 1.622172 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.578125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.736281 Loss1: 2.259473 Loss2: 1.476808 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.554067 Loss1: 2.079295 Loss2: 1.474773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.947766 Loss1: 2.841744 Loss2: 2.106022
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.484835 Loss1: 1.993093 Loss2: 1.491742
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.003450 Loss1: 2.432469 Loss2: 1.570980
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.408238 Loss1: 1.901568 Loss2: 1.506669
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.317237 Loss1: 1.816703 Loss2: 1.500534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.311379 Loss1: 1.804323 Loss2: 1.507056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.206451 Loss1: 1.688259 Loss2: 1.518191 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.511719
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.359444 Loss1: 1.794618 Loss2: 1.564826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.205518 Loss1: 1.629493 Loss2: 1.576025 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.601042
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.140761 Loss1: 1.556354 Loss2: 1.584407
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.953671 Loss1: 2.853422 Loss2: 2.100249
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.093243 Loss1: 2.510371 Loss2: 1.582872
-DEBUG flwr 2023-10-09 01:13:31,145 | server.py:236 | fit_round 21 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.927434 Loss1: 2.361162 Loss2: 1.566272
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.776367 Loss1: 2.202698 Loss2: 1.573669
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.635349 Loss1: 2.058756 Loss2: 1.576593
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.903560 Loss1: 2.870389 Loss2: 2.033171
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.600699 Loss1: 2.025699 Loss2: 1.575000
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.948336 Loss1: 2.451231 Loss2: 1.497105
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.478212 Loss1: 1.901897 Loss2: 1.576315
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.734981 Loss1: 2.245190 Loss2: 1.489792
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.430218 Loss1: 1.838869 Loss2: 1.591349
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.596434 Loss1: 2.108307 Loss2: 1.488127
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.500122 Loss1: 1.885708 Loss2: 1.614415
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.461488 Loss1: 1.964915 Loss2: 1.496574
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.321548 Loss1: 1.720245 Loss2: 1.601303
-(DefaultActor pid=3765) >> Training accuracy: 0.547917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.303675 Loss1: 1.797246 Loss2: 1.506429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.253475 Loss1: 1.736103 Loss2: 1.517372 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.234663 Loss1: 1.695396 Loss2: 1.539267
-(DefaultActor pid=3764) >> Training accuracy: 0.553125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.125733 Loss1: 3.080737 Loss2: 2.044995
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.079906 Loss1: 2.514880 Loss2: 1.565027
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.868138 Loss1: 2.347094 Loss2: 1.521045
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.703254 Loss1: 2.186810 Loss2: 1.516444
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.616628 Loss1: 2.095131 Loss2: 1.521497
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.193142 Loss1: 3.061593 Loss2: 2.131549
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.216269 Loss1: 2.616512 Loss2: 1.599757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.990658 Loss1: 2.409791 Loss2: 1.580867 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.746510 Loss1: 2.163371 Loss2: 1.583138 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.654098 Loss1: 2.063589 Loss2: 1.590510 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.557617
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.587909 Loss1: 1.982286 Loss2: 1.605624 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.542109 Loss1: 1.920471 Loss2: 1.621638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.347221 Loss1: 1.724894 Loss2: 1.622327 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.511719
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.769669 Loss1: 2.262466 Loss2: 1.507203 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.512110 Loss1: 2.000530 Loss2: 1.511580 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.811193 Loss1: 2.798653 Loss2: 2.012540
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.371513 Loss1: 1.852804 Loss2: 1.518709
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.904902 Loss1: 2.423140 Loss2: 1.481762
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.361454 Loss1: 1.833394 Loss2: 1.528060
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.642385 Loss1: 2.171506 Loss2: 1.470879
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.293674 Loss1: 1.750317 Loss2: 1.543356
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.448758 Loss1: 1.972427 Loss2: 1.476331
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.179495 Loss1: 1.626768 Loss2: 1.552727
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.324598 Loss1: 1.852163 Loss2: 1.472435
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.243640 Loss1: 1.695260 Loss2: 1.548380
-(DefaultActor pid=3765) >> Training accuracy: 0.572917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.270543 Loss1: 1.785576 Loss2: 1.484967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.144273 Loss1: 1.629335 Loss2: 1.514938 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.501042
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 01:13:31,145][flwr][DEBUG] - fit_round 21 received 50 results and 0 failures
-INFO flwr 2023-10-09 01:14:12,992 | server.py:125 | fit progress: (21, 3.1982289168019644, {'accuracy': 0.2382}, 48160.770377396)
->> Test accuracy: 0.238200
-[2023-10-09 01:14:12,992][flwr][INFO] - fit progress: (21, 3.1982289168019644, {'accuracy': 0.2382}, 48160.770377396)
-DEBUG flwr 2023-10-09 01:14:12,992 | server.py:173 | evaluate_round 21: strategy sampled 50 clients (out of 50)
-[2023-10-09 01:14:12,992][flwr][DEBUG] - evaluate_round 21: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 01:23:16,033 | server.py:187 | evaluate_round 21 received 50 results and 0 failures
-[2023-10-09 01:23:16,033][flwr][DEBUG] - evaluate_round 21 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 01:23:16,040 | server.py:222 | fit_round 22: strategy sampled 50 clients (out of 50)
-[2023-10-09 01:23:16,040][flwr][DEBUG] - fit_round 22: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.762805 Loss1: 2.726738 Loss2: 2.036067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.515542 Loss1: 2.010606 Loss2: 1.504936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.337188 Loss1: 1.829396 Loss2: 1.507791
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.812987 Loss1: 2.840249 Loss2: 1.972738
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.300224 Loss1: 1.787791 Loss2: 1.512434
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.841792 Loss1: 2.354946 Loss2: 1.486846
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.248475 Loss1: 1.729435 Loss2: 1.519041
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.613533 Loss1: 2.153459 Loss2: 1.460074
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.106811 Loss1: 1.586610 Loss2: 1.520201
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.492187 Loss1: 2.027235 Loss2: 1.464952
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.021254 Loss1: 1.490228 Loss2: 1.531026
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.350501 Loss1: 1.884397 Loss2: 1.466103
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.217690 Loss1: 1.664681 Loss2: 1.553009
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.210080 Loss1: 1.745395 Loss2: 1.464685
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.146457 Loss1: 1.588690 Loss2: 1.557767
-(DefaultActor pid=3765) >> Training accuracy: 0.590625
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.230136 Loss1: 1.735298 Loss2: 1.494838
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.163630 Loss1: 1.667618 Loss2: 1.496012
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.070602 Loss1: 1.567540 Loss2: 1.503063
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.087365 Loss1: 1.564326 Loss2: 1.523040
-(DefaultActor pid=3764) >> Training accuracy: 0.526042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.662243 Loss1: 2.658929 Loss2: 2.003314
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.776364 Loss1: 2.284774 Loss2: 1.491589
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.487143 Loss1: 2.014223 Loss2: 1.472920
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.391424 Loss1: 1.924136 Loss2: 1.467289
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.826313 Loss1: 2.795519 Loss2: 2.030794
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.922054 Loss1: 2.357933 Loss2: 1.564122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.704208 Loss1: 2.167972 Loss2: 1.536236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.533962 Loss1: 1.985359 Loss2: 1.548603 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.460681 Loss1: 1.907144 Loss2: 1.553538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.316969 Loss1: 1.767491 Loss2: 1.549478 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.623958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.165469 Loss1: 1.598583 Loss2: 1.566887 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.146647 Loss1: 1.559969 Loss2: 1.586678 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.589844
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.150800 Loss1: 2.530357 Loss2: 1.620443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.684347 Loss1: 2.115255 Loss2: 1.569092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.540963 Loss1: 1.956011 Loss2: 1.584952
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.846458 Loss1: 2.842253 Loss2: 2.004205
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.496576 Loss1: 1.916773 Loss2: 1.579803
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.867176 Loss1: 2.384434 Loss2: 1.482742
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.406571 Loss1: 1.812600 Loss2: 1.593970
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.651498 Loss1: 2.194099 Loss2: 1.457400
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.372796 Loss1: 1.784318 Loss2: 1.588478
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.518151 Loss1: 2.048439 Loss2: 1.469712
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.325375 Loss1: 1.705903 Loss2: 1.619472
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.354796 Loss1: 1.885261 Loss2: 1.469535
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.188069 Loss1: 1.576269 Loss2: 1.611800
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.281320 Loss1: 1.796109 Loss2: 1.485211
-(DefaultActor pid=3765) >> Training accuracy: 0.544792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.207505 Loss1: 1.718532 Loss2: 1.488973
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.042282 Loss1: 1.534357 Loss2: 1.507925
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.080499 Loss1: 1.567672 Loss2: 1.512828
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.079698 Loss1: 1.555540 Loss2: 1.524157
-(DefaultActor pid=3764) >> Training accuracy: 0.564583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.879180 Loss1: 2.985972 Loss2: 1.893208
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.974848 Loss1: 2.560990 Loss2: 1.413858
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.627830 Loss1: 2.227417 Loss2: 1.400413
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.515689 Loss1: 2.105032 Loss2: 1.410657
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.595586 Loss1: 2.639868 Loss2: 1.955718
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.593941 Loss1: 2.161554 Loss2: 1.432387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.289237 Loss1: 1.876535 Loss2: 1.412702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.228546 Loss1: 1.812257 Loss2: 1.416288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.113037 Loss1: 1.685060 Loss2: 1.427978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.957219 Loss1: 1.539860 Loss2: 1.417359 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.541992
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.931551 Loss1: 1.499966 Loss2: 1.431585 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.808458 Loss1: 1.348476 Loss2: 1.459982 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.596875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.807135 Loss1: 2.879880 Loss2: 1.927255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.682805 Loss1: 2.235759 Loss2: 1.447046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.519919 Loss1: 2.076406 Loss2: 1.443513
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.961016 Loss1: 2.856359 Loss2: 2.104658
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.967513 Loss1: 2.380353 Loss2: 1.587160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.635841 Loss1: 2.101340 Loss2: 1.534501 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.565221 Loss1: 2.003598 Loss2: 1.561623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.150623 Loss1: 1.672377 Loss2: 1.478246
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.429027 Loss1: 1.868435 Loss2: 1.560593
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.042759 Loss1: 1.560279 Loss2: 1.482480
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.301926 Loss1: 1.742716 Loss2: 1.559211
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.115949 Loss1: 1.631003 Loss2: 1.484946
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.244581 Loss1: 1.671788 Loss2: 1.572793
-(DefaultActor pid=3765) >> Training accuracy: 0.562500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.254719 Loss1: 1.663011 Loss2: 1.591708
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.107517 Loss1: 1.513166 Loss2: 1.594351
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.971714 Loss1: 1.381770 Loss2: 1.589944
-(DefaultActor pid=3764) >> Training accuracy: 0.555208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.017978 Loss1: 2.948685 Loss2: 2.069293
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.939285 Loss1: 2.449207 Loss2: 1.490078
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.657692 Loss1: 2.175023 Loss2: 1.482669
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.433623 Loss1: 1.961484 Loss2: 1.472139
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.801161 Loss1: 2.681894 Loss2: 2.119266
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.858540 Loss1: 2.309734 Loss2: 1.548806 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.618487 Loss1: 2.076547 Loss2: 1.541940 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.565387 Loss1: 2.010670 Loss2: 1.554718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.371803 Loss1: 1.819508 Loss2: 1.552295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.369974 Loss1: 1.799787 Loss2: 1.570187 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.538542
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.161290 Loss1: 1.633723 Loss2: 1.527567
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.301337 Loss1: 1.718245 Loss2: 1.583092
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.254940 Loss1: 1.672058 Loss2: 1.582882
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.179699 Loss1: 1.593124 Loss2: 1.586575
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.147550 Loss1: 1.547940 Loss2: 1.599609
-(DefaultActor pid=3764) >> Training accuracy: 0.548958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.870552 Loss1: 2.841334 Loss2: 2.029218
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.804941 Loss1: 2.292275 Loss2: 1.512666
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.522026 Loss1: 2.037826 Loss2: 1.484200
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.431883 Loss1: 1.944588 Loss2: 1.487295
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.853797 Loss1: 2.763050 Loss2: 2.090747
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.884640 Loss1: 2.289878 Loss2: 1.594763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.617577 Loss1: 2.059491 Loss2: 1.558086 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.500700 Loss1: 1.935443 Loss2: 1.565257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.417752 Loss1: 1.846377 Loss2: 1.571376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.400792 Loss1: 1.826255 Loss2: 1.574536 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.577083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.230136 Loss1: 1.645560 Loss2: 1.584576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.108097 Loss1: 1.511561 Loss2: 1.596537 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.579102
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.822185 Loss1: 2.708262 Loss2: 2.113923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.803465 Loss1: 2.230791 Loss2: 1.572673 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.771339 Loss1: 2.767898 Loss2: 2.003440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.726080 Loss1: 2.244521 Loss2: 1.481559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.508841 Loss1: 2.060944 Loss2: 1.447897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.305479 Loss1: 1.856053 Loss2: 1.449426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.229641 Loss1: 1.774186 Loss2: 1.455455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.136644 Loss1: 1.689745 Loss2: 1.446899 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.555208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.994542 Loss1: 1.530381 Loss2: 1.464161 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.818420 Loss1: 1.356790 Loss2: 1.461630 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.621875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.881985 Loss1: 2.378195 Loss2: 1.503790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.496107 Loss1: 1.998912 Loss2: 1.497195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.625890 Loss1: 2.554332 Loss2: 2.071558
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.434621 Loss1: 1.919976 Loss2: 1.514645
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.713187 Loss1: 2.158537 Loss2: 1.554649
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.306508 Loss1: 1.794061 Loss2: 1.512448
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.485089 Loss1: 1.964934 Loss2: 1.520155
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.310182 Loss1: 1.776465 Loss2: 1.533717
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.270510 Loss1: 1.749749 Loss2: 1.520761
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.234971 Loss1: 1.691991 Loss2: 1.542980
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.100794 Loss1: 1.589441 Loss2: 1.511353
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.104226 Loss1: 1.586351 Loss2: 1.517876
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.104598 Loss1: 1.577453 Loss2: 1.527146
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.149891 Loss1: 1.607219 Loss2: 1.542673
-(DefaultActor pid=3765) >> Training accuracy: 0.571875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.024708 Loss1: 1.494014 Loss2: 1.530694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.926528 Loss1: 1.379508 Loss2: 1.547019 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.629167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.951551 Loss1: 2.505205 Loss2: 1.446346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.602955 Loss1: 2.170814 Loss2: 1.432141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.461669 Loss1: 2.010867 Loss2: 1.450802
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.805842 Loss1: 2.783095 Loss2: 2.022748
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.367940 Loss1: 1.907497 Loss2: 1.460444
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.851176 Loss1: 2.303537 Loss2: 1.547639
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.305548 Loss1: 1.834903 Loss2: 1.470645
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.611202 Loss1: 2.087475 Loss2: 1.523728
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.263198 Loss1: 1.778508 Loss2: 1.484690
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.468245 Loss1: 1.951050 Loss2: 1.517195
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.378315 Loss1: 1.837800 Loss2: 1.540516 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.523958
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.182715 Loss1: 1.675723 Loss2: 1.506991
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.339175 Loss1: 1.788409 Loss2: 1.550765
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.247258 Loss1: 1.692599 Loss2: 1.554658
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.262491 Loss1: 1.692466 Loss2: 1.570025
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.224975 Loss1: 1.652133 Loss2: 1.572842
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.139327 Loss1: 1.564284 Loss2: 1.575043
-(DefaultActor pid=3764) >> Training accuracy: 0.546875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.812736 Loss1: 2.731002 Loss2: 2.081733
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.885562 Loss1: 2.358535 Loss2: 1.527027
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.678275 Loss1: 2.191810 Loss2: 1.486465
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.450887 Loss1: 1.973949 Loss2: 1.476938
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.353218 Loss1: 1.871524 Loss2: 1.481695
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.195667 Loss1: 1.699134 Loss2: 1.496532
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.673361 Loss1: 2.637891 Loss2: 2.035469
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.148108 Loss1: 1.656373 Loss2: 1.491735
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.665959 Loss1: 2.157653 Loss2: 1.508307
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.474739 Loss1: 1.971798 Loss2: 1.502941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.393909 Loss1: 1.889290 Loss2: 1.504618 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.583705
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.284287 Loss1: 1.773791 Loss2: 1.510495 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.216297 Loss1: 1.683410 Loss2: 1.532888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.895368 Loss1: 1.391110 Loss2: 1.504257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.853177 Loss1: 1.329138 Loss2: 1.524039 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.605208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.719254 Loss1: 2.223243 Loss2: 1.496011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.398120 Loss1: 1.894636 Loss2: 1.503483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.325909 Loss1: 1.818397 Loss2: 1.507512
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.008120 Loss1: 2.812854 Loss2: 2.195265
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.558343 Loss1: 2.043912 Loss2: 1.514431 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.147468 Loss1: 1.613342 Loss2: 1.534126 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.125841 Loss1: 1.585085 Loss2: 1.540756 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.568750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.124936 Loss1: 1.580157 Loss2: 1.544779 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.064548 Loss1: 1.493461 Loss2: 1.571087 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.589844
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.679044 Loss1: 2.169214 Loss2: 1.509829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.416382 Loss1: 1.924517 Loss2: 1.491865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.275355 Loss1: 1.765678 Loss2: 1.509677 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.191347 Loss1: 1.683787 Loss2: 1.507559 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.210815 Loss1: 1.699312 Loss2: 1.511503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.138549 Loss1: 1.611164 Loss2: 1.527385 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.137085 Loss1: 1.609621 Loss2: 1.527465 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.086612 Loss1: 1.540520 Loss2: 1.546092 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.505208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.138106 Loss1: 1.661001 Loss2: 1.477106 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.564732
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.878619 Loss1: 2.803029 Loss2: 2.075591 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.736839 Loss1: 2.170626 Loss2: 1.566213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.582125 Loss1: 2.020950 Loss2: 1.561175
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.851623 Loss1: 2.850593 Loss2: 2.001030
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.408303 Loss1: 1.836935 Loss2: 1.571368
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.959807 Loss1: 2.496506 Loss2: 1.463300
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.704569 Loss1: 2.265718 Loss2: 1.438850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.518839 Loss1: 2.061066 Loss2: 1.457773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.136674 Loss1: 1.563117 Loss2: 1.573557
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.482056 Loss1: 2.030758 Loss2: 1.451298
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.342971 Loss1: 1.885234 Loss2: 1.457736 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.616667
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.169139 Loss1: 1.566774 Loss2: 1.602365
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.286403 Loss1: 1.810787 Loss2: 1.475616
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.173504 Loss1: 1.691898 Loss2: 1.481606
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.132612 Loss1: 1.654202 Loss2: 1.478410
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.067506 Loss1: 1.576472 Loss2: 1.491033
-(DefaultActor pid=3764) >> Training accuracy: 0.529167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.839624 Loss1: 2.928381 Loss2: 1.911243
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.835877 Loss1: 2.407960 Loss2: 1.427917
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.554877 Loss1: 2.150443 Loss2: 1.404434
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.410867 Loss1: 2.005514 Loss2: 1.405353
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.046794 Loss1: 3.050846 Loss2: 1.995948
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.301304 Loss1: 1.884490 Loss2: 1.416814
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.052759 Loss1: 2.546267 Loss2: 1.506492
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.792415 Loss1: 2.299651 Loss2: 1.492763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.610624 Loss1: 2.124058 Loss2: 1.486566 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.531310 Loss1: 2.030080 Loss2: 1.501230 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.363659 Loss1: 1.853230 Loss2: 1.510429 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.591667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.314803 Loss1: 1.793396 Loss2: 1.521407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.267353 Loss1: 1.736507 Loss2: 1.530846 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.568359
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.905557 Loss1: 2.807394 Loss2: 2.098163 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.704193 Loss1: 2.141757 Loss2: 1.562436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.532029 Loss1: 2.649618 Loss2: 1.882411 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.624309 Loss1: 2.208652 Loss2: 1.415656 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.337683 Loss1: 1.924748 Loss2: 1.412935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.141618 Loss1: 1.754756 Loss2: 1.386862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.007205 Loss1: 1.614265 Loss2: 1.392940 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.927040 Loss1: 1.525063 Loss2: 1.401977 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.551042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.789631 Loss1: 1.378383 Loss2: 1.411248 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.720273 Loss1: 1.285773 Loss2: 1.434501 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.606250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.837915 Loss1: 2.732153 Loss2: 2.105761
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.862470 Loss1: 2.274185 Loss2: 1.588286
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.550899 Loss1: 1.980086 Loss2: 1.570813
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.496281 Loss1: 1.912453 Loss2: 1.583828
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.979119 Loss1: 2.952454 Loss2: 2.026665
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.084944 Loss1: 2.549554 Loss2: 1.535391 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.898154 Loss1: 2.351554 Loss2: 1.546600 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.719722 Loss1: 2.169951 Loss2: 1.549771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.186198 Loss1: 1.578742 Loss2: 1.607456
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.708067 Loss1: 2.146490 Loss2: 1.561576
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.201469 Loss1: 1.580590 Loss2: 1.620880
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.536973 Loss1: 1.972838 Loss2: 1.564135
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.054093 Loss1: 1.432095 Loss2: 1.621998
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.455871 Loss1: 1.873273 Loss2: 1.582598
-(DefaultActor pid=3765) >> Training accuracy: 0.623162
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.408282 Loss1: 1.826746 Loss2: 1.581535
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.332061 Loss1: 1.743402 Loss2: 1.588658
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.371577 Loss1: 1.773970 Loss2: 1.597608
-(DefaultActor pid=3764) >> Training accuracy: 0.490234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.070738 Loss1: 3.036139 Loss2: 2.034599
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.068473 Loss1: 2.569682 Loss2: 1.498791
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.798393 Loss1: 2.340294 Loss2: 1.458100
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.699282 Loss1: 2.227193 Loss2: 1.472088
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.777790 Loss1: 2.757742 Loss2: 2.020048
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.652089 Loss1: 2.168982 Loss2: 1.483107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.433117 Loss1: 1.975788 Loss2: 1.457328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.259733 Loss1: 1.793194 Loss2: 1.466539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.146041 Loss1: 1.682034 Loss2: 1.464007 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.077986 Loss1: 1.605039 Loss2: 1.472947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.133815 Loss1: 1.627711 Loss2: 1.506104
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.011828 Loss1: 1.530106 Loss2: 1.481721
-(DefaultActor pid=3765) >> Training accuracy: 0.530134
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.932054 Loss1: 1.447757 Loss2: 1.484297
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.906634 Loss1: 1.411872 Loss2: 1.494762
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.885194 Loss1: 1.384474 Loss2: 1.500720
-(DefaultActor pid=3764) >> Training accuracy: 0.592548
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.609170 Loss1: 2.679954 Loss2: 1.929216
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.611753 Loss1: 2.147261 Loss2: 1.464492
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.432072 Loss1: 1.975968 Loss2: 1.456105
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.905778 Loss1: 2.798043 Loss2: 2.107735
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.227759 Loss1: 1.780530 Loss2: 1.447228
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.979380 Loss1: 2.383749 Loss2: 1.595630
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.113841 Loss1: 1.667489 Loss2: 1.446352
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.032864 Loss1: 1.572906 Loss2: 1.459958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.051790 Loss1: 1.585062 Loss2: 1.466729 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.940043 Loss1: 1.469266 Loss2: 1.470777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.032636 Loss1: 1.542265 Loss2: 1.490371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.933223 Loss1: 1.442549 Loss2: 1.490674 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.567383
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.166952 Loss1: 1.575883 Loss2: 1.591069 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.586458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.937336 Loss1: 2.765341 Loss2: 2.171996 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.713993 Loss1: 2.157685 Loss2: 1.556309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.876129 Loss1: 2.785745 Loss2: 2.090384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.736376 Loss1: 2.214668 Loss2: 1.521707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.231025 Loss1: 1.640355 Loss2: 1.590670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.268254 Loss1: 1.660279 Loss2: 1.607975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.287041 Loss1: 1.696418 Loss2: 1.590623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.172524 Loss1: 1.551009 Loss2: 1.621515 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.570913
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.111646 Loss1: 1.602068 Loss2: 1.509578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.991435 Loss1: 1.464143 Loss2: 1.527292 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.583333
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.050106 Loss1: 1.507155 Loss2: 1.542951
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.990061 Loss1: 2.923356 Loss2: 2.066706
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.980067 Loss1: 2.458112 Loss2: 1.521955
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.766596 Loss1: 2.252587 Loss2: 1.514009
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.580586 Loss1: 2.056008 Loss2: 1.524578
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.432656 Loss1: 1.879924 Loss2: 1.552732
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.683766 Loss1: 2.706121 Loss2: 1.977645
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.806552 Loss1: 2.317349 Loss2: 1.489203 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.553633 Loss1: 2.099847 Loss2: 1.453787 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.420919 Loss1: 1.955111 Loss2: 1.465808 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.269465 Loss1: 1.791787 Loss2: 1.477678 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.560417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.199874 Loss1: 1.733618 Loss2: 1.466256 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.176918 Loss1: 1.658025 Loss2: 1.518894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.134864 Loss1: 1.620347 Loss2: 1.514517 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.562500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.895931 Loss1: 2.304711 Loss2: 1.591219 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.416657 Loss1: 1.857012 Loss2: 1.559645 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.322929 Loss1: 1.770755 Loss2: 1.552174
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.953490 Loss1: 2.894116 Loss2: 2.059374
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.954181 Loss1: 2.413726 Loss2: 1.540455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.705773 Loss1: 2.179732 Loss2: 1.526041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.643801 Loss1: 2.113879 Loss2: 1.529923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.458261 Loss1: 1.919528 Loss2: 1.538732 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.641667
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.065793 Loss1: 1.461954 Loss2: 1.603839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.394762 Loss1: 1.847673 Loss2: 1.547089
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.289203 Loss1: 1.740967 Loss2: 1.548236
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.243986 Loss1: 1.686131 Loss2: 1.557855
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.213157 Loss1: 1.645407 Loss2: 1.567750
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.225121 Loss1: 1.642914 Loss2: 1.582207
-(DefaultActor pid=3764) >> Training accuracy: 0.521875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.944157 Loss1: 2.902392 Loss2: 2.041765
-(DefaultActor pid=3765) Epoch: 1 Loss: 4.051422 Loss1: 2.529155 Loss2: 1.522267
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.771054 Loss1: 2.274821 Loss2: 1.496233
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.598490 Loss1: 2.099126 Loss2: 1.499364
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.477433 Loss1: 1.984784 Loss2: 1.492649
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.795529 Loss1: 2.741372 Loss2: 2.054158
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.893727 Loss1: 2.361533 Loss2: 1.532194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.694812 Loss1: 2.163372 Loss2: 1.531440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.459044 Loss1: 1.936875 Loss2: 1.522169 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.395745 Loss1: 1.875194 Loss2: 1.520551 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.532292
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.266998 Loss1: 1.705461 Loss2: 1.561537
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.258996 Loss1: 1.732639 Loss2: 1.526357
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.182657 Loss1: 1.638951 Loss2: 1.543706
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.145744 Loss1: 1.609143 Loss2: 1.536601
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.121147 Loss1: 1.569165 Loss2: 1.551983
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.101967 Loss1: 1.533115 Loss2: 1.568852
-(DefaultActor pid=3764) >> Training accuracy: 0.548958
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-09 01:51:51,667 | server.py:236 | fit_round 22 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.771830 Loss1: 2.815022 Loss2: 1.956808
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.776371 Loss1: 2.261215 Loss2: 1.515156
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.491980 Loss1: 2.005062 Loss2: 1.486918
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.407287 Loss1: 1.930649 Loss2: 1.476639
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.264186 Loss1: 1.773598 Loss2: 1.490587
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.608723 Loss1: 2.538752 Loss2: 2.069971
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.164188 Loss1: 1.665734 Loss2: 1.498454
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.684004 Loss1: 2.135801 Loss2: 1.548204
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.191023 Loss1: 1.675026 Loss2: 1.515997
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.449012 Loss1: 1.931825 Loss2: 1.517187
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.377651 Loss1: 1.847091 Loss2: 1.530560
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.203820 Loss1: 1.680156 Loss2: 1.523665
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.245813 Loss1: 1.709068 Loss2: 1.536745
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.046177 Loss1: 1.533299 Loss2: 1.512879
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.102719 Loss1: 1.572553 Loss2: 1.530166
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.926731 Loss1: 1.411947 Loss2: 1.514784
-(DefaultActor pid=3765) >> Training accuracy: 0.587891
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.092139 Loss1: 1.536494 Loss2: 1.555645 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.848278 Loss1: 1.289109 Loss2: 1.559169 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.630208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.957743 Loss1: 2.449150 Loss2: 1.508592 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.610742 Loss1: 2.115035 Loss2: 1.495707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.421621 Loss1: 1.924156 Loss2: 1.497466
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.726980 Loss1: 2.793489 Loss2: 1.933491
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.451915 Loss1: 1.942345 Loss2: 1.509571
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.825049 Loss1: 2.372133 Loss2: 1.452916
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.309696 Loss1: 1.797278 Loss2: 1.512418
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.519351 Loss1: 2.096909 Loss2: 1.422442
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.281753 Loss1: 1.744856 Loss2: 1.536898
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.375976 Loss1: 1.958213 Loss2: 1.417763
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.222848 Loss1: 1.690369 Loss2: 1.532478
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.296833 Loss1: 1.863982 Loss2: 1.432851
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.093775 Loss1: 1.546014 Loss2: 1.547761
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.205364 Loss1: 1.756860 Loss2: 1.448503
-(DefaultActor pid=3765) >> Training accuracy: 0.580208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.139442 Loss1: 1.693367 Loss2: 1.446075
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.046191 Loss1: 1.587248 Loss2: 1.458944
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.030569 Loss1: 1.558102 Loss2: 1.472467
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.919998 Loss1: 1.451400 Loss2: 1.468598
-(DefaultActor pid=3764) >> Training accuracy: 0.562500
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 01:51:51,667][flwr][DEBUG] - fit_round 22 received 50 results and 0 failures
-INFO flwr 2023-10-09 01:52:32,678 | server.py:125 | fit progress: (22, 3.1335429638719408, {'accuracy': 0.2531}, 50460.456895762)
->> Test accuracy: 0.253100
-[2023-10-09 01:52:32,678][flwr][INFO] - fit progress: (22, 3.1335429638719408, {'accuracy': 0.2531}, 50460.456895762)
-DEBUG flwr 2023-10-09 01:52:32,679 | server.py:173 | evaluate_round 22: strategy sampled 50 clients (out of 50)
-[2023-10-09 01:52:32,679][flwr][DEBUG] - evaluate_round 22: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 02:01:35,282 | server.py:187 | evaluate_round 22 received 50 results and 0 failures
-[2023-10-09 02:01:35,282][flwr][DEBUG] - evaluate_round 22 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 02:01:35,283 | server.py:222 | fit_round 23: strategy sampled 50 clients (out of 50)
-[2023-10-09 02:01:35,283][flwr][DEBUG] - fit_round 23: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.678517 Loss1: 2.698942 Loss2: 1.979575
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.716214 Loss1: 2.241053 Loss2: 1.475162
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.545252 Loss1: 2.086019 Loss2: 1.459233
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.364681 Loss1: 1.897292 Loss2: 1.467389
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.999355 Loss1: 2.846088 Loss2: 2.153267
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.225873 Loss1: 1.756555 Loss2: 1.469318
-(DefaultActor pid=3764) Epoch: 1 Loss: 4.094074 Loss1: 2.469896 Loss2: 1.624178
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.154124 Loss1: 1.692189 Loss2: 1.461935
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.835541 Loss1: 2.224403 Loss2: 1.611138
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.211791 Loss1: 1.712290 Loss2: 1.499501
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.659259 Loss1: 2.057448 Loss2: 1.601811
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.052070 Loss1: 1.574756 Loss2: 1.477314
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.554770 Loss1: 1.956399 Loss2: 1.598371
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.898516 Loss1: 1.422208 Loss2: 1.476308
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.440761 Loss1: 1.820555 Loss2: 1.620205
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.915078 Loss1: 1.428353 Loss2: 1.486724
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.433771 Loss1: 1.805938 Loss2: 1.627834
-(DefaultActor pid=3765) >> Training accuracy: 0.578125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.375306 Loss1: 1.744153 Loss2: 1.631153
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.214404 Loss1: 1.568983 Loss2: 1.645421
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.277167 Loss1: 1.622823 Loss2: 1.654343
-(DefaultActor pid=3764) >> Training accuracy: 0.535417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.660432 Loss1: 2.734943 Loss2: 1.925488
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.698181 Loss1: 2.242982 Loss2: 1.455199
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.433901 Loss1: 2.014204 Loss2: 1.419698
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.272030 Loss1: 1.847788 Loss2: 1.424241
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.827595 Loss1: 2.760081 Loss2: 2.067514
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.862262 Loss1: 2.323318 Loss2: 1.538944
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.169128 Loss1: 1.729309 Loss2: 1.439819
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.674340 Loss1: 2.170014 Loss2: 1.504326
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.059236 Loss1: 1.630333 Loss2: 1.428903
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.407317 Loss1: 1.898676 Loss2: 1.508641
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.981616 Loss1: 1.529488 Loss2: 1.452128
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.395285 Loss1: 1.881639 Loss2: 1.513646
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.922163 Loss1: 1.467832 Loss2: 1.454330
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.836585 Loss1: 1.372109 Loss2: 1.464476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.802055 Loss1: 1.348504 Loss2: 1.453551 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.644531
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.048362 Loss1: 1.513444 Loss2: 1.534918 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.606250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.870869 Loss1: 2.794209 Loss2: 2.076660 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.632702 Loss1: 2.106235 Loss2: 1.526467 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.481779 Loss1: 1.957640 Loss2: 1.524140
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.856189 Loss1: 2.946751 Loss2: 1.909438
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.428518 Loss1: 1.899122 Loss2: 1.529396
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.879270 Loss1: 2.453852 Loss2: 1.425419
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.291310 Loss1: 1.749376 Loss2: 1.541933
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.659937 Loss1: 2.240802 Loss2: 1.419135
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.555102 Loss1: 2.139366 Loss2: 1.415736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.376973 Loss1: 1.932588 Loss2: 1.444385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.346226 Loss1: 1.901027 Loss2: 1.445200 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.618750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.282583 Loss1: 1.814802 Loss2: 1.467782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.114343 Loss1: 1.636304 Loss2: 1.478040 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.552734
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.791074 Loss1: 2.697179 Loss2: 2.093895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.487375 Loss1: 1.958790 Loss2: 1.528585 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.889607 Loss1: 2.862356 Loss2: 2.027251 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.931426 Loss1: 2.418747 Loss2: 1.512679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.636396 Loss1: 2.141715 Loss2: 1.494681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.465233 Loss1: 1.984409 Loss2: 1.480824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.281403 Loss1: 1.790618 Loss2: 1.490785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.196122 Loss1: 1.700249 Loss2: 1.495873 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.671875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.179332 Loss1: 1.667076 Loss2: 1.512256 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.100273 Loss1: 1.578023 Loss2: 1.522249 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.536458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.777029 Loss1: 2.333942 Loss2: 1.443087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.393981 Loss1: 1.967807 Loss2: 1.426174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.838719 Loss1: 2.851385 Loss2: 1.987334
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.338226 Loss1: 1.903820 Loss2: 1.434406
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.880028 Loss1: 2.401094 Loss2: 1.478934
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.237374 Loss1: 1.785280 Loss2: 1.452094
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.573910 Loss1: 2.137310 Loss2: 1.436600
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.186351 Loss1: 1.722999 Loss2: 1.463352
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.384416 Loss1: 1.944110 Loss2: 1.440306
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.117195 Loss1: 1.650466 Loss2: 1.466729
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.249246 Loss1: 1.813373 Loss2: 1.435874
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.995196 Loss1: 1.527375 Loss2: 1.467821
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.192291 Loss1: 1.733355 Loss2: 1.458935
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.906781 Loss1: 1.433772 Loss2: 1.473009
-(DefaultActor pid=3765) >> Training accuracy: 0.542708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.183262 Loss1: 1.711500 Loss2: 1.471762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.974275 Loss1: 1.491207 Loss2: 1.483068 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.585417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.703044 Loss1: 2.251614 Loss2: 1.451430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.358043 Loss1: 1.920401 Loss2: 1.437642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.315500 Loss1: 1.866686 Loss2: 1.448814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.271333 Loss1: 1.790580 Loss2: 1.480753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.195694 Loss1: 1.730922 Loss2: 1.464772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.012600 Loss1: 1.544744 Loss2: 1.467856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.880335 Loss1: 1.420609 Loss2: 1.459726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.971764 Loss1: 1.477612 Loss2: 1.494152 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.582292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.045801 Loss1: 1.515828 Loss2: 1.529974 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.650670
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.454333 Loss1: 2.541705 Loss2: 1.912628 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.337007 Loss1: 1.918772 Loss2: 1.418235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.155951 Loss1: 1.742744 Loss2: 1.413207
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.825458 Loss1: 2.807048 Loss2: 2.018409
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.971935 Loss1: 1.550792 Loss2: 1.421144
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.814711 Loss1: 2.305494 Loss2: 1.509217
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.961495 Loss1: 1.537901 Loss2: 1.423594
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.533057 Loss1: 2.026511 Loss2: 1.506547
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.898215 Loss1: 1.464093 Loss2: 1.434122
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.433837 Loss1: 1.940577 Loss2: 1.493260
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.775600 Loss1: 1.351850 Loss2: 1.423750
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.286497 Loss1: 1.779536 Loss2: 1.506961
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.700851 Loss1: 1.261922 Loss2: 1.438929
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.196006 Loss1: 1.688569 Loss2: 1.507437
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.708094 Loss1: 1.252758 Loss2: 1.455336
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.128113 Loss1: 1.604711 Loss2: 1.523402
-(DefaultActor pid=3765) >> Training accuracy: 0.636458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.082994 Loss1: 1.569501 Loss2: 1.513493
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.076445 Loss1: 1.548023 Loss2: 1.528423
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.031317 Loss1: 1.482848 Loss2: 1.548469
-(DefaultActor pid=3764) >> Training accuracy: 0.605208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.605990 Loss1: 2.687290 Loss2: 1.918699
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.765481 Loss1: 2.294835 Loss2: 1.470646
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.565319 Loss1: 2.111886 Loss2: 1.453432
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.898873 Loss1: 2.872833 Loss2: 2.026040
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.371047 Loss1: 1.907424 Loss2: 1.463623
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.848276 Loss1: 2.345192 Loss2: 1.503084
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.256908 Loss1: 1.788317 Loss2: 1.468591
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.131526 Loss1: 1.652369 Loss2: 1.479157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.051332 Loss1: 1.558665 Loss2: 1.492667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.018138 Loss1: 1.527348 Loss2: 1.490791 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.980638 Loss1: 1.485716 Loss2: 1.494922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.945117 Loss1: 1.432878 Loss2: 1.512239 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.633789
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.037460 Loss1: 1.517065 Loss2: 1.520396 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.616667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.740144 Loss1: 2.632844 Loss2: 2.107300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.482335 Loss1: 1.961728 Loss2: 1.520606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.290100 Loss1: 1.773352 Loss2: 1.516748
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.767973 Loss1: 2.723382 Loss2: 2.044592
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.086489 Loss1: 1.596196 Loss2: 1.490293
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.668338 Loss1: 2.176530 Loss2: 1.491808
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.980113 Loss1: 1.476286 Loss2: 1.503827
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.470167 Loss1: 1.996257 Loss2: 1.473910
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.979744 Loss1: 1.467627 Loss2: 1.512116
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.315698 Loss1: 1.837561 Loss2: 1.478138
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.930317 Loss1: 1.407329 Loss2: 1.522988
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.227626 Loss1: 1.737424 Loss2: 1.490202
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.898587 Loss1: 1.362085 Loss2: 1.536502
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.198023 Loss1: 1.695130 Loss2: 1.502893
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.886515 Loss1: 1.358214 Loss2: 1.528301
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.153912 Loss1: 1.638406 Loss2: 1.515506
-(DefaultActor pid=3765) >> Training accuracy: 0.677083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.991105 Loss1: 1.468752 Loss2: 1.522353
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.924090 Loss1: 1.392210 Loss2: 1.531880
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.834982 Loss1: 1.311342 Loss2: 1.523641
-(DefaultActor pid=3764) >> Training accuracy: 0.667708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.859676 Loss1: 2.760308 Loss2: 2.099368
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.905759 Loss1: 2.357017 Loss2: 1.548742
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.713465 Loss1: 2.179401 Loss2: 1.534065
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.534166 Loss1: 1.984209 Loss2: 1.549957
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.942992 Loss1: 2.926102 Loss2: 2.016890
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.930827 Loss1: 2.411663 Loss2: 1.519164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.657094 Loss1: 2.165489 Loss2: 1.491605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.476736 Loss1: 1.974088 Loss2: 1.502648 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.401383 Loss1: 1.893337 Loss2: 1.508046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.313441 Loss1: 1.790929 Loss2: 1.522512 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.577083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.212320 Loss1: 1.691700 Loss2: 1.520621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.096985 Loss1: 1.560210 Loss2: 1.536774 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.612305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.571622 Loss1: 2.562492 Loss2: 2.009130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.464017 Loss1: 1.988811 Loss2: 1.475206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.699529 Loss1: 2.656045 Loss2: 2.043484 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.738246 Loss1: 2.231747 Loss2: 1.506499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.460566 Loss1: 1.946937 Loss2: 1.513629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.272845 Loss1: 1.771420 Loss2: 1.501425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.202275 Loss1: 1.699682 Loss2: 1.502594 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.092255 Loss1: 1.589509 Loss2: 1.502746 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.667708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.059644 Loss1: 1.536658 Loss2: 1.522987 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.867150 Loss1: 1.339173 Loss2: 1.527978 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.619792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.820961 Loss1: 2.278356 Loss2: 1.542605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.380678 Loss1: 1.855773 Loss2: 1.524905 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.951424 Loss1: 2.925895 Loss2: 2.025529
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.258489 Loss1: 1.713776 Loss2: 1.544713
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.881788 Loss1: 2.356036 Loss2: 1.525752
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.175495 Loss1: 1.634489 Loss2: 1.541006
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.645124 Loss1: 2.166107 Loss2: 1.479018
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.144585 Loss1: 1.597226 Loss2: 1.547359
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.490255 Loss1: 2.015784 Loss2: 1.474471
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.103779 Loss1: 1.540127 Loss2: 1.563652
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.397274 Loss1: 1.911268 Loss2: 1.486006
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.078158 Loss1: 1.493770 Loss2: 1.584388
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.295701 Loss1: 1.816443 Loss2: 1.479257
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.089501 Loss1: 1.507456 Loss2: 1.582045
-(DefaultActor pid=3765) >> Training accuracy: 0.563542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.188564 Loss1: 1.672367 Loss2: 1.516197 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.110832 Loss1: 1.574950 Loss2: 1.535883 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.601042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.796642 Loss1: 2.298773 Loss2: 1.497870 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.409746 Loss1: 1.914303 Loss2: 1.495444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.283613 Loss1: 1.773537 Loss2: 1.510075
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.773932 Loss1: 2.662630 Loss2: 2.111302
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.286288 Loss1: 1.771083 Loss2: 1.515204
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.741612 Loss1: 2.166905 Loss2: 1.574707
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.161147 Loss1: 1.642593 Loss2: 1.518554
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.545092 Loss1: 1.988045 Loss2: 1.557047
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.028729 Loss1: 1.505692 Loss2: 1.523036
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.394931 Loss1: 1.828856 Loss2: 1.566076
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.066596 Loss1: 1.547996 Loss2: 1.518600
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.292835 Loss1: 1.719749 Loss2: 1.573086
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.000159 Loss1: 1.441234 Loss2: 1.558925
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.191306 Loss1: 1.626859 Loss2: 1.564447
-(DefaultActor pid=3765) >> Training accuracy: 0.594792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.081603 Loss1: 1.501348 Loss2: 1.580255
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.091327 Loss1: 1.497773 Loss2: 1.593554
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.974510 Loss1: 1.375499 Loss2: 1.599012
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.959018 Loss1: 1.360820 Loss2: 1.598197
-(DefaultActor pid=3764) >> Training accuracy: 0.619792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.600028 Loss1: 2.714191 Loss2: 1.885837
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.667125 Loss1: 2.246555 Loss2: 1.420570
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.524342 Loss1: 2.094009 Loss2: 1.430333
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.355835 Loss1: 1.943838 Loss2: 1.411997
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.895191 Loss1: 2.747372 Loss2: 2.147819
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.786272 Loss1: 2.209898 Loss2: 1.576374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.499070 Loss1: 1.961483 Loss2: 1.537587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.391554 Loss1: 1.862124 Loss2: 1.529430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.296453 Loss1: 1.743712 Loss2: 1.552741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.181816 Loss1: 1.628851 Loss2: 1.552965 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.622070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.117192 Loss1: 1.548081 Loss2: 1.569112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.120758 Loss1: 1.547783 Loss2: 1.572975 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.593750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.723542 Loss1: 2.690619 Loss2: 2.032923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.559307 Loss1: 2.049407 Loss2: 1.509900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.381385 Loss1: 1.883190 Loss2: 1.498195
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.756902 Loss1: 2.660720 Loss2: 2.096182
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.774032 Loss1: 2.181067 Loss2: 1.592966 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.456889 Loss1: 1.923391 Loss2: 1.533498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.288813 Loss1: 1.753068 Loss2: 1.535745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.214566 Loss1: 1.675285 Loss2: 1.539281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.133134 Loss1: 1.581677 Loss2: 1.551457 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.613542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.983856 Loss1: 1.435383 Loss2: 1.548472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.902263 Loss1: 1.335644 Loss2: 1.566620 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.606250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.539230 Loss1: 2.540653 Loss2: 1.998577 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.276880 Loss1: 1.836692 Loss2: 1.440188 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.086895 Loss1: 1.662237 Loss2: 1.424657
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.074204 Loss1: 2.959089 Loss2: 2.115115
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.960055 Loss1: 2.360240 Loss2: 1.599815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.726660 Loss1: 2.174601 Loss2: 1.552058 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.573711 Loss1: 2.007437 Loss2: 1.566274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.501176 Loss1: 1.946003 Loss2: 1.555173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.425342 Loss1: 1.860282 Loss2: 1.565060 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.594792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.235530 Loss1: 1.655880 Loss2: 1.579650 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.180660 Loss1: 1.586750 Loss2: 1.593911 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.522461
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.706221 Loss1: 2.247052 Loss2: 1.459169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.295982 Loss1: 1.858827 Loss2: 1.437155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.104308 Loss1: 2.780006 Loss2: 2.324302
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.249825 Loss1: 1.793937 Loss2: 1.455888
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.182474 Loss1: 1.722558 Loss2: 1.459916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.042487 Loss1: 1.585687 Loss2: 1.456800 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.018429 Loss1: 1.552796 Loss2: 1.465633 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.230773 Loss1: 1.609428 Loss2: 1.621345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.289888 Loss1: 1.656080 Loss2: 1.633808 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.638542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.051670 Loss1: 1.422599 Loss2: 1.629071 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.563802
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.151806 Loss1: 3.044017 Loss2: 2.107790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.775054 Loss1: 2.231596 Loss2: 1.543457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.767051 Loss1: 2.704457 Loss2: 2.062594 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.707530 Loss1: 2.202868 Loss2: 1.504662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.486301 Loss1: 1.977025 Loss2: 1.509276 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.321753 Loss1: 1.813719 Loss2: 1.508034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.265944 Loss1: 1.751735 Loss2: 1.514209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.069694 Loss1: 1.458458 Loss2: 1.611236 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.587054
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.079440 Loss1: 1.549485 Loss2: 1.529955 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.925375 Loss1: 1.384615 Loss2: 1.540760 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.609375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.825807 Loss1: 2.356201 Loss2: 1.469605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.407874 Loss1: 1.954721 Loss2: 1.453154 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.295136 Loss1: 1.847771 Loss2: 1.447365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.348433 Loss1: 1.868160 Loss2: 1.480274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.213291 Loss1: 1.740688 Loss2: 1.472604 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.093810 Loss1: 1.613338 Loss2: 1.480471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.037645 Loss1: 1.553928 Loss2: 1.483717 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.096108 Loss1: 1.588668 Loss2: 1.507440 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.569336
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.899664 Loss1: 1.323672 Loss2: 1.575992 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.638542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.136145 Loss1: 3.031801 Loss2: 2.104344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.740990 Loss1: 2.191423 Loss2: 1.549566 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.575620 Loss1: 2.032757 Loss2: 1.542862
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.858954 Loss1: 2.661546 Loss2: 2.197407
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.690055 Loss1: 2.115358 Loss2: 1.574697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.556019 Loss1: 1.996165 Loss2: 1.559854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.392185 Loss1: 1.817349 Loss2: 1.574836 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.330602 Loss1: 1.737040 Loss2: 1.593562
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.269135 Loss1: 1.704637 Loss2: 1.564498
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.131695 Loss1: 1.550062 Loss2: 1.581633
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.244330 Loss1: 1.650706 Loss2: 1.593624
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.039152 Loss1: 1.449715 Loss2: 1.589437
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.137162 Loss1: 1.537211 Loss2: 1.599951
-(DefaultActor pid=3765) >> Training accuracy: 0.562500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.880636 Loss1: 1.313563 Loss2: 1.567073 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.581731
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.766543 Loss1: 2.548254 Loss2: 2.218289 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.448771 Loss1: 1.848510 Loss2: 1.600261 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.315084 Loss1: 1.729435 Loss2: 1.585649
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.794966 Loss1: 2.706330 Loss2: 2.088636
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.778489 Loss1: 2.249957 Loss2: 1.528532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.478585 Loss1: 1.958254 Loss2: 1.520330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.308807 Loss1: 1.793648 Loss2: 1.515159 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.334212 Loss1: 1.812434 Loss2: 1.521778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.181914 Loss1: 1.645741 Loss2: 1.536173 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.661458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.225937 Loss1: 1.688683 Loss2: 1.537254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.056753 Loss1: 1.509467 Loss2: 1.547286 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.583984
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.834894 Loss1: 2.726798 Loss2: 2.108096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.481646 Loss1: 1.988752 Loss2: 1.492895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.311981 Loss1: 1.817669 Loss2: 1.494312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.215373 Loss1: 1.705768 Loss2: 1.509605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.163329 Loss1: 1.645387 Loss2: 1.517942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.025509 Loss1: 1.499773 Loss2: 1.525735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.432630 Loss1: 1.948936 Loss2: 1.483694
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.916678 Loss1: 1.391695 Loss2: 1.524983
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.945383 Loss1: 1.407797 Loss2: 1.537587
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.322262 Loss1: 1.845333 Loss2: 1.476929
-(DefaultActor pid=3765) >> Training accuracy: 0.614183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.208467 Loss1: 1.706298 Loss2: 1.502169
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.126528 Loss1: 1.624435 Loss2: 1.502093
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.130511 Loss1: 1.618581 Loss2: 1.511931
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.090654 Loss1: 1.567704 Loss2: 1.522950
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.090636 Loss1: 1.556538 Loss2: 1.534098
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.684923 Loss1: 2.720704 Loss2: 1.964219
-(DefaultActor pid=3764) >> Training accuracy: 0.577083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.682239 Loss1: 2.208932 Loss2: 1.473306
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.365956 Loss1: 1.908952 Loss2: 1.457003
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.226600 Loss1: 1.759396 Loss2: 1.467204
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.154826 Loss1: 1.689350 Loss2: 1.465475
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.940195 Loss1: 2.840877 Loss2: 2.099318
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.949894 Loss1: 2.384259 Loss2: 1.565636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.073330 Loss1: 1.578076 Loss2: 1.495253
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.629219 Loss1: 2.066949 Loss2: 1.562270
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.088031 Loss1: 1.578487 Loss2: 1.509544
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.540771 Loss1: 1.979397 Loss2: 1.561374
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.541129 Loss1: 1.951403 Loss2: 1.589726
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.032377 Loss1: 1.508703 Loss2: 1.523674
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.480529 Loss1: 1.869278 Loss2: 1.611251
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.887202 Loss1: 1.380491 Loss2: 1.506711
-(DefaultActor pid=3765) >> Training accuracy: 0.581801
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.315475 Loss1: 1.713821 Loss2: 1.601654 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.127817 Loss1: 1.528516 Loss2: 1.599301 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.567708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.849079 Loss1: 2.300787 Loss2: 1.548292 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.382128 Loss1: 1.862470 Loss2: 1.519658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.911733 Loss1: 2.854893 Loss2: 2.056840
-DEBUG flwr 2023-10-09 02:30:35,118 | server.py:236 | fit_round 23 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.278056 Loss1: 1.742551 Loss2: 1.535505
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.959064 Loss1: 2.418882 Loss2: 1.540182
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.206755 Loss1: 1.653654 Loss2: 1.553100
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.661598 Loss1: 2.151645 Loss2: 1.509953
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.248317 Loss1: 1.705484 Loss2: 1.542833
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.486208 Loss1: 1.979367 Loss2: 1.506840
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.149174 Loss1: 1.586815 Loss2: 1.562360
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.483974 Loss1: 1.959511 Loss2: 1.524463
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.083745 Loss1: 1.526772 Loss2: 1.556974
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.365837 Loss1: 1.829101 Loss2: 1.536737
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.968821 Loss1: 1.403127 Loss2: 1.565694
-(DefaultActor pid=3765) >> Training accuracy: 0.598958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.133113 Loss1: 1.591019 Loss2: 1.542094 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.086823 Loss1: 1.514961 Loss2: 1.571862 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.607292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.606427 Loss1: 2.109801 Loss2: 1.496626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.229972 Loss1: 1.751908 Loss2: 1.478065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.822460 Loss1: 2.801486 Loss2: 2.020974
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.130042 Loss1: 1.644625 Loss2: 1.485417
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.018286 Loss1: 1.531564 Loss2: 1.486721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.949215 Loss1: 1.455454 Loss2: 1.493761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.065643 Loss1: 1.556841 Loss2: 1.508802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.842073 Loss1: 1.345942 Loss2: 1.496131 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.911275 Loss1: 1.399034 Loss2: 1.512241 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.606445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.146149 Loss1: 1.638715 Loss2: 1.507433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.031474 Loss1: 1.511608 Loss2: 1.519866 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.588542
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 02:30:35,118][flwr][DEBUG] - fit_round 23 received 50 results and 0 failures
-INFO flwr 2023-10-09 02:31:15,011 | server.py:125 | fit progress: (23, 3.1089451796711445, {'accuracy': 0.2637}, 52782.789583785)
->> Test accuracy: 0.263700
-[2023-10-09 02:31:15,011][flwr][INFO] - fit progress: (23, 3.1089451796711445, {'accuracy': 0.2637}, 52782.789583785)
-DEBUG flwr 2023-10-09 02:31:15,011 | server.py:173 | evaluate_round 23: strategy sampled 50 clients (out of 50)
-[2023-10-09 02:31:15,011][flwr][DEBUG] - evaluate_round 23: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 02:40:14,636 | server.py:187 | evaluate_round 23 received 50 results and 0 failures
-[2023-10-09 02:40:14,636][flwr][DEBUG] - evaluate_round 23 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 02:40:14,636 | server.py:222 | fit_round 24: strategy sampled 50 clients (out of 50)
-[2023-10-09 02:40:14,636][flwr][DEBUG] - fit_round 24: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.760952 Loss1: 2.653937 Loss2: 2.107015
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.756374 Loss1: 2.227787 Loss2: 1.528587
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.480698 Loss1: 1.975641 Loss2: 1.505057
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.328113 Loss1: 1.818558 Loss2: 1.509556
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.654900 Loss1: 2.590500 Loss2: 2.064400
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.100901 Loss1: 1.581601 Loss2: 1.519300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.964285 Loss1: 1.430750 Loss2: 1.533535 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.942502 Loss1: 1.416837 Loss2: 1.525665 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.897719 Loss1: 1.351867 Loss2: 1.545852 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.845396 Loss1: 1.304670 Loss2: 1.540726 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.618990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.966727 Loss1: 1.439391 Loss2: 1.527336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.889628 Loss1: 1.337384 Loss2: 1.552243 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.830977 Loss1: 1.292676 Loss2: 1.538301
-(DefaultActor pid=3764) >> Training accuracy: 0.656250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.709526 Loss1: 2.649017 Loss2: 2.060509
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.630948 Loss1: 2.129925 Loss2: 1.501024
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.355767 Loss1: 1.879169 Loss2: 1.476598
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.185230 Loss1: 1.713181 Loss2: 1.472049
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.048971 Loss1: 1.575955 Loss2: 1.473017
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.577066 Loss1: 2.537719 Loss2: 2.039348
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.918509 Loss1: 1.433055 Loss2: 1.485454
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.558899 Loss1: 2.043792 Loss2: 1.515107
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.957169 Loss1: 1.469877 Loss2: 1.487292
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.311287 Loss1: 1.802419 Loss2: 1.508868
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.843390 Loss1: 1.339533 Loss2: 1.503857
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.115310 Loss1: 1.619141 Loss2: 1.496168
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.769856 Loss1: 1.267646 Loss2: 1.502209
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.136625 Loss1: 1.623984 Loss2: 1.512640
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.734688 Loss1: 1.220000 Loss2: 1.514688
-(DefaultActor pid=3765) >> Training accuracy: 0.568750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.925198 Loss1: 1.402280 Loss2: 1.522918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.727222 Loss1: 1.199436 Loss2: 1.527786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.669192 Loss1: 1.150681 Loss2: 1.518511
-(DefaultActor pid=3764) >> Training accuracy: 0.723958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.632093 Loss1: 2.634803 Loss2: 1.997291
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.674421 Loss1: 2.161750 Loss2: 1.512671
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.382078 Loss1: 1.899684 Loss2: 1.482394
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.180078 Loss1: 1.702205 Loss2: 1.477874
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.066419 Loss1: 1.571532 Loss2: 1.494887
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.854767 Loss1: 2.828173 Loss2: 2.026594
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.988271 Loss1: 1.492007 Loss2: 1.496264
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.968479 Loss1: 1.462993 Loss2: 1.505486 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.845280 Loss1: 1.334903 Loss2: 1.510377 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.907830 Loss1: 1.389465 Loss2: 1.518365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.781183 Loss1: 1.241437 Loss2: 1.539747 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.616667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.234159 Loss1: 1.710235 Loss2: 1.523924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.975756 Loss1: 1.440681 Loss2: 1.535076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.056444 Loss1: 1.498985 Loss2: 1.557460
-(DefaultActor pid=3764) >> Training accuracy: 0.613542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.795912 Loss1: 2.710013 Loss2: 2.085899
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.696117 Loss1: 2.176776 Loss2: 1.519341
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.517716 Loss1: 2.009801 Loss2: 1.507915
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.291235 Loss1: 1.777868 Loss2: 1.513367
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.260477 Loss1: 1.734221 Loss2: 1.526256
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.440163 Loss1: 2.456429 Loss2: 1.983734
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.158412 Loss1: 1.610965 Loss2: 1.547447
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.538296 Loss1: 2.070217 Loss2: 1.468079
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.061166 Loss1: 1.512173 Loss2: 1.548993
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.087702 Loss1: 1.539106 Loss2: 1.548596 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.000485 Loss1: 1.424729 Loss2: 1.575756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.030856 Loss1: 1.449768 Loss2: 1.581088 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.579167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.842084 Loss1: 1.382659 Loss2: 1.459425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.753559 Loss1: 1.282841 Loss2: 1.470717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.735687 Loss1: 1.242542 Loss2: 1.493145
-(DefaultActor pid=3764) >> Training accuracy: 0.659375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.749488 Loss1: 2.634281 Loss2: 2.115208
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.804382 Loss1: 2.212358 Loss2: 1.592025
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.471003 Loss1: 1.906405 Loss2: 1.564598
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.428909 Loss1: 1.850133 Loss2: 1.578775
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.253322 Loss1: 1.674867 Loss2: 1.578456
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.506197 Loss1: 2.586615 Loss2: 1.919582
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.155174 Loss1: 1.563234 Loss2: 1.591940
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.632467 Loss1: 2.180873 Loss2: 1.451593
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.003011 Loss1: 1.403991 Loss2: 1.599020
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.090934 Loss1: 1.480800 Loss2: 1.610134
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.377103 Loss1: 1.947066 Loss2: 1.430037
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.043409 Loss1: 1.423541 Loss2: 1.619868
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.193023 Loss1: 1.748760 Loss2: 1.444263
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.879218 Loss1: 1.250492 Loss2: 1.628725
-(DefaultActor pid=3765) >> Training accuracy: 0.629167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.118245 Loss1: 1.673353 Loss2: 1.444892
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.962046 Loss1: 1.520837 Loss2: 1.441209
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.847292 Loss1: 1.397420 Loss2: 1.449872
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.842414 Loss1: 1.364974 Loss2: 1.477439
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.824685 Loss1: 1.350689 Loss2: 1.473996
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.910082 Loss1: 2.735390 Loss2: 2.174692
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.720992 Loss1: 1.249761 Loss2: 1.471231
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.823516 Loss1: 2.182997 Loss2: 1.640519
-(DefaultActor pid=3764) >> Training accuracy: 0.666360
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.542945 Loss1: 1.937664 Loss2: 1.605281
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.418702 Loss1: 1.803189 Loss2: 1.615512
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.292815 Loss1: 1.664213 Loss2: 1.628602
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.370539 Loss1: 1.732505 Loss2: 1.638034
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.158329 Loss1: 1.510512 Loss2: 1.647817
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.526834 Loss1: 2.460882 Loss2: 2.065951
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.217549 Loss1: 1.559270 Loss2: 1.658278
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.585669 Loss1: 2.041080 Loss2: 1.544589
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.094736 Loss1: 1.412840 Loss2: 1.681896
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.256716 Loss1: 1.746866 Loss2: 1.509851
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.988274 Loss1: 1.322840 Loss2: 1.665434
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.151195 Loss1: 1.645346 Loss2: 1.505849
-(DefaultActor pid=3765) >> Training accuracy: 0.623958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.063346 Loss1: 1.553976 Loss2: 1.509370
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.937916 Loss1: 1.416352 Loss2: 1.521564
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.024156 Loss1: 1.488117 Loss2: 1.536040
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.867481 Loss1: 1.336562 Loss2: 1.530919
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.587762 Loss1: 2.631049 Loss2: 1.956713
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.772717 Loss1: 1.237519 Loss2: 1.535198
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.577666 Loss1: 2.107014 Loss2: 1.470652
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.665087 Loss1: 1.142657 Loss2: 1.522430
-(DefaultActor pid=3764) >> Training accuracy: 0.739583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.247489 Loss1: 1.788878 Loss2: 1.458611 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.037451 Loss1: 1.567690 Loss2: 1.469760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.943248 Loss1: 1.461520 Loss2: 1.481728
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.923459 Loss1: 2.871076 Loss2: 2.052383
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.990794 Loss1: 2.430012 Loss2: 1.560781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.737077 Loss1: 2.171581 Loss2: 1.565495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.641667
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.847358 Loss1: 1.345099 Loss2: 1.502259
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.572315 Loss1: 2.013063 Loss2: 1.559252
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.634321 Loss1: 2.039103 Loss2: 1.595218
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.392876 Loss1: 1.816925 Loss2: 1.575951
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.252625 Loss1: 1.676907 Loss2: 1.575718
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.281191 Loss1: 1.669744 Loss2: 1.611447
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.722728 Loss1: 2.708818 Loss2: 2.013909
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.693928 Loss1: 2.212924 Loss2: 1.481004 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.587891
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.052710 Loss1: 1.438447 Loss2: 1.614263
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.480208 Loss1: 2.006821 Loss2: 1.473387
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.225162 Loss1: 1.743500 Loss2: 1.481661
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.210582 Loss1: 1.725079 Loss2: 1.485503
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.059752 Loss1: 1.565135 Loss2: 1.494617
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.925968 Loss1: 1.440451 Loss2: 1.485516
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.570323 Loss1: 2.611753 Loss2: 1.958569
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.881489 Loss1: 1.377452 Loss2: 1.504036
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.827559 Loss1: 1.325296 Loss2: 1.502263 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.803960 Loss1: 1.289866 Loss2: 1.514094 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.647917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.931683 Loss1: 1.505825 Loss2: 1.425858 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.778793 Loss1: 1.346058 Loss2: 1.432735 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.869031 Loss1: 2.725016 Loss2: 2.144015 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.736779
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.624475 Loss1: 2.051280 Loss2: 1.573194 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.219320 Loss1: 1.636171 Loss2: 1.583148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.177747 Loss1: 1.590736 Loss2: 1.587011
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.610595 Loss1: 2.642846 Loss2: 1.967749
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.736034 Loss1: 2.264317 Loss2: 1.471717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.409836 Loss1: 1.943594 Loss2: 1.466243 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.265493 Loss1: 1.794507 Loss2: 1.470986 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.610417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.136945 Loss1: 1.657047 Loss2: 1.479898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.957012 Loss1: 1.464210 Loss2: 1.492802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.922725 Loss1: 1.410518 Loss2: 1.512207 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.803699 Loss1: 2.289045 Loss2: 1.514654 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.645508
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.387040 Loss1: 1.884456 Loss2: 1.502585 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.163467 Loss1: 1.634552 Loss2: 1.528915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.121244 Loss1: 1.577027 Loss2: 1.544217
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.765023 Loss1: 2.670578 Loss2: 2.094445
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.044348 Loss1: 1.516947 Loss2: 1.527401
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.645140 Loss1: 2.113741 Loss2: 1.531399
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.038986 Loss1: 1.497339 Loss2: 1.541648
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.502873 Loss1: 1.966904 Loss2: 1.535969
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.047872 Loss1: 1.499593 Loss2: 1.548278
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.313233 Loss1: 1.777249 Loss2: 1.535984
-(DefaultActor pid=3765) >> Training accuracy: 0.540625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.213592 Loss1: 1.660540 Loss2: 1.553052
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.099666 Loss1: 1.547603 Loss2: 1.552063
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.972815 Loss1: 1.429947 Loss2: 1.542868
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.946101 Loss1: 1.379191 Loss2: 1.566910
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.843875 Loss1: 2.732601 Loss2: 2.111274
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.921628 Loss1: 1.352020 Loss2: 1.569608
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.999952 Loss1: 1.405714 Loss2: 1.594238
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.923596 Loss1: 2.326596 Loss2: 1.597000
-(DefaultActor pid=3764) >> Training accuracy: 0.606250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.641786 Loss1: 2.055655 Loss2: 1.586131
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.502565 Loss1: 1.911191 Loss2: 1.591374
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.415841 Loss1: 1.816188 Loss2: 1.599653
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.329178 Loss1: 1.720874 Loss2: 1.608305
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.803199 Loss1: 2.705055 Loss2: 2.098144
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.200786 Loss1: 1.579219 Loss2: 1.621568
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.125045 Loss1: 1.501463 Loss2: 1.623582 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.076364 Loss1: 1.444138 Loss2: 1.632226 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.064082 Loss1: 1.429194 Loss2: 1.634887 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.665039
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.973117 Loss1: 1.441653 Loss2: 1.531464 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.971971 Loss1: 1.428031 Loss2: 1.543940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.878644 Loss1: 2.814380 Loss2: 2.064264 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.687500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.772375 Loss1: 2.311804 Loss2: 1.460570 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.312039 Loss1: 1.862036 Loss2: 1.450003 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.177022 Loss1: 1.706829 Loss2: 1.470193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.072530 Loss1: 1.593129 Loss2: 1.479400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.995741 Loss1: 1.513246 Loss2: 1.482494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.946145 Loss1: 1.457183 Loss2: 1.488962 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.914244 Loss1: 1.417617 Loss2: 1.496627 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.637500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.887041 Loss1: 1.433489 Loss2: 1.453551 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.826751 Loss1: 1.337944 Loss2: 1.488807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.768451 Loss1: 1.278889 Loss2: 1.489562
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.777734 Loss1: 2.824436 Loss2: 1.953298
-(DefaultActor pid=3764) >> Training accuracy: 0.684375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.775557 Loss1: 2.245792 Loss2: 1.529765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.346744 Loss1: 1.858995 Loss2: 1.487749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.227268 Loss1: 1.711796 Loss2: 1.515472 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.161343 Loss1: 1.632296 Loss2: 1.529047 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.091826 Loss1: 1.555509 Loss2: 1.536317 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.883953 Loss1: 1.371239 Loss2: 1.512714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.504496 Loss1: 1.972817 Loss2: 1.531679
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.847846 Loss1: 1.312584 Loss2: 1.535261
-(DefaultActor pid=3765) >> Training accuracy: 0.635417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.259352 Loss1: 1.706147 Loss2: 1.553205 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.089986 Loss1: 1.508007 Loss2: 1.581980 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.741727 Loss1: 2.625441 Loss2: 2.116285
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.158520 Loss1: 1.574052 Loss2: 1.584468
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.126974 Loss1: 1.552461 Loss2: 1.574514 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.605469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.321328 Loss1: 1.794803 Loss2: 1.526525 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.052886 Loss1: 1.526480 Loss2: 1.526405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.786686 Loss1: 2.746680 Loss2: 2.040006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.859076 Loss1: 2.332905 Loss2: 1.526171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.844753 Loss1: 1.280379 Loss2: 1.564374 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.660714
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.271164 Loss1: 1.738414 Loss2: 1.532750 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.061200 Loss1: 1.501490 Loss2: 1.559710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.975934 Loss1: 1.416173 Loss2: 1.559761
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.903939 Loss1: 2.752022 Loss2: 2.151916
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.978754 Loss1: 1.409116 Loss2: 1.569638
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.885186 Loss1: 2.289344 Loss2: 1.595842
-(DefaultActor pid=3764) Epoch: 9 Loss: 3.021926 Loss1: 1.429039 Loss2: 1.592887
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.633518 Loss1: 2.035699 Loss2: 1.597819
-(DefaultActor pid=3764) >> Training accuracy: 0.604167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.495176 Loss1: 1.898106 Loss2: 1.597070
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.404701 Loss1: 1.793032 Loss2: 1.611669
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.316834 Loss1: 1.698018 Loss2: 1.618816
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.211492 Loss1: 1.572335 Loss2: 1.639156
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.151273 Loss1: 1.524934 Loss2: 1.626339
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.767759 Loss1: 2.686964 Loss2: 2.080795
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.115655 Loss1: 1.467385 Loss2: 1.648269
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.868019 Loss1: 2.323327 Loss2: 1.544693
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.030904 Loss1: 1.380888 Loss2: 1.650016
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.539444 Loss1: 2.002099 Loss2: 1.537345
-(DefaultActor pid=3765) >> Training accuracy: 0.567708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.403422 Loss1: 1.870697 Loss2: 1.532725
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.226189 Loss1: 1.683392 Loss2: 1.542798
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.186074 Loss1: 1.637904 Loss2: 1.548169
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.031495 Loss1: 1.481394 Loss2: 1.550101
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.048232 Loss1: 2.905198 Loss2: 2.143035
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.979531 Loss1: 1.415202 Loss2: 1.564329
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.953345 Loss1: 2.350194 Loss2: 1.603152
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.027137 Loss1: 1.443449 Loss2: 1.583688
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.619880 Loss1: 2.054167 Loss2: 1.565713
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.960865 Loss1: 1.377579 Loss2: 1.583286
-(DefaultActor pid=3764) >> Training accuracy: 0.543750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.347208 Loss1: 1.775227 Loss2: 1.571981 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.271257 Loss1: 1.679457 Loss2: 1.591800 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.161064 Loss1: 1.571512 Loss2: 1.589552
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.782841 Loss1: 2.875561 Loss2: 1.907281
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.129743 Loss1: 1.536848 Loss2: 1.592895
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.814550 Loss1: 2.378528 Loss2: 1.436023
-(DefaultActor pid=3765) >> Training accuracy: 0.652083
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.034093 Loss1: 1.447186 Loss2: 1.586906
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.529075 Loss1: 2.109676 Loss2: 1.419398
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.397598 Loss1: 1.962473 Loss2: 1.435124
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.336623 Loss1: 1.893569 Loss2: 1.443054
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.224268 Loss1: 1.777418 Loss2: 1.446849
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.197284 Loss1: 1.734333 Loss2: 1.462951
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.789549 Loss1: 2.724653 Loss2: 2.064896
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.781053 Loss1: 2.222330 Loss2: 1.558723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.573802 Loss1: 2.038736 Loss2: 1.535066 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.585938
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.930220 Loss1: 1.454443 Loss2: 1.475777
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.378567 Loss1: 1.838938 Loss2: 1.539628
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.207411 Loss1: 1.661772 Loss2: 1.545639
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.116123 Loss1: 1.570014 Loss2: 1.546109
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.018874 Loss1: 1.477750 Loss2: 1.541124
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.043614 Loss1: 1.479008 Loss2: 1.564606
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.751998 Loss1: 2.678760 Loss2: 2.073238
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.979604 Loss1: 1.410472 Loss2: 1.569131
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.751680 Loss1: 2.181803 Loss2: 1.569877
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.836971 Loss1: 1.258948 Loss2: 1.578023
-(DefaultActor pid=3765) >> Training accuracy: 0.654167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.202005 Loss1: 1.674648 Loss2: 1.527357 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.976904 Loss1: 1.425181 Loss2: 1.551723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.926328 Loss1: 1.373984 Loss2: 1.552344
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.798393 Loss1: 2.820883 Loss2: 1.977510
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.698879 Loss1: 2.244361 Loss2: 1.454518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.508242 Loss1: 2.065763 Loss2: 1.442480 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.667708
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.852364 Loss1: 1.286752 Loss2: 1.565611
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.320393 Loss1: 1.880255 Loss2: 1.440137
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.196310 Loss1: 1.740216 Loss2: 1.456094
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.073944 Loss1: 1.606920 Loss2: 1.467024
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.068125 Loss1: 1.595493 Loss2: 1.472632
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.962084 Loss1: 1.486506 Loss2: 1.475578
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.759611 Loss1: 2.711174 Loss2: 2.048437
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.956063 Loss1: 1.460454 Loss2: 1.495609
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.790632 Loss1: 2.298770 Loss2: 1.491862
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.025805 Loss1: 1.531510 Loss2: 1.494294
-(DefaultActor pid=3765) >> Training accuracy: 0.554167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.399642 Loss1: 1.901909 Loss2: 1.497734 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.156256 Loss1: 1.660869 Loss2: 1.495387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.049297 Loss1: 1.547845 Loss2: 1.501452
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.656156 Loss1: 2.622634 Loss2: 2.033522
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.022599 Loss1: 1.511630 Loss2: 1.510969
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.714068 Loss1: 2.212515 Loss2: 1.501553
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.990895 Loss1: 1.470103 Loss2: 1.520792
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.434066 Loss1: 1.932977 Loss2: 1.501090
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.903190 Loss1: 1.380811 Loss2: 1.522379
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.271795 Loss1: 1.768740 Loss2: 1.503055
-(DefaultActor pid=3764) >> Training accuracy: 0.620833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.057443 Loss1: 1.542299 Loss2: 1.515143
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.137899 Loss1: 1.613563 Loss2: 1.524336
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.015193 Loss1: 1.487211 Loss2: 1.527983
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.033293 Loss1: 1.507556 Loss2: 1.525737
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.870623 Loss1: 1.328491 Loss2: 1.542132
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.711986 Loss1: 2.648519 Loss2: 2.063468
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.871097 Loss1: 1.337112 Loss2: 1.533985
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.705684 Loss1: 2.161516 Loss2: 1.544168
-(DefaultActor pid=3765) >> Training accuracy: 0.660417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.567465 Loss1: 2.036169 Loss2: 1.531296
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.341816 Loss1: 1.799262 Loss2: 1.542554
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.185615 Loss1: 1.645687 Loss2: 1.539928
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.219562 Loss1: 1.662765 Loss2: 1.556797
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.874813 Loss1: 2.765985 Loss2: 2.108828
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.064480 Loss1: 1.494899 Loss2: 1.569582
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.028125 Loss1: 1.450323 Loss2: 1.577802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.959826 Loss1: 1.388828 Loss2: 1.570998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.952746 Loss1: 1.369863 Loss2: 1.582883 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.549805
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.181208 Loss1: 1.611835 Loss2: 1.569373 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.008472 Loss1: 1.438950 Loss2: 1.569522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.677942 Loss1: 2.742845 Loss2: 1.935097 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.543750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.464831 Loss1: 2.048328 Loss2: 1.416502 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.233877 Loss1: 1.802125 Loss2: 1.431751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.129751 Loss1: 1.699756 Loss2: 1.429994
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.513634 Loss1: 2.442469 Loss2: 2.071165
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.595893 Loss1: 2.044565 Loss2: 1.551328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.356823 Loss1: 1.814477 Loss2: 1.542346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.175158 Loss1: 1.645360 Loss2: 1.529798 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.582292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.120082 Loss1: 1.593034 Loss2: 1.527049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.935763 Loss1: 1.391202 Loss2: 1.544560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.780108 Loss1: 1.226352 Loss2: 1.553756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.737743 Loss1: 1.172119 Loss2: 1.565625 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.690625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.307804 Loss1: 1.867072 Loss2: 1.440732 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.959003 Loss1: 1.529993 Loss2: 1.429010 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.887926 Loss1: 1.426358 Loss2: 1.461568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.838713 Loss1: 1.371443 Loss2: 1.467270 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.709573 Loss1: 1.237189 Loss2: 1.472384 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.651042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.438383 Loss1: 1.871226 Loss2: 1.567158 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.196488 Loss1: 1.604235 Loss2: 1.592253 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.960876 Loss1: 2.898780 Loss2: 2.062096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.883437 Loss1: 2.356366 Loss2: 1.527071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.614380 Loss1: 2.093005 Loss2: 1.521375 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.582292
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.039478 Loss1: 1.426237 Loss2: 1.613242
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.426828 Loss1: 1.909762 Loss2: 1.517066
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.306353 Loss1: 1.782718 Loss2: 1.523635
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.149187 Loss1: 1.617412 Loss2: 1.531774
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.193702 Loss1: 1.648899 Loss2: 1.544803
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.274512 Loss1: 1.716117 Loss2: 1.558395
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.077722 Loss1: 1.507711 Loss2: 1.570011
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.646117 Loss1: 2.554205 Loss2: 2.091912
-(DefaultActor pid=3764) >> Training accuracy: 0.659598
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.992811 Loss1: 1.440634 Loss2: 1.552177
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.588864 Loss1: 2.001762 Loss2: 1.587102
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.388031 Loss1: 1.827495 Loss2: 1.560536
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.182998 Loss1: 1.626794 Loss2: 1.556204
-DEBUG flwr 2023-10-09 03:08:37,261 | server.py:236 | fit_round 24 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.110209 Loss1: 1.552025 Loss2: 1.558184
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.098277 Loss1: 1.532764 Loss2: 1.565512
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.017718 Loss1: 2.937537 Loss2: 2.080181
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.887947 Loss1: 2.374136 Loss2: 1.513811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.923068 Loss1: 1.346148 Loss2: 1.576920
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.622228 Loss1: 2.123494 Loss2: 1.498734
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.939910 Loss1: 1.346455 Loss2: 1.593455
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.516554 Loss1: 2.012503 Loss2: 1.504051
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.337034 Loss1: 1.830711 Loss2: 1.506323
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.748050 Loss1: 1.152349 Loss2: 1.595701
-(DefaultActor pid=3765) >> Training accuracy: 0.649414
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.126133 Loss1: 1.587418 Loss2: 1.538715 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.962413 Loss1: 1.413783 Loss2: 1.548630 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.579241
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.914360 Loss1: 1.364904 Loss2: 1.549456
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.627992 Loss1: 2.573908 Loss2: 2.054084
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.599432 Loss1: 2.066642 Loss2: 1.532789
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.405006 Loss1: 1.888168 Loss2: 1.516839
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.246356 Loss1: 1.725769 Loss2: 1.520587
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.149040 Loss1: 1.613334 Loss2: 1.535705
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.799669 Loss1: 2.630136 Loss2: 2.169533
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.048813 Loss1: 1.513845 Loss2: 1.534968
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.921118 Loss1: 1.379372 Loss2: 1.541746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.554183 Loss1: 1.921409 Loss2: 1.632774
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.794110 Loss1: 1.253735 Loss2: 1.540375
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.444706 Loss1: 1.795159 Loss2: 1.649547
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.780732 Loss1: 1.234329 Loss2: 1.546404
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.310333 Loss1: 1.657177 Loss2: 1.653156
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.750650 Loss1: 1.200558 Loss2: 1.550092
-(DefaultActor pid=3765) >> Training accuracy: 0.612500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.186302 Loss1: 1.528739 Loss2: 1.657563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.066412 Loss1: 1.395211 Loss2: 1.671201 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.850856 Loss1: 2.821658 Loss2: 2.029198
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.999234 Loss1: 1.316556 Loss2: 1.682679
-(DefaultActor pid=3764) >> Training accuracy: 0.625000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.559956 Loss1: 2.069715 Loss2: 1.490242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.293821 Loss1: 1.799928 Loss2: 1.493893 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.239318 Loss1: 1.733956 Loss2: 1.505362
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.686047 Loss1: 2.648707 Loss2: 2.037339
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.650478 Loss1: 2.105333 Loss2: 1.545145 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.504658 Loss1: 1.976226 Loss2: 1.528432 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.342972 Loss1: 1.792127 Loss2: 1.550845 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.610417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.161871 Loss1: 1.621385 Loss2: 1.540486 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.969333 Loss1: 1.411786 Loss2: 1.557547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.905695 Loss1: 1.338103 Loss2: 1.567593 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.648438
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 03:08:37,261][flwr][DEBUG] - fit_round 24 received 50 results and 0 failures
-INFO flwr 2023-10-09 03:09:18,444 | server.py:125 | fit progress: (24, 3.037447242691113, {'accuracy': 0.2814}, 55066.222770296)
->> Test accuracy: 0.281400
-[2023-10-09 03:09:18,444][flwr][INFO] - fit progress: (24, 3.037447242691113, {'accuracy': 0.2814}, 55066.222770296)
-DEBUG flwr 2023-10-09 03:09:18,445 | server.py:173 | evaluate_round 24: strategy sampled 50 clients (out of 50)
-[2023-10-09 03:09:18,445][flwr][DEBUG] - evaluate_round 24: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 03:18:23,968 | server.py:187 | evaluate_round 24 received 50 results and 0 failures
-[2023-10-09 03:18:23,968][flwr][DEBUG] - evaluate_round 24 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 03:18:23,968 | server.py:222 | fit_round 25: strategy sampled 50 clients (out of 50)
-[2023-10-09 03:18:23,968][flwr][DEBUG] - fit_round 25: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.758622 Loss1: 2.624612 Loss2: 2.134010 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.447285 Loss1: 1.890091 Loss2: 1.557194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.373739 Loss1: 1.822851 Loss2: 1.550888
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.700071 Loss1: 2.711642 Loss2: 1.988429
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.639695 Loss1: 2.166549 Loss2: 1.473146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.400738 Loss1: 1.938982 Loss2: 1.461756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.249397 Loss1: 1.765611 Loss2: 1.483786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.091157 Loss1: 1.609342 Loss2: 1.481815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.064099 Loss1: 1.576738 Loss2: 1.487361 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.641667
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.920360 Loss1: 1.310621 Loss2: 1.609739
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.935856 Loss1: 1.444374 Loss2: 1.491483
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.838853 Loss1: 1.335447 Loss2: 1.503407
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.779670 Loss1: 1.281564 Loss2: 1.498106
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.793441 Loss1: 1.276380 Loss2: 1.517061
-(DefaultActor pid=3764) >> Training accuracy: 0.623958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.805565 Loss1: 2.709743 Loss2: 2.095822
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.773263 Loss1: 2.250748 Loss2: 1.522515
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.534471 Loss1: 2.035545 Loss2: 1.498926
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.343902 Loss1: 1.834430 Loss2: 1.509472
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.700185 Loss1: 2.572584 Loss2: 2.127600
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.626774 Loss1: 2.074673 Loss2: 1.552102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.334753 Loss1: 1.832283 Loss2: 1.502470 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.116758 Loss1: 1.603466 Loss2: 1.513292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.030690 Loss1: 1.510270 Loss2: 1.520420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.913069 Loss1: 1.382056 Loss2: 1.531012 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.626042
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.879188 Loss1: 1.345668 Loss2: 1.533520
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.816927 Loss1: 1.287957 Loss2: 1.528970
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.779680 Loss1: 1.243672 Loss2: 1.536007
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.671570 Loss1: 1.134897 Loss2: 1.536673
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.789793 Loss1: 1.238224 Loss2: 1.551568
-(DefaultActor pid=3764) >> Training accuracy: 0.659375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.601666 Loss1: 2.564824 Loss2: 2.036842
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.636274 Loss1: 2.131801 Loss2: 1.504472
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.359240 Loss1: 1.865449 Loss2: 1.493791
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.134934 Loss1: 1.652797 Loss2: 1.482137
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.796732 Loss1: 2.731544 Loss2: 2.065189
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.051088 Loss1: 1.558660 Loss2: 1.492428
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.675278 Loss1: 2.177357 Loss2: 1.497921
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.992663 Loss1: 1.495474 Loss2: 1.497189
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.387780 Loss1: 1.907212 Loss2: 1.480567
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.814305 Loss1: 1.303729 Loss2: 1.510576
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.163201 Loss1: 1.676952 Loss2: 1.486250
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.742852 Loss1: 1.234258 Loss2: 1.508594
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.067976 Loss1: 1.578773 Loss2: 1.489203
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.672472 Loss1: 1.168641 Loss2: 1.503831
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.007327 Loss1: 1.504033 Loss2: 1.503294
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.603545 Loss1: 1.088876 Loss2: 1.514669
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.906742 Loss1: 1.404192 Loss2: 1.502550
-(DefaultActor pid=3765) >> Training accuracy: 0.650000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.879492 Loss1: 1.351562 Loss2: 1.527930
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.843535 Loss1: 1.320223 Loss2: 1.523312
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.798624 Loss1: 1.256512 Loss2: 1.542113
-(DefaultActor pid=3764) >> Training accuracy: 0.648958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.634760 Loss1: 2.699455 Loss2: 1.935305
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.563979 Loss1: 2.100035 Loss2: 1.463945
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.311240 Loss1: 1.874402 Loss2: 1.436838
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.452418 Loss1: 2.388442 Loss2: 2.063976
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.119477 Loss1: 1.672481 Loss2: 1.446997
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.487680 Loss1: 1.957788 Loss2: 1.529892
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.953657 Loss1: 1.495316 Loss2: 1.458341
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.214063 Loss1: 1.703168 Loss2: 1.510895
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.850950 Loss1: 1.402398 Loss2: 1.448552
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.078838 Loss1: 1.569553 Loss2: 1.509285
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.914297 Loss1: 1.441726 Loss2: 1.472571
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.811684 Loss1: 1.328703 Loss2: 1.482981 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.758066 Loss1: 1.269684 Loss2: 1.488382 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.661119 Loss1: 1.189735 Loss2: 1.471384 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.697266
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.805550 Loss1: 1.275353 Loss2: 1.530198 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.668750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.710633 Loss1: 2.659512 Loss2: 2.051122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.547262 Loss1: 2.061624 Loss2: 1.485639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.330540 Loss1: 1.839041 Loss2: 1.491499
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.505037 Loss1: 2.486282 Loss2: 2.018755
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.096238 Loss1: 1.631797 Loss2: 1.464441
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.494637 Loss1: 2.012455 Loss2: 1.482182
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.004830 Loss1: 1.512705 Loss2: 1.492125
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.250306 Loss1: 1.783433 Loss2: 1.466873
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.961162 Loss1: 1.467723 Loss2: 1.493438
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.205304 Loss1: 1.730286 Loss2: 1.475017
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.919269 Loss1: 1.431753 Loss2: 1.487515
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.004495 Loss1: 1.528780 Loss2: 1.475714
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.921963 Loss1: 1.420227 Loss2: 1.501737
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.843918 Loss1: 1.374824 Loss2: 1.469094
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.812537 Loss1: 1.308005 Loss2: 1.504532
-(DefaultActor pid=3765) >> Training accuracy: 0.630208
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.748509 Loss1: 1.268277 Loss2: 1.480231
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.845916 Loss1: 1.355832 Loss2: 1.490084
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.679001 Loss1: 1.180692 Loss2: 1.498309
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.658726 Loss1: 1.157916 Loss2: 1.500810
-(DefaultActor pid=3764) >> Training accuracy: 0.706250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.624840 Loss1: 2.578649 Loss2: 2.046191
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.597754 Loss1: 2.076392 Loss2: 1.521361
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.426688 Loss1: 1.904290 Loss2: 1.522398
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.621562 Loss1: 2.543661 Loss2: 2.077900
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.182736 Loss1: 1.648051 Loss2: 1.534685
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.067148 Loss1: 1.539484 Loss2: 1.527664 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.047584 Loss1: 1.519775 Loss2: 1.527809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.989873 Loss1: 1.432633 Loss2: 1.557240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.927116 Loss1: 1.380229 Loss2: 1.546887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.779491 Loss1: 1.264849 Loss2: 1.514642 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.677129 Loss1: 1.144465 Loss2: 1.532664 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.655331
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.680624 Loss1: 1.147660 Loss2: 1.532964 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.692708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.863483 Loss1: 2.841819 Loss2: 2.021663
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.760017 Loss1: 2.230489 Loss2: 1.529527
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.490939 Loss1: 2.004668 Loss2: 1.486271
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.899677 Loss1: 2.819067 Loss2: 2.080609
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.343421 Loss1: 1.848073 Loss2: 1.495348
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.833296 Loss1: 2.301821 Loss2: 1.531475
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.195880 Loss1: 1.698812 Loss2: 1.497068
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.494720 Loss1: 1.977995 Loss2: 1.516724
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.198267 Loss1: 1.688211 Loss2: 1.510056
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.074567 Loss1: 1.558776 Loss2: 1.515790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.015939 Loss1: 1.481437 Loss2: 1.534502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.960690 Loss1: 1.433078 Loss2: 1.527612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.945503 Loss1: 1.402550 Loss2: 1.542954 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.601562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.990705 Loss1: 1.430802 Loss2: 1.559904 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.639583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.925094 Loss1: 2.833470 Loss2: 2.091624 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.599072 Loss1: 2.025782 Loss2: 1.573289 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.586207 Loss1: 2.566081 Loss2: 2.020126
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.408130 Loss1: 1.816144 Loss2: 1.591986
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.659901 Loss1: 2.155415 Loss2: 1.504486
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.239921 Loss1: 1.653578 Loss2: 1.586343
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.386662 Loss1: 1.913625 Loss2: 1.473037
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.226671 Loss1: 1.629760 Loss2: 1.596911
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.181008 Loss1: 1.691180 Loss2: 1.489828
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.130844 Loss1: 1.522920 Loss2: 1.607924
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.036626 Loss1: 1.430134 Loss2: 1.606491 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.988179 Loss1: 1.362761 Loss2: 1.625418 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.901273 Loss1: 1.270650 Loss2: 1.630623 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.608398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.869861 Loss1: 1.353080 Loss2: 1.516781 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.639583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.929000 Loss1: 2.825696 Loss2: 2.103304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.564244 Loss1: 2.023338 Loss2: 1.540906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.404220 Loss1: 1.860373 Loss2: 1.543846
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.696196 Loss1: 2.519664 Loss2: 2.176532
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.179262 Loss1: 1.638966 Loss2: 1.540296
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.606072 Loss1: 2.011786 Loss2: 1.594286
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.134679 Loss1: 1.573369 Loss2: 1.561310
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.293278 Loss1: 1.732822 Loss2: 1.560455
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.118953 Loss1: 1.564045 Loss2: 1.554908
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.059838 Loss1: 1.518473 Loss2: 1.541365
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.967852 Loss1: 1.405263 Loss2: 1.562589
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.928965 Loss1: 1.391095 Loss2: 1.537870
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.941842 Loss1: 1.376349 Loss2: 1.565494
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.843185 Loss1: 1.295909 Loss2: 1.547276
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.999112 Loss1: 1.412028 Loss2: 1.587083
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.783645 Loss1: 1.224310 Loss2: 1.559335
-(DefaultActor pid=3765) >> Training accuracy: 0.602083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.691438 Loss1: 1.131125 Loss2: 1.560313
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.707772 Loss1: 1.146443 Loss2: 1.561329
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.582646 Loss1: 1.011193 Loss2: 1.571452
-(DefaultActor pid=3764) >> Training accuracy: 0.721875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.720674 Loss1: 2.637022 Loss2: 2.083652
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.587784 Loss1: 2.042808 Loss2: 1.544976
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.299823 Loss1: 1.794596 Loss2: 1.505227
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.115198 Loss1: 1.602639 Loss2: 1.512559
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.722489 Loss1: 2.611937 Loss2: 2.110552
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.046196 Loss1: 1.533120 Loss2: 1.513076
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.732376 Loss1: 2.226165 Loss2: 1.506211
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.440650 Loss1: 1.954812 Loss2: 1.485838
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.982909 Loss1: 1.459961 Loss2: 1.522948
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.829137 Loss1: 1.302508 Loss2: 1.526629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.737662 Loss1: 1.216052 Loss2: 1.521610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.847143 Loss1: 1.300232 Loss2: 1.546911 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.680512 Loss1: 1.132230 Loss2: 1.548282 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.682292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.694341 Loss1: 1.173825 Loss2: 1.520516 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.682692
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 5.047488 Loss1: 2.963862 Loss2: 2.083626
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.864638 Loss1: 2.345052 Loss2: 1.519586
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.610396 Loss1: 2.097882 Loss2: 1.512514
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.435427 Loss1: 1.910028 Loss2: 1.525399
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.847634 Loss1: 2.593246 Loss2: 2.254388
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.691803 Loss1: 2.112386 Loss2: 1.579417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.459346 Loss1: 1.908962 Loss2: 1.550384
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.195232 Loss1: 1.656770 Loss2: 1.538462
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.143506 Loss1: 1.582357 Loss2: 1.561148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.100083 Loss1: 1.537608 Loss2: 1.562475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 3.023564 Loss1: 1.466612 Loss2: 1.556952 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.892801 Loss1: 1.307586 Loss2: 1.585215 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.650670
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.729294 Loss1: 1.127558 Loss2: 1.601736 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.682292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.919187 Loss1: 2.806054 Loss2: 2.113134
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.933713 Loss1: 2.343413 Loss2: 1.590301
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.655206 Loss1: 2.075726 Loss2: 1.579480
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.384679 Loss1: 1.806409 Loss2: 1.578270
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.572667 Loss1: 2.486039 Loss2: 2.086628
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.583249 Loss1: 2.078702 Loss2: 1.504547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.190496 Loss1: 1.698426 Loss2: 1.492069 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.097812 Loss1: 1.617711 Loss2: 1.480101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.991910 Loss1: 1.506402 Loss2: 1.485508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.916311 Loss1: 1.409984 Loss2: 1.506327 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.604167
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.955842 Loss1: 1.343985 Loss2: 1.611857
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.787759 Loss1: 1.278714 Loss2: 1.509045
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.754906 Loss1: 1.239428 Loss2: 1.515478
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.641671 Loss1: 1.138362 Loss2: 1.503309
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.606056 Loss1: 1.096208 Loss2: 1.509848
-(DefaultActor pid=3764) >> Training accuracy: 0.744792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.937547 Loss1: 2.766107 Loss2: 2.171440
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.956941 Loss1: 2.335864 Loss2: 1.621077
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.651764 Loss1: 2.063499 Loss2: 1.588265
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.473945 Loss1: 1.877195 Loss2: 1.596750
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.702874 Loss1: 2.641433 Loss2: 2.061442
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.569916 Loss1: 2.072077 Loss2: 1.497839 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.333718 Loss1: 1.851761 Loss2: 1.481958 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.144838 Loss1: 1.659555 Loss2: 1.485284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.031351 Loss1: 1.535833 Loss2: 1.495519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.947111 Loss1: 1.443147 Loss2: 1.503964 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.636458
-(DefaultActor pid=3765) Epoch: 9 Loss: 3.030277 Loss1: 1.390605 Loss2: 1.639672
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.885417 Loss1: 1.385808 Loss2: 1.499609
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.827968 Loss1: 1.306837 Loss2: 1.521131
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.903989 Loss1: 1.380455 Loss2: 1.523534
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.786422 Loss1: 1.250227 Loss2: 1.536196
-(DefaultActor pid=3764) >> Training accuracy: 0.696875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.535926 Loss1: 2.560176 Loss2: 1.975750
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.586563 Loss1: 2.138969 Loss2: 1.447594
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.427794 Loss1: 1.985087 Loss2: 1.442707
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.230373 Loss1: 1.781694 Loss2: 1.448679
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.790292 Loss1: 2.717210 Loss2: 2.073082
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.114069 Loss1: 1.653256 Loss2: 1.460813
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.889366 Loss1: 2.349758 Loss2: 1.539608
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.907942 Loss1: 1.444886 Loss2: 1.463056
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.496041 Loss1: 1.997561 Loss2: 1.498480
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.933971 Loss1: 1.463755 Loss2: 1.470216
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.336055 Loss1: 1.833509 Loss2: 1.502545
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.954228 Loss1: 1.460663 Loss2: 1.493565
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.196054 Loss1: 1.699979 Loss2: 1.496075
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.798494 Loss1: 1.313585 Loss2: 1.484910
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.154202 Loss1: 1.651717 Loss2: 1.502485
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.734286 Loss1: 1.231764 Loss2: 1.502522
-(DefaultActor pid=3765) >> Training accuracy: 0.664583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.968865 Loss1: 1.467581 Loss2: 1.501284
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.874011 Loss1: 1.362645 Loss2: 1.511366
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.952754 Loss1: 1.430844 Loss2: 1.521910
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.856056 Loss1: 1.328839 Loss2: 1.527217
-(DefaultActor pid=3764) >> Training accuracy: 0.690625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.621748 Loss1: 2.587666 Loss2: 2.034081
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.634195 Loss1: 2.142757 Loss2: 1.491438
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.401257 Loss1: 1.917392 Loss2: 1.483864
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.151891 Loss1: 1.676148 Loss2: 1.475742
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.508019 Loss1: 2.623510 Loss2: 1.884509
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.628063 Loss1: 2.202696 Loss2: 1.425367 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.309590 Loss1: 1.889473 Loss2: 1.420117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.165517 Loss1: 1.740242 Loss2: 1.425275 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.088732 Loss1: 1.654301 Loss2: 1.434431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.968879 Loss1: 1.530753 Loss2: 1.438125 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.645833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.855750 Loss1: 1.403378 Loss2: 1.452372 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.746463 Loss1: 1.287498 Loss2: 1.458965 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.632812
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.668257 Loss1: 1.210252 Loss2: 1.458005
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.668800 Loss1: 2.639780 Loss2: 2.029020
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.694257 Loss1: 2.202345 Loss2: 1.491911
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.450609 Loss1: 1.974492 Loss2: 1.476117
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.239637 Loss1: 1.759843 Loss2: 1.479795
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.103347 Loss1: 1.617234 Loss2: 1.486113
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.715748 Loss1: 2.623295 Loss2: 2.092453
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.005249 Loss1: 1.519115 Loss2: 1.486133
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.596095 Loss1: 2.090628 Loss2: 1.505467
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.937705 Loss1: 1.432332 Loss2: 1.505373
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.353374 Loss1: 1.862017 Loss2: 1.491357
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.887550 Loss1: 1.388920 Loss2: 1.498630
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.251739 Loss1: 1.751113 Loss2: 1.500626
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.938554 Loss1: 1.419356 Loss2: 1.519198
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.112364 Loss1: 1.597681 Loss2: 1.514684
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.838039 Loss1: 1.320565 Loss2: 1.517475
-(DefaultActor pid=3765) >> Training accuracy: 0.670833
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.972315 Loss1: 1.466085 Loss2: 1.506230
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.881029 Loss1: 1.370756 Loss2: 1.510273
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.830039 Loss1: 1.306946 Loss2: 1.523094
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.747656 Loss1: 1.210760 Loss2: 1.536896
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.796925 Loss1: 1.262265 Loss2: 1.534660
-(DefaultActor pid=3764) >> Training accuracy: 0.628125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.739313 Loss1: 2.725661 Loss2: 2.013652
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.843252 Loss1: 2.355030 Loss2: 1.488222
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.481519 Loss1: 2.029264 Loss2: 1.452255
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.252355 Loss1: 1.782676 Loss2: 1.469680
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.128264 Loss1: 1.655263 Loss2: 1.473001
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.077348 Loss1: 1.595296 Loss2: 1.482052 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.059210 Loss1: 1.566886 Loss2: 1.492324 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.973033 Loss1: 1.473115 Loss2: 1.499918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.829166 Loss1: 1.339560 Loss2: 1.489606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.800454 Loss1: 1.305372 Loss2: 1.495081 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.626042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.074115 Loss1: 1.593474 Loss2: 1.480640 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 3.025514 Loss1: 1.526755 Loss2: 1.498759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.925345 Loss1: 1.409999 Loss2: 1.515346
-(DefaultActor pid=3764) >> Training accuracy: 0.598633
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.797218 Loss1: 2.632484 Loss2: 2.164734
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.750441 Loss1: 2.170611 Loss2: 1.579830
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.471419 Loss1: 1.899821 Loss2: 1.571598
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.244188 Loss1: 1.676851 Loss2: 1.567337
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.153002 Loss1: 1.582025 Loss2: 1.570977
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.437227 Loss1: 2.395507 Loss2: 2.041720
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.485342 Loss1: 1.993840 Loss2: 1.491502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.231645 Loss1: 1.749305 Loss2: 1.482339 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.968004 Loss1: 1.499247 Loss2: 1.468757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.921808 Loss1: 1.316831 Loss2: 1.604977
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.953210 Loss1: 1.476980 Loss2: 1.476229
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.904951 Loss1: 1.289335 Loss2: 1.615616
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.867349 Loss1: 1.393250 Loss2: 1.474099
-(DefaultActor pid=3765) >> Training accuracy: 0.622070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.773598 Loss1: 1.282709 Loss2: 1.490889
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.745622 Loss1: 1.254745 Loss2: 1.490877
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.720538 Loss1: 1.220002 Loss2: 1.500535
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.701650 Loss1: 1.191731 Loss2: 1.509919
-(DefaultActor pid=3764) >> Training accuracy: 0.679167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.836627 Loss1: 2.727757 Loss2: 2.108870
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.667911 Loss1: 2.121408 Loss2: 1.546504
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.378036 Loss1: 1.858628 Loss2: 1.519408
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.257736 Loss1: 1.721974 Loss2: 1.535761
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.202916 Loss1: 1.650185 Loss2: 1.552732
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.700792 Loss1: 2.679752 Loss2: 2.021040
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.034771 Loss1: 1.487253 Loss2: 1.547518
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.738746 Loss1: 2.247312 Loss2: 1.491433
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.389586 Loss1: 1.908440 Loss2: 1.481146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.303594 Loss1: 1.819243 Loss2: 1.484351 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.201986 Loss1: 1.698953 Loss2: 1.503033 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.655208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.122220 Loss1: 1.624192 Loss2: 1.498028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.949058 Loss1: 1.440583 Loss2: 1.508475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.743353 Loss1: 1.220317 Loss2: 1.523037 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.619141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.419665 Loss1: 1.834264 Loss2: 1.585401 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.179585 Loss1: 1.583235 Loss2: 1.596350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.742974 Loss1: 2.605635 Loss2: 2.137338
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.061367 Loss1: 1.454257 Loss2: 1.607110
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.637231 Loss1: 2.065468 Loss2: 1.571763
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.962526 Loss1: 1.353456 Loss2: 1.609070
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.453906 Loss1: 1.911084 Loss2: 1.542822
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.973464 Loss1: 1.367825 Loss2: 1.605639
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.262867 Loss1: 1.720921 Loss2: 1.541946
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.955017 Loss1: 1.328650 Loss2: 1.626367
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.209587 Loss1: 1.657097 Loss2: 1.552490
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.853344 Loss1: 1.225449 Loss2: 1.627895
-(DefaultActor pid=3765) >> Training accuracy: 0.636458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.048318 Loss1: 1.476088 Loss2: 1.572230 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.910057 Loss1: 1.332077 Loss2: 1.577980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.937038 Loss1: 1.347613 Loss2: 1.589425
-(DefaultActor pid=3764) >> Training accuracy: 0.635417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.521407 Loss1: 2.472238 Loss2: 2.049168
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.571314 Loss1: 2.046489 Loss2: 1.524825
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.266404 Loss1: 1.765477 Loss2: 1.500927
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.126989 Loss1: 1.625712 Loss2: 1.501277
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.947273 Loss1: 1.438841 Loss2: 1.508432
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.639433 Loss1: 2.649187 Loss2: 1.990247
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.556501 Loss1: 2.069743 Loss2: 1.486758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.282305 Loss1: 1.804780 Loss2: 1.477525 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.196995 Loss1: 1.721265 Loss2: 1.475729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.993958 Loss1: 1.530596 Loss2: 1.463361 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.678711
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.645231 Loss1: 1.102600 Loss2: 1.542631
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.857301 Loss1: 1.390803 Loss2: 1.466499
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.781587 Loss1: 1.310923 Loss2: 1.470665
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.651253 Loss1: 1.178190 Loss2: 1.473063
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.699885 Loss1: 1.200555 Loss2: 1.499330
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.739856 Loss1: 1.229548 Loss2: 1.510308
-(DefaultActor pid=3764) >> Training accuracy: 0.725000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.529736 Loss1: 2.577196 Loss2: 1.952540
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.626764 Loss1: 2.168267 Loss2: 1.458497
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.312363 Loss1: 1.868361 Loss2: 1.444002
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.134530 Loss1: 1.697718 Loss2: 1.436812
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.985590 Loss1: 1.544018 Loss2: 1.441572
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.704123 Loss1: 2.655407 Loss2: 2.048716
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.771843 Loss1: 2.244491 Loss2: 1.527351
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.980800 Loss1: 1.530139 Loss2: 1.450660
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.484269 Loss1: 1.958466 Loss2: 1.525803
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.986243 Loss1: 1.532379 Loss2: 1.453864
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.321310 Loss1: 1.789174 Loss2: 1.532136
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.871113 Loss1: 1.400269 Loss2: 1.470844
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.183537 Loss1: 1.651610 Loss2: 1.531927
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.797357 Loss1: 1.339212 Loss2: 1.458145
-DEBUG flwr 2023-10-09 03:47:28,943 | server.py:236 | fit_round 25 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.743318 Loss1: 1.262502 Loss2: 1.480815 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.670898
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.964291 Loss1: 1.407835 Loss2: 1.556456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.920217 Loss1: 1.345513 Loss2: 1.574704 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.602083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.600794 Loss1: 2.107359 Loss2: 1.493435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.263416 Loss1: 1.777136 Loss2: 1.486281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.762475 Loss1: 2.674292 Loss2: 2.088183
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.115043 Loss1: 1.626951 Loss2: 1.488092
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.717936 Loss1: 2.186090 Loss2: 1.531846
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.002014 Loss1: 1.498179 Loss2: 1.503835
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.457362 Loss1: 1.955069 Loss2: 1.502292
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.909575 Loss1: 1.401319 Loss2: 1.508255
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.293979 Loss1: 1.781527 Loss2: 1.512452
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.821326 Loss1: 1.303888 Loss2: 1.517438
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.070169 Loss1: 1.558452 Loss2: 1.511717
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.835520 Loss1: 1.305215 Loss2: 1.530305
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.085111 Loss1: 1.555556 Loss2: 1.529555
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.822913 Loss1: 1.301303 Loss2: 1.521611
-(DefaultActor pid=3765) >> Training accuracy: 0.671875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.897209 Loss1: 1.360096 Loss2: 1.537113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.857031 Loss1: 1.304056 Loss2: 1.552975 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.641667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.816317 Loss1: 2.330808 Loss2: 1.485509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.352427 Loss1: 1.875051 Loss2: 1.477376 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.195760 Loss1: 1.711872 Loss2: 1.483888 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.135302 Loss1: 1.649749 Loss2: 1.485553 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.154154 Loss1: 1.658199 Loss2: 1.495955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 3.069602 Loss1: 1.558976 Loss2: 1.510626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.944295 Loss1: 1.439881 Loss2: 1.504414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.946820 Loss1: 1.314751 Loss2: 1.632069 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.655208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.933457 Loss1: 1.272985 Loss2: 1.660472 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.695913
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.952699 Loss1: 2.824236 Loss2: 2.128463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.632518 Loss1: 2.110016 Loss2: 1.522502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.708544 Loss1: 2.613972 Loss2: 2.094572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.630904 Loss1: 2.114382 Loss2: 1.516523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.352510 Loss1: 1.856077 Loss2: 1.496433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.180838 Loss1: 1.674747 Loss2: 1.506092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.121997 Loss1: 1.605119 Loss2: 1.516878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.885643 Loss1: 1.373782 Loss2: 1.511861 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.627232
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.936573 Loss1: 1.401835 Loss2: 1.534739 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.787918 Loss1: 1.245072 Loss2: 1.542847 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.694196
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 03:47:28,943][flwr][DEBUG] - fit_round 25 received 50 results and 0 failures
-INFO flwr 2023-10-09 03:48:10,075 | server.py:125 | fit progress: (25, 2.9935917195420676, {'accuracy': 0.295}, 57397.853067455)
->> Test accuracy: 0.295000
-[2023-10-09 03:48:10,075][flwr][INFO] - fit progress: (25, 2.9935917195420676, {'accuracy': 0.295}, 57397.853067455)
-DEBUG flwr 2023-10-09 03:48:10,075 | server.py:173 | evaluate_round 25: strategy sampled 50 clients (out of 50)
-[2023-10-09 03:48:10,075][flwr][DEBUG] - evaluate_round 25: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 03:57:16,539 | server.py:187 | evaluate_round 25 received 50 results and 0 failures
-[2023-10-09 03:57:16,539][flwr][DEBUG] - evaluate_round 25 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 03:57:16,539 | server.py:222 | fit_round 26: strategy sampled 50 clients (out of 50)
-[2023-10-09 03:57:16,539][flwr][DEBUG] - fit_round 26: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.660271 Loss1: 2.646804 Loss2: 2.013467
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.632214 Loss1: 2.142981 Loss2: 1.489233
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.307941 Loss1: 1.855366 Loss2: 1.452575
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.100412 Loss1: 1.649711 Loss2: 1.450700
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.540738 Loss1: 2.562757 Loss2: 1.977980
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.535762 Loss1: 2.044041 Loss2: 1.491721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.317622 Loss1: 1.835988 Loss2: 1.481634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.127749 Loss1: 1.640698 Loss2: 1.487051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.018424 Loss1: 1.532230 Loss2: 1.486194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.932044 Loss1: 1.420818 Loss2: 1.511226 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.625000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.908481 Loss1: 1.397230 Loss2: 1.511251 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.729799 Loss1: 1.221604 Loss2: 1.508195 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.718750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.772119 Loss1: 2.651176 Loss2: 2.120943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.425149 Loss1: 1.898173 Loss2: 1.526976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.712814 Loss1: 2.555112 Loss2: 2.157702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.661181 Loss1: 2.107865 Loss2: 1.553315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.484666 Loss1: 1.951884 Loss2: 1.532782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.183115 Loss1: 1.640499 Loss2: 1.542617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.048678 Loss1: 1.495582 Loss2: 1.553096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.813494 Loss1: 1.251934 Loss2: 1.561559
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.002201 Loss1: 1.470187 Loss2: 1.532014
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.810448 Loss1: 1.260351 Loss2: 1.550097
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.840315 Loss1: 1.283825 Loss2: 1.556490
-(DefaultActor pid=3765) >> Training accuracy: 0.606250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.735866 Loss1: 1.159394 Loss2: 1.576472 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.670673
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.802859 Loss1: 2.661870 Loss2: 2.140988 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.578109 Loss1: 2.025375 Loss2: 1.552734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.328271 Loss1: 1.760919 Loss2: 1.567352
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.603985 Loss1: 2.594769 Loss2: 2.009216
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.544526 Loss1: 2.074781 Loss2: 1.469745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.235526 Loss1: 1.775754 Loss2: 1.459772 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.130137 Loss1: 1.668453 Loss2: 1.461684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.021911 Loss1: 1.558397 Loss2: 1.463513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.958258 Loss1: 1.467153 Loss2: 1.491105 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.629167
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.853497 Loss1: 1.241131 Loss2: 1.612366
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.939707 Loss1: 1.446403 Loss2: 1.493304
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.815334 Loss1: 1.323210 Loss2: 1.492124
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.868393 Loss1: 1.358077 Loss2: 1.510315
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.685112 Loss1: 1.179058 Loss2: 1.506053
-(DefaultActor pid=3764) >> Training accuracy: 0.656250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.735424 Loss1: 2.608798 Loss2: 2.126626
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.628557 Loss1: 2.076748 Loss2: 1.551808
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.419693 Loss1: 1.883848 Loss2: 1.535845
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.110947 Loss1: 1.582528 Loss2: 1.528419
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.707984 Loss1: 2.696107 Loss2: 2.011877
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.759380 Loss1: 2.254839 Loss2: 1.504541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.392100 Loss1: 1.891412 Loss2: 1.500688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.304162 Loss1: 1.800873 Loss2: 1.503288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.089821 Loss1: 1.588548 Loss2: 1.501273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.001091 Loss1: 1.471591 Loss2: 1.529500 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.604911
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.834413 Loss1: 1.295865 Loss2: 1.538548 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.814718 Loss1: 1.270998 Loss2: 1.543720 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.647917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.549853 Loss1: 2.029049 Loss2: 1.520804 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.097311 Loss1: 1.562272 Loss2: 1.535039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.628418 Loss1: 2.703066 Loss2: 1.925352
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.990473 Loss1: 1.477399 Loss2: 1.513074
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.657296 Loss1: 2.211974 Loss2: 1.445322
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.890533 Loss1: 1.349109 Loss2: 1.541424
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.396223 Loss1: 1.985033 Loss2: 1.411190
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.779235 Loss1: 1.219705 Loss2: 1.559530
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.199692 Loss1: 1.768003 Loss2: 1.431689
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.822535 Loss1: 1.278952 Loss2: 1.543582
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.047410 Loss1: 1.605498 Loss2: 1.441912
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.768687 Loss1: 1.203254 Loss2: 1.565432
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.997634 Loss1: 1.541384 Loss2: 1.456249
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.744575 Loss1: 1.190265 Loss2: 1.554310
-(DefaultActor pid=3765) >> Training accuracy: 0.676042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.879008 Loss1: 1.403707 Loss2: 1.475301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.738246 Loss1: 1.252036 Loss2: 1.486210 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.680208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.471446 Loss1: 1.970334 Loss2: 1.501112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.077525 Loss1: 1.594229 Loss2: 1.483297 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.886298 Loss1: 1.411919 Loss2: 1.474380
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.564854 Loss1: 2.596241 Loss2: 1.968613
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.837196 Loss1: 1.359220 Loss2: 1.477976
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.637204 Loss1: 2.159106 Loss2: 1.478098
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.740911 Loss1: 1.247088 Loss2: 1.493822
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.318048 Loss1: 1.859309 Loss2: 1.458739
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.163157 Loss1: 1.685550 Loss2: 1.477607 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.058009 Loss1: 1.569763 Loss2: 1.488247 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.654167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.981857 Loss1: 1.474131 Loss2: 1.507726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.806136 Loss1: 1.296429 Loss2: 1.509707 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.796706 Loss1: 1.280539 Loss2: 1.516166 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.656250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.627914 Loss1: 2.114444 Loss2: 1.513470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.138203 Loss1: 1.653718 Loss2: 1.484485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.627128 Loss1: 2.552920 Loss2: 2.074208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.602257 Loss1: 2.068956 Loss2: 1.533301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.401690 Loss1: 1.874027 Loss2: 1.527664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.219711 Loss1: 1.687158 Loss2: 1.532554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.087480 Loss1: 1.557073 Loss2: 1.530407 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.694792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.961168 Loss1: 1.396821 Loss2: 1.564347 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.746319 Loss1: 1.180375 Loss2: 1.565944 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.820464 Loss1: 1.248449 Loss2: 1.572015
-(DefaultActor pid=3764) >> Training accuracy: 0.679167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.632346 Loss1: 2.631100 Loss2: 2.001246
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.601669 Loss1: 2.125964 Loss2: 1.475705
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.316318 Loss1: 1.858902 Loss2: 1.457416
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.070891 Loss1: 1.603754 Loss2: 1.467137
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.932953 Loss1: 1.469117 Loss2: 1.463836
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.523646 Loss1: 2.529831 Loss2: 1.993815
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.027806 Loss1: 1.544929 Loss2: 1.482877
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.479008 Loss1: 1.968548 Loss2: 1.510460
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.846751 Loss1: 1.339772 Loss2: 1.506979
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.161233 Loss1: 1.670090 Loss2: 1.491143
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.811194 Loss1: 1.318348 Loss2: 1.492847
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.095298 Loss1: 1.589949 Loss2: 1.505349
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.707050 Loss1: 1.209787 Loss2: 1.497264
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.660442 Loss1: 1.150807 Loss2: 1.509636
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.946711 Loss1: 1.435245 Loss2: 1.511466
-(DefaultActor pid=3765) >> Training accuracy: 0.654167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.925337 Loss1: 1.415232 Loss2: 1.510104
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.767276 Loss1: 1.240905 Loss2: 1.526371
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.749655 Loss1: 1.219576 Loss2: 1.530079
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.612396 Loss1: 1.081826 Loss2: 1.530570
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.791992 Loss1: 2.703441 Loss2: 2.088551
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.619294 Loss1: 1.078403 Loss2: 1.540891
-(DefaultActor pid=3764) >> Training accuracy: 0.728516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.434793 Loss1: 1.915603 Loss2: 1.519190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.134113 Loss1: 1.610431 Loss2: 1.523683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.047092 Loss1: 1.500389 Loss2: 1.546703
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.552274 Loss1: 2.406583 Loss2: 2.145691
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.519830 Loss1: 1.955490 Loss2: 1.564341 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.266055 Loss1: 1.721066 Loss2: 1.544989 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.064252 Loss1: 1.512637 Loss2: 1.551614 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.663542
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.854908 Loss1: 1.300481 Loss2: 1.554427
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.966883 Loss1: 1.409755 Loss2: 1.557129
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.905599 Loss1: 1.339219 Loss2: 1.566380
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.849049 Loss1: 1.264515 Loss2: 1.584534
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.738936 Loss1: 1.160936 Loss2: 1.578000
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.709725 Loss1: 1.130024 Loss2: 1.579701
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.503669 Loss1: 2.537355 Loss2: 1.966314
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.633629 Loss1: 1.035314 Loss2: 1.598315
-(DefaultActor pid=3764) >> Training accuracy: 0.685417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.307595 Loss1: 1.842499 Loss2: 1.465096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.971827 Loss1: 1.508221 Loss2: 1.463606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.468697 Loss1: 2.453622 Loss2: 2.015075
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.848135 Loss1: 1.368632 Loss2: 1.479503
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.516648 Loss1: 1.991889 Loss2: 1.524758
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.818886 Loss1: 1.331747 Loss2: 1.487139
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.189817 Loss1: 1.672926 Loss2: 1.516891
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.742434 Loss1: 1.241699 Loss2: 1.500735
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.022741 Loss1: 1.514402 Loss2: 1.508339
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.696422 Loss1: 1.202662 Loss2: 1.493760
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.933363 Loss1: 1.424882 Loss2: 1.508481
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.779156 Loss1: 1.270524 Loss2: 1.508631
-(DefaultActor pid=3765) >> Training accuracy: 0.610352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.692832 Loss1: 1.171650 Loss2: 1.521182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.675913 Loss1: 1.144036 Loss2: 1.531877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.864168 Loss1: 2.756434 Loss2: 2.107734
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.652922 Loss1: 1.110224 Loss2: 1.542698
-(DefaultActor pid=3764) >> Training accuracy: 0.688477
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.447238 Loss1: 1.926343 Loss2: 1.520895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.209478 Loss1: 1.657178 Loss2: 1.552300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.579379 Loss1: 2.534659 Loss2: 2.044720
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.065849 Loss1: 1.502675 Loss2: 1.563173
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.390320 Loss1: 1.888521 Loss2: 1.501799
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.994750 Loss1: 1.433206 Loss2: 1.561543
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.954327 Loss1: 1.368398 Loss2: 1.585929 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.870506 Loss1: 1.296101 Loss2: 1.574405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.840550 Loss1: 1.253110 Loss2: 1.587440 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.656250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.689037 Loss1: 1.214465 Loss2: 1.474573 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.559913 Loss1: 1.073315 Loss2: 1.486598 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.701923
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.623045 Loss1: 2.085893 Loss2: 1.537152 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.191542 Loss1: 1.698122 Loss2: 1.493420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.585650 Loss1: 2.530614 Loss2: 2.055036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.575259 Loss1: 2.068141 Loss2: 1.507119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.805150 Loss1: 1.275785 Loss2: 1.529365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.101543 Loss1: 1.595918 Loss2: 1.505625 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.717448
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.982532 Loss1: 1.479052 Loss2: 1.503480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.817075 Loss1: 1.289823 Loss2: 1.527252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.699017 Loss1: 1.152825 Loss2: 1.546192 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.642453 Loss1: 1.103855 Loss2: 1.538598 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.728125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.283997 Loss1: 1.776185 Loss2: 1.507811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.995031 Loss1: 1.484660 Loss2: 1.510371 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.599215 Loss1: 2.575007 Loss2: 2.024209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.648765 Loss1: 2.147256 Loss2: 1.501509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.370713 Loss1: 1.873132 Loss2: 1.497581 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.166874 Loss1: 1.684170 Loss2: 1.482704 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.716667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.068622 Loss1: 1.563001 Loss2: 1.505620 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.890690 Loss1: 1.371002 Loss2: 1.519687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.733212 Loss1: 1.206857 Loss2: 1.526355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.631598 Loss1: 1.097639 Loss2: 1.533960 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.731250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.440699 Loss1: 1.881407 Loss2: 1.559292 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.166934 Loss1: 1.603163 Loss2: 1.563771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.323950 Loss1: 2.340011 Loss2: 1.983939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.292531 Loss1: 1.832482 Loss2: 1.460048 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.082715 Loss1: 1.641846 Loss2: 1.440869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.913581 Loss1: 1.476802 Loss2: 1.436779 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.672917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.764559 Loss1: 1.302757 Loss2: 1.461803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.667689 Loss1: 1.194624 Loss2: 1.473065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.598924 Loss1: 1.130703 Loss2: 1.468221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.518126 Loss1: 1.032333 Loss2: 1.485793 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.718750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.183477 Loss1: 1.671189 Loss2: 1.512288 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.978662 Loss1: 1.463655 Loss2: 1.515007 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.487080 Loss1: 2.518489 Loss2: 1.968591
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.963208 Loss1: 1.441702 Loss2: 1.521507
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.311355 Loss1: 1.900056 Loss2: 1.411299
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.836522 Loss1: 1.312629 Loss2: 1.523893
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.070211 Loss1: 1.673507 Loss2: 1.396704
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.691932 Loss1: 1.146508 Loss2: 1.545425
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.935083 Loss1: 1.521812 Loss2: 1.413271
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.624120 Loss1: 1.086699 Loss2: 1.537421
-(DefaultActor pid=3765) >> Training accuracy: 0.694792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.741136 Loss1: 1.319543 Loss2: 1.421592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.554657 Loss1: 1.132740 Loss2: 1.421917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.557387 Loss1: 1.121659 Loss2: 1.435727
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.653096 Loss1: 2.570085 Loss2: 2.083010
-(DefaultActor pid=3764) >> Training accuracy: 0.730208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.612543 Loss1: 2.049518 Loss2: 1.563025 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.240593 Loss1: 1.680657 Loss2: 1.559936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.919774 Loss1: 1.354668 Loss2: 1.565106 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.947828 Loss1: 1.379873 Loss2: 1.567955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.845472 Loss1: 1.259648 Loss2: 1.585824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.839163 Loss1: 1.251908 Loss2: 1.587255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.773444 Loss1: 1.173649 Loss2: 1.599795 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.678711
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.998556 Loss1: 1.463345 Loss2: 1.535211 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.819586 Loss1: 1.263133 Loss2: 1.556454 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.655333 Loss1: 2.655230 Loss2: 2.000104
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.790155 Loss1: 1.220699 Loss2: 1.569457
-(DefaultActor pid=3764) >> Training accuracy: 0.664583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.399312 Loss1: 1.931236 Loss2: 1.468076 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.123024 Loss1: 1.649561 Loss2: 1.473463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.113090 Loss1: 1.626875 Loss2: 1.486215
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.718956 Loss1: 2.726100 Loss2: 1.992856
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.747952 Loss1: 2.242910 Loss2: 1.505042 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.516652 Loss1: 2.013982 Loss2: 1.502670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.361280 Loss1: 1.845898 Loss2: 1.515382 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.664583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.209163 Loss1: 1.680199 Loss2: 1.528963 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.059566 Loss1: 1.521024 Loss2: 1.538542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.887061 Loss1: 1.324332 Loss2: 1.562729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.821787 Loss1: 1.259023 Loss2: 1.562764 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.565430
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.333804 Loss1: 1.754738 Loss2: 1.579066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.120805 Loss1: 1.508779 Loss2: 1.612026 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.963377 Loss1: 1.358088 Loss2: 1.605289
-(DefaultActor pid=3764) Epoch: 0 Loss: 5.001184 Loss1: 2.884897 Loss2: 2.116287
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.779694 Loss1: 2.238826 Loss2: 1.540867
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.822915 Loss1: 1.231324 Loss2: 1.591591
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.460212 Loss1: 1.929710 Loss2: 1.530502
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.870894 Loss1: 1.252424 Loss2: 1.618470
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.754084 Loss1: 1.145674 Loss2: 1.608410 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.696381 Loss1: 1.087032 Loss2: 1.609349 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.706801
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.040226 Loss1: 1.460401 Loss2: 1.579825 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.854422 Loss1: 1.277068 Loss2: 1.577355 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.647321
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.703431 Loss1: 2.230574 Loss2: 1.472858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.250231 Loss1: 1.792231 Loss2: 1.458000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.129741 Loss1: 1.647430 Loss2: 1.482311
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.672448 Loss1: 2.575571 Loss2: 2.096876
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.555867 Loss1: 2.025400 Loss2: 1.530466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.215857 Loss1: 1.694300 Loss2: 1.521556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.145762 Loss1: 1.617802 Loss2: 1.527959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.104596 Loss1: 1.569210 Loss2: 1.535386 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.648438
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.809608 Loss1: 1.266487 Loss2: 1.543120 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.694858 Loss1: 1.136037 Loss2: 1.558821 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.607823 Loss1: 1.046575 Loss2: 1.561248
-(DefaultActor pid=3764) >> Training accuracy: 0.718750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.686455 Loss1: 2.636714 Loss2: 2.049741
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.631676 Loss1: 2.095729 Loss2: 1.535947
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.358520 Loss1: 1.852183 Loss2: 1.506337
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.261299 Loss1: 1.742331 Loss2: 1.518968
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.113935 Loss1: 1.596681 Loss2: 1.517255
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.674066 Loss1: 2.655293 Loss2: 2.018773
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.625826 Loss1: 2.138033 Loss2: 1.487793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.389964 Loss1: 1.902903 Loss2: 1.487061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.311651 Loss1: 1.825249 Loss2: 1.486401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.119779 Loss1: 1.635639 Loss2: 1.484140 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.646875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.022153 Loss1: 1.528701 Loss2: 1.493452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.841986 Loss1: 1.338470 Loss2: 1.503516 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.728489 Loss1: 1.200378 Loss2: 1.528111 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.654167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.460737 Loss1: 1.926472 Loss2: 1.534265 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.968966 Loss1: 1.466757 Loss2: 1.502210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.875160 Loss1: 1.371984 Loss2: 1.503176
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.559153 Loss1: 2.476352 Loss2: 2.082801
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.605700 Loss1: 2.044924 Loss2: 1.560776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.249147 Loss1: 1.717138 Loss2: 1.532008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.082806 Loss1: 1.557988 Loss2: 1.524818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.906307 Loss1: 1.379511 Loss2: 1.526795 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.715625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.911285 Loss1: 1.376032 Loss2: 1.535253 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.714872 Loss1: 1.173571 Loss2: 1.541301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.639731 Loss1: 1.085324 Loss2: 1.554407 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.737500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.564905 Loss1: 2.087757 Loss2: 1.477148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.077169 Loss1: 1.609997 Loss2: 1.467172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.935898 Loss1: 1.472163 Loss2: 1.463736
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.691481 Loss1: 2.519438 Loss2: 2.172043
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.690428 Loss1: 2.087220 Loss2: 1.603208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.412985 Loss1: 1.811135 Loss2: 1.601851 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.304204 Loss1: 1.691385 Loss2: 1.612819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.206862 Loss1: 1.580354 Loss2: 1.626508 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.663542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.094891 Loss1: 1.469137 Loss2: 1.625755 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.034779 Loss1: 1.385705 Loss2: 1.649074 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.967578 Loss1: 1.321795 Loss2: 1.645784 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.610417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.434402 Loss1: 1.914660 Loss2: 1.519742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.948843 Loss1: 1.466920 Loss2: 1.481923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.822807 Loss1: 1.350287 Loss2: 1.472520
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.742306 Loss1: 2.738199 Loss2: 2.004107
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.816121 Loss1: 2.311640 Loss2: 1.504481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.428846 Loss1: 1.940460 Loss2: 1.488386 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.272581 Loss1: 1.783229 Loss2: 1.489351 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.150093 Loss1: 1.643146 Loss2: 1.506947 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.753125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.956249 Loss1: 1.432853 Loss2: 1.523396 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.848632 Loss1: 1.311061 Loss2: 1.537572 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.736680 Loss1: 2.580710 Loss2: 2.155970
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.718568 Loss1: 1.180585 Loss2: 1.537983
-(DefaultActor pid=3764) >> Training accuracy: 0.589844
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.379572 Loss1: 1.797578 Loss2: 1.581994 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.231874 Loss1: 1.623445 Loss2: 1.608430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.099298 Loss1: 1.483988 Loss2: 1.615310
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.992461 Loss1: 2.825600 Loss2: 2.166861
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.001624 Loss1: 1.385597 Loss2: 1.616026
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.873290 Loss1: 2.263111 Loss2: 1.610179
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.833068 Loss1: 1.215237 Loss2: 1.617831
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.549872 Loss1: 1.967445 Loss2: 1.582426
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.864361 Loss1: 1.244943 Loss2: 1.619418
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.386485 Loss1: 1.805514 Loss2: 1.580971
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.811198 Loss1: 1.180910 Loss2: 1.630288
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.336967 Loss1: 1.748697 Loss2: 1.588270
-(DefaultActor pid=3765) >> Training accuracy: 0.712500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.185742 Loss1: 1.600525 Loss2: 1.585216
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.130328 Loss1: 1.547891 Loss2: 1.582438
-(DefaultActor pid=3764) Epoch: 7 Loss: 3.004195 Loss1: 1.403764 Loss2: 1.600431
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.960940 Loss1: 1.370289 Loss2: 1.590651
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.865305 Loss1: 1.265103 Loss2: 1.600202
-DEBUG flwr 2023-10-09 04:25:49,168 | server.py:236 | fit_round 26 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.701109 Loss1: 2.614935 Loss2: 2.086174
-(DefaultActor pid=3764) >> Training accuracy: 0.637500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.728596 Loss1: 2.185852 Loss2: 1.542744
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.513810 Loss1: 1.975608 Loss2: 1.538202
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.330056 Loss1: 1.778107 Loss2: 1.551949
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.204759 Loss1: 1.657633 Loss2: 1.547127
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.074056 Loss1: 1.518925 Loss2: 1.555131
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.761382 Loss1: 2.568094 Loss2: 2.193288
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.084621 Loss1: 1.517981 Loss2: 1.566640
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.517912 Loss1: 1.937957 Loss2: 1.579955
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.943634 Loss1: 1.392518 Loss2: 1.551116
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.268473 Loss1: 1.724374 Loss2: 1.544098
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.891120 Loss1: 1.319124 Loss2: 1.571996
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.130178 Loss1: 1.580147 Loss2: 1.550031
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.862657 Loss1: 1.290723 Loss2: 1.571934
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.982664 Loss1: 1.432318 Loss2: 1.550346
-(DefaultActor pid=3765) >> Training accuracy: 0.596875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.920072 Loss1: 1.361052 Loss2: 1.559021
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.842452 Loss1: 1.264793 Loss2: 1.577660
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.796578 Loss1: 1.222308 Loss2: 1.574269
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.675688 Loss1: 1.095692 Loss2: 1.579996
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.632979 Loss1: 1.050895 Loss2: 1.582083
-(DefaultActor pid=3764) >> Training accuracy: 0.742708
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 04:25:49,168][flwr][DEBUG] - fit_round 26 received 50 results and 0 failures
-INFO flwr 2023-10-09 04:26:30,752 | server.py:125 | fit progress: (26, 2.974912224486232, {'accuracy': 0.3054}, 59698.530597688004)
->> Test accuracy: 0.305400
-[2023-10-09 04:26:30,752][flwr][INFO] - fit progress: (26, 2.974912224486232, {'accuracy': 0.3054}, 59698.530597688004)
-DEBUG flwr 2023-10-09 04:26:30,752 | server.py:173 | evaluate_round 26: strategy sampled 50 clients (out of 50)
-[2023-10-09 04:26:30,752][flwr][DEBUG] - evaluate_round 26: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 04:35:32,608 | server.py:187 | evaluate_round 26 received 50 results and 0 failures
-[2023-10-09 04:35:32,608][flwr][DEBUG] - evaluate_round 26 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 04:35:32,608 | server.py:222 | fit_round 27: strategy sampled 50 clients (out of 50)
-[2023-10-09 04:35:32,608][flwr][DEBUG] - fit_round 27: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.713092 Loss1: 2.677320 Loss2: 2.035771
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.644535 Loss1: 2.137809 Loss2: 1.506727
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.363990 Loss1: 1.870256 Loss2: 1.493734
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.172298 Loss1: 1.677563 Loss2: 1.494735
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.027373 Loss1: 1.532261 Loss2: 1.495112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.969047 Loss1: 1.464056 Loss2: 1.504992 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.952123 Loss1: 1.436918 Loss2: 1.515204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.788788 Loss1: 1.246154 Loss2: 1.542634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.709060 Loss1: 1.180747 Loss2: 1.528313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.597734 Loss1: 1.054955 Loss2: 1.542779 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.610417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.847406 Loss1: 1.269414 Loss2: 1.577993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.642200 Loss1: 1.052043 Loss2: 1.590157 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.738281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.363046 Loss1: 1.870741 Loss2: 1.492304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.925175 Loss1: 1.436214 Loss2: 1.488961 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.883371 Loss1: 1.393043 Loss2: 1.490328
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.509911 Loss1: 2.508742 Loss2: 2.001169
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.734878 Loss1: 1.238118 Loss2: 1.496761
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.634009 Loss1: 2.139250 Loss2: 1.494759
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.644038 Loss1: 1.144572 Loss2: 1.499467
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.261101 Loss1: 1.792908 Loss2: 1.468192
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.628747 Loss1: 1.109715 Loss2: 1.519033
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.117880 Loss1: 1.652057 Loss2: 1.465822
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.535052 Loss1: 1.025614 Loss2: 1.509439
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.980586 Loss1: 1.503596 Loss2: 1.476990
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.530913 Loss1: 1.001959 Loss2: 1.528954
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.923313 Loss1: 1.449563 Loss2: 1.473750
-(DefaultActor pid=3765) >> Training accuracy: 0.672917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.841173 Loss1: 1.356436 Loss2: 1.484736
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.794262 Loss1: 1.305033 Loss2: 1.489229
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.660779 Loss1: 1.171083 Loss2: 1.489696
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.589359 Loss1: 1.091580 Loss2: 1.497780
-(DefaultActor pid=3764) >> Training accuracy: 0.647917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.691495 Loss1: 2.689687 Loss2: 2.001808
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.732218 Loss1: 2.206433 Loss2: 1.525786
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.413630 Loss1: 1.903379 Loss2: 1.510251
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.232676 Loss1: 1.720980 Loss2: 1.511696
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.060973 Loss1: 1.533233 Loss2: 1.527739
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.684861 Loss1: 2.657636 Loss2: 2.027225
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.979451 Loss1: 1.456010 Loss2: 1.523441
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.637811 Loss1: 2.141032 Loss2: 1.496779
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.879115 Loss1: 1.339063 Loss2: 1.540052
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.347019 Loss1: 1.861857 Loss2: 1.485162
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.823424 Loss1: 1.285420 Loss2: 1.538004
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.179570 Loss1: 1.696197 Loss2: 1.483374
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.758059 Loss1: 1.197484 Loss2: 1.560575
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.112278 Loss1: 1.616918 Loss2: 1.495359
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.670972 Loss1: 1.122859 Loss2: 1.548113
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.977939 Loss1: 1.461317 Loss2: 1.516621
-(DefaultActor pid=3765) >> Training accuracy: 0.704102
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 3.034238 Loss1: 1.535064 Loss2: 1.499174
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.918629 Loss1: 1.382205 Loss2: 1.536425
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.728844 Loss1: 1.198684 Loss2: 1.530160
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.751865 Loss1: 1.240178 Loss2: 1.511688
-(DefaultActor pid=3764) >> Training accuracy: 0.706055
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.797431 Loss1: 2.674811 Loss2: 2.122620
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.901377 Loss1: 2.290733 Loss2: 1.610644
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.544046 Loss1: 1.960752 Loss2: 1.583294
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.269130 Loss1: 1.694014 Loss2: 1.575116
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.153436 Loss1: 1.577890 Loss2: 1.575545
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.501971 Loss1: 2.477091 Loss2: 2.024880
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.472518 Loss1: 2.010403 Loss2: 1.462115 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.171178 Loss1: 1.714023 Loss2: 1.457154 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.941982 Loss1: 1.491825 Loss2: 1.450158 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.850740 Loss1: 1.386066 Loss2: 1.464674 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.651042
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.808895 Loss1: 1.201913 Loss2: 1.606983
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.827893 Loss1: 1.357651 Loss2: 1.470242
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.728195 Loss1: 1.237106 Loss2: 1.491089
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.583870 Loss1: 1.107806 Loss2: 1.476064
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.580900 Loss1: 1.091308 Loss2: 1.489592
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.533969 Loss1: 1.043658 Loss2: 1.490311
-(DefaultActor pid=3764) >> Training accuracy: 0.700000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.698020 Loss1: 2.602638 Loss2: 2.095382
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.575467 Loss1: 2.061007 Loss2: 1.514460
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.299524 Loss1: 1.799256 Loss2: 1.500268
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.132939 Loss1: 1.614427 Loss2: 1.518512
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.013202 Loss1: 1.506168 Loss2: 1.507034
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.659546 Loss1: 2.523617 Loss2: 2.135929
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.644626 Loss1: 2.071145 Loss2: 1.573481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.301824 Loss1: 1.762973 Loss2: 1.538851 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.091607 Loss1: 1.524360 Loss2: 1.567247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.997439 Loss1: 1.438639 Loss2: 1.558800 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.678125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.685029 Loss1: 1.155865 Loss2: 1.529164
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.998813 Loss1: 1.428366 Loss2: 1.570447
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.930138 Loss1: 1.350473 Loss2: 1.579665
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.823888 Loss1: 1.247205 Loss2: 1.576682
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.843556 Loss1: 1.248538 Loss2: 1.595019
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.625412 Loss1: 1.038053 Loss2: 1.587360
-(DefaultActor pid=3764) >> Training accuracy: 0.730208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.425824 Loss1: 2.494988 Loss2: 1.930836
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.474947 Loss1: 2.031402 Loss2: 1.443545
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.270972 Loss1: 1.828319 Loss2: 1.442653
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.147188 Loss1: 1.678151 Loss2: 1.469037
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.579512 Loss1: 2.548540 Loss2: 2.030972
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.996864 Loss1: 1.536342 Loss2: 1.460521
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.514308 Loss1: 2.030202 Loss2: 1.484106
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.953467 Loss1: 1.495437 Loss2: 1.458031
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.202807 Loss1: 1.722090 Loss2: 1.480717
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.749909 Loss1: 1.282262 Loss2: 1.467647
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.045968 Loss1: 1.575982 Loss2: 1.469986
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.686926 Loss1: 1.220560 Loss2: 1.466367
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.629618 Loss1: 1.149312 Loss2: 1.480306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.520124 Loss1: 1.048829 Loss2: 1.471295 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.663086
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.708271 Loss1: 1.200949 Loss2: 1.507321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.543391 Loss1: 1.028973 Loss2: 1.514418 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.675000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.549840 Loss1: 1.973829 Loss2: 1.576011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.103551 Loss1: 1.533130 Loss2: 1.570421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.940905 Loss1: 1.375776 Loss2: 1.565129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.783437 Loss1: 1.206659 Loss2: 1.576778 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.832887 Loss1: 1.260627 Loss2: 1.572260 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.718142 Loss1: 1.120871 Loss2: 1.597271 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.675159 Loss1: 1.086578 Loss2: 1.588581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.565540 Loss1: 0.966051 Loss2: 1.599489 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.693750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.805780 Loss1: 1.231106 Loss2: 1.574674 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.661365 Loss1: 1.077989 Loss2: 1.583375 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.660417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.730340 Loss1: 2.639307 Loss2: 2.091033
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.699555 Loss1: 2.139927 Loss2: 1.559629
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.397769 Loss1: 1.862292 Loss2: 1.535477
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.213782 Loss1: 1.664476 Loss2: 1.549307
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.611801 Loss1: 2.509006 Loss2: 2.102795
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.495231 Loss1: 1.953900 Loss2: 1.541331 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.180125 Loss1: 1.652376 Loss2: 1.527750 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.073896 Loss1: 1.523830 Loss2: 1.550067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.900928 Loss1: 1.368069 Loss2: 1.532859 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.906664 Loss1: 1.322818 Loss2: 1.583846
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.832819 Loss1: 1.295191 Loss2: 1.537628
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.776442 Loss1: 1.165744 Loss2: 1.610698
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.728074 Loss1: 1.163699 Loss2: 1.564375
-(DefaultActor pid=3765) >> Training accuracy: 0.616211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.679603 Loss1: 1.121209 Loss2: 1.558394
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.730268 Loss1: 1.167428 Loss2: 1.562840
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.703971 Loss1: 1.118803 Loss2: 1.585168
-(DefaultActor pid=3764) >> Training accuracy: 0.605208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.604800 Loss1: 2.510443 Loss2: 2.094357
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.497321 Loss1: 1.971347 Loss2: 1.525974
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.271354 Loss1: 1.765610 Loss2: 1.505745
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.047399 Loss1: 1.532902 Loss2: 1.514496
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.452656 Loss1: 2.314573 Loss2: 2.138083
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.444763 Loss1: 1.870365 Loss2: 1.574398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.149176 Loss1: 1.615195 Loss2: 1.533981 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.929024 Loss1: 1.394787 Loss2: 1.534237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.853516 Loss1: 1.312613 Loss2: 1.540904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.715676 Loss1: 1.177970 Loss2: 1.537706 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.691667
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.703737 Loss1: 1.150867 Loss2: 1.552869
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.734494 Loss1: 1.181969 Loss2: 1.552525
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.675290 Loss1: 1.114862 Loss2: 1.560428
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.716312 Loss1: 1.145516 Loss2: 1.570795
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.661104 Loss1: 1.076915 Loss2: 1.584189
-(DefaultActor pid=3764) >> Training accuracy: 0.741667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.785231 Loss1: 2.648845 Loss2: 2.136386
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.680537 Loss1: 2.084652 Loss2: 1.595885
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.390518 Loss1: 1.801361 Loss2: 1.589157
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.181920 Loss1: 1.598440 Loss2: 1.583480
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.511681 Loss1: 2.470109 Loss2: 2.041573
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.520838 Loss1: 2.029444 Loss2: 1.491394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.320536 Loss1: 1.829430 Loss2: 1.491106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.108097 Loss1: 1.628723 Loss2: 1.479374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.024951 Loss1: 1.530111 Loss2: 1.494840 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.955025 Loss1: 1.459213 Loss2: 1.495812 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.705208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.840915 Loss1: 1.336733 Loss2: 1.504182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.791580 Loss1: 1.268990 Loss2: 1.522591 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.646875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.696771 Loss1: 2.603821 Loss2: 2.092949 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.342847 Loss1: 1.813131 Loss2: 1.529716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.108571 Loss1: 1.592234 Loss2: 1.516337
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.789954 Loss1: 2.652066 Loss2: 2.137888
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.628442 Loss1: 2.088028 Loss2: 1.540414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.318630 Loss1: 1.808887 Loss2: 1.509743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.108183 Loss1: 1.580571 Loss2: 1.527611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.000616 Loss1: 1.474723 Loss2: 1.525893 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.916697 Loss1: 1.374333 Loss2: 1.542364 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.680208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.747546 Loss1: 1.173887 Loss2: 1.573659
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.917239 Loss1: 1.357567 Loss2: 1.559671
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.838947 Loss1: 1.269490 Loss2: 1.569457
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.870821 Loss1: 1.293606 Loss2: 1.577215
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.795924 Loss1: 1.210336 Loss2: 1.585587
-(DefaultActor pid=3764) >> Training accuracy: 0.669792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.694525 Loss1: 2.534014 Loss2: 2.160511
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.598757 Loss1: 2.041612 Loss2: 1.557144
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.347334 Loss1: 1.807389 Loss2: 1.539945
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.202864 Loss1: 1.653915 Loss2: 1.548949
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.309908 Loss1: 2.317321 Loss2: 1.992587
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.304228 Loss1: 1.832610 Loss2: 1.471618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.057261 Loss1: 1.598187 Loss2: 1.459075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.891434 Loss1: 1.433308 Loss2: 1.458126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.816889 Loss1: 1.354886 Loss2: 1.462003 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.759206 Loss1: 1.283817 Loss2: 1.475390 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.693750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.626118 Loss1: 1.135912 Loss2: 1.490206 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.530716 Loss1: 1.051365 Loss2: 1.479352 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.717708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.541902 Loss1: 2.486062 Loss2: 2.055840
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.622368 Loss1: 2.122521 Loss2: 1.499846
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.355624 Loss1: 1.865180 Loss2: 1.490444
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.156076 Loss1: 1.659166 Loss2: 1.496910
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.669960 Loss1: 2.525027 Loss2: 2.144934
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.485907 Loss1: 2.005133 Loss2: 1.480774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.179093 Loss1: 1.734849 Loss2: 1.444244
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.988413 Loss1: 1.464646 Loss2: 1.523768
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.777009 Loss1: 1.249379 Loss2: 1.527630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.756304 Loss1: 1.235752 Loss2: 1.520553 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.706839 Loss1: 1.162297 Loss2: 1.544542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.741610 Loss1: 1.250180 Loss2: 1.491430 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.713542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.586051 Loss1: 1.087378 Loss2: 1.498673 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.766927
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.856191 Loss1: 2.789671 Loss2: 2.066520
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.700891 Loss1: 2.165005 Loss2: 1.535886
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.361284 Loss1: 1.840620 Loss2: 1.520664
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.208564 Loss1: 1.690312 Loss2: 1.518252
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.757350 Loss1: 2.559147 Loss2: 2.198202
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.103995 Loss1: 1.559518 Loss2: 1.544477
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.630087 Loss1: 2.066025 Loss2: 1.564062
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.345988 Loss1: 1.795132 Loss2: 1.550856
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.981049 Loss1: 1.441731 Loss2: 1.539318
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.950660 Loss1: 1.383355 Loss2: 1.567305 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.789840 Loss1: 1.226834 Loss2: 1.563006 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.805923 Loss1: 1.244942 Loss2: 1.560980 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.800018 Loss1: 1.234706 Loss2: 1.565312 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.680208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.630934 Loss1: 1.029641 Loss2: 1.601293 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.658654
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.726774 Loss1: 2.486494 Loss2: 2.240280
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.534642 Loss1: 1.907394 Loss2: 1.627248
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.272460 Loss1: 1.694591 Loss2: 1.577869
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.988601 Loss1: 1.398732 Loss2: 1.589869
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.679251 Loss1: 2.550539 Loss2: 2.128712
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.889506 Loss1: 1.286919 Loss2: 1.602587 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.752149 Loss1: 1.142902 Loss2: 1.609248 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.683884 Loss1: 1.093453 Loss2: 1.590431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.560048 Loss1: 0.945650 Loss2: 1.614399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.655325 Loss1: 1.040038 Loss2: 1.615288 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.625000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.866034 Loss1: 1.285731 Loss2: 1.580304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.697942 Loss1: 1.117261 Loss2: 1.580681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.555250 Loss1: 0.969712 Loss2: 1.585538
-(DefaultActor pid=3764) >> Training accuracy: 0.729167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.632187 Loss1: 2.540202 Loss2: 2.091985
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.518907 Loss1: 2.034426 Loss2: 1.484482
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.156987 Loss1: 1.690738 Loss2: 1.466249
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.062839 Loss1: 1.590971 Loss2: 1.471868
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.001955 Loss1: 1.511733 Loss2: 1.490222
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.736515 Loss1: 2.675362 Loss2: 2.061152
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.871751 Loss1: 1.393870 Loss2: 1.477880
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.832312 Loss1: 1.329990 Loss2: 1.502322 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.670838 Loss1: 1.168475 Loss2: 1.502363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.597197 Loss1: 1.113962 Loss2: 1.483236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.592782 Loss1: 1.095937 Loss2: 1.496845 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.660417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.960014 Loss1: 1.425678 Loss2: 1.534336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.863969 Loss1: 1.312975 Loss2: 1.550993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.847470 Loss1: 1.285100 Loss2: 1.562369
-(DefaultActor pid=3764) >> Training accuracy: 0.637500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.575305 Loss1: 2.453059 Loss2: 2.122245
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.541549 Loss1: 1.982926 Loss2: 1.558623
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.348894 Loss1: 1.816068 Loss2: 1.532826
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.037259 Loss1: 1.507195 Loss2: 1.530064
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.917481 Loss1: 1.378661 Loss2: 1.538821
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.565985 Loss1: 2.601684 Loss2: 1.964301
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.895927 Loss1: 1.358929 Loss2: 1.536998
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.828835 Loss1: 1.288270 Loss2: 1.540566 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.754159 Loss1: 1.208252 Loss2: 1.545908 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.616681 Loss1: 1.058109 Loss2: 1.558572 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.523251 Loss1: 0.972798 Loss2: 1.550453 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.690625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.763450 Loss1: 1.310088 Loss2: 1.453361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.696332 Loss1: 1.230271 Loss2: 1.466061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.628545 Loss1: 1.153147 Loss2: 1.475398
-(DefaultActor pid=3764) >> Training accuracy: 0.734375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.626081 Loss1: 2.445026 Loss2: 2.181055
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.469577 Loss1: 1.869333 Loss2: 1.600244
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.235806 Loss1: 1.672602 Loss2: 1.563205
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.929839 Loss1: 1.379504 Loss2: 1.550335
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.832717 Loss1: 1.271083 Loss2: 1.561634
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.358295 Loss1: 2.272322 Loss2: 2.085973
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.762649 Loss1: 1.204940 Loss2: 1.557709
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.574385 Loss1: 2.013216 Loss2: 1.561169
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.738732 Loss1: 1.171191 Loss2: 1.567541
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.217822 Loss1: 1.687305 Loss2: 1.530517
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.614264 Loss1: 1.041249 Loss2: 1.573015
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.017472 Loss1: 1.503498 Loss2: 1.513973
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.548006 Loss1: 0.979512 Loss2: 1.568494
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.872169 Loss1: 1.352558 Loss2: 1.519610
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.535300 Loss1: 0.949547 Loss2: 1.585753
-(DefaultActor pid=3765) >> Training accuracy: 0.665625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.647226 Loss1: 1.127593 Loss2: 1.519634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.677247 Loss1: 1.118135 Loss2: 1.559111 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.516505 Loss1: 0.977833 Loss2: 1.538672
-(DefaultActor pid=3764) >> Training accuracy: 0.736458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.540265 Loss1: 2.373479 Loss2: 2.166787
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.438168 Loss1: 1.824462 Loss2: 1.613706
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.162815 Loss1: 1.584036 Loss2: 1.578780
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.964385 Loss1: 1.382735 Loss2: 1.581649
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.925895 Loss1: 1.343053 Loss2: 1.582842
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.740650 Loss1: 2.672411 Loss2: 2.068239
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.787325 Loss1: 1.195838 Loss2: 1.591487
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.731163 Loss1: 1.126707 Loss2: 1.604456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.735211 Loss1: 1.140343 Loss2: 1.594868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.716280 Loss1: 1.096511 Loss2: 1.619769 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.620235 Loss1: 1.000122 Loss2: 1.620113 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.601562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.856485 Loss1: 1.301796 Loss2: 1.554690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.788331 Loss1: 1.213392 Loss2: 1.574938 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.703125
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.778893 Loss1: 1.213691 Loss2: 1.565202
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.631003 Loss1: 2.463728 Loss2: 2.167275
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.515008 Loss1: 1.941440 Loss2: 1.573569
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.151888 Loss1: 1.600389 Loss2: 1.551500
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.894008 Loss1: 1.352825 Loss2: 1.541183
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.967781 Loss1: 1.391986 Loss2: 1.575796
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.773856 Loss1: 2.669182 Loss2: 2.104674
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.715967 Loss1: 2.167453 Loss2: 1.548514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.485484 Loss1: 1.928369 Loss2: 1.557115 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.141898 Loss1: 1.604676 Loss2: 1.537221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.006491 Loss1: 1.474362 Loss2: 1.532129 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.681250
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.517457 Loss1: 0.950497 Loss2: 1.566960
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.916121 Loss1: 1.381165 Loss2: 1.534956
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.909041 Loss1: 1.365512 Loss2: 1.543528
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.743230 Loss1: 1.179153 Loss2: 1.564077
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.730592 Loss1: 1.177263 Loss2: 1.553328
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.600335 Loss1: 1.040285 Loss2: 1.560050
-(DefaultActor pid=3764) >> Training accuracy: 0.714583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.844787 Loss1: 2.747975 Loss2: 2.096812
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.722859 Loss1: 2.196089 Loss2: 1.526770
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.362793 Loss1: 1.857402 Loss2: 1.505390
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.129472 Loss1: 1.611325 Loss2: 1.518147
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.110578 Loss1: 1.581482 Loss2: 1.529096
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.544687 Loss1: 2.547072 Loss2: 1.997614
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.006552 Loss1: 1.460803 Loss2: 1.545749
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.977236 Loss1: 1.434165 Loss2: 1.543070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.165490 Loss1: 1.728837 Loss2: 1.436653
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.813685 Loss1: 1.265958 Loss2: 1.547727
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.937317 Loss1: 1.506444 Loss2: 1.430873
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.791388 Loss1: 1.249446 Loss2: 1.541942
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.712497 Loss1: 1.150145 Loss2: 1.562351
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.880181 Loss1: 1.443732 Loss2: 1.436449
-(DefaultActor pid=3765) >> Training accuracy: 0.705357
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.812754 Loss1: 1.348984 Loss2: 1.463770
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.581795 Loss1: 1.136107 Loss2: 1.445688
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.640321 Loss1: 1.190498 Loss2: 1.449823
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.580767 Loss1: 1.122680 Loss2: 1.458087
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.552700 Loss1: 1.074258 Loss2: 1.478442
-(DefaultActor pid=3764) >> Training accuracy: 0.693750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.683076 Loss1: 2.706327 Loss2: 1.976749
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.670808 Loss1: 2.169538 Loss2: 1.501270
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.447708 Loss1: 1.954462 Loss2: 1.493246
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.239885 Loss1: 1.728858 Loss2: 1.511027
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.080100 Loss1: 1.562820 Loss2: 1.517280
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.601798 Loss1: 2.536348 Loss2: 2.065449
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.538862 Loss1: 2.034984 Loss2: 1.503878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.012930 Loss1: 1.489092 Loss2: 1.523837
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.321914 Loss1: 1.834141 Loss2: 1.487772
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.853184 Loss1: 1.299162 Loss2: 1.554022
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.062014 Loss1: 1.564542 Loss2: 1.497472
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.780656 Loss1: 1.235791 Loss2: 1.544865
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.993831 Loss1: 1.501993 Loss2: 1.491838
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.912213 Loss1: 1.405329 Loss2: 1.506884
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.774188 Loss1: 1.225329 Loss2: 1.548859
-(DefaultActor pid=3765) >> Training accuracy: 0.710938
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.759149 Loss1: 1.238171 Loss2: 1.520978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.587662 Loss1: 1.056056 Loss2: 1.531606 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.643750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.572142 Loss1: 2.058277 Loss2: 1.513865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.054879 Loss1: 1.573109 Loss2: 1.481770 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.923857 Loss1: 1.428376 Loss2: 1.495481
-DEBUG flwr 2023-10-09 05:04:07,558 | server.py:236 | fit_round 27 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.532323 Loss1: 2.494618 Loss2: 2.037705
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.485085 Loss1: 1.952865 Loss2: 1.532221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.191747 Loss1: 1.677732 Loss2: 1.514015 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.962887 Loss1: 1.458698 Loss2: 1.504189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.657990 Loss1: 1.138439 Loss2: 1.519551 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.741071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.732485 Loss1: 1.208324 Loss2: 1.524161 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.594366 Loss1: 1.053738 Loss2: 1.540628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.563723 Loss1: 1.014704 Loss2: 1.549019 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.697266
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.253333 Loss1: 1.703472 Loss2: 1.549862 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 3.005219 Loss1: 1.435949 Loss2: 1.569269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 3.048890 Loss1: 1.459328 Loss2: 1.589562
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.398605 Loss1: 2.392662 Loss2: 2.005943
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.423427 Loss1: 1.940080 Loss2: 1.483348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.278956 Loss1: 1.796270 Loss2: 1.482686 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.603795
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.036236 Loss1: 1.552729 Loss2: 1.483506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.872589 Loss1: 1.382703 Loss2: 1.489886 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.378789 Loss1: 2.364115 Loss2: 2.014674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.446838 Loss1: 1.943316 Loss2: 1.503522 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.132827 Loss1: 1.657203 Loss2: 1.475624 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.727941
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.930736 Loss1: 1.465862 Loss2: 1.464874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.723597 Loss1: 1.235122 Loss2: 1.488476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.580628 Loss1: 1.094980 Loss2: 1.485648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.480303 Loss1: 0.972050 Loss2: 1.508253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.576101 Loss1: 1.065386 Loss2: 1.510715 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.662500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.153606 Loss1: 1.636580 Loss2: 1.517026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.922794 Loss1: 1.395245 Loss2: 1.527549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.709935 Loss1: 1.186891 Loss2: 1.523043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.719120 Loss1: 1.170088 Loss2: 1.549032 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.682617
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 05:04:07,558][flwr][DEBUG] - fit_round 27 received 50 results and 0 failures
-INFO flwr 2023-10-09 05:04:49,181 | server.py:125 | fit progress: (27, 2.8897676616431043, {'accuracy': 0.3206}, 61996.959205162)
->> Test accuracy: 0.320600
-[2023-10-09 05:04:49,181][flwr][INFO] - fit progress: (27, 2.8897676616431043, {'accuracy': 0.3206}, 61996.959205162)
-DEBUG flwr 2023-10-09 05:04:49,181 | server.py:173 | evaluate_round 27: strategy sampled 50 clients (out of 50)
-[2023-10-09 05:04:49,181][flwr][DEBUG] - evaluate_round 27: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 05:13:58,269 | server.py:187 | evaluate_round 27 received 50 results and 0 failures
-[2023-10-09 05:13:58,269][flwr][DEBUG] - evaluate_round 27 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 05:13:58,270 | server.py:222 | fit_round 28: strategy sampled 50 clients (out of 50)
-[2023-10-09 05:13:58,270][flwr][DEBUG] - fit_round 28: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.365372 Loss1: 2.301188 Loss2: 2.064185
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.316292 Loss1: 1.792139 Loss2: 1.524153
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.106327 Loss1: 1.622868 Loss2: 1.483459
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.985845 Loss1: 1.479769 Loss2: 1.506076
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.507830 Loss1: 2.451972 Loss2: 2.055858
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.520976 Loss1: 2.014362 Loss2: 1.506614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.211341 Loss1: 1.719061 Loss2: 1.492280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.056736 Loss1: 1.557756 Loss2: 1.498980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.919814 Loss1: 1.408379 Loss2: 1.511435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.878898 Loss1: 1.378716 Loss2: 1.500182 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.727083
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.435000 Loss1: 0.900683 Loss2: 1.534318
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.742664 Loss1: 1.229367 Loss2: 1.513297
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.648614 Loss1: 1.120947 Loss2: 1.527667
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.583202 Loss1: 1.066516 Loss2: 1.516686
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.565707 Loss1: 1.026016 Loss2: 1.539691
-(DefaultActor pid=3764) >> Training accuracy: 0.730208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.622533 Loss1: 2.571311 Loss2: 2.051222
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.552055 Loss1: 2.055052 Loss2: 1.497003
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.234959 Loss1: 1.770820 Loss2: 1.464139
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.991399 Loss1: 1.529012 Loss2: 1.462387
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.673960 Loss1: 2.588760 Loss2: 2.085200
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.702693 Loss1: 2.124326 Loss2: 1.578367 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.436245 Loss1: 1.864993 Loss2: 1.571253 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.269657 Loss1: 1.705877 Loss2: 1.563780 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.184757 Loss1: 1.596726 Loss2: 1.588031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 3.056522 Loss1: 1.464921 Loss2: 1.591602 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.679167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.919105 Loss1: 1.318959 Loss2: 1.600146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.817801 Loss1: 1.204920 Loss2: 1.612882 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.654297
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.562553 Loss1: 2.581524 Loss2: 1.981029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.230004 Loss1: 1.768081 Loss2: 1.461923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.965280 Loss1: 1.497367 Loss2: 1.467913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.887307 Loss1: 1.413094 Loss2: 1.474213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.895838 Loss1: 1.402903 Loss2: 1.492935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.758846 Loss1: 1.261339 Loss2: 1.497507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.672361 Loss1: 1.179067 Loss2: 1.493294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.571605 Loss1: 1.075243 Loss2: 1.496362 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.706250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.678120 Loss1: 1.167627 Loss2: 1.510493 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.561964 Loss1: 1.043328 Loss2: 1.518636 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.714583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.569738 Loss1: 2.129122 Loss2: 1.440616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.991370 Loss1: 1.548310 Loss2: 1.443060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.840284 Loss1: 1.414675 Loss2: 1.425608 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.863519 Loss1: 1.418076 Loss2: 1.445443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.740652 Loss1: 1.290633 Loss2: 1.450020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.035758 Loss1: 1.454135 Loss2: 1.581623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.624860 Loss1: 1.167055 Loss2: 1.457805 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.528225 Loss1: 1.074031 Loss2: 1.454194 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.740625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.738947 Loss1: 1.108622 Loss2: 1.630325 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.744792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.609554 Loss1: 2.624721 Loss2: 1.984833
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.547672 Loss1: 2.080868 Loss2: 1.466804
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.245447 Loss1: 1.790679 Loss2: 1.454768
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.048893 Loss1: 1.588722 Loss2: 1.460171
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.421519 Loss1: 2.411058 Loss2: 2.010462
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.313360 Loss1: 1.822456 Loss2: 1.490903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.040914 Loss1: 1.568420 Loss2: 1.472494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.831730 Loss1: 1.367012 Loss2: 1.464718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.745260 Loss1: 1.272446 Loss2: 1.472814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.634908 Loss1: 1.164439 Loss2: 1.470470 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.688542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.591462 Loss1: 1.109729 Loss2: 1.481733
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.565702 Loss1: 1.072801 Loss2: 1.492900
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.479111 Loss1: 0.996087 Loss2: 1.483024
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.442547 Loss1: 0.949396 Loss2: 1.493151
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.494684 Loss1: 0.988824 Loss2: 1.505860
-(DefaultActor pid=3764) >> Training accuracy: 0.663542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.438875 Loss1: 2.388548 Loss2: 2.050326
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.366840 Loss1: 1.838190 Loss2: 1.528649
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.227035 Loss1: 1.697102 Loss2: 1.529933
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.640431 Loss1: 2.557899 Loss2: 2.082531
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.931311 Loss1: 1.401032 Loss2: 1.530279
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.897209 Loss1: 1.372121 Loss2: 1.525088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.897114 Loss1: 1.344047 Loss2: 1.553067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.730179 Loss1: 1.192057 Loss2: 1.538122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.645919 Loss1: 1.087136 Loss2: 1.558783 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.615941 Loss1: 1.071938 Loss2: 1.544003 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.822257 Loss1: 1.282946 Loss2: 1.539311 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.650735
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.566419 Loss1: 1.019838 Loss2: 1.546581 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.662500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.454402 Loss1: 2.435796 Loss2: 2.018607
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.377093 Loss1: 1.851219 Loss2: 1.525874
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.064191 Loss1: 1.559104 Loss2: 1.505087
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.972554 Loss1: 1.446686 Loss2: 1.525867
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.297640 Loss1: 2.276067 Loss2: 2.021572
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.209336 Loss1: 1.739808 Loss2: 1.469529
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.788666 Loss1: 1.277204 Loss2: 1.511462
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.024875 Loss1: 1.567569 Loss2: 1.457306
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.788807 Loss1: 1.258414 Loss2: 1.530393
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.835179 Loss1: 1.374179 Loss2: 1.461001
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.657914 Loss1: 1.129473 Loss2: 1.528441
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.816415 Loss1: 1.342959 Loss2: 1.473456
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.560312 Loss1: 1.051249 Loss2: 1.509062
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.436921 Loss1: 0.896613 Loss2: 1.540308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.398151 Loss1: 0.864343 Loss2: 1.533808 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.766602
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.474928 Loss1: 0.968469 Loss2: 1.506459 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.652083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.566149 Loss1: 2.483672 Loss2: 2.082477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.283814 Loss1: 1.759928 Loss2: 1.523886 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.097469 Loss1: 1.556368 Loss2: 1.541101
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.713631 Loss1: 2.684408 Loss2: 2.029223
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.616481 Loss1: 2.104275 Loss2: 1.512207 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.370517 Loss1: 1.865707 Loss2: 1.504810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.182848 Loss1: 1.668297 Loss2: 1.514551 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.005225 Loss1: 1.484605 Loss2: 1.520620 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.888996 Loss1: 1.368921 Loss2: 1.520075 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.676042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.872123 Loss1: 1.340725 Loss2: 1.531398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.687316 Loss1: 1.130772 Loss2: 1.556544 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.701172
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.391982 Loss1: 2.424568 Loss2: 1.967414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.062104 Loss1: 1.627246 Loss2: 1.434858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.799234 Loss1: 1.346300 Loss2: 1.452935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.723291 Loss1: 2.186660 Loss2: 1.536631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.396305 Loss1: 1.875232 Loss2: 1.521073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.182203 Loss1: 1.662425 Loss2: 1.519777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.997508 Loss1: 1.472324 Loss2: 1.525183 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.877758 Loss1: 1.344774 Loss2: 1.532983 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.733333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.839116 Loss1: 1.266184 Loss2: 1.572932 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.728872 Loss1: 1.162105 Loss2: 1.566767 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.698661
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.576861 Loss1: 2.486254 Loss2: 2.090607
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.615868 Loss1: 2.076239 Loss2: 1.539629
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.262671 Loss1: 1.739944 Loss2: 1.522727
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.086008 Loss1: 1.565754 Loss2: 1.520254
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.385970 Loss1: 2.396061 Loss2: 1.989909
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.222797 Loss1: 1.769913 Loss2: 1.452884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.949722 Loss1: 1.396849 Loss2: 1.552873
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.085523 Loss1: 1.670809 Loss2: 1.414714
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.929566 Loss1: 1.485651 Loss2: 1.443915
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.821845 Loss1: 1.259168 Loss2: 1.562677
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.748277 Loss1: 1.324317 Loss2: 1.423960
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.725353 Loss1: 1.167652 Loss2: 1.557701
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.638993 Loss1: 1.088575 Loss2: 1.550418 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.659455 Loss1: 1.093905 Loss2: 1.565549 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.697917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.261763 Loss1: 0.819787 Loss2: 1.441976 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.762019
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.279238 Loss1: 2.331619 Loss2: 1.947619
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.254299 Loss1: 1.856817 Loss2: 1.397482
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.932914 Loss1: 1.556414 Loss2: 1.376500
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.730014 Loss1: 1.354768 Loss2: 1.375247
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.479751 Loss1: 2.485187 Loss2: 1.994565
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.490179 Loss1: 1.993636 Loss2: 1.496543 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.170582 Loss1: 1.687700 Loss2: 1.482882 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.021783 Loss1: 1.530772 Loss2: 1.491011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.820298 Loss1: 1.321765 Loss2: 1.498533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.811939 Loss1: 1.296358 Loss2: 1.515580 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.690625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.724017 Loss1: 1.197767 Loss2: 1.526250 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.487549 Loss1: 0.968142 Loss2: 1.519406 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.720703
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.452326 Loss1: 1.940682 Loss2: 1.511644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.006540 Loss1: 1.510480 Loss2: 1.496060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.911475 Loss1: 1.419383 Loss2: 1.492092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.749712 Loss1: 1.235172 Loss2: 1.514540 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.697503 Loss1: 1.181657 Loss2: 1.515846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.636659 Loss1: 1.118619 Loss2: 1.518040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.550612 Loss1: 1.035206 Loss2: 1.515406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.453743 Loss1: 0.939168 Loss2: 1.514574 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.771484
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.738369 Loss1: 1.120782 Loss2: 1.617587 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.577083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.717927 Loss1: 2.635449 Loss2: 2.082477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.300664 Loss1: 1.783809 Loss2: 1.516854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.176140 Loss1: 1.629882 Loss2: 1.546258
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.626212 Loss1: 2.626277 Loss2: 1.999935
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.524716 Loss1: 2.053384 Loss2: 1.471332
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.035733 Loss1: 1.487374 Loss2: 1.548359
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.354971 Loss1: 1.897159 Loss2: 1.457812
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.903380 Loss1: 1.349200 Loss2: 1.554180
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.194929 Loss1: 1.700071 Loss2: 1.494858
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.883527 Loss1: 1.324161 Loss2: 1.559366
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.062545 Loss1: 1.574237 Loss2: 1.488307
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.763848 Loss1: 1.191184 Loss2: 1.572665
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.699975 Loss1: 1.114422 Loss2: 1.585553 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.585044 Loss1: 1.016226 Loss2: 1.568818 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.729492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.788850 Loss1: 1.274997 Loss2: 1.513853 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.600000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.564151 Loss1: 2.455422 Loss2: 2.108729 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.086076 Loss1: 1.570281 Loss2: 1.515795 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.908997 Loss1: 1.396564 Loss2: 1.512433
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.787080 Loss1: 2.683522 Loss2: 2.103558
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.910073 Loss1: 1.390570 Loss2: 1.519503
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.705989 Loss1: 2.147616 Loss2: 1.558373
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.723050 Loss1: 1.194412 Loss2: 1.528639
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.387541 Loss1: 1.846339 Loss2: 1.541203
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.202036 Loss1: 1.647815 Loss2: 1.554221
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.559135 Loss1: 1.035777 Loss2: 1.523357
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.095631 Loss1: 1.536951 Loss2: 1.558680
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.559870 Loss1: 1.039080 Loss2: 1.520791
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.947391 Loss1: 1.399335 Loss2: 1.548056
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.541479 Loss1: 1.007461 Loss2: 1.534018
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.460407 Loss1: 0.931781 Loss2: 1.528626 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.661458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.839566 Loss1: 1.260282 Loss2: 1.579284 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.642857
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.590837 Loss1: 2.479707 Loss2: 2.111130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.318369 Loss1: 1.819344 Loss2: 1.499024 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.868502 Loss1: 1.378984 Loss2: 1.489517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.750572 Loss1: 1.253075 Loss2: 1.497497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.640257 Loss1: 1.137896 Loss2: 1.502360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.587655 Loss1: 1.086546 Loss2: 1.501109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.565922 Loss1: 1.050620 Loss2: 1.515302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.554788 Loss1: 1.032844 Loss2: 1.521944 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.703125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.853866 Loss1: 1.285077 Loss2: 1.568789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.587170 Loss1: 1.026975 Loss2: 1.560194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.481270 Loss1: 0.923355 Loss2: 1.557915
-(DefaultActor pid=3764) >> Training accuracy: 0.732292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.518194 Loss1: 2.524928 Loss2: 1.993266
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.525307 Loss1: 2.015941 Loss2: 1.509366
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.222757 Loss1: 1.730054 Loss2: 1.492703
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.008571 Loss1: 1.497137 Loss2: 1.511434
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.891473 Loss1: 1.388081 Loss2: 1.503393
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.556353 Loss1: 2.534512 Loss2: 2.021841
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.495881 Loss1: 2.017696 Loss2: 1.478185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.770696 Loss1: 1.252094 Loss2: 1.518602
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.256313 Loss1: 1.792801 Loss2: 1.463512
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.677517 Loss1: 1.146314 Loss2: 1.531203
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.081192 Loss1: 1.610340 Loss2: 1.470852
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.583622 Loss1: 1.046877 Loss2: 1.536745
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.931901 Loss1: 1.454744 Loss2: 1.477157
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.641237 Loss1: 1.102053 Loss2: 1.539185
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.783669 Loss1: 1.302709 Loss2: 1.480959
-(DefaultActor pid=3765) >> Training accuracy: 0.674805
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.817446 Loss1: 1.329869 Loss2: 1.487577
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.679318 Loss1: 1.167519 Loss2: 1.511800
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.687865 Loss1: 1.174153 Loss2: 1.513712
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.536632 Loss1: 1.022428 Loss2: 1.514204
-(DefaultActor pid=3764) >> Training accuracy: 0.680208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.579403 Loss1: 2.569015 Loss2: 2.010388
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.465915 Loss1: 1.987426 Loss2: 1.478489
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.155315 Loss1: 1.692198 Loss2: 1.463117
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.051572 Loss1: 1.590201 Loss2: 1.461371
-(DefaultActor pid=3765) Epoch: 4 Loss: 3.001347 Loss1: 1.520722 Loss2: 1.480625
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.907962 Loss1: 1.401161 Loss2: 1.506801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.850717 Loss1: 1.360966 Loss2: 1.489751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.677614 Loss1: 1.183751 Loss2: 1.493863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.710708 Loss1: 1.204741 Loss2: 1.505966 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.558325 Loss1: 1.048584 Loss2: 1.509741 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.706250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.603252 Loss1: 1.036841 Loss2: 1.566411 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.560296 Loss1: 0.991162 Loss2: 1.569134 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.758333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.425616 Loss1: 1.995101 Loss2: 1.430514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.849585 Loss1: 1.416022 Loss2: 1.433563 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.727230 Loss1: 1.293096 Loss2: 1.434134
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.760350 Loss1: 2.655733 Loss2: 2.104616
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.654146 Loss1: 1.203710 Loss2: 1.450437
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.662425 Loss1: 2.107860 Loss2: 1.554565
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.652056 Loss1: 1.203440 Loss2: 1.448617
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.315159 Loss1: 1.780860 Loss2: 1.534299
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.573924 Loss1: 1.119147 Loss2: 1.454777
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.111448 Loss1: 1.587756 Loss2: 1.523692
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.458265 Loss1: 1.002342 Loss2: 1.455923
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.008421 Loss1: 1.486422 Loss2: 1.521999
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.499710 Loss1: 1.020871 Loss2: 1.478839
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.939216 Loss1: 1.399892 Loss2: 1.539325
-(DefaultActor pid=3765) >> Training accuracy: 0.712500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.892224 Loss1: 1.348803 Loss2: 1.543421
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.850796 Loss1: 1.310898 Loss2: 1.539898
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.717423 Loss1: 1.162581 Loss2: 1.554841
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.692427 Loss1: 1.130629 Loss2: 1.561798
-(DefaultActor pid=3764) >> Training accuracy: 0.623958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.195312 Loss1: 2.276155 Loss2: 1.919157
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.125697 Loss1: 1.710954 Loss2: 1.414743
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.898023 Loss1: 1.516593 Loss2: 1.381430
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.649178 Loss1: 1.261672 Loss2: 1.387506
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.714608 Loss1: 1.321537 Loss2: 1.393072
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.401142 Loss1: 2.373812 Loss2: 2.027330
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.594182 Loss1: 1.181168 Loss2: 1.413014
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.378993 Loss1: 1.886089 Loss2: 1.492904
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.187318 Loss1: 1.695614 Loss2: 1.491703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.954901 Loss1: 1.442843 Loss2: 1.512059 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.893362 Loss1: 1.392043 Loss2: 1.501319 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.782292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.771197 Loss1: 1.249924 Loss2: 1.521274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.602310 Loss1: 1.066996 Loss2: 1.535314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.506383 Loss1: 0.964052 Loss2: 1.542331 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.714844
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.499273 Loss1: 2.031954 Loss2: 1.467319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.027935 Loss1: 1.577173 Loss2: 1.450762 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.884373 Loss1: 1.416187 Loss2: 1.468187
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.659949 Loss1: 2.502799 Loss2: 2.157150
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.366954 Loss1: 1.810114 Loss2: 1.556840 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.116915 Loss1: 1.599469 Loss2: 1.517446 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.944949 Loss1: 1.427074 Loss2: 1.517875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.827691 Loss1: 1.296745 Loss2: 1.530946 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.764583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.715276 Loss1: 1.176730 Loss2: 1.538546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.703804 Loss1: 1.162182 Loss2: 1.541622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.463129 Loss1: 0.911969 Loss2: 1.551160 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.763542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.251606 Loss1: 1.789406 Loss2: 1.462199 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.837413 Loss1: 1.393888 Loss2: 1.443525 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.759228 Loss1: 1.299722 Loss2: 1.459506
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.592394 Loss1: 2.511380 Loss2: 2.081014
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.584222 Loss1: 1.130937 Loss2: 1.453285
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.544168 Loss1: 2.003420 Loss2: 1.540748
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.426201 Loss1: 0.986895 Loss2: 1.439307
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.175763 Loss1: 1.667866 Loss2: 1.507897
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.524035 Loss1: 1.073197 Loss2: 1.450838
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.987349 Loss1: 1.468792 Loss2: 1.518557
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.841325 Loss1: 1.314604 Loss2: 1.526721
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.457327 Loss1: 1.003229 Loss2: 1.454097
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.674605 Loss1: 1.158684 Loss2: 1.515921
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.390083 Loss1: 0.925528 Loss2: 1.464555
-(DefaultActor pid=3765) >> Training accuracy: 0.750977
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.665930 Loss1: 1.113263 Loss2: 1.552667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.448246 Loss1: 0.907606 Loss2: 1.540640 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.736458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.599705 Loss1: 2.028312 Loss2: 1.571393 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.063092 Loss1: 1.503560 Loss2: 1.559532 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.944771 Loss1: 1.388736 Loss2: 1.556035
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.404190 Loss1: 2.396922 Loss2: 2.007268
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.371662 Loss1: 1.900755 Loss2: 1.470907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.094635 Loss1: 1.639337 Loss2: 1.455299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.901555 Loss1: 1.449173 Loss2: 1.452382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.792367 Loss1: 1.327309 Loss2: 1.465058 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.751116
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.733482 Loss1: 1.256124 Loss2: 1.477358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.692101 Loss1: 1.193637 Loss2: 1.498464 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.465727 Loss1: 0.977275 Loss2: 1.488452 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.718750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.399203 Loss1: 1.858577 Loss2: 1.540626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.920616 Loss1: 1.425231 Loss2: 1.495385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.375022 Loss1: 2.392186 Loss2: 1.982836 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.422953 Loss1: 1.947564 Loss2: 1.475388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.139899 Loss1: 1.683124 Loss2: 1.456775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.993625 Loss1: 1.522647 Loss2: 1.470978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.849943 Loss1: 1.387535 Loss2: 1.462409 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.670833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.746935 Loss1: 1.264392 Loss2: 1.482542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.747282 Loss1: 1.248531 Loss2: 1.498752 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.478205 Loss1: 0.982979 Loss2: 1.495226 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.706250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.384118 Loss1: 1.865442 Loss2: 1.518675 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.972059 Loss1: 1.463778 Loss2: 1.508281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.596941 Loss1: 2.525695 Loss2: 2.071246 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.502290 Loss1: 1.976885 Loss2: 1.525405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.216600 Loss1: 1.707213 Loss2: 1.509387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.033001 Loss1: 1.500063 Loss2: 1.532938 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.985173 Loss1: 1.453846 Loss2: 1.531327 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.698958
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-09 05:43:08,831 | server.py:236 | fit_round 28 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.736782 Loss1: 1.186086 Loss2: 1.550696 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.576852 Loss1: 1.014568 Loss2: 1.562283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.541062 Loss1: 0.986104 Loss2: 1.554957
-(DefaultActor pid=3764) >> Training accuracy: 0.736458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.584299 Loss1: 2.523987 Loss2: 2.060311
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.588875 Loss1: 2.020927 Loss2: 1.567948
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.293983 Loss1: 1.745828 Loss2: 1.548156
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.062799 Loss1: 1.501029 Loss2: 1.561769
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.884624 Loss1: 1.339680 Loss2: 1.544944
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.656877 Loss1: 2.616404 Loss2: 2.040474
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.875027 Loss1: 1.320625 Loss2: 1.554401
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.646779 Loss1: 2.131006 Loss2: 1.515774
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.830808 Loss1: 1.262638 Loss2: 1.568170
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.331243 Loss1: 1.831745 Loss2: 1.499498
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.759588 Loss1: 1.174909 Loss2: 1.584679
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.054494 Loss1: 1.565387 Loss2: 1.489107
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.723413 Loss1: 1.135692 Loss2: 1.587721
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.980435 Loss1: 1.482623 Loss2: 1.497813
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.608485 Loss1: 1.029025 Loss2: 1.579460
-(DefaultActor pid=3765) >> Training accuracy: 0.676042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.762551 Loss1: 1.262537 Loss2: 1.500013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.662378 Loss1: 1.125932 Loss2: 1.536446 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.709375
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 05:43:08,831][flwr][DEBUG] - fit_round 28 received 50 results and 0 failures
-INFO flwr 2023-10-09 05:43:50,143 | server.py:125 | fit progress: (28, 2.9036672896089644, {'accuracy': 0.3281}, 64337.92139886)
->> Test accuracy: 0.328100
-[2023-10-09 05:43:50,143][flwr][INFO] - fit progress: (28, 2.9036672896089644, {'accuracy': 0.3281}, 64337.92139886)
-DEBUG flwr 2023-10-09 05:43:50,143 | server.py:173 | evaluate_round 28: strategy sampled 50 clients (out of 50)
-[2023-10-09 05:43:50,143][flwr][DEBUG] - evaluate_round 28: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 05:52:55,855 | server.py:187 | evaluate_round 28 received 50 results and 0 failures
-[2023-10-09 05:52:55,855][flwr][DEBUG] - evaluate_round 28 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 05:52:55,855 | server.py:222 | fit_round 29: strategy sampled 50 clients (out of 50)
-[2023-10-09 05:52:55,855][flwr][DEBUG] - fit_round 29: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.759630 Loss1: 2.690108 Loss2: 2.069523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.311894 Loss1: 1.828917 Loss2: 1.482977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.384087 Loss1: 2.353710 Loss2: 2.030377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.294308 Loss1: 1.849680 Loss2: 1.444627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.110643 Loss1: 1.670384 Loss2: 1.440259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.901891 Loss1: 1.457521 Loss2: 1.444370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.756974 Loss1: 1.310167 Loss2: 1.446807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.633116 Loss1: 1.177630 Loss2: 1.455486 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.741071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.519179 Loss1: 1.040843 Loss2: 1.478336 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.340242 Loss1: 0.852567 Loss2: 1.487675 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.726562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.583391 Loss1: 2.409678 Loss2: 2.173713
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.483710 Loss1: 1.895971 Loss2: 1.587739
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.038539 Loss1: 1.485777 Loss2: 1.552763
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.937016 Loss1: 1.380634 Loss2: 1.556382
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.353486 Loss1: 2.413577 Loss2: 1.939910
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.329034 Loss1: 1.869215 Loss2: 1.459818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.056855 Loss1: 1.620318 Loss2: 1.436537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.870255 Loss1: 1.425707 Loss2: 1.444548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.739051 Loss1: 1.282153 Loss2: 1.456898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.633647 Loss1: 1.179662 Loss2: 1.453985 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.716667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.586637 Loss1: 1.084919 Loss2: 1.501719 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.428590 Loss1: 0.942107 Loss2: 1.486483 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.744141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.445902 Loss1: 1.959825 Loss2: 1.486077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.964016 Loss1: 1.478493 Loss2: 1.485523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.822177 Loss1: 1.341217 Loss2: 1.480960
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.422986 Loss1: 2.370660 Loss2: 2.052326
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.663499 Loss1: 1.171944 Loss2: 1.491555
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.331639 Loss1: 1.832112 Loss2: 1.499527
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.605430 Loss1: 1.100460 Loss2: 1.504970
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.054354 Loss1: 1.569681 Loss2: 1.484673
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.679867 Loss1: 1.154003 Loss2: 1.525864
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.930779 Loss1: 1.436579 Loss2: 1.494199
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.589266 Loss1: 1.064234 Loss2: 1.525033
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.833424 Loss1: 1.330383 Loss2: 1.503042
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.446355 Loss1: 0.933149 Loss2: 1.513206
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.702538 Loss1: 1.191235 Loss2: 1.511303
-(DefaultActor pid=3765) >> Training accuracy: 0.698958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.590656 Loss1: 1.078525 Loss2: 1.512130
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.421488 Loss1: 0.913306 Loss2: 1.508182
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.513621 Loss1: 0.988930 Loss2: 1.524691
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.376835 Loss1: 0.858030 Loss2: 1.518805
-(DefaultActor pid=3764) >> Training accuracy: 0.753125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.652812 Loss1: 2.446524 Loss2: 2.206288
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.516755 Loss1: 1.964553 Loss2: 1.552202
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.176509 Loss1: 1.684786 Loss2: 1.491723
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.021993 Loss1: 1.502757 Loss2: 1.519236
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.866460 Loss1: 1.342518 Loss2: 1.523943
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.673867 Loss1: 1.145445 Loss2: 1.528422
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.503532 Loss1: 2.277246 Loss2: 2.226285
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.395150 Loss1: 1.751034 Loss2: 1.644117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.130709 Loss1: 1.515993 Loss2: 1.614716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.617540 Loss1: 1.054088 Loss2: 1.563452 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.756510
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.760820 Loss1: 1.146957 Loss2: 1.613863 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.659095 Loss1: 1.031641 Loss2: 1.627454 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.430988 Loss1: 0.810176 Loss2: 1.620812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.491396 Loss1: 0.864849 Loss2: 1.626547 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.782292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.785951 Loss1: 1.333918 Loss2: 1.452032 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.615888 Loss1: 1.129809 Loss2: 1.486080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.523252 Loss1: 2.479130 Loss2: 2.044122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.492509 Loss1: 2.005032 Loss2: 1.487477 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.693510
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.996411 Loss1: 1.545391 Loss2: 1.451020 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.772642 Loss1: 1.294025 Loss2: 1.478617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.683613 Loss1: 1.197730 Loss2: 1.485883
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.476473 Loss1: 2.301393 Loss2: 2.175080
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.428084 Loss1: 1.822856 Loss2: 1.605228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.169091 Loss1: 1.578897 Loss2: 1.590195 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.767708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.837314 Loss1: 1.249555 Loss2: 1.587759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.707388 Loss1: 1.108422 Loss2: 1.598966 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.561439 Loss1: 0.957363 Loss2: 1.604076 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.503203 Loss1: 0.907981 Loss2: 1.595221 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.434268 Loss1: 0.817385 Loss2: 1.616883 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.781250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.875965 Loss1: 1.357532 Loss2: 1.518432 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.714237 Loss1: 1.178161 Loss2: 1.536076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.698575 Loss1: 1.156120 Loss2: 1.542455 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.252720 Loss1: 1.781874 Loss2: 1.470846
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.664946 Loss1: 1.103731 Loss2: 1.561215
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.022716 Loss1: 1.568589 Loss2: 1.454127
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.584035 Loss1: 1.025055 Loss2: 1.558980
-(DefaultActor pid=3764) >> Training accuracy: 0.678711
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.765487 Loss1: 1.280400 Loss2: 1.485087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.523383 Loss1: 1.048508 Loss2: 1.474875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.478749 Loss1: 2.482954 Loss2: 1.995795
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.469105 Loss1: 0.994282 Loss2: 1.474823
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.480398 Loss1: 0.989124 Loss2: 1.491274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.485876 Loss1: 0.972769 Loss2: 1.513108 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.724265
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.757454 Loss1: 1.322471 Loss2: 1.434983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.485721 Loss1: 1.041907 Loss2: 1.443814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.693945 Loss1: 2.605325 Loss2: 2.088620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.639204 Loss1: 2.089316 Loss2: 1.549888 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.769792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.244549 Loss1: 1.721155 Loss2: 1.523394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.922462 Loss1: 1.394806 Loss2: 1.527655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.837696 Loss1: 1.278959 Loss2: 1.558737 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.741507 Loss1: 1.172081 Loss2: 1.569425 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.595295 Loss1: 1.026033 Loss2: 1.569261 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.624917 Loss1: 1.052774 Loss2: 1.572143 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.653125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.713302 Loss1: 1.258779 Loss2: 1.454523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.522070 Loss1: 1.100208 Loss2: 1.421862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.473897 Loss1: 1.027924 Loss2: 1.445974
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.613074 Loss1: 2.519239 Loss2: 2.093835
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.642394 Loss1: 2.078869 Loss2: 1.563525 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.725000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.294944 Loss1: 1.753146 Loss2: 1.541798 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.901231 Loss1: 1.372783 Loss2: 1.528448 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.660569 Loss1: 1.117514 Loss2: 1.543055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.629184 Loss1: 1.085028 Loss2: 1.544156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.541422 Loss1: 0.984596 Loss2: 1.556825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.491233 Loss1: 0.930566 Loss2: 1.560667 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.731250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.637599 Loss1: 1.196448 Loss2: 1.441151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.528962 Loss1: 1.076696 Loss2: 1.452266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.443164 Loss1: 2.298388 Loss2: 2.144776 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.270821 Loss1: 1.692067 Loss2: 1.578754 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.807292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.895110 Loss1: 1.358274 Loss2: 1.536836 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.727991 Loss1: 1.179797 Loss2: 1.548194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.451385 Loss1: 2.447626 Loss2: 2.003759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.366906 Loss1: 1.909192 Loss2: 1.457714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.005798 Loss1: 1.570934 Loss2: 1.434864 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.767708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.768245 Loss1: 1.322762 Loss2: 1.445483 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.607788 Loss1: 1.145264 Loss2: 1.462524 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.651098 Loss1: 2.596390 Loss2: 2.054708
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.563313 Loss1: 1.088069 Loss2: 1.475244
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.515234 Loss1: 1.031248 Loss2: 1.483986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.423718 Loss1: 0.950348 Loss2: 1.473370 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.753906
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.955685 Loss1: 1.426404 Loss2: 1.529281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.777709 Loss1: 1.244595 Loss2: 1.533114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.742631 Loss1: 1.204479 Loss2: 1.538153
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.509922 Loss1: 2.456898 Loss2: 2.053024
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.432167 Loss1: 1.943550 Loss2: 1.488616 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.704167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.158860 Loss1: 1.684337 Loss2: 1.474523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.904440 Loss1: 1.425408 Loss2: 1.479031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.583354 Loss1: 1.091969 Loss2: 1.491385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.536552 Loss1: 1.052240 Loss2: 1.484313 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.488618 Loss1: 0.984964 Loss2: 1.503654 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.474385 Loss1: 0.961483 Loss2: 1.512903 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.707292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.812549 Loss1: 1.344836 Loss2: 1.467713 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.725630 Loss1: 1.223889 Loss2: 1.501741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.629562 Loss1: 2.575563 Loss2: 2.054000 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.675767 Loss1: 2.148297 Loss2: 1.527471 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.718750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.144072 Loss1: 1.627399 Loss2: 1.516673 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.760854 Loss1: 1.249769 Loss2: 1.511085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.763867 Loss1: 1.235186 Loss2: 1.528681
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.432555 Loss1: 2.333996 Loss2: 2.098559
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.430712 Loss1: 1.883343 Loss2: 1.547369 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.991532 Loss1: 1.470819 Loss2: 1.520713 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.712500
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.618655 Loss1: 1.057565 Loss2: 1.561090
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.830595 Loss1: 1.320688 Loss2: 1.509907
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.711518 Loss1: 1.190226 Loss2: 1.521292
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.576023 Loss1: 1.055218 Loss2: 1.520805
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.643076 Loss1: 1.122606 Loss2: 1.520470
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.603619 Loss1: 1.051848 Loss2: 1.551771
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.586349 Loss1: 2.454384 Loss2: 2.131965
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.502160 Loss1: 0.961577 Loss2: 1.540583
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.418733 Loss1: 1.862399 Loss2: 1.556334
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.469415 Loss1: 0.938952 Loss2: 1.530464
-(DefaultActor pid=3765) >> Training accuracy: 0.693750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.882410 Loss1: 1.354513 Loss2: 1.527897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.631884 Loss1: 1.090355 Loss2: 1.541529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.531990 Loss1: 0.997350 Loss2: 1.534640
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.528616 Loss1: 2.467546 Loss2: 2.061070
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.512650 Loss1: 0.963127 Loss2: 1.549523
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.477278 Loss1: 1.968794 Loss2: 1.508483
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.470481 Loss1: 0.909066 Loss2: 1.561416
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.120721 Loss1: 1.629361 Loss2: 1.491360
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.507934 Loss1: 0.938956 Loss2: 1.568978
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.016640 Loss1: 1.523740 Loss2: 1.492899
-(DefaultActor pid=3764) >> Training accuracy: 0.773958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.823416 Loss1: 1.325498 Loss2: 1.497917
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.656083 Loss1: 1.152423 Loss2: 1.503660
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.584249 Loss1: 1.076878 Loss2: 1.507371
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.617382 Loss1: 1.095107 Loss2: 1.522274
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.522637 Loss1: 2.545828 Loss2: 1.976809
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.510342 Loss1: 0.984621 Loss2: 1.525721
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.381116 Loss1: 1.913285 Loss2: 1.467831
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.558620 Loss1: 1.043954 Loss2: 1.514666
-(DefaultActor pid=3765) >> Training accuracy: 0.701042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.894865 Loss1: 1.434295 Loss2: 1.460570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.651657 Loss1: 1.182985 Loss2: 1.468672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.658534 Loss1: 1.166704 Loss2: 1.491831
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.320977 Loss1: 2.401852 Loss2: 1.919125
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.230923 Loss1: 1.797890 Loss2: 1.433034 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.928055 Loss1: 1.513101 Loss2: 1.414955 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.753125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.498182 Loss1: 1.002047 Loss2: 1.496136
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.772037 Loss1: 1.360679 Loss2: 1.411358
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.732889 Loss1: 1.304053 Loss2: 1.428836
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.684026 Loss1: 1.251782 Loss2: 1.432244
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.525984 Loss1: 1.098368 Loss2: 1.427616
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.460491 Loss1: 1.019317 Loss2: 1.441174
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.467200 Loss1: 2.487523 Loss2: 1.979677
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.492453 Loss1: 2.052170 Loss2: 1.440283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.353667 Loss1: 0.899074 Loss2: 1.454593
-(DefaultActor pid=3765) >> Training accuracy: 0.682617
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.154154 Loss1: 1.728623 Loss2: 1.425531
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.011466 Loss1: 1.574982 Loss2: 1.436484
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.788062 Loss1: 1.357249 Loss2: 1.430814
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.732061 Loss1: 1.288720 Loss2: 1.443341
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.675015 Loss1: 1.218466 Loss2: 1.456550
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.665684 Loss1: 1.214485 Loss2: 1.451199
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.496357 Loss1: 2.451484 Loss2: 2.044873
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.566830 Loss1: 1.093931 Loss2: 1.472899
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.432099 Loss1: 1.911113 Loss2: 1.520986
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.551837 Loss1: 1.067881 Loss2: 1.483956
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.202868 Loss1: 1.704596 Loss2: 1.498272
-(DefaultActor pid=3764) >> Training accuracy: 0.713542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.022777 Loss1: 1.510558 Loss2: 1.512219
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.771050 Loss1: 1.257042 Loss2: 1.514008
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.762052 Loss1: 1.250284 Loss2: 1.511768
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.699087 Loss1: 1.183459 Loss2: 1.515628
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.543028 Loss1: 2.392749 Loss2: 2.150280
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.572803 Loss1: 1.050598 Loss2: 1.522205
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.475444 Loss1: 0.949601 Loss2: 1.525844 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.482564 Loss1: 0.949902 Loss2: 1.532661 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.707031
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.851091 Loss1: 1.267397 Loss2: 1.583694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.723240 Loss1: 1.135363 Loss2: 1.587878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.549496 Loss1: 0.970097 Loss2: 1.579399
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.542804 Loss1: 2.413568 Loss2: 2.129236
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.350955 Loss1: 1.773295 Loss2: 1.577660 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.739583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.472625 Loss1: 0.878924 Loss2: 1.593701
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.987700 Loss1: 1.463974 Loss2: 1.523726
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.722690 Loss1: 1.193086 Loss2: 1.529604
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.738641 Loss1: 1.217154 Loss2: 1.521487
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.736883 Loss1: 1.189920 Loss2: 1.546963
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.573783 Loss1: 1.017397 Loss2: 1.556387
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.545816 Loss1: 2.436977 Loss2: 2.108839
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.416176 Loss1: 0.872372 Loss2: 1.543804
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.352074 Loss1: 0.806762 Loss2: 1.545312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.298048 Loss1: 0.750233 Loss2: 1.547816 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.779167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.904609 Loss1: 1.367734 Loss2: 1.536875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.696321 Loss1: 1.131386 Loss2: 1.564935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.730233 Loss1: 1.161570 Loss2: 1.568663
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.557751 Loss1: 2.489242 Loss2: 2.068508
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.574509 Loss1: 2.021371 Loss2: 1.553138 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.706250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.339691 Loss1: 1.803838 Loss2: 1.535853 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.922349 Loss1: 1.409764 Loss2: 1.512586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.689683 Loss1: 1.154403 Loss2: 1.535280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.729800 Loss1: 1.186964 Loss2: 1.542836 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.647951 Loss1: 1.101984 Loss2: 1.545967 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.595890 Loss1: 1.047821 Loss2: 1.548069 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.709375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.766860 Loss1: 1.301288 Loss2: 1.465572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.535305 Loss1: 1.049011 Loss2: 1.486294 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.447825 Loss1: 0.959304 Loss2: 1.488521
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.433686 Loss1: 2.377015 Loss2: 2.056671
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.363252 Loss1: 1.889627 Loss2: 1.473624 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.806250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.052502 Loss1: 1.600946 Loss2: 1.451556 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.779614 Loss1: 1.312004 Loss2: 1.467610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.603345 Loss1: 1.124834 Loss2: 1.478511 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.590704 Loss1: 1.101682 Loss2: 1.489022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.522381 Loss1: 2.012979 Loss2: 1.509403
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.516826 Loss1: 1.028070 Loss2: 1.488755
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.239776 Loss1: 1.756310 Loss2: 1.483465
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.398394 Loss1: 0.904556 Loss2: 1.493838
-(DefaultActor pid=3765) >> Training accuracy: 0.747917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.875381 Loss1: 1.380426 Loss2: 1.494955 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.755898 Loss1: 1.242585 Loss2: 1.513313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.533662 Loss1: 2.416834 Loss2: 2.116827
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.665780 Loss1: 1.137194 Loss2: 1.528587
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.433080 Loss1: 1.917451 Loss2: 1.515629
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.578953 Loss1: 1.062684 Loss2: 1.516269
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.109794 Loss1: 1.604429 Loss2: 1.505365
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.569473 Loss1: 1.051109 Loss2: 1.518364
-(DefaultActor pid=3764) >> Training accuracy: 0.717773
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.826657 Loss1: 1.306498 Loss2: 1.520159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.756139 Loss1: 1.216597 Loss2: 1.539542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.699801 Loss1: 1.162235 Loss2: 1.537566
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.655441 Loss1: 2.523661 Loss2: 2.131779
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.573178 Loss1: 1.995961 Loss2: 1.577217 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.696875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.584037 Loss1: 1.035240 Loss2: 1.548796
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.310207 Loss1: 1.726029 Loss2: 1.584179
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.119070 Loss1: 1.543022 Loss2: 1.576048
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.064608 Loss1: 1.486300 Loss2: 1.578308
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.901521 Loss1: 1.317191 Loss2: 1.584331
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.846205 Loss1: 1.243732 Loss2: 1.602473
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.496851 Loss1: 2.447075 Loss2: 2.049776
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.723391 Loss1: 1.124699 Loss2: 1.598692
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.725995 Loss1: 1.129032 Loss2: 1.596963 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.838139 Loss1: 1.209774 Loss2: 1.628365 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.654167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.902374 Loss1: 1.404719 Loss2: 1.497655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.682184 Loss1: 1.179597 Loss2: 1.502587 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.549324 Loss1: 1.037133 Loss2: 1.512191
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.565549 Loss1: 2.488839 Loss2: 2.076711
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.593025 Loss1: 2.085712 Loss2: 1.507313 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.729167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.198292 Loss1: 1.696582 Loss2: 1.501710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.860125 Loss1: 1.365659 Loss2: 1.494466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.700840 Loss1: 1.180884 Loss2: 1.519957 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.668928 Loss1: 1.155346 Loss2: 1.513583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.574202 Loss1: 1.043950 Loss2: 1.530252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.498781 Loss1: 0.966518 Loss2: 1.532262 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.748958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.676389 Loss1: 1.151295 Loss2: 1.525094 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.647722 Loss1: 1.104516 Loss2: 1.543206 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.460134 Loss1: 0.919028 Loss2: 1.541106
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.600466 Loss1: 2.470683 Loss2: 2.129783
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.607139 Loss1: 2.030719 Loss2: 1.576420 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.764583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.216293 Loss1: 1.656855 Loss2: 1.559439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.844393 Loss1: 1.297269 Loss2: 1.547124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.639024 Loss1: 1.071387 Loss2: 1.567637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.615724 Loss1: 1.050907 Loss2: 1.564817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.646443 Loss1: 1.057318 Loss2: 1.589125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.556990 Loss1: 0.954426 Loss2: 1.602564 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.636458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.935788 Loss1: 1.357344 Loss2: 1.578444 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.692882 Loss1: 1.095641 Loss2: 1.597241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.673501 Loss1: 2.453003 Loss2: 2.220498
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.627540 Loss1: 1.023652 Loss2: 1.603888
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.409654 Loss1: 1.809122 Loss2: 1.600532
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.604175 Loss1: 1.000280 Loss2: 1.603896
-(DefaultActor pid=3765) >> Training accuracy: 0.716518
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.816107 Loss1: 1.268359 Loss2: 1.547748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.677954 Loss1: 1.120616 Loss2: 1.557339 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.543769 Loss1: 0.949512 Loss2: 1.594258 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.397034 Loss1: 0.799493 Loss2: 1.597541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.457351 Loss1: 0.876713 Loss2: 1.580638 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.675481
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.031842 Loss1: 1.529230 Loss2: 1.502612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.891146 Loss1: 1.369275 Loss2: 1.521872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.782447 Loss1: 1.238158 Loss2: 1.544289
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.643344 Loss1: 2.483301 Loss2: 2.160043
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.666939 Loss1: 1.116991 Loss2: 1.549948
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.597986 Loss1: 2.000884 Loss2: 1.597102
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.701791 Loss1: 1.157243 Loss2: 1.544548
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.217528 Loss1: 1.657236 Loss2: 1.560292
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.627049 Loss1: 1.084838 Loss2: 1.542211
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.093503 Loss1: 1.522730 Loss2: 1.570773
-(DefaultActor pid=3765) >> Training accuracy: 0.666016
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.900754 Loss1: 1.323711 Loss2: 1.577043
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.914316 Loss1: 1.332007 Loss2: 1.582309
-DEBUG flwr 2023-10-09 06:21:11,143 | server.py:236 | fit_round 29 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.706029 Loss1: 1.102386 Loss2: 1.603642
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.613379 Loss1: 1.014911 Loss2: 1.598468
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.626757 Loss1: 1.031847 Loss2: 1.594910
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.628185 Loss1: 2.631646 Loss2: 1.996539
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.650684 Loss1: 1.023573 Loss2: 1.627112
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.589175 Loss1: 2.098646 Loss2: 1.490530
-(DefaultActor pid=3764) >> Training accuracy: 0.687500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.304669 Loss1: 1.832549 Loss2: 1.472120
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.089237 Loss1: 1.615837 Loss2: 1.473401
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.950359 Loss1: 1.463457 Loss2: 1.486902
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.896219 Loss1: 1.393061 Loss2: 1.503159
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.582149 Loss1: 2.487113 Loss2: 2.095036
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.831525 Loss1: 1.315308 Loss2: 1.516217
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.731339 Loss1: 1.231175 Loss2: 1.500164 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.728010 Loss1: 1.210621 Loss2: 1.517390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.722719 Loss1: 1.193717 Loss2: 1.529003 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.646484
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.693684 Loss1: 1.165425 Loss2: 1.528259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.546591 Loss1: 0.996950 Loss2: 1.549641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.418791 Loss1: 0.865002 Loss2: 1.553790 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.782292
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 06:21:11,143][flwr][DEBUG] - fit_round 29 received 50 results and 0 failures
-INFO flwr 2023-10-09 06:21:52,206 | server.py:125 | fit progress: (29, 2.8099579156016388, {'accuracy': 0.3431}, 66619.98491549399)
->> Test accuracy: 0.343100
-[2023-10-09 06:21:52,206][flwr][INFO] - fit progress: (29, 2.8099579156016388, {'accuracy': 0.3431}, 66619.98491549399)
-DEBUG flwr 2023-10-09 06:21:52,207 | server.py:173 | evaluate_round 29: strategy sampled 50 clients (out of 50)
-[2023-10-09 06:21:52,207][flwr][DEBUG] - evaluate_round 29: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 06:30:53,875 | server.py:187 | evaluate_round 29 received 50 results and 0 failures
-[2023-10-09 06:30:53,875][flwr][DEBUG] - evaluate_round 29 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 06:30:53,875 | server.py:222 | fit_round 30: strategy sampled 50 clients (out of 50)
-[2023-10-09 06:30:53,875][flwr][DEBUG] - fit_round 30: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.353549 Loss1: 2.276282 Loss2: 2.077266
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.436343 Loss1: 1.913829 Loss2: 1.522514
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.086424 Loss1: 1.572775 Loss2: 1.513649
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.782151 Loss1: 1.269480 Loss2: 1.512671
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.278353 Loss1: 2.324226 Loss2: 1.954128
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.757900 Loss1: 1.252558 Loss2: 1.505343
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.249419 Loss1: 1.772385 Loss2: 1.477035
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.577968 Loss1: 1.055818 Loss2: 1.522150
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.890840 Loss1: 1.452773 Loss2: 1.438067
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.436822 Loss1: 0.913250 Loss2: 1.523572
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.670553 Loss1: 1.233303 Loss2: 1.437249
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.430415 Loss1: 0.893972 Loss2: 1.536443
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.615157 Loss1: 1.181831 Loss2: 1.433326
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.477422 Loss1: 0.931202 Loss2: 1.546220
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.551840 Loss1: 1.087663 Loss2: 1.464177
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.431249 Loss1: 0.872769 Loss2: 1.558480
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.576670 Loss1: 1.113277 Loss2: 1.463393
-(DefaultActor pid=3765) >> Training accuracy: 0.792708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.391546 Loss1: 0.919459 Loss2: 1.472086
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.333502 Loss1: 0.867109 Loss2: 1.466392
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.414811 Loss1: 0.939314 Loss2: 1.475497
-(DefaultActor pid=3764) >> Training accuracy: 0.709375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.575865 Loss1: 2.474985 Loss2: 2.100879
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.579255 Loss1: 2.013487 Loss2: 1.565768
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.231861 Loss1: 1.674932 Loss2: 1.556929
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.977413 Loss1: 1.423975 Loss2: 1.553437
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.545009 Loss1: 2.495855 Loss2: 2.049154
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.561978 Loss1: 2.050080 Loss2: 1.511898 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.768667 Loss1: 1.199426 Loss2: 1.569241
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.262016 Loss1: 1.757057 Loss2: 1.504959
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.064510 Loss1: 1.554831 Loss2: 1.509680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.917238 Loss1: 1.407196 Loss2: 1.510042 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.811238 Loss1: 1.284105 Loss2: 1.527133 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.728125
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.581292 Loss1: 0.987967 Loss2: 1.593324
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.666543 Loss1: 1.129369 Loss2: 1.537174
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.637737 Loss1: 1.103639 Loss2: 1.534098
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.603425 Loss1: 1.042974 Loss2: 1.560451
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.604917 Loss1: 1.056157 Loss2: 1.548760
-(DefaultActor pid=3764) >> Training accuracy: 0.741667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.562856 Loss1: 2.406680 Loss2: 2.156176
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.471359 Loss1: 1.893596 Loss2: 1.577763
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.246560 Loss1: 1.676774 Loss2: 1.569786
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.046948 Loss1: 1.463899 Loss2: 1.583049
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.197156 Loss1: 2.158743 Loss2: 2.038414
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.238700 Loss1: 1.738882 Loss2: 1.499819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.929234 Loss1: 1.451969 Loss2: 1.477265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.757287 Loss1: 1.262657 Loss2: 1.494630 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.694909 Loss1: 1.201844 Loss2: 1.493065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.550722 Loss1: 1.048800 Loss2: 1.501922 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.703125
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.618859 Loss1: 1.012300 Loss2: 1.606559
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.431611 Loss1: 0.940487 Loss2: 1.491124
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.369801 Loss1: 0.870977 Loss2: 1.498824
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.346802 Loss1: 0.847003 Loss2: 1.499799
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.363726 Loss1: 0.856292 Loss2: 1.507434
-(DefaultActor pid=3764) >> Training accuracy: 0.761458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.419694 Loss1: 2.347664 Loss2: 2.072031
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.271574 Loss1: 1.779568 Loss2: 1.492007
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.966791 Loss1: 1.479002 Loss2: 1.487789
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.812898 Loss1: 1.327191 Loss2: 1.485707
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.355485 Loss1: 2.319688 Loss2: 2.035797
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.295212 Loss1: 1.775874 Loss2: 1.519338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.041651 Loss1: 1.543283 Loss2: 1.498368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.919297 Loss1: 1.410036 Loss2: 1.509261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.802706 Loss1: 1.293903 Loss2: 1.508803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.659380 Loss1: 1.133322 Loss2: 1.526057 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.690625
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.396387 Loss1: 0.873753 Loss2: 1.522634
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.576265 Loss1: 1.063660 Loss2: 1.512605
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.576287 Loss1: 1.046584 Loss2: 1.529703
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.422566 Loss1: 0.878806 Loss2: 1.543760
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.406973 Loss1: 0.875332 Loss2: 1.531642
-(DefaultActor pid=3764) >> Training accuracy: 0.776042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.656557 Loss1: 2.528642 Loss2: 2.127915
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.482549 Loss1: 1.932037 Loss2: 1.550512
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.177234 Loss1: 1.661156 Loss2: 1.516078
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.013918 Loss1: 1.477618 Loss2: 1.536300
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.426713 Loss1: 2.372234 Loss2: 2.054478
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.357910 Loss1: 1.830431 Loss2: 1.527479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.048630 Loss1: 1.521719 Loss2: 1.526911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.839103 Loss1: 1.312041 Loss2: 1.527062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.827893 Loss1: 1.301038 Loss2: 1.526855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.730148 Loss1: 1.178325 Loss2: 1.551823 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.730208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.586132 Loss1: 1.021011 Loss2: 1.565121 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.614016 Loss1: 1.024451 Loss2: 1.589565 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.678711
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.379975 Loss1: 1.765503 Loss2: 1.614472 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.873663 Loss1: 1.276085 Loss2: 1.597578 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.722346 Loss1: 1.116224 Loss2: 1.606122
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.476732 Loss1: 2.432877 Loss2: 2.043855
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.617446 Loss1: 0.998030 Loss2: 1.619416
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.389846 Loss1: 1.882473 Loss2: 1.507373
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.678969 Loss1: 1.068937 Loss2: 1.610032
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.157837 Loss1: 1.658419 Loss2: 1.499417
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.580985 Loss1: 0.964141 Loss2: 1.616844
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.897660 Loss1: 1.389245 Loss2: 1.508415
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.512350 Loss1: 0.887868 Loss2: 1.624482
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.753082 Loss1: 1.243316 Loss2: 1.509766
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.430090 Loss1: 0.787328 Loss2: 1.642762
-(DefaultActor pid=3765) >> Training accuracy: 0.795833
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.760516 Loss1: 1.231817 Loss2: 1.528699
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.673300 Loss1: 1.132588 Loss2: 1.540713
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.610597 Loss1: 1.054322 Loss2: 1.556275
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.452314 Loss1: 0.898499 Loss2: 1.553815
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.452824 Loss1: 0.904991 Loss2: 1.547833
-(DefaultActor pid=3764) >> Training accuracy: 0.707292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.361617 Loss1: 2.327404 Loss2: 2.034213
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.267905 Loss1: 1.767224 Loss2: 1.500681
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.017878 Loss1: 1.531078 Loss2: 1.486800
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.821968 Loss1: 1.345877 Loss2: 1.476090
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.369882 Loss1: 2.358950 Loss2: 2.010932
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.640236 Loss1: 1.172944 Loss2: 1.467292
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.197337 Loss1: 1.724096 Loss2: 1.473242
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.579032 Loss1: 1.108992 Loss2: 1.470040
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.514448 Loss1: 1.044011 Loss2: 1.470437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.464494 Loss1: 0.977433 Loss2: 1.487061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.346173 Loss1: 0.868732 Loss2: 1.477441 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.324676 Loss1: 0.847308 Loss2: 1.477368 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.685417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.294016 Loss1: 0.860225 Loss2: 1.433791 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.753606
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.303447 Loss1: 2.356814 Loss2: 1.946632 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.031398 Loss1: 1.588593 Loss2: 1.442806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.854888 Loss1: 1.391927 Loss2: 1.462961
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.589781 Loss1: 2.608483 Loss2: 1.981297
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.706959 Loss1: 1.243293 Loss2: 1.463666
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.573362 Loss1: 2.063825 Loss2: 1.509536
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.597029 Loss1: 1.128627 Loss2: 1.468402
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.207437 Loss1: 1.718928 Loss2: 1.488509
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.552703 Loss1: 1.076137 Loss2: 1.476566
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.961054 Loss1: 1.475024 Loss2: 1.486030
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.407834 Loss1: 0.937852 Loss2: 1.469982
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.896370 Loss1: 1.396140 Loss2: 1.500230
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.415458 Loss1: 0.931209 Loss2: 1.484249
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.892793 Loss1: 1.363051 Loss2: 1.529742
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.311075 Loss1: 0.834756 Loss2: 1.476319
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.685662 Loss1: 1.187552 Loss2: 1.498110
-(DefaultActor pid=3765) >> Training accuracy: 0.800781
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.559893 Loss1: 1.043368 Loss2: 1.516525
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.572898 Loss1: 1.052721 Loss2: 1.520177
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.480973 Loss1: 0.954504 Loss2: 1.526470
-(DefaultActor pid=3764) >> Training accuracy: 0.789062
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.439296 Loss1: 2.444893 Loss2: 1.994404
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.400500 Loss1: 1.925608 Loss2: 1.474891
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.059828 Loss1: 1.622194 Loss2: 1.437634
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.835756 Loss1: 1.395655 Loss2: 1.440100
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.703036 Loss1: 2.565544 Loss2: 2.137491
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.673592 Loss1: 1.234604 Loss2: 1.438988
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.541219 Loss1: 1.992457 Loss2: 1.548762
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.592346 Loss1: 1.145832 Loss2: 1.446514
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.287195 Loss1: 1.737199 Loss2: 1.549996
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.101660 Loss1: 1.554472 Loss2: 1.547188
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.436453 Loss1: 0.973794 Loss2: 1.462659
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.944738 Loss1: 1.382704 Loss2: 1.562035
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.436294 Loss1: 0.979529 Loss2: 1.456765
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.782136 Loss1: 1.233811 Loss2: 1.548325
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.389912 Loss1: 0.926515 Loss2: 1.463397
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.364363 Loss1: 0.881534 Loss2: 1.482829 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.794792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.623445 Loss1: 1.050392 Loss2: 1.573053 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.757812
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.425136 Loss1: 2.389197 Loss2: 2.035939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.180142 Loss1: 1.683591 Loss2: 1.496552 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.390603 Loss1: 2.356593 Loss2: 2.034010
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.934710 Loss1: 1.433309 Loss2: 1.501401
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.318226 Loss1: 1.823338 Loss2: 1.494887
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.831380 Loss1: 1.319261 Loss2: 1.512119
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.123181 Loss1: 1.652348 Loss2: 1.470833
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.749589 Loss1: 1.224454 Loss2: 1.525135
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.657469 Loss1: 1.120538 Loss2: 1.536931 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.572884 Loss1: 1.032226 Loss2: 1.540658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.522239 Loss1: 0.988143 Loss2: 1.534097 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.424560 Loss1: 0.890467 Loss2: 1.534093 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.744141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.384794 Loss1: 0.871756 Loss2: 1.513038 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.779167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.629985 Loss1: 2.431992 Loss2: 2.197992 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.212071 Loss1: 1.661805 Loss2: 1.550266 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.384837 Loss1: 2.348803 Loss2: 2.036035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.743277 Loss1: 1.155363 Loss2: 1.587913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.677600 Loss1: 1.088100 Loss2: 1.589500 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.643401 Loss1: 1.044256 Loss2: 1.599145 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.590833 Loss1: 0.978955 Loss2: 1.611878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.442350 Loss1: 0.840415 Loss2: 1.601935 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.774038
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.576771 Loss1: 1.082217 Loss2: 1.494554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.454316 Loss1: 0.951790 Loss2: 1.502526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.487208 Loss1: 0.966412 Loss2: 1.520796
-(DefaultActor pid=3764) >> Training accuracy: 0.727083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.447153 Loss1: 2.413970 Loss2: 2.033183
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.363238 Loss1: 1.866917 Loss2: 1.496322
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.109196 Loss1: 1.629151 Loss2: 1.480044
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.935533 Loss1: 1.450865 Loss2: 1.484668
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.750771 Loss1: 1.270092 Loss2: 1.480679
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.540968 Loss1: 2.412053 Loss2: 2.128915
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.725652 Loss1: 1.240457 Loss2: 1.485195
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.609894 Loss1: 1.108452 Loss2: 1.501442 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.654629 Loss1: 1.160382 Loss2: 1.494247 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.633253 Loss1: 1.133482 Loss2: 1.499771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.511408 Loss1: 0.991905 Loss2: 1.519503 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.717708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.705367 Loss1: 1.107839 Loss2: 1.597528 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.639421 Loss1: 1.039840 Loss2: 1.599582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.574704 Loss1: 0.970094 Loss2: 1.604610
-(DefaultActor pid=3764) >> Training accuracy: 0.743750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.528603 Loss1: 2.351422 Loss2: 2.177180
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.486575 Loss1: 1.842704 Loss2: 1.643870
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.111446 Loss1: 1.485322 Loss2: 1.626124
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.959508 Loss1: 1.336633 Loss2: 1.622875
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.872133 Loss1: 1.241848 Loss2: 1.630285
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.558950 Loss1: 2.437690 Loss2: 2.121261
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.258970 Loss1: 1.732766 Loss2: 1.526204
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.832588 Loss1: 1.200853 Loss2: 1.631735
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.908133 Loss1: 1.428487 Loss2: 1.479645
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.749254 Loss1: 1.101318 Loss2: 1.647936
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.833069 Loss1: 1.347240 Loss2: 1.485829
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.653289 Loss1: 0.999328 Loss2: 1.653961
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.586384 Loss1: 0.942631 Loss2: 1.643753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.552853 Loss1: 0.887318 Loss2: 1.665535 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.807904
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.476467 Loss1: 0.951292 Loss2: 1.525175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.378075 Loss1: 0.856825 Loss2: 1.521250 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.678125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.359638 Loss1: 2.332969 Loss2: 2.026669
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.245359 Loss1: 1.781622 Loss2: 1.463737
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.946366 Loss1: 1.507879 Loss2: 1.438487
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.736662 Loss1: 1.296564 Loss2: 1.440098
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.346007 Loss1: 2.334139 Loss2: 2.011869
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.226376 Loss1: 1.729705 Loss2: 1.496671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.825049 Loss1: 1.377520 Loss2: 1.447529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.708096 Loss1: 1.259269 Loss2: 1.448826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.561221 Loss1: 1.090369 Loss2: 1.470852 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.507038 Loss1: 1.040097 Loss2: 1.466941 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.780208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.479918 Loss1: 1.000105 Loss2: 1.479813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.364041 Loss1: 0.872284 Loss2: 1.491757 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.737500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.647880 Loss1: 2.609561 Loss2: 2.038319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.358250 Loss1: 1.818299 Loss2: 1.539951 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.118844 Loss1: 1.562569 Loss2: 1.556274
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.275499 Loss1: 2.189460 Loss2: 2.086040
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.216487 Loss1: 1.675641 Loss2: 1.540847 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.922107 Loss1: 1.413150 Loss2: 1.508956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.836659 Loss1: 1.273320 Loss2: 1.563340
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.699014 Loss1: 1.194485 Loss2: 1.504529
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.668041 Loss1: 1.095796 Loss2: 1.572244
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.704758 Loss1: 1.206813 Loss2: 1.497945
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.664698 Loss1: 1.082712 Loss2: 1.581986
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.541972 Loss1: 1.034512 Loss2: 1.507460
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.551437 Loss1: 0.968300 Loss2: 1.583136
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.427854 Loss1: 0.930540 Loss2: 1.497314
-(DefaultActor pid=3765) >> Training accuracy: 0.700195
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.402859 Loss1: 0.895212 Loss2: 1.507648
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.355787 Loss1: 0.845708 Loss2: 1.510078
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.319605 Loss1: 0.792746 Loss2: 1.526859
-(DefaultActor pid=3764) >> Training accuracy: 0.798958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.282206 Loss1: 2.284597 Loss2: 1.997609
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.215355 Loss1: 1.708721 Loss2: 1.506634
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.930934 Loss1: 1.457489 Loss2: 1.473445
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.359455 Loss1: 2.298170 Loss2: 2.061285
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.703414 Loss1: 1.239958 Loss2: 1.463457
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.317270 Loss1: 1.780427 Loss2: 1.536843
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.581000 Loss1: 1.108116 Loss2: 1.472884
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.003185 Loss1: 1.504096 Loss2: 1.499089
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.474117 Loss1: 0.989997 Loss2: 1.484120
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.475215 Loss1: 0.989804 Loss2: 1.485411 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.412051 Loss1: 0.921311 Loss2: 1.490740 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.378566 Loss1: 0.889951 Loss2: 1.488616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.308758 Loss1: 0.813249 Loss2: 1.495509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.790039
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.534781 Loss1: 0.992273 Loss2: 1.542508 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.741667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.520796 Loss1: 2.398175 Loss2: 2.122621 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.221549 Loss1: 1.682019 Loss2: 1.539531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 3.007557 Loss1: 1.467809 Loss2: 1.539748
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.280670 Loss1: 2.227209 Loss2: 2.053461
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.900950 Loss1: 1.337485 Loss2: 1.563466
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.281909 Loss1: 1.763119 Loss2: 1.518790
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.804072 Loss1: 1.244511 Loss2: 1.559562
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.950713 Loss1: 1.478849 Loss2: 1.471864
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.645122 Loss1: 1.069647 Loss2: 1.575476
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.735662 Loss1: 1.258335 Loss2: 1.477327
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.632617 Loss1: 1.060634 Loss2: 1.571983
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.608337 Loss1: 1.132351 Loss2: 1.475985
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.553033 Loss1: 0.967861 Loss2: 1.585173
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.510750 Loss1: 1.022409 Loss2: 1.488341
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.491117 Loss1: 0.906262 Loss2: 1.584855
-(DefaultActor pid=3765) >> Training accuracy: 0.713542
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.360202 Loss1: 0.894013 Loss2: 1.466189
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.384090 Loss1: 0.893193 Loss2: 1.490897
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.261360 Loss1: 0.760982 Loss2: 1.500378
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.271794 Loss1: 0.781009 Loss2: 1.490785
-(DefaultActor pid=3764) >> Training accuracy: 0.800000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.547841 Loss1: 2.517596 Loss2: 2.030245
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.393381 Loss1: 1.944802 Loss2: 1.448580
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.108843 Loss1: 1.664938 Loss2: 1.443904
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.865451 Loss1: 1.418186 Loss2: 1.447265
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.551572 Loss1: 2.477258 Loss2: 2.074314
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.394788 Loss1: 1.870096 Loss2: 1.524692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.134441 Loss1: 1.621150 Loss2: 1.513291 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.956117 Loss1: 1.432908 Loss2: 1.523209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.760028 Loss1: 1.242521 Loss2: 1.517507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.679714 Loss1: 1.149369 Loss2: 1.530345 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.711458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.346882 Loss1: 0.875583 Loss2: 1.471299
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.615292 Loss1: 1.073131 Loss2: 1.542161
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.607262 Loss1: 1.073884 Loss2: 1.533378
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.530612 Loss1: 0.984102 Loss2: 1.546510
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.477923 Loss1: 0.929762 Loss2: 1.548161
-(DefaultActor pid=3764) >> Training accuracy: 0.713542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.461617 Loss1: 2.440098 Loss2: 2.021519
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.335257 Loss1: 1.844401 Loss2: 1.490856
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.083886 Loss1: 1.612993 Loss2: 1.470894
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.813485 Loss1: 1.335747 Loss2: 1.477738
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.384487 Loss1: 2.309636 Loss2: 2.074851
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.345846 Loss1: 1.835786 Loss2: 1.510060 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.072428 Loss1: 1.548761 Loss2: 1.523667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.980537 Loss1: 1.455817 Loss2: 1.524720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.741255 Loss1: 1.208728 Loss2: 1.532527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.731959 Loss1: 1.194069 Loss2: 1.537890 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.769792
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.375691 Loss1: 0.858014 Loss2: 1.517677
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.676315 Loss1: 1.133753 Loss2: 1.542562
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.560924 Loss1: 1.008539 Loss2: 1.552385
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.484518 Loss1: 0.937382 Loss2: 1.547137
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.480923 Loss1: 0.925561 Loss2: 1.555362
-(DefaultActor pid=3764) >> Training accuracy: 0.705208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.535026 Loss1: 2.476644 Loss2: 2.058382
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.514520 Loss1: 1.984880 Loss2: 1.529640
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.209016 Loss1: 1.684656 Loss2: 1.524360
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.976332 Loss1: 1.463952 Loss2: 1.512380
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.721156 Loss1: 2.527065 Loss2: 2.194091
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.608670 Loss1: 1.991035 Loss2: 1.617635 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.248296 Loss1: 1.662502 Loss2: 1.585793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.047537 Loss1: 1.446584 Loss2: 1.600953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 3.008988 Loss1: 1.405235 Loss2: 1.603752 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.804316 Loss1: 1.201117 Loss2: 1.603199 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.748958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.496294 Loss1: 0.948926 Loss2: 1.547367
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.748802 Loss1: 1.149712 Loss2: 1.599089
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.748618 Loss1: 1.138745 Loss2: 1.609873
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.780007 Loss1: 1.133842 Loss2: 1.646165
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.526693 Loss1: 0.902394 Loss2: 1.624299
-(DefaultActor pid=3764) >> Training accuracy: 0.767708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.587109 Loss1: 2.545176 Loss2: 2.041933
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.493889 Loss1: 1.961382 Loss2: 1.532506
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.178274 Loss1: 1.672536 Loss2: 1.505738
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.908106 Loss1: 1.389702 Loss2: 1.518405
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.389937 Loss1: 2.330104 Loss2: 2.059832
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.335375 Loss1: 1.841391 Loss2: 1.493984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.940568 Loss1: 1.479298 Loss2: 1.461269 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.891598 Loss1: 1.430105 Loss2: 1.461493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.593763 Loss1: 1.039350 Loss2: 1.554414
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.759696 Loss1: 1.275374 Loss2: 1.484322
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.421780 Loss1: 0.879759 Loss2: 1.542021
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.574803 Loss1: 1.095967 Loss2: 1.478836
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.466478 Loss1: 0.984833 Loss2: 1.481645
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.426430 Loss1: 0.864404 Loss2: 1.562026
-(DefaultActor pid=3765) >> Training accuracy: 0.732292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.498234 Loss1: 0.981785 Loss2: 1.516448 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.784598
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.314563 Loss1: 2.269185 Loss2: 2.045378 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.029006 Loss1: 1.531173 Loss2: 1.497834 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.845491 Loss1: 1.348334 Loss2: 1.497157
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.376818 Loss1: 2.345307 Loss2: 2.031511
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.706491 Loss1: 1.207405 Loss2: 1.499086
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.299691 Loss1: 1.758142 Loss2: 1.541549
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.713804 Loss1: 1.195326 Loss2: 1.518478
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.029959 Loss1: 1.507665 Loss2: 1.522293
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.622444 Loss1: 1.104146 Loss2: 1.518298
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.811841 Loss1: 1.285750 Loss2: 1.526091
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.548968 Loss1: 1.016581 Loss2: 1.532388
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.649760 Loss1: 1.136429 Loss2: 1.513330
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.435814 Loss1: 0.900053 Loss2: 1.535762
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.554744 Loss1: 1.023084 Loss2: 1.531659
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.426772 Loss1: 0.896611 Loss2: 1.530161
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.530535 Loss1: 1.000071 Loss2: 1.530463
-(DefaultActor pid=3765) >> Training accuracy: 0.749023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.458964 Loss1: 0.913510 Loss2: 1.545454
-DEBUG flwr 2023-10-09 06:59:49,582 | server.py:236 | fit_round 30 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.406099 Loss1: 0.868087 Loss2: 1.538011
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.510045 Loss1: 0.959909 Loss2: 1.550135
-(DefaultActor pid=3764) >> Training accuracy: 0.691406
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.566852 Loss1: 2.510802 Loss2: 2.056051
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.493663 Loss1: 1.947088 Loss2: 1.546574
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.208920 Loss1: 1.698930 Loss2: 1.509990
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.974135 Loss1: 1.474203 Loss2: 1.499932
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.534829 Loss1: 2.486819 Loss2: 2.048011
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.545076 Loss1: 2.026859 Loss2: 1.518216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.190362 Loss1: 1.681775 Loss2: 1.508587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.890501 Loss1: 1.391106 Loss2: 1.499395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.889683 Loss1: 1.374322 Loss2: 1.515360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.773353 Loss1: 1.234570 Loss2: 1.538783 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.631250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.652236 Loss1: 1.122174 Loss2: 1.530063 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.443871 Loss1: 0.906497 Loss2: 1.537375 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.761719
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.465362 Loss1: 1.916234 Loss2: 1.549128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.907763 Loss1: 1.386503 Loss2: 1.521260 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.664894 Loss1: 1.145962 Loss2: 1.518932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.525234 Loss1: 0.986176 Loss2: 1.539058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.527250 Loss1: 0.977240 Loss2: 1.550010 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.426585 Loss1: 0.861847 Loss2: 1.564738 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.400255 Loss1: 0.851621 Loss2: 1.548635 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.718750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.808156 Loss1: 1.301531 Loss2: 1.506625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.619453 Loss1: 1.088636 Loss2: 1.530817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.350708 Loss1: 2.331487 Loss2: 2.019221 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.733259
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.088518 Loss1: 1.648494 Loss2: 1.440024 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.690572 Loss1: 1.235903 Loss2: 1.454669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.588711 Loss1: 1.126245 Loss2: 1.462466
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.480731 Loss1: 2.452847 Loss2: 2.027884
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.475193 Loss1: 1.940489 Loss2: 1.534704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.132364 Loss1: 1.605177 Loss2: 1.527187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.980536 Loss1: 1.431693 Loss2: 1.548844 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.763542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.838466 Loss1: 1.299327 Loss2: 1.539139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.762664 Loss1: 1.196383 Loss2: 1.566282 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.541884 Loss1: 0.984278 Loss2: 1.557606 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.777083
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 06:59:49,582][flwr][DEBUG] - fit_round 30 received 50 results and 0 failures
-INFO flwr 2023-10-09 07:00:31,787 | server.py:125 | fit progress: (30, 2.813382360881891, {'accuracy': 0.3485}, 68939.56565338999)
->> Test accuracy: 0.348500
-[2023-10-09 07:00:31,787][flwr][INFO] - fit progress: (30, 2.813382360881891, {'accuracy': 0.3485}, 68939.56565338999)
-DEBUG flwr 2023-10-09 07:00:31,787 | server.py:173 | evaluate_round 30: strategy sampled 50 clients (out of 50)
-[2023-10-09 07:00:31,787][flwr][DEBUG] - evaluate_round 30: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 07:09:35,722 | server.py:187 | evaluate_round 30 received 50 results and 0 failures
-[2023-10-09 07:09:35,722][flwr][DEBUG] - evaluate_round 30 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 07:09:35,722 | server.py:222 | fit_round 31: strategy sampled 50 clients (out of 50)
-[2023-10-09 07:09:35,722][flwr][DEBUG] - fit_round 31: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.386367 Loss1: 2.350478 Loss2: 2.035889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.053638 Loss1: 1.572814 Loss2: 1.480824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.829509 Loss1: 1.368429 Loss2: 1.461080
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.593356 Loss1: 2.536169 Loss2: 2.057187
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.701281 Loss1: 1.226000 Loss2: 1.475281
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.468189 Loss1: 1.978558 Loss2: 1.489631
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.151484 Loss1: 1.684710 Loss2: 1.466773
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.655647 Loss1: 1.179597 Loss2: 1.476050
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.938170 Loss1: 1.468556 Loss2: 1.469614
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.458786 Loss1: 0.976605 Loss2: 1.482181
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.754696 Loss1: 1.268391 Loss2: 1.486306
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.504259 Loss1: 1.010599 Loss2: 1.493660
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.705956 Loss1: 1.212679 Loss2: 1.493277
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.393418 Loss1: 0.881734 Loss2: 1.511684
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.343655 Loss1: 0.844867 Loss2: 1.498788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.736458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.505238 Loss1: 0.991468 Loss2: 1.513770 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.726562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.311514 Loss1: 2.276111 Loss2: 2.035403 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.955564 Loss1: 1.501227 Loss2: 1.454338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.768968 Loss1: 1.290093 Loss2: 1.478876
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.312017 Loss1: 2.385259 Loss2: 1.926758
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.262147 Loss1: 1.814410 Loss2: 1.447737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.884532 Loss1: 1.470396 Loss2: 1.414136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.802576 Loss1: 1.370515 Loss2: 1.432061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.630642 Loss1: 1.184771 Loss2: 1.445872 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.570289 Loss1: 1.114493 Loss2: 1.455796 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.787500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.374012 Loss1: 0.924373 Loss2: 1.449639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.204837 Loss1: 0.749201 Loss2: 1.455636 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.748047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.618468 Loss1: 2.300313 Loss2: 2.318155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.052315 Loss1: 1.434781 Loss2: 1.617534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.645387 Loss1: 1.025739 Loss2: 1.619648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.544901 Loss1: 0.914092 Loss2: 1.630810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.101532 Loss1: 1.622607 Loss2: 1.478925
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.484739 Loss1: 0.854816 Loss2: 1.629922
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.507032 Loss1: 0.865727 Loss2: 1.641305
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.840174 Loss1: 1.401294 Loss2: 1.438880
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.707501 Loss1: 1.266240 Loss2: 1.441261 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.806490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.575145 Loss1: 1.111728 Loss2: 1.463417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.381179 Loss1: 0.921368 Loss2: 1.459811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.303107 Loss1: 0.838933 Loss2: 1.464174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.289030 Loss1: 0.798305 Loss2: 1.490724 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.772917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.923206 Loss1: 1.485688 Loss2: 1.437518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.613505 Loss1: 1.155203 Loss2: 1.458302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.518898 Loss1: 1.057214 Loss2: 1.461684
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.362617 Loss1: 2.258495 Loss2: 2.104122
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.276070 Loss1: 1.732604 Loss2: 1.543466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.936118 Loss1: 1.416292 Loss2: 1.519826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.698338 Loss1: 1.170628 Loss2: 1.527710 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.700000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.571899 Loss1: 1.042749 Loss2: 1.529150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.520304 Loss1: 0.984968 Loss2: 1.535335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.361352 Loss1: 0.813504 Loss2: 1.547848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.319435 Loss1: 1.789034 Loss2: 1.530401 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.708008
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.853247 Loss1: 1.343290 Loss2: 1.509957 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.560247 Loss1: 1.040731 Loss2: 1.519515 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.528357 Loss1: 1.009722 Loss2: 1.518635
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.432114 Loss1: 2.304821 Loss2: 2.127293
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.453443 Loss1: 0.913846 Loss2: 1.539597
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.359615 Loss1: 1.829359 Loss2: 1.530256
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.415967 Loss1: 0.879351 Loss2: 1.536616
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.036711 Loss1: 1.524455 Loss2: 1.512256
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.409825 Loss1: 0.860737 Loss2: 1.549089
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.826297 Loss1: 1.309893 Loss2: 1.516403
-(DefaultActor pid=3765) >> Training accuracy: 0.694792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.730638 Loss1: 1.203545 Loss2: 1.527092
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.569486 Loss1: 1.034054 Loss2: 1.535433
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.475656 Loss1: 0.932911 Loss2: 1.542744
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.484156 Loss1: 0.936995 Loss2: 1.547160
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.467052 Loss1: 0.901276 Loss2: 1.565776
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.475201 Loss1: 2.390354 Loss2: 2.084846
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.453029 Loss1: 0.890702 Loss2: 1.562326
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.255929 Loss1: 1.761451 Loss2: 1.494478
-(DefaultActor pid=3764) >> Training accuracy: 0.764583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.056362 Loss1: 1.558835 Loss2: 1.497527
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.937592 Loss1: 1.438248 Loss2: 1.499344
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.705762 Loss1: 1.212764 Loss2: 1.492998
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.606246 Loss1: 1.099078 Loss2: 1.507169
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.493565 Loss1: 2.359102 Loss2: 2.134463
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.587435 Loss1: 1.062171 Loss2: 1.525264
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.535359 Loss1: 1.004115 Loss2: 1.531244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.429332 Loss1: 0.896878 Loss2: 1.532454 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.450144 Loss1: 0.927455 Loss2: 1.522689 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.780208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.441222 Loss1: 0.931877 Loss2: 1.509344 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.384844 Loss1: 0.858369 Loss2: 1.526474 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.792067
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.337555 Loss1: 0.802315 Loss2: 1.535240
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.318293 Loss1: 2.287372 Loss2: 2.030921
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.168922 Loss1: 1.688489 Loss2: 1.480433
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.941778 Loss1: 1.453255 Loss2: 1.488523
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.768722 Loss1: 1.290011 Loss2: 1.478711
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.434128 Loss1: 2.311652 Loss2: 2.122477
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.659948 Loss1: 1.173150 Loss2: 1.486798
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.231629 Loss1: 1.679300 Loss2: 1.552328
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.487670 Loss1: 0.997637 Loss2: 1.490033
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.532570 Loss1: 1.018342 Loss2: 1.514228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.457662 Loss1: 0.953470 Loss2: 1.504192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.438714 Loss1: 0.924667 Loss2: 1.514047 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.333323 Loss1: 0.814728 Loss2: 1.518595 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.687500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.378621 Loss1: 0.816984 Loss2: 1.561637 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.796875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.423593 Loss1: 2.439198 Loss2: 1.984395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.052994 Loss1: 1.615792 Loss2: 1.437202 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.812050 Loss1: 1.371082 Loss2: 1.440968
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.386801 Loss1: 2.258507 Loss2: 2.128294
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.394497 Loss1: 1.819071 Loss2: 1.575426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.988268 Loss1: 1.451649 Loss2: 1.536619 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.783766 Loss1: 1.268260 Loss2: 1.515506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.541605 Loss1: 1.026250 Loss2: 1.515355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.542438 Loss1: 1.028616 Loss2: 1.513821 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.773958
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.246129 Loss1: 0.794215 Loss2: 1.451914
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.442054 Loss1: 0.929888 Loss2: 1.512166
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.247581 Loss1: 0.724585 Loss2: 1.522995
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.158239 Loss1: 0.650281 Loss2: 1.507958
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.257661 Loss1: 0.741372 Loss2: 1.516290
-(DefaultActor pid=3764) >> Training accuracy: 0.791667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.384295 Loss1: 2.355806 Loss2: 2.028490
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.367956 Loss1: 1.902942 Loss2: 1.465014
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.091639 Loss1: 1.633378 Loss2: 1.458261
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.842056 Loss1: 1.378488 Loss2: 1.463569
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.516516 Loss1: 2.421833 Loss2: 2.094683
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.424747 Loss1: 1.863203 Loss2: 1.561544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.136173 Loss1: 1.600377 Loss2: 1.535796 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.938499 Loss1: 1.399669 Loss2: 1.538829 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.846291 Loss1: 1.294757 Loss2: 1.551534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.773212 Loss1: 1.210449 Loss2: 1.562762 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.781250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.623918 Loss1: 1.067468 Loss2: 1.556450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.464158 Loss1: 0.895271 Loss2: 1.568887 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.717708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.482283 Loss1: 2.473114 Loss2: 2.009169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.088252 Loss1: 1.626725 Loss2: 1.461527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.240282 Loss1: 2.358041 Loss2: 1.882242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.234814 Loss1: 1.815913 Loss2: 1.418901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.852052 Loss1: 1.449232 Loss2: 1.402819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.559481 Loss1: 1.168739 Loss2: 1.390741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.503437 Loss1: 1.100539 Loss2: 1.402898 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.433009 Loss1: 0.921671 Loss2: 1.511338 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.750000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.321063 Loss1: 0.888891 Loss2: 1.432172 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.140027 Loss1: 0.725595 Loss2: 1.414432 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.809570
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.225157 Loss1: 1.723094 Loss2: 1.502063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.665671 Loss1: 1.178971 Loss2: 1.486700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.616306 Loss1: 1.122403 Loss2: 1.493903
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.416090 Loss1: 2.290999 Loss2: 2.125090
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.222423 Loss1: 1.688968 Loss2: 1.533455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.935995 Loss1: 1.422941 Loss2: 1.513054 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.663587 Loss1: 1.163729 Loss2: 1.499858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.554200 Loss1: 1.056305 Loss2: 1.497895 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.790625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.207021 Loss1: 0.691094 Loss2: 1.515927
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.541951 Loss1: 1.027804 Loss2: 1.514147
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.439947 Loss1: 0.921462 Loss2: 1.518484
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.343050 Loss1: 0.811289 Loss2: 1.531761
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.289924 Loss1: 0.773877 Loss2: 1.516048
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.246636 Loss1: 0.709825 Loss2: 1.536811
-(DefaultActor pid=3764) >> Training accuracy: 0.813542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.271414 Loss1: 2.165826 Loss2: 2.105588
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.234581 Loss1: 1.700189 Loss2: 1.534393
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.840710 Loss1: 1.331128 Loss2: 1.509583
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.751359 Loss1: 1.254529 Loss2: 1.496830
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.660717 Loss1: 1.147585 Loss2: 1.513133
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.528846 Loss1: 2.446644 Loss2: 2.082202
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.386500 Loss1: 1.851196 Loss2: 1.535305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.085275 Loss1: 1.576707 Loss2: 1.508568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.939441 Loss1: 1.425222 Loss2: 1.514219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.735837 Loss1: 1.225929 Loss2: 1.509908 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.787500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.751927 Loss1: 1.235965 Loss2: 1.515962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.411345 Loss1: 0.882657 Loss2: 1.528688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.490330 Loss1: 0.947550 Loss2: 1.542780 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.755208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.394928 Loss1: 1.889938 Loss2: 1.504991 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.928941 Loss1: 1.448161 Loss2: 1.480780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.785947 Loss1: 1.290295 Loss2: 1.495652
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.566613 Loss1: 2.389061 Loss2: 2.177552
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.286302 Loss1: 1.766775 Loss2: 1.519527
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.661641 Loss1: 1.155174 Loss2: 1.506467
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.956345 Loss1: 1.467815 Loss2: 1.488530
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.412040 Loss1: 0.900246 Loss2: 1.511793 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.473520 Loss1: 0.960511 Loss2: 1.513009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.548369 Loss1: 1.011158 Loss2: 1.537211 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.766667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.431047 Loss1: 0.887802 Loss2: 1.543244 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.796875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.458712 Loss1: 2.296886 Loss2: 2.161826 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.995972 Loss1: 1.463812 Loss2: 1.532160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.963869 Loss1: 1.423757 Loss2: 1.540112
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.448322 Loss1: 2.386884 Loss2: 2.061438
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.313464 Loss1: 1.807560 Loss2: 1.505904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.979196 Loss1: 1.495216 Loss2: 1.483980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.713180 Loss1: 1.233180 Loss2: 1.480000 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.677671 Loss1: 1.184869 Loss2: 1.492803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.736380 Loss1: 1.230667 Loss2: 1.505713 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.809375
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.376482 Loss1: 0.814491 Loss2: 1.561991
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.564104 Loss1: 1.042602 Loss2: 1.521502
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.477284 Loss1: 0.960989 Loss2: 1.516295
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.468429 Loss1: 0.934056 Loss2: 1.534373
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.412050 Loss1: 0.872200 Loss2: 1.539851
-(DefaultActor pid=3764) >> Training accuracy: 0.784375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.422984 Loss1: 2.289618 Loss2: 2.133365
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.379575 Loss1: 1.826406 Loss2: 1.553169
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.172639 Loss1: 1.604686 Loss2: 1.567953
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.850094 Loss1: 1.285854 Loss2: 1.564240
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.595733 Loss1: 2.530717 Loss2: 2.065016
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.433125 Loss1: 1.873692 Loss2: 1.559432 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.122996 Loss1: 1.593503 Loss2: 1.529492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.001870 Loss1: 1.461774 Loss2: 1.540096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.802621 Loss1: 1.243074 Loss2: 1.559546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.679935 Loss1: 1.131457 Loss2: 1.548478 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.750000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.416805 Loss1: 0.824504 Loss2: 1.592300
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.719740 Loss1: 1.137916 Loss2: 1.581824
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.637119 Loss1: 1.064167 Loss2: 1.572952
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.537611 Loss1: 0.959794 Loss2: 1.577817
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.438568 Loss1: 0.861347 Loss2: 1.577221
-(DefaultActor pid=3764) >> Training accuracy: 0.730469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.470866 Loss1: 1.915856 Loss2: 1.555010 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.870138 Loss1: 1.322216 Loss2: 1.547923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.308120 Loss1: 2.299385 Loss2: 2.008735
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.744762 Loss1: 1.199324 Loss2: 1.545438
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.326239 Loss1: 1.860215 Loss2: 1.466024
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.698362 Loss1: 1.138087 Loss2: 1.560275
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.961262 Loss1: 1.492018 Loss2: 1.469243
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.615577 Loss1: 1.062364 Loss2: 1.553213
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.544573 Loss1: 0.978647 Loss2: 1.565926
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.736020 Loss1: 1.285901 Loss2: 1.450119
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.461249 Loss1: 0.894154 Loss2: 1.567095
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.610129 Loss1: 1.148750 Loss2: 1.461379
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.428992 Loss1: 0.863319 Loss2: 1.565673
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.668349 Loss1: 1.195254 Loss2: 1.473095
-(DefaultActor pid=3765) >> Training accuracy: 0.780208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.450060 Loss1: 0.973634 Loss2: 1.476426
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.388942 Loss1: 0.917838 Loss2: 1.471104
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.452953 Loss1: 0.969514 Loss2: 1.483439
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.349036 Loss1: 0.862989 Loss2: 1.486047
-(DefaultActor pid=3764) >> Training accuracy: 0.751953
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.420306 Loss1: 2.370042 Loss2: 2.050263
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.391161 Loss1: 1.916680 Loss2: 1.474481
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.088575 Loss1: 1.612723 Loss2: 1.475852
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.952218 Loss1: 1.465682 Loss2: 1.486536
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.850809 Loss1: 1.348075 Loss2: 1.502734
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.826838 Loss1: 2.658516 Loss2: 2.168321
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.642920 Loss1: 2.029301 Loss2: 1.613619 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.275646 Loss1: 1.674992 Loss2: 1.600655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.033248 Loss1: 1.426021 Loss2: 1.607227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.457060 Loss1: 0.934444 Loss2: 1.522616
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.957510 Loss1: 1.360304 Loss2: 1.597206
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.384305 Loss1: 0.864815 Loss2: 1.519490
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.810605 Loss1: 1.182508 Loss2: 1.628096
-(DefaultActor pid=3765) >> Training accuracy: 0.778125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.679262 Loss1: 1.056751 Loss2: 1.622510
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.580855 Loss1: 0.961983 Loss2: 1.618872
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.441082 Loss1: 0.802457 Loss2: 1.638625
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.462063 Loss1: 0.833054 Loss2: 1.629009
-(DefaultActor pid=3764) >> Training accuracy: 0.800223
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.573121 Loss1: 2.517812 Loss2: 2.055309
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.490495 Loss1: 1.979142 Loss2: 1.511352
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.178845 Loss1: 1.698200 Loss2: 1.480645
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.880541 Loss1: 1.372167 Loss2: 1.508374
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.482581 Loss1: 2.412514 Loss2: 2.070067
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.423938 Loss1: 1.937927 Loss2: 1.486011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.183242 Loss1: 1.715304 Loss2: 1.467938 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.892319 Loss1: 1.417533 Loss2: 1.474785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.782609 Loss1: 1.297124 Loss2: 1.485484 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.722420 Loss1: 1.240675 Loss2: 1.481745 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.797917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.479634 Loss1: 0.973317 Loss2: 1.506316 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.402031 Loss1: 0.903391 Loss2: 1.498640 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.710417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.522647 Loss1: 2.524908 Loss2: 1.997738
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.433729 Loss1: 1.958079 Loss2: 1.475649
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.077219 Loss1: 1.634534 Loss2: 1.442686
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.946594 Loss1: 1.494265 Loss2: 1.452329
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.314721 Loss1: 2.188104 Loss2: 2.126617
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.334552 Loss1: 1.762473 Loss2: 1.572079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.010372 Loss1: 1.465919 Loss2: 1.544453 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.894159 Loss1: 1.340320 Loss2: 1.553839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.620959 Loss1: 1.140444 Loss2: 1.480515
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.613194 Loss1: 1.067908 Loss2: 1.545286
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.524489 Loss1: 1.034391 Loss2: 1.490098
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.469864 Loss1: 0.938897 Loss2: 1.530967
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.454294 Loss1: 0.968302 Loss2: 1.485992
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.452180 Loss1: 0.906910 Loss2: 1.545270
-(DefaultActor pid=3765) >> Training accuracy: 0.686523
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.414847 Loss1: 0.866022 Loss2: 1.548826
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.425553 Loss1: 0.875266 Loss2: 1.550287
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.372651 Loss1: 0.803491 Loss2: 1.569159
-(DefaultActor pid=3764) >> Training accuracy: 0.804167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.569056 Loss1: 2.513035 Loss2: 2.056021
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.449259 Loss1: 1.934865 Loss2: 1.514394
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.224074 Loss1: 1.735322 Loss2: 1.488752
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.936897 Loss1: 1.450375 Loss2: 1.486522
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.553742 Loss1: 2.397101 Loss2: 2.156641
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.813524 Loss1: 1.325495 Loss2: 1.488029
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.467561 Loss1: 1.868091 Loss2: 1.599470
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.635165 Loss1: 1.148838 Loss2: 1.486327
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.089522 Loss1: 1.514519 Loss2: 1.575003
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.561071 Loss1: 1.057813 Loss2: 1.503257
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.927499 Loss1: 1.338624 Loss2: 1.588875
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.553373 Loss1: 1.052967 Loss2: 1.500406
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.791183 Loss1: 1.220456 Loss2: 1.570726
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.407221 Loss1: 0.909552 Loss2: 1.497669
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.634957 Loss1: 1.054746 Loss2: 1.580212
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.428580 Loss1: 0.925750 Loss2: 1.502830
-(DefaultActor pid=3765) >> Training accuracy: 0.760417
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.627720 Loss1: 1.035196 Loss2: 1.592524
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.480570 Loss1: 0.888913 Loss2: 1.591657
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.575276 Loss1: 0.977163 Loss2: 1.598113
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.492750 Loss1: 0.871932 Loss2: 1.620818
-(DefaultActor pid=3764) >> Training accuracy: 0.752083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.513213 Loss1: 2.405217 Loss2: 2.107995
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.398300 Loss1: 1.844468 Loss2: 1.553831
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.140265 Loss1: 1.609181 Loss2: 1.531083
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.964918 Loss1: 1.424543 Loss2: 1.540375
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.157508 Loss1: 2.119817 Loss2: 2.037691
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.807273 Loss1: 1.261648 Loss2: 1.545625
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.166947 Loss1: 1.665495 Loss2: 1.501452
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.717630 Loss1: 1.159186 Loss2: 1.558444
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.887562 Loss1: 1.399968 Loss2: 1.487594
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.536543 Loss1: 0.985905 Loss2: 1.550638
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.614643 Loss1: 1.122178 Loss2: 1.492464
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.537043 Loss1: 0.981590 Loss2: 1.555453
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.569715 Loss1: 1.080141 Loss2: 1.489574
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.355824 Loss1: 0.784336 Loss2: 1.571488
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.581140 Loss1: 1.074191 Loss2: 1.506948
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.364518 Loss1: 0.810500 Loss2: 1.554018
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.526693 Loss1: 1.008709 Loss2: 1.517984
-(DefaultActor pid=3765) >> Training accuracy: 0.788542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.476187 Loss1: 0.962682 Loss2: 1.513505
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.285824 Loss1: 0.768801 Loss2: 1.517023
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.251097 Loss1: 0.738851 Loss2: 1.512246
-(DefaultActor pid=3764) >> Training accuracy: 0.794792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.573516 Loss1: 2.357962 Loss2: 2.215554
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.409488 Loss1: 1.789509 Loss2: 1.619979
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.108154 Loss1: 1.530064 Loss2: 1.578090
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.862254 Loss1: 1.275903 Loss2: 1.586351
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.577045 Loss1: 2.477430 Loss2: 2.099615
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.427993 Loss1: 1.872670 Loss2: 1.555324 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.118364 Loss1: 1.590757 Loss2: 1.527608 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.507622 Loss1: 0.893549 Loss2: 1.614074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.547200 Loss1: 0.921282 Loss2: 1.625918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.479766 Loss1: 0.855602 Loss2: 1.624164 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.786830
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.558123 Loss1: 1.008450 Loss2: 1.549673 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.458095 Loss1: 0.886663 Loss2: 1.571432 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.770833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.315721 Loss1: 1.751394 Loss2: 1.564327 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.867238 Loss1: 1.321337 Loss2: 1.545900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.674495 Loss1: 1.118453 Loss2: 1.556042
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.582265 Loss1: 2.534895 Loss2: 2.047370
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.423505 Loss1: 1.896230 Loss2: 1.527274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.166550 Loss1: 1.664851 Loss2: 1.501699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.938625 Loss1: 1.433134 Loss2: 1.505491 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.741326 Loss1: 1.232483 Loss2: 1.508843 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.760417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.740235 Loss1: 1.215204 Loss2: 1.525031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.586812 Loss1: 1.049262 Loss2: 1.537550 [repeated 2x across cluster]
-DEBUG flwr 2023-10-09 07:38:25,396 | server.py:236 | fit_round 31 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.542377 Loss1: 0.977009 Loss2: 1.565367 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.739258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.963178 Loss1: 1.468204 Loss2: 1.494974 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.709087 Loss1: 1.216542 Loss2: 1.492545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.600682 Loss1: 1.090592 Loss2: 1.510090
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.408054 Loss1: 2.359103 Loss2: 2.048951
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.457989 Loss1: 0.949744 Loss2: 1.508245
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.366735 Loss1: 1.852646 Loss2: 1.514089
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.388427 Loss1: 0.880337 Loss2: 1.508091
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.105779 Loss1: 1.617533 Loss2: 1.488246
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.870747 Loss1: 1.386255 Loss2: 1.484491 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.760417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.314588 Loss1: 0.799370 Loss2: 1.515218
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.714327 Loss1: 1.205093 Loss2: 1.509234
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.668032 Loss1: 1.150480 Loss2: 1.517552
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.629249 Loss1: 1.109305 Loss2: 1.519944
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.510959 Loss1: 0.991061 Loss2: 1.519898
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.454163 Loss1: 0.928426 Loss2: 1.525737
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.477938 Loss1: 2.347618 Loss2: 2.130320
-(DefaultActor pid=3764) >> Training accuracy: 0.687500
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.569233 Loss1: 1.034036 Loss2: 1.535197
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.393047 Loss1: 1.818279 Loss2: 1.574768
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.062754 Loss1: 1.501067 Loss2: 1.561687
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.945989 Loss1: 1.370207 Loss2: 1.575782
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.868089 Loss1: 1.302624 Loss2: 1.565465
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.780574 Loss1: 1.200741 Loss2: 1.579833
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.484938 Loss1: 2.406784 Loss2: 2.078154
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.602634 Loss1: 1.011595 Loss2: 1.591038
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.443094 Loss1: 1.937131 Loss2: 1.505962
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.566378 Loss1: 0.986025 Loss2: 1.580353
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.996046 Loss1: 1.508415 Loss2: 1.487631
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.561619 Loss1: 0.971941 Loss2: 1.589677
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.822860 Loss1: 1.326850 Loss2: 1.496010
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.539951 Loss1: 0.949960 Loss2: 1.589991
-(DefaultActor pid=3765) >> Training accuracy: 0.751042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.606119 Loss1: 1.092192 Loss2: 1.513927 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.481346 Loss1: 0.956893 Loss2: 1.524453 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.326616 Loss1: 0.791823 Loss2: 1.534792 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.658333
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 07:38:25,396][flwr][DEBUG] - fit_round 31 received 50 results and 0 failures
-INFO flwr 2023-10-09 07:39:08,541 | server.py:125 | fit progress: (31, 2.7770677180335928, {'accuracy': 0.3589}, 71256.31940825199)
->> Test accuracy: 0.358900
-[2023-10-09 07:39:08,541][flwr][INFO] - fit progress: (31, 2.7770677180335928, {'accuracy': 0.3589}, 71256.31940825199)
-DEBUG flwr 2023-10-09 07:39:08,541 | server.py:173 | evaluate_round 31: strategy sampled 50 clients (out of 50)
-[2023-10-09 07:39:08,541][flwr][DEBUG] - evaluate_round 31: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 07:48:15,164 | server.py:187 | evaluate_round 31 received 50 results and 0 failures
-[2023-10-09 07:48:15,164][flwr][DEBUG] - evaluate_round 31 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 07:48:15,164 | server.py:222 | fit_round 32: strategy sampled 50 clients (out of 50)
-[2023-10-09 07:48:15,164][flwr][DEBUG] - fit_round 32: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.404019 Loss1: 2.383968 Loss2: 2.020051
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.352896 Loss1: 1.885405 Loss2: 1.467491
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.060808 Loss1: 1.599234 Loss2: 1.461574
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.881011 Loss1: 1.411356 Loss2: 1.469655
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.263204 Loss1: 2.184284 Loss2: 2.078920
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.119162 Loss1: 1.586200 Loss2: 1.532962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.891673 Loss1: 1.381549 Loss2: 1.510125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.645576 Loss1: 1.139120 Loss2: 1.506456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.602054 Loss1: 1.082727 Loss2: 1.519327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.545817 Loss1: 1.021292 Loss2: 1.524525 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.742708
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.414475 Loss1: 0.897701 Loss2: 1.516774
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.477138 Loss1: 0.948834 Loss2: 1.528305
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.353092 Loss1: 0.821022 Loss2: 1.532070
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.272598 Loss1: 0.740819 Loss2: 1.531778
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.280416 Loss1: 0.756314 Loss2: 1.524101
-(DefaultActor pid=3764) >> Training accuracy: 0.741667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.428863 Loss1: 2.422046 Loss2: 2.006816
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.252062 Loss1: 1.828564 Loss2: 1.423498
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.915699 Loss1: 1.502876 Loss2: 1.412824
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.801738 Loss1: 1.384102 Loss2: 1.417636
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.448062 Loss1: 2.380597 Loss2: 2.067466
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.287583 Loss1: 1.793374 Loss2: 1.494208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.117207 Loss1: 1.634275 Loss2: 1.482932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.947015 Loss1: 1.433121 Loss2: 1.513894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.733488 Loss1: 1.234039 Loss2: 1.499449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.609055 Loss1: 1.106444 Loss2: 1.502611 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.753125
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.252049 Loss1: 0.798910 Loss2: 1.453139
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.499295 Loss1: 0.993542 Loss2: 1.505753
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.415445 Loss1: 0.908827 Loss2: 1.506618
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.405508 Loss1: 0.879029 Loss2: 1.526479
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.373371 Loss1: 0.833496 Loss2: 1.539875
-(DefaultActor pid=3764) >> Training accuracy: 0.738542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.495524 Loss1: 2.428205 Loss2: 2.067319
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.311596 Loss1: 1.764476 Loss2: 1.547120
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.035924 Loss1: 1.534730 Loss2: 1.501193
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.864653 Loss1: 1.333756 Loss2: 1.530897
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.532396 Loss1: 2.411087 Loss2: 2.121309
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.386587 Loss1: 1.825838 Loss2: 1.560749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.112635 Loss1: 1.552855 Loss2: 1.559779 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 3.026463 Loss1: 1.453253 Loss2: 1.573209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.971089 Loss1: 1.354728 Loss2: 1.616361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.794516 Loss1: 1.196443 Loss2: 1.598073 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.721875
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.368061 Loss1: 0.823126 Loss2: 1.544935
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.559432 Loss1: 0.986491 Loss2: 1.572941
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.478598 Loss1: 0.901521 Loss2: 1.577078
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.323189 Loss1: 0.730665 Loss2: 1.592523
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.324183 Loss1: 0.726880 Loss2: 1.597303
-(DefaultActor pid=3764) >> Training accuracy: 0.750000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.273262 Loss1: 2.177638 Loss2: 2.095624
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.350050 Loss1: 1.809853 Loss2: 1.540197
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.017798 Loss1: 1.480600 Loss2: 1.537198
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.865371 Loss1: 1.318434 Loss2: 1.546937
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.346630 Loss1: 2.298508 Loss2: 2.048122
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.701992 Loss1: 1.158452 Loss2: 1.543540
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.268912 Loss1: 1.780886 Loss2: 1.488026
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.598852 Loss1: 1.043183 Loss2: 1.555669
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.915126 Loss1: 1.443572 Loss2: 1.471554
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.733513 Loss1: 1.255108 Loss2: 1.478405
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.462783 Loss1: 0.899687 Loss2: 1.563096
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.664055 Loss1: 1.175450 Loss2: 1.488605
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.528191 Loss1: 0.962262 Loss2: 1.565929
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.573132 Loss1: 1.072452 Loss2: 1.500680
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.496297 Loss1: 0.909558 Loss2: 1.586740
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.489252 Loss1: 0.977495 Loss2: 1.511756
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.403203 Loss1: 0.835192 Loss2: 1.568012
-(DefaultActor pid=3765) >> Training accuracy: 0.769531
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.382630 Loss1: 0.848915 Loss2: 1.533715 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.820833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.424715 Loss1: 2.315948 Loss2: 2.108767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.061613 Loss1: 1.540379 Loss2: 1.521235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.883276 Loss1: 1.358999 Loss2: 1.524276
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.437647 Loss1: 2.268525 Loss2: 2.169122
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.664168 Loss1: 1.135822 Loss2: 1.528346
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.274423 Loss1: 1.723871 Loss2: 1.550552
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.016782 Loss1: 1.481581 Loss2: 1.535202
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.611786 Loss1: 1.077079 Loss2: 1.534707
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.904964 Loss1: 1.357318 Loss2: 1.547646
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.535853 Loss1: 0.976412 Loss2: 1.559440
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.669095 Loss1: 1.112425 Loss2: 1.556670
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.528381 Loss1: 0.964617 Loss2: 1.563764
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.501628 Loss1: 0.937142 Loss2: 1.564486 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.357475 Loss1: 0.787897 Loss2: 1.569578 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.775000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.361838 Loss1: 0.788511 Loss2: 1.573327 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.776786
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.080883 Loss1: 2.111343 Loss2: 1.969540 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.758636 Loss1: 1.368978 Loss2: 1.389657 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.576900 Loss1: 1.163478 Loss2: 1.413421
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.357083 Loss1: 2.262950 Loss2: 2.094133
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.428876 Loss1: 1.033352 Loss2: 1.395525
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.298737 Loss1: 1.730461 Loss2: 1.568277
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.316966 Loss1: 0.913632 Loss2: 1.403334
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.102321 Loss1: 1.552463 Loss2: 1.549858
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.163014 Loss1: 0.751964 Loss2: 1.411050
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.926869 Loss1: 1.366738 Loss2: 1.560131
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.077214 Loss1: 0.678250 Loss2: 1.398964
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.802465 Loss1: 1.242221 Loss2: 1.560243
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.124242 Loss1: 0.713444 Loss2: 1.410798
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.637835 Loss1: 1.059620 Loss2: 1.578216
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.167169 Loss1: 0.742832 Loss2: 1.424337
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.619504 Loss1: 1.052440 Loss2: 1.567064
-(DefaultActor pid=3765) >> Training accuracy: 0.768750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.512875 Loss1: 0.921337 Loss2: 1.591538
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.501084 Loss1: 0.912134 Loss2: 1.588950
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.458398 Loss1: 0.866935 Loss2: 1.591463
-(DefaultActor pid=3764) >> Training accuracy: 0.713542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.445464 Loss1: 2.423129 Loss2: 2.022335
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.360349 Loss1: 1.827467 Loss2: 1.532882
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.126544 Loss1: 1.608793 Loss2: 1.517751
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.389700 Loss1: 2.250704 Loss2: 2.138996
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.868233 Loss1: 1.327839 Loss2: 1.540394
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.195749 Loss1: 1.661313 Loss2: 1.534436
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.847041 Loss1: 1.307963 Loss2: 1.539078
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.737989 Loss1: 1.177935 Loss2: 1.560054 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.689702 Loss1: 1.127772 Loss2: 1.561930 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.680468 Loss1: 1.118978 Loss2: 1.561489 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.543154 Loss1: 0.970325 Loss2: 1.572829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.495918 Loss1: 0.926879 Loss2: 1.569039 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.746094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.250005 Loss1: 0.691763 Loss2: 1.558242 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.810417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.250209 Loss1: 2.199197 Loss2: 2.051012 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.888567 Loss1: 1.391072 Loss2: 1.497495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.793557 Loss1: 1.272950 Loss2: 1.520607
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.369852 Loss1: 2.317239 Loss2: 2.052614
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.291249 Loss1: 1.802557 Loss2: 1.488692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.038688 Loss1: 1.550045 Loss2: 1.488643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.801259 Loss1: 1.298825 Loss2: 1.502434 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.731215 Loss1: 1.222621 Loss2: 1.508594 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.644790 Loss1: 1.130623 Loss2: 1.514167 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.736458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.438021 Loss1: 0.937896 Loss2: 1.500125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.311984 Loss1: 0.793211 Loss2: 1.518773 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.814583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.378421 Loss1: 2.363567 Loss2: 2.014854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.026422 Loss1: 1.566956 Loss2: 1.459466 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.826295 Loss1: 1.351277 Loss2: 1.475018
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.362628 Loss1: 2.291315 Loss2: 2.071313
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.222740 Loss1: 1.690694 Loss2: 1.532045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.938031 Loss1: 1.430614 Loss2: 1.507417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.849076 Loss1: 1.328244 Loss2: 1.520832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.534323 Loss1: 1.022833 Loss2: 1.511490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.586043 Loss1: 1.059532 Loss2: 1.526511 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.785417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.280751 Loss1: 0.781504 Loss2: 1.499247
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.491501 Loss1: 0.944936 Loss2: 1.546566
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.391277 Loss1: 0.850005 Loss2: 1.541273
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.298949 Loss1: 0.745444 Loss2: 1.553505
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.352802 Loss1: 0.810237 Loss2: 1.542565
-(DefaultActor pid=3764) >> Training accuracy: 0.805208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.330342 Loss1: 2.290225 Loss2: 2.040117
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.308504 Loss1: 1.810982 Loss2: 1.497522
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.992914 Loss1: 1.504479 Loss2: 1.488435
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.794985 Loss1: 1.302892 Loss2: 1.492093
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.683792 Loss1: 2.552687 Loss2: 2.131105
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.537392 Loss1: 2.011158 Loss2: 1.526234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.171204 Loss1: 1.674122 Loss2: 1.497081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.843355 Loss1: 1.326483 Loss2: 1.516871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.569055 Loss1: 1.057610 Loss2: 1.511445
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.728791 Loss1: 1.213991 Loss2: 1.514800
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.432092 Loss1: 0.915753 Loss2: 1.516339
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.611851 Loss1: 1.091206 Loss2: 1.520645
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.338063 Loss1: 0.820244 Loss2: 1.517819
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.644564 Loss1: 1.100369 Loss2: 1.544195
-(DefaultActor pid=3765) >> Training accuracy: 0.692708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.522389 Loss1: 0.985168 Loss2: 1.537222
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.460228 Loss1: 0.922968 Loss2: 1.537260
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.344734 Loss1: 0.799987 Loss2: 1.544746
-(DefaultActor pid=3764) >> Training accuracy: 0.758929
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.436614 Loss1: 2.406762 Loss2: 2.029852
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.203637 Loss1: 1.709338 Loss2: 1.494299
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.931011 Loss1: 1.454201 Loss2: 1.476810
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.885584 Loss1: 1.381629 Loss2: 1.503955
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.406447 Loss1: 2.283303 Loss2: 2.123144
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.649005 Loss1: 1.161884 Loss2: 1.487121
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.212759 Loss1: 1.688219 Loss2: 1.524540
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.539884 Loss1: 1.060100 Loss2: 1.479784
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.991596 Loss1: 1.498778 Loss2: 1.492818
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.510451 Loss1: 1.012658 Loss2: 1.497794
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.720216 Loss1: 1.227132 Loss2: 1.493083
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.451824 Loss1: 0.950946 Loss2: 1.500878
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.564860 Loss1: 1.077966 Loss2: 1.486894
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.372934 Loss1: 0.859299 Loss2: 1.513635
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.451912 Loss1: 0.958241 Loss2: 1.493671
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.362108 Loss1: 0.854092 Loss2: 1.508016
-(DefaultActor pid=3765) >> Training accuracy: 0.795833
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.344401 Loss1: 0.849003 Loss2: 1.495398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.457893 Loss1: 0.943032 Loss2: 1.514862
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.357492 Loss1: 0.826491 Loss2: 1.531001
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.254158 Loss1: 0.729953 Loss2: 1.524205
-(DefaultActor pid=3764) >> Training accuracy: 0.794792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.183288 Loss1: 2.104032 Loss2: 2.079256
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.178721 Loss1: 1.656307 Loss2: 1.522414
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.913000 Loss1: 1.403613 Loss2: 1.509387
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.682801 Loss1: 1.187258 Loss2: 1.495544
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.641235 Loss1: 2.341574 Loss2: 2.299661
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.442479 Loss1: 1.848101 Loss2: 1.594378 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.119093 Loss1: 1.568338 Loss2: 1.550755
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.400786 Loss1: 0.905873 Loss2: 1.494913
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.309194 Loss1: 0.817677 Loss2: 1.491517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.279280 Loss1: 0.760503 Loss2: 1.518777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.216318 Loss1: 0.705893 Loss2: 1.510426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.487379 Loss1: 0.912054 Loss2: 1.575325 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.791667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.287052 Loss1: 0.716523 Loss2: 1.570529 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.712240
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.375375 Loss1: 2.360126 Loss2: 2.015250
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.320110 Loss1: 1.864945 Loss2: 1.455165
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.900689 Loss1: 1.463281 Loss2: 1.437407
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.791414 Loss1: 1.358933 Loss2: 1.432481
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.345179 Loss1: 2.266658 Loss2: 2.078521
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.268144 Loss1: 1.736978 Loss2: 1.531166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.073751 Loss1: 1.544926 Loss2: 1.528825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.818927 Loss1: 1.283614 Loss2: 1.535313 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.750070 Loss1: 1.215266 Loss2: 1.534804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.568372 Loss1: 1.036937 Loss2: 1.531436 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.798958
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.280881 Loss1: 0.816681 Loss2: 1.464200
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.450873 Loss1: 0.916380 Loss2: 1.534492
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.440331 Loss1: 0.897599 Loss2: 1.542732
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.486567 Loss1: 0.932040 Loss2: 1.554526
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.343171 Loss1: 0.797854 Loss2: 1.545316
-(DefaultActor pid=3764) >> Training accuracy: 0.844792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.269153 Loss1: 2.214228 Loss2: 2.054924
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.089719 Loss1: 1.610717 Loss2: 1.479001
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.771973 Loss1: 1.367397 Loss2: 1.404575
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.541561 Loss1: 1.137962 Loss2: 1.403599
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.431422 Loss1: 1.008010 Loss2: 1.423413
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.373642 Loss1: 0.946387 Loss2: 1.427255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.325312 Loss1: 0.895060 Loss2: 1.430252 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.306179 Loss1: 0.862840 Loss2: 1.443340 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.262467 Loss1: 0.819578 Loss2: 1.442889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.109924 Loss1: 0.674486 Loss2: 1.435438 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.781250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.259775 Loss1: 0.825827 Loss2: 1.433948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.154898 Loss1: 0.712231 Loss2: 1.442667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.236064 Loss1: 0.782323 Loss2: 1.453741
-(DefaultActor pid=3764) >> Training accuracy: 0.773958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.256138 Loss1: 2.098140 Loss2: 2.157998
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.170372 Loss1: 1.603258 Loss2: 1.567114
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.912746 Loss1: 1.378053 Loss2: 1.534693
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.718626 Loss1: 1.164166 Loss2: 1.554459
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.552992 Loss1: 1.002450 Loss2: 1.550541
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.404766 Loss1: 2.314014 Loss2: 2.090752
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.452093 Loss1: 0.900098 Loss2: 1.551995
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.397172 Loss1: 1.875279 Loss2: 1.521893
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.444940 Loss1: 0.896044 Loss2: 1.548896
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.052540 Loss1: 1.531666 Loss2: 1.520874
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.390372 Loss1: 0.828083 Loss2: 1.562289
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.912916 Loss1: 1.383820 Loss2: 1.529096
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.380529 Loss1: 0.810452 Loss2: 1.570076
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.742658 Loss1: 1.236161 Loss2: 1.506497
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.295832 Loss1: 0.716794 Loss2: 1.579038
-(DefaultActor pid=3765) >> Training accuracy: 0.789583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.674533 Loss1: 1.134234 Loss2: 1.540298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.475343 Loss1: 0.931301 Loss2: 1.544042 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.381076 Loss1: 0.835703 Loss2: 1.545373
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.399511 Loss1: 2.314161 Loss2: 2.085350
-(DefaultActor pid=3764) >> Training accuracy: 0.812500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.186301 Loss1: 1.680695 Loss2: 1.505606
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.971348 Loss1: 1.481483 Loss2: 1.489865
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.797723 Loss1: 1.279431 Loss2: 1.518292
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.634923 Loss1: 1.128970 Loss2: 1.505952
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.440358 Loss1: 2.345879 Loss2: 2.094479
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.477843 Loss1: 0.973162 Loss2: 1.504681
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.254402 Loss1: 1.706382 Loss2: 1.548019
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.409955 Loss1: 0.898587 Loss2: 1.511368
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.904117 Loss1: 1.396178 Loss2: 1.507939
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.376271 Loss1: 0.846060 Loss2: 1.530211
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.679932 Loss1: 1.183109 Loss2: 1.496823
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.385697 Loss1: 0.842800 Loss2: 1.542898
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.493111 Loss1: 0.984835 Loss2: 1.508276
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.319837 Loss1: 0.773757 Loss2: 1.546079
-(DefaultActor pid=3765) >> Training accuracy: 0.740625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.305438 Loss1: 0.804141 Loss2: 1.501296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.234384 Loss1: 0.722902 Loss2: 1.511482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.230780 Loss1: 0.709785 Loss2: 1.520996
-(DefaultActor pid=3764) >> Training accuracy: 0.847917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.483039 Loss1: 2.497024 Loss2: 1.986015
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.366304 Loss1: 1.883849 Loss2: 1.482455
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.102711 Loss1: 1.646262 Loss2: 1.456449
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.788237 Loss1: 1.312642 Loss2: 1.475595
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.693533 Loss1: 1.220919 Loss2: 1.472614
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.521751 Loss1: 2.467010 Loss2: 2.054741
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.366647 Loss1: 1.847819 Loss2: 1.518828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.079784 Loss1: 1.562862 Loss2: 1.516922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.888553 Loss1: 1.370772 Loss2: 1.517781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.737389 Loss1: 1.216957 Loss2: 1.520432 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.749023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.298911 Loss1: 0.811534 Loss2: 1.487378
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.609516 Loss1: 1.093828 Loss2: 1.515688
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.561323 Loss1: 1.040589 Loss2: 1.520734
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.625868 Loss1: 1.083175 Loss2: 1.542693
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.484832 Loss1: 0.933039 Loss2: 1.551793
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.337733 Loss1: 0.799158 Loss2: 1.538574
-(DefaultActor pid=3764) >> Training accuracy: 0.744141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.238238 Loss1: 2.211643 Loss2: 2.026595
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.284062 Loss1: 1.769372 Loss2: 1.514690
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.868026 Loss1: 1.370547 Loss2: 1.497479
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.611396 Loss1: 1.125196 Loss2: 1.486200
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.585225 Loss1: 1.094908 Loss2: 1.490317
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.535969 Loss1: 2.450299 Loss2: 2.085669
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.313959 Loss1: 1.768196 Loss2: 1.545763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.081176 Loss1: 1.556632 Loss2: 1.524544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.858151 Loss1: 1.327155 Loss2: 1.530996 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.299312 Loss1: 0.776604 Loss2: 1.522709
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.656358 Loss1: 1.127257 Loss2: 1.529100
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.208861 Loss1: 0.690683 Loss2: 1.518178
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.611393 Loss1: 1.068272 Loss2: 1.543121
-(DefaultActor pid=3765) >> Training accuracy: 0.849609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.510294 Loss1: 0.957321 Loss2: 1.552973
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.403658 Loss1: 0.856706 Loss2: 1.546952
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.344301 Loss1: 0.803405 Loss2: 1.540896
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.333012 Loss1: 0.775067 Loss2: 1.557945
-(DefaultActor pid=3764) >> Training accuracy: 0.752083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.592925 Loss1: 2.505093 Loss2: 2.087832
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.490909 Loss1: 1.969861 Loss2: 1.521048
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.111992 Loss1: 1.607174 Loss2: 1.504818
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.830892 Loss1: 1.319320 Loss2: 1.511572
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.686054 Loss1: 1.181483 Loss2: 1.504570
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.614393 Loss1: 1.103224 Loss2: 1.511169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.510964 Loss1: 0.998453 Loss2: 1.512510 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.417889 Loss1: 0.900438 Loss2: 1.517451 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.415998 Loss1: 0.887530 Loss2: 1.528468 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.363536 Loss1: 0.810327 Loss2: 1.553209 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.774554
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.456400 Loss1: 0.943700 Loss2: 1.512700 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.430305 Loss1: 0.906005 Loss2: 1.524300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.359290 Loss1: 0.829110 Loss2: 1.530179
-(DefaultActor pid=3764) >> Training accuracy: 0.752083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.267057 Loss1: 2.285246 Loss2: 1.981811
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.236547 Loss1: 1.766648 Loss2: 1.469899
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.942776 Loss1: 1.487508 Loss2: 1.455267
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.690549 Loss1: 1.228275 Loss2: 1.462274
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.544447 Loss1: 1.073673 Loss2: 1.470774
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.451059 Loss1: 0.988697 Loss2: 1.462362
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.153228 Loss1: 2.171105 Loss2: 1.982124
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.441910 Loss1: 0.951775 Loss2: 1.490135
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.168532 Loss1: 1.686429 Loss2: 1.482104
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.333988 Loss1: 0.853325 Loss2: 1.480663
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.906409 Loss1: 1.452221 Loss2: 1.454189
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.776757 Loss1: 1.316596 Loss2: 1.460161 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.716667
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.248590 Loss1: 0.761122 Loss2: 1.487468
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.623727 Loss1: 1.165817 Loss2: 1.457910
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.487617 Loss1: 1.023464 Loss2: 1.464153
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.394322 Loss1: 0.917835 Loss2: 1.476486
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.457569 Loss1: 0.972057 Loss2: 1.485512
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.240252 Loss1: 2.170214 Loss2: 2.070037
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.357065 Loss1: 0.866164 Loss2: 1.490900
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.169741 Loss1: 1.687193 Loss2: 1.482549
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.218386 Loss1: 0.746492 Loss2: 1.471894
-(DefaultActor pid=3764) >> Training accuracy: 0.788603
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.598901 Loss1: 1.125275 Loss2: 1.473627 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.397386 Loss1: 0.919688 Loss2: 1.477698 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.378123 Loss1: 0.883795 Loss2: 1.494328
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.519500 Loss1: 2.440633 Loss2: 2.078867
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.420965 Loss1: 1.883034 Loss2: 1.537931 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.077657 Loss1: 1.556581 Loss2: 1.521075 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.739583
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.226918 Loss1: 0.725398 Loss2: 1.501520
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.909290 Loss1: 1.401483 Loss2: 1.507807
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.700358 Loss1: 1.184450 Loss2: 1.515908
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.613118 Loss1: 1.088112 Loss2: 1.525006
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.541166 Loss1: 1.002353 Loss2: 1.538813
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.425416 Loss1: 0.890234 Loss2: 1.535181
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.419838 Loss1: 2.370552 Loss2: 2.049285
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.441760 Loss1: 0.896618 Loss2: 1.545141
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.392155 Loss1: 1.823354 Loss2: 1.568802
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.421481 Loss1: 0.873885 Loss2: 1.547596
-(DefaultActor pid=3764) >> Training accuracy: 0.732292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.861079 Loss1: 1.313163 Loss2: 1.547916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.647540 Loss1: 1.096676 Loss2: 1.550865 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.357236 Loss1: 2.309960 Loss2: 2.047276
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.612374 Loss1: 1.043076 Loss2: 1.569297
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.277896 Loss1: 1.741029 Loss2: 1.536867
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.539389 Loss1: 0.969034 Loss2: 1.570355
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.986589 Loss1: 1.464984 Loss2: 1.521605
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.444678 Loss1: 0.859067 Loss2: 1.585611
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.718497 Loss1: 1.201203 Loss2: 1.517294
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.332325 Loss1: 0.766919 Loss2: 1.565405
-DEBUG flwr 2023-10-09 08:17:02,905 | server.py:236 | fit_round 32 received 50 results and 0 failures
-(DefaultActor pid=3765) >> Training accuracy: 0.744141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.732451 Loss1: 1.187469 Loss2: 1.544982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.436505 Loss1: 0.884313 Loss2: 1.552192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.419097 Loss1: 2.332030 Loss2: 2.087067
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.333701 Loss1: 0.805011 Loss2: 1.528690
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.311978 Loss1: 1.787518 Loss2: 1.524460
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.420676 Loss1: 0.883178 Loss2: 1.537499
-(DefaultActor pid=3764) >> Training accuracy: 0.782227
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.885242 Loss1: 1.353438 Loss2: 1.531804 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.607054 Loss1: 1.066057 Loss2: 1.540997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.570677 Loss1: 1.022187 Loss2: 1.548491
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.238298 Loss1: 2.192422 Loss2: 2.045876
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.457795 Loss1: 0.899871 Loss2: 1.557925
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.231914 Loss1: 1.731151 Loss2: 1.500762
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.457156 Loss1: 0.893316 Loss2: 1.563841
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.880719 Loss1: 1.402939 Loss2: 1.477780
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.420837 Loss1: 0.860287 Loss2: 1.560550
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.639412 Loss1: 1.158833 Loss2: 1.480579
-(DefaultActor pid=3765) >> Training accuracy: 0.731250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.559329 Loss1: 1.075617 Loss2: 1.483712
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.487440 Loss1: 0.979826 Loss2: 1.507614
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.421228 Loss1: 0.911201 Loss2: 1.510026
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.369062 Loss1: 0.851956 Loss2: 1.517106
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.240199 Loss1: 2.240012 Loss2: 2.000187
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.325741 Loss1: 0.813012 Loss2: 1.512729
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.170410 Loss1: 0.652884 Loss2: 1.517526
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.289452 Loss1: 1.783580 Loss2: 1.505872
-(DefaultActor pid=3764) >> Training accuracy: 0.748958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.949715 Loss1: 1.455901 Loss2: 1.493814
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.721215 Loss1: 1.209509 Loss2: 1.511706
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.664765 Loss1: 1.153596 Loss2: 1.511169
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.523647 Loss1: 1.010422 Loss2: 1.513225
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.374050 Loss1: 2.329184 Loss2: 2.044866
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.484985 Loss1: 0.963206 Loss2: 1.521779
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.388690 Loss1: 0.877079 Loss2: 1.511611 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.338010 Loss1: 0.811218 Loss2: 1.526792 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.330302 Loss1: 0.793644 Loss2: 1.536658 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.777344
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.493731 Loss1: 0.986430 Loss2: 1.507301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.481116 Loss1: 0.949359 Loss2: 1.531756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.271895 Loss1: 2.200834 Loss2: 2.071061 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.779167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.889140 Loss1: 1.348751 Loss2: 1.540389 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.566334 Loss1: 1.026431 Loss2: 1.539904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.502852 Loss1: 2.340203 Loss2: 2.162649
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.470126 Loss1: 0.922843 Loss2: 1.547283
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.406005 Loss1: 0.841396 Loss2: 1.564609 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.389176 Loss1: 0.826735 Loss2: 1.562441 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.641535 Loss1: 1.102149 Loss2: 1.539386 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.568528 Loss1: 1.029035 Loss2: 1.539493 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.819336
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.419965 Loss1: 0.864233 Loss2: 1.555732 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.306281 Loss1: 0.751209 Loss2: 1.555072 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.753606
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 08:17:02,905][flwr][DEBUG] - fit_round 32 received 50 results and 0 failures
-INFO flwr 2023-10-09 08:17:45,012 | server.py:125 | fit progress: (32, 2.7599665619694767, {'accuracy': 0.3679}, 73572.790189721)
->> Test accuracy: 0.367900
-[2023-10-09 08:17:45,012][flwr][INFO] - fit progress: (32, 2.7599665619694767, {'accuracy': 0.3679}, 73572.790189721)
-DEBUG flwr 2023-10-09 08:17:45,012 | server.py:173 | evaluate_round 32: strategy sampled 50 clients (out of 50)
-[2023-10-09 08:17:45,012][flwr][DEBUG] - evaluate_round 32: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 08:26:49,669 | server.py:187 | evaluate_round 32 received 50 results and 0 failures
-[2023-10-09 08:26:49,669][flwr][DEBUG] - evaluate_round 32 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 08:26:49,669 | server.py:222 | fit_round 33: strategy sampled 50 clients (out of 50)
-[2023-10-09 08:26:49,669][flwr][DEBUG] - fit_round 33: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.445686 Loss1: 2.404748 Loss2: 2.040938
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.335787 Loss1: 1.813025 Loss2: 1.522762
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.990020 Loss1: 1.513607 Loss2: 1.476413
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.812124 Loss1: 1.329160 Loss2: 1.482964
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.362882 Loss1: 2.297994 Loss2: 2.064888
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.190812 Loss1: 1.699054 Loss2: 1.491758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.920291 Loss1: 1.444139 Loss2: 1.476152 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.773655 Loss1: 1.287735 Loss2: 1.485921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.618606 Loss1: 1.129645 Loss2: 1.488961 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.489422 Loss1: 0.985625 Loss2: 1.503796 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.775000
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.215653 Loss1: 0.719413 Loss2: 1.496240
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.412814 Loss1: 0.905924 Loss2: 1.506890
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.383736 Loss1: 0.875781 Loss2: 1.507955
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.341984 Loss1: 0.823929 Loss2: 1.518056
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.357428 Loss1: 0.832399 Loss2: 1.525029
-(DefaultActor pid=3764) >> Training accuracy: 0.784375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.979161 Loss1: 2.024905 Loss2: 1.954256
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.992138 Loss1: 1.553108 Loss2: 1.439030
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.640812 Loss1: 1.232748 Loss2: 1.408064
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.513551 Loss1: 1.099854 Loss2: 1.413696
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.314280 Loss1: 2.208738 Loss2: 2.105542
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.137429 Loss1: 1.625736 Loss2: 1.511693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.005513 Loss1: 1.485371 Loss2: 1.520141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.661831 Loss1: 1.145261 Loss2: 1.516571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.598356 Loss1: 1.086808 Loss2: 1.511548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.459843 Loss1: 0.947065 Loss2: 1.512779 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.801042
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.135004 Loss1: 0.690338 Loss2: 1.444666
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.311616 Loss1: 0.800724 Loss2: 1.510892
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.328778 Loss1: 0.809205 Loss2: 1.519573
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.439938 Loss1: 0.898772 Loss2: 1.541166
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.286033 Loss1: 0.749094 Loss2: 1.536939
-(DefaultActor pid=3764) >> Training accuracy: 0.792708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.149742 Loss1: 2.121241 Loss2: 2.028501
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.087445 Loss1: 1.648972 Loss2: 1.438472
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.741939 Loss1: 1.298404 Loss2: 1.443535
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.516156 Loss1: 1.076038 Loss2: 1.440118
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.392297 Loss1: 2.238098 Loss2: 2.154199
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.246365 Loss1: 1.700054 Loss2: 1.546311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.369671 Loss1: 0.905051 Loss2: 1.464620
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.945150 Loss1: 1.431947 Loss2: 1.513202
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.393916 Loss1: 0.916928 Loss2: 1.476988
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.695782 Loss1: 1.177273 Loss2: 1.518510
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.532846 Loss1: 1.018177 Loss2: 1.514670
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.238404 Loss1: 0.765463 Loss2: 1.472941
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.439359 Loss1: 0.909382 Loss2: 1.529976
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.172662 Loss1: 0.712922 Loss2: 1.459740
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.388873 Loss1: 0.854136 Loss2: 1.534737
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.199209 Loss1: 0.717238 Loss2: 1.481971
-(DefaultActor pid=3765) >> Training accuracy: 0.835417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.307990 Loss1: 0.761434 Loss2: 1.546556 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.765625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.461326 Loss1: 2.423059 Loss2: 2.038268 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.960653 Loss1: 1.496579 Loss2: 1.464074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.654164 Loss1: 1.195683 Loss2: 1.458482
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.457612 Loss1: 2.371401 Loss2: 2.086211
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.436378 Loss1: 1.891916 Loss2: 1.544461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.997456 Loss1: 1.471139 Loss2: 1.526318 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.829187 Loss1: 1.299001 Loss2: 1.530185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.771431 Loss1: 1.240041 Loss2: 1.531390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.625107 Loss1: 1.075360 Loss2: 1.549747 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.776042
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.406949 Loss1: 0.893208 Loss2: 1.513741
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.530593 Loss1: 0.984532 Loss2: 1.546061
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.345367 Loss1: 0.788108 Loss2: 1.557258
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.373745 Loss1: 0.818285 Loss2: 1.555460
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.327404 Loss1: 0.754259 Loss2: 1.573145
-(DefaultActor pid=3764) >> Training accuracy: 0.828125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.320425 Loss1: 2.272690 Loss2: 2.047735
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.240170 Loss1: 1.735925 Loss2: 1.504245
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.102791 Loss1: 1.596731 Loss2: 1.506060
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.700030 Loss1: 1.202681 Loss2: 1.497349
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.317028 Loss1: 2.267838 Loss2: 2.049190
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.255865 Loss1: 1.756020 Loss2: 1.499845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.846183 Loss1: 1.375017 Loss2: 1.471166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.582624 Loss1: 1.112404 Loss2: 1.470220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.504094 Loss1: 1.035578 Loss2: 1.468517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.386151 Loss1: 0.911199 Loss2: 1.474951 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.773958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.309022 Loss1: 0.782200 Loss2: 1.526823
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.287362 Loss1: 0.812254 Loss2: 1.475108
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.270489 Loss1: 0.806341 Loss2: 1.464148
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.270022 Loss1: 0.777938 Loss2: 1.492084
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.190643 Loss1: 0.695711 Loss2: 1.494933
-(DefaultActor pid=3764) >> Training accuracy: 0.802083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.388349 Loss1: 2.312456 Loss2: 2.075893
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.386992 Loss1: 1.865944 Loss2: 1.521048
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.048336 Loss1: 1.534839 Loss2: 1.513497
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.851937 Loss1: 1.345048 Loss2: 1.506889
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.411611 Loss1: 2.317748 Loss2: 2.093863
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.262507 Loss1: 1.749402 Loss2: 1.513105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.822581 Loss1: 1.319998 Loss2: 1.502583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.768456 Loss1: 1.268667 Loss2: 1.499789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.598361 Loss1: 1.093961 Loss2: 1.504400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.480524 Loss1: 0.981629 Loss2: 1.498896 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.767708
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.381917 Loss1: 0.832152 Loss2: 1.549765
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.307088 Loss1: 0.798077 Loss2: 1.509011
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.344249 Loss1: 0.833331 Loss2: 1.510918
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.221197 Loss1: 0.706186 Loss2: 1.515011
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.164039 Loss1: 0.646197 Loss2: 1.517842
-(DefaultActor pid=3764) >> Training accuracy: 0.792708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.456300 Loss1: 2.363165 Loss2: 2.093135
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.265344 Loss1: 1.717935 Loss2: 1.547409
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.964598 Loss1: 1.454516 Loss2: 1.510081
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.839400 Loss1: 1.330670 Loss2: 1.508731
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.058310 Loss1: 1.999619 Loss2: 2.058692
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.100753 Loss1: 1.624338 Loss2: 1.476415 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.832935 Loss1: 1.354789 Loss2: 1.478147 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.643158 Loss1: 1.178501 Loss2: 1.464657 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.473365 Loss1: 1.000635 Loss2: 1.472730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.414332 Loss1: 0.944723 Loss2: 1.469608 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.782292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.299658 Loss1: 0.816139 Loss2: 1.483519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.251427 Loss1: 0.758805 Loss2: 1.492622 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.787500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.405630 Loss1: 2.274243 Loss2: 2.131387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.002514 Loss1: 1.430749 Loss2: 1.571765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.762651 Loss1: 1.215640 Loss2: 1.547011
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.459364 Loss1: 2.343313 Loss2: 2.116051
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.639291 Loss1: 1.082718 Loss2: 1.556574
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.321219 Loss1: 1.770657 Loss2: 1.550562
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.568683 Loss1: 1.002213 Loss2: 1.566470
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.086840 Loss1: 1.552524 Loss2: 1.534316
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.519910 Loss1: 0.953246 Loss2: 1.566664
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.841736 Loss1: 1.284179 Loss2: 1.557557
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.590914 Loss1: 1.039337 Loss2: 1.551577
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.349656 Loss1: 0.775621 Loss2: 1.574035
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.613595 Loss1: 1.075165 Loss2: 1.538430
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.439261 Loss1: 0.862531 Loss2: 1.576730
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.624276 Loss1: 1.053021 Loss2: 1.571255
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.430554 Loss1: 0.836386 Loss2: 1.594168
-(DefaultActor pid=3765) >> Training accuracy: 0.829102
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.500872 Loss1: 0.925969 Loss2: 1.574904 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.712500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.318654 Loss1: 2.314337 Loss2: 2.004316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.797864 Loss1: 1.386362 Loss2: 1.411502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.603728 Loss1: 1.195781 Loss2: 1.407947
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.445963 Loss1: 2.316633 Loss2: 2.129330
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.458686 Loss1: 1.044807 Loss2: 1.413879
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.312682 Loss1: 1.783906 Loss2: 1.528776
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.385426 Loss1: 0.969754 Loss2: 1.415672
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.982203 Loss1: 1.471906 Loss2: 1.510297
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.288926 Loss1: 0.867725 Loss2: 1.421201
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.772944 Loss1: 1.250713 Loss2: 1.522231
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.209056 Loss1: 0.788860 Loss2: 1.420196
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.613119 Loss1: 1.082102 Loss2: 1.531017
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.246370 Loss1: 0.814257 Loss2: 1.432113
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.498195 Loss1: 0.974233 Loss2: 1.523962
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.149771 Loss1: 0.706210 Loss2: 1.443562
-(DefaultActor pid=3765) >> Training accuracy: 0.787500
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.434143 Loss1: 0.899321 Loss2: 1.534821
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.359697 Loss1: 0.822552 Loss2: 1.537145
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.371700 Loss1: 0.830164 Loss2: 1.541536
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.351389 Loss1: 0.790688 Loss2: 1.560701
-(DefaultActor pid=3764) >> Training accuracy: 0.740625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.720040 Loss1: 2.560873 Loss2: 2.159167
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.529304 Loss1: 1.976651 Loss2: 1.552653
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.117187 Loss1: 1.583220 Loss2: 1.533967
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.872318 Loss1: 1.331324 Loss2: 1.540994
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.564008 Loss1: 2.462521 Loss2: 2.101487
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.277828 Loss1: 1.750093 Loss2: 1.527735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.999607 Loss1: 1.510668 Loss2: 1.488939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.794495 Loss1: 1.287658 Loss2: 1.506837 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.416043 Loss1: 0.839913 Loss2: 1.576130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.462254 Loss1: 0.881498 Loss2: 1.580755 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.802455
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.423260 Loss1: 0.882716 Loss2: 1.540545 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.310486 Loss1: 0.766213 Loss2: 1.544273 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.803125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.083481 Loss1: 1.606120 Loss2: 1.477362 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.577138 Loss1: 1.137346 Loss2: 1.439792 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.426866 Loss1: 0.970196 Loss2: 1.456669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.330223 Loss1: 0.868482 Loss2: 1.461741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.258958 Loss1: 0.793852 Loss2: 1.465106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.526659 Loss1: 0.990529 Loss2: 1.536130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.339129 Loss1: 0.802558 Loss2: 1.536571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.269260 Loss1: 0.710859 Loss2: 1.558401 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.798828
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.177038 Loss1: 0.613491 Loss2: 1.563547 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.822115
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.424885 Loss1: 2.354199 Loss2: 2.070686 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.956765 Loss1: 1.464483 Loss2: 1.492282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.795460 Loss1: 1.295535 Loss2: 1.499925
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.315682 Loss1: 2.336170 Loss2: 1.979512
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.171207 Loss1: 1.687650 Loss2: 1.483557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.848110 Loss1: 1.369135 Loss2: 1.478975 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.614274 Loss1: 1.148212 Loss2: 1.466062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.555635 Loss1: 1.079526 Loss2: 1.476109 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.482030 Loss1: 0.995944 Loss2: 1.486086 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.789583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.369859 Loss1: 0.876391 Loss2: 1.493468 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.385346 Loss1: 0.866404 Loss2: 1.518942 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.758789
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.319066 Loss1: 1.731311 Loss2: 1.587755 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.874240 Loss1: 1.297051 Loss2: 1.577189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.717600 Loss1: 1.138897 Loss2: 1.578703
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.229250 Loss1: 2.170209 Loss2: 2.059041
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.581005 Loss1: 0.996355 Loss2: 1.584651
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.077174 Loss1: 1.582791 Loss2: 1.494384
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.526846 Loss1: 0.940740 Loss2: 1.586106
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.727840 Loss1: 1.262075 Loss2: 1.465765
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.449131 Loss1: 0.864251 Loss2: 1.584879
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.625525 Loss1: 1.146462 Loss2: 1.479064
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.414236 Loss1: 0.816484 Loss2: 1.597752
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.492518 Loss1: 1.010513 Loss2: 1.482005
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.371368 Loss1: 0.781050 Loss2: 1.590318
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.432948 Loss1: 0.940864 Loss2: 1.492084
-(DefaultActor pid=3765) >> Training accuracy: 0.768750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.321117 Loss1: 0.823303 Loss2: 1.497814
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.396396 Loss1: 0.887569 Loss2: 1.508826
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.296616 Loss1: 0.782880 Loss2: 1.513735
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.254549 Loss1: 0.750895 Loss2: 1.503654
-(DefaultActor pid=3764) >> Training accuracy: 0.757292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.610090 Loss1: 2.508000 Loss2: 2.102090
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.389581 Loss1: 1.854513 Loss2: 1.535067
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.085076 Loss1: 1.567102 Loss2: 1.517974
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.957837 Loss1: 1.418028 Loss2: 1.539808
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.881220 Loss1: 1.330368 Loss2: 1.550852
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.620351 Loss1: 1.082745 Loss2: 1.537605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.455206 Loss1: 0.903378 Loss2: 1.551827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.406358 Loss1: 0.858251 Loss2: 1.548108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.298111 Loss1: 0.760869 Loss2: 1.537242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.368918 Loss1: 0.811546 Loss2: 1.557373 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.761161
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.349089 Loss1: 0.798976 Loss2: 1.550113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.200262 Loss1: 0.633853 Loss2: 1.566409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.201299 Loss1: 0.632496 Loss2: 1.568804
-(DefaultActor pid=3764) >> Training accuracy: 0.820833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.493597 Loss1: 2.359496 Loss2: 2.134101
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.377238 Loss1: 1.822819 Loss2: 1.554419
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.927918 Loss1: 1.395260 Loss2: 1.532658
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.730142 Loss1: 1.176513 Loss2: 1.553628
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.631959 Loss1: 1.094146 Loss2: 1.537814
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.412662 Loss1: 2.335797 Loss2: 2.076865
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.427569 Loss1: 1.923990 Loss2: 1.503579 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.963499 Loss1: 1.490337 Loss2: 1.473162 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.801795 Loss1: 1.322458 Loss2: 1.479337 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.629820 Loss1: 1.142469 Loss2: 1.487351 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.795833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.456636 Loss1: 0.967336 Loss2: 1.489300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.515773 Loss1: 0.993259 Loss2: 1.522514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.335466 Loss1: 0.823995 Loss2: 1.511471 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.818750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.278161 Loss1: 1.758167 Loss2: 1.519993 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.884640 Loss1: 1.384974 Loss2: 1.499666 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.748799 Loss1: 1.238437 Loss2: 1.510361
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.188014 Loss1: 2.183543 Loss2: 2.004471
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.631751 Loss1: 1.127901 Loss2: 1.503851
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.037576 Loss1: 1.557440 Loss2: 1.480135
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.510592 Loss1: 1.005282 Loss2: 1.505310
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.842809 Loss1: 1.374111 Loss2: 1.468698
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.434073 Loss1: 0.902366 Loss2: 1.531707
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.606447 Loss1: 1.120287 Loss2: 1.486160
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.392931 Loss1: 0.877484 Loss2: 1.515447
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.431682 Loss1: 0.969113 Loss2: 1.462569
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.376892 Loss1: 0.908662 Loss2: 1.468230
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.337612 Loss1: 0.814742 Loss2: 1.522870
-(DefaultActor pid=3765) >> Training accuracy: 0.724609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.212510 Loss1: 0.730128 Loss2: 1.482382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.175081 Loss1: 0.669239 Loss2: 1.505843 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.782292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.330341 Loss1: 1.821568 Loss2: 1.508773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.746034 Loss1: 1.225968 Loss2: 1.520066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.725040 Loss1: 1.213867 Loss2: 1.511173 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.544027 Loss1: 1.001129 Loss2: 1.542897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.454921 Loss1: 0.933984 Loss2: 1.520936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.419677 Loss1: 0.882020 Loss2: 1.537657 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.380559 Loss1: 0.825969 Loss2: 1.554591 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.419721 Loss1: 0.863108 Loss2: 1.556613 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.756250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.474316 Loss1: 0.902190 Loss2: 1.572127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.422257 Loss1: 0.832457 Loss2: 1.589801 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.759375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.460670 Loss1: 1.907044 Loss2: 1.553626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.877269 Loss1: 1.337907 Loss2: 1.539363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.685269 Loss1: 1.157334 Loss2: 1.527935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.583097 Loss1: 1.044446 Loss2: 1.538651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.513838 Loss1: 0.973729 Loss2: 1.540109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.467356 Loss1: 0.899913 Loss2: 1.567444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.368982 Loss1: 0.808361 Loss2: 1.560620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.373878 Loss1: 0.818180 Loss2: 1.555698 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.708008
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.325729 Loss1: 0.848694 Loss2: 1.477035 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.340141 Loss1: 0.849036 Loss2: 1.491105 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.734375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.262585 Loss1: 1.766709 Loss2: 1.495876 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.692108 Loss1: 1.203694 Loss2: 1.488414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.285492 Loss1: 2.215802 Loss2: 2.069690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.217072 Loss1: 1.717714 Loss2: 1.499358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.876637 Loss1: 1.384704 Loss2: 1.491932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.703977 Loss1: 1.223265 Loss2: 1.480711 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.275690 Loss1: 0.771267 Loss2: 1.504423 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.832933
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.255368 Loss1: 0.775326 Loss2: 1.480042 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.287248 Loss1: 0.779504 Loss2: 1.507744 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.363339 Loss1: 2.217705 Loss2: 2.145635
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.290531 Loss1: 0.768401 Loss2: 1.522130
-(DefaultActor pid=3764) >> Training accuracy: 0.805208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.842932 Loss1: 1.306229 Loss2: 1.536703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.500458 Loss1: 0.976597 Loss2: 1.523861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.477246 Loss1: 2.360861 Loss2: 2.116385
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.395195 Loss1: 0.866597 Loss2: 1.528598
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.327205 Loss1: 0.791147 Loss2: 1.536058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.263940 Loss1: 0.728717 Loss2: 1.535223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.196345 Loss1: 0.649104 Loss2: 1.547241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.457276 Loss1: 0.994378 Loss2: 1.462898 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.829167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.323580 Loss1: 0.856664 Loss2: 1.466916 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.148571 Loss1: 0.675716 Loss2: 1.472855 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.769531
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.185838 Loss1: 2.098918 Loss2: 2.086920
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.146533 Loss1: 1.609787 Loss2: 1.536746
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.750069 Loss1: 1.235305 Loss2: 1.514764
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.363953 Loss1: 2.267022 Loss2: 2.096931
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.676986 Loss1: 1.174068 Loss2: 1.502917
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.211517 Loss1: 1.680180 Loss2: 1.531337
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.457573 Loss1: 0.945659 Loss2: 1.511914
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.892704 Loss1: 1.385481 Loss2: 1.507223
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.335444 Loss1: 0.825759 Loss2: 1.509686
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.300935 Loss1: 0.777678 Loss2: 1.523257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.341205 Loss1: 0.819767 Loss2: 1.521439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.335934 Loss1: 0.815231 Loss2: 1.520703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.178864 Loss1: 0.646160 Loss2: 1.532704 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.816406
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.198077 Loss1: 0.688649 Loss2: 1.509428 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.785417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.252320 Loss1: 2.255686 Loss2: 1.996634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.911247 Loss1: 1.493515 Loss2: 1.417732 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.717238 Loss1: 1.284853 Loss2: 1.432385
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.175238 Loss1: 2.168623 Loss2: 2.006614
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.109844 Loss1: 1.606898 Loss2: 1.502947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.892926 Loss1: 1.436251 Loss2: 1.456675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.590018 Loss1: 1.114991 Loss2: 1.475027 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.459758 Loss1: 1.005170 Loss2: 1.454588 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.399602 Loss1: 0.938151 Loss2: 1.461451 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.729167
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.427925 Loss1: 0.946565 Loss2: 1.481360
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.396307 Loss1: 0.934596 Loss2: 1.461711
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.341359 Loss1: 0.863196 Loss2: 1.478164
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.158163 Loss1: 0.690880 Loss2: 1.467282
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.127539 Loss1: 0.651090 Loss2: 1.476449
-(DefaultActor pid=3764) >> Training accuracy: 0.790625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.174194 Loss1: 2.213169 Loss2: 1.961026
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.215269 Loss1: 1.756729 Loss2: 1.458539
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.838723 Loss1: 1.385708 Loss2: 1.453015
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.353975 Loss1: 2.355895 Loss2: 1.998080
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.608775 Loss1: 1.176084 Loss2: 1.432691
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.538847 Loss1: 1.090594 Loss2: 1.448253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.437865 Loss1: 0.981698 Loss2: 1.456168 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.355355 Loss1: 0.891119 Loss2: 1.464236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.219756 Loss1: 0.759728 Loss2: 1.460027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.334600 Loss1: 0.868711 Loss2: 1.465889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.392608 Loss1: 0.896032 Loss2: 1.496575 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.775735
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.222252 Loss1: 0.724354 Loss2: 1.497897 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.786458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.465827 Loss1: 2.425931 Loss2: 2.039897
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.367582 Loss1: 1.865840 Loss2: 1.501742
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.045182 Loss1: 1.551092 Loss2: 1.494090
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.334894 Loss1: 2.305036 Loss2: 2.029858
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.845480 Loss1: 1.342762 Loss2: 1.502717
-DEBUG flwr 2023-10-09 08:55:41,996 | server.py:236 | fit_round 33 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.219250 Loss1: 1.739828 Loss2: 1.479422
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.718253 Loss1: 1.201132 Loss2: 1.517121
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.805650 Loss1: 1.336537 Loss2: 1.469113
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.543593 Loss1: 1.023736 Loss2: 1.519858
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.648098 Loss1: 1.182489 Loss2: 1.465608
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.500526 Loss1: 0.986655 Loss2: 1.513871
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.567845 Loss1: 1.091245 Loss2: 1.476600
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.367952 Loss1: 0.844432 Loss2: 1.523520
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.426479 Loss1: 0.936909 Loss2: 1.489570
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.320003 Loss1: 0.780846 Loss2: 1.539157
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.305337 Loss1: 0.823873 Loss2: 1.481464
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.449621 Loss1: 0.909320 Loss2: 1.540301
-(DefaultActor pid=3765) >> Training accuracy: 0.697266
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.246913 Loss1: 0.758878 Loss2: 1.488035 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.772461
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.475298 Loss1: 2.295559 Loss2: 2.179739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.056462 Loss1: 1.476064 Loss2: 1.580397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.823719 Loss1: 1.246223 Loss2: 1.577496
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.421435 Loss1: 2.344734 Loss2: 2.076701
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.396794 Loss1: 1.865267 Loss2: 1.531526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.103901 Loss1: 1.569868 Loss2: 1.534033 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.916528 Loss1: 1.388366 Loss2: 1.528162 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.696877 Loss1: 1.164316 Loss2: 1.532560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.540074 Loss1: 1.011825 Loss2: 1.528249 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.825000
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.436869 Loss1: 0.818016 Loss2: 1.618852
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.532021 Loss1: 0.983724 Loss2: 1.548298
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.503122 Loss1: 0.950826 Loss2: 1.552296
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.461263 Loss1: 0.893916 Loss2: 1.567347
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.537008 Loss1: 0.966383 Loss2: 1.570625
-(DefaultActor pid=3764) >> Training accuracy: 0.721875
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 08:55:41,996][flwr][DEBUG] - fit_round 33 received 50 results and 0 failures
-INFO flwr 2023-10-09 08:56:23,658 | server.py:125 | fit progress: (33, 2.7204779908299064, {'accuracy': 0.3764}, 75891.436895839)
->> Test accuracy: 0.376400
-[2023-10-09 08:56:23,658][flwr][INFO] - fit progress: (33, 2.7204779908299064, {'accuracy': 0.3764}, 75891.436895839)
-DEBUG flwr 2023-10-09 08:56:23,659 | server.py:173 | evaluate_round 33: strategy sampled 50 clients (out of 50)
-[2023-10-09 08:56:23,659][flwr][DEBUG] - evaluate_round 33: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 09:05:27,014 | server.py:187 | evaluate_round 33 received 50 results and 0 failures
-[2023-10-09 09:05:27,014][flwr][DEBUG] - evaluate_round 33 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 09:05:27,015 | server.py:222 | fit_round 34: strategy sampled 50 clients (out of 50)
-[2023-10-09 09:05:27,015][flwr][DEBUG] - fit_round 34: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.222358 Loss1: 2.238348 Loss2: 1.984010
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.144200 Loss1: 1.674757 Loss2: 1.469443
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.906062 Loss1: 1.452050 Loss2: 1.454012
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.344052 Loss1: 2.125761 Loss2: 2.218291
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.626324 Loss1: 1.159369 Loss2: 1.466956
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.145256 Loss1: 1.536794 Loss2: 1.608462
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.510956 Loss1: 1.054332 Loss2: 1.456624
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.836997 Loss1: 1.254651 Loss2: 1.582346
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.389443 Loss1: 0.923503 Loss2: 1.465940
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.655923 Loss1: 1.078744 Loss2: 1.577178
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.287629 Loss1: 0.825406 Loss2: 1.462223
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.190309 Loss1: 0.729464 Loss2: 1.460845 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.117926 Loss1: 0.650903 Loss2: 1.467023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.259262 Loss1: 0.784540 Loss2: 1.474721 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.777344
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.348158 Loss1: 0.730868 Loss2: 1.617289 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.835417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.319429 Loss1: 2.169097 Loss2: 2.150332 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.974026 Loss1: 1.374623 Loss2: 1.599403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.366228 Loss1: 2.340827 Loss2: 2.025400
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.742180 Loss1: 1.149588 Loss2: 1.592592
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.229144 Loss1: 1.728124 Loss2: 1.501021
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.588378 Loss1: 0.981066 Loss2: 1.607312
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.867571 Loss1: 1.371827 Loss2: 1.495744
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.502888 Loss1: 0.890164 Loss2: 1.612723
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.627438 Loss1: 1.129618 Loss2: 1.497821
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.486702 Loss1: 0.875580 Loss2: 1.611122
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.426959 Loss1: 0.797586 Loss2: 1.629373 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.293016 Loss1: 0.676230 Loss2: 1.616786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.319538 Loss1: 0.695067 Loss2: 1.624471 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.819336
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.240991 Loss1: 0.728816 Loss2: 1.512175 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.830208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.421464 Loss1: 2.339249 Loss2: 2.082215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.007886 Loss1: 1.488344 Loss2: 1.519542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.822655 Loss1: 1.285468 Loss2: 1.537187
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.231240 Loss1: 2.207974 Loss2: 2.023266
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.671308 Loss1: 1.138092 Loss2: 1.533217
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.998110 Loss1: 1.511049 Loss2: 1.487061
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.707997 Loss1: 1.252955 Loss2: 1.455042
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.727205 Loss1: 1.173592 Loss2: 1.553613
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.500362 Loss1: 1.041905 Loss2: 1.458457
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.460099 Loss1: 0.909334 Loss2: 1.550766
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.394837 Loss1: 0.924029 Loss2: 1.470808
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.410310 Loss1: 0.853645 Loss2: 1.556665
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.312406 Loss1: 0.852668 Loss2: 1.459738
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.320319 Loss1: 0.757613 Loss2: 1.562706
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.272185 Loss1: 0.722169 Loss2: 1.550017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.784180
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.178972 Loss1: 0.700398 Loss2: 1.478574 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.778125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.200657 Loss1: 2.176517 Loss2: 2.024140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.789660 Loss1: 1.289939 Loss2: 1.499721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.610475 Loss1: 1.101563 Loss2: 1.508912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.478182 Loss1: 0.960641 Loss2: 1.517540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.653075 Loss1: 1.159636 Loss2: 1.493439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.533807 Loss1: 1.032193 Loss2: 1.501614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.463238 Loss1: 0.967620 Loss2: 1.495618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.301276 Loss1: 0.803020 Loss2: 1.498256 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.284904 Loss1: 0.774516 Loss2: 1.510388
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.187397 Loss1: 0.654231 Loss2: 1.533166
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.138257 Loss1: 0.619471 Loss2: 1.518786 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.797852
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.286033 Loss1: 2.206441 Loss2: 2.079592 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.833333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.868187 Loss1: 1.377159 Loss2: 1.491028 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.625435 Loss1: 1.129833 Loss2: 1.495602
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.186680 Loss1: 2.162583 Loss2: 2.024097
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.385129 Loss1: 0.909940 Loss2: 1.475190
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.124547 Loss1: 1.647821 Loss2: 1.476726
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.386791 Loss1: 0.911040 Loss2: 1.475751
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.780739 Loss1: 1.300624 Loss2: 1.480115
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.301201 Loss1: 0.801236 Loss2: 1.499964
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.522708 Loss1: 1.036040 Loss2: 1.486668
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.224468 Loss1: 0.736353 Loss2: 1.488115
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.556706 Loss1: 1.082702 Loss2: 1.474004
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.307460 Loss1: 0.806921 Loss2: 1.500539
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.404887 Loss1: 0.892498 Loss2: 1.512390
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.143795 Loss1: 0.629302 Loss2: 1.514493
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.344381 Loss1: 0.845749 Loss2: 1.498632
-(DefaultActor pid=3765) >> Training accuracy: 0.832292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.242935 Loss1: 0.751357 Loss2: 1.491578
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.215540 Loss1: 0.728021 Loss2: 1.487519
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.115423 Loss1: 0.612821 Loss2: 1.502602
-(DefaultActor pid=3764) >> Training accuracy: 0.846875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.364782 Loss1: 2.305702 Loss2: 2.059080
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.430463 Loss1: 1.922528 Loss2: 1.507935
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.006028 Loss1: 1.504450 Loss2: 1.501579
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.656440 Loss1: 1.172787 Loss2: 1.483653
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.289074 Loss1: 2.247091 Loss2: 2.041982
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.197469 Loss1: 1.719732 Loss2: 1.477737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.889318 Loss1: 1.434640 Loss2: 1.454678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.689864 Loss1: 1.218884 Loss2: 1.470980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.571060 Loss1: 1.097504 Loss2: 1.473556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.506066 Loss1: 1.009739 Loss2: 1.496328 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.751042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.418520 Loss1: 0.934789 Loss2: 1.483731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.227647 Loss1: 0.734080 Loss2: 1.493567 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.806250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.218866 Loss1: 2.193234 Loss2: 2.025632 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.770606 Loss1: 1.274383 Loss2: 1.496223 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.402710 Loss1: 2.340840 Loss2: 2.061870
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.703358 Loss1: 1.197197 Loss2: 1.506161
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.256725 Loss1: 1.753945 Loss2: 1.502781
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.496990 Loss1: 0.989430 Loss2: 1.507559
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.978718 Loss1: 1.500865 Loss2: 1.477853
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.455827 Loss1: 0.950537 Loss2: 1.505290
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.293013 Loss1: 0.790063 Loss2: 1.502950 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.311473 Loss1: 0.798687 Loss2: 1.512786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.190240 Loss1: 0.667464 Loss2: 1.522776 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.182120 Loss1: 0.671178 Loss2: 1.510943 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.846507
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.155380 Loss1: 0.654899 Loss2: 1.500482 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.803125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.269794 Loss1: 2.183401 Loss2: 2.086392
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.232942 Loss1: 1.678974 Loss2: 1.553968
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.898872 Loss1: 1.367211 Loss2: 1.531660
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.673956 Loss1: 1.131140 Loss2: 1.542816
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.406831 Loss1: 2.292334 Loss2: 2.114497
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.628022 Loss1: 1.076365 Loss2: 1.551657
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.409568 Loss1: 1.872035 Loss2: 1.537533
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.936545 Loss1: 1.432040 Loss2: 1.504506
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.558328 Loss1: 1.006894 Loss2: 1.551434
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.722165 Loss1: 1.208275 Loss2: 1.513890
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.324586 Loss1: 0.764223 Loss2: 1.560362
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.666114 Loss1: 1.140573 Loss2: 1.525542
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.279265 Loss1: 0.732851 Loss2: 1.546414
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.531962 Loss1: 1.011260 Loss2: 1.520702
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.313646 Loss1: 0.745772 Loss2: 1.567874
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.321221 Loss1: 0.746676 Loss2: 1.574545 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.764648
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.362305 Loss1: 0.823335 Loss2: 1.538969 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.750000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.307487 Loss1: 2.283973 Loss2: 2.023514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.922420 Loss1: 1.448583 Loss2: 1.473837 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.626814 Loss1: 1.142308 Loss2: 1.484507
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.327203 Loss1: 2.230212 Loss2: 2.096990
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.198937 Loss1: 1.677007 Loss2: 1.521930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.934719 Loss1: 1.409314 Loss2: 1.525404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.695234 Loss1: 1.165978 Loss2: 1.529256 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.540244 Loss1: 1.008939 Loss2: 1.531305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.419466 Loss1: 0.883816 Loss2: 1.535650 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.795833
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.119002 Loss1: 0.633267 Loss2: 1.485735
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.368558 Loss1: 0.843552 Loss2: 1.525006
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.327097 Loss1: 0.784582 Loss2: 1.542515
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.329441 Loss1: 0.777481 Loss2: 1.551960
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.247739 Loss1: 0.686051 Loss2: 1.561688
-(DefaultActor pid=3764) >> Training accuracy: 0.813542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.418702 Loss1: 2.384183 Loss2: 2.034519
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.222075 Loss1: 1.739603 Loss2: 1.482471
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.925990 Loss1: 1.452936 Loss2: 1.473054
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.673603 Loss1: 1.194474 Loss2: 1.479129
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.296359 Loss1: 2.204315 Loss2: 2.092043
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.229916 Loss1: 1.693056 Loss2: 1.536860 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.926533 Loss1: 1.415810 Loss2: 1.510723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.608104 Loss1: 1.089777 Loss2: 1.518326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.527514 Loss1: 1.014081 Loss2: 1.513433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.551385 Loss1: 1.013748 Loss2: 1.537636 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.812500
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.217906 Loss1: 0.717003 Loss2: 1.500902
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.512777 Loss1: 0.960607 Loss2: 1.552170
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.391159 Loss1: 0.847299 Loss2: 1.543860
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.223132 Loss1: 0.688805 Loss2: 1.534327
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.337669 Loss1: 0.789859 Loss2: 1.547810
-(DefaultActor pid=3764) >> Training accuracy: 0.755208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.511739 Loss1: 2.435189 Loss2: 2.076550
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.348617 Loss1: 1.816781 Loss2: 1.531836
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.015640 Loss1: 1.503062 Loss2: 1.512578
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.825475 Loss1: 1.317079 Loss2: 1.508397
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.474779 Loss1: 2.385989 Loss2: 2.088790
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.247836 Loss1: 1.749459 Loss2: 1.498377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.914876 Loss1: 1.427001 Loss2: 1.487875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.705044 Loss1: 1.217255 Loss2: 1.487789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.585290 Loss1: 1.087342 Loss2: 1.497948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.536491 Loss1: 1.038824 Loss2: 1.497667 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.812500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.285721 Loss1: 0.782224 Loss2: 1.503497 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.283343 Loss1: 0.766589 Loss2: 1.516754 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.822917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.351481 Loss1: 1.795869 Loss2: 1.555612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.933319 Loss1: 1.391087 Loss2: 1.542232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.706695 Loss1: 1.151375 Loss2: 1.555320
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.301691 Loss1: 2.239493 Loss2: 2.062197
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.485612 Loss1: 0.948740 Loss2: 1.536872
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.259527 Loss1: 1.746709 Loss2: 1.512818
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.902645 Loss1: 1.417872 Loss2: 1.484773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.749134 Loss1: 1.257224 Loss2: 1.491910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.552184 Loss1: 1.056986 Loss2: 1.495199 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.766741
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.488874 Loss1: 0.981603 Loss2: 1.507271 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.404052 Loss1: 0.886433 Loss2: 1.517619 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.332489 Loss1: 0.808616 Loss2: 1.523873 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.785417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.058387 Loss1: 1.519772 Loss2: 1.538615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.535332 Loss1: 0.999305 Loss2: 1.536027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.438446 Loss1: 0.909446 Loss2: 1.529001
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.288409 Loss1: 2.187247 Loss2: 2.101162
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.223479 Loss1: 1.676567 Loss2: 1.546911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.871527 Loss1: 1.346461 Loss2: 1.525067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.616773 Loss1: 1.082419 Loss2: 1.534354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.527501 Loss1: 0.993747 Loss2: 1.533754 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.744792
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.283644 Loss1: 0.729946 Loss2: 1.553698
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.517512 Loss1: 0.975271 Loss2: 1.542241
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.332457 Loss1: 0.786890 Loss2: 1.545566
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.253303 Loss1: 0.706196 Loss2: 1.547106
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.379985 Loss1: 0.817167 Loss2: 1.562817
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.342369 Loss1: 0.768742 Loss2: 1.573628
-(DefaultActor pid=3764) >> Training accuracy: 0.798958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.286006 Loss1: 2.272825 Loss2: 2.013181
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.161737 Loss1: 1.649379 Loss2: 1.512358
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.872651 Loss1: 1.390144 Loss2: 1.482508
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.649083 Loss1: 1.153184 Loss2: 1.495899
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.589626 Loss1: 1.093239 Loss2: 1.496387
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.235774 Loss1: 2.120121 Loss2: 2.115653
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.467996 Loss1: 0.969979 Loss2: 1.498017
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.415212 Loss1: 0.922102 Loss2: 1.493110 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.309586 Loss1: 0.797222 Loss2: 1.512364 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.317570 Loss1: 0.813692 Loss2: 1.503878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.217075 Loss1: 0.696777 Loss2: 1.520298 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.841667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.221060 Loss1: 0.707043 Loss2: 1.514017 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.160871 Loss1: 0.621813 Loss2: 1.539058 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.105183 Loss1: 0.578691 Loss2: 1.526492
-(DefaultActor pid=3764) >> Training accuracy: 0.860417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.472986 Loss1: 2.340649 Loss2: 2.132337
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.295750 Loss1: 1.765191 Loss2: 1.530559
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.048677 Loss1: 1.533447 Loss2: 1.515230
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.824646 Loss1: 1.287014 Loss2: 1.537632
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.701979 Loss1: 1.164218 Loss2: 1.537762
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.319053 Loss1: 2.306644 Loss2: 2.012409
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.603822 Loss1: 1.055965 Loss2: 1.547857
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.554852 Loss1: 1.003704 Loss2: 1.551149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.497853 Loss1: 0.948024 Loss2: 1.549828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.455079 Loss1: 0.898366 Loss2: 1.556713 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.353433 Loss1: 0.782846 Loss2: 1.570587 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.767708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.215629 Loss1: 0.735440 Loss2: 1.480189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.162829 Loss1: 0.681794 Loss2: 1.481035 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.171499 Loss1: 0.679628 Loss2: 1.491871
-(DefaultActor pid=3764) >> Training accuracy: 0.790625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.493800 Loss1: 2.353522 Loss2: 2.140277
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.384979 Loss1: 1.812480 Loss2: 1.572499
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.184834 Loss1: 1.602024 Loss2: 1.582810
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.829682 Loss1: 1.277451 Loss2: 1.552231
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.610324 Loss1: 1.063933 Loss2: 1.546391
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.091633 Loss1: 2.044318 Loss2: 2.047316
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.516559 Loss1: 0.958551 Loss2: 1.558008
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.468406 Loss1: 0.901808 Loss2: 1.566598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.370116 Loss1: 0.807880 Loss2: 1.562236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.385842 Loss1: 0.807144 Loss2: 1.578698 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.325080 Loss1: 0.742524 Loss2: 1.582556 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.752083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.219154 Loss1: 0.745672 Loss2: 1.473482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.185754 Loss1: 0.717677 Loss2: 1.468077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.258638 Loss1: 0.784644 Loss2: 1.473993
-(DefaultActor pid=3764) >> Training accuracy: 0.851042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.410369 Loss1: 2.403733 Loss2: 2.006636
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.370879 Loss1: 1.864414 Loss2: 1.506466
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.915472 Loss1: 1.440237 Loss2: 1.475235
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.693203 Loss1: 1.209694 Loss2: 1.483509
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.699238 Loss1: 1.202992 Loss2: 1.496246
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.258831 Loss1: 2.215816 Loss2: 2.043014
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.574536 Loss1: 1.069588 Loss2: 1.504948
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.258545 Loss1: 1.743828 Loss2: 1.514717
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.485564 Loss1: 0.978314 Loss2: 1.507250
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.904127 Loss1: 1.390541 Loss2: 1.513586
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.438650 Loss1: 0.913370 Loss2: 1.525280
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.709996 Loss1: 1.181239 Loss2: 1.528757
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.375067 Loss1: 0.855402 Loss2: 1.519665
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.603806 Loss1: 1.087971 Loss2: 1.515835
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.230924 Loss1: 0.700933 Loss2: 1.529991
-(DefaultActor pid=3765) >> Training accuracy: 0.788086
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.488457 Loss1: 0.970352 Loss2: 1.518105
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.558613 Loss1: 1.011494 Loss2: 1.547119
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.493433 Loss1: 0.933587 Loss2: 1.559846
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.401512 Loss1: 0.866454 Loss2: 1.535057
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.300583 Loss1: 0.749075 Loss2: 1.551508
-(DefaultActor pid=3764) >> Training accuracy: 0.821289
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.399257 Loss1: 2.382696 Loss2: 2.016561
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.317018 Loss1: 1.798329 Loss2: 1.518689
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.964030 Loss1: 1.456569 Loss2: 1.507461
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.812513 Loss1: 1.291326 Loss2: 1.521187
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.675937 Loss1: 1.154655 Loss2: 1.521282
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.172393 Loss1: 2.135632 Loss2: 2.036761
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.172771 Loss1: 1.705226 Loss2: 1.467545
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.572086 Loss1: 1.035893 Loss2: 1.536192
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.861162 Loss1: 1.405808 Loss2: 1.455354
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.557268 Loss1: 1.008922 Loss2: 1.548346
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.652105 Loss1: 1.172141 Loss2: 1.479964
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.515100 Loss1: 0.973901 Loss2: 1.541200
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.530563 Loss1: 1.045046 Loss2: 1.485517
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.553378 Loss1: 0.994929 Loss2: 1.558449
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.429778 Loss1: 0.855890 Loss2: 1.573888 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.713867
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.219885 Loss1: 0.728270 Loss2: 1.491615 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.186527 Loss1: 0.691317 Loss2: 1.495210 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.814583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.406486 Loss1: 2.287651 Loss2: 2.118835
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.298539 Loss1: 1.752418 Loss2: 1.546122
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.043228 Loss1: 1.505384 Loss2: 1.537844
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.898705 Loss1: 1.342920 Loss2: 1.555786
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.995836 Loss1: 2.042151 Loss2: 1.953685
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.998626 Loss1: 1.589878 Loss2: 1.408748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.686902 Loss1: 1.307699 Loss2: 1.379203 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.381107 Loss1: 1.018253 Loss2: 1.362853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.313751 Loss1: 0.928737 Loss2: 1.385014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.223667 Loss1: 0.840020 Loss2: 1.383647 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.785417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.122179 Loss1: 0.720566 Loss2: 1.401613 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.051391 Loss1: 0.651925 Loss2: 1.399466 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.853125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.985919 Loss1: 2.016486 Loss2: 1.969434
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.987619 Loss1: 1.516292 Loss2: 1.471327
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.664639 Loss1: 1.224291 Loss2: 1.440348
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.450215 Loss1: 1.017134 Loss2: 1.433081
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.258509 Loss1: 2.231464 Loss2: 2.027046
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.243565 Loss1: 1.759958 Loss2: 1.483607 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.289433 Loss1: 0.829250 Loss2: 1.460183
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.876513 Loss1: 1.405791 Loss2: 1.470722
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.194048 Loss1: 0.748804 Loss2: 1.445244
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.698405 Loss1: 1.234141 Loss2: 1.464264
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.178338 Loss1: 0.723196 Loss2: 1.455142
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.531405 Loss1: 1.046373 Loss2: 1.485032
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.206391 Loss1: 0.748196 Loss2: 1.458195
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.468332 Loss1: 0.986938 Loss2: 1.481394
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.183706 Loss1: 0.710259 Loss2: 1.473447
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.402760 Loss1: 0.903856 Loss2: 1.498904
-(DefaultActor pid=3765) >> Training accuracy: 0.847656
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.321890 Loss1: 0.835423 Loss2: 1.486467
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.302250 Loss1: 0.809924 Loss2: 1.492327
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.217847 Loss1: 0.728043 Loss2: 1.489804
-(DefaultActor pid=3764) >> Training accuracy: 0.789583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.109878 Loss1: 2.081811 Loss2: 2.028067
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.103558 Loss1: 1.588121 Loss2: 1.515437
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.798829 Loss1: 1.310797 Loss2: 1.488032
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.560976 Loss1: 1.091302 Loss2: 1.469673
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.215958 Loss1: 2.149858 Loss2: 2.066101
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.039607 Loss1: 1.571389 Loss2: 1.468218
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.441685 Loss1: 0.969589 Loss2: 1.472095
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.673932 Loss1: 1.257471 Loss2: 1.416461
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.327046 Loss1: 0.850191 Loss2: 1.476855
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.353148 Loss1: 0.857940 Loss2: 1.495208 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.260385 Loss1: 0.762259 Loss2: 1.498126 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.129168 Loss1: 0.631080 Loss2: 1.498088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.116655 Loss1: 0.622301 Loss2: 1.494354 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.810417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.062701 Loss1: 0.619972 Loss2: 1.442729 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.843750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.262967 Loss1: 2.127169 Loss2: 2.135799
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.093401 Loss1: 1.563938 Loss2: 1.529463
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.900824 Loss1: 1.379869 Loss2: 1.520954
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.606468 Loss1: 1.087403 Loss2: 1.519065
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.395145 Loss1: 2.270745 Loss2: 2.124400
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.436921 Loss1: 0.912178 Loss2: 1.524743
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.264599 Loss1: 1.728549 Loss2: 1.536049
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.348441 Loss1: 0.819324 Loss2: 1.529117
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.911001 Loss1: 1.393526 Loss2: 1.517475
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.322357 Loss1: 0.787619 Loss2: 1.534737
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.734583 Loss1: 1.202990 Loss2: 1.531593
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.369384 Loss1: 0.826384 Loss2: 1.543000
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.601772 Loss1: 1.075404 Loss2: 1.526368
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.335638 Loss1: 0.774189 Loss2: 1.561449
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.499080 Loss1: 0.957957 Loss2: 1.541123
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.293703 Loss1: 0.733857 Loss2: 1.559846
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.417189 Loss1: 0.873243 Loss2: 1.543946
-(DefaultActor pid=3765) >> Training accuracy: 0.795833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.436149 Loss1: 0.894169 Loss2: 1.541980
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.412517 Loss1: 0.840693 Loss2: 1.571825
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.289160 Loss1: 0.729182 Loss2: 1.559978
-(DefaultActor pid=3764) >> Training accuracy: 0.818750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.378419 Loss1: 2.243186 Loss2: 2.135232
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.204915 Loss1: 1.677742 Loss2: 1.527173
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.820675 Loss1: 1.333336 Loss2: 1.487339
-DEBUG flwr 2023-10-09 09:34:51,916 | server.py:236 | fit_round 34 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.657319 Loss1: 1.155376 Loss2: 1.501943
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.370656 Loss1: 2.210430 Loss2: 2.160226
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.322199 Loss1: 0.825355 Loss2: 1.496844 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.303796 Loss1: 0.791274 Loss2: 1.512522 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.360361 Loss1: 0.816328 Loss2: 1.544033 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.206547 Loss1: 0.674579 Loss2: 1.531968 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.141791 Loss1: 0.610253 Loss2: 1.531539 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.830529
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.278084 Loss1: 0.730133 Loss2: 1.547951 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.257700 Loss1: 0.692215 Loss2: 1.565485 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.723214
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.232559 Loss1: 1.722297 Loss2: 1.510262 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.675025 Loss1: 1.201496 Loss2: 1.473529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.323050 Loss1: 2.153659 Loss2: 2.169391
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.579024 Loss1: 1.088393 Loss2: 1.490630
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.148022 Loss1: 1.568029 Loss2: 1.579993
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.447677 Loss1: 0.965953 Loss2: 1.481724
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.847577 Loss1: 1.295848 Loss2: 1.551730
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.397595 Loss1: 0.886064 Loss2: 1.511530
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.673678 Loss1: 1.104606 Loss2: 1.569072
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.229017 Loss1: 0.732877 Loss2: 1.496140
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.511255 Loss1: 0.965547 Loss2: 1.545708
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.196350 Loss1: 0.701606 Loss2: 1.494744
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.436249 Loss1: 0.879165 Loss2: 1.557084
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.272236 Loss1: 0.766318 Loss2: 1.505919
-(DefaultActor pid=3765) >> Training accuracy: 0.732292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.286298 Loss1: 0.724329 Loss2: 1.561969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.122625 Loss1: 0.553773 Loss2: 1.568852 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.840625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.162796 Loss1: 1.606786 Loss2: 1.556010 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.618035 Loss1: 1.111787 Loss2: 1.506247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.432616 Loss1: 2.281058 Loss2: 2.151558
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.473197 Loss1: 0.947918 Loss2: 1.525280
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.271885 Loss1: 1.703095 Loss2: 1.568789
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.364959 Loss1: 0.851019 Loss2: 1.513940
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.018994 Loss1: 1.472422 Loss2: 1.546572
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.248724 Loss1: 0.726264 Loss2: 1.522460
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.771743 Loss1: 1.206356 Loss2: 1.565387
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.282307 Loss1: 0.751441 Loss2: 1.530865
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.660703 Loss1: 1.087325 Loss2: 1.573377
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.234515 Loss1: 0.682081 Loss2: 1.552434
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.548351 Loss1: 0.961314 Loss2: 1.587037
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.243926 Loss1: 0.702872 Loss2: 1.541054
-(DefaultActor pid=3765) >> Training accuracy: 0.820833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.393748 Loss1: 0.810047 Loss2: 1.583700 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.383805 Loss1: 0.793798 Loss2: 1.590006 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.818750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 09:34:51,916][flwr][DEBUG] - fit_round 34 received 50 results and 0 failures
-INFO flwr 2023-10-09 09:35:34,074 | server.py:125 | fit progress: (34, 2.68708546359699, {'accuracy': 0.3834}, 78241.852389781)
->> Test accuracy: 0.383400
-[2023-10-09 09:35:34,074][flwr][INFO] - fit progress: (34, 2.68708546359699, {'accuracy': 0.3834}, 78241.852389781)
-DEBUG flwr 2023-10-09 09:35:34,074 | server.py:173 | evaluate_round 34: strategy sampled 50 clients (out of 50)
-[2023-10-09 09:35:34,074][flwr][DEBUG] - evaluate_round 34: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 09:44:37,258 | server.py:187 | evaluate_round 34 received 50 results and 0 failures
-[2023-10-09 09:44:37,258][flwr][DEBUG] - evaluate_round 34 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 09:44:37,258 | server.py:222 | fit_round 35: strategy sampled 50 clients (out of 50)
-[2023-10-09 09:44:37,258][flwr][DEBUG] - fit_round 35: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.122047 Loss1: 1.999771 Loss2: 2.122276
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.985779 Loss1: 1.450234 Loss2: 1.535545
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.641013 Loss1: 1.127759 Loss2: 1.513254
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.545282 Loss1: 1.023113 Loss2: 1.522168
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.271664 Loss1: 2.143282 Loss2: 2.128382
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.236754 Loss1: 1.673676 Loss2: 1.563078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.948477 Loss1: 1.406340 Loss2: 1.542136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.762158 Loss1: 1.208015 Loss2: 1.554143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.550349 Loss1: 0.997863 Loss2: 1.552485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.651764 Loss1: 1.088936 Loss2: 1.562828 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.843750
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.186872 Loss1: 0.644523 Loss2: 1.542349
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.518187 Loss1: 0.949885 Loss2: 1.568302
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.397036 Loss1: 0.827388 Loss2: 1.569648
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.260921 Loss1: 0.695794 Loss2: 1.565127
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.230037 Loss1: 0.664809 Loss2: 1.565228
-(DefaultActor pid=3764) >> Training accuracy: 0.838542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.312375 Loss1: 2.294363 Loss2: 2.018012
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.191612 Loss1: 1.707794 Loss2: 1.483818
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.868278 Loss1: 1.420575 Loss2: 1.447703
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.671845 Loss1: 1.205113 Loss2: 1.466731
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.359090 Loss1: 2.287070 Loss2: 2.072020
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.160934 Loss1: 1.626783 Loss2: 1.534151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.865779 Loss1: 1.346299 Loss2: 1.519480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.632999 Loss1: 1.117669 Loss2: 1.515331 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.466646 Loss1: 0.947347 Loss2: 1.519299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.433695 Loss1: 0.904779 Loss2: 1.528915 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.815625
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.242755 Loss1: 0.768282 Loss2: 1.474473
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.358392 Loss1: 0.825223 Loss2: 1.533169
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.372097 Loss1: 0.815656 Loss2: 1.556441
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.310083 Loss1: 0.764535 Loss2: 1.545549
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.233097 Loss1: 0.684827 Loss2: 1.548270
-(DefaultActor pid=3764) >> Training accuracy: 0.818750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.404034 Loss1: 2.249441 Loss2: 2.154594
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.075589 Loss1: 1.539980 Loss2: 1.535609
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.815763 Loss1: 1.307200 Loss2: 1.508563
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.711745 Loss1: 1.194060 Loss2: 1.517685
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.274992 Loss1: 2.203559 Loss2: 2.071433
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.208507 Loss1: 1.679897 Loss2: 1.528610 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.862807 Loss1: 1.345701 Loss2: 1.517105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.629209 Loss1: 1.103880 Loss2: 1.525329 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.572725 Loss1: 1.043403 Loss2: 1.529322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.533950 Loss1: 0.997959 Loss2: 1.535991 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.821875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.280192 Loss1: 0.749939 Loss2: 1.530253 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.288957 Loss1: 0.745585 Loss2: 1.543372 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.782227
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.280292 Loss1: 1.718199 Loss2: 1.562093 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.798306 Loss1: 1.246194 Loss2: 1.552112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.424332 Loss1: 2.283360 Loss2: 2.140972
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.680740 Loss1: 1.132403 Loss2: 1.548337
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.223005 Loss1: 1.658922 Loss2: 1.564082
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.519167 Loss1: 0.961782 Loss2: 1.557384
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.530851 Loss1: 0.958299 Loss2: 1.572552 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.471090 Loss1: 0.880551 Loss2: 1.590539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.355812 Loss1: 0.784340 Loss2: 1.571472 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.307454 Loss1: 0.724250 Loss2: 1.583204 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.750000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.338252 Loss1: 0.764065 Loss2: 1.574187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.273289 Loss1: 0.702246 Loss2: 1.571043 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.769792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.452469 Loss1: 2.324601 Loss2: 2.127868
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.290467 Loss1: 1.771029 Loss2: 1.519438
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.991717 Loss1: 1.482294 Loss2: 1.509423
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.688357 Loss1: 1.174953 Loss2: 1.513403
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.250730 Loss1: 2.190326 Loss2: 2.060404
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.161685 Loss1: 1.664518 Loss2: 1.497167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.439406 Loss1: 0.909099 Loss2: 1.530307 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.387731 Loss1: 0.858744 Loss2: 1.528987 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.292597 Loss1: 0.733693 Loss2: 1.558904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.291356 Loss1: 0.745581 Loss2: 1.545775 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.834821
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.229295 Loss1: 0.722274 Loss2: 1.507021 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.073586 Loss1: 0.570402 Loss2: 1.503184 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.828125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.189279 Loss1: 1.698250 Loss2: 1.491029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.509824 Loss1: 1.031930 Loss2: 1.477894 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.459791 Loss1: 0.974957 Loss2: 1.484834
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.375721 Loss1: 2.291706 Loss2: 2.084015
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.059449 Loss1: 1.538879 Loss2: 1.520570
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.404194 Loss1: 0.897256 Loss2: 1.506937
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.778715 Loss1: 1.282174 Loss2: 1.496541
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.245409 Loss1: 0.746684 Loss2: 1.498726
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.583627 Loss1: 1.071147 Loss2: 1.512480
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.179823 Loss1: 0.678571 Loss2: 1.501252
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.488442 Loss1: 0.972813 Loss2: 1.515629
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.219100 Loss1: 0.706078 Loss2: 1.513022
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.235323 Loss1: 0.724180 Loss2: 1.511142 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.845703
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.285081 Loss1: 0.754720 Loss2: 1.530360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.157096 Loss1: 0.622743 Loss2: 1.534353 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.783333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.147702 Loss1: 1.659108 Loss2: 1.488594 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.633181 Loss1: 1.170727 Loss2: 1.462454 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.503654 Loss1: 1.030003 Loss2: 1.473651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.368233 Loss1: 0.886902 Loss2: 1.481331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.304287 Loss1: 0.822238 Loss2: 1.482049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.247679 Loss1: 0.763684 Loss2: 1.483995 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.192025 Loss1: 0.704470 Loss2: 1.487555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.116452 Loss1: 0.624277 Loss2: 1.492175 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.831250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.326867 Loss1: 0.789360 Loss2: 1.537507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.103654 Loss1: 0.568240 Loss2: 1.535414 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.853125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.299694 Loss1: 1.764888 Loss2: 1.534806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.715707 Loss1: 1.186011 Loss2: 1.529696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.044301 Loss1: 2.072455 Loss2: 1.971845
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.596476 Loss1: 1.039103 Loss2: 1.557374
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.886905 Loss1: 1.439768 Loss2: 1.447137
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.489127 Loss1: 0.937275 Loss2: 1.551853
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.675887 Loss1: 1.262569 Loss2: 1.413318
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.397844 Loss1: 0.839193 Loss2: 1.558651
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.477050 Loss1: 1.060345 Loss2: 1.416705
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.365613 Loss1: 0.812966 Loss2: 1.552647
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.338578 Loss1: 0.916560 Loss2: 1.422018
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.386748 Loss1: 0.797323 Loss2: 1.589425
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.315131 Loss1: 0.884035 Loss2: 1.431096
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.298638 Loss1: 0.710179 Loss2: 1.588459
-(DefaultActor pid=3765) >> Training accuracy: 0.761458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.136320 Loss1: 0.696213 Loss2: 1.440107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.010535 Loss1: 0.585326 Loss2: 1.425209 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.839583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.321292 Loss1: 1.779076 Loss2: 1.542216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.684919 Loss1: 1.159169 Loss2: 1.525750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.635306 Loss1: 1.094342 Loss2: 1.540963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.535410 Loss1: 0.979278 Loss2: 1.556132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.523051 Loss1: 0.974753 Loss2: 1.548298 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.442662 Loss1: 0.873222 Loss2: 1.569440 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.240698 Loss1: 0.680021 Loss2: 1.560677 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.278864 Loss1: 0.711412 Loss2: 1.567452 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.812500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.075659 Loss1: 0.595363 Loss2: 1.480296 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.848958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.198367 Loss1: 2.103298 Loss2: 2.095069 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.877417 Loss1: 1.353268 Loss2: 1.524149 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.316129 Loss1: 2.169981 Loss2: 2.146149
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.593809 Loss1: 1.059076 Loss2: 1.534733
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.406317 Loss1: 0.887939 Loss2: 1.518378 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.448449 Loss1: 0.928742 Loss2: 1.519707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.323738 Loss1: 0.781880 Loss2: 1.541858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.398488 Loss1: 0.880261 Loss2: 1.518228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.341143 Loss1: 0.816546 Loss2: 1.524597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.274813 Loss1: 0.755481 Loss2: 1.519332 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.778320
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.225021 Loss1: 0.700637 Loss2: 1.524384 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.835337
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.377612 Loss1: 2.252582 Loss2: 2.125029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.870479 Loss1: 1.333853 Loss2: 1.536626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.654708 Loss1: 1.123435 Loss2: 1.531273
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.147441 Loss1: 2.123389 Loss2: 2.024052
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.489898 Loss1: 0.941580 Loss2: 1.548317
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.993312 Loss1: 1.503606 Loss2: 1.489706
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.532691 Loss1: 0.975421 Loss2: 1.557270
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.642051 Loss1: 1.196012 Loss2: 1.446039
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.428673 Loss1: 0.864179 Loss2: 1.564493
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.445520 Loss1: 0.988100 Loss2: 1.457421
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.381710 Loss1: 0.808705 Loss2: 1.573005
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.401575 Loss1: 0.936292 Loss2: 1.465283
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.354030 Loss1: 0.774891 Loss2: 1.579139
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.345182 Loss1: 0.862620 Loss2: 1.482562
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.202284 Loss1: 0.628158 Loss2: 1.574127
-(DefaultActor pid=3765) >> Training accuracy: 0.853125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.208414 Loss1: 0.735452 Loss2: 1.472963
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.188615 Loss1: 0.706143 Loss2: 1.482472
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.187060 Loss1: 0.709014 Loss2: 1.478046
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.108677 Loss1: 0.625530 Loss2: 1.483148
-(DefaultActor pid=3764) >> Training accuracy: 0.871875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.303352 Loss1: 2.197572 Loss2: 2.105779
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.053806 Loss1: 1.534733 Loss2: 1.519073
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.803887 Loss1: 1.293267 Loss2: 1.510620
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.638822 Loss1: 1.126141 Loss2: 1.512682
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.357479 Loss1: 2.364691 Loss2: 1.992789
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.172921 Loss1: 1.710742 Loss2: 1.462178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.934582 Loss1: 1.470867 Loss2: 1.463714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.699431 Loss1: 1.223289 Loss2: 1.476142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.493105 Loss1: 1.041211 Loss2: 1.451894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.380333 Loss1: 0.904895 Loss2: 1.475438 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.838542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.408242 Loss1: 0.912713 Loss2: 1.495529 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.151289 Loss1: 0.660014 Loss2: 1.491275 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.838867
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.167643 Loss1: 1.664592 Loss2: 1.503051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.649216 Loss1: 1.147031 Loss2: 1.502185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.540517 Loss1: 1.047612 Loss2: 1.492905
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.366496 Loss1: 2.253113 Loss2: 2.113383
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.182930 Loss1: 1.626274 Loss2: 1.556655
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.435641 Loss1: 0.930152 Loss2: 1.505489
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.359836 Loss1: 0.847465 Loss2: 1.512371
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.815627 Loss1: 1.293664 Loss2: 1.521963
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.344306 Loss1: 0.824508 Loss2: 1.519798
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.658711 Loss1: 1.125245 Loss2: 1.533465
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.259959 Loss1: 0.755864 Loss2: 1.504094
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.468302 Loss1: 0.938342 Loss2: 1.529960
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.216364 Loss1: 0.692268 Loss2: 1.524097
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.479231 Loss1: 0.947709 Loss2: 1.531522
-(DefaultActor pid=3765) >> Training accuracy: 0.828125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.490671 Loss1: 0.931694 Loss2: 1.558977
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.351400 Loss1: 0.777524 Loss2: 1.573876
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.233799 Loss1: 0.679523 Loss2: 1.554276
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.345298 Loss1: 0.784950 Loss2: 1.560348
-(DefaultActor pid=3764) >> Training accuracy: 0.794792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.185753 Loss1: 2.006236 Loss2: 2.179517
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.082043 Loss1: 1.498182 Loss2: 1.583861
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.672560 Loss1: 1.125472 Loss2: 1.547089
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.484103 Loss1: 0.948955 Loss2: 1.535148
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.177395 Loss1: 2.076625 Loss2: 2.100769
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.963913 Loss1: 1.473533 Loss2: 1.490381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.736171 Loss1: 1.252204 Loss2: 1.483967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.503119 Loss1: 1.017109 Loss2: 1.486010 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.463474 Loss1: 0.964357 Loss2: 1.499117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.379802 Loss1: 0.863374 Loss2: 1.516428 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.814583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.234998 Loss1: 0.726245 Loss2: 1.508753 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.077930 Loss1: 0.579877 Loss2: 1.498053 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.838542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.131113 Loss1: 1.672068 Loss2: 1.459046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.581169 Loss1: 1.141854 Loss2: 1.439315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.139959 Loss1: 1.997741 Loss2: 2.142219
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.472035 Loss1: 1.004599 Loss2: 1.467437
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.014114 Loss1: 1.447584 Loss2: 1.566530
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.463627 Loss1: 0.989351 Loss2: 1.474275
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.756882 Loss1: 1.208459 Loss2: 1.548422
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.403739 Loss1: 0.925113 Loss2: 1.478627
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.552776 Loss1: 1.009414 Loss2: 1.543362
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.339134 Loss1: 0.860777 Loss2: 1.478357
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.451257 Loss1: 0.897795 Loss2: 1.553462
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.312413 Loss1: 0.827218 Loss2: 1.485195
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.454494 Loss1: 0.907258 Loss2: 1.547236
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.172642 Loss1: 0.686221 Loss2: 1.486420
-(DefaultActor pid=3765) >> Training accuracy: 0.834375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.176610 Loss1: 0.621040 Loss2: 1.555570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.139933 Loss1: 0.575258 Loss2: 1.564675 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.863542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.124725 Loss1: 1.616645 Loss2: 1.508080 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.527728 Loss1: 1.062486 Loss2: 1.465242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.977523 Loss1: 2.022622 Loss2: 1.954902
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.375739 Loss1: 0.897418 Loss2: 1.478322
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.045170 Loss1: 1.601484 Loss2: 1.443686
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.384573 Loss1: 0.904755 Loss2: 1.479818
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.655756 Loss1: 1.221073 Loss2: 1.434683
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.308083 Loss1: 0.825777 Loss2: 1.482306
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.467033 Loss1: 1.042910 Loss2: 1.424123
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.156172 Loss1: 0.668004 Loss2: 1.488168
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.469012 Loss1: 1.027394 Loss2: 1.441618
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.126695 Loss1: 0.642631 Loss2: 1.484064
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.109738 Loss1: 0.616704 Loss2: 1.493034
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.301937 Loss1: 0.854446 Loss2: 1.447491
-(DefaultActor pid=3765) >> Training accuracy: 0.786133
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.208592 Loss1: 0.770098 Loss2: 1.438493
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.086043 Loss1: 0.648606 Loss2: 1.437436
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.116473 Loss1: 0.675518 Loss2: 1.440955
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.182600 Loss1: 0.727530 Loss2: 1.455070
-(DefaultActor pid=3764) >> Training accuracy: 0.765625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.293410 Loss1: 2.157686 Loss2: 2.135724
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.121035 Loss1: 1.575872 Loss2: 1.545163
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.748664 Loss1: 1.242884 Loss2: 1.505780
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.645490 Loss1: 1.131468 Loss2: 1.514022
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.532207 Loss1: 0.989869 Loss2: 1.542338
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.332314 Loss1: 2.267782 Loss2: 2.064532
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.402297 Loss1: 1.889008 Loss2: 1.513289 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 3.063341 Loss1: 1.549136 Loss2: 1.514205 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.836157 Loss1: 1.332114 Loss2: 1.504044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.636455 Loss1: 1.126471 Loss2: 1.509984 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.797917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.480911 Loss1: 0.971252 Loss2: 1.509659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.368847 Loss1: 0.841961 Loss2: 1.526886 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.278079 Loss1: 0.735557 Loss2: 1.542522 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.778125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.988347 Loss1: 1.523947 Loss2: 1.464400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.513603 Loss1: 1.054129 Loss2: 1.459473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.231757 Loss1: 2.159284 Loss2: 2.072473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.994125 Loss1: 1.516711 Loss2: 1.477414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.660109 Loss1: 1.208024 Loss2: 1.452085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.579806 Loss1: 1.107372 Loss2: 1.472434 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.411192 Loss1: 0.934369 Loss2: 1.476823 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.821875
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.127366 Loss1: 0.646347 Loss2: 1.481020
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.310872 Loss1: 0.830328 Loss2: 1.480543
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.227609 Loss1: 0.751318 Loss2: 1.476290
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.200022 Loss1: 0.708153 Loss2: 1.491869
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.225623 Loss1: 0.728453 Loss2: 1.497170
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.160588 Loss1: 0.663102 Loss2: 1.497486
-(DefaultActor pid=3764) >> Training accuracy: 0.785714
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.600061 Loss1: 2.441676 Loss2: 2.158385
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.324298 Loss1: 1.769194 Loss2: 1.555104
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.956588 Loss1: 1.417360 Loss2: 1.539229
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.780436 Loss1: 1.218954 Loss2: 1.561482
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.658708 Loss1: 1.085936 Loss2: 1.572772
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.594252 Loss1: 1.012520 Loss2: 1.581732 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.401679 Loss1: 0.828361 Loss2: 1.573319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.342134 Loss1: 0.766210 Loss2: 1.575924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.291919 Loss1: 0.710321 Loss2: 1.581598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.221862 Loss1: 0.637914 Loss2: 1.583948 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.799107
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.410493 Loss1: 0.848163 Loss2: 1.562330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.182445 Loss1: 0.661684 Loss2: 1.520762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.201513 Loss1: 0.676731 Loss2: 1.524783
-(DefaultActor pid=3764) >> Training accuracy: 0.785417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.470110 Loss1: 2.321181 Loss2: 2.148929
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.248047 Loss1: 1.678823 Loss2: 1.569223
-(DefaultActor pid=3765) Epoch: 2 Loss: 3.035786 Loss1: 1.485546 Loss2: 1.550240
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.681336 Loss1: 1.117013 Loss2: 1.564323
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.598053 Loss1: 1.044818 Loss2: 1.553235
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.455368 Loss1: 2.172505 Loss2: 2.282864
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.624077 Loss1: 1.066089 Loss2: 1.557988
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.431925 Loss1: 0.861306 Loss2: 1.570619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.341586 Loss1: 0.774661 Loss2: 1.566925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.525637 Loss1: 0.971970 Loss2: 1.553666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.399948 Loss1: 0.833749 Loss2: 1.566199 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.804167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.361662 Loss1: 0.778334 Loss2: 1.583328 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.230710 Loss1: 0.653204 Loss2: 1.577507 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.802083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.335184 Loss1: 2.257864 Loss2: 2.077320
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.166925 Loss1: 1.645705 Loss2: 1.521221
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.872852 Loss1: 1.377960 Loss2: 1.494892
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.672706 Loss1: 1.174138 Loss2: 1.498568
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.414193 Loss1: 2.363901 Loss2: 2.050291
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.289822 Loss1: 1.757299 Loss2: 1.532524 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.924220 Loss1: 1.433702 Loss2: 1.490517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.727615 Loss1: 1.241241 Loss2: 1.486373 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.610313 Loss1: 1.120584 Loss2: 1.489729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.573167 Loss1: 1.062983 Loss2: 1.510183 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.803125
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.216143 Loss1: 0.700456 Loss2: 1.515687
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.452669 Loss1: 0.942987 Loss2: 1.509682
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.385519 Loss1: 0.887592 Loss2: 1.497926
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.229793 Loss1: 0.740962 Loss2: 1.488831
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.148747 Loss1: 0.654453 Loss2: 1.494294
-(DefaultActor pid=3764) >> Training accuracy: 0.815625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.207533 Loss1: 2.111807 Loss2: 2.095726
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.098901 Loss1: 1.537432 Loss2: 1.561469
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.815829 Loss1: 1.279267 Loss2: 1.536562
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.371268 Loss1: 2.117803 Loss2: 2.253465
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.586720 Loss1: 1.048766 Loss2: 1.537954
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.190644 Loss1: 1.609717 Loss2: 1.580927
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.414717 Loss1: 0.873410 Loss2: 1.541307
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.365496 Loss1: 0.840191 Loss2: 1.525305 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.296005 Loss1: 0.747758 Loss2: 1.548248 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.227734 Loss1: 0.686001 Loss2: 1.541732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.252474 Loss1: 0.701079 Loss2: 1.551394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.156616 Loss1: 0.596881 Loss2: 1.559735 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.802734
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.102095 Loss1: 0.550177 Loss2: 1.551918 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.818510
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.307417 Loss1: 2.182948 Loss2: 2.124469
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.092518 Loss1: 1.544703 Loss2: 1.547815
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.704718 Loss1: 1.204150 Loss2: 1.500567
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.531480 Loss1: 1.036164 Loss2: 1.495316
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.193075 Loss1: 2.143734 Loss2: 2.049341
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.172370 Loss1: 1.666883 Loss2: 1.505487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.839195 Loss1: 1.357156 Loss2: 1.482038 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.721830 Loss1: 1.238002 Loss2: 1.483828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.490873 Loss1: 1.014446 Loss2: 1.476427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.358412 Loss1: 0.876766 Loss2: 1.481647 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.786458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.367265 Loss1: 0.887040 Loss2: 1.480224 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.273667 Loss1: 0.778998 Loss2: 1.494669 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.806641
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.131151 Loss1: 2.106961 Loss2: 2.024190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.660235 Loss1: 1.218865 Loss2: 1.441371 [repeated 2x across cluster]
-DEBUG flwr 2023-10-09 10:13:50,019 | server.py:236 | fit_round 35 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.296898 Loss1: 2.225329 Loss2: 2.071570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.313269 Loss1: 1.816099 Loss2: 1.497170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.918511 Loss1: 1.432801 Loss2: 1.485709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.728394 Loss1: 1.237870 Loss2: 1.490524 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.587323 Loss1: 1.084830 Loss2: 1.502493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.440848 Loss1: 0.943311 Loss2: 1.497536 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.836458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.314972 Loss1: 0.803849 Loss2: 1.511123 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.182198 Loss1: 0.677027 Loss2: 1.505171 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.781250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.136185 Loss1: 1.602074 Loss2: 1.534112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.630380 Loss1: 1.106867 Loss2: 1.523513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.348381 Loss1: 2.263473 Loss2: 2.084908
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.544028 Loss1: 1.012486 Loss2: 1.531542
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.145397 Loss1: 1.652451 Loss2: 1.492947
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.439417 Loss1: 0.910463 Loss2: 1.528954
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.869996 Loss1: 1.385947 Loss2: 1.484049
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.329536 Loss1: 0.775886 Loss2: 1.553651
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.663426 Loss1: 1.186547 Loss2: 1.476879
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.347699 Loss1: 0.804972 Loss2: 1.542728
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.643998 Loss1: 1.158278 Loss2: 1.485720
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.265842 Loss1: 0.706818 Loss2: 1.559025
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.485308 Loss1: 0.989668 Loss2: 1.495640
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.297935 Loss1: 0.739901 Loss2: 1.558035
-(DefaultActor pid=3765) >> Training accuracy: 0.781250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.233117 Loss1: 0.750954 Loss2: 1.482162 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.268684 Loss1: 0.768871 Loss2: 1.499812 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.815625
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 10:13:50,019][flwr][DEBUG] - fit_round 35 received 50 results and 0 failures
-INFO flwr 2023-10-09 10:14:31,970 | server.py:125 | fit progress: (35, 2.6667960539412574, {'accuracy': 0.392}, 80579.74808721)
->> Test accuracy: 0.392000
-[2023-10-09 10:14:31,970][flwr][INFO] - fit progress: (35, 2.6667960539412574, {'accuracy': 0.392}, 80579.74808721)
-DEBUG flwr 2023-10-09 10:14:31,970 | server.py:173 | evaluate_round 35: strategy sampled 50 clients (out of 50)
-[2023-10-09 10:14:31,970][flwr][DEBUG] - evaluate_round 35: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 10:23:34,698 | server.py:187 | evaluate_round 35 received 50 results and 0 failures
-[2023-10-09 10:23:34,698][flwr][DEBUG] - evaluate_round 35 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 10:23:34,698 | server.py:222 | fit_round 36: strategy sampled 50 clients (out of 50)
-[2023-10-09 10:23:34,698][flwr][DEBUG] - fit_round 36: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.407950 Loss1: 2.231254 Loss2: 2.176696
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.215303 Loss1: 1.695213 Loss2: 1.520090
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.796550 Loss1: 1.334688 Loss2: 1.461862
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.528894 Loss1: 1.048436 Loss2: 1.480458
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.424044 Loss1: 0.941915 Loss2: 1.482128
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.257532 Loss1: 1.760725 Loss2: 1.496807 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.108526 Loss1: 0.632340 Loss2: 1.476186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.646433 Loss1: 1.141301 Loss2: 1.505132 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.824219
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.510615 Loss1: 1.000626 Loss2: 1.509989 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.404677 Loss1: 0.877792 Loss2: 1.526885 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.380037 Loss1: 0.843311 Loss2: 1.536725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.245327 Loss1: 1.750767 Loss2: 1.494561 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.766602
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.569611 Loss1: 1.087272 Loss2: 1.482339 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.380863 Loss1: 0.902324 Loss2: 1.478538 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.264802 Loss1: 0.789328 Loss2: 1.475474
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.189996 Loss1: 2.164681 Loss2: 2.025315
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.083938 Loss1: 1.616545 Loss2: 1.467393 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.822440 Loss1: 1.364950 Loss2: 1.457491 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.743304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.398619 Loss1: 0.935963 Loss2: 1.462656 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.255698 Loss1: 0.759717 Loss2: 1.495981 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.211598 Loss1: 0.720516 Loss2: 1.491082
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.390541 Loss1: 2.257216 Loss2: 2.133324
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.188689 Loss1: 1.622127 Loss2: 1.566562 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.825000
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.108410 Loss1: 0.624020 Loss2: 1.484391
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.870816 Loss1: 1.324194 Loss2: 1.546622
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.610556 Loss1: 1.065156 Loss2: 1.545400
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.487233 Loss1: 0.938379 Loss2: 1.548854
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.383242 Loss1: 0.834270 Loss2: 1.548972
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.337040 Loss1: 0.777064 Loss2: 1.559976
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.145879 Loss1: 2.186440 Loss2: 1.959439
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.142311 Loss1: 0.589287 Loss2: 1.553024
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.003706 Loss1: 1.563524 Loss2: 1.440183
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.150446 Loss1: 0.604053 Loss2: 1.546393
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.621891 Loss1: 1.211726 Loss2: 1.410165
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.217777 Loss1: 0.655454 Loss2: 1.562323
-(DefaultActor pid=3765) >> Training accuracy: 0.864583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.373303 Loss1: 0.961727 Loss2: 1.411576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.189521 Loss1: 0.758639 Loss2: 1.430882 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.056354 Loss1: 0.638416 Loss2: 1.417938
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.187401 Loss1: 2.228261 Loss2: 1.959140
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.054730 Loss1: 0.633163 Loss2: 1.421567
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.180452 Loss1: 1.728596 Loss2: 1.451855
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.996009 Loss1: 0.559648 Loss2: 1.436361
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.804847 Loss1: 1.368876 Loss2: 1.435971
-(DefaultActor pid=3764) >> Training accuracy: 0.831250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.463711 Loss1: 1.033666 Loss2: 1.430045
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.408815 Loss1: 0.970157 Loss2: 1.438659
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.334708 Loss1: 0.895743 Loss2: 1.438965
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.211077 Loss1: 0.764971 Loss2: 1.446105
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.244886 Loss1: 2.133740 Loss2: 2.111145
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.100467 Loss1: 0.666435 Loss2: 1.434032
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.121802 Loss1: 1.599042 Loss2: 1.522760
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.093255 Loss1: 0.654414 Loss2: 1.438840
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.753679 Loss1: 1.256554 Loss2: 1.497125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.970515 Loss1: 0.521557 Loss2: 1.448958
-(DefaultActor pid=3765) >> Training accuracy: 0.819792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.443453 Loss1: 0.954281 Loss2: 1.489172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.314322 Loss1: 0.803017 Loss2: 1.511304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.197495 Loss1: 0.670629 Loss2: 1.526865
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.030407 Loss1: 1.955859 Loss2: 2.074549
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.890910 Loss1: 1.406092 Loss2: 1.484818 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.840625
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.070234 Loss1: 0.560057 Loss2: 1.510177
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.615481 Loss1: 1.155059 Loss2: 1.460421
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.433747 Loss1: 0.980168 Loss2: 1.453578
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.296943 Loss1: 0.839402 Loss2: 1.457541
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.193150 Loss1: 0.739899 Loss2: 1.453251
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.101764 Loss1: 0.637103 Loss2: 1.464661
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.972413 Loss1: 1.901227 Loss2: 2.071185
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.141597 Loss1: 0.668070 Loss2: 1.473526
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.121134 Loss1: 0.628305 Loss2: 1.492829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.054988 Loss1: 0.575654 Loss2: 1.479334 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.798958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.275868 Loss1: 0.779611 Loss2: 1.496257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.164215 Loss1: 0.672640 Loss2: 1.491575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.165747 Loss1: 0.657691 Loss2: 1.508056
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.317211 Loss1: 2.283360 Loss2: 2.033851
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.112426 Loss1: 1.599135 Loss2: 1.513291 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.811458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.028914 Loss1: 0.523945 Loss2: 1.504969
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.862800 Loss1: 1.368149 Loss2: 1.494651
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.685802 Loss1: 1.169815 Loss2: 1.515987
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.468570 Loss1: 0.972137 Loss2: 1.496433
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.356045 Loss1: 0.853912 Loss2: 1.502133
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.246945 Loss1: 0.728915 Loss2: 1.518030
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.164651 Loss1: 2.131465 Loss2: 2.033186
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.247595 Loss1: 0.728594 Loss2: 1.519002
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.124655 Loss1: 1.574479 Loss2: 1.550175
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.250940 Loss1: 0.732142 Loss2: 1.518798
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.175646 Loss1: 0.658174 Loss2: 1.517471
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.789398 Loss1: 1.266528 Loss2: 1.522870
-(DefaultActor pid=3765) >> Training accuracy: 0.836458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.515835 Loss1: 1.005871 Loss2: 1.509964
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.463070 Loss1: 0.948193 Loss2: 1.514877
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.353694 Loss1: 0.832078 Loss2: 1.521616
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.286369 Loss1: 0.761908 Loss2: 1.524461
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.281759 Loss1: 2.192204 Loss2: 2.089555
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.125726 Loss1: 1.607928 Loss2: 1.517797 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.229585 Loss1: 0.686625 Loss2: 1.542960
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.783009 Loss1: 1.281167 Loss2: 1.501842
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.574773 Loss1: 1.066445 Loss2: 1.508328
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.214746 Loss1: 0.671059 Loss2: 1.543687
-(DefaultActor pid=3764) >> Training accuracy: 0.809743
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.401468 Loss1: 0.881869 Loss2: 1.519600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.132471 Loss1: 0.603765 Loss2: 1.528706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.414611 Loss1: 2.349174 Loss2: 2.065437
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.116273 Loss1: 0.593107 Loss2: 1.523166
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.228148 Loss1: 1.704103 Loss2: 1.524045
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.165317 Loss1: 0.631331 Loss2: 1.533986
-(DefaultActor pid=3765) >> Training accuracy: 0.850000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.626205 Loss1: 1.136223 Loss2: 1.489982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.371141 Loss1: 0.866143 Loss2: 1.504998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.407295 Loss1: 0.900494 Loss2: 1.506801
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.407875 Loss1: 2.345793 Loss2: 2.062082
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.212143 Loss1: 1.682665 Loss2: 1.529478 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.819329 Loss1: 1.318785 Loss2: 1.500544 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.796875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.628379 Loss1: 1.123511 Loss2: 1.504868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.461474 Loss1: 0.943794 Loss2: 1.517680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.343207 Loss1: 0.794038 Loss2: 1.549169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.216798 Loss1: 0.685920 Loss2: 1.530878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.387411 Loss1: 0.842968 Loss2: 1.544443 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.781250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.438181 Loss1: 0.944053 Loss2: 1.494129 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.126158 Loss1: 0.631702 Loss2: 1.494456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.089465 Loss1: 0.591457 Loss2: 1.498008
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.229278 Loss1: 2.182295 Loss2: 2.046983
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.198924 Loss1: 1.713522 Loss2: 1.485402 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.850446
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.194696 Loss1: 0.680926 Loss2: 1.513770
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.843062 Loss1: 1.367479 Loss2: 1.475583
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.535371 Loss1: 1.062743 Loss2: 1.472627
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.523916 Loss1: 1.037964 Loss2: 1.485952
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.431535 Loss1: 0.931634 Loss2: 1.499901
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.335012 Loss1: 0.822293 Loss2: 1.512719
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.152803 Loss1: 2.202968 Loss2: 1.949835
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.197573 Loss1: 0.697790 Loss2: 1.499783
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.110722 Loss1: 1.640262 Loss2: 1.470460
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.119265 Loss1: 0.631639 Loss2: 1.487626
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.087221 Loss1: 0.595601 Loss2: 1.491620
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.748959 Loss1: 1.317152 Loss2: 1.431807
-(DefaultActor pid=3765) >> Training accuracy: 0.818750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.520881 Loss1: 1.093086 Loss2: 1.427795
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.363619 Loss1: 0.923586 Loss2: 1.440032
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.268157 Loss1: 0.825263 Loss2: 1.442893
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.205307 Loss1: 0.771606 Loss2: 1.433701
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.157789 Loss1: 2.138147 Loss2: 2.019642
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.270244 Loss1: 0.810211 Loss2: 1.460033
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.048834 Loss1: 1.590390 Loss2: 1.458444
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.227009 Loss1: 0.759629 Loss2: 1.467380
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.164096 Loss1: 0.697695 Loss2: 1.466401 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.817383
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.462299 Loss1: 0.993411 Loss2: 1.468888 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.304415 Loss1: 0.823993 Loss2: 1.480422 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.177751 Loss1: 0.706074 Loss2: 1.471678
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.385568 Loss1: 2.311433 Loss2: 2.074135
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.258848 Loss1: 1.742814 Loss2: 1.516034 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.861458
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.108634 Loss1: 0.633160 Loss2: 1.475475
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.856509 Loss1: 1.377833 Loss2: 1.478675
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.637250 Loss1: 1.148715 Loss2: 1.488535
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.576320 Loss1: 1.082557 Loss2: 1.493763
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.397152 Loss1: 0.891449 Loss2: 1.505704
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.276121 Loss1: 0.776367 Loss2: 1.499754
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.176013 Loss1: 2.216321 Loss2: 1.959692
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.241686 Loss1: 0.725760 Loss2: 1.515926
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.060275 Loss1: 1.623843 Loss2: 1.436432
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.266120 Loss1: 0.746148 Loss2: 1.519972
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.740817 Loss1: 1.329795 Loss2: 1.411022
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.194819 Loss1: 0.671222 Loss2: 1.523597
-(DefaultActor pid=3764) >> Training accuracy: 0.754167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.385860 Loss1: 0.964236 Loss2: 1.421624 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.305545 Loss1: 0.870010 Loss2: 1.435535 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.217586 Loss1: 0.785654 Loss2: 1.431932
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.216173 Loss1: 2.162735 Loss2: 2.053438
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.948600 Loss1: 1.469355 Loss2: 1.479245 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.827083
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.023127 Loss1: 0.603544 Loss2: 1.419584
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.622863 Loss1: 1.162662 Loss2: 1.460200
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.397734 Loss1: 0.951990 Loss2: 1.445744
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.351205 Loss1: 0.895214 Loss2: 1.455991
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.265316 Loss1: 0.779960 Loss2: 1.485357
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.203663 Loss1: 0.743158 Loss2: 1.460505
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.147568 Loss1: 2.057566 Loss2: 2.090002
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.095989 Loss1: 0.622470 Loss2: 1.473520
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.031470 Loss1: 0.577872 Loss2: 1.453598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.023306 Loss1: 0.560764 Loss2: 1.462542 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.834375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.280240 Loss1: 0.801439 Loss2: 1.478801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.187598 Loss1: 0.694059 Loss2: 1.493539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.248908 Loss1: 0.743552 Loss2: 1.505355
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.025552 Loss1: 2.028630 Loss2: 1.996922
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.006192 Loss1: 1.506152 Loss2: 1.500040 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.851042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.647517 Loss1: 1.160308 Loss2: 1.487209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.369775 Loss1: 0.880974 Loss2: 1.488802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.362260 Loss1: 0.860329 Loss2: 1.501930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.237061 Loss1: 0.725155 Loss2: 1.511906 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.219744 Loss1: 0.708153 Loss2: 1.511591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.251480 Loss1: 0.742592 Loss2: 1.508889 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.772461
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.415324 Loss1: 0.982291 Loss2: 1.433032 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.182817 Loss1: 0.746502 Loss2: 1.436315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.167413 Loss1: 2.153286 Loss2: 2.014127
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.159363 Loss1: 0.719066 Loss2: 1.440297
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.069740 Loss1: 1.601353 Loss2: 1.468387
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.074340 Loss1: 0.620186 Loss2: 1.454155
-(DefaultActor pid=3765) >> Training accuracy: 0.844792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.652669 Loss1: 1.191254 Loss2: 1.461414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.471176 Loss1: 0.998683 Loss2: 1.472493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.303606 Loss1: 0.821253 Loss2: 1.482353
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.449665 Loss1: 2.263764 Loss2: 2.185901
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.083271 Loss1: 1.493408 Loss2: 1.589863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.763530 Loss1: 1.227968 Loss2: 1.535562 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.830208
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.130007 Loss1: 0.637267 Loss2: 1.492740
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.538388 Loss1: 0.988465 Loss2: 1.549923
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.445686 Loss1: 0.900071 Loss2: 1.545614
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.284808 Loss1: 0.743604 Loss2: 1.541204
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.305053 Loss1: 0.758537 Loss2: 1.546516
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.221529 Loss1: 0.682036 Loss2: 1.539493
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.935775 Loss1: 1.918544 Loss2: 2.017231
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.161736 Loss1: 0.600501 Loss2: 1.561235
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.848321 Loss1: 1.398747 Loss2: 1.449574
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.095954 Loss1: 0.543474 Loss2: 1.552480
-(DefaultActor pid=3765) >> Training accuracy: 0.817708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.380903 Loss1: 0.928012 Loss2: 1.452891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.126571 Loss1: 0.695578 Loss2: 1.430993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.071942 Loss1: 0.647910 Loss2: 1.424032
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.335669 Loss1: 2.255692 Loss2: 2.079977
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.094432 Loss1: 0.652522 Loss2: 1.441910
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.211648 Loss1: 1.717071 Loss2: 1.494578
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.080920 Loss1: 0.622633 Loss2: 1.458286
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.860250 Loss1: 1.381414 Loss2: 1.478836
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.043304 Loss1: 0.588254 Loss2: 1.455050
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.613975 Loss1: 1.138403 Loss2: 1.475572
-(DefaultActor pid=3764) >> Training accuracy: 0.848958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.512490 Loss1: 1.033186 Loss2: 1.479304
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.376657 Loss1: 0.889982 Loss2: 1.486674
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.333911 Loss1: 0.853399 Loss2: 1.480512
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.317593 Loss1: 0.823533 Loss2: 1.494060
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.302930 Loss1: 2.243879 Loss2: 2.059051
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.314004 Loss1: 0.803958 Loss2: 1.510046
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.153954 Loss1: 1.656676 Loss2: 1.497278
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.164757 Loss1: 0.662285 Loss2: 1.502472
-(DefaultActor pid=3765) >> Training accuracy: 0.814583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.527686 Loss1: 1.053211 Loss2: 1.474474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.324111 Loss1: 0.833094 Loss2: 1.491018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.283188 Loss1: 0.782699 Loss2: 1.500489
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.190643 Loss1: 2.051493 Loss2: 2.139151
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.261030 Loss1: 0.752979 Loss2: 1.508051
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.075573 Loss1: 1.529150 Loss2: 1.546424
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.234237 Loss1: 0.722513 Loss2: 1.511723
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.871214 Loss1: 1.329593 Loss2: 1.541621
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.242430 Loss1: 0.731941 Loss2: 1.510490
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.594169 Loss1: 1.055499 Loss2: 1.538669
-(DefaultActor pid=3764) >> Training accuracy: 0.834375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.500284 Loss1: 0.964991 Loss2: 1.535293
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.371590 Loss1: 0.818345 Loss2: 1.553245
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.180985 Loss1: 0.637800 Loss2: 1.543186
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.140387 Loss1: 0.605233 Loss2: 1.535154
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.490788 Loss1: 2.381577 Loss2: 2.109210
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.071261 Loss1: 0.532882 Loss2: 1.538379
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.353435 Loss1: 1.812643 Loss2: 1.540792
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.220795 Loss1: 0.668317 Loss2: 1.552479
-(DefaultActor pid=3765) >> Training accuracy: 0.845833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.647117 Loss1: 1.126925 Loss2: 1.520193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.391224 Loss1: 0.873095 Loss2: 1.518129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.167051 Loss1: 2.172444 Loss2: 1.994607 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.096529 Loss1: 1.630311 Loss2: 1.466217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.770589 Loss1: 1.325971 Loss2: 1.444619 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.809152
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.425502 Loss1: 0.973336 Loss2: 1.452166 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.132501 Loss1: 0.695310 Loss2: 1.437191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.109342 Loss1: 0.658730 Loss2: 1.450612
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.065263 Loss1: 2.017521 Loss2: 2.047742
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.023164 Loss1: 1.520571 Loss2: 1.502593 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.851042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.807804 Loss1: 1.303795 Loss2: 1.504009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.269405 Loss1: 0.783818 Loss2: 1.485587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.143928 Loss1: 0.652559 Loss2: 1.491368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.159382 Loss1: 0.667565 Loss2: 1.491817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.136576 Loss1: 0.632478 Loss2: 1.504099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.460532 Loss1: 1.000466 Loss2: 1.460067 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.790039
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.269801 Loss1: 0.799325 Loss2: 1.470477 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.189338 Loss1: 0.692497 Loss2: 1.496842 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.164123 Loss1: 0.687348 Loss2: 1.476775
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.258091 Loss1: 2.206825 Loss2: 2.051266
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.131490 Loss1: 0.653744 Loss2: 1.477745
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.196237 Loss1: 1.688107 Loss2: 1.508130
-(DefaultActor pid=3765) >> Training accuracy: 0.833333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.801089 Loss1: 1.327536 Loss2: 1.473553
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.580285 Loss1: 1.092155 Loss2: 1.488131
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.482719 Loss1: 0.998854 Loss2: 1.483866
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.320351 Loss1: 0.829473 Loss2: 1.490877
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.317620 Loss1: 2.182069 Loss2: 2.135551
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.320177 Loss1: 0.826413 Loss2: 1.493763
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.255033 Loss1: 0.753040 Loss2: 1.501993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.189365 Loss1: 0.683340 Loss2: 1.506026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.175637 Loss1: 0.660170 Loss2: 1.515467 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.816667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.203368 Loss1: 0.697132 Loss2: 1.506236 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.105716 Loss1: 0.589547 Loss2: 1.516168 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.876202
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.004117 Loss1: 0.487700 Loss2: 1.516417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.036440 Loss1: 2.065175 Loss2: 1.971265
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.052789 Loss1: 1.584447 Loss2: 1.468343
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.666095 Loss1: 1.219823 Loss2: 1.446272
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.444110 Loss1: 0.992576 Loss2: 1.451534
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.361588 Loss1: 2.248307 Loss2: 2.113280
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.432072 Loss1: 0.987022 Loss2: 1.445051
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.221797 Loss1: 1.676717 Loss2: 1.545080
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.295098 Loss1: 0.844138 Loss2: 1.450960
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.775406 Loss1: 1.263227 Loss2: 1.512179
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.197492 Loss1: 0.739867 Loss2: 1.457625
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.641117 Loss1: 1.119237 Loss2: 1.521880
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.090173 Loss1: 0.640658 Loss2: 1.449515
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.524440 Loss1: 0.981287 Loss2: 1.543153
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.094261 Loss1: 0.632214 Loss2: 1.462048
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.062504 Loss1: 0.596118 Loss2: 1.466386 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.811523
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.288725 Loss1: 0.747619 Loss2: 1.541106 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.199094 Loss1: 0.639438 Loss2: 1.559656 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.821875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.231379 Loss1: 2.142490 Loss2: 2.088890
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.209883 Loss1: 1.621261 Loss2: 1.588622
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.788483 Loss1: 1.221418 Loss2: 1.567065
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.641717 Loss1: 1.076493 Loss2: 1.565224
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.303130 Loss1: 2.292986 Loss2: 2.010145
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.114209 Loss1: 1.600034 Loss2: 1.514175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.428291 Loss1: 0.852582 Loss2: 1.575709
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.815535 Loss1: 1.318660 Loss2: 1.496875
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.624852 Loss1: 1.118701 Loss2: 1.506150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.507052 Loss1: 0.985899 Loss2: 1.521153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.468977 Loss1: 0.944812 Loss2: 1.524165 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.781250
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.180054 Loss1: 0.595831 Loss2: 1.584223
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.406061 Loss1: 0.885622 Loss2: 1.520439
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.286551 Loss1: 0.753425 Loss2: 1.533127
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.179741 Loss1: 0.662182 Loss2: 1.517559
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.116226 Loss1: 0.595857 Loss2: 1.520369
-(DefaultActor pid=3765) >> Training accuracy: 0.803711
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.036890 Loss1: 1.563540 Loss2: 1.473350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.517168 Loss1: 1.049856 Loss2: 1.467312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.338514 Loss1: 0.864159 Loss2: 1.474354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.216432 Loss1: 0.735590 Loss2: 1.480842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.237294 Loss1: 0.761695 Loss2: 1.475598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.197677 Loss1: 0.696310 Loss2: 1.501367 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.092075 Loss1: 0.605569 Loss2: 1.486506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.099269 Loss1: 0.602974 Loss2: 1.496295 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.793750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.182628 Loss1: 0.687400 Loss2: 1.495227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.105519 Loss1: 0.615301 Loss2: 1.490218 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.819792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.866259 Loss1: 1.394099 Loss2: 1.472160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.331986 Loss1: 0.905755 Loss2: 1.426231 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.259037 Loss1: 0.829022 Loss2: 1.430015
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.109828 Loss1: 2.093022 Loss2: 2.016806
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.062053 Loss1: 1.607202 Loss2: 1.454851 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.655224 Loss1: 1.216364 Loss2: 1.438859 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.452406 Loss1: 1.008085 Loss2: 1.444321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.336953 Loss1: 0.891755 Loss2: 1.445198 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.893029
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.228477 Loss1: 0.767399 Loss2: 1.461079 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.188215 Loss1: 0.700298 Loss2: 1.487916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.145388 Loss1: 0.667660 Loss2: 1.477728
-(DefaultActor pid=3765) >> Training accuracy: 0.767708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.097458 Loss1: 2.096232 Loss2: 2.001226
-DEBUG flwr 2023-10-09 10:51:53,629 | server.py:236 | fit_round 36 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.998584 Loss1: 1.467189 Loss2: 1.531395
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.666754 Loss1: 1.170010 Loss2: 1.496744
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.430889 Loss1: 0.925705 Loss2: 1.505184
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.350942 Loss1: 0.860648 Loss2: 1.490293
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.084629 Loss1: 1.950439 Loss2: 2.134190
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.054312 Loss1: 1.490291 Loss2: 1.564021 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.787730 Loss1: 1.251083 Loss2: 1.536647 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.487495 Loss1: 0.954553 Loss2: 1.532942 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.105350 Loss1: 0.598653 Loss2: 1.506697
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.333258 Loss1: 0.808303 Loss2: 1.524955
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.162237 Loss1: 0.658511 Loss2: 1.503726
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.245465 Loss1: 0.739271 Loss2: 1.506194
-(DefaultActor pid=3764) >> Training accuracy: 0.784180
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.188010 Loss1: 0.669773 Loss2: 1.518237
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.239061 Loss1: 0.704742 Loss2: 1.534318
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.145037 Loss1: 0.612119 Loss2: 1.532918
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.120670 Loss1: 0.582156 Loss2: 1.538514
-(DefaultActor pid=3765) >> Training accuracy: 0.832292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.488898 Loss1: 2.369114 Loss2: 2.119784
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.276779 Loss1: 1.747753 Loss2: 1.529026
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.914177 Loss1: 1.419383 Loss2: 1.494793
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.665974 Loss1: 1.165723 Loss2: 1.500250
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.526314 Loss1: 1.018686 Loss2: 1.507628
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.373405 Loss1: 0.859919 Loss2: 1.513486 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.398250 Loss1: 0.871116 Loss2: 1.527134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.278996 Loss1: 0.740846 Loss2: 1.538150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.259709 Loss1: 0.724662 Loss2: 1.535047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.195324 Loss1: 0.655300 Loss2: 1.540025 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.759375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.227599 Loss1: 0.688993 Loss2: 1.538606 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.083609 Loss1: 0.545721 Loss2: 1.537888 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.890625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.247196 Loss1: 1.685445 Loss2: 1.561752 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.756149 Loss1: 1.201147 Loss2: 1.555003 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.460016 Loss1: 0.902341 Loss2: 1.557675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.265969 Loss1: 0.681873 Loss2: 1.584096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.297336 Loss1: 0.725539 Loss2: 1.571797 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.770833
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 10:51:53,629][flwr][DEBUG] - fit_round 36 received 50 results and 0 failures
-INFO flwr 2023-10-09 10:52:35,576 | server.py:125 | fit progress: (36, 2.6492519972804254, {'accuracy': 0.3976}, 82863.354786335)
->> Test accuracy: 0.397600
-[2023-10-09 10:52:35,576][flwr][INFO] - fit progress: (36, 2.6492519972804254, {'accuracy': 0.3976}, 82863.354786335)
-DEBUG flwr 2023-10-09 10:52:35,577 | server.py:173 | evaluate_round 36: strategy sampled 50 clients (out of 50)
-[2023-10-09 10:52:35,577][flwr][DEBUG] - evaluate_round 36: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 11:01:38,899 | server.py:187 | evaluate_round 36 received 50 results and 0 failures
-[2023-10-09 11:01:38,899][flwr][DEBUG] - evaluate_round 36 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 11:01:38,900 | server.py:222 | fit_round 37: strategy sampled 50 clients (out of 50)
-[2023-10-09 11:01:38,900][flwr][DEBUG] - fit_round 37: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.388906 Loss1: 2.263984 Loss2: 2.124922
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.201628 Loss1: 1.673343 Loss2: 1.528285
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.816801 Loss1: 1.312576 Loss2: 1.504225
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.595514 Loss1: 1.091711 Loss2: 1.503803
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.164509 Loss1: 2.071459 Loss2: 2.093049
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.005406 Loss1: 1.500850 Loss2: 1.504556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.594514 Loss1: 1.098447 Loss2: 1.496067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.452124 Loss1: 0.956534 Loss2: 1.495590 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.302117 Loss1: 0.806659 Loss2: 1.495459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.237276 Loss1: 0.739682 Loss2: 1.497594 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.835938
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.186153 Loss1: 0.673033 Loss2: 1.513119 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.994035 Loss1: 0.488725 Loss2: 1.505309 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.890625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.110629 Loss1: 1.556018 Loss2: 1.554610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.700869 Loss1: 1.149695 Loss2: 1.551174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.300030 Loss1: 2.259035 Loss2: 2.040995
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.457407 Loss1: 0.931027 Loss2: 1.526380
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.179020 Loss1: 1.707168 Loss2: 1.471852
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.354528 Loss1: 0.822836 Loss2: 1.531692
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.826224 Loss1: 1.367742 Loss2: 1.458481
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.311558 Loss1: 0.768086 Loss2: 1.543471
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.531505 Loss1: 1.061432 Loss2: 1.470074
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.302551 Loss1: 0.754810 Loss2: 1.547741
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.315526 Loss1: 0.848903 Loss2: 1.466623
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.289486 Loss1: 0.722262 Loss2: 1.567224
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.195634 Loss1: 0.732023 Loss2: 1.463611
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.145870 Loss1: 0.592948 Loss2: 1.552922
-(DefaultActor pid=3765) >> Training accuracy: 0.862500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.108629 Loss1: 0.638958 Loss2: 1.469671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.034763 Loss1: 0.542167 Loss2: 1.492596 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.815625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.964752 Loss1: 1.507258 Loss2: 1.457494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.531188 Loss1: 1.096548 Loss2: 1.434640 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.273384 Loss1: 2.227166 Loss2: 2.046218
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.368667 Loss1: 0.948102 Loss2: 1.420565
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.062439 Loss1: 1.562437 Loss2: 1.500001
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.277794 Loss1: 0.851950 Loss2: 1.425844
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.839784 Loss1: 1.360399 Loss2: 1.479385
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.096766 Loss1: 0.675994 Loss2: 1.420771
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.680216 Loss1: 1.171872 Loss2: 1.508344
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.069540 Loss1: 0.653715 Loss2: 1.415826
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.561570 Loss1: 1.070070 Loss2: 1.491500
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.067420 Loss1: 0.634301 Loss2: 1.433119
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.406358 Loss1: 0.905534 Loss2: 1.500824
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.986988 Loss1: 0.549404 Loss2: 1.437584
-(DefaultActor pid=3765) >> Training accuracy: 0.883333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.139324 Loss1: 0.659026 Loss2: 1.480298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.072059 Loss1: 0.571777 Loss2: 1.500282 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.852083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.094579 Loss1: 1.571053 Loss2: 1.523526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.479380 Loss1: 0.953341 Loss2: 1.526039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.387611 Loss1: 0.884756 Loss2: 1.502855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.315585 Loss1: 0.800893 Loss2: 1.514692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.290170 Loss1: 0.767475 Loss2: 1.522695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.124748 Loss1: 0.595375 Loss2: 1.529373 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.167299 Loss1: 0.639809 Loss2: 1.527490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.197407 Loss1: 0.728952 Loss2: 1.468455
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.056436 Loss1: 0.522993 Loss2: 1.533443
-(DefaultActor pid=3765) >> Training accuracy: 0.841667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.156070 Loss1: 0.662761 Loss2: 1.493309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.103708 Loss1: 0.620946 Loss2: 1.482762 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.846875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.044948 Loss1: 1.500306 Loss2: 1.544642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.488848 Loss1: 0.981085 Loss2: 1.507764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.369825 Loss1: 0.852369 Loss2: 1.517456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.314888 Loss1: 0.796304 Loss2: 1.518585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.279444 Loss1: 0.758678 Loss2: 1.520766 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.170573 Loss1: 0.634645 Loss2: 1.535928 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.022003 Loss1: 0.510283 Loss2: 1.511720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.036872 Loss1: 0.520336 Loss2: 1.516536 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.858398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.129636 Loss1: 0.617021 Loss2: 1.512614 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.858333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.274657 Loss1: 2.171143 Loss2: 2.103514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.797949 Loss1: 1.294313 Loss2: 1.503636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.574048 Loss1: 1.057765 Loss2: 1.516282
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.212708 Loss1: 2.179620 Loss2: 2.033088
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.434738 Loss1: 0.928222 Loss2: 1.506515
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.008323 Loss1: 1.523443 Loss2: 1.484879
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.320141 Loss1: 0.813985 Loss2: 1.506156
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.739553 Loss1: 1.274341 Loss2: 1.465212
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.148940 Loss1: 0.641409 Loss2: 1.507531
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.601103 Loss1: 1.140474 Loss2: 1.460629
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.152990 Loss1: 0.650948 Loss2: 1.502041
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.506261 Loss1: 1.026800 Loss2: 1.479460
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.182569 Loss1: 0.656125 Loss2: 1.526444
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.300592 Loss1: 0.830914 Loss2: 1.469678
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.108323 Loss1: 0.589238 Loss2: 1.519085
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.204982 Loss1: 0.738864 Loss2: 1.466118
-(DefaultActor pid=3765) >> Training accuracy: 0.784375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.218876 Loss1: 0.740369 Loss2: 1.478507
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.185784 Loss1: 0.700015 Loss2: 1.485769
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.175794 Loss1: 0.682881 Loss2: 1.492913
-(DefaultActor pid=3764) >> Training accuracy: 0.818750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.070295 Loss1: 1.964275 Loss2: 2.106020
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.934883 Loss1: 1.404924 Loss2: 1.529959
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.679167 Loss1: 1.174103 Loss2: 1.505064
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.439769 Loss1: 0.935360 Loss2: 1.504409
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.225505 Loss1: 2.208803 Loss2: 2.016702
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.147798 Loss1: 1.658401 Loss2: 1.489397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.764100 Loss1: 1.292075 Loss2: 1.472025 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.594201 Loss1: 1.107723 Loss2: 1.486478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.341522 Loss1: 0.862423 Loss2: 1.479100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.337779 Loss1: 0.866140 Loss2: 1.471639 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.811458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.297227 Loss1: 0.807228 Loss2: 1.489999 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.158215 Loss1: 0.664175 Loss2: 1.494040 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.846875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.141875 Loss1: 2.058066 Loss2: 2.083809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.691916 Loss1: 1.168953 Loss2: 1.522963 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.276320 Loss1: 2.201149 Loss2: 2.075171
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.605443 Loss1: 1.075850 Loss2: 1.529593
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.159951 Loss1: 1.612628 Loss2: 1.547323
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.474850 Loss1: 0.941974 Loss2: 1.532877
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.682449 Loss1: 1.156347 Loss2: 1.526102
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.349347 Loss1: 0.809779 Loss2: 1.539568
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.529623 Loss1: 0.985408 Loss2: 1.544215
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.221103 Loss1: 0.694142 Loss2: 1.526961
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.427110 Loss1: 0.881681 Loss2: 1.545429
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.213966 Loss1: 0.679284 Loss2: 1.534682
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.094832 Loss1: 0.566405 Loss2: 1.528427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.061136 Loss1: 0.535147 Loss2: 1.525989 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.835478
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.301434 Loss1: 0.730311 Loss2: 1.571124 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.832031
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.235885 Loss1: 2.136555 Loss2: 2.099330 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.712869 Loss1: 1.202709 Loss2: 1.510160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.489084 Loss1: 0.979261 Loss2: 1.509823
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.435911 Loss1: 2.376945 Loss2: 2.058966
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.328255 Loss1: 0.830016 Loss2: 1.498239
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.248087 Loss1: 1.755998 Loss2: 1.492090
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.310681 Loss1: 0.791979 Loss2: 1.518702
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.846547 Loss1: 1.367189 Loss2: 1.479358
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.241727 Loss1: 0.724517 Loss2: 1.517210
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.483612 Loss1: 0.998010 Loss2: 1.485602
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.410193 Loss1: 0.942784 Loss2: 1.467409
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.147550 Loss1: 0.621532 Loss2: 1.526018
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.330202 Loss1: 0.839439 Loss2: 1.490763
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.127409 Loss1: 0.597635 Loss2: 1.529774
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.251460 Loss1: 0.763834 Loss2: 1.487626
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.104494 Loss1: 0.567364 Loss2: 1.537130
-(DefaultActor pid=3765) >> Training accuracy: 0.860417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.245742 Loss1: 0.736821 Loss2: 1.508920 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.799107
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.388254 Loss1: 2.323591 Loss2: 2.064663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.809979 Loss1: 1.292042 Loss2: 1.517937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.096273 Loss1: 2.076009 Loss2: 2.020264
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.589577 Loss1: 1.078742 Loss2: 1.510835
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.062506 Loss1: 1.578194 Loss2: 1.484312
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.402680 Loss1: 0.891191 Loss2: 1.511489
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.744287 Loss1: 1.280036 Loss2: 1.464251
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.354000 Loss1: 0.833161 Loss2: 1.520839
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.501180 Loss1: 1.032882 Loss2: 1.468298
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.284327 Loss1: 0.751815 Loss2: 1.532511
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.412732 Loss1: 0.954660 Loss2: 1.458072
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.330913 Loss1: 0.796238 Loss2: 1.534676
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.332778 Loss1: 0.855377 Loss2: 1.477401
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.213966 Loss1: 0.673310 Loss2: 1.540656
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.274924 Loss1: 0.792544 Loss2: 1.482381
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.241248 Loss1: 0.710600 Loss2: 1.530648
-(DefaultActor pid=3765) >> Training accuracy: 0.817383
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.153583 Loss1: 0.677228 Loss2: 1.476355 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.847656
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.226126 Loss1: 2.006352 Loss2: 2.219774 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.691962 Loss1: 1.193327 Loss2: 1.498635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.296496 Loss1: 0.804271 Loss2: 1.492225 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.239356 Loss1: 0.731540 Loss2: 1.507816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.259372 Loss1: 0.747967 Loss2: 1.511405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.147973 Loss1: 0.642211 Loss2: 1.505761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.019632 Loss1: 0.505747 Loss2: 1.513885 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.990822 Loss1: 0.483567 Loss2: 1.507255 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.843750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.076508 Loss1: 0.593739 Loss2: 1.482769 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.120056 Loss1: 0.609842 Loss2: 1.510215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.076519 Loss1: 0.557055 Loss2: 1.519463
-(DefaultActor pid=3764) >> Training accuracy: 0.864583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.169370 Loss1: 2.089664 Loss2: 2.079706
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.959767 Loss1: 1.450943 Loss2: 1.508824
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.698226 Loss1: 1.219651 Loss2: 1.478576
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.558726 Loss1: 1.057454 Loss2: 1.501273
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.390053 Loss1: 0.885783 Loss2: 1.504271
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.218251 Loss1: 2.071823 Loss2: 2.146428
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.295924 Loss1: 0.808948 Loss2: 1.486976
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.148087 Loss1: 0.658073 Loss2: 1.490014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.155649 Loss1: 0.665884 Loss2: 1.489765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.021117 Loss1: 0.526301 Loss2: 1.494816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.913784 Loss1: 0.441725 Loss2: 1.472059 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.878125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.235898 Loss1: 0.713316 Loss2: 1.522583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.143886 Loss1: 0.618337 Loss2: 1.525549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.063327 Loss1: 0.537404 Loss2: 1.525923
-(DefaultActor pid=3764) >> Training accuracy: 0.835417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.435198 Loss1: 2.285380 Loss2: 2.149819
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.213288 Loss1: 1.622255 Loss2: 1.591032
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.912903 Loss1: 1.356015 Loss2: 1.556887
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.633715 Loss1: 1.075097 Loss2: 1.558618
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.628866 Loss1: 1.073283 Loss2: 1.555583
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.096043 Loss1: 2.026558 Loss2: 2.069485
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.505433 Loss1: 0.930137 Loss2: 1.575296
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.348226 Loss1: 0.782092 Loss2: 1.566134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.361738 Loss1: 0.791770 Loss2: 1.569969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.437711 Loss1: 0.852325 Loss2: 1.585386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.283555 Loss1: 0.691239 Loss2: 1.592316 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.753125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.101459 Loss1: 0.624865 Loss2: 1.476594 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.108089 Loss1: 0.615173 Loss2: 1.492915 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.868750
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.119954 Loss1: 0.609869 Loss2: 1.510084
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.160640 Loss1: 2.138975 Loss2: 2.021665
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.980234 Loss1: 1.519198 Loss2: 1.461036
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.755032 Loss1: 1.310633 Loss2: 1.444399
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.450114 Loss1: 1.002177 Loss2: 1.447937
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.254675 Loss1: 0.818805 Loss2: 1.435869
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.345662 Loss1: 2.273643 Loss2: 2.072019
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.176112 Loss1: 1.641557 Loss2: 1.534554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.852446 Loss1: 1.346697 Loss2: 1.505749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.622607 Loss1: 1.114139 Loss2: 1.508468 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.404995 Loss1: 0.893387 Loss2: 1.511609 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.796875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.256690 Loss1: 0.744957 Loss2: 1.511733 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.224645 Loss1: 0.682322 Loss2: 1.542322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.209527 Loss1: 0.663185 Loss2: 1.546342
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.011833 Loss1: 1.947158 Loss2: 2.064675
-(DefaultActor pid=3764) >> Training accuracy: 0.853516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.987039 Loss1: 1.477874 Loss2: 1.509165
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.588280 Loss1: 1.093416 Loss2: 1.494864
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.412280 Loss1: 0.936333 Loss2: 1.475947
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.382275 Loss1: 0.899650 Loss2: 1.482625
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.148631 Loss1: 2.073746 Loss2: 2.074885
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.202859 Loss1: 0.721173 Loss2: 1.481687
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.084594 Loss1: 0.602833 Loss2: 1.481761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.106620 Loss1: 0.627304 Loss2: 1.479316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.143358 Loss1: 0.630990 Loss2: 1.512368 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.008309 Loss1: 0.509890 Loss2: 1.498419 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.837500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.110767 Loss1: 0.649510 Loss2: 1.461257 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.108636 Loss1: 0.632986 Loss2: 1.475650 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.844952
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.993513 Loss1: 1.479450 Loss2: 1.514062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.449779 Loss1: 0.955371 Loss2: 1.494408 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.439807 Loss1: 0.945057 Loss2: 1.494750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.248543 Loss1: 0.754087 Loss2: 1.494456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.226962 Loss1: 0.712592 Loss2: 1.514370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.422899 Loss1: 0.902668 Loss2: 1.520231 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.291502 Loss1: 0.772777 Loss2: 1.518725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.219565 Loss1: 0.704200 Loss2: 1.515365 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.854167
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.115465 Loss1: 0.595530 Loss2: 1.519935
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.132438 Loss1: 0.610293 Loss2: 1.522145
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.158630 Loss1: 0.638319 Loss2: 1.520311
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.086112 Loss1: 0.562788 Loss2: 1.523324
-(DefaultActor pid=3764) >> Training accuracy: 0.792969
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.239290 Loss1: 2.225748 Loss2: 2.013542
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.133087 Loss1: 1.646877 Loss2: 1.486210
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.795473 Loss1: 1.331064 Loss2: 1.464409
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.236665 Loss1: 2.022413 Loss2: 2.214252
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.122686 Loss1: 1.539341 Loss2: 1.583345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.804187 Loss1: 1.230936 Loss2: 1.573251 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.599342 Loss1: 1.027622 Loss2: 1.571720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.491932 Loss1: 0.929018 Loss2: 1.562914 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.260384 Loss1: 0.765938 Loss2: 1.494445
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.392160 Loss1: 0.817970 Loss2: 1.574189
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.179222 Loss1: 0.675170 Loss2: 1.504052
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.321186 Loss1: 0.739555 Loss2: 1.581631
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.191367 Loss1: 0.607021 Loss2: 1.584346
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.115197 Loss1: 0.617769 Loss2: 1.497428
-(DefaultActor pid=3765) >> Training accuracy: 0.806641
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.043381 Loss1: 0.474201 Loss2: 1.569180 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.864955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.226776 Loss1: 2.185888 Loss2: 2.040888
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.203574 Loss1: 1.718076 Loss2: 1.485498
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.871352 Loss1: 1.387884 Loss2: 1.483468
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.528727 Loss1: 1.057328 Loss2: 1.471398
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.226052 Loss1: 2.191200 Loss2: 2.034852
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.242119 Loss1: 1.756805 Loss2: 1.485314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.832227 Loss1: 1.362454 Loss2: 1.469773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.634426 Loss1: 1.164640 Loss2: 1.469786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.565661 Loss1: 1.070780 Loss2: 1.494881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.380056 Loss1: 0.902325 Loss2: 1.477731 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.795833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.245270 Loss1: 0.724641 Loss2: 1.520629
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.257329 Loss1: 0.777596 Loss2: 1.479733
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.354843 Loss1: 0.874145 Loss2: 1.480698
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.190921 Loss1: 0.681792 Loss2: 1.509129
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.084135 Loss1: 0.586603 Loss2: 1.497532
-(DefaultActor pid=3764) >> Training accuracy: 0.883333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.853673 Loss1: 1.841618 Loss2: 2.012055
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.895871 Loss1: 1.436150 Loss2: 1.459721
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.556698 Loss1: 1.088376 Loss2: 1.468322
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.408171 Loss1: 0.944002 Loss2: 1.464169
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.183605 Loss1: 2.166463 Loss2: 2.017142
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.105881 Loss1: 1.607570 Loss2: 1.498311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.720476 Loss1: 1.254155 Loss2: 1.466321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.474094 Loss1: 1.012263 Loss2: 1.461831 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.378475 Loss1: 0.912577 Loss2: 1.465898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.303037 Loss1: 0.830473 Loss2: 1.472564 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.831250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.251182 Loss1: 0.775914 Loss2: 1.475269 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.118104 Loss1: 0.638208 Loss2: 1.479896 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.839583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.237294 Loss1: 2.134486 Loss2: 2.102807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.854976 Loss1: 1.310125 Loss2: 1.544851 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.581798 Loss1: 1.048447 Loss2: 1.533350
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.986318 Loss1: 2.068350 Loss2: 1.917967
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.054300 Loss1: 1.629997 Loss2: 1.424303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.642919 Loss1: 1.233415 Loss2: 1.409504 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.462244 Loss1: 1.060560 Loss2: 1.401684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.304989 Loss1: 0.878588 Loss2: 1.426401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.139843 Loss1: 0.734960 Loss2: 1.404883 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.787500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.127076 Loss1: 0.706108 Loss2: 1.420968 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.923892 Loss1: 0.511844 Loss2: 1.412048 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.799805
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.052016 Loss1: 1.551763 Loss2: 1.500253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.570605 Loss1: 1.083315 Loss2: 1.487290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.376309 Loss1: 0.895683 Loss2: 1.480626
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.071248 Loss1: 2.010987 Loss2: 2.060261
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.280318 Loss1: 0.799185 Loss2: 1.481133
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.877523 Loss1: 1.390807 Loss2: 1.486716
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.698589 Loss1: 1.210865 Loss2: 1.487724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.432798 Loss1: 0.929968 Loss2: 1.502831 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.323772 Loss1: 0.846615 Loss2: 1.477157 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.839583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.183315 Loss1: 0.709245 Loss2: 1.474070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.073166 Loss1: 0.591496 Loss2: 1.481670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.990094 Loss1: 0.514162 Loss2: 1.475933 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.868164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.725499 Loss1: 1.218101 Loss2: 1.507398 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.495377 Loss1: 0.964480 Loss2: 1.530897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.314395 Loss1: 2.140981 Loss2: 2.173414
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.271662 Loss1: 0.724360 Loss2: 1.547302
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.273544 Loss1: 1.671920 Loss2: 1.601623
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.287292 Loss1: 0.767500 Loss2: 1.519792
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.857312 Loss1: 1.264740 Loss2: 1.592571
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.168141 Loss1: 0.629264 Loss2: 1.538877
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.716398 Loss1: 1.124965 Loss2: 1.591433
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.173327 Loss1: 0.636450 Loss2: 1.536877
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.547929 Loss1: 0.950938 Loss2: 1.596991
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.211157 Loss1: 0.663991 Loss2: 1.547166
-(DefaultActor pid=3765) >> Training accuracy: 0.818750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.338518 Loss1: 0.741812 Loss2: 1.596706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.261794 Loss1: 0.650513 Loss2: 1.611281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.207495 Loss1: 0.587991 Loss2: 1.619504
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.304576 Loss1: 2.235024 Loss2: 2.069553
-(DefaultActor pid=3764) >> Training accuracy: 0.809375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.112810 Loss1: 1.592054 Loss2: 1.520757
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.764895 Loss1: 1.257217 Loss2: 1.507678
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.653300 Loss1: 1.141872 Loss2: 1.511428
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.530140 Loss1: 1.007252 Loss2: 1.522887
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.296666 Loss1: 2.161130 Loss2: 2.135535
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.383580 Loss1: 0.854927 Loss2: 1.528654
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.111231 Loss1: 1.579317 Loss2: 1.531915
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.306359 Loss1: 0.786519 Loss2: 1.519839
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.819182 Loss1: 1.322275 Loss2: 1.496907
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.282091 Loss1: 0.733239 Loss2: 1.548852
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.709333 Loss1: 1.179580 Loss2: 1.529754
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.182393 Loss1: 0.644813 Loss2: 1.537580
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.596169 Loss1: 1.056794 Loss2: 1.539376
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.121201 Loss1: 0.572989 Loss2: 1.548212
-(DefaultActor pid=3765) >> Training accuracy: 0.861458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.299140 Loss1: 0.759143 Loss2: 1.539997 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.245664 Loss1: 0.701747 Loss2: 1.543917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.201497 Loss1: 0.657366 Loss2: 1.544131
-(DefaultActor pid=3764) >> Training accuracy: 0.808333
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-09 11:30:32,491 | server.py:236 | fit_round 37 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.218055 Loss1: 2.189287 Loss2: 2.028768
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.117063 Loss1: 1.645473 Loss2: 1.471590
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.787638 Loss1: 1.332415 Loss2: 1.455224
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.575069 Loss1: 1.127080 Loss2: 1.447989
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.420939 Loss1: 0.952502 Loss2: 1.468437
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.276022 Loss1: 2.171014 Loss2: 2.105008
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.219341 Loss1: 0.770968 Loss2: 1.448373
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.138276 Loss1: 0.688515 Loss2: 1.449760
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.065737 Loss1: 1.508603 Loss2: 1.557133
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.220843 Loss1: 0.747207 Loss2: 1.473636
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.764026 Loss1: 1.231896 Loss2: 1.532130
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.145830 Loss1: 0.663483 Loss2: 1.482347
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.557567 Loss1: 1.015756 Loss2: 1.541811
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.191817 Loss1: 0.711035 Loss2: 1.480782
-(DefaultActor pid=3765) >> Training accuracy: 0.825000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.402571 Loss1: 0.877079 Loss2: 1.525492
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.296868 Loss1: 0.755927 Loss2: 1.540941
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.188626 Loss1: 0.655351 Loss2: 1.533275
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.172454 Loss1: 0.628053 Loss2: 1.544401
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.137147 Loss1: 0.584635 Loss2: 1.552511
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.355804 Loss1: 2.212598 Loss2: 2.143207
-(DefaultActor pid=3764) >> Training accuracy: 0.857422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.171246 Loss1: 1.619932 Loss2: 1.551314 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.554289 Loss1: 1.013525 Loss2: 1.540763 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.366656 Loss1: 0.808593 Loss2: 1.558063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.335046 Loss1: 0.762697 Loss2: 1.572350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.288045 Loss1: 0.713335 Loss2: 1.574710 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.202340 Loss1: 0.628406 Loss2: 1.573935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.111384 Loss1: 0.539779 Loss2: 1.571605 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.868750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.256520 Loss1: 0.803597 Loss2: 1.452922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.058040 Loss1: 0.595775 Loss2: 1.462265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.027042 Loss1: 0.554930 Loss2: 1.472111 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.844792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 11:30:32,491][flwr][DEBUG] - fit_round 37 received 50 results and 0 failures
-INFO flwr 2023-10-09 11:31:12,846 | server.py:125 | fit progress: (37, 2.6360169702444596, {'accuracy': 0.4045}, 85180.624192099)
->> Test accuracy: 0.404500
-[2023-10-09 11:31:12,846][flwr][INFO] - fit progress: (37, 2.6360169702444596, {'accuracy': 0.4045}, 85180.624192099)
-DEBUG flwr 2023-10-09 11:31:12,846 | server.py:173 | evaluate_round 37: strategy sampled 50 clients (out of 50)
-[2023-10-09 11:31:12,846][flwr][DEBUG] - evaluate_round 37: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 11:40:20,253 | server.py:187 | evaluate_round 37 received 50 results and 0 failures
-[2023-10-09 11:40:20,253][flwr][DEBUG] - evaluate_round 37 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 11:40:20,253 | server.py:222 | fit_round 38: strategy sampled 50 clients (out of 50)
-[2023-10-09 11:40:20,253][flwr][DEBUG] - fit_round 38: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.091216 Loss1: 2.038684 Loss2: 2.052531
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.021559 Loss1: 1.531124 Loss2: 1.490436
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.556654 Loss1: 1.085217 Loss2: 1.471438
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.329188 Loss1: 0.874353 Loss2: 1.454836
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.398541 Loss1: 2.303997 Loss2: 2.094545
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.226944 Loss1: 0.781597 Loss2: 1.445347
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.196580 Loss1: 1.674918 Loss2: 1.521662
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.124879 Loss1: 0.657175 Loss2: 1.467703
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.774110 Loss1: 1.289378 Loss2: 1.484732
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.034765 Loss1: 0.570678 Loss2: 1.464087
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.594901 Loss1: 1.096628 Loss2: 1.498273
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.103234 Loss1: 0.638211 Loss2: 1.465023
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.418315 Loss1: 0.926200 Loss2: 1.492115
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.101696 Loss1: 0.634778 Loss2: 1.466918
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.397483 Loss1: 0.891742 Loss2: 1.505741
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.043020 Loss1: 0.570195 Loss2: 1.472825
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.352292 Loss1: 0.829838 Loss2: 1.522453
-(DefaultActor pid=3765) >> Training accuracy: 0.862500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.262136 Loss1: 0.722961 Loss2: 1.539174
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.197562 Loss1: 0.677785 Loss2: 1.519777
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.190770 Loss1: 0.669445 Loss2: 1.521325
-(DefaultActor pid=3764) >> Training accuracy: 0.805208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.044852 Loss1: 1.949577 Loss2: 2.095274
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.015638 Loss1: 1.489337 Loss2: 1.526302
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.664716 Loss1: 1.147245 Loss2: 1.517471
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.373567 Loss1: 0.855403 Loss2: 1.518165
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.145852 Loss1: 2.107962 Loss2: 2.037890
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.240936 Loss1: 0.738135 Loss2: 1.502801
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.110070 Loss1: 1.603951 Loss2: 1.506119
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.204734 Loss1: 0.703485 Loss2: 1.501249
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.789013 Loss1: 1.310375 Loss2: 1.478638
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.104461 Loss1: 0.588788 Loss2: 1.515673
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.528751 Loss1: 1.050351 Loss2: 1.478401
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.165134 Loss1: 0.651706 Loss2: 1.513429
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.322357 Loss1: 0.838834 Loss2: 1.483523
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.145987 Loss1: 0.611325 Loss2: 1.534662
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.256827 Loss1: 0.774405 Loss2: 1.482422
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.051526 Loss1: 0.523700 Loss2: 1.527825
-(DefaultActor pid=3765) >> Training accuracy: 0.889583
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.299451 Loss1: 0.804497 Loss2: 1.494954
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.194051 Loss1: 0.700521 Loss2: 1.493530
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.093907 Loss1: 0.591764 Loss2: 1.502143
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.160095 Loss1: 0.658012 Loss2: 1.502083
-(DefaultActor pid=3764) >> Training accuracy: 0.790625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.085805 Loss1: 2.028490 Loss2: 2.057314
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.980861 Loss1: 1.479513 Loss2: 1.501348
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.729870 Loss1: 1.248788 Loss2: 1.481082
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.508543 Loss1: 1.040510 Loss2: 1.468033
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.177719 Loss1: 2.040281 Loss2: 2.137437
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.090837 Loss1: 1.533595 Loss2: 1.557242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.705313 Loss1: 1.183344 Loss2: 1.521969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.475474 Loss1: 0.955003 Loss2: 1.520471 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.244362 Loss1: 0.738078 Loss2: 1.506285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.151907 Loss1: 0.644260 Loss2: 1.507648 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.860417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.916903 Loss1: 0.441916 Loss2: 1.474986
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.088206 Loss1: 0.570326 Loss2: 1.517880
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.073653 Loss1: 0.564902 Loss2: 1.508751
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.077117 Loss1: 0.563698 Loss2: 1.513419
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.039589 Loss1: 0.520734 Loss2: 1.518855
-(DefaultActor pid=3764) >> Training accuracy: 0.858333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.060901 Loss1: 2.046216 Loss2: 2.014685
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.044577 Loss1: 1.514425 Loss2: 1.530152
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.705082 Loss1: 1.204567 Loss2: 1.500515
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.489512 Loss1: 0.989316 Loss2: 1.500196
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.080268 Loss1: 2.048688 Loss2: 2.031580
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.324198 Loss1: 0.818385 Loss2: 1.505813
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.058151 Loss1: 1.524028 Loss2: 1.534123
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.305173 Loss1: 0.799472 Loss2: 1.505700
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.622387 Loss1: 1.131115 Loss2: 1.491272
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.149035 Loss1: 0.643853 Loss2: 1.505182
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.421682 Loss1: 0.926486 Loss2: 1.495196
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.111841 Loss1: 0.598583 Loss2: 1.513258
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.330185 Loss1: 0.827873 Loss2: 1.502312
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.102147 Loss1: 0.590983 Loss2: 1.511164
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.234005 Loss1: 0.721527 Loss2: 1.512478
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.051364 Loss1: 0.534091 Loss2: 1.517273
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.251317 Loss1: 0.746332 Loss2: 1.504985
-(DefaultActor pid=3765) >> Training accuracy: 0.862305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.177915 Loss1: 0.643633 Loss2: 1.534283
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.024082 Loss1: 0.508742 Loss2: 1.515340
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.022386 Loss1: 0.510946 Loss2: 1.511440
-(DefaultActor pid=3764) >> Training accuracy: 0.891602
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.126957 Loss1: 2.052028 Loss2: 2.074929
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.965753 Loss1: 1.489673 Loss2: 1.476080
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.615226 Loss1: 1.158786 Loss2: 1.456440
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.381743 Loss1: 0.931757 Loss2: 1.449985
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.124948 Loss1: 2.091911 Loss2: 2.033037
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.047962 Loss1: 1.570123 Loss2: 1.477838 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.733742 Loss1: 1.258380 Loss2: 1.475362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.543355 Loss1: 1.068921 Loss2: 1.474435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.402689 Loss1: 0.928669 Loss2: 1.474020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.234872 Loss1: 0.766904 Loss2: 1.467968 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.809152
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.189226 Loss1: 0.700089 Loss2: 1.489137 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.110269 Loss1: 0.621920 Loss2: 1.488348 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.837500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.880607 Loss1: 1.428743 Loss2: 1.451864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.445384 Loss1: 0.999429 Loss2: 1.445955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.188800 Loss1: 0.746207 Loss2: 1.442593 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.218374 Loss1: 0.777712 Loss2: 1.440661 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.119454 Loss1: 0.663924 Loss2: 1.455530 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.156628 Loss1: 0.689391 Loss2: 1.467237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.199219 Loss1: 0.724586 Loss2: 1.474633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.166695 Loss1: 0.693427 Loss2: 1.473268 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.811523
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.195181 Loss1: 0.584490 Loss2: 1.610690 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.840625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.239241 Loss1: 2.124876 Loss2: 2.114365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.804058 Loss1: 1.299868 Loss2: 1.504190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.648761 Loss1: 1.127704 Loss2: 1.521057
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.218946 Loss1: 2.226974 Loss2: 1.991972
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.159820 Loss1: 1.663779 Loss2: 1.496041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.810959 Loss1: 1.317989 Loss2: 1.492970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.646058 Loss1: 1.147861 Loss2: 1.498197 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.461316 Loss1: 0.970233 Loss2: 1.491083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.304605 Loss1: 0.801653 Loss2: 1.502952 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.777083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.235540 Loss1: 0.738338 Loss2: 1.497202 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.296874 Loss1: 0.767311 Loss2: 1.529563 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.834961
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.153187 Loss1: 0.629297 Loss2: 1.523890
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.299028 Loss1: 2.165354 Loss2: 2.133674
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.047958 Loss1: 1.500629 Loss2: 1.547329
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.727368 Loss1: 1.213472 Loss2: 1.513896
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.497589 Loss1: 0.960014 Loss2: 1.537575
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.391279 Loss1: 0.862553 Loss2: 1.528726
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.142823 Loss1: 2.055998 Loss2: 2.086824
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.313511 Loss1: 0.778018 Loss2: 1.535494
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.000482 Loss1: 1.473378 Loss2: 1.527104
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.233289 Loss1: 0.684951 Loss2: 1.548339
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.583748 Loss1: 1.060917 Loss2: 1.522831
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.386039 Loss1: 0.889859 Loss2: 1.496180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.239708 Loss1: 0.733003 Loss2: 1.506705 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.887500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.149218 Loss1: 0.594120 Loss2: 1.555097
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.178519 Loss1: 0.685807 Loss2: 1.492712
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.268999 Loss1: 0.746397 Loss2: 1.522602
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.212598 Loss1: 0.684316 Loss2: 1.528282
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.126143 Loss1: 0.602529 Loss2: 1.523614
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.020242 Loss1: 0.501709 Loss2: 1.518533
-(DefaultActor pid=3764) >> Training accuracy: 0.862305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.194900 Loss1: 2.111661 Loss2: 2.083239
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.037304 Loss1: 1.529604 Loss2: 1.507700
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.718373 Loss1: 1.226899 Loss2: 1.491474
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.501459 Loss1: 1.000775 Loss2: 1.500684
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.330070 Loss1: 0.836437 Loss2: 1.493633
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.229801 Loss1: 2.144513 Loss2: 2.085288
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.248731 Loss1: 0.748880 Loss2: 1.499851
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.130196 Loss1: 0.631676 Loss2: 1.498520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.110778 Loss1: 0.613853 Loss2: 1.496925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.489195 Loss1: 0.992682 Loss2: 1.496513
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.098572 Loss1: 0.591604 Loss2: 1.506968
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.049967 Loss1: 0.536565 Loss2: 1.513402 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.854167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.172737 Loss1: 0.657059 Loss2: 1.515677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.130019 Loss1: 0.602186 Loss2: 1.527834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.173244 Loss1: 0.641077 Loss2: 1.532167
-(DefaultActor pid=3764) >> Training accuracy: 0.831250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.223695 Loss1: 2.193725 Loss2: 2.029970
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.027499 Loss1: 1.551601 Loss2: 1.475898
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.673213 Loss1: 1.219861 Loss2: 1.453353
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.367031 Loss1: 0.907098 Loss2: 1.459933
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.306116 Loss1: 0.827969 Loss2: 1.478147
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.229433 Loss1: 2.190405 Loss2: 2.039028
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.220935 Loss1: 0.732519 Loss2: 1.488416
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.099413 Loss1: 1.586082 Loss2: 1.513331
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.183961 Loss1: 0.701149 Loss2: 1.482811
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.676774 Loss1: 1.188510 Loss2: 1.488264
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.169407 Loss1: 0.675495 Loss2: 1.493912
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.502953 Loss1: 1.019097 Loss2: 1.483857
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.113202 Loss1: 0.626700 Loss2: 1.486502
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.052830 Loss1: 0.558884 Loss2: 1.493946
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.335460 Loss1: 0.855703 Loss2: 1.479757
-(DefaultActor pid=3765) >> Training accuracy: 0.794792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.287882 Loss1: 0.792173 Loss2: 1.495710
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.181751 Loss1: 0.690313 Loss2: 1.491438
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.194853 Loss1: 0.687849 Loss2: 1.507003
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.033079 Loss1: 0.523618 Loss2: 1.509462
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.533491 Loss1: 2.359257 Loss2: 2.174234
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.092062 Loss1: 0.600641 Loss2: 1.491421
-(DefaultActor pid=3764) >> Training accuracy: 0.756836
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.812640 Loss1: 1.272232 Loss2: 1.540408 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.476686 Loss1: 0.915127 Loss2: 1.561559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.027082 Loss1: 1.906129 Loss2: 2.120952 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.226066 Loss1: 0.642275 Loss2: 1.583791 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.119128 Loss1: 0.558653 Loss2: 1.560475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.153247 Loss1: 0.596046 Loss2: 1.557201 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.800223
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.151873 Loss1: 0.653910 Loss2: 1.497963 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.163442 Loss1: 0.661059 Loss2: 1.502383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.284562 Loss1: 2.167929 Loss2: 2.116633
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.094983 Loss1: 0.578198 Loss2: 1.516785
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.129492 Loss1: 1.586427 Loss2: 1.543064
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.981461 Loss1: 0.479146 Loss2: 1.502316
-(DefaultActor pid=3764) >> Training accuracy: 0.879167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.496846 Loss1: 0.967595 Loss2: 1.529251 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.337250 Loss1: 0.792835 Loss2: 1.544416 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.375578 Loss1: 0.817759 Loss2: 1.557819
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.137417 Loss1: 2.031941 Loss2: 2.105477
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.865221 Loss1: 1.360364 Loss2: 1.504857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.521676 Loss1: 1.040638 Loss2: 1.481038 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.844792
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.117330 Loss1: 0.567514 Loss2: 1.549816
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.396710 Loss1: 0.910792 Loss2: 1.485918
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.328131 Loss1: 0.839122 Loss2: 1.489009
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.175142 Loss1: 0.668885 Loss2: 1.506257
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.185901 Loss1: 0.684392 Loss2: 1.501508
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.059848 Loss1: 0.555132 Loss2: 1.504716
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.168961 Loss1: 2.118547 Loss2: 2.050414
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.009807 Loss1: 0.507216 Loss2: 1.502591
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.017643 Loss1: 1.525433 Loss2: 1.492209
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.015276 Loss1: 0.520516 Loss2: 1.494759
-(DefaultActor pid=3764) >> Training accuracy: 0.842708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.363880 Loss1: 0.884798 Loss2: 1.479082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.252644 Loss1: 0.775735 Loss2: 1.476909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.210038 Loss1: 0.716450 Loss2: 1.493588
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.199652 Loss1: 2.189120 Loss2: 2.010532
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.114427 Loss1: 0.620563 Loss2: 1.493864
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.010632 Loss1: 1.592532 Loss2: 1.418100
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.060455 Loss1: 0.561103 Loss2: 1.499352
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.705720 Loss1: 1.293554 Loss2: 1.412166
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.982552 Loss1: 0.487490 Loss2: 1.495062
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.481870 Loss1: 1.070724 Loss2: 1.411145
-(DefaultActor pid=3765) >> Training accuracy: 0.851042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.331744 Loss1: 0.916912 Loss2: 1.414832
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.188770 Loss1: 0.772222 Loss2: 1.416548
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.147913 Loss1: 0.720796 Loss2: 1.427117
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.095478 Loss1: 0.657284 Loss2: 1.438194
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.257692 Loss1: 2.160512 Loss2: 2.097179
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.093317 Loss1: 0.667913 Loss2: 1.425403
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.090356 Loss1: 1.532650 Loss2: 1.557707
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.008659 Loss1: 0.571098 Loss2: 1.437561
-(DefaultActor pid=3764) >> Training accuracy: 0.844792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.511496 Loss1: 0.983306 Loss2: 1.528190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.275802 Loss1: 0.735887 Loss2: 1.539915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.312409 Loss1: 0.771534 Loss2: 1.540875
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.237347 Loss1: 2.226681 Loss2: 2.010667
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.221847 Loss1: 0.666214 Loss2: 1.555634
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.136092 Loss1: 1.663311 Loss2: 1.472781
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.144604 Loss1: 0.589007 Loss2: 1.555597
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.755186 Loss1: 1.292547 Loss2: 1.462640
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.166229 Loss1: 0.616672 Loss2: 1.549556
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.475692 Loss1: 1.017291 Loss2: 1.458400
-(DefaultActor pid=3765) >> Training accuracy: 0.812500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.423606 Loss1: 0.963607 Loss2: 1.459998
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.341286 Loss1: 0.867579 Loss2: 1.473708
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.258553 Loss1: 0.789917 Loss2: 1.468636
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.205143 Loss1: 0.728520 Loss2: 1.476623
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.446143 Loss1: 2.296440 Loss2: 2.149703
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.117796 Loss1: 0.649690 Loss2: 1.468107
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.029199 Loss1: 0.561341 Loss2: 1.467858 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.870833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.629104 Loss1: 1.079625 Loss2: 1.549479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.379363 Loss1: 0.838463 Loss2: 1.540901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.237317 Loss1: 2.193645 Loss2: 2.043673 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.057007 Loss1: 1.562031 Loss2: 1.494975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.209447 Loss1: 0.641294 Loss2: 1.568153 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.832589
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.414485 Loss1: 0.928445 Loss2: 1.486040 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.312836 Loss1: 0.817113 Loss2: 1.495723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.376969 Loss1: 2.207627 Loss2: 2.169342
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.180497 Loss1: 0.691077 Loss2: 1.489420
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.157263 Loss1: 1.587759 Loss2: 1.569503
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.200315 Loss1: 0.703551 Loss2: 1.496764
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.948068 Loss1: 1.391460 Loss2: 1.556608
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.081320 Loss1: 0.583403 Loss2: 1.497918
-(DefaultActor pid=3764) >> Training accuracy: 0.897917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.543133 Loss1: 0.988751 Loss2: 1.554382 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.232908 Loss1: 0.678075 Loss2: 1.554832 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.188129 Loss1: 0.632540 Loss2: 1.555588
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.239624 Loss1: 2.248674 Loss2: 1.990950
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.192261 Loss1: 0.632969 Loss2: 1.559292
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.193686 Loss1: 1.713664 Loss2: 1.480022
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.172965 Loss1: 0.600610 Loss2: 1.572355
-(DefaultActor pid=3765) >> Training accuracy: 0.810417
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.758851 Loss1: 1.302856 Loss2: 1.455995
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.471443 Loss1: 1.010634 Loss2: 1.460809
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.392204 Loss1: 0.934686 Loss2: 1.457518
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.371020 Loss1: 0.897871 Loss2: 1.473149
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.228167 Loss1: 0.739820 Loss2: 1.488347
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.107622 Loss1: 2.104524 Loss2: 2.003098
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.151634 Loss1: 0.686575 Loss2: 1.465059
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.148879 Loss1: 0.674771 Loss2: 1.474109 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.078982 Loss1: 0.588661 Loss2: 1.490321 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.873047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.273224 Loss1: 0.848426 Loss2: 1.424799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.134015 Loss1: 0.689958 Loss2: 1.444057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.143916 Loss1: 0.702075 Loss2: 1.441841
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.936687 Loss1: 1.860324 Loss2: 2.076364
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.903761 Loss1: 1.381954 Loss2: 1.521807 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.865625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.592770 Loss1: 1.089378 Loss2: 1.503392 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.274807 Loss1: 0.788454 Loss2: 1.486353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.149585 Loss1: 0.629058 Loss2: 1.520527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.999550 Loss1: 0.490810 Loss2: 1.508739 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.937900 Loss1: 0.432473 Loss2: 1.505427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.935134 Loss1: 0.444306 Loss2: 1.490828 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.863542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.346433 Loss1: 0.843130 Loss2: 1.503302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.198496 Loss1: 0.698573 Loss2: 1.499923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.243409 Loss1: 0.721643 Loss2: 1.521766
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.072300 Loss1: 2.026937 Loss2: 2.045363
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.983468 Loss1: 1.530661 Loss2: 1.452807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.057208 Loss1: 0.542638 Loss2: 1.514570
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.562250 Loss1: 1.144474 Loss2: 1.417776
-(DefaultActor pid=3765) >> Training accuracy: 0.856250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.314531 Loss1: 0.915813 Loss2: 1.398718
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.123602 Loss1: 0.725181 Loss2: 1.398422
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.995204 Loss1: 0.597648 Loss2: 1.397556
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.007618 Loss1: 0.601522 Loss2: 1.406096
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.959098 Loss1: 0.546214 Loss2: 1.412883
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.973557 Loss1: 1.957244 Loss2: 2.016314
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.950278 Loss1: 0.526297 Loss2: 1.423980 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.837740
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.647699 Loss1: 1.160041 Loss2: 1.487658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.259500 Loss1: 0.772405 Loss2: 1.487095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.247187 Loss1: 0.759504 Loss2: 1.487683
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.255701 Loss1: 2.168101 Loss2: 2.087600
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.093940 Loss1: 0.606570 Loss2: 1.487369
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.969833 Loss1: 1.450754 Loss2: 1.519079
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.718192 Loss1: 1.198828 Loss2: 1.519363
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.058755 Loss1: 0.565157 Loss2: 1.493598
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.513873 Loss1: 0.979646 Loss2: 1.534227
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.120764 Loss1: 0.630973 Loss2: 1.489791
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.270087 Loss1: 0.756226 Loss2: 1.513861
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.099634 Loss1: 0.589982 Loss2: 1.509651
-(DefaultActor pid=3765) >> Training accuracy: 0.813419
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.258348 Loss1: 0.726837 Loss2: 1.531512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.176392 Loss1: 0.635854 Loss2: 1.540538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.060198 Loss1: 0.520748 Loss2: 1.539450
-(DefaultActor pid=3764) >> Training accuracy: 0.865625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.226471 Loss1: 2.086883 Loss2: 2.139589
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.155288 Loss1: 1.595505 Loss2: 1.559783
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.740827 Loss1: 1.210076 Loss2: 1.530751
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.544074 Loss1: 0.998791 Loss2: 1.545282
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.486135 Loss1: 0.934492 Loss2: 1.551643
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.192944 Loss1: 2.044161 Loss2: 2.148783
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.384756 Loss1: 0.826013 Loss2: 1.558743
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.266102 Loss1: 0.711377 Loss2: 1.554725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.228249 Loss1: 0.673915 Loss2: 1.554334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.184266 Loss1: 0.625323 Loss2: 1.558942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.182213 Loss1: 0.604426 Loss2: 1.577787 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.860417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.157169 Loss1: 0.640350 Loss2: 1.516819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.055557 Loss1: 0.545328 Loss2: 1.510229 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.861458
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.003224 Loss1: 0.503284 Loss2: 1.499939
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.193733 Loss1: 2.103678 Loss2: 2.090055
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.084035 Loss1: 1.520647 Loss2: 1.563388
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.726528 Loss1: 1.194889 Loss2: 1.531640
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.530408 Loss1: 0.989392 Loss2: 1.541017
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.435006 Loss1: 0.889793 Loss2: 1.545213
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.352792 Loss1: 2.163071 Loss2: 2.189721
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.069740 Loss1: 1.508287 Loss2: 1.561453
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.274525 Loss1: 0.718383 Loss2: 1.556142
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.235232 Loss1: 0.679530 Loss2: 1.555702 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.179844 Loss1: 0.608318 Loss2: 1.571526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.147546 Loss1: 0.581382 Loss2: 1.566164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.180339 Loss1: 0.634672 Loss2: 1.545667 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.847656
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.185831 Loss1: 0.619921 Loss2: 1.565911 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.820913
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.190397 Loss1: 2.028029 Loss2: 2.162368 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.633819 Loss1: 1.062369 Loss2: 1.571450 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.616173 Loss1: 1.033122 Loss2: 1.583050
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.129860 Loss1: 2.170782 Loss2: 1.959078
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.445822 Loss1: 0.847096 Loss2: 1.598726
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.078978 Loss1: 1.640128 Loss2: 1.438850
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.325636 Loss1: 0.738199 Loss2: 1.587437
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.676979 Loss1: 1.255561 Loss2: 1.421418
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.297854 Loss1: 0.699347 Loss2: 1.598507
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.529179 Loss1: 1.113630 Loss2: 1.415549
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.185104 Loss1: 0.591439 Loss2: 1.593666
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.344090 Loss1: 0.918018 Loss2: 1.426072
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.123500 Loss1: 0.533526 Loss2: 1.589973
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.206119 Loss1: 0.788011 Loss2: 1.418108
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.132350 Loss1: 0.527186 Loss2: 1.605164
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.106109 Loss1: 0.685103 Loss2: 1.421006
-(DefaultActor pid=3765) >> Training accuracy: 0.846875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.012728 Loss1: 0.592120 Loss2: 1.420608
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.964604 Loss1: 0.547648 Loss2: 1.416956
-DEBUG flwr 2023-10-09 12:09:08,973 | server.py:236 | fit_round 38 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.040684 Loss1: 0.608553 Loss2: 1.432131
-(DefaultActor pid=3764) >> Training accuracy: 0.829167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.370579 Loss1: 2.242888 Loss2: 2.127691
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.167422 Loss1: 1.616494 Loss2: 1.550928
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.849157 Loss1: 1.324508 Loss2: 1.524648
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.598005 Loss1: 1.052390 Loss2: 1.545614
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.247605 Loss1: 2.115454 Loss2: 2.132152
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.458334 Loss1: 0.917249 Loss2: 1.541085
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.168607 Loss1: 1.579016 Loss2: 1.589591
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.414308 Loss1: 0.875113 Loss2: 1.539195
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.779821 Loss1: 1.207280 Loss2: 1.572541
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.610209 Loss1: 1.046557 Loss2: 1.563652
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.309092 Loss1: 0.763641 Loss2: 1.545450
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.468276 Loss1: 0.886727 Loss2: 1.581549
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.296983 Loss1: 0.741308 Loss2: 1.555675
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.309677 Loss1: 0.733394 Loss2: 1.576283
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.153898 Loss1: 0.602957 Loss2: 1.550941
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.381649 Loss1: 0.799554 Loss2: 1.582095
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.197422 Loss1: 0.652553 Loss2: 1.544869
-(DefaultActor pid=3765) >> Training accuracy: 0.775391
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.215604 Loss1: 0.627306 Loss2: 1.588298 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.865625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.114188 Loss1: 2.065345 Loss2: 2.048843 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.626671 Loss1: 1.175432 Loss2: 1.451239 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.523987 Loss1: 1.068955 Loss2: 1.455032
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.176568 Loss1: 2.090218 Loss2: 2.086350
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.248131 Loss1: 0.806181 Loss2: 1.441950
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.050092 Loss1: 1.541408 Loss2: 1.508684
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.186879 Loss1: 0.735798 Loss2: 1.451082
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.749522 Loss1: 1.232047 Loss2: 1.517475
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.140684 Loss1: 0.670050 Loss2: 1.470634
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.469317 Loss1: 0.951794 Loss2: 1.517523
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.045483 Loss1: 0.590590 Loss2: 1.454893
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.372576 Loss1: 0.849645 Loss2: 1.522930
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.018949 Loss1: 0.559276 Loss2: 1.459673
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.312277 Loss1: 0.800160 Loss2: 1.512116
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.065234 Loss1: 0.599084 Loss2: 1.466150
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.158233 Loss1: 0.641145 Loss2: 1.517089
-(DefaultActor pid=3765) >> Training accuracy: 0.823958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.179426 Loss1: 0.646680 Loss2: 1.532746
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.239062 Loss1: 0.710934 Loss2: 1.528127
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.132365 Loss1: 0.597316 Loss2: 1.535049
-(DefaultActor pid=3764) >> Training accuracy: 0.850000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.305501 Loss1: 2.117324 Loss2: 2.188178
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.178762 Loss1: 1.574096 Loss2: 1.604666
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.790747 Loss1: 1.181325 Loss2: 1.609421
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.558047 Loss1: 0.969168 Loss2: 1.588879
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.401532 Loss1: 2.167755 Loss2: 2.233777
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.095954 Loss1: 1.547097 Loss2: 1.548857
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.473887 Loss1: 0.881845 Loss2: 1.592042
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.331385 Loss1: 0.736267 Loss2: 1.595118 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.194894 Loss1: 0.597606 Loss2: 1.597288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.270173 Loss1: 0.768893 Loss2: 1.501280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.123737 Loss1: 0.618335 Loss2: 1.505402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.137457 Loss1: 0.626797 Loss2: 1.510660 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.842708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.081488 Loss1: 0.570915 Loss2: 1.510572 [repeated 3x across cluster]
-[2023-10-09 12:09:08,973][flwr][DEBUG] - fit_round 38 received 50 results and 0 failures
-INFO flwr 2023-10-09 12:09:51,103 | server.py:125 | fit progress: (38, 2.5861118205439166, {'accuracy': 0.412}, 87498.881200555)
->> Test accuracy: 0.412000
-[2023-10-09 12:09:51,103][flwr][INFO] - fit progress: (38, 2.5861118205439166, {'accuracy': 0.412}, 87498.881200555)
-DEBUG flwr 2023-10-09 12:09:51,103 | server.py:173 | evaluate_round 38: strategy sampled 50 clients (out of 50)
-[2023-10-09 12:09:51,103][flwr][DEBUG] - evaluate_round 38: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 12:18:56,444 | server.py:187 | evaluate_round 38 received 50 results and 0 failures
-[2023-10-09 12:18:56,444][flwr][DEBUG] - evaluate_round 38 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 12:18:56,444 | server.py:222 | fit_round 39: strategy sampled 50 clients (out of 50)
-[2023-10-09 12:18:56,444][flwr][DEBUG] - fit_round 39: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3764) >> Training accuracy: 0.812500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.293504 Loss1: 2.199001 Loss2: 2.094503
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.100369 Loss1: 1.609471 Loss2: 1.490897
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.729316 Loss1: 1.253802 Loss2: 1.475515
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.430743 Loss1: 0.961166 Loss2: 1.469576
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.194396 Loss1: 2.131031 Loss2: 2.063365
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.034906 Loss1: 1.526162 Loss2: 1.508743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.695080 Loss1: 1.205789 Loss2: 1.489290 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.469000 Loss1: 0.981852 Loss2: 1.487148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.284272 Loss1: 0.816562 Loss2: 1.467710 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.000323 Loss1: 0.519280 Loss2: 1.481042 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.849330
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.021468 Loss1: 0.537272 Loss2: 1.484196 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.959907 Loss1: 0.471162 Loss2: 1.488745 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.872917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.992690 Loss1: 1.522998 Loss2: 1.469693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.477742 Loss1: 0.996979 Loss2: 1.480764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.354956 Loss1: 0.883635 Loss2: 1.471321
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.209120 Loss1: 2.119059 Loss2: 2.090061
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.146465 Loss1: 0.681568 Loss2: 1.464896
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.076553 Loss1: 1.507172 Loss2: 1.569381
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.092289 Loss1: 0.631283 Loss2: 1.461006
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.767779 Loss1: 1.238445 Loss2: 1.529334
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.069255 Loss1: 0.596760 Loss2: 1.472495
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.570838 Loss1: 1.040842 Loss2: 1.529995
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.094291 Loss1: 0.609563 Loss2: 1.484727
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.384689 Loss1: 0.857000 Loss2: 1.527690
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.058801 Loss1: 0.564647 Loss2: 1.494154
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.290169 Loss1: 0.750350 Loss2: 1.539820
-(DefaultActor pid=3765) >> Training accuracy: 0.825000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.178867 Loss1: 0.631619 Loss2: 1.547249
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.183184 Loss1: 0.639106 Loss2: 1.544078
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.131200 Loss1: 0.591792 Loss2: 1.539408
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.143860 Loss1: 0.584350 Loss2: 1.559510
-(DefaultActor pid=3764) >> Training accuracy: 0.866667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.210236 Loss1: 2.156873 Loss2: 2.053363
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.017878 Loss1: 1.514773 Loss2: 1.503106
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.679779 Loss1: 1.199109 Loss2: 1.480671
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.404756 Loss1: 0.930526 Loss2: 1.474230
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.284519 Loss1: 0.813735 Loss2: 1.470784
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.278160 Loss1: 0.797536 Loss2: 1.480623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.206741 Loss1: 0.712800 Loss2: 1.493941 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.167690 Loss1: 0.688230 Loss2: 1.479460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.002756 Loss1: 0.518735 Loss2: 1.484021 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.997236 Loss1: 0.525922 Loss2: 1.471315 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.862500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.172504 Loss1: 0.716828 Loss2: 1.455675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.107686 Loss1: 0.629002 Loss2: 1.478685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.006039 Loss1: 0.555883 Loss2: 1.450156
-(DefaultActor pid=3764) >> Training accuracy: 0.824219
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.072454 Loss1: 1.902043 Loss2: 2.170411
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.902866 Loss1: 1.353377 Loss2: 1.549490
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.501792 Loss1: 0.988026 Loss2: 1.513766
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.400212 Loss1: 0.893891 Loss2: 1.506320
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.279118 Loss1: 0.764286 Loss2: 1.514832
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.131813 Loss1: 2.079542 Loss2: 2.052272
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.189855 Loss1: 0.681010 Loss2: 1.508845
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.170067 Loss1: 0.662554 Loss2: 1.507513 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.107761 Loss1: 0.572993 Loss2: 1.534768 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.016404 Loss1: 0.505462 Loss2: 1.510942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.951717 Loss1: 0.441638 Loss2: 1.510079 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.912500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.180293 Loss1: 0.703821 Loss2: 1.476471 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.120375 Loss1: 0.639614 Loss2: 1.480761 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.995594 Loss1: 0.519952 Loss2: 1.475641
-(DefaultActor pid=3764) >> Training accuracy: 0.837500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.133684 Loss1: 2.077564 Loss2: 2.056120
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.025416 Loss1: 1.543623 Loss2: 1.481794
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.601434 Loss1: 1.158249 Loss2: 1.443185
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.421210 Loss1: 0.970457 Loss2: 1.450753
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.338507 Loss1: 0.882265 Loss2: 1.456242
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.179836 Loss1: 2.042721 Loss2: 2.137115
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.002846 Loss1: 1.464897 Loss2: 1.537949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.658426 Loss1: 1.159263 Loss2: 1.499162 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.497015 Loss1: 0.992743 Loss2: 1.504272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.039523 Loss1: 0.561293 Loss2: 1.478230
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.258393 Loss1: 0.741246 Loss2: 1.517148
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.048680 Loss1: 0.581373 Loss2: 1.467307
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.254328 Loss1: 0.757913 Loss2: 1.496416
-(DefaultActor pid=3765) >> Training accuracy: 0.820833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.217640 Loss1: 0.689869 Loss2: 1.527771
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.140967 Loss1: 0.618704 Loss2: 1.522262
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.085238 Loss1: 0.564193 Loss2: 1.521045
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.044757 Loss1: 0.533619 Loss2: 1.511138
-(DefaultActor pid=3764) >> Training accuracy: 0.821429
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.021888 Loss1: 1.997570 Loss2: 2.024318
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.884271 Loss1: 1.432427 Loss2: 1.451844
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.492192 Loss1: 1.058914 Loss2: 1.433278
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.295408 Loss1: 0.873767 Loss2: 1.421641
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.300677 Loss1: 2.155037 Loss2: 2.145641
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.144345 Loss1: 1.585514 Loss2: 1.558831 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.745633 Loss1: 1.200661 Loss2: 1.544972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.595088 Loss1: 1.041373 Loss2: 1.553714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.422992 Loss1: 0.870838 Loss2: 1.552154 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.314803 Loss1: 0.751336 Loss2: 1.563468 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.886458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.167653 Loss1: 0.600987 Loss2: 1.566666 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.131704 Loss1: 0.566667 Loss2: 1.565037 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.848958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.822868 Loss1: 1.339963 Loss2: 1.482904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.347706 Loss1: 0.868424 Loss2: 1.479282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.141598 Loss1: 0.665258 Loss2: 1.476340 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.072764 Loss1: 0.594153 Loss2: 1.478611 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.100719 Loss1: 0.611891 Loss2: 1.488828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.044835 Loss1: 0.561342 Loss2: 1.483493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.038734 Loss1: 0.554064 Loss2: 1.484670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.914640 Loss1: 0.432136 Loss2: 1.482504 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.894531
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.168336 Loss1: 0.645981 Loss2: 1.522354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.074023 Loss1: 0.557921 Loss2: 1.516102 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.836458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.137292 Loss1: 2.081294 Loss2: 2.055998
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.915729 Loss1: 1.422355 Loss2: 1.493374
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.606246 Loss1: 1.136028 Loss2: 1.470218
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.306049 Loss1: 0.842443 Loss2: 1.463606
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.027046 Loss1: 2.059913 Loss2: 1.967133
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.948994 Loss1: 1.506679 Loss2: 1.442315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.509651 Loss1: 1.089005 Loss2: 1.420646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.329802 Loss1: 0.910540 Loss2: 1.419263 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.288432 Loss1: 0.862500 Loss2: 1.425933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.163488 Loss1: 0.738782 Loss2: 1.424706 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.885417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.043427 Loss1: 0.617568 Loss2: 1.425860 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.008494 Loss1: 0.559055 Loss2: 1.449440 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.844727
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.136334 Loss1: 1.581042 Loss2: 1.555292 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.530457 Loss1: 0.977283 Loss2: 1.553175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.105676 Loss1: 2.070015 Loss2: 2.035661
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.414223 Loss1: 0.869298 Loss2: 1.544924
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.835820 Loss1: 1.383142 Loss2: 1.452678
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.291337 Loss1: 0.737082 Loss2: 1.554255
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.469639 Loss1: 1.034390 Loss2: 1.435248
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.262503 Loss1: 0.708244 Loss2: 1.554259
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.338547 Loss1: 0.894182 Loss2: 1.444365
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.223323 Loss1: 0.665701 Loss2: 1.557622
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.179368 Loss1: 0.728796 Loss2: 1.450572
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.101837 Loss1: 0.534267 Loss2: 1.567570
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.139121 Loss1: 0.693043 Loss2: 1.446078
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.981912 Loss1: 0.446285 Loss2: 1.535627
-(DefaultActor pid=3765) >> Training accuracy: 0.873958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.088607 Loss1: 0.643199 Loss2: 1.445408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.955829 Loss1: 0.502377 Loss2: 1.453452 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.863542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.979722 Loss1: 1.497400 Loss2: 1.482322 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.515853 Loss1: 1.035904 Loss2: 1.479949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.207012 Loss1: 2.137728 Loss2: 2.069284
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.321732 Loss1: 0.852520 Loss2: 1.469212
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.194042 Loss1: 0.719457 Loss2: 1.474585
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.104012 Loss1: 1.584161 Loss2: 1.519851
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.071901 Loss1: 0.600787 Loss2: 1.471114
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.831662 Loss1: 1.336979 Loss2: 1.494683
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.035025 Loss1: 0.562015 Loss2: 1.473010
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.545995 Loss1: 1.034404 Loss2: 1.511592
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.999599 Loss1: 0.518712 Loss2: 1.480887
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.400006 Loss1: 0.891194 Loss2: 1.508812
-(DefaultActor pid=3765) >> Training accuracy: 0.868750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.003814 Loss1: 0.514897 Loss2: 1.488917
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.311894 Loss1: 0.807737 Loss2: 1.504157
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.302348 Loss1: 0.778555 Loss2: 1.523793
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.245888 Loss1: 0.723480 Loss2: 1.522408
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.151881 Loss1: 0.628927 Loss2: 1.522954
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.076853 Loss1: 0.558968 Loss2: 1.517885
-(DefaultActor pid=3764) >> Training accuracy: 0.842773
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.166123 Loss1: 2.067107 Loss2: 2.099016
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.048828 Loss1: 1.522463 Loss2: 1.526365
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.841293 Loss1: 1.339328 Loss2: 1.501965
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.522158 Loss1: 1.000028 Loss2: 1.522130
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.450588 Loss1: 0.933207 Loss2: 1.517380
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.056857 Loss1: 2.038198 Loss2: 2.018658
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.349842 Loss1: 0.821638 Loss2: 1.528205
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.008617 Loss1: 1.510942 Loss2: 1.497676
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.216728 Loss1: 0.692432 Loss2: 1.524296
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.602856 Loss1: 1.121066 Loss2: 1.481790
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.207221 Loss1: 0.681991 Loss2: 1.525230
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.165480 Loss1: 0.631383 Loss2: 1.534097
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.436016 Loss1: 0.962469 Loss2: 1.473547
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.100565 Loss1: 0.551852 Loss2: 1.548713
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.262627 Loss1: 0.775283 Loss2: 1.487344
-(DefaultActor pid=3765) >> Training accuracy: 0.898958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.164804 Loss1: 0.677558 Loss2: 1.487245
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.114691 Loss1: 0.638402 Loss2: 1.476289
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.048699 Loss1: 0.552047 Loss2: 1.496652
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.075207 Loss1: 0.576079 Loss2: 1.499129
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.242032 Loss1: 2.193658 Loss2: 2.048374
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.051629 Loss1: 0.545674 Loss2: 1.505956
-(DefaultActor pid=3764) >> Training accuracy: 0.875000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.703774 Loss1: 1.224957 Loss2: 1.478817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.352591 Loss1: 0.885317 Loss2: 1.467274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.205438 Loss1: 0.735955 Loss2: 1.469483
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.393138 Loss1: 2.279459 Loss2: 2.113679
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.133840 Loss1: 1.605012 Loss2: 1.528828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.749947 Loss1: 1.247621 Loss2: 1.502326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.471593 Loss1: 0.968109 Loss2: 1.503483 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.820833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.137589 Loss1: 0.643639 Loss2: 1.493950
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.412711 Loss1: 0.911494 Loss2: 1.501217
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.240169 Loss1: 0.712464 Loss2: 1.527705
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.131174 Loss1: 0.619042 Loss2: 1.512131
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.143719 Loss1: 0.633990 Loss2: 1.509729
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.107564 Loss1: 0.565833 Loss2: 1.541731
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.050343 Loss1: 0.520870 Loss2: 1.529474
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.102151 Loss1: 2.030658 Loss2: 2.071493
-(DefaultActor pid=3764) >> Training accuracy: 0.824777
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.991169 Loss1: 1.519237 Loss2: 1.471933
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.691260 Loss1: 1.245379 Loss2: 1.445881
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.473331 Loss1: 1.027132 Loss2: 1.446199
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.317112 Loss1: 0.876482 Loss2: 1.440629
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.201330 Loss1: 0.751686 Loss2: 1.449644
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.314021 Loss1: 2.184245 Loss2: 2.129775
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.040527 Loss1: 1.527425 Loss2: 1.513102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.591185 Loss1: 1.148032 Loss2: 1.443154 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.953038 Loss1: 0.490928 Loss2: 1.462111
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.376912 Loss1: 0.935812 Loss2: 1.441099
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.229516 Loss1: 0.784184 Loss2: 1.445332
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.889082 Loss1: 0.431189 Loss2: 1.457893
-(DefaultActor pid=3765) >> Training accuracy: 0.888221
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.117646 Loss1: 0.672035 Loss2: 1.445611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.014938 Loss1: 0.558790 Loss2: 1.456148 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.884115
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.147538 Loss1: 2.025480 Loss2: 2.122058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.692769 Loss1: 1.206841 Loss2: 1.485929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.874288 Loss1: 1.824703 Loss2: 2.049585 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.829089 Loss1: 1.352980 Loss2: 1.476109 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.540780 Loss1: 1.077449 Loss2: 1.463331 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.361823 Loss1: 0.896075 Loss2: 1.465748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.162593 Loss1: 0.706608 Loss2: 1.455986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.100634 Loss1: 0.642349 Loss2: 1.458285 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.814583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.052346 Loss1: 0.578130 Loss2: 1.474216 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.989689 Loss1: 0.502663 Loss2: 1.487026 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.858333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.057596 Loss1: 1.422233 Loss2: 1.635363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.453737 Loss1: 0.877549 Loss2: 1.576188 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.173445 Loss1: 2.089422 Loss2: 2.084023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.085424 Loss1: 1.570893 Loss2: 1.514531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.119949 Loss1: 0.541361 Loss2: 1.578588 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.091694 Loss1: 0.505293 Loss2: 1.586401 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.152630 Loss1: 0.555349 Loss2: 1.597281 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.786058
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.349281 Loss1: 0.842094 Loss2: 1.507187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.177339 Loss1: 0.652095 Loss2: 1.525243 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.202192 Loss1: 2.130923 Loss2: 2.071269 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.872917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.539029 Loss1: 1.072564 Loss2: 1.466465 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.308462 Loss1: 0.820674 Loss2: 1.487788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.248809 Loss1: 0.767967 Loss2: 1.480842
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.047080 Loss1: 1.955839 Loss2: 2.091241
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.212206 Loss1: 0.717695 Loss2: 1.494510
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.960717 Loss1: 1.458972 Loss2: 1.501745
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.150548 Loss1: 0.638674 Loss2: 1.511874
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.635271 Loss1: 1.123440 Loss2: 1.511831
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.007877 Loss1: 0.506622 Loss2: 1.501255
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.432124 Loss1: 0.927377 Loss2: 1.504747
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.968348 Loss1: 0.484519 Loss2: 1.483829
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.316614 Loss1: 0.814316 Loss2: 1.502298
-(DefaultActor pid=3765) >> Training accuracy: 0.879167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.186865 Loss1: 0.666792 Loss2: 1.520074
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.164518 Loss1: 0.663219 Loss2: 1.501299
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.124468 Loss1: 0.606596 Loss2: 1.517872
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.019237 Loss1: 0.500259 Loss2: 1.518977
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.994126 Loss1: 0.481730 Loss2: 1.512396
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.148103 Loss1: 2.141963 Loss2: 2.006140
-(DefaultActor pid=3764) >> Training accuracy: 0.904167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.982991 Loss1: 1.518194 Loss2: 1.464797
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.719321 Loss1: 1.258615 Loss2: 1.460705
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.485892 Loss1: 1.020848 Loss2: 1.465044
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.350494 Loss1: 0.888720 Loss2: 1.461774
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.015519 Loss1: 2.026001 Loss2: 1.989518
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.200187 Loss1: 0.736259 Loss2: 1.463928
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.160987 Loss1: 0.699514 Loss2: 1.461473 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.149552 Loss1: 0.676730 Loss2: 1.472823 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.147196 Loss1: 0.666552 Loss2: 1.480644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.016435 Loss1: 0.535865 Loss2: 1.480570 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.818359
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.042032 Loss1: 0.644233 Loss2: 1.397798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.095068 Loss1: 0.659795 Loss2: 1.435273 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.846875
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.001844 Loss1: 0.568813 Loss2: 1.433031
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.106715 Loss1: 1.988871 Loss2: 2.117844
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.986384 Loss1: 1.466841 Loss2: 1.519543
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.511517 Loss1: 1.020297 Loss2: 1.491219
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.334790 Loss1: 0.858405 Loss2: 1.476385
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.350158 Loss1: 0.870237 Loss2: 1.479921
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.300158 Loss1: 2.239717 Loss2: 2.060440
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.266194 Loss1: 0.771907 Loss2: 1.494286
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.031310 Loss1: 0.542492 Loss2: 1.488818 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.022630 Loss1: 0.535471 Loss2: 1.487159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.973958 Loss1: 0.492417 Loss2: 1.481541 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.933140 Loss1: 0.443743 Loss2: 1.489397 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.873958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.111672 Loss1: 0.643854 Loss2: 1.467818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.055517 Loss1: 0.570616 Loss2: 1.484901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.027105 Loss1: 0.552322 Loss2: 1.474783
-(DefaultActor pid=3764) >> Training accuracy: 0.878125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.963324 Loss1: 1.965402 Loss2: 1.997921
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.861200 Loss1: 1.366717 Loss2: 1.494483
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.535329 Loss1: 1.071630 Loss2: 1.463699
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.406869 Loss1: 0.950486 Loss2: 1.456383
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.285639 Loss1: 0.809299 Loss2: 1.476339
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.037062 Loss1: 1.922343 Loss2: 2.114719
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.895403 Loss1: 1.379359 Loss2: 1.516044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.575128 Loss1: 1.078046 Loss2: 1.497082 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.398700 Loss1: 0.891588 Loss2: 1.507112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.251175 Loss1: 0.749618 Loss2: 1.501557 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.839844
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.001504 Loss1: 0.516722 Loss2: 1.484782
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.173763 Loss1: 0.661020 Loss2: 1.512743
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.168243 Loss1: 0.642789 Loss2: 1.525453
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.104786 Loss1: 0.587429 Loss2: 1.517357
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.048011 Loss1: 0.522637 Loss2: 1.525374
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.030908 Loss1: 0.499605 Loss2: 1.531303
-(DefaultActor pid=3764) >> Training accuracy: 0.853125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.153963 Loss1: 2.003892 Loss2: 2.150071
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.027258 Loss1: 1.459944 Loss2: 1.567314
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.694005 Loss1: 1.128654 Loss2: 1.565351
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.443218 Loss1: 0.872158 Loss2: 1.571060
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.249245 Loss1: 2.195710 Loss2: 2.053535
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.420325 Loss1: 0.836768 Loss2: 1.583557
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.264353 Loss1: 1.758188 Loss2: 1.506165
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.379492 Loss1: 0.800921 Loss2: 1.578571
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.233299 Loss1: 0.670641 Loss2: 1.562658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.184688 Loss1: 0.611893 Loss2: 1.572794 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.081568 Loss1: 0.511599 Loss2: 1.569969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.080452 Loss1: 0.513516 Loss2: 1.566936 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.861328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.211990 Loss1: 0.698525 Loss2: 1.513465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.162791 Loss1: 0.647754 Loss2: 1.515036 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.790625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.052938 Loss1: 1.940534 Loss2: 2.112404
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.910680 Loss1: 1.400974 Loss2: 1.509707
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.672831 Loss1: 1.181595 Loss2: 1.491237
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.454326 Loss1: 0.941391 Loss2: 1.512935
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.258744 Loss1: 2.148240 Loss2: 2.110504
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.184709 Loss1: 1.635941 Loss2: 1.548768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.749670 Loss1: 1.222546 Loss2: 1.527124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.574423 Loss1: 1.045774 Loss2: 1.528649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.473377 Loss1: 0.936574 Loss2: 1.536803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.293119 Loss1: 0.758074 Loss2: 1.535044 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.800000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.147223 Loss1: 0.612677 Loss2: 1.534546 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.080010 Loss1: 0.542321 Loss2: 1.537689 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.853125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.840591 Loss1: 1.321208 Loss2: 1.519383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.330923 Loss1: 0.827945 Loss2: 1.502978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.236072 Loss1: 0.733799 Loss2: 1.502272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.178158 Loss1: 0.672359 Loss2: 1.505799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.094756 Loss1: 0.584110 Loss2: 1.510645 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.084330 Loss1: 0.577035 Loss2: 1.507295 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.035577 Loss1: 0.523433 Loss2: 1.512144 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.981890 Loss1: 0.465376 Loss2: 1.516514 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.854167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.174419 Loss1: 0.602310 Loss2: 1.572109 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.170563 Loss1: 0.587438 Loss2: 1.583125 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.773958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.744670 Loss1: 1.296018 Loss2: 1.448652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.363916 Loss1: 0.908947 Loss2: 1.454970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.363093 Loss1: 2.250942 Loss2: 2.112152
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.161207 Loss1: 0.725559 Loss2: 1.435648
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.138230 Loss1: 1.565839 Loss2: 1.572391
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.087227 Loss1: 0.650022 Loss2: 1.437205
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.826611 Loss1: 1.270653 Loss2: 1.555958
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.056784 Loss1: 0.604695 Loss2: 1.452089
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.567588 Loss1: 1.008870 Loss2: 1.558719
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.008958 Loss1: 0.561494 Loss2: 1.447464
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.950169 Loss1: 0.494027 Loss2: 1.456142
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.361934 Loss1: 0.811071 Loss2: 1.550863
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.878019 Loss1: 0.426470 Loss2: 1.451550
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.279014 Loss1: 0.723129 Loss2: 1.555885
-(DefaultActor pid=3765) >> Training accuracy: 0.883333
-DEBUG flwr 2023-10-09 12:47:05,215 | server.py:236 | fit_round 39 received 50 results and 0 failures
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.188780 Loss1: 0.634206 Loss2: 1.554574
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.248420 Loss1: 0.681049 Loss2: 1.567371
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.239370 Loss1: 0.676661 Loss2: 1.562709
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.121510 Loss1: 0.544623 Loss2: 1.576887
-(DefaultActor pid=3764) >> Training accuracy: 0.819336
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.342266 Loss1: 2.210231 Loss2: 2.132036
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.169453 Loss1: 1.616303 Loss2: 1.553150
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.677606 Loss1: 1.166435 Loss2: 1.511172
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.497532 Loss1: 0.981017 Loss2: 1.516514
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.477943 Loss1: 0.949354 Loss2: 1.528589
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.932141 Loss1: 1.900091 Loss2: 2.032049
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.924642 Loss1: 1.417873 Loss2: 1.506768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.599734 Loss1: 1.103180 Loss2: 1.496554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.435813 Loss1: 0.933870 Loss2: 1.501943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.024343 Loss1: 0.483402 Loss2: 1.540941 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.864583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.110423 Loss1: 0.610137 Loss2: 1.500286 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.026803 Loss1: 0.526488 Loss2: 1.500315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.043885 Loss1: 0.538504 Loss2: 1.505380 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.865809
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.570098 Loss1: 1.072951 Loss2: 1.497147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.262558 Loss1: 0.763164 Loss2: 1.499394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.139310 Loss1: 2.127994 Loss2: 2.011315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.083441 Loss1: 1.623277 Loss2: 1.460164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.690022 Loss1: 1.251069 Loss2: 1.438953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.500313 Loss1: 1.054305 Loss2: 1.446008 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.876042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.223557 Loss1: 0.768669 Loss2: 1.454888 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.085759 Loss1: 0.630765 Loss2: 1.454995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.932792 Loss1: 0.485872 Loss2: 1.446920 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.871875
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 12:47:05,215][flwr][DEBUG] - fit_round 39 received 50 results and 0 failures
-INFO flwr 2023-10-09 12:47:45,935 | server.py:125 | fit progress: (39, 2.5775355591941564, {'accuracy': 0.4201}, 89773.71312695)
->> Test accuracy: 0.420100
-[2023-10-09 12:47:45,935][flwr][INFO] - fit progress: (39, 2.5775355591941564, {'accuracy': 0.4201}, 89773.71312695)
-DEBUG flwr 2023-10-09 12:47:45,935 | server.py:173 | evaluate_round 39: strategy sampled 50 clients (out of 50)
-[2023-10-09 12:47:45,935][flwr][DEBUG] - evaluate_round 39: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 12:56:49,087 | server.py:187 | evaluate_round 39 received 50 results and 0 failures
-[2023-10-09 12:56:49,087][flwr][DEBUG] - evaluate_round 39 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 12:56:49,087 | server.py:222 | fit_round 40: strategy sampled 50 clients (out of 50)
-[2023-10-09 12:56:49,087][flwr][DEBUG] - fit_round 40: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.164358 Loss1: 2.135789 Loss2: 2.028570
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.059771 Loss1: 1.588662 Loss2: 1.471108
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.662705 Loss1: 1.222569 Loss2: 1.440136
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.466969 Loss1: 1.009117 Loss2: 1.457852
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.290765 Loss1: 2.205651 Loss2: 2.085113
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.036830 Loss1: 1.517504 Loss2: 1.519326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.678863 Loss1: 1.188168 Loss2: 1.490695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.455354 Loss1: 0.965226 Loss2: 1.490127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.347224 Loss1: 0.850665 Loss2: 1.496559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.149303 Loss1: 0.648960 Loss2: 1.500343 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.845833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.959335 Loss1: 0.489671 Loss2: 1.469665
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.173797 Loss1: 0.678637 Loss2: 1.495159
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.108809 Loss1: 0.597697 Loss2: 1.511112
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.003167 Loss1: 0.494173 Loss2: 1.508994
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.000929 Loss1: 0.495277 Loss2: 1.505652
-(DefaultActor pid=3764) >> Training accuracy: 0.825000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.306053 Loss1: 2.198566 Loss2: 2.107487
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.109818 Loss1: 1.581032 Loss2: 1.528786
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.734002 Loss1: 1.221340 Loss2: 1.512662
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.575522 Loss1: 1.049565 Loss2: 1.525957
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.963161 Loss1: 1.949554 Loss2: 2.013607
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.818687 Loss1: 1.367394 Loss2: 1.451293 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.597907 Loss1: 1.175554 Loss2: 1.422353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.280842 Loss1: 0.837325 Loss2: 1.443517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.144908 Loss1: 0.718268 Loss2: 1.426640 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.087492 Loss1: 0.651441 Loss2: 1.436051 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.780208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.131758 Loss1: 0.590436 Loss2: 1.541322
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.010343 Loss1: 0.571348 Loss2: 1.438996
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.969523 Loss1: 0.524823 Loss2: 1.444699
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.006640 Loss1: 0.556817 Loss2: 1.449823
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.986311 Loss1: 0.529987 Loss2: 1.456324
-(DefaultActor pid=3764) >> Training accuracy: 0.879167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.934450 Loss1: 1.958042 Loss2: 1.976408
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.932482 Loss1: 1.484697 Loss2: 1.447785
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.508626 Loss1: 1.063147 Loss2: 1.445478
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.405845 Loss1: 0.960239 Loss2: 1.445607
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.100098 Loss1: 2.066173 Loss2: 2.033924
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.182909 Loss1: 0.735556 Loss2: 1.447353
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.928231 Loss1: 1.435948 Loss2: 1.492283
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.666721 Loss1: 1.206509 Loss2: 1.460212
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.134526 Loss1: 0.698580 Loss2: 1.435946
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.397410 Loss1: 0.922916 Loss2: 1.474494
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.030470 Loss1: 0.578317 Loss2: 1.452153
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.226576 Loss1: 0.765688 Loss2: 1.460888
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.997849 Loss1: 0.533762 Loss2: 1.464086
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.122379 Loss1: 0.664850 Loss2: 1.457529
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.946782 Loss1: 0.498090 Loss2: 1.448693
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.985357 Loss1: 0.529466 Loss2: 1.455891 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.876953
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.960367 Loss1: 0.479513 Loss2: 1.480853 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.880208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.120790 Loss1: 2.053109 Loss2: 2.067680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.618580 Loss1: 1.128819 Loss2: 1.489761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.425334 Loss1: 0.921784 Loss2: 1.503550
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.077439 Loss1: 1.932998 Loss2: 2.144442
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.893078 Loss1: 1.368735 Loss2: 1.524343
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.326886 Loss1: 0.810457 Loss2: 1.516429
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.294655 Loss1: 0.774943 Loss2: 1.519712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.177395 Loss1: 0.657766 Loss2: 1.519630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.107069 Loss1: 0.579449 Loss2: 1.527620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.097066 Loss1: 0.583451 Loss2: 1.513615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.072790 Loss1: 0.535497 Loss2: 1.537292 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.798958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.930126 Loss1: 0.461256 Loss2: 1.468870 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.897837
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.238692 Loss1: 2.174303 Loss2: 2.064389
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.979382 Loss1: 1.469375 Loss2: 1.510007
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.635913 Loss1: 1.148229 Loss2: 1.487684
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.439858 Loss1: 0.953490 Loss2: 1.486368
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.958082 Loss1: 1.908758 Loss2: 2.049324
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.364799 Loss1: 0.861680 Loss2: 1.503119
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.875171 Loss1: 1.390142 Loss2: 1.485029
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.244602 Loss1: 0.730115 Loss2: 1.514486
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.515360 Loss1: 1.037821 Loss2: 1.477539
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.315867 Loss1: 0.800072 Loss2: 1.515795
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.298410 Loss1: 0.833643 Loss2: 1.464768
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.217034 Loss1: 0.685693 Loss2: 1.531341
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.221237 Loss1: 0.755252 Loss2: 1.465985
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.045345 Loss1: 0.526833 Loss2: 1.518512
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.132518 Loss1: 0.658465 Loss2: 1.474052
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.005061 Loss1: 0.504431 Loss2: 1.500630
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.033507 Loss1: 0.570606 Loss2: 1.462901
-(DefaultActor pid=3765) >> Training accuracy: 0.890625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.010865 Loss1: 0.535217 Loss2: 1.475648
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.069988 Loss1: 0.588760 Loss2: 1.481228
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.995675 Loss1: 0.513658 Loss2: 1.482017
-(DefaultActor pid=3764) >> Training accuracy: 0.877083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.184162 Loss1: 2.156576 Loss2: 2.027587
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.009009 Loss1: 1.520407 Loss2: 1.488602
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.637504 Loss1: 1.146580 Loss2: 1.490924
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.473335 Loss1: 0.986359 Loss2: 1.486976
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.170683 Loss1: 2.104838 Loss2: 2.065845
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.401848 Loss1: 0.911937 Loss2: 1.489910
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.147370 Loss1: 1.641388 Loss2: 1.505982
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.294272 Loss1: 0.810577 Loss2: 1.483695
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.716773 Loss1: 1.242973 Loss2: 1.473800
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.249492 Loss1: 0.750228 Loss2: 1.499264
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.568420 Loss1: 1.086721 Loss2: 1.481699
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.164187 Loss1: 0.670026 Loss2: 1.494161
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.297637 Loss1: 0.819204 Loss2: 1.478433
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.130715 Loss1: 0.633991 Loss2: 1.496724
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.144564 Loss1: 0.675114 Loss2: 1.469450
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.138260 Loss1: 0.638742 Loss2: 1.499518
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.111739 Loss1: 0.640418 Loss2: 1.471321
-(DefaultActor pid=3765) >> Training accuracy: 0.870833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.023729 Loss1: 0.555946 Loss2: 1.467783
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.033411 Loss1: 0.555714 Loss2: 1.477697
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.057746 Loss1: 0.573785 Loss2: 1.483961
-(DefaultActor pid=3764) >> Training accuracy: 0.726042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.081274 Loss1: 2.079579 Loss2: 2.001695
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.958683 Loss1: 1.475473 Loss2: 1.483210
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.606802 Loss1: 1.147055 Loss2: 1.459747
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.406003 Loss1: 0.944456 Loss2: 1.461547
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.228404 Loss1: 2.128110 Loss2: 2.100294
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.115672 Loss1: 1.535696 Loss2: 1.579977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.755023 Loss1: 1.203114 Loss2: 1.551909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.485273 Loss1: 0.929443 Loss2: 1.555829 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.343027 Loss1: 0.790356 Loss2: 1.552672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.281371 Loss1: 0.723283 Loss2: 1.558088 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.857292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.227871 Loss1: 0.674951 Loss2: 1.552920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.156614 Loss1: 0.597828 Loss2: 1.558785 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.827148
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.175230 Loss1: 2.071625 Loss2: 2.103605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.759064 Loss1: 1.203706 Loss2: 1.555358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.118976 Loss1: 2.016311 Loss2: 2.102666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.855700 Loss1: 1.357966 Loss2: 1.497734 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.511062 Loss1: 1.059081 Loss2: 1.451981 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.314206 Loss1: 0.861189 Loss2: 1.453017 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.167725 Loss1: 0.722006 Loss2: 1.445718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.044164 Loss1: 0.589076 Loss2: 1.455088 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.863542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.960156 Loss1: 0.503029 Loss2: 1.457127 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.876072 Loss1: 0.421290 Loss2: 1.454782 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.896875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.948691 Loss1: 1.461075 Loss2: 1.487616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.363107 Loss1: 0.885190 Loss2: 1.477918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.175670 Loss1: 2.110591 Loss2: 2.065080
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.124812 Loss1: 0.654864 Loss2: 1.469948
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.071762 Loss1: 1.570123 Loss2: 1.501639
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.027009 Loss1: 0.556580 Loss2: 1.470430
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.630609 Loss1: 1.146258 Loss2: 1.484351
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.056572 Loss1: 0.581474 Loss2: 1.475098
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.480146 Loss1: 0.986689 Loss2: 1.493457
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.164022 Loss1: 0.671608 Loss2: 1.492414
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.439876 Loss1: 0.939432 Loss2: 1.500444
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.131716 Loss1: 0.621878 Loss2: 1.509839
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.360687 Loss1: 0.857076 Loss2: 1.503611
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.043378 Loss1: 0.521163 Loss2: 1.522215
-(DefaultActor pid=3765) >> Training accuracy: 0.810417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.110823 Loss1: 0.608663 Loss2: 1.502160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.030150 Loss1: 0.510387 Loss2: 1.519762 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.846875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.919457 Loss1: 1.444232 Loss2: 1.475224 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.314080 Loss1: 0.856069 Loss2: 1.458011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.206806 Loss1: 0.743886 Loss2: 1.462920
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.077993 Loss1: 2.022737 Loss2: 2.055256
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.039639 Loss1: 0.570415 Loss2: 1.469224
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.870720 Loss1: 1.361614 Loss2: 1.509106
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.025954 Loss1: 0.558413 Loss2: 1.467541
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.458638 Loss1: 0.967374 Loss2: 1.491264
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.047445 Loss1: 0.581604 Loss2: 1.465841
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.334623 Loss1: 0.850901 Loss2: 1.483722
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.937715 Loss1: 0.470664 Loss2: 1.467052
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.328153 Loss1: 0.831210 Loss2: 1.496943
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.001487 Loss1: 0.546944 Loss2: 1.454543
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.291736 Loss1: 0.777423 Loss2: 1.514313
-(DefaultActor pid=3765) >> Training accuracy: 0.858333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.071596 Loss1: 0.565898 Loss2: 1.505698
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.004480 Loss1: 0.506243 Loss2: 1.498237
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.058223 Loss1: 0.546735 Loss2: 1.511488
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.997977 Loss1: 0.477246 Loss2: 1.520731
-(DefaultActor pid=3764) >> Training accuracy: 0.870833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.053590 Loss1: 1.988261 Loss2: 2.065330
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.824026 Loss1: 1.311242 Loss2: 1.512785
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.594443 Loss1: 1.109549 Loss2: 1.484894
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.469152 Loss1: 0.959341 Loss2: 1.509811
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.284704 Loss1: 0.781548 Loss2: 1.503156
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.135221 Loss1: 0.641291 Loss2: 1.493930 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.098179 Loss1: 0.611854 Loss2: 1.486325 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.074228 Loss1: 0.581034 Loss2: 1.493193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.984076 Loss1: 0.487226 Loss2: 1.496850 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.999802 Loss1: 0.498821 Loss2: 1.500982 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.901042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.080913 Loss1: 0.596812 Loss2: 1.484100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.002065 Loss1: 0.519360 Loss2: 1.482705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.068903 Loss1: 2.020551 Loss2: 2.048352
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.102491 Loss1: 0.603017 Loss2: 1.499474
-(DefaultActor pid=3764) >> Training accuracy: 0.857537
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.527938 Loss1: 1.036313 Loss2: 1.491626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.305491 Loss1: 0.814644 Loss2: 1.490847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.258744 Loss1: 0.755849 Loss2: 1.502894
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.984201 Loss1: 2.041573 Loss2: 1.942628
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.006868 Loss1: 1.578761 Loss2: 1.428107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.548279 Loss1: 1.151712 Loss2: 1.396567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.331767 Loss1: 0.930537 Loss2: 1.401231 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.870833
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.071874 Loss1: 0.579500 Loss2: 1.492374
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.202178 Loss1: 0.806224 Loss2: 1.395953
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.217899 Loss1: 0.807547 Loss2: 1.410352
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.080289 Loss1: 0.668154 Loss2: 1.412135
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.975971 Loss1: 0.572610 Loss2: 1.403361
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.982903 Loss1: 0.580380 Loss2: 1.402522
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.893019 Loss1: 0.470457 Loss2: 1.422563
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.175707 Loss1: 2.144391 Loss2: 2.031316
-(DefaultActor pid=3764) >> Training accuracy: 0.878125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.014761 Loss1: 1.489903 Loss2: 1.524858
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.709614 Loss1: 1.206861 Loss2: 1.502753
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.483617 Loss1: 0.971916 Loss2: 1.511702
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.366784 Loss1: 0.861486 Loss2: 1.505298
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.995693 Loss1: 1.935992 Loss2: 2.059701
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.249260 Loss1: 0.737542 Loss2: 1.511718
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.923578 Loss1: 1.423575 Loss2: 1.500003
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.123645 Loss1: 0.618174 Loss2: 1.505471
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.700269 Loss1: 1.205375 Loss2: 1.494894
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.085131 Loss1: 0.578079 Loss2: 1.507052
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.003707 Loss1: 0.491178 Loss2: 1.512529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.007838 Loss1: 0.485646 Loss2: 1.522193 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.833984
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.121381 Loss1: 0.609903 Loss2: 1.511478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.052433 Loss1: 0.543169 Loss2: 1.509264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.019123 Loss1: 0.513622 Loss2: 1.505501
-(DefaultActor pid=3764) >> Training accuracy: 0.869792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.365027 Loss1: 2.248913 Loss2: 2.116114
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.125208 Loss1: 1.603293 Loss2: 1.521915
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.728415 Loss1: 1.253820 Loss2: 1.474595
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.472441 Loss1: 0.994257 Loss2: 1.478184
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.337278 Loss1: 0.850966 Loss2: 1.486313
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.264308 Loss1: 0.780215 Loss2: 1.484093
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.142450 Loss1: 2.031303 Loss2: 2.111148
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.002454 Loss1: 1.468656 Loss2: 1.533798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.668152 Loss1: 1.162850 Loss2: 1.505302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.489617 Loss1: 0.978130 Loss2: 1.511487 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.828125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.351943 Loss1: 0.826165 Loss2: 1.525778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.105975 Loss1: 0.590526 Loss2: 1.515449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.114795 Loss1: 0.594423 Loss2: 1.520371 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.130396 Loss1: 0.599343 Loss2: 1.531053 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.835417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.638785 Loss1: 1.120300 Loss2: 1.518484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.304732 Loss1: 0.795969 Loss2: 1.508763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.371254 Loss1: 2.098956 Loss2: 2.272298
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.257207 Loss1: 0.742574 Loss2: 1.514633
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.210260 Loss1: 0.677804 Loss2: 1.532456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.147220 Loss1: 0.607718 Loss2: 1.539502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.381248 Loss1: 0.838648 Loss2: 1.542600 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.312994 Loss1: 0.767255 Loss2: 1.545740 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.884766
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.083449 Loss1: 0.540075 Loss2: 1.543374 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.018986 Loss1: 0.474029 Loss2: 1.544957 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.903646
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.937169 Loss1: 1.909383 Loss2: 2.027786
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.886138 Loss1: 1.425781 Loss2: 1.460357
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.375053 Loss1: 0.959591 Loss2: 1.415462
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.242670 Loss1: 0.837487 Loss2: 1.405184
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.096083 Loss1: 2.063308 Loss2: 2.032775
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.897432 Loss1: 1.427929 Loss2: 1.469503 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.568160 Loss1: 1.124990 Loss2: 1.443170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.391830 Loss1: 0.927163 Loss2: 1.464666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.263044 Loss1: 0.785407 Loss2: 1.477637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.126390 Loss1: 0.662387 Loss2: 1.464003 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.892708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.880133 Loss1: 0.446520 Loss2: 1.433613
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.146834 Loss1: 0.680448 Loss2: 1.466387
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.982811 Loss1: 0.509782 Loss2: 1.473028
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.942868 Loss1: 0.481044 Loss2: 1.461824
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.939301 Loss1: 0.474591 Loss2: 1.464710
-(DefaultActor pid=3764) >> Training accuracy: 0.873958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.161165 Loss1: 2.077180 Loss2: 2.083986
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.965622 Loss1: 1.449153 Loss2: 1.516469
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.754357 Loss1: 1.262540 Loss2: 1.491817
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.523651 Loss1: 1.015114 Loss2: 1.508538
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.010924 Loss1: 1.955870 Loss2: 2.055054
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.406295 Loss1: 0.893849 Loss2: 1.512445
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.776860 Loss1: 1.313891 Loss2: 1.462969
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.225733 Loss1: 0.709610 Loss2: 1.516123
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.539044 Loss1: 1.101787 Loss2: 1.437257
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.189125 Loss1: 0.678977 Loss2: 1.510148
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.285383 Loss1: 0.831216 Loss2: 1.454166
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.212175 Loss1: 0.764586 Loss2: 1.447589
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.177134 Loss1: 0.666171 Loss2: 1.510964
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.089734 Loss1: 0.643460 Loss2: 1.446274
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.141684 Loss1: 0.630398 Loss2: 1.511286
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.093333 Loss1: 0.634465 Loss2: 1.458868
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.049132 Loss1: 0.522704 Loss2: 1.526429
-(DefaultActor pid=3765) >> Training accuracy: 0.876042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.957996 Loss1: 0.504370 Loss2: 1.453626 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.885045
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.961243 Loss1: 2.020228 Loss2: 1.941014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.557624 Loss1: 1.166049 Loss2: 1.391575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.298450 Loss1: 0.916795 Loss2: 1.381655
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.057950 Loss1: 2.057932 Loss2: 2.000018
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.877852 Loss1: 1.405483 Loss2: 1.472369 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.617857 Loss1: 1.171974 Loss2: 1.445883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.371205 Loss1: 0.911428 Loss2: 1.459778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.255547 Loss1: 0.795481 Loss2: 1.460066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.101065 Loss1: 0.663915 Loss2: 1.437151 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.776042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.915627 Loss1: 0.519107 Loss2: 1.396520
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.107940 Loss1: 0.654414 Loss2: 1.453526
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.054404 Loss1: 0.586967 Loss2: 1.467437
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.959965 Loss1: 0.500324 Loss2: 1.459641
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.050323 Loss1: 0.587909 Loss2: 1.462414
-(DefaultActor pid=3764) >> Training accuracy: 0.854167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.343098 Loss1: 2.202766 Loss2: 2.140332
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.097913 Loss1: 1.571918 Loss2: 1.525995
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.731414 Loss1: 1.205687 Loss2: 1.525727
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.474912 Loss1: 0.965722 Loss2: 1.509190
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.940942 Loss1: 1.880894 Loss2: 2.060048
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.769735 Loss1: 1.304450 Loss2: 1.465285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.489407 Loss1: 1.023292 Loss2: 1.466114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.166050 Loss1: 0.617952 Loss2: 1.548099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.120940 Loss1: 0.585547 Loss2: 1.535392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.986578 Loss1: 0.454287 Loss2: 1.532291 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.892857
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.903788 Loss1: 0.449207 Loss2: 1.454581 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.923980 Loss1: 0.473967 Loss2: 1.450013 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.901042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.997204 Loss1: 1.417791 Loss2: 1.579413 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.433021 Loss1: 0.919463 Loss2: 1.513558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.206907 Loss1: 2.049013 Loss2: 2.157894
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.349233 Loss1: 0.819226 Loss2: 1.530006
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.168593 Loss1: 0.638193 Loss2: 1.530400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.144649 Loss1: 0.614275 Loss2: 1.530374 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.084839 Loss1: 0.549218 Loss2: 1.535621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.179425 Loss1: 0.674055 Loss2: 1.505370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.117372 Loss1: 0.606473 Loss2: 1.510900 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.862305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.066235 Loss1: 0.542591 Loss2: 1.523644 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.870192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.947540 Loss1: 1.869754 Loss2: 2.077786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.512366 Loss1: 1.036050 Loss2: 1.476316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.315022 Loss1: 0.834937 Loss2: 1.480085
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.943945 Loss1: 1.925038 Loss2: 2.018907
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.970598 Loss1: 1.425909 Loss2: 1.544689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.561280 Loss1: 1.046832 Loss2: 1.514448 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.442930 Loss1: 0.936684 Loss2: 1.506246 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.266685 Loss1: 0.747013 Loss2: 1.519672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.159514 Loss1: 0.646815 Loss2: 1.512699 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.887500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.048302 Loss1: 0.546766 Loss2: 1.501536 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.064284 Loss1: 0.556563 Loss2: 1.507721 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.846680
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.037643 Loss1: 2.096909 Loss2: 1.940733 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.678434 Loss1: 1.222366 Loss2: 1.456068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.431812 Loss1: 0.985951 Loss2: 1.445860
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.229542 Loss1: 2.175597 Loss2: 2.053946
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.026621 Loss1: 1.507543 Loss2: 1.519078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.656679 Loss1: 1.166593 Loss2: 1.490087 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.482224 Loss1: 0.975980 Loss2: 1.506244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.430724 Loss1: 0.932490 Loss2: 1.498234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.295347 Loss1: 0.794978 Loss2: 1.500369 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.850586
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.228730 Loss1: 0.726306 Loss2: 1.502424 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.091723 Loss1: 0.596289 Loss2: 1.495435 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.834961
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.179531 Loss1: 2.180525 Loss2: 1.999006 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.541211 Loss1: 1.115735 Loss2: 1.425476 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.146913 Loss1: 2.120504 Loss2: 2.026409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.100844 Loss1: 1.659043 Loss2: 1.441801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.713818 Loss1: 1.283459 Loss2: 1.430358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.356376 Loss1: 0.927772 Loss2: 1.428603 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.154102 Loss1: 0.740004 Loss2: 1.414097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.165150 Loss1: 0.741305 Loss2: 1.423845 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.895833
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-09 13:25:48,586 | server.py:236 | fit_round 40 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.006104 Loss1: 0.569090 Loss2: 1.437014 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.905583 Loss1: 0.469120 Loss2: 1.436463 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.847917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.836660 Loss1: 1.348283 Loss2: 1.488377 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.463990 Loss1: 0.997765 Loss2: 1.466225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.936075 Loss1: 1.901204 Loss2: 2.034871
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.254805 Loss1: 0.783403 Loss2: 1.471402
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.957884 Loss1: 1.468966 Loss2: 1.488918
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.128414 Loss1: 0.663577 Loss2: 1.464837
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.631195 Loss1: 1.143055 Loss2: 1.488140
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.973984 Loss1: 0.525957 Loss2: 1.448027
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.003115 Loss1: 0.541894 Loss2: 1.461221
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.317084 Loss1: 0.829178 Loss2: 1.487905
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.956859 Loss1: 0.500156 Loss2: 1.456703
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.198218 Loss1: 0.738136 Loss2: 1.460082
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.875810 Loss1: 0.416003 Loss2: 1.459807
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.146041 Loss1: 0.668601 Loss2: 1.477440
-(DefaultActor pid=3765) >> Training accuracy: 0.892708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.048797 Loss1: 0.580909 Loss2: 1.467889
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.934163 Loss1: 0.459944 Loss2: 1.474219
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.947154 Loss1: 0.480788 Loss2: 1.466366
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.021385 Loss1: 0.547078 Loss2: 1.474308
-(DefaultActor pid=3764) >> Training accuracy: 0.859375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.156804 Loss1: 2.042761 Loss2: 2.114043
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.953303 Loss1: 1.381935 Loss2: 1.571368
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.655663 Loss1: 1.108608 Loss2: 1.547055
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.487629 Loss1: 0.935997 Loss2: 1.551632
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.356087 Loss1: 0.816296 Loss2: 1.539791
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.844857 Loss1: 1.770001 Loss2: 2.074856
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.807275 Loss1: 1.304154 Loss2: 1.503121 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.531803 Loss1: 1.043666 Loss2: 1.488138 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.325079 Loss1: 0.841437 Loss2: 1.483642 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.236051 Loss1: 0.756663 Loss2: 1.479388 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.881250
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.028913 Loss1: 0.473423 Loss2: 1.555489
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.134617 Loss1: 0.644860 Loss2: 1.489757
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.023457 Loss1: 0.531663 Loss2: 1.491794
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.961181 Loss1: 0.482091 Loss2: 1.479090
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.961144 Loss1: 0.463914 Loss2: 1.497231
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.938062 Loss1: 0.459767 Loss2: 1.478295
-(DefaultActor pid=3764) >> Training accuracy: 0.832292
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 13:25:48,586][flwr][DEBUG] - fit_round 40 received 50 results and 0 failures
-INFO flwr 2023-10-09 13:26:29,768 | server.py:125 | fit progress: (40, 2.5703621779006127, {'accuracy': 0.4248}, 92097.546352868)
->> Test accuracy: 0.424800
-[2023-10-09 13:26:29,768][flwr][INFO] - fit progress: (40, 2.5703621779006127, {'accuracy': 0.4248}, 92097.546352868)
-DEBUG flwr 2023-10-09 13:26:29,768 | server.py:173 | evaluate_round 40: strategy sampled 50 clients (out of 50)
-[2023-10-09 13:26:29,768][flwr][DEBUG] - evaluate_round 40: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 13:35:37,089 | server.py:187 | evaluate_round 40 received 50 results and 0 failures
-[2023-10-09 13:35:37,089][flwr][DEBUG] - evaluate_round 40 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 13:35:37,089 | server.py:222 | fit_round 41: strategy sampled 50 clients (out of 50)
-[2023-10-09 13:35:37,089][flwr][DEBUG] - fit_round 41: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.029467 Loss1: 1.959127 Loss2: 2.070340
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.916623 Loss1: 1.425392 Loss2: 1.491231
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.580515 Loss1: 1.099210 Loss2: 1.481305
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.325493 Loss1: 0.845462 Loss2: 1.480031
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.992454 Loss1: 1.869414 Loss2: 2.123040
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.218203 Loss1: 0.732269 Loss2: 1.485934
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.820516 Loss1: 1.314699 Loss2: 1.505818
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.152797 Loss1: 0.658545 Loss2: 1.494252
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.480245 Loss1: 0.987987 Loss2: 1.492258
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.074360 Loss1: 0.583066 Loss2: 1.491294
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.233596 Loss1: 0.749959 Loss2: 1.483637
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.984058 Loss1: 0.493319 Loss2: 1.490740
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.092962 Loss1: 0.622981 Loss2: 1.469981
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.933200 Loss1: 0.437658 Loss2: 1.495542
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.040110 Loss1: 0.569344 Loss2: 1.470766
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.889660 Loss1: 0.394102 Loss2: 1.495558
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.004992 Loss1: 0.523851 Loss2: 1.481141
-(DefaultActor pid=3765) >> Training accuracy: 0.877083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.994775 Loss1: 0.518699 Loss2: 1.476076
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.118707 Loss1: 0.610463 Loss2: 1.508244
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.102364 Loss1: 0.593492 Loss2: 1.508871
-(DefaultActor pid=3764) >> Training accuracy: 0.796875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.153081 Loss1: 2.084759 Loss2: 2.068322
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.990231 Loss1: 1.492551 Loss2: 1.497681
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.701201 Loss1: 1.231590 Loss2: 1.469611
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.413377 Loss1: 0.932836 Loss2: 1.480542
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.244167 Loss1: 2.167663 Loss2: 2.076504
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.284576 Loss1: 0.803570 Loss2: 1.481006
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.054871 Loss1: 1.565165 Loss2: 1.489707
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.157972 Loss1: 0.680335 Loss2: 1.477637
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.843639 Loss1: 1.398227 Loss2: 1.445412
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.497371 Loss1: 1.037691 Loss2: 1.459680
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.189265 Loss1: 0.698488 Loss2: 1.490777
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.330326 Loss1: 0.879971 Loss2: 1.450355
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.152570 Loss1: 0.654668 Loss2: 1.497902
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.156262 Loss1: 0.695422 Loss2: 1.460841
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.010586 Loss1: 0.505809 Loss2: 1.504777
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.947861 Loss1: 0.466859 Loss2: 1.481002 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.888542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.954773 Loss1: 0.501890 Loss2: 1.452883 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.831473
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.101859 Loss1: 2.032850 Loss2: 2.069009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.681377 Loss1: 1.161307 Loss2: 1.520070 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.440901 Loss1: 0.921030 Loss2: 1.519871
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.178180 Loss1: 2.118144 Loss2: 2.060036
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.372680 Loss1: 0.845497 Loss2: 1.527182
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.096759 Loss1: 1.596059 Loss2: 1.500700
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.368960 Loss1: 0.837584 Loss2: 1.531376
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.656675 Loss1: 1.172064 Loss2: 1.484611
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.212917 Loss1: 0.663777 Loss2: 1.549140
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.529287 Loss1: 1.035215 Loss2: 1.494072
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.114962 Loss1: 0.586110 Loss2: 1.528852
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.309051 Loss1: 0.809211 Loss2: 1.499840
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.053733 Loss1: 0.527256 Loss2: 1.526477
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.274815 Loss1: 0.775370 Loss2: 1.499445
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.923705 Loss1: 0.398354 Loss2: 1.525351
-(DefaultActor pid=3765) >> Training accuracy: 0.896875
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.145996 Loss1: 0.633984 Loss2: 1.512012
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.131891 Loss1: 0.622099 Loss2: 1.509792
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.106364 Loss1: 0.583536 Loss2: 1.522828
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.093075 Loss1: 0.563870 Loss2: 1.529205
-(DefaultActor pid=3764) >> Training accuracy: 0.869792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.264735 Loss1: 2.194481 Loss2: 2.070254
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.049217 Loss1: 1.535987 Loss2: 1.513231
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.732540 Loss1: 1.255320 Loss2: 1.477220
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.463571 Loss1: 0.969449 Loss2: 1.494122
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.174825 Loss1: 1.966709 Loss2: 2.208116
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.851034 Loss1: 1.321495 Loss2: 1.529539
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.367023 Loss1: 0.871830 Loss2: 1.495193
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.221904 Loss1: 0.729805 Loss2: 1.492099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.116424 Loss1: 0.609132 Loss2: 1.507292 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.980187 Loss1: 0.485412 Loss2: 1.494775 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.921915 Loss1: 0.429992 Loss2: 1.491923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.950394 Loss1: 0.455395 Loss2: 1.494998 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.848958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.939075 Loss1: 0.432757 Loss2: 1.506318 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.887019
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.966996 Loss1: 1.905123 Loss2: 2.061874
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.816495 Loss1: 1.300088 Loss2: 1.516407
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.597309 Loss1: 1.106181 Loss2: 1.491128
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.082755 Loss1: 2.056699 Loss2: 2.026056
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.292370 Loss1: 0.800415 Loss2: 1.491955
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.839938 Loss1: 1.381774 Loss2: 1.458163
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.136396 Loss1: 0.661406 Loss2: 1.474990
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.494735 Loss1: 1.063117 Loss2: 1.431619
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.086651 Loss1: 0.603825 Loss2: 1.482825
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.056835 Loss1: 0.573089 Loss2: 1.483746 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.001563 Loss1: 0.510472 Loss2: 1.491091 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.938455 Loss1: 0.438726 Loss2: 1.499729 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.920746 Loss1: 0.431511 Loss2: 1.489235 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.898438
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.942512 Loss1: 0.504818 Loss2: 1.437694 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.829167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.122839 Loss1: 2.017077 Loss2: 2.105762 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.616017 Loss1: 1.091686 Loss2: 1.524331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.384890 Loss1: 0.866455 Loss2: 1.518435
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.158353 Loss1: 2.109308 Loss2: 2.049044
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.961916 Loss1: 1.459100 Loss2: 1.502816 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.715620 Loss1: 1.231391 Loss2: 1.484229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.442902 Loss1: 0.935863 Loss2: 1.507039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.995613 Loss1: 0.473899 Loss2: 1.521714
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.302026 Loss1: 0.823207 Loss2: 1.478819
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.166844 Loss1: 0.678074 Loss2: 1.488770 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.974145 Loss1: 0.462214 Loss2: 1.511931
-(DefaultActor pid=3765) >> Training accuracy: 0.853125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.119576 Loss1: 0.644999 Loss2: 1.474577
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.044123 Loss1: 0.563084 Loss2: 1.481039
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.032097 Loss1: 0.541800 Loss2: 1.490298
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.068481 Loss1: 0.580753 Loss2: 1.487728
-(DefaultActor pid=3764) >> Training accuracy: 0.864583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.873752 Loss1: 1.818110 Loss2: 2.055642
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.867339 Loss1: 1.350556 Loss2: 1.516783
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.524703 Loss1: 1.019015 Loss2: 1.505688
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.049610 Loss1: 1.906341 Loss2: 2.143269
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.330434 Loss1: 0.836040 Loss2: 1.494395
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.188340 Loss1: 0.689583 Loss2: 1.498758 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.095588 Loss1: 0.595352 Loss2: 1.500236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.123393 Loss1: 0.618707 Loss2: 1.504686 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.210483 Loss1: 0.687117 Loss2: 1.523366 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.085431 Loss1: 0.560340 Loss2: 1.525092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.922195 Loss1: 0.446486 Loss2: 1.475708 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.817096
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.889025 Loss1: 0.409340 Loss2: 1.479685 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.848958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.092665 Loss1: 1.987369 Loss2: 2.105296
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.981309 Loss1: 1.468239 Loss2: 1.513070
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.673752 Loss1: 1.167221 Loss2: 1.506531
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.347289 Loss1: 0.850424 Loss2: 1.496865
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.977328 Loss1: 1.810373 Loss2: 2.166955
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.306354 Loss1: 0.811712 Loss2: 1.494641
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.834670 Loss1: 1.280886 Loss2: 1.553784
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.201717 Loss1: 0.698496 Loss2: 1.503221
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.532062 Loss1: 0.987436 Loss2: 1.544626
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.058684 Loss1: 0.557102 Loss2: 1.501582
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.328865 Loss1: 0.788902 Loss2: 1.539963
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.030008 Loss1: 0.537393 Loss2: 1.492616
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.105677 Loss1: 0.564462 Loss2: 1.541214
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.037732 Loss1: 0.530647 Loss2: 1.507084
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.159435 Loss1: 0.622215 Loss2: 1.537221
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.930764 Loss1: 0.422810 Loss2: 1.507954
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.077659 Loss1: 0.528873 Loss2: 1.548786
-(DefaultActor pid=3765) >> Training accuracy: 0.932292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.011030 Loss1: 0.465352 Loss2: 1.545678
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.949396 Loss1: 0.400513 Loss2: 1.548883
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.936052 Loss1: 0.391070 Loss2: 1.544982
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.074624 Loss1: 1.992505 Loss2: 2.082120
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.937790 Loss1: 1.423662 Loss2: 1.514128
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.537973 Loss1: 1.045674 Loss2: 1.492299
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.133322 Loss1: 2.060068 Loss2: 2.073254
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.399189 Loss1: 0.896853 Loss2: 1.502336
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.106343 Loss1: 1.571461 Loss2: 1.534882
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.253380 Loss1: 0.751509 Loss2: 1.501871
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.619916 Loss1: 1.107808 Loss2: 1.512107
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.151085 Loss1: 0.659293 Loss2: 1.491792
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.387144 Loss1: 0.895871 Loss2: 1.491273
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.185060 Loss1: 0.682971 Loss2: 1.502089
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.127550 Loss1: 0.616776 Loss2: 1.510773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.068224 Loss1: 0.570357 Loss2: 1.497866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.997614 Loss1: 0.499106 Loss2: 1.498509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.854492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.127734 Loss1: 0.630256 Loss2: 1.497478 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.862500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.945913 Loss1: 1.942987 Loss2: 2.002926 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.494911 Loss1: 1.042328 Loss2: 1.452583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.293992 Loss1: 0.830082 Loss2: 1.463910
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.065247 Loss1: 2.011778 Loss2: 2.053468
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.014746 Loss1: 1.507780 Loss2: 1.506967
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.243023 Loss1: 0.776471 Loss2: 1.466552
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.635814 Loss1: 1.147100 Loss2: 1.488714
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.167511 Loss1: 0.695605 Loss2: 1.471906
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.388545 Loss1: 0.896566 Loss2: 1.491979
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.039144 Loss1: 0.562904 Loss2: 1.476240
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.264213 Loss1: 0.772602 Loss2: 1.491611
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.959909 Loss1: 0.501714 Loss2: 1.458196
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.024916 Loss1: 0.553890 Loss2: 1.471026 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.974417 Loss1: 0.493807 Loss2: 1.480610 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.875977
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.921455 Loss1: 0.432349 Loss2: 1.489107 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.864583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.136476 Loss1: 2.022006 Loss2: 2.114469 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.586062 Loss1: 1.127246 Loss2: 1.458816 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.068759 Loss1: 2.044280 Loss2: 2.024479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.122666 Loss1: 0.657391 Loss2: 1.465275 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.093744 Loss1: 0.626071 Loss2: 1.467673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.089294 Loss1: 0.615781 Loss2: 1.473513 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.017667 Loss1: 0.523559 Loss2: 1.494108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.901539 Loss1: 0.426790 Loss2: 1.474749 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.877404
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.252039 Loss1: 0.757034 Loss2: 1.495006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.050703 Loss1: 0.569861 Loss2: 1.480842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.029387 Loss1: 0.543315 Loss2: 1.486072
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.079140 Loss1: 2.064505 Loss2: 2.014635
-(DefaultActor pid=3764) >> Training accuracy: 0.881836
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.872030 Loss1: 1.418769 Loss2: 1.453262
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.479438 Loss1: 1.069820 Loss2: 1.409619
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.268853 Loss1: 0.853446 Loss2: 1.415407
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.114249 Loss1: 0.706585 Loss2: 1.407664
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.024947 Loss1: 0.613012 Loss2: 1.411935
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.071137 Loss1: 2.021340 Loss2: 2.049797
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.031609 Loss1: 0.602872 Loss2: 1.428737
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.079291 Loss1: 1.533222 Loss2: 1.546069
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.923362 Loss1: 0.497768 Loss2: 1.425594
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.626606 Loss1: 1.102659 Loss2: 1.523946
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.960699 Loss1: 0.525480 Loss2: 1.435218
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.367300 Loss1: 0.842762 Loss2: 1.524538
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.940127 Loss1: 0.507743 Loss2: 1.432384
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.309412 Loss1: 0.774810 Loss2: 1.534601
-(DefaultActor pid=3765) >> Training accuracy: 0.883333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.260726 Loss1: 0.727074 Loss2: 1.533652
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.211768 Loss1: 0.671646 Loss2: 1.540122
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.111077 Loss1: 0.581109 Loss2: 1.529969
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.107006 Loss1: 0.570453 Loss2: 1.536553
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.040301 Loss1: 0.499716 Loss2: 1.540585
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.116042 Loss1: 1.998229 Loss2: 2.117813
-(DefaultActor pid=3764) >> Training accuracy: 0.831250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.926883 Loss1: 1.424686 Loss2: 1.502196
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.638729 Loss1: 1.144168 Loss2: 1.494561
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.330961 Loss1: 0.830734 Loss2: 1.500227
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.218937 Loss1: 0.727372 Loss2: 1.491565
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.186661 Loss1: 0.696982 Loss2: 1.489679
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.105192 Loss1: 2.068433 Loss2: 2.036759
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.129315 Loss1: 0.626950 Loss2: 1.502365
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.100058 Loss1: 1.598324 Loss2: 1.501734
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.692029 Loss1: 1.185636 Loss2: 1.506394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.417300 Loss1: 0.934857 Loss2: 1.482443 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.891667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.927075 Loss1: 0.418864 Loss2: 1.508211
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.283532 Loss1: 0.795662 Loss2: 1.487870
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.169808 Loss1: 0.680254 Loss2: 1.489554
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.074063 Loss1: 0.578086 Loss2: 1.495978
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.027887 Loss1: 0.535751 Loss2: 1.492136
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.985368 Loss1: 0.493016 Loss2: 1.492352
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.176904 Loss1: 2.109502 Loss2: 2.067402
-(DefaultActor pid=3764) >> Training accuracy: 0.839844
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.922402 Loss1: 0.434069 Loss2: 1.488333
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.970568 Loss1: 1.486428 Loss2: 1.484140
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.628500 Loss1: 1.137991 Loss2: 1.490509
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.480311 Loss1: 0.986735 Loss2: 1.493575
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.351671 Loss1: 0.845450 Loss2: 1.506221
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.289778 Loss1: 0.769430 Loss2: 1.520348
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.296687 Loss1: 2.180554 Loss2: 2.116133
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.173078 Loss1: 0.668829 Loss2: 1.504248
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.131381 Loss1: 0.621412 Loss2: 1.509969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.007357 Loss1: 0.500573 Loss2: 1.506784 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.014868 Loss1: 0.503673 Loss2: 1.511195 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.868750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.148434 Loss1: 0.638623 Loss2: 1.509811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.054363 Loss1: 0.539183 Loss2: 1.515180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.006133 Loss1: 0.481569 Loss2: 1.524564
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.205087 Loss1: 2.119962 Loss2: 2.085125
-(DefaultActor pid=3764) >> Training accuracy: 0.881250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.926946 Loss1: 0.405406 Loss2: 1.521541
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.959267 Loss1: 1.464420 Loss2: 1.494847
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.559800 Loss1: 1.088110 Loss2: 1.471691
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.406228 Loss1: 0.944943 Loss2: 1.461285
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.242485 Loss1: 0.770196 Loss2: 1.472289
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.164294 Loss1: 0.683806 Loss2: 1.480488
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.082311 Loss1: 2.016862 Loss2: 2.065448
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.078120 Loss1: 0.602280 Loss2: 1.475840
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.059310 Loss1: 0.570090 Loss2: 1.489220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.080124 Loss1: 0.593892 Loss2: 1.486232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.329755 Loss1: 0.846770 Loss2: 1.482984
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.046726 Loss1: 0.549333 Loss2: 1.497394
-(DefaultActor pid=3765) >> Training accuracy: 0.887500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.151314 Loss1: 0.670868 Loss2: 1.480446 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.995426 Loss1: 0.517354 Loss2: 1.478072 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.972843 Loss1: 0.490863 Loss2: 1.481980
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.284574 Loss1: 2.131005 Loss2: 2.153569
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.196059 Loss1: 1.634435 Loss2: 1.561624
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.007091 Loss1: 0.506688 Loss2: 1.500404
-(DefaultActor pid=3764) >> Training accuracy: 0.832031
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.493429 Loss1: 0.963403 Loss2: 1.530026 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.363222 Loss1: 0.811469 Loss2: 1.551753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.246334 Loss1: 0.702548 Loss2: 1.543785
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.101135 Loss1: 1.995936 Loss2: 2.105198
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.116971 Loss1: 0.571865 Loss2: 1.545106
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.891979 Loss1: 1.376059 Loss2: 1.515919
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.095762 Loss1: 0.540140 Loss2: 1.555622
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.556212 Loss1: 1.062492 Loss2: 1.493720
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.095650 Loss1: 0.544341 Loss2: 1.551309
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.351091 Loss1: 0.848353 Loss2: 1.502738
-(DefaultActor pid=3765) >> Training accuracy: 0.873958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.176114 Loss1: 0.666827 Loss2: 1.509287
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.189817 Loss1: 0.679289 Loss2: 1.510528
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.175903 Loss1: 0.647869 Loss2: 1.528034
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.101056 Loss1: 0.580237 Loss2: 1.520819
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.120529 Loss1: 0.599873 Loss2: 1.520655
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.274420 Loss1: 2.049031 Loss2: 2.225389
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.945695 Loss1: 0.429111 Loss2: 1.516584
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.165182 Loss1: 1.534828 Loss2: 1.630354
-(DefaultActor pid=3764) >> Training accuracy: 0.904167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.829010 Loss1: 1.218665 Loss2: 1.610344
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.607074 Loss1: 1.003247 Loss2: 1.603826
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.378913 Loss1: 0.781238 Loss2: 1.597675
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.324114 Loss1: 0.722061 Loss2: 1.602054
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.719673 Loss1: 1.789765 Loss2: 1.929908
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.182416 Loss1: 0.583132 Loss2: 1.599284
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.690089 Loss1: 1.283860 Loss2: 1.406228
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.134094 Loss1: 0.536992 Loss2: 1.597102
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.299432 Loss1: 0.916233 Loss2: 1.383200
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.059491 Loss1: 0.450409 Loss2: 1.609082
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.139106 Loss1: 0.744154 Loss2: 1.394952
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.016389 Loss1: 0.419984 Loss2: 1.596404
-(DefaultActor pid=3765) >> Training accuracy: 0.870833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.024051 Loss1: 0.620360 Loss2: 1.403691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.834865 Loss1: 0.428253 Loss2: 1.406612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.854826 Loss1: 0.457217 Loss2: 1.397609
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.913343 Loss1: 1.871560 Loss2: 2.041783
-(DefaultActor pid=3764) >> Training accuracy: 0.863542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.849246 Loss1: 1.319622 Loss2: 1.529624 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.319459 Loss1: 0.820567 Loss2: 1.498892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.011015 Loss1: 0.530395 Loss2: 1.480620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.031983 Loss1: 0.539245 Loss2: 1.492738 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.988591 Loss1: 0.481020 Loss2: 1.507572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.296923 Loss1: 0.804040 Loss2: 1.492883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.187975 Loss1: 0.699173 Loss2: 1.488803 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.922852
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.086434 Loss1: 0.581281 Loss2: 1.505153 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.059762 Loss1: 0.555747 Loss2: 1.504015 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.912946
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.978491 Loss1: 0.468979 Loss2: 1.509513
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.059226 Loss1: 2.012782 Loss2: 2.046444
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.863436 Loss1: 1.417883 Loss2: 1.445553
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.529350 Loss1: 1.097296 Loss2: 1.432054
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.299266 Loss1: 0.845664 Loss2: 1.453601
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.128926 Loss1: 0.675017 Loss2: 1.453909
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.142999 Loss1: 2.102201 Loss2: 2.040798
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.091564 Loss1: 0.653180 Loss2: 1.438384
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.028515 Loss1: 0.578783 Loss2: 1.449732 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.063975 Loss1: 0.583706 Loss2: 1.480269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.009223 Loss1: 0.540929 Loss2: 1.468293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.051054 Loss1: 0.555713 Loss2: 1.495341 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.787500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.092315 Loss1: 0.623274 Loss2: 1.469041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.994813 Loss1: 0.536278 Loss2: 1.458535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.935933 Loss1: 0.473769 Loss2: 1.462164
-(DefaultActor pid=3764) >> Training accuracy: 0.845833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.346930 Loss1: 2.226744 Loss2: 2.120186
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.018012 Loss1: 1.478838 Loss2: 1.539174
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.707925 Loss1: 1.194396 Loss2: 1.513528
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.488370 Loss1: 0.974771 Loss2: 1.513599
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.311104 Loss1: 0.792007 Loss2: 1.519097
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.179140 Loss1: 0.650736 Loss2: 1.528404
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.876021 Loss1: 1.883146 Loss2: 1.992875
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.849543 Loss1: 1.401031 Loss2: 1.448511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.524574 Loss1: 1.084936 Loss2: 1.439638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.241859 Loss1: 0.822647 Loss2: 1.419212 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.862723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.146875 Loss1: 0.727196 Loss2: 1.419679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.015472 Loss1: 0.584679 Loss2: 1.430793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.852791 Loss1: 0.424860 Loss2: 1.427931 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.905359 Loss1: 0.484438 Loss2: 1.420921 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.883333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.531277 Loss1: 1.042418 Loss2: 1.488859 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.252442 Loss1: 0.779234 Loss2: 1.473208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.862857 Loss1: 1.797935 Loss2: 2.064922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.849732 Loss1: 1.349042 Loss2: 1.500690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.498276 Loss1: 1.021133 Loss2: 1.477143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.238895 Loss1: 0.769746 Loss2: 1.469148 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.846875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.143568 Loss1: 0.674922 Loss2: 1.468646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.994638 Loss1: 0.520979 Loss2: 1.473659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.927386 Loss1: 0.465415 Loss2: 1.461971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.854338 Loss1: 0.379683 Loss2: 1.474656 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.902083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.522811 Loss1: 1.009744 Loss2: 1.513067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.321530 Loss1: 0.791019 Loss2: 1.530511 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.266373 Loss1: 0.729798 Loss2: 1.536575
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.182066 Loss1: 2.146178 Loss2: 2.035888
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.057930 Loss1: 1.537885 Loss2: 1.520045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.745377 Loss1: 1.250624 Loss2: 1.494753 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.491587 Loss1: 0.987469 Loss2: 1.504119 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.880208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.205894 Loss1: 0.721006 Loss2: 1.484888 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.018983 Loss1: 0.510880 Loss2: 1.508103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.081182 Loss1: 2.002722 Loss2: 2.078460
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.997448 Loss1: 0.503128 Loss2: 1.494320
-DEBUG flwr 2023-10-09 14:04:49,974 | server.py:236 | fit_round 41 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.848415 Loss1: 1.343279 Loss2: 1.505137
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.993700 Loss1: 0.499797 Loss2: 1.493903
-(DefaultActor pid=3764) >> Training accuracy: 0.890625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.341955 Loss1: 0.867647 Loss2: 1.474308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.063234 Loss1: 0.594124 Loss2: 1.469110 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.046378 Loss1: 0.570282 Loss2: 1.476096
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.011938 Loss1: 1.914347 Loss2: 2.097591
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.071436 Loss1: 0.597647 Loss2: 1.473789
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.860419 Loss1: 1.325046 Loss2: 1.535373
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.957391 Loss1: 0.473055 Loss2: 1.484336
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.552972 Loss1: 1.051968 Loss2: 1.501005
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.904202 Loss1: 0.419521 Loss2: 1.484681
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.296782 Loss1: 0.805223 Loss2: 1.491559
-(DefaultActor pid=3765) >> Training accuracy: 0.873958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.181577 Loss1: 0.678111 Loss2: 1.503465
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.099556 Loss1: 0.592819 Loss2: 1.506737
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.995521 Loss1: 0.493752 Loss2: 1.501769
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.963560 Loss1: 0.461848 Loss2: 1.501712
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.999986 Loss1: 2.080735 Loss2: 1.919251
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.965536 Loss1: 0.463367 Loss2: 1.502168
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.974548 Loss1: 1.551909 Loss2: 1.422638
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.920403 Loss1: 0.410612 Loss2: 1.509790
-(DefaultActor pid=3764) >> Training accuracy: 0.856250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.366497 Loss1: 0.980472 Loss2: 1.386025 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.073943 Loss1: 0.687575 Loss2: 1.386368 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.985563 Loss1: 0.594744 Loss2: 1.390819
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.015316 Loss1: 1.920117 Loss2: 2.095199
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.865193 Loss1: 0.475792 Loss2: 1.389400
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.898450 Loss1: 1.403773 Loss2: 1.494677
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.863195 Loss1: 0.476051 Loss2: 1.387144
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.514862 Loss1: 1.015344 Loss2: 1.499517
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.940864 Loss1: 0.552373 Loss2: 1.388491
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.351681 Loss1: 0.852001 Loss2: 1.499681
-(DefaultActor pid=3765) >> Training accuracy: 0.868750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.199866 Loss1: 0.698500 Loss2: 1.501367
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.111056 Loss1: 0.613706 Loss2: 1.497349
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.084489 Loss1: 0.573679 Loss2: 1.510811
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.069675 Loss1: 0.574526 Loss2: 1.495149
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.166929 Loss1: 2.031840 Loss2: 2.135088
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.076074 Loss1: 0.570035 Loss2: 1.506038
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.039239 Loss1: 0.514602 Loss2: 1.524637 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.887500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.265053 Loss1: 0.823075 Loss2: 1.441978 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.091349 Loss1: 0.633814 Loss2: 1.457536 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.906712 Loss1: 0.452502 Loss2: 1.454209 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.834635
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.870611 Loss1: 0.408683 Loss2: 1.461928 [repeated 2x across cluster]
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.441183 Loss1: 0.936529 Loss2: 1.504653 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.207400 Loss1: 0.697754 Loss2: 1.509646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.149089 Loss1: 0.634516 Loss2: 1.514573 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.124177 Loss1: 0.599037 Loss2: 1.525139 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.779297
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 14:04:49,974][flwr][DEBUG] - fit_round 41 received 50 results and 0 failures
-INFO flwr 2023-10-09 14:05:31,774 | server.py:125 | fit progress: (41, 2.547065445409415, {'accuracy': 0.4319}, 94439.55233840099)
->> Test accuracy: 0.431900
-[2023-10-09 14:05:31,774][flwr][INFO] - fit progress: (41, 2.547065445409415, {'accuracy': 0.4319}, 94439.55233840099)
-DEBUG flwr 2023-10-09 14:05:31,774 | server.py:173 | evaluate_round 41: strategy sampled 50 clients (out of 50)
-[2023-10-09 14:05:31,774][flwr][DEBUG] - evaluate_round 41: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 14:14:39,084 | server.py:187 | evaluate_round 41 received 50 results and 0 failures
-[2023-10-09 14:14:39,084][flwr][DEBUG] - evaluate_round 41 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 14:14:39,084 | server.py:222 | fit_round 42: strategy sampled 50 clients (out of 50)
-[2023-10-09 14:14:39,084][flwr][DEBUG] - fit_round 42: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.217871 Loss1: 2.123617 Loss2: 2.094254
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.065605 Loss1: 1.521396 Loss2: 1.544209
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.670205 Loss1: 1.161960 Loss2: 1.508245
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.395288 Loss1: 0.890797 Loss2: 1.504491
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.140532 Loss1: 2.087916 Loss2: 2.052616
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.326606 Loss1: 0.811288 Loss2: 1.515318
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.966481 Loss1: 1.475607 Loss2: 1.490874
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.633236 Loss1: 1.159671 Loss2: 1.473565
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.294745 Loss1: 0.772500 Loss2: 1.522246
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.490363 Loss1: 1.015978 Loss2: 1.474384
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.160577 Loss1: 0.620394 Loss2: 1.540182
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.341042 Loss1: 0.849901 Loss2: 1.491142
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.152276 Loss1: 0.629702 Loss2: 1.522573
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.132864 Loss1: 0.654878 Loss2: 1.477987
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.138906 Loss1: 0.607442 Loss2: 1.531464
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.101853 Loss1: 0.565955 Loss2: 1.535898 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.879883
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.979736 Loss1: 0.502566 Loss2: 1.477170 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.871875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.091055 Loss1: 1.998006 Loss2: 2.093049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.669378 Loss1: 1.148597 Loss2: 1.520781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.424112 Loss1: 0.917615 Loss2: 1.506497
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.088048 Loss1: 1.936764 Loss2: 2.151284
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.410887 Loss1: 0.886266 Loss2: 1.524622
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.819238 Loss1: 1.276974 Loss2: 1.542264
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.228095 Loss1: 0.708229 Loss2: 1.519866
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.574857 Loss1: 1.078875 Loss2: 1.495982
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.064918 Loss1: 0.543578 Loss2: 1.521340
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.329715 Loss1: 0.830679 Loss2: 1.499037
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.994464 Loss1: 0.476455 Loss2: 1.518009
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.261157 Loss1: 0.764426 Loss2: 1.496731
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.956708 Loss1: 0.447044 Loss2: 1.509664
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.079826 Loss1: 0.576778 Loss2: 1.503048
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.890910 Loss1: 0.372091 Loss2: 1.518820
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.990633 Loss1: 0.494770 Loss2: 1.495863
-(DefaultActor pid=3765) >> Training accuracy: 0.870833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.014479 Loss1: 0.515244 Loss2: 1.499236
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.061795 Loss1: 0.562413 Loss2: 1.499382
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.929177 Loss1: 0.425018 Loss2: 1.504159
-(DefaultActor pid=3764) >> Training accuracy: 0.879167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.048688 Loss1: 1.972483 Loss2: 2.076205
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.935667 Loss1: 1.428631 Loss2: 1.507037
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.646987 Loss1: 1.158699 Loss2: 1.488289
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.405512 Loss1: 0.909350 Loss2: 1.496162
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.162036 Loss1: 2.002674 Loss2: 2.159361
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.849446 Loss1: 1.318680 Loss2: 1.530767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.537567 Loss1: 1.041213 Loss2: 1.496354
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.125263 Loss1: 0.625793 Loss2: 1.499469
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.299291 Loss1: 0.805285 Loss2: 1.494007
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.984367 Loss1: 0.493409 Loss2: 1.490958
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.014001 Loss1: 0.530463 Loss2: 1.483537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.051719 Loss1: 0.560554 Loss2: 1.491166 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.900902 Loss1: 0.396209 Loss2: 1.504693 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.909375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.775961 Loss1: 0.302810 Loss2: 1.473151 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.918269
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.056170 Loss1: 2.002462 Loss2: 2.053708
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.915564 Loss1: 1.412763 Loss2: 1.502801
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.649253 Loss1: 1.174215 Loss2: 1.475038
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.481236 Loss1: 0.981983 Loss2: 1.499252
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.931365 Loss1: 1.910714 Loss2: 2.020651
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.904540 Loss1: 1.420105 Loss2: 1.484435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.532185 Loss1: 1.048791 Loss2: 1.483393 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.243046 Loss1: 0.775027 Loss2: 1.468018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.227690 Loss1: 0.754470 Loss2: 1.473220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.079271 Loss1: 0.608220 Loss2: 1.471051 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.859375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.953030 Loss1: 0.451816 Loss2: 1.501214
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.004914 Loss1: 0.529216 Loss2: 1.475699
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.000620 Loss1: 0.521709 Loss2: 1.478911
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.929591 Loss1: 0.453807 Loss2: 1.475784
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.811459 Loss1: 0.333735 Loss2: 1.477724
-(DefaultActor pid=3764) >> Training accuracy: 0.893750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.760078 Loss1: 1.711210 Loss2: 2.048867
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.805540 Loss1: 1.306285 Loss2: 1.499255
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.571969 Loss1: 1.087175 Loss2: 1.484793
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.197789 Loss1: 0.735471 Loss2: 1.462318
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.084879 Loss1: 2.037854 Loss2: 2.047025
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.853473 Loss1: 1.339307 Loss2: 1.514166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.595855 Loss1: 1.106160 Loss2: 1.489695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.455107 Loss1: 0.935533 Loss2: 1.519574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.227698 Loss1: 0.724217 Loss2: 1.503481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.166267 Loss1: 0.672832 Loss2: 1.493435 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.909375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.814410 Loss1: 0.357430 Loss2: 1.456980
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.131411 Loss1: 0.627735 Loss2: 1.503676
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.972820 Loss1: 0.473336 Loss2: 1.499483
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.951689 Loss1: 0.457491 Loss2: 1.494198
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.955827 Loss1: 0.449731 Loss2: 1.506096
-(DefaultActor pid=3764) >> Training accuracy: 0.912500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.809678 Loss1: 1.770281 Loss2: 2.039397
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.668408 Loss1: 1.224115 Loss2: 1.444292
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.279017 Loss1: 0.861402 Loss2: 1.417615
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.222571 Loss1: 0.795884 Loss2: 1.426686
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.143493 Loss1: 2.002002 Loss2: 2.141491
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.056371 Loss1: 0.629586 Loss2: 1.426784
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.907160 Loss1: 1.382403 Loss2: 1.524756
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.568989 Loss1: 1.094404 Loss2: 1.474586
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.046366 Loss1: 0.617932 Loss2: 1.428434
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.972325 Loss1: 0.539393 Loss2: 1.432932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.908684 Loss1: 0.480639 Loss2: 1.428045 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.780358 Loss1: 0.352836 Loss2: 1.427522 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.780608 Loss1: 0.362849 Loss2: 1.417759 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.860417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.884889 Loss1: 0.396210 Loss2: 1.488679 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.913462
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.955796 Loss1: 1.934589 Loss2: 2.021207
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.814653 Loss1: 1.352666 Loss2: 1.461987
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.515288 Loss1: 1.076018 Loss2: 1.439269
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.263648 Loss1: 0.807381 Loss2: 1.456266
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.080851 Loss1: 2.046593 Loss2: 2.034258
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.922744 Loss1: 1.407590 Loss2: 1.515155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.541106 Loss1: 1.050682 Loss2: 1.490424 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.335741 Loss1: 0.832263 Loss2: 1.503478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.309308 Loss1: 0.812429 Loss2: 1.496880 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.149025 Loss1: 0.636937 Loss2: 1.512087 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.858333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.143723 Loss1: 0.650477 Loss2: 1.493245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.056730 Loss1: 0.548760 Loss2: 1.507970 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.819336
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.062699 Loss1: 2.031252 Loss2: 2.031447 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.495814 Loss1: 1.052526 Loss2: 1.443288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.002626 Loss1: 1.973708 Loss2: 2.028918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.840037 Loss1: 1.313853 Loss2: 1.526184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.625288 Loss1: 1.109808 Loss2: 1.515480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.316799 Loss1: 0.801974 Loss2: 1.514825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.994190 Loss1: 0.511250 Loss2: 1.482940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.913733 Loss1: 0.442845 Loss2: 1.470888 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.914583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.092387 Loss1: 0.568442 Loss2: 1.523945 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.980569 Loss1: 0.450380 Loss2: 1.530189 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.888672
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.840641 Loss1: 1.360281 Loss2: 1.480360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.276487 Loss1: 0.818915 Loss2: 1.457573 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.133341 Loss1: 0.675119 Loss2: 1.458222 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.105420 Loss1: 0.641313 Loss2: 1.464108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.990001 Loss1: 0.525519 Loss2: 1.464482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.975623 Loss1: 0.506431 Loss2: 1.469192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.890793 Loss1: 0.433306 Loss2: 1.457487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.049020 Loss1: 0.602602 Loss2: 1.446419 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.921875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.928575 Loss1: 0.477767 Loss2: 1.450808 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.905208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.045082 Loss1: 1.953011 Loss2: 2.092071 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.515688 Loss1: 1.032159 Loss2: 1.483530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.883175 Loss1: 1.936152 Loss2: 1.947023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.746599 Loss1: 1.342059 Loss2: 1.404540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.526357 Loss1: 1.151880 Loss2: 1.374478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.300338 Loss1: 0.895448 Loss2: 1.404890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.091891 Loss1: 0.705217 Loss2: 1.386674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.963199 Loss1: 0.460368 Loss2: 1.502831 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.868304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.927640 Loss1: 0.531369 Loss2: 1.396271 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.910516 Loss1: 0.510205 Loss2: 1.400311 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.876042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.934936 Loss1: 1.404050 Loss2: 1.530886 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.389962 Loss1: 0.903094 Loss2: 1.486868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.096564 Loss1: 0.604182 Loss2: 1.492382 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.958353 Loss1: 0.480563 Loss2: 1.477790 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.773446 Loss1: 1.302474 Loss2: 1.470972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.528913 Loss1: 1.059294 Loss2: 1.469619 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.872396
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.382194 Loss1: 0.912360 Loss2: 1.469835 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.124227 Loss1: 0.651093 Loss2: 1.473134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.942776 Loss1: 0.464617 Loss2: 1.478159 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.921080 Loss1: 0.450481 Loss2: 1.470599 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.945213 Loss1: 0.460555 Loss2: 1.484658 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.864258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.300590 Loss1: 0.757954 Loss2: 1.542636 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.095610 Loss1: 0.556574 Loss2: 1.539036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.153303 Loss1: 2.123670 Loss2: 2.029633
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.096615 Loss1: 0.560790 Loss2: 1.535825
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.991935 Loss1: 1.523555 Loss2: 1.468380
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.076778 Loss1: 0.530095 Loss2: 1.546683
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.661369 Loss1: 1.204310 Loss2: 1.457059
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.083612 Loss1: 0.533536 Loss2: 1.550077
-(DefaultActor pid=3765) >> Training accuracy: 0.895833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.157404 Loss1: 0.706858 Loss2: 1.450546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.068946 Loss1: 0.607283 Loss2: 1.461664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.061258 Loss1: 0.597311 Loss2: 1.463947
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.948874 Loss1: 1.957664 Loss2: 1.991209
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.836914 Loss1: 1.389779 Loss2: 1.447135 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.834375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.015085 Loss1: 0.553460 Loss2: 1.461625
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.559802 Loss1: 1.140566 Loss2: 1.419236
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.319596 Loss1: 0.890710 Loss2: 1.428886
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.076531 Loss1: 0.655402 Loss2: 1.421129
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.026005 Loss1: 0.606913 Loss2: 1.419092
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.009789 Loss1: 0.576854 Loss2: 1.432934
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.213882 Loss1: 2.015657 Loss2: 2.198225
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.953205 Loss1: 0.518870 Loss2: 1.434335
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.928242 Loss1: 0.493373 Loss2: 1.434870 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.907771 Loss1: 0.471881 Loss2: 1.435890 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.876042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.388323 Loss1: 0.813800 Loss2: 1.574523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.280349 Loss1: 0.682916 Loss2: 1.597433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.188392 Loss1: 0.594447 Loss2: 1.593945
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.003441 Loss1: 1.924883 Loss2: 2.078557
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.959315 Loss1: 1.426133 Loss2: 1.533182 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.879167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.549044 Loss1: 1.035305 Loss2: 1.513739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.130359 Loss1: 0.624694 Loss2: 1.505665 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.004902 Loss1: 0.502262 Loss2: 1.502640 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.918052 Loss1: 0.409075 Loss2: 1.508977 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.935110 Loss1: 0.430002 Loss2: 1.505108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.986818 Loss1: 0.479493 Loss2: 1.507325 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.809375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.109229 Loss1: 0.688918 Loss2: 1.420311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.903404 Loss1: 0.473765 Loss2: 1.429639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.860225 Loss1: 0.427800 Loss2: 1.432425
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.144867 Loss1: 2.107444 Loss2: 2.037423
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.950499 Loss1: 1.457718 Loss2: 1.492781 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.922917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.672744 Loss1: 1.186072 Loss2: 1.486671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.292945 Loss1: 0.807292 Loss2: 1.485653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.151124 Loss1: 0.645796 Loss2: 1.505328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.112999 Loss1: 0.612217 Loss2: 1.500782 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.037883 Loss1: 0.525324 Loss2: 1.512559 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.937942 Loss1: 0.444045 Loss2: 1.493897 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.912109
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.057508 Loss1: 0.605502 Loss2: 1.452006 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.885058 Loss1: 0.411808 Loss2: 1.473250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.959116 Loss1: 0.499776 Loss2: 1.459340
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.913511 Loss1: 1.904624 Loss2: 2.008887
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.941052 Loss1: 0.444833 Loss2: 1.496219
-(DefaultActor pid=3764) >> Training accuracy: 0.851042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.787550 Loss1: 1.277085 Loss2: 1.510465
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.484465 Loss1: 0.987024 Loss2: 1.497441
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.255883 Loss1: 0.770300 Loss2: 1.485583
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.136254 Loss1: 0.653758 Loss2: 1.482496
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.078395 Loss1: 0.599901 Loss2: 1.478494
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.077333 Loss1: 2.030331 Loss2: 2.047003
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.035607 Loss1: 1.526092 Loss2: 1.509515 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.575496 Loss1: 1.107964 Loss2: 1.467533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.355868 Loss1: 0.893864 Loss2: 1.462004 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.880859
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.857869 Loss1: 0.375135 Loss2: 1.482734
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.213384 Loss1: 0.744108 Loss2: 1.469276
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.083864 Loss1: 0.610392 Loss2: 1.473471
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.153742 Loss1: 0.668902 Loss2: 1.484840
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.061176 Loss1: 0.562912 Loss2: 1.498264
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.974109 Loss1: 0.485930 Loss2: 1.488180
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.818395 Loss1: 1.834832 Loss2: 1.983562
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.000029 Loss1: 0.516021 Loss2: 1.484007
-(DefaultActor pid=3764) >> Training accuracy: 0.885417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.335619 Loss1: 0.947587 Loss2: 1.388031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.107047 Loss1: 0.712375 Loss2: 1.394673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.983129 Loss1: 0.585556 Loss2: 1.397573
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.083029 Loss1: 2.003742 Loss2: 2.079287
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.874410 Loss1: 1.376041 Loss2: 1.498369 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.524409 Loss1: 1.051042 Loss2: 1.473366 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.328670 Loss1: 0.843748 Loss2: 1.484922 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.935417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.773406 Loss1: 0.378775 Loss2: 1.394631
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.236802 Loss1: 0.759721 Loss2: 1.477081
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.241670 Loss1: 0.755697 Loss2: 1.485973
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.211760 Loss1: 0.707460 Loss2: 1.504300
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.137535 Loss1: 0.629863 Loss2: 1.507672
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.015295 Loss1: 0.517496 Loss2: 1.497799
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.030407 Loss1: 2.016168 Loss2: 2.014238
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.960766 Loss1: 0.473950 Loss2: 1.486815
-(DefaultActor pid=3764) >> Training accuracy: 0.902083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.537567 Loss1: 1.091862 Loss2: 1.445705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.210782 Loss1: 0.774580 Loss2: 1.436202 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.155930 Loss1: 0.697937 Loss2: 1.457993
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.012783 Loss1: 1.889347 Loss2: 2.123436
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.944592 Loss1: 1.391219 Loss2: 1.553374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.556904 Loss1: 1.002521 Loss2: 1.554382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.374147 Loss1: 0.829130 Loss2: 1.545016 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.886458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.946324 Loss1: 0.484224 Loss2: 1.462100
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.197162 Loss1: 0.659616 Loss2: 1.537546
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.099917 Loss1: 0.568607 Loss2: 1.531310
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.059272 Loss1: 0.522060 Loss2: 1.537213
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.021084 Loss1: 0.483991 Loss2: 1.537093
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.996982 Loss1: 0.457443 Loss2: 1.539539
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.161250 Loss1: 2.101755 Loss2: 2.059496
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.985636 Loss1: 0.441252 Loss2: 1.544384
-(DefaultActor pid=3764) >> Training accuracy: 0.831250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.560437 Loss1: 1.092565 Loss2: 1.467871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.265264 Loss1: 0.788417 Loss2: 1.476848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.178934 Loss1: 0.699751 Loss2: 1.479183
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.971939 Loss1: 2.006008 Loss2: 1.965931
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.979599 Loss1: 1.490202 Loss2: 1.489397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.538934 Loss1: 1.060071 Loss2: 1.478863 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.377818 Loss1: 0.912598 Loss2: 1.465220 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.881250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.272789 Loss1: 0.801934 Loss2: 1.470856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.109906 Loss1: 0.622389 Loss2: 1.487517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.980425 Loss1: 0.497420 Loss2: 1.483005 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.959094 Loss1: 0.477209 Loss2: 1.481885 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.868164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.477711 Loss1: 0.978290 Loss2: 1.499421 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.160219 Loss1: 0.652812 Loss2: 1.507407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.840389 Loss1: 1.829588 Loss2: 2.010801
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.144280 Loss1: 0.628774 Loss2: 1.515507
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.703846 Loss1: 1.230956 Loss2: 1.472889
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.160563 Loss1: 0.632437 Loss2: 1.528126
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.031951 Loss1: 0.517019 Loss2: 1.514931
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.324318 Loss1: 0.881349 Loss2: 1.442969
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.008695 Loss1: 0.485609 Loss2: 1.523085
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.220231 Loss1: 0.771650 Loss2: 1.448580
-(DefaultActor pid=3765) >> Training accuracy: 0.815625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.117593 Loss1: 0.668355 Loss2: 1.449238
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.022115 Loss1: 0.575864 Loss2: 1.446251
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.949666 Loss1: 0.500598 Loss2: 1.449068
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.882264 Loss1: 0.435937 Loss2: 1.446327
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.949199 Loss1: 1.951741 Loss2: 1.997458
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.902502 Loss1: 0.451002 Loss2: 1.451500
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.778633 Loss1: 0.330067 Loss2: 1.448566 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.903320
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.212805 Loss1: 0.769345 Loss2: 1.443461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.132541 Loss1: 0.678236 Loss2: 1.454305 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.034308 Loss1: 0.576177 Loss2: 1.458131
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.896424 Loss1: 1.838864 Loss2: 2.057560
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.738274 Loss1: 1.241773 Loss2: 1.496500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.452197 Loss1: 0.975583 Loss2: 1.476614 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.901042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.822760 Loss1: 0.375484 Loss2: 1.447276
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.329399 Loss1: 0.848839 Loss2: 1.480559
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.082189 Loss1: 0.599134 Loss2: 1.483055
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.998309 Loss1: 0.529350 Loss2: 1.468960
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.978865 Loss1: 0.503927 Loss2: 1.474939
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.917586 Loss1: 0.439562 Loss2: 1.478024
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.187780 Loss1: 2.051003 Loss2: 2.136778
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.842309 Loss1: 0.363746 Loss2: 1.478564
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.837457 Loss1: 0.370375 Loss2: 1.467082 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.932292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.504307 Loss1: 0.982026 Loss2: 1.522282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.198178 Loss1: 0.683205 Loss2: 1.514974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.097640 Loss1: 0.570949 Loss2: 1.526691
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.941054 Loss1: 1.843494 Loss2: 2.097560
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.782542 Loss1: 1.285231 Loss2: 1.497311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.511660 Loss1: 1.025440 Loss2: 1.486219 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.875000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.275254 Loss1: 0.781568 Loss2: 1.493685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.184254 Loss1: 0.687743 Loss2: 1.496511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.003482 Loss1: 0.505029 Loss2: 1.498453 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.980198 Loss1: 0.477425 Loss2: 1.502773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.902872 Loss1: 0.399354 Loss2: 1.503518 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.829167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.225524 Loss1: 0.742834 Loss2: 1.482690 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.172783 Loss1: 0.693482 Loss2: 1.479301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.081607 Loss1: 0.597580 Loss2: 1.484026
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.068926 Loss1: 1.968920 Loss2: 2.100006
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.906095 Loss1: 1.396136 Loss2: 1.509959 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.873884
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.941271 Loss1: 0.470696 Loss2: 1.470575
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.616228 Loss1: 1.115354 Loss2: 1.500874
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.370372 Loss1: 0.847754 Loss2: 1.522618
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.131993 Loss1: 0.627703 Loss2: 1.504290
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.203807 Loss1: 0.702103 Loss2: 1.501704
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.163245 Loss1: 0.647547 Loss2: 1.515697
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.936534 Loss1: 1.982714 Loss2: 1.953820
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.070462 Loss1: 0.553320 Loss2: 1.517142
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.887073 Loss1: 1.418625 Loss2: 1.468447
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.002149 Loss1: 0.484152 Loss2: 1.517997
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.479441 Loss1: 1.036962 Loss2: 1.442479
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.903300 Loss1: 0.390437 Loss2: 1.512863
-(DefaultActor pid=3764) >> Training accuracy: 0.930208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.227271 Loss1: 0.796546 Loss2: 1.430725 [repeated 2x across cluster]
-DEBUG flwr 2023-10-09 14:43:16,469 | server.py:236 | fit_round 42 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.018711 Loss1: 0.567759 Loss2: 1.450953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.874161 Loss1: 1.842393 Loss2: 2.031769
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.982091 Loss1: 0.535288 Loss2: 1.446804
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.808616 Loss1: 1.332244 Loss2: 1.476372
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.022723 Loss1: 0.569495 Loss2: 1.453228
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.985981 Loss1: 0.531017 Loss2: 1.454964 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.857422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.154793 Loss1: 0.689863 Loss2: 1.464930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.944828 Loss1: 0.488612 Loss2: 1.456217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.956807 Loss1: 0.499488 Loss2: 1.457319
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.287256 Loss1: 2.173367 Loss2: 2.113890
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.065525 Loss1: 1.537320 Loss2: 1.528204 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.886458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.913887 Loss1: 0.445322 Loss2: 1.468565
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.654650 Loss1: 1.139676 Loss2: 1.514974
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.462007 Loss1: 0.934112 Loss2: 1.527895
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.259792 Loss1: 0.747463 Loss2: 1.512328
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.198420 Loss1: 0.702522 Loss2: 1.495898
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.138105 Loss1: 0.617797 Loss2: 1.520308
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.081015 Loss1: 0.559482 Loss2: 1.521532
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.901198 Loss1: 1.897205 Loss2: 2.003992
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.051025 Loss1: 0.526446 Loss2: 1.524578
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.830504 Loss1: 1.363128 Loss2: 1.467376
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.011719 Loss1: 0.494178 Loss2: 1.517541
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.506630 Loss1: 1.053340 Loss2: 1.453290
-(DefaultActor pid=3765) >> Training accuracy: 0.880580
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.272280 Loss1: 0.834320 Loss2: 1.437960
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.171525 Loss1: 0.731509 Loss2: 1.440016
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.124863 Loss1: 0.686060 Loss2: 1.438802
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.113501 Loss1: 0.654761 Loss2: 1.458740
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.084731 Loss1: 0.624018 Loss2: 1.460713
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.983005 Loss1: 0.513882 Loss2: 1.469123
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.932427 Loss1: 0.473589 Loss2: 1.458839
-(DefaultActor pid=3764) >> Training accuracy: 0.836458
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 14:43:16,469][flwr][DEBUG] - fit_round 42 received 50 results and 0 failures
-INFO flwr 2023-10-09 14:43:58,568 | server.py:125 | fit progress: (42, 2.541129877772956, {'accuracy': 0.4394}, 96746.3462451)
->> Test accuracy: 0.439400
-[2023-10-09 14:43:58,568][flwr][INFO] - fit progress: (42, 2.541129877772956, {'accuracy': 0.4394}, 96746.3462451)
-DEBUG flwr 2023-10-09 14:43:58,568 | server.py:173 | evaluate_round 42: strategy sampled 50 clients (out of 50)
-[2023-10-09 14:43:58,568][flwr][DEBUG] - evaluate_round 42: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 14:53:05,181 | server.py:187 | evaluate_round 42 received 50 results and 0 failures
-[2023-10-09 14:53:05,181][flwr][DEBUG] - evaluate_round 42 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 14:53:05,182 | server.py:222 | fit_round 43: strategy sampled 50 clients (out of 50)
-[2023-10-09 14:53:05,182][flwr][DEBUG] - fit_round 43: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.009983 Loss1: 2.006484 Loss2: 2.003499
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.803456 Loss1: 1.323936 Loss2: 1.479520
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.464917 Loss1: 1.005729 Loss2: 1.459188
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.908839 Loss1: 1.879812 Loss2: 2.029027
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.308293 Loss1: 0.846108 Loss2: 1.462185
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.747280 Loss1: 1.263266 Loss2: 1.484014
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.195895 Loss1: 0.730255 Loss2: 1.465641
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.137946 Loss1: 0.674299 Loss2: 1.463647 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.153248 Loss1: 0.678062 Loss2: 1.475186 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.961335 Loss1: 0.482257 Loss2: 1.479078 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.914777 Loss1: 0.443069 Loss2: 1.471707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.854464 Loss1: 0.395116 Loss2: 1.459348 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.917969
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.917110 Loss1: 0.465494 Loss2: 1.451616 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.901042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.019286 Loss1: 1.966015 Loss2: 2.053270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.454909 Loss1: 1.007489 Loss2: 1.447420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.244881 Loss1: 0.800284 Loss2: 1.444597
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.009382 Loss1: 1.985120 Loss2: 2.024262
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.844768 Loss1: 1.376244 Loss2: 1.468525 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.383280 Loss1: 0.943884 Loss2: 1.439396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.204192 Loss1: 0.765525 Loss2: 1.438667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.086782 Loss1: 0.646747 Loss2: 1.440034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.062991 Loss1: 0.609367 Loss2: 1.453624 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.918750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.781367 Loss1: 0.345634 Loss2: 1.435733
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.096816 Loss1: 0.628873 Loss2: 1.467943
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.971271 Loss1: 0.507123 Loss2: 1.464148
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.964193 Loss1: 0.496004 Loss2: 1.468189
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.961401 Loss1: 0.492533 Loss2: 1.468868
-(DefaultActor pid=3764) >> Training accuracy: 0.875000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.992411 Loss1: 1.936600 Loss2: 2.055811
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.966921 Loss1: 1.468235 Loss2: 1.498685
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.651303 Loss1: 1.166702 Loss2: 1.484602
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.436439 Loss1: 0.951447 Loss2: 1.484992
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.900651 Loss1: 1.867061 Loss2: 2.033590
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.694602 Loss1: 1.211127 Loss2: 1.483474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.080391 Loss1: 0.595367 Loss2: 1.485025 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.048275 Loss1: 0.550462 Loss2: 1.497813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.031781 Loss1: 0.540321 Loss2: 1.491461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.980391 Loss1: 0.470651 Loss2: 1.509740 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.843750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.960358 Loss1: 0.488923 Loss2: 1.471435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.927523 Loss1: 0.451632 Loss2: 1.475891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.927201 Loss1: 0.455860 Loss2: 1.471341
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.148326 Loss1: 2.032903 Loss2: 2.115423
-(DefaultActor pid=3764) >> Training accuracy: 0.904412
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.882079 Loss1: 1.349919 Loss2: 1.532161
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.557446 Loss1: 1.041795 Loss2: 1.515651
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.380537 Loss1: 0.864952 Loss2: 1.515585
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.234857 Loss1: 0.716816 Loss2: 1.518042
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.109318 Loss1: 1.945346 Loss2: 2.163971
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.230258 Loss1: 0.698245 Loss2: 1.532013
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.968920 Loss1: 1.394776 Loss2: 1.574143
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.082592 Loss1: 0.552547 Loss2: 1.530046
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.596885 Loss1: 1.046603 Loss2: 1.550281
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.063983 Loss1: 0.544193 Loss2: 1.519790
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.379959 Loss1: 0.822673 Loss2: 1.557286
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.111794 Loss1: 0.572207 Loss2: 1.539586
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.300049 Loss1: 0.761594 Loss2: 1.538456
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.027664 Loss1: 0.486227 Loss2: 1.541437
-(DefaultActor pid=3765) >> Training accuracy: 0.902083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.136036 Loss1: 0.578106 Loss2: 1.557930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.026613 Loss1: 0.461413 Loss2: 1.565200 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.983044 Loss1: 0.426293 Loss2: 1.556751
-(DefaultActor pid=3764) >> Training accuracy: 0.904167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.101761 Loss1: 2.039463 Loss2: 2.062299
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.864638 Loss1: 1.370264 Loss2: 1.494374
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.612633 Loss1: 1.135540 Loss2: 1.477092
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.382182 Loss1: 0.914043 Loss2: 1.468139
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.185486 Loss1: 0.701197 Loss2: 1.484290
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.228711 Loss1: 2.168610 Loss2: 2.060101
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.169837 Loss1: 0.687686 Loss2: 1.482150
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.111016 Loss1: 0.619211 Loss2: 1.491805 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.043740 Loss1: 0.539701 Loss2: 1.504039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.001309 Loss1: 0.507692 Loss2: 1.493617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.921729 Loss1: 0.427532 Loss2: 1.494197 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.891667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.943305 Loss1: 0.490433 Loss2: 1.452872 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.897904 Loss1: 0.434465 Loss2: 1.463439 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.899554
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.740877 Loss1: 1.257019 Loss2: 1.483858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.424597 Loss1: 0.932066 Loss2: 1.492531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.246362 Loss1: 0.762692 Loss2: 1.483669
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.831068 Loss1: 1.829031 Loss2: 2.002038
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.082521 Loss1: 0.612292 Loss2: 1.470229
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.754052 Loss1: 1.308587 Loss2: 1.445464
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.016611 Loss1: 0.538323 Loss2: 1.478289
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.311336 Loss1: 0.888184 Loss2: 1.423152
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.989371 Loss1: 0.520528 Loss2: 1.468843
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.085873 Loss1: 0.681107 Loss2: 1.404766
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.990665 Loss1: 0.506727 Loss2: 1.483937
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.084912 Loss1: 0.674905 Loss2: 1.410007
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.998085 Loss1: 0.508617 Loss2: 1.489468
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.972401 Loss1: 0.549749 Loss2: 1.422652
-(DefaultActor pid=3765) >> Training accuracy: 0.853125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.001197 Loss1: 0.578451 Loss2: 1.422746
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.851955 Loss1: 0.424711 Loss2: 1.427243
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.831951 Loss1: 0.420662 Loss2: 1.411289
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.823438 Loss1: 0.408501 Loss2: 1.414937
-(DefaultActor pid=3764) >> Training accuracy: 0.882292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.094040 Loss1: 2.079248 Loss2: 2.014792
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.850448 Loss1: 1.361277 Loss2: 1.489171
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.470091 Loss1: 1.012962 Loss2: 1.457129
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.290485 Loss1: 0.845747 Loss2: 1.444738
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.122382 Loss1: 2.030155 Loss2: 2.092227
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.160190 Loss1: 0.699532 Loss2: 1.460658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.074394 Loss1: 0.620558 Loss2: 1.453836 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.076153 Loss1: 0.616926 Loss2: 1.459227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.065744 Loss1: 0.597829 Loss2: 1.467914 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.928175 Loss1: 0.458335 Loss2: 1.469841 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.883333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.085504 Loss1: 0.558206 Loss2: 1.527298 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.964209 Loss1: 0.429196 Loss2: 1.535013 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.847917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.848269 Loss1: 1.367987 Loss2: 1.480283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.315674 Loss1: 0.882934 Loss2: 1.432739 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.117169 Loss1: 2.002343 Loss2: 2.114826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.923377 Loss1: 1.362662 Loss2: 1.560715 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.925128 Loss1: 0.488170 Loss2: 1.436958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.891826 Loss1: 0.442469 Loss2: 1.449357 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.803089 Loss1: 0.351242 Loss2: 1.451846 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.882212
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.202137 Loss1: 0.648288 Loss2: 1.553849 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.067100 Loss1: 0.514880 Loss2: 1.552220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.035963 Loss1: 1.993210 Loss2: 2.042753 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.904167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 3.048540 Loss1: 1.526157 Loss2: 1.522384 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.373528 Loss1: 0.877986 Loss2: 1.495542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.194342 Loss1: 0.695943 Loss2: 1.498400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.800154 Loss1: 1.301319 Loss2: 1.498835 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.411174 Loss1: 0.931540 Loss2: 1.479634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.269372 Loss1: 0.796422 Loss2: 1.472950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.126132 Loss1: 0.657127 Loss2: 1.469006 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.968633 Loss1: 0.489431 Loss2: 1.479202 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.897681 Loss1: 0.411000 Loss2: 1.486680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.821453 Loss1: 0.341220 Loss2: 1.480234
-(DefaultActor pid=3764) >> Training accuracy: 0.887500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.162173 Loss1: 2.062037 Loss2: 2.100136
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.873160 Loss1: 1.340543 Loss2: 1.532617
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.413391 Loss1: 0.916494 Loss2: 1.496897
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.186601 Loss1: 0.683476 Loss2: 1.503126
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.194600 Loss1: 0.697030 Loss2: 1.497570
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.819457 Loss1: 1.833563 Loss2: 1.985894
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.174580 Loss1: 0.665081 Loss2: 1.509499
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.091537 Loss1: 0.553719 Loss2: 1.537817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.954146 Loss1: 0.435848 Loss2: 1.518298 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.916985 Loss1: 0.409546 Loss2: 1.507439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.956986 Loss1: 0.433351 Loss2: 1.523635 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.861458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.927057 Loss1: 0.520259 Loss2: 1.406797 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.857047 Loss1: 0.446460 Loss2: 1.410587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.925153 Loss1: 0.516952 Loss2: 1.408201
-(DefaultActor pid=3764) >> Training accuracy: 0.868750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.233738 Loss1: 2.153852 Loss2: 2.079886
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.957481 Loss1: 1.416781 Loss2: 1.540700
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.695013 Loss1: 1.176836 Loss2: 1.518177
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.497341 Loss1: 0.978232 Loss2: 1.519110
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.294655 Loss1: 0.784582 Loss2: 1.510073
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.021988 Loss1: 1.973775 Loss2: 2.048213
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.915657 Loss1: 1.443362 Loss2: 1.472295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.486681 Loss1: 1.041503 Loss2: 1.445177 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.258624 Loss1: 0.805218 Loss2: 1.453406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.159617 Loss1: 0.729082 Loss2: 1.430535 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.859375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.066245 Loss1: 0.627013 Loss2: 1.439233 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.047857 Loss1: 0.590507 Loss2: 1.457351 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.853093 Loss1: 0.396518 Loss2: 1.456574 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.897917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.921817 Loss1: 1.455148 Loss2: 1.466669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.319423 Loss1: 0.866264 Loss2: 1.453159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.154695 Loss1: 0.707300 Loss2: 1.447395
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.955030 Loss1: 1.912776 Loss2: 2.042253
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.821197 Loss1: 1.367226 Loss2: 1.453971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.484974 Loss1: 1.052298 Loss2: 1.432676 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.261615 Loss1: 0.826451 Loss2: 1.435164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.110150 Loss1: 0.690394 Loss2: 1.419756 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.896875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.000614 Loss1: 0.588434 Loss2: 1.412180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.862284 Loss1: 0.441931 Loss2: 1.420352 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.883302 Loss1: 0.448317 Loss2: 1.434985 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.891667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.999605 Loss1: 1.486527 Loss2: 1.513078 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.357421 Loss1: 0.864936 Loss2: 1.492485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.174427 Loss1: 0.667504 Loss2: 1.506923
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.314428 Loss1: 2.168743 Loss2: 2.145684
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.048041 Loss1: 1.488428 Loss2: 1.559613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.605666 Loss1: 1.078926 Loss2: 1.526741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.983734 Loss1: 0.493505 Loss2: 1.490229
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.430090 Loss1: 0.878712 Loss2: 1.551379
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.941146 Loss1: 0.445953 Loss2: 1.495194
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.257424 Loss1: 0.720389 Loss2: 1.537035
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.221353 Loss1: 0.681874 Loss2: 1.539479
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.876714 Loss1: 0.379621 Loss2: 1.497094
-(DefaultActor pid=3765) >> Training accuracy: 0.869792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.085915 Loss1: 0.540244 Loss2: 1.545671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.989013 Loss1: 0.438504 Loss2: 1.550509 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.856027
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.810039 Loss1: 1.321020 Loss2: 1.489019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.283869 Loss1: 0.809867 Loss2: 1.474001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.743448 Loss1: 1.737070 Loss2: 2.006378
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.164495 Loss1: 0.671235 Loss2: 1.493260
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.641806 Loss1: 1.191572 Loss2: 1.450234
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.087267 Loss1: 0.604734 Loss2: 1.482532
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.300282 Loss1: 0.854337 Loss2: 1.445945
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.049210 Loss1: 0.549196 Loss2: 1.500015
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.126012 Loss1: 0.689311 Loss2: 1.436701
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.040053 Loss1: 0.535849 Loss2: 1.504204
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.113440 Loss1: 0.665537 Loss2: 1.447902
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.009476 Loss1: 0.519051 Loss2: 1.490425
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.040572 Loss1: 0.580701 Loss2: 1.459871
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.984963 Loss1: 0.485290 Loss2: 1.499673
-(DefaultActor pid=3765) >> Training accuracy: 0.896875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.862941 Loss1: 0.402726 Loss2: 1.460215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.823353 Loss1: 0.370322 Loss2: 1.453031 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.897393 Loss1: 1.413300 Loss2: 1.484092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.350247 Loss1: 0.861810 Loss2: 1.488438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.255457 Loss1: 0.788821 Loss2: 1.466636 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.880528 Loss1: 1.424571 Loss2: 1.455957
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.155381 Loss1: 0.678315 Loss2: 1.477066
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.556107 Loss1: 1.114321 Loss2: 1.441786
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.044420 Loss1: 0.570779 Loss2: 1.473641
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.297573 Loss1: 0.856109 Loss2: 1.441465
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.081636 Loss1: 0.598697 Loss2: 1.482938
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.007917 Loss1: 0.527354 Loss2: 1.480564
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.266317 Loss1: 0.830987 Loss2: 1.435329
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.932094 Loss1: 0.448471 Loss2: 1.483623
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.131227 Loss1: 0.682713 Loss2: 1.448514
-(DefaultActor pid=3765) >> Training accuracy: 0.868750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.062201 Loss1: 0.622973 Loss2: 1.439228
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.975617 Loss1: 0.527231 Loss2: 1.448386
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.921373 Loss1: 0.482938 Loss2: 1.438435
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.955727 Loss1: 0.509387 Loss2: 1.446340
-(DefaultActor pid=3764) >> Training accuracy: 0.877930
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.759709 Loss1: 1.802821 Loss2: 1.956888
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.737767 Loss1: 1.255582 Loss2: 1.482185
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.348448 Loss1: 0.903585 Loss2: 1.444863
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.221732 Loss1: 0.769765 Loss2: 1.451966
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.129762 Loss1: 0.679167 Loss2: 1.450595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.069967 Loss1: 0.619200 Loss2: 1.450768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.293986 Loss1: 0.822608 Loss2: 1.471378 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.893822 Loss1: 0.446627 Loss2: 1.447196 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.879407 Loss1: 0.444542 Loss2: 1.434865 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.981545 Loss1: 0.490192 Loss2: 1.491353 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.903320
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.922542 Loss1: 0.446269 Loss2: 1.476272 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.895833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.944605 Loss1: 1.941261 Loss2: 2.003344
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.814308 Loss1: 1.349010 Loss2: 1.465298
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.457094 Loss1: 0.998956 Loss2: 1.458138
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.325301 Loss1: 0.859667 Loss2: 1.465634
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.144105 Loss1: 2.051720 Loss2: 2.092386
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.889381 Loss1: 1.392892 Loss2: 1.496489 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.450445 Loss1: 0.987041 Loss2: 1.463403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.246979 Loss1: 0.780001 Loss2: 1.466978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.134536 Loss1: 0.670240 Loss2: 1.464296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.070110 Loss1: 0.595737 Loss2: 1.474373 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.908333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.800654 Loss1: 0.346360 Loss2: 1.454294
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.052441 Loss1: 0.580193 Loss2: 1.472248
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.985878 Loss1: 0.504534 Loss2: 1.481344
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.933585 Loss1: 0.450249 Loss2: 1.483336
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.954894 Loss1: 0.476827 Loss2: 1.478066
-(DefaultActor pid=3764) >> Training accuracy: 0.904167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.999378 Loss1: 1.849277 Loss2: 2.150101
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.723394 Loss1: 1.256410 Loss2: 1.466983
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.361833 Loss1: 0.938496 Loss2: 1.423338
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.179497 Loss1: 0.741912 Loss2: 1.437585
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.041062 Loss1: 0.591379 Loss2: 1.449684
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.970059 Loss1: 0.530276 Loss2: 1.439783 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.883385 Loss1: 0.441091 Loss2: 1.442294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.881940 Loss1: 0.447895 Loss2: 1.434046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.861970 Loss1: 0.425734 Loss2: 1.436236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.816575 Loss1: 0.375015 Loss2: 1.441559 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.915865
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.120383 Loss1: 0.547248 Loss2: 1.573135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.979465 Loss1: 0.413948 Loss2: 1.565517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.974142 Loss1: 0.408494 Loss2: 1.565647
-(DefaultActor pid=3764) >> Training accuracy: 0.902083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.925377 Loss1: 1.887854 Loss2: 2.037523
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.833029 Loss1: 1.330308 Loss2: 1.502721
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.546448 Loss1: 1.052557 Loss2: 1.493891
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.327289 Loss1: 0.832991 Loss2: 1.494298
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.226048 Loss1: 0.727016 Loss2: 1.499032
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.785818 Loss1: 1.796509 Loss2: 1.989308
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.684839 Loss1: 1.238992 Loss2: 1.445848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.333521 Loss1: 0.908517 Loss2: 1.425004 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.989732 Loss1: 0.487663 Loss2: 1.502069
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.100498 Loss1: 0.679909 Loss2: 1.420589
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.893183 Loss1: 0.393986 Loss2: 1.499197
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.040235 Loss1: 0.623218 Loss2: 1.417018
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.864327 Loss1: 0.371080 Loss2: 1.493247
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.037971 Loss1: 0.614838 Loss2: 1.423133
-(DefaultActor pid=3765) >> Training accuracy: 0.942383
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.879078 Loss1: 0.453626 Loss2: 1.425453
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.866236 Loss1: 0.441773 Loss2: 1.424463
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.892772 Loss1: 0.462497 Loss2: 1.430275
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.821591 Loss1: 0.382304 Loss2: 1.439287
-(DefaultActor pid=3764) >> Training accuracy: 0.907292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.024801 Loss1: 1.990303 Loss2: 2.034497
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.973943 Loss1: 1.487428 Loss2: 1.486515
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.526497 Loss1: 1.052158 Loss2: 1.474339
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.386896 Loss1: 0.916001 Loss2: 1.470895
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.178366 Loss1: 0.710716 Loss2: 1.467650
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.128840 Loss1: 0.669670 Loss2: 1.459170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.055115 Loss1: 0.589277 Loss2: 1.465839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.009765 Loss1: 0.539129 Loss2: 1.470636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.951838 Loss1: 0.473367 Loss2: 1.478471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.949230 Loss1: 0.481317 Loss2: 1.467913 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.864583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.942176 Loss1: 0.488181 Loss2: 1.453994 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.794852 Loss1: 0.340704 Loss2: 1.454148 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.885094 Loss1: 1.362443 Loss2: 1.522651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.421508 Loss1: 0.928315 Loss2: 1.493193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.212258 Loss1: 0.714427 Loss2: 1.497831
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.800984 Loss1: 1.758471 Loss2: 2.042513
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.652801 Loss1: 1.165886 Loss2: 1.486915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.370494 Loss1: 0.890617 Loss2: 1.479877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.231057 Loss1: 0.741218 Loss2: 1.489839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.897801 Loss1: 0.400407 Loss2: 1.497395 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.911830
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.980773 Loss1: 0.503068 Loss2: 1.477705 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.863194 Loss1: 0.393866 Loss2: 1.469328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.996785 Loss1: 1.959989 Loss2: 2.036796
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.849305 Loss1: 0.381818 Loss2: 1.467487
-(DefaultActor pid=3764) >> Training accuracy: 0.895508
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.591039 Loss1: 1.073684 Loss2: 1.517355 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.292421 Loss1: 0.776117 Loss2: 1.516304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.196674 Loss1: 0.673225 Loss2: 1.523449
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.095959 Loss1: 2.053910 Loss2: 2.042049
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.938997 Loss1: 1.464850 Loss2: 1.474147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.167522 Loss1: 0.626494 Loss2: 1.541028
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.526657 Loss1: 1.060390 Loss2: 1.466267
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.084167 Loss1: 0.557092 Loss2: 1.527075
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.395412 Loss1: 0.924657 Loss2: 1.470755
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.965783 Loss1: 0.442248 Loss2: 1.523535
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.237451 Loss1: 0.756039 Loss2: 1.481412
-(DefaultActor pid=3765) >> Training accuracy: 0.887695
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.104187 Loss1: 0.635207 Loss2: 1.468980
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.023667 Loss1: 0.556973 Loss2: 1.466694
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.087357 Loss1: 0.606061 Loss2: 1.481296
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.978388 Loss1: 0.484905 Loss2: 1.493483
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.943966 Loss1: 0.463323 Loss2: 1.480643
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.987571 Loss1: 1.949402 Loss2: 2.038169
-(DefaultActor pid=3764) >> Training accuracy: 0.894792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.848727 Loss1: 1.360423 Loss2: 1.488303
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.424611 Loss1: 0.942094 Loss2: 1.482517
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.236432 Loss1: 0.774312 Loss2: 1.462120
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.136426 Loss1: 0.673685 Loss2: 1.462741
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.983096 Loss1: 1.929805 Loss2: 2.053291
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.086518 Loss1: 0.615814 Loss2: 1.470704
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.003829 Loss1: 0.531849 Loss2: 1.471980
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.886482 Loss1: 1.387101 Loss2: 1.499381
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.998249 Loss1: 0.519672 Loss2: 1.478577
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.566763 Loss1: 1.080646 Loss2: 1.486117
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.926597 Loss1: 0.447506 Loss2: 1.479091
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.358706 Loss1: 0.874489 Loss2: 1.484217
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.879398 Loss1: 0.401425 Loss2: 1.477973
-(DefaultActor pid=3765) >> Training accuracy: 0.910417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.133218 Loss1: 0.661267 Loss2: 1.471951
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.052226 Loss1: 0.582912 Loss2: 1.469313
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.007194 Loss1: 0.536030 Loss2: 1.471165
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.042762 Loss1: 0.554128 Loss2: 1.488635
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.063117 Loss1: 0.567557 Loss2: 1.495560
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.034621 Loss1: 1.978511 Loss2: 2.056110
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.040041 Loss1: 0.539625 Loss2: 1.500416
-DEBUG flwr 2023-10-09 15:21:39,998 | server.py:236 | fit_round 43 received 50 results and 0 failures
-(DefaultActor pid=3764) >> Training accuracy: 0.858398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.497194 Loss1: 1.011626 Loss2: 1.485568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.243666 Loss1: 0.754385 Loss2: 1.489281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.064711 Loss1: 0.576089 Loss2: 1.488622
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.116911 Loss1: 1.988948 Loss2: 2.127963
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.043540 Loss1: 0.559837 Loss2: 1.483703
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.952641 Loss1: 1.417338 Loss2: 1.535303
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.037507 Loss1: 0.539887 Loss2: 1.497620
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.624146 Loss1: 1.113835 Loss2: 1.510311
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.335558 Loss1: 0.805433 Loss2: 1.530125
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.992970 Loss1: 0.494641 Loss2: 1.498329
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.175793 Loss1: 0.663646 Loss2: 1.512147
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.896043 Loss1: 0.402662 Loss2: 1.493381
-(DefaultActor pid=3765) >> Training accuracy: 0.845703
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.020174 Loss1: 0.493236 Loss2: 1.526937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.893538 Loss1: 0.379059 Loss2: 1.514479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.914834 Loss1: 0.407545 Loss2: 1.507289
-(DefaultActor pid=3764) >> Training accuracy: 0.854167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.989079 Loss1: 1.927598 Loss2: 2.061481
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.784980 Loss1: 1.275356 Loss2: 1.509624
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.493274 Loss1: 1.012687 Loss2: 1.480587
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.261443 Loss1: 0.783289 Loss2: 1.478153
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.105523 Loss1: 0.627922 Loss2: 1.477600
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.886387 Loss1: 1.728244 Loss2: 2.158143
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.981454 Loss1: 0.506104 Loss2: 1.475349
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.746665 Loss1: 1.210215 Loss2: 1.536451
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.924131 Loss1: 0.443290 Loss2: 1.480842
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.485088 Loss1: 0.973299 Loss2: 1.511789
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.947194 Loss1: 0.467743 Loss2: 1.479451
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.235337 Loss1: 0.726389 Loss2: 1.508947
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.012148 Loss1: 0.518656 Loss2: 1.493492
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.105201 Loss1: 0.607072 Loss2: 1.498130
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.892964 Loss1: 0.399932 Loss2: 1.493032
-(DefaultActor pid=3765) >> Training accuracy: 0.881250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.973737 Loss1: 0.476040 Loss2: 1.497697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.964344 Loss1: 0.459490 Loss2: 1.504854 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.895833
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 15:21:39,998][flwr][DEBUG] - fit_round 43 received 50 results and 0 failures
-INFO flwr 2023-10-09 15:22:22,122 | server.py:125 | fit progress: (43, 2.521814847144837, {'accuracy': 0.4441}, 99049.900299082)
->> Test accuracy: 0.444100
-[2023-10-09 15:22:22,122][flwr][INFO] - fit progress: (43, 2.521814847144837, {'accuracy': 0.4441}, 99049.900299082)
-DEBUG flwr 2023-10-09 15:22:22,122 | server.py:173 | evaluate_round 43: strategy sampled 50 clients (out of 50)
-[2023-10-09 15:22:22,122][flwr][DEBUG] - evaluate_round 43: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 15:31:27,128 | server.py:187 | evaluate_round 43 received 50 results and 0 failures
-[2023-10-09 15:31:27,128][flwr][DEBUG] - evaluate_round 43 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 15:31:27,128 | server.py:222 | fit_round 44: strategy sampled 50 clients (out of 50)
-[2023-10-09 15:31:27,128][flwr][DEBUG] - fit_round 44: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.160709 Loss1: 2.036126 Loss2: 2.124583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.646797 Loss1: 1.084617 Loss2: 1.562180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.928122 Loss1: 1.894784 Loss2: 2.033338
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.436261 Loss1: 0.872854 Loss2: 1.563407
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.783617 Loss1: 1.306993 Loss2: 1.476624
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.326332 Loss1: 0.774760 Loss2: 1.551573
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.506259 Loss1: 1.056874 Loss2: 1.449384
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.233699 Loss1: 0.673866 Loss2: 1.559833
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.275895 Loss1: 0.827841 Loss2: 1.448054
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.097962 Loss1: 0.531222 Loss2: 1.566739
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.028166 Loss1: 0.476975 Loss2: 1.551191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.984806 Loss1: 0.432076 Loss2: 1.552730 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.927112 Loss1: 0.376847 Loss2: 1.550265 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.882812
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.935844 Loss1: 0.476237 Loss2: 1.459607 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.935417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.947380 Loss1: 1.904224 Loss2: 2.043156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.401931 Loss1: 0.955442 Loss2: 1.446489 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.234977 Loss1: 0.768282 Loss2: 1.466696
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.940473 Loss1: 1.934001 Loss2: 2.006471
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.855220 Loss1: 1.363174 Loss2: 1.492047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.477958 Loss1: 0.998327 Loss2: 1.479632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.219108 Loss1: 0.744661 Loss2: 1.474447 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.121127 Loss1: 0.647922 Loss2: 1.473205 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.015394 Loss1: 0.546527 Loss2: 1.468866 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.884375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.036357 Loss1: 0.545752 Loss2: 1.490605 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.959918 Loss1: 0.463124 Loss2: 1.496794 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.863281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.839457 Loss1: 1.372765 Loss2: 1.466693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.230162 Loss1: 0.817751 Loss2: 1.412411 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.055570 Loss1: 0.639742 Loss2: 1.415828
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.056066 Loss1: 2.055350 Loss2: 2.000717
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.881138 Loss1: 1.423868 Loss2: 1.457270 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.450769 Loss1: 1.006385 Loss2: 1.444383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.184924 Loss1: 0.761427 Loss2: 1.423498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.043415 Loss1: 0.615705 Loss2: 1.427710 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.873884
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.979944 Loss1: 0.540872 Loss2: 1.439071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.986974 Loss1: 0.536063 Loss2: 1.450911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.827599 Loss1: 0.388187 Loss2: 1.439413 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.893750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.972297 Loss1: 1.498783 Loss2: 1.473514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.412095 Loss1: 0.968070 Loss2: 1.444025 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.100725 Loss1: 1.996281 Loss2: 2.104444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.939524 Loss1: 1.387185 Loss2: 1.552339 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.528145 Loss1: 1.019422 Loss2: 1.508723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.355655 Loss1: 0.843665 Loss2: 1.511990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.169746 Loss1: 0.646549 Loss2: 1.523197 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.882292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.013960 Loss1: 0.496032 Loss2: 1.517928 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.917592 Loss1: 0.406975 Loss2: 1.510618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.001059 Loss1: 0.471956 Loss2: 1.529103
-(DefaultActor pid=3764) >> Training accuracy: 0.840625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.950299 Loss1: 1.927716 Loss2: 2.022583
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.799688 Loss1: 1.347959 Loss2: 1.451729
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.589478 Loss1: 1.117709 Loss2: 1.471769
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.289998 Loss1: 0.840671 Loss2: 1.449328
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.180095 Loss1: 0.742385 Loss2: 1.437710
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.802151 Loss1: 1.689604 Loss2: 2.112547
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.961403 Loss1: 0.514544 Loss2: 1.446859
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.948301 Loss1: 0.518206 Loss2: 1.430095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.958499 Loss1: 0.518410 Loss2: 1.440089 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.003862 Loss1: 0.543822 Loss2: 1.460040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.883395 Loss1: 0.435562 Loss2: 1.447833 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.876042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.998684 Loss1: 0.485855 Loss2: 1.512829 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.976989 Loss1: 0.454836 Loss2: 1.522153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.899691 Loss1: 0.383067 Loss2: 1.516624
-(DefaultActor pid=3764) >> Training accuracy: 0.939583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.828069 Loss1: 1.832765 Loss2: 1.995304
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.849076 Loss1: 1.349532 Loss2: 1.499544
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.543714 Loss1: 1.083142 Loss2: 1.460573
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.227221 Loss1: 0.743893 Loss2: 1.483329
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.206057 Loss1: 0.740038 Loss2: 1.466019
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.079446 Loss1: 2.026740 Loss2: 2.052707
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.008373 Loss1: 1.480239 Loss2: 1.528134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.576958 Loss1: 1.063441 Loss2: 1.513517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.354339 Loss1: 0.846533 Loss2: 1.507807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.313861 Loss1: 0.802008 Loss2: 1.511852 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.877930
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.928411 Loss1: 0.442433 Loss2: 1.485978
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.166471 Loss1: 0.652713 Loss2: 1.513758
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.088674 Loss1: 0.590549 Loss2: 1.498124
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.988144 Loss1: 0.476217 Loss2: 1.511927
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.048251 Loss1: 0.533329 Loss2: 1.514922
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.992774 Loss1: 0.474012 Loss2: 1.518762
-(DefaultActor pid=3764) >> Training accuracy: 0.871094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.855652 Loss1: 1.774897 Loss2: 2.080755
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.645884 Loss1: 1.167531 Loss2: 1.478353
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.375321 Loss1: 0.908616 Loss2: 1.466705
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.173065 Loss1: 0.711776 Loss2: 1.461288
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.055257 Loss1: 0.591719 Loss2: 1.463538
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.813352 Loss1: 1.782761 Loss2: 2.030592
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.675138 Loss1: 1.162014 Loss2: 1.513124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.417974 Loss1: 0.927468 Loss2: 1.490506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.259657 Loss1: 0.770612 Loss2: 1.489045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.078708 Loss1: 0.583278 Loss2: 1.495430 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.905208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.937494 Loss1: 0.456144 Loss2: 1.481350 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.988503 Loss1: 0.496579 Loss2: 1.491924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.127258 Loss1: 2.051378 Loss2: 2.075880
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.859710 Loss1: 0.369092 Loss2: 1.490617
-(DefaultActor pid=3764) >> Training accuracy: 0.918945
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.629913 Loss1: 1.142275 Loss2: 1.487638 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.179157 Loss1: 0.689173 Loss2: 1.489984 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.077565 Loss1: 0.586447 Loss2: 1.491117
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.032743 Loss1: 1.919801 Loss2: 2.112942
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.839086 Loss1: 1.297264 Loss2: 1.541822 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.478949 Loss1: 0.953420 Loss2: 1.525529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.336545 Loss1: 0.805389 Loss2: 1.531156 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.827083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.206284 Loss1: 0.675565 Loss2: 1.530719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.062539 Loss1: 0.529747 Loss2: 1.532792 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.995390 Loss1: 0.468167 Loss2: 1.527223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.741811 Loss1: 1.727050 Loss2: 2.014761
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.093661 Loss1: 0.553725 Loss2: 1.539937
-(DefaultActor pid=3764) >> Training accuracy: 0.869792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.445008 Loss1: 0.964587 Loss2: 1.480421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.132445 Loss1: 0.645643 Loss2: 1.486803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.943588 Loss1: 1.935917 Loss2: 2.007671
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.031438 Loss1: 0.554513 Loss2: 1.476925
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.990493 Loss1: 0.505960 Loss2: 1.484533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.925309 Loss1: 0.438980 Loss2: 1.486330 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.910080 Loss1: 0.422833 Loss2: 1.487248 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.944758 Loss1: 0.455281 Loss2: 1.489477 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.874081
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.868317 Loss1: 0.447395 Loss2: 1.420922 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.765942 Loss1: 0.343893 Loss2: 1.422049 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.908333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.948479 Loss1: 1.444621 Loss2: 1.503858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.278763 Loss1: 0.809725 Loss2: 1.469037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.165725 Loss1: 0.711417 Loss2: 1.454307
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.823139 Loss1: 1.767891 Loss2: 2.055248
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.081111 Loss1: 0.616145 Loss2: 1.464966
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.801419 Loss1: 1.292808 Loss2: 1.508611
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.058733 Loss1: 0.586189 Loss2: 1.472544
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.526223 Loss1: 1.018034 Loss2: 1.508190
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.928609 Loss1: 0.454103 Loss2: 1.474507
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.276818 Loss1: 0.775220 Loss2: 1.501597
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.913947 Loss1: 0.452721 Loss2: 1.461226
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.175869 Loss1: 0.698271 Loss2: 1.477599
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.832499 Loss1: 0.372289 Loss2: 1.460210
-(DefaultActor pid=3765) >> Training accuracy: 0.872917
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.076101 Loss1: 0.596649 Loss2: 1.479452
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.950208 Loss1: 0.468236 Loss2: 1.481972
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.883995 Loss1: 0.413076 Loss2: 1.470919
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.928011 Loss1: 0.444427 Loss2: 1.483585
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.815886 Loss1: 0.326664 Loss2: 1.489222
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.125634 Loss1: 1.986237 Loss2: 2.139397
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.916392 Loss1: 1.384153 Loss2: 1.532239
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.573794 Loss1: 1.093426 Loss2: 1.480368
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.297447 Loss1: 0.803918 Loss2: 1.493529
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.173950 Loss1: 0.678242 Loss2: 1.495708
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.037851 Loss1: 0.542950 Loss2: 1.494900
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.132418 Loss1: 2.006709 Loss2: 2.125709
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.848014 Loss1: 1.313756 Loss2: 1.534258 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.508152 Loss1: 1.017463 Loss2: 1.490689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.328211 Loss1: 0.824249 Loss2: 1.503961 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.919471
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.144517 Loss1: 0.629528 Loss2: 1.514989 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.998164 Loss1: 0.477433 Loss2: 1.520731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.040505 Loss1: 0.529049 Loss2: 1.511456
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.144371 Loss1: 1.925766 Loss2: 2.218605
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.954067 Loss1: 0.442363 Loss2: 1.511704
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.955119 Loss1: 1.385959 Loss2: 1.569160
-(DefaultActor pid=3764) >> Training accuracy: 0.833333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.551512 Loss1: 1.018245 Loss2: 1.533267
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.246538 Loss1: 0.717048 Loss2: 1.529490
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.181220 Loss1: 0.664412 Loss2: 1.516808
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.024991 Loss1: 0.494221 Loss2: 1.530770
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.991053 Loss1: 0.463256 Loss2: 1.527797
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.100100 Loss1: 1.997768 Loss2: 2.102332
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.962996 Loss1: 1.439943 Loss2: 1.523053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.522544 Loss1: 1.010943 Loss2: 1.511600 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.913462
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.217518 Loss1: 0.703696 Loss2: 1.513822 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.073778 Loss1: 0.552534 Loss2: 1.521244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.071283 Loss1: 0.563554 Loss2: 1.507729
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.890333 Loss1: 1.850490 Loss2: 2.039843
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.854576 Loss1: 1.374574 Loss2: 1.480002 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.872768
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.516256 Loss1: 1.035307 Loss2: 1.480948 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.197088 Loss1: 0.733362 Loss2: 1.463726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.035160 Loss1: 0.567765 Loss2: 1.467395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.003179 Loss1: 0.526738 Loss2: 1.476441 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.994746 Loss1: 0.517356 Loss2: 1.477390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.969014 Loss1: 0.499977 Loss2: 1.469037 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.879167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.140869 Loss1: 0.685879 Loss2: 1.454990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.035989 Loss1: 0.556296 Loss2: 1.479694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.087412 Loss1: 1.939436 Loss2: 2.147976 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.940403 Loss1: 1.351867 Loss2: 1.588536 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.859375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.594086 Loss1: 1.034776 Loss2: 1.559310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.259242 Loss1: 0.710678 Loss2: 1.548564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.023442 Loss1: 0.474610 Loss2: 1.548833 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.911739 Loss1: 0.377553 Loss2: 1.534186 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.972944 Loss1: 0.427244 Loss2: 1.545699 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.947139 Loss1: 0.392483 Loss2: 1.554656 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.875000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.038825 Loss1: 0.584703 Loss2: 1.454122 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.924432 Loss1: 0.449966 Loss2: 1.474466 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.923404 Loss1: 1.761878 Loss2: 2.161526
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.871266 Loss1: 0.404972 Loss2: 1.466293
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.800624 Loss1: 1.277639 Loss2: 1.522984
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.874634 Loss1: 0.395527 Loss2: 1.479107
-(DefaultActor pid=3764) >> Training accuracy: 0.914583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.251838 Loss1: 0.756774 Loss2: 1.495064 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.957650 Loss1: 0.469896 Loss2: 1.487754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.886347 Loss1: 0.406528 Loss2: 1.479819
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.065768 Loss1: 1.941436 Loss2: 2.124332
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.835913 Loss1: 1.321871 Loss2: 1.514042 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.401430 Loss1: 0.912341 Loss2: 1.489089 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.895833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.887527 Loss1: 0.390751 Loss2: 1.496776
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.200670 Loss1: 0.739076 Loss2: 1.461593
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.093835 Loss1: 0.621359 Loss2: 1.472475
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.976769 Loss1: 0.501582 Loss2: 1.475187
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.968432 Loss1: 0.502295 Loss2: 1.466137
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.951768 Loss1: 0.482410 Loss2: 1.469357
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.767129 Loss1: 1.708214 Loss2: 2.058916
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.882264 Loss1: 0.406812 Loss2: 1.475452
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.758431 Loss1: 1.292245 Loss2: 1.466186
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.866997 Loss1: 0.387701 Loss2: 1.479296
-(DefaultActor pid=3764) >> Training accuracy: 0.892708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.178566 Loss1: 0.729860 Loss2: 1.448706 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.035536 Loss1: 0.579440 Loss2: 1.456096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.965746 Loss1: 0.495396 Loss2: 1.470350
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.978150 Loss1: 1.918797 Loss2: 2.059354
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.844952 Loss1: 1.318692 Loss2: 1.526260 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.530610 Loss1: 1.026771 Loss2: 1.503839 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.927083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.849361 Loss1: 0.385971 Loss2: 1.463390
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.216410 Loss1: 0.715312 Loss2: 1.501098
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.082254 Loss1: 0.601970 Loss2: 1.480284
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.061239 Loss1: 0.573489 Loss2: 1.487750
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.989698 Loss1: 0.495513 Loss2: 1.494185
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.992328 Loss1: 0.505006 Loss2: 1.487322
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.139517 Loss1: 2.027653 Loss2: 2.111864
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.952010 Loss1: 0.452575 Loss2: 1.499435
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.917296 Loss1: 1.365940 Loss2: 1.551356
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.819178 Loss1: 0.323795 Loss2: 1.495383
-(DefaultActor pid=3764) >> Training accuracy: 0.920833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.370405 Loss1: 0.853853 Loss2: 1.516552 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.057918 Loss1: 0.549460 Loss2: 1.508458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.023646 Loss1: 0.512747 Loss2: 1.510899
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.979888 Loss1: 1.930668 Loss2: 2.049219
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.881944 Loss1: 1.396195 Loss2: 1.485749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.521922 Loss1: 1.053147 Loss2: 1.468775 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.911143 Loss1: 0.392364 Loss2: 1.518778
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.414637 Loss1: 0.938187 Loss2: 1.476449
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.225209 Loss1: 0.745934 Loss2: 1.479275
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.088789 Loss1: 0.603336 Loss2: 1.485454
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.045046 Loss1: 0.568107 Loss2: 1.476939
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.960983 Loss1: 0.474851 Loss2: 1.486132
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.888877 Loss1: 1.844595 Loss2: 2.044282
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.973791 Loss1: 0.493655 Loss2: 1.480137
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.593079 Loss1: 1.125152 Loss2: 1.467927
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.946767 Loss1: 0.449618 Loss2: 1.497149
-(DefaultActor pid=3764) >> Training accuracy: 0.875000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.147300 Loss1: 0.704510 Loss2: 1.442789 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.957088 Loss1: 0.533101 Loss2: 1.423987 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.948078 Loss1: 0.518171 Loss2: 1.429908
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.091565 Loss1: 2.025552 Loss2: 2.066013
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.868199 Loss1: 0.427728 Loss2: 1.440471
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.866439 Loss1: 1.353229 Loss2: 1.513210
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.787575 Loss1: 0.347298 Loss2: 1.440277
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.539619 Loss1: 1.048101 Loss2: 1.491518
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.765711 Loss1: 0.339856 Loss2: 1.425855
-(DefaultActor pid=3765) >> Training accuracy: 0.915625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.261930 Loss1: 0.769293 Loss2: 1.492638
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.114928 Loss1: 0.620867 Loss2: 1.494061
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.053722 Loss1: 0.573327 Loss2: 1.480395
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.034779 Loss1: 0.540077 Loss2: 1.494703
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.943128 Loss1: 0.455289 Loss2: 1.487839
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.845676 Loss1: 0.364135 Loss2: 1.481541
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.053209 Loss1: 2.015544 Loss2: 2.037665
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.864938 Loss1: 0.389615 Loss2: 1.475323
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.860602 Loss1: 1.389188 Loss2: 1.471414
-(DefaultActor pid=3764) >> Training accuracy: 0.910417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.512891 Loss1: 1.059799 Loss2: 1.453092
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.306653 Loss1: 0.844674 Loss2: 1.461979
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.101009 Loss1: 0.646377 Loss2: 1.454632
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.013891 Loss1: 0.562818 Loss2: 1.451073
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.135130 Loss1: 2.035735 Loss2: 2.099395
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.989060 Loss1: 0.528866 Loss2: 1.460194
-(DefaultActor pid=3764) Epoch: 1 Loss: 3.026684 Loss1: 1.525202 Loss2: 1.501482
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.040590 Loss1: 0.577982 Loss2: 1.462608
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.562076 Loss1: 1.080272 Loss2: 1.481804
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.941623 Loss1: 0.476424 Loss2: 1.465198
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.371637 Loss1: 0.881373 Loss2: 1.490264
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.831174 Loss1: 0.370852 Loss2: 1.460323
-(DefaultActor pid=3765) >> Training accuracy: 0.880208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.181398 Loss1: 0.678826 Loss2: 1.502572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.149187 Loss1: 0.635800 Loss2: 1.513387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.012920 Loss1: 0.503310 Loss2: 1.509610
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.976375 Loss1: 1.873575 Loss2: 2.102800
-(DefaultActor pid=3764) >> Training accuracy: 0.830208
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.987822 Loss1: 0.481189 Loss2: 1.506633
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.949656 Loss1: 1.419231 Loss2: 1.530425
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.410774 Loss1: 0.898815 Loss2: 1.511959
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.262421 Loss1: 0.759622 Loss2: 1.502799
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.196708 Loss1: 0.675394 Loss2: 1.521314
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.157442 Loss1: 0.636810 Loss2: 1.520632
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.989528 Loss1: 1.862334 Loss2: 2.127194
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.055324 Loss1: 0.536655 Loss2: 1.518669
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.945759 Loss1: 0.421468 Loss2: 1.524291 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.950735 Loss1: 0.443155 Loss2: 1.507579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.871625 Loss1: 0.357844 Loss2: 1.513781 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.904167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.102360 Loss1: 0.602898 Loss2: 1.499462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.955437 Loss1: 0.451563 Loss2: 1.503874 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.906973 Loss1: 0.398574 Loss2: 1.508399 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.920759
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.595209 Loss1: 1.116323 Loss2: 1.478886 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.139413 Loss1: 0.681449 Loss2: 1.457964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.047207 Loss1: 0.580576 Loss2: 1.466631
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.839722 Loss1: 1.863140 Loss2: 1.976582
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.079889 Loss1: 0.603401 Loss2: 1.476488
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.841259 Loss1: 1.350097 Loss2: 1.491162
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.022435 Loss1: 0.534845 Loss2: 1.487590
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.346002 Loss1: 0.882865 Loss2: 1.463137
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.191074 Loss1: 0.739813 Loss2: 1.451261 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.925000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.954514 Loss1: 0.470723 Loss2: 1.483791
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.119249 Loss1: 0.657669 Loss2: 1.461580
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.024218 Loss1: 0.556272 Loss2: 1.467946
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.001942 Loss1: 0.527815 Loss2: 1.474127
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.974348 Loss1: 0.500099 Loss2: 1.474248
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.016985 Loss1: 0.535678 Loss2: 1.481307
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.029419 Loss1: 1.907838 Loss2: 2.121582
-(DefaultActor pid=3764) >> Training accuracy: 0.857422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.889791 Loss1: 1.355904 Loss2: 1.533887 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.385661 Loss1: 0.864773 Loss2: 1.520888 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.020632 Loss1: 0.522459 Loss2: 1.498174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.921375 Loss1: 1.892839 Loss2: 2.028536
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.019671 Loss1: 0.522085 Loss2: 1.497586
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.761120 Loss1: 1.268533 Loss2: 1.492586
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.976430 Loss1: 0.468518 Loss2: 1.507912
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.479794 Loss1: 1.010933 Loss2: 1.468862
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.941410 Loss1: 0.429646 Loss2: 1.511764
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.884014 Loss1: 0.384204 Loss2: 1.499810
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.334855 Loss1: 0.844641 Loss2: 1.490214
-(DefaultActor pid=3765) >> Training accuracy: 0.883333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.111627 Loss1: 0.636324 Loss2: 1.475303
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.027708 Loss1: 0.566706 Loss2: 1.461002
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.992885 Loss1: 0.527539 Loss2: 1.465346
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.996073 Loss1: 0.520404 Loss2: 1.475668
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.082307 Loss1: 1.984239 Loss2: 2.098069
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.887398 Loss1: 0.414012 Loss2: 1.473386
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.883493 Loss1: 1.333952 Loss2: 1.549541
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.945627 Loss1: 0.481034 Loss2: 1.464593
-(DefaultActor pid=3764) >> Training accuracy: 0.882812
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.374096 Loss1: 0.850985 Loss2: 1.523110 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.184213 Loss1: 0.665993 Loss2: 1.518221 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.073143 Loss1: 0.549737 Loss2: 1.523406
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.299875 Loss1: 2.058188 Loss2: 2.241687
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.907601 Loss1: 1.336705 Loss2: 1.570896 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.627961 Loss1: 1.126015 Loss2: 1.501946
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.959666 Loss1: 0.448207 Loss2: 1.511458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.969585 Loss1: 0.452196 Loss2: 1.517390 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.837500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.054873 Loss1: 0.548383 Loss2: 1.506489 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.827675 Loss1: 0.333276 Loss2: 1.494400 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.910156
-DEBUG flwr 2023-10-09 15:59:43,670 | server.py:236 | fit_round 44 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.895454 Loss1: 0.393353 Loss2: 1.502101
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.199282 Loss1: 2.108859 Loss2: 2.090424
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.864873 Loss1: 1.334497 Loss2: 1.530376
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.601654 Loss1: 1.089573 Loss2: 1.512081
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.465691 Loss1: 0.940930 Loss2: 1.524762
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.222964 Loss1: 0.715142 Loss2: 1.507822
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.799320 Loss1: 1.773116 Loss2: 2.026203
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.156325 Loss1: 0.638504 Loss2: 1.517821
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.105403 Loss1: 0.578944 Loss2: 1.526459 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.141395 Loss1: 0.630800 Loss2: 1.510595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.202767 Loss1: 0.741992 Loss2: 1.460775
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.045065 Loss1: 0.523948 Loss2: 1.521118
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.066742 Loss1: 0.597769 Loss2: 1.468974
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.016002 Loss1: 0.491574 Loss2: 1.524428
-(DefaultActor pid=3765) >> Training accuracy: 0.835417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.976733 Loss1: 0.502899 Loss2: 1.473834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.934740 Loss1: 0.463555 Loss2: 1.471185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.975328 Loss1: 1.928097 Loss2: 2.047232
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.871899 Loss1: 0.401816 Loss2: 1.470082
-(DefaultActor pid=3764) >> Training accuracy: 0.885742
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.514614 Loss1: 1.046040 Loss2: 1.468574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.165383 Loss1: 0.686366 Loss2: 1.479017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.997690 Loss1: 0.527423 Loss2: 1.470268
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.992210 Loss1: 1.985492 Loss2: 2.006718
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.930324 Loss1: 1.424221 Loss2: 1.506103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.565404 Loss1: 1.070052 Loss2: 1.495353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.457352 Loss1: 0.943477 Loss2: 1.513875 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.873958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.281792 Loss1: 0.779611 Loss2: 1.502181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.067863 Loss1: 0.559322 Loss2: 1.508541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.964662 Loss1: 0.465597 Loss2: 1.499065 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.904297
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 15:59:43,670][flwr][DEBUG] - fit_round 44 received 50 results and 0 failures
-INFO flwr 2023-10-09 16:00:24,719 | server.py:125 | fit progress: (44, 2.493049050672367, {'accuracy': 0.4467}, 101332.49732903899)
->> Test accuracy: 0.446700
-[2023-10-09 16:00:24,719][flwr][INFO] - fit progress: (44, 2.493049050672367, {'accuracy': 0.4467}, 101332.49732903899)
-DEBUG flwr 2023-10-09 16:00:24,719 | server.py:173 | evaluate_round 44: strategy sampled 50 clients (out of 50)
-[2023-10-09 16:00:24,719][flwr][DEBUG] - evaluate_round 44: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 16:09:27,129 | server.py:187 | evaluate_round 44 received 50 results and 0 failures
-[2023-10-09 16:09:27,129][flwr][DEBUG] - evaluate_round 44 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 16:09:27,130 | server.py:222 | fit_round 45: strategy sampled 50 clients (out of 50)
-[2023-10-09 16:09:27,130][flwr][DEBUG] - fit_round 45: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.848211 Loss1: 1.847998 Loss2: 2.000213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.372534 Loss1: 0.906929 Loss2: 1.465604 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.922325 Loss1: 1.814255 Loss2: 2.108070
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.239572 Loss1: 0.781661 Loss2: 1.457912
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.798010 Loss1: 1.289453 Loss2: 1.508557
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.045095 Loss1: 0.586264 Loss2: 1.458831
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.957793 Loss1: 0.501519 Loss2: 1.456274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.900906 Loss1: 0.438232 Loss2: 1.462674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.854426 Loss1: 0.403221 Loss2: 1.451205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.811161 Loss1: 0.356365 Loss2: 1.454796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.741084 Loss1: 0.292007 Loss2: 1.449077 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.929688
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.899934 Loss1: 0.407600 Loss2: 1.492334 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.868750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.778713 Loss1: 1.776410 Loss2: 2.002303 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.401260 Loss1: 0.930385 Loss2: 1.470875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.859391 Loss1: 1.784575 Loss2: 2.074817
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.266052 Loss1: 0.797684 Loss2: 1.468368
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.085114 Loss1: 0.614866 Loss2: 1.470249 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.110976 Loss1: 0.645371 Loss2: 1.465605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.018285 Loss1: 0.549185 Loss2: 1.469101 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.893634 Loss1: 0.424661 Loss2: 1.468973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.854958 Loss1: 0.395870 Loss2: 1.459088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.953599 Loss1: 0.468141 Loss2: 1.485458 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.915441
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.899267 Loss1: 0.421691 Loss2: 1.477577 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.896875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.890507 Loss1: 1.869701 Loss2: 2.020806
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.771235 Loss1: 1.331757 Loss2: 1.439478
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.422687 Loss1: 0.995247 Loss2: 1.427440
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.263843 Loss1: 0.832183 Loss2: 1.431661
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.931568 Loss1: 1.777972 Loss2: 2.153596
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.091739 Loss1: 0.671331 Loss2: 1.420408
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.756894 Loss1: 1.225573 Loss2: 1.531321
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.032414 Loss1: 0.608405 Loss2: 1.424009
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.391125 Loss1: 0.890065 Loss2: 1.501060
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.937847 Loss1: 0.499935 Loss2: 1.437912
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.258965 Loss1: 0.753097 Loss2: 1.505867
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.852455 Loss1: 0.432001 Loss2: 1.420454
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.089114 Loss1: 0.587704 Loss2: 1.501410
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.759776 Loss1: 0.340116 Loss2: 1.419659
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.001159 Loss1: 0.490342 Loss2: 1.510817
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.795794 Loss1: 0.380797 Loss2: 1.414997
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.889914 Loss1: 0.401420 Loss2: 1.488494
-(DefaultActor pid=3765) >> Training accuracy: 0.909375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.938128 Loss1: 0.440118 Loss2: 1.498010
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.871262 Loss1: 0.375232 Loss2: 1.496030
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.794432 Loss1: 0.305069 Loss2: 1.489363
-(DefaultActor pid=3764) >> Training accuracy: 0.917708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.904620 Loss1: 1.890294 Loss2: 2.014326
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.708509 Loss1: 1.230314 Loss2: 1.478195
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.455675 Loss1: 0.993438 Loss2: 1.462237
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.936395 Loss1: 1.859688 Loss2: 2.076707
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.189027 Loss1: 0.741863 Loss2: 1.447164
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.687961 Loss1: 1.191684 Loss2: 1.496278
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.127127 Loss1: 0.682166 Loss2: 1.444961
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.303001 Loss1: 0.852069 Loss2: 1.450932
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.085413 Loss1: 0.638283 Loss2: 1.447130
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.095057 Loss1: 0.651949 Loss2: 1.443108
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.053679 Loss1: 0.593572 Loss2: 1.460107
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.898620 Loss1: 0.444255 Loss2: 1.454364 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.988931 Loss1: 0.532764 Loss2: 1.456167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.985335 Loss1: 0.520610 Loss2: 1.464725 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.863281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.794468 Loss1: 0.361791 Loss2: 1.432677 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.882292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.038474 Loss1: 1.949292 Loss2: 2.089182 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.485378 Loss1: 0.978041 Loss2: 1.507337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.321973 Loss1: 0.811657 Loss2: 1.510316
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.990812 Loss1: 1.945452 Loss2: 2.045360
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.809094 Loss1: 1.346193 Loss2: 1.462901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.407574 Loss1: 0.961542 Loss2: 1.446032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.293818 Loss1: 0.838711 Loss2: 1.455107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.167022 Loss1: 0.701600 Loss2: 1.465422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.046671 Loss1: 0.583671 Loss2: 1.463000 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.884375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.891099 Loss1: 0.385559 Loss2: 1.505540
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.022514 Loss1: 0.551269 Loss2: 1.471244
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.980012 Loss1: 0.510545 Loss2: 1.469468
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.996452 Loss1: 0.524260 Loss2: 1.472192
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.916711 Loss1: 0.449023 Loss2: 1.467688
-(DefaultActor pid=3764) >> Training accuracy: 0.886458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.851625 Loss1: 1.814541 Loss2: 2.037084
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.773554 Loss1: 1.298980 Loss2: 1.474574
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.395101 Loss1: 0.927003 Loss2: 1.468098
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.216687 Loss1: 0.763965 Loss2: 1.452722
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.090127 Loss1: 2.032334 Loss2: 2.057793
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.919811 Loss1: 1.405289 Loss2: 1.514523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.569639 Loss1: 1.061601 Loss2: 1.508039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.303282 Loss1: 0.818482 Loss2: 1.484799 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.131794 Loss1: 0.655379 Loss2: 1.476414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.090423 Loss1: 0.612253 Loss2: 1.478170 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.897917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.902590 Loss1: 0.436433 Loss2: 1.466157
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.030651 Loss1: 0.541995 Loss2: 1.488656
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.979330 Loss1: 0.476679 Loss2: 1.502652
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.923807 Loss1: 0.434850 Loss2: 1.488956
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.803530 Loss1: 0.329569 Loss2: 1.473961
-(DefaultActor pid=3764) >> Training accuracy: 0.919792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.852853 Loss1: 1.731159 Loss2: 2.121694
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.717680 Loss1: 1.181582 Loss2: 1.536098
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.399909 Loss1: 0.879523 Loss2: 1.520385
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.228389 Loss1: 0.712440 Loss2: 1.515949
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.942789 Loss1: 1.834204 Loss2: 2.108585
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.731055 Loss1: 1.220179 Loss2: 1.510877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.036738 Loss1: 0.526601 Loss2: 1.510137
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.463555 Loss1: 0.984224 Loss2: 1.479331
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.915576 Loss1: 0.391989 Loss2: 1.523586
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.306411 Loss1: 0.817402 Loss2: 1.489009
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.920239 Loss1: 0.405378 Loss2: 1.514861
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.184266 Loss1: 0.694645 Loss2: 1.489621
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.103811 Loss1: 0.615001 Loss2: 1.488810
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.925936 Loss1: 0.409151 Loss2: 1.516786
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.048208 Loss1: 0.554121 Loss2: 1.494087
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.905888 Loss1: 0.391578 Loss2: 1.514310
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.889945 Loss1: 0.405986 Loss2: 1.483958 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.922991
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.206758 Loss1: 2.052028 Loss2: 2.154730 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.551940 Loss1: 1.022282 Loss2: 1.529658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.414386 Loss1: 0.878398 Loss2: 1.535988
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.887100 Loss1: 1.834870 Loss2: 2.052230
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.754866 Loss1: 1.252359 Loss2: 1.502507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.445611 Loss1: 0.951348 Loss2: 1.494263 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.183249 Loss1: 0.700680 Loss2: 1.482569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.000950 Loss1: 0.449748 Loss2: 1.551202 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.932099 Loss1: 0.388359 Loss2: 1.543741 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.912946
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.877723 Loss1: 0.411409 Loss2: 1.466314 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.871488 Loss1: 0.399425 Loss2: 1.472063 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.895508
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.797365 Loss1: 1.357600 Loss2: 1.439765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.322550 Loss1: 0.896964 Loss2: 1.425586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.172005 Loss1: 0.757910 Loss2: 1.414095
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.944285 Loss1: 1.866548 Loss2: 2.077737
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.735807 Loss1: 1.254555 Loss2: 1.481252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.312506 Loss1: 0.849916 Loss2: 1.462591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.248140 Loss1: 0.787951 Loss2: 1.460189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.087658 Loss1: 0.609011 Loss2: 1.478647 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.908333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.985434 Loss1: 0.526200 Loss2: 1.459234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.867155 Loss1: 0.392987 Loss2: 1.474169 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.750936 Loss1: 0.297189 Loss2: 1.453748 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.881250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.796363 Loss1: 1.305145 Loss2: 1.491218 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.188498 Loss1: 0.721714 Loss2: 1.466784 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.108707 Loss1: 0.645362 Loss2: 1.463345
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.828668 Loss1: 1.766951 Loss2: 2.061717
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.746987 Loss1: 1.265283 Loss2: 1.481704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.479418 Loss1: 0.991559 Loss2: 1.487859 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.205802 Loss1: 0.704572 Loss2: 1.501230 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.138306 Loss1: 0.657168 Loss2: 1.481138 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.920833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.809379 Loss1: 0.333167 Loss2: 1.476213
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.009913 Loss1: 0.523837 Loss2: 1.486076
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.913773 Loss1: 0.443693 Loss2: 1.470081
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.912709 Loss1: 0.431438 Loss2: 1.481271
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.887302 Loss1: 0.398749 Loss2: 1.488553
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.848395 Loss1: 0.361391 Loss2: 1.487004
-(DefaultActor pid=3764) >> Training accuracy: 0.914583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.936416 Loss1: 1.943658 Loss2: 1.992758
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.805021 Loss1: 1.345338 Loss2: 1.459683
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.443638 Loss1: 0.997164 Loss2: 1.446474
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.202473 Loss1: 0.747420 Loss2: 1.455053
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.026622 Loss1: 0.587771 Loss2: 1.438850
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.865574 Loss1: 1.766488 Loss2: 2.099086
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.959205 Loss1: 0.521305 Loss2: 1.437900
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.688418 Loss1: 1.159106 Loss2: 1.529312
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.948927 Loss1: 0.500945 Loss2: 1.447982
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.412265 Loss1: 0.893939 Loss2: 1.518326
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.916132 Loss1: 0.456887 Loss2: 1.459244
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.231641 Loss1: 0.715948 Loss2: 1.515693
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.121541 Loss1: 0.612254 Loss2: 1.509287
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.915803 Loss1: 0.461363 Loss2: 1.454440
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.070454 Loss1: 0.561855 Loss2: 1.508599
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.024607 Loss1: 0.569489 Loss2: 1.455118
-(DefaultActor pid=3765) >> Training accuracy: 0.853516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.944601 Loss1: 0.423005 Loss2: 1.521596 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.903762 Loss1: 0.388456 Loss2: 1.515305 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.906250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.772297 Loss1: 1.292544 Loss2: 1.479753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.237821 Loss1: 0.766603 Loss2: 1.471218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.893233 Loss1: 1.823174 Loss2: 2.070059
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.082023 Loss1: 0.619027 Loss2: 1.462996
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.751732 Loss1: 1.291794 Loss2: 1.459938
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.013627 Loss1: 0.545547 Loss2: 1.468080
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.403413 Loss1: 0.960677 Loss2: 1.442736
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.959814 Loss1: 0.485079 Loss2: 1.474735
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.159439 Loss1: 0.716025 Loss2: 1.443414
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.034567 Loss1: 0.559528 Loss2: 1.475040
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.052501 Loss1: 0.628013 Loss2: 1.424488
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.989604 Loss1: 0.491287 Loss2: 1.498316
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.970500 Loss1: 0.538243 Loss2: 1.432258
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.946135 Loss1: 0.468912 Loss2: 1.477224
-(DefaultActor pid=3765) >> Training accuracy: 0.907292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.904178 Loss1: 0.462483 Loss2: 1.441695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.838289 Loss1: 0.398072 Loss2: 1.440217 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.848958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.862309 Loss1: 1.355606 Loss2: 1.506703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.289012 Loss1: 0.795122 Loss2: 1.493890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.106531 Loss1: 2.018430 Loss2: 2.088101
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.168633 Loss1: 0.665333 Loss2: 1.503300
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.931941 Loss1: 1.411158 Loss2: 1.520784
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.049062 Loss1: 0.544078 Loss2: 1.504984
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.589324 Loss1: 1.098904 Loss2: 1.490421
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.970404 Loss1: 0.488870 Loss2: 1.481535
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.320543 Loss1: 0.816408 Loss2: 1.504135
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.980560 Loss1: 0.493061 Loss2: 1.487499
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.184025 Loss1: 0.688219 Loss2: 1.495806
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.897012 Loss1: 0.403649 Loss2: 1.493363
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.153228 Loss1: 0.665984 Loss2: 1.487244
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.877720 Loss1: 0.378676 Loss2: 1.499044
-(DefaultActor pid=3765) >> Training accuracy: 0.891667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.046084 Loss1: 0.538305 Loss2: 1.507779 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.882763 Loss1: 0.389035 Loss2: 1.493728 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.865625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.960329 Loss1: 1.444653 Loss2: 1.515676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.406814 Loss1: 0.904192 Loss2: 1.502622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.831819 Loss1: 1.833684 Loss2: 1.998135
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.236554 Loss1: 0.730967 Loss2: 1.505587
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.590990 Loss1: 1.165779 Loss2: 1.425211
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.164750 Loss1: 0.673332 Loss2: 1.491419
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.302363 Loss1: 0.889878 Loss2: 1.412485
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.092821 Loss1: 0.589179 Loss2: 1.503642
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.127862 Loss1: 0.713889 Loss2: 1.413973
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.087755 Loss1: 0.578792 Loss2: 1.508963
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.096845 Loss1: 0.669749 Loss2: 1.427096
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.998210 Loss1: 0.488325 Loss2: 1.509885
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.942891 Loss1: 0.513649 Loss2: 1.429242
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.948764 Loss1: 0.446159 Loss2: 1.502605
-(DefaultActor pid=3765) >> Training accuracy: 0.875000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.819621 Loss1: 0.391017 Loss2: 1.428604 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.843961 Loss1: 0.412607 Loss2: 1.431353 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.931250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.902655 Loss1: 1.318928 Loss2: 1.583727 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.327834 Loss1: 0.766374 Loss2: 1.561461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.222686 Loss1: 0.673942 Loss2: 1.548744 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.093349 Loss1: 0.523438 Loss2: 1.569912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.001627 Loss1: 0.441207 Loss2: 1.560420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.050367 Loss1: 0.494850 Loss2: 1.555517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.998674 Loss1: 0.431803 Loss2: 1.566872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.907885 Loss1: 0.349781 Loss2: 1.558104 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.905273
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.844467 Loss1: 0.426472 Loss2: 1.417994 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.888542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.153979 Loss1: 2.051108 Loss2: 2.102872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.539010 Loss1: 0.996018 Loss2: 1.542992 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.306730 Loss1: 0.759453 Loss2: 1.547277
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.912710 Loss1: 1.844906 Loss2: 2.067804
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.193704 Loss1: 0.642458 Loss2: 1.551246
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.707506 Loss1: 1.234325 Loss2: 1.473181
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.126828 Loss1: 0.577962 Loss2: 1.548867
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.327874 Loss1: 0.868448 Loss2: 1.459426
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.135943 Loss1: 0.687382 Loss2: 1.448561
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.089994 Loss1: 0.531274 Loss2: 1.558720
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.052353 Loss1: 0.591592 Loss2: 1.460761
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.074168 Loss1: 0.508574 Loss2: 1.565593
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.113290 Loss1: 0.634383 Loss2: 1.478907
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.064757 Loss1: 0.494600 Loss2: 1.570157
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.907165 Loss1: 0.419475 Loss2: 1.487691
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.994183 Loss1: 0.414691 Loss2: 1.579492
-(DefaultActor pid=3765) >> Training accuracy: 0.866211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.972093 Loss1: 0.493632 Loss2: 1.478460 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.897917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.971797 Loss1: 1.955432 Loss2: 2.016365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.480180 Loss1: 1.029027 Loss2: 1.451153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.304057 Loss1: 0.841860 Loss2: 1.462197
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.027255 Loss1: 1.896186 Loss2: 2.131068
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.859304 Loss1: 1.383036 Loss2: 1.476268
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.135618 Loss1: 0.678568 Loss2: 1.457050
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.010775 Loss1: 0.559159 Loss2: 1.451616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.971311 Loss1: 0.516446 Loss2: 1.454865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.993558 Loss1: 0.548115 Loss2: 1.445443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.899815 Loss1: 0.473321 Loss2: 1.426494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.889810 Loss1: 0.462199 Loss2: 1.427611 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.888542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.776422 Loss1: 0.344369 Loss2: 1.432053 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.010773 Loss1: 1.888938 Loss2: 2.121835
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.802127 Loss1: 1.325594 Loss2: 1.476533
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.536117 Loss1: 1.085819 Loss2: 1.450298
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.171757 Loss1: 0.727085 Loss2: 1.444672
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.074840 Loss1: 1.937773 Loss2: 2.137067
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.931669 Loss1: 0.497293 Loss2: 1.434376 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.848239 Loss1: 0.395213 Loss2: 1.453026 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.835310 Loss1: 0.394169 Loss2: 1.441141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.845587 Loss1: 0.403990 Loss2: 1.441597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.830535 Loss1: 0.383626 Loss2: 1.446909 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.918269
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.216776 Loss1: 0.663333 Loss2: 1.553443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.032966 Loss1: 0.468801 Loss2: 1.564165 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.949915 Loss1: 0.400417 Loss2: 1.549498
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.772841 Loss1: 1.716901 Loss2: 2.055941
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.538397 Loss1: 1.065256 Loss2: 1.473141
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.290609 Loss1: 0.831191 Loss2: 1.459418
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.174725 Loss1: 0.713862 Loss2: 1.460862
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.989841 Loss1: 0.528301 Loss2: 1.461540
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.013779 Loss1: 1.810872 Loss2: 2.202907
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.762087 Loss1: 1.244082 Loss2: 1.518005 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.939462 Loss1: 0.490278 Loss2: 1.449184
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.437826 Loss1: 0.961135 Loss2: 1.476692
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.190217 Loss1: 0.709700 Loss2: 1.480516
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.845329 Loss1: 0.389848 Loss2: 1.455481
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.068133 Loss1: 0.592042 Loss2: 1.476091
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.778755 Loss1: 0.316893 Loss2: 1.461862
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.798508 Loss1: 0.338880 Loss2: 1.459628 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.944792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.918278 Loss1: 0.439798 Loss2: 1.478480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.864308 Loss1: 0.375295 Loss2: 1.489013 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.915865
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.905192 Loss1: 1.820909 Loss2: 2.084283
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.854543 Loss1: 1.369263 Loss2: 1.485280
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.538855 Loss1: 1.069827 Loss2: 1.469027
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.266214 Loss1: 0.800011 Loss2: 1.466203
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.923939 Loss1: 1.892797 Loss2: 2.031142
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.781311 Loss1: 1.286169 Loss2: 1.495142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.421640 Loss1: 0.937313 Loss2: 1.484327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.244620 Loss1: 0.751385 Loss2: 1.493235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.161098 Loss1: 0.681123 Loss2: 1.479975 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.142550 Loss1: 0.641808 Loss2: 1.500742 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.875000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.897071 Loss1: 0.420241 Loss2: 1.476830
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.068011 Loss1: 0.569959 Loss2: 1.498051
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.045984 Loss1: 0.550212 Loss2: 1.495772
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.951274 Loss1: 0.449781 Loss2: 1.501493
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.883310 Loss1: 0.393529 Loss2: 1.489782
-(DefaultActor pid=3764) >> Training accuracy: 0.888542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.959383 Loss1: 1.975316 Loss2: 1.984067
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.908611 Loss1: 1.462817 Loss2: 1.445795
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.541277 Loss1: 1.110373 Loss2: 1.430904
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.236079 Loss1: 0.820564 Loss2: 1.415515
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.950553 Loss1: 1.883037 Loss2: 2.067516
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.835178 Loss1: 1.309335 Loss2: 1.525843 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.485517 Loss1: 0.976535 Loss2: 1.508982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.229267 Loss1: 0.718080 Loss2: 1.511188 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.132063 Loss1: 0.633905 Loss2: 1.498158 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.047911 Loss1: 0.548160 Loss2: 1.499751 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.884766
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.810536 Loss1: 0.398792 Loss2: 1.411744
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.914998 Loss1: 0.418947 Loss2: 1.496051
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.942464 Loss1: 0.448097 Loss2: 1.494367
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.925819 Loss1: 0.415732 Loss2: 1.510087
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.934483 Loss1: 0.421996 Loss2: 1.512488
-(DefaultActor pid=3764) >> Training accuracy: 0.902344
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.020127 Loss1: 1.931961 Loss2: 2.088166
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.833735 Loss1: 1.327401 Loss2: 1.506334
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.515796 Loss1: 1.022817 Loss2: 1.492979
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.356723 Loss1: 0.864099 Loss2: 1.492624
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.903852 Loss1: 1.863741 Loss2: 2.040110
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.702813 Loss1: 1.246022 Loss2: 1.456791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.396306 Loss1: 0.953959 Loss2: 1.442347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.168417 Loss1: 0.736150 Loss2: 1.432267 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.052970 Loss1: 0.627665 Loss2: 1.425305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.901978 Loss1: 0.472345 Loss2: 1.429633 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.881250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.842098 Loss1: 0.413758 Loss2: 1.428340 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.890792 Loss1: 0.443402 Loss2: 1.447391 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.878125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.982428 Loss1: 1.927948 Loss2: 2.054480
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.793779 Loss1: 1.315952 Loss2: 1.477828
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.453583 Loss1: 0.975238 Loss2: 1.478345
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.221968 Loss1: 0.753001 Loss2: 1.468967
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.129344 Loss1: 2.013746 Loss2: 2.115598
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.958277 Loss1: 1.433515 Loss2: 1.524763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.572446 Loss1: 1.053882 Loss2: 1.518564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.317445 Loss1: 0.800850 Loss2: 1.516595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.230680 Loss1: 0.714213 Loss2: 1.516467 [repeated 2x across cluster]
-DEBUG flwr 2023-10-09 16:38:42,534 | server.py:236 | fit_round 45 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.114012 Loss1: 0.600070 Loss2: 1.513942 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.863542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.099179 Loss1: 0.572028 Loss2: 1.527152 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 2.002026 Loss1: 0.476846 Loss2: 1.525180 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.851562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.930941 Loss1: 1.408220 Loss2: 1.522721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.397557 Loss1: 0.905192 Loss2: 1.492365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.959239 Loss1: 1.923944 Loss2: 2.035295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.815218 Loss1: 1.333165 Loss2: 1.482053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.414990 Loss1: 0.971162 Loss2: 1.443828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.913162 Loss1: 0.418089 Loss2: 1.495072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.933082 Loss1: 0.444073 Loss2: 1.489009 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.940848
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.923064 Loss1: 0.466331 Loss2: 1.456733 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.901534 Loss1: 0.450962 Loss2: 1.450571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.883933 Loss1: 0.425304 Loss2: 1.458629
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.733806 Loss1: 1.694151 Loss2: 2.039655
-(DefaultActor pid=3764) >> Training accuracy: 0.908333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.717681 Loss1: 1.239741 Loss2: 1.477940
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.369369 Loss1: 0.910503 Loss2: 1.458866
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.122126 Loss1: 0.663088 Loss2: 1.459038
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.028328 Loss1: 0.585995 Loss2: 1.442333
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.047798 Loss1: 0.589971 Loss2: 1.457827
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.016135 Loss1: 1.976942 Loss2: 2.039193
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.940121 Loss1: 0.479833 Loss2: 1.460288
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.839946 Loss1: 1.345745 Loss2: 1.494201
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.868665 Loss1: 0.406350 Loss2: 1.462315
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.441653 Loss1: 0.965310 Loss2: 1.476342
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.783778 Loss1: 0.332840 Loss2: 1.450938
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.292794 Loss1: 0.809876 Loss2: 1.482918
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.837136 Loss1: 0.390187 Loss2: 1.446949
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.171737 Loss1: 0.679073 Loss2: 1.492663
-(DefaultActor pid=3765) >> Training accuracy: 0.870833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.164195 Loss1: 0.670835 Loss2: 1.493360
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.040769 Loss1: 0.542416 Loss2: 1.498353
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.899012 Loss1: 0.424283 Loss2: 1.474730
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.929188 Loss1: 0.442238 Loss2: 1.486950
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.862679 Loss1: 0.373677 Loss2: 1.489002
-(DefaultActor pid=3764) >> Training accuracy: 0.872917
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 16:38:42,534][flwr][DEBUG] - fit_round 45 received 50 results and 0 failures
-INFO flwr 2023-10-09 16:39:24,842 | server.py:125 | fit progress: (45, 2.4865793888561263, {'accuracy': 0.4568}, 103672.620332282)
->> Test accuracy: 0.456800
-[2023-10-09 16:39:24,842][flwr][INFO] - fit progress: (45, 2.4865793888561263, {'accuracy': 0.4568}, 103672.620332282)
-DEBUG flwr 2023-10-09 16:39:24,842 | server.py:173 | evaluate_round 45: strategy sampled 50 clients (out of 50)
-[2023-10-09 16:39:24,842][flwr][DEBUG] - evaluate_round 45: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 16:48:28,679 | server.py:187 | evaluate_round 45 received 50 results and 0 failures
-[2023-10-09 16:48:28,679][flwr][DEBUG] - evaluate_round 45 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 16:48:28,680 | server.py:222 | fit_round 46: strategy sampled 50 clients (out of 50)
-[2023-10-09 16:48:28,680][flwr][DEBUG] - fit_round 46: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.898112 Loss1: 1.853889 Loss2: 2.044223
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.729719 Loss1: 1.186862 Loss2: 1.542857
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.466418 Loss1: 0.952810 Loss2: 1.513608
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.222674 Loss1: 0.715911 Loss2: 1.506763
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.943204 Loss1: 1.428497 Loss2: 1.514708 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.574735 Loss1: 1.096975 Loss2: 1.477760 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.295067 Loss1: 0.813656 Loss2: 1.481411 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.142012 Loss1: 0.675186 Loss2: 1.466826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.017083 Loss1: 0.539378 Loss2: 1.477705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.997761 Loss1: 0.515450 Loss2: 1.482311 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.880859
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.001556 Loss1: 0.516017 Loss2: 1.485539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.932500 Loss1: 0.441700 Loss2: 1.490800 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.910417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.761184 Loss1: 1.734121 Loss2: 2.027063
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.687831 Loss1: 1.190025 Loss2: 1.497806
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.469809 Loss1: 0.965423 Loss2: 1.504385
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.760040 Loss1: 1.765523 Loss2: 1.994516
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.153301 Loss1: 0.658214 Loss2: 1.495087
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.739263 Loss1: 1.241146 Loss2: 1.498117
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.962698 Loss1: 0.481518 Loss2: 1.481180
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.394155 Loss1: 0.904240 Loss2: 1.489915
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.920449 Loss1: 0.454009 Loss2: 1.466440
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.127734 Loss1: 0.672360 Loss2: 1.455375
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.946062 Loss1: 0.462725 Loss2: 1.483337
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.022594 Loss1: 0.558440 Loss2: 1.464154
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.864460 Loss1: 0.390952 Loss2: 1.473508
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.899104 Loss1: 0.444021 Loss2: 1.455082
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.862593 Loss1: 0.388539 Loss2: 1.474054
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.870282 Loss1: 0.422048 Loss2: 1.448235
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.794030 Loss1: 0.314279 Loss2: 1.479751
-(DefaultActor pid=3765) >> Training accuracy: 0.902344
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.838687 Loss1: 0.376897 Loss2: 1.461791 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.904297
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.952092 Loss1: 1.801029 Loss2: 2.151063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.382622 Loss1: 0.869013 Loss2: 1.513608 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.152899 Loss1: 0.655167 Loss2: 1.497732
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.021287 Loss1: 1.923733 Loss2: 2.097554
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.080146 Loss1: 0.592120 Loss2: 1.488026
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.754514 Loss1: 1.270256 Loss2: 1.484258
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.028690 Loss1: 0.537629 Loss2: 1.491061
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.456511 Loss1: 0.991750 Loss2: 1.464761
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.026053 Loss1: 0.521466 Loss2: 1.504587
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.218508 Loss1: 0.748778 Loss2: 1.469730
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.879998 Loss1: 0.375404 Loss2: 1.504594
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.117776 Loss1: 0.643708 Loss2: 1.474069
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.876797 Loss1: 0.370074 Loss2: 1.506723
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.088037 Loss1: 0.604149 Loss2: 1.483887
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.822104 Loss1: 0.315657 Loss2: 1.506448
-(DefaultActor pid=3765) >> Training accuracy: 0.927083
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.010313 Loss1: 0.530039 Loss2: 1.480274
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.923296 Loss1: 0.448125 Loss2: 1.475171
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.909444 Loss1: 0.439379 Loss2: 1.470066
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.838018 Loss1: 0.369944 Loss2: 1.468073
-(DefaultActor pid=3764) >> Training accuracy: 0.903125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.798731 Loss1: 1.688574 Loss2: 2.110157
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.736223 Loss1: 1.247539 Loss2: 1.488683
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.396585 Loss1: 0.934815 Loss2: 1.461769
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.274651 Loss1: 0.800336 Loss2: 1.474315
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.965348 Loss1: 1.948297 Loss2: 2.017051
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.855283 Loss1: 1.371183 Loss2: 1.484100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.452585 Loss1: 1.021079 Loss2: 1.431506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.194080 Loss1: 0.752641 Loss2: 1.441439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.116601 Loss1: 0.681828 Loss2: 1.434773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.984943 Loss1: 0.559300 Loss2: 1.425642 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.920833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.793470 Loss1: 0.335128 Loss2: 1.458342
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.946595 Loss1: 0.510644 Loss2: 1.435951
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.872771 Loss1: 0.439430 Loss2: 1.433341
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.846747 Loss1: 0.412711 Loss2: 1.434037
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.763374 Loss1: 0.323919 Loss2: 1.439455
-(DefaultActor pid=3764) >> Training accuracy: 0.905208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.822930 Loss1: 1.781192 Loss2: 2.041738
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.730099 Loss1: 1.285597 Loss2: 1.444502
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.442970 Loss1: 0.982172 Loss2: 1.460798
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.259761 Loss1: 0.820008 Loss2: 1.439753
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.901593 Loss1: 1.814455 Loss2: 2.087138
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.714659 Loss1: 1.208829 Loss2: 1.505830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.362696 Loss1: 0.878741 Loss2: 1.483955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.800000 Loss1: 0.367221 Loss2: 1.432779 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.786968 Loss1: 0.355605 Loss2: 1.431363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.784056 Loss1: 0.346139 Loss2: 1.437917 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.896994 Loss1: 0.418001 Loss2: 1.478993 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.870639 Loss1: 0.375567 Loss2: 1.495073 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.894792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.703884 Loss1: 1.193327 Loss2: 1.510557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.132811 Loss1: 0.632931 Loss2: 1.499880 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.069207 Loss1: 0.583921 Loss2: 1.485286
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.056048 Loss1: 1.957619 Loss2: 2.098429
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.005622 Loss1: 0.510077 Loss2: 1.495546
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.966457 Loss1: 1.414370 Loss2: 1.552087
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.991923 Loss1: 0.485648 Loss2: 1.506274
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.503494 Loss1: 0.975238 Loss2: 1.528255
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.001945 Loss1: 0.491729 Loss2: 1.510216
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.293108 Loss1: 0.777190 Loss2: 1.515918
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.972821 Loss1: 0.452855 Loss2: 1.519966
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.183175 Loss1: 0.672694 Loss2: 1.510482
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.829841 Loss1: 0.334412 Loss2: 1.495429
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.059354 Loss1: 0.542534 Loss2: 1.516820
-(DefaultActor pid=3765) >> Training accuracy: 0.891667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.981674 Loss1: 0.471103 Loss2: 1.510571
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.986509 Loss1: 0.484972 Loss2: 1.501537
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.970793 Loss1: 0.451921 Loss2: 1.518871
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.881460 Loss1: 0.365808 Loss2: 1.515652
-(DefaultActor pid=3764) >> Training accuracy: 0.905208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.759562 Loss1: 1.748079 Loss2: 2.011483
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.713004 Loss1: 1.236733 Loss2: 1.476271
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.366985 Loss1: 0.930972 Loss2: 1.436014
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.141586 Loss1: 0.707234 Loss2: 1.434352
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.999723 Loss1: 0.569518 Loss2: 1.430206
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.936485 Loss1: 0.503339 Loss2: 1.433146 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.836137 Loss1: 0.408508 Loss2: 1.427629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.855326 Loss1: 0.427198 Loss2: 1.428128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.830481 Loss1: 0.385930 Loss2: 1.444551 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.764466 Loss1: 0.329375 Loss2: 1.435091 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.909375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.003573 Loss1: 0.551560 Loss2: 1.452013 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.876245 Loss1: 0.418450 Loss2: 1.457795 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.876042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.745130 Loss1: 1.271311 Loss2: 1.473819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.204307 Loss1: 0.732171 Loss2: 1.472136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.104231 Loss1: 1.940797 Loss2: 2.163434
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.122934 Loss1: 0.645607 Loss2: 1.477327
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.786621 Loss1: 1.257559 Loss2: 1.529062
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.048146 Loss1: 0.579238 Loss2: 1.468908
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.002113 Loss1: 0.530750 Loss2: 1.471363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.006913 Loss1: 0.524743 Loss2: 1.482170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.002109 Loss1: 0.507704 Loss2: 1.494405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.951222 Loss1: 0.462837 Loss2: 1.488385 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.875000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.896553 Loss1: 0.391563 Loss2: 1.504990 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.907452
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.102697 Loss1: 1.896855 Loss2: 2.205841 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.343864 Loss1: 0.844264 Loss2: 1.499600 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.781297 Loss1: 1.774658 Loss2: 2.006639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.954221 Loss1: 0.448383 Loss2: 1.505837 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.827008 Loss1: 0.324298 Loss2: 1.502709 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.832753 Loss1: 0.343118 Loss2: 1.489635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.829280 Loss1: 0.341120 Loss2: 1.488160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.782683 Loss1: 0.290830 Loss2: 1.491853 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.933894
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.840127 Loss1: 0.434384 Loss2: 1.405743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.898800 Loss1: 0.459086 Loss2: 1.439715 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.877083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.874962 Loss1: 0.437599 Loss2: 1.437362
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.774345 Loss1: 1.780132 Loss2: 1.994213
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.720804 Loss1: 1.258075 Loss2: 1.462730
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.378753 Loss1: 0.935597 Loss2: 1.443155
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.096433 Loss1: 0.661850 Loss2: 1.434583
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.996576 Loss1: 0.565094 Loss2: 1.431481
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.934219 Loss1: 1.887049 Loss2: 2.047170
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.728556 Loss1: 1.244622 Loss2: 1.483934 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.413945 Loss1: 0.941542 Loss2: 1.472403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.152652 Loss1: 0.693937 Loss2: 1.458715 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.983963 Loss1: 0.533468 Loss2: 1.450495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.881250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.846735 Loss1: 0.394339 Loss2: 1.452396
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.898618 Loss1: 0.452873 Loss2: 1.445744
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.844945 Loss1: 0.399012 Loss2: 1.445933
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.855870 Loss1: 0.398822 Loss2: 1.457048
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.871061 Loss1: 0.398461 Loss2: 1.472600
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.765573 Loss1: 0.299948 Loss2: 1.465624
-(DefaultActor pid=3764) >> Training accuracy: 0.898958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.967009 Loss1: 1.939254 Loss2: 2.027755
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.814081 Loss1: 1.348866 Loss2: 1.465215
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.498866 Loss1: 1.059515 Loss2: 1.439351
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.361052 Loss1: 0.913634 Loss2: 1.447419
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.117704 Loss1: 0.667853 Loss2: 1.449851
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.906858 Loss1: 1.864788 Loss2: 2.042070
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.807700 Loss1: 1.315105 Loss2: 1.492595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.367294 Loss1: 0.897303 Loss2: 1.469991 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.181674 Loss1: 0.716871 Loss2: 1.464803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.130904 Loss1: 0.668174 Loss2: 1.462730 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.862500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.046350 Loss1: 0.566820 Loss2: 1.479530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.999098 Loss1: 0.516321 Loss2: 1.482777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.828949 Loss1: 0.358870 Loss2: 1.470079 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.878125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.669049 Loss1: 1.206748 Loss2: 1.462301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.169721 Loss1: 0.710688 Loss2: 1.459033 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.060433 Loss1: 0.614206 Loss2: 1.446227
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.798177 Loss1: 1.832458 Loss2: 1.965719
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.793725 Loss1: 1.347364 Loss2: 1.446361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.439410 Loss1: 1.014108 Loss2: 1.425301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.153268 Loss1: 0.735346 Loss2: 1.417922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.042981 Loss1: 0.647630 Loss2: 1.395351 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.926042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.812118 Loss1: 0.371569 Loss2: 1.440549
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.047189 Loss1: 0.641235 Loss2: 1.405953
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.935068 Loss1: 0.515401 Loss2: 1.419667
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.834791 Loss1: 0.423289 Loss2: 1.411502
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.832428 Loss1: 0.419232 Loss2: 1.413196
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.775148 Loss1: 0.365675 Loss2: 1.409473
-(DefaultActor pid=3764) >> Training accuracy: 0.918750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.903865 Loss1: 1.866701 Loss2: 2.037165
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.740885 Loss1: 1.247951 Loss2: 1.492934
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.515479 Loss1: 1.027189 Loss2: 1.488290
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.298930 Loss1: 0.807473 Loss2: 1.491457
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.137060 Loss1: 0.657316 Loss2: 1.479744
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.872790 Loss1: 1.832693 Loss2: 2.040097
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.783940 Loss1: 1.252525 Loss2: 1.531416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.436412 Loss1: 0.928568 Loss2: 1.507844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.318637 Loss1: 0.803290 Loss2: 1.515347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.170900 Loss1: 0.644370 Loss2: 1.526529 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.862500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.115410 Loss1: 0.589792 Loss2: 1.525618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.985932 Loss1: 0.479515 Loss2: 1.506418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.938073 Loss1: 0.415289 Loss2: 1.522785 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.903320
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.573339 Loss1: 1.061679 Loss2: 1.511660 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.086903 Loss1: 0.587257 Loss2: 1.499646 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.133257 Loss1: 0.636083 Loss2: 1.497174
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.937540 Loss1: 1.969403 Loss2: 1.968137
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.826829 Loss1: 1.358869 Loss2: 1.467960 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.507961 Loss1: 1.043702 Loss2: 1.464259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.313412 Loss1: 0.849382 Loss2: 1.464029 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.889509
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.008430 Loss1: 0.550594 Loss2: 1.457836 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.972873 Loss1: 0.502554 Loss2: 1.470320 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.682660 Loss1: 1.650550 Loss2: 2.032110
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.934203 Loss1: 0.461414 Loss2: 1.472790
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.646712 Loss1: 1.160518 Loss2: 1.486194
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.855400 Loss1: 0.392320 Loss2: 1.463080
-(DefaultActor pid=3764) >> Training accuracy: 0.881836
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.037761 Loss1: 0.587337 Loss2: 1.450424 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.864653 Loss1: 0.438908 Loss2: 1.425745 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.794499 Loss1: 0.370766 Loss2: 1.423733
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.883989 Loss1: 1.882288 Loss2: 2.001701
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.731990 Loss1: 0.294884 Loss2: 1.437105
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.652939 Loss1: 1.200398 Loss2: 1.452542
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.845136 Loss1: 0.409563 Loss2: 1.435574
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.399782 Loss1: 0.958991 Loss2: 1.440791
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.857284 Loss1: 0.401290 Loss2: 1.455994
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.263404 Loss1: 0.808039 Loss2: 1.455365
-(DefaultActor pid=3765) >> Training accuracy: 0.901042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.162523 Loss1: 0.703802 Loss2: 1.458722
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.941673 Loss1: 0.490507 Loss2: 1.451165
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.929045 Loss1: 0.480720 Loss2: 1.448325
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.882411 Loss1: 0.434099 Loss2: 1.448312
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.864601 Loss1: 0.411611 Loss2: 1.452990
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.939220 Loss1: 1.914346 Loss2: 2.024874
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.858746 Loss1: 0.398716 Loss2: 1.460031
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.895163 Loss1: 1.397313 Loss2: 1.497850
-(DefaultActor pid=3764) >> Training accuracy: 0.879167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.523257 Loss1: 1.056213 Loss2: 1.467044
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.233707 Loss1: 0.766382 Loss2: 1.467325
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.123882 Loss1: 0.671353 Loss2: 1.452529
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.948390 Loss1: 0.489786 Loss2: 1.458604
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.912617 Loss1: 0.454104 Loss2: 1.458512
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.755719 Loss1: 1.799876 Loss2: 1.955843
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.855370 Loss1: 0.411618 Loss2: 1.443752
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.720306 Loss1: 1.280361 Loss2: 1.439945
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.813842 Loss1: 0.355482 Loss2: 1.458360
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.370550 Loss1: 0.959834 Loss2: 1.410716
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.852050 Loss1: 0.400694 Loss2: 1.451356
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.180609 Loss1: 0.774557 Loss2: 1.406052
-(DefaultActor pid=3765) >> Training accuracy: 0.917708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.093911 Loss1: 0.700953 Loss2: 1.392957
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.946921 Loss1: 0.542085 Loss2: 1.404836
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.827727 Loss1: 0.429661 Loss2: 1.398066
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.782659 Loss1: 0.381370 Loss2: 1.401288
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.189286 Loss1: 1.939614 Loss2: 2.249672
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.715488 Loss1: 0.324046 Loss2: 1.391441
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.671909 Loss1: 0.286893 Loss2: 1.385015 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.123789 Loss1: 0.642905 Loss2: 1.480884 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.990531 Loss1: 0.502238 Loss2: 1.488294 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.829190 Loss1: 1.840104 Loss2: 1.989087 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.624927 Loss1: 1.191596 Loss2: 1.433332 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.888021
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.196831 Loss1: 0.763094 Loss2: 1.433736 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.892877 Loss1: 0.472777 Loss2: 1.420100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.898632 Loss1: 0.485611 Loss2: 1.413021
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.905751 Loss1: 1.864723 Loss2: 2.041029
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.881599 Loss1: 0.452196 Loss2: 1.429402
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.704667 Loss1: 1.202579 Loss2: 1.502088
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.838989 Loss1: 0.413171 Loss2: 1.425817
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.433337 Loss1: 0.955411 Loss2: 1.477926
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.809755 Loss1: 0.387329 Loss2: 1.422427
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.334342 Loss1: 0.843748 Loss2: 1.490594
-(DefaultActor pid=3764) >> Training accuracy: 0.905208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.147926 Loss1: 0.652310 Loss2: 1.495615
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.065688 Loss1: 0.585499 Loss2: 1.480189
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.996472 Loss1: 0.517512 Loss2: 1.478960
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.931385 Loss1: 0.443220 Loss2: 1.488165
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.855582 Loss1: 0.374653 Loss2: 1.480930
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.790922 Loss1: 1.730082 Loss2: 2.060839
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.884810 Loss1: 0.402757 Loss2: 1.482053
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.632691 Loss1: 1.151099 Loss2: 1.481591
-(DefaultActor pid=3765) >> Training accuracy: 0.879167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.356589 Loss1: 0.904006 Loss2: 1.452583
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.133600 Loss1: 0.694945 Loss2: 1.438655
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.918369 Loss1: 0.483643 Loss2: 1.434725
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.979985 Loss1: 0.551092 Loss2: 1.428893
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.858816 Loss1: 0.409998 Loss2: 1.448818
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.924503 Loss1: 1.948090 Loss2: 1.976412
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.772133 Loss1: 0.335919 Loss2: 1.436213
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.783810 Loss1: 1.320372 Loss2: 1.463438
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.787311 Loss1: 0.351634 Loss2: 1.435677
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.444229 Loss1: 1.000709 Loss2: 1.443520
-(DefaultActor pid=3764) >> Training accuracy: 0.859375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.841943 Loss1: 0.393677 Loss2: 1.448266
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.260168 Loss1: 0.824624 Loss2: 1.435544
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.109192 Loss1: 0.671561 Loss2: 1.437631
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.977887 Loss1: 0.549645 Loss2: 1.428242
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.969335 Loss1: 0.537126 Loss2: 1.432209
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.886079 Loss1: 0.443798 Loss2: 1.442281
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.654952 Loss1: 1.669385 Loss2: 1.985566
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.654212 Loss1: 1.223553 Loss2: 1.430659 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.882812
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.836055 Loss1: 0.398690 Loss2: 1.437365
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.258174 Loss1: 0.826425 Loss2: 1.431749
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.026243 Loss1: 0.628954 Loss2: 1.397289
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.939182 Loss1: 0.541887 Loss2: 1.397295
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.902359 Loss1: 0.503813 Loss2: 1.398546
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.849378 Loss1: 0.447421 Loss2: 1.401957
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.852172 Loss1: 1.836610 Loss2: 2.015562
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.765211 Loss1: 0.362941 Loss2: 1.402270
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.730244 Loss1: 1.272923 Loss2: 1.457321
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.733883 Loss1: 0.346472 Loss2: 1.387411
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.375218 Loss1: 0.938159 Loss2: 1.437059
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.733567 Loss1: 0.336894 Loss2: 1.396673
-(DefaultActor pid=3764) >> Training accuracy: 0.862500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.026577 Loss1: 0.597640 Loss2: 1.428936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.864445 Loss1: 0.431002 Loss2: 1.433442 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.848087 Loss1: 0.416514 Loss2: 1.431573
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.952802 Loss1: 1.898438 Loss2: 2.054364
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.794348 Loss1: 1.292123 Loss2: 1.502225 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.877083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.871252 Loss1: 0.441092 Loss2: 1.430160
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.336301 Loss1: 0.856517 Loss2: 1.479784
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.176808 Loss1: 0.710003 Loss2: 1.466805
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.031765 Loss1: 0.564174 Loss2: 1.467591
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.009280 Loss1: 0.544523 Loss2: 1.464757
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.913466 Loss1: 0.443247 Loss2: 1.470219
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.757613 Loss1: 1.802718 Loss2: 1.954894
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.902840 Loss1: 0.430703 Loss2: 1.472136
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.752102 Loss1: 1.309503 Loss2: 1.442599
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.820767 Loss1: 0.341967 Loss2: 1.478800
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.845701 Loss1: 0.386193 Loss2: 1.459509
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.343934 Loss1: 0.907953 Loss2: 1.435981
-(DefaultActor pid=3764) >> Training accuracy: 0.933333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.177248 Loss1: 0.754685 Loss2: 1.422562
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.954215 Loss1: 0.537100 Loss2: 1.417115
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.850309 Loss1: 0.433859 Loss2: 1.416450
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.861303 Loss1: 0.442704 Loss2: 1.418599
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.224396 Loss1: 2.098600 Loss2: 2.125795
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.921940 Loss1: 1.415423 Loss2: 1.506517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.543493 Loss1: 1.078570 Loss2: 1.464923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.826751 Loss1: 0.389182 Loss2: 1.437568
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.327575 Loss1: 0.862584 Loss2: 1.464991
-(DefaultActor pid=3765) >> Training accuracy: 0.891602
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.078690 Loss1: 0.624841 Loss2: 1.453849
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.014383 Loss1: 0.554980 Loss2: 1.459403
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.972388 Loss1: 0.505004 Loss2: 1.467384
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.885919 Loss1: 0.424816 Loss2: 1.461103
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.830684 Loss1: 0.366135 Loss2: 1.464549
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.132836 Loss1: 2.005347 Loss2: 2.127490
-(DefaultActor pid=3764) >> Training accuracy: 0.920759
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.827715 Loss1: 0.372511 Loss2: 1.455204
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.934103 Loss1: 1.380920 Loss2: 1.553184
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.558032 Loss1: 1.012246 Loss2: 1.545786
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.298647 Loss1: 0.766598 Loss2: 1.532049
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.200774 Loss1: 0.665140 Loss2: 1.535634
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.109251 Loss1: 0.574462 Loss2: 1.534790
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.842736 Loss1: 1.822837 Loss2: 2.019899
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.030510 Loss1: 0.495014 Loss2: 1.535496
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.020685 Loss1: 0.488614 Loss2: 1.532071
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.724726 Loss1: 1.246626 Loss2: 1.478100
-(DefaultActor pid=3765) Epoch: 8 Loss: 2.048561 Loss1: 0.508131 Loss2: 1.540430
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.380960 Loss1: 0.916185 Loss2: 1.464775
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.020846 Loss1: 0.473109 Loss2: 1.547737
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.260916 Loss1: 0.801564 Loss2: 1.459352
-(DefaultActor pid=3765) >> Training accuracy: 0.873958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.141287 Loss1: 0.679433 Loss2: 1.461854
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.964344 Loss1: 0.502976 Loss2: 1.461369
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.876099 Loss1: 0.427637 Loss2: 1.448462
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.930780 Loss1: 0.476618 Loss2: 1.454162
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.136747 Loss1: 2.055446 Loss2: 2.081301
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.896153 Loss1: 0.435922 Loss2: 1.460231
-DEBUG flwr 2023-10-09 17:17:08,335 | server.py:236 | fit_round 46 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.869756 Loss1: 0.402940 Loss2: 1.466815 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.903320
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.333425 Loss1: 0.834991 Loss2: 1.498434 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.095223 Loss1: 0.606360 Loss2: 1.488863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.052557 Loss1: 0.559616 Loss2: 1.492940
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.935006 Loss1: 1.888418 Loss2: 2.046588
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.778430 Loss1: 1.288593 Loss2: 1.489837 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.381809 Loss1: 0.912798 Loss2: 1.469011 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.919792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.209491 Loss1: 0.741788 Loss2: 1.467703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.060124 Loss1: 0.589431 Loss2: 1.470694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.918670 Loss1: 0.444394 Loss2: 1.474276 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.899051 Loss1: 0.432890 Loss2: 1.466161 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.867857 Loss1: 0.396696 Loss2: 1.471161 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.888542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.308039 Loss1: 0.762677 Loss2: 1.545362 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.109694 Loss1: 0.567282 Loss2: 1.542412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.146847 Loss1: 0.605626 Loss2: 1.541221
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.017790 Loss1: 1.992411 Loss2: 2.025379
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.845920 Loss1: 1.343522 Loss2: 1.502398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.458677 Loss1: 0.988512 Loss2: 1.470164 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.246405 Loss1: 0.784713 Loss2: 1.461693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.098636 Loss1: 0.638459 Loss2: 1.460178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.942630 Loss1: 0.461804 Loss2: 1.480827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.889634 Loss1: 0.421086 Loss2: 1.468548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.786976 Loss1: 0.318114 Loss2: 1.468862 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.125373 Loss1: 0.662103 Loss2: 1.463270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.915239 Loss1: 0.454671 Loss2: 1.460568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.901996 Loss1: 0.443794 Loss2: 1.458203
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.918361 Loss1: 1.886153 Loss2: 2.032208
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.706065 Loss1: 1.243240 Loss2: 1.462826
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.847821 Loss1: 0.395745 Loss2: 1.452076
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.467386 Loss1: 1.025058 Loss2: 1.442328
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.882789 Loss1: 0.432147 Loss2: 1.450642
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.260493 Loss1: 0.805369 Loss2: 1.455124
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.836658 Loss1: 0.376684 Loss2: 1.459974
-(DefaultActor pid=3765) >> Training accuracy: 0.905331
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.947265 Loss1: 0.504642 Loss2: 1.442623 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.828933 Loss1: 0.379247 Loss2: 1.449686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.794212 Loss1: 0.352019 Loss2: 1.442193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.864583
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 17:17:08,335][flwr][DEBUG] - fit_round 46 received 50 results and 0 failures
-INFO flwr 2023-10-09 17:17:48,699 | server.py:125 | fit progress: (46, 2.4787738273699826, {'accuracy': 0.4587}, 105976.477566338)
->> Test accuracy: 0.458700
-[2023-10-09 17:17:48,699][flwr][INFO] - fit progress: (46, 2.4787738273699826, {'accuracy': 0.4587}, 105976.477566338)
-DEBUG flwr 2023-10-09 17:17:48,699 | server.py:173 | evaluate_round 46: strategy sampled 50 clients (out of 50)
-[2023-10-09 17:17:48,699][flwr][DEBUG] - evaluate_round 46: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 17:26:51,884 | server.py:187 | evaluate_round 46 received 50 results and 0 failures
-[2023-10-09 17:26:51,884][flwr][DEBUG] - evaluate_round 46 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 17:26:51,885 | server.py:222 | fit_round 47: strategy sampled 50 clients (out of 50)
-[2023-10-09 17:26:51,885][flwr][DEBUG] - fit_round 47: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.940602 Loss1: 1.887008 Loss2: 2.053594
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.837054 Loss1: 1.336933 Loss2: 1.500121
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.459444 Loss1: 0.963679 Loss2: 1.495765
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.337841 Loss1: 0.842763 Loss2: 1.495078
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.687968 Loss1: 1.666684 Loss2: 2.021284
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.698159 Loss1: 1.233550 Loss2: 1.464609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.263088 Loss1: 0.792946 Loss2: 1.470142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.140859 Loss1: 0.684973 Loss2: 1.455887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.004093 Loss1: 0.548117 Loss2: 1.455976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.008871 Loss1: 0.538412 Loss2: 1.470459 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.900000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.795492 Loss1: 0.344491 Loss2: 1.451001 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.788151 Loss1: 0.327333 Loss2: 1.460819 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.889706
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.740587 Loss1: 1.211647 Loss2: 1.528940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.158102 Loss1: 0.653613 Loss2: 1.504489 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.016937 Loss1: 0.525128 Loss2: 1.491809
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.845049 Loss1: 1.878989 Loss2: 1.966060
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.756342 Loss1: 1.299618 Loss2: 1.456724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.351043 Loss1: 0.915211 Loss2: 1.435832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.130769 Loss1: 0.702046 Loss2: 1.428724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.052073 Loss1: 0.621766 Loss2: 1.430308 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.927083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.921444 Loss1: 0.488922 Loss2: 1.432521 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.893942 Loss1: 0.447069 Loss2: 1.446873 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.799186 Loss1: 0.359909 Loss2: 1.439277 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.924805
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.344887 Loss1: 0.865185 Loss2: 1.479702 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.019203 Loss1: 0.564644 Loss2: 1.454559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.909982 Loss1: 1.864032 Loss2: 2.045950
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.980283 Loss1: 0.519681 Loss2: 1.460601
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.809648 Loss1: 1.333059 Loss2: 1.476589
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.925172 Loss1: 0.455465 Loss2: 1.469707
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.503099 Loss1: 1.045550 Loss2: 1.457549
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.857541 Loss1: 0.385778 Loss2: 1.471763
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.228512 Loss1: 0.754301 Loss2: 1.474210
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.850075 Loss1: 0.395041 Loss2: 1.455034
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.059881 Loss1: 0.607964 Loss2: 1.451917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.891035 Loss1: 0.424485 Loss2: 1.466550
-(DefaultActor pid=3765) >> Training accuracy: 0.830208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.935460 Loss1: 0.470091 Loss2: 1.465368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.903931 Loss1: 0.447585 Loss2: 1.456346 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.829344 Loss1: 0.360887 Loss2: 1.468457
-(DefaultActor pid=3764) >> Training accuracy: 0.908333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.823755 Loss1: 1.800680 Loss2: 2.023075
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.612568 Loss1: 1.162382 Loss2: 1.450186
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.313926 Loss1: 0.870621 Loss2: 1.443305
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.233166 Loss1: 0.781937 Loss2: 1.451229
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.982638 Loss1: 0.535789 Loss2: 1.446849
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.983803 Loss1: 1.907742 Loss2: 2.076061
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.002308 Loss1: 0.556938 Loss2: 1.445370
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.945254 Loss1: 0.498775 Loss2: 1.446479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.928985 Loss1: 0.478385 Loss2: 1.450600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.785487 Loss1: 0.334287 Loss2: 1.451200 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.770026 Loss1: 0.324558 Loss2: 1.445469 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.916667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.980877 Loss1: 0.490370 Loss2: 1.490507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.844382 Loss1: 0.361528 Loss2: 1.482855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.899873 Loss1: 0.416036 Loss2: 1.483837
-(DefaultActor pid=3764) >> Training accuracy: 0.910417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.971724 Loss1: 1.873796 Loss2: 2.097928
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.812271 Loss1: 1.302410 Loss2: 1.509862
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.470845 Loss1: 0.986806 Loss2: 1.484039
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.213899 Loss1: 0.744553 Loss2: 1.469346
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.112562 Loss1: 0.638601 Loss2: 1.473961
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.796954 Loss1: 1.723377 Loss2: 2.073577
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.974223 Loss1: 0.499927 Loss2: 1.474296
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.951150 Loss1: 0.480008 Loss2: 1.471142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.904979 Loss1: 0.429107 Loss2: 1.475872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.847188 Loss1: 0.358889 Loss2: 1.488299 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.830587 Loss1: 0.357763 Loss2: 1.472823 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.894792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.921000 Loss1: 0.441259 Loss2: 1.479741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.804127 Loss1: 0.332086 Loss2: 1.472042 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.762208 Loss1: 0.279354 Loss2: 1.482854
-(DefaultActor pid=3764) >> Training accuracy: 0.917708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.967509 Loss1: 1.940739 Loss2: 2.026770
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.905734 Loss1: 1.394025 Loss2: 1.511709
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.553204 Loss1: 1.057423 Loss2: 1.495781
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.279926 Loss1: 0.788709 Loss2: 1.491217
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.110810 Loss1: 0.629529 Loss2: 1.481281
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.836198 Loss1: 1.836505 Loss2: 1.999692
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.733823 Loss1: 1.260954 Loss2: 1.472869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.462037 Loss1: 1.015111 Loss2: 1.446926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.265104 Loss1: 0.806942 Loss2: 1.458162 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.084045 Loss1: 0.628225 Loss2: 1.455820 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.840657 Loss1: 0.349240 Loss2: 1.491416
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.002476 Loss1: 0.563829 Loss2: 1.438647
-(DefaultActor pid=3765) >> Training accuracy: 0.903320
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.977219 Loss1: 0.527743 Loss2: 1.449475
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.891906 Loss1: 0.443351 Loss2: 1.448555
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.893904 Loss1: 0.439459 Loss2: 1.454445
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.743070 Loss1: 0.303228 Loss2: 1.439842
-(DefaultActor pid=3764) >> Training accuracy: 0.940625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.906139 Loss1: 1.842239 Loss2: 2.063899
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.676515 Loss1: 1.223178 Loss2: 1.453336
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.361950 Loss1: 0.947814 Loss2: 1.414136
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.193904 Loss1: 0.764226 Loss2: 1.429678
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.996118 Loss1: 0.573853 Loss2: 1.422265
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.808421 Loss1: 0.396631 Loss2: 1.411790
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.790741 Loss1: 1.651152 Loss2: 2.139589
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.632510 Loss1: 1.120010 Loss2: 1.512500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.260984 Loss1: 0.776958 Loss2: 1.484027 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.021968 Loss1: 0.548639 Loss2: 1.473329 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962740
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.909619 Loss1: 0.437328 Loss2: 1.472291 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.789104 Loss1: 0.314030 Loss2: 1.475074 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.805312 Loss1: 0.340822 Loss2: 1.464489
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.818011 Loss1: 1.814994 Loss2: 2.003017
-(DefaultActor pid=3764) >> Training accuracy: 0.936458
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.765079 Loss1: 0.291770 Loss2: 1.473308
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.702426 Loss1: 1.233856 Loss2: 1.468570
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.397983 Loss1: 0.953032 Loss2: 1.444951
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.128689 Loss1: 0.673653 Loss2: 1.455036
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.059828 Loss1: 0.616384 Loss2: 1.443443
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.983439 Loss1: 0.534403 Loss2: 1.449036
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.954789 Loss1: 1.857525 Loss2: 2.097264
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.738251 Loss1: 1.233314 Loss2: 1.504937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.337653 Loss1: 0.870297 Loss2: 1.467356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.145056 Loss1: 0.664538 Loss2: 1.480518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.836053 Loss1: 0.392306 Loss2: 1.443747
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.075548 Loss1: 0.606506 Loss2: 1.469042
-(DefaultActor pid=3765) >> Training accuracy: 0.872070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.983420 Loss1: 0.519791 Loss2: 1.463628
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.909775 Loss1: 0.448389 Loss2: 1.461386
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.927574 Loss1: 0.460270 Loss2: 1.467305
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.855345 Loss1: 0.380258 Loss2: 1.475087
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.809071 Loss1: 1.735142 Loss2: 2.073928
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.763933 Loss1: 0.296451 Loss2: 1.467483
-(DefaultActor pid=3764) >> Training accuracy: 0.922917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.382158 Loss1: 0.866668 Loss2: 1.515490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.074706 Loss1: 0.575988 Loss2: 1.498718 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.965333 Loss1: 0.469450 Loss2: 1.495883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.899082 Loss1: 0.406233 Loss2: 1.492848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.873270 Loss1: 0.380025 Loss2: 1.493245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.775908 Loss1: 0.284813 Loss2: 1.491095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.803355 Loss1: 0.312457 Loss2: 1.490899 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.916016
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.874764 Loss1: 0.443899 Loss2: 1.430865 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.719887 Loss1: 0.294140 Loss2: 1.425747 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.644923 Loss1: 1.611742 Loss2: 2.033181 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.264006 Loss1: 0.799778 Loss2: 1.464228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.846452 Loss1: 1.760898 Loss2: 2.085554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.710073 Loss1: 1.208334 Loss2: 1.501738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.337613 Loss1: 0.856031 Loss2: 1.481582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.159153 Loss1: 0.687158 Loss2: 1.471996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.015275 Loss1: 0.554451 Loss2: 1.460824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.937618 Loss1: 0.465459 Loss2: 1.472159 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.913542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.854595 Loss1: 0.385634 Loss2: 1.468961 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.757144 Loss1: 0.295384 Loss2: 1.461760 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.923958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.836479 Loss1: 1.321751 Loss2: 1.514729 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.343057 Loss1: 0.842021 Loss2: 1.501036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.966444 Loss1: 1.859178 Loss2: 2.107266
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.199490 Loss1: 0.699341 Loss2: 1.500150
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.798121 Loss1: 1.262044 Loss2: 1.536077
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.118173 Loss1: 0.603941 Loss2: 1.514232
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.467926 Loss1: 0.969880 Loss2: 1.498046
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.086438 Loss1: 0.573694 Loss2: 1.512743
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.250807 Loss1: 0.753561 Loss2: 1.497246
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.022914 Loss1: 0.504646 Loss2: 1.518267
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.019049 Loss1: 0.534645 Loss2: 1.484404
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.942518 Loss1: 0.428584 Loss2: 1.513934
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.932794 Loss1: 0.438209 Loss2: 1.494585
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.965468 Loss1: 0.452707 Loss2: 1.512762
-(DefaultActor pid=3765) >> Training accuracy: 0.910417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.924243 Loss1: 0.435758 Loss2: 1.488485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.791561 Loss1: 0.316005 Loss2: 1.475557 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.928125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.646106 Loss1: 1.196060 Loss2: 1.450046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.134581 Loss1: 0.716969 Loss2: 1.417612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.942738 Loss1: 0.543198 Loss2: 1.399540 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.903111 Loss1: 0.509169 Loss2: 1.393942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.933040 Loss1: 0.536256 Loss2: 1.396784 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.842662 Loss1: 0.429216 Loss2: 1.413445 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.871334 Loss1: 0.473008 Loss2: 1.398326 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.762612 Loss1: 0.368174 Loss2: 1.394438 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.896875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.826252 Loss1: 0.394718 Loss2: 1.431534 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.919643
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.860965 Loss1: 1.805368 Loss2: 2.055597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.444749 Loss1: 0.974018 Loss2: 1.470732 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.185516 Loss1: 0.732569 Loss2: 1.452947
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.050073 Loss1: 2.036828 Loss2: 2.013245
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.901120 Loss1: 1.403850 Loss2: 1.497270 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.496592 Loss1: 1.039720 Loss2: 1.456872 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.283244 Loss1: 0.835523 Loss2: 1.447721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.058603 Loss1: 0.600709 Loss2: 1.457894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.976052 Loss1: 0.539936 Loss2: 1.436116 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.928125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.825402 Loss1: 0.379708 Loss2: 1.445693
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.904188 Loss1: 0.463273 Loss2: 1.440915
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.923208 Loss1: 0.480475 Loss2: 1.442733
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.903676 Loss1: 0.450258 Loss2: 1.453418
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.830194 Loss1: 0.377662 Loss2: 1.452532
-(DefaultActor pid=3764) >> Training accuracy: 0.890625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.964086 Loss1: 1.920647 Loss2: 2.043439
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.753348 Loss1: 1.274954 Loss2: 1.478394
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.447427 Loss1: 0.997281 Loss2: 1.450146
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.246491 Loss1: 0.800213 Loss2: 1.446278
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.898418 Loss1: 1.923086 Loss2: 1.975332
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.755110 Loss1: 1.311578 Loss2: 1.443531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.342258 Loss1: 0.922627 Loss2: 1.419631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.149249 Loss1: 0.730426 Loss2: 1.418823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.950663 Loss1: 0.526982 Loss2: 1.423681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.999985 Loss1: 0.579050 Loss2: 1.420935 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.889583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.892710 Loss1: 0.466152 Loss2: 1.426559 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.875597 Loss1: 0.441702 Loss2: 1.433895 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.889648
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.738838 Loss1: 1.298990 Loss2: 1.439849 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.230894 Loss1: 0.822166 Loss2: 1.408728 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.997198 Loss1: 0.582576 Loss2: 1.414623
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.844893 Loss1: 1.770095 Loss2: 2.074798
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.986798 Loss1: 0.566982 Loss2: 1.419816
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.680268 Loss1: 1.153618 Loss2: 1.526650
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.924880 Loss1: 0.499800 Loss2: 1.425080
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.495537 Loss1: 0.995127 Loss2: 1.500410
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.923861 Loss1: 0.499105 Loss2: 1.424756
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.176617 Loss1: 0.671437 Loss2: 1.505180
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.000458 Loss1: 0.519313 Loss2: 1.481145 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.729690 Loss1: 0.326353 Loss2: 1.403337
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.929157 Loss1: 0.448172 Loss2: 1.480984
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.954161 Loss1: 0.472473 Loss2: 1.481688
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.849491 Loss1: 0.366702 Loss2: 1.482789
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.856834 Loss1: 0.384498 Loss2: 1.472336
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.798109 Loss1: 0.315204 Loss2: 1.482904
-(DefaultActor pid=3764) >> Training accuracy: 0.942383
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.992043 Loss1: 1.925936 Loss2: 2.066107
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.757522 Loss1: 1.276263 Loss2: 1.481259
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.324008 Loss1: 0.860376 Loss2: 1.463632
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.108746 Loss1: 0.657064 Loss2: 1.451682
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.029675 Loss1: 0.577349 Loss2: 1.452326
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.903972 Loss1: 1.874248 Loss2: 2.029724
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.819546 Loss1: 1.308708 Loss2: 1.510838 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.416236 Loss1: 0.904967 Loss2: 1.511269 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.197295 Loss1: 0.701627 Loss2: 1.495668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.033742 Loss1: 0.551237 Loss2: 1.482505 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.915625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.947797 Loss1: 0.462633 Loss2: 1.485164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 2.025430 Loss1: 0.527857 Loss2: 1.497573 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.857556 Loss1: 0.359674 Loss2: 1.497882 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.917708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.760447 Loss1: 1.220452 Loss2: 1.539995 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.223870 Loss1: 0.721913 Loss2: 1.501957 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.148225 Loss1: 0.645359 Loss2: 1.502866
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.992678 Loss1: 1.849320 Loss2: 2.143358
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.017089 Loss1: 0.494099 Loss2: 1.522990
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.720168 Loss1: 1.184933 Loss2: 1.535235
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.488187 Loss1: 0.988080 Loss2: 1.500107
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.907735 Loss1: 0.406543 Loss2: 1.501192
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.228942 Loss1: 0.725107 Loss2: 1.503835
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.939794 Loss1: 0.447087 Loss2: 1.492708
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.864235 Loss1: 0.350815 Loss2: 1.513420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.886505 Loss1: 0.384140 Loss2: 1.502365 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.894531
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.778344 Loss1: 0.297429 Loss2: 1.480915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.820713 Loss1: 0.333471 Loss2: 1.487241 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.919643
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.926424 Loss1: 1.921604 Loss2: 2.004820
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.835801 Loss1: 1.364711 Loss2: 1.471090
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.483144 Loss1: 1.019159 Loss2: 1.463985
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.045386 Loss1: 1.968747 Loss2: 2.076639
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.198963 Loss1: 0.737527 Loss2: 1.461437
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.881532 Loss1: 1.352891 Loss2: 1.528641
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.081744 Loss1: 0.627247 Loss2: 1.454497
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.614134 Loss1: 1.107293 Loss2: 1.506841
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.015996 Loss1: 0.555956 Loss2: 1.460040
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.332360 Loss1: 0.811520 Loss2: 1.520839
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.977938 Loss1: 0.507862 Loss2: 1.470077
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.912056 Loss1: 0.456853 Loss2: 1.455203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.879739 Loss1: 0.418357 Loss2: 1.461382 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.814070 Loss1: 0.355216 Loss2: 1.458853 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.894531
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.887663 Loss1: 0.385719 Loss2: 1.501944 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.110715 Loss1: 2.016639 Loss2: 2.094075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.507711 Loss1: 1.001774 Loss2: 1.505937 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.285612 Loss1: 0.776719 Loss2: 1.508894
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.199659 Loss1: 2.087698 Loss2: 2.111961
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.931320 Loss1: 1.404776 Loss2: 1.526543
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.165092 Loss1: 0.657607 Loss2: 1.507485
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.509332 Loss1: 1.001745 Loss2: 1.507587
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.091048 Loss1: 0.572408 Loss2: 1.518640
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.226154 Loss1: 0.736765 Loss2: 1.489389
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.018818 Loss1: 0.507407 Loss2: 1.511411
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.000180 Loss1: 0.491662 Loss2: 1.508518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.973193 Loss1: 0.451043 Loss2: 1.522150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 2.016222 Loss1: 0.484923 Loss2: 1.531299 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.826042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.903888 Loss1: 0.409231 Loss2: 1.494656 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.915762 Loss1: 1.822445 Loss2: 2.093317 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.426304 Loss1: 1.016887 Loss2: 1.409417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.731978 Loss1: 1.656042 Loss2: 2.075936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.845069 Loss1: 0.437090 Loss2: 1.407978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.806218 Loss1: 0.408051 Loss2: 1.398167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.771355 Loss1: 0.368160 Loss2: 1.403195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.722957 Loss1: 0.326379 Loss2: 1.396579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.697108 Loss1: 0.300619 Loss2: 1.396489 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.923077
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.873746 Loss1: 0.405364 Loss2: 1.468382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.819897 Loss1: 0.354049 Loss2: 1.465848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.758442 Loss1: 0.295460 Loss2: 1.462981
-(DefaultActor pid=3764) >> Training accuracy: 0.926042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.094367 Loss1: 1.989475 Loss2: 2.104892
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.803102 Loss1: 1.295634 Loss2: 1.507468
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.350186 Loss1: 0.865580 Loss2: 1.484606
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.172823 Loss1: 0.698010 Loss2: 1.474813
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.121349 Loss1: 0.639031 Loss2: 1.482318
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.977203 Loss1: 1.891413 Loss2: 2.085790
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.033976 Loss1: 0.548375 Loss2: 1.485601
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.072983 Loss1: 0.578424 Loss2: 1.494558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.979521 Loss1: 0.485525 Loss2: 1.493996 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.879722 Loss1: 0.396065 Loss2: 1.483657 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.877519 Loss1: 0.401941 Loss2: 1.475578 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.939583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.025475 Loss1: 0.521892 Loss2: 1.503582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.838366 Loss1: 0.335185 Loss2: 1.503182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.817405 Loss1: 0.325105 Loss2: 1.492299
-(DefaultActor pid=3764) >> Training accuracy: 0.884375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.898334 Loss1: 1.821504 Loss2: 2.076831
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.755651 Loss1: 1.267366 Loss2: 1.488285
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.515237 Loss1: 1.035826 Loss2: 1.479411
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.245156 Loss1: 0.755547 Loss2: 1.489609
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.041519 Loss1: 0.567991 Loss2: 1.473528
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.776636 Loss1: 1.695424 Loss2: 2.081212
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.655034 Loss1: 1.161658 Loss2: 1.493376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.292687 Loss1: 0.818968 Loss2: 1.473719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.143827 Loss1: 0.672620 Loss2: 1.471206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.036158 Loss1: 0.576305 Loss2: 1.459853 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.976093 Loss1: 0.508729 Loss2: 1.467364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.834847 Loss1: 0.369631 Loss2: 1.465216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.782826 Loss1: 0.320835 Loss2: 1.461991 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.633091 Loss1: 1.108243 Loss2: 1.524848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.091996 Loss1: 0.616420 Loss2: 1.475576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.801312 Loss1: 1.758904 Loss2: 2.042408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.783272 Loss1: 1.290410 Loss2: 1.492862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.433251 Loss1: 0.952495 Loss2: 1.480756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.212689 Loss1: 0.729393 Loss2: 1.483296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.992884 Loss1: 0.520678 Loss2: 1.472206 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.918750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.967556 Loss1: 0.484479 Loss2: 1.483077 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.834104 Loss1: 0.342297 Loss2: 1.491807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.806657 Loss1: 0.328299 Loss2: 1.478359 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.905273
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.303035 Loss1: 0.841160 Loss2: 1.461875 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.997283 Loss1: 0.541461 Loss2: 1.455822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.943586 Loss1: 0.503467 Loss2: 1.440119
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.907618 Loss1: 1.878975 Loss2: 2.028643
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.807772 Loss1: 1.344458 Loss2: 1.463314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.466313 Loss1: 1.011295 Loss2: 1.455018 [repeated 2x across cluster]
-DEBUG flwr 2023-10-09 17:55:35,424 | server.py:236 | fit_round 47 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.280451 Loss1: 0.827987 Loss2: 1.452464 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.735691 Loss1: 0.301283 Loss2: 1.434408
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.125632 Loss1: 0.669212 Loss2: 1.456420
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.979516 Loss1: 0.531763 Loss2: 1.447753
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.943680 Loss1: 0.494714 Loss2: 1.448966
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.886436 Loss1: 0.430448 Loss2: 1.455989
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.856533 Loss1: 0.409217 Loss2: 1.447316
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.159525 Loss1: 1.946974 Loss2: 2.212552
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.866664 Loss1: 0.409651 Loss2: 1.457013
-(DefaultActor pid=3764) >> Training accuracy: 0.888542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.248781 Loss1: 0.765847 Loss2: 1.482934 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.936031 Loss1: 0.457029 Loss2: 1.479002 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.829076 Loss1: 0.356371 Loss2: 1.472705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.791014 Loss1: 0.311622 Loss2: 1.479391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.818821 Loss1: 0.337305 Loss2: 1.481516 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.898438
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.018500 Loss1: 0.607498 Loss2: 1.411001 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.940462 Loss1: 0.511134 Loss2: 1.429327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.765381 Loss1: 0.351932 Loss2: 1.413449 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 17:55:35,424][flwr][DEBUG] - fit_round 47 received 50 results and 0 failures
-INFO flwr 2023-10-09 17:56:17,586 | server.py:125 | fit progress: (47, 2.4710606367062455, {'accuracy': 0.4661}, 108285.36502193799)
->> Test accuracy: 0.466100
-[2023-10-09 17:56:17,586][flwr][INFO] - fit progress: (47, 2.4710606367062455, {'accuracy': 0.4661}, 108285.36502193799)
-DEBUG flwr 2023-10-09 17:56:17,587 | server.py:173 | evaluate_round 47: strategy sampled 50 clients (out of 50)
-[2023-10-09 17:56:17,587][flwr][DEBUG] - evaluate_round 47: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 18:05:26,013 | server.py:187 | evaluate_round 47 received 50 results and 0 failures
-[2023-10-09 18:05:26,013][flwr][DEBUG] - evaluate_round 47 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 18:05:26,013 | server.py:222 | fit_round 48: strategy sampled 50 clients (out of 50)
-[2023-10-09 18:05:26,013][flwr][DEBUG] - fit_round 48: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.830594 Loss1: 1.817152 Loss2: 2.013442 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.329906 Loss1: 0.869905 Loss2: 1.460001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.781638 Loss1: 1.728705 Loss2: 2.052933
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.242293 Loss1: 0.783992 Loss2: 1.458301
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.697314 Loss1: 1.205224 Loss2: 1.492091
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.085824 Loss1: 0.627570 Loss2: 1.458254
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.442687 Loss1: 0.973330 Loss2: 1.469357
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.015390 Loss1: 0.568895 Loss2: 1.446495
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.901157 Loss1: 0.449947 Loss2: 1.451210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.809175 Loss1: 0.353305 Loss2: 1.455870 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.775326 Loss1: 0.333259 Loss2: 1.442067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.771737 Loss1: 0.330756 Loss2: 1.440981 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.918945
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.860997 Loss1: 0.394040 Loss2: 1.466957 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.915625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.804182 Loss1: 1.760464 Loss2: 2.043719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.215219 Loss1: 0.782946 Loss2: 1.432274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.081206 Loss1: 0.649234 Loss2: 1.431972
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.095448 Loss1: 1.997119 Loss2: 2.098329
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.957283 Loss1: 0.525900 Loss2: 1.431382
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.834967 Loss1: 1.311913 Loss2: 1.523054
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.943010 Loss1: 0.503540 Loss2: 1.439470
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.492886 Loss1: 0.980595 Loss2: 1.512291
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.242039 Loss1: 0.732959 Loss2: 1.509080
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.886181 Loss1: 0.446927 Loss2: 1.439253
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.131418 Loss1: 0.634533 Loss2: 1.496885
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.852768 Loss1: 0.411978 Loss2: 1.440790
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.036459 Loss1: 0.545222 Loss2: 1.491237
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.848594 Loss1: 0.409525 Loss2: 1.439069
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.744959 Loss1: 0.303157 Loss2: 1.441802 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.931250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.903681 Loss1: 0.400077 Loss2: 1.503604 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.920759
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.819904 Loss1: 1.759644 Loss2: 2.060260 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.379137 Loss1: 0.892742 Loss2: 1.486395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.279823 Loss1: 0.808984 Loss2: 1.470839
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.107259 Loss1: 1.932818 Loss2: 2.174440
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.419771 Loss1: 0.974971 Loss2: 1.444800 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.909517 Loss1: 0.451398 Loss2: 1.458119 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.806704 Loss1: 0.353983 Loss2: 1.452721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.779409 Loss1: 0.322742 Loss2: 1.456667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.848549 Loss1: 0.390032 Loss2: 1.458517 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.915179
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.824701 Loss1: 0.374251 Loss2: 1.450450 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.928385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.844671 Loss1: 1.865004 Loss2: 1.979667
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.745175 Loss1: 1.294257 Loss2: 1.450917
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.322381 Loss1: 0.869636 Loss2: 1.452745
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.757709 Loss1: 1.692599 Loss2: 2.065111
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.204604 Loss1: 0.763146 Loss2: 1.441457
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.729045 Loss1: 1.233542 Loss2: 1.495503
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.029098 Loss1: 0.576527 Loss2: 1.452571
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.382740 Loss1: 0.881347 Loss2: 1.501393
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.984913 Loss1: 0.543324 Loss2: 1.441588
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.135986 Loss1: 0.656508 Loss2: 1.479477
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.963757 Loss1: 0.507972 Loss2: 1.455785
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.880847 Loss1: 0.431656 Loss2: 1.449191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.870943 Loss1: 0.419768 Loss2: 1.451175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.802913 Loss1: 0.346855 Loss2: 1.456058 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.933594
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.823220 Loss1: 0.358468 Loss2: 1.464752 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.909375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.798561 Loss1: 1.762435 Loss2: 2.036126 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.234406 Loss1: 0.792683 Loss2: 1.441723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.056675 Loss1: 0.623891 Loss2: 1.432785
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.867017 Loss1: 1.771205 Loss2: 2.095812
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.797256 Loss1: 1.248067 Loss2: 1.549189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.434174 Loss1: 0.904618 Loss2: 1.529556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.224191 Loss1: 0.701359 Loss2: 1.522832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.014938 Loss1: 0.505666 Loss2: 1.509272 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.998186 Loss1: 0.491769 Loss2: 1.506418 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.894792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.768811 Loss1: 0.334307 Loss2: 1.434504
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.943477 Loss1: 0.435889 Loss2: 1.507588
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.964578 Loss1: 0.454694 Loss2: 1.509884
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.994979 Loss1: 0.463755 Loss2: 1.531224
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.870643 Loss1: 0.339296 Loss2: 1.531347
-(DefaultActor pid=3764) >> Training accuracy: 0.909375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.903061 Loss1: 1.837661 Loss2: 2.065400
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.734923 Loss1: 1.239346 Loss2: 1.495578
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.455851 Loss1: 0.972977 Loss2: 1.482873
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.273701 Loss1: 0.791973 Loss2: 1.481728
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.021214 Loss1: 1.888217 Loss2: 2.132997
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.690994 Loss1: 1.150792 Loss2: 1.540201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.351597 Loss1: 0.859651 Loss2: 1.491947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.105534 Loss1: 0.623497 Loss2: 1.482036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.982591 Loss1: 0.517871 Loss2: 1.464720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.882549 Loss1: 0.414101 Loss2: 1.468448 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.904167
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.864567 Loss1: 0.391836 Loss2: 1.472731
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.810444 Loss1: 0.344850 Loss2: 1.465594
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.838253 Loss1: 0.374350 Loss2: 1.463903
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.830125 Loss1: 0.363925 Loss2: 1.466200
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.815570 Loss1: 0.337082 Loss2: 1.478488
-(DefaultActor pid=3764) >> Training accuracy: 0.920833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.748714 Loss1: 1.648471 Loss2: 2.100243
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.716092 Loss1: 1.154894 Loss2: 1.561198
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.353847 Loss1: 0.834596 Loss2: 1.519251
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.097506 Loss1: 0.587961 Loss2: 1.509545
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.000710 Loss1: 1.936558 Loss2: 2.064153
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.791416 Loss1: 1.298435 Loss2: 1.492981 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.533342 Loss1: 1.065582 Loss2: 1.467760 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.281580 Loss1: 0.802949 Loss2: 1.478631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.101388 Loss1: 0.633096 Loss2: 1.468293 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.944658 Loss1: 0.490707 Loss2: 1.453951 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.905208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.912798 Loss1: 0.456265 Loss2: 1.456533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.927696 Loss1: 0.455324 Loss2: 1.472372 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.913542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.933245 Loss1: 1.874601 Loss2: 2.058645 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.460671 Loss1: 0.976080 Loss2: 1.484591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.896571 Loss1: 1.839015 Loss2: 2.057556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.840147 Loss1: 1.319253 Loss2: 1.520894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.416213 Loss1: 0.924193 Loss2: 1.492020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.187452 Loss1: 0.698336 Loss2: 1.489116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.040202 Loss1: 0.567693 Loss2: 1.472508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.941366 Loss1: 0.462973 Loss2: 1.478392 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.892483 Loss1: 0.405499 Loss2: 1.486983 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.824767 Loss1: 0.334900 Loss2: 1.489867 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.907292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.836444 Loss1: 1.761508 Loss2: 2.074937
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.689491 Loss1: 1.188237 Loss2: 1.501253
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.251695 Loss1: 0.787745 Loss2: 1.463950
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.077010 Loss1: 0.627591 Loss2: 1.449419
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.682130 Loss1: 1.660789 Loss2: 2.021341
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.690105 Loss1: 1.234403 Loss2: 1.455702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.364245 Loss1: 0.930675 Loss2: 1.433569 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.091737 Loss1: 0.656809 Loss2: 1.434927 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.926994 Loss1: 0.504128 Loss2: 1.422867 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.861527 Loss1: 0.437648 Loss2: 1.423880 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.933333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.823024 Loss1: 0.396914 Loss2: 1.426110 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.772762 Loss1: 0.348375 Loss2: 1.424387 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.939583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.754562 Loss1: 1.719280 Loss2: 2.035281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.273858 Loss1: 0.828802 Loss2: 1.445056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.104134 Loss1: 0.655985 Loss2: 1.448149
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.836563 Loss1: 1.800260 Loss2: 2.036303
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.687599 Loss1: 1.223406 Loss2: 1.464193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.350506 Loss1: 0.893215 Loss2: 1.457291 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.200996 Loss1: 0.734692 Loss2: 1.466305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.976822 Loss1: 0.527858 Loss2: 1.448965 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.962868 Loss1: 0.508875 Loss2: 1.453993 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.909375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.885390 Loss1: 0.425559 Loss2: 1.459831 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.758997 Loss1: 0.309593 Loss2: 1.449404 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.922917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.969950 Loss1: 1.945011 Loss2: 2.024939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.521349 Loss1: 1.058302 Loss2: 1.463048 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.193559 Loss1: 0.739314 Loss2: 1.454245
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.764270 Loss1: 1.768237 Loss2: 1.996033
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.582512 Loss1: 1.074383 Loss2: 1.508130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.256749 Loss1: 0.778361 Loss2: 1.478388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.151393 Loss1: 0.669977 Loss2: 1.481417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.025004 Loss1: 0.556675 Loss2: 1.468328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.932490 Loss1: 0.459702 Loss2: 1.472788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.925000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.758424 Loss1: 0.297520 Loss2: 1.460904 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.716805 Loss1: 0.271913 Loss2: 1.444892 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.917969
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.861107 Loss1: 1.385275 Loss2: 1.475832 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.262984 Loss1: 0.795014 Loss2: 1.467970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.754451 Loss1: 1.838063 Loss2: 1.916389
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.135083 Loss1: 0.656402 Loss2: 1.478681
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.606195 Loss1: 1.167883 Loss2: 1.438311
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.016719 Loss1: 0.538205 Loss2: 1.478514
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.376609 Loss1: 0.959611 Loss2: 1.416998
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.903203 Loss1: 0.433288 Loss2: 1.469915
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.116365 Loss1: 0.698339 Loss2: 1.418026
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.946106 Loss1: 0.474241 Loss2: 1.471865
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.928109 Loss1: 0.530306 Loss2: 1.397803
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.913917 Loss1: 0.433531 Loss2: 1.480385
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.843927 Loss1: 0.451157 Loss2: 1.392770
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.814586 Loss1: 0.342263 Loss2: 1.472322
-(DefaultActor pid=3765) >> Training accuracy: 0.904297
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.821262 Loss1: 0.410534 Loss2: 1.410728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.726240 Loss1: 0.317651 Loss2: 1.408589 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.923828
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.844157 Loss1: 1.302836 Loss2: 1.541321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.259655 Loss1: 0.762301 Loss2: 1.497355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.757906 Loss1: 1.792762 Loss2: 1.965144 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.704165 Loss1: 1.268531 Loss2: 1.435634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.289374 Loss1: 0.874250 Loss2: 1.415124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.084639 Loss1: 0.684186 Loss2: 1.400453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.869026 Loss1: 0.371862 Loss2: 1.497164 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.917766 Loss1: 0.490671 Loss2: 1.427095 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.809239 Loss1: 0.396602 Loss2: 1.412637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.787618 Loss1: 0.382127 Loss2: 1.405491
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.985168 Loss1: 1.927201 Loss2: 2.057967
-(DefaultActor pid=3764) >> Training accuracy: 0.895833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.726137 Loss1: 1.252708 Loss2: 1.473429
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.450316 Loss1: 0.997125 Loss2: 1.453191
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.182769 Loss1: 0.732110 Loss2: 1.450658
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.017383 Loss1: 0.574446 Loss2: 1.442936
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.990082 Loss1: 1.920504 Loss2: 2.069578
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.982594 Loss1: 0.541929 Loss2: 1.440664
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.731120 Loss1: 1.208384 Loss2: 1.522736
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.924092 Loss1: 0.470888 Loss2: 1.453204
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.414717 Loss1: 0.919157 Loss2: 1.495560
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.906707 Loss1: 0.447544 Loss2: 1.459163
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.211063 Loss1: 0.717037 Loss2: 1.494026
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.799792 Loss1: 0.344332 Loss2: 1.455460
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.981757 Loss1: 0.510599 Loss2: 1.471158
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.826107 Loss1: 0.377487 Loss2: 1.448619
-(DefaultActor pid=3765) >> Training accuracy: 0.936458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.922898 Loss1: 0.440260 Loss2: 1.482638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.865627 Loss1: 0.393320 Loss2: 1.472307 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.867782 Loss1: 0.383942 Loss2: 1.483840
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.982774 Loss1: 1.810626 Loss2: 2.172148
-(DefaultActor pid=3764) >> Training accuracy: 0.887500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.721830 Loss1: 1.185350 Loss2: 1.536480
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.487449 Loss1: 0.985122 Loss2: 1.502327
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.157832 Loss1: 0.653273 Loss2: 1.504559
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.099693 Loss1: 0.590808 Loss2: 1.508884
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.995865 Loss1: 0.484131 Loss2: 1.511735
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.735149 Loss1: 1.715854 Loss2: 2.019295
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.674288 Loss1: 1.196735 Loss2: 1.477553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.433431 Loss1: 0.979195 Loss2: 1.454236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.789551 Loss1: 0.281594 Loss2: 1.507957 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.899038
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.948676 Loss1: 0.486101 Loss2: 1.462575 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.803740 Loss1: 0.347211 Loss2: 1.456529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.815418 Loss1: 1.782877 Loss2: 2.032540
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.785266 Loss1: 0.330755 Loss2: 1.454511
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.754052 Loss1: 1.267311 Loss2: 1.486741
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.743101 Loss1: 0.284645 Loss2: 1.458456
-(DefaultActor pid=3764) >> Training accuracy: 0.892708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.239766 Loss1: 0.772626 Loss2: 1.467141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.930660 Loss1: 0.485468 Loss2: 1.445193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.968069 Loss1: 0.519830 Loss2: 1.448238
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.819484 Loss1: 1.801029 Loss2: 2.018455
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.888933 Loss1: 0.426403 Loss2: 1.462530
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.859665 Loss1: 1.351579 Loss2: 1.508085
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.542196 Loss1: 1.019617 Loss2: 1.522580 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.901042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.812196 Loss1: 0.366317 Loss2: 1.445879
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.260051 Loss1: 0.769700 Loss2: 1.490351
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.059583 Loss1: 0.576157 Loss2: 1.483426
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.055609 Loss1: 0.564134 Loss2: 1.491475
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.952746 Loss1: 0.459830 Loss2: 1.492916
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.920803 Loss1: 0.431690 Loss2: 1.489113
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.897504 Loss1: 1.908033 Loss2: 1.989471
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.727095 Loss1: 1.250851 Loss2: 1.476245 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.878906
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.397256 Loss1: 0.950843 Loss2: 1.446413 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.996700 Loss1: 0.549852 Loss2: 1.446849 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.850452 Loss1: 0.428635 Loss2: 1.421817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.812258 Loss1: 0.383993 Loss2: 1.428266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.800199 Loss1: 0.375407 Loss2: 1.424792 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.722896 Loss1: 0.303993 Loss2: 1.418904 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.948958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.085280 Loss1: 0.574903 Loss2: 1.510377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.025838 Loss1: 0.527919 Loss2: 1.497919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.885829 Loss1: 0.386192 Loss2: 1.499638
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.713502 Loss1: 1.751519 Loss2: 1.961983
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.685918 Loss1: 1.233871 Loss2: 1.452046 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.908333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.287842 Loss1: 0.840942 Loss2: 1.446900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.009823 Loss1: 0.578432 Loss2: 1.431391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.829469 Loss1: 0.397369 Loss2: 1.432101 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.782093 Loss1: 0.345568 Loss2: 1.436526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.859593 Loss1: 0.432468 Loss2: 1.427125 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.778208 Loss1: 0.339794 Loss2: 1.438414 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.896484
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.064723 Loss1: 0.546452 Loss2: 1.518271 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.921803 Loss1: 0.399013 Loss2: 1.522790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.819710 Loss1: 1.836104 Loss2: 1.983606
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.965000 Loss1: 0.446564 Loss2: 1.518436
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.781903 Loss1: 1.349605 Loss2: 1.432298
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.907247 Loss1: 0.377081 Loss2: 1.530167
-(DefaultActor pid=3764) >> Training accuracy: 0.909375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.128203 Loss1: 0.702503 Loss2: 1.425700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.811209 Loss1: 0.409827 Loss2: 1.401382 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.779655 Loss1: 0.380167 Loss2: 1.399488
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.773786 Loss1: 1.718201 Loss2: 2.055585
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.625759 Loss1: 1.159605 Loss2: 1.466155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.438343 Loss1: 0.977461 Loss2: 1.460883 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.909375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.733872 Loss1: 0.322888 Loss2: 1.410984
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.110461 Loss1: 0.645869 Loss2: 1.464592
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.031464 Loss1: 0.576166 Loss2: 1.455298
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.084646 Loss1: 0.621229 Loss2: 1.463417
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.986846 Loss1: 0.494959 Loss2: 1.491887
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.869384 Loss1: 0.400535 Loss2: 1.468848
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.688607 Loss1: 1.649808 Loss2: 2.038799
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.829013 Loss1: 0.375392 Loss2: 1.453621
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.777978 Loss1: 0.321914 Loss2: 1.456064
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.657946 Loss1: 1.173812 Loss2: 1.484134
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.411220 Loss1: 0.912536 Loss2: 1.498684
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.137975 Loss1: 0.653644 Loss2: 1.484331
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.041690 Loss1: 0.566388 Loss2: 1.475302
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.957898 Loss1: 0.483497 Loss2: 1.474401
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.878344 Loss1: 1.867726 Loss2: 2.010617
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.871725 Loss1: 0.405565 Loss2: 1.466159
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.824371 Loss1: 0.359462 Loss2: 1.464909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.814450 Loss1: 0.348038 Loss2: 1.466412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.762887 Loss1: 0.295932 Loss2: 1.466955 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.927734
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.973774 Loss1: 0.545535 Loss2: 1.428239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.813831 Loss1: 0.385813 Loss2: 1.428018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.839088 Loss1: 0.410331 Loss2: 1.428757
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.728093 Loss1: 1.717195 Loss2: 2.010897
-(DefaultActor pid=3764) >> Training accuracy: 0.920833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.649995 Loss1: 1.175223 Loss2: 1.474773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.128168 Loss1: 0.681192 Loss2: 1.446976 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.990850 Loss1: 0.548816 Loss2: 1.442034 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.867143 Loss1: 0.422855 Loss2: 1.444288 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.785481 Loss1: 0.351373 Loss2: 1.434108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.763596 Loss1: 0.334808 Loss2: 1.428788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.721014 Loss1: 0.298215 Loss2: 1.422800 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.947064 Loss1: 0.484302 Loss2: 1.462763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.834595 Loss1: 0.374806 Loss2: 1.459788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.898422 Loss1: 1.917922 Loss2: 1.980501 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.858333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.302137 Loss1: 0.889129 Loss2: 1.413008 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.955195 Loss1: 0.531169 Loss2: 1.424027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.798579 Loss1: 0.387661 Loss2: 1.410918
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.821791 Loss1: 1.759262 Loss2: 2.062530
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.789217 Loss1: 1.305216 Loss2: 1.484001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.372163 Loss1: 0.889478 Loss2: 1.482685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.050547 Loss1: 0.587501 Loss2: 1.463046 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.923958
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.799424 Loss1: 0.365783 Loss2: 1.433641
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.957686 Loss1: 0.514580 Loss2: 1.443106
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.902692 Loss1: 0.453989 Loss2: 1.448702
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.818991 Loss1: 0.375073 Loss2: 1.443918
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.839224 Loss1: 0.398107 Loss2: 1.441116
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.823992 Loss1: 0.369880 Loss2: 1.454113
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.633348 Loss1: 1.591559 Loss2: 2.041789
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.695894 Loss1: 0.253920 Loss2: 1.441975
-(DefaultActor pid=3764) >> Training accuracy: 0.940625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.277638 Loss1: 0.853576 Loss2: 1.424062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.949925 Loss1: 0.537436 Loss2: 1.412489 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.814146 Loss1: 0.408511 Loss2: 1.405635
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.720557 Loss1: 1.742177 Loss2: 1.978380
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.548950 Loss1: 1.082676 Loss2: 1.466274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.247996 Loss1: 0.790594 Loss2: 1.457401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.145912 Loss1: 0.688550 Loss2: 1.457361 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.940625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.906578 Loss1: 0.465496 Loss2: 1.441082 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.843574 Loss1: 0.400684 Loss2: 1.442890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.803314 Loss1: 0.354272 Loss2: 1.449042 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.771487 Loss1: 0.328979 Loss2: 1.442507 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.909007
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.126207 Loss1: 0.655138 Loss2: 1.471069 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.945059 Loss1: 0.479985 Loss2: 1.465074 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.015929 Loss1: 1.886195 Loss2: 2.129734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.737340 Loss1: 0.282302 Loss2: 1.455038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.819769 Loss1: 0.372307 Loss2: 1.447462 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.882292
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-09 18:34:03,017 | server.py:236 | fit_round 48 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.159705 Loss1: 0.612960 Loss2: 1.546745 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.010095 Loss1: 0.480707 Loss2: 1.529389 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.927513 Loss1: 0.392145 Loss2: 1.535368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.954847 Loss1: 0.415742 Loss2: 1.539105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.904946 Loss1: 0.346743 Loss2: 1.558203 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.916016
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.843273 Loss1: 0.388470 Loss2: 1.454804 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.732851 Loss1: 0.281142 Loss2: 1.451709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.967174 Loss1: 1.953680 Loss2: 2.013495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.909856
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.471255 Loss1: 1.035341 Loss2: 1.435914 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.146531 Loss1: 0.699619 Loss2: 1.446912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.947476 Loss1: 0.516752 Loss2: 1.430724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.894487 Loss1: 0.451715 Loss2: 1.442772 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.932292
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 18:34:03,017][flwr][DEBUG] - fit_round 48 received 50 results and 0 failures
-INFO flwr 2023-10-09 18:34:44,478 | server.py:125 | fit progress: (48, 2.4517499086575008, {'accuracy': 0.47}, 110592.256799552)
->> Test accuracy: 0.470000
-[2023-10-09 18:34:44,478][flwr][INFO] - fit progress: (48, 2.4517499086575008, {'accuracy': 0.47}, 110592.256799552)
-DEBUG flwr 2023-10-09 18:34:44,479 | server.py:173 | evaluate_round 48: strategy sampled 50 clients (out of 50)
-[2023-10-09 18:34:44,479][flwr][DEBUG] - evaluate_round 48: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 18:43:47,396 | server.py:187 | evaluate_round 48 received 50 results and 0 failures
-[2023-10-09 18:43:47,396][flwr][DEBUG] - evaluate_round 48 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 18:43:47,396 | server.py:222 | fit_round 49: strategy sampled 50 clients (out of 50)
-[2023-10-09 18:43:47,396][flwr][DEBUG] - fit_round 49: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.819391 Loss1: 1.772309 Loss2: 2.047082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.364190 Loss1: 0.894387 Loss2: 1.469803 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.177684 Loss1: 0.695625 Loss2: 1.482059
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.892109 Loss1: 1.834559 Loss2: 2.057550
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.069691 Loss1: 0.598580 Loss2: 1.471111
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.736827 Loss1: 1.266804 Loss2: 1.470023
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.931409 Loss1: 0.456415 Loss2: 1.474994
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.396915 Loss1: 0.933263 Loss2: 1.463651
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.896259 Loss1: 0.432316 Loss2: 1.463943
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.069763 Loss1: 0.619684 Loss2: 1.450080
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.926131 Loss1: 0.455847 Loss2: 1.470284
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.990005 Loss1: 0.543428 Loss2: 1.446577
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.849525 Loss1: 0.375996 Loss2: 1.473529
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.997618 Loss1: 0.541633 Loss2: 1.455986
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.758623 Loss1: 0.297912 Loss2: 1.460711
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.983864 Loss1: 0.520960 Loss2: 1.462903
-(DefaultActor pid=3765) >> Training accuracy: 0.916667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.835935 Loss1: 0.367475 Loss2: 1.468460
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.825995 Loss1: 0.372642 Loss2: 1.453352
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.738976 Loss1: 0.286640 Loss2: 1.452336
-(DefaultActor pid=3764) >> Training accuracy: 0.926042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.052822 Loss1: 1.896919 Loss2: 2.155904
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.901300 Loss1: 1.327927 Loss2: 1.573373
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.432939 Loss1: 0.904318 Loss2: 1.528622
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.225456 Loss1: 0.710030 Loss2: 1.515426
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.568474 Loss1: 1.567856 Loss2: 2.000619
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.075930 Loss1: 0.567060 Loss2: 1.508870
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.504737 Loss1: 1.048666 Loss2: 1.456071
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.999587 Loss1: 0.500837 Loss2: 1.498750
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.204251 Loss1: 0.765842 Loss2: 1.438409
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.968039 Loss1: 0.453767 Loss2: 1.514272
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.992979 Loss1: 0.566141 Loss2: 1.426837
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.916545 Loss1: 0.409117 Loss2: 1.507427
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.921979 Loss1: 0.485537 Loss2: 1.436443
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.910556 Loss1: 0.409857 Loss2: 1.500699
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.863332 Loss1: 0.429595 Loss2: 1.433737
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.815043 Loss1: 0.306427 Loss2: 1.508616
-(DefaultActor pid=3765) >> Training accuracy: 0.931250
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.849906 Loss1: 0.422495 Loss2: 1.427411
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.816494 Loss1: 0.383944 Loss2: 1.432550
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.710030 Loss1: 0.285400 Loss2: 1.424629
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.693803 Loss1: 0.273328 Loss2: 1.420474
-(DefaultActor pid=3764) >> Training accuracy: 0.923958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.107934 Loss1: 1.869652 Loss2: 2.238282
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.770318 Loss1: 1.187308 Loss2: 1.583011
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.377339 Loss1: 0.859113 Loss2: 1.518226
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.181520 Loss1: 0.675178 Loss2: 1.506343
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.175128 Loss1: 0.663729 Loss2: 1.511399
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.053352 Loss1: 0.523698 Loss2: 1.529653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.917381 Loss1: 0.398768 Loss2: 1.518614 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.934585 Loss1: 0.423153 Loss2: 1.511432 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.859511 Loss1: 0.347403 Loss2: 1.512109
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.165918 Loss1: 0.724013 Loss2: 1.441904
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.829275 Loss1: 0.316364 Loss2: 1.512911
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.978777 Loss1: 0.545218 Loss2: 1.433559
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.945822 Loss1: 0.519112 Loss2: 1.426710
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.962144 Loss1: 0.524211 Loss2: 1.437933
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.924294 Loss1: 0.468956 Loss2: 1.455338
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.837254 Loss1: 0.392062 Loss2: 1.445192
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.781606 Loss1: 1.802721 Loss2: 1.978885
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.723604 Loss1: 0.297892 Loss2: 1.425712
-(DefaultActor pid=3764) >> Training accuracy: 0.910417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.337684 Loss1: 0.887783 Loss2: 1.449901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.078445 Loss1: 0.662994 Loss2: 1.415450 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.917680 Loss1: 0.489976 Loss2: 1.427704
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.939001 Loss1: 1.834331 Loss2: 2.104670
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.711564 Loss1: 1.186600 Loss2: 1.524964 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.314018 Loss1: 0.792178 Loss2: 1.521841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.231441 Loss1: 0.709565 Loss2: 1.521876 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.116935 Loss1: 0.605610 Loss2: 1.511324 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.955186 Loss1: 0.439143 Loss2: 1.516043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.853223 Loss1: 0.333017 Loss2: 1.520206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.808495 Loss1: 0.308895 Loss2: 1.499600 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.907292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.423108 Loss1: 0.959724 Loss2: 1.463384 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.014388 Loss1: 0.566586 Loss2: 1.447802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.972791 Loss1: 1.722331 Loss2: 2.250460
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.975181 Loss1: 0.526173 Loss2: 1.449008
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.982159 Loss1: 0.517839 Loss2: 1.464320 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.948865 Loss1: 0.485130 Loss2: 1.463735 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.933739 Loss1: 0.457255 Loss2: 1.476484 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.996646 Loss1: 0.507459 Loss2: 1.489188 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.926758
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.849663 Loss1: 0.353190 Loss2: 1.496473 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.896887 Loss1: 0.406484 Loss2: 1.490403 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.908654
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.794487 Loss1: 1.672733 Loss2: 2.121753
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.748384 Loss1: 1.205485 Loss2: 1.542899
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.454752 Loss1: 0.930137 Loss2: 1.524615
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.194549 Loss1: 0.678129 Loss2: 1.516420
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.886794 Loss1: 1.758551 Loss2: 2.128243
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.008239 Loss1: 0.499961 Loss2: 1.508279
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.693637 Loss1: 1.164962 Loss2: 1.528675
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.970792 Loss1: 0.466982 Loss2: 1.503810
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.346350 Loss1: 0.858522 Loss2: 1.487828
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.927583 Loss1: 0.425087 Loss2: 1.502497
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.127902 Loss1: 0.638569 Loss2: 1.489332
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.069187 Loss1: 0.578212 Loss2: 1.490975
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.903037 Loss1: 0.393869 Loss2: 1.509167
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.918097 Loss1: 0.441632 Loss2: 1.476465
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.775383 Loss1: 0.267074 Loss2: 1.508309
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.809107 Loss1: 0.339439 Loss2: 1.469669
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.753189 Loss1: 0.264714 Loss2: 1.488475
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.848365 Loss1: 0.380211 Loss2: 1.468154 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.914062
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.145518 Loss1: 2.040906 Loss2: 2.104611 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.436837 Loss1: 0.955314 Loss2: 1.481523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.839056 Loss1: 1.719571 Loss2: 2.119486 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.648902 Loss1: 1.119675 Loss2: 1.529227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.315580 Loss1: 0.804288 Loss2: 1.511292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.125560 Loss1: 0.623613 Loss2: 1.501947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.047830 Loss1: 0.552524 Loss2: 1.495306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.017305 Loss1: 0.523101 Loss2: 1.494204 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.905134
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.872149 Loss1: 0.372738 Loss2: 1.499411 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.834448 Loss1: 0.325970 Loss2: 1.508478 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.913542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.635769 Loss1: 1.148082 Loss2: 1.487687 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.139620 Loss1: 0.679336 Loss2: 1.460284 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.983766 Loss1: 0.522961 Loss2: 1.460805 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.935619 Loss1: 0.474868 Loss2: 1.460751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.963840 Loss1: 0.512340 Loss2: 1.451500 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.932863 Loss1: 0.464701 Loss2: 1.468162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.812961 Loss1: 0.351601 Loss2: 1.461360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.002708 Loss1: 0.499815 Loss2: 1.502893 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.914522
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.846696 Loss1: 0.351777 Loss2: 1.494919 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.910156
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.889499 Loss1: 1.857135 Loss2: 2.032365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.447207 Loss1: 0.968821 Loss2: 1.478386 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.925315 Loss1: 1.829580 Loss2: 2.095734
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.182529 Loss1: 0.707313 Loss2: 1.475217
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.888977 Loss1: 1.351875 Loss2: 1.537103
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.011596 Loss1: 0.540458 Loss2: 1.471138
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.612109 Loss1: 1.072605 Loss2: 1.539504
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.029526 Loss1: 0.562652 Loss2: 1.466875
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.304412 Loss1: 0.776535 Loss2: 1.527877
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.888848 Loss1: 0.421503 Loss2: 1.467345
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.831743 Loss1: 0.365248 Loss2: 1.466495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.865280 Loss1: 0.390034 Loss2: 1.475246 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.837671 Loss1: 0.367810 Loss2: 1.469862 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.905273
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.949340 Loss1: 0.420843 Loss2: 1.528497 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.910417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.981290 Loss1: 1.937746 Loss2: 2.043545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.338007 Loss1: 0.894076 Loss2: 1.443931 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.130657 Loss1: 0.682263 Loss2: 1.448393
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.862665 Loss1: 1.820834 Loss2: 2.041831
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.757667 Loss1: 1.245566 Loss2: 1.512101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.415062 Loss1: 0.910553 Loss2: 1.504508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.167100 Loss1: 0.666454 Loss2: 1.500646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.997200 Loss1: 0.513795 Loss2: 1.483404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.891297 Loss1: 0.417112 Loss2: 1.474185 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.857292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.868442 Loss1: 0.417234 Loss2: 1.451207
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.928418 Loss1: 0.452983 Loss2: 1.475435
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.892102 Loss1: 0.411587 Loss2: 1.480515
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.877242 Loss1: 0.399100 Loss2: 1.478142
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.831554 Loss1: 0.351400 Loss2: 1.480154
-(DefaultActor pid=3764) >> Training accuracy: 0.892708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.777422 Loss1: 1.734444 Loss2: 2.042978
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.659290 Loss1: 1.213569 Loss2: 1.445721
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.399409 Loss1: 0.995967 Loss2: 1.403442
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.180801 Loss1: 0.751046 Loss2: 1.429754
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.967651 Loss1: 0.557932 Loss2: 1.409720
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.890835 Loss1: 0.487630 Loss2: 1.403205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.845165 Loss1: 0.433896 Loss2: 1.411270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.784277 Loss1: 0.378899 Loss2: 1.405379 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.683405 Loss1: 0.281229 Loss2: 1.402176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.648860 Loss1: 0.251162 Loss2: 1.397698 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.950721
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.885909 Loss1: 0.412608 Loss2: 1.473301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.838538 Loss1: 0.355969 Loss2: 1.482568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.838631 Loss1: 0.360405 Loss2: 1.478226
-(DefaultActor pid=3764) >> Training accuracy: 0.915625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.060241 Loss1: 1.920024 Loss2: 2.140217
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.830524 Loss1: 1.292095 Loss2: 1.538430
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.400109 Loss1: 0.873446 Loss2: 1.526663
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.163237 Loss1: 0.647554 Loss2: 1.515683
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.025434 Loss1: 0.513998 Loss2: 1.511436
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.784777 Loss1: 1.778577 Loss2: 2.006201
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.937610 Loss1: 0.422301 Loss2: 1.515310
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.609939 Loss1: 1.161074 Loss2: 1.448865
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.888949 Loss1: 0.376211 Loss2: 1.512738
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.356051 Loss1: 0.926373 Loss2: 1.429678
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.861261 Loss1: 0.353568 Loss2: 1.507693
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.148863 Loss1: 0.711255 Loss2: 1.437608
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.819793 Loss1: 0.305745 Loss2: 1.514049
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.049762 Loss1: 0.617560 Loss2: 1.432202
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.797234 Loss1: 0.290749 Loss2: 1.506484
-(DefaultActor pid=3765) >> Training accuracy: 0.939583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.880651 Loss1: 0.452865 Loss2: 1.427786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.760280 Loss1: 0.332854 Loss2: 1.427426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.742169 Loss1: 0.315813 Loss2: 1.426356
-(DefaultActor pid=3764) >> Training accuracy: 0.942708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.809150 Loss1: 1.809652 Loss2: 1.999497
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.639333 Loss1: 1.182770 Loss2: 1.456563
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.307248 Loss1: 0.855242 Loss2: 1.452006
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.097864 Loss1: 0.671795 Loss2: 1.426070
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.956063 Loss1: 0.541361 Loss2: 1.414702
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.892142 Loss1: 0.479413 Loss2: 1.412728
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.879903 Loss1: 1.837348 Loss2: 2.042555
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.842456 Loss1: 0.427129 Loss2: 1.415326
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.693735 Loss1: 1.181090 Loss2: 1.512646
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.802287 Loss1: 0.381308 Loss2: 1.420979
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.305022 Loss1: 0.805416 Loss2: 1.499606
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.835903 Loss1: 0.415479 Loss2: 1.420424
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.171347 Loss1: 0.686343 Loss2: 1.485004
-(DefaultActor pid=3765) >> Training accuracy: 0.893750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.883577 Loss1: 0.446279 Loss2: 1.437297
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.114100 Loss1: 0.617002 Loss2: 1.497099
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.985255 Loss1: 0.477197 Loss2: 1.508058
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.932910 Loss1: 0.444298 Loss2: 1.488612
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.889735 Loss1: 0.393930 Loss2: 1.495804
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.815776 Loss1: 0.323208 Loss2: 1.492568
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.053208 Loss1: 1.930494 Loss2: 2.122714
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.746357 Loss1: 0.260137 Loss2: 1.486221
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.836065 Loss1: 1.284664 Loss2: 1.551402
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.564422 Loss1: 1.032102 Loss2: 1.532321
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.343305 Loss1: 0.781225 Loss2: 1.562080
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.183831 Loss1: 0.651765 Loss2: 1.532065
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.118734 Loss1: 0.586611 Loss2: 1.532123
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.728281 Loss1: 1.717550 Loss2: 2.010731
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.009973 Loss1: 0.472712 Loss2: 1.537261
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.592749 Loss1: 1.148210 Loss2: 1.444539
-(DefaultActor pid=3765) Epoch: 7 Loss: 2.016122 Loss1: 0.489547 Loss2: 1.526576
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.353014 Loss1: 0.895459 Loss2: 1.457555
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.969054 Loss1: 0.421531 Loss2: 1.547523
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.070380 Loss1: 0.624642 Loss2: 1.445738
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.893564 Loss1: 0.356273 Loss2: 1.537292
-(DefaultActor pid=3765) >> Training accuracy: 0.926042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.854711 Loss1: 0.421253 Loss2: 1.433458 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.768847 Loss1: 0.334415 Loss2: 1.434432 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.772625 Loss1: 0.332961 Loss2: 1.439664
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.754133 Loss1: 1.715079 Loss2: 2.039054
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.682263 Loss1: 0.249596 Loss2: 1.432667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.613936 Loss1: 1.116017 Loss2: 1.497919
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.250479 Loss1: 0.776936 Loss2: 1.473543
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.032738 Loss1: 0.577159 Loss2: 1.455578
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.921414 Loss1: 0.467442 Loss2: 1.453972
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.901035 Loss1: 0.449993 Loss2: 1.451042
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.797469 Loss1: 1.736533 Loss2: 2.060936
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.687813 Loss1: 1.224018 Loss2: 1.463795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.328820 Loss1: 0.883716 Loss2: 1.445103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.135840 Loss1: 0.690740 Loss2: 1.445099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.816417 Loss1: 0.356920 Loss2: 1.459497
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.019743 Loss1: 0.578408 Loss2: 1.441334
-(DefaultActor pid=3765) >> Training accuracy: 0.885742
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.941925 Loss1: 0.504050 Loss2: 1.437875
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.812783 Loss1: 0.380387 Loss2: 1.432395
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.777089 Loss1: 0.353009 Loss2: 1.424080
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.784633 Loss1: 0.344741 Loss2: 1.439892
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.833789 Loss1: 1.807568 Loss2: 2.026221
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.753252 Loss1: 0.320530 Loss2: 1.432722
-(DefaultActor pid=3764) >> Training accuracy: 0.928125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.318264 Loss1: 0.894911 Loss2: 1.423353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.073906 Loss1: 0.642579 Loss2: 1.431327 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.012488 Loss1: 0.564832 Loss2: 1.447656
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.879287 Loss1: 1.796202 Loss2: 2.083085
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.841462 Loss1: 0.392905 Loss2: 1.448558
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.810514 Loss1: 1.284314 Loss2: 1.526200
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.416555 Loss1: 0.911191 Loss2: 1.505364
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.760922 Loss1: 0.334557 Loss2: 1.426365
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.727930 Loss1: 0.299459 Loss2: 1.428471
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.167243 Loss1: 0.664977 Loss2: 1.502267
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.781315 Loss1: 0.356137 Loss2: 1.425178
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.067067 Loss1: 0.566883 Loss2: 1.500184
-(DefaultActor pid=3765) >> Training accuracy: 0.920833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.979922 Loss1: 0.488803 Loss2: 1.491119
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.923391 Loss1: 0.435876 Loss2: 1.487515
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.965804 Loss1: 0.467193 Loss2: 1.498610
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.877246 Loss1: 0.376554 Loss2: 1.500691
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.793838 Loss1: 0.300231 Loss2: 1.493607
-(DefaultActor pid=3764) >> Training accuracy: 0.954167
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.816495 Loss1: 1.839122 Loss2: 1.977373
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.633886 Loss1: 1.143891 Loss2: 1.489994
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.201831 Loss1: 0.756322 Loss2: 1.445509
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.987176 Loss1: 0.545198 Loss2: 1.441978
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.887103 Loss1: 0.463593 Loss2: 1.423509
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.989428 Loss1: 1.869516 Loss2: 2.119912
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.782758 Loss1: 1.254419 Loss2: 1.528339 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.497876 Loss1: 0.964497 Loss2: 1.533379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.287658 Loss1: 0.772770 Loss2: 1.514888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.048663 Loss1: 0.534773 Loss2: 1.513890 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.855469
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.759847 Loss1: 0.325377 Loss2: 1.434470
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.942638 Loss1: 0.436075 Loss2: 1.506564
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.926714 Loss1: 0.406452 Loss2: 1.520262
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.932887 Loss1: 0.412576 Loss2: 1.520311
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.847502 Loss1: 0.329580 Loss2: 1.517922
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.812213 Loss1: 0.307543 Loss2: 1.504671
-(DefaultActor pid=3764) >> Training accuracy: 0.918750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.897015 Loss1: 1.720615 Loss2: 2.176400
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.530861 Loss1: 0.966086 Loss2: 1.564775
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.339680 Loss1: 0.812930 Loss2: 1.526750
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.154120 Loss1: 0.621139 Loss2: 1.532981
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.019459 Loss1: 0.506752 Loss2: 1.512707
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.730448 Loss1: 1.674124 Loss2: 2.056324
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.934858 Loss1: 0.424437 Loss2: 1.510420
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.652745 Loss1: 1.176063 Loss2: 1.476683
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.919575 Loss1: 0.410510 Loss2: 1.509065
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.263836 Loss1: 0.788379 Loss2: 1.475457
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.871640 Loss1: 0.345381 Loss2: 1.526259
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.003111 Loss1: 0.547684 Loss2: 1.455427
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.869820 Loss1: 0.365829 Loss2: 1.503991
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.925142 Loss1: 0.481587 Loss2: 1.443554
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.844803 Loss1: 0.326549 Loss2: 1.518254
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.899832 Loss1: 0.451246 Loss2: 1.448586
-(DefaultActor pid=3765) >> Training accuracy: 0.885417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.754661 Loss1: 0.303770 Loss2: 1.450891
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.737451 Loss1: 0.294835 Loss2: 1.442615
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.734596 Loss1: 0.287280 Loss2: 1.447316
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.794315 Loss1: 0.348733 Loss2: 1.445582
-(DefaultActor pid=3764) >> Training accuracy: 0.920833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.782316 Loss1: 1.700201 Loss2: 2.082115
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.684542 Loss1: 1.192204 Loss2: 1.492338
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.320112 Loss1: 0.830671 Loss2: 1.489441
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.124356 Loss1: 0.649020 Loss2: 1.475336
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.036367 Loss1: 0.563560 Loss2: 1.472806
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.928078 Loss1: 0.456772 Loss2: 1.471306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.867641 Loss1: 0.393250 Loss2: 1.474391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.816783 Loss1: 0.358658 Loss2: 1.458125 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.755382 Loss1: 0.295851 Loss2: 1.459530 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.789123 Loss1: 0.324632 Loss2: 1.464490 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.919792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.877789 Loss1: 0.395163 Loss2: 1.482626 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.807729 Loss1: 0.334651 Loss2: 1.473077 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.905208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.796115 Loss1: 1.307415 Loss2: 1.488700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.228495 Loss1: 0.753155 Loss2: 1.475339 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.772436 Loss1: 1.701946 Loss2: 2.070490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.773055 Loss1: 1.227992 Loss2: 1.545063 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.400475 Loss1: 0.888238 Loss2: 1.512236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.864064 Loss1: 0.392047 Loss2: 1.472017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.806361 Loss1: 0.333557 Loss2: 1.472804 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.868304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.955477 Loss1: 0.446740 Loss2: 1.508738 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.784470 Loss1: 0.289967 Loss2: 1.494502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.745394 Loss1: 0.251721 Loss2: 1.493673 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.944336
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.526859 Loss1: 1.061856 Loss2: 1.465003 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.026534 Loss1: 0.569717 Loss2: 1.456817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.926945 Loss1: 0.477346 Loss2: 1.449599
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.806616 Loss1: 1.773735 Loss2: 2.032882
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.756265 Loss1: 1.280577 Loss2: 1.475688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.338124 Loss1: 0.895832 Loss2: 1.442292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.127827 Loss1: 0.684602 Loss2: 1.443225 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.896875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.008977 Loss1: 0.567367 Loss2: 1.441611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.827253 Loss1: 0.405143 Loss2: 1.422110 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.775491 Loss1: 0.347441 Loss2: 1.428050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.764343 Loss1: 0.334347 Loss2: 1.429996 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.907292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.224385 Loss1: 0.747210 Loss2: 1.477175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.907421 Loss1: 0.465474 Loss2: 1.441946 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.840938 Loss1: 0.399323 Loss2: 1.441615
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.915974 Loss1: 1.822292 Loss2: 2.093682
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.810973 Loss1: 1.276362 Loss2: 1.534611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.566109 Loss1: 1.022259 Loss2: 1.543850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.217311 Loss1: 0.690912 Loss2: 1.526399 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.936458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.064800 Loss1: 0.546355 Loss2: 1.518445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.996771 Loss1: 0.470457 Loss2: 1.526314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 2.003144 Loss1: 0.453497 Loss2: 1.549647 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.958091 Loss1: 0.417481 Loss2: 1.540610 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.906250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.330448 Loss1: 0.922997 Loss2: 1.407451 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.025599 Loss1: 0.613122 Loss2: 1.412477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.995316 Loss1: 0.564567 Loss2: 1.430749
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.732989 Loss1: 1.723115 Loss2: 2.009874DEBUG flwr 2023-10-09 19:12:18,080 | server.py:236 | fit_round 49 received 50 results and 0 failures
-
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.648320 Loss1: 1.202978 Loss2: 1.445342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.288589 Loss1: 0.848481 Loss2: 1.440108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.159115 Loss1: 0.725796 Loss2: 1.433319 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.854167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.990878 Loss1: 0.560458 Loss2: 1.430420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.800266 Loss1: 0.363564 Loss2: 1.436702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.716448 Loss1: 0.283757 Loss2: 1.432692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.751739 Loss1: 0.324520 Loss2: 1.427219 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.930208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.300789 Loss1: 0.831728 Loss2: 1.469060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.018034 Loss1: 0.561861 Loss2: 1.456173 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.841213 Loss1: 0.391016 Loss2: 1.450197
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.973468 Loss1: 1.933581 Loss2: 2.039886
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.786596 Loss1: 0.345421 Loss2: 1.441174
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.834192 Loss1: 1.342908 Loss2: 1.491284
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.416159 Loss1: 0.942442 Loss2: 1.473717
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.742227 Loss1: 0.298551 Loss2: 1.443676
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.180132 Loss1: 0.710785 Loss2: 1.469347
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.811941 Loss1: 0.361836 Loss2: 1.450105
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.035878 Loss1: 0.568525 Loss2: 1.467353
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.788232 Loss1: 0.334789 Loss2: 1.453443
-(DefaultActor pid=3765) >> Training accuracy: 0.858398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.898227 Loss1: 0.435397 Loss2: 1.462830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.839178 Loss1: 0.379793 Loss2: 1.459385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.794836 Loss1: 0.326969 Loss2: 1.467867
-(DefaultActor pid=3764) >> Training accuracy: 0.918750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.793537 Loss1: 1.623137 Loss2: 2.170400
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.604894 Loss1: 1.059550 Loss2: 1.545344
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.370088 Loss1: 0.835394 Loss2: 1.534694
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.143037 Loss1: 0.622770 Loss2: 1.520268
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.991351 Loss1: 0.478668 Loss2: 1.512683
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.891053 Loss1: 0.388648 Loss2: 1.502405
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.866888 Loss1: 1.850517 Loss2: 2.016372
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.816331 Loss1: 0.321625 Loss2: 1.494706
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.765823 Loss1: 1.260176 Loss2: 1.505647
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.761601 Loss1: 0.274681 Loss2: 1.486919
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.396814 Loss1: 0.911688 Loss2: 1.485126
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.806966 Loss1: 0.321543 Loss2: 1.485423
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.142470 Loss1: 0.667416 Loss2: 1.475053
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.800055 Loss1: 0.298722 Loss2: 1.501332
-(DefaultActor pid=3765) >> Training accuracy: 0.893750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.998485 Loss1: 0.523409 Loss2: 1.475076
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.955679 Loss1: 0.478728 Loss2: 1.476951
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.896873 Loss1: 0.416611 Loss2: 1.480263
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.880168 Loss1: 0.395913 Loss2: 1.484256
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.838587 Loss1: 0.349528 Loss2: 1.489060
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.771574 Loss1: 0.294239 Loss2: 1.477335
-(DefaultActor pid=3764) >> Training accuracy: 0.934570
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 19:12:18,080][flwr][DEBUG] - fit_round 49 received 50 results and 0 failures
-INFO flwr 2023-10-09 19:13:00,199 | server.py:125 | fit progress: (49, 2.436963511732059, {'accuracy': 0.4738}, 112887.97707739999)
->> Test accuracy: 0.473800
-[2023-10-09 19:13:00,199][flwr][INFO] - fit progress: (49, 2.436963511732059, {'accuracy': 0.4738}, 112887.97707739999)
-DEBUG flwr 2023-10-09 19:13:00,199 | server.py:173 | evaluate_round 49: strategy sampled 50 clients (out of 50)
-[2023-10-09 19:13:00,199][flwr][DEBUG] - evaluate_round 49: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 19:22:08,911 | server.py:187 | evaluate_round 49 received 50 results and 0 failures
-[2023-10-09 19:22:08,911][flwr][DEBUG] - evaluate_round 49 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 19:22:08,912 | server.py:222 | fit_round 50: strategy sampled 50 clients (out of 50)
-[2023-10-09 19:22:08,912][flwr][DEBUG] - fit_round 50: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.773367 Loss1: 1.785448 Loss2: 1.987919
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.768236 Loss1: 1.268820 Loss2: 1.499416
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.374965 Loss1: 0.885865 Loss2: 1.489101
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.625046 Loss1: 1.589398 Loss2: 2.035648
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.663706 Loss1: 1.190584 Loss2: 1.473122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.333692 Loss1: 0.865836 Loss2: 1.467856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.119584 Loss1: 0.660200 Loss2: 1.459384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.018646 Loss1: 0.562784 Loss2: 1.455862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.930371 Loss1: 0.476992 Loss2: 1.453379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.907962 Loss1: 0.448683 Loss2: 1.459279 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.915039
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.817003 Loss1: 0.361979 Loss2: 1.455024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.749171 Loss1: 0.296716 Loss2: 1.452455 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.915625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.691789 Loss1: 1.721158 Loss2: 1.970631
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.621496 Loss1: 1.185643 Loss2: 1.435853
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.285068 Loss1: 0.873101 Loss2: 1.411967
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.103896 Loss1: 0.683593 Loss2: 1.420303
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.900323 Loss1: 1.825604 Loss2: 2.074719
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.711352 Loss1: 1.222993 Loss2: 1.488359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.497280 Loss1: 1.017596 Loss2: 1.479684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.194305 Loss1: 0.709457 Loss2: 1.484848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.086832 Loss1: 0.615177 Loss2: 1.471655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.971095 Loss1: 0.502216 Loss2: 1.468879 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.890625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.748809 Loss1: 0.351368 Loss2: 1.397441
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.975698 Loss1: 0.505935 Loss2: 1.469763
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.910771 Loss1: 0.436953 Loss2: 1.473818
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.840680 Loss1: 0.362945 Loss2: 1.477734
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.780937 Loss1: 0.322407 Loss2: 1.458530
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.778757 Loss1: 1.729168 Loss2: 2.049588
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.628938 Loss1: 1.153657 Loss2: 1.475281
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.372976 Loss1: 0.906487 Loss2: 1.466490
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.184553 Loss1: 0.706015 Loss2: 1.478538
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.837552 Loss1: 1.761109 Loss2: 2.076443
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.728427 Loss1: 1.236374 Loss2: 1.492053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.435778 Loss1: 0.947795 Loss2: 1.487983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.207589 Loss1: 0.718219 Loss2: 1.489370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.028322 Loss1: 0.558018 Loss2: 1.470304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.019399 Loss1: 0.544163 Loss2: 1.475236 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.934375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.740983 Loss1: 0.281691 Loss2: 1.459292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.978362 Loss1: 0.492498 Loss2: 1.485863
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.878947 Loss1: 0.393671 Loss2: 1.485276
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.856223 Loss1: 0.382464 Loss2: 1.473759
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.861428 Loss1: 0.381474 Loss2: 1.479955
-(DefaultActor pid=3764) >> Training accuracy: 0.901042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.870721 Loss1: 1.812654 Loss2: 2.058067
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.625794 Loss1: 1.136548 Loss2: 1.489246
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.332171 Loss1: 0.859914 Loss2: 1.472256
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.095438 Loss1: 0.633077 Loss2: 1.462361
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.669222 Loss1: 1.706862 Loss2: 1.962360
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.515801 Loss1: 1.043604 Loss2: 1.472197 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.128512 Loss1: 0.691228 Loss2: 1.437284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.992281 Loss1: 0.559399 Loss2: 1.432882 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.829604 Loss1: 0.405881 Loss2: 1.423723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.759821 Loss1: 0.346863 Loss2: 1.412958 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.929167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.760093 Loss1: 0.343333 Loss2: 1.416760 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.720853 Loss1: 0.300912 Loss2: 1.419941 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.906250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.746843 Loss1: 1.697481 Loss2: 2.049363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.201204 Loss1: 0.771252 Loss2: 1.429952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.803451 Loss1: 1.724250 Loss2: 2.079201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.638572 Loss1: 1.119493 Loss2: 1.519079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.292106 Loss1: 0.787822 Loss2: 1.504284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.063158 Loss1: 0.567325 Loss2: 1.495833 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.984679 Loss1: 0.492346 Loss2: 1.492333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.942866 Loss1: 0.452448 Loss2: 1.490418 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.946875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.887560 Loss1: 0.385766 Loss2: 1.501794 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.781393 Loss1: 0.289044 Loss2: 1.492349 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.887500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.795364 Loss1: 1.250950 Loss2: 1.544414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.208945 Loss1: 0.723722 Loss2: 1.485222 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.953493 Loss1: 0.468783 Loss2: 1.484711 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.844149 Loss1: 0.372656 Loss2: 1.471494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.794727 Loss1: 0.319843 Loss2: 1.474884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.837408 Loss1: 0.365991 Loss2: 1.471417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.725561 Loss1: 0.247051 Loss2: 1.478510 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.012112 Loss1: 0.576152 Loss2: 1.435959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.803039 Loss1: 0.376444 Loss2: 1.426595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.818899 Loss1: 1.717144 Loss2: 2.101755
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.699804 Loss1: 0.288811 Loss2: 1.410992
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.681493 Loss1: 1.199886 Loss2: 1.481607
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.680740 Loss1: 0.274387 Loss2: 1.406353
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.190070 Loss1: 0.711765 Loss2: 1.478305 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.923950 Loss1: 0.445904 Loss2: 1.478047 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.797621 Loss1: 0.325153 Loss2: 1.472468
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.981976 Loss1: 1.909951 Loss2: 2.072025
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.851983 Loss1: 1.327493 Loss2: 1.524490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.541476 Loss1: 0.994433 Loss2: 1.547044 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.926339
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.056582 Loss1: 0.548435 Loss2: 1.508148 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.981275 Loss1: 0.469105 Loss2: 1.512170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.922324 Loss1: 0.412029 Loss2: 1.510295
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.758849 Loss1: 1.733384 Loss2: 2.025465
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.672486 Loss1: 1.212322 Loss2: 1.460164 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.905208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.268486 Loss1: 0.822217 Loss2: 1.446269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.959852 Loss1: 0.517034 Loss2: 1.442818 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.905540 Loss1: 0.453268 Loss2: 1.452272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.888163 Loss1: 0.436103 Loss2: 1.452060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.853964 Loss1: 0.395675 Loss2: 1.458289 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.817708 Loss1: 0.361169 Loss2: 1.456539 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.894792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.060424 Loss1: 0.577902 Loss2: 1.482522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.845403 Loss1: 0.378418 Loss2: 1.466985 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.899926 Loss1: 1.807316 Loss2: 2.092610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.780100 Loss1: 1.263096 Loss2: 1.517004 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.928125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.167250 Loss1: 0.676037 Loss2: 1.491213 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.060630 Loss1: 0.550713 Loss2: 1.509917 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.984798 Loss1: 0.478022 Loss2: 1.506776
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.019315 Loss1: 1.861052 Loss2: 2.158263
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.709886 Loss1: 1.133958 Loss2: 1.575928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.471681 Loss1: 0.907025 Loss2: 1.564656 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.944792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.799815 Loss1: 0.306531 Loss2: 1.493284
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.288971 Loss1: 0.719029 Loss2: 1.569942
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.178214 Loss1: 0.621005 Loss2: 1.557209
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.116607 Loss1: 0.564219 Loss2: 1.552387
-(DefaultActor pid=3764) Epoch: 6 Loss: 2.019212 Loss1: 0.455250 Loss2: 1.563963
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.921201 Loss1: 0.372136 Loss2: 1.549065
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.763554 Loss1: 1.726703 Loss2: 2.036850
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.902352 Loss1: 0.357055 Loss2: 1.545297
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.681783 Loss1: 1.206488 Loss2: 1.475295
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.912906 Loss1: 0.367827 Loss2: 1.545079
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.024231 Loss1: 0.578400 Loss2: 1.445832 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.895472 Loss1: 0.431483 Loss2: 1.463989 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.822171 Loss1: 0.372113 Loss2: 1.450058
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.840351 Loss1: 1.815216 Loss2: 2.025135
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.758129 Loss1: 1.277470 Loss2: 1.480659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.395501 Loss1: 0.938446 Loss2: 1.457056 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.897917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.795661 Loss1: 0.340237 Loss2: 1.455425
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.204861 Loss1: 0.742020 Loss2: 1.462841
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.039926 Loss1: 0.583016 Loss2: 1.456911
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.973651 Loss1: 0.518889 Loss2: 1.454762
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.863227 Loss1: 0.404038 Loss2: 1.459189
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.861122 Loss1: 0.409066 Loss2: 1.452055
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.810874 Loss1: 1.721486 Loss2: 2.089388
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.722262 Loss1: 0.262698 Loss2: 1.459564
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.757507 Loss1: 1.215536 Loss2: 1.541972
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.725085 Loss1: 0.283712 Loss2: 1.441373
-(DefaultActor pid=3764) >> Training accuracy: 0.915625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.161078 Loss1: 0.645546 Loss2: 1.515532 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.000855 Loss1: 0.487932 Loss2: 1.512922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.875165 Loss1: 0.370448 Loss2: 1.504717
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.813504 Loss1: 1.754418 Loss2: 2.059086
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.909738 Loss1: 0.408873 Loss2: 1.500865
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.610562 Loss1: 1.146092 Loss2: 1.464470
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.778105 Loss1: 0.280310 Loss2: 1.497794
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.388760 Loss1: 0.937610 Loss2: 1.451151
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.763870 Loss1: 0.274769 Loss2: 1.489101
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.108329 Loss1: 0.638385 Loss2: 1.469943
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.922942 Loss1: 0.472613 Loss2: 1.450329
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.873554 Loss1: 0.439789 Loss2: 1.433765
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.931823 Loss1: 0.483699 Loss2: 1.448124
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.935603 Loss1: 0.477327 Loss2: 1.458275
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.759187 Loss1: 0.300633 Loss2: 1.458554
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.954116 Loss1: 1.837499 Loss2: 2.116617
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.710559 Loss1: 0.268793 Loss2: 1.441766
-(DefaultActor pid=3764) >> Training accuracy: 0.906250
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.822771 Loss1: 1.272857 Loss2: 1.549914
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.425691 Loss1: 0.892159 Loss2: 1.533532
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.147098 Loss1: 0.616274 Loss2: 1.530824
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.068143 Loss1: 0.544948 Loss2: 1.523196
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.965152 Loss1: 0.441383 Loss2: 1.523769
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.922853 Loss1: 1.854068 Loss2: 2.068785
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.915121 Loss1: 0.387467 Loss2: 1.527654
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.946584 Loss1: 0.420454 Loss2: 1.526130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.924320 Loss1: 0.394719 Loss2: 1.529601 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.920072 Loss1: 0.402389 Loss2: 1.517684 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.926758
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.024267 Loss1: 0.545039 Loss2: 1.479228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.931257 Loss1: 0.431625 Loss2: 1.499633 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.820380 Loss1: 0.333888 Loss2: 1.486492
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.740181 Loss1: 1.679363 Loss2: 2.060817
-(DefaultActor pid=3764) >> Training accuracy: 0.931250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.644770 Loss1: 1.147027 Loss2: 1.497743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.010680 Loss1: 0.547664 Loss2: 1.463016 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.845598 Loss1: 0.402490 Loss2: 1.443107 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.834711 Loss1: 0.379611 Loss2: 1.455100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.768046 Loss1: 0.312582 Loss2: 1.455465 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.712469 Loss1: 0.267092 Loss2: 1.445377 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.718613 Loss1: 0.277308 Loss2: 1.441305 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.917708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.006961 Loss1: 0.506241 Loss2: 1.500721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.982651 Loss1: 0.474029 Loss2: 1.508622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.750121 Loss1: 1.828470 Loss2: 1.921652 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.907292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.139487 Loss1: 0.757935 Loss2: 1.381552 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.838418 Loss1: 0.469526 Loss2: 1.368892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.702033 Loss1: 1.695189 Loss2: 2.006844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.585672 Loss1: 1.112227 Loss2: 1.473445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.248428 Loss1: 0.783429 Loss2: 1.464999 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.128243 Loss1: 0.667796 Loss2: 1.460446 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.915625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.912052 Loss1: 0.445091 Loss2: 1.466960 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.807676 Loss1: 0.351165 Loss2: 1.456511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.788869 Loss1: 0.326983 Loss2: 1.461886
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.785843 Loss1: 1.811111 Loss2: 1.974731
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.779077 Loss1: 0.323735 Loss2: 1.455342
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.741477 Loss1: 1.327074 Loss2: 1.414403
-(DefaultActor pid=3764) >> Training accuracy: 0.924805
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.440320 Loss1: 1.026893 Loss2: 1.413427
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.161658 Loss1: 0.742513 Loss2: 1.419145
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.974348 Loss1: 0.575070 Loss2: 1.399278
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.898227 Loss1: 0.499609 Loss2: 1.398617
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.828104 Loss1: 0.433448 Loss2: 1.394656
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.892550 Loss1: 1.740883 Loss2: 2.151667
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.779375 Loss1: 0.385619 Loss2: 1.393756
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.628133 Loss1: 1.110908 Loss2: 1.517225
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.686732 Loss1: 0.292377 Loss2: 1.394355
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.374880 Loss1: 0.884325 Loss2: 1.490555
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.642453 Loss1: 0.251227 Loss2: 1.391226
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.145898 Loss1: 0.646084 Loss2: 1.499814
-(DefaultActor pid=3765) >> Training accuracy: 0.933333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.057223 Loss1: 0.574902 Loss2: 1.482321
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.932620 Loss1: 0.458490 Loss2: 1.474130
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.916003 Loss1: 0.441415 Loss2: 1.474588
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.861579 Loss1: 0.375377 Loss2: 1.486202
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.481570 Loss1: 1.489513 Loss2: 1.992057
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.759836 Loss1: 0.276907 Loss2: 1.482928
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.571556 Loss1: 1.114104 Loss2: 1.457452
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.761942 Loss1: 0.296305 Loss2: 1.465638
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.013459 Loss1: 0.581259 Loss2: 1.432200 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.854190 Loss1: 0.436640 Loss2: 1.417550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.810767 Loss1: 0.385791 Loss2: 1.424977
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.706111 Loss1: 1.612310 Loss2: 2.093801
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.691815 Loss1: 0.276270 Loss2: 1.415545
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.627540 Loss1: 1.124687 Loss2: 1.502853
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.698090 Loss1: 0.278255 Loss2: 1.419835
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.312905 Loss1: 0.803080 Loss2: 1.509825
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.665793 Loss1: 0.248172 Loss2: 1.417621
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.105707 Loss1: 0.603072 Loss2: 1.502636
-(DefaultActor pid=3765) >> Training accuracy: 0.940625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.992121 Loss1: 0.502092 Loss2: 1.490029
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.880702 Loss1: 0.390105 Loss2: 1.490597
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.859261 Loss1: 0.376989 Loss2: 1.482272
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.920804 Loss1: 0.425521 Loss2: 1.495283
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.886552 Loss1: 1.819585 Loss2: 2.066967
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.856017 Loss1: 0.354767 Loss2: 1.501250
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.763550 Loss1: 1.249017 Loss2: 1.514533
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.771792 Loss1: 0.286292 Loss2: 1.485500
-(DefaultActor pid=3764) >> Training accuracy: 0.932292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.202115 Loss1: 0.720482 Loss2: 1.481633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.974564 Loss1: 0.501754 Loss2: 1.472810 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.969057 Loss1: 0.495164 Loss2: 1.473893
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.920029 Loss1: 1.859406 Loss2: 2.060623
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.885746 Loss1: 0.408060 Loss2: 1.477686
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.765421 Loss1: 1.259978 Loss2: 1.505443
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.917812 Loss1: 0.439863 Loss2: 1.477949
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.424971 Loss1: 0.956581 Loss2: 1.468390
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.884185 Loss1: 0.398856 Loss2: 1.485330
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.243112 Loss1: 0.770688 Loss2: 1.472424
-(DefaultActor pid=3765) >> Training accuracy: 0.903125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.154236 Loss1: 0.680989 Loss2: 1.473246
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.051039 Loss1: 0.567934 Loss2: 1.483106
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.956470 Loss1: 0.485605 Loss2: 1.470865
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.886848 Loss1: 0.412906 Loss2: 1.473942
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.691634 Loss1: 1.705973 Loss2: 1.985661
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.800591 Loss1: 0.326399 Loss2: 1.474191
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.495898 Loss1: 1.079196 Loss2: 1.416703
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.787759 Loss1: 0.327110 Loss2: 1.460649
-(DefaultActor pid=3764) >> Training accuracy: 0.925000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.976585 Loss1: 0.588648 Loss2: 1.387936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.875826 Loss1: 0.488708 Loss2: 1.387119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.720647 Loss1: 0.332975 Loss2: 1.387672
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.747306 Loss1: 1.776276 Loss2: 1.971030
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.684852 Loss1: 0.303097 Loss2: 1.381754
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.590410 Loss1: 1.130762 Loss2: 1.459648
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.214673 Loss1: 0.768702 Loss2: 1.445971 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.646048 Loss1: 0.261266 Loss2: 1.384782
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.992060 Loss1: 0.552254 Loss2: 1.439807
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.930348 Loss1: 0.498381 Loss2: 1.431967
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.878961 Loss1: 0.438550 Loss2: 1.440411
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.783230 Loss1: 0.346743 Loss2: 1.436487
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.754474 Loss1: 0.324102 Loss2: 1.430372
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.860553 Loss1: 1.786114 Loss2: 2.074439
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.763752 Loss1: 1.249842 Loss2: 1.513910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.808324 Loss1: 0.379116 Loss2: 1.429208
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.363059 Loss1: 0.867970 Loss2: 1.495089
-(DefaultActor pid=3764) >> Training accuracy: 0.908203
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.108525 Loss1: 0.618081 Loss2: 1.490444
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.011535 Loss1: 0.526369 Loss2: 1.485166
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.993246 Loss1: 0.506671 Loss2: 1.486574
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.970948 Loss1: 0.472254 Loss2: 1.498694
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.979701 Loss1: 0.482051 Loss2: 1.497650
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.582422 Loss1: 1.632898 Loss2: 1.949524
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.900694 Loss1: 0.408961 Loss2: 1.491733
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.622499 Loss1: 1.172084 Loss2: 1.450415
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.838923 Loss1: 0.345419 Loss2: 1.493504
-(DefaultActor pid=3765) >> Training accuracy: 0.900000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.241595 Loss1: 0.795341 Loss2: 1.446254
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.065179 Loss1: 0.636611 Loss2: 1.428568
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.034415 Loss1: 0.606278 Loss2: 1.428137
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.877300 Loss1: 0.452495 Loss2: 1.424805
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.719276 Loss1: 0.309172 Loss2: 1.410105
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.879926 Loss1: 1.721035 Loss2: 2.158890
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.673410 Loss1: 1.155101 Loss2: 1.518310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.278801 Loss1: 0.803385 Loss2: 1.475416
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.824078 Loss1: 0.404589 Loss2: 1.419490
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.762350 Loss1: 0.334454 Loss2: 1.427896 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.906250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.840479 Loss1: 0.382649 Loss2: 1.457830 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.672003 Loss1: 0.211439 Loss2: 1.460564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.723273 Loss1: 0.264507 Loss2: 1.458766 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.936298
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.320326 Loss1: 0.851964 Loss2: 1.468361 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.928792 Loss1: 0.481941 Loss2: 1.446850 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.784704 Loss1: 1.788578 Loss2: 1.996126
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.826312 Loss1: 0.380213 Loss2: 1.446100
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.636012 Loss1: 1.193465 Loss2: 1.442547
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.810887 Loss1: 0.364726 Loss2: 1.446160
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.327442 Loss1: 0.898775 Loss2: 1.428667
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.794400 Loss1: 0.353289 Loss2: 1.441111
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.019563 Loss1: 0.584426 Loss2: 1.435137
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.793637 Loss1: 0.345874 Loss2: 1.447763
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.911692 Loss1: 0.490074 Loss2: 1.421618
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.916134 Loss1: 0.452847 Loss2: 1.463287
-(DefaultActor pid=3764) >> Training accuracy: 0.881250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.781477 Loss1: 0.361573 Loss2: 1.419904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.760548 Loss1: 0.339064 Loss2: 1.421484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.744874 Loss1: 0.317780 Loss2: 1.427094
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.088958 Loss1: 2.013983 Loss2: 2.074974
-(DefaultActor pid=3765) >> Training accuracy: 0.929167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.778436 Loss1: 1.285337 Loss2: 1.493100
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.425565 Loss1: 0.957068 Loss2: 1.468497
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.160927 Loss1: 0.693919 Loss2: 1.467008
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.039389 Loss1: 0.577557 Loss2: 1.461832
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.945468 Loss1: 0.491675 Loss2: 1.453793
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.072748 Loss1: 2.012233 Loss2: 2.060515
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.876984 Loss1: 0.417201 Loss2: 1.459783
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.802925 Loss1: 1.339698 Loss2: 1.463227
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.810265 Loss1: 0.357225 Loss2: 1.453040
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.393132 Loss1: 0.959415 Loss2: 1.433717
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.813432 Loss1: 0.373219 Loss2: 1.440213
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.149742 Loss1: 0.732610 Loss2: 1.417132
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.763208 Loss1: 0.312782 Loss2: 1.450426
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.977390 Loss1: 0.563895 Loss2: 1.413495
-(DefaultActor pid=3764) >> Training accuracy: 0.896205
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.854165 Loss1: 0.437244 Loss2: 1.416921
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.807769 Loss1: 0.394047 Loss2: 1.413722
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.767491 Loss1: 0.357188 Loss2: 1.410303
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.726570 Loss1: 0.310502 Loss2: 1.416067
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.726381 Loss1: 0.316500 Loss2: 1.409881
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.826026 Loss1: 1.735342 Loss2: 2.090683
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.651327 Loss1: 1.159331 Loss2: 1.491996
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.337051 Loss1: 0.884780 Loss2: 1.452271
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.121302 Loss1: 0.673353 Loss2: 1.447949
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.985291 Loss1: 0.524834 Loss2: 1.460457
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.830459 Loss1: 0.385262 Loss2: 1.445197
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.796945 Loss1: 0.345297 Loss2: 1.451647
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.778483 Loss1: 0.327149 Loss2: 1.451334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.704666 Loss1: 0.267498 Loss2: 1.437168 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.698378 Loss1: 0.262542 Loss2: 1.435836 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.927885
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.902407 Loss1: 0.484090 Loss2: 1.418318 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.932628 Loss1: 0.490543 Loss2: 1.442085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.790836 Loss1: 1.805038 Loss2: 1.985798
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.843858 Loss1: 0.411056 Loss2: 1.432802
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.886144 Loss1: 1.408055 Loss2: 1.478089
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.759979 Loss1: 0.340064 Loss2: 1.419915
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.436203 Loss1: 0.954352 Loss2: 1.481851
-DEBUG flwr 2023-10-09 19:50:49,254 | server.py:236 | fit_round 50 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.749195 Loss1: 0.322067 Loss2: 1.427128
-(DefaultActor pid=3765) >> Training accuracy: 0.936581
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.988890 Loss1: 0.532048 Loss2: 1.456842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.875753 Loss1: 0.417372 Loss2: 1.458381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.767324 Loss1: 0.322412 Loss2: 1.444912
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.658296 Loss1: 1.614547 Loss2: 2.043749
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.855745 Loss1: 0.409572 Loss2: 1.446173
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.511278 Loss1: 1.035402 Loss2: 1.475876
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.862348 Loss1: 0.398599 Loss2: 1.463750
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.210540 Loss1: 0.767942 Loss2: 1.442597
-(DefaultActor pid=3764) >> Training accuracy: 0.927734
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.003343 Loss1: 0.558739 Loss2: 1.444603
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.907713 Loss1: 0.466571 Loss2: 1.441141
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.795724 Loss1: 0.363569 Loss2: 1.432155
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.778274 Loss1: 0.338814 Loss2: 1.439460
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.799829 Loss1: 0.366434 Loss2: 1.433396
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.671197 Loss1: 1.705365 Loss2: 1.965832
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.667696 Loss1: 1.188167 Loss2: 1.479528 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.944792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.696798 Loss1: 0.267114 Loss2: 1.429684
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.247625 Loss1: 0.798880 Loss2: 1.448745
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.102547 Loss1: 0.658144 Loss2: 1.444402
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.014232 Loss1: 0.569705 Loss2: 1.444527
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.898521 Loss1: 0.445192 Loss2: 1.453329
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.887190 Loss1: 0.446210 Loss2: 1.440981
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.723901 Loss1: 1.661300 Loss2: 2.062601
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.572815 Loss1: 1.077382 Loss2: 1.495433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.228793 Loss1: 0.764483 Loss2: 1.464311 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.875000
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.785596 Loss1: 0.347738 Loss2: 1.437858
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.094503 Loss1: 0.622759 Loss2: 1.471745
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.041866 Loss1: 0.568820 Loss2: 1.473046
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.896494 Loss1: 0.425941 Loss2: 1.470552
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.896045 Loss1: 0.428233 Loss2: 1.467813
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.835081 Loss1: 0.361435 Loss2: 1.473646
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.875267 Loss1: 1.843606 Loss2: 2.031661
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.774333 Loss1: 0.314673 Loss2: 1.459660
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.761859 Loss1: 0.295051 Loss2: 1.466809
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.740459 Loss1: 1.243592 Loss2: 1.496867
-(DefaultActor pid=3765) >> Training accuracy: 0.934375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.387483 Loss1: 0.897236 Loss2: 1.490247
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.239011 Loss1: 0.746527 Loss2: 1.492484
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.104793 Loss1: 0.623769 Loss2: 1.481024
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.949469 Loss1: 0.461284 Loss2: 1.488185
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.892782 Loss1: 0.418012 Loss2: 1.474770
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.835641 Loss1: 0.357884 Loss2: 1.477757
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.801920 Loss1: 0.325763 Loss2: 1.476157
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.787427 Loss1: 0.316555 Loss2: 1.470873
-(DefaultActor pid=3764) >> Training accuracy: 0.925781
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 19:50:49,254][flwr][DEBUG] - fit_round 50 received 50 results and 0 failures
-INFO flwr 2023-10-09 19:51:29,754 | server.py:125 | fit progress: (50, 2.4152099930059414, {'accuracy': 0.4764}, 115197.53232752299)
->> Test accuracy: 0.476400
-[2023-10-09 19:51:29,754][flwr][INFO] - fit progress: (50, 2.4152099930059414, {'accuracy': 0.4764}, 115197.53232752299)
-DEBUG flwr 2023-10-09 19:51:29,754 | server.py:173 | evaluate_round 50: strategy sampled 50 clients (out of 50)
-[2023-10-09 19:51:29,754][flwr][DEBUG] - evaluate_round 50: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 20:00:34,307 | server.py:187 | evaluate_round 50 received 50 results and 0 failures
-[2023-10-09 20:00:34,307][flwr][DEBUG] - evaluate_round 50 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 20:00:34,307 | server.py:222 | fit_round 51: strategy sampled 50 clients (out of 50)
-[2023-10-09 20:00:34,307][flwr][DEBUG] - fit_round 51: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 4.016628 Loss1: 1.932497 Loss2: 2.084130
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.851809 Loss1: 1.311681 Loss2: 1.540129
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.418402 Loss1: 0.910998 Loss2: 1.507404
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.251455 Loss1: 0.758436 Loss2: 1.493019
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.994693 Loss1: 0.496564 Loss2: 1.498129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.918124 Loss1: 0.427569 Loss2: 1.490554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.839432 Loss1: 0.354486 Loss2: 1.484946 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.824559 Loss1: 0.338971 Loss2: 1.485588 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.830594 Loss1: 0.350372 Loss2: 1.480221 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.869247 Loss1: 0.372670 Loss2: 1.496577 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.835417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.780733 Loss1: 0.331848 Loss2: 1.448885 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.770852 Loss1: 0.314541 Loss2: 1.456311 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.906250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.630595 Loss1: 1.174986 Loss2: 1.455609 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.100720 Loss1: 0.647592 Loss2: 1.453128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.961859 Loss1: 0.514370 Loss2: 1.447489
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.933970 Loss1: 1.884497 Loss2: 2.049474
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.905188 Loss1: 0.471885 Loss2: 1.433303
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.846202 Loss1: 1.349260 Loss2: 1.496942
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.820511 Loss1: 0.385503 Loss2: 1.435009
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.446812 Loss1: 0.965212 Loss2: 1.481601
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.770042 Loss1: 0.331729 Loss2: 1.438314
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.192467 Loss1: 0.710106 Loss2: 1.482362
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.742299 Loss1: 0.300870 Loss2: 1.441429
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.076971 Loss1: 0.602468 Loss2: 1.474503
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.727005 Loss1: 0.299373 Loss2: 1.427631
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.985382 Loss1: 0.506872 Loss2: 1.478510
-(DefaultActor pid=3765) >> Training accuracy: 0.920833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.910520 Loss1: 0.441799 Loss2: 1.468721
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.836105 Loss1: 0.366447 Loss2: 1.469658
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.812591 Loss1: 0.339071 Loss2: 1.473520
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.883029 Loss1: 0.411812 Loss2: 1.471217
-(DefaultActor pid=3764) >> Training accuracy: 0.853125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.781321 Loss1: 1.704854 Loss2: 2.076466
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.684799 Loss1: 1.163193 Loss2: 1.521607
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.369990 Loss1: 0.837226 Loss2: 1.532763
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.165859 Loss1: 0.650076 Loss2: 1.515783
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.862972 Loss1: 1.708635 Loss2: 2.154336
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.586080 Loss1: 1.116464 Loss2: 1.469617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.239238 Loss1: 0.802492 Loss2: 1.436747 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.028743 Loss1: 0.583045 Loss2: 1.445698 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.890777 Loss1: 0.452501 Loss2: 1.438275 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.779150 Loss1: 0.356223 Loss2: 1.422928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.734774 Loss1: 0.308341 Loss2: 1.426433
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.768685 Loss1: 0.272762 Loss2: 1.495923
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.648350 Loss1: 0.239168 Loss2: 1.409182 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.956731
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.778002 Loss1: 1.676808 Loss2: 2.101195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.274935 Loss1: 0.818685 Loss2: 1.456250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.085444 Loss1: 0.633444 Loss2: 1.452000
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.712415 Loss1: 1.661584 Loss2: 2.050831
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.597062 Loss1: 1.084103 Loss2: 1.512959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.316696 Loss1: 0.802550 Loss2: 1.514146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.126366 Loss1: 0.618529 Loss2: 1.507836 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.031542 Loss1: 0.529115 Loss2: 1.502427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.967766 Loss1: 0.464047 Loss2: 1.503719 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.935268
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.829779 Loss1: 0.343150 Loss2: 1.486629 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.775672 Loss1: 0.289607 Loss2: 1.486065 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.928125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.664804 Loss1: 1.157248 Loss2: 1.507556 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.053054 Loss1: 0.571086 Loss2: 1.481968 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.949862 Loss1: 0.480012 Loss2: 1.469850 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.953311 Loss1: 0.479180 Loss2: 1.474132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.928290 Loss1: 0.441512 Loss2: 1.486777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.843682 Loss1: 0.357708 Loss2: 1.485974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.785470 Loss1: 0.312587 Loss2: 1.472883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.763577 Loss1: 0.290568 Loss2: 1.473010 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.919792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.780370 Loss1: 0.328141 Loss2: 1.452229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.826034 Loss1: 0.363262 Loss2: 1.462772 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.884375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.813215 Loss1: 1.762428 Loss2: 2.050786
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.696641 Loss1: 1.191512 Loss2: 1.505130
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.348719 Loss1: 0.854972 Loss2: 1.493747
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.163989 Loss1: 0.674001 Loss2: 1.489988
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.759521 Loss1: 1.661393 Loss2: 2.098128
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.731326 Loss1: 1.162943 Loss2: 1.568384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.372247 Loss1: 0.831034 Loss2: 1.541212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.061705 Loss1: 0.540471 Loss2: 1.521233 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.972830 Loss1: 0.450797 Loss2: 1.522033 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.860344 Loss1: 0.362603 Loss2: 1.497741 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.932617
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.873790 Loss1: 0.374663 Loss2: 1.499127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.924363 Loss1: 0.419824 Loss2: 1.504540 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.898438
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.762774 Loss1: 1.717610 Loss2: 2.045164 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.309398 Loss1: 0.854049 Loss2: 1.455348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.826930 Loss1: 1.812055 Loss2: 2.014874 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.766419 Loss1: 1.300515 Loss2: 1.465904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.335076 Loss1: 0.894372 Loss2: 1.440703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.089468 Loss1: 0.642053 Loss2: 1.447415 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.018068 Loss1: 0.591920 Loss2: 1.426148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.919574 Loss1: 0.475077 Loss2: 1.444496 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.938542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.836875 Loss1: 0.397565 Loss2: 1.439310 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.783891 Loss1: 0.349296 Loss2: 1.434595 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.919792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.590654 Loss1: 1.134912 Loss2: 1.455743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.976023 Loss1: 0.565833 Loss2: 1.410191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.831832 Loss1: 0.431770 Loss2: 1.400062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.844867 Loss1: 0.443295 Loss2: 1.401572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.401177 Loss1: 0.927261 Loss2: 1.473916
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.778640 Loss1: 0.363652 Loss2: 1.414988
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.104550 Loss1: 0.629626 Loss2: 1.474924
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.717585 Loss1: 0.314764 Loss2: 1.402820
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.920561 Loss1: 0.466865 Loss2: 1.453696
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.684155 Loss1: 0.283925 Loss2: 1.400231
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.716993 Loss1: 0.310283 Loss2: 1.406710
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.826106 Loss1: 0.376582 Loss2: 1.449524
-(DefaultActor pid=3765) >> Training accuracy: 0.927083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.824325 Loss1: 0.368357 Loss2: 1.455968
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.800986 Loss1: 0.351037 Loss2: 1.449949
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.841916 Loss1: 0.387155 Loss2: 1.454761
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.778178 Loss1: 0.315509 Loss2: 1.462668
-(DefaultActor pid=3764) >> Training accuracy: 0.894531
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.716531 Loss1: 1.697013 Loss2: 2.019518
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.548641 Loss1: 1.081056 Loss2: 1.467585
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.329209 Loss1: 0.866894 Loss2: 1.462315
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.185640 Loss1: 0.710866 Loss2: 1.474774
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.986610 Loss1: 1.841948 Loss2: 2.144662
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.909605 Loss1: 0.448111 Loss2: 1.461494
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.886879 Loss1: 0.441872 Loss2: 1.445007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.816847 Loss1: 0.369499 Loss2: 1.447348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.981737 Loss1: 0.550807 Loss2: 1.430930 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.750937 Loss1: 0.305305 Loss2: 1.445632 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.713276 Loss1: 0.270910 Loss2: 1.442366 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.732572 Loss1: 0.315839 Loss2: 1.416734 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.924479
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.803648 Loss1: 1.818619 Loss2: 1.985029
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.626385 Loss1: 1.177973 Loss2: 1.448412
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.336004 Loss1: 0.891800 Loss2: 1.444205
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.118275 Loss1: 0.680338 Loss2: 1.437937
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.939751 Loss1: 1.899307 Loss2: 2.040444
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.891424 Loss1: 0.459153 Loss2: 1.432272
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.749127 Loss1: 1.255422 Loss2: 1.493706
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.867652 Loss1: 0.450961 Loss2: 1.416691
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.435322 Loss1: 0.956900 Loss2: 1.478422
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.855104 Loss1: 0.437980 Loss2: 1.417124
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.155471 Loss1: 0.686912 Loss2: 1.468559
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.830811 Loss1: 0.392195 Loss2: 1.438616
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.059057 Loss1: 0.603534 Loss2: 1.455524
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.834597 Loss1: 0.405388 Loss2: 1.429209
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.027377 Loss1: 0.556611 Loss2: 1.470766
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.810305 Loss1: 0.378007 Loss2: 1.432298
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.922445 Loss1: 0.456727 Loss2: 1.465718
-(DefaultActor pid=3765) >> Training accuracy: 0.925000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.822343 Loss1: 0.364151 Loss2: 1.458193
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.776137 Loss1: 0.320751 Loss2: 1.455386
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.790531 Loss1: 0.326462 Loss2: 1.464069
-(DefaultActor pid=3764) >> Training accuracy: 0.935417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.875036 Loss1: 1.832135 Loss2: 2.042901
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.683008 Loss1: 1.179707 Loss2: 1.503301
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.286478 Loss1: 0.784685 Loss2: 1.501792
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.070355 Loss1: 0.588690 Loss2: 1.481664
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.714657 Loss1: 1.689628 Loss2: 2.025028
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.693071 Loss1: 1.229910 Loss2: 1.463161 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.290475 Loss1: 0.824857 Loss2: 1.465618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.115116 Loss1: 0.656403 Loss2: 1.458713 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.938268 Loss1: 0.492937 Loss2: 1.445330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.857847 Loss1: 0.412968 Loss2: 1.444879 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.939583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.724088 Loss1: 0.262976 Loss2: 1.461112
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.854578 Loss1: 0.405210 Loss2: 1.449368
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.821855 Loss1: 0.372241 Loss2: 1.449614
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.761562 Loss1: 0.313212 Loss2: 1.448350
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.735561 Loss1: 0.294111 Loss2: 1.441450
-(DefaultActor pid=3764) >> Training accuracy: 0.937500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.843202 Loss1: 1.758165 Loss2: 2.085038
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.833115 Loss1: 1.303914 Loss2: 1.529201
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.396592 Loss1: 0.884721 Loss2: 1.511872
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.233199 Loss1: 0.746824 Loss2: 1.486375
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.722273 Loss1: 1.721671 Loss2: 2.000602
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.097062 Loss1: 0.603871 Loss2: 1.493191
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.660889 Loss1: 1.211475 Loss2: 1.449415
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.953952 Loss1: 0.468270 Loss2: 1.485682
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.300790 Loss1: 0.892834 Loss2: 1.407956
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.863781 Loss1: 0.389767 Loss2: 1.474014
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.017073 Loss1: 0.611438 Loss2: 1.405635
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.912946 Loss1: 0.431838 Loss2: 1.481108
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.852853 Loss1: 0.458979 Loss2: 1.393874
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.826710 Loss1: 0.341965 Loss2: 1.484745
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.781995 Loss1: 0.388257 Loss2: 1.393738
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.805875 Loss1: 0.325583 Loss2: 1.480292
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.840319 Loss1: 0.437624 Loss2: 1.402695
-(DefaultActor pid=3765) >> Training accuracy: 0.948958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.823027 Loss1: 0.416560 Loss2: 1.406466
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.792289 Loss1: 0.382350 Loss2: 1.409939
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.677290 Loss1: 0.273711 Loss2: 1.403579
-(DefaultActor pid=3764) >> Training accuracy: 0.915625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.754885 Loss1: 1.767457 Loss2: 1.987427
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.698759 Loss1: 1.235034 Loss2: 1.463726
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.263245 Loss1: 0.801946 Loss2: 1.461299
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.885164 Loss1: 1.848980 Loss2: 2.036184
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.134923 Loss1: 0.687486 Loss2: 1.447437
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.520120 Loss1: 1.055653 Loss2: 1.464467
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.975972 Loss1: 0.526031 Loss2: 1.449941
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.938682 Loss1: 0.492040 Loss2: 1.446641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.843048 Loss1: 0.391575 Loss2: 1.451473 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.840664 Loss1: 0.401245 Loss2: 1.439419 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.756228 Loss1: 0.306505 Loss2: 1.449723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.696645 Loss1: 0.258925 Loss2: 1.437719 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.905273
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.810430 Loss1: 0.355407 Loss2: 1.455023 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.907292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.714062 Loss1: 1.652062 Loss2: 2.062001 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.294662 Loss1: 0.818362 Loss2: 1.476300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.081812 Loss1: 0.592787 Loss2: 1.489026
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.829965 Loss1: 1.720190 Loss2: 2.109775
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.984507 Loss1: 0.511682 Loss2: 1.472825
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.561675 Loss1: 1.071761 Loss2: 1.489914
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.882565 Loss1: 0.412844 Loss2: 1.469722
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.119143 Loss1: 0.664954 Loss2: 1.454190
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.847741 Loss1: 0.379119 Loss2: 1.468622
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.027403 Loss1: 0.602311 Loss2: 1.425091
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.775875 Loss1: 0.315095 Loss2: 1.460781
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.857518 Loss1: 0.422962 Loss2: 1.434556
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.732203 Loss1: 0.267487 Loss2: 1.464716
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.838326 Loss1: 0.412126 Loss2: 1.426200
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.725891 Loss1: 0.264376 Loss2: 1.461516
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.809749 Loss1: 0.374329 Loss2: 1.435420
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.790241 Loss1: 0.342779 Loss2: 1.447462
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.703214 Loss1: 0.273146 Loss2: 1.430068
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.696231 Loss1: 0.272947 Loss2: 1.423284
-(DefaultActor pid=3764) >> Training accuracy: 0.925000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.707663 Loss1: 1.688117 Loss2: 2.019546
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.612307 Loss1: 1.167268 Loss2: 1.445039
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.237704 Loss1: 0.815404 Loss2: 1.422301
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.113726 Loss1: 0.684338 Loss2: 1.429388
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.895790 Loss1: 1.857146 Loss2: 2.038644
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.993343 Loss1: 0.554778 Loss2: 1.438565
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.644714 Loss1: 1.142445 Loss2: 1.502268
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.897325 Loss1: 0.473429 Loss2: 1.423895
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.458877 Loss1: 0.974007 Loss2: 1.484870
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.909789 Loss1: 0.482537 Loss2: 1.427252
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.191693 Loss1: 0.698599 Loss2: 1.493094
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.857207 Loss1: 0.427210 Loss2: 1.429997
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.023762 Loss1: 0.534773 Loss2: 1.488989
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.745157 Loss1: 0.314888 Loss2: 1.430269
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.943587 Loss1: 0.451796 Loss2: 1.491791
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.673488 Loss1: 0.250775 Loss2: 1.422713
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.922550 Loss1: 0.443686 Loss2: 1.478864
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.825325 Loss1: 0.352925 Loss2: 1.472400
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.861352 Loss1: 0.378569 Loss2: 1.482784
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.807770 Loss1: 0.319394 Loss2: 1.488376
-(DefaultActor pid=3764) >> Training accuracy: 0.913542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.654495 Loss1: 1.639405 Loss2: 2.015089
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.608377 Loss1: 1.141096 Loss2: 1.467280
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.237503 Loss1: 0.764871 Loss2: 1.472632
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.001517 Loss1: 0.572653 Loss2: 1.428864
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.808870 Loss1: 1.781108 Loss2: 2.027761
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.876489 Loss1: 0.447931 Loss2: 1.428558
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.695309 Loss1: 1.229564 Loss2: 1.465746
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.833331 Loss1: 0.405133 Loss2: 1.428198
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.374057 Loss1: 0.918752 Loss2: 1.455305
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.757794 Loss1: 0.336050 Loss2: 1.421744
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.141845 Loss1: 0.699282 Loss2: 1.442563
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.680211 Loss1: 0.258757 Loss2: 1.421454
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.989252 Loss1: 0.548035 Loss2: 1.441216
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.687417 Loss1: 0.260677 Loss2: 1.426740
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.839224 Loss1: 0.416938 Loss2: 1.422286
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.667313 Loss1: 0.236851 Loss2: 1.430462
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.778625 Loss1: 0.351131 Loss2: 1.427493
-(DefaultActor pid=3765) >> Training accuracy: 0.928125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.796342 Loss1: 0.367272 Loss2: 1.429070
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.793618 Loss1: 0.360736 Loss2: 1.432882
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.768630 Loss1: 0.324190 Loss2: 1.444440
-(DefaultActor pid=3764) >> Training accuracy: 0.917708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.707964 Loss1: 1.639119 Loss2: 2.068845
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.527196 Loss1: 1.092930 Loss2: 1.434266
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.263494 Loss1: 0.853318 Loss2: 1.410177
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.082449 Loss1: 0.667968 Loss2: 1.414482
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.780826 Loss1: 1.748005 Loss2: 2.032821
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.837822 Loss1: 0.410553 Loss2: 1.427269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.817061 Loss1: 0.395956 Loss2: 1.421105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.748029 Loss1: 0.336836 Loss2: 1.411194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.704051 Loss1: 0.293343 Loss2: 1.410708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.651410 Loss1: 0.237622 Loss2: 1.413788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.930288
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.828710 Loss1: 0.369014 Loss2: 1.459695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.797842 Loss1: 0.338900 Loss2: 1.458943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.743202 Loss1: 0.288120 Loss2: 1.455081
-(DefaultActor pid=3764) >> Training accuracy: 0.923958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.563821 Loss1: 1.553800 Loss2: 2.010021
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.569344 Loss1: 1.082426 Loss2: 1.486919
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.230387 Loss1: 0.749580 Loss2: 1.480807
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.066623 Loss1: 0.606243 Loss2: 1.460380
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.958990 Loss1: 1.911906 Loss2: 2.047085
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.942884 Loss1: 0.484673 Loss2: 1.458210
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.806950 Loss1: 1.324359 Loss2: 1.482591
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.880796 Loss1: 0.414810 Loss2: 1.465986
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.851110 Loss1: 0.397179 Loss2: 1.453932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.790631 Loss1: 0.329559 Loss2: 1.461072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.800000 Loss1: 0.340939 Loss2: 1.459061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.916784 Loss1: 0.456554 Loss2: 1.460229 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.948529
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.777058 Loss1: 0.332202 Loss2: 1.444857 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.938616
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.722222 Loss1: 1.730725 Loss2: 1.991497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.334559 Loss1: 0.883148 Loss2: 1.451411 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.103641 Loss1: 0.643314 Loss2: 1.460327
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.647783 Loss1: 1.611785 Loss2: 2.035998
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.645794 Loss1: 1.129822 Loss2: 1.515972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.231266 Loss1: 0.725014 Loss2: 1.506252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.059671 Loss1: 0.566915 Loss2: 1.492756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.910886 Loss1: 0.432110 Loss2: 1.478776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.863697 Loss1: 0.393282 Loss2: 1.470415 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.804906 Loss1: 0.335168 Loss2: 1.469737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.795562 Loss1: 0.320712 Loss2: 1.474850 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.922852
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.805764 Loss1: 0.332079 Loss2: 1.473685
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.873014 Loss1: 1.761331 Loss2: 2.111683
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.607093 Loss1: 1.088992 Loss2: 1.518102
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.329264 Loss1: 0.847366 Loss2: 1.481899
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.136090 Loss1: 0.642027 Loss2: 1.494063
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.969657 Loss1: 0.485819 Loss2: 1.483839
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.695968 Loss1: 1.735626 Loss2: 1.960342
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.503017 Loss1: 1.080841 Loss2: 1.422175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.184865 Loss1: 0.778147 Loss2: 1.406718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.082224 Loss1: 0.676903 Loss2: 1.405321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.867733 Loss1: 0.466095 Loss2: 1.401638 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.940625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.786268 Loss1: 0.325547 Loss2: 1.460721
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.820789 Loss1: 0.425804 Loss2: 1.394986
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.772628 Loss1: 0.374771 Loss2: 1.397857
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.649816 Loss1: 0.256255 Loss2: 1.393561
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.632003 Loss1: 0.249530 Loss2: 1.382472
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.623006 Loss1: 0.233652 Loss2: 1.389355
-(DefaultActor pid=3764) >> Training accuracy: 0.947917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.809575 Loss1: 1.787642 Loss2: 2.021933
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.606542 Loss1: 1.136252 Loss2: 1.470290
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.398897 Loss1: 0.949294 Loss2: 1.449604
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.130301 Loss1: 0.678675 Loss2: 1.451625
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.946524 Loss1: 0.507014 Loss2: 1.439509
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.769223 Loss1: 1.734633 Loss2: 2.034590
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.578784 Loss1: 1.097330 Loss2: 1.481454 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.237825 Loss1: 0.773263 Loss2: 1.464563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.069769 Loss1: 0.631370 Loss2: 1.438399 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.938443 Loss1: 0.503567 Loss2: 1.434876 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.913542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.754924 Loss1: 0.317954 Loss2: 1.436970
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.859146 Loss1: 0.423245 Loss2: 1.435901
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.851093 Loss1: 0.410042 Loss2: 1.441052
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.775570 Loss1: 0.345131 Loss2: 1.430439
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.778102 Loss1: 0.344287 Loss2: 1.433815
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.716823 Loss1: 0.285873 Loss2: 1.430949
-(DefaultActor pid=3764) >> Training accuracy: 0.938542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.724214 Loss1: 1.741112 Loss2: 1.983101
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.564584 Loss1: 1.126269 Loss2: 1.438315
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.326015 Loss1: 0.895324 Loss2: 1.430692
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.138346 Loss1: 0.701703 Loss2: 1.436643
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.998113 Loss1: 0.565205 Loss2: 1.432909
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.783501 Loss1: 1.732537 Loss2: 2.050965
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.706819 Loss1: 1.207418 Loss2: 1.499402
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.904284 Loss1: 0.483048 Loss2: 1.421236
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.313194 Loss1: 0.801520 Loss2: 1.511673
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.809262 Loss1: 0.381936 Loss2: 1.427327
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.148643 Loss1: 0.657973 Loss2: 1.490670
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.849388 Loss1: 0.427030 Loss2: 1.422358
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.061343 Loss1: 0.579141 Loss2: 1.482201
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.863238 Loss1: 0.436800 Loss2: 1.426438
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.751241 Loss1: 0.314786 Loss2: 1.436455 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.933594
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.866420 Loss1: 0.391985 Loss2: 1.474435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.779597 Loss1: 0.307846 Loss2: 1.471751 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.813111 Loss1: 1.760952 Loss2: 2.052159
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.654809 Loss1: 1.149351 Loss2: 1.505458
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.421875 Loss1: 0.943666 Loss2: 1.478209
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.159949 Loss1: 0.675355 Loss2: 1.484594
-DEBUG flwr 2023-10-09 20:29:20,865 | server.py:236 | fit_round 51 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.730119 Loss1: 1.629655 Loss2: 2.100464
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.518743 Loss1: 1.002091 Loss2: 1.516653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.210043 Loss1: 0.721201 Loss2: 1.488842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.078781 Loss1: 0.579790 Loss2: 1.498992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.035011 Loss1: 0.533488 Loss2: 1.501522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.943225 Loss1: 0.441243 Loss2: 1.501981 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.857571 Loss1: 0.365972 Loss2: 1.491600 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.743751 Loss1: 0.264631 Loss2: 1.479120 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.951042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.651863 Loss1: 1.550921 Loss2: 2.100942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.302494 Loss1: 0.819773 Loss2: 1.482721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.040992 Loss1: 0.550499 Loss2: 1.490492
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.160250 Loss1: 1.997390 Loss2: 2.162860
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.869236 Loss1: 1.310004 Loss2: 1.559232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.466254 Loss1: 0.940087 Loss2: 1.526167 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.204245 Loss1: 0.673784 Loss2: 1.530461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.695862 Loss1: 0.238135 Loss2: 1.457727
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.062740 Loss1: 0.534777 Loss2: 1.527962
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.737460 Loss1: 0.292966 Loss2: 1.444494
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.998824 Loss1: 0.467711 Loss2: 1.531113
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.698815 Loss1: 0.240006 Loss2: 1.458810
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.959369 Loss1: 0.427695 Loss2: 1.531675
-(DefaultActor pid=3765) >> Training accuracy: 0.923958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.903520 Loss1: 0.368397 Loss2: 1.535123
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.843883 Loss1: 0.318832 Loss2: 1.525051
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.831418 Loss1: 0.317476 Loss2: 1.513942
-(DefaultActor pid=3764) >> Training accuracy: 0.909598
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.692131 Loss1: 1.665932 Loss2: 2.026199
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.630442 Loss1: 1.138299 Loss2: 1.492144
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.284484 Loss1: 0.799954 Loss2: 1.484531
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.956006 Loss1: 1.847772 Loss2: 2.108233
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.084975 Loss1: 0.612280 Loss2: 1.472695
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.743526 Loss1: 1.210085 Loss2: 1.533440
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.995015 Loss1: 0.523677 Loss2: 1.471338
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.410017 Loss1: 0.911336 Loss2: 1.498681
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.905988 Loss1: 0.439963 Loss2: 1.466025
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.142975 Loss1: 0.628182 Loss2: 1.514793
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.888342 Loss1: 0.407108 Loss2: 1.481234
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.897582 Loss1: 0.420696 Loss2: 1.476886 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.816527 Loss1: 0.338256 Loss2: 1.478271 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.855701 Loss1: 0.380614 Loss2: 1.475087 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.862305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.819305 Loss1: 0.321869 Loss2: 1.497435 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.906250
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 20:29:20,865][flwr][DEBUG] - fit_round 51 received 50 results and 0 failures
-INFO flwr 2023-10-09 20:30:02,969 | server.py:125 | fit progress: (51, 2.3997931175719436, {'accuracy': 0.4815}, 117510.747286994)
->> Test accuracy: 0.481500
-[2023-10-09 20:30:02,969][flwr][INFO] - fit progress: (51, 2.3997931175719436, {'accuracy': 0.4815}, 117510.747286994)
-DEBUG flwr 2023-10-09 20:30:02,969 | server.py:173 | evaluate_round 51: strategy sampled 50 clients (out of 50)
-[2023-10-09 20:30:02,969][flwr][DEBUG] - evaluate_round 51: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 20:39:09,425 | server.py:187 | evaluate_round 51 received 50 results and 0 failures
-[2023-10-09 20:39:09,425][flwr][DEBUG] - evaluate_round 51 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 20:39:09,425 | server.py:222 | fit_round 52: strategy sampled 50 clients (out of 50)
-[2023-10-09 20:39:09,425][flwr][DEBUG] - fit_round 52: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.797124 Loss1: 1.771976 Loss2: 2.025149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.318046 Loss1: 0.867626 Loss2: 1.450421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.036815 Loss1: 0.594989 Loss2: 1.441826
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.689877 Loss1: 1.650800 Loss2: 2.039077
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.587639 Loss1: 1.105255 Loss2: 1.482384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.257818 Loss1: 0.816967 Loss2: 1.440852 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.010696 Loss1: 0.575918 Loss2: 1.434778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.889472 Loss1: 0.464850 Loss2: 1.424622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.786444 Loss1: 0.361298 Loss2: 1.425145 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.914583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.749943 Loss1: 0.303451 Loss2: 1.446492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.740738 Loss1: 0.320395 Loss2: 1.420343
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.743944 Loss1: 0.330644 Loss2: 1.413300
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.712671 Loss1: 0.293150 Loss2: 1.419521
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.658292 Loss1: 0.237961 Loss2: 1.420331
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.624404 Loss1: 1.584656 Loss2: 2.039748
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.478083 Loss1: 1.040290 Loss2: 1.437792
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.377075 Loss1: 0.931061 Loss2: 1.446014
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.015666 Loss1: 1.781704 Loss2: 2.233962
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.977309 Loss1: 0.531017 Loss2: 1.446292
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.775223 Loss1: 0.373160 Loss2: 1.402063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.759794 Loss1: 0.362648 Loss2: 1.397146 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.707067 Loss1: 0.307661 Loss2: 1.399406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.922190 Loss1: 0.439547 Loss2: 1.482644 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.870793 Loss1: 0.393542 Loss2: 1.477250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.828397 Loss1: 0.344766 Loss2: 1.483630 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.709162 Loss1: 0.233114 Loss2: 1.476048 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.708662 Loss1: 1.648217 Loss2: 2.060444
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.612708 Loss1: 1.157449 Loss2: 1.455259
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.276732 Loss1: 0.814149 Loss2: 1.462583
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.070317 Loss1: 0.624308 Loss2: 1.446009
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.557959 Loss1: 1.507685 Loss2: 2.050274
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.486832 Loss1: 1.006217 Loss2: 1.480615 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.268646 Loss1: 0.791717 Loss2: 1.476928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.083801 Loss1: 0.607459 Loss2: 1.476342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.944451 Loss1: 0.493452 Loss2: 1.450999 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.804588 Loss1: 0.359395 Loss2: 1.445194 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.895089
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.715115 Loss1: 0.283212 Loss2: 1.431903 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.637262 Loss1: 0.207296 Loss2: 1.429967 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.942708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.664408 Loss1: 1.148158 Loss2: 1.516250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.148246 Loss1: 0.667831 Loss2: 1.480415 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.711088 Loss1: 1.723349 Loss2: 1.987739
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.002522 Loss1: 0.531164 Loss2: 1.471358
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.714734 Loss1: 1.253617 Loss2: 1.461117
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.971283 Loss1: 0.502003 Loss2: 1.469280
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.383897 Loss1: 0.935386 Loss2: 1.448511
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.875795 Loss1: 0.397709 Loss2: 1.478086
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.156782 Loss1: 0.710215 Loss2: 1.446568
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.862844 Loss1: 0.397820 Loss2: 1.465025
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.007616 Loss1: 0.578151 Loss2: 1.429465
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.835235 Loss1: 0.362215 Loss2: 1.473020
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.927894 Loss1: 0.498818 Loss2: 1.429077
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.765840 Loss1: 0.295175 Loss2: 1.470665
-(DefaultActor pid=3765) >> Training accuracy: 0.919792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.796233 Loss1: 0.384028 Loss2: 1.412205 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.714764 Loss1: 0.302806 Loss2: 1.411958 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.723119 Loss1: 1.248803 Loss2: 1.474316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.130140 Loss1: 0.655721 Loss2: 1.474419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.787801 Loss1: 1.775319 Loss2: 2.012482
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.002965 Loss1: 0.551282 Loss2: 1.451683
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.686913 Loss1: 1.237416 Loss2: 1.449497
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.909831 Loss1: 0.460395 Loss2: 1.449436
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.271603 Loss1: 0.846067 Loss2: 1.425537
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.863708 Loss1: 0.414771 Loss2: 1.448936
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.017105 Loss1: 0.587490 Loss2: 1.429615
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.775196 Loss1: 0.327076 Loss2: 1.448120
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.898456 Loss1: 0.491429 Loss2: 1.407027
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.759413 Loss1: 0.305908 Loss2: 1.453504
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.837489 Loss1: 0.417990 Loss2: 1.419499
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.802811 Loss1: 0.357107 Loss2: 1.445704
-(DefaultActor pid=3765) >> Training accuracy: 0.933333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.805196 Loss1: 0.383422 Loss2: 1.421774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.767088 Loss1: 0.338710 Loss2: 1.428378 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.842708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.648282 Loss1: 1.201204 Loss2: 1.447078 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.027057 Loss1: 0.607206 Loss2: 1.419851 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.914282 Loss1: 0.498327 Loss2: 1.415955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.813521 Loss1: 0.393503 Loss2: 1.420018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.774342 Loss1: 0.364886 Loss2: 1.409456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.708518 Loss1: 0.295120 Loss2: 1.413398 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.727076 Loss1: 0.315079 Loss2: 1.411997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.703266 Loss1: 0.291155 Loss2: 1.412112 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.923828
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.819789 Loss1: 0.379030 Loss2: 1.440758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.714509 Loss1: 0.275476 Loss2: 1.439033 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.554459 Loss1: 1.606086 Loss2: 1.948373
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.477374 Loss1: 1.007157 Loss2: 1.470217
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.234579 Loss1: 0.775913 Loss2: 1.458666
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.982953 Loss1: 0.536880 Loss2: 1.446073
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.666615 Loss1: 1.695332 Loss2: 1.971283
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.824006 Loss1: 0.404822 Loss2: 1.419184
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.594215 Loss1: 1.151691 Loss2: 1.442524
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.778582 Loss1: 0.359503 Loss2: 1.419079
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.245327 Loss1: 0.795787 Loss2: 1.449539
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.825501 Loss1: 0.402945 Loss2: 1.422556
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.993039 Loss1: 0.559823 Loss2: 1.433216
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.821867 Loss1: 0.382080 Loss2: 1.439787
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.944911 Loss1: 0.526127 Loss2: 1.418784
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.823830 Loss1: 0.373776 Loss2: 1.450053
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.896724 Loss1: 0.459456 Loss2: 1.437269
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.714360 Loss1: 0.283252 Loss2: 1.431108
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.837779 Loss1: 0.405637 Loss2: 1.432142
-(DefaultActor pid=3765) >> Training accuracy: 0.958008
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.747050 Loss1: 0.321783 Loss2: 1.425267
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.709785 Loss1: 0.293968 Loss2: 1.415818
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.663248 Loss1: 0.241806 Loss2: 1.421442
-(DefaultActor pid=3764) >> Training accuracy: 0.944336
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.861995 Loss1: 1.845722 Loss2: 2.016273
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.803420 Loss1: 1.334230 Loss2: 1.469190
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.352444 Loss1: 0.853676 Loss2: 1.498768
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.131946 Loss1: 0.673501 Loss2: 1.458445
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.950547 Loss1: 1.874306 Loss2: 2.076241
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.749373 Loss1: 1.276296 Loss2: 1.473077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.293804 Loss1: 0.855492 Loss2: 1.438312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.814318 Loss1: 0.370498 Loss2: 1.443820
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.065678 Loss1: 0.635758 Loss2: 1.429919
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.837475 Loss1: 0.388322 Loss2: 1.449154
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.018890 Loss1: 0.592385 Loss2: 1.426506
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.828255 Loss1: 0.368684 Loss2: 1.459571
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.867136 Loss1: 0.430659 Loss2: 1.436477
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.801361 Loss1: 0.376697 Loss2: 1.424664
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.841199 Loss1: 0.390445 Loss2: 1.450754
-(DefaultActor pid=3765) >> Training accuracy: 0.887500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.794953 Loss1: 0.360556 Loss2: 1.434397 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.928571
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.696971 Loss1: 1.660571 Loss2: 2.036399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.295740 Loss1: 0.822889 Loss2: 1.472850 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.063646 Loss1: 0.603432 Loss2: 1.460214
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.662905 Loss1: 1.606858 Loss2: 2.056047
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.508936 Loss1: 1.035824 Loss2: 1.473111 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.312258 Loss1: 0.843725 Loss2: 1.468533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.100296 Loss1: 0.628855 Loss2: 1.471441 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.937970 Loss1: 0.477746 Loss2: 1.460224 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.901873 Loss1: 0.447246 Loss2: 1.454627 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.745059 Loss1: 0.310070 Loss2: 1.434989
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.852084 Loss1: 0.400861 Loss2: 1.451224
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.740229 Loss1: 0.279879 Loss2: 1.460350
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.714615 Loss1: 0.265736 Loss2: 1.448880
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.685912 Loss1: 0.250318 Loss2: 1.435594
-(DefaultActor pid=3764) >> Training accuracy: 0.943750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.604438 Loss1: 1.568512 Loss2: 2.035926
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.514557 Loss1: 1.081996 Loss2: 1.432561
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.159514 Loss1: 0.753061 Loss2: 1.406452
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.954034 Loss1: 0.536525 Loss2: 1.417509
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.735413 Loss1: 1.729006 Loss2: 2.006407
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.574554 Loss1: 1.113845 Loss2: 1.460708 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.264878 Loss1: 0.825179 Loss2: 1.439700 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.055928 Loss1: 0.609323 Loss2: 1.446605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.947995 Loss1: 0.494661 Loss2: 1.453334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.927614 Loss1: 0.479830 Loss2: 1.447784 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.934375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.686236 Loss1: 0.282563 Loss2: 1.403673
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.909550 Loss1: 0.448573 Loss2: 1.460977
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.821759 Loss1: 0.366962 Loss2: 1.454797
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.805036 Loss1: 0.352406 Loss2: 1.452630
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.695777 Loss1: 0.252149 Loss2: 1.443627
-(DefaultActor pid=3764) >> Training accuracy: 0.938542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.723661 Loss1: 1.699679 Loss2: 2.023982
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.546635 Loss1: 1.080027 Loss2: 1.466609
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.216361 Loss1: 0.775790 Loss2: 1.440572
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.048338 Loss1: 0.593927 Loss2: 1.454411
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.854527 Loss1: 1.742366 Loss2: 2.112160
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.720245 Loss1: 1.189262 Loss2: 1.530983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.336677 Loss1: 0.823542 Loss2: 1.513135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.136869 Loss1: 0.617651 Loss2: 1.519218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.024933 Loss1: 0.513270 Loss2: 1.511663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.947604 Loss1: 0.440056 Loss2: 1.507547 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.677944 Loss1: 0.234397 Loss2: 1.443546
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.838040 Loss1: 0.338841 Loss2: 1.499200
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.768215 Loss1: 0.270094 Loss2: 1.498121
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.741433 Loss1: 0.254654 Loss2: 1.486779
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.785055 Loss1: 0.293470 Loss2: 1.491585
-(DefaultActor pid=3764) >> Training accuracy: 0.919792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.615849 Loss1: 1.551866 Loss2: 2.063982
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.611508 Loss1: 1.113404 Loss2: 1.498104
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.298609 Loss1: 0.782820 Loss2: 1.515789
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.029746 Loss1: 0.546301 Loss2: 1.483445
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.967721 Loss1: 1.857504 Loss2: 2.110217
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.739785 Loss1: 1.213160 Loss2: 1.526625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.340055 Loss1: 0.853012 Loss2: 1.487043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.105843 Loss1: 0.610567 Loss2: 1.495276 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.015582 Loss1: 0.536995 Loss2: 1.478587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.008217 Loss1: 0.509283 Loss2: 1.498935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.674228 Loss1: 0.212695 Loss2: 1.461533
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.892974 Loss1: 0.394597 Loss2: 1.498377
-(DefaultActor pid=3765) >> Training accuracy: 0.929167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.870664 Loss1: 0.393789 Loss2: 1.476874
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.807109 Loss1: 0.321262 Loss2: 1.485848
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.718262 Loss1: 0.242830 Loss2: 1.475432
-(DefaultActor pid=3764) >> Training accuracy: 0.920759
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.985749 Loss1: 1.902277 Loss2: 2.083472
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.828770 Loss1: 1.312566 Loss2: 1.516204
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.341986 Loss1: 0.855842 Loss2: 1.486143
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.123100 Loss1: 0.626970 Loss2: 1.496130
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.743050 Loss1: 1.711977 Loss2: 2.031074
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.962539 Loss1: 0.496597 Loss2: 1.465942
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.558035 Loss1: 1.102878 Loss2: 1.455157
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.882871 Loss1: 0.422921 Loss2: 1.459950
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.273844 Loss1: 0.833793 Loss2: 1.440051
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.927190 Loss1: 0.453881 Loss2: 1.473308
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.113029 Loss1: 0.661997 Loss2: 1.451031
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.874358 Loss1: 0.391660 Loss2: 1.482697
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.005362 Loss1: 0.556822 Loss2: 1.448540
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.829891 Loss1: 0.353306 Loss2: 1.476585
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.865829 Loss1: 0.422655 Loss2: 1.443173
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.777273 Loss1: 0.312557 Loss2: 1.464716
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.861713 Loss1: 0.425067 Loss2: 1.436645
-(DefaultActor pid=3765) >> Training accuracy: 0.918750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.799161 Loss1: 0.354811 Loss2: 1.444350
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.766803 Loss1: 0.324926 Loss2: 1.441877
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.780199 Loss1: 0.331422 Loss2: 1.448777
-(DefaultActor pid=3764) >> Training accuracy: 0.942708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.955627 Loss1: 1.907195 Loss2: 2.048432
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.794084 Loss1: 1.284463 Loss2: 1.509622
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.364081 Loss1: 0.900486 Loss2: 1.463595
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.196065 Loss1: 0.728684 Loss2: 1.467381
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.689771 Loss1: 1.616102 Loss2: 2.073670
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.601136 Loss1: 1.109956 Loss2: 1.491180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.473267 Loss1: 0.963311 Loss2: 1.509956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.237868 Loss1: 0.726142 Loss2: 1.511726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.037889 Loss1: 0.543489 Loss2: 1.494400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.978039 Loss1: 0.477815 Loss2: 1.500224 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.886458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.821515 Loss1: 0.348857 Loss2: 1.472658
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.950913 Loss1: 0.460212 Loss2: 1.490701
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.910913 Loss1: 0.428024 Loss2: 1.482889
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.802694 Loss1: 0.307899 Loss2: 1.494795
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.698513 Loss1: 0.222275 Loss2: 1.476239
-(DefaultActor pid=3764) >> Training accuracy: 0.912500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.769230 Loss1: 1.731044 Loss2: 2.038186
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.584079 Loss1: 1.066928 Loss2: 1.517151
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.311376 Loss1: 0.825025 Loss2: 1.486351
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.036405 Loss1: 0.566800 Loss2: 1.469605
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.728479 Loss1: 1.746554 Loss2: 1.981925
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.546858 Loss1: 1.059286 Loss2: 1.487572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.214004 Loss1: 0.754505 Loss2: 1.459498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.073937 Loss1: 0.625167 Loss2: 1.448771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.990953 Loss1: 0.530405 Loss2: 1.460548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.853163 Loss1: 0.392652 Loss2: 1.460511 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.950000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.781202 Loss1: 0.338345 Loss2: 1.442857 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.746247 Loss1: 0.302180 Loss2: 1.444067 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.917969
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.734804 Loss1: 1.252267 Loss2: 1.482537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.059060 Loss1: 0.589985 Loss2: 1.469075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.890604 Loss1: 1.797299 Loss2: 2.093305
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.017348 Loss1: 0.554753 Loss2: 1.462594
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.801515 Loss1: 1.293400 Loss2: 1.508115
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.935335 Loss1: 0.466975 Loss2: 1.468360
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.881867 Loss1: 0.407966 Loss2: 1.473900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.840198 Loss1: 0.368492 Loss2: 1.471706 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.780326 Loss1: 0.312576 Loss2: 1.467750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.791332 Loss1: 0.319929 Loss2: 1.471404 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.904297
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.906736 Loss1: 0.416713 Loss2: 1.490023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.811455 Loss1: 0.327291 Loss2: 1.484164 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.926042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.563360 Loss1: 1.579409 Loss2: 1.983950
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.585812 Loss1: 1.105477 Loss2: 1.480335
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.261401 Loss1: 0.794308 Loss2: 1.467092
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.648192 Loss1: 1.622267 Loss2: 2.025924
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.084287 Loss1: 0.624307 Loss2: 1.459980
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.536680 Loss1: 1.076975 Loss2: 1.459705
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.956326 Loss1: 0.507750 Loss2: 1.448576
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.239017 Loss1: 0.799680 Loss2: 1.439337
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.873207 Loss1: 0.422241 Loss2: 1.450966
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.816311 Loss1: 0.366421 Loss2: 1.449890 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.722863 Loss1: 0.282985 Loss2: 1.439878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.735730 Loss1: 0.295995 Loss2: 1.439735 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.774756 Loss1: 0.326128 Loss2: 1.448628 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.944853
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.725552 Loss1: 0.293249 Loss2: 1.432303 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.894792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.696648 Loss1: 1.659999 Loss2: 2.036649
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.566471 Loss1: 1.100418 Loss2: 1.466054
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.223931 Loss1: 0.767806 Loss2: 1.456125
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.796801 Loss1: 1.686128 Loss2: 2.110672
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.014185 Loss1: 0.558251 Loss2: 1.455934
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.602406 Loss1: 1.100749 Loss2: 1.501657
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.822933 Loss1: 0.383067 Loss2: 1.439867
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.821966 Loss1: 0.385358 Loss2: 1.436608 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.821512 Loss1: 0.380234 Loss2: 1.441278 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.771061 Loss1: 0.315611 Loss2: 1.455451 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.796172 Loss1: 0.341983 Loss2: 1.454189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.822917 Loss1: 0.369478 Loss2: 1.453439 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.908333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.701297 Loss1: 0.242735 Loss2: 1.458562 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.949519
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.788075 Loss1: 1.757616 Loss2: 2.030459
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.668403 Loss1: 1.176663 Loss2: 1.491739
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.274305 Loss1: 0.801864 Loss2: 1.472441
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.132612 Loss1: 0.664934 Loss2: 1.467678
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.768573 Loss1: 1.720139 Loss2: 2.048434
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.963958 Loss1: 0.502556 Loss2: 1.461402
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.556166 Loss1: 1.071620 Loss2: 1.484545
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.958085 Loss1: 0.492764 Loss2: 1.465321
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.289984 Loss1: 0.819629 Loss2: 1.470355
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.959264 Loss1: 0.481441 Loss2: 1.477823
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.098603 Loss1: 0.610210 Loss2: 1.488393
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.903326 Loss1: 0.435409 Loss2: 1.467917
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.004078 Loss1: 0.526217 Loss2: 1.477862
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.826997 Loss1: 0.367704 Loss2: 1.459293
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.949695 Loss1: 0.471992 Loss2: 1.477703
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.788439 Loss1: 0.323017 Loss2: 1.465422
-(DefaultActor pid=3765) >> Training accuracy: 0.904167
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.946160 Loss1: 0.467172 Loss2: 1.478989
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.861485 Loss1: 0.377566 Loss2: 1.483920
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.803082 Loss1: 0.326288 Loss2: 1.476794
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.753859 Loss1: 0.281448 Loss2: 1.472410
-(DefaultActor pid=3764) >> Training accuracy: 0.927083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.795949 Loss1: 1.785532 Loss2: 2.010417
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.703091 Loss1: 1.211911 Loss2: 1.491180
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.354104 Loss1: 0.865396 Loss2: 1.488708
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.132188 Loss1: 0.661296 Loss2: 1.470892
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.686997 Loss1: 1.697079 Loss2: 1.989918
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.097589 Loss1: 0.600158 Loss2: 1.497431
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.677361 Loss1: 1.186139 Loss2: 1.491222
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.035407 Loss1: 0.547645 Loss2: 1.487762
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.347398 Loss1: 0.857914 Loss2: 1.489484
-(DefaultActor pid=3765) Epoch: 6 Loss: 2.002440 Loss1: 0.505417 Loss2: 1.497023
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.064885 Loss1: 0.605781 Loss2: 1.459104
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.962164 Loss1: 0.458922 Loss2: 1.503242
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.928197 Loss1: 0.475033 Loss2: 1.453164
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.913034 Loss1: 0.416528 Loss2: 1.496506
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.818324 Loss1: 0.375630 Loss2: 1.442694
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.856317 Loss1: 0.364053 Loss2: 1.492264
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.872245 Loss1: 0.413864 Loss2: 1.458381
-(DefaultActor pid=3765) >> Training accuracy: 0.910156
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.782709 Loss1: 0.333241 Loss2: 1.449468
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.751291 Loss1: 0.309900 Loss2: 1.441391
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.764940 Loss1: 0.320007 Loss2: 1.444932
-(DefaultActor pid=3764) >> Training accuracy: 0.927734
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.698257 Loss1: 1.626771 Loss2: 2.071486
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.638036 Loss1: 1.149440 Loss2: 1.488596
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.305754 Loss1: 0.815594 Loss2: 1.490160
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.059764 Loss1: 0.577570 Loss2: 1.482194
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.869590 Loss1: 1.786900 Loss2: 2.082690
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.758932 Loss1: 1.229705 Loss2: 1.529227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.349168 Loss1: 0.838202 Loss2: 1.510966 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.175543 Loss1: 0.665363 Loss2: 1.510180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.074180 Loss1: 0.566978 Loss2: 1.507202 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.992186 Loss1: 0.492711 Loss2: 1.499476 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.921390 Loss1: 0.422039 Loss2: 1.499351 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.809443 Loss1: 0.292266 Loss2: 1.517178 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.932617
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.689471 Loss1: 1.197719 Loss2: 1.491752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.131474 Loss1: 0.632609 Loss2: 1.498865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.139860 Loss1: 0.659970 Loss2: 1.479890
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.720290 Loss1: 1.669318 Loss2: 2.050972
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.418490 Loss1: 0.950274 Loss2: 1.468216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.118856 Loss1: 0.692349 Loss2: 1.426508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.945789 Loss1: 0.512418 Loss2: 1.433371 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.872735 Loss1: 0.441500 Loss2: 1.431235 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.930208
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.767500 Loss1: 0.294774 Loss2: 1.472727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.845719 Loss1: 0.409551 Loss2: 1.436169
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.809936 Loss1: 0.365118 Loss2: 1.444818
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.757628 Loss1: 0.325900 Loss2: 1.431729
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.741044 Loss1: 0.315698 Loss2: 1.425345
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.720896 Loss1: 0.284760 Loss2: 1.436137
-(DefaultActor pid=3764) >> Training accuracy: 0.954167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.734793 Loss1: 1.744596 Loss2: 1.990197
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.657896 Loss1: 1.176450 Loss2: 1.481446
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.278306 Loss1: 0.816539 Loss2: 1.461766
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.035593 Loss1: 0.583819 Loss2: 1.451773
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.886417 Loss1: 0.451495 Loss2: 1.434922
-DEBUG flwr 2023-10-09 21:08:10,917 | server.py:236 | fit_round 52 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.724197 Loss1: 1.659378 Loss2: 2.064819
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.856152 Loss1: 0.421480 Loss2: 1.434671
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.604509 Loss1: 1.108108 Loss2: 1.496401
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.796777 Loss1: 0.366876 Loss2: 1.429901
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.244078 Loss1: 0.743585 Loss2: 1.500493
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.722686 Loss1: 0.297630 Loss2: 1.425056
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.117847 Loss1: 0.633334 Loss2: 1.484513
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.721798 Loss1: 0.299041 Loss2: 1.422757
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.997314 Loss1: 0.519398 Loss2: 1.477916
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.699162 Loss1: 0.275843 Loss2: 1.423319
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.928263 Loss1: 0.451797 Loss2: 1.476466
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.879529 Loss1: 0.397596 Loss2: 1.481933
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.787672 Loss1: 0.316253 Loss2: 1.471419
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.759675 Loss1: 0.293461 Loss2: 1.466215
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.713737 Loss1: 0.244642 Loss2: 1.469094
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.660937 Loss1: 1.661143 Loss2: 1.999794
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.517873 Loss1: 1.076034 Loss2: 1.441839
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.189825 Loss1: 0.750349 Loss2: 1.439476
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.988919 Loss1: 0.550971 Loss2: 1.437948
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.884516 Loss1: 1.763098 Loss2: 2.121418
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.930125 Loss1: 0.499999 Loss2: 1.430127
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.677446 Loss1: 1.182672 Loss2: 1.494774
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.876977 Loss1: 0.441551 Loss2: 1.435426
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.847031 Loss1: 0.411695 Loss2: 1.435336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.809326 Loss1: 0.372196 Loss2: 1.437130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.755682 Loss1: 0.318530 Loss2: 1.437152 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.734242 Loss1: 0.300370 Loss2: 1.433871 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.672508 Loss1: 0.254003 Loss2: 1.418506 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.956731
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.795394 Loss1: 1.681396 Loss2: 2.113998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.171847 Loss1: 0.713069 Loss2: 1.458777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.004992 Loss1: 0.546943 Loss2: 1.458049
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.608047 Loss1: 1.599643 Loss2: 2.008404
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.632341 Loss1: 1.149074 Loss2: 1.483268 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.254748 Loss1: 0.772238 Loss2: 1.482509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.043726 Loss1: 0.584325 Loss2: 1.459400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.983998 Loss1: 0.526387 Loss2: 1.457611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.878594 Loss1: 0.430216 Loss2: 1.448378 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.735307 Loss1: 0.298262 Loss2: 1.437045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.643091 Loss1: 0.209092 Loss2: 1.433998 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962891
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 21:08:10,917][flwr][DEBUG] - fit_round 52 received 50 results and 0 failures
-INFO flwr 2023-10-09 21:08:52,520 | server.py:125 | fit progress: (52, 2.3937867083869424, {'accuracy': 0.4836}, 119840.298075107)
->> Test accuracy: 0.483600
-[2023-10-09 21:08:52,520][flwr][INFO] - fit progress: (52, 2.3937867083869424, {'accuracy': 0.4836}, 119840.298075107)
-DEBUG flwr 2023-10-09 21:08:52,520 | server.py:173 | evaluate_round 52: strategy sampled 50 clients (out of 50)
-[2023-10-09 21:08:52,520][flwr][DEBUG] - evaluate_round 52: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 21:17:58,156 | server.py:187 | evaluate_round 52 received 50 results and 0 failures
-[2023-10-09 21:17:58,156][flwr][DEBUG] - evaluate_round 52 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 21:17:58,157 | server.py:222 | fit_round 53: strategy sampled 50 clients (out of 50)
-[2023-10-09 21:17:58,157][flwr][DEBUG] - fit_round 53: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.925190 Loss1: 1.771658 Loss2: 2.153532 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.269745 Loss1: 0.823687 Loss2: 1.446057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.910876 Loss1: 0.478924 Loss2: 1.431951 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.718502 Loss1: 1.173357 Loss2: 1.545145 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.699405 Loss1: 0.286705 Loss2: 1.412700 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.073564 Loss1: 0.564193 Loss2: 1.509371 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.936198
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.016798 Loss1: 0.509981 Loss2: 1.506818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.845833 Loss1: 0.345713 Loss2: 1.500121 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.866200 Loss1: 0.372403 Loss2: 1.493797 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.857754 Loss1: 0.345127 Loss2: 1.512627 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.866211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.944833 Loss1: 0.515309 Loss2: 1.429524 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.797884 Loss1: 0.383320 Loss2: 1.414564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.766637 Loss1: 0.350697 Loss2: 1.415940
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.762880 Loss1: 1.707143 Loss2: 2.055737
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.766083 Loss1: 0.341044 Loss2: 1.425039
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.641690 Loss1: 1.164346 Loss2: 1.477344
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.739077 Loss1: 0.317779 Loss2: 1.421298
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.269240 Loss1: 0.785368 Loss2: 1.483873
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.699603 Loss1: 0.282393 Loss2: 1.417211
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.063675 Loss1: 0.586229 Loss2: 1.477446
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.011485 Loss1: 0.548368 Loss2: 1.463117
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.932424 Loss1: 0.456719 Loss2: 1.475705
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.895595 Loss1: 0.422209 Loss2: 1.473386
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.797318 Loss1: 0.334240 Loss2: 1.463078
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.838314 Loss1: 1.763506 Loss2: 2.074808
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.759393 Loss1: 0.298752 Loss2: 1.460640
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.556727 Loss1: 1.067458 Loss2: 1.489269
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.691667 Loss1: 0.225474 Loss2: 1.466193
-(DefaultActor pid=3764) >> Training accuracy: 0.926042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.076285 Loss1: 0.601044 Loss2: 1.475241 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.910152 Loss1: 0.445677 Loss2: 1.464475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.891467 Loss1: 0.423613 Loss2: 1.467854
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.838280 Loss1: 1.781528 Loss2: 2.056752
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.890167 Loss1: 0.408151 Loss2: 1.482016
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.604599 Loss1: 1.121488 Loss2: 1.483111
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.826246 Loss1: 0.351628 Loss2: 1.474618
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.333401 Loss1: 0.865439 Loss2: 1.467963
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.785717 Loss1: 0.322535 Loss2: 1.463182
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.138049 Loss1: 0.656174 Loss2: 1.481875
-(DefaultActor pid=3765) >> Training accuracy: 0.947917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.022120 Loss1: 0.552085 Loss2: 1.470035
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.947994 Loss1: 0.482703 Loss2: 1.465291
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.849637 Loss1: 0.385140 Loss2: 1.464498
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.852917 Loss1: 0.392624 Loss2: 1.460293
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.822517 Loss1: 1.699173 Loss2: 2.123344
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.745838 Loss1: 0.281146 Loss2: 1.464691
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.664890 Loss1: 1.161858 Loss2: 1.503032
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.693426 Loss1: 0.247095 Loss2: 1.446331
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.103913 Loss1: 0.620193 Loss2: 1.483720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.879918 Loss1: 0.399620 Loss2: 1.480298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.631281 Loss1: 1.639198 Loss2: 1.992082 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.489435 Loss1: 1.027484 Loss2: 1.461951 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.318198 Loss1: 0.868402 Loss2: 1.449797 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.916295
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.849237 Loss1: 0.419821 Loss2: 1.429416 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.773661 Loss1: 0.344913 Loss2: 1.428748 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.853567 Loss1: 1.664193 Loss2: 2.189374
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.721593 Loss1: 0.298910 Loss2: 1.422683
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.526508 Loss1: 1.022403 Loss2: 1.504104
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.702372 Loss1: 0.279240 Loss2: 1.423132
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.649888 Loss1: 0.235793 Loss2: 1.414095 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.887830 Loss1: 0.428542 Loss2: 1.459288 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.740229 Loss1: 0.294877 Loss2: 1.445352 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.765597 Loss1: 0.295795 Loss2: 1.469802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.775404 Loss1: 0.311208 Loss2: 1.464197 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.078266 Loss1: 0.601033 Loss2: 1.477233 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.934261 Loss1: 0.463702 Loss2: 1.470559 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.659508 Loss1: 1.541665 Loss2: 2.117842
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.912130 Loss1: 0.418845 Loss2: 1.493285
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.579604 Loss1: 1.046928 Loss2: 1.532677
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.807400 Loss1: 0.323224 Loss2: 1.484176
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.118167 Loss1: 0.609988 Loss2: 1.508179
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.729841 Loss1: 0.274703 Loss2: 1.455138
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.004176 Loss1: 0.509699 Loss2: 1.494477
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.705780 Loss1: 0.240833 Loss2: 1.464947
-(DefaultActor pid=3764) >> Training accuracy: 0.946875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.955150 Loss1: 0.438173 Loss2: 1.516977 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.784877 Loss1: 0.288744 Loss2: 1.496133 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.825254 Loss1: 0.326481 Loss2: 1.498773
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.732425 Loss1: 1.666932 Loss2: 2.065493
-(DefaultActor pid=3765) >> Training accuracy: 0.919792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.765589 Loss1: 0.264854 Loss2: 1.500735
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.589699 Loss1: 1.061244 Loss2: 1.528455
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.299573 Loss1: 0.798369 Loss2: 1.501204
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.031537 Loss1: 0.534933 Loss2: 1.496604
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.940507 Loss1: 0.449954 Loss2: 1.490553
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.910941 Loss1: 0.435885 Loss2: 1.475056
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.765914 Loss1: 1.717484 Loss2: 2.048430
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.853212 Loss1: 0.355314 Loss2: 1.497898
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.531621 Loss1: 1.073155 Loss2: 1.458466
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.773171 Loss1: 0.292316 Loss2: 1.480855
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.211572 Loss1: 0.762514 Loss2: 1.449058
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.788148 Loss1: 0.312384 Loss2: 1.475764
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.045749 Loss1: 0.607871 Loss2: 1.437878
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.856287 Loss1: 0.426657 Loss2: 1.429629
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.776121 Loss1: 0.297375 Loss2: 1.478746
-(DefaultActor pid=3764) >> Training accuracy: 0.930664
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.729910 Loss1: 0.303615 Loss2: 1.426295 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.677947 Loss1: 0.249406 Loss2: 1.428541 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.702897 Loss1: 0.278475 Loss2: 1.424422
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.722387 Loss1: 1.704950 Loss2: 2.017438
-(DefaultActor pid=3765) >> Training accuracy: 0.946875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.698814 Loss1: 1.228561 Loss2: 1.470253
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.237328 Loss1: 0.787468 Loss2: 1.449860
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.032145 Loss1: 0.590671 Loss2: 1.441474
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.916548 Loss1: 0.484229 Loss2: 1.432319
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.886305 Loss1: 1.834374 Loss2: 2.051930
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.847312 Loss1: 0.413828 Loss2: 1.433484
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.664102 Loss1: 1.180117 Loss2: 1.483985
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.836496 Loss1: 0.407933 Loss2: 1.428564
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.323517 Loss1: 0.853587 Loss2: 1.469930
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.766682 Loss1: 0.327449 Loss2: 1.439233
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.180117 Loss1: 0.695777 Loss2: 1.484340
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.717263 Loss1: 0.288884 Loss2: 1.428378
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.004672 Loss1: 0.533166 Loss2: 1.471507
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.712744 Loss1: 0.283215 Loss2: 1.429529
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.870943 Loss1: 0.403938 Loss2: 1.467005 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.784865 Loss1: 0.320275 Loss2: 1.464589 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.734837 Loss1: 0.277072 Loss2: 1.457766
-(DefaultActor pid=3765) >> Training accuracy: 0.931250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.600443 Loss1: 1.575186 Loss2: 2.025257
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.555019 Loss1: 1.072481 Loss2: 1.482537
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.195124 Loss1: 0.727030 Loss2: 1.468093
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.071449 Loss1: 0.621870 Loss2: 1.449579
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.001948 Loss1: 0.539137 Loss2: 1.462811
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.585680 Loss1: 1.541823 Loss2: 2.043857
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.969375 Loss1: 0.502491 Loss2: 1.466884
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.532394 Loss1: 1.023664 Loss2: 1.508729
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.847274 Loss1: 0.372193 Loss2: 1.475081
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.213914 Loss1: 0.709719 Loss2: 1.504194
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.759328 Loss1: 0.295308 Loss2: 1.464019
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.732891 Loss1: 0.272266 Loss2: 1.460625
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.031725 Loss1: 0.524700 Loss2: 1.507025
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.714831 Loss1: 0.255681 Loss2: 1.459151
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.852240 Loss1: 0.358952 Loss2: 1.493288
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.810076 Loss1: 0.341229 Loss2: 1.468847
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.763072 Loss1: 0.288020 Loss2: 1.475052
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.776194 Loss1: 0.288839 Loss2: 1.487356
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.887733 Loss1: 0.402763 Loss2: 1.484970
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.580285 Loss1: 1.549769 Loss2: 2.030515
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.762629 Loss1: 0.268442 Loss2: 1.494187
-(DefaultActor pid=3765) >> Training accuracy: 0.934570
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.251334 Loss1: 0.765741 Loss2: 1.485593 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.925231 Loss1: 0.452102 Loss2: 1.473129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.734090 Loss1: 1.696545 Loss2: 2.037545
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.829129 Loss1: 0.374041 Loss2: 1.455088
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.688604 Loss1: 1.177089 Loss2: 1.511515
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.831073 Loss1: 0.366837 Loss2: 1.464236
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.332123 Loss1: 0.836777 Loss2: 1.495346
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.831487 Loss1: 0.356701 Loss2: 1.474786
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.784469 Loss1: 0.312635 Loss2: 1.471834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.764121 Loss1: 0.298519 Loss2: 1.465602 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.941176
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.886052 Loss1: 0.398896 Loss2: 1.487156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.898996 Loss1: 0.403765 Loss2: 1.495231 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.814286 Loss1: 0.326767 Loss2: 1.487519
-(DefaultActor pid=3765) >> Training accuracy: 0.914062
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.588029 Loss1: 1.536904 Loss2: 2.051125
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.609817 Loss1: 1.124148 Loss2: 1.485669
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.296242 Loss1: 0.816073 Loss2: 1.480169
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.083985 Loss1: 0.604039 Loss2: 1.479946
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.865089 Loss1: 0.401704 Loss2: 1.463385
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.751036 Loss1: 1.729800 Loss2: 2.021236
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.640339 Loss1: 1.147504 Loss2: 1.492835 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.300179 Loss1: 0.794287 Loss2: 1.505893 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.051751 Loss1: 0.564509 Loss2: 1.487242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.715287 Loss1: 0.275239 Loss2: 1.440048
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.910493 Loss1: 0.439483 Loss2: 1.471010
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.701618 Loss1: 0.254165 Loss2: 1.447453
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.849976 Loss1: 0.379083 Loss2: 1.470892
-(DefaultActor pid=3764) >> Training accuracy: 0.935547
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.860529 Loss1: 0.396141 Loss2: 1.464388
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.919666 Loss1: 0.447073 Loss2: 1.472592
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.777354 Loss1: 0.298670 Loss2: 1.478684
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.730992 Loss1: 0.266213 Loss2: 1.464779
-(DefaultActor pid=3765) >> Training accuracy: 0.911458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.765621 Loss1: 1.786782 Loss2: 1.978839
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.703496 Loss1: 1.259203 Loss2: 1.444293
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.370252 Loss1: 0.925500 Loss2: 1.444752
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.125249 Loss1: 0.684995 Loss2: 1.440255
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.710202 Loss1: 1.585217 Loss2: 2.124986
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.436177 Loss1: 0.936253 Loss2: 1.499924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.282141 Loss1: 0.795768 Loss2: 1.486373 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.195372 Loss1: 0.691448 Loss2: 1.503924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.976868 Loss1: 0.488104 Loss2: 1.488765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.807834 Loss1: 0.350069 Loss2: 1.457765 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.934570
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.758506 Loss1: 0.343700 Loss2: 1.414807
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.772300 Loss1: 0.322209 Loss2: 1.450091
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.704327 Loss1: 0.255949 Loss2: 1.448378
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.704487 Loss1: 0.252450 Loss2: 1.452037
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.722417 Loss1: 0.269053 Loss2: 1.453364
-(DefaultActor pid=3765) >> Training accuracy: 0.934375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.800234 Loss1: 1.740543 Loss2: 2.059691
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.632414 Loss1: 1.133607 Loss2: 1.498807
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.347818 Loss1: 0.853141 Loss2: 1.494677
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.156253 Loss1: 0.645499 Loss2: 1.510754
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.920739 Loss1: 1.898259 Loss2: 2.022480
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.580063 Loss1: 1.157436 Loss2: 1.422627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.958044 Loss1: 0.472955 Loss2: 1.485089
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.169632 Loss1: 0.756422 Loss2: 1.413211
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.924610 Loss1: 0.422479 Loss2: 1.502131
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.048888 Loss1: 0.634810 Loss2: 1.414079
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.916282 Loss1: 0.510170 Loss2: 1.406112
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.791561 Loss1: 0.295166 Loss2: 1.496395
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.887153 Loss1: 0.473371 Loss2: 1.413782
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.816103 Loss1: 0.324986 Loss2: 1.491117
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.802047 Loss1: 0.388350 Loss2: 1.413697
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.847024 Loss1: 0.351160 Loss2: 1.495864
-(DefaultActor pid=3764) >> Training accuracy: 0.931250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.756541 Loss1: 0.355055 Loss2: 1.401486 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.911830
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.936814 Loss1: 1.786783 Loss2: 2.150032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.453714 Loss1: 0.912293 Loss2: 1.541421 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.220650 Loss1: 0.686177 Loss2: 1.534473
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.724058 Loss1: 1.731243 Loss2: 1.992815
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.076367 Loss1: 0.547743 Loss2: 1.528624
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.552457 Loss1: 1.125342 Loss2: 1.427115
-(DefaultActor pid=3764) Epoch: 5 Loss: 2.053701 Loss1: 0.519654 Loss2: 1.534047
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.155957 Loss1: 0.759132 Loss2: 1.396824
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.910241 Loss1: 0.373355 Loss2: 1.536887
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.958118 Loss1: 0.554559 Loss2: 1.403558
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.848344 Loss1: 0.329120 Loss2: 1.519224
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.854258 Loss1: 0.456071 Loss2: 1.398187
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.868952 Loss1: 0.354250 Loss2: 1.514702
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.758960 Loss1: 0.367294 Loss2: 1.391666
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.909615 Loss1: 0.382723 Loss2: 1.526891
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.710162 Loss1: 0.326648 Loss2: 1.383514
-(DefaultActor pid=3764) >> Training accuracy: 0.908333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.667119 Loss1: 0.275712 Loss2: 1.391407
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.733999 Loss1: 0.340551 Loss2: 1.393449
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.721769 Loss1: 0.321601 Loss2: 1.400168
-(DefaultActor pid=3765) >> Training accuracy: 0.929167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.797432 Loss1: 1.768636 Loss2: 2.028796
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.534438 Loss1: 1.052733 Loss2: 1.481704
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.244200 Loss1: 0.778620 Loss2: 1.465580
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.048932 Loss1: 0.564732 Loss2: 1.484199
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.682659 Loss1: 1.650351 Loss2: 2.032308
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.924519 Loss1: 0.455423 Loss2: 1.469096
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.625319 Loss1: 1.140920 Loss2: 1.484399
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.951338 Loss1: 0.489708 Loss2: 1.461630
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.258226 Loss1: 0.777352 Loss2: 1.480874
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.899139 Loss1: 0.414495 Loss2: 1.484644
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.121554 Loss1: 0.644177 Loss2: 1.477378
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.826471 Loss1: 0.353765 Loss2: 1.472706
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.046895 Loss1: 0.587682 Loss2: 1.459213
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.726365 Loss1: 0.262393 Loss2: 1.463973
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.940369 Loss1: 0.460765 Loss2: 1.479605
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.708386 Loss1: 0.247583 Loss2: 1.460803
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.880593 Loss1: 0.412229 Loss2: 1.468364
-(DefaultActor pid=3764) >> Training accuracy: 0.914583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.855718 Loss1: 0.389468 Loss2: 1.466250
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.779499 Loss1: 0.309114 Loss2: 1.470386
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.698965 Loss1: 0.237027 Loss2: 1.461937
-(DefaultActor pid=3765) >> Training accuracy: 0.933333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.659448 Loss1: 1.580020 Loss2: 2.079428
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.532695 Loss1: 1.026791 Loss2: 1.505904
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.250451 Loss1: 0.779496 Loss2: 1.470955
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.100094 Loss1: 0.625558 Loss2: 1.474536
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.694312 Loss1: 1.666038 Loss2: 2.028274
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.930377 Loss1: 0.454907 Loss2: 1.475470
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.646617 Loss1: 1.175499 Loss2: 1.471118
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.828804 Loss1: 0.366076 Loss2: 1.462728
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.334124 Loss1: 0.883703 Loss2: 1.450422
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.703746 Loss1: 0.248025 Loss2: 1.455721
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.085670 Loss1: 0.637927 Loss2: 1.447742
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.651102 Loss1: 0.206837 Loss2: 1.444264
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.894384 Loss1: 0.460723 Loss2: 1.433660
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.724642 Loss1: 0.288006 Loss2: 1.436636
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.830077 Loss1: 0.420545 Loss2: 1.409532
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.757733 Loss1: 0.311483 Loss2: 1.446250
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.759312 Loss1: 0.326557 Loss2: 1.432755
-(DefaultActor pid=3764) >> Training accuracy: 0.942708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.703436 Loss1: 0.291724 Loss2: 1.411713
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.656683 Loss1: 0.247666 Loss2: 1.409017
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.658461 Loss1: 0.254765 Loss2: 1.403696
-(DefaultActor pid=3765) >> Training accuracy: 0.929167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.980123 Loss1: 1.849443 Loss2: 2.130680
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.755450 Loss1: 1.205432 Loss2: 1.550019
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.381533 Loss1: 0.863904 Loss2: 1.517629
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.193436 Loss1: 0.684282 Loss2: 1.509154
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.647065 Loss1: 1.625050 Loss2: 2.022015
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.563204 Loss1: 1.117024 Loss2: 1.446180 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.239533 Loss1: 0.800892 Loss2: 1.438641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.011636 Loss1: 0.584443 Loss2: 1.427193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.815547 Loss1: 0.328060 Loss2: 1.487487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.811443 Loss1: 0.315564 Loss2: 1.495880 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.943080
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.792732 Loss1: 0.383314 Loss2: 1.409418 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.642816 Loss1: 0.234624 Loss2: 1.408192 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.923958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.682253 Loss1: 1.203835 Loss2: 1.478418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.146986 Loss1: 0.702254 Loss2: 1.444731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.938020 Loss1: 0.499116 Loss2: 1.438904
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.657808 Loss1: 1.609924 Loss2: 2.047884
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.480296 Loss1: 0.990948 Loss2: 1.489348 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.203715 Loss1: 0.731745 Loss2: 1.471969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.030089 Loss1: 0.568156 Loss2: 1.461933 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.916056 Loss1: 0.454501 Loss2: 1.461555 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.884375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.761324 Loss1: 0.321884 Loss2: 1.439441
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.869957 Loss1: 0.421170 Loss2: 1.448787
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.820815 Loss1: 0.366778 Loss2: 1.454037
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.757971 Loss1: 0.297008 Loss2: 1.460963
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.752017 Loss1: 0.291927 Loss2: 1.460090
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.717252 Loss1: 0.261141 Loss2: 1.456111
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.655683 Loss1: 1.659900 Loss2: 1.995784
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.582080 Loss1: 1.125095 Loss2: 1.456985
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.276189 Loss1: 0.826609 Loss2: 1.449579
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.017053 Loss1: 0.577645 Loss2: 1.439408
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.845795 Loss1: 0.414054 Loss2: 1.431741
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.767328 Loss1: 1.683045 Loss2: 2.084283
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.641618 Loss1: 1.170933 Loss2: 1.470685
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.826506 Loss1: 0.396137 Loss2: 1.430370
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.282181 Loss1: 0.852263 Loss2: 1.429918
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.820697 Loss1: 0.385962 Loss2: 1.434735
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.794434 Loss1: 0.358303 Loss2: 1.436132 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.719168 Loss1: 0.285740 Loss2: 1.433429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.735627 Loss1: 0.300167 Loss2: 1.435460 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.946875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.701473 Loss1: 0.265811 Loss2: 1.435662 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.842152 Loss1: 1.756658 Loss2: 2.085494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.372639 Loss1: 0.879780 Loss2: 1.492859 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.071093 Loss1: 0.593140 Loss2: 1.477953
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.788929 Loss1: 1.730080 Loss2: 2.058848
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.699160 Loss1: 1.198865 Loss2: 1.500295 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.300768 Loss1: 0.812073 Loss2: 1.488696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.159570 Loss1: 0.682503 Loss2: 1.477067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.028763 Loss1: 0.551770 Loss2: 1.476993 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.917830 Loss1: 0.447350 Loss2: 1.470480 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.933333
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.834463 Loss1: 0.362558 Loss2: 1.471905
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.875857 Loss1: 0.403945 Loss2: 1.471912
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.793798 Loss1: 0.329167 Loss2: 1.464630
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.768282 Loss1: 0.301941 Loss2: 1.466341
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.812050 Loss1: 0.342376 Loss2: 1.469674
-(DefaultActor pid=3765) >> Training accuracy: 0.928125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.726553 Loss1: 1.707714 Loss2: 2.018839
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.642221 Loss1: 1.146017 Loss2: 1.496204
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.229960 Loss1: 0.737164 Loss2: 1.492796
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.679603 Loss1: 1.685629 Loss2: 1.993974
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.008563 Loss1: 0.540942 Loss2: 1.467621
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.573124 Loss1: 1.114900 Loss2: 1.458223
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.002879 Loss1: 0.535583 Loss2: 1.467295
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.355742 Loss1: 0.888279 Loss2: 1.467463
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.986291 Loss1: 0.499397 Loss2: 1.486894
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.150476 Loss1: 0.681807 Loss2: 1.468669
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.892149 Loss1: 0.408588 Loss2: 1.483560
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.786221 Loss1: 0.299054 Loss2: 1.487166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.836485 Loss1: 0.367156 Loss2: 1.469329 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.857072 Loss1: 0.378844 Loss2: 1.478228 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.919922
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.706745 Loss1: 0.288143 Loss2: 1.418601 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.916667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.624992 Loss1: 1.617728 Loss2: 2.007264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.100147 Loss1: 0.654192 Loss2: 1.445954 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.588638 Loss1: 1.627739 Loss2: 1.960899
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.969784 Loss1: 0.530736 Loss2: 1.439048
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.397357 Loss1: 0.986472 Loss2: 1.410885
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.946746 Loss1: 0.503517 Loss2: 1.443229
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.172276 Loss1: 0.755496 Loss2: 1.416779
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.853394 Loss1: 0.408022 Loss2: 1.445372
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.991757 Loss1: 0.572964 Loss2: 1.418793
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.769482 Loss1: 0.334114 Loss2: 1.435369
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.685041 Loss1: 0.246132 Loss2: 1.438909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.692954 Loss1: 0.258286 Loss2: 1.434667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.703135 Loss1: 0.270528 Loss2: 1.432607 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.931641
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.729338 Loss1: 0.326814 Loss2: 1.402523 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.919792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.703568 Loss1: 1.636614 Loss2: 2.066954 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.283002 Loss1: 0.780891 Loss2: 1.502111 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.131849 Loss1: 0.636660 Loss2: 1.495189
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.859340 Loss1: 1.775437 Loss2: 2.083903
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.633060 Loss1: 1.144736 Loss2: 1.488325 [repeated 2x across cluster]
-DEBUG flwr 2023-10-09 21:46:36,358 | server.py:236 | fit_round 53 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.387072 Loss1: 0.885571 Loss2: 1.501501 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.155079 Loss1: 0.660214 Loss2: 1.494865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.029920 Loss1: 0.537817 Loss2: 1.492103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.945450 Loss1: 0.474529 Loss2: 1.470920 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.875301 Loss1: 0.378553 Loss2: 1.496748
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.852581 Loss1: 0.376917 Loss2: 1.475663
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.769666 Loss1: 0.294127 Loss2: 1.475539
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.741850 Loss1: 0.273790 Loss2: 1.468060
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.704341 Loss1: 0.233888 Loss2: 1.470452
-(DefaultActor pid=3765) >> Training accuracy: 0.923958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.388130 Loss1: 1.432627 Loss2: 1.955503
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.320691 Loss1: 0.916451 Loss2: 1.404240
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.074862 Loss1: 0.681008 Loss2: 1.393854
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.969204 Loss1: 0.568908 Loss2: 1.400296
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.648934 Loss1: 1.668370 Loss2: 1.980564
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.600084 Loss1: 1.135379 Loss2: 1.464705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.245227 Loss1: 0.777707 Loss2: 1.467519 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.091908 Loss1: 0.638841 Loss2: 1.453067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.946893 Loss1: 0.499292 Loss2: 1.447601 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.863137 Loss1: 0.422584 Loss2: 1.440553 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.784899 Loss1: 0.347374 Loss2: 1.437525 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.734489 Loss1: 0.296215 Loss2: 1.438274 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.921875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.836185 Loss1: 1.816167 Loss2: 2.020018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.446014 Loss1: 0.960197 Loss2: 1.485817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.575076 Loss1: 1.530454 Loss2: 2.044622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.543634 Loss1: 1.078129 Loss2: 1.465505 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.202582 Loss1: 0.737513 Loss2: 1.465069 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.789030 Loss1: 0.326004 Loss2: 1.463026 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.884333 Loss1: 0.426972 Loss2: 1.457361 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.875957 Loss1: 0.425292 Loss2: 1.450665 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.913542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.697175 Loss1: 0.251825 Loss2: 1.445350 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.697797 Loss1: 0.246877 Loss2: 1.450920 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.927083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.374118 Loss1: 0.952851 Loss2: 1.421267 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.925604 Loss1: 0.548073 Loss2: 1.377531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.755984 Loss1: 0.374379 Loss2: 1.381605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.721721 Loss1: 0.343692 Loss2: 1.378029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.595389 Loss1: 0.229287 Loss2: 1.366102 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.922917
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 21:46:36,358][flwr][DEBUG] - fit_round 53 received 50 results and 0 failures
-INFO flwr 2023-10-09 21:47:17,782 | server.py:125 | fit progress: (53, 2.385063742296383, {'accuracy': 0.4882}, 122145.56038520999)
->> Test accuracy: 0.488200
-[2023-10-09 21:47:17,782][flwr][INFO] - fit progress: (53, 2.385063742296383, {'accuracy': 0.4882}, 122145.56038520999)
-DEBUG flwr 2023-10-09 21:47:17,782 | server.py:173 | evaluate_round 53: strategy sampled 50 clients (out of 50)
-[2023-10-09 21:47:17,782][flwr][DEBUG] - evaluate_round 53: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 21:56:27,005 | server.py:187 | evaluate_round 53 received 50 results and 0 failures
-[2023-10-09 21:56:27,005][flwr][DEBUG] - evaluate_round 53 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 21:56:27,005 | server.py:222 | fit_round 54: strategy sampled 50 clients (out of 50)
-[2023-10-09 21:56:27,005][flwr][DEBUG] - fit_round 54: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.816752 Loss1: 1.782007 Loss2: 2.034746
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.583467 Loss1: 1.117697 Loss2: 1.465770
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.333219 Loss1: 0.888765 Loss2: 1.444454
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.089603 Loss1: 0.630571 Loss2: 1.459032
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.573051 Loss1: 1.528375 Loss2: 2.044676
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.466689 Loss1: 1.020514 Loss2: 1.446175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.200960 Loss1: 0.776729 Loss2: 1.424232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.003972 Loss1: 0.572808 Loss2: 1.431164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.881400 Loss1: 0.463864 Loss2: 1.417536 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.778345 Loss1: 0.369387 Loss2: 1.408958 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.931250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.694038 Loss1: 0.281956 Loss2: 1.412083
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.730699 Loss1: 0.314922 Loss2: 1.415776
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.692410 Loss1: 0.277965 Loss2: 1.414446
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.606132 Loss1: 0.202876 Loss2: 1.403257
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.644738 Loss1: 0.242030 Loss2: 1.402708
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.634632 Loss1: 1.616643 Loss2: 2.017990
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.569897 Loss1: 1.124382 Loss2: 1.445515
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.278563 Loss1: 0.839373 Loss2: 1.439190
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.985311 Loss1: 0.542974 Loss2: 1.442337
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.625422 Loss1: 1.638876 Loss2: 1.986545
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.612522 Loss1: 1.137990 Loss2: 1.474532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.248110 Loss1: 0.776507 Loss2: 1.471603 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.971175 Loss1: 0.513319 Loss2: 1.457856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.884751 Loss1: 0.434196 Loss2: 1.450555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.780444 Loss1: 0.336315 Loss2: 1.444129 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.833376 Loss1: 0.387208 Loss2: 1.446168 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.796621 Loss1: 0.332003 Loss2: 1.464617 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.908203
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.609235 Loss1: 1.184099 Loss2: 1.425136 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.979808 Loss1: 0.559337 Loss2: 1.420471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.811226 Loss1: 0.395302 Loss2: 1.415924
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.696354 Loss1: 1.623569 Loss2: 2.072785
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.706619 Loss1: 1.163842 Loss2: 1.542777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.333522 Loss1: 0.807192 Loss2: 1.526330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.031410 Loss1: 0.517038 Loss2: 1.514371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.595806 Loss1: 0.193264 Loss2: 1.402542 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.944196
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.789357 Loss1: 0.303220 Loss2: 1.486137 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.811656 Loss1: 0.313145 Loss2: 1.498511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.851977 Loss1: 0.354211 Loss2: 1.497766 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.909180
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.360129 Loss1: 0.860822 Loss2: 1.499307 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.079348 Loss1: 0.582364 Loss2: 1.496983 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 2.016754 Loss1: 0.510419 Loss2: 1.506335
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.842374 Loss1: 1.803089 Loss2: 2.039285
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.930053 Loss1: 0.440586 Loss2: 1.489467
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.727774 Loss1: 1.230298 Loss2: 1.497477
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.368123 Loss1: 0.900636 Loss2: 1.467487
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.854584 Loss1: 0.362403 Loss2: 1.492181
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.084537 Loss1: 0.624541 Loss2: 1.459997
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.878792 Loss1: 0.384748 Loss2: 1.494045
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.987572 Loss1: 0.538584 Loss2: 1.448988
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.861812 Loss1: 0.362658 Loss2: 1.499154
-(DefaultActor pid=3765) >> Training accuracy: 0.920898
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.852037 Loss1: 0.408535 Loss2: 1.443502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.812814 Loss1: 0.355104 Loss2: 1.457710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.692526 Loss1: 0.235769 Loss2: 1.456757
-(DefaultActor pid=3764) >> Training accuracy: 0.927083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.736777 Loss1: 1.750444 Loss2: 1.986333
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.603951 Loss1: 1.163766 Loss2: 1.440185
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.173658 Loss1: 0.745863 Loss2: 1.427795
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.953343 Loss1: 0.532502 Loss2: 1.420841
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.891831 Loss1: 0.473842 Loss2: 1.417989
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.839068 Loss1: 1.665549 Loss2: 2.173519
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.813871 Loss1: 0.390615 Loss2: 1.423257
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.745552 Loss1: 0.317121 Loss2: 1.428430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.711672 Loss1: 0.288817 Loss2: 1.422854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.707705 Loss1: 0.289613 Loss2: 1.418092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.815730 Loss1: 0.348338 Loss2: 1.467393 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.727055 Loss1: 0.266245 Loss2: 1.460810 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.620427 Loss1: 0.163558 Loss2: 1.456869 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972356
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.711842 Loss1: 1.634123 Loss2: 2.077720
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.603195 Loss1: 1.094168 Loss2: 1.509028
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.265791 Loss1: 0.778642 Loss2: 1.487149
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.033848 Loss1: 0.539719 Loss2: 1.494129
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.681267 Loss1: 1.588864 Loss2: 2.092403
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.495417 Loss1: 1.018421 Loss2: 1.476996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.132664 Loss1: 0.680415 Loss2: 1.452248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.022145 Loss1: 0.578606 Loss2: 1.443540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.894858 Loss1: 0.442010 Loss2: 1.452848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.865245 Loss1: 0.433793 Loss2: 1.431451 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.847618 Loss1: 0.361683 Loss2: 1.485936
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.809858 Loss1: 0.375735 Loss2: 1.434123
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.778589 Loss1: 0.341112 Loss2: 1.437477
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.688895 Loss1: 0.251645 Loss2: 1.437250
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.623999 Loss1: 0.200609 Loss2: 1.423389
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.762363 Loss1: 1.685750 Loss2: 2.076613
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.561901 Loss1: 1.058664 Loss2: 1.503237
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.213024 Loss1: 0.714982 Loss2: 1.498042
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.032217 Loss1: 0.539243 Loss2: 1.492974
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.939087 Loss1: 1.931115 Loss2: 2.007971
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.678430 Loss1: 1.204494 Loss2: 1.473936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.329298 Loss1: 0.826809 Loss2: 1.502489 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.065031 Loss1: 0.605783 Loss2: 1.459248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.010102 Loss1: 0.552268 Loss2: 1.457834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.898860 Loss1: 0.430948 Loss2: 1.467912 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.929167
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.705576 Loss1: 0.227653 Loss2: 1.477923
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.861705 Loss1: 0.405707 Loss2: 1.455998
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.872103 Loss1: 0.417794 Loss2: 1.454309
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.794765 Loss1: 0.333820 Loss2: 1.460945
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.744873 Loss1: 0.290185 Loss2: 1.454688
-(DefaultActor pid=3764) >> Training accuracy: 0.919792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.593150 Loss1: 1.518888 Loss2: 2.074262
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.623959 Loss1: 1.124523 Loss2: 1.499436
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.249842 Loss1: 0.741239 Loss2: 1.508603
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.974867 Loss1: 0.500718 Loss2: 1.474149
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.699029 Loss1: 1.694935 Loss2: 2.004094
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.581132 Loss1: 1.101559 Loss2: 1.479573 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.281028 Loss1: 0.821231 Loss2: 1.459797 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.130680 Loss1: 0.668202 Loss2: 1.462478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.990792 Loss1: 0.538214 Loss2: 1.452578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.846982 Loss1: 0.397169 Loss2: 1.449813 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.912500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.775377 Loss1: 0.309391 Loss2: 1.465986
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.856589 Loss1: 0.415313 Loss2: 1.441276
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.794267 Loss1: 0.341123 Loss2: 1.453144
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.724428 Loss1: 0.277075 Loss2: 1.447353
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.716532 Loss1: 0.285102 Loss2: 1.431429
-(DefaultActor pid=3764) >> Training accuracy: 0.933333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.724465 Loss1: 1.707102 Loss2: 2.017363
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.583682 Loss1: 1.138953 Loss2: 1.444730
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.291139 Loss1: 0.843606 Loss2: 1.447534
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.119452 Loss1: 0.679812 Loss2: 1.439640
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.572706 Loss1: 1.521326 Loss2: 2.051379
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.498948 Loss1: 1.044237 Loss2: 1.454712 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.093063 Loss1: 0.656647 Loss2: 1.436416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.920670 Loss1: 0.496352 Loss2: 1.424318 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.812951 Loss1: 0.399507 Loss2: 1.413445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.772135 Loss1: 0.369257 Loss2: 1.402878 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.934375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.713092 Loss1: 0.279624 Loss2: 1.433468
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.738842 Loss1: 0.335323 Loss2: 1.403519
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.746819 Loss1: 0.328037 Loss2: 1.418782
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.697226 Loss1: 0.284056 Loss2: 1.413169
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.647324 Loss1: 0.245480 Loss2: 1.401844
-(DefaultActor pid=3764) >> Training accuracy: 0.928125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.639267 Loss1: 1.647898 Loss2: 1.991369
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.419071 Loss1: 0.966185 Loss2: 1.452886
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.208094 Loss1: 0.791119 Loss2: 1.416976
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.021632 Loss1: 0.580343 Loss2: 1.441290
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.578655 Loss1: 1.606764 Loss2: 1.971891
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.478103 Loss1: 1.048828 Loss2: 1.429275 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.135832 Loss1: 0.714884 Loss2: 1.420948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.990354 Loss1: 0.579056 Loss2: 1.411298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.929360 Loss1: 0.518039 Loss2: 1.411321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.871598 Loss1: 0.455148 Loss2: 1.416450 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.575333 Loss1: 0.185541 Loss2: 1.389792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.800065 Loss1: 0.386164 Loss2: 1.413901
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.770312 Loss1: 0.365544 Loss2: 1.404768
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.806854 Loss1: 0.381836 Loss2: 1.425018
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.757019 Loss1: 0.340122 Loss2: 1.416897
-(DefaultActor pid=3764) >> Training accuracy: 0.920833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.634371 Loss1: 1.659592 Loss2: 1.974779
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.554950 Loss1: 1.128858 Loss2: 1.426092
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.277085 Loss1: 0.862453 Loss2: 1.414632
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.038533 Loss1: 0.619640 Loss2: 1.418893
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.959456 Loss1: 1.800914 Loss2: 2.158542
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.756584 Loss1: 1.226976 Loss2: 1.529607 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.369075 Loss1: 0.872502 Loss2: 1.496573 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.728903 Loss1: 0.333003 Loss2: 1.395900
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.137102 Loss1: 0.628948 Loss2: 1.508154
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.747209 Loss1: 0.345049 Loss2: 1.402159
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.012237 Loss1: 0.514871 Loss2: 1.497366
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.701027 Loss1: 0.296523 Loss2: 1.404504
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.960684 Loss1: 0.465925 Loss2: 1.494759
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.875596 Loss1: 0.383519 Loss2: 1.492077
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.706663 Loss1: 0.307814 Loss2: 1.398849
-(DefaultActor pid=3765) >> Training accuracy: 0.916667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.777666 Loss1: 0.289743 Loss2: 1.487923 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.677070 Loss1: 1.641406 Loss2: 2.035663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.128441 Loss1: 0.663098 Loss2: 1.465343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.994466 Loss1: 0.524365 Loss2: 1.470101
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.743724 Loss1: 1.625610 Loss2: 2.118114
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.645916 Loss1: 1.114817 Loss2: 1.531100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.222084 Loss1: 0.714502 Loss2: 1.507582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.068086 Loss1: 0.569001 Loss2: 1.499085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.923843 Loss1: 0.419345 Loss2: 1.504498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.865445 Loss1: 0.371676 Loss2: 1.493769 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.943750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.747293 Loss1: 0.275657 Loss2: 1.471636
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.833917 Loss1: 0.347894 Loss2: 1.486023
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.802264 Loss1: 0.313439 Loss2: 1.488825
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.752768 Loss1: 0.259645 Loss2: 1.493123
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.744371 Loss1: 0.251726 Loss2: 1.492645
-(DefaultActor pid=3764) >> Training accuracy: 0.922917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.605926 Loss1: 1.577599 Loss2: 2.028327
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.510172 Loss1: 1.003398 Loss2: 1.506774
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.211153 Loss1: 0.703155 Loss2: 1.507998
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.702882 Loss1: 1.652746 Loss2: 2.050136
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.017569 Loss1: 0.528387 Loss2: 1.489182
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.690689 Loss1: 1.194242 Loss2: 1.496447
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.969865 Loss1: 0.495064 Loss2: 1.474802
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.849719 Loss1: 0.379968 Loss2: 1.469751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.800153 Loss1: 0.335394 Loss2: 1.464759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.817752 Loss1: 0.340493 Loss2: 1.477258 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.810335 Loss1: 0.332446 Loss2: 1.477889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.782854 Loss1: 0.314138 Loss2: 1.468716 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.934570
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.731577 Loss1: 0.279726 Loss2: 1.451851 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.946875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.584148 Loss1: 1.568133 Loss2: 2.016015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.198175 Loss1: 0.725712 Loss2: 1.472463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.969065 Loss1: 0.521348 Loss2: 1.447717
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.710669 Loss1: 1.687655 Loss2: 2.023015
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.878744 Loss1: 0.450702 Loss2: 1.428042
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.567885 Loss1: 1.097082 Loss2: 1.470802
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.833078 Loss1: 0.392899 Loss2: 1.440179
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.310662 Loss1: 0.861581 Loss2: 1.449081
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.729861 Loss1: 0.298975 Loss2: 1.430886
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.140364 Loss1: 0.695688 Loss2: 1.444675
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.725254 Loss1: 0.297129 Loss2: 1.428125
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.025513 Loss1: 0.568132 Loss2: 1.457381
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.701318 Loss1: 0.273730 Loss2: 1.427588
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.819216 Loss1: 0.383519 Loss2: 1.435697
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.692704 Loss1: 0.253996 Loss2: 1.438708
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.828851 Loss1: 0.402075 Loss2: 1.426776
-(DefaultActor pid=3765) >> Training accuracy: 0.943750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.777626 Loss1: 0.340159 Loss2: 1.437467
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.808508 Loss1: 0.368617 Loss2: 1.439890
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.752357 Loss1: 0.308315 Loss2: 1.444042
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.671525 Loss1: 1.646225 Loss2: 2.025300
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.568251 Loss1: 1.085200 Loss2: 1.483051
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.253393 Loss1: 0.774761 Loss2: 1.478632
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.027993 Loss1: 0.562043 Loss2: 1.465950
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.674720 Loss1: 1.620998 Loss2: 2.053722
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.883810 Loss1: 0.427723 Loss2: 1.456087
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.497725 Loss1: 0.996390 Loss2: 1.501335
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.836252 Loss1: 0.381414 Loss2: 1.454838
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.263690 Loss1: 0.807681 Loss2: 1.456009
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.792627 Loss1: 0.335805 Loss2: 1.456822
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.059501 Loss1: 0.583308 Loss2: 1.476193
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.766153 Loss1: 0.308970 Loss2: 1.457183
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.828135 Loss1: 0.372135 Loss2: 1.456000
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.677227 Loss1: 0.228627 Loss2: 1.448599
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.817639 Loss1: 0.373049 Loss2: 1.444590
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.734917 Loss1: 0.288456 Loss2: 1.446461
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.755270 Loss1: 0.305744 Loss2: 1.449526
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.725293 Loss1: 0.289059 Loss2: 1.436234
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.675634 Loss1: 0.234326 Loss2: 1.441308
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.672791 Loss1: 0.237375 Loss2: 1.435416
-(DefaultActor pid=3764) >> Training accuracy: 0.906250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.549789 Loss1: 1.568834 Loss2: 1.980955
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.593286 Loss1: 1.122934 Loss2: 1.470352
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.259012 Loss1: 0.796352 Loss2: 1.462661
-(DefaultActor pid=3764) Epoch: 0 Loss: 4.030767 Loss1: 1.745041 Loss2: 2.285726
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.033575 Loss1: 0.579601 Loss2: 1.453974
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.966064 Loss1: 0.516830 Loss2: 1.449235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.864639 Loss1: 0.404916 Loss2: 1.459722 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.005291 Loss1: 0.473604 Loss2: 1.531687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.887289 Loss1: 0.368808 Loss2: 1.518482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.874439 Loss1: 0.362127 Loss2: 1.512312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.731488 Loss1: 0.287118 Loss2: 1.444371
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.830827 Loss1: 0.325020 Loss2: 1.505807
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.781677 Loss1: 0.277899 Loss2: 1.503778
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.686153 Loss1: 0.246479 Loss2: 1.439675
-(DefaultActor pid=3765) >> Training accuracy: 0.967773
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.531910 Loss1: 1.560431 Loss2: 1.971479 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964844
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.199330 Loss1: 0.729874 Loss2: 1.469456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.602267 Loss1: 1.598584 Loss2: 2.003684
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.006877 Loss1: 0.545292 Loss2: 1.461584
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.488579 Loss1: 0.984249 Loss2: 1.504330
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.898211 Loss1: 0.444701 Loss2: 1.453510
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.151850 Loss1: 0.679113 Loss2: 1.472737
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.907782 Loss1: 0.447866 Loss2: 1.459916
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.945384 Loss1: 0.490187 Loss2: 1.455197
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.847432 Loss1: 0.387867 Loss2: 1.459564
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.798037 Loss1: 0.338135 Loss2: 1.459903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.749142 Loss1: 0.306480 Loss2: 1.442662 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.742765 Loss1: 0.289064 Loss2: 1.453701 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.929228
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.647149 Loss1: 0.212000 Loss2: 1.435149 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.912109
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.719146 Loss1: 1.615090 Loss2: 2.104057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.345898 Loss1: 0.866751 Loss2: 1.479147 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.732227 Loss1: 1.721624 Loss2: 2.010603 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.832022 Loss1: 0.386817 Loss2: 1.445206 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.849693 Loss1: 0.403506 Loss2: 1.446187 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.754190 Loss1: 0.297209 Loss2: 1.456982 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.769471 Loss1: 0.315048 Loss2: 1.454423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.685772 Loss1: 0.230148 Loss2: 1.455624 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963942
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.836911 Loss1: 0.392161 Loss2: 1.444750 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.723510 Loss1: 0.286927 Loss2: 1.436583 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.938542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.724980 Loss1: 0.291828 Loss2: 1.433151
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.801426 Loss1: 1.729491 Loss2: 2.071935
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.569428 Loss1: 1.070075 Loss2: 1.499353
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.328842 Loss1: 0.845190 Loss2: 1.483652
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.080611 Loss1: 0.580134 Loss2: 1.500477
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.040303 Loss1: 0.561597 Loss2: 1.478706
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.505579 Loss1: 1.444891 Loss2: 2.060688
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.524378 Loss1: 1.032866 Loss2: 1.491512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.238598 Loss1: 0.742433 Loss2: 1.496165 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.011760 Loss1: 0.520405 Loss2: 1.491355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.890049 Loss1: 0.424751 Loss2: 1.465298 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.932292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.843860 Loss1: 0.386126 Loss2: 1.457734 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.724842 Loss1: 0.266798 Loss2: 1.458044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.666647 Loss1: 0.224636 Loss2: 1.442011 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.619977 Loss1: 1.155423 Loss2: 1.464554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.035802 Loss1: 0.586824 Loss2: 1.448978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.891251 Loss1: 0.445182 Loss2: 1.446069
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.984993 Loss1: 1.896621 Loss2: 2.088372
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.652196 Loss1: 1.144595 Loss2: 1.507601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.332692 Loss1: 0.835668 Loss2: 1.497024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.085425 Loss1: 0.590377 Loss2: 1.495048 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.742705 Loss1: 0.306245 Loss2: 1.436460
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.931197 Loss1: 0.455226 Loss2: 1.475970
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.735953 Loss1: 0.301155 Loss2: 1.434798
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.916370 Loss1: 0.433311 Loss2: 1.483058
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.822275 Loss1: 0.345326 Loss2: 1.476949
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.787604 Loss1: 0.311554 Loss2: 1.476050
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.865985 Loss1: 0.387821 Loss2: 1.478164
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.790018 Loss1: 0.303643 Loss2: 1.486375
-(DefaultActor pid=3764) >> Training accuracy: 0.891741
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.752852 Loss1: 1.752755 Loss2: 2.000097
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.639472 Loss1: 1.152167 Loss2: 1.487304
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.278962 Loss1: 0.798500 Loss2: 1.480462
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.082508 Loss1: 0.602383 Loss2: 1.480125
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.795931 Loss1: 1.685100 Loss2: 2.110831
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.666760 Loss1: 1.158744 Loss2: 1.508016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.357557 Loss1: 0.839744 Loss2: 1.517813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.069805 Loss1: 0.564960 Loss2: 1.504846 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.921643 Loss1: 0.435070 Loss2: 1.486573 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.793234 Loss1: 0.323379 Loss2: 1.469856
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.902782 Loss1: 0.412828 Loss2: 1.489954
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.774742 Loss1: 0.314994 Loss2: 1.459748
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.896061 Loss1: 0.398458 Loss2: 1.497603
-(DefaultActor pid=3765) >> Training accuracy: 0.936523
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.876088 Loss1: 0.367803 Loss2: 1.508285
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.825613 Loss1: 0.319739 Loss2: 1.505874
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.761743 Loss1: 0.265700 Loss2: 1.496043
-(DefaultActor pid=3764) >> Training accuracy: 0.943750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.662245 Loss1: 1.575435 Loss2: 2.086810
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.647474 Loss1: 1.145659 Loss2: 1.501815
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.276180 Loss1: 0.774349 Loss2: 1.501831
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.042674 Loss1: 0.546364 Loss2: 1.496310
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.747767 Loss1: 1.684344 Loss2: 2.063422
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.592997 Loss1: 1.051056 Loss2: 1.541941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.272055 Loss1: 0.725317 Loss2: 1.546738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.186923 Loss1: 0.651901 Loss2: 1.535022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.070506 Loss1: 0.546223 Loss2: 1.524283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.967516 Loss1: 0.447097 Loss2: 1.520419 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.849822 Loss1: 0.326062 Loss2: 1.523761 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.727976 Loss1: 0.219785 Loss2: 1.508191 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.905273
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.855194 Loss1: 1.820645 Loss2: 2.034549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.253549 Loss1: 0.761304 Loss2: 1.492245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.665090 Loss1: 1.626565 Loss2: 2.038525 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.525074 Loss1: 1.036329 Loss2: 1.488745 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.854704 Loss1: 0.388198 Loss2: 1.466505 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.766729 Loss1: 0.308516 Loss2: 1.458214 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.743043 Loss1: 0.285864 Loss2: 1.457179 [repeated 2x across cluster]
-DEBUG flwr 2023-10-09 22:25:21,915 | server.py:236 | fit_round 54 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.672864 Loss1: 0.221700 Loss2: 1.451165 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.938542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.775943 Loss1: 0.314206 Loss2: 1.461737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.832555 Loss1: 0.352362 Loss2: 1.480193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.948242
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.761160 Loss1: 0.278990 Loss2: 1.482171
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.765002 Loss1: 1.637475 Loss2: 2.127527
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.630785 Loss1: 1.098640 Loss2: 1.532144
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.387834 Loss1: 0.855509 Loss2: 1.532325
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.171984 Loss1: 0.637492 Loss2: 1.534492
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.000367 Loss1: 0.485036 Loss2: 1.515331
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.779612 Loss1: 1.753333 Loss2: 2.026279
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.653015 Loss1: 1.175675 Loss2: 1.477340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.277685 Loss1: 0.820207 Loss2: 1.457478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.052459 Loss1: 0.585101 Loss2: 1.467358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.969911 Loss1: 0.513344 Loss2: 1.456567 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.891497 Loss1: 0.436129 Loss2: 1.455368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.795945 Loss1: 0.338418 Loss2: 1.457527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.765829 Loss1: 0.317203 Loss2: 1.448626 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.923958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.591483 Loss1: 1.135533 Loss2: 1.455950 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.981719 Loss1: 0.571926 Loss2: 1.409793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.873210 Loss1: 0.458498 Loss2: 1.414712
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.689563 Loss1: 1.686409 Loss2: 2.003154
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.690174 Loss1: 1.227197 Loss2: 1.462977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.331839 Loss1: 0.879572 Loss2: 1.452268 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.047227 Loss1: 0.624484 Loss2: 1.422743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.973706 Loss1: 0.549856 Loss2: 1.423850 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.823306 Loss1: 0.403906 Loss2: 1.419401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.794344 Loss1: 0.384218 Loss2: 1.410126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.832596 Loss1: 0.390510 Loss2: 1.442086 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 22:25:21,915][flwr][DEBUG] - fit_round 54 received 50 results and 0 failures
-INFO flwr 2023-10-09 22:26:03,541 | server.py:125 | fit progress: (54, 2.387508314638473, {'accuracy': 0.4907}, 124471.31985545199)
->> Test accuracy: 0.490700
-[2023-10-09 22:26:03,541][flwr][INFO] - fit progress: (54, 2.387508314638473, {'accuracy': 0.4907}, 124471.31985545199)
-DEBUG flwr 2023-10-09 22:26:03,542 | server.py:173 | evaluate_round 54: strategy sampled 50 clients (out of 50)
-[2023-10-09 22:26:03,542][flwr][DEBUG] - evaluate_round 54: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 22:35:09,793 | server.py:187 | evaluate_round 54 received 50 results and 0 failures
-[2023-10-09 22:35:09,793][flwr][DEBUG] - evaluate_round 54 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 22:35:09,794 | server.py:222 | fit_round 55: strategy sampled 50 clients (out of 50)
-[2023-10-09 22:35:09,794][flwr][DEBUG] - fit_round 55: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.753373 Loss1: 1.766172 Loss2: 1.987201
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.501021 Loss1: 1.053705 Loss2: 1.447316
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.302134 Loss1: 0.865624 Loss2: 1.436510
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.071799 Loss1: 0.625904 Loss2: 1.445895
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.607318 Loss1: 1.546136 Loss2: 2.061182
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.974239 Loss1: 0.548337 Loss2: 1.425902
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.501448 Loss1: 0.988520 Loss2: 1.512929
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.845328 Loss1: 0.427141 Loss2: 1.418187
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.190574 Loss1: 0.716558 Loss2: 1.474016
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.796488 Loss1: 0.380054 Loss2: 1.416434
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.011374 Loss1: 0.541417 Loss2: 1.469957
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.719786 Loss1: 0.304969 Loss2: 1.414816
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.868537 Loss1: 0.423950 Loss2: 1.444587
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.683256 Loss1: 0.273226 Loss2: 1.410030
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.808265 Loss1: 0.362139 Loss2: 1.446126
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.647535 Loss1: 0.244707 Loss2: 1.402829
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.777317 Loss1: 0.324998 Loss2: 1.452319
-(DefaultActor pid=3765) >> Training accuracy: 0.935417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.755574 Loss1: 0.309586 Loss2: 1.445988
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.659600 Loss1: 0.219969 Loss2: 1.439631
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.638329 Loss1: 0.198611 Loss2: 1.439719
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.738528 Loss1: 1.699662 Loss2: 2.038866
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.489803 Loss1: 1.033431 Loss2: 1.456373
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.228875 Loss1: 0.789657 Loss2: 1.439217
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.952062 Loss1: 0.513512 Loss2: 1.438550
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.695102 Loss1: 1.623972 Loss2: 2.071130
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.847425 Loss1: 0.418277 Loss2: 1.429148
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.430887 Loss1: 0.974262 Loss2: 1.456625
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.778925 Loss1: 0.353926 Loss2: 1.424999
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.196552 Loss1: 0.756359 Loss2: 1.440194
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.716426 Loss1: 0.299437 Loss2: 1.416989
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.943271 Loss1: 0.514393 Loss2: 1.428878
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.690496 Loss1: 0.272920 Loss2: 1.417576
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.791074 Loss1: 0.375322 Loss2: 1.415752
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.625772 Loss1: 0.210286 Loss2: 1.415486
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.768869 Loss1: 0.362674 Loss2: 1.406195
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.645550 Loss1: 0.236458 Loss2: 1.409092
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.754449 Loss1: 0.347742 Loss2: 1.406706
-(DefaultActor pid=3765) >> Training accuracy: 0.938542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.685637 Loss1: 0.277720 Loss2: 1.407917
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.592235 Loss1: 0.193989 Loss2: 1.398246
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.591926 Loss1: 0.203590 Loss2: 1.388335
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.806389 Loss1: 1.706931 Loss2: 2.099458
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.404552 Loss1: 0.978123 Loss2: 1.426429
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.232130 Loss1: 0.815929 Loss2: 1.416201
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.960224 Loss1: 0.540276 Loss2: 1.419949
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.566972 Loss1: 1.577664 Loss2: 1.989308
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.806836 Loss1: 0.395015 Loss2: 1.411821 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.763488 Loss1: 0.350804 Loss2: 1.412684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.697686 Loss1: 0.291405 Loss2: 1.406280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.601077 Loss1: 0.203488 Loss2: 1.397589 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.540259 Loss1: 0.148453 Loss2: 1.391807 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955529
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.725833 Loss1: 0.297617 Loss2: 1.428216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.725036 Loss1: 0.291935 Loss2: 1.433101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.691576 Loss1: 0.261981 Loss2: 1.429595
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.727647 Loss1: 1.696488 Loss2: 2.031159
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.613495 Loss1: 1.141282 Loss2: 1.472213
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.317984 Loss1: 0.878680 Loss2: 1.439305
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.090425 Loss1: 0.645058 Loss2: 1.445367
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.934999 Loss1: 0.505728 Loss2: 1.429270
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.415773 Loss1: 1.447668 Loss2: 1.968105
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.755451 Loss1: 0.335445 Loss2: 1.420006
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.741077 Loss1: 0.319159 Loss2: 1.421918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.694944 Loss1: 0.278243 Loss2: 1.416701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.668218 Loss1: 0.259400 Loss2: 1.408818 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.673985 Loss1: 0.258597 Loss2: 1.415388 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.947917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.671213 Loss1: 0.286760 Loss2: 1.384454 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.602347 Loss1: 0.225268 Loss2: 1.377079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.584180 Loss1: 0.208067 Loss2: 1.376113
-(DefaultActor pid=3764) >> Training accuracy: 0.930208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.822140 Loss1: 1.796504 Loss2: 2.025636
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.660366 Loss1: 1.202305 Loss2: 1.458061
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.244989 Loss1: 0.797894 Loss2: 1.447095
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.053084 Loss1: 0.608012 Loss2: 1.445072
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.863619 Loss1: 0.432731 Loss2: 1.430888
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.494966 Loss1: 1.505108 Loss2: 1.989858
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.574767 Loss1: 1.150851 Loss2: 1.423916 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.203436 Loss1: 0.719869 Loss2: 1.483567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.017600 Loss1: 0.560066 Loss2: 1.457534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.917013 Loss1: 0.478198 Loss2: 1.438815 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.943750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.818766 Loss1: 0.382176 Loss2: 1.436590 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.741193 Loss1: 0.323219 Loss2: 1.417974 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.657563 Loss1: 0.242822 Loss2: 1.414741 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.284927 Loss1: 0.853259 Loss2: 1.431668 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.963490 Loss1: 0.511098 Loss2: 1.452392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.817533 Loss1: 0.375444 Loss2: 1.442089 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.757567 Loss1: 0.320529 Loss2: 1.437038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.696637 Loss1: 0.262527 Loss2: 1.434111 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.668682 Loss1: 0.240993 Loss2: 1.427689 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.933594
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.930484 Loss1: 0.454532 Loss2: 1.475952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.819100 Loss1: 0.343816 Loss2: 1.475284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.828147 Loss1: 0.349265 Loss2: 1.478882
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.680080 Loss1: 1.700213 Loss2: 1.979867
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.444032 Loss1: 1.001126 Loss2: 1.442906
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.853383 Loss1: 0.371507 Loss2: 1.481877
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.149789 Loss1: 0.737053 Loss2: 1.412736
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.840752 Loss1: 0.345956 Loss2: 1.494796
-(DefaultActor pid=3764) >> Training accuracy: 0.944336
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.835385 Loss1: 0.439121 Loss2: 1.396264 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.660413 Loss1: 0.267693 Loss2: 1.392720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.655200 Loss1: 0.270421 Loss2: 1.384779
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.543721 Loss1: 1.595422 Loss2: 1.948299
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.592577 Loss1: 0.213234 Loss2: 1.379343
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.437698 Loss1: 1.040273 Loss2: 1.397425
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.545003 Loss1: 0.171725 Loss2: 1.373278
-(DefaultActor pid=3765) >> Training accuracy: 0.923958
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.149552 Loss1: 0.768251 Loss2: 1.381300
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.896210 Loss1: 0.516012 Loss2: 1.380198
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.784458 Loss1: 0.426512 Loss2: 1.357947
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.690473 Loss1: 0.340008 Loss2: 1.350465
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.682657 Loss1: 0.323162 Loss2: 1.359495
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.563144 Loss1: 1.511864 Loss2: 2.051280
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.676200 Loss1: 0.306717 Loss2: 1.369484
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.479692 Loss1: 1.002481 Loss2: 1.477211
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.589760 Loss1: 0.235841 Loss2: 1.353919
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.116304 Loss1: 0.660750 Loss2: 1.455554
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.584035 Loss1: 0.223494 Loss2: 1.360541
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.811063 Loss1: 0.370988 Loss2: 1.440075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.681492 Loss1: 0.261643 Loss2: 1.419848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.698610 Loss1: 0.272247 Loss2: 1.426364
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.898335 Loss1: 1.864556 Loss2: 2.033779
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.718453 Loss1: 0.283460 Loss2: 1.434992
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.614499 Loss1: 1.125854 Loss2: 1.488645
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.704592 Loss1: 0.261973 Loss2: 1.442619
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.253698 Loss1: 0.772366 Loss2: 1.481332
-(DefaultActor pid=3765) >> Training accuracy: 0.910417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.037560 Loss1: 0.573824 Loss2: 1.463736
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.906804 Loss1: 0.451221 Loss2: 1.455583
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.860330 Loss1: 0.397894 Loss2: 1.462436
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.794741 Loss1: 0.350291 Loss2: 1.444450
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.674260 Loss1: 0.224582 Loss2: 1.449678
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.678507 Loss1: 1.656975 Loss2: 2.021532
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.637957 Loss1: 0.206162 Loss2: 1.431794
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.587356 Loss1: 1.098594 Loss2: 1.488762
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.651703 Loss1: 0.220431 Loss2: 1.431271
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.188034 Loss1: 0.712713 Loss2: 1.475321
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.123155 Loss1: 0.644504 Loss2: 1.478651
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.061500 Loss1: 0.563703 Loss2: 1.497797
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.931682 Loss1: 0.446039 Loss2: 1.485643
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.856747 Loss1: 0.383215 Loss2: 1.473532
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.945594 Loss1: 1.886113 Loss2: 2.059482
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.675360 Loss1: 1.173902 Loss2: 1.501458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.777825 Loss1: 0.306719 Loss2: 1.471105
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.285321 Loss1: 0.795470 Loss2: 1.489852
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.792494 Loss1: 0.322337 Loss2: 1.470157
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.119554 Loss1: 0.646615 Loss2: 1.472939
-(DefaultActor pid=3765) >> Training accuracy: 0.964844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.933280 Loss1: 0.462863 Loss2: 1.470418
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.860455 Loss1: 0.407166 Loss2: 1.453288
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.799043 Loss1: 0.345101 Loss2: 1.453942
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.781503 Loss1: 0.321127 Loss2: 1.460376
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.755105 Loss1: 0.303887 Loss2: 1.451218
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.668913 Loss1: 1.691973 Loss2: 1.976939
-(DefaultActor pid=3764) >> Training accuracy: 0.940848
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.185265 Loss1: 0.736491 Loss2: 1.448774 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.942938 Loss1: 0.495105 Loss2: 1.447833 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.840264 Loss1: 0.410410 Loss2: 1.429854
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.601038 Loss1: 1.532413 Loss2: 2.068625
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.783820 Loss1: 0.352939 Loss2: 1.430881
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.604176 Loss1: 1.102343 Loss2: 1.501833
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.337504 Loss1: 0.850400 Loss2: 1.487104
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.693500 Loss1: 0.256143 Loss2: 1.437357
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.127192 Loss1: 0.620891 Loss2: 1.506301
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.673567 Loss1: 0.253115 Loss2: 1.420452
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.075401 Loss1: 0.582670 Loss2: 1.492731
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.711899 Loss1: 0.292806 Loss2: 1.419094
-(DefaultActor pid=3765) >> Training accuracy: 0.893555
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.843238 Loss1: 0.360441 Loss2: 1.482798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.827517 Loss1: 0.352872 Loss2: 1.474645 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.726462 Loss1: 0.240450 Loss2: 1.486012
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.636186 Loss1: 1.573417 Loss2: 2.062770
-(DefaultActor pid=3764) >> Training accuracy: 0.935417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.520486 Loss1: 1.031003 Loss2: 1.489483
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.125381 Loss1: 0.650245 Loss2: 1.475135
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.995121 Loss1: 0.549053 Loss2: 1.446069
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.916633 Loss1: 0.465033 Loss2: 1.451600
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.814897 Loss1: 1.789262 Loss2: 2.025635
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.804247 Loss1: 0.360133 Loss2: 1.444115
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.753133 Loss1: 1.266417 Loss2: 1.486716
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.831654 Loss1: 0.381164 Loss2: 1.450490
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.374291 Loss1: 0.881817 Loss2: 1.492474
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.777571 Loss1: 0.317153 Loss2: 1.460418
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.108436 Loss1: 0.638071 Loss2: 1.470364
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.699147 Loss1: 0.248861 Loss2: 1.450286
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.003721 Loss1: 0.544851 Loss2: 1.458870
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.609144 Loss1: 0.179664 Loss2: 1.429479
-(DefaultActor pid=3765) >> Training accuracy: 0.948958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.880937 Loss1: 0.411493 Loss2: 1.469444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.765272 Loss1: 0.307248 Loss2: 1.458023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.820865 Loss1: 0.360460 Loss2: 1.460405
-(DefaultActor pid=3764) >> Training accuracy: 0.898958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.821228 Loss1: 1.739974 Loss2: 2.081254
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.701334 Loss1: 1.180595 Loss2: 1.520738
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.276516 Loss1: 0.774315 Loss2: 1.502201
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.100497 Loss1: 0.606263 Loss2: 1.494233
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.026491 Loss1: 0.532917 Loss2: 1.493575
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.694271 Loss1: 1.678478 Loss2: 2.015793
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.907575 Loss1: 0.414277 Loss2: 1.493298
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.929731 Loss1: 0.431527 Loss2: 1.498204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.894597 Loss1: 0.395909 Loss2: 1.498688 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.803746 Loss1: 0.302360 Loss2: 1.501387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.809399 Loss1: 0.320731 Loss2: 1.488668 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.933333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.779777 Loss1: 0.329755 Loss2: 1.450023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.720489 Loss1: 0.284621 Loss2: 1.435868 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.735576 Loss1: 0.293151 Loss2: 1.442425
-(DefaultActor pid=3764) >> Training accuracy: 0.925000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.549999 Loss1: 1.461067 Loss2: 2.088932
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.446612 Loss1: 0.949128 Loss2: 1.497484
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.124711 Loss1: 0.636860 Loss2: 1.487852
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.957136 Loss1: 0.471407 Loss2: 1.485728
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.902532 Loss1: 0.431092 Loss2: 1.471440
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.516533 Loss1: 1.539684 Loss2: 1.976850
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.494986 Loss1: 1.022051 Loss2: 1.472935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.128000 Loss1: 0.654511 Loss2: 1.473489 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.924034 Loss1: 0.463687 Loss2: 1.460348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.802046 Loss1: 0.351049 Loss2: 1.450997 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.694369 Loss1: 0.249969 Loss2: 1.444400 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.674417 Loss1: 0.242600 Loss2: 1.431817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.780354 Loss1: 1.767937 Loss2: 2.012418
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.758725 Loss1: 0.318952 Loss2: 1.439773
-(DefaultActor pid=3764) >> Training accuracy: 0.919922
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.204231 Loss1: 0.790284 Loss2: 1.413947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.925893 Loss1: 0.501753 Loss2: 1.424140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.790049 Loss1: 0.374107 Loss2: 1.415942
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.768601 Loss1: 1.646691 Loss2: 2.121909
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.741915 Loss1: 0.329646 Loss2: 1.412269
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.666613 Loss1: 1.185453 Loss2: 1.481159
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.293621 Loss1: 0.818199 Loss2: 1.475422
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.725502 Loss1: 0.322664 Loss2: 1.402838
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.052793 Loss1: 0.568309 Loss2: 1.484484
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.703988 Loss1: 0.293549 Loss2: 1.410439
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.688117 Loss1: 0.272401 Loss2: 1.415716 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.923958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.811216 Loss1: 0.356878 Loss2: 1.454338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.711610 Loss1: 0.264956 Loss2: 1.446654 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.955529
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.639214 Loss1: 1.619964 Loss2: 2.019249 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.289383 Loss1: 0.829072 Loss2: 1.460311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.743992 Loss1: 1.709539 Loss2: 2.034452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.543905 Loss1: 1.082495 Loss2: 1.461409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.168223 Loss1: 0.717811 Loss2: 1.450412 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.074522 Loss1: 0.618497 Loss2: 1.456024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.004241 Loss1: 0.537261 Loss2: 1.466980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.917010 Loss1: 0.457323 Loss2: 1.459688 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.921875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.820118 Loss1: 0.361771 Loss2: 1.458347 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.712208 Loss1: 0.261848 Loss2: 1.450360 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.588871 Loss1: 1.092711 Loss2: 1.496160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.063004 Loss1: 0.572142 Loss2: 1.490862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.857570 Loss1: 1.783563 Loss2: 2.074007
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.945624 Loss1: 0.462974 Loss2: 1.482650
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.898758 Loss1: 0.410571 Loss2: 1.488186 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.792524 Loss1: 0.314932 Loss2: 1.477593 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.815311 Loss1: 0.342294 Loss2: 1.473017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.794428 Loss1: 0.314853 Loss2: 1.479576 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.737252 Loss1: 0.256632 Loss2: 1.480620 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.780205 Loss1: 0.307742 Loss2: 1.472463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.710541 Loss1: 0.232250 Loss2: 1.478291 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.947917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.610746 Loss1: 1.631293 Loss2: 1.979452
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.465969 Loss1: 1.025751 Loss2: 1.440218
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.182772 Loss1: 0.750242 Loss2: 1.432531
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.055416 Loss1: 0.631150 Loss2: 1.424266
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.629649 Loss1: 1.572647 Loss2: 2.057001
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.555165 Loss1: 1.076397 Loss2: 1.478768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.221007 Loss1: 0.729825 Loss2: 1.491182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.029609 Loss1: 0.542783 Loss2: 1.486826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.884657 Loss1: 0.406977 Loss2: 1.477680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.862024 Loss1: 0.397115 Loss2: 1.464910 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.923958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.828269 Loss1: 0.353269 Loss2: 1.475000 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.788383 Loss1: 0.315355 Loss2: 1.473028 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.951042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.592955 Loss1: 1.668807 Loss2: 1.924148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.151011 Loss1: 0.709755 Loss2: 1.441256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.999734 Loss1: 0.587052 Loss2: 1.412682
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.661050 Loss1: 1.688530 Loss2: 1.972519
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.942965 Loss1: 0.518053 Loss2: 1.424912
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.476420 Loss1: 1.028784 Loss2: 1.447636
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.860188 Loss1: 0.444887 Loss2: 1.415301
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.250866 Loss1: 0.814536 Loss2: 1.436330
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.798771 Loss1: 0.380085 Loss2: 1.418686
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.987561 Loss1: 0.554459 Loss2: 1.433102
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.893494 Loss1: 0.487149 Loss2: 1.406345
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.792159 Loss1: 0.368538 Loss2: 1.423621
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.743972 Loss1: 0.344182 Loss2: 1.399790
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.788417 Loss1: 0.362833 Loss2: 1.425583
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.747560 Loss1: 0.363845 Loss2: 1.383715
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.723667 Loss1: 0.308495 Loss2: 1.415172
-(DefaultActor pid=3765) >> Training accuracy: 0.916016
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.583985 Loss1: 0.202097 Loss2: 1.381888 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.564387 Loss1: 1.556549 Loss2: 2.007838 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.211224 Loss1: 0.742199 Loss2: 1.469025 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.965199 Loss1: 0.510119 Loss2: 1.455080
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.625401 Loss1: 1.639061 Loss2: 1.986339
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.857058 Loss1: 0.429424 Loss2: 1.427634
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.499267 Loss1: 1.018932 Loss2: 1.480335
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.800114 Loss1: 0.363479 Loss2: 1.436634
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.171107 Loss1: 0.703632 Loss2: 1.467475
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.800493 Loss1: 0.369547 Loss2: 1.430947
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.008770 Loss1: 0.571846 Loss2: 1.436924
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.785285 Loss1: 0.344385 Loss2: 1.440900
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.844908 Loss1: 0.409683 Loss2: 1.435225
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.760377 Loss1: 0.318222 Loss2: 1.442155
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.872963 Loss1: 0.424978 Loss2: 1.447985
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.706133 Loss1: 0.269138 Loss2: 1.436995
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.868469 Loss1: 0.418632 Loss2: 1.449837
-(DefaultActor pid=3765) >> Training accuracy: 0.921875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.852911 Loss1: 0.407337 Loss2: 1.445574
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.796162 Loss1: 0.343639 Loss2: 1.452523
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.764526 Loss1: 0.310867 Loss2: 1.453659
-(DefaultActor pid=3764) >> Training accuracy: 0.949219
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.837185 Loss1: 1.696414 Loss2: 2.140771
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.682279 Loss1: 1.133958 Loss2: 1.548321
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.315009 Loss1: 0.803067 Loss2: 1.511942
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.125326 Loss1: 0.612096 Loss2: 1.513230
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.638392 Loss1: 1.558386 Loss2: 2.080006
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.534903 Loss1: 1.029249 Loss2: 1.505654 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.208153 Loss1: 0.720136 Loss2: 1.488017 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.036745 Loss1: 0.560223 Loss2: 1.476523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.878453 Loss1: 0.411230 Loss2: 1.467223 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.879366 Loss1: 0.410519 Loss2: 1.468847 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.927083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.810118 Loss1: 0.301394 Loss2: 1.508724
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.835348 Loss1: 0.367992 Loss2: 1.467356
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.832705 Loss1: 0.359034 Loss2: 1.473671
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.806385 Loss1: 0.338395 Loss2: 1.467991
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.750398 Loss1: 0.286150 Loss2: 1.464248
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.803333 Loss1: 1.730997 Loss2: 2.072336
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.615968 Loss1: 1.107088 Loss2: 1.508880
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.240031 Loss1: 0.744714 Loss2: 1.495316
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.064445 Loss1: 0.568918 Loss2: 1.495527
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.637716 Loss1: 1.631334 Loss2: 2.006382
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.756278 Loss1: 1.263838 Loss2: 1.492440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.316307 Loss1: 0.813316 Loss2: 1.502990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.079547 Loss1: 0.619969 Loss2: 1.459578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.987533 Loss1: 0.517476 Loss2: 1.470057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.877086 Loss1: 0.413153 Loss2: 1.463933 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.794326 Loss1: 0.301368 Loss2: 1.492958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.790596 Loss1: 0.336219 Loss2: 1.454377
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.789189 Loss1: 0.336347 Loss2: 1.452843
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.775484 Loss1: 0.319263 Loss2: 1.456221
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.678368 Loss1: 0.221267 Loss2: 1.457101
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.355789 Loss1: 1.421819 Loss2: 1.933970
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.523327 Loss1: 1.078540 Loss2: 1.444787
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.098202 Loss1: 0.658876 Loss2: 1.439326
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.596888 Loss1: 1.578099 Loss2: 2.018789
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.463133 Loss1: 1.009729 Loss2: 1.453404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.297112 Loss1: 0.855137 Loss2: 1.441975 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.044724 Loss1: 0.591648 Loss2: 1.453076 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.773673 Loss1: 0.363094 Loss2: 1.410578
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.921773 Loss1: 0.477043 Loss2: 1.444730
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.840073 Loss1: 0.400627 Loss2: 1.439446
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.684737 Loss1: 0.280253 Loss2: 1.404484
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.777484 Loss1: 0.345487 Loss2: 1.431997
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.677121 Loss1: 0.287014 Loss2: 1.390106
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.717660 Loss1: 0.290420 Loss2: 1.427239
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.591877 Loss1: 0.200680 Loss2: 1.391197
-(DefaultActor pid=3765) >> Training accuracy: 0.941176
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.618130 Loss1: 0.193937 Loss2: 1.424193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.612745 Loss1: 1.585639 Loss2: 2.027106
-DEBUG flwr 2023-10-09 23:03:45,563 | server.py:236 | fit_round 55 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.458169 Loss1: 0.983528 Loss2: 1.474641
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.143406 Loss1: 0.673678 Loss2: 1.469728
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.783602 Loss1: 1.703581 Loss2: 2.080021
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.032042 Loss1: 0.561887 Loss2: 1.470155
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.426031 Loss1: 0.953122 Loss2: 1.472909
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.883929 Loss1: 0.433359 Loss2: 1.450570
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.764207 Loss1: 0.325091 Loss2: 1.439115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.785585 Loss1: 0.345065 Loss2: 1.440520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.794909 Loss1: 0.361630 Loss2: 1.433280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.716194 Loss1: 0.269494 Loss2: 1.446701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.651783 Loss1: 0.226090 Loss2: 1.425693 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.938477
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.659838 Loss1: 0.227504 Loss2: 1.432334 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.956473
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.991141 Loss1: 1.862706 Loss2: 2.128436
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.628699 Loss1: 1.103207 Loss2: 1.525492
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.354515 Loss1: 0.850375 Loss2: 1.504140
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.094541 Loss1: 0.575431 Loss2: 1.519109
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.746328 Loss1: 1.726778 Loss2: 2.019550
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.544702 Loss1: 1.094003 Loss2: 1.450699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.217107 Loss1: 0.787609 Loss2: 1.429498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.938502 Loss1: 0.518910 Loss2: 1.419591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.828126 Loss1: 0.417373 Loss2: 1.410753 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.793437 Loss1: 0.382285 Loss2: 1.411152 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.929688
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.718599 Loss1: 0.305928 Loss2: 1.412671 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.622351 Loss1: 0.213744 Loss2: 1.408607 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.429951 Loss1: 0.985215 Loss2: 1.444737 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.929498 Loss1: 0.495097 Loss2: 1.434402 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.814690 Loss1: 0.396522 Loss2: 1.418168
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.775153 Loss1: 1.671406 Loss2: 2.103747
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.744808 Loss1: 0.318928 Loss2: 1.425879
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.682866 Loss1: 1.132498 Loss2: 1.550368
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.732714 Loss1: 0.313094 Loss2: 1.419620
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.348282 Loss1: 0.786717 Loss2: 1.561565
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.726283 Loss1: 0.308684 Loss2: 1.417598
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.132207 Loss1: 0.597832 Loss2: 1.534375
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.667403 Loss1: 0.249814 Loss2: 1.417589
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.003623 Loss1: 0.483451 Loss2: 1.520172
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.658544 Loss1: 0.243440 Loss2: 1.415105
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.920431 Loss1: 0.400258 Loss2: 1.520173
-(DefaultActor pid=3765) >> Training accuracy: 0.948958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.824570 Loss1: 0.293761 Loss2: 1.530809
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.780243 Loss1: 0.260447 Loss2: 1.519796
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.741984 Loss1: 0.233461 Loss2: 1.508523
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.749335 Loss1: 0.242854 Loss2: 1.506482
-(DefaultActor pid=3764) >> Training accuracy: 0.929167
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 23:03:45,563][flwr][DEBUG] - fit_round 55 received 50 results and 0 failures
-INFO flwr 2023-10-09 23:04:27,636 | server.py:125 | fit progress: (55, 2.378667508832182, {'accuracy': 0.4939}, 126775.4142645)
->> Test accuracy: 0.493900
-[2023-10-09 23:04:27,636][flwr][INFO] - fit progress: (55, 2.378667508832182, {'accuracy': 0.4939}, 126775.4142645)
-DEBUG flwr 2023-10-09 23:04:27,636 | server.py:173 | evaluate_round 55: strategy sampled 50 clients (out of 50)
-[2023-10-09 23:04:27,636][flwr][DEBUG] - evaluate_round 55: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 23:13:31,942 | server.py:187 | evaluate_round 55 received 50 results and 0 failures
-[2023-10-09 23:13:31,942][flwr][DEBUG] - evaluate_round 55 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 23:13:31,943 | server.py:222 | fit_round 56: strategy sampled 50 clients (out of 50)
-[2023-10-09 23:13:31,943][flwr][DEBUG] - fit_round 56: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.652994 Loss1: 1.701230 Loss2: 1.951764
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.522298 Loss1: 1.067517 Loss2: 1.454782
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.121492 Loss1: 0.677578 Loss2: 1.443914
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.690599 Loss1: 1.657116 Loss2: 2.033483
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.917784 Loss1: 0.477316 Loss2: 1.440468
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.691479 Loss1: 1.202866 Loss2: 1.488614
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.925471 Loss1: 0.492993 Loss2: 1.432479
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.288081 Loss1: 0.806720 Loss2: 1.481361
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.804754 Loss1: 0.356444 Loss2: 1.448310
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.070006 Loss1: 0.592544 Loss2: 1.477462
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.820232 Loss1: 0.383955 Loss2: 1.436277
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.843264 Loss1: 0.391122 Loss2: 1.452142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.738064 Loss1: 0.294040 Loss2: 1.444024 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.653311 Loss1: 0.225097 Loss2: 1.428214 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958008
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.761154 Loss1: 0.303643 Loss2: 1.457511 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.651482 Loss1: 1.644268 Loss2: 2.007214 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.220916 Loss1: 0.775420 Loss2: 1.445496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.034177 Loss1: 0.597294 Loss2: 1.436882
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.509414 Loss1: 1.537761 Loss2: 1.971652
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.869517 Loss1: 0.442490 Loss2: 1.427027
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.431814 Loss1: 0.998203 Loss2: 1.433611
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.810394 Loss1: 0.387533 Loss2: 1.422861
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.081662 Loss1: 0.637014 Loss2: 1.444648
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.742392 Loss1: 0.321895 Loss2: 1.420497
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.954560 Loss1: 0.537550 Loss2: 1.417010
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.679623 Loss1: 0.258473 Loss2: 1.421150
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.840513 Loss1: 0.427430 Loss2: 1.413083
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.668841 Loss1: 0.261068 Loss2: 1.407772
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.746121 Loss1: 0.343823 Loss2: 1.402298
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.696239 Loss1: 0.285315 Loss2: 1.410924
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.692494 Loss1: 0.285130 Loss2: 1.407364
-(DefaultActor pid=3765) >> Training accuracy: 0.935417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.638367 Loss1: 0.241974 Loss2: 1.396393
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.637871 Loss1: 0.230845 Loss2: 1.407026
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.668088 Loss1: 0.265051 Loss2: 1.403037
-(DefaultActor pid=3764) >> Training accuracy: 0.938542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.483932 Loss1: 1.518752 Loss2: 1.965180
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.439459 Loss1: 0.980725 Loss2: 1.458734
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.093915 Loss1: 0.638811 Loss2: 1.455104
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.736065 Loss1: 1.725964 Loss2: 2.010101
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.938843 Loss1: 0.487696 Loss2: 1.451147
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.592978 Loss1: 1.127021 Loss2: 1.465957
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.870321 Loss1: 0.434845 Loss2: 1.435476
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.809556 Loss1: 0.368403 Loss2: 1.441153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.699528 Loss1: 0.268539 Loss2: 1.430989 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.779325 Loss1: 0.353252 Loss2: 1.426073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.666745 Loss1: 0.229205 Loss2: 1.437540 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.653303 Loss1: 0.224406 Loss2: 1.428898 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966912
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.702275 Loss1: 0.271594 Loss2: 1.430681 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.808860 Loss1: 1.704962 Loss2: 2.103898
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.560377 Loss1: 1.066288 Loss2: 1.494088
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.211878 Loss1: 0.764562 Loss2: 1.447316
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.043563 Loss1: 0.584493 Loss2: 1.459070
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.915271 Loss1: 0.451655 Loss2: 1.463616
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.788332 Loss1: 0.335818 Loss2: 1.452513 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.772331 Loss1: 0.334064 Loss2: 1.438267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.711519 Loss1: 0.274461 Loss2: 1.437058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.657582 Loss1: 0.220285 Loss2: 1.437298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.880528 Loss1: 0.431896 Loss2: 1.448633
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.675002 Loss1: 0.238213 Loss2: 1.436789
-(DefaultActor pid=3765) >> Training accuracy: 0.956731
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.771634 Loss1: 0.325509 Loss2: 1.446125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.662448 Loss1: 0.220964 Loss2: 1.441484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.630953 Loss1: 1.638911 Loss2: 1.992042
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.622003 Loss1: 0.196319 Loss2: 1.425684
-(DefaultActor pid=3764) >> Training accuracy: 0.923828
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.150242 Loss1: 0.679128 Loss2: 1.471114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.924789 Loss1: 0.483036 Loss2: 1.441753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.794036 Loss1: 0.353487 Loss2: 1.440549
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.468459 Loss1: 1.506317 Loss2: 1.962142
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.818370 Loss1: 0.375864 Loss2: 1.442506
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.383437 Loss1: 0.968619 Loss2: 1.414818
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.756626 Loss1: 0.305825 Loss2: 1.450801
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.143657 Loss1: 0.726062 Loss2: 1.417596
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.739493 Loss1: 0.296360 Loss2: 1.443133
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.831349 Loss1: 0.432244 Loss2: 1.399104
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.696492 Loss1: 0.247928 Loss2: 1.448564
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.730189 Loss1: 0.355058 Loss2: 1.375131
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.723836 Loss1: 0.346424 Loss2: 1.377412
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.645145 Loss1: 0.273037 Loss2: 1.372108
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.645990 Loss1: 0.271565 Loss2: 1.374425
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.662051 Loss1: 0.286429 Loss2: 1.375622
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.649605 Loss1: 0.278857 Loss2: 1.370747
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.632039 Loss1: 1.520051 Loss2: 2.111988
-(DefaultActor pid=3764) >> Training accuracy: 0.943750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.460195 Loss1: 0.960443 Loss2: 1.499752
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.164333 Loss1: 0.670876 Loss2: 1.493457
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.013456 Loss1: 0.533700 Loss2: 1.479756
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.888459 Loss1: 0.421569 Loss2: 1.466890
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.428299 Loss1: 1.395047 Loss2: 2.033251
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.857331 Loss1: 0.394232 Loss2: 1.463099
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.446925 Loss1: 0.978100 Loss2: 1.468825
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.802818 Loss1: 0.344099 Loss2: 1.458719
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.124296 Loss1: 0.652166 Loss2: 1.472131
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.725340 Loss1: 0.273818 Loss2: 1.451522
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.894567 Loss1: 0.428388 Loss2: 1.466179
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.663141 Loss1: 0.206652 Loss2: 1.456488
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.767416 Loss1: 0.334951 Loss2: 1.432465
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.644688 Loss1: 0.206305 Loss2: 1.438383
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.748570 Loss1: 0.311777 Loss2: 1.436793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.627905 Loss1: 0.193359 Loss2: 1.434546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.667508 Loss1: 1.593822 Loss2: 2.073686
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.628020 Loss1: 0.202032 Loss2: 1.425988
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.236582 Loss1: 0.793367 Loss2: 1.443215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.858820 Loss1: 0.422558 Loss2: 1.436262 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.753052 Loss1: 0.328529 Loss2: 1.424523
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.704942 Loss1: 1.758238 Loss2: 1.946705
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.438149 Loss1: 1.032718 Loss2: 1.405432 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.147813 Loss1: 0.734153 Loss2: 1.413659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.916789 Loss1: 0.504915 Loss2: 1.411874 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.928125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.737156 Loss1: 0.310949 Loss2: 1.426207
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.885331 Loss1: 0.483521 Loss2: 1.401810
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.764079 Loss1: 0.357339 Loss2: 1.406740
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.692851 Loss1: 0.297932 Loss2: 1.394919
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.701248 Loss1: 0.313669 Loss2: 1.387579
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.597163 Loss1: 0.195773 Loss2: 1.401389
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.841733 Loss1: 1.741914 Loss2: 2.099819
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.573442 Loss1: 0.184242 Loss2: 1.389200
-(DefaultActor pid=3764) >> Training accuracy: 0.947917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.316800 Loss1: 0.790865 Loss2: 1.525936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.980314 Loss1: 0.483776 Loss2: 1.496538 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.926545 Loss1: 0.422940 Loss2: 1.503605
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.627323 Loss1: 1.581200 Loss2: 2.046123
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.649077 Loss1: 1.140721 Loss2: 1.508356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.237386 Loss1: 0.705094 Loss2: 1.532293 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.944303 Loss1: 0.470301 Loss2: 1.474002 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.745217 Loss1: 0.269423 Loss2: 1.475794
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.863485 Loss1: 0.411614 Loss2: 1.451870
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.802918 Loss1: 0.350580 Loss2: 1.452338
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.769107 Loss1: 0.310147 Loss2: 1.458960
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.756849 Loss1: 0.295307 Loss2: 1.461541
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.716905 Loss1: 0.263217 Loss2: 1.453688
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.705887 Loss1: 1.655284 Loss2: 2.050603
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.725897 Loss1: 0.277953 Loss2: 1.447944
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.317410 Loss1: 0.837344 Loss2: 1.480067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.893876 Loss1: 0.437998 Loss2: 1.455878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.861354 Loss1: 0.405999 Loss2: 1.455356
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.639114 Loss1: 1.650645 Loss2: 1.988469
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.879806 Loss1: 0.408654 Loss2: 1.471152
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.570844 Loss1: 1.086122 Loss2: 1.484722
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.859822 Loss1: 0.388940 Loss2: 1.470883
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.245336 Loss1: 0.746728 Loss2: 1.498608
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.038698 Loss1: 0.565882 Loss2: 1.472817 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.915625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.760963 Loss1: 0.287718 Loss2: 1.473245
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.003132 Loss1: 0.524418 Loss2: 1.478714
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.850930 Loss1: 0.380992 Loss2: 1.469938
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.747431 Loss1: 0.293258 Loss2: 1.454173
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.747697 Loss1: 0.296737 Loss2: 1.450959
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.759172 Loss1: 0.295002 Loss2: 1.464169
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.633471 Loss1: 1.604813 Loss2: 2.028657
-(DefaultActor pid=3764) >> Training accuracy: 0.954102
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.621668 Loss1: 1.139432 Loss2: 1.482236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.020448 Loss1: 0.558597 Loss2: 1.461851 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.831094 Loss1: 0.367028 Loss2: 1.464066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.774192 Loss1: 0.312508 Loss2: 1.461684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.728898 Loss1: 0.274786 Loss2: 1.454111 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.300360 Loss1: 0.797988 Loss2: 1.502371
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.691511 Loss1: 0.231979 Loss2: 1.459532
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.105718 Loss1: 0.603008 Loss2: 1.502710
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.693981 Loss1: 0.250339 Loss2: 1.443643
-(DefaultActor pid=3765) >> Training accuracy: 0.939583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.816500 Loss1: 0.330173 Loss2: 1.486327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.874230 Loss1: 0.393168 Loss2: 1.481062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.574383 Loss1: 1.547708 Loss2: 2.026675
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.816409 Loss1: 0.322335 Loss2: 1.494074
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.499800 Loss1: 1.035738 Loss2: 1.464062
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.819862 Loss1: 0.331161 Loss2: 1.488701
-(DefaultActor pid=3764) >> Training accuracy: 0.911133
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.951644 Loss1: 0.497028 Loss2: 1.454617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.741788 Loss1: 0.312018 Loss2: 1.429770 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.670633 Loss1: 0.245913 Loss2: 1.424720
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.932898 Loss1: 1.742027 Loss2: 2.190871
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.511924 Loss1: 1.019199 Loss2: 1.492725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.258740 Loss1: 0.801190 Loss2: 1.457549
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.818841 Loss1: 0.390118 Loss2: 1.428723
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.778963 Loss1: 0.334917 Loss2: 1.444046 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.935417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.811412 Loss1: 0.348706 Loss2: 1.462705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.699442 Loss1: 0.256080 Loss2: 1.443362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.666410 Loss1: 0.221276 Loss2: 1.445134 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.954427
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.567391 Loss1: 1.139116 Loss2: 1.428274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.088237 Loss1: 0.653746 Loss2: 1.434492 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.870721 Loss1: 0.444723 Loss2: 1.425998
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.925617 Loss1: 1.839961 Loss2: 2.085657
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.564474 Loss1: 1.099375 Loss2: 1.465099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.287606 Loss1: 0.831035 Loss2: 1.456572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.057608 Loss1: 0.595465 Loss2: 1.462144 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.911920 Loss1: 0.462919 Loss2: 1.449001 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.618095 Loss1: 0.209627 Loss2: 1.408468
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.821674 Loss1: 0.381269 Loss2: 1.440405
-(DefaultActor pid=3765) >> Training accuracy: 0.930208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.752882 Loss1: 0.303126 Loss2: 1.449756
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.722607 Loss1: 0.290104 Loss2: 1.432503
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.723818 Loss1: 0.290673 Loss2: 1.433144
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.741127 Loss1: 0.293241 Loss2: 1.447886
-(DefaultActor pid=3764) >> Training accuracy: 0.909598
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.629797 Loss1: 1.609587 Loss2: 2.020210
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.515402 Loss1: 1.042528 Loss2: 1.472873
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.208796 Loss1: 0.734670 Loss2: 1.474126
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.004448 Loss1: 0.531310 Loss2: 1.473139
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.517535 Loss1: 1.449922 Loss2: 2.067613
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.497835 Loss1: 1.032508 Loss2: 1.465326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.241527 Loss1: 0.746117 Loss2: 1.495410 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.026248 Loss1: 0.558066 Loss2: 1.468182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.890920 Loss1: 0.456935 Loss2: 1.433985 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.846297 Loss1: 0.388753 Loss2: 1.457544 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.931250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.718829 Loss1: 0.282568 Loss2: 1.436261 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.671389 Loss1: 0.242253 Loss2: 1.429136 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.927083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.581841 Loss1: 1.100178 Loss2: 1.481663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.960078 Loss1: 0.497966 Loss2: 1.462113 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.832361 Loss1: 0.377498 Loss2: 1.454863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.813752 Loss1: 0.360925 Loss2: 1.452826 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.884737 Loss1: 0.432070 Loss2: 1.452667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.792436 Loss1: 0.333366 Loss2: 1.459069 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.731848 Loss1: 0.276218 Loss2: 1.455630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.707324 Loss1: 0.264311 Loss2: 1.443013 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.934570
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.773522 Loss1: 0.341735 Loss2: 1.431787 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.684664 Loss1: 0.260038 Loss2: 1.424626 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.607845 Loss1: 1.583152 Loss2: 2.024692
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.475964 Loss1: 1.009230 Loss2: 1.466734
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.188211 Loss1: 0.719406 Loss2: 1.468805
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.006098 Loss1: 0.551884 Loss2: 1.454214
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.793954 Loss1: 1.745291 Loss2: 2.048663
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.654491 Loss1: 1.171707 Loss2: 1.482784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.270617 Loss1: 0.746855 Loss2: 1.523762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.046297 Loss1: 0.553767 Loss2: 1.492530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.015674 Loss1: 0.546313 Loss2: 1.469362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.923062 Loss1: 0.432468 Loss2: 1.490595 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.957292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.852422 Loss1: 0.377867 Loss2: 1.474556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.769946 Loss1: 0.293097 Loss2: 1.476849 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.923958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.678664 Loss1: 1.611840 Loss2: 2.066824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.078660 Loss1: 0.689350 Loss2: 1.389310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.776162 Loss1: 0.386099 Loss2: 1.390063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.719225 Loss1: 0.336124 Loss2: 1.383101 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.677154 Loss1: 0.291036 Loss2: 1.386118 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.585768 Loss1: 0.195249 Loss2: 1.390520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.563295 Loss1: 0.193001 Loss2: 1.370294
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.002251 Loss1: 0.531807 Loss2: 1.470444
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.541696 Loss1: 0.174537 Loss2: 1.367159
-(DefaultActor pid=3765) >> Training accuracy: 0.960337
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.890163 Loss1: 0.443289 Loss2: 1.446874
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.823832 Loss1: 0.368031 Loss2: 1.455801
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.818834 Loss1: 0.358533 Loss2: 1.460301
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.847280 Loss1: 0.379478 Loss2: 1.467803
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.811086 Loss1: 0.334031 Loss2: 1.477055
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.469704 Loss1: 1.481869 Loss2: 1.987835
-(DefaultActor pid=3764) >> Training accuracy: 0.949219
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.420105 Loss1: 0.945284 Loss2: 1.474821 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.999211 Loss1: 0.532072 Loss2: 1.467139 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.810890 Loss1: 0.350133 Loss2: 1.460757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.745095 Loss1: 0.296036 Loss2: 1.449059 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.719548 Loss1: 0.286381 Loss2: 1.433167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.711554 Loss1: 0.273017 Loss2: 1.438536 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.954975 Loss1: 0.518682 Loss2: 1.436293 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.796844 Loss1: 0.342599 Loss2: 1.454245 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.697418 Loss1: 0.269353 Loss2: 1.428065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.644192 Loss1: 0.219766 Loss2: 1.424426
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.758855 Loss1: 1.735854 Loss2: 2.023001
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.637634 Loss1: 1.143422 Loss2: 1.494212
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.303771 Loss1: 0.840891 Loss2: 1.462880
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.026324 Loss1: 0.563956 Loss2: 1.462368
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.986053 Loss1: 0.534476 Loss2: 1.451577
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.665096 Loss1: 1.701487 Loss2: 1.963609
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.863006 Loss1: 0.416278 Loss2: 1.446727
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.710709 Loss1: 1.237903 Loss2: 1.472806
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.787399 Loss1: 0.343161 Loss2: 1.444238
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.385063 Loss1: 0.857937 Loss2: 1.527127
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.751717 Loss1: 0.310847 Loss2: 1.440870
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.004819 Loss1: 0.561511 Loss2: 1.443308
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.757374 Loss1: 0.318497 Loss2: 1.438876
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.972030 Loss1: 0.521817 Loss2: 1.450213
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.709213 Loss1: 0.265803 Loss2: 1.443409
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.800674 Loss1: 0.345760 Loss2: 1.454914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.747833 Loss1: 0.289720 Loss2: 1.458113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.747274 Loss1: 0.297025 Loss2: 1.450249
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.506730 Loss1: 1.563469 Loss2: 1.943261
-(DefaultActor pid=3764) >> Training accuracy: 0.937500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.481769 Loss1: 1.074707 Loss2: 1.407062
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.185584 Loss1: 0.755384 Loss2: 1.430200
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.951712 Loss1: 0.529561 Loss2: 1.422151
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.816756 Loss1: 0.414685 Loss2: 1.402071
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.412427 Loss1: 1.516206 Loss2: 1.896221
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.807235 Loss1: 0.397957 Loss2: 1.409278
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.581816 Loss1: 1.154501 Loss2: 1.427314
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.793241 Loss1: 0.395406 Loss2: 1.397834
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.687398 Loss1: 0.283739 Loss2: 1.403659
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.224252 Loss1: 0.792663 Loss2: 1.431589
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.723604 Loss1: 0.326121 Loss2: 1.397483
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.989730 Loss1: 0.581737 Loss2: 1.407994
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.687494 Loss1: 0.287117 Loss2: 1.400377
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.838995 Loss1: 0.442344 Loss2: 1.396652
-(DefaultActor pid=3765) >> Training accuracy: 0.934375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.815098 Loss1: 0.410775 Loss2: 1.404323
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.813303 Loss1: 0.395641 Loss2: 1.417662
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.757780 Loss1: 0.348865 Loss2: 1.408915
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.660354 Loss1: 0.264879 Loss2: 1.395474
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.593192 Loss1: 1.554046 Loss2: 2.039146
-(DefaultActor pid=3764) >> Training accuracy: 0.951172
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.406657 Loss1: 0.930461 Loss2: 1.476196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.985142 Loss1: 0.538605 Loss2: 1.446537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.778047 Loss1: 0.341349 Loss2: 1.436699 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.781838 Loss1: 0.351738 Loss2: 1.430100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.685583 Loss1: 0.259827 Loss2: 1.425757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.732002 Loss1: 0.301101 Loss2: 1.430901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.684265 Loss1: 0.248854 Loss2: 1.435411 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.921875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.819540 Loss1: 0.378920 Loss2: 1.440620 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.752080 Loss1: 0.313514 Loss2: 1.438565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.620057 Loss1: 1.615659 Loss2: 2.004399 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.941667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.202368 Loss1: 0.761396 Loss2: 1.440972 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.868090 Loss1: 0.440415 Loss2: 1.427675 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.867701 Loss1: 0.445635 Loss2: 1.422065
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.850803 Loss1: 1.797094 Loss2: 2.053709
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.720548 Loss1: 1.202959 Loss2: 1.517589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.273707 Loss1: 0.765680 Loss2: 1.508027 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.076654 Loss1: 0.593734 Loss2: 1.482920 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.671789 Loss1: 0.271658 Loss2: 1.400131
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.131711 Loss1: 0.652896 Loss2: 1.478816
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.998676 Loss1: 0.484151 Loss2: 1.514525
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.947498 Loss1: 0.460147 Loss2: 1.487352
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.895894 Loss1: 0.426910 Loss2: 1.468983
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.795875 Loss1: 0.320220 Loss2: 1.475655
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.597248 Loss1: 1.650637 Loss2: 1.946610
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.740285 Loss1: 0.273557 Loss2: 1.466728
-(DefaultActor pid=3764) >> Training accuracy: 0.936458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.194163 Loss1: 0.762199 Loss2: 1.431964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.810023 Loss1: 0.395529 Loss2: 1.414494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.630472 Loss1: 1.594992 Loss2: 2.035480
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.869306 Loss1: 0.450494 Loss2: 1.418812
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.548008 Loss1: 1.091347 Loss2: 1.456661
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.750471 Loss1: 0.331331 Loss2: 1.419140
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.215310 Loss1: 0.718294 Loss2: 1.497016
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.732050 Loss1: 0.320374 Loss2: 1.411676
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.807817 Loss1: 0.389676 Loss2: 1.418140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.716016 Loss1: 0.275966 Loss2: 1.440050 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.925781
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.709747 Loss1: 0.269106 Loss2: 1.440641 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.617630 Loss1: 0.178849 Loss2: 1.438780 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.960938
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.606104 Loss1: 1.071935 Loss2: 1.534169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.074659 Loss1: 0.553523 Loss2: 1.521137 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.884296 Loss1: 0.370851 Loss2: 1.513445
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.619081 Loss1: 1.631526 Loss2: 1.987555
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.819968 Loss1: 0.329594 Loss2: 1.490374
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.480571 Loss1: 1.035674 Loss2: 1.444897
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.808173 Loss1: 0.307918 Loss2: 1.500255
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.162194 Loss1: 0.716300 Loss2: 1.445894
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.738969 Loss1: 0.247350 Loss2: 1.491620
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.982398 Loss1: 0.547010 Loss2: 1.435388
-DEBUG flwr 2023-10-09 23:42:12,573 | server.py:236 | fit_round 56 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.754610 Loss1: 0.264616 Loss2: 1.489994
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.828722 Loss1: 0.400363 Loss2: 1.428358
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.732791 Loss1: 0.247192 Loss2: 1.485599
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.706508 Loss1: 0.294744 Loss2: 1.411764
-(DefaultActor pid=3765) >> Training accuracy: 0.931250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.710541 Loss1: 0.300388 Loss2: 1.410153
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.686699 Loss1: 0.271996 Loss2: 1.414703
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.628039 Loss1: 0.213580 Loss2: 1.414459
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.614797 Loss1: 0.205885 Loss2: 1.408912
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.580310 Loss1: 1.628773 Loss2: 1.951537
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.406321 Loss1: 0.988133 Loss2: 1.418187
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.155130 Loss1: 0.742000 Loss2: 1.413131
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.946013 Loss1: 0.539790 Loss2: 1.406224
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.824893 Loss1: 0.432727 Loss2: 1.392166
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.764807 Loss1: 0.375102 Loss2: 1.389705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.315952 Loss1: 0.798228 Loss2: 1.517724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.128941 Loss1: 0.609927 Loss2: 1.519014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.972916 Loss1: 0.475339 Loss2: 1.497577 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.851502 Loss1: 0.363276 Loss2: 1.488226 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.915625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.791131 Loss1: 0.306343 Loss2: 1.484789 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.750010 Loss1: 0.268219 Loss2: 1.481791 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.938616
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.671135 Loss1: 1.631420 Loss2: 2.039715
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.599679 Loss1: 1.110484 Loss2: 1.489195
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.371290 Loss1: 0.869155 Loss2: 1.502135
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.065632 Loss1: 0.586698 Loss2: 1.478934
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.596952 Loss1: 1.611043 Loss2: 1.985909
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.527718 Loss1: 1.080112 Loss2: 1.447606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.181404 Loss1: 0.732930 Loss2: 1.448474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.021360 Loss1: 0.585583 Loss2: 1.435777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.913935 Loss1: 0.506397 Loss2: 1.407538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.833392 Loss1: 0.400247 Loss2: 1.433145 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.920833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.727954 Loss1: 0.290040 Loss2: 1.437914
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.794471 Loss1: 0.369926 Loss2: 1.424545
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.812055 Loss1: 0.395661 Loss2: 1.416393
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.803137 Loss1: 0.372381 Loss2: 1.430756
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.666323 Loss1: 0.250190 Loss2: 1.416133
-(DefaultActor pid=3764) >> Training accuracy: 0.947917
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-09 23:42:12,573][flwr][DEBUG] - fit_round 56 received 50 results and 0 failures
-INFO flwr 2023-10-09 23:42:53,929 | server.py:125 | fit progress: (56, 2.3453345260681053, {'accuracy': 0.4999}, 129081.707892689)
->> Test accuracy: 0.499900
-[2023-10-09 23:42:53,929][flwr][INFO] - fit progress: (56, 2.3453345260681053, {'accuracy': 0.4999}, 129081.707892689)
-DEBUG flwr 2023-10-09 23:42:53,930 | server.py:173 | evaluate_round 56: strategy sampled 50 clients (out of 50)
-[2023-10-09 23:42:53,930][flwr][DEBUG] - evaluate_round 56: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-09 23:52:01,138 | server.py:187 | evaluate_round 56 received 50 results and 0 failures
-[2023-10-09 23:52:01,138][flwr][DEBUG] - evaluate_round 56 received 50 results and 0 failures
-DEBUG flwr 2023-10-09 23:52:01,139 | server.py:222 | fit_round 57: strategy sampled 50 clients (out of 50)
-[2023-10-09 23:52:01,139][flwr][DEBUG] - fit_round 57: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.501036 Loss1: 1.534974 Loss2: 1.966062
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.456984 Loss1: 1.023481 Loss2: 1.433504
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.132692 Loss1: 0.686607 Loss2: 1.446085
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.881050 Loss1: 0.456349 Loss2: 1.424700
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.778287 Loss1: 1.730027 Loss2: 2.048261
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.566606 Loss1: 1.076835 Loss2: 1.489770
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.829794 Loss1: 0.423508 Loss2: 1.406286
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.203989 Loss1: 0.747184 Loss2: 1.456805
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.734412 Loss1: 0.325206 Loss2: 1.409206
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.925204 Loss1: 0.477596 Loss2: 1.447608
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.722640 Loss1: 0.319505 Loss2: 1.403135
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.874402 Loss1: 0.434042 Loss2: 1.440361
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.710023 Loss1: 0.295581 Loss2: 1.414443
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.633261 Loss1: 0.223336 Loss2: 1.409925 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.628499 Loss1: 0.227307 Loss2: 1.401191 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.940430
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.664440 Loss1: 0.230657 Loss2: 1.433782 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.524226 Loss1: 1.588685 Loss2: 1.935541 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.124831 Loss1: 0.701148 Loss2: 1.423682 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.934765 Loss1: 0.519402 Loss2: 1.415363
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.269313 Loss1: 1.335151 Loss2: 1.934163
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.380720 Loss1: 0.950266 Loss2: 1.430454 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.249275 Loss1: 0.794958 Loss2: 1.454317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.987490 Loss1: 0.564556 Loss2: 1.422934 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.859225 Loss1: 0.436104 Loss2: 1.423121 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.593023 Loss1: 0.223965 Loss2: 1.369058 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.923958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.660069 Loss1: 0.254572 Loss2: 1.405497 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.602111 Loss1: 0.204387 Loss2: 1.397724 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.955882
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.583329 Loss1: 1.166838 Loss2: 1.416491 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.966837 Loss1: 0.571940 Loss2: 1.394897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.832892 Loss1: 0.447045 Loss2: 1.385846
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.589257 Loss1: 1.567786 Loss2: 2.021471
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.689973 Loss1: 1.178983 Loss2: 1.510990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.356192 Loss1: 0.785928 Loss2: 1.570264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.993870 Loss1: 0.490375 Loss2: 1.503495 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.922653 Loss1: 0.445670 Loss2: 1.476984 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.939583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.861240 Loss1: 0.376279 Loss2: 1.484961 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.764342 Loss1: 0.292313 Loss2: 1.472029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.665877 Loss1: 0.194641 Loss2: 1.471236 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.649812 Loss1: 1.106367 Loss2: 1.543445 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.031757 Loss1: 0.527702 Loss2: 1.504054 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.520457 Loss1: 1.503122 Loss2: 2.017335
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.988313 Loss1: 0.452483 Loss2: 1.535831
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.340629 Loss1: 0.889966 Loss2: 1.450663
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.943566 Loss1: 0.420694 Loss2: 1.522872
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.088780 Loss1: 0.661030 Loss2: 1.427750
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.867242 Loss1: 0.349386 Loss2: 1.517856
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.891466 Loss1: 0.359270 Loss2: 1.532196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.833355 Loss1: 0.305996 Loss2: 1.527360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.773787 Loss1: 0.264609 Loss2: 1.509178 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.931641
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.680059 Loss1: 0.267232 Loss2: 1.412827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.684889 Loss1: 0.267447 Loss2: 1.417443 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.940625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.483615 Loss1: 1.042636 Loss2: 1.440978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.886310 Loss1: 0.474669 Loss2: 1.411641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.803882 Loss1: 0.407301 Loss2: 1.396582 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.745912 Loss1: 0.336719 Loss2: 1.409193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.694987 Loss1: 0.296440 Loss2: 1.398547 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.701683 Loss1: 0.308653 Loss2: 1.393029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.656700 Loss1: 0.256253 Loss2: 1.400448 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.599419 Loss1: 0.203660 Loss2: 1.395759 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.944792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.602970 Loss1: 0.208825 Loss2: 1.394146 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975446
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.778097 Loss1: 1.706306 Loss2: 2.071791 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.404384 Loss1: 0.839570 Loss2: 1.564814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.165095 Loss1: 0.652058 Loss2: 1.513037
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.725253 Loss1: 1.708053 Loss2: 2.017201
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.979582 Loss1: 0.479298 Loss2: 1.500284
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.552580 Loss1: 1.083515 Loss2: 1.469064
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.861375 Loss1: 0.357959 Loss2: 1.503416
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.182801 Loss1: 0.704902 Loss2: 1.477900
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.847099 Loss1: 0.350444 Loss2: 1.496655
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.982928 Loss1: 0.534992 Loss2: 1.447936
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.866541 Loss1: 0.367508 Loss2: 1.499033
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.874048 Loss1: 0.438425 Loss2: 1.435623
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.812951 Loss1: 0.311798 Loss2: 1.501153
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.823002 Loss1: 0.376413 Loss2: 1.446589
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.823833 Loss1: 0.322361 Loss2: 1.501472
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.779200 Loss1: 0.333945 Loss2: 1.445255
-(DefaultActor pid=3765) >> Training accuracy: 0.925000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.727499 Loss1: 0.294781 Loss2: 1.432718
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.721837 Loss1: 0.280694 Loss2: 1.441142
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.695055 Loss1: 0.251801 Loss2: 1.443254
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.625131 Loss1: 1.590031 Loss2: 2.035100
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.600151 Loss1: 1.094167 Loss2: 1.505984
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.248339 Loss1: 0.729864 Loss2: 1.518475
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.018849 Loss1: 0.526448 Loss2: 1.492400
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.598418 Loss1: 1.533926 Loss2: 2.064492
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.931280 Loss1: 0.450671 Loss2: 1.480609
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.492448 Loss1: 1.001144 Loss2: 1.491303
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.883968 Loss1: 0.397427 Loss2: 1.486541
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.163192 Loss1: 0.675707 Loss2: 1.487485
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.937934 Loss1: 0.471492 Loss2: 1.466442
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.843657 Loss1: 0.353338 Loss2: 1.490319
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.859022 Loss1: 0.405830 Loss2: 1.453192
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.848323 Loss1: 0.356433 Loss2: 1.491890
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.753802 Loss1: 0.304802 Loss2: 1.449000
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.770168 Loss1: 0.278447 Loss2: 1.491720
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.760656 Loss1: 0.313147 Loss2: 1.447509
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.708746 Loss1: 0.226869 Loss2: 1.481877
-(DefaultActor pid=3765) >> Training accuracy: 0.944336
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.675350 Loss1: 0.223054 Loss2: 1.452296 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.670151 Loss1: 1.738519 Loss2: 1.931632 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.139259 Loss1: 0.701094 Loss2: 1.438165 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.042462 Loss1: 0.618877 Loss2: 1.423584
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.559396 Loss1: 1.658640 Loss2: 1.900757
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.865686 Loss1: 0.446171 Loss2: 1.419515
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.419743 Loss1: 0.994568 Loss2: 1.425174
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.788868 Loss1: 0.356582 Loss2: 1.432286
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.151871 Loss1: 0.712372 Loss2: 1.439499
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.819005 Loss1: 0.391325 Loss2: 1.427680
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.985787 Loss1: 0.574882 Loss2: 1.410904
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.772104 Loss1: 0.361112 Loss2: 1.410993
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.851068 Loss1: 0.430529 Loss2: 1.420539
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.729980 Loss1: 0.301999 Loss2: 1.427980
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.766997 Loss1: 0.355059 Loss2: 1.411938
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.727609 Loss1: 0.311465 Loss2: 1.416144
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.702900 Loss1: 0.293917 Loss2: 1.408983
-(DefaultActor pid=3765) >> Training accuracy: 0.943359
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.698921 Loss1: 0.297180 Loss2: 1.401741
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.681156 Loss1: 0.268595 Loss2: 1.412560
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.638058 Loss1: 0.230202 Loss2: 1.407856
-(DefaultActor pid=3764) >> Training accuracy: 0.913086
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.404108 Loss1: 1.428840 Loss2: 1.975269
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.344483 Loss1: 0.922219 Loss2: 1.422264
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.111344 Loss1: 0.673953 Loss2: 1.437391
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.935701 Loss1: 0.520265 Loss2: 1.415435
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.760399 Loss1: 1.734221 Loss2: 2.026178
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.539826 Loss1: 1.059898 Loss2: 1.479928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.335076 Loss1: 0.870564 Loss2: 1.464512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.096684 Loss1: 0.618303 Loss2: 1.478381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.948968 Loss1: 0.499845 Loss2: 1.449123 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.822569 Loss1: 0.373278 Loss2: 1.449291 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.947917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.824795 Loss1: 0.373713 Loss2: 1.451082 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.757939 Loss1: 0.314915 Loss2: 1.443024 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.929167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.562736 Loss1: 1.509953 Loss2: 2.052783 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.255862 Loss1: 0.688460 Loss2: 1.567402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.537902 Loss1: 1.582864 Loss2: 1.955037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.571334 Loss1: 1.142025 Loss2: 1.429309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.226397 Loss1: 0.783984 Loss2: 1.442413 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.970140 Loss1: 0.551099 Loss2: 1.419040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.825346 Loss1: 0.430820 Loss2: 1.394526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.754250 Loss1: 0.356755 Loss2: 1.397495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.892708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.706965 Loss1: 0.318180 Loss2: 1.388785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.660809 Loss1: 0.278716 Loss2: 1.382093 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.587744 Loss1: 1.595236 Loss2: 1.992508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.152526 Loss1: 0.722155 Loss2: 1.430371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.909159 Loss1: 0.476657 Loss2: 1.432502
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.374428 Loss1: 1.420734 Loss2: 1.953695
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.318423 Loss1: 0.899245 Loss2: 1.419178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.058654 Loss1: 0.643365 Loss2: 1.415289 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.931384 Loss1: 0.508109 Loss2: 1.423274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.834718 Loss1: 0.430779 Loss2: 1.403938 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.748916 Loss1: 0.353597 Loss2: 1.395319 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.631350 Loss1: 0.239040 Loss2: 1.392310 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.557627 Loss1: 0.180347 Loss2: 1.377280 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.781558 Loss1: 1.790855 Loss2: 1.990703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.303037 Loss1: 0.822367 Loss2: 1.480669 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.485390 Loss1: 1.500146 Loss2: 1.985244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.428014 Loss1: 0.981077 Loss2: 1.446938 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.193699 Loss1: 0.718261 Loss2: 1.475438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.694913 Loss1: 0.279456 Loss2: 1.415458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.643678 Loss1: 0.229892 Loss2: 1.413786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.660837 Loss1: 0.248354 Loss2: 1.412483 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.934152
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.741184 Loss1: 0.307688 Loss2: 1.433496 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.683926 Loss1: 0.257439 Loss2: 1.426488 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.523025 Loss1: 1.059548 Loss2: 1.463477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.054297 Loss1: 0.579162 Loss2: 1.475135 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.846984 Loss1: 0.407355 Loss2: 1.439630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.740962 Loss1: 0.310634 Loss2: 1.430329 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.809774 Loss1: 0.378446 Loss2: 1.431328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.764250 Loss1: 0.321091 Loss2: 1.443159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.635515 Loss1: 0.200776 Loss2: 1.434739 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.742873 Loss1: 0.328680 Loss2: 1.414192 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.626904 Loss1: 0.224634 Loss2: 1.402270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.492695 Loss1: 1.568690 Loss2: 1.924005 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966346
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.140700 Loss1: 0.757940 Loss2: 1.382760 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.725230 Loss1: 0.342367 Loss2: 1.382863 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.624383 Loss1: 1.630684 Loss2: 1.993699
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.712658 Loss1: 0.341221 Loss2: 1.371437
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.615186 Loss1: 1.146265 Loss2: 1.468921
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.614396 Loss1: 0.257294 Loss2: 1.357102
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.291039 Loss1: 0.791444 Loss2: 1.499594
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.573236 Loss1: 0.216411 Loss2: 1.356825
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.965574 Loss1: 0.502060 Loss2: 1.463515
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.519377 Loss1: 0.163868 Loss2: 1.355509
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.855931 Loss1: 0.405901 Loss2: 1.450030
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.555356 Loss1: 0.202654 Loss2: 1.352703
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.746588 Loss1: 0.287458 Loss2: 1.459130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.740898 Loss1: 0.283258 Loss2: 1.457640 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.886268 Loss1: 1.789988 Loss2: 2.096280
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.706921 Loss1: 0.247900 Loss2: 1.459021
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.200044 Loss1: 0.724754 Loss2: 1.475291 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.960741 Loss1: 0.501506 Loss2: 1.459236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.865234 Loss1: 0.400417 Loss2: 1.464817
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.547922 Loss1: 1.539159 Loss2: 2.008763
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.487745 Loss1: 1.040683 Loss2: 1.447062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.282955 Loss1: 0.815272 Loss2: 1.467683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.058153 Loss1: 0.583622 Loss2: 1.474531 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.868666 Loss1: 0.429760 Loss2: 1.438906 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.774567 Loss1: 0.332597 Loss2: 1.441970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.748372 Loss1: 0.311548 Loss2: 1.436824
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.425218 Loss1: 1.450010 Loss2: 1.975208
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.513024 Loss1: 1.015128 Loss2: 1.497897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.955925 Loss1: 0.489176 Loss2: 1.466749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.816182 Loss1: 0.351897 Loss2: 1.464285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.789850 Loss1: 0.328395 Loss2: 1.461455 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.728122 Loss1: 0.267336 Loss2: 1.460786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.667304 Loss1: 0.212810 Loss2: 1.454494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.738489 Loss1: 0.283351 Loss2: 1.455138 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.927734
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.755689 Loss1: 0.321219 Loss2: 1.434470 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.647370 Loss1: 0.221265 Loss2: 1.426105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.610143 Loss1: 1.598411 Loss2: 2.011731
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.616140 Loss1: 0.198467 Loss2: 1.417673
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.187480 Loss1: 0.701069 Loss2: 1.486411 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.994696 Loss1: 0.518962 Loss2: 1.475734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.830395 Loss1: 0.373181 Loss2: 1.457215
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.508077 Loss1: 1.506424 Loss2: 2.001653
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.816022 Loss1: 0.365062 Loss2: 1.450961
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.553221 Loss1: 1.068324 Loss2: 1.484898
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.807268 Loss1: 0.356604 Loss2: 1.450664
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.304104 Loss1: 0.806351 Loss2: 1.497753
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.739714 Loss1: 0.291216 Loss2: 1.448498
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.085291 Loss1: 0.601200 Loss2: 1.484092
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.730024 Loss1: 0.284359 Loss2: 1.445666
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.918218 Loss1: 0.454579 Loss2: 1.463639
-(DefaultActor pid=3765) >> Training accuracy: 0.954167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.865706 Loss1: 0.415960 Loss2: 1.449746
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.765266 Loss1: 0.308645 Loss2: 1.456621
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.745535 Loss1: 0.286621 Loss2: 1.458913
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.676547 Loss1: 0.227443 Loss2: 1.449104
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.632856 Loss1: 0.184890 Loss2: 1.447965
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.537992 Loss1: 1.613048 Loss2: 1.924944
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.587024 Loss1: 1.154989 Loss2: 1.432035
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.279085 Loss1: 0.803175 Loss2: 1.475910
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.969894 Loss1: 0.543591 Loss2: 1.426303
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.885055 Loss1: 0.463635 Loss2: 1.421420
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.586716 Loss1: 1.541483 Loss2: 2.045233
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.839378 Loss1: 0.419785 Loss2: 1.419592
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.862851 Loss1: 0.442540 Loss2: 1.420311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.765057 Loss1: 0.330304 Loss2: 1.434753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.707674 Loss1: 0.290382 Loss2: 1.417291 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.674307 Loss1: 0.259565 Loss2: 1.414742 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.932617
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.798766 Loss1: 0.348050 Loss2: 1.450716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.779965 Loss1: 0.308731 Loss2: 1.471234 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.912500
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.768313 Loss1: 0.316527 Loss2: 1.451787
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.718915 Loss1: 1.689081 Loss2: 2.029834
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.494040 Loss1: 1.044463 Loss2: 1.449578
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.132365 Loss1: 0.700796 Loss2: 1.431569
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.969744 Loss1: 0.528106 Loss2: 1.441638
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.841619 Loss1: 0.412822 Loss2: 1.428797
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.500077 Loss1: 1.522684 Loss2: 1.977393
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.452482 Loss1: 1.030321 Loss2: 1.422162 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.156154 Loss1: 0.706729 Loss2: 1.449425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.938371 Loss1: 0.507454 Loss2: 1.430918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.852858 Loss1: 0.446467 Loss2: 1.406390 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.927083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.651456 Loss1: 0.232314 Loss2: 1.419141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.810821 Loss1: 0.384731 Loss2: 1.426090
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.770175 Loss1: 0.352297 Loss2: 1.417879
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.718258 Loss1: 0.304318 Loss2: 1.413941
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.632687 Loss1: 0.215813 Loss2: 1.416874
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.666293 Loss1: 0.255126 Loss2: 1.411167
-(DefaultActor pid=3764) >> Training accuracy: 0.907292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.581441 Loss1: 1.614870 Loss2: 1.966571
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.611463 Loss1: 1.164134 Loss2: 1.447329
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.266299 Loss1: 0.770582 Loss2: 1.495716
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.998184 Loss1: 0.545843 Loss2: 1.452341
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.802879 Loss1: 0.373470 Loss2: 1.429409
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.651097 Loss1: 1.657223 Loss2: 1.993874
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.553586 Loss1: 1.071692 Loss2: 1.481894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.223812 Loss1: 0.727127 Loss2: 1.496685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.089817 Loss1: 0.613129 Loss2: 1.476688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.864267 Loss1: 0.398065 Loss2: 1.466202 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.959375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.807223 Loss1: 0.346745 Loss2: 1.460478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.718616 Loss1: 0.262525 Loss2: 1.456091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.678145 Loss1: 0.226701 Loss2: 1.451444 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.414150 Loss1: 0.957612 Loss2: 1.456538 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.905194 Loss1: 0.457351 Loss2: 1.447843 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.731269 Loss1: 0.311931 Loss2: 1.419337
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.753078 Loss1: 1.736561 Loss2: 2.016517
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.510750 Loss1: 1.040056 Loss2: 1.470694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.126163 Loss1: 0.663168 Loss2: 1.462995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.922169 Loss1: 0.475608 Loss2: 1.446561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.906198 Loss1: 0.449073 Loss2: 1.457125 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.857321 Loss1: 0.398490 Loss2: 1.458830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.807245 Loss1: 0.359203 Loss2: 1.448043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.701844 Loss1: 0.262612 Loss2: 1.439232 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.432499 Loss1: 0.984457 Loss2: 1.448042 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.994222 Loss1: 0.547448 Loss2: 1.446775 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.829397 Loss1: 0.394141 Loss2: 1.435256
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.645378 Loss1: 1.662155 Loss2: 1.983224
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.472798 Loss1: 1.043661 Loss2: 1.429137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.125630 Loss1: 0.700969 Loss2: 1.424661 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.941943 Loss1: 0.525380 Loss2: 1.416563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.882823 Loss1: 0.469084 Loss2: 1.413740 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.792379 Loss1: 0.383688 Loss2: 1.408691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.829610 Loss1: 0.413594 Loss2: 1.416016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.701456 Loss1: 0.292379 Loss2: 1.409076 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.936458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.498324 Loss1: 1.016346 Loss2: 1.481979 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.899213 Loss1: 0.450619 Loss2: 1.448594 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.795795 Loss1: 0.352848 Loss2: 1.442946
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.682283 Loss1: 1.609814 Loss2: 2.072469
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.407890 Loss1: 0.973388 Loss2: 1.434502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.776233 Loss1: 0.341613 Loss2: 1.434620
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.174670 Loss1: 0.753240 Loss2: 1.421430
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.946442 Loss1: 0.518608 Loss2: 1.427833
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.774078 Loss1: 0.321073 Loss2: 1.453005
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.884601 Loss1: 0.479782 Loss2: 1.404818
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.783323 Loss1: 0.340394 Loss2: 1.442928
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.825370 Loss1: 0.361457 Loss2: 1.463913 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.943750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.674710 Loss1: 0.260525 Loss2: 1.414185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.578756 Loss1: 0.193908 Loss2: 1.384848 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.954327
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.647289 Loss1: 1.630025 Loss2: 2.017265
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.599539 Loss1: 1.132886 Loss2: 1.466653
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.424611 Loss1: 0.938154 Loss2: 1.486458
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.097497 Loss1: 0.621991 Loss2: 1.475507
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.456313 Loss1: 1.537242 Loss2: 1.919071
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.530087 Loss1: 1.092804 Loss2: 1.437283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.143277 Loss1: 0.721733 Loss2: 1.421543 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 00:20:54,582 | server.py:236 | fit_round 57 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.891341 Loss1: 0.490886 Loss2: 1.400455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.746720 Loss1: 0.356996 Loss2: 1.389724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.692766 Loss1: 0.313632 Loss2: 1.379134 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.944792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.679626 Loss1: 0.288607 Loss2: 1.391019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.689744 Loss1: 0.292808 Loss2: 1.396935 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.938477
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.402110 Loss1: 1.443475 Loss2: 1.958635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.074071 Loss1: 0.666543 Loss2: 1.407528 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.651423 Loss1: 1.575098 Loss2: 2.076326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.494055 Loss1: 0.998649 Loss2: 1.495406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.157695 Loss1: 0.654731 Loss2: 1.502964 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.013370 Loss1: 0.529329 Loss2: 1.484040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.965091 Loss1: 0.489674 Loss2: 1.475417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.912792 Loss1: 0.418845 Loss2: 1.493947 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941406
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.677217 Loss1: 0.294373 Loss2: 1.382844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.793975 Loss1: 0.305974 Loss2: 1.488001
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.776714 Loss1: 0.298942 Loss2: 1.477772
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.694919 Loss1: 0.221974 Loss2: 1.472945
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.660129 Loss1: 0.198368 Loss2: 1.461761
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 00:20:54,582][flwr][DEBUG] - fit_round 57 received 50 results and 0 failures
-INFO flwr 2023-10-10 00:21:36,020 | server.py:125 | fit progress: (57, 2.3549529428299243, {'accuracy': 0.4983}, 131403.798305177)
->> Test accuracy: 0.498300
-[2023-10-10 00:21:36,020][flwr][INFO] - fit progress: (57, 2.3549529428299243, {'accuracy': 0.4983}, 131403.798305177)
-DEBUG flwr 2023-10-10 00:21:36,020 | server.py:173 | evaluate_round 57: strategy sampled 50 clients (out of 50)
-[2023-10-10 00:21:36,020][flwr][DEBUG] - evaluate_round 57: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 00:30:40,098 | server.py:187 | evaluate_round 57 received 50 results and 0 failures
-[2023-10-10 00:30:40,098][flwr][DEBUG] - evaluate_round 57 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 00:30:40,098 | server.py:222 | fit_round 58: strategy sampled 50 clients (out of 50)
-[2023-10-10 00:30:40,098][flwr][DEBUG] - fit_round 58: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.458826 Loss1: 1.567334 Loss2: 1.891492
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.466060 Loss1: 1.068170 Loss2: 1.397890
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.065189 Loss1: 0.616997 Loss2: 1.448192
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.899287 Loss1: 0.510152 Loss2: 1.389135
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.674814 Loss1: 1.692601 Loss2: 1.982213
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.925376 Loss1: 0.524915 Loss2: 1.400460
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.545214 Loss1: 1.075557 Loss2: 1.469657
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.878743 Loss1: 0.466890 Loss2: 1.411853
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.297775 Loss1: 0.824191 Loss2: 1.473584
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.756528 Loss1: 0.344274 Loss2: 1.412253
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.036887 Loss1: 0.596080 Loss2: 1.440807
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.642542 Loss1: 0.247499 Loss2: 1.395043
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.915653 Loss1: 0.476899 Loss2: 1.438754
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.649129 Loss1: 0.262945 Loss2: 1.386184
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.869272 Loss1: 0.425494 Loss2: 1.443778
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.613237 Loss1: 0.214925 Loss2: 1.398313
-(DefaultActor pid=3765) >> Training accuracy: 0.936458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.777807 Loss1: 0.341159 Loss2: 1.436648
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.731022 Loss1: 0.292597 Loss2: 1.438425
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.736053 Loss1: 0.302704 Loss2: 1.433349
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.682801 Loss1: 0.252475 Loss2: 1.430326
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.596257 Loss1: 1.554472 Loss2: 2.041785
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.618142 Loss1: 1.147376 Loss2: 1.470767
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.327152 Loss1: 0.825428 Loss2: 1.501724
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.066920 Loss1: 0.574853 Loss2: 1.492067
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.501405 Loss1: 1.504483 Loss2: 1.996922
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.848816 Loss1: 0.389652 Loss2: 1.459164
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.412088 Loss1: 0.902613 Loss2: 1.509475
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.123210 Loss1: 0.615715 Loss2: 1.507494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.882359 Loss1: 0.410338 Loss2: 1.472021 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.853223 Loss1: 0.376013 Loss2: 1.477209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.799769 Loss1: 0.317101 Loss2: 1.482667 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.933333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.754757 Loss1: 0.283985 Loss2: 1.470773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.775145 Loss1: 0.304916 Loss2: 1.470229 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.893555
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.624300 Loss1: 1.622859 Loss2: 2.001441 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.279538 Loss1: 0.775909 Loss2: 1.503629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.911036 Loss1: 0.452657 Loss2: 1.458379 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.913002 Loss1: 0.468328 Loss2: 1.444674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.806816 Loss1: 0.349700 Loss2: 1.457116 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.724624 Loss1: 0.272670 Loss2: 1.451954 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.725045 Loss1: 0.283572 Loss2: 1.441474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.699927 Loss1: 0.252076 Loss2: 1.447852 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.912500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.596622 Loss1: 0.214560 Loss2: 1.382062 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.560308 Loss1: 0.169528 Loss2: 1.390780 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.523226 Loss1: 1.105584 Loss2: 1.417642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.032642 Loss1: 0.572939 Loss2: 1.459703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.892871 Loss1: 0.474919 Loss2: 1.417952
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.624137 Loss1: 1.623591 Loss2: 2.000546
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.539139 Loss1: 1.058056 Loss2: 1.481083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.173401 Loss1: 0.655026 Loss2: 1.518375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.944706 Loss1: 0.470791 Loss2: 1.473915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.858707 Loss1: 0.375671 Loss2: 1.483037 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973214
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.811813 Loss1: 0.333524 Loss2: 1.478289 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.689461 Loss1: 0.221804 Loss2: 1.467657 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.684101 Loss1: 0.225255 Loss2: 1.458846
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.346182 Loss1: 1.376118 Loss2: 1.970064
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.306855 Loss1: 0.885916 Loss2: 1.420938
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.086701 Loss1: 0.624571 Loss2: 1.462129
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.850231 Loss1: 0.451282 Loss2: 1.398949
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.857702 Loss1: 0.467083 Loss2: 1.390619
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.686436 Loss1: 1.682424 Loss2: 2.004012
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.821500 Loss1: 0.402529 Loss2: 1.418971
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.610140 Loss1: 1.145675 Loss2: 1.464465
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.660921 Loss1: 0.247075 Loss2: 1.413846
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.139967 Loss1: 0.646588 Loss2: 1.493378
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.588090 Loss1: 0.203773 Loss2: 1.384317
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.987763 Loss1: 0.552583 Loss2: 1.435180
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.553523 Loss1: 0.175285 Loss2: 1.378237
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.992193 Loss1: 0.540742 Loss2: 1.451451
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.538420 Loss1: 0.156951 Loss2: 1.381470
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.785409 Loss1: 0.331378 Loss2: 1.454032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.747662 Loss1: 0.308618 Loss2: 1.439044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.706420 Loss1: 0.260868 Loss2: 1.445552
-(DefaultActor pid=3764) >> Training accuracy: 0.942708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.680623 Loss1: 1.662816 Loss2: 2.017807
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.596606 Loss1: 1.106588 Loss2: 1.490017
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.269826 Loss1: 0.768335 Loss2: 1.501491
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.051164 Loss1: 0.569670 Loss2: 1.481495
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.989501 Loss1: 0.512413 Loss2: 1.477088
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.357275 Loss1: 1.422012 Loss2: 1.935263
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.309768 Loss1: 0.866693 Loss2: 1.443075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.063846 Loss1: 0.596410 Loss2: 1.467436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.917128 Loss1: 0.477033 Loss2: 1.440095 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.832709 Loss1: 0.400659 Loss2: 1.432050 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952148
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.783599 Loss1: 0.345151 Loss2: 1.438449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.757345 Loss1: 0.336235 Loss2: 1.421110 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.593334 Loss1: 0.179255 Loss2: 1.414080 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.949219
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.287653 Loss1: 0.775622 Loss2: 1.512031 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.963888 Loss1: 0.481056 Loss2: 1.482832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.581121 Loss1: 1.597461 Loss2: 1.983660
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.874802 Loss1: 0.382215 Loss2: 1.492587
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.482202 Loss1: 1.004248 Loss2: 1.477953
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.757602 Loss1: 0.272741 Loss2: 1.484861
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.115344 Loss1: 0.650009 Loss2: 1.465336
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.711465 Loss1: 0.243321 Loss2: 1.468143
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.874140 Loss1: 0.435821 Loss2: 1.438319
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.644817 Loss1: 0.183820 Loss2: 1.460996
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.840752 Loss1: 0.403033 Loss2: 1.437719
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.641164 Loss1: 0.178307 Loss2: 1.462857
-(DefaultActor pid=3765) >> Training accuracy: 0.957292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.743604 Loss1: 0.297227 Loss2: 1.446377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.618231 Loss1: 0.198911 Loss2: 1.419320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.696410 Loss1: 0.264750 Loss2: 1.431660
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.549577 Loss1: 1.567132 Loss2: 1.982445
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.546750 Loss1: 1.104779 Loss2: 1.441971
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.182325 Loss1: 0.730554 Loss2: 1.451771
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.950060 Loss1: 0.503415 Loss2: 1.446645
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.876357 Loss1: 0.455978 Loss2: 1.420379
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.674071 Loss1: 1.669153 Loss2: 2.004918
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.752168 Loss1: 0.318206 Loss2: 1.433963
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.741787 Loss1: 0.313980 Loss2: 1.427807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.695032 Loss1: 0.280632 Loss2: 1.414399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.647990 Loss1: 0.231747 Loss2: 1.416244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.691087 Loss1: 0.273146 Loss2: 1.417941 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.932292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.817352 Loss1: 0.343624 Loss2: 1.473728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.731763 Loss1: 0.275942 Loss2: 1.455821 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.690636 Loss1: 0.235165 Loss2: 1.455471
-(DefaultActor pid=3764) >> Training accuracy: 0.932292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.503374 Loss1: 1.543136 Loss2: 1.960238
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.516447 Loss1: 1.071060 Loss2: 1.445387
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.108841 Loss1: 0.653092 Loss2: 1.455749
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.885658 Loss1: 0.474290 Loss2: 1.411369
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.855222 Loss1: 0.455701 Loss2: 1.399521
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.473576 Loss1: 1.506332 Loss2: 1.967244
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.781090 Loss1: 0.359819 Loss2: 1.421271
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.720674 Loss1: 0.321580 Loss2: 1.399094 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.681835 Loss1: 0.284255 Loss2: 1.397580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.662821 Loss1: 0.267879 Loss2: 1.394942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.682774 Loss1: 0.291212 Loss2: 1.391561 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.928125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.643862 Loss1: 0.229268 Loss2: 1.414594 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.592548 Loss1: 0.190414 Loss2: 1.402134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.632866 Loss1: 0.220822 Loss2: 1.412044
-(DefaultActor pid=3764) >> Training accuracy: 0.939583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.521171 Loss1: 1.608537 Loss2: 1.912634
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.536162 Loss1: 1.114460 Loss2: 1.421702
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.199806 Loss1: 0.763172 Loss2: 1.436634
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.932833 Loss1: 0.542706 Loss2: 1.390127
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.829112 Loss1: 0.436903 Loss2: 1.392209
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.603636 Loss1: 1.636707 Loss2: 1.966929
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.731991 Loss1: 0.347460 Loss2: 1.384531
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.668940 Loss1: 0.283594 Loss2: 1.385346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.678296 Loss1: 0.295060 Loss2: 1.383235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.636508 Loss1: 0.253379 Loss2: 1.383129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.644328 Loss1: 0.264623 Loss2: 1.379705 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.803984 Loss1: 0.347970 Loss2: 1.456015 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.665307 Loss1: 0.216999 Loss2: 1.448309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.663920 Loss1: 0.212784 Loss2: 1.451137
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.324739 Loss1: 1.402689 Loss2: 1.922050
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.321797 Loss1: 0.880833 Loss2: 1.440964
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.034958 Loss1: 0.602773 Loss2: 1.432185
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.824139 Loss1: 0.417368 Loss2: 1.406771
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.791698 Loss1: 0.365621 Loss2: 1.426077
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.555076 Loss1: 1.527925 Loss2: 2.027151
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.543241 Loss1: 1.066827 Loss2: 1.476414
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.730897 Loss1: 0.314611 Loss2: 1.416286
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.175888 Loss1: 0.694893 Loss2: 1.480995
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.720918 Loss1: 0.300850 Loss2: 1.420068
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.716402 Loss1: 0.298920 Loss2: 1.417482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.693369 Loss1: 0.269272 Loss2: 1.424098 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.680651 Loss1: 0.264563 Loss2: 1.416088 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941176
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.635671 Loss1: 0.200349 Loss2: 1.435321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.622883 Loss1: 0.186185 Loss2: 1.436697 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.716316 Loss1: 1.771141 Loss2: 1.945175
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.757658 Loss1: 1.253661 Loss2: 1.503997
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.323732 Loss1: 0.818179 Loss2: 1.505554
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.045444 Loss1: 0.564289 Loss2: 1.481155
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.876238 Loss1: 1.724798 Loss2: 2.151440
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.956420 Loss1: 0.485231 Loss2: 1.471189
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.553472 Loss1: 1.091062 Loss2: 1.462410
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.190163 Loss1: 0.718532 Loss2: 1.471631
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.858061 Loss1: 0.388467 Loss2: 1.469594
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.769350 Loss1: 0.305062 Loss2: 1.464288 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.714205 Loss1: 0.266716 Loss2: 1.447489 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.726324 Loss1: 0.268272 Loss2: 1.458053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.745759 Loss1: 0.304536 Loss2: 1.441223 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.913542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.654729 Loss1: 0.218959 Loss2: 1.435769 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.955729
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.503118 Loss1: 1.592731 Loss2: 1.910387
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.563345 Loss1: 1.110101 Loss2: 1.453245
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.193804 Loss1: 0.695358 Loss2: 1.498446
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.018741 Loss1: 0.587587 Loss2: 1.431154
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.431295 Loss1: 1.478989 Loss2: 1.952306
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.883539 Loss1: 0.449749 Loss2: 1.433790
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.392994 Loss1: 0.984241 Loss2: 1.408753
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.788178 Loss1: 0.375051 Loss2: 1.413127
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.151111 Loss1: 0.720969 Loss2: 1.430141
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.699796 Loss1: 0.290694 Loss2: 1.409101
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.877039 Loss1: 0.469743 Loss2: 1.407297
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.721172 Loss1: 0.304369 Loss2: 1.416803
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.830526 Loss1: 0.429354 Loss2: 1.401172
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.720091 Loss1: 0.296718 Loss2: 1.423373
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.842130 Loss1: 0.436698 Loss2: 1.405432
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.652613 Loss1: 0.249394 Loss2: 1.403220
-(DefaultActor pid=3765) >> Training accuracy: 0.933333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.911309 Loss1: 0.478780 Loss2: 1.432529
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.819817 Loss1: 0.402039 Loss2: 1.417778
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.739665 Loss1: 0.336894 Loss2: 1.402771
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.683775 Loss1: 0.280716 Loss2: 1.403058
-(DefaultActor pid=3764) >> Training accuracy: 0.932292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.699539 Loss1: 1.734037 Loss2: 1.965502
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.559470 Loss1: 1.096506 Loss2: 1.462964
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.201529 Loss1: 0.718724 Loss2: 1.482805
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.995548 Loss1: 0.552431 Loss2: 1.443117
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.602040 Loss1: 1.593534 Loss2: 2.008505
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.521947 Loss1: 1.012319 Loss2: 1.509629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.226353 Loss1: 0.697516 Loss2: 1.528837 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.979337 Loss1: 0.483252 Loss2: 1.496085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.857071 Loss1: 0.373016 Loss2: 1.484055 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.821765 Loss1: 0.347724 Loss2: 1.474041 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.905208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.740198 Loss1: 0.262070 Loss2: 1.478128 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.758440 Loss1: 0.280980 Loss2: 1.477460 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.924805
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.337328 Loss1: 0.921728 Loss2: 1.415600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.855275 Loss1: 0.447866 Loss2: 1.407409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.726576 Loss1: 1.638755 Loss2: 2.087821
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.695744 Loss1: 0.304771 Loss2: 1.390973
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.576922 Loss1: 1.033820 Loss2: 1.543102
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.719100 Loss1: 0.331537 Loss2: 1.387563
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.180151 Loss1: 0.620571 Loss2: 1.559580
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.657538 Loss1: 0.267126 Loss2: 1.390411
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.038157 Loss1: 0.522039 Loss2: 1.516118
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.598991 Loss1: 0.212230 Loss2: 1.386761
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.027142 Loss1: 0.504118 Loss2: 1.523024
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.584953 Loss1: 0.206928 Loss2: 1.378025
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.933932 Loss1: 0.392528 Loss2: 1.541404
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.585578 Loss1: 0.196010 Loss2: 1.389568
-(DefaultActor pid=3765) >> Training accuracy: 0.943750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.870450 Loss1: 0.360096 Loss2: 1.510354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.776021 Loss1: 0.254300 Loss2: 1.521721 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.943750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.429321 Loss1: 0.977010 Loss2: 1.452311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.919731 Loss1: 0.483891 Loss2: 1.435839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.810893 Loss1: 0.399735 Loss2: 1.411158
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.636471 Loss1: 1.521080 Loss2: 2.115391
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.744333 Loss1: 0.325788 Loss2: 1.418544
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.504447 Loss1: 0.960760 Loss2: 1.543687
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.692710 Loss1: 0.281931 Loss2: 1.410779
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.143193 Loss1: 0.611200 Loss2: 1.531992
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.660584 Loss1: 0.248604 Loss2: 1.411980
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.970086 Loss1: 0.462980 Loss2: 1.507106
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.684272 Loss1: 0.278507 Loss2: 1.405765
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.853120 Loss1: 0.364950 Loss2: 1.488171
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.618496 Loss1: 0.209268 Loss2: 1.409228
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.809043 Loss1: 0.305302 Loss2: 1.503741
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.768367 Loss1: 0.272095 Loss2: 1.496272
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.778543 Loss1: 0.286460 Loss2: 1.492083
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.742366 Loss1: 0.251963 Loss2: 1.490402
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.720834 Loss1: 0.223227 Loss2: 1.497607
-(DefaultActor pid=3764) >> Training accuracy: 0.916667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.653954 Loss1: 1.630020 Loss2: 2.023934
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.395073 Loss1: 0.971088 Loss2: 1.423985
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.067130 Loss1: 0.683796 Loss2: 1.383334
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.887584 Loss1: 0.489171 Loss2: 1.398413
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.735892 Loss1: 0.354149 Loss2: 1.381743
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.666502 Loss1: 0.296981 Loss2: 1.369521
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.441534 Loss1: 1.498798 Loss2: 1.942736
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.624143 Loss1: 0.254736 Loss2: 1.369407
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.457576 Loss1: 0.972486 Loss2: 1.485091
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.151433 Loss1: 0.668325 Loss2: 1.483108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.995548 Loss1: 0.538630 Loss2: 1.456918 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962740
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.811990 Loss1: 0.351721 Loss2: 1.460268 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.757281 Loss1: 0.306091 Loss2: 1.451189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.746298 Loss1: 1.702282 Loss2: 2.044016
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.714424 Loss1: 0.254090 Loss2: 1.460334
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.705274 Loss1: 1.244077 Loss2: 1.461197
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.673167 Loss1: 0.220641 Loss2: 1.452526
-(DefaultActor pid=3764) >> Training accuracy: 0.935547
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.042248 Loss1: 0.568459 Loss2: 1.473789 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.768872 Loss1: 0.337792 Loss2: 1.431079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.625986 Loss1: 1.608913 Loss2: 2.017073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.492608 Loss1: 0.998194 Loss2: 1.494414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.230473 Loss1: 0.719572 Loss2: 1.510901 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956473
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.876762 Loss1: 0.412565 Loss2: 1.464197 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.794887 Loss1: 0.332649 Loss2: 1.462237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.820554 Loss1: 0.343892 Loss2: 1.476663
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.476360 Loss1: 1.525414 Loss2: 1.950947
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.371524 Loss1: 0.948851 Loss2: 1.422673 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.939583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.738742 Loss1: 0.274840 Loss2: 1.463902
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.091713 Loss1: 0.656885 Loss2: 1.434829
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.951160 Loss1: 0.525410 Loss2: 1.425750
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.878400 Loss1: 0.466462 Loss2: 1.411938
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.805002 Loss1: 0.378248 Loss2: 1.426754
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.703661 Loss1: 0.299342 Loss2: 1.404319
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.399754 Loss1: 1.482602 Loss2: 1.917151
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.651396 Loss1: 0.254603 Loss2: 1.396794
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.643210 Loss1: 0.242062 Loss2: 1.401147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.605047 Loss1: 0.213126 Loss2: 1.391921 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.752379 Loss1: 0.360492 Loss2: 1.391887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.692593 Loss1: 0.303698 Loss2: 1.388895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.649270 Loss1: 0.273215 Loss2: 1.376055
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.386442 Loss1: 1.404811 Loss2: 1.981631
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.368566 Loss1: 0.912787 Loss2: 1.455779 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.941667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.668821 Loss1: 0.281149 Loss2: 1.387672
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.232216 Loss1: 0.754200 Loss2: 1.478016
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.008480 Loss1: 0.564939 Loss2: 1.443541
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.851565 Loss1: 0.416412 Loss2: 1.435153
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.813153 Loss1: 0.385047 Loss2: 1.428106
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.716743 Loss1: 0.295865 Loss2: 1.420878
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.443420 Loss1: 1.464874 Loss2: 1.978546
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.611407 Loss1: 0.202711 Loss2: 1.408696
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.580975 Loss1: 0.179987 Loss2: 1.400988 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.609115 Loss1: 0.210561 Loss2: 1.398554 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.946875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.774754 Loss1: 0.345189 Loss2: 1.429565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.706305 Loss1: 0.280079 Loss2: 1.426226 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.706185 Loss1: 0.284650 Loss2: 1.421535
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.613721 Loss1: 1.545529 Loss2: 2.068192
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.394997 Loss1: 0.954472 Loss2: 1.440525
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.657558 Loss1: 0.223863 Loss2: 1.433695
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.155841 Loss1: 0.729642 Loss2: 1.426199
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.584318 Loss1: 0.163617 Loss2: 1.420701
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.785961 Loss1: 0.361469 Loss2: 1.424492 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.790160 Loss1: 0.354736 Loss2: 1.435424 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.396868 Loss1: 1.469081 Loss2: 1.927787 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.725324 Loss1: 0.295680 Loss2: 1.429643 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941106
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.923923 Loss1: 0.492220 Loss2: 1.431703 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.843303 Loss1: 0.397649 Loss2: 1.445654 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.487710 Loss1: 1.573782 Loss2: 1.913928
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.766639 Loss1: 0.325283 Loss2: 1.441356
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.704669 Loss1: 0.275343 Loss2: 1.429326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.650435 Loss1: 0.219598 Loss2: 1.430837 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.674178 Loss1: 0.243088 Loss2: 1.431090 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957031
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.756163 Loss1: 0.369541 Loss2: 1.386622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.734570 Loss1: 0.327684 Loss2: 1.406887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.583344 Loss1: 1.622332 Loss2: 1.961012 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.929167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.405871 Loss1: 0.988362 Loss2: 1.417509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.891104 Loss1: 0.468508 Loss2: 1.422596 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.776212 Loss1: 0.357289 Loss2: 1.418923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.723342 Loss1: 0.315155 Loss2: 1.408187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.704832 Loss1: 0.303527 Loss2: 1.401306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.654959 Loss1: 0.250937 Loss2: 1.404023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.684732 Loss1: 0.281426 Loss2: 1.403305 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.913542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.798134 Loss1: 0.361707 Loss2: 1.436428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.722385 Loss1: 0.301458 Loss2: 1.420927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.723461 Loss1: 0.292123 Loss2: 1.431339
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.545741 Loss1: 1.615560 Loss2: 1.930181
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.533679 Loss1: 1.077467 Loss2: 1.456212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.985119 Loss1: 0.547400 Loss2: 1.437719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.718486 Loss1: 0.284508 Loss2: 1.433978 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 00:59:19,082 | server.py:236 | fit_round 58 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.704470 Loss1: 0.278615 Loss2: 1.425855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.651618 Loss1: 0.231889 Loss2: 1.419729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.653967 Loss1: 0.230627 Loss2: 1.423340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.677797 Loss1: 0.256408 Loss2: 1.421390 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.952148
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.713454 Loss1: 0.312404 Loss2: 1.401050 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.638133 Loss1: 0.253027 Loss2: 1.385106 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.651894 Loss1: 0.256214 Loss2: 1.395681 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.947266
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.149982 Loss1: 0.696661 Loss2: 1.453321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.808405 Loss1: 0.366086 Loss2: 1.442319 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.751524 Loss1: 0.318710 Loss2: 1.432814
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.441820 Loss1: 1.518625 Loss2: 1.923195
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.731243 Loss1: 0.296226 Loss2: 1.435017
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.490217 Loss1: 1.071037 Loss2: 1.419180
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.712721 Loss1: 0.271407 Loss2: 1.441314
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.141756 Loss1: 0.707631 Loss2: 1.434125
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.818608 Loss1: 0.417526 Loss2: 1.401082
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.718474 Loss1: 0.278617 Loss2: 1.439857
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.818365 Loss1: 0.422773 Loss2: 1.395591
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.669782 Loss1: 0.235044 Loss2: 1.434739
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.723523 Loss1: 0.302411 Loss2: 1.421112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.552222 Loss1: 0.165079 Loss2: 1.387143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.545885 Loss1: 0.166748 Loss2: 1.379137
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.779474 Loss1: 1.744953 Loss2: 2.034521
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.629186 Loss1: 1.167483 Loss2: 1.461703
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.217494 Loss1: 0.737942 Loss2: 1.479551
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.014176 Loss1: 0.573881 Loss2: 1.440294
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.888426 Loss1: 0.452945 Loss2: 1.435481
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.822797 Loss1: 0.366218 Loss2: 1.456580
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.714973 Loss1: 0.288145 Loss2: 1.426829
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.749708 Loss1: 0.319267 Loss2: 1.430441
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.702528 Loss1: 0.268783 Loss2: 1.433746
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.658767 Loss1: 0.238527 Loss2: 1.420240
-(DefaultActor pid=3764) >> Training accuracy: 0.957589
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 00:59:19,082][flwr][DEBUG] - fit_round 58 received 50 results and 0 failures
-INFO flwr 2023-10-10 01:00:00,261 | server.py:125 | fit progress: (58, 2.3542091282792748, {'accuracy': 0.5016}, 133708.03983640502)
->> Test accuracy: 0.501600
-[2023-10-10 01:00:00,261][flwr][INFO] - fit progress: (58, 2.3542091282792748, {'accuracy': 0.5016}, 133708.03983640502)
-DEBUG flwr 2023-10-10 01:00:00,262 | server.py:173 | evaluate_round 58: strategy sampled 50 clients (out of 50)
-[2023-10-10 01:00:00,262][flwr][DEBUG] - evaluate_round 58: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 01:09:04,862 | server.py:187 | evaluate_round 58 received 50 results and 0 failures
-[2023-10-10 01:09:04,862][flwr][DEBUG] - evaluate_round 58 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 01:09:04,863 | server.py:222 | fit_round 59: strategy sampled 50 clients (out of 50)
-[2023-10-10 01:09:04,863][flwr][DEBUG] - fit_round 59: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.512553 Loss1: 1.483892 Loss2: 2.028662
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.460824 Loss1: 0.977246 Loss2: 1.483578
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.141724 Loss1: 0.645793 Loss2: 1.495930
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.919438 Loss1: 0.453245 Loss2: 1.466193
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.376723 Loss1: 1.390795 Loss2: 1.985929
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.367837 Loss1: 0.921560 Loss2: 1.446278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.083878 Loss1: 0.595846 Loss2: 1.488031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.833841 Loss1: 0.391120 Loss2: 1.442721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.762176 Loss1: 0.341395 Loss2: 1.420781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.723789 Loss1: 0.287558 Loss2: 1.436231 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.597352 Loss1: 0.158291 Loss2: 1.439061
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.660811 Loss1: 0.227901 Loss2: 1.432911
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.658653 Loss1: 0.231643 Loss2: 1.427009
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.619746 Loss1: 0.198516 Loss2: 1.421230
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.668445 Loss1: 0.240461 Loss2: 1.427984
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.528743 Loss1: 1.578802 Loss2: 1.949941
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.625891 Loss1: 1.117517 Loss2: 1.508373
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.242423 Loss1: 0.714653 Loss2: 1.527770
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.044542 Loss1: 0.554100 Loss2: 1.490443
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.547198 Loss1: 1.577066 Loss2: 1.970132
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.986710 Loss1: 0.526401 Loss2: 1.460309
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.537136 Loss1: 1.081466 Loss2: 1.455671
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.189781 Loss1: 0.698379 Loss2: 1.491401
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.886873 Loss1: 0.409292 Loss2: 1.477580
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.979128 Loss1: 0.539401 Loss2: 1.439727
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.843843 Loss1: 0.372931 Loss2: 1.470912
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.888225 Loss1: 0.441774 Loss2: 1.446451
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.848103 Loss1: 0.378315 Loss2: 1.469788
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.864613 Loss1: 0.416414 Loss2: 1.448199
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.730598 Loss1: 0.259363 Loss2: 1.471235
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.652793 Loss1: 0.191433 Loss2: 1.461360 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.943359
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.736913 Loss1: 0.288577 Loss2: 1.448336 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.692562 Loss1: 1.658900 Loss2: 2.033663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.179903 Loss1: 0.623192 Loss2: 1.556711 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.020260 Loss1: 0.521396 Loss2: 1.498864
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.383511 Loss1: 1.469250 Loss2: 1.914261
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.889234 Loss1: 0.369753 Loss2: 1.519481
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.384912 Loss1: 0.954093 Loss2: 1.430819
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.835265 Loss1: 0.337465 Loss2: 1.497800
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.085574 Loss1: 0.640409 Loss2: 1.445165
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.886323 Loss1: 0.377388 Loss2: 1.508935
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.923705 Loss1: 0.515344 Loss2: 1.408362
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.899182 Loss1: 0.379472 Loss2: 1.519710
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.838180 Loss1: 0.410419 Loss2: 1.427761
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.806525 Loss1: 0.290626 Loss2: 1.515899
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.729689 Loss1: 0.321189 Loss2: 1.408499
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.783898 Loss1: 0.271388 Loss2: 1.512510
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.646206 Loss1: 0.244046 Loss2: 1.402160
-(DefaultActor pid=3765) >> Training accuracy: 0.947266
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.698525 Loss1: 0.300357 Loss2: 1.398168
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.737168 Loss1: 0.322274 Loss2: 1.414894
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.699496 Loss1: 0.285404 Loss2: 1.414092
-(DefaultActor pid=3764) >> Training accuracy: 0.893555
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.691733 Loss1: 1.616517 Loss2: 2.075216
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.600787 Loss1: 1.066417 Loss2: 1.534370
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.329878 Loss1: 0.785138 Loss2: 1.544740
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.116497 Loss1: 0.602916 Loss2: 1.513581
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.616650 Loss1: 1.669570 Loss2: 1.947080
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.990745 Loss1: 0.456205 Loss2: 1.534540
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.513994 Loss1: 1.072860 Loss2: 1.441134
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.891587 Loss1: 0.376714 Loss2: 1.514873
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.117452 Loss1: 0.641783 Loss2: 1.475669
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.858507 Loss1: 0.336512 Loss2: 1.521995
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.951966 Loss1: 0.521605 Loss2: 1.430361
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.795074 Loss1: 0.282727 Loss2: 1.512347
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.851994 Loss1: 0.396600 Loss2: 1.455394
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.810741 Loss1: 0.303816 Loss2: 1.506926
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.890839 Loss1: 0.441893 Loss2: 1.448947
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.741302 Loss1: 0.229049 Loss2: 1.512253
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.779517 Loss1: 0.332399 Loss2: 1.447118
-(DefaultActor pid=3765) >> Training accuracy: 0.925000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.738856 Loss1: 0.293071 Loss2: 1.445785
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.726113 Loss1: 0.278771 Loss2: 1.447341
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.650200 Loss1: 0.217468 Loss2: 1.432732
-(DefaultActor pid=3764) >> Training accuracy: 0.947917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.563697 Loss1: 1.614494 Loss2: 1.949203
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.658438 Loss1: 1.113993 Loss2: 1.544445
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.129473 Loss1: 0.621857 Loss2: 1.507616
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.548380 Loss1: 1.648880 Loss2: 1.899500
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.001542 Loss1: 0.533806 Loss2: 1.467735
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.504445 Loss1: 1.093284 Loss2: 1.411161
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.922284 Loss1: 0.440821 Loss2: 1.481463
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.095945 Loss1: 0.671838 Loss2: 1.424107
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.818633 Loss1: 0.357644 Loss2: 1.460989
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.785655 Loss1: 0.323319 Loss2: 1.462336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.748260 Loss1: 0.272863 Loss2: 1.475397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.728148 Loss1: 0.273377 Loss2: 1.454771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.691221 Loss1: 0.236159 Loss2: 1.455063 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.957031
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.697662 Loss1: 0.322198 Loss2: 1.375464 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.938542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.396600 Loss1: 1.480374 Loss2: 1.916225 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.120687 Loss1: 0.596704 Loss2: 1.523983 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.961553 Loss1: 0.551618 Loss2: 1.409936
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.448259 Loss1: 1.443512 Loss2: 2.004748
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.425721 Loss1: 0.957277 Loss2: 1.468444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.161006 Loss1: 0.654655 Loss2: 1.506351 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.913341 Loss1: 0.451599 Loss2: 1.461742 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.821969 Loss1: 0.365708 Loss2: 1.456262 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.759255 Loss1: 0.298380 Loss2: 1.460875 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.627475 Loss1: 0.227267 Loss2: 1.400209
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.719250 Loss1: 0.266912 Loss2: 1.452338
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.729024 Loss1: 0.278729 Loss2: 1.450295
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.711868 Loss1: 0.265998 Loss2: 1.445870
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.690784 Loss1: 0.240235 Loss2: 1.450549
-(DefaultActor pid=3764) >> Training accuracy: 0.941667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.589992 Loss1: 1.558658 Loss2: 2.031334
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.556146 Loss1: 1.058473 Loss2: 1.497673
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.149897 Loss1: 0.636528 Loss2: 1.513369
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.979475 Loss1: 0.492661 Loss2: 1.486814
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.427813 Loss1: 1.482360 Loss2: 1.945452
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.354929 Loss1: 0.926731 Loss2: 1.428197 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.204210 Loss1: 0.738570 Loss2: 1.465640 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.894759 Loss1: 0.487280 Loss2: 1.407479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.763373 Loss1: 0.360395 Loss2: 1.402979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.758845 Loss1: 0.346752 Loss2: 1.412092 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.929167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.735030 Loss1: 0.258417 Loss2: 1.476612
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.699634 Loss1: 0.295226 Loss2: 1.404409
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.639663 Loss1: 0.233582 Loss2: 1.406082
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.670703 Loss1: 0.268576 Loss2: 1.402128
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.667740 Loss1: 0.255214 Loss2: 1.412526
-(DefaultActor pid=3764) >> Training accuracy: 0.935417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.734897 Loss1: 1.704320 Loss2: 2.030577
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.676150 Loss1: 1.187538 Loss2: 1.488612
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.207350 Loss1: 0.694319 Loss2: 1.513031
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.063868 Loss1: 0.588040 Loss2: 1.475828
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.516489 Loss1: 1.557387 Loss2: 1.959102
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.570364 Loss1: 1.117620 Loss2: 1.452744 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.190565 Loss1: 0.681571 Loss2: 1.508994 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.898573 Loss1: 0.461492 Loss2: 1.437080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.840071 Loss1: 0.415508 Loss2: 1.424563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.704061 Loss1: 0.275128 Loss2: 1.428933 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.761467 Loss1: 0.334287 Loss2: 1.427180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.682586 Loss1: 0.255648 Loss2: 1.426938 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.939583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.438478 Loss1: 1.527562 Loss2: 1.910916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.195720 Loss1: 0.702062 Loss2: 1.493658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.952192 Loss1: 0.513556 Loss2: 1.438636
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.591585 Loss1: 1.544221 Loss2: 2.047364
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.618280 Loss1: 1.080496 Loss2: 1.537784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.292642 Loss1: 0.725521 Loss2: 1.567121 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.091616 Loss1: 0.573922 Loss2: 1.517695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.969385 Loss1: 0.450113 Loss2: 1.519271 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.878608 Loss1: 0.366204 Loss2: 1.512403 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.948958
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.608656 Loss1: 0.194088 Loss2: 1.414568
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.802984 Loss1: 0.293524 Loss2: 1.509460
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.758117 Loss1: 0.261334 Loss2: 1.496783
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.721369 Loss1: 0.224011 Loss2: 1.497358
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.697803 Loss1: 0.206978 Loss2: 1.490825
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.656263 Loss1: 1.670360 Loss2: 1.985903
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.615957 Loss1: 1.112399 Loss2: 1.503558
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.134499 Loss1: 0.631064 Loss2: 1.503434
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.741432 Loss1: 1.670260 Loss2: 2.071172
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.995457 Loss1: 0.517053 Loss2: 1.478405
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.948633 Loss1: 0.466444 Loss2: 1.482189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.843684 Loss1: 0.359319 Loss2: 1.484365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.862078 Loss1: 0.443304 Loss2: 1.418775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.775884 Loss1: 0.369856 Loss2: 1.406028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.723520 Loss1: 0.317371 Loss2: 1.406149 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.685788 Loss1: 0.268719 Loss2: 1.417069 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.657215 Loss1: 0.247498 Loss2: 1.409717
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.780859 Loss1: 0.287975 Loss2: 1.492883
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.269753 Loss1: 1.348365 Loss2: 1.921388 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.928385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.110337 Loss1: 0.624524 Loss2: 1.485812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.973352 Loss1: 0.542827 Loss2: 1.430526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.857276 Loss1: 0.409537 Loss2: 1.447739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.729026 Loss1: 0.307782 Loss2: 1.421244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.755597 Loss1: 0.331137 Loss2: 1.424460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.650947 Loss1: 0.224528 Loss2: 1.426419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.658722 Loss1: 0.255201 Loss2: 1.403521 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.627779 Loss1: 0.233445 Loss2: 1.394334 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955882
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.598200 Loss1: 0.200833 Loss2: 1.397367 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.636189 Loss1: 1.726133 Loss2: 1.910056
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.471094 Loss1: 1.045767 Loss2: 1.425327
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.102467 Loss1: 0.671984 Loss2: 1.430482
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.836351 Loss1: 0.441751 Loss2: 1.394600
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.580434 Loss1: 1.588021 Loss2: 1.992413
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.790715 Loss1: 0.389365 Loss2: 1.401349
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.645284 Loss1: 1.168842 Loss2: 1.476442
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.674662 Loss1: 0.284246 Loss2: 1.390416
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.236938 Loss1: 0.697828 Loss2: 1.539111
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.659522 Loss1: 0.275240 Loss2: 1.384282
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.125340 Loss1: 0.658610 Loss2: 1.466730
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.742465 Loss1: 0.346866 Loss2: 1.395599
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.090468 Loss1: 0.581534 Loss2: 1.508935
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.654173 Loss1: 0.255235 Loss2: 1.398938
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.913378 Loss1: 0.421787 Loss2: 1.491591
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.585502 Loss1: 0.204699 Loss2: 1.380803
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.808148 Loss1: 0.344318 Loss2: 1.463830
-(DefaultActor pid=3765) >> Training accuracy: 0.960417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.728806 Loss1: 0.262062 Loss2: 1.466744
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.788481 Loss1: 0.319935 Loss2: 1.468546
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.754525 Loss1: 0.277621 Loss2: 1.476904
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.499882 Loss1: 1.605076 Loss2: 1.894806
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.428676 Loss1: 1.018961 Loss2: 1.409715
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.950972 Loss1: 0.531702 Loss2: 1.419270
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.803025 Loss1: 0.431812 Loss2: 1.371213
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.502872 Loss1: 1.580762 Loss2: 1.922110
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.504256 Loss1: 1.012940 Loss2: 1.491316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.067996 Loss1: 0.578892 Loss2: 1.489105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.927589 Loss1: 0.481646 Loss2: 1.445943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.827225 Loss1: 0.365170 Loss2: 1.462056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.796894 Loss1: 0.343796 Loss2: 1.453099 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.722341 Loss1: 0.267895 Loss2: 1.454446 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.742728 Loss1: 0.282304 Loss2: 1.460424 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.896484
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.574134 Loss1: 1.100854 Loss2: 1.473279 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.994754 Loss1: 0.540249 Loss2: 1.454506 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.915542 Loss1: 0.455057 Loss2: 1.460485
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.449477 Loss1: 1.443484 Loss2: 2.005993
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.814417 Loss1: 0.342107 Loss2: 1.472310
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.460182 Loss1: 0.980522 Loss2: 1.479660
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.757654 Loss1: 0.298519 Loss2: 1.459136
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.136006 Loss1: 0.636103 Loss2: 1.499904
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.886283 Loss1: 0.435169 Loss2: 1.451115 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.809950 Loss1: 0.363856 Loss2: 1.446094 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.700157 Loss1: 0.236263 Loss2: 1.463893
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.694165 Loss1: 0.247637 Loss2: 1.446528
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.718918 Loss1: 0.282949 Loss2: 1.435969
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.755221 Loss1: 0.312745 Loss2: 1.442476
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.738887 Loss1: 0.298848 Loss2: 1.440039
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.678894 Loss1: 0.236396 Loss2: 1.442497
-(DefaultActor pid=3764) >> Training accuracy: 0.955078
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.725103 Loss1: 1.739404 Loss2: 1.985699
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.473030 Loss1: 1.024432 Loss2: 1.448597
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.105948 Loss1: 0.648523 Loss2: 1.457424
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.923791 Loss1: 0.506395 Loss2: 1.417396
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.810686 Loss1: 0.389370 Loss2: 1.421316
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.607982 Loss1: 1.598116 Loss2: 2.009866
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.711385 Loss1: 0.292876 Loss2: 1.418509
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.667211 Loss1: 0.257487 Loss2: 1.409724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.650662 Loss1: 0.238304 Loss2: 1.412359 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.693764 Loss1: 0.275202 Loss2: 1.418562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.588848 Loss1: 0.169666 Loss2: 1.419182 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.691221 Loss1: 0.281677 Loss2: 1.409543 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.624155 Loss1: 0.223623 Loss2: 1.400531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.597486 Loss1: 0.204954 Loss2: 1.392532
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.474444 Loss1: 1.598100 Loss2: 1.876344
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.408408 Loss1: 1.021810 Loss2: 1.386598
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.985817 Loss1: 0.617319 Loss2: 1.368498
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.854468 Loss1: 0.504945 Loss2: 1.349523
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.750011 Loss1: 0.394589 Loss2: 1.355422
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.563020 Loss1: 1.591249 Loss2: 1.971772
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.646095 Loss1: 0.301701 Loss2: 1.344393
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.669197 Loss1: 1.181296 Loss2: 1.487901
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.673411 Loss1: 0.325421 Loss2: 1.347990
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.344830 Loss1: 0.817489 Loss2: 1.527341
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.625086 Loss1: 0.286424 Loss2: 1.338662
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.083768 Loss1: 0.626934 Loss2: 1.456833
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.989752 Loss1: 0.513582 Loss2: 1.476170
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.580720 Loss1: 0.227094 Loss2: 1.353626
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.885928 Loss1: 0.412306 Loss2: 1.473622
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.539816 Loss1: 0.194355 Loss2: 1.345460
-(DefaultActor pid=3765) >> Training accuracy: 0.967773
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.780992 Loss1: 0.312811 Loss2: 1.468181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.668411 Loss1: 0.218478 Loss2: 1.449933 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.927083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.499432 Loss1: 1.006039 Loss2: 1.493393 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.847155 Loss1: 0.396308 Loss2: 1.450847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.786096 Loss1: 0.324269 Loss2: 1.461828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.713564 Loss1: 0.266194 Loss2: 1.447370 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.666143 Loss1: 0.226000 Loss2: 1.440144 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.630716 Loss1: 0.192154 Loss2: 1.438562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.619075 Loss1: 0.183326 Loss2: 1.435748 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.643621 Loss1: 0.217575 Loss2: 1.426046 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.944336
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.698165 Loss1: 0.255296 Loss2: 1.442869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.683054 Loss1: 0.261405 Loss2: 1.421649 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.787538 Loss1: 1.783112 Loss2: 2.004426
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.424968 Loss1: 0.999422 Loss2: 1.425546
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.192643 Loss1: 0.766827 Loss2: 1.425816
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.912862 Loss1: 0.496617 Loss2: 1.416245
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.478810 Loss1: 1.501322 Loss2: 1.977488
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.382780 Loss1: 0.931921 Loss2: 1.450859 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.083601 Loss1: 0.636948 Loss2: 1.446653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.948077 Loss1: 0.509765 Loss2: 1.438312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.808234 Loss1: 0.381237 Loss2: 1.426997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.768137 Loss1: 0.316212 Loss2: 1.451925 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.909598
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.650784 Loss1: 0.234288 Loss2: 1.416496 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.632376 Loss1: 0.212970 Loss2: 1.419406 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.505014 Loss1: 1.049954 Loss2: 1.455060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.873516 Loss1: 0.426480 Loss2: 1.447035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.816080 Loss1: 0.390532 Loss2: 1.425548
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.364622 Loss1: 1.413565 Loss2: 1.951057
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.348579 Loss1: 0.931044 Loss2: 1.417534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.024924 Loss1: 0.613005 Loss2: 1.411919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.839658 Loss1: 0.448975 Loss2: 1.390683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.798376 Loss1: 0.408001 Loss2: 1.390375 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.924107
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.607912 Loss1: 0.218754 Loss2: 1.389158 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.598598 Loss1: 0.220944 Loss2: 1.377654 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.605516 Loss1: 0.226273 Loss2: 1.379243
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.585131 Loss1: 1.582501 Loss2: 2.002630
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.494481 Loss1: 1.045622 Loss2: 1.448859
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.222300 Loss1: 0.769821 Loss2: 1.452479
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.005689 Loss1: 0.570044 Loss2: 1.435646
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.876222 Loss1: 0.447663 Loss2: 1.428559
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.623205 Loss1: 1.636105 Loss2: 1.987101
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.758536 Loss1: 0.334537 Loss2: 1.423999
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.686123 Loss1: 0.265675 Loss2: 1.420448 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.670778 Loss1: 0.255067 Loss2: 1.415712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.694595 Loss1: 0.276341 Loss2: 1.418253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.704965 Loss1: 0.284838 Loss2: 1.420127 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.916667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.849292 Loss1: 0.404655 Loss2: 1.444637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.713107 Loss1: 0.263629 Loss2: 1.449477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.691872 Loss1: 0.259717 Loss2: 1.432155
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.462502 Loss1: 1.512471 Loss2: 1.950031
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.403449 Loss1: 0.981843 Loss2: 1.421606
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.119155 Loss1: 0.694578 Loss2: 1.424577
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.897227 Loss1: 0.494502 Loss2: 1.402724
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.795088 Loss1: 0.397515 Loss2: 1.397573
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.595025 Loss1: 1.666770 Loss2: 1.928255
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.475371 Loss1: 1.035082 Loss2: 1.440289 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.186995 Loss1: 0.732485 Loss2: 1.454510 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.897037 Loss1: 0.468608 Loss2: 1.428430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.807279 Loss1: 0.394756 Loss2: 1.412523 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.746161 Loss1: 0.335862 Loss2: 1.410298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.646109 Loss1: 0.241992 Loss2: 1.404117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.606420 Loss1: 0.209826 Loss2: 1.396593 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.442169 Loss1: 1.023643 Loss2: 1.418526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.981795 Loss1: 0.580979 Loss2: 1.400816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.853701 Loss1: 0.444507 Loss2: 1.409193
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.634217 Loss1: 1.627476 Loss2: 2.006741
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.609559 Loss1: 1.162755 Loss2: 1.446804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.303187 Loss1: 0.814545 Loss2: 1.488642
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.736190 Loss1: 0.342357 Loss2: 1.393833
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.990394 Loss1: 0.531628 Loss2: 1.458766
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.643396 Loss1: 0.238027 Loss2: 1.405369
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.634364 Loss1: 0.238408 Loss2: 1.395956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.626671 Loss1: 0.234853 Loss2: 1.391818 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.950000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.672203 Loss1: 0.246476 Loss2: 1.425727 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.951923
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.626978 Loss1: 1.574868 Loss2: 2.052110 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.085279 Loss1: 0.640042 Loss2: 1.445238 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.620158 Loss1: 1.626901 Loss2: 1.993257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.642762 Loss1: 0.238140 Loss2: 1.404622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.704044 Loss1: 0.294473 Loss2: 1.409571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.655546 Loss1: 0.243014 Loss2: 1.412532 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.629296 Loss1: 0.218121 Loss2: 1.411175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.576690 Loss1: 0.176282 Loss2: 1.400408 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961538
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-10 01:37:53,314 | server.py:236 | fit_round 59 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.745239 Loss1: 0.311948 Loss2: 1.433290 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.695295 Loss1: 0.262840 Loss2: 1.432455 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.651648 Loss1: 0.216117 Loss2: 1.435531
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.390883 Loss1: 1.449385 Loss2: 1.941497
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.354627 Loss1: 0.924471 Loss2: 1.430156
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.047809 Loss1: 0.589833 Loss2: 1.457976
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.809281 Loss1: 0.404452 Loss2: 1.404829
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.687982 Loss1: 0.284263 Loss2: 1.403719
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.675038 Loss1: 1.683155 Loss2: 1.991882
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.587094 Loss1: 1.136312 Loss2: 1.450782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.206834 Loss1: 0.726174 Loss2: 1.480659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.951933 Loss1: 0.504492 Loss2: 1.447441 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.884233 Loss1: 0.440904 Loss2: 1.443328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.636271 Loss1: 0.231362 Loss2: 1.404910
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.802154 Loss1: 0.350109 Loss2: 1.452045
-(DefaultActor pid=3765) >> Training accuracy: 0.936458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.782048 Loss1: 0.332862 Loss2: 1.449185
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.678987 Loss1: 0.244760 Loss2: 1.434227
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.643248 Loss1: 0.215663 Loss2: 1.427584
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.596285 Loss1: 0.170351 Loss2: 1.425933
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.477217 Loss1: 1.521258 Loss2: 1.955959
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.324278 Loss1: 0.913585 Loss2: 1.410692
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.111346 Loss1: 0.670700 Loss2: 1.440646
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.826068 Loss1: 0.451159 Loss2: 1.374909
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.463941 Loss1: 1.509225 Loss2: 1.954716
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.408850 Loss1: 0.949157 Loss2: 1.459693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.111094 Loss1: 0.611056 Loss2: 1.500037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.953186 Loss1: 0.516979 Loss2: 1.436207 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.875680 Loss1: 0.415809 Loss2: 1.459871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.762846 Loss1: 0.318612 Loss2: 1.444234 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.633747 Loss1: 0.203174 Loss2: 1.430573 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.649991 Loss1: 0.222598 Loss2: 1.427393 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 01:37:53,314][flwr][DEBUG] - fit_round 59 received 50 results and 0 failures
-INFO flwr 2023-10-10 01:38:35,167 | server.py:125 | fit progress: (59, 2.3416105348842975, {'accuracy': 0.5068}, 136022.945519467)
->> Test accuracy: 0.506800
-[2023-10-10 01:38:35,167][flwr][INFO] - fit progress: (59, 2.3416105348842975, {'accuracy': 0.5068}, 136022.945519467)
-DEBUG flwr 2023-10-10 01:38:35,167 | server.py:173 | evaluate_round 59: strategy sampled 50 clients (out of 50)
-[2023-10-10 01:38:35,167][flwr][DEBUG] - evaluate_round 59: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 01:47:37,191 | server.py:187 | evaluate_round 59 received 50 results and 0 failures
-[2023-10-10 01:47:37,191][flwr][DEBUG] - evaluate_round 59 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 01:47:37,192 | server.py:222 | fit_round 60: strategy sampled 50 clients (out of 50)
-[2023-10-10 01:47:37,192][flwr][DEBUG] - fit_round 60: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.591018 Loss1: 1.557674 Loss2: 2.033343
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.573174 Loss1: 1.031042 Loss2: 1.542132
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.235093 Loss1: 0.658286 Loss2: 1.576807
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.010146 Loss1: 0.483423 Loss2: 1.526723
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.368605 Loss1: 1.387827 Loss2: 1.980778
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.934868 Loss1: 0.403980 Loss2: 1.530888
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.293733 Loss1: 0.854592 Loss2: 1.439141
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.857129 Loss1: 0.340881 Loss2: 1.516247
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.049654 Loss1: 0.586050 Loss2: 1.463604
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.788684 Loss1: 0.266473 Loss2: 1.522211
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.912155 Loss1: 0.488811 Loss2: 1.423344
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.726733 Loss1: 0.209900 Loss2: 1.516833
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.819793 Loss1: 0.396205 Loss2: 1.423588
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.716777 Loss1: 0.206534 Loss2: 1.510243
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.785842 Loss1: 0.351839 Loss2: 1.434003
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.695291 Loss1: 0.189033 Loss2: 1.506258
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.722421 Loss1: 0.300275 Loss2: 1.422146
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.712706 Loss1: 0.286975 Loss2: 1.425731
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.649129 Loss1: 0.236434 Loss2: 1.412695
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.638417 Loss1: 0.226933 Loss2: 1.411484
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.602215 Loss1: 1.694100 Loss2: 1.908115
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.555700 Loss1: 1.074570 Loss2: 1.481130
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.249902 Loss1: 0.756300 Loss2: 1.493602
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.992614 Loss1: 0.547217 Loss2: 1.445397
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.272541 Loss1: 1.328680 Loss2: 1.943861
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.358979 Loss1: 0.880452 Loss2: 1.478527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.057229 Loss1: 0.586809 Loss2: 1.470419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.861894 Loss1: 0.421510 Loss2: 1.440384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.884861 Loss1: 0.422888 Loss2: 1.461974 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.822833 Loss1: 0.387009 Loss2: 1.435823 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.936458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.679779 Loss1: 0.247560 Loss2: 1.432219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.639522 Loss1: 0.216404 Loss2: 1.423118 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958984
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.431381 Loss1: 1.412368 Loss2: 2.019013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.137634 Loss1: 0.603398 Loss2: 1.534237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.858894 Loss1: 0.372009 Loss2: 1.486885 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.790790 Loss1: 0.312064 Loss2: 1.478725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.809976 Loss1: 0.331427 Loss2: 1.478549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.805967 Loss1: 0.310057 Loss2: 1.495909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.724585 Loss1: 0.242643 Loss2: 1.481942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.690735 Loss1: 0.213290 Loss2: 1.477445 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.657674 Loss1: 0.210861 Loss2: 1.446813 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.645679 Loss1: 0.205963 Loss2: 1.439716 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.943750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.479837 Loss1: 1.021352 Loss2: 1.458484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.947893 Loss1: 0.492425 Loss2: 1.455468 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.847835 Loss1: 0.400294 Loss2: 1.447541
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.665079 Loss1: 1.669837 Loss2: 1.995242
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.587802 Loss1: 1.101802 Loss2: 1.486001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.220220 Loss1: 0.682035 Loss2: 1.538185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.956585 Loss1: 0.510593 Loss2: 1.445992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.915854 Loss1: 0.459405 Loss2: 1.456448 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.571058 Loss1: 0.151141 Loss2: 1.419917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.853316 Loss1: 0.383582 Loss2: 1.469734
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.779847 Loss1: 0.325383 Loss2: 1.454464
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.766306 Loss1: 0.309808 Loss2: 1.456498
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.734788 Loss1: 0.278972 Loss2: 1.455816
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.670291 Loss1: 0.224190 Loss2: 1.446101
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.470502 Loss1: 1.489856 Loss2: 1.980646
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.339582 Loss1: 0.883063 Loss2: 1.456519
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.997435 Loss1: 0.552054 Loss2: 1.445381
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.923093 Loss1: 0.507967 Loss2: 1.415125
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.820197 Loss1: 0.393223 Loss2: 1.426974
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.434399 Loss1: 1.502907 Loss2: 1.931491
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.488541 Loss1: 1.060528 Loss2: 1.428013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.153130 Loss1: 0.682654 Loss2: 1.470476 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.946245 Loss1: 0.537224 Loss2: 1.409021 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.828071 Loss1: 0.421784 Loss2: 1.406287 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.959375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.627589 Loss1: 0.223546 Loss2: 1.404043
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.766755 Loss1: 0.350946 Loss2: 1.415809
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.721053 Loss1: 0.310412 Loss2: 1.410641
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.665547 Loss1: 0.252742 Loss2: 1.412805
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.579718 Loss1: 0.187692 Loss2: 1.392027
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.608136 Loss1: 0.218154 Loss2: 1.389982
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.538531 Loss1: 1.528406 Loss2: 2.010124
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.436049 Loss1: 1.010004 Loss2: 1.426045
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.216762 Loss1: 0.771133 Loss2: 1.445629
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.908288 Loss1: 0.470080 Loss2: 1.438208
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.835865 Loss1: 0.423110 Loss2: 1.412755
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.709817 Loss1: 0.286517 Loss2: 1.423301
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.486316 Loss1: 1.509245 Loss2: 1.977071
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.338665 Loss1: 0.873978 Loss2: 1.464687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.082963 Loss1: 0.584837 Loss2: 1.498126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.902373 Loss1: 0.444986 Loss2: 1.457387 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.878606
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.731096 Loss1: 0.279018 Loss2: 1.452078 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.643587 Loss1: 0.200628 Loss2: 1.442958 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.702026 Loss1: 0.253877 Loss2: 1.448149
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.880520 Loss1: 1.769917 Loss2: 2.110603
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.661062 Loss1: 0.201835 Loss2: 1.459227
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.786039 Loss1: 1.218822 Loss2: 1.567217
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.366056 Loss1: 0.753631 Loss2: 1.612425
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.034374 Loss1: 0.509880 Loss2: 1.524494
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.976192 Loss1: 0.434927 Loss2: 1.541264
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.865269 Loss1: 0.321571 Loss2: 1.543698
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.890649 Loss1: 0.372465 Loss2: 1.518183
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.444615 Loss1: 1.411127 Loss2: 2.033488
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.835881 Loss1: 0.286628 Loss2: 1.549253
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.469701 Loss1: 0.935244 Loss2: 1.534457
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.162607 Loss1: 0.610078 Loss2: 1.552528 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.960938
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.726204 Loss1: 0.212981 Loss2: 1.513223
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.002385 Loss1: 0.487585 Loss2: 1.514799
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.889982 Loss1: 0.366393 Loss2: 1.523589
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.771988 Loss1: 0.279339 Loss2: 1.492649
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.770850 Loss1: 0.276074 Loss2: 1.494775
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.773040 Loss1: 0.260794 Loss2: 1.512246
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.567468 Loss1: 1.564104 Loss2: 2.003364
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.810095 Loss1: 0.310900 Loss2: 1.499195
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.754885 Loss1: 0.248238 Loss2: 1.506647 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.762396 Loss1: 0.389240 Loss2: 1.373156 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.633358 Loss1: 0.249543 Loss2: 1.383815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.599525 Loss1: 0.228570 Loss2: 1.370954
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.430163 Loss1: 1.514791 Loss2: 1.915372
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.411461 Loss1: 0.962438 Loss2: 1.449024 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978365
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.841958 Loss1: 0.426560 Loss2: 1.415399 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.737415 Loss1: 0.321851 Loss2: 1.415564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.480561 Loss1: 1.577487 Loss2: 1.903075
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.728155 Loss1: 0.316361 Loss2: 1.411794
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.504454 Loss1: 1.088040 Loss2: 1.416414
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.751513 Loss1: 0.321745 Loss2: 1.429768
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.102909 Loss1: 0.651172 Loss2: 1.451736
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.729444 Loss1: 0.302886 Loss2: 1.426557
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.875673 Loss1: 0.487062 Loss2: 1.388611
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.664932 Loss1: 0.239782 Loss2: 1.425149
-(DefaultActor pid=3764) >> Training accuracy: 0.946289
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.691104 Loss1: 0.315212 Loss2: 1.375892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.618946 Loss1: 0.241279 Loss2: 1.377667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.570277 Loss1: 0.193826 Loss2: 1.376451
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.543464 Loss1: 1.599926 Loss2: 1.943538
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.422049 Loss1: 0.954643 Loss2: 1.467406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.973519 Loss1: 0.534984 Loss2: 1.438534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.821878 Loss1: 0.383599 Loss2: 1.438279 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.708031 Loss1: 0.262951 Loss2: 1.445080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.661526 Loss1: 0.232428 Loss2: 1.429098 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.639623 Loss1: 0.206812 Loss2: 1.432811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.600026 Loss1: 0.175462 Loss2: 1.424564 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.931641
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.702100 Loss1: 0.305373 Loss2: 1.396727 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.663986 Loss1: 0.254854 Loss2: 1.409132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.630606 Loss1: 0.227404 Loss2: 1.403202
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.394898 Loss1: 1.476353 Loss2: 1.918546
-(DefaultActor pid=3765) >> Training accuracy: 0.936458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.475029 Loss1: 1.038470 Loss2: 1.436559
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.134874 Loss1: 0.669117 Loss2: 1.465757
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.980562 Loss1: 0.555985 Loss2: 1.424577
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.811325 Loss1: 0.376147 Loss2: 1.435178
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.777998 Loss1: 0.365970 Loss2: 1.412027
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.494171 Loss1: 1.570405 Loss2: 1.923766
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.820585 Loss1: 0.397902 Loss2: 1.422683
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.476138 Loss1: 1.044660 Loss2: 1.431479
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.738387 Loss1: 0.313405 Loss2: 1.424982
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.099438 Loss1: 0.640823 Loss2: 1.458616
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.729526 Loss1: 0.306798 Loss2: 1.422728
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.955688 Loss1: 0.549435 Loss2: 1.406253
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.634595 Loss1: 0.224788 Loss2: 1.409807
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.828918 Loss1: 0.405671 Loss2: 1.423247
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.844507 Loss1: 0.425628 Loss2: 1.418879
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.785988 Loss1: 0.365493 Loss2: 1.420495
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.726780 Loss1: 0.314099 Loss2: 1.412682
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.658360 Loss1: 0.247818 Loss2: 1.410543
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.651781 Loss1: 0.245783 Loss2: 1.405998
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.272278 Loss1: 1.304430 Loss2: 1.967848
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.382754 Loss1: 0.939325 Loss2: 1.443429
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.135522 Loss1: 0.622788 Loss2: 1.512733
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.850233 Loss1: 0.422118 Loss2: 1.428115
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.797266 Loss1: 0.375355 Loss2: 1.421911
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.692119 Loss1: 0.263017 Loss2: 1.429102
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.285503 Loss1: 1.311008 Loss2: 1.974495
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.708404 Loss1: 0.286161 Loss2: 1.422242
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.296370 Loss1: 0.850763 Loss2: 1.445607
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.670245 Loss1: 0.251679 Loss2: 1.418567
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.084778 Loss1: 0.619890 Loss2: 1.464888
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.566544 Loss1: 0.148721 Loss2: 1.417823
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.872979 Loss1: 0.438050 Loss2: 1.434929
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.572212 Loss1: 0.175167 Loss2: 1.397045
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.835514 Loss1: 0.398350 Loss2: 1.437164
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.772514 Loss1: 0.335430 Loss2: 1.437084
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.690846 Loss1: 0.267503 Loss2: 1.423343
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.631499 Loss1: 0.209530 Loss2: 1.421969
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.615971 Loss1: 0.201420 Loss2: 1.414552
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.597798 Loss1: 0.184116 Loss2: 1.413682
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.420544 Loss1: 1.447484 Loss2: 1.973060
-(DefaultActor pid=3765) >> Training accuracy: 0.950000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.418469 Loss1: 0.931082 Loss2: 1.487387
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.117079 Loss1: 0.588977 Loss2: 1.528102
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.984793 Loss1: 0.506878 Loss2: 1.477915
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.884537 Loss1: 0.386718 Loss2: 1.497819
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.481527 Loss1: 1.544364 Loss2: 1.937163
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.811502 Loss1: 0.329196 Loss2: 1.482306
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.843600 Loss1: 0.351596 Loss2: 1.492004 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.838303 Loss1: 0.341771 Loss2: 1.496531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.736123 Loss1: 0.251212 Loss2: 1.484911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.700049 Loss1: 0.216185 Loss2: 1.483864 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964844
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.775803 Loss1: 0.326632 Loss2: 1.449171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.684774 Loss1: 0.244080 Loss2: 1.440694 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.646264 Loss1: 0.203196 Loss2: 1.443069
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.474227 Loss1: 1.564819 Loss2: 1.909408
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.511070 Loss1: 1.046070 Loss2: 1.465000
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.132024 Loss1: 0.649704 Loss2: 1.482320
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.948874 Loss1: 0.527713 Loss2: 1.421161
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.472526 Loss1: 1.510498 Loss2: 1.962027
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.911838 Loss1: 0.456835 Loss2: 1.455004
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.824421 Loss1: 0.375107 Loss2: 1.449314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.867236 Loss1: 0.424591 Loss2: 1.442646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.731699 Loss1: 0.281085 Loss2: 1.450614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.721010 Loss1: 0.297997 Loss2: 1.423013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.676614 Loss1: 0.226667 Loss2: 1.449947 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.942383
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.582711 Loss1: 0.168445 Loss2: 1.414265 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965402
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.500204 Loss1: 1.590294 Loss2: 1.909910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.093507 Loss1: 0.639760 Loss2: 1.453746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.921825 Loss1: 0.526948 Loss2: 1.394877
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.592154 Loss1: 1.634791 Loss2: 1.957363
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.472831 Loss1: 1.017068 Loss2: 1.455763 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.118158 Loss1: 0.628084 Loss2: 1.490074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.866029 Loss1: 0.433952 Loss2: 1.432077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.799776 Loss1: 0.372254 Loss2: 1.427523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.816166 Loss1: 0.374685 Loss2: 1.441480 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.650302 Loss1: 0.256905 Loss2: 1.393397
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.756335 Loss1: 0.312799 Loss2: 1.443537
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.699163 Loss1: 0.267521 Loss2: 1.431642
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.682566 Loss1: 0.255405 Loss2: 1.427161
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.692940 Loss1: 0.247751 Loss2: 1.445190
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.459866 Loss1: 1.514339 Loss2: 1.945527
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.564451 Loss1: 1.151002 Loss2: 1.413449
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.251165 Loss1: 0.763996 Loss2: 1.487169
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.991090 Loss1: 0.570996 Loss2: 1.420094
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.332498 Loss1: 1.421633 Loss2: 1.910865
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.380440 Loss1: 0.929370 Loss2: 1.451069 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.993330 Loss1: 0.554680 Loss2: 1.438650 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.850241 Loss1: 0.435845 Loss2: 1.414396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.550264 Loss1: 0.162089 Loss2: 1.388175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.562633 Loss1: 0.178841 Loss2: 1.383792 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.942708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.750058 Loss1: 0.326189 Loss2: 1.423868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.637053 Loss1: 0.209299 Loss2: 1.427754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.602569 Loss1: 0.194093 Loss2: 1.408476
-(DefaultActor pid=3765) >> Training accuracy: 0.915441
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.625830 Loss1: 1.532811 Loss2: 2.093019
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.650473 Loss1: 1.086903 Loss2: 1.563570
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.348512 Loss1: 0.726421 Loss2: 1.622091
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.116890 Loss1: 0.569124 Loss2: 1.547766
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.875610 Loss1: 0.323765 Loss2: 1.551845
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.411846 Loss1: 1.444986 Loss2: 1.966860
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.805533 Loss1: 0.279675 Loss2: 1.525858
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.459594 Loss1: 0.982011 Loss2: 1.477582
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.790590 Loss1: 0.260335 Loss2: 1.530255
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.044161 Loss1: 0.561030 Loss2: 1.483131
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.770955 Loss1: 0.243359 Loss2: 1.527596
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.810132 Loss1: 0.277171 Loss2: 1.532960
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.894367 Loss1: 0.443113 Loss2: 1.451254
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.756990 Loss1: 0.224972 Loss2: 1.532018
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.881019 Loss1: 0.428969 Loss2: 1.452050
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.757571 Loss1: 0.305590 Loss2: 1.451981
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.761031 Loss1: 0.313225 Loss2: 1.447805
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.730819 Loss1: 0.276453 Loss2: 1.454366
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.673043 Loss1: 0.220868 Loss2: 1.452175
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.453266 Loss1: 1.539683 Loss2: 1.913584
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.654691 Loss1: 0.216804 Loss2: 1.437887
-(DefaultActor pid=3765) >> Training accuracy: 0.944336
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.143917 Loss1: 0.670806 Loss2: 1.473111 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.780540 Loss1: 0.320411 Loss2: 1.460129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.706590 Loss1: 0.272570 Loss2: 1.434021
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.555393 Loss1: 1.609174 Loss2: 1.946218
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.708600 Loss1: 0.275649 Loss2: 1.432952
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.455619 Loss1: 0.995880 Loss2: 1.459740
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.713555 Loss1: 0.266964 Loss2: 1.446591
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.071709 Loss1: 0.602802 Loss2: 1.468907
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.787574 Loss1: 0.337204 Loss2: 1.450370
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.982002 Loss1: 0.549804 Loss2: 1.432198
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.622966 Loss1: 0.177740 Loss2: 1.445226
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.907070 Loss1: 0.457390 Loss2: 1.449680
-(DefaultActor pid=3764) >> Training accuracy: 0.958008
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.827043 Loss1: 0.374476 Loss2: 1.452568
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.785270 Loss1: 0.352582 Loss2: 1.432688
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.718783 Loss1: 0.284943 Loss2: 1.433840
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.735204 Loss1: 0.301405 Loss2: 1.433799
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.460823 Loss1: 1.550155 Loss2: 1.910668
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.731786 Loss1: 0.303229 Loss2: 1.428558
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.190181 Loss1: 0.712362 Loss2: 1.477819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.809550 Loss1: 0.395178 Loss2: 1.414372 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.769902 Loss1: 0.378031 Loss2: 1.391872
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.577201 Loss1: 1.635920 Loss2: 1.941281
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.427786 Loss1: 0.967042 Loss2: 1.460744 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.052666 Loss1: 0.588971 Loss2: 1.463695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.870427 Loss1: 0.434493 Loss2: 1.435935 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.938542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.838026 Loss1: 0.392939 Loss2: 1.445087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.724269 Loss1: 0.285958 Loss2: 1.438310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.606883 Loss1: 0.172371 Loss2: 1.434511 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.581370 Loss1: 0.152927 Loss2: 1.428443 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.286339 Loss1: 0.744185 Loss2: 1.542154 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.863729 Loss1: 0.376366 Loss2: 1.487363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.657668 Loss1: 1.712121 Loss2: 1.945546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.544154 Loss1: 1.138357 Loss2: 1.405798 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.166419 Loss1: 0.743201 Loss2: 1.423218 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.940303 Loss1: 0.556311 Loss2: 1.383992 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.828651 Loss1: 0.444156 Loss2: 1.384495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.688882 Loss1: 0.307421 Loss2: 1.381461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.636437 Loss1: 0.252461 Loss2: 1.383977 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.596325 Loss1: 0.219957 Loss2: 1.376368
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.533762 Loss1: 1.498497 Loss2: 2.035266
-(DefaultActor pid=3765) >> Training accuracy: 0.959821
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.574549 Loss1: 1.040021 Loss2: 1.534528
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.127707 Loss1: 0.590322 Loss2: 1.537385
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.004363 Loss1: 0.513813 Loss2: 1.490549
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.916742 Loss1: 0.411244 Loss2: 1.505498
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.722992 Loss1: 1.597600 Loss2: 2.125392
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.855567 Loss1: 0.358596 Loss2: 1.496971
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.802818 Loss1: 0.309372 Loss2: 1.493446 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.786930 Loss1: 0.295024 Loss2: 1.491907 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.912487 Loss1: 0.457271 Loss2: 1.455217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.789611 Loss1: 0.333696 Loss2: 1.455916 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.947917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.754358 Loss1: 0.307758 Loss2: 1.446600 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.691690 Loss1: 0.230776 Loss2: 1.460914 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.930990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.637563 Loss1: 1.741293 Loss2: 1.896270
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.521618 Loss1: 1.105707 Loss2: 1.415911
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.096877 Loss1: 0.638051 Loss2: 1.458826
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.913776 Loss1: 0.522926 Loss2: 1.390850
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.480711 Loss1: 1.567071 Loss2: 1.913641
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.625090 Loss1: 1.161527 Loss2: 1.463563 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.189042 Loss1: 0.688824 Loss2: 1.500217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.977431 Loss1: 0.543156 Loss2: 1.434275 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.814128 Loss1: 0.364242 Loss2: 1.449886 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.643349 Loss1: 0.222325 Loss2: 1.421024 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.918750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.628857 Loss1: 0.226589 Loss2: 1.402269
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.653786 Loss1: 0.249317 Loss2: 1.404469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.630109 Loss1: 0.217496 Loss2: 1.412613
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.609398 Loss1: 0.200107 Loss2: 1.409291
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.545104 Loss1: 0.138046 Loss2: 1.407058
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.394230 Loss1: 1.447399 Loss2: 1.946830
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.377552 Loss1: 0.943415 Loss2: 1.434137
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.029330 Loss1: 0.573426 Loss2: 1.455904
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.869050 Loss1: 0.442008 Loss2: 1.427043
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.498381 Loss1: 1.541189 Loss2: 1.957192
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.575900 Loss1: 1.110520 Loss2: 1.465380 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.207096 Loss1: 0.686570 Loss2: 1.520526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.947844 Loss1: 0.518808 Loss2: 1.429036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.852454 Loss1: 0.407886 Loss2: 1.444568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.779392 Loss1: 0.331527 Loss2: 1.447865 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.726512 Loss1: 0.292107 Loss2: 1.434405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.605825 Loss1: 0.174974 Loss2: 1.430851 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-DEBUG flwr 2023-10-10 02:16:10,359 | server.py:236 | fit_round 60 received 50 results and 0 failures
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.374302 Loss1: 1.332958 Loss2: 2.041344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.205337 Loss1: 0.666861 Loss2: 1.538476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.579728 Loss1: 1.519577 Loss2: 2.060151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.371007 Loss1: 0.887883 Loss2: 1.483124 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.111014 Loss1: 0.616834 Loss2: 1.494180 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.927242 Loss1: 0.466297 Loss2: 1.460945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.826353 Loss1: 0.379846 Loss2: 1.446507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.776752 Loss1: 0.316868 Loss2: 1.459883 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.737456 Loss1: 0.289013 Loss2: 1.448443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.699231 Loss1: 0.250567 Loss2: 1.448664 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.948958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.443163 Loss1: 1.495850 Loss2: 1.947313 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.128119 Loss1: 0.597409 Loss2: 1.530710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.974671 Loss1: 0.506385 Loss2: 1.468285
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.405445 Loss1: 1.534082 Loss2: 1.871362
-(DefaultActor pid=3764) Epoch: 4 Loss: 2.021409 Loss1: 0.526646 Loss2: 1.494763
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.396758 Loss1: 1.017846 Loss2: 1.378912
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.916531 Loss1: 0.435353 Loss2: 1.481178
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.097982 Loss1: 0.676574 Loss2: 1.421408
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.801881 Loss1: 0.303749 Loss2: 1.498132
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.866265 Loss1: 0.502537 Loss2: 1.363728
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.768292 Loss1: 0.398101 Loss2: 1.370191
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.823428 Loss1: 0.340566 Loss2: 1.482861
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.789901 Loss1: 0.413313 Loss2: 1.376588
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.724997 Loss1: 0.240350 Loss2: 1.484647
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.699286 Loss1: 0.325694 Loss2: 1.373593
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.676847 Loss1: 0.210182 Loss2: 1.466666
-(DefaultActor pid=3764) >> Training accuracy: 0.956055
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.659860 Loss1: 0.277465 Loss2: 1.382395 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.940625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.403273 Loss1: 1.504921 Loss2: 1.898352 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.074720 Loss1: 0.652070 Loss2: 1.422651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.775289 Loss1: 0.404954 Loss2: 1.370335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.635311 Loss1: 0.274886 Loss2: 1.360425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.621175 Loss1: 0.257480 Loss2: 1.363696 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.926042
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 02:16:10,359][flwr][DEBUG] - fit_round 60 received 50 results and 0 failures
-INFO flwr 2023-10-10 02:16:51,856 | server.py:125 | fit progress: (60, 2.347771980891974, {'accuracy': 0.5081}, 138319.634262642)
->> Test accuracy: 0.508100
-[2023-10-10 02:16:51,856][flwr][INFO] - fit progress: (60, 2.347771980891974, {'accuracy': 0.5081}, 138319.634262642)
-DEBUG flwr 2023-10-10 02:16:51,856 | server.py:173 | evaluate_round 60: strategy sampled 50 clients (out of 50)
-[2023-10-10 02:16:51,856][flwr][DEBUG] - evaluate_round 60: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 02:25:59,976 | server.py:187 | evaluate_round 60 received 50 results and 0 failures
-[2023-10-10 02:25:59,976][flwr][DEBUG] - evaluate_round 60 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 02:25:59,977 | server.py:222 | fit_round 61: strategy sampled 50 clients (out of 50)
-[2023-10-10 02:25:59,977][flwr][DEBUG] - fit_round 61: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.521081 Loss1: 1.555654 Loss2: 1.965427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.155638 Loss1: 0.717498 Loss2: 1.438140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.899818 Loss1: 0.498976 Loss2: 1.400843
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.448607 Loss1: 1.572675 Loss2: 1.875932
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.475314 Loss1: 1.055052 Loss2: 1.420262 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.111981 Loss1: 0.671192 Loss2: 1.440790 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.893759 Loss1: 0.505505 Loss2: 1.388254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.833680 Loss1: 0.422672 Loss2: 1.411008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.802579 Loss1: 0.401625 Loss2: 1.400954 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966518
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.682701 Loss1: 0.292066 Loss2: 1.390635 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.574931 Loss1: 0.183258 Loss2: 1.391673 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.935417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.549164 Loss1: 1.083912 Loss2: 1.465252 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.818087 Loss1: 0.460288 Loss2: 1.357799 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.588834 Loss1: 1.669861 Loss2: 1.918972
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.680210 Loss1: 0.309006 Loss2: 1.371204
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.497326 Loss1: 1.060988 Loss2: 1.436338
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.683656 Loss1: 0.337472 Loss2: 1.346185
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.219182 Loss1: 0.748795 Loss2: 1.470387
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.664510 Loss1: 0.303694 Loss2: 1.360816
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.933436 Loss1: 0.521287 Loss2: 1.412149
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.620130 Loss1: 0.245550 Loss2: 1.374581
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.870006 Loss1: 0.442307 Loss2: 1.427699
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.569637 Loss1: 0.214242 Loss2: 1.355395
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.764446 Loss1: 0.342370 Loss2: 1.422076
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.521748 Loss1: 0.171597 Loss2: 1.350151
-(DefaultActor pid=3765) >> Training accuracy: 0.950000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.684349 Loss1: 0.268712 Loss2: 1.415638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.696722 Loss1: 0.276333 Loss2: 1.420390 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.632916 Loss1: 1.164907 Loss2: 1.468010 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.054976 Loss1: 0.627532 Loss2: 1.427443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.931100 Loss1: 0.468081 Loss2: 1.463019
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.358515 Loss1: 1.510886 Loss2: 1.847629
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.807762 Loss1: 0.375080 Loss2: 1.432682
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.509163 Loss1: 1.063982 Loss2: 1.445181
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.723176 Loss1: 0.293062 Loss2: 1.430114
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.100075 Loss1: 0.669231 Loss2: 1.430844
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.680078 Loss1: 0.258933 Loss2: 1.421145
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.936580 Loss1: 0.524593 Loss2: 1.411987
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.860827 Loss1: 0.449439 Loss2: 1.411389 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.632710 Loss1: 0.214981 Loss2: 1.417729
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.856879 Loss1: 0.438234 Loss2: 1.418645
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.774044 Loss1: 0.358282 Loss2: 1.415762
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.658123 Loss1: 0.257652 Loss2: 1.400471
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.680320 Loss1: 0.278736 Loss2: 1.401585
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.599860 Loss1: 0.198909 Loss2: 1.400951
-(DefaultActor pid=3764) >> Training accuracy: 0.957031
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.229587 Loss1: 1.358254 Loss2: 1.871333
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.303733 Loss1: 0.892472 Loss2: 1.411261
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.049482 Loss1: 0.624199 Loss2: 1.425283
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.866980 Loss1: 0.473499 Loss2: 1.393481
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.735022 Loss1: 0.351490 Loss2: 1.383531
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.611686 Loss1: 1.655294 Loss2: 1.956392
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.665055 Loss1: 0.286093 Loss2: 1.378962
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.494146 Loss1: 1.010395 Loss2: 1.483751
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.612518 Loss1: 0.232656 Loss2: 1.379861
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.150942 Loss1: 0.664703 Loss2: 1.486238
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.585356 Loss1: 0.203303 Loss2: 1.382053
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.974011 Loss1: 0.516279 Loss2: 1.457732
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.541350 Loss1: 0.175976 Loss2: 1.365375
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.884191 Loss1: 0.436571 Loss2: 1.447619
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.515928 Loss1: 0.153508 Loss2: 1.362419
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.658665 Loss1: 0.229202 Loss2: 1.429462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.647349 Loss1: 0.225905 Loss2: 1.421445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.673824 Loss1: 0.229902 Loss2: 1.443923
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.396648 Loss1: 1.516362 Loss2: 1.880286
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.364439 Loss1: 0.943718 Loss2: 1.420721
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.112326 Loss1: 0.668018 Loss2: 1.444309
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.831669 Loss1: 0.427395 Loss2: 1.404274
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.753353 Loss1: 0.343075 Loss2: 1.410279
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.603934 Loss1: 1.707960 Loss2: 1.895974
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.697971 Loss1: 0.303561 Loss2: 1.394409
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.660499 Loss1: 0.258228 Loss2: 1.402270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.669392 Loss1: 0.269329 Loss2: 1.400063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.641835 Loss1: 0.244203 Loss2: 1.397633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.631598 Loss1: 0.237333 Loss2: 1.394265 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.938542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.679286 Loss1: 0.297183 Loss2: 1.382103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.602691 Loss1: 0.218814 Loss2: 1.383877 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.927455
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.477992 Loss1: 1.557068 Loss2: 1.920924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.180868 Loss1: 0.674329 Loss2: 1.506539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.907696 Loss1: 0.440768 Loss2: 1.466929 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.843570 Loss1: 0.391237 Loss2: 1.452333 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.790601 Loss1: 0.335672 Loss2: 1.454929 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.783375 Loss1: 0.319044 Loss2: 1.464332 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.717701 Loss1: 0.267817 Loss2: 1.449884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.713074 Loss1: 0.261610 Loss2: 1.451464 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.940625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.759217 Loss1: 0.356919 Loss2: 1.402298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.730431 Loss1: 0.340477 Loss2: 1.389954 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.664639 Loss1: 0.260015 Loss2: 1.404624
-(DefaultActor pid=3764) >> Training accuracy: 0.924805
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.276958 Loss1: 1.383804 Loss2: 1.893154
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.309834 Loss1: 0.827893 Loss2: 1.481941
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.051283 Loss1: 0.560557 Loss2: 1.490726
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.839468 Loss1: 0.384898 Loss2: 1.454570
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.865265 Loss1: 0.410212 Loss2: 1.455053
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.371475 Loss1: 1.494605 Loss2: 1.876870
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.402556 Loss1: 0.998878 Loss2: 1.403677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.102582 Loss1: 0.685553 Loss2: 1.417029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.870094 Loss1: 0.485854 Loss2: 1.384240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.582734 Loss1: 0.146498 Loss2: 1.436236
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.757754 Loss1: 0.373538 Loss2: 1.384216
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.598313 Loss1: 0.171623 Loss2: 1.426690
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.721278 Loss1: 0.327998 Loss2: 1.393280
-(DefaultActor pid=3765) >> Training accuracy: 0.975586
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.628461 Loss1: 0.246069 Loss2: 1.382392
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.579490 Loss1: 0.202210 Loss2: 1.377279
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.569853 Loss1: 0.197779 Loss2: 1.372074
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.597817 Loss1: 0.227308 Loss2: 1.370509
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.508549 Loss1: 1.601523 Loss2: 1.907026
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.470102 Loss1: 0.984927 Loss2: 1.485176
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.141033 Loss1: 0.675112 Loss2: 1.465921
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.023359 Loss1: 0.562946 Loss2: 1.460412
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.306385 Loss1: 1.469747 Loss2: 1.836637
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.306176 Loss1: 0.940091 Loss2: 1.366085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.075248 Loss1: 0.669263 Loss2: 1.405985 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.994516 Loss1: 0.644185 Loss2: 1.350332 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.811242 Loss1: 0.434554 Loss2: 1.376689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.642581 Loss1: 0.296279 Loss2: 1.346302 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973633
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.539751 Loss1: 0.205087 Loss2: 1.334665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.583935 Loss1: 0.241679 Loss2: 1.342256 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.613021 Loss1: 1.675208 Loss2: 1.937812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.110810 Loss1: 0.653275 Loss2: 1.457535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.480042 Loss1: 1.628371 Loss2: 1.851671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.404691 Loss1: 0.986684 Loss2: 1.418007 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.137590 Loss1: 0.715897 Loss2: 1.421693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.904994 Loss1: 0.511568 Loss2: 1.393426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.817037 Loss1: 0.423809 Loss2: 1.393228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.566497 Loss1: 0.173023 Loss2: 1.393475 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958705
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.700688 Loss1: 0.308903 Loss2: 1.391785 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.692229 Loss1: 0.309129 Loss2: 1.383100 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.413737 Loss1: 0.996714 Loss2: 1.417023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.935464 Loss1: 0.525144 Loss2: 1.410321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.523644 Loss1: 1.500191 Loss2: 2.023452
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.766106 Loss1: 0.353034 Loss2: 1.413073
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.671136 Loss1: 1.101926 Loss2: 1.569210
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.697547 Loss1: 0.303504 Loss2: 1.394043
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.672242 Loss1: 0.280194 Loss2: 1.392049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.661053 Loss1: 0.275773 Loss2: 1.385280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.633900 Loss1: 0.247703 Loss2: 1.386197 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.583334 Loss1: 0.194110 Loss2: 1.389224 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958008
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.840293 Loss1: 0.291684 Loss2: 1.548609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.736326 Loss1: 0.199108 Loss2: 1.537218 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.443271 Loss1: 1.483892 Loss2: 1.959379
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.533184 Loss1: 1.028171 Loss2: 1.505013
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.182679 Loss1: 0.663294 Loss2: 1.519386
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.992891 Loss1: 0.524781 Loss2: 1.468111
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.543156 Loss1: 1.521830 Loss2: 2.021326
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.510755 Loss1: 1.083446 Loss2: 1.427309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.088161 Loss1: 0.597330 Loss2: 1.490830
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.880722 Loss1: 0.410334 Loss2: 1.470388
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.854133 Loss1: 0.450700 Loss2: 1.403432
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.817689 Loss1: 0.337390 Loss2: 1.480299
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.734192 Loss1: 0.259503 Loss2: 1.474690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.695128 Loss1: 0.234273 Loss2: 1.460854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.636186 Loss1: 0.177614 Loss2: 1.458572 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.597254 Loss1: 0.199075 Loss2: 1.398179 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957933
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.483955 Loss1: 1.573820 Loss2: 1.910135
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.387592 Loss1: 0.985780 Loss2: 1.401812
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.102806 Loss1: 0.674674 Loss2: 1.428132
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.889306 Loss1: 0.514043 Loss2: 1.375263
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.327974 Loss1: 1.350515 Loss2: 1.977459
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.820553 Loss1: 0.430934 Loss2: 1.389619
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.467721 Loss1: 0.997676 Loss2: 1.470044
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.702400 Loss1: 0.317679 Loss2: 1.384721
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.196276 Loss1: 0.655478 Loss2: 1.540798
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.639590 Loss1: 0.276276 Loss2: 1.363314
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.929544 Loss1: 0.477561 Loss2: 1.451983
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.682182 Loss1: 0.300600 Loss2: 1.381581
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.793629 Loss1: 0.345887 Loss2: 1.447743
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.643285 Loss1: 0.268517 Loss2: 1.374769
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.756848 Loss1: 0.302569 Loss2: 1.454279
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.594768 Loss1: 0.222816 Loss2: 1.371952
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.724610 Loss1: 0.275951 Loss2: 1.448659
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.669249 Loss1: 0.227835 Loss2: 1.441414
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.669693 Loss1: 0.229717 Loss2: 1.439976
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.653824 Loss1: 0.209315 Loss2: 1.444509
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.398914 Loss1: 1.530798 Loss2: 1.868115
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.504796 Loss1: 1.024407 Loss2: 1.480390
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.177653 Loss1: 0.702117 Loss2: 1.475536
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.428892 Loss1: 1.473044 Loss2: 1.955849
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.966777 Loss1: 0.536791 Loss2: 1.429986
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.347588 Loss1: 0.900974 Loss2: 1.446614
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.908801 Loss1: 0.465861 Loss2: 1.442940
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.025214 Loss1: 0.567457 Loss2: 1.457757
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.713837 Loss1: 0.294564 Loss2: 1.419273
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.811951 Loss1: 0.390120 Loss2: 1.421831
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.636507 Loss1: 0.223718 Loss2: 1.412789
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.738605 Loss1: 0.323271 Loss2: 1.415334
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.614149 Loss1: 0.206165 Loss2: 1.407984
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.572020 Loss1: 0.161171 Loss2: 1.410848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.596434 Loss1: 0.198664 Loss2: 1.397771 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952148
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.606277 Loss1: 0.197312 Loss2: 1.408965 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.931250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.510627 Loss1: 1.567739 Loss2: 1.942888 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.169872 Loss1: 0.682457 Loss2: 1.487414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.048818 Loss1: 0.589263 Loss2: 1.459555
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.440284 Loss1: 1.531857 Loss2: 1.908427
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.873980 Loss1: 0.409387 Loss2: 1.464593
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.472342 Loss1: 1.001393 Loss2: 1.470950
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.780708 Loss1: 0.328923 Loss2: 1.451785
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.083089 Loss1: 0.597337 Loss2: 1.485753
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.667621 Loss1: 0.223191 Loss2: 1.444430
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.980024 Loss1: 0.533446 Loss2: 1.446578
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.698592 Loss1: 0.255370 Loss2: 1.443222
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.801083 Loss1: 0.342368 Loss2: 1.458715
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.638494 Loss1: 0.195186 Loss2: 1.443308
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.690461 Loss1: 0.253708 Loss2: 1.436753
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.659333 Loss1: 0.220575 Loss2: 1.438758
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.721038 Loss1: 0.287108 Loss2: 1.433930
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.652889 Loss1: 0.213410 Loss2: 1.439478
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.698906 Loss1: 0.265751 Loss2: 1.433155
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.666064 Loss1: 0.217944 Loss2: 1.448121
-(DefaultActor pid=3764) >> Training accuracy: 0.928125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.694688 Loss1: 1.696663 Loss2: 1.998025
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.530542 Loss1: 1.053678 Loss2: 1.476863
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.231797 Loss1: 0.744050 Loss2: 1.487746
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.958481 Loss1: 0.518904 Loss2: 1.439577
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.519006 Loss1: 1.582635 Loss2: 1.936370
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.831475 Loss1: 0.378970 Loss2: 1.452504
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.494301 Loss1: 1.008262 Loss2: 1.486038
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.242248 Loss1: 0.776297 Loss2: 1.465951 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.947799 Loss1: 0.500264 Loss2: 1.447535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.805795 Loss1: 0.376006 Loss2: 1.429789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.728718 Loss1: 0.305269 Loss2: 1.423449 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.664666 Loss1: 0.232214 Loss2: 1.432452
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.712585 Loss1: 0.285955 Loss2: 1.426630
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.825253 Loss1: 0.377118 Loss2: 1.448135
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.773077 Loss1: 0.325974 Loss2: 1.447103
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.724520 Loss1: 0.290963 Loss2: 1.433558
-(DefaultActor pid=3764) >> Training accuracy: 0.889583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.679393 Loss1: 1.611004 Loss2: 2.068389
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.459985 Loss1: 1.026570 Loss2: 1.433415
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.125147 Loss1: 0.651284 Loss2: 1.473863
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.915625 Loss1: 0.469463 Loss2: 1.446162
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.845154 Loss1: 0.413007 Loss2: 1.432147
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.801003 Loss1: 0.369991 Loss2: 1.431012 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.695342 Loss1: 0.262194 Loss2: 1.433148
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.374355 Loss1: 0.949865 Loss2: 1.424490
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.914033 Loss1: 0.512156 Loss2: 1.401877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.854295 Loss1: 0.483057 Loss2: 1.371237 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.919271
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.748920 Loss1: 0.379152 Loss2: 1.369768 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.648766 Loss1: 0.286016 Loss2: 1.362750 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.605022 Loss1: 0.258022 Loss2: 1.347000 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.597392 Loss1: 0.233387 Loss2: 1.364005 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957721
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.805113 Loss1: 0.411219 Loss2: 1.393894 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.616251 Loss1: 0.245034 Loss2: 1.371217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.590164 Loss1: 0.225252 Loss2: 1.364913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.616378 Loss1: 0.240264 Loss2: 1.376115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.580267 Loss1: 0.207022 Loss2: 1.373245 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.760981 Loss1: 0.377959 Loss2: 1.383023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.635613 Loss1: 0.268083 Loss2: 1.367529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.571811 Loss1: 0.207214 Loss2: 1.364598
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.391015 Loss1: 1.453631 Loss2: 1.937384
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.363245 Loss1: 0.911999 Loss2: 1.451246 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.038637 Loss1: 0.559733 Loss2: 1.478904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.805321 Loss1: 0.367447 Loss2: 1.437874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.754351 Loss1: 0.327610 Loss2: 1.426742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.681531 Loss1: 0.246700 Loss2: 1.434832 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.638520 Loss1: 0.223593 Loss2: 1.414927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.613930 Loss1: 0.192310 Loss2: 1.421620 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952148
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.774219 Loss1: 0.341062 Loss2: 1.433157 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.668867 Loss1: 0.236840 Loss2: 1.432027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.432563 Loss1: 1.497753 Loss2: 1.934809
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.624016 Loss1: 0.199064 Loss2: 1.424952
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.616046 Loss1: 1.121757 Loss2: 1.494289
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.596836 Loss1: 0.164551 Loss2: 1.432285
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.983760 Loss1: 0.546659 Loss2: 1.437102 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.742617 Loss1: 0.313467 Loss2: 1.429150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.716327 Loss1: 0.283858 Loss2: 1.432469
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.460105 Loss1: 1.561632 Loss2: 1.898472
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.671182 Loss1: 0.231829 Loss2: 1.439353
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.356593 Loss1: 0.940878 Loss2: 1.415715
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.635650 Loss1: 0.209830 Loss2: 1.425820
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.113437 Loss1: 0.646851 Loss2: 1.466585
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.613468 Loss1: 0.186340 Loss2: 1.427128
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.874193 Loss1: 0.459884 Loss2: 1.414308
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.786969 Loss1: 0.369016 Loss2: 1.417953
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.695363 Loss1: 0.285100 Loss2: 1.410262
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.646567 Loss1: 0.236671 Loss2: 1.409896
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.603337 Loss1: 0.199903 Loss2: 1.403433
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.547063 Loss1: 1.604816 Loss2: 1.942247
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.574224 Loss1: 0.178581 Loss2: 1.395643
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.416829 Loss1: 0.971525 Loss2: 1.445304
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.540962 Loss1: 0.143777 Loss2: 1.397184
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.880188 Loss1: 0.468948 Loss2: 1.411240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.702562 Loss1: 0.302436 Loss2: 1.400126 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.642370 Loss1: 0.231314 Loss2: 1.411056
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.339845 Loss1: 1.451160 Loss2: 1.888685
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.629937 Loss1: 0.224034 Loss2: 1.405903
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.386225 Loss1: 0.979464 Loss2: 1.406761
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.606046 Loss1: 0.198842 Loss2: 1.407204
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.065474 Loss1: 0.623239 Loss2: 1.442235
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.601060 Loss1: 0.199956 Loss2: 1.401105
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.840418 Loss1: 0.462255 Loss2: 1.378163
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687307 Loss1: 0.313336 Loss2: 1.373971
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.634572 Loss1: 0.271997 Loss2: 1.362575
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.641249 Loss1: 0.272816 Loss2: 1.368433
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.604510 Loss1: 0.232045 Loss2: 1.372465
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.560289 Loss1: 0.198353 Loss2: 1.361936
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.397966 Loss1: 1.493661 Loss2: 1.904304
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.583927 Loss1: 0.216903 Loss2: 1.367024
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.381209 Loss1: 0.960817 Loss2: 1.420392
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.067893 Loss1: 0.620139 Loss2: 1.447754
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.885717 Loss1: 0.484455 Loss2: 1.401261
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.763806 Loss1: 0.352083 Loss2: 1.411723
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.751160 Loss1: 0.355319 Loss2: 1.395841
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.739138 Loss1: 0.336271 Loss2: 1.402867
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.440134 Loss1: 1.543803 Loss2: 1.896332
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.672242 Loss1: 0.269488 Loss2: 1.402754
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.378266 Loss1: 0.941749 Loss2: 1.436517
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.693540 Loss1: 0.296169 Loss2: 1.397371
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.028241 Loss1: 0.591712 Loss2: 1.436529
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.608637 Loss1: 0.221220 Loss2: 1.387416
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.818391 Loss1: 0.409537 Loss2: 1.408854
-(DefaultActor pid=3765) >> Training accuracy: 0.936458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.707098 Loss1: 0.299170 Loss2: 1.407928
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.714000 Loss1: 0.321513 Loss2: 1.392488
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.698959 Loss1: 0.289141 Loss2: 1.409817
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.662776 Loss1: 0.264119 Loss2: 1.398656
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.728690 Loss1: 0.327679 Loss2: 1.401011
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.394686 Loss1: 1.526072 Loss2: 1.868614
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.659901 Loss1: 0.247133 Loss2: 1.412767
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.325805 Loss1: 0.917255 Loss2: 1.408550
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.114321 Loss1: 0.704976 Loss2: 1.409345
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.845521 Loss1: 0.462967 Loss2: 1.382554
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.772457 Loss1: 0.387003 Loss2: 1.385454
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.765146 Loss1: 0.384876 Loss2: 1.380270
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.312345 Loss1: 1.370098 Loss2: 1.942247
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.403560 Loss1: 0.935152 Loss2: 1.468408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.110723 Loss1: 0.620789 Loss2: 1.489934 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.953236 Loss1: 0.500931 Loss2: 1.452305 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942383
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.583814 Loss1: 0.206246 Loss2: 1.377569
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.831387 Loss1: 0.373925 Loss2: 1.457462
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.759579 Loss1: 0.311858 Loss2: 1.447721
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.720355 Loss1: 0.277824 Loss2: 1.442532
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.721188 Loss1: 0.289496 Loss2: 1.431692
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.689393 Loss1: 0.245071 Loss2: 1.444322
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.497289 Loss1: 1.593853 Loss2: 1.903436
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.686111 Loss1: 0.238908 Loss2: 1.447204
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.175799 Loss1: 0.701944 Loss2: 1.473855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.867857 Loss1: 0.419315 Loss2: 1.448542 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 02:54:36,703 | server.py:236 | fit_round 61 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.774876 Loss1: 0.338069 Loss2: 1.436807
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.372412 Loss1: 1.468446 Loss2: 1.903966
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.490773 Loss1: 1.066048 Loss2: 1.424725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.185743 Loss1: 0.707735 Loss2: 1.478008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.983286 Loss1: 0.553998 Loss2: 1.429288 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.806241 Loss1: 0.354781 Loss2: 1.451460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.788927 Loss1: 0.350049 Loss2: 1.438878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.668380 Loss1: 0.251528 Loss2: 1.416852 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.670542 Loss1: 0.254459 Loss2: 1.416084 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.034811 Loss1: 0.613421 Loss2: 1.421390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.774397 Loss1: 0.381188 Loss2: 1.393209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.683382 Loss1: 0.291208 Loss2: 1.392174
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.266981 Loss1: 1.371932 Loss2: 1.895050
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.248550 Loss1: 0.847433 Loss2: 1.401117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.092860 Loss1: 0.650734 Loss2: 1.442126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.895837 Loss1: 0.496565 Loss2: 1.399273 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.776203 Loss1: 0.370657 Loss2: 1.405547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.629232 Loss1: 0.243118 Loss2: 1.386114 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.660182 Loss1: 0.275569 Loss2: 1.384613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.632652 Loss1: 0.245640 Loss2: 1.387012 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.930208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.201674 Loss1: 0.729939 Loss2: 1.471735 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.777524 Loss1: 0.387182 Loss2: 1.390342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.655280 Loss1: 0.258672 Loss2: 1.396608 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.598268 Loss1: 0.208385 Loss2: 1.389883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.578972 Loss1: 0.195274 Loss2: 1.383698 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.573632 Loss1: 0.195056 Loss2: 1.378576 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972356
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.966176 Loss1: 0.427563 Loss2: 1.538613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.816486 Loss1: 0.284926 Loss2: 1.531560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.787858 Loss1: 0.261760 Loss2: 1.526098 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.945312
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 02:54:36,703][flwr][DEBUG] - fit_round 61 received 50 results and 0 failures
-INFO flwr 2023-10-10 02:55:17,873 | server.py:125 | fit progress: (61, 2.3367708978561548, {'accuracy': 0.51}, 140625.651772201)
->> Test accuracy: 0.510000
-[2023-10-10 02:55:17,873][flwr][INFO] - fit progress: (61, 2.3367708978561548, {'accuracy': 0.51}, 140625.651772201)
-DEBUG flwr 2023-10-10 02:55:17,874 | server.py:173 | evaluate_round 61: strategy sampled 50 clients (out of 50)
-[2023-10-10 02:55:17,874][flwr][DEBUG] - evaluate_round 61: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 03:04:22,586 | server.py:187 | evaluate_round 61 received 50 results and 0 failures
-[2023-10-10 03:04:22,586][flwr][DEBUG] - evaluate_round 61 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 03:04:22,586 | server.py:222 | fit_round 62: strategy sampled 50 clients (out of 50)
-[2023-10-10 03:04:22,586][flwr][DEBUG] - fit_round 62: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.755579 Loss1: 1.792551 Loss2: 1.963028 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.138409 Loss1: 0.659016 Loss2: 1.479394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.372878 Loss1: 1.449100 Loss2: 1.923778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.295813 Loss1: 0.839930 Loss2: 1.455883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.047529 Loss1: 0.587817 Loss2: 1.459712 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.835167 Loss1: 0.402103 Loss2: 1.433064 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.708856 Loss1: 0.287728 Loss2: 1.421127 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.659558 Loss1: 0.247913 Loss2: 1.411645 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.944196
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.615709 Loss1: 0.188147 Loss2: 1.427562 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.593723 Loss1: 0.184658 Loss2: 1.409065 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.381048 Loss1: 0.896994 Loss2: 1.484054 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.885662 Loss1: 0.441272 Loss2: 1.444390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.828096 Loss1: 0.369466 Loss2: 1.458629
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.347452 Loss1: 1.469538 Loss2: 1.877913
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.776704 Loss1: 0.317016 Loss2: 1.459688
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.517117 Loss1: 1.067969 Loss2: 1.449147
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.723699 Loss1: 0.272221 Loss2: 1.451479
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.126368 Loss1: 0.658730 Loss2: 1.467639
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.665644 Loss1: 0.225666 Loss2: 1.439978
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.994981 Loss1: 0.572711 Loss2: 1.422270
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.640952 Loss1: 0.206253 Loss2: 1.434699
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.848503 Loss1: 0.415202 Loss2: 1.433301
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.662758 Loss1: 0.221711 Loss2: 1.441047
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.809762 Loss1: 0.395243 Loss2: 1.414519
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.746893 Loss1: 0.327451 Loss2: 1.419441
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.677582 Loss1: 0.263944 Loss2: 1.413638
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.701367 Loss1: 0.284780 Loss2: 1.416587
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.670503 Loss1: 0.238716 Loss2: 1.431787
-(DefaultActor pid=3764) >> Training accuracy: 0.929167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.293093 Loss1: 1.328877 Loss2: 1.964216
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.365772 Loss1: 0.836631 Loss2: 1.529141
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.094224 Loss1: 0.572432 Loss2: 1.521792
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.961309 Loss1: 0.455488 Loss2: 1.505821
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.432060 Loss1: 1.475430 Loss2: 1.956630
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.420495 Loss1: 0.947280 Loss2: 1.473216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.154961 Loss1: 0.646816 Loss2: 1.508145 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.781168 Loss1: 0.283967 Loss2: 1.497201
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.003668 Loss1: 0.554862 Loss2: 1.448806
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.862252 Loss1: 0.396868 Loss2: 1.465385
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.688605 Loss1: 0.207026 Loss2: 1.481578
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.803125 Loss1: 0.349631 Loss2: 1.453494
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.704492 Loss1: 0.222067 Loss2: 1.482425
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.746319 Loss1: 0.300130 Loss2: 1.446189
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.754424 Loss1: 0.268719 Loss2: 1.485704
-(DefaultActor pid=3765) >> Training accuracy: 0.956801
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.657989 Loss1: 0.212381 Loss2: 1.445608 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.483839 Loss1: 1.532911 Loss2: 1.950928 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.171965 Loss1: 0.678802 Loss2: 1.493163 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.942112 Loss1: 0.487171 Loss2: 1.454941
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.526768 Loss1: 1.556389 Loss2: 1.970379
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.545262 Loss1: 1.015464 Loss2: 1.529798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.159408 Loss1: 0.620052 Loss2: 1.539356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.002079 Loss1: 0.497149 Loss2: 1.504929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.850356 Loss1: 0.353929 Loss2: 1.496426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.824102 Loss1: 0.333622 Loss2: 1.490480 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.646681 Loss1: 0.204788 Loss2: 1.441893
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.797541 Loss1: 0.293934 Loss2: 1.503607
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.789774 Loss1: 0.300608 Loss2: 1.489165
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.807876 Loss1: 0.317747 Loss2: 1.490130
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.704306 Loss1: 0.210127 Loss2: 1.494179
-(DefaultActor pid=3764) >> Training accuracy: 0.954167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.336641 Loss1: 1.483774 Loss2: 1.852867
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.322430 Loss1: 0.893346 Loss2: 1.429085
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.931809 Loss1: 0.519969 Loss2: 1.411840
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.804153 Loss1: 0.411554 Loss2: 1.392598
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.668209 Loss1: 1.590416 Loss2: 2.077793
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.768472 Loss1: 0.372239 Loss2: 1.396233
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.484278 Loss1: 0.953589 Loss2: 1.530689
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.788717 Loss1: 0.383641 Loss2: 1.405077
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.235810 Loss1: 0.684682 Loss2: 1.551128
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.689828 Loss1: 0.289762 Loss2: 1.400066
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.930852 Loss1: 0.444304 Loss2: 1.486548
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.804590 Loss1: 0.310852 Loss2: 1.493738
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.638431 Loss1: 0.252177 Loss2: 1.386254
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.775249 Loss1: 0.294931 Loss2: 1.480318
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.619525 Loss1: 0.232009 Loss2: 1.387515
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.784824 Loss1: 0.295369 Loss2: 1.489455
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.615672 Loss1: 0.226830 Loss2: 1.388843
-(DefaultActor pid=3765) >> Training accuracy: 0.915039
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.668658 Loss1: 0.194389 Loss2: 1.474269 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.446906 Loss1: 1.510289 Loss2: 1.936617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.228423 Loss1: 0.715969 Loss2: 1.512455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.386457 Loss1: 1.441212 Loss2: 1.945245
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.089117 Loss1: 0.590046 Loss2: 1.499071
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.513353 Loss1: 1.062887 Loss2: 1.450466
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.931644 Loss1: 0.428001 Loss2: 1.503643
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.185826 Loss1: 0.667924 Loss2: 1.517903
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.856258 Loss1: 0.365155 Loss2: 1.491103
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.728467 Loss1: 0.255162 Loss2: 1.473305 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.718772 Loss1: 0.240357 Loss2: 1.478415 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.735506 Loss1: 0.257945 Loss2: 1.477562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.726074 Loss1: 0.252416 Loss2: 1.473659 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942383
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.653698 Loss1: 0.216801 Loss2: 1.436897 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.939583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.408560 Loss1: 1.574896 Loss2: 1.833664 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.094010 Loss1: 0.699236 Loss2: 1.394774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.234470 Loss1: 1.352715 Loss2: 1.881755
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.899869 Loss1: 0.526946 Loss2: 1.372923
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.264843 Loss1: 0.865237 Loss2: 1.399606
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.840701 Loss1: 0.449062 Loss2: 1.391639
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.038381 Loss1: 0.613148 Loss2: 1.425232
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.691718 Loss1: 0.324341 Loss2: 1.367377
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.604117 Loss1: 0.240998 Loss2: 1.363120 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.602002 Loss1: 0.245683 Loss2: 1.356319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.546435 Loss1: 0.187230 Loss2: 1.359204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.555426 Loss1: 0.201717 Loss2: 1.353709 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.950195
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.646120 Loss1: 0.283549 Loss2: 1.362571 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.721484 Loss1: 1.594842 Loss2: 2.126642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.379542 Loss1: 0.761115 Loss2: 1.618427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.485373 Loss1: 1.551143 Loss2: 1.934230 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.611452 Loss1: 1.126216 Loss2: 1.485237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.210281 Loss1: 0.711108 Loss2: 1.499173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.017635 Loss1: 0.567728 Loss2: 1.449906 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.922393 Loss1: 0.451443 Loss2: 1.470949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.804478 Loss1: 0.361888 Loss2: 1.442590 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.728943 Loss1: 0.274470 Loss2: 1.454473 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.659475 Loss1: 0.226279 Loss2: 1.433197 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.516664 Loss1: 1.067884 Loss2: 1.448780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.934836 Loss1: 0.512987 Loss2: 1.421849 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.903772 Loss1: 0.466782 Loss2: 1.436990
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.318513 Loss1: 1.448579 Loss2: 1.869935
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.829867 Loss1: 0.394181 Loss2: 1.435686
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.290404 Loss1: 0.867236 Loss2: 1.423167
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.714581 Loss1: 0.279241 Loss2: 1.435339
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.985245 Loss1: 0.564014 Loss2: 1.421231
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.725008 Loss1: 0.295255 Loss2: 1.429753
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.794662 Loss1: 0.403617 Loss2: 1.391045
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.745939 Loss1: 0.353448 Loss2: 1.392490 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.934375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.620219 Loss1: 0.206604 Loss2: 1.413615
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.739258 Loss1: 0.359068 Loss2: 1.380190
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.722057 Loss1: 0.318833 Loss2: 1.403224
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.684401 Loss1: 0.297878 Loss2: 1.386523
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.635310 Loss1: 0.238808 Loss2: 1.396502
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.539420 Loss1: 0.158659 Loss2: 1.380761
-(DefaultActor pid=3764) >> Training accuracy: 0.944336
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.270889 Loss1: 1.472386 Loss2: 1.798502
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.370645 Loss1: 0.958369 Loss2: 1.412276
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.964660 Loss1: 0.562770 Loss2: 1.401890
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.843552 Loss1: 0.459465 Loss2: 1.384087
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.735972 Loss1: 0.350065 Loss2: 1.385907
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.387847 Loss1: 1.476995 Loss2: 1.910852
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.413175 Loss1: 0.964872 Loss2: 1.448302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.110548 Loss1: 0.639218 Loss2: 1.471330 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.577357 Loss1: 0.218080 Loss2: 1.359276
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.953888 Loss1: 0.528288 Loss2: 1.425599
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.594187 Loss1: 0.220620 Loss2: 1.373566
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.792962 Loss1: 0.357663 Loss2: 1.435299
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.589481 Loss1: 0.218500 Loss2: 1.370981
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.731358 Loss1: 0.312714 Loss2: 1.418644
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.683733 Loss1: 0.258619 Loss2: 1.425114
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.611410 Loss1: 0.197861 Loss2: 1.413548
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.615963 Loss1: 0.207525 Loss2: 1.408439
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.625247 Loss1: 0.218277 Loss2: 1.406970
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.349816 Loss1: 1.460627 Loss2: 1.889189
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.394907 Loss1: 0.977897 Loss2: 1.417010
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.095133 Loss1: 0.634656 Loss2: 1.460476
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.835087 Loss1: 0.432170 Loss2: 1.402917
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.400934 Loss1: 1.444315 Loss2: 1.956619
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.399662 Loss1: 0.934121 Loss2: 1.465541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.128327 Loss1: 0.629694 Loss2: 1.498633 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.938815 Loss1: 0.485351 Loss2: 1.453463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.796865 Loss1: 0.322723 Loss2: 1.474142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.728253 Loss1: 0.283552 Loss2: 1.444701 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.659200 Loss1: 0.222665 Loss2: 1.436535 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.582500 Loss1: 0.157732 Loss2: 1.424768 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.444614 Loss1: 0.978642 Loss2: 1.465973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.890536 Loss1: 0.470783 Loss2: 1.419753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.750573 Loss1: 0.347931 Loss2: 1.402642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.663219 Loss1: 0.278418 Loss2: 1.384800 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.608423 Loss1: 0.216533 Loss2: 1.391890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.790293 Loss1: 0.367631 Loss2: 1.422662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.720541 Loss1: 0.290744 Loss2: 1.429796 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.710547 Loss1: 0.302428 Loss2: 1.408119 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.693988 Loss1: 0.285261 Loss2: 1.408727 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.633367 Loss1: 0.217608 Loss2: 1.415759 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.954427
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.514232 Loss1: 1.646901 Loss2: 1.867331
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.503076 Loss1: 1.098733 Loss2: 1.404343
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.109735 Loss1: 0.671089 Loss2: 1.438646
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.888350 Loss1: 0.502976 Loss2: 1.385374
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.366564 Loss1: 1.492614 Loss2: 1.873950
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.319056 Loss1: 0.879518 Loss2: 1.439539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.052357 Loss1: 0.620724 Loss2: 1.431633 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.888126 Loss1: 0.453508 Loss2: 1.434618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.823447 Loss1: 0.396847 Loss2: 1.426600 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.799370 Loss1: 0.373327 Loss2: 1.426044 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.726846 Loss1: 0.301760 Loss2: 1.425086 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.723952 Loss1: 0.297977 Loss2: 1.425975 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973633
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.273504 Loss1: 1.408756 Loss2: 1.864747 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.047577 Loss1: 0.616661 Loss2: 1.430916 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.413106 Loss1: 1.429808 Loss2: 1.983297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.555924 Loss1: 1.086109 Loss2: 1.469814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.197023 Loss1: 0.673200 Loss2: 1.523823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.921850 Loss1: 0.468018 Loss2: 1.453832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.913674 Loss1: 0.442880 Loss2: 1.470794 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.812024 Loss1: 0.345489 Loss2: 1.466535 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.936458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.705617 Loss1: 0.253008 Loss2: 1.452609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.606205 Loss1: 0.163181 Loss2: 1.443025 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970982
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.632022 Loss1: 1.596730 Loss2: 2.035292 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.012540 Loss1: 0.552878 Loss2: 1.459662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.343701 Loss1: 1.478488 Loss2: 1.865214 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.686502 Loss1: 0.263786 Loss2: 1.422716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.682948 Loss1: 0.265774 Loss2: 1.417174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.671618 Loss1: 0.246471 Loss2: 1.425147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.633092 Loss1: 0.211256 Loss2: 1.421836 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.596083 Loss1: 0.173197 Loss2: 1.422886 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965144
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.663607 Loss1: 0.245536 Loss2: 1.418071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.607950 Loss1: 0.200513 Loss2: 1.407436 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.665606 Loss1: 1.655535 Loss2: 2.010071
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.669027 Loss1: 0.255763 Loss2: 1.413264
-(DefaultActor pid=3764) >> Training accuracy: 0.917969
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.275179 Loss1: 0.705665 Loss2: 1.569515 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 2.060646 Loss1: 0.522344 Loss2: 1.538302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.967406 Loss1: 0.449798 Loss2: 1.517608
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.410270 Loss1: 1.493125 Loss2: 1.917145
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.461389 Loss1: 0.972519 Loss2: 1.488870 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.140218 Loss1: 0.665129 Loss2: 1.475089 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.835970 Loss1: 0.394419 Loss2: 1.441550 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.920833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.777282 Loss1: 0.268930 Loss2: 1.508352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.791169 Loss1: 0.346133 Loss2: 1.445036
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.756921 Loss1: 0.316926 Loss2: 1.439995
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.697395 Loss1: 0.256170 Loss2: 1.441225
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.678575 Loss1: 0.243187 Loss2: 1.435388
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.654954 Loss1: 0.220051 Loss2: 1.434904
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.394473 Loss1: 1.491965 Loss2: 1.902508
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.572224 Loss1: 0.146100 Loss2: 1.426124
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.129640 Loss1: 0.667078 Loss2: 1.462562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.809906 Loss1: 0.379306 Loss2: 1.430600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.757720 Loss1: 0.339563 Loss2: 1.418157
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.435921 Loss1: 1.459715 Loss2: 1.976206
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.720361 Loss1: 0.298982 Loss2: 1.421379
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.384525 Loss1: 0.890856 Loss2: 1.493669
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.712554 Loss1: 0.308519 Loss2: 1.404035
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.148604 Loss1: 0.634169 Loss2: 1.514434
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.642858 Loss1: 0.242909 Loss2: 1.399949
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.954776 Loss1: 0.483095 Loss2: 1.471682
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.617741 Loss1: 0.208613 Loss2: 1.409128
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.872936 Loss1: 0.369892 Loss2: 1.503044
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.751920 Loss1: 0.277847 Loss2: 1.474073
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.657246 Loss1: 0.201191 Loss2: 1.456055
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.682618 Loss1: 0.227931 Loss2: 1.454687
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.652742 Loss1: 0.186095 Loss2: 1.466647
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.639356 Loss1: 0.177890 Loss2: 1.461466
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.399986 Loss1: 1.532226 Loss2: 1.867761
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.452472 Loss1: 0.985793 Loss2: 1.466678
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.076649 Loss1: 0.606015 Loss2: 1.470634
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.933140 Loss1: 0.502694 Loss2: 1.430446
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.803640 Loss1: 0.358882 Loss2: 1.444758
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.298224 Loss1: 1.321741 Loss2: 1.976483
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.379347 Loss1: 0.882354 Loss2: 1.496993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.144105 Loss1: 0.605074 Loss2: 1.539030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.919333 Loss1: 0.450735 Loss2: 1.468598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.821423 Loss1: 0.343798 Loss2: 1.477625 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.673347 Loss1: 0.256651 Loss2: 1.416696
-(DefaultActor pid=3765) >> Training accuracy: 0.916016
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.806122 Loss1: 0.332851 Loss2: 1.473271
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.747314 Loss1: 0.271590 Loss2: 1.475724
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.720083 Loss1: 0.248396 Loss2: 1.471688
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.647638 Loss1: 0.181730 Loss2: 1.465908
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.612757 Loss1: 0.151391 Loss2: 1.461366
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.582286 Loss1: 1.598241 Loss2: 1.984044
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.563127 Loss1: 1.026119 Loss2: 1.537008
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.163281 Loss1: 0.634561 Loss2: 1.528720
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.973206 Loss1: 0.464180 Loss2: 1.509026
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.910194 Loss1: 0.403435 Loss2: 1.506759
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.248722 Loss1: 1.338388 Loss2: 1.910335
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.828538 Loss1: 0.327472 Loss2: 1.501066
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.261307 Loss1: 0.824225 Loss2: 1.437082
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.825573 Loss1: 0.317320 Loss2: 1.508253
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.930808 Loss1: 0.485878 Loss2: 1.444930
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.815854 Loss1: 0.307583 Loss2: 1.508271
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.905136 Loss1: 0.500476 Loss2: 1.404659
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.759591 Loss1: 0.250389 Loss2: 1.509202
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.769563 Loss1: 0.349419 Loss2: 1.420144
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.719022 Loss1: 0.215509 Loss2: 1.503512
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.644565 Loss1: 0.236125 Loss2: 1.408440
-(DefaultActor pid=3765) >> Training accuracy: 0.940625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.576235 Loss1: 0.180629 Loss2: 1.395607
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.578865 Loss1: 0.185342 Loss2: 1.393523
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.542840 Loss1: 0.146029 Loss2: 1.396812
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.555043 Loss1: 0.167842 Loss2: 1.387201
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.498754 Loss1: 1.603589 Loss2: 1.895164
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.501058 Loss1: 1.048486 Loss2: 1.452572
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.065858 Loss1: 0.625512 Loss2: 1.440346
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.887638 Loss1: 0.474409 Loss2: 1.413228
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.771578 Loss1: 0.363513 Loss2: 1.408065
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.736847 Loss1: 0.336859 Loss2: 1.399988 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.672627 Loss1: 0.268391 Loss2: 1.404236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.597251 Loss1: 0.204140 Loss2: 1.393111 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.553982 Loss1: 0.166047 Loss2: 1.387935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.544706 Loss1: 0.154296 Loss2: 1.390410 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.642111 Loss1: 0.242722 Loss2: 1.399389 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.571701 Loss1: 0.165139 Loss2: 1.406562 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.410975 Loss1: 0.977967 Loss2: 1.433008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.848908 Loss1: 0.439993 Loss2: 1.408915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.801016 Loss1: 0.381016 Loss2: 1.420001
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.572068 Loss1: 1.549803 Loss2: 2.022265
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.699017 Loss1: 0.284322 Loss2: 1.414694
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.547561 Loss1: 1.002070 Loss2: 1.545491
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.685318 Loss1: 0.285971 Loss2: 1.399348
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.126189 Loss1: 0.578676 Loss2: 1.547513
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.673598 Loss1: 0.262110 Loss2: 1.411488
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.993821 Loss1: 0.482218 Loss2: 1.511603
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.671462 Loss1: 0.261458 Loss2: 1.410004
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.914071 Loss1: 0.382598 Loss2: 1.531472
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.638037 Loss1: 0.225393 Loss2: 1.412643
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.810796 Loss1: 0.309171 Loss2: 1.501625
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.774763 Loss1: 0.269653 Loss2: 1.505110
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.748450 Loss1: 0.244755 Loss2: 1.503694
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.714057 Loss1: 0.218682 Loss2: 1.495375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.690790 Loss1: 0.197330 Loss2: 1.493460
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.567012 Loss1: 1.539850 Loss2: 2.027162
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.451572 Loss1: 0.975278 Loss2: 1.476294
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.165708 Loss1: 0.659711 Loss2: 1.505997
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.933160 Loss1: 0.472242 Loss2: 1.460918
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.801627 Loss1: 0.348948 Loss2: 1.452679
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.740868 Loss1: 0.272276 Loss2: 1.468591
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.326505 Loss1: 1.447043 Loss2: 1.879462
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.518717 Loss1: 1.065236 Loss2: 1.453481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.135991 Loss1: 0.634988 Loss2: 1.501003 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.888396 Loss1: 0.499256 Loss2: 1.389140 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.906250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.739540 Loss1: 0.349762 Loss2: 1.389778 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.564830 Loss1: 0.173603 Loss2: 1.391227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.687333 Loss1: 0.297270 Loss2: 1.390063
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.214873 Loss1: 1.361943 Loss2: 1.852930
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.325965 Loss1: 0.903817 Loss2: 1.422148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.782624 Loss1: 0.396215 Loss2: 1.386409 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.635284 Loss1: 0.255720 Loss2: 1.379564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.586094 Loss1: 0.208122 Loss2: 1.377972 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.572532 Loss1: 0.191361 Loss2: 1.381171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.522450 Loss1: 0.148624 Loss2: 1.373827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.514379 Loss1: 0.144328 Loss2: 1.370051 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971680
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-10 03:33:10,833 | server.py:236 | fit_round 62 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.680147 Loss1: 0.308588 Loss2: 1.371559 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.619343 Loss1: 0.255269 Loss2: 1.364074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.553843 Loss1: 1.709867 Loss2: 1.843976
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.523898 Loss1: 0.168081 Loss2: 1.355818
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.111575 Loss1: 0.691433 Loss2: 1.420142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.735911 Loss1: 0.352157 Loss2: 1.383754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.686566 Loss1: 0.304979 Loss2: 1.381587
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.384313 Loss1: 1.534637 Loss2: 1.849677
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.644499 Loss1: 0.269721 Loss2: 1.374778
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.383549 Loss1: 0.982794 Loss2: 1.400754
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.614022 Loss1: 0.233110 Loss2: 1.380912
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.090524 Loss1: 0.673024 Loss2: 1.417500
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.557063 Loss1: 0.193130 Loss2: 1.363933
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.874136 Loss1: 0.487132 Loss2: 1.387003
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.535213 Loss1: 0.163008 Loss2: 1.372205
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.698520 Loss1: 0.302085 Loss2: 1.396435
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.660148 Loss1: 0.297698 Loss2: 1.362450
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.588561 Loss1: 0.212496 Loss2: 1.376064
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.565890 Loss1: 0.191437 Loss2: 1.374453
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.546579 Loss1: 0.182335 Loss2: 1.364244
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.545632 Loss1: 0.179930 Loss2: 1.365702
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.480565 Loss1: 1.583876 Loss2: 1.896689
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.450578 Loss1: 1.012685 Loss2: 1.437893
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.081623 Loss1: 0.619812 Loss2: 1.461811
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.975402 Loss1: 0.531933 Loss2: 1.443469
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.875868 Loss1: 0.445007 Loss2: 1.430861
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.320897 Loss1: 1.366942 Loss2: 1.953955
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.708328 Loss1: 0.287687 Loss2: 1.420641
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.356891 Loss1: 0.911423 Loss2: 1.445468
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.678929 Loss1: 0.261201 Loss2: 1.417728
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.060101 Loss1: 0.564943 Loss2: 1.495158
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.725830 Loss1: 0.307411 Loss2: 1.418419
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.858926 Loss1: 0.444887 Loss2: 1.414039
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.668885 Loss1: 0.250026 Loss2: 1.418858
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.812313 Loss1: 0.375357 Loss2: 1.436956
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.640543 Loss1: 0.225630 Loss2: 1.414913
-(DefaultActor pid=3765) >> Training accuracy: 0.943750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.684674 Loss1: 0.271046 Loss2: 1.413628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.606245 Loss1: 0.196261 Loss2: 1.409984 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 03:33:10,833][flwr][DEBUG] - fit_round 62 received 50 results and 0 failures
-INFO flwr 2023-10-10 03:33:52,321 | server.py:125 | fit progress: (62, 2.3237575894346634, {'accuracy': 0.5111}, 142940.099831166)
->> Test accuracy: 0.511100
-[2023-10-10 03:33:52,321][flwr][INFO] - fit progress: (62, 2.3237575894346634, {'accuracy': 0.5111}, 142940.099831166)
-DEBUG flwr 2023-10-10 03:33:52,322 | server.py:173 | evaluate_round 62: strategy sampled 50 clients (out of 50)
-[2023-10-10 03:33:52,322][flwr][DEBUG] - evaluate_round 62: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 03:43:04,043 | server.py:187 | evaluate_round 62 received 50 results and 0 failures
-[2023-10-10 03:43:04,043][flwr][DEBUG] - evaluate_round 62 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 03:43:04,044 | server.py:222 | fit_round 63: strategy sampled 50 clients (out of 50)
-[2023-10-10 03:43:04,044][flwr][DEBUG] - fit_round 63: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.262390 Loss1: 1.421120 Loss2: 1.841269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.030064 Loss1: 0.621101 Loss2: 1.408963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.789543 Loss1: 0.448905 Loss2: 1.340637
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.415964 Loss1: 1.486789 Loss2: 1.929176
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.310617 Loss1: 0.912890 Loss2: 1.397727 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.592568 Loss1: 0.257868 Loss2: 1.334699
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.008680 Loss1: 0.590738 Loss2: 1.417941
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.598217 Loss1: 0.253942 Loss2: 1.344276
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.800355 Loss1: 0.414611 Loss2: 1.385745
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.668254 Loss1: 0.286516 Loss2: 1.381739
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.598043 Loss1: 0.258066 Loss2: 1.339977
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.684203 Loss1: 0.294758 Loss2: 1.389445
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.574291 Loss1: 0.227072 Loss2: 1.347219
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.629211 Loss1: 0.242880 Loss2: 1.386331
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.581612 Loss1: 0.229483 Loss2: 1.352129
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.586474 Loss1: 0.215532 Loss2: 1.370943 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957589
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.381895 Loss1: 1.493026 Loss2: 1.888869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.092988 Loss1: 0.628195 Loss2: 1.464793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.898138 Loss1: 0.485444 Loss2: 1.412694
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.369833 Loss1: 1.399693 Loss2: 1.970140
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.354149 Loss1: 0.884094 Loss2: 1.470055 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.099642 Loss1: 0.627564 Loss2: 1.472078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.932866 Loss1: 0.494476 Loss2: 1.438390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.782341 Loss1: 0.338023 Loss2: 1.444317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.697887 Loss1: 0.283380 Loss2: 1.414507 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.607077 Loss1: 0.206182 Loss2: 1.400895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.559450 Loss1: 0.161894 Loss2: 1.397556 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.499651 Loss1: 1.525497 Loss2: 1.974154 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.149988 Loss1: 0.648605 Loss2: 1.501382 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.857853 Loss1: 0.399283 Loss2: 1.458570 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.729039 Loss1: 0.289625 Loss2: 1.439414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.742072 Loss1: 0.315684 Loss2: 1.426388 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.659531 Loss1: 0.208099 Loss2: 1.451432 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.598111 Loss1: 0.176397 Loss2: 1.421715 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.562848 Loss1: 0.145101 Loss2: 1.417748 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956731
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.713675 Loss1: 0.261563 Loss2: 1.452112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.589390 Loss1: 0.155687 Loss2: 1.433703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.591882 Loss1: 0.164290 Loss2: 1.427593
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.504554 Loss1: 1.500609 Loss2: 2.003944
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.478784 Loss1: 1.047480 Loss2: 1.431304
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.032612 Loss1: 0.548274 Loss2: 1.484338
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.887519 Loss1: 0.490710 Loss2: 1.396809
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.815161 Loss1: 0.396596 Loss2: 1.418564
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.675777 Loss1: 0.266792 Loss2: 1.408985
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.461833 Loss1: 1.637771 Loss2: 1.824062
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.464524 Loss1: 1.073667 Loss2: 1.390857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.065105 Loss1: 0.665237 Loss2: 1.399869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.532835 Loss1: 0.153104 Loss2: 1.379731 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975962
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.724507 Loss1: 0.347929 Loss2: 1.376578 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.549629 Loss1: 0.196969 Loss2: 1.352660 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.542926 Loss1: 0.188269 Loss2: 1.354656
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.333282 Loss1: 1.476303 Loss2: 1.856979
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.545140 Loss1: 0.189285 Loss2: 1.355855
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.552630 Loss1: 1.094904 Loss2: 1.457726
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.025367 Loss1: 0.578404 Loss2: 1.446963
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.920415 Loss1: 0.504968 Loss2: 1.415447
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.865769 Loss1: 0.433931 Loss2: 1.431838
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.757854 Loss1: 0.333579 Loss2: 1.424276
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.442234 Loss1: 1.536741 Loss2: 1.905494
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.415182 Loss1: 0.952582 Loss2: 1.462601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.045428 Loss1: 0.556180 Loss2: 1.489248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.937169 Loss1: 0.500720 Loss2: 1.436449 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955078
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.616804 Loss1: 0.216603 Loss2: 1.400201
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.842951 Loss1: 0.382470 Loss2: 1.460481
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.822685 Loss1: 0.363380 Loss2: 1.459304
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.757026 Loss1: 0.310911 Loss2: 1.446115
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.677032 Loss1: 0.237517 Loss2: 1.439515
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.712894 Loss1: 0.266356 Loss2: 1.446538
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.369913 Loss1: 1.417160 Loss2: 1.952753
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.293401 Loss1: 0.823650 Loss2: 1.469751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.969158 Loss1: 0.502702 Loss2: 1.466457 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.720408 Loss1: 0.264158 Loss2: 1.456250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.666324 Loss1: 0.215034 Loss2: 1.451290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.612193 Loss1: 0.171296 Loss2: 1.440897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.589480 Loss1: 0.151811 Loss2: 1.437669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.557773 Loss1: 0.128210 Loss2: 1.429563 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.802299 Loss1: 0.340417 Loss2: 1.461882 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.729317 Loss1: 0.269743 Loss2: 1.459574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.433826 Loss1: 1.549314 Loss2: 1.884512 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.043156 Loss1: 0.577511 Loss2: 1.465645 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.884220 Loss1: 0.426456 Loss2: 1.457763 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.808579 Loss1: 0.364326 Loss2: 1.444253
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.393600 Loss1: 1.536421 Loss2: 1.857179
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.813555 Loss1: 0.364759 Loss2: 1.448796
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.448244 Loss1: 1.012734 Loss2: 1.435511
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.180052 Loss1: 0.749868 Loss2: 1.430183
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.692663 Loss1: 0.250368 Loss2: 1.442296
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.969381 Loss1: 0.557949 Loss2: 1.411432
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.629808 Loss1: 0.199293 Loss2: 1.430515
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.837266 Loss1: 0.429547 Loss2: 1.407719
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.593816 Loss1: 0.166019 Loss2: 1.427798
-(DefaultActor pid=3765) >> Training accuracy: 0.961914
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.755956 Loss1: 0.362835 Loss2: 1.393121 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.667016 Loss1: 0.272868 Loss2: 1.394148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.605656 Loss1: 0.220090 Loss2: 1.385566
-(DefaultActor pid=3764) >> Training accuracy: 0.932292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.585346 Loss1: 1.646689 Loss2: 1.938657
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.494072 Loss1: 1.011858 Loss2: 1.482214
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.160522 Loss1: 0.682672 Loss2: 1.477850
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.925777 Loss1: 0.471019 Loss2: 1.454758
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.795016 Loss1: 0.343234 Loss2: 1.451782
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.342250 Loss1: 1.500798 Loss2: 1.841452
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.763968 Loss1: 0.328242 Loss2: 1.435725
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.230644 Loss1: 0.812964 Loss2: 1.417680
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.745260 Loss1: 0.299911 Loss2: 1.445349
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.931085 Loss1: 0.518497 Loss2: 1.412588
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.770299 Loss1: 0.324182 Loss2: 1.446117
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.834281 Loss1: 0.468030 Loss2: 1.366251
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.700421 Loss1: 0.253213 Loss2: 1.447208
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.698303 Loss1: 0.311726 Loss2: 1.386577
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.726553 Loss1: 0.283678 Loss2: 1.442875
-(DefaultActor pid=3765) >> Training accuracy: 0.940625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.657200 Loss1: 0.262278 Loss2: 1.394922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.554603 Loss1: 0.195151 Loss2: 1.359452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.562268 Loss1: 0.204037 Loss2: 1.358231
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.515440 Loss1: 1.539347 Loss2: 1.976093
-(DefaultActor pid=3764) >> Training accuracy: 0.926042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.447609 Loss1: 0.963439 Loss2: 1.484170
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.186543 Loss1: 0.648615 Loss2: 1.537928
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.938998 Loss1: 0.457396 Loss2: 1.481602
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.840536 Loss1: 0.354660 Loss2: 1.485876
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.352199 Loss1: 1.473591 Loss2: 1.878608
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.800608 Loss1: 0.320641 Loss2: 1.479967
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.375815 Loss1: 0.936265 Loss2: 1.439550
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.847792 Loss1: 0.367728 Loss2: 1.480065
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.091083 Loss1: 0.667031 Loss2: 1.424052
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.797737 Loss1: 0.299696 Loss2: 1.498042
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.898343 Loss1: 0.477890 Loss2: 1.420453
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.725472 Loss1: 0.256668 Loss2: 1.468804
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.747376 Loss1: 0.359733 Loss2: 1.387642
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.692346 Loss1: 0.217002 Loss2: 1.475344
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.597423 Loss1: 0.216326 Loss2: 1.381097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.582906 Loss1: 0.202665 Loss2: 1.380242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.543248 Loss1: 0.167557 Loss2: 1.375691
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.528651 Loss1: 1.494105 Loss2: 2.034546
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.491174 Loss1: 0.986018 Loss2: 1.505155
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.185392 Loss1: 0.633161 Loss2: 1.552231
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.050086 Loss1: 0.541863 Loss2: 1.508223
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.896209 Loss1: 0.366847 Loss2: 1.529362
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.795436 Loss1: 0.298852 Loss2: 1.496584
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.301688 Loss1: 1.367239 Loss2: 1.934449
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.749852 Loss1: 0.240479 Loss2: 1.509373
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.451752 Loss1: 0.986514 Loss2: 1.465238
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.672212 Loss1: 0.185829 Loss2: 1.486383
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.171743 Loss1: 0.679765 Loss2: 1.491977
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.655482 Loss1: 0.166281 Loss2: 1.489201
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.927946 Loss1: 0.487002 Loss2: 1.440943
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.703353 Loss1: 0.217132 Loss2: 1.486221
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.827596 Loss1: 0.375920 Loss2: 1.451676
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.707299 Loss1: 0.277464 Loss2: 1.429836
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.683378 Loss1: 0.257549 Loss2: 1.425829
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.702610 Loss1: 0.265546 Loss2: 1.437064
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.670588 Loss1: 0.242562 Loss2: 1.428026
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.425114 Loss1: 1.506411 Loss2: 1.918703
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.622135 Loss1: 0.200871 Loss2: 1.421264
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.101636 Loss1: 0.629552 Loss2: 1.472084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.871947 Loss1: 0.418809 Loss2: 1.453138 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.270385 Loss1: 1.451394 Loss2: 1.818992
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.791596 Loss1: 0.333209 Loss2: 1.458387
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.329029 Loss1: 0.954862 Loss2: 1.374167
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.731012 Loss1: 0.280858 Loss2: 1.450155
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.689357 Loss1: 0.243844 Loss2: 1.445513 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.640200 Loss1: 0.196192 Loss2: 1.444008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.593749 Loss1: 0.161141 Loss2: 1.432609 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.586496 Loss1: 0.232730 Loss2: 1.353766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.562903 Loss1: 0.213839 Loss2: 1.349065 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.514556 Loss1: 0.160307 Loss2: 1.354249
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.528049 Loss1: 1.503417 Loss2: 2.024632
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.407045 Loss1: 0.992171 Loss2: 1.414873
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.196948 Loss1: 0.719135 Loss2: 1.477813
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.931187 Loss1: 0.500831 Loss2: 1.430356
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.806365 Loss1: 0.392243 Loss2: 1.414121
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.742989 Loss1: 0.315929 Loss2: 1.427060
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.441861 Loss1: 1.506601 Loss2: 1.935260
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.672490 Loss1: 0.263677 Loss2: 1.408813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.686595 Loss1: 0.274522 Loss2: 1.412072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.700588 Loss1: 0.276234 Loss2: 1.424354 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.919271
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.963620 Loss1: 0.470588 Loss2: 1.493032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.791378 Loss1: 0.311843 Loss2: 1.479535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.690492 Loss1: 0.224039 Loss2: 1.466454
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.596831 Loss1: 1.673374 Loss2: 1.923457
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.470642 Loss1: 1.024179 Loss2: 1.446463 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.925781
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.690124 Loss1: 0.230398 Loss2: 1.459726
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.091924 Loss1: 0.647319 Loss2: 1.444604
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.949045 Loss1: 0.535800 Loss2: 1.413246
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.890313 Loss1: 0.453690 Loss2: 1.436623
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.790882 Loss1: 0.376652 Loss2: 1.414230
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.736360 Loss1: 0.307579 Loss2: 1.428781
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.368783 Loss1: 1.458094 Loss2: 1.910689
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.695636 Loss1: 0.291379 Loss2: 1.404257
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.312047 Loss1: 0.872814 Loss2: 1.439233
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.653113 Loss1: 0.237935 Loss2: 1.415178
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.158105 Loss1: 0.691023 Loss2: 1.467082
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.595735 Loss1: 0.180312 Loss2: 1.415423
-(DefaultActor pid=3765) >> Training accuracy: 0.960417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.859860 Loss1: 0.429005 Loss2: 1.430856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.627577 Loss1: 0.228860 Loss2: 1.398716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.643555 Loss1: 0.246174 Loss2: 1.397381
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.307069 Loss1: 1.372184 Loss2: 1.934884
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.363048 Loss1: 0.917478 Loss2: 1.445570 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.935417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.610993 Loss1: 0.212436 Loss2: 1.398556
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.100733 Loss1: 0.639366 Loss2: 1.461368
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.888075 Loss1: 0.470895 Loss2: 1.417180
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.767281 Loss1: 0.335344 Loss2: 1.431937
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.693208 Loss1: 0.286901 Loss2: 1.406307
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.647017 Loss1: 0.237010 Loss2: 1.410008
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.251163 Loss1: 1.401234 Loss2: 1.849929
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.662987 Loss1: 0.261217 Loss2: 1.401770
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.585949 Loss1: 0.185196 Loss2: 1.400753
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.342166 Loss1: 0.904554 Loss2: 1.437612
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.561748 Loss1: 0.168237 Loss2: 1.393512
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.003851 Loss1: 0.585044 Loss2: 1.418807
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.809605 Loss1: 0.418327 Loss2: 1.391278
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.696358 Loss1: 0.305379 Loss2: 1.390979
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.658109 Loss1: 0.275126 Loss2: 1.382983
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.612456 Loss1: 0.235434 Loss2: 1.377022
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.290003 Loss1: 1.407502 Loss2: 1.882501
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.249278 Loss1: 0.854871 Loss2: 1.394407 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.971555 Loss1: 0.568658 Loss2: 1.402898 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.935547
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.623256 Loss1: 0.237541 Loss2: 1.385715
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.865871 Loss1: 0.479252 Loss2: 1.386619
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.787132 Loss1: 0.395415 Loss2: 1.391716
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.720009 Loss1: 0.326524 Loss2: 1.393484
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.634158 Loss1: 0.262391 Loss2: 1.371768
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.585835 Loss1: 0.211193 Loss2: 1.374642
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.418299 Loss1: 1.503619 Loss2: 1.914680
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.570147 Loss1: 0.197166 Loss2: 1.372981
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.362206 Loss1: 0.902607 Loss2: 1.459599
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.605208 Loss1: 0.220049 Loss2: 1.385160
-(DefaultActor pid=3765) >> Training accuracy: 0.926042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.932856 Loss1: 0.491644 Loss2: 1.441212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.785922 Loss1: 0.340521 Loss2: 1.445402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.689874 Loss1: 0.250031 Loss2: 1.439842
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.255428 Loss1: 1.377272 Loss2: 1.878156
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.362827 Loss1: 0.881110 Loss2: 1.481717 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.967037 Loss1: 0.507296 Loss2: 1.459741 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.601101 Loss1: 0.165811 Loss2: 1.435290
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.791740 Loss1: 0.362552 Loss2: 1.429188
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.750623 Loss1: 0.317114 Loss2: 1.433509
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.777945 Loss1: 0.333791 Loss2: 1.444154
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.647065 Loss1: 0.217873 Loss2: 1.429191
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.629682 Loss1: 0.211629 Loss2: 1.418053
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.496790 Loss1: 1.568477 Loss2: 1.928313
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.574957 Loss1: 1.085999 Loss2: 1.488958 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972656
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.191050 Loss1: 0.705270 Loss2: 1.485781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.863950 Loss1: 0.417733 Loss2: 1.446217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.616801 Loss1: 0.195841 Loss2: 1.420959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.670739 Loss1: 0.252877 Loss2: 1.417862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.689285 Loss1: 0.266710 Loss2: 1.422575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.651372 Loss1: 0.216565 Loss2: 1.434808 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.939583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.769289 Loss1: 0.382513 Loss2: 1.386776 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.603352 Loss1: 0.237886 Loss2: 1.365466 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.558071 Loss1: 0.188697 Loss2: 1.369374
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.499887 Loss1: 1.609763 Loss2: 1.890124
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.342937 Loss1: 0.926492 Loss2: 1.416444 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.048693 Loss1: 0.600383 Loss2: 1.448309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.749768 Loss1: 0.351215 Loss2: 1.398553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.736512 Loss1: 0.329754 Loss2: 1.406758 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.469714 Loss1: 1.532468 Loss2: 1.937245
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.739863 Loss1: 0.329951 Loss2: 1.409912
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.484008 Loss1: 1.008704 Loss2: 1.475303
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.643239 Loss1: 0.239542 Loss2: 1.403697
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.128354 Loss1: 0.655430 Loss2: 1.472924
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.640946 Loss1: 0.243068 Loss2: 1.397878
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.907806 Loss1: 0.451435 Loss2: 1.456371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.680384 Loss1: 0.247189 Loss2: 1.433195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.635104 Loss1: 0.213586 Loss2: 1.421518
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.380591 Loss1: 1.493539 Loss2: 1.887052
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.407677 Loss1: 0.954916 Loss2: 1.452761 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.938542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.667652 Loss1: 0.231708 Loss2: 1.435944
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.967803 Loss1: 0.522171 Loss2: 1.445632
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.866172 Loss1: 0.450948 Loss2: 1.415224
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.860710 Loss1: 0.434839 Loss2: 1.425871
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.766592 Loss1: 0.331583 Loss2: 1.435009
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.725740 Loss1: 0.302894 Loss2: 1.422845
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.294635 Loss1: 1.388367 Loss2: 1.906268
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.730933 Loss1: 0.314005 Loss2: 1.416928
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.301804 Loss1: 0.843016 Loss2: 1.458789
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.671874 Loss1: 0.252985 Loss2: 1.418889
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.044304 Loss1: 0.593414 Loss2: 1.450889
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.554368 Loss1: 0.149800 Loss2: 1.404569
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.822282 Loss1: 0.396599 Loss2: 1.425684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.643368 Loss1: 0.225740 Loss2: 1.417628 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.616502 Loss1: 0.202539 Loss2: 1.413963
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.431506 Loss1: 1.525375 Loss2: 1.906132
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.564737 Loss1: 1.095657 Loss2: 1.469080 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.954167
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.555188 Loss1: 0.150076 Loss2: 1.405112
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.202055 Loss1: 0.727143 Loss2: 1.474912
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.976845 Loss1: 0.522276 Loss2: 1.454570
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.875389 Loss1: 0.421252 Loss2: 1.454137
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.839863 Loss1: 0.391341 Loss2: 1.448522
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.731909 Loss1: 0.285841 Loss2: 1.446068
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.155624 Loss1: 1.259207 Loss2: 1.896417
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.772936 Loss1: 0.324127 Loss2: 1.448809
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.662786 Loss1: 0.229250 Loss2: 1.433536 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.659786 Loss1: 0.225898 Loss2: 1.433889 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.927083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.752378 Loss1: 0.348239 Loss2: 1.404140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.722577 Loss1: 0.307364 Loss2: 1.415212 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.705868 Loss1: 0.312056 Loss2: 1.393812
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.475789 Loss1: 1.551390 Loss2: 1.924399
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.609299 Loss1: 1.141763 Loss2: 1.467535 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.544852 Loss1: 0.157756 Loss2: 1.387096
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.201372 Loss1: 0.713979 Loss2: 1.487394
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.908732 Loss1: 0.465082 Loss2: 1.443650
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.840923 Loss1: 0.380579 Loss2: 1.460344
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.835873 Loss1: 0.393001 Loss2: 1.442871
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.704257 Loss1: 0.262377 Loss2: 1.441881
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.416832 Loss1: 1.416567 Loss2: 2.000265
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.670992 Loss1: 0.228161 Loss2: 1.442831
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.444102 Loss1: 0.935602 Loss2: 1.508501
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.646889 Loss1: 0.216570 Loss2: 1.430320
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.132859 Loss1: 0.595775 Loss2: 1.537084
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.675216 Loss1: 0.232432 Loss2: 1.442784
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.913936 Loss1: 0.405485 Loss2: 1.508450 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.765441 Loss1: 0.288301 Loss2: 1.477140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.745020 Loss1: 0.255089 Loss2: 1.489931
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.373431 Loss1: 1.482180 Loss2: 1.891251
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.477819 Loss1: 1.004921 Loss2: 1.472898 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.632517 Loss1: 0.173876 Loss2: 1.458641
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.114867 Loss1: 0.635906 Loss2: 1.478960
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.896608 Loss1: 0.486734 Loss2: 1.409874
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.765806 Loss1: 0.342325 Loss2: 1.423481
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.697691 Loss1: 0.286678 Loss2: 1.411014
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.672574 Loss1: 0.270671 Loss2: 1.401903
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.603432 Loss1: 1.633184 Loss2: 1.970248
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.676011 Loss1: 0.269942 Loss2: 1.406069
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.687639 Loss1: 0.278917 Loss2: 1.408722 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.644437 Loss1: 0.234883 Loss2: 1.409554 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.802749 Loss1: 0.334804 Loss2: 1.467944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.769623 Loss1: 0.331160 Loss2: 1.438464 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.771830 Loss1: 0.313216 Loss2: 1.458614
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.555869 Loss1: 1.636354 Loss2: 1.919515
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.442438 Loss1: 1.019856 Loss2: 1.422582 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.730812 Loss1: 0.284571 Loss2: 1.446241
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.051527 Loss1: 0.612600 Loss2: 1.438927
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.870129 Loss1: 0.456387 Loss2: 1.413742
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.777387 Loss1: 0.373061 Loss2: 1.404327
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.733291 Loss1: 0.335208 Loss2: 1.398083
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.744815 Loss1: 0.343851 Loss2: 1.400963
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.670729 Loss1: 0.255497 Loss2: 1.415232
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.412875 Loss1: 1.536262 Loss2: 1.876613
-DEBUG flwr 2023-10-10 04:11:26,766 | server.py:236 | fit_round 63 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.436092 Loss1: 0.991430 Loss2: 1.444663 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.575679 Loss1: 0.184277 Loss2: 1.391402
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.059916 Loss1: 0.611556 Loss2: 1.448361
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.868155 Loss1: 0.459797 Loss2: 1.408358
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.719529 Loss1: 0.313397 Loss2: 1.406132
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.651779 Loss1: 0.242973 Loss2: 1.408806
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.771606 Loss1: 0.373888 Loss2: 1.397718
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.317422 Loss1: 1.512180 Loss2: 1.805241
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.778391 Loss1: 0.344842 Loss2: 1.433549
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.410397 Loss1: 0.972332 Loss2: 1.438064
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.641227 Loss1: 0.230232 Loss2: 1.410994
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.600608 Loss1: 0.205098 Loss2: 1.395510
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.015828 Loss1: 0.631759 Loss2: 1.384069
-(DefaultActor pid=3765) >> Training accuracy: 0.962500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.748629 Loss1: 0.391788 Loss2: 1.356840
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.756919 Loss1: 0.403785 Loss2: 1.353134
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.690111 Loss1: 0.328388 Loss2: 1.361723
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.595919 Loss1: 0.248548 Loss2: 1.347371
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.600730 Loss1: 0.247078 Loss2: 1.353653
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.412438 Loss1: 1.461050 Loss2: 1.951388
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.558145 Loss1: 0.214254 Loss2: 1.343891
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.532886 Loss1: 1.008210 Loss2: 1.524676
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.556612 Loss1: 0.202843 Loss2: 1.353769
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.082166 Loss1: 0.582072 Loss2: 1.500094
-(DefaultActor pid=3764) >> Training accuracy: 0.947266
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.907645 Loss1: 0.438945 Loss2: 1.468701
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.824531 Loss1: 0.338318 Loss2: 1.486213
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.807467 Loss1: 0.327653 Loss2: 1.479814
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.776490 Loss1: 0.301795 Loss2: 1.474695
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.241120 Loss1: 1.339920 Loss2: 1.901200
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.750500 Loss1: 0.273365 Loss2: 1.477135
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.716648 Loss1: 0.248550 Loss2: 1.468098
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.415248 Loss1: 0.929727 Loss2: 1.485522
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.670241 Loss1: 0.196748 Loss2: 1.473493
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.045786 Loss1: 0.596950 Loss2: 1.448836
-(DefaultActor pid=3765) >> Training accuracy: 0.965820
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.822575 Loss1: 0.395600 Loss2: 1.426975
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.750462 Loss1: 0.329971 Loss2: 1.420492
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.742978 Loss1: 0.321444 Loss2: 1.421534
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.690703 Loss1: 0.269384 Loss2: 1.421319
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.316008 Loss1: 1.470613 Loss2: 1.845394
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.306000 Loss1: 0.866741 Loss2: 1.439260 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.040182 Loss1: 0.593260 Loss2: 1.446922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.571513 Loss1: 0.168471 Loss2: 1.403042
-(DefaultActor pid=3764) >> Training accuracy: 0.965074
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.854502 Loss1: 0.446398 Loss2: 1.408104
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.772352 Loss1: 0.354292 Loss2: 1.418061
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.825285 Loss1: 0.414250 Loss2: 1.411034
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.785944 Loss1: 0.364955 Loss2: 1.420989
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.710568 Loss1: 0.299917 Loss2: 1.410651
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.696536 Loss1: 1.708597 Loss2: 1.987940
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.676408 Loss1: 0.262122 Loss2: 1.414285
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.628899 Loss1: 0.228366 Loss2: 1.400533 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.927734
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.860789 Loss1: 0.377112 Loss2: 1.483677 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.694040 Loss1: 0.240873 Loss2: 1.453167 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.663591 Loss1: 0.210344 Loss2: 1.453247 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.955357
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 04:11:26,766][flwr][DEBUG] - fit_round 63 received 50 results and 0 failures
-INFO flwr 2023-10-10 04:12:07,644 | server.py:125 | fit progress: (63, 2.3184024251688022, {'accuracy': 0.517}, 145235.42257321402)
->> Test accuracy: 0.517000
-[2023-10-10 04:12:07,644][flwr][INFO] - fit progress: (63, 2.3184024251688022, {'accuracy': 0.517}, 145235.42257321402)
-DEBUG flwr 2023-10-10 04:12:07,644 | server.py:173 | evaluate_round 63: strategy sampled 50 clients (out of 50)
-[2023-10-10 04:12:07,644][flwr][DEBUG] - evaluate_round 63: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 04:21:10,351 | server.py:187 | evaluate_round 63 received 50 results and 0 failures
-[2023-10-10 04:21:10,351][flwr][DEBUG] - evaluate_round 63 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 04:21:10,352 | server.py:222 | fit_round 64: strategy sampled 50 clients (out of 50)
-[2023-10-10 04:21:10,352][flwr][DEBUG] - fit_round 64: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.208803 Loss1: 1.352252 Loss2: 1.856551 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.961838 Loss1: 0.541889 Loss2: 1.419949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.414799 Loss1: 1.569210 Loss2: 1.845589
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.824253 Loss1: 0.398180 Loss2: 1.426073
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.452486 Loss1: 1.005056 Loss2: 1.447429
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.768416 Loss1: 0.346190 Loss2: 1.422227
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.089680 Loss1: 0.674365 Loss2: 1.415315
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.694587 Loss1: 0.288127 Loss2: 1.406460
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.674966 Loss1: 0.265861 Loss2: 1.409105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.630674 Loss1: 0.227432 Loss2: 1.403242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.576564 Loss1: 0.174556 Loss2: 1.402009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.599663 Loss1: 0.203253 Loss2: 1.396410 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.954044
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.658757 Loss1: 0.283942 Loss2: 1.374815 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.939453
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.598421 Loss1: 1.566996 Loss2: 2.031425 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.116178 Loss1: 0.616975 Loss2: 1.499203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.955186 Loss1: 0.482394 Loss2: 1.472792
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.346752 Loss1: 1.407413 Loss2: 1.939338
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.764536 Loss1: 0.305032 Loss2: 1.459504
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.390342 Loss1: 0.936873 Loss2: 1.453469
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.691573 Loss1: 0.241801 Loss2: 1.449773
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.123462 Loss1: 0.635845 Loss2: 1.487617
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.693976 Loss1: 0.245659 Loss2: 1.448317
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.877628 Loss1: 0.438024 Loss2: 1.439604
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.662084 Loss1: 0.218074 Loss2: 1.444011
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.777723 Loss1: 0.339412 Loss2: 1.438311
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.677561 Loss1: 0.224950 Loss2: 1.452611
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.699546 Loss1: 0.270982 Loss2: 1.428564
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.662958 Loss1: 0.213289 Loss2: 1.449669
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.653282 Loss1: 0.228841 Loss2: 1.424441
-(DefaultActor pid=3765) >> Training accuracy: 0.947917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.601039 Loss1: 0.185020 Loss2: 1.416019
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.594833 Loss1: 0.191833 Loss2: 1.403001
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.573258 Loss1: 0.161237 Loss2: 1.412021
-(DefaultActor pid=3764) >> Training accuracy: 0.946875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.146866 Loss1: 1.332931 Loss2: 1.813935
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.157519 Loss1: 0.765071 Loss2: 1.392448
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.882586 Loss1: 0.484070 Loss2: 1.398515
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.559350 Loss1: 1.565927 Loss2: 1.993423
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.727489 Loss1: 0.362939 Loss2: 1.364549
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.738363 Loss1: 0.358193 Loss2: 1.380170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.699873 Loss1: 0.332257 Loss2: 1.367616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.595093 Loss1: 0.218092 Loss2: 1.377001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.700165 Loss1: 0.290628 Loss2: 1.409537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.598151 Loss1: 0.198159 Loss2: 1.399992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.567873 Loss1: 0.172255 Loss2: 1.395618 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.542112 Loss1: 0.150639 Loss2: 1.391473 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.274476 Loss1: 1.470014 Loss2: 1.804462 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.057991 Loss1: 0.652802 Loss2: 1.405189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.371926 Loss1: 1.449434 Loss2: 1.922492
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.841408 Loss1: 0.452349 Loss2: 1.389059
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.453133 Loss1: 0.971247 Loss2: 1.481887
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.732907 Loss1: 0.357440 Loss2: 1.375466
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.619629 Loss1: 0.245452 Loss2: 1.374177 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547871 Loss1: 0.184572 Loss2: 1.363299 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.568712 Loss1: 0.202178 Loss2: 1.366534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.614500 Loss1: 0.237464 Loss2: 1.377036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.564156 Loss1: 0.181151 Loss2: 1.383005 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.947266
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.641544 Loss1: 0.192426 Loss2: 1.449118 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.115365 Loss1: 1.209811 Loss2: 1.905554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.117477 Loss1: 0.654900 Loss2: 1.462577 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.916236 Loss1: 0.495574 Loss2: 1.420663
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.624962 Loss1: 1.727943 Loss2: 1.897019
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.755171 Loss1: 0.343346 Loss2: 1.411825
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.458872 Loss1: 0.993355 Loss2: 1.465517
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.725208 Loss1: 0.319858 Loss2: 1.405350
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.055799 Loss1: 0.602482 Loss2: 1.453318
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.649503 Loss1: 0.255032 Loss2: 1.394472
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.871939 Loss1: 0.455304 Loss2: 1.416635
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.572737 Loss1: 0.177614 Loss2: 1.395124
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.856870 Loss1: 0.434438 Loss2: 1.422432
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.611811 Loss1: 0.225376 Loss2: 1.386435
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.821679 Loss1: 0.385612 Loss2: 1.436067
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.569267 Loss1: 0.166699 Loss2: 1.402567
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.741650 Loss1: 0.322415 Loss2: 1.419235
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.680188 Loss1: 0.264425 Loss2: 1.415762
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.654408 Loss1: 0.235652 Loss2: 1.418756
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.604013 Loss1: 0.193817 Loss2: 1.410195
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.359547 Loss1: 1.461585 Loss2: 1.897962
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.492454 Loss1: 1.033062 Loss2: 1.459392
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.050516 Loss1: 0.582630 Loss2: 1.467886
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.891947 Loss1: 0.478158 Loss2: 1.413789
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.437995 Loss1: 1.451765 Loss2: 1.986231
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.796037 Loss1: 0.354982 Loss2: 1.441055
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.475693 Loss1: 0.986527 Loss2: 1.489166
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.694233 Loss1: 0.278346 Loss2: 1.415887
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.103540 Loss1: 0.587931 Loss2: 1.515609
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.886412 Loss1: 0.438357 Loss2: 1.448055
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.687972 Loss1: 0.272811 Loss2: 1.415160
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.772313 Loss1: 0.303852 Loss2: 1.468462
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.693510 Loss1: 0.271116 Loss2: 1.422394
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.744131 Loss1: 0.283646 Loss2: 1.460485
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.652998 Loss1: 0.234962 Loss2: 1.418036
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.585610 Loss1: 0.172119 Loss2: 1.413491 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.635589 Loss1: 0.180803 Loss2: 1.454786 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958705
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.589494 Loss1: 1.656209 Loss2: 1.933285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.195485 Loss1: 0.730417 Loss2: 1.465068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.933238 Loss1: 0.507386 Loss2: 1.425852
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.170636 Loss1: 1.323218 Loss2: 1.847418
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.226903 Loss1: 0.836754 Loss2: 1.390150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.926409 Loss1: 0.529595 Loss2: 1.396815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.745827 Loss1: 0.374516 Loss2: 1.371311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.791338 Loss1: 0.410658 Loss2: 1.380681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.680028 Loss1: 0.302716 Loss2: 1.377312 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967634
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548451 Loss1: 0.189268 Loss2: 1.359183 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.490890 Loss1: 0.135120 Loss2: 1.355770 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.271420 Loss1: 1.391743 Loss2: 1.879677
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.396566 Loss1: 0.923891 Loss2: 1.472674
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.051560 Loss1: 0.598445 Loss2: 1.453115
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.814702 Loss1: 0.401402 Loss2: 1.413300
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.680783 Loss1: 1.537438 Loss2: 2.143345
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.566776 Loss1: 1.037555 Loss2: 1.529221
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.729522 Loss1: 0.312212 Loss2: 1.417311
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.280475 Loss1: 0.704890 Loss2: 1.575586
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.014752 Loss1: 0.490758 Loss2: 1.523995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.861575 Loss1: 0.343740 Loss2: 1.517835 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.584269 Loss1: 0.179939 Loss2: 1.404330 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.595135 Loss1: 0.195183 Loss2: 1.399952 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963867
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.263808 Loss1: 1.373629 Loss2: 1.890179 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.956676 Loss1: 0.552882 Loss2: 1.403794 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.797651 Loss1: 0.405210 Loss2: 1.392441
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.271628 Loss1: 1.405191 Loss2: 1.866437
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.259569 Loss1: 0.870602 Loss2: 1.388967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.979746 Loss1: 0.560972 Loss2: 1.418774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.754082 Loss1: 0.394888 Loss2: 1.359195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.624678 Loss1: 0.256860 Loss2: 1.367818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567175 Loss1: 0.214545 Loss2: 1.352631 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.938542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.605884 Loss1: 0.235531 Loss2: 1.370353
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.548765 Loss1: 0.199703 Loss2: 1.349062
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.608375 Loss1: 0.248203 Loss2: 1.360172
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.570677 Loss1: 0.204352 Loss2: 1.366326
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.553667 Loss1: 0.202574 Loss2: 1.351094
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.327654 Loss1: 1.440341 Loss2: 1.887313
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.344230 Loss1: 0.873752 Loss2: 1.470478
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.023681 Loss1: 0.568004 Loss2: 1.455677
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.867127 Loss1: 0.442510 Loss2: 1.424617
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.370790 Loss1: 1.495808 Loss2: 1.874981
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.764160 Loss1: 0.347971 Loss2: 1.416189
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.496950 Loss1: 1.012149 Loss2: 1.484801
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.717599 Loss1: 0.301389 Loss2: 1.416211
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.135266 Loss1: 0.660992 Loss2: 1.474273
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.672938 Loss1: 0.263095 Loss2: 1.409843
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.879593 Loss1: 0.441056 Loss2: 1.438537
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.630851 Loss1: 0.225909 Loss2: 1.404942
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.811227 Loss1: 0.367040 Loss2: 1.444187
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.595158 Loss1: 0.191925 Loss2: 1.403234
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.792437 Loss1: 0.349200 Loss2: 1.443236
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.602917 Loss1: 0.200095 Loss2: 1.402822
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.709758 Loss1: 0.269222 Loss2: 1.440536
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.704340 Loss1: 0.265413 Loss2: 1.438927
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.663560 Loss1: 0.230267 Loss2: 1.433293
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.625615 Loss1: 0.200184 Loss2: 1.425430
-(DefaultActor pid=3764) >> Training accuracy: 0.958984
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.433984 Loss1: 1.552517 Loss2: 1.881467
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.331148 Loss1: 0.918760 Loss2: 1.412389
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.039476 Loss1: 0.592058 Loss2: 1.447419
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.820578 Loss1: 0.419974 Loss2: 1.400604
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.420405 Loss1: 1.513806 Loss2: 1.906599
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.394712 Loss1: 0.941652 Loss2: 1.453060 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.220928 Loss1: 0.760107 Loss2: 1.460821 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.040360 Loss1: 0.598382 Loss2: 1.441977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.857381 Loss1: 0.423991 Loss2: 1.433390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.801840 Loss1: 0.378746 Loss2: 1.423094 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.929167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.680418 Loss1: 0.262922 Loss2: 1.417496 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.620251 Loss1: 0.213734 Loss2: 1.406517 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.393175 Loss1: 1.546542 Loss2: 1.846632 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.024094 Loss1: 0.585503 Loss2: 1.438591 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.903365 Loss1: 0.486375 Loss2: 1.416990
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.268207 Loss1: 1.378235 Loss2: 1.889973
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.777318 Loss1: 0.358331 Loss2: 1.418987
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.247097 Loss1: 0.832455 Loss2: 1.414642
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.789252 Loss1: 0.369928 Loss2: 1.419325
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.016854 Loss1: 0.587619 Loss2: 1.429235
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.679312 Loss1: 0.259701 Loss2: 1.419611
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.754642 Loss1: 0.389628 Loss2: 1.365015
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.633601 Loss1: 0.264932 Loss2: 1.368668
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.684150 Loss1: 0.274049 Loss2: 1.410101
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.606782 Loss1: 0.244212 Loss2: 1.362571
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.660418 Loss1: 0.238134 Loss2: 1.422283
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.630174 Loss1: 0.264550 Loss2: 1.365625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.605903 Loss1: 0.202637 Loss2: 1.403267
-(DefaultActor pid=3765) >> Training accuracy: 0.963867
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.539275 Loss1: 0.193426 Loss2: 1.345849 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.488122 Loss1: 1.585645 Loss2: 1.902477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.033799 Loss1: 0.589464 Loss2: 1.444334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.864459 Loss1: 0.441925 Loss2: 1.422534
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.410040 Loss1: 1.522555 Loss2: 1.887485
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.737385 Loss1: 0.321005 Loss2: 1.416380
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.436157 Loss1: 1.010388 Loss2: 1.425769
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.746882 Loss1: 0.336388 Loss2: 1.410494
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.071897 Loss1: 0.600803 Loss2: 1.471094
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.644345 Loss1: 0.229839 Loss2: 1.414506
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.877902 Loss1: 0.481574 Loss2: 1.396328
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.683534 Loss1: 0.277379 Loss2: 1.406155
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.785523 Loss1: 0.367452 Loss2: 1.418072
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.614767 Loss1: 0.196134 Loss2: 1.418633
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.700288 Loss1: 0.305275 Loss2: 1.395013
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.564077 Loss1: 0.164475 Loss2: 1.399601
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.678835 Loss1: 0.275352 Loss2: 1.403484
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.633547 Loss1: 0.240032 Loss2: 1.393515
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.579173 Loss1: 0.184176 Loss2: 1.394997
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.564137 Loss1: 0.176309 Loss2: 1.387828
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.374679 Loss1: 1.501028 Loss2: 1.873651
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.463906 Loss1: 1.019947 Loss2: 1.443958
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.179105 Loss1: 0.712755 Loss2: 1.466350
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.940236 Loss1: 0.523844 Loss2: 1.416392
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.252084 Loss1: 1.411346 Loss2: 1.840738
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.815893 Loss1: 0.404043 Loss2: 1.411851
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.340990 Loss1: 0.949658 Loss2: 1.391333
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.751152 Loss1: 0.349640 Loss2: 1.401512
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.035420 Loss1: 0.609100 Loss2: 1.426319
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.730694 Loss1: 0.320035 Loss2: 1.410659
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.824408 Loss1: 0.445536 Loss2: 1.378872
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.674090 Loss1: 0.271736 Loss2: 1.402354
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.674428 Loss1: 0.283149 Loss2: 1.391279
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.627400 Loss1: 0.229167 Loss2: 1.398233
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.643189 Loss1: 0.269599 Loss2: 1.373589
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.561475 Loss1: 0.173598 Loss2: 1.387878
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.683250 Loss1: 0.298493 Loss2: 1.384757
-(DefaultActor pid=3765) >> Training accuracy: 0.943750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.579009 Loss1: 0.199301 Loss2: 1.379708
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.602903 Loss1: 0.231614 Loss2: 1.371289
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.589126 Loss1: 0.215635 Loss2: 1.373491
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.526922 Loss1: 1.616661 Loss2: 1.910261
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.512165 Loss1: 1.039880 Loss2: 1.472284
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.149855 Loss1: 0.664815 Loss2: 1.485040
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.992491 Loss1: 0.532502 Loss2: 1.459989
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.426516 Loss1: 1.544278 Loss2: 1.882237
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.846885 Loss1: 0.381940 Loss2: 1.464944
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.453716 Loss1: 0.985042 Loss2: 1.468673
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.062654 Loss1: 0.632429 Loss2: 1.430225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.888232 Loss1: 0.462500 Loss2: 1.425732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.783145 Loss1: 0.358636 Loss2: 1.424509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.741176 Loss1: 0.322542 Loss2: 1.418634 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.947917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.642759 Loss1: 0.210953 Loss2: 1.431805
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.693349 Loss1: 0.277976 Loss2: 1.415373
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.631998 Loss1: 0.223742 Loss2: 1.408255
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.588463 Loss1: 0.191801 Loss2: 1.396662
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.559744 Loss1: 0.170661 Loss2: 1.389083
-(DefaultActor pid=3764) >> Training accuracy: 0.951042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.507253 Loss1: 1.663039 Loss2: 1.844214
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.639406 Loss1: 1.189321 Loss2: 1.450086
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.100719 Loss1: 0.694588 Loss2: 1.406131
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.866476 Loss1: 0.464207 Loss2: 1.402270
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.691871 Loss1: 1.716603 Loss2: 1.975269
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.559285 Loss1: 1.087310 Loss2: 1.471975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.672134 Loss1: 0.290891 Loss2: 1.381243
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.099514 Loss1: 0.604392 Loss2: 1.495122
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.632990 Loss1: 0.253593 Loss2: 1.379397
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.913655 Loss1: 0.475205 Loss2: 1.438451
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.586928 Loss1: 0.213790 Loss2: 1.373137
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.852033 Loss1: 0.400891 Loss2: 1.451142
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.744721 Loss1: 0.304767 Loss2: 1.439954
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.612363 Loss1: 0.231835 Loss2: 1.380528
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.700917 Loss1: 0.265573 Loss2: 1.435344
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.567730 Loss1: 0.194520 Loss2: 1.373211
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.623767 Loss1: 0.192017 Loss2: 1.431750 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958705
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.487831 Loss1: 1.615229 Loss2: 1.872601 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.067532 Loss1: 0.639761 Loss2: 1.427771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.844650 Loss1: 0.441733 Loss2: 1.402917
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.405280 Loss1: 1.510595 Loss2: 1.894685
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.378305 Loss1: 0.964149 Loss2: 1.414155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.113680 Loss1: 0.651385 Loss2: 1.462295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.855889 Loss1: 0.453683 Loss2: 1.402206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.832195 Loss1: 0.405352 Loss2: 1.426843 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.717675 Loss1: 0.318629 Loss2: 1.399047 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.516396 Loss1: 0.139224 Loss2: 1.377173
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.687190 Loss1: 0.286570 Loss2: 1.400621
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.637389 Loss1: 0.233220 Loss2: 1.404168
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.662217 Loss1: 0.264640 Loss2: 1.397577
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.692093 Loss1: 0.288602 Loss2: 1.403491
-(DefaultActor pid=3764) >> Training accuracy: 0.954167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.376463 Loss1: 1.469915 Loss2: 1.906548
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.333147 Loss1: 0.856016 Loss2: 1.477131
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.078990 Loss1: 0.622123 Loss2: 1.456868
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.846866 Loss1: 0.410447 Loss2: 1.436419
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.221543 Loss1: 1.386716 Loss2: 1.834827
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.486314 Loss1: 1.025589 Loss2: 1.460726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.054207 Loss1: 0.626396 Loss2: 1.427811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.840823 Loss1: 0.418947 Loss2: 1.421876 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.765301 Loss1: 0.363267 Loss2: 1.402034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.683167 Loss1: 0.281380 Loss2: 1.401787 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.957292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.657589 Loss1: 0.255004 Loss2: 1.402585 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.590259 Loss1: 0.188012 Loss2: 1.402247 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.935547
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.398569 Loss1: 1.446529 Loss2: 1.952040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.194892 Loss1: 0.674607 Loss2: 1.520285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.917376 Loss1: 0.436832 Loss2: 1.480544 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.798117 Loss1: 0.321974 Loss2: 1.476143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.818937 Loss1: 0.354598 Loss2: 1.464339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.719329 Loss1: 0.245530 Loss2: 1.473799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.666324 Loss1: 0.205406 Loss2: 1.460918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.682272 Loss1: 0.221589 Loss2: 1.460683 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.938542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.521732 Loss1: 0.201470 Loss2: 1.320262 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.507561 Loss1: 0.193767 Loss2: 1.313793 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.942708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.284667 Loss1: 0.840310 Loss2: 1.444358 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.820082 Loss1: 0.417486 Loss2: 1.402597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.748674 Loss1: 0.318614 Loss2: 1.430060
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.327942 Loss1: 1.419839 Loss2: 1.908102
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.672122 Loss1: 0.261532 Loss2: 1.410590
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.416479 Loss1: 0.982784 Loss2: 1.433695
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.632338 Loss1: 0.234892 Loss2: 1.397445
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.139161 Loss1: 0.674904 Loss2: 1.464256
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.616261 Loss1: 0.209007 Loss2: 1.407254
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.935462 Loss1: 0.514741 Loss2: 1.420721
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.603546 Loss1: 0.204706 Loss2: 1.398840
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.782546 Loss1: 0.356414 Loss2: 1.426132
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.601920 Loss1: 0.198061 Loss2: 1.403859
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.731538 Loss1: 0.315079 Loss2: 1.416459
-(DefaultActor pid=3765) >> Training accuracy: 0.936458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.703802 Loss1: 0.285656 Loss2: 1.418146
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.673374 Loss1: 0.261299 Loss2: 1.412076
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.623995 Loss1: 0.212606 Loss2: 1.411389
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.611477 Loss1: 0.207326 Loss2: 1.404150
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.429097 Loss1: 1.513585 Loss2: 1.915512
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.379764 Loss1: 0.983971 Loss2: 1.395793
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.098985 Loss1: 0.660093 Loss2: 1.438892
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.837958 Loss1: 0.451291 Loss2: 1.386667
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.726331 Loss1: 0.343275 Loss2: 1.383055
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.653131 Loss1: 0.275390 Loss2: 1.377742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.575248 Loss1: 0.195838 Loss2: 1.379409
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.438266 Loss1: 0.964396 Loss2: 1.473870
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.606799 Loss1: 0.235644 Loss2: 1.371155
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.111537 Loss1: 0.662697 Loss2: 1.448840
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.855565 Loss1: 0.451187 Loss2: 1.404378 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.951923
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.818693 Loss1: 0.398839 Loss2: 1.419854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.795474 Loss1: 0.361084 Loss2: 1.434390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.630778 Loss1: 0.219738 Loss2: 1.411040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.632494 Loss1: 0.226505 Loss2: 1.405989 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.110849 Loss1: 0.584537 Loss2: 1.526311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.918979 Loss1: 0.416006 Loss2: 1.502974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.782661 Loss1: 0.289515 Loss2: 1.493145
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.335756 Loss1: 1.495958 Loss2: 1.839798
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.425257 Loss1: 1.004114 Loss2: 1.421143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.111101 Loss1: 0.685683 Loss2: 1.425418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.891825 Loss1: 0.509723 Loss2: 1.382101 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.734366 Loss1: 0.351668 Loss2: 1.382698 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.702646 Loss1: 0.325702 Loss2: 1.376944 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.614730 Loss1: 0.243125 Loss2: 1.371605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.591236 Loss1: 0.215599 Loss2: 1.375636 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.951042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.950433 Loss1: 0.551130 Loss2: 1.399304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.704484 Loss1: 0.327758 Loss2: 1.376725 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 04:50:32,089 | server.py:236 | fit_round 64 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.518837 Loss1: 1.587601 Loss2: 1.931237
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.596188 Loss1: 0.238654 Loss2: 1.357534
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.542823 Loss1: 1.061964 Loss2: 1.480859
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544208 Loss1: 0.180263 Loss2: 1.363945
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.167999 Loss1: 0.675011 Loss2: 1.492987
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.573575 Loss1: 0.213975 Loss2: 1.359600
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.985296 Loss1: 0.528702 Loss2: 1.456594
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.585423 Loss1: 0.217313 Loss2: 1.368110
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.609837 Loss1: 0.230480 Loss2: 1.379357 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.936523
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.744220 Loss1: 0.296331 Loss2: 1.447889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.664352 Loss1: 0.215048 Loss2: 1.449304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.620535 Loss1: 0.181456 Loss2: 1.439079
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.291766 Loss1: 1.390857 Loss2: 1.900910
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.416634 Loss1: 0.945210 Loss2: 1.471424
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.075215 Loss1: 0.630614 Loss2: 1.444601
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.911891 Loss1: 0.467187 Loss2: 1.444704
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.726838 Loss1: 0.306309 Loss2: 1.420529
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.324141 Loss1: 1.332656 Loss2: 1.991486
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.689412 Loss1: 0.266163 Loss2: 1.423249
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.661798 Loss1: 0.249536 Loss2: 1.412262 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.601797 Loss1: 0.188305 Loss2: 1.413493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.618451 Loss1: 0.208938 Loss2: 1.409513 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.599276 Loss1: 0.189662 Loss2: 1.409613 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.674092 Loss1: 0.195034 Loss2: 1.479058 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.655128 Loss1: 0.193048 Loss2: 1.462080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.663602 Loss1: 0.189943 Loss2: 1.473659
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.503108 Loss1: 1.579791 Loss2: 1.923317
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.528353 Loss1: 0.979070 Loss2: 1.549283
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.066242 Loss1: 0.597873 Loss2: 1.468369
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.951473 Loss1: 0.485653 Loss2: 1.465820
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.855444 Loss1: 0.385813 Loss2: 1.469631
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.370746 Loss1: 1.485903 Loss2: 1.884843
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.781596 Loss1: 0.330866 Loss2: 1.450730
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.740052 Loss1: 0.284486 Loss2: 1.455566 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.669097 Loss1: 0.210732 Loss2: 1.458365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.608492 Loss1: 0.175990 Loss2: 1.432502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.590556 Loss1: 0.157892 Loss2: 1.432664 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.635409 Loss1: 0.229836 Loss2: 1.405574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.594987 Loss1: 0.197138 Loss2: 1.397849 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 04:50:32,089][flwr][DEBUG] - fit_round 64 received 50 results and 0 failures
-INFO flwr 2023-10-10 04:51:14,059 | server.py:125 | fit progress: (64, 2.3150291172460244, {'accuracy': 0.5197}, 147581.83765539702)
->> Test accuracy: 0.519700
-[2023-10-10 04:51:14,059][flwr][INFO] - fit progress: (64, 2.3150291172460244, {'accuracy': 0.5197}, 147581.83765539702)
-DEBUG flwr 2023-10-10 04:51:14,059 | server.py:173 | evaluate_round 64: strategy sampled 50 clients (out of 50)
-[2023-10-10 04:51:14,059][flwr][DEBUG] - evaluate_round 64: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 05:00:15,154 | server.py:187 | evaluate_round 64 received 50 results and 0 failures
-[2023-10-10 05:00:15,154][flwr][DEBUG] - evaluate_round 64 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 05:00:15,154 | server.py:222 | fit_round 65: strategy sampled 50 clients (out of 50)
-[2023-10-10 05:00:15,154][flwr][DEBUG] - fit_round 65: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.368101 Loss1: 1.499589 Loss2: 1.868512 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.975815 Loss1: 0.558605 Loss2: 1.417210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.838668 Loss1: 0.459975 Loss2: 1.378693
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.288523 Loss1: 1.334729 Loss2: 1.953794
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.439390 Loss1: 0.925169 Loss2: 1.514221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.122644 Loss1: 0.621538 Loss2: 1.501106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.870443 Loss1: 0.396918 Loss2: 1.473525 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.809888 Loss1: 0.343779 Loss2: 1.466108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.777223 Loss1: 0.309467 Loss2: 1.467756 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.623769 Loss1: 0.166892 Loss2: 1.456877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.630357 Loss1: 0.178121 Loss2: 1.452236 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969727
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.419371 Loss1: 1.410563 Loss2: 2.008809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.127633 Loss1: 0.668290 Loss2: 1.459342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.885697 Loss1: 0.470996 Loss2: 1.414701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.727919 Loss1: 0.314643 Loss2: 1.413277 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.676451 Loss1: 0.277709 Loss2: 1.398743
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.251313 Loss1: 0.823353 Loss2: 1.427960
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.593392 Loss1: 0.210842 Loss2: 1.382550
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.991288 Loss1: 0.561191 Loss2: 1.430097
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.801303 Loss1: 0.396135 Loss2: 1.405168 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.950721
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.577466 Loss1: 0.197881 Loss2: 1.379585
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.747084 Loss1: 0.342437 Loss2: 1.404647
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.682106 Loss1: 0.281273 Loss2: 1.400833
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.625372 Loss1: 0.241443 Loss2: 1.383929
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.595554 Loss1: 0.216643 Loss2: 1.378911
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.561471 Loss1: 0.180244 Loss2: 1.381227
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.323249 Loss1: 1.428838 Loss2: 1.894411
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.556215 Loss1: 0.175186 Loss2: 1.381029
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.126722 Loss1: 0.653475 Loss2: 1.473248 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.762333 Loss1: 0.349326 Loss2: 1.413008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.668449 Loss1: 0.265658 Loss2: 1.402791
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.211382 Loss1: 1.341255 Loss2: 1.870127
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.287111 Loss1: 0.881916 Loss2: 1.405194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.096967 Loss1: 0.639046 Loss2: 1.457920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.898704 Loss1: 0.513254 Loss2: 1.385451 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.778126 Loss1: 0.374633 Loss2: 1.403492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.579546 Loss1: 0.204738 Loss2: 1.374808 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.557278 Loss1: 0.175422 Loss2: 1.381856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.558914 Loss1: 0.179871 Loss2: 1.379042 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.057386 Loss1: 0.538841 Loss2: 1.518545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.787890 Loss1: 0.312127 Loss2: 1.475763 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.697427 Loss1: 0.221095 Loss2: 1.476332
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.611490 Loss1: 1.540747 Loss2: 2.070743
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.590748 Loss1: 1.016216 Loss2: 1.574531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.141699 Loss1: 0.550820 Loss2: 1.590879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.054509 Loss1: 0.495559 Loss2: 1.558950 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.574648 Loss1: 0.120478 Loss2: 1.454170
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.957744 Loss1: 0.398268 Loss2: 1.559476
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.890006 Loss1: 0.330901 Loss2: 1.559105
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.834073 Loss1: 0.285479 Loss2: 1.548594
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.834434 Loss1: 0.274320 Loss2: 1.560114
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.741647 Loss1: 0.194658 Loss2: 1.546989
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.432278 Loss1: 1.580458 Loss2: 1.851821
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.701686 Loss1: 0.171567 Loss2: 1.530118
-(DefaultActor pid=3764) >> Training accuracy: 0.951042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.051846 Loss1: 0.648901 Loss2: 1.402945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.720774 Loss1: 0.356627 Loss2: 1.364147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.619661 Loss1: 0.267054 Loss2: 1.352607
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.196430 Loss1: 1.263772 Loss2: 1.932658
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.239446 Loss1: 0.800282 Loss2: 1.439164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.008181 Loss1: 0.554170 Loss2: 1.454010 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.865610 Loss1: 0.470742 Loss2: 1.394868 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.821784 Loss1: 0.384899 Loss2: 1.436885 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.672623 Loss1: 0.251718 Loss2: 1.420905 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.579712 Loss1: 0.191760 Loss2: 1.387953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.573509 Loss1: 0.180824 Loss2: 1.392685 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.262067 Loss1: 0.674308 Loss2: 1.587759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.856223 Loss1: 0.326338 Loss2: 1.529885 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.786829 Loss1: 0.279424 Loss2: 1.507405
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.503573 Loss1: 1.564022 Loss2: 1.939551
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.679183 Loss1: 1.111876 Loss2: 1.567308 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.227708 Loss1: 0.731364 Loss2: 1.496344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.109036 Loss1: 0.613097 Loss2: 1.495939 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.900255 Loss1: 0.409922 Loss2: 1.490333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.741801 Loss1: 0.273629 Loss2: 1.468172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.762343 Loss1: 0.294116 Loss2: 1.468227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.227390 Loss1: 1.361826 Loss2: 1.865564
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.694580 Loss1: 0.233491 Loss2: 1.461089
-(DefaultActor pid=3764) >> Training accuracy: 0.941667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.043952 Loss1: 0.615825 Loss2: 1.428127 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.696580 Loss1: 0.308101 Loss2: 1.388479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.617871 Loss1: 0.244136 Loss2: 1.373735
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.455319 Loss1: 1.508828 Loss2: 1.946492
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.509632 Loss1: 0.978441 Loss2: 1.531192 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.151375 Loss1: 0.649070 Loss2: 1.502305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.034148 Loss1: 0.516847 Loss2: 1.517301 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.882885 Loss1: 0.400530 Loss2: 1.482355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.722425 Loss1: 0.251555 Loss2: 1.470871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.727034 Loss1: 0.251217 Loss2: 1.475817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.667672 Loss1: 0.195653 Loss2: 1.472020 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.952148
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.736136 Loss1: 0.373759 Loss2: 1.362377 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.631979 Loss1: 0.267534 Loss2: 1.364445 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.672202 Loss1: 0.304974 Loss2: 1.367227
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.296859 Loss1: 1.427907 Loss2: 1.868953
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.583747 Loss1: 0.217035 Loss2: 1.366712
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.339233 Loss1: 0.896066 Loss2: 1.443168
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.551996 Loss1: 0.189555 Loss2: 1.362441
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.064700 Loss1: 0.614148 Loss2: 1.450552
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.570618 Loss1: 0.209889 Loss2: 1.360729
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.957190 Loss1: 0.518739 Loss2: 1.438451
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.813854 Loss1: 0.384902 Loss2: 1.428952
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.750673 Loss1: 0.337456 Loss2: 1.413216
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.698168 Loss1: 0.280261 Loss2: 1.417907
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.658191 Loss1: 0.242188 Loss2: 1.416004
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.467382 Loss1: 1.500332 Loss2: 1.967050
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.523463 Loss1: 1.000984 Loss2: 1.522478 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.943359
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.169059 Loss1: 0.618007 Loss2: 1.551052 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.870241 Loss1: 0.364091 Loss2: 1.506150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.715451 Loss1: 0.222950 Loss2: 1.492500 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.792066 Loss1: 0.307354 Loss2: 1.484712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.804159 Loss1: 0.305152 Loss2: 1.499007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.754905 Loss1: 0.274612 Loss2: 1.480293 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.896484
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.707582 Loss1: 0.271598 Loss2: 1.435984 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.619887 Loss1: 0.179550 Loss2: 1.440338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.579772 Loss1: 0.154497 Loss2: 1.425274
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.283813 Loss1: 1.463447 Loss2: 1.820366
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.561314 Loss1: 0.134414 Loss2: 1.426900
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.241870 Loss1: 0.851356 Loss2: 1.390514
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.937238 Loss1: 0.520969 Loss2: 1.416269
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.814533 Loss1: 0.436586 Loss2: 1.377947
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.772836 Loss1: 0.377696 Loss2: 1.395140
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.710457 Loss1: 0.329241 Loss2: 1.381216
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.583799 Loss1: 1.617856 Loss2: 1.965943
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.493752 Loss1: 0.983044 Loss2: 1.510708 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.092675 Loss1: 0.561117 Loss2: 1.531558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.016310 Loss1: 0.543692 Loss2: 1.472617 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.951172
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.636513 Loss1: 0.251783 Loss2: 1.384730
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.835155 Loss1: 0.340859 Loss2: 1.494297
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.804831 Loss1: 0.329231 Loss2: 1.475600
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.800482 Loss1: 0.324335 Loss2: 1.476147
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.788116 Loss1: 0.312033 Loss2: 1.476083
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.751239 Loss1: 0.268672 Loss2: 1.482567
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.480498 Loss1: 1.519344 Loss2: 1.961154
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.754772 Loss1: 0.279317 Loss2: 1.475454
-(DefaultActor pid=3764) >> Training accuracy: 0.943750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.141920 Loss1: 0.609255 Loss2: 1.532666 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.855111 Loss1: 0.392099 Loss2: 1.463012 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.843842 Loss1: 0.384367 Loss2: 1.459474
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.311042 Loss1: 1.431942 Loss2: 1.879100
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.279996 Loss1: 0.795269 Loss2: 1.484727 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.976213 Loss1: 0.535176 Loss2: 1.441037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.792662 Loss1: 0.358004 Loss2: 1.434658 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.661709 Loss1: 0.246665 Loss2: 1.415043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.592811 Loss1: 0.185938 Loss2: 1.406873 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.538985 Loss1: 0.142724 Loss2: 1.396262 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.545983 Loss1: 0.148865 Loss2: 1.397117 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958008
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.862938 Loss1: 0.467135 Loss2: 1.395804 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.743316 Loss1: 0.349098 Loss2: 1.394218 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.638462 Loss1: 0.255431 Loss2: 1.383031
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.197636 Loss1: 1.279635 Loss2: 1.918000
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.610628 Loss1: 0.226954 Loss2: 1.383674
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.228968 Loss1: 0.767886 Loss2: 1.461082
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.593212 Loss1: 0.210425 Loss2: 1.382787
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.089845 Loss1: 0.620823 Loss2: 1.469022
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.543517 Loss1: 0.167701 Loss2: 1.375816
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.936560 Loss1: 0.491707 Loss2: 1.444853
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.786044 Loss1: 0.344206 Loss2: 1.441838
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.727896 Loss1: 0.296360 Loss2: 1.431535
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.705785 Loss1: 0.265466 Loss2: 1.440319
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.639662 Loss1: 0.216321 Loss2: 1.423341
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.610300 Loss1: 0.189848 Loss2: 1.420451
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.740223 Loss1: 1.649688 Loss2: 2.090535
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.549487 Loss1: 0.133905 Loss2: 1.415582
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.751676 Loss1: 1.100418 Loss2: 1.651258
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.253098 Loss1: 0.654222 Loss2: 1.598876
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.060719 Loss1: 0.487070 Loss2: 1.573649
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.948032 Loss1: 0.363136 Loss2: 1.584896
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.954702 Loss1: 0.387650 Loss2: 1.567052
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.358301 Loss1: 1.367770 Loss2: 1.990530
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.806520 Loss1: 0.237954 Loss2: 1.568566
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.356265 Loss1: 0.854225 Loss2: 1.502041
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.768752 Loss1: 0.218403 Loss2: 1.550348
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.085187 Loss1: 0.570134 Loss2: 1.515053
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.761183 Loss1: 0.207901 Loss2: 1.553282
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.948991 Loss1: 0.468007 Loss2: 1.480984
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.764092 Loss1: 0.214328 Loss2: 1.549764
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.827887 Loss1: 0.353947 Loss2: 1.473941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.726343 Loss1: 0.257428 Loss2: 1.468915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.507664 Loss1: 1.531272 Loss2: 1.976392
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.683031 Loss1: 0.210285 Loss2: 1.472746
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.484178 Loss1: 1.048073 Loss2: 1.436105
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.701405 Loss1: 0.232914 Loss2: 1.468491
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.864376 Loss1: 0.454429 Loss2: 1.409947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.684295 Loss1: 0.273643 Loss2: 1.410652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.424144 Loss1: 1.526279 Loss2: 1.897866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.390786 Loss1: 0.935561 Loss2: 1.455225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.058070 Loss1: 0.604159 Loss2: 1.453912 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970982
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.805378 Loss1: 0.380807 Loss2: 1.424571 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.720905 Loss1: 0.305390 Loss2: 1.415515 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.663755 Loss1: 0.245301 Loss2: 1.418455
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.546955 Loss1: 1.627178 Loss2: 1.919777
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.551054 Loss1: 1.118206 Loss2: 1.432848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.572485 Loss1: 0.168954 Loss2: 1.403531
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.090332 Loss1: 0.611097 Loss2: 1.479235
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.929957 Loss1: 0.532350 Loss2: 1.397607
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.786189 Loss1: 0.360521 Loss2: 1.425668
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.737255 Loss1: 0.334095 Loss2: 1.403161
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.654306 Loss1: 0.257097 Loss2: 1.397209
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.668979 Loss1: 0.261983 Loss2: 1.406996
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.355373 Loss1: 1.388421 Loss2: 1.966952
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.455841 Loss1: 0.957331 Loss2: 1.498509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.929688
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.118295 Loss1: 0.618673 Loss2: 1.499622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.881529 Loss1: 0.404932 Loss2: 1.476597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.758227 Loss1: 0.291864 Loss2: 1.466363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.803163 Loss1: 0.326824 Loss2: 1.476340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.774381 Loss1: 0.313623 Loss2: 1.460757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.667325 Loss1: 0.195548 Loss2: 1.471777 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.739921 Loss1: 0.345055 Loss2: 1.394865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.621254 Loss1: 0.241106 Loss2: 1.380147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.568091 Loss1: 0.182520 Loss2: 1.385572
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.287320 Loss1: 1.363834 Loss2: 1.923486
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.352435 Loss1: 0.910253 Loss2: 1.442182 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.517299 Loss1: 0.152572 Loss2: 1.364727
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.055212 Loss1: 0.557528 Loss2: 1.497684
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.799611 Loss1: 0.374973 Loss2: 1.424638
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.752943 Loss1: 0.319447 Loss2: 1.433496
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.707285 Loss1: 0.270959 Loss2: 1.436326
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.631857 Loss1: 0.219842 Loss2: 1.412014
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.474244 Loss1: 1.536547 Loss2: 1.937697
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.647809 Loss1: 0.225444 Loss2: 1.422365
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.660285 Loss1: 0.232012 Loss2: 1.428274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.604341 Loss1: 0.183764 Loss2: 1.420577 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.647484 Loss1: 0.241483 Loss2: 1.406001 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.576496 Loss1: 0.192993 Loss2: 1.383503 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.486757 Loss1: 1.363675 Loss2: 2.123083 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980769
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.260307 Loss1: 0.622490 Loss2: 1.637817 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.918027 Loss1: 0.365531 Loss2: 1.552496 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.815831 Loss1: 0.271663 Loss2: 1.544169
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.190210 Loss1: 1.277235 Loss2: 1.912975
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.335277 Loss1: 0.894095 Loss2: 1.441183 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.991325 Loss1: 0.548997 Loss2: 1.442328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.818526 Loss1: 0.402940 Loss2: 1.415586 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.686035 Loss1: 0.160761 Loss2: 1.525274
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.740462 Loss1: 0.329124 Loss2: 1.411339
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.693115 Loss1: 0.291494 Loss2: 1.401621
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.654170 Loss1: 0.248940 Loss2: 1.405229
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.574026 Loss1: 0.170334 Loss2: 1.403692
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.584200 Loss1: 0.183504 Loss2: 1.400697
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.320912 Loss1: 1.449511 Loss2: 1.871401
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.582656 Loss1: 0.184822 Loss2: 1.397834
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.064893 Loss1: 0.625841 Loss2: 1.439051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.749896 Loss1: 0.338385 Loss2: 1.411511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.646716 Loss1: 0.243438 Loss2: 1.403279
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.419682 Loss1: 1.518290 Loss2: 1.901392
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.499435 Loss1: 1.021828 Loss2: 1.477607
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.655530 Loss1: 0.255535 Loss2: 1.399996
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.188431 Loss1: 0.726534 Loss2: 1.461897
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.637773 Loss1: 0.225194 Loss2: 1.412579
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.040942 Loss1: 0.585131 Loss2: 1.455810
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.637601 Loss1: 0.235992 Loss2: 1.401609
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.877235 Loss1: 0.448224 Loss2: 1.429011
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.610292 Loss1: 0.203108 Loss2: 1.407184
-(DefaultActor pid=3764) >> Training accuracy: 0.949219
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.642121 Loss1: 0.216165 Loss2: 1.425957 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.637243 Loss1: 0.214235 Loss2: 1.423009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.590067 Loss1: 0.173538 Loss2: 1.416529
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.417943 Loss1: 1.453937 Loss2: 1.964006
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.490982 Loss1: 0.963278 Loss2: 1.527703
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.085693 Loss1: 0.566327 Loss2: 1.519365
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.939094 Loss1: 0.460758 Loss2: 1.478336
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.833326 Loss1: 0.357969 Loss2: 1.475356
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.463338 Loss1: 1.554920 Loss2: 1.908417
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.804465 Loss1: 0.339703 Loss2: 1.464763
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.683122 Loss1: 0.204040 Loss2: 1.479082 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.646113 Loss1: 0.191350 Loss2: 1.454763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.680377 Loss1: 0.222400 Loss2: 1.457977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.617531 Loss1: 0.148886 Loss2: 1.468645 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.701071 Loss1: 0.303717 Loss2: 1.397354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.624025 Loss1: 0.232295 Loss2: 1.391730 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.599553 Loss1: 0.200368 Loss2: 1.399185
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.310152 Loss1: 1.424718 Loss2: 1.885434
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.377616 Loss1: 0.931048 Loss2: 1.446568
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.060305 Loss1: 0.597939 Loss2: 1.462367
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.866083 Loss1: 0.446973 Loss2: 1.419110
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.751358 Loss1: 0.333791 Loss2: 1.417567
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.492112 Loss1: 1.551556 Loss2: 1.940556
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.422665 Loss1: 0.954522 Loss2: 1.468143
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.764657 Loss1: 0.354179 Loss2: 1.410479
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.071205 Loss1: 0.600821 Loss2: 1.470384
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.696079 Loss1: 0.289074 Loss2: 1.407006
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.921911 Loss1: 0.496467 Loss2: 1.425444
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.611201 Loss1: 0.207408 Loss2: 1.403793
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.825703 Loss1: 0.365853 Loss2: 1.459850
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.607148 Loss1: 0.204461 Loss2: 1.402687
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.701553 Loss1: 0.275568 Loss2: 1.425985
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.630694 Loss1: 0.224562 Loss2: 1.406131
-(DefaultActor pid=3764) >> Training accuracy: 0.959961
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.618774 Loss1: 0.197660 Loss2: 1.421114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.649496 Loss1: 0.233233 Loss2: 1.416263 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.379866 Loss1: 0.929967 Loss2: 1.449899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.055457 Loss1: 0.624853 Loss2: 1.430604 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.873889 Loss1: 0.423812 Loss2: 1.450076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.748439 Loss1: 0.317792 Loss2: 1.430648 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.715696 Loss1: 0.287687 Loss2: 1.428009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.678674 Loss1: 0.260037 Loss2: 1.418637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.658451 Loss1: 0.229808 Loss2: 1.428643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.588561 Loss1: 0.168950 Loss2: 1.419611 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.645566 Loss1: 0.195335 Loss2: 1.450231 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.629774 Loss1: 0.189748 Loss2: 1.440026 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.403057 Loss1: 0.961328 Loss2: 1.441729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.893911 Loss1: 0.482372 Loss2: 1.411539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.772559 Loss1: 0.349069 Loss2: 1.423490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.361704 Loss1: 0.930787 Loss2: 1.430916
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.738842 Loss1: 0.336318 Loss2: 1.402524
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.006262 Loss1: 0.561247 Loss2: 1.445015
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.670004 Loss1: 0.261627 Loss2: 1.408377
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.856663 Loss1: 0.449733 Loss2: 1.406930
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.675180 Loss1: 0.265907 Loss2: 1.409273
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.614333 Loss1: 0.208267 Loss2: 1.406066
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.776519 Loss1: 0.361553 Loss2: 1.414966
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.576697 Loss1: 0.182987 Loss2: 1.393710
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.794284 Loss1: 0.372803 Loss2: 1.421481
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.678933 Loss1: 0.273767 Loss2: 1.405166
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.611089 Loss1: 0.215254 Loss2: 1.395834
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.558573 Loss1: 0.164077 Loss2: 1.394496
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.584033 Loss1: 0.187854 Loss2: 1.396179
-(DefaultActor pid=3765) >> Training accuracy: 0.945312
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.645327 Loss1: 1.539260 Loss2: 2.106067
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.379412 Loss1: 0.915682 Loss2: 1.463729
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.099839 Loss1: 0.622729 Loss2: 1.477110
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.000166 Loss1: 0.505583 Loss2: 1.494583
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.887315 Loss1: 0.431019 Loss2: 1.456295
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.828015 Loss1: 0.371331 Loss2: 1.456684
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.336270 Loss1: 1.337963 Loss2: 1.998307
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.723261 Loss1: 0.260232 Loss2: 1.463029 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 05:28:29,767 | server.py:236 | fit_round 65 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.640795 Loss1: 0.183075 Loss2: 1.457720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.629154 Loss1: 0.179815 Loss2: 1.449339 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.956558 Loss1: 0.420780 Loss2: 1.535778 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.821957 Loss1: 0.309830 Loss2: 1.512127 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.767303 Loss1: 0.257766 Loss2: 1.509538
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.355838 Loss1: 1.451399 Loss2: 1.904439
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.465467 Loss1: 1.012286 Loss2: 1.453181 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.129707 Loss1: 0.629192 Loss2: 1.500515 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.824462 Loss1: 0.378053 Loss2: 1.446409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.725836 Loss1: 0.297526 Loss2: 1.428310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.101285 Loss1: 1.260399 Loss2: 1.840886
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.713121 Loss1: 0.281335 Loss2: 1.431787
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.626678 Loss1: 0.189402 Loss2: 1.437276
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.241159 Loss1: 0.822408 Loss2: 1.418751
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.594139 Loss1: 0.174811 Loss2: 1.419328
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.917307 Loss1: 0.493837 Loss2: 1.423470
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.854473 Loss1: 0.457325 Loss2: 1.397148
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.819312 Loss1: 0.404636 Loss2: 1.414676
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.711246 Loss1: 0.318801 Loss2: 1.392445
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.630517 Loss1: 0.239569 Loss2: 1.390949
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.686354 Loss1: 1.704270 Loss2: 1.982084
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.588254 Loss1: 0.201409 Loss2: 1.386845
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.576429 Loss1: 1.093916 Loss2: 1.482513
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.234839 Loss1: 0.744980 Loss2: 1.489859
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.611153 Loss1: 0.231556 Loss2: 1.379597
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.941972 Loss1: 0.498244 Loss2: 1.443728
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.606831 Loss1: 0.213631 Loss2: 1.393199
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.696584 Loss1: 0.268190 Loss2: 1.428394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.721409 Loss1: 0.281844 Loss2: 1.439565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.711263 Loss1: 0.268840 Loss2: 1.442423 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.943080
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 05:28:29,767][flwr][DEBUG] - fit_round 65 received 50 results and 0 failures
-INFO flwr 2023-10-10 05:29:10,054 | server.py:125 | fit progress: (65, 2.3072678543889102, {'accuracy': 0.5214}, 149857.832570231)
->> Test accuracy: 0.521400
-[2023-10-10 05:29:10,054][flwr][INFO] - fit progress: (65, 2.3072678543889102, {'accuracy': 0.5214}, 149857.832570231)
-DEBUG flwr 2023-10-10 05:29:10,054 | server.py:173 | evaluate_round 65: strategy sampled 50 clients (out of 50)
-[2023-10-10 05:29:10,054][flwr][DEBUG] - evaluate_round 65: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 05:38:11,548 | server.py:187 | evaluate_round 65 received 50 results and 0 failures
-[2023-10-10 05:38:11,548][flwr][DEBUG] - evaluate_round 65 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 05:38:11,549 | server.py:222 | fit_round 66: strategy sampled 50 clients (out of 50)
-[2023-10-10 05:38:11,549][flwr][DEBUG] - fit_round 66: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.605107 Loss1: 1.538463 Loss2: 2.066644
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.517367 Loss1: 1.073290 Loss2: 1.444076
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.144483 Loss1: 0.619107 Loss2: 1.525377
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.916786 Loss1: 0.466747 Loss2: 1.450039
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.814510 Loss1: 0.369770 Loss2: 1.444740
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.736239 Loss1: 0.278563 Loss2: 1.457676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.685610 Loss1: 0.249583 Loss2: 1.436027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.639505 Loss1: 0.213934 Loss2: 1.425571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.659921 Loss1: 0.231212 Loss2: 1.428709
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.787886 Loss1: 0.416797 Loss2: 1.371090
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.630088 Loss1: 0.192134 Loss2: 1.437954
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.716537 Loss1: 0.336137 Loss2: 1.380400
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.638242 Loss1: 0.262555 Loss2: 1.375687
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.599932 Loss1: 0.237700 Loss2: 1.362232
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.579640 Loss1: 0.214756 Loss2: 1.364884
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.612045 Loss1: 0.242786 Loss2: 1.369259
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.491269 Loss1: 1.501988 Loss2: 1.989281
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.574449 Loss1: 0.213943 Loss2: 1.360506
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.003509 Loss1: 0.547556 Loss2: 1.455953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.723601 Loss1: 0.316506 Loss2: 1.407094 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.642080 Loss1: 0.238201 Loss2: 1.403879 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.572778 Loss1: 0.173183 Loss2: 1.399595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.543048 Loss1: 0.148433 Loss2: 1.394616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.496241 Loss1: 0.101811 Loss2: 1.394431 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985577
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.644975 Loss1: 0.345366 Loss2: 1.299609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486208 Loss1: 0.204364 Loss2: 1.281843 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416210 Loss1: 0.141140 Loss2: 1.275069
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.676186 Loss1: 1.604955 Loss2: 2.071231
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.602671 Loss1: 1.036801 Loss2: 1.565870 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404474 Loss1: 0.145817 Loss2: 1.258657
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.267445 Loss1: 0.725520 Loss2: 1.541925
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.063828 Loss1: 0.527657 Loss2: 1.536170
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.962262 Loss1: 0.457684 Loss2: 1.504578
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.951041 Loss1: 0.434203 Loss2: 1.516838
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.820800 Loss1: 0.309554 Loss2: 1.511246
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.819019 Loss1: 0.318378 Loss2: 1.500641
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.229023 Loss1: 1.373103 Loss2: 1.855920
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.358684 Loss1: 0.897622 Loss2: 1.461062 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973214
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.056276 Loss1: 0.619098 Loss2: 1.437178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.777465 Loss1: 0.355471 Loss2: 1.421993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.666109 Loss1: 0.256098 Loss2: 1.410012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.641895 Loss1: 0.238511 Loss2: 1.403384 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.985998 Loss1: 0.535481 Loss2: 1.450518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.873314 Loss1: 0.470345 Loss2: 1.402969 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.950195
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.676424 Loss1: 0.278398 Loss2: 1.398025 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.602185 Loss1: 0.215934 Loss2: 1.386251 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.555114 Loss1: 0.170884 Loss2: 1.384230
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.300587 Loss1: 1.490599 Loss2: 1.809988
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.570841 Loss1: 0.191653 Loss2: 1.379187
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.281993 Loss1: 0.857374 Loss2: 1.424619
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.967373 Loss1: 0.568788 Loss2: 1.398584
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.811331 Loss1: 0.420487 Loss2: 1.390844
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.747644 Loss1: 0.358086 Loss2: 1.389558
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.752183 Loss1: 0.360805 Loss2: 1.391378
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.297578 Loss1: 1.367182 Loss2: 1.930396
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.423554 Loss1: 0.945472 Loss2: 1.478082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.149048 Loss1: 0.639584 Loss2: 1.509464 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.611882 Loss1: 0.230916 Loss2: 1.380966
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.938168 Loss1: 0.495247 Loss2: 1.442921
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.565407 Loss1: 0.185637 Loss2: 1.379770
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.860609 Loss1: 0.405525 Loss2: 1.455083
-(DefaultActor pid=3764) >> Training accuracy: 0.973633
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.748201 Loss1: 0.303312 Loss2: 1.444889
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.675652 Loss1: 0.246102 Loss2: 1.429550
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.636575 Loss1: 0.202660 Loss2: 1.433915
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.586322 Loss1: 0.163021 Loss2: 1.423300
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.592262 Loss1: 0.174783 Loss2: 1.417479
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.450603 Loss1: 1.557551 Loss2: 1.893052
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.553086 Loss1: 1.052892 Loss2: 1.500194
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.127243 Loss1: 0.690430 Loss2: 1.436812
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.983886 Loss1: 0.549324 Loss2: 1.434561
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.835985 Loss1: 0.405752 Loss2: 1.430232
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.342267 Loss1: 1.552837 Loss2: 1.789430
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.784431 Loss1: 0.360535 Loss2: 1.423897
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.425776 Loss1: 1.019558 Loss2: 1.406218
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.774515 Loss1: 0.357904 Loss2: 1.416611
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.997019 Loss1: 0.631067 Loss2: 1.365951
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.700177 Loss1: 0.282291 Loss2: 1.417886
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.795158 Loss1: 0.435572 Loss2: 1.359585
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.635500 Loss1: 0.227431 Loss2: 1.408070
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.646149 Loss1: 0.302356 Loss2: 1.343792
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.624076 Loss1: 0.212565 Loss2: 1.411511
-(DefaultActor pid=3764) >> Training accuracy: 0.940625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.574903 Loss1: 0.239536 Loss2: 1.335367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489552 Loss1: 0.161683 Loss2: 1.327869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.526923 Loss1: 1.486081 Loss2: 2.040842
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453754 Loss1: 0.134892 Loss2: 1.318862
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.210887 Loss1: 0.657261 Loss2: 1.553626 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.758129 Loss1: 0.274034 Loss2: 1.484095 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.699738 Loss1: 0.230000 Loss2: 1.469738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.684019 Loss1: 0.218922 Loss2: 1.465097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.600392 Loss1: 0.133590 Loss2: 1.466802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.586552 Loss1: 0.131284 Loss2: 1.455268 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967548
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.783644 Loss1: 0.320169 Loss2: 1.463476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.771249 Loss1: 0.324671 Loss2: 1.446579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.843249 Loss1: 0.390202 Loss2: 1.453048
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.410119 Loss1: 1.517430 Loss2: 1.892689
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.410681 Loss1: 0.946707 Loss2: 1.463974 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.946875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.719488 Loss1: 0.262225 Loss2: 1.457263
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.951008 Loss1: 0.522650 Loss2: 1.428357
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.768141 Loss1: 0.354461 Loss2: 1.413680
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.702187 Loss1: 0.288359 Loss2: 1.413827
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.665669 Loss1: 0.263089 Loss2: 1.402581
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.700754 Loss1: 0.297262 Loss2: 1.403492
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.289315 Loss1: 1.397698 Loss2: 1.891617
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.652273 Loss1: 0.236448 Loss2: 1.415825
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.326372 Loss1: 0.896092 Loss2: 1.430280
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.610185 Loss1: 0.210118 Loss2: 1.400068
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.069699 Loss1: 0.614540 Loss2: 1.455158
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.625830 Loss1: 0.216888 Loss2: 1.408943
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.828449 Loss1: 0.410827 Loss2: 1.417623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.682010 Loss1: 0.271895 Loss2: 1.410115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.667516 Loss1: 0.256287 Loss2: 1.411229
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.357141 Loss1: 1.457909 Loss2: 1.899232
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.600265 Loss1: 0.201715 Loss2: 1.398550
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.335837 Loss1: 0.872548 Loss2: 1.463289
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.608592 Loss1: 0.212664 Loss2: 1.395928
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.127711 Loss1: 0.650688 Loss2: 1.477023
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.872748 Loss1: 0.452507 Loss2: 1.420241
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.805541 Loss1: 0.365361 Loss2: 1.440180
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.684933 Loss1: 0.268387 Loss2: 1.416546
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.635880 Loss1: 0.229038 Loss2: 1.406842
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.582187 Loss1: 0.174010 Loss2: 1.408177
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.451107 Loss1: 1.567873 Loss2: 1.883234
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.557907 Loss1: 0.146847 Loss2: 1.411060
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.541480 Loss1: 1.051913 Loss2: 1.489567
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.536664 Loss1: 0.136965 Loss2: 1.399699
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.130192 Loss1: 0.696782 Loss2: 1.433411
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.971487 Loss1: 0.544367 Loss2: 1.427120
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.852114 Loss1: 0.435302 Loss2: 1.416812
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.775367 Loss1: 0.346554 Loss2: 1.428812
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.683488 Loss1: 0.275120 Loss2: 1.408368
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.657362 Loss1: 0.252737 Loss2: 1.404625
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.302453 Loss1: 1.325514 Loss2: 1.976939
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.570517 Loss1: 0.169798 Loss2: 1.400719
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.410597 Loss1: 0.933937 Loss2: 1.476660
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.556877 Loss1: 0.161361 Loss2: 1.395516
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.024298 Loss1: 0.517643 Loss2: 1.506656
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.829807 Loss1: 0.393644 Loss2: 1.436162
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.760637 Loss1: 0.311466 Loss2: 1.449171
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.668958 Loss1: 0.223977 Loss2: 1.444981
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.693857 Loss1: 0.256318 Loss2: 1.437539
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.404985 Loss1: 1.502465 Loss2: 1.902520
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.603266 Loss1: 0.163823 Loss2: 1.439443
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.339212 Loss1: 0.916367 Loss2: 1.422845
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.586093 Loss1: 0.165132 Loss2: 1.420961
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.966249 Loss1: 0.544368 Loss2: 1.421880
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.583392 Loss1: 0.163799 Loss2: 1.419594
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.690233 Loss1: 0.296902 Loss2: 1.393331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.602690 Loss1: 0.226598 Loss2: 1.376091 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.651834 Loss1: 0.273109 Loss2: 1.378725
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.308616 Loss1: 1.424909 Loss2: 1.883707
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.620203 Loss1: 0.225744 Loss2: 1.394459
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.377246 Loss1: 0.948409 Loss2: 1.428837
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.546219 Loss1: 0.164101 Loss2: 1.382118
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.066877 Loss1: 0.614487 Loss2: 1.452389
-(DefaultActor pid=3765) >> Training accuracy: 0.946875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.880826 Loss1: 0.483905 Loss2: 1.396921
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.766541 Loss1: 0.351459 Loss2: 1.415082
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.716102 Loss1: 0.310609 Loss2: 1.405493
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.620648 Loss1: 0.225566 Loss2: 1.395082
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.364252 Loss1: 1.425217 Loss2: 1.939035
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.586554 Loss1: 0.209751 Loss2: 1.376804
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.413455 Loss1: 0.982713 Loss2: 1.430742
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.582635 Loss1: 0.189155 Loss2: 1.393481
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.146781 Loss1: 0.658850 Loss2: 1.487931
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.624055 Loss1: 0.241100 Loss2: 1.382955
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.726390 Loss1: 0.310589 Loss2: 1.415801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.629647 Loss1: 0.230314 Loss2: 1.399333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.286902 Loss1: 1.432288 Loss2: 1.854614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.360514 Loss1: 0.897747 Loss2: 1.462767 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.926339
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.767839 Loss1: 0.380140 Loss2: 1.387698 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.614776 Loss1: 0.242697 Loss2: 1.372079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.188852 Loss1: 1.318829 Loss2: 1.870023
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.566832 Loss1: 0.204372 Loss2: 1.362459
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.177632 Loss1: 0.780587 Loss2: 1.397045
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.549677 Loss1: 0.189576 Loss2: 1.360101
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.056773 Loss1: 0.643286 Loss2: 1.413487
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.559038 Loss1: 0.199274 Loss2: 1.359764
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.845550 Loss1: 0.455201 Loss2: 1.390349
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.544797 Loss1: 0.180237 Loss2: 1.364560
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.568159 Loss1: 0.209147 Loss2: 1.359011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.531071 Loss1: 0.177015 Loss2: 1.354056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488991 Loss1: 0.141784 Loss2: 1.347207
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.396624 Loss1: 1.377069 Loss2: 2.019555
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508861 Loss1: 0.164870 Loss2: 1.343991
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.540867 Loss1: 1.048809 Loss2: 1.492057
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.270399 Loss1: 0.607482 Loss2: 1.662917
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.899970 Loss1: 0.390128 Loss2: 1.509842
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.774561 Loss1: 0.313213 Loss2: 1.461348
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.776064 Loss1: 0.316336 Loss2: 1.459728
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.256044 Loss1: 1.348316 Loss2: 1.907729
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.729407 Loss1: 0.255918 Loss2: 1.473488
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.289907 Loss1: 0.800574 Loss2: 1.489333
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.692265 Loss1: 0.227223 Loss2: 1.465042
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.644313 Loss1: 0.179991 Loss2: 1.464322
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.051061 Loss1: 0.593997 Loss2: 1.457064
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.654176 Loss1: 0.203017 Loss2: 1.451159
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.840612 Loss1: 0.375809 Loss2: 1.464803
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.786220 Loss1: 0.333721 Loss2: 1.452498
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.709597 Loss1: 0.255398 Loss2: 1.454199
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.613923 Loss1: 0.191098 Loss2: 1.422825
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.613356 Loss1: 0.186627 Loss2: 1.426729
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.228505 Loss1: 1.365075 Loss2: 1.863429
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.584895 Loss1: 0.155800 Loss2: 1.429095
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.592518 Loss1: 0.161285 Loss2: 1.431233 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.939453
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.799283 Loss1: 0.422823 Loss2: 1.376460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.624249 Loss1: 0.259080 Loss2: 1.365168 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.566007 Loss1: 0.205285 Loss2: 1.360723
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.131966 Loss1: 1.198235 Loss2: 1.933731
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.164993 Loss1: 0.740013 Loss2: 1.424980 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.011898 Loss1: 0.559268 Loss2: 1.452630 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.515696 Loss1: 0.162421 Loss2: 1.353275
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.953117 Loss1: 0.502453 Loss2: 1.450664
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.823733 Loss1: 0.395782 Loss2: 1.427951
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.721784 Loss1: 0.284744 Loss2: 1.437040
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.621432 Loss1: 0.215997 Loss2: 1.405435
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.614861 Loss1: 0.201921 Loss2: 1.412940
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.395998 Loss1: 1.473122 Loss2: 1.922876
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.600168 Loss1: 0.193703 Loss2: 1.406466
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.560382 Loss1: 0.156956 Loss2: 1.403427 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.954167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.917334 Loss1: 0.458384 Loss2: 1.458950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.754603 Loss1: 0.295371 Loss2: 1.459233 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.685918 Loss1: 0.236122 Loss2: 1.449796
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.434140 Loss1: 1.479894 Loss2: 1.954245
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.319995 Loss1: 0.855512 Loss2: 1.464483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.122352 Loss1: 0.624810 Loss2: 1.497542 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.602638 Loss1: 0.175682 Loss2: 1.426957
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.907848 Loss1: 0.449405 Loss2: 1.458443
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.726712 Loss1: 0.283168 Loss2: 1.443544
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.697206 Loss1: 0.259049 Loss2: 1.438156
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.660319 Loss1: 0.221700 Loss2: 1.438619
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.671683 Loss1: 0.229502 Loss2: 1.442181
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.374055 Loss1: 1.429503 Loss2: 1.944552
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.681334 Loss1: 0.242143 Loss2: 1.439191
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.390830 Loss1: 0.915994 Loss2: 1.474836
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.635615 Loss1: 0.193249 Loss2: 1.442366
-(DefaultActor pid=3765) >> Training accuracy: 0.954167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.920866 Loss1: 0.461201 Loss2: 1.459665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.790617 Loss1: 0.344324 Loss2: 1.446293 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.695799 Loss1: 0.243901 Loss2: 1.451898
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.238990 Loss1: 1.322857 Loss2: 1.916133
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.342115 Loss1: 0.829543 Loss2: 1.512572 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.951101 Loss1: 0.482190 Loss2: 1.468911 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.868494 Loss1: 0.408328 Loss2: 1.460166 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.719129 Loss1: 0.273931 Loss2: 1.445199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.259006 Loss1: 1.429697 Loss2: 1.829309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.383879 Loss1: 0.998125 Loss2: 1.385754 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.080727 Loss1: 0.660265 Loss2: 1.420462 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965074
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.920718 Loss1: 0.533452 Loss2: 1.387266 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.677154 Loss1: 0.299822 Loss2: 1.377333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.558513 Loss1: 0.204770 Loss2: 1.353743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.533838 Loss1: 0.180830 Loss2: 1.353009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.569369 Loss1: 0.210239 Loss2: 1.359130 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.952254 Loss1: 0.472033 Loss2: 1.480222 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.854246 Loss1: 0.351918 Loss2: 1.502328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.390845 Loss1: 1.448961 Loss2: 1.941884 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.480019 Loss1: 0.950613 Loss2: 1.529406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.077293 Loss1: 0.559702 Loss2: 1.517591 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.841734 Loss1: 0.336306 Loss2: 1.505428 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.734765 Loss1: 0.252875 Loss2: 1.481889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.733256 Loss1: 0.257259 Loss2: 1.475997
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.367525 Loss1: 1.427050 Loss2: 1.940476
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.732216 Loss1: 0.245810 Loss2: 1.486405
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.408124 Loss1: 0.939622 Loss2: 1.468502
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.116552 Loss1: 0.607741 Loss2: 1.508810
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.715119 Loss1: 0.232541 Loss2: 1.482578
-(DefaultActor pid=3764) >> Training accuracy: 0.962891
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.822718 Loss1: 0.359919 Loss2: 1.462799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.751775 Loss1: 0.303423 Loss2: 1.448352 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.717052 Loss1: 0.260544 Loss2: 1.456508
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.288250 Loss1: 1.413739 Loss2: 1.874511
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.284816 Loss1: 0.852889 Loss2: 1.431927 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.591771 Loss1: 0.156858 Loss2: 1.434913
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.042123 Loss1: 0.595241 Loss2: 1.446882
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.916353 Loss1: 0.500422 Loss2: 1.415930
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.789538 Loss1: 0.358153 Loss2: 1.431386
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.712573 Loss1: 0.314791 Loss2: 1.397782
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.614781 Loss1: 0.208142 Loss2: 1.406639
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.572570 Loss1: 1.543335 Loss2: 2.029235
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.648090 Loss1: 0.244617 Loss2: 1.403474
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.609086 Loss1: 0.202868 Loss2: 1.406218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.549732 Loss1: 0.157823 Loss2: 1.391909 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.874659 Loss1: 0.372940 Loss2: 1.501718 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.760465 Loss1: 0.263418 Loss2: 1.497047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.241224 Loss1: 1.407294 Loss2: 1.833930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.180845 Loss1: 0.792542 Loss2: 1.388303 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955357
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699049 Loss1: 0.337118 Loss2: 1.361931 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.617216 Loss1: 0.257870 Loss2: 1.359346 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.650435 Loss1: 0.282506 Loss2: 1.367929
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.393373 Loss1: 1.439281 Loss2: 1.954092
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.378942 Loss1: 0.869511 Loss2: 1.509431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.083134 Loss1: 0.576176 Loss2: 1.506958 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.939081 Loss1: 0.444485 Loss2: 1.494597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.764529 Loss1: 0.291945 Loss2: 1.472584 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.693629 Loss1: 0.233036 Loss2: 1.460593 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.687420 Loss1: 0.217484 Loss2: 1.469936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.649047 Loss1: 0.181003 Loss2: 1.468044 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.946289
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.953518 Loss1: 0.477202 Loss2: 1.476316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.806783 Loss1: 0.339234 Loss2: 1.467549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.710478 Loss1: 0.247611 Loss2: 1.462867
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.389568 Loss1: 1.372439 Loss2: 2.017129
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.450434 Loss1: 0.870167 Loss2: 1.580267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.122590 Loss1: 0.557279 Loss2: 1.565311 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958984
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.943210 Loss1: 0.386343 Loss2: 1.556867 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.826907 Loss1: 0.296211 Loss2: 1.530696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.771349 Loss1: 0.236035 Loss2: 1.535313 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.180550 Loss1: 0.798124 Loss2: 1.382426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.924253 Loss1: 0.538036 Loss2: 1.386217 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.949219
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.636495 Loss1: 0.284083 Loss2: 1.352412 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.600677 Loss1: 0.247159 Loss2: 1.353518 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.553622 Loss1: 0.206028 Loss2: 1.347595
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.309367 Loss1: 1.329973 Loss2: 1.979395
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.527698 Loss1: 0.184347 Loss2: 1.343351
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.324341 Loss1: 0.833990 Loss2: 1.490351
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.570650 Loss1: 0.223830 Loss2: 1.346820
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.023725 Loss1: 0.500553 Loss2: 1.523173
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.918613 Loss1: 0.437863 Loss2: 1.480750
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.829823 Loss1: 0.346765 Loss2: 1.483057
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.722785 Loss1: 0.242910 Loss2: 1.479875
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.694973 Loss1: 0.231453 Loss2: 1.463520
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.412753 Loss1: 1.498717 Loss2: 1.914036
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.678462 Loss1: 0.202559 Loss2: 1.475903
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.483812 Loss1: 1.042712 Loss2: 1.441100
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.632911 Loss1: 0.170770 Loss2: 1.462140
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.059823 Loss1: 0.606326 Loss2: 1.453496
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.589384 Loss1: 0.135549 Loss2: 1.453835
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.807535 Loss1: 0.372091 Loss2: 1.435444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.667592 Loss1: 0.243679 Loss2: 1.423913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.612965 Loss1: 0.203507 Loss2: 1.409457
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.355871 Loss1: 1.503996 Loss2: 1.851874
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.629539 Loss1: 0.226167 Loss2: 1.403371
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.475578 Loss1: 1.020209 Loss2: 1.455369
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.583086 Loss1: 0.179901 Loss2: 1.403186
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.989469 Loss1: 0.576911 Loss2: 1.412559
-(DefaultActor pid=3764) >> Training accuracy: 0.946875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.848669 Loss1: 0.450165 Loss2: 1.398504
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.696107 Loss1: 0.309695 Loss2: 1.386413
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.742516 Loss1: 0.354396 Loss2: 1.388120
-DEBUG flwr 2023-10-10 06:06:36,986 | server.py:236 | fit_round 66 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.644080 Loss1: 0.256143 Loss2: 1.387937
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.265004 Loss1: 1.430103 Loss2: 1.834900
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.627945 Loss1: 0.245080 Loss2: 1.382865
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.406692 Loss1: 1.021245 Loss2: 1.385447
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.559525 Loss1: 0.180674 Loss2: 1.378851
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.057420 Loss1: 0.620905 Loss2: 1.436515
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.549918 Loss1: 0.179754 Loss2: 1.370164
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.762520 Loss1: 0.373454 Loss2: 1.389066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.662324 Loss1: 0.300690 Loss2: 1.361633 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.572015 Loss1: 0.193072 Loss2: 1.378942
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.216657 Loss1: 1.366364 Loss2: 1.850292
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.225408 Loss1: 0.843752 Loss2: 1.381656 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.872833 Loss1: 0.508147 Loss2: 1.364686 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.604549 Loss1: 0.266818 Loss2: 1.337731 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.546019 Loss1: 0.219142 Loss2: 1.326877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.551904 Loss1: 0.222655 Loss2: 1.329249 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.532665 Loss1: 0.195481 Loss2: 1.337184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.501111 Loss1: 0.168579 Loss2: 1.332533 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.743812 Loss1: 0.349451 Loss2: 1.394361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.627624 Loss1: 0.242826 Loss2: 1.384799 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.596324 Loss1: 0.208862 Loss2: 1.387462
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.462100 Loss1: 1.514303 Loss2: 1.947798
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.454328 Loss1: 0.962740 Loss2: 1.491588 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.541024 Loss1: 0.157749 Loss2: 1.383275
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.025119 Loss1: 0.513391 Loss2: 1.511728
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.856642 Loss1: 0.396068 Loss2: 1.460575
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.768416 Loss1: 0.295994 Loss2: 1.472422
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.741302 Loss1: 0.283573 Loss2: 1.457729
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.726780 Loss1: 0.256623 Loss2: 1.470157
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.229078 Loss1: 1.346255 Loss2: 1.882823
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.695229 Loss1: 0.232241 Loss2: 1.462988
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.399247 Loss1: 0.931819 Loss2: 1.467428
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.608441 Loss1: 0.149628 Loss2: 1.458813
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.991959 Loss1: 0.512721 Loss2: 1.479237
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.592983 Loss1: 0.147604 Loss2: 1.445379
-(DefaultActor pid=3765) >> Training accuracy: 0.962500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.692789 Loss1: 0.251121 Loss2: 1.441668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.640280 Loss1: 0.207013 Loss2: 1.433267 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.730552 Loss1: 0.282402 Loss2: 1.448150 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.943359
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 06:06:36,986][flwr][DEBUG] - fit_round 66 received 50 results and 0 failures
-INFO flwr 2023-10-10 06:07:18,962 | server.py:125 | fit progress: (66, 2.3170356525780673, {'accuracy': 0.5231}, 152146.740390885)
->> Test accuracy: 0.523100
-[2023-10-10 06:07:18,962][flwr][INFO] - fit progress: (66, 2.3170356525780673, {'accuracy': 0.5231}, 152146.740390885)
-DEBUG flwr 2023-10-10 06:07:18,962 | server.py:173 | evaluate_round 66: strategy sampled 50 clients (out of 50)
-[2023-10-10 06:07:18,962][flwr][DEBUG] - evaluate_round 66: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 06:16:21,950 | server.py:187 | evaluate_round 66 received 50 results and 0 failures
-[2023-10-10 06:16:21,950][flwr][DEBUG] - evaluate_round 66 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 06:16:21,951 | server.py:222 | fit_round 67: strategy sampled 50 clients (out of 50)
-[2023-10-10 06:16:21,951][flwr][DEBUG] - fit_round 67: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.311802 Loss1: 1.278225 Loss2: 2.033576 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.999931 Loss1: 0.509084 Loss2: 1.490847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.874905 Loss1: 0.420846 Loss2: 1.454059
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.606767 Loss1: 1.628635 Loss2: 1.978132
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.782306 Loss1: 0.324451 Loss2: 1.457855
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.492178 Loss1: 1.023614 Loss2: 1.468564
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.695427 Loss1: 0.255211 Loss2: 1.440216
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.059056 Loss1: 0.585024 Loss2: 1.474032
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.663874 Loss1: 0.225166 Loss2: 1.438708
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.872241 Loss1: 0.435519 Loss2: 1.436723
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.774176 Loss1: 0.342548 Loss2: 1.431628
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.587227 Loss1: 0.156464 Loss2: 1.430763
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.711844 Loss1: 0.285100 Loss2: 1.426745
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.573421 Loss1: 0.151154 Loss2: 1.422267
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.677199 Loss1: 0.249759 Loss2: 1.427441
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.546836 Loss1: 0.123162 Loss2: 1.423674
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.659903 Loss1: 0.235302 Loss2: 1.424602 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.950893
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.154382 Loss1: 1.294351 Loss2: 1.860031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.952151 Loss1: 0.527911 Loss2: 1.424241 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.795868 Loss1: 0.399799 Loss2: 1.396070 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.662552 Loss1: 0.266483 Loss2: 1.396069 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.595160 Loss1: 0.217069 Loss2: 1.378091 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.646862 Loss1: 0.265297 Loss2: 1.381566 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.606272 Loss1: 0.221032 Loss2: 1.385240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.533468 Loss1: 0.151506 Loss2: 1.381962 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.509156 Loss1: 0.132611 Loss2: 1.376544 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981618
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.642647 Loss1: 0.221573 Loss2: 1.421073 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.959961
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.626427 Loss1: 1.657535 Loss2: 1.968893 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.054955 Loss1: 0.582843 Loss2: 1.472112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.513124 Loss1: 1.496717 Loss2: 2.016407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.657126 Loss1: 1.100291 Loss2: 1.556834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.279771 Loss1: 0.728591 Loss2: 1.551181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.079499 Loss1: 0.561496 Loss2: 1.518003 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.896539 Loss1: 0.394630 Loss2: 1.501909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.765013 Loss1: 0.265375 Loss2: 1.499639 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.960938
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.701383 Loss1: 0.223456 Loss2: 1.477927 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.680166 Loss1: 0.201659 Loss2: 1.478508 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.322478 Loss1: 0.818242 Loss2: 1.504236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.889847 Loss1: 0.425926 Loss2: 1.463921 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.814179 Loss1: 0.333316 Loss2: 1.480863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.688021 Loss1: 0.236297 Loss2: 1.451724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.684951 Loss1: 0.228750 Loss2: 1.456200 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.643641 Loss1: 0.200248 Loss2: 1.443394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.673244 Loss1: 0.217442 Loss2: 1.455802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.669413 Loss1: 0.218514 Loss2: 1.450899 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.943359
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.645867 Loss1: 0.173442 Loss2: 1.472425 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.310072 Loss1: 1.434584 Loss2: 1.875488 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.981928 Loss1: 0.577332 Loss2: 1.404595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.831908 Loss1: 0.451668 Loss2: 1.380240
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.415003 Loss1: 1.418534 Loss2: 1.996469
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.460040 Loss1: 0.946059 Loss2: 1.513981 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.113086 Loss1: 0.592780 Loss2: 1.520306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.904805 Loss1: 0.438107 Loss2: 1.466697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.788276 Loss1: 0.307526 Loss2: 1.480751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.842946 Loss1: 0.376905 Loss2: 1.466042 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.567229 Loss1: 0.195230 Loss2: 1.372000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.742219 Loss1: 0.268626 Loss2: 1.473593
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.784859 Loss1: 0.317549 Loss2: 1.467310
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.736376 Loss1: 0.265709 Loss2: 1.470667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.635820 Loss1: 0.177634 Loss2: 1.458186
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.209263 Loss1: 1.334739 Loss2: 1.874525
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.349257 Loss1: 0.910944 Loss2: 1.438313
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.083684 Loss1: 0.627467 Loss2: 1.456217
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.393483 Loss1: 1.498718 Loss2: 1.894766
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.830807 Loss1: 0.407092 Loss2: 1.423715
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.446893 Loss1: 0.993940 Loss2: 1.452953
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.710780 Loss1: 0.294777 Loss2: 1.416002
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.140117 Loss1: 0.665899 Loss2: 1.474218
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.638733 Loss1: 0.238362 Loss2: 1.400370
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.589471 Loss1: 0.188959 Loss2: 1.400512 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.572112 Loss1: 0.173244 Loss2: 1.398868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.624678 Loss1: 0.223544 Loss2: 1.401134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.555447 Loss1: 0.147127 Loss2: 1.408320 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.606231 Loss1: 0.206482 Loss2: 1.399749 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.527338 Loss1: 1.551855 Loss2: 1.975483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.136219 Loss1: 0.627734 Loss2: 1.508484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.891923 Loss1: 0.424267 Loss2: 1.467656
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.403444 Loss1: 1.488708 Loss2: 1.914736
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.808723 Loss1: 0.339803 Loss2: 1.468920
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.348879 Loss1: 0.915537 Loss2: 1.433342
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.748320 Loss1: 0.277082 Loss2: 1.471238
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.056440 Loss1: 0.601972 Loss2: 1.454468
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.738001 Loss1: 0.281037 Loss2: 1.456964
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.831879 Loss1: 0.410153 Loss2: 1.421726
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.708734 Loss1: 0.246183 Loss2: 1.462551
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.742294 Loss1: 0.319527 Loss2: 1.422766
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.700608 Loss1: 0.235516 Loss2: 1.465092
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.693262 Loss1: 0.284825 Loss2: 1.408438
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.710376 Loss1: 0.258147 Loss2: 1.452230
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.660101 Loss1: 0.251215 Loss2: 1.408886
-(DefaultActor pid=3765) >> Training accuracy: 0.944792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.630255 Loss1: 0.222817 Loss2: 1.407438
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.609237 Loss1: 0.199256 Loss2: 1.409981
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.532126 Loss1: 0.131423 Loss2: 1.400704
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.172314 Loss1: 1.303249 Loss2: 1.869066
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.174437 Loss1: 0.783719 Loss2: 1.390718
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.994330 Loss1: 0.582045 Loss2: 1.412285
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.783440 Loss1: 0.394164 Loss2: 1.389277
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.340526 Loss1: 1.403686 Loss2: 1.936840
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.707138 Loss1: 0.337692 Loss2: 1.369446
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.395895 Loss1: 0.930932 Loss2: 1.464963
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.619748 Loss1: 0.248253 Loss2: 1.371495
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.036269 Loss1: 0.559077 Loss2: 1.477192
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.597364 Loss1: 0.235121 Loss2: 1.362243
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.910604 Loss1: 0.476615 Loss2: 1.433989
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.560443 Loss1: 0.196256 Loss2: 1.364187
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.815407 Loss1: 0.360157 Loss2: 1.455250
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.530517 Loss1: 0.180342 Loss2: 1.350175
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.693148 Loss1: 0.259621 Loss2: 1.433527
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.511986 Loss1: 0.156606 Loss2: 1.355379
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.607489 Loss1: 0.188769 Loss2: 1.418720
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.604822 Loss1: 0.195099 Loss2: 1.409723
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.597540 Loss1: 0.188646 Loss2: 1.408893
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.568268 Loss1: 0.155568 Loss2: 1.412700
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.309799 Loss1: 1.432348 Loss2: 1.877452
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.270545 Loss1: 0.863934 Loss2: 1.406612
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.017728 Loss1: 0.559305 Loss2: 1.458423
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.833382 Loss1: 0.443754 Loss2: 1.389628
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.357028 Loss1: 1.381898 Loss2: 1.975131
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.733675 Loss1: 0.310030 Loss2: 1.423644
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.366785 Loss1: 0.858359 Loss2: 1.508426
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.651969 Loss1: 0.259408 Loss2: 1.392561
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.962776 Loss1: 0.481489 Loss2: 1.481287
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.635779 Loss1: 0.242636 Loss2: 1.393143
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.947780 Loss1: 0.465040 Loss2: 1.482739
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.634763 Loss1: 0.234858 Loss2: 1.399905
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.797433 Loss1: 0.323820 Loss2: 1.473613
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.582711 Loss1: 0.188777 Loss2: 1.393934
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.761870 Loss1: 0.298183 Loss2: 1.463687
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.557622 Loss1: 0.166489 Loss2: 1.391133
-(DefaultActor pid=3765) >> Training accuracy: 0.957292
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.724549 Loss1: 0.256536 Loss2: 1.468012
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.636395 Loss1: 0.187349 Loss2: 1.449046
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.641561 Loss1: 0.194355 Loss2: 1.447206
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.600896 Loss1: 0.149839 Loss2: 1.451057
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.325315 Loss1: 1.374988 Loss2: 1.950327
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.381535 Loss1: 0.893868 Loss2: 1.487667
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.083365 Loss1: 0.606665 Loss2: 1.476700
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.869787 Loss1: 0.420859 Loss2: 1.448928
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.156466 Loss1: 1.299933 Loss2: 1.856533
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.224369 Loss1: 0.780653 Loss2: 1.443716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.972775 Loss1: 0.518538 Loss2: 1.454237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.829734 Loss1: 0.406136 Loss2: 1.423598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.676421 Loss1: 0.271351 Loss2: 1.405070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.672316 Loss1: 0.264983 Loss2: 1.407333 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.638646 Loss1: 0.239444 Loss2: 1.399203 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.544226 Loss1: 0.161113 Loss2: 1.383113 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965820
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.253391 Loss1: 1.314003 Loss2: 1.939388 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.988391 Loss1: 0.503593 Loss2: 1.484798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.379797 Loss1: 1.518786 Loss2: 1.861011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.388541 Loss1: 0.979488 Loss2: 1.409053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.134826 Loss1: 0.713567 Loss2: 1.421259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.899364 Loss1: 0.492647 Loss2: 1.406717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.741217 Loss1: 0.352045 Loss2: 1.389172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.676111 Loss1: 0.304502 Loss2: 1.371610 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.626095 Loss1: 0.250368 Loss2: 1.375727 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.555671 Loss1: 0.193770 Loss2: 1.361901 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.447593 Loss1: 1.041492 Loss2: 1.406102 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.874374 Loss1: 0.487694 Loss2: 1.386680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.679313 Loss1: 0.307743 Loss2: 1.371570
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.339871 Loss1: 1.444148 Loss2: 1.895723
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.681648 Loss1: 0.324749 Loss2: 1.356899
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.396690 Loss1: 0.913930 Loss2: 1.482760
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.098110 Loss1: 0.651667 Loss2: 1.446443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.943643 Loss1: 0.489266 Loss2: 1.454377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.794124 Loss1: 0.377865 Loss2: 1.416259 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.669131 Loss1: 0.250271 Loss2: 1.418860 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.579739 Loss1: 0.186258 Loss2: 1.393481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.547090 Loss1: 0.158942 Loss2: 1.388148 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.024891 Loss1: 0.603712 Loss2: 1.421180 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.681066 Loss1: 0.306989 Loss2: 1.374078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.212961 Loss1: 1.394416 Loss2: 1.818545
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.631820 Loss1: 0.270951 Loss2: 1.360870
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.382226 Loss1: 0.952715 Loss2: 1.429511
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.561533 Loss1: 0.211761 Loss2: 1.349772
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.001418 Loss1: 0.604382 Loss2: 1.397036
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.517770 Loss1: 0.162305 Loss2: 1.355465
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.508801 Loss1: 0.162092 Loss2: 1.346709
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.865691 Loss1: 0.460797 Loss2: 1.404894
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.546185 Loss1: 0.195892 Loss2: 1.350293
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.740825 Loss1: 0.359745 Loss2: 1.381080
-(DefaultActor pid=3765) >> Training accuracy: 0.943750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.693264 Loss1: 0.325648 Loss2: 1.367616
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.683097 Loss1: 0.313857 Loss2: 1.369240
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.589495 Loss1: 0.223043 Loss2: 1.366451
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.592975 Loss1: 0.214977 Loss2: 1.377998
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.304201 Loss1: 1.435246 Loss2: 1.868955
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.588019 Loss1: 0.213833 Loss2: 1.374186
-(DefaultActor pid=3764) >> Training accuracy: 0.960938
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.019372 Loss1: 0.579744 Loss2: 1.439628 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.810620 Loss1: 0.391425 Loss2: 1.419196 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.269958 Loss1: 1.432000 Loss2: 1.837958
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.759157 Loss1: 0.360875 Loss2: 1.398282
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.290059 Loss1: 0.933288 Loss2: 1.356771
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.708624 Loss1: 0.306140 Loss2: 1.402484
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.902700 Loss1: 0.548028 Loss2: 1.354671
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.650846 Loss1: 0.248186 Loss2: 1.402660
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.585499 Loss1: 0.195943 Loss2: 1.389556 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.565387 Loss1: 0.178651 Loss2: 1.386736 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.553521 Loss1: 0.231903 Loss2: 1.321618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493477 Loss1: 0.177552 Loss2: 1.315925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.501251 Loss1: 0.182570 Loss2: 1.318681
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.443194 Loss1: 1.533090 Loss2: 1.910104
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.376371 Loss1: 0.942922 Loss2: 1.433449
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.996630 Loss1: 0.552185 Loss2: 1.444445
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.794258 Loss1: 0.398053 Loss2: 1.396205
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.693104 Loss1: 0.273184 Loss2: 1.419920
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.295610 Loss1: 1.390226 Loss2: 1.905384
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.672112 Loss1: 0.282298 Loss2: 1.389814
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.646467 Loss1: 0.242129 Loss2: 1.404338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.618824 Loss1: 0.220087 Loss2: 1.398737 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.582061 Loss1: 0.189724 Loss2: 1.392337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.531811 Loss1: 0.151053 Loss2: 1.380758 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.618612 Loss1: 0.243125 Loss2: 1.375487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.570631 Loss1: 0.200538 Loss2: 1.370092 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.946875
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.569589 Loss1: 0.195680 Loss2: 1.373909
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.213678 Loss1: 1.325269 Loss2: 1.888409
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.174348 Loss1: 0.767689 Loss2: 1.406659
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.926692 Loss1: 0.496684 Loss2: 1.430008
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.761595 Loss1: 0.377133 Loss2: 1.384462
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.626552 Loss1: 0.229800 Loss2: 1.396753
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.169482 Loss1: 1.238697 Loss2: 1.930785
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.297591 Loss1: 0.862075 Loss2: 1.435516 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.928981 Loss1: 0.455921 Loss2: 1.473060 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.832556 Loss1: 0.416877 Loss2: 1.415679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.712635 Loss1: 0.259813 Loss2: 1.452822 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.520732 Loss1: 0.145525 Loss2: 1.375207
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.667039 Loss1: 0.251809 Loss2: 1.415231
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.645035 Loss1: 0.225455 Loss2: 1.419579
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.605019 Loss1: 0.185322 Loss2: 1.419697
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.581188 Loss1: 0.167613 Loss2: 1.413575
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.622812 Loss1: 0.201148 Loss2: 1.421664
-(DefaultActor pid=3764) >> Training accuracy: 0.943750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.379813 Loss1: 1.471206 Loss2: 1.908607
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.329103 Loss1: 0.962512 Loss2: 1.366591
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.022248 Loss1: 0.616134 Loss2: 1.406115
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.816508 Loss1: 0.444868 Loss2: 1.371640
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.705969 Loss1: 0.348701 Loss2: 1.357268
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.660789 Loss1: 0.288094 Loss2: 1.372696
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.538812 Loss1: 1.558070 Loss2: 1.980742
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.533179 Loss1: 0.995484 Loss2: 1.537695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.095200 Loss1: 0.609572 Loss2: 1.485627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.894084 Loss1: 0.410465 Loss2: 1.483619 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972356
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.741877 Loss1: 0.277531 Loss2: 1.464346 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.609472 Loss1: 0.169626 Loss2: 1.439846 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.582353 Loss1: 0.141223 Loss2: 1.441130
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.332201 Loss1: 1.390513 Loss2: 1.941688
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.346658 Loss1: 0.887912 Loss2: 1.458747 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.847555 Loss1: 0.417619 Loss2: 1.429936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.662366 Loss1: 0.233542 Loss2: 1.428823 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.661501 Loss1: 0.237654 Loss2: 1.423848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.594332 Loss1: 0.163104 Loss2: 1.431228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.899713 Loss1: 0.485800 Loss2: 1.413912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.832080 Loss1: 0.420097 Loss2: 1.411983 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.690452 Loss1: 0.265426 Loss2: 1.425025 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.696163 Loss1: 0.279841 Loss2: 1.416322 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.422951 Loss1: 1.461736 Loss2: 1.961215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.080547 Loss1: 0.574813 Loss2: 1.505734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.886254 Loss1: 0.422693 Loss2: 1.463562
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.262215 Loss1: 1.415508 Loss2: 1.846707
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.343880 Loss1: 0.929061 Loss2: 1.414819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.958162 Loss1: 0.539509 Loss2: 1.418652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.789081 Loss1: 0.420914 Loss2: 1.368166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.677475 Loss1: 0.303842 Loss2: 1.373632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.602412 Loss1: 0.232623 Loss2: 1.369789 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.620692 Loss1: 0.248119 Loss2: 1.372573 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.649029 Loss1: 0.276007 Loss2: 1.373022 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.920833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.542169 Loss1: 1.601315 Loss2: 1.940854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.059944 Loss1: 0.592483 Loss2: 1.467461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.197403 Loss1: 1.260177 Loss2: 1.937226 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.393372 Loss1: 0.930639 Loss2: 1.462733 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.104277 Loss1: 0.593517 Loss2: 1.510760 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.916955 Loss1: 0.474834 Loss2: 1.442121 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.716933 Loss1: 0.271114 Loss2: 1.445819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.622079 Loss1: 0.202005 Loss2: 1.420074 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.603439 Loss1: 0.184882 Loss2: 1.418557 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.578962 Loss1: 0.157659 Loss2: 1.421303 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.542393 Loss1: 1.055022 Loss2: 1.487371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.855957 Loss1: 0.401632 Loss2: 1.454325 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.760922 Loss1: 0.298920 Loss2: 1.462002
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.307935 Loss1: 1.378227 Loss2: 1.929708
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.689805 Loss1: 0.230434 Loss2: 1.459371
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.417048 Loss1: 0.946790 Loss2: 1.470258
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.688782 Loss1: 0.238382 Loss2: 1.450399
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.176200 Loss1: 0.685101 Loss2: 1.491099
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.999833 Loss1: 0.552714 Loss2: 1.447120 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.803245 Loss1: 0.355776 Loss2: 1.447469 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958705
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.617885 Loss1: 0.167046 Loss2: 1.450839
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.763152 Loss1: 0.333036 Loss2: 1.430117
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.700194 Loss1: 0.261753 Loss2: 1.438441
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.691529 Loss1: 0.266465 Loss2: 1.425064
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.681210 Loss1: 0.253941 Loss2: 1.427269
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.591829 Loss1: 0.162571 Loss2: 1.429258
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.341247 Loss1: 1.408046 Loss2: 1.933200
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.412491 Loss1: 0.902854 Loss2: 1.509637
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.073052 Loss1: 0.587899 Loss2: 1.485153
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.894070 Loss1: 0.424933 Loss2: 1.469137
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.819536 Loss1: 0.353591 Loss2: 1.465944
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.217498 Loss1: 1.433021 Loss2: 1.784477
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.708390 Loss1: 0.257817 Loss2: 1.450573
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.328924 Loss1: 0.974504 Loss2: 1.354421
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.970029 Loss1: 0.588716 Loss2: 1.381313
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.702713 Loss1: 0.244907 Loss2: 1.457807
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.806210 Loss1: 0.480167 Loss2: 1.326043
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.676855 Loss1: 0.232458 Loss2: 1.444397
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.681967 Loss1: 0.340013 Loss2: 1.341955
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.616053 Loss1: 0.171935 Loss2: 1.444118
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.676083 Loss1: 0.344459 Loss2: 1.331623
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.615696 Loss1: 0.187573 Loss2: 1.428124
-(DefaultActor pid=3765) >> Training accuracy: 0.963867
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.560346 Loss1: 0.240794 Loss2: 1.319552 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.564627 Loss1: 0.242232 Loss2: 1.322396 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.953125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.418985 Loss1: 1.004302 Loss2: 1.414682 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.863987 Loss1: 0.472326 Loss2: 1.391662 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.726217 Loss1: 0.334340 Loss2: 1.391876
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.336018 Loss1: 1.448850 Loss2: 1.887167
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.371522 Loss1: 0.951125 Loss2: 1.420396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.017044 Loss1: 0.580197 Loss2: 1.436847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.586106 Loss1: 0.202891 Loss2: 1.383215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.524985 Loss1: 0.144051 Loss2: 1.380934 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967548
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.653732 Loss1: 0.266149 Loss2: 1.387583 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.602157 Loss1: 0.204296 Loss2: 1.397861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.445393 Loss1: 1.371351 Loss2: 2.074042
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.562378 Loss1: 0.178428 Loss2: 1.383950
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-10 06:45:02,196 | server.py:236 | fit_round 67 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.132732 Loss1: 0.543221 Loss2: 1.589511 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.870300 Loss1: 0.318928 Loss2: 1.551371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.819623 Loss1: 0.287168 Loss2: 1.532455
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.163758 Loss1: 1.247355 Loss2: 1.916403
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.322313 Loss1: 0.895964 Loss2: 1.426350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.066537 Loss1: 0.604305 Loss2: 1.462232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.887371 Loss1: 0.464142 Loss2: 1.423230 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.932292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.777757 Loss1: 0.240126 Loss2: 1.537631
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.735594 Loss1: 0.304266 Loss2: 1.431329
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.643715 Loss1: 0.235886 Loss2: 1.407828
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.672793 Loss1: 0.275567 Loss2: 1.397226
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.615333 Loss1: 0.206185 Loss2: 1.409148
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.596969 Loss1: 0.195372 Loss2: 1.401597
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.260081 Loss1: 1.392550 Loss2: 1.867532
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.560502 Loss1: 0.158932 Loss2: 1.401569
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.976871 Loss1: 0.563325 Loss2: 1.413546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.760592 Loss1: 0.344892 Loss2: 1.415700 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.519872 Loss1: 1.612874 Loss2: 1.906998
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.677020 Loss1: 0.284511 Loss2: 1.392509
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.592125 Loss1: 1.080241 Loss2: 1.511883
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.616390 Loss1: 0.226075 Loss2: 1.390314
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.568182 Loss1: 0.189212 Loss2: 1.378970 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.533984 Loss1: 0.159702 Loss2: 1.374282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.548176 Loss1: 0.171272 Loss2: 1.376904 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.960938
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.648401 Loss1: 0.229515 Loss2: 1.418886 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.624419 Loss1: 0.210098 Loss2: 1.414321 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 06:45:02,196][flwr][DEBUG] - fit_round 67 received 50 results and 0 failures
-INFO flwr 2023-10-10 06:45:44,683 | server.py:125 | fit progress: (67, 2.2957204646957567, {'accuracy': 0.5234}, 154452.462019717)
->> Test accuracy: 0.523400
-[2023-10-10 06:45:44,683][flwr][INFO] - fit progress: (67, 2.2957204646957567, {'accuracy': 0.5234}, 154452.462019717)
-DEBUG flwr 2023-10-10 06:45:44,684 | server.py:173 | evaluate_round 67: strategy sampled 50 clients (out of 50)
-[2023-10-10 06:45:44,684][flwr][DEBUG] - evaluate_round 67: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 06:54:51,009 | server.py:187 | evaluate_round 67 received 50 results and 0 failures
-[2023-10-10 06:54:51,009][flwr][DEBUG] - evaluate_round 67 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 06:54:51,009 | server.py:222 | fit_round 68: strategy sampled 50 clients (out of 50)
-[2023-10-10 06:54:51,009][flwr][DEBUG] - fit_round 68: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.448779 Loss1: 1.588919 Loss2: 1.859860 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.998602 Loss1: 0.588806 Loss2: 1.409795 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.845527 Loss1: 0.428597 Loss2: 1.416930
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.322443 Loss1: 1.493485 Loss2: 1.828958
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.292485 Loss1: 0.881444 Loss2: 1.411041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.990513 Loss1: 0.603988 Loss2: 1.386526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.801458 Loss1: 0.428100 Loss2: 1.373358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.680642 Loss1: 0.323437 Loss2: 1.357206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.587689 Loss1: 0.242949 Loss2: 1.344740 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.547791 Loss1: 0.161560 Loss2: 1.386231
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.585951 Loss1: 0.237779 Loss2: 1.348173
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.597688 Loss1: 0.253195 Loss2: 1.344493
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.529223 Loss1: 0.189602 Loss2: 1.339621
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.513445 Loss1: 0.177212 Loss2: 1.336232
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.220283 Loss1: 1.355557 Loss2: 1.864725
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.372025 Loss1: 0.898787 Loss2: 1.473239
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.088938 Loss1: 0.616104 Loss2: 1.472834
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.922879 Loss1: 0.485070 Loss2: 1.437809
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.348419 Loss1: 1.361194 Loss2: 1.987224
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.746418 Loss1: 0.317195 Loss2: 1.429223
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.319675 Loss1: 0.798510 Loss2: 1.521166
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.053154 Loss1: 0.525235 Loss2: 1.527919
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.708348 Loss1: 0.277047 Loss2: 1.431301
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.848091 Loss1: 0.363084 Loss2: 1.485007
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.676327 Loss1: 0.256151 Loss2: 1.420177
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.789580 Loss1: 0.295945 Loss2: 1.493635
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.586829 Loss1: 0.173441 Loss2: 1.413388
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.726518 Loss1: 0.240990 Loss2: 1.485528
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.621058 Loss1: 0.207012 Loss2: 1.414046
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.587744 Loss1: 0.180439 Loss2: 1.407305 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958984
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.659699 Loss1: 0.182921 Loss2: 1.476779 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.156062 Loss1: 1.297515 Loss2: 1.858547 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.860634 Loss1: 0.455028 Loss2: 1.405606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.181575 Loss1: 1.335419 Loss2: 1.846156
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.801360 Loss1: 0.390776 Loss2: 1.410584
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.364105 Loss1: 0.934465 Loss2: 1.429640
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.690086 Loss1: 0.281702 Loss2: 1.408384
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.645741 Loss1: 0.255468 Loss2: 1.390273 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.674132 Loss1: 0.265115 Loss2: 1.409017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.653086 Loss1: 0.243231 Loss2: 1.409856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.630567 Loss1: 0.218461 Loss2: 1.412106 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.598880 Loss1: 0.199284 Loss2: 1.399596 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.934570
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.535682 Loss1: 0.166127 Loss2: 1.369556 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.921875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.300385 Loss1: 1.446015 Loss2: 1.854369 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.079667 Loss1: 0.648384 Loss2: 1.431282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.791049 Loss1: 0.385977 Loss2: 1.405072
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.368357 Loss1: 1.471373 Loss2: 1.896985
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.401686 Loss1: 0.952447 Loss2: 1.449239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.140609 Loss1: 0.662359 Loss2: 1.478249 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.923844 Loss1: 0.496800 Loss2: 1.427044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.788959 Loss1: 0.340183 Loss2: 1.448776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.679531 Loss1: 0.266233 Loss2: 1.413297 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.493532 Loss1: 0.122932 Loss2: 1.370600
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.669831 Loss1: 0.254286 Loss2: 1.415545
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.596041 Loss1: 0.181336 Loss2: 1.414705
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.602390 Loss1: 0.192492 Loss2: 1.409898
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.576547 Loss1: 0.177107 Loss2: 1.399440
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.329036 Loss1: 1.503501 Loss2: 1.825535
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.279823 Loss1: 0.829816 Loss2: 1.450007
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.948603 Loss1: 0.551633 Loss2: 1.396970
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.785102 Loss1: 0.398389 Loss2: 1.386712
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.114391 Loss1: 1.241698 Loss2: 1.872694
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.167881 Loss1: 0.778199 Loss2: 1.389682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.935366 Loss1: 0.512526 Loss2: 1.422840 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.706540 Loss1: 0.339784 Loss2: 1.366756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.652006 Loss1: 0.279104 Loss2: 1.372902 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.569784 Loss1: 0.201806 Loss2: 1.367978 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.619254 Loss1: 0.250812 Loss2: 1.368442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.620688 Loss1: 0.248170 Loss2: 1.372518 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.322315 Loss1: 1.456773 Loss2: 1.865542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.045398 Loss1: 0.605162 Loss2: 1.440236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.792656 Loss1: 0.378003 Loss2: 1.414653
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.390156 Loss1: 1.474482 Loss2: 1.915675
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.463531 Loss1: 0.951005 Loss2: 1.512526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.097464 Loss1: 0.628907 Loss2: 1.468557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.874857 Loss1: 0.423916 Loss2: 1.450941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.782747 Loss1: 0.342064 Loss2: 1.440683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.746769 Loss1: 0.305984 Loss2: 1.440786 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.684066 Loss1: 0.240642 Loss2: 1.443424 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.635432 Loss1: 0.214503 Loss2: 1.420929 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.229138 Loss1: 1.387358 Loss2: 1.841780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.017787 Loss1: 0.584875 Loss2: 1.432913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.287278 Loss1: 1.360141 Loss2: 1.927137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.390449 Loss1: 0.869436 Loss2: 1.521013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.081043 Loss1: 0.592195 Loss2: 1.488848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.919110 Loss1: 0.447515 Loss2: 1.471595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.755052 Loss1: 0.303227 Loss2: 1.451825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.560994 Loss1: 0.204102 Loss2: 1.356892 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.655879 Loss1: 0.217305 Loss2: 1.438574 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.562249 Loss1: 0.144115 Loss2: 1.418134 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.952148
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.447548 Loss1: 0.990928 Loss2: 1.456620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.918303 Loss1: 0.489766 Loss2: 1.428536 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.866363 Loss1: 0.402854 Loss2: 1.463509
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.304508 Loss1: 1.456732 Loss2: 1.847776
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.314257 Loss1: 0.926540 Loss2: 1.387718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.007819 Loss1: 0.587213 Loss2: 1.420606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.781671 Loss1: 0.400452 Loss2: 1.381219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.695337 Loss1: 0.296101 Loss2: 1.399236 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.946875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.681253 Loss1: 0.247438 Loss2: 1.433815
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.634850 Loss1: 0.267593 Loss2: 1.367257
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.598176 Loss1: 0.221355 Loss2: 1.376822
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.562369 Loss1: 0.185700 Loss2: 1.376669
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487925 Loss1: 0.129197 Loss2: 1.358728
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491311 Loss1: 0.137352 Loss2: 1.353959
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.385847 Loss1: 1.448913 Loss2: 1.936934
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.567360 Loss1: 1.070615 Loss2: 1.496746
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.225207 Loss1: 0.722894 Loss2: 1.502313
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.931194 Loss1: 0.472427 Loss2: 1.458768
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.765835 Loss1: 0.298700 Loss2: 1.467135
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.301582 Loss1: 1.395635 Loss2: 1.905947
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.207652 Loss1: 0.781098 Loss2: 1.426555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.882152 Loss1: 0.464547 Loss2: 1.417606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.797438 Loss1: 0.399869 Loss2: 1.397569 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.695523 Loss1: 0.293891 Loss2: 1.401633 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.579164 Loss1: 0.137294 Loss2: 1.441870
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.626393 Loss1: 0.227868 Loss2: 1.398525
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.558255 Loss1: 0.171997 Loss2: 1.386258
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.565007 Loss1: 0.187366 Loss2: 1.377641
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.548668 Loss1: 0.164516 Loss2: 1.384152
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.587109 Loss1: 0.202911 Loss2: 1.384199
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.203221 Loss1: 1.417198 Loss2: 1.786023
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.358448 Loss1: 0.985391 Loss2: 1.373057
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.943434 Loss1: 0.569734 Loss2: 1.373700
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.762250 Loss1: 0.425533 Loss2: 1.336717
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.704444 Loss1: 0.345693 Loss2: 1.358751
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.277597 Loss1: 1.439341 Loss2: 1.838256
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.255801 Loss1: 0.840375 Loss2: 1.415426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.977153 Loss1: 0.559702 Loss2: 1.417450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.754996 Loss1: 0.371915 Loss2: 1.383081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.720259 Loss1: 0.335825 Loss2: 1.384434 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.631636 Loss1: 0.242405 Loss2: 1.389231 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.564788 Loss1: 0.196787 Loss2: 1.368001 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.170158 Loss1: 1.344240 Loss2: 1.825918
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.504947 Loss1: 0.140421 Loss2: 1.364526
-(DefaultActor pid=3764) >> Training accuracy: 0.971680
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.989219 Loss1: 0.584409 Loss2: 1.404811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.684224 Loss1: 0.303482 Loss2: 1.380742 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.294170 Loss1: 1.426076 Loss2: 1.868094
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.636784 Loss1: 0.247374 Loss2: 1.389410
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.275522 Loss1: 0.841254 Loss2: 1.434268
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.572559 Loss1: 0.191376 Loss2: 1.381182
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.962760 Loss1: 0.533265 Loss2: 1.429495
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.596186 Loss1: 0.217239 Loss2: 1.378947
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.568617 Loss1: 0.182909 Loss2: 1.385707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508745 Loss1: 0.139648 Loss2: 1.369098 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.549115 Loss1: 0.172934 Loss2: 1.376181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.470801 Loss1: 0.108873 Loss2: 1.361928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491340 Loss1: 0.130349 Loss2: 1.360991
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.248234 Loss1: 1.246230 Loss2: 2.002004
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.411296 Loss1: 0.898377 Loss2: 1.512919
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.193101 Loss1: 0.612812 Loss2: 1.580290
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.074835 Loss1: 0.567284 Loss2: 1.507552
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.959381 Loss1: 0.430609 Loss2: 1.528772
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.367774 Loss1: 1.504360 Loss2: 1.863413
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.797653 Loss1: 0.302948 Loss2: 1.494704
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.422646 Loss1: 0.937067 Loss2: 1.485579
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.720939 Loss1: 0.227615 Loss2: 1.493324
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.078425 Loss1: 0.635995 Loss2: 1.442430
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.630510 Loss1: 0.154620 Loss2: 1.475889
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.841982 Loss1: 0.417219 Loss2: 1.424763
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.599879 Loss1: 0.130513 Loss2: 1.469366
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.574506 Loss1: 0.111312 Loss2: 1.463194
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.753769 Loss1: 0.332654 Loss2: 1.421115
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.677783 Loss1: 0.267617 Loss2: 1.410165
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.662954 Loss1: 0.250865 Loss2: 1.412089
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.647038 Loss1: 0.233235 Loss2: 1.413803
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.607188 Loss1: 0.199212 Loss2: 1.407976
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.233487 Loss1: 1.398183 Loss2: 1.835304
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.625705 Loss1: 0.213118 Loss2: 1.412587
-(DefaultActor pid=3764) >> Training accuracy: 0.965820
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.013283 Loss1: 0.592054 Loss2: 1.421229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.736764 Loss1: 0.351862 Loss2: 1.384903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.649818 Loss1: 0.279360 Loss2: 1.370458
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.276273 Loss1: 1.414050 Loss2: 1.862224
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.613649 Loss1: 0.241078 Loss2: 1.372571
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.321449 Loss1: 0.913430 Loss2: 1.408019
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.545256 Loss1: 0.181176 Loss2: 1.364080
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.011253 Loss1: 0.586666 Loss2: 1.424588
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.497845 Loss1: 0.134661 Loss2: 1.363184
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.804560 Loss1: 0.406122 Loss2: 1.398438
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484958 Loss1: 0.129503 Loss2: 1.355456
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.694661 Loss1: 0.298968 Loss2: 1.395693
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.629907 Loss1: 0.248552 Loss2: 1.381355
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.584712 Loss1: 0.198812 Loss2: 1.385900
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546228 Loss1: 0.171403 Loss2: 1.374825
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.549318 Loss1: 0.165783 Loss2: 1.383536
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.536987 Loss1: 0.164309 Loss2: 1.372678
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.446272 Loss1: 1.528498 Loss2: 1.917774
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.390278 Loss1: 0.928667 Loss2: 1.461610
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.087627 Loss1: 0.611686 Loss2: 1.475942
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.925762 Loss1: 0.493176 Loss2: 1.432585
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.791769 Loss1: 0.357133 Loss2: 1.434636
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.741102 Loss1: 0.324318 Loss2: 1.416783
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.266188 Loss1: 1.391546 Loss2: 1.874641
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.628289 Loss1: 0.219972 Loss2: 1.408317
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.286546 Loss1: 0.868384 Loss2: 1.418162
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.589069 Loss1: 0.180477 Loss2: 1.408592
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.952672 Loss1: 0.523650 Loss2: 1.429022
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.558135 Loss1: 0.163265 Loss2: 1.394870
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.884535 Loss1: 0.499868 Loss2: 1.384667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.528577 Loss1: 0.131233 Loss2: 1.397344
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.773750 Loss1: 0.341986 Loss2: 1.431765
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.673465 Loss1: 0.294513 Loss2: 1.378953
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.607607 Loss1: 0.221587 Loss2: 1.386020
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.608965 Loss1: 0.224688 Loss2: 1.384277
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.547882 Loss1: 0.171439 Loss2: 1.376443
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.414378 Loss1: 1.524212 Loss2: 1.890166
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527897 Loss1: 0.160297 Loss2: 1.367600
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.099103 Loss1: 0.666129 Loss2: 1.432974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.694381 Loss1: 0.317320 Loss2: 1.377061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.361972 Loss1: 1.362014 Loss2: 1.999958 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.262493 Loss1: 0.834253 Loss2: 1.428240 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.038855 Loss1: 0.577288 Loss2: 1.461567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.913763 Loss1: 0.473564 Loss2: 1.440200 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.762434 Loss1: 0.330206 Loss2: 1.432228 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.637317 Loss1: 0.229516 Loss2: 1.407800 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.571834 Loss1: 0.170477 Loss2: 1.401357 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978365
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.530643 Loss1: 0.136623 Loss2: 1.394019
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.101548 Loss1: 1.154868 Loss2: 1.946680
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.361143 Loss1: 0.881145 Loss2: 1.479998
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.071674 Loss1: 0.558021 Loss2: 1.513653
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.807156 Loss1: 0.353319 Loss2: 1.453837
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.771909 Loss1: 0.319160 Loss2: 1.452749
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.341157 Loss1: 1.480196 Loss2: 1.860960
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.452902 Loss1: 1.011929 Loss2: 1.440973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.125628 Loss1: 0.698347 Loss2: 1.427281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.856344 Loss1: 0.444684 Loss2: 1.411660 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.745550 Loss1: 0.358762 Loss2: 1.386788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.622991 Loss1: 0.191835 Loss2: 1.431156
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.703939 Loss1: 0.312400 Loss2: 1.391539
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.654221 Loss1: 0.261021 Loss2: 1.393200
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.668415 Loss1: 0.276164 Loss2: 1.392252
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.618127 Loss1: 0.224672 Loss2: 1.393455
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.612211 Loss1: 0.226034 Loss2: 1.386177
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.210388 Loss1: 1.421683 Loss2: 1.788705
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.328111 Loss1: 0.930351 Loss2: 1.397761
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.089247 Loss1: 0.712163 Loss2: 1.377084
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.823388 Loss1: 0.463180 Loss2: 1.360208
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.724094 Loss1: 0.374904 Loss2: 1.349190
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.390503 Loss1: 1.520500 Loss2: 1.870003
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.289882 Loss1: 0.869982 Loss2: 1.419901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.050839 Loss1: 0.625323 Loss2: 1.425516 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.821025 Loss1: 0.430450 Loss2: 1.390575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.744044 Loss1: 0.346402 Loss2: 1.397642 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.587419 Loss1: 0.238458 Loss2: 1.348961
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.650758 Loss1: 0.273217 Loss2: 1.377540
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.576561 Loss1: 0.204389 Loss2: 1.372172
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.562121 Loss1: 0.190247 Loss2: 1.371875
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.511274 Loss1: 0.145929 Loss2: 1.365345
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.488244 Loss1: 0.128888 Loss2: 1.359356
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.101671 Loss1: 1.277273 Loss2: 1.824398
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.311417 Loss1: 0.848995 Loss2: 1.462421
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.965958 Loss1: 0.539824 Loss2: 1.426134
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.779311 Loss1: 0.369425 Loss2: 1.409886
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.738267 Loss1: 0.330772 Loss2: 1.407495
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.237758 Loss1: 1.380542 Loss2: 1.857216
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.702687 Loss1: 0.284587 Loss2: 1.418100
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.270307 Loss1: 0.852847 Loss2: 1.417460
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.662851 Loss1: 0.267863 Loss2: 1.394987
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.976320 Loss1: 0.552491 Loss2: 1.423829
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.798672 Loss1: 0.407119 Loss2: 1.391553
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.658761 Loss1: 0.246535 Loss2: 1.412226
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.653654 Loss1: 0.271894 Loss2: 1.381760
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.650485 Loss1: 0.246332 Loss2: 1.404153
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.592420 Loss1: 0.226606 Loss2: 1.365814
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.616318 Loss1: 0.206696 Loss2: 1.409621
-(DefaultActor pid=3765) >> Training accuracy: 0.931641
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511727 Loss1: 0.152091 Loss2: 1.359635 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.469916 Loss1: 0.121819 Loss2: 1.348097 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.563038 Loss1: 1.068855 Loss2: 1.494183 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.984683 Loss1: 0.489788 Loss2: 1.494896 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.524947 Loss1: 1.596593 Loss2: 1.928353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.410172 Loss1: 0.984548 Loss2: 1.425624 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.674179 Loss1: 0.202741 Loss2: 1.471437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.622328 Loss1: 0.154464 Loss2: 1.467864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.582030 Loss1: 0.124705 Loss2: 1.457325 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977865
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.608913 Loss1: 0.222916 Loss2: 1.385997 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.574354 Loss1: 0.182342 Loss2: 1.392012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.559956 Loss1: 0.172977 Loss2: 1.386979
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.275436 Loss1: 1.384238 Loss2: 1.891198
-(DefaultActor pid=3764) >> Training accuracy: 0.977679
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.456882 Loss1: 0.944380 Loss2: 1.512502
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.068903 Loss1: 0.627785 Loss2: 1.441118
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.811941 Loss1: 0.369533 Loss2: 1.442409
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.687802 Loss1: 0.257543 Loss2: 1.430258
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.158588 Loss1: 1.292059 Loss2: 1.866529
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.424800 Loss1: 0.959415 Loss2: 1.465385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.922083 Loss1: 0.500421 Loss2: 1.421662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.754936 Loss1: 0.359753 Loss2: 1.395183 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.708148 Loss1: 0.313039 Loss2: 1.395110 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.531235 Loss1: 0.129736 Loss2: 1.401499
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.604342 Loss1: 0.224359 Loss2: 1.379982
-(DefaultActor pid=3765) >> Training accuracy: 0.975586
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.563672 Loss1: 0.192292 Loss2: 1.371380
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.550785 Loss1: 0.185177 Loss2: 1.365608
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.515145 Loss1: 0.144733 Loss2: 1.370412
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.561329 Loss1: 0.187845 Loss2: 1.373484
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.212912 Loss1: 1.338368 Loss2: 1.874544
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.375250 Loss1: 0.910798 Loss2: 1.464452
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.044447 Loss1: 0.585045 Loss2: 1.459402
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.887264 Loss1: 0.448855 Loss2: 1.438409
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.723698 Loss1: 0.284533 Loss2: 1.439165
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.268770 Loss1: 1.412322 Loss2: 1.856448
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.306051 Loss1: 0.889274 Loss2: 1.416777
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.673180 Loss1: 0.255871 Loss2: 1.417309
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.996728 Loss1: 0.573085 Loss2: 1.423643
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.632474 Loss1: 0.210029 Loss2: 1.422445
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.791001 Loss1: 0.390099 Loss2: 1.400901
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.616721 Loss1: 0.195925 Loss2: 1.420796
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.721510 Loss1: 0.331649 Loss2: 1.389862
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.604202 Loss1: 0.186178 Loss2: 1.418024
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.591956 Loss1: 0.172651 Loss2: 1.419305 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.949219
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.585988 Loss1: 0.189246 Loss2: 1.396742 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.562717 Loss1: 0.180657 Loss2: 1.382060 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.299014 Loss1: 1.390707 Loss2: 1.908307
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.264465 Loss1: 0.863092 Loss2: 1.401372
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.945295 Loss1: 0.544886 Loss2: 1.400409
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.763267 Loss1: 0.383699 Loss2: 1.379568
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.368792 Loss1: 1.490180 Loss2: 1.878612
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.351620 Loss1: 0.922905 Loss2: 1.428715 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.982231 Loss1: 0.539264 Loss2: 1.442967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.827808 Loss1: 0.418355 Loss2: 1.409453 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.721920 Loss1: 0.320119 Loss2: 1.401801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.638346 Loss1: 0.234729 Loss2: 1.403617 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548731 Loss1: 0.168778 Loss2: 1.379952 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.500016 Loss1: 0.127217 Loss2: 1.372799 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.250834 Loss1: 0.867921 Loss2: 1.382913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.821008 Loss1: 0.465510 Loss2: 1.355498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.014328 Loss1: 1.209707 Loss2: 1.804622
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.730382 Loss1: 0.377507 Loss2: 1.352876
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.634174 Loss1: 0.290776 Loss2: 1.343399
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.152417 Loss1: 0.742339 Loss2: 1.410077
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.597443 Loss1: 0.254232 Loss2: 1.343211
-DEBUG flwr 2023-10-10 07:23:35,716 | server.py:236 | fit_round 68 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.895045 Loss1: 0.499629 Loss2: 1.395416
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.524579 Loss1: 0.188885 Loss2: 1.335694
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.809632 Loss1: 0.424023 Loss2: 1.385608
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.715106 Loss1: 0.332733 Loss2: 1.382373 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.473764 Loss1: 0.146767 Loss2: 1.326997
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.679950 Loss1: 0.303392 Loss2: 1.376558
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.632012 Loss1: 0.257625 Loss2: 1.374388
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.593842 Loss1: 0.220565 Loss2: 1.373277
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.611059 Loss1: 0.242772 Loss2: 1.368288
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.288831 Loss1: 1.513098 Loss2: 1.775733
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.528663 Loss1: 0.166568 Loss2: 1.362095
-(DefaultActor pid=3764) >> Training accuracy: 0.955882
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.937604 Loss1: 0.578161 Loss2: 1.359442 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.674566 Loss1: 0.344070 Loss2: 1.330496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.602538 Loss1: 0.274388 Loss2: 1.328150
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.403474 Loss1: 1.367784 Loss2: 2.035690
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.520033 Loss1: 0.951352 Loss2: 1.568681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.535833 Loss1: 0.209362 Loss2: 1.326471
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.135179 Loss1: 0.557468 Loss2: 1.577711
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.965409 Loss1: 0.425978 Loss2: 1.539432 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474885 Loss1: 0.157289 Loss2: 1.317596
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.844494 Loss1: 0.309447 Loss2: 1.535047
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.805857 Loss1: 0.285676 Loss2: 1.520181
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.800339 Loss1: 0.272358 Loss2: 1.527981
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.751033 Loss1: 0.234096 Loss2: 1.516937
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.725472 Loss1: 0.206890 Loss2: 1.518581
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.300640 Loss1: 1.465159 Loss2: 1.835480
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.716114 Loss1: 0.204619 Loss2: 1.511496
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.881087 Loss1: 0.510455 Loss2: 1.370632 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.659747 Loss1: 0.330556 Loss2: 1.329191 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.433178 Loss1: 1.471587 Loss2: 1.961591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.451958 Loss1: 1.011988 Loss2: 1.439970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.101660 Loss1: 0.603855 Loss2: 1.497805 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.820872 Loss1: 0.393699 Loss2: 1.427172 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975446
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.495364 Loss1: 0.184164 Loss2: 1.311200
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.709120 Loss1: 0.280604 Loss2: 1.428515
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.670714 Loss1: 0.240273 Loss2: 1.430442
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.628991 Loss1: 0.209174 Loss2: 1.419817
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.659279 Loss1: 0.237567 Loss2: 1.421711
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.602145 Loss1: 0.175940 Loss2: 1.426205
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.588213 Loss1: 0.165987 Loss2: 1.422227
-(DefaultActor pid=3764) >> Training accuracy: 0.969952
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 07:23:35,716][flwr][DEBUG] - fit_round 68 received 50 results and 0 failures
-INFO flwr 2023-10-10 07:24:17,270 | server.py:125 | fit progress: (68, 2.3044485558336154, {'accuracy': 0.5227}, 156765.04836486402)
->> Test accuracy: 0.522700
-[2023-10-10 07:24:17,270][flwr][INFO] - fit progress: (68, 2.3044485558336154, {'accuracy': 0.5227}, 156765.04836486402)
-DEBUG flwr 2023-10-10 07:24:17,270 | server.py:173 | evaluate_round 68: strategy sampled 50 clients (out of 50)
-[2023-10-10 07:24:17,270][flwr][DEBUG] - evaluate_round 68: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 07:33:24,811 | server.py:187 | evaluate_round 68 received 50 results and 0 failures
-[2023-10-10 07:33:24,811][flwr][DEBUG] - evaluate_round 68 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 07:33:24,812 | server.py:222 | fit_round 69: strategy sampled 50 clients (out of 50)
-[2023-10-10 07:33:24,812][flwr][DEBUG] - fit_round 69: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.373848 Loss1: 1.420487 Loss2: 1.953361
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.402347 Loss1: 0.912187 Loss2: 1.490160
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.047090 Loss1: 0.565633 Loss2: 1.481457
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.951999 Loss1: 0.493622 Loss2: 1.458377
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.831821 Loss1: 0.369443 Loss2: 1.462378 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.752603 Loss1: 0.298691 Loss2: 1.453912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.688104 Loss1: 0.239610 Loss2: 1.448494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.659479 Loss1: 0.213384 Loss2: 1.446096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.614995 Loss1: 0.176732 Loss2: 1.438263 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.587372 Loss1: 0.158193 Loss2: 1.429179 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.650451 Loss1: 0.223803 Loss2: 1.426647 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.372592 Loss1: 1.497731 Loss2: 1.874861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.035918 Loss1: 0.614949 Loss2: 1.420969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.846240 Loss1: 0.451560 Loss2: 1.394680
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.244455 Loss1: 1.365728 Loss2: 1.878727
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.366081 Loss1: 0.888855 Loss2: 1.477227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.053237 Loss1: 0.564745 Loss2: 1.488492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.785216 Loss1: 0.338041 Loss2: 1.447176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.786905 Loss1: 0.334568 Loss2: 1.452337 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.712882 Loss1: 0.262398 Loss2: 1.450485 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.639262 Loss1: 0.201034 Loss2: 1.438228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.551564 Loss1: 0.122663 Loss2: 1.428901 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.955078
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.334028 Loss1: 1.434995 Loss2: 1.899034 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.116380 Loss1: 0.668879 Loss2: 1.447500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.387245 Loss1: 1.473726 Loss2: 1.913520 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.378817 Loss1: 0.885618 Loss2: 1.493199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.052979 Loss1: 0.618829 Loss2: 1.434150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.860870 Loss1: 0.425590 Loss2: 1.435280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.727542 Loss1: 0.305284 Loss2: 1.422258 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.657038 Loss1: 0.256871 Loss2: 1.400167 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.957292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.577464 Loss1: 0.177238 Loss2: 1.400226 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.586254 Loss1: 0.184636 Loss2: 1.401618 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.250165 Loss1: 0.795994 Loss2: 1.454171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.776384 Loss1: 0.362530 Loss2: 1.413855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.668727 Loss1: 0.240979 Loss2: 1.427748
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.419239 Loss1: 1.465608 Loss2: 1.953631
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.615574 Loss1: 0.206224 Loss2: 1.409350
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.399210 Loss1: 0.916817 Loss2: 1.482393
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.584048 Loss1: 0.179888 Loss2: 1.404160
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.173787 Loss1: 0.672774 Loss2: 1.501014
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.594055 Loss1: 0.195343 Loss2: 1.398712
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.984119 Loss1: 0.495980 Loss2: 1.488139
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.565709 Loss1: 0.165958 Loss2: 1.399751
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.828530 Loss1: 0.352972 Loss2: 1.475558
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.583404 Loss1: 0.179080 Loss2: 1.404323
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.805384 Loss1: 0.344695 Loss2: 1.460688
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.736645 Loss1: 0.273747 Loss2: 1.462898
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.685801 Loss1: 0.234615 Loss2: 1.451186
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.673989 Loss1: 0.222174 Loss2: 1.451816
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.632603 Loss1: 0.184313 Loss2: 1.448291
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.316712 Loss1: 1.342389 Loss2: 1.974324
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.340361 Loss1: 0.843021 Loss2: 1.497340
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.071867 Loss1: 0.562819 Loss2: 1.509048
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.854840 Loss1: 0.393153 Loss2: 1.461687
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.734781 Loss1: 0.270639 Loss2: 1.464142
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.415129 Loss1: 0.968298 Loss2: 1.446831 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.039206 Loss1: 0.563023 Loss2: 1.476183 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.868991 Loss1: 0.443002 Loss2: 1.425990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.735544 Loss1: 0.299025 Loss2: 1.436518 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.715508 Loss1: 0.279568 Loss2: 1.435940 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.613569 Loss1: 0.195356 Loss2: 1.418214 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.633702 Loss1: 0.210964 Loss2: 1.422738 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.947545
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.253672 Loss1: 1.289923 Loss2: 1.963748
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.393170 Loss1: 0.886093 Loss2: 1.507077
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.101333 Loss1: 0.606044 Loss2: 1.495289
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.865848 Loss1: 0.393374 Loss2: 1.472473
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.082937 Loss1: 1.226293 Loss2: 1.856644
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.130658 Loss1: 0.699179 Loss2: 1.431479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.923481 Loss1: 0.489958 Loss2: 1.433522 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.739872 Loss1: 0.291789 Loss2: 1.448083 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.700302 Loss1: 0.227472 Loss2: 1.472830 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.620291 Loss1: 0.169367 Loss2: 1.450924 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.608408 Loss1: 0.216560 Loss2: 1.391848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.570186 Loss1: 0.180028 Loss2: 1.390158 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.594580 Loss1: 0.203648 Loss2: 1.390932
-(DefaultActor pid=3764) >> Training accuracy: 0.930147
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.388922 Loss1: 1.429056 Loss2: 1.959866
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.368900 Loss1: 0.879076 Loss2: 1.489824
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.127197 Loss1: 0.622576 Loss2: 1.504620
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.881158 Loss1: 0.414394 Loss2: 1.466764
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.788390 Loss1: 0.326541 Loss2: 1.461849
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.062599 Loss1: 1.234883 Loss2: 1.827717
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.734043 Loss1: 0.280769 Loss2: 1.453274
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.138552 Loss1: 0.752136 Loss2: 1.386416
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.719087 Loss1: 0.269650 Loss2: 1.449436
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.659969 Loss1: 0.215325 Loss2: 1.444644
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.908109 Loss1: 0.500602 Loss2: 1.407507
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.623640 Loss1: 0.182467 Loss2: 1.441173
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.787047 Loss1: 0.425255 Loss2: 1.361792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.589879 Loss1: 0.155235 Loss2: 1.434644
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.674329 Loss1: 0.306841 Loss2: 1.367488
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.635249 Loss1: 0.278916 Loss2: 1.356333
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.612913 Loss1: 0.261608 Loss2: 1.351305
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.537649 Loss1: 0.183440 Loss2: 1.354209
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.526948 Loss1: 0.184083 Loss2: 1.342864
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.435686 Loss1: 1.509219 Loss2: 1.926467
-(DefaultActor pid=3764) >> Training accuracy: 0.975586
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.448004 Loss1: 0.972230 Loss2: 1.475773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.874287 Loss1: 0.431739 Loss2: 1.442548 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.662805 Loss1: 0.235505 Loss2: 1.427300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.616479 Loss1: 0.203552 Loss2: 1.412927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.595405 Loss1: 0.180190 Loss2: 1.415215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.595904 Loss1: 0.174203 Loss2: 1.421701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.574285 Loss1: 0.158048 Loss2: 1.416237 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.724850 Loss1: 0.267984 Loss2: 1.456866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.726920 Loss1: 0.241029 Loss2: 1.485891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.641566 Loss1: 0.176765 Loss2: 1.464801
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.306232 Loss1: 1.404717 Loss2: 1.901515
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.307125 Loss1: 0.832809 Loss2: 1.474316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.788819 Loss1: 0.371615 Loss2: 1.417204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.666125 Loss1: 0.279531 Loss2: 1.386593 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.645546 Loss1: 0.243920 Loss2: 1.401626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.606228 Loss1: 0.206532 Loss2: 1.399696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.546348 Loss1: 0.153102 Loss2: 1.393246 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.577558 Loss1: 0.186515 Loss2: 1.391043 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.608143 Loss1: 0.213514 Loss2: 1.394629 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.555716 Loss1: 0.162422 Loss2: 1.393293 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973558
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527249 Loss1: 0.142886 Loss2: 1.384363
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.283713 Loss1: 1.393098 Loss2: 1.890614
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.360920 Loss1: 0.908121 Loss2: 1.452799
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.063824 Loss1: 0.618645 Loss2: 1.445179
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.818111 Loss1: 0.406499 Loss2: 1.411611
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.755379 Loss1: 0.338787 Loss2: 1.416591
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.302320 Loss1: 1.309655 Loss2: 1.992664
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.456929 Loss1: 0.889609 Loss2: 1.567320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.142915 Loss1: 0.608570 Loss2: 1.534346 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.985385 Loss1: 0.451158 Loss2: 1.534226 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.910873 Loss1: 0.391864 Loss2: 1.519010 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.834992 Loss1: 0.306353 Loss2: 1.528639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.807511 Loss1: 0.294160 Loss2: 1.513351 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.680675 Loss1: 0.186876 Loss2: 1.493799 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.945312
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.008130 Loss1: 0.563267 Loss2: 1.444864 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.758342 Loss1: 0.343453 Loss2: 1.414890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.409973 Loss1: 1.504334 Loss2: 1.905639
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.718957 Loss1: 0.317164 Loss2: 1.401793
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.274558 Loss1: 0.812667 Loss2: 1.461891
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.710192 Loss1: 0.308534 Loss2: 1.401658
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.954299 Loss1: 0.502303 Loss2: 1.451997
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.607536 Loss1: 0.205792 Loss2: 1.401744
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.841883 Loss1: 0.412454 Loss2: 1.429430
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.602627 Loss1: 0.217611 Loss2: 1.385016
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.723036 Loss1: 0.286668 Loss2: 1.436367
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.581382 Loss1: 0.195759 Loss2: 1.385623
-(DefaultActor pid=3765) >> Training accuracy: 0.960417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.643619 Loss1: 0.230431 Loss2: 1.413189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.565414 Loss1: 0.157174 Loss2: 1.408241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527148 Loss1: 0.128857 Loss2: 1.398291
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.291443 Loss1: 1.390001 Loss2: 1.901442
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.227755 Loss1: 0.794130 Loss2: 1.433625
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.934783 Loss1: 0.485352 Loss2: 1.449431
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.828429 Loss1: 0.415238 Loss2: 1.413191
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.767275 Loss1: 0.338383 Loss2: 1.428893
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.410552 Loss1: 1.446748 Loss2: 1.963804
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.687049 Loss1: 0.271992 Loss2: 1.415057
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.409520 Loss1: 0.919821 Loss2: 1.489699
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.654417 Loss1: 0.234641 Loss2: 1.419776
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.061317 Loss1: 0.558991 Loss2: 1.502326
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.616234 Loss1: 0.207030 Loss2: 1.409204
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.981085 Loss1: 0.494905 Loss2: 1.486180
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.626841 Loss1: 0.208627 Loss2: 1.418214
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.815179 Loss1: 0.347601 Loss2: 1.467578
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.566461 Loss1: 0.155178 Loss2: 1.411283
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.672888 Loss1: 0.218435 Loss2: 1.454453 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.593847 Loss1: 0.153622 Loss2: 1.440225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.569977 Loss1: 0.134143 Loss2: 1.435833
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.127670 Loss1: 1.307383 Loss2: 1.820286
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.141645 Loss1: 0.714175 Loss2: 1.427470
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.882552 Loss1: 0.472733 Loss2: 1.409819
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.704791 Loss1: 0.321427 Loss2: 1.383364
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.628717 Loss1: 0.244238 Loss2: 1.384479
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.397312 Loss1: 1.504844 Loss2: 1.892467
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.611201 Loss1: 0.231949 Loss2: 1.379252
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524036 Loss1: 0.153854 Loss2: 1.370181 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500968 Loss1: 0.139849 Loss2: 1.361120 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.546998 Loss1: 0.186206 Loss2: 1.360793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.526798 Loss1: 0.164590 Loss2: 1.362208 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.660926 Loss1: 0.248403 Loss2: 1.412524 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.570920 Loss1: 0.173115 Loss2: 1.397805 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.954167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.264102 Loss1: 0.850711 Loss2: 1.413391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.778062 Loss1: 0.391759 Loss2: 1.386303 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.717001 Loss1: 0.314505 Loss2: 1.402496
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.311818 Loss1: 1.409303 Loss2: 1.902515
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.445202 Loss1: 0.929987 Loss2: 1.515215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.124427 Loss1: 0.643446 Loss2: 1.480980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.954200 Loss1: 0.480526 Loss2: 1.473673 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.755375 Loss1: 0.300846 Loss2: 1.454529 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.685928 Loss1: 0.230831 Loss2: 1.455097 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.647198 Loss1: 0.214501 Loss2: 1.432697 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.154503 Loss1: 1.261364 Loss2: 1.893140
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.644161 Loss1: 0.201673 Loss2: 1.442488
-(DefaultActor pid=3764) >> Training accuracy: 0.967773
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.945867 Loss1: 0.494589 Loss2: 1.451278 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.675799 Loss1: 0.263262 Loss2: 1.412537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.664398 Loss1: 0.265160 Loss2: 1.399238
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.326999 Loss1: 1.394930 Loss2: 1.932069
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.663040 Loss1: 0.253093 Loss2: 1.409947
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.348106 Loss1: 0.897714 Loss2: 1.450392
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.569804 Loss1: 0.173350 Loss2: 1.396454
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.003275 Loss1: 0.541209 Loss2: 1.462066
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.570974 Loss1: 0.178034 Loss2: 1.392940
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.839937 Loss1: 0.409764 Loss2: 1.430172
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.539094 Loss1: 0.146116 Loss2: 1.392978
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.731312 Loss1: 0.299100 Loss2: 1.432212
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.748995 Loss1: 0.312807 Loss2: 1.436188
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.736457 Loss1: 0.297836 Loss2: 1.438621
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.662523 Loss1: 0.240669 Loss2: 1.421854
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.588623 Loss1: 0.176527 Loss2: 1.412096
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.316505 Loss1: 1.384513 Loss2: 1.931993
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.567046 Loss1: 0.152419 Loss2: 1.414627
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.050408 Loss1: 0.549650 Loss2: 1.500757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.763764 Loss1: 0.270095 Loss2: 1.493669 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.244352 Loss1: 1.241112 Loss2: 2.003241
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.695033 Loss1: 0.230502 Loss2: 1.464531
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.382151 Loss1: 0.888341 Loss2: 1.493810
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.724075 Loss1: 0.255975 Loss2: 1.468100
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.107102 Loss1: 0.610587 Loss2: 1.496515
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.761197 Loss1: 0.273438 Loss2: 1.487760
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.910509 Loss1: 0.459472 Loss2: 1.451037
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.688014 Loss1: 0.217265 Loss2: 1.470749
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.685257 Loss1: 0.216271 Loss2: 1.468986 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965820
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.692133 Loss1: 0.249550 Loss2: 1.442583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.555585 Loss1: 0.126695 Loss2: 1.428890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.531796 Loss1: 0.122927 Loss2: 1.408868
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.524767 Loss1: 1.567217 Loss2: 1.957550
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.490684 Loss1: 0.991619 Loss2: 1.499065
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.101103 Loss1: 0.642577 Loss2: 1.458526
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.864613 Loss1: 0.413656 Loss2: 1.450957
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.827747 Loss1: 0.386680 Loss2: 1.441067
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.530921 Loss1: 1.572408 Loss2: 1.958512
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.698392 Loss1: 0.258526 Loss2: 1.439865
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.710056 Loss1: 0.285471 Loss2: 1.424585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.674127 Loss1: 0.250243 Loss2: 1.423883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.615610 Loss1: 0.190265 Loss2: 1.425345 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.620284 Loss1: 0.192331 Loss2: 1.427953 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.605095 Loss1: 0.178969 Loss2: 1.426127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.594811 Loss1: 0.187167 Loss2: 1.407644 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978795
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.242470 Loss1: 1.432290 Loss2: 1.810180 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.959142 Loss1: 0.569370 Loss2: 1.389771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.358495 Loss1: 1.444029 Loss2: 1.914466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.306361 Loss1: 0.878714 Loss2: 1.427647 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.045797 Loss1: 0.581278 Loss2: 1.464519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.804932 Loss1: 0.399833 Loss2: 1.405099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.711732 Loss1: 0.298961 Loss2: 1.412771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647091 Loss1: 0.241157 Loss2: 1.405934 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.653189 Loss1: 0.240042 Loss2: 1.413147 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.602378 Loss1: 0.191637 Loss2: 1.410741 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.267566 Loss1: 0.860986 Loss2: 1.406580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.760049 Loss1: 0.378921 Loss2: 1.381128 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.154271 Loss1: 1.254650 Loss2: 1.899621
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.671659 Loss1: 0.281816 Loss2: 1.389843
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.418882 Loss1: 0.956632 Loss2: 1.462250
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.588039 Loss1: 0.205313 Loss2: 1.382725
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.573261 Loss1: 0.195166 Loss2: 1.378095
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.140123 Loss1: 0.654475 Loss2: 1.485649
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.565415 Loss1: 0.186021 Loss2: 1.379394
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.840180 Loss1: 0.396702 Loss2: 1.443479
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.498099 Loss1: 0.125583 Loss2: 1.372516
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.754042 Loss1: 0.305616 Loss2: 1.448426
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.483559 Loss1: 0.120530 Loss2: 1.363029
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.690646 Loss1: 0.266147 Loss2: 1.424499
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.675128 Loss1: 0.247826 Loss2: 1.427302
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.671529 Loss1: 0.234132 Loss2: 1.437397
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.598708 Loss1: 0.168372 Loss2: 1.430337
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.581425 Loss1: 0.158851 Loss2: 1.422575
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.316493 Loss1: 1.346132 Loss2: 1.970361
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.322171 Loss1: 0.836786 Loss2: 1.485385
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.086043 Loss1: 0.584975 Loss2: 1.501068
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.963662 Loss1: 0.497230 Loss2: 1.466431
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.780713 Loss1: 0.301458 Loss2: 1.479256
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.345012 Loss1: 1.479951 Loss2: 1.865061
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.354580 Loss1: 0.926006 Loss2: 1.428574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.959041 Loss1: 0.548492 Loss2: 1.410549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.753516 Loss1: 0.383833 Loss2: 1.369683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.762408 Loss1: 0.382125 Loss2: 1.380283 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.954167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.667830 Loss1: 0.297562 Loss2: 1.370269 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.489537 Loss1: 0.141549 Loss2: 1.347988 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.526608 Loss1: 0.169039 Loss2: 1.357569 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.272583 Loss1: 0.835659 Loss2: 1.436924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.842020 Loss1: 0.424152 Loss2: 1.417869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.771700 Loss1: 0.349285 Loss2: 1.422415
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.219447 Loss1: 1.317709 Loss2: 1.901738
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.390186 Loss1: 0.941518 Loss2: 1.448668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.151361 Loss1: 0.644277 Loss2: 1.507083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.860867 Loss1: 0.430643 Loss2: 1.430224 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.823260 Loss1: 0.375598 Loss2: 1.447662 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.721006 Loss1: 0.282777 Loss2: 1.438228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.632512 Loss1: 0.212304 Loss2: 1.420208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.639136 Loss1: 0.212545 Loss2: 1.426591 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.363362 Loss1: 0.902801 Loss2: 1.460561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.844963 Loss1: 0.391841 Loss2: 1.453122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.779469 Loss1: 0.313305 Loss2: 1.466165 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.790280 Loss1: 0.311117 Loss2: 1.479163 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.731546 Loss1: 0.259387 Loss2: 1.472159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.674777 Loss1: 0.211487 Loss2: 1.463290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.667830 Loss1: 0.213317 Loss2: 1.454513 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.950893
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.662948 Loss1: 0.273591 Loss2: 1.389357 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.561467 Loss1: 0.186601 Loss2: 1.374866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.498482 Loss1: 0.126454 Loss2: 1.372028
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.454569 Loss1: 1.535973 Loss2: 1.918596
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.493289 Loss1: 0.132260 Loss2: 1.361029
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.395854 Loss1: 0.906027 Loss2: 1.489827
-(DefaultActor pid=3764) >> Training accuracy: 0.960938
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.971419 Loss1: 0.536592 Loss2: 1.434827
-(DefaultActor pid=3765) Epoch: 3 Loss: 2.016770 Loss1: 0.580059 Loss2: 1.436711
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.995961 Loss1: 0.526396 Loss2: 1.469565
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.808521 Loss1: 0.388904 Loss2: 1.419616
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.420875 Loss1: 1.365274 Loss2: 2.055601
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.711108 Loss1: 0.286104 Loss2: 1.425004
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.377037 Loss1: 0.808952 Loss2: 1.568085
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.622934 Loss1: 0.207050 Loss2: 1.415884
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.236713 Loss1: 0.645065 Loss2: 1.591648
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.609150 Loss1: 0.198015 Loss2: 1.411136
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.987656 Loss1: 0.436330 Loss2: 1.551325
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.609103 Loss1: 0.204206 Loss2: 1.404897
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.810147 Loss1: 0.261050 Loss2: 1.549098 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.764240 Loss1: 0.226786 Loss2: 1.537453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.393361 Loss1: 1.445624 Loss2: 1.947737
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.754052 Loss1: 0.230127 Loss2: 1.523925
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.705289 Loss1: 0.178349 Loss2: 1.526940 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.800544 Loss1: 0.454115 Loss2: 1.346430 [repeated 3x across cluster]
-DEBUG flwr 2023-10-10 08:01:53,566 | server.py:236 | fit_round 69 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.601465 Loss1: 0.251145 Loss2: 1.350320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.250461 Loss1: 1.286809 Loss2: 1.963653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.538527 Loss1: 0.196046 Loss2: 1.342481 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.959635
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.804070 Loss1: 0.374856 Loss2: 1.429214 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.625561 Loss1: 0.213138 Loss2: 1.412423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.438631 Loss1: 1.472307 Loss2: 1.966324
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.616866 Loss1: 0.205637 Loss2: 1.411229
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.233629 Loss1: 0.839220 Loss2: 1.394409
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.648597 Loss1: 0.231194 Loss2: 1.417403
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.558130 Loss1: 0.140810 Loss2: 1.417320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529353 Loss1: 0.129679 Loss2: 1.399674 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.566634 Loss1: 0.205733 Loss2: 1.360901 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.536375 Loss1: 0.169784 Loss2: 1.366591 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.521525 Loss1: 0.177404 Loss2: 1.344120
-(DefaultActor pid=3765) >> Training accuracy: 0.965144
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.219767 Loss1: 1.372613 Loss2: 1.847154
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.337599 Loss1: 0.906867 Loss2: 1.430732
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.035381 Loss1: 0.606846 Loss2: 1.428535
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.855587 Loss1: 0.446560 Loss2: 1.409027
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.794122 Loss1: 0.393244 Loss2: 1.400878
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.687904 Loss1: 0.284056 Loss2: 1.403847
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.720438 Loss1: 0.321897 Loss2: 1.398541
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.582803 Loss1: 0.195170 Loss2: 1.387633
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.558573 Loss1: 0.179579 Loss2: 1.378994
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.517771 Loss1: 0.146247 Loss2: 1.371524
-(DefaultActor pid=3764) >> Training accuracy: 0.965820
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 08:01:53,566][flwr][DEBUG] - fit_round 69 received 50 results and 0 failures
-INFO flwr 2023-10-10 08:02:36,713 | server.py:125 | fit progress: (69, 2.284203640949993, {'accuracy': 0.5269}, 159064.49114573802)
->> Test accuracy: 0.526900
-[2023-10-10 08:02:36,713][flwr][INFO] - fit progress: (69, 2.284203640949993, {'accuracy': 0.5269}, 159064.49114573802)
-DEBUG flwr 2023-10-10 08:02:36,713 | server.py:173 | evaluate_round 69: strategy sampled 50 clients (out of 50)
-[2023-10-10 08:02:36,713][flwr][DEBUG] - evaluate_round 69: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 08:11:42,347 | server.py:187 | evaluate_round 69 received 50 results and 0 failures
-[2023-10-10 08:11:42,347][flwr][DEBUG] - evaluate_round 69 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 08:11:42,348 | server.py:222 | fit_round 70: strategy sampled 50 clients (out of 50)
-[2023-10-10 08:11:42,348][flwr][DEBUG] - fit_round 70: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.274978 Loss1: 1.367127 Loss2: 1.907851
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.386666 Loss1: 0.878949 Loss2: 1.507718
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.955120 Loss1: 0.516671 Loss2: 1.438448
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.837638 Loss1: 0.407437 Loss2: 1.430201
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.316904 Loss1: 1.355468 Loss2: 1.961436
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.305395 Loss1: 0.837145 Loss2: 1.468250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.007396 Loss1: 0.515567 Loss2: 1.491829 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.802524 Loss1: 0.343165 Loss2: 1.459359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.777256 Loss1: 0.318510 Loss2: 1.458746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.725509 Loss1: 0.270493 Loss2: 1.455015 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.587311 Loss1: 0.171590 Loss2: 1.415721
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.705059 Loss1: 0.260233 Loss2: 1.444826
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.682103 Loss1: 0.230606 Loss2: 1.451497
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.632586 Loss1: 0.184747 Loss2: 1.447839
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.675335 Loss1: 0.232204 Loss2: 1.443131
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.548153 Loss1: 1.573119 Loss2: 1.975034
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.386446 Loss1: 0.919699 Loss2: 1.466748
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.133436 Loss1: 0.654918 Loss2: 1.478518
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.850366 Loss1: 0.423486 Loss2: 1.426880
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.008355 Loss1: 1.199329 Loss2: 1.809026
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.024706 Loss1: 0.680172 Loss2: 1.344534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.793906 Loss1: 0.433378 Loss2: 1.360528 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.739646 Loss1: 0.418624 Loss2: 1.321022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.614624 Loss1: 0.275016 Loss2: 1.339608 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.579426 Loss1: 0.260072 Loss2: 1.319354 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.930804
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465789 Loss1: 0.154651 Loss2: 1.311138 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462229 Loss1: 0.150684 Loss2: 1.311545 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.287465 Loss1: 0.866726 Loss2: 1.420740 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765710 Loss1: 0.388534 Loss2: 1.377176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.930844 Loss1: 1.157198 Loss2: 1.773646
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.679595 Loss1: 0.290627 Loss2: 1.388968
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.141411 Loss1: 0.808393 Loss2: 1.333018
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.688813 Loss1: 0.313549 Loss2: 1.375264
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.781101 Loss1: 0.428600 Loss2: 1.352502
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.573292 Loss1: 0.196109 Loss2: 1.377184
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.610738 Loss1: 0.304861 Loss2: 1.305878
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.547268 Loss1: 0.181108 Loss2: 1.366159
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.638906 Loss1: 0.327002 Loss2: 1.311903
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.556077 Loss1: 0.187751 Loss2: 1.368326
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.613595 Loss1: 0.301559 Loss2: 1.312036
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.535024 Loss1: 0.162769 Loss2: 1.372255
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515791 Loss1: 0.211618 Loss2: 1.304173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445175 Loss1: 0.156525 Loss2: 1.288650 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.329352 Loss1: 0.827400 Loss2: 1.501952 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.901603 Loss1: 0.428430 Loss2: 1.473174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.801978 Loss1: 0.330347 Loss2: 1.471631 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.759726 Loss1: 0.290565 Loss2: 1.469162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.736630 Loss1: 0.270040 Loss2: 1.466590 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.850016 Loss1: 0.323177 Loss2: 1.526839 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.790749 Loss1: 0.259937 Loss2: 1.530812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.799440 Loss1: 0.274979 Loss2: 1.524461 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.921875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.714350 Loss1: 0.187491 Loss2: 1.526859 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.533141 Loss1: 1.504381 Loss2: 2.028760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.149708 Loss1: 0.585168 Loss2: 1.564539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.931745 Loss1: 0.436788 Loss2: 1.494957
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.146104 Loss1: 1.235667 Loss2: 1.910437
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.234042 Loss1: 0.775484 Loss2: 1.458558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.970583 Loss1: 0.527498 Loss2: 1.443084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.690139 Loss1: 0.187678 Loss2: 1.502461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.692714 Loss1: 0.216345 Loss2: 1.476369 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.674940 Loss1: 0.187447 Loss2: 1.487493 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.949777
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.660912 Loss1: 0.242195 Loss2: 1.418717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.601201 Loss1: 0.199722 Loss2: 1.401479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.642790 Loss1: 0.234187 Loss2: 1.408603
-(DefaultActor pid=3764) >> Training accuracy: 0.929688
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.353562 Loss1: 1.360854 Loss2: 1.992708
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.296813 Loss1: 0.803199 Loss2: 1.493614
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.010077 Loss1: 0.514004 Loss2: 1.496073
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.777006 Loss1: 0.327624 Loss2: 1.449382
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.778943 Loss1: 0.317963 Loss2: 1.460980
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.249467 Loss1: 1.345730 Loss2: 1.903737
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.651731 Loss1: 0.213802 Loss2: 1.437928
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.654323 Loss1: 0.214286 Loss2: 1.440036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.640209 Loss1: 0.186382 Loss2: 1.453827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.603121 Loss1: 0.163587 Loss2: 1.439534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.579206 Loss1: 0.142264 Loss2: 1.436942 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.624505 Loss1: 0.245005 Loss2: 1.379500 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.520353 Loss1: 0.139118 Loss2: 1.381235 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.277087 Loss1: 0.828987 Loss2: 1.448100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.745625 Loss1: 0.337774 Loss2: 1.407850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.277198 Loss1: 1.389185 Loss2: 1.888013
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.685495 Loss1: 0.280044 Loss2: 1.405451
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.320389 Loss1: 0.871470 Loss2: 1.448919
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.639300 Loss1: 0.233460 Loss2: 1.405840
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.979022 Loss1: 0.514379 Loss2: 1.464643
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.640255 Loss1: 0.238678 Loss2: 1.401577
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.845160 Loss1: 0.419812 Loss2: 1.425348
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.582807 Loss1: 0.172296 Loss2: 1.410510
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.756886 Loss1: 0.333761 Loss2: 1.423125
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.554054 Loss1: 0.150717 Loss2: 1.403337
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.731884 Loss1: 0.305211 Loss2: 1.426673
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.515889 Loss1: 0.115999 Loss2: 1.399890
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.593538 Loss1: 0.188914 Loss2: 1.404624 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.545417 Loss1: 0.144543 Loss2: 1.400874 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.412182 Loss1: 0.930986 Loss2: 1.481196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.911249 Loss1: 0.460547 Loss2: 1.450701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.811219 Loss1: 0.360483 Loss2: 1.450736 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.741863 Loss1: 0.298385 Loss2: 1.443478 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.707253 Loss1: 0.260161 Loss2: 1.447092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.666137 Loss1: 0.226580 Loss2: 1.439557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.632219 Loss1: 0.188740 Loss2: 1.443480 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.633081 Loss1: 0.195737 Loss2: 1.437344 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.933594
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.601839 Loss1: 0.219241 Loss2: 1.382598 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.201279 Loss1: 1.310208 Loss2: 1.891071 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.911913 Loss1: 0.475361 Loss2: 1.436553 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.735439 Loss1: 0.355935 Loss2: 1.379505
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.233506 Loss1: 1.421756 Loss2: 1.811749
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.383791 Loss1: 0.994207 Loss2: 1.389584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.000070 Loss1: 0.605671 Loss2: 1.394399 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.826575 Loss1: 0.468199 Loss2: 1.358376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.652633 Loss1: 0.281217 Loss2: 1.371416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.600910 Loss1: 0.264203 Loss2: 1.336708 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.600375 Loss1: 0.243255 Loss2: 1.357120 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.535230 Loss1: 0.192273 Loss2: 1.342957 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.391818 Loss1: 1.411281 Loss2: 1.980537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.010303 Loss1: 0.476864 Loss2: 1.533439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.254608 Loss1: 1.356525 Loss2: 1.898083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.363166 Loss1: 0.916556 Loss2: 1.446610 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.959316 Loss1: 0.476613 Loss2: 1.482703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.806996 Loss1: 0.388518 Loss2: 1.418478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.707255 Loss1: 0.277530 Loss2: 1.429726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.643593 Loss1: 0.231848 Loss2: 1.411745 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.658234 Loss1: 0.254544 Loss2: 1.403691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.633398 Loss1: 0.220873 Loss2: 1.412524 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.276777 Loss1: 1.344354 Loss2: 1.932423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.939124 Loss1: 0.464934 Loss2: 1.474190 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.179297 Loss1: 1.314197 Loss2: 1.865100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.271376 Loss1: 0.800724 Loss2: 1.470653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.937853 Loss1: 0.513966 Loss2: 1.423887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.764296 Loss1: 0.342857 Loss2: 1.421440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.708554 Loss1: 0.287946 Loss2: 1.420608 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.571867 Loss1: 0.178449 Loss2: 1.393418 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.959375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.641801 Loss1: 0.220976 Loss2: 1.420824 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.558553 Loss1: 0.156912 Loss2: 1.401641 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965820
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.268535 Loss1: 0.852147 Loss2: 1.416388 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.775086 Loss1: 0.393191 Loss2: 1.381895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.223007 Loss1: 1.362292 Loss2: 1.860715
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.704571 Loss1: 0.322986 Loss2: 1.381585
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.257392 Loss1: 0.843803 Loss2: 1.413589
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.705859 Loss1: 0.315934 Loss2: 1.389925
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.975333 Loss1: 0.548598 Loss2: 1.426735
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.688778 Loss1: 0.303916 Loss2: 1.384861
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.628893 Loss1: 0.241376 Loss2: 1.387517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.586548 Loss1: 0.208086 Loss2: 1.378461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.562570 Loss1: 0.188248 Loss2: 1.374322 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.566988 Loss1: 0.196356 Loss2: 1.370632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.558605 Loss1: 0.186845 Loss2: 1.371759 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.186099 Loss1: 1.324660 Loss2: 1.861439
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.257865 Loss1: 0.820177 Loss2: 1.437688
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.035565 Loss1: 0.591235 Loss2: 1.444330
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.858943 Loss1: 0.464441 Loss2: 1.394502
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.110612 Loss1: 1.250660 Loss2: 1.859952
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.687591 Loss1: 0.278870 Loss2: 1.408721
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.282664 Loss1: 0.865389 Loss2: 1.417275
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.946879 Loss1: 0.505569 Loss2: 1.441309
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.560005 Loss1: 0.176063 Loss2: 1.383942
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.766555 Loss1: 0.388186 Loss2: 1.378369
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.596190 Loss1: 0.218728 Loss2: 1.377461
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.722147 Loss1: 0.327658 Loss2: 1.394489
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.579531 Loss1: 0.193913 Loss2: 1.385618
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.655218 Loss1: 0.278800 Loss2: 1.376418
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.601141 Loss1: 0.219014 Loss2: 1.382127
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.581718 Loss1: 0.194567 Loss2: 1.387150 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956055
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.576719 Loss1: 0.204041 Loss2: 1.372678 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.334876 Loss1: 1.444232 Loss2: 1.890644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.064293 Loss1: 0.612282 Loss2: 1.452011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.850846 Loss1: 0.433113 Loss2: 1.417733
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.359776 Loss1: 1.505627 Loss2: 1.854149
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.278517 Loss1: 0.876638 Loss2: 1.401879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.974500 Loss1: 0.561824 Loss2: 1.412676 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.842080 Loss1: 0.451576 Loss2: 1.390503 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.689920 Loss1: 0.313621 Loss2: 1.376299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582185 Loss1: 0.220937 Loss2: 1.361247 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.521466 Loss1: 0.137797 Loss2: 1.383668
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.602515 Loss1: 0.233064 Loss2: 1.369452
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.606066 Loss1: 0.237959 Loss2: 1.368106
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.552854 Loss1: 0.188052 Loss2: 1.364802
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.520795 Loss1: 0.163083 Loss2: 1.357712
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.227334 Loss1: 1.338092 Loss2: 1.889242
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.220088 Loss1: 0.788752 Loss2: 1.431335
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.102124 Loss1: 0.615645 Loss2: 1.486479
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.889366 Loss1: 0.472547 Loss2: 1.416819
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.285924 Loss1: 1.421649 Loss2: 1.864275
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.297908 Loss1: 0.865884 Loss2: 1.432024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.948863 Loss1: 0.514133 Loss2: 1.434730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.825854 Loss1: 0.416754 Loss2: 1.409100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.764907 Loss1: 0.342147 Loss2: 1.422759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.556888 Loss1: 0.164077 Loss2: 1.392810
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.629353 Loss1: 0.229377 Loss2: 1.399976
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.541013 Loss1: 0.143635 Loss2: 1.397378
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.588534 Loss1: 0.185951 Loss2: 1.402583
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.576178 Loss1: 0.186496 Loss2: 1.389682
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.546812 Loss1: 0.149033 Loss2: 1.397779
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.548063 Loss1: 0.154397 Loss2: 1.393666
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.371502 Loss1: 1.517226 Loss2: 1.854275
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.333369 Loss1: 0.894077 Loss2: 1.439292
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.952833 Loss1: 0.531318 Loss2: 1.421515
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.852349 Loss1: 0.462960 Loss2: 1.389388
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.227526 Loss1: 1.358460 Loss2: 1.869066
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.775800 Loss1: 0.378589 Loss2: 1.397211
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.283505 Loss1: 0.851037 Loss2: 1.432468
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.658842 Loss1: 0.281915 Loss2: 1.376927
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.017516 Loss1: 0.560260 Loss2: 1.457256
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.655510 Loss1: 0.272252 Loss2: 1.383259
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.809834 Loss1: 0.411099 Loss2: 1.398736
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.599860 Loss1: 0.231524 Loss2: 1.368335
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.675733 Loss1: 0.266672 Loss2: 1.409061
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.585970 Loss1: 0.221439 Loss2: 1.364531
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.606176 Loss1: 0.221309 Loss2: 1.384867
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.563474 Loss1: 0.190947 Loss2: 1.372527
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.622111 Loss1: 0.229586 Loss2: 1.392526
-(DefaultActor pid=3765) >> Training accuracy: 0.943750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.577824 Loss1: 0.182097 Loss2: 1.395728
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.570386 Loss1: 0.192208 Loss2: 1.378178
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.545257 Loss1: 0.162938 Loss2: 1.382319
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.042616 Loss1: 1.264403 Loss2: 1.778213
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.189333 Loss1: 0.801082 Loss2: 1.388251
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.957004 Loss1: 0.572959 Loss2: 1.384045
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.189837 Loss1: 1.308993 Loss2: 1.880845
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.718592 Loss1: 0.358380 Loss2: 1.360211
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.266454 Loss1: 0.822330 Loss2: 1.444125
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.658354 Loss1: 0.296750 Loss2: 1.361604
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.892565 Loss1: 0.469591 Loss2: 1.422974
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.658948 Loss1: 0.302385 Loss2: 1.356563
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.810790 Loss1: 0.399915 Loss2: 1.410875
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.567936 Loss1: 0.210387 Loss2: 1.357549
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.626178 Loss1: 0.276332 Loss2: 1.349846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.523835 Loss1: 0.170234 Loss2: 1.353601 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.525122 Loss1: 0.177741 Loss2: 1.347381 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962891
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.576594 Loss1: 0.186580 Loss2: 1.390015 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.161592 Loss1: 1.259311 Loss2: 1.902281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.970569 Loss1: 0.515216 Loss2: 1.455353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.812541 Loss1: 0.404471 Loss2: 1.408070
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.338381 Loss1: 1.386007 Loss2: 1.952374
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.500358 Loss1: 0.994605 Loss2: 1.505753 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.176758 Loss1: 0.639483 Loss2: 1.537275 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.897993 Loss1: 0.434662 Loss2: 1.463331 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.781025 Loss1: 0.307799 Loss2: 1.473226 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.705263 Loss1: 0.242132 Loss2: 1.463131 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.557607 Loss1: 0.162820 Loss2: 1.394787
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.696148 Loss1: 0.236456 Loss2: 1.459693
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.664451 Loss1: 0.203398 Loss2: 1.461053
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.698218 Loss1: 0.239595 Loss2: 1.458623
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.687359 Loss1: 0.220410 Loss2: 1.466949
-(DefaultActor pid=3764) >> Training accuracy: 0.954167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.255145 Loss1: 1.388538 Loss2: 1.866608
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.338709 Loss1: 0.888080 Loss2: 1.450629
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.036908 Loss1: 0.593600 Loss2: 1.443308
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.436482 Loss1: 1.505919 Loss2: 1.930563
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.806166 Loss1: 0.386971 Loss2: 1.419194
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.469622 Loss1: 0.962783 Loss2: 1.506839
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.786808 Loss1: 0.381529 Loss2: 1.405280
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.093700 Loss1: 0.615301 Loss2: 1.478399
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.679155 Loss1: 0.261848 Loss2: 1.417307
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.995877 Loss1: 0.522034 Loss2: 1.473843
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.643932 Loss1: 0.243402 Loss2: 1.400530
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.611898 Loss1: 0.205458 Loss2: 1.406440 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.629566 Loss1: 0.227058 Loss2: 1.402508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.590165 Loss1: 0.181033 Loss2: 1.409132 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967773
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.655039 Loss1: 0.213371 Loss2: 1.441669 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.199201 Loss1: 1.311800 Loss2: 1.887400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.963369 Loss1: 0.512822 Loss2: 1.450547 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.775315 Loss1: 0.355512 Loss2: 1.419803
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.125872 Loss1: 1.286479 Loss2: 1.839393
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.160517 Loss1: 0.721621 Loss2: 1.438896 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.930432 Loss1: 0.507465 Loss2: 1.422967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.756569 Loss1: 0.354558 Loss2: 1.402011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.701616 Loss1: 0.307656 Loss2: 1.393960 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.637120 Loss1: 0.241121 Loss2: 1.395999 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.571742 Loss1: 0.186521 Loss2: 1.385220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.509526 Loss1: 0.138297 Loss2: 1.371229 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.207921 Loss1: 1.300685 Loss2: 1.907236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.980032 Loss1: 0.482591 Loss2: 1.497441 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.875297 Loss1: 0.414344 Loss2: 1.460954
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.276130 Loss1: 1.402740 Loss2: 1.873390
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.245424 Loss1: 0.878953 Loss2: 1.366470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.744369 Loss1: 0.299019 Loss2: 1.445350
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.898024 Loss1: 0.508998 Loss2: 1.389026
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.709401 Loss1: 0.352200 Loss2: 1.357201
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.688954 Loss1: 0.228073 Loss2: 1.460881
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.627333 Loss1: 0.185025 Loss2: 1.442307 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.603526 Loss1: 0.165120 Loss2: 1.438406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.584920 Loss1: 0.140320 Loss2: 1.444600 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.467849 Loss1: 0.136980 Loss2: 1.330869 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966346
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.289051 Loss1: 1.419829 Loss2: 1.869222
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.292364 Loss1: 0.834044 Loss2: 1.458320
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.041786 Loss1: 0.597011 Loss2: 1.444775
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.793329 Loss1: 0.385525 Loss2: 1.407804
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.113950 Loss1: 1.221513 Loss2: 1.892437
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.266747 Loss1: 0.770381 Loss2: 1.496366 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.036101 Loss1: 0.568642 Loss2: 1.467459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.891135 Loss1: 0.419657 Loss2: 1.471478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.767873 Loss1: 0.323078 Loss2: 1.444795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.716482 Loss1: 0.273184 Loss2: 1.443298 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.655470 Loss1: 0.219128 Loss2: 1.436342 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.571575 Loss1: 0.149314 Loss2: 1.422261 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978860
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.296682 Loss1: 0.864683 Loss2: 1.431999 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.725373 Loss1: 0.313959 Loss2: 1.411414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.650024 Loss1: 0.230871 Loss2: 1.419153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.600030 Loss1: 0.196837 Loss2: 1.403193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.556796 Loss1: 0.160227 Loss2: 1.396568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.524541 Loss1: 0.137850 Loss2: 1.386691 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476161 Loss1: 0.091665 Loss2: 1.384497 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.570276 Loss1: 0.189815 Loss2: 1.380460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.572696 Loss1: 0.182507 Loss2: 1.390189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.534314 Loss1: 0.153185 Loss2: 1.381130
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.019380 Loss1: 1.201712 Loss2: 1.817669
-(DefaultActor pid=3764) >> Training accuracy: 0.934375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.537589 Loss1: 0.156512 Loss2: 1.381077
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.216959 Loss1: 0.841045 Loss2: 1.375914
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.860920 Loss1: 0.499101 Loss2: 1.361819
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.887694 Loss1: 0.523568 Loss2: 1.364127
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.783897 Loss1: 0.426278 Loss2: 1.357619
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.609066 Loss1: 0.265746 Loss2: 1.343320
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.094049 Loss1: 1.183437 Loss2: 1.910612
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.508707 Loss1: 0.182601 Loss2: 1.326106
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454457 Loss1: 0.131742 Loss2: 1.322714 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 08:40:53,677 | server.py:236 | fit_round 70 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404930 Loss1: 0.095688 Loss2: 1.309241 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400933 Loss1: 0.094718 Loss2: 1.306214 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.648010 Loss1: 0.238185 Loss2: 1.409825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.718435 Loss1: 0.315499 Loss2: 1.402936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.657485 Loss1: 0.231987 Loss2: 1.425498
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.256426 Loss1: 1.460853 Loss2: 1.795572
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.572101 Loss1: 0.157252 Loss2: 1.414849
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.228183 Loss1: 0.881163 Loss2: 1.347019
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.028610 Loss1: 0.668488 Loss2: 1.360122
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.849945 Loss1: 0.518368 Loss2: 1.331578
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.733383 Loss1: 0.391015 Loss2: 1.342368
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.598139 Loss1: 0.279218 Loss2: 1.318921
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.301824 Loss1: 1.433842 Loss2: 1.867982
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.533145 Loss1: 0.220182 Loss2: 1.312963
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.232201 Loss1: 0.807348 Loss2: 1.424853
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.520421 Loss1: 0.213750 Loss2: 1.306671
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.003295 Loss1: 0.562253 Loss2: 1.441041
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509826 Loss1: 0.201843 Loss2: 1.307983
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.900751 Loss1: 0.497413 Loss2: 1.403338
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.538503 Loss1: 0.231626 Loss2: 1.306877
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.712012 Loss1: 0.307931 Loss2: 1.404081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.592977 Loss1: 0.200713 Loss2: 1.392264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.621894 Loss1: 0.236296 Loss2: 1.385598 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 08:40:53,677][flwr][DEBUG] - fit_round 70 received 50 results and 0 failures
-INFO flwr 2023-10-10 08:41:35,096 | server.py:125 | fit progress: (70, 2.281081163654693, {'accuracy': 0.5317}, 161402.874152568)
->> Test accuracy: 0.531700
-[2023-10-10 08:41:35,096][flwr][INFO] - fit progress: (70, 2.281081163654693, {'accuracy': 0.5317}, 161402.874152568)
-DEBUG flwr 2023-10-10 08:41:35,096 | server.py:173 | evaluate_round 70: strategy sampled 50 clients (out of 50)
-[2023-10-10 08:41:35,096][flwr][DEBUG] - evaluate_round 70: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 08:50:37,056 | server.py:187 | evaluate_round 70 received 50 results and 0 failures
-[2023-10-10 08:50:37,056][flwr][DEBUG] - evaluate_round 70 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 08:50:37,056 | server.py:222 | fit_round 71: strategy sampled 50 clients (out of 50)
-[2023-10-10 08:50:37,056][flwr][DEBUG] - fit_round 71: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.210094 Loss1: 1.284618 Loss2: 1.925476
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.336055 Loss1: 0.948595 Loss2: 1.387461
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.074109 Loss1: 0.605601 Loss2: 1.468508
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.742769 Loss1: 0.356366 Loss2: 1.386403
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.282714 Loss1: 1.310975 Loss2: 1.971739
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.626161 Loss1: 0.227000 Loss2: 1.399161 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.567428 Loss1: 0.185115 Loss2: 1.382313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.574814 Loss1: 0.194708 Loss2: 1.380106 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.511539 Loss1: 0.139124 Loss2: 1.372415 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.493038 Loss1: 0.129397 Loss2: 1.363641 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.654749 Loss1: 0.206541 Loss2: 1.448209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.610599 Loss1: 0.171570 Loss2: 1.439028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.575020 Loss1: 0.140455 Loss2: 1.434565
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.179012 Loss1: 1.342407 Loss2: 1.836605
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.386704 Loss1: 0.921682 Loss2: 1.465022
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.976339 Loss1: 0.548247 Loss2: 1.428092
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.818355 Loss1: 0.411796 Loss2: 1.406559
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.676005 Loss1: 0.273146 Loss2: 1.402859
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.202525 Loss1: 1.309873 Loss2: 1.892652
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.633858 Loss1: 0.245559 Loss2: 1.388299
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.262530 Loss1: 0.843505 Loss2: 1.419024
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.578486 Loss1: 0.187896 Loss2: 1.390590
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.944974 Loss1: 0.515127 Loss2: 1.429847
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.557543 Loss1: 0.175982 Loss2: 1.381561
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.802450 Loss1: 0.411995 Loss2: 1.390454
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.545380 Loss1: 0.164588 Loss2: 1.380792
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.772649 Loss1: 0.373489 Loss2: 1.399160
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.655743 Loss1: 0.263333 Loss2: 1.392410
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.560956 Loss1: 0.177126 Loss2: 1.383829
-(DefaultActor pid=3765) >> Training accuracy: 0.964844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.634299 Loss1: 0.246724 Loss2: 1.387575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527239 Loss1: 0.157863 Loss2: 1.369376 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.368707 Loss1: 0.877617 Loss2: 1.491089 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.770871 Loss1: 0.314711 Loss2: 1.456160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.701587 Loss1: 0.244621 Loss2: 1.456966
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.150718 Loss1: 1.270634 Loss2: 1.880084
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.680041 Loss1: 0.217550 Loss2: 1.462491
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.357953 Loss1: 0.852264 Loss2: 1.505689
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.637988 Loss1: 0.187031 Loss2: 1.450957
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.942277 Loss1: 0.514858 Loss2: 1.427419
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.812645 Loss1: 0.373844 Loss2: 1.438801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.650837 Loss1: 0.245022 Loss2: 1.405815 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.603425 Loss1: 0.204743 Loss2: 1.398681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.537215 Loss1: 0.150603 Loss2: 1.386612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.525028 Loss1: 0.138387 Loss2: 1.386641 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977022
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.994231 Loss1: 0.526627 Loss2: 1.467604 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.772223 Loss1: 0.311332 Loss2: 1.460891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.279569 Loss1: 1.312027 Loss2: 1.967542
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.686107 Loss1: 0.252318 Loss2: 1.433789
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.507719 Loss1: 0.967050 Loss2: 1.540669
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.659802 Loss1: 0.232487 Loss2: 1.427314
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.644309 Loss1: 0.207340 Loss2: 1.436969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.642748 Loss1: 0.215317 Loss2: 1.427431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.656644 Loss1: 0.224016 Loss2: 1.432628 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971680
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.705632 Loss1: 0.240757 Loss2: 1.464875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.613695 Loss1: 0.161188 Loss2: 1.452508 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.591049 Loss1: 0.146712 Loss2: 1.444337
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.328535 Loss1: 1.435591 Loss2: 1.892944
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.330309 Loss1: 0.883944 Loss2: 1.446365
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.997141 Loss1: 0.559151 Loss2: 1.437990
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.782937 Loss1: 0.382516 Loss2: 1.400422
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.647379 Loss1: 0.256249 Loss2: 1.391129
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.706694 Loss1: 1.588303 Loss2: 2.118391
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.490867 Loss1: 0.914474 Loss2: 1.576393 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.212415 Loss1: 0.611507 Loss2: 1.600909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.581146 Loss1: 0.195939 Loss2: 1.385207
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.973040 Loss1: 0.424345 Loss2: 1.548694
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.579333 Loss1: 0.188641 Loss2: 1.390692
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.851433 Loss1: 0.291809 Loss2: 1.559624
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.599514 Loss1: 0.210432 Loss2: 1.389082
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.824020 Loss1: 0.279670 Loss2: 1.544350
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.779715 Loss1: 0.233065 Loss2: 1.546650
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.809055 Loss1: 0.263919 Loss2: 1.545136
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.733312 Loss1: 0.193158 Loss2: 1.540154
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.703504 Loss1: 0.163666 Loss2: 1.539837
-(DefaultActor pid=3764) >> Training accuracy: 0.967634
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.125685 Loss1: 1.215552 Loss2: 1.910133
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.274612 Loss1: 0.833466 Loss2: 1.441146
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.011576 Loss1: 0.547680 Loss2: 1.463896
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.778342 Loss1: 0.372220 Loss2: 1.406122
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.072864 Loss1: 1.240378 Loss2: 1.832486
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.095417 Loss1: 0.726316 Loss2: 1.369101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.911991 Loss1: 0.514616 Loss2: 1.397375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.782964 Loss1: 0.423290 Loss2: 1.359674 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.655020 Loss1: 0.291928 Loss2: 1.363092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.589090 Loss1: 0.240195 Loss2: 1.348896 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.959375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.526566 Loss1: 0.189621 Loss2: 1.336944 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462297 Loss1: 0.129951 Loss2: 1.332347 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.159162 Loss1: 1.253245 Loss2: 1.905917
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.290753 Loss1: 0.813350 Loss2: 1.477404
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.956155 Loss1: 0.485567 Loss2: 1.470588
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.877808 Loss1: 0.443197 Loss2: 1.434611
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.242812 Loss1: 1.391566 Loss2: 1.851246
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.322311 Loss1: 0.855419 Loss2: 1.466892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.988431 Loss1: 0.573180 Loss2: 1.415251 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.854918 Loss1: 0.431376 Loss2: 1.423541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.795176 Loss1: 0.377517 Loss2: 1.417659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.713963 Loss1: 0.295648 Loss2: 1.418316 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975586
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.703820 Loss1: 0.307919 Loss2: 1.395901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.551342 Loss1: 0.160304 Loss2: 1.391038 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971680
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.224211 Loss1: 1.344716 Loss2: 1.879495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.014090 Loss1: 0.549959 Loss2: 1.464131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.185838 Loss1: 1.305763 Loss2: 1.880074 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.355125 Loss1: 0.912635 Loss2: 1.442490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.120030 Loss1: 0.671141 Loss2: 1.448889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.890147 Loss1: 0.468276 Loss2: 1.421870 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.768809 Loss1: 0.369343 Loss2: 1.399466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.694107 Loss1: 0.291109 Loss2: 1.402997 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.600913 Loss1: 0.209737 Loss2: 1.391176 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.581212 Loss1: 0.190234 Loss2: 1.390978 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.166633 Loss1: 1.258496 Loss2: 1.908136
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.299359 Loss1: 0.807626 Loss2: 1.491733
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.968481 Loss1: 0.494887 Loss2: 1.473594
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.809961 Loss1: 0.364144 Loss2: 1.445817
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.431572 Loss1: 1.441120 Loss2: 1.990452
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.394377 Loss1: 0.890109 Loss2: 1.504268 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.059624 Loss1: 0.568526 Loss2: 1.491098 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.947152 Loss1: 0.458903 Loss2: 1.488249 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.589625 Loss1: 0.164567 Loss2: 1.425057
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.843291 Loss1: 0.357817 Loss2: 1.485474
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.558977 Loss1: 0.135851 Loss2: 1.423126
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.802390 Loss1: 0.316945 Loss2: 1.485445
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.536734 Loss1: 0.120370 Loss2: 1.416364
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.677399 Loss1: 0.206326 Loss2: 1.471073
-(DefaultActor pid=3765) >> Training accuracy: 0.977539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.633859 Loss1: 0.167594 Loss2: 1.466264
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.629214 Loss1: 0.170031 Loss2: 1.459183
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.619693 Loss1: 0.159262 Loss2: 1.460430
-(DefaultActor pid=3764) >> Training accuracy: 0.933333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.129571 Loss1: 1.227981 Loss2: 1.901590
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.306580 Loss1: 0.837781 Loss2: 1.468799
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.957025 Loss1: 0.498721 Loss2: 1.458303
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.013172 Loss1: 1.161114 Loss2: 1.852058
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.848515 Loss1: 0.422888 Loss2: 1.425627
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.089404 Loss1: 0.710183 Loss2: 1.379221
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.733745 Loss1: 0.288555 Loss2: 1.445190
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.874018 Loss1: 0.468634 Loss2: 1.405384
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.682528 Loss1: 0.258788 Loss2: 1.423740
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.628518 Loss1: 0.199305 Loss2: 1.429213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.651765 Loss1: 0.230326 Loss2: 1.421438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.620032 Loss1: 0.193259 Loss2: 1.426773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.599893 Loss1: 0.190918 Loss2: 1.408974 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.469831 Loss1: 0.124351 Loss2: 1.345479 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.195832 Loss1: 1.347310 Loss2: 1.848521 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.930451 Loss1: 0.508601 Loss2: 1.421849 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.852847 Loss1: 0.465923 Loss2: 1.386924
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.360484 Loss1: 1.420510 Loss2: 1.939974
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.412070 Loss1: 0.930133 Loss2: 1.481937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.041929 Loss1: 0.562987 Loss2: 1.478941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.915276 Loss1: 0.453651 Loss2: 1.461625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.779773 Loss1: 0.318411 Loss2: 1.461362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.694173 Loss1: 0.260676 Loss2: 1.433497 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.518172 Loss1: 0.144431 Loss2: 1.373741
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.665146 Loss1: 0.219594 Loss2: 1.445552
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.671586 Loss1: 0.233211 Loss2: 1.438375
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.679349 Loss1: 0.226160 Loss2: 1.453189
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.646095 Loss1: 0.203208 Loss2: 1.442887
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.355025 Loss1: 1.397783 Loss2: 1.957242
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.288159 Loss1: 0.923511 Loss2: 1.364648
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.015013 Loss1: 0.592474 Loss2: 1.422538
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.828759 Loss1: 0.432165 Loss2: 1.396594
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.720924 Loss1: 0.358368 Loss2: 1.362557
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.537910 Loss1: 1.010282 Loss2: 1.527628 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.169188 Loss1: 0.666139 Loss2: 1.503049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.494811 Loss1: 0.147303 Loss2: 1.347508 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484566 Loss1: 0.138771 Loss2: 1.345795 [repeated 2x across cluster]
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.649744 Loss1: 0.196628 Loss2: 1.453116 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.674771 Loss1: 0.231023 Loss2: 1.443748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.633945 Loss1: 0.198193 Loss2: 1.435752 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.990011 Loss1: 0.544842 Loss2: 1.445169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.707643 Loss1: 0.283125 Loss2: 1.424518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.696324 Loss1: 0.284299 Loss2: 1.412025
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.381509 Loss1: 1.317934 Loss2: 2.063575
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.429752 Loss1: 0.848924 Loss2: 1.580828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.115365 Loss1: 0.496405 Loss2: 1.618960 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.911954 Loss1: 0.369412 Loss2: 1.542541 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.845979 Loss1: 0.283615 Loss2: 1.562364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.760969 Loss1: 0.204687 Loss2: 1.556282 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.646841 Loss1: 0.119544 Loss2: 1.527297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.612143 Loss1: 0.093539 Loss2: 1.518604 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.992912 Loss1: 0.522228 Loss2: 1.470685 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.792352 Loss1: 0.341602 Loss2: 1.450749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.684153 Loss1: 0.239197 Loss2: 1.444956
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.407897 Loss1: 1.452588 Loss2: 1.955309
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.434003 Loss1: 0.931343 Loss2: 1.502659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.177598 Loss1: 0.656938 Loss2: 1.520660 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.003852 Loss1: 0.532483 Loss2: 1.471368 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.863745 Loss1: 0.390567 Loss2: 1.473178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.717027 Loss1: 0.241564 Loss2: 1.475463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.644212 Loss1: 0.187191 Loss2: 1.457021 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.132096 Loss1: 1.335583 Loss2: 1.796513
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.609820 Loss1: 0.154413 Loss2: 1.455407
-(DefaultActor pid=3764) >> Training accuracy: 0.951042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.986365 Loss1: 0.589883 Loss2: 1.396483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.635777 Loss1: 0.258460 Loss2: 1.377317 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.572931 Loss1: 0.215179 Loss2: 1.357752
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.220426 Loss1: 1.340251 Loss2: 1.880176
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.209884 Loss1: 0.794904 Loss2: 1.414981
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.566631 Loss1: 0.210266 Loss2: 1.356365
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.007130 Loss1: 0.559187 Loss2: 1.447943
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.563594 Loss1: 0.205942 Loss2: 1.357652
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.796054 Loss1: 0.408383 Loss2: 1.387671
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.492259 Loss1: 0.145898 Loss2: 1.346360
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.715647 Loss1: 0.310732 Loss2: 1.404914
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.500409 Loss1: 0.150222 Loss2: 1.350187
-(DefaultActor pid=3765) >> Training accuracy: 0.959961
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.595781 Loss1: 0.214829 Loss2: 1.380952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.532462 Loss1: 0.162758 Loss2: 1.369704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.520814 Loss1: 0.140127 Loss2: 1.380687
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.325646 Loss1: 1.430910 Loss2: 1.894736
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.314672 Loss1: 0.812991 Loss2: 1.501681
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.003643 Loss1: 0.580576 Loss2: 1.423067
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.826386 Loss1: 0.394939 Loss2: 1.431447
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.668149 Loss1: 0.257434 Loss2: 1.410715
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.928960 Loss1: 1.103299 Loss2: 1.825661
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.594723 Loss1: 0.193969 Loss2: 1.400755
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.542208 Loss1: 0.148160 Loss2: 1.394048 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.530294 Loss1: 0.150916 Loss2: 1.379378 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.531458 Loss1: 0.144516 Loss2: 1.386942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.538330 Loss1: 0.145056 Loss2: 1.393274 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545062 Loss1: 0.211184 Loss2: 1.333878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476930 Loss1: 0.147786 Loss2: 1.329144 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460136 Loss1: 0.134264 Loss2: 1.325872
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.286257 Loss1: 1.343579 Loss2: 1.942678
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.281500 Loss1: 0.828836 Loss2: 1.452663
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.895191 Loss1: 0.459333 Loss2: 1.435857
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.726775 Loss1: 0.345678 Loss2: 1.381097
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.770050 Loss1: 0.363326 Loss2: 1.406724
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.174255 Loss1: 1.301284 Loss2: 1.872971
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.216237 Loss1: 0.806204 Loss2: 1.410033 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.931609 Loss1: 0.495387 Loss2: 1.436221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.722449 Loss1: 0.337068 Loss2: 1.385381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.650982 Loss1: 0.264948 Loss2: 1.386035 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.550663 Loss1: 0.177654 Loss2: 1.373009
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.619181 Loss1: 0.237337 Loss2: 1.381844
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.556059 Loss1: 0.186057 Loss2: 1.370002
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548906 Loss1: 0.168671 Loss2: 1.380235
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.537303 Loss1: 0.159611 Loss2: 1.377692
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527045 Loss1: 0.154371 Loss2: 1.372674
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.112807 Loss1: 1.234141 Loss2: 1.878665
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.301924 Loss1: 0.849911 Loss2: 1.452013
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.043251 Loss1: 0.562658 Loss2: 1.480593
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.872292 Loss1: 0.439415 Loss2: 1.432878
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.818153 Loss1: 0.374749 Loss2: 1.443404
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.293538 Loss1: 1.381271 Loss2: 1.912266
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.296233 Loss1: 0.856069 Loss2: 1.440165 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.058968 Loss1: 0.581319 Loss2: 1.477649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.835790 Loss1: 0.412945 Loss2: 1.422845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.748847 Loss1: 0.309631 Loss2: 1.439215 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.699160 Loss1: 0.278764 Loss2: 1.420396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.674052 Loss1: 0.246229 Loss2: 1.427823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.536779 Loss1: 0.129635 Loss2: 1.407144 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.321584 Loss1: 0.898242 Loss2: 1.423342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.805833 Loss1: 0.402113 Loss2: 1.403720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.244162 Loss1: 1.392371 Loss2: 1.851791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.261209 Loss1: 0.855938 Loss2: 1.405270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.642119 Loss1: 0.251923 Loss2: 1.390196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.591340 Loss1: 0.199370 Loss2: 1.391971 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.574969 Loss1: 0.188522 Loss2: 1.386447 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977679
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550901 Loss1: 0.202990 Loss2: 1.347912 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481500 Loss1: 0.142032 Loss2: 1.339468 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.569242 Loss1: 0.221524 Loss2: 1.347718
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.198745 Loss1: 1.360489 Loss2: 1.838257
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.429149 Loss1: 0.980712 Loss2: 1.448436
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.013559 Loss1: 0.567172 Loss2: 1.446387
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.752760 Loss1: 0.371010 Loss2: 1.381750
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.738810 Loss1: 0.346489 Loss2: 1.392321
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.670659 Loss1: 0.280863 Loss2: 1.389796
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.244804 Loss1: 1.367286 Loss2: 1.877519
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.599980 Loss1: 0.222649 Loss2: 1.377331
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.259326 Loss1: 0.849118 Loss2: 1.410208
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.586829 Loss1: 0.203331 Loss2: 1.383499
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.922115 Loss1: 0.507323 Loss2: 1.414792
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.778538 Loss1: 0.383198 Loss2: 1.395339
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.549520 Loss1: 0.181084 Loss2: 1.368436
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.544543 Loss1: 0.175723 Loss2: 1.368820
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.719382 Loss1: 0.324309 Loss2: 1.395073
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.672167 Loss1: 0.284808 Loss2: 1.387360
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.582082 Loss1: 0.194436 Loss2: 1.387646
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.635685 Loss1: 0.250196 Loss2: 1.385488
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.569543 Loss1: 0.191283 Loss2: 1.378261
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.537421 Loss1: 0.152969 Loss2: 1.384452
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.373882 Loss1: 1.451381 Loss2: 1.922501
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.343456 Loss1: 0.851093 Loss2: 1.492363
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.974947 Loss1: 0.498957 Loss2: 1.475989
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.829602 Loss1: 0.388886 Loss2: 1.440717
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.738129 Loss1: 0.297951 Loss2: 1.440178
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.299496 Loss1: 1.307039 Loss2: 1.992457
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.685746 Loss1: 0.261528 Loss2: 1.424218
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.633960 Loss1: 0.204607 Loss2: 1.429353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.639041 Loss1: 0.218896 Loss2: 1.420144 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.633843 Loss1: 0.210508 Loss2: 1.423335 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.611879 Loss1: 0.182260 Loss2: 1.429619 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.600674 Loss1: 0.232795 Loss2: 1.367880 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.476430 Loss1: 0.115627 Loss2: 1.360803 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961538
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.306627 Loss1: 1.365552 Loss2: 1.941075
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.378554 Loss1: 0.878004 Loss2: 1.500550
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.061433 Loss1: 0.559440 Loss2: 1.501993
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.863114 Loss1: 0.402135 Loss2: 1.460979
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.177839 Loss1: 1.272944 Loss2: 1.904895
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.409225 Loss1: 0.894427 Loss2: 1.514798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.057293 Loss1: 0.612630 Loss2: 1.444662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.816756 Loss1: 0.365186 Loss2: 1.451570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.796712 Loss1: 0.358410 Loss2: 1.438302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.697341 Loss1: 0.253706 Loss2: 1.443635 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.614287 Loss1: 0.194165 Loss2: 1.420122 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.560858 Loss1: 0.143001 Loss2: 1.417858 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.326588 Loss1: 0.856665 Loss2: 1.469922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.872515 Loss1: 0.420714 Loss2: 1.451801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.386947 Loss1: 1.469332 Loss2: 1.917614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.290652 Loss1: 0.845522 Loss2: 1.445131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.925816 Loss1: 0.485839 Loss2: 1.439977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.789977 Loss1: 0.383153 Loss2: 1.406823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.671159 Loss1: 0.265136 Loss2: 1.406024 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.937500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.640430 Loss1: 0.238243 Loss2: 1.402187 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.617275 Loss1: 0.225987 Loss2: 1.391288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.590620 Loss1: 0.194730 Loss2: 1.395890
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.349173 Loss1: 1.440407 Loss2: 1.908766
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.375561 Loss1: 0.908585 Loss2: 1.466977
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.004256 Loss1: 0.558816 Loss2: 1.445440
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.819797 Loss1: 0.391319 Loss2: 1.428478
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.746449 Loss1: 0.324392 Loss2: 1.422057
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.714160 Loss1: 0.299474 Loss2: 1.414686
-DEBUG flwr 2023-10-10 09:19:02,090 | server.py:236 | fit_round 71 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.270097 Loss1: 1.373483 Loss2: 1.896615
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.333974 Loss1: 0.842377 Loss2: 1.491597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.051509 Loss1: 0.594085 Loss2: 1.457423 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.939796 Loss1: 0.477496 Loss2: 1.462300 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.934375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.828968 Loss1: 0.378439 Loss2: 1.450529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.752510 Loss1: 0.295289 Loss2: 1.457221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.621704 Loss1: 0.186476 Loss2: 1.435228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.590501 Loss1: 0.163387 Loss2: 1.427114 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971680
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.978392 Loss1: 0.482152 Loss2: 1.496240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.853990 Loss1: 0.383847 Loss2: 1.470143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.225011 Loss1: 1.257716 Loss2: 1.967295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.382132 Loss1: 0.870980 Loss2: 1.511152 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.048049 Loss1: 0.517421 Loss2: 1.530627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.864955 Loss1: 0.387391 Loss2: 1.477563 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.750814 Loss1: 0.286862 Loss2: 1.463952 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.678935 Loss1: 0.209725 Loss2: 1.469211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.624683 Loss1: 0.174403 Loss2: 1.450280 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 09:19:02,090][flwr][DEBUG] - fit_round 71 received 50 results and 0 failures
-INFO flwr 2023-10-10 09:19:44,350 | server.py:125 | fit progress: (71, 2.287786943463091, {'accuracy': 0.5305}, 163692.128101278)
->> Test accuracy: 0.530500
-[2023-10-10 09:19:44,350][flwr][INFO] - fit progress: (71, 2.287786943463091, {'accuracy': 0.5305}, 163692.128101278)
-DEBUG flwr 2023-10-10 09:19:44,350 | server.py:173 | evaluate_round 71: strategy sampled 50 clients (out of 50)
-[2023-10-10 09:19:44,350][flwr][DEBUG] - evaluate_round 71: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 09:28:51,092 | server.py:187 | evaluate_round 71 received 50 results and 0 failures
-[2023-10-10 09:28:51,092][flwr][DEBUG] - evaluate_round 71 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 09:28:51,092 | server.py:222 | fit_round 72: strategy sampled 50 clients (out of 50)
-[2023-10-10 09:28:51,092][flwr][DEBUG] - fit_round 72: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.968079 Loss1: 1.058500 Loss2: 1.909579
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.202438 Loss1: 0.774802 Loss2: 1.427636
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.913509 Loss1: 0.437428 Loss2: 1.476082
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.770521 Loss1: 0.366931 Loss2: 1.403590
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.253713 Loss1: 1.335396 Loss2: 1.918317
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.314568 Loss1: 0.831132 Loss2: 1.483436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.017792 Loss1: 0.546106 Loss2: 1.471686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.876026 Loss1: 0.427494 Loss2: 1.448532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.759969 Loss1: 0.316917 Loss2: 1.443052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.712450 Loss1: 0.285172 Loss2: 1.427278 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.681765 Loss1: 0.242739 Loss2: 1.439026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.591229 Loss1: 0.163096 Loss2: 1.428133 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.175585 Loss1: 1.296202 Loss2: 1.879383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.911225 Loss1: 0.487707 Loss2: 1.423518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.736410 Loss1: 0.351954 Loss2: 1.384456
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.355738 Loss1: 1.384967 Loss2: 1.970771
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.405942 Loss1: 0.882318 Loss2: 1.523623 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.105478 Loss1: 0.580484 Loss2: 1.524994 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.860491 Loss1: 0.370878 Loss2: 1.489613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.809315 Loss1: 0.331319 Loss2: 1.477996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.685403 Loss1: 0.212701 Loss2: 1.472702 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.493976 Loss1: 0.116767 Loss2: 1.377208
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.641573 Loss1: 0.177660 Loss2: 1.463913
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.628908 Loss1: 0.168137 Loss2: 1.460772
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.623770 Loss1: 0.165016 Loss2: 1.458754
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.596791 Loss1: 0.134470 Loss2: 1.462321
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.525726 Loss1: 1.547060 Loss2: 1.978667
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.379506 Loss1: 0.911116 Loss2: 1.468389
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.020202 Loss1: 0.530050 Loss2: 1.490153
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.865444 Loss1: 0.417766 Loss2: 1.447678
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.184982 Loss1: 1.330813 Loss2: 1.854170
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.189790 Loss1: 0.759595 Loss2: 1.430195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.005971 Loss1: 0.586741 Loss2: 1.419230 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.890008 Loss1: 0.470909 Loss2: 1.419099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.724433 Loss1: 0.328283 Loss2: 1.396150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.638679 Loss1: 0.259087 Loss2: 1.379592 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981027
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.580524 Loss1: 0.202910 Loss2: 1.377614 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.493711 Loss1: 0.128135 Loss2: 1.365576 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.208441 Loss1: 0.852496 Loss2: 1.355944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.747102 Loss1: 0.412321 Loss2: 1.334781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.028047 Loss1: 1.170102 Loss2: 1.857945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500689 Loss1: 0.177975 Loss2: 1.322714 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488452 Loss1: 0.159601 Loss2: 1.328851 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454773 Loss1: 0.135885 Loss2: 1.318887 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442872 Loss1: 0.128641 Loss2: 1.314231 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969952
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.739847 Loss1: 0.361817 Loss2: 1.378030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.598247 Loss1: 0.235500 Loss2: 1.362747 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.527932 Loss1: 0.165433 Loss2: 1.362498
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.257983 Loss1: 1.349127 Loss2: 1.908855
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463342 Loss1: 0.109653 Loss2: 1.353688
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.443582 Loss1: 0.926593 Loss2: 1.516989
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.036491 Loss1: 0.582359 Loss2: 1.454133
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.889191 Loss1: 0.420945 Loss2: 1.468246
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.802552 Loss1: 0.358454 Loss2: 1.444098
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.798554 Loss1: 0.347296 Loss2: 1.451258
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.672469 Loss1: 0.236204 Loss2: 1.436266
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.021737 Loss1: 1.198918 Loss2: 1.822819
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.168279 Loss1: 0.775611 Loss2: 1.392668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.896924 Loss1: 0.454474 Loss2: 1.442450 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.643469 Loss1: 0.212204 Loss2: 1.431266
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.761705 Loss1: 0.372133 Loss2: 1.389571
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.695230 Loss1: 0.310483 Loss2: 1.384747
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.629015 Loss1: 0.241345 Loss2: 1.387669
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.662808 Loss1: 0.282609 Loss2: 1.380200
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.631937 Loss1: 0.239627 Loss2: 1.392310
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.278371 Loss1: 1.409793 Loss2: 1.868578
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.316618 Loss1: 0.861709 Loss2: 1.454910 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958984
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.553927 Loss1: 0.172285 Loss2: 1.381642
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.052242 Loss1: 0.594138 Loss2: 1.458104
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.792862 Loss1: 0.394163 Loss2: 1.398699
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.754273 Loss1: 0.337838 Loss2: 1.416435
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.676366 Loss1: 0.275820 Loss2: 1.400546
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.650626 Loss1: 0.250684 Loss2: 1.399942
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.195758 Loss1: 1.359728 Loss2: 1.836030
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.590518 Loss1: 0.190417 Loss2: 1.400101
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.543140 Loss1: 0.154449 Loss2: 1.388691 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.518243 Loss1: 0.135390 Loss2: 1.382853 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.722910 Loss1: 0.340935 Loss2: 1.381975 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.564311 Loss1: 0.199226 Loss2: 1.365085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.150166 Loss1: 1.307959 Loss2: 1.842207 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.339432 Loss1: 0.877648 Loss2: 1.461784 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.770267 Loss1: 0.350587 Loss2: 1.419680 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.713839 Loss1: 0.303259 Loss2: 1.410580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.673765 Loss1: 0.253322 Loss2: 1.420443
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.220018 Loss1: 1.294380 Loss2: 1.925639
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.575818 Loss1: 0.170440 Loss2: 1.405378
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.172924 Loss1: 0.763863 Loss2: 1.409061
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.594538 Loss1: 0.197030 Loss2: 1.397508
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.998506 Loss1: 0.561913 Loss2: 1.436593
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.817237 Loss1: 0.420268 Loss2: 1.396968
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.592014 Loss1: 0.190209 Loss2: 1.401805
-(DefaultActor pid=3765) >> Training accuracy: 0.959961
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.580841 Loss1: 0.195096 Loss2: 1.385745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.517127 Loss1: 0.144948 Loss2: 1.372180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519081 Loss1: 0.155095 Loss2: 1.363986
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.368782 Loss1: 1.402408 Loss2: 1.966374
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457221 Loss1: 0.093114 Loss2: 1.364107
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.349020 Loss1: 0.830546 Loss2: 1.518474
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.095437 Loss1: 0.583730 Loss2: 1.511706
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.919592 Loss1: 0.422920 Loss2: 1.496672
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.778681 Loss1: 0.297253 Loss2: 1.481428
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.714416 Loss1: 0.236482 Loss2: 1.477934
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.201326 Loss1: 1.297395 Loss2: 1.903930
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.739564 Loss1: 0.252329 Loss2: 1.487235
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.347979 Loss1: 0.889893 Loss2: 1.458086
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.689651 Loss1: 0.214815 Loss2: 1.474836
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.026820 Loss1: 0.548252 Loss2: 1.478568
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.767364 Loss1: 0.294590 Loss2: 1.472774
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.780926 Loss1: 0.352176 Loss2: 1.428751
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.763625 Loss1: 0.269384 Loss2: 1.494241
-(DefaultActor pid=3765) >> Training accuracy: 0.934375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.693447 Loss1: 0.263601 Loss2: 1.429845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.584206 Loss1: 0.163645 Loss2: 1.420561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.576407 Loss1: 0.156378 Loss2: 1.420028
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.287665 Loss1: 1.434742 Loss2: 1.852923
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.547359 Loss1: 0.138104 Loss2: 1.409255
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.380163 Loss1: 0.932873 Loss2: 1.447290
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.929690 Loss1: 0.533550 Loss2: 1.396140
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.729035 Loss1: 0.331156 Loss2: 1.397880
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.597611 Loss1: 0.225387 Loss2: 1.372224
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.546057 Loss1: 0.171046 Loss2: 1.375011
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.101013 Loss1: 1.245104 Loss2: 1.855909
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547253 Loss1: 0.168653 Loss2: 1.378600
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.084779 Loss1: 0.718624 Loss2: 1.366155
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.586831 Loss1: 0.216263 Loss2: 1.370568
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834290 Loss1: 0.430854 Loss2: 1.403435
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.491325 Loss1: 0.114736 Loss2: 1.376589
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.666681 Loss1: 0.325273 Loss2: 1.341408
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.487181 Loss1: 0.124952 Loss2: 1.362229
-(DefaultActor pid=3765) >> Training accuracy: 0.962500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539704 Loss1: 0.205272 Loss2: 1.334432 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.517306 Loss1: 0.179322 Loss2: 1.337984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.554948 Loss1: 0.216005 Loss2: 1.338944
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.243660 Loss1: 1.309321 Loss2: 1.934339
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.531652 Loss1: 0.187029 Loss2: 1.344624
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.368687 Loss1: 0.868068 Loss2: 1.500619
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.026473 Loss1: 0.532886 Loss2: 1.493587
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.865607 Loss1: 0.387220 Loss2: 1.478387
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.765099 Loss1: 0.298547 Loss2: 1.466552
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.709716 Loss1: 0.249138 Loss2: 1.460578
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.993472 Loss1: 1.177388 Loss2: 1.816083
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.163405 Loss1: 0.753340 Loss2: 1.410065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.862604 Loss1: 0.453276 Loss2: 1.409328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.753065 Loss1: 0.370679 Loss2: 1.382386 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961914
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.721779 Loss1: 0.324086 Loss2: 1.397693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.557950 Loss1: 0.187958 Loss2: 1.369993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.478272 Loss1: 0.126614 Loss2: 1.351658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458583 Loss1: 0.107891 Loss2: 1.350693 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.049647 Loss1: 0.595061 Loss2: 1.454586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.677009 Loss1: 0.263393 Loss2: 1.413616 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.214705 Loss1: 1.360105 Loss2: 1.854601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.332416 Loss1: 0.906035 Loss2: 1.426382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.034932 Loss1: 0.608252 Loss2: 1.426680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.795865 Loss1: 0.401749 Loss2: 1.394117 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.701291 Loss1: 0.321072 Loss2: 1.380219 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.606122 Loss1: 0.229442 Loss2: 1.376680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.566031 Loss1: 0.196186 Loss2: 1.369846
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.184279 Loss1: 1.373802 Loss2: 1.810477
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529679 Loss1: 0.159635 Loss2: 1.370044
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.298327 Loss1: 0.883282 Loss2: 1.415045
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.988228 Loss1: 0.592349 Loss2: 1.395879
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.858961 Loss1: 0.469517 Loss2: 1.389444
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.748400 Loss1: 0.360513 Loss2: 1.387887
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.630494 Loss1: 0.263321 Loss2: 1.367173
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.281768 Loss1: 1.450085 Loss2: 1.831684
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.305164 Loss1: 0.918129 Loss2: 1.387034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.043200 Loss1: 0.624833 Loss2: 1.418367 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.795481 Loss1: 0.422647 Loss2: 1.372834 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961914
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484345 Loss1: 0.124794 Loss2: 1.359550
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.664216 Loss1: 0.293420 Loss2: 1.370795
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.594212 Loss1: 0.237780 Loss2: 1.356432
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.572765 Loss1: 0.214142 Loss2: 1.358622
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.557235 Loss1: 0.202399 Loss2: 1.354835
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.576380 Loss1: 0.228473 Loss2: 1.347906
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.285421 Loss1: 1.389978 Loss2: 1.895442
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.523582 Loss1: 0.168270 Loss2: 1.355312
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.104352 Loss1: 0.618759 Loss2: 1.485593 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.873490 Loss1: 0.436973 Loss2: 1.436517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.808447 Loss1: 0.378340 Loss2: 1.430107
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.132263 Loss1: 1.247821 Loss2: 1.884442
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.281742 Loss1: 0.796159 Loss2: 1.485583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.096596 Loss1: 0.613570 Loss2: 1.483026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.921402 Loss1: 0.452930 Loss2: 1.468472 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.798162 Loss1: 0.326231 Loss2: 1.471931 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.678952 Loss1: 0.214812 Loss2: 1.464140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.620319 Loss1: 0.167440 Loss2: 1.452880 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.339339 Loss1: 0.873725 Loss2: 1.465614 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965820
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.859291 Loss1: 0.423597 Loss2: 1.435693 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.773361 Loss1: 0.342372 Loss2: 1.430989 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.665011 Loss1: 0.234188 Loss2: 1.430824
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.253790 Loss1: 1.372085 Loss2: 1.881705
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.256754 Loss1: 0.890178 Loss2: 1.366576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.950621 Loss1: 0.533878 Loss2: 1.416743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.541305 Loss1: 0.138890 Loss2: 1.402415
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.760976 Loss1: 0.390059 Loss2: 1.370918
-(DefaultActor pid=3765) >> Training accuracy: 0.965402
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.645042 Loss1: 0.294317 Loss2: 1.350726
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.548615 Loss1: 0.198896 Loss2: 1.349719
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487848 Loss1: 0.149813 Loss2: 1.338035
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460323 Loss1: 0.126123 Loss2: 1.334200
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464433 Loss1: 0.133904 Loss2: 1.330528
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.173361 Loss1: 1.254983 Loss2: 1.918377
-(DefaultActor pid=3764) >> Training accuracy: 0.972356
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.004535 Loss1: 0.540997 Loss2: 1.463538 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.660962 Loss1: 0.241602 Loss2: 1.419360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.597183 Loss1: 0.195546 Loss2: 1.401637
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.285600 Loss1: 1.342278 Loss2: 1.943322
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.324668 Loss1: 0.840479 Loss2: 1.484189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.041929 Loss1: 0.550812 Loss2: 1.491117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.895586 Loss1: 0.436249 Loss2: 1.459337 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.536422 Loss1: 0.135301 Loss2: 1.401121
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.842899 Loss1: 0.371595 Loss2: 1.471304
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.810299 Loss1: 0.356153 Loss2: 1.454146
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.724015 Loss1: 0.266218 Loss2: 1.457797
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.693405 Loss1: 0.248786 Loss2: 1.444619
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.625044 Loss1: 0.185210 Loss2: 1.439833
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.376087 Loss1: 1.422851 Loss2: 1.953236
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.583584 Loss1: 0.154628 Loss2: 1.428957
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.959962 Loss1: 0.498492 Loss2: 1.461470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.736339 Loss1: 0.296453 Loss2: 1.439886 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.667892 Loss1: 0.229209 Loss2: 1.438683
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.158009 Loss1: 1.275447 Loss2: 1.882562
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.651810 Loss1: 0.216672 Loss2: 1.435139
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.173261 Loss1: 0.780716 Loss2: 1.392545
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.625232 Loss1: 0.189435 Loss2: 1.435796
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.922525 Loss1: 0.516354 Loss2: 1.406170
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.575067 Loss1: 0.147393 Loss2: 1.427674
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.768025 Loss1: 0.402120 Loss2: 1.365905
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.553839 Loss1: 0.135676 Loss2: 1.418163
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.640497 Loss1: 0.273525 Loss2: 1.366972
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.596723 Loss1: 0.233996 Loss2: 1.362727
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.537915 Loss1: 0.187796 Loss2: 1.350118
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.550072 Loss1: 0.193955 Loss2: 1.356116
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.474545 Loss1: 0.121993 Loss2: 1.352552
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.146915 Loss1: 1.238748 Loss2: 1.908166
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.447100 Loss1: 0.106283 Loss2: 1.340817
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.017930 Loss1: 0.565292 Loss2: 1.452638 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.712938 Loss1: 0.295463 Loss2: 1.417476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.655074 Loss1: 0.250323 Loss2: 1.404751
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.141881 Loss1: 1.246758 Loss2: 1.895123
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.254145 Loss1: 0.823592 Loss2: 1.430553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.931449 Loss1: 0.480455 Loss2: 1.450994 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.721690 Loss1: 0.314185 Loss2: 1.407506 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.549763 Loss1: 0.139844 Loss2: 1.409919
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.727194 Loss1: 0.323631 Loss2: 1.403563
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.609494 Loss1: 0.206187 Loss2: 1.403307
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.559526 Loss1: 0.169633 Loss2: 1.389893
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.502214 Loss1: 0.121681 Loss2: 1.380532
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.557903 Loss1: 0.176578 Loss2: 1.381325
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.089361 Loss1: 1.258802 Loss2: 1.830559
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.555548 Loss1: 0.168380 Loss2: 1.387168
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.119540 Loss1: 0.710002 Loss2: 1.409537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.681594 Loss1: 0.291893 Loss2: 1.389701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.368669 Loss1: 1.412750 Loss2: 1.955920
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.634535 Loss1: 0.245097 Loss2: 1.389438
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.484937 Loss1: 0.953222 Loss2: 1.531714
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.576945 Loss1: 0.184913 Loss2: 1.392032
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.089107 Loss1: 0.571776 Loss2: 1.517330
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.514108 Loss1: 0.139087 Loss2: 1.375021
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.899838 Loss1: 0.418541 Loss2: 1.481298
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499088 Loss1: 0.131555 Loss2: 1.367533
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447542 Loss1: 0.086075 Loss2: 1.361467 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.767335 Loss1: 0.298281 Loss2: 1.469054 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.656178 Loss1: 0.188268 Loss2: 1.467910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.674064 Loss1: 0.215616 Loss2: 1.458447
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.279457 Loss1: 1.369811 Loss2: 1.909646
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.251021 Loss1: 0.816205 Loss2: 1.434816
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.995792 Loss1: 0.520474 Loss2: 1.475318
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.802249 Loss1: 0.382201 Loss2: 1.420048
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.733677 Loss1: 0.309505 Loss2: 1.424172
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.094637 Loss1: 1.175749 Loss2: 1.918887
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.678515 Loss1: 0.262751 Loss2: 1.415764
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.669478 Loss1: 0.254859 Loss2: 1.414619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.626971 Loss1: 0.213630 Loss2: 1.413342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.601338 Loss1: 0.189933 Loss2: 1.411405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.620497 Loss1: 0.202531 Loss2: 1.417966 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.651065 Loss1: 0.234989 Loss2: 1.416076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.596184 Loss1: 0.179570 Loss2: 1.416614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.534774 Loss1: 0.131038 Loss2: 1.403736
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.320006 Loss1: 1.403725 Loss2: 1.916281
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.417838 Loss1: 0.880243 Loss2: 1.537595
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.001976 Loss1: 0.536980 Loss2: 1.464996
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.853794 Loss1: 0.373620 Loss2: 1.480174
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.704587 Loss1: 0.253742 Loss2: 1.450845
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.172579 Loss1: 1.287567 Loss2: 1.885012
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.139412 Loss1: 0.661771 Loss2: 1.477641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.923630 Loss1: 0.481717 Loss2: 1.441912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.779202 Loss1: 0.346772 Loss2: 1.432430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.732297 Loss1: 0.305994 Loss2: 1.426303 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961914
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.693585 Loss1: 0.261653 Loss2: 1.431932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.594840 Loss1: 0.175668 Loss2: 1.419172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.352271 Loss1: 1.363540 Loss2: 1.988731 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.943015
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.115670 Loss1: 0.651912 Loss2: 1.463757 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.762036 Loss1: 0.369862 Loss2: 1.392174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547498 Loss1: 0.162641 Loss2: 1.384857 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.512883 Loss1: 0.138896 Loss2: 1.373987
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.289784 Loss1: 1.467230 Loss2: 1.822554
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.334449 Loss1: 0.950163 Loss2: 1.384286 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.992069 Loss1: 0.588952 Loss2: 1.403117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.709471 Loss1: 0.351369 Loss2: 1.358102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.572163 Loss1: 0.227342 Loss2: 1.344821 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.555255 Loss1: 0.215378 Loss2: 1.339877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.553031 Loss1: 0.209895 Loss2: 1.343136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.495354 Loss1: 0.150436 Loss2: 1.344918 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606566 Loss1: 0.269358 Loss2: 1.337208 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485461 Loss1: 0.169312 Loss2: 1.316149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.442555 Loss1: 0.129393 Loss2: 1.313162
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.427796 Loss1: 1.513092 Loss2: 1.914703
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.278309 Loss1: 0.872907 Loss2: 1.405402 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.509536 Loss1: 0.199124 Loss2: 1.310412
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.936065 Loss1: 0.504740 Loss2: 1.431326
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.712426 Loss1: 0.339106 Loss2: 1.373320
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.693612 Loss1: 0.319958 Loss2: 1.373654
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647220 Loss1: 0.262676 Loss2: 1.384544
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.625046 Loss1: 0.253932 Loss2: 1.371115
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.560122 Loss1: 0.186752 Loss2: 1.373370
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.299926 Loss1: 1.427339 Loss2: 1.872588
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.517279 Loss1: 0.157618 Loss2: 1.359661
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.364105 Loss1: 0.916135 Loss2: 1.447970
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.547565 Loss1: 0.184817 Loss2: 1.362748
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.964058 Loss1: 0.544087 Loss2: 1.419972
-(DefaultActor pid=3764) >> Training accuracy: 0.962054
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.808773 Loss1: 0.401232 Loss2: 1.407541
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.803271 Loss1: 0.401830 Loss2: 1.401441
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.680601 Loss1: 0.273911 Loss2: 1.406690
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.657868 Loss1: 0.262221 Loss2: 1.395647
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.483993 Loss1: 1.415152 Loss2: 2.068842
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.659436 Loss1: 0.264263 Loss2: 1.395173
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.442291 Loss1: 0.865956 Loss2: 1.576335
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.616693 Loss1: 0.230969 Loss2: 1.385724
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.092764 Loss1: 0.514623 Loss2: 1.578141
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.571024 Loss1: 0.178858 Loss2: 1.392166
-(DefaultActor pid=3765) >> Training accuracy: 0.947917
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-10 09:57:03,044 | server.py:236 | fit_round 72 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.824148 Loss1: 0.286994 Loss2: 1.537154 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.764744 Loss1: 0.235713 Loss2: 1.529031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.784028 Loss1: 0.240492 Loss2: 1.543536
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.256291 Loss1: 1.311746 Loss2: 1.944545
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.365416 Loss1: 0.881612 Loss2: 1.483804 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.727103 Loss1: 0.197080 Loss2: 1.530023
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.056642 Loss1: 0.551435 Loss2: 1.505207
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.897456 Loss1: 0.428426 Loss2: 1.469030
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.869840 Loss1: 0.380916 Loss2: 1.488924
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.798531 Loss1: 0.319940 Loss2: 1.478592
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.767434 Loss1: 0.293177 Loss2: 1.474257
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.311461 Loss1: 1.366691 Loss2: 1.944771
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.656734 Loss1: 0.191017 Loss2: 1.465717
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.332858 Loss1: 0.856784 Loss2: 1.476073
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.650188 Loss1: 0.185197 Loss2: 1.464991
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.065613 Loss1: 0.553533 Loss2: 1.512080
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.616088 Loss1: 0.163060 Loss2: 1.453028
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.853871 Loss1: 0.364950 Loss2: 1.488921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.718539 Loss1: 0.245772 Loss2: 1.472768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.651796 Loss1: 0.190665 Loss2: 1.461131
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.181539 Loss1: 1.334628 Loss2: 1.846910
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.165426 Loss1: 0.760003 Loss2: 1.405423 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.608374 Loss1: 0.149308 Loss2: 1.459065
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.948359 Loss1: 0.543391 Loss2: 1.404968
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.715783 Loss1: 0.341496 Loss2: 1.374287
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.687018 Loss1: 0.307099 Loss2: 1.379919
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.657249 Loss1: 0.265412 Loss2: 1.391837
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.613677 Loss1: 0.238479 Loss2: 1.375198
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.294492 Loss1: 1.333541 Loss2: 1.960951
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.587382 Loss1: 0.216132 Loss2: 1.371250
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.544756 Loss1: 0.175788 Loss2: 1.368969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.562022 Loss1: 0.200841 Loss2: 1.361180 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.761811 Loss1: 0.288539 Loss2: 1.473272 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.692605 Loss1: 0.241433 Loss2: 1.451171 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.705698 Loss1: 0.255083 Loss2: 1.450615 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 09:57:03,044][flwr][DEBUG] - fit_round 72 received 50 results and 0 failures
-INFO flwr 2023-10-10 09:57:43,564 | server.py:125 | fit progress: (72, 2.2798919079783624, {'accuracy': 0.5342}, 165971.342123449)
->> Test accuracy: 0.534200
-[2023-10-10 09:57:43,564][flwr][INFO] - fit progress: (72, 2.2798919079783624, {'accuracy': 0.5342}, 165971.342123449)
-DEBUG flwr 2023-10-10 09:57:43,564 | server.py:173 | evaluate_round 72: strategy sampled 50 clients (out of 50)
-[2023-10-10 09:57:43,564][flwr][DEBUG] - evaluate_round 72: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 10:06:48,900 | server.py:187 | evaluate_round 72 received 50 results and 0 failures
-[2023-10-10 10:06:48,900][flwr][DEBUG] - evaluate_round 72 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 10:06:48,901 | server.py:222 | fit_round 73: strategy sampled 50 clients (out of 50)
-[2023-10-10 10:06:48,901][flwr][DEBUG] - fit_round 73: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.123547 Loss1: 1.243335 Loss2: 1.880212 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.849819 Loss1: 0.439233 Loss2: 1.410586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.674467 Loss1: 0.302846 Loss2: 1.371621
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.268794 Loss1: 1.354372 Loss2: 1.914422
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.419835 Loss1: 0.925999 Loss2: 1.493836 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.935088 Loss1: 0.481300 Loss2: 1.453788 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.858021 Loss1: 0.417947 Loss2: 1.440075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.768582 Loss1: 0.325490 Loss2: 1.443092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.683475 Loss1: 0.263220 Loss2: 1.420255 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.957292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.536042 Loss1: 0.181232 Loss2: 1.354810
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.646967 Loss1: 0.222651 Loss2: 1.424315
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.627222 Loss1: 0.209803 Loss2: 1.417419
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.607541 Loss1: 0.184935 Loss2: 1.422606
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.542244 Loss1: 0.129185 Loss2: 1.413059
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.380293 Loss1: 1.489845 Loss2: 1.890448
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.359150 Loss1: 0.877249 Loss2: 1.481901
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.968235 Loss1: 0.505706 Loss2: 1.462529
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.882366 Loss1: 0.447426 Loss2: 1.434941
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.112839 Loss1: 1.244384 Loss2: 1.868454
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.212113 Loss1: 0.786320 Loss2: 1.425793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.948798 Loss1: 0.503893 Loss2: 1.444905 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.839590 Loss1: 0.433947 Loss2: 1.405643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.674829 Loss1: 0.266622 Loss2: 1.408207 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.587353 Loss1: 0.200285 Loss2: 1.387069 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.551406 Loss1: 0.170795 Loss2: 1.380611 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.503976 Loss1: 0.133082 Loss2: 1.370894 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.308996 Loss1: 0.896742 Loss2: 1.412254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.763407 Loss1: 0.379210 Loss2: 1.384197 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.116905 Loss1: 1.261826 Loss2: 1.855079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.228110 Loss1: 0.823990 Loss2: 1.404119 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.005079 Loss1: 0.578482 Loss2: 1.426597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.799290 Loss1: 0.403556 Loss2: 1.395734 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.758910 Loss1: 0.365047 Loss2: 1.393863 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972098
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.596289 Loss1: 0.215516 Loss2: 1.380773 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.489560 Loss1: 0.115260 Loss2: 1.374300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.499628 Loss1: 0.137201 Loss2: 1.362427
-(DefaultActor pid=3764) >> Training accuracy: 0.942708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.158056 Loss1: 1.316102 Loss2: 1.841954
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.125495 Loss1: 0.754910 Loss2: 1.370585
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.891281 Loss1: 0.498487 Loss2: 1.392794
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.704143 Loss1: 0.353595 Loss2: 1.350548
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.612079 Loss1: 0.253429 Loss2: 1.358650
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.418466 Loss1: 1.353713 Loss2: 2.064753
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.598060 Loss1: 0.253863 Loss2: 1.344197
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531473 Loss1: 0.180290 Loss2: 1.351184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.482932 Loss1: 0.143809 Loss2: 1.339123 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477239 Loss1: 0.143017 Loss2: 1.334222 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458125 Loss1: 0.121201 Loss2: 1.336924 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.790495 Loss1: 0.242900 Loss2: 1.547595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.748634 Loss1: 0.212930 Loss2: 1.535704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.750217 Loss1: 0.203717 Loss2: 1.546500
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.041089 Loss1: 1.186254 Loss2: 1.854835
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.207193 Loss1: 0.749259 Loss2: 1.457934
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.890427 Loss1: 0.459311 Loss2: 1.431115
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.763719 Loss1: 0.347256 Loss2: 1.416463
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.668360 Loss1: 0.259711 Loss2: 1.408648
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.503619 Loss1: 1.514842 Loss2: 1.988777
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.661590 Loss1: 0.255384 Loss2: 1.406206
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.439125 Loss1: 0.949080 Loss2: 1.490045
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.087932 Loss1: 0.585787 Loss2: 1.502145
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.631859 Loss1: 0.225723 Loss2: 1.406136
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.911514 Loss1: 0.455689 Loss2: 1.455825
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.594581 Loss1: 0.191611 Loss2: 1.402970
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.533324 Loss1: 0.137584 Loss2: 1.395740 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474048 Loss1: 0.094534 Loss2: 1.379514 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.607198 Loss1: 0.165184 Loss2: 1.442014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.549275 Loss1: 0.130844 Loss2: 1.418431 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970982
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.159777 Loss1: 1.247110 Loss2: 1.912667
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.187863 Loss1: 0.762242 Loss2: 1.425621
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.010250 Loss1: 0.552736 Loss2: 1.457514
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765516 Loss1: 0.354176 Loss2: 1.411339
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.102355 Loss1: 1.199562 Loss2: 1.902793
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.265735 Loss1: 0.776007 Loss2: 1.489729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.887773 Loss1: 0.455128 Loss2: 1.432645 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.786834 Loss1: 0.360453 Loss2: 1.426381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.707800 Loss1: 0.293354 Loss2: 1.414446 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.531930 Loss1: 0.141082 Loss2: 1.390848 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.582478 Loss1: 0.186468 Loss2: 1.396010 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.545310 Loss1: 0.151640 Loss2: 1.393670 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970588
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.202262 Loss1: 0.806840 Loss2: 1.395422 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.700094 Loss1: 0.330181 Loss2: 1.369913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.666016 Loss1: 0.292376 Loss2: 1.373640
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.257647 Loss1: 1.415381 Loss2: 1.842266
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.235546 Loss1: 0.831167 Loss2: 1.404379
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.610119 Loss1: 0.248721 Loss2: 1.361397
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.957518 Loss1: 0.554419 Loss2: 1.403099
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.604012 Loss1: 0.235560 Loss2: 1.368452
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.727732 Loss1: 0.352988 Loss2: 1.374744
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.550387 Loss1: 0.184777 Loss2: 1.365610
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.613815 Loss1: 0.236210 Loss2: 1.377605
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.519155 Loss1: 0.162855 Loss2: 1.356299
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.557292 Loss1: 0.195220 Loss2: 1.362072 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.941406
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487523 Loss1: 0.125962 Loss2: 1.361562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460166 Loss1: 0.117454 Loss2: 1.342713 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.341945 Loss1: 0.867170 Loss2: 1.474775 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.773848 Loss1: 0.334858 Loss2: 1.438990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.742429 Loss1: 0.296918 Loss2: 1.445511 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.702299 Loss1: 0.271371 Loss2: 1.430928 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.698946 Loss1: 0.258131 Loss2: 1.440815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.747144 Loss1: 0.300851 Loss2: 1.446294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.674126 Loss1: 0.244249 Loss2: 1.429877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.638666 Loss1: 0.207223 Loss2: 1.431444 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.932292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.610623 Loss1: 0.231163 Loss2: 1.379460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.496506 Loss1: 0.120866 Loss2: 1.375640 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.380560 Loss1: 0.941427 Loss2: 1.439132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.787086 Loss1: 0.376153 Loss2: 1.410933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.048762 Loss1: 1.244332 Loss2: 1.804430
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.708178 Loss1: 0.292680 Loss2: 1.415497
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.171591 Loss1: 0.745263 Loss2: 1.426328
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.750228 Loss1: 0.353147 Loss2: 1.397081
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.667133 Loss1: 0.257489 Loss2: 1.409645
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.944701 Loss1: 0.559750 Loss2: 1.384951
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.651103 Loss1: 0.250855 Loss2: 1.400249
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.761494 Loss1: 0.392900 Loss2: 1.368594
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.613568 Loss1: 0.219965 Loss2: 1.393603
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.684485 Loss1: 0.327555 Loss2: 1.356929
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.589783 Loss1: 0.203455 Loss2: 1.386328
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.678479 Loss1: 0.312516 Loss2: 1.365963
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.588380 Loss1: 0.235402 Loss2: 1.352978
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.573994 Loss1: 0.210138 Loss2: 1.363855
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.509634 Loss1: 0.167394 Loss2: 1.342240
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.471522 Loss1: 0.134911 Loss2: 1.336611
-(DefaultActor pid=3764) >> Training accuracy: 0.978516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.284747 Loss1: 1.444396 Loss2: 1.840350
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.353063 Loss1: 0.938341 Loss2: 1.414722
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.971647 Loss1: 0.556732 Loss2: 1.414914
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.798769 Loss1: 0.411616 Loss2: 1.387152
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.736401 Loss1: 0.337106 Loss2: 1.399295
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.160307 Loss1: 1.270852 Loss2: 1.889454
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.613721 Loss1: 0.230896 Loss2: 1.382825
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.239364 Loss1: 0.807319 Loss2: 1.432044
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.557546 Loss1: 0.182995 Loss2: 1.374550
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.016969 Loss1: 0.580939 Loss2: 1.436030
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.533957 Loss1: 0.167487 Loss2: 1.366470
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.869397 Loss1: 0.451424 Loss2: 1.417974
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.506756 Loss1: 0.146374 Loss2: 1.360382
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.720497 Loss1: 0.315308 Loss2: 1.405189
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.540317 Loss1: 0.178869 Loss2: 1.361448
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.628902 Loss1: 0.228983 Loss2: 1.399919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.555424 Loss1: 0.167918 Loss2: 1.387505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.544610 Loss1: 0.159778 Loss2: 1.384832
-(DefaultActor pid=3764) >> Training accuracy: 0.947917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.341583 Loss1: 1.394226 Loss2: 1.947357
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.602147 Loss1: 1.084849 Loss2: 1.517298
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.151017 Loss1: 0.642293 Loss2: 1.508724
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.942343 Loss1: 0.461056 Loss2: 1.481287
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.741065 Loss1: 0.277840 Loss2: 1.463225
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.170810 Loss1: 1.287007 Loss2: 1.883803
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.715314 Loss1: 0.274993 Loss2: 1.440320
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.187761 Loss1: 0.720465 Loss2: 1.467295
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.666646 Loss1: 0.215148 Loss2: 1.451497
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.918120 Loss1: 0.472572 Loss2: 1.445548
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.654100 Loss1: 0.208240 Loss2: 1.445860
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.602811 Loss1: 0.164063 Loss2: 1.438748
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.776198 Loss1: 0.349615 Loss2: 1.426583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.589630 Loss1: 0.152184 Loss2: 1.437446
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.693515 Loss1: 0.268957 Loss2: 1.424559
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.669288 Loss1: 0.242443 Loss2: 1.426845
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.597285 Loss1: 0.186162 Loss2: 1.411123
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.580475 Loss1: 0.170043 Loss2: 1.410431
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.591044 Loss1: 0.181872 Loss2: 1.409172
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.382598 Loss1: 1.385013 Loss2: 1.997585
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.565685 Loss1: 0.154718 Loss2: 1.410967
-(DefaultActor pid=3764) >> Training accuracy: 0.970703
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.141783 Loss1: 0.599875 Loss2: 1.541908 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.837807 Loss1: 0.339296 Loss2: 1.498511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.000780 Loss1: 1.149122 Loss2: 1.851658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.096088 Loss1: 0.709173 Loss2: 1.386915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.930884 Loss1: 0.524817 Loss2: 1.406067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.773317 Loss1: 0.393047 Loss2: 1.380270 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.671630 Loss1: 0.297179 Loss2: 1.374451 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.567716 Loss1: 0.211112 Loss2: 1.356604 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.492104 Loss1: 0.146442 Loss2: 1.345663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.467430 Loss1: 0.122173 Loss2: 1.345257 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.841780 Loss1: 0.418473 Loss2: 1.423308 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.695094 Loss1: 0.269028 Loss2: 1.426066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.637893 Loss1: 0.216758 Loss2: 1.421134
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.279787 Loss1: 1.399942 Loss2: 1.879845
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.587235 Loss1: 0.171446 Loss2: 1.415789
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.289660 Loss1: 0.856874 Loss2: 1.432786
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.569625 Loss1: 0.165938 Loss2: 1.403686
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.925444 Loss1: 0.508937 Loss2: 1.416507
-(DefaultActor pid=3765) >> Training accuracy: 0.935268
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.637863 Loss1: 0.228592 Loss2: 1.409271
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.793598 Loss1: 0.405912 Loss2: 1.387686
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.738754 Loss1: 0.345627 Loss2: 1.393128
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.635659 Loss1: 0.264475 Loss2: 1.371184
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.536463 Loss1: 0.164208 Loss2: 1.372255
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.529789 Loss1: 0.165595 Loss2: 1.364194
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.169680 Loss1: 1.321696 Loss2: 1.847985
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.558567 Loss1: 0.193569 Loss2: 1.364997
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.524617 Loss1: 0.147910 Loss2: 1.376707 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.913542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.744167 Loss1: 0.370474 Loss2: 1.373694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.574350 Loss1: 0.212075 Loss2: 1.362274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.594863 Loss1: 0.226843 Loss2: 1.368019
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.197683 Loss1: 1.228219 Loss2: 1.969464
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.207698 Loss1: 0.722535 Loss2: 1.485163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.990264 Loss1: 0.477002 Loss2: 1.513262 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491890 Loss1: 0.146024 Loss2: 1.345867
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.786494 Loss1: 0.328878 Loss2: 1.457616
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.745341 Loss1: 0.282559 Loss2: 1.462782
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.660823 Loss1: 0.207850 Loss2: 1.452974
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.678673 Loss1: 0.231162 Loss2: 1.447511
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.670076 Loss1: 0.208487 Loss2: 1.461589
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.214262 Loss1: 1.317699 Loss2: 1.896563
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.584907 Loss1: 0.141615 Loss2: 1.443292
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.239797 Loss1: 0.798757 Loss2: 1.441040
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.585034 Loss1: 0.146568 Loss2: 1.438466
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.751664 Loss1: 0.351517 Loss2: 1.400148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.656810 Loss1: 0.269501 Loss2: 1.387309 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.562323 Loss1: 0.181621 Loss2: 1.380702
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.155401 Loss1: 1.264644 Loss2: 1.890757
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.239427 Loss1: 0.827617 Loss2: 1.411810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.921902 Loss1: 0.462910 Loss2: 1.458992 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.503180 Loss1: 0.126002 Loss2: 1.377178
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.781567 Loss1: 0.385653 Loss2: 1.395914
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.629577 Loss1: 0.234502 Loss2: 1.395075
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.573262 Loss1: 0.187254 Loss2: 1.386008
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.556417 Loss1: 0.170005 Loss2: 1.386412
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.529843 Loss1: 0.148079 Loss2: 1.381764
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.226465 Loss1: 1.297319 Loss2: 1.929146
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519819 Loss1: 0.139901 Loss2: 1.379918
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.408456 Loss1: 0.920850 Loss2: 1.487606
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.505372 Loss1: 0.137509 Loss2: 1.367862
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.803278 Loss1: 0.369542 Loss2: 1.433736 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.677848 Loss1: 0.258592 Loss2: 1.419256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.636051 Loss1: 0.208026 Loss2: 1.428025
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.129114 Loss1: 1.310023 Loss2: 1.819091
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.253746 Loss1: 0.862247 Loss2: 1.391499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.946487 Loss1: 0.516570 Loss2: 1.429917 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.954167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.560258 Loss1: 0.147339 Loss2: 1.412918
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.718581 Loss1: 0.354618 Loss2: 1.363963
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.621261 Loss1: 0.248655 Loss2: 1.372607
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.592069 Loss1: 0.230768 Loss2: 1.361301
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.562941 Loss1: 0.205903 Loss2: 1.357038
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548168 Loss1: 0.188640 Loss2: 1.359528
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.448672 Loss1: 1.454745 Loss2: 1.993928
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485026 Loss1: 0.140507 Loss2: 1.344519
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460614 Loss1: 0.112311 Loss2: 1.348303 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.844041 Loss1: 0.350235 Loss2: 1.493806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.727682 Loss1: 0.248407 Loss2: 1.479275 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.740322 Loss1: 0.269551 Loss2: 1.470772
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.060417 Loss1: 1.222328 Loss2: 1.838089
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.253069 Loss1: 0.824534 Loss2: 1.428535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.914141 Loss1: 0.501253 Loss2: 1.412888 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.832534 Loss1: 0.437045 Loss2: 1.395489 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.665700 Loss1: 0.284442 Loss2: 1.381258 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.506417 Loss1: 0.134276 Loss2: 1.372142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.475494 Loss1: 0.111876 Loss2: 1.363618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.493962 Loss1: 0.131686 Loss2: 1.362276 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970703
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.667295 Loss1: 0.234710 Loss2: 1.432584 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.617342 Loss1: 0.194749 Loss2: 1.422593 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.588922 Loss1: 0.175728 Loss2: 1.413194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.587371 Loss1: 0.170241 Loss2: 1.417129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.566999 Loss1: 0.153044 Loss2: 1.413955 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.954102
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.793434 Loss1: 0.310399 Loss2: 1.483035 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.740324 Loss1: 0.273667 Loss2: 1.466657 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.428330 Loss1: 1.435310 Loss2: 1.993020 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.186591 Loss1: 0.766325 Loss2: 1.420266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.889337 Loss1: 0.445971 Loss2: 1.443366 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.630921 Loss1: 0.246894 Loss2: 1.384027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.578870 Loss1: 0.202023 Loss2: 1.376847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.537652 Loss1: 0.150227 Loss2: 1.387425
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.263075 Loss1: 1.394387 Loss2: 1.868688
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.202616 Loss1: 0.787756 Loss2: 1.414860 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987981
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.724092 Loss1: 0.334724 Loss2: 1.389368 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.636924 Loss1: 0.250652 Loss2: 1.386273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.573040 Loss1: 0.204654 Loss2: 1.368386
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.367318 Loss1: 1.400108 Loss2: 1.967210
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.296003 Loss1: 0.816078 Loss2: 1.479925 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.988027 Loss1: 0.496044 Loss2: 1.491983 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.842578 Loss1: 0.371691 Loss2: 1.470887 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.732308 Loss1: 0.268835 Loss2: 1.463473 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.680013 Loss1: 0.226014 Loss2: 1.453998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.655039 Loss1: 0.201709 Loss2: 1.453330 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.596143 Loss1: 0.146615 Loss2: 1.449529 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.821304 Loss1: 0.369238 Loss2: 1.452066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.706145 Loss1: 0.252838 Loss2: 1.453307 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.616299 Loss1: 0.170905 Loss2: 1.445394
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.492608 Loss1: 1.446457 Loss2: 2.046152
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.375772 Loss1: 0.959996 Loss2: 1.415776
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.582169 Loss1: 0.141132 Loss2: 1.441037
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.634849 Loss1: 0.199672 Loss2: 1.435178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.638932 Loss1: 0.188752 Loss2: 1.450180 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.723410 Loss1: 0.293657 Loss2: 1.429753 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.625853 Loss1: 0.204663 Loss2: 1.421190 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.179671 Loss1: 1.324667 Loss2: 1.855004 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.997594 Loss1: 0.556362 Loss2: 1.441232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.718191 Loss1: 0.323279 Loss2: 1.394912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.621714 Loss1: 0.242751 Loss2: 1.378963 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.575681 Loss1: 0.201837 Loss2: 1.373844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.506110 Loss1: 0.141685 Loss2: 1.364425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.492337 Loss1: 0.136598 Loss2: 1.355738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441870 Loss1: 0.083522 Loss2: 1.358347 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.842466 Loss1: 0.266555 Loss2: 1.575912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.780312 Loss1: 0.211396 Loss2: 1.568916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.744713 Loss1: 0.183419 Loss2: 1.561294
-(DefaultActor pid=3765) >> Training accuracy: 0.938477
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.369579 Loss1: 1.262939 Loss2: 2.106641
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.394668 Loss1: 0.798412 Loss2: 1.596256
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.098599 Loss1: 0.467610 Loss2: 1.630989
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.988504 Loss1: 0.410045 Loss2: 1.578460
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.931917 Loss1: 0.330648 Loss2: 1.601268
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.125577 Loss1: 1.282310 Loss2: 1.843267
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.792671 Loss1: 0.225404 Loss2: 1.567267
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.201498 Loss1: 0.782524 Loss2: 1.418973
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.725593 Loss1: 0.158078 Loss2: 1.567515
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.912206 Loss1: 0.502185 Loss2: 1.410021
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.748020 Loss1: 0.188675 Loss2: 1.559344
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.741364 Loss1: 0.360092 Loss2: 1.381271
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.699584 Loss1: 0.140236 Loss2: 1.559348
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.684525 Loss1: 0.298962 Loss2: 1.385563
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.660413 Loss1: 0.108294 Loss2: 1.552118
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.554766 Loss1: 0.179207 Loss2: 1.375559 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.492448 Loss1: 0.133748 Loss2: 1.358700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.505969 Loss1: 0.152539 Loss2: 1.353430
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.192329 Loss1: 1.352611 Loss2: 1.839719
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.321108 Loss1: 0.873612 Loss2: 1.447496
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.969011 Loss1: 0.573624 Loss2: 1.395387
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.784566 Loss1: 0.388484 Loss2: 1.396083
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.652670 Loss1: 0.270468 Loss2: 1.382202
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.190247 Loss1: 1.330436 Loss2: 1.859811
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.215051 Loss1: 0.816712 Loss2: 1.398339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.905200 Loss1: 0.475564 Loss2: 1.429637 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.741837 Loss1: 0.373869 Loss2: 1.367968 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 10:35:03,270 | server.py:236 | fit_round 73 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.698442 Loss1: 0.307142 Loss2: 1.391300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.515435 Loss1: 0.148414 Loss2: 1.367021
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564368 Loss1: 0.200098 Loss2: 1.364269
-(DefaultActor pid=3764) >> Training accuracy: 0.960938
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.571774 Loss1: 0.211851 Loss2: 1.359923
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.523187 Loss1: 0.167279 Loss2: 1.355908
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.523534 Loss1: 0.171047 Loss2: 1.352487
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.486932 Loss1: 0.133429 Loss2: 1.353503
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.023220 Loss1: 1.153442 Loss2: 1.869778
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.192844 Loss1: 0.785418 Loss2: 1.407425
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.042488 Loss1: 0.593362 Loss2: 1.449126
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.786249 Loss1: 0.400500 Loss2: 1.385749
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.666709 Loss1: 0.274845 Loss2: 1.391864
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.391137 Loss1: 1.460055 Loss2: 1.931082
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.633771 Loss1: 0.266696 Loss2: 1.367076
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.530642 Loss1: 1.031654 Loss2: 1.498989
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.540375 Loss1: 0.163022 Loss2: 1.377353
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.992985 Loss1: 0.556640 Loss2: 1.436345
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473915 Loss1: 0.113923 Loss2: 1.359992
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.803613 Loss1: 0.377149 Loss2: 1.426463
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493324 Loss1: 0.138751 Loss2: 1.354573
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.743944 Loss1: 0.321606 Loss2: 1.422339
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443201 Loss1: 0.085335 Loss2: 1.357866
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.659675 Loss1: 0.248516 Loss2: 1.411159
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.617204 Loss1: 0.209769 Loss2: 1.407435
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.600158 Loss1: 0.198600 Loss2: 1.401558
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.580919 Loss1: 0.175131 Loss2: 1.405788
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.533173 Loss1: 0.134628 Loss2: 1.398545
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.291699 Loss1: 1.338695 Loss2: 1.953003
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.266905 Loss1: 0.854809 Loss2: 1.412097
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.045230 Loss1: 0.556170 Loss2: 1.489060
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.801404 Loss1: 0.395904 Loss2: 1.405500
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.699496 Loss1: 0.287882 Loss2: 1.411613
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.654661 Loss1: 0.232740 Loss2: 1.421920
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.653369 Loss1: 0.246670 Loss2: 1.406699
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.589291 Loss1: 0.188911 Loss2: 1.400380
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.523636 Loss1: 0.125660 Loss2: 1.397976
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512733 Loss1: 0.125423 Loss2: 1.387310
-(DefaultActor pid=3764) >> Training accuracy: 0.978365
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 10:35:03,270][flwr][DEBUG] - fit_round 73 received 50 results and 0 failures
-INFO flwr 2023-10-10 10:35:45,952 | server.py:125 | fit progress: (73, 2.2661834475331415, {'accuracy': 0.5364}, 168253.73014172702)
->> Test accuracy: 0.536400
-[2023-10-10 10:35:45,952][flwr][INFO] - fit progress: (73, 2.2661834475331415, {'accuracy': 0.5364}, 168253.73014172702)
-DEBUG flwr 2023-10-10 10:35:45,952 | server.py:173 | evaluate_round 73: strategy sampled 50 clients (out of 50)
-[2023-10-10 10:35:45,952][flwr][DEBUG] - evaluate_round 73: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 10:44:48,060 | server.py:187 | evaluate_round 73 received 50 results and 0 failures
-[2023-10-10 10:44:48,060][flwr][DEBUG] - evaluate_round 73 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 10:44:48,060 | server.py:222 | fit_round 74: strategy sampled 50 clients (out of 50)
-[2023-10-10 10:44:48,060][flwr][DEBUG] - fit_round 74: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.096249 Loss1: 1.230054 Loss2: 1.866195
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.190723 Loss1: 0.775437 Loss2: 1.415286
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.913805 Loss1: 0.484757 Loss2: 1.429048
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.701172 Loss1: 0.325619 Loss2: 1.375553
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.038323 Loss1: 1.201103 Loss2: 1.837221
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.172168 Loss1: 0.766087 Loss2: 1.406081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.908415 Loss1: 0.475867 Loss2: 1.432548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.743183 Loss1: 0.366523 Loss2: 1.376660 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.711718 Loss1: 0.333786 Loss2: 1.377933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.678136 Loss1: 0.314365 Loss2: 1.363771 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.530181 Loss1: 0.174231 Loss2: 1.355950 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441368 Loss1: 0.102360 Loss2: 1.339009 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.271640 Loss1: 0.822002 Loss2: 1.449638 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.747357 Loss1: 0.327530 Loss2: 1.419827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.231487 Loss1: 1.385526 Loss2: 1.845961
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.657110 Loss1: 0.239689 Loss2: 1.417421
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.335575 Loss1: 0.902084 Loss2: 1.433491
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.655920 Loss1: 0.252960 Loss2: 1.402960
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.924583 Loss1: 0.515221 Loss2: 1.409362
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.660885 Loss1: 0.250825 Loss2: 1.410060
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.783064 Loss1: 0.389518 Loss2: 1.393546
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.690281 Loss1: 0.274935 Loss2: 1.415347
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687752 Loss1: 0.297339 Loss2: 1.390413
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.641231 Loss1: 0.238685 Loss2: 1.402546
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.597764 Loss1: 0.223414 Loss2: 1.374350
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.563569 Loss1: 0.150854 Loss2: 1.412715
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.520043 Loss1: 0.151758 Loss2: 1.368285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482596 Loss1: 0.129673 Loss2: 1.352923 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.395471 Loss1: 0.895645 Loss2: 1.499826 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.835105 Loss1: 0.372766 Loss2: 1.462339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.816928 Loss1: 0.357981 Loss2: 1.458948 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.743437 Loss1: 0.282478 Loss2: 1.460959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.653398 Loss1: 0.200891 Loss2: 1.452507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.622908 Loss1: 0.188144 Loss2: 1.434764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.544971 Loss1: 0.109365 Loss2: 1.435606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.554273 Loss1: 0.131055 Loss2: 1.423218 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.574952 Loss1: 0.154495 Loss2: 1.420456 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970982
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.986689 Loss1: 1.105353 Loss2: 1.881336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.892149 Loss1: 0.468941 Loss2: 1.423208 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.679568 Loss1: 0.296684 Loss2: 1.382884
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.322481 Loss1: 1.433292 Loss2: 1.889189
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.618415 Loss1: 0.234945 Loss2: 1.383470
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.289492 Loss1: 0.820801 Loss2: 1.468691
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.540536 Loss1: 0.168573 Loss2: 1.371963
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.957438 Loss1: 0.531782 Loss2: 1.425656
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531300 Loss1: 0.170764 Loss2: 1.360536
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.837715 Loss1: 0.426206 Loss2: 1.411509
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.496988 Loss1: 0.137447 Loss2: 1.359541
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.789755 Loss1: 0.365794 Loss2: 1.423961
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.485416 Loss1: 0.132608 Loss2: 1.352808
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.692337 Loss1: 0.293849 Loss2: 1.398488
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452889 Loss1: 0.098720 Loss2: 1.354169
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.571289 Loss1: 0.165896 Loss2: 1.405393
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.556624 Loss1: 0.173154 Loss2: 1.383470
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.568532 Loss1: 0.187319 Loss2: 1.381213
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.593945 Loss1: 0.197659 Loss2: 1.396287
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.175731 Loss1: 1.277527 Loss2: 1.898204
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.121523 Loss1: 0.703267 Loss2: 1.418256
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.937283 Loss1: 0.499711 Loss2: 1.437572
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.720212 Loss1: 0.304092 Loss2: 1.416120
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.278609 Loss1: 1.397777 Loss2: 1.880832
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.294194 Loss1: 0.822496 Loss2: 1.471698 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.019437 Loss1: 0.562949 Loss2: 1.456488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.829537 Loss1: 0.413512 Loss2: 1.416025 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.723044 Loss1: 0.299654 Loss2: 1.423391 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.645123 Loss1: 0.240514 Loss2: 1.404609 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.959375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.606072 Loss1: 0.213882 Loss2: 1.392190
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.672662 Loss1: 0.264175 Loss2: 1.408487
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.600799 Loss1: 0.188646 Loss2: 1.412153
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.635950 Loss1: 0.220437 Loss2: 1.415513
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.620237 Loss1: 0.211962 Loss2: 1.408275
-(DefaultActor pid=3764) >> Training accuracy: 0.927083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.087554 Loss1: 1.228986 Loss2: 1.858568
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.205077 Loss1: 0.803094 Loss2: 1.401982
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.808333 Loss1: 0.386696 Loss2: 1.421637
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.713420 Loss1: 0.336858 Loss2: 1.376562
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.259902 Loss1: 1.294452 Loss2: 1.965450
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.282139 Loss1: 0.732905 Loss2: 1.549234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.052051 Loss1: 0.524581 Loss2: 1.527470 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.868925 Loss1: 0.374693 Loss2: 1.494232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.777668 Loss1: 0.279461 Loss2: 1.498207 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.700075 Loss1: 0.210752 Loss2: 1.489323 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.648139 Loss1: 0.165009 Loss2: 1.483130 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.573945 Loss1: 0.109104 Loss2: 1.464841 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.339038 Loss1: 0.870241 Loss2: 1.468797 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.729169 Loss1: 0.318634 Loss2: 1.410535 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.673714 Loss1: 0.278151 Loss2: 1.395563
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.983474 Loss1: 1.174905 Loss2: 1.808569
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.582104 Loss1: 0.186257 Loss2: 1.395847
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.053309 Loss1: 0.664498 Loss2: 1.388811
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.828575 Loss1: 0.427515 Loss2: 1.401060 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.690904 Loss1: 0.316620 Loss2: 1.374284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.601387 Loss1: 0.228149 Loss2: 1.373239 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.552175 Loss1: 0.194902 Loss2: 1.357272 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487186 Loss1: 0.141004 Loss2: 1.346181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442998 Loss1: 0.104358 Loss2: 1.338640 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.960938
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.307371 Loss1: 0.831410 Loss2: 1.475960 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.839159 Loss1: 0.377650 Loss2: 1.461509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.228103 Loss1: 1.342020 Loss2: 1.886084 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.279051 Loss1: 0.803392 Loss2: 1.475659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.985806 Loss1: 0.540796 Loss2: 1.445010 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.831230 Loss1: 0.398355 Loss2: 1.432876 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.581328 Loss1: 0.139664 Loss2: 1.441663 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.629568 Loss1: 0.220818 Loss2: 1.408750 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.557568 Loss1: 0.157974 Loss2: 1.399594 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.598158 Loss1: 1.547141 Loss2: 2.051017
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.553897 Loss1: 0.155611 Loss2: 1.398286
-(DefaultActor pid=3764) >> Training accuracy: 0.959961
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.071363 Loss1: 0.536450 Loss2: 1.534913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.752562 Loss1: 0.253603 Loss2: 1.498959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.253929 Loss1: 1.317728 Loss2: 1.936201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.128758 Loss1: 0.748238 Loss2: 1.380520 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.890297 Loss1: 0.459140 Loss2: 1.431157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.683482 Loss1: 0.320235 Loss2: 1.363246 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.630997 Loss1: 0.149393 Loss2: 1.481604
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.659237 Loss1: 0.291909 Loss2: 1.367328
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.622925 Loss1: 0.245101 Loss2: 1.377824
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.537935 Loss1: 0.181331 Loss2: 1.356604
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510189 Loss1: 0.146342 Loss2: 1.363847
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485166 Loss1: 0.125480 Loss2: 1.359687
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487824 Loss1: 0.127007 Loss2: 1.360817
-(DefaultActor pid=3764) >> Training accuracy: 0.975962
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.165707 Loss1: 1.303461 Loss2: 1.862246
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.368980 Loss1: 0.915248 Loss2: 1.453731
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.844387 Loss1: 0.441596 Loss2: 1.402791
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.706443 Loss1: 0.308040 Loss2: 1.398402
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.692178 Loss1: 0.303413 Loss2: 1.388766
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.191104 Loss1: 1.240081 Loss2: 1.951024
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.588049 Loss1: 0.212075 Loss2: 1.375974
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.556997 Loss1: 0.179000 Loss2: 1.377997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.515501 Loss1: 0.142458 Loss2: 1.373043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.507259 Loss1: 0.138038 Loss2: 1.369222 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.492535 Loss1: 0.126077 Loss2: 1.366458 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.574114 Loss1: 0.160140 Loss2: 1.413974 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.527582 Loss1: 0.130463 Loss2: 1.397119 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.498885 Loss1: 0.099117 Loss2: 1.399768
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.162851 Loss1: 1.212886 Loss2: 1.949965
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.305876 Loss1: 0.796717 Loss2: 1.509159
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.077009 Loss1: 0.516273 Loss2: 1.560736
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.839191 Loss1: 0.345275 Loss2: 1.493917
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.718014 Loss1: 0.220522 Loss2: 1.497492
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.397378 Loss1: 1.474338 Loss2: 1.923040
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.690961 Loss1: 0.201749 Loss2: 1.489213
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.280564 Loss1: 0.821050 Loss2: 1.459513
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.637231 Loss1: 0.145218 Loss2: 1.492013
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.975298 Loss1: 0.529302 Loss2: 1.445995
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.616025 Loss1: 0.139771 Loss2: 1.476254
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.804344 Loss1: 0.372549 Loss2: 1.431795
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.685066 Loss1: 0.271558 Loss2: 1.413508
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.571206 Loss1: 0.099612 Loss2: 1.471594
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.686882 Loss1: 0.282996 Loss2: 1.403886
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.597232 Loss1: 0.132502 Loss2: 1.464731
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.562148 Loss1: 0.164382 Loss2: 1.397766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.549827 Loss1: 0.156541 Loss2: 1.393286 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.368164 Loss1: 0.953250 Loss2: 1.414913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.747974 Loss1: 0.394105 Loss2: 1.353870 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.154228 Loss1: 1.209335 Loss2: 1.944893
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.613971 Loss1: 0.271873 Loss2: 1.342098
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.313166 Loss1: 0.849769 Loss2: 1.463397
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.616130 Loss1: 0.283985 Loss2: 1.332145
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.076775 Loss1: 0.566299 Loss2: 1.510476
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.537931 Loss1: 0.191416 Loss2: 1.346515
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.853372 Loss1: 0.398586 Loss2: 1.454786
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.507183 Loss1: 0.178622 Loss2: 1.328561
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.773304 Loss1: 0.323312 Loss2: 1.449992
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.480855 Loss1: 0.162711 Loss2: 1.318144
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.690727 Loss1: 0.251205 Loss2: 1.439522
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445454 Loss1: 0.118581 Loss2: 1.326873
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.672167 Loss1: 0.231444 Loss2: 1.440723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.600871 Loss1: 0.169925 Loss2: 1.430946 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.171267 Loss1: 0.714397 Loss2: 1.456870 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.769309 Loss1: 0.338940 Loss2: 1.430369 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.819577 Loss1: 0.356652 Loss2: 1.462926 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.787999 Loss1: 0.346320 Loss2: 1.441679 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.756648 Loss1: 0.307678 Loss2: 1.448970 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.664864 Loss1: 0.230153 Loss2: 1.434712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.614332 Loss1: 0.187685 Loss2: 1.426648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.635972 Loss1: 0.200769 Loss2: 1.435203 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.949219
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.546342 Loss1: 0.205919 Loss2: 1.340423 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.195783 Loss1: 1.323802 Loss2: 1.871981 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.806092 Loss1: 0.392024 Loss2: 1.414068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.643830 Loss1: 0.287562 Loss2: 1.356268
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.293459 Loss1: 1.346657 Loss2: 1.946802
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.429495 Loss1: 0.942357 Loss2: 1.487138 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.095928 Loss1: 0.576686 Loss2: 1.519242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.875142 Loss1: 0.390204 Loss2: 1.484938 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.851131 Loss1: 0.359542 Loss2: 1.491589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.758741 Loss1: 0.281314 Loss2: 1.477426 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.493182 Loss1: 0.145636 Loss2: 1.347546
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.698849 Loss1: 0.224106 Loss2: 1.474742
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.666237 Loss1: 0.198046 Loss2: 1.468191
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.612543 Loss1: 0.150002 Loss2: 1.462541
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.621981 Loss1: 0.165016 Loss2: 1.456965
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.397708 Loss1: 1.509855 Loss2: 1.887854
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.343559 Loss1: 0.897186 Loss2: 1.446373
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.065463 Loss1: 0.622185 Loss2: 1.443278
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.799080 Loss1: 0.392664 Loss2: 1.406417
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.095354 Loss1: 1.266023 Loss2: 1.829331
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.274414 Loss1: 0.865315 Loss2: 1.409100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.871766 Loss1: 0.515161 Loss2: 1.356605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.756554 Loss1: 0.398335 Loss2: 1.358219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.651387 Loss1: 0.300746 Loss2: 1.350641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.561119 Loss1: 0.225604 Loss2: 1.335515 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.548738 Loss1: 0.215961 Loss2: 1.332777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.505588 Loss1: 0.172835 Loss2: 1.332753 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.153951 Loss1: 1.283494 Loss2: 1.870457 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.899033 Loss1: 0.448665 Loss2: 1.450368 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.700160 Loss1: 0.284759 Loss2: 1.415401 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.689508 Loss1: 0.286106 Loss2: 1.403402 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.584114 Loss1: 0.179457 Loss2: 1.404657 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.605971 Loss1: 0.200414 Loss2: 1.405558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.584610 Loss1: 0.169558 Loss2: 1.415053 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.563056 Loss1: 0.170388 Loss2: 1.392668 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488512 Loss1: 0.160283 Loss2: 1.328229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464917 Loss1: 0.136286 Loss2: 1.328631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428449 Loss1: 0.105072 Loss2: 1.323377
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.182034 Loss1: 1.311115 Loss2: 1.870919
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.173793 Loss1: 0.701913 Loss2: 1.471879
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.873237 Loss1: 0.423353 Loss2: 1.449885
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.767383 Loss1: 0.343781 Loss2: 1.423602
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.676002 Loss1: 0.246154 Loss2: 1.429848
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.915715 Loss1: 1.087824 Loss2: 1.827891
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.193413 Loss1: 0.747367 Loss2: 1.446046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.941529 Loss1: 0.534572 Loss2: 1.406957 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.885669 Loss1: 0.472297 Loss2: 1.413372 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.740179 Loss1: 0.336669 Loss2: 1.403509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962891
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.600467 Loss1: 0.215230 Loss2: 1.385237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511559 Loss1: 0.146864 Loss2: 1.364695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.163485 Loss1: 1.276173 Loss2: 1.887312 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980699
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.004206 Loss1: 0.552223 Loss2: 1.451982 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.729383 Loss1: 0.315977 Loss2: 1.413406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.648915 Loss1: 0.252972 Loss2: 1.395943
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.143371 Loss1: 1.279397 Loss2: 1.863974
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.373416 Loss1: 0.916580 Loss2: 1.456837 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.082477 Loss1: 0.628209 Loss2: 1.454267 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.885407 Loss1: 0.453757 Loss2: 1.431650 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.747356 Loss1: 0.329481 Loss2: 1.417875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.599778 Loss1: 0.196977 Loss2: 1.402801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.537990 Loss1: 0.146099 Loss2: 1.391891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.550979 Loss1: 0.162958 Loss2: 1.388021 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.959961
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.893574 Loss1: 0.457637 Loss2: 1.435937 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.669444 Loss1: 0.248123 Loss2: 1.421321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.375179 Loss1: 1.344915 Loss2: 2.030264
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.614775 Loss1: 0.193075 Loss2: 1.421699
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.605543 Loss1: 0.184328 Loss2: 1.421216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.594363 Loss1: 0.175536 Loss2: 1.418827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.534229 Loss1: 0.117125 Loss2: 1.417104 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.565988 Loss1: 0.163400 Loss2: 1.402588 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.552665 Loss1: 0.162040 Loss2: 1.390626 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962240
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.182044 Loss1: 1.296593 Loss2: 1.885451 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.903063 Loss1: 0.470691 Loss2: 1.432372 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.599954 Loss1: 0.218292 Loss2: 1.381662 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.539394 Loss1: 0.161395 Loss2: 1.378000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480809 Loss1: 0.108770 Loss2: 1.372039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500351 Loss1: 0.134965 Loss2: 1.365386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.549482 Loss1: 0.181890 Loss2: 1.367592 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.563269 Loss1: 0.184939 Loss2: 1.378330 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.960417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.617997 Loss1: 0.216320 Loss2: 1.401677 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.595195 Loss1: 0.202362 Loss2: 1.392834 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.244669 Loss1: 0.818138 Loss2: 1.426531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765375 Loss1: 0.367987 Loss2: 1.397388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.230387 Loss1: 1.341227 Loss2: 1.889160
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.724532 Loss1: 0.313210 Loss2: 1.411322
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.264994 Loss1: 0.882923 Loss2: 1.382072
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.657920 Loss1: 0.266637 Loss2: 1.391284
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.620621 Loss1: 0.216793 Loss2: 1.403828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.514353 Loss1: 0.127326 Loss2: 1.387028 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.551636 Loss1: 0.167911 Loss2: 1.383725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.498865 Loss1: 0.114361 Loss2: 1.384504 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.534439 Loss1: 0.172088 Loss2: 1.362350 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980769
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.353103 Loss1: 1.312023 Loss2: 2.041080 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.038121 Loss1: 0.475583 Loss2: 1.562537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.840479 Loss1: 0.322257 Loss2: 1.518222
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.154182 Loss1: 1.251448 Loss2: 1.902734
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.784047 Loss1: 0.259030 Loss2: 1.525017
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.375349 Loss1: 0.914132 Loss2: 1.461217
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.698401 Loss1: 0.191684 Loss2: 1.506717
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.098062 Loss1: 0.599932 Loss2: 1.498130
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.739215 Loss1: 0.224539 Loss2: 1.514676
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.933918 Loss1: 0.492970 Loss2: 1.440948
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.687680 Loss1: 0.173412 Loss2: 1.514268
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.778315 Loss1: 0.323029 Loss2: 1.455286
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.670284 Loss1: 0.168378 Loss2: 1.501906
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.675810 Loss1: 0.251937 Loss2: 1.423873
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.641385 Loss1: 0.143513 Loss2: 1.497872
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.678149 Loss1: 0.248864 Loss2: 1.429285
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.576780 Loss1: 0.158658 Loss2: 1.418122
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.558982 Loss1: 0.144167 Loss2: 1.414815
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.561241 Loss1: 0.148977 Loss2: 1.412264
-(DefaultActor pid=3764) >> Training accuracy: 0.944792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.154447 Loss1: 1.204241 Loss2: 1.950206
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.171073 Loss1: 0.741133 Loss2: 1.429939
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.000172 Loss1: 0.535673 Loss2: 1.464499
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.810473 Loss1: 0.392735 Loss2: 1.417738
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.171292 Loss1: 1.291123 Loss2: 1.880169
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.676246 Loss1: 0.263060 Loss2: 1.413186
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.215239 Loss1: 0.777419 Loss2: 1.437821
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.637333 Loss1: 0.229071 Loss2: 1.408262
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.916621 Loss1: 0.491839 Loss2: 1.424782
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.584901 Loss1: 0.168390 Loss2: 1.416511
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.780553 Loss1: 0.380911 Loss2: 1.399642
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.544451 Loss1: 0.146521 Loss2: 1.397930
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.635507 Loss1: 0.231652 Loss2: 1.403856
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.541663 Loss1: 0.143181 Loss2: 1.398481
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.601949 Loss1: 0.218420 Loss2: 1.383528
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.513313 Loss1: 0.118829 Loss2: 1.394484
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.576541 Loss1: 0.184062 Loss2: 1.392479
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.558720 Loss1: 0.176718 Loss2: 1.382001
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.514385 Loss1: 0.133323 Loss2: 1.381062
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.502335 Loss1: 0.129338 Loss2: 1.372998
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.397545 Loss1: 1.442843 Loss2: 1.954703
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.361259 Loss1: 0.910370 Loss2: 1.450889
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.042029 Loss1: 0.573633 Loss2: 1.468396
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.846931 Loss1: 0.437376 Loss2: 1.409555
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.111089 Loss1: 1.270053 Loss2: 1.841035
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.251174 Loss1: 0.845054 Loss2: 1.406120 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.006599 Loss1: 0.559873 Loss2: 1.446725 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 11:13:21,113 | server.py:236 | fit_round 74 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699449 Loss1: 0.321923 Loss2: 1.377526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.636631 Loss1: 0.259625 Loss2: 1.377006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.632251 Loss1: 0.249582 Loss2: 1.382669 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.940848
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.529513 Loss1: 0.165013 Loss2: 1.364500 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478325 Loss1: 0.123978 Loss2: 1.354347 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.487732 Loss1: 0.988474 Loss2: 1.499257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.810312 Loss1: 0.365151 Loss2: 1.445162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.773741 Loss1: 0.336857 Loss2: 1.436884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.637043 Loss1: 0.210971 Loss2: 1.426073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.587390 Loss1: 0.176887 Loss2: 1.410503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.577086 Loss1: 0.170963 Loss2: 1.406123 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.568765 Loss1: 0.165683 Loss2: 1.403082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.530241 Loss1: 0.126373 Loss2: 1.403868 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.542769 Loss1: 0.197308 Loss2: 1.345461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.511351 Loss1: 0.171404 Loss2: 1.339947 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 11:13:21,113][flwr][DEBUG] - fit_round 74 received 50 results and 0 failures
-INFO flwr 2023-10-10 11:14:01,896 | server.py:125 | fit progress: (74, 2.2724047929715043, {'accuracy': 0.541}, 170549.674130211)
->> Test accuracy: 0.541000
-[2023-10-10 11:14:01,896][flwr][INFO] - fit progress: (74, 2.2724047929715043, {'accuracy': 0.541}, 170549.674130211)
-DEBUG flwr 2023-10-10 11:14:01,896 | server.py:173 | evaluate_round 74: strategy sampled 50 clients (out of 50)
-[2023-10-10 11:14:01,896][flwr][DEBUG] - evaluate_round 74: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 11:23:08,976 | server.py:187 | evaluate_round 74 received 50 results and 0 failures
-[2023-10-10 11:23:08,976][flwr][DEBUG] - evaluate_round 74 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 11:23:08,976 | server.py:222 | fit_round 75: strategy sampled 50 clients (out of 50)
-[2023-10-10 11:23:08,976][flwr][DEBUG] - fit_round 75: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.259031 Loss1: 1.360205 Loss2: 1.898826
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.207546 Loss1: 0.762417 Loss2: 1.445129
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.949751 Loss1: 0.520105 Loss2: 1.429646
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.785077 Loss1: 0.361247 Loss2: 1.423830
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.215848 Loss1: 1.358763 Loss2: 1.857085
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.655175 Loss1: 0.257239 Loss2: 1.397936
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.163909 Loss1: 0.753066 Loss2: 1.410844
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.615091 Loss1: 0.219317 Loss2: 1.395774
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.985942 Loss1: 0.565681 Loss2: 1.420262
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.618722 Loss1: 0.230182 Loss2: 1.388539
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.809676 Loss1: 0.434740 Loss2: 1.374936
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.555016 Loss1: 0.172493 Loss2: 1.382523
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.758306 Loss1: 0.366051 Loss2: 1.392255
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.542998 Loss1: 0.157739 Loss2: 1.385259
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.619236 Loss1: 0.252056 Loss2: 1.367179
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.541064 Loss1: 0.159557 Loss2: 1.381508
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.569811 Loss1: 0.209366 Loss2: 1.360444
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510125 Loss1: 0.158424 Loss2: 1.351701
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.455245 Loss1: 0.110096 Loss2: 1.345149
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464072 Loss1: 0.122300 Loss2: 1.341772
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.204881 Loss1: 1.290080 Loss2: 1.914802
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.277091 Loss1: 0.820647 Loss2: 1.456444
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.957338 Loss1: 0.514192 Loss2: 1.443146
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.812253 Loss1: 0.392488 Loss2: 1.419765
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.177030 Loss1: 1.299749 Loss2: 1.877281
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.719517 Loss1: 0.294422 Loss2: 1.425095
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.217530 Loss1: 0.821355 Loss2: 1.396175
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.651274 Loss1: 0.244528 Loss2: 1.406746
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.877749 Loss1: 0.438722 Loss2: 1.439028
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.649441 Loss1: 0.238341 Loss2: 1.411099
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.700745 Loss1: 0.326961 Loss2: 1.373784
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.591459 Loss1: 0.194099 Loss2: 1.397360
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.667899 Loss1: 0.291077 Loss2: 1.376822
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.560061 Loss1: 0.161585 Loss2: 1.398476
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.672261 Loss1: 0.281361 Loss2: 1.390900
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.581719 Loss1: 0.188036 Loss2: 1.393683
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.688149 Loss1: 0.299994 Loss2: 1.388155
-(DefaultActor pid=3765) >> Training accuracy: 0.955208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.580649 Loss1: 0.202261 Loss2: 1.378388
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.547828 Loss1: 0.170115 Loss2: 1.377713
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.552950 Loss1: 0.180715 Loss2: 1.372235
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.273588 Loss1: 1.339896 Loss2: 1.933692
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.357694 Loss1: 0.861313 Loss2: 1.496381
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.004331 Loss1: 0.543457 Loss2: 1.460874
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.843900 Loss1: 0.406867 Loss2: 1.437034
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.058868 Loss1: 1.237213 Loss2: 1.821655
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.268179 Loss1: 0.877169 Loss2: 1.391009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.922755 Loss1: 0.504535 Loss2: 1.418220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.820288 Loss1: 0.448789 Loss2: 1.371499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.683062 Loss1: 0.305955 Loss2: 1.377107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.664714 Loss1: 0.297697 Loss2: 1.367017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.571028 Loss1: 0.161414 Loss2: 1.409614
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.613762 Loss1: 0.251146 Loss2: 1.362616
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.528852 Loss1: 0.179658 Loss2: 1.349193
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493038 Loss1: 0.149714 Loss2: 1.343324
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.500497 Loss1: 0.158168 Loss2: 1.342329
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.219323 Loss1: 1.263781 Loss2: 1.955541
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.275726 Loss1: 0.790831 Loss2: 1.484895
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.959929 Loss1: 0.454554 Loss2: 1.505375
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.786717 Loss1: 0.338633 Loss2: 1.448085
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.366543 Loss1: 1.417113 Loss2: 1.949430
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.339988 Loss1: 0.856732 Loss2: 1.483256 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.055491 Loss1: 0.546896 Loss2: 1.508595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.872809 Loss1: 0.411024 Loss2: 1.461785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.782357 Loss1: 0.301294 Loss2: 1.481064 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.703273 Loss1: 0.244293 Loss2: 1.458980 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.509299 Loss1: 0.092811 Loss2: 1.416487
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.641374 Loss1: 0.182575 Loss2: 1.458798
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.639774 Loss1: 0.193515 Loss2: 1.446259
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.645499 Loss1: 0.201225 Loss2: 1.444274
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.680699 Loss1: 0.227057 Loss2: 1.453642
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.216657 Loss1: 1.392612 Loss2: 1.824045
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.344363 Loss1: 0.926662 Loss2: 1.417701
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.897021 Loss1: 0.499739 Loss2: 1.397282
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.672797 Loss1: 0.320080 Loss2: 1.352716
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.253814 Loss1: 1.331200 Loss2: 1.922614
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593900 Loss1: 0.227645 Loss2: 1.366255
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.333089 Loss1: 0.915737 Loss2: 1.417352
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.583828 Loss1: 0.225760 Loss2: 1.358069
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.105235 Loss1: 0.626765 Loss2: 1.478470
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.549190 Loss1: 0.196941 Loss2: 1.352249
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.799124 Loss1: 0.400878 Loss2: 1.398246
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.750038 Loss1: 0.358617 Loss2: 1.391421
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.512453 Loss1: 0.166804 Loss2: 1.345649
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.672569 Loss1: 0.261679 Loss2: 1.410889
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462888 Loss1: 0.120027 Loss2: 1.342862
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.591867 Loss1: 0.205562 Loss2: 1.386305
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460699 Loss1: 0.129937 Loss2: 1.330762
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.505468 Loss1: 0.135150 Loss2: 1.370319 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962054
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.176828 Loss1: 1.201652 Loss2: 1.975176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.898817 Loss1: 0.443010 Loss2: 1.455807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.792308 Loss1: 0.362591 Loss2: 1.429717
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.035642 Loss1: 1.215164 Loss2: 1.820478
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.199396 Loss1: 0.786653 Loss2: 1.412742 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.868833 Loss1: 0.449300 Loss2: 1.419534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.718747 Loss1: 0.348420 Loss2: 1.370328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.686847 Loss1: 0.296829 Loss2: 1.390018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.604052 Loss1: 0.229781 Loss2: 1.374271 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.567231 Loss1: 0.200406 Loss2: 1.366825 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.515646 Loss1: 0.155467 Loss2: 1.360179 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966797
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.301822 Loss1: 0.793863 Loss2: 1.507959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.845645 Loss1: 0.386942 Loss2: 1.458703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.272987 Loss1: 1.313664 Loss2: 1.959322
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.750804 Loss1: 0.306344 Loss2: 1.444460
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.330760 Loss1: 0.832305 Loss2: 1.498455
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.657141 Loss1: 0.228136 Loss2: 1.429005
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.618407 Loss1: 0.191864 Loss2: 1.426543 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.568203 Loss1: 0.139601 Loss2: 1.428602 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.568857 Loss1: 0.150671 Loss2: 1.418187 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.584932 Loss1: 0.165564 Loss2: 1.419368 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546620 Loss1: 0.113348 Loss2: 1.433272 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.559717 Loss1: 0.133747 Loss2: 1.425970 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.951620 Loss1: 1.119553 Loss2: 1.832067
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.206994 Loss1: 0.802163 Loss2: 1.404831
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.863330 Loss1: 0.476786 Loss2: 1.386545
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.719394 Loss1: 0.356663 Loss2: 1.362731
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.198035 Loss1: 1.260474 Loss2: 1.937561
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.346885 Loss1: 0.827988 Loss2: 1.518897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.991726 Loss1: 0.530906 Loss2: 1.460821 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.777626 Loss1: 0.327075 Loss2: 1.450551 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687320 Loss1: 0.255676 Loss2: 1.431645 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.614109 Loss1: 0.191720 Loss2: 1.422389 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.496158 Loss1: 0.164479 Loss2: 1.331679
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.554156 Loss1: 0.138809 Loss2: 1.415348
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.554646 Loss1: 0.138622 Loss2: 1.416024
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.542287 Loss1: 0.134003 Loss2: 1.408284
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529169 Loss1: 0.114674 Loss2: 1.414495
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.108142 Loss1: 1.240822 Loss2: 1.867320
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.199907 Loss1: 0.783301 Loss2: 1.416605
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.837610 Loss1: 0.429521 Loss2: 1.408088
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765531 Loss1: 0.394848 Loss2: 1.370683
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.322630 Loss1: 1.295047 Loss2: 2.027583
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.273683 Loss1: 0.843867 Loss2: 1.429816 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.904112 Loss1: 0.427491 Loss2: 1.476621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.689046 Loss1: 0.295881 Loss2: 1.393164
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.562538 Loss1: 0.192566 Loss2: 1.369973
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.607721 Loss1: 0.225727 Loss2: 1.381995
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.518707 Loss1: 0.162897 Loss2: 1.355811
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484662 Loss1: 0.130101 Loss2: 1.354561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472304 Loss1: 0.124102 Loss2: 1.348202 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.483784 Loss1: 0.114622 Loss2: 1.369162 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.951923
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.210806 Loss1: 1.299312 Loss2: 1.911494
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.347237 Loss1: 0.894460 Loss2: 1.452777
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.024495 Loss1: 0.516972 Loss2: 1.507523
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.808117 Loss1: 0.378689 Loss2: 1.429428
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.208233 Loss1: 1.290503 Loss2: 1.917730
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.749784 Loss1: 0.295372 Loss2: 1.454412
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.202806 Loss1: 0.807288 Loss2: 1.395519
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.978612 Loss1: 0.532127 Loss2: 1.446485
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.716697 Loss1: 0.276103 Loss2: 1.440594
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.829295 Loss1: 0.438148 Loss2: 1.391147
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.649829 Loss1: 0.209271 Loss2: 1.440559
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.700158 Loss1: 0.300447 Loss2: 1.399711
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.625815 Loss1: 0.183261 Loss2: 1.442553
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.569887 Loss1: 0.145751 Loss2: 1.424136 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.569322 Loss1: 0.141873 Loss2: 1.427450 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.522131 Loss1: 0.151559 Loss2: 1.370572 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972098
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.325967 Loss1: 1.314734 Loss2: 2.011233 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.155024 Loss1: 0.580456 Loss2: 1.574568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.937672 Loss1: 0.413162 Loss2: 1.524510
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.220662 Loss1: 1.334422 Loss2: 1.886240
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.785003 Loss1: 0.252470 Loss2: 1.532533
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.242910 Loss1: 0.833643 Loss2: 1.409267
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.722766 Loss1: 0.228862 Loss2: 1.493904
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.935570 Loss1: 0.511290 Loss2: 1.424279
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.688722 Loss1: 0.200346 Loss2: 1.488376
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.753922 Loss1: 0.365307 Loss2: 1.388615
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.658532 Loss1: 0.167027 Loss2: 1.491505
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.700314 Loss1: 0.310355 Loss2: 1.389959
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.661016 Loss1: 0.166412 Loss2: 1.494605
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647372 Loss1: 0.263413 Loss2: 1.383959
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.702183 Loss1: 0.215337 Loss2: 1.486846
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.599109 Loss1: 0.222672 Loss2: 1.376437
-(DefaultActor pid=3765) >> Training accuracy: 0.954167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.554810 Loss1: 0.183768 Loss2: 1.371042
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.529288 Loss1: 0.173284 Loss2: 1.356004
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529104 Loss1: 0.159510 Loss2: 1.369594
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.221169 Loss1: 1.294506 Loss2: 1.926663
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.296250 Loss1: 0.826382 Loss2: 1.469867
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.021703 Loss1: 0.541666 Loss2: 1.480037
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.852432 Loss1: 0.410907 Loss2: 1.441526
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.091691 Loss1: 1.208584 Loss2: 1.883107
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.735605 Loss1: 0.290333 Loss2: 1.445272
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.149822 Loss1: 0.739989 Loss2: 1.409833
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.680039 Loss1: 0.253221 Loss2: 1.426818
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.964557 Loss1: 0.532529 Loss2: 1.432028
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.634981 Loss1: 0.200691 Loss2: 1.434290
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.816803 Loss1: 0.423980 Loss2: 1.392822
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.610348 Loss1: 0.188040 Loss2: 1.422308
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.750027 Loss1: 0.353785 Loss2: 1.396242
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.596023 Loss1: 0.184602 Loss2: 1.411421
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.702810 Loss1: 0.319922 Loss2: 1.382888
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.545125 Loss1: 0.134567 Loss2: 1.410558
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.625984 Loss1: 0.232821 Loss2: 1.393163
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.556644 Loss1: 0.174990 Loss2: 1.381655
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519107 Loss1: 0.143811 Loss2: 1.375296
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509192 Loss1: 0.137501 Loss2: 1.371691
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.080248 Loss1: 1.294037 Loss2: 1.786211
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.168946 Loss1: 0.819580 Loss2: 1.349367
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.933884 Loss1: 0.545191 Loss2: 1.388694
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.784349 Loss1: 0.448674 Loss2: 1.335674
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.143072 Loss1: 1.181761 Loss2: 1.961311
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.222429 Loss1: 0.757647 Loss2: 1.464782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.993036 Loss1: 0.498655 Loss2: 1.494381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.848149 Loss1: 0.401721 Loss2: 1.446428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.720839 Loss1: 0.264449 Loss2: 1.456390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.656131 Loss1: 0.215546 Loss2: 1.440585 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.492507 Loss1: 0.176203 Loss2: 1.316304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.620927 Loss1: 0.185678 Loss2: 1.435249
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.553944 Loss1: 0.118417 Loss2: 1.435527
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.571516 Loss1: 0.142563 Loss2: 1.428953
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.569690 Loss1: 0.143633 Loss2: 1.426057
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.016694 Loss1: 1.149546 Loss2: 1.867148
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.097884 Loss1: 0.723260 Loss2: 1.374624
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.823532 Loss1: 0.431175 Loss2: 1.392357
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.668943 Loss1: 0.316533 Loss2: 1.352410
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.538690 Loss1: 1.573148 Loss2: 1.965542
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.549523 Loss1: 0.206638 Loss2: 1.342885
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.292170 Loss1: 0.822431 Loss2: 1.469739
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513120 Loss1: 0.177844 Loss2: 1.335277
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.958201 Loss1: 0.474153 Loss2: 1.484048
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.815542 Loss1: 0.378603 Loss2: 1.436939
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495470 Loss1: 0.156157 Loss2: 1.339313
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.747694 Loss1: 0.302768 Loss2: 1.444926
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.555614 Loss1: 0.213676 Loss2: 1.341939
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.717666 Loss1: 0.284552 Loss2: 1.433114
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.579601 Loss1: 0.217970 Loss2: 1.361631
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.515029 Loss1: 0.176499 Loss2: 1.338530 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.619265 Loss1: 0.199743 Loss2: 1.419522 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.954241
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.203816 Loss1: 1.326435 Loss2: 1.877381 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.970965 Loss1: 0.544035 Loss2: 1.426930 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.840619 Loss1: 0.449339 Loss2: 1.391280
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.120582 Loss1: 1.301673 Loss2: 1.818909
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.122853 Loss1: 0.728554 Loss2: 1.394299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.879747 Loss1: 0.491456 Loss2: 1.388291 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.775081 Loss1: 0.403328 Loss2: 1.371753 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.675965 Loss1: 0.306451 Loss2: 1.369514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.687320 Loss1: 0.323390 Loss2: 1.363930 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.605516 Loss1: 0.243200 Loss2: 1.362316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.514407 Loss1: 0.153616 Loss2: 1.360792 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970703
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.978715 Loss1: 1.128073 Loss2: 1.850642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.927181 Loss1: 0.501879 Loss2: 1.425302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617488 Loss1: 0.252509 Loss2: 1.364979 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.622119 Loss1: 0.260175 Loss2: 1.361944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.602871 Loss1: 0.231539 Loss2: 1.371332 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.498089 Loss1: 0.146790 Loss2: 1.351300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499424 Loss1: 0.154385 Loss2: 1.345038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.468080 Loss1: 0.128189 Loss2: 1.339891 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.652798 Loss1: 0.257594 Loss2: 1.395204 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.560580 Loss1: 0.169486 Loss2: 1.391094 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.533679 Loss1: 0.153594 Loss2: 1.380084
-(DefaultActor pid=3764) >> Training accuracy: 0.969727
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.367091 Loss1: 1.410682 Loss2: 1.956408
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.385814 Loss1: 0.886127 Loss2: 1.499688
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.042564 Loss1: 0.546975 Loss2: 1.495589
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.881373 Loss1: 0.413976 Loss2: 1.467397
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.767031 Loss1: 0.287630 Loss2: 1.479401
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.391735 Loss1: 1.378812 Loss2: 2.012923
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.296683 Loss1: 0.898384 Loss2: 1.398299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.132067 Loss1: 0.626225 Loss2: 1.505842
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.693248 Loss1: 0.235427 Loss2: 1.457821
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.650408 Loss1: 0.192089 Loss2: 1.458319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.618376 Loss1: 0.169423 Loss2: 1.448953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.567122 Loss1: 0.122674 Loss2: 1.444448 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.583410 Loss1: 0.191545 Loss2: 1.391865 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962240
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.192438 Loss1: 1.286980 Loss2: 1.905458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.009630 Loss1: 0.548834 Loss2: 1.460796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.836663 Loss1: 0.419604 Loss2: 1.417060
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.337608 Loss1: 1.471179 Loss2: 1.866429
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.275173 Loss1: 0.840400 Loss2: 1.434773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.919201 Loss1: 0.507291 Loss2: 1.411910 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.672276 Loss1: 0.267068 Loss2: 1.405208
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.760231 Loss1: 0.369952 Loss2: 1.390279
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.665124 Loss1: 0.270360 Loss2: 1.394764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.630697 Loss1: 0.251070 Loss2: 1.379627 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.538186 Loss1: 0.149308 Loss2: 1.388878
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.572722 Loss1: 0.193032 Loss2: 1.379690
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.583044 Loss1: 0.211107 Loss2: 1.371936
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.567889 Loss1: 0.188371 Loss2: 1.379518
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.559513 Loss1: 0.181926 Loss2: 1.377586
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.149394 Loss1: 1.255374 Loss2: 1.894020
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.267977 Loss1: 0.815742 Loss2: 1.452236
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.984852 Loss1: 0.530988 Loss2: 1.453864
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.826464 Loss1: 0.387995 Loss2: 1.438469
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.003248 Loss1: 1.149653 Loss2: 1.853595
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.692060 Loss1: 0.257186 Loss2: 1.434874
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.196493 Loss1: 0.794273 Loss2: 1.402220
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.652933 Loss1: 0.235786 Loss2: 1.417147
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.963886 Loss1: 0.539138 Loss2: 1.424748
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.652524 Loss1: 0.232847 Loss2: 1.419677
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.743691 Loss1: 0.355412 Loss2: 1.388279
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.723620 Loss1: 0.295622 Loss2: 1.427999
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.622008 Loss1: 0.242892 Loss2: 1.379116
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.744252 Loss1: 0.302781 Loss2: 1.441472
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.591612 Loss1: 0.217987 Loss2: 1.373625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.614352 Loss1: 0.188850 Loss2: 1.425502
-(DefaultActor pid=3765) >> Training accuracy: 0.963867
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.568899 Loss1: 0.191584 Loss2: 1.377315
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.572915 Loss1: 0.210812 Loss2: 1.362103
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.521149 Loss1: 0.148976 Loss2: 1.372172
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.504042 Loss1: 0.139536 Loss2: 1.364507
-(DefaultActor pid=3764) >> Training accuracy: 0.972656
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.100781 Loss1: 1.231080 Loss2: 1.869701
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.236264 Loss1: 0.749310 Loss2: 1.486954
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.880886 Loss1: 0.433685 Loss2: 1.447201
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.793962 Loss1: 0.353269 Loss2: 1.440693
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.995260 Loss1: 1.149623 Loss2: 1.845637
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.766656 Loss1: 0.318326 Loss2: 1.448330
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.137225 Loss1: 0.719556 Loss2: 1.417669
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.724799 Loss1: 0.287053 Loss2: 1.437746
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.968789 Loss1: 0.546929 Loss2: 1.421860
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.642463 Loss1: 0.212254 Loss2: 1.430208
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.780024 Loss1: 0.367199 Loss2: 1.412825
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.667221 Loss1: 0.231144 Loss2: 1.436076
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.664973 Loss1: 0.281756 Loss2: 1.383217
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.679393 Loss1: 0.245648 Loss2: 1.433745
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.580640 Loss1: 0.198365 Loss2: 1.382274
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.617989 Loss1: 0.178173 Loss2: 1.439816
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500930 Loss1: 0.131732 Loss2: 1.369197
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.528027 Loss1: 0.165158 Loss2: 1.362869
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473187 Loss1: 0.105355 Loss2: 1.367833
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470677 Loss1: 0.114829 Loss2: 1.355848
-(DefaultActor pid=3764) >> Training accuracy: 0.973633
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.191896 Loss1: 1.305551 Loss2: 1.886345
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.249664 Loss1: 0.812778 Loss2: 1.436886
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.873346 Loss1: 0.438538 Loss2: 1.434807
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.691059 Loss1: 0.291706 Loss2: 1.399352
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.159575 Loss1: 1.250148 Loss2: 1.909427
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.153507 Loss1: 0.727588 Loss2: 1.425919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.951368 Loss1: 0.486380 Loss2: 1.464988 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.796140 Loss1: 0.385090 Loss2: 1.411050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.651109 Loss1: 0.231246 Loss2: 1.419863 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.540543 Loss1: 0.142120 Loss2: 1.398423 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.540554 Loss1: 0.152460 Loss2: 1.388093 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.520099 Loss1: 0.127552 Loss2: 1.392547 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.376209 Loss1: 1.384094 Loss2: 1.992115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.030411 Loss1: 0.525182 Loss2: 1.505229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.654684 Loss1: 0.229542 Loss2: 1.425141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.643666 Loss1: 0.212806 Loss2: 1.430861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.622840 Loss1: 0.188806 Loss2: 1.434034 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.592650 Loss1: 0.164454 Loss2: 1.428196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.577533 Loss1: 0.159496 Loss2: 1.418037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.546020 Loss1: 0.129354 Loss2: 1.416666
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.729314 Loss1: 0.297648 Loss2: 1.431666
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-10 11:52:12,986 | server.py:236 | fit_round 75 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.655697 Loss1: 0.235996 Loss2: 1.419701
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.626909 Loss1: 0.208230 Loss2: 1.418679
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.553874 Loss1: 0.150779 Loss2: 1.403095
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.538899 Loss1: 0.137667 Loss2: 1.401232
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.520183 Loss1: 0.125506 Loss2: 1.394676
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.038873 Loss1: 1.247489 Loss2: 1.791384
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.251319 Loss1: 0.850317 Loss2: 1.401001
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.967674 Loss1: 0.565881 Loss2: 1.401793
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.784675 Loss1: 0.415205 Loss2: 1.369470
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.615789 Loss1: 0.255537 Loss2: 1.360252
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.041400 Loss1: 1.147606 Loss2: 1.893794
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.557850 Loss1: 0.218551 Loss2: 1.339299
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.170939 Loss1: 0.706367 Loss2: 1.464572
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500007 Loss1: 0.149257 Loss2: 1.350750
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.836028 Loss1: 0.402790 Loss2: 1.433238
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.528660 Loss1: 0.188974 Loss2: 1.339686
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.695182 Loss1: 0.284794 Loss2: 1.410388
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488002 Loss1: 0.140587 Loss2: 1.347416
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.515271 Loss1: 0.176000 Loss2: 1.339270
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.697396 Loss1: 0.282565 Loss2: 1.414832
-(DefaultActor pid=3765) >> Training accuracy: 0.951172
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.630773 Loss1: 0.212748 Loss2: 1.418025
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.576333 Loss1: 0.175955 Loss2: 1.400378
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.597020 Loss1: 0.194644 Loss2: 1.402377
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.549528 Loss1: 0.142325 Loss2: 1.407203
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.209263 Loss1: 1.342160 Loss2: 1.867103
-(DefaultActor pid=3764) >> Training accuracy: 0.963235
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.501106 Loss1: 0.112999 Loss2: 1.388108
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.337676 Loss1: 0.891690 Loss2: 1.445986
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.000831 Loss1: 0.572504 Loss2: 1.428327
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.729939 Loss1: 0.340728 Loss2: 1.389212
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.709825 Loss1: 0.323100 Loss2: 1.386724
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.619902 Loss1: 0.232681 Loss2: 1.387221
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.637967 Loss1: 0.260375 Loss2: 1.377591
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.260816 Loss1: 1.354795 Loss2: 1.906021
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.573995 Loss1: 0.184837 Loss2: 1.389158
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.336779 Loss1: 0.860271 Loss2: 1.476508
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.573728 Loss1: 0.192269 Loss2: 1.381458
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.081931 Loss1: 0.607659 Loss2: 1.474272
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.587165 Loss1: 0.196184 Loss2: 1.390981
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.878094 Loss1: 0.413907 Loss2: 1.464186
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.814936 Loss1: 0.362823 Loss2: 1.452113
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.730452 Loss1: 0.284651 Loss2: 1.445801
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.645257 Loss1: 0.209950 Loss2: 1.435307
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.632972 Loss1: 0.198823 Loss2: 1.434149
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.595856 Loss1: 0.168664 Loss2: 1.427193
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.392466 Loss1: 1.468100 Loss2: 1.924365
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.585181 Loss1: 0.156635 Loss2: 1.428546
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.455470 Loss1: 0.957173 Loss2: 1.498298
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.085390 Loss1: 0.633637 Loss2: 1.451752
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.920083 Loss1: 0.452006 Loss2: 1.468078
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.789450 Loss1: 0.362935 Loss2: 1.426516
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.718891 Loss1: 0.284517 Loss2: 1.434374
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.730044 Loss1: 0.300399 Loss2: 1.429646
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.183759 Loss1: 1.258734 Loss2: 1.925025
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.702681 Loss1: 0.272907 Loss2: 1.429774
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.353871 Loss1: 0.847352 Loss2: 1.506518
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.630303 Loss1: 0.208179 Loss2: 1.422123
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.953629 Loss1: 0.471526 Loss2: 1.482103
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.566983 Loss1: 0.150470 Loss2: 1.416513
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.905227 Loss1: 0.447075 Loss2: 1.458152
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.740504 Loss1: 0.286916 Loss2: 1.453589
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.721759 Loss1: 0.259138 Loss2: 1.462621
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.623950 Loss1: 0.183875 Loss2: 1.440075
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.575619 Loss1: 0.142583 Loss2: 1.433036
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.624781 Loss1: 0.191672 Loss2: 1.433109
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.604750 Loss1: 0.171624 Loss2: 1.433126
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 11:52:12,986][flwr][DEBUG] - fit_round 75 received 50 results and 0 failures
-INFO flwr 2023-10-10 11:52:54,426 | server.py:125 | fit progress: (75, 2.2630984912665126, {'accuracy': 0.5379}, 172882.20411751402)
->> Test accuracy: 0.537900
-[2023-10-10 11:52:54,426][flwr][INFO] - fit progress: (75, 2.2630984912665126, {'accuracy': 0.5379}, 172882.20411751402)
-DEBUG flwr 2023-10-10 11:52:54,426 | server.py:173 | evaluate_round 75: strategy sampled 50 clients (out of 50)
-[2023-10-10 11:52:54,426][flwr][DEBUG] - evaluate_round 75: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 12:02:02,633 | server.py:187 | evaluate_round 75 received 50 results and 0 failures
-[2023-10-10 12:02:02,633][flwr][DEBUG] - evaluate_round 75 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 12:02:02,633 | server.py:222 | fit_round 76: strategy sampled 50 clients (out of 50)
-[2023-10-10 12:02:02,633][flwr][DEBUG] - fit_round 76: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.223422 Loss1: 1.340909 Loss2: 1.882514
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.189848 Loss1: 0.707823 Loss2: 1.482025
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.976236 Loss1: 0.532300 Loss2: 1.443936
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.345797 Loss1: 1.332001 Loss2: 2.013796
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.775142 Loss1: 0.346210 Loss2: 1.428932
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.610420 Loss1: 0.204502 Loss2: 1.405918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.573863 Loss1: 0.173712 Loss2: 1.400151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.677625 Loss1: 0.267781 Loss2: 1.409844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.644709 Loss1: 0.245531 Loss2: 1.399178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.648680 Loss1: 0.251663 Loss2: 1.397017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.537949 Loss1: 0.147262 Loss2: 1.390688
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.542685 Loss1: 0.151838 Loss2: 1.390847
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.521676 Loss1: 0.140532 Loss2: 1.381144
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.563494 Loss1: 0.171111 Loss2: 1.392383
-(DefaultActor pid=3765) >> Training accuracy: 0.964844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.119836 Loss1: 1.255567 Loss2: 1.864269 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957031
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.969883 Loss1: 0.515717 Loss2: 1.454166 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.774196 Loss1: 0.351208 Loss2: 1.422987
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.156666 Loss1: 1.295586 Loss2: 1.861081
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.154382 Loss1: 0.739478 Loss2: 1.414904
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.689142 Loss1: 0.264625 Loss2: 1.424517
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.975604 Loss1: 0.533723 Loss2: 1.441881
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.647239 Loss1: 0.237429 Loss2: 1.409811
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.711080 Loss1: 0.314814 Loss2: 1.396265
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.648467 Loss1: 0.233601 Loss2: 1.414866
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.656360 Loss1: 0.263940 Loss2: 1.392420
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.591529 Loss1: 0.186878 Loss2: 1.404651
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.556756 Loss1: 0.158271 Loss2: 1.398485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.544978 Loss1: 0.147048 Loss2: 1.397930 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.542095 Loss1: 0.166667 Loss2: 1.375428 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.996142 Loss1: 1.135226 Loss2: 1.860916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.949637 Loss1: 0.492143 Loss2: 1.457494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.056005 Loss1: 1.217362 Loss2: 1.838643
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.749031 Loss1: 0.331784 Loss2: 1.417248
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.135313 Loss1: 0.726273 Loss2: 1.409040
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.662943 Loss1: 0.244330 Loss2: 1.418613
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.901759 Loss1: 0.471394 Loss2: 1.430365
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569811 Loss1: 0.169203 Loss2: 1.400609
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.519890 Loss1: 0.128233 Loss2: 1.391658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.560956 Loss1: 0.168100 Loss2: 1.392855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.582630 Loss1: 0.180060 Loss2: 1.402570 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.536408 Loss1: 0.138103 Loss2: 1.398305 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.564692 Loss1: 0.187361 Loss2: 1.377331 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.088288 Loss1: 1.276224 Loss2: 1.812063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.935709 Loss1: 0.540139 Loss2: 1.395570 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.763667 Loss1: 0.409417 Loss2: 1.354250
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.263032 Loss1: 1.278794 Loss2: 1.984237
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.724617 Loss1: 0.355136 Loss2: 1.369481
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.374702 Loss1: 0.845420 Loss2: 1.529282
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.541791 Loss1: 0.197031 Loss2: 1.344760
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.103786 Loss1: 0.574628 Loss2: 1.529159
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544683 Loss1: 0.213267 Loss2: 1.331416
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.879967 Loss1: 0.379530 Loss2: 1.500438
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.512849 Loss1: 0.174797 Loss2: 1.338053
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.775176 Loss1: 0.283576 Loss2: 1.491601
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.534052 Loss1: 0.199029 Loss2: 1.335023
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.695239 Loss1: 0.213572 Loss2: 1.481668
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481077 Loss1: 0.149208 Loss2: 1.331869
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.690765 Loss1: 0.211648 Loss2: 1.479116
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.649510 Loss1: 0.174223 Loss2: 1.475286
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.609835 Loss1: 0.140355 Loss2: 1.469481
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.609592 Loss1: 0.148601 Loss2: 1.460991
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.352533 Loss1: 1.495439 Loss2: 1.857094
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.305764 Loss1: 0.862209 Loss2: 1.443555
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.874435 Loss1: 0.473067 Loss2: 1.401367
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.805916 Loss1: 0.392997 Loss2: 1.412919
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.172357 Loss1: 1.270639 Loss2: 1.901718
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.700195 Loss1: 0.307076 Loss2: 1.393120
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.306029 Loss1: 0.860418 Loss2: 1.445611
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.628152 Loss1: 0.249339 Loss2: 1.378813
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.129779 Loss1: 0.628101 Loss2: 1.501678
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.605132 Loss1: 0.222000 Loss2: 1.383132
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.928759 Loss1: 0.494821 Loss2: 1.433938
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.539074 Loss1: 0.166887 Loss2: 1.372188
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.812405 Loss1: 0.356567 Loss2: 1.455838
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.508937 Loss1: 0.143777 Loss2: 1.365161
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.694023 Loss1: 0.262870 Loss2: 1.431153
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.456252 Loss1: 0.094090 Loss2: 1.362162
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.716491 Loss1: 0.291233 Loss2: 1.425258
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.618949 Loss1: 0.189254 Loss2: 1.429695
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.554463 Loss1: 0.147040 Loss2: 1.407423
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.537983 Loss1: 0.133409 Loss2: 1.404574
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.081631 Loss1: 1.274303 Loss2: 1.807327
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.192725 Loss1: 0.789979 Loss2: 1.402746
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.866300 Loss1: 0.462780 Loss2: 1.403520
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.736803 Loss1: 0.360426 Loss2: 1.376377
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.286148 Loss1: 1.409280 Loss2: 1.876868
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.232273 Loss1: 0.787095 Loss2: 1.445178
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.658727 Loss1: 0.280784 Loss2: 1.377943
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.944014 Loss1: 0.529690 Loss2: 1.414324
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.553524 Loss1: 0.187482 Loss2: 1.366042
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.749167 Loss1: 0.344929 Loss2: 1.404238
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.527149 Loss1: 0.172880 Loss2: 1.354269
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.657522 Loss1: 0.271464 Loss2: 1.386058
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.585054 Loss1: 0.220249 Loss2: 1.364805
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.620090 Loss1: 0.255502 Loss2: 1.364588 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.565009 Loss1: 0.195156 Loss2: 1.369852 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.502842 Loss1: 0.131198 Loss2: 1.371644 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.011642 Loss1: 1.116461 Loss2: 1.895181 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.955416 Loss1: 0.505524 Loss2: 1.449892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.792909 Loss1: 0.378457 Loss2: 1.414452
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.028818 Loss1: 1.170646 Loss2: 1.858172
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.129896 Loss1: 0.741509 Loss2: 1.388388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.979359 Loss1: 0.544525 Loss2: 1.434834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.894083 Loss1: 0.502407 Loss2: 1.391677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.780114 Loss1: 0.371942 Loss2: 1.408172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.676103 Loss1: 0.298173 Loss2: 1.377930 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476364 Loss1: 0.102079 Loss2: 1.374285
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.651589 Loss1: 0.265293 Loss2: 1.386296
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.562373 Loss1: 0.189734 Loss2: 1.372639
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.500143 Loss1: 0.137424 Loss2: 1.362719
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.493080 Loss1: 0.135722 Loss2: 1.357358
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.042916 Loss1: 1.214004 Loss2: 1.828912
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.034141 Loss1: 0.694840 Loss2: 1.339301
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.838830 Loss1: 0.460542 Loss2: 1.378288
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.673012 Loss1: 0.341011 Loss2: 1.332001
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.277241 Loss1: 1.290880 Loss2: 1.986361
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.609190 Loss1: 0.282970 Loss2: 1.326220
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.353764 Loss1: 0.797348 Loss2: 1.556416
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.588704 Loss1: 0.248202 Loss2: 1.340502
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.956025 Loss1: 0.447031 Loss2: 1.508994
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.869647 Loss1: 0.372860 Loss2: 1.496786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.528310 Loss1: 0.200457 Loss2: 1.327853
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.750751 Loss1: 0.264661 Loss2: 1.486090
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.471378 Loss1: 0.148340 Loss2: 1.323038
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.755372 Loss1: 0.269191 Loss2: 1.486181
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465967 Loss1: 0.150229 Loss2: 1.315738
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.732601 Loss1: 0.248202 Loss2: 1.484399
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.651928 Loss1: 0.177559 Loss2: 1.474369
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.635400 Loss1: 0.167768 Loss2: 1.467632
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.650683 Loss1: 0.174426 Loss2: 1.476257
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.124413 Loss1: 1.224921 Loss2: 1.899492
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.282705 Loss1: 0.797178 Loss2: 1.485527
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.982774 Loss1: 0.549113 Loss2: 1.433661
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.850226 Loss1: 0.419514 Loss2: 1.430712
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.337293 Loss1: 1.346863 Loss2: 1.990430
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.288101 Loss1: 0.864865 Loss2: 1.423236
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.668188 Loss1: 0.254458 Loss2: 1.413730
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587936 Loss1: 0.197760 Loss2: 1.390176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.558651 Loss1: 0.177016 Loss2: 1.381635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.526583 Loss1: 0.139040 Loss2: 1.387543 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.552974 Loss1: 0.171438 Loss2: 1.381537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491488 Loss1: 0.110927 Loss2: 1.380561 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444962 Loss1: 0.076807 Loss2: 1.368154 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987981
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.293795 Loss1: 1.280458 Loss2: 2.013337
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.325327 Loss1: 0.769626 Loss2: 1.555701
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.024848 Loss1: 0.487309 Loss2: 1.537540
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.873800 Loss1: 0.355604 Loss2: 1.518196
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.020409 Loss1: 1.113821 Loss2: 1.906588
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.152625 Loss1: 0.724641 Loss2: 1.427984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.875891 Loss1: 0.432825 Loss2: 1.443067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.772402 Loss1: 0.360529 Loss2: 1.411872 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.719965 Loss1: 0.297632 Loss2: 1.422333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.613445 Loss1: 0.206529 Loss2: 1.406917 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.591127 Loss1: 0.194652 Loss2: 1.396475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.539217 Loss1: 0.153535 Loss2: 1.385682 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.104255 Loss1: 1.207103 Loss2: 1.897152 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.899709 Loss1: 0.448453 Loss2: 1.451256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.762701 Loss1: 0.353626 Loss2: 1.409075
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.253182 Loss1: 1.334603 Loss2: 1.918579
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.253072 Loss1: 0.831956 Loss2: 1.421116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.081477 Loss1: 0.626485 Loss2: 1.454992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.870065 Loss1: 0.458143 Loss2: 1.411922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.574019 Loss1: 0.178528 Loss2: 1.395491
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.748451 Loss1: 0.342726 Loss2: 1.405725
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.578677 Loss1: 0.178412 Loss2: 1.400266
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.645240 Loss1: 0.261056 Loss2: 1.384185
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.494685 Loss1: 0.100105 Loss2: 1.394580
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.609921 Loss1: 0.224720 Loss2: 1.385200
-(DefaultActor pid=3765) >> Training accuracy: 0.962500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.601234 Loss1: 0.211186 Loss2: 1.390048
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.550660 Loss1: 0.167403 Loss2: 1.383257
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.504473 Loss1: 0.119404 Loss2: 1.385069
-(DefaultActor pid=3764) >> Training accuracy: 0.979911
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.138711 Loss1: 1.257681 Loss2: 1.881030
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.238578 Loss1: 0.825267 Loss2: 1.413311
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.007380 Loss1: 0.560288 Loss2: 1.447092
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765323 Loss1: 0.373162 Loss2: 1.392161
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.337749 Loss1: 1.339825 Loss2: 1.997924
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.275372 Loss1: 0.762266 Loss2: 1.513106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.058255 Loss1: 0.540740 Loss2: 1.517515 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.816223 Loss1: 0.328126 Loss2: 1.488096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.730133 Loss1: 0.248068 Loss2: 1.482066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.700351 Loss1: 0.224046 Loss2: 1.476304 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.585730 Loss1: 0.208256 Loss2: 1.377474
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.686567 Loss1: 0.210527 Loss2: 1.476040
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.641907 Loss1: 0.167390 Loss2: 1.474517
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.632859 Loss1: 0.161781 Loss2: 1.471078
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.574615 Loss1: 0.109948 Loss2: 1.464667
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.093231 Loss1: 1.129467 Loss2: 1.963763
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.145844 Loss1: 0.676589 Loss2: 1.469255
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.902904 Loss1: 0.411327 Loss2: 1.491576
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.784086 Loss1: 0.327342 Loss2: 1.456744
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.160430 Loss1: 1.312640 Loss2: 1.847791
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.210340 Loss1: 0.788378 Loss2: 1.421962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.056847 Loss1: 0.626210 Loss2: 1.430637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.877956 Loss1: 0.470313 Loss2: 1.407643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.746007 Loss1: 0.353569 Loss2: 1.392437 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.599447 Loss1: 0.190749 Loss2: 1.408698 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.541183 Loss1: 0.115739 Loss2: 1.425443
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.548378 Loss1: 0.170017 Loss2: 1.378360
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.533044 Loss1: 0.163309 Loss2: 1.369735
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.542296 Loss1: 0.164829 Loss2: 1.377467
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.508253 Loss1: 0.134316 Loss2: 1.373937
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.515461 Loss1: 1.505389 Loss2: 2.010072
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.382040 Loss1: 0.889090 Loss2: 1.492950
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.006738 Loss1: 0.518834 Loss2: 1.487903
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.890224 Loss1: 0.442852 Loss2: 1.447373
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.137541 Loss1: 1.301968 Loss2: 1.835573
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.187214 Loss1: 0.782054 Loss2: 1.405160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.889790 Loss1: 0.502977 Loss2: 1.386813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.729759 Loss1: 0.361198 Loss2: 1.368561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.631792 Loss1: 0.195566 Loss2: 1.436226 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.599509 Loss1: 0.162419 Loss2: 1.437090 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.946429
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.494601 Loss1: 0.142426 Loss2: 1.352175 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.508443 Loss1: 0.159223 Loss2: 1.349220 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.263581 Loss1: 0.747237 Loss2: 1.516344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.756515 Loss1: 0.280022 Loss2: 1.476493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.302012 Loss1: 1.358315 Loss2: 1.943696
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.683781 Loss1: 0.223930 Loss2: 1.459851
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.289850 Loss1: 0.805580 Loss2: 1.484270
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.637935 Loss1: 0.183810 Loss2: 1.454125
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.004366 Loss1: 0.505201 Loss2: 1.499165
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.656572 Loss1: 0.196713 Loss2: 1.459859
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.648079 Loss1: 0.193141 Loss2: 1.454938 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.679340 Loss1: 0.218969 Loss2: 1.460371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.656019 Loss1: 0.195655 Loss2: 1.460364 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952148
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.622707 Loss1: 0.184977 Loss2: 1.437730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.599438 Loss1: 0.151823 Loss2: 1.447614 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.245301 Loss1: 1.333472 Loss2: 1.911829
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.344180 Loss1: 0.863321 Loss2: 1.480859
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.098195 Loss1: 0.594286 Loss2: 1.503909
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.902041 Loss1: 0.444524 Loss2: 1.457517
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.104866 Loss1: 1.249347 Loss2: 1.855519
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.183379 Loss1: 0.775785 Loss2: 1.407594 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.845663 Loss1: 0.416212 Loss2: 1.429451 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.721848 Loss1: 0.331274 Loss2: 1.390574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.651111 Loss1: 0.244760 Loss2: 1.406351 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582986 Loss1: 0.208578 Loss2: 1.374408 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.582880 Loss1: 0.205989 Loss2: 1.376892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.569198 Loss1: 0.200589 Loss2: 1.368610 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.253874 Loss1: 1.371896 Loss2: 1.881978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.981652 Loss1: 0.544510 Loss2: 1.437142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.360414 Loss1: 1.389467 Loss2: 1.970947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.307846 Loss1: 0.799297 Loss2: 1.508549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.977185 Loss1: 0.478757 Loss2: 1.498428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.897645 Loss1: 0.415294 Loss2: 1.482352 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.744377 Loss1: 0.265585 Loss2: 1.478792 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.679077 Loss1: 0.224519 Loss2: 1.454558 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.639432 Loss1: 0.180548 Loss2: 1.458884 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.629524 Loss1: 0.166814 Loss2: 1.462710 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.943828 Loss1: 1.152647 Loss2: 1.791180
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.188933 Loss1: 0.774600 Loss2: 1.414333
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.816211 Loss1: 0.456436 Loss2: 1.359775
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.660186 Loss1: 0.301402 Loss2: 1.358783
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.209610 Loss1: 1.273873 Loss2: 1.935737
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.153303 Loss1: 0.717753 Loss2: 1.435549
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565997 Loss1: 0.222481 Loss2: 1.343515
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.928970 Loss1: 0.468615 Loss2: 1.460355
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520419 Loss1: 0.184070 Loss2: 1.336349
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.505903 Loss1: 0.172482 Loss2: 1.333422 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471430 Loss1: 0.132828 Loss2: 1.338602 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.486831 Loss1: 0.156580 Loss2: 1.330251 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475664 Loss1: 0.140632 Loss2: 1.335033 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970588
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.506286 Loss1: 0.118993 Loss2: 1.387293 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.107611 Loss1: 1.223570 Loss2: 1.884041
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.202139 Loss1: 0.763339 Loss2: 1.438799
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.849195 Loss1: 0.423247 Loss2: 1.425949
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.518577 Loss1: 1.493142 Loss2: 2.025435
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.675926 Loss1: 0.295033 Loss2: 1.380892
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.400707 Loss1: 0.855409 Loss2: 1.545298
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606572 Loss1: 0.220520 Loss2: 1.386052
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.090910 Loss1: 0.561594 Loss2: 1.529316
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.596536 Loss1: 0.220280 Loss2: 1.376256
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.876297 Loss1: 0.382246 Loss2: 1.494051
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.542884 Loss1: 0.168270 Loss2: 1.374615
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.551785 Loss1: 0.186353 Loss2: 1.365432 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.480763 Loss1: 0.114267 Loss2: 1.366496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.532496 Loss1: 0.168564 Loss2: 1.363932 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952148
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.608772 Loss1: 0.143109 Loss2: 1.465663 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.081066 Loss1: 1.194343 Loss2: 1.886723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.876425 Loss1: 0.442534 Loss2: 1.433891 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.709959 Loss1: 0.346457 Loss2: 1.363502
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.076049 Loss1: 1.242205 Loss2: 1.833844
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.635286 Loss1: 0.261392 Loss2: 1.373894
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.242785 Loss1: 0.777011 Loss2: 1.465774
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.878953 Loss1: 0.458282 Loss2: 1.420671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.770825 Loss1: 0.353980 Loss2: 1.416844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.733921 Loss1: 0.327492 Loss2: 1.406429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.638218 Loss1: 0.239297 Loss2: 1.398921 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.557455 Loss1: 0.170934 Loss2: 1.386521 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.547104 Loss1: 0.158511 Loss2: 1.388592 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970703
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.295870 Loss1: 1.381222 Loss2: 1.914648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.940734 Loss1: 0.498962 Loss2: 1.441772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.678210 Loss1: 0.270788 Loss2: 1.407422 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.626843 Loss1: 0.233594 Loss2: 1.393249 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.599132 Loss1: 0.208810 Loss2: 1.390322 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.554078 Loss1: 0.157689 Loss2: 1.396389 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.524490 Loss1: 0.133036 Loss2: 1.391454 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.527591 Loss1: 0.143145 Loss2: 1.384447 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967634
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.622236 Loss1: 0.228657 Loss2: 1.393579 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.525259 Loss1: 0.144708 Loss2: 1.380551 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.560263 Loss1: 0.191184 Loss2: 1.369079
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.245116 Loss1: 1.286269 Loss2: 1.958846
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.363695 Loss1: 0.883467 Loss2: 1.480228
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.991194 Loss1: 0.494698 Loss2: 1.496497
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.817164 Loss1: 0.368178 Loss2: 1.448986
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.711865 Loss1: 0.260001 Loss2: 1.451863
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.960091 Loss1: 1.180087 Loss2: 1.780004
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.668988 Loss1: 0.226093 Loss2: 1.442895
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.146206 Loss1: 0.739521 Loss2: 1.406685
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.682229 Loss1: 0.230416 Loss2: 1.451813
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.792442 Loss1: 0.425279 Loss2: 1.367163
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.656621 Loss1: 0.211230 Loss2: 1.445391
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.605083 Loss1: 0.167866 Loss2: 1.437217
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.682842 Loss1: 0.330599 Loss2: 1.352242
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.588350 Loss1: 0.156982 Loss2: 1.431368
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.625097 Loss1: 0.265744 Loss2: 1.359353
-(DefaultActor pid=3765) >> Training accuracy: 0.950000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560972 Loss1: 0.219152 Loss2: 1.341821
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.501978 Loss1: 0.155056 Loss2: 1.346922
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477024 Loss1: 0.141288 Loss2: 1.335736
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.456972 Loss1: 0.123493 Loss2: 1.333479
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.116059 Loss1: 1.249051 Loss2: 1.867008
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443704 Loss1: 0.118273 Loss2: 1.325431
-(DefaultActor pid=3764) >> Training accuracy: 0.960938
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.917712 Loss1: 0.501895 Loss2: 1.415817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.600974 Loss1: 0.210986 Loss2: 1.389988 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587263 Loss1: 0.217303 Loss2: 1.369960
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.095833 Loss1: 1.243360 Loss2: 1.852473
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.240732 Loss1: 0.768686 Loss2: 1.472046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.012467 Loss1: 0.575393 Loss2: 1.437074 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 12:31:07,597 | server.py:236 | fit_round 76 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.832601 Loss1: 0.372448 Loss2: 1.460153 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.689138 Loss1: 0.271814 Loss2: 1.417325 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.549364 Loss1: 0.142667 Loss2: 1.406696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.592207 Loss1: 0.189764 Loss2: 1.402443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.203118 Loss1: 0.790663 Loss2: 1.412456 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966797
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.736236 Loss1: 0.380393 Loss2: 1.355843 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.558673 Loss1: 0.208939 Loss2: 1.349734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.580871 Loss1: 0.220020 Loss2: 1.360851
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.329317 Loss1: 1.354435 Loss2: 1.974881
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.519650 Loss1: 0.170640 Loss2: 1.349010
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.307593 Loss1: 0.871957 Loss2: 1.435637
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.959082 Loss1: 0.496269 Loss2: 1.462813
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.481015 Loss1: 0.137553 Loss2: 1.343461
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.486378 Loss1: 0.141635 Loss2: 1.344743 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.637536 Loss1: 0.217066 Loss2: 1.420470 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.616313 Loss1: 0.197687 Loss2: 1.418626 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.522458 Loss1: 0.116981 Loss2: 1.405477 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987981
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.888405 Loss1: 0.453625 Loss2: 1.434780 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.673795 Loss1: 0.276720 Loss2: 1.397076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.164898 Loss1: 1.266137 Loss2: 1.898762
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.580708 Loss1: 0.189957 Loss2: 1.390752
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.363337 Loss1: 0.921192 Loss2: 1.442144
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539739 Loss1: 0.166583 Loss2: 1.373157
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.009017 Loss1: 0.516921 Loss2: 1.492096
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.566753 Loss1: 0.190176 Loss2: 1.376576
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.788003 Loss1: 0.368941 Loss2: 1.419062
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509007 Loss1: 0.130351 Loss2: 1.378656
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.704959 Loss1: 0.279861 Loss2: 1.425097
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.514362 Loss1: 0.140277 Loss2: 1.374085
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.634750 Loss1: 0.223084 Loss2: 1.411666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.592260 Loss1: 0.176737 Loss2: 1.415523 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 12:31:07,597][flwr][DEBUG] - fit_round 76 received 50 results and 0 failures
-INFO flwr 2023-10-10 12:31:48,855 | server.py:125 | fit progress: (76, 2.255889182273572, {'accuracy': 0.5413}, 175216.633607881)
->> Test accuracy: 0.541300
-[2023-10-10 12:31:48,855][flwr][INFO] - fit progress: (76, 2.255889182273572, {'accuracy': 0.5413}, 175216.633607881)
-DEBUG flwr 2023-10-10 12:31:48,855 | server.py:173 | evaluate_round 76: strategy sampled 50 clients (out of 50)
-[2023-10-10 12:31:48,855][flwr][DEBUG] - evaluate_round 76: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 12:40:54,782 | server.py:187 | evaluate_round 76 received 50 results and 0 failures
-[2023-10-10 12:40:54,782][flwr][DEBUG] - evaluate_round 76 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 12:40:54,783 | server.py:222 | fit_round 77: strategy sampled 50 clients (out of 50)
-[2023-10-10 12:40:54,783][flwr][DEBUG] - fit_round 77: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.918155 Loss1: 1.099646 Loss2: 1.818510 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.915300 Loss1: 0.487453 Loss2: 1.427847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.740982 Loss1: 0.356701 Loss2: 1.384281
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.183848 Loss1: 1.375096 Loss2: 1.808751
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.669212 Loss1: 0.284127 Loss2: 1.385085
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.229108 Loss1: 0.843422 Loss2: 1.385686
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.944424 Loss1: 0.555075 Loss2: 1.389349
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.669933 Loss1: 0.288521 Loss2: 1.381412
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.762207 Loss1: 0.398005 Loss2: 1.364202
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.611588 Loss1: 0.212907 Loss2: 1.398681
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.657838 Loss1: 0.291069 Loss2: 1.366768
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.539920 Loss1: 0.174741 Loss2: 1.365179
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.618691 Loss1: 0.259191 Loss2: 1.359500
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.518541 Loss1: 0.151445 Loss2: 1.367097
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.529094 Loss1: 0.163155 Loss2: 1.365939 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.946289
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.537848 Loss1: 0.179115 Loss2: 1.358734 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.087417 Loss1: 1.178663 Loss2: 1.908755 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.889663 Loss1: 0.432575 Loss2: 1.457088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.751220 Loss1: 0.353604 Loss2: 1.397616
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.185205 Loss1: 1.202266 Loss2: 1.982939
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.696249 Loss1: 0.283875 Loss2: 1.412373
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.212835 Loss1: 0.823426 Loss2: 1.389409
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.903985 Loss1: 0.488434 Loss2: 1.415550
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.619720 Loss1: 0.215831 Loss2: 1.403889
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.560964 Loss1: 0.174242 Loss2: 1.386722 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.555120 Loss1: 0.169117 Loss2: 1.386003 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.518734 Loss1: 0.128497 Loss2: 1.390237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.497654 Loss1: 0.114697 Loss2: 1.382956 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404699 Loss1: 0.062689 Loss2: 1.342010 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.172966 Loss1: 1.320175 Loss2: 1.852790
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.391830 Loss1: 0.943649 Loss2: 1.448181
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.037096 Loss1: 0.593901 Loss2: 1.443195
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.794667 Loss1: 0.378042 Loss2: 1.416625
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.270646 Loss1: 1.336595 Loss2: 1.934050
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.400326 Loss1: 0.920229 Loss2: 1.480097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.097916 Loss1: 0.588972 Loss2: 1.508944 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.879419 Loss1: 0.417959 Loss2: 1.461460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.749817 Loss1: 0.299646 Loss2: 1.450170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.707729 Loss1: 0.264430 Loss2: 1.443299 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.490881 Loss1: 0.114734 Loss2: 1.376146
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.629487 Loss1: 0.194389 Loss2: 1.435098
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.649849 Loss1: 0.219060 Loss2: 1.430790
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.652105 Loss1: 0.225023 Loss2: 1.427082
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.578574 Loss1: 0.150732 Loss2: 1.427842
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.286340 Loss1: 1.389259 Loss2: 1.897081
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.169964 Loss1: 0.744295 Loss2: 1.425668
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.845887 Loss1: 0.411164 Loss2: 1.434723
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.738918 Loss1: 0.344378 Loss2: 1.394540
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.124571 Loss1: 1.175821 Loss2: 1.948750
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.235440 Loss1: 0.738514 Loss2: 1.496926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.930077 Loss1: 0.423164 Loss2: 1.506913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.848974 Loss1: 0.370262 Loss2: 1.478712 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.758428 Loss1: 0.276211 Loss2: 1.482218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.691068 Loss1: 0.239732 Loss2: 1.451336 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.621814 Loss1: 0.163895 Loss2: 1.457920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.571927 Loss1: 0.132447 Loss2: 1.439480 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.978867 Loss1: 1.143556 Loss2: 1.835311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.800975 Loss1: 0.445085 Loss2: 1.355890 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638946 Loss1: 0.312025 Loss2: 1.326921
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.124217 Loss1: 1.227406 Loss2: 1.896812
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.237793 Loss1: 0.806076 Loss2: 1.431717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.903800 Loss1: 0.455293 Loss2: 1.448507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.728317 Loss1: 0.328097 Loss2: 1.400219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.624978 Loss1: 0.217577 Loss2: 1.407401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.589994 Loss1: 0.199447 Loss2: 1.390548 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.563268 Loss1: 0.178826 Loss2: 1.384442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.524869 Loss1: 0.146207 Loss2: 1.378662 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.151751 Loss1: 1.264571 Loss2: 1.887181 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.970265 Loss1: 0.529088 Loss2: 1.441177 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.087995 Loss1: 1.233058 Loss2: 1.854938 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.193844 Loss1: 0.781163 Loss2: 1.412681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.893539 Loss1: 0.456340 Loss2: 1.437199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.700206 Loss1: 0.313474 Loss2: 1.386732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.630714 Loss1: 0.246464 Loss2: 1.384250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.614454 Loss1: 0.229846 Loss2: 1.384608 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.553310 Loss1: 0.178604 Loss2: 1.374707 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.577144 Loss1: 0.200667 Loss2: 1.376476 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.054582 Loss1: 1.171882 Loss2: 1.882700
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.164396 Loss1: 0.748179 Loss2: 1.416216
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.997624 Loss1: 0.537483 Loss2: 1.460140
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.768386 Loss1: 0.365279 Loss2: 1.403108
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.160297 Loss1: 1.243673 Loss2: 1.916624
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.242704 Loss1: 0.790527 Loss2: 1.452177 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.970590 Loss1: 0.517098 Loss2: 1.453492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.755035 Loss1: 0.326675 Loss2: 1.428361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.684935 Loss1: 0.269902 Loss2: 1.415033 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.598819 Loss1: 0.186038 Loss2: 1.412781 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.605218 Loss1: 0.203700 Loss2: 1.401518 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.586274 Loss1: 0.180776 Loss2: 1.405498 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.206447 Loss1: 1.276041 Loss2: 1.930405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.039198 Loss1: 0.542387 Loss2: 1.496811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.872073 Loss1: 0.382378 Loss2: 1.489695
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.257530 Loss1: 1.388504 Loss2: 1.869026
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.265202 Loss1: 0.847925 Loss2: 1.417277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.931158 Loss1: 0.491201 Loss2: 1.439957 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.821023 Loss1: 0.429372 Loss2: 1.391650 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.634357 Loss1: 0.174951 Loss2: 1.459406
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.699806 Loss1: 0.313853 Loss2: 1.385953
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.644388 Loss1: 0.190700 Loss2: 1.453688
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.618739 Loss1: 0.244698 Loss2: 1.374041
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.623088 Loss1: 0.166108 Loss2: 1.456979
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.584763 Loss1: 0.219753 Loss2: 1.365010
-(DefaultActor pid=3765) >> Training accuracy: 0.963867
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.607891 Loss1: 0.235067 Loss2: 1.372824
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.523646 Loss1: 0.160739 Loss2: 1.362907
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529028 Loss1: 0.164827 Loss2: 1.364200
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.124880 Loss1: 1.263735 Loss2: 1.861145
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.216703 Loss1: 0.818709 Loss2: 1.397994
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.922922 Loss1: 0.498496 Loss2: 1.424426
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.821083 Loss1: 0.430918 Loss2: 1.390166
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.251259 Loss1: 1.358466 Loss2: 1.892793
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.698269 Loss1: 0.302462 Loss2: 1.395807
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.317465 Loss1: 0.854769 Loss2: 1.462696
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.571683 Loss1: 0.206584 Loss2: 1.365099
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.927929 Loss1: 0.483653 Loss2: 1.444276
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.626774 Loss1: 0.251539 Loss2: 1.375236
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.757795 Loss1: 0.343023 Loss2: 1.414772
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.590866 Loss1: 0.215140 Loss2: 1.375726
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.681430 Loss1: 0.270739 Loss2: 1.410692
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.565651 Loss1: 0.205815 Loss2: 1.359836
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.635261 Loss1: 0.233932 Loss2: 1.401329
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.558295 Loss1: 0.191946 Loss2: 1.366348
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.622705 Loss1: 0.230768 Loss2: 1.391937
-(DefaultActor pid=3765) >> Training accuracy: 0.933333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.617452 Loss1: 0.221499 Loss2: 1.395953
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.577047 Loss1: 0.174322 Loss2: 1.402726
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.536075 Loss1: 0.142367 Loss2: 1.393708
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.216025 Loss1: 1.418723 Loss2: 1.797301
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.234195 Loss1: 0.860525 Loss2: 1.373670
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.889275 Loss1: 0.528451 Loss2: 1.360824
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.690676 Loss1: 0.355228 Loss2: 1.335448
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.010479 Loss1: 1.141549 Loss2: 1.868930
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.153015 Loss1: 0.740748 Loss2: 1.412267 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.882298 Loss1: 0.476447 Loss2: 1.405851 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.771293 Loss1: 0.378607 Loss2: 1.392686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.599445 Loss1: 0.215524 Loss2: 1.383921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.533973 Loss1: 0.177729 Loss2: 1.356243 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416144 Loss1: 0.110689 Loss2: 1.305455
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.496673 Loss1: 0.135566 Loss2: 1.361107
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467447 Loss1: 0.111409 Loss2: 1.356038
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438972 Loss1: 0.093751 Loss2: 1.345221
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429738 Loss1: 0.089496 Loss2: 1.340242
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.162634 Loss1: 1.271295 Loss2: 1.891338
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.280135 Loss1: 0.826598 Loss2: 1.453536
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.975982 Loss1: 0.524520 Loss2: 1.451462
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.772264 Loss1: 0.348690 Loss2: 1.423574
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.235788 Loss1: 1.264293 Loss2: 1.971495
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.200265 Loss1: 0.714299 Loss2: 1.485966 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.052173 Loss1: 0.530412 Loss2: 1.521762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.822957 Loss1: 0.353440 Loss2: 1.469517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.728458 Loss1: 0.261197 Loss2: 1.467261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.707301 Loss1: 0.252644 Loss2: 1.454657 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.581972 Loss1: 0.187572 Loss2: 1.394400
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.626728 Loss1: 0.169403 Loss2: 1.457326
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.632861 Loss1: 0.187024 Loss2: 1.445837
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.601060 Loss1: 0.153319 Loss2: 1.447741
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.597734 Loss1: 0.150027 Loss2: 1.447707
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.299660 Loss1: 1.370391 Loss2: 1.929269
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.248978 Loss1: 0.818283 Loss2: 1.430694
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.924117 Loss1: 0.463752 Loss2: 1.460365
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.862319 Loss1: 0.454677 Loss2: 1.407642
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.063100 Loss1: 1.192655 Loss2: 1.870445
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.247374 Loss1: 0.840643 Loss2: 1.406730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.902189 Loss1: 0.454237 Loss2: 1.447953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.709260 Loss1: 0.324621 Loss2: 1.384638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.704001 Loss1: 0.314682 Loss2: 1.389319 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.686428 Loss1: 0.304839 Loss2: 1.381588 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969866
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.592774 Loss1: 0.212935 Loss2: 1.379839 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529355 Loss1: 0.144729 Loss2: 1.384625 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.318089 Loss1: 0.837401 Loss2: 1.480688 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.781848 Loss1: 0.323067 Loss2: 1.458781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.709425 Loss1: 0.254710 Loss2: 1.454716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.092395 Loss1: 0.583874 Loss2: 1.508520 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.882463 Loss1: 0.454542 Loss2: 1.427921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.774437 Loss1: 0.343521 Loss2: 1.430916 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.734899 Loss1: 0.281335 Loss2: 1.453564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.670983 Loss1: 0.260202 Loss2: 1.410781
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.578510 Loss1: 0.136833 Loss2: 1.441677
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.545876 Loss1: 0.116793 Loss2: 1.429083 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.020990 Loss1: 1.060532 Loss2: 1.960458 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971354
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.941165 Loss1: 0.445312 Loss2: 1.495853 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.851834 Loss1: 0.368014 Loss2: 1.483819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.704852 Loss1: 0.239972 Loss2: 1.464879 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.647418 Loss1: 0.189560 Loss2: 1.457858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.629439 Loss1: 0.177689 Loss2: 1.451750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.600071 Loss1: 0.148385 Loss2: 1.451686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.589770 Loss1: 0.216005 Loss2: 1.373765 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.545512 Loss1: 0.183157 Loss2: 1.362355 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.515240 Loss1: 0.157900 Loss2: 1.357340 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.101726 Loss1: 1.227340 Loss2: 1.874386
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.235625 Loss1: 0.785332 Loss2: 1.450293
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.932291 Loss1: 0.507357 Loss2: 1.424934
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.803945 Loss1: 0.402157 Loss2: 1.401787
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.291354 Loss1: 1.295454 Loss2: 1.995900
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.251047 Loss1: 0.732390 Loss2: 1.518657 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.018003 Loss1: 0.476115 Loss2: 1.541888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.836353 Loss1: 0.338543 Loss2: 1.497810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.755136 Loss1: 0.264662 Loss2: 1.490474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.709346 Loss1: 0.224453 Loss2: 1.484893 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454700 Loss1: 0.095580 Loss2: 1.359120
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.705327 Loss1: 0.219972 Loss2: 1.485356
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.649822 Loss1: 0.168962 Loss2: 1.480860
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.618566 Loss1: 0.140444 Loss2: 1.478122
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.647176 Loss1: 0.175403 Loss2: 1.471773
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.157553 Loss1: 1.237767 Loss2: 1.919786
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.079449 Loss1: 0.602425 Loss2: 1.477023
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.918323 Loss1: 0.443556 Loss2: 1.474768
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.829579 Loss1: 0.377486 Loss2: 1.452094
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.192056 Loss1: 1.293862 Loss2: 1.898194
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.172351 Loss1: 0.700206 Loss2: 1.472145 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.971152 Loss1: 0.501938 Loss2: 1.469214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.864623 Loss1: 0.420737 Loss2: 1.443885 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.752248 Loss1: 0.307662 Loss2: 1.444586 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.630979 Loss1: 0.210841 Loss2: 1.420138 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.565871 Loss1: 0.151820 Loss2: 1.414051 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.526619 Loss1: 0.123847 Loss2: 1.402772 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965820
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.173231 Loss1: 0.774721 Loss2: 1.398510 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630530 Loss1: 0.272034 Loss2: 1.358496 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.077567 Loss1: 1.114141 Loss2: 1.963426
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.602814 Loss1: 0.252557 Loss2: 1.350257
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.145815 Loss1: 0.667450 Loss2: 1.478365
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587427 Loss1: 0.239398 Loss2: 1.348028
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.904354 Loss1: 0.419854 Loss2: 1.484499
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.561232 Loss1: 0.220245 Loss2: 1.340987
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.745541 Loss1: 0.317713 Loss2: 1.427828
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.504522 Loss1: 0.162338 Loss2: 1.342184
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.679140 Loss1: 0.244373 Loss2: 1.434767
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.492793 Loss1: 0.154971 Loss2: 1.337822
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.623000 Loss1: 0.191200 Loss2: 1.431801
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463236 Loss1: 0.127304 Loss2: 1.335932
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.545787 Loss1: 0.126722 Loss2: 1.419065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.489867 Loss1: 0.081928 Loss2: 1.407940 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.137329 Loss1: 0.699750 Loss2: 1.437579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.703290 Loss1: 0.297723 Loss2: 1.405567 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.644496 Loss1: 0.251175 Loss2: 1.393321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.580371 Loss1: 0.192058 Loss2: 1.388314 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.609790 Loss1: 0.213225 Loss2: 1.396565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.658692 Loss1: 0.248698 Loss2: 1.409994 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.585467 Loss1: 0.198134 Loss2: 1.387332 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.622710 Loss1: 0.224746 Loss2: 1.397964 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.633398 Loss1: 0.173249 Loss2: 1.460150 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.940625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.157859 Loss1: 1.234136 Loss2: 1.923723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.913208 Loss1: 0.427636 Loss2: 1.485572 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.849917 Loss1: 0.375013 Loss2: 1.474904
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.993127 Loss1: 1.224829 Loss2: 1.768299
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.753764 Loss1: 0.276298 Loss2: 1.477466
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.226034 Loss1: 0.857414 Loss2: 1.368620
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.713606 Loss1: 0.250796 Loss2: 1.462810
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.895768 Loss1: 0.552768 Loss2: 1.343000
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.655585 Loss1: 0.187427 Loss2: 1.468158
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.653185 Loss1: 0.329624 Loss2: 1.323561
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.615961 Loss1: 0.154127 Loss2: 1.461834
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541193 Loss1: 0.225793 Loss2: 1.315400
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.587271 Loss1: 0.136794 Loss2: 1.450477
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512962 Loss1: 0.202716 Loss2: 1.310246
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.589711 Loss1: 0.134561 Loss2: 1.455151
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458116 Loss1: 0.149490 Loss2: 1.308626
-(DefaultActor pid=3765) >> Training accuracy: 0.972656
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440557 Loss1: 0.134394 Loss2: 1.306163
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402989 Loss1: 0.104414 Loss2: 1.298575
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421754 Loss1: 0.125809 Loss2: 1.295946
-(DefaultActor pid=3764) >> Training accuracy: 0.973633
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.203796 Loss1: 1.329315 Loss2: 1.874481
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.177711 Loss1: 0.789471 Loss2: 1.388240
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.894464 Loss1: 0.477265 Loss2: 1.417199
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.725351 Loss1: 0.342304 Loss2: 1.383047
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.295854 Loss1: 1.309218 Loss2: 1.986636
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.224520 Loss1: 0.715360 Loss2: 1.509160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.962495 Loss1: 0.431250 Loss2: 1.531245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.874356 Loss1: 0.391288 Loss2: 1.483068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.487042 Loss1: 0.131215 Loss2: 1.355827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.464700 Loss1: 0.115149 Loss2: 1.349551 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.648964 Loss1: 0.173591 Loss2: 1.475372 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.589115 Loss1: 0.131206 Loss2: 1.457909 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.059849 Loss1: 0.632493 Loss2: 1.427356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.724765 Loss1: 0.333461 Loss2: 1.391303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.123167 Loss1: 1.215905 Loss2: 1.907262
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.640975 Loss1: 0.229810 Loss2: 1.411165
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.206743 Loss1: 0.778706 Loss2: 1.428037
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.581665 Loss1: 0.190811 Loss2: 1.390854
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.541525 Loss1: 0.155540 Loss2: 1.385985 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.514993 Loss1: 0.133625 Loss2: 1.381367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489330 Loss1: 0.117245 Loss2: 1.372085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.513882 Loss1: 0.139268 Loss2: 1.374614 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965820
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540898 Loss1: 0.136324 Loss2: 1.404574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.506029 Loss1: 0.116296 Loss2: 1.389733 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.069260 Loss1: 1.263759 Loss2: 1.805502
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.160087 Loss1: 0.786631 Loss2: 1.373455
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.916280 Loss1: 0.513766 Loss2: 1.402514
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.785663 Loss1: 0.429163 Loss2: 1.356500
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.348864 Loss1: 1.461382 Loss2: 1.887482
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.331537 Loss1: 0.896383 Loss2: 1.435155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.958120 Loss1: 0.526147 Loss2: 1.431973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.510902 Loss1: 0.169578 Loss2: 1.341323
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.826272 Loss1: 0.435995 Loss2: 1.390277
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.520741 Loss1: 0.196033 Loss2: 1.324708
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.760087 Loss1: 0.359814 Loss2: 1.400272
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.696551 Loss1: 0.315311 Loss2: 1.381241
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460735 Loss1: 0.135088 Loss2: 1.325647
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.637852 Loss1: 0.247836 Loss2: 1.390016
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477136 Loss1: 0.155735 Loss2: 1.321401
-(DefaultActor pid=3765) >> Training accuracy: 0.962500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.498492 Loss1: 0.133970 Loss2: 1.364523 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978795
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.196869 Loss1: 1.259122 Loss2: 1.937747 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.950332 Loss1: 0.497385 Loss2: 1.452947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.114759 Loss1: 1.175866 Loss2: 1.938892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.622281 Loss1: 0.221738 Loss2: 1.400542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.571235 Loss1: 0.176848 Loss2: 1.394387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.552579 Loss1: 0.164093 Loss2: 1.388486 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.570334 Loss1: 0.183728 Loss2: 1.386606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.504410 Loss1: 0.109175 Loss2: 1.395235 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.506399 Loss1: 0.087789 Loss2: 1.418610 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485151 Loss1: 0.086337 Loss2: 1.398814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456152 Loss1: 0.054503 Loss2: 1.401649
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.166074 Loss1: 1.350856 Loss2: 1.815217
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.220609 Loss1: 0.831450 Loss2: 1.389159
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.886095 Loss1: 0.480389 Loss2: 1.405706
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.732977 Loss1: 0.371576 Loss2: 1.361400
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.666089 Loss1: 0.282325 Loss2: 1.383764
-DEBUG flwr 2023-10-10 13:09:58,999 | server.py:236 | fit_round 77 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.059720 Loss1: 1.244199 Loss2: 1.815522
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.161642 Loss1: 0.787366 Loss2: 1.374276 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.919108 Loss1: 0.512369 Loss2: 1.406739 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.752295 Loss1: 0.399108 Loss2: 1.353188 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.620538 Loss1: 0.266206 Loss2: 1.354332 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.589086 Loss1: 0.236214 Loss2: 1.352872 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.497374 Loss1: 0.155520 Loss2: 1.341854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433297 Loss1: 0.103940 Loss2: 1.329357 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.200055 Loss1: 0.691550 Loss2: 1.508505 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.828379 Loss1: 0.349955 Loss2: 1.478424 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.701860 Loss1: 0.242109 Loss2: 1.459751
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.121098 Loss1: 1.235116 Loss2: 1.885982
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.658956 Loss1: 0.205255 Loss2: 1.453702
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.110851 Loss1: 0.703924 Loss2: 1.406927
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.643943 Loss1: 0.187428 Loss2: 1.456516
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.826253 Loss1: 0.416580 Loss2: 1.409674
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.656674 Loss1: 0.278418 Loss2: 1.378256
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.600894 Loss1: 0.152776 Loss2: 1.448118
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.594114 Loss1: 0.219382 Loss2: 1.374732
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.581192 Loss1: 0.136541 Loss2: 1.444652
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478377 Loss1: 0.124417 Loss2: 1.353960
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.577371 Loss1: 0.143899 Loss2: 1.433472
-(DefaultActor pid=3765) >> Training accuracy: 0.969727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510068 Loss1: 0.149162 Loss2: 1.360905 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444621 Loss1: 0.101759 Loss2: 1.342862 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 13:09:58,999][flwr][DEBUG] - fit_round 77 received 50 results and 0 failures
-INFO flwr 2023-10-10 13:10:39,620 | server.py:125 | fit progress: (77, 2.267773052374014, {'accuracy': 0.5416}, 177547.39856439602)
->> Test accuracy: 0.541600
-[2023-10-10 13:10:39,620][flwr][INFO] - fit progress: (77, 2.267773052374014, {'accuracy': 0.5416}, 177547.39856439602)
-DEBUG flwr 2023-10-10 13:10:39,620 | server.py:173 | evaluate_round 77: strategy sampled 50 clients (out of 50)
-[2023-10-10 13:10:39,620][flwr][DEBUG] - evaluate_round 77: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 13:19:47,750 | server.py:187 | evaluate_round 77 received 50 results and 0 failures
-[2023-10-10 13:19:47,750][flwr][DEBUG] - evaluate_round 77 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 13:19:47,750 | server.py:222 | fit_round 78: strategy sampled 50 clients (out of 50)
-[2023-10-10 13:19:47,750][flwr][DEBUG] - fit_round 78: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.300586 Loss1: 1.271876 Loss2: 2.028709
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.412655 Loss1: 0.856167 Loss2: 1.556488
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.089703 Loss1: 0.536414 Loss2: 1.553289
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.879215 Loss1: 0.349326 Loss2: 1.529889
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.090203 Loss1: 1.219533 Loss2: 1.870670
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.189735 Loss1: 0.755497 Loss2: 1.434239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.955192 Loss1: 0.502994 Loss2: 1.452198 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.809613 Loss1: 0.372615 Loss2: 1.436998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.698397 Loss1: 0.271234 Loss2: 1.427163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.631018 Loss1: 0.215289 Loss2: 1.415729 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.577164 Loss1: 0.175396 Loss2: 1.401768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519809 Loss1: 0.113236 Loss2: 1.406574 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967773
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.168656 Loss1: 1.280908 Loss2: 1.887748 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.936692 Loss1: 0.474967 Loss2: 1.461724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.192932 Loss1: 1.277126 Loss2: 1.915806 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.195435 Loss1: 0.734892 Loss2: 1.460543 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.960162 Loss1: 0.486900 Loss2: 1.473261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.802194 Loss1: 0.363597 Loss2: 1.438597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.672704 Loss1: 0.238531 Loss2: 1.434174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.639623 Loss1: 0.214156 Loss2: 1.425466 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.631958 Loss1: 0.208082 Loss2: 1.423876 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491446 Loss1: 0.082330 Loss2: 1.409116 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.163690 Loss1: 0.737831 Loss2: 1.425859 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765267 Loss1: 0.366198 Loss2: 1.399069 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.644758 Loss1: 0.252415 Loss2: 1.392343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.578851 Loss1: 0.203799 Loss2: 1.375051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539204 Loss1: 0.159454 Loss2: 1.379750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.553491 Loss1: 0.183152 Loss2: 1.370338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.516282 Loss1: 0.141919 Loss2: 1.374364 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.486582 Loss1: 0.116279 Loss2: 1.370302 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.627032 Loss1: 0.258952 Loss2: 1.368080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.493038 Loss1: 0.142522 Loss2: 1.350517 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.249043 Loss1: 0.741259 Loss2: 1.507784 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.810309 Loss1: 0.344152 Loss2: 1.466157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.074153 Loss1: 1.179013 Loss2: 1.895141
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.709318 Loss1: 0.229329 Loss2: 1.479989
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.328874 Loss1: 0.848397 Loss2: 1.480477
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.654899 Loss1: 0.205406 Loss2: 1.449492
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.017027 Loss1: 0.526377 Loss2: 1.490649
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.639067 Loss1: 0.181471 Loss2: 1.457596
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.651182 Loss1: 0.201444 Loss2: 1.449738
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.784434 Loss1: 0.337304 Loss2: 1.447130
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.629215 Loss1: 0.179146 Loss2: 1.450069
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.709498 Loss1: 0.259218 Loss2: 1.450280
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.595962 Loss1: 0.141460 Loss2: 1.454501
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.639796 Loss1: 0.202720 Loss2: 1.437076
-(DefaultActor pid=3765) >> Training accuracy: 0.950000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.598405 Loss1: 0.166482 Loss2: 1.431923
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.592707 Loss1: 0.167892 Loss2: 1.424815
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.549693 Loss1: 0.122137 Loss2: 1.427557
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.524513 Loss1: 0.106275 Loss2: 1.418238
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.143179 Loss1: 1.248877 Loss2: 1.894302
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.232078 Loss1: 0.769997 Loss2: 1.462081
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.933498 Loss1: 0.468966 Loss2: 1.464532
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.740944 Loss1: 0.321599 Loss2: 1.419346
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.652850 Loss1: 0.233651 Loss2: 1.419199
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.047687 Loss1: 1.177320 Loss2: 1.870367
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.610016 Loss1: 0.205755 Loss2: 1.404260
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.588586 Loss1: 0.177505 Loss2: 1.411081 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.611684 Loss1: 0.206109 Loss2: 1.405575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.551467 Loss1: 0.156947 Loss2: 1.394520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.620108 Loss1: 0.210581 Loss2: 1.409527 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.496818 Loss1: 0.150606 Loss2: 1.346212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428761 Loss1: 0.094182 Loss2: 1.334579 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450584 Loss1: 0.120424 Loss2: 1.330160
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.011446 Loss1: 1.147552 Loss2: 1.863895
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.113627 Loss1: 0.720868 Loss2: 1.392760
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.969550 Loss1: 0.512679 Loss2: 1.456871
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.729380 Loss1: 0.353477 Loss2: 1.375903
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.641131 Loss1: 0.244531 Loss2: 1.396600
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.238699 Loss1: 1.311513 Loss2: 1.927185
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569742 Loss1: 0.199988 Loss2: 1.369754
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.548354 Loss1: 0.186518 Loss2: 1.361836 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.542966 Loss1: 0.180549 Loss2: 1.362417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.675800 Loss1: 0.279900 Loss2: 1.395900 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.611413 Loss1: 0.203740 Loss2: 1.407673 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.528345 Loss1: 0.145057 Loss2: 1.383289 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.514566 Loss1: 0.134466 Loss2: 1.380101 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972356
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.910667 Loss1: 1.078220 Loss2: 1.832447
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.251743 Loss1: 0.797197 Loss2: 1.454546
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.919849 Loss1: 0.496049 Loss2: 1.423800
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.816100 Loss1: 0.382009 Loss2: 1.434091
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.018408 Loss1: 1.197883 Loss2: 1.820525
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.037280 Loss1: 0.672676 Loss2: 1.364604
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.692159 Loss1: 0.288740 Loss2: 1.403420
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.766282 Loss1: 0.383637 Loss2: 1.382644
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.647148 Loss1: 0.250752 Loss2: 1.396396
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.668493 Loss1: 0.320465 Loss2: 1.348028
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.599904 Loss1: 0.205861 Loss2: 1.394043
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.605626 Loss1: 0.248955 Loss2: 1.356671
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.558069 Loss1: 0.167470 Loss2: 1.390600
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.514295 Loss1: 0.130897 Loss2: 1.383398 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.536407 Loss1: 0.154583 Loss2: 1.381824 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966797
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.496274 Loss1: 0.155943 Loss2: 1.340330 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.181916 Loss1: 1.303401 Loss2: 1.878515 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.997757 Loss1: 0.558944 Loss2: 1.438813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.844684 Loss1: 0.431591 Loss2: 1.413094
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.110483 Loss1: 1.198237 Loss2: 1.912245
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.676597 Loss1: 0.282587 Loss2: 1.394010
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.228142 Loss1: 0.767571 Loss2: 1.460572
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.649711 Loss1: 0.256823 Loss2: 1.392889
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.992280 Loss1: 0.564707 Loss2: 1.427573
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.577048 Loss1: 0.186808 Loss2: 1.390240
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.767975 Loss1: 0.354890 Loss2: 1.413085
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.530486 Loss1: 0.151697 Loss2: 1.378789
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.690168 Loss1: 0.288062 Loss2: 1.402106
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.508020 Loss1: 0.135333 Loss2: 1.372687
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.569067 Loss1: 0.189886 Loss2: 1.379181
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.519466 Loss1: 0.142806 Loss2: 1.376660
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.511527 Loss1: 0.133462 Loss2: 1.378065
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.522188 Loss1: 0.148757 Loss2: 1.373431
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454027 Loss1: 0.082142 Loss2: 1.371886
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445362 Loss1: 0.083851 Loss2: 1.361511
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.100594 Loss1: 1.235466 Loss2: 1.865128
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.108395 Loss1: 0.706635 Loss2: 1.401760
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.901520 Loss1: 0.473670 Loss2: 1.427850
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.758757 Loss1: 0.362140 Loss2: 1.396617
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.986304 Loss1: 1.163085 Loss2: 1.823219
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.139955 Loss1: 0.708718 Loss2: 1.431237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.918050 Loss1: 0.507047 Loss2: 1.411003 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.762274 Loss1: 0.366529 Loss2: 1.395746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.645418 Loss1: 0.259746 Loss2: 1.385673 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.596308 Loss1: 0.216573 Loss2: 1.379735 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500046 Loss1: 0.139550 Loss2: 1.360496 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.498390 Loss1: 0.130189 Loss2: 1.368201 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958008
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.274377 Loss1: 0.858051 Loss2: 1.416326 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.703245 Loss1: 0.333050 Loss2: 1.370194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.633094 Loss1: 0.281864 Loss2: 1.351231
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.314157 Loss1: 1.446385 Loss2: 1.867772
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542227 Loss1: 0.193612 Loss2: 1.348615
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.337381 Loss1: 0.875330 Loss2: 1.462050
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.537505 Loss1: 0.195236 Loss2: 1.342269
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.914812 Loss1: 0.476074 Loss2: 1.438738
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.508520 Loss1: 0.175741 Loss2: 1.332778
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.776385 Loss1: 0.370179 Loss2: 1.406207
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456311 Loss1: 0.120320 Loss2: 1.335990
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.738550 Loss1: 0.321934 Loss2: 1.416616
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.468981 Loss1: 0.142012 Loss2: 1.326969
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.620900 Loss1: 0.228004 Loss2: 1.392896
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545223 Loss1: 0.155954 Loss2: 1.389269
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.513467 Loss1: 0.138811 Loss2: 1.374656
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.495505 Loss1: 0.124675 Loss2: 1.370830
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.532748 Loss1: 0.160429 Loss2: 1.372319
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.298274 Loss1: 1.326366 Loss2: 1.971908
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.232307 Loss1: 0.841351 Loss2: 1.390956
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.932416 Loss1: 0.490361 Loss2: 1.442055
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.742649 Loss1: 0.336452 Loss2: 1.406197
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.706968 Loss1: 0.333817 Loss2: 1.373151
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.620979 Loss1: 0.221353 Loss2: 1.399627
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.178141 Loss1: 1.299535 Loss2: 1.878606
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.603137 Loss1: 0.216520 Loss2: 1.386617
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.835410 Loss1: 0.401986 Loss2: 1.433425 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474587 Loss1: 0.113191 Loss2: 1.361396 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981771
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.637001 Loss1: 0.243549 Loss2: 1.393453 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.525352 Loss1: 0.147593 Loss2: 1.377759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.509713 Loss1: 1.484152 Loss2: 2.025561
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.514602 Loss1: 0.139470 Loss2: 1.375132
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.361935 Loss1: 0.839916 Loss2: 1.522019
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.492281 Loss1: 0.116730 Loss2: 1.375551
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.879743 Loss1: 0.390632 Loss2: 1.489111 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.655101 Loss1: 0.181125 Loss2: 1.473977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.190153 Loss1: 1.224517 Loss2: 1.965636 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.229585 Loss1: 0.754984 Loss2: 1.474602 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.060688 Loss1: 0.545114 Loss2: 1.515574 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.695105 Loss1: 0.234137 Loss2: 1.460968 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.630588 Loss1: 0.182681 Loss2: 1.447907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.600328 Loss1: 0.159222 Loss2: 1.441106
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.996273 Loss1: 1.157768 Loss2: 1.838505
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.019837 Loss1: 0.654067 Loss2: 1.365770 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.601122 Loss1: 0.158751 Loss2: 1.442371
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.802890 Loss1: 0.415434 Loss2: 1.387457
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.655073 Loss1: 0.311284 Loss2: 1.343789
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567810 Loss1: 0.226243 Loss2: 1.341567
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458617 Loss1: 0.135779 Loss2: 1.322838
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418585 Loss1: 0.100292 Loss2: 1.318294
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.200073 Loss1: 1.345434 Loss2: 1.854640
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444681 Loss1: 0.130521 Loss2: 1.314160
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.334680 Loss1: 0.909451 Loss2: 1.425230
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438154 Loss1: 0.120642 Loss2: 1.317512
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.987664 Loss1: 0.557969 Loss2: 1.429695
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434519 Loss1: 0.118380 Loss2: 1.316139
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.643931 Loss1: 0.253275 Loss2: 1.390656 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.605870 Loss1: 0.250707 Loss2: 1.355164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.594484 Loss1: 0.223155 Loss2: 1.371329
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.260056 Loss1: 1.313181 Loss2: 1.946875
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.315122 Loss1: 0.802346 Loss2: 1.512775 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509035 Loss1: 0.143917 Loss2: 1.365118
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.022416 Loss1: 0.512892 Loss2: 1.509524
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.831113 Loss1: 0.352778 Loss2: 1.478335
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.806805 Loss1: 0.317505 Loss2: 1.489300
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.760872 Loss1: 0.283438 Loss2: 1.477434
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.710128 Loss1: 0.237418 Loss2: 1.472710
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.289099 Loss1: 1.336767 Loss2: 1.952332
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.726755 Loss1: 0.256842 Loss2: 1.469912
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.737406 Loss1: 0.257507 Loss2: 1.479900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.733252 Loss1: 0.253361 Loss2: 1.479891 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.939583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.748987 Loss1: 0.301918 Loss2: 1.447069 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.600956 Loss1: 0.191747 Loss2: 1.409209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.125798 Loss1: 1.194785 Loss2: 1.931013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.511928 Loss1: 0.117283 Loss2: 1.394645 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.788219 Loss1: 0.315432 Loss2: 1.472787 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.755923 Loss1: 0.297564 Loss2: 1.458360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.095754 Loss1: 1.199226 Loss2: 1.896528
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.721699 Loss1: 0.248516 Loss2: 1.473183
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.159990 Loss1: 0.715145 Loss2: 1.444845
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.628458 Loss1: 0.168522 Loss2: 1.459935
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.862518 Loss1: 0.440813 Loss2: 1.421705
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.587573 Loss1: 0.136365 Loss2: 1.451209
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.591749 Loss1: 0.155083 Loss2: 1.436667 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.570822 Loss1: 0.193364 Loss2: 1.377458 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540026 Loss1: 0.162672 Loss2: 1.377354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.513387 Loss1: 0.142654 Loss2: 1.370733
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.226433 Loss1: 1.289967 Loss2: 1.936466
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.224263 Loss1: 0.752077 Loss2: 1.472186 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.833090 Loss1: 0.383616 Loss2: 1.449474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.743433 Loss1: 0.288400 Loss2: 1.455033 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.712316 Loss1: 0.263498 Loss2: 1.448818 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.681009 Loss1: 0.231730 Loss2: 1.449280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.604807 Loss1: 0.162115 Loss2: 1.442692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.593118 Loss1: 0.163250 Loss2: 1.429868 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.565245 Loss1: 0.153853 Loss2: 1.411392 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548123 Loss1: 0.138738 Loss2: 1.409385 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.084840 Loss1: 1.227045 Loss2: 1.857795 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.930916 Loss1: 0.489161 Loss2: 1.441754 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.639513 Loss1: 0.235502 Loss2: 1.404011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.584516 Loss1: 0.190337 Loss2: 1.394179
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.101348 Loss1: 1.252223 Loss2: 1.849126
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.602783 Loss1: 0.210760 Loss2: 1.392023
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.104681 Loss1: 0.682604 Loss2: 1.422077
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.563066 Loss1: 0.168904 Loss2: 1.394162
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.826176 Loss1: 0.414629 Loss2: 1.411547
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.530077 Loss1: 0.143646 Loss2: 1.386431
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.736709 Loss1: 0.340970 Loss2: 1.395738
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.576236 Loss1: 0.189028 Loss2: 1.387208
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.614965 Loss1: 0.218348 Loss2: 1.396617
-(DefaultActor pid=3765) >> Training accuracy: 0.969727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546979 Loss1: 0.163635 Loss2: 1.383344
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.559965 Loss1: 0.180929 Loss2: 1.379036
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515700 Loss1: 0.131816 Loss2: 1.383884
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.545718 Loss1: 0.163364 Loss2: 1.382354
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.094869 Loss1: 1.183994 Loss2: 1.910875
-(DefaultActor pid=3764) >> Training accuracy: 0.970703
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.344652 Loss1: 0.882077 Loss2: 1.462575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.794586 Loss1: 0.359031 Loss2: 1.435555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.645815 Loss1: 0.222274 Loss2: 1.423541 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.615064 Loss1: 0.195411 Loss2: 1.419654 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.571508 Loss1: 0.148361 Loss2: 1.423147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.513846 Loss1: 0.107716 Loss2: 1.406130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.511367 Loss1: 0.111519 Loss2: 1.399848 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.585780 Loss1: 0.210287 Loss2: 1.375493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501627 Loss1: 0.141935 Loss2: 1.359692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.921831 Loss1: 1.110852 Loss2: 1.810979 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.050841 Loss1: 0.650952 Loss2: 1.399889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.648626 Loss1: 0.282167 Loss2: 1.366458 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.180563 Loss1: 1.267061 Loss2: 1.913502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.324611 Loss1: 0.830186 Loss2: 1.494426 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.878664 Loss1: 0.423551 Loss2: 1.455113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.760330 Loss1: 0.334266 Loss2: 1.426064 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.796754 Loss1: 0.358523 Loss2: 1.438231 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.974265
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474754 Loss1: 0.123099 Loss2: 1.351655
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.721861 Loss1: 0.279809 Loss2: 1.442052
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.649178 Loss1: 0.218664 Loss2: 1.430514
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.617243 Loss1: 0.196059 Loss2: 1.421183
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.597955 Loss1: 0.175309 Loss2: 1.422645
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.581236 Loss1: 0.162255 Loss2: 1.418981
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.966407 Loss1: 1.139082 Loss2: 1.827325
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.042314 Loss1: 0.620904 Loss2: 1.421410
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.849353 Loss1: 0.444603 Loss2: 1.404750
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.675354 Loss1: 0.291927 Loss2: 1.383427
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.976734 Loss1: 1.127934 Loss2: 1.848800
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.671671 Loss1: 0.293676 Loss2: 1.377995
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.180225 Loss1: 0.757099 Loss2: 1.423126
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.589783 Loss1: 0.217803 Loss2: 1.371979
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.917795 Loss1: 0.502013 Loss2: 1.415782
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534764 Loss1: 0.174655 Loss2: 1.360109
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.788818 Loss1: 0.398436 Loss2: 1.390382
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.541860 Loss1: 0.171350 Loss2: 1.370510
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.680749 Loss1: 0.292324 Loss2: 1.388425
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.579870 Loss1: 0.204435 Loss2: 1.375435
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.682494 Loss1: 0.311193 Loss2: 1.371301
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.538970 Loss1: 0.172807 Loss2: 1.366164
-(DefaultActor pid=3765) >> Training accuracy: 0.969727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.573464 Loss1: 0.202767 Loss2: 1.370697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.501205 Loss1: 0.144123 Loss2: 1.357082 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.224099 Loss1: 0.799973 Loss2: 1.424126 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.754027 Loss1: 0.364172 Loss2: 1.389855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.640820 Loss1: 0.247155 Loss2: 1.393665
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.011886 Loss1: 1.133790 Loss2: 1.878096
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.586623 Loss1: 0.196503 Loss2: 1.390119
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.218519 Loss1: 0.793537 Loss2: 1.424982
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544331 Loss1: 0.167067 Loss2: 1.377265
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.922011 Loss1: 0.473041 Loss2: 1.448970
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.502964 Loss1: 0.124086 Loss2: 1.378878
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.711705 Loss1: 0.300749 Loss2: 1.410956
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472808 Loss1: 0.103579 Loss2: 1.369230
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.648543 Loss1: 0.252689 Loss2: 1.395854
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442022 Loss1: 0.075340 Loss2: 1.366682
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.599862 Loss1: 0.214863 Loss2: 1.384999
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.579943 Loss1: 0.189311 Loss2: 1.390631
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.555755 Loss1: 0.176379 Loss2: 1.379376
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.520384 Loss1: 0.136763 Loss2: 1.383620
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512427 Loss1: 0.133732 Loss2: 1.378695
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.139158 Loss1: 1.171376 Loss2: 1.967782
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.179976 Loss1: 0.711853 Loss2: 1.468123
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.932692 Loss1: 0.462148 Loss2: 1.470544
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.731607 Loss1: 0.287639 Loss2: 1.443969
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.663133 Loss1: 0.228402 Loss2: 1.434730
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.654450 Loss1: 0.222749 Loss2: 1.431701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.577986 Loss1: 0.153114 Loss2: 1.424872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.551705 Loss1: 0.127637 Loss2: 1.424067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.540628 Loss1: 0.124571 Loss2: 1.416057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.586466 Loss1: 0.167038 Loss2: 1.419428 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.550727 Loss1: 0.160139 Loss2: 1.390588 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.543872 Loss1: 0.158619 Loss2: 1.385253 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.308659 Loss1: 0.848946 Loss2: 1.459713 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.779334 Loss1: 0.379253 Loss2: 1.400081 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.674160 Loss1: 0.264113 Loss2: 1.410047
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.985019 Loss1: 1.142989 Loss2: 1.842030
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.646865 Loss1: 0.249538 Loss2: 1.397328
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.233713 Loss1: 0.831915 Loss2: 1.401798
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.602860 Loss1: 0.204739 Loss2: 1.398121
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.881140 Loss1: 0.472227 Loss2: 1.408913
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.546389 Loss1: 0.161499 Loss2: 1.384890
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.736346 Loss1: 0.371936 Loss2: 1.364409
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.533442 Loss1: 0.157891 Loss2: 1.375551
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.628603 Loss1: 0.260589 Loss2: 1.368014
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.482979 Loss1: 0.102391 Loss2: 1.380588
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.572169 Loss1: 0.220717 Loss2: 1.351452
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-10 13:48:09,796 | server.py:236 | fit_round 78 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.546313 Loss1: 0.188297 Loss2: 1.358016
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487761 Loss1: 0.143063 Loss2: 1.344697
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.530259 Loss1: 0.189231 Loss2: 1.341027
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.510350 Loss1: 0.157952 Loss2: 1.352398
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.437240 Loss1: 1.439727 Loss2: 1.997512
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.380831 Loss1: 0.901798 Loss2: 1.479033
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.972988 Loss1: 0.472815 Loss2: 1.500173
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.782095 Loss1: 0.349412 Loss2: 1.432684
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.740749 Loss1: 0.295803 Loss2: 1.444946
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.143223 Loss1: 1.227498 Loss2: 1.915725
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.073571 Loss1: 0.702693 Loss2: 1.370878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.849411 Loss1: 0.446884 Loss2: 1.402526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.709958 Loss1: 0.356448 Loss2: 1.353510 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.528952 Loss1: 0.115791 Loss2: 1.413161
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.632124 Loss1: 0.281069 Loss2: 1.351055
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549662 Loss1: 0.195186 Loss2: 1.354477
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508757 Loss1: 0.099953 Loss2: 1.408804
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491137 Loss1: 0.148048 Loss2: 1.343089 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445026 Loss1: 0.116442 Loss2: 1.328583 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965144
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.022568 Loss1: 1.182580 Loss2: 1.839988
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.186047 Loss1: 0.827558 Loss2: 1.358489
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.907181 Loss1: 0.517916 Loss2: 1.389264
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.703409 Loss1: 0.330470 Loss2: 1.372939
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.349568 Loss1: 1.369483 Loss2: 1.980085
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.333295 Loss1: 0.783977 Loss2: 1.549318 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.994049 Loss1: 0.484119 Loss2: 1.509931 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.798678 Loss1: 0.317199 Loss2: 1.481479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.731107 Loss1: 0.250747 Loss2: 1.480361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.656877 Loss1: 0.181071 Loss2: 1.475806 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.622154 Loss1: 0.158799 Loss2: 1.463355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.631159 Loss1: 0.163584 Loss2: 1.467574 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 13:48:09,796][flwr][DEBUG] - fit_round 78 received 50 results and 0 failures
-INFO flwr 2023-10-10 13:48:52,019 | server.py:125 | fit progress: (78, 2.254478170848883, {'accuracy': 0.5425}, 179839.797407425)
->> Test accuracy: 0.542500
-[2023-10-10 13:48:52,019][flwr][INFO] - fit progress: (78, 2.254478170848883, {'accuracy': 0.5425}, 179839.797407425)
-DEBUG flwr 2023-10-10 13:48:52,019 | server.py:173 | evaluate_round 78: strategy sampled 50 clients (out of 50)
-[2023-10-10 13:48:52,019][flwr][DEBUG] - evaluate_round 78: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 13:57:58,819 | server.py:187 | evaluate_round 78 received 50 results and 0 failures
-[2023-10-10 13:57:58,819][flwr][DEBUG] - evaluate_round 78 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 13:57:58,820 | server.py:222 | fit_round 79: strategy sampled 50 clients (out of 50)
-[2023-10-10 13:57:58,820][flwr][DEBUG] - fit_round 79: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.256388 Loss1: 1.296245 Loss2: 1.960144 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.977891 Loss1: 0.468447 Loss2: 1.509444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.799019 Loss1: 0.329845 Loss2: 1.469174
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.286845 Loss1: 1.346178 Loss2: 1.940667
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.696106 Loss1: 0.227819 Loss2: 1.468287
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.404994 Loss1: 0.911298 Loss2: 1.493696
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.631036 Loss1: 0.190660 Loss2: 1.440377
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.159205 Loss1: 0.660438 Loss2: 1.498767
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.636690 Loss1: 0.196228 Loss2: 1.440462
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.861740 Loss1: 0.384612 Loss2: 1.477129
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.605292 Loss1: 0.159455 Loss2: 1.445838
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.753297 Loss1: 0.303588 Loss2: 1.449709
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.604056 Loss1: 0.163869 Loss2: 1.440187
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.628568 Loss1: 0.175398 Loss2: 1.453170
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.586021 Loss1: 0.154950 Loss2: 1.431071
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.594746 Loss1: 0.163848 Loss2: 1.430898
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.593199 Loss1: 0.163167 Loss2: 1.430032
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.561438 Loss1: 0.128067 Loss2: 1.433371
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.575731 Loss1: 0.143592 Loss2: 1.432138
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.131888 Loss1: 1.251924 Loss2: 1.879964
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.206717 Loss1: 0.783469 Loss2: 1.423247
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.903096 Loss1: 0.439437 Loss2: 1.463659
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.721991 Loss1: 0.320723 Loss2: 1.401269
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.168624 Loss1: 1.258908 Loss2: 1.909716
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.188590 Loss1: 0.737620 Loss2: 1.450971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.886341 Loss1: 0.430343 Loss2: 1.455998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.665415 Loss1: 0.260833 Loss2: 1.404583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.627325 Loss1: 0.223594 Loss2: 1.403730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.655463 Loss1: 0.246090 Loss2: 1.409373 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.524294 Loss1: 0.138120 Loss2: 1.386174
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.601108 Loss1: 0.191810 Loss2: 1.409298
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.593690 Loss1: 0.181320 Loss2: 1.412370
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.564265 Loss1: 0.160736 Loss2: 1.403529
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.534209 Loss1: 0.134611 Loss2: 1.399598
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.022386 Loss1: 1.175336 Loss2: 1.847050
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.073572 Loss1: 0.688970 Loss2: 1.384602
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.807230 Loss1: 0.447269 Loss2: 1.359961
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.696951 Loss1: 0.337796 Loss2: 1.359156
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.822271 Loss1: 1.012691 Loss2: 1.809580
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.985945 Loss1: 0.620289 Loss2: 1.365656 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.757614 Loss1: 0.378715 Loss2: 1.378899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.668430 Loss1: 0.322760 Loss2: 1.345670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.690123 Loss1: 0.332990 Loss2: 1.357133 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.616625 Loss1: 0.269974 Loss2: 1.346651 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.552061 Loss1: 0.212260 Loss2: 1.339800 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499328 Loss1: 0.161630 Loss2: 1.337697 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.170164 Loss1: 1.204499 Loss2: 1.965665 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.982016 Loss1: 0.483216 Loss2: 1.498800 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.877420 Loss1: 0.987753 Loss2: 1.889667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.635862 Loss1: 0.195242 Loss2: 1.440620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.693198 Loss1: 0.246049 Loss2: 1.447150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.683973 Loss1: 0.239192 Loss2: 1.444781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.654164 Loss1: 0.193926 Loss2: 1.460238 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.584467 Loss1: 0.140928 Loss2: 1.443539 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.540304 Loss1: 0.147418 Loss2: 1.392886 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.552940 Loss1: 0.159107 Loss2: 1.393833 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.523204 Loss1: 0.140092 Loss2: 1.383112
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.159612 Loss1: 1.201141 Loss2: 1.958471
-(DefaultActor pid=3764) >> Training accuracy: 0.974265
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.287422 Loss1: 0.796747 Loss2: 1.490675
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.022381 Loss1: 0.500232 Loss2: 1.522149
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.816027 Loss1: 0.343474 Loss2: 1.472553
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.708885 Loss1: 0.242379 Loss2: 1.466506
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.250908 Loss1: 1.304112 Loss2: 1.946796
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.598054 Loss1: 0.143261 Loss2: 1.454794
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.285220 Loss1: 0.753389 Loss2: 1.531832
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.632013 Loss1: 0.188360 Loss2: 1.443653
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.612827 Loss1: 0.166342 Loss2: 1.446485
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.137222 Loss1: 0.636334 Loss2: 1.500888
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.659720 Loss1: 0.206252 Loss2: 1.453467
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.964346 Loss1: 0.443924 Loss2: 1.520422
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.595262 Loss1: 0.152127 Loss2: 1.443135
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.710243 Loss1: 0.254501 Loss2: 1.455742
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.664842 Loss1: 0.202511 Loss2: 1.462332
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.667939 Loss1: 0.208141 Loss2: 1.459798
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.632431 Loss1: 0.175406 Loss2: 1.457024
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.612243 Loss1: 0.161323 Loss2: 1.450920
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.013908 Loss1: 1.119776 Loss2: 1.894132
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.579495 Loss1: 0.136044 Loss2: 1.443451
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.882189 Loss1: 0.434310 Loss2: 1.447879 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.691657 Loss1: 0.257031 Loss2: 1.434626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.668274 Loss1: 0.248998 Loss2: 1.419277
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.238256 Loss1: 1.316648 Loss2: 1.921609
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.344067 Loss1: 0.855190 Loss2: 1.488877
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.644719 Loss1: 0.220980 Loss2: 1.423739
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.057005 Loss1: 0.611701 Loss2: 1.445305
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.630568 Loss1: 0.204357 Loss2: 1.426211
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.902830 Loss1: 0.467527 Loss2: 1.435303
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.624096 Loss1: 0.205522 Loss2: 1.418574
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.799231 Loss1: 0.357469 Loss2: 1.441762
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.564438 Loss1: 0.154110 Loss2: 1.410328
-(DefaultActor pid=3765) >> Training accuracy: 0.954102
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.596163 Loss1: 0.182470 Loss2: 1.413693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.559099 Loss1: 0.156339 Loss2: 1.402760 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.536474 Loss1: 0.140485 Loss2: 1.395989
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.091382 Loss1: 1.288083 Loss2: 1.803299
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.321266 Loss1: 0.853578 Loss2: 1.467688
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.910798 Loss1: 0.530195 Loss2: 1.380604
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.791240 Loss1: 0.403167 Loss2: 1.388073
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.754162 Loss1: 0.370877 Loss2: 1.383285
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.212526 Loss1: 1.339638 Loss2: 1.872888
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.187544 Loss1: 0.763290 Loss2: 1.424254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.955159 Loss1: 0.518375 Loss2: 1.436784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.772547 Loss1: 0.374904 Loss2: 1.397643 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.539598 Loss1: 0.173611 Loss2: 1.365987
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.645266 Loss1: 0.243205 Loss2: 1.402061
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465941 Loss1: 0.111371 Loss2: 1.354569
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.581218 Loss1: 0.202280 Loss2: 1.378938
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.615329 Loss1: 0.240946 Loss2: 1.374383
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.571505 Loss1: 0.184526 Loss2: 1.386979
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.542721 Loss1: 0.167878 Loss2: 1.374842
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527725 Loss1: 0.153353 Loss2: 1.374372
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.060721 Loss1: 1.157405 Loss2: 1.903316
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.128994 Loss1: 0.707005 Loss2: 1.421988
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.938183 Loss1: 0.484935 Loss2: 1.453248
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765080 Loss1: 0.359410 Loss2: 1.405670
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.689686 Loss1: 0.272244 Loss2: 1.417443
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.622465 Loss1: 0.216158 Loss2: 1.406307 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544401 Loss1: 0.148819 Loss2: 1.395582 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.516336 Loss1: 0.127971 Loss2: 1.388365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489859 Loss1: 0.102188 Loss2: 1.387671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.473877 Loss1: 0.088401 Loss2: 1.385476 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.640801 Loss1: 0.247202 Loss2: 1.393599 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.515549 Loss1: 0.155551 Loss2: 1.359998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487364 Loss1: 0.117763 Loss2: 1.369601
-(DefaultActor pid=3764) >> Training accuracy: 0.956055
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.056462 Loss1: 1.031441 Loss2: 2.025021
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.250350 Loss1: 0.734493 Loss2: 1.515857
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.073499 Loss1: 0.503255 Loss2: 1.570244
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.918278 Loss1: 0.411565 Loss2: 1.506713
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.823234 Loss1: 0.299018 Loss2: 1.524216
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.966197 Loss1: 1.145642 Loss2: 1.820555
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.735902 Loss1: 0.235102 Loss2: 1.500800
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.202301 Loss1: 0.761942 Loss2: 1.440359
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.759782 Loss1: 0.255041 Loss2: 1.504740
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.833646 Loss1: 0.460084 Loss2: 1.373563
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.700506 Loss1: 0.201939 Loss2: 1.498567
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.667708 Loss1: 0.293298 Loss2: 1.374410
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.647969 Loss1: 0.160937 Loss2: 1.487032
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.628619 Loss1: 0.140299 Loss2: 1.488320
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.613912 Loss1: 0.247778 Loss2: 1.366134
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.631865 Loss1: 0.275393 Loss2: 1.356472
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.567740 Loss1: 0.196645 Loss2: 1.371095
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.536966 Loss1: 0.187092 Loss2: 1.349875
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.538553 Loss1: 0.187080 Loss2: 1.351473
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.152118 Loss1: 1.261596 Loss2: 1.890521
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464034 Loss1: 0.120509 Loss2: 1.343525
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.914971 Loss1: 0.451915 Loss2: 1.463056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.689668 Loss1: 0.265126 Loss2: 1.424542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562431 Loss1: 0.155393 Loss2: 1.407038
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.255846 Loss1: 1.286940 Loss2: 1.968905
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.377831 Loss1: 0.867180 Loss2: 1.510651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.133119 Loss1: 0.625032 Loss2: 1.508086 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.909775 Loss1: 0.430988 Loss2: 1.478786 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465901 Loss1: 0.084754 Loss2: 1.381147
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.806831 Loss1: 0.320097 Loss2: 1.486734
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.713725 Loss1: 0.238625 Loss2: 1.475100
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.647304 Loss1: 0.194435 Loss2: 1.452869
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.591003 Loss1: 0.142577 Loss2: 1.448426
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.559944 Loss1: 0.119194 Loss2: 1.440750
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.049173 Loss1: 1.209427 Loss2: 1.839746
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.540523 Loss1: 0.097209 Loss2: 1.443314
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.922509 Loss1: 0.493286 Loss2: 1.429224 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.676934 Loss1: 0.268584 Loss2: 1.408350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.575347 Loss1: 0.181967 Loss2: 1.393380
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.065088 Loss1: 1.134579 Loss2: 1.930508
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.555751 Loss1: 0.161506 Loss2: 1.394245
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.192401 Loss1: 0.695504 Loss2: 1.496898
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.530507 Loss1: 0.151355 Loss2: 1.379152
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.907716 Loss1: 0.391149 Loss2: 1.516567
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.540996 Loss1: 0.154961 Loss2: 1.386034
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.837609 Loss1: 0.362187 Loss2: 1.475422
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.535874 Loss1: 0.145105 Loss2: 1.390769
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.745256 Loss1: 0.256141 Loss2: 1.489115
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.673337 Loss1: 0.211638 Loss2: 1.461699
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.633713 Loss1: 0.166101 Loss2: 1.467612
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.618462 Loss1: 0.161504 Loss2: 1.456958
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.600089 Loss1: 0.139338 Loss2: 1.460751
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.084884 Loss1: 1.191712 Loss2: 1.893172
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.191100 Loss1: 0.742092 Loss2: 1.449007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.706613 Loss1: 0.295437 Loss2: 1.411176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569957 Loss1: 0.179011 Loss2: 1.390946 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.553201 Loss1: 0.159655 Loss2: 1.393547 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.552562 Loss1: 0.160921 Loss2: 1.391641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.503733 Loss1: 0.114457 Loss2: 1.389276 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477006 Loss1: 0.104253 Loss2: 1.372754 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.683591 Loss1: 0.227255 Loss2: 1.456336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.635222 Loss1: 0.181617 Loss2: 1.453604 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.556483 Loss1: 0.113618 Loss2: 1.442865
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.182077 Loss1: 1.279051 Loss2: 1.903026
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.099964 Loss1: 0.678797 Loss2: 1.421167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.736782 Loss1: 0.335668 Loss2: 1.401114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.603117 Loss1: 0.205736 Loss2: 1.397381 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547276 Loss1: 0.157524 Loss2: 1.389753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479448 Loss1: 0.104363 Loss2: 1.375085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.471133 Loss1: 0.099743 Loss2: 1.371390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475405 Loss1: 0.108600 Loss2: 1.366804 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.661890 Loss1: 0.222196 Loss2: 1.439694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.648613 Loss1: 0.211174 Loss2: 1.437440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.607755 Loss1: 0.166967 Loss2: 1.440789
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.125778 Loss1: 1.220042 Loss2: 1.905737
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.580002 Loss1: 0.151757 Loss2: 1.428245
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.225674 Loss1: 0.755483 Loss2: 1.470191
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.983985 Loss1: 0.512980 Loss2: 1.471005
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.802889 Loss1: 0.355518 Loss2: 1.447371
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.670902 Loss1: 0.239109 Loss2: 1.431793
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.644304 Loss1: 0.223543 Loss2: 1.420761
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.186478 Loss1: 1.292609 Loss2: 1.893869
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.588893 Loss1: 0.162161 Loss2: 1.426732
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.125847 Loss1: 0.703239 Loss2: 1.422608
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.617957 Loss1: 0.195964 Loss2: 1.421993
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.829906 Loss1: 0.406133 Loss2: 1.423773
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.602006 Loss1: 0.172035 Loss2: 1.429971
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.756657 Loss1: 0.358672 Loss2: 1.397985
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.591810 Loss1: 0.163067 Loss2: 1.428743
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.554558 Loss1: 0.176385 Loss2: 1.378173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.516128 Loss1: 0.148916 Loss2: 1.367213 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.512646 Loss1: 0.144550 Loss2: 1.368096
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.250215 Loss1: 1.263786 Loss2: 1.986429
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.304400 Loss1: 0.933793 Loss2: 1.370607
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.515126 Loss1: 0.145798 Loss2: 1.369327
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.005670 Loss1: 0.539492 Loss2: 1.466178
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.760903 Loss1: 0.380695 Loss2: 1.380207
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631228 Loss1: 0.256559 Loss2: 1.374669
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.645584 Loss1: 0.266308 Loss2: 1.379276
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.619928 Loss1: 0.244511 Loss2: 1.375417
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.538269 Loss1: 0.170454 Loss2: 1.367816
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.038948 Loss1: 1.124839 Loss2: 1.914109
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.150821 Loss1: 0.719815 Loss2: 1.431006 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.960938
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.763082 Loss1: 0.336798 Loss2: 1.426283 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.611345 Loss1: 0.204223 Loss2: 1.407122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.587115 Loss1: 0.179677 Loss2: 1.407438
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.113761 Loss1: 1.233878 Loss2: 1.879883
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.537497 Loss1: 0.139081 Loss2: 1.398415
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.184086 Loss1: 0.760315 Loss2: 1.423771
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501375 Loss1: 0.111678 Loss2: 1.389696
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.896742 Loss1: 0.448499 Loss2: 1.448243
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478541 Loss1: 0.087144 Loss2: 1.391397
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.822721 Loss1: 0.427135 Loss2: 1.395586
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.722600 Loss1: 0.312578 Loss2: 1.410022
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.609779 Loss1: 0.217775 Loss2: 1.392004
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547733 Loss1: 0.155473 Loss2: 1.392260
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.531746 Loss1: 0.160136 Loss2: 1.371610
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.558230 Loss1: 0.180050 Loss2: 1.378180
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.274241 Loss1: 1.275922 Loss2: 1.998319
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508685 Loss1: 0.129692 Loss2: 1.378993
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.345419 Loss1: 0.818508 Loss2: 1.526910
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.000828 Loss1: 0.481712 Loss2: 1.519115
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.791114 Loss1: 0.311505 Loss2: 1.479609
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.757516 Loss1: 0.268930 Loss2: 1.488586
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.663733 Loss1: 0.190111 Loss2: 1.473622
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.121390 Loss1: 1.248340 Loss2: 1.873051
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.649090 Loss1: 0.186170 Loss2: 1.462919
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.095160 Loss1: 0.729076 Loss2: 1.366084
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.615055 Loss1: 0.153581 Loss2: 1.461474
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.932289 Loss1: 0.527779 Loss2: 1.404509
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.592013 Loss1: 0.138891 Loss2: 1.453122
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.623038 Loss1: 0.171641 Loss2: 1.451397 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.646345 Loss1: 0.296700 Loss2: 1.349645 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.579079 Loss1: 0.224222 Loss2: 1.354857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.244717 Loss1: 1.391597 Loss2: 1.853120 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978795
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.870000 Loss1: 0.501202 Loss2: 1.368798 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.626799 Loss1: 0.279706 Loss2: 1.347093 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.057703 Loss1: 1.157995 Loss2: 1.899707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.215858 Loss1: 0.767292 Loss2: 1.448566 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.021451 Loss1: 0.535519 Loss2: 1.485933 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.758463 Loss1: 0.326152 Loss2: 1.432311 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973214
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.626236 Loss1: 0.207315 Loss2: 1.418921 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.660071 Loss1: 0.234412 Loss2: 1.425659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.628821 Loss1: 0.212237 Loss2: 1.416584
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.236516 Loss1: 1.306146 Loss2: 1.930370
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.583311 Loss1: 0.167221 Loss2: 1.416091
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.299724 Loss1: 0.812967 Loss2: 1.486757
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.959767 Loss1: 0.500193 Loss2: 1.459574
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.709514 Loss1: 0.295517 Loss2: 1.413997
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.635777 Loss1: 0.217985 Loss2: 1.417792
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.637623 Loss1: 0.232769 Loss2: 1.404854
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.969434 Loss1: 1.069323 Loss2: 1.900110
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.627085 Loss1: 0.214860 Loss2: 1.412225
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.067120 Loss1: 0.598013 Loss2: 1.469108
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.612185 Loss1: 0.199926 Loss2: 1.412260
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.815230 Loss1: 0.356966 Loss2: 1.458264
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.590302 Loss1: 0.182667 Loss2: 1.407635
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.550428 Loss1: 0.142992 Loss2: 1.407436
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.633517 Loss1: 0.202854 Loss2: 1.430663
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.616277 Loss1: 0.191107 Loss2: 1.425169
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.621534 Loss1: 0.202537 Loss2: 1.418997
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.614625 Loss1: 0.181964 Loss2: 1.432661
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.546310 Loss1: 0.130346 Loss2: 1.415964
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.316585 Loss1: 1.369377 Loss2: 1.947207
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.582606 Loss1: 0.163150 Loss2: 1.419457
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.554564 Loss1: 0.133683 Loss2: 1.420882 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699919 Loss1: 0.299536 Loss2: 1.400383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.577913 Loss1: 0.181011 Loss2: 1.396902 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.148694 Loss1: 1.238793 Loss2: 1.909900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.185614 Loss1: 0.744118 Loss2: 1.441497 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.550846 Loss1: 0.166735 Loss2: 1.384111 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977679
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.709752 Loss1: 0.279903 Loss2: 1.429849 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.687481 Loss1: 0.255125 Loss2: 1.432357 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.617254 Loss1: 0.193159 Loss2: 1.424096
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.052874 Loss1: 1.266479 Loss2: 1.786395
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.574876 Loss1: 0.162813 Loss2: 1.412062
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.232565 Loss1: 0.860821 Loss2: 1.371744
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.551154 Loss1: 0.141726 Loss2: 1.409428
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.902973 Loss1: 0.523327 Loss2: 1.379646
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.711203 Loss1: 0.371825 Loss2: 1.339378
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.617896 Loss1: 0.276541 Loss2: 1.341355
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.569558 Loss1: 0.242807 Loss2: 1.326751
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.512467 Loss1: 0.189250 Loss2: 1.323217
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439244 Loss1: 0.132969 Loss2: 1.306275
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.089782 Loss1: 1.171591 Loss2: 1.918191
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429171 Loss1: 0.125555 Loss2: 1.303616
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.109080 Loss1: 0.698899 Loss2: 1.410181
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433284 Loss1: 0.137298 Loss2: 1.295985
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.838570 Loss1: 0.434612 Loss2: 1.403958
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.636003 Loss1: 0.272980 Loss2: 1.363023
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.576431 Loss1: 0.222939 Loss2: 1.353492
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547120 Loss1: 0.202476 Loss2: 1.344645
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534128 Loss1: 0.182729 Loss2: 1.351399
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.124995 Loss1: 1.112417 Loss2: 2.012578
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.503973 Loss1: 0.157598 Loss2: 1.346375
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.259517 Loss1: 0.755643 Loss2: 1.503874
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.571482 Loss1: 0.218284 Loss2: 1.353198
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.027958 Loss1: 0.466553 Loss2: 1.561405
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.534577 Loss1: 0.192555 Loss2: 1.342022
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.754843 Loss1: 0.265331 Loss2: 1.489512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.630953 Loss1: 0.156705 Loss2: 1.474248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.605483 Loss1: 0.137353 Loss2: 1.468131
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.122766 Loss1: 1.249094 Loss2: 1.873672
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.229514 Loss1: 0.805125 Loss2: 1.424389 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.580447 Loss1: 0.120737 Loss2: 1.459710
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.961113 Loss1: 0.494147 Loss2: 1.466967
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.804053 Loss1: 0.405876 Loss2: 1.398177
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.637746 Loss1: 0.237866 Loss2: 1.399881
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.605279 Loss1: 0.213059 Loss2: 1.392220
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.597838 Loss1: 0.201890 Loss2: 1.395948
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.165029 Loss1: 1.278359 Loss2: 1.886670
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.587342 Loss1: 0.192359 Loss2: 1.394983
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.270836 Loss1: 0.838215 Loss2: 1.432621
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.572056 Loss1: 0.179786 Loss2: 1.392270
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.996376 Loss1: 0.542502 Loss2: 1.453873
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.517311 Loss1: 0.129952 Loss2: 1.387360
-(DefaultActor pid=3765) >> Training accuracy: 0.954167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.676693 Loss1: 0.268376 Loss2: 1.408317 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 14:26:26,108 | server.py:236 | fit_round 79 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.586303 Loss1: 0.196069 Loss2: 1.390233 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.615694 Loss1: 0.215711 Loss2: 1.399983
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.174655 Loss1: 1.235045 Loss2: 1.939610
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.266707 Loss1: 0.799855 Loss2: 1.466851 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.546300 Loss1: 0.151497 Loss2: 1.394802
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.933943 Loss1: 0.448910 Loss2: 1.485033
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.788006 Loss1: 0.355314 Loss2: 1.432692
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.706547 Loss1: 0.251189 Loss2: 1.455358
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.641682 Loss1: 0.209793 Loss2: 1.431889
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.559543 Loss1: 0.141862 Loss2: 1.417681
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.200534 Loss1: 1.227093 Loss2: 1.973441
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.540367 Loss1: 0.129273 Loss2: 1.411094
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.547448 Loss1: 0.137462 Loss2: 1.409986 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.523712 Loss1: 0.117801 Loss2: 1.405911 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.629344 Loss1: 0.205273 Loss2: 1.424071 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548520 Loss1: 0.143096 Loss2: 1.405424 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.144114 Loss1: 1.253209 Loss2: 1.890905 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967548
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.987814 Loss1: 0.538088 Loss2: 1.449726 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.754692 Loss1: 0.323155 Loss2: 1.431537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.204891 Loss1: 1.226347 Loss2: 1.978544
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.675771 Loss1: 0.247559 Loss2: 1.428212
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.229129 Loss1: 0.838179 Loss2: 1.390950
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.647413 Loss1: 0.220243 Loss2: 1.427171
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.632765 Loss1: 0.210194 Loss2: 1.422572 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.645425 Loss1: 0.222619 Loss2: 1.422806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.602573 Loss1: 0.182125 Loss2: 1.420448 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496551 Loss1: 0.140166 Loss2: 1.356385 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434537 Loss1: 0.089528 Loss2: 1.345009 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979567
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.015150 Loss1: 1.165348 Loss2: 1.849803
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.142365 Loss1: 0.739400 Loss2: 1.402965
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.930343 Loss1: 0.499694 Loss2: 1.430649
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.718715 Loss1: 0.344843 Loss2: 1.373873
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.218224 Loss1: 1.369093 Loss2: 1.849131
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.310344 Loss1: 0.869116 Loss2: 1.441229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.980026 Loss1: 0.582994 Loss2: 1.397032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.764230 Loss1: 0.372016 Loss2: 1.392214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.616236 Loss1: 0.244904 Loss2: 1.371333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.575303 Loss1: 0.209635 Loss2: 1.365669 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.558729 Loss1: 0.198705 Loss2: 1.360023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476975 Loss1: 0.131507 Loss2: 1.345467 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 14:26:26,108][flwr][DEBUG] - fit_round 79 received 50 results and 0 failures
-INFO flwr 2023-10-10 14:27:07,462 | server.py:125 | fit progress: (79, 2.2412412355121334, {'accuracy': 0.546}, 182135.241001466)
->> Test accuracy: 0.546000
-[2023-10-10 14:27:07,462][flwr][INFO] - fit progress: (79, 2.2412412355121334, {'accuracy': 0.546}, 182135.241001466)
-DEBUG flwr 2023-10-10 14:27:07,463 | server.py:173 | evaluate_round 79: strategy sampled 50 clients (out of 50)
-[2023-10-10 14:27:07,463][flwr][DEBUG] - evaluate_round 79: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 14:36:11,090 | server.py:187 | evaluate_round 79 received 50 results and 0 failures
-[2023-10-10 14:36:11,090][flwr][DEBUG] - evaluate_round 79 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 14:36:11,090 | server.py:222 | fit_round 80: strategy sampled 50 clients (out of 50)
-[2023-10-10 14:36:11,090][flwr][DEBUG] - fit_round 80: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.946489 Loss1: 1.061548 Loss2: 1.884941 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.830811 Loss1: 0.406087 Loss2: 1.424724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.662114 Loss1: 0.262915 Loss2: 1.399199
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.110371 Loss1: 1.216273 Loss2: 1.894098
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.240248 Loss1: 0.802024 Loss2: 1.438224 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.922387 Loss1: 0.470606 Loss2: 1.451780 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.723638 Loss1: 0.309327 Loss2: 1.414311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.711100 Loss1: 0.299455 Loss2: 1.411645 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.601666 Loss1: 0.194520 Loss2: 1.407146 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.487629 Loss1: 0.109113 Loss2: 1.378516
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.595592 Loss1: 0.190968 Loss2: 1.404624
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.567561 Loss1: 0.171435 Loss2: 1.396126
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.536964 Loss1: 0.137178 Loss2: 1.399786
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.503335 Loss1: 0.121383 Loss2: 1.381952
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.976228 Loss1: 1.079392 Loss2: 1.896837
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.240009 Loss1: 0.787714 Loss2: 1.452295
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.881017 Loss1: 0.448301 Loss2: 1.432716
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.732805 Loss1: 0.315198 Loss2: 1.417607
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.369958 Loss1: 1.441909 Loss2: 1.928049
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.264858 Loss1: 0.812288 Loss2: 1.452570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.927168 Loss1: 0.488640 Loss2: 1.438528 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.808923 Loss1: 0.389291 Loss2: 1.419632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.688968 Loss1: 0.276653 Loss2: 1.412315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.648933 Loss1: 0.251985 Loss2: 1.396948 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.501280 Loss1: 0.111244 Loss2: 1.390036
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.661899 Loss1: 0.249075 Loss2: 1.412823
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.601153 Loss1: 0.202484 Loss2: 1.398669
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.577121 Loss1: 0.186962 Loss2: 1.390159
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.516623 Loss1: 0.130095 Loss2: 1.386529
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.182581 Loss1: 1.277345 Loss2: 1.905237
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.276930 Loss1: 0.807495 Loss2: 1.469435
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.922908 Loss1: 0.448031 Loss2: 1.474878
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.780332 Loss1: 0.358613 Loss2: 1.421719
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.861485 Loss1: 1.132101 Loss2: 1.729384
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.730023 Loss1: 0.297760 Loss2: 1.432262
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.002986 Loss1: 0.690995 Loss2: 1.311991
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.649172 Loss1: 0.220209 Loss2: 1.428963
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.787585 Loss1: 0.443537 Loss2: 1.344048
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.637826 Loss1: 0.217877 Loss2: 1.419949
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.660705 Loss1: 0.367948 Loss2: 1.292757
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.623805 Loss1: 0.208140 Loss2: 1.415665
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574015 Loss1: 0.269763 Loss2: 1.304253
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.574891 Loss1: 0.157494 Loss2: 1.417397
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582049 Loss1: 0.279390 Loss2: 1.302659
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.524755 Loss1: 0.119426 Loss2: 1.405329
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509660 Loss1: 0.209429 Loss2: 1.300231
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515940 Loss1: 0.230982 Loss2: 1.284959
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.515302 Loss1: 0.225650 Loss2: 1.289652
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438945 Loss1: 0.151247 Loss2: 1.287698
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.954955 Loss1: 1.078758 Loss2: 1.876197
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.048482 Loss1: 0.603412 Loss2: 1.445070
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.793353 Loss1: 0.351724 Loss2: 1.441630
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.645541 Loss1: 0.243144 Loss2: 1.402397
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.993745 Loss1: 1.099459 Loss2: 1.894286
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.634742 Loss1: 0.220820 Loss2: 1.413923
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.166508 Loss1: 0.722897 Loss2: 1.443612
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.995513 Loss1: 0.548180 Loss2: 1.447333
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.615443 Loss1: 0.218389 Loss2: 1.397053
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.786459 Loss1: 0.364172 Loss2: 1.422287
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.631389 Loss1: 0.207785 Loss2: 1.423605
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.700224 Loss1: 0.295347 Loss2: 1.404877
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.587829 Loss1: 0.187636 Loss2: 1.400194
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.660184 Loss1: 0.256802 Loss2: 1.403382
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.566479 Loss1: 0.156660 Loss2: 1.409819
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.600102 Loss1: 0.201113 Loss2: 1.398990
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.536846 Loss1: 0.139646 Loss2: 1.397200
-(DefaultActor pid=3765) >> Training accuracy: 0.974609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.504326 Loss1: 0.128625 Loss2: 1.375701 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.092842 Loss1: 1.199028 Loss2: 1.893814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.017109 Loss1: 0.524996 Loss2: 1.492113 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.820538 Loss1: 0.396705 Loss2: 1.423833
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.007134 Loss1: 1.146541 Loss2: 1.860592
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.146287 Loss1: 0.730421 Loss2: 1.415866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.862506 Loss1: 0.461205 Loss2: 1.401301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.721166 Loss1: 0.342508 Loss2: 1.378658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.582034 Loss1: 0.208255 Loss2: 1.373779 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523155 Loss1: 0.170680 Loss2: 1.352475 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.530769 Loss1: 0.125569 Loss2: 1.405199
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.511024 Loss1: 0.163674 Loss2: 1.347350
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486356 Loss1: 0.140404 Loss2: 1.345952
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444426 Loss1: 0.102779 Loss2: 1.341646
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462078 Loss1: 0.121110 Loss2: 1.340968
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.068509 Loss1: 1.185438 Loss2: 1.883071
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.208564 Loss1: 0.716943 Loss2: 1.491621
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.890545 Loss1: 0.430108 Loss2: 1.460437
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.769302 Loss1: 0.328817 Loss2: 1.440486
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.939014 Loss1: 1.077880 Loss2: 1.861134
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.054806 Loss1: 0.672311 Loss2: 1.382494
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.643867 Loss1: 0.205273 Loss2: 1.438594
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.794703 Loss1: 0.371261 Loss2: 1.423441
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.625029 Loss1: 0.204848 Loss2: 1.420181
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.667685 Loss1: 0.299907 Loss2: 1.367778
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.605588 Loss1: 0.187858 Loss2: 1.417731
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.742029 Loss1: 0.352472 Loss2: 1.389556
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.570189 Loss1: 0.155953 Loss2: 1.414236
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.571380 Loss1: 0.150212 Loss2: 1.421169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.553994 Loss1: 0.139297 Loss2: 1.414696 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479762 Loss1: 0.125265 Loss2: 1.354498 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.096770 Loss1: 1.245625 Loss2: 1.851145 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.895948 Loss1: 0.477829 Loss2: 1.418119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.747894 Loss1: 0.351049 Loss2: 1.396844
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.064037 Loss1: 1.239751 Loss2: 1.824287
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.191513 Loss1: 0.780623 Loss2: 1.410890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.813225 Loss1: 0.431120 Loss2: 1.382105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.725125 Loss1: 0.356986 Loss2: 1.368139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.604788 Loss1: 0.245068 Loss2: 1.359719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.597019 Loss1: 0.249015 Loss2: 1.348004 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.488336 Loss1: 0.115996 Loss2: 1.372341
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.585068 Loss1: 0.212098 Loss2: 1.372970
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518196 Loss1: 0.175374 Loss2: 1.342822
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.557842 Loss1: 0.215041 Loss2: 1.342801
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.524107 Loss1: 0.176803 Loss2: 1.347304
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.385943 Loss1: 1.318093 Loss2: 2.067850
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.226496 Loss1: 0.769948 Loss2: 1.456548
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.114042 Loss1: 0.586197 Loss2: 1.527845
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.870473 Loss1: 0.398406 Loss2: 1.472067
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.715767 Loss1: 0.265002 Loss2: 1.450765
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.661953 Loss1: 0.220045 Loss2: 1.441907 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.860848 Loss1: 0.420376 Loss2: 1.440472 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.724954 Loss1: 0.316613 Loss2: 1.408341 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.558261 Loss1: 0.160771 Loss2: 1.397489 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.517890 Loss1: 0.133200 Loss2: 1.384689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.511526 Loss1: 0.130863 Loss2: 1.380662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.497065 Loss1: 0.117057 Loss2: 1.380008 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988051
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.711714 Loss1: 0.342192 Loss2: 1.369522 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.586862 Loss1: 0.244179 Loss2: 1.342684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.545370 Loss1: 0.196289 Loss2: 1.349082
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.997524 Loss1: 1.214940 Loss2: 1.782584
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.139448 Loss1: 0.751511 Loss2: 1.387937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.862785 Loss1: 0.483766 Loss2: 1.379019 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962891
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448182 Loss1: 0.125902 Loss2: 1.322279
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.712038 Loss1: 0.343188 Loss2: 1.368850
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.656125 Loss1: 0.285999 Loss2: 1.370126
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.569831 Loss1: 0.215912 Loss2: 1.353918
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.517520 Loss1: 0.169443 Loss2: 1.348078
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540397 Loss1: 0.191212 Loss2: 1.349185
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.005763 Loss1: 1.152095 Loss2: 1.853668
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.092158 Loss1: 0.688965 Loss2: 1.403193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966797
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478767 Loss1: 0.131490 Loss2: 1.347278
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.843912 Loss1: 0.440759 Loss2: 1.403154
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.747379 Loss1: 0.365762 Loss2: 1.381617
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.659485 Loss1: 0.269606 Loss2: 1.389879
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.580947 Loss1: 0.207126 Loss2: 1.373822
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514121 Loss1: 0.143694 Loss2: 1.370427
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.063920 Loss1: 1.238899 Loss2: 1.825021
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497074 Loss1: 0.137784 Loss2: 1.359290
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.494359 Loss1: 0.137075 Loss2: 1.357284 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.504789 Loss1: 0.139861 Loss2: 1.364927 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598563 Loss1: 0.232462 Loss2: 1.366101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.574471 Loss1: 0.220266 Loss2: 1.354205 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.506548 Loss1: 0.158505 Loss2: 1.348043
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.960743 Loss1: 1.115938 Loss2: 1.844805
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.963640 Loss1: 0.603137 Loss2: 1.360502 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461129 Loss1: 0.125039 Loss2: 1.336090
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.764366 Loss1: 0.376687 Loss2: 1.387679
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.633392 Loss1: 0.289259 Loss2: 1.344132
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.557094 Loss1: 0.223692 Loss2: 1.333402
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.540183 Loss1: 0.203031 Loss2: 1.337152
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.526960 Loss1: 0.195066 Loss2: 1.331894
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.256164 Loss1: 1.311930 Loss2: 1.944234
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.507533 Loss1: 0.174294 Loss2: 1.333239
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.495478 Loss1: 0.161916 Loss2: 1.333562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448460 Loss1: 0.117581 Loss2: 1.330879 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.758829 Loss1: 0.318290 Loss2: 1.440539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.590897 Loss1: 0.176395 Loss2: 1.414502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.050917 Loss1: 1.167480 Loss2: 1.883437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.124010 Loss1: 0.696781 Loss2: 1.427229 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978795
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.753347 Loss1: 0.344986 Loss2: 1.408360 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.615145 Loss1: 0.216290 Loss2: 1.398854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.536772 Loss1: 0.147399 Loss2: 1.389373
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.115753 Loss1: 1.226139 Loss2: 1.889615
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.527146 Loss1: 0.150045 Loss2: 1.377101
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.180202 Loss1: 0.748566 Loss2: 1.431636
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.508130 Loss1: 0.126414 Loss2: 1.381716
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.946839 Loss1: 0.466548 Loss2: 1.480292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.506663 Loss1: 0.125357 Loss2: 1.381306
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.795724 Loss1: 0.377578 Loss2: 1.418147
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.743139 Loss1: 0.297945 Loss2: 1.445194
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.697560 Loss1: 0.274731 Loss2: 1.422830
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.617291 Loss1: 0.196939 Loss2: 1.420351
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.573714 Loss1: 0.173783 Loss2: 1.399931
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.200451 Loss1: 1.304189 Loss2: 1.896262
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.561985 Loss1: 0.154274 Loss2: 1.407711
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.558735 Loss1: 0.158121 Loss2: 1.400614 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.952083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.695645 Loss1: 0.362003 Loss2: 1.333642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484346 Loss1: 0.158897 Loss2: 1.325449 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450376 Loss1: 0.128053 Loss2: 1.322323 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402387 Loss1: 0.089021 Loss2: 1.313365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392205 Loss1: 0.084034 Loss2: 1.308171 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986779
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.729155 Loss1: 0.339495 Loss2: 1.389661 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549459 Loss1: 0.161441 Loss2: 1.388018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.059168 Loss1: 1.181184 Loss2: 1.877984
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.555195 Loss1: 0.184647 Loss2: 1.370548
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.169730 Loss1: 0.752081 Loss2: 1.417650
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.497402 Loss1: 0.123963 Loss2: 1.373439
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.844692 Loss1: 0.427208 Loss2: 1.417484
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461418 Loss1: 0.108518 Loss2: 1.352900
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623076 Loss1: 0.245254 Loss2: 1.377822
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448034 Loss1: 0.091229 Loss2: 1.356805
-(DefaultActor pid=3764) >> Training accuracy: 0.978516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497138 Loss1: 0.138204 Loss2: 1.358933 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471993 Loss1: 0.114370 Loss2: 1.357622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470871 Loss1: 0.124120 Loss2: 1.346751
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.952740 Loss1: 1.134018 Loss2: 1.818721
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.532437 Loss1: 0.184880 Loss2: 1.347557
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.988764 Loss1: 0.653567 Loss2: 1.335197
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.745374 Loss1: 0.390182 Loss2: 1.355193
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.630983 Loss1: 0.313454 Loss2: 1.317529
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568655 Loss1: 0.240147 Loss2: 1.328508
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.533113 Loss1: 0.214973 Loss2: 1.318140
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.986646 Loss1: 1.146790 Loss2: 1.839856
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480370 Loss1: 0.173128 Loss2: 1.307242
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.269348 Loss1: 0.807599 Loss2: 1.461750
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441618 Loss1: 0.129322 Loss2: 1.312296
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.831042 Loss1: 0.425029 Loss2: 1.406013
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429445 Loss1: 0.129872 Loss2: 1.299573
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.669114 Loss1: 0.279673 Loss2: 1.389441
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398763 Loss1: 0.100537 Loss2: 1.298226
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.571351 Loss1: 0.187600 Loss2: 1.383752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.501671 Loss1: 0.122354 Loss2: 1.379317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.015164 Loss1: 1.172298 Loss2: 1.842866
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.517716 Loss1: 0.147265 Loss2: 1.370451
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.126518 Loss1: 0.698255 Loss2: 1.428263
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.518067 Loss1: 0.144476 Loss2: 1.373591
-(DefaultActor pid=3765) >> Training accuracy: 0.949219
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.734115 Loss1: 0.327686 Loss2: 1.406428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.596830 Loss1: 0.205407 Loss2: 1.391424 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.036635 Loss1: 1.215246 Loss2: 1.821389
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.554162 Loss1: 0.166755 Loss2: 1.387407
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.180548 Loss1: 0.765037 Loss2: 1.415511
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548900 Loss1: 0.163971 Loss2: 1.384929
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.977781 Loss1: 0.573955 Loss2: 1.403826
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519356 Loss1: 0.140503 Loss2: 1.378853
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.502421 Loss1: 0.126939 Loss2: 1.375482 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525635 Loss1: 0.175206 Loss2: 1.350429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480279 Loss1: 0.141412 Loss2: 1.338867 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474033 Loss1: 0.146675 Loss2: 1.327358
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.185446 Loss1: 1.295000 Loss2: 1.890446
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.279179 Loss1: 0.832085 Loss2: 1.447094 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.800558 Loss1: 0.367576 Loss2: 1.432982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.613312 Loss1: 0.195068 Loss2: 1.418244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.077932 Loss1: 1.246594 Loss2: 1.831338
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.638807 Loss1: 0.222010 Loss2: 1.416798
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.179250 Loss1: 0.773693 Loss2: 1.405557
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.559424 Loss1: 0.142416 Loss2: 1.417008
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.841339 Loss1: 0.423406 Loss2: 1.417933
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.537367 Loss1: 0.135708 Loss2: 1.401659
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.755967 Loss1: 0.362875 Loss2: 1.393092
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.590511 Loss1: 0.185708 Loss2: 1.404803
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.554523 Loss1: 0.175308 Loss2: 1.379216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.535043 Loss1: 0.159929 Loss2: 1.375114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477080 Loss1: 0.120777 Loss2: 1.356302
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.203472 Loss1: 1.272917 Loss2: 1.930555
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.473585 Loss1: 0.113342 Loss2: 1.360243
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.200871 Loss1: 0.800034 Loss2: 1.400836
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.936938 Loss1: 0.490463 Loss2: 1.446476
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.805126 Loss1: 0.402268 Loss2: 1.402858
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.712315 Loss1: 0.295186 Loss2: 1.417129
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.554010 Loss1: 0.172154 Loss2: 1.381856
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.542989 Loss1: 0.164635 Loss2: 1.378354
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.514954 Loss1: 0.139077 Loss2: 1.375876
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490433 Loss1: 0.110691 Loss2: 1.379742 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.471554 Loss1: 0.110327 Loss2: 1.361227 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.775949 Loss1: 0.312351 Loss2: 1.463598 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.676929 Loss1: 0.233646 Loss2: 1.443284 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.625608 Loss1: 0.179941 Loss2: 1.445667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.591796 Loss1: 0.161283 Loss2: 1.430513 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.562081 Loss1: 0.128421 Loss2: 1.433660 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.751958 Loss1: 0.265346 Loss2: 1.486613 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.656703 Loss1: 0.171681 Loss2: 1.485022 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.191653 Loss1: 1.240654 Loss2: 1.950998
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.623168 Loss1: 0.144104 Loss2: 1.479064
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.261160 Loss1: 0.821202 Loss2: 1.439958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.598284 Loss1: 0.131340 Loss2: 1.466945
-(DefaultActor pid=3764) >> Training accuracy: 0.964286
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.732980 Loss1: 0.321443 Loss2: 1.411537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.648923 Loss1: 0.244737 Loss2: 1.404186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.050275 Loss1: 1.193413 Loss2: 1.856862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.161441 Loss1: 0.737423 Loss2: 1.424018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.890457 Loss1: 0.465315 Loss2: 1.425141 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981027
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.655773 Loss1: 0.277991 Loss2: 1.377782 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.591611 Loss1: 0.214989 Loss2: 1.376622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.533357 Loss1: 0.157850 Loss2: 1.375506
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.000226 Loss1: 1.128069 Loss2: 1.872156
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.069885 Loss1: 0.683464 Loss2: 1.386421 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457386 Loss1: 0.097956 Loss2: 1.359430
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.889355 Loss1: 0.469237 Loss2: 1.420118
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.721262 Loss1: 0.360177 Loss2: 1.361085
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.584041 Loss1: 0.218627 Loss2: 1.365414
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498207 Loss1: 0.149847 Loss2: 1.348361
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478193 Loss1: 0.132808 Loss2: 1.345385
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.008613 Loss1: 1.206205 Loss2: 1.802407
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439157 Loss1: 0.094197 Loss2: 1.344960
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.217451 Loss1: 0.835553 Loss2: 1.381898
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455697 Loss1: 0.122959 Loss2: 1.332738
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.855555 Loss1: 0.459110 Loss2: 1.396445
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.480948 Loss1: 0.145241 Loss2: 1.335707
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.620860 Loss1: 0.272254 Loss2: 1.348605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.566739 Loss1: 0.233038 Loss2: 1.333701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.492936 Loss1: 0.163030 Loss2: 1.329906
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.156980 Loss1: 1.234901 Loss2: 1.922079
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.205398 Loss1: 0.762989 Loss2: 1.442409 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440064 Loss1: 0.116481 Loss2: 1.323583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.924601 Loss1: 0.488839 Loss2: 1.435763
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.699666 Loss1: 0.303014 Loss2: 1.396652
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593228 Loss1: 0.194491 Loss2: 1.398738
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.534265 Loss1: 0.153324 Loss2: 1.380941
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.567958 Loss1: 0.181916 Loss2: 1.386042
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.066771 Loss1: 1.203830 Loss2: 1.862941
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.575755 Loss1: 0.194297 Loss2: 1.381458
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.178198 Loss1: 0.767001 Loss2: 1.411197
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.473454 Loss1: 0.096951 Loss2: 1.376503
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.897006 Loss1: 0.468568 Loss2: 1.428438
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474011 Loss1: 0.098660 Loss2: 1.375351
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.656380 Loss1: 0.265999 Loss2: 1.390381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.569703 Loss1: 0.185044 Loss2: 1.384659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.587236 Loss1: 0.208047 Loss2: 1.379188
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.100123 Loss1: 1.222510 Loss2: 1.877613
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.169639 Loss1: 0.728605 Loss2: 1.441034 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.019110 Loss1: 0.569415 Loss2: 1.449695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.687620 Loss1: 0.267496 Loss2: 1.420124 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.697116 Loss1: 0.268061 Loss2: 1.429055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.620240 Loss1: 0.213854 Loss2: 1.406386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.587412 Loss1: 0.183814 Loss2: 1.403598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.558763 Loss1: 0.154689 Loss2: 1.404074 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973633
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.625980 Loss1: 0.251034 Loss2: 1.374946 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.547233 Loss1: 0.181677 Loss2: 1.365557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477939 Loss1: 0.121593 Loss2: 1.356346
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.034280 Loss1: 1.111958 Loss2: 1.922323
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.116494 Loss1: 0.667871 Loss2: 1.448623 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465064 Loss1: 0.112218 Loss2: 1.352846
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.866509 Loss1: 0.395961 Loss2: 1.470548
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.731883 Loss1: 0.313938 Loss2: 1.417945
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.636021 Loss1: 0.215018 Loss2: 1.421003
-DEBUG flwr 2023-10-10 15:04:49,444 | server.py:236 | fit_round 80 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.619154 Loss1: 0.200773 Loss2: 1.418381
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.614608 Loss1: 0.203763 Loss2: 1.410845
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.077236 Loss1: 1.259783 Loss2: 1.817452
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.596824 Loss1: 0.191449 Loss2: 1.405375
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.186194 Loss1: 0.765259 Loss2: 1.420935
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.571650 Loss1: 0.167247 Loss2: 1.404403
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.882366 Loss1: 0.494425 Loss2: 1.387942
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.540423 Loss1: 0.139849 Loss2: 1.400573
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.679456 Loss1: 0.310759 Loss2: 1.368697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.527097 Loss1: 0.181772 Loss2: 1.345325 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546608 Loss1: 0.204769 Loss2: 1.341839
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.147678 Loss1: 1.232744 Loss2: 1.914934
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480231 Loss1: 0.131694 Loss2: 1.348537
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.229221 Loss1: 0.763781 Loss2: 1.465440
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.468128 Loss1: 0.134880 Loss2: 1.333248
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.074734 Loss1: 0.589929 Loss2: 1.484805
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.799890 Loss1: 0.361685 Loss2: 1.438204
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.699244 Loss1: 0.265577 Loss2: 1.433666
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.668320 Loss1: 0.247512 Loss2: 1.420808
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.640449 Loss1: 0.217571 Loss2: 1.422877
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.607996 Loss1: 0.190744 Loss2: 1.417252
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.253141 Loss1: 1.312246 Loss2: 1.940896
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.573368 Loss1: 0.158752 Loss2: 1.414616
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.300138 Loss1: 0.841416 Loss2: 1.458722
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.560830 Loss1: 0.148184 Loss2: 1.412646
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.991591 Loss1: 0.533911 Loss2: 1.457680
-(DefaultActor pid=3765) >> Training accuracy: 0.956250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.798755 Loss1: 0.366205 Loss2: 1.432551
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.700522 Loss1: 0.273991 Loss2: 1.426531
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.646462 Loss1: 0.239902 Loss2: 1.406561
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.616457 Loss1: 0.200872 Loss2: 1.415585
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.627019 Loss1: 0.217262 Loss2: 1.409757
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.217052 Loss1: 1.297952 Loss2: 1.919101
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.601149 Loss1: 0.189834 Loss2: 1.411315
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.361627 Loss1: 0.861469 Loss2: 1.500158
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.563649 Loss1: 0.155725 Loss2: 1.407924
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.034240 Loss1: 0.555845 Loss2: 1.478395
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.747593 Loss1: 0.301618 Loss2: 1.445975
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.690276 Loss1: 0.254786 Loss2: 1.435490
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.687030 Loss1: 0.255778 Loss2: 1.431252
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.633550 Loss1: 0.204105 Loss2: 1.429445
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.099192 Loss1: 1.260755 Loss2: 1.838437
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.573952 Loss1: 0.150587 Loss2: 1.423365
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.262585 Loss1: 0.840199 Loss2: 1.422385
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.561306 Loss1: 0.142767 Loss2: 1.418539
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.995170 Loss1: 0.589231 Loss2: 1.405939
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.555468 Loss1: 0.140120 Loss2: 1.415348
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.639124 Loss1: 0.266784 Loss2: 1.372340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.522787 Loss1: 0.166832 Loss2: 1.355955 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466141 Loss1: 0.109813 Loss2: 1.356327 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 15:04:49,444][flwr][DEBUG] - fit_round 80 received 50 results and 0 failures
-INFO flwr 2023-10-10 15:05:31,319 | server.py:125 | fit progress: (80, 2.2419653856716217, {'accuracy': 0.5467}, 184439.09719714)
->> Test accuracy: 0.546700
-[2023-10-10 15:05:31,319][flwr][INFO] - fit progress: (80, 2.2419653856716217, {'accuracy': 0.5467}, 184439.09719714)
-DEBUG flwr 2023-10-10 15:05:31,319 | server.py:173 | evaluate_round 80: strategy sampled 50 clients (out of 50)
-[2023-10-10 15:05:31,319][flwr][DEBUG] - evaluate_round 80: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 15:14:32,916 | server.py:187 | evaluate_round 80 received 50 results and 0 failures
-[2023-10-10 15:14:32,916][flwr][DEBUG] - evaluate_round 80 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 15:14:32,916 | server.py:222 | fit_round 81: strategy sampled 50 clients (out of 50)
-[2023-10-10 15:14:32,916][flwr][DEBUG] - fit_round 81: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.274305 Loss1: 1.321049 Loss2: 1.953256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.037398 Loss1: 0.542660 Loss2: 1.494737 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.848668 Loss1: 0.376491 Loss2: 1.472177
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.094718 Loss1: 1.265260 Loss2: 1.829457
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.065419 Loss1: 0.684017 Loss2: 1.381403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.817116 Loss1: 0.409035 Loss2: 1.408081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.716123 Loss1: 0.358173 Loss2: 1.357950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.630882 Loss1: 0.272340 Loss2: 1.358542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559711 Loss1: 0.212895 Loss2: 1.346816 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475421 Loss1: 0.134140 Loss2: 1.341281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463906 Loss1: 0.128384 Loss2: 1.335523 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.033650 Loss1: 1.043878 Loss2: 1.989772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.033115 Loss1: 0.502930 Loss2: 1.530185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.881282 Loss1: 0.407575 Loss2: 1.473708
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.088924 Loss1: 1.219411 Loss2: 1.869513
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.109790 Loss1: 0.708554 Loss2: 1.401236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.873049 Loss1: 0.482091 Loss2: 1.390957 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.701225 Loss1: 0.322634 Loss2: 1.378591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.601482 Loss1: 0.243207 Loss2: 1.358275 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.517499 Loss1: 0.167109 Loss2: 1.350390 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484442 Loss1: 0.135640 Loss2: 1.348802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.469535 Loss1: 0.125260 Loss2: 1.344275 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.159521 Loss1: 1.298201 Loss2: 1.861320 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.925734 Loss1: 0.521332 Loss2: 1.404401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.190962 Loss1: 1.225463 Loss2: 1.965500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.138628 Loss1: 0.769747 Loss2: 1.368881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.910170 Loss1: 0.485830 Loss2: 1.424340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.663467 Loss1: 0.310429 Loss2: 1.353038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.610928 Loss1: 0.217493 Loss2: 1.393435
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609880 Loss1: 0.260601 Loss2: 1.349280
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.657535 Loss1: 0.278133 Loss2: 1.379402
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.533728 Loss1: 0.154526 Loss2: 1.379201
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491899 Loss1: 0.123781 Loss2: 1.368118 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519590 Loss1: 0.171436 Loss2: 1.348153 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971154
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.946623 Loss1: 1.066793 Loss2: 1.879830 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.940326 Loss1: 0.468991 Loss2: 1.471335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.099616 Loss1: 1.160286 Loss2: 1.939330
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.719469 Loss1: 0.302196 Loss2: 1.417273
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.336071 Loss1: 0.796486 Loss2: 1.539585
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.612134 Loss1: 0.196901 Loss2: 1.415233
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.976117 Loss1: 0.514492 Loss2: 1.461625
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.554875 Loss1: 0.151170 Loss2: 1.403705
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.578412 Loss1: 0.179278 Loss2: 1.399133 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.583847 Loss1: 0.182834 Loss2: 1.401013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.598309 Loss1: 0.194020 Loss2: 1.404289 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.585348 Loss1: 0.184977 Loss2: 1.400371 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962891
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.585337 Loss1: 0.152917 Loss2: 1.432420 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.977718 Loss1: 1.094067 Loss2: 1.883651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.914669 Loss1: 0.436614 Loss2: 1.478056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.070408 Loss1: 1.187756 Loss2: 1.882652
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.774392 Loss1: 0.326863 Loss2: 1.447529
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.027467 Loss1: 0.607886 Loss2: 1.419580
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.715239 Loss1: 0.266317 Loss2: 1.448923
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.877131 Loss1: 0.423995 Loss2: 1.453136
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.643332 Loss1: 0.199587 Loss2: 1.443745
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.708228 Loss1: 0.303068 Loss2: 1.405159
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.626754 Loss1: 0.194344 Loss2: 1.432409
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.599589 Loss1: 0.159730 Loss2: 1.439858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.607075 Loss1: 0.176204 Loss2: 1.430871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.574263 Loss1: 0.144078 Loss2: 1.430184 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965820
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.536990 Loss1: 0.132094 Loss2: 1.404896 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.264869 Loss1: 1.354699 Loss2: 1.910169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.926438 Loss1: 0.502792 Loss2: 1.423646 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.751341 Loss1: 0.333457 Loss2: 1.417885
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.027377 Loss1: 1.223402 Loss2: 1.803975
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.160692 Loss1: 0.770696 Loss2: 1.389996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.951960 Loss1: 0.546282 Loss2: 1.405678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.803316 Loss1: 0.434695 Loss2: 1.368621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.680867 Loss1: 0.307062 Loss2: 1.373805 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.616107 Loss1: 0.261321 Loss2: 1.354786 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.502640 Loss1: 0.122633 Loss2: 1.380008
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.529566 Loss1: 0.173160 Loss2: 1.356405
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.520248 Loss1: 0.172008 Loss2: 1.348240
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457947 Loss1: 0.116330 Loss2: 1.341616
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470360 Loss1: 0.132898 Loss2: 1.337462
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.161089 Loss1: 1.254578 Loss2: 1.906510
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.266371 Loss1: 0.826098 Loss2: 1.440273
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.017553 Loss1: 0.552478 Loss2: 1.465075
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765900 Loss1: 0.361206 Loss2: 1.404694
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.044292 Loss1: 1.180863 Loss2: 1.863428
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.098329 Loss1: 0.692304 Loss2: 1.406025 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.829019 Loss1: 0.425718 Loss2: 1.403301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.641515 Loss1: 0.272071 Loss2: 1.369444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.575570 Loss1: 0.209753 Loss2: 1.365817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511578 Loss1: 0.154051 Loss2: 1.357528 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459400 Loss1: 0.086331 Loss2: 1.373069
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533470 Loss1: 0.172704 Loss2: 1.360766
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.535480 Loss1: 0.172836 Loss2: 1.362645
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.488024 Loss1: 0.133999 Loss2: 1.354024
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.534358 Loss1: 0.179880 Loss2: 1.354478
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.113102 Loss1: 1.268722 Loss2: 1.844380
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.167322 Loss1: 0.752098 Loss2: 1.415224
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.850812 Loss1: 0.418813 Loss2: 1.432000
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.702141 Loss1: 0.306814 Loss2: 1.395327
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.132456 Loss1: 1.179548 Loss2: 1.952907
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.644313 Loss1: 0.263779 Loss2: 1.380534
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.264712 Loss1: 0.777916 Loss2: 1.486797
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.552624 Loss1: 0.171447 Loss2: 1.381177
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.000960 Loss1: 0.513892 Loss2: 1.487068
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514389 Loss1: 0.148605 Loss2: 1.365784
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.773243 Loss1: 0.311389 Loss2: 1.461853
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.519395 Loss1: 0.144323 Loss2: 1.375072
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.696687 Loss1: 0.246925 Loss2: 1.449762
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.525273 Loss1: 0.154378 Loss2: 1.370894
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.658896 Loss1: 0.222260 Loss2: 1.436635
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.522007 Loss1: 0.147476 Loss2: 1.374531
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.657040 Loss1: 0.217265 Loss2: 1.439775
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.625271 Loss1: 0.196120 Loss2: 1.429151
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.581940 Loss1: 0.146412 Loss2: 1.435528
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.566245 Loss1: 0.139297 Loss2: 1.426948
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.999402 Loss1: 1.136550 Loss2: 1.862852
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.139205 Loss1: 0.723862 Loss2: 1.415343
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.941266 Loss1: 0.485879 Loss2: 1.455388
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.712755 Loss1: 0.326595 Loss2: 1.386160
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.937558 Loss1: 1.099679 Loss2: 1.837879
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.152457 Loss1: 0.745229 Loss2: 1.407228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.969693 Loss1: 0.532223 Loss2: 1.437469 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.839951 Loss1: 0.437861 Loss2: 1.402089 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.653240 Loss1: 0.256662 Loss2: 1.396578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.569599 Loss1: 0.191286 Loss2: 1.378313 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513734 Loss1: 0.143367 Loss2: 1.370367 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.531102 Loss1: 0.158105 Loss2: 1.372997 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.053227 Loss1: 1.181776 Loss2: 1.871451 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731965 Loss1: 0.340480 Loss2: 1.391486 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.310157 Loss1: 1.285844 Loss2: 2.024313 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.195294 Loss1: 0.796523 Loss2: 1.398771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.983205 Loss1: 0.475386 Loss2: 1.507819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.802590 Loss1: 0.386664 Loss2: 1.415926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.759304 Loss1: 0.352867 Loss2: 1.406437
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.510753 Loss1: 0.165117 Loss2: 1.345636
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.570323 Loss1: 0.209583 Loss2: 1.360740 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.520484 Loss1: 0.166659 Loss2: 1.353825 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.951042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.493375 Loss1: 0.114922 Loss2: 1.378453 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.119087 Loss1: 1.253387 Loss2: 1.865701
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.242572 Loss1: 0.834441 Loss2: 1.408131
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.930800 Loss1: 0.489158 Loss2: 1.441642
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.776283 Loss1: 0.377744 Loss2: 1.398539
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.221050 Loss1: 1.326204 Loss2: 1.894846
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.646225 Loss1: 0.247082 Loss2: 1.399143
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.135656 Loss1: 0.769899 Loss2: 1.365757
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.028012 Loss1: 0.612374 Loss2: 1.415637
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.622262 Loss1: 0.230940 Loss2: 1.391322
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.689584 Loss1: 0.320601 Loss2: 1.368983
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.607123 Loss1: 0.209386 Loss2: 1.397736
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.573177 Loss1: 0.185008 Loss2: 1.388169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.534579 Loss1: 0.149941 Loss2: 1.384638 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481026 Loss1: 0.106635 Loss2: 1.374391 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.488548 Loss1: 0.145635 Loss2: 1.342914 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967548
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.030326 Loss1: 1.187300 Loss2: 1.843026
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.195556 Loss1: 0.774805 Loss2: 1.420751
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.991995 Loss1: 0.564838 Loss2: 1.427157
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.153108 Loss1: 1.129350 Loss2: 2.023758
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.803199 Loss1: 0.392638 Loss2: 1.410561
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.276850 Loss1: 0.715845 Loss2: 1.561005
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.664815 Loss1: 0.266476 Loss2: 1.398339
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.962012 Loss1: 0.390971 Loss2: 1.571041
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.609038 Loss1: 0.228104 Loss2: 1.380934
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.832270 Loss1: 0.317104 Loss2: 1.515166
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.554017 Loss1: 0.168760 Loss2: 1.385257
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.579472 Loss1: 0.200500 Loss2: 1.378972 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.555208 Loss1: 0.167699 Loss2: 1.387509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.523235 Loss1: 0.147882 Loss2: 1.375353 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971680
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.626359 Loss1: 0.138234 Loss2: 1.488125 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.993904 Loss1: 1.168344 Loss2: 1.825560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.839701 Loss1: 0.431914 Loss2: 1.407787 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.157167 Loss1: 1.238701 Loss2: 1.918465
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.802362 Loss1: 0.410420 Loss2: 1.391943
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.229280 Loss1: 0.753306 Loss2: 1.475974
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.666886 Loss1: 0.265136 Loss2: 1.401750
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.936489 Loss1: 0.468839 Loss2: 1.467650
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.606501 Loss1: 0.229284 Loss2: 1.377217
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.787529 Loss1: 0.372768 Loss2: 1.414761
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.554968 Loss1: 0.180801 Loss2: 1.374167
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.510669 Loss1: 0.145860 Loss2: 1.364809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482377 Loss1: 0.115685 Loss2: 1.366693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459519 Loss1: 0.103198 Loss2: 1.356321 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.609791 Loss1: 0.191316 Loss2: 1.418476 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.129224 Loss1: 1.263089 Loss2: 1.866135 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.845497 Loss1: 0.435616 Loss2: 1.409881 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.628208 Loss1: 0.261828 Loss2: 1.366380
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.202633 Loss1: 1.255319 Loss2: 1.947314
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.271809 Loss1: 0.783213 Loss2: 1.488596 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.999416 Loss1: 0.498147 Loss2: 1.501270 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.850900 Loss1: 0.386438 Loss2: 1.464463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.717745 Loss1: 0.251401 Loss2: 1.466344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.660310 Loss1: 0.213246 Loss2: 1.447064 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425228 Loss1: 0.081006 Loss2: 1.344222
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.626991 Loss1: 0.176967 Loss2: 1.450024
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.642731 Loss1: 0.193319 Loss2: 1.449411
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.611921 Loss1: 0.173552 Loss2: 1.438369
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.596962 Loss1: 0.152414 Loss2: 1.444548
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.894691 Loss1: 1.009891 Loss2: 1.884800
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.048614 Loss1: 0.605305 Loss2: 1.443309
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.895861 Loss1: 0.454449 Loss2: 1.441411
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.046848 Loss1: 1.090658 Loss2: 1.956190
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.715823 Loss1: 0.299066 Loss2: 1.416756
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.119528 Loss1: 0.656142 Loss2: 1.463385
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.678307 Loss1: 0.271621 Loss2: 1.406686
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.621579 Loss1: 0.213659 Loss2: 1.407920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.588377 Loss1: 0.186849 Loss2: 1.401528 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.567168 Loss1: 0.169011 Loss2: 1.398157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.525524 Loss1: 0.128623 Loss2: 1.396901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.483628 Loss1: 0.097861 Loss2: 1.385767 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989890
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.569197 Loss1: 0.142895 Loss2: 1.426302 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.016394 Loss1: 1.121715 Loss2: 1.894679
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.194883 Loss1: 0.745735 Loss2: 1.449147
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.863460 Loss1: 0.436698 Loss2: 1.426762
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.723709 Loss1: 0.322667 Loss2: 1.401042
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.930253 Loss1: 1.087798 Loss2: 1.842455
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.025051 Loss1: 0.604624 Loss2: 1.420427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.803624 Loss1: 0.378261 Loss2: 1.425363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.647767 Loss1: 0.256483 Loss2: 1.391284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.631863 Loss1: 0.243353 Loss2: 1.388510 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556244 Loss1: 0.176134 Loss2: 1.380110 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.552374 Loss1: 0.173652 Loss2: 1.378721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.504053 Loss1: 0.136461 Loss2: 1.367592 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971680
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.218288 Loss1: 1.220921 Loss2: 1.997367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.948530 Loss1: 0.396607 Loss2: 1.551924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.115154 Loss1: 1.255893 Loss2: 1.859262 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.184554 Loss1: 0.764494 Loss2: 1.420059 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.921188 Loss1: 0.501666 Loss2: 1.419523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.767194 Loss1: 0.375078 Loss2: 1.392116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.749601 Loss1: 0.340604 Loss2: 1.408997 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.598555 Loss1: 0.207998 Loss2: 1.390557 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.547843 Loss1: 0.164702 Loss2: 1.383140 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.525043 Loss1: 0.156834 Loss2: 1.368209 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.157561 Loss1: 0.731273 Loss2: 1.426288 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.733333 Loss1: 0.324135 Loss2: 1.409198 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.663375 Loss1: 0.256351 Loss2: 1.407025
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.126796 Loss1: 1.173020 Loss2: 1.953777
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.156225 Loss1: 0.685123 Loss2: 1.471102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.951657 Loss1: 0.445280 Loss2: 1.506376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.776158 Loss1: 0.321515 Loss2: 1.454644 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687883 Loss1: 0.235538 Loss2: 1.452345 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966518
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.633761 Loss1: 0.190862 Loss2: 1.442899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.692874 Loss1: 0.252879 Loss2: 1.439994 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.544342 Loss1: 0.112834 Loss2: 1.431508 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.109875 Loss1: 0.714903 Loss2: 1.394973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.667985 Loss1: 0.314191 Loss2: 1.353793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.624363 Loss1: 0.271447 Loss2: 1.352916
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.202122 Loss1: 1.238888 Loss2: 1.963234
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.181312 Loss1: 0.712540 Loss2: 1.468772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496801 Loss1: 0.156676 Loss2: 1.340125
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.979688 Loss1: 0.523002 Loss2: 1.456686
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.503260 Loss1: 0.162505 Loss2: 1.340756
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.762333 Loss1: 0.323028 Loss2: 1.439305
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509681 Loss1: 0.170652 Loss2: 1.339029
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.668656 Loss1: 0.264250 Loss2: 1.404406
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.619677 Loss1: 0.215590 Loss2: 1.404088
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476699 Loss1: 0.144365 Loss2: 1.332334
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.560638 Loss1: 0.158415 Loss2: 1.402223 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487892 Loss1: 0.099468 Loss2: 1.388424 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.359979 Loss1: 0.831954 Loss2: 1.528025 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.881654 Loss1: 0.437573 Loss2: 1.444081 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.789060 Loss1: 0.364069 Loss2: 1.424991 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.684163 Loss1: 0.259577 Loss2: 1.424585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.602264 Loss1: 0.184611 Loss2: 1.417652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.516319 Loss1: 0.116380 Loss2: 1.399939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.517546 Loss1: 0.128663 Loss2: 1.388884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.504542 Loss1: 0.113611 Loss2: 1.390931 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.631514 Loss1: 0.173433 Loss2: 1.458081 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.063041 Loss1: 1.230644 Loss2: 1.832397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.984067 Loss1: 0.555640 Loss2: 1.428427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.721374 Loss1: 0.359083 Loss2: 1.362291
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.083791 Loss1: 1.144656 Loss2: 1.939135
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.096215 Loss1: 0.646114 Loss2: 1.450101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.907925 Loss1: 0.452822 Loss2: 1.455102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.783418 Loss1: 0.350172 Loss2: 1.433246 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.683311 Loss1: 0.260732 Loss2: 1.422579 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.649566 Loss1: 0.228356 Loss2: 1.421210 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.516467 Loss1: 0.149655 Loss2: 1.366812
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.604057 Loss1: 0.177035 Loss2: 1.427022
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.551307 Loss1: 0.143392 Loss2: 1.407915
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519463 Loss1: 0.117865 Loss2: 1.401598
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.553327 Loss1: 0.150292 Loss2: 1.403036
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.094748 Loss1: 1.208725 Loss2: 1.886023
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.125181 Loss1: 0.690377 Loss2: 1.434804
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.852983 Loss1: 0.404438 Loss2: 1.448545
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.680301 Loss1: 0.271042 Loss2: 1.409260
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.101094 Loss1: 1.177182 Loss2: 1.923912
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.243111 Loss1: 0.775195 Loss2: 1.467916 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.905432 Loss1: 0.419022 Loss2: 1.486410 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.765978 Loss1: 0.329700 Loss2: 1.436278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.691760 Loss1: 0.250567 Loss2: 1.441193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.593663 Loss1: 0.154463 Loss2: 1.439200 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.530071 Loss1: 0.139982 Loss2: 1.390089
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.579560 Loss1: 0.154935 Loss2: 1.424625
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.582625 Loss1: 0.158581 Loss2: 1.424044
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.570450 Loss1: 0.142838 Loss2: 1.427612
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.543619 Loss1: 0.122669 Loss2: 1.420950
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.271340 Loss1: 1.380449 Loss2: 1.890891
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.164802 Loss1: 0.759945 Loss2: 1.404857
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.908636 Loss1: 0.502725 Loss2: 1.405911
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.746519 Loss1: 0.371974 Loss2: 1.374545
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.340825 Loss1: 1.383264 Loss2: 1.957561
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.257425 Loss1: 0.828163 Loss2: 1.429261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.016293 Loss1: 0.530045 Loss2: 1.486248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.748341 Loss1: 0.340842 Loss2: 1.407499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.624303 Loss1: 0.217813 Loss2: 1.406490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.627609 Loss1: 0.224562 Loss2: 1.403047 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.583510 Loss1: 0.187782 Loss2: 1.395728 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.517774 Loss1: 0.123748 Loss2: 1.394025 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965402
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.912130 Loss1: 1.117943 Loss2: 1.794187
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.128897 Loss1: 0.727798 Loss2: 1.401099
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.841556 Loss1: 0.478877 Loss2: 1.362679
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.699121 Loss1: 0.336115 Loss2: 1.363005
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.080425 Loss1: 1.257457 Loss2: 1.822968
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631527 Loss1: 0.283870 Loss2: 1.347657
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.103302 Loss1: 0.736255 Loss2: 1.367047
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.603756 Loss1: 0.257812 Loss2: 1.345943
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804670 Loss1: 0.420607 Loss2: 1.384063
-DEBUG flwr 2023-10-10 15:43:57,601 | server.py:236 | fit_round 81 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.560299 Loss1: 0.211944 Loss2: 1.348356
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.722007 Loss1: 0.362851 Loss2: 1.359157
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.624781 Loss1: 0.276939 Loss2: 1.347843
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.544848 Loss1: 0.202136 Loss2: 1.342712
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.624833 Loss1: 0.280183 Loss2: 1.344650
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.500896 Loss1: 0.168648 Loss2: 1.332248
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533346 Loss1: 0.186010 Loss2: 1.347337
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.479647 Loss1: 0.144340 Loss2: 1.335307
-(DefaultActor pid=3765) >> Training accuracy: 0.965820
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.465218 Loss1: 0.138519 Loss2: 1.326699 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.179512 Loss1: 1.222240 Loss2: 1.957272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.925923 Loss1: 0.439427 Loss2: 1.486497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.753604 Loss1: 0.281238 Loss2: 1.472366
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.897973 Loss1: 1.047958 Loss2: 1.850015
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.700843 Loss1: 0.234857 Loss2: 1.465986
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.010255 Loss1: 0.619381 Loss2: 1.390873
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.647960 Loss1: 0.197770 Loss2: 1.450190
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.813459 Loss1: 0.396299 Loss2: 1.417160
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.602944 Loss1: 0.156440 Loss2: 1.446505
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.673241 Loss1: 0.300940 Loss2: 1.372301
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.567028 Loss1: 0.123810 Loss2: 1.443218
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.561920 Loss1: 0.185881 Loss2: 1.376039
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.578965 Loss1: 0.138082 Loss2: 1.440882
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.554074 Loss1: 0.203207 Loss2: 1.350867
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.532582 Loss1: 0.087529 Loss2: 1.445053
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.572660 Loss1: 0.199841 Loss2: 1.372818
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.527525 Loss1: 0.158740 Loss2: 1.368786
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.527239 Loss1: 0.161915 Loss2: 1.365323
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.531136 Loss1: 0.171801 Loss2: 1.359334
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 15:43:57,601][flwr][DEBUG] - fit_round 81 received 50 results and 0 failures
-INFO flwr 2023-10-10 15:44:39,875 | server.py:125 | fit progress: (81, 2.2340269601002287, {'accuracy': 0.5476}, 186787.65366741602)
->> Test accuracy: 0.547600
-[2023-10-10 15:44:39,875][flwr][INFO] - fit progress: (81, 2.2340269601002287, {'accuracy': 0.5476}, 186787.65366741602)
-DEBUG flwr 2023-10-10 15:44:39,875 | server.py:173 | evaluate_round 81: strategy sampled 50 clients (out of 50)
-[2023-10-10 15:44:39,875][flwr][DEBUG] - evaluate_round 81: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 15:53:45,333 | server.py:187 | evaluate_round 81 received 50 results and 0 failures
-[2023-10-10 15:53:45,333][flwr][DEBUG] - evaluate_round 81 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 15:53:45,333 | server.py:222 | fit_round 82: strategy sampled 50 clients (out of 50)
-[2023-10-10 15:53:45,333][flwr][DEBUG] - fit_round 82: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.153625 Loss1: 1.236755 Loss2: 1.916870
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.185194 Loss1: 0.716241 Loss2: 1.468954
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.929168 Loss1: 0.450277 Loss2: 1.478891
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.767782 Loss1: 0.327241 Loss2: 1.440542
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.916289 Loss1: 1.053607 Loss2: 1.862682
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.126444 Loss1: 0.697891 Loss2: 1.428553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.851787 Loss1: 0.407484 Loss2: 1.444303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.764524 Loss1: 0.347075 Loss2: 1.417449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.683036 Loss1: 0.272480 Loss2: 1.410557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.637926 Loss1: 0.232190 Loss2: 1.405736 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.582800 Loss1: 0.183910 Loss2: 1.398890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.526208 Loss1: 0.130914 Loss2: 1.395294 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.166946 Loss1: 1.254433 Loss2: 1.912513 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.089713 Loss1: 0.605504 Loss2: 1.484210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.729710 Loss1: 0.277819 Loss2: 1.451891 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.648099 Loss1: 0.225862 Loss2: 1.422237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.603027 Loss1: 0.176783 Loss2: 1.426245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.562208 Loss1: 0.144123 Loss2: 1.418085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.541668 Loss1: 0.124655 Loss2: 1.417014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.526698 Loss1: 0.118659 Loss2: 1.408038 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.513606 Loss1: 0.166879 Loss2: 1.346726 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.520145 Loss1: 0.168882 Loss2: 1.351262 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.196580 Loss1: 0.780374 Loss2: 1.416205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.803831 Loss1: 0.400754 Loss2: 1.403077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.681823 Loss1: 0.269275 Loss2: 1.412548
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.185851 Loss1: 1.345621 Loss2: 1.840230
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.094048 Loss1: 0.688035 Loss2: 1.406013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.752468 Loss1: 0.355986 Loss2: 1.396482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.670941 Loss1: 0.303124 Loss2: 1.367816 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.602689 Loss1: 0.222093 Loss2: 1.380596 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.545594 Loss1: 0.185035 Loss2: 1.360559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.522651 Loss1: 0.163172 Loss2: 1.359479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.480094 Loss1: 0.136030 Loss2: 1.344064 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.158918 Loss1: 0.717645 Loss2: 1.441273 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.693747 Loss1: 0.294361 Loss2: 1.399386 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.078208 Loss1: 1.201298 Loss2: 1.876911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.119349 Loss1: 0.712135 Loss2: 1.407214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.796234 Loss1: 0.407737 Loss2: 1.388497 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.674572 Loss1: 0.324469 Loss2: 1.350103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569710 Loss1: 0.203693 Loss2: 1.366017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439397 Loss1: 0.111188 Loss2: 1.328209 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448665 Loss1: 0.122539 Loss2: 1.326126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425301 Loss1: 0.105970 Loss2: 1.319331
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.138077 Loss1: 1.193454 Loss2: 1.944623
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.235210 Loss1: 0.733296 Loss2: 1.501913
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.069968 Loss1: 0.543637 Loss2: 1.526331
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.883379 Loss1: 0.412066 Loss2: 1.471313
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.772335 Loss1: 0.289564 Loss2: 1.482770
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.105547 Loss1: 1.230055 Loss2: 1.875492
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.646775 Loss1: 0.183486 Loss2: 1.463289
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.615906 Loss1: 0.166002 Loss2: 1.449904
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.312635 Loss1: 0.820268 Loss2: 1.492367
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.577353 Loss1: 0.129745 Loss2: 1.447608
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.910103 Loss1: 0.453441 Loss2: 1.456662
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.562074 Loss1: 0.112413 Loss2: 1.449661
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.705718 Loss1: 0.285931 Loss2: 1.419788
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.564390 Loss1: 0.127534 Loss2: 1.436856
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.664894 Loss1: 0.245904 Loss2: 1.418991
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.662834 Loss1: 0.236745 Loss2: 1.426089
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.635510 Loss1: 0.203726 Loss2: 1.431783
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568158 Loss1: 0.146308 Loss2: 1.421850
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.520226 Loss1: 0.112271 Loss2: 1.407956
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.226024 Loss1: 1.262703 Loss2: 1.963322
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.297888 Loss1: 0.846162 Loss2: 1.451726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.784150 Loss1: 0.342622 Loss2: 1.441528 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.667614 Loss1: 0.229544 Loss2: 1.438070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.051675 Loss1: 1.197556 Loss2: 1.854119 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.117240 Loss1: 0.720701 Loss2: 1.396540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.912702 Loss1: 0.491978 Loss2: 1.420724 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.630758 Loss1: 0.252647 Loss2: 1.378111 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480770 Loss1: 0.125985 Loss2: 1.354786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501132 Loss1: 0.144414 Loss2: 1.356717
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.002978 Loss1: 1.074006 Loss2: 1.928972
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.076790 Loss1: 0.648479 Loss2: 1.428311 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461996 Loss1: 0.112986 Loss2: 1.349010
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.857570 Loss1: 0.391264 Loss2: 1.466306
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.663666 Loss1: 0.258135 Loss2: 1.405531
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.625996 Loss1: 0.216402 Loss2: 1.409593
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569246 Loss1: 0.168181 Loss2: 1.401065
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.515088 Loss1: 0.124833 Loss2: 1.390256
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.211170 Loss1: 1.277739 Loss2: 1.933431
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.513056 Loss1: 0.123912 Loss2: 1.389144
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.200490 Loss1: 0.770009 Loss2: 1.430481
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474943 Loss1: 0.082354 Loss2: 1.392589
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475137 Loss1: 0.094315 Loss2: 1.380823 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.674567 Loss1: 0.271929 Loss2: 1.402638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.620440 Loss1: 0.214806 Loss2: 1.405634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.546870 Loss1: 0.153366 Loss2: 1.393504 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.505594 Loss1: 0.125775 Loss2: 1.379820 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979911
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.786451 Loss1: 0.370295 Loss2: 1.416156 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.522816 Loss1: 0.143121 Loss2: 1.379695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517420 Loss1: 0.151279 Loss2: 1.366141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.507983 Loss1: 0.142305 Loss2: 1.365678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.528374 Loss1: 0.159164 Loss2: 1.369210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.499726 Loss1: 0.132720 Loss2: 1.367005 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.957031
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.610300 Loss1: 0.215889 Loss2: 1.394411 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.521177 Loss1: 0.143908 Loss2: 1.377269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.088153 Loss1: 1.218258 Loss2: 1.869895 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.891105 Loss1: 0.472442 Loss2: 1.418663 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.597168 Loss1: 0.207141 Loss2: 1.390026 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.536771 Loss1: 0.163780 Loss2: 1.372991
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.025797 Loss1: 1.151694 Loss2: 1.874103
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.119825 Loss1: 0.733132 Loss2: 1.386693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.839047 Loss1: 0.417617 Loss2: 1.421431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.678662 Loss1: 0.311436 Loss2: 1.367226 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.495931 Loss1: 0.126755 Loss2: 1.369176
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.720246 Loss1: 0.326860 Loss2: 1.393386
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.613549 Loss1: 0.241672 Loss2: 1.371877
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.637113 Loss1: 0.266867 Loss2: 1.370245
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.539975 Loss1: 0.165945 Loss2: 1.374030
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476002 Loss1: 0.120856 Loss2: 1.355145
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.218605 Loss1: 1.397155 Loss2: 1.821451
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433246 Loss1: 0.085846 Loss2: 1.347400
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.885757 Loss1: 0.495719 Loss2: 1.390038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.576016 Loss1: 0.239412 Loss2: 1.336603 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.498325 Loss1: 0.179421 Loss2: 1.318904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423419 Loss1: 0.113570 Loss2: 1.309849 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414046 Loss1: 0.110374 Loss2: 1.303672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390988 Loss1: 0.091583 Loss2: 1.299405 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975446
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.698979 Loss1: 0.265184 Loss2: 1.433795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.593545 Loss1: 0.170779 Loss2: 1.422767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.551949 Loss1: 0.139717 Loss2: 1.412232
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.128773 Loss1: 1.265196 Loss2: 1.863577
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.191952 Loss1: 0.772090 Loss2: 1.419862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509260 Loss1: 0.103296 Loss2: 1.405964
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.881472 Loss1: 0.461873 Loss2: 1.419599
-(DefaultActor pid=3764) >> Training accuracy: 0.961914
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.701551 Loss1: 0.320685 Loss2: 1.380866
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605897 Loss1: 0.221636 Loss2: 1.384261
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576980 Loss1: 0.204889 Loss2: 1.372091
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.526095 Loss1: 0.157397 Loss2: 1.368698
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.058881 Loss1: 1.089775 Loss2: 1.969106
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.523730 Loss1: 0.157876 Loss2: 1.365854
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.277323 Loss1: 0.767321 Loss2: 1.510002
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482582 Loss1: 0.116557 Loss2: 1.366025
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.055800 Loss1: 0.534810 Loss2: 1.520990
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484220 Loss1: 0.124169 Loss2: 1.360052
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.723631 Loss1: 0.242966 Loss2: 1.480665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.626418 Loss1: 0.166729 Loss2: 1.459689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.610431 Loss1: 0.162104 Loss2: 1.448326
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.085525 Loss1: 1.193559 Loss2: 1.891967
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.185614 Loss1: 0.737718 Loss2: 1.447896 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.568517 Loss1: 0.134420 Loss2: 1.434097
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.922655 Loss1: 0.469754 Loss2: 1.452901
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.762964 Loss1: 0.336087 Loss2: 1.426877
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.700806 Loss1: 0.272403 Loss2: 1.428403
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.630016 Loss1: 0.214434 Loss2: 1.415582
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517807 Loss1: 0.110700 Loss2: 1.407106
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.520302 Loss1: 0.126946 Loss2: 1.393356
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.194371 Loss1: 1.315487 Loss2: 1.878884
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.532280 Loss1: 0.131704 Loss2: 1.400576
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.116577 Loss1: 0.691370 Loss2: 1.425207
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.489230 Loss1: 0.103203 Loss2: 1.386027
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.867691 Loss1: 0.434455 Loss2: 1.433236
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.776144 Loss1: 0.373129 Loss2: 1.403015
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.692591 Loss1: 0.273676 Loss2: 1.418915
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647944 Loss1: 0.246699 Loss2: 1.401245
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.584959 Loss1: 0.184995 Loss2: 1.399964
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.978011 Loss1: 1.165562 Loss2: 1.812449
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568739 Loss1: 0.178547 Loss2: 1.390192
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.999398 Loss1: 0.636672 Loss2: 1.362727
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.510556 Loss1: 0.120343 Loss2: 1.390213
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.781799 Loss1: 0.404071 Loss2: 1.377728
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487627 Loss1: 0.100264 Loss2: 1.387363
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.554053 Loss1: 0.203178 Loss2: 1.350874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.558150 Loss1: 0.213828 Loss2: 1.344321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.478502 Loss1: 0.137671 Loss2: 1.340831
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.904959 Loss1: 1.080049 Loss2: 1.824910
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.051716 Loss1: 0.650641 Loss2: 1.401075 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.489166 Loss1: 0.156442 Loss2: 1.332724
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834763 Loss1: 0.439663 Loss2: 1.395099
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.770455 Loss1: 0.391575 Loss2: 1.378880
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.670900 Loss1: 0.309048 Loss2: 1.361852
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.643418 Loss1: 0.269300 Loss2: 1.374118
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.568180 Loss1: 0.221985 Loss2: 1.346196
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.233802 Loss1: 1.261673 Loss2: 1.972129
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.528140 Loss1: 0.950466 Loss2: 1.577673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.042286 Loss1: 0.534754 Loss2: 1.507532 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969727
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.488361 Loss1: 0.142812 Loss2: 1.345550
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.880885 Loss1: 0.380581 Loss2: 1.500305
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.775238 Loss1: 0.287916 Loss2: 1.487322
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.737613 Loss1: 0.257585 Loss2: 1.480028
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.765603 Loss1: 0.277195 Loss2: 1.488409
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.655656 Loss1: 0.175707 Loss2: 1.479949
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.199969 Loss1: 1.234014 Loss2: 1.965955
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.620587 Loss1: 0.155784 Loss2: 1.464803
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.579805 Loss1: 0.116511 Loss2: 1.463294 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.962500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.685534 Loss1: 0.296424 Loss2: 1.389110 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513964 Loss1: 0.158268 Loss2: 1.355695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.511600 Loss1: 0.155254 Loss2: 1.356345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.476348 Loss1: 0.124193 Loss2: 1.352155 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.879552 Loss1: 0.485552 Loss2: 1.394001 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.648993 Loss1: 0.284511 Loss2: 1.364482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.574981 Loss1: 0.211773 Loss2: 1.363209
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.036790 Loss1: 1.202283 Loss2: 1.834508
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.577805 Loss1: 0.224979 Loss2: 1.352826
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.231104 Loss1: 0.826699 Loss2: 1.404405
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.506452 Loss1: 0.151792 Loss2: 1.354660
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.849298 Loss1: 0.500169 Loss2: 1.349129
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.480947 Loss1: 0.135281 Loss2: 1.345666
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.740494 Loss1: 0.387174 Loss2: 1.353320
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.610609 Loss1: 0.288477 Loss2: 1.322131
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460612 Loss1: 0.126656 Loss2: 1.333956
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491432 Loss1: 0.178947 Loss2: 1.312485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416016 Loss1: 0.113852 Loss2: 1.302164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398209 Loss1: 0.100399 Loss2: 1.297810
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.976432 Loss1: 1.060378 Loss2: 1.916054
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.049933 Loss1: 0.595327 Loss2: 1.454607
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.848464 Loss1: 0.392860 Loss2: 1.455604
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.720003 Loss1: 0.289000 Loss2: 1.431003
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.657502 Loss1: 0.237583 Loss2: 1.419920
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.608972 Loss1: 0.192539 Loss2: 1.416434
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.913933 Loss1: 1.075746 Loss2: 1.838187
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.606508 Loss1: 0.193450 Loss2: 1.413059
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.060591 Loss1: 0.667679 Loss2: 1.392912
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.570852 Loss1: 0.164273 Loss2: 1.406579
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.855386 Loss1: 0.444001 Loss2: 1.411385
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.549854 Loss1: 0.149126 Loss2: 1.400728
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.666573 Loss1: 0.298719 Loss2: 1.367855
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.526084 Loss1: 0.118768 Loss2: 1.407316
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.577630 Loss1: 0.210330 Loss2: 1.367301
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528818 Loss1: 0.170242 Loss2: 1.358576
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471047 Loss1: 0.117782 Loss2: 1.353265
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460508 Loss1: 0.113958 Loss2: 1.346551
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.477346 Loss1: 0.135842 Loss2: 1.341505
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.477267 Loss1: 0.135432 Loss2: 1.341835
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.042301 Loss1: 1.152881 Loss2: 1.889420
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.146540 Loss1: 0.737979 Loss2: 1.408562
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.931094 Loss1: 0.473604 Loss2: 1.457490
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.747223 Loss1: 0.352043 Loss2: 1.395180
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.682983 Loss1: 0.273728 Loss2: 1.409254
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.580426 Loss1: 0.182950 Loss2: 1.397476
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.927096 Loss1: 1.136719 Loss2: 1.790376
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531547 Loss1: 0.136932 Loss2: 1.394615
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.917881 Loss1: 0.581423 Loss2: 1.336459
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.535620 Loss1: 0.147068 Loss2: 1.388552
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.790066 Loss1: 0.428483 Loss2: 1.361582
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.504161 Loss1: 0.120968 Loss2: 1.383194
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.629926 Loss1: 0.312300 Loss2: 1.317626
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466163 Loss1: 0.090064 Loss2: 1.376099
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535960 Loss1: 0.215167 Loss2: 1.320793
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467281 Loss1: 0.158660 Loss2: 1.308621
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476229 Loss1: 0.174359 Loss2: 1.301870
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457016 Loss1: 0.159511 Loss2: 1.297505
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429842 Loss1: 0.128785 Loss2: 1.301057
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.109909 Loss1: 1.181574 Loss2: 1.928335
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422525 Loss1: 0.127304 Loss2: 1.295221
-(DefaultActor pid=3764) >> Training accuracy: 0.950000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.920260 Loss1: 0.430471 Loss2: 1.489789 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.775550 Loss1: 0.327343 Loss2: 1.448207 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.660129 Loss1: 0.216292 Loss2: 1.443838
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.962380 Loss1: 1.028294 Loss2: 1.934086
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.633054 Loss1: 0.204424 Loss2: 1.428630
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.150305 Loss1: 0.708097 Loss2: 1.442208
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.590585 Loss1: 0.165852 Loss2: 1.424733
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.938529 Loss1: 0.450720 Loss2: 1.487809
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.591022 Loss1: 0.162707 Loss2: 1.428315
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699494 Loss1: 0.284428 Loss2: 1.415066
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.596332 Loss1: 0.172522 Loss2: 1.423810
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.681647 Loss1: 0.246261 Loss2: 1.435386
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.625324 Loss1: 0.202214 Loss2: 1.423110
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.572424 Loss1: 0.165686 Loss2: 1.406738
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.547007 Loss1: 0.143185 Loss2: 1.403822
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.526145 Loss1: 0.124528 Loss2: 1.401617
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.913174 Loss1: 1.044474 Loss2: 1.868701
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.567713 Loss1: 0.164238 Loss2: 1.403475
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.787387 Loss1: 0.380721 Loss2: 1.406666 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.626628 Loss1: 0.260513 Loss2: 1.366115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562645 Loss1: 0.216641 Loss2: 1.346004
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.867980 Loss1: 1.082180 Loss2: 1.785800
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509693 Loss1: 0.164153 Loss2: 1.345540
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.035842 Loss1: 0.700142 Loss2: 1.335701
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469714 Loss1: 0.126942 Loss2: 1.342773
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.850536 Loss1: 0.457016 Loss2: 1.393520
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443943 Loss1: 0.106665 Loss2: 1.337278
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.710207 Loss1: 0.371303 Loss2: 1.338903
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398076 Loss1: 0.068525 Loss2: 1.329551
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.636960 Loss1: 0.286587 Loss2: 1.350372
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.536537 Loss1: 0.206955 Loss2: 1.329583
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.522098 Loss1: 0.196038 Loss2: 1.326061
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484922 Loss1: 0.163823 Loss2: 1.321099
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446011 Loss1: 0.126402 Loss2: 1.319609
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.170472 Loss1: 1.114348 Loss2: 2.056124
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414231 Loss1: 0.100979 Loss2: 1.313252
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.997783 Loss1: 0.463369 Loss2: 1.534414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.747137 Loss1: 0.288602 Loss2: 1.458535 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.616399 Loss1: 0.173554 Loss2: 1.442844 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.595147 Loss1: 0.147635 Loss2: 1.447512 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.619902 Loss1: 0.165988 Loss2: 1.453913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.905236 Loss1: 0.364445 Loss2: 1.540791
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.602152 Loss1: 0.159461 Loss2: 1.442691
-(DefaultActor pid=3765) >> Training accuracy: 0.961538
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.726024 Loss1: 0.230437 Loss2: 1.495586 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.619181 Loss1: 0.153017 Loss2: 1.466164 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.921452 Loss1: 1.061849 Loss2: 1.859603
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.575958 Loss1: 0.114564 Loss2: 1.461394
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.114216 Loss1: 0.714946 Loss2: 1.399269
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.573390 Loss1: 0.118052 Loss2: 1.455338
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.570672 Loss1: 0.108408 Loss2: 1.462264 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.955078
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.645182 Loss1: 0.267862 Loss2: 1.377320 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.498121 Loss1: 0.148165 Loss2: 1.349955 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.035987 Loss1: 1.155657 Loss2: 1.880330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.149910 Loss1: 0.733540 Loss2: 1.416370 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.670717 Loss1: 0.274355 Loss2: 1.396361 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.592544 Loss1: 0.204656 Loss2: 1.387889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.569049 Loss1: 0.186617 Loss2: 1.382432
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.047751 Loss1: 1.192330 Loss2: 1.855421
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.169690 Loss1: 0.775745 Loss2: 1.393945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.911212 Loss1: 0.482097 Loss2: 1.429115 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.703283 Loss1: 0.329720 Loss2: 1.373564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.579208 Loss1: 0.210791 Loss2: 1.368417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.510424 Loss1: 0.151628 Loss2: 1.358797 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449894 Loss1: 0.104177 Loss2: 1.345718 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467109 Loss1: 0.121915 Loss2: 1.345194 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.705193 Loss1: 0.271006 Loss2: 1.434187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.661732 Loss1: 0.225847 Loss2: 1.435885 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.609188 Loss1: 0.174217 Loss2: 1.434971
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.841988 Loss1: 1.024563 Loss2: 1.817425
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.053283 Loss1: 0.625268 Loss2: 1.428014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.859774 Loss1: 0.447043 Loss2: 1.412731 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551522 Loss1: 0.169871 Loss2: 1.381651 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.533810 Loss1: 0.165879 Loss2: 1.367932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.908046 Loss1: 1.085882 Loss2: 1.822164
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.527025 Loss1: 0.163592 Loss2: 1.363432
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.082969 Loss1: 0.652844 Loss2: 1.430125
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.511263 Loss1: 0.139207 Loss2: 1.372057
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.871255 Loss1: 0.466925 Loss2: 1.404330
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.482473 Loss1: 0.110026 Loss2: 1.372447
-(DefaultActor pid=3765) >> Training accuracy: 0.975184
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687116 Loss1: 0.289345 Loss2: 1.397772 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 16:21:57,506 | server.py:236 | fit_round 82 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.525485 Loss1: 0.144683 Loss2: 1.380802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.294049 Loss1: 1.353157 Loss2: 1.940892
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491352 Loss1: 0.125477 Loss2: 1.365875
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.221584 Loss1: 0.772059 Loss2: 1.449525
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497915 Loss1: 0.138136 Loss2: 1.359780
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.050775 Loss1: 0.578041 Loss2: 1.472734
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429772 Loss1: 0.068776 Loss2: 1.360995
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.710616 Loss1: 0.274981 Loss2: 1.435635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.590670 Loss1: 0.178163 Loss2: 1.412507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.603346 Loss1: 0.195873 Loss2: 1.407473
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.041407 Loss1: 1.047872 Loss2: 1.993536
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.568175 Loss1: 0.156354 Loss2: 1.411821
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.202767 Loss1: 0.713458 Loss2: 1.489309
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.538093 Loss1: 0.132506 Loss2: 1.405588
-(DefaultActor pid=3765) >> Training accuracy: 0.960417
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.060174 Loss1: 0.502045 Loss2: 1.558129
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.848149 Loss1: 0.383254 Loss2: 1.464895
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.742755 Loss1: 0.260904 Loss2: 1.481852
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.636088 Loss1: 0.171356 Loss2: 1.464732
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.625205 Loss1: 0.161816 Loss2: 1.463389
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.658834 Loss1: 0.191347 Loss2: 1.467487
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.990366 Loss1: 1.134227 Loss2: 1.856139
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.648800 Loss1: 0.186060 Loss2: 1.462740
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.121632 Loss1: 0.655477 Loss2: 1.466155
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.606465 Loss1: 0.135501 Loss2: 1.470964
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.938547 Loss1: 0.495710 Loss2: 1.442837
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.800058 Loss1: 0.375120 Loss2: 1.424938
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.722017 Loss1: 0.301474 Loss2: 1.420543
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.609826 Loss1: 0.203265 Loss2: 1.406561
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.605558 Loss1: 0.202086 Loss2: 1.403471
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.980541 Loss1: 1.139259 Loss2: 1.841282
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.562407 Loss1: 0.148341 Loss2: 1.414066
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.518463 Loss1: 0.126771 Loss2: 1.391692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.517443 Loss1: 0.127529 Loss2: 1.389914 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.680831 Loss1: 0.288675 Loss2: 1.392156 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.538590 Loss1: 0.174986 Loss2: 1.363604 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.492274 Loss1: 0.129698 Loss2: 1.362575 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 16:21:57,506][flwr][DEBUG] - fit_round 82 received 50 results and 0 failures
-INFO flwr 2023-10-10 16:22:38,908 | server.py:125 | fit progress: (82, 2.2478357490640097, {'accuracy': 0.5485}, 189066.686781659)
->> Test accuracy: 0.548500
-[2023-10-10 16:22:38,908][flwr][INFO] - fit progress: (82, 2.2478357490640097, {'accuracy': 0.5485}, 189066.686781659)
-DEBUG flwr 2023-10-10 16:22:38,909 | server.py:173 | evaluate_round 82: strategy sampled 50 clients (out of 50)
-[2023-10-10 16:22:38,909][flwr][DEBUG] - evaluate_round 82: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 16:31:40,452 | server.py:187 | evaluate_round 82 received 50 results and 0 failures
-[2023-10-10 16:31:40,452][flwr][DEBUG] - evaluate_round 82 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 16:31:40,452 | server.py:222 | fit_round 83: strategy sampled 50 clients (out of 50)
-[2023-10-10 16:31:40,452][flwr][DEBUG] - fit_round 83: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.146647 Loss1: 1.188510 Loss2: 1.958138 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.010189 Loss1: 0.525772 Loss2: 1.484417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.801587 Loss1: 0.347830 Loss2: 1.453757
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.121623 Loss1: 1.210239 Loss2: 1.911384
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.703829 Loss1: 0.253169 Loss2: 1.450660
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.224671 Loss1: 0.742142 Loss2: 1.482529
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.706298 Loss1: 0.264132 Loss2: 1.442166
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.917511 Loss1: 0.477843 Loss2: 1.439668
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.623745 Loss1: 0.176794 Loss2: 1.446951
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.726454 Loss1: 0.296235 Loss2: 1.430219
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.605589 Loss1: 0.167964 Loss2: 1.437625
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.684959 Loss1: 0.263423 Loss2: 1.421535
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.553633 Loss1: 0.115597 Loss2: 1.438036
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.586532 Loss1: 0.178455 Loss2: 1.408077
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.548968 Loss1: 0.128362 Loss2: 1.420606
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.535622 Loss1: 0.137750 Loss2: 1.397872
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482755 Loss1: 0.091027 Loss2: 1.391728
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479707 Loss1: 0.091255 Loss2: 1.388452
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.524625 Loss1: 0.145803 Loss2: 1.378823
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.043126 Loss1: 1.175933 Loss2: 1.867193
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.242440 Loss1: 0.774436 Loss2: 1.468004
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.957762 Loss1: 0.522789 Loss2: 1.434973
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.106059 Loss1: 1.167431 Loss2: 1.938628
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.763971 Loss1: 0.331315 Loss2: 1.432656
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.223983 Loss1: 0.764390 Loss2: 1.459593
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.678619 Loss1: 0.267705 Loss2: 1.410915
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.914273 Loss1: 0.450880 Loss2: 1.463392
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.593005 Loss1: 0.191093 Loss2: 1.401912
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.629286 Loss1: 0.229886 Loss2: 1.399400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.573146 Loss1: 0.170460 Loss2: 1.402686 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.516988 Loss1: 0.128948 Loss2: 1.388040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.504683 Loss1: 0.113952 Loss2: 1.390731 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971680
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.488113 Loss1: 0.099547 Loss2: 1.388567 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.154518 Loss1: 1.267487 Loss2: 1.887031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.873479 Loss1: 0.427493 Loss2: 1.445986 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.702682 Loss1: 0.309681 Loss2: 1.393001
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.012202 Loss1: 1.137284 Loss2: 1.874918
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.664252 Loss1: 0.270325 Loss2: 1.393927
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.069934 Loss1: 0.675178 Loss2: 1.394756
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.643470 Loss1: 0.256749 Loss2: 1.386721
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.908536 Loss1: 0.484345 Loss2: 1.424191
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.575229 Loss1: 0.185993 Loss2: 1.389236
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.747359 Loss1: 0.354226 Loss2: 1.393133
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.487679 Loss1: 0.114477 Loss2: 1.373202
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.674677 Loss1: 0.287750 Loss2: 1.386927
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472451 Loss1: 0.107822 Loss2: 1.364629
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.604668 Loss1: 0.225590 Loss2: 1.379078
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.470650 Loss1: 0.109719 Loss2: 1.360932
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.562144 Loss1: 0.187130 Loss2: 1.375014
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.535871 Loss1: 0.164416 Loss2: 1.371455
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480318 Loss1: 0.114608 Loss2: 1.365711
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.502599 Loss1: 0.137834 Loss2: 1.364765
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.020696 Loss1: 1.135175 Loss2: 1.885521
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.077519 Loss1: 0.610652 Loss2: 1.466867
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.823541 Loss1: 0.376867 Loss2: 1.446674
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.025413 Loss1: 1.214444 Loss2: 1.810970
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.722134 Loss1: 0.297512 Loss2: 1.424623
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.052713 Loss1: 0.698472 Loss2: 1.354241
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.680422 Loss1: 0.261947 Loss2: 1.418475
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.826020 Loss1: 0.450081 Loss2: 1.375939
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.632876 Loss1: 0.208172 Loss2: 1.424704
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.656518 Loss1: 0.243233 Loss2: 1.413285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.582263 Loss1: 0.171201 Loss2: 1.411062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.566876 Loss1: 0.153873 Loss2: 1.413003 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.509943 Loss1: 0.107623 Loss2: 1.402320 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967773
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425332 Loss1: 0.119788 Loss2: 1.305544 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.007224 Loss1: 1.205045 Loss2: 1.802179 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.830051 Loss1: 0.437880 Loss2: 1.392171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.662215 Loss1: 0.315414 Loss2: 1.346801
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.054770 Loss1: 1.111806 Loss2: 1.942964
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.137216 Loss1: 0.668131 Loss2: 1.469085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.930413 Loss1: 0.429227 Loss2: 1.501186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.823428 Loss1: 0.365804 Loss2: 1.457624 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.765888 Loss1: 0.313511 Loss2: 1.452378 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.654447 Loss1: 0.204550 Loss2: 1.449897 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438174 Loss1: 0.109421 Loss2: 1.328753
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.615239 Loss1: 0.174360 Loss2: 1.440878
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.588052 Loss1: 0.151844 Loss2: 1.436208
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.594733 Loss1: 0.163537 Loss2: 1.431196
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.567786 Loss1: 0.136893 Loss2: 1.430893
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.100627 Loss1: 1.147721 Loss2: 1.952906
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.079637 Loss1: 0.616634 Loss2: 1.463003
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.842891 Loss1: 0.422154 Loss2: 1.420737
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.742869 Loss1: 0.331165 Loss2: 1.411704
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.188536 Loss1: 1.259227 Loss2: 1.929309
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.156139 Loss1: 0.739520 Loss2: 1.416619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.584038 Loss1: 0.192982 Loss2: 1.391055
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.897469 Loss1: 0.435979 Loss2: 1.461490
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524911 Loss1: 0.140141 Loss2: 1.384770
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.720211 Loss1: 0.313291 Loss2: 1.406919
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.530243 Loss1: 0.152957 Loss2: 1.377286
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.662120 Loss1: 0.257117 Loss2: 1.405003
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.634429 Loss1: 0.237358 Loss2: 1.397071
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.515493 Loss1: 0.138449 Loss2: 1.377044
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.582233 Loss1: 0.190731 Loss2: 1.391502
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475594 Loss1: 0.104398 Loss2: 1.371196
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490434 Loss1: 0.114348 Loss2: 1.376086 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965402
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.105046 Loss1: 1.120660 Loss2: 1.984386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.038720 Loss1: 0.506324 Loss2: 1.532396 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.809666 Loss1: 0.329616 Loss2: 1.480050
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.025297 Loss1: 1.104999 Loss2: 1.920298
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.694289 Loss1: 0.220113 Loss2: 1.474176
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.132374 Loss1: 0.675007 Loss2: 1.457366
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.644601 Loss1: 0.187304 Loss2: 1.457296
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.907918 Loss1: 0.439368 Loss2: 1.468550
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.609545 Loss1: 0.152114 Loss2: 1.457431
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.726401 Loss1: 0.308888 Loss2: 1.417513
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.571535 Loss1: 0.124084 Loss2: 1.447451
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.686763 Loss1: 0.256231 Loss2: 1.430531
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.586610 Loss1: 0.131974 Loss2: 1.454636
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.583474 Loss1: 0.172909 Loss2: 1.410566
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.591577 Loss1: 0.141113 Loss2: 1.450464
-(DefaultActor pid=3765) >> Training accuracy: 0.953125
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.629844 Loss1: 0.218769 Loss2: 1.411075
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.600085 Loss1: 0.187546 Loss2: 1.412540
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.627301 Loss1: 0.218391 Loss2: 1.408910
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.573328 Loss1: 0.158197 Loss2: 1.415131
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.223853 Loss1: 1.274877 Loss2: 1.948976
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.141636 Loss1: 0.722297 Loss2: 1.419339
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.927032 Loss1: 0.447348 Loss2: 1.479684
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.760979 Loss1: 0.332454 Loss2: 1.428524
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.968984 Loss1: 1.133313 Loss2: 1.835671
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.625683 Loss1: 0.198671 Loss2: 1.427012 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.622066 Loss1: 0.208851 Loss2: 1.413214 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.581862 Loss1: 0.160553 Loss2: 1.421309 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.556441 Loss1: 0.141158 Loss2: 1.415283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.511543 Loss1: 0.109633 Loss2: 1.401910 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980769
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.565937 Loss1: 0.169023 Loss2: 1.396913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.631573 Loss1: 0.229412 Loss2: 1.402162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.096639 Loss1: 1.132315 Loss2: 1.964324
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.642115 Loss1: 0.231770 Loss2: 1.410345
-(DefaultActor pid=3764) >> Training accuracy: 0.978516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.955232 Loss1: 0.437542 Loss2: 1.517689 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.706161 Loss1: 0.245765 Loss2: 1.460396 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.645262 Loss1: 0.196654 Loss2: 1.448608
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.009175 Loss1: 1.168518 Loss2: 1.840657
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.099822 Loss1: 0.661406 Loss2: 1.438416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.903551 Loss1: 0.487770 Loss2: 1.415781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.753284 Loss1: 0.350656 Loss2: 1.402627 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.633411 Loss1: 0.245128 Loss2: 1.388283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.539165 Loss1: 0.163796 Loss2: 1.375369 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.518457 Loss1: 0.157780 Loss2: 1.360676 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.504637 Loss1: 0.135838 Loss2: 1.368799 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.998327 Loss1: 0.531509 Loss2: 1.466817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.764527 Loss1: 0.319932 Loss2: 1.444595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.679421 Loss1: 0.245283 Loss2: 1.434138
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.879792 Loss1: 0.957574 Loss2: 1.922219
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.072701 Loss1: 0.631079 Loss2: 1.441623 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.927186 Loss1: 0.459181 Loss2: 1.468005 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.595792 Loss1: 0.183246 Loss2: 1.412546
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.787647 Loss1: 0.367048 Loss2: 1.420598
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.552910 Loss1: 0.137884 Loss2: 1.415026
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.657462 Loss1: 0.225044 Loss2: 1.432418
-(DefaultActor pid=3765) >> Training accuracy: 0.962891
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546357 Loss1: 0.141123 Loss2: 1.405234
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.526572 Loss1: 0.132884 Loss2: 1.393688
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.509522 Loss1: 0.115870 Loss2: 1.393651
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487271 Loss1: 0.095535 Loss2: 1.391736
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.475166 Loss1: 0.089350 Loss2: 1.385816
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.016224 Loss1: 1.133919 Loss2: 1.882304
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.268407 Loss1: 0.803685 Loss2: 1.464722
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.904234 Loss1: 0.450093 Loss2: 1.454141
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.782336 Loss1: 0.356600 Loss2: 1.425736
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.668736 Loss1: 0.241752 Loss2: 1.426985
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.667864 Loss1: 0.247301 Loss2: 1.420563
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.904853 Loss1: 1.088119 Loss2: 1.816734
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.566116 Loss1: 0.158239 Loss2: 1.407878
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.188530 Loss1: 0.773189 Loss2: 1.415341
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.549090 Loss1: 0.147041 Loss2: 1.402049
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.867734 Loss1: 0.437011 Loss2: 1.430723
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.707109 Loss1: 0.319544 Loss2: 1.387565 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.549360 Loss1: 0.145846 Loss2: 1.403514
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.628719 Loss1: 0.244357 Loss2: 1.384363
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.632742 Loss1: 0.247305 Loss2: 1.385437
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.581852 Loss1: 0.204232 Loss2: 1.377620
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493388 Loss1: 0.124730 Loss2: 1.368658
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.469522 Loss1: 0.119160 Loss2: 1.350363
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.102138 Loss1: 1.216523 Loss2: 1.885615
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.266182 Loss1: 0.826783 Loss2: 1.439399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.686191 Loss1: 0.293177 Loss2: 1.393015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562859 Loss1: 0.182357 Loss2: 1.380502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512913 Loss1: 0.143753 Loss2: 1.369160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488753 Loss1: 0.124097 Loss2: 1.364656 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441120 Loss1: 0.079019 Loss2: 1.362100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434009 Loss1: 0.082172 Loss2: 1.351838 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.565418 Loss1: 0.192999 Loss2: 1.372419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.550167 Loss1: 0.188448 Loss2: 1.361719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.504209 Loss1: 0.144514 Loss2: 1.359696
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.940094 Loss1: 1.076897 Loss2: 1.863197
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.034806 Loss1: 0.585457 Loss2: 1.449349 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.707681 Loss1: 0.304812 Loss2: 1.402869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.193895 Loss1: 1.243706 Loss2: 1.950189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.234304 Loss1: 0.811192 Loss2: 1.423113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.001595 Loss1: 0.500902 Loss2: 1.500694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.795127 Loss1: 0.372956 Loss2: 1.422170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.530642 Loss1: 0.155577 Loss2: 1.375065
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.659783 Loss1: 0.237722 Loss2: 1.422061
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.683439 Loss1: 0.269634 Loss2: 1.413806
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.514896 Loss1: 0.136036 Loss2: 1.378859
-(DefaultActor pid=3765) >> Training accuracy: 0.982537
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.580661 Loss1: 0.169148 Loss2: 1.411514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.537006 Loss1: 0.133374 Loss2: 1.403632 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978795
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.113955 Loss1: 0.731306 Loss2: 1.382649 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.653075 Loss1: 0.279092 Loss2: 1.373983 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.609632 Loss1: 0.233194 Loss2: 1.376437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530209 Loss1: 0.163086 Loss2: 1.367123 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.878880 Loss1: 0.430982 Loss2: 1.447898
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.527760 Loss1: 0.171073 Loss2: 1.356687
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.720635 Loss1: 0.289918 Loss2: 1.430717
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480350 Loss1: 0.122975 Loss2: 1.357375
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.668525 Loss1: 0.253257 Loss2: 1.415268
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456006 Loss1: 0.104666 Loss2: 1.351340
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428889 Loss1: 0.080650 Loss2: 1.348239
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.607155 Loss1: 0.187007 Loss2: 1.420148
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.552184 Loss1: 0.147568 Loss2: 1.404617
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.504287 Loss1: 0.106772 Loss2: 1.397515
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485348 Loss1: 0.093187 Loss2: 1.392160
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.511406 Loss1: 0.116513 Loss2: 1.394893
-(DefaultActor pid=3764) >> Training accuracy: 0.972656
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.130817 Loss1: 1.301541 Loss2: 1.829276
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.209948 Loss1: 0.810312 Loss2: 1.399635
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.947136 Loss1: 0.552647 Loss2: 1.394489
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.745782 Loss1: 0.352707 Loss2: 1.393075
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.649702 Loss1: 0.287310 Loss2: 1.362392
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.092302 Loss1: 1.243543 Loss2: 1.848760
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.157467 Loss1: 0.780209 Loss2: 1.377257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.956253 Loss1: 0.524071 Loss2: 1.432182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.800658 Loss1: 0.426304 Loss2: 1.374353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.692500 Loss1: 0.305947 Loss2: 1.386553 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.584260 Loss1: 0.227531 Loss2: 1.356730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496662 Loss1: 0.148717 Loss2: 1.347945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.485323 Loss1: 0.125815 Loss2: 1.359508 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.325189 Loss1: 0.747484 Loss2: 1.577705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.894987 Loss1: 0.345077 Loss2: 1.549909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.789756 Loss1: 0.229023 Loss2: 1.560734
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.071345 Loss1: 1.197781 Loss2: 1.873564
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.121031 Loss1: 0.658953 Loss2: 1.462079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.895338 Loss1: 0.435773 Loss2: 1.459564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.800246 Loss1: 0.366145 Loss2: 1.434101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.695270 Loss1: 0.264634 Loss2: 1.430636 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.616642 Loss1: 0.192723 Loss2: 1.423920 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.567039 Loss1: 0.155523 Loss2: 1.411516 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.043016 Loss1: 1.071130 Loss2: 1.971886
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.542428 Loss1: 0.142331 Loss2: 1.400096
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.000167 Loss1: 0.507295 Loss2: 1.492871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.795273 Loss1: 0.337269 Loss2: 1.458004 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.216174 Loss1: 1.284468 Loss2: 1.931706
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.646501 Loss1: 0.188063 Loss2: 1.458438
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.210575 Loss1: 0.773633 Loss2: 1.436942
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.609319 Loss1: 0.168740 Loss2: 1.440579
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.057745 Loss1: 0.590529 Loss2: 1.467216
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.551479 Loss1: 0.120429 Loss2: 1.431050
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.529852 Loss1: 0.110292 Loss2: 1.419560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.521407 Loss1: 0.104580 Loss2: 1.416827 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.586572 Loss1: 0.190170 Loss2: 1.396402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.524761 Loss1: 0.131832 Loss2: 1.392929 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979911
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.494724 Loss1: 0.114159 Loss2: 1.380566
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.007988 Loss1: 1.064399 Loss2: 1.943589
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.011507 Loss1: 0.572302 Loss2: 1.439205
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.832371 Loss1: 0.358351 Loss2: 1.474020
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.714260 Loss1: 0.273323 Loss2: 1.440937
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.692004 Loss1: 0.257355 Loss2: 1.434649
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.103090 Loss1: 1.219198 Loss2: 1.883892
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.295655 Loss1: 0.873206 Loss2: 1.422448 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.864734 Loss1: 0.445829 Loss2: 1.418904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.719563 Loss1: 0.338204 Loss2: 1.381359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.661246 Loss1: 0.271628 Loss2: 1.389618 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.506315 Loss1: 0.099956 Loss2: 1.406360
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.578355 Loss1: 0.202893 Loss2: 1.375462
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550328 Loss1: 0.178513 Loss2: 1.371815
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.514061 Loss1: 0.151246 Loss2: 1.362814
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480323 Loss1: 0.121958 Loss2: 1.358365
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449884 Loss1: 0.098305 Loss2: 1.351579
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.000764 Loss1: 1.151400 Loss2: 1.849364
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.206711 Loss1: 0.787639 Loss2: 1.419072
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.920737 Loss1: 0.498008 Loss2: 1.422730
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.778301 Loss1: 0.387518 Loss2: 1.390783
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.653803 Loss1: 0.262143 Loss2: 1.391659
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.061483 Loss1: 1.208961 Loss2: 1.852522
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.091931 Loss1: 0.707631 Loss2: 1.384300
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.528497 Loss1: 0.154635 Loss2: 1.373862
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.848329 Loss1: 0.414978 Loss2: 1.433350
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475277 Loss1: 0.113559 Loss2: 1.361718
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.643079 Loss1: 0.272124 Loss2: 1.370955
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491528 Loss1: 0.132322 Loss2: 1.359206
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.650586 Loss1: 0.267663 Loss2: 1.382922
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.490376 Loss1: 0.131035 Loss2: 1.359341
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466446 Loss1: 0.112294 Loss2: 1.354152 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501727 Loss1: 0.146096 Loss2: 1.355631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.501236 Loss1: 0.151103 Loss2: 1.350133 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.139682 Loss1: 1.298066 Loss2: 1.841616
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.188089 Loss1: 0.773879 Loss2: 1.414210
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.830619 Loss1: 0.446906 Loss2: 1.383713
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.664970 Loss1: 0.312177 Loss2: 1.352793
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.988570 Loss1: 1.112356 Loss2: 1.876214
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.169674 Loss1: 0.747157 Loss2: 1.422517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.825397 Loss1: 0.401207 Loss2: 1.424190 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.766473 Loss1: 0.382420 Loss2: 1.384053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.634017 Loss1: 0.227117 Loss2: 1.406900 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.588370 Loss1: 0.211650 Loss2: 1.376720 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550601 Loss1: 0.176042 Loss2: 1.374559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467374 Loss1: 0.097707 Loss2: 1.369667 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.004859 Loss1: 1.175371 Loss2: 1.829488 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.847712 Loss1: 0.442149 Loss2: 1.405563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.999805 Loss1: 1.102001 Loss2: 1.897804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.253109 Loss1: 0.772943 Loss2: 1.480166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.863326 Loss1: 0.445698 Loss2: 1.417628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.724688 Loss1: 0.306421 Loss2: 1.418267 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.644497 Loss1: 0.233447 Loss2: 1.411050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.672413 Loss1: 0.264612 Loss2: 1.407801 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.578181 Loss1: 0.179074 Loss2: 1.399107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486658 Loss1: 0.107316 Loss2: 1.379342 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.131605 Loss1: 1.229524 Loss2: 1.902082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.939387 Loss1: 0.484424 Loss2: 1.454963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.809276 Loss1: 0.391243 Loss2: 1.418032
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.225499 Loss1: 1.181676 Loss2: 2.043823
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.252556 Loss1: 0.832222 Loss2: 1.420334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.998887 Loss1: 0.518697 Loss2: 1.480190
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.617265 Loss1: 0.212264 Loss2: 1.405001
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.607449 Loss1: 0.196386 Loss2: 1.411063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.551855 Loss1: 0.146886 Loss2: 1.404969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.520056 Loss1: 0.132431 Loss2: 1.387625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.576105 Loss1: 0.173699 Loss2: 1.402406 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.568947 Loss1: 0.172636 Loss2: 1.396311 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977865
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.192563 Loss1: 1.203844 Loss2: 1.988719
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.092415 Loss1: 0.688233 Loss2: 1.404182
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.876706 Loss1: 0.441273 Loss2: 1.435432
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.681490 Loss1: 0.279570 Loss2: 1.401920
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.937309 Loss1: 1.089691 Loss2: 1.847618
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.536775 Loss1: 0.156591 Loss2: 1.380184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.549034 Loss1: 0.172462 Loss2: 1.376572 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 17:00:31,426 | server.py:236 | fit_round 83 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.558380 Loss1: 0.183197 Loss2: 1.375182 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.505051 Loss1: 0.129598 Loss2: 1.375452 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.496606 Loss1: 0.131588 Loss2: 1.365018 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986779
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.539861 Loss1: 0.170950 Loss2: 1.368911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.483200 Loss1: 0.123805 Loss2: 1.359395 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.514051 Loss1: 0.151051 Loss2: 1.363001
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.088063 Loss1: 1.240325 Loss2: 1.847738
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.201832 Loss1: 0.765214 Loss2: 1.436618
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.809977 Loss1: 0.395452 Loss2: 1.414525
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.747193 Loss1: 0.356055 Loss2: 1.391138
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.692080 Loss1: 0.286498 Loss2: 1.405582
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.087610 Loss1: 1.230136 Loss2: 1.857474
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.242071 Loss1: 0.818049 Loss2: 1.424022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.940978 Loss1: 0.498653 Loss2: 1.442326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.718673 Loss1: 0.336666 Loss2: 1.382007 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.601658 Loss1: 0.209971 Loss2: 1.391688 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528038 Loss1: 0.157536 Loss2: 1.370502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.506432 Loss1: 0.136389 Loss2: 1.370043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.488482 Loss1: 0.118245 Loss2: 1.370237 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.315555 Loss1: 0.850498 Loss2: 1.465057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765005 Loss1: 0.337922 Loss2: 1.427083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.934560 Loss1: 1.050101 Loss2: 1.884459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.059261 Loss1: 0.654998 Loss2: 1.404263 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.791705 Loss1: 0.379824 Loss2: 1.411881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.711774 Loss1: 0.336594 Loss2: 1.375179 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.660364 Loss1: 0.273073 Loss2: 1.387292 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.546310 Loss1: 0.181044 Loss2: 1.365266 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458712 Loss1: 0.108674 Loss2: 1.350038 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 17:00:31,426][flwr][DEBUG] - fit_round 83 received 50 results and 0 failures
-INFO flwr 2023-10-10 17:01:15,237 | server.py:125 | fit progress: (83, 2.2357876489337642, {'accuracy': 0.5508}, 191383.015105836)
->> Test accuracy: 0.550800
-[2023-10-10 17:01:15,237][flwr][INFO] - fit progress: (83, 2.2357876489337642, {'accuracy': 0.5508}, 191383.015105836)
-DEBUG flwr 2023-10-10 17:01:15,237 | server.py:173 | evaluate_round 83: strategy sampled 50 clients (out of 50)
-[2023-10-10 17:01:15,237][flwr][DEBUG] - evaluate_round 83: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 17:10:19,173 | server.py:187 | evaluate_round 83 received 50 results and 0 failures
-[2023-10-10 17:10:19,173][flwr][DEBUG] - evaluate_round 83 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 17:10:19,173 | server.py:222 | fit_round 84: strategy sampled 50 clients (out of 50)
-[2023-10-10 17:10:19,173][flwr][DEBUG] - fit_round 84: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.076846 Loss1: 1.224578 Loss2: 1.852268 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.865184 Loss1: 0.469992 Loss2: 1.395192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.624753 Loss1: 0.257425 Loss2: 1.367329
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.941911 Loss1: 1.119502 Loss2: 1.822409
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.206439 Loss1: 0.757982 Loss2: 1.448457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.973520 Loss1: 0.557938 Loss2: 1.415582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.783534 Loss1: 0.384495 Loss2: 1.399040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.616578 Loss1: 0.232288 Loss2: 1.384290 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.585610 Loss1: 0.216200 Loss2: 1.369410 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509241 Loss1: 0.137177 Loss2: 1.372063 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467212 Loss1: 0.122444 Loss2: 1.344768 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.113159 Loss1: 1.229103 Loss2: 1.884056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.910179 Loss1: 0.461762 Loss2: 1.448417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.125601 Loss1: 1.197592 Loss2: 1.928009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.257361 Loss1: 0.824011 Loss2: 1.433350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.976209 Loss1: 0.516532 Loss2: 1.459677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.752446 Loss1: 0.349637 Loss2: 1.402809 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.656886 Loss1: 0.251223 Loss2: 1.405663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.586118 Loss1: 0.189005 Loss2: 1.397113 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.516480 Loss1: 0.137933 Loss2: 1.378547
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.542134 Loss1: 0.156688 Loss2: 1.385446
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.535958 Loss1: 0.151051 Loss2: 1.384908
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.511994 Loss1: 0.130741 Loss2: 1.381253
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.484679 Loss1: 0.105634 Loss2: 1.379045
-(DefaultActor pid=3764) >> Training accuracy: 0.977679
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.794070 Loss1: 0.964873 Loss2: 1.829197
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.020192 Loss1: 0.604335 Loss2: 1.415857
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.799007 Loss1: 0.395362 Loss2: 1.403645
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.012422 Loss1: 1.100047 Loss2: 1.912376
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.744986 Loss1: 0.360401 Loss2: 1.384585
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.125139 Loss1: 0.628840 Loss2: 1.496298
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587443 Loss1: 0.205620 Loss2: 1.381823
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.893034 Loss1: 0.399825 Loss2: 1.493209
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.558016 Loss1: 0.180650 Loss2: 1.377365
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.785582 Loss1: 0.317360 Loss2: 1.468222
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539680 Loss1: 0.173559 Loss2: 1.366121
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.498336 Loss1: 0.133807 Loss2: 1.364528 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465927 Loss1: 0.104917 Loss2: 1.361010 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.444963 Loss1: 0.086961 Loss2: 1.358002 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.576702 Loss1: 0.139303 Loss2: 1.437399 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.001256 Loss1: 1.213822 Loss2: 1.787434 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.839003 Loss1: 0.486788 Loss2: 1.352215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.673875 Loss1: 0.337444 Loss2: 1.336431
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.867753 Loss1: 0.996536 Loss2: 1.871217
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.575520 Loss1: 0.233796 Loss2: 1.341724
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.114116 Loss1: 0.695461 Loss2: 1.418654
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506846 Loss1: 0.187548 Loss2: 1.319298
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.876307 Loss1: 0.441490 Loss2: 1.434817
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.540735 Loss1: 0.216877 Loss2: 1.323858
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.801300 Loss1: 0.402294 Loss2: 1.399007
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499479 Loss1: 0.182626 Loss2: 1.316853
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.688088 Loss1: 0.288550 Loss2: 1.399538
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452036 Loss1: 0.139878 Loss2: 1.312158
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.665995 Loss1: 0.276156 Loss2: 1.389839
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447088 Loss1: 0.135583 Loss2: 1.311505
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.568980 Loss1: 0.185959 Loss2: 1.383021
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.552696 Loss1: 0.177486 Loss2: 1.375210
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486288 Loss1: 0.122323 Loss2: 1.363965
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439386 Loss1: 0.082939 Loss2: 1.356447
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.957444 Loss1: 0.994401 Loss2: 1.963043
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.135039 Loss1: 0.688573 Loss2: 1.446466
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.976522 Loss1: 0.480210 Loss2: 1.496312
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.794278 Loss1: 0.338250 Loss2: 1.456028
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.965155 Loss1: 1.012488 Loss2: 1.952666
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.114688 Loss1: 0.654181 Loss2: 1.460507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.945206 Loss1: 0.445621 Loss2: 1.499584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.758146 Loss1: 0.319103 Loss2: 1.439043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.711435 Loss1: 0.269106 Loss2: 1.442329 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.659569 Loss1: 0.224792 Loss2: 1.434777 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.583048 Loss1: 0.149582 Loss2: 1.433467
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.589204 Loss1: 0.152134 Loss2: 1.437071
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.531720 Loss1: 0.106992 Loss2: 1.424728
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.517870 Loss1: 0.103063 Loss2: 1.414807
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.526559 Loss1: 0.113202 Loss2: 1.413357
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.966843 Loss1: 1.128125 Loss2: 1.838718
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.144224 Loss1: 0.738352 Loss2: 1.405872
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.848544 Loss1: 0.456977 Loss2: 1.391567
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.664404 Loss1: 0.296568 Loss2: 1.367835
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.178562 Loss1: 1.192630 Loss2: 1.985932
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.212225 Loss1: 0.723576 Loss2: 1.488649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.927165 Loss1: 0.423690 Loss2: 1.503475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.786073 Loss1: 0.306128 Loss2: 1.479945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.765846 Loss1: 0.284002 Loss2: 1.481844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.674133 Loss1: 0.183318 Loss2: 1.490816 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452231 Loss1: 0.114287 Loss2: 1.337944
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.616059 Loss1: 0.152045 Loss2: 1.464014
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.588170 Loss1: 0.129039 Loss2: 1.459131
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.593387 Loss1: 0.144812 Loss2: 1.448575
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.561155 Loss1: 0.101808 Loss2: 1.459346
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.373978 Loss1: 1.301932 Loss2: 2.072046
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.197896 Loss1: 0.768182 Loss2: 1.429714
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.943169 Loss1: 0.470462 Loss2: 1.472707
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.784776 Loss1: 0.338044 Loss2: 1.446732
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.716798 Loss1: 0.286170 Loss2: 1.430629
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.151271 Loss1: 0.683097 Loss2: 1.468174 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.562062 Loss1: 0.152978 Loss2: 1.409084 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.714166 Loss1: 0.287631 Loss2: 1.426535 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981771
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.678451 Loss1: 0.257369 Loss2: 1.421083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.541638 Loss1: 0.132281 Loss2: 1.409356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.498859 Loss1: 0.108719 Loss2: 1.390141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.481321 Loss1: 0.091058 Loss2: 1.390263 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.747744 Loss1: 0.335294 Loss2: 1.412449 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.630069 Loss1: 0.224127 Loss2: 1.405942 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.824397 Loss1: 0.959872 Loss2: 1.864525
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.616655 Loss1: 0.212433 Loss2: 1.404222
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.009082 Loss1: 0.620064 Loss2: 1.389018
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.527959 Loss1: 0.133234 Loss2: 1.394725
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.818029 Loss1: 0.388384 Loss2: 1.429645
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.539328 Loss1: 0.152505 Loss2: 1.386823
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.480868 Loss1: 0.097262 Loss2: 1.383606 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.563408 Loss1: 0.189790 Loss2: 1.373618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.506938 Loss1: 0.145503 Loss2: 1.361435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.566020 Loss1: 0.197500 Loss2: 1.368520
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.014748 Loss1: 1.082977 Loss2: 1.931771
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.036255 Loss1: 0.609224 Loss2: 1.427032 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.662829 Loss1: 0.253290 Loss2: 1.409539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.605478 Loss1: 0.195576 Loss2: 1.409902 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.574771 Loss1: 0.168818 Loss2: 1.405954 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.103585 Loss1: 0.676047 Loss2: 1.427538
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.546393 Loss1: 0.147808 Loss2: 1.398586
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.808992 Loss1: 0.366237 Loss2: 1.442755
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.526431 Loss1: 0.131980 Loss2: 1.394451
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.766769 Loss1: 0.351740 Loss2: 1.415029
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.524545 Loss1: 0.124455 Loss2: 1.400090
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.612772 Loss1: 0.213120 Loss2: 1.399652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.533619 Loss1: 0.144865 Loss2: 1.388754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.354254 Loss1: 1.397068 Loss2: 1.957187
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.514068 Loss1: 0.121696 Loss2: 1.392372
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.280128 Loss1: 0.779822 Loss2: 1.500306
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.531048 Loss1: 0.143434 Loss2: 1.387614
-(DefaultActor pid=3764) >> Training accuracy: 0.974609
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.796168 Loss1: 0.338407 Loss2: 1.457761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.698608 Loss1: 0.258164 Loss2: 1.440444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.633719 Loss1: 0.186309 Loss2: 1.447410
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.944485 Loss1: 1.071866 Loss2: 1.872619
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.092489 Loss1: 0.695366 Loss2: 1.397123 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.877925 Loss1: 0.456935 Loss2: 1.420990 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.731827 Loss1: 0.348611 Loss2: 1.383216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.571888 Loss1: 0.208652 Loss2: 1.363236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.514582 Loss1: 0.159551 Loss2: 1.355032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448374 Loss1: 0.096643 Loss2: 1.351731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.467637 Loss1: 0.123263 Loss2: 1.344374 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.721617 Loss1: 0.298459 Loss2: 1.423158 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.620102 Loss1: 0.205630 Loss2: 1.414472 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.604813 Loss1: 0.189682 Loss2: 1.415131
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.961956 Loss1: 1.062387 Loss2: 1.899569
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.162810 Loss1: 0.749359 Loss2: 1.413451 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.917354 Loss1: 0.428718 Loss2: 1.488636 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.779083 Loss1: 0.364809 Loss2: 1.414274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.669780 Loss1: 0.250824 Loss2: 1.418956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.634489 Loss1: 0.207869 Loss2: 1.426620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.031550 Loss1: 1.115639 Loss2: 1.915911
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.582502 Loss1: 0.166294 Loss2: 1.416208
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.072337 Loss1: 0.664446 Loss2: 1.407891
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.572748 Loss1: 0.160639 Loss2: 1.412110
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.673998 Loss1: 0.291170 Loss2: 1.382828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569372 Loss1: 0.186955 Loss2: 1.382417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.546567 Loss1: 0.164659 Loss2: 1.381907
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.225653 Loss1: 1.284199 Loss2: 1.941454
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.271813 Loss1: 0.755518 Loss2: 1.516295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.957827 Loss1: 0.467867 Loss2: 1.489959 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.959375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.518109 Loss1: 0.141481 Loss2: 1.376628
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.824302 Loss1: 0.352086 Loss2: 1.472216
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.746117 Loss1: 0.284401 Loss2: 1.461716
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.678760 Loss1: 0.233733 Loss2: 1.445027
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.732845 Loss1: 0.280575 Loss2: 1.452270
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.675375 Loss1: 0.217999 Loss2: 1.457376
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.952974 Loss1: 1.166261 Loss2: 1.786713
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.671329 Loss1: 0.223383 Loss2: 1.447946
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.582070 Loss1: 0.133521 Loss2: 1.448549
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.996102 Loss1: 0.634016 Loss2: 1.362086
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.810414 Loss1: 0.419595 Loss2: 1.390819
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.690150 Loss1: 0.348771 Loss2: 1.341380
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.600626 Loss1: 0.253705 Loss2: 1.346921
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513823 Loss1: 0.181088 Loss2: 1.332735
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.286076 Loss1: 1.314041 Loss2: 1.972035
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.214991 Loss1: 0.769440 Loss2: 1.445551 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.905269 Loss1: 0.440757 Loss2: 1.464513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.706797 Loss1: 0.286982 Loss2: 1.419815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.514750 Loss1: 0.191354 Loss2: 1.323396
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.719674 Loss1: 0.297920 Loss2: 1.421755
-(DefaultActor pid=3765) >> Training accuracy: 0.955078
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.685886 Loss1: 0.238410 Loss2: 1.447476
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.637007 Loss1: 0.210434 Loss2: 1.426574
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.585942 Loss1: 0.169935 Loss2: 1.416006
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.538539 Loss1: 0.121454 Loss2: 1.417085
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529663 Loss1: 0.121306 Loss2: 1.408357
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.198666 Loss1: 1.247840 Loss2: 1.950826
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.214104 Loss1: 0.724417 Loss2: 1.489687
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.015413 Loss1: 0.517639 Loss2: 1.497774
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.821825 Loss1: 0.366833 Loss2: 1.454991
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.757802 Loss1: 0.294640 Loss2: 1.463161
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.070801 Loss1: 1.142726 Loss2: 1.928076
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.690954 Loss1: 0.237427 Loss2: 1.453527
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.140894 Loss1: 0.647625 Loss2: 1.493269
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.688819 Loss1: 0.233107 Loss2: 1.455711
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.873765 Loss1: 0.403397 Loss2: 1.470369
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.643489 Loss1: 0.191167 Loss2: 1.452322
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.750021 Loss1: 0.304167 Loss2: 1.445854
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.614438 Loss1: 0.163503 Loss2: 1.450936
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.632636 Loss1: 0.205040 Loss2: 1.427596
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.547410 Loss1: 0.102849 Loss2: 1.444561
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.542374 Loss1: 0.122617 Loss2: 1.419757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.548925 Loss1: 0.133257 Loss2: 1.415668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.504860 Loss1: 0.099787 Loss2: 1.405073
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.115521 Loss1: 1.196289 Loss2: 1.919231
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.124159 Loss1: 0.647954 Loss2: 1.476204
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.854561 Loss1: 0.380098 Loss2: 1.474463
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.752272 Loss1: 0.310854 Loss2: 1.441418
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.645068 Loss1: 0.197140 Loss2: 1.447928
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.109518 Loss1: 1.243577 Loss2: 1.865941
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.603926 Loss1: 0.166937 Loss2: 1.436989
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.577209 Loss1: 0.154524 Loss2: 1.422685 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.579646 Loss1: 0.156175 Loss2: 1.423471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.573059 Loss1: 0.148103 Loss2: 1.424956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.568390 Loss1: 0.206259 Loss2: 1.362131 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.957292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496103 Loss1: 0.162493 Loss2: 1.333610 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449397 Loss1: 0.120505 Loss2: 1.328892 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977163
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.151008 Loss1: 1.227735 Loss2: 1.923273
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.227312 Loss1: 0.768814 Loss2: 1.458498
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.951265 Loss1: 0.500487 Loss2: 1.450778
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.743960 Loss1: 0.314619 Loss2: 1.429341
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.017915 Loss1: 1.070098 Loss2: 1.947817
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.424644 Loss1: 0.861144 Loss2: 1.563500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.034732 Loss1: 0.495729 Loss2: 1.539002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.909885 Loss1: 0.367914 Loss2: 1.541971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.882032 Loss1: 0.362046 Loss2: 1.519987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.768037 Loss1: 0.263545 Loss2: 1.504492 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.695215 Loss1: 0.192979 Loss2: 1.502236 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.598680 Loss1: 0.105519 Loss2: 1.493161 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.227249 Loss1: 0.845600 Loss2: 1.381649 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694961 Loss1: 0.322696 Loss2: 1.372265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.140050 Loss1: 1.177784 Loss2: 1.962266
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589075 Loss1: 0.216035 Loss2: 1.373040
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.211494 Loss1: 0.758834 Loss2: 1.452659
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.550471 Loss1: 0.186803 Loss2: 1.363668
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.987946 Loss1: 0.492111 Loss2: 1.495834
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511390 Loss1: 0.149639 Loss2: 1.361752
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.798471 Loss1: 0.348977 Loss2: 1.449494
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448205 Loss1: 0.099094 Loss2: 1.349111
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.694723 Loss1: 0.232001 Loss2: 1.462722
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444871 Loss1: 0.103226 Loss2: 1.341645
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647412 Loss1: 0.212710 Loss2: 1.434702
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434891 Loss1: 0.095037 Loss2: 1.339854
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.535538 Loss1: 0.109258 Loss2: 1.426280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.554857 Loss1: 0.130995 Loss2: 1.423863 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.064106 Loss1: 0.677933 Loss2: 1.386173 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.687861 Loss1: 0.320780 Loss2: 1.367081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.909887 Loss1: 1.104941 Loss2: 1.804947
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.641733 Loss1: 0.267170 Loss2: 1.374563
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.131571 Loss1: 0.746627 Loss2: 1.384943
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.598435 Loss1: 0.223920 Loss2: 1.374515
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.886371 Loss1: 0.512336 Loss2: 1.374035
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.567605 Loss1: 0.196013 Loss2: 1.371592
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.700583 Loss1: 0.354365 Loss2: 1.346218
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.564090 Loss1: 0.197556 Loss2: 1.366534
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.594024 Loss1: 0.246798 Loss2: 1.347226
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.503635 Loss1: 0.138054 Loss2: 1.365582
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.625952 Loss1: 0.295477 Loss2: 1.330475
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477356 Loss1: 0.128425 Loss2: 1.348932
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449170 Loss1: 0.131948 Loss2: 1.317222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.477109 Loss1: 0.155497 Loss2: 1.321611 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.216824 Loss1: 0.779554 Loss2: 1.437270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.856384 Loss1: 0.445691 Loss2: 1.410693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.719340 Loss1: 0.293635 Loss2: 1.425705
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.030252 Loss1: 1.127414 Loss2: 1.902838
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.673296 Loss1: 0.272865 Loss2: 1.400431
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.098093 Loss1: 0.651919 Loss2: 1.446174
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.572674 Loss1: 0.180078 Loss2: 1.392596
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.839253 Loss1: 0.384408 Loss2: 1.454845
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.552335 Loss1: 0.163883 Loss2: 1.388452
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.753103 Loss1: 0.334984 Loss2: 1.418120
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.521659 Loss1: 0.145485 Loss2: 1.376174
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.707292 Loss1: 0.276787 Loss2: 1.430505
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.512673 Loss1: 0.126446 Loss2: 1.386227
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.592190 Loss1: 0.179441 Loss2: 1.412748
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.561750 Loss1: 0.154488 Loss2: 1.407262
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.552892 Loss1: 0.151348 Loss2: 1.401543
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.524594 Loss1: 0.125251 Loss2: 1.399344
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.486696 Loss1: 0.094447 Loss2: 1.392248
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.993544 Loss1: 1.141504 Loss2: 1.852040
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.101834 Loss1: 0.699992 Loss2: 1.401841
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.864266 Loss1: 0.435595 Loss2: 1.428671
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.814966 Loss1: 0.433643 Loss2: 1.381322
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.040590 Loss1: 1.141175 Loss2: 1.899415
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.206425 Loss1: 0.775933 Loss2: 1.430492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.007356 Loss1: 0.510882 Loss2: 1.496473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.756034 Loss1: 0.336500 Loss2: 1.419534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.638838 Loss1: 0.217273 Loss2: 1.421565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.588124 Loss1: 0.181380 Loss2: 1.406744 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515189 Loss1: 0.129495 Loss2: 1.385695 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512833 Loss1: 0.122724 Loss2: 1.390109 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.074467 Loss1: 0.640947 Loss2: 1.433521 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.729556 Loss1: 0.331541 Loss2: 1.398015 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.995198 Loss1: 1.182147 Loss2: 1.813051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.066980 Loss1: 0.638908 Loss2: 1.428073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.539876 Loss1: 0.151078 Loss2: 1.388799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.532447 Loss1: 0.133391 Loss2: 1.399055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.504179 Loss1: 0.114392 Loss2: 1.389787 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981971
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.607170 Loss1: 0.223408 Loss2: 1.383762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.523776 Loss1: 0.135620 Loss2: 1.388156 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.495907 Loss1: 0.118476 Loss2: 1.377432
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.946069 Loss1: 1.090139 Loss2: 1.855930
-(DefaultActor pid=3764) >> Training accuracy: 0.972656
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463032 Loss1: 0.098401 Loss2: 1.364631
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.019559 Loss1: 0.654644 Loss2: 1.364914
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.786064 Loss1: 0.406956 Loss2: 1.379108
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.609176 Loss1: 0.249462 Loss2: 1.359714
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567750 Loss1: 0.223232 Loss2: 1.344519
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569026 Loss1: 0.221863 Loss2: 1.347163
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.041751 Loss1: 1.154158 Loss2: 1.887593
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531668 Loss1: 0.183497 Loss2: 1.348171
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.119962 Loss1: 0.672314 Loss2: 1.447648
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451502 Loss1: 0.118238 Loss2: 1.333264
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.884919 Loss1: 0.423212 Loss2: 1.461707
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461335 Loss1: 0.138094 Loss2: 1.323241
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.674712 Loss1: 0.270907 Loss2: 1.403805
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.489577 Loss1: 0.153501 Loss2: 1.336076
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.655919 Loss1: 0.239471 Loss2: 1.416448 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.613634 Loss1: 0.208940 Loss2: 1.404694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.544445 Loss1: 0.140691 Loss2: 1.403754
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.903987 Loss1: 1.129403 Loss2: 1.774584
-(DefaultActor pid=3764) >> Training accuracy: 0.945833
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.497255 Loss1: 0.106643 Loss2: 1.390612
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.128823 Loss1: 0.784142 Loss2: 1.344681
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.879133 Loss1: 0.513127 Loss2: 1.366006
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.724482 Loss1: 0.383459 Loss2: 1.341023
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.698820 Loss1: 0.350277 Loss2: 1.348543
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.568891 Loss1: 0.241598 Loss2: 1.327293
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.988094 Loss1: 1.139797 Loss2: 1.848297
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.601972 Loss1: 0.266721 Loss2: 1.335251
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.537249 Loss1: 0.210704 Loss2: 1.326545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422386 Loss1: 0.105286 Loss2: 1.317101 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 17:38:50,417 | server.py:236 | fit_round 84 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447263 Loss1: 0.141009 Loss2: 1.306254 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531320 Loss1: 0.171490 Loss2: 1.359830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.456297 Loss1: 0.109352 Loss2: 1.346946 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434246 Loss1: 0.093010 Loss2: 1.341236
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.935845 Loss1: 1.007985 Loss2: 1.927861
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.124768 Loss1: 0.623573 Loss2: 1.501195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.755810 Loss1: 0.296618 Loss2: 1.459193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.603665 Loss1: 0.167183 Loss2: 1.436482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.571762 Loss1: 0.137725 Loss2: 1.434037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.567651 Loss1: 0.128548 Loss2: 1.439103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.565264 Loss1: 0.137454 Loss2: 1.427809 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576721 Loss1: 0.219256 Loss2: 1.357465 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513786 Loss1: 0.168354 Loss2: 1.345432 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446701 Loss1: 0.109801 Loss2: 1.336900 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465240 Loss1: 0.123975 Loss2: 1.341265
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.983011 Loss1: 1.121381 Loss2: 1.861630
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.000885 Loss1: 0.604896 Loss2: 1.395990
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.860578 Loss1: 0.426785 Loss2: 1.433792
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.677636 Loss1: 0.284932 Loss2: 1.392705
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.619141 Loss1: 0.226280 Loss2: 1.392860
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.234518 Loss1: 1.284345 Loss2: 1.950173
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.585877 Loss1: 0.193775 Loss2: 1.392102
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544561 Loss1: 0.165412 Loss2: 1.379149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.541146 Loss1: 0.156827 Loss2: 1.384319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469857 Loss1: 0.097301 Loss2: 1.372557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469750 Loss1: 0.102817 Loss2: 1.366933 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.608246 Loss1: 0.190012 Loss2: 1.418233 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.530598 Loss1: 0.121855 Loss2: 1.408743 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.974330
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 17:38:50,417][flwr][DEBUG] - fit_round 84 received 50 results and 0 failures
-INFO flwr 2023-10-10 17:39:33,002 | server.py:125 | fit progress: (84, 2.2436922419185454, {'accuracy': 0.55}, 193680.780604483)
->> Test accuracy: 0.550000
-[2023-10-10 17:39:33,002][flwr][INFO] - fit progress: (84, 2.2436922419185454, {'accuracy': 0.55}, 193680.780604483)
-DEBUG flwr 2023-10-10 17:39:33,002 | server.py:173 | evaluate_round 84: strategy sampled 50 clients (out of 50)
-[2023-10-10 17:39:33,002][flwr][DEBUG] - evaluate_round 84: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 17:48:36,794 | server.py:187 | evaluate_round 84 received 50 results and 0 failures
-[2023-10-10 17:48:36,794][flwr][DEBUG] - evaluate_round 84 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 17:48:36,794 | server.py:222 | fit_round 85: strategy sampled 50 clients (out of 50)
-[2023-10-10 17:48:36,794][flwr][DEBUG] - fit_round 85: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.800937 Loss1: 0.954712 Loss2: 1.846225
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.041704 Loss1: 0.601705 Loss2: 1.439998
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.827145 Loss1: 0.383457 Loss2: 1.443688
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.946200 Loss1: 1.104253 Loss2: 1.841947
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610122 Loss1: 0.207513 Loss2: 1.402609
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.080785 Loss1: 0.679786 Loss2: 1.400999
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577158 Loss1: 0.174574 Loss2: 1.402584
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.575548 Loss1: 0.188764 Loss2: 1.386783 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528812 Loss1: 0.132048 Loss2: 1.396764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.515457 Loss1: 0.122751 Loss2: 1.392706 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509753 Loss1: 0.126242 Loss2: 1.383510 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.495203 Loss1: 0.115103 Loss2: 1.380100 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.521923 Loss1: 0.181118 Loss2: 1.340806 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.950302 Loss1: 1.083715 Loss2: 1.866587 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.867913 Loss1: 0.414769 Loss2: 1.453144 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.863861 Loss1: 0.975610 Loss2: 1.888251
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.767715 Loss1: 0.349467 Loss2: 1.418248
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.242043 Loss1: 0.783141 Loss2: 1.458902
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.663554 Loss1: 0.250148 Loss2: 1.413405
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.860282 Loss1: 0.412678 Loss2: 1.447604
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.610111 Loss1: 0.202015 Loss2: 1.408095
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.677439 Loss1: 0.269313 Loss2: 1.408126
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.538797 Loss1: 0.135697 Loss2: 1.403099
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499696 Loss1: 0.114881 Loss2: 1.384815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.495903 Loss1: 0.108235 Loss2: 1.387669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.530369 Loss1: 0.138503 Loss2: 1.391866 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.482465 Loss1: 0.113052 Loss2: 1.369413 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.006528 Loss1: 1.138191 Loss2: 1.868337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.940316 Loss1: 0.485529 Loss2: 1.454787 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.843261 Loss1: 0.435898 Loss2: 1.407363
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.092699 Loss1: 1.180276 Loss2: 1.912422
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.745243 Loss1: 0.331577 Loss2: 1.413666
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.113215 Loss1: 0.718621 Loss2: 1.394594
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.862475 Loss1: 0.428517 Loss2: 1.433958
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.608492 Loss1: 0.222458 Loss2: 1.386034
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.659464 Loss1: 0.272174 Loss2: 1.387290
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.587604 Loss1: 0.206115 Loss2: 1.381489
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.602287 Loss1: 0.221386 Loss2: 1.380901
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.511723 Loss1: 0.122128 Loss2: 1.389595
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.498352 Loss1: 0.125045 Loss2: 1.373307 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.517545 Loss1: 0.141297 Loss2: 1.376248 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499249 Loss1: 0.131404 Loss2: 1.367845 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.974330
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.079545 Loss1: 1.220672 Loss2: 1.858873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.916822 Loss1: 0.471513 Loss2: 1.445310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.703237 Loss1: 0.311222 Loss2: 1.392015
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.076035 Loss1: 1.248912 Loss2: 1.827123
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.347309 Loss1: 0.912279 Loss2: 1.435031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.925818 Loss1: 0.524857 Loss2: 1.400961 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.733134 Loss1: 0.346423 Loss2: 1.386711 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.626832 Loss1: 0.254524 Loss2: 1.372307 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.571869 Loss1: 0.203694 Loss2: 1.368175 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.541585 Loss1: 0.162886 Loss2: 1.378699
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486505 Loss1: 0.129166 Loss2: 1.357339
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.497367 Loss1: 0.143221 Loss2: 1.354147
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431026 Loss1: 0.091659 Loss2: 1.339367
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457408 Loss1: 0.120971 Loss2: 1.336437
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.165546 Loss1: 1.231846 Loss2: 1.933700
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.144834 Loss1: 0.773708 Loss2: 1.371126
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.878315 Loss1: 0.451581 Loss2: 1.426734
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654011 Loss1: 0.307272 Loss2: 1.346740
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.991967 Loss1: 1.134557 Loss2: 1.857410
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.512322 Loss1: 0.161186 Loss2: 1.351136 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.453344 Loss1: 0.119389 Loss2: 1.333956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458415 Loss1: 0.133248 Loss2: 1.325167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.415634 Loss1: 0.092942 Loss2: 1.322692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393662 Loss1: 0.080438 Loss2: 1.313224 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.501960 Loss1: 0.149678 Loss2: 1.352282 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.475819 Loss1: 0.128936 Loss2: 1.346883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.477559 Loss1: 0.136597 Loss2: 1.340962
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.936343 Loss1: 1.122945 Loss2: 1.813398
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.252380 Loss1: 0.806648 Loss2: 1.445733
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.892936 Loss1: 0.497844 Loss2: 1.395092
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.724076 Loss1: 0.335825 Loss2: 1.388251
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.658830 Loss1: 0.276614 Loss2: 1.382216
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.046541 Loss1: 1.164579 Loss2: 1.881962
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.580439 Loss1: 0.207848 Loss2: 1.372591
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.112522 Loss1: 0.696260 Loss2: 1.416262
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.558782 Loss1: 0.187384 Loss2: 1.371398
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.829603 Loss1: 0.386297 Loss2: 1.443305
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.669402 Loss1: 0.276049 Loss2: 1.393352
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.560830 Loss1: 0.180478 Loss2: 1.380352
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.621343 Loss1: 0.221785 Loss2: 1.399558
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.522452 Loss1: 0.150829 Loss2: 1.371622
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.568599 Loss1: 0.178652 Loss2: 1.389947
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484436 Loss1: 0.130654 Loss2: 1.353782
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493737 Loss1: 0.118116 Loss2: 1.375622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.502610 Loss1: 0.117481 Loss2: 1.385129 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.856183 Loss1: 0.975341 Loss2: 1.880841
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.004044 Loss1: 0.607095 Loss2: 1.396949
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.757643 Loss1: 0.346208 Loss2: 1.411436
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.631572 Loss1: 0.260296 Loss2: 1.371276
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.115952 Loss1: 1.232714 Loss2: 1.883238
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.229330 Loss1: 0.767719 Loss2: 1.461611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.906994 Loss1: 0.444148 Loss2: 1.462846 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.739861 Loss1: 0.323859 Loss2: 1.416002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.712304 Loss1: 0.280695 Loss2: 1.431609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.613063 Loss1: 0.200701 Loss2: 1.412362 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.564893 Loss1: 0.167584 Loss2: 1.397309 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.540136 Loss1: 0.140240 Loss2: 1.399896 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.995554 Loss1: 0.619482 Loss2: 1.376072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629001 Loss1: 0.286674 Loss2: 1.342327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.032972 Loss1: 1.116288 Loss2: 1.916684
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.542447 Loss1: 0.199028 Loss2: 1.343419
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.220783 Loss1: 0.760904 Loss2: 1.459879
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518686 Loss1: 0.187900 Loss2: 1.330786
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.905313 Loss1: 0.411585 Loss2: 1.493729
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473335 Loss1: 0.147597 Loss2: 1.325739
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.752461 Loss1: 0.319333 Loss2: 1.433128
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438034 Loss1: 0.119618 Loss2: 1.318416
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.721026 Loss1: 0.270100 Loss2: 1.450926
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435599 Loss1: 0.118013 Loss2: 1.317586
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.671270 Loss1: 0.234406 Loss2: 1.436864
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452175 Loss1: 0.135185 Loss2: 1.316990
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.609277 Loss1: 0.177967 Loss2: 1.431310 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.555263 Loss1: 0.128399 Loss2: 1.426864 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.946875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.059179 Loss1: 0.663206 Loss2: 1.395973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.715213 Loss1: 0.341599 Loss2: 1.373613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.657516 Loss1: 0.307685 Loss2: 1.349831 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.566805 Loss1: 0.209182 Loss2: 1.357623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.551241 Loss1: 0.213847 Loss2: 1.337394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500371 Loss1: 0.167506 Loss2: 1.332865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.464758 Loss1: 0.137464 Loss2: 1.327294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.444740 Loss1: 0.119419 Loss2: 1.325321 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467737 Loss1: 0.126614 Loss2: 1.341123 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.973574 Loss1: 1.119286 Loss2: 1.854288 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.818054 Loss1: 0.383592 Loss2: 1.434462 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.735636 Loss1: 0.339191 Loss2: 1.396445
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.939439 Loss1: 1.075607 Loss2: 1.863832
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.660496 Loss1: 0.242445 Loss2: 1.418051
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.074811 Loss1: 0.676615 Loss2: 1.398196
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.862826 Loss1: 0.439091 Loss2: 1.423735
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.570223 Loss1: 0.186275 Loss2: 1.383948
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.748494 Loss1: 0.359535 Loss2: 1.388959
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.527146 Loss1: 0.146307 Loss2: 1.380839
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.742106 Loss1: 0.343587 Loss2: 1.398520
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.531825 Loss1: 0.149332 Loss2: 1.382492
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.638543 Loss1: 0.252995 Loss2: 1.385548
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499926 Loss1: 0.126113 Loss2: 1.373812
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.507525 Loss1: 0.130633 Loss2: 1.376893 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.955078
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501768 Loss1: 0.143621 Loss2: 1.358147 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.954318 Loss1: 1.112017 Loss2: 1.842302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.898816 Loss1: 0.483279 Loss2: 1.415538 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.761560 Loss1: 0.380186 Loss2: 1.381375
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.924363 Loss1: 1.082306 Loss2: 1.842057
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.715479 Loss1: 0.316526 Loss2: 1.398953
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.959399 Loss1: 0.587555 Loss2: 1.371844
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.592246 Loss1: 0.223397 Loss2: 1.368849
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.757744 Loss1: 0.366389 Loss2: 1.391354
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.558086 Loss1: 0.186156 Loss2: 1.371930
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.655166 Loss1: 0.298658 Loss2: 1.356508
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.504456 Loss1: 0.139459 Loss2: 1.364997
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.617059 Loss1: 0.253549 Loss2: 1.363510
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.528021 Loss1: 0.167096 Loss2: 1.360925
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582475 Loss1: 0.229871 Loss2: 1.352604
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.464002 Loss1: 0.104375 Loss2: 1.359627
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.575767 Loss1: 0.223329 Loss2: 1.352438
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.567977 Loss1: 0.204488 Loss2: 1.363489
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.514891 Loss1: 0.166461 Loss2: 1.348430
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441070 Loss1: 0.098255 Loss2: 1.342815
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.218979 Loss1: 1.160913 Loss2: 2.058066
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.223825 Loss1: 0.641717 Loss2: 1.582108
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.029382 Loss1: 0.501827 Loss2: 1.527555
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.864621 Loss1: 0.331775 Loss2: 1.532845
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.005446 Loss1: 1.052731 Loss2: 1.952715
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.059109 Loss1: 0.596022 Loss2: 1.463087 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.897050 Loss1: 0.422887 Loss2: 1.474163 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.622292 Loss1: 0.137451 Loss2: 1.484840
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.775360 Loss1: 0.331117 Loss2: 1.444243
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687344 Loss1: 0.235272 Loss2: 1.452072 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.607629 Loss1: 0.176378 Loss2: 1.431251 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.590165 Loss1: 0.111761 Loss2: 1.478404
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.636830 Loss1: 0.204530 Loss2: 1.432299
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.620413 Loss1: 0.191499 Loss2: 1.428914
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.566641 Loss1: 0.145210 Loss2: 1.421431
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.550516 Loss1: 0.131895 Loss2: 1.418621
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.066520 Loss1: 1.224250 Loss2: 1.842270
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.047430 Loss1: 0.651863 Loss2: 1.395566
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.796491 Loss1: 0.394921 Loss2: 1.401570
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.695760 Loss1: 0.343688 Loss2: 1.352071
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.098346 Loss1: 1.229400 Loss2: 1.868946
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.274683 Loss1: 0.839540 Loss2: 1.435142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.997028 Loss1: 0.550553 Loss2: 1.446475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.749336 Loss1: 0.343413 Loss2: 1.405923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.677988 Loss1: 0.296577 Loss2: 1.381411 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567450 Loss1: 0.185399 Loss2: 1.382050 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424528 Loss1: 0.093854 Loss2: 1.330674
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.568493 Loss1: 0.190856 Loss2: 1.377637
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540262 Loss1: 0.163305 Loss2: 1.376957
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.530118 Loss1: 0.156319 Loss2: 1.373799
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.562248 Loss1: 0.184948 Loss2: 1.377300
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.966248 Loss1: 1.024130 Loss2: 1.942119
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.153373 Loss1: 0.637502 Loss2: 1.515871
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.875141 Loss1: 0.401029 Loss2: 1.474112
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.180561 Loss1: 1.257653 Loss2: 1.922908
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.160599 Loss1: 0.748863 Loss2: 1.411736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.953007 Loss1: 0.498696 Loss2: 1.454312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.578133 Loss1: 0.135723 Loss2: 1.442411
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.762121 Loss1: 0.366266 Loss2: 1.395856
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.664202 Loss1: 0.270181 Loss2: 1.394021
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.587333 Loss1: 0.144594 Loss2: 1.442739
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.583452 Loss1: 0.144222 Loss2: 1.439230 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.550244 Loss1: 0.112383 Loss2: 1.437861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.545569 Loss1: 0.107752 Loss2: 1.437817 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981618
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.874664 Loss1: 1.068254 Loss2: 1.806410 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.894613 Loss1: 0.487693 Loss2: 1.406920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.795201 Loss1: 0.406113 Loss2: 1.389088
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.054402 Loss1: 1.195731 Loss2: 1.858671
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.634132 Loss1: 0.236673 Loss2: 1.397459
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.070148 Loss1: 0.656411 Loss2: 1.413737
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524844 Loss1: 0.166918 Loss2: 1.357926
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.871034 Loss1: 0.456854 Loss2: 1.414180
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.846660 Loss1: 0.449227 Loss2: 1.397433
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511822 Loss1: 0.151005 Loss2: 1.360817
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.627211 Loss1: 0.229972 Loss2: 1.397239
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.517024 Loss1: 0.154126 Loss2: 1.362898
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530205 Loss1: 0.160941 Loss2: 1.369264
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.490062 Loss1: 0.136280 Loss2: 1.353782
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.505873 Loss1: 0.146753 Loss2: 1.359120
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434479 Loss1: 0.084597 Loss2: 1.349882
-(DefaultActor pid=3765) >> Training accuracy: 0.966797
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.455967 Loss1: 0.100225 Loss2: 1.355742 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.899630 Loss1: 1.057869 Loss2: 1.841761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.860746 Loss1: 0.455903 Loss2: 1.404843 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.651785 Loss1: 0.285501 Loss2: 1.366285
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.103906 Loss1: 1.149529 Loss2: 1.954378
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.616054 Loss1: 0.249443 Loss2: 1.366611
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.209292 Loss1: 0.705197 Loss2: 1.504095
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480248 Loss1: 0.137846 Loss2: 1.342402
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.904749 Loss1: 0.402661 Loss2: 1.502088
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495295 Loss1: 0.155584 Loss2: 1.339711
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.788466 Loss1: 0.337790 Loss2: 1.450675
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443543 Loss1: 0.106224 Loss2: 1.337319
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.671572 Loss1: 0.199058 Loss2: 1.472513
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408722 Loss1: 0.082019 Loss2: 1.326703
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.687711 Loss1: 0.243714 Loss2: 1.443996
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420118 Loss1: 0.089727 Loss2: 1.330391
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.671789 Loss1: 0.218109 Loss2: 1.453680
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.621942 Loss1: 0.175727 Loss2: 1.446215
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.575849 Loss1: 0.122790 Loss2: 1.453059
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.563646 Loss1: 0.122288 Loss2: 1.441358
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.135627 Loss1: 1.107585 Loss2: 2.028043
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.284806 Loss1: 0.736788 Loss2: 1.548018
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.957766 Loss1: 0.423340 Loss2: 1.534426
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.755009 Loss1: 0.266636 Loss2: 1.488372
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.286207 Loss1: 1.350538 Loss2: 1.935669
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.724437 Loss1: 0.234784 Loss2: 1.489652
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.186206 Loss1: 0.726657 Loss2: 1.459549
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.657452 Loss1: 0.169763 Loss2: 1.487688
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.929512 Loss1: 0.478429 Loss2: 1.451083
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.722773 Loss1: 0.314984 Loss2: 1.407789
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.645236 Loss1: 0.168615 Loss2: 1.476621
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.637237 Loss1: 0.231272 Loss2: 1.405964
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.603293 Loss1: 0.131308 Loss2: 1.471985
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.578576 Loss1: 0.188665 Loss2: 1.389911
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.574845 Loss1: 0.106287 Loss2: 1.468559
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.547253 Loss1: 0.086916 Loss2: 1.460337 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.477129 Loss1: 0.104175 Loss2: 1.372953 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.960938
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.148312 Loss1: 1.212875 Loss2: 1.935436 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.941356 Loss1: 0.499723 Loss2: 1.441633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561737 Loss1: 0.214929 Loss2: 1.346808 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.090289 Loss1: 0.677999 Loss2: 1.412290 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471850 Loss1: 0.146901 Loss2: 1.324949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.730062 Loss1: 0.331612 Loss2: 1.398450 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975260
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.660818 Loss1: 0.271168 Loss2: 1.389650 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.564625 Loss1: 0.190731 Loss2: 1.373894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.527771 Loss1: 0.157803 Loss2: 1.369967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.500776 Loss1: 0.131545 Loss2: 1.369231 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971680
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.712964 Loss1: 0.337817 Loss2: 1.375147 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.593240 Loss1: 0.219677 Loss2: 1.373563 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.535630 Loss1: 0.186668 Loss2: 1.348962
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.156691 Loss1: 1.208607 Loss2: 1.948083
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.132608 Loss1: 0.715490 Loss2: 1.417117
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497552 Loss1: 0.138365 Loss2: 1.359188
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.910194 Loss1: 0.422627 Loss2: 1.487567
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472882 Loss1: 0.127401 Loss2: 1.345481
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408796 Loss1: 0.071010 Loss2: 1.337786 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.595236 Loss1: 0.183396 Loss2: 1.411840 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510758 Loss1: 0.116411 Loss2: 1.394347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462830 Loss1: 0.076123 Loss2: 1.386707 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985577
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.172372 Loss1: 0.757413 Loss2: 1.414959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.758496 Loss1: 0.382343 Loss2: 1.376153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.076397 Loss1: 1.179676 Loss2: 1.896720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.103014 Loss1: 0.694418 Loss2: 1.408595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.897219 Loss1: 0.432011 Loss2: 1.465208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.719950 Loss1: 0.324128 Loss2: 1.395821 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.651634 Loss1: 0.245048 Loss2: 1.406585 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516902 Loss1: 0.139403 Loss2: 1.377499 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.532584 Loss1: 0.157360 Loss2: 1.375224 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.526919 Loss1: 0.149504 Loss2: 1.377415
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.119815 Loss1: 1.167316 Loss2: 1.952499
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.166577 Loss1: 0.696033 Loss2: 1.470544
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.907733 Loss1: 0.436254 Loss2: 1.471478
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.739502 Loss1: 0.294047 Loss2: 1.445456
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.646923 Loss1: 0.209917 Loss2: 1.437005
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.179399 Loss1: 1.292756 Loss2: 1.886643
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.640431 Loss1: 0.209926 Loss2: 1.430505
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.249306 Loss1: 0.789493 Loss2: 1.459813
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.573501 Loss1: 0.149677 Loss2: 1.423824
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.969661 Loss1: 0.540527 Loss2: 1.429134
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.551089 Loss1: 0.129996 Loss2: 1.421094
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.559308 Loss1: 0.142541 Loss2: 1.416767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.636931 Loss1: 0.215452 Loss2: 1.421479 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.917708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530021 Loss1: 0.130712 Loss2: 1.399309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.525654 Loss1: 0.142050 Loss2: 1.383604 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.530763 Loss1: 0.137576 Loss2: 1.393187
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.854965 Loss1: 1.007962 Loss2: 1.847003
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.150804 Loss1: 0.699709 Loss2: 1.451095
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.891534 Loss1: 0.470291 Loss2: 1.421243
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.756112 Loss1: 0.348494 Loss2: 1.407618
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611881 Loss1: 0.211565 Loss2: 1.400316
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.925604 Loss1: 1.082454 Loss2: 1.843150
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.205508 Loss1: 0.755700 Loss2: 1.449808 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.920778 Loss1: 0.468020 Loss2: 1.452758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.801460 Loss1: 0.386203 Loss2: 1.415256 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.722324 Loss1: 0.304776 Loss2: 1.417548 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.622065 Loss1: 0.215691 Loss2: 1.406375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.524883 Loss1: 0.139922 Loss2: 1.384961 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466094 Loss1: 0.094727 Loss2: 1.371367 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.831286 Loss1: 0.378947 Loss2: 1.452339 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.607640 Loss1: 0.205128 Loss2: 1.402512 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.557044 Loss1: 0.155975 Loss2: 1.401069
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.931377 Loss1: 1.047493 Loss2: 1.883884
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529933 Loss1: 0.146317 Loss2: 1.383616
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.289039 Loss1: 0.849389 Loss2: 1.439650
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480289 Loss1: 0.096179 Loss2: 1.384110
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.892847 Loss1: 0.445031 Loss2: 1.447816
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458804 Loss1: 0.079667 Loss2: 1.379137
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.729994 Loss1: 0.321523 Loss2: 1.408470
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429121 Loss1: 0.058754 Loss2: 1.370367
-DEBUG flwr 2023-10-10 18:17:53,745 | server.py:236 | fit_round 85 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.747203 Loss1: 0.339162 Loss2: 1.408042
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.589259 Loss1: 0.192821 Loss2: 1.396438
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.546109 Loss1: 0.162969 Loss2: 1.383140
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510456 Loss1: 0.129337 Loss2: 1.381120
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.520952 Loss1: 0.147768 Loss2: 1.373185
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.536059 Loss1: 0.159528 Loss2: 1.376530
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.080601 Loss1: 1.175389 Loss2: 1.905212
-(DefaultActor pid=3764) >> Training accuracy: 0.951042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.191175 Loss1: 0.732334 Loss2: 1.458842
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.936827 Loss1: 0.461340 Loss2: 1.475487
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.812477 Loss1: 0.367719 Loss2: 1.444758
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.748502 Loss1: 0.308927 Loss2: 1.439575
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.668894 Loss1: 0.232428 Loss2: 1.436466
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.981593 Loss1: 1.030046 Loss2: 1.951547
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.632896 Loss1: 0.201168 Loss2: 1.431728
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.078302 Loss1: 0.628727 Loss2: 1.449575
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.599550 Loss1: 0.174105 Loss2: 1.425445
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.962696 Loss1: 0.462507 Loss2: 1.500189
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.529160 Loss1: 0.105739 Loss2: 1.423421
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.753905 Loss1: 0.302877 Loss2: 1.451028
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.553738 Loss1: 0.141997 Loss2: 1.411740
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.674429 Loss1: 0.226971 Loss2: 1.447459
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.631807 Loss1: 0.186354 Loss2: 1.445454
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.554357 Loss1: 0.120443 Loss2: 1.433914
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.559272 Loss1: 0.129638 Loss2: 1.429634
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.523930 Loss1: 0.103997 Loss2: 1.419934
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.943089 Loss1: 1.113950 Loss2: 1.829139
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527756 Loss1: 0.108439 Loss2: 1.419318
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.768444 Loss1: 0.361509 Loss2: 1.406934 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.560943 Loss1: 0.201252 Loss2: 1.359691 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542280 Loss1: 0.187673 Loss2: 1.354607
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.036041 Loss1: 1.146256 Loss2: 1.889785
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.557382 Loss1: 0.206804 Loss2: 1.350579
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.204926 Loss1: 0.764123 Loss2: 1.440803
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.562199 Loss1: 0.210437 Loss2: 1.351762
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.910019 Loss1: 0.467515 Loss2: 1.442504
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.519372 Loss1: 0.173403 Loss2: 1.345969
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.792170 Loss1: 0.384511 Loss2: 1.407659
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.471285 Loss1: 0.123086 Loss2: 1.348199
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.678947 Loss1: 0.275957 Loss2: 1.402990
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.589867 Loss1: 0.201619 Loss2: 1.388248
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.547415 Loss1: 0.163629 Loss2: 1.383786
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511675 Loss1: 0.131181 Loss2: 1.380494
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.532399 Loss1: 0.157580 Loss2: 1.374819
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529968 Loss1: 0.152084 Loss2: 1.377884
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 18:17:53,745][flwr][DEBUG] - fit_round 85 received 50 results and 0 failures
-INFO flwr 2023-10-10 18:18:35,399 | server.py:125 | fit progress: (85, 2.2410841007202196, {'accuracy': 0.552}, 196023.177882296)
->> Test accuracy: 0.552000
-[2023-10-10 18:18:35,399][flwr][INFO] - fit progress: (85, 2.2410841007202196, {'accuracy': 0.552}, 196023.177882296)
-DEBUG flwr 2023-10-10 18:18:35,400 | server.py:173 | evaluate_round 85: strategy sampled 50 clients (out of 50)
-[2023-10-10 18:18:35,400][flwr][DEBUG] - evaluate_round 85: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 18:27:39,110 | server.py:187 | evaluate_round 85 received 50 results and 0 failures
-[2023-10-10 18:27:39,110][flwr][DEBUG] - evaluate_round 85 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 18:27:39,111 | server.py:222 | fit_round 86: strategy sampled 50 clients (out of 50)
-[2023-10-10 18:27:39,111][flwr][DEBUG] - fit_round 86: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.140318 Loss1: 1.270333 Loss2: 1.869985
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.176108 Loss1: 0.781465 Loss2: 1.394643
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.849023 Loss1: 0.436900 Loss2: 1.412122
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.692152 Loss1: 0.335372 Loss2: 1.356781
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.670463 Loss1: 0.285919 Loss2: 1.384544 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.617694 Loss1: 0.268017 Loss2: 1.349677 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544201 Loss1: 0.181340 Loss2: 1.362861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495779 Loss1: 0.153423 Loss2: 1.342356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.732374 Loss1: 0.313687 Loss2: 1.418687
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.506549 Loss1: 0.164520 Loss2: 1.342029
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.639253 Loss1: 0.242032 Loss2: 1.397222
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.490304 Loss1: 0.143774 Loss2: 1.346530
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.575582 Loss1: 0.181434 Loss2: 1.394148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.578316 Loss1: 0.173003 Loss2: 1.405313 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.099565 Loss1: 0.655740 Loss2: 1.443825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.655446 Loss1: 0.250406 Loss2: 1.405039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.598777 Loss1: 0.209109 Loss2: 1.389668
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.082849 Loss1: 1.162817 Loss2: 1.920032
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.568750 Loss1: 0.181023 Loss2: 1.387727
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.183702 Loss1: 0.700405 Loss2: 1.483297
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534038 Loss1: 0.147702 Loss2: 1.386336
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.932178 Loss1: 0.427318 Loss2: 1.504860
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488502 Loss1: 0.117021 Loss2: 1.371481
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.784162 Loss1: 0.348491 Loss2: 1.435671
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484718 Loss1: 0.107286 Loss2: 1.377432
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.745049 Loss1: 0.304238 Loss2: 1.440812
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.480120 Loss1: 0.109343 Loss2: 1.370778
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.746783 Loss1: 0.301795 Loss2: 1.444988
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.695047 Loss1: 0.258589 Loss2: 1.436458
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.619295 Loss1: 0.189401 Loss2: 1.429894
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.554588 Loss1: 0.133703 Loss2: 1.420885
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.508972 Loss1: 0.107050 Loss2: 1.401922
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.978975 Loss1: 1.024959 Loss2: 1.954016
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.055045 Loss1: 0.594785 Loss2: 1.460260
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.900033 Loss1: 0.406303 Loss2: 1.493730
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.741520 Loss1: 0.283532 Loss2: 1.457988
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.717737 Loss1: 0.261566 Loss2: 1.456171
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.620205 Loss1: 0.172917 Loss2: 1.447288 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.597596 Loss1: 0.162633 Loss2: 1.434963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.547915 Loss1: 0.117634 Loss2: 1.430281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.531887 Loss1: 0.112511 Loss2: 1.419376 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.529085 Loss1: 0.102751 Loss2: 1.426334 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.402311 Loss1: 0.090329 Loss2: 1.311983 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370337 Loss1: 0.071687 Loss2: 1.298650 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.126023 Loss1: 0.695988 Loss2: 1.430036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.735021 Loss1: 0.321725 Loss2: 1.413295 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.624257 Loss1: 0.196024 Loss2: 1.428233
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.722584 Loss1: 0.917226 Loss2: 1.805358
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.984746 Loss1: 0.583743 Loss2: 1.401003 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.798530 Loss1: 0.408280 Loss2: 1.390250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.620095 Loss1: 0.240388 Loss2: 1.379707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491130 Loss1: 0.103987 Loss2: 1.387143 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.524420 Loss1: 0.166697 Loss2: 1.357722 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466704 Loss1: 0.119202 Loss2: 1.347502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461881 Loss1: 0.117045 Loss2: 1.344836 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973346
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.884559 Loss1: 0.470601 Loss2: 1.413958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.581154 Loss1: 0.213862 Loss2: 1.367292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.983874 Loss1: 1.133341 Loss2: 1.850534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.229561 Loss1: 0.740890 Loss2: 1.488671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.819181 Loss1: 0.407085 Loss2: 1.412096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.721285 Loss1: 0.304771 Loss2: 1.416513 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.557848 Loss1: 0.166815 Loss2: 1.391033 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481357 Loss1: 0.101545 Loss2: 1.379812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.204708 Loss1: 1.286101 Loss2: 1.918607
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480217 Loss1: 0.101367 Loss2: 1.378850
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.498800 Loss1: 0.118127 Loss2: 1.380673 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969727
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.788792 Loss1: 0.365825 Loss2: 1.422967 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587951 Loss1: 0.182730 Loss2: 1.405221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.128633 Loss1: 1.228426 Loss2: 1.900207 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.279481 Loss1: 0.910370 Loss2: 1.369111 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.877649 Loss1: 0.422631 Loss2: 1.455018 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.698741 Loss1: 0.344132 Loss2: 1.354609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.562390 Loss1: 0.195460 Loss2: 1.366930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469845 Loss1: 0.131613 Loss2: 1.338232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.132133 Loss1: 1.180043 Loss2: 1.952090 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.887831 Loss1: 0.412036 Loss2: 1.475795 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.696635 Loss1: 0.230708 Loss2: 1.465927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.659805 Loss1: 0.202437 Loss2: 1.457368
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.852168 Loss1: 1.027468 Loss2: 1.824700
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.126990 Loss1: 0.673787 Loss2: 1.453203 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.921632 Loss1: 0.483056 Loss2: 1.438576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.747982 Loss1: 0.328565 Loss2: 1.419417 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.652442 Loss1: 0.242060 Loss2: 1.410382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.553926 Loss1: 0.156879 Loss2: 1.397047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.482909 Loss1: 0.101983 Loss2: 1.380927 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.505189 Loss1: 0.124076 Loss2: 1.381114 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.891482 Loss1: 0.415911 Loss2: 1.475571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.653166 Loss1: 0.220822 Loss2: 1.432344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.060435 Loss1: 1.161763 Loss2: 1.898672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.044019 Loss1: 0.631385 Loss2: 1.412634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.858920 Loss1: 0.409337 Loss2: 1.449583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.639119 Loss1: 0.240717 Loss2: 1.398402 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556777 Loss1: 0.153439 Loss2: 1.403338 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.550130 Loss1: 0.166166 Loss2: 1.383964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.293992 Loss1: 1.284787 Loss2: 2.009206 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.888777 Loss1: 0.387104 Loss2: 1.501672 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.763504 Loss1: 0.305887 Loss2: 1.457617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.927293 Loss1: 1.032978 Loss2: 1.894314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.059882 Loss1: 0.657340 Loss2: 1.402542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.799171 Loss1: 0.374192 Loss2: 1.424979 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.538854 Loss1: 0.116943 Loss2: 1.421911 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.974330
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.573552 Loss1: 0.201774 Loss2: 1.371778 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483919 Loss1: 0.115836 Loss2: 1.368083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487697 Loss1: 0.126938 Loss2: 1.360759
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.953130 Loss1: 1.050048 Loss2: 1.903082
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.494218 Loss1: 0.138932 Loss2: 1.355285
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.184933 Loss1: 0.739162 Loss2: 1.445771
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.908962 Loss1: 0.427181 Loss2: 1.481781
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.814323 Loss1: 0.384126 Loss2: 1.430197
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.683379 Loss1: 0.243689 Loss2: 1.439691
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.625978 Loss1: 0.209632 Loss2: 1.416346
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.924332 Loss1: 1.026710 Loss2: 1.897622
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.572280 Loss1: 0.165816 Loss2: 1.406464
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.997553 Loss1: 0.614385 Loss2: 1.383168
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.536103 Loss1: 0.129877 Loss2: 1.406227
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.891541 Loss1: 0.451942 Loss2: 1.439599
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.516275 Loss1: 0.115527 Loss2: 1.400749
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.707782 Loss1: 0.337447 Loss2: 1.370334
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.504930 Loss1: 0.111431 Loss2: 1.393499
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.558951 Loss1: 0.195328 Loss2: 1.363623 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496702 Loss1: 0.133041 Loss2: 1.363662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462200 Loss1: 0.107266 Loss2: 1.354934
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.073827 Loss1: 1.219820 Loss2: 1.854008
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.155574 Loss1: 0.709759 Loss2: 1.445815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.724489 Loss1: 0.313194 Loss2: 1.411294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576092 Loss1: 0.193345 Loss2: 1.382746 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.551713 Loss1: 0.168057 Loss2: 1.383656 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.482451 Loss1: 0.108698 Loss2: 1.373753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443116 Loss1: 0.076808 Loss2: 1.366308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430281 Loss1: 0.066395 Loss2: 1.363886 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502077 Loss1: 0.158645 Loss2: 1.343432 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417661 Loss1: 0.086849 Loss2: 1.330812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.065382 Loss1: 1.183491 Loss2: 1.881892
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.455297 Loss1: 0.133646 Loss2: 1.321651
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.787511 Loss1: 0.346623 Loss2: 1.440888 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.582955 Loss1: 0.181714 Loss2: 1.401241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.044325 Loss1: 1.127147 Loss2: 1.917177
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.591221 Loss1: 0.191352 Loss2: 1.399869
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.191778 Loss1: 0.739928 Loss2: 1.451850
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.597277 Loss1: 0.195103 Loss2: 1.402174
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.973659 Loss1: 0.480980 Loss2: 1.492679
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.569682 Loss1: 0.163629 Loss2: 1.406053
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.817044 Loss1: 0.380173 Loss2: 1.436871
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.558804 Loss1: 0.153289 Loss2: 1.405514
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.497427 Loss1: 0.105992 Loss2: 1.391435 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.586982 Loss1: 0.166063 Loss2: 1.420919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.513874 Loss1: 0.113809 Loss2: 1.400065 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.515585 Loss1: 0.119626 Loss2: 1.395959
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.052319 Loss1: 1.197471 Loss2: 1.854848
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.106842 Loss1: 0.695990 Loss2: 1.410851
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.891222 Loss1: 0.456580 Loss2: 1.434641
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.708818 Loss1: 0.321753 Loss2: 1.387064
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.582496 Loss1: 0.195289 Loss2: 1.387207
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.008967 Loss1: 1.098897 Loss2: 1.910070
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.116235 Loss1: 0.647895 Loss2: 1.468340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.908306 Loss1: 0.431007 Loss2: 1.477299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.783518 Loss1: 0.353844 Loss2: 1.429674 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.701577 Loss1: 0.264661 Loss2: 1.436915 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.959375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647382 Loss1: 0.224848 Loss2: 1.422535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548909 Loss1: 0.151466 Loss2: 1.397443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.548586 Loss1: 0.151568 Loss2: 1.397018 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.226243 Loss1: 0.728475 Loss2: 1.497768 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.835263 Loss1: 0.383479 Loss2: 1.451784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.053141 Loss1: 1.116648 Loss2: 1.936493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.070960 Loss1: 0.681011 Loss2: 1.389949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804125 Loss1: 0.395684 Loss2: 1.408442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.757350 Loss1: 0.381408 Loss2: 1.375943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.630242 Loss1: 0.194191 Loss2: 1.436051
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.572042 Loss1: 0.186340 Loss2: 1.385701
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502157 Loss1: 0.142362 Loss2: 1.359795
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.563636 Loss1: 0.135737 Loss2: 1.427899
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419655 Loss1: 0.076249 Loss2: 1.343405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419535 Loss1: 0.085557 Loss2: 1.333977 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985577
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.134920 Loss1: 1.234275 Loss2: 1.900645
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.174678 Loss1: 0.736140 Loss2: 1.438538
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.776876 Loss1: 0.389936 Loss2: 1.386940
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629821 Loss1: 0.244105 Loss2: 1.385716
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.986023 Loss1: 1.047073 Loss2: 1.938950
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.307706 Loss1: 0.813781 Loss2: 1.493926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.982490 Loss1: 0.476566 Loss2: 1.505924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.820136 Loss1: 0.363610 Loss2: 1.456527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.729848 Loss1: 0.260482 Loss2: 1.469366 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.664943 Loss1: 0.230169 Loss2: 1.434774 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.690163 Loss1: 0.249480 Loss2: 1.440683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.561096 Loss1: 0.128878 Loss2: 1.432218 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.131408 Loss1: 1.209088 Loss2: 1.922320 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.003790 Loss1: 0.527759 Loss2: 1.476031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.804805 Loss1: 0.353539 Loss2: 1.451266
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.974287 Loss1: 1.099049 Loss2: 1.875238
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.194016 Loss1: 0.739413 Loss2: 1.454603 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.881170 Loss1: 0.460854 Loss2: 1.420316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.713523 Loss1: 0.310601 Loss2: 1.402922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.631202 Loss1: 0.242923 Loss2: 1.388279 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.565402 Loss1: 0.177853 Loss2: 1.387549 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.543767 Loss1: 0.162850 Loss2: 1.380917 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.495050 Loss1: 0.123496 Loss2: 1.371554 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.170461 Loss1: 0.698078 Loss2: 1.472383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.751940 Loss1: 0.311952 Loss2: 1.439988 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.982911 Loss1: 1.124208 Loss2: 1.858703
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.674939 Loss1: 0.233717 Loss2: 1.441222
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.101558 Loss1: 0.690153 Loss2: 1.411405
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.640326 Loss1: 0.217940 Loss2: 1.422386
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.872267 Loss1: 0.438886 Loss2: 1.433381
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.566137 Loss1: 0.139852 Loss2: 1.426285
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.725952 Loss1: 0.338309 Loss2: 1.387643
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.529524 Loss1: 0.105745 Loss2: 1.423778
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.626486 Loss1: 0.245505 Loss2: 1.380981
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.543811 Loss1: 0.130039 Loss2: 1.413773
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.584040 Loss1: 0.206688 Loss2: 1.377351
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.530233 Loss1: 0.116214 Loss2: 1.414019
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.495001 Loss1: 0.132543 Loss2: 1.362457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448927 Loss1: 0.096812 Loss2: 1.352114 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.016535 Loss1: 1.095396 Loss2: 1.921139
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.102003 Loss1: 0.604800 Loss2: 1.497203
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.857931 Loss1: 0.382629 Loss2: 1.475302
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.720550 Loss1: 0.270726 Loss2: 1.449824
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.127472 Loss1: 1.230446 Loss2: 1.897026
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.270671 Loss1: 0.803512 Loss2: 1.467160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.972620 Loss1: 0.522537 Loss2: 1.450083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.772661 Loss1: 0.331652 Loss2: 1.441009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.662333 Loss1: 0.243827 Loss2: 1.418505 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.494453 Loss1: 0.082854 Loss2: 1.411598
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.594253 Loss1: 0.183212 Loss2: 1.411042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.486993 Loss1: 0.079563 Loss2: 1.407430
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.547221 Loss1: 0.144198 Loss2: 1.403023
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490699 Loss1: 0.092219 Loss2: 1.398480
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499026 Loss1: 0.111973 Loss2: 1.387052
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.479786 Loss1: 0.093006 Loss2: 1.386780
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.993988 Loss1: 1.078590 Loss2: 1.915398
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.107020 Loss1: 0.656105 Loss2: 1.450915
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.871716 Loss1: 0.398786 Loss2: 1.472930
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.693606 Loss1: 0.277843 Loss2: 1.415762
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.871560 Loss1: 1.093574 Loss2: 1.777986
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.097564 Loss1: 0.698268 Loss2: 1.399296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.832924 Loss1: 0.466771 Loss2: 1.366153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.767144 Loss1: 0.391627 Loss2: 1.375517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.604049 Loss1: 0.254038 Loss2: 1.350011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510023 Loss1: 0.162291 Loss2: 1.347733 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478885 Loss1: 0.146731 Loss2: 1.332154 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432151 Loss1: 0.108971 Loss2: 1.323180 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.970596 Loss1: 1.101645 Loss2: 1.868951 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.859144 Loss1: 0.414649 Loss2: 1.444495 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.906958 Loss1: 1.039525 Loss2: 1.867434 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.931824 Loss1: 0.572296 Loss2: 1.359528 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701808 Loss1: 0.320709 Loss2: 1.381099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.645220 Loss1: 0.309908 Loss2: 1.335313 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.592131 Loss1: 0.240369 Loss2: 1.351762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.565553 Loss1: 0.224695 Loss2: 1.340858 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.521987 Loss1: 0.179208 Loss2: 1.342779 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.520379 Loss1: 0.178255 Loss2: 1.342124 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.955184 Loss1: 1.038752 Loss2: 1.916433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.894033 Loss1: 0.443684 Loss2: 1.450349 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.738227 Loss1: 0.324104 Loss2: 1.414124
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.995747 Loss1: 1.130036 Loss2: 1.865711
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.185991 Loss1: 0.760041 Loss2: 1.425950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.940969 Loss1: 0.497556 Loss2: 1.443413 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.721458 Loss1: 0.317869 Loss2: 1.403589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.643740 Loss1: 0.249498 Loss2: 1.394241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567497 Loss1: 0.183234 Loss2: 1.384263 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.546284 Loss1: 0.166536 Loss2: 1.379748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.489889 Loss1: 0.125435 Loss2: 1.364454 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.084060 Loss1: 1.175128 Loss2: 1.908932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.918908 Loss1: 0.462839 Loss2: 1.456069 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.108981 Loss1: 1.215187 Loss2: 1.893794 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.574513 Loss1: 0.200788 Loss2: 1.373725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.584061 Loss1: 0.197564 Loss2: 1.386497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.533730 Loss1: 0.146384 Loss2: 1.387345 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.555147 Loss1: 0.188915 Loss2: 1.366232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.492432 Loss1: 0.125982 Loss2: 1.366450 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972098
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.611115 Loss1: 0.207426 Loss2: 1.403689 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.526381 Loss1: 0.139873 Loss2: 1.386508 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.027977 Loss1: 0.651643 Loss2: 1.376334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.690495 Loss1: 0.330164 Loss2: 1.360331 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.919832 Loss1: 1.058120 Loss2: 1.861712
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.586099 Loss1: 0.221726 Loss2: 1.364373
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.106318 Loss1: 0.697232 Loss2: 1.409087
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.550877 Loss1: 0.182218 Loss2: 1.368659
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.828238 Loss1: 0.405955 Loss2: 1.422283
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518813 Loss1: 0.170083 Loss2: 1.348730
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.678622 Loss1: 0.306868 Loss2: 1.371754
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457390 Loss1: 0.106461 Loss2: 1.350929
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430875 Loss1: 0.092809 Loss2: 1.338066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446002 Loss1: 0.111351 Loss2: 1.334651 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483082 Loss1: 0.128542 Loss2: 1.354541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439290 Loss1: 0.091306 Loss2: 1.347984 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.206313 Loss1: 0.787053 Loss2: 1.419260 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.837189 Loss1: 0.388848 Loss2: 1.448341 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.073583 Loss1: 1.213796 Loss2: 1.859787 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.130287 Loss1: 0.726676 Loss2: 1.403610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.588832 Loss1: 0.180066 Loss2: 1.408766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.817391 Loss1: 0.421795 Loss2: 1.395597 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972656
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.603306 Loss1: 0.224534 Loss2: 1.378772 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500574 Loss1: 0.129667 Loss2: 1.370907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461975 Loss1: 0.097259 Loss2: 1.364716
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.974263 Loss1: 0.991357 Loss2: 1.982906
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460226 Loss1: 0.107768 Loss2: 1.352458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.335353 Loss1: 0.847355 Loss2: 1.487998
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.045640 Loss1: 0.512737 Loss2: 1.532903
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.878695 Loss1: 0.403655 Loss2: 1.475040
-DEBUG flwr 2023-10-10 18:56:02,170 | server.py:236 | fit_round 86 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.730886 Loss1: 0.256390 Loss2: 1.474496
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.607843 Loss1: 0.155072 Loss2: 1.452771
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.017348 Loss1: 1.124054 Loss2: 1.893294
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.550876 Loss1: 0.112241 Loss2: 1.438635
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.244304 Loss1: 0.730064 Loss2: 1.514240
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.553926 Loss1: 0.120534 Loss2: 1.433392
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.538090 Loss1: 0.105709 Loss2: 1.432382
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.919019 Loss1: 0.461194 Loss2: 1.457825
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.522117 Loss1: 0.090114 Loss2: 1.432003
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.877702 Loss1: 0.420936 Loss2: 1.456767
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.760448 Loss1: 0.320739 Loss2: 1.439709
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.729061 Loss1: 0.274980 Loss2: 1.454081
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.587778 Loss1: 0.159921 Loss2: 1.427857
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.513899 Loss1: 0.098372 Loss2: 1.415528
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.506823 Loss1: 0.096090 Loss2: 1.410733
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.494253 Loss1: 0.084340 Loss2: 1.409912
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 18:56:02,170][flwr][DEBUG] - fit_round 86 received 50 results and 0 failures
-INFO flwr 2023-10-10 18:56:42,398 | server.py:125 | fit progress: (86, 2.234298116863726, {'accuracy': 0.5525}, 198310.17647703202)
->> Test accuracy: 0.552500
-[2023-10-10 18:56:42,398][flwr][INFO] - fit progress: (86, 2.234298116863726, {'accuracy': 0.5525}, 198310.17647703202)
-DEBUG flwr 2023-10-10 18:56:42,398 | server.py:173 | evaluate_round 86: strategy sampled 50 clients (out of 50)
-[2023-10-10 18:56:42,398][flwr][DEBUG] - evaluate_round 86: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 19:05:46,675 | server.py:187 | evaluate_round 86 received 50 results and 0 failures
-[2023-10-10 19:05:46,675][flwr][DEBUG] - evaluate_round 86 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 19:05:46,675 | server.py:222 | fit_round 87: strategy sampled 50 clients (out of 50)
-[2023-10-10 19:05:46,675][flwr][DEBUG] - fit_round 87: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.123142 Loss1: 1.155201 Loss2: 1.967941
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.334102 Loss1: 0.792193 Loss2: 1.541909
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.999898 Loss1: 0.492751 Loss2: 1.507147
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.826729 Loss1: 0.341843 Loss2: 1.484886
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.237907 Loss1: 1.233943 Loss2: 2.003964
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.052682 Loss1: 0.652435 Loss2: 1.400247
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.790454 Loss1: 0.301870 Loss2: 1.488584
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.805969 Loss1: 0.367733 Loss2: 1.438236
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.636381 Loss1: 0.168893 Loss2: 1.467487
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.598193 Loss1: 0.144110 Loss2: 1.454083 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.597802 Loss1: 0.142650 Loss2: 1.455152 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.608898 Loss1: 0.158259 Loss2: 1.450639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.583480 Loss1: 0.129130 Loss2: 1.454350 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527219 Loss1: 0.158986 Loss2: 1.368232 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973558
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.963377 Loss1: 1.083073 Loss2: 1.880304
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.128378 Loss1: 0.732273 Loss2: 1.396104
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.858283 Loss1: 0.419545 Loss2: 1.438739
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694153 Loss1: 0.318064 Loss2: 1.376090
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.995508 Loss1: 1.157705 Loss2: 1.837804
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568171 Loss1: 0.183584 Loss2: 1.384587
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.171778 Loss1: 0.746189 Loss2: 1.425589
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569931 Loss1: 0.211378 Loss2: 1.358553
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.893239 Loss1: 0.475485 Loss2: 1.417754
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502163 Loss1: 0.134189 Loss2: 1.367973
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.730501 Loss1: 0.339551 Loss2: 1.390950
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.494375 Loss1: 0.134362 Loss2: 1.360014
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.611573 Loss1: 0.220736 Loss2: 1.390837
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484606 Loss1: 0.127206 Loss2: 1.357400
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.562482 Loss1: 0.189794 Loss2: 1.372688
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453533 Loss1: 0.096664 Loss2: 1.356869
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.585929 Loss1: 0.221297 Loss2: 1.364632
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568234 Loss1: 0.199788 Loss2: 1.368446
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.535039 Loss1: 0.168162 Loss2: 1.366877
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.538508 Loss1: 0.177983 Loss2: 1.360525
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.151003 Loss1: 1.255878 Loss2: 1.895124
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.129543 Loss1: 0.698526 Loss2: 1.431017
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.868366 Loss1: 0.423004 Loss2: 1.445362
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.702908 Loss1: 0.299143 Loss2: 1.403765
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.011038 Loss1: 1.189029 Loss2: 1.822009
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605788 Loss1: 0.199626 Loss2: 1.406161
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.167648 Loss1: 0.779409 Loss2: 1.388239
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.589734 Loss1: 0.197212 Loss2: 1.392522
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.865963 Loss1: 0.439676 Loss2: 1.426287
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.592955 Loss1: 0.186713 Loss2: 1.406241
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.717655 Loss1: 0.351234 Loss2: 1.366421
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.515176 Loss1: 0.116668 Loss2: 1.398507
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.653102 Loss1: 0.286410 Loss2: 1.366692
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.502885 Loss1: 0.118459 Loss2: 1.384426
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.601202 Loss1: 0.237629 Loss2: 1.363573
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485755 Loss1: 0.099740 Loss2: 1.386015
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.579076 Loss1: 0.219159 Loss2: 1.359917
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.517145 Loss1: 0.169655 Loss2: 1.347490
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490269 Loss1: 0.150134 Loss2: 1.340135
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465405 Loss1: 0.129868 Loss2: 1.335536
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.996516 Loss1: 1.154056 Loss2: 1.842460
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.103322 Loss1: 0.678339 Loss2: 1.424982
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.845299 Loss1: 0.428060 Loss2: 1.417239
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.871064 Loss1: 1.024639 Loss2: 1.846425
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.666068 Loss1: 0.276350 Loss2: 1.389718
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.121671 Loss1: 0.683827 Loss2: 1.437844
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.614628 Loss1: 0.227037 Loss2: 1.387590
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.822779 Loss1: 0.419962 Loss2: 1.402817
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.581862 Loss1: 0.200834 Loss2: 1.381028
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.785340 Loss1: 0.382393 Loss2: 1.402947
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503257 Loss1: 0.130404 Loss2: 1.372853
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.681038 Loss1: 0.277518 Loss2: 1.403520
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.527316 Loss1: 0.158989 Loss2: 1.368327
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.643249 Loss1: 0.249077 Loss2: 1.394172
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.494682 Loss1: 0.129351 Loss2: 1.365331
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.584859 Loss1: 0.198616 Loss2: 1.386243
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485966 Loss1: 0.118540 Loss2: 1.367426
-(DefaultActor pid=3765) >> Training accuracy: 0.977539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.506063 Loss1: 0.128202 Loss2: 1.377861 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.095399 Loss1: 1.187154 Loss2: 1.908245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.920981 Loss1: 0.448642 Loss2: 1.472339 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.904711 Loss1: 1.042144 Loss2: 1.862567
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.798634 Loss1: 0.324625 Loss2: 1.474009
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.183058 Loss1: 0.749702 Loss2: 1.433356
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.726494 Loss1: 0.265362 Loss2: 1.461132
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.865175 Loss1: 0.422366 Loss2: 1.442809
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.664006 Loss1: 0.205600 Loss2: 1.458406
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.620435 Loss1: 0.170636 Loss2: 1.449799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.603160 Loss1: 0.161307 Loss2: 1.441852 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.584603 Loss1: 0.144926 Loss2: 1.439678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.543380 Loss1: 0.104988 Loss2: 1.438392 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.468591 Loss1: 0.103603 Loss2: 1.364988 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.994748 Loss1: 1.139540 Loss2: 1.855208 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.789413 Loss1: 0.375880 Loss2: 1.413533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.735925 Loss1: 0.361792 Loss2: 1.374133
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.011265 Loss1: 1.149359 Loss2: 1.861907
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.022654 Loss1: 0.606811 Loss2: 1.415843 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.740471 Loss1: 0.318974 Loss2: 1.421497 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.651720 Loss1: 0.264199 Loss2: 1.387521 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.629830 Loss1: 0.226074 Loss2: 1.403756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.639067 Loss1: 0.258119 Loss2: 1.380947 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.515033 Loss1: 0.166794 Loss2: 1.348239
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.611690 Loss1: 0.213262 Loss2: 1.398428
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.564068 Loss1: 0.180133 Loss2: 1.383934
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.504698 Loss1: 0.122267 Loss2: 1.382431
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.467937 Loss1: 0.093200 Loss2: 1.374738
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.853087 Loss1: 1.011718 Loss2: 1.841370
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.085716 Loss1: 0.679890 Loss2: 1.405826
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.855687 Loss1: 0.435491 Loss2: 1.420196
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.766423 Loss1: 0.395498 Loss2: 1.370925
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.827414 Loss1: 0.991093 Loss2: 1.836321
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.938486 Loss1: 0.539637 Loss2: 1.398849 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.832212 Loss1: 0.420771 Loss2: 1.411441 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.710756 Loss1: 0.324094 Loss2: 1.386663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.724654 Loss1: 0.318076 Loss2: 1.406578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.597972 Loss1: 0.216200 Loss2: 1.381772 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.578210 Loss1: 0.193800 Loss2: 1.384409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490243 Loss1: 0.130630 Loss2: 1.359613 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.920994 Loss1: 1.043834 Loss2: 1.877160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.958992 Loss1: 0.499462 Loss2: 1.459530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.880495 Loss1: 1.004879 Loss2: 1.875615 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.133833 Loss1: 0.722001 Loss2: 1.411832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.865467 Loss1: 0.422532 Loss2: 1.442935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.688600 Loss1: 0.300745 Loss2: 1.387855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.689359 Loss1: 0.294580 Loss2: 1.394779 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.635498 Loss1: 0.244813 Loss2: 1.390685 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546851 Loss1: 0.165896 Loss2: 1.380955 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452456 Loss1: 0.092133 Loss2: 1.360323 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.016631 Loss1: 0.617013 Loss2: 1.399618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591104 Loss1: 0.233091 Loss2: 1.358013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517831 Loss1: 0.164222 Loss2: 1.353609 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551895 Loss1: 0.199013 Loss2: 1.352883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.501510 Loss1: 0.141543 Loss2: 1.359968 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495161 Loss1: 0.146518 Loss2: 1.348642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454326 Loss1: 0.106559 Loss2: 1.347767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.514907 Loss1: 0.163692 Loss2: 1.351215
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.482984 Loss1: 0.140583 Loss2: 1.342401
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431093 Loss1: 0.094452 Loss2: 1.336641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396370 Loss1: 0.066421 Loss2: 1.329949 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.175390 Loss1: 0.703898 Loss2: 1.471492 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.891364 Loss1: 0.414005 Loss2: 1.477359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.867849 Loss1: 1.026928 Loss2: 1.840921
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.779388 Loss1: 0.318622 Loss2: 1.460766
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.678558 Loss1: 0.231712 Loss2: 1.446846
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.117276 Loss1: 0.691562 Loss2: 1.425714
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.606772 Loss1: 0.165254 Loss2: 1.441518
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.860973 Loss1: 0.433863 Loss2: 1.427110
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.618968 Loss1: 0.182463 Loss2: 1.436506
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.664006 Loss1: 0.261889 Loss2: 1.402117
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.620638 Loss1: 0.181571 Loss2: 1.439067
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.562916 Loss1: 0.175404 Loss2: 1.387511
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.601799 Loss1: 0.167946 Loss2: 1.433853
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521895 Loss1: 0.142726 Loss2: 1.379169
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487895 Loss1: 0.110260 Loss2: 1.377635
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.463794 Loss1: 0.103565 Loss2: 1.360229
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453574 Loss1: 0.093402 Loss2: 1.360172
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440943 Loss1: 0.084184 Loss2: 1.356759
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.888995 Loss1: 1.068322 Loss2: 1.820673
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.072275 Loss1: 0.683512 Loss2: 1.388763
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.856626 Loss1: 0.462698 Loss2: 1.393928
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.690798 Loss1: 0.332206 Loss2: 1.358592
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.659440 Loss1: 0.296997 Loss2: 1.362444
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.950553 Loss1: 1.008326 Loss2: 1.942227
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576989 Loss1: 0.212047 Loss2: 1.364942
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.202800 Loss1: 0.695831 Loss2: 1.506970
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.543951 Loss1: 0.205604 Loss2: 1.338347
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479655 Loss1: 0.127622 Loss2: 1.352033
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.913789 Loss1: 0.398620 Loss2: 1.515170
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.473060 Loss1: 0.140271 Loss2: 1.332789
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.759216 Loss1: 0.289891 Loss2: 1.469325
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463858 Loss1: 0.124813 Loss2: 1.339045
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.681204 Loss1: 0.208871 Loss2: 1.472333
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.642146 Loss1: 0.174807 Loss2: 1.467339
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.594293 Loss1: 0.138821 Loss2: 1.455472
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568299 Loss1: 0.115452 Loss2: 1.452847
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.540753 Loss1: 0.091620 Loss2: 1.449132
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.815461 Loss1: 1.032986 Loss2: 1.782474
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509437 Loss1: 0.064179 Loss2: 1.445258
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.750223 Loss1: 0.379565 Loss2: 1.370658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.575439 Loss1: 0.229247 Loss2: 1.346192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.500381 Loss1: 0.166007 Loss2: 1.334374
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.882182 Loss1: 1.059409 Loss2: 1.822773
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468873 Loss1: 0.146489 Loss2: 1.322384
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.060609 Loss1: 0.649495 Loss2: 1.411115
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.465153 Loss1: 0.141783 Loss2: 1.323370
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.820805 Loss1: 0.408397 Loss2: 1.412408
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433920 Loss1: 0.116333 Loss2: 1.317587
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.688160 Loss1: 0.304688 Loss2: 1.383472
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421458 Loss1: 0.109481 Loss2: 1.311977
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609279 Loss1: 0.222976 Loss2: 1.386303
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.599012 Loss1: 0.225286 Loss2: 1.373726
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.536291 Loss1: 0.163195 Loss2: 1.373096
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.541074 Loss1: 0.168074 Loss2: 1.373000
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497327 Loss1: 0.134966 Loss2: 1.362361
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.801662 Loss1: 0.933856 Loss2: 1.867806
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.479900 Loss1: 0.122944 Loss2: 1.356955
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.824589 Loss1: 0.407816 Loss2: 1.416773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.581023 Loss1: 0.212503 Loss2: 1.368520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538452 Loss1: 0.186798 Loss2: 1.351654
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.167753 Loss1: 1.176009 Loss2: 1.991744
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.189701 Loss1: 0.717556 Loss2: 1.472144 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.485705 Loss1: 0.122571 Loss2: 1.363134
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.908699 Loss1: 0.410788 Loss2: 1.497911
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.442942 Loss1: 0.094690 Loss2: 1.348253
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.780257 Loss1: 0.326762 Loss2: 1.453495
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.470456 Loss1: 0.127568 Loss2: 1.342888
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.683989 Loss1: 0.222550 Loss2: 1.461439
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.587102 Loss1: 0.141936 Loss2: 1.445166
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.575343 Loss1: 0.140043 Loss2: 1.435300
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.551509 Loss1: 0.120362 Loss2: 1.431147
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.568242 Loss1: 0.141457 Loss2: 1.426785
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.528883 Loss1: 0.098338 Loss2: 1.430545
-(DefaultActor pid=3764) >> Training accuracy: 0.985491
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.023888 Loss1: 1.164051 Loss2: 1.859837
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.182480 Loss1: 0.745989 Loss2: 1.436492
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.879539 Loss1: 0.443832 Loss2: 1.435707
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.813665 Loss1: 0.401034 Loss2: 1.412631
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.648310 Loss1: 0.239305 Loss2: 1.409006
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.004156 Loss1: 1.165693 Loss2: 1.838464
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.593909 Loss1: 0.208395 Loss2: 1.385514
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.563153 Loss1: 0.169944 Loss2: 1.393209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.538040 Loss1: 0.153725 Loss2: 1.384315 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.511626 Loss1: 0.129637 Loss2: 1.381989 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481227 Loss1: 0.098342 Loss2: 1.382885 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.570410 Loss1: 0.177229 Loss2: 1.393182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.491470 Loss1: 0.125921 Loss2: 1.365549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.495117 Loss1: 0.123015 Loss2: 1.372101
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.007217 Loss1: 1.098055 Loss2: 1.909163
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.146294 Loss1: 0.687012 Loss2: 1.459282
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.956629 Loss1: 0.509813 Loss2: 1.446816
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.781765 Loss1: 0.347949 Loss2: 1.433816
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.651409 Loss1: 0.232645 Loss2: 1.418764
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.054601 Loss1: 1.221791 Loss2: 1.832810
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.590951 Loss1: 0.184834 Loss2: 1.406116
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534339 Loss1: 0.143279 Loss2: 1.391060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484868 Loss1: 0.099830 Loss2: 1.385038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.471009 Loss1: 0.095518 Loss2: 1.375491 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.548398 Loss1: 0.202957 Loss2: 1.345441 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.494582 Loss1: 0.168323 Loss2: 1.326260 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410069 Loss1: 0.087959 Loss2: 1.322110 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.919780 Loss1: 1.075036 Loss2: 1.844744
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.038304 Loss1: 0.650102 Loss2: 1.388202
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.826197 Loss1: 0.413628 Loss2: 1.412569
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.738429 Loss1: 0.366313 Loss2: 1.372117
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.796667 Loss1: 0.940556 Loss2: 1.856112
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.146816 Loss1: 0.730568 Loss2: 1.416249 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.923379 Loss1: 0.483669 Loss2: 1.439710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.725566 Loss1: 0.338875 Loss2: 1.386691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558176 Loss1: 0.172286 Loss2: 1.385890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502234 Loss1: 0.137947 Loss2: 1.364288 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483351 Loss1: 0.126070 Loss2: 1.357280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464376 Loss1: 0.121562 Loss2: 1.342814 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.028250 Loss1: 1.112942 Loss2: 1.915308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.024370 Loss1: 0.509755 Loss2: 1.514616 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.114707 Loss1: 1.246896 Loss2: 1.867812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.122982 Loss1: 0.722678 Loss2: 1.400304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.920146 Loss1: 0.507972 Loss2: 1.412174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.703580 Loss1: 0.322149 Loss2: 1.381431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.635479 Loss1: 0.266857 Loss2: 1.368621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527456 Loss1: 0.161496 Loss2: 1.365960 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.520421 Loss1: 0.169593 Loss2: 1.350828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446873 Loss1: 0.100673 Loss2: 1.346200 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.816734 Loss1: 0.920368 Loss2: 1.896367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.905163 Loss1: 0.448370 Loss2: 1.456793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.797993 Loss1: 0.352788 Loss2: 1.445205
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.881670 Loss1: 1.007967 Loss2: 1.873704
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.671139 Loss1: 0.228634 Loss2: 1.442505
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.247844 Loss1: 0.794939 Loss2: 1.452906
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.614954 Loss1: 0.187411 Loss2: 1.427543
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.948901 Loss1: 0.456151 Loss2: 1.492750
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.743749 Loss1: 0.337126 Loss2: 1.406623
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.602411 Loss1: 0.175088 Loss2: 1.427323
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.729014 Loss1: 0.311735 Loss2: 1.417279
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.523061 Loss1: 0.103791 Loss2: 1.419270
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.517825 Loss1: 0.104340 Loss2: 1.413485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474560 Loss1: 0.066766 Loss2: 1.407794 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988971
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.582404 Loss1: 0.184247 Loss2: 1.398157 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.075307 Loss1: 1.041809 Loss2: 2.033498 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.988755 Loss1: 0.486704 Loss2: 1.502051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.806744 Loss1: 0.313740 Loss2: 1.493004
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.866302 Loss1: 1.041500 Loss2: 1.824802
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.724829 Loss1: 0.247780 Loss2: 1.477049
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.186320 Loss1: 0.778429 Loss2: 1.407891
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.714098 Loss1: 0.244855 Loss2: 1.469243
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.847214 Loss1: 0.451944 Loss2: 1.395270
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.636841 Loss1: 0.164394 Loss2: 1.472447
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.691859 Loss1: 0.330291 Loss2: 1.361567
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.583779 Loss1: 0.113637 Loss2: 1.470142
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571952 Loss1: 0.212276 Loss2: 1.359676
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.591436 Loss1: 0.137393 Loss2: 1.454042
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485453 Loss1: 0.143428 Loss2: 1.342025
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.558923 Loss1: 0.099847 Loss2: 1.459076
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436751 Loss1: 0.101051 Loss2: 1.335700
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405182 Loss1: 0.081067 Loss2: 1.324114
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410112 Loss1: 0.096526 Loss2: 1.313586
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387928 Loss1: 0.072271 Loss2: 1.315657
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.059705 Loss1: 1.190905 Loss2: 1.868800
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.107492 Loss1: 0.687858 Loss2: 1.419634
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.852778 Loss1: 0.409224 Loss2: 1.443555
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.224318 Loss1: 1.237630 Loss2: 1.986688
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.667598 Loss1: 0.273136 Loss2: 1.394462
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.638950 Loss1: 0.242188 Loss2: 1.396762 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.613481 Loss1: 0.225387 Loss2: 1.388094 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.622984 Loss1: 0.227304 Loss2: 1.395680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.631885 Loss1: 0.238966 Loss2: 1.392920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.546999 Loss1: 0.165224 Loss2: 1.381775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.545937 Loss1: 0.178028 Loss2: 1.367909 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464608 Loss1: 0.101975 Loss2: 1.362633 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.248846 Loss1: 1.332403 Loss2: 1.916443
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.197725 Loss1: 0.767177 Loss2: 1.430548
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.894033 Loss1: 0.465433 Loss2: 1.428600
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.719393 Loss1: 0.319678 Loss2: 1.399715
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.062884 Loss1: 1.155246 Loss2: 1.907638
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.164075 Loss1: 0.702126 Loss2: 1.461949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.930784 Loss1: 0.469545 Loss2: 1.461238 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.747594 Loss1: 0.323914 Loss2: 1.423679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.666534 Loss1: 0.242561 Loss2: 1.423973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.646152 Loss1: 0.242707 Loss2: 1.403446 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972098
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548619 Loss1: 0.143292 Loss2: 1.405327 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.542084 Loss1: 0.152393 Loss2: 1.389691 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.139492 Loss1: 0.682917 Loss2: 1.456575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.779591 Loss1: 0.318116 Loss2: 1.461475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.728706 Loss1: 0.287547 Loss2: 1.441160
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.923044 Loss1: 1.162544 Loss2: 1.760500
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.050282 Loss1: 0.693360 Loss2: 1.356921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.753329 Loss1: 0.402121 Loss2: 1.351208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699795 Loss1: 0.362548 Loss2: 1.337247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.620637 Loss1: 0.297538 Loss2: 1.323100 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.565489 Loss1: 0.244574 Loss2: 1.320915 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464800 Loss1: 0.150035 Loss2: 1.314765 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437362 Loss1: 0.131949 Loss2: 1.305412
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.892532 Loss1: 1.007908 Loss2: 1.884624
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.137114 Loss1: 0.654127 Loss2: 1.482987
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.848989 Loss1: 0.380705 Loss2: 1.468283
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.786942 Loss1: 0.345216 Loss2: 1.441726
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.687156 Loss1: 0.240960 Loss2: 1.446195
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.997116 Loss1: 1.125269 Loss2: 1.871848
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.132814 Loss1: 0.702264 Loss2: 1.430550 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 19:34:47,276 | server.py:236 | fit_round 87 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.581102 Loss1: 0.159072 Loss2: 1.422030
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.793932 Loss1: 0.382984 Loss2: 1.410948
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.531536 Loss1: 0.111597 Loss2: 1.419939
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.664623 Loss1: 0.280017 Loss2: 1.384605
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.528136 Loss1: 0.115821 Loss2: 1.412315
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.584009 Loss1: 0.209731 Loss2: 1.374278
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.499144 Loss1: 0.091995 Loss2: 1.407149
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556773 Loss1: 0.193355 Loss2: 1.363418
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.529115 Loss1: 0.165309 Loss2: 1.363807
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518849 Loss1: 0.149650 Loss2: 1.369199
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.482090 Loss1: 0.129607 Loss2: 1.352483
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.495949 Loss1: 0.143827 Loss2: 1.352122
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.950017 Loss1: 1.139450 Loss2: 1.810567
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.168611 Loss1: 0.773738 Loss2: 1.394873
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.857307 Loss1: 0.474357 Loss2: 1.382950
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.670003 Loss1: 0.329238 Loss2: 1.340765
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.600205 Loss1: 0.251516 Loss2: 1.348689
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511098 Loss1: 0.181455 Loss2: 1.329643 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450708 Loss1: 0.123751 Loss2: 1.326956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467555 Loss1: 0.145406 Loss2: 1.322149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449415 Loss1: 0.128331 Loss2: 1.321084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433541 Loss1: 0.116628 Loss2: 1.316913 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.541902 Loss1: 0.134564 Loss2: 1.407338 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430696 Loss1: 0.037630 Loss2: 1.393066 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.031865 Loss1: 0.646156 Loss2: 1.385708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623948 Loss1: 0.244926 Loss2: 1.379022 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568421 Loss1: 0.197688 Loss2: 1.370733
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.919731 Loss1: 0.999388 Loss2: 1.920343
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.573846 Loss1: 0.202015 Loss2: 1.371831
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.972387 Loss1: 0.560247 Loss2: 1.412140
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528887 Loss1: 0.159592 Loss2: 1.369295
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.772344 Loss1: 0.337602 Loss2: 1.434742
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.501122 Loss1: 0.137370 Loss2: 1.363752
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.624939 Loss1: 0.227236 Loss2: 1.397704
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499365 Loss1: 0.144810 Loss2: 1.354556
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571660 Loss1: 0.176727 Loss2: 1.394933
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481371 Loss1: 0.122419 Loss2: 1.358951
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.541567 Loss1: 0.149491 Loss2: 1.392076
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530973 Loss1: 0.143977 Loss2: 1.386996
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481857 Loss1: 0.101927 Loss2: 1.379930
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447333 Loss1: 0.077445 Loss2: 1.369888
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430487 Loss1: 0.067069 Loss2: 1.363418
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 19:34:47,276][flwr][DEBUG] - fit_round 87 received 50 results and 0 failures
-INFO flwr 2023-10-10 19:35:29,077 | server.py:125 | fit progress: (87, 2.233296902796712, {'accuracy': 0.5543}, 200636.85539069702)
->> Test accuracy: 0.554300
-[2023-10-10 19:35:29,077][flwr][INFO] - fit progress: (87, 2.233296902796712, {'accuracy': 0.5543}, 200636.85539069702)
-DEBUG flwr 2023-10-10 19:35:29,077 | server.py:173 | evaluate_round 87: strategy sampled 50 clients (out of 50)
-[2023-10-10 19:35:29,077][flwr][DEBUG] - evaluate_round 87: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 19:44:35,176 | server.py:187 | evaluate_round 87 received 50 results and 0 failures
-[2023-10-10 19:44:35,176][flwr][DEBUG] - evaluate_round 87 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 19:44:35,176 | server.py:222 | fit_round 88: strategy sampled 50 clients (out of 50)
-[2023-10-10 19:44:35,176][flwr][DEBUG] - fit_round 88: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.924679 Loss1: 1.058438 Loss2: 1.866241
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.985379 Loss1: 0.585901 Loss2: 1.399478
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.730162 Loss1: 0.321948 Loss2: 1.408213
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589130 Loss1: 0.224111 Loss2: 1.365019
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.925676 Loss1: 1.106193 Loss2: 1.819483
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.594679 Loss1: 0.233128 Loss2: 1.361551
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.096936 Loss1: 0.716777 Loss2: 1.380160
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.559030 Loss1: 0.197869 Loss2: 1.361161
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.801331 Loss1: 0.404087 Loss2: 1.397244
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.564759 Loss1: 0.205461 Loss2: 1.359298
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.691243 Loss1: 0.324144 Loss2: 1.367099
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.523170 Loss1: 0.154928 Loss2: 1.368243
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557478 Loss1: 0.192982 Loss2: 1.364496
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488764 Loss1: 0.135837 Loss2: 1.352927
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499981 Loss1: 0.152069 Loss2: 1.347912
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.534398 Loss1: 0.175136 Loss2: 1.359262
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.454729 Loss1: 0.114899 Loss2: 1.339830
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439580 Loss1: 0.103174 Loss2: 1.336406
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.469063 Loss1: 0.129777 Loss2: 1.339286
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429986 Loss1: 0.096136 Loss2: 1.333850
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.059348 Loss1: 1.168553 Loss2: 1.890795
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.213468 Loss1: 0.743325 Loss2: 1.470143
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.937880 Loss1: 0.484545 Loss2: 1.453335
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.731087 Loss1: 0.310922 Loss2: 1.420165
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.031033 Loss1: 1.151368 Loss2: 1.879665
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.628199 Loss1: 0.219437 Loss2: 1.408762
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.090553 Loss1: 0.684464 Loss2: 1.406088
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.646099 Loss1: 0.247155 Loss2: 1.398944
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.819964 Loss1: 0.388706 Loss2: 1.431258
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.578778 Loss1: 0.174677 Loss2: 1.404101
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.665902 Loss1: 0.266229 Loss2: 1.399672
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.547448 Loss1: 0.157557 Loss2: 1.389892
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.590123 Loss1: 0.191278 Loss2: 1.398845
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.500371 Loss1: 0.111948 Loss2: 1.388423
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.563507 Loss1: 0.181629 Loss2: 1.381878
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.507049 Loss1: 0.126158 Loss2: 1.380892
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.537972 Loss1: 0.163440 Loss2: 1.374532
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510074 Loss1: 0.130772 Loss2: 1.379302
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.512022 Loss1: 0.140218 Loss2: 1.371804
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.477398 Loss1: 0.107920 Loss2: 1.369477
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.987645 Loss1: 1.106650 Loss2: 1.880995
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.154396 Loss1: 0.739224 Loss2: 1.415172
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.913458 Loss1: 0.446459 Loss2: 1.466999
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.727576 Loss1: 0.313630 Loss2: 1.413946
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.127240 Loss1: 1.252170 Loss2: 1.875071
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.090618 Loss1: 0.707922 Loss2: 1.382697 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.592492 Loss1: 0.195488 Loss2: 1.397004
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.861978 Loss1: 0.437316 Loss2: 1.424663
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547553 Loss1: 0.151026 Loss2: 1.396527
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.617964 Loss1: 0.244529 Loss2: 1.373435
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.510650 Loss1: 0.123354 Loss2: 1.387296
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.566442 Loss1: 0.197947 Loss2: 1.368495
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513632 Loss1: 0.155920 Loss2: 1.357713
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.478860 Loss1: 0.087566 Loss2: 1.391294
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483106 Loss1: 0.130099 Loss2: 1.353008
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461249 Loss1: 0.087041 Loss2: 1.374208
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414382 Loss1: 0.074725 Loss2: 1.339657 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978795
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.914197 Loss1: 1.050302 Loss2: 1.863895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.771447 Loss1: 0.328188 Loss2: 1.443259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.127407 Loss1: 1.286298 Loss2: 1.841108
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.679946 Loss1: 0.271946 Loss2: 1.408000
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.164263 Loss1: 0.747605 Loss2: 1.416657
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.578079 Loss1: 0.170283 Loss2: 1.407796
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.880956 Loss1: 0.443134 Loss2: 1.437822
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.539367 Loss1: 0.143299 Loss2: 1.396068
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.663635 Loss1: 0.275906 Loss2: 1.387729
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516320 Loss1: 0.121073 Loss2: 1.395247
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.510673 Loss1: 0.122041 Loss2: 1.388632 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.510861 Loss1: 0.120330 Loss2: 1.390530 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475149 Loss1: 0.085819 Loss2: 1.389330 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493556 Loss1: 0.123488 Loss2: 1.370068 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.979607 Loss1: 1.114848 Loss2: 1.864759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.829169 Loss1: 0.386071 Loss2: 1.443098 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.748638 Loss1: 0.349836 Loss2: 1.398803
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.131169 Loss1: 1.191256 Loss2: 1.939913
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.683728 Loss1: 0.273605 Loss2: 1.410123
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.182777 Loss1: 0.690839 Loss2: 1.491938
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.567422 Loss1: 0.172325 Loss2: 1.395097
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.856821 Loss1: 0.363301 Loss2: 1.493520
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.562713 Loss1: 0.178884 Loss2: 1.383829
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.719546 Loss1: 0.266859 Loss2: 1.452687
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.545820 Loss1: 0.160792 Loss2: 1.385027
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.659395 Loss1: 0.205163 Loss2: 1.454232
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509865 Loss1: 0.133362 Loss2: 1.376503
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.568097 Loss1: 0.135506 Loss2: 1.432592
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.489800 Loss1: 0.115825 Loss2: 1.373975
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.560172 Loss1: 0.135492 Loss2: 1.424681
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.592213 Loss1: 0.164780 Loss2: 1.427433
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.566269 Loss1: 0.138365 Loss2: 1.427904
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.535365 Loss1: 0.108558 Loss2: 1.426807
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.885275 Loss1: 1.050679 Loss2: 1.834596
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.015959 Loss1: 0.602613 Loss2: 1.413347
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.797019 Loss1: 0.373360 Loss2: 1.423659
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.634698 Loss1: 0.244435 Loss2: 1.390263
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.010803 Loss1: 1.221962 Loss2: 1.788841
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574414 Loss1: 0.196776 Loss2: 1.377638
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.057655 Loss1: 0.660319 Loss2: 1.397336
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538738 Loss1: 0.165843 Loss2: 1.372895
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.728057 Loss1: 0.367597 Loss2: 1.360460
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.606815 Loss1: 0.268718 Loss2: 1.338097
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.526560 Loss1: 0.158515 Loss2: 1.368044
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.578032 Loss1: 0.244573 Loss2: 1.333459
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.518274 Loss1: 0.149801 Loss2: 1.368473
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487183 Loss1: 0.164504 Loss2: 1.322679
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.519789 Loss1: 0.154399 Loss2: 1.365389
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452926 Loss1: 0.136554 Loss2: 1.316373
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.506160 Loss1: 0.141008 Loss2: 1.365152
-(DefaultActor pid=3765) >> Training accuracy: 0.957031
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445704 Loss1: 0.139203 Loss2: 1.306501 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.102656 Loss1: 1.198474 Loss2: 1.904182 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.906662 Loss1: 0.457124 Loss2: 1.449537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.053104 Loss1: 1.139616 Loss2: 1.913488
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.775546 Loss1: 0.319650 Loss2: 1.455896
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.762280 Loss1: 0.332636 Loss2: 1.429643 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.685766 Loss1: 0.244687 Loss2: 1.441079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.641647 Loss1: 0.217095 Loss2: 1.424552 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647444 Loss1: 0.239765 Loss2: 1.407678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.524121 Loss1: 0.153107 Loss2: 1.371014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.512020 Loss1: 0.138605 Loss2: 1.373415 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969727
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.507055 Loss1: 0.139839 Loss2: 1.367216 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978365
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.746783 Loss1: 0.954519 Loss2: 1.792264 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.739735 Loss1: 0.366672 Loss2: 1.373064 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.620986 Loss1: 0.264529 Loss2: 1.356457 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.564386 Loss1: 0.215556 Loss2: 1.348830 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487461 Loss1: 0.145463 Loss2: 1.341998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436375 Loss1: 0.098806 Loss2: 1.337569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448886 Loss1: 0.124248 Loss2: 1.324638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.572865 Loss1: 0.184435 Loss2: 1.388430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.507126 Loss1: 0.134226 Loss2: 1.372900 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970588
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461740 Loss1: 0.097774 Loss2: 1.363966 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.127381 Loss1: 1.256224 Loss2: 1.871157
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.067806 Loss1: 0.698910 Loss2: 1.368896
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.903955 Loss1: 0.487484 Loss2: 1.416471
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.708510 Loss1: 0.355587 Loss2: 1.352923
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.030927 Loss1: 1.206767 Loss2: 1.824160
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.049275 Loss1: 0.648915 Loss2: 1.400360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.800305 Loss1: 0.386823 Loss2: 1.413481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.616303 Loss1: 0.244765 Loss2: 1.371538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571363 Loss1: 0.191602 Loss2: 1.379761 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.566448 Loss1: 0.195412 Loss2: 1.371036 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.522214 Loss1: 0.159650 Loss2: 1.362564 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.477511 Loss1: 0.123413 Loss2: 1.354099 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.127163 Loss1: 0.740246 Loss2: 1.386918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.655328 Loss1: 0.297686 Loss2: 1.357642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.598943 Loss1: 0.243048 Loss2: 1.355895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.549470 Loss1: 0.200911 Loss2: 1.348558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496573 Loss1: 0.156129 Loss2: 1.340444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.478972 Loss1: 0.137372 Loss2: 1.341600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400291 Loss1: 0.070240 Loss2: 1.330051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392577 Loss1: 0.070684 Loss2: 1.321892 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466414 Loss1: 0.127464 Loss2: 1.338950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465016 Loss1: 0.130814 Loss2: 1.334203 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.028283 Loss1: 1.170086 Loss2: 1.858197
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.174607 Loss1: 0.716315 Loss2: 1.458292
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.926926 Loss1: 0.508564 Loss2: 1.418362
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.723461 Loss1: 0.326612 Loss2: 1.396848
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.942908 Loss1: 1.124727 Loss2: 1.818181
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.066403 Loss1: 0.692383 Loss2: 1.374020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.878579 Loss1: 0.487624 Loss2: 1.390955 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.683524 Loss1: 0.314554 Loss2: 1.368970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.547443 Loss1: 0.201298 Loss2: 1.346145 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.535950 Loss1: 0.195644 Loss2: 1.340307 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.528991 Loss1: 0.183526 Loss2: 1.345465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442472 Loss1: 0.113922 Loss2: 1.328549 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.239516 Loss1: 1.177819 Loss2: 2.061697 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.907530 Loss1: 0.386964 Loss2: 1.520566 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.662486 Loss1: 0.209980 Loss2: 1.452506 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.616535 Loss1: 0.163065 Loss2: 1.453470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.594194 Loss1: 0.155087 Loss2: 1.439107 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.542013 Loss1: 0.103857 Loss2: 1.438155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.562172 Loss1: 0.128121 Loss2: 1.434051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.543511 Loss1: 0.114407 Loss2: 1.429104
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.614840 Loss1: 0.184124 Loss2: 1.430716
-(DefaultActor pid=3765) >> Training accuracy: 0.975962
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559362 Loss1: 0.147110 Loss2: 1.412252
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.560962 Loss1: 0.153528 Loss2: 1.407434
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.538951 Loss1: 0.135049 Loss2: 1.403901
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.504931 Loss1: 0.101736 Loss2: 1.403195
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.486556 Loss1: 0.082839 Loss2: 1.403717
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.111843 Loss1: 1.210302 Loss2: 1.901541
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.193866 Loss1: 0.769479 Loss2: 1.424387
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.859907 Loss1: 0.427996 Loss2: 1.431911
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.685432 Loss1: 0.303893 Loss2: 1.381539
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565275 Loss1: 0.176307 Loss2: 1.388968
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.094697 Loss1: 1.162204 Loss2: 1.932493
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524343 Loss1: 0.146569 Loss2: 1.377775
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.283474 Loss1: 0.819319 Loss2: 1.464155
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.526986 Loss1: 0.168455 Loss2: 1.358532
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.977748 Loss1: 0.490931 Loss2: 1.486817
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.519913 Loss1: 0.144469 Loss2: 1.375444
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.811079 Loss1: 0.375170 Loss2: 1.435909
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.504976 Loss1: 0.136512 Loss2: 1.368465
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.661450 Loss1: 0.225451 Loss2: 1.435999
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.492489 Loss1: 0.125155 Loss2: 1.367335
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.556219 Loss1: 0.141558 Loss2: 1.414661 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.598155 Loss1: 0.176072 Loss2: 1.422083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.587890 Loss1: 0.173830 Loss2: 1.414059
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.917050 Loss1: 0.969042 Loss2: 1.948009
-(DefaultActor pid=3764) >> Training accuracy: 0.948958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.093405 Loss1: 0.648109 Loss2: 1.445297
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.885762 Loss1: 0.401842 Loss2: 1.483920
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.689325 Loss1: 0.265182 Loss2: 1.424143
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.680966 Loss1: 0.254504 Loss2: 1.426462
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.646577 Loss1: 0.219505 Loss2: 1.427072
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.870730 Loss1: 1.006810 Loss2: 1.863920
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.575712 Loss1: 0.157376 Loss2: 1.418336
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.949187 Loss1: 0.574272 Loss2: 1.374915
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.513758 Loss1: 0.102828 Loss2: 1.410931
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.734735 Loss1: 0.324586 Loss2: 1.410149
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493601 Loss1: 0.094392 Loss2: 1.399208
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605307 Loss1: 0.251495 Loss2: 1.353812
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.482433 Loss1: 0.085697 Loss2: 1.396737
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555345 Loss1: 0.205155 Loss2: 1.350190
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.507683 Loss1: 0.152491 Loss2: 1.355192
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490023 Loss1: 0.148628 Loss2: 1.341395
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481336 Loss1: 0.137796 Loss2: 1.343540
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448639 Loss1: 0.111640 Loss2: 1.336999
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440113 Loss1: 0.106686 Loss2: 1.333427
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.708071 Loss1: 0.947033 Loss2: 1.761038
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.970412 Loss1: 0.620760 Loss2: 1.349652
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.793418 Loss1: 0.411128 Loss2: 1.382290
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.668389 Loss1: 0.319252 Loss2: 1.349137
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535350 Loss1: 0.195368 Loss2: 1.339981
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.876261 Loss1: 0.968350 Loss2: 1.907911
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503611 Loss1: 0.178984 Loss2: 1.324627
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502556 Loss1: 0.177983 Loss2: 1.324573 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473149 Loss1: 0.144470 Loss2: 1.328680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414948 Loss1: 0.094005 Loss2: 1.320943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398501 Loss1: 0.086294 Loss2: 1.312207 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533628 Loss1: 0.157236 Loss2: 1.376392 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.521300 Loss1: 0.146999 Loss2: 1.374301 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509837 Loss1: 0.133803 Loss2: 1.376034
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.832471 Loss1: 0.991291 Loss2: 1.841180
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.004686 Loss1: 0.632517 Loss2: 1.372168
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.824816 Loss1: 0.413321 Loss2: 1.411495
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.670761 Loss1: 0.308527 Loss2: 1.362234
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587241 Loss1: 0.221551 Loss2: 1.365690
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.058748 Loss1: 1.177874 Loss2: 1.880874
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.154096 Loss1: 0.709780 Loss2: 1.444316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.865241 Loss1: 0.421091 Loss2: 1.444150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.740219 Loss1: 0.332493 Loss2: 1.407726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.629922 Loss1: 0.230612 Loss2: 1.399310 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425671 Loss1: 0.095570 Loss2: 1.330101
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.569588 Loss1: 0.177138 Loss2: 1.392450
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545046 Loss1: 0.157249 Loss2: 1.387797
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.527562 Loss1: 0.136999 Loss2: 1.390563
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.512837 Loss1: 0.127437 Loss2: 1.385400
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460742 Loss1: 0.082155 Loss2: 1.378588
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.830584 Loss1: 1.046883 Loss2: 1.783701
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.987977 Loss1: 0.667710 Loss2: 1.320268
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.785180 Loss1: 0.415404 Loss2: 1.369776
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572942 Loss1: 0.272544 Loss2: 1.300398
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503212 Loss1: 0.198753 Loss2: 1.304459
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.048081 Loss1: 1.164572 Loss2: 1.883510
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.030917 Loss1: 0.584685 Loss2: 1.446232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.757526 Loss1: 0.327279 Loss2: 1.430247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.653142 Loss1: 0.253906 Loss2: 1.399236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.661799 Loss1: 0.260097 Loss2: 1.401702 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.578405 Loss1: 0.177595 Loss2: 1.400810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.520201 Loss1: 0.129471 Loss2: 1.390730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482174 Loss1: 0.103175 Loss2: 1.378999 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.184532 Loss1: 0.744643 Loss2: 1.439889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.866433 Loss1: 0.422642 Loss2: 1.443791 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.628491 Loss1: 0.188763 Loss2: 1.439728 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.550768 Loss1: 0.129868 Loss2: 1.420900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.540411 Loss1: 0.123736 Loss2: 1.416675
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.039900 Loss1: 0.602489 Loss2: 1.437411
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.513065 Loss1: 0.102913 Loss2: 1.410151
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.813840 Loss1: 0.347899 Loss2: 1.465940
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.706849 Loss1: 0.274117 Loss2: 1.432732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.575688 Loss1: 0.154111 Loss2: 1.421577 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.547703 Loss1: 0.133095 Loss2: 1.414608 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.527260 Loss1: 0.121643 Loss2: 1.405617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.496683 Loss1: 0.087935 Loss2: 1.408748 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.965906 Loss1: 0.501947 Loss2: 1.463959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.654613 Loss1: 0.233360 Loss2: 1.421254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.601881 Loss1: 0.180469 Loss2: 1.421412
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.028118 Loss1: 1.157350 Loss2: 1.870768
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.066679 Loss1: 0.666429 Loss2: 1.400250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.878599 Loss1: 0.456407 Loss2: 1.422191 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.538113 Loss1: 0.140966 Loss2: 1.397147
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.748970 Loss1: 0.362361 Loss2: 1.386609
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.660204 Loss1: 0.277549 Loss2: 1.382654
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.568611 Loss1: 0.202471 Loss2: 1.366140
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.492381 Loss1: 0.129741 Loss2: 1.362640
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.479154 Loss1: 0.125755 Loss2: 1.353399
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.808001 Loss1: 1.003740 Loss2: 1.804262
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.478559 Loss1: 0.124854 Loss2: 1.353704
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.094703 Loss1: 0.728134 Loss2: 1.366569
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.473912 Loss1: 0.124985 Loss2: 1.348927
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.636266 Loss1: 0.298955 Loss2: 1.337310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504619 Loss1: 0.173813 Loss2: 1.330806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450569 Loss1: 0.130197 Loss2: 1.320371
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.985547 Loss1: 1.031961 Loss2: 1.953586
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401479 Loss1: 0.083201 Loss2: 1.318277
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.044651 Loss1: 0.567554 Loss2: 1.477097
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.387893 Loss1: 0.077591 Loss2: 1.310302
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.784735 Loss1: 0.324026 Loss2: 1.460709
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378395 Loss1: 0.070046 Loss2: 1.308349
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.718495 Loss1: 0.275172 Loss2: 1.443323
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.625792 Loss1: 0.190502 Loss2: 1.435290
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.576705 Loss1: 0.151766 Loss2: 1.424939
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.565176 Loss1: 0.141895 Loss2: 1.423280
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.590301 Loss1: 0.169122 Loss2: 1.421179
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.535664 Loss1: 0.114428 Loss2: 1.421236
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.854578 Loss1: 1.094672 Loss2: 1.759906
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.520267 Loss1: 0.106648 Loss2: 1.413619
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.006884 Loss1: 0.647021 Loss2: 1.359863
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.782173 Loss1: 0.421704 Loss2: 1.360469
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.622558 Loss1: 0.284579 Loss2: 1.337979
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506734 Loss1: 0.169932 Loss2: 1.336802
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461008 Loss1: 0.141134 Loss2: 1.319875
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.961494 Loss1: 1.044893 Loss2: 1.916602
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512488 Loss1: 0.184692 Loss2: 1.327796
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.482443 Loss1: 0.152653 Loss2: 1.329790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.457635 Loss1: 0.136365 Loss2: 1.321270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474658 Loss1: 0.149585 Loss2: 1.325073 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.670516 Loss1: 0.245461 Loss2: 1.425055 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568190 Loss1: 0.152721 Loss2: 1.415470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.933255 Loss1: 1.100142 Loss2: 1.833112 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.926661 Loss1: 0.508979 Loss2: 1.417682 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.599664 Loss1: 0.237373 Loss2: 1.362292 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508803 Loss1: 0.144495 Loss2: 1.364309
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.869888 Loss1: 1.009125 Loss2: 1.860763
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.114218 Loss1: 0.673568 Loss2: 1.440649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.775237 Loss1: 0.367546 Loss2: 1.407691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.708222 Loss1: 0.311389 Loss2: 1.396832 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.629030 Loss1: 0.243047 Loss2: 1.385983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.537048 Loss1: 0.158340 Loss2: 1.378708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.010090 Loss1: 1.116709 Loss2: 1.893381 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.129866 Loss1: 0.736456 Loss2: 1.393409 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.707885 Loss1: 0.331713 Loss2: 1.376172 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547722 Loss1: 0.169465 Loss2: 1.378257 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 20:13:35,345 | server.py:236 | fit_round 88 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.881189 Loss1: 1.064200 Loss2: 1.816989 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.102759 Loss1: 0.661838 Loss2: 1.440921 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.473257 Loss1: 0.123186 Loss2: 1.350071 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.636834 Loss1: 0.234730 Loss2: 1.402103 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.558262 Loss1: 0.179195 Loss2: 1.379067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.079546 Loss1: 1.262218 Loss2: 1.817328
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.579122 Loss1: 0.187314 Loss2: 1.391808
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501098 Loss1: 0.131290 Loss2: 1.369808 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464048 Loss1: 0.098636 Loss2: 1.365412 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551947 Loss1: 0.190123 Loss2: 1.361824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436011 Loss1: 0.098953 Loss2: 1.337058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414748 Loss1: 0.084639 Loss2: 1.330109
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.847428 Loss1: 1.027014 Loss2: 1.820414
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.095966 Loss1: 0.669663 Loss2: 1.426303 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.807062 Loss1: 0.395113 Loss2: 1.411949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.636690 Loss1: 0.238176 Loss2: 1.398513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.522782 Loss1: 0.142402 Loss2: 1.380380 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486470 Loss1: 0.114660 Loss2: 1.371810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.520954 Loss1: 0.144774 Loss2: 1.376180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487051 Loss1: 0.118819 Loss2: 1.368232 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.639834 Loss1: 0.248016 Loss2: 1.391818 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.586339 Loss1: 0.216842 Loss2: 1.369498 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.574838 Loss1: 0.196459 Loss2: 1.378379
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.888152 Loss1: 1.025784 Loss2: 1.862368
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.973903 Loss1: 0.589385 Loss2: 1.384518 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.548178 Loss1: 0.169726 Loss2: 1.378452
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.758383 Loss1: 0.370598 Loss2: 1.387785
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.622884 Loss1: 0.270180 Loss2: 1.352704
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543298 Loss1: 0.191404 Loss2: 1.351894
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474128 Loss1: 0.129177 Loss2: 1.344951
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.466337 Loss1: 0.132999 Loss2: 1.333338
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471223 Loss1: 0.131079 Loss2: 1.340144
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419486 Loss1: 0.090390 Loss2: 1.329096
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452734 Loss1: 0.128216 Loss2: 1.324518
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 20:13:35,345][flwr][DEBUG] - fit_round 88 received 50 results and 0 failures
-INFO flwr 2023-10-10 20:14:17,254 | server.py:125 | fit progress: (88, 2.2306158236040474, {'accuracy': 0.5567}, 202965.03208381802)
->> Test accuracy: 0.556700
-[2023-10-10 20:14:17,254][flwr][INFO] - fit progress: (88, 2.2306158236040474, {'accuracy': 0.5567}, 202965.03208381802)
-DEBUG flwr 2023-10-10 20:14:17,254 | server.py:173 | evaluate_round 88: strategy sampled 50 clients (out of 50)
-[2023-10-10 20:14:17,254][flwr][DEBUG] - evaluate_round 88: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 20:23:21,965 | server.py:187 | evaluate_round 88 received 50 results and 0 failures
-[2023-10-10 20:23:21,965][flwr][DEBUG] - evaluate_round 88 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 20:23:21,966 | server.py:222 | fit_round 89: strategy sampled 50 clients (out of 50)
-[2023-10-10 20:23:21,966][flwr][DEBUG] - fit_round 89: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.284616 Loss1: 1.391065 Loss2: 1.893551
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.294996 Loss1: 0.835745 Loss2: 1.459251
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.848403 Loss1: 0.455162 Loss2: 1.393241
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.705968 Loss1: 0.312988 Loss2: 1.392980
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.962718 Loss1: 1.054606 Loss2: 1.908112
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.036971 Loss1: 0.616024 Loss2: 1.420947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.774406 Loss1: 0.313364 Loss2: 1.461043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.670603 Loss1: 0.257021 Loss2: 1.413582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.616991 Loss1: 0.203541 Loss2: 1.413450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.568158 Loss1: 0.155600 Loss2: 1.412558 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981027
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.561626 Loss1: 0.169663 Loss2: 1.391964 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.481254 Loss1: 0.095175 Loss2: 1.386079 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.074215 Loss1: 0.641925 Loss2: 1.432291 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.730555 Loss1: 0.306732 Loss2: 1.423823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.003679 Loss1: 1.151676 Loss2: 1.852002
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.683110 Loss1: 0.292130 Loss2: 1.390980
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.620699 Loss1: 0.224616 Loss2: 1.396083 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.535951 Loss1: 0.151502 Loss2: 1.384449 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474030 Loss1: 0.097124 Loss2: 1.376906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447488 Loss1: 0.082028 Loss2: 1.365460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465796 Loss1: 0.101842 Loss2: 1.363954 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.523072 Loss1: 0.138163 Loss2: 1.384909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512909 Loss1: 0.133079 Loss2: 1.379829 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.018283 Loss1: 1.069561 Loss2: 1.948722
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.159454 Loss1: 0.688018 Loss2: 1.471436
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.911414 Loss1: 0.403805 Loss2: 1.507610
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.782085 Loss1: 0.328959 Loss2: 1.453126
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.758093 Loss1: 0.927284 Loss2: 1.830809
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.976281 Loss1: 0.561628 Loss2: 1.414653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.774856 Loss1: 0.364174 Loss2: 1.410682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636570 Loss1: 0.244272 Loss2: 1.392298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539033 Loss1: 0.162934 Loss2: 1.376100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.536788 Loss1: 0.103819 Loss2: 1.432970 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.524678 Loss1: 0.154396 Loss2: 1.370282 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.496165 Loss1: 0.130968 Loss2: 1.365197 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988051
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.158379 Loss1: 0.701143 Loss2: 1.457236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.727427 Loss1: 0.313968 Loss2: 1.413460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.939039 Loss1: 0.954191 Loss2: 1.984847 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.050054 Loss1: 0.572527 Loss2: 1.477526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.819939 Loss1: 0.320878 Loss2: 1.499061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.778659 Loss1: 0.321911 Loss2: 1.456748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.727313 Loss1: 0.246280 Loss2: 1.481033 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.573751 Loss1: 0.118909 Loss2: 1.454842 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.548608 Loss1: 0.107015 Loss2: 1.441592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529399 Loss1: 0.091446 Loss2: 1.437953
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.948523 Loss1: 1.018491 Loss2: 1.930032
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.170071 Loss1: 0.655315 Loss2: 1.514756
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.972957 Loss1: 0.471335 Loss2: 1.501622
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.819880 Loss1: 0.360971 Loss2: 1.458909
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.747397 Loss1: 0.295898 Loss2: 1.451500
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.967932 Loss1: 1.064886 Loss2: 1.903045
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.127729 Loss1: 0.692309 Loss2: 1.435420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.921094 Loss1: 0.451062 Loss2: 1.470032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.738587 Loss1: 0.320761 Loss2: 1.417825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.655536 Loss1: 0.229223 Loss2: 1.426313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.541386 Loss1: 0.113283 Loss2: 1.428103
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.586168 Loss1: 0.168713 Loss2: 1.417454
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.566095 Loss1: 0.163245 Loss2: 1.402850
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.569794 Loss1: 0.160141 Loss2: 1.409653
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.527056 Loss1: 0.129173 Loss2: 1.397883
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.498121 Loss1: 0.097786 Loss2: 1.400335
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.745839 Loss1: 0.926703 Loss2: 1.819135
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.961502 Loss1: 0.611556 Loss2: 1.349947
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.848420 Loss1: 0.436163 Loss2: 1.412257
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.650734 Loss1: 0.315026 Loss2: 1.335708
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.961542 Loss1: 1.135818 Loss2: 1.825724
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.041368 Loss1: 0.637317 Loss2: 1.404051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.917902 Loss1: 0.505101 Loss2: 1.412801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.757039 Loss1: 0.373685 Loss2: 1.383355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.660768 Loss1: 0.282330 Loss2: 1.378437 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.536261 Loss1: 0.177234 Loss2: 1.359027 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.550110 Loss1: 0.187921 Loss2: 1.362189 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.510002 Loss1: 0.144790 Loss2: 1.365212 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.956250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.107434 Loss1: 0.701897 Loss2: 1.405537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.661550 Loss1: 0.289268 Loss2: 1.372282 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.916942 Loss1: 1.073162 Loss2: 1.843780
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.602281 Loss1: 0.235760 Loss2: 1.366521
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.059048 Loss1: 0.633978 Loss2: 1.425070
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526780 Loss1: 0.167363 Loss2: 1.359417
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524667 Loss1: 0.166370 Loss2: 1.358298
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.867116 Loss1: 0.452726 Loss2: 1.414390
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500358 Loss1: 0.144926 Loss2: 1.355431
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.776557 Loss1: 0.367231 Loss2: 1.409327
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.513349 Loss1: 0.163615 Loss2: 1.349734
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.695429 Loss1: 0.299866 Loss2: 1.395563
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476126 Loss1: 0.116656 Loss2: 1.359469
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.667039 Loss1: 0.272193 Loss2: 1.394846
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.586410 Loss1: 0.199076 Loss2: 1.387334
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546585 Loss1: 0.175306 Loss2: 1.371279
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.508417 Loss1: 0.135795 Loss2: 1.372622
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.507650 Loss1: 0.140296 Loss2: 1.367354
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.184584 Loss1: 1.257342 Loss2: 1.927243
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.153777 Loss1: 0.743237 Loss2: 1.410540
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.902284 Loss1: 0.440475 Loss2: 1.461810
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.689171 Loss1: 0.289334 Loss2: 1.399837
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.608100 Loss1: 0.216805 Loss2: 1.391295
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.870698 Loss1: 1.023141 Loss2: 1.847557
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.553292 Loss1: 0.160602 Loss2: 1.392689
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.545270 Loss1: 0.169182 Loss2: 1.376088
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.010389 Loss1: 0.638906 Loss2: 1.371483
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.514085 Loss1: 0.133948 Loss2: 1.380137
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.798657 Loss1: 0.399897 Loss2: 1.398760
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.457353 Loss1: 0.090877 Loss2: 1.366475
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.658893 Loss1: 0.290923 Loss2: 1.367970
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462745 Loss1: 0.097015 Loss2: 1.365729
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.642196 Loss1: 0.274143 Loss2: 1.368054
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.587343 Loss1: 0.236510 Loss2: 1.350832
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516426 Loss1: 0.164698 Loss2: 1.351729
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.503032 Loss1: 0.153261 Loss2: 1.349771
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460109 Loss1: 0.116586 Loss2: 1.343523
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.115954 Loss1: 1.159281 Loss2: 1.956673
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428586 Loss1: 0.087559 Loss2: 1.341026
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694323 Loss1: 0.323941 Loss2: 1.370383 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.640250 Loss1: 0.279627 Loss2: 1.360623 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.835225 Loss1: 0.982685 Loss2: 1.852540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.011612 Loss1: 0.620180 Loss2: 1.391432 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465958 Loss1: 0.131066 Loss2: 1.334892 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986979
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609962 Loss1: 0.232384 Loss2: 1.377578 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509289 Loss1: 0.145508 Loss2: 1.363781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.827424 Loss1: 1.017068 Loss2: 1.810356
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475271 Loss1: 0.118974 Loss2: 1.356296
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.989775 Loss1: 0.600187 Loss2: 1.389589
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442276 Loss1: 0.092241 Loss2: 1.350034
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.798001 Loss1: 0.407494 Loss2: 1.390507
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444583 Loss1: 0.095081 Loss2: 1.349502
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.637188 Loss1: 0.271955 Loss2: 1.365233 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539019 Loss1: 0.181954 Loss2: 1.357065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.960509 Loss1: 1.098132 Loss2: 1.862378
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464345 Loss1: 0.126896 Loss2: 1.337448
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.019260 Loss1: 0.633054 Loss2: 1.386206
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461862 Loss1: 0.129823 Loss2: 1.332039
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.819507 Loss1: 0.419060 Loss2: 1.400447
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420382 Loss1: 0.083222 Loss2: 1.337161
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.604929 Loss1: 0.229740 Loss2: 1.375189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.554241 Loss1: 0.187782 Loss2: 1.366459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.517828 Loss1: 0.172035 Loss2: 1.345793
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.954241 Loss1: 1.099943 Loss2: 1.854299
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.086990 Loss1: 0.685632 Loss2: 1.401358 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463256 Loss1: 0.119649 Loss2: 1.343608
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.815172 Loss1: 0.403210 Loss2: 1.411962
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.704902 Loss1: 0.329627 Loss2: 1.375276
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.609677 Loss1: 0.225950 Loss2: 1.383727
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.589439 Loss1: 0.230544 Loss2: 1.358895
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.499919 Loss1: 0.139118 Loss2: 1.360801
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.490113 Loss1: 0.131874 Loss2: 1.358239
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.803585 Loss1: 1.046851 Loss2: 1.756734
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477582 Loss1: 0.126282 Loss2: 1.351300
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.082946 Loss1: 0.689165 Loss2: 1.393781
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476950 Loss1: 0.128301 Loss2: 1.348649
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.779940 Loss1: 0.409068 Loss2: 1.370873
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.681170 Loss1: 0.326265 Loss2: 1.354905
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.639212 Loss1: 0.282085 Loss2: 1.357127
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538508 Loss1: 0.198743 Loss2: 1.339765
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.501514 Loss1: 0.171867 Loss2: 1.329647
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.029662 Loss1: 1.156141 Loss2: 1.873520
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.187794 Loss1: 0.730677 Loss2: 1.457117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.862602 Loss1: 0.414436 Loss2: 1.448166 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403404 Loss1: 0.094208 Loss2: 1.309196
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.745274 Loss1: 0.351077 Loss2: 1.394197
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.651543 Loss1: 0.235829 Loss2: 1.415714
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.577307 Loss1: 0.182092 Loss2: 1.395215
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.550345 Loss1: 0.155032 Loss2: 1.395313
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.514228 Loss1: 0.132868 Loss2: 1.381360
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509178 Loss1: 0.129463 Loss2: 1.379715
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.123034 Loss1: 1.167600 Loss2: 1.955434
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.502773 Loss1: 0.124071 Loss2: 1.378702
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.155881 Loss1: 0.664285 Loss2: 1.491596
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.975749 Loss1: 0.476169 Loss2: 1.499580
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.744948 Loss1: 0.273825 Loss2: 1.471123
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.646073 Loss1: 0.194755 Loss2: 1.451318
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.671809 Loss1: 0.213900 Loss2: 1.457909
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.628035 Loss1: 0.186543 Loss2: 1.441492
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.084732 Loss1: 1.205682 Loss2: 1.879050
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.582972 Loss1: 0.144529 Loss2: 1.438443
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.035203 Loss1: 0.600689 Loss2: 1.434514
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.557474 Loss1: 0.126110 Loss2: 1.431364
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.794005 Loss1: 0.376576 Loss2: 1.417429
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.581450 Loss1: 0.153264 Loss2: 1.428185
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.635562 Loss1: 0.253936 Loss2: 1.381625
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.569839 Loss1: 0.179306 Loss2: 1.390534
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.552997 Loss1: 0.170934 Loss2: 1.382063
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502656 Loss1: 0.131544 Loss2: 1.371112
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.472579 Loss1: 0.106833 Loss2: 1.365746
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470131 Loss1: 0.112332 Loss2: 1.357799
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.912631 Loss1: 1.086304 Loss2: 1.826327
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441823 Loss1: 0.084408 Loss2: 1.357414
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.936507 Loss1: 0.567605 Loss2: 1.368902
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.787105 Loss1: 0.383427 Loss2: 1.403678
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.711341 Loss1: 0.341362 Loss2: 1.369980
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.597693 Loss1: 0.229768 Loss2: 1.367925
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.518980 Loss1: 0.171434 Loss2: 1.347546
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509972 Loss1: 0.166131 Loss2: 1.343840
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.906093 Loss1: 1.019908 Loss2: 1.886185
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.504939 Loss1: 0.158673 Loss2: 1.346266
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.037971 Loss1: 0.620501 Loss2: 1.417470
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480430 Loss1: 0.141338 Loss2: 1.339092
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.816070 Loss1: 0.369121 Loss2: 1.446949
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453372 Loss1: 0.112390 Loss2: 1.340982
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.689494 Loss1: 0.292814 Loss2: 1.396681
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606008 Loss1: 0.208685 Loss2: 1.397323
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537316 Loss1: 0.144787 Loss2: 1.392529
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.501581 Loss1: 0.121052 Loss2: 1.380529
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467107 Loss1: 0.099918 Loss2: 1.367189
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.149389 Loss1: 1.234881 Loss2: 1.914508
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472738 Loss1: 0.105758 Loss2: 1.366979
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.287690 Loss1: 0.805240 Loss2: 1.482450
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467635 Loss1: 0.104717 Loss2: 1.362918
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.747129 Loss1: 0.317466 Loss2: 1.429663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.569193 Loss1: 0.160614 Loss2: 1.408579 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545441 Loss1: 0.134607 Loss2: 1.410834
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.847484 Loss1: 0.997389 Loss2: 1.850096
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500531 Loss1: 0.098930 Loss2: 1.401601
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.026872 Loss1: 0.612368 Loss2: 1.414503
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.828826 Loss1: 0.390164 Loss2: 1.438662 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.537850 Loss1: 0.136785 Loss2: 1.401065
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.670218 Loss1: 0.290936 Loss2: 1.379282
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.585084 Loss1: 0.211989 Loss2: 1.373094
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.600972 Loss1: 0.228728 Loss2: 1.372243
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.572137 Loss1: 0.190971 Loss2: 1.381166
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.521152 Loss1: 0.156703 Loss2: 1.364448
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.938284 Loss1: 1.021579 Loss2: 1.916705
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.109906 Loss1: 0.669289 Loss2: 1.440616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.482935 Loss1: 0.130221 Loss2: 1.352715
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.937932 Loss1: 0.449787 Loss2: 1.488146
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.724334 Loss1: 0.288382 Loss2: 1.435952
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.642980 Loss1: 0.218030 Loss2: 1.424950
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582742 Loss1: 0.166092 Loss2: 1.416650
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.606758 Loss1: 0.195429 Loss2: 1.411329
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.063501 Loss1: 1.124663 Loss2: 1.938838
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.551681 Loss1: 0.138818 Loss2: 1.412863
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.195823 Loss1: 0.695540 Loss2: 1.500283
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.522522 Loss1: 0.120253 Loss2: 1.402269
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.972196 Loss1: 0.482444 Loss2: 1.489752
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.486655 Loss1: 0.088203 Loss2: 1.398452
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.647164 Loss1: 0.190892 Loss2: 1.456272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.631977 Loss1: 0.190623 Loss2: 1.441354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.554037 Loss1: 0.124510 Loss2: 1.429527
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.237653 Loss1: 1.304764 Loss2: 1.932889
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.531554 Loss1: 0.103063 Loss2: 1.428491
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.218601 Loss1: 0.729181 Loss2: 1.489420
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.526893 Loss1: 0.106292 Loss2: 1.420601
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.957673 Loss1: 0.470246 Loss2: 1.487426
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.822831 Loss1: 0.368248 Loss2: 1.454582
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.754034 Loss1: 0.300708 Loss2: 1.453326
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.750334 Loss1: 0.294279 Loss2: 1.456055
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.683017 Loss1: 0.231850 Loss2: 1.451167
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.603119 Loss1: 0.169178 Loss2: 1.433942
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.995614 Loss1: 1.124686 Loss2: 1.870928
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.566586 Loss1: 0.137428 Loss2: 1.429158
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.186551 Loss1: 0.745983 Loss2: 1.440568
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.565471 Loss1: 0.137588 Loss2: 1.427883
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.935306 Loss1: 0.517021 Loss2: 1.418285
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.727135 Loss1: 0.326013 Loss2: 1.401122
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.622873 Loss1: 0.250901 Loss2: 1.371971
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.594748 Loss1: 0.220854 Loss2: 1.373894
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.543242 Loss1: 0.179245 Loss2: 1.363997
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.520759 Loss1: 0.165157 Loss2: 1.355602
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.923105 Loss1: 1.010415 Loss2: 1.912690
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482985 Loss1: 0.124940 Loss2: 1.358045
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.091137 Loss1: 0.663832 Loss2: 1.427305
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.470696 Loss1: 0.119695 Loss2: 1.351001
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.902451 Loss1: 0.439789 Loss2: 1.462662
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.738402 Loss1: 0.332395 Loss2: 1.406007
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.615429 Loss1: 0.214383 Loss2: 1.401046
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.601231 Loss1: 0.205141 Loss2: 1.396090
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.522951 Loss1: 0.133398 Loss2: 1.389554
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.002421 Loss1: 1.096286 Loss2: 1.906135
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.495428 Loss1: 0.116495 Loss2: 1.378934
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.151289 Loss1: 0.777102 Loss2: 1.374187
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466584 Loss1: 0.091432 Loss2: 1.375152
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456051 Loss1: 0.088815 Loss2: 1.367235 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.569950 Loss1: 0.200795 Loss2: 1.369155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.535920 Loss1: 0.180363 Loss2: 1.355557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453063 Loss1: 0.098190 Loss2: 1.354873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.530022 Loss1: 0.173545 Loss2: 1.356477 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973558
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.707340 Loss1: 0.301688 Loss2: 1.405652 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.592840 Loss1: 0.203926 Loss2: 1.388914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.579498 Loss1: 0.180106 Loss2: 1.399392
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.946206 Loss1: 1.060717 Loss2: 1.885489
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.013132 Loss1: 0.586188 Loss2: 1.426944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.770789 Loss1: 0.344026 Loss2: 1.426764 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.690407 Loss1: 0.297292 Loss2: 1.393115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.597156 Loss1: 0.199293 Loss2: 1.397863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.540360 Loss1: 0.145646 Loss2: 1.394714 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.530085 Loss1: 0.139260 Loss2: 1.390825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.519527 Loss1: 0.125470 Loss2: 1.394057 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.767177 Loss1: 0.340472 Loss2: 1.426706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.615276 Loss1: 0.202132 Loss2: 1.413143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.846148 Loss1: 1.023668 Loss2: 1.822480 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.991621 Loss1: 0.618853 Loss2: 1.372767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.718539 Loss1: 0.375542 Loss2: 1.342996 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484774 Loss1: 0.156242 Loss2: 1.328532 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.438311 Loss1: 0.121754 Loss2: 1.316558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.406715 Loss1: 0.095627 Loss2: 1.311087
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.130285 Loss1: 1.164630 Loss2: 1.965656
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.380848 Loss1: 0.083104 Loss2: 1.297745
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.198975 Loss1: 0.785252 Loss2: 1.413724
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.895260 Loss1: 0.420336 Loss2: 1.474923
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379927 Loss1: 0.084746 Loss2: 1.295181
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.665225 Loss1: 0.265828 Loss2: 1.399397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497302 Loss1: 0.132602 Loss2: 1.364699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439098 Loss1: 0.085509 Loss2: 1.353589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415275 Loss1: 0.072486 Loss2: 1.342789 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986779
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.791137 Loss1: 0.357795 Loss2: 1.433342 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.629619 Loss1: 0.236155 Loss2: 1.393463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.994622 Loss1: 1.046819 Loss2: 1.947803
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.550577 Loss1: 0.167900 Loss2: 1.382677
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.081294 Loss1: 0.620371 Loss2: 1.460924
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.494485 Loss1: 0.126207 Loss2: 1.368279
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.929038 Loss1: 0.444550 Loss2: 1.484488
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484241 Loss1: 0.114996 Loss2: 1.369244
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.853875 Loss1: 0.392974 Loss2: 1.460901
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.507802 Loss1: 0.137673 Loss2: 1.370129
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.639964 Loss1: 0.205140 Loss2: 1.434825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.600238 Loss1: 0.179035 Loss2: 1.421203 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.560352 Loss1: 0.137036 Loss2: 1.423316
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.058931 Loss1: 1.183438 Loss2: 1.875493
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527331 Loss1: 0.105904 Loss2: 1.421427
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.146751 Loss1: 0.698884 Loss2: 1.447867
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.844032 Loss1: 0.406648 Loss2: 1.437383
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.727789 Loss1: 0.324567 Loss2: 1.403222
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.652911 Loss1: 0.253142 Loss2: 1.399769
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.596842 Loss1: 0.205160 Loss2: 1.391682
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531613 Loss1: 0.139668 Loss2: 1.391945
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.031190 Loss1: 1.131408 Loss2: 1.899782
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.513652 Loss1: 0.136217 Loss2: 1.377435
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.019052 Loss1: 0.595118 Loss2: 1.423934
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.457493 Loss1: 0.081135 Loss2: 1.376357
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.856919 Loss1: 0.415737 Loss2: 1.441182
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440747 Loss1: 0.081756 Loss2: 1.358991
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.698613 Loss1: 0.284646 Loss2: 1.413966
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.690920 Loss1: 0.271047 Loss2: 1.419873
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.656942 Loss1: 0.241196 Loss2: 1.415746
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.611572 Loss1: 0.196564 Loss2: 1.415009
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568197 Loss1: 0.167704 Loss2: 1.400493
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.552330 Loss1: 0.154314 Loss2: 1.398016
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.811822 Loss1: 1.049025 Loss2: 1.762798
-(DefaultActor pid=3764) >> Training accuracy: 0.937500
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.548586 Loss1: 0.148132 Loss2: 1.400454
-DEBUG flwr 2023-10-10 20:51:47,694 | server.py:236 | fit_round 89 received 50 results and 0 failures
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.076919 Loss1: 0.706620 Loss2: 1.370299
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.737626 Loss1: 0.370431 Loss2: 1.367195
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.657270 Loss1: 0.322883 Loss2: 1.334387
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572042 Loss1: 0.228287 Loss2: 1.343755
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564150 Loss1: 0.229695 Loss2: 1.334455
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.755865 Loss1: 0.940258 Loss2: 1.815607
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.950181 Loss1: 0.534797 Loss2: 1.415384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.754197 Loss1: 0.348633 Loss2: 1.405564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.662030 Loss1: 0.272941 Loss2: 1.389089 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569906 Loss1: 0.195558 Loss2: 1.374348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.561909 Loss1: 0.198747 Loss2: 1.363162 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485076 Loss1: 0.125547 Loss2: 1.359529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.506227 Loss1: 0.149792 Loss2: 1.356435 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.817579 Loss1: 0.341466 Loss2: 1.476113 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.675783 Loss1: 0.226057 Loss2: 1.449726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.637909 Loss1: 0.203309 Loss2: 1.434601
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.996172 Loss1: 1.163828 Loss2: 1.832344
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.146949 Loss1: 0.678780 Loss2: 1.468168 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.895793 Loss1: 0.492987 Loss2: 1.402806 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.734365 Loss1: 0.352909 Loss2: 1.381457 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.556204 Loss1: 0.130009 Loss2: 1.426195
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.583375 Loss1: 0.205694 Loss2: 1.377680
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484801 Loss1: 0.125596 Loss2: 1.359205
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438923 Loss1: 0.088066 Loss2: 1.350857
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418684 Loss1: 0.074059 Loss2: 1.344625
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385088 Loss1: 0.053597 Loss2: 1.331491
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.037120 Loss1: 1.170638 Loss2: 1.866481
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390316 Loss1: 0.064861 Loss2: 1.325455
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.795574 Loss1: 0.383837 Loss2: 1.411737 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605818 Loss1: 0.225646 Loss2: 1.380172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.608830 Loss1: 0.240311 Loss2: 1.368519
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.961356 Loss1: 1.024467 Loss2: 1.936889
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.058925 Loss1: 0.626346 Loss2: 1.432579 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.773608 Loss1: 0.311247 Loss2: 1.462361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.672592 Loss1: 0.239448 Loss2: 1.433144 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.498200 Loss1: 0.131561 Loss2: 1.366638
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576011 Loss1: 0.154089 Loss2: 1.421922
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.553646 Loss1: 0.145685 Loss2: 1.407961
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.572100 Loss1: 0.159353 Loss2: 1.412747
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540137 Loss1: 0.124965 Loss2: 1.415172
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.514757 Loss1: 0.109098 Loss2: 1.405658
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.513763 Loss1: 0.108796 Loss2: 1.404967
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 20:51:47,694][flwr][DEBUG] - fit_round 89 received 50 results and 0 failures
-INFO flwr 2023-10-10 20:52:29,505 | server.py:125 | fit progress: (89, 2.2304537201080077, {'accuracy': 0.5568}, 205257.283233468)
->> Test accuracy: 0.556800
-[2023-10-10 20:52:29,505][flwr][INFO] - fit progress: (89, 2.2304537201080077, {'accuracy': 0.5568}, 205257.283233468)
-DEBUG flwr 2023-10-10 20:52:29,505 | server.py:173 | evaluate_round 89: strategy sampled 50 clients (out of 50)
-[2023-10-10 20:52:29,505][flwr][DEBUG] - evaluate_round 89: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 21:01:38,802 | server.py:187 | evaluate_round 89 received 50 results and 0 failures
-[2023-10-10 21:01:38,802][flwr][DEBUG] - evaluate_round 89 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 21:01:38,803 | server.py:222 | fit_round 90: strategy sampled 50 clients (out of 50)
-[2023-10-10 21:01:38,803][flwr][DEBUG] - fit_round 90: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.810796 Loss1: 0.983006 Loss2: 1.827790
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.053737 Loss1: 0.645279 Loss2: 1.408458
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.871689 Loss1: 0.424789 Loss2: 1.446899
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.872757 Loss1: 1.024199 Loss2: 1.848557
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.963298 Loss1: 0.579538 Loss2: 1.383760 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804612 Loss1: 0.398533 Loss2: 1.406079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.662399 Loss1: 0.296104 Loss2: 1.366295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557995 Loss1: 0.196662 Loss2: 1.361333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.564342 Loss1: 0.211417 Loss2: 1.352925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.547590 Loss1: 0.190406 Loss2: 1.357184 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515261 Loss1: 0.161570 Loss2: 1.353692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446554 Loss1: 0.105643 Loss2: 1.340911 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.188030 Loss1: 1.252146 Loss2: 1.935884
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.209589 Loss1: 0.800563 Loss2: 1.409025
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.935581 Loss1: 0.468874 Loss2: 1.466708
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.671312 Loss1: 0.270696 Loss2: 1.400617
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.899674 Loss1: 1.039624 Loss2: 1.860050
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.583785 Loss1: 0.192467 Loss2: 1.391318 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516648 Loss1: 0.137162 Loss2: 1.379486 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481452 Loss1: 0.101452 Loss2: 1.380000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456440 Loss1: 0.081513 Loss2: 1.374927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451279 Loss1: 0.083597 Loss2: 1.367683 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.562587 Loss1: 0.195474 Loss2: 1.367113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.510184 Loss1: 0.146844 Loss2: 1.363340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527463 Loss1: 0.165534 Loss2: 1.361929
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.775094 Loss1: 1.015748 Loss2: 1.759346
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.998091 Loss1: 0.644659 Loss2: 1.353431
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741511 Loss1: 0.394753 Loss2: 1.346758
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604486 Loss1: 0.290151 Loss2: 1.314334
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.522979 Loss1: 0.209708 Loss2: 1.313271
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.951664 Loss1: 1.085696 Loss2: 1.865967
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.447718 Loss1: 0.149940 Loss2: 1.297778
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420633 Loss1: 0.120729 Loss2: 1.299903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.374394 Loss1: 0.085153 Loss2: 1.289242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371414 Loss1: 0.088897 Loss2: 1.282517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386174 Loss1: 0.104058 Loss2: 1.282116 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474055 Loss1: 0.111668 Loss2: 1.362387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476814 Loss1: 0.118726 Loss2: 1.358088 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465495 Loss1: 0.110104 Loss2: 1.355391
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.965088 Loss1: 1.092194 Loss2: 1.872894
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.029710 Loss1: 0.591406 Loss2: 1.438304
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.813594 Loss1: 0.380188 Loss2: 1.433405
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.697532 Loss1: 0.290060 Loss2: 1.407472
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571518 Loss1: 0.169662 Loss2: 1.401856
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.892451 Loss1: 0.963541 Loss2: 1.928910
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537961 Loss1: 0.146641 Loss2: 1.391321
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.025206 Loss1: 0.541159 Loss2: 1.484047
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.492411 Loss1: 0.097654 Loss2: 1.394757
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.820909 Loss1: 0.337174 Loss2: 1.483735
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499445 Loss1: 0.116395 Loss2: 1.383050
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.757877 Loss1: 0.290509 Loss2: 1.467368
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472223 Loss1: 0.093068 Loss2: 1.379155
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.698440 Loss1: 0.224503 Loss2: 1.473937
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452651 Loss1: 0.069739 Loss2: 1.382912
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.621564 Loss1: 0.170832 Loss2: 1.450732
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.626950 Loss1: 0.178154 Loss2: 1.448796
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.589617 Loss1: 0.142898 Loss2: 1.446719
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.587600 Loss1: 0.147125 Loss2: 1.440476
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.561281 Loss1: 0.124742 Loss2: 1.436539
-(DefaultActor pid=3764) >> Training accuracy: 0.972656
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.984917 Loss1: 1.093389 Loss2: 1.891528
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.045005 Loss1: 0.631552 Loss2: 1.413453
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.795112 Loss1: 0.389206 Loss2: 1.405906
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.691507 Loss1: 0.307379 Loss2: 1.384128
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.594161 Loss1: 0.200231 Loss2: 1.393930
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.875251 Loss1: 1.014292 Loss2: 1.860960
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.051614 Loss1: 0.681963 Loss2: 1.369651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.833008 Loss1: 0.424535 Loss2: 1.408473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.625171 Loss1: 0.275479 Loss2: 1.349692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.603159 Loss1: 0.250889 Loss2: 1.352270 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446474 Loss1: 0.091429 Loss2: 1.355045
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.576313 Loss1: 0.227978 Loss2: 1.348335
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.555393 Loss1: 0.211078 Loss2: 1.344315
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.479176 Loss1: 0.139067 Loss2: 1.340109
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440217 Loss1: 0.110284 Loss2: 1.329933
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414040 Loss1: 0.086314 Loss2: 1.327726
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.872114 Loss1: 0.927886 Loss2: 1.944227
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.154734 Loss1: 0.685204 Loss2: 1.469530
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.813131 Loss1: 0.318548 Loss2: 1.494584
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.670043 Loss1: 0.238399 Loss2: 1.431644
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.626372 Loss1: 0.190334 Loss2: 1.436038
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.597995 Loss1: 0.169723 Loss2: 1.428272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.566875 Loss1: 0.143809 Loss2: 1.423066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.570194 Loss1: 0.148669 Loss2: 1.421526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.510749 Loss1: 0.094627 Loss2: 1.416122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.526641 Loss1: 0.108449 Loss2: 1.418192 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.555498 Loss1: 0.157592 Loss2: 1.397905 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.505727 Loss1: 0.115147 Loss2: 1.390581 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.013049 Loss1: 0.599270 Loss2: 1.413779 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.633118 Loss1: 0.243374 Loss2: 1.389744 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.954426 Loss1: 1.148965 Loss2: 1.805461
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.543875 Loss1: 0.157876 Loss2: 1.385999
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.221795 Loss1: 0.790305 Loss2: 1.431490
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530750 Loss1: 0.160638 Loss2: 1.370112
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.843984 Loss1: 0.488589 Loss2: 1.355395
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480439 Loss1: 0.108183 Loss2: 1.372256
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.707846 Loss1: 0.352949 Loss2: 1.354898
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497289 Loss1: 0.129458 Loss2: 1.367830
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.594112 Loss1: 0.259364 Loss2: 1.334748
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.478686 Loss1: 0.111399 Loss2: 1.367288
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556428 Loss1: 0.226708 Loss2: 1.329719
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449502 Loss1: 0.090716 Loss2: 1.358785
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460275 Loss1: 0.145531 Loss2: 1.314744 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401977 Loss1: 0.091807 Loss2: 1.310170 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.067858 Loss1: 0.670331 Loss2: 1.397528 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654450 Loss1: 0.277542 Loss2: 1.376908 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.949478 Loss1: 1.076277 Loss2: 1.873201
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.569043 Loss1: 0.195971 Loss2: 1.373072
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.066369 Loss1: 0.651622 Loss2: 1.414747
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.536834 Loss1: 0.172845 Loss2: 1.363989
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.826845 Loss1: 0.369465 Loss2: 1.457380
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.555669 Loss1: 0.191424 Loss2: 1.364245
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.690629 Loss1: 0.303696 Loss2: 1.386933
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.482145 Loss1: 0.125598 Loss2: 1.356546
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.611321 Loss1: 0.215857 Loss2: 1.395464
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448667 Loss1: 0.099811 Loss2: 1.348857
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.579361 Loss1: 0.182483 Loss2: 1.396878
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426266 Loss1: 0.081468 Loss2: 1.344798
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.537862 Loss1: 0.147923 Loss2: 1.389939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470550 Loss1: 0.097239 Loss2: 1.373311 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.040853 Loss1: 0.622273 Loss2: 1.418580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.631312 Loss1: 0.237698 Loss2: 1.393614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.730277 Loss1: 0.887293 Loss2: 1.842984
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.573098 Loss1: 0.184564 Loss2: 1.388534
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.957624 Loss1: 0.564873 Loss2: 1.392751
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494126 Loss1: 0.117132 Loss2: 1.376994
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478205 Loss1: 0.110558 Loss2: 1.367647
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.826806 Loss1: 0.400630 Loss2: 1.426176
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.494833 Loss1: 0.125035 Loss2: 1.369799
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.661285 Loss1: 0.280965 Loss2: 1.380320
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.596812 Loss1: 0.213333 Loss2: 1.383479 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.534337 Loss1: 0.165779 Loss2: 1.368558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.476770 Loss1: 0.118010 Loss2: 1.358760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.108090 Loss1: 1.143383 Loss2: 1.964707 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.889855 Loss1: 0.415984 Loss2: 1.473871 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555714 Loss1: 0.168560 Loss2: 1.387154 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491489 Loss1: 0.127300 Loss2: 1.364189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.501670 Loss1: 0.145302 Loss2: 1.356368 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469584 Loss1: 0.113361 Loss2: 1.356223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413337 Loss1: 0.060123 Loss2: 1.353214 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513030 Loss1: 0.139907 Loss2: 1.373123 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510250 Loss1: 0.148336 Loss2: 1.361914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.475365 Loss1: 0.107008 Loss2: 1.368357
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.871808 Loss1: 1.034646 Loss2: 1.837162
-(DefaultActor pid=3764) >> Training accuracy: 0.983259
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457371 Loss1: 0.100792 Loss2: 1.356579
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.100223 Loss1: 0.653871 Loss2: 1.446352
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.849433 Loss1: 0.411977 Loss2: 1.437456
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.729732 Loss1: 0.316920 Loss2: 1.412812
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.621512 Loss1: 0.213779 Loss2: 1.407733
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.572490 Loss1: 0.177937 Loss2: 1.394552
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.930822 Loss1: 1.021268 Loss2: 1.909553
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.079190 Loss1: 0.639208 Loss2: 1.439982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.836225 Loss1: 0.390411 Loss2: 1.445815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.507376 Loss1: 0.118855 Loss2: 1.388521
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.701446 Loss1: 0.296106 Loss2: 1.405340
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.497218 Loss1: 0.114303 Loss2: 1.382915
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.617529 Loss1: 0.211564 Loss2: 1.405965
-(DefaultActor pid=3765) >> Training accuracy: 0.974609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.544306 Loss1: 0.161104 Loss2: 1.383202
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491396 Loss1: 0.114796 Loss2: 1.376600
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501691 Loss1: 0.127916 Loss2: 1.373775
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447709 Loss1: 0.070040 Loss2: 1.377669
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.042783 Loss1: 1.137752 Loss2: 1.905030
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.484348 Loss1: 0.116415 Loss2: 1.367933
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.795214 Loss1: 0.360338 Loss2: 1.434876 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.624447 Loss1: 0.218144 Loss2: 1.406303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.906997 Loss1: 1.005442 Loss2: 1.901555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.046819 Loss1: 0.651627 Loss2: 1.395192 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.850879 Loss1: 0.397358 Loss2: 1.453522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699709 Loss1: 0.313753 Loss2: 1.385955 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981027
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.554265 Loss1: 0.173881 Loss2: 1.380384 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470772 Loss1: 0.102961 Loss2: 1.367812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479284 Loss1: 0.111485 Loss2: 1.367799
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.098942 Loss1: 1.260443 Loss2: 1.838500
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478089 Loss1: 0.110639 Loss2: 1.367451
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.106411 Loss1: 0.685227 Loss2: 1.421184
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.791692 Loss1: 0.400244 Loss2: 1.391448
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.687861 Loss1: 0.318936 Loss2: 1.368925
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.624810 Loss1: 0.253298 Loss2: 1.371512
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.592939 Loss1: 0.221397 Loss2: 1.371541
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.046325 Loss1: 1.197002 Loss2: 1.849323
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.541887 Loss1: 0.192804 Loss2: 1.349083
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.118182 Loss1: 0.700467 Loss2: 1.417715
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486991 Loss1: 0.132854 Loss2: 1.354138
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.784104 Loss1: 0.370773 Loss2: 1.413331
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449497 Loss1: 0.105857 Loss2: 1.343640
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.728117 Loss1: 0.336498 Loss2: 1.391620
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433127 Loss1: 0.096769 Loss2: 1.336358
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.575988 Loss1: 0.190303 Loss2: 1.385686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.536314 Loss1: 0.162682 Loss2: 1.373632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.509051 Loss1: 0.134768 Loss2: 1.374283
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.801802 Loss1: 0.969204 Loss2: 1.832598
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.469418 Loss1: 0.104900 Loss2: 1.364518
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.889502 Loss1: 0.550413 Loss2: 1.339089
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.834113 Loss1: 0.471161 Loss2: 1.362951
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.688412 Loss1: 0.351967 Loss2: 1.336445
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572565 Loss1: 0.239093 Loss2: 1.333472
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492223 Loss1: 0.180687 Loss2: 1.311536
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.069789 Loss1: 1.144976 Loss2: 1.924813
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428341 Loss1: 0.130771 Loss2: 1.297570
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.181820 Loss1: 0.721477 Loss2: 1.460343
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404800 Loss1: 0.102455 Loss2: 1.302345
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.892962 Loss1: 0.406939 Loss2: 1.486023
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.380293 Loss1: 0.087098 Loss2: 1.293195
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.737610 Loss1: 0.309947 Loss2: 1.427662
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378100 Loss1: 0.087445 Loss2: 1.290655
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.637651 Loss1: 0.218745 Loss2: 1.418905 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.603169 Loss1: 0.184210 Loss2: 1.418959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.584536 Loss1: 0.173565 Loss2: 1.410971
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.923039 Loss1: 1.045421 Loss2: 1.877617
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.556602 Loss1: 0.142306 Loss2: 1.414296
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.006953 Loss1: 0.600675 Loss2: 1.406278
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.790585 Loss1: 0.361502 Loss2: 1.429083
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642386 Loss1: 0.252066 Loss2: 1.390319
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593271 Loss1: 0.203184 Loss2: 1.390087
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533893 Loss1: 0.151220 Loss2: 1.382673
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.925746 Loss1: 1.140466 Loss2: 1.785280
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487152 Loss1: 0.116588 Loss2: 1.370564
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.032693 Loss1: 0.669084 Loss2: 1.363609
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491024 Loss1: 0.123987 Loss2: 1.367038
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.751487 Loss1: 0.388833 Loss2: 1.362654
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.486808 Loss1: 0.120467 Loss2: 1.366340
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.639128 Loss1: 0.309671 Loss2: 1.329456
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475194 Loss1: 0.111441 Loss2: 1.363752
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519641 Loss1: 0.191716 Loss2: 1.327925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.403636 Loss1: 0.101218 Loss2: 1.302418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.380866 Loss1: 0.076209 Loss2: 1.304656
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.878904 Loss1: 1.018300 Loss2: 1.860604
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369874 Loss1: 0.074505 Loss2: 1.295369
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.993450 Loss1: 0.604972 Loss2: 1.388478
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.774211 Loss1: 0.368578 Loss2: 1.405633
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.687145 Loss1: 0.313005 Loss2: 1.374140
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.612347 Loss1: 0.237142 Loss2: 1.375205
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547227 Loss1: 0.189855 Loss2: 1.357372
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.156360 Loss1: 1.200861 Loss2: 1.955499
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.499202 Loss1: 0.134679 Loss2: 1.364522
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486224 Loss1: 0.133531 Loss2: 1.352693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.487136 Loss1: 0.136380 Loss2: 1.350756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.478662 Loss1: 0.128588 Loss2: 1.350074 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.660947 Loss1: 0.217994 Loss2: 1.442953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.542433 Loss1: 0.122682 Loss2: 1.419751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.524899 Loss1: 0.113286 Loss2: 1.411613
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.870205 Loss1: 1.034668 Loss2: 1.835537
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.043812 Loss1: 0.637638 Loss2: 1.406175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.732440 Loss1: 0.353516 Loss2: 1.378925 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.601470 Loss1: 0.225622 Loss2: 1.375848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529090 Loss1: 0.160555 Loss2: 1.368535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.852203 Loss1: 0.409039 Loss2: 1.443164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.724259 Loss1: 0.349005 Loss2: 1.375254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.622143 Loss1: 0.242764 Loss2: 1.379379 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487760 Loss1: 0.125745 Loss2: 1.362015 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461468 Loss1: 0.104809 Loss2: 1.356658 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978795
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465664 Loss1: 0.115593 Loss2: 1.350070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.803918 Loss1: 1.003277 Loss2: 1.800641
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.982984 Loss1: 0.615698 Loss2: 1.367286
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.694444 Loss1: 0.342230 Loss2: 1.352215
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.628318 Loss1: 0.298868 Loss2: 1.329450
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.540479 Loss1: 0.199645 Loss2: 1.340833
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.987386 Loss1: 1.085409 Loss2: 1.901977
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.253643 Loss1: 0.808227 Loss2: 1.445416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.976733 Loss1: 0.485860 Loss2: 1.490874 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.740069 Loss1: 0.310784 Loss2: 1.429285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.719620 Loss1: 0.297088 Loss2: 1.422532 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438155 Loss1: 0.126172 Loss2: 1.311984
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.633355 Loss1: 0.211528 Loss2: 1.421828
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.538267 Loss1: 0.136288 Loss2: 1.401979
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.530021 Loss1: 0.130651 Loss2: 1.399370
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.520023 Loss1: 0.120524 Loss2: 1.399499
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491919 Loss1: 0.098865 Loss2: 1.393054
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.120510 Loss1: 1.207250 Loss2: 1.913260
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.297035 Loss1: 0.813480 Loss2: 1.483555
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.945309 Loss1: 0.489617 Loss2: 1.455691
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.752945 Loss1: 0.338965 Loss2: 1.413980
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.640011 Loss1: 0.228676 Loss2: 1.411335
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.895260 Loss1: 1.070382 Loss2: 1.824879
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.030929 Loss1: 0.652252 Loss2: 1.378677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.879923 Loss1: 0.477335 Loss2: 1.402587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.663928 Loss1: 0.299087 Loss2: 1.364841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609742 Loss1: 0.241857 Loss2: 1.367885 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.527295 Loss1: 0.151682 Loss2: 1.375613
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.536969 Loss1: 0.188782 Loss2: 1.348188
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473131 Loss1: 0.121949 Loss2: 1.351182
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453113 Loss1: 0.112894 Loss2: 1.340219
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454855 Loss1: 0.117803 Loss2: 1.337052
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427595 Loss1: 0.093809 Loss2: 1.333786
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.056372 Loss1: 1.170453 Loss2: 1.885919
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.149857 Loss1: 0.683230 Loss2: 1.466627
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.907072 Loss1: 0.470994 Loss2: 1.436078
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.805432 Loss1: 0.381490 Loss2: 1.423941
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631378 Loss1: 0.212538 Loss2: 1.418840
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.931833 Loss1: 1.036718 Loss2: 1.895115
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.105760 Loss1: 0.664699 Loss2: 1.441061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.844175 Loss1: 0.375089 Loss2: 1.469086 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.667591 Loss1: 0.245071 Loss2: 1.422521 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.630193 Loss1: 0.214489 Loss2: 1.415704 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.625882 Loss1: 0.209931 Loss2: 1.415951 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.563132 Loss1: 0.161358 Loss2: 1.401774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.499270 Loss1: 0.103407 Loss2: 1.395864 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.128160 Loss1: 0.613689 Loss2: 1.514472 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.819930 Loss1: 0.342961 Loss2: 1.476969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.696263 Loss1: 0.245639 Loss2: 1.450624
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.813209 Loss1: 1.006946 Loss2: 1.806263
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.032372 Loss1: 0.666567 Loss2: 1.365804
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.602895 Loss1: 0.165289 Loss2: 1.437607
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.813527 Loss1: 0.427854 Loss2: 1.385673
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.557009 Loss1: 0.120616 Loss2: 1.436393
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.686857 Loss1: 0.325673 Loss2: 1.361184
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.543812 Loss1: 0.114790 Loss2: 1.429022
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.667900 Loss1: 0.314871 Loss2: 1.353030
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.549498 Loss1: 0.128489 Loss2: 1.421009
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.532191 Loss1: 0.105753 Loss2: 1.426438 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.960938
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482414 Loss1: 0.143024 Loss2: 1.339390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458677 Loss1: 0.116698 Loss2: 1.341979 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.025418 Loss1: 0.615124 Loss2: 1.410293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.627614 Loss1: 0.238005 Loss2: 1.389610 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.996391 Loss1: 1.084493 Loss2: 1.911898
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.613073 Loss1: 0.233618 Loss2: 1.379455
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.567777 Loss1: 0.189716 Loss2: 1.378061
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.165019 Loss1: 0.660745 Loss2: 1.504274
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514415 Loss1: 0.142339 Loss2: 1.372077
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.906504 Loss1: 0.412196 Loss2: 1.494307
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484833 Loss1: 0.119717 Loss2: 1.365115
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.753153 Loss1: 0.286665 Loss2: 1.466488
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.492194 Loss1: 0.131371 Loss2: 1.360823
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.751746 Loss1: 0.284966 Loss2: 1.466780
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.464449 Loss1: 0.101883 Loss2: 1.362566
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.737639 Loss1: 0.259041 Loss2: 1.478598
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.607060 Loss1: 0.154729 Loss2: 1.452332
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.595319 Loss1: 0.148399 Loss2: 1.446920
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.577965 Loss1: 0.136720 Loss2: 1.441245
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.555344 Loss1: 0.119636 Loss2: 1.435708
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.934253 Loss1: 0.942315 Loss2: 1.991938
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.133403 Loss1: 0.591421 Loss2: 1.541982
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.961151 Loss1: 0.400209 Loss2: 1.560942
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.809226 Loss1: 0.294185 Loss2: 1.515042
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.701362 Loss1: 0.187254 Loss2: 1.514108
-DEBUG flwr 2023-10-10 21:30:05,935 | server.py:236 | fit_round 90 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.079656 Loss1: 1.122970 Loss2: 1.956686
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.681565 Loss1: 0.177951 Loss2: 1.503614
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.140646 Loss1: 0.653240 Loss2: 1.487405
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.648145 Loss1: 0.154832 Loss2: 1.493313
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.945512 Loss1: 0.452026 Loss2: 1.493486
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.750804 Loss1: 0.287366 Loss2: 1.463439
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.603272 Loss1: 0.107745 Loss2: 1.495527
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.686826 Loss1: 0.225939 Loss2: 1.460887
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.607619 Loss1: 0.127778 Loss2: 1.479841
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.676718 Loss1: 0.218159 Loss2: 1.458559
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.620113 Loss1: 0.134483 Loss2: 1.485630
-(DefaultActor pid=3765) >> Training accuracy: 0.965820
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.615485 Loss1: 0.163350 Loss2: 1.452135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.555110 Loss1: 0.112927 Loss2: 1.442183 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.083689 Loss1: 0.694648 Loss2: 1.389040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.620823 Loss1: 0.257542 Loss2: 1.363280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555664 Loss1: 0.209651 Loss2: 1.346013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491509 Loss1: 0.152145 Loss2: 1.339365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467431 Loss1: 0.135858 Loss2: 1.331573 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439441 Loss1: 0.112960 Loss2: 1.326482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.464233 Loss1: 0.144149 Loss2: 1.320084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428198 Loss1: 0.100625 Loss2: 1.327573 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.568893 Loss1: 0.149114 Loss2: 1.419780 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.314325 Loss1: 1.148246 Loss2: 2.166079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.124292 Loss1: 0.531170 Loss2: 1.593122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.744463 Loss1: 0.244664 Loss2: 1.499799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.723416 Loss1: 0.204275 Loss2: 1.519141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.638666 Loss1: 0.143254 Loss2: 1.495412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.612582 Loss1: 0.125251 Loss2: 1.487331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.649892 Loss1: 0.161581 Loss2: 1.488311
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.686269 Loss1: 0.310495 Loss2: 1.375774
-(DefaultActor pid=3765) >> Training accuracy: 0.977865
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.588303 Loss1: 0.102571 Loss2: 1.485731
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.608557 Loss1: 0.251776 Loss2: 1.356781
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.520542 Loss1: 0.172931 Loss2: 1.347611
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471851 Loss1: 0.134064 Loss2: 1.337787
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445844 Loss1: 0.103360 Loss2: 1.342484
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426306 Loss1: 0.093698 Loss2: 1.332608
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409590 Loss1: 0.081725 Loss2: 1.327865
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 21:30:05,935][flwr][DEBUG] - fit_round 90 received 50 results and 0 failures
-INFO flwr 2023-10-10 21:30:47,521 | server.py:125 | fit progress: (90, 2.2210369241504244, {'accuracy': 0.5571}, 207555.299687149)
->> Test accuracy: 0.557100
-[2023-10-10 21:30:47,521][flwr][INFO] - fit progress: (90, 2.2210369241504244, {'accuracy': 0.5571}, 207555.299687149)
-DEBUG flwr 2023-10-10 21:30:47,521 | server.py:173 | evaluate_round 90: strategy sampled 50 clients (out of 50)
-[2023-10-10 21:30:47,521][flwr][DEBUG] - evaluate_round 90: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 21:39:51,467 | server.py:187 | evaluate_round 90 received 50 results and 0 failures
-[2023-10-10 21:39:51,467][flwr][DEBUG] - evaluate_round 90 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 21:39:51,468 | server.py:222 | fit_round 91: strategy sampled 50 clients (out of 50)
-[2023-10-10 21:39:51,468][flwr][DEBUG] - fit_round 91: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.867967 Loss1: 1.018204 Loss2: 1.849763
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.114334 Loss1: 0.652242 Loss2: 1.462091
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.850079 Loss1: 0.424558 Loss2: 1.425521
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.858704 Loss1: 0.999092 Loss2: 1.859612
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.088812 Loss1: 0.691277 Loss2: 1.397535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.866834 Loss1: 0.418372 Loss2: 1.448462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.719736 Loss1: 0.325113 Loss2: 1.394623 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.664469 Loss1: 0.259439 Loss2: 1.405030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.574134 Loss1: 0.194983 Loss2: 1.379151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490640 Loss1: 0.110943 Loss2: 1.379697 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.474658 Loss1: 0.110613 Loss2: 1.364045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.480128 Loss1: 0.115166 Loss2: 1.364962 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.083119 Loss1: 1.054804 Loss2: 2.028315
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.237600 Loss1: 0.833801 Loss2: 1.403799
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.976208 Loss1: 0.482822 Loss2: 1.493386
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.805945 Loss1: 0.385362 Loss2: 1.420583
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.760926 Loss1: 0.340142 Loss2: 1.420785
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.150359 Loss1: 0.719500 Loss2: 1.430859 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.859140 Loss1: 0.363577 Loss2: 1.495563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.670895 Loss1: 0.253728 Loss2: 1.417167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.518789 Loss1: 0.129976 Loss2: 1.388813 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.537157 Loss1: 0.134577 Loss2: 1.402580 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.500434 Loss1: 0.102774 Loss2: 1.397660 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.774753 Loss1: 0.947862 Loss2: 1.826890
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472873 Loss1: 0.083419 Loss2: 1.389454
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.679510 Loss1: 0.303472 Loss2: 1.376037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534559 Loss1: 0.180455 Loss2: 1.354104 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503422 Loss1: 0.173708 Loss2: 1.329715
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.053397 Loss1: 1.176354 Loss2: 1.877043
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.077658 Loss1: 0.648573 Loss2: 1.429085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.795612 Loss1: 0.372714 Loss2: 1.422898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.676950 Loss1: 0.279568 Loss2: 1.397382 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.952083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431686 Loss1: 0.105065 Loss2: 1.326621
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.619030 Loss1: 0.229561 Loss2: 1.389469
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.535547 Loss1: 0.157212 Loss2: 1.378335
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.544820 Loss1: 0.173017 Loss2: 1.371803
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568104 Loss1: 0.196233 Loss2: 1.371871
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.588078 Loss1: 0.200005 Loss2: 1.388072
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.812205 Loss1: 0.983109 Loss2: 1.829095
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.531226 Loss1: 0.155240 Loss2: 1.375987
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.693360 Loss1: 0.311119 Loss2: 1.382241 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513634 Loss1: 0.158022 Loss2: 1.355612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.447472 Loss1: 0.124325 Loss2: 1.323148
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.938115 Loss1: 1.124995 Loss2: 1.813120
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447114 Loss1: 0.126320 Loss2: 1.320794
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.067360 Loss1: 0.669277 Loss2: 1.398083
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423398 Loss1: 0.101173 Loss2: 1.322225
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.862814 Loss1: 0.474337 Loss2: 1.388478
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409153 Loss1: 0.094396 Loss2: 1.314757
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.721695 Loss1: 0.340727 Loss2: 1.380968
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383821 Loss1: 0.069979 Loss2: 1.313841
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.691865 Loss1: 0.315847 Loss2: 1.376018
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.573676 Loss1: 0.210041 Loss2: 1.363634
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.525507 Loss1: 0.174731 Loss2: 1.350776
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481078 Loss1: 0.132549 Loss2: 1.348529
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450085 Loss1: 0.104372 Loss2: 1.345713
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462489 Loss1: 0.134500 Loss2: 1.327989
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.965909 Loss1: 1.051838 Loss2: 1.914072
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.052428 Loss1: 0.600837 Loss2: 1.451591
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.843361 Loss1: 0.415182 Loss2: 1.428179
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.680249 Loss1: 0.266914 Loss2: 1.413335
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.594106 Loss1: 0.198692 Loss2: 1.395414
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538095 Loss1: 0.143406 Loss2: 1.394689
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.057519 Loss1: 1.173890 Loss2: 1.883630
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528229 Loss1: 0.142255 Loss2: 1.385974
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.043414 Loss1: 0.635561 Loss2: 1.407853
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.501532 Loss1: 0.126322 Loss2: 1.375210
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.916509 Loss1: 0.475003 Loss2: 1.441506
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445452 Loss1: 0.065827 Loss2: 1.379625
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.732315 Loss1: 0.338582 Loss2: 1.393733
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425090 Loss1: 0.056247 Loss2: 1.368843
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.614066 Loss1: 0.215129 Loss2: 1.398937
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.624796 Loss1: 0.235785 Loss2: 1.389011
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.567873 Loss1: 0.182640 Loss2: 1.385233
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.575418 Loss1: 0.193572 Loss2: 1.381846
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.552974 Loss1: 0.167079 Loss2: 1.385895
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.516734 Loss1: 0.131562 Loss2: 1.385173
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.784460 Loss1: 1.027716 Loss2: 1.756744
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.041508 Loss1: 0.683471 Loss2: 1.358036
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731244 Loss1: 0.371235 Loss2: 1.360009
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601194 Loss1: 0.278200 Loss2: 1.322993
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526062 Loss1: 0.201120 Loss2: 1.324942
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.862134 Loss1: 0.993818 Loss2: 1.868316
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475969 Loss1: 0.164245 Loss2: 1.311724
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435304 Loss1: 0.130995 Loss2: 1.304310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430920 Loss1: 0.125038 Loss2: 1.305882 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417430 Loss1: 0.112026 Loss2: 1.305404 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403099 Loss1: 0.095019 Loss2: 1.308079 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480978 Loss1: 0.126550 Loss2: 1.354428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440046 Loss1: 0.094792 Loss2: 1.345254 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429164 Loss1: 0.091937 Loss2: 1.337226
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.945673 Loss1: 1.025114 Loss2: 1.920559
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.135716 Loss1: 0.685838 Loss2: 1.449878
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.903390 Loss1: 0.415770 Loss2: 1.487619
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.696653 Loss1: 0.272584 Loss2: 1.424069
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.644522 Loss1: 0.222137 Loss2: 1.422385
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.953368 Loss1: 1.013299 Loss2: 1.940069
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.045953 Loss1: 0.602529 Loss2: 1.443424 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.797194 Loss1: 0.338952 Loss2: 1.458242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.713275 Loss1: 0.292626 Loss2: 1.420649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.612400 Loss1: 0.192125 Loss2: 1.420274 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.494012 Loss1: 0.101668 Loss2: 1.392344
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531757 Loss1: 0.127268 Loss2: 1.404489
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513467 Loss1: 0.118067 Loss2: 1.395399
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.502256 Loss1: 0.108797 Loss2: 1.393459
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.470506 Loss1: 0.079036 Loss2: 1.391469
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.481370 Loss1: 0.098290 Loss2: 1.383080
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.111561 Loss1: 1.247778 Loss2: 1.863782
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.122225 Loss1: 0.696313 Loss2: 1.425912
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.875389 Loss1: 0.443426 Loss2: 1.431963
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.700111 Loss1: 0.311021 Loss2: 1.389090
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611352 Loss1: 0.229956 Loss2: 1.381395
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.853903 Loss1: 1.016675 Loss2: 1.837229
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.109114 Loss1: 0.686552 Loss2: 1.422562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834924 Loss1: 0.412504 Loss2: 1.422419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.693390 Loss1: 0.301012 Loss2: 1.392378 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.624972 Loss1: 0.237212 Loss2: 1.387760 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567446 Loss1: 0.188105 Loss2: 1.379341 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.542217 Loss1: 0.165581 Loss2: 1.376636 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.492171 Loss1: 0.120159 Loss2: 1.372011 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.830963 Loss1: 0.368348 Loss2: 1.462614 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.656060 Loss1: 0.238813 Loss2: 1.417247 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.560107 Loss1: 0.167102 Loss2: 1.393005
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.680452 Loss1: 0.851990 Loss2: 1.828462
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.885428 Loss1: 0.496275 Loss2: 1.389153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.730030 Loss1: 0.322974 Loss2: 1.407056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475306 Loss1: 0.093239 Loss2: 1.382067 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522244 Loss1: 0.156544 Loss2: 1.365700 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.497762 Loss1: 0.133575 Loss2: 1.364187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453343 Loss1: 0.099006 Loss2: 1.354337 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425887 Loss1: 0.076282 Loss2: 1.349604 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587233 Loss1: 0.171899 Loss2: 1.415333 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.548375 Loss1: 0.151698 Loss2: 1.396677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.142903 Loss1: 1.218340 Loss2: 1.924563
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.514886 Loss1: 0.124133 Loss2: 1.390752
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.531074 Loss1: 0.142026 Loss2: 1.389048 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491314 Loss1: 0.098845 Loss2: 1.392469 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.614343 Loss1: 0.224661 Loss2: 1.389682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.521691 Loss1: 0.142877 Loss2: 1.378814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.942188 Loss1: 1.067212 Loss2: 1.874976 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.046022 Loss1: 0.624876 Loss2: 1.421147 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.608308 Loss1: 0.230843 Loss2: 1.377466 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489251 Loss1: 0.125736 Loss2: 1.363515 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.550635 Loss1: 0.191194 Loss2: 1.359441
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.991315 Loss1: 1.060183 Loss2: 1.931132
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481574 Loss1: 0.108769 Loss2: 1.372806
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.145566 Loss1: 0.675528 Loss2: 1.470038
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.480011 Loss1: 0.124556 Loss2: 1.355455
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.948887 Loss1: 0.437660 Loss2: 1.511227
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469646 Loss1: 0.113150 Loss2: 1.356496
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.863002 Loss1: 0.410622 Loss2: 1.452381
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.699216 Loss1: 0.230683 Loss2: 1.468533
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.642017 Loss1: 0.199222 Loss2: 1.442795
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.607104 Loss1: 0.176861 Loss2: 1.430242
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.601276 Loss1: 0.161873 Loss2: 1.439403
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.945527 Loss1: 1.042376 Loss2: 1.903151
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.561258 Loss1: 0.130712 Loss2: 1.430545
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.147967 Loss1: 0.690923 Loss2: 1.457044
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.553233 Loss1: 0.133168 Loss2: 1.420066
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.663255 Loss1: 0.264755 Loss2: 1.398500 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.549124 Loss1: 0.158325 Loss2: 1.390799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.569662 Loss1: 0.182225 Loss2: 1.387437
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.918841 Loss1: 0.901841 Loss2: 2.017000
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.298025 Loss1: 0.757884 Loss2: 1.540141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.074973 Loss1: 0.507529 Loss2: 1.567444 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.495028 Loss1: 0.120452 Loss2: 1.374576
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.966284 Loss1: 0.453459 Loss2: 1.512825
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.776899 Loss1: 0.277594 Loss2: 1.499305
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.697482 Loss1: 0.220467 Loss2: 1.477015
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.637539 Loss1: 0.154071 Loss2: 1.483469
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.579773 Loss1: 0.116338 Loss2: 1.463435
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.048889 Loss1: 1.100989 Loss2: 1.947900
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.587735 Loss1: 0.133694 Loss2: 1.454041
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.576085 Loss1: 0.115418 Loss2: 1.460667 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.597421 Loss1: 0.197136 Loss2: 1.400285 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.584923 Loss1: 0.188663 Loss2: 1.396260 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.004919 Loss1: 1.063232 Loss2: 1.941687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.323717 Loss1: 0.843019 Loss2: 1.480698 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977163
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.911602 Loss1: 0.418104 Loss2: 1.493499 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.676917 Loss1: 0.217639 Loss2: 1.459278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.643685 Loss1: 0.187769 Loss2: 1.455916
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.954093 Loss1: 1.083838 Loss2: 1.870255
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.002067 Loss1: 0.548783 Loss2: 1.453285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.889517 Loss1: 0.458252 Loss2: 1.431265 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.726370 Loss1: 0.302135 Loss2: 1.424235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.608303 Loss1: 0.216553 Loss2: 1.391749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.511670 Loss1: 0.127675 Loss2: 1.383995 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.491278 Loss1: 0.119284 Loss2: 1.371993 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435551 Loss1: 0.068102 Loss2: 1.367448 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569333 Loss1: 0.211057 Loss2: 1.358276 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483565 Loss1: 0.153863 Loss2: 1.329702 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.896739 Loss1: 1.021429 Loss2: 1.875310
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455820 Loss1: 0.129417 Loss2: 1.326404
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.105353 Loss1: 0.663765 Loss2: 1.441588
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447446 Loss1: 0.126087 Loss2: 1.321359
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.951021 Loss1: 0.498559 Loss2: 1.452462
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403631 Loss1: 0.083372 Loss2: 1.320259
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.658599 Loss1: 0.239349 Loss2: 1.419250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.560727 Loss1: 0.159483 Loss2: 1.401244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.545961 Loss1: 0.141495 Loss2: 1.404467
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.063660 Loss1: 1.207402 Loss2: 1.856258
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.041453 Loss1: 0.638474 Loss2: 1.402979 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.480643 Loss1: 0.094311 Loss2: 1.386332
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.821292 Loss1: 0.429904 Loss2: 1.391388
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.676028 Loss1: 0.298914 Loss2: 1.377114
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598852 Loss1: 0.233302 Loss2: 1.365550
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.534024 Loss1: 0.169913 Loss2: 1.364111
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478417 Loss1: 0.127831 Loss2: 1.350586
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.923552 Loss1: 1.047365 Loss2: 1.876187
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473578 Loss1: 0.130333 Loss2: 1.343245
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.042558 Loss1: 0.637533 Loss2: 1.405025
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425271 Loss1: 0.090969 Loss2: 1.334302
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.821271 Loss1: 0.391697 Loss2: 1.429575
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410317 Loss1: 0.079860 Loss2: 1.330457
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.706819 Loss1: 0.300274 Loss2: 1.406545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.570445 Loss1: 0.196161 Loss2: 1.374284 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.502976 Loss1: 0.132046 Loss2: 1.370930
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.206670 Loss1: 1.207918 Loss2: 1.998752
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.093758 Loss1: 0.685861 Loss2: 1.407897
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.495601 Loss1: 0.134738 Loss2: 1.360862
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.778453 Loss1: 0.339243 Loss2: 1.439210
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.456999 Loss1: 0.092979 Loss2: 1.364020
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.594340 Loss1: 0.228628 Loss2: 1.365712 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465129 Loss1: 0.105168 Loss2: 1.359961 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463062 Loss1: 0.111834 Loss2: 1.351228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.471485 Loss1: 0.117661 Loss2: 1.353824 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973558
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.709112 Loss1: 0.303414 Loss2: 1.405698 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.507542 Loss1: 0.146129 Loss2: 1.361413 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.131501 Loss1: 1.204944 Loss2: 1.926557
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514889 Loss1: 0.166584 Loss2: 1.348305
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.320846 Loss1: 0.820735 Loss2: 1.500111
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.478117 Loss1: 0.124804 Loss2: 1.353313
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.964741 Loss1: 0.500480 Loss2: 1.464261
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.442740 Loss1: 0.098887 Loss2: 1.343853
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.834869 Loss1: 0.366681 Loss2: 1.468188
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407357 Loss1: 0.065232 Loss2: 1.342125
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.661742 Loss1: 0.246087 Loss2: 1.415655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.549473 Loss1: 0.144633 Loss2: 1.404839 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.526359 Loss1: 0.122743 Loss2: 1.403617
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.136371 Loss1: 1.281944 Loss2: 1.854427
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.591989 Loss1: 0.191276 Loss2: 1.400713
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.158194 Loss1: 0.769322 Loss2: 1.388872
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.753865 Loss1: 0.379995 Loss2: 1.373870
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642038 Loss1: 0.308684 Loss2: 1.333354
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574614 Loss1: 0.235691 Loss2: 1.338923
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.483175 Loss1: 0.156235 Loss2: 1.326940
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432069 Loss1: 0.114099 Loss2: 1.317970
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.829207 Loss1: 0.985327 Loss2: 1.843880
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.978466 Loss1: 0.548780 Loss2: 1.429686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.755388 Loss1: 0.318102 Loss2: 1.437286 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.649883 Loss1: 0.261338 Loss2: 1.388545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500331 Loss1: 0.129578 Loss2: 1.370753 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485683 Loss1: 0.119414 Loss2: 1.366269 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.474119 Loss1: 0.110599 Loss2: 1.363520 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.469990 Loss1: 0.107899 Loss2: 1.362092 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.637276 Loss1: 0.298573 Loss2: 1.338703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.529057 Loss1: 0.180561 Loss2: 1.348495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514965 Loss1: 0.175597 Loss2: 1.339368
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.807613 Loss1: 0.946697 Loss2: 1.860916
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.981513 Loss1: 0.596328 Loss2: 1.385186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.776381 Loss1: 0.366926 Loss2: 1.409456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428504 Loss1: 0.102481 Loss2: 1.326023
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.621683 Loss1: 0.249318 Loss2: 1.372365
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.627581 Loss1: 0.255976 Loss2: 1.371605
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531006 Loss1: 0.163147 Loss2: 1.367859
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.526109 Loss1: 0.165685 Loss2: 1.360425
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466147 Loss1: 0.110605 Loss2: 1.355542
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.898502 Loss1: 1.003856 Loss2: 1.894647
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434358 Loss1: 0.085460 Loss2: 1.348898
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.157686 Loss1: 0.663898 Loss2: 1.493788
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422204 Loss1: 0.080756 Loss2: 1.341449
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.705340 Loss1: 0.275878 Loss2: 1.429461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.679538 Loss1: 0.253180 Loss2: 1.426357 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.823379 Loss1: 0.996580 Loss2: 1.826799
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.601662 Loss1: 0.183439 Loss2: 1.418223
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.959542 Loss1: 0.576408 Loss2: 1.383134
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.528996 Loss1: 0.122513 Loss2: 1.406483
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.744847 Loss1: 0.327494 Loss2: 1.417353
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.554165 Loss1: 0.156804 Loss2: 1.397361
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.635563 Loss1: 0.275535 Loss2: 1.360028
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.492336 Loss1: 0.088723 Loss2: 1.403613
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516207 Loss1: 0.159126 Loss2: 1.357081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483016 Loss1: 0.125329 Loss2: 1.357687 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.050230 Loss1: 1.099511 Loss2: 1.950719
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447282 Loss1: 0.101779 Loss2: 1.345503
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.153904 Loss1: 0.650752 Loss2: 1.503152
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.451555 Loss1: 0.112448 Loss2: 1.339107
-(DefaultActor pid=3764) >> Training accuracy: 0.967773
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.772544 Loss1: 0.301529 Loss2: 1.471015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.676967 Loss1: 0.219252 Loss2: 1.457716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.625981 Loss1: 0.169968 Loss2: 1.456013
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.004969 Loss1: 1.151769 Loss2: 1.853199
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.079598 Loss1: 0.672177 Loss2: 1.407421 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.789251 Loss1: 0.359750 Loss2: 1.429501 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.577405 Loss1: 0.130248 Loss2: 1.447157
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.627735 Loss1: 0.254780 Loss2: 1.372955
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.588321 Loss1: 0.206500 Loss2: 1.381821
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.580723 Loss1: 0.208872 Loss2: 1.371851
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513270 Loss1: 0.149306 Loss2: 1.363963
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459119 Loss1: 0.100663 Loss2: 1.358456
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.028089 Loss1: 1.160610 Loss2: 1.867478
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431362 Loss1: 0.084610 Loss2: 1.346752
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413598 Loss1: 0.068179 Loss2: 1.345420 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.700155 Loss1: 0.295521 Loss2: 1.404634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562106 Loss1: 0.177496 Loss2: 1.384610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517549 Loss1: 0.136529 Loss2: 1.381020
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.981592 Loss1: 0.990913 Loss2: 1.990679
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.147480 Loss1: 0.652349 Loss2: 1.495131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.906265 Loss1: 0.389974 Loss2: 1.516291 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.770627 Loss1: 0.294039 Loss2: 1.476588 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.687496 Loss1: 0.224272 Loss2: 1.463224 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.563930 Loss1: 0.108457 Loss2: 1.455473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.584945 Loss1: 0.133557 Loss2: 1.451387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.578227 Loss1: 0.120966 Loss2: 1.457260 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.643218 Loss1: 0.292372 Loss2: 1.350846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514409 Loss1: 0.177587 Loss2: 1.336822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.561626 Loss1: 0.220676 Loss2: 1.340950
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.102399 Loss1: 1.239536 Loss2: 1.862863
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.129156 Loss1: 0.678781 Loss2: 1.450375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.825136 Loss1: 0.442267 Loss2: 1.382869 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.942708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.696049 Loss1: 0.306481 Loss2: 1.389568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.553868 Loss1: 0.189619 Loss2: 1.364250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.480769 Loss1: 0.126807 Loss2: 1.353962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447770 Loss1: 0.099739 Loss2: 1.348031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439665 Loss1: 0.094289 Loss2: 1.345376 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 22:08:20,670 | server.py:236 | fit_round 91 received 50 results and 0 failures
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.733553 Loss1: 0.269153 Loss2: 1.464400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.631232 Loss1: 0.185693 Loss2: 1.445539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.588885 Loss1: 0.143522 Loss2: 1.445363
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.957511 Loss1: 1.065362 Loss2: 1.892150
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.032697 Loss1: 0.623046 Loss2: 1.409651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.886298 Loss1: 0.472381 Loss2: 1.413917 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.752068 Loss1: 0.321859 Loss2: 1.430209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512086 Loss1: 0.133351 Loss2: 1.378735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465448 Loss1: 0.105703 Loss2: 1.359745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452079 Loss1: 0.090288 Loss2: 1.361791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428628 Loss1: 0.072136 Loss2: 1.356492 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.759739 Loss1: 0.360213 Loss2: 1.399526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.565455 Loss1: 0.200288 Loss2: 1.365167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.521179 Loss1: 0.151250 Loss2: 1.369930
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.977539 Loss1: 1.138964 Loss2: 1.838575
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.501523 Loss1: 0.144447 Loss2: 1.357076
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.099006 Loss1: 0.701818 Loss2: 1.397188
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436374 Loss1: 0.087132 Loss2: 1.349242
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.761040 Loss1: 0.354014 Loss2: 1.407026
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.660837 Loss1: 0.302237 Loss2: 1.358600
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420728 Loss1: 0.075344 Loss2: 1.345384
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.570028 Loss1: 0.202670 Loss2: 1.367358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466269 Loss1: 0.116905 Loss2: 1.349364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404162 Loss1: 0.076651 Loss2: 1.327511 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 22:08:20,670][flwr][DEBUG] - fit_round 91 received 50 results and 0 failures
-INFO flwr 2023-10-10 22:09:02,220 | server.py:125 | fit progress: (91, 2.2214489318311403, {'accuracy': 0.5571}, 209849.998660804)
->> Test accuracy: 0.557100
-[2023-10-10 22:09:02,220][flwr][INFO] - fit progress: (91, 2.2214489318311403, {'accuracy': 0.5571}, 209849.998660804)
-DEBUG flwr 2023-10-10 22:09:02,220 | server.py:173 | evaluate_round 91: strategy sampled 50 clients (out of 50)
-[2023-10-10 22:09:02,220][flwr][DEBUG] - evaluate_round 91: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 22:18:07,786 | server.py:187 | evaluate_round 91 received 50 results and 0 failures
-[2023-10-10 22:18:07,786][flwr][DEBUG] - evaluate_round 91 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 22:18:07,786 | server.py:222 | fit_round 92: strategy sampled 50 clients (out of 50)
-[2023-10-10 22:18:07,786][flwr][DEBUG] - fit_round 92: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.889109 Loss1: 1.072547 Loss2: 1.816562
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.998324 Loss1: 0.561428 Loss2: 1.436896
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.762121 Loss1: 0.360605 Loss2: 1.401516
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.672616 Loss1: 0.271321 Loss2: 1.401295
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.763677 Loss1: 0.995215 Loss2: 1.768462
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.618638 Loss1: 0.231566 Loss2: 1.387072
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.919303 Loss1: 0.560691 Loss2: 1.358612
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.594774 Loss1: 0.209978 Loss2: 1.384796
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.766136 Loss1: 0.405287 Loss2: 1.360849
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.554916 Loss1: 0.171269 Loss2: 1.383647
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.628907 Loss1: 0.283805 Loss2: 1.345101
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.546614 Loss1: 0.167942 Loss2: 1.378672
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.580495 Loss1: 0.235836 Loss2: 1.344659
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.540837 Loss1: 0.160525 Loss2: 1.380312
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530127 Loss1: 0.183963 Loss2: 1.346164
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508140 Loss1: 0.128840 Loss2: 1.379300
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441036 Loss1: 0.107640 Loss2: 1.333395
-(DefaultActor pid=3765) >> Training accuracy: 0.970703
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.402543 Loss1: 0.084227 Loss2: 1.318316
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388489 Loss1: 0.076387 Loss2: 1.312103
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377994 Loss1: 0.072445 Loss2: 1.305549
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.102538 Loss1: 1.135145 Loss2: 1.967393
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.191988 Loss1: 0.771519 Loss2: 1.420469
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.927543 Loss1: 0.431726 Loss2: 1.495817
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694173 Loss1: 0.275578 Loss2: 1.418595
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.682745 Loss1: 0.264188 Loss2: 1.418557
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.640437 Loss1: 0.215304 Loss2: 1.425133 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.572091 Loss1: 0.160896 Loss2: 1.411194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.537303 Loss1: 0.134369 Loss2: 1.402934 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.519368 Loss1: 0.119689 Loss2: 1.399679 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.528761 Loss1: 0.121939 Loss2: 1.406821 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985577
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.515143 Loss1: 0.140782 Loss2: 1.374361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486775 Loss1: 0.124539 Loss2: 1.362236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.508224 Loss1: 0.136327 Loss2: 1.371898
-(DefaultActor pid=3764) >> Training accuracy: 0.955208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.883900 Loss1: 1.064682 Loss2: 1.819218
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.990731 Loss1: 0.611180 Loss2: 1.379551
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.730891 Loss1: 0.349962 Loss2: 1.380929
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623162 Loss1: 0.264468 Loss2: 1.358694
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558987 Loss1: 0.205965 Loss2: 1.353021
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.851548 Loss1: 0.991696 Loss2: 1.859852
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.535067 Loss1: 0.184181 Loss2: 1.350886
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.028156 Loss1: 0.596415 Loss2: 1.431741
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516284 Loss1: 0.171049 Loss2: 1.345235
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452583 Loss1: 0.116880 Loss2: 1.335703
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.759585 Loss1: 0.332682 Loss2: 1.426903
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428029 Loss1: 0.095418 Loss2: 1.332612
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.674814 Loss1: 0.271052 Loss2: 1.403762
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450814 Loss1: 0.121776 Loss2: 1.329038
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.606542 Loss1: 0.208027 Loss2: 1.398515
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.564275 Loss1: 0.173540 Loss2: 1.390735
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500651 Loss1: 0.111843 Loss2: 1.388809
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483828 Loss1: 0.099534 Loss2: 1.384294
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444514 Loss1: 0.071295 Loss2: 1.373219
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.822141 Loss1: 0.987246 Loss2: 1.834896
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445641 Loss1: 0.076542 Loss2: 1.369099
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.708606 Loss1: 0.346763 Loss2: 1.361843 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.502023 Loss1: 0.168311 Loss2: 1.333712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513318 Loss1: 0.194507 Loss2: 1.318812
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.965701 Loss1: 1.117504 Loss2: 1.848197
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.038824 Loss1: 0.628289 Loss2: 1.410535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.860604 Loss1: 0.443422 Loss2: 1.417182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.664817 Loss1: 0.300295 Loss2: 1.364521 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368307 Loss1: 0.067740 Loss2: 1.300567
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598464 Loss1: 0.225343 Loss2: 1.373121
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.550930 Loss1: 0.182307 Loss2: 1.368623
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516048 Loss1: 0.160704 Loss2: 1.355344
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471255 Loss1: 0.116970 Loss2: 1.354286
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447190 Loss1: 0.104577 Loss2: 1.342613
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.886932 Loss1: 0.961395 Loss2: 1.925537
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422601 Loss1: 0.083404 Loss2: 1.339197
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.914803 Loss1: 0.432729 Loss2: 1.482074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.651897 Loss1: 0.219412 Loss2: 1.432485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.578523 Loss1: 0.160475 Loss2: 1.418048
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.013666 Loss1: 1.137602 Loss2: 1.876063
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.013502 Loss1: 0.604303 Loss2: 1.409199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.806948 Loss1: 0.392017 Loss2: 1.414931 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.661850 Loss1: 0.270184 Loss2: 1.391666 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.604796 Loss1: 0.222074 Loss2: 1.382722 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.567313 Loss1: 0.200634 Loss2: 1.366679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.472937 Loss1: 0.112796 Loss2: 1.360141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457408 Loss1: 0.099478 Loss2: 1.357931 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.880657 Loss1: 0.398148 Loss2: 1.482509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.603069 Loss1: 0.166775 Loss2: 1.436294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.568516 Loss1: 0.131344 Loss2: 1.437172
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.942354 Loss1: 1.094338 Loss2: 1.848016
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.102033 Loss1: 0.683406 Loss2: 1.418626 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.821425 Loss1: 0.395263 Loss2: 1.426163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.704479 Loss1: 0.310499 Loss2: 1.393980 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.640120 Loss1: 0.248587 Loss2: 1.391533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.511141 Loss1: 0.139941 Loss2: 1.371199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434554 Loss1: 0.077097 Loss2: 1.357457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453866 Loss1: 0.101103 Loss2: 1.352763 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.757433 Loss1: 0.350024 Loss2: 1.407408 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.617178 Loss1: 0.219775 Loss2: 1.397402 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.578180 Loss1: 0.183359 Loss2: 1.394821
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.889065 Loss1: 1.012141 Loss2: 1.876923
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.588461 Loss1: 0.197319 Loss2: 1.391142
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.036421 Loss1: 0.620630 Loss2: 1.415790
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.511736 Loss1: 0.114757 Loss2: 1.396979
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.829033 Loss1: 0.395095 Loss2: 1.433938
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491684 Loss1: 0.109610 Loss2: 1.382074
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.646480 Loss1: 0.250021 Loss2: 1.396458
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.652269 Loss1: 0.252368 Loss2: 1.399901
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.627057 Loss1: 0.223046 Loss2: 1.404011
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.603587 Loss1: 0.210908 Loss2: 1.392679
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.534125 Loss1: 0.141657 Loss2: 1.392468
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.857231 Loss1: 1.028613 Loss2: 1.828618
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.483288 Loss1: 0.103764 Loss2: 1.379524
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.033679 Loss1: 0.660769 Loss2: 1.372909
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.475891 Loss1: 0.100728 Loss2: 1.375163
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649206 Loss1: 0.282953 Loss2: 1.366253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.563537 Loss1: 0.199430 Loss2: 1.364107 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516745 Loss1: 0.151183 Loss2: 1.365562
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.013156 Loss1: 1.053343 Loss2: 1.959813
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.237600 Loss1: 0.709353 Loss2: 1.528248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.872215 Loss1: 0.391516 Loss2: 1.480699 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449318 Loss1: 0.104287 Loss2: 1.345031
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.776354 Loss1: 0.316071 Loss2: 1.460284
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.666239 Loss1: 0.204424 Loss2: 1.461815
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.580499 Loss1: 0.132775 Loss2: 1.447724
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.561627 Loss1: 0.127985 Loss2: 1.433643
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.523949 Loss1: 0.099595 Loss2: 1.424354
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.093901 Loss1: 1.111562 Loss2: 1.982339
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.511309 Loss1: 0.087556 Loss2: 1.423753
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.524246 Loss1: 0.104794 Loss2: 1.419452 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631484 Loss1: 0.260734 Loss2: 1.370750 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.543926 Loss1: 0.165664 Loss2: 1.378263 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.512838 Loss1: 0.156413 Loss2: 1.356425 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476024 Loss1: 0.119058 Loss2: 1.356965 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986979
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.953492 Loss1: 0.466531 Loss2: 1.486961 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.672483 Loss1: 0.233453 Loss2: 1.439030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.634393 Loss1: 0.220882 Loss2: 1.413511
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.948669 Loss1: 1.107022 Loss2: 1.841646
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.125397 Loss1: 0.741254 Loss2: 1.384143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.790401 Loss1: 0.402516 Loss2: 1.387884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.686435 Loss1: 0.327492 Loss2: 1.358943 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.579302 Loss1: 0.212024 Loss2: 1.367278 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.575735 Loss1: 0.215819 Loss2: 1.359916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472134 Loss1: 0.138122 Loss2: 1.334013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446760 Loss1: 0.114195 Loss2: 1.332565 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.734805 Loss1: 0.385428 Loss2: 1.349376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505054 Loss1: 0.182465 Loss2: 1.322589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487215 Loss1: 0.171234 Loss2: 1.315981
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.023982 Loss1: 1.116506 Loss2: 1.907476
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.148464 Loss1: 0.670895 Loss2: 1.477569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.910025 Loss1: 0.431622 Loss2: 1.478403 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.791458 Loss1: 0.348047 Loss2: 1.443412 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423397 Loss1: 0.123731 Loss2: 1.299666
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.700305 Loss1: 0.261068 Loss2: 1.439236
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.671223 Loss1: 0.248707 Loss2: 1.422516
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.543351 Loss1: 0.120476 Loss2: 1.422876
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.533035 Loss1: 0.124861 Loss2: 1.408174
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.513844 Loss1: 0.101606 Loss2: 1.412238
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.905606 Loss1: 1.014812 Loss2: 1.890794
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.512673 Loss1: 0.103887 Loss2: 1.408787
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.932661 Loss1: 0.445019 Loss2: 1.487643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.670286 Loss1: 0.212040 Loss2: 1.458246 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.651381 Loss1: 0.212070 Loss2: 1.439311
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.881539 Loss1: 1.020164 Loss2: 1.861375
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.115472 Loss1: 0.716401 Loss2: 1.399071
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.636938 Loss1: 0.194111 Loss2: 1.442827
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.779217 Loss1: 0.377595 Loss2: 1.401622
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.638169 Loss1: 0.200238 Loss2: 1.437931
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.712417 Loss1: 0.342947 Loss2: 1.369470
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.588890 Loss1: 0.152700 Loss2: 1.436190
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589560 Loss1: 0.212719 Loss2: 1.376841
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.545122 Loss1: 0.123197 Loss2: 1.421926
-(DefaultActor pid=3764) >> Training accuracy: 0.974609
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.533757 Loss1: 0.171339 Loss2: 1.362418 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462180 Loss1: 0.118854 Loss2: 1.343326 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447309 Loss1: 0.102521 Loss2: 1.344788
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.041857 Loss1: 1.081407 Loss2: 1.960450
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.250613 Loss1: 0.747696 Loss2: 1.502917
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.962131 Loss1: 0.432711 Loss2: 1.529420
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.768626 Loss1: 0.300196 Loss2: 1.468430
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.712072 Loss1: 0.234824 Loss2: 1.477248
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.885486 Loss1: 0.985950 Loss2: 1.899536
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.660622 Loss1: 0.195550 Loss2: 1.465072
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.636923 Loss1: 0.188767 Loss2: 1.448156
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.071998 Loss1: 0.641729 Loss2: 1.430269
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.563865 Loss1: 0.108623 Loss2: 1.455242
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.807323 Loss1: 0.371824 Loss2: 1.435500
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.538979 Loss1: 0.097115 Loss2: 1.441864
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.728587 Loss1: 0.330227 Loss2: 1.398361
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.538391 Loss1: 0.098652 Loss2: 1.439738
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.632539 Loss1: 0.226444 Loss2: 1.406096
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.603960 Loss1: 0.201896 Loss2: 1.402063
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.507452 Loss1: 0.114955 Loss2: 1.392497
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489898 Loss1: 0.103490 Loss2: 1.386408
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.501749 Loss1: 0.121982 Loss2: 1.379768
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.881392 Loss1: 0.983924 Loss2: 1.897468
-(DefaultActor pid=3765) >> Training accuracy: 0.977539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.063314 Loss1: 0.652562 Loss2: 1.410752 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611607 Loss1: 0.211800 Loss2: 1.399807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506016 Loss1: 0.128517 Loss2: 1.377498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.489103 Loss1: 0.112767 Loss2: 1.376336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.503254 Loss1: 0.132523 Loss2: 1.370731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.502783 Loss1: 0.125630 Loss2: 1.377153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487292 Loss1: 0.112974 Loss2: 1.374318 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576139 Loss1: 0.200366 Loss2: 1.375772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.514021 Loss1: 0.145707 Loss2: 1.368314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.955995 Loss1: 1.097426 Loss2: 1.858569 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.146249 Loss1: 0.724270 Loss2: 1.421979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594227 Loss1: 0.225885 Loss2: 1.368342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.561984 Loss1: 0.196024 Loss2: 1.365959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.537130 Loss1: 0.175356 Loss2: 1.361774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472856 Loss1: 0.112412 Loss2: 1.360444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433911 Loss1: 0.079704 Loss2: 1.354208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441482 Loss1: 0.096816 Loss2: 1.344665 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.545748 Loss1: 0.167227 Loss2: 1.378521 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.526668 Loss1: 0.136117 Loss2: 1.390551 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.521876 Loss1: 0.144869 Loss2: 1.377007
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.921708 Loss1: 0.997071 Loss2: 1.924637
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.002887 Loss1: 0.590481 Loss2: 1.412406
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477318 Loss1: 0.098524 Loss2: 1.378793
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.678321 Loss1: 0.279222 Loss2: 1.399099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.597104 Loss1: 0.206964 Loss2: 1.390140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.613468 Loss1: 0.214128 Loss2: 1.399340
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.933643 Loss1: 1.073133 Loss2: 1.860510
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.148081 Loss1: 0.700737 Loss2: 1.447344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.903328 Loss1: 0.464839 Loss2: 1.438489 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.806611 Loss1: 0.385702 Loss2: 1.420909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.594385 Loss1: 0.194476 Loss2: 1.399909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.502531 Loss1: 0.114587 Loss2: 1.387944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.478051 Loss1: 0.103366 Loss2: 1.374685 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462223 Loss1: 0.096391 Loss2: 1.365832 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.627656 Loss1: 0.210525 Loss2: 1.417131 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.532974 Loss1: 0.139232 Loss2: 1.393743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.066726 Loss1: 1.106945 Loss2: 1.959780
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490897 Loss1: 0.104651 Loss2: 1.386246
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.160628 Loss1: 0.681769 Loss2: 1.478859
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448480 Loss1: 0.077207 Loss2: 1.371273
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.945258 Loss1: 0.450102 Loss2: 1.495156
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413899 Loss1: 0.052171 Loss2: 1.361729
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.655238 Loss1: 0.192530 Loss2: 1.462707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.616066 Loss1: 0.180979 Loss2: 1.435086 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.610705 Loss1: 0.167237 Loss2: 1.443469
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.764033 Loss1: 0.906395 Loss2: 1.857638
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.570566 Loss1: 0.127789 Loss2: 1.442776
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.193414 Loss1: 0.767072 Loss2: 1.426342
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.563864 Loss1: 0.128920 Loss2: 1.434944
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.872903 Loss1: 0.425484 Loss2: 1.447420
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.725688 Loss1: 0.307828 Loss2: 1.417860
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687104 Loss1: 0.273783 Loss2: 1.413321
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542944 Loss1: 0.145802 Loss2: 1.397142
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.529546 Loss1: 0.136584 Loss2: 1.392962
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.711333 Loss1: 0.871118 Loss2: 1.840214
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491802 Loss1: 0.105460 Loss2: 1.386342
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.876581 Loss1: 0.478317 Loss2: 1.398265
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446651 Loss1: 0.070976 Loss2: 1.375676
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460646 Loss1: 0.089518 Loss2: 1.371128
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.725695 Loss1: 0.314304 Loss2: 1.411391
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.635489 Loss1: 0.243333 Loss2: 1.392156
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593436 Loss1: 0.210391 Loss2: 1.383045
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.559381 Loss1: 0.177379 Loss2: 1.382002
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.553733 Loss1: 0.178022 Loss2: 1.375711
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.837075 Loss1: 0.972887 Loss2: 1.864188
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.081536 Loss1: 0.666640 Loss2: 1.414895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.541855 Loss1: 0.175155 Loss2: 1.366700
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.906188 Loss1: 0.482971 Loss2: 1.423217
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.552964 Loss1: 0.173151 Loss2: 1.379814
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.818202 Loss1: 0.407270 Loss2: 1.410932
-(DefaultActor pid=3765) >> Training accuracy: 0.977022
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.713746 Loss1: 0.318967 Loss2: 1.394779
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556550 Loss1: 0.167311 Loss2: 1.389239
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.481497 Loss1: 0.120836 Loss2: 1.360661
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471577 Loss1: 0.116234 Loss2: 1.355343
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453601 Loss1: 0.094352 Loss2: 1.359249
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.938722 Loss1: 1.045657 Loss2: 1.893065
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428455 Loss1: 0.075867 Loss2: 1.352588
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.118402 Loss1: 0.694114 Loss2: 1.424288
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.874341 Loss1: 0.422133 Loss2: 1.452209
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.708808 Loss1: 0.316739 Loss2: 1.392069
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.583283 Loss1: 0.180794 Loss2: 1.402489
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537318 Loss1: 0.160612 Loss2: 1.376706
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.103962 Loss1: 1.188205 Loss2: 1.915757
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513845 Loss1: 0.145476 Loss2: 1.368369
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.119919 Loss1: 0.706574 Loss2: 1.413345
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.468724 Loss1: 0.095500 Loss2: 1.373224
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448617 Loss1: 0.087515 Loss2: 1.361102 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434901 Loss1: 0.081036 Loss2: 1.353866 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556177 Loss1: 0.183877 Loss2: 1.372300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.512153 Loss1: 0.136395 Loss2: 1.375758 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.824297 Loss1: 0.943235 Loss2: 1.881062 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979911
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.833652 Loss1: 0.414529 Loss2: 1.419123 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.597189 Loss1: 0.229835 Loss2: 1.367353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516179 Loss1: 0.158811 Loss2: 1.357369
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.973489 Loss1: 1.082194 Loss2: 1.891295
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.026031 Loss1: 0.607324 Loss2: 1.418707 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.816967 Loss1: 0.371117 Loss2: 1.445850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.696493 Loss1: 0.302699 Loss2: 1.393794 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463935 Loss1: 0.123160 Loss2: 1.340775
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.650576 Loss1: 0.250725 Loss2: 1.399851
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.591067 Loss1: 0.196259 Loss2: 1.394808
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508546 Loss1: 0.126006 Loss2: 1.382540
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484823 Loss1: 0.106852 Loss2: 1.377971
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.488751 Loss1: 0.114812 Loss2: 1.373939
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.975123 Loss1: 1.110124 Loss2: 1.864999
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512148 Loss1: 0.131657 Loss2: 1.380491
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.864797 Loss1: 0.407741 Loss2: 1.457056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.702160 Loss1: 0.283449 Loss2: 1.418711 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.616700 Loss1: 0.213420 Loss2: 1.403280
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.729604 Loss1: 0.875473 Loss2: 1.854131
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.935846 Loss1: 0.539817 Loss2: 1.396029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.781303 Loss1: 0.348395 Loss2: 1.432908 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631449 Loss1: 0.252001 Loss2: 1.379448 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.509736 Loss1: 0.121881 Loss2: 1.387854
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.585400 Loss1: 0.201284 Loss2: 1.384116
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.565100 Loss1: 0.191941 Loss2: 1.373159
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497196 Loss1: 0.135100 Loss2: 1.362096
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464733 Loss1: 0.106991 Loss2: 1.357743
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452270 Loss1: 0.096375 Loss2: 1.355895
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.865243 Loss1: 1.011645 Loss2: 1.853598
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420972 Loss1: 0.076543 Loss2: 1.344429
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.854368 Loss1: 0.400704 Loss2: 1.453664 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.638451 Loss1: 0.232769 Loss2: 1.405682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.847711 Loss1: 0.979554 Loss2: 1.868157
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.608849 Loss1: 0.204031 Loss2: 1.404818
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.065074 Loss1: 0.642735 Loss2: 1.422339
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547668 Loss1: 0.145667 Loss2: 1.402001
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497041 Loss1: 0.105869 Loss2: 1.391172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461242 Loss1: 0.082623 Loss2: 1.378619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466499 Loss1: 0.090405 Loss2: 1.376094 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.515968 Loss1: 0.142201 Loss2: 1.373767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452545 Loss1: 0.090985 Loss2: 1.361560 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462803 Loss1: 0.102256 Loss2: 1.360547
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.895589 Loss1: 0.990407 Loss2: 1.905182
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.071470 Loss1: 0.663564 Loss2: 1.407907
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.956785 Loss1: 0.473013 Loss2: 1.483771
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.730910 Loss1: 0.324700 Loss2: 1.406209
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.661596 Loss1: 0.238718 Loss2: 1.422878
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.151986 Loss1: 1.255270 Loss2: 1.896716
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.229323 Loss1: 0.789536 Loss2: 1.439786 [repeated 2x across cluster]
-DEBUG flwr 2023-10-10 22:46:22,633 | server.py:236 | fit_round 92 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.998663 Loss1: 0.544675 Loss2: 1.453989 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.823642 Loss1: 0.397953 Loss2: 1.425689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.683354 Loss1: 0.283105 Loss2: 1.400249 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476402 Loss1: 0.090845 Loss2: 1.385558
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.627123 Loss1: 0.227765 Loss2: 1.399358
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.611755 Loss1: 0.222032 Loss2: 1.389723
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518652 Loss1: 0.130297 Loss2: 1.388355
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458626 Loss1: 0.091729 Loss2: 1.366897
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466392 Loss1: 0.099914 Loss2: 1.366478
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.255228 Loss1: 1.164179 Loss2: 2.091049
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.236739 Loss1: 0.728082 Loss2: 1.508657
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.962396 Loss1: 0.422471 Loss2: 1.539925
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.692520 Loss1: 0.213978 Loss2: 1.478542
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.655147 Loss1: 0.191212 Loss2: 1.463935
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.620433 Loss1: 0.150341 Loss2: 1.470092
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.924096 Loss1: 1.014116 Loss2: 1.909980
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.025473 Loss1: 0.627860 Loss2: 1.397613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.843722 Loss1: 0.397988 Loss2: 1.445734 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612251 Loss1: 0.236443 Loss2: 1.375809 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556125 Loss1: 0.184138 Loss2: 1.371988 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466619 Loss1: 0.109341 Loss2: 1.357278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458304 Loss1: 0.106235 Loss2: 1.352069
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.794437 Loss1: 0.910692 Loss2: 1.883746
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.965769 Loss1: 0.563086 Loss2: 1.402683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.710277 Loss1: 0.308355 Loss2: 1.401922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.625483 Loss1: 0.226515 Loss2: 1.398968 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539715 Loss1: 0.143495 Loss2: 1.396220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.487843 Loss1: 0.099913 Loss2: 1.387931 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.490465 Loss1: 0.103733 Loss2: 1.386733 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.503316 Loss1: 0.122529 Loss2: 1.380787 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.529281 Loss1: 0.150701 Loss2: 1.378581 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.518870 Loss1: 0.155466 Loss2: 1.363404 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977679
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 22:46:22,633][flwr][DEBUG] - fit_round 92 received 50 results and 0 failures
-INFO flwr 2023-10-10 22:47:04,524 | server.py:125 | fit progress: (92, 2.20932971498075, {'accuracy': 0.561}, 212132.302083312)
->> Test accuracy: 0.561000
-[2023-10-10 22:47:04,524][flwr][INFO] - fit progress: (92, 2.20932971498075, {'accuracy': 0.561}, 212132.302083312)
-DEBUG flwr 2023-10-10 22:47:04,524 | server.py:173 | evaluate_round 92: strategy sampled 50 clients (out of 50)
-[2023-10-10 22:47:04,524][flwr][DEBUG] - evaluate_round 92: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 22:56:12,759 | server.py:187 | evaluate_round 92 received 50 results and 0 failures
-[2023-10-10 22:56:12,759][flwr][DEBUG] - evaluate_round 92 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 22:56:12,760 | server.py:222 | fit_round 93: strategy sampled 50 clients (out of 50)
-[2023-10-10 22:56:12,760][flwr][DEBUG] - fit_round 93: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.716243 Loss1: 0.909041 Loss2: 1.807202 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.734289 Loss1: 0.343208 Loss2: 1.391081 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.668005 Loss1: 0.302522 Loss2: 1.365483
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.875608 Loss1: 1.003338 Loss2: 1.872270
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567956 Loss1: 0.204738 Loss2: 1.363218
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.074874 Loss1: 0.622574 Loss2: 1.452300
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493242 Loss1: 0.137511 Loss2: 1.355731
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.889389 Loss1: 0.439873 Loss2: 1.449515
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455236 Loss1: 0.110153 Loss2: 1.345083
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.772216 Loss1: 0.342019 Loss2: 1.430197
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.468980 Loss1: 0.128619 Loss2: 1.340361
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.728127 Loss1: 0.305265 Loss2: 1.422862
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460593 Loss1: 0.120576 Loss2: 1.340018
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.663186 Loss1: 0.243953 Loss2: 1.419232
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445685 Loss1: 0.108182 Loss2: 1.337503
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.591222 Loss1: 0.190891 Loss2: 1.400331
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.525938 Loss1: 0.126392 Loss2: 1.399546
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501773 Loss1: 0.115137 Loss2: 1.386636
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.474935 Loss1: 0.090506 Loss2: 1.384429
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.918596 Loss1: 1.045376 Loss2: 1.873220
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.059158 Loss1: 0.623015 Loss2: 1.436143
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.807380 Loss1: 0.372454 Loss2: 1.434926
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.745545 Loss1: 0.326542 Loss2: 1.419003
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.645515 Loss1: 0.835572 Loss2: 1.809943
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.862800 Loss1: 0.484562 Loss2: 1.378238 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.729001 Loss1: 0.339984 Loss2: 1.389017 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640862 Loss1: 0.278644 Loss2: 1.362218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539058 Loss1: 0.181630 Loss2: 1.357428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463572 Loss1: 0.118603 Loss2: 1.344969 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400348 Loss1: 0.064880 Loss2: 1.335468 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388517 Loss1: 0.065420 Loss2: 1.323097 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.017610 Loss1: 0.697271 Loss2: 1.320339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.626774 Loss1: 0.300554 Loss2: 1.326220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.819144 Loss1: 1.029654 Loss2: 1.789490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.973944 Loss1: 0.607429 Loss2: 1.366515 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.762401 Loss1: 0.375286 Loss2: 1.387115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433596 Loss1: 0.127804 Loss2: 1.305791 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408323 Loss1: 0.107361 Loss2: 1.300962 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497881 Loss1: 0.163829 Loss2: 1.334052 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487305 Loss1: 0.156099 Loss2: 1.331206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438401 Loss1: 0.112686 Loss2: 1.325715 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969727
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.828197 Loss1: 0.405804 Loss2: 1.422393 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558785 Loss1: 0.184700 Loss2: 1.374085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504891 Loss1: 0.139098 Loss2: 1.365794
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.866247 Loss1: 1.019576 Loss2: 1.846671
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.008700 Loss1: 0.626093 Loss2: 1.382608 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.798450 Loss1: 0.363776 Loss2: 1.434674 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.700035 Loss1: 0.311538 Loss2: 1.388497 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467376 Loss1: 0.100338 Loss2: 1.367038
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.645990 Loss1: 0.248975 Loss2: 1.397015
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.629212 Loss1: 0.241741 Loss2: 1.387471
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.588622 Loss1: 0.194541 Loss2: 1.394081
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.525091 Loss1: 0.147880 Loss2: 1.377211
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467498 Loss1: 0.096764 Loss2: 1.370733
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.817290 Loss1: 0.940397 Loss2: 1.876893
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453111 Loss1: 0.085675 Loss2: 1.367435
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.798437 Loss1: 0.372381 Loss2: 1.426056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.626731 Loss1: 0.249609 Loss2: 1.377122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520169 Loss1: 0.151926 Loss2: 1.368243
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.868146 Loss1: 0.998763 Loss2: 1.869383
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.002765 Loss1: 0.602079 Loss2: 1.400687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.702041 Loss1: 0.276718 Loss2: 1.425323 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586421 Loss1: 0.211951 Loss2: 1.374469 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412749 Loss1: 0.069431 Loss2: 1.343318
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.580804 Loss1: 0.206516 Loss2: 1.374289
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.590917 Loss1: 0.212816 Loss2: 1.378101
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484060 Loss1: 0.105259 Loss2: 1.378801
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460493 Loss1: 0.103012 Loss2: 1.357481
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450314 Loss1: 0.092102 Loss2: 1.358212
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.824944 Loss1: 0.913068 Loss2: 1.911876
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434893 Loss1: 0.081347 Loss2: 1.353546
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.775195 Loss1: 0.327830 Loss2: 1.447365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.614279 Loss1: 0.194644 Loss2: 1.419634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.595724 Loss1: 0.175426 Loss2: 1.420297 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516903 Loss1: 0.105016 Loss2: 1.411887 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497626 Loss1: 0.100809 Loss2: 1.396817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482547 Loss1: 0.083468 Loss2: 1.399079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457873 Loss1: 0.070045 Loss2: 1.387827 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986213
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568236 Loss1: 0.134828 Loss2: 1.433409 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.536158 Loss1: 0.115148 Loss2: 1.421010 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.105022 Loss1: 0.665965 Loss2: 1.439057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.709132 Loss1: 0.306061 Loss2: 1.403071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.913053 Loss1: 1.079855 Loss2: 1.833198
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577881 Loss1: 0.204957 Loss2: 1.372924
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.002894 Loss1: 0.636475 Loss2: 1.366419
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537679 Loss1: 0.167795 Loss2: 1.369885
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.710403 Loss1: 0.339628 Loss2: 1.370776
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.520543 Loss1: 0.156981 Loss2: 1.363562
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.641394 Loss1: 0.291056 Loss2: 1.350338
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.493738 Loss1: 0.125014 Loss2: 1.368723
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.610572 Loss1: 0.256704 Loss2: 1.353868
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.498413 Loss1: 0.139807 Loss2: 1.358606
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.564121 Loss1: 0.224415 Loss2: 1.339706
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460623 Loss1: 0.103567 Loss2: 1.357056
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482058 Loss1: 0.149637 Loss2: 1.332422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433711 Loss1: 0.110804 Loss2: 1.322907 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.103701 Loss1: 0.692118 Loss2: 1.411583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.711686 Loss1: 0.318427 Loss2: 1.393259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.783678 Loss1: 0.956077 Loss2: 1.827602
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.648741 Loss1: 0.253014 Loss2: 1.395727
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.871853 Loss1: 0.515323 Loss2: 1.356530
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542694 Loss1: 0.159857 Loss2: 1.382837
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.698224 Loss1: 0.334802 Loss2: 1.363422
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.507173 Loss1: 0.139454 Loss2: 1.367719
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.598052 Loss1: 0.262711 Loss2: 1.335341
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489050 Loss1: 0.121170 Loss2: 1.367880
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524464 Loss1: 0.201454 Loss2: 1.323011
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454099 Loss1: 0.096738 Loss2: 1.357361
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.445883 Loss1: 0.128257 Loss2: 1.317626
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451409 Loss1: 0.097886 Loss2: 1.353524
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450778 Loss1: 0.131029 Loss2: 1.319749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457398 Loss1: 0.133017 Loss2: 1.324382 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.946767 Loss1: 0.586523 Loss2: 1.360243 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569169 Loss1: 0.226156 Loss2: 1.343014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.570543 Loss1: 0.222720 Loss2: 1.347824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511738 Loss1: 0.172592 Loss2: 1.339146 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428333 Loss1: 0.092615 Loss2: 1.335718 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422641 Loss1: 0.101814 Loss2: 1.320828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419002 Loss1: 0.096719 Loss2: 1.322283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383724 Loss1: 0.067193 Loss2: 1.316531 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.508371 Loss1: 0.157130 Loss2: 1.351240 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431222 Loss1: 0.089609 Loss2: 1.341613 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.966991 Loss1: 0.538501 Loss2: 1.428490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.627958 Loss1: 0.203787 Loss2: 1.424170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605244 Loss1: 0.194359 Loss2: 1.410885 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.573378 Loss1: 0.160156 Loss2: 1.413223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.603692 Loss1: 0.191976 Loss2: 1.411716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.650698 Loss1: 0.227623 Loss2: 1.423075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.598685 Loss1: 0.179896 Loss2: 1.418789 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.560959 Loss1: 0.146585 Loss2: 1.414374 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.526796 Loss1: 0.142274 Loss2: 1.384522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.578309 Loss1: 0.194288 Loss2: 1.384022 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.992327 Loss1: 0.566040 Loss2: 1.426287 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.728291 Loss1: 0.312957 Loss2: 1.415334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.874268 Loss1: 0.914328 Loss2: 1.959940
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617053 Loss1: 0.210445 Loss2: 1.406608
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.140089 Loss1: 0.668258 Loss2: 1.471831
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.595689 Loss1: 0.201449 Loss2: 1.394239
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.898533 Loss1: 0.394022 Loss2: 1.504511
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516951 Loss1: 0.126833 Loss2: 1.390118
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.798197 Loss1: 0.347375 Loss2: 1.450822
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499365 Loss1: 0.122736 Loss2: 1.376630
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.727153 Loss1: 0.257171 Loss2: 1.469982
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470148 Loss1: 0.092676 Loss2: 1.377472
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.620168 Loss1: 0.167097 Loss2: 1.453071
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447306 Loss1: 0.073654 Loss2: 1.373652
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.555861 Loss1: 0.120369 Loss2: 1.435492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.517258 Loss1: 0.089292 Loss2: 1.427966 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.090847 Loss1: 0.657313 Loss2: 1.433534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.639939 Loss1: 0.252656 Loss2: 1.387283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.022700 Loss1: 1.087447 Loss2: 1.935252
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.563566 Loss1: 0.181333 Loss2: 1.382233
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.124931 Loss1: 0.771098 Loss2: 1.353833
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.495106 Loss1: 0.113783 Loss2: 1.381322
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488043 Loss1: 0.115288 Loss2: 1.372755 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436911 Loss1: 0.073893 Loss2: 1.363018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462328 Loss1: 0.098624 Loss2: 1.363703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.478179 Loss1: 0.113234 Loss2: 1.364945 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417798 Loss1: 0.098892 Loss2: 1.318906 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.694290 Loss1: 0.837124 Loss2: 1.857167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.755745 Loss1: 0.319955 Loss2: 1.435790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.718097 Loss1: 0.337248 Loss2: 1.380849
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.012639 Loss1: 1.134312 Loss2: 1.878327
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.040211 Loss1: 0.603169 Loss2: 1.437041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.935367 Loss1: 0.497796 Loss2: 1.437572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.703362 Loss1: 0.277262 Loss2: 1.426100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.582463 Loss1: 0.179314 Loss2: 1.403148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.543390 Loss1: 0.142797 Loss2: 1.400593 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460090 Loss1: 0.096679 Loss2: 1.363411
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485713 Loss1: 0.101005 Loss2: 1.384708
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500324 Loss1: 0.127121 Loss2: 1.373203
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457449 Loss1: 0.079244 Loss2: 1.378205
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445288 Loss1: 0.073874 Loss2: 1.371414
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.891478 Loss1: 0.908659 Loss2: 1.982819
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.057806 Loss1: 0.593494 Loss2: 1.464312
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.884513 Loss1: 0.382694 Loss2: 1.501819
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.735469 Loss1: 0.274411 Loss2: 1.461057
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.062662 Loss1: 1.112284 Loss2: 1.950378
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.228375 Loss1: 0.712491 Loss2: 1.515883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.941141 Loss1: 0.436479 Loss2: 1.504662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.784998 Loss1: 0.331254 Loss2: 1.453743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.713710 Loss1: 0.250097 Loss2: 1.463613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.622614 Loss1: 0.177005 Loss2: 1.445609 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.584647 Loss1: 0.129808 Loss2: 1.454840
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.594708 Loss1: 0.155861 Loss2: 1.438847
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.577125 Loss1: 0.145957 Loss2: 1.431169
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.574511 Loss1: 0.138751 Loss2: 1.435760
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.550525 Loss1: 0.109096 Loss2: 1.441429
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.879921 Loss1: 1.030098 Loss2: 1.849823
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.001236 Loss1: 0.612907 Loss2: 1.388330
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.843978 Loss1: 0.400998 Loss2: 1.442980
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.683835 Loss1: 0.291333 Loss2: 1.392502
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.954231 Loss1: 1.073656 Loss2: 1.880575
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.111996 Loss1: 0.680698 Loss2: 1.431299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.843935 Loss1: 0.401017 Loss2: 1.442918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.713107 Loss1: 0.311320 Loss2: 1.401786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.680829 Loss1: 0.279375 Loss2: 1.401454 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559771 Loss1: 0.170344 Loss2: 1.389427 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.497266 Loss1: 0.133940 Loss2: 1.363326
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509042 Loss1: 0.128524 Loss2: 1.380518
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.479232 Loss1: 0.103980 Loss2: 1.375252
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480627 Loss1: 0.106600 Loss2: 1.374027
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.459613 Loss1: 0.091842 Loss2: 1.367772
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.883031 Loss1: 1.067423 Loss2: 1.815608
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.014020 Loss1: 0.644107 Loss2: 1.369914
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.831999 Loss1: 0.434248 Loss2: 1.397751
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.670492 Loss1: 0.305337 Loss2: 1.365155
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.978359 Loss1: 1.113442 Loss2: 1.864916
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.038479 Loss1: 0.626717 Loss2: 1.411762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.809085 Loss1: 0.391380 Loss2: 1.417705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.671124 Loss1: 0.301224 Loss2: 1.369899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598790 Loss1: 0.220251 Loss2: 1.378539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510523 Loss1: 0.140266 Loss2: 1.370257 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.493795 Loss1: 0.136935 Loss2: 1.356860 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414917 Loss1: 0.067473 Loss2: 1.347444 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.229660 Loss1: 1.162907 Loss2: 2.066753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.996859 Loss1: 0.482455 Loss2: 1.514404 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.652888 Loss1: 0.221578 Loss2: 1.431309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.000215 Loss1: 0.571041 Loss2: 1.429174 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.665367 Loss1: 0.220853 Loss2: 1.444514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.626222 Loss1: 0.243375 Loss2: 1.382847 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.964844
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.514606 Loss1: 0.136318 Loss2: 1.378289 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477724 Loss1: 0.110729 Loss2: 1.366995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438747 Loss1: 0.076815 Loss2: 1.361932
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.974990 Loss1: 1.039005 Loss2: 1.935985
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.119495 Loss1: 0.655122 Loss2: 1.464374 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.758185 Loss1: 0.334321 Loss2: 1.423864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.583985 Loss1: 0.176164 Loss2: 1.407821 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547657 Loss1: 0.155863 Loss2: 1.391794 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.507545 Loss1: 0.118411 Loss2: 1.389134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472873 Loss1: 0.092085 Loss2: 1.380788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467084 Loss1: 0.089610 Loss2: 1.377475 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.538780 Loss1: 0.120820 Loss2: 1.417960 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486468 Loss1: 0.084993 Loss2: 1.401475 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985577
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509990 Loss1: 0.108622 Loss2: 1.401368
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.196707 Loss1: 1.168121 Loss2: 2.028586
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.190877 Loss1: 0.683787 Loss2: 1.507089
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.996576 Loss1: 0.439259 Loss2: 1.557317
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.837648 Loss1: 0.346866 Loss2: 1.490782
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.748447 Loss1: 0.253469 Loss2: 1.494977
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.943291 Loss1: 1.114690 Loss2: 1.828601
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.675911 Loss1: 0.195229 Loss2: 1.480682
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.618929 Loss1: 0.148325 Loss2: 1.470603 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.785406 Loss1: 0.372127 Loss2: 1.413278
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.604176 Loss1: 0.141121 Loss2: 1.463055
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.652596 Loss1: 0.291240 Loss2: 1.361356
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.605478 Loss1: 0.138118 Loss2: 1.467360
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.585837 Loss1: 0.124152 Loss2: 1.461685
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569332 Loss1: 0.199998 Loss2: 1.369335
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.514957 Loss1: 0.157705 Loss2: 1.357252
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.551963 Loss1: 0.190748 Loss2: 1.361215
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.480709 Loss1: 0.129434 Loss2: 1.351275
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454710 Loss1: 0.108411 Loss2: 1.346300
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.473487 Loss1: 0.124617 Loss2: 1.348870
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.878552 Loss1: 1.005070 Loss2: 1.873481
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.936445 Loss1: 0.516207 Loss2: 1.420238
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.783603 Loss1: 0.359960 Loss2: 1.423643
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.667448 Loss1: 0.273903 Loss2: 1.393545
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561539 Loss1: 0.175047 Loss2: 1.386492
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.601049 Loss1: 0.213161 Loss2: 1.387888
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.777669 Loss1: 1.009857 Loss2: 1.767812
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509374 Loss1: 0.132511 Loss2: 1.376863
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.056065 Loss1: 0.667299 Loss2: 1.388766
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.513981 Loss1: 0.139402 Loss2: 1.374579
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.756035 Loss1: 0.386676 Loss2: 1.369359
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.620519 Loss1: 0.283647 Loss2: 1.336871 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.478654 Loss1: 0.116055 Loss2: 1.362599
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521972 Loss1: 0.180542 Loss2: 1.341430
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.505919 Loss1: 0.176455 Loss2: 1.329464
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.505848 Loss1: 0.173603 Loss2: 1.332244
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511616 Loss1: 0.178469 Loss2: 1.333147
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487140 Loss1: 0.156945 Loss2: 1.330195
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.042273 Loss1: 1.203508 Loss2: 1.838765
-(DefaultActor pid=3764) >> Training accuracy: 0.975586
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.074815 Loss1: 0.673280 Loss2: 1.401535 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.741818 Loss1: 0.359574 Loss2: 1.382244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516828 Loss1: 0.150017 Loss2: 1.366811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.499477 Loss1: 0.139426 Loss2: 1.360051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.513204 Loss1: 0.157088 Loss2: 1.356116 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484760 Loss1: 0.129031 Loss2: 1.355729 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.507440 Loss1: 0.157017 Loss2: 1.350423 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523002 Loss1: 0.152447 Loss2: 1.370554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465674 Loss1: 0.104401 Loss2: 1.361272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.013977 Loss1: 1.106912 Loss2: 1.907065 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.249363 Loss1: 0.746095 Loss2: 1.503268 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.732322 Loss1: 0.309846 Loss2: 1.422476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.588923 Loss1: 0.181904 Loss2: 1.407019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.546770 Loss1: 0.132445 Loss2: 1.414324 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.531969 Loss1: 0.133210 Loss2: 1.398759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.505038 Loss1: 0.111860 Loss2: 1.393179 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.471442 Loss1: 0.078718 Loss2: 1.392725 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.503882 Loss1: 0.130584 Loss2: 1.373298 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481711 Loss1: 0.115249 Loss2: 1.366463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439434 Loss1: 0.072685 Loss2: 1.366750
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.010538 Loss1: 1.148237 Loss2: 1.862301
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.200315 Loss1: 0.754726 Loss2: 1.445589
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.890924 Loss1: 0.466005 Loss2: 1.424919
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.697918 Loss1: 0.302573 Loss2: 1.395346
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.622305 Loss1: 0.236011 Loss2: 1.386294
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.660413 Loss1: 0.863704 Loss2: 1.796709
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.591314 Loss1: 0.220517 Loss2: 1.370796
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.949661 Loss1: 0.572032 Loss2: 1.377629
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.570499 Loss1: 0.190296 Loss2: 1.380203
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.757763 Loss1: 0.365664 Loss2: 1.392099
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.548614 Loss1: 0.173342 Loss2: 1.375272
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.506816 Loss1: 0.137614 Loss2: 1.369202
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.686857 Loss1: 0.311512 Loss2: 1.375345
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.503007 Loss1: 0.136018 Loss2: 1.366989
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.631188 Loss1: 0.266401 Loss2: 1.364786
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.543436 Loss1: 0.175110 Loss2: 1.368327
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488308 Loss1: 0.140439 Loss2: 1.347869
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.494542 Loss1: 0.146107 Loss2: 1.348435
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.520952 Loss1: 0.171500 Loss2: 1.349452
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.952077 Loss1: 1.065770 Loss2: 1.886307
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454134 Loss1: 0.104760 Loss2: 1.349374
-(DefaultActor pid=3764) >> Training accuracy: 0.961914
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.852397 Loss1: 0.394768 Loss2: 1.457630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.633517 Loss1: 0.208323 Loss2: 1.425194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.601232 Loss1: 0.172131 Loss2: 1.429102
-DEBUG flwr 2023-10-10 23:24:48,369 | server.py:236 | fit_round 93 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.834095 Loss1: 1.052465 Loss2: 1.781630
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523281 Loss1: 0.103275 Loss2: 1.420006
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.972273 Loss1: 0.595682 Loss2: 1.376591
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470685 Loss1: 0.063106 Loss2: 1.407579
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.731416 Loss1: 0.365002 Loss2: 1.366414
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509749 Loss1: 0.110623 Loss2: 1.399126
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612951 Loss1: 0.268330 Loss2: 1.344622
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477047 Loss1: 0.076278 Loss2: 1.400768
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.578147 Loss1: 0.233680 Loss2: 1.344466
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567235 Loss1: 0.227236 Loss2: 1.339999
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.511692 Loss1: 0.168503 Loss2: 1.343188
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481672 Loss1: 0.145511 Loss2: 1.336161
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.465695 Loss1: 0.137173 Loss2: 1.328522
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.869212 Loss1: 1.013137 Loss2: 1.856075
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438561 Loss1: 0.116312 Loss2: 1.322250
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.777319 Loss1: 0.369334 Loss2: 1.407985 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.628616 Loss1: 0.239722 Loss2: 1.388894 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.567030 Loss1: 0.183113 Loss2: 1.383917
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.890158 Loss1: 1.022410 Loss2: 1.867748
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513652 Loss1: 0.143273 Loss2: 1.370380
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.001154 Loss1: 0.599273 Loss2: 1.401881
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473919 Loss1: 0.105869 Loss2: 1.368050
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.803650 Loss1: 0.371541 Loss2: 1.432109
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.688820 Loss1: 0.298930 Loss2: 1.389890
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.473935 Loss1: 0.111748 Loss2: 1.362186
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.600672 Loss1: 0.214888 Loss2: 1.385783
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441331 Loss1: 0.076387 Loss2: 1.364944
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513554 Loss1: 0.129147 Loss2: 1.384407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.483861 Loss1: 0.116568 Loss2: 1.367292 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-10 23:24:48,369][flwr][DEBUG] - fit_round 93 received 50 results and 0 failures
-INFO flwr 2023-10-10 23:25:29,351 | server.py:125 | fit progress: (93, 2.22590211443246, {'accuracy': 0.5612}, 214437.129805915)
->> Test accuracy: 0.561200
-[2023-10-10 23:25:29,351][flwr][INFO] - fit progress: (93, 2.22590211443246, {'accuracy': 0.5612}, 214437.129805915)
-DEBUG flwr 2023-10-10 23:25:29,352 | server.py:173 | evaluate_round 93: strategy sampled 50 clients (out of 50)
-[2023-10-10 23:25:29,352][flwr][DEBUG] - evaluate_round 93: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-10 23:34:37,226 | server.py:187 | evaluate_round 93 received 50 results and 0 failures
-[2023-10-10 23:34:37,226][flwr][DEBUG] - evaluate_round 93 received 50 results and 0 failures
-DEBUG flwr 2023-10-10 23:34:37,226 | server.py:222 | fit_round 94: strategy sampled 50 clients (out of 50)
-[2023-10-10 23:34:37,226][flwr][DEBUG] - fit_round 94: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.958269 Loss1: 1.073842 Loss2: 1.884427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.842630 Loss1: 0.426817 Loss2: 1.415813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.851483 Loss1: 0.904900 Loss2: 1.946583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.111795 Loss1: 0.636679 Loss2: 1.475117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.861653 Loss1: 0.383910 Loss2: 1.477743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.775460 Loss1: 0.347849 Loss2: 1.427610 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.664752 Loss1: 0.226244 Loss2: 1.438508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.587440 Loss1: 0.169045 Loss2: 1.418395 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986607
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.508488 Loss1: 0.109804 Loss2: 1.398684 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.519892 Loss1: 0.122615 Loss2: 1.397276 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.057199 Loss1: 0.595562 Loss2: 1.461637 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765773 Loss1: 0.315821 Loss2: 1.449951 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.676306 Loss1: 0.252173 Loss2: 1.424133 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.604731 Loss1: 0.175108 Loss2: 1.429623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.596031 Loss1: 0.180781 Loss2: 1.415250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.534444 Loss1: 0.120905 Loss2: 1.413539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.516725 Loss1: 0.112247 Loss2: 1.404478 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.513049 Loss1: 0.110498 Loss2: 1.402551 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519415 Loss1: 0.170623 Loss2: 1.348792 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.963960 Loss1: 1.079969 Loss2: 1.883991 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.930941 Loss1: 0.470774 Loss2: 1.460168 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.785247 Loss1: 0.347883 Loss2: 1.437365
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.825408 Loss1: 0.941400 Loss2: 1.884008
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.004691 Loss1: 0.615748 Loss2: 1.388943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.828196 Loss1: 0.384850 Loss2: 1.443346 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.647465 Loss1: 0.283197 Loss2: 1.364269 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.577062 Loss1: 0.205767 Loss2: 1.371295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537116 Loss1: 0.174499 Loss2: 1.362617 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472616 Loss1: 0.124067 Loss2: 1.348549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.472840 Loss1: 0.123753 Loss2: 1.349087 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.966412 Loss1: 1.063404 Loss2: 1.903009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.855271 Loss1: 0.399645 Loss2: 1.455627 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.724683 Loss1: 0.304411 Loss2: 1.420272
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.833104 Loss1: 1.001624 Loss2: 1.831480
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.057483 Loss1: 0.662035 Loss2: 1.395448 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.802262 Loss1: 0.409275 Loss2: 1.392986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.642080 Loss1: 0.262486 Loss2: 1.379594 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548851 Loss1: 0.184915 Loss2: 1.363936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542390 Loss1: 0.181277 Loss2: 1.361113 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.495982 Loss1: 0.145845 Loss2: 1.350137 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435556 Loss1: 0.092311 Loss2: 1.343246 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972656
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.150078 Loss1: 0.719528 Loss2: 1.430550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.826479 Loss1: 0.389524 Loss2: 1.436955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.673760 Loss1: 0.262846 Loss2: 1.410914
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.628767 Loss1: 0.864753 Loss2: 1.764014
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.614935 Loss1: 0.211396 Loss2: 1.403539
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.884897 Loss1: 0.491220 Loss2: 1.393677
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.731056 Loss1: 0.381244 Loss2: 1.349812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.626848 Loss1: 0.287089 Loss2: 1.339759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.580150 Loss1: 0.239256 Loss2: 1.340894 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.533093 Loss1: 0.199576 Loss2: 1.333517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484478 Loss1: 0.158340 Loss2: 1.326138 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.089261 Loss1: 1.126336 Loss2: 1.962924 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977022
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.935667 Loss1: 0.467113 Loss2: 1.468554 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.660946 Loss1: 0.224697 Loss2: 1.436249 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.888673 Loss1: 0.977289 Loss2: 1.911384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.021173 Loss1: 0.599988 Loss2: 1.421185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.832034 Loss1: 0.371358 Loss2: 1.460676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.468848 Loss1: 0.072577 Loss2: 1.396271 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.578249 Loss1: 0.178343 Loss2: 1.399906 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.513507 Loss1: 0.125662 Loss2: 1.387845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479230 Loss1: 0.100312 Loss2: 1.378918
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.943806 Loss1: 1.042369 Loss2: 1.901437
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458205 Loss1: 0.087423 Loss2: 1.370782
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.198285 Loss1: 0.710561 Loss2: 1.487723
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.884847 Loss1: 0.416045 Loss2: 1.468803
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.764582 Loss1: 0.312363 Loss2: 1.452220
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.619308 Loss1: 0.188967 Loss2: 1.430341
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.548194 Loss1: 0.132141 Loss2: 1.416054
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.034172 Loss1: 1.136751 Loss2: 1.897420
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.867930 Loss1: 0.466763 Loss2: 1.401167 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.481946 Loss1: 0.082197 Loss2: 1.399749 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.497095 Loss1: 0.098312 Loss2: 1.398783 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970703
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.402330 Loss1: 0.108324 Loss2: 1.294006 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387757 Loss1: 0.100996 Loss2: 1.286762 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985677
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.972547 Loss1: 1.079646 Loss2: 1.892901
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.085659 Loss1: 0.630977 Loss2: 1.454681
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.843523 Loss1: 0.381644 Loss2: 1.461879
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.738246 Loss1: 0.318801 Loss2: 1.419445
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.849761 Loss1: 0.995778 Loss2: 1.853982
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.649051 Loss1: 0.222761 Loss2: 1.426289
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.961358 Loss1: 0.564591 Loss2: 1.396767
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.643462 Loss1: 0.234858 Loss2: 1.408604
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.752066 Loss1: 0.336623 Loss2: 1.415444
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.617726 Loss1: 0.198984 Loss2: 1.418741
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.669375 Loss1: 0.293191 Loss2: 1.376184
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.566451 Loss1: 0.152715 Loss2: 1.413737
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.672677 Loss1: 0.294460 Loss2: 1.378216
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.574787 Loss1: 0.173101 Loss2: 1.401686
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.576217 Loss1: 0.193611 Loss2: 1.382606
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.569739 Loss1: 0.160651 Loss2: 1.409088
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.528959 Loss1: 0.166441 Loss2: 1.362519
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470366 Loss1: 0.116450 Loss2: 1.353915
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430512 Loss1: 0.078871 Loss2: 1.351641
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401870 Loss1: 0.062763 Loss2: 1.339107
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.848204 Loss1: 0.985014 Loss2: 1.863190
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.092802 Loss1: 0.696168 Loss2: 1.396634
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.918795 Loss1: 0.479192 Loss2: 1.439603
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.702029 Loss1: 0.307927 Loss2: 1.394102
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.958895 Loss1: 1.112185 Loss2: 1.846710
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.628530 Loss1: 0.261119 Loss2: 1.367411
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.081956 Loss1: 0.693123 Loss2: 1.388833
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.592274 Loss1: 0.219103 Loss2: 1.373170
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.842163 Loss1: 0.432929 Loss2: 1.409235
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544427 Loss1: 0.184943 Loss2: 1.359484
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.687917 Loss1: 0.312102 Loss2: 1.375815
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484180 Loss1: 0.124663 Loss2: 1.359518
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.573317 Loss1: 0.201210 Loss2: 1.372107
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.471155 Loss1: 0.118403 Loss2: 1.352752
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530625 Loss1: 0.170223 Loss2: 1.360402
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459674 Loss1: 0.110732 Loss2: 1.348942
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508367 Loss1: 0.156532 Loss2: 1.351835
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452451 Loss1: 0.104153 Loss2: 1.348298
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.456607 Loss1: 0.120241 Loss2: 1.336366
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443375 Loss1: 0.100017 Loss2: 1.343358
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.922026 Loss1: 1.050703 Loss2: 1.871323
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.107915 Loss1: 0.704765 Loss2: 1.403150
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.849503 Loss1: 0.429530 Loss2: 1.419973
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.695728 Loss1: 0.313678 Loss2: 1.382049
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.791433 Loss1: 0.964170 Loss2: 1.827262
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.888614 Loss1: 0.542675 Loss2: 1.345939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.728456 Loss1: 0.338006 Loss2: 1.390450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.618995 Loss1: 0.274071 Loss2: 1.344923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524714 Loss1: 0.175603 Loss2: 1.349111 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468626 Loss1: 0.133823 Loss2: 1.334802 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502775 Loss1: 0.170843 Loss2: 1.331931 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.507345 Loss1: 0.169064 Loss2: 1.338282 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.745718 Loss1: 0.891804 Loss2: 1.853914 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.696571 Loss1: 0.236751 Loss2: 1.459819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606047 Loss1: 0.209085 Loss2: 1.396962
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.898366 Loss1: 1.053975 Loss2: 1.844391
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.040745 Loss1: 0.615426 Loss2: 1.425319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513108 Loss1: 0.128731 Loss2: 1.384377
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.813867 Loss1: 0.418001 Loss2: 1.395866
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.586356 Loss1: 0.203453 Loss2: 1.382904
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.629692 Loss1: 0.255284 Loss2: 1.374409
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.577141 Loss1: 0.175483 Loss2: 1.401658
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535170 Loss1: 0.175444 Loss2: 1.359726
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500858 Loss1: 0.149401 Loss2: 1.351458
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.534447 Loss1: 0.146558 Loss2: 1.387890
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460281 Loss1: 0.112823 Loss2: 1.347457
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467634 Loss1: 0.078887 Loss2: 1.388747
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420983 Loss1: 0.086539 Loss2: 1.334443 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.811577 Loss1: 0.930947 Loss2: 1.880630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.828805 Loss1: 0.390186 Loss2: 1.438619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.712788 Loss1: 0.303738 Loss2: 1.409050
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.036584 Loss1: 1.019842 Loss2: 2.016742
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.265473 Loss1: 0.740352 Loss2: 1.525121
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.613535 Loss1: 0.213382 Loss2: 1.400153
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.005646 Loss1: 0.462349 Loss2: 1.543296
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.556952 Loss1: 0.171860 Loss2: 1.385092
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.833513 Loss1: 0.340073 Loss2: 1.493439
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.565355 Loss1: 0.178094 Loss2: 1.387260
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.740119 Loss1: 0.236585 Loss2: 1.503533
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.520668 Loss1: 0.139966 Loss2: 1.380702
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.524635 Loss1: 0.156334 Loss2: 1.368301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.483970 Loss1: 0.111253 Loss2: 1.372717 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.564225 Loss1: 0.097924 Loss2: 1.466301 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.777708 Loss1: 0.897832 Loss2: 1.879876 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.773791 Loss1: 0.344527 Loss2: 1.429264 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.633579 Loss1: 0.247515 Loss2: 1.386064
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.847317 Loss1: 0.916317 Loss2: 1.931001
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.599465 Loss1: 0.216317 Loss2: 1.383147
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.122711 Loss1: 0.684935 Loss2: 1.437775
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518882 Loss1: 0.142102 Loss2: 1.376780
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.887414 Loss1: 0.415649 Loss2: 1.471765
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474328 Loss1: 0.110374 Loss2: 1.363954
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.701594 Loss1: 0.279286 Loss2: 1.422308
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499453 Loss1: 0.132769 Loss2: 1.366684
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.617975 Loss1: 0.188077 Loss2: 1.429897
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453789 Loss1: 0.093853 Loss2: 1.359936
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.544395 Loss1: 0.136805 Loss2: 1.407590
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450064 Loss1: 0.094397 Loss2: 1.355667
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.531490 Loss1: 0.124370 Loss2: 1.407119
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.628876 Loss1: 0.210080 Loss2: 1.418796
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.597953 Loss1: 0.167005 Loss2: 1.430947
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.570567 Loss1: 0.150294 Loss2: 1.420273
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.913732 Loss1: 1.054617 Loss2: 1.859115
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.055107 Loss1: 0.650724 Loss2: 1.404383
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.767244 Loss1: 0.349937 Loss2: 1.417308
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.681641 Loss1: 0.301818 Loss2: 1.379823
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.920598 Loss1: 1.067592 Loss2: 1.853006
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.944584 Loss1: 0.549811 Loss2: 1.394773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.806719 Loss1: 0.399509 Loss2: 1.407210 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.715960 Loss1: 0.333545 Loss2: 1.382415 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.669600 Loss1: 0.288457 Loss2: 1.381143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.548013 Loss1: 0.174179 Loss2: 1.373834 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472365 Loss1: 0.107053 Loss2: 1.365312
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516722 Loss1: 0.146025 Loss2: 1.370697
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440603 Loss1: 0.087240 Loss2: 1.353363
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426744 Loss1: 0.081603 Loss2: 1.345141
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439413 Loss1: 0.098168 Loss2: 1.341245
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.752331 Loss1: 0.976806 Loss2: 1.775526
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.927582 Loss1: 0.574227 Loss2: 1.353355
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736825 Loss1: 0.372197 Loss2: 1.364628
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602101 Loss1: 0.256972 Loss2: 1.345129
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.204316 Loss1: 1.245978 Loss2: 1.958339
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518678 Loss1: 0.181817 Loss2: 1.336861
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.230264 Loss1: 0.774573 Loss2: 1.455690
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.869231 Loss1: 0.411347 Loss2: 1.457884
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473077 Loss1: 0.137235 Loss2: 1.335842
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.681799 Loss1: 0.281004 Loss2: 1.400795
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448504 Loss1: 0.129753 Loss2: 1.318751
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419776 Loss1: 0.098794 Loss2: 1.320982 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395873 Loss1: 0.083632 Loss2: 1.312241 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367929 Loss1: 0.062167 Loss2: 1.305762 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464570 Loss1: 0.087497 Loss2: 1.377074 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.985313 Loss1: 1.062512 Loss2: 1.922801
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.087300 Loss1: 0.624649 Loss2: 1.462651
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.833221 Loss1: 0.362721 Loss2: 1.470500
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.696398 Loss1: 0.266054 Loss2: 1.430344
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.926898 Loss1: 1.010233 Loss2: 1.916664
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.649177 Loss1: 0.215626 Loss2: 1.433550
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.060046 Loss1: 0.591948 Loss2: 1.468097
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.661775 Loss1: 0.238797 Loss2: 1.422978
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.881210 Loss1: 0.390272 Loss2: 1.490938
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.608346 Loss1: 0.180130 Loss2: 1.428216
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.712768 Loss1: 0.283415 Loss2: 1.429353
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.517534 Loss1: 0.104162 Loss2: 1.413372
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.709731 Loss1: 0.256389 Loss2: 1.453341
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489089 Loss1: 0.085894 Loss2: 1.403195
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.638860 Loss1: 0.209128 Loss2: 1.429732
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466137 Loss1: 0.062686 Loss2: 1.403451
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.597911 Loss1: 0.166332 Loss2: 1.431579
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.588730 Loss1: 0.165909 Loss2: 1.422821
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.533035 Loss1: 0.118162 Loss2: 1.414874
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.501033 Loss1: 0.086961 Loss2: 1.414072
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.030054 Loss1: 1.114214 Loss2: 1.915840
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.112307 Loss1: 0.670304 Loss2: 1.442003
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741022 Loss1: 0.325736 Loss2: 1.415287
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.664942 Loss1: 0.266840 Loss2: 1.398103
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.867837 Loss1: 1.014401 Loss2: 1.853436
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.021546 Loss1: 0.621817 Loss2: 1.399729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.852808 Loss1: 0.415998 Loss2: 1.436810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.750581 Loss1: 0.345179 Loss2: 1.405402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.635739 Loss1: 0.223385 Loss2: 1.412353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.548129 Loss1: 0.173361 Loss2: 1.374768 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508900 Loss1: 0.142135 Loss2: 1.366764
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.561735 Loss1: 0.177705 Loss2: 1.384029
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.524702 Loss1: 0.144086 Loss2: 1.380615
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487938 Loss1: 0.118314 Loss2: 1.369624
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.485489 Loss1: 0.112900 Loss2: 1.372589
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.811876 Loss1: 0.991815 Loss2: 1.820061
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.993003 Loss1: 0.573998 Loss2: 1.419005
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.734201 Loss1: 0.342340 Loss2: 1.391861
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.631607 Loss1: 0.258712 Loss2: 1.372896
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.910229 Loss1: 1.018843 Loss2: 1.891387
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566616 Loss1: 0.198679 Loss2: 1.367937
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.067940 Loss1: 0.651608 Loss2: 1.416332
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506624 Loss1: 0.149102 Loss2: 1.357522
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.793169 Loss1: 0.348076 Loss2: 1.445093
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.695562 Loss1: 0.298280 Loss2: 1.397282
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.507933 Loss1: 0.152449 Loss2: 1.355484
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.699992 Loss1: 0.295598 Loss2: 1.404394
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.503813 Loss1: 0.156368 Loss2: 1.347445
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.649191 Loss1: 0.237920 Loss2: 1.411270
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452346 Loss1: 0.105507 Loss2: 1.346839
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.579573 Loss1: 0.190715 Loss2: 1.388858
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.468385 Loss1: 0.121641 Loss2: 1.346744
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.494691 Loss1: 0.107013 Loss2: 1.387678 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.770745 Loss1: 0.888679 Loss2: 1.882066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.843844 Loss1: 0.416277 Loss2: 1.427567 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.689381 Loss1: 0.296086 Loss2: 1.393294
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.953032 Loss1: 0.997676 Loss2: 1.955356
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.010600 Loss1: 0.592504 Loss2: 1.418096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.789062 Loss1: 0.348262 Loss2: 1.440799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.497264 Loss1: 0.129256 Loss2: 1.368008
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.670736 Loss1: 0.256504 Loss2: 1.414232
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.689297 Loss1: 0.277542 Loss2: 1.411756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437561 Loss1: 0.087630 Loss2: 1.349930
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.550137 Loss1: 0.146728 Loss2: 1.403408
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429828 Loss1: 0.079083 Loss2: 1.350745
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504947 Loss1: 0.117782 Loss2: 1.387164
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518422 Loss1: 0.128370 Loss2: 1.390053
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.468708 Loss1: 0.079536 Loss2: 1.389172
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454024 Loss1: 0.072787 Loss2: 1.381237
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.864497 Loss1: 1.005887 Loss2: 1.858610
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.107051 Loss1: 0.684696 Loss2: 1.422355
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.858844 Loss1: 0.450501 Loss2: 1.408342
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.700762 Loss1: 0.300165 Loss2: 1.400597
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.132702 Loss1: 1.137439 Loss2: 1.995264
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.113916 Loss1: 0.686915 Loss2: 1.427001
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.576398 Loss1: 0.193626 Loss2: 1.382772
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.842608 Loss1: 0.388029 Loss2: 1.454578
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.529997 Loss1: 0.155453 Loss2: 1.374544
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470067 Loss1: 0.108062 Loss2: 1.362005 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477705 Loss1: 0.118575 Loss2: 1.359130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439875 Loss1: 0.080010 Loss2: 1.359864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451107 Loss1: 0.100457 Loss2: 1.350650 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450926 Loss1: 0.073173 Loss2: 1.377753 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.041151 Loss1: 1.077606 Loss2: 1.963545
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.155015 Loss1: 0.695191 Loss2: 1.459823
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.966897 Loss1: 0.465950 Loss2: 1.500947
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.810031 Loss1: 0.367794 Loss2: 1.442237
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.043023 Loss1: 1.109749 Loss2: 1.933274
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.746654 Loss1: 0.285631 Loss2: 1.461024
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.118494 Loss1: 0.687815 Loss2: 1.430679
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.649925 Loss1: 0.213335 Loss2: 1.436591
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.922746 Loss1: 0.436503 Loss2: 1.486243
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.635813 Loss1: 0.206329 Loss2: 1.429484
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.715837 Loss1: 0.296075 Loss2: 1.419762
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.628101 Loss1: 0.196914 Loss2: 1.431187
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.652555 Loss1: 0.231638 Loss2: 1.420917
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.586623 Loss1: 0.162049 Loss2: 1.424573
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.604213 Loss1: 0.200593 Loss2: 1.403620
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.531170 Loss1: 0.108718 Loss2: 1.422452
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.549332 Loss1: 0.152766 Loss2: 1.396566
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496479 Loss1: 0.098394 Loss2: 1.398085
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499122 Loss1: 0.117683 Loss2: 1.381439
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448388 Loss1: 0.066907 Loss2: 1.381481
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.855976 Loss1: 0.975996 Loss2: 1.879980
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.984403 Loss1: 0.583090 Loss2: 1.401313
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.833612 Loss1: 0.424716 Loss2: 1.408896
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.708887 Loss1: 0.321759 Loss2: 1.387129
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.039606 Loss1: 1.169901 Loss2: 1.869705
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.100625 Loss1: 0.693844 Loss2: 1.406781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.807868 Loss1: 0.397055 Loss2: 1.410813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.670364 Loss1: 0.289587 Loss2: 1.380776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.552031 Loss1: 0.175105 Loss2: 1.376925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.545343 Loss1: 0.181293 Loss2: 1.364050 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466558 Loss1: 0.127104 Loss2: 1.339454
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533258 Loss1: 0.170785 Loss2: 1.362472
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.530111 Loss1: 0.164067 Loss2: 1.366044
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.559469 Loss1: 0.198308 Loss2: 1.361161
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.514858 Loss1: 0.146721 Loss2: 1.368138
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.943033 Loss1: 1.058482 Loss2: 1.884550
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.077751 Loss1: 0.596566 Loss2: 1.481185
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.840339 Loss1: 0.375619 Loss2: 1.464719
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.698766 Loss1: 0.257454 Loss2: 1.441312
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.038157 Loss1: 1.074971 Loss2: 1.963185
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.144929 Loss1: 0.736519 Loss2: 1.408410
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.622977 Loss1: 0.184423 Loss2: 1.438554
-DEBUG flwr 2023-10-11 00:03:46,361 | server.py:236 | fit_round 94 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.658171 Loss1: 0.236023 Loss2: 1.422148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.614432 Loss1: 0.171637 Loss2: 1.442794 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.637411 Loss1: 0.210623 Loss2: 1.426788 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516809 Loss1: 0.117532 Loss2: 1.399278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.532242 Loss1: 0.142890 Loss2: 1.389353 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973633
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.479831 Loss1: 0.099497 Loss2: 1.380334 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975962
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.041119 Loss1: 1.137238 Loss2: 1.903881
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.173423 Loss1: 0.692209 Loss2: 1.481214
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.876219 Loss1: 0.423264 Loss2: 1.452955
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.793394 Loss1: 0.343003 Loss2: 1.450391
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.069406 Loss1: 1.119702 Loss2: 1.949704
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.651851 Loss1: 0.219009 Loss2: 1.432841
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.192946 Loss1: 0.703196 Loss2: 1.489750
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.616481 Loss1: 0.202247 Loss2: 1.414234
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.880825 Loss1: 0.364350 Loss2: 1.516475
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.546964 Loss1: 0.135336 Loss2: 1.411628
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.763986 Loss1: 0.309029 Loss2: 1.454958
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.559152 Loss1: 0.151974 Loss2: 1.407178
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.694646 Loss1: 0.220715 Loss2: 1.473931
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.576836 Loss1: 0.168211 Loss2: 1.408625
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.588541 Loss1: 0.140912 Loss2: 1.447630
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.537356 Loss1: 0.127650 Loss2: 1.409707
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.541135 Loss1: 0.107659 Loss2: 1.433476
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546361 Loss1: 0.118434 Loss2: 1.427927
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.558717 Loss1: 0.128047 Loss2: 1.430670
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.544676 Loss1: 0.114402 Loss2: 1.430274
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.846647 Loss1: 1.022829 Loss2: 1.823818
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.977326 Loss1: 0.571220 Loss2: 1.406106
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.779660 Loss1: 0.368077 Loss2: 1.411583
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.878867 Loss1: 1.011518 Loss2: 1.867349
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.643775 Loss1: 0.257204 Loss2: 1.386571
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.126799 Loss1: 0.715264 Loss2: 1.411534
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.583234 Loss1: 0.211157 Loss2: 1.372077
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834010 Loss1: 0.405594 Loss2: 1.428416
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.546331 Loss1: 0.168222 Loss2: 1.378109
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.746578 Loss1: 0.348291 Loss2: 1.398287
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.499981 Loss1: 0.133254 Loss2: 1.366727
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568136 Loss1: 0.183884 Loss2: 1.384252
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460967 Loss1: 0.098168 Loss2: 1.362799
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447406 Loss1: 0.090997 Loss2: 1.356409 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437815 Loss1: 0.083485 Loss2: 1.354330 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425903 Loss1: 0.077731 Loss2: 1.348172 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 00:03:46,361][flwr][DEBUG] - fit_round 94 received 50 results and 0 failures
-INFO flwr 2023-10-11 00:04:28,687 | server.py:125 | fit progress: (94, 2.218318693744489, {'accuracy': 0.5643}, 216776.465405886)
->> Test accuracy: 0.564300
-[2023-10-11 00:04:28,687][flwr][INFO] - fit progress: (94, 2.218318693744489, {'accuracy': 0.5643}, 216776.465405886)
-DEBUG flwr 2023-10-11 00:04:28,687 | server.py:173 | evaluate_round 94: strategy sampled 50 clients (out of 50)
-[2023-10-11 00:04:28,687][flwr][DEBUG] - evaluate_round 94: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 00:13:31,844 | server.py:187 | evaluate_round 94 received 50 results and 0 failures
-[2023-10-11 00:13:31,844][flwr][DEBUG] - evaluate_round 94 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 00:13:31,844 | server.py:222 | fit_round 95: strategy sampled 50 clients (out of 50)
-[2023-10-11 00:13:31,844][flwr][DEBUG] - fit_round 95: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.822954 Loss1: 0.987660 Loss2: 1.835294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.851840 Loss1: 0.417095 Loss2: 1.434745 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.697189 Loss1: 0.333671 Loss2: 1.363517
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.810873 Loss1: 0.957553 Loss2: 1.853320
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.626987 Loss1: 0.248032 Loss2: 1.378956
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.055518 Loss1: 0.666728 Loss2: 1.388790
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.550351 Loss1: 0.193823 Loss2: 1.356527
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.902580 Loss1: 0.449637 Loss2: 1.452943
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483449 Loss1: 0.126118 Loss2: 1.357331
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.648437 Loss1: 0.280998 Loss2: 1.367439
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438745 Loss1: 0.093255 Loss2: 1.345491
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598802 Loss1: 0.216607 Loss2: 1.382195
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423174 Loss1: 0.085252 Loss2: 1.337922
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.577547 Loss1: 0.211477 Loss2: 1.366070
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419505 Loss1: 0.082658 Loss2: 1.336846
-(DefaultActor pid=3765) >> Training accuracy: 0.960417
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485885 Loss1: 0.128270 Loss2: 1.357615
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485801 Loss1: 0.134144 Loss2: 1.351657
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462811 Loss1: 0.112356 Loss2: 1.350455
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456013 Loss1: 0.116190 Loss2: 1.339823
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.004930 Loss1: 1.086794 Loss2: 1.918136
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.117869 Loss1: 0.671846 Loss2: 1.446023
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.860419 Loss1: 0.423764 Loss2: 1.436655
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638038 Loss1: 0.224115 Loss2: 1.413923
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.820917 Loss1: 0.993461 Loss2: 1.827455
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558948 Loss1: 0.166236 Loss2: 1.392712
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.054543 Loss1: 0.680939 Loss2: 1.373604
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530118 Loss1: 0.145563 Loss2: 1.384554
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.822629 Loss1: 0.440894 Loss2: 1.381735
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.522198 Loss1: 0.138523 Loss2: 1.383675
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699464 Loss1: 0.334691 Loss2: 1.364772
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.537906 Loss1: 0.144697 Loss2: 1.393210
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556515 Loss1: 0.209586 Loss2: 1.346930
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.506329 Loss1: 0.118180 Loss2: 1.388149
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.547494 Loss1: 0.206388 Loss2: 1.341105
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477955 Loss1: 0.099306 Loss2: 1.378649
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476641 Loss1: 0.133143 Loss2: 1.343498
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440563 Loss1: 0.117827 Loss2: 1.322737
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406166 Loss1: 0.084126 Loss2: 1.322040
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401974 Loss1: 0.083776 Loss2: 1.318199
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.618934 Loss1: 0.826908 Loss2: 1.792026
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.928462 Loss1: 0.554042 Loss2: 1.374420
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684211 Loss1: 0.307228 Loss2: 1.376983
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.993223 Loss1: 1.073312 Loss2: 1.919911
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591034 Loss1: 0.250717 Loss2: 1.340318
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516313 Loss1: 0.172323 Loss2: 1.343990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.500461 Loss1: 0.168964 Loss2: 1.331497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473145 Loss1: 0.144781 Loss2: 1.328363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433660 Loss1: 0.102073 Loss2: 1.331587 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419850 Loss1: 0.097317 Loss2: 1.322532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.489497 Loss1: 0.115951 Loss2: 1.373546 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460201 Loss1: 0.093477 Loss2: 1.366724 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985491
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.832293 Loss1: 1.006532 Loss2: 1.825761
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.033377 Loss1: 0.667005 Loss2: 1.366372
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.747040 Loss1: 0.371194 Loss2: 1.375846
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583593 Loss1: 0.256171 Loss2: 1.327422
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.874619 Loss1: 0.995445 Loss2: 1.879174
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.973997 Loss1: 0.562322 Loss2: 1.411675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.789163 Loss1: 0.349669 Loss2: 1.439494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.675067 Loss1: 0.282930 Loss2: 1.392137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.613646 Loss1: 0.210574 Loss2: 1.403072 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.590455 Loss1: 0.204119 Loss2: 1.386336 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371039 Loss1: 0.073575 Loss2: 1.297464
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.521133 Loss1: 0.129149 Loss2: 1.391984
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488637 Loss1: 0.106001 Loss2: 1.382636
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449463 Loss1: 0.074482 Loss2: 1.374981
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428235 Loss1: 0.059960 Loss2: 1.368275
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.791327 Loss1: 0.914757 Loss2: 1.876569
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.234569 Loss1: 0.750491 Loss2: 1.484079
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.902513 Loss1: 0.445601 Loss2: 1.456912
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.763659 Loss1: 0.331186 Loss2: 1.432473
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.841867 Loss1: 0.940989 Loss2: 1.900878
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.934780 Loss1: 0.489071 Loss2: 1.445709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.798815 Loss1: 0.339183 Loss2: 1.459632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.679774 Loss1: 0.252492 Loss2: 1.427281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.644187 Loss1: 0.215085 Loss2: 1.429102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.631000 Loss1: 0.216945 Loss2: 1.414055 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484191 Loss1: 0.088575 Loss2: 1.395616
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.569010 Loss1: 0.149264 Loss2: 1.419746
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.555474 Loss1: 0.147624 Loss2: 1.407850
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.537555 Loss1: 0.128115 Loss2: 1.409439
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.499035 Loss1: 0.101703 Loss2: 1.397332
-(DefaultActor pid=3764) >> Training accuracy: 0.966797
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.798308 Loss1: 0.880141 Loss2: 1.918168
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.106989 Loss1: 0.661036 Loss2: 1.445953
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.892801 Loss1: 0.398572 Loss2: 1.494229
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.761386 Loss1: 0.321546 Loss2: 1.439840
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.771107 Loss1: 0.879254 Loss2: 1.891854
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.952181 Loss1: 0.536871 Loss2: 1.415310 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.821113 Loss1: 0.347022 Loss2: 1.474091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.685026 Loss1: 0.284569 Loss2: 1.400458 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687564 Loss1: 0.257101 Loss2: 1.430463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.625360 Loss1: 0.214628 Loss2: 1.410731 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467699 Loss1: 0.065765 Loss2: 1.401934
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.669170 Loss1: 0.248643 Loss2: 1.420526
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.631566 Loss1: 0.220938 Loss2: 1.410628
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.566880 Loss1: 0.158151 Loss2: 1.408728
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.521671 Loss1: 0.122807 Loss2: 1.398864
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.680880 Loss1: 0.919044 Loss2: 1.761836
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.966440 Loss1: 0.595787 Loss2: 1.370653
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.755927 Loss1: 0.377626 Loss2: 1.378301
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599336 Loss1: 0.251088 Loss2: 1.348247
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.997474 Loss1: 1.111733 Loss2: 1.885741
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.104526 Loss1: 0.661055 Loss2: 1.443471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505375 Loss1: 0.176811 Loss2: 1.328564
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.822929 Loss1: 0.367307 Loss2: 1.455622
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467699 Loss1: 0.128718 Loss2: 1.338981
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.747110 Loss1: 0.335492 Loss2: 1.411618
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469898 Loss1: 0.141071 Loss2: 1.328827
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.614624 Loss1: 0.188679 Loss2: 1.425945
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.599546 Loss1: 0.195898 Loss2: 1.403648
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448155 Loss1: 0.109590 Loss2: 1.338566
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.555379 Loss1: 0.145414 Loss2: 1.409965
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442125 Loss1: 0.112318 Loss2: 1.329807
-(DefaultActor pid=3765) >> Training accuracy: 0.952148
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.496698 Loss1: 0.101194 Loss2: 1.395504 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.122241 Loss1: 1.184103 Loss2: 1.938138 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.934152 Loss1: 0.467016 Loss2: 1.467136 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.738123 Loss1: 0.285065 Loss2: 1.453058
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.017528 Loss1: 1.068329 Loss2: 1.949199
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.012711 Loss1: 0.655453 Loss2: 1.357258
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.624448 Loss1: 0.191270 Loss2: 1.433178
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.719002 Loss1: 0.320264 Loss2: 1.398738
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.582777 Loss1: 0.154270 Loss2: 1.428507
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.571751 Loss1: 0.144941 Loss2: 1.426810 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.570552 Loss1: 0.145913 Loss2: 1.424639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.553879 Loss1: 0.136086 Loss2: 1.417793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.522736 Loss1: 0.108856 Loss2: 1.413880 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408305 Loss1: 0.091448 Loss2: 1.316857 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.963155 Loss1: 1.019931 Loss2: 1.943223
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.116606 Loss1: 0.612930 Loss2: 1.503677
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.910607 Loss1: 0.420349 Loss2: 1.490259
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.898854 Loss1: 0.962569 Loss2: 1.936285
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.826756 Loss1: 0.344946 Loss2: 1.481811
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.089267 Loss1: 0.610535 Loss2: 1.478733
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.712198 Loss1: 0.248808 Loss2: 1.463390
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.700944 Loss1: 0.248415 Loss2: 1.452528 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.633813 Loss1: 0.165841 Loss2: 1.467973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.560169 Loss1: 0.113053 Loss2: 1.447116 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509382 Loss1: 0.079159 Loss2: 1.430224 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.487042 Loss1: 0.061655 Loss2: 1.425387 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.526995 Loss1: 0.104682 Loss2: 1.422313 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.731452 Loss1: 0.901742 Loss2: 1.829709 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.810542 Loss1: 0.403810 Loss2: 1.406732 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.679415 Loss1: 0.309119 Loss2: 1.370295
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.834557 Loss1: 0.988284 Loss2: 1.846273
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547206 Loss1: 0.170482 Loss2: 1.376724
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.892324 Loss1: 0.505421 Loss2: 1.386903
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.774542 Loss1: 0.382921 Loss2: 1.391620
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542041 Loss1: 0.181997 Loss2: 1.360044
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.673028 Loss1: 0.311865 Loss2: 1.361163
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481364 Loss1: 0.125659 Loss2: 1.355706
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.536009 Loss1: 0.179994 Loss2: 1.356016
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483375 Loss1: 0.130049 Loss2: 1.353326
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493233 Loss1: 0.152676 Loss2: 1.340557
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461774 Loss1: 0.104156 Loss2: 1.357618
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422771 Loss1: 0.070305 Loss2: 1.352466 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410018 Loss1: 0.082925 Loss2: 1.327093 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.979319 Loss1: 1.082389 Loss2: 1.896929 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.885440 Loss1: 0.421675 Loss2: 1.463765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.752434 Loss1: 0.329356 Loss2: 1.423078
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.804338 Loss1: 0.977725 Loss2: 1.826613
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.936179 Loss1: 0.569812 Loss2: 1.366367 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.807751 Loss1: 0.392149 Loss2: 1.415602 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.600182 Loss1: 0.233596 Loss2: 1.366586 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549349 Loss1: 0.190990 Loss2: 1.358359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542542 Loss1: 0.187741 Loss2: 1.354801 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.521857 Loss1: 0.123219 Loss2: 1.398638
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478418 Loss1: 0.130225 Loss2: 1.348193
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473665 Loss1: 0.128108 Loss2: 1.345558
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444658 Loss1: 0.104396 Loss2: 1.340261
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433779 Loss1: 0.093653 Loss2: 1.340127
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.964091 Loss1: 1.093760 Loss2: 1.870331
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.043904 Loss1: 0.629578 Loss2: 1.414325
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.872447 Loss1: 0.445807 Loss2: 1.426639
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694481 Loss1: 0.303336 Loss2: 1.391145
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.815605 Loss1: 0.951262 Loss2: 1.864342
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.866556 Loss1: 0.462270 Loss2: 1.404286 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.660790 Loss1: 0.273626 Loss2: 1.387164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.628911 Loss1: 0.254318 Loss2: 1.374593 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.581829 Loss1: 0.218188 Loss2: 1.363641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434232 Loss1: 0.085999 Loss2: 1.348233 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467983 Loss1: 0.117737 Loss2: 1.350246 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423718 Loss1: 0.076555 Loss2: 1.347163 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987132
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.063693 Loss1: 0.630160 Loss2: 1.433533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.669063 Loss1: 0.269071 Loss2: 1.399992 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.557900 Loss1: 0.160638 Loss2: 1.397261
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.902986 Loss1: 1.026634 Loss2: 1.876352
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.119285 Loss1: 0.684164 Loss2: 1.435121 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.910454 Loss1: 0.450184 Loss2: 1.460269 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.751461 Loss1: 0.339143 Loss2: 1.412318 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.705989 Loss1: 0.274046 Loss2: 1.431943 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442668 Loss1: 0.076521 Loss2: 1.366147
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.610444 Loss1: 0.205619 Loss2: 1.404825
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.616559 Loss1: 0.215480 Loss2: 1.401079
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.563985 Loss1: 0.174752 Loss2: 1.389233
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.542707 Loss1: 0.152061 Loss2: 1.390646
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.683862 Loss1: 0.265323 Loss2: 1.418539
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.738149 Loss1: 0.903856 Loss2: 1.834293
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.982460 Loss1: 0.603766 Loss2: 1.378694
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.806821 Loss1: 0.392597 Loss2: 1.414224
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642889 Loss1: 0.281995 Loss2: 1.360894
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.584908 Loss1: 0.207789 Loss2: 1.377120
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509357 Loss1: 0.158916 Loss2: 1.350441 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511479 Loss1: 0.161117 Loss2: 1.350362 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495687 Loss1: 0.151242 Loss2: 1.344445 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450694 Loss1: 0.107119 Loss2: 1.343576 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435291 Loss1: 0.099599 Loss2: 1.335692 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530461 Loss1: 0.157709 Loss2: 1.372752 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519071 Loss1: 0.142370 Loss2: 1.376701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.511420 Loss1: 0.147108 Loss2: 1.364312
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.789178 Loss1: 0.943986 Loss2: 1.845192
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.897059 Loss1: 0.541430 Loss2: 1.355629
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.692691 Loss1: 0.318289 Loss2: 1.374402
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555602 Loss1: 0.215991 Loss2: 1.339611
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519373 Loss1: 0.184713 Loss2: 1.334660
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509595 Loss1: 0.183232 Loss2: 1.326362
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.032346 Loss1: 1.064103 Loss2: 1.968243
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441855 Loss1: 0.119365 Loss2: 1.322490
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.122232 Loss1: 0.613578 Loss2: 1.508654
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429198 Loss1: 0.120618 Loss2: 1.308580
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.983249 Loss1: 0.446364 Loss2: 1.536886
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.382741 Loss1: 0.076345 Loss2: 1.306396
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.813576 Loss1: 0.327649 Loss2: 1.485927
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393040 Loss1: 0.091800 Loss2: 1.301240
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.742569 Loss1: 0.249937 Loss2: 1.492632
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.734907 Loss1: 0.251878 Loss2: 1.483028
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.728013 Loss1: 0.243918 Loss2: 1.484095
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.653814 Loss1: 0.168479 Loss2: 1.485335
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.633682 Loss1: 0.159256 Loss2: 1.474427
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.030204 Loss1: 1.112182 Loss2: 1.918022
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.660511 Loss1: 0.187108 Loss2: 1.473403
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.805253 Loss1: 0.349076 Loss2: 1.456177 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587564 Loss1: 0.170217 Loss2: 1.417347 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530381 Loss1: 0.126367 Loss2: 1.404014
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.865062 Loss1: 1.019237 Loss2: 1.845825
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.572387 Loss1: 0.170929 Loss2: 1.401458
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.952119 Loss1: 0.594337 Loss2: 1.357783
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.533864 Loss1: 0.131983 Loss2: 1.401881
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769917 Loss1: 0.378505 Loss2: 1.391412
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488853 Loss1: 0.092368 Loss2: 1.396484
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636893 Loss1: 0.299352 Loss2: 1.337541
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484212 Loss1: 0.085482 Loss2: 1.398730
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556060 Loss1: 0.204409 Loss2: 1.351650
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528470 Loss1: 0.191866 Loss2: 1.336603
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.525050 Loss1: 0.193617 Loss2: 1.331433
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467144 Loss1: 0.140966 Loss2: 1.326179
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451032 Loss1: 0.128056 Loss2: 1.322976
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.974626 Loss1: 1.017650 Loss2: 1.956977
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390404 Loss1: 0.070068 Loss2: 1.320335
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.049840 Loss1: 0.507249 Loss2: 1.542592 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.684325 Loss1: 0.270064 Loss2: 1.414261 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.603443 Loss1: 0.205702 Loss2: 1.397741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479249 Loss1: 0.083368 Loss2: 1.395882 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463596 Loss1: 0.082314 Loss2: 1.381282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.487069 Loss1: 0.111215 Loss2: 1.375854 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.573501 Loss1: 0.194008 Loss2: 1.379494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.524111 Loss1: 0.153918 Loss2: 1.370192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.039775 Loss1: 1.173807 Loss2: 1.865967 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.066302 Loss1: 0.643891 Loss2: 1.422411 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.639771 Loss1: 0.264752 Loss2: 1.375019 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.521511 Loss1: 0.157073 Loss2: 1.364438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490826 Loss1: 0.127702 Loss2: 1.363124
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.815635 Loss1: 0.955311 Loss2: 1.860324
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.919016 Loss1: 0.541770 Loss2: 1.377246 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.765971 Loss1: 0.349882 Loss2: 1.416089 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640013 Loss1: 0.263264 Loss2: 1.376749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503032 Loss1: 0.139543 Loss2: 1.363490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417164 Loss1: 0.075925 Loss2: 1.341239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395958 Loss1: 0.060959 Loss2: 1.334999 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413107 Loss1: 0.079785 Loss2: 1.333323 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.776751 Loss1: 0.264110 Loss2: 1.512641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.698684 Loss1: 0.192210 Loss2: 1.506474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.636348 Loss1: 0.136659 Loss2: 1.499690
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.842322 Loss1: 1.011612 Loss2: 1.830711
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.002303 Loss1: 0.607722 Loss2: 1.394582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.823614 Loss1: 0.415427 Loss2: 1.408187 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.556339 Loss1: 0.077390 Loss2: 1.478949
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.668101 Loss1: 0.306635 Loss2: 1.361466
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.623442 Loss1: 0.260542 Loss2: 1.362901
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582960 Loss1: 0.224705 Loss2: 1.358255
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513380 Loss1: 0.145239 Loss2: 1.368141
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466586 Loss1: 0.122570 Loss2: 1.344016
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.063841 Loss1: 1.094701 Loss2: 1.969140
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420943 Loss1: 0.085043 Loss2: 1.335901
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.063860 Loss1: 0.556325 Loss2: 1.507535
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422384 Loss1: 0.089387 Loss2: 1.332997
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.819856 Loss1: 0.333243 Loss2: 1.486613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.685664 Loss1: 0.220311 Loss2: 1.465352 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.605724 Loss1: 0.143025 Loss2: 1.462700
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.807414 Loss1: 1.032208 Loss2: 1.775207
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.019534 Loss1: 0.614516 Loss2: 1.405018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.731310 Loss1: 0.379712 Loss2: 1.351597 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.597925 Loss1: 0.261136 Loss2: 1.336789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478198 Loss1: 0.154285 Loss2: 1.323912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453420 Loss1: 0.137484 Loss2: 1.315936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395988 Loss1: 0.084500 Loss2: 1.311488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378035 Loss1: 0.070450 Loss2: 1.307585 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.543899 Loss1: 0.160888 Loss2: 1.383011 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478163 Loss1: 0.107974 Loss2: 1.370189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488425 Loss1: 0.122582 Loss2: 1.365843 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462357 Loss1: 0.105153 Loss2: 1.357204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447517 Loss1: 0.090079 Loss2: 1.357438 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.602609 Loss1: 0.172187 Loss2: 1.430422 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.519194 Loss1: 0.111748 Loss2: 1.407445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482687 Loss1: 0.083504 Loss2: 1.399183 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975260
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.809137 Loss1: 0.390446 Loss2: 1.418691 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.569141 Loss1: 0.179143 Loss2: 1.389997 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.991994 Loss1: 1.100382 Loss2: 1.891611
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523292 Loss1: 0.146825 Loss2: 1.376468
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.021283 Loss1: 0.607827 Loss2: 1.413455
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483023 Loss1: 0.117080 Loss2: 1.365943
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.805916 Loss1: 0.387004 Loss2: 1.418912
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457306 Loss1: 0.097519 Loss2: 1.359787
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.727614 Loss1: 0.332333 Loss2: 1.395282
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.446865 Loss1: 0.094641 Loss2: 1.352224
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.632950 Loss1: 0.229511 Loss2: 1.403438
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.559469 Loss1: 0.185054 Loss2: 1.374416
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518986 Loss1: 0.143089 Loss2: 1.375897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463783 Loss1: 0.102011 Loss2: 1.361771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431806 Loss1: 0.073474 Loss2: 1.358331
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.930217 Loss1: 1.107120 Loss2: 1.823097
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.045166 Loss1: 0.613741 Loss2: 1.431425
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.866382 Loss1: 0.455175 Loss2: 1.411207
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.728501 Loss1: 0.333013 Loss2: 1.395488
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.619433 Loss1: 0.236818 Loss2: 1.382614
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.909173 Loss1: 1.147548 Loss2: 1.761626
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.024971 Loss1: 0.682644 Loss2: 1.342326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.797694 Loss1: 0.445447 Loss2: 1.352247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.644372 Loss1: 0.328661 Loss2: 1.315711 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.605529 Loss1: 0.279711 Loss2: 1.325818 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971680
-DEBUG flwr 2023-10-11 00:42:34,448 | server.py:236 | fit_round 95 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435599 Loss1: 0.086387 Loss2: 1.349213
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.532568 Loss1: 0.223262 Loss2: 1.309306
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.492238 Loss1: 0.179881 Loss2: 1.312357
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453016 Loss1: 0.145386 Loss2: 1.307630
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438957 Loss1: 0.142786 Loss2: 1.296172
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428654 Loss1: 0.129348 Loss2: 1.299306
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.991952 Loss1: 1.100472 Loss2: 1.891480
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.016379 Loss1: 0.608633 Loss2: 1.407746
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.877707 Loss1: 0.437612 Loss2: 1.440095
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649455 Loss1: 0.257527 Loss2: 1.391928
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589560 Loss1: 0.199325 Loss2: 1.390235
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.095850 Loss1: 1.198527 Loss2: 1.897323
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.157588 Loss1: 0.753413 Loss2: 1.404176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.925637 Loss1: 0.488370 Loss2: 1.437267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480206 Loss1: 0.112449 Loss2: 1.367757
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.739309 Loss1: 0.366618 Loss2: 1.372691
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.500101 Loss1: 0.129690 Loss2: 1.370410
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.616891 Loss1: 0.220776 Loss2: 1.396115
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467617 Loss1: 0.105975 Loss2: 1.361642
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.553862 Loss1: 0.205040 Loss2: 1.348822
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483023 Loss1: 0.127378 Loss2: 1.355645
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459846 Loss1: 0.119320 Loss2: 1.340526
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436405 Loss1: 0.102039 Loss2: 1.334366
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424070 Loss1: 0.088721 Loss2: 1.335349
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.057047 Loss1: 1.122039 Loss2: 1.935008
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.005792 Loss1: 0.609485 Loss2: 1.396307
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.813355 Loss1: 0.382941 Loss2: 1.430414
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.692810 Loss1: 0.301698 Loss2: 1.391111
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.628564 Loss1: 0.234815 Loss2: 1.393749
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.553556 Loss1: 0.169032 Loss2: 1.384524 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484479 Loss1: 0.109673 Loss2: 1.374806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460161 Loss1: 0.098059 Loss2: 1.362102 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439057 Loss1: 0.082671 Loss2: 1.356386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431543 Loss1: 0.080944 Loss2: 1.350599 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986607
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.532032 Loss1: 0.138967 Loss2: 1.393065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.506105 Loss1: 0.120409 Loss2: 1.385697 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 00:42:34,448][flwr][DEBUG] - fit_round 95 received 50 results and 0 failures
-INFO flwr 2023-10-11 00:43:15,405 | server.py:125 | fit progress: (95, 2.222508845047448, {'accuracy': 0.5634}, 219103.184059672)
->> Test accuracy: 0.563400
-[2023-10-11 00:43:15,405][flwr][INFO] - fit progress: (95, 2.222508845047448, {'accuracy': 0.5634}, 219103.184059672)
-DEBUG flwr 2023-10-11 00:43:15,406 | server.py:173 | evaluate_round 95: strategy sampled 50 clients (out of 50)
-[2023-10-11 00:43:15,406][flwr][DEBUG] - evaluate_round 95: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 00:52:22,688 | server.py:187 | evaluate_round 95 received 50 results and 0 failures
-[2023-10-11 00:52:22,688][flwr][DEBUG] - evaluate_round 95 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 00:52:22,689 | server.py:222 | fit_round 96: strategy sampled 50 clients (out of 50)
-[2023-10-11 00:52:22,689][flwr][DEBUG] - fit_round 96: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.861972 Loss1: 0.990679 Loss2: 1.871293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.841317 Loss1: 0.402289 Loss2: 1.439028 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.719242 Loss1: 0.324303 Loss2: 1.394939
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.984330 Loss1: 1.128538 Loss2: 1.855792
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.185322 Loss1: 0.741339 Loss2: 1.443983
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.644125 Loss1: 0.241638 Loss2: 1.402487
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.852685 Loss1: 0.440713 Loss2: 1.411972
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.567630 Loss1: 0.185419 Loss2: 1.382211
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.713266 Loss1: 0.310342 Loss2: 1.402923
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528704 Loss1: 0.143923 Loss2: 1.384781
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.587673 Loss1: 0.202409 Loss2: 1.385265
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.466964 Loss1: 0.095989 Loss2: 1.370975
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459889 Loss1: 0.093779 Loss2: 1.366110 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469004 Loss1: 0.107644 Loss2: 1.361360 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.478643 Loss1: 0.120428 Loss2: 1.358215 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.890360 Loss1: 1.033130 Loss2: 1.857230 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.760214 Loss1: 0.343102 Loss2: 1.417112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.710249 Loss1: 0.330731 Loss2: 1.379518
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.924347 Loss1: 0.980556 Loss2: 1.943792
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.569633 Loss1: 0.186750 Loss2: 1.382883
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.009634 Loss1: 0.567278 Loss2: 1.442356
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489177 Loss1: 0.126490 Loss2: 1.362687
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.833846 Loss1: 0.352762 Loss2: 1.481085
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455424 Loss1: 0.096028 Loss2: 1.359396
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.717526 Loss1: 0.284293 Loss2: 1.433233
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480687 Loss1: 0.126511 Loss2: 1.354176
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.606289 Loss1: 0.174813 Loss2: 1.431476
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.479023 Loss1: 0.119475 Loss2: 1.359548
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.583522 Loss1: 0.168702 Loss2: 1.414820
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.479499 Loss1: 0.126224 Loss2: 1.353275
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.575124 Loss1: 0.166424 Loss2: 1.408700
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.531112 Loss1: 0.113029 Loss2: 1.418084
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493668 Loss1: 0.094637 Loss2: 1.399032
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.485911 Loss1: 0.085837 Loss2: 1.400074
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.689856 Loss1: 0.871765 Loss2: 1.818092
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.951668 Loss1: 0.517467 Loss2: 1.434201
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.733310 Loss1: 0.345129 Loss2: 1.388182
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.651085 Loss1: 0.263848 Loss2: 1.387237
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.895328 Loss1: 1.050257 Loss2: 1.845071
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.030297 Loss1: 0.632103 Loss2: 1.398194
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.644021 Loss1: 0.259528 Loss2: 1.384493
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.796379 Loss1: 0.380557 Loss2: 1.415822
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.544945 Loss1: 0.168677 Loss2: 1.376269
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.651782 Loss1: 0.282413 Loss2: 1.369369
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486961 Loss1: 0.120949 Loss2: 1.366012
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.642731 Loss1: 0.268578 Loss2: 1.374154
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438978 Loss1: 0.085652 Loss2: 1.353325
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.415424 Loss1: 0.065611 Loss2: 1.349813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426379 Loss1: 0.080544 Loss2: 1.345834 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.533434 Loss1: 0.173574 Loss2: 1.359860 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.963542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.986517 Loss1: 1.118534 Loss2: 1.867982 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.817043 Loss1: 0.399868 Loss2: 1.417175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.704775 Loss1: 0.318800 Loss2: 1.385975
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.936860 Loss1: 1.108907 Loss2: 1.827953
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.615905 Loss1: 0.227221 Loss2: 1.388684
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.054302 Loss1: 0.654079 Loss2: 1.400222
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587127 Loss1: 0.214711 Loss2: 1.372415
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.900811 Loss1: 0.466767 Loss2: 1.434044
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.566103 Loss1: 0.197839 Loss2: 1.368264
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.710043 Loss1: 0.330694 Loss2: 1.379349
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491293 Loss1: 0.124404 Loss2: 1.366890
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.615279 Loss1: 0.245109 Loss2: 1.370170
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484951 Loss1: 0.135352 Loss2: 1.349599
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530847 Loss1: 0.170592 Loss2: 1.360254
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452279 Loss1: 0.092501 Loss2: 1.359778
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475965 Loss1: 0.121583 Loss2: 1.354382
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469586 Loss1: 0.121495 Loss2: 1.348091
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485433 Loss1: 0.133631 Loss2: 1.351802
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456261 Loss1: 0.109940 Loss2: 1.346321
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.904294 Loss1: 0.923246 Loss2: 1.981048
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.982043 Loss1: 0.520990 Loss2: 1.461053
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.862639 Loss1: 0.369746 Loss2: 1.492892
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.702798 Loss1: 0.252482 Loss2: 1.450316
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.058182 Loss1: 1.130200 Loss2: 1.927982
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605824 Loss1: 0.169868 Loss2: 1.435956
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.089305 Loss1: 0.682164 Loss2: 1.407142
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576676 Loss1: 0.145460 Loss2: 1.431215
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.902331 Loss1: 0.445073 Loss2: 1.457259
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.697556 Loss1: 0.303172 Loss2: 1.394384
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534488 Loss1: 0.111697 Loss2: 1.422791
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.663027 Loss1: 0.259758 Loss2: 1.403269
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.529200 Loss1: 0.110178 Loss2: 1.419022
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.581206 Loss1: 0.189266 Loss2: 1.391940
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.503386 Loss1: 0.085573 Loss2: 1.417813
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.512565 Loss1: 0.096621 Loss2: 1.415944 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462673 Loss1: 0.092422 Loss2: 1.370251 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.861778 Loss1: 1.041789 Loss2: 1.819989 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.779906 Loss1: 0.375400 Loss2: 1.404506 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.621396 Loss1: 0.258041 Loss2: 1.363355
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.849407 Loss1: 0.942642 Loss2: 1.906764
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.032011 Loss1: 0.614186 Loss2: 1.417825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.814544 Loss1: 0.369594 Loss2: 1.444950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.693607 Loss1: 0.295650 Loss2: 1.397957 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.635152 Loss1: 0.226469 Loss2: 1.408684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.543124 Loss1: 0.150946 Loss2: 1.392178 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410298 Loss1: 0.075028 Loss2: 1.335270
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.523960 Loss1: 0.139280 Loss2: 1.384679
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510690 Loss1: 0.126898 Loss2: 1.383792
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.478074 Loss1: 0.103685 Loss2: 1.374389
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449427 Loss1: 0.077298 Loss2: 1.372129
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.859497 Loss1: 0.963733 Loss2: 1.895764
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.090044 Loss1: 0.667123 Loss2: 1.422922
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.819990 Loss1: 0.360294 Loss2: 1.459696
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.720767 Loss1: 0.311211 Loss2: 1.409555
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.949897 Loss1: 1.063438 Loss2: 1.886459
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.653996 Loss1: 0.237934 Loss2: 1.416063
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.030723 Loss1: 0.675726 Loss2: 1.354997
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.798631 Loss1: 0.392750 Loss2: 1.405880
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.612241 Loss1: 0.210092 Loss2: 1.402149
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529651 Loss1: 0.123916 Loss2: 1.405736 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.504522 Loss1: 0.110094 Loss2: 1.394428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489094 Loss1: 0.104022 Loss2: 1.385072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454020 Loss1: 0.079278 Loss2: 1.374742 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433411 Loss1: 0.108078 Loss2: 1.325334 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980769
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.089612 Loss1: 1.155418 Loss2: 1.934193
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.017424 Loss1: 0.608662 Loss2: 1.408762
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.823074 Loss1: 0.378025 Loss2: 1.445048
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629022 Loss1: 0.236629 Loss2: 1.392393
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.984753 Loss1: 1.047618 Loss2: 1.937135
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.000662 Loss1: 0.615990 Loss2: 1.384672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.787770 Loss1: 0.353794 Loss2: 1.433976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.615062 Loss1: 0.243198 Loss2: 1.371864 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558600 Loss1: 0.199047 Loss2: 1.359553 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.485744 Loss1: 0.118230 Loss2: 1.367514
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501561 Loss1: 0.133847 Loss2: 1.367714
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459175 Loss1: 0.089157 Loss2: 1.370017
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.507933 Loss1: 0.151259 Loss2: 1.356674
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546947 Loss1: 0.190749 Loss2: 1.356198
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497726 Loss1: 0.134294 Loss2: 1.363432
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454983 Loss1: 0.105993 Loss2: 1.348990
-(DefaultActor pid=3764) >> Training accuracy: 0.981971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.752916 Loss1: 0.958082 Loss2: 1.794835
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.883102 Loss1: 0.520398 Loss2: 1.362704
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648318 Loss1: 0.295446 Loss2: 1.352872
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.537418 Loss1: 0.220502 Loss2: 1.316917
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.752348 Loss1: 0.885651 Loss2: 1.866697
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.082270 Loss1: 0.685128 Loss2: 1.397142
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487916 Loss1: 0.171309 Loss2: 1.316608
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.845206 Loss1: 0.393907 Loss2: 1.451299
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480020 Loss1: 0.166923 Loss2: 1.313096
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.718550 Loss1: 0.330577 Loss2: 1.387973
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441240 Loss1: 0.135903 Loss2: 1.305337
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.583935 Loss1: 0.178926 Loss2: 1.405009
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414165 Loss1: 0.105882 Loss2: 1.308283
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395896 Loss1: 0.095995 Loss2: 1.299901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452342 Loss1: 0.147981 Loss2: 1.304361 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.517369 Loss1: 0.136406 Loss2: 1.380963 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.832963 Loss1: 1.005777 Loss2: 1.827187 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.685335 Loss1: 0.288676 Loss2: 1.396659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.657386 Loss1: 0.305006 Loss2: 1.352381
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.894545 Loss1: 0.986986 Loss2: 1.907559
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.097350 Loss1: 0.618005 Loss2: 1.479345 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569461 Loss1: 0.213723 Loss2: 1.355738
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.903979 Loss1: 0.411767 Loss2: 1.492212
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.728955 Loss1: 0.284198 Loss2: 1.444758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.694544 Loss1: 0.251535 Loss2: 1.443009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647423 Loss1: 0.205521 Loss2: 1.441902 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457151 Loss1: 0.116358 Loss2: 1.340793
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.611614 Loss1: 0.169520 Loss2: 1.442093
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.597070 Loss1: 0.160875 Loss2: 1.436196
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.567581 Loss1: 0.139433 Loss2: 1.428148
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529573 Loss1: 0.108359 Loss2: 1.421214
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.756410 Loss1: 0.850780 Loss2: 1.905630
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.896959 Loss1: 0.502869 Loss2: 1.394090
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741671 Loss1: 0.325730 Loss2: 1.415941
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.140869 Loss1: 1.139434 Loss2: 2.001436
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629723 Loss1: 0.241909 Loss2: 1.387814
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.590407 Loss1: 0.205417 Loss2: 1.384990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503213 Loss1: 0.133051 Loss2: 1.370162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.498040 Loss1: 0.132557 Loss2: 1.365483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.563603 Loss1: 0.185301 Loss2: 1.378301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449098 Loss1: 0.095652 Loss2: 1.353447 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431060 Loss1: 0.079693 Loss2: 1.351367 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.682263 Loss1: 0.930520 Loss2: 1.751743 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.697286 Loss1: 0.339325 Loss2: 1.357961 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.259885 Loss1: 1.262556 Loss2: 1.997329
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.645839 Loss1: 0.294116 Loss2: 1.351723
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.239852 Loss1: 0.721385 Loss2: 1.518467
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.600887 Loss1: 0.243238 Loss2: 1.357649
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.962596 Loss1: 0.437172 Loss2: 1.525424
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.577641 Loss1: 0.234591 Loss2: 1.343051
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491355 Loss1: 0.153008 Loss2: 1.338347 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499400 Loss1: 0.174527 Loss2: 1.324873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.511793 Loss1: 0.175991 Loss2: 1.335801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452598 Loss1: 0.125410 Loss2: 1.327188 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.974609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.542404 Loss1: 0.102542 Loss2: 1.439862 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.888994 Loss1: 1.030987 Loss2: 1.858006 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.801078 Loss1: 0.377445 Loss2: 1.423634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.849311 Loss1: 1.033983 Loss2: 1.815328
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.618783 Loss1: 0.227766 Loss2: 1.391017
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.996328 Loss1: 0.605381 Loss2: 1.390947
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562491 Loss1: 0.185316 Loss2: 1.377175
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.772610 Loss1: 0.402686 Loss2: 1.369924
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.517496 Loss1: 0.142428 Loss2: 1.375069
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.515114 Loss1: 0.144391 Loss2: 1.370723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.505715 Loss1: 0.131756 Loss2: 1.373960 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472908 Loss1: 0.110016 Loss2: 1.362892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469858 Loss1: 0.107726 Loss2: 1.362131 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476148 Loss1: 0.143938 Loss2: 1.332210 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.776917 Loss1: 0.910151 Loss2: 1.866766 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.794112 Loss1: 0.344355 Loss2: 1.449758 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.687299 Loss1: 0.301464 Loss2: 1.385835
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.915715 Loss1: 1.056556 Loss2: 1.859159
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.635908 Loss1: 0.242705 Loss2: 1.393203
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.037819 Loss1: 0.646645 Loss2: 1.391174
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.536770 Loss1: 0.167514 Loss2: 1.369256
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.786983 Loss1: 0.349902 Loss2: 1.437081
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483689 Loss1: 0.123045 Loss2: 1.360644
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.682686 Loss1: 0.307642 Loss2: 1.375045
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.512635 Loss1: 0.145878 Loss2: 1.366757
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.617973 Loss1: 0.235466 Loss2: 1.382507
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458648 Loss1: 0.098726 Loss2: 1.359922
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.581707 Loss1: 0.197291 Loss2: 1.384416
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420540 Loss1: 0.068650 Loss2: 1.351890
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.554536 Loss1: 0.186360 Loss2: 1.368176
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.499165 Loss1: 0.131521 Loss2: 1.367643
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.541577 Loss1: 0.176410 Loss2: 1.365167
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.499588 Loss1: 0.135326 Loss2: 1.364262
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.824756 Loss1: 0.973680 Loss2: 1.851076
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.067460 Loss1: 0.641356 Loss2: 1.426104
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.761734 Loss1: 0.340515 Loss2: 1.421219
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.633636 Loss1: 0.241690 Loss2: 1.391946
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.227443 Loss1: 1.043379 Loss2: 2.184064
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.591088 Loss1: 0.195082 Loss2: 1.396005
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.370016 Loss1: 0.680472 Loss2: 1.689544
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538196 Loss1: 0.166954 Loss2: 1.371242
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.184090 Loss1: 0.446893 Loss2: 1.737197
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512991 Loss1: 0.143920 Loss2: 1.369071
-(DefaultActor pid=3764) Epoch: 3 Loss: 2.032748 Loss1: 0.370563 Loss2: 1.662185
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481561 Loss1: 0.117253 Loss2: 1.364308
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.944613 Loss1: 0.264126 Loss2: 1.680487
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.475807 Loss1: 0.116428 Loss2: 1.359379
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.871290 Loss1: 0.219990 Loss2: 1.651300
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415684 Loss1: 0.054296 Loss2: 1.361388
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.813845 Loss1: 0.175105 Loss2: 1.638740
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.739844 Loss1: 0.109211 Loss2: 1.630632
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.731516 Loss1: 0.114167 Loss2: 1.617349
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.693149 Loss1: 0.080749 Loss2: 1.612400
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.874216 Loss1: 0.967013 Loss2: 1.907204
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.117784 Loss1: 0.693991 Loss2: 1.423793
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.885875 Loss1: 0.432813 Loss2: 1.453062
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.723955 Loss1: 0.310898 Loss2: 1.413057
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.952359 Loss1: 1.102297 Loss2: 1.850061
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.936292 Loss1: 0.532713 Loss2: 1.403580 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.746224 Loss1: 0.344024 Loss2: 1.402200 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.645836 Loss1: 0.275458 Loss2: 1.370378 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576387 Loss1: 0.209306 Loss2: 1.367081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519589 Loss1: 0.156294 Loss2: 1.363295 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459539 Loss1: 0.085410 Loss2: 1.374129
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484021 Loss1: 0.133523 Loss2: 1.350498
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454007 Loss1: 0.103866 Loss2: 1.350142
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418038 Loss1: 0.072536 Loss2: 1.345502
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410084 Loss1: 0.072041 Loss2: 1.338043
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.628872 Loss1: 0.873504 Loss2: 1.755368
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.841315 Loss1: 0.540856 Loss2: 1.300458
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.689226 Loss1: 0.341981 Loss2: 1.347245
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591237 Loss1: 0.294606 Loss2: 1.296631
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.768561 Loss1: 0.840683 Loss2: 1.927878
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.977698 Loss1: 0.564401 Loss2: 1.413297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.818365 Loss1: 0.346790 Loss2: 1.471574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.688724 Loss1: 0.281166 Loss2: 1.407558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.653144 Loss1: 0.225253 Loss2: 1.427891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.562505 Loss1: 0.159269 Loss2: 1.403236 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380454 Loss1: 0.110940 Loss2: 1.269515
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.557819 Loss1: 0.157957 Loss2: 1.399863
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.505763 Loss1: 0.103893 Loss2: 1.401870
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.532758 Loss1: 0.133186 Loss2: 1.399573
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.498973 Loss1: 0.098859 Loss2: 1.400114
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.965592 Loss1: 1.073552 Loss2: 1.892040
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.041686 Loss1: 0.613023 Loss2: 1.428663
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.773378 Loss1: 0.362987 Loss2: 1.410391
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.676854 Loss1: 0.277139 Loss2: 1.399715
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.635446 Loss1: 0.901202 Loss2: 1.734244
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.973618 Loss1: 0.621988 Loss2: 1.351631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.768428 Loss1: 0.412891 Loss2: 1.355536 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.625140 Loss1: 0.302199 Loss2: 1.322941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.533136 Loss1: 0.220767 Loss2: 1.312369 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484078 Loss1: 0.180915 Loss2: 1.303163 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.394998 Loss1: 0.097357 Loss2: 1.297641 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.346592 Loss1: 0.066600 Loss2: 1.279992 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.033578 Loss1: 0.642884 Loss2: 1.390694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.737218 Loss1: 0.342871 Loss2: 1.394347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.067032 Loss1: 1.130683 Loss2: 1.936348
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.645435 Loss1: 0.268238 Loss2: 1.377197
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.100397 Loss1: 0.665423 Loss2: 1.434974
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.552795 Loss1: 0.185296 Loss2: 1.367499
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.925705 Loss1: 0.452803 Loss2: 1.472902
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.515909 Loss1: 0.148036 Loss2: 1.367873
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.713808 Loss1: 0.295952 Loss2: 1.417856
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467065 Loss1: 0.109553 Loss2: 1.357512
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.628818 Loss1: 0.205955 Loss2: 1.422863
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432234 Loss1: 0.089454 Loss2: 1.342780
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.580931 Loss1: 0.173909 Loss2: 1.407021
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441585 Loss1: 0.098409 Loss2: 1.343176
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.507634 Loss1: 0.112671 Loss2: 1.394963 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.474578 Loss1: 0.089532 Loss2: 1.385046 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.916298 Loss1: 0.502550 Loss2: 1.413748 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587862 Loss1: 0.196993 Loss2: 1.390869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521112 Loss1: 0.144723 Loss2: 1.376389
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.585458 Loss1: 0.829737 Loss2: 1.755722
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.490964 Loss1: 0.120211 Loss2: 1.370753
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.842512 Loss1: 0.507612 Loss2: 1.334900
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684056 Loss1: 0.330768 Loss2: 1.353288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.638047 Loss1: 0.305870 Loss2: 1.332177 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544127 Loss1: 0.216275 Loss2: 1.327852 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513917 Loss1: 0.186143 Loss2: 1.327774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482309 Loss1: 0.166596 Loss2: 1.315713 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.856458 Loss1: 1.005970 Loss2: 1.850488 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981618
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.795691 Loss1: 0.356817 Loss2: 1.438873 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.588667 Loss1: 0.202606 Loss2: 1.386061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.517686 Loss1: 0.147852 Loss2: 1.369834
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.935313 Loss1: 1.035336 Loss2: 1.899977
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481771 Loss1: 0.118426 Loss2: 1.363345
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.055486 Loss1: 0.638267 Loss2: 1.417219
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457696 Loss1: 0.100317 Loss2: 1.357380
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.830871 Loss1: 0.365384 Loss2: 1.465487
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.479125 Loss1: 0.117568 Loss2: 1.361557
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.710906 Loss1: 0.292323 Loss2: 1.418584
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.503103 Loss1: 0.145822 Loss2: 1.357281
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598508 Loss1: 0.169999 Loss2: 1.428509
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.574310 Loss1: 0.170235 Loss2: 1.404076
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.527229 Loss1: 0.122712 Loss2: 1.404517
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.534534 Loss1: 0.143116 Loss2: 1.391418
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.522407 Loss1: 0.124896 Loss2: 1.397511
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.177670 Loss1: 1.233525 Loss2: 1.944145
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.538010 Loss1: 0.131124 Loss2: 1.406886
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.894133 Loss1: 0.434313 Loss2: 1.459820 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.615348 Loss1: 0.204939 Loss2: 1.410409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.022524 Loss1: 1.078441 Loss2: 1.944084 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.161945 Loss1: 0.672414 Loss2: 1.489531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.796382 Loss1: 0.380659 Loss2: 1.415723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.727540 Loss1: 0.295406 Loss2: 1.432134 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560759 Loss1: 0.156092 Loss2: 1.404666 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465965 Loss1: 0.080034 Loss2: 1.385931 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499170 Loss1: 0.115471 Loss2: 1.383700
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.733284 Loss1: 0.900160 Loss2: 1.833124
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460574 Loss1: 0.078705 Loss2: 1.381869
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.152701 Loss1: 0.718852 Loss2: 1.433849
-DEBUG flwr 2023-10-11 01:21:32,481 | server.py:236 | fit_round 96 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.788774 Loss1: 0.372534 Loss2: 1.416239
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.678606 Loss1: 0.282020 Loss2: 1.396585
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.576647 Loss1: 0.184751 Loss2: 1.391896
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.556978 Loss1: 0.174347 Loss2: 1.382631
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.982371 Loss1: 1.099676 Loss2: 1.882694
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.126630 Loss1: 0.701248 Loss2: 1.425382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.914270 Loss1: 0.441812 Loss2: 1.472459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.707214 Loss1: 0.291028 Loss2: 1.416186 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481043 Loss1: 0.105494 Loss2: 1.375549
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.643994 Loss1: 0.228598 Loss2: 1.415396
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.586936 Loss1: 0.183871 Loss2: 1.403066
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.568223 Loss1: 0.170347 Loss2: 1.397877
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.560340 Loss1: 0.151835 Loss2: 1.408506
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.549117 Loss1: 0.162755 Loss2: 1.386361
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.682698 Loss1: 0.834256 Loss2: 1.848441
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.530551 Loss1: 0.139445 Loss2: 1.391106
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.740172 Loss1: 0.320165 Loss2: 1.420007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577835 Loss1: 0.201600 Loss2: 1.376235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538009 Loss1: 0.172622 Loss2: 1.365387
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.789730 Loss1: 1.000118 Loss2: 1.789612
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.890901 Loss1: 0.529467 Loss2: 1.361433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677907 Loss1: 0.308533 Loss2: 1.369374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.608386 Loss1: 0.257588 Loss2: 1.350799 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513090 Loss1: 0.170272 Loss2: 1.342818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435490 Loss1: 0.109460 Loss2: 1.326030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405427 Loss1: 0.089103 Loss2: 1.316324 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430232 Loss1: 0.114234 Loss2: 1.315998 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647931 Loss1: 0.279645 Loss2: 1.368286 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.540293 Loss1: 0.172400 Loss2: 1.367894 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.507869 Loss1: 0.143265 Loss2: 1.364603
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.806608 Loss1: 1.008162 Loss2: 1.798446
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.463365 Loss1: 0.106578 Loss2: 1.356787
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.021193 Loss1: 0.592799 Loss2: 1.428395
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465835 Loss1: 0.113029 Loss2: 1.352806
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.792132 Loss1: 0.391865 Loss2: 1.400267
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426220 Loss1: 0.084153 Loss2: 1.342067
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.660535 Loss1: 0.269397 Loss2: 1.391138
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.632110 Loss1: 0.254056 Loss2: 1.378053
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.581391 Loss1: 0.204762 Loss2: 1.376629
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516595 Loss1: 0.146458 Loss2: 1.370137
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.492214 Loss1: 0.130084 Loss2: 1.362130
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487352 Loss1: 0.120139 Loss2: 1.367213
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.504399 Loss1: 0.143926 Loss2: 1.360473
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 01:21:32,481][flwr][DEBUG] - fit_round 96 received 50 results and 0 failures
-INFO flwr 2023-10-11 01:22:13,443 | server.py:125 | fit progress: (96, 2.205584313351506, {'accuracy': 0.5652}, 221441.221870161)
->> Test accuracy: 0.565200
-[2023-10-11 01:22:13,443][flwr][INFO] - fit progress: (96, 2.205584313351506, {'accuracy': 0.5652}, 221441.221870161)
-DEBUG flwr 2023-10-11 01:22:13,444 | server.py:173 | evaluate_round 96: strategy sampled 50 clients (out of 50)
-[2023-10-11 01:22:13,444][flwr][DEBUG] - evaluate_round 96: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 01:31:20,981 | server.py:187 | evaluate_round 96 received 50 results and 0 failures
-[2023-10-11 01:31:20,981][flwr][DEBUG] - evaluate_round 96 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 01:31:20,981 | server.py:222 | fit_round 97: strategy sampled 50 clients (out of 50)
-[2023-10-11 01:31:20,981][flwr][DEBUG] - fit_round 97: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.067949 Loss1: 1.154553 Loss2: 1.913396
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.064240 Loss1: 0.672277 Loss2: 1.391963
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.885521 Loss1: 0.446770 Loss2: 1.438751
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.688976 Loss1: 0.308943 Loss2: 1.380033
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.824553 Loss1: 0.978364 Loss2: 1.846190
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.073387 Loss1: 0.652526 Loss2: 1.420861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.878587 Loss1: 0.438048 Loss2: 1.440539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430106 Loss1: 0.077452 Loss2: 1.352654 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428275 Loss1: 0.081306 Loss2: 1.346969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413755 Loss1: 0.073421 Loss2: 1.340334 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.572598 Loss1: 0.178216 Loss2: 1.394381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487610 Loss1: 0.110173 Loss2: 1.377438 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.476721 Loss1: 0.104716 Loss2: 1.372004
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.941494 Loss1: 1.079815 Loss2: 1.861680
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.051745 Loss1: 0.632767 Loss2: 1.418978
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.782821 Loss1: 0.386856 Loss2: 1.395964
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.691665 Loss1: 0.310610 Loss2: 1.381055
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.629187 Loss1: 0.236632 Loss2: 1.392555
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.795795 Loss1: 0.960750 Loss2: 1.835045
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.561389 Loss1: 0.201344 Loss2: 1.360046
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496157 Loss1: 0.131684 Loss2: 1.364473 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.431117 Loss1: 0.078818 Loss2: 1.352299 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411344 Loss1: 0.065334 Loss2: 1.346010 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396577 Loss1: 0.060238 Loss2: 1.336339 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.551715 Loss1: 0.188989 Loss2: 1.362726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431090 Loss1: 0.091327 Loss2: 1.339763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419213 Loss1: 0.078531 Loss2: 1.340682
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.864949 Loss1: 1.059603 Loss2: 1.805345
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.008753 Loss1: 0.642378 Loss2: 1.366375
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.744149 Loss1: 0.374645 Loss2: 1.369504
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.619145 Loss1: 0.271209 Loss2: 1.347936
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.493127 Loss1: 0.156006 Loss2: 1.337121
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.754117 Loss1: 0.875510 Loss2: 1.878607
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471284 Loss1: 0.148475 Loss2: 1.322809
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.988432 Loss1: 0.531179 Loss2: 1.457253
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418299 Loss1: 0.101931 Loss2: 1.316369
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.852251 Loss1: 0.379605 Loss2: 1.472646
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401267 Loss1: 0.093573 Loss2: 1.307694
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377610 Loss1: 0.077045 Loss2: 1.300565
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.745459 Loss1: 0.292094 Loss2: 1.453365
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377897 Loss1: 0.072930 Loss2: 1.304967
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.690907 Loss1: 0.244313 Loss2: 1.446594
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.613766 Loss1: 0.178183 Loss2: 1.435583
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.538524 Loss1: 0.109675 Loss2: 1.428850
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.513380 Loss1: 0.095118 Loss2: 1.418262
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.540845 Loss1: 0.121791 Loss2: 1.419054
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.956355 Loss1: 1.060407 Loss2: 1.895948
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.524797 Loss1: 0.106223 Loss2: 1.418574
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.784171 Loss1: 0.344385 Loss2: 1.439786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.594306 Loss1: 0.183848 Loss2: 1.410458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.579797 Loss1: 0.181895 Loss2: 1.397901
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.809999 Loss1: 0.923597 Loss2: 1.886401
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.939472 Loss1: 0.536307 Loss2: 1.403166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.763077 Loss1: 0.330956 Loss2: 1.432121 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.679749 Loss1: 0.294350 Loss2: 1.385398 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.672765 Loss1: 0.275742 Loss2: 1.397023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.531918 Loss1: 0.152152 Loss2: 1.379766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497862 Loss1: 0.123792 Loss2: 1.374069 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464297 Loss1: 0.098932 Loss2: 1.365365 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.780209 Loss1: 0.382356 Loss2: 1.397852 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.620812 Loss1: 0.247555 Loss2: 1.373257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.557657 Loss1: 0.204878 Loss2: 1.352778
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.748737 Loss1: 0.845683 Loss2: 1.903055
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.894706 Loss1: 0.499476 Loss2: 1.395231 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.782195 Loss1: 0.342146 Loss2: 1.440049 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.683987 Loss1: 0.291393 Loss2: 1.392594 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418991 Loss1: 0.087988 Loss2: 1.331004
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.614406 Loss1: 0.209444 Loss2: 1.404962
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.558189 Loss1: 0.172562 Loss2: 1.385627
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.531578 Loss1: 0.141279 Loss2: 1.390299
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490817 Loss1: 0.109886 Loss2: 1.380931
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464330 Loss1: 0.091449 Loss2: 1.372881
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.777164 Loss1: 0.919999 Loss2: 1.857166
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452368 Loss1: 0.085030 Loss2: 1.367338
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.823443 Loss1: 0.402529 Loss2: 1.420914 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.576813 Loss1: 0.200042 Loss2: 1.376771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537102 Loss1: 0.167143 Loss2: 1.369959
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.700250 Loss1: 0.815123 Loss2: 1.885127
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.988489 Loss1: 0.565336 Loss2: 1.423152 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.776636 Loss1: 0.347457 Loss2: 1.429179 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.671913 Loss1: 0.249591 Loss2: 1.422322 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.589103 Loss1: 0.185429 Loss2: 1.403674 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487370 Loss1: 0.098339 Loss2: 1.389031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.862269 Loss1: 0.952600 Loss2: 1.909669
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462789 Loss1: 0.082025 Loss2: 1.380763
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452646 Loss1: 0.077830 Loss2: 1.374816 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972426
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.666654 Loss1: 0.256554 Loss2: 1.410100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.585065 Loss1: 0.178119 Loss2: 1.406946 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.959606 Loss1: 1.031588 Loss2: 1.928018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.141182 Loss1: 0.650643 Loss2: 1.490539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.888534 Loss1: 0.406608 Loss2: 1.481926 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.653611 Loss1: 0.211551 Loss2: 1.442060 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.528568 Loss1: 0.103065 Loss2: 1.425502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510912 Loss1: 0.095891 Loss2: 1.415022
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.009204 Loss1: 1.067452 Loss2: 1.941752
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.064848 Loss1: 0.643257 Loss2: 1.421591 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.473095 Loss1: 0.068850 Loss2: 1.404246
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.847953 Loss1: 0.377853 Loss2: 1.470100
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.682804 Loss1: 0.268630 Loss2: 1.414174
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.600264 Loss1: 0.186367 Loss2: 1.413897
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.563018 Loss1: 0.158045 Loss2: 1.404973
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.543338 Loss1: 0.142493 Loss2: 1.400845
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.542410 Loss1: 0.143701 Loss2: 1.398709
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.850593 Loss1: 0.991052 Loss2: 1.859540
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.917729 Loss1: 0.519402 Loss2: 1.398327 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477287 Loss1: 0.094080 Loss2: 1.383207
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.820962 Loss1: 0.379078 Loss2: 1.441884
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.782094 Loss1: 0.395830 Loss2: 1.386264
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.632012 Loss1: 0.230970 Loss2: 1.401042
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.599837 Loss1: 0.221340 Loss2: 1.378497
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.580017 Loss1: 0.202907 Loss2: 1.377110
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.063351 Loss1: 1.078898 Loss2: 1.984452
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546573 Loss1: 0.164225 Loss2: 1.382348
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.535036 Loss1: 0.159703 Loss2: 1.375333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.474160 Loss1: 0.102132 Loss2: 1.372028 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.565538 Loss1: 0.197193 Loss2: 1.368346 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.476122 Loss1: 0.126072 Loss2: 1.350050 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461993 Loss1: 0.121722 Loss2: 1.340270 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.801149 Loss1: 0.348021 Loss2: 1.453128 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.719348 Loss1: 0.272773 Loss2: 1.446575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.671465 Loss1: 0.825845 Loss2: 1.845620
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.670801 Loss1: 0.247016 Loss2: 1.423785
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.973832 Loss1: 0.584825 Loss2: 1.389007
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.596394 Loss1: 0.171373 Loss2: 1.425021
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.812313 Loss1: 0.369391 Loss2: 1.442922
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.567020 Loss1: 0.155363 Loss2: 1.411657
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647792 Loss1: 0.273479 Loss2: 1.374313
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.530338 Loss1: 0.120784 Loss2: 1.409555
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.679343 Loss1: 0.283338 Loss2: 1.396005
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.529851 Loss1: 0.129349 Loss2: 1.400502
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525905 Loss1: 0.154946 Loss2: 1.370959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462759 Loss1: 0.110328 Loss2: 1.352431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411775 Loss1: 0.061627 Loss2: 1.350148
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.953798 Loss1: 1.117495 Loss2: 1.836304
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.009736 Loss1: 0.594096 Loss2: 1.415640
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.852098 Loss1: 0.442091 Loss2: 1.410007
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.790134 Loss1: 0.394256 Loss2: 1.395878
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.738430 Loss1: 0.334097 Loss2: 1.404332
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.853291 Loss1: 1.006465 Loss2: 1.846826
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.653944 Loss1: 0.265810 Loss2: 1.388134
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.023615 Loss1: 0.578480 Loss2: 1.445136
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.559319 Loss1: 0.187407 Loss2: 1.371912
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.513036 Loss1: 0.142373 Loss2: 1.370663
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.811381 Loss1: 0.376379 Loss2: 1.435002
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.475564 Loss1: 0.114478 Loss2: 1.361085
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.702533 Loss1: 0.303416 Loss2: 1.399118
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472892 Loss1: 0.118653 Loss2: 1.354239
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.637008 Loss1: 0.234516 Loss2: 1.402492
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.594825 Loss1: 0.203365 Loss2: 1.391460
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.554852 Loss1: 0.163865 Loss2: 1.390987
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.541318 Loss1: 0.148627 Loss2: 1.392692
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.557253 Loss1: 0.159121 Loss2: 1.398132
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.894229 Loss1: 0.956316 Loss2: 1.937913
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485252 Loss1: 0.103282 Loss2: 1.381970
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.973671 Loss1: 0.470557 Loss2: 1.503113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.737084 Loss1: 0.270051 Loss2: 1.467033 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647487 Loss1: 0.201702 Loss2: 1.445785
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.720589 Loss1: 0.868822 Loss2: 1.851767
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.847918 Loss1: 0.448706 Loss2: 1.399212 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684396 Loss1: 0.286802 Loss2: 1.397594 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.615261 Loss1: 0.234150 Loss2: 1.381111 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547382 Loss1: 0.170430 Loss2: 1.376952 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473166 Loss1: 0.109047 Loss2: 1.364118 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431380 Loss1: 0.075561 Loss2: 1.355819
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.893446 Loss1: 1.084614 Loss2: 1.808832
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.030447 Loss1: 0.659303 Loss2: 1.371145
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416283 Loss1: 0.064505 Loss2: 1.351778
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.638779 Loss1: 0.292228 Loss2: 1.346552 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519061 Loss1: 0.186289 Loss2: 1.332773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545390 Loss1: 0.205105 Loss2: 1.340285
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.766742 Loss1: 1.006789 Loss2: 1.759953
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470130 Loss1: 0.132603 Loss2: 1.337527
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.932364 Loss1: 0.630280 Loss2: 1.302084
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453720 Loss1: 0.128465 Loss2: 1.325254
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.698822 Loss1: 0.374164 Loss2: 1.324658
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434603 Loss1: 0.105440 Loss2: 1.329162
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569970 Loss1: 0.284900 Loss2: 1.285070
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.501092 Loss1: 0.207829 Loss2: 1.293263
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.453074 Loss1: 0.177487 Loss2: 1.275587
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404479 Loss1: 0.129133 Loss2: 1.275345
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394824 Loss1: 0.127888 Loss2: 1.266936
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.358116 Loss1: 0.090808 Loss2: 1.267308
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.796647 Loss1: 0.947730 Loss2: 1.848917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.345380 Loss1: 0.084736 Loss2: 1.260644
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.933294 Loss1: 0.548337 Loss2: 1.384957
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.752798 Loss1: 0.328473 Loss2: 1.424325
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.715108 Loss1: 0.354169 Loss2: 1.360939
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.623732 Loss1: 0.240873 Loss2: 1.382859
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.571459 Loss1: 0.216706 Loss2: 1.354753
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518642 Loss1: 0.156215 Loss2: 1.362427
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.922939 Loss1: 0.988815 Loss2: 1.934124
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466537 Loss1: 0.119227 Loss2: 1.347311
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.139813 Loss1: 0.679281 Loss2: 1.460532
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.455627 Loss1: 0.116063 Loss2: 1.339564
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.866046 Loss1: 0.357619 Loss2: 1.508427
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.451441 Loss1: 0.121234 Loss2: 1.330207
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.761301 Loss1: 0.317275 Loss2: 1.444027
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.700294 Loss1: 0.232553 Loss2: 1.467741
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.585373 Loss1: 0.155094 Loss2: 1.430279
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.560764 Loss1: 0.133310 Loss2: 1.427454
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.583285 Loss1: 0.154381 Loss2: 1.428904
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.722511 Loss1: 0.893115 Loss2: 1.829396
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.571270 Loss1: 0.148215 Loss2: 1.423055
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.554463 Loss1: 0.127418 Loss2: 1.427046
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.937809 Loss1: 0.541649 Loss2: 1.396160
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.699199 Loss1: 0.271065 Loss2: 1.428134
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619386 Loss1: 0.242760 Loss2: 1.376626
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549566 Loss1: 0.163699 Loss2: 1.385867
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531753 Loss1: 0.155262 Loss2: 1.376491
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.945948 Loss1: 1.005600 Loss2: 1.940348
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.523685 Loss1: 0.149332 Loss2: 1.374353
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.562459 Loss1: 0.180334 Loss2: 1.382125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.590736 Loss1: 0.210902 Loss2: 1.379834 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.646808 Loss1: 0.238193 Loss2: 1.408615 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964844
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514667 Loss1: 0.126456 Loss2: 1.388211 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470502 Loss1: 0.088914 Loss2: 1.381588 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.950205 Loss1: 0.991163 Loss2: 1.959041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.993213 Loss1: 0.452586 Loss2: 1.540627 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.691509 Loss1: 0.874112 Loss2: 1.817397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.940429 Loss1: 0.585386 Loss2: 1.355043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.729205 Loss1: 0.320143 Loss2: 1.409061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.603846 Loss1: 0.264815 Loss2: 1.339031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526609 Loss1: 0.184221 Loss2: 1.342387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.532098 Loss1: 0.189621 Loss2: 1.342477 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441951 Loss1: 0.110789 Loss2: 1.331162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385695 Loss1: 0.068802 Loss2: 1.316893 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.965610 Loss1: 1.117622 Loss2: 1.847987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726065 Loss1: 0.327616 Loss2: 1.398449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.598264 Loss1: 0.237976 Loss2: 1.360287
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.195703 Loss1: 1.257553 Loss2: 1.938150
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.116622 Loss1: 0.663195 Loss2: 1.453427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.820343 Loss1: 0.378376 Loss2: 1.441967 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.729567 Loss1: 0.308764 Loss2: 1.420803 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.651898 Loss1: 0.248469 Loss2: 1.403429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447828 Loss1: 0.108351 Loss2: 1.339477
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.553014 Loss1: 0.156914 Loss2: 1.396099
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414540 Loss1: 0.081935 Loss2: 1.332605
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511696 Loss1: 0.120414 Loss2: 1.391282
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497085 Loss1: 0.112350 Loss2: 1.384735
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458304 Loss1: 0.076893 Loss2: 1.381411
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438565 Loss1: 0.064845 Loss2: 1.373721
-(DefaultActor pid=3765) >> Training accuracy: 0.975446
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.894579 Loss1: 1.034838 Loss2: 1.859742
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.993601 Loss1: 0.608814 Loss2: 1.384787
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.808755 Loss1: 0.398507 Loss2: 1.410247
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.661102 Loss1: 0.281631 Loss2: 1.379472
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.895110 Loss1: 1.008312 Loss2: 1.886798
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.594730 Loss1: 0.224148 Loss2: 1.370583
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.986739 Loss1: 0.552954 Loss2: 1.433785
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506245 Loss1: 0.144267 Loss2: 1.361978
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.858909 Loss1: 0.391242 Loss2: 1.467667
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443291 Loss1: 0.088460 Loss2: 1.354831
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.745659 Loss1: 0.323857 Loss2: 1.421802
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439130 Loss1: 0.094942 Loss2: 1.344187
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.647812 Loss1: 0.208858 Loss2: 1.438954
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438434 Loss1: 0.096500 Loss2: 1.341934
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.573938 Loss1: 0.149313 Loss2: 1.424625
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436941 Loss1: 0.091846 Loss2: 1.345095
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.541856 Loss1: 0.134690 Loss2: 1.407166
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.524543 Loss1: 0.121654 Loss2: 1.402889
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.513077 Loss1: 0.101347 Loss2: 1.411731
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.525223 Loss1: 0.123034 Loss2: 1.402189
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.784867 Loss1: 0.945267 Loss2: 1.839599
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.822223 Loss1: 0.452872 Loss2: 1.369352
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.679811 Loss1: 0.304492 Loss2: 1.375319
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.587425 Loss1: 0.243211 Loss2: 1.344215
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.971087 Loss1: 1.083745 Loss2: 1.887342
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.004593 Loss1: 0.574033 Loss2: 1.430560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.759559 Loss1: 0.341000 Loss2: 1.418559 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.657511 Loss1: 0.265338 Loss2: 1.392173 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.578760 Loss1: 0.196140 Loss2: 1.382620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.535965 Loss1: 0.158642 Loss2: 1.377323 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379448 Loss1: 0.056013 Loss2: 1.323434
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488213 Loss1: 0.113003 Loss2: 1.375210
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484201 Loss1: 0.121600 Loss2: 1.362601
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.480086 Loss1: 0.118417 Loss2: 1.361669
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.480481 Loss1: 0.111566 Loss2: 1.368916
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.684053 Loss1: 0.885209 Loss2: 1.798844
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.898615 Loss1: 0.533408 Loss2: 1.365207
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.739010 Loss1: 0.333259 Loss2: 1.405751
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619307 Loss1: 0.264467 Loss2: 1.354841
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.788159 Loss1: 0.843002 Loss2: 1.945157
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549468 Loss1: 0.187521 Loss2: 1.361947
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.107503 Loss1: 0.645109 Loss2: 1.462394
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523417 Loss1: 0.176606 Loss2: 1.346811
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.889467 Loss1: 0.402559 Loss2: 1.486908
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.773738 Loss1: 0.318292 Loss2: 1.455446
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448587 Loss1: 0.107958 Loss2: 1.340629
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.682542 Loss1: 0.240554 Loss2: 1.441989
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461599 Loss1: 0.127192 Loss2: 1.334406
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.638419 Loss1: 0.204770 Loss2: 1.433649
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426629 Loss1: 0.092339 Loss2: 1.334290
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.586662 Loss1: 0.154371 Loss2: 1.432291
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403811 Loss1: 0.082273 Loss2: 1.321537
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.524086 Loss1: 0.097481 Loss2: 1.426606 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.956096 Loss1: 1.082172 Loss2: 1.873924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.797714 Loss1: 0.365734 Loss2: 1.431980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.644136 Loss1: 0.255971 Loss2: 1.388164
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.959974 Loss1: 1.148501 Loss2: 1.811474
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.934579 Loss1: 0.535615 Loss2: 1.398965 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.761715 Loss1: 0.420737 Loss2: 1.340978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.598507 Loss1: 0.255366 Loss2: 1.343141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.509019 Loss1: 0.188482 Loss2: 1.320536 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437399 Loss1: 0.115457 Loss2: 1.321942 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419963 Loss1: 0.065197 Loss2: 1.354767
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402496 Loss1: 0.094046 Loss2: 1.308450
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378669 Loss1: 0.082368 Loss2: 1.296301
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396596 Loss1: 0.100349 Loss2: 1.296247
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408180 Loss1: 0.111446 Loss2: 1.296734
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.851114 Loss1: 0.970010 Loss2: 1.881104
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.056680 Loss1: 0.596260 Loss2: 1.460420
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.816919 Loss1: 0.389324 Loss2: 1.427595
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.090329 Loss1: 1.087394 Loss2: 2.002935
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.688619 Loss1: 0.268843 Loss2: 1.419776
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.984541 Loss1: 0.576380 Loss2: 1.408161
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.652265 Loss1: 0.246697 Loss2: 1.405567
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567354 Loss1: 0.158722 Loss2: 1.408632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.556521 Loss1: 0.166625 Loss2: 1.389896 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.540879 Loss1: 0.155908 Loss2: 1.384971 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461539 Loss1: 0.093733 Loss2: 1.367806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495299 Loss1: 0.129345 Loss2: 1.365954 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428800 Loss1: 0.075756 Loss2: 1.353045 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.843295 Loss1: 1.014745 Loss2: 1.828550
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.012922 Loss1: 0.619422 Loss2: 1.393500
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.788952 Loss1: 0.360236 Loss2: 1.428716
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.660937 Loss1: 0.295551 Loss2: 1.365386
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.987249 Loss1: 1.063177 Loss2: 1.924071
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.622188 Loss1: 0.232454 Loss2: 1.389734
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.072092 Loss1: 0.631693 Loss2: 1.440399
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524162 Loss1: 0.154064 Loss2: 1.370097
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.880034 Loss1: 0.419583 Loss2: 1.460451
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456623 Loss1: 0.107118 Loss2: 1.349505
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.759078 Loss1: 0.333221 Loss2: 1.425857
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472352 Loss1: 0.123651 Loss2: 1.348701
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.616136 Loss1: 0.195377 Loss2: 1.420759
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458726 Loss1: 0.113618 Loss2: 1.345109
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526351 Loss1: 0.125753 Loss2: 1.400598
-DEBUG flwr 2023-10-11 02:00:27,690 | server.py:236 | fit_round 97 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449128 Loss1: 0.109452 Loss2: 1.339677
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503298 Loss1: 0.110562 Loss2: 1.392735
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474443 Loss1: 0.086495 Loss2: 1.387948
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465451 Loss1: 0.084208 Loss2: 1.381243
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.473863 Loss1: 0.095377 Loss2: 1.378485
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.917522 Loss1: 1.011192 Loss2: 1.906330
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.207788 Loss1: 0.757917 Loss2: 1.449871
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.865987 Loss1: 0.388172 Loss2: 1.477814
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.715751 Loss1: 0.289849 Loss2: 1.425901
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.994353 Loss1: 1.052377 Loss2: 1.941976
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.981018 Loss1: 0.539179 Loss2: 1.441839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.748174 Loss1: 0.311596 Loss2: 1.436578 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.668587 Loss1: 0.258978 Loss2: 1.409609 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.597165 Loss1: 0.200616 Loss2: 1.396549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.541901 Loss1: 0.148250 Loss2: 1.393651 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.522340 Loss1: 0.134958 Loss2: 1.387382
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534362 Loss1: 0.151734 Loss2: 1.382628
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.502592 Loss1: 0.127343 Loss2: 1.375248
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.503378 Loss1: 0.128726 Loss2: 1.374652
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477984 Loss1: 0.098999 Loss2: 1.378985
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.889258 Loss1: 0.968776 Loss2: 1.920482
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.056073 Loss1: 0.607966 Loss2: 1.448106
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834591 Loss1: 0.359742 Loss2: 1.474849
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.752873 Loss1: 0.305279 Loss2: 1.447594
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.808914 Loss1: 0.952057 Loss2: 1.856857
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.988971 Loss1: 0.561490 Loss2: 1.427480 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.796318 Loss1: 0.369042 Loss2: 1.427276 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.711322 Loss1: 0.314790 Loss2: 1.396531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.618606 Loss1: 0.221093 Loss2: 1.397513 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.571224 Loss1: 0.181563 Loss2: 1.389661 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.508132 Loss1: 0.119755 Loss2: 1.388378 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442740 Loss1: 0.078473 Loss2: 1.364267 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.905566 Loss1: 0.526674 Loss2: 1.378891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.650177 Loss1: 0.290692 Loss2: 1.359485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.514059 Loss1: 0.160924 Loss2: 1.353135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468225 Loss1: 0.125066 Loss2: 1.343159 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458880 Loss1: 0.117373 Loss2: 1.341507 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 02:00:27,690][flwr][DEBUG] - fit_round 97 received 50 results and 0 failures
-INFO flwr 2023-10-11 02:01:11,210 | server.py:125 | fit progress: (97, 2.2052834205353222, {'accuracy': 0.5673}, 223778.98901012202)
->> Test accuracy: 0.567300
-[2023-10-11 02:01:11,210][flwr][INFO] - fit progress: (97, 2.2052834205353222, {'accuracy': 0.5673}, 223778.98901012202)
-DEBUG flwr 2023-10-11 02:01:11,211 | server.py:173 | evaluate_round 97: strategy sampled 50 clients (out of 50)
-[2023-10-11 02:01:11,211][flwr][DEBUG] - evaluate_round 97: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 02:10:21,087 | server.py:187 | evaluate_round 97 received 50 results and 0 failures
-[2023-10-11 02:10:21,087][flwr][DEBUG] - evaluate_round 97 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 02:10:21,088 | server.py:222 | fit_round 98: strategy sampled 50 clients (out of 50)
-[2023-10-11 02:10:21,088][flwr][DEBUG] - fit_round 98: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.811126 Loss1: 0.915043 Loss2: 1.896082
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.006571 Loss1: 0.558173 Loss2: 1.448398
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.850102 Loss1: 0.366221 Loss2: 1.483881
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.668043 Loss1: 0.236848 Loss2: 1.431195
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.953009 Loss1: 1.068865 Loss2: 1.884144
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.636035 Loss1: 0.211694 Loss2: 1.424341
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.980677 Loss1: 0.587586 Loss2: 1.393091
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.552202 Loss1: 0.125684 Loss2: 1.426518
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.714900 Loss1: 0.295003 Loss2: 1.419897
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525498 Loss1: 0.115633 Loss2: 1.409865
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.621039 Loss1: 0.243275 Loss2: 1.377765
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.482159 Loss1: 0.075520 Loss2: 1.406639
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.628710 Loss1: 0.240087 Loss2: 1.388623
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.487840 Loss1: 0.090946 Loss2: 1.396894
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546553 Loss1: 0.174966 Loss2: 1.371587
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485478 Loss1: 0.092370 Loss2: 1.393108
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.505673 Loss1: 0.137138 Loss2: 1.368534
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487213 Loss1: 0.124688 Loss2: 1.362525
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.512129 Loss1: 0.147979 Loss2: 1.364150
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440257 Loss1: 0.077506 Loss2: 1.362751
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.981365 Loss1: 1.090837 Loss2: 1.890528
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.219940 Loss1: 0.721192 Loss2: 1.498748
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.829186 Loss1: 0.396751 Loss2: 1.432435
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.765808 Loss1: 0.332237 Loss2: 1.433571
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.743724 Loss1: 0.892525 Loss2: 1.851199
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.171798 Loss1: 0.708723 Loss2: 1.463075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.840934 Loss1: 0.406306 Loss2: 1.434628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.659074 Loss1: 0.231320 Loss2: 1.427753 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.579243 Loss1: 0.171063 Loss2: 1.408180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542956 Loss1: 0.143820 Loss2: 1.399136 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.570477 Loss1: 0.174295 Loss2: 1.396182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.556853 Loss1: 0.148595 Loss2: 1.408258 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971680
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.742889 Loss1: 0.899860 Loss2: 1.843029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.686480 Loss1: 0.295011 Loss2: 1.391470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594404 Loss1: 0.234577 Loss2: 1.359827
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.143268 Loss1: 1.175781 Loss2: 1.967487
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.099151 Loss1: 0.681250 Loss2: 1.417900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511010 Loss1: 0.152321 Loss2: 1.358689
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.798120 Loss1: 0.348234 Loss2: 1.449885
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.643795 Loss1: 0.258547 Loss2: 1.385248
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523161 Loss1: 0.171633 Loss2: 1.351527
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.478866 Loss1: 0.125601 Loss2: 1.353265 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451142 Loss1: 0.117084 Loss2: 1.334058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441145 Loss1: 0.101146 Loss2: 1.339999 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973633
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440034 Loss1: 0.085714 Loss2: 1.354320 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.043390 Loss1: 1.178085 Loss2: 1.865305
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.088396 Loss1: 0.683960 Loss2: 1.404436
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.895669 Loss1: 0.433423 Loss2: 1.462246
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.728685 Loss1: 0.338873 Loss2: 1.389812
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.820915 Loss1: 0.976990 Loss2: 1.843925
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.663884 Loss1: 0.278119 Loss2: 1.385765
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.014382 Loss1: 0.600204 Loss2: 1.414178
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.577469 Loss1: 0.194950 Loss2: 1.382520
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.806617 Loss1: 0.363998 Loss2: 1.442619
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490642 Loss1: 0.127316 Loss2: 1.363326
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.658537 Loss1: 0.279610 Loss2: 1.378927
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499180 Loss1: 0.142137 Loss2: 1.357044
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.572921 Loss1: 0.187170 Loss2: 1.385751
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.511645 Loss1: 0.149424 Loss2: 1.362221
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556963 Loss1: 0.189353 Loss2: 1.367610
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460847 Loss1: 0.101060 Loss2: 1.359787
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.582455 Loss1: 0.206751 Loss2: 1.375704
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548376 Loss1: 0.170739 Loss2: 1.377637
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.506679 Loss1: 0.140763 Loss2: 1.365916
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.475964 Loss1: 0.109013 Loss2: 1.366952
-(DefaultActor pid=3764) >> Training accuracy: 0.960417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.756134 Loss1: 0.900291 Loss2: 1.855843
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.000528 Loss1: 0.603304 Loss2: 1.397225
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.862674 Loss1: 0.408504 Loss2: 1.454170
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.669344 Loss1: 0.287357 Loss2: 1.381987
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.899412 Loss1: 0.972654 Loss2: 1.926758
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.162400 Loss1: 0.671771 Loss2: 1.490629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.943102 Loss1: 0.483152 Loss2: 1.459949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.720172 Loss1: 0.266851 Loss2: 1.453321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.600977 Loss1: 0.165927 Loss2: 1.435050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.570022 Loss1: 0.144524 Loss2: 1.425498 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442669 Loss1: 0.088093 Loss2: 1.354575
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.556296 Loss1: 0.138936 Loss2: 1.417360
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.532263 Loss1: 0.117358 Loss2: 1.414904
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.510884 Loss1: 0.106906 Loss2: 1.403977
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.542198 Loss1: 0.136038 Loss2: 1.406160
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.726804 Loss1: 0.946129 Loss2: 1.780676
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.870130 Loss1: 0.514062 Loss2: 1.356069
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.753848 Loss1: 0.380249 Loss2: 1.373598
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.747886 Loss1: 0.932119 Loss2: 1.815766
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.676879 Loss1: 0.325859 Loss2: 1.351020
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.971993 Loss1: 0.610251 Loss2: 1.361742
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577726 Loss1: 0.228081 Loss2: 1.349646
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.673253 Loss1: 0.293289 Loss2: 1.379964
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.552485 Loss1: 0.210288 Loss2: 1.342197
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579436 Loss1: 0.250139 Loss2: 1.329297
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488399 Loss1: 0.152024 Loss2: 1.336375
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432010 Loss1: 0.104104 Loss2: 1.327906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417625 Loss1: 0.094155 Loss2: 1.323470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416818 Loss1: 0.092382 Loss2: 1.324436 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973633
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.474743 Loss1: 0.147854 Loss2: 1.326889 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.826435 Loss1: 0.938067 Loss2: 1.888368 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.749760 Loss1: 0.315896 Loss2: 1.433864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.632240 Loss1: 0.246550 Loss2: 1.385690
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.787085 Loss1: 0.952324 Loss2: 1.834761
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.924369 Loss1: 0.539083 Loss2: 1.385286 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.774088 Loss1: 0.369678 Loss2: 1.404410 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640757 Loss1: 0.277537 Loss2: 1.363220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521811 Loss1: 0.154113 Loss2: 1.367698 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495363 Loss1: 0.139692 Loss2: 1.355671 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427022 Loss1: 0.063016 Loss2: 1.364006
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445820 Loss1: 0.089708 Loss2: 1.356112
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.478544 Loss1: 0.138882 Loss2: 1.339662
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464157 Loss1: 0.113022 Loss2: 1.351136
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.459661 Loss1: 0.116425 Loss2: 1.343235
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.766877 Loss1: 0.904062 Loss2: 1.862815
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.954504 Loss1: 0.575721 Loss2: 1.378784
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.761624 Loss1: 0.341167 Loss2: 1.420457
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638447 Loss1: 0.275040 Loss2: 1.363407
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.036254 Loss1: 1.153904 Loss2: 1.882349
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571981 Loss1: 0.203229 Loss2: 1.368752
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.062714 Loss1: 0.682335 Loss2: 1.380380
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498366 Loss1: 0.144732 Loss2: 1.353633
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.900256 Loss1: 0.500205 Loss2: 1.400051
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.679333 Loss1: 0.325415 Loss2: 1.353918
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476955 Loss1: 0.129076 Loss2: 1.347879
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.599600 Loss1: 0.237145 Loss2: 1.362455
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440447 Loss1: 0.089934 Loss2: 1.350514
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.541842 Loss1: 0.195997 Loss2: 1.345845
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417897 Loss1: 0.077639 Loss2: 1.340258
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390893 Loss1: 0.056888 Loss2: 1.334005 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436319 Loss1: 0.109023 Loss2: 1.327296 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.939732
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.788058 Loss1: 0.970608 Loss2: 1.817450 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.745973 Loss1: 0.361724 Loss2: 1.384248 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638587 Loss1: 0.280834 Loss2: 1.357753
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.825882 Loss1: 0.973367 Loss2: 1.852514
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.988576 Loss1: 0.604601 Loss2: 1.383976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769477 Loss1: 0.337761 Loss2: 1.431716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631018 Loss1: 0.259159 Loss2: 1.371858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.579927 Loss1: 0.206888 Loss2: 1.373039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523213 Loss1: 0.156696 Loss2: 1.366518 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423325 Loss1: 0.101614 Loss2: 1.321711
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470143 Loss1: 0.108141 Loss2: 1.362001
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467275 Loss1: 0.110613 Loss2: 1.356662
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466731 Loss1: 0.115927 Loss2: 1.350804
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466217 Loss1: 0.114495 Loss2: 1.351722
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.896325 Loss1: 1.027084 Loss2: 1.869241
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.954178 Loss1: 0.577174 Loss2: 1.377004
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.751849 Loss1: 0.366636 Loss2: 1.385212
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586681 Loss1: 0.214668 Loss2: 1.372013
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.782936 Loss1: 0.918689 Loss2: 1.864247
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.025963 Loss1: 0.630849 Loss2: 1.395114 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.866931 Loss1: 0.405603 Loss2: 1.461328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.707795 Loss1: 0.324913 Loss2: 1.382882 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.641658 Loss1: 0.234341 Loss2: 1.407316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.563842 Loss1: 0.181786 Loss2: 1.382057 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405109 Loss1: 0.073927 Loss2: 1.331181
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.540486 Loss1: 0.155927 Loss2: 1.384559
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487167 Loss1: 0.114014 Loss2: 1.373153
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493628 Loss1: 0.129003 Loss2: 1.364625
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450975 Loss1: 0.085719 Loss2: 1.365256
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.790624 Loss1: 0.883687 Loss2: 1.906937
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.909199 Loss1: 0.503499 Loss2: 1.405700
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.758213 Loss1: 0.315069 Loss2: 1.443144
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.626250 Loss1: 0.229393 Loss2: 1.396857
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.861106 Loss1: 0.975656 Loss2: 1.885450
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.919932 Loss1: 0.513875 Loss2: 1.406057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727141 Loss1: 0.292745 Loss2: 1.434397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.622291 Loss1: 0.233148 Loss2: 1.389144 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.607998 Loss1: 0.221610 Loss2: 1.386388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.580677 Loss1: 0.187004 Loss2: 1.393672 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.579068 Loss1: 0.193682 Loss2: 1.385386 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.470950 Loss1: 0.101707 Loss2: 1.369242 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.616628 Loss1: 0.813796 Loss2: 1.802833 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.727071 Loss1: 0.341500 Loss2: 1.385571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.632434 Loss1: 0.313019 Loss2: 1.319415
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.891657 Loss1: 1.053078 Loss2: 1.838578
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.026353 Loss1: 0.608423 Loss2: 1.417930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.756093 Loss1: 0.382858 Loss2: 1.373235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.683326 Loss1: 0.320132 Loss2: 1.363194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.654202 Loss1: 0.277406 Loss2: 1.376797 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.563515 Loss1: 0.200548 Loss2: 1.362967 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.362045 Loss1: 0.073950 Loss2: 1.288096
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.527352 Loss1: 0.169441 Loss2: 1.357911
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491978 Loss1: 0.126602 Loss2: 1.365376
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.495019 Loss1: 0.147320 Loss2: 1.347699
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464039 Loss1: 0.110412 Loss2: 1.353626
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.822679 Loss1: 0.966488 Loss2: 1.856191
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.983105 Loss1: 0.609048 Loss2: 1.374058
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.800523 Loss1: 0.377345 Loss2: 1.423178
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.683729 Loss1: 0.309705 Loss2: 1.374025
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.954235 Loss1: 1.080610 Loss2: 1.873624
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.959000 Loss1: 0.574496 Loss2: 1.384504 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.737832 Loss1: 0.338720 Loss2: 1.399112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.625882 Loss1: 0.266064 Loss2: 1.359818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.573890 Loss1: 0.211495 Loss2: 1.362395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527100 Loss1: 0.170812 Loss2: 1.356288 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.462245 Loss1: 0.110791 Loss2: 1.351454 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426022 Loss1: 0.095258 Loss2: 1.330764 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.127923 Loss1: 1.126823 Loss2: 2.001100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.954369 Loss1: 0.470969 Loss2: 1.483400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.645793 Loss1: 0.232332 Loss2: 1.413461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.565184 Loss1: 0.179825 Loss2: 1.385359 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.503825 Loss1: 0.117004 Loss2: 1.386822 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.754256 Loss1: 0.350179 Loss2: 1.404077 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.574809 Loss1: 0.181390 Loss2: 1.393418 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500086 Loss1: 0.126529 Loss2: 1.373558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.861071 Loss1: 1.004314 Loss2: 1.856756
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449992 Loss1: 0.077142 Loss2: 1.372850
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.921867 Loss1: 0.535103 Loss2: 1.386764
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446108 Loss1: 0.086621 Loss2: 1.359487
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606861 Loss1: 0.229343 Loss2: 1.377518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.590484 Loss1: 0.218091 Loss2: 1.372393 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517571 Loss1: 0.154866 Loss2: 1.362705
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.806962 Loss1: 1.033505 Loss2: 1.773457
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.929555 Loss1: 0.572923 Loss2: 1.356632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.733378 Loss1: 0.371358 Loss2: 1.362019 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454300 Loss1: 0.096531 Loss2: 1.357768
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.671089 Loss1: 0.338805 Loss2: 1.332283
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554271 Loss1: 0.219647 Loss2: 1.334625
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511900 Loss1: 0.197810 Loss2: 1.314090
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452923 Loss1: 0.140430 Loss2: 1.312493
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411309 Loss1: 0.104700 Loss2: 1.306608
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.801718 Loss1: 0.937656 Loss2: 1.864061
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397906 Loss1: 0.099889 Loss2: 1.298017
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.880174 Loss1: 0.510714 Loss2: 1.369460
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401777 Loss1: 0.106117 Loss2: 1.295659
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.684978 Loss1: 0.309021 Loss2: 1.375957 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.553478 Loss1: 0.172773 Loss2: 1.380705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.482664 Loss1: 0.131536 Loss2: 1.351128
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.957862 Loss1: 1.094590 Loss2: 1.863272
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.105464 Loss1: 0.675739 Loss2: 1.429725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.800545 Loss1: 0.370497 Loss2: 1.430049 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400870 Loss1: 0.059623 Loss2: 1.341248
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640207 Loss1: 0.241652 Loss2: 1.398555
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.601387 Loss1: 0.204668 Loss2: 1.396718
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.543046 Loss1: 0.166565 Loss2: 1.376482
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.507043 Loss1: 0.130167 Loss2: 1.376876
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460370 Loss1: 0.085339 Loss2: 1.375031
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.916436 Loss1: 1.066359 Loss2: 1.850077
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434663 Loss1: 0.074874 Loss2: 1.359789
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431526 Loss1: 0.079073 Loss2: 1.352453 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.670782 Loss1: 0.328683 Loss2: 1.342099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504343 Loss1: 0.160277 Loss2: 1.344066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.478143 Loss1: 0.159888 Loss2: 1.318256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401723 Loss1: 0.097627 Loss2: 1.304096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395808 Loss1: 0.091811 Loss2: 1.303997 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.753395 Loss1: 0.319893 Loss2: 1.433502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.636962 Loss1: 0.205644 Loss2: 1.431318 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.949630 Loss1: 1.048175 Loss2: 1.901455 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.130034 Loss1: 0.706341 Loss2: 1.423693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.893436 Loss1: 0.438290 Loss2: 1.455146 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.600072 Loss1: 0.201314 Loss2: 1.398757 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.546685 Loss1: 0.157391 Loss2: 1.389294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.504914 Loss1: 0.119972 Loss2: 1.384942
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.940721 Loss1: 1.035171 Loss2: 1.905550
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.149580 Loss1: 0.770638 Loss2: 1.378942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442815 Loss1: 0.068080 Loss2: 1.374734
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.893365 Loss1: 0.439238 Loss2: 1.454127
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.679593 Loss1: 0.312655 Loss2: 1.366937
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548208 Loss1: 0.178126 Loss2: 1.370082
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490183 Loss1: 0.134492 Loss2: 1.355691
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471067 Loss1: 0.121518 Loss2: 1.349549
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427205 Loss1: 0.082573 Loss2: 1.344633
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.691497 Loss1: 0.862178 Loss2: 1.829319
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.019584 Loss1: 0.583353 Loss2: 1.436231 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.794272 Loss1: 0.378016 Loss2: 1.416256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.541492 Loss1: 0.165778 Loss2: 1.375714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.666474 Loss1: 0.881227 Loss2: 1.785247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.859667 Loss1: 0.508811 Loss2: 1.350856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.704791 Loss1: 0.322568 Loss2: 1.382223 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.589568 Loss1: 0.245114 Loss2: 1.344454 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989890
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549242 Loss1: 0.200612 Loss2: 1.348629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.479470 Loss1: 0.133296 Loss2: 1.346174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433867 Loss1: 0.108546 Loss2: 1.325321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414822 Loss1: 0.093881 Loss2: 1.320940 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973633
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.774199 Loss1: 0.372277 Loss2: 1.401922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611223 Loss1: 0.234816 Loss2: 1.376406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.541576 Loss1: 0.169554 Loss2: 1.372021
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.658385 Loss1: 0.823363 Loss2: 1.835022
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.882463 Loss1: 0.505634 Loss2: 1.376828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671682 Loss1: 0.277137 Loss2: 1.394545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553267 Loss1: 0.201213 Loss2: 1.352055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449482 Loss1: 0.099849 Loss2: 1.349633
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505490 Loss1: 0.157856 Loss2: 1.347634
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.475188 Loss1: 0.135848 Loss2: 1.339340
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.462072 Loss1: 0.121655 Loss2: 1.340416
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434205 Loss1: 0.102437 Loss2: 1.331768
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415049 Loss1: 0.084208 Loss2: 1.330841
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.984946 Loss1: 1.109085 Loss2: 1.875862
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397210 Loss1: 0.069759 Loss2: 1.327451
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.763113 Loss1: 0.357257 Loss2: 1.405856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.597338 Loss1: 0.228877 Loss2: 1.368461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.662244 Loss1: 0.865879 Loss2: 1.796365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.996734 Loss1: 0.585186 Loss2: 1.411548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.806993 Loss1: 0.415735 Loss2: 1.391259 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397532 Loss1: 0.057486 Loss2: 1.340047 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994420
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.557783 Loss1: 0.189639 Loss2: 1.368144 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.507885 Loss1: 0.146166 Loss2: 1.361719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.494747 Loss1: 0.133110 Loss2: 1.361637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.451343 Loss1: 0.091879 Loss2: 1.359464 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.571070 Loss1: 0.210246 Loss2: 1.360824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506053 Loss1: 0.143899 Loss2: 1.362154 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.809934 Loss1: 0.947041 Loss2: 1.862893 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.992623 Loss1: 0.591812 Loss2: 1.400812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.873057 Loss1: 0.418803 Loss2: 1.454254 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.729775 Loss1: 0.319239 Loss2: 1.410536 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.541454 Loss1: 0.158759 Loss2: 1.382696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.505794 Loss1: 0.134526 Loss2: 1.371268
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.927377 Loss1: 0.989256 Loss2: 1.938120
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.020006 Loss1: 0.564836 Loss2: 1.455171 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.798770 Loss1: 0.389017 Loss2: 1.409753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.600422 Loss1: 0.200350 Loss2: 1.400073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.557968 Loss1: 0.156026 Loss2: 1.401942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480933 Loss1: 0.101695 Loss2: 1.379238 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477589 Loss1: 0.102432 Loss2: 1.375156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.492270 Loss1: 0.116444 Loss2: 1.375826 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.663967 Loss1: 0.224748 Loss2: 1.439219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.581545 Loss1: 0.155399 Loss2: 1.426146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540027 Loss1: 0.127835 Loss2: 1.412192
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.790859 Loss1: 0.928902 Loss2: 1.861957
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.985098 Loss1: 0.557564 Loss2: 1.427533 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-11 02:39:11,326 | server.py:236 | fit_round 98 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.720096 Loss1: 0.305741 Loss2: 1.414355 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.559709 Loss1: 0.155082 Loss2: 1.404627 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509337 Loss1: 0.113722 Loss2: 1.395615
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.796061 Loss1: 0.969095 Loss2: 1.826965
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.532212 Loss1: 0.139460 Loss2: 1.392751
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.917925 Loss1: 0.546524 Loss2: 1.371401
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.746691 Loss1: 0.346060 Loss2: 1.400632
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.511808 Loss1: 0.119039 Loss2: 1.392769
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605430 Loss1: 0.254040 Loss2: 1.351389
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467733 Loss1: 0.084310 Loss2: 1.383423
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.515278 Loss1: 0.165226 Loss2: 1.350052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450525 Loss1: 0.107555 Loss2: 1.342970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428312 Loss1: 0.096030 Loss2: 1.332282
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.029267 Loss1: 1.180666 Loss2: 1.848601
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430893 Loss1: 0.097259 Loss2: 1.333634
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.066447 Loss1: 0.617826 Loss2: 1.448621
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.818285 Loss1: 0.418756 Loss2: 1.399529
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.685361 Loss1: 0.290038 Loss2: 1.395322
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.695124 Loss1: 0.289756 Loss2: 1.405367
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.606865 Loss1: 0.211582 Loss2: 1.395284
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.627501 Loss1: 0.831532 Loss2: 1.795969
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528993 Loss1: 0.163558 Loss2: 1.365435
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471268 Loss1: 0.104394 Loss2: 1.366873
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.872966 Loss1: 0.490402 Loss2: 1.382563
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454538 Loss1: 0.096356 Loss2: 1.358182
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.752462 Loss1: 0.357027 Loss2: 1.395435
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465770 Loss1: 0.112247 Loss2: 1.353523
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631507 Loss1: 0.254517 Loss2: 1.376990
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569342 Loss1: 0.213156 Loss2: 1.356187
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.488441 Loss1: 0.135514 Loss2: 1.352927
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439442 Loss1: 0.097302 Loss2: 1.342140
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429231 Loss1: 0.092460 Loss2: 1.336771
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410901 Loss1: 0.084852 Loss2: 1.326049
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418913 Loss1: 0.095495 Loss2: 1.323418
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 02:39:11,326][flwr][DEBUG] - fit_round 98 received 50 results and 0 failures
-INFO flwr 2023-10-11 02:39:53,371 | server.py:125 | fit progress: (98, 2.1971591758651856, {'accuracy': 0.5667}, 226101.149762787)
->> Test accuracy: 0.566700
-[2023-10-11 02:39:53,371][flwr][INFO] - fit progress: (98, 2.1971591758651856, {'accuracy': 0.5667}, 226101.149762787)
-DEBUG flwr 2023-10-11 02:39:53,372 | server.py:173 | evaluate_round 98: strategy sampled 50 clients (out of 50)
-[2023-10-11 02:39:53,372][flwr][DEBUG] - evaluate_round 98: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 02:49:01,799 | server.py:187 | evaluate_round 98 received 50 results and 0 failures
-[2023-10-11 02:49:01,799][flwr][DEBUG] - evaluate_round 98 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 02:49:01,799 | server.py:222 | fit_round 99: strategy sampled 50 clients (out of 50)
-[2023-10-11 02:49:01,799][flwr][DEBUG] - fit_round 99: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.920294 Loss1: 1.009401 Loss2: 1.910893
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.068249 Loss1: 0.610990 Loss2: 1.457258
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.956522 Loss1: 0.455532 Loss2: 1.500990
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.752265 Loss1: 0.306590 Loss2: 1.445675
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.862420 Loss1: 0.981099 Loss2: 1.881321
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.642954 Loss1: 0.197482 Loss2: 1.445472
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.020170 Loss1: 0.595395 Loss2: 1.424775
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.612932 Loss1: 0.180536 Loss2: 1.432397
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.795809 Loss1: 0.349631 Loss2: 1.446178
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.584284 Loss1: 0.152135 Loss2: 1.432150
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.661857 Loss1: 0.262198 Loss2: 1.399659
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.592246 Loss1: 0.166417 Loss2: 1.425829
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.578316 Loss1: 0.182539 Loss2: 1.395777
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.555286 Loss1: 0.132400 Loss2: 1.422886
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.583955 Loss1: 0.195747 Loss2: 1.388208
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.589515 Loss1: 0.166387 Loss2: 1.423128
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.551447 Loss1: 0.157261 Loss2: 1.394186
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515585 Loss1: 0.136339 Loss2: 1.379246
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.511642 Loss1: 0.126090 Loss2: 1.385553
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464780 Loss1: 0.086760 Loss2: 1.378020
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.991654 Loss1: 1.022762 Loss2: 1.968892
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.991371 Loss1: 0.624802 Loss2: 1.366569
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.792068 Loss1: 0.388685 Loss2: 1.403383
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.657397 Loss1: 0.265743 Loss2: 1.391654
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.597961 Loss1: 0.225380 Loss2: 1.372580
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.565263 Loss1: 0.188490 Loss2: 1.376774 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.538351 Loss1: 0.168174 Loss2: 1.370178 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.490866 Loss1: 0.132799 Loss2: 1.358068
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727142 Loss1: 0.330229 Loss2: 1.396913
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636885 Loss1: 0.255759 Loss2: 1.381126 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.606852 Loss1: 0.220713 Loss2: 1.386139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486972 Loss1: 0.120934 Loss2: 1.366037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443954 Loss1: 0.096021 Loss2: 1.347933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454356 Loss1: 0.110507 Loss2: 1.343850 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.690561 Loss1: 0.239273 Loss2: 1.451289 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.579642 Loss1: 0.136227 Loss2: 1.443415 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534506 Loss1: 0.097549 Loss2: 1.436957
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.842263 Loss1: 0.928661 Loss2: 1.913602
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.549104 Loss1: 0.116807 Loss2: 1.432297
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.037449 Loss1: 0.566219 Loss2: 1.471230
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.511269 Loss1: 0.081497 Loss2: 1.429772
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.925240 Loss1: 0.436576 Loss2: 1.488664
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491360 Loss1: 0.068242 Loss2: 1.423118
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.850548 Loss1: 0.377707 Loss2: 1.472842
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.755384 Loss1: 0.288648 Loss2: 1.466736
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.705698 Loss1: 0.234883 Loss2: 1.470815
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.661826 Loss1: 0.209916 Loss2: 1.451910
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.611830 Loss1: 0.149672 Loss2: 1.462158
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.631874 Loss1: 0.780715 Loss2: 1.851159
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.922127 Loss1: 0.540055 Loss2: 1.382072 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.744968 Loss1: 0.309427 Loss2: 1.435542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593423 Loss1: 0.215010 Loss2: 1.378414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528725 Loss1: 0.161601 Loss2: 1.367124 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.517628 Loss1: 0.152806 Loss2: 1.364822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.502660 Loss1: 0.139879 Loss2: 1.362780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.548838 Loss1: 0.178201 Loss2: 1.370637 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527078 Loss1: 0.143408 Loss2: 1.383670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490540 Loss1: 0.112207 Loss2: 1.378333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486974 Loss1: 0.116049 Loss2: 1.370924
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.746856 Loss1: 0.915707 Loss2: 1.831149
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.008088 Loss1: 0.626693 Loss2: 1.381395 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470751 Loss1: 0.105409 Loss2: 1.365341
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.903924 Loss1: 0.470113 Loss2: 1.433811
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.714912 Loss1: 0.337114 Loss2: 1.377799
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.634306 Loss1: 0.259469 Loss2: 1.374836
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516253 Loss1: 0.159943 Loss2: 1.356310
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503547 Loss1: 0.151939 Loss2: 1.351607
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.056085 Loss1: 1.151646 Loss2: 1.904439
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.466953 Loss1: 0.119991 Loss2: 1.346961
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.471628 Loss1: 0.131845 Loss2: 1.339784 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474581 Loss1: 0.129085 Loss2: 1.345497 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574478 Loss1: 0.190782 Loss2: 1.383697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.496773 Loss1: 0.147857 Loss2: 1.348917 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.892997 Loss1: 1.042964 Loss2: 1.850033 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.062708 Loss1: 0.659299 Loss2: 1.403409 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982143
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.641509 Loss1: 0.253538 Loss2: 1.387971 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.605533 Loss1: 0.215322 Loss2: 1.390212 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.555177 Loss1: 0.169762 Loss2: 1.385415
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.977999 Loss1: 1.025333 Loss2: 1.952666
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.073390 Loss1: 0.651736 Loss2: 1.421654
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.501636 Loss1: 0.129755 Loss2: 1.371880
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.861904 Loss1: 0.370579 Loss2: 1.491325
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.491977 Loss1: 0.119489 Loss2: 1.372488
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.494322 Loss1: 0.123326 Loss2: 1.370996 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.628559 Loss1: 0.188463 Loss2: 1.440096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.539486 Loss1: 0.131577 Loss2: 1.407909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.467463 Loss1: 0.072116 Loss2: 1.395347 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.952844 Loss1: 0.484402 Loss2: 1.468442 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.684121 Loss1: 0.249315 Loss2: 1.434806 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.929756 Loss1: 1.101253 Loss2: 1.828503
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.667394 Loss1: 0.246747 Loss2: 1.420646
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.937936 Loss1: 0.548008 Loss2: 1.389928
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.614263 Loss1: 0.183052 Loss2: 1.431211
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.774240 Loss1: 0.390678 Loss2: 1.383563
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.560341 Loss1: 0.149205 Loss2: 1.411136
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.621586 Loss1: 0.263710 Loss2: 1.357876
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.524882 Loss1: 0.117727 Loss2: 1.407155
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.601715 Loss1: 0.247839 Loss2: 1.353876
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.511239 Loss1: 0.113516 Loss2: 1.397722
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.538573 Loss1: 0.185388 Loss2: 1.353184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446897 Loss1: 0.107791 Loss2: 1.339105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423017 Loss1: 0.085990 Loss2: 1.337026
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.819767 Loss1: 0.949694 Loss2: 1.870073
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.034495 Loss1: 0.622934 Loss2: 1.411561
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.874163 Loss1: 0.450730 Loss2: 1.423432
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.726195 Loss1: 0.329674 Loss2: 1.396520
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.654608 Loss1: 0.252133 Loss2: 1.402475
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.969649 Loss1: 1.052888 Loss2: 1.916761
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.622055 Loss1: 0.231548 Loss2: 1.390507
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.611029 Loss1: 0.217164 Loss2: 1.393866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.520523 Loss1: 0.129665 Loss2: 1.390858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.457931 Loss1: 0.085581 Loss2: 1.372350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472876 Loss1: 0.113899 Loss2: 1.358977 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516010 Loss1: 0.149390 Loss2: 1.366620 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404445 Loss1: 0.053507 Loss2: 1.350938 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.772355 Loss1: 0.978562 Loss2: 1.793793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716061 Loss1: 0.338576 Loss2: 1.377485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562189 Loss1: 0.216291 Loss2: 1.345897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.923957 Loss1: 0.500387 Loss2: 1.423570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.871852 Loss1: 0.423954 Loss2: 1.447898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.643721 Loss1: 0.218643 Loss2: 1.425078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.593245 Loss1: 0.188364 Loss2: 1.404881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.561297 Loss1: 0.149260 Loss2: 1.412037 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.517696 Loss1: 0.122229 Loss2: 1.395468 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.482848 Loss1: 0.095738 Loss2: 1.387110 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.831810 Loss1: 0.979266 Loss2: 1.852544 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.788129 Loss1: 0.380246 Loss2: 1.407883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.730204 Loss1: 0.885394 Loss2: 1.844810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.030654 Loss1: 0.641383 Loss2: 1.389271 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.787843 Loss1: 0.344142 Loss2: 1.443701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.690319 Loss1: 0.310140 Loss2: 1.380179 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.593422 Loss1: 0.201876 Loss2: 1.391546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.547965 Loss1: 0.173847 Loss2: 1.374118 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.580304 Loss1: 0.191183 Loss2: 1.389121 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.480878 Loss1: 0.120303 Loss2: 1.360576 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.876273 Loss1: 0.919788 Loss2: 1.956485
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.128045 Loss1: 0.657004 Loss2: 1.471041
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.863827 Loss1: 0.343718 Loss2: 1.520110
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.704849 Loss1: 0.261603 Loss2: 1.443245
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.862983 Loss1: 0.938930 Loss2: 1.924053
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.002378 Loss1: 0.542598 Loss2: 1.459780 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.798348 Loss1: 0.327496 Loss2: 1.470852 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.710328 Loss1: 0.273031 Loss2: 1.437297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609930 Loss1: 0.171497 Loss2: 1.438434 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.585371 Loss1: 0.168326 Loss2: 1.417045 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530434 Loss1: 0.115189 Loss2: 1.415244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501208 Loss1: 0.098123 Loss2: 1.403085 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.750848 Loss1: 0.885264 Loss2: 1.865583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.779768 Loss1: 0.350975 Loss2: 1.428793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.695459 Loss1: 0.314091 Loss2: 1.381368
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.838823 Loss1: 1.000268 Loss2: 1.838555
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.908938 Loss1: 0.524762 Loss2: 1.384176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.743414 Loss1: 0.332078 Loss2: 1.411337 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.601819 Loss1: 0.227364 Loss2: 1.374455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576249 Loss1: 0.211419 Loss2: 1.364830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.557411 Loss1: 0.186679 Loss2: 1.370731 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.479144 Loss1: 0.126071 Loss2: 1.353073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439965 Loss1: 0.090242 Loss2: 1.349722 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.955693 Loss1: 1.058318 Loss2: 1.897374 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.737042 Loss1: 0.315884 Loss2: 1.421158 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.993434 Loss1: 1.100304 Loss2: 1.893129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.170028 Loss1: 0.718566 Loss2: 1.451462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.832732 Loss1: 0.387696 Loss2: 1.445037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.776411 Loss1: 0.365768 Loss2: 1.410642 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.701789 Loss1: 0.287488 Loss2: 1.414301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.595699 Loss1: 0.194411 Loss2: 1.401288 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.531725 Loss1: 0.143548 Loss2: 1.388177 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.469087 Loss1: 0.080309 Loss2: 1.388777 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.795993 Loss1: 0.951458 Loss2: 1.844535
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.018617 Loss1: 0.575770 Loss2: 1.442848
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.832782 Loss1: 0.416601 Loss2: 1.416181
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.723601 Loss1: 0.312901 Loss2: 1.410701
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.782383 Loss1: 0.886263 Loss2: 1.896120
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.991012 Loss1: 0.563293 Loss2: 1.427719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.842155 Loss1: 0.382622 Loss2: 1.459533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.527805 Loss1: 0.134255 Loss2: 1.393550
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.760618 Loss1: 0.329850 Loss2: 1.430768
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.622835 Loss1: 0.242696 Loss2: 1.380139
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.636013 Loss1: 0.225298 Loss2: 1.410715
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.506703 Loss1: 0.109396 Loss2: 1.397307
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.562802 Loss1: 0.154684 Loss2: 1.408117
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.532239 Loss1: 0.136507 Loss2: 1.395732
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.488560 Loss1: 0.118046 Loss2: 1.370514
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.531014 Loss1: 0.142092 Loss2: 1.388922 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.872509 Loss1: 1.048570 Loss2: 1.823939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.683881 Loss1: 0.309579 Loss2: 1.374302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596811 Loss1: 0.239753 Loss2: 1.357058
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.870359 Loss1: 0.994712 Loss2: 1.875648
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546646 Loss1: 0.197133 Loss2: 1.349513
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.942661 Loss1: 0.541793 Loss2: 1.400867
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519460 Loss1: 0.173964 Loss2: 1.345495
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.785827 Loss1: 0.362992 Loss2: 1.422835
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.471744 Loss1: 0.135207 Loss2: 1.336537
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.688835 Loss1: 0.314142 Loss2: 1.374693
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411488 Loss1: 0.084341 Loss2: 1.327147
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.648968 Loss1: 0.259334 Loss2: 1.389634
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421968 Loss1: 0.101547 Loss2: 1.320421
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.564961 Loss1: 0.195367 Loss2: 1.369595
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398511 Loss1: 0.080660 Loss2: 1.317851
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469938 Loss1: 0.114984 Loss2: 1.354955
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485876 Loss1: 0.135812 Loss2: 1.350064
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486032 Loss1: 0.132674 Loss2: 1.353358
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456333 Loss1: 0.110172 Loss2: 1.346161
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.775231 Loss1: 0.885230 Loss2: 1.890002
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.960477 Loss1: 0.557941 Loss2: 1.402535
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.808023 Loss1: 0.361503 Loss2: 1.446520
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649602 Loss1: 0.255338 Loss2: 1.394264
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.014919 Loss1: 1.040864 Loss2: 1.974055
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.123416 Loss1: 0.627747 Loss2: 1.495669 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.919017 Loss1: 0.417976 Loss2: 1.501040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.763724 Loss1: 0.280804 Loss2: 1.482920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.705174 Loss1: 0.241669 Loss2: 1.463505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.662687 Loss1: 0.208426 Loss2: 1.454261 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453568 Loss1: 0.080947 Loss2: 1.372622
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.614894 Loss1: 0.166432 Loss2: 1.448462
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.552760 Loss1: 0.114736 Loss2: 1.438024
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.500826 Loss1: 0.069829 Loss2: 1.430997
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491718 Loss1: 0.073749 Loss2: 1.417970
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.765486 Loss1: 0.911729 Loss2: 1.853757
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.866819 Loss1: 0.493616 Loss2: 1.373204
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.707867 Loss1: 0.309021 Loss2: 1.398847
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.625394 Loss1: 0.266867 Loss2: 1.358526
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.905715 Loss1: 0.983132 Loss2: 1.922584
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.552640 Loss1: 0.186429 Loss2: 1.366211
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.103592 Loss1: 0.667773 Loss2: 1.435819
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476855 Loss1: 0.125809 Loss2: 1.351046
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.871234 Loss1: 0.385687 Loss2: 1.485547
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440260 Loss1: 0.099764 Loss2: 1.340496
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.797149 Loss1: 0.349993 Loss2: 1.447157
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.442585 Loss1: 0.103046 Loss2: 1.339539
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.658246 Loss1: 0.212529 Loss2: 1.445717
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.420430 Loss1: 0.089455 Loss2: 1.330974
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.571999 Loss1: 0.142898 Loss2: 1.429101
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440480 Loss1: 0.110618 Loss2: 1.329862
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.582217 Loss1: 0.162086 Loss2: 1.420132
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.533523 Loss1: 0.111116 Loss2: 1.422407
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.518071 Loss1: 0.101873 Loss2: 1.416198
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482552 Loss1: 0.070964 Loss2: 1.411588
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.789828 Loss1: 0.968981 Loss2: 1.820848
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.051203 Loss1: 0.663708 Loss2: 1.387495
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.823077 Loss1: 0.411435 Loss2: 1.411641
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642089 Loss1: 0.286246 Loss2: 1.355843
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.775320 Loss1: 0.946248 Loss2: 1.829072
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.991607 Loss1: 0.602301 Loss2: 1.389306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.767564 Loss1: 0.374733 Loss2: 1.392831 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.639282 Loss1: 0.263777 Loss2: 1.375505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.617032 Loss1: 0.250534 Loss2: 1.366498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.532583 Loss1: 0.167755 Loss2: 1.364828 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.504681 Loss1: 0.154845 Loss2: 1.349836 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463624 Loss1: 0.112048 Loss2: 1.351577 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.934520 Loss1: 0.527243 Loss2: 1.407278 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.648445 Loss1: 0.267516 Loss2: 1.380929 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.592178 Loss1: 0.195736 Loss2: 1.396442
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.910073 Loss1: 0.957553 Loss2: 1.952521
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514608 Loss1: 0.141816 Loss2: 1.372792
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.078217 Loss1: 0.592480 Loss2: 1.485737
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.499755 Loss1: 0.138021 Loss2: 1.361733
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.888198 Loss1: 0.376431 Loss2: 1.511768
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459042 Loss1: 0.104397 Loss2: 1.354645
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.804120 Loss1: 0.333744 Loss2: 1.470376
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435203 Loss1: 0.084373 Loss2: 1.350830
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.719226 Loss1: 0.234841 Loss2: 1.484386
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437249 Loss1: 0.095812 Loss2: 1.341437
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.616538 Loss1: 0.154907 Loss2: 1.461631
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.607018 Loss1: 0.158813 Loss2: 1.448205
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.578041 Loss1: 0.130746 Loss2: 1.447295
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.585081 Loss1: 0.137639 Loss2: 1.447442
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.542961 Loss1: 0.103581 Loss2: 1.439380
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.840110 Loss1: 0.972870 Loss2: 1.867240
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.049545 Loss1: 0.600785 Loss2: 1.448759
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.916783 Loss1: 0.471039 Loss2: 1.445743
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.821125 Loss1: 0.385055 Loss2: 1.436070
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.698917 Loss1: 0.884073 Loss2: 1.814843
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.967950 Loss1: 0.569641 Loss2: 1.398309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.770639 Loss1: 0.342891 Loss2: 1.427748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631127 Loss1: 0.256569 Loss2: 1.374558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549312 Loss1: 0.170093 Loss2: 1.379219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521117 Loss1: 0.156216 Loss2: 1.364901 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473216 Loss1: 0.125642 Loss2: 1.347575 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431508 Loss1: 0.087992 Loss2: 1.343516 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.048795 Loss1: 0.672105 Loss2: 1.376690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.667280 Loss1: 0.302216 Loss2: 1.365064 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.906159 Loss1: 1.044460 Loss2: 1.861699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.070582 Loss1: 0.656630 Loss2: 1.413952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.894577 Loss1: 0.469233 Loss2: 1.425344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.695447 Loss1: 0.315075 Loss2: 1.380372 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.499108 Loss1: 0.138987 Loss2: 1.360121 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975962
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494499 Loss1: 0.128289 Loss2: 1.366210 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464693 Loss1: 0.109999 Loss2: 1.354694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.476205 Loss1: 0.116924 Loss2: 1.359281
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.683834 Loss1: 0.836329 Loss2: 1.847505
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.829675 Loss1: 0.432313 Loss2: 1.397362
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684788 Loss1: 0.257036 Loss2: 1.427751
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558850 Loss1: 0.175420 Loss2: 1.383430
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534189 Loss1: 0.163382 Loss2: 1.370807
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.720690 Loss1: 0.817819 Loss2: 1.902871
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564038 Loss1: 0.187160 Loss2: 1.376878
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531709 Loss1: 0.152672 Loss2: 1.379038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.538723 Loss1: 0.171297 Loss2: 1.367427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.517910 Loss1: 0.137992 Loss2: 1.379918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.500582 Loss1: 0.130400 Loss2: 1.370181 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966797
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.466750 Loss1: 0.083843 Loss2: 1.382907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450312 Loss1: 0.077738 Loss2: 1.372573 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436597 Loss1: 0.067296 Loss2: 1.369301
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.960640 Loss1: 1.104006 Loss2: 1.856634
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.957211 Loss1: 0.572344 Loss2: 1.384868
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.726792 Loss1: 0.333680 Loss2: 1.393112
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582168 Loss1: 0.223059 Loss2: 1.359109
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544614 Loss1: 0.192295 Loss2: 1.352320
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.678042 Loss1: 0.892832 Loss2: 1.785210
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.838670 Loss1: 0.527052 Loss2: 1.311618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.638721 Loss1: 0.279668 Loss2: 1.359053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555629 Loss1: 0.251647 Loss2: 1.303981 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.517125 Loss1: 0.206975 Loss2: 1.310150 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503637 Loss1: 0.205067 Loss2: 1.298569 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376141 Loss1: 0.087244 Loss2: 1.288897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376502 Loss1: 0.095884 Loss2: 1.280618 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.056565 Loss1: 0.659574 Loss2: 1.396991 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 03:17:44,020 | server.py:236 | fit_round 99 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642346 Loss1: 0.271398 Loss2: 1.370948 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481209 Loss1: 0.133480 Loss2: 1.347729 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518437 Loss1: 0.168481 Loss2: 1.349956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.487434 Loss1: 0.136537 Loss2: 1.350897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.479641 Loss1: 0.131898 Loss2: 1.347742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447871 Loss1: 0.104499 Loss2: 1.343373 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444181 Loss1: 0.071756 Loss2: 1.372425 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454458 Loss1: 0.090039 Loss2: 1.364419 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.596046 Loss1: 0.766634 Loss2: 1.829412
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435019 Loss1: 0.077457 Loss2: 1.357561
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.763871 Loss1: 0.360554 Loss2: 1.403317 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531517 Loss1: 0.166647 Loss2: 1.364869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523620 Loss1: 0.166853 Loss2: 1.356766 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466954 Loss1: 0.116082 Loss2: 1.350872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456192 Loss1: 0.113876 Loss2: 1.342316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.481938 Loss1: 0.136521 Loss2: 1.345417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435759 Loss1: 0.090217 Loss2: 1.345542 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986213
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518990 Loss1: 0.123729 Loss2: 1.395261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.511009 Loss1: 0.113974 Loss2: 1.397035 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 03:17:44,020][flwr][DEBUG] - fit_round 99 received 50 results and 0 failures
-INFO flwr 2023-10-11 03:18:26,620 | server.py:125 | fit progress: (99, 2.2024877379877497, {'accuracy': 0.5666}, 228414.399005038)
->> Test accuracy: 0.566600
-[2023-10-11 03:18:26,620][flwr][INFO] - fit progress: (99, 2.2024877379877497, {'accuracy': 0.5666}, 228414.399005038)
-DEBUG flwr 2023-10-11 03:18:26,621 | server.py:173 | evaluate_round 99: strategy sampled 50 clients (out of 50)
-[2023-10-11 03:18:26,621][flwr][DEBUG] - evaluate_round 99: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 03:27:29,176 | server.py:187 | evaluate_round 99 received 50 results and 0 failures
-[2023-10-11 03:27:29,176][flwr][DEBUG] - evaluate_round 99 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 03:27:29,176 | server.py:222 | fit_round 100: strategy sampled 50 clients (out of 50)
-[2023-10-11 03:27:29,176][flwr][DEBUG] - fit_round 100: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.865358 Loss1: 1.041977 Loss2: 1.823381 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.809730 Loss1: 0.401109 Loss2: 1.408621 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638140 Loss1: 0.298792 Loss2: 1.339348
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.125170 Loss1: 1.181259 Loss2: 1.943911
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.103731 Loss1: 0.674316 Loss2: 1.429415 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.909857 Loss1: 0.437087 Loss2: 1.472770 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.724176 Loss1: 0.325762 Loss2: 1.398414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.603326 Loss1: 0.191396 Loss2: 1.411930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546531 Loss1: 0.145327 Loss2: 1.401204 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533816 Loss1: 0.143148 Loss2: 1.390668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.503468 Loss1: 0.116130 Loss2: 1.387338 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967634
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.892776 Loss1: 1.051836 Loss2: 1.840939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.779324 Loss1: 0.371040 Loss2: 1.408283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629292 Loss1: 0.239964 Loss2: 1.389328
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.016863 Loss1: 1.027504 Loss2: 1.989359
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.233814 Loss1: 0.682749 Loss2: 1.551065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.913386 Loss1: 0.397899 Loss2: 1.515487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.798393 Loss1: 0.301357 Loss2: 1.497036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.688534 Loss1: 0.198287 Loss2: 1.490247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.605623 Loss1: 0.128292 Loss2: 1.477331 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.501618 Loss1: 0.128568 Loss2: 1.373050
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.554079 Loss1: 0.093138 Loss2: 1.460941
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.559961 Loss1: 0.109307 Loss2: 1.450654
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.574590 Loss1: 0.115651 Loss2: 1.458939
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.537640 Loss1: 0.083305 Loss2: 1.454335
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.831510 Loss1: 0.965167 Loss2: 1.866344
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.964435 Loss1: 0.577272 Loss2: 1.387163
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.793172 Loss1: 0.376868 Loss2: 1.416304
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.665181 Loss1: 0.281076 Loss2: 1.384105
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.855543 Loss1: 0.972666 Loss2: 1.882877
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.036593 Loss1: 0.635104 Loss2: 1.401489 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.790148 Loss1: 0.349927 Loss2: 1.440221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.661073 Loss1: 0.271975 Loss2: 1.389097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.641637 Loss1: 0.251603 Loss2: 1.390033 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.640745 Loss1: 0.250391 Loss2: 1.390354 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.542713 Loss1: 0.152301 Loss2: 1.390412 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.471286 Loss1: 0.098358 Loss2: 1.372928 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.929802 Loss1: 1.007783 Loss2: 1.922019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.886746 Loss1: 0.393478 Loss2: 1.493268 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.784964 Loss1: 0.331431 Loss2: 1.453533
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.955674 Loss1: 1.053151 Loss2: 1.902523
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.109245 Loss1: 0.678741 Loss2: 1.430504 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.839602 Loss1: 0.383168 Loss2: 1.456433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.700423 Loss1: 0.279627 Loss2: 1.420796 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.566225 Loss1: 0.151778 Loss2: 1.414446 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523696 Loss1: 0.120763 Loss2: 1.402933 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494179 Loss1: 0.098729 Loss2: 1.395449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487084 Loss1: 0.098603 Loss2: 1.388481 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.785661 Loss1: 0.706717 Loss2: 2.078944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 2.031012 Loss1: 0.404759 Loss2: 1.626253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.908460 Loss1: 0.344627 Loss2: 1.563833
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.692580 Loss1: 0.899209 Loss2: 1.793371
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.793507 Loss1: 0.465777 Loss2: 1.327730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.663604 Loss1: 0.311826 Loss2: 1.351777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.656847 Loss1: 0.339944 Loss2: 1.316903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568106 Loss1: 0.225969 Loss2: 1.342137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.448142 Loss1: 0.149086 Loss2: 1.299056 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435410 Loss1: 0.132088 Loss2: 1.303322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366850 Loss1: 0.071601 Loss2: 1.295249 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.795898 Loss1: 0.944658 Loss2: 1.851240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.866403 Loss1: 0.431372 Loss2: 1.435031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.782098 Loss1: 0.345274 Loss2: 1.436825
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.673525 Loss1: 0.822526 Loss2: 1.850999
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.923038 Loss1: 0.486903 Loss2: 1.436134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.747512 Loss1: 0.310968 Loss2: 1.436544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.641596 Loss1: 0.241039 Loss2: 1.400557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550169 Loss1: 0.151789 Loss2: 1.398379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.558799 Loss1: 0.169621 Loss2: 1.389178 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500900 Loss1: 0.111992 Loss2: 1.388908 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442270 Loss1: 0.066230 Loss2: 1.376041 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.655594 Loss1: 0.817781 Loss2: 1.837812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.788778 Loss1: 0.384246 Loss2: 1.404531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.789741 Loss1: 0.924844 Loss2: 1.864898 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475991 Loss1: 0.145837 Loss2: 1.330154 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426378 Loss1: 0.103814 Loss2: 1.322565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430906 Loss1: 0.104362 Loss2: 1.326544 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400963 Loss1: 0.082351 Loss2: 1.318612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397327 Loss1: 0.086677 Loss2: 1.310650 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.553516 Loss1: 0.161895 Loss2: 1.391621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.491167 Loss1: 0.105106 Loss2: 1.386061 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.473365 Loss1: 0.092054 Loss2: 1.381311
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.886520 Loss1: 1.028781 Loss2: 1.857739
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.045082 Loss1: 0.626347 Loss2: 1.418735
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.861702 Loss1: 0.414435 Loss2: 1.447266
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.696883 Loss1: 0.310850 Loss2: 1.386033
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.652966 Loss1: 0.249029 Loss2: 1.403937
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.888682 Loss1: 1.034065 Loss2: 1.854617
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.116871 Loss1: 0.698024 Loss2: 1.418847 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.777855 Loss1: 0.335080 Loss2: 1.442776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.663093 Loss1: 0.278023 Loss2: 1.385071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.607053 Loss1: 0.209644 Loss2: 1.397409 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537631 Loss1: 0.154554 Loss2: 1.383076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.517305 Loss1: 0.143165 Loss2: 1.374140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.474396 Loss1: 0.107037 Loss2: 1.367359 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.926376 Loss1: 0.507919 Loss2: 1.418457 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.657582 Loss1: 0.255903 Loss2: 1.401679 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631811 Loss1: 0.222414 Loss2: 1.409396
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.799379 Loss1: 0.866754 Loss2: 1.932625
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.579055 Loss1: 0.178775 Loss2: 1.400281
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.942540 Loss1: 0.489979 Loss2: 1.452561
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509392 Loss1: 0.119123 Loss2: 1.390269
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.772051 Loss1: 0.295498 Loss2: 1.476553
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.485144 Loss1: 0.106411 Loss2: 1.378733
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.672201 Loss1: 0.240542 Loss2: 1.431659
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.481912 Loss1: 0.103892 Loss2: 1.378019
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.610226 Loss1: 0.175957 Loss2: 1.434269
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477625 Loss1: 0.096149 Loss2: 1.381475
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.631733 Loss1: 0.214258 Loss2: 1.417475
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.624095 Loss1: 0.188535 Loss2: 1.435560
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.565613 Loss1: 0.151935 Loss2: 1.413679
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.577047 Loss1: 0.161280 Loss2: 1.415767
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.574735 Loss1: 0.165127 Loss2: 1.409608
-(DefaultActor pid=3764) >> Training accuracy: 0.960938
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.780057 Loss1: 0.895952 Loss2: 1.884105
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.840328 Loss1: 0.453477 Loss2: 1.386850
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.735338 Loss1: 0.335745 Loss2: 1.399593
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.593187 Loss1: 0.216548 Loss2: 1.376639
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544979 Loss1: 0.179705 Loss2: 1.365274
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.686370 Loss1: 0.891767 Loss2: 1.794602
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.964519 Loss1: 0.599852 Loss2: 1.364667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.667200 Loss1: 0.300919 Loss2: 1.366281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540709 Loss1: 0.205084 Loss2: 1.335626 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495189 Loss1: 0.161927 Loss2: 1.333261 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469802 Loss1: 0.123055 Loss2: 1.346747
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.462449 Loss1: 0.142893 Loss2: 1.319556
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423346 Loss1: 0.103199 Loss2: 1.320147
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431368 Loss1: 0.112694 Loss2: 1.318675
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420665 Loss1: 0.106923 Loss2: 1.313742
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413202 Loss1: 0.101367 Loss2: 1.311834
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.805851 Loss1: 0.967962 Loss2: 1.837889
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.004764 Loss1: 0.586858 Loss2: 1.417905
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.827899 Loss1: 0.399387 Loss2: 1.428512
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.628337 Loss1: 0.245077 Loss2: 1.383260
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.624621 Loss1: 0.230328 Loss2: 1.394293
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509318 Loss1: 0.133237 Loss2: 1.376082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483331 Loss1: 0.114226 Loss2: 1.369104 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449340 Loss1: 0.083075 Loss2: 1.366265 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423530 Loss1: 0.074177 Loss2: 1.349353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421419 Loss1: 0.074591 Loss2: 1.346827 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500119 Loss1: 0.183283 Loss2: 1.316836 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422872 Loss1: 0.112197 Loss2: 1.310674 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.954167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.135157 Loss1: 0.715587 Loss2: 1.419570 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.653054 Loss1: 0.278822 Loss2: 1.374232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.942953 Loss1: 0.973168 Loss2: 1.969785
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606181 Loss1: 0.254347 Loss2: 1.351834
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502774 Loss1: 0.166231 Loss2: 1.336543 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479791 Loss1: 0.145401 Loss2: 1.334389 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.465825 Loss1: 0.137475 Loss2: 1.328350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436836 Loss1: 0.108193 Loss2: 1.328644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396739 Loss1: 0.074509 Loss2: 1.322230 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480964 Loss1: 0.099479 Loss2: 1.381485 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986779
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.825128 Loss1: 0.954339 Loss2: 1.870789 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.759102 Loss1: 0.324823 Loss2: 1.434279 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.616936 Loss1: 0.214639 Loss2: 1.402297
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.784372 Loss1: 0.932460 Loss2: 1.851912
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.554204 Loss1: 0.156686 Loss2: 1.397518
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.936563 Loss1: 0.551532 Loss2: 1.385031
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519390 Loss1: 0.133934 Loss2: 1.385456
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.819764 Loss1: 0.387432 Loss2: 1.432332
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472790 Loss1: 0.092012 Loss2: 1.380779
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.600280 Loss1: 0.223944 Loss2: 1.376336
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440159 Loss1: 0.066126 Loss2: 1.374033
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518188 Loss1: 0.140973 Loss2: 1.377215
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430240 Loss1: 0.060788 Loss2: 1.369452
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491430 Loss1: 0.133876 Loss2: 1.357554
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437856 Loss1: 0.073545 Loss2: 1.364311
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458619 Loss1: 0.100507 Loss2: 1.358113
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447416 Loss1: 0.094035 Loss2: 1.353381
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449120 Loss1: 0.102904 Loss2: 1.346216
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427070 Loss1: 0.081260 Loss2: 1.345810
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.865250 Loss1: 1.028010 Loss2: 1.837241
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.914002 Loss1: 0.596548 Loss2: 1.317454
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.874457 Loss1: 0.491236 Loss2: 1.383221
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.614816 Loss1: 0.278515 Loss2: 1.336301
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.607068 Loss1: 0.793986 Loss2: 1.813082
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471604 Loss1: 0.151478 Loss2: 1.320126 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.437136 Loss1: 0.127356 Loss2: 1.309780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.387730 Loss1: 0.084824 Loss2: 1.302906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.370575 Loss1: 0.076112 Loss2: 1.294463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.325925 Loss1: 0.038263 Loss2: 1.287662 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.507926 Loss1: 0.189534 Loss2: 1.318392 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460276 Loss1: 0.141303 Loss2: 1.318973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404126 Loss1: 0.086547 Loss2: 1.317579
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.662885 Loss1: 0.816201 Loss2: 1.846684
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.920977 Loss1: 0.513211 Loss2: 1.407766
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.779997 Loss1: 0.339694 Loss2: 1.440304
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.658019 Loss1: 0.260987 Loss2: 1.397032
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.597181 Loss1: 0.198378 Loss2: 1.398803
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.821530 Loss1: 0.940888 Loss2: 1.880642
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587698 Loss1: 0.200382 Loss2: 1.387315
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.960941 Loss1: 0.535896 Loss2: 1.425045
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.772306 Loss1: 0.314408 Loss2: 1.457898
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.559764 Loss1: 0.169819 Loss2: 1.389945
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.655768 Loss1: 0.241346 Loss2: 1.414422
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495011 Loss1: 0.111309 Loss2: 1.383702
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.503247 Loss1: 0.124376 Loss2: 1.378871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484408 Loss1: 0.101504 Loss2: 1.382904 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991728
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546895 Loss1: 0.151733 Loss2: 1.395162 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.510308 Loss1: 0.125268 Loss2: 1.385040 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.970833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.825334 Loss1: 0.941523 Loss2: 1.883811
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.851374 Loss1: 0.460485 Loss2: 1.390888
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645137 Loss1: 0.265255 Loss2: 1.379882
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.674823 Loss1: 0.313656 Loss2: 1.361167
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.823833 Loss1: 0.985251 Loss2: 1.838582
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.986929 Loss1: 0.610835 Loss2: 1.376095 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804069 Loss1: 0.392825 Loss2: 1.411244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.700190 Loss1: 0.330394 Loss2: 1.369795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571746 Loss1: 0.202772 Loss2: 1.368974 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546262 Loss1: 0.190679 Loss2: 1.355583 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445838 Loss1: 0.104500 Loss2: 1.341338
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494794 Loss1: 0.144933 Loss2: 1.349861
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462138 Loss1: 0.119834 Loss2: 1.342304
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462720 Loss1: 0.123741 Loss2: 1.338980
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427936 Loss1: 0.090346 Loss2: 1.337590
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.742399 Loss1: 0.933715 Loss2: 1.808684
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.913290 Loss1: 0.512397 Loss2: 1.400893
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.710263 Loss1: 0.302156 Loss2: 1.408108
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638455 Loss1: 0.269577 Loss2: 1.368878
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.745975 Loss1: 0.900253 Loss2: 1.845723
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568830 Loss1: 0.194467 Loss2: 1.374363
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.968316 Loss1: 0.615903 Loss2: 1.352413
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508540 Loss1: 0.150349 Loss2: 1.358191
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.758166 Loss1: 0.359269 Loss2: 1.398897
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.608073 Loss1: 0.260480 Loss2: 1.347593
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473073 Loss1: 0.120707 Loss2: 1.352366
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519052 Loss1: 0.164605 Loss2: 1.354447
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436542 Loss1: 0.086188 Loss2: 1.350354
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.454178 Loss1: 0.121734 Loss2: 1.332445
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462018 Loss1: 0.118305 Loss2: 1.343713
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436937 Loss1: 0.109598 Loss2: 1.327338
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.468540 Loss1: 0.118472 Loss2: 1.350068
-(DefaultActor pid=3765) >> Training accuracy: 0.974609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425244 Loss1: 0.107771 Loss2: 1.317473 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.752205 Loss1: 0.906099 Loss2: 1.846105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.735001 Loss1: 0.326941 Loss2: 1.408060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647337 Loss1: 0.275501 Loss2: 1.371836
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.737414 Loss1: 0.937425 Loss2: 1.799988
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.131864 Loss1: 0.715216 Loss2: 1.416647 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.770358 Loss1: 0.384842 Loss2: 1.385516 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.682941 Loss1: 0.333700 Loss2: 1.349241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.588861 Loss1: 0.230001 Loss2: 1.358860 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.562276 Loss1: 0.221409 Loss2: 1.340866 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437083 Loss1: 0.086676 Loss2: 1.350406
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474170 Loss1: 0.145498 Loss2: 1.328671
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462290 Loss1: 0.144759 Loss2: 1.317531
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412533 Loss1: 0.092985 Loss2: 1.319548
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407290 Loss1: 0.092748 Loss2: 1.314542
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.754004 Loss1: 1.021838 Loss2: 1.732166
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.922737 Loss1: 0.591295 Loss2: 1.331442
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.680894 Loss1: 0.347660 Loss2: 1.333233
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520365 Loss1: 0.215387 Loss2: 1.304978
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.860129 Loss1: 1.026396 Loss2: 1.833732
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.029715 Loss1: 0.646027 Loss2: 1.383689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.774044 Loss1: 0.404415 Loss2: 1.369629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.596102 Loss1: 0.240679 Loss2: 1.355423 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.533686 Loss1: 0.183145 Loss2: 1.350542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474967 Loss1: 0.140542 Loss2: 1.334424 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361546 Loss1: 0.095489 Loss2: 1.266057
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437108 Loss1: 0.108949 Loss2: 1.328160
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422497 Loss1: 0.090633 Loss2: 1.331863
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418092 Loss1: 0.094576 Loss2: 1.323515
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390429 Loss1: 0.072163 Loss2: 1.318266
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.724673 Loss1: 0.872084 Loss2: 1.852590
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.851297 Loss1: 0.499330 Loss2: 1.351968
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.739664 Loss1: 0.341718 Loss2: 1.397946
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.603659 Loss1: 0.246417 Loss2: 1.357242
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.796461 Loss1: 0.967999 Loss2: 1.828462
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.016785 Loss1: 0.624442 Loss2: 1.392343 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.813676 Loss1: 0.422520 Loss2: 1.391156 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.737489 Loss1: 0.346818 Loss2: 1.390671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.621715 Loss1: 0.257045 Loss2: 1.364670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529249 Loss1: 0.175297 Loss2: 1.353952 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419389 Loss1: 0.097992 Loss2: 1.321397
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.503758 Loss1: 0.155642 Loss2: 1.348116
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.514794 Loss1: 0.163960 Loss2: 1.350835
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.489346 Loss1: 0.146033 Loss2: 1.343313
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482636 Loss1: 0.141401 Loss2: 1.341235
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.754057 Loss1: 0.848558 Loss2: 1.905499
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.944494 Loss1: 0.538726 Loss2: 1.405767
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.750351 Loss1: 0.302967 Loss2: 1.447385
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.712466 Loss1: 0.315760 Loss2: 1.396706
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.857655 Loss1: 1.023994 Loss2: 1.833661
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.965438 Loss1: 0.559519 Loss2: 1.405920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.765472 Loss1: 0.342530 Loss2: 1.422942 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.653637 Loss1: 0.267037 Loss2: 1.386600 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.620562 Loss1: 0.233064 Loss2: 1.387498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556876 Loss1: 0.170804 Loss2: 1.386072 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.513884 Loss1: 0.135626 Loss2: 1.378258 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.486901 Loss1: 0.126157 Loss2: 1.360744 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.977897 Loss1: 0.551414 Loss2: 1.426483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.727290 Loss1: 0.301560 Loss2: 1.425730 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.659972 Loss1: 0.232527 Loss2: 1.427445
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.948692 Loss1: 1.105387 Loss2: 1.843305
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.605692 Loss1: 0.199969 Loss2: 1.405723
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.037040 Loss1: 0.660856 Loss2: 1.376184
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.552878 Loss1: 0.141487 Loss2: 1.411391
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.805707 Loss1: 0.413827 Loss2: 1.391880
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500797 Loss1: 0.110061 Loss2: 1.390736
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.626840 Loss1: 0.285018 Loss2: 1.341822
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465815 Loss1: 0.074242 Loss2: 1.391574
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.534171 Loss1: 0.193662 Loss2: 1.340509
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.504107 Loss1: 0.113727 Loss2: 1.390379
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538164 Loss1: 0.196889 Loss2: 1.341275
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472307 Loss1: 0.137440 Loss2: 1.334866
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445584 Loss1: 0.123654 Loss2: 1.321930
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439956 Loss1: 0.119852 Loss2: 1.320104
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401599 Loss1: 0.080418 Loss2: 1.321181
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.980199 Loss1: 1.048878 Loss2: 1.931322
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.010150 Loss1: 0.557372 Loss2: 1.452778
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.813693 Loss1: 0.343718 Loss2: 1.469975
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694482 Loss1: 0.256112 Loss2: 1.438370
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.911904 Loss1: 1.007605 Loss2: 1.904299
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.090237 Loss1: 0.706300 Loss2: 1.383937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.859053 Loss1: 0.431933 Loss2: 1.427119 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.653562 Loss1: 0.293199 Loss2: 1.360363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554277 Loss1: 0.173961 Loss2: 1.380316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494912 Loss1: 0.136335 Loss2: 1.358577 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443411 Loss1: 0.096774 Loss2: 1.346637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430653 Loss1: 0.097712 Loss2: 1.332941 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 03:56:20,067 | server.py:236 | fit_round 100 received 50 results and 0 failures
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.697562 Loss1: 0.869867 Loss2: 1.827694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.714948 Loss1: 0.281153 Loss2: 1.433795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.883447 Loss1: 0.978281 Loss2: 1.905166
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649821 Loss1: 0.257568 Loss2: 1.392253
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.001560 Loss1: 0.593429 Loss2: 1.408130
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611336 Loss1: 0.202175 Loss2: 1.409160
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.709711 Loss1: 0.283114 Loss2: 1.426597
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523561 Loss1: 0.146041 Loss2: 1.377520
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.645048 Loss1: 0.247484 Loss2: 1.397564
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.551072 Loss1: 0.167641 Loss2: 1.383431
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500784 Loss1: 0.115829 Loss2: 1.384956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.495092 Loss1: 0.117432 Loss2: 1.377660 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.493356 Loss1: 0.127404 Loss2: 1.365952 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.477439 Loss1: 0.103095 Loss2: 1.374344 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.083631 Loss1: 1.063814 Loss2: 2.019817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.954320 Loss1: 0.458660 Loss2: 1.495660 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.669941 Loss1: 0.274176 Loss2: 1.395765 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.945961 Loss1: 0.555915 Loss2: 1.390046 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.543415 Loss1: 0.150660 Loss2: 1.392754 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.677806 Loss1: 0.297226 Loss2: 1.380579 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528605 Loss1: 0.171256 Loss2: 1.357349 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.509108 Loss1: 0.148664 Loss2: 1.360444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470216 Loss1: 0.116048 Loss2: 1.354169 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975586
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 03:56:20,067][flwr][DEBUG] - fit_round 100 received 50 results and 0 failures
-INFO flwr 2023-10-11 03:57:02,805 | server.py:125 | fit progress: (100, 2.2043136573447204, {'accuracy': 0.5667}, 230730.583792349)
->> Test accuracy: 0.566700
-[2023-10-11 03:57:02,805][flwr][INFO] - fit progress: (100, 2.2043136573447204, {'accuracy': 0.5667}, 230730.583792349)
-DEBUG flwr 2023-10-11 03:57:02,806 | server.py:173 | evaluate_round 100: strategy sampled 50 clients (out of 50)
-[2023-10-11 03:57:02,806][flwr][DEBUG] - evaluate_round 100: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 04:06:07,050 | server.py:187 | evaluate_round 100 received 50 results and 0 failures
-[2023-10-11 04:06:07,050][flwr][DEBUG] - evaluate_round 100 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 04:06:07,050 | server.py:222 | fit_round 101: strategy sampled 50 clients (out of 50)
-[2023-10-11 04:06:07,050][flwr][DEBUG] - fit_round 101: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.744296 Loss1: 0.912524 Loss2: 1.831771
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.051886 Loss1: 0.660426 Loss2: 1.391460
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.887061 Loss1: 0.506079 Loss2: 1.380981
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647543 Loss1: 0.281962 Loss2: 1.365581
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.044514 Loss1: 1.107124 Loss2: 1.937390
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.583577 Loss1: 0.224629 Loss2: 1.358948
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.090629 Loss1: 0.621181 Loss2: 1.469448
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503713 Loss1: 0.167327 Loss2: 1.336385
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.902232 Loss1: 0.413073 Loss2: 1.489158
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483504 Loss1: 0.147393 Loss2: 1.336111
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.735154 Loss1: 0.299224 Loss2: 1.435930
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.472663 Loss1: 0.142652 Loss2: 1.330011
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.670471 Loss1: 0.225102 Loss2: 1.445369
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418248 Loss1: 0.087884 Loss2: 1.330363
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.606315 Loss1: 0.175635 Loss2: 1.430680
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393663 Loss1: 0.074047 Loss2: 1.319615
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.601663 Loss1: 0.176877 Loss2: 1.424785
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.579821 Loss1: 0.149898 Loss2: 1.429923
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.558529 Loss1: 0.141819 Loss2: 1.416711
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.506564 Loss1: 0.095023 Loss2: 1.411541
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.875908 Loss1: 0.960230 Loss2: 1.915677
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.987915 Loss1: 0.544034 Loss2: 1.443880
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.862320 Loss1: 0.373675 Loss2: 1.488645
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.769950 Loss1: 0.347514 Loss2: 1.422436
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.951340 Loss1: 1.064259 Loss2: 1.887081
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.073302 Loss1: 0.621506 Loss2: 1.451795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.732234 Loss1: 0.293335 Loss2: 1.438899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.570634 Loss1: 0.178185 Loss2: 1.392449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554355 Loss1: 0.155332 Loss2: 1.399023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498776 Loss1: 0.117403 Loss2: 1.381373 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452552 Loss1: 0.060968 Loss2: 1.391584
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.524017 Loss1: 0.138703 Loss2: 1.385314
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490964 Loss1: 0.104555 Loss2: 1.386409
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466001 Loss1: 0.088932 Loss2: 1.377069
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464365 Loss1: 0.092669 Loss2: 1.371696
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.889437 Loss1: 0.979626 Loss2: 1.909811
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.186245 Loss1: 0.726601 Loss2: 1.459644
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.978718 Loss1: 0.523650 Loss2: 1.455069
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.721412 Loss1: 0.311788 Loss2: 1.409624
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.823518 Loss1: 0.960426 Loss2: 1.863092
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.958850 Loss1: 0.569099 Loss2: 1.389751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.715034 Loss1: 0.301130 Loss2: 1.413904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.638495 Loss1: 0.262747 Loss2: 1.375748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527497 Loss1: 0.161052 Loss2: 1.366445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503161 Loss1: 0.137229 Loss2: 1.365932 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468424 Loss1: 0.121102 Loss2: 1.347322 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434043 Loss1: 0.091547 Loss2: 1.342496 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.060896 Loss1: 0.639332 Loss2: 1.421564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.667690 Loss1: 0.271468 Loss2: 1.396222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.981256 Loss1: 0.997413 Loss2: 1.983843
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.573750 Loss1: 0.176415 Loss2: 1.397335
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.244380 Loss1: 0.740963 Loss2: 1.503417
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551148 Loss1: 0.163502 Loss2: 1.387646
-(DefaultActor pid=3764) Epoch: 2 Loss: 2.031981 Loss1: 0.529255 Loss2: 1.502726
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.526485 Loss1: 0.140540 Loss2: 1.385945
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.815049 Loss1: 0.329904 Loss2: 1.485145
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.502945 Loss1: 0.122594 Loss2: 1.380351
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.771786 Loss1: 0.310886 Loss2: 1.460900
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.475346 Loss1: 0.096632 Loss2: 1.378714
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.696220 Loss1: 0.223681 Loss2: 1.472539
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.479621 Loss1: 0.108179 Loss2: 1.371442
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.581321 Loss1: 0.137471 Loss2: 1.443851 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.519210 Loss1: 0.092110 Loss2: 1.427100 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.960722 Loss1: 0.557641 Loss2: 1.403081 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654172 Loss1: 0.278072 Loss2: 1.376099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.780731 Loss1: 0.920762 Loss2: 1.859968
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.599741 Loss1: 0.211894 Loss2: 1.387847
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.947659 Loss1: 0.522082 Loss2: 1.425576
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.589672 Loss1: 0.211971 Loss2: 1.377701
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804656 Loss1: 0.387248 Loss2: 1.417408
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.530078 Loss1: 0.161839 Loss2: 1.368239
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.657989 Loss1: 0.276482 Loss2: 1.381508
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.510712 Loss1: 0.142738 Loss2: 1.367974
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.575843 Loss1: 0.204010 Loss2: 1.371833
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.495572 Loss1: 0.129960 Loss2: 1.365612
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503541 Loss1: 0.145435 Loss2: 1.358106
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454720 Loss1: 0.095710 Loss2: 1.359010
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469176 Loss1: 0.108358 Loss2: 1.360818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419457 Loss1: 0.070666 Loss2: 1.348791 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.992556 Loss1: 0.580892 Loss2: 1.411664 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.640699 Loss1: 0.256199 Loss2: 1.384499 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566465 Loss1: 0.183487 Loss2: 1.382977
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.863954 Loss1: 0.910729 Loss2: 1.953225
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505544 Loss1: 0.139420 Loss2: 1.366124
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.052345 Loss1: 0.541916 Loss2: 1.510429
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.878956 Loss1: 0.354001 Loss2: 1.524955 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.741775 Loss1: 0.269453 Loss2: 1.472322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.729806 Loss1: 0.248748 Loss2: 1.481058 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.644084 Loss1: 0.176356 Loss2: 1.467728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.570921 Loss1: 0.116613 Loss2: 1.454308 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.524161 Loss1: 0.077079 Loss2: 1.447082 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.924748 Loss1: 0.568803 Loss2: 1.355944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601942 Loss1: 0.250193 Loss2: 1.351749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.822456 Loss1: 0.980757 Loss2: 1.841699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.022287 Loss1: 0.583362 Loss2: 1.438924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.773385 Loss1: 0.355418 Loss2: 1.417967 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433405 Loss1: 0.120420 Loss2: 1.312985 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400570 Loss1: 0.079940 Loss2: 1.320630 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491920 Loss1: 0.125865 Loss2: 1.366055 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448150 Loss1: 0.089195 Loss2: 1.358955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.552975 Loss1: 0.777049 Loss2: 1.775926
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440627 Loss1: 0.083663 Loss2: 1.356965
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.680885 Loss1: 0.317012 Loss2: 1.363873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524830 Loss1: 0.192530 Loss2: 1.332300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.888582 Loss1: 0.992235 Loss2: 1.896347
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492551 Loss1: 0.157149 Loss2: 1.335402
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448802 Loss1: 0.128788 Loss2: 1.320014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404961 Loss1: 0.086560 Loss2: 1.318401 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372558 Loss1: 0.065499 Loss2: 1.307058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355157 Loss1: 0.054116 Loss2: 1.301042 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993566
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.480082 Loss1: 0.099699 Loss2: 1.380383 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405610 Loss1: 0.042047 Loss2: 1.363563 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.999729 Loss1: 0.579008 Loss2: 1.420721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.662340 Loss1: 0.265869 Loss2: 1.396471 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.814213 Loss1: 0.922293 Loss2: 1.891920
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547646 Loss1: 0.161770 Loss2: 1.385876
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.090234 Loss1: 0.648544 Loss2: 1.441690
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.496124 Loss1: 0.119021 Loss2: 1.377103
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.820345 Loss1: 0.349080 Loss2: 1.471266
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485370 Loss1: 0.109652 Loss2: 1.375718
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452981 Loss1: 0.091639 Loss2: 1.361342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463511 Loss1: 0.096637 Loss2: 1.366874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443022 Loss1: 0.082590 Loss2: 1.360433 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.519574 Loss1: 0.115004 Loss2: 1.404570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.467268 Loss1: 0.084417 Loss2: 1.382851 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.009587 Loss1: 1.036230 Loss2: 1.973357
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.018373 Loss1: 0.564589 Loss2: 1.453784
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.782795 Loss1: 0.305544 Loss2: 1.477251
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.708496 Loss1: 0.270494 Loss2: 1.438002
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.789021 Loss1: 0.908675 Loss2: 1.880346
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.618814 Loss1: 0.199714 Loss2: 1.419100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.627836 Loss1: 0.198600 Loss2: 1.429237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.567899 Loss1: 0.139345 Loss2: 1.428554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.571336 Loss1: 0.159004 Loss2: 1.412331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.568645 Loss1: 0.147323 Loss2: 1.421322 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986607
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.514904 Loss1: 0.160888 Loss2: 1.354016 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439893 Loss1: 0.102061 Loss2: 1.337832 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.911000 Loss1: 0.501385 Loss2: 1.409615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647597 Loss1: 0.254460 Loss2: 1.393136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.788256 Loss1: 0.941081 Loss2: 1.847175
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.633927 Loss1: 0.245415 Loss2: 1.388512
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.937613 Loss1: 0.538425 Loss2: 1.399188
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.563515 Loss1: 0.176584 Loss2: 1.386931
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.760056 Loss1: 0.338295 Loss2: 1.421761
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529593 Loss1: 0.151118 Loss2: 1.378474
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.482548 Loss1: 0.111684 Loss2: 1.370864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509439 Loss1: 0.139056 Loss2: 1.370383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.497349 Loss1: 0.124895 Loss2: 1.372454 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436752 Loss1: 0.082157 Loss2: 1.354595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391232 Loss1: 0.050121 Loss2: 1.341111 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.901065 Loss1: 0.986161 Loss2: 1.914905
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.016354 Loss1: 0.564580 Loss2: 1.451774
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.857341 Loss1: 0.395502 Loss2: 1.461839
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.673501 Loss1: 0.243143 Loss2: 1.430358
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.994484 Loss1: 1.021949 Loss2: 1.972535
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.044178 Loss1: 0.563100 Loss2: 1.481078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.911050 Loss1: 0.418642 Loss2: 1.492408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.774295 Loss1: 0.304078 Loss2: 1.470217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.709497 Loss1: 0.251540 Loss2: 1.457956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.678432 Loss1: 0.219790 Loss2: 1.458642 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.639206 Loss1: 0.175799 Loss2: 1.463407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.576181 Loss1: 0.135056 Loss2: 1.441125 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.731933 Loss1: 0.798159 Loss2: 1.933774 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.833170 Loss1: 0.365629 Loss2: 1.467541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.829856 Loss1: 0.971909 Loss2: 1.857947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.071838 Loss1: 0.642645 Loss2: 1.429193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834799 Loss1: 0.419458 Loss2: 1.415341 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.658039 Loss1: 0.269103 Loss2: 1.388937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554849 Loss1: 0.174023 Loss2: 1.380826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539509 Loss1: 0.168470 Loss2: 1.371040 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510484 Loss1: 0.134094 Loss2: 1.376389 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.469990 Loss1: 0.114644 Loss2: 1.355345 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.862088 Loss1: 1.043716 Loss2: 1.818373
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.952160 Loss1: 0.595859 Loss2: 1.356301
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.742614 Loss1: 0.357076 Loss2: 1.385538
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576142 Loss1: 0.234931 Loss2: 1.341211
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.813290 Loss1: 0.919983 Loss2: 1.893307
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.884280 Loss1: 0.478326 Loss2: 1.405955 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.751484 Loss1: 0.327310 Loss2: 1.424173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.648238 Loss1: 0.248226 Loss2: 1.400012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548618 Loss1: 0.154064 Loss2: 1.394554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538139 Loss1: 0.151629 Loss2: 1.386510 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488699 Loss1: 0.116962 Loss2: 1.371737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.488878 Loss1: 0.110540 Loss2: 1.378338 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.966667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.097999 Loss1: 1.036717 Loss2: 2.061282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.910987 Loss1: 0.407674 Loss2: 1.503313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.632715 Loss1: 0.189946 Loss2: 1.442770 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.565911 Loss1: 0.134020 Loss2: 1.431891 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524511 Loss1: 0.107934 Loss2: 1.416577 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.524961 Loss1: 0.108254 Loss2: 1.416706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.629065 Loss1: 0.260598 Loss2: 1.368467
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.535260 Loss1: 0.121003 Loss2: 1.414257
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.502621 Loss1: 0.093222 Loss2: 1.409399
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554513 Loss1: 0.197324 Loss2: 1.357189
-(DefaultActor pid=3765) >> Training accuracy: 0.986779
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492424 Loss1: 0.149219 Loss2: 1.343205
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472174 Loss1: 0.128958 Loss2: 1.343216
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421639 Loss1: 0.088276 Loss2: 1.333364
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396654 Loss1: 0.070813 Loss2: 1.325841
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395409 Loss1: 0.080162 Loss2: 1.315248
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.825948 Loss1: 0.972839 Loss2: 1.853109
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.923512 Loss1: 0.540652 Loss2: 1.382859
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.732100 Loss1: 0.332837 Loss2: 1.399263
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.615942 Loss1: 0.257174 Loss2: 1.358768
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566159 Loss1: 0.212094 Loss2: 1.354065
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.787964 Loss1: 0.983254 Loss2: 1.804710
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537350 Loss1: 0.195067 Loss2: 1.342283
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.015157 Loss1: 0.650851 Loss2: 1.364307
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.507564 Loss1: 0.159605 Loss2: 1.347959
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.763674 Loss1: 0.384537 Loss2: 1.379137
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.442525 Loss1: 0.113804 Loss2: 1.328721
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588936 Loss1: 0.252408 Loss2: 1.336528
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453544 Loss1: 0.125933 Loss2: 1.327611
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503942 Loss1: 0.174967 Loss2: 1.328975
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406591 Loss1: 0.077743 Loss2: 1.328848
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450777 Loss1: 0.137945 Loss2: 1.312833 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402589 Loss1: 0.088199 Loss2: 1.314390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418498 Loss1: 0.114021 Loss2: 1.304477
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.716159 Loss1: 0.884763 Loss2: 1.831395
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.992164 Loss1: 0.580546 Loss2: 1.411618
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.766432 Loss1: 0.365325 Loss2: 1.401107
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.633856 Loss1: 0.251859 Loss2: 1.381997
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.625364 Loss1: 0.249164 Loss2: 1.376200
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.718042 Loss1: 0.827034 Loss2: 1.891008
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.216402 Loss1: 0.720347 Loss2: 1.496055 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.940335 Loss1: 0.481473 Loss2: 1.458861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.775730 Loss1: 0.321622 Loss2: 1.454108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.683717 Loss1: 0.253715 Loss2: 1.430002 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.688033 Loss1: 0.254296 Loss2: 1.433736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.573854 Loss1: 0.157910 Loss2: 1.415944 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.557470 Loss1: 0.143652 Loss2: 1.413818 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965820
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.845334 Loss1: 0.450016 Loss2: 1.395318 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565202 Loss1: 0.178887 Loss2: 1.386315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.778662 Loss1: 0.938196 Loss2: 1.840466
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498929 Loss1: 0.119471 Loss2: 1.379457
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.988444 Loss1: 0.605481 Loss2: 1.382963
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428858 Loss1: 0.060353 Loss2: 1.368505
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.827034 Loss1: 0.418576 Loss2: 1.408458
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432749 Loss1: 0.083056 Loss2: 1.349693
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636231 Loss1: 0.268250 Loss2: 1.367981
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432654 Loss1: 0.082230 Loss2: 1.350424
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569969 Loss1: 0.197226 Loss2: 1.372743
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421332 Loss1: 0.070200 Loss2: 1.351132
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485147 Loss1: 0.132230 Loss2: 1.352917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486140 Loss1: 0.131565 Loss2: 1.354575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465418 Loss1: 0.111119 Loss2: 1.354299
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.976176 Loss1: 1.098761 Loss2: 1.877415
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.064292 Loss1: 0.675401 Loss2: 1.388891
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.866788 Loss1: 0.459474 Loss2: 1.407315
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.645278 Loss1: 0.293085 Loss2: 1.352193
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587842 Loss1: 0.235750 Loss2: 1.352093
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511175 Loss1: 0.171603 Loss2: 1.339571
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.844578 Loss1: 0.996568 Loss2: 1.848010
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441754 Loss1: 0.112877 Loss2: 1.328878
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.968098 Loss1: 0.589027 Loss2: 1.379071
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.739980 Loss1: 0.323694 Loss2: 1.416286 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.607422 Loss1: 0.246031 Loss2: 1.361391 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386709 Loss1: 0.071701 Loss2: 1.315008
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539740 Loss1: 0.173662 Loss2: 1.366078
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480701 Loss1: 0.129327 Loss2: 1.351374
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461528 Loss1: 0.116047 Loss2: 1.345481
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428376 Loss1: 0.083088 Loss2: 1.345288
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429241 Loss1: 0.090786 Loss2: 1.338454
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.764333 Loss1: 0.946255 Loss2: 1.818078
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427328 Loss1: 0.092022 Loss2: 1.335306
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.812685 Loss1: 0.386196 Loss2: 1.426489 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.570647 Loss1: 0.209258 Loss2: 1.361389 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.546128 Loss1: 0.187513 Loss2: 1.358615
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.624147 Loss1: 0.794854 Loss2: 1.829294
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448496 Loss1: 0.100777 Loss2: 1.347718
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.951377 Loss1: 0.577106 Loss2: 1.374271
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432646 Loss1: 0.096662 Loss2: 1.335984
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.829478 Loss1: 0.404553 Loss2: 1.424925
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395285 Loss1: 0.070235 Loss2: 1.325050
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.707245 Loss1: 0.337772 Loss2: 1.369473
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404887 Loss1: 0.081935 Loss2: 1.322952
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.604782 Loss1: 0.225618 Loss2: 1.379164
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.520854 Loss1: 0.168019 Loss2: 1.352835
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487934 Loss1: 0.139223 Loss2: 1.348710
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417367 Loss1: 0.075998 Loss2: 1.341369
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431069 Loss1: 0.098941 Loss2: 1.332128
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.837961 Loss1: 0.953135 Loss2: 1.884826
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408609 Loss1: 0.077879 Loss2: 1.330730
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.769126 Loss1: 0.330512 Loss2: 1.438614 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.620738 Loss1: 0.207069 Loss2: 1.413670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576892 Loss1: 0.186370 Loss2: 1.390521
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.919137 Loss1: 0.963564 Loss2: 1.955573
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.017845 Loss1: 0.612112 Loss2: 1.405733
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.561101 Loss1: 0.168676 Loss2: 1.392424
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488201 Loss1: 0.104239 Loss2: 1.383962 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465553 Loss1: 0.093074 Loss2: 1.372479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451360 Loss1: 0.076014 Loss2: 1.375346 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487861 Loss1: 0.094590 Loss2: 1.393271 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466906 Loss1: 0.078763 Loss2: 1.388143 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.826839 Loss1: 0.940922 Loss2: 1.885917
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.986047 Loss1: 0.512691 Loss2: 1.473356
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.812431 Loss1: 0.386002 Loss2: 1.426428
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.670708 Loss1: 0.231239 Loss2: 1.439469
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.647923 Loss1: 0.897630 Loss2: 1.750294
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.940621 Loss1: 0.570905 Loss2: 1.369716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.733983 Loss1: 0.371674 Loss2: 1.362308 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594164 Loss1: 0.249439 Loss2: 1.344725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513565 Loss1: 0.178866 Loss2: 1.334699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468590 Loss1: 0.146601 Loss2: 1.321989 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443269 Loss1: 0.126881 Loss2: 1.316388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366204 Loss1: 0.056559 Loss2: 1.309645 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.971501 Loss1: 1.021032 Loss2: 1.950468 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.973667 Loss1: 0.480813 Loss2: 1.492854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.607659 Loss1: 0.192142 Loss2: 1.415517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525803 Loss1: 0.126275 Loss2: 1.399528 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514213 Loss1: 0.122886 Loss2: 1.391326 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474353 Loss1: 0.091120 Loss2: 1.383234 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.506820 Loss1: 0.127551 Loss2: 1.379270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465319 Loss1: 0.081861 Loss2: 1.383458 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404141 Loss1: 0.072359 Loss2: 1.331782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397533 Loss1: 0.078923 Loss2: 1.318610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.067097 Loss1: 1.092464 Loss2: 1.974633
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417258 Loss1: 0.095614 Loss2: 1.321643
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-11 04:34:47,253 | server.py:236 | fit_round 101 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.682455 Loss1: 0.302801 Loss2: 1.379655 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.543860 Loss1: 0.170562 Loss2: 1.373298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.788187 Loss1: 0.905787 Loss2: 1.882400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.507823 Loss1: 0.150339 Loss2: 1.357483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508621 Loss1: 0.154953 Loss2: 1.353668 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977865
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.583937 Loss1: 0.189465 Loss2: 1.394471 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504272 Loss1: 0.140209 Loss2: 1.364062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.591979 Loss1: 0.787478 Loss2: 1.804501
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.498951 Loss1: 0.135040 Loss2: 1.363911
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.877970 Loss1: 0.535088 Loss2: 1.342883
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447345 Loss1: 0.082159 Loss2: 1.365186
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731618 Loss1: 0.338659 Loss2: 1.392959
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419515 Loss1: 0.064093 Loss2: 1.355422
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498797 Loss1: 0.165968 Loss2: 1.332830 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406851 Loss1: 0.093070 Loss2: 1.313781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.387950 Loss1: 0.078095 Loss2: 1.309856
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.877919 Loss1: 1.018030 Loss2: 1.859889
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.022277 Loss1: 0.616359 Loss2: 1.405918 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367689 Loss1: 0.063866 Loss2: 1.303822
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.831575 Loss1: 0.375989 Loss2: 1.455586
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.719532 Loss1: 0.326082 Loss2: 1.393450
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.628084 Loss1: 0.218526 Loss2: 1.409559
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.596059 Loss1: 0.198213 Loss2: 1.397846
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.549153 Loss1: 0.157236 Loss2: 1.391917
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.512488 Loss1: 0.120814 Loss2: 1.391674
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.508291 Loss1: 0.128700 Loss2: 1.379591
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491259 Loss1: 0.109937 Loss2: 1.381322
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 04:34:47,253][flwr][DEBUG] - fit_round 101 received 50 results and 0 failures
-INFO flwr 2023-10-11 04:35:28,895 | server.py:125 | fit progress: (101, 2.207833128044019, {'accuracy': 0.5696}, 233036.673442934)
->> Test accuracy: 0.569600
-[2023-10-11 04:35:28,895][flwr][INFO] - fit progress: (101, 2.207833128044019, {'accuracy': 0.5696}, 233036.673442934)
-DEBUG flwr 2023-10-11 04:35:28,895 | server.py:173 | evaluate_round 101: strategy sampled 50 clients (out of 50)
-[2023-10-11 04:35:28,895][flwr][DEBUG] - evaluate_round 101: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 04:44:35,805 | server.py:187 | evaluate_round 101 received 50 results and 0 failures
-[2023-10-11 04:44:35,805][flwr][DEBUG] - evaluate_round 101 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 04:44:35,805 | server.py:222 | fit_round 102: strategy sampled 50 clients (out of 50)
-[2023-10-11 04:44:35,805][flwr][DEBUG] - fit_round 102: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.928432 Loss1: 0.976942 Loss2: 1.951490
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.086212 Loss1: 0.612961 Loss2: 1.473251
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.884005 Loss1: 0.390936 Loss2: 1.493069
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.781752 Loss1: 0.317450 Loss2: 1.464303
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.855217 Loss1: 1.116873 Loss2: 1.738345
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.946189 Loss1: 0.621552 Loss2: 1.324638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671153 Loss1: 0.352387 Loss2: 1.318766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556522 Loss1: 0.263476 Loss2: 1.293045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.466119 Loss1: 0.181865 Loss2: 1.284254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.393824 Loss1: 0.116220 Loss2: 1.277604 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.527975 Loss1: 0.096546 Loss2: 1.431429
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.421824 Loss1: 0.148674 Loss2: 1.273150
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368303 Loss1: 0.102824 Loss2: 1.265479
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372003 Loss1: 0.109119 Loss2: 1.262884
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.343794 Loss1: 0.082972 Loss2: 1.260822
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.983633 Loss1: 1.088248 Loss2: 1.895385
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.079556 Loss1: 0.647815 Loss2: 1.431741
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.793204 Loss1: 0.350713 Loss2: 1.442491
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.753307 Loss1: 0.347589 Loss2: 1.405717
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.789617 Loss1: 0.984243 Loss2: 1.805373
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.897412 Loss1: 0.504149 Loss2: 1.393264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.659812 Loss1: 0.267058 Loss2: 1.392754 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584859 Loss1: 0.225291 Loss2: 1.359568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535422 Loss1: 0.180464 Loss2: 1.354958 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478967 Loss1: 0.143154 Loss2: 1.335813 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448552 Loss1: 0.113672 Loss2: 1.334880 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444267 Loss1: 0.112294 Loss2: 1.331973 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.970851 Loss1: 0.616134 Loss2: 1.354716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606549 Loss1: 0.276953 Loss2: 1.329597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.504384 Loss1: 0.168599 Loss2: 1.335784
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.786036 Loss1: 0.986221 Loss2: 1.799815
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.441317 Loss1: 0.124707 Loss2: 1.316610
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.842442 Loss1: 0.480193 Loss2: 1.362249
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419049 Loss1: 0.110545 Loss2: 1.308505
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.735753 Loss1: 0.339003 Loss2: 1.396750
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.402885 Loss1: 0.090931 Loss2: 1.311953
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563259 Loss1: 0.223714 Loss2: 1.339546
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451330 Loss1: 0.147222 Loss2: 1.304108
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549441 Loss1: 0.201362 Loss2: 1.348079
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407566 Loss1: 0.097263 Loss2: 1.310303
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512278 Loss1: 0.175346 Loss2: 1.336932
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460122 Loss1: 0.130635 Loss2: 1.329487
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460281 Loss1: 0.136392 Loss2: 1.323889
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423316 Loss1: 0.098911 Loss2: 1.324405
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417132 Loss1: 0.107457 Loss2: 1.309675
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.832458 Loss1: 0.993463 Loss2: 1.838995
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.042195 Loss1: 0.659836 Loss2: 1.382359
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.775064 Loss1: 0.361156 Loss2: 1.413908
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.597862 Loss1: 0.238803 Loss2: 1.359058
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.512712 Loss1: 0.148277 Loss2: 1.364435
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518753 Loss1: 0.170434 Loss2: 1.348319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.492915 Loss1: 0.142006 Loss2: 1.350908 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450692 Loss1: 0.099065 Loss2: 1.351627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.610982 Loss1: 0.232145 Loss2: 1.378837 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542698 Loss1: 0.166055 Loss2: 1.376643 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443055 Loss1: 0.089367 Loss2: 1.353688 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408223 Loss1: 0.062595 Loss2: 1.345628 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983259
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.835288 Loss1: 0.965321 Loss2: 1.869967
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.057672 Loss1: 0.616764 Loss2: 1.440907
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.784140 Loss1: 0.362078 Loss2: 1.422062
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.700439 Loss1: 0.274262 Loss2: 1.426177
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.660808 Loss1: 0.832984 Loss2: 1.827823
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.816929 Loss1: 0.409554 Loss2: 1.407375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.698368 Loss1: 0.291455 Loss2: 1.406913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.575741 Loss1: 0.190605 Loss2: 1.385135 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.548404 Loss1: 0.155907 Loss2: 1.392497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.506471 Loss1: 0.118070 Loss2: 1.388401 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427739 Loss1: 0.067336 Loss2: 1.360403 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423271 Loss1: 0.069538 Loss2: 1.353733 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.960007 Loss1: 0.531531 Loss2: 1.428476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.754222 Loss1: 0.323878 Loss2: 1.430345 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.689087 Loss1: 0.256452 Loss2: 1.432635
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.689914 Loss1: 0.796679 Loss2: 1.893236
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.945661 Loss1: 0.531206 Loss2: 1.414456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.765062 Loss1: 0.326960 Loss2: 1.438102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.666564 Loss1: 0.269442 Loss2: 1.397122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.623777 Loss1: 0.213560 Loss2: 1.410218 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.569741 Loss1: 0.169659 Loss2: 1.400082 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.492742 Loss1: 0.112515 Loss2: 1.380227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.459067 Loss1: 0.086832 Loss2: 1.372235
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.931655 Loss1: 0.969127 Loss2: 1.962528
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.050487 Loss1: 0.584605 Loss2: 1.465882
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.776298 Loss1: 0.323462 Loss2: 1.452836
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.665643 Loss1: 0.248216 Loss2: 1.417427
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.649132 Loss1: 0.229656 Loss2: 1.419476
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.545722 Loss1: 0.137898 Loss2: 1.407823
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.791239 Loss1: 0.862196 Loss2: 1.929043
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523113 Loss1: 0.120856 Loss2: 1.402257
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.958770 Loss1: 0.530863 Loss2: 1.427906
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.530543 Loss1: 0.130515 Loss2: 1.400027
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.800115 Loss1: 0.334229 Loss2: 1.465885
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.536441 Loss1: 0.138288 Loss2: 1.398153
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.633711 Loss1: 0.210596 Loss2: 1.423115
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.513969 Loss1: 0.112956 Loss2: 1.401013
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.586160 Loss1: 0.171290 Loss2: 1.414870
-(DefaultActor pid=3765) >> Training accuracy: 0.964583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559584 Loss1: 0.149699 Loss2: 1.409886
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.525792 Loss1: 0.112231 Loss2: 1.413561
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.524705 Loss1: 0.114928 Loss2: 1.409777
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519725 Loss1: 0.111231 Loss2: 1.408494
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.500448 Loss1: 0.098788 Loss2: 1.401660
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.719877 Loss1: 0.883968 Loss2: 1.835909
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.026691 Loss1: 0.588821 Loss2: 1.437870
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.713842 Loss1: 0.297567 Loss2: 1.416274
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.618837 Loss1: 0.227484 Loss2: 1.391353
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606215 Loss1: 0.222171 Loss2: 1.384044
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.770898 Loss1: 0.858608 Loss2: 1.912290
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.527004 Loss1: 0.142118 Loss2: 1.384886
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.504376 Loss1: 0.119176 Loss2: 1.385201 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.475050 Loss1: 0.096743 Loss2: 1.378307 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450912 Loss1: 0.082892 Loss2: 1.368020 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435918 Loss1: 0.071652 Loss2: 1.364266 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.525184 Loss1: 0.126070 Loss2: 1.399113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.474934 Loss1: 0.086526 Loss2: 1.388408 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465215 Loss1: 0.081198 Loss2: 1.384018
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.794338 Loss1: 0.902952 Loss2: 1.891386
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.885263 Loss1: 0.481297 Loss2: 1.403966
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.781568 Loss1: 0.340445 Loss2: 1.441123
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.614457 Loss1: 0.218705 Loss2: 1.395752
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.599044 Loss1: 0.208711 Loss2: 1.390333
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.642653 Loss1: 0.760040 Loss2: 1.882613
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.898360 Loss1: 0.496819 Loss2: 1.401541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.717529 Loss1: 0.301850 Loss2: 1.415680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581161 Loss1: 0.205084 Loss2: 1.376077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569941 Loss1: 0.189194 Loss2: 1.380747 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.480509 Loss1: 0.110469 Loss2: 1.370040
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499112 Loss1: 0.135562 Loss2: 1.363550
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476187 Loss1: 0.112334 Loss2: 1.363853
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.503996 Loss1: 0.141134 Loss2: 1.362863
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481594 Loss1: 0.122348 Loss2: 1.359246
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464125 Loss1: 0.106471 Loss2: 1.357654
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.764244 Loss1: 0.853480 Loss2: 1.910764
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.843065 Loss1: 0.428237 Loss2: 1.414829
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.744254 Loss1: 0.296758 Loss2: 1.447495
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.605530 Loss1: 0.199329 Loss2: 1.406201
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.588941 Loss1: 0.194358 Loss2: 1.394583
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.927526 Loss1: 1.057248 Loss2: 1.870278
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.970492 Loss1: 0.587012 Loss2: 1.383480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.676498 Loss1: 0.279055 Loss2: 1.397443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.570475 Loss1: 0.209730 Loss2: 1.360745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545821 Loss1: 0.190498 Loss2: 1.355323 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.950000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.541155 Loss1: 0.153625 Loss2: 1.387530
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.514948 Loss1: 0.156308 Loss2: 1.358639
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463499 Loss1: 0.106835 Loss2: 1.356664
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424926 Loss1: 0.083904 Loss2: 1.341022
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416750 Loss1: 0.079344 Loss2: 1.337406
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428628 Loss1: 0.096084 Loss2: 1.332544
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.713290 Loss1: 0.857714 Loss2: 1.855575
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.973277 Loss1: 0.573688 Loss2: 1.399589
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.800418 Loss1: 0.355119 Loss2: 1.445299
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.677615 Loss1: 0.290128 Loss2: 1.387488
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.649983 Loss1: 0.248530 Loss2: 1.401453
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.930855 Loss1: 1.047043 Loss2: 1.883812
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.037027 Loss1: 0.612881 Loss2: 1.424146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.851242 Loss1: 0.402491 Loss2: 1.448751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.687621 Loss1: 0.284539 Loss2: 1.403082 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.657820 Loss1: 0.253774 Loss2: 1.404046 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.586734 Loss1: 0.193425 Loss2: 1.393309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.503585 Loss1: 0.113394 Loss2: 1.390191 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470497 Loss1: 0.095377 Loss2: 1.375120 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.141718 Loss1: 0.745057 Loss2: 1.396661 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.657969 Loss1: 0.297011 Loss2: 1.360958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.585394 Loss1: 0.234399 Loss2: 1.350996
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.655869 Loss1: 0.819478 Loss2: 1.836391
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.871962 Loss1: 0.454371 Loss2: 1.417591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.694462 Loss1: 0.295370 Loss2: 1.399093 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434609 Loss1: 0.111091 Loss2: 1.323519 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416085 Loss1: 0.093337 Loss2: 1.322748 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.607638 Loss1: 0.210233 Loss2: 1.397405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496269 Loss1: 0.122933 Loss2: 1.373336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479108 Loss1: 0.108292 Loss2: 1.370816
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.668064 Loss1: 0.910550 Loss2: 1.757514
-(DefaultActor pid=3764) >> Training accuracy: 0.991728
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433016 Loss1: 0.069327 Loss2: 1.363689
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.875748 Loss1: 0.505427 Loss2: 1.370321
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.746065 Loss1: 0.364355 Loss2: 1.381710
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.611563 Loss1: 0.256707 Loss2: 1.354856
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562236 Loss1: 0.213730 Loss2: 1.348506
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506934 Loss1: 0.162838 Loss2: 1.344096
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.739308 Loss1: 0.900100 Loss2: 1.839208
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.075171 Loss1: 0.659632 Loss2: 1.415539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.797379 Loss1: 0.344068 Loss2: 1.453311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640937 Loss1: 0.249800 Loss2: 1.391137 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571907 Loss1: 0.185347 Loss2: 1.386560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.499685 Loss1: 0.118045 Loss2: 1.381641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.491554 Loss1: 0.128933 Loss2: 1.362621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464536 Loss1: 0.094803 Loss2: 1.369733 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973633
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563555 Loss1: 0.215602 Loss2: 1.347952 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488558 Loss1: 0.154550 Loss2: 1.334009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461353 Loss1: 0.127069 Loss2: 1.334283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469102 Loss1: 0.132283 Loss2: 1.336819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474180 Loss1: 0.134171 Loss2: 1.340008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437277 Loss1: 0.104245 Loss2: 1.333032 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467902 Loss1: 0.105807 Loss2: 1.362095 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480001 Loss1: 0.116712 Loss2: 1.363289 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979567
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453975 Loss1: 0.091553 Loss2: 1.362422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.772098 Loss1: 0.909725 Loss2: 1.862373
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.857651 Loss1: 0.470454 Loss2: 1.387197
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667022 Loss1: 0.253020 Loss2: 1.414002
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629648 Loss1: 0.250360 Loss2: 1.379288
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.538487 Loss1: 0.163521 Loss2: 1.374966
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.832661 Loss1: 0.976773 Loss2: 1.855888
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.078185 Loss1: 0.670400 Loss2: 1.407785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.805898 Loss1: 0.349565 Loss2: 1.456333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.707661 Loss1: 0.318496 Loss2: 1.389165 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.636508 Loss1: 0.232789 Loss2: 1.403719 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.572500 Loss1: 0.180654 Loss2: 1.391846 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477431 Loss1: 0.105926 Loss2: 1.371505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.432104 Loss1: 0.071458 Loss2: 1.360646 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.042014 Loss1: 0.611315 Loss2: 1.430699 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.665158 Loss1: 0.264835 Loss2: 1.400324 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.564545 Loss1: 0.167226 Loss2: 1.397320
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.661206 Loss1: 0.868616 Loss2: 1.792590
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.907245 Loss1: 0.559243 Loss2: 1.348001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.738605 Loss1: 0.350273 Loss2: 1.388332 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.624188 Loss1: 0.285273 Loss2: 1.338915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609465 Loss1: 0.253145 Loss2: 1.356321 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490518 Loss1: 0.152375 Loss2: 1.338142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429933 Loss1: 0.102807 Loss2: 1.327125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421302 Loss1: 0.106518 Loss2: 1.314784 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.009732 Loss1: 0.596935 Loss2: 1.412797 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.697186 Loss1: 0.304480 Loss2: 1.392706 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.620446 Loss1: 0.217259 Loss2: 1.403188
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.770992 Loss1: 0.901965 Loss2: 1.869027
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551302 Loss1: 0.177257 Loss2: 1.374045
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.943741 Loss1: 0.579999 Loss2: 1.363741
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513300 Loss1: 0.132343 Loss2: 1.380958
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.772060 Loss1: 0.343523 Loss2: 1.428537
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.697395 Loss1: 0.334137 Loss2: 1.363258
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480521 Loss1: 0.109838 Loss2: 1.370684
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570679 Loss1: 0.204026 Loss2: 1.366653
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454304 Loss1: 0.093954 Loss2: 1.360351
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516724 Loss1: 0.164557 Loss2: 1.352167
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460204 Loss1: 0.097647 Loss2: 1.362556
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452949 Loss1: 0.110435 Loss2: 1.342514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418825 Loss1: 0.083990 Loss2: 1.334835 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.136138 Loss1: 0.685446 Loss2: 1.450691 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.680011 Loss1: 0.277927 Loss2: 1.402084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606119 Loss1: 0.199332 Loss2: 1.406787
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.862197 Loss1: 0.984361 Loss2: 1.877836
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.069039 Loss1: 0.639451 Loss2: 1.429588 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.842253 Loss1: 0.399277 Loss2: 1.442976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699172 Loss1: 0.280866 Loss2: 1.418305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.610186 Loss1: 0.200170 Loss2: 1.410017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.507026 Loss1: 0.114642 Loss2: 1.392384 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443935 Loss1: 0.064512 Loss2: 1.379423 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458213 Loss1: 0.086227 Loss2: 1.371986
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.948135 Loss1: 1.056916 Loss2: 1.891219
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.050700 Loss1: 0.685434 Loss2: 1.365266
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.872687 Loss1: 0.431047 Loss2: 1.441640
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.684062 Loss1: 0.324351 Loss2: 1.359711
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.586455 Loss1: 0.220607 Loss2: 1.365848
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.550133 Loss1: 0.189712 Loss2: 1.360421
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.731357 Loss1: 0.844938 Loss2: 1.886420
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.988648 Loss1: 0.584138 Loss2: 1.404510 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413662 Loss1: 0.068842 Loss2: 1.344820 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383443 Loss1: 0.050365 Loss2: 1.333077 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500079 Loss1: 0.127626 Loss2: 1.372454 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423707 Loss1: 0.072354 Loss2: 1.351352 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.856248 Loss1: 1.006973 Loss2: 1.849275
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414870 Loss1: 0.068014 Loss2: 1.346856
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.961005 Loss1: 0.571834 Loss2: 1.389171
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401884 Loss1: 0.057231 Loss2: 1.344653
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.627149 Loss1: 0.261904 Loss2: 1.365245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473228 Loss1: 0.119688 Loss2: 1.353540 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447598 Loss1: 0.101502 Loss2: 1.346096
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.789690 Loss1: 0.971402 Loss2: 1.818287
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.983184 Loss1: 0.578600 Loss2: 1.404583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.745519 Loss1: 0.369114 Loss2: 1.376405 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.733751 Loss1: 0.348597 Loss2: 1.385154 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.593569 Loss1: 0.231095 Loss2: 1.362474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461253 Loss1: 0.110798 Loss2: 1.350455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431300 Loss1: 0.089444 Loss2: 1.341856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409423 Loss1: 0.075297 Loss2: 1.334126 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.635978 Loss1: 0.204549 Loss2: 1.431429 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523969 Loss1: 0.124265 Loss2: 1.399704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.903593 Loss1: 0.984518 Loss2: 1.919076
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.493125 Loss1: 0.099196 Loss2: 1.393929
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.086203 Loss1: 0.630670 Loss2: 1.455533
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.481737 Loss1: 0.093879 Loss2: 1.387858
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.881100 Loss1: 0.398675 Loss2: 1.482425
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476774 Loss1: 0.086749 Loss2: 1.390025
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.706301 Loss1: 0.264418 Loss2: 1.441883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.592418 Loss1: 0.168203 Loss2: 1.424215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.571300 Loss1: 0.150174 Loss2: 1.421125
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.755893 Loss1: 0.902554 Loss2: 1.853339
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.950743 Loss1: 0.565643 Loss2: 1.385100 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.488345 Loss1: 0.081130 Loss2: 1.407214
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.710599 Loss1: 0.302877 Loss2: 1.407722
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.611467 Loss1: 0.244611 Loss2: 1.366856
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.564517 Loss1: 0.196059 Loss2: 1.368458
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.548732 Loss1: 0.187585 Loss2: 1.361147
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486497 Loss1: 0.134879 Loss2: 1.351618
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.695839 Loss1: 0.796960 Loss2: 1.898879
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.533268 Loss1: 0.187756 Loss2: 1.345512
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.965399 Loss1: 0.565381 Loss2: 1.400017
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.515548 Loss1: 0.143167 Loss2: 1.372381
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.835441 Loss1: 0.389227 Loss2: 1.446214
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481704 Loss1: 0.127382 Loss2: 1.354322
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.622428 Loss1: 0.220789 Loss2: 1.401638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467597 Loss1: 0.095851 Loss2: 1.371746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464033 Loss1: 0.099520 Loss2: 1.364513
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.951060 Loss1: 1.070621 Loss2: 1.880439
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.107095 Loss1: 0.665403 Loss2: 1.441692 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426602 Loss1: 0.066669 Loss2: 1.359933
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.801576 Loss1: 0.377079 Loss2: 1.424497
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.774917 Loss1: 0.352428 Loss2: 1.422489
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.637035 Loss1: 0.230668 Loss2: 1.406367
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569907 Loss1: 0.180190 Loss2: 1.389718
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.548527 Loss1: 0.156658 Loss2: 1.391870
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.045884 Loss1: 0.968217 Loss2: 2.077667
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.533143 Loss1: 0.147097 Loss2: 1.386046
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.560537 Loss1: 0.169421 Loss2: 1.391116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.754150 Loss1: 0.243032 Loss2: 1.511118 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.708294 Loss1: 0.243042 Loss2: 1.465252 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.598941 Loss1: 0.147543 Loss2: 1.451398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.558670 Loss1: 0.123827 Loss2: 1.434843 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972656
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.795262 Loss1: 0.350436 Loss2: 1.444826 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.578438 Loss1: 0.175892 Loss2: 1.402545 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 05:12:54,725 | server.py:236 | fit_round 102 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.527990 Loss1: 0.133799 Loss2: 1.394191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528592 Loss1: 0.130644 Loss2: 1.397948 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.504574 Loss1: 0.117916 Loss2: 1.386659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.483303 Loss1: 0.101260 Loss2: 1.382043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458763 Loss1: 0.086577 Loss2: 1.372186 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.523044 Loss1: 0.137763 Loss2: 1.385281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.505187 Loss1: 0.134751 Loss2: 1.370435 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978795
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.477954 Loss1: 0.102282 Loss2: 1.375672
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.761586 Loss1: 0.889076 Loss2: 1.872510
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.062294 Loss1: 0.659824 Loss2: 1.402470
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.904285 Loss1: 0.454222 Loss2: 1.450062
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.707213 Loss1: 0.329084 Loss2: 1.378129
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.643346 Loss1: 0.244110 Loss2: 1.399236
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.574490 Loss1: 0.823834 Loss2: 1.750657
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.847290 Loss1: 0.506508 Loss2: 1.340782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.581214 Loss1: 0.243449 Loss2: 1.337765 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565353 Loss1: 0.262037 Loss2: 1.303316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.561084 Loss1: 0.235665 Loss2: 1.325419 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.948958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494875 Loss1: 0.170484 Loss2: 1.324390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400904 Loss1: 0.102218 Loss2: 1.298686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367405 Loss1: 0.079186 Loss2: 1.288218 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 05:12:54,725][flwr][DEBUG] - fit_round 102 received 50 results and 0 failures
-INFO flwr 2023-10-11 05:13:36,032 | server.py:125 | fit progress: (102, 2.1906376145899107, {'accuracy': 0.5702}, 235323.810206367)
->> Test accuracy: 0.570200
-[2023-10-11 05:13:36,032][flwr][INFO] - fit progress: (102, 2.1906376145899107, {'accuracy': 0.5702}, 235323.810206367)
-DEBUG flwr 2023-10-11 05:13:36,032 | server.py:173 | evaluate_round 102: strategy sampled 50 clients (out of 50)
-[2023-10-11 05:13:36,032][flwr][DEBUG] - evaluate_round 102: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 05:22:42,238 | server.py:187 | evaluate_round 102 received 50 results and 0 failures
-[2023-10-11 05:22:42,238][flwr][DEBUG] - evaluate_round 102 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 05:22:42,238 | server.py:222 | fit_round 103: strategy sampled 50 clients (out of 50)
-[2023-10-11 05:22:42,238][flwr][DEBUG] - fit_round 103: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.778473 Loss1: 0.895703 Loss2: 1.882769
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.984881 Loss1: 0.565067 Loss2: 1.419814
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.825053 Loss1: 0.355582 Loss2: 1.469472
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.671543 Loss1: 0.281071 Loss2: 1.390472
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.658888 Loss1: 0.860656 Loss2: 1.798233
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.630841 Loss1: 0.215935 Loss2: 1.414906
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.893371 Loss1: 0.549050 Loss2: 1.344321
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562335 Loss1: 0.169559 Loss2: 1.392776
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.729622 Loss1: 0.367857 Loss2: 1.361765
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531204 Loss1: 0.144171 Loss2: 1.387032
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.613511 Loss1: 0.282354 Loss2: 1.331157
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.508402 Loss1: 0.128831 Loss2: 1.379571
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543508 Loss1: 0.203414 Loss2: 1.340094
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.502312 Loss1: 0.126414 Loss2: 1.375898
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529257 Loss1: 0.200792 Loss2: 1.328465
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.497196 Loss1: 0.117843 Loss2: 1.379353
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.466103 Loss1: 0.144479 Loss2: 1.321624
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475692 Loss1: 0.147056 Loss2: 1.328636
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.411426 Loss1: 0.096785 Loss2: 1.314641
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375977 Loss1: 0.067103 Loss2: 1.308874
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.708051 Loss1: 0.910217 Loss2: 1.797834
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.979968 Loss1: 0.583873 Loss2: 1.396095
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.698597 Loss1: 0.303891 Loss2: 1.394706
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.785885 Loss1: 0.873678 Loss2: 1.912208
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.646556 Loss1: 0.265486 Loss2: 1.381070
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.961224 Loss1: 0.546577 Loss2: 1.414647
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.607838 Loss1: 0.234792 Loss2: 1.373047
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.780919 Loss1: 0.349225 Loss2: 1.431694
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.559918 Loss1: 0.198714 Loss2: 1.361204
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479478 Loss1: 0.120062 Loss2: 1.359415 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469537 Loss1: 0.118414 Loss2: 1.351123 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443965 Loss1: 0.097420 Loss2: 1.346545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.488996 Loss1: 0.142131 Loss2: 1.346865 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.437567 Loss1: 0.065607 Loss2: 1.371959 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.744469 Loss1: 0.873984 Loss2: 1.870485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.835691 Loss1: 0.397201 Loss2: 1.438491 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.668885 Loss1: 0.271324 Loss2: 1.397561
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.875699 Loss1: 0.951780 Loss2: 1.923919
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.614429 Loss1: 0.229602 Loss2: 1.384827
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.089184 Loss1: 0.630483 Loss2: 1.458701
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.608492 Loss1: 0.209505 Loss2: 1.398987
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.851796 Loss1: 0.396864 Loss2: 1.454932
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544336 Loss1: 0.164711 Loss2: 1.379624
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.687334 Loss1: 0.267653 Loss2: 1.419681
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.521387 Loss1: 0.143960 Loss2: 1.377427
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.622448 Loss1: 0.205772 Loss2: 1.416676
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.473348 Loss1: 0.108634 Loss2: 1.364714
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.545571 Loss1: 0.147995 Loss2: 1.397576
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476606 Loss1: 0.114655 Loss2: 1.361950
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504477 Loss1: 0.106228 Loss2: 1.398249
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481399 Loss1: 0.098293 Loss2: 1.383106
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458156 Loss1: 0.079527 Loss2: 1.378629
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.495960 Loss1: 0.112395 Loss2: 1.383564
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.785780 Loss1: 0.922601 Loss2: 1.863179
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.950958 Loss1: 0.545674 Loss2: 1.405284
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.729954 Loss1: 0.321230 Loss2: 1.408724
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623395 Loss1: 0.226268 Loss2: 1.397127
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.848704 Loss1: 1.001167 Loss2: 1.847536
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.957633 Loss1: 0.546621 Loss2: 1.411012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726608 Loss1: 0.336973 Loss2: 1.389635 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564164 Loss1: 0.202424 Loss2: 1.361740 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.531892 Loss1: 0.175689 Loss2: 1.356203 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486734 Loss1: 0.131904 Loss2: 1.354830 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467363 Loss1: 0.120601 Loss2: 1.346761 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414222 Loss1: 0.072801 Loss2: 1.341421 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.774053 Loss1: 0.932449 Loss2: 1.841604 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.824808 Loss1: 0.389401 Loss2: 1.435407 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.576943 Loss1: 0.199295 Loss2: 1.377648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504208 Loss1: 0.136250 Loss2: 1.367958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528139 Loss1: 0.163991 Loss2: 1.364148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473815 Loss1: 0.120018 Loss2: 1.353798 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.468678 Loss1: 0.118861 Loss2: 1.349817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467331 Loss1: 0.116359 Loss2: 1.350971 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448803 Loss1: 0.098857 Loss2: 1.349946 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440807 Loss1: 0.099067 Loss2: 1.341741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406239 Loss1: 0.070207 Loss2: 1.336032
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.757039 Loss1: 0.853913 Loss2: 1.903126
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.012985 Loss1: 0.551007 Loss2: 1.461978
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.820019 Loss1: 0.354534 Loss2: 1.465485
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.715269 Loss1: 0.279918 Loss2: 1.435352
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.719226 Loss1: 0.284424 Loss2: 1.434801
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.858191 Loss1: 1.052296 Loss2: 1.805895
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.013693 Loss1: 0.631801 Loss2: 1.381892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.683782 Loss1: 0.237932 Loss2: 1.445850
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.781034 Loss1: 0.398044 Loss2: 1.382990
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.601725 Loss1: 0.172920 Loss2: 1.428805
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.645329 Loss1: 0.290395 Loss2: 1.354934
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.522502 Loss1: 0.108541 Loss2: 1.413961
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509173 Loss1: 0.161540 Loss2: 1.347633
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491757 Loss1: 0.165578 Loss2: 1.326179
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.511940 Loss1: 0.099180 Loss2: 1.412760
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.492876 Loss1: 0.165176 Loss2: 1.327700 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.507592 Loss1: 0.168089 Loss2: 1.339503 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.098708 Loss1: 0.628849 Loss2: 1.469859 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.713051 Loss1: 0.273894 Loss2: 1.439157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.682828 Loss1: 0.902060 Loss2: 1.780768
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617803 Loss1: 0.178578 Loss2: 1.439225
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.573982 Loss1: 0.153031 Loss2: 1.420951
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.894746 Loss1: 0.532369 Loss2: 1.362377
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523666 Loss1: 0.114585 Loss2: 1.409080
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.756838 Loss1: 0.382280 Loss2: 1.374558
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.515276 Loss1: 0.099377 Loss2: 1.415899
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.725425 Loss1: 0.368491 Loss2: 1.356935
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482077 Loss1: 0.074321 Loss2: 1.407756
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.580158 Loss1: 0.228162 Loss2: 1.351996
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481301 Loss1: 0.079703 Loss2: 1.401598
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502878 Loss1: 0.168460 Loss2: 1.334418
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438290 Loss1: 0.112775 Loss2: 1.325515
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449209 Loss1: 0.128371 Loss2: 1.320838
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398873 Loss1: 0.080227 Loss2: 1.318646
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404583 Loss1: 0.092854 Loss2: 1.311729
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.809791 Loss1: 0.927178 Loss2: 1.882613
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.910294 Loss1: 0.520310 Loss2: 1.389983
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.790002 Loss1: 0.373691 Loss2: 1.416311
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.661636 Loss1: 0.279541 Loss2: 1.382095
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568003 Loss1: 0.194320 Loss2: 1.373683
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.676527 Loss1: 0.820969 Loss2: 1.855558
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504558 Loss1: 0.139600 Loss2: 1.364958
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467441 Loss1: 0.105843 Loss2: 1.361598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451735 Loss1: 0.099012 Loss2: 1.352723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432507 Loss1: 0.082345 Loss2: 1.350161 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426932 Loss1: 0.084915 Loss2: 1.342017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464380 Loss1: 0.111085 Loss2: 1.353295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449754 Loss1: 0.103651 Loss2: 1.346103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409230 Loss1: 0.070705 Loss2: 1.338525
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.773676 Loss1: 0.864401 Loss2: 1.909275
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.943623 Loss1: 0.525875 Loss2: 1.417748
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.851858 Loss1: 0.392999 Loss2: 1.458860
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.775786 Loss1: 0.340333 Loss2: 1.435453
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.665720 Loss1: 0.227227 Loss2: 1.438493
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.827393 Loss1: 0.916977 Loss2: 1.910416
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.536620 Loss1: 0.126726 Loss2: 1.409894
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.521833 Loss1: 0.113215 Loss2: 1.408618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.519606 Loss1: 0.116204 Loss2: 1.403403 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484433 Loss1: 0.091476 Loss2: 1.392957 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.473443 Loss1: 0.087728 Loss2: 1.385715 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.502871 Loss1: 0.124529 Loss2: 1.378342 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463946 Loss1: 0.091954 Loss2: 1.371992 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982143
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.964070 Loss1: 0.598212 Loss2: 1.365859 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630856 Loss1: 0.275708 Loss2: 1.355148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.581138 Loss1: 0.228784 Loss2: 1.352354
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.679612 Loss1: 0.812161 Loss2: 1.867451
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.876511 Loss1: 0.450399 Loss2: 1.426112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671844 Loss1: 0.244939 Loss2: 1.426904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.632252 Loss1: 0.227841 Loss2: 1.404412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454073 Loss1: 0.120589 Loss2: 1.333484 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.492391 Loss1: 0.108142 Loss2: 1.384249 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466424 Loss1: 0.091530 Loss2: 1.374894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439119 Loss1: 0.075775 Loss2: 1.363344 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.742249 Loss1: 0.312271 Loss2: 1.429977 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536310 Loss1: 0.163210 Loss2: 1.373100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.528608 Loss1: 0.166925 Loss2: 1.361682
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.977162 Loss1: 1.099367 Loss2: 1.877795
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.981649 Loss1: 0.598066 Loss2: 1.383583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.705095 Loss1: 0.323461 Loss2: 1.381634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458681 Loss1: 0.101226 Loss2: 1.357455
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.589983 Loss1: 0.237994 Loss2: 1.351989
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485401 Loss1: 0.127084 Loss2: 1.358317
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543227 Loss1: 0.192529 Loss2: 1.350698
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496075 Loss1: 0.149966 Loss2: 1.346109
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480581 Loss1: 0.149817 Loss2: 1.330764
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446148 Loss1: 0.113233 Loss2: 1.332915
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451279 Loss1: 0.119908 Loss2: 1.331371
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410973 Loss1: 0.084321 Loss2: 1.326653
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.891768 Loss1: 1.049885 Loss2: 1.841882
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.996178 Loss1: 0.587973 Loss2: 1.408205
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.820578 Loss1: 0.413256 Loss2: 1.407323
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.627694 Loss1: 0.253338 Loss2: 1.374356
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536191 Loss1: 0.164677 Loss2: 1.371515
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.986550 Loss1: 0.993036 Loss2: 1.993514
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487691 Loss1: 0.137440 Loss2: 1.350251
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486535 Loss1: 0.134583 Loss2: 1.351952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.628109 Loss1: 0.222892 Loss2: 1.405217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.682247 Loss1: 0.306634 Loss2: 1.375614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.581476 Loss1: 0.186426 Loss2: 1.395049 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.537794 Loss1: 0.155031 Loss2: 1.382763 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.539926 Loss1: 0.164632 Loss2: 1.375294 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975260
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.854307 Loss1: 0.936383 Loss2: 1.917924
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.957556 Loss1: 0.536389 Loss2: 1.421167
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.765369 Loss1: 0.352435 Loss2: 1.412934
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.677555 Loss1: 0.278610 Loss2: 1.398945
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.723021 Loss1: 0.928496 Loss2: 1.794525
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.944473 Loss1: 0.582064 Loss2: 1.362409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.751870 Loss1: 0.361134 Loss2: 1.390736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.621420 Loss1: 0.278428 Loss2: 1.342992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518553 Loss1: 0.173694 Loss2: 1.344859 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473650 Loss1: 0.151765 Loss2: 1.321885 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438875 Loss1: 0.091387 Loss2: 1.347488
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472597 Loss1: 0.144922 Loss2: 1.327675
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442137 Loss1: 0.119314 Loss2: 1.322824
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434107 Loss1: 0.116986 Loss2: 1.317121
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.447845 Loss1: 0.124153 Loss2: 1.323692
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.855621 Loss1: 0.947515 Loss2: 1.908105
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.053661 Loss1: 0.638362 Loss2: 1.415298
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.786824 Loss1: 0.339824 Loss2: 1.447000
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629211 Loss1: 0.232991 Loss2: 1.396220
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.772614 Loss1: 0.902213 Loss2: 1.870401
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561167 Loss1: 0.174156 Loss2: 1.387011
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.995111 Loss1: 0.587709 Loss2: 1.407403
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524023 Loss1: 0.141287 Loss2: 1.382736
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.767498 Loss1: 0.332248 Loss2: 1.435250
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480107 Loss1: 0.102179 Loss2: 1.377928
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.691660 Loss1: 0.301196 Loss2: 1.390464
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457816 Loss1: 0.087620 Loss2: 1.370197
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.592651 Loss1: 0.194180 Loss2: 1.398471
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447418 Loss1: 0.080526 Loss2: 1.366893
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.505141 Loss1: 0.131170 Loss2: 1.373972
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420271 Loss1: 0.058677 Loss2: 1.361594
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487197 Loss1: 0.114786 Loss2: 1.372411
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466494 Loss1: 0.104356 Loss2: 1.362138
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433093 Loss1: 0.077331 Loss2: 1.355762
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457043 Loss1: 0.101218 Loss2: 1.355825
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.790784 Loss1: 0.960080 Loss2: 1.830704
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.059812 Loss1: 0.659020 Loss2: 1.400792
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.833672 Loss1: 0.408052 Loss2: 1.425620
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.726046 Loss1: 0.330693 Loss2: 1.395353
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.763550 Loss1: 0.891026 Loss2: 1.872524
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.049129 Loss1: 0.630809 Loss2: 1.418320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.811616 Loss1: 0.360304 Loss2: 1.451311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.691687 Loss1: 0.285659 Loss2: 1.406027 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.612211 Loss1: 0.220222 Loss2: 1.391989 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529990 Loss1: 0.144669 Loss2: 1.385321 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410484 Loss1: 0.077410 Loss2: 1.333074
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.538154 Loss1: 0.153249 Loss2: 1.384905
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.533327 Loss1: 0.153241 Loss2: 1.380086
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.488106 Loss1: 0.106731 Loss2: 1.381376
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458595 Loss1: 0.094023 Loss2: 1.364572
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.933082 Loss1: 0.986974 Loss2: 1.946108
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.939131 Loss1: 0.572654 Loss2: 1.366477
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.775103 Loss1: 0.383903 Loss2: 1.391200
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.680975 Loss1: 0.291203 Loss2: 1.389772
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.706641 Loss1: 0.893299 Loss2: 1.813342
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.521067 Loss1: 0.159132 Loss2: 1.361935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479816 Loss1: 0.129762 Loss2: 1.350053 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440366 Loss1: 0.097116 Loss2: 1.343250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399038 Loss1: 0.070921 Loss2: 1.328117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421887 Loss1: 0.095537 Loss2: 1.326350 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508062 Loss1: 0.163589 Loss2: 1.344474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387618 Loss1: 0.059164 Loss2: 1.328454 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387939 Loss1: 0.063290 Loss2: 1.324649
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.684788 Loss1: 0.757894 Loss2: 1.926894
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.015103 Loss1: 0.572010 Loss2: 1.443093
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.912010 Loss1: 0.446854 Loss2: 1.465156
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.714232 Loss1: 0.290086 Loss2: 1.424146
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631535 Loss1: 0.207276 Loss2: 1.424259
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.805001 Loss1: 0.946110 Loss2: 1.858890
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.631950 Loss1: 0.211909 Loss2: 1.420041
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.521795 Loss1: 0.115776 Loss2: 1.406019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.731073 Loss1: 0.293878 Loss2: 1.437195
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.465362 Loss1: 0.074650 Loss2: 1.390712
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.614374 Loss1: 0.214908 Loss2: 1.399466
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459567 Loss1: 0.070563 Loss2: 1.389004
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.604118 Loss1: 0.203589 Loss2: 1.400528
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438206 Loss1: 0.057665 Loss2: 1.380541
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.562756 Loss1: 0.167593 Loss2: 1.395163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.484537 Loss1: 0.101558 Loss2: 1.382979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.523746 Loss1: 0.141048 Loss2: 1.382699 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.889403 Loss1: 0.428247 Loss2: 1.461156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558987 Loss1: 0.162600 Loss2: 1.396388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.828596 Loss1: 0.981486 Loss2: 1.847110 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.988772 Loss1: 0.586234 Loss2: 1.402538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.798074 Loss1: 0.360709 Loss2: 1.437365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.712195 Loss1: 0.339362 Loss2: 1.372833 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.663544 Loss1: 0.273406 Loss2: 1.390138 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.495356 Loss1: 0.134206 Loss2: 1.361150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.504060 Loss1: 0.139116 Loss2: 1.364944 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.501487 Loss1: 0.140539 Loss2: 1.360949 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.765504 Loss1: 0.370407 Loss2: 1.395097 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.585721 Loss1: 0.221755 Loss2: 1.363966 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530721 Loss1: 0.168452 Loss2: 1.362268
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.725635 Loss1: 0.851662 Loss2: 1.873973
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.939896 Loss1: 0.540539 Loss2: 1.399357 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677601 Loss1: 0.243312 Loss2: 1.434289 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574043 Loss1: 0.207089 Loss2: 1.366955 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405973 Loss1: 0.067540 Loss2: 1.338433
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.586796 Loss1: 0.212354 Loss2: 1.374442
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519076 Loss1: 0.149455 Loss2: 1.369621
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.537422 Loss1: 0.174620 Loss2: 1.362802
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488881 Loss1: 0.118044 Loss2: 1.370837
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.468499 Loss1: 0.110457 Loss2: 1.358043
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.618830 Loss1: 0.778691 Loss2: 1.840139
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450166 Loss1: 0.095369 Loss2: 1.354797
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.754801 Loss1: 0.361725 Loss2: 1.393075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524624 Loss1: 0.184180 Loss2: 1.340443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.474429 Loss1: 0.137770 Loss2: 1.336660
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.594986 Loss1: 0.764860 Loss2: 1.830125
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.835201 Loss1: 0.450049 Loss2: 1.385153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.758272 Loss1: 0.342012 Loss2: 1.416260 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.677104 Loss1: 0.283835 Loss2: 1.393269 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.605575 Loss1: 0.215647 Loss2: 1.389928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509507 Loss1: 0.130417 Loss2: 1.379091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464519 Loss1: 0.098703 Loss2: 1.365816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.139578 Loss1: 0.678803 Loss2: 1.460774 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694690 Loss1: 0.262294 Loss2: 1.432396 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.574346 Loss1: 0.148730 Loss2: 1.425616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.548519 Loss1: 0.132553 Loss2: 1.415966
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.716334 Loss1: 0.864740 Loss2: 1.851594
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.902349 Loss1: 0.510580 Loss2: 1.391768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.704125 Loss1: 0.293658 Loss2: 1.410467 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.520132 Loss1: 0.116888 Loss2: 1.403244
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.570603 Loss1: 0.209374 Loss2: 1.361229
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543913 Loss1: 0.176929 Loss2: 1.366983
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446511 Loss1: 0.095868 Loss2: 1.350643
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470609 Loss1: 0.135144 Loss2: 1.335464
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445122 Loss1: 0.103709 Loss2: 1.341412
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420699 Loss1: 0.081141 Loss2: 1.339558
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.668728 Loss1: 0.787831 Loss2: 1.880897
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440541 Loss1: 0.100603 Loss2: 1.339938
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.055638 Loss1: 0.612691 Loss2: 1.442947
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.827480 Loss1: 0.347147 Loss2: 1.480333
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.707366 Loss1: 0.279942 Loss2: 1.427424
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611470 Loss1: 0.175699 Loss2: 1.435771
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.541995 Loss1: 0.121979 Loss2: 1.420016
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.754368 Loss1: 1.001920 Loss2: 1.752448
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.543833 Loss1: 0.122833 Loss2: 1.420999
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.541935 Loss1: 0.124278 Loss2: 1.417658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509517 Loss1: 0.094043 Loss2: 1.415474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.544026 Loss1: 0.135839 Loss2: 1.408187 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506216 Loss1: 0.203640 Loss2: 1.302576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.361478 Loss1: 0.075329 Loss2: 1.286149 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.348207 Loss1: 0.073762 Loss2: 1.274445
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.898558 Loss1: 1.033794 Loss2: 1.864764
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.029130 Loss1: 0.600672 Loss2: 1.428458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606758 Loss1: 0.220184 Loss2: 1.386574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.517985 Loss1: 0.148735 Loss2: 1.369251 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490732 Loss1: 0.122590 Loss2: 1.368142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460534 Loss1: 0.093878 Loss2: 1.366656 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 05:51:14,294 | server.py:236 | fit_round 103 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458041 Loss1: 0.099865 Loss2: 1.358176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445934 Loss1: 0.088851 Loss2: 1.357083 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582448 Loss1: 0.134210 Loss2: 1.448237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.524496 Loss1: 0.090087 Loss2: 1.434409 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.886841 Loss1: 1.045462 Loss2: 1.841380 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.937579 Loss1: 0.550044 Loss2: 1.387535 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602380 Loss1: 0.238482 Loss2: 1.363898 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484455 Loss1: 0.126906 Loss2: 1.357549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456160 Loss1: 0.101734 Loss2: 1.354426 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439366 Loss1: 0.096172 Loss2: 1.343194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.693834 Loss1: 0.297701 Loss2: 1.396132 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.592135 Loss1: 0.205758 Loss2: 1.386377 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.575533 Loss1: 0.195463 Loss2: 1.380070 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501724 Loss1: 0.113135 Loss2: 1.388589 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.793088 Loss1: 0.944942 Loss2: 1.848146 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.770591 Loss1: 0.373928 Loss2: 1.396663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.630631 Loss1: 0.244809 Loss2: 1.385822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.548419 Loss1: 0.175107 Loss2: 1.373312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.482140 Loss1: 0.122896 Loss2: 1.359244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489854 Loss1: 0.135604 Loss2: 1.354250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439692 Loss1: 0.088800 Loss2: 1.350892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431049 Loss1: 0.075406 Loss2: 1.355643 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470952 Loss1: 0.111178 Loss2: 1.359774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421518 Loss1: 0.069773 Loss2: 1.351745 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994485
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 05:51:14,294][flwr][DEBUG] - fit_round 103 received 50 results and 0 failures
-INFO flwr 2023-10-11 05:51:55,181 | server.py:125 | fit progress: (103, 2.201942985431074, {'accuracy': 0.5712}, 237622.959630363)
->> Test accuracy: 0.571200
-[2023-10-11 05:51:55,181][flwr][INFO] - fit progress: (103, 2.201942985431074, {'accuracy': 0.5712}, 237622.959630363)
-DEBUG flwr 2023-10-11 05:51:55,181 | server.py:173 | evaluate_round 103: strategy sampled 50 clients (out of 50)
-[2023-10-11 05:51:55,181][flwr][DEBUG] - evaluate_round 103: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 06:00:59,470 | server.py:187 | evaluate_round 103 received 50 results and 0 failures
-[2023-10-11 06:00:59,470][flwr][DEBUG] - evaluate_round 103 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 06:00:59,470 | server.py:222 | fit_round 104: strategy sampled 50 clients (out of 50)
-[2023-10-11 06:00:59,470][flwr][DEBUG] - fit_round 104: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.929769 Loss1: 1.089953 Loss2: 1.839816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.778159 Loss1: 0.376161 Loss2: 1.401998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.624868 Loss1: 0.240768 Loss2: 1.384101
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.718823 Loss1: 0.893453 Loss2: 1.825371
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.588538 Loss1: 0.218441 Loss2: 1.370097
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.888609 Loss1: 0.471504 Loss2: 1.417105
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.707965 Loss1: 0.302139 Loss2: 1.405825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.628171 Loss1: 0.235501 Loss2: 1.392670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.564284 Loss1: 0.185507 Loss2: 1.378776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537724 Loss1: 0.158402 Loss2: 1.379322 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509229 Loss1: 0.140904 Loss2: 1.368326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434402 Loss1: 0.075849 Loss2: 1.358553 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.690898 Loss1: 0.837425 Loss2: 1.853473 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.784061 Loss1: 0.356451 Loss2: 1.427610 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.725726 Loss1: 0.871246 Loss2: 1.854479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.026553 Loss1: 0.596700 Loss2: 1.429853 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524825 Loss1: 0.149821 Loss2: 1.375004 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.517952 Loss1: 0.145651 Loss2: 1.372301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451671 Loss1: 0.086507 Loss2: 1.365164 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.503740 Loss1: 0.148903 Loss2: 1.354837 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441773 Loss1: 0.064741 Loss2: 1.377032 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436204 Loss1: 0.071873 Loss2: 1.364331 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.761285 Loss1: 0.429230 Loss2: 1.332055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573364 Loss1: 0.237757 Loss2: 1.335608 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.853183 Loss1: 0.987976 Loss2: 1.865207
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526718 Loss1: 0.178386 Loss2: 1.348331
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.848622 Loss1: 0.483172 Loss2: 1.365450
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461563 Loss1: 0.135089 Loss2: 1.326474
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.452311 Loss1: 0.128828 Loss2: 1.323483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422649 Loss1: 0.099413 Loss2: 1.323236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441580 Loss1: 0.123103 Loss2: 1.318477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419798 Loss1: 0.099157 Loss2: 1.320641 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977022
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386360 Loss1: 0.059535 Loss2: 1.326825 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.746722 Loss1: 0.839966 Loss2: 1.906757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.829136 Loss1: 0.413165 Loss2: 1.415970 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.686053 Loss1: 0.290471 Loss2: 1.395583
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.810319 Loss1: 0.908167 Loss2: 1.902152
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.989713 Loss1: 0.560711 Loss2: 1.429002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.905022 Loss1: 0.404192 Loss2: 1.500830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.667451 Loss1: 0.240741 Loss2: 1.426711 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687668 Loss1: 0.261786 Loss2: 1.425883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.595328 Loss1: 0.165351 Loss2: 1.429976 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.520333 Loss1: 0.149751 Loss2: 1.370582
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.558890 Loss1: 0.142201 Loss2: 1.416689
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.514124 Loss1: 0.104770 Loss2: 1.409354
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.483628 Loss1: 0.079282 Loss2: 1.404346
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.518299 Loss1: 0.119063 Loss2: 1.399237
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.101655 Loss1: 1.121042 Loss2: 1.980613
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.060622 Loss1: 0.684713 Loss2: 1.375909
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.728349 Loss1: 0.297405 Loss2: 1.430944
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.611137 Loss1: 0.236411 Loss2: 1.374727
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565626 Loss1: 0.205613 Loss2: 1.360013
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564917 Loss1: 0.178726 Loss2: 1.386191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490287 Loss1: 0.134913 Loss2: 1.355375 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451221 Loss1: 0.104167 Loss2: 1.347055
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.808428 Loss1: 0.379107 Loss2: 1.429321
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699006 Loss1: 0.318920 Loss2: 1.380086 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.610170 Loss1: 0.224750 Loss2: 1.385420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.544609 Loss1: 0.177939 Loss2: 1.366670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.945013 Loss1: 0.984729 Loss2: 1.960284 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.040679 Loss1: 0.650166 Loss2: 1.390513 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.667853 Loss1: 0.286202 Loss2: 1.381651 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.585631 Loss1: 0.188232 Loss2: 1.397400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452925 Loss1: 0.081835 Loss2: 1.371090 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450207 Loss1: 0.088180 Loss2: 1.362027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418324 Loss1: 0.061168 Loss2: 1.357156 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998798
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514636 Loss1: 0.200247 Loss2: 1.314388 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440299 Loss1: 0.128447 Loss2: 1.311852 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.604726 Loss1: 0.831388 Loss2: 1.773338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377382 Loss1: 0.084148 Loss2: 1.293234 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987981
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.584315 Loss1: 0.248300 Loss2: 1.336014 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493413 Loss1: 0.164307 Loss2: 1.329105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.584119 Loss1: 0.826173 Loss2: 1.757946
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.482546 Loss1: 0.151267 Loss2: 1.331279
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.740122 Loss1: 0.396017 Loss2: 1.344106
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422184 Loss1: 0.091581 Loss2: 1.330604
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.634141 Loss1: 0.271677 Loss2: 1.362464
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.382489 Loss1: 0.064722 Loss2: 1.317766
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532301 Loss1: 0.212699 Loss2: 1.319601
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369396 Loss1: 0.055684 Loss2: 1.313711
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.448639 Loss1: 0.126707 Loss2: 1.321932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411096 Loss1: 0.099725 Loss2: 1.311371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.810571 Loss1: 0.965911 Loss2: 1.844660
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.353892 Loss1: 0.047472 Loss2: 1.306420
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.916267 Loss1: 0.508624 Loss2: 1.407642
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.359201 Loss1: 0.059142 Loss2: 1.300060
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.641785 Loss1: 0.265106 Loss2: 1.376679 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.548608 Loss1: 0.178068 Loss2: 1.370540 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.526573 Loss1: 0.157037 Loss2: 1.369536
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.629911 Loss1: 0.724449 Loss2: 1.905462
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.921997 Loss1: 0.519122 Loss2: 1.402875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.815821 Loss1: 0.368625 Loss2: 1.447196 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.746269 Loss1: 0.339592 Loss2: 1.406677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530746 Loss1: 0.132483 Loss2: 1.398263 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.497782 Loss1: 0.111587 Loss2: 1.386195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.777244 Loss1: 0.878248 Loss2: 1.898996
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450525 Loss1: 0.068745 Loss2: 1.381780
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.932468 Loss1: 0.523210 Loss2: 1.409258
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434002 Loss1: 0.066515 Loss2: 1.367487
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.548563 Loss1: 0.172233 Loss2: 1.376329 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471822 Loss1: 0.101844 Loss2: 1.369978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512607 Loss1: 0.152766 Loss2: 1.359841
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.941034 Loss1: 0.948062 Loss2: 1.992972
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471972 Loss1: 0.105514 Loss2: 1.366459
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.027841 Loss1: 0.577259 Loss2: 1.450583
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.476063 Loss1: 0.111968 Loss2: 1.364095
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.802205 Loss1: 0.318629 Loss2: 1.483577
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460581 Loss1: 0.105925 Loss2: 1.354656
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.689004 Loss1: 0.252740 Loss2: 1.436264
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.671108 Loss1: 0.232378 Loss2: 1.438730
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.594372 Loss1: 0.159973 Loss2: 1.434399
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.556494 Loss1: 0.132402 Loss2: 1.424092
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.553197 Loss1: 0.131208 Loss2: 1.421989
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.553916 Loss1: 0.128876 Loss2: 1.425039
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.589504 Loss1: 0.730792 Loss2: 1.858712
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.518162 Loss1: 0.097540 Loss2: 1.420622
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.798974 Loss1: 0.424519 Loss2: 1.374455
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.780502 Loss1: 0.362436 Loss2: 1.418066
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.688314 Loss1: 0.314024 Loss2: 1.374290
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593428 Loss1: 0.216227 Loss2: 1.377201
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509379 Loss1: 0.151187 Loss2: 1.358192
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.692379 Loss1: 0.864702 Loss2: 1.827677
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.465472 Loss1: 0.108383 Loss2: 1.357088
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.475499 Loss1: 0.127315 Loss2: 1.348184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431938 Loss1: 0.079821 Loss2: 1.352117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391387 Loss1: 0.050565 Loss2: 1.340823 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495047 Loss1: 0.164277 Loss2: 1.330771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437780 Loss1: 0.113634 Loss2: 1.324146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418197 Loss1: 0.104797 Loss2: 1.313400
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.706690 Loss1: 0.896019 Loss2: 1.810671
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.897868 Loss1: 0.513407 Loss2: 1.384460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.579280 Loss1: 0.215730 Loss2: 1.363550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542911 Loss1: 0.181278 Loss2: 1.361633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503582 Loss1: 0.143929 Loss2: 1.359653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489314 Loss1: 0.131724 Loss2: 1.357590 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459389 Loss1: 0.112870 Loss2: 1.346519 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481341 Loss1: 0.135178 Loss2: 1.346163 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.622505 Loss1: 0.165437 Loss2: 1.457068 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.588925 Loss1: 0.124017 Loss2: 1.464908 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.559962 Loss1: 0.115155 Loss2: 1.444807
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.889019 Loss1: 0.988227 Loss2: 1.900792
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.539367 Loss1: 0.095798 Loss2: 1.443569
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.967107 Loss1: 0.526593 Loss2: 1.440513
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.816274 Loss1: 0.353653 Loss2: 1.462621
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.644974 Loss1: 0.224647 Loss2: 1.420327
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.615130 Loss1: 0.192390 Loss2: 1.422740
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.545727 Loss1: 0.135874 Loss2: 1.409852
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.620211 Loss1: 0.809055 Loss2: 1.811156
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524135 Loss1: 0.113848 Loss2: 1.410286
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.512890 Loss1: 0.109946 Loss2: 1.402945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.541884 Loss1: 0.139543 Loss2: 1.402341 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.497135 Loss1: 0.092183 Loss2: 1.404952 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519824 Loss1: 0.177353 Loss2: 1.342471 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433110 Loss1: 0.109072 Loss2: 1.324038 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418122 Loss1: 0.089427 Loss2: 1.328695
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.773461 Loss1: 0.888949 Loss2: 1.884512
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405309 Loss1: 0.083092 Loss2: 1.322217
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.959385 Loss1: 0.576333 Loss2: 1.383052
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.693917 Loss1: 0.279231 Loss2: 1.414687
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540742 Loss1: 0.183230 Loss2: 1.357512
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488223 Loss1: 0.127442 Loss2: 1.360780
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458246 Loss1: 0.109488 Loss2: 1.348758
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.727638 Loss1: 0.875728 Loss2: 1.851910
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450346 Loss1: 0.107212 Loss2: 1.343134
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452234 Loss1: 0.111361 Loss2: 1.340873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444284 Loss1: 0.098707 Loss2: 1.345577 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413070 Loss1: 0.078024 Loss2: 1.335046 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465538 Loss1: 0.101421 Loss2: 1.364117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484492 Loss1: 0.121585 Loss2: 1.362906 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.456094 Loss1: 0.098073 Loss2: 1.358021
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.840011 Loss1: 0.956074 Loss2: 1.883937
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446767 Loss1: 0.088441 Loss2: 1.358326
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.936156 Loss1: 0.562070 Loss2: 1.374086
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.725992 Loss1: 0.317383 Loss2: 1.408609
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.616907 Loss1: 0.249564 Loss2: 1.367342
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.585153 Loss1: 0.205461 Loss2: 1.379691
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.575943 Loss1: 0.208756 Loss2: 1.367187
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.734505 Loss1: 0.810565 Loss2: 1.923941
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502536 Loss1: 0.140842 Loss2: 1.361694
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.056045 Loss1: 0.607389 Loss2: 1.448656
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461139 Loss1: 0.102594 Loss2: 1.358545
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.813152 Loss1: 0.368554 Loss2: 1.444598
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443640 Loss1: 0.094338 Loss2: 1.349302
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.724615 Loss1: 0.315271 Loss2: 1.409344
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418352 Loss1: 0.075111 Loss2: 1.343241
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567981 Loss1: 0.167518 Loss2: 1.400463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548319 Loss1: 0.157480 Loss2: 1.390840 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497309 Loss1: 0.110430 Loss2: 1.386879
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.840712 Loss1: 0.966047 Loss2: 1.874665
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463777 Loss1: 0.087655 Loss2: 1.376122
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.941817 Loss1: 0.527247 Loss2: 1.414570
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.839926 Loss1: 0.428150 Loss2: 1.411776
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.640132 Loss1: 0.236932 Loss2: 1.403200
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.541234 Loss1: 0.148996 Loss2: 1.392238
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513122 Loss1: 0.136761 Loss2: 1.376361
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.087577 Loss1: 1.088814 Loss2: 1.998763
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475192 Loss1: 0.095393 Loss2: 1.379799
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.233568 Loss1: 0.717562 Loss2: 1.516006
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437138 Loss1: 0.069717 Loss2: 1.367421
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421441 Loss1: 0.059789 Loss2: 1.361652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417094 Loss1: 0.059693 Loss2: 1.357401 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.620882 Loss1: 0.162779 Loss2: 1.458103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.547861 Loss1: 0.103571 Loss2: 1.444290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.767671 Loss1: 0.928852 Loss2: 1.838819 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.791507 Loss1: 0.372784 Loss2: 1.418723 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514076 Loss1: 0.159600 Loss2: 1.354477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491214 Loss1: 0.145704 Loss2: 1.345510
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.892426 Loss1: 0.957826 Loss2: 1.934599
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.031595 Loss1: 0.587831 Loss2: 1.443764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.881015 Loss1: 0.386322 Loss2: 1.494693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.673894 Loss1: 0.244304 Loss2: 1.429590 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424941 Loss1: 0.089139 Loss2: 1.335803
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.649039 Loss1: 0.210555 Loss2: 1.438483
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.568769 Loss1: 0.140532 Loss2: 1.428238
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.520320 Loss1: 0.115562 Loss2: 1.404758
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.521876 Loss1: 0.114546 Loss2: 1.407331
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490446 Loss1: 0.084423 Loss2: 1.406023
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.750814 Loss1: 0.879788 Loss2: 1.871026
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.518825 Loss1: 0.118787 Loss2: 1.400038
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716995 Loss1: 0.298863 Loss2: 1.418131 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561954 Loss1: 0.181620 Loss2: 1.380333 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492724 Loss1: 0.121332 Loss2: 1.371392
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.763051 Loss1: 0.867418 Loss2: 1.895633
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.028097 Loss1: 0.594387 Loss2: 1.433710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.846045 Loss1: 0.411336 Loss2: 1.434709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.662571 Loss1: 0.255858 Loss2: 1.406713 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421440 Loss1: 0.068655 Loss2: 1.352785
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.593735 Loss1: 0.195384 Loss2: 1.398351
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531463 Loss1: 0.137475 Loss2: 1.393988
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485914 Loss1: 0.101927 Loss2: 1.383987
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438707 Loss1: 0.067476 Loss2: 1.371231
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432170 Loss1: 0.067196 Loss2: 1.364974
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.658638 Loss1: 0.795391 Loss2: 1.863247
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454613 Loss1: 0.089096 Loss2: 1.365517
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.887054 Loss1: 0.414903 Loss2: 1.472151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.591186 Loss1: 0.200046 Loss2: 1.391140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564686 Loss1: 0.189771 Loss2: 1.374914
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.795498 Loss1: 0.949985 Loss2: 1.845513
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.914478 Loss1: 0.551743 Loss2: 1.362735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.705294 Loss1: 0.310103 Loss2: 1.395191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488535 Loss1: 0.120297 Loss2: 1.368239
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573440 Loss1: 0.218320 Loss2: 1.355120
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434558 Loss1: 0.075245 Loss2: 1.359312
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.561303 Loss1: 0.205657 Loss2: 1.355645
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472600 Loss1: 0.118790 Loss2: 1.353810
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465458 Loss1: 0.124181 Loss2: 1.341278
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426006 Loss1: 0.087130 Loss2: 1.338875
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408702 Loss1: 0.076976 Loss2: 1.331727
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.790197 Loss1: 0.889249 Loss2: 1.900948
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378695 Loss1: 0.052688 Loss2: 1.326007
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.851714 Loss1: 0.353647 Loss2: 1.498067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.748652 Loss1: 0.282144 Loss2: 1.466508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.750366 Loss1: 0.864818 Loss2: 1.885548
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.670322 Loss1: 0.203348 Loss2: 1.466974
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.059931 Loss1: 0.629318 Loss2: 1.430614
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.669099 Loss1: 0.217806 Loss2: 1.451293
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834629 Loss1: 0.375242 Loss2: 1.459387
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.588576 Loss1: 0.135625 Loss2: 1.452951
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.541116 Loss1: 0.101393 Loss2: 1.439723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.522854 Loss1: 0.087335 Loss2: 1.435520 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.542272 Loss1: 0.137333 Loss2: 1.404939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.482369 Loss1: 0.096203 Loss2: 1.386166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465255 Loss1: 0.083899 Loss2: 1.381356
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.952828 Loss1: 1.037583 Loss2: 1.915245
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.112304 Loss1: 0.623443 Loss2: 1.488860
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.809877 Loss1: 0.353191 Loss2: 1.456686
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.734412 Loss1: 0.287113 Loss2: 1.447299
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.717836 Loss1: 0.257401 Loss2: 1.460435
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.873568 Loss1: 1.004145 Loss2: 1.869423
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.704629 Loss1: 0.259523 Loss2: 1.445106
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.634212 Loss1: 0.195460 Loss2: 1.438751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.567909 Loss1: 0.139702 Loss2: 1.428207 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.541126 Loss1: 0.118882 Loss2: 1.422244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508761 Loss1: 0.094850 Loss2: 1.413911 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474557 Loss1: 0.122089 Loss2: 1.352468 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.472517 Loss1: 0.121578 Loss2: 1.350939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431507 Loss1: 0.083845 Loss2: 1.347662
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.809931 Loss1: 0.900398 Loss2: 1.909534
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.997577 Loss1: 0.561209 Loss2: 1.436368
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.846085 Loss1: 0.386527 Loss2: 1.459559
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.684934 Loss1: 0.273724 Loss2: 1.411210
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.664557 Loss1: 0.254476 Loss2: 1.410081
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.028210 Loss1: 1.054620 Loss2: 1.973591
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.592707 Loss1: 0.185061 Loss2: 1.407646
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.546025 Loss1: 0.145169 Loss2: 1.400856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.524202 Loss1: 0.129186 Loss2: 1.395016 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499632 Loss1: 0.116664 Loss2: 1.382968 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.480870 Loss1: 0.095322 Loss2: 1.385547 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.598545 Loss1: 0.149495 Loss2: 1.449050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.559867 Loss1: 0.117604 Loss2: 1.442263 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.532698 Loss1: 0.092164 Loss2: 1.440533
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.940954 Loss1: 1.044640 Loss2: 1.896314
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.037365 Loss1: 0.620215 Loss2: 1.417150
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.764661 Loss1: 0.325852 Loss2: 1.438809
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.662450 Loss1: 0.257184 Loss2: 1.405266
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.607765 Loss1: 0.202498 Loss2: 1.405267
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.776649 Loss1: 1.005936 Loss2: 1.770713
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.584673 Loss1: 0.184827 Loss2: 1.399846
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.593624 Loss1: 0.188155 Loss2: 1.405469 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.561323 Loss1: 0.154138 Loss2: 1.407185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.550501 Loss1: 0.158753 Loss2: 1.391749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.522489 Loss1: 0.125046 Loss2: 1.397444 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.432470 Loss1: 0.122880 Loss2: 1.309590 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423841 Loss1: 0.122628 Loss2: 1.301213 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.359756 Loss1: 0.060525 Loss2: 1.299231
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.703370 Loss1: 0.872428 Loss2: 1.830943
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.842005 Loss1: 0.492982 Loss2: 1.349023
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.677691 Loss1: 0.281919 Loss2: 1.395772
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533914 Loss1: 0.182718 Loss2: 1.351196
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491772 Loss1: 0.154856 Loss2: 1.336916
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.027748 Loss1: 1.113328 Loss2: 1.914420
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.496417 Loss1: 0.157270 Loss2: 1.339147
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.429387 Loss1: 0.095241 Loss2: 1.334146 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420998 Loss1: 0.094899 Loss2: 1.326099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394061 Loss1: 0.074437 Loss2: 1.319624 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398569 Loss1: 0.081720 Loss2: 1.316849 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.506117 Loss1: 0.139949 Loss2: 1.366168 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434354 Loss1: 0.081003 Loss2: 1.353351 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-DEBUG flwr 2023-10-11 06:29:21,456 | server.py:236 | fit_round 104 received 50 results and 0 failures
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.954503 Loss1: 0.555921 Loss2: 1.398581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.680183 Loss1: 0.295338 Loss2: 1.384844 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.627717 Loss1: 0.216198 Loss2: 1.411519
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.774576 Loss1: 0.873182 Loss2: 1.901394
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.560921 Loss1: 0.175738 Loss2: 1.385183
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.951285 Loss1: 0.518336 Loss2: 1.432950
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.477004 Loss1: 0.097181 Loss2: 1.379823
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.941515 Loss1: 0.468616 Loss2: 1.472899
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469518 Loss1: 0.101010 Loss2: 1.368508
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.632019 Loss1: 0.215939 Loss2: 1.416080
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450251 Loss1: 0.085207 Loss2: 1.365044
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.611231 Loss1: 0.200567 Loss2: 1.410664
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443684 Loss1: 0.079942 Loss2: 1.363742
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537212 Loss1: 0.128235 Loss2: 1.408977
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530752 Loss1: 0.129681 Loss2: 1.401070
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511767 Loss1: 0.116579 Loss2: 1.395188
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476092 Loss1: 0.084809 Loss2: 1.391283
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.527442 Loss1: 0.133922 Loss2: 1.393520
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.798455 Loss1: 0.944892 Loss2: 1.853563
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.952215 Loss1: 0.566389 Loss2: 1.385827
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.766361 Loss1: 0.342248 Loss2: 1.424114
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.650531 Loss1: 0.278118 Loss2: 1.372412
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544962 Loss1: 0.155168 Loss2: 1.389794
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.852797 Loss1: 0.961987 Loss2: 1.890810
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.055749 Loss1: 0.586346 Loss2: 1.469403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.814418 Loss1: 0.355623 Loss2: 1.458795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.685898 Loss1: 0.247684 Loss2: 1.438214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.634807 Loss1: 0.210249 Loss2: 1.424558 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.604893 Loss1: 0.175715 Loss2: 1.429178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.557029 Loss1: 0.140706 Loss2: 1.416323 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.576039 Loss1: 0.156629 Loss2: 1.419410 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 06:29:21,456][flwr][DEBUG] - fit_round 104 received 50 results and 0 failures
-INFO flwr 2023-10-11 06:30:03,449 | server.py:125 | fit progress: (104, 2.207348462110891, {'accuracy': 0.5699}, 239911.22773559002)
->> Test accuracy: 0.569900
-[2023-10-11 06:30:03,449][flwr][INFO] - fit progress: (104, 2.207348462110891, {'accuracy': 0.5699}, 239911.22773559002)
-DEBUG flwr 2023-10-11 06:30:03,450 | server.py:173 | evaluate_round 104: strategy sampled 50 clients (out of 50)
-[2023-10-11 06:30:03,450][flwr][DEBUG] - evaluate_round 104: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 06:39:07,247 | server.py:187 | evaluate_round 104 received 50 results and 0 failures
-[2023-10-11 06:39:07,247][flwr][DEBUG] - evaluate_round 104 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 06:39:07,248 | server.py:222 | fit_round 105: strategy sampled 50 clients (out of 50)
-[2023-10-11 06:39:07,248][flwr][DEBUG] - fit_round 105: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.704990 Loss1: 0.810588 Loss2: 1.894402
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.069108 Loss1: 0.651020 Loss2: 1.418088
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.893941 Loss1: 0.417519 Loss2: 1.476422
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.679731 Loss1: 0.277142 Loss2: 1.402589
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.946470 Loss1: 1.064075 Loss2: 1.882395
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.588677 Loss1: 0.184592 Loss2: 1.404085
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.023537 Loss1: 0.636189 Loss2: 1.387348
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587663 Loss1: 0.195733 Loss2: 1.391930
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.827323 Loss1: 0.432653 Loss2: 1.394670
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.533449 Loss1: 0.130003 Loss2: 1.403447
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.667838 Loss1: 0.299752 Loss2: 1.368086
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548310 Loss1: 0.190905 Loss2: 1.357404
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489236 Loss1: 0.107316 Loss2: 1.381920
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.533683 Loss1: 0.185082 Loss2: 1.348601
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488186 Loss1: 0.104900 Loss2: 1.383287
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.482994 Loss1: 0.131317 Loss2: 1.351677
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.557399 Loss1: 0.163761 Loss2: 1.393638
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432047 Loss1: 0.099475 Loss2: 1.332572 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.023920 Loss1: 1.063371 Loss2: 1.960549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.794615 Loss1: 0.338917 Loss2: 1.455698 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.832292 Loss1: 0.926246 Loss2: 1.906046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.900784 Loss1: 0.481937 Loss2: 1.418847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.497296 Loss1: 0.096930 Loss2: 1.400367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.509120 Loss1: 0.117272 Loss2: 1.391848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.475780 Loss1: 0.079219 Loss2: 1.396561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.470490 Loss1: 0.083929 Loss2: 1.386561 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485727 Loss1: 0.116196 Loss2: 1.369531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405110 Loss1: 0.045449 Loss2: 1.359662 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405960 Loss1: 0.052912 Loss2: 1.353048
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.767476 Loss1: 0.939723 Loss2: 1.827753
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.904753 Loss1: 0.534596 Loss2: 1.370157
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.713313 Loss1: 0.317154 Loss2: 1.396159
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.626237 Loss1: 0.264235 Loss2: 1.362002
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.608280 Loss1: 0.247753 Loss2: 1.360527
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.746541 Loss1: 0.888535 Loss2: 1.858006
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.882995 Loss1: 0.485912 Loss2: 1.397083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.796249 Loss1: 0.350730 Loss2: 1.445519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.647579 Loss1: 0.261922 Loss2: 1.385656 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.668227 Loss1: 0.261786 Loss2: 1.406441 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421018 Loss1: 0.083870 Loss2: 1.337149
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.562436 Loss1: 0.166671 Loss2: 1.395765
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.526629 Loss1: 0.147625 Loss2: 1.379004
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.530575 Loss1: 0.151002 Loss2: 1.379573
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480955 Loss1: 0.098207 Loss2: 1.382748
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466201 Loss1: 0.097050 Loss2: 1.369151
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.077102 Loss1: 1.086885 Loss2: 1.990217
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.982613 Loss1: 0.610976 Loss2: 1.371637
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.764072 Loss1: 0.339007 Loss2: 1.425065
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.656068 Loss1: 0.272231 Loss2: 1.383837
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.564359 Loss1: 0.194835 Loss2: 1.369524
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.501228 Loss1: 0.134191 Loss2: 1.367037
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.699628 Loss1: 0.830177 Loss2: 1.869451
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446434 Loss1: 0.096506 Loss2: 1.349928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.730772 Loss1: 0.299198 Loss2: 1.431574 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.589442 Loss1: 0.205308 Loss2: 1.384134 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502087 Loss1: 0.129451 Loss2: 1.372636 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460392 Loss1: 0.092247 Loss2: 1.368145
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.659922 Loss1: 0.830964 Loss2: 1.828958
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.808105 Loss1: 0.463254 Loss2: 1.344851 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.520559 Loss1: 0.152726 Loss2: 1.367833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.617539 Loss1: 0.243030 Loss2: 1.374509
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562894 Loss1: 0.231637 Loss2: 1.331258
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544424 Loss1: 0.206226 Loss2: 1.338197
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506720 Loss1: 0.171508 Loss2: 1.335212
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456621 Loss1: 0.125179 Loss2: 1.331441
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.943531 Loss1: 0.968392 Loss2: 1.975139
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415076 Loss1: 0.089940 Loss2: 1.325136
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402406 Loss1: 0.081820 Loss2: 1.320586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404699 Loss1: 0.086623 Loss2: 1.318075 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491796 Loss1: 0.140122 Loss2: 1.351674 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.525596 Loss1: 0.173561 Loss2: 1.352036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.477093 Loss1: 0.117162 Loss2: 1.359931
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.731036 Loss1: 0.839107 Loss2: 1.891929
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.984097 Loss1: 0.574550 Loss2: 1.409546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.598663 Loss1: 0.212366 Loss2: 1.386296 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.563550 Loss1: 0.171588 Loss2: 1.391962 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.545025 Loss1: 0.154724 Loss2: 1.390301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.511097 Loss1: 0.131367 Loss2: 1.379730 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451280 Loss1: 0.082112 Loss2: 1.369169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454922 Loss1: 0.088569 Loss2: 1.366353 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.536079 Loss1: 0.124903 Loss2: 1.411176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.506982 Loss1: 0.113785 Loss2: 1.393196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.587563 Loss1: 0.790587 Loss2: 1.796976
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.529306 Loss1: 0.125203 Loss2: 1.404103
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.852802 Loss1: 0.509684 Loss2: 1.343118
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.510878 Loss1: 0.119773 Loss2: 1.391106
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.603526 Loss1: 0.267204 Loss2: 1.336322 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503006 Loss1: 0.159473 Loss2: 1.343533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509154 Loss1: 0.179625 Loss2: 1.329529
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.705705 Loss1: 0.859212 Loss2: 1.846493
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.026424 Loss1: 0.627550 Loss2: 1.398875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.762127 Loss1: 0.329828 Loss2: 1.432299 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378877 Loss1: 0.062733 Loss2: 1.316144
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.601972 Loss1: 0.236638 Loss2: 1.365334
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.494224 Loss1: 0.127501 Loss2: 1.366723
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484781 Loss1: 0.131404 Loss2: 1.353376
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491182 Loss1: 0.140591 Loss2: 1.350592
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.463829 Loss1: 0.110784 Loss2: 1.353045
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.946464 Loss1: 1.013604 Loss2: 1.932860
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453324 Loss1: 0.108642 Loss2: 1.344682
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.471490 Loss1: 0.122584 Loss2: 1.348906 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.686097 Loss1: 0.305166 Loss2: 1.380931 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.588252 Loss1: 0.195622 Loss2: 1.392630 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.755654 Loss1: 0.850016 Loss2: 1.905638 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.478763 Loss1: 0.116547 Loss2: 1.362216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461982 Loss1: 0.104572 Loss2: 1.357409 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.592790 Loss1: 0.201813 Loss2: 1.390977 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.515008 Loss1: 0.130692 Loss2: 1.384315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488599 Loss1: 0.115908 Loss2: 1.372691
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.788957 Loss1: 0.900982 Loss2: 1.887975
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.500570 Loss1: 0.122348 Loss2: 1.378222
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.955209 Loss1: 0.541935 Loss2: 1.413274
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472142 Loss1: 0.096166 Loss2: 1.375976
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.739542 Loss1: 0.293578 Loss2: 1.445964
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.770011 Loss1: 0.355973 Loss2: 1.414039
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.632920 Loss1: 0.201664 Loss2: 1.431256
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.617506 Loss1: 0.212738 Loss2: 1.404768
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.555559 Loss1: 0.153116 Loss2: 1.402443
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.886381 Loss1: 1.022106 Loss2: 1.864275
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461556 Loss1: 0.075501 Loss2: 1.386055
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.949571 Loss1: 0.549323 Loss2: 1.400248
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469746 Loss1: 0.093394 Loss2: 1.376352
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.753188 Loss1: 0.344263 Loss2: 1.408925
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443538 Loss1: 0.067546 Loss2: 1.375992
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.578280 Loss1: 0.200684 Loss2: 1.377596 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502185 Loss1: 0.146390 Loss2: 1.355795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493246 Loss1: 0.121524 Loss2: 1.371722
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.689228 Loss1: 0.832705 Loss2: 1.856523
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444711 Loss1: 0.088457 Loss2: 1.356254
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.918539 Loss1: 0.494224 Loss2: 1.424315
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426221 Loss1: 0.080163 Loss2: 1.346058
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.856939 Loss1: 0.407687 Loss2: 1.449252
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.683847 Loss1: 0.264542 Loss2: 1.419306
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.680277 Loss1: 0.268407 Loss2: 1.411870
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.566831 Loss1: 0.155003 Loss2: 1.411828
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531997 Loss1: 0.136946 Loss2: 1.395051
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.636753 Loss1: 0.825539 Loss2: 1.811213
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.866632 Loss1: 0.491746 Loss2: 1.374887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677018 Loss1: 0.276728 Loss2: 1.400290 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.620160 Loss1: 0.267350 Loss2: 1.352811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522930 Loss1: 0.168371 Loss2: 1.354559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477849 Loss1: 0.126913 Loss2: 1.350936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476990 Loss1: 0.137556 Loss2: 1.339434 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690251 Loss1: 0.330161 Loss2: 1.360090 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.578854 Loss1: 0.231349 Loss2: 1.347505 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458797 Loss1: 0.132479 Loss2: 1.326317 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453437 Loss1: 0.137059 Loss2: 1.316379
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.919884 Loss1: 0.971513 Loss2: 1.948370
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.416040 Loss1: 0.095975 Loss2: 1.320065
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.104415 Loss1: 0.557777 Loss2: 1.546639
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395940 Loss1: 0.081364 Loss2: 1.314576
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.927699 Loss1: 0.428862 Loss2: 1.498837
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.787505 Loss1: 0.285938 Loss2: 1.501567
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.692919 Loss1: 0.214828 Loss2: 1.478092
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.672874 Loss1: 0.188139 Loss2: 1.484735
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.580356 Loss1: 0.111222 Loss2: 1.469134
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.881915 Loss1: 1.013494 Loss2: 1.868422
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.592142 Loss1: 0.130844 Loss2: 1.461298
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.566506 Loss1: 0.103687 Loss2: 1.462819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.558737 Loss1: 0.104695 Loss2: 1.454042 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566239 Loss1: 0.186163 Loss2: 1.380076 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472724 Loss1: 0.115256 Loss2: 1.357467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.768805 Loss1: 0.925720 Loss2: 1.843085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.914322 Loss1: 0.533584 Loss2: 1.380738 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604530 Loss1: 0.235991 Loss2: 1.368539 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473870 Loss1: 0.111413 Loss2: 1.362458 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473063 Loss1: 0.115433 Loss2: 1.357631
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.642672 Loss1: 0.804628 Loss2: 1.838044
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.864674 Loss1: 0.504747 Loss2: 1.359927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.704026 Loss1: 0.323389 Loss2: 1.380636 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450565 Loss1: 0.100488 Loss2: 1.350077
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.622844 Loss1: 0.286444 Loss2: 1.336400
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516515 Loss1: 0.167550 Loss2: 1.348965
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508086 Loss1: 0.181380 Loss2: 1.326706
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460023 Loss1: 0.140417 Loss2: 1.319606
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423172 Loss1: 0.100140 Loss2: 1.323032
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.858226 Loss1: 1.007586 Loss2: 1.850640
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407911 Loss1: 0.097837 Loss2: 1.310074
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401420 Loss1: 0.091933 Loss2: 1.309487 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.670115 Loss1: 0.282089 Loss2: 1.388026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529941 Loss1: 0.173524 Loss2: 1.356417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.462725 Loss1: 0.110026 Loss2: 1.352699
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.762939 Loss1: 0.920167 Loss2: 1.842772
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.969079 Loss1: 0.585862 Loss2: 1.383217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.796796 Loss1: 0.375200 Loss2: 1.421596 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638257 Loss1: 0.272490 Loss2: 1.365767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.496816 Loss1: 0.138364 Loss2: 1.358452 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411443 Loss1: 0.066889 Loss2: 1.344554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398661 Loss1: 0.065201 Loss2: 1.333460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420147 Loss1: 0.084267 Loss2: 1.335881 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604599 Loss1: 0.221971 Loss2: 1.382627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521599 Loss1: 0.147257 Loss2: 1.374342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.752581 Loss1: 0.922792 Loss2: 1.829789 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.008750 Loss1: 0.579379 Loss2: 1.429371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.742175 Loss1: 0.328567 Loss2: 1.413607 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.596853 Loss1: 0.223509 Loss2: 1.373344 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.541121 Loss1: 0.165137 Loss2: 1.375984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.723652 Loss1: 0.898760 Loss2: 1.824891
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.551347 Loss1: 0.173815 Loss2: 1.377532
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.538232 Loss1: 0.170027 Loss2: 1.368205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445051 Loss1: 0.082056 Loss2: 1.362994 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.546661 Loss1: 0.177156 Loss2: 1.369505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516669 Loss1: 0.163872 Loss2: 1.352797 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.917166 Loss1: 0.967588 Loss2: 1.949578 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.932726 Loss1: 0.472707 Loss2: 1.460019 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.831482 Loss1: 0.357508 Loss2: 1.473973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.685543 Loss1: 0.246217 Loss2: 1.439326 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.555581 Loss1: 0.146061 Loss2: 1.409520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.533203 Loss1: 0.126803 Loss2: 1.406400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.978778 Loss1: 0.533884 Loss2: 1.444894
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.522859 Loss1: 0.123764 Loss2: 1.399095
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.526830 Loss1: 0.130416 Loss2: 1.396414
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.722990 Loss1: 0.292011 Loss2: 1.430979
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.671496 Loss1: 0.257320 Loss2: 1.414176
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556176 Loss1: 0.146538 Loss2: 1.409638
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500154 Loss1: 0.105885 Loss2: 1.394269
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504581 Loss1: 0.115171 Loss2: 1.389411
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.924492 Loss1: 1.041299 Loss2: 1.883193
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.046729 Loss1: 0.627222 Loss2: 1.419507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.772991 Loss1: 0.335077 Loss2: 1.437914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487753 Loss1: 0.102510 Loss2: 1.385243
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.679815 Loss1: 0.291375 Loss2: 1.388440
-(DefaultActor pid=3764) >> Training accuracy: 0.988971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.596011 Loss1: 0.199968 Loss2: 1.396043
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.512338 Loss1: 0.136740 Loss2: 1.375597
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525103 Loss1: 0.148822 Loss2: 1.376282
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.541751 Loss1: 0.173962 Loss2: 1.367789
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.739211 Loss1: 0.947689 Loss2: 1.791522
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.467232 Loss1: 0.092495 Loss2: 1.374737
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.906157 Loss1: 0.564928 Loss2: 1.341230
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443857 Loss1: 0.082155 Loss2: 1.361702
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611317 Loss1: 0.285887 Loss2: 1.325431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528709 Loss1: 0.197222 Loss2: 1.331487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.510045 Loss1: 0.195295 Loss2: 1.314750
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.750069 Loss1: 0.908323 Loss2: 1.841746
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.893305 Loss1: 0.524871 Loss2: 1.368434 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.709701 Loss1: 0.305890 Loss2: 1.403811 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436388 Loss1: 0.124771 Loss2: 1.311617
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573112 Loss1: 0.220707 Loss2: 1.352406
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519604 Loss1: 0.165437 Loss2: 1.354167
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462041 Loss1: 0.115636 Loss2: 1.346405
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419952 Loss1: 0.087457 Loss2: 1.332495
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.397179 Loss1: 0.068172 Loss2: 1.329007
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.562031 Loss1: 0.800603 Loss2: 1.761428
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394377 Loss1: 0.071028 Loss2: 1.323349
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.788553 Loss1: 0.482742 Loss2: 1.305811
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386620 Loss1: 0.058847 Loss2: 1.327773
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530378 Loss1: 0.228015 Loss2: 1.302363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.405185 Loss1: 0.120540 Loss2: 1.284645 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.394624 Loss1: 0.114186 Loss2: 1.280438
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.582866 Loss1: 0.801826 Loss2: 1.781040
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.825640 Loss1: 0.474071 Loss2: 1.351568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.702964 Loss1: 0.310616 Loss2: 1.392347 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.338423 Loss1: 0.061566 Loss2: 1.276857
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.593469 Loss1: 0.247823 Loss2: 1.345646
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589094 Loss1: 0.240826 Loss2: 1.348269
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.540089 Loss1: 0.180092 Loss2: 1.359997
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534935 Loss1: 0.187654 Loss2: 1.347281
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467354 Loss1: 0.126182 Loss2: 1.341172
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.886000 Loss1: 0.965419 Loss2: 1.920582
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.061805 Loss1: 0.601800 Loss2: 1.460004 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391238 Loss1: 0.066332 Loss2: 1.324905
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.897626 Loss1: 0.409226 Loss2: 1.488400
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.717917 Loss1: 0.283551 Loss2: 1.434366
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.641832 Loss1: 0.205972 Loss2: 1.435859
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.577736 Loss1: 0.157617 Loss2: 1.420119
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.577783 Loss1: 0.162339 Loss2: 1.415444
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.858696 Loss1: 0.940419 Loss2: 1.918277
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.534707 Loss1: 0.115284 Loss2: 1.419423
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.510737 Loss1: 0.105204 Loss2: 1.405533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.522519 Loss1: 0.117940 Loss2: 1.404578 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.590310 Loss1: 0.213274 Loss2: 1.377035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.537687 Loss1: 0.167224 Loss2: 1.370463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.631922 Loss1: 0.794199 Loss2: 1.837723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463603 Loss1: 0.098984 Loss2: 1.364619 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.625341 Loss1: 0.244334 Loss2: 1.381006 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523680 Loss1: 0.148261 Loss2: 1.375418 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.636315 Loss1: 0.803002 Loss2: 1.833313
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448154 Loss1: 0.077069 Loss2: 1.371084
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437348 Loss1: 0.080451 Loss2: 1.356898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433106 Loss1: 0.084493 Loss2: 1.348613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405803 Loss1: 0.059835 Loss2: 1.345968 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.474748 Loss1: 0.126552 Loss2: 1.348196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433276 Loss1: 0.091239 Loss2: 1.342038 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.779556 Loss1: 0.862169 Loss2: 1.917387 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.831407 Loss1: 0.344247 Loss2: 1.487160 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.665901 Loss1: 0.229604 Loss2: 1.436297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.573921 Loss1: 0.166262 Loss2: 1.407659
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.665933 Loss1: 0.765220 Loss2: 1.900714
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.030780 Loss1: 0.573490 Loss2: 1.457290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.823376 Loss1: 0.330635 Loss2: 1.492741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.744329 Loss1: 0.308422 Loss2: 1.435908 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.667436 Loss1: 0.216123 Loss2: 1.451313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.564610 Loss1: 0.137593 Loss2: 1.427017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.581205 Loss1: 0.156628 Loss2: 1.424577 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.537841 Loss1: 0.114856 Loss2: 1.422985 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.652492 Loss1: 0.271572 Loss2: 1.380920 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484906 Loss1: 0.122626 Loss2: 1.362281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472776 Loss1: 0.114929 Loss2: 1.357847
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.685378 Loss1: 0.844962 Loss2: 1.840415
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470305 Loss1: 0.114261 Loss2: 1.356044
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.875090 Loss1: 0.475087 Loss2: 1.400003
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479162 Loss1: 0.120018 Loss2: 1.359145
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.695350 Loss1: 0.297876 Loss2: 1.397475
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431353 Loss1: 0.086180 Loss2: 1.345173
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.643353 Loss1: 0.252664 Loss2: 1.390689
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.550515 Loss1: 0.181604 Loss2: 1.368911
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.527273 Loss1: 0.157649 Loss2: 1.369624
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480302 Loss1: 0.117425 Loss2: 1.362877
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499371 Loss1: 0.140764 Loss2: 1.358607
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.808688 Loss1: 0.867330 Loss2: 1.941358
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.056901 Loss1: 0.578706 Loss2: 1.478194 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.786904 Loss1: 0.321438 Loss2: 1.465466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.644267 Loss1: 0.193193 Loss2: 1.451074 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488796 Loss1: 0.071741 Loss2: 1.417054 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483311 Loss1: 0.072579 Loss2: 1.410733 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.478270 Loss1: 0.075037 Loss2: 1.403233 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.507427 Loss1: 0.102489 Loss2: 1.404938 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.663261 Loss1: 0.242050 Loss2: 1.421212 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 07:07:33,861 | server.py:236 | fit_round 105 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496215 Loss1: 0.101309 Loss2: 1.394906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461179 Loss1: 0.074135 Loss2: 1.387044
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.733099 Loss1: 0.837265 Loss2: 1.895835
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.985977 Loss1: 0.577941 Loss2: 1.408036 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.920637 Loss1: 0.426664 Loss2: 1.493972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.644741 Loss1: 0.212155 Loss2: 1.432586 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.560722 Loss1: 0.164839 Loss2: 1.395883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482661 Loss1: 0.088239 Loss2: 1.394422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448939 Loss1: 0.065391 Loss2: 1.383548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420560 Loss1: 0.042602 Loss2: 1.377958 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567793 Loss1: 0.163537 Loss2: 1.404256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509547 Loss1: 0.133383 Loss2: 1.376164 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491366 Loss1: 0.107410 Loss2: 1.383956
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.736929 Loss1: 0.907590 Loss2: 1.829338
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.107040 Loss1: 0.722161 Loss2: 1.384879 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.839277 Loss1: 0.407305 Loss2: 1.431973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.632637 Loss1: 0.234836 Loss2: 1.397801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.529429 Loss1: 0.163497 Loss2: 1.365932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452651 Loss1: 0.097636 Loss2: 1.355015 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 07:07:33,861][flwr][DEBUG] - fit_round 105 received 50 results and 0 failures
-INFO flwr 2023-10-11 07:08:15,351 | server.py:125 | fit progress: (105, 2.1982187847740735, {'accuracy': 0.5705}, 242203.12937831102)
->> Test accuracy: 0.570500
-[2023-10-11 07:08:15,351][flwr][INFO] - fit progress: (105, 2.1982187847740735, {'accuracy': 0.5705}, 242203.12937831102)
-DEBUG flwr 2023-10-11 07:08:15,351 | server.py:173 | evaluate_round 105: strategy sampled 50 clients (out of 50)
-[2023-10-11 07:08:15,351][flwr][DEBUG] - evaluate_round 105: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 07:17:20,498 | server.py:187 | evaluate_round 105 received 50 results and 0 failures
-[2023-10-11 07:17:20,498][flwr][DEBUG] - evaluate_round 105 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 07:17:20,498 | server.py:222 | fit_round 106: strategy sampled 50 clients (out of 50)
-[2023-10-11 07:17:20,498][flwr][DEBUG] - fit_round 106: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.719383 Loss1: 0.873246 Loss2: 1.846137 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.808833 Loss1: 0.391621 Loss2: 1.417212 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.679863 Loss1: 0.304549 Loss2: 1.375314
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.581107 Loss1: 0.711345 Loss2: 1.869762
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.898700 Loss1: 0.476638 Loss2: 1.422062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.709759 Loss1: 0.284840 Loss2: 1.424919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.618965 Loss1: 0.226300 Loss2: 1.392665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.591367 Loss1: 0.190864 Loss2: 1.400503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463362 Loss1: 0.118972 Loss2: 1.344390 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.552439 Loss1: 0.170360 Loss2: 1.382079 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.488161 Loss1: 0.118431 Loss2: 1.369730 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981618
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.817077 Loss1: 0.376038 Loss2: 1.441039 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571664 Loss1: 0.177968 Loss2: 1.393697 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.517979 Loss1: 0.140757 Loss2: 1.377222
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.580638 Loss1: 0.789933 Loss2: 1.790705
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.939013 Loss1: 0.546607 Loss2: 1.392406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.729735 Loss1: 0.353089 Loss2: 1.376646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.597682 Loss1: 0.233456 Loss2: 1.364226 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469161 Loss1: 0.127546 Loss2: 1.341615 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452982 Loss1: 0.116937 Loss2: 1.336044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420584 Loss1: 0.094226 Loss2: 1.326358
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.635485 Loss1: 0.882923 Loss2: 1.752562
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374238 Loss1: 0.048615 Loss2: 1.325623
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.869455 Loss1: 0.498386 Loss2: 1.371068
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.712113 Loss1: 0.359066 Loss2: 1.353047
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580256 Loss1: 0.240237 Loss2: 1.340019
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514315 Loss1: 0.187947 Loss2: 1.326369
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459618 Loss1: 0.141494 Loss2: 1.318123
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.019321 Loss1: 1.025829 Loss2: 1.993492
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.029341 Loss1: 0.643933 Loss2: 1.385408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.791418 Loss1: 0.331469 Loss2: 1.459949
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384713 Loss1: 0.086620 Loss2: 1.298093
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.365033 Loss1: 0.065884 Loss2: 1.299149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.341302 Loss1: 0.046383 Loss2: 1.294920 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485329 Loss1: 0.119578 Loss2: 1.365751 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452489 Loss1: 0.083621 Loss2: 1.368867 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985677
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.750201 Loss1: 0.871702 Loss2: 1.878499
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.978582 Loss1: 0.570247 Loss2: 1.408336
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.794353 Loss1: 0.361308 Loss2: 1.433045
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.734804 Loss1: 0.328501 Loss2: 1.406302
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.894283 Loss1: 1.037969 Loss2: 1.856314
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.626401 Loss1: 0.219000 Loss2: 1.407402
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.990637 Loss1: 0.601661 Loss2: 1.388976
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515321 Loss1: 0.128958 Loss2: 1.386363
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.863441 Loss1: 0.429412 Loss2: 1.434029
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.510692 Loss1: 0.125381 Loss2: 1.385312
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.668874 Loss1: 0.284547 Loss2: 1.384327
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484916 Loss1: 0.106279 Loss2: 1.378637
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.553483 Loss1: 0.180992 Loss2: 1.372491
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.457611 Loss1: 0.079753 Loss2: 1.377858
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500114 Loss1: 0.142167 Loss2: 1.357947
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459293 Loss1: 0.086529 Loss2: 1.372763
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.489745 Loss1: 0.135270 Loss2: 1.354476
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472172 Loss1: 0.120175 Loss2: 1.351996
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461579 Loss1: 0.114334 Loss2: 1.347245
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461567 Loss1: 0.115229 Loss2: 1.346338
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.867564 Loss1: 0.973087 Loss2: 1.894477
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.000503 Loss1: 0.564306 Loss2: 1.436197
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.805578 Loss1: 0.356123 Loss2: 1.449455
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.711128 Loss1: 0.273715 Loss2: 1.437413
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.719978 Loss1: 0.885742 Loss2: 1.834237
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589965 Loss1: 0.174484 Loss2: 1.415481
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.946780 Loss1: 0.578327 Loss2: 1.368453
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.554552 Loss1: 0.150160 Loss2: 1.404392
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.725278 Loss1: 0.311801 Loss2: 1.413477
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.489800 Loss1: 0.094540 Loss2: 1.395260
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.583530 Loss1: 0.226406 Loss2: 1.357125
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.498861 Loss1: 0.103094 Loss2: 1.395766
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542137 Loss1: 0.177143 Loss2: 1.364994
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.466659 Loss1: 0.075364 Loss2: 1.391295
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511240 Loss1: 0.158250 Loss2: 1.352991
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.468770 Loss1: 0.077964 Loss2: 1.390806
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478224 Loss1: 0.129266 Loss2: 1.348958
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434196 Loss1: 0.090758 Loss2: 1.343438
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453093 Loss1: 0.113989 Loss2: 1.339104
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444916 Loss1: 0.111581 Loss2: 1.333336
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.855876 Loss1: 1.010367 Loss2: 1.845510
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.098573 Loss1: 0.635877 Loss2: 1.462696
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.787689 Loss1: 0.360848 Loss2: 1.426841
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.677283 Loss1: 0.279709 Loss2: 1.397575
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.906005 Loss1: 0.993213 Loss2: 1.912792
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.934660 Loss1: 0.568397 Loss2: 1.366263 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.575343 Loss1: 0.193114 Loss2: 1.382228
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.819693 Loss1: 0.412319 Loss2: 1.407375
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538190 Loss1: 0.181097 Loss2: 1.357093
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.501155 Loss1: 0.125878 Loss2: 1.375277
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.489764 Loss1: 0.154531 Loss2: 1.335233
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441726 Loss1: 0.077742 Loss2: 1.363984
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443632 Loss1: 0.082594 Loss2: 1.361038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466968 Loss1: 0.107195 Loss2: 1.359774 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.360186 Loss1: 0.044487 Loss2: 1.315699 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986779
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.606765 Loss1: 0.802558 Loss2: 1.804207
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.843033 Loss1: 0.460311 Loss2: 1.382722
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.723000 Loss1: 0.325515 Loss2: 1.397485
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.788520 Loss1: 0.962299 Loss2: 1.826221
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.648874 Loss1: 0.267718 Loss2: 1.381156
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.979585 Loss1: 0.569025 Loss2: 1.410560
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572718 Loss1: 0.194424 Loss2: 1.378294
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.723419 Loss1: 0.322148 Loss2: 1.401271
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.575627 Loss1: 0.207096 Loss2: 1.368531
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509615 Loss1: 0.142923 Loss2: 1.366692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.527556 Loss1: 0.163396 Loss2: 1.364160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.531247 Loss1: 0.151497 Loss2: 1.379750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.498701 Loss1: 0.138847 Loss2: 1.359854 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412081 Loss1: 0.064347 Loss2: 1.347734 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.617991 Loss1: 0.791429 Loss2: 1.826562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690920 Loss1: 0.309246 Loss2: 1.381674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557395 Loss1: 0.207184 Loss2: 1.350211
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.760818 Loss1: 0.967145 Loss2: 1.793673
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491754 Loss1: 0.139383 Loss2: 1.352371
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.895232 Loss1: 0.515665 Loss2: 1.379566
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515203 Loss1: 0.168593 Loss2: 1.346610
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.692275 Loss1: 0.319065 Loss2: 1.373210
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.553129 Loss1: 0.204675 Loss2: 1.348454
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.548716 Loss1: 0.209558 Loss2: 1.339158
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489279 Loss1: 0.134540 Loss2: 1.354740
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.490436 Loss1: 0.160827 Loss2: 1.329608
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470046 Loss1: 0.127273 Loss2: 1.342774
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460427 Loss1: 0.133264 Loss2: 1.327163
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431248 Loss1: 0.092154 Loss2: 1.339094
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451189 Loss1: 0.124637 Loss2: 1.326552
-(DefaultActor pid=3765) >> Training accuracy: 0.970703
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420718 Loss1: 0.103620 Loss2: 1.317098
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400404 Loss1: 0.089141 Loss2: 1.311262
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411747 Loss1: 0.100514 Loss2: 1.311233
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.576430 Loss1: 0.784952 Loss2: 1.791478
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.861582 Loss1: 0.496482 Loss2: 1.365101
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.718761 Loss1: 0.338281 Loss2: 1.380479
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.669360 Loss1: 0.308994 Loss2: 1.360366
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.759008 Loss1: 0.874392 Loss2: 1.884616
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.581051 Loss1: 0.225753 Loss2: 1.355298
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.016590 Loss1: 0.564028 Loss2: 1.452563
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.740687 Loss1: 0.272472 Loss2: 1.468216
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477108 Loss1: 0.135484 Loss2: 1.341624
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.677716 Loss1: 0.260624 Loss2: 1.417092
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448857 Loss1: 0.118823 Loss2: 1.330034
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.603205 Loss1: 0.183720 Loss2: 1.419485
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405529 Loss1: 0.087076 Loss2: 1.318453
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.561431 Loss1: 0.159264 Loss2: 1.402167
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.393069 Loss1: 0.076031 Loss2: 1.317039
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378828 Loss1: 0.065165 Loss2: 1.313663 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479269 Loss1: 0.089483 Loss2: 1.389786 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.744429 Loss1: 0.881474 Loss2: 1.862955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.807373 Loss1: 0.343607 Loss2: 1.463766 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.660490 Loss1: 0.263861 Loss2: 1.396628
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.807575 Loss1: 0.871629 Loss2: 1.935946
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.596076 Loss1: 0.197144 Loss2: 1.398932
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.002714 Loss1: 0.560042 Loss2: 1.442671
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.541454 Loss1: 0.151243 Loss2: 1.390210
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.781108 Loss1: 0.304091 Loss2: 1.477017
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.545728 Loss1: 0.159794 Loss2: 1.385934
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.625021 Loss1: 0.199996 Loss2: 1.425025
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.541154 Loss1: 0.155188 Loss2: 1.385966
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.623741 Loss1: 0.197513 Loss2: 1.426229
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.496746 Loss1: 0.116676 Loss2: 1.380070
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549655 Loss1: 0.135912 Loss2: 1.413743
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.503047 Loss1: 0.129504 Loss2: 1.373544
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487060 Loss1: 0.090072 Loss2: 1.396988
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461799 Loss1: 0.072207 Loss2: 1.389592
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450560 Loss1: 0.062038 Loss2: 1.388522
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482250 Loss1: 0.099945 Loss2: 1.382305
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.761076 Loss1: 0.941490 Loss2: 1.819586
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.965421 Loss1: 0.595870 Loss2: 1.369551
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.735942 Loss1: 0.351971 Loss2: 1.383971
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580789 Loss1: 0.236323 Loss2: 1.344466
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.922890 Loss1: 0.974934 Loss2: 1.947956
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.501851 Loss1: 0.166980 Loss2: 1.334871
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.972471 Loss1: 0.538899 Loss2: 1.433572
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465415 Loss1: 0.137379 Loss2: 1.328035
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.796260 Loss1: 0.332853 Loss2: 1.463407
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444588 Loss1: 0.120804 Loss2: 1.323785
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.644671 Loss1: 0.230422 Loss2: 1.414249
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433660 Loss1: 0.113650 Loss2: 1.320009
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.668707 Loss1: 0.243451 Loss2: 1.425257
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396501 Loss1: 0.079204 Loss2: 1.317297
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559478 Loss1: 0.149063 Loss2: 1.410416
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363392 Loss1: 0.051026 Loss2: 1.312366
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550359 Loss1: 0.145474 Loss2: 1.404885
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.534804 Loss1: 0.127802 Loss2: 1.407001
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.483192 Loss1: 0.089722 Loss2: 1.393471
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.480000 Loss1: 0.086388 Loss2: 1.393612
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.657924 Loss1: 0.785469 Loss2: 1.872455
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.021080 Loss1: 0.582556 Loss2: 1.438523
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.819188 Loss1: 0.353536 Loss2: 1.465652
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.725125 Loss1: 0.836214 Loss2: 1.888911
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.735963 Loss1: 0.308623 Loss2: 1.427340
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.930396 Loss1: 0.542836 Loss2: 1.387560
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.629621 Loss1: 0.202023 Loss2: 1.427598
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.729017 Loss1: 0.295684 Loss2: 1.433334
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.561344 Loss1: 0.151348 Loss2: 1.409996
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509758 Loss1: 0.106893 Loss2: 1.402865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.505793 Loss1: 0.105154 Loss2: 1.400639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.497139 Loss1: 0.095040 Loss2: 1.402099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508759 Loss1: 0.110167 Loss2: 1.398592 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389797 Loss1: 0.047251 Loss2: 1.342547 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.705057 Loss1: 0.797804 Loss2: 1.907254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.663704 Loss1: 0.255509 Loss2: 1.408196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623362 Loss1: 0.237210 Loss2: 1.386153
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.699927 Loss1: 0.863754 Loss2: 1.836173
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572487 Loss1: 0.174575 Loss2: 1.397912
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.813694 Loss1: 0.482931 Loss2: 1.330763
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504504 Loss1: 0.124086 Loss2: 1.380418
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.672418 Loss1: 0.321972 Loss2: 1.350446
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.506559 Loss1: 0.124244 Loss2: 1.382316
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.542011 Loss1: 0.221479 Loss2: 1.320532
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457832 Loss1: 0.085703 Loss2: 1.372129
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543607 Loss1: 0.222367 Loss2: 1.321240
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453686 Loss1: 0.082026 Loss2: 1.371660
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467698 Loss1: 0.147065 Loss2: 1.320633
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426985 Loss1: 0.058767 Loss2: 1.368219
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441809 Loss1: 0.137406 Loss2: 1.304404
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389343 Loss1: 0.085755 Loss2: 1.303588
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368295 Loss1: 0.071113 Loss2: 1.297182
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.359224 Loss1: 0.063338 Loss2: 1.295886
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.586211 Loss1: 0.826892 Loss2: 1.759318
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.937881 Loss1: 0.579029 Loss2: 1.358852
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.705829 Loss1: 0.336866 Loss2: 1.368963
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574291 Loss1: 0.233931 Loss2: 1.340359
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.783342 Loss1: 0.846637 Loss2: 1.936705
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487883 Loss1: 0.149284 Loss2: 1.338599
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.997828 Loss1: 0.549500 Loss2: 1.448328
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461267 Loss1: 0.134821 Loss2: 1.326446
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.858524 Loss1: 0.354996 Loss2: 1.503527
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460829 Loss1: 0.139461 Loss2: 1.321368
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.732524 Loss1: 0.280590 Loss2: 1.451935
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.712111 Loss1: 0.244626 Loss2: 1.467485
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449082 Loss1: 0.127217 Loss2: 1.321865
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.633837 Loss1: 0.189203 Loss2: 1.444634
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413871 Loss1: 0.096874 Loss2: 1.316997
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.560247 Loss1: 0.126007 Loss2: 1.434239
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415682 Loss1: 0.106040 Loss2: 1.309642
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490401 Loss1: 0.073895 Loss2: 1.416506 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.616833 Loss1: 0.790230 Loss2: 1.826603 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.808494 Loss1: 0.390102 Loss2: 1.418392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.671251 Loss1: 0.277967 Loss2: 1.393284
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.794779 Loss1: 0.947627 Loss2: 1.847152
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.039352 Loss1: 0.658415 Loss2: 1.380937
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.586432 Loss1: 0.196197 Loss2: 1.390235
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.754081 Loss1: 0.355669 Loss2: 1.398412
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.546066 Loss1: 0.157203 Loss2: 1.388864
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576921 Loss1: 0.223560 Loss2: 1.353361
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502394 Loss1: 0.116509 Loss2: 1.385885
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528591 Loss1: 0.178351 Loss2: 1.350240
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.533894 Loss1: 0.158080 Loss2: 1.375814
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470657 Loss1: 0.091541 Loss2: 1.379116 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447004 Loss1: 0.083617 Loss2: 1.363387 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422372 Loss1: 0.087655 Loss2: 1.334717 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.747718 Loss1: 0.860929 Loss2: 1.886790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.714616 Loss1: 0.270998 Loss2: 1.443618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578382 Loss1: 0.206869 Loss2: 1.371513
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.694024 Loss1: 0.819559 Loss2: 1.874466
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.007254 Loss1: 0.588567 Loss2: 1.418687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.823268 Loss1: 0.385611 Loss2: 1.437657 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.663310 Loss1: 0.256137 Loss2: 1.407172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.615595 Loss1: 0.203851 Loss2: 1.411744 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.561144 Loss1: 0.160722 Loss2: 1.400422 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431987 Loss1: 0.082053 Loss2: 1.349934
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.548573 Loss1: 0.155543 Loss2: 1.393030
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540234 Loss1: 0.140576 Loss2: 1.399658
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.559088 Loss1: 0.168913 Loss2: 1.390175
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512729 Loss1: 0.120539 Loss2: 1.392190
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.988695 Loss1: 1.028070 Loss2: 1.960626
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.079815 Loss1: 0.645657 Loss2: 1.434158
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.870628 Loss1: 0.379892 Loss2: 1.490736
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.769561 Loss1: 0.337484 Loss2: 1.432077
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.900066 Loss1: 1.004183 Loss2: 1.895883
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.051585 Loss1: 0.690253 Loss2: 1.361332 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.843545 Loss1: 0.404896 Loss2: 1.438649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.628891 Loss1: 0.277240 Loss2: 1.351651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467074 Loss1: 0.077473 Loss2: 1.389601
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527459 Loss1: 0.171438 Loss2: 1.356021
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487068 Loss1: 0.137145 Loss2: 1.349923
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452906 Loss1: 0.072481 Loss2: 1.380425
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437142 Loss1: 0.103431 Loss2: 1.333710
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.455406 Loss1: 0.073402 Loss2: 1.382004
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369279 Loss1: 0.046215 Loss2: 1.323064 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980769
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.786265 Loss1: 0.895386 Loss2: 1.890879 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.815136 Loss1: 0.361997 Loss2: 1.453139 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623717 Loss1: 0.211883 Loss2: 1.411834
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.765027 Loss1: 0.879497 Loss2: 1.885529
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562027 Loss1: 0.158205 Loss2: 1.403822
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.005609 Loss1: 0.600708 Loss2: 1.404901
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514134 Loss1: 0.114566 Loss2: 1.399568
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.871887 Loss1: 0.442839 Loss2: 1.429048
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478281 Loss1: 0.088222 Loss2: 1.390059
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.730533 Loss1: 0.334507 Loss2: 1.396026
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462560 Loss1: 0.085993 Loss2: 1.376567
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.627490 Loss1: 0.235955 Loss2: 1.391535
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453701 Loss1: 0.074587 Loss2: 1.379114
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.583727 Loss1: 0.198116 Loss2: 1.385611
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440492 Loss1: 0.062232 Loss2: 1.378260
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.532617 Loss1: 0.154774 Loss2: 1.377843
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488268 Loss1: 0.125801 Loss2: 1.362467
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.496177 Loss1: 0.130664 Loss2: 1.365513
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.493485 Loss1: 0.137283 Loss2: 1.356202
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.006208 Loss1: 1.024740 Loss2: 1.981467
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.011751 Loss1: 0.564082 Loss2: 1.447669
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.791686 Loss1: 0.308383 Loss2: 1.483303
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.722026 Loss1: 0.283370 Loss2: 1.438656
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.814094 Loss1: 0.805362 Loss2: 2.008732
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.990108 Loss1: 0.471761 Loss2: 1.518347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.918800 Loss1: 0.359488 Loss2: 1.559312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.779144 Loss1: 0.267867 Loss2: 1.511277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.685960 Loss1: 0.185401 Loss2: 1.500559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.713583 Loss1: 0.213079 Loss2: 1.500504 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.611763 Loss1: 0.120921 Loss2: 1.490842 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.598440 Loss1: 0.123190 Loss2: 1.475250 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.962853 Loss1: 0.561855 Loss2: 1.400998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.609895 Loss1: 0.226640 Loss2: 1.383255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567606 Loss1: 0.194018 Loss2: 1.373588
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.752776 Loss1: 0.912273 Loss2: 1.840503
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481920 Loss1: 0.109106 Loss2: 1.372814
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.021759 Loss1: 0.627212 Loss2: 1.394547
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.494668 Loss1: 0.139831 Loss2: 1.354838
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.702970 Loss1: 0.304563 Loss2: 1.398406
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448307 Loss1: 0.092518 Loss2: 1.355790
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592480 Loss1: 0.236928 Loss2: 1.355552
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429444 Loss1: 0.076446 Loss2: 1.352997
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570876 Loss1: 0.203182 Loss2: 1.367694
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410304 Loss1: 0.068492 Loss2: 1.341812
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560012 Loss1: 0.194729 Loss2: 1.365283
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500596 Loss1: 0.140804 Loss2: 1.359792
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500315 Loss1: 0.145936 Loss2: 1.354379
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.477270 Loss1: 0.131365 Loss2: 1.345905
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419130 Loss1: 0.079043 Loss2: 1.340086
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.849237 Loss1: 0.916454 Loss2: 1.932783
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.008522 Loss1: 0.571907 Loss2: 1.436615
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.848689 Loss1: 0.375519 Loss2: 1.473170
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.660516 Loss1: 0.252361 Loss2: 1.408155
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587494 Loss1: 0.179041 Loss2: 1.408454
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.536109 Loss1: 0.144749 Loss2: 1.391360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503427 Loss1: 0.117957 Loss2: 1.385470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499366 Loss1: 0.116253 Loss2: 1.383114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.471589 Loss1: 0.091202 Loss2: 1.380387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422378 Loss1: 0.053290 Loss2: 1.369088 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.538452 Loss1: 0.158584 Loss2: 1.379868 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.523719 Loss1: 0.135491 Loss2: 1.388228 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.750744 Loss1: 0.413008 Loss2: 1.337736 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582510 Loss1: 0.246081 Loss2: 1.336429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491279 Loss1: 0.156951 Loss2: 1.334328
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.718926 Loss1: 0.892843 Loss2: 1.826083
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.868134 Loss1: 0.494219 Loss2: 1.373915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.650805 Loss1: 0.267340 Loss2: 1.383465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.589803 Loss1: 0.237429 Loss2: 1.352374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.538458 Loss1: 0.182812 Loss2: 1.355647 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-11 07:46:30,212 | server.py:236 | fit_round 106 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485911 Loss1: 0.137252 Loss2: 1.348659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.479621 Loss1: 0.130933 Loss2: 1.348687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431946 Loss1: 0.094620 Loss2: 1.337327 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.040227 Loss1: 0.573639 Loss2: 1.466589 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.740335 Loss1: 0.292892 Loss2: 1.447443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.655548 Loss1: 0.202224 Loss2: 1.453324
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.679121 Loss1: 0.861176 Loss2: 1.817946
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.933680 Loss1: 0.583668 Loss2: 1.350012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.728893 Loss1: 0.328846 Loss2: 1.400047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.614868 Loss1: 0.269769 Loss2: 1.345099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571198 Loss1: 0.210888 Loss2: 1.360310 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508519 Loss1: 0.169115 Loss2: 1.339403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471883 Loss1: 0.126865 Loss2: 1.345017 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393917 Loss1: 0.072040 Loss2: 1.321877 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.923340 Loss1: 0.539426 Loss2: 1.383914 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561617 Loss1: 0.204486 Loss2: 1.357131 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470996 Loss1: 0.124108 Loss2: 1.346888
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.857841 Loss1: 0.992248 Loss2: 1.865593
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.994435 Loss1: 0.559708 Loss2: 1.434726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.844190 Loss1: 0.389630 Loss2: 1.454561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.661089 Loss1: 0.251598 Loss2: 1.409491 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.597398 Loss1: 0.193058 Loss2: 1.404340 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.526964 Loss1: 0.135458 Loss2: 1.391506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.499338 Loss1: 0.118058 Loss2: 1.381281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.504809 Loss1: 0.127338 Loss2: 1.377470 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.923978 Loss1: 0.567928 Loss2: 1.356049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572797 Loss1: 0.243322 Loss2: 1.329474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.495619 Loss1: 0.172330 Loss2: 1.323289
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.601179 Loss1: 0.738553 Loss2: 1.862626
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.886657 Loss1: 0.498963 Loss2: 1.387694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769048 Loss1: 0.347155 Loss2: 1.421892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.625730 Loss1: 0.244252 Loss2: 1.381478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.572881 Loss1: 0.195902 Loss2: 1.376978 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350604 Loss1: 0.061520 Loss2: 1.289084
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.570626 Loss1: 0.195297 Loss2: 1.375330
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.555607 Loss1: 0.177713 Loss2: 1.377893
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.538960 Loss1: 0.171888 Loss2: 1.367072
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.522147 Loss1: 0.159621 Loss2: 1.362526
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.493553 Loss1: 0.125960 Loss2: 1.367593
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 07:46:30,212][flwr][DEBUG] - fit_round 106 received 50 results and 0 failures
-INFO flwr 2023-10-11 07:47:11,216 | server.py:125 | fit progress: (106, 2.199014896401963, {'accuracy': 0.5705}, 244538.994493704)
->> Test accuracy: 0.570500
-[2023-10-11 07:47:11,216][flwr][INFO] - fit progress: (106, 2.199014896401963, {'accuracy': 0.5705}, 244538.994493704)
-DEBUG flwr 2023-10-11 07:47:11,216 | server.py:173 | evaluate_round 106: strategy sampled 50 clients (out of 50)
-[2023-10-11 07:47:11,216][flwr][DEBUG] - evaluate_round 106: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 07:56:17,161 | server.py:187 | evaluate_round 106 received 50 results and 0 failures
-[2023-10-11 07:56:17,161][flwr][DEBUG] - evaluate_round 106 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 07:56:17,161 | server.py:222 | fit_round 107: strategy sampled 50 clients (out of 50)
-[2023-10-11 07:56:17,161][flwr][DEBUG] - fit_round 107: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.766414 Loss1: 0.902424 Loss2: 1.863990
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.910279 Loss1: 0.506617 Loss2: 1.403663
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.685939 Loss1: 0.270829 Loss2: 1.415110
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.615692 Loss1: 0.235571 Loss2: 1.380121
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.788242 Loss1: 0.839147 Loss2: 1.949094
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.613665 Loss1: 0.226594 Loss2: 1.387071
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.060491 Loss1: 0.591637 Loss2: 1.468854
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.605230 Loss1: 0.217655 Loss2: 1.387575
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.925142 Loss1: 0.376536 Loss2: 1.548606
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.598023 Loss1: 0.216094 Loss2: 1.381929
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.760335 Loss1: 0.287417 Loss2: 1.472918
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.601856 Loss1: 0.208863 Loss2: 1.392993
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.690734 Loss1: 0.213428 Loss2: 1.477306
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.539379 Loss1: 0.160926 Loss2: 1.378453
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.649275 Loss1: 0.183198 Loss2: 1.466077
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.521018 Loss1: 0.141144 Loss2: 1.379874
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.650549 Loss1: 0.181756 Loss2: 1.468793
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.605611 Loss1: 0.149473 Loss2: 1.456138
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.561800 Loss1: 0.112154 Loss2: 1.449646
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.578131 Loss1: 0.130767 Loss2: 1.447364
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.797069 Loss1: 0.925394 Loss2: 1.871674
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.991525 Loss1: 0.613997 Loss2: 1.377528
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.797412 Loss1: 0.356804 Loss2: 1.440608
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.612620 Loss1: 0.236081 Loss2: 1.376538
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.722763 Loss1: 0.915961 Loss2: 1.806803
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.576737 Loss1: 0.199345 Loss2: 1.377393
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.975021 Loss1: 0.574571 Loss2: 1.400450
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.687349 Loss1: 0.322948 Loss2: 1.364401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561801 Loss1: 0.219347 Loss2: 1.342455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516109 Loss1: 0.168730 Loss2: 1.347379 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.471720 Loss1: 0.119975 Loss2: 1.351745
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474877 Loss1: 0.152455 Loss2: 1.322422
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432308 Loss1: 0.080389 Loss2: 1.351920
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443591 Loss1: 0.121964 Loss2: 1.321627
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439590 Loss1: 0.116423 Loss2: 1.323167
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454207 Loss1: 0.137875 Loss2: 1.316332
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392415 Loss1: 0.078436 Loss2: 1.313979
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.853533 Loss1: 0.920054 Loss2: 1.933479
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.928134 Loss1: 0.492775 Loss2: 1.435359
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.748074 Loss1: 0.297025 Loss2: 1.451048
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.605824 Loss1: 0.179897 Loss2: 1.425927
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.772966 Loss1: 0.882691 Loss2: 1.890275
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.018306 Loss1: 0.548594 Loss2: 1.469712 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.760774 Loss1: 0.322815 Loss2: 1.437959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.734793 Loss1: 0.302589 Loss2: 1.432204 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.687437 Loss1: 0.260170 Loss2: 1.427267 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.633191 Loss1: 0.212051 Loss2: 1.421141 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.575659 Loss1: 0.164294 Loss2: 1.411365 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.496538 Loss1: 0.093748 Loss2: 1.402790 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.837756 Loss1: 0.458136 Loss2: 1.379620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.616777 Loss1: 0.248917 Loss2: 1.367860 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.643702 Loss1: 0.262975 Loss2: 1.380727 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547473 Loss1: 0.180995 Loss2: 1.366477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.521446 Loss1: 0.159290 Loss2: 1.362155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461399 Loss1: 0.101527 Loss2: 1.359872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419523 Loss1: 0.070513 Loss2: 1.349010 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406366 Loss1: 0.066182 Loss2: 1.340184 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386554 Loss1: 0.060911 Loss2: 1.325644 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.858656 Loss1: 0.988724 Loss2: 1.869932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.732912 Loss1: 0.324022 Loss2: 1.408890 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.600154 Loss1: 0.230245 Loss2: 1.369908
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.823074 Loss1: 0.937030 Loss2: 1.886044
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.009439 Loss1: 0.582267 Loss2: 1.427172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.848635 Loss1: 0.396747 Loss2: 1.451888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.669848 Loss1: 0.262277 Loss2: 1.407571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570189 Loss1: 0.167923 Loss2: 1.402265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538927 Loss1: 0.145217 Loss2: 1.393710 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432768 Loss1: 0.090470 Loss2: 1.342298
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.506041 Loss1: 0.120855 Loss2: 1.385186
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490416 Loss1: 0.106323 Loss2: 1.384094
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.494563 Loss1: 0.114727 Loss2: 1.379836
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449691 Loss1: 0.072500 Loss2: 1.377191
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.827855 Loss1: 0.918805 Loss2: 1.909049
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.980835 Loss1: 0.544748 Loss2: 1.436087
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.765609 Loss1: 0.311185 Loss2: 1.454424
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.673274 Loss1: 0.255493 Loss2: 1.417781
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.742522 Loss1: 0.858865 Loss2: 1.883658
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.916873 Loss1: 0.466575 Loss2: 1.450298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.768739 Loss1: 0.314056 Loss2: 1.454683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.624380 Loss1: 0.191219 Loss2: 1.433160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557922 Loss1: 0.148525 Loss2: 1.409397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521980 Loss1: 0.115014 Loss2: 1.406966 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475000 Loss1: 0.079789 Loss2: 1.395211 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465124 Loss1: 0.080574 Loss2: 1.384550 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.951937 Loss1: 0.589894 Loss2: 1.362043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561459 Loss1: 0.206067 Loss2: 1.355392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.500944 Loss1: 0.145421 Loss2: 1.355523
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.609296 Loss1: 0.754379 Loss2: 1.854916
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.811636 Loss1: 0.432857 Loss2: 1.378779 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.743800 Loss1: 0.332751 Loss2: 1.411049 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577420 Loss1: 0.210829 Loss2: 1.366591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.547553 Loss1: 0.185196 Loss2: 1.362356 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390999 Loss1: 0.067481 Loss2: 1.323518
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473736 Loss1: 0.111349 Loss2: 1.362386
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453636 Loss1: 0.100343 Loss2: 1.353293
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431160 Loss1: 0.083266 Loss2: 1.347894
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422199 Loss1: 0.075466 Loss2: 1.346732
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405906 Loss1: 0.065118 Loss2: 1.340788
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.659253 Loss1: 0.806855 Loss2: 1.852398
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.918957 Loss1: 0.540709 Loss2: 1.378248
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.826169 Loss1: 0.394631 Loss2: 1.431538
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.643331 Loss1: 0.269550 Loss2: 1.373781
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.595128 Loss1: 0.230433 Loss2: 1.364695
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.748445 Loss1: 0.926511 Loss2: 1.821934
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.967529 Loss1: 0.575201 Loss2: 1.392328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.778159 Loss1: 0.374451 Loss2: 1.403708 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.685287 Loss1: 0.312207 Loss2: 1.373081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.584947 Loss1: 0.208667 Loss2: 1.376280 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516095 Loss1: 0.159870 Loss2: 1.356225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472106 Loss1: 0.118641 Loss2: 1.353465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405251 Loss1: 0.067356 Loss2: 1.337895 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.797612 Loss1: 0.321311 Loss2: 1.476302 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.576966 Loss1: 0.171184 Loss2: 1.405782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.894403 Loss1: 0.999062 Loss2: 1.895341
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.517945 Loss1: 0.121996 Loss2: 1.395948
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.077135 Loss1: 0.633251 Loss2: 1.443884
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.506603 Loss1: 0.117193 Loss2: 1.389410
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.846676 Loss1: 0.389040 Loss2: 1.457636
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489525 Loss1: 0.099903 Loss2: 1.389622
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.752355 Loss1: 0.325115 Loss2: 1.427240
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456756 Loss1: 0.081016 Loss2: 1.375740
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.665612 Loss1: 0.244540 Loss2: 1.421072
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434277 Loss1: 0.059422 Loss2: 1.374855
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.531345 Loss1: 0.131409 Loss2: 1.399935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466393 Loss1: 0.077037 Loss2: 1.389356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.468347 Loss1: 0.081266 Loss2: 1.387081
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.683380 Loss1: 0.809020 Loss2: 1.874360
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.999564 Loss1: 0.602584 Loss2: 1.396980
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.785660 Loss1: 0.334276 Loss2: 1.451384
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.644379 Loss1: 0.245864 Loss2: 1.398515
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.581796 Loss1: 0.175335 Loss2: 1.406461
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.802380 Loss1: 0.968047 Loss2: 1.834333
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.539159 Loss1: 0.136885 Loss2: 1.402274
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.901162 Loss1: 0.536854 Loss2: 1.364308
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.526911 Loss1: 0.145548 Loss2: 1.381363
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.738144 Loss1: 0.336444 Loss2: 1.401700
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.505104 Loss1: 0.114091 Loss2: 1.391012
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.681463 Loss1: 0.321144 Loss2: 1.360319
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.487319 Loss1: 0.108756 Loss2: 1.378562
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.583703 Loss1: 0.197784 Loss2: 1.385918
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.489107 Loss1: 0.118752 Loss2: 1.370355
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474196 Loss1: 0.124073 Loss2: 1.350124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452305 Loss1: 0.119377 Loss2: 1.332928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428662 Loss1: 0.100889 Loss2: 1.327773
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.836734 Loss1: 0.904927 Loss2: 1.931808
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.012272 Loss1: 0.575309 Loss2: 1.436963
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.863321 Loss1: 0.383216 Loss2: 1.480105
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.748083 Loss1: 0.313100 Loss2: 1.434983
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.620940 Loss1: 0.201812 Loss2: 1.419128
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.900802 Loss1: 0.950232 Loss2: 1.950570
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.626133 Loss1: 0.214116 Loss2: 1.412017
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.999029 Loss1: 0.520407 Loss2: 1.478622
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528610 Loss1: 0.116027 Loss2: 1.412583
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.839148 Loss1: 0.326744 Loss2: 1.512404
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.509809 Loss1: 0.108736 Loss2: 1.401073
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.716906 Loss1: 0.259575 Loss2: 1.457331
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493154 Loss1: 0.094117 Loss2: 1.399037
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.685826 Loss1: 0.232487 Loss2: 1.453339
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.522717 Loss1: 0.128074 Loss2: 1.394643
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.551372 Loss1: 0.112210 Loss2: 1.439163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.502842 Loss1: 0.081771 Loss2: 1.421070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.488686 Loss1: 0.070945 Loss2: 1.417741
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.691016 Loss1: 0.897594 Loss2: 1.793422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.927838 Loss1: 0.588403 Loss2: 1.339435
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.744332 Loss1: 0.351389 Loss2: 1.392942
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.618183 Loss1: 0.289639 Loss2: 1.328544
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.613442 Loss1: 0.259543 Loss2: 1.353899
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.749341 Loss1: 0.782344 Loss2: 1.966997
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520718 Loss1: 0.178169 Loss2: 1.342549
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.988536 Loss1: 0.544384 Loss2: 1.444152
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502488 Loss1: 0.167401 Loss2: 1.335086
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.855773 Loss1: 0.355677 Loss2: 1.500095
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434742 Loss1: 0.103546 Loss2: 1.331196
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.727621 Loss1: 0.268079 Loss2: 1.459542
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.426785 Loss1: 0.108545 Loss2: 1.318239
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.655131 Loss1: 0.199355 Loss2: 1.455776
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393685 Loss1: 0.079614 Loss2: 1.314071
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.566252 Loss1: 0.130559 Loss2: 1.435694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.530090 Loss1: 0.101312 Loss2: 1.428777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.578053 Loss1: 0.138585 Loss2: 1.439468
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.758152 Loss1: 0.917261 Loss2: 1.840891
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.990195 Loss1: 0.589947 Loss2: 1.400248
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731735 Loss1: 0.318027 Loss2: 1.413708
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.597635 Loss1: 0.225986 Loss2: 1.371649
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561416 Loss1: 0.186583 Loss2: 1.374833
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.880611 Loss1: 0.969199 Loss2: 1.911413
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.568002 Loss1: 0.202312 Loss2: 1.365691
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.552821 Loss1: 0.180997 Loss2: 1.371824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469507 Loss1: 0.111911 Loss2: 1.357596 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469126 Loss1: 0.115432 Loss2: 1.353694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448370 Loss1: 0.102119 Loss2: 1.346251 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453994 Loss1: 0.083535 Loss2: 1.370459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457074 Loss1: 0.102773 Loss2: 1.354301 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.675840 Loss1: 0.810199 Loss2: 1.865641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.698467 Loss1: 0.297268 Loss2: 1.401199 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546729 Loss1: 0.191667 Loss2: 1.355062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503158 Loss1: 0.151727 Loss2: 1.351432 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486655 Loss1: 0.138756 Loss2: 1.347899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436581 Loss1: 0.097509 Loss2: 1.339072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394436 Loss1: 0.065340 Loss2: 1.329096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391577 Loss1: 0.063095 Loss2: 1.328482 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397182 Loss1: 0.108419 Loss2: 1.288763 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.355176 Loss1: 0.072193 Loss2: 1.282983 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.941417 Loss1: 0.500048 Loss2: 1.441369 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576970 Loss1: 0.196093 Loss2: 1.380877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.727789 Loss1: 0.891236 Loss2: 1.836553
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.570619 Loss1: 0.204093 Loss2: 1.366526
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.955039 Loss1: 0.564580 Loss2: 1.390458
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.571999 Loss1: 0.194061 Loss2: 1.377938
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.538390 Loss1: 0.170426 Loss2: 1.367963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432033 Loss1: 0.076595 Loss2: 1.355438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396905 Loss1: 0.053700 Loss2: 1.343206 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407987 Loss1: 0.068638 Loss2: 1.339349 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475612 Loss1: 0.139176 Loss2: 1.336437 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367883 Loss1: 0.053380 Loss2: 1.314503 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.742228 Loss1: 0.790433 Loss2: 1.951795
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.023226 Loss1: 0.556480 Loss2: 1.466745
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.870978 Loss1: 0.389436 Loss2: 1.481541
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.736187 Loss1: 0.306531 Loss2: 1.429656
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.539500 Loss1: 0.662926 Loss2: 1.876574
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.876098 Loss1: 0.459467 Loss2: 1.416631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.744032 Loss1: 0.300950 Loss2: 1.443082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497533 Loss1: 0.086919 Loss2: 1.410614 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.498692 Loss1: 0.088493 Loss2: 1.410200 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474225 Loss1: 0.066958 Loss2: 1.407267 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.554993 Loss1: 0.146961 Loss2: 1.408032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.498347 Loss1: 0.109034 Loss2: 1.389313 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.488204 Loss1: 0.101903 Loss2: 1.386300
-(DefaultActor pid=3764) >> Training accuracy: 0.985294
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.590924 Loss1: 0.752374 Loss2: 1.838550
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.919220 Loss1: 0.507107 Loss2: 1.412113
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.727174 Loss1: 0.287328 Loss2: 1.439846
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.678717 Loss1: 0.291130 Loss2: 1.387587
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.638480 Loss1: 0.240483 Loss2: 1.397997
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.018255 Loss1: 1.012078 Loss2: 2.006178
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.959713 Loss1: 0.577640 Loss2: 1.382073
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.557651 Loss1: 0.178130 Loss2: 1.379521
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.505126 Loss1: 0.125373 Loss2: 1.379752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.465145 Loss1: 0.087375 Loss2: 1.377770 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560895 Loss1: 0.188998 Loss2: 1.371897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.525996 Loss1: 0.141371 Loss2: 1.384625 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481428 Loss1: 0.113394 Loss2: 1.368034 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.711645 Loss1: 0.868850 Loss2: 1.842795 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.754824 Loss1: 0.324912 Loss2: 1.429912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.657200 Loss1: 0.298456 Loss2: 1.358744
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.693488 Loss1: 0.865141 Loss2: 1.828347
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605954 Loss1: 0.230201 Loss2: 1.375753
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831543 Loss1: 0.485388 Loss2: 1.346154
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537543 Loss1: 0.182145 Loss2: 1.355398
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677654 Loss1: 0.287270 Loss2: 1.390384
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516222 Loss1: 0.168173 Loss2: 1.348049
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562847 Loss1: 0.236444 Loss2: 1.326403
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500147 Loss1: 0.149392 Loss2: 1.350755
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518334 Loss1: 0.184819 Loss2: 1.333516
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427814 Loss1: 0.078367 Loss2: 1.349447
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.448419 Loss1: 0.124757 Loss2: 1.323663
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406500 Loss1: 0.072737 Loss2: 1.333763
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480653 Loss1: 0.162962 Loss2: 1.317691
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428890 Loss1: 0.107883 Loss2: 1.321007
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.468748 Loss1: 0.155610 Loss2: 1.313138
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408264 Loss1: 0.082365 Loss2: 1.325899
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.947324 Loss1: 1.041965 Loss2: 1.905359
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.948418 Loss1: 0.507858 Loss2: 1.440560
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.761091 Loss1: 0.333521 Loss2: 1.427570
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604302 Loss1: 0.212874 Loss2: 1.391428
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.859113 Loss1: 0.886888 Loss2: 1.972225
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.933767 Loss1: 0.550478 Loss2: 1.383289
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.539183 Loss1: 0.154128 Loss2: 1.385055
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491094 Loss1: 0.110122 Loss2: 1.380972 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451361 Loss1: 0.080705 Loss2: 1.370656 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456496 Loss1: 0.091557 Loss2: 1.364939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452616 Loss1: 0.089274 Loss2: 1.363343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449395 Loss1: 0.084505 Loss2: 1.364891 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419535 Loss1: 0.075024 Loss2: 1.344511 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.685502 Loss1: 0.863094 Loss2: 1.822407
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.816555 Loss1: 0.465649 Loss2: 1.350906
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.653278 Loss1: 0.273725 Loss2: 1.379553
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556825 Loss1: 0.221249 Loss2: 1.335576
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.881167 Loss1: 1.015393 Loss2: 1.865774
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.958491 Loss1: 0.603258 Loss2: 1.355233
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498987 Loss1: 0.151830 Loss2: 1.347157
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472692 Loss1: 0.139955 Loss2: 1.332736 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446782 Loss1: 0.118875 Loss2: 1.327907 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467052 Loss1: 0.137089 Loss2: 1.329963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455836 Loss1: 0.131278 Loss2: 1.324557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414074 Loss1: 0.087546 Loss2: 1.326528 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384807 Loss1: 0.056933 Loss2: 1.327874 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.678893 Loss1: 0.843250 Loss2: 1.835643
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.914848 Loss1: 0.538406 Loss2: 1.376442
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.681504 Loss1: 0.293527 Loss2: 1.387977
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.593612 Loss1: 0.237655 Loss2: 1.355957
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.615047 Loss1: 0.828004 Loss2: 1.787043
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.855330 Loss1: 0.492926 Loss2: 1.362405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.737987 Loss1: 0.383252 Loss2: 1.354735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619313 Loss1: 0.266769 Loss2: 1.352544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.474203 Loss1: 0.145345 Loss2: 1.328858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.488261 Loss1: 0.162551 Loss2: 1.325709 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433116 Loss1: 0.115545 Loss2: 1.317571 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389445 Loss1: 0.081607 Loss2: 1.307839 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.880745 Loss1: 0.521907 Loss2: 1.358838 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.560901 Loss1: 0.203729 Loss2: 1.357173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.818817 Loss1: 0.887272 Loss2: 1.931544
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498403 Loss1: 0.162240 Loss2: 1.336163
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.081341 Loss1: 0.538404 Loss2: 1.542937
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505164 Loss1: 0.164657 Loss2: 1.340507
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.790162 Loss1: 0.278594 Loss2: 1.511568
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476209 Loss1: 0.139786 Loss2: 1.336424
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425320 Loss1: 0.093390 Loss2: 1.331929
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.741368 Loss1: 0.252670 Loss2: 1.488699
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405943 Loss1: 0.082343 Loss2: 1.323600
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.688995 Loss1: 0.206919 Loss2: 1.482076
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390869 Loss1: 0.075352 Loss2: 1.315517
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.725236 Loss1: 0.234986 Loss2: 1.490250
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.692200 Loss1: 0.207051 Loss2: 1.485149
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.649920 Loss1: 0.179499 Loss2: 1.470422
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.598466 Loss1: 0.125667 Loss2: 1.472799
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.579445 Loss1: 0.114831 Loss2: 1.464613
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.918558 Loss1: 0.976944 Loss2: 1.941615
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.008056 Loss1: 0.609118 Loss2: 1.398938
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.825021 Loss1: 0.385384 Loss2: 1.439637
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.707570 Loss1: 0.317770 Loss2: 1.389800
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606741 Loss1: 0.205297 Loss2: 1.401443
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.669233 Loss1: 0.800197 Loss2: 1.869036
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.570688 Loss1: 0.186066 Loss2: 1.384622
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.928640 Loss1: 0.493540 Loss2: 1.435100
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.492446 Loss1: 0.105171 Loss2: 1.387275
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447699 Loss1: 0.076096 Loss2: 1.371603
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.792961 Loss1: 0.345453 Loss2: 1.447508
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460955 Loss1: 0.094273 Loss2: 1.366682
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.705162 Loss1: 0.285155 Loss2: 1.420007
-DEBUG flwr 2023-10-11 08:25:10,956 | server.py:236 | fit_round 107 received 50 results and 0 failures
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436026 Loss1: 0.070344 Loss2: 1.365682
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.658209 Loss1: 0.241436 Loss2: 1.416773
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.626922 Loss1: 0.213907 Loss2: 1.413015
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.610583 Loss1: 0.198524 Loss2: 1.412059
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.542032 Loss1: 0.133147 Loss2: 1.408885
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.465125 Loss1: 0.072858 Loss2: 1.392267
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.686520 Loss1: 0.868705 Loss2: 1.817815
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.790162 Loss1: 0.451497 Loss2: 1.338665 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.609554 Loss1: 0.269939 Loss2: 1.339615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513052 Loss1: 0.171181 Loss2: 1.341871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.687288 Loss1: 0.852066 Loss2: 1.835221
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445734 Loss1: 0.111101 Loss2: 1.334633
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.903550 Loss1: 0.515893 Loss2: 1.387658
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443480 Loss1: 0.118347 Loss2: 1.325133
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.694471 Loss1: 0.295898 Loss2: 1.398572
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419699 Loss1: 0.096666 Loss2: 1.323033
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.608665 Loss1: 0.241919 Loss2: 1.366746
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375419 Loss1: 0.059426 Loss2: 1.315993
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.594161 Loss1: 0.232244 Loss2: 1.361917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490529 Loss1: 0.132948 Loss2: 1.357581 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436790 Loss1: 0.092058 Loss2: 1.344732
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.635210 Loss1: 0.775320 Loss2: 1.859890
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416838 Loss1: 0.077600 Loss2: 1.339237
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.853377 Loss1: 0.491152 Loss2: 1.362225
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700552 Loss1: 0.305859 Loss2: 1.394694
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.552167 Loss1: 0.199873 Loss2: 1.352294
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462765 Loss1: 0.111275 Loss2: 1.351490
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450865 Loss1: 0.112017 Loss2: 1.338848
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.941283 Loss1: 1.050043 Loss2: 1.891241
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.465582 Loss1: 0.127110 Loss2: 1.338472
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.442920 Loss1: 0.103293 Loss2: 1.339627 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441073 Loss1: 0.104126 Loss2: 1.336947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415950 Loss1: 0.087075 Loss2: 1.328876 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.499309 Loss1: 0.136376 Loss2: 1.362933 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409594 Loss1: 0.062038 Loss2: 1.347556 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 08:25:10,956][flwr][DEBUG] - fit_round 107 received 50 results and 0 failures
-INFO flwr 2023-10-11 08:25:52,279 | server.py:125 | fit progress: (107, 2.205110744546397, {'accuracy': 0.5746}, 246860.058023771)
->> Test accuracy: 0.574600
-[2023-10-11 08:25:52,279][flwr][INFO] - fit progress: (107, 2.205110744546397, {'accuracy': 0.5746}, 246860.058023771)
-DEBUG flwr 2023-10-11 08:25:52,280 | server.py:173 | evaluate_round 107: strategy sampled 50 clients (out of 50)
-[2023-10-11 08:25:52,280][flwr][DEBUG] - evaluate_round 107: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 08:34:53,870 | server.py:187 | evaluate_round 107 received 50 results and 0 failures
-[2023-10-11 08:34:53,870][flwr][DEBUG] - evaluate_round 107 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 08:34:53,871 | server.py:222 | fit_round 108: strategy sampled 50 clients (out of 50)
-[2023-10-11 08:34:53,871][flwr][DEBUG] - fit_round 108: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.616460 Loss1: 0.780404 Loss2: 1.836056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.719982 Loss1: 0.339141 Loss2: 1.380841 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.628162 Loss1: 0.268513 Loss2: 1.359649
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.754510 Loss1: 0.934771 Loss2: 1.819739
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562181 Loss1: 0.206497 Loss2: 1.355684
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.963870 Loss1: 0.548888 Loss2: 1.414982
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.692600 Loss1: 0.316666 Loss2: 1.375935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594481 Loss1: 0.226177 Loss2: 1.368304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.559496 Loss1: 0.199710 Loss2: 1.359785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.550992 Loss1: 0.188200 Loss2: 1.362792 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.559940 Loss1: 0.201951 Loss2: 1.357988 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.494807 Loss1: 0.135392 Loss2: 1.359415 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.762000 Loss1: 0.914111 Loss2: 1.847889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.737267 Loss1: 0.328328 Loss2: 1.408940 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.614278 Loss1: 0.815951 Loss2: 1.798327 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.567321 Loss1: 0.199160 Loss2: 1.368162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517330 Loss1: 0.146830 Loss2: 1.370500 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.492963 Loss1: 0.129010 Loss2: 1.363953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493171 Loss1: 0.137157 Loss2: 1.356014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472630 Loss1: 0.119986 Loss2: 1.352644 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430108 Loss1: 0.103020 Loss2: 1.327088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407821 Loss1: 0.089462 Loss2: 1.318359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397294 Loss1: 0.082255 Loss2: 1.315038
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.814358 Loss1: 0.946581 Loss2: 1.867777
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.902065 Loss1: 0.487316 Loss2: 1.414749
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.747400 Loss1: 0.312172 Loss2: 1.435228
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578463 Loss1: 0.207621 Loss2: 1.370842
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606874 Loss1: 0.228953 Loss2: 1.377921
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.794423 Loss1: 0.831259 Loss2: 1.963164
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.023626 Loss1: 0.551241 Loss2: 1.472386 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.789224 Loss1: 0.304963 Loss2: 1.484261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.712947 Loss1: 0.266148 Loss2: 1.446798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.668633 Loss1: 0.201128 Loss2: 1.467505 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.596583 Loss1: 0.163362 Loss2: 1.433221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.514205 Loss1: 0.083969 Loss2: 1.430235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.545409 Loss1: 0.123162 Loss2: 1.422247 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.919721 Loss1: 0.515818 Loss2: 1.403903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610411 Loss1: 0.219521 Loss2: 1.390890 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617472 Loss1: 0.226177 Loss2: 1.391295
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.759476 Loss1: 0.870254 Loss2: 1.889222
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.943181 Loss1: 0.530440 Loss2: 1.412741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.754449 Loss1: 0.310822 Loss2: 1.443627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612549 Loss1: 0.217365 Loss2: 1.395185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.582689 Loss1: 0.191184 Loss2: 1.391505 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506965 Loss1: 0.123796 Loss2: 1.383169 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491703 Loss1: 0.114331 Loss2: 1.377372 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433106 Loss1: 0.071353 Loss2: 1.361753 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.959648 Loss1: 0.606173 Loss2: 1.353476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.605668 Loss1: 0.282419 Loss2: 1.323249 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.785266 Loss1: 0.978523 Loss2: 1.806743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.899508 Loss1: 0.534134 Loss2: 1.365375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.740879 Loss1: 0.331340 Loss2: 1.409539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.645097 Loss1: 0.288576 Loss2: 1.356520 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574070 Loss1: 0.203773 Loss2: 1.370297 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455740 Loss1: 0.119458 Loss2: 1.336282 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396335 Loss1: 0.072185 Loss2: 1.324149 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387393 Loss1: 0.065935 Loss2: 1.321458
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.703381 Loss1: 0.840504 Loss2: 1.862877
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.000611 Loss1: 0.553975 Loss2: 1.446636
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.782385 Loss1: 0.331484 Loss2: 1.450901
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.661380 Loss1: 0.236659 Loss2: 1.424722
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.609766 Loss1: 0.191068 Loss2: 1.418698
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.709651 Loss1: 0.820319 Loss2: 1.889331
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.945408 Loss1: 0.535257 Loss2: 1.410151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.823064 Loss1: 0.359231 Loss2: 1.463832 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481969 Loss1: 0.085444 Loss2: 1.396524
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.649384 Loss1: 0.241324 Loss2: 1.408060
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.522363 Loss1: 0.127902 Loss2: 1.394461
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.604887 Loss1: 0.198786 Loss2: 1.406101
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472648 Loss1: 0.083452 Loss2: 1.389197
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539306 Loss1: 0.138257 Loss2: 1.401049
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518142 Loss1: 0.127053 Loss2: 1.391089
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.522904 Loss1: 0.132372 Loss2: 1.390532
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476720 Loss1: 0.094760 Loss2: 1.381960
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452003 Loss1: 0.073273 Loss2: 1.378729
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.747550 Loss1: 0.934254 Loss2: 1.813296
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.025334 Loss1: 0.639301 Loss2: 1.386033
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.775187 Loss1: 0.373622 Loss2: 1.401565
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.626252 Loss1: 0.271471 Loss2: 1.354780
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.651863 Loss1: 0.765954 Loss2: 1.885909
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454177 Loss1: 0.114138 Loss2: 1.340039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434295 Loss1: 0.106373 Loss2: 1.327922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433106 Loss1: 0.104107 Loss2: 1.328999 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411567 Loss1: 0.080233 Loss2: 1.331334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435913 Loss1: 0.109720 Loss2: 1.326193 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.534114 Loss1: 0.160902 Loss2: 1.373213 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.495531 Loss1: 0.126875 Loss2: 1.368656 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460130 Loss1: 0.090974 Loss2: 1.369157
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.685864 Loss1: 0.906339 Loss2: 1.779524
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.704566 Loss1: 0.383417 Loss2: 1.321149
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684908 Loss1: 0.340293 Loss2: 1.344615
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554827 Loss1: 0.233175 Loss2: 1.321652
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.469048 Loss1: 0.161163 Loss2: 1.307885
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.722492 Loss1: 0.870230 Loss2: 1.852262
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.409464 Loss1: 0.109884 Loss2: 1.299579
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.380791 Loss1: 0.087965 Loss2: 1.292825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.353645 Loss1: 0.065114 Loss2: 1.288531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.358283 Loss1: 0.074949 Loss2: 1.283333 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.348503 Loss1: 0.070252 Loss2: 1.278250 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508304 Loss1: 0.137859 Loss2: 1.370445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454484 Loss1: 0.093928 Loss2: 1.360556 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457427 Loss1: 0.097116 Loss2: 1.360311
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.684985 Loss1: 0.929742 Loss2: 1.755243
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.930268 Loss1: 0.557253 Loss2: 1.373016
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700277 Loss1: 0.358674 Loss2: 1.341603
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.545492 Loss1: 0.231323 Loss2: 1.314169
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475964 Loss1: 0.153592 Loss2: 1.322372
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.834198 Loss1: 0.945878 Loss2: 1.888320
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.062735 Loss1: 0.634426 Loss2: 1.428309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.839889 Loss1: 0.386179 Loss2: 1.453710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.687186 Loss1: 0.262310 Loss2: 1.424875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.647549 Loss1: 0.239788 Loss2: 1.407761 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.339837 Loss1: 0.054173 Loss2: 1.285664
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.599645 Loss1: 0.189926 Loss2: 1.409719
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550268 Loss1: 0.146858 Loss2: 1.403410
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.502731 Loss1: 0.109371 Loss2: 1.393361
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461915 Loss1: 0.081880 Loss2: 1.380035
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470371 Loss1: 0.094496 Loss2: 1.375875
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.761017 Loss1: 0.877211 Loss2: 1.883806
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.896775 Loss1: 0.502583 Loss2: 1.394192
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.745915 Loss1: 0.320995 Loss2: 1.424921
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.640582 Loss1: 0.260377 Loss2: 1.380205
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.612274 Loss1: 0.209145 Loss2: 1.403128
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.672212 Loss1: 0.838350 Loss2: 1.833862
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.925685 Loss1: 0.553902 Loss2: 1.371783 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.750798 Loss1: 0.354962 Loss2: 1.395837 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.617310 Loss1: 0.240091 Loss2: 1.377219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.596114 Loss1: 0.224277 Loss2: 1.371837 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549300 Loss1: 0.191828 Loss2: 1.357472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452830 Loss1: 0.104313 Loss2: 1.348518 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491049 Loss1: 0.149856 Loss2: 1.341193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.970323 Loss1: 0.569589 Loss2: 1.400734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.626119 Loss1: 0.236385 Loss2: 1.389734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.507035 Loss1: 0.132906 Loss2: 1.374129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470709 Loss1: 0.104958 Loss2: 1.365751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441871 Loss1: 0.084860 Loss2: 1.357011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421530 Loss1: 0.067186 Loss2: 1.354344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461580 Loss1: 0.108292 Loss2: 1.353288 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986779
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468165 Loss1: 0.148459 Loss2: 1.319706 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394817 Loss1: 0.083132 Loss2: 1.311685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374778 Loss1: 0.074215 Loss2: 1.300563
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.857520 Loss1: 0.937895 Loss2: 1.919625
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.945410 Loss1: 0.561401 Loss2: 1.384008
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.749913 Loss1: 0.328417 Loss2: 1.421496
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.718367 Loss1: 0.315679 Loss2: 1.402688
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605133 Loss1: 0.217758 Loss2: 1.387375
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.570289 Loss1: 0.191592 Loss2: 1.378697
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.906667 Loss1: 1.019368 Loss2: 1.887299
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.971534 Loss1: 0.595788 Loss2: 1.375746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.857398 Loss1: 0.437444 Loss2: 1.419953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390234 Loss1: 0.039166 Loss2: 1.351068 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.550189 Loss1: 0.197190 Loss2: 1.353000 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487312 Loss1: 0.143581 Loss2: 1.343731 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.780672 Loss1: 0.831938 Loss2: 1.948733 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.964488 Loss1: 0.499867 Loss2: 1.464621 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.672794 Loss1: 0.225885 Loss2: 1.446909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547014 Loss1: 0.112773 Loss2: 1.434241 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547297 Loss1: 0.119502 Loss2: 1.427794 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.530907 Loss1: 0.099661 Loss2: 1.431245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.511202 Loss1: 0.087347 Loss2: 1.423855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.523931 Loss1: 0.105978 Loss2: 1.417953 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.575147 Loss1: 0.190704 Loss2: 1.384443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462152 Loss1: 0.091117 Loss2: 1.371035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.662099 Loss1: 0.814415 Loss2: 1.847684 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.654106 Loss1: 0.260122 Loss2: 1.393984 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.520746 Loss1: 0.140284 Loss2: 1.380462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.798436 Loss1: 0.950008 Loss2: 1.848427
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514940 Loss1: 0.132322 Loss2: 1.382618
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.980744 Loss1: 0.563023 Loss2: 1.417721
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472846 Loss1: 0.098678 Loss2: 1.374169
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.757964 Loss1: 0.331997 Loss2: 1.425966
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.506464 Loss1: 0.140328 Loss2: 1.366135
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636660 Loss1: 0.246351 Loss2: 1.390309
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460308 Loss1: 0.086564 Loss2: 1.373744
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.520692 Loss1: 0.142929 Loss2: 1.377763 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963867
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478901 Loss1: 0.113108 Loss2: 1.365793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466789 Loss1: 0.101817 Loss2: 1.364972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443871 Loss1: 0.086492 Loss2: 1.357379
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.804656 Loss1: 0.900681 Loss2: 1.903975
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.977523 Loss1: 0.564842 Loss2: 1.412681
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.831685 Loss1: 0.370942 Loss2: 1.460743
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.675584 Loss1: 0.285171 Loss2: 1.390413
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.637358 Loss1: 0.228221 Loss2: 1.409138
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.654474 Loss1: 0.833850 Loss2: 1.820624
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.922461 Loss1: 0.520956 Loss2: 1.401505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769071 Loss1: 0.356291 Loss2: 1.412780 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.641981 Loss1: 0.254310 Loss2: 1.387672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.562161 Loss1: 0.184868 Loss2: 1.377293 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504599 Loss1: 0.132554 Loss2: 1.372045 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462325 Loss1: 0.106838 Loss2: 1.355487 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.830026 Loss1: 0.883772 Loss2: 1.946254
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436969 Loss1: 0.084233 Loss2: 1.352736
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.838326 Loss1: 0.350198 Loss2: 1.488128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.688012 Loss1: 0.244545 Loss2: 1.443466 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.595595 Loss1: 0.166771 Loss2: 1.428824
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.491539 Loss1: 0.712176 Loss2: 1.779363
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.781201 Loss1: 0.433712 Loss2: 1.347488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.664903 Loss1: 0.296329 Loss2: 1.368574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567650 Loss1: 0.228632 Loss2: 1.339018 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455447 Loss1: 0.128851 Loss2: 1.326596 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442420 Loss1: 0.119390 Loss2: 1.323030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434709 Loss1: 0.112579 Loss2: 1.322130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400919 Loss1: 0.085219 Loss2: 1.315700 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977941
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.792299 Loss1: 0.380309 Loss2: 1.411990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.603373 Loss1: 0.194240 Loss2: 1.409133 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.759187 Loss1: 0.884879 Loss2: 1.874308 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.913408 Loss1: 0.529957 Loss2: 1.383452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.698935 Loss1: 0.273535 Loss2: 1.425400 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541781 Loss1: 0.157276 Loss2: 1.384504 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478664 Loss1: 0.130712 Loss2: 1.347952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464645 Loss1: 0.110332 Loss2: 1.354312
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.819273 Loss1: 0.953768 Loss2: 1.865505
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.954092 Loss1: 0.558004 Loss2: 1.396088 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.804068 Loss1: 0.380388 Loss2: 1.423680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.549842 Loss1: 0.163700 Loss2: 1.386143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525996 Loss1: 0.153132 Loss2: 1.372864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.493435 Loss1: 0.132888 Loss2: 1.360547 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454062 Loss1: 0.093140 Loss2: 1.360922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476266 Loss1: 0.113596 Loss2: 1.362670 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560255 Loss1: 0.148472 Loss2: 1.411782 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518275 Loss1: 0.112276 Loss2: 1.405999 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.468839 Loss1: 0.068467 Loss2: 1.400372
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.791207 Loss1: 0.937789 Loss2: 1.853418
-(DefaultActor pid=3764) >> Training accuracy: 0.994420
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444372 Loss1: 0.052630 Loss2: 1.391742
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.963619 Loss1: 0.584319 Loss2: 1.379300
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.793106 Loss1: 0.380120 Loss2: 1.412986
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694354 Loss1: 0.326275 Loss2: 1.368079
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551253 Loss1: 0.191367 Loss2: 1.359886
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502575 Loss1: 0.158942 Loss2: 1.343633
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.757302 Loss1: 0.746598 Loss2: 2.010704
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486753 Loss1: 0.145743 Loss2: 1.341010
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.088732 Loss1: 0.574619 Loss2: 1.514113
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469411 Loss1: 0.130122 Loss2: 1.339289
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.878364 Loss1: 0.326536 Loss2: 1.551828
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452427 Loss1: 0.117790 Loss2: 1.334636
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.788567 Loss1: 0.276569 Loss2: 1.511998
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427052 Loss1: 0.094800 Loss2: 1.332253
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.637782 Loss1: 0.155910 Loss2: 1.481871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.585561 Loss1: 0.114823 Loss2: 1.470738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.560371 Loss1: 0.094670 Loss2: 1.465701
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.777949 Loss1: 0.887639 Loss2: 1.890309
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.549325 Loss1: 0.084229 Loss2: 1.465097
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.846034 Loss1: 0.465916 Loss2: 1.380118
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.708628 Loss1: 0.313218 Loss2: 1.395409
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583237 Loss1: 0.200435 Loss2: 1.382803
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.552019 Loss1: 0.187377 Loss2: 1.364642
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.510247 Loss1: 0.143293 Loss2: 1.366954
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.606844 Loss1: 0.733184 Loss2: 1.873661
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523983 Loss1: 0.162970 Loss2: 1.361012
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.815909 Loss1: 0.419809 Loss2: 1.396100
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454084 Loss1: 0.090718 Loss2: 1.363367
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.719145 Loss1: 0.292351 Loss2: 1.426794
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434163 Loss1: 0.081530 Loss2: 1.352633
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.642236 Loss1: 0.261770 Loss2: 1.380465
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429308 Loss1: 0.086484 Loss2: 1.342824
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480570 Loss1: 0.104254 Loss2: 1.376317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435785 Loss1: 0.075217 Loss2: 1.360568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428710 Loss1: 0.072733 Loss2: 1.355977
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.902983 Loss1: 0.917253 Loss2: 1.985731
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420118 Loss1: 0.065435 Loss2: 1.354683
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.915451 Loss1: 0.442836 Loss2: 1.472615
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.880978 Loss1: 0.400128 Loss2: 1.480850
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.687302 Loss1: 0.236928 Loss2: 1.450374
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.612612 Loss1: 0.174274 Loss2: 1.438338
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.585149 Loss1: 0.144866 Loss2: 1.440283
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.730275 Loss1: 0.941215 Loss2: 1.789060
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547014 Loss1: 0.128608 Loss2: 1.418406
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.917169 Loss1: 0.521541 Loss2: 1.395627
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.506251 Loss1: 0.088365 Loss2: 1.417886
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.638133 Loss1: 0.289126 Loss2: 1.349007
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.535808 Loss1: 0.120053 Loss2: 1.415755
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.583120 Loss1: 0.247605 Loss2: 1.335515
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.537408 Loss1: 0.120037 Loss2: 1.417371
-(DefaultActor pid=3765) >> Training accuracy: 0.960417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468898 Loss1: 0.143514 Loss2: 1.325384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432848 Loss1: 0.112006 Loss2: 1.320841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421337 Loss1: 0.104203 Loss2: 1.317134
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.706252 Loss1: 0.847656 Loss2: 1.858595
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409877 Loss1: 0.098608 Loss2: 1.311269
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.858002 Loss1: 0.483492 Loss2: 1.374509
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.764887 Loss1: 0.325038 Loss2: 1.439849
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586586 Loss1: 0.226620 Loss2: 1.359966
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.583093 Loss1: 0.212486 Loss2: 1.370606
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.554046 Loss1: 0.189636 Loss2: 1.364409
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451614 Loss1: 0.099550 Loss2: 1.352064
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.739449 Loss1: 0.860161 Loss2: 1.879288
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460704 Loss1: 0.114784 Loss2: 1.345920
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.928975 Loss1: 0.535945 Loss2: 1.393030
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452913 Loss1: 0.110131 Loss2: 1.342781
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.697265 Loss1: 0.261010 Loss2: 1.436255
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406294 Loss1: 0.068649 Loss2: 1.337645
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.633185 Loss1: 0.241060 Loss2: 1.392125
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.572125 Loss1: 0.178104 Loss2: 1.394021
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.580059 Loss1: 0.186316 Loss2: 1.393743
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.526213 Loss1: 0.141812 Loss2: 1.384401
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.528636 Loss1: 0.138268 Loss2: 1.390368
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.533530 Loss1: 0.151666 Loss2: 1.381864
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.531765 Loss1: 0.725979 Loss2: 1.805787
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.484983 Loss1: 0.103382 Loss2: 1.381602
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.961188 Loss1: 0.584494 Loss2: 1.376694
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.748716 Loss1: 0.322788 Loss2: 1.425928
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.640062 Loss1: 0.266709 Loss2: 1.373353
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.650422 Loss1: 0.253997 Loss2: 1.396425
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.579567 Loss1: 0.200255 Loss2: 1.379312
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.645333 Loss1: 0.851980 Loss2: 1.793353
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.558066 Loss1: 0.182722 Loss2: 1.375344
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.511608 Loss1: 0.141191 Loss2: 1.370417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.467456 Loss1: 0.101778 Loss2: 1.365678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445582 Loss1: 0.089986 Loss2: 1.355595 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.457820 Loss1: 0.114987 Loss2: 1.342833 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412424 Loss1: 0.082239 Loss2: 1.330185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.906999 Loss1: 0.925857 Loss2: 1.981142
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429828 Loss1: 0.097849 Loss2: 1.331979
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385690 Loss1: 0.058036 Loss2: 1.327654 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587237 Loss1: 0.204311 Loss2: 1.382926 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.553756 Loss1: 0.184868 Loss2: 1.368888 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 09:03:22,411 | server.py:236 | fit_round 108 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.868328 Loss1: 1.011181 Loss2: 1.857147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438049 Loss1: 0.073824 Loss2: 1.364225 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586731 Loss1: 0.225764 Loss2: 1.360966 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.534119 Loss1: 0.182468 Loss2: 1.351650 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.662099 Loss1: 0.855724 Loss2: 1.806375
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.506067 Loss1: 0.156467 Loss2: 1.349601
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.006967 Loss1: 0.629659 Loss2: 1.377308
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449600 Loss1: 0.104497 Loss2: 1.345104
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.781728 Loss1: 0.360565 Loss2: 1.421163
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408829 Loss1: 0.072566 Loss2: 1.336263
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576940 Loss1: 0.223381 Loss2: 1.353558
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386841 Loss1: 0.056127 Loss2: 1.330714
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442240 Loss1: 0.110009 Loss2: 1.332231 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.396014 Loss1: 0.076261 Loss2: 1.319754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.387287 Loss1: 0.069770 Loss2: 1.317517
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.580112 Loss1: 0.768044 Loss2: 1.812067
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359721 Loss1: 0.048307 Loss2: 1.311414
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.897932 Loss1: 0.516559 Loss2: 1.381373
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726231 Loss1: 0.303399 Loss2: 1.422833
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.663599 Loss1: 0.282173 Loss2: 1.381427
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.577915 Loss1: 0.195364 Loss2: 1.382551
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521777 Loss1: 0.140078 Loss2: 1.381699
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486730 Loss1: 0.115272 Loss2: 1.371458
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.476417 Loss1: 0.115627 Loss2: 1.360789
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442965 Loss1: 0.083701 Loss2: 1.359264
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453010 Loss1: 0.097535 Loss2: 1.355475
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 09:03:22,411][flwr][DEBUG] - fit_round 108 received 50 results and 0 failures
-INFO flwr 2023-10-11 09:04:03,728 | server.py:125 | fit progress: (108, 2.2027086655552774, {'accuracy': 0.5734}, 249151.506683776)
->> Test accuracy: 0.573400
-[2023-10-11 09:04:03,728][flwr][INFO] - fit progress: (108, 2.2027086655552774, {'accuracy': 0.5734}, 249151.506683776)
-DEBUG flwr 2023-10-11 09:04:03,728 | server.py:173 | evaluate_round 108: strategy sampled 50 clients (out of 50)
-[2023-10-11 09:04:03,728][flwr][DEBUG] - evaluate_round 108: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 09:13:05,386 | server.py:187 | evaluate_round 108 received 50 results and 0 failures
-[2023-10-11 09:13:05,386][flwr][DEBUG] - evaluate_round 108 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 09:13:05,386 | server.py:222 | fit_round 109: strategy sampled 50 clients (out of 50)
-[2023-10-11 09:13:05,386][flwr][DEBUG] - fit_round 109: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.735014 Loss1: 0.928740 Loss2: 1.806274
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.895976 Loss1: 0.535552 Loss2: 1.360425
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.843333 Loss1: 0.445985 Loss2: 1.397348
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.757091 Loss1: 0.393862 Loss2: 1.363229
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.626719 Loss1: 0.259940 Loss2: 1.366779 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537218 Loss1: 0.191623 Loss2: 1.345595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.510461 Loss1: 0.157710 Loss2: 1.352752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477421 Loss1: 0.136300 Loss2: 1.341120 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.475883 Loss1: 0.145936 Loss2: 1.329947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408786 Loss1: 0.080697 Loss2: 1.328089 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490263 Loss1: 0.081569 Loss2: 1.408694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.488618 Loss1: 0.089050 Loss2: 1.399568 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.986026 Loss1: 0.575228 Loss2: 1.410798 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.634668 Loss1: 0.228665 Loss2: 1.406002 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.579751 Loss1: 0.178701 Loss2: 1.401050 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.550198 Loss1: 0.156619 Loss2: 1.393579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512738 Loss1: 0.127649 Loss2: 1.385090 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.524916 Loss1: 0.144017 Loss2: 1.380898 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484162 Loss1: 0.100051 Loss2: 1.384111 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448914 Loss1: 0.070560 Loss2: 1.378354 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.516333 Loss1: 0.138199 Loss2: 1.378135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435216 Loss1: 0.067373 Loss2: 1.367843 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.891627 Loss1: 0.456063 Loss2: 1.435564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.636508 Loss1: 0.212666 Loss2: 1.423842 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.575609 Loss1: 0.148465 Loss2: 1.427144 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523484 Loss1: 0.117328 Loss2: 1.406156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495752 Loss1: 0.091014 Loss2: 1.404738 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470477 Loss1: 0.072019 Loss2: 1.398458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.466009 Loss1: 0.071631 Loss2: 1.394378 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465552 Loss1: 0.073953 Loss2: 1.391599 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460273 Loss1: 0.097988 Loss2: 1.362285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442626 Loss1: 0.096267 Loss2: 1.346359 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.642431 Loss1: 0.838720 Loss2: 1.803712
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.792803 Loss1: 0.435176 Loss2: 1.357627
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638854 Loss1: 0.261839 Loss2: 1.377015
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.501133 Loss1: 0.181700 Loss2: 1.319433
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.795428 Loss1: 0.866949 Loss2: 1.928479
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.942240 Loss1: 0.499022 Loss2: 1.443218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804828 Loss1: 0.329979 Loss2: 1.474849 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.699748 Loss1: 0.270044 Loss2: 1.429704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.626838 Loss1: 0.188156 Loss2: 1.438682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.617595 Loss1: 0.193273 Loss2: 1.424321 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.547180 Loss1: 0.117569 Loss2: 1.429610 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.518899 Loss1: 0.111545 Loss2: 1.407354 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 3.035384 Loss1: 1.027527 Loss2: 2.007858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.867946 Loss1: 0.418057 Loss2: 1.449890 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.582387 Loss1: 0.199706 Loss2: 1.382681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.527881 Loss1: 0.136133 Loss2: 1.391748 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.497847 Loss1: 0.123569 Loss2: 1.374278 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461278 Loss1: 0.091604 Loss2: 1.369673
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.768272 Loss1: 0.324621 Loss2: 1.443652
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.646799 Loss1: 0.209627 Loss2: 1.437172 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557717 Loss1: 0.139055 Loss2: 1.418662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.569638 Loss1: 0.153189 Loss2: 1.416450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.531016 Loss1: 0.120559 Loss2: 1.410457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.510903 Loss1: 0.101570 Loss2: 1.409333 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.720543 Loss1: 0.255334 Loss2: 1.465209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.619603 Loss1: 0.183684 Loss2: 1.435918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.650221 Loss1: 0.817439 Loss2: 1.832782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.952987 Loss1: 0.548228 Loss2: 1.404758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.755955 Loss1: 0.340470 Loss2: 1.415485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.629909 Loss1: 0.255473 Loss2: 1.374435 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.534760 Loss1: 0.159654 Loss2: 1.375105 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422870 Loss1: 0.071031 Loss2: 1.351839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.826087 Loss1: 0.897012 Loss2: 1.929075
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401459 Loss1: 0.058512 Loss2: 1.342947
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398677 Loss1: 0.059067 Loss2: 1.339610 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.732389 Loss1: 0.271123 Loss2: 1.461266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.592387 Loss1: 0.154338 Loss2: 1.438049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.548747 Loss1: 0.119617 Loss2: 1.429129
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.787998 Loss1: 0.911706 Loss2: 1.876292
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.960482 Loss1: 0.557943 Loss2: 1.402539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.751287 Loss1: 0.350157 Loss2: 1.401130 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.486099 Loss1: 0.071444 Loss2: 1.414655
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.621712 Loss1: 0.253743 Loss2: 1.367969
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530156 Loss1: 0.152284 Loss2: 1.377872
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502429 Loss1: 0.147964 Loss2: 1.354465
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436181 Loss1: 0.085295 Loss2: 1.350887
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416092 Loss1: 0.072851 Loss2: 1.343241
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.811635 Loss1: 0.953381 Loss2: 1.858254
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420973 Loss1: 0.081271 Loss2: 1.339702
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.940420 Loss1: 0.560930 Loss2: 1.379490
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397411 Loss1: 0.063789 Loss2: 1.333622
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.585922 Loss1: 0.218689 Loss2: 1.367233 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478533 Loss1: 0.133610 Loss2: 1.344924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448784 Loss1: 0.098593 Loss2: 1.350191
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.856124 Loss1: 0.929479 Loss2: 1.926645
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.982374 Loss1: 0.521767 Loss2: 1.460607 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.799176 Loss1: 0.310186 Loss2: 1.488990 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421509 Loss1: 0.088996 Loss2: 1.332514
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.658496 Loss1: 0.225568 Loss2: 1.432928
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.596493 Loss1: 0.166032 Loss2: 1.430461
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.517656 Loss1: 0.098629 Loss2: 1.419027
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.527888 Loss1: 0.118785 Loss2: 1.409103
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.524650 Loss1: 0.109472 Loss2: 1.415178
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.830528 Loss1: 0.940993 Loss2: 1.889535
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480149 Loss1: 0.073403 Loss2: 1.406746
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.966514 Loss1: 0.591516 Loss2: 1.374998
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.507911 Loss1: 0.105641 Loss2: 1.402269
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.621856 Loss1: 0.261140 Loss2: 1.360716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.496952 Loss1: 0.136817 Loss2: 1.360135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.770307 Loss1: 0.923057 Loss2: 1.847250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.874035 Loss1: 0.454843 Loss2: 1.419191 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.722734 Loss1: 0.302766 Loss2: 1.419969 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979911
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.619436 Loss1: 0.226111 Loss2: 1.393325 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.519505 Loss1: 0.137854 Loss2: 1.381651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.986661 Loss1: 0.993152 Loss2: 1.993510
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.476546 Loss1: 0.104903 Loss2: 1.371643
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.517862 Loss1: 0.142786 Loss2: 1.375075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462805 Loss1: 0.088504 Loss2: 1.374301 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525531 Loss1: 0.137294 Loss2: 1.388237 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.475436 Loss1: 0.106754 Loss2: 1.368682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.772084 Loss1: 0.846688 Loss2: 1.925396 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979567
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.851186 Loss1: 0.375673 Loss2: 1.475512 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.604862 Loss1: 0.198761 Loss2: 1.406101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.547411 Loss1: 0.159627 Loss2: 1.387784
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.771311 Loss1: 0.884073 Loss2: 1.887238
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.948958 Loss1: 0.499459 Loss2: 1.449499 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.749076 Loss1: 0.292140 Loss2: 1.456936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.672836 Loss1: 0.256024 Loss2: 1.416812 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.577902 Loss1: 0.161825 Loss2: 1.416077 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486992 Loss1: 0.079206 Loss2: 1.407786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465665 Loss1: 0.068201 Loss2: 1.397464
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.491622 Loss1: 0.670380 Loss2: 1.821242
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475035 Loss1: 0.085370 Loss2: 1.389665
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.824485 Loss1: 0.479129 Loss2: 1.345356
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.697054 Loss1: 0.307971 Loss2: 1.389083
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619551 Loss1: 0.270306 Loss2: 1.349245
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543835 Loss1: 0.186544 Loss2: 1.357291
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461530 Loss1: 0.117461 Loss2: 1.344068
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412568 Loss1: 0.074474 Loss2: 1.338094
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.517894 Loss1: 0.721271 Loss2: 1.796624
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.886360 Loss1: 0.506466 Loss2: 1.379895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.764653 Loss1: 0.348109 Loss2: 1.416545 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388998 Loss1: 0.069257 Loss2: 1.319742
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.696723 Loss1: 0.310888 Loss2: 1.385835
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.645432 Loss1: 0.254386 Loss2: 1.391045
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.663244 Loss1: 0.275206 Loss2: 1.388038
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.530387 Loss1: 0.147724 Loss2: 1.382663
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477290 Loss1: 0.112197 Loss2: 1.365093
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.755424 Loss1: 0.906559 Loss2: 1.848866
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.017140 Loss1: 0.614021 Loss2: 1.403119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462120 Loss1: 0.104540 Loss2: 1.357580
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.849241 Loss1: 0.422584 Loss2: 1.426657
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.644730 Loss1: 0.254854 Loss2: 1.389876
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519996 Loss1: 0.143793 Loss2: 1.376203
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485554 Loss1: 0.123416 Loss2: 1.362138
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.481571 Loss1: 0.119313 Loss2: 1.362257
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448527 Loss1: 0.086053 Loss2: 1.362474
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.587671 Loss1: 0.748418 Loss2: 1.839254
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460821 Loss1: 0.110544 Loss2: 1.350277
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.808604 Loss1: 0.415949 Loss2: 1.392654
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457326 Loss1: 0.103851 Loss2: 1.353475
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731276 Loss1: 0.292027 Loss2: 1.439248
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601105 Loss1: 0.211857 Loss2: 1.389248
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567334 Loss1: 0.179525 Loss2: 1.387809
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524642 Loss1: 0.144842 Loss2: 1.379800
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514368 Loss1: 0.136453 Loss2: 1.377914
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.754601 Loss1: 0.886663 Loss2: 1.867938
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.920174 Loss1: 0.511991 Loss2: 1.408182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.841785 Loss1: 0.399017 Loss2: 1.442768 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481793 Loss1: 0.115275 Loss2: 1.366517
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.621488 Loss1: 0.240970 Loss2: 1.380517
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.601507 Loss1: 0.210037 Loss2: 1.391470
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.555222 Loss1: 0.179567 Loss2: 1.375655
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.505272 Loss1: 0.135588 Loss2: 1.369683
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486518 Loss1: 0.118925 Loss2: 1.367593
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.681403 Loss1: 0.828500 Loss2: 1.852903
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.891204 Loss1: 0.514467 Loss2: 1.376737 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655296 Loss1: 0.255407 Loss2: 1.399889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.500261 Loss1: 0.156742 Loss2: 1.343519 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481775 Loss1: 0.148206 Loss2: 1.333569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.502437 Loss1: 0.162367 Loss2: 1.340070 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452616 Loss1: 0.120116 Loss2: 1.332500 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.470986 Loss1: 0.134073 Loss2: 1.336913 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530863 Loss1: 0.170325 Loss2: 1.360538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468918 Loss1: 0.122163 Loss2: 1.346755 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.642523 Loss1: 0.836506 Loss2: 1.806017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.927561 Loss1: 0.565002 Loss2: 1.362559 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.738312 Loss1: 0.331454 Loss2: 1.406858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.659940 Loss1: 0.292241 Loss2: 1.367699 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.506404 Loss1: 0.164168 Loss2: 1.342236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454713 Loss1: 0.115723 Loss2: 1.338990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460256 Loss1: 0.130355 Loss2: 1.329901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402027 Loss1: 0.070945 Loss2: 1.331082 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527841 Loss1: 0.160535 Loss2: 1.367307 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494194 Loss1: 0.129283 Loss2: 1.364911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470476 Loss1: 0.109700 Loss2: 1.360776
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.732210 Loss1: 0.917312 Loss2: 1.814898
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.840527 Loss1: 0.527093 Loss2: 1.313434 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425421 Loss1: 0.071011 Loss2: 1.354410
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.657867 Loss1: 0.305200 Loss2: 1.352667
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513557 Loss1: 0.210436 Loss2: 1.303121
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.455823 Loss1: 0.145285 Loss2: 1.310538
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449302 Loss1: 0.157412 Loss2: 1.291890
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.399363 Loss1: 0.109037 Loss2: 1.290327
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.376026 Loss1: 0.089424 Loss2: 1.286602
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.561247 Loss1: 0.747557 Loss2: 1.813690
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.871018 Loss1: 0.473763 Loss2: 1.397255 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979911
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.723772 Loss1: 0.308801 Loss2: 1.414971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544411 Loss1: 0.174389 Loss2: 1.370021 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494419 Loss1: 0.136436 Loss2: 1.357984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452293 Loss1: 0.096295 Loss2: 1.355998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448263 Loss1: 0.089357 Loss2: 1.358906 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.486015 Loss1: 0.140109 Loss2: 1.345907 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465328 Loss1: 0.146752 Loss2: 1.318576 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393442 Loss1: 0.082801 Loss2: 1.310641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372933 Loss1: 0.073181 Loss2: 1.299752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391423 Loss1: 0.086200 Loss2: 1.305223 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557341 Loss1: 0.190840 Loss2: 1.366501 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485315 Loss1: 0.123239 Loss2: 1.362076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462595 Loss1: 0.122525 Loss2: 1.340070
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.733834 Loss1: 0.864570 Loss2: 1.869264
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.953302 Loss1: 0.556860 Loss2: 1.396442 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575874 Loss1: 0.211314 Loss2: 1.364559 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470451 Loss1: 0.112464 Loss2: 1.357987 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455879 Loss1: 0.116791 Loss2: 1.339088
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.797656 Loss1: 0.928389 Loss2: 1.869267
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.972269 Loss1: 0.567817 Loss2: 1.404452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.830597 Loss1: 0.384475 Loss2: 1.446122 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431978 Loss1: 0.092730 Loss2: 1.339248
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.698269 Loss1: 0.283239 Loss2: 1.415030
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.562884 Loss1: 0.181254 Loss2: 1.381630
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494154 Loss1: 0.120990 Loss2: 1.373164
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463613 Loss1: 0.095197 Loss2: 1.368416
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441719 Loss1: 0.083134 Loss2: 1.358584
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.707839 Loss1: 0.848601 Loss2: 1.859238
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442536 Loss1: 0.083454 Loss2: 1.359081
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438142 Loss1: 0.080591 Loss2: 1.357551 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610445 Loss1: 0.245118 Loss2: 1.365328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494308 Loss1: 0.138977 Loss2: 1.355331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456916 Loss1: 0.111059 Loss2: 1.345857
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.907958 Loss1: 1.085085 Loss2: 1.822872
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.940411 Loss1: 0.596536 Loss2: 1.343875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671751 Loss1: 0.314843 Loss2: 1.356907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.494635 Loss1: 0.184944 Loss2: 1.309691 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.440613 Loss1: 0.140205 Loss2: 1.300408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389804 Loss1: 0.098738 Loss2: 1.291066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.364041 Loss1: 0.073828 Loss2: 1.290214
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.542179 Loss1: 0.693445 Loss2: 1.848734
-(DefaultActor pid=3764) >> Training accuracy: 0.994420
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.341670 Loss1: 0.053138 Loss2: 1.288532
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.906405 Loss1: 0.548474 Loss2: 1.357931
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.696032 Loss1: 0.297132 Loss2: 1.398900
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.592152 Loss1: 0.237931 Loss2: 1.354221
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566251 Loss1: 0.209327 Loss2: 1.356924
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494559 Loss1: 0.138298 Loss2: 1.356261
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.558192 Loss1: 0.770388 Loss2: 1.787804
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464455 Loss1: 0.124374 Loss2: 1.340081
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.784158 Loss1: 0.455838 Loss2: 1.328320
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.416592 Loss1: 0.084068 Loss2: 1.332524
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.705203 Loss1: 0.336459 Loss2: 1.368744
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417401 Loss1: 0.085381 Loss2: 1.332020
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604410 Loss1: 0.274822 Loss2: 1.329587
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396379 Loss1: 0.065324 Loss2: 1.331055
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452861 Loss1: 0.142155 Loss2: 1.310706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376887 Loss1: 0.074918 Loss2: 1.301969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.351054 Loss1: 0.052979 Loss2: 1.298076
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.673041 Loss1: 0.777475 Loss2: 1.895566
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.860769 Loss1: 0.474881 Loss2: 1.385889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.568602 Loss1: 0.197471 Loss2: 1.371132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491648 Loss1: 0.125583 Loss2: 1.366065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.623896 Loss1: 0.753359 Loss2: 1.870536
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460636 Loss1: 0.100407 Loss2: 1.360229
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.910640 Loss1: 0.524882 Loss2: 1.385759
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445219 Loss1: 0.089246 Loss2: 1.355973
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.744610 Loss1: 0.291900 Loss2: 1.452710
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447716 Loss1: 0.099405 Loss2: 1.348312
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619493 Loss1: 0.239217 Loss2: 1.380276
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430159 Loss1: 0.077552 Loss2: 1.352607
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.507349 Loss1: 0.132547 Loss2: 1.374802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.451964 Loss1: 0.092317 Loss2: 1.359646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473014 Loss1: 0.113722 Loss2: 1.359292
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.682593 Loss1: 0.811344 Loss2: 1.871249
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438465 Loss1: 0.078171 Loss2: 1.360294
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.846587 Loss1: 0.455580 Loss2: 1.391007
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.739362 Loss1: 0.309960 Loss2: 1.429402
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649376 Loss1: 0.266902 Loss2: 1.382474
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.643655 Loss1: 0.244998 Loss2: 1.398657
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.550466 Loss1: 0.151981 Loss2: 1.398485
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.709163 Loss1: 0.855451 Loss2: 1.853711
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524908 Loss1: 0.142864 Loss2: 1.382044
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491560 Loss1: 0.116964 Loss2: 1.374596
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.932245 Loss1: 0.516672 Loss2: 1.415572
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.467102 Loss1: 0.082891 Loss2: 1.384211
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.712970 Loss1: 0.286780 Loss2: 1.426189
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442646 Loss1: 0.075659 Loss2: 1.366987
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.647203 Loss1: 0.256142 Loss2: 1.391061
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574275 Loss1: 0.178900 Loss2: 1.395375
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513970 Loss1: 0.136411 Loss2: 1.377559
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513972 Loss1: 0.143818 Loss2: 1.370154
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464186 Loss1: 0.085839 Loss2: 1.378347
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.557304 Loss1: 0.746940 Loss2: 1.810364
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444210 Loss1: 0.080947 Loss2: 1.363263
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452626 Loss1: 0.088981 Loss2: 1.363645 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649983 Loss1: 0.288491 Loss2: 1.361492 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525813 Loss1: 0.158988 Loss2: 1.366825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.520482 Loss1: 0.159913 Loss2: 1.360568
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.694558 Loss1: 0.794694 Loss2: 1.899864
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.001271 Loss1: 0.562469 Loss2: 1.438802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.758906 Loss1: 0.292251 Loss2: 1.466655 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.693443 Loss1: 0.259353 Loss2: 1.434090 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.526732 Loss1: 0.115217 Loss2: 1.411515 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.513302 Loss1: 0.102897 Loss2: 1.410405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.503175 Loss1: 0.103424 Loss2: 1.399751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512337 Loss1: 0.112372 Loss2: 1.399965 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581720 Loss1: 0.222911 Loss2: 1.358809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502051 Loss1: 0.154722 Loss2: 1.347329 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456768 Loss1: 0.109007 Loss2: 1.347761
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.590914 Loss1: 0.748668 Loss2: 1.842246
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.905346 Loss1: 0.464749 Loss2: 1.440597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727636 Loss1: 0.303041 Loss2: 1.424595 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 09:41:25,648 | server.py:236 | fit_round 109 received 50 results and 0 failures
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.668586 Loss1: 0.264515 Loss2: 1.404071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.541717 Loss1: 0.152755 Loss2: 1.388962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461510 Loss1: 0.092785 Loss2: 1.368725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476052 Loss1: 0.103350 Loss2: 1.372702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.484730 Loss1: 0.113823 Loss2: 1.370907 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965820
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577724 Loss1: 0.184963 Loss2: 1.392761 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488665 Loss1: 0.117052 Loss2: 1.371613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.524301 Loss1: 0.144542 Loss2: 1.379759
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.531778 Loss1: 0.735661 Loss2: 1.796117
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.798864 Loss1: 0.418261 Loss2: 1.380603 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.639675 Loss1: 0.269085 Loss2: 1.370590 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.533990 Loss1: 0.178791 Loss2: 1.355199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456339 Loss1: 0.111158 Loss2: 1.345181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441693 Loss1: 0.101305 Loss2: 1.340387 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992647
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 09:41:25,648][flwr][DEBUG] - fit_round 109 received 50 results and 0 failures
-INFO flwr 2023-10-11 09:42:09,239 | server.py:125 | fit progress: (109, 2.2131336215205084, {'accuracy': 0.5739}, 251437.017252407)
->> Test accuracy: 0.573900
-[2023-10-11 09:42:09,239][flwr][INFO] - fit progress: (109, 2.2131336215205084, {'accuracy': 0.5739}, 251437.017252407)
-DEBUG flwr 2023-10-11 09:42:09,239 | server.py:173 | evaluate_round 109: strategy sampled 50 clients (out of 50)
-[2023-10-11 09:42:09,239][flwr][DEBUG] - evaluate_round 109: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 09:51:16,347 | server.py:187 | evaluate_round 109 received 50 results and 0 failures
-[2023-10-11 09:51:16,347][flwr][DEBUG] - evaluate_round 109 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 09:51:16,347 | server.py:222 | fit_round 110: strategy sampled 50 clients (out of 50)
-[2023-10-11 09:51:16,347][flwr][DEBUG] - fit_round 110: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.742540 Loss1: 0.820059 Loss2: 1.922481 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736004 Loss1: 0.269109 Loss2: 1.466895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629098 Loss1: 0.206557 Loss2: 1.422541
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.858670 Loss1: 1.018418 Loss2: 1.840252
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589384 Loss1: 0.157475 Loss2: 1.431909
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.027905 Loss1: 0.641047 Loss2: 1.386858
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538986 Loss1: 0.123992 Loss2: 1.414994
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.779959 Loss1: 0.377697 Loss2: 1.402262
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.507550 Loss1: 0.095114 Loss2: 1.412436
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604329 Loss1: 0.246944 Loss2: 1.357385
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484357 Loss1: 0.074910 Loss2: 1.409447
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512026 Loss1: 0.144162 Loss2: 1.367863
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.481271 Loss1: 0.074509 Loss2: 1.406762
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467536 Loss1: 0.123328 Loss2: 1.344209
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472576 Loss1: 0.070887 Loss2: 1.401689
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476980 Loss1: 0.128760 Loss2: 1.348220
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466403 Loss1: 0.124964 Loss2: 1.341439
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462181 Loss1: 0.125438 Loss2: 1.336742
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410127 Loss1: 0.074533 Loss2: 1.335594
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.543573 Loss1: 0.761912 Loss2: 1.781661
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.907069 Loss1: 0.538353 Loss2: 1.368716
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.755008 Loss1: 0.368205 Loss2: 1.386803
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.738078 Loss1: 0.907994 Loss2: 1.830084
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610612 Loss1: 0.259214 Loss2: 1.351398
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.884586 Loss1: 0.514257 Loss2: 1.370329
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.545041 Loss1: 0.177800 Loss2: 1.367240
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.736053 Loss1: 0.324996 Loss2: 1.411057
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520805 Loss1: 0.189030 Loss2: 1.331776
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.560341 Loss1: 0.193943 Loss2: 1.366399
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.519398 Loss1: 0.180463 Loss2: 1.338935
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471601 Loss1: 0.137670 Loss2: 1.333931 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444352 Loss1: 0.114616 Loss2: 1.329736 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403121 Loss1: 0.077370 Loss2: 1.325751 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420388 Loss1: 0.077007 Loss2: 1.343381 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.847872 Loss1: 0.917755 Loss2: 1.930117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.908887 Loss1: 0.421023 Loss2: 1.487865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.680876 Loss1: 0.230761 Loss2: 1.450115
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.735318 Loss1: 0.881997 Loss2: 1.853321
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.818549 Loss1: 0.434302 Loss2: 1.384248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.688447 Loss1: 0.297729 Loss2: 1.390719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.601389 Loss1: 0.229941 Loss2: 1.371449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475028 Loss1: 0.123792 Loss2: 1.351236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459102 Loss1: 0.112530 Loss2: 1.346572 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472361 Loss1: 0.065696 Loss2: 1.406665
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435436 Loss1: 0.096916 Loss2: 1.338521
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425391 Loss1: 0.086461 Loss2: 1.338930
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381585 Loss1: 0.046396 Loss2: 1.335189
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409094 Loss1: 0.080616 Loss2: 1.328479
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.599345 Loss1: 0.694930 Loss2: 1.904415
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.944405 Loss1: 0.522916 Loss2: 1.421489
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.819202 Loss1: 0.348188 Loss2: 1.471014
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.700702 Loss1: 0.287808 Loss2: 1.412894
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.830695 Loss1: 0.984474 Loss2: 1.846221
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.956462 Loss1: 0.549362 Loss2: 1.407099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.795765 Loss1: 0.375653 Loss2: 1.420112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.685288 Loss1: 0.287632 Loss2: 1.397655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.634253 Loss1: 0.245119 Loss2: 1.389134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.591468 Loss1: 0.211743 Loss2: 1.379725 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447015 Loss1: 0.069385 Loss2: 1.377630
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.553008 Loss1: 0.166214 Loss2: 1.386794
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540915 Loss1: 0.170217 Loss2: 1.370698
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481514 Loss1: 0.110057 Loss2: 1.371457
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434250 Loss1: 0.071913 Loss2: 1.362337
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.847983 Loss1: 0.900654 Loss2: 1.947329
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.916648 Loss1: 0.482343 Loss2: 1.434305
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.796791 Loss1: 0.321609 Loss2: 1.475182
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.637665 Loss1: 0.212408 Loss2: 1.425257
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.652169 Loss1: 0.748719 Loss2: 1.903449
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.969694 Loss1: 0.504820 Loss2: 1.464874 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.793198 Loss1: 0.309057 Loss2: 1.484141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.687476 Loss1: 0.244316 Loss2: 1.443160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.651063 Loss1: 0.197537 Loss2: 1.453526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.598591 Loss1: 0.168824 Loss2: 1.429767 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.522203 Loss1: 0.089592 Loss2: 1.432611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497779 Loss1: 0.078892 Loss2: 1.418887 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.935818 Loss1: 0.962087 Loss2: 1.973731 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.828051 Loss1: 0.349580 Loss2: 1.478471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617466 Loss1: 0.205757 Loss2: 1.411709 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.603476 Loss1: 0.189538 Loss2: 1.413938 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.805388 Loss1: 0.362687 Loss2: 1.442701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.647031 Loss1: 0.270698 Loss2: 1.376333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492857 Loss1: 0.121418 Loss2: 1.371439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471631 Loss1: 0.111227 Loss2: 1.360405 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978795
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425249 Loss1: 0.079493 Loss2: 1.345756 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381821 Loss1: 0.047259 Loss2: 1.334562 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.840888 Loss1: 0.896143 Loss2: 1.944744
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.994498 Loss1: 0.512313 Loss2: 1.482185
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.802025 Loss1: 0.297217 Loss2: 1.504808
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.729081 Loss1: 0.264382 Loss2: 1.464699
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.654214 Loss1: 0.771394 Loss2: 1.882820
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.684531 Loss1: 0.222128 Loss2: 1.462404
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.057388 Loss1: 0.639235 Loss2: 1.418153
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.614123 Loss1: 0.157402 Loss2: 1.456721
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.873847 Loss1: 0.399209 Loss2: 1.474637
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.676179 Loss1: 0.271461 Loss2: 1.404718
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.596306 Loss1: 0.151338 Loss2: 1.444968
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.613064 Loss1: 0.199295 Loss2: 1.413770
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.571084 Loss1: 0.120887 Loss2: 1.450197
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.533218 Loss1: 0.138150 Loss2: 1.395068
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.549257 Loss1: 0.115625 Loss2: 1.433633
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.514036 Loss1: 0.127052 Loss2: 1.386985
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.522057 Loss1: 0.093975 Loss2: 1.428082
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501145 Loss1: 0.110042 Loss2: 1.391103 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.883025 Loss1: 0.977962 Loss2: 1.905063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.782403 Loss1: 0.330890 Loss2: 1.451512 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.659057 Loss1: 0.249242 Loss2: 1.409815
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.874737 Loss1: 0.948431 Loss2: 1.926306
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.607051 Loss1: 0.193241 Loss2: 1.413810
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.076544 Loss1: 0.583304 Loss2: 1.493240
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526094 Loss1: 0.128399 Loss2: 1.397694
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834661 Loss1: 0.374238 Loss2: 1.460422
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529000 Loss1: 0.141759 Loss2: 1.387241
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.659197 Loss1: 0.231286 Loss2: 1.427911
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.527953 Loss1: 0.137108 Loss2: 1.390845
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.587084 Loss1: 0.163972 Loss2: 1.423112
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.533909 Loss1: 0.145857 Loss2: 1.388052
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542886 Loss1: 0.133783 Loss2: 1.409103
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.547208 Loss1: 0.148812 Loss2: 1.398396
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550628 Loss1: 0.150872 Loss2: 1.399755
-(DefaultActor pid=3765) >> Training accuracy: 0.961458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.559302 Loss1: 0.149026 Loss2: 1.410276
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.509979 Loss1: 0.104194 Loss2: 1.405785
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.513396 Loss1: 0.109249 Loss2: 1.404147
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.878585 Loss1: 0.916491 Loss2: 1.962094
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.974114 Loss1: 0.546502 Loss2: 1.427612
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.798470 Loss1: 0.308722 Loss2: 1.489748
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.730882 Loss1: 0.305848 Loss2: 1.425034
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.775695 Loss1: 0.878206 Loss2: 1.897489
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.023199 Loss1: 0.592824 Loss2: 1.430375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.831250 Loss1: 0.386105 Loss2: 1.445145 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.732342 Loss1: 0.301456 Loss2: 1.430886 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.664361 Loss1: 0.241732 Loss2: 1.422629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.551631 Loss1: 0.146167 Loss2: 1.405463 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972098
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486421 Loss1: 0.094595 Loss2: 1.391826 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466278 Loss1: 0.082195 Loss2: 1.384083 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.870020 Loss1: 0.465826 Loss2: 1.404193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.612962 Loss1: 0.226248 Loss2: 1.386714 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.533802 Loss1: 0.149121 Loss2: 1.384681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515662 Loss1: 0.133256 Loss2: 1.382406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.471219 Loss1: 0.100586 Loss2: 1.370633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444333 Loss1: 0.073953 Loss2: 1.370380 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445685 Loss1: 0.080332 Loss2: 1.365353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451924 Loss1: 0.092477 Loss2: 1.359448 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469795 Loss1: 0.121037 Loss2: 1.348757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466408 Loss1: 0.122318 Loss2: 1.344090 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.965625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.967103 Loss1: 0.551607 Loss2: 1.415496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.658977 Loss1: 0.255882 Loss2: 1.403095 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.690308 Loss1: 0.861568 Loss2: 1.828740
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.608791 Loss1: 0.202511 Loss2: 1.406281
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.914056 Loss1: 0.526375 Loss2: 1.387681
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.557239 Loss1: 0.161795 Loss2: 1.395444
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.760475 Loss1: 0.332250 Loss2: 1.428226
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.557066 Loss1: 0.165265 Loss2: 1.391801
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.580281 Loss1: 0.216417 Loss2: 1.363864
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.537604 Loss1: 0.136850 Loss2: 1.400755
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485877 Loss1: 0.127654 Loss2: 1.358223
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.524174 Loss1: 0.131359 Loss2: 1.392814
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499233 Loss1: 0.143674 Loss2: 1.355559
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491347 Loss1: 0.104825 Loss2: 1.386522
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433894 Loss1: 0.092457 Loss2: 1.341437 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431741 Loss1: 0.090806 Loss2: 1.340935 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.905413 Loss1: 0.513984 Loss2: 1.391429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572550 Loss1: 0.202257 Loss2: 1.370293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555124 Loss1: 0.187066 Loss2: 1.368057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.528431 Loss1: 0.160583 Loss2: 1.367848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514442 Loss1: 0.153216 Loss2: 1.361227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.465147 Loss1: 0.098204 Loss2: 1.366943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462324 Loss1: 0.109001 Loss2: 1.353323 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451833 Loss1: 0.096763 Loss2: 1.355070 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455435 Loss1: 0.110152 Loss2: 1.345282 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452827 Loss1: 0.108403 Loss2: 1.344424 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.605406 Loss1: 0.799978 Loss2: 1.805427
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.904189 Loss1: 0.509789 Loss2: 1.394400
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.748690 Loss1: 0.334976 Loss2: 1.413714
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606314 Loss1: 0.231199 Loss2: 1.375115
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.780903 Loss1: 0.884776 Loss2: 1.896127
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.980223 Loss1: 0.539922 Loss2: 1.440301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.824892 Loss1: 0.352619 Loss2: 1.472273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.741489 Loss1: 0.320216 Loss2: 1.421273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.611138 Loss1: 0.178001 Loss2: 1.433136 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474965 Loss1: 0.120247 Loss2: 1.354717
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.600493 Loss1: 0.184676 Loss2: 1.415817
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454348 Loss1: 0.103860 Loss2: 1.350488
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.552776 Loss1: 0.134003 Loss2: 1.418773
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.517181 Loss1: 0.105422 Loss2: 1.411759
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.491930 Loss1: 0.088032 Loss2: 1.403899
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491200 Loss1: 0.097396 Loss2: 1.393804
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.643213 Loss1: 0.799931 Loss2: 1.843283
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.791032 Loss1: 0.443867 Loss2: 1.347164
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.661655 Loss1: 0.278164 Loss2: 1.383491
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580335 Loss1: 0.253217 Loss2: 1.327119
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.667656 Loss1: 0.762618 Loss2: 1.905038
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.939266 Loss1: 0.534753 Loss2: 1.404513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.828160 Loss1: 0.367486 Loss2: 1.460675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.703840 Loss1: 0.290574 Loss2: 1.413267 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.681675 Loss1: 0.266224 Loss2: 1.415450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.600738 Loss1: 0.183848 Loss2: 1.416890 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439902 Loss1: 0.121135 Loss2: 1.318767
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545835 Loss1: 0.152902 Loss2: 1.392933
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454751 Loss1: 0.066491 Loss2: 1.388260
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452504 Loss1: 0.075210 Loss2: 1.377294
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444398 Loss1: 0.071989 Loss2: 1.372409
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.811582 Loss1: 0.897390 Loss2: 1.914192
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.967399 Loss1: 0.530878 Loss2: 1.436521
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.833929 Loss1: 0.380206 Loss2: 1.453723
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.660836 Loss1: 0.233773 Loss2: 1.427063
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.679157 Loss1: 0.777365 Loss2: 1.901792
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.870770 Loss1: 0.485373 Loss2: 1.385397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682349 Loss1: 0.271896 Loss2: 1.410452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584758 Loss1: 0.217481 Loss2: 1.367277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571949 Loss1: 0.194374 Loss2: 1.377575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559903 Loss1: 0.188327 Loss2: 1.371576 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446278 Loss1: 0.063106 Loss2: 1.383172
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494627 Loss1: 0.132526 Loss2: 1.362102
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484448 Loss1: 0.117308 Loss2: 1.367141
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440704 Loss1: 0.076949 Loss2: 1.363755
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417152 Loss1: 0.066773 Loss2: 1.350379
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.644525 Loss1: 0.802925 Loss2: 1.841600
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.786785 Loss1: 0.387830 Loss2: 1.398954
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.730050 Loss1: 0.311881 Loss2: 1.418169
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.627626 Loss1: 0.233027 Loss2: 1.394598
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.479798 Loss1: 0.703524 Loss2: 1.776274
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605260 Loss1: 0.204785 Loss2: 1.400475
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.818875 Loss1: 0.473046 Loss2: 1.345829
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.611827 Loss1: 0.216089 Loss2: 1.395738
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.766979 Loss1: 0.383205 Loss2: 1.383774
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529610 Loss1: 0.138293 Loss2: 1.391317
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.624247 Loss1: 0.278106 Loss2: 1.346141
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474834 Loss1: 0.096065 Loss2: 1.378769
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556634 Loss1: 0.206833 Loss2: 1.349801
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.467070 Loss1: 0.093565 Loss2: 1.373504
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516196 Loss1: 0.173255 Loss2: 1.342941
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438006 Loss1: 0.070403 Loss2: 1.367603
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447203 Loss1: 0.115442 Loss2: 1.331761
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448437 Loss1: 0.120552 Loss2: 1.327885
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428748 Loss1: 0.104637 Loss2: 1.324111
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392451 Loss1: 0.071073 Loss2: 1.321378
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.717342 Loss1: 0.810972 Loss2: 1.906370
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.963316 Loss1: 0.529482 Loss2: 1.433834
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.794217 Loss1: 0.338364 Loss2: 1.455853
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.621626 Loss1: 0.203732 Loss2: 1.417893
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.609515 Loss1: 0.750150 Loss2: 1.859365
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.955545 Loss1: 0.496824 Loss2: 1.458721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.689100 Loss1: 0.259012 Loss2: 1.430087 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.585329 Loss1: 0.182593 Loss2: 1.402737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.538540 Loss1: 0.139197 Loss2: 1.399342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502442 Loss1: 0.117392 Loss2: 1.385050 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.505509 Loss1: 0.108557 Loss2: 1.396952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436893 Loss1: 0.061398 Loss2: 1.375495 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.615887 Loss1: 0.792323 Loss2: 1.823564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.755614 Loss1: 0.355194 Loss2: 1.400420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518858 Loss1: 0.173611 Loss2: 1.345247 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498987 Loss1: 0.166364 Loss2: 1.332623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525754 Loss1: 0.192005 Loss2: 1.333749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483036 Loss1: 0.150557 Loss2: 1.332480 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.563804 Loss1: 0.219889 Loss2: 1.343915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465170 Loss1: 0.119454 Loss2: 1.345716 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.542867 Loss1: 0.158573 Loss2: 1.384294 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461735 Loss1: 0.100318 Loss2: 1.361417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448217 Loss1: 0.083382 Loss2: 1.364835
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.924079 Loss1: 0.987389 Loss2: 1.936690
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.154603 Loss1: 0.642368 Loss2: 1.512235
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.795288 Loss1: 0.354342 Loss2: 1.440946
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.713449 Loss1: 0.280514 Loss2: 1.432935
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617402 Loss1: 0.176794 Loss2: 1.440609
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.483880 Loss1: 0.723652 Loss2: 1.760228
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.608352 Loss1: 0.192671 Loss2: 1.415680
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.546411 Loss1: 0.125601 Loss2: 1.420810 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483907 Loss1: 0.072098 Loss2: 1.411810 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482106 Loss1: 0.086389 Loss2: 1.395717 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485717 Loss1: 0.087650 Loss2: 1.398067 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.466957 Loss1: 0.172379 Loss2: 1.294578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413931 Loss1: 0.115017 Loss2: 1.298914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385681 Loss1: 0.092339 Loss2: 1.293342
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.700309 Loss1: 0.861175 Loss2: 1.839134
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.984330 Loss1: 0.590432 Loss2: 1.393898
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.765261 Loss1: 0.357774 Loss2: 1.407486
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.661561 Loss1: 0.281519 Loss2: 1.380042
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572505 Loss1: 0.195460 Loss2: 1.377045
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.567173 Loss1: 0.744717 Loss2: 1.822456
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488133 Loss1: 0.126265 Loss2: 1.361869
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480791 Loss1: 0.126249 Loss2: 1.354542
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.884195 Loss1: 0.494524 Loss2: 1.389671
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437732 Loss1: 0.082776 Loss2: 1.354956
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726418 Loss1: 0.339367 Loss2: 1.387050
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436717 Loss1: 0.091209 Loss2: 1.345508
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605914 Loss1: 0.243472 Loss2: 1.362442
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395844 Loss1: 0.054447 Loss2: 1.341398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.510990 Loss1: 0.155465 Loss2: 1.355525
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530923 Loss1: 0.177977 Loss2: 1.352945
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.562319 Loss1: 0.197889 Loss2: 1.364430
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.506125 Loss1: 0.154384 Loss2: 1.351741
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449976 Loss1: 0.100479 Loss2: 1.349496
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.761086 Loss1: 0.899642 Loss2: 1.861443
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413558 Loss1: 0.078137 Loss2: 1.335421
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.956831 Loss1: 0.522316 Loss2: 1.434515
-(DefaultActor pid=3764) >> Training accuracy: 0.993566
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.744496 Loss1: 0.332627 Loss2: 1.411869
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.568123 Loss1: 0.184830 Loss2: 1.383293
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577124 Loss1: 0.182099 Loss2: 1.395026
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518793 Loss1: 0.135856 Loss2: 1.382937
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.710548 Loss1: 0.871527 Loss2: 1.839020
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.494327 Loss1: 0.123201 Loss2: 1.371127
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464911 Loss1: 0.088346 Loss2: 1.376565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.475707 Loss1: 0.112559 Loss2: 1.363148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450981 Loss1: 0.080517 Loss2: 1.370464 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465245 Loss1: 0.116688 Loss2: 1.348557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440636 Loss1: 0.106649 Loss2: 1.333987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432650 Loss1: 0.100135 Loss2: 1.332516
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.709567 Loss1: 0.814172 Loss2: 1.895396
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.867075 Loss1: 0.478369 Loss2: 1.388705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.705369 Loss1: 0.313355 Loss2: 1.392014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.573222 Loss1: 0.181914 Loss2: 1.391308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.537786 Loss1: 0.157079 Loss2: 1.380706 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.540152 Loss1: 0.153562 Loss2: 1.386590 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484441 Loss1: 0.106771 Loss2: 1.377670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436691 Loss1: 0.070978 Loss2: 1.365713 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468436 Loss1: 0.111783 Loss2: 1.356654 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.463649 Loss1: 0.106881 Loss2: 1.356768 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.891839 Loss1: 0.994446 Loss2: 1.897394 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.812439 Loss1: 0.372494 Loss2: 1.439944 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544389 Loss1: 0.171356 Loss2: 1.373033 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498162 Loss1: 0.126208 Loss2: 1.371954
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.087434 Loss1: 0.991554 Loss2: 2.095880
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.087932 Loss1: 0.619892 Loss2: 1.468040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.918522 Loss1: 0.403123 Loss2: 1.515399
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467339 Loss1: 0.111224 Loss2: 1.356114
-DEBUG flwr 2023-10-11 10:19:55,773 | server.py:236 | fit_round 110 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436874 Loss1: 0.090654 Loss2: 1.346220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407179 Loss1: 0.066447 Loss2: 1.340732 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.561563 Loss1: 0.112676 Loss2: 1.448886 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.534631 Loss1: 0.095015 Loss2: 1.439617 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983073
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.069952 Loss1: 0.649366 Loss2: 1.420586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.704538 Loss1: 0.286874 Loss2: 1.417664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.655692 Loss1: 0.840341 Loss2: 1.815351
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.588129 Loss1: 0.164180 Loss2: 1.423949
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.543939 Loss1: 0.134840 Loss2: 1.409099
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.847866 Loss1: 0.511482 Loss2: 1.336384
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.696937 Loss1: 0.328497 Loss2: 1.368440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.583027 Loss1: 0.250045 Loss2: 1.332983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542583 Loss1: 0.208317 Loss2: 1.334266 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983173
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452132 Loss1: 0.129517 Loss2: 1.322615 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392718 Loss1: 0.075840 Loss2: 1.316878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403994 Loss1: 0.098282 Loss2: 1.305712
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.645197 Loss1: 0.779096 Loss2: 1.866101
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.837073 Loss1: 0.462014 Loss2: 1.375058
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.682445 Loss1: 0.277364 Loss2: 1.405081
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596624 Loss1: 0.224750 Loss2: 1.371874
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.627929 Loss1: 0.236321 Loss2: 1.391607
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.005488 Loss1: 0.920352 Loss2: 2.085136
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.535318 Loss1: 0.164608 Loss2: 1.370710
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495985 Loss1: 0.138222 Loss2: 1.357763 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464835 Loss1: 0.110816 Loss2: 1.354019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448910 Loss1: 0.099188 Loss2: 1.349722 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406897 Loss1: 0.066151 Loss2: 1.340745 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.654869 Loss1: 0.138441 Loss2: 1.516428 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.646633 Loss1: 0.142185 Loss2: 1.504448 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 10:19:55,773][flwr][DEBUG] - fit_round 110 received 50 results and 0 failures
-INFO flwr 2023-10-11 10:20:36,510 | server.py:125 | fit progress: (110, 2.194226622581482, {'accuracy': 0.5761}, 253744.288186102)
->> Test accuracy: 0.576100
-[2023-10-11 10:20:36,510][flwr][INFO] - fit progress: (110, 2.194226622581482, {'accuracy': 0.5761}, 253744.288186102)
-DEBUG flwr 2023-10-11 10:20:36,510 | server.py:173 | evaluate_round 110: strategy sampled 50 clients (out of 50)
-[2023-10-11 10:20:36,510][flwr][DEBUG] - evaluate_round 110: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 10:29:39,966 | server.py:187 | evaluate_round 110 received 50 results and 0 failures
-[2023-10-11 10:29:39,966][flwr][DEBUG] - evaluate_round 110 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 10:29:39,967 | server.py:222 | fit_round 111: strategy sampled 50 clients (out of 50)
-[2023-10-11 10:29:39,967][flwr][DEBUG] - fit_round 111: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.644603 Loss1: 0.806579 Loss2: 1.838023
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.960231 Loss1: 0.565252 Loss2: 1.394979
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.799988 Loss1: 0.393496 Loss2: 1.406493
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.648741 Loss1: 0.275376 Loss2: 1.373365
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.741061 Loss1: 0.851490 Loss2: 1.889571
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.080960 Loss1: 0.595806 Loss2: 1.485153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.833065 Loss1: 0.392137 Loss2: 1.440928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.748289 Loss1: 0.288838 Loss2: 1.459451 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.645848 Loss1: 0.227483 Loss2: 1.418365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.598296 Loss1: 0.182253 Loss2: 1.416043 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.562135 Loss1: 0.153593 Loss2: 1.408542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.506914 Loss1: 0.111733 Loss2: 1.395181 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.476160 Loss1: 0.082604 Loss2: 1.393556
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.868336 Loss1: 0.904864 Loss2: 1.963472
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.951499 Loss1: 0.508881 Loss2: 1.442617
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.733518 Loss1: 0.270133 Loss2: 1.463385
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.639582 Loss1: 0.211807 Loss2: 1.427775
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.601094 Loss1: 0.178563 Loss2: 1.422531
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.822445 Loss1: 0.901233 Loss2: 1.921212
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.961749 Loss1: 0.639663 Loss2: 1.322085
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.534762 Loss1: 0.121831 Loss2: 1.412931
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.520609 Loss1: 0.119756 Loss2: 1.400853 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456558 Loss1: 0.064072 Loss2: 1.392485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463862 Loss1: 0.067934 Loss2: 1.395929 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466834 Loss1: 0.072654 Loss2: 1.394180 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396042 Loss1: 0.084852 Loss2: 1.311190 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986979
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.631184 Loss1: 0.763788 Loss2: 1.867396 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.750600 Loss1: 0.306300 Loss2: 1.444300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.687297 Loss1: 0.280438 Loss2: 1.406859
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.554095 Loss1: 0.710889 Loss2: 1.843205
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.771788 Loss1: 0.419401 Loss2: 1.352387
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589389 Loss1: 0.186566 Loss2: 1.402824
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.707497 Loss1: 0.312608 Loss2: 1.394889
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.529135 Loss1: 0.127360 Loss2: 1.401775
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563124 Loss1: 0.212895 Loss2: 1.350229
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.492070 Loss1: 0.104580 Loss2: 1.387490
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.531461 Loss1: 0.180872 Loss2: 1.350590
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486042 Loss1: 0.104936 Loss2: 1.381106
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.491317 Loss1: 0.112253 Loss2: 1.379064 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.507601 Loss1: 0.127087 Loss2: 1.380514 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972656
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414149 Loss1: 0.079723 Loss2: 1.334425 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.647404 Loss1: 0.779391 Loss2: 1.868012 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.686781 Loss1: 0.267604 Loss2: 1.419176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582019 Loss1: 0.219726 Loss2: 1.362293
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.660527 Loss1: 0.849819 Loss2: 1.810708
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.894857 Loss1: 0.522862 Loss2: 1.371995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.700482 Loss1: 0.303000 Loss2: 1.397482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.634678 Loss1: 0.288206 Loss2: 1.346472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.668669 Loss1: 0.290794 Loss2: 1.377875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.566628 Loss1: 0.216537 Loss2: 1.350091 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408668 Loss1: 0.075295 Loss2: 1.333373
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.514813 Loss1: 0.166768 Loss2: 1.348045
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467649 Loss1: 0.126724 Loss2: 1.340925
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424343 Loss1: 0.089334 Loss2: 1.335008
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393307 Loss1: 0.074722 Loss2: 1.318585
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.654197 Loss1: 0.772333 Loss2: 1.881864
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.871819 Loss1: 0.485601 Loss2: 1.386218
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.695830 Loss1: 0.275216 Loss2: 1.420614
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.593608 Loss1: 0.220828 Loss2: 1.372780
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.626058 Loss1: 0.767632 Loss2: 1.858426
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.989331 Loss1: 0.557063 Loss2: 1.432268 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.776515 Loss1: 0.310538 Loss2: 1.465977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.696381 Loss1: 0.270253 Loss2: 1.426127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.653232 Loss1: 0.226161 Loss2: 1.427071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.578476 Loss1: 0.165415 Loss2: 1.413061 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.532512 Loss1: 0.133644 Loss2: 1.398867 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.481609 Loss1: 0.084318 Loss2: 1.397291 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.731162 Loss1: 0.429202 Loss2: 1.301961 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599341 Loss1: 0.307479 Loss2: 1.291862 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510135 Loss1: 0.215136 Loss2: 1.294998
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.514337 Loss1: 0.730622 Loss2: 1.783715
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.464469 Loss1: 0.174679 Loss2: 1.289790
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.705313 Loss1: 0.359838 Loss2: 1.345476
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383955 Loss1: 0.109127 Loss2: 1.274827
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.582009 Loss1: 0.227328 Loss2: 1.354681
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.365071 Loss1: 0.095546 Loss2: 1.269524
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586396 Loss1: 0.244674 Loss2: 1.341722
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.551111 Loss1: 0.205989 Loss2: 1.345121 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.322555 Loss1: 0.060000 Loss2: 1.262555
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500916 Loss1: 0.158571 Loss2: 1.342344
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473152 Loss1: 0.146889 Loss2: 1.326263
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465048 Loss1: 0.136071 Loss2: 1.328977
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429485 Loss1: 0.101499 Loss2: 1.327986
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428760 Loss1: 0.104769 Loss2: 1.323992
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.665651 Loss1: 0.825819 Loss2: 1.839832
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.927730 Loss1: 0.548673 Loss2: 1.379057
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.859768 Loss1: 0.404354 Loss2: 1.455415
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.651426 Loss1: 0.277095 Loss2: 1.374331
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.559478 Loss1: 0.184007 Loss2: 1.375470
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.807056 Loss1: 0.951548 Loss2: 1.855508
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524497 Loss1: 0.160790 Loss2: 1.363707
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464681 Loss1: 0.103528 Loss2: 1.361153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.468273 Loss1: 0.115507 Loss2: 1.352765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424995 Loss1: 0.077409 Loss2: 1.347586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445120 Loss1: 0.092101 Loss2: 1.353020 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427984 Loss1: 0.103833 Loss2: 1.324151 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375022 Loss1: 0.059569 Loss2: 1.315453 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.825429 Loss1: 0.469710 Loss2: 1.355719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578540 Loss1: 0.228113 Loss2: 1.350427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.483926 Loss1: 0.141120 Loss2: 1.342806
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.740515 Loss1: 0.855137 Loss2: 1.885378
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.044340 Loss1: 0.619346 Loss2: 1.424994 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.765214 Loss1: 0.328380 Loss2: 1.436834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.615917 Loss1: 0.213921 Loss2: 1.401996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.560916 Loss1: 0.156683 Loss2: 1.404232 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393451 Loss1: 0.079590 Loss2: 1.313861
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.548425 Loss1: 0.160958 Loss2: 1.387467
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484495 Loss1: 0.099803 Loss2: 1.384692
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477093 Loss1: 0.100903 Loss2: 1.376190
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.471399 Loss1: 0.093091 Loss2: 1.378308
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446634 Loss1: 0.072732 Loss2: 1.373902
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.607923 Loss1: 0.769621 Loss2: 1.838302
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.835692 Loss1: 0.427272 Loss2: 1.408420
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655466 Loss1: 0.263115 Loss2: 1.392350
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551771 Loss1: 0.182610 Loss2: 1.369161
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518844 Loss1: 0.155249 Loss2: 1.363595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458903 Loss1: 0.101209 Loss2: 1.357694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456647 Loss1: 0.100578 Loss2: 1.356068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447994 Loss1: 0.099875 Loss2: 1.348119 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.554889 Loss1: 0.172717 Loss2: 1.382172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.505335 Loss1: 0.130800 Loss2: 1.374535 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996324
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.459903 Loss1: 0.099829 Loss2: 1.360074 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.818585 Loss1: 0.975649 Loss2: 1.842936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.721143 Loss1: 0.321856 Loss2: 1.399287 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578926 Loss1: 0.236065 Loss2: 1.342861
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.737043 Loss1: 0.869127 Loss2: 1.867915
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.560193 Loss1: 0.220779 Loss2: 1.339414
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.946350 Loss1: 0.526872 Loss2: 1.419478
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458614 Loss1: 0.125144 Loss2: 1.333470
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.736884 Loss1: 0.294201 Loss2: 1.442683
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.429901 Loss1: 0.110716 Loss2: 1.319185
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.648079 Loss1: 0.254305 Loss2: 1.393774
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.396134 Loss1: 0.083124 Loss2: 1.313010
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.592319 Loss1: 0.179976 Loss2: 1.412343
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.367583 Loss1: 0.059093 Loss2: 1.308490
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.517695 Loss1: 0.127572 Loss2: 1.390123
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350882 Loss1: 0.050200 Loss2: 1.300683
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486772 Loss1: 0.107210 Loss2: 1.379562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452213 Loss1: 0.076006 Loss2: 1.376207
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430031 Loss1: 0.063726 Loss2: 1.366305
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421324 Loss1: 0.063242 Loss2: 1.358083
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.609619 Loss1: 0.789765 Loss2: 1.819854
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.788931 Loss1: 0.437252 Loss2: 1.351680
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.668595 Loss1: 0.298447 Loss2: 1.370148
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.609943 Loss1: 0.264727 Loss2: 1.345216
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.524014 Loss1: 0.728823 Loss2: 1.795191
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.808743 Loss1: 0.473353 Loss2: 1.335390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.700693 Loss1: 0.313962 Loss2: 1.386731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.580923 Loss1: 0.237339 Loss2: 1.343583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512905 Loss1: 0.177927 Loss2: 1.334977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449681 Loss1: 0.123670 Loss2: 1.326010 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428207 Loss1: 0.103018 Loss2: 1.325189
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436337 Loss1: 0.110611 Loss2: 1.325726
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414959 Loss1: 0.093317 Loss2: 1.321642
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439605 Loss1: 0.128021 Loss2: 1.311583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418636 Loss1: 0.098417 Loss2: 1.320219
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.600364 Loss1: 0.778432 Loss2: 1.821932
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.829308 Loss1: 0.419963 Loss2: 1.409344
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.734795 Loss1: 0.325720 Loss2: 1.409075
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.841770 Loss1: 0.919251 Loss2: 1.922519
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.645397 Loss1: 0.244919 Loss2: 1.400479
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631444 Loss1: 0.236874 Loss2: 1.394571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.565177 Loss1: 0.176596 Loss2: 1.388581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.510764 Loss1: 0.129406 Loss2: 1.381359 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484647 Loss1: 0.115994 Loss2: 1.368653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443777 Loss1: 0.079527 Loss2: 1.364250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481299 Loss1: 0.108045 Loss2: 1.373255 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445888 Loss1: 0.082704 Loss2: 1.363184 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983259
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.837718 Loss1: 0.937018 Loss2: 1.900700
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.893724 Loss1: 0.464453 Loss2: 1.429270
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.748384 Loss1: 0.306067 Loss2: 1.442317
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.652920 Loss1: 0.259389 Loss2: 1.393531
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.670463 Loss1: 0.789723 Loss2: 1.880740
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.618483 Loss1: 0.205792 Loss2: 1.412690
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.978857 Loss1: 0.542106 Loss2: 1.436751
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.601702 Loss1: 0.209161 Loss2: 1.392541
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.869711 Loss1: 0.403848 Loss2: 1.465863
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.563505 Loss1: 0.163896 Loss2: 1.399609
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.822438 Loss1: 0.381099 Loss2: 1.441339
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481285 Loss1: 0.094153 Loss2: 1.387132
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.730154 Loss1: 0.285791 Loss2: 1.444363
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.473105 Loss1: 0.096523 Loss2: 1.376582
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.701612 Loss1: 0.267328 Loss2: 1.434283
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469559 Loss1: 0.093128 Loss2: 1.376431
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.549674 Loss1: 0.136981 Loss2: 1.412693
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.536319 Loss1: 0.125614 Loss2: 1.410705
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.489469 Loss1: 0.093700 Loss2: 1.395768
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482428 Loss1: 0.093091 Loss2: 1.389337
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.910031 Loss1: 0.967479 Loss2: 1.942552
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.898700 Loss1: 0.547329 Loss2: 1.351372
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.686965 Loss1: 0.290724 Loss2: 1.396242
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549231 Loss1: 0.200785 Loss2: 1.348446
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527757 Loss1: 0.195292 Loss2: 1.332465
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487406 Loss1: 0.144581 Loss2: 1.342825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462462 Loss1: 0.134098 Loss2: 1.328364 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414861 Loss1: 0.094943 Loss2: 1.319917 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421026 Loss1: 0.102054 Loss2: 1.318972 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377834 Loss1: 0.063375 Loss2: 1.314459
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543901 Loss1: 0.188078 Loss2: 1.355823
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479292 Loss1: 0.138268 Loss2: 1.341024
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.477944 Loss1: 0.137641 Loss2: 1.340304
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441724 Loss1: 0.101268 Loss2: 1.340456
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410713 Loss1: 0.079691 Loss2: 1.331022
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.635401 Loss1: 0.821828 Loss2: 1.813573
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390934 Loss1: 0.064356 Loss2: 1.326578
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.775814 Loss1: 0.373705 Loss2: 1.402109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.539059 Loss1: 0.192419 Loss2: 1.346640 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478513 Loss1: 0.144366 Loss2: 1.334147
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.669092 Loss1: 0.844222 Loss2: 1.824870
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.879908 Loss1: 0.522020 Loss2: 1.357888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.706722 Loss1: 0.311831 Loss2: 1.394891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538621 Loss1: 0.191231 Loss2: 1.347390 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364872 Loss1: 0.051337 Loss2: 1.313535
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484245 Loss1: 0.140642 Loss2: 1.343603
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468223 Loss1: 0.136739 Loss2: 1.331484
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450509 Loss1: 0.110530 Loss2: 1.339980
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424905 Loss1: 0.099155 Loss2: 1.325750
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389912 Loss1: 0.069110 Loss2: 1.320802
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.920926 Loss1: 0.965022 Loss2: 1.955904
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377897 Loss1: 0.058024 Loss2: 1.319873
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.769313 Loss1: 0.275650 Loss2: 1.493663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.684918 Loss1: 0.237693 Loss2: 1.447225 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.615253 Loss1: 0.175787 Loss2: 1.439466
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.729476 Loss1: 0.856520 Loss2: 1.872956
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.979869 Loss1: 0.554595 Loss2: 1.425274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.863106 Loss1: 0.393279 Loss2: 1.469827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.751640 Loss1: 0.355224 Loss2: 1.396416 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609799 Loss1: 0.206671 Loss2: 1.403128 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502495 Loss1: 0.116382 Loss2: 1.386113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445405 Loss1: 0.081250 Loss2: 1.364155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452968 Loss1: 0.084633 Loss2: 1.368336 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741249 Loss1: 0.287554 Loss2: 1.453695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.560274 Loss1: 0.165676 Loss2: 1.394598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.521424 Loss1: 0.142537 Loss2: 1.378887
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.757647 Loss1: 0.906932 Loss2: 1.850715
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.913106 Loss1: 0.525522 Loss2: 1.387584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.720848 Loss1: 0.303948 Loss2: 1.416900 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592414 Loss1: 0.226040 Loss2: 1.366375 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.537996 Loss1: 0.171865 Loss2: 1.366131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.519994 Loss1: 0.163137 Loss2: 1.356857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.465699 Loss1: 0.111390 Loss2: 1.354309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434554 Loss1: 0.088930 Loss2: 1.345623 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690886 Loss1: 0.291596 Loss2: 1.399290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536808 Loss1: 0.170620 Loss2: 1.366188 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.482590 Loss1: 0.117725 Loss2: 1.364865
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.711488 Loss1: 0.860069 Loss2: 1.851419
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434957 Loss1: 0.080265 Loss2: 1.354692
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.912744 Loss1: 0.509918 Loss2: 1.402827
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423149 Loss1: 0.079067 Loss2: 1.344081
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.655885 Loss1: 0.251439 Loss2: 1.404446
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.642541 Loss1: 0.267257 Loss2: 1.375284
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402216 Loss1: 0.057402 Loss2: 1.344814
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512588 Loss1: 0.133606 Loss2: 1.378981
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407744 Loss1: 0.071268 Loss2: 1.336476
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476487 Loss1: 0.117035 Loss2: 1.359452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436700 Loss1: 0.085571 Loss2: 1.351130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409978 Loss1: 0.064704 Loss2: 1.345274
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.637557 Loss1: 0.866179 Loss2: 1.771378
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.938235 Loss1: 0.563676 Loss2: 1.374559
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.683202 Loss1: 0.318756 Loss2: 1.364446
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589989 Loss1: 0.243618 Loss2: 1.346371
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468872 Loss1: 0.141215 Loss2: 1.327657
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.869798 Loss1: 0.937375 Loss2: 1.932423
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.166756 Loss1: 0.699865 Loss2: 1.466891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.850087 Loss1: 0.340017 Loss2: 1.510070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.732870 Loss1: 0.284870 Loss2: 1.448000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407321 Loss1: 0.092496 Loss2: 1.314826
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.656472 Loss1: 0.198803 Loss2: 1.457669
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373104 Loss1: 0.068712 Loss2: 1.304391
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.579406 Loss1: 0.141648 Loss2: 1.437758
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.526482 Loss1: 0.099272 Loss2: 1.427210
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486604 Loss1: 0.064270 Loss2: 1.422333
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473939 Loss1: 0.063473 Loss2: 1.410465
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450516 Loss1: 0.042015 Loss2: 1.408501
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.557534 Loss1: 0.748130 Loss2: 1.809404
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.765876 Loss1: 0.393868 Loss2: 1.372008
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.586000 Loss1: 0.211032 Loss2: 1.374968
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534402 Loss1: 0.189062 Loss2: 1.345340
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.783851 Loss1: 0.895076 Loss2: 1.888774
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.972074 Loss1: 0.570516 Loss2: 1.401558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.744254 Loss1: 0.287225 Loss2: 1.457030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.660861 Loss1: 0.269253 Loss2: 1.391609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.610067 Loss1: 0.208184 Loss2: 1.401882 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527471 Loss1: 0.138714 Loss2: 1.388756 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485987 Loss1: 0.105830 Loss2: 1.380157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443589 Loss1: 0.073637 Loss2: 1.369952 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.722773 Loss1: 0.877310 Loss2: 1.845463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736844 Loss1: 0.316480 Loss2: 1.420364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.651894 Loss1: 0.814288 Loss2: 1.837606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.813204 Loss1: 0.468615 Loss2: 1.344589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.630073 Loss1: 0.261271 Loss2: 1.368802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551245 Loss1: 0.219964 Loss2: 1.331281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574105 Loss1: 0.238530 Loss2: 1.335575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522997 Loss1: 0.179476 Loss2: 1.343521 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.573647 Loss1: 0.230323 Loss2: 1.343324 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410063 Loss1: 0.083668 Loss2: 1.326394 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.980606 Loss1: 0.621984 Loss2: 1.358622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602266 Loss1: 0.241609 Loss2: 1.360657 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.765328 Loss1: 0.827528 Loss2: 1.937800 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.943752 Loss1: 0.514358 Loss2: 1.429393 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.805724 Loss1: 0.330936 Loss2: 1.474788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399237 Loss1: 0.067525 Loss2: 1.331712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397713 Loss1: 0.066815 Loss2: 1.330899 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.558566 Loss1: 0.151757 Loss2: 1.406809 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490775 Loss1: 0.101817 Loss2: 1.388958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.633019 Loss1: 0.839742 Loss2: 1.793276
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.483505 Loss1: 0.091310 Loss2: 1.392195
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.719310 Loss1: 0.338437 Loss2: 1.380874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574335 Loss1: 0.230200 Loss2: 1.344135 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.517391 Loss1: 0.182943 Loss2: 1.334448
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.776544 Loss1: 0.851688 Loss2: 1.924856
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464936 Loss1: 0.137750 Loss2: 1.327186
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.964286 Loss1: 0.529564 Loss2: 1.434721
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474486 Loss1: 0.153654 Loss2: 1.320832
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.745717 Loss1: 0.282220 Loss2: 1.463497
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448335 Loss1: 0.122116 Loss2: 1.326219
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.651808 Loss1: 0.238826 Loss2: 1.412982
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393470 Loss1: 0.079638 Loss2: 1.313832
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.630132 Loss1: 0.201846 Loss2: 1.428286
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.557421 Loss1: 0.153045 Loss2: 1.404376
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.527055 Loss1: 0.129510 Loss2: 1.397546
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510227 Loss1: 0.107014 Loss2: 1.403213
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486201 Loss1: 0.093528 Loss2: 1.392673
-DEBUG flwr 2023-10-11 10:58:31,459 | server.py:236 | fit_round 111 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.471704 Loss1: 0.083509 Loss2: 1.388195
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.813048 Loss1: 0.940248 Loss2: 1.872800
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.873320 Loss1: 0.444704 Loss2: 1.428615
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.687117 Loss1: 0.279429 Loss2: 1.407689
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604715 Loss1: 0.216085 Loss2: 1.388630
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589067 Loss1: 0.199274 Loss2: 1.389792
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.577980 Loss1: 0.788964 Loss2: 1.789016
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537534 Loss1: 0.153553 Loss2: 1.383981
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.859608 Loss1: 0.494739 Loss2: 1.364869
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511679 Loss1: 0.143756 Loss2: 1.367923
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.739537 Loss1: 0.344152 Loss2: 1.395385
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500848 Loss1: 0.123945 Loss2: 1.376902
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574692 Loss1: 0.234681 Loss2: 1.340011
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.497893 Loss1: 0.131623 Loss2: 1.366270
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502243 Loss1: 0.167015 Loss2: 1.335228
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.492800 Loss1: 0.122003 Loss2: 1.370797
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427203 Loss1: 0.099233 Loss2: 1.327969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386155 Loss1: 0.073088 Loss2: 1.313067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351449 Loss1: 0.044697 Loss2: 1.306753
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.796029 Loss1: 0.858770 Loss2: 1.937259
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.023874 Loss1: 0.573292 Loss2: 1.450582
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.870766 Loss1: 0.372512 Loss2: 1.498254
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.725325 Loss1: 0.281634 Loss2: 1.443690
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.668231 Loss1: 0.217662 Loss2: 1.450568
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.565019 Loss1: 0.123229 Loss2: 1.441790
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.778941 Loss1: 0.922713 Loss2: 1.856228
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534740 Loss1: 0.105175 Loss2: 1.429565
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.965857 Loss1: 0.545288 Loss2: 1.420569
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480338 Loss1: 0.062875 Loss2: 1.417462
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.760502 Loss1: 0.356406 Loss2: 1.404096
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.495471 Loss1: 0.087412 Loss2: 1.408059
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.698880 Loss1: 0.316345 Loss2: 1.382535
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.471421 Loss1: 0.061402 Loss2: 1.410019
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.592410 Loss1: 0.213606 Loss2: 1.378804
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539680 Loss1: 0.167534 Loss2: 1.372145
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446744 Loss1: 0.091970 Loss2: 1.354773
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418182 Loss1: 0.069364 Loss2: 1.348818
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426997 Loss1: 0.084626 Loss2: 1.342370
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411450 Loss1: 0.070965 Loss2: 1.340485
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 10:58:31,459][flwr][DEBUG] - fit_round 111 received 50 results and 0 failures
-INFO flwr 2023-10-11 10:59:13,095 | server.py:125 | fit progress: (111, 2.1984924363632934, {'accuracy': 0.5772}, 256060.873497265)
->> Test accuracy: 0.577200
-[2023-10-11 10:59:13,095][flwr][INFO] - fit progress: (111, 2.1984924363632934, {'accuracy': 0.5772}, 256060.873497265)
-DEBUG flwr 2023-10-11 10:59:13,095 | server.py:173 | evaluate_round 111: strategy sampled 50 clients (out of 50)
-[2023-10-11 10:59:13,095][flwr][DEBUG] - evaluate_round 111: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 11:08:18,545 | server.py:187 | evaluate_round 111 received 50 results and 0 failures
-[2023-10-11 11:08:18,545][flwr][DEBUG] - evaluate_round 111 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 11:08:18,546 | server.py:222 | fit_round 112: strategy sampled 50 clients (out of 50)
-[2023-10-11 11:08:18,546][flwr][DEBUG] - fit_round 112: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.625170 Loss1: 0.780257 Loss2: 1.844913
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.900276 Loss1: 0.494278 Loss2: 1.405998
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.718017 Loss1: 0.285741 Loss2: 1.432276
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.662390 Loss1: 0.811975 Loss2: 1.850415
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.903011 Loss1: 0.504598 Loss2: 1.398413 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.739361 Loss1: 0.314279 Loss2: 1.425081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.616010 Loss1: 0.220980 Loss2: 1.395030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.526764 Loss1: 0.138153 Loss2: 1.388611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469066 Loss1: 0.101719 Loss2: 1.367347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430137 Loss1: 0.072702 Loss2: 1.357435 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428847 Loss1: 0.072612 Loss2: 1.356235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423284 Loss1: 0.072507 Loss2: 1.350776 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.654681 Loss1: 0.816230 Loss2: 1.838451
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.836888 Loss1: 0.479546 Loss2: 1.357342
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.717899 Loss1: 0.295541 Loss2: 1.422358
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.598524 Loss1: 0.246992 Loss2: 1.351533
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.624416 Loss1: 0.778230 Loss2: 1.846187
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544357 Loss1: 0.184248 Loss2: 1.360110
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.901142 Loss1: 0.525699 Loss2: 1.375443
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.535349 Loss1: 0.169213 Loss2: 1.366135
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.731689 Loss1: 0.337271 Loss2: 1.394418
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502182 Loss1: 0.154562 Loss2: 1.347619
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.654053 Loss1: 0.274205 Loss2: 1.379849
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457748 Loss1: 0.112111 Loss2: 1.345637
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.603469 Loss1: 0.220747 Loss2: 1.382722
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450026 Loss1: 0.111968 Loss2: 1.338058
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.497137 Loss1: 0.130393 Loss2: 1.366744
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429039 Loss1: 0.093065 Loss2: 1.335974
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475367 Loss1: 0.113853 Loss2: 1.361513
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443573 Loss1: 0.088278 Loss2: 1.355295
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467249 Loss1: 0.115123 Loss2: 1.352126
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448451 Loss1: 0.101082 Loss2: 1.347369
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.696867 Loss1: 0.874744 Loss2: 1.822123
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.960682 Loss1: 0.583547 Loss2: 1.377135
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.810135 Loss1: 0.398533 Loss2: 1.411602
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.635056 Loss1: 0.255318 Loss2: 1.379738
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.678946 Loss1: 0.822950 Loss2: 1.855997
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525333 Loss1: 0.167982 Loss2: 1.357352
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.113456 Loss1: 0.694695 Loss2: 1.418761
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485607 Loss1: 0.133765 Loss2: 1.351842
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.900445 Loss1: 0.454075 Loss2: 1.446370
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461764 Loss1: 0.113364 Loss2: 1.348399
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.714369 Loss1: 0.328726 Loss2: 1.385642
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459978 Loss1: 0.123716 Loss2: 1.336262
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.629114 Loss1: 0.243282 Loss2: 1.385832
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412100 Loss1: 0.078776 Loss2: 1.333324
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.532264 Loss1: 0.158420 Loss2: 1.373844
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370509 Loss1: 0.042659 Loss2: 1.327850
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460696 Loss1: 0.103541 Loss2: 1.357156
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414766 Loss1: 0.070529 Loss2: 1.344237
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416419 Loss1: 0.069147 Loss2: 1.347271
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394842 Loss1: 0.054930 Loss2: 1.339912
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.709875 Loss1: 0.840425 Loss2: 1.869450
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.857771 Loss1: 0.464465 Loss2: 1.393306
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690203 Loss1: 0.271931 Loss2: 1.418271
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574747 Loss1: 0.195691 Loss2: 1.379057
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.657909 Loss1: 0.811598 Loss2: 1.846311
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.868545 Loss1: 0.484337 Loss2: 1.384208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.665352 Loss1: 0.251329 Loss2: 1.414023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554252 Loss1: 0.187712 Loss2: 1.366540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.551905 Loss1: 0.177179 Loss2: 1.374726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560263 Loss1: 0.179957 Loss2: 1.380307 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448427 Loss1: 0.088660 Loss2: 1.359767
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.522410 Loss1: 0.144699 Loss2: 1.377710
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.519524 Loss1: 0.151361 Loss2: 1.368163
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476439 Loss1: 0.111050 Loss2: 1.365389
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478119 Loss1: 0.113191 Loss2: 1.364928
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.872837 Loss1: 0.941579 Loss2: 1.931257
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.976020 Loss1: 0.559409 Loss2: 1.416611
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.881103 Loss1: 0.426185 Loss2: 1.454918
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.667459 Loss1: 0.261406 Loss2: 1.406053
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.805161 Loss1: 0.840056 Loss2: 1.965104
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.875829 Loss1: 0.430745 Loss2: 1.445084 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.766632 Loss1: 0.290932 Loss2: 1.475700 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.630666 Loss1: 0.200908 Loss2: 1.429759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.624021 Loss1: 0.186721 Loss2: 1.437300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.595063 Loss1: 0.162896 Loss2: 1.432168 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.536959 Loss1: 0.111523 Loss2: 1.425436 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.481959 Loss1: 0.070760 Loss2: 1.411200 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.873349 Loss1: 0.507609 Loss2: 1.365741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587544 Loss1: 0.246272 Loss2: 1.341272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525367 Loss1: 0.175791 Loss2: 1.349576 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451060 Loss1: 0.123780 Loss2: 1.327280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422083 Loss1: 0.098460 Loss2: 1.323624 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404975 Loss1: 0.080774 Loss2: 1.324201 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394134 Loss1: 0.076891 Loss2: 1.317243 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383509 Loss1: 0.073015 Loss2: 1.310495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.528376 Loss1: 0.111134 Loss2: 1.417242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.513103 Loss1: 0.119001 Loss2: 1.394102 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.868792 Loss1: 0.481188 Loss2: 1.387604 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.622068 Loss1: 0.258553 Loss2: 1.363516 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.570355 Loss1: 0.204056 Loss2: 1.366299
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.600480 Loss1: 0.739006 Loss2: 1.861474
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.571724 Loss1: 0.214728 Loss2: 1.356996
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.866312 Loss1: 0.445668 Loss2: 1.420644
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.699654 Loss1: 0.294808 Loss2: 1.404845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581236 Loss1: 0.194541 Loss2: 1.386695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.547803 Loss1: 0.164477 Loss2: 1.383326 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529597 Loss1: 0.159396 Loss2: 1.370201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482787 Loss1: 0.124149 Loss2: 1.358638 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.715372 Loss1: 0.862179 Loss2: 1.853193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987132
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731339 Loss1: 0.333058 Loss2: 1.398281 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.563528 Loss1: 0.192928 Loss2: 1.370600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463710 Loss1: 0.102582 Loss2: 1.361127
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.910330 Loss1: 1.006841 Loss2: 1.903489
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462974 Loss1: 0.119558 Loss2: 1.343416
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.007366 Loss1: 0.612042 Loss2: 1.395324
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426560 Loss1: 0.078233 Loss2: 1.348327
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.734924 Loss1: 0.338244 Loss2: 1.396680
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.646151 Loss1: 0.277103 Loss2: 1.369048
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.410793 Loss1: 0.073099 Loss2: 1.337694
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544430 Loss1: 0.184977 Loss2: 1.359453
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401713 Loss1: 0.065865 Loss2: 1.335848
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415998 Loss1: 0.074775 Loss2: 1.341223 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381685 Loss1: 0.048580 Loss2: 1.333104 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388432 Loss1: 0.061195 Loss2: 1.327238
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.776366 Loss1: 0.883320 Loss2: 1.893046
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.995722 Loss1: 0.581792 Loss2: 1.413930
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.826503 Loss1: 0.376097 Loss2: 1.450406
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654400 Loss1: 0.247459 Loss2: 1.406941
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631093 Loss1: 0.222275 Loss2: 1.408818
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.805061 Loss1: 0.863972 Loss2: 1.941089
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.872691 Loss1: 0.495516 Loss2: 1.377175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490003 Loss1: 0.096279 Loss2: 1.393724
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677096 Loss1: 0.279958 Loss2: 1.397137
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599387 Loss1: 0.225421 Loss2: 1.373967
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461595 Loss1: 0.079267 Loss2: 1.382327
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463113 Loss1: 0.084888 Loss2: 1.378225 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.464640 Loss1: 0.091725 Loss2: 1.372916 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427407 Loss1: 0.073911 Loss2: 1.353496 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443783 Loss1: 0.106339 Loss2: 1.337444 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987981
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.558630 Loss1: 0.760328 Loss2: 1.798302
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.834750 Loss1: 0.439973 Loss2: 1.394777
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.670365 Loss1: 0.287386 Loss2: 1.382979
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.696198 Loss1: 0.853516 Loss2: 1.842683
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599725 Loss1: 0.228628 Loss2: 1.371097
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.938471 Loss1: 0.560781 Loss2: 1.377690
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544336 Loss1: 0.185843 Loss2: 1.358493
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.830852 Loss1: 0.402858 Loss2: 1.427994
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484424 Loss1: 0.125573 Loss2: 1.358851
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433693 Loss1: 0.087494 Loss2: 1.346199 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424657 Loss1: 0.082905 Loss2: 1.341751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.415202 Loss1: 0.079645 Loss2: 1.335557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422825 Loss1: 0.083555 Loss2: 1.339270 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439593 Loss1: 0.106135 Loss2: 1.333458 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.693744 Loss1: 0.839525 Loss2: 1.854220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638687 Loss1: 0.266448 Loss2: 1.372239 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562751 Loss1: 0.212189 Loss2: 1.350562
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.651840 Loss1: 0.757360 Loss2: 1.894480
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.529006 Loss1: 0.170220 Loss2: 1.358786
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.908107 Loss1: 0.516905 Loss2: 1.391203
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.510215 Loss1: 0.158239 Loss2: 1.351976
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.794005 Loss1: 0.331623 Loss2: 1.462382
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425688 Loss1: 0.084222 Loss2: 1.341466
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591065 Loss1: 0.209134 Loss2: 1.381931
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.431616 Loss1: 0.102047 Loss2: 1.329569
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.532114 Loss1: 0.147935 Loss2: 1.384179
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384828 Loss1: 0.057044 Loss2: 1.327784
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506417 Loss1: 0.121861 Loss2: 1.384556
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375625 Loss1: 0.055090 Loss2: 1.320535
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470279 Loss1: 0.099323 Loss2: 1.370956
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462256 Loss1: 0.092131 Loss2: 1.370125
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448727 Loss1: 0.086661 Loss2: 1.362066
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430008 Loss1: 0.073287 Loss2: 1.356720
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.580659 Loss1: 0.728472 Loss2: 1.852187
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.806424 Loss1: 0.388005 Loss2: 1.418419
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.695606 Loss1: 0.262061 Loss2: 1.433544
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.612953 Loss1: 0.211587 Loss2: 1.401366
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.796933 Loss1: 0.927160 Loss2: 1.869773
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.564296 Loss1: 0.163896 Loss2: 1.400399
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.911617 Loss1: 0.498352 Loss2: 1.413265
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.532040 Loss1: 0.135261 Loss2: 1.396779
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.731166 Loss1: 0.302757 Loss2: 1.428409
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.654867 Loss1: 0.259050 Loss2: 1.395817
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.543210 Loss1: 0.152570 Loss2: 1.390640
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.575382 Loss1: 0.183230 Loss2: 1.392152
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.532976 Loss1: 0.143644 Loss2: 1.389333
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531984 Loss1: 0.150475 Loss2: 1.381509
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482883 Loss1: 0.103196 Loss2: 1.379687
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.521902 Loss1: 0.138532 Loss2: 1.383369
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452321 Loss1: 0.078252 Loss2: 1.374069
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481501 Loss1: 0.108436 Loss2: 1.373065 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.699097 Loss1: 0.782999 Loss2: 1.916098 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.889567 Loss1: 0.418295 Loss2: 1.471272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.663267 Loss1: 0.274857 Loss2: 1.388410
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.796311 Loss1: 0.945288 Loss2: 1.851023
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.985358 Loss1: 0.555669 Loss2: 1.429689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769965 Loss1: 0.357554 Loss2: 1.412411 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636413 Loss1: 0.244047 Loss2: 1.392366 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.533030 Loss1: 0.153859 Loss2: 1.379171 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.507192 Loss1: 0.136569 Loss2: 1.370622 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445902 Loss1: 0.083429 Loss2: 1.362473
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502054 Loss1: 0.137468 Loss2: 1.364586
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.507568 Loss1: 0.142148 Loss2: 1.365420
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481000 Loss1: 0.118177 Loss2: 1.362823
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438742 Loss1: 0.082592 Loss2: 1.356150
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.656983 Loss1: 0.810313 Loss2: 1.846671
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.944004 Loss1: 0.574482 Loss2: 1.369522
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.778557 Loss1: 0.346088 Loss2: 1.432468
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.703407 Loss1: 0.334249 Loss2: 1.369158
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.657002 Loss1: 0.708609 Loss2: 1.948393
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.965959 Loss1: 0.507908 Loss2: 1.458050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.830484 Loss1: 0.312032 Loss2: 1.518452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.780058 Loss1: 0.318388 Loss2: 1.461670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.693105 Loss1: 0.221209 Loss2: 1.471895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.651756 Loss1: 0.203713 Loss2: 1.448043 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415724 Loss1: 0.073807 Loss2: 1.341917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550664 Loss1: 0.099966 Loss2: 1.450698
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510740 Loss1: 0.075433 Loss2: 1.435308
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481439 Loss1: 0.052142 Loss2: 1.429297
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.492923 Loss1: 0.077164 Loss2: 1.415759
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.788611 Loss1: 0.790320 Loss2: 1.998291
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.984282 Loss1: 0.461854 Loss2: 1.522428
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.820788 Loss1: 0.298642 Loss2: 1.522146
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.750670 Loss1: 0.898685 Loss2: 1.851985
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.724496 Loss1: 0.230454 Loss2: 1.494042
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.848208 Loss1: 0.460055 Loss2: 1.388153
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.739266 Loss1: 0.243402 Loss2: 1.495864
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.751781 Loss1: 0.325795 Loss2: 1.425987
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.696400 Loss1: 0.190715 Loss2: 1.505685
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.675346 Loss1: 0.287968 Loss2: 1.387378
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.654829 Loss1: 0.175557 Loss2: 1.479273
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.631346 Loss1: 0.149330 Loss2: 1.482015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.572869 Loss1: 0.100538 Loss2: 1.472331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.547291 Loss1: 0.080074 Loss2: 1.467217 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445391 Loss1: 0.083707 Loss2: 1.361684 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.625875 Loss1: 0.766645 Loss2: 1.859230 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.733812 Loss1: 0.323553 Loss2: 1.410259 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.665098 Loss1: 0.274133 Loss2: 1.390964
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.679312 Loss1: 0.810538 Loss2: 1.868773
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.584662 Loss1: 0.205284 Loss2: 1.379379
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.926826 Loss1: 0.516117 Loss2: 1.410709
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520345 Loss1: 0.158199 Loss2: 1.362145
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769495 Loss1: 0.323141 Loss2: 1.446354
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478643 Loss1: 0.115096 Loss2: 1.363548
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.660700 Loss1: 0.274774 Loss2: 1.385926
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453353 Loss1: 0.095014 Loss2: 1.358339
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.723450 Loss1: 0.297658 Loss2: 1.425792
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434250 Loss1: 0.082405 Loss2: 1.351845
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.599779 Loss1: 0.204213 Loss2: 1.395566
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417211 Loss1: 0.071211 Loss2: 1.346000
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.612538 Loss1: 0.212779 Loss2: 1.399760
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.573190 Loss1: 0.166926 Loss2: 1.406263
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.528251 Loss1: 0.143193 Loss2: 1.385058
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.521785 Loss1: 0.142563 Loss2: 1.379222
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.917858 Loss1: 0.944397 Loss2: 1.973460
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.911008 Loss1: 0.422800 Loss2: 1.488208
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.782903 Loss1: 0.295516 Loss2: 1.487387
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654157 Loss1: 0.195950 Loss2: 1.458207
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.753000 Loss1: 0.810106 Loss2: 1.942894
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.652774 Loss1: 0.196470 Loss2: 1.456304
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.953335 Loss1: 0.516520 Loss2: 1.436814
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.613372 Loss1: 0.151246 Loss2: 1.462125
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.859115 Loss1: 0.340395 Loss2: 1.518720
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.612309 Loss1: 0.153939 Loss2: 1.458370
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.666541 Loss1: 0.235444 Loss2: 1.431097
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.569683 Loss1: 0.127081 Loss2: 1.442601
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.590497 Loss1: 0.158274 Loss2: 1.432223
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.529640 Loss1: 0.091060 Loss2: 1.438581
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.557895 Loss1: 0.129129 Loss2: 1.428767
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.530381 Loss1: 0.099544 Loss2: 1.430837
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518532 Loss1: 0.101272 Loss2: 1.417260
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.509432 Loss1: 0.096265 Loss2: 1.413167
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.482755 Loss1: 0.073269 Loss2: 1.409486
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465146 Loss1: 0.058586 Loss2: 1.406560
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.673271 Loss1: 0.853187 Loss2: 1.820083
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.850699 Loss1: 0.475244 Loss2: 1.375455
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.760565 Loss1: 0.352176 Loss2: 1.408389
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.707521 Loss1: 0.340307 Loss2: 1.367215
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.563170 Loss1: 0.732128 Loss2: 1.831043
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.920215 Loss1: 0.514735 Loss2: 1.405480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.791580 Loss1: 0.361292 Loss2: 1.430288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.680994 Loss1: 0.289231 Loss2: 1.391762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598343 Loss1: 0.208536 Loss2: 1.389807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559221 Loss1: 0.170728 Loss2: 1.388493 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.517260 Loss1: 0.133003 Loss2: 1.384257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438703 Loss1: 0.078725 Loss2: 1.359977 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.578725 Loss1: 0.728657 Loss2: 1.850068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.754145 Loss1: 0.329872 Loss2: 1.424273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.739711 Loss1: 0.905538 Loss2: 1.834173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.890519 Loss1: 0.497706 Loss2: 1.392813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.691045 Loss1: 0.306541 Loss2: 1.384504 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.600312 Loss1: 0.234020 Loss2: 1.366292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509455 Loss1: 0.149535 Loss2: 1.359920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481678 Loss1: 0.127749 Loss2: 1.353929 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447222 Loss1: 0.108260 Loss2: 1.338962 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424552 Loss1: 0.087060 Loss2: 1.337491 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.812172 Loss1: 0.456695 Loss2: 1.355478 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599814 Loss1: 0.247350 Loss2: 1.352464 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.557916 Loss1: 0.204616 Loss2: 1.353299 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503997 Loss1: 0.142042 Loss2: 1.361955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.482232 Loss1: 0.131647 Loss2: 1.350585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458693 Loss1: 0.114768 Loss2: 1.343924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453891 Loss1: 0.106515 Loss2: 1.347376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500631 Loss1: 0.151895 Loss2: 1.348736 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438549 Loss1: 0.087875 Loss2: 1.350674 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987981
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.942136 Loss1: 0.871125 Loss2: 2.071011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.923973 Loss1: 0.418485 Loss2: 1.505488 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.793065 Loss1: 0.337833 Loss2: 1.455232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.690338 Loss1: 0.216141 Loss2: 1.474197 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.559588 Loss1: 0.131081 Loss2: 1.428507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.530902 Loss1: 0.111401 Loss2: 1.419501
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.722538 Loss1: 0.316599 Loss2: 1.405939
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.527110 Loss1: 0.104420 Loss2: 1.422690
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.642637 Loss1: 0.266282 Loss2: 1.376354
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.545716 Loss1: 0.129256 Loss2: 1.416460
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.623322 Loss1: 0.237112 Loss2: 1.386210
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538511 Loss1: 0.159974 Loss2: 1.378537
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.505799 Loss1: 0.135581 Loss2: 1.370219
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467939 Loss1: 0.102503 Loss2: 1.365436
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451354 Loss1: 0.096180 Loss2: 1.355174
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.747009 Loss1: 0.879779 Loss2: 1.867230
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427781 Loss1: 0.075678 Loss2: 1.352103
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.805355 Loss1: 0.351309 Loss2: 1.454046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.662505 Loss1: 0.237090 Loss2: 1.425414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.540179 Loss1: 0.135287 Loss2: 1.404892
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.759804 Loss1: 0.893423 Loss2: 1.866381
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.881777 Loss1: 0.475922 Loss2: 1.405856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.720717 Loss1: 0.295366 Loss2: 1.425351 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599828 Loss1: 0.227929 Loss2: 1.371899 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.480108 Loss1: 0.097439 Loss2: 1.382668
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563032 Loss1: 0.180353 Loss2: 1.382679
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503564 Loss1: 0.129194 Loss2: 1.374370
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.477512 Loss1: 0.104872 Loss2: 1.372639
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501950 Loss1: 0.130820 Loss2: 1.371129
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436140 Loss1: 0.075264 Loss2: 1.360877
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.706711 Loss1: 0.795638 Loss2: 1.911073
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448190 Loss1: 0.090283 Loss2: 1.357907
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.873502 Loss1: 0.419030 Loss2: 1.454472 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617687 Loss1: 0.211930 Loss2: 1.405757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.601469 Loss1: 0.189855 Loss2: 1.411614
-DEBUG flwr 2023-10-11 11:37:32,543 | server.py:236 | fit_round 112 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.652854 Loss1: 0.853927 Loss2: 1.798928
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.865788 Loss1: 0.490428 Loss2: 1.375360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682903 Loss1: 0.300298 Loss2: 1.382605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.602864 Loss1: 0.257119 Loss2: 1.345746 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.552797 Loss1: 0.189293 Loss2: 1.363505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.547756 Loss1: 0.191031 Loss2: 1.356725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421864 Loss1: 0.086861 Loss2: 1.335003 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406268 Loss1: 0.079474 Loss2: 1.326794 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.605864 Loss1: 0.248697 Loss2: 1.357167 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516839 Loss1: 0.166410 Loss2: 1.350428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490901 Loss1: 0.133482 Loss2: 1.357419
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.663716 Loss1: 0.800804 Loss2: 1.862911
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444138 Loss1: 0.104403 Loss2: 1.339735
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.878163 Loss1: 0.480044 Loss2: 1.398119
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421378 Loss1: 0.086678 Loss2: 1.334700
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.733302 Loss1: 0.311685 Loss2: 1.421617
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403738 Loss1: 0.070024 Loss2: 1.333714
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.695074 Loss1: 0.309581 Loss2: 1.385493
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.565083 Loss1: 0.177785 Loss2: 1.387298
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496582 Loss1: 0.120613 Loss2: 1.375969
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.492660 Loss1: 0.127335 Loss2: 1.365325
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445590 Loss1: 0.079186 Loss2: 1.366405
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.787199 Loss1: 0.837305 Loss2: 1.949894
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430151 Loss1: 0.070440 Loss2: 1.359711
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.050317 Loss1: 0.620520 Loss2: 1.429797
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422133 Loss1: 0.067088 Loss2: 1.355045
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623241 Loss1: 0.207837 Loss2: 1.415404 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.574475 Loss1: 0.162084 Loss2: 1.412390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.663893 Loss1: 0.858947 Loss2: 1.804946 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.935563 Loss1: 0.510503 Loss2: 1.425061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.502825 Loss1: 0.116450 Loss2: 1.386375 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981027
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548291 Loss1: 0.180389 Loss2: 1.367901 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508955 Loss1: 0.154671 Loss2: 1.354284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410101 Loss1: 0.067276 Loss2: 1.342825 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 11:37:32,543][flwr][DEBUG] - fit_round 112 received 50 results and 0 failures
-INFO flwr 2023-10-11 11:38:15,479 | server.py:125 | fit progress: (112, 2.2133301115645385, {'accuracy': 0.5783}, 258403.2577254)
->> Test accuracy: 0.578300
-[2023-10-11 11:38:15,479][flwr][INFO] - fit progress: (112, 2.2133301115645385, {'accuracy': 0.5783}, 258403.2577254)
-DEBUG flwr 2023-10-11 11:38:15,480 | server.py:173 | evaluate_round 112: strategy sampled 50 clients (out of 50)
-[2023-10-11 11:38:15,480][flwr][DEBUG] - evaluate_round 112: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 11:47:21,954 | server.py:187 | evaluate_round 112 received 50 results and 0 failures
-[2023-10-11 11:47:21,954][flwr][DEBUG] - evaluate_round 112 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 11:47:21,955 | server.py:222 | fit_round 113: strategy sampled 50 clients (out of 50)
-[2023-10-11 11:47:21,955][flwr][DEBUG] - fit_round 113: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.535149 Loss1: 0.685920 Loss2: 1.849229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.720501 Loss1: 0.308383 Loss2: 1.412119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601953 Loss1: 0.237817 Loss2: 1.364136
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.621870 Loss1: 0.799286 Loss2: 1.822585
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514071 Loss1: 0.156590 Loss2: 1.357481
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.017334 Loss1: 0.589831 Loss2: 1.427504
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.760750 Loss1: 0.354607 Loss2: 1.406143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.668075 Loss1: 0.275316 Loss2: 1.392759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.594842 Loss1: 0.207279 Loss2: 1.387563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.603486 Loss1: 0.218609 Loss2: 1.384877 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.551277 Loss1: 0.174005 Loss2: 1.377272 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.469339 Loss1: 0.104597 Loss2: 1.364741 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.652808 Loss1: 0.815613 Loss2: 1.837195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.607111 Loss1: 0.220628 Loss2: 1.386483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490648 Loss1: 0.137510 Loss2: 1.353137 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457030 Loss1: 0.124832 Loss2: 1.332198 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.465328 Loss1: 0.127962 Loss2: 1.337366 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414620 Loss1: 0.083882 Loss2: 1.330738 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400405 Loss1: 0.075725 Loss2: 1.324680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381626 Loss1: 0.063159 Loss2: 1.318468 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.654959 Loss1: 0.229909 Loss2: 1.425050 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491218 Loss1: 0.086706 Loss2: 1.404511 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.936727 Loss1: 0.553885 Loss2: 1.382841 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.761277 Loss1: 0.340194 Loss2: 1.421083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.686238 Loss1: 0.798165 Loss2: 1.888073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476187 Loss1: 0.104666 Loss2: 1.371522 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458165 Loss1: 0.100796 Loss2: 1.357369 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440510 Loss1: 0.085924 Loss2: 1.354586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434981 Loss1: 0.080455 Loss2: 1.354525 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985577
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538534 Loss1: 0.140595 Loss2: 1.397939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.476423 Loss1: 0.091915 Loss2: 1.384509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454299 Loss1: 0.080283 Loss2: 1.374016
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.743872 Loss1: 0.854733 Loss2: 1.889138
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.833346 Loss1: 0.451384 Loss2: 1.381962 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.584751 Loss1: 0.215280 Loss2: 1.369471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471881 Loss1: 0.114886 Loss2: 1.356995 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451024 Loss1: 0.098501 Loss2: 1.352523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439493 Loss1: 0.087613 Loss2: 1.351880 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418565 Loss1: 0.075995 Loss2: 1.342571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389729 Loss1: 0.055004 Loss2: 1.334725 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467663 Loss1: 0.112380 Loss2: 1.355284 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409286 Loss1: 0.070487 Loss2: 1.338799 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387001 Loss1: 0.053017 Loss2: 1.333983
-(DefaultActor pid=3764) >> Training accuracy: 0.983259
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.409292 Loss1: 0.607152 Loss2: 1.802140
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.833012 Loss1: 0.467367 Loss2: 1.365645
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.657912 Loss1: 0.279897 Loss2: 1.378015
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.612103 Loss1: 0.250084 Loss2: 1.362019
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.543884 Loss1: 0.193789 Loss2: 1.350096
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.665189 Loss1: 0.762041 Loss2: 1.903148
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.517256 Loss1: 0.164293 Loss2: 1.352963
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.962609 Loss1: 0.533859 Loss2: 1.428750
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.799055 Loss1: 0.331289 Loss2: 1.467766
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.494950 Loss1: 0.150936 Loss2: 1.344013
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.695593 Loss1: 0.291028 Loss2: 1.404566
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.490106 Loss1: 0.149949 Loss2: 1.340157
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.643501 Loss1: 0.226396 Loss2: 1.417105
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458296 Loss1: 0.107454 Loss2: 1.350842
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430410 Loss1: 0.084438 Loss2: 1.345972 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975184
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.534790 Loss1: 0.136942 Loss2: 1.397848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.500497 Loss1: 0.112037 Loss2: 1.388460 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.869610 Loss1: 0.476919 Loss2: 1.392692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.665953 Loss1: 0.276201 Loss2: 1.389752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.612035 Loss1: 0.224538 Loss2: 1.387497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.567937 Loss1: 0.170560 Loss2: 1.397377 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502577 Loss1: 0.124766 Loss2: 1.377811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484386 Loss1: 0.107901 Loss2: 1.376485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424250 Loss1: 0.063099 Loss2: 1.361151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403143 Loss1: 0.049613 Loss2: 1.353530 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432624 Loss1: 0.107486 Loss2: 1.325138 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392985 Loss1: 0.074472 Loss2: 1.318514 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.578901 Loss1: 0.778707 Loss2: 1.800194
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.838122 Loss1: 0.461074 Loss2: 1.377049
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.613849 Loss1: 0.217837 Loss2: 1.396012
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.603632 Loss1: 0.244101 Loss2: 1.359531
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.750937 Loss1: 0.866241 Loss2: 1.884697
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.985869 Loss1: 0.551156 Loss2: 1.434713 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.755717 Loss1: 0.301527 Loss2: 1.454190 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.649000 Loss1: 0.243854 Loss2: 1.405146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568893 Loss1: 0.153218 Loss2: 1.415675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524384 Loss1: 0.125609 Loss2: 1.398775 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427837 Loss1: 0.086326 Loss2: 1.341511
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.477941 Loss1: 0.089087 Loss2: 1.388854
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475194 Loss1: 0.097340 Loss2: 1.377854
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462593 Loss1: 0.082534 Loss2: 1.380060
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436374 Loss1: 0.061224 Loss2: 1.375150
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.670111 Loss1: 0.789848 Loss2: 1.880263
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.925949 Loss1: 0.531853 Loss2: 1.394096
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.752468 Loss1: 0.303880 Loss2: 1.448588
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.625290 Loss1: 0.237874 Loss2: 1.387416
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.611181 Loss1: 0.760642 Loss2: 1.850540
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.971189 Loss1: 0.554750 Loss2: 1.416439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.807447 Loss1: 0.375328 Loss2: 1.432119 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565453 Loss1: 0.190738 Loss2: 1.374715 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523868 Loss1: 0.155686 Loss2: 1.368182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.525956 Loss1: 0.163754 Loss2: 1.362202 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.492707 Loss1: 0.129437 Loss2: 1.363270 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445611 Loss1: 0.103278 Loss2: 1.342334 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.649439 Loss1: 0.828780 Loss2: 1.820658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.742281 Loss1: 0.342607 Loss2: 1.399674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.714989 Loss1: 0.345651 Loss2: 1.369338
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.907644 Loss1: 1.033968 Loss2: 1.873676
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.970371 Loss1: 0.557049 Loss2: 1.413322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.841044 Loss1: 0.397958 Loss2: 1.443086 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.673400 Loss1: 0.280608 Loss2: 1.392792 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570315 Loss1: 0.175281 Loss2: 1.395034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.583770 Loss1: 0.197760 Loss2: 1.386011 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509348 Loss1: 0.132361 Loss2: 1.376987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485851 Loss1: 0.115771 Loss2: 1.370080 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.711380 Loss1: 0.838361 Loss2: 1.873019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.762596 Loss1: 0.330730 Loss2: 1.431866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.688718 Loss1: 0.797684 Loss2: 1.891034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.964762 Loss1: 0.525350 Loss2: 1.439412 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769839 Loss1: 0.316778 Loss2: 1.453061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.797865 Loss1: 0.358415 Loss2: 1.439449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.681912 Loss1: 0.242435 Loss2: 1.439477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582484 Loss1: 0.166384 Loss2: 1.416101 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.530466 Loss1: 0.129292 Loss2: 1.401174 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.500725 Loss1: 0.105225 Loss2: 1.395500 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.825315 Loss1: 0.474917 Loss2: 1.350398 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.634343 Loss1: 0.295193 Loss2: 1.339150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.542356 Loss1: 0.190696 Loss2: 1.351660 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525085 Loss1: 0.186554 Loss2: 1.338530 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.492426 Loss1: 0.149922 Loss2: 1.342504 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409078 Loss1: 0.077401 Loss2: 1.331677 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.415622 Loss1: 0.095971 Loss2: 1.319651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.394606 Loss1: 0.073821 Loss2: 1.320784 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491638 Loss1: 0.117636 Loss2: 1.374002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429504 Loss1: 0.065686 Loss2: 1.363819 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.792159 Loss1: 0.374642 Loss2: 1.417518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.615608 Loss1: 0.221297 Loss2: 1.394311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.982489 Loss1: 0.609955 Loss2: 1.372534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.880174 Loss1: 0.421064 Loss2: 1.459110 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.661532 Loss1: 0.264797 Loss2: 1.396735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598220 Loss1: 0.205139 Loss2: 1.393081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.594954 Loss1: 0.191834 Loss2: 1.403121
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462808 Loss1: 0.089727 Loss2: 1.373081
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439036 Loss1: 0.074219 Loss2: 1.364816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416704 Loss1: 0.057994 Loss2: 1.358710 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.625961 Loss1: 0.784131 Loss2: 1.841830 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.728939 Loss1: 0.303188 Loss2: 1.425752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.650125 Loss1: 0.275392 Loss2: 1.374733
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.726734 Loss1: 0.914719 Loss2: 1.812015
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.559381 Loss1: 0.188607 Loss2: 1.370774
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.950765 Loss1: 0.559456 Loss2: 1.391308
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465157 Loss1: 0.105080 Loss2: 1.360077
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726952 Loss1: 0.347321 Loss2: 1.379631
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422576 Loss1: 0.069802 Loss2: 1.352774
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.593817 Loss1: 0.260728 Loss2: 1.333089
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413240 Loss1: 0.060533 Loss2: 1.352707
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.613347 Loss1: 0.249933 Loss2: 1.363414
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418237 Loss1: 0.074391 Loss2: 1.343846
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504902 Loss1: 0.176037 Loss2: 1.328865
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436033 Loss1: 0.097107 Loss2: 1.338925
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438196 Loss1: 0.114454 Loss2: 1.323742
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431072 Loss1: 0.107890 Loss2: 1.323182
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406038 Loss1: 0.091302 Loss2: 1.314736
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390929 Loss1: 0.082849 Loss2: 1.308080
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.595832 Loss1: 0.728790 Loss2: 1.867042
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.944631 Loss1: 0.541833 Loss2: 1.402798
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.751668 Loss1: 0.317801 Loss2: 1.433867
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638602 Loss1: 0.239349 Loss2: 1.399252
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.671218 Loss1: 0.845858 Loss2: 1.825360
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.870397 Loss1: 0.503491 Loss2: 1.366906 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.695943 Loss1: 0.291873 Loss2: 1.404070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550399 Loss1: 0.201943 Loss2: 1.348457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508355 Loss1: 0.161538 Loss2: 1.346817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506889 Loss1: 0.159693 Loss2: 1.347195 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450873 Loss1: 0.083143 Loss2: 1.367730
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497250 Loss1: 0.144955 Loss2: 1.352295
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437741 Loss1: 0.098515 Loss2: 1.339226
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426062 Loss1: 0.095843 Loss2: 1.330218
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390472 Loss1: 0.064457 Loss2: 1.326015
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.672033 Loss1: 0.849247 Loss2: 1.822786
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.887583 Loss1: 0.531024 Loss2: 1.356559
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.657918 Loss1: 0.282153 Loss2: 1.375765
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555110 Loss1: 0.222093 Loss2: 1.333017
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.794037 Loss1: 0.853039 Loss2: 1.940998
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536138 Loss1: 0.194274 Loss2: 1.341865
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.979810 Loss1: 0.589274 Loss2: 1.390536
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834773 Loss1: 0.395167 Loss2: 1.439607
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492506 Loss1: 0.153509 Loss2: 1.338996
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.628614 Loss1: 0.212441 Loss2: 1.416172
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425051 Loss1: 0.101418 Loss2: 1.323633
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.396391 Loss1: 0.080184 Loss2: 1.316207 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.370757 Loss1: 0.058280 Loss2: 1.312477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360690 Loss1: 0.054941 Loss2: 1.305750 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.459901 Loss1: 0.089058 Loss2: 1.370843 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.613792 Loss1: 0.772156 Loss2: 1.841636
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.736748 Loss1: 0.383740 Loss2: 1.353009
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716393 Loss1: 0.331299 Loss2: 1.385095
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581870 Loss1: 0.233767 Loss2: 1.348104
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.791778 Loss1: 0.816137 Loss2: 1.975642
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503125 Loss1: 0.159786 Loss2: 1.343339
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.996050 Loss1: 0.543646 Loss2: 1.452403
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470174 Loss1: 0.138888 Loss2: 1.331286
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.851558 Loss1: 0.343695 Loss2: 1.507863
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416465 Loss1: 0.089963 Loss2: 1.326502
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.804246 Loss1: 0.357319 Loss2: 1.446927
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.743856 Loss1: 0.265571 Loss2: 1.478286
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451526 Loss1: 0.130199 Loss2: 1.321327
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.608724 Loss1: 0.166806 Loss2: 1.441918
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434864 Loss1: 0.111041 Loss2: 1.323823
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.578457 Loss1: 0.141243 Loss2: 1.437215
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421402 Loss1: 0.096102 Loss2: 1.325300
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.512067 Loss1: 0.088331 Loss2: 1.423736 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.846986 Loss1: 0.968267 Loss2: 1.878718 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.775896 Loss1: 0.343585 Loss2: 1.432311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.665841 Loss1: 0.275934 Loss2: 1.389907
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.768014 Loss1: 0.801159 Loss2: 1.966855
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.583644 Loss1: 0.190589 Loss2: 1.393055
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.930812 Loss1: 0.486612 Loss2: 1.444201
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519985 Loss1: 0.142589 Loss2: 1.377396
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.702165 Loss1: 0.252124 Loss2: 1.450041
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466970 Loss1: 0.100701 Loss2: 1.366269
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588903 Loss1: 0.176606 Loss2: 1.412297
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440918 Loss1: 0.081723 Loss2: 1.359196
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.510868 Loss1: 0.099562 Loss2: 1.411305
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423315 Loss1: 0.076068 Loss2: 1.347247
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538703 Loss1: 0.140622 Loss2: 1.398081
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441420 Loss1: 0.094614 Loss2: 1.346806
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488598 Loss1: 0.092545 Loss2: 1.396052
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.479972 Loss1: 0.087491 Loss2: 1.392481
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.498998 Loss1: 0.105850 Loss2: 1.393148
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.511751 Loss1: 0.118084 Loss2: 1.393667
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.640421 Loss1: 0.801666 Loss2: 1.838755
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.801441 Loss1: 0.407402 Loss2: 1.394039
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.726888 Loss1: 0.312499 Loss2: 1.414389
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.640775 Loss1: 0.744051 Loss2: 1.896724
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.667664 Loss1: 0.290402 Loss2: 1.377262
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.837673 Loss1: 0.444789 Loss2: 1.392884
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.655941 Loss1: 0.264525 Loss2: 1.391416
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538442 Loss1: 0.168864 Loss2: 1.369579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.477664 Loss1: 0.114261 Loss2: 1.363403 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460870 Loss1: 0.107727 Loss2: 1.353143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427157 Loss1: 0.078027 Loss2: 1.349131 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422344 Loss1: 0.078580 Loss2: 1.343764 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451618 Loss1: 0.085493 Loss2: 1.366125 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.696497 Loss1: 0.856448 Loss2: 1.840049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.625953 Loss1: 0.253744 Loss2: 1.372209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535692 Loss1: 0.200163 Loss2: 1.335529
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.569237 Loss1: 0.747469 Loss2: 1.821768
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.939654 Loss1: 0.516320 Loss2: 1.423335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.738953 Loss1: 0.298796 Loss2: 1.440157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.664415 Loss1: 0.266234 Loss2: 1.398181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543284 Loss1: 0.144332 Loss2: 1.398952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.540116 Loss1: 0.159678 Loss2: 1.380437 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518887 Loss1: 0.142629 Loss2: 1.376258 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.455084 Loss1: 0.085667 Loss2: 1.369416 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.815287 Loss1: 0.851966 Loss2: 1.963320 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.871276 Loss1: 0.360066 Loss2: 1.511210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.665339 Loss1: 0.201981 Loss2: 1.463358 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.637643 Loss1: 0.180239 Loss2: 1.457405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.600274 Loss1: 0.157012 Loss2: 1.443262 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.608529 Loss1: 0.146737 Loss2: 1.461792 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.560398 Loss1: 0.120196 Loss2: 1.440202 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.524544 Loss1: 0.096114 Loss2: 1.428431 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.519682 Loss1: 0.159976 Loss2: 1.359706 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470340 Loss1: 0.101945 Loss2: 1.368395 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.981661 Loss1: 0.594630 Loss2: 1.387031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.657673 Loss1: 0.284083 Loss2: 1.373589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.695567 Loss1: 0.784278 Loss2: 1.911290
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.554390 Loss1: 0.168418 Loss2: 1.385972
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.995589 Loss1: 0.566960 Loss2: 1.428629
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518928 Loss1: 0.168879 Loss2: 1.350049
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.755313 Loss1: 0.287148 Loss2: 1.468166
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495452 Loss1: 0.145847 Loss2: 1.349606
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.689577 Loss1: 0.263394 Loss2: 1.426183
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.463452 Loss1: 0.113205 Loss2: 1.350248
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.591803 Loss1: 0.158807 Loss2: 1.432996
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423675 Loss1: 0.078879 Loss2: 1.344797
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523934 Loss1: 0.108200 Loss2: 1.415735
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416627 Loss1: 0.081697 Loss2: 1.334930
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493460 Loss1: 0.085927 Loss2: 1.407534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454215 Loss1: 0.059362 Loss2: 1.394853 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.898974 Loss1: 0.537178 Loss2: 1.361796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587029 Loss1: 0.231426 Loss2: 1.355602 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566445 Loss1: 0.213745 Loss2: 1.352700
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.568946 Loss1: 0.765422 Loss2: 1.803525
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.844197 Loss1: 0.476695 Loss2: 1.367502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.631181 Loss1: 0.267754 Loss2: 1.363427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.608452 Loss1: 0.272865 Loss2: 1.335587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527848 Loss1: 0.185006 Loss2: 1.342842 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995536
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433110 Loss1: 0.110794 Loss2: 1.322316 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386980 Loss1: 0.074765 Loss2: 1.312215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.645806 Loss1: 0.848784 Loss2: 1.797022
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405666 Loss1: 0.099235 Loss2: 1.306432
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.659833 Loss1: 0.285049 Loss2: 1.374784 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475342 Loss1: 0.150093 Loss2: 1.325249 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.420056 Loss1: 0.103111 Loss2: 1.316944
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.528551 Loss1: 0.694789 Loss2: 1.833762
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.806012 Loss1: 0.453658 Loss2: 1.352354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684717 Loss1: 0.278237 Loss2: 1.406480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588088 Loss1: 0.226006 Loss2: 1.362082 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.329405 Loss1: 0.040760 Loss2: 1.288646
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.583191 Loss1: 0.210541 Loss2: 1.372650
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510619 Loss1: 0.141563 Loss2: 1.369056
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475632 Loss1: 0.123089 Loss2: 1.352543
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449611 Loss1: 0.106173 Loss2: 1.343438
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428548 Loss1: 0.085523 Loss2: 1.343025
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.585689 Loss1: 0.706152 Loss2: 1.879537
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412054 Loss1: 0.068053 Loss2: 1.344001
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700042 Loss1: 0.257443 Loss2: 1.442599 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499714 Loss1: 0.099741 Loss2: 1.399972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.784023 Loss1: 0.904706 Loss2: 1.879316
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485782 Loss1: 0.090068 Loss2: 1.395714
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.925135 Loss1: 0.533635 Loss2: 1.391500
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474002 Loss1: 0.087699 Loss2: 1.386302
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.711663 Loss1: 0.283923 Loss2: 1.427740
-DEBUG flwr 2023-10-11 12:16:05,021 | server.py:236 | fit_round 113 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464926 Loss1: 0.079588 Loss2: 1.385339
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611675 Loss1: 0.225369 Loss2: 1.386306
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.467622 Loss1: 0.082363 Loss2: 1.385259
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466226 Loss1: 0.081583 Loss2: 1.384643 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.477665 Loss1: 0.119143 Loss2: 1.358521 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.471890 Loss1: 0.119449 Loss2: 1.352440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456634 Loss1: 0.094342 Loss2: 1.362292
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.658354 Loss1: 0.799375 Loss2: 1.858979
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.041120 Loss1: 0.597627 Loss2: 1.443494
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.728138 Loss1: 0.299420 Loss2: 1.428718
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647613 Loss1: 0.255028 Loss2: 1.392585
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577332 Loss1: 0.171933 Loss2: 1.405398
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.761746 Loss1: 0.888564 Loss2: 1.873182
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.947252 Loss1: 0.522625 Loss2: 1.424627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.820862 Loss1: 0.346314 Loss2: 1.474547 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.562737 Loss1: 0.176894 Loss2: 1.385843
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.666809 Loss1: 0.260471 Loss2: 1.406338
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.581321 Loss1: 0.189603 Loss2: 1.391718
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.626724 Loss1: 0.211787 Loss2: 1.414937
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.490339 Loss1: 0.091326 Loss2: 1.399013
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.525148 Loss1: 0.134035 Loss2: 1.391113
-(DefaultActor pid=3765) >> Training accuracy: 0.971680
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.551477 Loss1: 0.156943 Loss2: 1.394534
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.514011 Loss1: 0.119225 Loss2: 1.394785
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.504274 Loss1: 0.118780 Loss2: 1.385494
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.541090 Loss1: 0.157846 Loss2: 1.383244
-(DefaultActor pid=3764) >> Training accuracy: 0.957292
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 12:16:05,021][flwr][DEBUG] - fit_round 113 received 50 results and 0 failures
-INFO flwr 2023-10-11 12:16:45,585 | server.py:125 | fit progress: (113, 2.1971218222246383, {'accuracy': 0.579}, 260713.36380087602)
->> Test accuracy: 0.579000
-[2023-10-11 12:16:45,585][flwr][INFO] - fit progress: (113, 2.1971218222246383, {'accuracy': 0.579}, 260713.36380087602)
-DEBUG flwr 2023-10-11 12:16:45,586 | server.py:173 | evaluate_round 113: strategy sampled 50 clients (out of 50)
-[2023-10-11 12:16:45,586][flwr][DEBUG] - evaluate_round 113: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 12:25:47,515 | server.py:187 | evaluate_round 113 received 50 results and 0 failures
-[2023-10-11 12:25:47,515][flwr][DEBUG] - evaluate_round 113 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 12:25:47,515 | server.py:222 | fit_round 114: strategy sampled 50 clients (out of 50)
-[2023-10-11 12:25:47,515][flwr][DEBUG] - fit_round 114: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.515881 Loss1: 0.730689 Loss2: 1.785192
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.782824 Loss1: 0.409773 Loss2: 1.373052
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.601986 Loss1: 0.233279 Loss2: 1.368707
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.727789 Loss1: 0.843824 Loss2: 1.883965
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518830 Loss1: 0.185178 Loss2: 1.333653
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.946829 Loss1: 0.502373 Loss2: 1.444456
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507323 Loss1: 0.170659 Loss2: 1.336665
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.752552 Loss1: 0.320132 Loss2: 1.432420
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493653 Loss1: 0.164283 Loss2: 1.329370
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611729 Loss1: 0.199483 Loss2: 1.412246
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.421679 Loss1: 0.099330 Loss2: 1.322349
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556481 Loss1: 0.151283 Loss2: 1.405198
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450674 Loss1: 0.125664 Loss2: 1.325011
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384010 Loss1: 0.066519 Loss2: 1.317490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.349179 Loss1: 0.042432 Loss2: 1.306748 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.500806 Loss1: 0.105625 Loss2: 1.395181 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.639243 Loss1: 0.850193 Loss2: 1.789051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.632671 Loss1: 0.271845 Loss2: 1.360826 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526072 Loss1: 0.202614 Loss2: 1.323457
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.615019 Loss1: 0.812283 Loss2: 1.802737
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492952 Loss1: 0.164946 Loss2: 1.328005
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.852892 Loss1: 0.492569 Loss2: 1.360323
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449862 Loss1: 0.126257 Loss2: 1.323606
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635801 Loss1: 0.262161 Loss2: 1.373640
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406877 Loss1: 0.087502 Loss2: 1.319374
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509461 Loss1: 0.169732 Loss2: 1.339729
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403011 Loss1: 0.093797 Loss2: 1.309214
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.482761 Loss1: 0.147990 Loss2: 1.334772
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379960 Loss1: 0.074375 Loss2: 1.305585
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466945 Loss1: 0.136173 Loss2: 1.330772
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374227 Loss1: 0.073565 Loss2: 1.300663
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.411589 Loss1: 0.090559 Loss2: 1.321031
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.384384 Loss1: 0.067478 Loss2: 1.316906
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381965 Loss1: 0.071635 Loss2: 1.310330
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381630 Loss1: 0.073219 Loss2: 1.308411
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.835527 Loss1: 0.916414 Loss2: 1.919113
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.887931 Loss1: 0.512686 Loss2: 1.375244
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.755337 Loss1: 0.344363 Loss2: 1.410975
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.639510 Loss1: 0.259668 Loss2: 1.379842
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.664647 Loss1: 0.697567 Loss2: 1.967080
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538225 Loss1: 0.152142 Loss2: 1.386083 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491648 Loss1: 0.130998 Loss2: 1.360650 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448157 Loss1: 0.096820 Loss2: 1.351338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437452 Loss1: 0.088481 Loss2: 1.348971 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.444966 Loss1: 0.096558 Loss2: 1.348408 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987981
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.569906 Loss1: 0.147845 Loss2: 1.422061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497539 Loss1: 0.076871 Loss2: 1.420668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.498646 Loss1: 0.093469 Loss2: 1.405177
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.607631 Loss1: 0.758852 Loss2: 1.848779
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.900850 Loss1: 0.538301 Loss2: 1.362549
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.711006 Loss1: 0.293081 Loss2: 1.417926
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.613212 Loss1: 0.256910 Loss2: 1.356302
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499472 Loss1: 0.130159 Loss2: 1.369314
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.564792 Loss1: 0.762814 Loss2: 1.801978
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457536 Loss1: 0.109388 Loss2: 1.348148
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.817850 Loss1: 0.436271 Loss2: 1.381579
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435282 Loss1: 0.097872 Loss2: 1.337410
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.745633 Loss1: 0.359456 Loss2: 1.386177
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418782 Loss1: 0.082247 Loss2: 1.336535
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.587749 Loss1: 0.228509 Loss2: 1.359241
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408706 Loss1: 0.074964 Loss2: 1.333743
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428262 Loss1: 0.102255 Loss2: 1.326007
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.560550 Loss1: 0.204800 Loss2: 1.355749
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487397 Loss1: 0.145486 Loss2: 1.341911
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444386 Loss1: 0.109186 Loss2: 1.335201
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411297 Loss1: 0.083169 Loss2: 1.328127
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394843 Loss1: 0.077791 Loss2: 1.317052
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.712713 Loss1: 0.896328 Loss2: 1.816386
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388958 Loss1: 0.069101 Loss2: 1.319857
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.755241 Loss1: 0.354254 Loss2: 1.400987 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506532 Loss1: 0.144241 Loss2: 1.362291 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.447194 Loss1: 0.110918 Loss2: 1.336276
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.537920 Loss1: 0.715265 Loss2: 1.822655
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.819077 Loss1: 0.472624 Loss2: 1.346453 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669136 Loss1: 0.293619 Loss2: 1.375517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631810 Loss1: 0.294996 Loss2: 1.336814 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.590008 Loss1: 0.236612 Loss2: 1.353396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451286 Loss1: 0.124077 Loss2: 1.327209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416793 Loss1: 0.104026 Loss2: 1.312767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388441 Loss1: 0.072160 Loss2: 1.316281 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.708922 Loss1: 0.309529 Loss2: 1.399393 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.600928 Loss1: 0.217789 Loss2: 1.383140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523454 Loss1: 0.148373 Loss2: 1.375081
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.625208 Loss1: 0.757047 Loss2: 1.868161
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.925572 Loss1: 0.529855 Loss2: 1.395717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.724149 Loss1: 0.272668 Loss2: 1.451481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.659160 Loss1: 0.271374 Loss2: 1.387787 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.603980 Loss1: 0.210932 Loss2: 1.393048 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.537994 Loss1: 0.157159 Loss2: 1.380835 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.456100 Loss1: 0.085806 Loss2: 1.370294 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461517 Loss1: 0.098164 Loss2: 1.363352 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.693609 Loss1: 0.278352 Loss2: 1.415257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.660608 Loss1: 0.277780 Loss2: 1.382828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.571397 Loss1: 0.193356 Loss2: 1.378041
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.697122 Loss1: 0.832556 Loss2: 1.864566
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.879742 Loss1: 0.501084 Loss2: 1.378659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.733136 Loss1: 0.302690 Loss2: 1.430446 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.634298 Loss1: 0.253439 Loss2: 1.380859 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.596111 Loss1: 0.195757 Loss2: 1.400354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480213 Loss1: 0.108484 Loss2: 1.371729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458806 Loss1: 0.099164 Loss2: 1.359643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426226 Loss1: 0.061130 Loss2: 1.365095 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.742888 Loss1: 0.356439 Loss2: 1.386449 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.632504 Loss1: 0.262274 Loss2: 1.370230 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.507832 Loss1: 0.163099 Loss2: 1.344732
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.748189 Loss1: 0.858605 Loss2: 1.889584
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.882473 Loss1: 0.473834 Loss2: 1.408639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.799241 Loss1: 0.358516 Loss2: 1.440724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.676120 Loss1: 0.270809 Loss2: 1.405311 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.566000 Loss1: 0.166243 Loss2: 1.399756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504232 Loss1: 0.119160 Loss2: 1.385072 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461076 Loss1: 0.088159 Loss2: 1.372917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437915 Loss1: 0.066711 Loss2: 1.371204 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.607017 Loss1: 0.231559 Loss2: 1.375458 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497692 Loss1: 0.140374 Loss2: 1.357318 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.715808 Loss1: 0.880291 Loss2: 1.835517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.853075 Loss1: 0.471905 Loss2: 1.381170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386534 Loss1: 0.048859 Loss2: 1.337675 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548955 Loss1: 0.166714 Loss2: 1.382241 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473172 Loss1: 0.118235 Loss2: 1.354936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434210 Loss1: 0.085649 Loss2: 1.348561
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.747664 Loss1: 0.863097 Loss2: 1.884567
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414988 Loss1: 0.069402 Loss2: 1.345586
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.893553 Loss1: 0.503716 Loss2: 1.389837
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407493 Loss1: 0.064140 Loss2: 1.343352
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.734004 Loss1: 0.304821 Loss2: 1.429182
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.628294 Loss1: 0.243063 Loss2: 1.385231
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.618172 Loss1: 0.225597 Loss2: 1.392575
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.567245 Loss1: 0.164961 Loss2: 1.402284
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.536418 Loss1: 0.156230 Loss2: 1.380189
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.516246 Loss1: 0.130029 Loss2: 1.386217
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.546737 Loss1: 0.753476 Loss2: 1.793261
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.471046 Loss1: 0.092899 Loss2: 1.378147
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.837122 Loss1: 0.471060 Loss2: 1.366062
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439030 Loss1: 0.071207 Loss2: 1.367822
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682286 Loss1: 0.292785 Loss2: 1.389502
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550325 Loss1: 0.200249 Loss2: 1.350076
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.572729 Loss1: 0.211199 Loss2: 1.361530
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513706 Loss1: 0.166170 Loss2: 1.347536
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458702 Loss1: 0.117441 Loss2: 1.341260
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.754574 Loss1: 0.824474 Loss2: 1.930100
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.910812 Loss1: 0.490521 Loss2: 1.420291 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.773880 Loss1: 0.311737 Loss2: 1.462143 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397237 Loss1: 0.070680 Loss2: 1.326558
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642591 Loss1: 0.248715 Loss2: 1.393876
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.604536 Loss1: 0.184464 Loss2: 1.420073
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.550281 Loss1: 0.154272 Loss2: 1.396009
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516926 Loss1: 0.123790 Loss2: 1.393136
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488231 Loss1: 0.099852 Loss2: 1.388379
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.640328 Loss1: 0.788227 Loss2: 1.852101
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.507891 Loss1: 0.119327 Loss2: 1.388563
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476226 Loss1: 0.090807 Loss2: 1.385419 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.627288 Loss1: 0.254546 Loss2: 1.372742 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529869 Loss1: 0.154598 Loss2: 1.375271 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.541014 Loss1: 0.165167 Loss2: 1.375847
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.571858 Loss1: 0.737884 Loss2: 1.833974
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.857249 Loss1: 0.479202 Loss2: 1.378046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.733211 Loss1: 0.323508 Loss2: 1.409703 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450102 Loss1: 0.098211 Loss2: 1.351891
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.810191 Loss1: 0.421995 Loss2: 1.388195
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.718053 Loss1: 0.327499 Loss2: 1.390554
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.580701 Loss1: 0.215376 Loss2: 1.365326
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456498 Loss1: 0.096311 Loss2: 1.360187
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423145 Loss1: 0.074405 Loss2: 1.348740
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.708622 Loss1: 0.886330 Loss2: 1.822293
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409248 Loss1: 0.071749 Loss2: 1.337499
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.933209 Loss1: 0.555258 Loss2: 1.377951
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.382797 Loss1: 0.049195 Loss2: 1.333602
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.575093 Loss1: 0.238003 Loss2: 1.337091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500606 Loss1: 0.164912 Loss2: 1.335694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.489396 Loss1: 0.152301 Loss2: 1.337095
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.866368 Loss1: 0.976353 Loss2: 1.890015
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.032131 Loss1: 0.611320 Loss2: 1.420811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.836335 Loss1: 0.397017 Loss2: 1.439317 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424615 Loss1: 0.094566 Loss2: 1.330048
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.681014 Loss1: 0.273830 Loss2: 1.407184
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589806 Loss1: 0.194293 Loss2: 1.395514
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562769 Loss1: 0.175422 Loss2: 1.387347
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511515 Loss1: 0.129162 Loss2: 1.382353
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477543 Loss1: 0.106813 Loss2: 1.370730
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.683218 Loss1: 0.877859 Loss2: 1.805359
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.485227 Loss1: 0.113090 Loss2: 1.372137
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477431 Loss1: 0.111266 Loss2: 1.366165 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.639898 Loss1: 0.296501 Loss2: 1.343397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.454926 Loss1: 0.130541 Loss2: 1.324385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.403497 Loss1: 0.083003 Loss2: 1.320494
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.754297 Loss1: 0.840430 Loss2: 1.913867
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.044782 Loss1: 0.572280 Loss2: 1.472502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.918324 Loss1: 0.411081 Loss2: 1.507243 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.816640 Loss1: 0.351863 Loss2: 1.464777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.579826 Loss1: 0.140559 Loss2: 1.439267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.525744 Loss1: 0.103443 Loss2: 1.422301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.502398 Loss1: 0.076831 Loss2: 1.425568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.470007 Loss1: 0.051712 Loss2: 1.418295 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.656193 Loss1: 0.265514 Loss2: 1.390678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.551856 Loss1: 0.184658 Loss2: 1.367199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.535516 Loss1: 0.164683 Loss2: 1.370833
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.471815 Loss1: 0.655450 Loss2: 1.816365
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.860822 Loss1: 0.510585 Loss2: 1.350236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.737018 Loss1: 0.327118 Loss2: 1.409900 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.588016 Loss1: 0.244107 Loss2: 1.343909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488267 Loss1: 0.154415 Loss2: 1.333852 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435430 Loss1: 0.099828 Loss2: 1.335602 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433136 Loss1: 0.110361 Loss2: 1.322774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.923725 Loss1: 0.490938 Loss2: 1.432787
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422208 Loss1: 0.092835 Loss2: 1.329373
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574433 Loss1: 0.167686 Loss2: 1.406748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.533354 Loss1: 0.140018 Loss2: 1.393336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.620993 Loss1: 0.832936 Loss2: 1.788057
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.501574 Loss1: 0.107918 Loss2: 1.393655
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.905670 Loss1: 0.540481 Loss2: 1.365189
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.495556 Loss1: 0.108333 Loss2: 1.387223
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.670908 Loss1: 0.286933 Loss2: 1.383974
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473206 Loss1: 0.087062 Loss2: 1.386144
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581264 Loss1: 0.233256 Loss2: 1.348008
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.468866 Loss1: 0.087107 Loss2: 1.381759
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506057 Loss1: 0.174803 Loss2: 1.331254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440674 Loss1: 0.114025 Loss2: 1.326649 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.393281 Loss1: 0.069971 Loss2: 1.323309
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.649312 Loss1: 0.849184 Loss2: 1.800128
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.920081 Loss1: 0.522030 Loss2: 1.398051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586062 Loss1: 0.213397 Loss2: 1.372664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473366 Loss1: 0.117882 Loss2: 1.355485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451804 Loss1: 0.102041 Loss2: 1.349763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461227 Loss1: 0.111626 Loss2: 1.349601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427772 Loss1: 0.085003 Loss2: 1.342769 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406282 Loss1: 0.074381 Loss2: 1.331901 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509847 Loss1: 0.136255 Loss2: 1.373592 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445811 Loss1: 0.084723 Loss2: 1.361088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430363 Loss1: 0.075065 Loss2: 1.355298 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.791976 Loss1: 0.389740 Loss2: 1.402236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550173 Loss1: 0.218061 Loss2: 1.332112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.692883 Loss1: 0.847103 Loss2: 1.845780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.858587 Loss1: 0.474420 Loss2: 1.384167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736848 Loss1: 0.322799 Loss2: 1.414049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575639 Loss1: 0.202338 Loss2: 1.373301 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506572 Loss1: 0.143123 Loss2: 1.363449 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.515621 Loss1: 0.153508 Loss2: 1.362112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.490700 Loss1: 0.123314 Loss2: 1.367386
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.688667 Loss1: 0.776247 Loss2: 1.912421
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441097 Loss1: 0.090833 Loss2: 1.350264
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.020572 Loss1: 0.599870 Loss2: 1.420703
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.834590 Loss1: 0.373262 Loss2: 1.461328
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.718465 Loss1: 0.301491 Loss2: 1.416974
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.636667 Loss1: 0.220054 Loss2: 1.416613
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.605081 Loss1: 0.193132 Loss2: 1.411949
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.729925 Loss1: 0.857071 Loss2: 1.872854
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.528888 Loss1: 0.121018 Loss2: 1.407870
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.480760 Loss1: 0.091948 Loss2: 1.388812
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.048763 Loss1: 0.597215 Loss2: 1.451549
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460092 Loss1: 0.077671 Loss2: 1.382421
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.787274 Loss1: 0.370755 Loss2: 1.416519
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448610 Loss1: 0.071374 Loss2: 1.377236
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.628611 Loss1: 0.207975 Loss2: 1.420636
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.570782 Loss1: 0.177631 Loss2: 1.393151
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.529928 Loss1: 0.135380 Loss2: 1.394548
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.499715 Loss1: 0.122966 Loss2: 1.376749
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451228 Loss1: 0.078827 Loss2: 1.372401
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.758347 Loss1: 0.877909 Loss2: 1.880438
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408904 Loss1: 0.048489 Loss2: 1.360415
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435731 Loss1: 0.081244 Loss2: 1.354487 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.560543 Loss1: 0.198578 Loss2: 1.361965 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.457063 Loss1: 0.105497 Loss2: 1.351566 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.811598 Loss1: 0.906724 Loss2: 1.904874
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462870 Loss1: 0.112322 Loss2: 1.350548
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.893612 Loss1: 0.512516 Loss2: 1.381096
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415329 Loss1: 0.076942 Loss2: 1.338387
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.755926 Loss1: 0.333643 Loss2: 1.422283
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423823 Loss1: 0.090046 Loss2: 1.333778
-(DefaultActor pid=3764) >> Training accuracy: 0.979911
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567964 Loss1: 0.187062 Loss2: 1.380902 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.552216 Loss1: 0.181503 Loss2: 1.370713 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488906 Loss1: 0.109052 Loss2: 1.379854
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.381660 Loss1: 0.678753 Loss2: 1.702907
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.784918 Loss1: 0.474985 Loss2: 1.309933 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.574812 Loss1: 0.238682 Loss2: 1.336130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.454327 Loss1: 0.164281 Loss2: 1.290046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.363049 Loss1: 0.084733 Loss2: 1.278316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396634 Loss1: 0.131479 Loss2: 1.265155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.686944 Loss1: 0.311600 Loss2: 1.375344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554952 Loss1: 0.212010 Loss2: 1.342943 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975586
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475964 Loss1: 0.140057 Loss2: 1.335908 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422276 Loss1: 0.097487 Loss2: 1.324789 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408525 Loss1: 0.088308 Loss2: 1.320218
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.482616 Loss1: 0.637054 Loss2: 1.845563
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.839865 Loss1: 0.433636 Loss2: 1.406229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.641948 Loss1: 0.235575 Loss2: 1.406373 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523540 Loss1: 0.140317 Loss2: 1.383224 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494628 Loss1: 0.112880 Loss2: 1.381749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467987 Loss1: 0.087323 Loss2: 1.380663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580167 Loss1: 0.209586 Loss2: 1.370581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536010 Loss1: 0.160186 Loss2: 1.375824 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991728
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432240 Loss1: 0.078919 Loss2: 1.353322 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434335 Loss1: 0.087114 Loss2: 1.347221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.779298 Loss1: 0.869723 Loss2: 1.909575
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435738 Loss1: 0.091929 Loss2: 1.343809
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.771810 Loss1: 0.333749 Loss2: 1.438061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.606421 Loss1: 0.190266 Loss2: 1.416155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.577889 Loss1: 0.167265 Loss2: 1.410625
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.746893 Loss1: 0.928554 Loss2: 1.818339
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.536351 Loss1: 0.134789 Loss2: 1.401562
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.938478 Loss1: 0.539755 Loss2: 1.398723
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481937 Loss1: 0.085855 Loss2: 1.396082
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656300 Loss1: 0.265835 Loss2: 1.390465
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461608 Loss1: 0.076624 Loss2: 1.384984
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.571382 Loss1: 0.214227 Loss2: 1.357155
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453495 Loss1: 0.071861 Loss2: 1.381634
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521199 Loss1: 0.157066 Loss2: 1.364133
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502519 Loss1: 0.152115 Loss2: 1.350404
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474980 Loss1: 0.123752 Loss2: 1.351228
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.463873 Loss1: 0.118363 Loss2: 1.345510
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427956 Loss1: 0.082508 Loss2: 1.345448
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.652171 Loss1: 0.799010 Loss2: 1.853161
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405494 Loss1: 0.068134 Loss2: 1.337360
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.681754 Loss1: 0.295048 Loss2: 1.386706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558190 Loss1: 0.202673 Loss2: 1.355517 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481286 Loss1: 0.135165 Loss2: 1.346121
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.614372 Loss1: 0.753096 Loss2: 1.861275
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.840402 Loss1: 0.436107 Loss2: 1.404296 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.653696 Loss1: 0.237178 Loss2: 1.416518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583556 Loss1: 0.194996 Loss2: 1.388560 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 12:54:13,365 | server.py:236 | fit_round 114 received 50 results and 0 failures
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.590767 Loss1: 0.190646 Loss2: 1.400121 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466374 Loss1: 0.092258 Loss2: 1.374116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.886900 Loss1: 0.934332 Loss2: 1.952568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.975137 Loss1: 0.613978 Loss2: 1.361160 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.965820
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.683479 Loss1: 0.298916 Loss2: 1.384563 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.557674 Loss1: 0.187535 Loss2: 1.370140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458625 Loss1: 0.119488 Loss2: 1.339137 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.636966 Loss1: 0.777049 Loss2: 1.859917 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400543 Loss1: 0.074218 Loss2: 1.326325
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.812432 Loss1: 0.455369 Loss2: 1.357063
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.660898 Loss1: 0.259971 Loss2: 1.400927
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.494031 Loss1: 0.142496 Loss2: 1.351535
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475410 Loss1: 0.131309 Loss2: 1.344101
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451300 Loss1: 0.109752 Loss2: 1.341548
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.636243 Loss1: 0.834989 Loss2: 1.801254
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410505 Loss1: 0.076284 Loss2: 1.334221
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.791644 Loss1: 0.462304 Loss2: 1.329340
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424552 Loss1: 0.088481 Loss2: 1.336070
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.652331 Loss1: 0.303580 Loss2: 1.348751
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431644 Loss1: 0.099483 Loss2: 1.332161
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.614646 Loss1: 0.299005 Loss2: 1.315641
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414547 Loss1: 0.083741 Loss2: 1.330806
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474306 Loss1: 0.164169 Loss2: 1.310137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.384280 Loss1: 0.090636 Loss2: 1.293644 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.349569 Loss1: 0.069405 Loss2: 1.280164 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 12:54:13,365][flwr][DEBUG] - fit_round 114 received 50 results and 0 failures
-INFO flwr 2023-10-11 12:54:54,932 | server.py:125 | fit progress: (114, 2.199429733303789, {'accuracy': 0.5787}, 263002.71083863097)
->> Test accuracy: 0.578700
-[2023-10-11 12:54:54,932][flwr][INFO] - fit progress: (114, 2.199429733303789, {'accuracy': 0.5787}, 263002.71083863097)
-DEBUG flwr 2023-10-11 12:54:54,933 | server.py:173 | evaluate_round 114: strategy sampled 50 clients (out of 50)
-[2023-10-11 12:54:54,933][flwr][DEBUG] - evaluate_round 114: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 13:03:59,677 | server.py:187 | evaluate_round 114 received 50 results and 0 failures
-[2023-10-11 13:03:59,677][flwr][DEBUG] - evaluate_round 114 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 13:03:59,677 | server.py:222 | fit_round 115: strategy sampled 50 clients (out of 50)
-[2023-10-11 13:03:59,677][flwr][DEBUG] - fit_round 115: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.706543 Loss1: 0.799641 Loss2: 1.906902
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.973225 Loss1: 0.529686 Loss2: 1.443540
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.828500 Loss1: 0.346591 Loss2: 1.481909
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.707411 Loss1: 0.835424 Loss2: 1.871987
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.739395 Loss1: 0.307880 Loss2: 1.431515
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.984538 Loss1: 0.575936 Loss2: 1.408602
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.619951 Loss1: 0.177834 Loss2: 1.442116
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.846611 Loss1: 0.390517 Loss2: 1.456094
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.561945 Loss1: 0.139712 Loss2: 1.422233
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631932 Loss1: 0.236767 Loss2: 1.395164
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529939 Loss1: 0.106633 Loss2: 1.423306
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477308 Loss1: 0.067980 Loss2: 1.409328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482842 Loss1: 0.080333 Loss2: 1.402509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457568 Loss1: 0.064713 Loss2: 1.392855 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.470887 Loss1: 0.098574 Loss2: 1.372313 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.784849 Loss1: 0.895917 Loss2: 1.888932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644984 Loss1: 0.286148 Loss2: 1.358836 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.681092 Loss1: 0.852407 Loss2: 1.828686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.784316 Loss1: 0.435526 Loss2: 1.348790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383794 Loss1: 0.090559 Loss2: 1.293235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382035 Loss1: 0.094873 Loss2: 1.287162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.349688 Loss1: 0.066426 Loss2: 1.283262 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.339198 Loss1: 0.062496 Loss2: 1.276701 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995192
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443588 Loss1: 0.115538 Loss2: 1.328051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409276 Loss1: 0.097101 Loss2: 1.312175 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386816 Loss1: 0.075242 Loss2: 1.311573
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.524501 Loss1: 0.643478 Loss2: 1.881023
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.844453 Loss1: 0.439519 Loss2: 1.404934
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.767602 Loss1: 0.310590 Loss2: 1.457012
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.612664 Loss1: 0.209875 Loss2: 1.402789
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.584357 Loss1: 0.178402 Loss2: 1.405955
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.644576 Loss1: 0.809240 Loss2: 1.835336
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.917530 Loss1: 0.560957 Loss2: 1.356573 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.766014 Loss1: 0.369101 Loss2: 1.396914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.613197 Loss1: 0.260089 Loss2: 1.353109 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522626 Loss1: 0.167179 Loss2: 1.355447 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.444829 Loss1: 0.069606 Loss2: 1.375223
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485909 Loss1: 0.150427 Loss2: 1.335482
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446810 Loss1: 0.110410 Loss2: 1.336400
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406678 Loss1: 0.076534 Loss2: 1.330144
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419796 Loss1: 0.098562 Loss2: 1.321234
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441685 Loss1: 0.115596 Loss2: 1.326089
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.872049 Loss1: 1.008672 Loss2: 1.863377
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.001344 Loss1: 0.591325 Loss2: 1.410019
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.756606 Loss1: 0.331848 Loss2: 1.424758
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.628149 Loss1: 0.243350 Loss2: 1.384799
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.584640 Loss1: 0.198079 Loss2: 1.386561
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.731190 Loss1: 0.879962 Loss2: 1.851228
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537742 Loss1: 0.167576 Loss2: 1.370166
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458211 Loss1: 0.097146 Loss2: 1.361065 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451155 Loss1: 0.090514 Loss2: 1.360642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447056 Loss1: 0.095289 Loss2: 1.351767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451681 Loss1: 0.103664 Loss2: 1.348017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419115 Loss1: 0.081980 Loss2: 1.337135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381501 Loss1: 0.056214 Loss2: 1.325287 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391489 Loss1: 0.066106 Loss2: 1.325383
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.634072 Loss1: 0.756471 Loss2: 1.877601
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.918810 Loss1: 0.514632 Loss2: 1.404178
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.754066 Loss1: 0.286907 Loss2: 1.467159
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.600826 Loss1: 0.203942 Loss2: 1.396884
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.580067 Loss1: 0.178344 Loss2: 1.401723
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.688068 Loss1: 0.815707 Loss2: 1.872361
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.995121 Loss1: 0.561530 Loss2: 1.433592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.748036 Loss1: 0.298369 Loss2: 1.449666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.698209 Loss1: 0.282982 Loss2: 1.415227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.620752 Loss1: 0.194562 Loss2: 1.426190 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.511488 Loss1: 0.111104 Loss2: 1.400383 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.469788 Loss1: 0.080190 Loss2: 1.389598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458029 Loss1: 0.078156 Loss2: 1.379873
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.856198 Loss1: 0.885784 Loss2: 1.970413
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.062555 Loss1: 0.569351 Loss2: 1.493204
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.872825 Loss1: 0.358653 Loss2: 1.514172
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.717449 Loss1: 0.243176 Loss2: 1.474273
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.674638 Loss1: 0.204496 Loss2: 1.470142
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.835296 Loss1: 0.982002 Loss2: 1.853293
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.597890 Loss1: 0.138318 Loss2: 1.459572
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.579468 Loss1: 0.125394 Loss2: 1.454074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.560963 Loss1: 0.110150 Loss2: 1.450814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.566696 Loss1: 0.118075 Loss2: 1.448621 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.552427 Loss1: 0.102260 Loss2: 1.450166 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.462083 Loss1: 0.127528 Loss2: 1.334554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443865 Loss1: 0.112322 Loss2: 1.331543 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406400 Loss1: 0.080159 Loss2: 1.326240
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.785225 Loss1: 0.900530 Loss2: 1.884694
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.861669 Loss1: 0.470981 Loss2: 1.390688
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.722735 Loss1: 0.320969 Loss2: 1.401766
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.648707 Loss1: 0.275219 Loss2: 1.373488
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567673 Loss1: 0.187787 Loss2: 1.379886
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.767630 Loss1: 0.847711 Loss2: 1.919919
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530894 Loss1: 0.158557 Loss2: 1.372337
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466159 Loss1: 0.105271 Loss2: 1.360887 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426892 Loss1: 0.075483 Loss2: 1.351409 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399833 Loss1: 0.056053 Loss2: 1.343780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420175 Loss1: 0.078155 Loss2: 1.342020 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457281 Loss1: 0.091452 Loss2: 1.365829 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.476215 Loss1: 0.114575 Loss2: 1.361640 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.942758 Loss1: 0.536212 Loss2: 1.406546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580959 Loss1: 0.197810 Loss2: 1.383148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.557022 Loss1: 0.177063 Loss2: 1.379958
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.729932 Loss1: 0.869213 Loss2: 1.860720
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520596 Loss1: 0.150913 Loss2: 1.369682
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.867420 Loss1: 0.463927 Loss2: 1.403493
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486126 Loss1: 0.120970 Loss2: 1.365156
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.686892 Loss1: 0.251496 Loss2: 1.435395
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488716 Loss1: 0.130220 Loss2: 1.358496
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.641906 Loss1: 0.254292 Loss2: 1.387614
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.466046 Loss1: 0.106747 Loss2: 1.359299
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568494 Loss1: 0.173326 Loss2: 1.395168
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428513 Loss1: 0.074666 Loss2: 1.353847
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.563997 Loss1: 0.188543 Loss2: 1.375455
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518475 Loss1: 0.140271 Loss2: 1.378203
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484735 Loss1: 0.109988 Loss2: 1.374747
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501238 Loss1: 0.128593 Loss2: 1.372645
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458340 Loss1: 0.088036 Loss2: 1.370303
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.594256 Loss1: 0.727575 Loss2: 1.866680
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.819046 Loss1: 0.429073 Loss2: 1.389973
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.749645 Loss1: 0.304867 Loss2: 1.444779
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.644293 Loss1: 0.248760 Loss2: 1.395533
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.584720 Loss1: 0.186275 Loss2: 1.398445
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.531491 Loss1: 0.142345 Loss2: 1.389146 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.558034 Loss1: 0.169332 Loss2: 1.388702 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.527521 Loss1: 0.130248 Loss2: 1.397273 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.485839 Loss1: 0.110485 Loss2: 1.375353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477506 Loss1: 0.098321 Loss2: 1.379185 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405488 Loss1: 0.046575 Loss2: 1.358913 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411393 Loss1: 0.067788 Loss2: 1.343606 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.866648 Loss1: 0.455589 Loss2: 1.411059 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.646270 Loss1: 0.240399 Loss2: 1.405871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.545963 Loss1: 0.143533 Loss2: 1.402430
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.601943 Loss1: 0.721115 Loss2: 1.880828
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471368 Loss1: 0.098387 Loss2: 1.372981
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.858997 Loss1: 0.431890 Loss2: 1.427106
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466785 Loss1: 0.098887 Loss2: 1.367898
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.785332 Loss1: 0.294120 Loss2: 1.491212
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.692454 Loss1: 0.268380 Loss2: 1.424075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.647560 Loss1: 0.196530 Loss2: 1.451030 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426629 Loss1: 0.068352 Loss2: 1.358277
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.669902 Loss1: 0.243841 Loss2: 1.426061
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.605519 Loss1: 0.166134 Loss2: 1.439385
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.545915 Loss1: 0.132661 Loss2: 1.413254
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.498769 Loss1: 0.096831 Loss2: 1.401939
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470847 Loss1: 0.076414 Loss2: 1.394432
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.686903 Loss1: 0.852613 Loss2: 1.834290
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.807465 Loss1: 0.441003 Loss2: 1.366462
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.704972 Loss1: 0.303018 Loss2: 1.401954
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.634234 Loss1: 0.279305 Loss2: 1.354929
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.607436 Loss1: 0.235799 Loss2: 1.371637
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.570957 Loss1: 0.742686 Loss2: 1.828271
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.864862 Loss1: 0.506822 Loss2: 1.358040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.680315 Loss1: 0.295443 Loss2: 1.384872 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.547022 Loss1: 0.201161 Loss2: 1.345861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483049 Loss1: 0.137573 Loss2: 1.345477 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474414 Loss1: 0.142786 Loss2: 1.331628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427023 Loss1: 0.099957 Loss2: 1.327066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386034 Loss1: 0.063505 Loss2: 1.322530 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.920220 Loss1: 0.484483 Loss2: 1.435737 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594996 Loss1: 0.184635 Loss2: 1.410361 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536036 Loss1: 0.132175 Loss2: 1.403860
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.799689 Loss1: 0.902542 Loss2: 1.897147
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.932008 Loss1: 0.495360 Loss2: 1.436649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769779 Loss1: 0.321610 Loss2: 1.448169 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.645266 Loss1: 0.226744 Loss2: 1.418522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.631476 Loss1: 0.215687 Loss2: 1.415789 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450597 Loss1: 0.071140 Loss2: 1.379456
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.571642 Loss1: 0.159525 Loss2: 1.412116
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502363 Loss1: 0.098782 Loss2: 1.403581
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462764 Loss1: 0.072351 Loss2: 1.390414
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438293 Loss1: 0.059153 Loss2: 1.379140
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428508 Loss1: 0.056014 Loss2: 1.372494
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.734325 Loss1: 0.832615 Loss2: 1.901710
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.882507 Loss1: 0.473114 Loss2: 1.409393
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.757257 Loss1: 0.312865 Loss2: 1.444391
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.631970 Loss1: 0.236526 Loss2: 1.395444
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.550448 Loss1: 0.148572 Loss2: 1.401875
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.900982 Loss1: 0.901793 Loss2: 1.999189
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.950092 Loss1: 0.575578 Loss2: 1.374514
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564601 Loss1: 0.175738 Loss2: 1.388863
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.780060 Loss1: 0.341420 Loss2: 1.438640
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453099 Loss1: 0.086558 Loss2: 1.366541 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455724 Loss1: 0.092325 Loss2: 1.363399 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.564187 Loss1: 0.184761 Loss2: 1.379426 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445254 Loss1: 0.082083 Loss2: 1.363171 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.470888 Loss1: 0.625987 Loss2: 1.844902 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.688195 Loss1: 0.258528 Loss2: 1.429667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.625444 Loss1: 0.246599 Loss2: 1.378846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593336 Loss1: 0.204758 Loss2: 1.388579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.581222 Loss1: 0.193379 Loss2: 1.387843 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491777 Loss1: 0.109311 Loss2: 1.382466 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483707 Loss1: 0.112429 Loss2: 1.371278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.557537 Loss1: 0.163458 Loss2: 1.394079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540045 Loss1: 0.150073 Loss2: 1.389972 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462911 Loss1: 0.088990 Loss2: 1.373922 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.493429 Loss1: 0.657171 Loss2: 1.836258
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.753590 Loss1: 0.378370 Loss2: 1.375220
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.625937 Loss1: 0.214684 Loss2: 1.411253
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.603461 Loss1: 0.735885 Loss2: 1.867576
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569567 Loss1: 0.211242 Loss2: 1.358325
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.879531 Loss1: 0.499805 Loss2: 1.379726
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519458 Loss1: 0.148024 Loss2: 1.371435
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472581 Loss1: 0.106963 Loss2: 1.365618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486204 Loss1: 0.132615 Loss2: 1.353589 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483930 Loss1: 0.128664 Loss2: 1.355266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452852 Loss1: 0.101469 Loss2: 1.351383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446248 Loss1: 0.089670 Loss2: 1.356577 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460875 Loss1: 0.105126 Loss2: 1.355749 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.797192 Loss1: 0.932270 Loss2: 1.864922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.758018 Loss1: 0.342866 Loss2: 1.415153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629037 Loss1: 0.230368 Loss2: 1.398669
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.734491 Loss1: 0.905626 Loss2: 1.828865
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.982402 Loss1: 0.607845 Loss2: 1.374557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.735199 Loss1: 0.345598 Loss2: 1.389601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562746 Loss1: 0.214053 Loss2: 1.348693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503595 Loss1: 0.156411 Loss2: 1.347185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474352 Loss1: 0.138026 Loss2: 1.336326 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406161 Loss1: 0.056938 Loss2: 1.349223
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.466386 Loss1: 0.127442 Loss2: 1.338944
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446000 Loss1: 0.112272 Loss2: 1.333728
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397925 Loss1: 0.074521 Loss2: 1.323405
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378016 Loss1: 0.061195 Loss2: 1.316820
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.652248 Loss1: 0.809602 Loss2: 1.842646
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.941810 Loss1: 0.525833 Loss2: 1.415977
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684567 Loss1: 0.258280 Loss2: 1.426286
-(DefaultActor pid=3764) Epoch: 0 Loss: 3.027996 Loss1: 1.042986 Loss2: 1.985010
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528313 Loss1: 0.158790 Loss2: 1.369524
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.057008 Loss1: 0.569313 Loss2: 1.487695
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480060 Loss1: 0.110352 Loss2: 1.369707
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.467685 Loss1: 0.109041 Loss2: 1.358644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484975 Loss1: 0.123427 Loss2: 1.361547 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461501 Loss1: 0.102116 Loss2: 1.359385 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493127 Loss1: 0.139860 Loss2: 1.353267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431088 Loss1: 0.078215 Loss2: 1.352873 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.502566 Loss1: 0.083288 Loss2: 1.419277 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.542877 Loss1: 0.755750 Loss2: 1.787126
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.869050 Loss1: 0.498074 Loss2: 1.370976
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.675081 Loss1: 0.284412 Loss2: 1.390669
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604623 Loss1: 0.251153 Loss2: 1.353470
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.673859 Loss1: 0.763597 Loss2: 1.910261
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.528263 Loss1: 0.172465 Loss2: 1.355798
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.865894 Loss1: 0.457027 Loss2: 1.408867
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.783437 Loss1: 0.342005 Loss2: 1.441431
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489738 Loss1: 0.144949 Loss2: 1.344789
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.623410 Loss1: 0.212059 Loss2: 1.411351
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490595 Loss1: 0.145912 Loss2: 1.344683
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.600515 Loss1: 0.191930 Loss2: 1.408585
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467348 Loss1: 0.127058 Loss2: 1.340290
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528468 Loss1: 0.126000 Loss2: 1.402467
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434781 Loss1: 0.095565 Loss2: 1.339216
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393204 Loss1: 0.065979 Loss2: 1.327225 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467536 Loss1: 0.084982 Loss2: 1.382555 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.586153 Loss1: 0.720480 Loss2: 1.865673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716360 Loss1: 0.291581 Loss2: 1.424779 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.641648 Loss1: 0.740899 Loss2: 1.900749
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.631068 Loss1: 0.244118 Loss2: 1.386949
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.868881 Loss1: 0.471339 Loss2: 1.397542
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.542393 Loss1: 0.143548 Loss2: 1.398844
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.807977 Loss1: 0.362933 Loss2: 1.445045
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551957 Loss1: 0.175402 Loss2: 1.376555
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.691375 Loss1: 0.282455 Loss2: 1.408920
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.499021 Loss1: 0.123348 Loss2: 1.375673
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488140 Loss1: 0.115617 Loss2: 1.372523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429497 Loss1: 0.070869 Loss2: 1.358627 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443084 Loss1: 0.081127 Loss2: 1.361957 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462536 Loss1: 0.083725 Loss2: 1.378811 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.544555 Loss1: 0.589251 Loss2: 1.955304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.862796 Loss1: 0.363781 Loss2: 1.499015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.793698 Loss1: 0.339525 Loss2: 1.454173
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.861724 Loss1: 0.934062 Loss2: 1.927662
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.655342 Loss1: 0.200343 Loss2: 1.454999
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.979636 Loss1: 0.577193 Loss2: 1.402443
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.812403 Loss1: 0.349054 Loss2: 1.463349
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.606271 Loss1: 0.164070 Loss2: 1.442201
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.620213 Loss1: 0.224922 Loss2: 1.395291
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.616420 Loss1: 0.177206 Loss2: 1.439214
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.567287 Loss1: 0.173802 Loss2: 1.393485
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.553415 Loss1: 0.114132 Loss2: 1.439283
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.485343 Loss1: 0.057986 Loss2: 1.427357 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.523117 Loss1: 0.105629 Loss2: 1.417488 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446680 Loss1: 0.087671 Loss2: 1.359009 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.675022 Loss1: 0.880121 Loss2: 1.794902 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.729599 Loss1: 0.352861 Loss2: 1.376738 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.643935 Loss1: 0.297817 Loss2: 1.346117
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.543431 Loss1: 0.741499 Loss2: 1.801933
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.523442 Loss1: 0.183071 Loss2: 1.340370
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.777629 Loss1: 0.455672 Loss2: 1.321956
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.464103 Loss1: 0.136420 Loss2: 1.327682
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.628954 Loss1: 0.260613 Loss2: 1.368342
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450958 Loss1: 0.134219 Loss2: 1.316740
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.522978 Loss1: 0.211613 Loss2: 1.311366
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446616 Loss1: 0.128444 Loss2: 1.318172
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476399 Loss1: 0.153735 Loss2: 1.322664
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391168 Loss1: 0.079107 Loss2: 1.312061
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.399740 Loss1: 0.095597 Loss2: 1.304143
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366563 Loss1: 0.060310 Loss2: 1.306253
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.398334 Loss1: 0.099703 Loss2: 1.298631
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.355128 Loss1: 0.054126 Loss2: 1.301002
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.333798 Loss1: 0.043201 Loss2: 1.290597
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.341263 Loss1: 0.054174 Loss2: 1.287088
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.816547 Loss1: 0.876904 Loss2: 1.939643
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.040250 Loss1: 0.594000 Loss2: 1.446250
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.803885 Loss1: 0.366578 Loss2: 1.437307
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.621085 Loss1: 0.223185 Loss2: 1.397900
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.758792 Loss1: 0.892585 Loss2: 1.866207
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.539868 Loss1: 0.143657 Loss2: 1.396211
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.907465 Loss1: 0.528555 Loss2: 1.378910
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.532612 Loss1: 0.137200 Loss2: 1.395412
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.721958 Loss1: 0.301640 Loss2: 1.420318
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503660 Loss1: 0.115624 Loss2: 1.388036
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.663120 Loss1: 0.304820 Loss2: 1.358301
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462734 Loss1: 0.087587 Loss2: 1.375147
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.593229 Loss1: 0.208617 Loss2: 1.384611
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436513 Loss1: 0.062908 Loss2: 1.373605
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.532114 Loss1: 0.170467 Loss2: 1.361647
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429797 Loss1: 0.064905 Loss2: 1.364891
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467179 Loss1: 0.111123 Loss2: 1.356057
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433542 Loss1: 0.087860 Loss2: 1.345682
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401111 Loss1: 0.066959 Loss2: 1.334151
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397176 Loss1: 0.066475 Loss2: 1.330702
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.491092 Loss1: 0.711156 Loss2: 1.779936
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.737305 Loss1: 0.385033 Loss2: 1.352272
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.637091 Loss1: 0.264348 Loss2: 1.372743
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.647490 Loss1: 0.805040 Loss2: 1.842450
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524352 Loss1: 0.174576 Loss2: 1.349777
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.051488 Loss1: 0.645728 Loss2: 1.405760
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514795 Loss1: 0.171729 Loss2: 1.343066
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.865808 Loss1: 0.408346 Loss2: 1.457462
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514500 Loss1: 0.170972 Loss2: 1.343528
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.740538 Loss1: 0.358282 Loss2: 1.382257
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.519273 Loss1: 0.167633 Loss2: 1.351640
-DEBUG flwr 2023-10-11 13:33:09,447 | server.py:236 | fit_round 115 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477922 Loss1: 0.134040 Loss2: 1.343882 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499704 Loss1: 0.161028 Loss2: 1.338676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443907 Loss1: 0.107613 Loss2: 1.336294 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973633
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449889 Loss1: 0.093940 Loss2: 1.355949 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.576477 Loss1: 0.737924 Loss2: 1.838553 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.646444 Loss1: 0.229756 Loss2: 1.416689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.615592 Loss1: 0.785595 Loss2: 1.829997
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555047 Loss1: 0.188152 Loss2: 1.366895
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.823690 Loss1: 0.462387 Loss2: 1.361303
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547651 Loss1: 0.177954 Loss2: 1.369697
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.707280 Loss1: 0.312956 Loss2: 1.394323
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.512733 Loss1: 0.144397 Loss2: 1.368336
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604061 Loss1: 0.242948 Loss2: 1.361113
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500855 Loss1: 0.139781 Loss2: 1.361074
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.475788 Loss1: 0.110255 Loss2: 1.365533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477916 Loss1: 0.122566 Loss2: 1.355350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.477433 Loss1: 0.120207 Loss2: 1.357226 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423772 Loss1: 0.090840 Loss2: 1.332931 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.659897 Loss1: 0.812618 Loss2: 1.847279 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.785663 Loss1: 0.379800 Loss2: 1.405863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.650685 Loss1: 0.272981 Loss2: 1.377704
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.812332 Loss1: 0.886143 Loss2: 1.926189
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.923248 Loss1: 0.524690 Loss2: 1.398559
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.575856 Loss1: 0.199180 Loss2: 1.376676
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463368 Loss1: 0.106587 Loss2: 1.356781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445660 Loss1: 0.099399 Loss2: 1.346261 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409027 Loss1: 0.067998 Loss2: 1.341028 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406788 Loss1: 0.068168 Loss2: 1.338620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374314 Loss1: 0.046891 Loss2: 1.327422 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472171 Loss1: 0.099427 Loss2: 1.372743 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978365
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 13:33:09,447][flwr][DEBUG] - fit_round 115 received 50 results and 0 failures
-INFO flwr 2023-10-11 13:33:50,426 | server.py:125 | fit progress: (115, 2.1888895084301883, {'accuracy': 0.5787}, 265338.20431725896)
->> Test accuracy: 0.578700
-[2023-10-11 13:33:50,426][flwr][INFO] - fit progress: (115, 2.1888895084301883, {'accuracy': 0.5787}, 265338.20431725896)
-DEBUG flwr 2023-10-11 13:33:50,426 | server.py:173 | evaluate_round 115: strategy sampled 50 clients (out of 50)
-[2023-10-11 13:33:50,426][flwr][DEBUG] - evaluate_round 115: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 13:42:55,317 | server.py:187 | evaluate_round 115 received 50 results and 0 failures
-[2023-10-11 13:42:55,317][flwr][DEBUG] - evaluate_round 115 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 13:42:55,318 | server.py:222 | fit_round 116: strategy sampled 50 clients (out of 50)
-[2023-10-11 13:42:55,318][flwr][DEBUG] - fit_round 116: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.906143 Loss1: 0.907155 Loss2: 1.998989
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.946148 Loss1: 0.578437 Loss2: 1.367711
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.813106 Loss1: 0.381679 Loss2: 1.431427
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.650857 Loss1: 0.247068 Loss2: 1.403789
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.623057 Loss1: 0.258060 Loss2: 1.364997
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551842 Loss1: 0.177523 Loss2: 1.374319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529006 Loss1: 0.164012 Loss2: 1.364995 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500520 Loss1: 0.147403 Loss2: 1.353118
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682597 Loss1: 0.276488 Loss2: 1.406109
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472270 Loss1: 0.110440 Loss2: 1.361830
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576947 Loss1: 0.222620 Loss2: 1.354327
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521167 Loss1: 0.166698 Loss2: 1.354468 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.429173 Loss1: 0.090722 Loss2: 1.338450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414879 Loss1: 0.080673 Loss2: 1.334206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403497 Loss1: 0.073032 Loss2: 1.330465 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.786802 Loss1: 0.368635 Loss2: 1.418167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.608275 Loss1: 0.236921 Loss2: 1.371354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551689 Loss1: 0.199494 Loss2: 1.352195
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.655852 Loss1: 0.796243 Loss2: 1.859609
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.832066 Loss1: 0.448205 Loss2: 1.383861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.765758 Loss1: 0.330871 Loss2: 1.434887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640294 Loss1: 0.268875 Loss2: 1.371419 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563155 Loss1: 0.168741 Loss2: 1.394414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.429675 Loss1: 0.069190 Loss2: 1.360485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400586 Loss1: 0.052198 Loss2: 1.348388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405677 Loss1: 0.063293 Loss2: 1.342384 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.711623 Loss1: 0.290822 Loss2: 1.420801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521616 Loss1: 0.130871 Loss2: 1.390745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.651928 Loss1: 0.725951 Loss2: 1.925977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.007794 Loss1: 0.591866 Loss2: 1.415928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.748248 Loss1: 0.278738 Loss2: 1.469510 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.638270 Loss1: 0.226578 Loss2: 1.411692 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.564428 Loss1: 0.150738 Loss2: 1.413690 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485482 Loss1: 0.077757 Loss2: 1.407725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.486164 Loss1: 0.709748 Loss2: 1.776416 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.775791 Loss1: 0.447399 Loss2: 1.328392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542612 Loss1: 0.207739 Loss2: 1.334874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.447377 Loss1: 0.130041 Loss2: 1.317336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409695 Loss1: 0.104389 Loss2: 1.305306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.402999 Loss1: 0.096180 Loss2: 1.306819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.359604 Loss1: 0.059561 Loss2: 1.300043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361228 Loss1: 0.066140 Loss2: 1.295088 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443049 Loss1: 0.096155 Loss2: 1.346895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442564 Loss1: 0.102516 Loss2: 1.340048 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.667056 Loss1: 0.826016 Loss2: 1.841040 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.699489 Loss1: 0.290060 Loss2: 1.409429 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587957 Loss1: 0.213274 Loss2: 1.374682 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530608 Loss1: 0.171749 Loss2: 1.358858
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.633628 Loss1: 0.738329 Loss2: 1.895299
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.847694 Loss1: 0.461692 Loss2: 1.386002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.835026 Loss1: 0.376868 Loss2: 1.458158 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.703043 Loss1: 0.328512 Loss2: 1.374531 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.645663 Loss1: 0.248838 Loss2: 1.396825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513276 Loss1: 0.142005 Loss2: 1.371271 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412047 Loss1: 0.060577 Loss2: 1.351470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.510568 Loss1: 0.659932 Loss2: 1.850636
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403048 Loss1: 0.061859 Loss2: 1.341189
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.800005 Loss1: 0.340565 Loss2: 1.459440 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.607970 Loss1: 0.188828 Loss2: 1.419143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.561427 Loss1: 0.161534 Loss2: 1.399893
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.618448 Loss1: 0.662465 Loss2: 1.955983
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.960737 Loss1: 0.514305 Loss2: 1.446432 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469099 Loss1: 0.081034 Loss2: 1.388065
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.768286 Loss1: 0.262877 Loss2: 1.505408
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434643 Loss1: 0.055436 Loss2: 1.379207
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.737307 Loss1: 0.282009 Loss2: 1.455298
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454321 Loss1: 0.084244 Loss2: 1.370078
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.658933 Loss1: 0.200134 Loss2: 1.458798
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.612378 Loss1: 0.163717 Loss2: 1.448662
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.565849 Loss1: 0.120061 Loss2: 1.445788
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.593323 Loss1: 0.150341 Loss2: 1.442982
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.600266 Loss1: 0.158800 Loss2: 1.441466
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.534948 Loss1: 0.093800 Loss2: 1.441148
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.553920 Loss1: 0.756984 Loss2: 1.796936
-(DefaultActor pid=3764) >> Training accuracy: 0.962500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.770356 Loss1: 0.435693 Loss2: 1.334663
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655330 Loss1: 0.275427 Loss2: 1.379903
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488823 Loss1: 0.162589 Loss2: 1.326234
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.500506 Loss1: 0.179400 Loss2: 1.321106
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.508403 Loss1: 0.645774 Loss2: 1.862629
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478698 Loss1: 0.152776 Loss2: 1.325922
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455470 Loss1: 0.131813 Loss2: 1.323657
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.922021 Loss1: 0.512889 Loss2: 1.409132
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435742 Loss1: 0.116766 Loss2: 1.318975
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.724880 Loss1: 0.256727 Loss2: 1.468153
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424663 Loss1: 0.111473 Loss2: 1.313189
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.685219 Loss1: 0.275945 Loss2: 1.409274
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407336 Loss1: 0.091900 Loss2: 1.315436
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.703147 Loss1: 0.277868 Loss2: 1.425280
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.667207 Loss1: 0.252869 Loss2: 1.414338
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.576737 Loss1: 0.158500 Loss2: 1.418237
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.575723 Loss1: 0.174081 Loss2: 1.401642
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.525798 Loss1: 0.122202 Loss2: 1.403596
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.701300 Loss1: 0.848624 Loss2: 1.852676
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.869923 Loss1: 0.494953 Loss2: 1.374970 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578765 Loss1: 0.218024 Loss2: 1.360741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484278 Loss1: 0.121610 Loss2: 1.362668 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446473 Loss1: 0.094259 Loss2: 1.352214 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425733 Loss1: 0.080860 Loss2: 1.344873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477343 Loss1: 0.127986 Loss2: 1.349357 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427836 Loss1: 0.076594 Loss2: 1.351242 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.544297 Loss1: 0.145673 Loss2: 1.398624 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493958 Loss1: 0.110069 Loss2: 1.383889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460636 Loss1: 0.082377 Loss2: 1.378259
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.635285 Loss1: 0.851794 Loss2: 1.783491
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.789548 Loss1: 0.450429 Loss2: 1.339119
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.680920 Loss1: 0.310924 Loss2: 1.369997
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.615798 Loss1: 0.286638 Loss2: 1.329160
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.550820 Loss1: 0.212349 Loss2: 1.338471
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.660704 Loss1: 0.778330 Loss2: 1.882374
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.531529 Loss1: 0.206775 Loss2: 1.324754
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.865152 Loss1: 0.480224 Loss2: 1.384928
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.497219 Loss1: 0.171321 Loss2: 1.325898
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684719 Loss1: 0.291652 Loss2: 1.393067
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454586 Loss1: 0.136783 Loss2: 1.317803
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592966 Loss1: 0.238304 Loss2: 1.354662
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431530 Loss1: 0.113635 Loss2: 1.317895
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.480297 Loss1: 0.128930 Loss2: 1.351368
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411355 Loss1: 0.107322 Loss2: 1.304033
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434447 Loss1: 0.098581 Loss2: 1.335866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390482 Loss1: 0.061711 Loss2: 1.328771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.371784 Loss1: 0.045948 Loss2: 1.325835
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.562258 Loss1: 0.727144 Loss2: 1.835113
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.871267 Loss1: 0.501509 Loss2: 1.369759
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.768372 Loss1: 0.342450 Loss2: 1.425922
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.686091 Loss1: 0.303791 Loss2: 1.382301
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.599935 Loss1: 0.216157 Loss2: 1.383778
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538356 Loss1: 0.172688 Loss2: 1.365667
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.928969 Loss1: 0.983294 Loss2: 1.945675
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529773 Loss1: 0.173750 Loss2: 1.356024
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.080188 Loss1: 0.619494 Loss2: 1.460695
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.496773 Loss1: 0.134119 Loss2: 1.362655
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.870652 Loss1: 0.379318 Loss2: 1.491334
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465187 Loss1: 0.109552 Loss2: 1.355635
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.754738 Loss1: 0.309361 Loss2: 1.445376
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448095 Loss1: 0.099042 Loss2: 1.349053
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.674177 Loss1: 0.218153 Loss2: 1.456023
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.572360 Loss1: 0.146121 Loss2: 1.426239
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.526671 Loss1: 0.100443 Loss2: 1.426228
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.492110 Loss1: 0.073986 Loss2: 1.418124
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485544 Loss1: 0.074973 Loss2: 1.410571
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472723 Loss1: 0.071667 Loss2: 1.401056
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.571861 Loss1: 0.757691 Loss2: 1.814171
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.912784 Loss1: 0.539654 Loss2: 1.373130
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.762376 Loss1: 0.361322 Loss2: 1.401055
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649335 Loss1: 0.283960 Loss2: 1.365375
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524031 Loss1: 0.166460 Loss2: 1.357571
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462691 Loss1: 0.126524 Loss2: 1.336167
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.710974 Loss1: 0.822220 Loss2: 1.888754
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428128 Loss1: 0.095969 Loss2: 1.332159
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.964372 Loss1: 0.533590 Loss2: 1.430782
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417461 Loss1: 0.088709 Loss2: 1.328752
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.861786 Loss1: 0.375645 Loss2: 1.486141
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398444 Loss1: 0.075046 Loss2: 1.323399
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.714248 Loss1: 0.287867 Loss2: 1.426381
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370335 Loss1: 0.052704 Loss2: 1.317631
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.625644 Loss1: 0.194789 Loss2: 1.430855
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.586960 Loss1: 0.169375 Loss2: 1.417586
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504427 Loss1: 0.090719 Loss2: 1.413708
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518204 Loss1: 0.114303 Loss2: 1.403901
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.514194 Loss1: 0.110235 Loss2: 1.403959
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.480122 Loss1: 0.082404 Loss2: 1.397718
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.644557 Loss1: 0.824895 Loss2: 1.819662
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.801972 Loss1: 0.445137 Loss2: 1.356835
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.668891 Loss1: 0.277209 Loss2: 1.391682
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513556 Loss1: 0.171442 Loss2: 1.342115
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.489293 Loss1: 0.148055 Loss2: 1.341238
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.467162 Loss1: 0.124797 Loss2: 1.342365
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.571294 Loss1: 0.676843 Loss2: 1.894450
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476621 Loss1: 0.145946 Loss2: 1.330675
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.994924 Loss1: 0.519149 Loss2: 1.475776
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477349 Loss1: 0.142913 Loss2: 1.334436
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.760041 Loss1: 0.260131 Loss2: 1.499910
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612278 Loss1: 0.177591 Loss2: 1.434687 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.958333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440616 Loss1: 0.110167 Loss2: 1.330449
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.650522 Loss1: 0.202906 Loss2: 1.447616
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.602971 Loss1: 0.165141 Loss2: 1.437830
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533018 Loss1: 0.104745 Loss2: 1.428272
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.554421 Loss1: 0.123641 Loss2: 1.430780
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485335 Loss1: 0.060283 Loss2: 1.425052
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.687279 Loss1: 0.772140 Loss2: 1.915139
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.910777 Loss1: 0.486647 Loss2: 1.424129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.676742 Loss1: 0.264600 Loss2: 1.412141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562584 Loss1: 0.159797 Loss2: 1.402788 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525161 Loss1: 0.127350 Loss2: 1.397811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484393 Loss1: 0.091423 Loss2: 1.392970 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488122 Loss1: 0.098993 Loss2: 1.389129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.517792 Loss1: 0.125748 Loss2: 1.392044 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458030 Loss1: 0.136267 Loss2: 1.321764 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434064 Loss1: 0.130392 Loss2: 1.303672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.798477 Loss1: 0.892885 Loss2: 1.905592
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409264 Loss1: 0.105040 Loss2: 1.304224
-(DefaultActor pid=3764) >> Training accuracy: 0.985491
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.760049 Loss1: 0.325553 Loss2: 1.434496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.552154 Loss1: 0.179142 Loss2: 1.373012 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542826 Loss1: 0.173553 Loss2: 1.369273
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.645646 Loss1: 0.787828 Loss2: 1.857818
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831101 Loss1: 0.417142 Loss2: 1.413959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.621712 Loss1: 0.196463 Loss2: 1.425249 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567074 Loss1: 0.170442 Loss2: 1.396632 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535593 Loss1: 0.144617 Loss2: 1.390977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.522497 Loss1: 0.131778 Loss2: 1.390719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460164 Loss1: 0.078830 Loss2: 1.381335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430405 Loss1: 0.057169 Loss2: 1.373236 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.780300 Loss1: 0.371993 Loss2: 1.408308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562492 Loss1: 0.187906 Loss2: 1.374585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520132 Loss1: 0.147495 Loss2: 1.372637
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.632309 Loss1: 0.784671 Loss2: 1.847638
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.822812 Loss1: 0.443867 Loss2: 1.378945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.742976 Loss1: 0.318572 Loss2: 1.424405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.667129 Loss1: 0.286758 Loss2: 1.380371 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.464029 Loss1: 0.114350 Loss2: 1.349679
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.616563 Loss1: 0.221764 Loss2: 1.394799
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527124 Loss1: 0.153842 Loss2: 1.373282
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464710 Loss1: 0.094022 Loss2: 1.370688
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434360 Loss1: 0.069620 Loss2: 1.364741
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424999 Loss1: 0.068976 Loss2: 1.356023
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.671257 Loss1: 0.840121 Loss2: 1.831136
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422804 Loss1: 0.069273 Loss2: 1.353531
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.761526 Loss1: 0.359914 Loss2: 1.401613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572385 Loss1: 0.199074 Loss2: 1.373310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.535473 Loss1: 0.171440 Loss2: 1.364033
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.531611 Loss1: 0.751089 Loss2: 1.780522
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.802278 Loss1: 0.469846 Loss2: 1.332432 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.708267 Loss1: 0.356174 Loss2: 1.352093 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.607577 Loss1: 0.277166 Loss2: 1.330411 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427961 Loss1: 0.080846 Loss2: 1.347114
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542167 Loss1: 0.228514 Loss2: 1.313654
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495666 Loss1: 0.175312 Loss2: 1.320354
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.421304 Loss1: 0.114555 Loss2: 1.306749
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416498 Loss1: 0.115878 Loss2: 1.300620
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395030 Loss1: 0.101610 Loss2: 1.293420
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.689188 Loss1: 0.863210 Loss2: 1.825978
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364842 Loss1: 0.074106 Loss2: 1.290736
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.704884 Loss1: 0.291161 Loss2: 1.413724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526184 Loss1: 0.159122 Loss2: 1.367062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.423375 Loss1: 0.612750 Loss2: 1.810624 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.789123 Loss1: 0.461357 Loss2: 1.327766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633333 Loss1: 0.269176 Loss2: 1.364157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529834 Loss1: 0.198164 Loss2: 1.331670 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512258 Loss1: 0.186701 Loss2: 1.325557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.547200 Loss1: 0.227139 Loss2: 1.320061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391224 Loss1: 0.072504 Loss2: 1.318720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364780 Loss1: 0.058230 Loss2: 1.306550 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.735900 Loss1: 0.308261 Loss2: 1.427639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567403 Loss1: 0.198553 Loss2: 1.368850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.509814 Loss1: 0.772631 Loss2: 1.737183 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.792215 Loss1: 0.459891 Loss2: 1.332324 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.594348 Loss1: 0.242899 Loss2: 1.351449 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431037 Loss1: 0.094044 Loss2: 1.336993 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452316 Loss1: 0.141394 Loss2: 1.310922 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.380066 Loss1: 0.082239 Loss2: 1.297827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.729136 Loss1: 0.867813 Loss2: 1.861323
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381120 Loss1: 0.087881 Loss2: 1.293239
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.867943 Loss1: 0.476943 Loss2: 1.391000
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406511 Loss1: 0.107466 Loss2: 1.299046
-(DefaultActor pid=3764) >> Training accuracy: 0.964844
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.660875 Loss1: 0.273094 Loss2: 1.387781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523863 Loss1: 0.147464 Loss2: 1.376399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488620 Loss1: 0.120353 Loss2: 1.368267
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.703100 Loss1: 0.776449 Loss2: 1.926651
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.847065 Loss1: 0.425239 Loss2: 1.421826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.705764 Loss1: 0.253768 Loss2: 1.451997 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383448 Loss1: 0.032420 Loss2: 1.351028
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631904 Loss1: 0.222468 Loss2: 1.409436
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556342 Loss1: 0.143563 Loss2: 1.412779
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503960 Loss1: 0.096765 Loss2: 1.407194
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.498356 Loss1: 0.108076 Loss2: 1.390280
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471416 Loss1: 0.081393 Loss2: 1.390024
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.720768 Loss1: 0.876189 Loss2: 1.844579
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441312 Loss1: 0.057424 Loss2: 1.383887
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.953036 Loss1: 0.551518 Loss2: 1.401518
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.459338 Loss1: 0.077550 Loss2: 1.381788
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606067 Loss1: 0.224146 Loss2: 1.381921 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516263 Loss1: 0.141646 Loss2: 1.374617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469305 Loss1: 0.103667 Loss2: 1.365638
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.420432 Loss1: 0.603865 Loss2: 1.816567
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.760698 Loss1: 0.391001 Loss2: 1.369698 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.667877 Loss1: 0.275089 Loss2: 1.392788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577200 Loss1: 0.214509 Loss2: 1.362691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486877 Loss1: 0.130853 Loss2: 1.356023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500000 Loss1: 0.151167 Loss2: 1.348833 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.808293 Loss1: 0.400067 Loss2: 1.408225 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.663220 Loss1: 0.235725 Loss2: 1.427495 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983456
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491519 Loss1: 0.111784 Loss2: 1.379735 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.498336 Loss1: 0.116859 Loss2: 1.381476 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.798855 Loss1: 0.863344 Loss2: 1.935511
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483181 Loss1: 0.106984 Loss2: 1.376196
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.909495 Loss1: 0.489252 Loss2: 1.420244
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.464761 Loss1: 0.088700 Loss2: 1.376061
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428476 Loss1: 0.065940 Loss2: 1.362536 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.607076 Loss1: 0.201370 Loss2: 1.405706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516014 Loss1: 0.127909 Loss2: 1.388105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.897331 Loss1: 0.911932 Loss2: 1.985399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.892091 Loss1: 0.488977 Loss2: 1.403114 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.624990 Loss1: 0.223646 Loss2: 1.401344 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506538 Loss1: 0.130372 Loss2: 1.376166 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436810 Loss1: 0.073750 Loss2: 1.363060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411944 Loss1: 0.058080 Loss2: 1.353864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420473 Loss1: 0.067228 Loss2: 1.353244 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.491393 Loss1: 0.162519 Loss2: 1.328874 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444336 Loss1: 0.117300 Loss2: 1.327036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.607876 Loss1: 0.829306 Loss2: 1.778571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.828564 Loss1: 0.471696 Loss2: 1.356867 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655402 Loss1: 0.288574 Loss2: 1.366827 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516179 Loss1: 0.177193 Loss2: 1.338986 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469746 Loss1: 0.148830 Loss2: 1.320916 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.763848 Loss1: 0.796293 Loss2: 1.967555
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439630 Loss1: 0.121387 Loss2: 1.318243
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.882029 Loss1: 0.440745 Loss2: 1.441284
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427034 Loss1: 0.110808 Loss2: 1.316226
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.753847 Loss1: 0.292033 Loss2: 1.461813
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403657 Loss1: 0.089554 Loss2: 1.314103
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.605869 Loss1: 0.171040 Loss2: 1.434829 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.539264 Loss1: 0.115059 Loss2: 1.424204 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.507728 Loss1: 0.083084 Loss2: 1.424644
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.531088 Loss1: 0.731426 Loss2: 1.799662
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.840338 Loss1: 0.470035 Loss2: 1.370303 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.475749 Loss1: 0.060092 Loss2: 1.415657
-DEBUG flwr 2023-10-11 14:11:11,621 | server.py:236 | fit_round 116 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648944 Loss1: 0.252509 Loss2: 1.396435
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570818 Loss1: 0.210096 Loss2: 1.360722
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.596006 Loss1: 0.224684 Loss2: 1.371322
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.581220 Loss1: 0.210137 Loss2: 1.371082
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.521324 Loss1: 0.157249 Loss2: 1.364075
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.662196 Loss1: 0.793559 Loss2: 1.868636
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.895674 Loss1: 0.525052 Loss2: 1.370622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.746348 Loss1: 0.328713 Loss2: 1.417635 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435607 Loss1: 0.088842 Loss2: 1.346765
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.658760 Loss1: 0.279826 Loss2: 1.378933
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.567504 Loss1: 0.187539 Loss2: 1.379966
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472174 Loss1: 0.108830 Loss2: 1.363344
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440756 Loss1: 0.091265 Loss2: 1.349492
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398101 Loss1: 0.053913 Loss2: 1.344187
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.885538 Loss1: 0.992652 Loss2: 1.892886
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383540 Loss1: 0.044496 Loss2: 1.339044
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374352 Loss1: 0.038103 Loss2: 1.336249 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604365 Loss1: 0.243879 Loss2: 1.360487 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462291 Loss1: 0.111572 Loss2: 1.350719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389610 Loss1: 0.059084 Loss2: 1.330526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388643 Loss1: 0.063955 Loss2: 1.324688 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387783 Loss1: 0.071913 Loss2: 1.315869 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577209 Loss1: 0.223781 Loss2: 1.353428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468861 Loss1: 0.124948 Loss2: 1.343914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425261 Loss1: 0.088379 Loss2: 1.336882 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406180 Loss1: 0.073426 Loss2: 1.332754 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 14:11:11,621][flwr][DEBUG] - fit_round 116 received 50 results and 0 failures
-INFO flwr 2023-10-11 14:11:52,821 | server.py:125 | fit progress: (116, 2.2013852497259268, {'accuracy': 0.5796}, 267620.599708241)
->> Test accuracy: 0.579600
-[2023-10-11 14:11:52,821][flwr][INFO] - fit progress: (116, 2.2013852497259268, {'accuracy': 0.5796}, 267620.599708241)
-DEBUG flwr 2023-10-11 14:11:52,821 | server.py:173 | evaluate_round 116: strategy sampled 50 clients (out of 50)
-[2023-10-11 14:11:52,821][flwr][DEBUG] - evaluate_round 116: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 14:20:55,356 | server.py:187 | evaluate_round 116 received 50 results and 0 failures
-[2023-10-11 14:20:55,356][flwr][DEBUG] - evaluate_round 116 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 14:20:55,357 | server.py:222 | fit_round 117: strategy sampled 50 clients (out of 50)
-[2023-10-11 14:20:55,357][flwr][DEBUG] - fit_round 117: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.568193 Loss1: 0.641608 Loss2: 1.926586
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.815686 Loss1: 0.391947 Loss2: 1.423738
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.691189 Loss1: 0.232786 Loss2: 1.458403
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562930 Loss1: 0.148521 Loss2: 1.414409
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.564264 Loss1: 0.776131 Loss2: 1.788133
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.874353 Loss1: 0.531635 Loss2: 1.342718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.837331 Loss1: 0.431067 Loss2: 1.406264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.615142 Loss1: 0.275143 Loss2: 1.339999 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.566318 Loss1: 0.213824 Loss2: 1.352494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470320 Loss1: 0.138593 Loss2: 1.331728 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.497416 Loss1: 0.097561 Loss2: 1.399856
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458783 Loss1: 0.137194 Loss2: 1.321589
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472069 Loss1: 0.142661 Loss2: 1.329408
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438429 Loss1: 0.114772 Loss2: 1.323656
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378562 Loss1: 0.063359 Loss2: 1.315203
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.777227 Loss1: 0.894554 Loss2: 1.882674
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.911899 Loss1: 0.540224 Loss2: 1.371675
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.709425 Loss1: 0.305080 Loss2: 1.404345
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.595853 Loss1: 0.229235 Loss2: 1.366618
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.622775 Loss1: 0.761224 Loss2: 1.861551
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.805667 Loss1: 0.424736 Loss2: 1.380932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.676093 Loss1: 0.291781 Loss2: 1.384312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.664877 Loss1: 0.293007 Loss2: 1.371869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541611 Loss1: 0.181677 Loss2: 1.359934 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405047 Loss1: 0.073902 Loss2: 1.331145 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434113 Loss1: 0.091702 Loss2: 1.342411 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398121 Loss1: 0.070012 Loss2: 1.328108 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.846313 Loss1: 0.475282 Loss2: 1.371032 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573895 Loss1: 0.225877 Loss2: 1.348018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.535959 Loss1: 0.695110 Loss2: 1.840849
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.542053 Loss1: 0.172883 Loss2: 1.369170
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.886735 Loss1: 0.463410 Loss2: 1.423325
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463333 Loss1: 0.116831 Loss2: 1.346501
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.430612 Loss1: 0.094592 Loss2: 1.336020
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.781471 Loss1: 0.336897 Loss2: 1.444574
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.381969 Loss1: 0.054780 Loss2: 1.327189
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.665919 Loss1: 0.251900 Loss2: 1.414019
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.376597 Loss1: 0.051545 Loss2: 1.325051
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.640167 Loss1: 0.220082 Loss2: 1.420085
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368601 Loss1: 0.047695 Loss2: 1.320906
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.563940 Loss1: 0.160333 Loss2: 1.403606
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.535972 Loss1: 0.129639 Loss2: 1.406333
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.557184 Loss1: 0.164953 Loss2: 1.392232
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.507183 Loss1: 0.112089 Loss2: 1.395094
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478793 Loss1: 0.085281 Loss2: 1.393513
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.632616 Loss1: 0.774047 Loss2: 1.858569
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.923869 Loss1: 0.501725 Loss2: 1.422144
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.699644 Loss1: 0.275313 Loss2: 1.424331
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.577871 Loss1: 0.198428 Loss2: 1.379443
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.549319 Loss1: 0.153152 Loss2: 1.396167
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.733577 Loss1: 0.896728 Loss2: 1.836849
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.919356 Loss1: 0.540385 Loss2: 1.378971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701440 Loss1: 0.302069 Loss2: 1.399371 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631528 Loss1: 0.271161 Loss2: 1.360368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521979 Loss1: 0.170017 Loss2: 1.351961 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465309 Loss1: 0.125564 Loss2: 1.339745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418247 Loss1: 0.088740 Loss2: 1.329508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401721 Loss1: 0.081942 Loss2: 1.319779 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.847311 Loss1: 0.480478 Loss2: 1.366834 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629393 Loss1: 0.269387 Loss2: 1.360006 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.602320 Loss1: 0.221975 Loss2: 1.380345
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.703371 Loss1: 0.846006 Loss2: 1.857365
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.851291 Loss1: 0.435497 Loss2: 1.415794 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.784046 Loss1: 0.356548 Loss2: 1.427498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.662129 Loss1: 0.266449 Loss2: 1.395680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.602380 Loss1: 0.191637 Loss2: 1.410744 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527618 Loss1: 0.154284 Loss2: 1.373334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488301 Loss1: 0.118281 Loss2: 1.370020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430260 Loss1: 0.062979 Loss2: 1.367281 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.767347 Loss1: 0.413950 Loss2: 1.353397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.673426 Loss1: 0.312435 Loss2: 1.360991 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534605 Loss1: 0.179278 Loss2: 1.355327
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.605446 Loss1: 0.778261 Loss2: 1.827185
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.914630 Loss1: 0.549565 Loss2: 1.365064 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.785032 Loss1: 0.354923 Loss2: 1.430108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.598457 Loss1: 0.224881 Loss2: 1.373576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521510 Loss1: 0.156072 Loss2: 1.365438 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474152 Loss1: 0.115303 Loss2: 1.358849 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427487 Loss1: 0.082719 Loss2: 1.344768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424134 Loss1: 0.090528 Loss2: 1.333606 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.835634 Loss1: 0.422048 Loss2: 1.413586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.631056 Loss1: 0.225925 Loss2: 1.405132 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.562436 Loss1: 0.733695 Loss2: 1.828741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.717111 Loss1: 0.397735 Loss2: 1.319376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.637424 Loss1: 0.296415 Loss2: 1.341009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529784 Loss1: 0.215567 Loss2: 1.314218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557142 Loss1: 0.235579 Loss2: 1.321563 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.401360 Loss1: 0.105125 Loss2: 1.296235 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.351343 Loss1: 0.059953 Loss2: 1.291390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.335476 Loss1: 0.057135 Loss2: 1.278341
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.650672 Loss1: 0.787297 Loss2: 1.863375
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.899019 Loss1: 0.474818 Loss2: 1.424200
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.796743 Loss1: 0.331154 Loss2: 1.465589
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654757 Loss1: 0.250137 Loss2: 1.404620
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606615 Loss1: 0.189408 Loss2: 1.417207
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.733625 Loss1: 0.854655 Loss2: 1.878970
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.891354 Loss1: 0.511827 Loss2: 1.379527 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.515028 Loss1: 0.123157 Loss2: 1.391871
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670127 Loss1: 0.266926 Loss2: 1.403201
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491692 Loss1: 0.095381 Loss2: 1.396311
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.590479 Loss1: 0.224093 Loss2: 1.366386
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447722 Loss1: 0.062820 Loss2: 1.384903
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550119 Loss1: 0.175712 Loss2: 1.374407
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447164 Loss1: 0.066032 Loss2: 1.381132
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.483750 Loss1: 0.116588 Loss2: 1.367162
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447985 Loss1: 0.095798 Loss2: 1.352186
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420538 Loss1: 0.074710 Loss2: 1.345827
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.411516 Loss1: 0.069188 Loss2: 1.342328
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394196 Loss1: 0.060084 Loss2: 1.334112
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.452539 Loss1: 0.648882 Loss2: 1.803657
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.745505 Loss1: 0.406251 Loss2: 1.339254
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.676293 Loss1: 0.296024 Loss2: 1.380269
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547653 Loss1: 0.206771 Loss2: 1.340883
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.574108 Loss1: 0.783522 Loss2: 1.790586
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.867141 Loss1: 0.520474 Loss2: 1.346667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.706765 Loss1: 0.339380 Loss2: 1.367385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.583169 Loss1: 0.247453 Loss2: 1.335716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530292 Loss1: 0.186747 Loss2: 1.343545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459607 Loss1: 0.132322 Loss2: 1.327285 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412252 Loss1: 0.091140 Loss2: 1.321112 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376765 Loss1: 0.066534 Loss2: 1.310231 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.974380 Loss1: 0.560566 Loss2: 1.413814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.663390 Loss1: 0.272319 Loss2: 1.391071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.520161 Loss1: 0.691232 Loss2: 1.828930
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.666710 Loss1: 0.260831 Loss2: 1.405879
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.787752 Loss1: 0.437886 Loss2: 1.349866
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562345 Loss1: 0.174342 Loss2: 1.388004
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.675548 Loss1: 0.283732 Loss2: 1.391816
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.533007 Loss1: 0.145368 Loss2: 1.387639
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.533246 Loss1: 0.184748 Loss2: 1.348498
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449738 Loss1: 0.070561 Loss2: 1.379177
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460441 Loss1: 0.122118 Loss2: 1.338323
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458136 Loss1: 0.093446 Loss2: 1.364690
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459364 Loss1: 0.131566 Loss2: 1.327797
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.488343 Loss1: 0.121444 Loss2: 1.366900
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406106 Loss1: 0.086787 Loss2: 1.319320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404010 Loss1: 0.084724 Loss2: 1.319286 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.853987 Loss1: 0.458914 Loss2: 1.395072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.621471 Loss1: 0.239759 Loss2: 1.381712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.538753 Loss1: 0.157439 Loss2: 1.381314
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.777070 Loss1: 0.900856 Loss2: 1.876214
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.848006 Loss1: 0.494865 Loss2: 1.353141
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502090 Loss1: 0.135173 Loss2: 1.366917
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.703509 Loss1: 0.294052 Loss2: 1.409458
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512268 Loss1: 0.144916 Loss2: 1.367352
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564955 Loss1: 0.218710 Loss2: 1.346245
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.493993 Loss1: 0.119504 Loss2: 1.374489
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.475738 Loss1: 0.114228 Loss2: 1.361510 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.456802 Loss1: 0.097860 Loss2: 1.358942 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.377932 Loss1: 0.056655 Loss2: 1.321277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379858 Loss1: 0.072381 Loss2: 1.307477 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.795220 Loss1: 0.853159 Loss2: 1.942060
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.928019 Loss1: 0.529948 Loss2: 1.398071
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.757951 Loss1: 0.323724 Loss2: 1.434226
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.615213 Loss1: 0.201039 Loss2: 1.414174
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558848 Loss1: 0.168833 Loss2: 1.390015
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.527601 Loss1: 0.138434 Loss2: 1.389166 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483578 Loss1: 0.099644 Loss2: 1.383933 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461853 Loss1: 0.086781 Loss2: 1.375072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.468831 Loss1: 0.090396 Loss2: 1.378435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440732 Loss1: 0.070219 Loss2: 1.370513 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.477428 Loss1: 0.099235 Loss2: 1.378193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449949 Loss1: 0.081246 Loss2: 1.368703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420839 Loss1: 0.052697 Loss2: 1.368142
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.535629 Loss1: 0.686284 Loss2: 1.849345
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.732965 Loss1: 0.331788 Loss2: 1.401177
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.631619 Loss1: 0.230207 Loss2: 1.401412
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540952 Loss1: 0.166708 Loss2: 1.374244
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498084 Loss1: 0.125038 Loss2: 1.373046
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.599483 Loss1: 0.736245 Loss2: 1.863238
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.507312 Loss1: 0.135271 Loss2: 1.372042
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.864037 Loss1: 0.474337 Loss2: 1.389700
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.764233 Loss1: 0.332589 Loss2: 1.431644
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463626 Loss1: 0.092201 Loss2: 1.371425
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.600105 Loss1: 0.216548 Loss2: 1.383556
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.442851 Loss1: 0.080466 Loss2: 1.362385
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529205 Loss1: 0.151272 Loss2: 1.377933
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433370 Loss1: 0.070147 Loss2: 1.363223
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429736 Loss1: 0.074239 Loss2: 1.355497 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991728
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483724 Loss1: 0.118764 Loss2: 1.364960 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436555 Loss1: 0.084079 Loss2: 1.352476 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.533993 Loss1: 0.715819 Loss2: 1.818174
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.890307 Loss1: 0.478052 Loss2: 1.412256
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.686842 Loss1: 0.270042 Loss2: 1.416800
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594803 Loss1: 0.220914 Loss2: 1.373889
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.671431 Loss1: 0.890148 Loss2: 1.781282
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.875077 Loss1: 0.545112 Loss2: 1.329965 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.761153 Loss1: 0.377979 Loss2: 1.383174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.505949 Loss1: 0.134987 Loss2: 1.370963
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.638690 Loss1: 0.301349 Loss2: 1.337341
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451834 Loss1: 0.093740 Loss2: 1.358094
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.547475 Loss1: 0.213366 Loss2: 1.334109
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438835 Loss1: 0.085331 Loss2: 1.353504
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480985 Loss1: 0.160793 Loss2: 1.320192
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460660 Loss1: 0.146907 Loss2: 1.313753
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442101 Loss1: 0.094924 Loss2: 1.347176
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388539 Loss1: 0.083016 Loss2: 1.305523 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.617290 Loss1: 0.838571 Loss2: 1.778718 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.737844 Loss1: 0.378726 Loss2: 1.359118 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.609786 Loss1: 0.287741 Loss2: 1.322045
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.868957 Loss1: 0.930359 Loss2: 1.938598
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.917138 Loss1: 0.547691 Loss2: 1.369447
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484510 Loss1: 0.169874 Loss2: 1.314635
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.423507 Loss1: 0.129824 Loss2: 1.293683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.438009 Loss1: 0.144267 Loss2: 1.293742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389371 Loss1: 0.101922 Loss2: 1.287448 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.361294 Loss1: 0.079287 Loss2: 1.282007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.348716 Loss1: 0.069119 Loss2: 1.279597 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356994 Loss1: 0.040185 Loss2: 1.316809 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.800038 Loss1: 0.856251 Loss2: 1.943787
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.021114 Loss1: 0.686522 Loss2: 1.334592
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.815841 Loss1: 0.385634 Loss2: 1.430207
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.650424 Loss1: 0.290068 Loss2: 1.360356
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.582411 Loss1: 0.234787 Loss2: 1.347624
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542018 Loss1: 0.175707 Loss2: 1.366311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.533557 Loss1: 0.186500 Loss2: 1.347056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446316 Loss1: 0.105792 Loss2: 1.340524 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397224 Loss1: 0.072593 Loss2: 1.324631
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605699 Loss1: 0.256205 Loss2: 1.349495
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380563 Loss1: 0.057641 Loss2: 1.322922
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527199 Loss1: 0.179999 Loss2: 1.347199
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431932 Loss1: 0.091566 Loss2: 1.340366
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416506 Loss1: 0.086512 Loss2: 1.329994
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.403727 Loss1: 0.080442 Loss2: 1.323284
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400455 Loss1: 0.079666 Loss2: 1.320789
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.539083 Loss1: 0.666686 Loss2: 1.872397
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381287 Loss1: 0.067670 Loss2: 1.313617
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716207 Loss1: 0.286968 Loss2: 1.429240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.595130 Loss1: 0.186555 Loss2: 1.408575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542789 Loss1: 0.156113 Loss2: 1.386677
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.628388 Loss1: 0.842583 Loss2: 1.785805
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.742497 Loss1: 0.414667 Loss2: 1.327830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.637540 Loss1: 0.282065 Loss2: 1.355475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.495467 Loss1: 0.171049 Loss2: 1.324418 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514646 Loss1: 0.192910 Loss2: 1.321736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455592 Loss1: 0.143987 Loss2: 1.311605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386514 Loss1: 0.088382 Loss2: 1.298132 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375754 Loss1: 0.079239 Loss2: 1.296515 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.664785 Loss1: 0.300301 Loss2: 1.364485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.469403 Loss1: 0.152394 Loss2: 1.317009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.632015 Loss1: 0.821003 Loss2: 1.811013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.878284 Loss1: 0.524669 Loss2: 1.353615 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.648169 Loss1: 0.240761 Loss2: 1.407408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567053 Loss1: 0.223504 Loss2: 1.343550 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523423 Loss1: 0.175856 Loss2: 1.347567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426261 Loss1: 0.098408 Loss2: 1.327853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402389 Loss1: 0.078459 Loss2: 1.323930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398543 Loss1: 0.078455 Loss2: 1.320088 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.766065 Loss1: 0.313292 Loss2: 1.452773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589638 Loss1: 0.169979 Loss2: 1.419659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.549577 Loss1: 0.139877 Loss2: 1.409700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.513768 Loss1: 0.111073 Loss2: 1.402695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469061 Loss1: 0.074522 Loss2: 1.394539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445511 Loss1: 0.056005 Loss2: 1.389506 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.566443 Loss1: 0.212164 Loss2: 1.354280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.454026 Loss1: 0.110816 Loss2: 1.343211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439493 Loss1: 0.116594 Loss2: 1.322899
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.723122 Loss1: 0.851440 Loss2: 1.871682
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.999138 Loss1: 0.553360 Loss2: 1.445778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405278 Loss1: 0.082614 Loss2: 1.322663
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.862237 Loss1: 0.415102 Loss2: 1.447136
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.746087 Loss1: 0.341547 Loss2: 1.404540
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.635218 Loss1: 0.218510 Loss2: 1.416708
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533769 Loss1: 0.144905 Loss2: 1.388864
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503981 Loss1: 0.119115 Loss2: 1.384866
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.822654 Loss1: 0.845018 Loss2: 1.977636
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453923 Loss1: 0.079059 Loss2: 1.374865
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.089523 Loss1: 0.579165 Loss2: 1.510357
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428891 Loss1: 0.056840 Loss2: 1.372051
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.868721 Loss1: 0.344436 Loss2: 1.524285
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446570 Loss1: 0.086124 Loss2: 1.360446
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.700432 Loss1: 0.221804 Loss2: 1.478628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.566124 Loss1: 0.108528 Loss2: 1.457596 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.537077 Loss1: 0.092068 Loss2: 1.445008
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.570451 Loss1: 0.792519 Loss2: 1.777933
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.891516 Loss1: 0.511151 Loss2: 1.380366 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.497474 Loss1: 0.067027 Loss2: 1.430447
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.662373 Loss1: 0.277223 Loss2: 1.385151
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575684 Loss1: 0.231445 Loss2: 1.344240
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.528933 Loss1: 0.184922 Loss2: 1.344011
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487034 Loss1: 0.147355 Loss2: 1.339678
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446441 Loss1: 0.109121 Loss2: 1.337320
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.451292 Loss1: 0.641656 Loss2: 1.809636
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.890806 Loss1: 0.499044 Loss2: 1.391762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.770780 Loss1: 0.354447 Loss2: 1.416332 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640242 Loss1: 0.257926 Loss2: 1.382317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506121 Loss1: 0.144751 Loss2: 1.361370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481410 Loss1: 0.134961 Loss2: 1.346449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444647 Loss1: 0.099398 Loss2: 1.345248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460872 Loss1: 0.121436 Loss2: 1.339436 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.608105 Loss1: 0.230236 Loss2: 1.377869 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475190 Loss1: 0.124646 Loss2: 1.350545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461348 Loss1: 0.113179 Loss2: 1.348169
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.751714 Loss1: 0.828208 Loss2: 1.923506
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438734 Loss1: 0.095676 Loss2: 1.343057
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.009237 Loss1: 0.548926 Loss2: 1.460311
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412583 Loss1: 0.074908 Loss2: 1.337676
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.794420 Loss1: 0.324707 Loss2: 1.469714
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.652412 Loss1: 0.214646 Loss2: 1.437766
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.599334 Loss1: 0.167328 Loss2: 1.432006
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.544095 Loss1: 0.123006 Loss2: 1.421089
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.551654 Loss1: 0.134554 Loss2: 1.417100
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.661863 Loss1: 0.802773 Loss2: 1.859090
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.536362 Loss1: 0.124465 Loss2: 1.411897
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.789555 Loss1: 0.399290 Loss2: 1.390266
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.495279 Loss1: 0.087970 Loss2: 1.407310
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.669478 Loss1: 0.250164 Loss2: 1.419313
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512678 Loss1: 0.106106 Loss2: 1.406572
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.584386 Loss1: 0.196564 Loss2: 1.387823 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523418 Loss1: 0.152044 Loss2: 1.371374 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453030 Loss1: 0.091554 Loss2: 1.361476
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.566797 Loss1: 0.688020 Loss2: 1.878777
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.870537 Loss1: 0.432983 Loss2: 1.437554 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432321 Loss1: 0.077946 Loss2: 1.354375
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.739816 Loss1: 0.272323 Loss2: 1.467493
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.568118 Loss1: 0.157183 Loss2: 1.410935
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558424 Loss1: 0.144566 Loss2: 1.413858
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.532297 Loss1: 0.129755 Loss2: 1.402542
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.534336 Loss1: 0.132090 Loss2: 1.402245
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.660779 Loss1: 0.867745 Loss2: 1.793035
-DEBUG flwr 2023-10-11 14:49:24,532 | server.py:236 | fit_round 117 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.813195 Loss1: 0.458065 Loss2: 1.355130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.671754 Loss1: 0.306142 Loss2: 1.365611 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.538747 Loss1: 0.130882 Loss2: 1.407865
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556855 Loss1: 0.229506 Loss2: 1.327349
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.500410 Loss1: 0.170516 Loss2: 1.329894
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.418047 Loss1: 0.098728 Loss2: 1.319319
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402482 Loss1: 0.085600 Loss2: 1.316883
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.385845 Loss1: 0.074758 Loss2: 1.311087
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.648640 Loss1: 0.815523 Loss2: 1.833117
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.360702 Loss1: 0.059924 Loss2: 1.300779
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.891974 Loss1: 0.491490 Loss2: 1.400484
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.347598 Loss1: 0.048315 Loss2: 1.299283
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.655150 Loss1: 0.269035 Loss2: 1.386115 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485573 Loss1: 0.117883 Loss2: 1.367690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.490663 Loss1: 0.689233 Loss2: 1.801429
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475921 Loss1: 0.114734 Loss2: 1.361186
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.754383 Loss1: 0.414124 Loss2: 1.340259
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448964 Loss1: 0.088364 Loss2: 1.360600
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638759 Loss1: 0.259691 Loss2: 1.379068
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418008 Loss1: 0.071543 Loss2: 1.346465
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524736 Loss1: 0.190486 Loss2: 1.334250
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442559 Loss1: 0.094588 Loss2: 1.347971
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459422 Loss1: 0.130758 Loss2: 1.328665 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421378 Loss1: 0.103067 Loss2: 1.318311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437632 Loss1: 0.109791 Loss2: 1.327841
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.524471 Loss1: 0.727555 Loss2: 1.796916
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404824 Loss1: 0.087325 Loss2: 1.317499
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.757020 Loss1: 0.398510 Loss2: 1.358510
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588829 Loss1: 0.222943 Loss2: 1.365886
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550393 Loss1: 0.208866 Loss2: 1.341527
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.459888 Loss1: 0.122414 Loss2: 1.337474
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464576 Loss1: 0.142918 Loss2: 1.321659
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.454102 Loss1: 0.119416 Loss2: 1.334686
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397797 Loss1: 0.080437 Loss2: 1.317360
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395079 Loss1: 0.076167 Loss2: 1.318913
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404277 Loss1: 0.091694 Loss2: 1.312583
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 14:49:24,532][flwr][DEBUG] - fit_round 117 received 50 results and 0 failures
-INFO flwr 2023-10-11 14:50:04,664 | server.py:125 | fit progress: (117, 2.210739805104253, {'accuracy': 0.5816}, 269912.44244217797)
->> Test accuracy: 0.581600
-[2023-10-11 14:50:04,664][flwr][INFO] - fit progress: (117, 2.210739805104253, {'accuracy': 0.5816}, 269912.44244217797)
-DEBUG flwr 2023-10-11 14:50:04,664 | server.py:173 | evaluate_round 117: strategy sampled 50 clients (out of 50)
-[2023-10-11 14:50:04,664][flwr][DEBUG] - evaluate_round 117: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 14:59:10,074 | server.py:187 | evaluate_round 117 received 50 results and 0 failures
-[2023-10-11 14:59:10,074][flwr][DEBUG] - evaluate_round 117 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 14:59:10,075 | server.py:222 | fit_round 118: strategy sampled 50 clients (out of 50)
-[2023-10-11 14:59:10,075][flwr][DEBUG] - fit_round 118: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.580240 Loss1: 0.706629 Loss2: 1.873611
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.861077 Loss1: 0.491414 Loss2: 1.369662
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.699847 Loss1: 0.292896 Loss2: 1.406950
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602700 Loss1: 0.232118 Loss2: 1.370582
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555010 Loss1: 0.189774 Loss2: 1.365236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451632 Loss1: 0.090973 Loss2: 1.360659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433989 Loss1: 0.091126 Loss2: 1.342863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.412793 Loss1: 0.069935 Loss2: 1.342858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461742 Loss1: 0.125001 Loss2: 1.336741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452654 Loss1: 0.107217 Loss2: 1.345437 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368115 Loss1: 0.037804 Loss2: 1.330311 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.713885 Loss1: 0.837827 Loss2: 1.876058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.774318 Loss1: 0.317303 Loss2: 1.457015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630606 Loss1: 0.237980 Loss2: 1.392626
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.762843 Loss1: 0.861057 Loss2: 1.901786
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.943683 Loss1: 0.517977 Loss2: 1.425706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.753268 Loss1: 0.273308 Loss2: 1.479960 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.683947 Loss1: 0.261658 Loss2: 1.422289 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.616810 Loss1: 0.194883 Loss2: 1.421927 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549245 Loss1: 0.129288 Loss2: 1.419957 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466492 Loss1: 0.093987 Loss2: 1.372505
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.542162 Loss1: 0.134449 Loss2: 1.407713
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.557010 Loss1: 0.140488 Loss2: 1.416523
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.508051 Loss1: 0.109476 Loss2: 1.398575
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454883 Loss1: 0.060883 Loss2: 1.394000
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.839929 Loss1: 0.928654 Loss2: 1.911275
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.030738 Loss1: 0.608249 Loss2: 1.422490
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.782372 Loss1: 0.334507 Loss2: 1.447865
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.627634 Loss1: 0.227450 Loss2: 1.400184
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.756795 Loss1: 0.847226 Loss2: 1.909569
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.085728 Loss1: 0.653756 Loss2: 1.431972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.817171 Loss1: 0.328609 Loss2: 1.488561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.644303 Loss1: 0.221255 Loss2: 1.423048 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430089 Loss1: 0.066933 Loss2: 1.363156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403793 Loss1: 0.047652 Loss2: 1.356141 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995536
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.558409 Loss1: 0.141555 Loss2: 1.416854 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.490385 Loss1: 0.095740 Loss2: 1.394644 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.896501 Loss1: 0.552636 Loss2: 1.343864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591597 Loss1: 0.272090 Loss2: 1.319508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.483755 Loss1: 0.155063 Loss2: 1.328691
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.709368 Loss1: 0.825768 Loss2: 1.883600
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456713 Loss1: 0.143129 Loss2: 1.313584
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.805481 Loss1: 0.406528 Loss2: 1.398952
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448758 Loss1: 0.140630 Loss2: 1.308127
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.790290 Loss1: 0.336268 Loss2: 1.454022
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405103 Loss1: 0.097984 Loss2: 1.307118
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.705075 Loss1: 0.296640 Loss2: 1.408435
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394553 Loss1: 0.091884 Loss2: 1.302669
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.665163 Loss1: 0.241943 Loss2: 1.423221
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372425 Loss1: 0.078582 Loss2: 1.293843
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.628222 Loss1: 0.212075 Loss2: 1.416146
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.531490 Loss1: 0.130417 Loss2: 1.401073
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.509812 Loss1: 0.120113 Loss2: 1.389700
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.470233 Loss1: 0.082167 Loss2: 1.388066
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463462 Loss1: 0.079819 Loss2: 1.383643
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.616520 Loss1: 0.791916 Loss2: 1.824605
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.850084 Loss1: 0.498815 Loss2: 1.351269
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.742372 Loss1: 0.341440 Loss2: 1.400932
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561696 Loss1: 0.208473 Loss2: 1.353223
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498016 Loss1: 0.155795 Loss2: 1.342221
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.761795 Loss1: 0.860787 Loss2: 1.901008
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468013 Loss1: 0.124350 Loss2: 1.343663
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.842776 Loss1: 0.441263 Loss2: 1.401513
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425920 Loss1: 0.092598 Loss2: 1.333322
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.703392 Loss1: 0.289916 Loss2: 1.413475
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426701 Loss1: 0.092927 Loss2: 1.333774
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.601145 Loss1: 0.215486 Loss2: 1.385658
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406562 Loss1: 0.075176 Loss2: 1.331386
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519155 Loss1: 0.141818 Loss2: 1.377337
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381222 Loss1: 0.052672 Loss2: 1.328550
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472455 Loss1: 0.106244 Loss2: 1.366211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.442773 Loss1: 0.075999 Loss2: 1.366774
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446547 Loss1: 0.095741 Loss2: 1.350807
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429687 Loss1: 0.080821 Loss2: 1.348866
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399066 Loss1: 0.051434 Loss2: 1.347633
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.765943 Loss1: 0.885316 Loss2: 1.880627
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.907842 Loss1: 0.553681 Loss2: 1.354161
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.657524 Loss1: 0.235942 Loss2: 1.421582
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.507498 Loss1: 0.163718 Loss2: 1.343781
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.523706 Loss1: 0.182060 Loss2: 1.341647
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.640581 Loss1: 0.745348 Loss2: 1.895234
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481863 Loss1: 0.131446 Loss2: 1.350417
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451793 Loss1: 0.111170 Loss2: 1.340623
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.775818 Loss1: 0.385166 Loss2: 1.390652
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441686 Loss1: 0.114082 Loss2: 1.327604
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.664243 Loss1: 0.243204 Loss2: 1.421039
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574536 Loss1: 0.179085 Loss2: 1.395451 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.947115
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529704 Loss1: 0.141055 Loss2: 1.388649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460553 Loss1: 0.087117 Loss2: 1.373436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.455675 Loss1: 0.099960 Loss2: 1.355714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436975 Loss1: 0.082005 Loss2: 1.354970 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635125 Loss1: 0.246512 Loss2: 1.388613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506082 Loss1: 0.143726 Loss2: 1.362357 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.528480 Loss1: 0.178319 Loss2: 1.350161
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.897000 Loss1: 0.919708 Loss2: 1.977292
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467654 Loss1: 0.111225 Loss2: 1.356429
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.959132 Loss1: 0.532671 Loss2: 1.426461
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.766115 Loss1: 0.290006 Loss2: 1.476110
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436029 Loss1: 0.090205 Loss2: 1.345824
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.708145 Loss1: 0.285065 Loss2: 1.423080
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430480 Loss1: 0.090630 Loss2: 1.339849
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430978 Loss1: 0.094345 Loss2: 1.336633 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.557322 Loss1: 0.152982 Loss2: 1.404340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499222 Loss1: 0.095804 Loss2: 1.403418 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.451426 Loss1: 0.057650 Loss2: 1.393776
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.691332 Loss1: 0.807114 Loss2: 1.884218
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.831003 Loss1: 0.430089 Loss2: 1.400914
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.668134 Loss1: 0.242371 Loss2: 1.425763
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629160 Loss1: 0.230509 Loss2: 1.398651
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617123 Loss1: 0.208832 Loss2: 1.408291
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.778193 Loss1: 0.909717 Loss2: 1.868476
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.934614 Loss1: 0.523561 Loss2: 1.411053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.722517 Loss1: 0.313774 Loss2: 1.408743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.580268 Loss1: 0.207375 Loss2: 1.372893 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505245 Loss1: 0.132481 Loss2: 1.372764 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439334 Loss1: 0.069026 Loss2: 1.370308
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470025 Loss1: 0.112295 Loss2: 1.357729
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.432437 Loss1: 0.078344 Loss2: 1.354093
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405060 Loss1: 0.056954 Loss2: 1.348106
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423430 Loss1: 0.079774 Loss2: 1.343656
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410794 Loss1: 0.069747 Loss2: 1.341046
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.555925 Loss1: 0.725166 Loss2: 1.830758
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.906222 Loss1: 0.522904 Loss2: 1.383318
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.689502 Loss1: 0.277310 Loss2: 1.412192
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.669133 Loss1: 0.305419 Loss2: 1.363714
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587977 Loss1: 0.215312 Loss2: 1.372665
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.753050 Loss1: 0.916619 Loss2: 1.836431
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.886230 Loss1: 0.546694 Loss2: 1.339536 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495758 Loss1: 0.138629 Loss2: 1.357129
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701601 Loss1: 0.323404 Loss2: 1.378196
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449411 Loss1: 0.090386 Loss2: 1.359025
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573336 Loss1: 0.259217 Loss2: 1.314120
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419339 Loss1: 0.077415 Loss2: 1.341924
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524821 Loss1: 0.190141 Loss2: 1.334680
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513124 Loss1: 0.186845 Loss2: 1.326279
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406349 Loss1: 0.075087 Loss2: 1.331262
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430474 Loss1: 0.123018 Loss2: 1.307456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375768 Loss1: 0.076041 Loss2: 1.299726 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987723
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.903957 Loss1: 0.480968 Loss2: 1.422989 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.639649 Loss1: 0.220622 Loss2: 1.419027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.600046 Loss1: 0.179449 Loss2: 1.420597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.556976 Loss1: 0.151918 Loss2: 1.405057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.519803 Loss1: 0.117947 Loss2: 1.401856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.475774 Loss1: 0.086035 Loss2: 1.389739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459482 Loss1: 0.074141 Loss2: 1.385341 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.468414 Loss1: 0.085453 Loss2: 1.382961 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501574 Loss1: 0.127539 Loss2: 1.374035 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.823190 Loss1: 0.881831 Loss2: 1.941360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741995 Loss1: 0.271367 Loss2: 1.470629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.645751 Loss1: 0.225204 Loss2: 1.420547
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.497707 Loss1: 0.612544 Loss2: 1.885163
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.850129 Loss1: 0.463166 Loss2: 1.386963 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.768456 Loss1: 0.322473 Loss2: 1.445983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.651127 Loss1: 0.265706 Loss2: 1.385421 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.649073 Loss1: 0.240776 Loss2: 1.408297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.659631 Loss1: 0.252211 Loss2: 1.407420 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.471662 Loss1: 0.073786 Loss2: 1.397876
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.596649 Loss1: 0.195963 Loss2: 1.400686
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.519803 Loss1: 0.130944 Loss2: 1.388859
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.484331 Loss1: 0.099740 Loss2: 1.384590
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.473214 Loss1: 0.100848 Loss2: 1.372367
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.397514 Loss1: 0.609256 Loss2: 1.788258
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.792694 Loss1: 0.438820 Loss2: 1.353874
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.698557 Loss1: 0.292308 Loss2: 1.406249
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.840624 Loss1: 0.929598 Loss2: 1.911025
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580048 Loss1: 0.220128 Loss2: 1.359920
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555281 Loss1: 0.191503 Loss2: 1.363778 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508495 Loss1: 0.161784 Loss2: 1.346711 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.471600 Loss1: 0.123712 Loss2: 1.347889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501720 Loss1: 0.177590 Loss2: 1.324130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.499958 Loss1: 0.168486 Loss2: 1.331473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435821 Loss1: 0.113192 Loss2: 1.322629 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409913 Loss1: 0.091321 Loss2: 1.318592 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.670900 Loss1: 0.809865 Loss2: 1.861036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667319 Loss1: 0.260103 Loss2: 1.407216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.590634 Loss1: 0.210039 Loss2: 1.380595
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.523924 Loss1: 0.655808 Loss2: 1.868116
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.835642 Loss1: 0.428141 Loss2: 1.407501 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.673251 Loss1: 0.243580 Loss2: 1.429671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599854 Loss1: 0.199573 Loss2: 1.400281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.532586 Loss1: 0.137972 Loss2: 1.394614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521070 Loss1: 0.127945 Loss2: 1.393125 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480999 Loss1: 0.094761 Loss2: 1.386239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454424 Loss1: 0.079080 Loss2: 1.375344 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.969727
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.485513 Loss1: 0.654668 Loss2: 1.830845 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.566037 Loss1: 0.200486 Loss2: 1.365552 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.682443 Loss1: 0.710522 Loss2: 1.971921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.011471 Loss1: 0.535006 Loss2: 1.476465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.908268 Loss1: 0.375924 Loss2: 1.532344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.768713 Loss1: 0.283046 Loss2: 1.485667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.668257 Loss1: 0.194318 Loss2: 1.473939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.617226 Loss1: 0.159298 Loss2: 1.457928 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.586250 Loss1: 0.134834 Loss2: 1.451416 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.551718 Loss1: 0.109935 Loss2: 1.441783 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.729752 Loss1: 0.389201 Loss2: 1.340552 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.532308 Loss1: 0.192717 Loss2: 1.339591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.599219 Loss1: 0.783689 Loss2: 1.815530
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.529785 Loss1: 0.188915 Loss2: 1.340870
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.877070 Loss1: 0.500735 Loss2: 1.376335
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498927 Loss1: 0.164493 Loss2: 1.334435
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.692787 Loss1: 0.290081 Loss2: 1.402706
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428802 Loss1: 0.099377 Loss2: 1.329425
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591934 Loss1: 0.227873 Loss2: 1.364061
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444331 Loss1: 0.119548 Loss2: 1.324783
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512141 Loss1: 0.143127 Loss2: 1.369013
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399504 Loss1: 0.075034 Loss2: 1.324470
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476409 Loss1: 0.129869 Loss2: 1.346540
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381835 Loss1: 0.062744 Loss2: 1.319091
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419282 Loss1: 0.085783 Loss2: 1.333499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373562 Loss1: 0.053884 Loss2: 1.319678 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.827062 Loss1: 0.472576 Loss2: 1.354485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555222 Loss1: 0.215420 Loss2: 1.339802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.677393 Loss1: 0.816350 Loss2: 1.861043
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515780 Loss1: 0.170013 Loss2: 1.345767
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.888130 Loss1: 0.502671 Loss2: 1.385460
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492289 Loss1: 0.154309 Loss2: 1.337981
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727454 Loss1: 0.314428 Loss2: 1.413026
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449228 Loss1: 0.117552 Loss2: 1.331676
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555923 Loss1: 0.177006 Loss2: 1.378916
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426622 Loss1: 0.100072 Loss2: 1.326550
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507730 Loss1: 0.139367 Loss2: 1.368363
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414596 Loss1: 0.088227 Loss2: 1.326369
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503281 Loss1: 0.145230 Loss2: 1.358050
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385220 Loss1: 0.061278 Loss2: 1.323942
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448896 Loss1: 0.095034 Loss2: 1.353862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399889 Loss1: 0.053932 Loss2: 1.345958 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.868673 Loss1: 0.477384 Loss2: 1.391289 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589775 Loss1: 0.220952 Loss2: 1.368823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.605936 Loss1: 0.746838 Loss2: 1.859098
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.532631 Loss1: 0.159358 Loss2: 1.373273
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.809706 Loss1: 0.425645 Loss2: 1.384062
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503002 Loss1: 0.136589 Loss2: 1.366413
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701338 Loss1: 0.278019 Loss2: 1.423319
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463380 Loss1: 0.104858 Loss2: 1.358523
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579957 Loss1: 0.202977 Loss2: 1.376980
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434792 Loss1: 0.085904 Loss2: 1.348888
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.536370 Loss1: 0.165459 Loss2: 1.370910
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427264 Loss1: 0.079162 Loss2: 1.348102
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504979 Loss1: 0.143821 Loss2: 1.361158
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441272 Loss1: 0.096907 Loss2: 1.344365
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417922 Loss1: 0.071052 Loss2: 1.346869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443622 Loss1: 0.102482 Loss2: 1.341141 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.829145 Loss1: 0.466203 Loss2: 1.362942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694871 Loss1: 0.321456 Loss2: 1.373415 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.484110 Loss1: 0.635980 Loss2: 1.848129
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572076 Loss1: 0.195655 Loss2: 1.376420
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.819102 Loss1: 0.407202 Loss2: 1.411900
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.522627 Loss1: 0.156745 Loss2: 1.365882
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484515 Loss1: 0.125322 Loss2: 1.359192
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.675326 Loss1: 0.242382 Loss2: 1.432945
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464973 Loss1: 0.109935 Loss2: 1.355039
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605162 Loss1: 0.205788 Loss2: 1.399374
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428183 Loss1: 0.078948 Loss2: 1.349236
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.581389 Loss1: 0.169087 Loss2: 1.412302
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425921 Loss1: 0.085548 Loss2: 1.340373
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.507125 Loss1: 0.106647 Loss2: 1.400478
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473329 Loss1: 0.083093 Loss2: 1.390236
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436250 Loss1: 0.056244 Loss2: 1.380005
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439725 Loss1: 0.065515 Loss2: 1.374210
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438068 Loss1: 0.067667 Loss2: 1.370401
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.607895 Loss1: 0.783804 Loss2: 1.824090
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.722122 Loss1: 0.366751 Loss2: 1.355371
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656837 Loss1: 0.289950 Loss2: 1.366887
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540207 Loss1: 0.190999 Loss2: 1.349208
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514325 Loss1: 0.169146 Loss2: 1.345179
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.459313 Loss1: 0.720139 Loss2: 1.739174
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492012 Loss1: 0.157374 Loss2: 1.334638
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.756291 Loss1: 0.443470 Loss2: 1.312821
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469841 Loss1: 0.138256 Loss2: 1.331585
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684236 Loss1: 0.336861 Loss2: 1.347375
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452548 Loss1: 0.122424 Loss2: 1.330125
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398925 Loss1: 0.074503 Loss2: 1.324422
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528217 Loss1: 0.216886 Loss2: 1.311331
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399576 Loss1: 0.079276 Loss2: 1.320301
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509911 Loss1: 0.195416 Loss2: 1.314495
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474504 Loss1: 0.169669 Loss2: 1.304834
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428136 Loss1: 0.121810 Loss2: 1.306326
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.385708 Loss1: 0.089210 Loss2: 1.296498
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.345691 Loss1: 0.061170 Loss2: 1.284521
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.727882 Loss1: 0.817721 Loss2: 1.910161
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.905025 Loss1: 0.468911 Loss2: 1.436114 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649522 Loss1: 0.215960 Loss2: 1.433562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.600495 Loss1: 0.172823 Loss2: 1.427671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.538261 Loss1: 0.109841 Loss2: 1.428420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.795647 Loss1: 0.409070 Loss2: 1.386577
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491489 Loss1: 0.072446 Loss2: 1.419043
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.639524 Loss1: 0.255577 Loss2: 1.383947
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460351 Loss1: 0.053274 Loss2: 1.407077
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.473883 Loss1: 0.077548 Loss2: 1.396335
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604542 Loss1: 0.233580 Loss2: 1.370962
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.559683 Loss1: 0.188634 Loss2: 1.371049
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531576 Loss1: 0.167480 Loss2: 1.364096
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475745 Loss1: 0.121369 Loss2: 1.354376
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433855 Loss1: 0.088422 Loss2: 1.345433
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.644833 Loss1: 0.790511 Loss2: 1.854322
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.932923 Loss1: 0.519948 Loss2: 1.412975 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993566
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418313 Loss1: 0.076923 Loss2: 1.341390
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.833373 Loss1: 0.392777 Loss2: 1.440596
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.700114 Loss1: 0.290438 Loss2: 1.409676
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.583203 Loss1: 0.182707 Loss2: 1.400496
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547566 Loss1: 0.166175 Loss2: 1.381391
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481850 Loss1: 0.099884 Loss2: 1.381966
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.631893 Loss1: 0.810753 Loss2: 1.821141
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.900554 Loss1: 0.511171 Loss2: 1.389382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.715118 Loss1: 0.325837 Loss2: 1.389280 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419428 Loss1: 0.055401 Loss2: 1.364027
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604042 Loss1: 0.234390 Loss2: 1.369652
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.566598 Loss1: 0.209253 Loss2: 1.357345
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464183 Loss1: 0.110863 Loss2: 1.353320
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447353 Loss1: 0.102488 Loss2: 1.344865
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423099 Loss1: 0.086681 Loss2: 1.336418
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.527206 Loss1: 0.715079 Loss2: 1.812126
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406094 Loss1: 0.073029 Loss2: 1.333065
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.885324 Loss1: 0.486766 Loss2: 1.398557
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377040 Loss1: 0.051437 Loss2: 1.325603
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.663331 Loss1: 0.287191 Loss2: 1.376140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.589415 Loss1: 0.208958 Loss2: 1.380457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.712248 Loss1: 0.832523 Loss2: 1.879725
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.505865 Loss1: 0.132679 Loss2: 1.373187
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.989070 Loss1: 0.573658 Loss2: 1.415412
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452994 Loss1: 0.091698 Loss2: 1.361296
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411515 Loss1: 0.057222 Loss2: 1.354293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401842 Loss1: 0.058555 Loss2: 1.343287 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.533041 Loss1: 0.152690 Loss2: 1.380351 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458089 Loss1: 0.086740 Loss2: 1.371350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.650583 Loss1: 0.786015 Loss2: 1.864568 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.983645 Loss1: 0.553062 Loss2: 1.430582 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.695885 Loss1: 0.290300 Loss2: 1.405585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.555635 Loss1: 0.160951 Loss2: 1.394684 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 15:27:43,747 | server.py:236 | fit_round 118 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.510100 Loss1: 0.121361 Loss2: 1.388739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460764 Loss1: 0.082978 Loss2: 1.377785 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455251 Loss1: 0.087153 Loss2: 1.368098 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440322 Loss1: 0.074678 Loss2: 1.365643 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.609576 Loss1: 0.152538 Loss2: 1.457039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.612717 Loss1: 0.162082 Loss2: 1.450635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.497380 Loss1: 0.711139 Loss2: 1.786241 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.653406 Loss1: 0.255602 Loss2: 1.397804 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.489192 Loss1: 0.143575 Loss2: 1.345616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506469 Loss1: 0.169188 Loss2: 1.337281
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.572660 Loss1: 0.743347 Loss2: 1.829313
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448093 Loss1: 0.104567 Loss2: 1.343526
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.821786 Loss1: 0.470281 Loss2: 1.351506
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.699132 Loss1: 0.315091 Loss2: 1.384040
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437252 Loss1: 0.105594 Loss2: 1.331658
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.570729 Loss1: 0.239326 Loss2: 1.331403
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444464 Loss1: 0.113780 Loss2: 1.330685
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518560 Loss1: 0.165439 Loss2: 1.353121
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418996 Loss1: 0.089217 Loss2: 1.329779
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395177 Loss1: 0.080621 Loss2: 1.314557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366409 Loss1: 0.066379 Loss2: 1.300029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.353456 Loss1: 0.055535 Loss2: 1.297921
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.969248 Loss1: 0.922547 Loss2: 2.046701
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.039157 Loss1: 0.535872 Loss2: 1.503285
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.817601 Loss1: 0.273952 Loss2: 1.543649
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.730119 Loss1: 0.250256 Loss2: 1.479863
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.648691 Loss1: 0.151643 Loss2: 1.497048
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.769530 Loss1: 0.798428 Loss2: 1.971102
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.580555 Loss1: 0.111142 Loss2: 1.469412
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.588798 Loss1: 0.120239 Loss2: 1.468559 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.580453 Loss1: 0.110730 Loss2: 1.469723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.551267 Loss1: 0.086174 Loss2: 1.465093 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559557 Loss1: 0.178327 Loss2: 1.381230 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482779 Loss1: 0.142097 Loss2: 1.340682 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396794 Loss1: 0.064355 Loss2: 1.332439 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 15:27:43,747][flwr][DEBUG] - fit_round 118 received 50 results and 0 failures
-INFO flwr 2023-10-11 15:28:25,629 | server.py:125 | fit progress: (118, 2.2009005855066706, {'accuracy': 0.5821}, 272213.407915922)
->> Test accuracy: 0.582100
-[2023-10-11 15:28:25,629][flwr][INFO] - fit progress: (118, 2.2009005855066706, {'accuracy': 0.5821}, 272213.407915922)
-DEBUG flwr 2023-10-11 15:28:25,630 | server.py:173 | evaluate_round 118: strategy sampled 50 clients (out of 50)
-[2023-10-11 15:28:25,630][flwr][DEBUG] - evaluate_round 118: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 15:37:27,804 | server.py:187 | evaluate_round 118 received 50 results and 0 failures
-[2023-10-11 15:37:27,804][flwr][DEBUG] - evaluate_round 118 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 15:37:27,805 | server.py:222 | fit_round 119: strategy sampled 50 clients (out of 50)
-[2023-10-11 15:37:27,805][flwr][DEBUG] - fit_round 119: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.511798 Loss1: 0.678606 Loss2: 1.833192
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.796235 Loss1: 0.436231 Loss2: 1.360004
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.633239 Loss1: 0.234414 Loss2: 1.398825
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534812 Loss1: 0.184769 Loss2: 1.350043
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.739259 Loss1: 0.880668 Loss2: 1.858591
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498993 Loss1: 0.151424 Loss2: 1.347570
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.982554 Loss1: 0.623804 Loss2: 1.358750
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470060 Loss1: 0.132030 Loss2: 1.338030
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.711197 Loss1: 0.309582 Loss2: 1.401615
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581511 Loss1: 0.229792 Loss2: 1.351719
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431108 Loss1: 0.097233 Loss2: 1.333875
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.551866 Loss1: 0.207335 Loss2: 1.344531
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425542 Loss1: 0.086970 Loss2: 1.338572
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.535934 Loss1: 0.188074 Loss2: 1.347860
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398646 Loss1: 0.076309 Loss2: 1.322337
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399364 Loss1: 0.075997 Loss2: 1.323366 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394099 Loss1: 0.064748 Loss2: 1.329350 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.569602 Loss1: 0.706879 Loss2: 1.862723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.734374 Loss1: 0.289265 Loss2: 1.445109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596568 Loss1: 0.202813 Loss2: 1.393755
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.636744 Loss1: 0.735146 Loss2: 1.901597
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.520666 Loss1: 0.124493 Loss2: 1.396174
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.887004 Loss1: 0.441693 Loss2: 1.445311
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461055 Loss1: 0.079763 Loss2: 1.381292
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.716927 Loss1: 0.275740 Loss2: 1.441187
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.454645 Loss1: 0.088062 Loss2: 1.366583
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594279 Loss1: 0.171354 Loss2: 1.422925
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469292 Loss1: 0.098201 Loss2: 1.371091
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.531030 Loss1: 0.119942 Loss2: 1.411088
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.457014 Loss1: 0.087919 Loss2: 1.369095
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472126 Loss1: 0.068901 Loss2: 1.403225
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439919 Loss1: 0.078589 Loss2: 1.361330
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471514 Loss1: 0.078943 Loss2: 1.392571
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.476550 Loss1: 0.084576 Loss2: 1.391974
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476954 Loss1: 0.081789 Loss2: 1.395165
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439637 Loss1: 0.050118 Loss2: 1.389519
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.605750 Loss1: 0.776244 Loss2: 1.829506
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.872924 Loss1: 0.517277 Loss2: 1.355647
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.694121 Loss1: 0.288293 Loss2: 1.405828
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.579421 Loss1: 0.235625 Loss2: 1.343795
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.425499 Loss1: 0.596049 Loss2: 1.829450
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.769449 Loss1: 0.389902 Loss2: 1.379547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.673697 Loss1: 0.266070 Loss2: 1.407628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567693 Loss1: 0.203308 Loss2: 1.364384 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452280 Loss1: 0.130812 Loss2: 1.321468 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407450 Loss1: 0.091598 Loss2: 1.315853 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.457761 Loss1: 0.103609 Loss2: 1.354151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426769 Loss1: 0.076103 Loss2: 1.350666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430842 Loss1: 0.086371 Loss2: 1.344471
-(DefaultActor pid=3764) >> Training accuracy: 0.988051
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.504211 Loss1: 0.658452 Loss2: 1.845759
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.853019 Loss1: 0.492867 Loss2: 1.360152
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.762870 Loss1: 0.334349 Loss2: 1.428521
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582882 Loss1: 0.212940 Loss2: 1.369943
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524756 Loss1: 0.162512 Loss2: 1.362244
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.579213 Loss1: 0.728379 Loss2: 1.850834
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499231 Loss1: 0.144235 Loss2: 1.354996
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.883069 Loss1: 0.458075 Loss2: 1.424994
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470834 Loss1: 0.115623 Loss2: 1.355211
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.465520 Loss1: 0.114934 Loss2: 1.350586
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.732641 Loss1: 0.292995 Loss2: 1.439646
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436861 Loss1: 0.091179 Loss2: 1.345682
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.641975 Loss1: 0.237114 Loss2: 1.404862
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395904 Loss1: 0.053794 Loss2: 1.342109
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570117 Loss1: 0.156518 Loss2: 1.413600
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.564796 Loss1: 0.167319 Loss2: 1.397477
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.546982 Loss1: 0.152365 Loss2: 1.394617
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.527869 Loss1: 0.131871 Loss2: 1.395998
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.506789 Loss1: 0.114367 Loss2: 1.392422
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.622469 Loss1: 0.811516 Loss2: 1.810954
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.848009 Loss1: 0.487980 Loss2: 1.360029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555415 Loss1: 0.220347 Loss2: 1.335068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494842 Loss1: 0.156622 Loss2: 1.338220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425233 Loss1: 0.105581 Loss2: 1.319652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.392935 Loss1: 0.079272 Loss2: 1.313664 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.383781 Loss1: 0.078610 Loss2: 1.305171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361548 Loss1: 0.063213 Loss2: 1.298335 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.553667 Loss1: 0.179148 Loss2: 1.374519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464070 Loss1: 0.109779 Loss2: 1.354290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.567247 Loss1: 0.730703 Loss2: 1.836544 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.812418 Loss1: 0.430334 Loss2: 1.382084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558006 Loss1: 0.193566 Loss2: 1.364440 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.474881 Loss1: 0.116183 Loss2: 1.358698 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434669 Loss1: 0.086184 Loss2: 1.348485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434431 Loss1: 0.090564 Loss2: 1.343867 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550489 Loss1: 0.165084 Loss2: 1.385405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.594665 Loss1: 0.228675 Loss2: 1.365990 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508000 Loss1: 0.135792 Loss2: 1.372207 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427262 Loss1: 0.080155 Loss2: 1.347107 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.635691 Loss1: 0.840016 Loss2: 1.795675 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.686392 Loss1: 0.308523 Loss2: 1.377869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462363 Loss1: 0.146730 Loss2: 1.315632 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.406857 Loss1: 0.095934 Loss2: 1.310923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406246 Loss1: 0.100010 Loss2: 1.306236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.359271 Loss1: 0.058920 Loss2: 1.300351 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.353626 Loss1: 0.059233 Loss2: 1.294394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.357141 Loss1: 0.061984 Loss2: 1.295157 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374584 Loss1: 0.067196 Loss2: 1.307387 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356125 Loss1: 0.062015 Loss2: 1.294110 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.968403 Loss1: 0.570967 Loss2: 1.397436 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.696006 Loss1: 0.317242 Loss2: 1.378764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.622625 Loss1: 0.727683 Loss2: 1.894942
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.610264 Loss1: 0.225020 Loss2: 1.385244
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.806838 Loss1: 0.429844 Loss2: 1.376995
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.600748 Loss1: 0.228827 Loss2: 1.371921
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635185 Loss1: 0.259744 Loss2: 1.375441
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539634 Loss1: 0.168948 Loss2: 1.370685
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592278 Loss1: 0.226003 Loss2: 1.366275
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477365 Loss1: 0.121342 Loss2: 1.356023
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539150 Loss1: 0.163955 Loss2: 1.375196
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450133 Loss1: 0.097071 Loss2: 1.353062
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528145 Loss1: 0.173425 Loss2: 1.354720
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422176 Loss1: 0.076621 Loss2: 1.345555
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.476793 Loss1: 0.127161 Loss2: 1.349632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439540 Loss1: 0.093504 Loss2: 1.346037 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.804714 Loss1: 0.492006 Loss2: 1.312708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.644370 Loss1: 0.285782 Loss2: 1.358588 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564885 Loss1: 0.231847 Loss2: 1.333039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441341 Loss1: 0.116523 Loss2: 1.324817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.379613 Loss1: 0.079793 Loss2: 1.299820 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401154 Loss1: 0.101037 Loss2: 1.300117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373828 Loss1: 0.076246 Loss2: 1.297582 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490058 Loss1: 0.124133 Loss2: 1.365924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.532456 Loss1: 0.162887 Loss2: 1.369570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446920 Loss1: 0.080579 Loss2: 1.366341
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.704517 Loss1: 0.808004 Loss2: 1.896513
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.455578 Loss1: 0.101155 Loss2: 1.354423
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.864373 Loss1: 0.508857 Loss2: 1.355516
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690265 Loss1: 0.285728 Loss2: 1.404537
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.537213 Loss1: 0.183215 Loss2: 1.353998
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.552984 Loss1: 0.193589 Loss2: 1.359395
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513066 Loss1: 0.152378 Loss2: 1.360689
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467450 Loss1: 0.124944 Loss2: 1.342505
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.707313 Loss1: 0.850947 Loss2: 1.856366
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.867041 Loss1: 0.464716 Loss2: 1.402325 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.703760 Loss1: 0.266620 Loss2: 1.437140 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.583250 Loss1: 0.203687 Loss2: 1.379563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499877 Loss1: 0.125666 Loss2: 1.374211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443799 Loss1: 0.073915 Loss2: 1.369883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416590 Loss1: 0.060132 Loss2: 1.356458 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424525 Loss1: 0.067391 Loss2: 1.357134 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562252 Loss1: 0.195154 Loss2: 1.367099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.501629 Loss1: 0.139054 Loss2: 1.362575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.613807 Loss1: 0.771021 Loss2: 1.842786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.877491 Loss1: 0.512260 Loss2: 1.365231 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.693165 Loss1: 0.279925 Loss2: 1.413240 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529015 Loss1: 0.170289 Loss2: 1.358726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455713 Loss1: 0.106956 Loss2: 1.348757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422149 Loss1: 0.078489 Loss2: 1.343660 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389571 Loss1: 0.053573 Loss2: 1.335998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400720 Loss1: 0.071198 Loss2: 1.329521 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.600008 Loss1: 0.257684 Loss2: 1.342323 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484890 Loss1: 0.144511 Loss2: 1.340379 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448509 Loss1: 0.112451 Loss2: 1.336058
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.621892 Loss1: 0.711518 Loss2: 1.910374
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.776614 Loss1: 0.380363 Loss2: 1.396251 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.715694 Loss1: 0.297182 Loss2: 1.418512 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576479 Loss1: 0.190116 Loss2: 1.386363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464827 Loss1: 0.086856 Loss2: 1.377971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440536 Loss1: 0.070116 Loss2: 1.370420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445465 Loss1: 0.078519 Loss2: 1.366947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408380 Loss1: 0.051631 Loss2: 1.356749 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554647 Loss1: 0.237685 Loss2: 1.316962 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.446545 Loss1: 0.143405 Loss2: 1.303140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.507913 Loss1: 0.688852 Loss2: 1.819061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.837004 Loss1: 0.451269 Loss2: 1.385735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.746423 Loss1: 0.321019 Loss2: 1.425404 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.564014 Loss1: 0.187692 Loss2: 1.376322 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.536015 Loss1: 0.169326 Loss2: 1.366690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.650362 Loss1: 0.833345 Loss2: 1.817017
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472114 Loss1: 0.109691 Loss2: 1.362423
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.944875 Loss1: 0.610618 Loss2: 1.334258
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452812 Loss1: 0.092626 Loss2: 1.360186
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.770141 Loss1: 0.365456 Loss2: 1.404685
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450640 Loss1: 0.099097 Loss2: 1.351543
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.523270 Loss1: 0.194575 Loss2: 1.328695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473451 Loss1: 0.167912 Loss2: 1.305538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.770136 Loss1: 0.898443 Loss2: 1.871693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.015381 Loss1: 0.586877 Loss2: 1.428504 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.616071 Loss1: 0.245613 Loss2: 1.370458 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492638 Loss1: 0.138500 Loss2: 1.354138 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473003 Loss1: 0.118201 Loss2: 1.354802
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.681213 Loss1: 0.848071 Loss2: 1.833142
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419473 Loss1: 0.074345 Loss2: 1.345128
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.849838 Loss1: 0.489911 Loss2: 1.359927
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394156 Loss1: 0.060277 Loss2: 1.333880
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.705859 Loss1: 0.317503 Loss2: 1.388357
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382599 Loss1: 0.054794 Loss2: 1.327804
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.617140 Loss1: 0.276210 Loss2: 1.340929
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534882 Loss1: 0.180922 Loss2: 1.353960
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473956 Loss1: 0.138210 Loss2: 1.335746
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419477 Loss1: 0.088922 Loss2: 1.330555
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398698 Loss1: 0.077677 Loss2: 1.321022
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.578387 Loss1: 0.683626 Loss2: 1.894760
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378197 Loss1: 0.062079 Loss2: 1.316118
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.886094 Loss1: 0.478561 Loss2: 1.407533
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355305 Loss1: 0.047427 Loss2: 1.307878
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.638989 Loss1: 0.240208 Loss2: 1.398781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.483478 Loss1: 0.095832 Loss2: 1.387647 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475149 Loss1: 0.093011 Loss2: 1.382138
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.603228 Loss1: 0.810617 Loss2: 1.792611
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426774 Loss1: 0.055843 Loss2: 1.370932
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.840050 Loss1: 0.489983 Loss2: 1.350067
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405426 Loss1: 0.039814 Loss2: 1.365611
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.708524 Loss1: 0.321503 Loss2: 1.387021
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389358 Loss1: 0.030446 Loss2: 1.358912
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.590044 Loss1: 0.256075 Loss2: 1.333968
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507545 Loss1: 0.173773 Loss2: 1.333772
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.439003 Loss1: 0.114997 Loss2: 1.324006
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426390 Loss1: 0.110462 Loss2: 1.315928
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.355415 Loss1: 0.048356 Loss2: 1.307058
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.357204 Loss1: 0.058591 Loss2: 1.298612
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.606196 Loss1: 0.820581 Loss2: 1.785615
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360297 Loss1: 0.064913 Loss2: 1.295384
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.778179 Loss1: 0.442598 Loss2: 1.335581
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614855 Loss1: 0.254185 Loss2: 1.360669
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.569213 Loss1: 0.244034 Loss2: 1.325179
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.486402 Loss1: 0.157700 Loss2: 1.328702
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441641 Loss1: 0.122189 Loss2: 1.319452
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392615 Loss1: 0.084657 Loss2: 1.307958
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.692750 Loss1: 0.833358 Loss2: 1.859392
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376240 Loss1: 0.073882 Loss2: 1.302358
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.886001 Loss1: 0.490344 Loss2: 1.395656
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376210 Loss1: 0.078644 Loss2: 1.297566
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.653877 Loss1: 0.240529 Loss2: 1.413348
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.342866 Loss1: 0.053586 Loss2: 1.289279
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.631154 Loss1: 0.257722 Loss2: 1.373432
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571762 Loss1: 0.191651 Loss2: 1.380111
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506713 Loss1: 0.145731 Loss2: 1.360982
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.520337 Loss1: 0.160420 Loss2: 1.359917
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.490711 Loss1: 0.118005 Loss2: 1.372705
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.823990 Loss1: 0.858099 Loss2: 1.965891
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463896 Loss1: 0.108783 Loss2: 1.355113
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463961 Loss1: 0.107817 Loss2: 1.356144 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543520 Loss1: 0.137783 Loss2: 1.405737 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.526235 Loss1: 0.130457 Loss2: 1.395778 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.691308 Loss1: 0.798166 Loss2: 1.893142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458335 Loss1: 0.066661 Loss2: 1.391674 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604985 Loss1: 0.173749 Loss2: 1.431236 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503369 Loss1: 0.088679 Loss2: 1.414690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.498733 Loss1: 0.092774 Loss2: 1.405959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.485002 Loss1: 0.081574 Loss2: 1.403428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474027 Loss1: 0.070906 Loss2: 1.403121 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458042 Loss1: 0.061142 Loss2: 1.396900 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.540521 Loss1: 0.142916 Loss2: 1.397605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.505958 Loss1: 0.116681 Loss2: 1.389277 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.551105 Loss1: 0.679182 Loss2: 1.871924 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.707622 Loss1: 0.279757 Loss2: 1.427865 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.489745 Loss1: 0.116315 Loss2: 1.373430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506858 Loss1: 0.142032 Loss2: 1.364826
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.477287 Loss1: 0.681475 Loss2: 1.795813
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.793306 Loss1: 0.430825 Loss2: 1.362481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.599504 Loss1: 0.219499 Loss2: 1.380004 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.522682 Loss1: 0.196811 Loss2: 1.325871 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.443975 Loss1: 0.111692 Loss2: 1.332283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450740 Loss1: 0.115792 Loss2: 1.334948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409072 Loss1: 0.093591 Loss2: 1.315481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428415 Loss1: 0.117428 Loss2: 1.310987 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551261 Loss1: 0.195988 Loss2: 1.355273 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472229 Loss1: 0.121732 Loss2: 1.350497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441636 Loss1: 0.095312 Loss2: 1.346324
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.524314 Loss1: 0.670164 Loss2: 1.854150
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409072 Loss1: 0.069462 Loss2: 1.339609
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.812127 Loss1: 0.434667 Loss2: 1.377460
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390283 Loss1: 0.065334 Loss2: 1.324950
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.663393 Loss1: 0.261203 Loss2: 1.402190
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377151 Loss1: 0.052822 Loss2: 1.324329
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551265 Loss1: 0.184863 Loss2: 1.366401
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500721 Loss1: 0.142828 Loss2: 1.357893
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477067 Loss1: 0.115677 Loss2: 1.361391
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.409866 Loss1: 0.067429 Loss2: 1.342438
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.403758 Loss1: 0.064474 Loss2: 1.339284
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.875580 Loss1: 0.959589 Loss2: 1.915990
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405723 Loss1: 0.066779 Loss2: 1.338944
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.879185 Loss1: 0.445641 Loss2: 1.433544
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401496 Loss1: 0.060573 Loss2: 1.340924
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596612 Loss1: 0.189329 Loss2: 1.407283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.495880 Loss1: 0.105726 Loss2: 1.390153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529634 Loss1: 0.143280 Loss2: 1.386353
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.631960 Loss1: 0.778345 Loss2: 1.853616
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484341 Loss1: 0.102413 Loss2: 1.381929
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.927789 Loss1: 0.544273 Loss2: 1.383516
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469393 Loss1: 0.095882 Loss2: 1.373511
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.823475 Loss1: 0.367754 Loss2: 1.455721
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439391 Loss1: 0.066140 Loss2: 1.373250
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.675564 Loss1: 0.298900 Loss2: 1.376664
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.652140 Loss1: 0.253297 Loss2: 1.398843
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542280 Loss1: 0.163886 Loss2: 1.378393
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455020 Loss1: 0.095997 Loss2: 1.359023
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467187 Loss1: 0.105220 Loss2: 1.361967
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412923 Loss1: 0.065293 Loss2: 1.347631
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.497593 Loss1: 0.642801 Loss2: 1.854792
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392348 Loss1: 0.049973 Loss2: 1.342375
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.736087 Loss1: 0.367950 Loss2: 1.368137
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.666489 Loss1: 0.264057 Loss2: 1.402432
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599881 Loss1: 0.238972 Loss2: 1.360910
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.529278 Loss1: 0.167109 Loss2: 1.362169
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.596347 Loss1: 0.230384 Loss2: 1.365963
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525162 Loss1: 0.162398 Loss2: 1.362764
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.489511 Loss1: 0.674756 Loss2: 1.814755
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.835269 Loss1: 0.447760 Loss2: 1.387509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.697679 Loss1: 0.277995 Loss2: 1.419683 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430617 Loss1: 0.087423 Loss2: 1.343194
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.603941 Loss1: 0.218132 Loss2: 1.385809
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.552396 Loss1: 0.173245 Loss2: 1.379150
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531795 Loss1: 0.152136 Loss2: 1.379659
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473547 Loss1: 0.108255 Loss2: 1.365292
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427385 Loss1: 0.070210 Loss2: 1.357174
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.517104 Loss1: 0.685031 Loss2: 1.832073
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.783476 Loss1: 0.413142 Loss2: 1.370334 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376593 Loss1: 0.032694 Loss2: 1.343899
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.668336 Loss1: 0.272418 Loss2: 1.395919
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549445 Loss1: 0.197106 Loss2: 1.352339
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507927 Loss1: 0.168206 Loss2: 1.339721
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530869 Loss1: 0.187693 Loss2: 1.343177
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469419 Loss1: 0.129475 Loss2: 1.339944
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448373 Loss1: 0.115216 Loss2: 1.333158
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.717942 Loss1: 0.876609 Loss2: 1.841333
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453972 Loss1: 0.122805 Loss2: 1.331168
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.847328 Loss1: 0.443477 Loss2: 1.403851
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427328 Loss1: 0.094724 Loss2: 1.332604
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.723954 Loss1: 0.318083 Loss2: 1.405871
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.655624 Loss1: 0.273481 Loss2: 1.382143
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558302 Loss1: 0.180466 Loss2: 1.377836
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.489008 Loss1: 0.126466 Loss2: 1.362542
-DEBUG flwr 2023-10-11 16:06:01,233 | server.py:236 | fit_round 119 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438512 Loss1: 0.072448 Loss2: 1.366064
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.496632 Loss1: 0.662381 Loss2: 1.834251
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427502 Loss1: 0.074102 Loss2: 1.353400
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.859501 Loss1: 0.483032 Loss2: 1.376469
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404023 Loss1: 0.058881 Loss2: 1.345142
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.751116 Loss1: 0.320756 Loss2: 1.430359
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396847 Loss1: 0.054915 Loss2: 1.341932
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.616041 Loss1: 0.222048 Loss2: 1.393993 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481879 Loss1: 0.114733 Loss2: 1.367146 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460223 Loss1: 0.097362 Loss2: 1.362861
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.721104 Loss1: 0.849982 Loss2: 1.871122
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458470 Loss1: 0.104795 Loss2: 1.353675
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.866940 Loss1: 0.478284 Loss2: 1.388656
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449001 Loss1: 0.100650 Loss2: 1.348351
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.697376 Loss1: 0.314519 Loss2: 1.382857
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543055 Loss1: 0.173906 Loss2: 1.369149
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497755 Loss1: 0.150414 Loss2: 1.347341
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476312 Loss1: 0.132700 Loss2: 1.343611
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.393669 Loss1: 0.055869 Loss2: 1.337800
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.601103 Loss1: 0.762530 Loss2: 1.838573
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408785 Loss1: 0.076279 Loss2: 1.332506
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.902571 Loss1: 0.515679 Loss2: 1.386892
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404127 Loss1: 0.076563 Loss2: 1.327564
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.668795 Loss1: 0.262759 Loss2: 1.406036
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366009 Loss1: 0.044023 Loss2: 1.321986
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.599823 Loss1: 0.220483 Loss2: 1.379340 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467552 Loss1: 0.106254 Loss2: 1.361298 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486737 Loss1: 0.130923 Loss2: 1.355814
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.540321 Loss1: 0.721312 Loss2: 1.819009
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428179 Loss1: 0.079321 Loss2: 1.348858
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.872309 Loss1: 0.511192 Loss2: 1.361117
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421667 Loss1: 0.076713 Loss2: 1.344954
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.717286 Loss1: 0.299008 Loss2: 1.418278
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.544123 Loss1: 0.188604 Loss2: 1.355519
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544801 Loss1: 0.188988 Loss2: 1.355813
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479744 Loss1: 0.123756 Loss2: 1.355987
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497164 Loss1: 0.152218 Loss2: 1.344946
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473420 Loss1: 0.130068 Loss2: 1.343351
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.498798 Loss1: 0.157355 Loss2: 1.341443
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431154 Loss1: 0.090136 Loss2: 1.341018
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 16:06:01,233][flwr][DEBUG] - fit_round 119 received 50 results and 0 failures
-INFO flwr 2023-10-11 16:06:41,589 | server.py:125 | fit progress: (119, 2.20066045934019, {'accuracy': 0.5794}, 274509.36708156497)
->> Test accuracy: 0.579400
-[2023-10-11 16:06:41,589][flwr][INFO] - fit progress: (119, 2.20066045934019, {'accuracy': 0.5794}, 274509.36708156497)
-DEBUG flwr 2023-10-11 16:06:41,589 | server.py:173 | evaluate_round 119: strategy sampled 50 clients (out of 50)
-[2023-10-11 16:06:41,589][flwr][DEBUG] - evaluate_round 119: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 16:15:48,017 | server.py:187 | evaluate_round 119 received 50 results and 0 failures
-[2023-10-11 16:15:48,017][flwr][DEBUG] - evaluate_round 119 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 16:15:48,018 | server.py:222 | fit_round 120: strategy sampled 50 clients (out of 50)
-[2023-10-11 16:15:48,018][flwr][DEBUG] - fit_round 120: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.554561 Loss1: 0.712557 Loss2: 1.842004
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.747517 Loss1: 0.382555 Loss2: 1.364962
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.654988 Loss1: 0.253666 Loss2: 1.401322
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563171 Loss1: 0.201377 Loss2: 1.361794
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.552059 Loss1: 0.684097 Loss2: 1.867962
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.926081 Loss1: 0.534709 Loss2: 1.391371 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.742841 Loss1: 0.287152 Loss2: 1.455689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.645522 Loss1: 0.255101 Loss2: 1.390421 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.582292 Loss1: 0.193384 Loss2: 1.388909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.547747 Loss1: 0.173621 Loss2: 1.374126 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385449 Loss1: 0.054846 Loss2: 1.330603
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459573 Loss1: 0.096064 Loss2: 1.363509
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433903 Loss1: 0.074981 Loss2: 1.358922
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399599 Loss1: 0.045354 Loss2: 1.354244
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410741 Loss1: 0.069421 Loss2: 1.341320
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.864972 Loss1: 0.959897 Loss2: 1.905075
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.838676 Loss1: 0.476404 Loss2: 1.362271
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.729278 Loss1: 0.324598 Loss2: 1.404679
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591587 Loss1: 0.225111 Loss2: 1.366475
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.620886 Loss1: 0.771422 Loss2: 1.849463
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.853738 Loss1: 0.486500 Loss2: 1.367238 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.648647 Loss1: 0.236560 Loss2: 1.412088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534386 Loss1: 0.184754 Loss2: 1.349632 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456194 Loss1: 0.108689 Loss2: 1.347505 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412498 Loss1: 0.078153 Loss2: 1.334344 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.456009 Loss1: 0.113829 Loss2: 1.342180 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392222 Loss1: 0.060226 Loss2: 1.331996 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.889907 Loss1: 0.467350 Loss2: 1.422557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630071 Loss1: 0.220610 Loss2: 1.409461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566294 Loss1: 0.150324 Loss2: 1.415970
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.624544 Loss1: 0.826012 Loss2: 1.798532
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.893498 Loss1: 0.529718 Loss2: 1.363779 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671256 Loss1: 0.297191 Loss2: 1.374064 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605525 Loss1: 0.260371 Loss2: 1.345154 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530701 Loss1: 0.177510 Loss2: 1.353192 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981027
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433344 Loss1: 0.099855 Loss2: 1.333489 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453481 Loss1: 0.132518 Loss2: 1.320963 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396947 Loss1: 0.070213 Loss2: 1.326735
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.789241 Loss1: 0.878304 Loss2: 1.910936
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.922282 Loss1: 0.484168 Loss2: 1.438115
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.758632 Loss1: 0.294055 Loss2: 1.464577
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.685717 Loss1: 0.253569 Loss2: 1.432147
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.661909 Loss1: 0.217442 Loss2: 1.444467
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.746588 Loss1: 0.895664 Loss2: 1.850924
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.555107 Loss1: 0.137763 Loss2: 1.417344
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.545737 Loss1: 0.132199 Loss2: 1.413539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.518082 Loss1: 0.098003 Loss2: 1.420080 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488370 Loss1: 0.078066 Loss2: 1.410304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476618 Loss1: 0.070164 Loss2: 1.406454 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441415 Loss1: 0.085098 Loss2: 1.356317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397587 Loss1: 0.060191 Loss2: 1.337395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380779 Loss1: 0.044256 Loss2: 1.336523
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.545850 Loss1: 0.698580 Loss2: 1.847270
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.826584 Loss1: 0.455465 Loss2: 1.371119
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.711356 Loss1: 0.326630 Loss2: 1.384725
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.625058 Loss1: 0.246762 Loss2: 1.378296
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.596796 Loss1: 0.237335 Loss2: 1.359460
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.770742 Loss1: 0.845369 Loss2: 1.925373
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.527778 Loss1: 0.162554 Loss2: 1.365224
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479533 Loss1: 0.137748 Loss2: 1.341785 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417015 Loss1: 0.079919 Loss2: 1.337096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384916 Loss1: 0.062296 Loss2: 1.322620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392726 Loss1: 0.067113 Loss2: 1.325613 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.558787 Loss1: 0.151405 Loss2: 1.407381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.518226 Loss1: 0.112711 Loss2: 1.405515 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.502450 Loss1: 0.106117 Loss2: 1.396333
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.581641 Loss1: 0.716968 Loss2: 1.864673
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.857438 Loss1: 0.488111 Loss2: 1.369327
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690037 Loss1: 0.270023 Loss2: 1.420014
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551552 Loss1: 0.189962 Loss2: 1.361591
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499374 Loss1: 0.137843 Loss2: 1.361531
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.568966 Loss1: 0.718679 Loss2: 1.850287
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468102 Loss1: 0.119498 Loss2: 1.348604
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.821765 Loss1: 0.420894 Loss2: 1.400871
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472729 Loss1: 0.130933 Loss2: 1.341796
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404650 Loss1: 0.061909 Loss2: 1.342741
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.646776 Loss1: 0.222343 Loss2: 1.424433
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.383700 Loss1: 0.049368 Loss2: 1.334331
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581868 Loss1: 0.184390 Loss2: 1.397478
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367409 Loss1: 0.041204 Loss2: 1.326206
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558086 Loss1: 0.160720 Loss2: 1.397366
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.518062 Loss1: 0.129862 Loss2: 1.388200
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550293 Loss1: 0.163974 Loss2: 1.386319
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493892 Loss1: 0.107376 Loss2: 1.386515
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.468935 Loss1: 0.083442 Loss2: 1.385494
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.539310 Loss1: 0.671324 Loss2: 1.867986
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426510 Loss1: 0.055928 Loss2: 1.370582
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645051 Loss1: 0.218652 Loss2: 1.426399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.523444 Loss1: 0.123793 Loss2: 1.399651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.501908 Loss1: 0.116543 Loss2: 1.385365
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.743778 Loss1: 0.750951 Loss2: 1.992828
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456192 Loss1: 0.070758 Loss2: 1.385434
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.890782 Loss1: 0.429942 Loss2: 1.460840
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473585 Loss1: 0.095757 Loss2: 1.377828
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.822842 Loss1: 0.316487 Loss2: 1.506355
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.678633 Loss1: 0.228374 Loss2: 1.450259
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.491463 Loss1: 0.109871 Loss2: 1.381592
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.613916 Loss1: 0.164248 Loss2: 1.449668
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458669 Loss1: 0.080019 Loss2: 1.378650
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.531350 Loss1: 0.090021 Loss2: 1.441330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.484176 Loss1: 0.065778 Loss2: 1.418398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.471787 Loss1: 0.058537 Loss2: 1.413249
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.516428 Loss1: 0.681405 Loss2: 1.835023
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.756256 Loss1: 0.395508 Loss2: 1.360748
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648529 Loss1: 0.259330 Loss2: 1.389199
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554078 Loss1: 0.192147 Loss2: 1.361931
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526402 Loss1: 0.173226 Loss2: 1.353176
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.656911 Loss1: 0.773687 Loss2: 1.883224
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.428620 Loss1: 0.080705 Loss2: 1.347915
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414005 Loss1: 0.077099 Loss2: 1.336906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400594 Loss1: 0.069988 Loss2: 1.330606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.383987 Loss1: 0.052507 Loss2: 1.331480 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383723 Loss1: 0.062879 Loss2: 1.320844 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485107 Loss1: 0.131112 Loss2: 1.353996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457891 Loss1: 0.106347 Loss2: 1.351544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431257 Loss1: 0.086699 Loss2: 1.344558
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.738486 Loss1: 0.804295 Loss2: 1.934192
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.952334 Loss1: 0.499259 Loss2: 1.453074
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.816731 Loss1: 0.312508 Loss2: 1.504223
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.665390 Loss1: 0.217625 Loss2: 1.447765
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587877 Loss1: 0.143179 Loss2: 1.444698
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.575552 Loss1: 0.749793 Loss2: 1.825760
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.572150 Loss1: 0.136802 Loss2: 1.435348
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529254 Loss1: 0.101077 Loss2: 1.428177 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.493543 Loss1: 0.071434 Loss2: 1.422109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493364 Loss1: 0.079664 Loss2: 1.413700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.471954 Loss1: 0.057901 Loss2: 1.414052 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434900 Loss1: 0.087968 Loss2: 1.346932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416522 Loss1: 0.072456 Loss2: 1.344066 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407163 Loss1: 0.066180 Loss2: 1.340983
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.758289 Loss1: 0.877406 Loss2: 1.880884
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.909653 Loss1: 0.507566 Loss2: 1.402088
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.711284 Loss1: 0.278110 Loss2: 1.433174
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.621246 Loss1: 0.232588 Loss2: 1.388658
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.563859 Loss1: 0.174005 Loss2: 1.389854
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.534544 Loss1: 0.692615 Loss2: 1.841929
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.749480 Loss1: 0.401154 Loss2: 1.348326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.696952 Loss1: 0.287396 Loss2: 1.409556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.570759 Loss1: 0.224583 Loss2: 1.346176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.537072 Loss1: 0.192379 Loss2: 1.344694 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472004 Loss1: 0.122038 Loss2: 1.349966 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441652 Loss1: 0.103932 Loss2: 1.337720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370993 Loss1: 0.051184 Loss2: 1.319809 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.955524 Loss1: 0.547744 Loss2: 1.407780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589619 Loss1: 0.208457 Loss2: 1.381162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536940 Loss1: 0.154365 Loss2: 1.382575
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.557548 Loss1: 0.717378 Loss2: 1.840170
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.924057 Loss1: 0.549938 Loss2: 1.374119 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804680 Loss1: 0.358271 Loss2: 1.446409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599052 Loss1: 0.235612 Loss2: 1.363440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550095 Loss1: 0.173091 Loss2: 1.377004 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446160 Loss1: 0.085035 Loss2: 1.361125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.550689 Loss1: 0.187326 Loss2: 1.363362
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444235 Loss1: 0.080330 Loss2: 1.363905
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406908 Loss1: 0.055911 Loss2: 1.350996
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399427 Loss1: 0.054939 Loss2: 1.344489
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380788 Loss1: 0.045710 Loss2: 1.335078
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.641776 Loss1: 0.805990 Loss2: 1.835786
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.822747 Loss1: 0.486211 Loss2: 1.336536
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.701576 Loss1: 0.306289 Loss2: 1.395288
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511045 Loss1: 0.177262 Loss2: 1.333783
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479682 Loss1: 0.157685 Loss2: 1.321997
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.446800 Loss1: 0.657102 Loss2: 1.789698
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472215 Loss1: 0.144458 Loss2: 1.327757
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441319 Loss1: 0.121780 Loss2: 1.319539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.720913 Loss1: 0.327609 Loss2: 1.393304
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.466476 Loss1: 0.146577 Loss2: 1.319899
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419800 Loss1: 0.097190 Loss2: 1.322610
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.624474 Loss1: 0.246290 Loss2: 1.378184
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378660 Loss1: 0.068801 Loss2: 1.309859
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496721 Loss1: 0.137257 Loss2: 1.359464
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441411 Loss1: 0.099070 Loss2: 1.342341
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441289 Loss1: 0.107014 Loss2: 1.334274
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418844 Loss1: 0.085155 Loss2: 1.333689
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396800 Loss1: 0.074397 Loss2: 1.322403
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.640191 Loss1: 0.755328 Loss2: 1.884863
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.909933 Loss1: 0.501782 Loss2: 1.408151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694389 Loss1: 0.294963 Loss2: 1.399427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.558898 Loss1: 0.169494 Loss2: 1.389404 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512248 Loss1: 0.129323 Loss2: 1.382925 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488853 Loss1: 0.107394 Loss2: 1.381459 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.426441 Loss1: 0.065535 Loss2: 1.360906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409414 Loss1: 0.047144 Loss2: 1.362270 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.644750 Loss1: 0.204086 Loss2: 1.440664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.540846 Loss1: 0.113336 Loss2: 1.427510 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.593707 Loss1: 0.795091 Loss2: 1.798616 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.742636 Loss1: 0.352137 Loss2: 1.390499 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558761 Loss1: 0.219333 Loss2: 1.339428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515159 Loss1: 0.174380 Loss2: 1.340779
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.752432 Loss1: 0.835412 Loss2: 1.917020
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.012707 Loss1: 0.633102 Loss2: 1.379605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.828468 Loss1: 0.394641 Loss2: 1.433827
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481712 Loss1: 0.153608 Loss2: 1.328104
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.659120 Loss1: 0.276943 Loss2: 1.382176
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419659 Loss1: 0.098190 Loss2: 1.321470
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405317 Loss1: 0.085742 Loss2: 1.319574 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460719 Loss1: 0.096878 Loss2: 1.363841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404450 Loss1: 0.055571 Loss2: 1.348879 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.687900 Loss1: 0.771884 Loss2: 1.916016 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.811696 Loss1: 0.338919 Loss2: 1.472777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.814744 Loss1: 0.879144 Loss2: 1.935600 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.887633 Loss1: 0.465948 Loss2: 1.421685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.693925 Loss1: 0.247049 Loss2: 1.446876 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.607536 Loss1: 0.213284 Loss2: 1.394252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555429 Loss1: 0.148639 Loss2: 1.406789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529199 Loss1: 0.138058 Loss2: 1.391141 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458478 Loss1: 0.078537 Loss2: 1.379941 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449908 Loss1: 0.074950 Loss2: 1.374957 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.863767 Loss1: 0.463589 Loss2: 1.400178 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.616496 Loss1: 0.235024 Loss2: 1.381472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.729576 Loss1: 0.785150 Loss2: 1.944426
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562679 Loss1: 0.178553 Loss2: 1.384126
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.500978 Loss1: 0.123463 Loss2: 1.377515 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455884 Loss1: 0.095807 Loss2: 1.360077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436704 Loss1: 0.076763 Loss2: 1.359941 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.426185 Loss1: 0.070650 Loss2: 1.355535 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404834 Loss1: 0.050991 Loss2: 1.353843 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387712 Loss1: 0.048964 Loss2: 1.338748 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.481905 Loss1: 0.730385 Loss2: 1.751520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615246 Loss1: 0.269281 Loss2: 1.345966 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522780 Loss1: 0.212793 Loss2: 1.309987
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.520937 Loss1: 0.706121 Loss2: 1.814816
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.799156 Loss1: 0.415603 Loss2: 1.383553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629768 Loss1: 0.232869 Loss2: 1.396899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543089 Loss1: 0.189658 Loss2: 1.353431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493685 Loss1: 0.139098 Loss2: 1.354587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477430 Loss1: 0.120078 Loss2: 1.357352 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451416 Loss1: 0.108475 Loss2: 1.342941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399916 Loss1: 0.065583 Loss2: 1.334333 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.574380 Loss1: 0.693573 Loss2: 1.880807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.714194 Loss1: 0.276040 Loss2: 1.438153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.658107 Loss1: 0.778820 Loss2: 1.879287 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.859490 Loss1: 0.470557 Loss2: 1.388932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.757501 Loss1: 0.332640 Loss2: 1.424861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.673682 Loss1: 0.287076 Loss2: 1.386607 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.582987 Loss1: 0.190934 Loss2: 1.392053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512207 Loss1: 0.145893 Loss2: 1.366315 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.444140 Loss1: 0.079285 Loss2: 1.364855 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407428 Loss1: 0.058856 Loss2: 1.348572 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.771031 Loss1: 0.435578 Loss2: 1.335454 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.525462 Loss1: 0.190757 Loss2: 1.334705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498346 Loss1: 0.171863 Loss2: 1.326483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.428253 Loss1: 0.106405 Loss2: 1.321848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410978 Loss1: 0.100000 Loss2: 1.310979 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.387795 Loss1: 0.074182 Loss2: 1.313613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.393867 Loss1: 0.087014 Loss2: 1.306853 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.362196 Loss1: 0.061477 Loss2: 1.300718 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445528 Loss1: 0.098322 Loss2: 1.347206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423591 Loss1: 0.087655 Loss2: 1.335936 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) 
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.716694 Loss1: 0.405360 Loss2: 1.311334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562464 Loss1: 0.257657 Loss2: 1.304808 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.481142 Loss1: 0.169802 Loss2: 1.311341 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.394783 Loss1: 0.099287 Loss2: 1.295496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.396877 Loss1: 0.109097 Loss2: 1.287780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380046 Loss1: 0.090642 Loss2: 1.289404 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.347737 Loss1: 0.065222 Loss2: 1.282515 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.323035 Loss1: 0.048442 Loss2: 1.274592 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451374 Loss1: 0.082899 Loss2: 1.368475 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.546657 Loss1: 0.718315 Loss2: 1.828342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.719446 Loss1: 0.276318 Loss2: 1.443128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563307 Loss1: 0.177764 Loss2: 1.385543
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.567898 Loss1: 0.733495 Loss2: 1.834403
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499805 Loss1: 0.122926 Loss2: 1.376879
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.929176 Loss1: 0.526054 Loss2: 1.403122
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461865 Loss1: 0.089450 Loss2: 1.372415
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.785606 Loss1: 0.357001 Loss2: 1.428604
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424792 Loss1: 0.060618 Loss2: 1.364173
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.695721 Loss1: 0.313101 Loss2: 1.382620
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441327 Loss1: 0.088481 Loss2: 1.352846
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.587030 Loss1: 0.201334 Loss2: 1.385695
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458301 Loss1: 0.098284 Loss2: 1.360017
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472697 Loss1: 0.112429 Loss2: 1.360268
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461038 Loss1: 0.091228 Loss2: 1.369810
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459341 Loss1: 0.109757 Loss2: 1.349584
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447823 Loss1: 0.102085 Loss2: 1.345737
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430207 Loss1: 0.080841 Loss2: 1.349366
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.455534 Loss1: 0.104693 Loss2: 1.350841
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.495809 Loss1: 0.655923 Loss2: 1.839886
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.768210 Loss1: 0.416020 Loss2: 1.352190
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.646534 Loss1: 0.253326 Loss2: 1.393208
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574457 Loss1: 0.216747 Loss2: 1.357711
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.735078 Loss1: 0.857361 Loss2: 1.877717
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.877267 Loss1: 0.483918 Loss2: 1.393349 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.707121 Loss1: 0.274809 Loss2: 1.432312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584152 Loss1: 0.200418 Loss2: 1.383734 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529353 Loss1: 0.151112 Loss2: 1.378241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508258 Loss1: 0.129040 Loss2: 1.379218 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473293 Loss1: 0.094734 Loss2: 1.378559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430922 Loss1: 0.067146 Loss2: 1.363776 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.472787 Loss1: 0.674287 Loss2: 1.798500 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684789 Loss1: 0.280243 Loss2: 1.404546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586881 Loss1: 0.216611 Loss2: 1.370270
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.578563 Loss1: 0.747099 Loss2: 1.831464
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.779906 Loss1: 0.434991 Loss2: 1.344914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.639863 Loss1: 0.269678 Loss2: 1.370185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500965 Loss1: 0.148383 Loss2: 1.352581
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509240 Loss1: 0.185124 Loss2: 1.324116
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452639 Loss1: 0.096417 Loss2: 1.356222
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.456763 Loss1: 0.130768 Loss2: 1.325995
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493187 Loss1: 0.139758 Loss2: 1.353429
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473103 Loss1: 0.152060 Loss2: 1.321043
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439316 Loss1: 0.088200 Loss2: 1.351117
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423369 Loss1: 0.108503 Loss2: 1.314866
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406012 Loss1: 0.105182 Loss2: 1.300830
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418892 Loss1: 0.117153 Loss2: 1.301739
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376102 Loss1: 0.070682 Loss2: 1.305420
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.903521 Loss1: 0.835337 Loss2: 2.068184
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.057379 Loss1: 0.631251 Loss2: 1.426128
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.919259 Loss1: 0.410561 Loss2: 1.508699
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.785374 Loss1: 0.336591 Loss2: 1.448783
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.659504 Loss1: 0.230853 Loss2: 1.428651
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.838873 Loss1: 0.475489 Loss2: 1.363384 [repeated 3x across cluster]
-DEBUG flwr 2023-10-11 16:44:04,487 | server.py:236 | fit_round 120 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.657822 Loss1: 0.304274 Loss2: 1.353548 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.487386 Loss1: 0.079082 Loss2: 1.408304 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983073
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.503254 Loss1: 0.094248 Loss2: 1.409006 [repeated 2x across cluster]
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425916 Loss1: 0.117371 Loss2: 1.308545 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368632 Loss1: 0.067041 Loss2: 1.301591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351883 Loss1: 0.054450 Loss2: 1.297434 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.628851 Loss1: 0.224911 Loss2: 1.403940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544295 Loss1: 0.173597 Loss2: 1.370698 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503792 Loss1: 0.142502 Loss2: 1.361290
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.593604 Loss1: 0.675284 Loss2: 1.918320
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.818228 Loss1: 0.401996 Loss2: 1.416232
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442994 Loss1: 0.087512 Loss2: 1.355482
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.718564 Loss1: 0.261596 Loss2: 1.456969
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429069 Loss1: 0.078591 Loss2: 1.350478
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.694222 Loss1: 0.280118 Loss2: 1.414104
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450574 Loss1: 0.106669 Loss2: 1.343905
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438805 Loss1: 0.095149 Loss2: 1.343656 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988971
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.525107 Loss1: 0.123021 Loss2: 1.402085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444765 Loss1: 0.057959 Loss2: 1.386806 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 16:44:04,487][flwr][DEBUG] - fit_round 120 received 50 results and 0 failures
-INFO flwr 2023-10-11 16:44:46,252 | server.py:125 | fit progress: (120, 2.2117004977247587, {'accuracy': 0.5816}, 276794.030562)
->> Test accuracy: 0.581600
-[2023-10-11 16:44:46,252][flwr][INFO] - fit progress: (120, 2.2117004977247587, {'accuracy': 0.5816}, 276794.030562)
-DEBUG flwr 2023-10-11 16:44:46,252 | server.py:173 | evaluate_round 120: strategy sampled 50 clients (out of 50)
-[2023-10-11 16:44:46,252][flwr][DEBUG] - evaluate_round 120: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 16:53:51,591 | server.py:187 | evaluate_round 120 received 50 results and 0 failures
-[2023-10-11 16:53:51,591][flwr][DEBUG] - evaluate_round 120 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 16:53:51,592 | server.py:222 | fit_round 121: strategy sampled 50 clients (out of 50)
-[2023-10-11 16:53:51,592][flwr][DEBUG] - fit_round 121: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.677700 Loss1: 0.799180 Loss2: 1.878521 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.865612 Loss1: 0.392350 Loss2: 1.473262 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.449436 Loss1: 0.617994 Loss2: 1.831442
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647401 Loss1: 0.210317 Loss2: 1.437084
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.733803 Loss1: 0.351450 Loss2: 1.382353
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.554066 Loss1: 0.133143 Loss2: 1.420923
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622260 Loss1: 0.229019 Loss2: 1.393241
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520568 Loss1: 0.118081 Loss2: 1.402486
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529648 Loss1: 0.158611 Loss2: 1.371037
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485859 Loss1: 0.086521 Loss2: 1.399338
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507339 Loss1: 0.138483 Loss2: 1.368856
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481097 Loss1: 0.084695 Loss2: 1.396402
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468331 Loss1: 0.105092 Loss2: 1.363239
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489869 Loss1: 0.093036 Loss2: 1.396833
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437068 Loss1: 0.081339 Loss2: 1.355729
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.471418 Loss1: 0.079360 Loss2: 1.392058
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429496 Loss1: 0.080218 Loss2: 1.349278 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.524345 Loss1: 0.700670 Loss2: 1.823675 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.865229 Loss1: 0.428180 Loss2: 1.437049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599609 Loss1: 0.238315 Loss2: 1.361294
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.596027 Loss1: 0.766395 Loss2: 1.829632
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.881952 Loss1: 0.528127 Loss2: 1.353824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670395 Loss1: 0.291018 Loss2: 1.379377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.602937 Loss1: 0.266822 Loss2: 1.336115 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529661 Loss1: 0.180291 Loss2: 1.349370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485474 Loss1: 0.149086 Loss2: 1.336389 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374831 Loss1: 0.045456 Loss2: 1.329375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433809 Loss1: 0.114245 Loss2: 1.319563
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400511 Loss1: 0.082705 Loss2: 1.317806
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383788 Loss1: 0.070069 Loss2: 1.313719
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379655 Loss1: 0.066325 Loss2: 1.313330
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.826197 Loss1: 0.829002 Loss2: 1.997195
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.903871 Loss1: 0.529037 Loss2: 1.374834
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.775985 Loss1: 0.354814 Loss2: 1.421172
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.707353 Loss1: 0.304699 Loss2: 1.402655
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.625549 Loss1: 0.239112 Loss2: 1.386437
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831260 Loss1: 0.449658 Loss2: 1.381602 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.639732 Loss1: 0.226340 Loss2: 1.413392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477844 Loss1: 0.111176 Loss2: 1.366668 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986979
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449170 Loss1: 0.080275 Loss2: 1.368895 [repeated 2x across cluster]
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.531443 Loss1: 0.163697 Loss2: 1.367746 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461901 Loss1: 0.110295 Loss2: 1.351606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.472546 Loss1: 0.636769 Loss2: 1.835777
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433063 Loss1: 0.084546 Loss2: 1.348517
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.765860 Loss1: 0.347083 Loss2: 1.418777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.559112 Loss1: 0.174693 Loss2: 1.384419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.752709 Loss1: 0.822677 Loss2: 1.930033
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.510213 Loss1: 0.139638 Loss2: 1.370575
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.929647 Loss1: 0.500681 Loss2: 1.428966
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461427 Loss1: 0.089627 Loss2: 1.371800
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.782242 Loss1: 0.322589 Loss2: 1.459653
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422605 Loss1: 0.070711 Loss2: 1.351894
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.639479 Loss1: 0.220851 Loss2: 1.418629
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400222 Loss1: 0.050138 Loss2: 1.350083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374308 Loss1: 0.034252 Loss2: 1.340056 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.546430 Loss1: 0.144367 Loss2: 1.402063 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519611 Loss1: 0.120796 Loss2: 1.398814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.474016 Loss1: 0.085684 Loss2: 1.388331
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.657975 Loss1: 0.800098 Loss2: 1.857877
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.764820 Loss1: 0.397484 Loss2: 1.367336
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.657020 Loss1: 0.261014 Loss2: 1.396005
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517526 Loss1: 0.169520 Loss2: 1.348006
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521050 Loss1: 0.170007 Loss2: 1.351043
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.476615 Loss1: 0.657780 Loss2: 1.818836
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.512444 Loss1: 0.160229 Loss2: 1.352214
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.859618 Loss1: 0.454324 Loss2: 1.405294
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.501167 Loss1: 0.142599 Loss2: 1.358568
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.686890 Loss1: 0.270588 Loss2: 1.416302
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.476028 Loss1: 0.126991 Loss2: 1.349037
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.578365 Loss1: 0.192319 Loss2: 1.386046
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422330 Loss1: 0.087327 Loss2: 1.335003
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429335 Loss1: 0.088126 Loss2: 1.341209
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.532730 Loss1: 0.154703 Loss2: 1.378027
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.517373 Loss1: 0.148871 Loss2: 1.368501
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464865 Loss1: 0.100566 Loss2: 1.364298
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429957 Loss1: 0.069336 Loss2: 1.360621
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442003 Loss1: 0.089351 Loss2: 1.352652
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.583347 Loss1: 0.773292 Loss2: 1.810056
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420611 Loss1: 0.063680 Loss2: 1.356932
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736689 Loss1: 0.325329 Loss2: 1.411360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.583532 Loss1: 0.224499 Loss2: 1.359032 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520717 Loss1: 0.160541 Loss2: 1.360177
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.670022 Loss1: 0.853064 Loss2: 1.816958
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.027181 Loss1: 0.629632 Loss2: 1.397549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.739517 Loss1: 0.346703 Loss2: 1.392814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550146 Loss1: 0.202937 Loss2: 1.347209 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497042 Loss1: 0.149100 Loss2: 1.347942 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464088 Loss1: 0.133987 Loss2: 1.330101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386446 Loss1: 0.066503 Loss2: 1.319943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380755 Loss1: 0.069134 Loss2: 1.311621 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.553772 Loss1: 0.196019 Loss2: 1.357753 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438233 Loss1: 0.105192 Loss2: 1.333041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.738293 Loss1: 0.893057 Loss2: 1.845236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.912673 Loss1: 0.519436 Loss2: 1.393238 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.768091 Loss1: 0.346538 Loss2: 1.421553 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.573148 Loss1: 0.195095 Loss2: 1.378053 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464409 Loss1: 0.104154 Loss2: 1.360255 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458321 Loss1: 0.100641 Loss2: 1.357680
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.765492 Loss1: 0.960689 Loss2: 1.804803
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.786006 Loss1: 0.433164 Loss2: 1.352843 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.641468 Loss1: 0.293606 Loss2: 1.347862 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461667 Loss1: 0.131564 Loss2: 1.330104 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.364923 Loss1: 0.056695 Loss2: 1.308228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.359953 Loss1: 0.058711 Loss2: 1.301242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.360880 Loss1: 0.064546 Loss2: 1.296334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.349692 Loss1: 0.059050 Loss2: 1.290641 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484933 Loss1: 0.128802 Loss2: 1.356131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.503851 Loss1: 0.162844 Loss2: 1.341007 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455874 Loss1: 0.109507 Loss2: 1.346366
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.897664 Loss1: 1.023799 Loss2: 1.873865
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.903654 Loss1: 0.532913 Loss2: 1.370741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.727412 Loss1: 0.322692 Loss2: 1.404720 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535347 Loss1: 0.190596 Loss2: 1.344751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.423132 Loss1: 0.101226 Loss2: 1.321906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.408058 Loss1: 0.081241 Loss2: 1.326816
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.738528 Loss1: 0.839189 Loss2: 1.899339
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.837238 Loss1: 0.458754 Loss2: 1.378484
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.393999 Loss1: 0.073812 Loss2: 1.320188
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.747828 Loss1: 0.342592 Loss2: 1.405236
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395124 Loss1: 0.084238 Loss2: 1.310886
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555676 Loss1: 0.192307 Loss2: 1.363370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.514243 Loss1: 0.139360 Loss2: 1.374883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461770 Loss1: 0.104393 Loss2: 1.357376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443314 Loss1: 0.083559 Loss2: 1.359756 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499620 Loss1: 0.185202 Loss2: 1.314418 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.391007 Loss1: 0.093733 Loss2: 1.297274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.360551 Loss1: 0.072216 Loss2: 1.288336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.355057 Loss1: 0.071437 Loss2: 1.283620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373576 Loss1: 0.085460 Loss2: 1.288117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412520 Loss1: 0.123125 Loss2: 1.289395 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504109 Loss1: 0.171834 Loss2: 1.332275 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414777 Loss1: 0.098093 Loss2: 1.316684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409055 Loss1: 0.085863 Loss2: 1.323192
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.583810 Loss1: 0.709099 Loss2: 1.874711
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379720 Loss1: 0.059540 Loss2: 1.320180
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.816866 Loss1: 0.436397 Loss2: 1.380469
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.699487 Loss1: 0.284920 Loss2: 1.414567
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580425 Loss1: 0.201530 Loss2: 1.378895
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514268 Loss1: 0.153692 Loss2: 1.360576
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513327 Loss1: 0.136047 Loss2: 1.377280
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.636532 Loss1: 0.834449 Loss2: 1.802082
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459917 Loss1: 0.103768 Loss2: 1.356148
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.806985 Loss1: 0.447770 Loss2: 1.359215
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404038 Loss1: 0.055378 Loss2: 1.348661
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.676656 Loss1: 0.294172 Loss2: 1.382484
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432252 Loss1: 0.086047 Loss2: 1.346205
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562281 Loss1: 0.221042 Loss2: 1.341239
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400022 Loss1: 0.059672 Loss2: 1.340349
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443105 Loss1: 0.114300 Loss2: 1.328804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.385532 Loss1: 0.072264 Loss2: 1.313268 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372254 Loss1: 0.067037 Loss2: 1.305217
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.802452 Loss1: 0.863469 Loss2: 1.938983
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367801 Loss1: 0.063928 Loss2: 1.303873
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.032048 Loss1: 0.610643 Loss2: 1.421405
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.795376 Loss1: 0.324654 Loss2: 1.470721
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.645826 Loss1: 0.241690 Loss2: 1.404135
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.620637 Loss1: 0.206292 Loss2: 1.414344
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511858 Loss1: 0.115011 Loss2: 1.396847
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484039 Loss1: 0.096090 Loss2: 1.387949
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.517645 Loss1: 0.734055 Loss2: 1.783590
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.750100 Loss1: 0.432819 Loss2: 1.317281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.644127 Loss1: 0.298763 Loss2: 1.345365 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520359 Loss1: 0.210351 Loss2: 1.310009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.438412 Loss1: 0.129571 Loss2: 1.308841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420222 Loss1: 0.122244 Loss2: 1.297977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373717 Loss1: 0.083003 Loss2: 1.290714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372135 Loss1: 0.081021 Loss2: 1.291115 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.570613 Loss1: 0.159363 Loss2: 1.411251 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511198 Loss1: 0.115643 Loss2: 1.395556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.489208 Loss1: 0.688130 Loss2: 1.801078
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489095 Loss1: 0.087223 Loss2: 1.401872
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452726 Loss1: 0.059401 Loss2: 1.393325
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.778896 Loss1: 0.434958 Loss2: 1.343938
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.444066 Loss1: 0.061112 Loss2: 1.382954
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.652520 Loss1: 0.285684 Loss2: 1.366837
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.569873 Loss1: 0.228210 Loss2: 1.341663
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500072 Loss1: 0.158397 Loss2: 1.341674
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510082 Loss1: 0.180833 Loss2: 1.329248
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441826 Loss1: 0.112431 Loss2: 1.329395
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.665729 Loss1: 0.816436 Loss2: 1.849292
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407317 Loss1: 0.079764 Loss2: 1.327553
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.884108 Loss1: 0.498567 Loss2: 1.385541
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.382990 Loss1: 0.066562 Loss2: 1.316428
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.720932 Loss1: 0.283847 Loss2: 1.437085
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465808 Loss1: 0.145838 Loss2: 1.319970
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565253 Loss1: 0.193119 Loss2: 1.372134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447172 Loss1: 0.091722 Loss2: 1.355450 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422120 Loss1: 0.078230 Loss2: 1.343890
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.657383 Loss1: 0.740732 Loss2: 1.916652
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394905 Loss1: 0.056112 Loss2: 1.338794
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.855768 Loss1: 0.426434 Loss2: 1.429334
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396809 Loss1: 0.061789 Loss2: 1.335020
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.723247 Loss1: 0.254196 Loss2: 1.469050
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584065 Loss1: 0.182921 Loss2: 1.401144
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516360 Loss1: 0.109361 Loss2: 1.406999
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485767 Loss1: 0.088400 Loss2: 1.397367
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.449423 Loss1: 0.068889 Loss2: 1.380534
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434344 Loss1: 0.061081 Loss2: 1.373263
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.627243 Loss1: 0.760818 Loss2: 1.866425
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414389 Loss1: 0.049993 Loss2: 1.364396
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.967347 Loss1: 0.522426 Loss2: 1.444921
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399546 Loss1: 0.038431 Loss2: 1.361115
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.804982 Loss1: 0.352111 Loss2: 1.452871
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.692467 Loss1: 0.273071 Loss2: 1.419396
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605279 Loss1: 0.198785 Loss2: 1.406494
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.581844 Loss1: 0.173014 Loss2: 1.408830
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544812 Loss1: 0.135455 Loss2: 1.409357
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.805135 Loss1: 0.881908 Loss2: 1.923227
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.020528 Loss1: 0.552348 Loss2: 1.468180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.725830 Loss1: 0.266553 Loss2: 1.459277 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.471603 Loss1: 0.083910 Loss2: 1.387693
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.620717 Loss1: 0.194653 Loss2: 1.426064
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.525113 Loss1: 0.110751 Loss2: 1.414362
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538339 Loss1: 0.140556 Loss2: 1.397783
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.520586 Loss1: 0.119447 Loss2: 1.401138
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.527339 Loss1: 0.125553 Loss2: 1.401786
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.490347 Loss1: 0.632533 Loss2: 1.857815
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.489181 Loss1: 0.083370 Loss2: 1.405811
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.800256 Loss1: 0.422841 Loss2: 1.377415
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.501018 Loss1: 0.098862 Loss2: 1.402156
-(DefaultActor pid=3764) >> Training accuracy: 0.969792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.659900 Loss1: 0.288747 Loss2: 1.371153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.556813 Loss1: 0.185910 Loss2: 1.370903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.465819 Loss1: 0.103154 Loss2: 1.362665
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.462731 Loss1: 0.690846 Loss2: 1.771886
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.850179 Loss1: 0.497760 Loss2: 1.352419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.708100 Loss1: 0.295563 Loss2: 1.412537 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.603579 Loss1: 0.264492 Loss2: 1.339087 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.433715 Loss1: 0.099793 Loss2: 1.333922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409576 Loss1: 0.088666 Loss2: 1.320909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395325 Loss1: 0.076982 Loss2: 1.318344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412513 Loss1: 0.095074 Loss2: 1.317439 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.648410 Loss1: 0.221432 Loss2: 1.426978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.553609 Loss1: 0.131933 Loss2: 1.421676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518178 Loss1: 0.108879 Loss2: 1.409299
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.459658 Loss1: 0.620100 Loss2: 1.839558
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.819905 Loss1: 0.421470 Loss2: 1.398435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.658903 Loss1: 0.261443 Loss2: 1.397460 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.552779 Loss1: 0.178520 Loss2: 1.374259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.526442 Loss1: 0.144060 Loss2: 1.382381 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.606140 Loss1: 0.747096 Loss2: 1.859044 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.913853 Loss1: 0.531158 Loss2: 1.382695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.746923 Loss1: 0.310486 Loss2: 1.436438 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.971507
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.624172 Loss1: 0.249365 Loss2: 1.374807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508042 Loss1: 0.140560 Loss2: 1.367483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479073 Loss1: 0.116644 Loss2: 1.362429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444708 Loss1: 0.087645 Loss2: 1.357062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426906 Loss1: 0.073879 Loss2: 1.353027 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.659261 Loss1: 0.239518 Loss2: 1.419743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.574587 Loss1: 0.157662 Loss2: 1.416925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.514439 Loss1: 0.108719 Loss2: 1.405720
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.599655 Loss1: 0.727956 Loss2: 1.871699
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.938525 Loss1: 0.496357 Loss2: 1.442168 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.766474 Loss1: 0.311135 Loss2: 1.455339 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566588 Loss1: 0.146389 Loss2: 1.420199 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518377 Loss1: 0.122441 Loss2: 1.395936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495028 Loss1: 0.098901 Loss2: 1.396127
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.517447 Loss1: 0.741163 Loss2: 1.776284
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.778075 Loss1: 0.436645 Loss2: 1.341430
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.466141 Loss1: 0.073141 Loss2: 1.393000
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653703 Loss1: 0.295011 Loss2: 1.358691
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451626 Loss1: 0.064673 Loss2: 1.386952
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508765 Loss1: 0.176713 Loss2: 1.332052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463088 Loss1: 0.148693 Loss2: 1.314395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.393679 Loss1: 0.084311 Loss2: 1.309368
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.538828 Loss1: 0.763889 Loss2: 1.774939
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.777537 Loss1: 0.429355 Loss2: 1.348182 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.363235 Loss1: 0.063839 Loss2: 1.299396
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.627963 Loss1: 0.267994 Loss2: 1.359969
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562522 Loss1: 0.224342 Loss2: 1.338180
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.548915 Loss1: 0.214448 Loss2: 1.334466
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.479902 Loss1: 0.154064 Loss2: 1.325838
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444249 Loss1: 0.120914 Loss2: 1.323335
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.641209 Loss1: 0.818933 Loss2: 1.822276
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.840973 Loss1: 0.475688 Loss2: 1.365285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.693626 Loss1: 0.300471 Loss2: 1.393156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372366 Loss1: 0.071415 Loss2: 1.300951
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.623955 Loss1: 0.262858 Loss2: 1.361096
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.611359 Loss1: 0.240488 Loss2: 1.370871
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.505251 Loss1: 0.160603 Loss2: 1.344648
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438744 Loss1: 0.098559 Loss2: 1.340185
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417090 Loss1: 0.084148 Loss2: 1.332942
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.506393 Loss1: 0.699275 Loss2: 1.807118
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401435 Loss1: 0.076131 Loss2: 1.325305
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.819532 Loss1: 0.482159 Loss2: 1.337373
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429025 Loss1: 0.103021 Loss2: 1.326004
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.644065 Loss1: 0.301743 Loss2: 1.342322 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.557235 Loss1: 0.212564 Loss2: 1.344671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.482021 Loss1: 0.146317 Loss2: 1.335704
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.594633 Loss1: 0.761761 Loss2: 1.832872
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.496259 Loss1: 0.168143 Loss2: 1.328116
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.747749 Loss1: 0.384794 Loss2: 1.362956
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456374 Loss1: 0.120165 Loss2: 1.336209
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642921 Loss1: 0.261201 Loss2: 1.381720
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374203 Loss1: 0.056916 Loss2: 1.317287
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554655 Loss1: 0.193580 Loss2: 1.361075
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524116 Loss1: 0.168005 Loss2: 1.356112
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501452 Loss1: 0.142858 Loss2: 1.358594
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434416 Loss1: 0.078946 Loss2: 1.355470
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418348 Loss1: 0.081635 Loss2: 1.336713
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395500 Loss1: 0.062182 Loss2: 1.333318
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.621520 Loss1: 0.800287 Loss2: 1.821233
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386124 Loss1: 0.062126 Loss2: 1.323998
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.762796 Loss1: 0.405813 Loss2: 1.356983
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.703228 Loss1: 0.342286 Loss2: 1.360941
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601075 Loss1: 0.257308 Loss2: 1.343767
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491155 Loss1: 0.156120 Loss2: 1.335036
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459530 Loss1: 0.140018 Loss2: 1.319512
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450781 Loss1: 0.131438 Loss2: 1.319344
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.489995 Loss1: 0.731089 Loss2: 1.758906
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437409 Loss1: 0.114554 Loss2: 1.322855
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.770118 Loss1: 0.453882 Loss2: 1.316235
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391300 Loss1: 0.079843 Loss2: 1.311457
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.658584 Loss1: 0.297732 Loss2: 1.360852
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374577 Loss1: 0.062281 Loss2: 1.312296
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.582086 Loss1: 0.267464 Loss2: 1.314621
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541232 Loss1: 0.214019 Loss2: 1.327214
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461186 Loss1: 0.152037 Loss2: 1.309150
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437295 Loss1: 0.133457 Loss2: 1.303837
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386917 Loss1: 0.089147 Loss2: 1.297770
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.378158 Loss1: 0.088879 Loss2: 1.289279
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.549716 Loss1: 0.698100 Loss2: 1.851616
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356353 Loss1: 0.071050 Loss2: 1.285303
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.779331 Loss1: 0.419439 Loss2: 1.359892
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.666476 Loss1: 0.278032 Loss2: 1.388443
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.607530 Loss1: 0.255181 Loss2: 1.352349
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535585 Loss1: 0.185084 Loss2: 1.350500
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.495795 Loss1: 0.140723 Loss2: 1.355072
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442286 Loss1: 0.106778 Loss2: 1.335509
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.444860 Loss1: 0.572549 Loss2: 1.872311
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432897 Loss1: 0.098885 Loss2: 1.334012
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.832440 Loss1: 0.454513 Loss2: 1.377927
-DEBUG flwr 2023-10-11 17:22:07,102 | server.py:236 | fit_round 121 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408593 Loss1: 0.076609 Loss2: 1.331984
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.732984 Loss1: 0.320522 Loss2: 1.412462
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391134 Loss1: 0.062681 Loss2: 1.328452
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.616674 Loss1: 0.216282 Loss2: 1.400392
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563356 Loss1: 0.178237 Loss2: 1.385119
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556838 Loss1: 0.179258 Loss2: 1.377581
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467282 Loss1: 0.082637 Loss2: 1.384646
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441765 Loss1: 0.074019 Loss2: 1.367746
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.405797 Loss1: 0.592954 Loss2: 1.812843
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413590 Loss1: 0.052529 Loss2: 1.361060
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.710354 Loss1: 0.365512 Loss2: 1.344843
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395863 Loss1: 0.039726 Loss2: 1.356137
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547689 Loss1: 0.197814 Loss2: 1.349875 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487341 Loss1: 0.143948 Loss2: 1.343394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446610 Loss1: 0.102451 Loss2: 1.344158
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.551722 Loss1: 0.674335 Loss2: 1.877387
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.813147 Loss1: 0.420936 Loss2: 1.392211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.698530 Loss1: 0.283260 Loss2: 1.415270 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.362004 Loss1: 0.035780 Loss2: 1.326224
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.617236 Loss1: 0.252226 Loss2: 1.365010
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.608813 Loss1: 0.222252 Loss2: 1.386561
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484624 Loss1: 0.115207 Loss2: 1.369416
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473888 Loss1: 0.118547 Loss2: 1.355341
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430742 Loss1: 0.076099 Loss2: 1.354642
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.613134 Loss1: 0.735571 Loss2: 1.877563
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415864 Loss1: 0.064240 Loss2: 1.351624
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431924 Loss1: 0.089778 Loss2: 1.342147 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.620937 Loss1: 0.239033 Loss2: 1.381903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505826 Loss1: 0.134515 Loss2: 1.371311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467336 Loss1: 0.093627 Loss2: 1.373710
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.488430 Loss1: 0.666771 Loss2: 1.821659
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.899270 Loss1: 0.535381 Loss2: 1.363888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.772830 Loss1: 0.360949 Loss2: 1.411881 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.609069 Loss1: 0.240440 Loss2: 1.368629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502792 Loss1: 0.160827 Loss2: 1.341964 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486385 Loss1: 0.148882 Loss2: 1.337503 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403259 Loss1: 0.069998 Loss2: 1.333261 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 17:22:07,102][flwr][DEBUG] - fit_round 121 received 50 results and 0 failures
-INFO flwr 2023-10-11 17:22:47,444 | server.py:125 | fit progress: (121, 2.2130620091106183, {'accuracy': 0.5797}, 279075.222091128)
->> Test accuracy: 0.579700
-[2023-10-11 17:22:47,444][flwr][INFO] - fit progress: (121, 2.2130620091106183, {'accuracy': 0.5797}, 279075.222091128)
-DEBUG flwr 2023-10-11 17:22:47,444 | server.py:173 | evaluate_round 121: strategy sampled 50 clients (out of 50)
-[2023-10-11 17:22:47,444][flwr][DEBUG] - evaluate_round 121: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 17:31:52,201 | server.py:187 | evaluate_round 121 received 50 results and 0 failures
-[2023-10-11 17:31:52,201][flwr][DEBUG] - evaluate_round 121 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 17:31:52,201 | server.py:222 | fit_round 122: strategy sampled 50 clients (out of 50)
-[2023-10-11 17:31:52,201][flwr][DEBUG] - fit_round 122: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.617665 Loss1: 0.797766 Loss2: 1.819899
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.880841 Loss1: 0.526684 Loss2: 1.354157
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.683406 Loss1: 0.297566 Loss2: 1.385839
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.552487 Loss1: 0.214271 Loss2: 1.338215
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.574964 Loss1: 0.762329 Loss2: 1.812636
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487005 Loss1: 0.150376 Loss2: 1.336629
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.883872 Loss1: 0.518764 Loss2: 1.365109
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442006 Loss1: 0.119407 Loss2: 1.322600
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726590 Loss1: 0.321036 Loss2: 1.405553
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410289 Loss1: 0.090232 Loss2: 1.320056
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.658915 Loss1: 0.299671 Loss2: 1.359244
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.399866 Loss1: 0.081351 Loss2: 1.318516
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.588850 Loss1: 0.229736 Loss2: 1.359114
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374621 Loss1: 0.063634 Loss2: 1.310987
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511548 Loss1: 0.161130 Loss2: 1.350418
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377565 Loss1: 0.064936 Loss2: 1.312630
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440668 Loss1: 0.103785 Loss2: 1.336883
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442155 Loss1: 0.107935 Loss2: 1.334220
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394135 Loss1: 0.067957 Loss2: 1.326178
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377932 Loss1: 0.059865 Loss2: 1.318067
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.553439 Loss1: 0.667307 Loss2: 1.886132
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.960394 Loss1: 0.501749 Loss2: 1.458646
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.828219 Loss1: 0.344242 Loss2: 1.483977
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.686701 Loss1: 0.242514 Loss2: 1.444187
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.708153 Loss1: 0.771803 Loss2: 1.936350
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.823690 Loss1: 0.402985 Loss2: 1.420704
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.665573 Loss1: 0.215481 Loss2: 1.450092
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.693036 Loss1: 0.257459 Loss2: 1.435578
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.645452 Loss1: 0.209189 Loss2: 1.436264
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.672549 Loss1: 0.268738 Loss2: 1.403811
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.566744 Loss1: 0.131032 Loss2: 1.435712
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.573176 Loss1: 0.169291 Loss2: 1.403884
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.528753 Loss1: 0.104843 Loss2: 1.423910
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.508376 Loss1: 0.089214 Loss2: 1.419162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.506628 Loss1: 0.091968 Loss2: 1.414659 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485645 Loss1: 0.104036 Loss2: 1.381609 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.473796 Loss1: 0.694952 Loss2: 1.778844 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.612283 Loss1: 0.235322 Loss2: 1.376961 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.611824 Loss1: 0.263078 Loss2: 1.348746
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.695613 Loss1: 0.838738 Loss2: 1.856875
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.891393 Loss1: 0.515371 Loss2: 1.376022
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.533481 Loss1: 0.183165 Loss2: 1.350316
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.844539 Loss1: 0.409488 Loss2: 1.435051
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498453 Loss1: 0.160286 Loss2: 1.338167
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.785122 Loss1: 0.397291 Loss2: 1.387831
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.438608 Loss1: 0.103531 Loss2: 1.335077
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.790538 Loss1: 0.357847 Loss2: 1.432691
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451028 Loss1: 0.121157 Loss2: 1.329871
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425292 Loss1: 0.098866 Loss2: 1.326426 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430155 Loss1: 0.108368 Loss2: 1.321788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418940 Loss1: 0.073084 Loss2: 1.345856 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.595001 Loss1: 0.732467 Loss2: 1.862534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.771795 Loss1: 0.346763 Loss2: 1.425032 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580275 Loss1: 0.199076 Loss2: 1.381199
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.692608 Loss1: 0.744285 Loss2: 1.948323
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.553976 Loss1: 0.166897 Loss2: 1.387079
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.941160 Loss1: 0.494471 Loss2: 1.446688
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491279 Loss1: 0.114855 Loss2: 1.376424
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.910981 Loss1: 0.427106 Loss2: 1.483875
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.465920 Loss1: 0.099009 Loss2: 1.366911
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.737089 Loss1: 0.285190 Loss2: 1.451898
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.468215 Loss1: 0.104248 Loss2: 1.363968
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.622794 Loss1: 0.194626 Loss2: 1.428168
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454234 Loss1: 0.092116 Loss2: 1.362118
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.579166 Loss1: 0.151758 Loss2: 1.427409
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457075 Loss1: 0.096613 Loss2: 1.360462
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518180 Loss1: 0.099267 Loss2: 1.418913
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.504099 Loss1: 0.092802 Loss2: 1.411297
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.500640 Loss1: 0.091304 Loss2: 1.409336
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472467 Loss1: 0.065215 Loss2: 1.407253
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.511622 Loss1: 0.692917 Loss2: 1.818705
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.848930 Loss1: 0.484605 Loss2: 1.364325
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.682421 Loss1: 0.282624 Loss2: 1.399797
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513853 Loss1: 0.168946 Loss2: 1.344907
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.497625 Loss1: 0.633369 Loss2: 1.864256
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.790123 Loss1: 0.425012 Loss2: 1.365111 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.660949 Loss1: 0.252601 Loss2: 1.408348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.569425 Loss1: 0.197109 Loss2: 1.372315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515267 Loss1: 0.154236 Loss2: 1.361030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470234 Loss1: 0.107853 Loss2: 1.362382 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414996 Loss1: 0.084320 Loss2: 1.330677
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464264 Loss1: 0.113339 Loss2: 1.350925
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437848 Loss1: 0.092420 Loss2: 1.345428
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426353 Loss1: 0.081960 Loss2: 1.344393
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427716 Loss1: 0.080166 Loss2: 1.347551
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.593666 Loss1: 0.728499 Loss2: 1.865167
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.904070 Loss1: 0.505814 Loss2: 1.398255
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.729849 Loss1: 0.314195 Loss2: 1.415653
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606292 Loss1: 0.215815 Loss2: 1.390478
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.496314 Loss1: 0.645642 Loss2: 1.850672
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.784964 Loss1: 0.440432 Loss2: 1.344531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.699702 Loss1: 0.300079 Loss2: 1.399622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.571800 Loss1: 0.228335 Loss2: 1.343465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545893 Loss1: 0.199150 Loss2: 1.346743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496039 Loss1: 0.153300 Loss2: 1.342740 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392956 Loss1: 0.050661 Loss2: 1.342295
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438353 Loss1: 0.103570 Loss2: 1.334783
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417632 Loss1: 0.085064 Loss2: 1.332568
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394408 Loss1: 0.066175 Loss2: 1.328234
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373432 Loss1: 0.053595 Loss2: 1.319836
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.454509 Loss1: 0.647745 Loss2: 1.806763
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.783359 Loss1: 0.455706 Loss2: 1.327654
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.600570 Loss1: 0.248128 Loss2: 1.352442
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.553963 Loss1: 0.230792 Loss2: 1.323171
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.601145 Loss1: 0.714664 Loss2: 1.886481
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.858366 Loss1: 0.451182 Loss2: 1.407184
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516583 Loss1: 0.186561 Loss2: 1.330022
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.754301 Loss1: 0.287591 Loss2: 1.466711
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437132 Loss1: 0.121307 Loss2: 1.315825
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.639825 Loss1: 0.243904 Loss2: 1.395921
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410642 Loss1: 0.101412 Loss2: 1.309230
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598648 Loss1: 0.190021 Loss2: 1.408626
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410478 Loss1: 0.105792 Loss2: 1.304687
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403597 Loss1: 0.095050 Loss2: 1.308547 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385009 Loss1: 0.082461 Loss2: 1.302548 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493474 Loss1: 0.106276 Loss2: 1.387198 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.617290 Loss1: 0.732311 Loss2: 1.884978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.727886 Loss1: 0.292005 Loss2: 1.435881 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.614415 Loss1: 0.219563 Loss2: 1.394852
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.441214 Loss1: 0.648526 Loss2: 1.792688
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.824624 Loss1: 0.458575 Loss2: 1.366049 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.681502 Loss1: 0.290320 Loss2: 1.391182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.596809 Loss1: 0.250196 Loss2: 1.346612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.517897 Loss1: 0.159451 Loss2: 1.358447 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472205 Loss1: 0.131469 Loss2: 1.340736 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468106 Loss1: 0.133077 Loss2: 1.335029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423751 Loss1: 0.091686 Loss2: 1.332066 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975586
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.445134 Loss1: 0.669882 Loss2: 1.775252 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.636652 Loss1: 0.266598 Loss2: 1.370055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.600140 Loss1: 0.254215 Loss2: 1.345926
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.727131 Loss1: 0.865427 Loss2: 1.861704
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.985673 Loss1: 0.580917 Loss2: 1.404757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.753948 Loss1: 0.315962 Loss2: 1.437987 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460943 Loss1: 0.129319 Loss2: 1.331625
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.665705 Loss1: 0.296706 Loss2: 1.368999
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438169 Loss1: 0.105611 Loss2: 1.332558
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544512 Loss1: 0.166742 Loss2: 1.377770
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549451 Loss1: 0.190756 Loss2: 1.358695
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.387136 Loss1: 0.068524 Loss2: 1.318612
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530438 Loss1: 0.170137 Loss2: 1.360301
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365390 Loss1: 0.049663 Loss2: 1.315727
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409411 Loss1: 0.073647 Loss2: 1.335763 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.616606 Loss1: 0.768960 Loss2: 1.847646 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.681061 Loss1: 0.271936 Loss2: 1.409125 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599002 Loss1: 0.224795 Loss2: 1.374207
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.500550 Loss1: 0.732043 Loss2: 1.768507
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.768591 Loss1: 0.430539 Loss2: 1.338052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.659600 Loss1: 0.280884 Loss2: 1.378717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554415 Loss1: 0.221158 Loss2: 1.333256 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469215 Loss1: 0.139906 Loss2: 1.329309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421899 Loss1: 0.093201 Loss2: 1.328698 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.400946 Loss1: 0.083946 Loss2: 1.317000 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385387 Loss1: 0.077794 Loss2: 1.307593 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958008
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.695658 Loss1: 0.683029 Loss2: 2.012629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.891567 Loss1: 0.334687 Loss2: 1.556880 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.719628 Loss1: 0.861770 Loss2: 1.857858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.866988 Loss1: 0.469671 Loss2: 1.397317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.704970 Loss1: 0.295186 Loss2: 1.409784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.621889 Loss1: 0.243187 Loss2: 1.378702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523440 Loss1: 0.139221 Loss2: 1.384219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471248 Loss1: 0.100924 Loss2: 1.370324 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450626 Loss1: 0.094480 Loss2: 1.356146 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411515 Loss1: 0.063569 Loss2: 1.347946 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.877379 Loss1: 0.485925 Loss2: 1.391454 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583905 Loss1: 0.204767 Loss2: 1.379137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.545773 Loss1: 0.720551 Loss2: 1.825222
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515519 Loss1: 0.131099 Loss2: 1.384420
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.931506 Loss1: 0.541622 Loss2: 1.389885
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462830 Loss1: 0.098056 Loss2: 1.364775
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442680 Loss1: 0.083211 Loss2: 1.359469
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804555 Loss1: 0.382613 Loss2: 1.421941
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.431874 Loss1: 0.075137 Loss2: 1.356738
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.735539 Loss1: 0.353021 Loss2: 1.382518
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431816 Loss1: 0.076717 Loss2: 1.355099
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.562479 Loss1: 0.190124 Loss2: 1.372355
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400199 Loss1: 0.053347 Loss2: 1.346853
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484564 Loss1: 0.128796 Loss2: 1.355768
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464397 Loss1: 0.119163 Loss2: 1.345234
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436259 Loss1: 0.092741 Loss2: 1.343518
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425969 Loss1: 0.090775 Loss2: 1.335194
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405676 Loss1: 0.070248 Loss2: 1.335428
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.662622 Loss1: 0.709172 Loss2: 1.953450
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.925288 Loss1: 0.477779 Loss2: 1.447509
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.767143 Loss1: 0.270522 Loss2: 1.496621
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.659153 Loss1: 0.217286 Loss2: 1.441867
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.629142 Loss1: 0.192602 Loss2: 1.436540
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.660206 Loss1: 0.754991 Loss2: 1.905216
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.757156 Loss1: 0.360803 Loss2: 1.396353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.697375 Loss1: 0.277980 Loss2: 1.419395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.570172 Loss1: 0.190835 Loss2: 1.379337 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.537272 Loss1: 0.153820 Loss2: 1.383452 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481395 Loss1: 0.060124 Loss2: 1.421271
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504764 Loss1: 0.132488 Loss2: 1.372275
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452128 Loss1: 0.086893 Loss2: 1.365235
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432944 Loss1: 0.074779 Loss2: 1.358165
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396419 Loss1: 0.044615 Loss2: 1.351804
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382984 Loss1: 0.031944 Loss2: 1.351041
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.559956 Loss1: 0.718836 Loss2: 1.841121
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.713708 Loss1: 0.378333 Loss2: 1.335375
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.581218 Loss1: 0.226362 Loss2: 1.354856
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491453 Loss1: 0.163427 Loss2: 1.328027
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480087 Loss1: 0.153796 Loss2: 1.326291
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.901932 Loss1: 0.898112 Loss2: 2.003820
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.951815 Loss1: 0.568535 Loss2: 1.383280
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455976 Loss1: 0.134426 Loss2: 1.321549
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.430012 Loss1: 0.116451 Loss2: 1.313561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434344 Loss1: 0.118972 Loss2: 1.315372 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409277 Loss1: 0.092301 Loss2: 1.316976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437854 Loss1: 0.077384 Loss2: 1.360471 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443676 Loss1: 0.092173 Loss2: 1.351504 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977865
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.683478 Loss1: 0.844500 Loss2: 1.838978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.670185 Loss1: 0.277738 Loss2: 1.392447 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.584553 Loss1: 0.220857 Loss2: 1.363697
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.686539 Loss1: 0.791104 Loss2: 1.895434
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547309 Loss1: 0.186387 Loss2: 1.360922
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.824104 Loss1: 0.410295 Loss2: 1.413809
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.517098 Loss1: 0.157889 Loss2: 1.359209
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.723171 Loss1: 0.308999 Loss2: 1.414172
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.454345 Loss1: 0.106370 Loss2: 1.347976
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.676104 Loss1: 0.270647 Loss2: 1.405457
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447756 Loss1: 0.108556 Loss2: 1.339201
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.564000 Loss1: 0.172968 Loss2: 1.391032
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401995 Loss1: 0.067777 Loss2: 1.334218
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.551167 Loss1: 0.168951 Loss2: 1.382216
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419853 Loss1: 0.091711 Loss2: 1.328142
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.552051 Loss1: 0.162910 Loss2: 1.389142
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477252 Loss1: 0.100721 Loss2: 1.376530
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.524253 Loss1: 0.145973 Loss2: 1.378280
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.513227 Loss1: 0.128427 Loss2: 1.384799
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.749110 Loss1: 0.865989 Loss2: 1.883120
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.941220 Loss1: 0.538127 Loss2: 1.403093
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.689483 Loss1: 0.277728 Loss2: 1.411754
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498081 Loss1: 0.141363 Loss2: 1.356719
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.688132 Loss1: 0.771377 Loss2: 1.916755
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.925128 Loss1: 0.511744 Loss2: 1.413384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.773044 Loss1: 0.304583 Loss2: 1.468460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.717849 Loss1: 0.303737 Loss2: 1.414112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.589720 Loss1: 0.170508 Loss2: 1.419212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546643 Loss1: 0.134658 Loss2: 1.411985 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379753 Loss1: 0.047322 Loss2: 1.332431
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.498360 Loss1: 0.101582 Loss2: 1.396778
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466539 Loss1: 0.075439 Loss2: 1.391100
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436142 Loss1: 0.048376 Loss2: 1.387766
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433593 Loss1: 0.056405 Loss2: 1.377188
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.871454 Loss1: 0.879871 Loss2: 1.991583
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.889525 Loss1: 0.482178 Loss2: 1.407347
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647847 Loss1: 0.232073 Loss2: 1.415775
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558509 Loss1: 0.168798 Loss2: 1.389711
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.626354 Loss1: 0.746034 Loss2: 1.880320
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.549268 Loss1: 0.162294 Loss2: 1.386974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486857 Loss1: 0.101243 Loss2: 1.385614 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.466910 Loss1: 0.097910 Loss2: 1.369000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438102 Loss1: 0.073387 Loss2: 1.364715 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412191 Loss1: 0.053609 Loss2: 1.358582 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463579 Loss1: 0.108698 Loss2: 1.354881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444042 Loss1: 0.092988 Loss2: 1.351054 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429574 Loss1: 0.086331 Loss2: 1.343243
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.741175 Loss1: 0.844404 Loss2: 1.896771
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.903941 Loss1: 0.523792 Loss2: 1.380149
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.657925 Loss1: 0.244594 Loss2: 1.413331
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569687 Loss1: 0.198147 Loss2: 1.371540
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503772 Loss1: 0.141036 Loss2: 1.362736
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.565749 Loss1: 0.722668 Loss2: 1.843080
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463008 Loss1: 0.101847 Loss2: 1.361161
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.810455 Loss1: 0.419678 Loss2: 1.390777
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451532 Loss1: 0.104814 Loss2: 1.346718
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670925 Loss1: 0.255106 Loss2: 1.415820
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427169 Loss1: 0.078709 Loss2: 1.348460
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414517 Loss1: 0.074063 Loss2: 1.340454
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576160 Loss1: 0.203941 Loss2: 1.372219
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371340 Loss1: 0.042523 Loss2: 1.328816
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554600 Loss1: 0.177243 Loss2: 1.377357
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512189 Loss1: 0.153066 Loss2: 1.359123
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478135 Loss1: 0.115159 Loss2: 1.362976
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443117 Loss1: 0.089735 Loss2: 1.353382
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413944 Loss1: 0.066421 Loss2: 1.347523
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.397342 Loss1: 0.554101 Loss2: 1.843241
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.775405 Loss1: 0.386369 Loss2: 1.389035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536810 Loss1: 0.151422 Loss2: 1.385389 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466478 Loss1: 0.089356 Loss2: 1.377122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479137 Loss1: 0.103433 Loss2: 1.375704 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.482491 Loss1: 0.114565 Loss2: 1.367926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.645553 Loss1: 0.286980 Loss2: 1.358573 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522791 Loss1: 0.152299 Loss2: 1.370492 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485256 Loss1: 0.141885 Loss2: 1.343370 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449467 Loss1: 0.113328 Loss2: 1.336139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414031 Loss1: 0.088138 Loss2: 1.325893
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.358174 Loss1: 0.556312 Loss2: 1.801861
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.782277 Loss1: 0.419284 Loss2: 1.362993
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.659751 Loss1: 0.257278 Loss2: 1.402473
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563841 Loss1: 0.200489 Loss2: 1.363352
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479587 Loss1: 0.124213 Loss2: 1.355373
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.705837 Loss1: 0.831251 Loss2: 1.874587
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.849098 Loss1: 0.500203 Loss2: 1.348895
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460704 Loss1: 0.116368 Loss2: 1.344336
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.786418 Loss1: 0.365993 Loss2: 1.420425
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.423235 Loss1: 0.080981 Loss2: 1.342253
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391827 Loss1: 0.059576 Loss2: 1.332251 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373970 Loss1: 0.047289 Loss2: 1.326682 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395266 Loss1: 0.074727 Loss2: 1.320538 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990809
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433566 Loss1: 0.107452 Loss2: 1.326114 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.717585 Loss1: 0.781795 Loss2: 1.935790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.799844 Loss1: 0.394571 Loss2: 1.405273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.635172 Loss1: 0.725431 Loss2: 1.909740 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.855316 Loss1: 0.447107 Loss2: 1.408210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513972 Loss1: 0.133436 Loss2: 1.380536 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483249 Loss1: 0.111312 Loss2: 1.371938 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451513 Loss1: 0.089373 Loss2: 1.362141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428203 Loss1: 0.070873 Loss2: 1.357330 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995192
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.549251 Loss1: 0.144784 Loss2: 1.404467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.491856 Loss1: 0.098637 Loss2: 1.393219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465143 Loss1: 0.073627 Loss2: 1.391516
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.752353 Loss1: 0.853537 Loss2: 1.898816
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.120621 Loss1: 0.669887 Loss2: 1.450733
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.765841 Loss1: 0.316708 Loss2: 1.449133
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649127 Loss1: 0.243155 Loss2: 1.405972
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.621882 Loss1: 0.201128 Loss2: 1.420754
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.594701 Loss1: 0.788458 Loss2: 1.806243
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.529706 Loss1: 0.139600 Loss2: 1.390105
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487083 Loss1: 0.106017 Loss2: 1.381066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444720 Loss1: 0.069762 Loss2: 1.374958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430664 Loss1: 0.060638 Loss2: 1.370025 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410847 Loss1: 0.049090 Loss2: 1.361757 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425223 Loss1: 0.091452 Loss2: 1.333771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431644 Loss1: 0.114955 Loss2: 1.316688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410382 Loss1: 0.095975 Loss2: 1.314408
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.567548 Loss1: 0.770550 Loss2: 1.796997
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.749186 Loss1: 0.410414 Loss2: 1.338771
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.626869 Loss1: 0.266016 Loss2: 1.360854
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.689317 Loss1: 0.342999 Loss2: 1.346318
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565860 Loss1: 0.215450 Loss2: 1.350410
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.508724 Loss1: 0.686321 Loss2: 1.822403
-DEBUG flwr 2023-10-11 18:00:49,744 | server.py:236 | fit_round 122 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.725038 Loss1: 0.390533 Loss2: 1.334505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.658885 Loss1: 0.300034 Loss2: 1.358850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567376 Loss1: 0.239362 Loss2: 1.328015 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.468289 Loss1: 0.143164 Loss2: 1.325125 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429323 Loss1: 0.117578 Loss2: 1.311745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421021 Loss1: 0.109484 Loss2: 1.311537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394662 Loss1: 0.086831 Loss2: 1.307831 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.887665 Loss1: 0.432434 Loss2: 1.455232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.687263 Loss1: 0.225820 Loss2: 1.461442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.708564 Loss1: 0.841030 Loss2: 1.867534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.958181 Loss1: 0.519854 Loss2: 1.438326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.706146 Loss1: 0.278626 Loss2: 1.427521 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.685324 Loss1: 0.269513 Loss2: 1.415811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598197 Loss1: 0.177482 Loss2: 1.420715 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.578190 Loss1: 0.168711 Loss2: 1.409478 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.502500 Loss1: 0.115974 Loss2: 1.386525 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.525432 Loss1: 0.123737 Loss2: 1.401695
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.864253 Loss1: 0.873850 Loss2: 1.990403
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.001077 Loss1: 0.551350 Loss2: 1.449727
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.794371 Loss1: 0.296019 Loss2: 1.498352
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.671719 Loss1: 0.239214 Loss2: 1.432506
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.620980 Loss1: 0.184759 Loss2: 1.436221
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.555605 Loss1: 0.128413 Loss2: 1.427192
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.631341 Loss1: 0.771355 Loss2: 1.859986
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.573975 Loss1: 0.153499 Loss2: 1.420475
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.833377 Loss1: 0.457676 Loss2: 1.375701
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.541176 Loss1: 0.127392 Loss2: 1.413784
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669571 Loss1: 0.266084 Loss2: 1.403487
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588370 Loss1: 0.223041 Loss2: 1.365329 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.503296 Loss1: 0.093926 Loss2: 1.409370
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512354 Loss1: 0.154255 Loss2: 1.358100
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516371 Loss1: 0.157093 Loss2: 1.359278
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.522489 Loss1: 0.155906 Loss2: 1.366583
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485085 Loss1: 0.127816 Loss2: 1.357269
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441490 Loss1: 0.092436 Loss2: 1.349054
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437586 Loss1: 0.096490 Loss2: 1.341096
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 18:00:49,744][flwr][DEBUG] - fit_round 122 received 50 results and 0 failures
-INFO flwr 2023-10-11 18:01:30,831 | server.py:125 | fit progress: (122, 2.2014924769584363, {'accuracy': 0.5839}, 281398.609859069)
->> Test accuracy: 0.583900
-[2023-10-11 18:01:30,831][flwr][INFO] - fit progress: (122, 2.2014924769584363, {'accuracy': 0.5839}, 281398.609859069)
-DEBUG flwr 2023-10-11 18:01:30,832 | server.py:173 | evaluate_round 122: strategy sampled 50 clients (out of 50)
-[2023-10-11 18:01:30,832][flwr][DEBUG] - evaluate_round 122: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 18:10:42,537 | server.py:187 | evaluate_round 122 received 50 results and 0 failures
-[2023-10-11 18:10:42,537][flwr][DEBUG] - evaluate_round 122 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 18:10:42,538 | server.py:222 | fit_round 123: strategy sampled 50 clients (out of 50)
-[2023-10-11 18:10:42,538][flwr][DEBUG] - fit_round 123: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.588495 Loss1: 0.694753 Loss2: 1.893742
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.821124 Loss1: 0.406362 Loss2: 1.414763
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.710857 Loss1: 0.251138 Loss2: 1.459719
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.632009 Loss1: 0.211875 Loss2: 1.420134
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.870830 Loss1: 0.493933 Loss2: 1.376897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.687880 Loss1: 0.278308 Loss2: 1.409572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.673437 Loss1: 0.303057 Loss2: 1.370381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.580271 Loss1: 0.202025 Loss2: 1.378245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482522 Loss1: 0.123934 Loss2: 1.358587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437302 Loss1: 0.085815 Loss2: 1.351487 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433674 Loss1: 0.093693 Loss2: 1.339981 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.537901 Loss1: 0.675398 Loss2: 1.862503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.767156 Loss1: 0.326606 Loss2: 1.440550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.641430 Loss1: 0.264592 Loss2: 1.376838
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.507419 Loss1: 0.695191 Loss2: 1.812228
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.809719 Loss1: 0.474445 Loss2: 1.335274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.665442 Loss1: 0.286734 Loss2: 1.378708 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565389 Loss1: 0.232267 Loss2: 1.333122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513566 Loss1: 0.181764 Loss2: 1.331802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.453458 Loss1: 0.134714 Loss2: 1.318745 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438074 Loss1: 0.082060 Loss2: 1.356014
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450702 Loss1: 0.135409 Loss2: 1.315294
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429381 Loss1: 0.109425 Loss2: 1.319956
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408108 Loss1: 0.093423 Loss2: 1.314685
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410607 Loss1: 0.100865 Loss2: 1.309741
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.559499 Loss1: 0.723886 Loss2: 1.835613
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.850791 Loss1: 0.482784 Loss2: 1.368007
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.686313 Loss1: 0.283404 Loss2: 1.402909
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.544541 Loss1: 0.189152 Loss2: 1.355388
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.625534 Loss1: 0.778807 Loss2: 1.846727
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.858401 Loss1: 0.478833 Loss2: 1.379568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669369 Loss1: 0.261323 Loss2: 1.408047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611067 Loss1: 0.249278 Loss2: 1.361789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503062 Loss1: 0.132712 Loss2: 1.370350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455355 Loss1: 0.107875 Loss2: 1.347481 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400757 Loss1: 0.060113 Loss2: 1.340644
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441233 Loss1: 0.096025 Loss2: 1.345208
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415624 Loss1: 0.074460 Loss2: 1.341164
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388117 Loss1: 0.051191 Loss2: 1.336926
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380652 Loss1: 0.054862 Loss2: 1.325790
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.558077 Loss1: 0.733023 Loss2: 1.825054
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.894855 Loss1: 0.491600 Loss2: 1.403255
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.708183 Loss1: 0.308347 Loss2: 1.399836
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.662128 Loss1: 0.275712 Loss2: 1.386416
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.678732 Loss1: 0.797772 Loss2: 1.880959
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.884823 Loss1: 0.464510 Loss2: 1.420312
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.608224 Loss1: 0.226953 Loss2: 1.381270
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.758052 Loss1: 0.307123 Loss2: 1.450930
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.561520 Loss1: 0.186616 Loss2: 1.374904
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.654824 Loss1: 0.256833 Loss2: 1.397991
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524676 Loss1: 0.158411 Loss2: 1.366265
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.559212 Loss1: 0.160387 Loss2: 1.398825
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458359 Loss1: 0.093452 Loss2: 1.364907
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411080 Loss1: 0.060380 Loss2: 1.350700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402275 Loss1: 0.057485 Loss2: 1.344790 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452940 Loss1: 0.080036 Loss2: 1.372904 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.555903 Loss1: 0.733032 Loss2: 1.822871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.691042 Loss1: 0.287238 Loss2: 1.403804 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.619440 Loss1: 0.261257 Loss2: 1.358184
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.702600 Loss1: 0.793879 Loss2: 1.908721
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.881359 Loss1: 0.521807 Loss2: 1.359552
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531602 Loss1: 0.164302 Loss2: 1.367299
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.678424 Loss1: 0.305586 Loss2: 1.372838
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.482441 Loss1: 0.133825 Loss2: 1.348617
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470401 Loss1: 0.129958 Loss2: 1.340444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437053 Loss1: 0.095409 Loss2: 1.341644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.446272 Loss1: 0.113900 Loss2: 1.332373 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406339 Loss1: 0.071389 Loss2: 1.334949 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.346286 Loss1: 0.050297 Loss2: 1.295989 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.588012 Loss1: 0.683638 Loss2: 1.904374
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.840804 Loss1: 0.397558 Loss2: 1.443246
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.677386 Loss1: 0.209199 Loss2: 1.468187
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610805 Loss1: 0.189290 Loss2: 1.421515
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.585161 Loss1: 0.695748 Loss2: 1.889413
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.804998 Loss1: 0.421337 Loss2: 1.383660
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.573967 Loss1: 0.150390 Loss2: 1.423576
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669005 Loss1: 0.236863 Loss2: 1.432142
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569443 Loss1: 0.152017 Loss2: 1.417426
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591118 Loss1: 0.212626 Loss2: 1.378492
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.559212 Loss1: 0.141981 Loss2: 1.417231
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569196 Loss1: 0.188544 Loss2: 1.380652
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.496347 Loss1: 0.077426 Loss2: 1.418920
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448247 Loss1: 0.049900 Loss2: 1.398346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.471735 Loss1: 0.078898 Loss2: 1.392837 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447177 Loss1: 0.084487 Loss2: 1.362690 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.779609 Loss1: 0.801693 Loss2: 1.977915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.769567 Loss1: 0.288730 Loss2: 1.480837 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.646041 Loss1: 0.209406 Loss2: 1.436635
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.702457 Loss1: 0.799238 Loss2: 1.903219
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.853859 Loss1: 0.430639 Loss2: 1.423220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.776659 Loss1: 0.325061 Loss2: 1.451598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.630813 Loss1: 0.210498 Loss2: 1.420315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557853 Loss1: 0.138631 Loss2: 1.419223 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.564298 Loss1: 0.157224 Loss2: 1.407074 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998884
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468159 Loss1: 0.072763 Loss2: 1.395396 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.455736 Loss1: 0.074506 Loss2: 1.381230 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.639127 Loss1: 0.317142 Loss2: 1.321986 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520113 Loss1: 0.213366 Loss2: 1.306747 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.482202 Loss1: 0.619381 Loss2: 1.862821
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491391 Loss1: 0.171613 Loss2: 1.319778
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.763633 Loss1: 0.362573 Loss2: 1.401061
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.399192 Loss1: 0.091114 Loss2: 1.308079
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.378417 Loss1: 0.079770 Loss2: 1.298648
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684969 Loss1: 0.250800 Loss2: 1.434169
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394386 Loss1: 0.093394 Loss2: 1.300992
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619455 Loss1: 0.216387 Loss2: 1.403068
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381811 Loss1: 0.086672 Loss2: 1.295139
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.577384 Loss1: 0.175834 Loss2: 1.401550
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359905 Loss1: 0.062833 Loss2: 1.297071
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.532015 Loss1: 0.134555 Loss2: 1.397461
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.498585 Loss1: 0.119105 Loss2: 1.379479
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.498250 Loss1: 0.114555 Loss2: 1.383695
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490715 Loss1: 0.108366 Loss2: 1.382348
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465815 Loss1: 0.087720 Loss2: 1.378095
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.668061 Loss1: 0.810523 Loss2: 1.857538
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.980134 Loss1: 0.585231 Loss2: 1.394903
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.837555 Loss1: 0.400475 Loss2: 1.437079
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630408 Loss1: 0.244179 Loss2: 1.386229
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535045 Loss1: 0.141251 Loss2: 1.393794
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.641438 Loss1: 0.700364 Loss2: 1.941074
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480092 Loss1: 0.116349 Loss2: 1.363743
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424969 Loss1: 0.067274 Loss2: 1.357695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439238 Loss1: 0.082659 Loss2: 1.356580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443198 Loss1: 0.094163 Loss2: 1.349034 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447784 Loss1: 0.091735 Loss2: 1.356049 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.579797 Loss1: 0.158886 Loss2: 1.420911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.528469 Loss1: 0.110114 Loss2: 1.418355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509163 Loss1: 0.098279 Loss2: 1.410884
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.469354 Loss1: 0.625475 Loss2: 1.843879
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.789283 Loss1: 0.428811 Loss2: 1.360473
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.712536 Loss1: 0.290062 Loss2: 1.422474
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527637 Loss1: 0.174582 Loss2: 1.353055
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518031 Loss1: 0.165878 Loss2: 1.352153
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.592506 Loss1: 0.720528 Loss2: 1.871979
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.860059 Loss1: 0.468437 Loss2: 1.391622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.683743 Loss1: 0.259640 Loss2: 1.424102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.568601 Loss1: 0.200900 Loss2: 1.367702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.479971 Loss1: 0.112400 Loss2: 1.367572 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427232 Loss1: 0.077792 Loss2: 1.349440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446520 Loss1: 0.097504 Loss2: 1.349016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433419 Loss1: 0.086878 Loss2: 1.346541 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.649384 Loss1: 0.326766 Loss2: 1.322618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.470131 Loss1: 0.161789 Loss2: 1.308341 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.695268 Loss1: 0.746587 Loss2: 1.948681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.962083 Loss1: 0.458734 Loss2: 1.503349 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.826674 Loss1: 0.312606 Loss2: 1.514068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.327515 Loss1: 0.049355 Loss2: 1.278160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.313954 Loss1: 0.042558 Loss2: 1.271396 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.622019 Loss1: 0.156605 Loss2: 1.465415 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.578021 Loss1: 0.123917 Loss2: 1.454103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.518287 Loss1: 0.073451 Loss2: 1.444836 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.652807 Loss1: 0.254188 Loss2: 1.398619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.493965 Loss1: 0.116050 Loss2: 1.377915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.467594 Loss1: 0.099041 Loss2: 1.368553
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.569623 Loss1: 0.617430 Loss2: 1.952193
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831206 Loss1: 0.400667 Loss2: 1.430539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.828660 Loss1: 0.319060 Loss2: 1.509601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.710084 Loss1: 0.273452 Loss2: 1.436632 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.671629 Loss1: 0.221554 Loss2: 1.450075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.639566 Loss1: 0.204191 Loss2: 1.435374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.522208 Loss1: 0.097700 Loss2: 1.424509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.505803 Loss1: 0.091593 Loss2: 1.414210 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644596 Loss1: 0.289631 Loss2: 1.354965 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.483255 Loss1: 0.162584 Loss2: 1.320672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.429133 Loss1: 0.122466 Loss2: 1.306667
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.600431 Loss1: 0.775967 Loss2: 1.824465
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.837166 Loss1: 0.462889 Loss2: 1.374277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633365 Loss1: 0.236230 Loss2: 1.397135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526335 Loss1: 0.176217 Loss2: 1.350117 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.346735 Loss1: 0.060842 Loss2: 1.285893
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.572138 Loss1: 0.220756 Loss2: 1.351382
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527179 Loss1: 0.176506 Loss2: 1.350674
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494556 Loss1: 0.138879 Loss2: 1.355676
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415882 Loss1: 0.076718 Loss2: 1.339164
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403436 Loss1: 0.069029 Loss2: 1.334408
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.527746 Loss1: 0.734325 Loss2: 1.793420
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413523 Loss1: 0.083759 Loss2: 1.329763
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638797 Loss1: 0.283004 Loss2: 1.355793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488237 Loss1: 0.157753 Loss2: 1.330484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.414370 Loss1: 0.095579 Loss2: 1.318791
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.739695 Loss1: 0.922420 Loss2: 1.817275
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.836346 Loss1: 0.498370 Loss2: 1.337976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.620552 Loss1: 0.259458 Loss2: 1.361095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.368061 Loss1: 0.068494 Loss2: 1.299567
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584700 Loss1: 0.280236 Loss2: 1.304464
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363775 Loss1: 0.066760 Loss2: 1.297015
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.464400 Loss1: 0.152987 Loss2: 1.311413
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.445229 Loss1: 0.137877 Loss2: 1.307352
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446156 Loss1: 0.141016 Loss2: 1.305140
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.380740 Loss1: 0.080217 Loss2: 1.300523
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354490 Loss1: 0.066588 Loss2: 1.287902
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356608 Loss1: 0.071510 Loss2: 1.285099
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.643262 Loss1: 0.810163 Loss2: 1.833099
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.897368 Loss1: 0.529844 Loss2: 1.367524
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.773927 Loss1: 0.354892 Loss2: 1.419035
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591425 Loss1: 0.228621 Loss2: 1.362804
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.596256 Loss1: 0.228432 Loss2: 1.367824
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.605921 Loss1: 0.716848 Loss2: 1.889073
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.768034 Loss1: 0.362072 Loss2: 1.405962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.679230 Loss1: 0.246419 Loss2: 1.432811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540181 Loss1: 0.150146 Loss2: 1.390035 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530122 Loss1: 0.149981 Loss2: 1.380142 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.563673 Loss1: 0.181864 Loss2: 1.381809 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.492437 Loss1: 0.120779 Loss2: 1.371658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462368 Loss1: 0.093716 Loss2: 1.368651 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.968028 Loss1: 0.547064 Loss2: 1.420964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.613173 Loss1: 0.227656 Loss2: 1.385517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571311 Loss1: 0.180746 Loss2: 1.390565
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.520658 Loss1: 0.756425 Loss2: 1.764233
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.799336 Loss1: 0.473800 Loss2: 1.325535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.611533 Loss1: 0.272737 Loss2: 1.338795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.501058 Loss1: 0.183409 Loss2: 1.317649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.412408 Loss1: 0.110664 Loss2: 1.301744 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427851 Loss1: 0.075392 Loss2: 1.352460
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.400800 Loss1: 0.108309 Loss2: 1.292491
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425384 Loss1: 0.128112 Loss2: 1.297272
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.370793 Loss1: 0.079973 Loss2: 1.290820
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.352025 Loss1: 0.070514 Loss2: 1.281512
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.345374 Loss1: 0.068390 Loss2: 1.276984
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.890472 Loss1: 0.886588 Loss2: 2.003884
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.924634 Loss1: 0.553453 Loss2: 1.371181
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.758323 Loss1: 0.322682 Loss2: 1.435641
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606416 Loss1: 0.210806 Loss2: 1.395611
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534072 Loss1: 0.161122 Loss2: 1.372950
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.486352 Loss1: 0.115155 Loss2: 1.371197
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.401624 Loss1: 0.663487 Loss2: 1.738137
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449711 Loss1: 0.096471 Loss2: 1.353239 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437576 Loss1: 0.079350 Loss2: 1.358226 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433992 Loss1: 0.079439 Loss2: 1.354553 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.468995 Loss1: 0.159094 Loss2: 1.309901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406461 Loss1: 0.104610 Loss2: 1.301851 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.365617 Loss1: 0.075413 Loss2: 1.290204
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.515599 Loss1: 0.701111 Loss2: 1.814488
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392748 Loss1: 0.108873 Loss2: 1.283875
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.806359 Loss1: 0.432615 Loss2: 1.373744
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351528 Loss1: 0.063528 Loss2: 1.288000
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.596413 Loss1: 0.201136 Loss2: 1.395278
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547836 Loss1: 0.201095 Loss2: 1.346741
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479478 Loss1: 0.122239 Loss2: 1.357239
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.428554 Loss1: 0.087010 Loss2: 1.341544
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432778 Loss1: 0.087159 Loss2: 1.345619
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.535428 Loss1: 0.673481 Loss2: 1.861948
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.849028 Loss1: 0.471835 Loss2: 1.377193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.647049 Loss1: 0.241009 Loss2: 1.406040 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398752 Loss1: 0.070319 Loss2: 1.328433
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577265 Loss1: 0.211749 Loss2: 1.365516
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491206 Loss1: 0.126599 Loss2: 1.364607
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461923 Loss1: 0.109413 Loss2: 1.352510
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451337 Loss1: 0.101421 Loss2: 1.349916
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485626 Loss1: 0.133399 Loss2: 1.352227
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.409723 Loss1: 0.615660 Loss2: 1.794063
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447096 Loss1: 0.098299 Loss2: 1.348797
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.809738 Loss1: 0.437337 Loss2: 1.372401
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400322 Loss1: 0.056610 Loss2: 1.343711
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.645129 Loss1: 0.286188 Loss2: 1.358942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497591 Loss1: 0.140181 Loss2: 1.357410 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466929 Loss1: 0.114513 Loss2: 1.352416
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.419955 Loss1: 0.580705 Loss2: 1.839250
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.750352 Loss1: 0.392546 Loss2: 1.357807
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423826 Loss1: 0.078561 Loss2: 1.345265
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.685524 Loss1: 0.274888 Loss2: 1.410636
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403823 Loss1: 0.070875 Loss2: 1.332948
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559867 Loss1: 0.205648 Loss2: 1.354219
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390302 Loss1: 0.060511 Loss2: 1.329791
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466694 Loss1: 0.112301 Loss2: 1.354393 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447820 Loss1: 0.101789 Loss2: 1.346030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436389 Loss1: 0.096665 Loss2: 1.339724
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.426014 Loss1: 0.642161 Loss2: 1.783853
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401312 Loss1: 0.067688 Loss2: 1.333624
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.675061 Loss1: 0.370768 Loss2: 1.304294
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.527669 Loss1: 0.198889 Loss2: 1.328780
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496637 Loss1: 0.204375 Loss2: 1.292262
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516719 Loss1: 0.217201 Loss2: 1.299518
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.474801 Loss1: 0.181815 Loss2: 1.292987
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.477570 Loss1: 0.630367 Loss2: 1.847203
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.363499 Loss1: 0.077531 Loss2: 1.285967
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.859862 Loss1: 0.462679 Loss2: 1.397183
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.337792 Loss1: 0.062505 Loss2: 1.275287
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.326263 Loss1: 0.060518 Loss2: 1.265746
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.751286 Loss1: 0.306918 Loss2: 1.444368
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.326242 Loss1: 0.063981 Loss2: 1.262261
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.689324 Loss1: 0.296501 Loss2: 1.392823
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.602904 Loss1: 0.203300 Loss2: 1.399605
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.592152 Loss1: 0.201796 Loss2: 1.390355
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.538626 Loss1: 0.150371 Loss2: 1.388255
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568336 Loss1: 0.180585 Loss2: 1.387751
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.689053 Loss1: 0.790308 Loss2: 1.898745
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.526389 Loss1: 0.144860 Loss2: 1.381529
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456608 Loss1: 0.086778 Loss2: 1.369830 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587737 Loss1: 0.193373 Loss2: 1.394364 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505597 Loss1: 0.131237 Loss2: 1.374360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476323 Loss1: 0.107578 Loss2: 1.368745
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.511863 Loss1: 0.665409 Loss2: 1.846453
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.789576 Loss1: 0.437009 Loss2: 1.352567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.661127 Loss1: 0.267195 Loss2: 1.393932 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584467 Loss1: 0.232050 Loss2: 1.352417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482764 Loss1: 0.137597 Loss2: 1.345167 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470248 Loss1: 0.124593 Loss2: 1.345655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419310 Loss1: 0.084172 Loss2: 1.335137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453129 Loss1: 0.115791 Loss2: 1.337338 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451114 Loss1: 0.110083 Loss2: 1.341031 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.471707 Loss1: 0.131078 Loss2: 1.340629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.584292 Loss1: 0.699831 Loss2: 1.884461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.902699 Loss1: 0.494722 Loss2: 1.407977 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981971
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.617996 Loss1: 0.230089 Loss2: 1.387907 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508104 Loss1: 0.111520 Loss2: 1.396584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487603 Loss1: 0.109802 Loss2: 1.377801
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.465413 Loss1: 0.618053 Loss2: 1.847360
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.922338 Loss1: 0.570080 Loss2: 1.352258 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.805494 Loss1: 0.359888 Loss2: 1.445605 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.648094 Loss1: 0.294599 Loss2: 1.353495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576774 Loss1: 0.204633 Loss2: 1.372141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464721 Loss1: 0.109228 Loss2: 1.355493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433932 Loss1: 0.091001 Loss2: 1.342931 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389884 Loss1: 0.055610 Loss2: 1.334273 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577311 Loss1: 0.205397 Loss2: 1.371914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522840 Loss1: 0.163509 Loss2: 1.359331 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471054 Loss1: 0.114659 Loss2: 1.356395
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.592649 Loss1: 0.756943 Loss2: 1.835707
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469182 Loss1: 0.119298 Loss2: 1.349884
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.745529 Loss1: 0.382642 Loss2: 1.362887
-DEBUG flwr 2023-10-11 18:39:25,526 | server.py:236 | fit_round 123 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644450 Loss1: 0.250100 Loss2: 1.394350
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415318 Loss1: 0.070271 Loss2: 1.345047
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520440 Loss1: 0.169594 Loss2: 1.350846
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393293 Loss1: 0.058653 Loss2: 1.334640
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452237 Loss1: 0.105881 Loss2: 1.346356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.476736 Loss1: 0.133380 Loss2: 1.343356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489635 Loss1: 0.153526 Loss2: 1.336109
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.615167 Loss1: 0.750967 Loss2: 1.864200
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463467 Loss1: 0.118373 Loss2: 1.345094
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.992882 Loss1: 0.582643 Loss2: 1.410239
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.758224 Loss1: 0.355712 Loss2: 1.402512
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.670407 Loss1: 0.297625 Loss2: 1.372783
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563388 Loss1: 0.185687 Loss2: 1.377701
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473905 Loss1: 0.133074 Loss2: 1.340832
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.622294 Loss1: 0.795193 Loss2: 1.827101
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425273 Loss1: 0.086337 Loss2: 1.338936
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.822560 Loss1: 0.451359 Loss2: 1.371201
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411690 Loss1: 0.082539 Loss2: 1.329151
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.640967 Loss1: 0.230651 Loss2: 1.410316
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384035 Loss1: 0.062228 Loss2: 1.321806
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535230 Loss1: 0.169014 Loss2: 1.366215
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369540 Loss1: 0.052165 Loss2: 1.317376
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.532772 Loss1: 0.154798 Loss2: 1.377973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.514166 Loss1: 0.147424 Loss2: 1.366742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.504953 Loss1: 0.145683 Loss2: 1.359271
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.763370 Loss1: 0.842590 Loss2: 1.920780
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491728 Loss1: 0.129124 Loss2: 1.362604
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.908797 Loss1: 0.493387 Loss2: 1.415410
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.827775 Loss1: 0.366282 Loss2: 1.461493
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.644017 Loss1: 0.239915 Loss2: 1.404102
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.594437 Loss1: 0.194447 Loss2: 1.399990
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.497267 Loss1: 0.110717 Loss2: 1.386550
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471261 Loss1: 0.089056 Loss2: 1.382206
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441937 Loss1: 0.069821 Loss2: 1.372116
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446082 Loss1: 0.075385 Loss2: 1.370698
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441497 Loss1: 0.073801 Loss2: 1.367695
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 18:39:25,526][flwr][DEBUG] - fit_round 123 received 50 results and 0 failures
-INFO flwr 2023-10-11 18:40:05,828 | server.py:125 | fit progress: (123, 2.200183067268457, {'accuracy': 0.5825}, 283713.606855665)
->> Test accuracy: 0.582500
-[2023-10-11 18:40:05,828][flwr][INFO] - fit progress: (123, 2.200183067268457, {'accuracy': 0.5825}, 283713.606855665)
-DEBUG flwr 2023-10-11 18:40:05,829 | server.py:173 | evaluate_round 123: strategy sampled 50 clients (out of 50)
-[2023-10-11 18:40:05,829][flwr][DEBUG] - evaluate_round 123: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 18:49:10,229 | server.py:187 | evaluate_round 123 received 50 results and 0 failures
-[2023-10-11 18:49:10,229][flwr][DEBUG] - evaluate_round 123 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 18:49:10,229 | server.py:222 | fit_round 124: strategy sampled 50 clients (out of 50)
-[2023-10-11 18:49:10,229][flwr][DEBUG] - fit_round 124: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.466810 Loss1: 0.590347 Loss2: 1.876463
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.818023 Loss1: 0.394176 Loss2: 1.423846
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.697503 Loss1: 0.236396 Loss2: 1.461107
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.648653 Loss1: 0.773769 Loss2: 1.874884
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.845338 Loss1: 0.464186 Loss2: 1.381152 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.667636 Loss1: 0.244924 Loss2: 1.422712 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.589451 Loss1: 0.223480 Loss2: 1.365971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512155 Loss1: 0.139927 Loss2: 1.372228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481202 Loss1: 0.122643 Loss2: 1.358559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455723 Loss1: 0.104048 Loss2: 1.351675 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429238 Loss1: 0.075009 Loss2: 1.354228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461252 Loss1: 0.109281 Loss2: 1.351972 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.416135 Loss1: 0.561326 Loss2: 1.854808
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.750519 Loss1: 0.345499 Loss2: 1.405020
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.643979 Loss1: 0.219765 Loss2: 1.424213
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.883994 Loss1: 0.921664 Loss2: 1.962330
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.969261 Loss1: 0.539666 Loss2: 1.429595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.776150 Loss1: 0.297054 Loss2: 1.479097 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.563895 Loss1: 0.168449 Loss2: 1.395446
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.658896 Loss1: 0.247890 Loss2: 1.411006
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.629309 Loss1: 0.205007 Loss2: 1.424302
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481668 Loss1: 0.095721 Loss2: 1.385948
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.592731 Loss1: 0.182164 Loss2: 1.410567
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.494208 Loss1: 0.114745 Loss2: 1.379463
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433720 Loss1: 0.058103 Loss2: 1.375617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418324 Loss1: 0.048772 Loss2: 1.369552 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996324
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487726 Loss1: 0.094877 Loss2: 1.392849 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.872298 Loss1: 0.850970 Loss2: 2.021329
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.910518 Loss1: 0.518336 Loss2: 1.392182
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.697717 Loss1: 0.271130 Loss2: 1.426587
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.673790 Loss1: 0.271456 Loss2: 1.402334
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631258 Loss1: 0.239398 Loss2: 1.391861
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.820647 Loss1: 0.462832 Loss2: 1.357815 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489033 Loss1: 0.104581 Loss2: 1.384452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.571044 Loss1: 0.236832 Loss2: 1.334212 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.537035 Loss1: 0.213232 Loss2: 1.323803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480847 Loss1: 0.160636 Loss2: 1.320211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415936 Loss1: 0.110679 Loss2: 1.305257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377460 Loss1: 0.070286 Loss2: 1.307174 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.643744 Loss1: 0.238133 Loss2: 1.405611 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.455734 Loss1: 0.111648 Loss2: 1.344086 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438067 Loss1: 0.098841 Loss2: 1.339226
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.623450 Loss1: 0.737195 Loss2: 1.886254
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.890216 Loss1: 0.485455 Loss2: 1.404761 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.709241 Loss1: 0.278401 Loss2: 1.430839 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.544534 Loss1: 0.154865 Loss2: 1.389669 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.525753 Loss1: 0.139766 Loss2: 1.385987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460650 Loss1: 0.097315 Loss2: 1.363336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438962 Loss1: 0.078641 Loss2: 1.360321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453903 Loss1: 0.094078 Loss2: 1.359826 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.921354 Loss1: 0.422330 Loss2: 1.499023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.683812 Loss1: 0.250429 Loss2: 1.433383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.608385 Loss1: 0.190034 Loss2: 1.418350
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.492141 Loss1: 0.662841 Loss2: 1.829301
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.813887 Loss1: 0.447054 Loss2: 1.366832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.664097 Loss1: 0.253592 Loss2: 1.410506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612747 Loss1: 0.261605 Loss2: 1.351142 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555235 Loss1: 0.189071 Loss2: 1.366165 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.424376 Loss1: 0.089154 Loss2: 1.335222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390770 Loss1: 0.067198 Loss2: 1.323572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405823 Loss1: 0.082811 Loss2: 1.323012 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.740567 Loss1: 0.285978 Loss2: 1.454589 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.675506 Loss1: 0.234364 Loss2: 1.441142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.588848 Loss1: 0.184110 Loss2: 1.404737
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.417604 Loss1: 0.653893 Loss2: 1.763711
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.732968 Loss1: 0.394958 Loss2: 1.338010 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.577922 Loss1: 0.225589 Loss2: 1.352333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563522 Loss1: 0.244137 Loss2: 1.319385 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.590153 Loss1: 0.264271 Loss2: 1.325883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422538 Loss1: 0.106846 Loss2: 1.315693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354285 Loss1: 0.058772 Loss2: 1.295513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356017 Loss1: 0.069886 Loss2: 1.286131 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564353 Loss1: 0.183205 Loss2: 1.381148 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516371 Loss1: 0.140947 Loss2: 1.375424 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.708341 Loss1: 0.708262 Loss2: 2.000078
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.489932 Loss1: 0.118120 Loss2: 1.371812
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.986017 Loss1: 0.489383 Loss2: 1.496634
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.472726 Loss1: 0.095669 Loss2: 1.377058
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.888917 Loss1: 0.330471 Loss2: 1.558446
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437182 Loss1: 0.068540 Loss2: 1.368642
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.712109 Loss1: 0.222214 Loss2: 1.489896
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435626 Loss1: 0.073211 Loss2: 1.362415
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.624731 Loss1: 0.138494 Loss2: 1.486237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.583014 Loss1: 0.111525 Loss2: 1.471488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.580746 Loss1: 0.106132 Loss2: 1.474613
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.504243 Loss1: 0.660690 Loss2: 1.843553
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.533193 Loss1: 0.063387 Loss2: 1.469806
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.779697 Loss1: 0.382377 Loss2: 1.397321
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.669062 Loss1: 0.241883 Loss2: 1.427179
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.592136 Loss1: 0.202615 Loss2: 1.389521
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514997 Loss1: 0.121404 Loss2: 1.393594
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493231 Loss1: 0.120098 Loss2: 1.373133
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.689846 Loss1: 0.855599 Loss2: 1.834247
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.834536 Loss1: 0.443480 Loss2: 1.391057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604787 Loss1: 0.209337 Loss2: 1.395450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592762 Loss1: 0.229179 Loss2: 1.363583 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438757 Loss1: 0.077893 Loss2: 1.360864
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544033 Loss1: 0.171887 Loss2: 1.372146
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484473 Loss1: 0.128707 Loss2: 1.355766
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478687 Loss1: 0.128792 Loss2: 1.349895
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445009 Loss1: 0.093901 Loss2: 1.351108
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405443 Loss1: 0.067344 Loss2: 1.338099
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.767783 Loss1: 0.881606 Loss2: 1.886176
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386260 Loss1: 0.054493 Loss2: 1.331767
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.692157 Loss1: 0.266551 Loss2: 1.425605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.563737 Loss1: 0.166530 Loss2: 1.397207 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519577 Loss1: 0.128887 Loss2: 1.390690
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.582042 Loss1: 0.731667 Loss2: 1.850376
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476215 Loss1: 0.093550 Loss2: 1.382665
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.795494 Loss1: 0.467877 Loss2: 1.327618
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.709360 Loss1: 0.332170 Loss2: 1.377190
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486484 Loss1: 0.110714 Loss2: 1.375771
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557498 Loss1: 0.224202 Loss2: 1.333296
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456414 Loss1: 0.083235 Loss2: 1.373179
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516999 Loss1: 0.188485 Loss2: 1.328514
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463833 Loss1: 0.088311 Loss2: 1.375522
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445315 Loss1: 0.126082 Loss2: 1.319233 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.365519 Loss1: 0.062316 Loss2: 1.303203 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.360035 Loss1: 0.063482 Loss2: 1.296553
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.433001 Loss1: 0.665017 Loss2: 1.767984
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.767166 Loss1: 0.424278 Loss2: 1.342888
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.669768 Loss1: 0.307773 Loss2: 1.361994
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563345 Loss1: 0.242691 Loss2: 1.320654
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.504906 Loss1: 0.173125 Loss2: 1.331781
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.543732 Loss1: 0.665110 Loss2: 1.878622
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471266 Loss1: 0.152238 Loss2: 1.319028
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.935582 Loss1: 0.534451 Loss2: 1.401131
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.398235 Loss1: 0.084788 Loss2: 1.313447
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.799481 Loss1: 0.344070 Loss2: 1.455410
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.685100 Loss1: 0.291098 Loss2: 1.394002
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.395358 Loss1: 0.088007 Loss2: 1.307351
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.853239 Loss1: 0.423389 Loss2: 1.429850
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386122 Loss1: 0.082482 Loss2: 1.303640
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.660715 Loss1: 0.256363 Loss2: 1.404351
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387034 Loss1: 0.081213 Loss2: 1.305821
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.530297 Loss1: 0.141957 Loss2: 1.388340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461881 Loss1: 0.090753 Loss2: 1.371128 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.637578 Loss1: 0.320915 Loss2: 1.316663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506675 Loss1: 0.192305 Loss2: 1.314370 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484008 Loss1: 0.177545 Loss2: 1.306463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519874 Loss1: 0.193814 Loss2: 1.326059 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426501 Loss1: 0.105941 Loss2: 1.320560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.396733 Loss1: 0.097039 Loss2: 1.299695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.514978 Loss1: 0.118847 Loss2: 1.396131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475964 Loss1: 0.099686 Loss2: 1.376278 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431763 Loss1: 0.064321 Loss2: 1.367443 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.612990 Loss1: 0.803452 Loss2: 1.809538 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.678888 Loss1: 0.285305 Loss2: 1.393583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.597548 Loss1: 0.249660 Loss2: 1.347887
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.492410 Loss1: 0.729468 Loss2: 1.762942
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.743512 Loss1: 0.417432 Loss2: 1.326081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.644102 Loss1: 0.296688 Loss2: 1.347414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.515388 Loss1: 0.195956 Loss2: 1.319431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.444986 Loss1: 0.125511 Loss2: 1.319475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.381499 Loss1: 0.079618 Loss2: 1.301882 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.370715 Loss1: 0.068168 Loss2: 1.302547 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.336520 Loss1: 0.049300 Loss2: 1.287219 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.832390 Loss1: 0.465669 Loss2: 1.366721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.530699 Loss1: 0.167479 Loss2: 1.363220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.498074 Loss1: 0.686503 Loss2: 1.811570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.815363 Loss1: 0.441152 Loss2: 1.374211 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450012 Loss1: 0.107004 Loss2: 1.343008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440431 Loss1: 0.099372 Loss2: 1.341059 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405506 Loss1: 0.075792 Loss2: 1.329713 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416101 Loss1: 0.076210 Loss2: 1.339891 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390880 Loss1: 0.057482 Loss2: 1.333398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400731 Loss1: 0.069240 Loss2: 1.331491 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.723921 Loss1: 0.312488 Loss2: 1.411433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.467608 Loss1: 0.129186 Loss2: 1.338422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.673646 Loss1: 0.790085 Loss2: 1.883560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.893981 Loss1: 0.493545 Loss2: 1.400436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701773 Loss1: 0.285156 Loss2: 1.416617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591805 Loss1: 0.207729 Loss2: 1.384076 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499218 Loss1: 0.131315 Loss2: 1.367903 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417429 Loss1: 0.058909 Loss2: 1.358520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.598972 Loss1: 0.806027 Loss2: 1.792945 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.653930 Loss1: 0.290950 Loss2: 1.362980 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448848 Loss1: 0.134882 Loss2: 1.313966 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.390950 Loss1: 0.080801 Loss2: 1.310149
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.760131 Loss1: 0.810325 Loss2: 1.949807
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.892424 Loss1: 0.487412 Loss2: 1.405011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.792931 Loss1: 0.325516 Loss2: 1.467415 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.359775 Loss1: 0.066042 Loss2: 1.293734
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.719576 Loss1: 0.312475 Loss2: 1.407101
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.353363 Loss1: 0.065919 Loss2: 1.287444
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.616263 Loss1: 0.188549 Loss2: 1.427713
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.587130 Loss1: 0.184343 Loss2: 1.402786
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.523122 Loss1: 0.121331 Loss2: 1.401790
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.495958 Loss1: 0.097099 Loss2: 1.398858
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466669 Loss1: 0.076600 Loss2: 1.390068
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436250 Loss1: 0.057106 Loss2: 1.379144
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.913006 Loss1: 0.924472 Loss2: 1.988534
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.911721 Loss1: 0.447749 Loss2: 1.463972
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.747103 Loss1: 0.291292 Loss2: 1.455811
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.671500 Loss1: 0.233706 Loss2: 1.437794
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.629306 Loss1: 0.193295 Loss2: 1.436011
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564413 Loss1: 0.135283 Loss2: 1.429130
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.548949 Loss1: 0.663386 Loss2: 1.885562
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.555192 Loss1: 0.131296 Loss2: 1.423896
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.797660 Loss1: 0.418586 Loss2: 1.379074
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.504768 Loss1: 0.096767 Loss2: 1.408002
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.654215 Loss1: 0.229079 Loss2: 1.425136
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499066 Loss1: 0.087051 Loss2: 1.412015
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.580487 Loss1: 0.212284 Loss2: 1.368204
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453816 Loss1: 0.047624 Loss2: 1.406191
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501625 Loss1: 0.132924 Loss2: 1.368701
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466210 Loss1: 0.105937 Loss2: 1.360273
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485345 Loss1: 0.131965 Loss2: 1.353380
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455810 Loss1: 0.106053 Loss2: 1.349758
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433690 Loss1: 0.077931 Loss2: 1.355759
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437723 Loss1: 0.096448 Loss2: 1.341275
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.745414 Loss1: 0.834403 Loss2: 1.911011
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.833155 Loss1: 0.443147 Loss2: 1.390008
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634249 Loss1: 0.209250 Loss2: 1.424999
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.566771 Loss1: 0.198335 Loss2: 1.368436
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513534 Loss1: 0.146772 Loss2: 1.366762
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.559867 Loss1: 0.758937 Loss2: 1.800930
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515585 Loss1: 0.147576 Loss2: 1.368009
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503827 Loss1: 0.132839 Loss2: 1.370988
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.827306 Loss1: 0.483770 Loss2: 1.343536
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484539 Loss1: 0.117403 Loss2: 1.367136
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.741443 Loss1: 0.349066 Loss2: 1.392377
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456955 Loss1: 0.097585 Loss2: 1.359369
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595828 Loss1: 0.239621 Loss2: 1.356206
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421243 Loss1: 0.066228 Loss2: 1.355016
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491905 Loss1: 0.153857 Loss2: 1.338047
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455862 Loss1: 0.118818 Loss2: 1.337045
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412273 Loss1: 0.088424 Loss2: 1.323849
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.403878 Loss1: 0.086153 Loss2: 1.317726
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423040 Loss1: 0.107353 Loss2: 1.315686
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.476248 Loss1: 0.659220 Loss2: 1.817028
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400201 Loss1: 0.090378 Loss2: 1.309823
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595823 Loss1: 0.226816 Loss2: 1.369007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.501408 Loss1: 0.163536 Loss2: 1.337872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444056 Loss1: 0.104040 Loss2: 1.340016
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.673783 Loss1: 0.769003 Loss2: 1.904780
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.858599 Loss1: 0.436765 Loss2: 1.421834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.768402 Loss1: 0.304516 Loss2: 1.463886 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.656763 Loss1: 0.238489 Loss2: 1.418274 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576916 Loss1: 0.147992 Loss2: 1.428924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.523676 Loss1: 0.117863 Loss2: 1.405813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462911 Loss1: 0.071388 Loss2: 1.391522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457238 Loss1: 0.070410 Loss2: 1.386828 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.728047 Loss1: 0.289371 Loss2: 1.438676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.495797 Loss1: 0.125573 Loss2: 1.370224 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466946 Loss1: 0.108400 Loss2: 1.358546
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.629068 Loss1: 0.743364 Loss2: 1.885704
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.906072 Loss1: 0.507707 Loss2: 1.398365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.832661 Loss1: 0.372570 Loss2: 1.460090 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.674822 Loss1: 0.275318 Loss2: 1.399504 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.620676 Loss1: 0.213549 Loss2: 1.407127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497232 Loss1: 0.113170 Loss2: 1.384062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439202 Loss1: 0.063455 Loss2: 1.375747 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436660 Loss1: 0.066553 Loss2: 1.370107 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.663272 Loss1: 0.249905 Loss2: 1.413367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506872 Loss1: 0.139099 Loss2: 1.367772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.486750 Loss1: 0.125799 Loss2: 1.360951
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.576710 Loss1: 0.666882 Loss2: 1.909827
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.896263 Loss1: 0.488544 Loss2: 1.407719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.744879 Loss1: 0.287127 Loss2: 1.457752 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.737208 Loss1: 0.312468 Loss2: 1.424740 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.624769 Loss1: 0.195725 Loss2: 1.429044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518974 Loss1: 0.108428 Loss2: 1.410546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.494185 Loss1: 0.098572 Loss2: 1.395613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449659 Loss1: 0.056407 Loss2: 1.393252 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.621357 Loss1: 0.212761 Loss2: 1.408596 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.552135 Loss1: 0.173326 Loss2: 1.378809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.549995 Loss1: 0.177319 Loss2: 1.372675
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.571343 Loss1: 0.677699 Loss2: 1.893643
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.497997 Loss1: 0.125204 Loss2: 1.372793
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.807592 Loss1: 0.433075 Loss2: 1.374517
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.490505 Loss1: 0.131903 Loss2: 1.358602
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.655800 Loss1: 0.264438 Loss2: 1.391362
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595514 Loss1: 0.220206 Loss2: 1.375308
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.466477 Loss1: 0.105256 Loss2: 1.361221
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544598 Loss1: 0.171663 Loss2: 1.372936
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447042 Loss1: 0.090159 Loss2: 1.356883
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.544120 Loss1: 0.167774 Loss2: 1.376346 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442121 Loss1: 0.089848 Loss2: 1.352273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408848 Loss1: 0.064044 Loss2: 1.344804
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.402522 Loss1: 0.607394 Loss2: 1.795127
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.758669 Loss1: 0.416305 Loss2: 1.342364
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.720909 Loss1: 0.332178 Loss2: 1.388731
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.661379 Loss1: 0.320193 Loss2: 1.341185
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.581056 Loss1: 0.227037 Loss2: 1.354018
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.671214 Loss1: 0.780609 Loss2: 1.890605
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.799634 Loss1: 0.407101 Loss2: 1.392534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.732392 Loss1: 0.299828 Loss2: 1.432563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636375 Loss1: 0.246825 Loss2: 1.389550 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576991 Loss1: 0.185391 Loss2: 1.391600 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375345 Loss1: 0.075008 Loss2: 1.300337
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.497458 Loss1: 0.118598 Loss2: 1.378860
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494552 Loss1: 0.120985 Loss2: 1.373566
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468934 Loss1: 0.092031 Loss2: 1.376903
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438254 Loss1: 0.076494 Loss2: 1.361760
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411429 Loss1: 0.056300 Loss2: 1.355129
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.577351 Loss1: 0.663912 Loss2: 1.913439
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.773627 Loss1: 0.363958 Loss2: 1.409669
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.696396 Loss1: 0.251926 Loss2: 1.444471
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.607652 Loss1: 0.202804 Loss2: 1.404848
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.550284 Loss1: 0.153058 Loss2: 1.397226
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.556925 Loss1: 0.706817 Loss2: 1.850108
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524849 Loss1: 0.125056 Loss2: 1.399792
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.789644 Loss1: 0.403482 Loss2: 1.386162
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517238 Loss1: 0.124905 Loss2: 1.392333
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.751240 Loss1: 0.318963 Loss2: 1.432277
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450197 Loss1: 0.061747 Loss2: 1.388450
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.571619 Loss1: 0.187959 Loss2: 1.383660
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433614 Loss1: 0.051592 Loss2: 1.382022
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516607 Loss1: 0.143113 Loss2: 1.373494
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417742 Loss1: 0.044266 Loss2: 1.373476
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490584 Loss1: 0.120603 Loss2: 1.369981
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490388 Loss1: 0.118798 Loss2: 1.371590
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.534064 Loss1: 0.166965 Loss2: 1.367100
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.475999 Loss1: 0.102669 Loss2: 1.373330
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446600 Loss1: 0.082524 Loss2: 1.364077
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.781466 Loss1: 0.656282 Loss2: 2.125184
-(DefaultActor pid=3765) Epoch: 1 Loss: 2.012204 Loss1: 0.440305 Loss2: 1.571899
-DEBUG flwr 2023-10-11 19:17:58,049 | server.py:236 | fit_round 124 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.885147 Loss1: 0.261764 Loss2: 1.623383
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.722750 Loss1: 0.161280 Loss2: 1.561470
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.723190 Loss1: 0.802633 Loss2: 1.920556
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.870977 Loss1: 0.505539 Loss2: 1.365439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.713213 Loss1: 0.324039 Loss2: 1.389173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554973 Loss1: 0.197434 Loss2: 1.357539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.482957 Loss1: 0.146933 Loss2: 1.336023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427500 Loss1: 0.092138 Loss2: 1.335362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.403456 Loss1: 0.077953 Loss2: 1.325503 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363206 Loss1: 0.047283 Loss2: 1.315923 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.553373 Loss1: 0.718394 Loss2: 1.834979 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.733021 Loss1: 0.324172 Loss2: 1.408849 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.624329 Loss1: 0.252285 Loss2: 1.372044
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.737385 Loss1: 0.813416 Loss2: 1.923970
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.002066 Loss1: 0.552929 Loss2: 1.449137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.764445 Loss1: 0.267083 Loss2: 1.497362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612109 Loss1: 0.177965 Loss2: 1.434144 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563863 Loss1: 0.132488 Loss2: 1.431375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530078 Loss1: 0.110731 Loss2: 1.419348 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415337 Loss1: 0.075047 Loss2: 1.340290
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.529349 Loss1: 0.114750 Loss2: 1.414599
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.499417 Loss1: 0.090766 Loss2: 1.408651
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490111 Loss1: 0.079807 Loss2: 1.410304
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487399 Loss1: 0.080675 Loss2: 1.406724
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 19:17:58,049][flwr][DEBUG] - fit_round 124 received 50 results and 0 failures
-INFO flwr 2023-10-11 19:18:40,644 | server.py:125 | fit progress: (124, 2.2044151994747856, {'accuracy': 0.5851}, 286028.422550948)
->> Test accuracy: 0.585100
-[2023-10-11 19:18:40,644][flwr][INFO] - fit progress: (124, 2.2044151994747856, {'accuracy': 0.5851}, 286028.422550948)
-DEBUG flwr 2023-10-11 19:18:40,644 | server.py:173 | evaluate_round 124: strategy sampled 50 clients (out of 50)
-[2023-10-11 19:18:40,644][flwr][DEBUG] - evaluate_round 124: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 19:27:45,219 | server.py:187 | evaluate_round 124 received 50 results and 0 failures
-[2023-10-11 19:27:45,219][flwr][DEBUG] - evaluate_round 124 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 19:27:45,219 | server.py:222 | fit_round 125: strategy sampled 50 clients (out of 50)
-[2023-10-11 19:27:45,219][flwr][DEBUG] - fit_round 125: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.443535 Loss1: 0.654620 Loss2: 1.788914
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.730356 Loss1: 0.421198 Loss2: 1.309157
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645501 Loss1: 0.286574 Loss2: 1.358928
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.584707 Loss1: 0.271466 Loss2: 1.313240
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.773465 Loss1: 0.857707 Loss2: 1.915758
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.857536 Loss1: 0.467886 Loss2: 1.389650 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440176 Loss1: 0.136827 Loss2: 1.303349
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.683834 Loss1: 0.250008 Loss2: 1.433825
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.371115 Loss1: 0.072279 Loss2: 1.298836
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586978 Loss1: 0.210763 Loss2: 1.376215
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394865 Loss1: 0.099602 Loss2: 1.295263
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.586160 Loss1: 0.201607 Loss2: 1.384553
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.557441 Loss1: 0.174841 Loss2: 1.382600
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.415563 Loss1: 0.118536 Loss2: 1.297027
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483574 Loss1: 0.114093 Loss2: 1.369480
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391461 Loss1: 0.103323 Loss2: 1.288138
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461007 Loss1: 0.100234 Loss2: 1.360773 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982143
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.669436 Loss1: 0.831685 Loss2: 1.837751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.726755 Loss1: 0.315899 Loss2: 1.410857 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.568081 Loss1: 0.216183 Loss2: 1.351898
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.534529 Loss1: 0.702047 Loss2: 1.832482
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.767979 Loss1: 0.401245 Loss2: 1.366733 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.693065 Loss1: 0.294398 Loss2: 1.398667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.607623 Loss1: 0.238941 Loss2: 1.368682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528458 Loss1: 0.162746 Loss2: 1.365711 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486489 Loss1: 0.120949 Loss2: 1.365540 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372598 Loss1: 0.055457 Loss2: 1.317141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447824 Loss1: 0.098001 Loss2: 1.349823
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413590 Loss1: 0.068412 Loss2: 1.345178
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385854 Loss1: 0.049268 Loss2: 1.336585
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370414 Loss1: 0.040660 Loss2: 1.329754
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.589670 Loss1: 0.772898 Loss2: 1.816772
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.721571 Loss1: 0.372125 Loss2: 1.349445
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.587100 Loss1: 0.213880 Loss2: 1.373220
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513435 Loss1: 0.178380 Loss2: 1.335055
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.548981 Loss1: 0.744627 Loss2: 1.804354
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.884791 Loss1: 0.537308 Loss2: 1.347484 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.796064 Loss1: 0.379332 Loss2: 1.416733 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.648889 Loss1: 0.307717 Loss2: 1.341173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.584157 Loss1: 0.219877 Loss2: 1.364280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528151 Loss1: 0.193236 Loss2: 1.334914 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.362341 Loss1: 0.050096 Loss2: 1.312244
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.506545 Loss1: 0.160771 Loss2: 1.345775
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.463567 Loss1: 0.123142 Loss2: 1.340425
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.411652 Loss1: 0.085955 Loss2: 1.325698
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438031 Loss1: 0.114380 Loss2: 1.323650
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.443483 Loss1: 0.574607 Loss2: 1.868876
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784992 Loss1: 0.397989 Loss2: 1.387003
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.787041 Loss1: 0.337915 Loss2: 1.449125
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.620185 Loss1: 0.225591 Loss2: 1.394594
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.735906 Loss1: 0.873610 Loss2: 1.862295
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.837596 Loss1: 0.480587 Loss2: 1.357009
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534934 Loss1: 0.132340 Loss2: 1.402594
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.664623 Loss1: 0.266027 Loss2: 1.398596
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491447 Loss1: 0.108777 Loss2: 1.382671
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562191 Loss1: 0.227553 Loss2: 1.334639
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461528 Loss1: 0.086083 Loss2: 1.375446
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452408 Loss1: 0.080903 Loss2: 1.371505 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438645 Loss1: 0.071011 Loss2: 1.367634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430314 Loss1: 0.063994 Loss2: 1.366320 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389596 Loss1: 0.077387 Loss2: 1.312209 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.764146 Loss1: 0.890226 Loss2: 1.873919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.639865 Loss1: 0.232052 Loss2: 1.407813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572278 Loss1: 0.195176 Loss2: 1.377102
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.579813 Loss1: 0.767075 Loss2: 1.812739
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518458 Loss1: 0.143352 Loss2: 1.375107
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.833159 Loss1: 0.495962 Loss2: 1.337198
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477996 Loss1: 0.113282 Loss2: 1.364714
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.692934 Loss1: 0.307197 Loss2: 1.385737
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456873 Loss1: 0.100467 Loss2: 1.356406
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528965 Loss1: 0.197973 Loss2: 1.330992
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.487747 Loss1: 0.137565 Loss2: 1.350183
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446639 Loss1: 0.121883 Loss2: 1.324756
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425589 Loss1: 0.074008 Loss2: 1.351582
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.425059 Loss1: 0.112670 Loss2: 1.312389
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413576 Loss1: 0.066899 Loss2: 1.346677
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.394258 Loss1: 0.085322 Loss2: 1.308936
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.404970 Loss1: 0.103604 Loss2: 1.301366
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392325 Loss1: 0.084722 Loss2: 1.307602
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352510 Loss1: 0.055481 Loss2: 1.297029
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.567178 Loss1: 0.733618 Loss2: 1.833560
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.811979 Loss1: 0.450699 Loss2: 1.361281
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.631142 Loss1: 0.234595 Loss2: 1.396547
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.609083 Loss1: 0.255610 Loss2: 1.353473
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.694014 Loss1: 0.820183 Loss2: 1.873831
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.852334 Loss1: 0.445797 Loss2: 1.406536 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.686506 Loss1: 0.262730 Loss2: 1.423776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.655100 Loss1: 0.273941 Loss2: 1.381159 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.566676 Loss1: 0.170686 Loss2: 1.395991 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.535821 Loss1: 0.158155 Loss2: 1.377666 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399223 Loss1: 0.070563 Loss2: 1.328661
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.479597 Loss1: 0.110516 Loss2: 1.369081
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468916 Loss1: 0.105459 Loss2: 1.363457
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447898 Loss1: 0.089497 Loss2: 1.358401
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424278 Loss1: 0.067248 Loss2: 1.357030
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.507398 Loss1: 0.611150 Loss2: 1.896247
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.855710 Loss1: 0.464440 Loss2: 1.391270
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.737352 Loss1: 0.286920 Loss2: 1.450432
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.609069 Loss1: 0.217200 Loss2: 1.391869
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.585615 Loss1: 0.706772 Loss2: 1.878843
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.762780 Loss1: 0.371606 Loss2: 1.391174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.692611 Loss1: 0.260714 Loss2: 1.431897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.613158 Loss1: 0.216701 Loss2: 1.396457 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.670372 Loss1: 0.273807 Loss2: 1.396565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.585152 Loss1: 0.173621 Loss2: 1.411531 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432544 Loss1: 0.069009 Loss2: 1.363535
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.534083 Loss1: 0.148513 Loss2: 1.385570
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.533435 Loss1: 0.146465 Loss2: 1.386969
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.534704 Loss1: 0.144010 Loss2: 1.390694
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.528193 Loss1: 0.140851 Loss2: 1.387342
-(DefaultActor pid=3764) >> Training accuracy: 0.929167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.620158 Loss1: 0.785471 Loss2: 1.834687
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.917949 Loss1: 0.522451 Loss2: 1.395498
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700593 Loss1: 0.295602 Loss2: 1.404991
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.639273 Loss1: 0.277257 Loss2: 1.362016
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.736079 Loss1: 0.868316 Loss2: 1.867763
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514040 Loss1: 0.147024 Loss2: 1.367017
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.844029 Loss1: 0.485678 Loss2: 1.358352
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.675233 Loss1: 0.274875 Loss2: 1.400358
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.467170 Loss1: 0.104763 Loss2: 1.362406
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567695 Loss1: 0.217472 Loss2: 1.350223
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.454577 Loss1: 0.100587 Loss2: 1.353990
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.498343 Loss1: 0.151456 Loss2: 1.346886
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471519 Loss1: 0.117153 Loss2: 1.354365
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412160 Loss1: 0.068092 Loss2: 1.344068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387301 Loss1: 0.050143 Loss2: 1.337158 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414322 Loss1: 0.092038 Loss2: 1.322284 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.683760 Loss1: 0.774371 Loss2: 1.909389 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.735065 Loss1: 0.279875 Loss2: 1.455190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642926 Loss1: 0.250137 Loss2: 1.392789
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.517285 Loss1: 0.739420 Loss2: 1.777865
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.810643 Loss1: 0.475171 Loss2: 1.335472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.607831 Loss1: 0.240114 Loss2: 1.367717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.517515 Loss1: 0.208452 Loss2: 1.309063 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.466304 Loss1: 0.154700 Loss2: 1.311604 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473204 Loss1: 0.159948 Loss2: 1.313256 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484211 Loss1: 0.114257 Loss2: 1.369954
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.449394 Loss1: 0.139674 Loss2: 1.309719
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397047 Loss1: 0.092289 Loss2: 1.304759
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363769 Loss1: 0.066126 Loss2: 1.297643
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400981 Loss1: 0.109388 Loss2: 1.291592
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.592136 Loss1: 0.720941 Loss2: 1.871195
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.777612 Loss1: 0.383163 Loss2: 1.394449
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.682580 Loss1: 0.256525 Loss2: 1.426055
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596006 Loss1: 0.207019 Loss2: 1.388987
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.776048 Loss1: 0.812133 Loss2: 1.963915
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.891280 Loss1: 0.535401 Loss2: 1.355879
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546008 Loss1: 0.149652 Loss2: 1.396356
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505607 Loss1: 0.121253 Loss2: 1.384354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.519570 Loss1: 0.138350 Loss2: 1.381220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.522665 Loss1: 0.140050 Loss2: 1.382616 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469538 Loss1: 0.119565 Loss2: 1.349973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.480654 Loss1: 0.131415 Loss2: 1.349239 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425971 Loss1: 0.081414 Loss2: 1.344557 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.647362 Loss1: 0.798667 Loss2: 1.848695
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.746515 Loss1: 0.376157 Loss2: 1.370358
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.662969 Loss1: 0.272480 Loss2: 1.390488
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534035 Loss1: 0.173135 Loss2: 1.360900
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.693331 Loss1: 0.765793 Loss2: 1.927538
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.894848 Loss1: 0.445005 Loss2: 1.449842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.729290 Loss1: 0.285489 Loss2: 1.443801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612243 Loss1: 0.195767 Loss2: 1.416476 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576756 Loss1: 0.175473 Loss2: 1.401284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524172 Loss1: 0.127835 Loss2: 1.396337 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430961 Loss1: 0.082882 Loss2: 1.348079
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480653 Loss1: 0.095682 Loss2: 1.384971
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452557 Loss1: 0.066904 Loss2: 1.385653
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439382 Loss1: 0.065990 Loss2: 1.373392
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417314 Loss1: 0.047081 Loss2: 1.370233
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.713344 Loss1: 0.789838 Loss2: 1.923505
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.882818 Loss1: 0.494399 Loss2: 1.388419
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.745288 Loss1: 0.331964 Loss2: 1.413324
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.598976 Loss1: 0.181769 Loss2: 1.417208
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562252 Loss1: 0.176426 Loss2: 1.385825
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551253 Loss1: 0.162440 Loss2: 1.388813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484489 Loss1: 0.102808 Loss2: 1.381681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443072 Loss1: 0.070160 Loss2: 1.372911 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451788 Loss1: 0.095084 Loss2: 1.356703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450601 Loss1: 0.082615 Loss2: 1.367986 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.410421 Loss1: 0.083539 Loss2: 1.326882 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362342 Loss1: 0.046012 Loss2: 1.316330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348575 Loss1: 0.041118 Loss2: 1.307457
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.430959 Loss1: 0.578115 Loss2: 1.852844
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.837201 Loss1: 0.436013 Loss2: 1.401188
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.694922 Loss1: 0.268780 Loss2: 1.426142
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.685136 Loss1: 0.284338 Loss2: 1.400798
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.684376 Loss1: 0.254625 Loss2: 1.429751
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.506693 Loss1: 0.691550 Loss2: 1.815143
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.572802 Loss1: 0.165609 Loss2: 1.407193
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.526392 Loss1: 0.129516 Loss2: 1.396877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.492558 Loss1: 0.103797 Loss2: 1.388761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472871 Loss1: 0.081432 Loss2: 1.391439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466325 Loss1: 0.082080 Loss2: 1.384245 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451499 Loss1: 0.097049 Loss2: 1.354450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404684 Loss1: 0.059461 Loss2: 1.345223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.610424 Loss1: 0.723084 Loss2: 1.887340
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399813 Loss1: 0.062068 Loss2: 1.337745
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.770041 Loss1: 0.331675 Loss2: 1.438366 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519311 Loss1: 0.124949 Loss2: 1.394363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.510230 Loss1: 0.133294 Loss2: 1.376936
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.672948 Loss1: 0.664282 Loss2: 2.008666
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513903 Loss1: 0.128009 Loss2: 1.385894
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.893027 Loss1: 0.410978 Loss2: 1.482050
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495132 Loss1: 0.113570 Loss2: 1.381562
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.796711 Loss1: 0.268443 Loss2: 1.528267
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451385 Loss1: 0.079835 Loss2: 1.371550
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.701215 Loss1: 0.227050 Loss2: 1.474165
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424487 Loss1: 0.057178 Loss2: 1.367310
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.738389 Loss1: 0.255976 Loss2: 1.482412
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.723810 Loss1: 0.220706 Loss2: 1.503105
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.651298 Loss1: 0.174211 Loss2: 1.477087
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.603990 Loss1: 0.134145 Loss2: 1.469845
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.560335 Loss1: 0.104353 Loss2: 1.455982
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.595425 Loss1: 0.828536 Loss2: 1.766888
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.562365 Loss1: 0.107179 Loss2: 1.455185
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.549496 Loss1: 0.252050 Loss2: 1.297446 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.408908 Loss1: 0.139304 Loss2: 1.269605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.361921 Loss1: 0.100792 Loss2: 1.261129
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.470962 Loss1: 0.621438 Loss2: 1.849525
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.370000 Loss1: 0.117628 Loss2: 1.252372
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.702137 Loss1: 0.299855 Loss2: 1.402282
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.338107 Loss1: 0.081316 Loss2: 1.256791
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.597181 Loss1: 0.193108 Loss2: 1.404073
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.522955 Loss1: 0.148352 Loss2: 1.374603 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.289688 Loss1: 0.042330 Loss2: 1.247359
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496078 Loss1: 0.119946 Loss2: 1.376132
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485887 Loss1: 0.115205 Loss2: 1.370682
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430697 Loss1: 0.068707 Loss2: 1.361990
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438794 Loss1: 0.080010 Loss2: 1.358783
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467830 Loss1: 0.107059 Loss2: 1.360771
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.485134 Loss1: 0.676166 Loss2: 1.808967
-(DefaultActor pid=3764) >> Training accuracy: 0.977539
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.910588 Loss1: 0.505440 Loss2: 1.405148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.637130 Loss1: 0.239985 Loss2: 1.397145 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497505 Loss1: 0.122276 Loss2: 1.375229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479093 Loss1: 0.104587 Loss2: 1.374506 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.463795 Loss1: 0.100143 Loss2: 1.363652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430054 Loss1: 0.071329 Loss2: 1.358725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400844 Loss1: 0.049614 Loss2: 1.351230 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.482718 Loss1: 0.107082 Loss2: 1.375636 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.500514 Loss1: 0.127091 Loss2: 1.373423 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462758 Loss1: 0.085261 Loss2: 1.377496 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634678 Loss1: 0.197803 Loss2: 1.436874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.560705 Loss1: 0.159816 Loss2: 1.400890 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.541345 Loss1: 0.136405 Loss2: 1.404941
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.516123 Loss1: 0.674082 Loss2: 1.842042
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.515678 Loss1: 0.116324 Loss2: 1.399354
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.855317 Loss1: 0.439564 Loss2: 1.415754
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.534992 Loss1: 0.135879 Loss2: 1.399113
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769353 Loss1: 0.318529 Loss2: 1.450824
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482143 Loss1: 0.086598 Loss2: 1.395545
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.691349 Loss1: 0.293082 Loss2: 1.398267
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.473519 Loss1: 0.084738 Loss2: 1.388780
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609290 Loss1: 0.208868 Loss2: 1.400422
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.552539 Loss1: 0.151835 Loss2: 1.400705
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.527999 Loss1: 0.142892 Loss2: 1.385107
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.528378 Loss1: 0.138886 Loss2: 1.389492
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.465829 Loss1: 0.082924 Loss2: 1.382905
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.666652 Loss1: 0.780839 Loss2: 1.885813
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.910419 Loss1: 0.483295 Loss2: 1.427124 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576720 Loss1: 0.194592 Loss2: 1.382128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478086 Loss1: 0.113188 Loss2: 1.364899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476474 Loss1: 0.114418 Loss2: 1.362056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449752 Loss1: 0.091313 Loss2: 1.358439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453812 Loss1: 0.098247 Loss2: 1.355564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447233 Loss1: 0.087445 Loss2: 1.359788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.534388 Loss1: 0.091617 Loss2: 1.442771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.499877 Loss1: 0.075443 Loss2: 1.424434 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.344288 Loss1: 0.585807 Loss2: 1.758481 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.605165 Loss1: 0.285585 Loss2: 1.319581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.448694 Loss1: 0.141274 Loss2: 1.307420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.634365 Loss1: 0.757333 Loss2: 1.877032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831294 Loss1: 0.455558 Loss2: 1.375736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.634300 Loss1: 0.269516 Loss2: 1.364784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604956 Loss1: 0.252555 Loss2: 1.352401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.593058 Loss1: 0.243657 Loss2: 1.349401 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988051
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361105 Loss1: 0.073358 Loss2: 1.287747
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.526702 Loss1: 0.172311 Loss2: 1.354391
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448036 Loss1: 0.107559 Loss2: 1.340477
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435607 Loss1: 0.092817 Loss2: 1.342790
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413820 Loss1: 0.087503 Loss2: 1.326317
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365785 Loss1: 0.047434 Loss2: 1.318351
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.447479 Loss1: 0.619099 Loss2: 1.828380
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.701858 Loss1: 0.361265 Loss2: 1.340593
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656521 Loss1: 0.290502 Loss2: 1.366019
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518902 Loss1: 0.180187 Loss2: 1.338715
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492417 Loss1: 0.163542 Loss2: 1.328875
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.513703 Loss1: 0.696139 Loss2: 1.817564
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504264 Loss1: 0.162368 Loss2: 1.341896
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.879491 Loss1: 0.514212 Loss2: 1.365280
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439858 Loss1: 0.110103 Loss2: 1.329755
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670381 Loss1: 0.274455 Loss2: 1.395927
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592415 Loss1: 0.244368 Loss2: 1.348047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515194 Loss1: 0.153896 Loss2: 1.361298 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386371 Loss1: 0.071012 Loss2: 1.315360
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446161 Loss1: 0.105388 Loss2: 1.340773
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425870 Loss1: 0.095166 Loss2: 1.330704
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.402953 Loss1: 0.073138 Loss2: 1.329815
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.375751 Loss1: 0.056599 Loss2: 1.319152
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.353278 Loss1: 0.040926 Loss2: 1.312352
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.556310 Loss1: 0.746182 Loss2: 1.810129
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.861586 Loss1: 0.509934 Loss2: 1.351652
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.694812 Loss1: 0.288768 Loss2: 1.406044
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533677 Loss1: 0.188994 Loss2: 1.344682
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480015 Loss1: 0.136109 Loss2: 1.343906
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.595967 Loss1: 0.755899 Loss2: 1.840068
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442697 Loss1: 0.113649 Loss2: 1.329048
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.834943 Loss1: 0.455016 Loss2: 1.379927
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.407292 Loss1: 0.083668 Loss2: 1.323624
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.723430 Loss1: 0.305814 Loss2: 1.417616
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411241 Loss1: 0.086882 Loss2: 1.324359
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636343 Loss1: 0.262986 Loss2: 1.373357
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423862 Loss1: 0.103761 Loss2: 1.320101
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.708423 Loss1: 0.319683 Loss2: 1.388740
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399794 Loss1: 0.079786 Loss2: 1.320008
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.526828 Loss1: 0.154085 Loss2: 1.372743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.456828 Loss1: 0.103254 Loss2: 1.353574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438644 Loss1: 0.086152 Loss2: 1.352492
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.695199 Loss1: 0.753900 Loss2: 1.941299
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.962132 Loss1: 0.504825 Loss2: 1.457307
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.779176 Loss1: 0.269303 Loss2: 1.509873
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647270 Loss1: 0.203056 Loss2: 1.444214
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.625421 Loss1: 0.172542 Loss2: 1.452879
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.812923 Loss1: 0.826814 Loss2: 1.986109
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.554246 Loss1: 0.116091 Loss2: 1.438155
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.538844 Loss1: 0.107667 Loss2: 1.431177 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.518952 Loss1: 0.097188 Loss2: 1.421764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.514979 Loss1: 0.086298 Loss2: 1.428681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.482947 Loss1: 0.062801 Loss2: 1.420146 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431585 Loss1: 0.074819 Loss2: 1.356766 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458823 Loss1: 0.100307 Loss2: 1.358516 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.580237 Loss1: 0.722077 Loss2: 1.858160
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.921303 Loss1: 0.526735 Loss2: 1.394567
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.701342 Loss1: 0.301798 Loss2: 1.399544
-DEBUG flwr 2023-10-11 19:56:44,136 | server.py:236 | fit_round 125 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.640326 Loss1: 0.281214 Loss2: 1.359112
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.568251 Loss1: 0.688993 Loss2: 1.879258
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.891278 Loss1: 0.483415 Loss2: 1.407863 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.754474 Loss1: 0.308849 Loss2: 1.445625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.596627 Loss1: 0.209369 Loss2: 1.387257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535304 Loss1: 0.144246 Loss2: 1.391059 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487329 Loss1: 0.109276 Loss2: 1.378053 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413032 Loss1: 0.072967 Loss2: 1.340065
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474035 Loss1: 0.106681 Loss2: 1.367354
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445301 Loss1: 0.078708 Loss2: 1.366593
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426694 Loss1: 0.067020 Loss2: 1.359674
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434779 Loss1: 0.078058 Loss2: 1.356720
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.661679 Loss1: 0.787220 Loss2: 1.874459
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.730982 Loss1: 0.329963 Loss2: 1.401018
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.649976 Loss1: 0.251339 Loss2: 1.398637
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.560467 Loss1: 0.181674 Loss2: 1.378794
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.571357 Loss1: 0.722489 Loss2: 1.848867
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.874796 Loss1: 0.488921 Loss2: 1.385875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727277 Loss1: 0.289628 Loss2: 1.437649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.587819 Loss1: 0.209329 Loss2: 1.378490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.622508 Loss1: 0.220041 Loss2: 1.402467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499882 Loss1: 0.114978 Loss2: 1.384904 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417692 Loss1: 0.066858 Loss2: 1.350834
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.495760 Loss1: 0.123168 Loss2: 1.372592
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467884 Loss1: 0.098093 Loss2: 1.369790
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450037 Loss1: 0.085522 Loss2: 1.364514
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422405 Loss1: 0.063771 Loss2: 1.358634
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.428784 Loss1: 0.655038 Loss2: 1.773746
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.679881 Loss1: 0.375336 Loss2: 1.304545
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.571767 Loss1: 0.240874 Loss2: 1.330892
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.503047 Loss1: 0.206636 Loss2: 1.296411
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.474169 Loss1: 0.639692 Loss2: 1.834477
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.849768 Loss1: 0.463120 Loss2: 1.386648 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.728493 Loss1: 0.280598 Loss2: 1.447895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526245 Loss1: 0.149040 Loss2: 1.377206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515162 Loss1: 0.141853 Loss2: 1.373309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487564 Loss1: 0.120090 Loss2: 1.367474 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.451017 Loss1: 0.086291 Loss2: 1.364727 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405422 Loss1: 0.052920 Loss2: 1.352502 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 19:56:44,136][flwr][DEBUG] - fit_round 125 received 50 results and 0 failures
-INFO flwr 2023-10-11 19:57:25,673 | server.py:125 | fit progress: (125, 2.2017363711667897, {'accuracy': 0.5871}, 288353.45192956197)
->> Test accuracy: 0.587100
-[2023-10-11 19:57:25,673][flwr][INFO] - fit progress: (125, 2.2017363711667897, {'accuracy': 0.5871}, 288353.45192956197)
-DEBUG flwr 2023-10-11 19:57:25,674 | server.py:173 | evaluate_round 125: strategy sampled 50 clients (out of 50)
-[2023-10-11 19:57:25,674][flwr][DEBUG] - evaluate_round 125: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 20:06:31,840 | server.py:187 | evaluate_round 125 received 50 results and 0 failures
-[2023-10-11 20:06:31,840][flwr][DEBUG] - evaluate_round 125 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 20:06:31,841 | server.py:222 | fit_round 126: strategy sampled 50 clients (out of 50)
-[2023-10-11 20:06:31,841][flwr][DEBUG] - fit_round 126: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.576233 Loss1: 0.737990 Loss2: 1.838243
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.758718 Loss1: 0.408260 Loss2: 1.350458
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645540 Loss1: 0.266268 Loss2: 1.379272
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.501947 Loss1: 0.162962 Loss2: 1.338986
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.438728 Loss1: 0.626583 Loss2: 1.812145
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.692581 Loss1: 0.356973 Loss2: 1.335608 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.574129 Loss1: 0.196592 Loss2: 1.377536 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.590264 Loss1: 0.248974 Loss2: 1.341290 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514117 Loss1: 0.162264 Loss2: 1.351853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486287 Loss1: 0.146203 Loss2: 1.340084 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.353976 Loss1: 0.045063 Loss2: 1.308913
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448255 Loss1: 0.115142 Loss2: 1.333112
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419721 Loss1: 0.087875 Loss2: 1.331846
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435119 Loss1: 0.113681 Loss2: 1.321438
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417398 Loss1: 0.093756 Loss2: 1.323642
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.566994 Loss1: 0.736652 Loss2: 1.830343
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.753369 Loss1: 0.377566 Loss2: 1.375804
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.659102 Loss1: 0.268109 Loss2: 1.390993
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.446415 Loss1: 0.584788 Loss2: 1.861627
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574309 Loss1: 0.219490 Loss2: 1.354819
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.823802 Loss1: 0.424000 Loss2: 1.399802
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.469061 Loss1: 0.118196 Loss2: 1.350865
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.712865 Loss1: 0.265763 Loss2: 1.447102
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475950 Loss1: 0.128557 Loss2: 1.347393
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.706162 Loss1: 0.312446 Loss2: 1.393716
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434797 Loss1: 0.099182 Loss2: 1.335616
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.597999 Loss1: 0.170301 Loss2: 1.427698
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432087 Loss1: 0.096683 Loss2: 1.335404
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538103 Loss1: 0.151898 Loss2: 1.386205
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399350 Loss1: 0.068727 Loss2: 1.330623
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.506557 Loss1: 0.115878 Loss2: 1.390679
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418944 Loss1: 0.093045 Loss2: 1.325898
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480395 Loss1: 0.099619 Loss2: 1.380776 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.603346 Loss1: 0.779272 Loss2: 1.824073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.665549 Loss1: 0.278051 Loss2: 1.387498 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.603689 Loss1: 0.253216 Loss2: 1.350472
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.693368 Loss1: 0.881772 Loss2: 1.811595
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.995763 Loss1: 0.558471 Loss2: 1.437292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.713724 Loss1: 0.335841 Loss2: 1.377883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.635941 Loss1: 0.269077 Loss2: 1.366864 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.594517 Loss1: 0.229790 Loss2: 1.364727 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.526301 Loss1: 0.181231 Loss2: 1.345070 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383778 Loss1: 0.056250 Loss2: 1.327527
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437304 Loss1: 0.099924 Loss2: 1.337380
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422715 Loss1: 0.090102 Loss2: 1.332613
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434120 Loss1: 0.102368 Loss2: 1.331752
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390500 Loss1: 0.059179 Loss2: 1.331320
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.964240 Loss1: 0.588465 Loss2: 1.375775 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.684814 Loss1: 0.279397 Loss2: 1.405418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.421508 Loss1: 0.624141 Loss2: 1.797367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484132 Loss1: 0.122516 Loss2: 1.361617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.507527 Loss1: 0.139974 Loss2: 1.367553 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.506473 Loss1: 0.135850 Loss2: 1.370623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451886 Loss1: 0.086881 Loss2: 1.365006 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.526876 Loss1: 0.162205 Loss2: 1.364671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424317 Loss1: 0.082018 Loss2: 1.342299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415847 Loss1: 0.075376 Loss2: 1.340471
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.539658 Loss1: 0.695641 Loss2: 1.844016
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415274 Loss1: 0.080876 Loss2: 1.334399
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.876511 Loss1: 0.476806 Loss2: 1.399706
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.729818 Loss1: 0.318676 Loss2: 1.411141
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.637369 Loss1: 0.243999 Loss2: 1.393370
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577472 Loss1: 0.193748 Loss2: 1.383724
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.543235 Loss1: 0.171455 Loss2: 1.371780
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.511715 Loss1: 0.666403 Loss2: 1.845312
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.506254 Loss1: 0.137594 Loss2: 1.368660
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452853 Loss1: 0.088874 Loss2: 1.363978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459807 Loss1: 0.100274 Loss2: 1.359533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438717 Loss1: 0.081605 Loss2: 1.357112 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.462793 Loss1: 0.123741 Loss2: 1.339052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381236 Loss1: 0.060656 Loss2: 1.320580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.558490 Loss1: 0.690075 Loss2: 1.868414 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.896186 Loss1: 0.462722 Loss2: 1.433464 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.619297 Loss1: 0.203394 Loss2: 1.415903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.633035 Loss1: 0.721014 Loss2: 1.912021 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.939519 Loss1: 0.505569 Loss2: 1.433950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.775895 Loss1: 0.297263 Loss2: 1.478631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.725024 Loss1: 0.291280 Loss2: 1.433744 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.628057 Loss1: 0.199593 Loss2: 1.428464 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.528187 Loss1: 0.124466 Loss2: 1.403721 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463945 Loss1: 0.076680 Loss2: 1.387265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478164 Loss1: 0.092045 Loss2: 1.386118
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.553133 Loss1: 0.735864 Loss2: 1.817269
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.780567 Loss1: 0.417174 Loss2: 1.363393
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.651324 Loss1: 0.264544 Loss2: 1.386781
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582880 Loss1: 0.234121 Loss2: 1.348758
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507381 Loss1: 0.155135 Loss2: 1.352246
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.493239 Loss1: 0.668339 Loss2: 1.824899
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.755032 Loss1: 0.415139 Loss2: 1.339892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.667549 Loss1: 0.290805 Loss2: 1.376744 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.525095 Loss1: 0.196360 Loss2: 1.328735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.498235 Loss1: 0.168276 Loss2: 1.329959 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400435 Loss1: 0.074802 Loss2: 1.325632
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479262 Loss1: 0.149193 Loss2: 1.330069
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.462233 Loss1: 0.144279 Loss2: 1.317955
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.403353 Loss1: 0.080352 Loss2: 1.323001
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413811 Loss1: 0.095551 Loss2: 1.318260
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402607 Loss1: 0.089541 Loss2: 1.313067
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.648821 Loss1: 0.772100 Loss2: 1.876720
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.961799 Loss1: 0.556402 Loss2: 1.405397
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.762426 Loss1: 0.307111 Loss2: 1.455315
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.670340 Loss1: 0.265529 Loss2: 1.404811
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.606847 Loss1: 0.200291 Loss2: 1.406556
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.539589 Loss1: 0.712893 Loss2: 1.826696
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.560505 Loss1: 0.174194 Loss2: 1.386311
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.698966 Loss1: 0.359301 Loss2: 1.339665
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518984 Loss1: 0.134498 Loss2: 1.384487
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.557853 Loss1: 0.196146 Loss2: 1.361707
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495586 Loss1: 0.120150 Loss2: 1.375436
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520846 Loss1: 0.186164 Loss2: 1.334682
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461326 Loss1: 0.087186 Loss2: 1.374140
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.444367 Loss1: 0.107929 Loss2: 1.336438
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431927 Loss1: 0.063507 Loss2: 1.368420
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431814 Loss1: 0.109807 Loss2: 1.322008
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444662 Loss1: 0.118883 Loss2: 1.325779
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471583 Loss1: 0.145405 Loss2: 1.326179
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.492869 Loss1: 0.157765 Loss2: 1.335104
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434740 Loss1: 0.104955 Loss2: 1.329785
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.846644 Loss1: 0.845763 Loss2: 2.000882
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.890285 Loss1: 0.426706 Loss2: 1.463579
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.742048 Loss1: 0.267860 Loss2: 1.474189
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606773 Loss1: 0.167983 Loss2: 1.438790
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525510 Loss1: 0.092093 Loss2: 1.433417
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509141 Loss1: 0.085762 Loss2: 1.423379 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491997 Loss1: 0.073422 Loss2: 1.418575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.472251 Loss1: 0.060449 Loss2: 1.411802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.483922 Loss1: 0.081511 Loss2: 1.402410 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462467 Loss1: 0.057332 Loss2: 1.405135 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.529910 Loss1: 0.081726 Loss2: 1.448185 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.475076 Loss1: 0.037617 Loss2: 1.437459 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.968935 Loss1: 0.564095 Loss2: 1.404840 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.632924 Loss1: 0.248815 Loss2: 1.384109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567422 Loss1: 0.181060 Loss2: 1.386362
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.694652 Loss1: 0.777791 Loss2: 1.916861
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.544479 Loss1: 0.168263 Loss2: 1.376216
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.814107 Loss1: 0.401890 Loss2: 1.412217
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485988 Loss1: 0.110984 Loss2: 1.375005
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635201 Loss1: 0.190676 Loss2: 1.444525
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456615 Loss1: 0.095710 Loss2: 1.360905
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595869 Loss1: 0.195260 Loss2: 1.400609
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444594 Loss1: 0.083905 Loss2: 1.360689
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.581909 Loss1: 0.183454 Loss2: 1.398455
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390078 Loss1: 0.042159 Loss2: 1.347919
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522263 Loss1: 0.116176 Loss2: 1.406087
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480012 Loss1: 0.082967 Loss2: 1.397045
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465195 Loss1: 0.075357 Loss2: 1.389838
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432895 Loss1: 0.048340 Loss2: 1.384555
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419471 Loss1: 0.042207 Loss2: 1.377264
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.503084 Loss1: 0.675934 Loss2: 1.827150
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.758215 Loss1: 0.411220 Loss2: 1.346995
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.701078 Loss1: 0.309468 Loss2: 1.391610
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500626 Loss1: 0.160638 Loss2: 1.339988
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.538635 Loss1: 0.200986 Loss2: 1.337648
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488863 Loss1: 0.140963 Loss2: 1.347899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436138 Loss1: 0.112429 Loss2: 1.323709 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.396105 Loss1: 0.077686 Loss2: 1.318419 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371159 Loss1: 0.060115 Loss2: 1.311044 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350204 Loss1: 0.048428 Loss2: 1.301777 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.505212 Loss1: 0.130329 Loss2: 1.374882 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.455773 Loss1: 0.092807 Loss2: 1.362966 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.858417 Loss1: 0.470740 Loss2: 1.387677 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649431 Loss1: 0.259479 Loss2: 1.389951 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558558 Loss1: 0.160137 Loss2: 1.398421
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.554114 Loss1: 0.693040 Loss2: 1.861074
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499131 Loss1: 0.116805 Loss2: 1.382325
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.850839 Loss1: 0.465090 Loss2: 1.385748
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478005 Loss1: 0.108440 Loss2: 1.369565
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.662352 Loss1: 0.237677 Loss2: 1.424674
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.502845 Loss1: 0.135170 Loss2: 1.367675
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.616693 Loss1: 0.231901 Loss2: 1.384792
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.491447 Loss1: 0.112164 Loss2: 1.379283
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514521 Loss1: 0.132382 Loss2: 1.382140
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435050 Loss1: 0.066832 Loss2: 1.368218
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.475226 Loss1: 0.112420 Loss2: 1.362806
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.432802 Loss1: 0.080377 Loss2: 1.352425
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433443 Loss1: 0.078768 Loss2: 1.354675
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416613 Loss1: 0.069500 Loss2: 1.347113
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414620 Loss1: 0.069582 Loss2: 1.345038
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.652483 Loss1: 0.794655 Loss2: 1.857828
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.859810 Loss1: 0.472427 Loss2: 1.387383
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716220 Loss1: 0.286431 Loss2: 1.429789
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602782 Loss1: 0.232795 Loss2: 1.369988
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551370 Loss1: 0.171860 Loss2: 1.379510
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.529841 Loss1: 0.161880 Loss2: 1.367961 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486982 Loss1: 0.124357 Loss2: 1.362625 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.478200 Loss1: 0.117872 Loss2: 1.360328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423863 Loss1: 0.072257 Loss2: 1.351607 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421308 Loss1: 0.072933 Loss2: 1.348375 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494053 Loss1: 0.107308 Loss2: 1.386745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.503449 Loss1: 0.115433 Loss2: 1.388016 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.420911 Loss1: 0.571382 Loss2: 1.849529
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.451935 Loss1: 0.063236 Loss2: 1.388699
-(DefaultActor pid=3764) >> Training accuracy: 0.990809
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655162 Loss1: 0.258564 Loss2: 1.396598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.538820 Loss1: 0.182248 Loss2: 1.356573 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468756 Loss1: 0.111261 Loss2: 1.357495
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.527094 Loss1: 0.652678 Loss2: 1.874416
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.827060 Loss1: 0.391150 Loss2: 1.435910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.724422 Loss1: 0.269608 Loss2: 1.454815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.629331 Loss1: 0.213189 Loss2: 1.416141 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501951 Loss1: 0.097154 Loss2: 1.404796 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434604 Loss1: 0.042554 Loss2: 1.392051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439165 Loss1: 0.053471 Loss2: 1.385694
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.404572 Loss1: 0.628497 Loss2: 1.776074
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425572 Loss1: 0.043831 Loss2: 1.381741
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.741664 Loss1: 0.402912 Loss2: 1.338752
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573865 Loss1: 0.200699 Loss2: 1.373165
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.507935 Loss1: 0.182561 Loss2: 1.325374
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490135 Loss1: 0.161662 Loss2: 1.328473
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458423 Loss1: 0.132144 Loss2: 1.326279
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.726102 Loss1: 0.846014 Loss2: 1.880088
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.438317 Loss1: 0.116099 Loss2: 1.322218
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.386763 Loss1: 0.065789 Loss2: 1.320974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.367095 Loss1: 0.056770 Loss2: 1.310325 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.345234 Loss1: 0.038527 Loss2: 1.306707 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.453493 Loss1: 0.087205 Loss2: 1.366287 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483651 Loss1: 0.115871 Loss2: 1.367780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.634151 Loss1: 0.766309 Loss2: 1.867842 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.688493 Loss1: 0.274683 Loss2: 1.413810 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.549706 Loss1: 0.172501 Loss2: 1.377205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508424 Loss1: 0.151624 Loss2: 1.356799
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.702889 Loss1: 0.775048 Loss2: 1.927840
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.914064 Loss1: 0.532431 Loss2: 1.381633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462153 Loss1: 0.124656 Loss2: 1.337497
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.832541 Loss1: 0.373484 Loss2: 1.459057
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.689266 Loss1: 0.285762 Loss2: 1.403503
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407697 Loss1: 0.066478 Loss2: 1.341219
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.582988 Loss1: 0.194992 Loss2: 1.387996
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410155 Loss1: 0.075403 Loss2: 1.334753
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516517 Loss1: 0.140438 Loss2: 1.376079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442638 Loss1: 0.074550 Loss2: 1.368088 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.575639 Loss1: 0.685059 Loss2: 1.890581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.810867 Loss1: 0.331230 Loss2: 1.479637 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.645711 Loss1: 0.224419 Loss2: 1.421292 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.591227 Loss1: 0.190305 Loss2: 1.400922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.515908 Loss1: 0.127189 Loss2: 1.388720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.490906 Loss1: 0.101709 Loss2: 1.389197 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460463 Loss1: 0.082603 Loss2: 1.377860 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469435 Loss1: 0.096383 Loss2: 1.373052 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.517701 Loss1: 0.116715 Loss2: 1.400986 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.531535 Loss1: 0.127176 Loss2: 1.404359 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.856308 Loss1: 0.468775 Loss2: 1.387533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.532314 Loss1: 0.158133 Loss2: 1.374181 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.501449 Loss1: 0.129078 Loss2: 1.372371
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.753703 Loss1: 0.848536 Loss2: 1.905167
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449723 Loss1: 0.084580 Loss2: 1.365143
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831128 Loss1: 0.442497 Loss2: 1.388631
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458223 Loss1: 0.099413 Loss2: 1.358810
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653713 Loss1: 0.277777 Loss2: 1.375937
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459217 Loss1: 0.099258 Loss2: 1.359959
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.585176 Loss1: 0.231194 Loss2: 1.353982
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423620 Loss1: 0.069941 Loss2: 1.353679
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509432 Loss1: 0.156640 Loss2: 1.352792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412570 Loss1: 0.061782 Loss2: 1.350789
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467587 Loss1: 0.128848 Loss2: 1.338739
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450885 Loss1: 0.115647 Loss2: 1.335238
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422450 Loss1: 0.085927 Loss2: 1.336523
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385227 Loss1: 0.062700 Loss2: 1.322528
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384963 Loss1: 0.063311 Loss2: 1.321652
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.727946 Loss1: 0.849127 Loss2: 1.878819
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.872898 Loss1: 0.511907 Loss2: 1.360991
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.794145 Loss1: 0.370876 Loss2: 1.423269
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.579518 Loss1: 0.225508 Loss2: 1.354010
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.573423 Loss1: 0.221743 Loss2: 1.351680
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.682797 Loss1: 0.785840 Loss2: 1.896957
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509522 Loss1: 0.144503 Loss2: 1.365019
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448809 Loss1: 0.107114 Loss2: 1.341695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.468175 Loss1: 0.134561 Loss2: 1.333615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.426903 Loss1: 0.097574 Loss2: 1.329329 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396896 Loss1: 0.073153 Loss2: 1.323743 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463863 Loss1: 0.099761 Loss2: 1.364103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422160 Loss1: 0.074030 Loss2: 1.348131 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385201 Loss1: 0.039117 Loss2: 1.346084
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.407709 Loss1: 0.597306 Loss2: 1.810403
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.705992 Loss1: 0.378479 Loss2: 1.327513
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.620700 Loss1: 0.250855 Loss2: 1.369845
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526376 Loss1: 0.197666 Loss2: 1.328710
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.523153 Loss1: 0.199185 Loss2: 1.323967
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.561553 Loss1: 0.683331 Loss2: 1.878221
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456206 Loss1: 0.133751 Loss2: 1.322454
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831774 Loss1: 0.437528 Loss2: 1.394246
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402556 Loss1: 0.096547 Loss2: 1.306009
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.703274 Loss1: 0.280136 Loss2: 1.423138
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.369942 Loss1: 0.067706 Loss2: 1.302236
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.623322 Loss1: 0.236326 Loss2: 1.386996
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395453 Loss1: 0.096563 Loss2: 1.298890
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.606637 Loss1: 0.206119 Loss2: 1.400518
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375142 Loss1: 0.069617 Loss2: 1.305525
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542601 Loss1: 0.153953 Loss2: 1.388648
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490085 Loss1: 0.111946 Loss2: 1.378138
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470723 Loss1: 0.105020 Loss2: 1.365702
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464880 Loss1: 0.097563 Loss2: 1.367317
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446903 Loss1: 0.079340 Loss2: 1.367563
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.515608 Loss1: 0.633791 Loss2: 1.881817
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.912815 Loss1: 0.537647 Loss2: 1.375168
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.807549 Loss1: 0.331665 Loss2: 1.475884
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.632011 Loss1: 0.262714 Loss2: 1.369297
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.603952 Loss1: 0.212615 Loss2: 1.391337
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.555047 Loss1: 0.167393 Loss2: 1.387654 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473493 Loss1: 0.106234 Loss2: 1.367259 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439327 Loss1: 0.077356 Loss2: 1.361971 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413413 Loss1: 0.058235 Loss2: 1.355179 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407803 Loss1: 0.060140 Loss2: 1.347663 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485048 Loss1: 0.117053 Loss2: 1.367995 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414556 Loss1: 0.058728 Loss2: 1.355827 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.936945 Loss1: 0.552629 Loss2: 1.384315 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580682 Loss1: 0.205631 Loss2: 1.375050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.650185 Loss1: 0.729755 Loss2: 1.920430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.959645 Loss1: 0.510039 Loss2: 1.449606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421062 Loss1: 0.070189 Loss2: 1.350873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.393459 Loss1: 0.048075 Loss2: 1.345383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414607 Loss1: 0.075849 Loss2: 1.338759 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.541367 Loss1: 0.117472 Loss2: 1.423895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490014 Loss1: 0.082355 Loss2: 1.407659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.629200 Loss1: 0.790299 Loss2: 1.838901 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644126 Loss1: 0.264353 Loss2: 1.379772 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536617 Loss1: 0.188099 Loss2: 1.348518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.482897 Loss1: 0.150011 Loss2: 1.332886
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.751527 Loss1: 0.825127 Loss2: 1.926400
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.947000 Loss1: 0.515550 Loss2: 1.431449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.811136 Loss1: 0.332213 Loss2: 1.478922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.711525 Loss1: 0.283842 Loss2: 1.427684 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.344993 Loss1: 0.039235 Loss2: 1.305758
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.643640 Loss1: 0.204556 Loss2: 1.439084
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.620827 Loss1: 0.201564 Loss2: 1.419264
-DEBUG flwr 2023-10-11 20:34:53,769 | server.py:236 | fit_round 126 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.565481 Loss1: 0.151177 Loss2: 1.414305
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.549699 Loss1: 0.139182 Loss2: 1.410517
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.549709 Loss1: 0.139748 Loss2: 1.409961
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.559834 Loss1: 0.727963 Loss2: 1.831872
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.504092 Loss1: 0.097287 Loss2: 1.406805
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700980 Loss1: 0.275516 Loss2: 1.425464 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514027 Loss1: 0.149247 Loss2: 1.364779 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492748 Loss1: 0.144663 Loss2: 1.348085
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.401730 Loss1: 0.620403 Loss2: 1.781327
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.725184 Loss1: 0.383448 Loss2: 1.341736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.662588 Loss1: 0.291541 Loss2: 1.371047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529670 Loss1: 0.182996 Loss2: 1.346673 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.458272 Loss1: 0.121178 Loss2: 1.337094 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.417345 Loss1: 0.098010 Loss2: 1.319335 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.696757 Loss1: 0.765834 Loss2: 1.930923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.924863 Loss1: 0.521899 Loss2: 1.402964 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610065 Loss1: 0.205370 Loss2: 1.404695 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551112 Loss1: 0.150764 Loss2: 1.400348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.508168 Loss1: 0.678223 Loss2: 1.829945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.779188 Loss1: 0.402686 Loss2: 1.376502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416617 Loss1: 0.055817 Loss2: 1.360800 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515618 Loss1: 0.148752 Loss2: 1.366865 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476086 Loss1: 0.115443 Loss2: 1.360643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394448 Loss1: 0.055389 Loss2: 1.339059 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 20:34:53,769][flwr][DEBUG] - fit_round 126 received 50 results and 0 failures
-INFO flwr 2023-10-11 20:35:35,315 | server.py:125 | fit progress: (126, 2.198208836701731, {'accuracy': 0.5847}, 290643.09371704096)
->> Test accuracy: 0.584700
-[2023-10-11 20:35:35,315][flwr][INFO] - fit progress: (126, 2.198208836701731, {'accuracy': 0.5847}, 290643.09371704096)
-DEBUG flwr 2023-10-11 20:35:35,316 | server.py:173 | evaluate_round 126: strategy sampled 50 clients (out of 50)
-[2023-10-11 20:35:35,316][flwr][DEBUG] - evaluate_round 126: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 20:44:42,301 | server.py:187 | evaluate_round 126 received 50 results and 0 failures
-[2023-10-11 20:44:42,301][flwr][DEBUG] - evaluate_round 126 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 20:44:42,301 | server.py:222 | fit_round 127: strategy sampled 50 clients (out of 50)
-[2023-10-11 20:44:42,301][flwr][DEBUG] - fit_round 127: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.525568 Loss1: 0.699176 Loss2: 1.826392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.704776 Loss1: 0.277625 Loss2: 1.427151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564030 Loss1: 0.202734 Loss2: 1.361296
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.522604 Loss1: 0.730080 Loss2: 1.792524
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574586 Loss1: 0.204529 Loss2: 1.370057
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.774397 Loss1: 0.434035 Loss2: 1.340363
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.512585 Loss1: 0.142214 Loss2: 1.370371
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.665365 Loss1: 0.311559 Loss2: 1.353806
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500436 Loss1: 0.135085 Loss2: 1.365350
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.522490 Loss1: 0.198099 Loss2: 1.324391
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440150 Loss1: 0.087518 Loss2: 1.352632
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.457749 Loss1: 0.134025 Loss2: 1.323724
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.446388 Loss1: 0.095746 Loss2: 1.350642
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.410850 Loss1: 0.103747 Loss2: 1.307102
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428935 Loss1: 0.080479 Loss2: 1.348456
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.369155 Loss1: 0.064745 Loss2: 1.304410
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368963 Loss1: 0.071027 Loss2: 1.297936
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.375224 Loss1: 0.071662 Loss2: 1.303562
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385644 Loss1: 0.091263 Loss2: 1.294381
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.388922 Loss1: 0.599842 Loss2: 1.789080
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.727892 Loss1: 0.373078 Loss2: 1.354814
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.662982 Loss1: 0.276420 Loss2: 1.386562
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.633195 Loss1: 0.753618 Loss2: 1.879577
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558511 Loss1: 0.190521 Loss2: 1.367991
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.820743 Loss1: 0.471857 Loss2: 1.348886
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519525 Loss1: 0.161409 Loss2: 1.358116
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463247 Loss1: 0.113947 Loss2: 1.349301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448926 Loss1: 0.104583 Loss2: 1.344343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.468472 Loss1: 0.126933 Loss2: 1.341539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.457287 Loss1: 0.108597 Loss2: 1.348690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429382 Loss1: 0.090178 Loss2: 1.339204 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391892 Loss1: 0.066557 Loss2: 1.325336 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982143
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.611079 Loss1: 0.710618 Loss2: 1.900461
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.795369 Loss1: 0.387256 Loss2: 1.408112
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.681706 Loss1: 0.254608 Loss2: 1.427098
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604827 Loss1: 0.203275 Loss2: 1.401552
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.636918 Loss1: 0.770781 Loss2: 1.866137
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574346 Loss1: 0.168127 Loss2: 1.406218
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.855455 Loss1: 0.435981 Loss2: 1.419473
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526094 Loss1: 0.119064 Loss2: 1.407030
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.640359 Loss1: 0.247684 Loss2: 1.392675
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516014 Loss1: 0.123630 Loss2: 1.392384
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528040 Loss1: 0.167632 Loss2: 1.360409
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484193 Loss1: 0.095480 Loss2: 1.388714
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.457500 Loss1: 0.099057 Loss2: 1.358443
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463986 Loss1: 0.081157 Loss2: 1.382829
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.435263 Loss1: 0.088302 Loss2: 1.346960
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.455837 Loss1: 0.073839 Loss2: 1.381998
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.396457 Loss1: 0.065242 Loss2: 1.331215
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388214 Loss1: 0.060505 Loss2: 1.327709
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366551 Loss1: 0.042348 Loss2: 1.324204
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352390 Loss1: 0.032659 Loss2: 1.319731
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.547452 Loss1: 0.658024 Loss2: 1.889428
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751920 Loss1: 0.335823 Loss2: 1.416097
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.605812 Loss1: 0.177164 Loss2: 1.428648
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.569390 Loss1: 0.667282 Loss2: 1.902109
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582365 Loss1: 0.184518 Loss2: 1.397848
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.025953 Loss1: 0.595612 Loss2: 1.430340
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.557206 Loss1: 0.149015 Loss2: 1.408191
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.730824 Loss1: 0.262968 Loss2: 1.467856
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513852 Loss1: 0.120785 Loss2: 1.393067
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480575 Loss1: 0.089371 Loss2: 1.391204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.487382 Loss1: 0.102084 Loss2: 1.385298 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.496193 Loss1: 0.111694 Loss2: 1.384499 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.488167 Loss1: 0.094356 Loss2: 1.393811 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973633
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.474505 Loss1: 0.085263 Loss2: 1.389243 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.540514 Loss1: 0.712497 Loss2: 1.828017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.661631 Loss1: 0.266057 Loss2: 1.395574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541474 Loss1: 0.184577 Loss2: 1.356897
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.395909 Loss1: 0.596814 Loss2: 1.799095
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521053 Loss1: 0.163312 Loss2: 1.357741
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.706806 Loss1: 0.378947 Loss2: 1.327859
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.469830 Loss1: 0.119203 Loss2: 1.350627
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636123 Loss1: 0.270532 Loss2: 1.365591
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476432 Loss1: 0.136655 Loss2: 1.339776
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498197 Loss1: 0.174923 Loss2: 1.323273
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432821 Loss1: 0.091115 Loss2: 1.341706
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.438033 Loss1: 0.114739 Loss2: 1.323294
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414621 Loss1: 0.080801 Loss2: 1.333820
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.406639 Loss1: 0.093880 Loss2: 1.312759
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390543 Loss1: 0.059892 Loss2: 1.330651
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.382916 Loss1: 0.076479 Loss2: 1.306437
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376567 Loss1: 0.074878 Loss2: 1.301689
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371295 Loss1: 0.074451 Loss2: 1.296844
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.349724 Loss1: 0.050309 Loss2: 1.299415
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.491964 Loss1: 0.626872 Loss2: 1.865093
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.801854 Loss1: 0.426374 Loss2: 1.375480
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.675787 Loss1: 0.270769 Loss2: 1.405018
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.578701 Loss1: 0.746275 Loss2: 1.832426
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.577871 Loss1: 0.203237 Loss2: 1.374635
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.799041 Loss1: 0.479267 Loss2: 1.319774
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546422 Loss1: 0.169365 Loss2: 1.377057
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488776 Loss1: 0.124356 Loss2: 1.364420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439545 Loss1: 0.081460 Loss2: 1.358085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456777 Loss1: 0.102289 Loss2: 1.354488 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413855 Loss1: 0.061024 Loss2: 1.352831 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410852 Loss1: 0.061295 Loss2: 1.349557 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382757 Loss1: 0.086601 Loss2: 1.296155 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987981
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.719044 Loss1: 0.788659 Loss2: 1.930385
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.843193 Loss1: 0.465439 Loss2: 1.377755
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.669224 Loss1: 0.250214 Loss2: 1.419009
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.509262 Loss1: 0.135288 Loss2: 1.373974
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.586657 Loss1: 0.748724 Loss2: 1.837933
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.802780 Loss1: 0.442253 Loss2: 1.360528 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.655973 Loss1: 0.265436 Loss2: 1.390537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526673 Loss1: 0.180793 Loss2: 1.345881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501826 Loss1: 0.149815 Loss2: 1.352011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444794 Loss1: 0.107091 Loss2: 1.337704 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432733 Loss1: 0.099849 Loss2: 1.332884 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413323 Loss1: 0.088550 Loss2: 1.324773 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.715510 Loss1: 0.335338 Loss2: 1.380172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.471584 Loss1: 0.112607 Loss2: 1.358977 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.425916 Loss1: 0.073029 Loss2: 1.352887
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.579105 Loss1: 0.695877 Loss2: 1.883227
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.424408 Loss1: 0.074316 Loss2: 1.350092
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.800975 Loss1: 0.406808 Loss2: 1.394166
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.430722 Loss1: 0.090320 Loss2: 1.340402
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.714343 Loss1: 0.268462 Loss2: 1.445881
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418584 Loss1: 0.073044 Loss2: 1.345541
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.560579 Loss1: 0.200633 Loss2: 1.359947
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418179 Loss1: 0.078486 Loss2: 1.339693
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502654 Loss1: 0.125797 Loss2: 1.376857
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403833 Loss1: 0.060772 Loss2: 1.343061
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521416 Loss1: 0.153964 Loss2: 1.367452
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.442848 Loss1: 0.084175 Loss2: 1.358673
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441750 Loss1: 0.083445 Loss2: 1.358305
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402790 Loss1: 0.054491 Loss2: 1.348299
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398543 Loss1: 0.052240 Loss2: 1.346304
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.545948 Loss1: 0.667091 Loss2: 1.878858
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.788416 Loss1: 0.350036 Loss2: 1.438380
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.652706 Loss1: 0.227278 Loss2: 1.425429
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569287 Loss1: 0.163011 Loss2: 1.406276
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.549522 Loss1: 0.754477 Loss2: 1.795044
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.701076 Loss1: 0.351614 Loss2: 1.349462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.640570 Loss1: 0.262881 Loss2: 1.377689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.568953 Loss1: 0.216242 Loss2: 1.352711 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.553436 Loss1: 0.207313 Loss2: 1.346122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501921 Loss1: 0.150261 Loss2: 1.351660 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430663 Loss1: 0.093018 Loss2: 1.337645 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389775 Loss1: 0.073820 Loss2: 1.315955 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.722820 Loss1: 0.346398 Loss2: 1.376422 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.645723 Loss1: 0.271541 Loss2: 1.374182 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.599501 Loss1: 0.221965 Loss2: 1.377535
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.571355 Loss1: 0.742934 Loss2: 1.828420
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.574016 Loss1: 0.200560 Loss2: 1.373456
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.830961 Loss1: 0.412030 Loss2: 1.418931
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502255 Loss1: 0.137446 Loss2: 1.364809
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.660452 Loss1: 0.264675 Loss2: 1.395777
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538590 Loss1: 0.160901 Loss2: 1.377689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516817 Loss1: 0.145518 Loss2: 1.371299 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452273 Loss1: 0.097517 Loss2: 1.354755
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480126 Loss1: 0.111433 Loss2: 1.368693
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464226 Loss1: 0.109502 Loss2: 1.354724
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471110 Loss1: 0.114097 Loss2: 1.357013
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.475435 Loss1: 0.110478 Loss2: 1.364958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458819 Loss1: 0.101465 Loss2: 1.357353
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.491673 Loss1: 0.672935 Loss2: 1.818738
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.739653 Loss1: 0.379696 Loss2: 1.359957
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.619428 Loss1: 0.242157 Loss2: 1.377271
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564408 Loss1: 0.219252 Loss2: 1.345157
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.570800 Loss1: 0.218728 Loss2: 1.352072
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.408853 Loss1: 0.565349 Loss2: 1.843504
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.726245 Loss1: 0.341613 Loss2: 1.384632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578678 Loss1: 0.175907 Loss2: 1.402771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.548021 Loss1: 0.178281 Loss2: 1.369740 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.453530 Loss1: 0.086966 Loss2: 1.366564 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367168 Loss1: 0.048011 Loss2: 1.319157
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443586 Loss1: 0.081306 Loss2: 1.362281
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436996 Loss1: 0.083230 Loss2: 1.353766
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423756 Loss1: 0.063762 Loss2: 1.359995
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395106 Loss1: 0.045989 Loss2: 1.349117
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407579 Loss1: 0.061879 Loss2: 1.345701
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.592214 Loss1: 0.752417 Loss2: 1.839798
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.835691 Loss1: 0.456641 Loss2: 1.379051
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.715236 Loss1: 0.280220 Loss2: 1.435017
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555928 Loss1: 0.183989 Loss2: 1.371939
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.540804 Loss1: 0.173176 Loss2: 1.367628
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.532104 Loss1: 0.678629 Loss2: 1.853475
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454420 Loss1: 0.090768 Loss2: 1.363653
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442271 Loss1: 0.089013 Loss2: 1.353258 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425223 Loss1: 0.080917 Loss2: 1.344306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459496 Loss1: 0.118732 Loss2: 1.340764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443738 Loss1: 0.084422 Loss2: 1.359315 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431900 Loss1: 0.091318 Loss2: 1.340581 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432261 Loss1: 0.094379 Loss2: 1.337882 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422737 Loss1: 0.087809 Loss2: 1.334928
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.612245 Loss1: 0.828738 Loss2: 1.783507
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.771228 Loss1: 0.411924 Loss2: 1.359304
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.627638 Loss1: 0.256612 Loss2: 1.371027
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.567145 Loss1: 0.238830 Loss2: 1.328315
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514651 Loss1: 0.174057 Loss2: 1.340594
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.752710 Loss1: 0.802247 Loss2: 1.950463
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514264 Loss1: 0.192539 Loss2: 1.321726
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449959 Loss1: 0.124136 Loss2: 1.325824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453440 Loss1: 0.133254 Loss2: 1.320186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541174 Loss1: 0.169075 Loss2: 1.372098 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.488198 Loss1: 0.130500 Loss2: 1.357698 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466951 Loss1: 0.122096 Loss2: 1.344855 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443359 Loss1: 0.101970 Loss2: 1.341389 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.548158 Loss1: 0.684940 Loss2: 1.863218
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.747742 Loss1: 0.376106 Loss2: 1.371636
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.746824 Loss1: 0.325425 Loss2: 1.421399
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.607054 Loss1: 0.242438 Loss2: 1.364616
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.517987 Loss1: 0.659869 Loss2: 1.858118
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571559 Loss1: 0.195910 Loss2: 1.375649
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.775040 Loss1: 0.405750 Loss2: 1.369291
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505489 Loss1: 0.137655 Loss2: 1.367834
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635522 Loss1: 0.230791 Loss2: 1.404731
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469275 Loss1: 0.115424 Loss2: 1.353851
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577904 Loss1: 0.217150 Loss2: 1.360754
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474155 Loss1: 0.124630 Loss2: 1.349524
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550739 Loss1: 0.174327 Loss2: 1.376412
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451070 Loss1: 0.097681 Loss2: 1.353389
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472281 Loss1: 0.112090 Loss2: 1.360191
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411411 Loss1: 0.068435 Loss2: 1.342976
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438297 Loss1: 0.087438 Loss2: 1.350858
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442303 Loss1: 0.093921 Loss2: 1.348382
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425553 Loss1: 0.082403 Loss2: 1.343150
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.432706 Loss1: 0.083537 Loss2: 1.349169
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.526477 Loss1: 0.706844 Loss2: 1.819633
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.849426 Loss1: 0.489375 Loss2: 1.360051
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667547 Loss1: 0.268779 Loss2: 1.398769
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587707 Loss1: 0.237017 Loss2: 1.350689
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.532842 Loss1: 0.699631 Loss2: 1.833210
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.775182 Loss1: 0.409518 Loss2: 1.365663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670994 Loss1: 0.264531 Loss2: 1.406462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535234 Loss1: 0.175785 Loss2: 1.359449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484852 Loss1: 0.129702 Loss2: 1.355150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492116 Loss1: 0.142274 Loss2: 1.349843 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413932 Loss1: 0.079845 Loss2: 1.334087
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468012 Loss1: 0.122120 Loss2: 1.345892
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436232 Loss1: 0.086979 Loss2: 1.349253
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432986 Loss1: 0.092879 Loss2: 1.340106
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399608 Loss1: 0.065571 Loss2: 1.334038
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.409688 Loss1: 0.589763 Loss2: 1.819925
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.886273 Loss1: 0.500986 Loss2: 1.385288
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.746138 Loss1: 0.294607 Loss2: 1.451531
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.681420 Loss1: 0.278469 Loss2: 1.402951
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.607254 Loss1: 0.698314 Loss2: 1.908940
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.692520 Loss1: 0.278386 Loss2: 1.414135
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.823869 Loss1: 0.384971 Loss2: 1.438898
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.546523 Loss1: 0.155322 Loss2: 1.391201
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.796057 Loss1: 0.345619 Loss2: 1.450438
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.712115 Loss1: 0.276313 Loss2: 1.435802
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487714 Loss1: 0.112972 Loss2: 1.374742
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.644906 Loss1: 0.215317 Loss2: 1.429589
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453578 Loss1: 0.081337 Loss2: 1.372241
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.576494 Loss1: 0.157985 Loss2: 1.418509
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425191 Loss1: 0.061060 Loss2: 1.364131
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.512843 Loss1: 0.114665 Loss2: 1.398178
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399740 Loss1: 0.045756 Loss2: 1.353984
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.471502 Loss1: 0.078059 Loss2: 1.393443 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.621932 Loss1: 0.806512 Loss2: 1.815420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.623633 Loss1: 0.255018 Loss2: 1.368615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518446 Loss1: 0.187366 Loss2: 1.331081
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.488589 Loss1: 0.643935 Loss2: 1.844654
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.775718 Loss1: 0.429285 Loss2: 1.346433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.637590 Loss1: 0.259464 Loss2: 1.378126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.593398 Loss1: 0.257796 Loss2: 1.335602 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518272 Loss1: 0.177349 Loss2: 1.340923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485107 Loss1: 0.144742 Loss2: 1.340365 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419554 Loss1: 0.103983 Loss2: 1.315571
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455895 Loss1: 0.132739 Loss2: 1.323156
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427368 Loss1: 0.101384 Loss2: 1.325984
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406962 Loss1: 0.086454 Loss2: 1.320508
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386103 Loss1: 0.066598 Loss2: 1.319505
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.486230 Loss1: 0.632899 Loss2: 1.853331
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.686131 Loss1: 0.316887 Loss2: 1.369244
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.588544 Loss1: 0.208653 Loss2: 1.379891
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.483198 Loss1: 0.113632 Loss2: 1.369566
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.347094 Loss1: 0.577029 Loss2: 1.770065
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.657782 Loss1: 0.332352 Loss2: 1.325430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578696 Loss1: 0.247899 Loss2: 1.330797 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441848 Loss1: 0.097861 Loss2: 1.343987 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401852 Loss1: 0.068446 Loss2: 1.333406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412927 Loss1: 0.080498 Loss2: 1.332428 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.396621 Loss1: 0.093291 Loss2: 1.303330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.344359 Loss1: 0.049448 Loss2: 1.294911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.332020 Loss1: 0.037739 Loss2: 1.294281
-(DefaultActor pid=3764) >> Training accuracy: 0.992647
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.685624 Loss1: 0.832882 Loss2: 1.852742
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.931668 Loss1: 0.546634 Loss2: 1.385033
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736615 Loss1: 0.293398 Loss2: 1.443217
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642269 Loss1: 0.258760 Loss2: 1.383510
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.569786 Loss1: 0.183738 Loss2: 1.386048
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.816197 Loss1: 0.860159 Loss2: 1.956038
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542498 Loss1: 0.164307 Loss2: 1.378192
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502445 Loss1: 0.126509 Loss2: 1.375936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.596909 Loss1: 0.213113 Loss2: 1.383796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440082 Loss1: 0.079012 Loss2: 1.361070 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425870 Loss1: 0.072500 Loss2: 1.353369 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415895 Loss1: 0.073412 Loss2: 1.342483 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993490
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.451524 Loss1: 0.633015 Loss2: 1.818509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.702818 Loss1: 0.290865 Loss2: 1.411953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.446068 Loss1: 0.664555 Loss2: 1.781513
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.661721 Loss1: 0.284099 Loss2: 1.377622
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.747495 Loss1: 0.428603 Loss2: 1.318892
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.542184 Loss1: 0.169116 Loss2: 1.373067
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701289 Loss1: 0.345397 Loss2: 1.355892
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480992 Loss1: 0.121537 Loss2: 1.359455
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456098 Loss1: 0.101390 Loss2: 1.354708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423676 Loss1: 0.079777 Loss2: 1.343899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394180 Loss1: 0.054344 Loss2: 1.339836 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390184 Loss1: 0.054224 Loss2: 1.335959 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.342403 Loss1: 0.057498 Loss2: 1.284905 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.586543 Loss1: 0.733782 Loss2: 1.852761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.773950 Loss1: 0.337017 Loss2: 1.436933 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.543209 Loss1: 0.171752 Loss2: 1.371457
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.626346 Loss1: 0.691718 Loss2: 1.934628
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.869441 Loss1: 0.430819 Loss2: 1.438623 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.739338 Loss1: 0.259478 Loss2: 1.479860 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.623263 Loss1: 0.191425 Loss2: 1.431837 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.605948 Loss1: 0.170756 Loss2: 1.435192 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.532587 Loss1: 0.110911 Loss2: 1.421676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389619 Loss1: 0.043801 Loss2: 1.345818
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.546946 Loss1: 0.136701 Loss2: 1.410245
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.541299 Loss1: 0.115327 Loss2: 1.425971
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487086 Loss1: 0.081977 Loss2: 1.405109
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462664 Loss1: 0.065653 Loss2: 1.397011
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.679601 Loss1: 0.714680 Loss2: 1.964922
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.916598 Loss1: 0.442068 Loss2: 1.474530
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.932825 Loss1: 0.403574 Loss2: 1.529250
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.791474 Loss1: 0.321959 Loss2: 1.469515
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.900264 Loss1: 0.902181 Loss2: 1.998084
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.913716 Loss1: 0.424550 Loss2: 1.489166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.738939 Loss1: 0.249272 Loss2: 1.489667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.702344 Loss1: 0.238104 Loss2: 1.464240 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.637334 Loss1: 0.179437 Loss2: 1.457897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.580629 Loss1: 0.133737 Loss2: 1.446892 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.559813 Loss1: 0.122004 Loss2: 1.437809 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.488691 Loss1: 0.061078 Loss2: 1.427613 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.467722 Loss1: 0.697763 Loss2: 1.769959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.590594 Loss1: 0.250602 Loss2: 1.339992 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.530815 Loss1: 0.223596 Loss2: 1.307219
-DEBUG flwr 2023-10-11 21:13:44,827 | server.py:236 | fit_round 127 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.460217 Loss1: 0.661284 Loss2: 1.798933
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.747319 Loss1: 0.424829 Loss2: 1.322490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.644970 Loss1: 0.280853 Loss2: 1.364117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557768 Loss1: 0.238293 Loss2: 1.319475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.499112 Loss1: 0.178704 Loss2: 1.320408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478849 Loss1: 0.164047 Loss2: 1.314802 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.329741 Loss1: 0.058064 Loss2: 1.271677
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434313 Loss1: 0.127961 Loss2: 1.306353
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421475 Loss1: 0.112863 Loss2: 1.308612
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384420 Loss1: 0.083080 Loss2: 1.301340
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352183 Loss1: 0.055823 Loss2: 1.296360
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.546857 Loss1: 0.748664 Loss2: 1.798193
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.742865 Loss1: 0.410195 Loss2: 1.332670
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638664 Loss1: 0.269992 Loss2: 1.368672
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508790 Loss1: 0.186319 Loss2: 1.322471
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.615725 Loss1: 0.743072 Loss2: 1.872653
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.886542 Loss1: 0.475978 Loss2: 1.410564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.722081 Loss1: 0.304641 Loss2: 1.417440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612807 Loss1: 0.221611 Loss2: 1.391197 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545683 Loss1: 0.162173 Loss2: 1.383510 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469131 Loss1: 0.094753 Loss2: 1.374379 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390506 Loss1: 0.089648 Loss2: 1.300858
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.454962 Loss1: 0.088095 Loss2: 1.366866
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434667 Loss1: 0.076878 Loss2: 1.357789
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416930 Loss1: 0.062375 Loss2: 1.354555
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387581 Loss1: 0.039892 Loss2: 1.347689
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.705236 Loss1: 0.813036 Loss2: 1.892200
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.827418 Loss1: 0.455776 Loss2: 1.371643
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.721607 Loss1: 0.299908 Loss2: 1.421699
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.678312 Loss1: 0.306047 Loss2: 1.372265
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.476340 Loss1: 0.671620 Loss2: 1.804720
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.793600 Loss1: 0.447539 Loss2: 1.346061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.652223 Loss1: 0.261661 Loss2: 1.390561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573282 Loss1: 0.235391 Loss2: 1.337891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.538007 Loss1: 0.177951 Loss2: 1.360056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421366 Loss1: 0.077362 Loss2: 1.344004 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454800 Loss1: 0.127046 Loss2: 1.327753 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.432969 Loss1: 0.104101 Loss2: 1.328868 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 21:13:44,827][flwr][DEBUG] - fit_round 127 received 50 results and 0 failures
-INFO flwr 2023-10-11 21:14:26,756 | server.py:125 | fit progress: (127, 2.2097703696439823, {'accuracy': 0.5875}, 292974.534956929)
->> Test accuracy: 0.587500
-[2023-10-11 21:14:26,756][flwr][INFO] - fit progress: (127, 2.2097703696439823, {'accuracy': 0.5875}, 292974.534956929)
-DEBUG flwr 2023-10-11 21:14:26,757 | server.py:173 | evaluate_round 127: strategy sampled 50 clients (out of 50)
-[2023-10-11 21:14:26,757][flwr][DEBUG] - evaluate_round 127: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 21:23:34,535 | server.py:187 | evaluate_round 127 received 50 results and 0 failures
-[2023-10-11 21:23:34,535][flwr][DEBUG] - evaluate_round 127 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 21:23:34,536 | server.py:222 | fit_round 128: strategy sampled 50 clients (out of 50)
-[2023-10-11 21:23:34,536][flwr][DEBUG] - fit_round 128: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.457145 Loss1: 0.592992 Loss2: 1.864153
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.740107 Loss1: 0.381462 Loss2: 1.358645
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684749 Loss1: 0.298709 Loss2: 1.386039
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630691 Loss1: 0.270325 Loss2: 1.360366
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.514857 Loss1: 0.639855 Loss2: 1.875002
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.710278 Loss1: 0.352222 Loss2: 1.358056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.533394 Loss1: 0.167793 Loss2: 1.365601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.479986 Loss1: 0.133776 Loss2: 1.346210 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.425680 Loss1: 0.095264 Loss2: 1.330416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439483 Loss1: 0.110740 Loss2: 1.328743 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399198 Loss1: 0.068355 Loss2: 1.330843
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.418855 Loss1: 0.092970 Loss2: 1.325885
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.402230 Loss1: 0.078687 Loss2: 1.323543
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403571 Loss1: 0.082191 Loss2: 1.321380
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395150 Loss1: 0.075231 Loss2: 1.319919
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.698664 Loss1: 0.806294 Loss2: 1.892370
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.931724 Loss1: 0.549523 Loss2: 1.382201
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.712732 Loss1: 0.308686 Loss2: 1.404046
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542855 Loss1: 0.181516 Loss2: 1.361339
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.535912 Loss1: 0.654387 Loss2: 1.881526
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.787063 Loss1: 0.410693 Loss2: 1.376370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.626533 Loss1: 0.214445 Loss2: 1.412088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.593168 Loss1: 0.227149 Loss2: 1.366019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.584997 Loss1: 0.190212 Loss2: 1.394784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510711 Loss1: 0.140384 Loss2: 1.370327 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404741 Loss1: 0.068339 Loss2: 1.336402
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459269 Loss1: 0.096758 Loss2: 1.362511
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485957 Loss1: 0.119556 Loss2: 1.366401
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435746 Loss1: 0.073839 Loss2: 1.361907
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408378 Loss1: 0.054504 Loss2: 1.353874
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.502385 Loss1: 0.621097 Loss2: 1.881288
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.792468 Loss1: 0.392532 Loss2: 1.399936
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.683247 Loss1: 0.250462 Loss2: 1.432785
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.632108 Loss1: 0.238549 Loss2: 1.393559
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.623689 Loss1: 0.733241 Loss2: 1.890447
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.834862 Loss1: 0.440041 Loss2: 1.394822
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547525 Loss1: 0.156487 Loss2: 1.391037
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.654709 Loss1: 0.231180 Loss2: 1.423529
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.522184 Loss1: 0.139071 Loss2: 1.383114
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.587081 Loss1: 0.198099 Loss2: 1.388982
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488963 Loss1: 0.109498 Loss2: 1.379464
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.562847 Loss1: 0.178919 Loss2: 1.383928
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.503731 Loss1: 0.132385 Loss2: 1.371346
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462757 Loss1: 0.090045 Loss2: 1.372712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434297 Loss1: 0.064334 Loss2: 1.369964 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425568 Loss1: 0.059672 Loss2: 1.365896 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.445466 Loss1: 0.658848 Loss2: 1.786618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.770239 Loss1: 0.369782 Loss2: 1.400457 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563382 Loss1: 0.210612 Loss2: 1.352770
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.679342 Loss1: 0.703270 Loss2: 1.976072
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.947680 Loss1: 0.479574 Loss2: 1.468107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769516 Loss1: 0.266877 Loss2: 1.502639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.704770 Loss1: 0.246358 Loss2: 1.458412 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.654563 Loss1: 0.197932 Loss2: 1.456632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.585427 Loss1: 0.125930 Loss2: 1.459497 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381380 Loss1: 0.056791 Loss2: 1.324589
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.555797 Loss1: 0.109401 Loss2: 1.446395
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.545187 Loss1: 0.104570 Loss2: 1.440617
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501496 Loss1: 0.060368 Loss2: 1.441128
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512011 Loss1: 0.085687 Loss2: 1.426324
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.570832 Loss1: 0.755925 Loss2: 1.814907
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.820339 Loss1: 0.454271 Loss2: 1.366068
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700533 Loss1: 0.305230 Loss2: 1.395303
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581844 Loss1: 0.215335 Loss2: 1.366509
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.487336 Loss1: 0.670425 Loss2: 1.816911
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.774962 Loss1: 0.395900 Loss2: 1.379062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.714845 Loss1: 0.291770 Loss2: 1.423075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.656980 Loss1: 0.282700 Loss2: 1.374280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509000 Loss1: 0.135491 Loss2: 1.373510 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467175 Loss1: 0.106616 Loss2: 1.360560 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465571 Loss1: 0.110783 Loss2: 1.354789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424652 Loss1: 0.077925 Loss2: 1.346727 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.437678 Loss1: 0.614608 Loss2: 1.823070 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573115 Loss1: 0.210657 Loss2: 1.362458 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.460666 Loss1: 0.598237 Loss2: 1.862429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.781811 Loss1: 0.420456 Loss2: 1.361355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.696635 Loss1: 0.286792 Loss2: 1.409842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573494 Loss1: 0.213964 Loss2: 1.359530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518103 Loss1: 0.160209 Loss2: 1.357894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449843 Loss1: 0.101331 Loss2: 1.348512 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415180 Loss1: 0.076099 Loss2: 1.339082 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408708 Loss1: 0.074380 Loss2: 1.334329 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.851912 Loss1: 0.455220 Loss2: 1.396692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.619481 Loss1: 0.224738 Loss2: 1.394743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.603948 Loss1: 0.212364 Loss2: 1.391584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.822405 Loss1: 0.347010 Loss2: 1.475396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.692349 Loss1: 0.242296 Loss2: 1.450053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.623609 Loss1: 0.204046 Loss2: 1.419563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.596268 Loss1: 0.161651 Loss2: 1.434617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.528770 Loss1: 0.111674 Loss2: 1.417096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427231 Loss1: 0.066993 Loss2: 1.360237
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481174 Loss1: 0.073634 Loss2: 1.407540
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454943 Loss1: 0.050586 Loss2: 1.404358
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427387 Loss1: 0.032298 Loss2: 1.395090
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.618432 Loss1: 0.683665 Loss2: 1.934767
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.878939 Loss1: 0.434254 Loss2: 1.444685
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.724597 Loss1: 0.236776 Loss2: 1.487821
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594899 Loss1: 0.168953 Loss2: 1.425946
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.583620 Loss1: 0.154836 Loss2: 1.428785 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.583266 Loss1: 0.157271 Loss2: 1.425996 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511038 Loss1: 0.092371 Loss2: 1.418668 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488662 Loss1: 0.079577 Loss2: 1.409085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469426 Loss1: 0.064141 Loss2: 1.405284 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.465706 Loss1: 0.061781 Loss2: 1.403924 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.482255 Loss1: 0.116499 Loss2: 1.365756 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.448857 Loss1: 0.601511 Loss2: 1.847346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561011 Loss1: 0.173876 Loss2: 1.387135 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535627 Loss1: 0.192267 Loss2: 1.343360
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.541740 Loss1: 0.733898 Loss2: 1.807842
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.755957 Loss1: 0.408457 Loss2: 1.347500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605576 Loss1: 0.237777 Loss2: 1.367800 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.489698 Loss1: 0.156748 Loss2: 1.332950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441012 Loss1: 0.111505 Loss2: 1.329507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.436760 Loss1: 0.113035 Loss2: 1.323724 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433812 Loss1: 0.117052 Loss2: 1.316760 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368545 Loss1: 0.055617 Loss2: 1.312928 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.676958 Loss1: 0.779155 Loss2: 1.897802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.682794 Loss1: 0.232408 Loss2: 1.450387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.810037 Loss1: 0.845503 Loss2: 1.964534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.892444 Loss1: 0.539811 Loss2: 1.352633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.575454 Loss1: 0.162556 Loss2: 1.412897
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.778894 Loss1: 0.356020 Loss2: 1.422874
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.646583 Loss1: 0.270477 Loss2: 1.376106
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.506801 Loss1: 0.109447 Loss2: 1.397354
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469387 Loss1: 0.076988 Loss2: 1.392399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.442088 Loss1: 0.053387 Loss2: 1.388701 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426142 Loss1: 0.050983 Loss2: 1.375159 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431763 Loss1: 0.101690 Loss2: 1.330073 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.648186 Loss1: 0.773649 Loss2: 1.874537
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.889604 Loss1: 0.465565 Loss2: 1.424039
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716735 Loss1: 0.277776 Loss2: 1.438959
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.616724 Loss1: 0.211708 Loss2: 1.405017
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.528421 Loss1: 0.627701 Loss2: 1.900720
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565232 Loss1: 0.154246 Loss2: 1.410986
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.807308 Loss1: 0.401240 Loss2: 1.406068
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519456 Loss1: 0.125129 Loss2: 1.394328
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.679958 Loss1: 0.214470 Loss2: 1.465487
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.504287 Loss1: 0.115997 Loss2: 1.388289
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.580573 Loss1: 0.182680 Loss2: 1.397894
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474778 Loss1: 0.085504 Loss2: 1.389274
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.547872 Loss1: 0.149769 Loss2: 1.398103
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.481473 Loss1: 0.103396 Loss2: 1.378077
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522954 Loss1: 0.119602 Loss2: 1.403352
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448349 Loss1: 0.069815 Loss2: 1.378533
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478509 Loss1: 0.086772 Loss2: 1.391738
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.444364 Loss1: 0.059672 Loss2: 1.384691
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.437396 Loss1: 0.059564 Loss2: 1.377832
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436753 Loss1: 0.065759 Loss2: 1.370995
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.672187 Loss1: 0.811346 Loss2: 1.860841
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.823299 Loss1: 0.474546 Loss2: 1.348753
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.751101 Loss1: 0.348437 Loss2: 1.402664
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557637 Loss1: 0.209977 Loss2: 1.347660
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.479191 Loss1: 0.618673 Loss2: 1.860518
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.775946 Loss1: 0.357841 Loss2: 1.418105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.703837 Loss1: 0.272291 Loss2: 1.431546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425551 Loss1: 0.102360 Loss2: 1.323192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394373 Loss1: 0.066936 Loss2: 1.327437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372287 Loss1: 0.056456 Loss2: 1.315831 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.539138 Loss1: 0.139237 Loss2: 1.399902 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463480 Loss1: 0.083901 Loss2: 1.379579 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453295 Loss1: 0.077245 Loss2: 1.376050
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.446730 Loss1: 0.559461 Loss2: 1.887269
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.785255 Loss1: 0.360302 Loss2: 1.424953
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.636669 Loss1: 0.190846 Loss2: 1.445824
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564504 Loss1: 0.152902 Loss2: 1.411603
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.606678 Loss1: 0.717434 Loss2: 1.889244
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561473 Loss1: 0.142306 Loss2: 1.419166
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831857 Loss1: 0.431314 Loss2: 1.400543
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.594306 Loss1: 0.172350 Loss2: 1.421956
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.594634 Loss1: 0.168729 Loss2: 1.425905 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.552178 Loss1: 0.140470 Loss2: 1.411708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.593604 Loss1: 0.185282 Loss2: 1.408321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.576452 Loss1: 0.159250 Loss2: 1.417201 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985294
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.459763 Loss1: 0.106243 Loss2: 1.353520 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.521215 Loss1: 0.665523 Loss2: 1.855692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.695111 Loss1: 0.261681 Loss2: 1.433431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581974 Loss1: 0.209466 Loss2: 1.372508
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.551577 Loss1: 0.700467 Loss2: 1.851111
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.869201 Loss1: 0.487672 Loss2: 1.381529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.743939 Loss1: 0.301941 Loss2: 1.441998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631577 Loss1: 0.252315 Loss2: 1.379261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.559755 Loss1: 0.161134 Loss2: 1.398621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493453 Loss1: 0.115007 Loss2: 1.378446 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.538767 Loss1: 0.169135 Loss2: 1.369631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458105 Loss1: 0.088204 Loss2: 1.369901 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.479424 Loss1: 0.621662 Loss2: 1.857763 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.646622 Loss1: 0.231164 Loss2: 1.415458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583892 Loss1: 0.221480 Loss2: 1.362412
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.672429 Loss1: 0.811451 Loss2: 1.860978
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.872166 Loss1: 0.496970 Loss2: 1.375196 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.698037 Loss1: 0.287261 Loss2: 1.410776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605887 Loss1: 0.233096 Loss2: 1.372791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563526 Loss1: 0.186867 Loss2: 1.376659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.517413 Loss1: 0.148204 Loss2: 1.369209 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500244 Loss1: 0.134175 Loss2: 1.366068 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422995 Loss1: 0.077854 Loss2: 1.345141 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.489986 Loss1: 0.643624 Loss2: 1.846362 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.605542 Loss1: 0.209487 Loss2: 1.396055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529596 Loss1: 0.156211 Loss2: 1.373385
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.505705 Loss1: 0.690495 Loss2: 1.815210
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.817369 Loss1: 0.463791 Loss2: 1.353578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.704276 Loss1: 0.300973 Loss2: 1.403303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559657 Loss1: 0.213478 Loss2: 1.346179 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569008 Loss1: 0.209519 Loss2: 1.359489 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528539 Loss1: 0.179891 Loss2: 1.348648 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396709 Loss1: 0.057217 Loss2: 1.339492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.521119 Loss1: 0.177258 Loss2: 1.343862
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443302 Loss1: 0.098469 Loss2: 1.344833
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425861 Loss1: 0.092116 Loss2: 1.333745
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404862 Loss1: 0.074162 Loss2: 1.330700
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.616349 Loss1: 0.721311 Loss2: 1.895038
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.879437 Loss1: 0.485823 Loss2: 1.393614
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.689891 Loss1: 0.252023 Loss2: 1.437868
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559955 Loss1: 0.178406 Loss2: 1.381549
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.469028 Loss1: 0.662263 Loss2: 1.806766
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.745479 Loss1: 0.400085 Loss2: 1.345394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608304 Loss1: 0.235168 Loss2: 1.373136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588266 Loss1: 0.242051 Loss2: 1.346214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508106 Loss1: 0.154336 Loss2: 1.353770 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.505375 Loss1: 0.163173 Loss2: 1.342202 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441737 Loss1: 0.074100 Loss2: 1.367638
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.399374 Loss1: 0.062264 Loss2: 1.337110
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372667 Loss1: 0.050567 Loss2: 1.322100
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.358180 Loss1: 0.043794 Loss2: 1.314386
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.341480 Loss1: 0.030980 Loss2: 1.310499
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.546399 Loss1: 0.624151 Loss2: 1.922248
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.776258 Loss1: 0.360347 Loss2: 1.415911
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.664822 Loss1: 0.212610 Loss2: 1.452212
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.537402 Loss1: 0.130824 Loss2: 1.406578
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.487927 Loss1: 0.656193 Loss2: 1.831734
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.833750 Loss1: 0.488193 Loss2: 1.345557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.691419 Loss1: 0.299107 Loss2: 1.392311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.560737 Loss1: 0.216156 Loss2: 1.344581 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541859 Loss1: 0.191150 Loss2: 1.350709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476286 Loss1: 0.129024 Loss2: 1.347263 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449171 Loss1: 0.063452 Loss2: 1.385719
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450197 Loss1: 0.111432 Loss2: 1.338764
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413131 Loss1: 0.074428 Loss2: 1.338703
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397035 Loss1: 0.067654 Loss2: 1.329381
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403975 Loss1: 0.077537 Loss2: 1.326438
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.457047 Loss1: 0.634843 Loss2: 1.822204
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.800699 Loss1: 0.440926 Loss2: 1.359773
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.725182 Loss1: 0.325146 Loss2: 1.400036
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.593545 Loss1: 0.242832 Loss2: 1.350713
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.819115 Loss1: 0.868279 Loss2: 1.950836
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.934795 Loss1: 0.517613 Loss2: 1.417182
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.543500 Loss1: 0.184498 Loss2: 1.359002
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.801034 Loss1: 0.336411 Loss2: 1.464624
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462202 Loss1: 0.120572 Loss2: 1.341631
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.679840 Loss1: 0.271393 Loss2: 1.408447
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439777 Loss1: 0.106264 Loss2: 1.333512
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417182 Loss1: 0.085439 Loss2: 1.331743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399750 Loss1: 0.074367 Loss2: 1.325383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375685 Loss1: 0.059629 Loss2: 1.316056 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.490446 Loss1: 0.098368 Loss2: 1.392078 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.974330
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.647190 Loss1: 0.792684 Loss2: 1.854506
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.919922 Loss1: 0.511513 Loss2: 1.408409
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.734725 Loss1: 0.295209 Loss2: 1.439517
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604768 Loss1: 0.222731 Loss2: 1.382036
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.458236 Loss1: 0.665113 Loss2: 1.793123
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.848956 Loss1: 0.473475 Loss2: 1.375481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.650182 Loss1: 0.263175 Loss2: 1.387006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.566345 Loss1: 0.205849 Loss2: 1.360496 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.498079 Loss1: 0.149094 Loss2: 1.348985 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500317 Loss1: 0.147874 Loss2: 1.352443 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467615 Loss1: 0.115389 Loss2: 1.352226 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.437052 Loss1: 0.096679 Loss2: 1.340373 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.670936 Loss1: 0.776161 Loss2: 1.894775 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731442 Loss1: 0.274283 Loss2: 1.457159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.541033 Loss1: 0.166093 Loss2: 1.374939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.500376 Loss1: 0.125101 Loss2: 1.375274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.492964 Loss1: 0.124994 Loss2: 1.367971 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445009 Loss1: 0.083719 Loss2: 1.361290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436199 Loss1: 0.083653 Loss2: 1.352546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.435261 Loss1: 0.103367 Loss2: 1.331894
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460734 Loss1: 0.110290 Loss2: 1.350444
-(DefaultActor pid=3765) >> Training accuracy: 0.986607
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.382907 Loss1: 0.068170 Loss2: 1.314736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.365722 Loss1: 0.054077 Loss2: 1.311645 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.464883 Loss1: 0.644707 Loss2: 1.820176
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.346503 Loss1: 0.045556 Loss2: 1.300947
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.663582 Loss1: 0.261971 Loss2: 1.401610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.605423 Loss1: 0.205862 Loss2: 1.399561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.534708 Loss1: 0.156412 Loss2: 1.378295
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.655724 Loss1: 0.737884 Loss2: 1.917839
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.822979 Loss1: 0.407442 Loss2: 1.415537
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503700 Loss1: 0.127734 Loss2: 1.375967
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684153 Loss1: 0.227316 Loss2: 1.456837
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.496162 Loss1: 0.124775 Loss2: 1.371388
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.648059 Loss1: 0.247736 Loss2: 1.400323
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.467890 Loss1: 0.101684 Loss2: 1.366206
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.626958 Loss1: 0.191151 Loss2: 1.435807
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438277 Loss1: 0.075307 Loss2: 1.362970
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.571052 Loss1: 0.158772 Loss2: 1.412279 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.513607 Loss1: 0.110193 Loss2: 1.403414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465847 Loss1: 0.072460 Loss2: 1.393387
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.676710 Loss1: 0.726765 Loss2: 1.949946
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.940606 Loss1: 0.552116 Loss2: 1.388490
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741007 Loss1: 0.300488 Loss2: 1.440519
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474813 Loss1: 0.104281 Loss2: 1.370532
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478252 Loss1: 0.119407 Loss2: 1.358845
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442084 Loss1: 0.089334 Loss2: 1.352750
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.546415 Loss1: 0.675617 Loss2: 1.870798
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.817019 Loss1: 0.445645 Loss2: 1.371374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.704633 Loss1: 0.270967 Loss2: 1.433666 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431165 Loss1: 0.098871 Loss2: 1.332294 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.580328 Loss1: 0.196929 Loss2: 1.383399 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483622 Loss1: 0.116816 Loss2: 1.366806 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458125 Loss1: 0.100527 Loss2: 1.357598
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.658333 Loss1: 0.762173 Loss2: 1.896160
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421235 Loss1: 0.065913 Loss2: 1.355322
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.748565 Loss1: 0.364348 Loss2: 1.384217
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.669219 Loss1: 0.259133 Loss2: 1.410087
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547529 Loss1: 0.163455 Loss2: 1.384074
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.493754 Loss1: 0.117076 Loss2: 1.376678
-DEBUG flwr 2023-10-11 21:52:37,010 | server.py:236 | fit_round 128 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504110 Loss1: 0.131150 Loss2: 1.372960
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.517564 Loss1: 0.693552 Loss2: 1.824012
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462316 Loss1: 0.093045 Loss2: 1.369271
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.842515 Loss1: 0.482005 Loss2: 1.360510
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458625 Loss1: 0.099377 Loss2: 1.359248
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.695520 Loss1: 0.282553 Loss2: 1.412966
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445284 Loss1: 0.086115 Loss2: 1.359169
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.596991 Loss1: 0.229823 Loss2: 1.367168
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445111 Loss1: 0.086964 Loss2: 1.358147
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464798 Loss1: 0.113092 Loss2: 1.351706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408759 Loss1: 0.070339 Loss2: 1.338420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401410 Loss1: 0.067127 Loss2: 1.334283
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.447289 Loss1: 0.635136 Loss2: 1.812153
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387262 Loss1: 0.058935 Loss2: 1.328327
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.722744 Loss1: 0.398375 Loss2: 1.324370
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.669089 Loss1: 0.296582 Loss2: 1.372507
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575768 Loss1: 0.240059 Loss2: 1.335709
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.538083 Loss1: 0.188135 Loss2: 1.349947
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511212 Loss1: 0.184948 Loss2: 1.326264
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.420519 Loss1: 0.589128 Loss2: 1.831390
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490496 Loss1: 0.164720 Loss2: 1.325776
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443899 Loss1: 0.115011 Loss2: 1.328888
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.795650 Loss1: 0.393608 Loss2: 1.402042
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403134 Loss1: 0.086434 Loss2: 1.316700
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.649472 Loss1: 0.239787 Loss2: 1.409685
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375074 Loss1: 0.063755 Loss2: 1.311319
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.546858 Loss1: 0.172033 Loss2: 1.374825
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522910 Loss1: 0.143924 Loss2: 1.378986
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471096 Loss1: 0.102940 Loss2: 1.368156
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465296 Loss1: 0.104325 Loss2: 1.360972
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408663 Loss1: 0.048077 Loss2: 1.360586
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389846 Loss1: 0.039717 Loss2: 1.350129
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383956 Loss1: 0.039235 Loss2: 1.344721
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 21:52:37,010][flwr][DEBUG] - fit_round 128 received 50 results and 0 failures
-INFO flwr 2023-10-11 21:53:18,684 | server.py:125 | fit progress: (128, 2.208596081969837, {'accuracy': 0.5867}, 295306.462969138)
->> Test accuracy: 0.586700
-[2023-10-11 21:53:18,684][flwr][INFO] - fit progress: (128, 2.208596081969837, {'accuracy': 0.5867}, 295306.462969138)
-DEBUG flwr 2023-10-11 21:53:18,685 | server.py:173 | evaluate_round 128: strategy sampled 50 clients (out of 50)
-[2023-10-11 21:53:18,685][flwr][DEBUG] - evaluate_round 128: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 22:02:27,797 | server.py:187 | evaluate_round 128 received 50 results and 0 failures
-[2023-10-11 22:02:27,797][flwr][DEBUG] - evaluate_round 128 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 22:02:27,798 | server.py:222 | fit_round 129: strategy sampled 50 clients (out of 50)
-[2023-10-11 22:02:27,798][flwr][DEBUG] - fit_round 129: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.518712 Loss1: 0.678186 Loss2: 1.840526
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.940663 Loss1: 0.502480 Loss2: 1.438183
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.712715 Loss1: 0.296847 Loss2: 1.415868
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.515229 Loss1: 0.631178 Loss2: 1.884051
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.636201 Loss1: 0.241714 Loss2: 1.394487
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.830140 Loss1: 0.429910 Loss2: 1.400230
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.601612 Loss1: 0.190828 Loss2: 1.410783
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.616354 Loss1: 0.183798 Loss2: 1.432557
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.529311 Loss1: 0.144349 Loss2: 1.384962
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.537873 Loss1: 0.150713 Loss2: 1.387159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.504848 Loss1: 0.123847 Loss2: 1.381000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.501278 Loss1: 0.120183 Loss2: 1.381095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449457 Loss1: 0.075658 Loss2: 1.373799 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402788 Loss1: 0.046446 Loss2: 1.356342 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.556007 Loss1: 0.695549 Loss2: 1.860457 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.709806 Loss1: 0.287285 Loss2: 1.422522 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589688 Loss1: 0.186822 Loss2: 1.402866
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.388919 Loss1: 0.566007 Loss2: 1.822912
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.774089 Loss1: 0.394758 Loss2: 1.379330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.666223 Loss1: 0.257475 Loss2: 1.408747 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.609002 Loss1: 0.241527 Loss2: 1.367475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576627 Loss1: 0.204124 Loss2: 1.372503 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512733 Loss1: 0.146953 Loss2: 1.365781 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.495285 Loss1: 0.130628 Loss2: 1.364658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415843 Loss1: 0.069824 Loss2: 1.346019 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.441551 Loss1: 0.627996 Loss2: 1.813555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.578705 Loss1: 0.204899 Loss2: 1.373807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.450815 Loss1: 0.127570 Loss2: 1.323245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.423937 Loss1: 0.104447 Loss2: 1.319489 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432974 Loss1: 0.111704 Loss2: 1.321270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.390663 Loss1: 0.075105 Loss2: 1.315558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609768 Loss1: 0.231260 Loss2: 1.378508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354838 Loss1: 0.055117 Loss2: 1.299720 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481981 Loss1: 0.125632 Loss2: 1.356349 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410513 Loss1: 0.066823 Loss2: 1.343690 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.830588 Loss1: 0.412570 Loss2: 1.418017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.592433 Loss1: 0.200244 Loss2: 1.392189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.438599 Loss1: 0.578651 Loss2: 1.859948
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513434 Loss1: 0.120490 Loss2: 1.392944
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.743877 Loss1: 0.371662 Loss2: 1.372215
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494073 Loss1: 0.118780 Loss2: 1.375293
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677487 Loss1: 0.266799 Loss2: 1.410687
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447476 Loss1: 0.077823 Loss2: 1.369652
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550658 Loss1: 0.194158 Loss2: 1.356499
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432484 Loss1: 0.071894 Loss2: 1.360590
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.547426 Loss1: 0.190248 Loss2: 1.357178
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412015 Loss1: 0.054295 Loss2: 1.357719
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537327 Loss1: 0.172141 Loss2: 1.365186
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421391 Loss1: 0.064563 Loss2: 1.356828
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439546 Loss1: 0.091013 Loss2: 1.348533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426466 Loss1: 0.079116 Loss2: 1.347350 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.742513 Loss1: 0.391489 Loss2: 1.351024 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526200 Loss1: 0.171211 Loss2: 1.354990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.626018 Loss1: 0.758874 Loss2: 1.867144
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488868 Loss1: 0.142327 Loss2: 1.346541
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.829807 Loss1: 0.446231 Loss2: 1.383576
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.483252 Loss1: 0.132365 Loss2: 1.350887
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682713 Loss1: 0.266869 Loss2: 1.415844
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469562 Loss1: 0.130563 Loss2: 1.338999
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567803 Loss1: 0.187203 Loss2: 1.380600
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413366 Loss1: 0.082140 Loss2: 1.331226
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509428 Loss1: 0.131858 Loss2: 1.377570
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402581 Loss1: 0.070930 Loss2: 1.331651
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474893 Loss1: 0.105607 Loss2: 1.369286
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381541 Loss1: 0.052161 Loss2: 1.329380
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461059 Loss1: 0.096724 Loss2: 1.364335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396246 Loss1: 0.049391 Loss2: 1.346854 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.847260 Loss1: 0.492742 Loss2: 1.354518 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563053 Loss1: 0.223203 Loss2: 1.339850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.716619 Loss1: 0.784529 Loss2: 1.932089
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484724 Loss1: 0.149179 Loss2: 1.335544
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.962130 Loss1: 0.478622 Loss2: 1.483507
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470802 Loss1: 0.141077 Loss2: 1.329725
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.754922 Loss1: 0.290708 Loss2: 1.464214
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441112 Loss1: 0.106396 Loss2: 1.334716
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.647236 Loss1: 0.216449 Loss2: 1.430787
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432293 Loss1: 0.111068 Loss2: 1.321226
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570113 Loss1: 0.133796 Loss2: 1.436317
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411777 Loss1: 0.093952 Loss2: 1.317825
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.525804 Loss1: 0.112056 Loss2: 1.413748
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439490 Loss1: 0.119495 Loss2: 1.319995
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.534079 Loss1: 0.119668 Loss2: 1.414411 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.518516 Loss1: 0.109511 Loss2: 1.409004 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.829905 Loss1: 0.477289 Loss2: 1.352616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.590913 Loss1: 0.234203 Loss2: 1.356710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.533546 Loss1: 0.654804 Loss2: 1.878741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.794198 Loss1: 0.413848 Loss2: 1.380350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.602442 Loss1: 0.199708 Loss2: 1.402734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401950 Loss1: 0.082328 Loss2: 1.319622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390012 Loss1: 0.067756 Loss2: 1.322257 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508345 Loss1: 0.129764 Loss2: 1.378581 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497543 Loss1: 0.122644 Loss2: 1.374899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.508964 Loss1: 0.710744 Loss2: 1.798220
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434823 Loss1: 0.076184 Loss2: 1.358639
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.697328 Loss1: 0.318098 Loss2: 1.379230 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.512029 Loss1: 0.180265 Loss2: 1.331764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.761479 Loss1: 0.750039 Loss2: 2.011440
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.469062 Loss1: 0.137494 Loss2: 1.331568
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472389 Loss1: 0.158693 Loss2: 1.313696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471646 Loss1: 0.151263 Loss2: 1.320383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417211 Loss1: 0.103536 Loss2: 1.313675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522038 Loss1: 0.129540 Loss2: 1.392498 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518915 Loss1: 0.132212 Loss2: 1.386703 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509961 Loss1: 0.136465 Loss2: 1.373496 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977865
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.520480 Loss1: 0.723981 Loss2: 1.796499
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.868052 Loss1: 0.503125 Loss2: 1.364927
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.715911 Loss1: 0.341771 Loss2: 1.374141
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.568279 Loss1: 0.212653 Loss2: 1.355626
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.475301 Loss1: 0.635028 Loss2: 1.840272
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.841457 Loss1: 0.468690 Loss2: 1.372767
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462109 Loss1: 0.129034 Loss2: 1.333076
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.747352 Loss1: 0.300074 Loss2: 1.447278
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.411990 Loss1: 0.093404 Loss2: 1.318586
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.633955 Loss1: 0.255866 Loss2: 1.378089
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.417256 Loss1: 0.100780 Loss2: 1.316475
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.607678 Loss1: 0.223663 Loss2: 1.384015
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404094 Loss1: 0.085873 Loss2: 1.318221
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.362615 Loss1: 0.049315 Loss2: 1.313300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.341429 Loss1: 0.037734 Loss2: 1.303695 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462511 Loss1: 0.102477 Loss2: 1.360034 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.536294 Loss1: 0.720131 Loss2: 1.816163 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.738191 Loss1: 0.331425 Loss2: 1.406765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.621466 Loss1: 0.257734 Loss2: 1.363732
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.697977 Loss1: 0.732640 Loss2: 1.965337
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.906958 Loss1: 0.444097 Loss2: 1.462862 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.827027 Loss1: 0.317679 Loss2: 1.509349 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.748244 Loss1: 0.287286 Loss2: 1.460958 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.656855 Loss1: 0.192216 Loss2: 1.464639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582939 Loss1: 0.137255 Loss2: 1.445684 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.376239 Loss1: 0.053518 Loss2: 1.322721
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.592747 Loss1: 0.148257 Loss2: 1.444490
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.547058 Loss1: 0.099229 Loss2: 1.447829
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490124 Loss1: 0.057218 Loss2: 1.432906
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.519682 Loss1: 0.097693 Loss2: 1.421989
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.404905 Loss1: 0.563318 Loss2: 1.841587
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.728652 Loss1: 0.371670 Loss2: 1.356981
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.712616 Loss1: 0.301505 Loss2: 1.411111
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581849 Loss1: 0.220945 Loss2: 1.360904
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.449161 Loss1: 0.691218 Loss2: 1.757944
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.706775 Loss1: 0.411600 Loss2: 1.295175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.602880 Loss1: 0.255796 Loss2: 1.347084 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523490 Loss1: 0.229133 Loss2: 1.294357 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.448636 Loss1: 0.152666 Loss2: 1.295970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423959 Loss1: 0.131215 Loss2: 1.292743 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392645 Loss1: 0.051220 Loss2: 1.341425
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.379666 Loss1: 0.094300 Loss2: 1.285366
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.357651 Loss1: 0.084052 Loss2: 1.273599
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379441 Loss1: 0.103898 Loss2: 1.275543
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.333801 Loss1: 0.055695 Loss2: 1.278107
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.692603 Loss1: 0.730783 Loss2: 1.961820
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.900400 Loss1: 0.517006 Loss2: 1.383394
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.768771 Loss1: 0.327447 Loss2: 1.441324
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604258 Loss1: 0.230081 Loss2: 1.374178
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.523599 Loss1: 0.695404 Loss2: 1.828194
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.571038 Loss1: 0.183134 Loss2: 1.387903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485986 Loss1: 0.128157 Loss2: 1.357829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460550 Loss1: 0.104056 Loss2: 1.356493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433243 Loss1: 0.081844 Loss2: 1.351399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403640 Loss1: 0.059427 Loss2: 1.344213 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408051 Loss1: 0.082072 Loss2: 1.325979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407379 Loss1: 0.091018 Loss2: 1.316361 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414028 Loss1: 0.100013 Loss2: 1.314016
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.567471 Loss1: 0.727380 Loss2: 1.840091
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.870574 Loss1: 0.498262 Loss2: 1.372312
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.706128 Loss1: 0.307374 Loss2: 1.398754
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570773 Loss1: 0.213033 Loss2: 1.357739
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503002 Loss1: 0.146157 Loss2: 1.356845
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.537519 Loss1: 0.685570 Loss2: 1.851949
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.907645 Loss1: 0.522913 Loss2: 1.384732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.759798 Loss1: 0.306603 Loss2: 1.453195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.590869 Loss1: 0.220049 Loss2: 1.370820 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541637 Loss1: 0.160498 Loss2: 1.381139 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.520507 Loss1: 0.156078 Loss2: 1.364429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457128 Loss1: 0.102712 Loss2: 1.354416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401280 Loss1: 0.060631 Loss2: 1.340649 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.929383 Loss1: 0.517821 Loss2: 1.411562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.639630 Loss1: 0.235316 Loss2: 1.404314 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564919 Loss1: 0.172703 Loss2: 1.392216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472780 Loss1: 0.097676 Loss2: 1.375103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439619 Loss1: 0.067189 Loss2: 1.372431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419304 Loss1: 0.058974 Loss2: 1.360330 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407197 Loss1: 0.052362 Loss2: 1.354834 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994420
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512992 Loss1: 0.131586 Loss2: 1.381406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.524976 Loss1: 0.148946 Loss2: 1.376030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485393 Loss1: 0.113754 Loss2: 1.371639
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.560460 Loss1: 0.727083 Loss2: 1.833377
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454884 Loss1: 0.091956 Loss2: 1.362928
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.915540 Loss1: 0.487969 Loss2: 1.427571
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.726124 Loss1: 0.322267 Loss2: 1.403857
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.611476 Loss1: 0.223326 Loss2: 1.388150
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568893 Loss1: 0.188106 Loss2: 1.380787
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525319 Loss1: 0.160401 Loss2: 1.364918
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.706511 Loss1: 0.771639 Loss2: 1.934872
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.883764 Loss1: 0.477720 Loss2: 1.406044 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430267 Loss1: 0.074000 Loss2: 1.356267
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.769286 Loss1: 0.310884 Loss2: 1.458402
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411288 Loss1: 0.058196 Loss2: 1.353093
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.627141 Loss1: 0.219105 Loss2: 1.408036
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574112 Loss1: 0.177316 Loss2: 1.396796
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403182 Loss1: 0.057106 Loss2: 1.346076
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.526214 Loss1: 0.140538 Loss2: 1.385676 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479411 Loss1: 0.097325 Loss2: 1.382086 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465708 Loss1: 0.090288 Loss2: 1.375420
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.500787 Loss1: 0.698839 Loss2: 1.801948
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.677093 Loss1: 0.330152 Loss2: 1.346941
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.570118 Loss1: 0.215633 Loss2: 1.354484
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580058 Loss1: 0.243648 Loss2: 1.336410
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535643 Loss1: 0.193073 Loss2: 1.342570
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.531836 Loss1: 0.672710 Loss2: 1.859127
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.834407 Loss1: 0.442097 Loss2: 1.392310
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.479037 Loss1: 0.154010 Loss2: 1.325028
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726695 Loss1: 0.286671 Loss2: 1.440025
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446764 Loss1: 0.122363 Loss2: 1.324401
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.655367 Loss1: 0.253973 Loss2: 1.401393
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422182 Loss1: 0.100771 Loss2: 1.321410
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.644975 Loss1: 0.234025 Loss2: 1.410951
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.382127 Loss1: 0.069696 Loss2: 1.312431
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378406 Loss1: 0.072484 Loss2: 1.305922 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.495403 Loss1: 0.104226 Loss2: 1.391177 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.480896 Loss1: 0.097280 Loss2: 1.383615 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.610244 Loss1: 0.747227 Loss2: 1.863017
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.847596 Loss1: 0.470321 Loss2: 1.377275
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.750116 Loss1: 0.325420 Loss2: 1.424696
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630589 Loss1: 0.261534 Loss2: 1.369055
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.632985 Loss1: 0.744870 Loss2: 1.888115
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.898432 Loss1: 0.493214 Loss2: 1.405218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.747536 Loss1: 0.302426 Loss2: 1.445109 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.709835 Loss1: 0.291327 Loss2: 1.418508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.659517 Loss1: 0.233563 Loss2: 1.425953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516172 Loss1: 0.119365 Loss2: 1.396807 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454157 Loss1: 0.071100 Loss2: 1.383057 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398790 Loss1: 0.039682 Loss2: 1.359109 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.710373 Loss1: 0.346010 Loss2: 1.364363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.585063 Loss1: 0.215675 Loss2: 1.369388 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546246 Loss1: 0.181974 Loss2: 1.364271
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.405563 Loss1: 0.596890 Loss2: 1.808673
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509699 Loss1: 0.130465 Loss2: 1.379234
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.703351 Loss1: 0.351839 Loss2: 1.351511
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.452313 Loss1: 0.105114 Loss2: 1.347198
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.586053 Loss1: 0.208728 Loss2: 1.377325
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417538 Loss1: 0.073795 Loss2: 1.343742
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581949 Loss1: 0.237056 Loss2: 1.344893
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403720 Loss1: 0.064328 Loss2: 1.339392
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.472738 Loss1: 0.131707 Loss2: 1.341031
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392282 Loss1: 0.056845 Loss2: 1.335437
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.440713 Loss1: 0.111090 Loss2: 1.329623
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425567 Loss1: 0.096387 Loss2: 1.329179
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407286 Loss1: 0.090488 Loss2: 1.316799
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376199 Loss1: 0.064714 Loss2: 1.311486
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365221 Loss1: 0.058285 Loss2: 1.306936
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.529858 Loss1: 0.652549 Loss2: 1.877308
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.834287 Loss1: 0.426422 Loss2: 1.407866
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.706208 Loss1: 0.247678 Loss2: 1.458530
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649947 Loss1: 0.248554 Loss2: 1.401393
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631540 Loss1: 0.209696 Loss2: 1.421845
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.513050 Loss1: 0.610515 Loss2: 1.902535
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569362 Loss1: 0.159825 Loss2: 1.409537
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.707000 Loss1: 0.317867 Loss2: 1.389133
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.625126 Loss1: 0.222533 Loss2: 1.402593
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516696 Loss1: 0.115517 Loss2: 1.401179
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.568620 Loss1: 0.184366 Loss2: 1.384254
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460147 Loss1: 0.068032 Loss2: 1.392116
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.547213 Loss1: 0.157146 Loss2: 1.390067
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458390 Loss1: 0.068723 Loss2: 1.389667
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508560 Loss1: 0.132458 Loss2: 1.376102
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457200 Loss1: 0.069660 Loss2: 1.387540
-(DefaultActor pid=3765) >> Training accuracy: 0.975586
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484684 Loss1: 0.106921 Loss2: 1.377763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444460 Loss1: 0.076723 Loss2: 1.367736 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.733519 Loss1: 0.360719 Loss2: 1.372800 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.546370 Loss1: 0.192722 Loss2: 1.353648 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.584732 Loss1: 0.748889 Loss2: 1.835843
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515482 Loss1: 0.164105 Loss2: 1.351376
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.895358 Loss1: 0.538452 Loss2: 1.356906
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.428048 Loss1: 0.084833 Loss2: 1.343215
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.767402 Loss1: 0.356221 Loss2: 1.411180
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410178 Loss1: 0.073177 Loss2: 1.337001
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.622957 Loss1: 0.266982 Loss2: 1.355975
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420746 Loss1: 0.089873 Loss2: 1.330873
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539100 Loss1: 0.184202 Loss2: 1.354898
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406702 Loss1: 0.077533 Loss2: 1.329169
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512146 Loss1: 0.164574 Loss2: 1.347572
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393592 Loss1: 0.070883 Loss2: 1.322709
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430308 Loss1: 0.098191 Loss2: 1.332117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402749 Loss1: 0.076795 Loss2: 1.325953 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.745839 Loss1: 0.405261 Loss2: 1.340579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533091 Loss1: 0.189652 Loss2: 1.343439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.478610 Loss1: 0.651381 Loss2: 1.827229
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.482536 Loss1: 0.136746 Loss2: 1.345790
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.806051 Loss1: 0.402336 Loss2: 1.403715
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491232 Loss1: 0.143355 Loss2: 1.347877
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.471535 Loss1: 0.138356 Loss2: 1.333179
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.665410 Loss1: 0.243160 Loss2: 1.422249
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483420 Loss1: 0.139368 Loss2: 1.344052
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591457 Loss1: 0.200561 Loss2: 1.390896
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.513868 Loss1: 0.164898 Loss2: 1.348970
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571191 Loss1: 0.173959 Loss2: 1.397232
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426402 Loss1: 0.088547 Loss2: 1.337856
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.547016 Loss1: 0.161651 Loss2: 1.385365
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516350 Loss1: 0.132526 Loss2: 1.383824
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461683 Loss1: 0.086042 Loss2: 1.375641
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438351 Loss1: 0.066559 Loss2: 1.371792
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421584 Loss1: 0.054179 Loss2: 1.367405
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.506146 Loss1: 0.661928 Loss2: 1.844219
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.795467 Loss1: 0.428076 Loss2: 1.367391
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.699141 Loss1: 0.293483 Loss2: 1.405658
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.519734 Loss1: 0.176716 Loss2: 1.343018
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471762 Loss1: 0.134470 Loss2: 1.337292
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.667530 Loss1: 0.767848 Loss2: 1.899682
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.767453 Loss1: 0.398966 Loss2: 1.368487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.731234 Loss1: 0.324476 Loss2: 1.406758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588118 Loss1: 0.211417 Loss2: 1.376702 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377198 Loss1: 0.067424 Loss2: 1.309774
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.486225 Loss1: 0.128551 Loss2: 1.357674
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372046 Loss1: 0.063558 Loss2: 1.308488
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449773 Loss1: 0.087816 Loss2: 1.361957
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.413471 Loss1: 0.068649 Loss2: 1.344822
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407296 Loss1: 0.071266 Loss2: 1.336030
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412636 Loss1: 0.074721 Loss2: 1.337915
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406951 Loss1: 0.072351 Loss2: 1.334600
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.570546 Loss1: 0.742524 Loss2: 1.828023
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.829170 Loss1: 0.452009 Loss2: 1.377161
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.743224 Loss1: 0.332553 Loss2: 1.410671
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.614765 Loss1: 0.265827 Loss2: 1.348937
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.533326 Loss1: 0.179870 Loss2: 1.353456
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451501 Loss1: 0.116674 Loss2: 1.334827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.407040 Loss1: 0.081903 Loss2: 1.325137 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410314 Loss1: 0.090330 Loss2: 1.319984 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 22:31:04,741 | server.py:236 | fit_round 129 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371743 Loss1: 0.046834 Loss2: 1.324908 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388551 Loss1: 0.070828 Loss2: 1.317723 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.466557 Loss1: 0.104246 Loss2: 1.362311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.470563 Loss1: 0.106364 Loss2: 1.364199 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.590712 Loss1: 0.744784 Loss2: 1.845928
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445453 Loss1: 0.084571 Loss2: 1.360882
-(DefaultActor pid=3764) >> Training accuracy: 0.988051
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.660246 Loss1: 0.272595 Loss2: 1.387652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492489 Loss1: 0.148638 Loss2: 1.343851 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462250 Loss1: 0.127843 Loss2: 1.334407
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.577791 Loss1: 0.708759 Loss2: 1.869032
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479897 Loss1: 0.150357 Loss2: 1.329540
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.839116 Loss1: 0.461381 Loss2: 1.377735
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437659 Loss1: 0.114916 Loss2: 1.322743
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.685002 Loss1: 0.269634 Loss2: 1.415369
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419152 Loss1: 0.097684 Loss2: 1.321468
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.552931 Loss1: 0.179588 Loss2: 1.373343
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393001 Loss1: 0.072798 Loss2: 1.320203
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500921 Loss1: 0.134863 Loss2: 1.366059
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499214 Loss1: 0.140875 Loss2: 1.358339
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504202 Loss1: 0.147366 Loss2: 1.356836
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458644 Loss1: 0.098722 Loss2: 1.359921
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433099 Loss1: 0.084687 Loss2: 1.348412
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.517604 Loss1: 0.613859 Loss2: 1.903745
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389231 Loss1: 0.045043 Loss2: 1.344189
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.753815 Loss1: 0.287390 Loss2: 1.466425 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.615629 Loss1: 0.183706 Loss2: 1.431922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.555434 Loss1: 0.129253 Loss2: 1.426181
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.615862 Loss1: 0.722800 Loss2: 1.893062
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525865 Loss1: 0.115944 Loss2: 1.409921
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.808602 Loss1: 0.397072 Loss2: 1.411530
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.524417 Loss1: 0.116773 Loss2: 1.407645
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682090 Loss1: 0.245405 Loss2: 1.436686
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499824 Loss1: 0.095176 Loss2: 1.404648
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.650913 Loss1: 0.248768 Loss2: 1.402145
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.486106 Loss1: 0.088021 Loss2: 1.398085
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.551300 Loss1: 0.152896 Loss2: 1.398403
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529854 Loss1: 0.138630 Loss2: 1.391224
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491963 Loss1: 0.101166 Loss2: 1.390797
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486866 Loss1: 0.099025 Loss2: 1.387841
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479563 Loss1: 0.098787 Loss2: 1.380777
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429446 Loss1: 0.048728 Loss2: 1.380718
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 22:31:04,741][flwr][DEBUG] - fit_round 129 received 50 results and 0 failures
-INFO flwr 2023-10-11 22:31:46,998 | server.py:125 | fit progress: (129, 2.204728903480993, {'accuracy': 0.5893}, 297614.776855937)
->> Test accuracy: 0.589300
-[2023-10-11 22:31:46,998][flwr][INFO] - fit progress: (129, 2.204728903480993, {'accuracy': 0.5893}, 297614.776855937)
-DEBUG flwr 2023-10-11 22:31:46,999 | server.py:173 | evaluate_round 129: strategy sampled 50 clients (out of 50)
-[2023-10-11 22:31:46,999][flwr][DEBUG] - evaluate_round 129: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 22:40:50,713 | server.py:187 | evaluate_round 129 received 50 results and 0 failures
-[2023-10-11 22:40:50,713][flwr][DEBUG] - evaluate_round 129 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 22:40:50,713 | server.py:222 | fit_round 130: strategy sampled 50 clients (out of 50)
-[2023-10-11 22:40:50,713][flwr][DEBUG] - fit_round 130: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.800936 Loss1: 0.895898 Loss2: 1.905039
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.866630 Loss1: 0.481495 Loss2: 1.385135
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.735574 Loss1: 0.313371 Loss2: 1.422203
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606793 Loss1: 0.243740 Loss2: 1.363053
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555570 Loss1: 0.177702 Loss2: 1.377867 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511968 Loss1: 0.149180 Loss2: 1.362788 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.624508 Loss1: 0.229659 Loss2: 1.394849
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.452821 Loss1: 0.095938 Loss2: 1.356883
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458855 Loss1: 0.102463 Loss2: 1.356392
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.477053 Loss1: 0.120772 Loss2: 1.356280
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428772 Loss1: 0.078316 Loss2: 1.350456
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475624 Loss1: 0.130082 Loss2: 1.345541
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401955 Loss1: 0.062848 Loss2: 1.339107
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447422 Loss1: 0.100943 Loss2: 1.346479
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430806 Loss1: 0.092670 Loss2: 1.338136
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390223 Loss1: 0.052982 Loss2: 1.337241
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394325 Loss1: 0.058729 Loss2: 1.335596
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369590 Loss1: 0.046499 Loss2: 1.323092
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.528725 Loss1: 0.660917 Loss2: 1.867808
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.749085 Loss1: 0.364108 Loss2: 1.384976
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.737746 Loss1: 0.312173 Loss2: 1.425573
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.600053 Loss1: 0.228200 Loss2: 1.371852
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547958 Loss1: 0.159127 Loss2: 1.388831
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.548324 Loss1: 0.727884 Loss2: 1.820440
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516218 Loss1: 0.145552 Loss2: 1.370665
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474878 Loss1: 0.100944 Loss2: 1.373933 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464955 Loss1: 0.097520 Loss2: 1.367435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423963 Loss1: 0.061120 Loss2: 1.362843 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423096 Loss1: 0.066942 Loss2: 1.356154 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408497 Loss1: 0.081508 Loss2: 1.326989 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369110 Loss1: 0.041897 Loss2: 1.327213 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388302 Loss1: 0.065515 Loss2: 1.322787
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.560879 Loss1: 0.665283 Loss2: 1.895596
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.885195 Loss1: 0.499592 Loss2: 1.385603
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.759004 Loss1: 0.291624 Loss2: 1.467380
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.579639 Loss1: 0.205245 Loss2: 1.374394
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.508340 Loss1: 0.136982 Loss2: 1.371358
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.584582 Loss1: 0.707951 Loss2: 1.876631
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.796103 Loss1: 0.384139 Loss2: 1.411964 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.663103 Loss1: 0.252032 Loss2: 1.411071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.597645 Loss1: 0.207236 Loss2: 1.390409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545330 Loss1: 0.153551 Loss2: 1.391779 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465962 Loss1: 0.094395 Loss2: 1.371567 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449025 Loss1: 0.084565 Loss2: 1.364460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.723035 Loss1: 0.685104 Loss2: 2.037931
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436577 Loss1: 0.075825 Loss2: 1.360752
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.836822 Loss1: 0.280687 Loss2: 1.556136 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.717462 Loss1: 0.201398 Loss2: 1.516064 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.716759 Loss1: 0.187617 Loss2: 1.529142
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.566513 Loss1: 0.666135 Loss2: 1.900378
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.790142 Loss1: 0.408263 Loss2: 1.381879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.724029 Loss1: 0.316358 Loss2: 1.407671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619094 Loss1: 0.240291 Loss2: 1.378803 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.612455 Loss1: 0.112350 Loss2: 1.500105
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529870 Loss1: 0.164837 Loss2: 1.365033
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481439 Loss1: 0.121461 Loss2: 1.359978
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.462751 Loss1: 0.109550 Loss2: 1.353202
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.456499 Loss1: 0.101741 Loss2: 1.354759
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438170 Loss1: 0.083877 Loss2: 1.354293
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.611675 Loss1: 0.759573 Loss2: 1.852102
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393324 Loss1: 0.050914 Loss2: 1.342409
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.780342 Loss1: 0.356796 Loss2: 1.423546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506669 Loss1: 0.151607 Loss2: 1.355062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455767 Loss1: 0.113700 Loss2: 1.342067
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.423144 Loss1: 0.579351 Loss2: 1.843793
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.734669 Loss1: 0.375584 Loss2: 1.359085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.654622 Loss1: 0.248266 Loss2: 1.406356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.628223 Loss1: 0.275212 Loss2: 1.353011 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.613440 Loss1: 0.229012 Loss2: 1.384428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445222 Loss1: 0.093739 Loss2: 1.351483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381018 Loss1: 0.046789 Loss2: 1.334229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367272 Loss1: 0.038922 Loss2: 1.328350 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.572068 Loss1: 0.191176 Loss2: 1.380892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476890 Loss1: 0.133816 Loss2: 1.343074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460412 Loss1: 0.121212 Loss2: 1.339199
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.643005 Loss1: 0.769906 Loss2: 1.873100
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441545 Loss1: 0.104980 Loss2: 1.336564
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.802474 Loss1: 0.422692 Loss2: 1.379782
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419172 Loss1: 0.091172 Loss2: 1.328000
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622487 Loss1: 0.228728 Loss2: 1.393759
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419919 Loss1: 0.096871 Loss2: 1.323048
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498064 Loss1: 0.128834 Loss2: 1.369230
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.486675 Loss1: 0.127893 Loss2: 1.358783
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436242 Loss1: 0.112740 Loss2: 1.323502
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.424890 Loss1: 0.081072 Loss2: 1.343818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400642 Loss1: 0.060687 Loss2: 1.339955 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399536 Loss1: 0.065842 Loss2: 1.333694
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.740295 Loss1: 0.828452 Loss2: 1.911842
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.750289 Loss1: 0.406253 Loss2: 1.344036
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.558356 Loss1: 0.199669 Loss2: 1.358687
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.509045 Loss1: 0.168423 Loss2: 1.340622
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.464655 Loss1: 0.142593 Loss2: 1.322062
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.404143 Loss1: 0.082208 Loss2: 1.321935
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.513500 Loss1: 0.638670 Loss2: 1.874830
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.811326 Loss1: 0.428458 Loss2: 1.382868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394271 Loss1: 0.088596 Loss2: 1.305675 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400935 Loss1: 0.094810 Loss2: 1.306125 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997596
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.639060 Loss1: 0.240445 Loss2: 1.398614 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.507298 Loss1: 0.129625 Loss2: 1.377672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.557148 Loss1: 0.687430 Loss2: 1.869718
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467918 Loss1: 0.094364 Loss2: 1.373554
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.914702 Loss1: 0.520554 Loss2: 1.394148
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456079 Loss1: 0.100589 Loss2: 1.355490
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.648093 Loss1: 0.254224 Loss2: 1.393869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.567591 Loss1: 0.181396 Loss2: 1.386194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.535659 Loss1: 0.154291 Loss2: 1.381367
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.607832 Loss1: 0.793921 Loss2: 1.813911
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.783839 Loss1: 0.443821 Loss2: 1.340018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.683689 Loss1: 0.288902 Loss2: 1.394786 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449212 Loss1: 0.082367 Loss2: 1.366845
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561260 Loss1: 0.227470 Loss2: 1.333790
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503867 Loss1: 0.169072 Loss2: 1.334795
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479281 Loss1: 0.144027 Loss2: 1.335254
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440742 Loss1: 0.105397 Loss2: 1.335345
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.391565 Loss1: 0.071217 Loss2: 1.320348
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.394338 Loss1: 0.598563 Loss2: 1.795775
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385602 Loss1: 0.072856 Loss2: 1.312747
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.702825 Loss1: 0.347565 Loss2: 1.355259
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415959 Loss1: 0.103072 Loss2: 1.312887
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527110 Loss1: 0.189088 Loss2: 1.338022 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.435944 Loss1: 0.104567 Loss2: 1.331376 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.430396 Loss1: 0.099973 Loss2: 1.330423
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.455593 Loss1: 0.635226 Loss2: 1.820367
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.744030 Loss1: 0.406732 Loss2: 1.337298
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.397324 Loss1: 0.070576 Loss2: 1.326748
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.631454 Loss1: 0.258818 Loss2: 1.372636
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401618 Loss1: 0.077877 Loss2: 1.323741
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.549198 Loss1: 0.200768 Loss2: 1.348429
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378161 Loss1: 0.063358 Loss2: 1.314803
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461291 Loss1: 0.127242 Loss2: 1.334049 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409397 Loss1: 0.083827 Loss2: 1.325570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410523 Loss1: 0.079655 Loss2: 1.330867
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.537705 Loss1: 0.716588 Loss2: 1.821116
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370095 Loss1: 0.049766 Loss2: 1.320329
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.710369 Loss1: 0.370689 Loss2: 1.339680
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.620743 Loss1: 0.252424 Loss2: 1.368319
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.560007 Loss1: 0.224843 Loss2: 1.335164
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.520099 Loss1: 0.170483 Loss2: 1.349616
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515423 Loss1: 0.179988 Loss2: 1.335435
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484351 Loss1: 0.147747 Loss2: 1.336604
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.330119 Loss1: 0.550274 Loss2: 1.779845
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445647 Loss1: 0.109388 Loss2: 1.336259
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.665856 Loss1: 0.331106 Loss2: 1.334750
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454437 Loss1: 0.127881 Loss2: 1.326556
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.646556 Loss1: 0.259221 Loss2: 1.387335
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428194 Loss1: 0.105125 Loss2: 1.323069
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519737 Loss1: 0.182913 Loss2: 1.336824
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491552 Loss1: 0.140035 Loss2: 1.351516
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495597 Loss1: 0.159338 Loss2: 1.336258
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484399 Loss1: 0.140834 Loss2: 1.343566
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405133 Loss1: 0.077500 Loss2: 1.327633
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.635190 Loss1: 0.762894 Loss2: 1.872296
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400640 Loss1: 0.078269 Loss2: 1.322372
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381694 Loss1: 0.060487 Loss2: 1.321207 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594429 Loss1: 0.239844 Loss2: 1.354585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494180 Loss1: 0.148663 Loss2: 1.345517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.553018 Loss1: 0.199469 Loss2: 1.353549
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.448996 Loss1: 0.641685 Loss2: 1.807310
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.707631 Loss1: 0.376189 Loss2: 1.331442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.596347 Loss1: 0.215997 Loss2: 1.380350 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441588 Loss1: 0.096965 Loss2: 1.344624
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.501409 Loss1: 0.167274 Loss2: 1.334135
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.490045 Loss1: 0.158471 Loss2: 1.331574
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474121 Loss1: 0.145135 Loss2: 1.328985
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446528 Loss1: 0.114728 Loss2: 1.331800
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415587 Loss1: 0.095433 Loss2: 1.320154
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.732040 Loss1: 0.784511 Loss2: 1.947529
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420167 Loss1: 0.096617 Loss2: 1.323550
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.838141 Loss1: 0.357251 Loss2: 1.480890
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384463 Loss1: 0.072360 Loss2: 1.312104
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586041 Loss1: 0.139780 Loss2: 1.446261 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.590462 Loss1: 0.144741 Loss2: 1.445721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.530043 Loss1: 0.091510 Loss2: 1.438533
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.435813 Loss1: 0.616407 Loss2: 1.819406
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.792946 Loss1: 0.453480 Loss2: 1.339466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.646452 Loss1: 0.247800 Loss2: 1.398652 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.521699 Loss1: 0.089051 Loss2: 1.432648
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554626 Loss1: 0.221869 Loss2: 1.332757
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491651 Loss1: 0.145724 Loss2: 1.345927
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439122 Loss1: 0.115066 Loss2: 1.324056
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.411901 Loss1: 0.086589 Loss2: 1.325313
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.362924 Loss1: 0.051207 Loss2: 1.311717
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.595059 Loss1: 0.695928 Loss2: 1.899132
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373005 Loss1: 0.065074 Loss2: 1.307931
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.859069 Loss1: 0.447920 Loss2: 1.411149
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.335230 Loss1: 0.031825 Loss2: 1.303404
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557597 Loss1: 0.151539 Loss2: 1.406058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503074 Loss1: 0.112764 Loss2: 1.390310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.493402 Loss1: 0.106777 Loss2: 1.386626
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.730956 Loss1: 0.796588 Loss2: 1.934367
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.974803 Loss1: 0.577856 Loss2: 1.396947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462316 Loss1: 0.084441 Loss2: 1.377875
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.741922 Loss1: 0.287308 Loss2: 1.454613
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.490654 Loss1: 0.115843 Loss2: 1.374811
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.626820 Loss1: 0.234887 Loss2: 1.391933
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.575919 Loss1: 0.170695 Loss2: 1.405224
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503130 Loss1: 0.115857 Loss2: 1.387272
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465702 Loss1: 0.083313 Loss2: 1.382389
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453093 Loss1: 0.085278 Loss2: 1.367815
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433762 Loss1: 0.066505 Loss2: 1.367257
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.556454 Loss1: 0.702484 Loss2: 1.853971
-(DefaultActor pid=3764) >> Training accuracy: 0.997768
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.908381 Loss1: 0.507663 Loss2: 1.400719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.597947 Loss1: 0.218000 Loss2: 1.379947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516586 Loss1: 0.126249 Loss2: 1.390337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459384 Loss1: 0.088364 Loss2: 1.371020 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.428039 Loss1: 0.066739 Loss2: 1.361299 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408486 Loss1: 0.053394 Loss2: 1.355092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433280 Loss1: 0.076730 Loss2: 1.356551 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496797 Loss1: 0.126883 Loss2: 1.369913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438473 Loss1: 0.082110 Loss2: 1.356363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.433494 Loss1: 0.638326 Loss2: 1.795168 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.632265 Loss1: 0.255815 Loss2: 1.376450 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499731 Loss1: 0.144039 Loss2: 1.355692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.556333 Loss1: 0.695220 Loss2: 1.861113
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480855 Loss1: 0.125970 Loss2: 1.354885
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.847221 Loss1: 0.466770 Loss2: 1.380451
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.437335 Loss1: 0.096361 Loss2: 1.340974
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411869 Loss1: 0.073986 Loss2: 1.337883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388474 Loss1: 0.057896 Loss2: 1.330578 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373657 Loss1: 0.049679 Loss2: 1.323978 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488654 Loss1: 0.133441 Loss2: 1.355213 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.478361 Loss1: 0.120547 Loss2: 1.357815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466118 Loss1: 0.113774 Loss2: 1.352345
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.587065 Loss1: 0.753587 Loss2: 1.833478
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.851610 Loss1: 0.481293 Loss2: 1.370317
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.694208 Loss1: 0.279850 Loss2: 1.414358
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516696 Loss1: 0.166810 Loss2: 1.349886
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.458827 Loss1: 0.106682 Loss2: 1.352145
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.451482 Loss1: 0.656414 Loss2: 1.795068
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.690710 Loss1: 0.343153 Loss2: 1.347556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568689 Loss1: 0.198564 Loss2: 1.370125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534660 Loss1: 0.199912 Loss2: 1.334749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.467546 Loss1: 0.133240 Loss2: 1.334306 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463557 Loss1: 0.133649 Loss2: 1.329907 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433728 Loss1: 0.104753 Loss2: 1.328975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.644864 Loss1: 0.710357 Loss2: 1.934508
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414702 Loss1: 0.096991 Loss2: 1.317711
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.717871 Loss1: 0.242371 Loss2: 1.475499 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.585150 Loss1: 0.142103 Loss2: 1.443047 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.553338 Loss1: 0.122509 Loss2: 1.430828
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.596948 Loss1: 0.795440 Loss2: 1.801508
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.571011 Loss1: 0.144100 Loss2: 1.426911
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.771202 Loss1: 0.417515 Loss2: 1.353687
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.510434 Loss1: 0.083541 Loss2: 1.426893
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.689863 Loss1: 0.310486 Loss2: 1.379377
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.521409 Loss1: 0.101361 Loss2: 1.420048
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562264 Loss1: 0.222861 Loss2: 1.339403
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.517237 Loss1: 0.103892 Loss2: 1.413345
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496983 Loss1: 0.151353 Loss2: 1.345630
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465487 Loss1: 0.142700 Loss2: 1.322787
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404489 Loss1: 0.080718 Loss2: 1.323772
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400400 Loss1: 0.085694 Loss2: 1.314706
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.359106 Loss1: 0.049759 Loss2: 1.309346
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.565260 Loss1: 0.739326 Loss2: 1.825934
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.338458 Loss1: 0.034649 Loss2: 1.303808
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.691071 Loss1: 0.274174 Loss2: 1.416897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.603419 Loss1: 0.229399 Loss2: 1.374020 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.521637 Loss1: 0.168896 Loss2: 1.352741
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.410646 Loss1: 0.596950 Loss2: 1.813696
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.734194 Loss1: 0.372621 Loss2: 1.361573 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.652379 Loss1: 0.258907 Loss2: 1.393472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.621571 Loss1: 0.254399 Loss2: 1.367172 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.632721 Loss1: 0.251346 Loss2: 1.381375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.527904 Loss1: 0.155959 Loss2: 1.371945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414267 Loss1: 0.067449 Loss2: 1.346818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395374 Loss1: 0.056750 Loss2: 1.338624 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630512 Loss1: 0.259775 Loss2: 1.370737 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.521827 Loss1: 0.156580 Loss2: 1.365247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.739626 Loss1: 0.781127 Loss2: 1.958499
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.530917 Loss1: 0.166473 Loss2: 1.364445
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483878 Loss1: 0.122396 Loss2: 1.361481 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.475260 Loss1: 0.123078 Loss2: 1.352182 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445031 Loss1: 0.091007 Loss2: 1.354024 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.482340 Loss1: 0.130839 Loss2: 1.351500 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415767 Loss1: 0.069998 Loss2: 1.345770 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.449190 Loss1: 0.619373 Loss2: 1.829817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.642955 Loss1: 0.256595 Loss2: 1.386360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484863 Loss1: 0.150046 Loss2: 1.334817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462041 Loss1: 0.129418 Loss2: 1.332623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434967 Loss1: 0.110622 Loss2: 1.324345 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398407 Loss1: 0.073008 Loss2: 1.325399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.368857 Loss1: 0.060506 Loss2: 1.308350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.343823 Loss1: 0.033612 Loss2: 1.310211 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381263 Loss1: 0.068072 Loss2: 1.313191 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357404 Loss1: 0.049616 Loss2: 1.307788 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.794749 Loss1: 0.450950 Loss2: 1.343799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.577797 Loss1: 0.235764 Loss2: 1.342033 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525699 Loss1: 0.173785 Loss2: 1.351914
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.570061 Loss1: 0.746353 Loss2: 1.823709
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485882 Loss1: 0.147172 Loss2: 1.338710
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.747731 Loss1: 0.430346 Loss2: 1.317385
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467771 Loss1: 0.143520 Loss2: 1.324250
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.621512 Loss1: 0.262683 Loss2: 1.358829
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555603 Loss1: 0.228195 Loss2: 1.327408
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420052 Loss1: 0.087775 Loss2: 1.332277
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493378 Loss1: 0.178392 Loss2: 1.314986
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391851 Loss1: 0.074235 Loss2: 1.317616
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.448805 Loss1: 0.130856 Loss2: 1.317949
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375989 Loss1: 0.064419 Loss2: 1.311570
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389480 Loss1: 0.091543 Loss2: 1.297937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.338557 Loss1: 0.052286 Loss2: 1.286272 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.507395 Loss1: 0.677291 Loss2: 1.830104
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.797884 Loss1: 0.422472 Loss2: 1.375412
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647605 Loss1: 0.252412 Loss2: 1.395193
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570253 Loss1: 0.207691 Loss2: 1.362562
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.512804 Loss1: 0.702134 Loss2: 1.810670
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.733529 Loss1: 0.386571 Loss2: 1.346959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.615385 Loss1: 0.236991 Loss2: 1.378394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497751 Loss1: 0.171968 Loss2: 1.325782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.478412 Loss1: 0.143515 Loss2: 1.334897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.432153 Loss1: 0.109149 Loss2: 1.323005 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.400322 Loss1: 0.094355 Loss2: 1.305967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.353185 Loss1: 0.055862 Loss2: 1.297323 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.519270 Loss1: 0.638986 Loss2: 1.880285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.666356 Loss1: 0.241800 Loss2: 1.424556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.707440 Loss1: 0.784356 Loss2: 1.923084 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.951014 Loss1: 0.567036 Loss2: 1.383978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.759893 Loss1: 0.298874 Loss2: 1.461019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496045 Loss1: 0.109591 Loss2: 1.386453
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564711 Loss1: 0.175307 Loss2: 1.389405
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503307 Loss1: 0.134180 Loss2: 1.369127
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.490522 Loss1: 0.104347 Loss2: 1.386175
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.488000 Loss1: 0.111120 Loss2: 1.376880
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461960 Loss1: 0.083339 Loss2: 1.378621
-DEBUG flwr 2023-10-11 23:09:16,659 | server.py:236 | fit_round 130 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427644 Loss1: 0.050666 Loss2: 1.376978 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445370 Loss1: 0.085004 Loss2: 1.360367 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.321627 Loss1: 0.520111 Loss2: 1.801517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.707103 Loss1: 0.302135 Loss2: 1.404968 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.617693 Loss1: 0.722675 Loss2: 1.895018
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.539448 Loss1: 0.184531 Loss2: 1.354917
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.522953 Loss1: 0.174153 Loss2: 1.348800 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465450 Loss1: 0.123541 Loss2: 1.341909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419110 Loss1: 0.086204 Loss2: 1.332906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.377378 Loss1: 0.055478 Loss2: 1.321900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379953 Loss1: 0.060203 Loss2: 1.319750 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460908 Loss1: 0.084329 Loss2: 1.376580 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992647
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.486790 Loss1: 0.107748 Loss2: 1.379042 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.620290 Loss1: 0.726050 Loss2: 1.894240
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.842931 Loss1: 0.405603 Loss2: 1.437328
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.703500 Loss1: 0.239792 Loss2: 1.463708
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.643113 Loss1: 0.775656 Loss2: 1.867457
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.608521 Loss1: 0.192132 Loss2: 1.416389
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831783 Loss1: 0.458173 Loss2: 1.373610
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.553328 Loss1: 0.132676 Loss2: 1.420652
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.740018 Loss1: 0.324195 Loss2: 1.415823
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542660 Loss1: 0.130997 Loss2: 1.411663
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611673 Loss1: 0.235032 Loss2: 1.376641
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516480 Loss1: 0.100515 Loss2: 1.415964
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.527153 Loss1: 0.119163 Loss2: 1.407991 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465607 Loss1: 0.062009 Loss2: 1.403598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463814 Loss1: 0.070421 Loss2: 1.393394 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410587 Loss1: 0.065663 Loss2: 1.344923 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 23:09:16,659][flwr][DEBUG] - fit_round 130 received 50 results and 0 failures
-INFO flwr 2023-10-11 23:09:57,233 | server.py:125 | fit progress: (130, 2.2075375242355153, {'accuracy': 0.59}, 299905.01109869697)
->> Test accuracy: 0.590000
-[2023-10-11 23:09:57,233][flwr][INFO] - fit progress: (130, 2.2075375242355153, {'accuracy': 0.59}, 299905.01109869697)
-DEBUG flwr 2023-10-11 23:09:57,233 | server.py:173 | evaluate_round 130: strategy sampled 50 clients (out of 50)
-[2023-10-11 23:09:57,233][flwr][DEBUG] - evaluate_round 130: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 23:18:59,952 | server.py:187 | evaluate_round 130 received 50 results and 0 failures
-[2023-10-11 23:18:59,952][flwr][DEBUG] - evaluate_round 130 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 23:18:59,953 | server.py:222 | fit_round 131: strategy sampled 50 clients (out of 50)
-[2023-10-11 23:18:59,953][flwr][DEBUG] - fit_round 131: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.655447 Loss1: 0.741645 Loss2: 1.913802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.639519 Loss1: 0.189385 Loss2: 1.450134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575051 Loss1: 0.157119 Loss2: 1.417932
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.596476 Loss1: 0.747446 Loss2: 1.849030
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.579809 Loss1: 0.162617 Loss2: 1.417192
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.723144 Loss1: 0.365974 Loss2: 1.357170
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547536 Loss1: 0.128478 Loss2: 1.419058
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600007 Loss1: 0.230904 Loss2: 1.369103
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511143 Loss1: 0.101059 Loss2: 1.410085
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.502456 Loss1: 0.159896 Loss2: 1.342560
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.498117 Loss1: 0.091330 Loss2: 1.406787
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.504488 Loss1: 0.161807 Loss2: 1.342681
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.495805 Loss1: 0.094175 Loss2: 1.401630
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450952 Loss1: 0.111278 Loss2: 1.339675
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.479402 Loss1: 0.075204 Loss2: 1.404198
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419000 Loss1: 0.092460 Loss2: 1.326540
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428781 Loss1: 0.100377 Loss2: 1.328404
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444868 Loss1: 0.111755 Loss2: 1.333113
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390287 Loss1: 0.063630 Loss2: 1.326657
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.414217 Loss1: 0.588850 Loss2: 1.825367
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.851522 Loss1: 0.495036 Loss2: 1.356487
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.699647 Loss1: 0.317143 Loss2: 1.382503
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573163 Loss1: 0.249471 Loss2: 1.323692
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.595285 Loss1: 0.712859 Loss2: 1.882426
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.832670 Loss1: 0.458416 Loss2: 1.374254
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.538283 Loss1: 0.206169 Loss2: 1.332114
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614618 Loss1: 0.214013 Loss2: 1.400605
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473574 Loss1: 0.156140 Loss2: 1.317434
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573481 Loss1: 0.210704 Loss2: 1.362777
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.399753 Loss1: 0.091881 Loss2: 1.307873
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.376656 Loss1: 0.074862 Loss2: 1.301794 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381632 Loss1: 0.083686 Loss2: 1.297946 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423631 Loss1: 0.125257 Loss2: 1.298374 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406833 Loss1: 0.062342 Loss2: 1.344491 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994420
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.501044 Loss1: 0.655360 Loss2: 1.845684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.704701 Loss1: 0.275093 Loss2: 1.429608 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.653301 Loss1: 0.289382 Loss2: 1.363919
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.545647 Loss1: 0.706387 Loss2: 1.839260
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593877 Loss1: 0.213778 Loss2: 1.380099
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.720653 Loss1: 0.352984 Loss2: 1.367669
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491259 Loss1: 0.130050 Loss2: 1.361209
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605386 Loss1: 0.232468 Loss2: 1.372917
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434864 Loss1: 0.071475 Loss2: 1.363389
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599619 Loss1: 0.246698 Loss2: 1.352921
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425014 Loss1: 0.082471 Loss2: 1.342543
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522419 Loss1: 0.164132 Loss2: 1.358287
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455055 Loss1: 0.113913 Loss2: 1.341142
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494158 Loss1: 0.147796 Loss2: 1.346362
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463867 Loss1: 0.113397 Loss2: 1.350469
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446500 Loss1: 0.107955 Loss2: 1.338545
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396701 Loss1: 0.059854 Loss2: 1.336847
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408914 Loss1: 0.082093 Loss2: 1.326821
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361877 Loss1: 0.036858 Loss2: 1.325019
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.520774 Loss1: 0.601928 Loss2: 1.918846
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.783756 Loss1: 0.380591 Loss2: 1.403164
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.739026 Loss1: 0.281825 Loss2: 1.457201
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.643433 Loss1: 0.239344 Loss2: 1.404088
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.708355 Loss1: 0.683639 Loss2: 2.024715
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.667645 Loss1: 0.243644 Loss2: 1.424001
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.946440 Loss1: 0.451038 Loss2: 1.495401
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.596745 Loss1: 0.191270 Loss2: 1.405476
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804574 Loss1: 0.265716 Loss2: 1.538857
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.654336 Loss1: 0.235937 Loss2: 1.418399
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.708254 Loss1: 0.204386 Loss2: 1.503868
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.547979 Loss1: 0.139829 Loss2: 1.408151
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.674163 Loss1: 0.177459 Loss2: 1.496704
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.492673 Loss1: 0.093084 Loss2: 1.399590
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.647987 Loss1: 0.145094 Loss2: 1.502893
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476239 Loss1: 0.086286 Loss2: 1.389953
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.597949 Loss1: 0.102223 Loss2: 1.495726
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.558430 Loss1: 0.085313 Loss2: 1.473117
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.536553 Loss1: 0.056851 Loss2: 1.479702
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.542902 Loss1: 0.072645 Loss2: 1.470256
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.531740 Loss1: 0.599636 Loss2: 1.932104
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.899102 Loss1: 0.472555 Loss2: 1.426547
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.792196 Loss1: 0.302158 Loss2: 1.490038
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.653138 Loss1: 0.239056 Loss2: 1.414082
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.692781 Loss1: 0.801759 Loss2: 1.891022
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.633811 Loss1: 0.211658 Loss2: 1.422154
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.877030 Loss1: 0.492772 Loss2: 1.384259
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.559408 Loss1: 0.143870 Loss2: 1.415538
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633180 Loss1: 0.240247 Loss2: 1.392934
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.594653 Loss1: 0.190124 Loss2: 1.404529
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.547821 Loss1: 0.189050 Loss2: 1.358771
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.567703 Loss1: 0.155916 Loss2: 1.411787
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476084 Loss1: 0.125319 Loss2: 1.350766
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.522046 Loss1: 0.124206 Loss2: 1.397841
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.428048 Loss1: 0.088745 Loss2: 1.339303
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.534427 Loss1: 0.130479 Loss2: 1.403948
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.413582 Loss1: 0.078729 Loss2: 1.334852
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414923 Loss1: 0.088266 Loss2: 1.326657
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397997 Loss1: 0.066948 Loss2: 1.331049
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378858 Loss1: 0.053380 Loss2: 1.325477
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.699188 Loss1: 0.769592 Loss2: 1.929596
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.845011 Loss1: 0.414518 Loss2: 1.430493
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684179 Loss1: 0.222376 Loss2: 1.461804
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.571334 Loss1: 0.151208 Loss2: 1.420126
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.547495 Loss1: 0.695535 Loss2: 1.851960
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.801933 Loss1: 0.432262 Loss2: 1.369671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.706128 Loss1: 0.277207 Loss2: 1.428921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574404 Loss1: 0.206385 Loss2: 1.368019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.578422 Loss1: 0.196370 Loss2: 1.382052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567924 Loss1: 0.190848 Loss2: 1.377075 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.493818 Loss1: 0.102742 Loss2: 1.391076
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.515522 Loss1: 0.138961 Loss2: 1.376560
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452012 Loss1: 0.084663 Loss2: 1.367349
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412272 Loss1: 0.058573 Loss2: 1.353699
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392734 Loss1: 0.047413 Loss2: 1.345320
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.903734 Loss1: 0.835387 Loss2: 2.068347
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.958015 Loss1: 0.506195 Loss2: 1.451820
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731828 Loss1: 0.236740 Loss2: 1.495088
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.646155 Loss1: 0.201123 Loss2: 1.445032
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.514191 Loss1: 0.632003 Loss2: 1.882189
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.718892 Loss1: 0.336554 Loss2: 1.382338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509452 Loss1: 0.085247 Loss2: 1.424205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.523990 Loss1: 0.100873 Loss2: 1.423117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.487849 Loss1: 0.075190 Loss2: 1.412658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476824 Loss1: 0.065750 Loss2: 1.411074 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419809 Loss1: 0.057658 Loss2: 1.362151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400200 Loss1: 0.047753 Loss2: 1.352447 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386410 Loss1: 0.039104 Loss2: 1.347306
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.643246 Loss1: 0.727253 Loss2: 1.915994
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.913276 Loss1: 0.540826 Loss2: 1.372450
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.715015 Loss1: 0.283007 Loss2: 1.432008
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.641260 Loss1: 0.261879 Loss2: 1.379381
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.567795 Loss1: 0.183362 Loss2: 1.384433
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.501723 Loss1: 0.124839 Loss2: 1.376884
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.686646 Loss1: 0.794631 Loss2: 1.892015
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.780503 Loss1: 0.421736 Loss2: 1.358768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.617561 Loss1: 0.226939 Loss2: 1.390622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.503267 Loss1: 0.157392 Loss2: 1.345874 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.453346 Loss1: 0.115281 Loss2: 1.338065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.424181 Loss1: 0.094355 Loss2: 1.329825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.382398 Loss1: 0.065224 Loss2: 1.317174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387497 Loss1: 0.067622 Loss2: 1.319874
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.425699 Loss1: 0.647424 Loss2: 1.778275
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.819293 Loss1: 0.460127 Loss2: 1.359166
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684889 Loss1: 0.299190 Loss2: 1.385699
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526298 Loss1: 0.193883 Loss2: 1.332415
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.467571 Loss1: 0.130534 Loss2: 1.337036
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.481967 Loss1: 0.650983 Loss2: 1.830984
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.399668 Loss1: 0.081486 Loss2: 1.318182
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.398539 Loss1: 0.083216 Loss2: 1.315323 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394111 Loss1: 0.088340 Loss2: 1.305771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.393851 Loss1: 0.083002 Loss2: 1.310848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.376527 Loss1: 0.071678 Loss2: 1.304849 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484250 Loss1: 0.126491 Loss2: 1.357760 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462971 Loss1: 0.112448 Loss2: 1.350523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415537 Loss1: 0.069334 Loss2: 1.346204
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.427714 Loss1: 0.614710 Loss2: 1.813004
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.773687 Loss1: 0.439608 Loss2: 1.334079
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.654490 Loss1: 0.273280 Loss2: 1.381210
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.539197 Loss1: 0.195657 Loss2: 1.343539
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503345 Loss1: 0.165786 Loss2: 1.337559
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.620448 Loss1: 0.730352 Loss2: 1.890095
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.806042 Loss1: 0.413294 Loss2: 1.392748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.687310 Loss1: 0.240413 Loss2: 1.446897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.617545 Loss1: 0.226003 Loss2: 1.391542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544069 Loss1: 0.146891 Loss2: 1.397179 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360601 Loss1: 0.056269 Loss2: 1.304332
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531598 Loss1: 0.145074 Loss2: 1.386525
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.462171 Loss1: 0.086181 Loss2: 1.375990
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442551 Loss1: 0.075674 Loss2: 1.366877
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429510 Loss1: 0.065219 Loss2: 1.364291
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429019 Loss1: 0.065280 Loss2: 1.363739
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.645707 Loss1: 0.734312 Loss2: 1.911395
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.746400 Loss1: 0.344836 Loss2: 1.401564
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.631535 Loss1: 0.213144 Loss2: 1.418391
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574695 Loss1: 0.192981 Loss2: 1.381714
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.541611 Loss1: 0.162338 Loss2: 1.379273
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.565656 Loss1: 0.647981 Loss2: 1.917674
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.822958 Loss1: 0.342488 Loss2: 1.480470 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.695160 Loss1: 0.239002 Loss2: 1.456158 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.646602 Loss1: 0.209053 Loss2: 1.437549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.567560 Loss1: 0.134327 Loss2: 1.433233 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549290 Loss1: 0.127160 Loss2: 1.422130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.509755 Loss1: 0.091714 Loss2: 1.418041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458773 Loss1: 0.054873 Loss2: 1.403901 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736122 Loss1: 0.294389 Loss2: 1.441733 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587862 Loss1: 0.194008 Loss2: 1.393854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551241 Loss1: 0.164103 Loss2: 1.387137
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.429551 Loss1: 0.669896 Loss2: 1.759655
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518356 Loss1: 0.132920 Loss2: 1.385436
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.832261 Loss1: 0.484216 Loss2: 1.348044
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479735 Loss1: 0.103527 Loss2: 1.376207
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.648719 Loss1: 0.276922 Loss2: 1.371797
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448377 Loss1: 0.081502 Loss2: 1.366875
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.484835 Loss1: 0.157014 Loss2: 1.327821
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436799 Loss1: 0.074857 Loss2: 1.361942
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.422367 Loss1: 0.111130 Loss2: 1.311237
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439355 Loss1: 0.131832 Loss2: 1.307522
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.421814 Loss1: 0.115840 Loss2: 1.305974
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.387021 Loss1: 0.085529 Loss2: 1.301492
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381849 Loss1: 0.080776 Loss2: 1.301074
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.794551 Loss1: 0.750987 Loss2: 2.043564
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.346210 Loss1: 0.057822 Loss2: 1.288388
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.823520 Loss1: 0.242949 Loss2: 1.580571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.732509 Loss1: 0.190713 Loss2: 1.541796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.719112 Loss1: 0.187470 Loss2: 1.531642
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.428803 Loss1: 0.581483 Loss2: 1.847320
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.772499 Loss1: 0.407580 Loss2: 1.364919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.639727 Loss1: 0.241828 Loss2: 1.397899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.623928 Loss1: 0.250951 Loss2: 1.372977 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.608160 Loss1: 0.231816 Loss2: 1.376344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497200 Loss1: 0.132685 Loss2: 1.364515 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454543 Loss1: 0.109507 Loss2: 1.345035 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442194 Loss1: 0.094851 Loss2: 1.347343 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.634832 Loss1: 0.250945 Loss2: 1.383887 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533386 Loss1: 0.175829 Loss2: 1.357557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.428117 Loss1: 0.093080 Loss2: 1.335036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427618 Loss1: 0.093828 Loss2: 1.333790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419233 Loss1: 0.087688 Loss2: 1.331544 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983073
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.566011 Loss1: 0.189792 Loss2: 1.376219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464045 Loss1: 0.103639 Loss2: 1.360406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.406611 Loss1: 0.588794 Loss2: 1.817817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.725080 Loss1: 0.382813 Loss2: 1.342267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.621295 Loss1: 0.234792 Loss2: 1.386503 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529267 Loss1: 0.179043 Loss2: 1.350224 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498424 Loss1: 0.146808 Loss2: 1.351616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414970 Loss1: 0.084357 Loss2: 1.330613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400731 Loss1: 0.072039 Loss2: 1.328692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.394692 Loss1: 0.069447 Loss2: 1.325245 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557981 Loss1: 0.170953 Loss2: 1.387028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499353 Loss1: 0.109862 Loss2: 1.389490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.613654 Loss1: 0.749817 Loss2: 1.863837 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.836630 Loss1: 0.464456 Loss2: 1.372174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.696455 Loss1: 0.282220 Loss2: 1.414235 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.585666 Loss1: 0.214430 Loss2: 1.371236 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503986 Loss1: 0.134792 Loss2: 1.369194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460198 Loss1: 0.103858 Loss2: 1.356339
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.525611 Loss1: 0.700592 Loss2: 1.825019
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.802310 Loss1: 0.446144 Loss2: 1.356166 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433528 Loss1: 0.081866 Loss2: 1.351662
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726073 Loss1: 0.323794 Loss2: 1.402278
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636482 Loss1: 0.265864 Loss2: 1.370619
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.608458 Loss1: 0.240257 Loss2: 1.368201
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504615 Loss1: 0.142016 Loss2: 1.362599
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478089 Loss1: 0.130689 Loss2: 1.347400
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.434957 Loss1: 0.642450 Loss2: 1.792508
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.476744 Loss1: 0.125238 Loss2: 1.351506
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473585 Loss1: 0.125590 Loss2: 1.347996
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.746988 Loss1: 0.398044 Loss2: 1.348944
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452551 Loss1: 0.106886 Loss2: 1.345664
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.613052 Loss1: 0.235392 Loss2: 1.377660
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527439 Loss1: 0.196802 Loss2: 1.330636
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.502939 Loss1: 0.159823 Loss2: 1.343117
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.501489 Loss1: 0.173506 Loss2: 1.327983
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467809 Loss1: 0.145793 Loss2: 1.322016
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.390704 Loss1: 0.566913 Loss2: 1.823790
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.788441 Loss1: 0.420150 Loss2: 1.368290 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.637949 Loss1: 0.235267 Loss2: 1.402682 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581910 Loss1: 0.227514 Loss2: 1.354396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470859 Loss1: 0.116482 Loss2: 1.354377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400241 Loss1: 0.061574 Loss2: 1.338667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425737 Loss1: 0.084118 Loss2: 1.341619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.681457 Loss1: 0.257939 Loss2: 1.423519 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975586
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555098 Loss1: 0.166674 Loss2: 1.388424 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459142 Loss1: 0.088815 Loss2: 1.370327 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448081 Loss1: 0.076891 Loss2: 1.371189
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.395191 Loss1: 0.596168 Loss2: 1.799024
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435689 Loss1: 0.064722 Loss2: 1.370967
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.747535 Loss1: 0.386744 Loss2: 1.360791
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430323 Loss1: 0.067559 Loss2: 1.362763
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.690707 Loss1: 0.286452 Loss2: 1.404256
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.582186 Loss1: 0.226401 Loss2: 1.355785
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.540087 Loss1: 0.180981 Loss2: 1.359106
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519669 Loss1: 0.161298 Loss2: 1.358370
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486453 Loss1: 0.130976 Loss2: 1.355476
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.399231 Loss1: 0.548805 Loss2: 1.850426
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.718156 Loss1: 0.368029 Loss2: 1.350127 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.664093 Loss1: 0.266918 Loss2: 1.397175 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374979 Loss1: 0.046157 Loss2: 1.328821
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547401 Loss1: 0.189426 Loss2: 1.357975
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.512857 Loss1: 0.163934 Loss2: 1.348923
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475994 Loss1: 0.120493 Loss2: 1.355501
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464798 Loss1: 0.119530 Loss2: 1.345269
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429923 Loss1: 0.083532 Loss2: 1.346390
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.381397 Loss1: 0.528582 Loss2: 1.852816
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408499 Loss1: 0.068903 Loss2: 1.339595
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.771271 Loss1: 0.378380 Loss2: 1.392892
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414686 Loss1: 0.078139 Loss2: 1.336547
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.585723 Loss1: 0.201662 Loss2: 1.384062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.545747 Loss1: 0.155316 Loss2: 1.390430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.561334 Loss1: 0.174638 Loss2: 1.386697
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.550110 Loss1: 0.676292 Loss2: 1.873819
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.727577 Loss1: 0.369778 Loss2: 1.357800
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485187 Loss1: 0.107754 Loss2: 1.377433
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.569762 Loss1: 0.192104 Loss2: 1.377658
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.437997 Loss1: 0.067298 Loss2: 1.370699
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.507676 Loss1: 0.154267 Loss2: 1.353409
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412271 Loss1: 0.053533 Loss2: 1.358737
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.568443 Loss1: 0.209863 Loss2: 1.358580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.431841 Loss1: 0.092494 Loss2: 1.339347 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412613 Loss1: 0.079196 Loss2: 1.333416
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.434871 Loss1: 0.621201 Loss2: 1.813670
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.803046 Loss1: 0.420143 Loss2: 1.382903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.587495 Loss1: 0.210340 Loss2: 1.377155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513281 Loss1: 0.147179 Loss2: 1.366101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500916 Loss1: 0.129427 Loss2: 1.371488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484680 Loss1: 0.118730 Loss2: 1.365950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.492156 Loss1: 0.127851 Loss2: 1.364305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416668 Loss1: 0.057541 Loss2: 1.359128 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523584 Loss1: 0.153291 Loss2: 1.370293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443064 Loss1: 0.087580 Loss2: 1.355484 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.521542 Loss1: 0.691366 Loss2: 1.830175
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460806 Loss1: 0.107495 Loss2: 1.353311
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.827271 Loss1: 0.466657 Loss2: 1.360614
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430937 Loss1: 0.078719 Loss2: 1.352218
-(DefaultActor pid=3765) >> Training accuracy: 0.987132
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554158 Loss1: 0.192179 Loss2: 1.361978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427902 Loss1: 0.086817 Loss2: 1.341085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428867 Loss1: 0.096196 Loss2: 1.332671
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.562532 Loss1: 0.666217 Loss2: 1.896315
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.861249 Loss1: 0.459860 Loss2: 1.401390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.672473 Loss1: 0.245429 Loss2: 1.427044 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.353134 Loss1: 0.037226 Loss2: 1.315907
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554378 Loss1: 0.167642 Loss2: 1.386736
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515197 Loss1: 0.127031 Loss2: 1.388166
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485177 Loss1: 0.112148 Loss2: 1.373029
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464890 Loss1: 0.094707 Loss2: 1.370183
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462878 Loss1: 0.086943 Loss2: 1.375935
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.489221 Loss1: 0.674302 Loss2: 1.814919
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.483667 Loss1: 0.110769 Loss2: 1.372898
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.855215 Loss1: 0.489477 Loss2: 1.365738
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420819 Loss1: 0.052966 Loss2: 1.367852
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.598531 Loss1: 0.247175 Loss2: 1.351356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481544 Loss1: 0.134012 Loss2: 1.347533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431872 Loss1: 0.091639 Loss2: 1.340232
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.466264 Loss1: 0.655468 Loss2: 1.810796
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.737839 Loss1: 0.377984 Loss2: 1.359855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.668206 Loss1: 0.268232 Loss2: 1.399974 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583500 Loss1: 0.216542 Loss2: 1.366958 [repeated 2x across cluster]
-DEBUG flwr 2023-10-11 23:47:38,409 | server.py:236 | fit_round 131 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438124 Loss1: 0.085785 Loss2: 1.352339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423021 Loss1: 0.083676 Loss2: 1.339345 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402561 Loss1: 0.070618 Loss2: 1.331943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.672240 Loss1: 0.255190 Loss2: 1.417050 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518979 Loss1: 0.147128 Loss2: 1.371851 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455180 Loss1: 0.104915 Loss2: 1.350265 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.489630 Loss1: 0.639223 Loss2: 1.850407 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.800525 Loss1: 0.436594 Loss2: 1.363932 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.477148 Loss1: 0.137941 Loss2: 1.339207 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.422774 Loss1: 0.094135 Loss2: 1.328639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.556248 Loss1: 0.709541 Loss2: 1.846708 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.813435 Loss1: 0.436887 Loss2: 1.376548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.693746 Loss1: 0.264896 Loss2: 1.428850 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514975 Loss1: 0.142493 Loss2: 1.372482 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430389 Loss1: 0.077021 Loss2: 1.353368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412389 Loss1: 0.063516 Loss2: 1.348874
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.508610 Loss1: 0.665301 Loss2: 1.843309
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.755356 Loss1: 0.387861 Loss2: 1.367495 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656179 Loss1: 0.265088 Loss2: 1.391091 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515240 Loss1: 0.164742 Loss2: 1.350497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450774 Loss1: 0.113346 Loss2: 1.337427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419913 Loss1: 0.093523 Loss2: 1.326390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398615 Loss1: 0.076163 Loss2: 1.322452 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406401 Loss1: 0.085038 Loss2: 1.321363 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548908 Loss1: 0.181138 Loss2: 1.367770 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488081 Loss1: 0.115162 Loss2: 1.372919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480838 Loss1: 0.128306 Loss2: 1.352532 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-11 23:47:38,409][flwr][DEBUG] - fit_round 131 received 50 results and 0 failures
-INFO flwr 2023-10-11 23:48:19,830 | server.py:125 | fit progress: (131, 2.212314099168625, {'accuracy': 0.5878}, 302207.60819046496)
->> Test accuracy: 0.587800
-[2023-10-11 23:48:19,830][flwr][INFO] - fit progress: (131, 2.212314099168625, {'accuracy': 0.5878}, 302207.60819046496)
-DEBUG flwr 2023-10-11 23:48:19,830 | server.py:173 | evaluate_round 131: strategy sampled 50 clients (out of 50)
-[2023-10-11 23:48:19,830][flwr][DEBUG] - evaluate_round 131: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-11 23:57:23,062 | server.py:187 | evaluate_round 131 received 50 results and 0 failures
-[2023-10-11 23:57:23,062][flwr][DEBUG] - evaluate_round 131 received 50 results and 0 failures
-DEBUG flwr 2023-10-11 23:57:23,063 | server.py:222 | fit_round 132: strategy sampled 50 clients (out of 50)
-[2023-10-11 23:57:23,063][flwr][DEBUG] - fit_round 132: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.573851 Loss1: 0.692672 Loss2: 1.881179 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690794 Loss1: 0.269910 Loss2: 1.420884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587510 Loss1: 0.212556 Loss2: 1.374955
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.562005 Loss1: 0.660835 Loss2: 1.901170
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.528557 Loss1: 0.149665 Loss2: 1.378892
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.811514 Loss1: 0.378983 Loss2: 1.432531
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513099 Loss1: 0.140849 Loss2: 1.372250
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701455 Loss1: 0.228951 Loss2: 1.472504
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447403 Loss1: 0.080379 Loss2: 1.367024
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.608665 Loss1: 0.192196 Loss2: 1.416469
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429088 Loss1: 0.068547 Loss2: 1.360541
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.595399 Loss1: 0.164591 Loss2: 1.430807
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445888 Loss1: 0.090631 Loss2: 1.355258
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524717 Loss1: 0.111541 Loss2: 1.413176
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427225 Loss1: 0.068755 Loss2: 1.358470
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.535854 Loss1: 0.129087 Loss2: 1.406767
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.522576 Loss1: 0.109054 Loss2: 1.413523
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497852 Loss1: 0.092395 Loss2: 1.405456
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.490743 Loss1: 0.096791 Loss2: 1.393952
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.454506 Loss1: 0.603796 Loss2: 1.850710
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.781761 Loss1: 0.424862 Loss2: 1.356899
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.732683 Loss1: 0.310726 Loss2: 1.421956
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557654 Loss1: 0.211505 Loss2: 1.346149
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.330679 Loss1: 0.532985 Loss2: 1.797694
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.494990 Loss1: 0.146456 Loss2: 1.348534
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.692945 Loss1: 0.332517 Loss2: 1.360428
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592182 Loss1: 0.207465 Loss2: 1.384717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.506265 Loss1: 0.160344 Loss2: 1.345921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549059 Loss1: 0.194026 Loss2: 1.355033 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530686 Loss1: 0.180343 Loss2: 1.350343 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.528817 Loss1: 0.175380 Loss2: 1.353437 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444482 Loss1: 0.098302 Loss2: 1.346181 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.680507 Loss1: 0.775732 Loss2: 1.904776 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.682669 Loss1: 0.271377 Loss2: 1.411292 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.509185 Loss1: 0.135014 Loss2: 1.374171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472674 Loss1: 0.108357 Loss2: 1.364317 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460459 Loss1: 0.106413 Loss2: 1.354046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419348 Loss1: 0.066789 Loss2: 1.352559 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397595 Loss1: 0.054726 Loss2: 1.342869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370382 Loss1: 0.030434 Loss2: 1.339948 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445563 Loss1: 0.113393 Loss2: 1.332170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369705 Loss1: 0.046623 Loss2: 1.323082 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377749 Loss1: 0.061557 Loss2: 1.316192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.741776 Loss1: 0.817254 Loss2: 1.924522
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.892703 Loss1: 0.513849 Loss2: 1.378853
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.747019 Loss1: 0.338705 Loss2: 1.408314
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649169 Loss1: 0.255708 Loss2: 1.393460
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589741 Loss1: 0.204094 Loss2: 1.385647
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493869 Loss1: 0.122416 Loss2: 1.371454
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.462598 Loss1: 0.635286 Loss2: 1.827312
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.673015 Loss1: 0.341525 Loss2: 1.331490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608690 Loss1: 0.245668 Loss2: 1.363022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526038 Loss1: 0.194742 Loss2: 1.331296 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997596
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477167 Loss1: 0.146809 Loss2: 1.330358 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390972 Loss1: 0.081801 Loss2: 1.309171 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.360109 Loss1: 0.056481 Loss2: 1.303628
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.539800 Loss1: 0.631380 Loss2: 1.908420
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.325888 Loss1: 0.033695 Loss2: 1.292193
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.824097 Loss1: 0.423557 Loss2: 1.400540
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.753007 Loss1: 0.300974 Loss2: 1.452033
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602317 Loss1: 0.213515 Loss2: 1.388802
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513293 Loss1: 0.123125 Loss2: 1.390169
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478125 Loss1: 0.094506 Loss2: 1.383619
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.532382 Loss1: 0.676638 Loss2: 1.855745
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450124 Loss1: 0.080891 Loss2: 1.369234
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.771085 Loss1: 0.368209 Loss2: 1.402876
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415792 Loss1: 0.055216 Loss2: 1.360575
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.648384 Loss1: 0.235813 Loss2: 1.412570
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409201 Loss1: 0.054508 Loss2: 1.354693
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397444 Loss1: 0.044206 Loss2: 1.353238
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.590029 Loss1: 0.207481 Loss2: 1.382547
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.551086 Loss1: 0.159039 Loss2: 1.392047
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.541140 Loss1: 0.158213 Loss2: 1.382927
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483400 Loss1: 0.106303 Loss2: 1.377097
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.479851 Loss1: 0.105883 Loss2: 1.373967
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.836623 Loss1: 0.798403 Loss2: 2.038220
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463186 Loss1: 0.088337 Loss2: 1.374849
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437870 Loss1: 0.071922 Loss2: 1.365948 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.582972 Loss1: 0.164256 Loss2: 1.418716 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.541387 Loss1: 0.130773 Loss2: 1.410614 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472517 Loss1: 0.075513 Loss2: 1.397004 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440967 Loss1: 0.057036 Loss2: 1.383931 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.616085 Loss1: 0.248357 Loss2: 1.367728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545071 Loss1: 0.186587 Loss2: 1.358484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.799134 Loss1: 0.879127 Loss2: 1.920007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.828935 Loss1: 0.448545 Loss2: 1.380390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741497 Loss1: 0.331511 Loss2: 1.409986 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569617 Loss1: 0.193211 Loss2: 1.376407 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370370 Loss1: 0.046315 Loss2: 1.324055
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.545768 Loss1: 0.179252 Loss2: 1.366515
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.521141 Loss1: 0.145868 Loss2: 1.375274
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467934 Loss1: 0.102947 Loss2: 1.364987
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.455675 Loss1: 0.100022 Loss2: 1.355652
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453304 Loss1: 0.099134 Loss2: 1.354170
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427488 Loss1: 0.073429 Loss2: 1.354059
-(DefaultActor pid=3765) >> Training accuracy: 0.983259
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.675024 Loss1: 0.747759 Loss2: 1.927266
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.785889 Loss1: 0.377312 Loss2: 1.408577
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.706619 Loss1: 0.269367 Loss2: 1.437252
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.668264 Loss1: 0.253871 Loss2: 1.414393
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.580302 Loss1: 0.169704 Loss2: 1.410598
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.522106 Loss1: 0.710119 Loss2: 1.811988
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.526397 Loss1: 0.127378 Loss2: 1.399019
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.711244 Loss1: 0.366628 Loss2: 1.344617
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487592 Loss1: 0.098461 Loss2: 1.389130
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.637912 Loss1: 0.268148 Loss2: 1.369764
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486649 Loss1: 0.095053 Loss2: 1.391596
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.553491 Loss1: 0.213434 Loss2: 1.340057
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457588 Loss1: 0.075327 Loss2: 1.382261
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527030 Loss1: 0.183075 Loss2: 1.343955
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441638 Loss1: 0.064409 Loss2: 1.377229
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459910 Loss1: 0.131807 Loss2: 1.328103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.392281 Loss1: 0.076940 Loss2: 1.315341 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364772 Loss1: 0.052354 Loss2: 1.312418
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.698767 Loss1: 0.726529 Loss2: 1.972238
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.970354 Loss1: 0.486820 Loss2: 1.483534
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.838415 Loss1: 0.317073 Loss2: 1.521342
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.738804 Loss1: 0.265117 Loss2: 1.473687
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.692390 Loss1: 0.220616 Loss2: 1.471775
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.564193 Loss1: 0.704082 Loss2: 1.860110
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.583096 Loss1: 0.117904 Loss2: 1.465192
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.575106 Loss1: 0.117799 Loss2: 1.457307 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.560322 Loss1: 0.110700 Loss2: 1.449622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.525807 Loss1: 0.076263 Loss2: 1.449543 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.514842 Loss1: 0.078685 Loss2: 1.436157 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.954167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476045 Loss1: 0.103050 Loss2: 1.372994 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433663 Loss1: 0.074348 Loss2: 1.359315 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414186 Loss1: 0.060025 Loss2: 1.354161
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.654144 Loss1: 0.788410 Loss2: 1.865734
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.836048 Loss1: 0.459472 Loss2: 1.376576
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.717969 Loss1: 0.299057 Loss2: 1.418912
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.585679 Loss1: 0.212988 Loss2: 1.372691
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.562643 Loss1: 0.189430 Loss2: 1.373213
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.357620 Loss1: 0.572018 Loss2: 1.785603
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495554 Loss1: 0.132400 Loss2: 1.363154
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488877 Loss1: 0.127382 Loss2: 1.361495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.630610 Loss1: 0.255051 Loss2: 1.375558
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469334 Loss1: 0.112171 Loss2: 1.357163
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529226 Loss1: 0.188141 Loss2: 1.341085
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486236 Loss1: 0.126048 Loss2: 1.360188
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487123 Loss1: 0.138491 Loss2: 1.348632
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452118 Loss1: 0.096652 Loss2: 1.355466
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410631 Loss1: 0.075808 Loss2: 1.334823 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391934 Loss1: 0.066621 Loss2: 1.325312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378417 Loss1: 0.059397 Loss2: 1.319021
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.549217 Loss1: 0.701074 Loss2: 1.848143
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.821085 Loss1: 0.467292 Loss2: 1.353793
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.702012 Loss1: 0.302780 Loss2: 1.399232
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567301 Loss1: 0.203957 Loss2: 1.363344
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550536 Loss1: 0.187036 Loss2: 1.363500
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484819 Loss1: 0.128954 Loss2: 1.355865
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.495644 Loss1: 0.704424 Loss2: 1.791220
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463714 Loss1: 0.115288 Loss2: 1.348425
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.762262 Loss1: 0.410203 Loss2: 1.352059
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429694 Loss1: 0.088288 Loss2: 1.341406
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.679837 Loss1: 0.275680 Loss2: 1.404157
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454717 Loss1: 0.116849 Loss2: 1.337868
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.567810 Loss1: 0.220083 Loss2: 1.347727
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439601 Loss1: 0.097856 Loss2: 1.341745
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.539491 Loss1: 0.189587 Loss2: 1.349904
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475573 Loss1: 0.132432 Loss2: 1.343141
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461999 Loss1: 0.135553 Loss2: 1.326446
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418135 Loss1: 0.095490 Loss2: 1.322646
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.387949 Loss1: 0.064005 Loss2: 1.323944
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.632380 Loss1: 0.636354 Loss2: 1.996026
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363718 Loss1: 0.052987 Loss2: 1.310731
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.919414 Loss1: 0.404341 Loss2: 1.515073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.627462 Loss1: 0.162013 Loss2: 1.465449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.579565 Loss1: 0.136562 Loss2: 1.443003
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.421045 Loss1: 0.582557 Loss2: 1.838489
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.767843 Loss1: 0.374063 Loss2: 1.393780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.728639 Loss1: 0.293456 Loss2: 1.435183 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642274 Loss1: 0.254429 Loss2: 1.387845 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.622032 Loss1: 0.213302 Loss2: 1.408730 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479861 Loss1: 0.107940 Loss2: 1.371921 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444249 Loss1: 0.085842 Loss2: 1.358407 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448421 Loss1: 0.087509 Loss2: 1.360912 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.623819 Loss1: 0.237335 Loss2: 1.386484 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519787 Loss1: 0.132584 Loss2: 1.387203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.603607 Loss1: 0.744459 Loss2: 1.859147
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446615 Loss1: 0.077175 Loss2: 1.369440
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.871362 Loss1: 0.495528 Loss2: 1.375835
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429141 Loss1: 0.062002 Loss2: 1.367139
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.745468 Loss1: 0.325636 Loss2: 1.419831
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410201 Loss1: 0.048980 Loss2: 1.361222
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.700098 Loss1: 0.318941 Loss2: 1.381157
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393550 Loss1: 0.038271 Loss2: 1.355279
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.539100 Loss1: 0.171262 Loss2: 1.367838 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439177 Loss1: 0.088680 Loss2: 1.350496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.376592 Loss1: 0.033007 Loss2: 1.343586
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.584692 Loss1: 0.773101 Loss2: 1.811590
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.362248 Loss1: 0.030889 Loss2: 1.331359
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.744123 Loss1: 0.398480 Loss2: 1.345644
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.627317 Loss1: 0.268309 Loss2: 1.359008
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528609 Loss1: 0.188287 Loss2: 1.340322
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.470337 Loss1: 0.131052 Loss2: 1.339286
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431270 Loss1: 0.101715 Loss2: 1.329555
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.424743 Loss1: 0.524044 Loss2: 1.900698
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.421751 Loss1: 0.097044 Loss2: 1.324708
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.815190 Loss1: 0.431034 Loss2: 1.384156
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425132 Loss1: 0.109140 Loss2: 1.315993
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.771703 Loss1: 0.311658 Loss2: 1.460045
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402320 Loss1: 0.081543 Loss2: 1.320777
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.634374 Loss1: 0.233725 Loss2: 1.400648
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386900 Loss1: 0.068700 Loss2: 1.318200
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492294 Loss1: 0.104224 Loss2: 1.388069 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.476542 Loss1: 0.097889 Loss2: 1.378653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440107 Loss1: 0.068740 Loss2: 1.371367
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.630629 Loss1: 0.766467 Loss2: 1.864162
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427531 Loss1: 0.059242 Loss2: 1.368289
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.870277 Loss1: 0.478108 Loss2: 1.392170
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.688987 Loss1: 0.247814 Loss2: 1.441173
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579672 Loss1: 0.201041 Loss2: 1.378630
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.595404 Loss1: 0.208019 Loss2: 1.387385
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.564586 Loss1: 0.190498 Loss2: 1.374088
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.578751 Loss1: 0.675163 Loss2: 1.903588
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550156 Loss1: 0.173570 Loss2: 1.376586
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.867336 Loss1: 0.455876 Loss2: 1.411460
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483635 Loss1: 0.109505 Loss2: 1.374130
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.760077 Loss1: 0.292948 Loss2: 1.467129
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453944 Loss1: 0.093710 Loss2: 1.360233
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654401 Loss1: 0.245854 Loss2: 1.408546
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444227 Loss1: 0.087204 Loss2: 1.357023
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.559005 Loss1: 0.151079 Loss2: 1.407926 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477337 Loss1: 0.088648 Loss2: 1.388689 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.475253 Loss1: 0.095814 Loss2: 1.379439
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.495767 Loss1: 0.690128 Loss2: 1.805639
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426846 Loss1: 0.047959 Loss2: 1.378888
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.737838 Loss1: 0.412873 Loss2: 1.324965
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642813 Loss1: 0.268055 Loss2: 1.374758
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537661 Loss1: 0.209691 Loss2: 1.327969
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.463619 Loss1: 0.117586 Loss2: 1.346033
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485807 Loss1: 0.160176 Loss2: 1.325630
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.403740 Loss1: 0.088639 Loss2: 1.315101
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.443096 Loss1: 0.622087 Loss2: 1.821008
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.403077 Loss1: 0.088132 Loss2: 1.314945
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.707595 Loss1: 0.363026 Loss2: 1.344569
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395783 Loss1: 0.081166 Loss2: 1.314617
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.593001 Loss1: 0.222953 Loss2: 1.370048
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405813 Loss1: 0.095161 Loss2: 1.310651
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520837 Loss1: 0.174226 Loss2: 1.346611
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.481312 Loss1: 0.142023 Loss2: 1.339290
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.490375 Loss1: 0.145708 Loss2: 1.344667
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476151 Loss1: 0.135985 Loss2: 1.340166
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429291 Loss1: 0.098003 Loss2: 1.331287
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.551214 Loss1: 0.621503 Loss2: 1.929711
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405739 Loss1: 0.082006 Loss2: 1.323733
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.804704 Loss1: 0.378776 Loss2: 1.425928
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412359 Loss1: 0.088770 Loss2: 1.323589
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619311 Loss1: 0.180777 Loss2: 1.438534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560281 Loss1: 0.125720 Loss2: 1.434561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.570020 Loss1: 0.147334 Loss2: 1.422686
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.587279 Loss1: 0.715036 Loss2: 1.872243
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.760036 Loss1: 0.342947 Loss2: 1.417089 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.671257 Loss1: 0.245044 Loss2: 1.426214 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.597587 Loss1: 0.188242 Loss2: 1.409345 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.507286 Loss1: 0.110770 Loss2: 1.396517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425854 Loss1: 0.037969 Loss2: 1.387886 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409358 Loss1: 0.033967 Loss2: 1.375392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397609 Loss1: 0.026719 Loss2: 1.370890 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553925 Loss1: 0.191200 Loss2: 1.362725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.436835 Loss1: 0.080959 Loss2: 1.355876 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.410205 Loss1: 0.060920 Loss2: 1.349285
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.517969 Loss1: 0.659209 Loss2: 1.858760
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.836557 Loss1: 0.457224 Loss2: 1.379333 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.770715 Loss1: 0.317356 Loss2: 1.453358 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381885 Loss1: 0.046382 Loss2: 1.335502
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.622271 Loss1: 0.240423 Loss2: 1.381848
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574638 Loss1: 0.181350 Loss2: 1.393288
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513897 Loss1: 0.148339 Loss2: 1.365558
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463796 Loss1: 0.099087 Loss2: 1.364709
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469617 Loss1: 0.106168 Loss2: 1.363449
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.694711 Loss1: 0.812615 Loss2: 1.882096
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419001 Loss1: 0.066299 Loss2: 1.352701
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402703 Loss1: 0.059378 Loss2: 1.343324 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523176 Loss1: 0.177570 Loss2: 1.345605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471710 Loss1: 0.121182 Loss2: 1.350528 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.435160 Loss1: 0.575133 Loss2: 1.860027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.834228 Loss1: 0.472570 Loss2: 1.361658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398428 Loss1: 0.073777 Loss2: 1.324650 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561690 Loss1: 0.187434 Loss2: 1.374256 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486165 Loss1: 0.138454 Loss2: 1.347711 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446733 Loss1: 0.100233 Loss2: 1.346500
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.488385 Loss1: 0.655331 Loss2: 1.833054
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396779 Loss1: 0.060041 Loss2: 1.336738
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.734740 Loss1: 0.351632 Loss2: 1.383107
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420294 Loss1: 0.086210 Loss2: 1.334084
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629399 Loss1: 0.229681 Loss2: 1.399718
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554390 Loss1: 0.189915 Loss2: 1.364475
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513716 Loss1: 0.151248 Loss2: 1.362469
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477285 Loss1: 0.126830 Loss2: 1.350455
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439549 Loss1: 0.088318 Loss2: 1.351232
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.625868 Loss1: 0.759328 Loss2: 1.866539
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.781500 Loss1: 0.409375 Loss2: 1.372125 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624117 Loss1: 0.219507 Loss2: 1.404609 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382234 Loss1: 0.046936 Loss2: 1.335298
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541217 Loss1: 0.179509 Loss2: 1.361708
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461302 Loss1: 0.110195 Loss2: 1.351107
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466032 Loss1: 0.118482 Loss2: 1.347549
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449413 Loss1: 0.103639 Loss2: 1.345774
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424200 Loss1: 0.085988 Loss2: 1.338212
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.512834 Loss1: 0.620859 Loss2: 1.891975
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.376437 Loss1: 0.044132 Loss2: 1.332305
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.760748 Loss1: 0.347704 Loss2: 1.413045
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360095 Loss1: 0.033241 Loss2: 1.326854
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.637610 Loss1: 0.220654 Loss2: 1.416956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.614686 Loss1: 0.184413 Loss2: 1.430273 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.641795 Loss1: 0.682804 Loss2: 1.958990
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.583025 Loss1: 0.170746 Loss2: 1.412280
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.822255 Loss1: 0.369572 Loss2: 1.452683
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.546021 Loss1: 0.138832 Loss2: 1.407189
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.769731 Loss1: 0.285011 Loss2: 1.484719
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493794 Loss1: 0.100367 Loss2: 1.393427
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.497361 Loss1: 0.106408 Loss2: 1.390953 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.559422 Loss1: 0.126687 Loss2: 1.432735 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.587950 Loss1: 0.155967 Loss2: 1.431983 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.543427 Loss1: 0.109005 Loss2: 1.434422
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.271235 Loss1: 0.482001 Loss2: 1.789234
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.714964 Loss1: 0.365036 Loss2: 1.349928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.514357 Loss1: 0.170535 Loss2: 1.343822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.695626 Loss1: 0.809045 Loss2: 1.886582 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.773305 Loss1: 0.450726 Loss2: 1.322579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.715437 Loss1: 0.320825 Loss2: 1.394612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381872 Loss1: 0.053957 Loss2: 1.327916
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594457 Loss1: 0.273288 Loss2: 1.321169
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534933 Loss1: 0.212254 Loss2: 1.322679
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363843 Loss1: 0.041751 Loss2: 1.322092
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362571 Loss1: 0.043194 Loss2: 1.319377 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385007 Loss1: 0.086372 Loss2: 1.298635 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.600286 Loss1: 0.710243 Loss2: 1.890044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.660765 Loss1: 0.240755 Loss2: 1.420010 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.566957 Loss1: 0.207835 Loss2: 1.359123
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.618613 Loss1: 0.758524 Loss2: 1.860089
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.812197 Loss1: 0.412321 Loss2: 1.399876 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.705683 Loss1: 0.272526 Loss2: 1.433156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.595950 Loss1: 0.213095 Loss2: 1.382855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.570432 Loss1: 0.180630 Loss2: 1.389802 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 00:25:47,411 | server.py:236 | fit_round 132 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.522388 Loss1: 0.139363 Loss2: 1.383025 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402075 Loss1: 0.060814 Loss2: 1.341261
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490165 Loss1: 0.114047 Loss2: 1.376118
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495734 Loss1: 0.126578 Loss2: 1.369156
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499087 Loss1: 0.124293 Loss2: 1.374794
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434783 Loss1: 0.070479 Loss2: 1.364305
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.485087 Loss1: 0.665422 Loss2: 1.819665
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.791004 Loss1: 0.437547 Loss2: 1.353457
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.752932 Loss1: 0.349862 Loss2: 1.403070
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595732 Loss1: 0.239361 Loss2: 1.356371
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.417858 Loss1: 0.583253 Loss2: 1.834606
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.781364 Loss1: 0.404632 Loss2: 1.376732 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.665631 Loss1: 0.245418 Loss2: 1.420213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541471 Loss1: 0.166545 Loss2: 1.374926 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517281 Loss1: 0.141001 Loss2: 1.376279 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.500934 Loss1: 0.135398 Loss2: 1.365536 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534340 Loss1: 0.164361 Loss2: 1.369978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499750 Loss1: 0.137094 Loss2: 1.362656 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.437673 Loss1: 0.687596 Loss2: 1.750077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.544203 Loss1: 0.214112 Loss2: 1.330091 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.497162 Loss1: 0.633703 Loss2: 1.863459 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.790085 Loss1: 0.432186 Loss2: 1.357899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648631 Loss1: 0.249646 Loss2: 1.398985 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561457 Loss1: 0.200460 Loss2: 1.360997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519914 Loss1: 0.154289 Loss2: 1.365625 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.495320 Loss1: 0.123492 Loss2: 1.371828 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424520 Loss1: 0.078587 Loss2: 1.345932 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404775 Loss1: 0.064968 Loss2: 1.339806 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.892778 Loss1: 0.443506 Loss2: 1.449273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.678226 Loss1: 0.226903 Loss2: 1.451322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.591210 Loss1: 0.150873 Loss2: 1.440337 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515550 Loss1: 0.086090 Loss2: 1.429461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.525129 Loss1: 0.101966 Loss2: 1.423163 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 00:25:47,411][flwr][DEBUG] - fit_round 132 received 50 results and 0 failures
-INFO flwr 2023-10-12 00:26:29,342 | server.py:125 | fit progress: (132, 2.2114045372405373, {'accuracy': 0.5901}, 304497.120365695)
->> Test accuracy: 0.590100
-[2023-10-12 00:26:29,342][flwr][INFO] - fit progress: (132, 2.2114045372405373, {'accuracy': 0.5901}, 304497.120365695)
-DEBUG flwr 2023-10-12 00:26:29,342 | server.py:173 | evaluate_round 132: strategy sampled 50 clients (out of 50)
-[2023-10-12 00:26:29,342][flwr][DEBUG] - evaluate_round 132: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 00:35:31,724 | server.py:187 | evaluate_round 132 received 50 results and 0 failures
-[2023-10-12 00:35:31,724][flwr][DEBUG] - evaluate_round 132 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 00:35:31,724 | server.py:222 | fit_round 133: strategy sampled 50 clients (out of 50)
-[2023-10-12 00:35:31,724][flwr][DEBUG] - fit_round 133: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.501698 Loss1: 0.663436 Loss2: 1.838262
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.812044 Loss1: 0.440654 Loss2: 1.371390
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667290 Loss1: 0.266884 Loss2: 1.400407
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.550483 Loss1: 0.197517 Loss2: 1.352966
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.501815 Loss1: 0.657143 Loss2: 1.844672
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.678148 Loss1: 0.328239 Loss2: 1.349908 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.549544 Loss1: 0.178998 Loss2: 1.370547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.508907 Loss1: 0.161266 Loss2: 1.347641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491329 Loss1: 0.153290 Loss2: 1.338039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.415112 Loss1: 0.082013 Loss2: 1.333099 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365198 Loss1: 0.041714 Loss2: 1.323483
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.393249 Loss1: 0.067916 Loss2: 1.325332
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376266 Loss1: 0.055387 Loss2: 1.320879
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374142 Loss1: 0.059965 Loss2: 1.314176
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367266 Loss1: 0.055391 Loss2: 1.311876
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.736110 Loss1: 0.737989 Loss2: 1.998121
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.944799 Loss1: 0.545102 Loss2: 1.399697
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.757923 Loss1: 0.282216 Loss2: 1.475707
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556055 Loss1: 0.164930 Loss2: 1.391125
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516050 Loss1: 0.136318 Loss2: 1.379732
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450749 Loss1: 0.071821 Loss2: 1.378927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440748 Loss1: 0.068513 Loss2: 1.372235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437002 Loss1: 0.071836 Loss2: 1.365166 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.420731 Loss1: 0.055153 Loss2: 1.365578 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395216 Loss1: 0.041709 Loss2: 1.353508 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497524 Loss1: 0.115768 Loss2: 1.381757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454058 Loss1: 0.082594 Loss2: 1.371464 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418493 Loss1: 0.058469 Loss2: 1.360024
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.494995 Loss1: 0.648693 Loss2: 1.846302
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.744181 Loss1: 0.400516 Loss2: 1.343665
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647194 Loss1: 0.282101 Loss2: 1.365094
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561885 Loss1: 0.226819 Loss2: 1.335066
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.501909 Loss1: 0.169000 Loss2: 1.332909
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.555014 Loss1: 0.639120 Loss2: 1.915894
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.483287 Loss1: 0.161767 Loss2: 1.321521
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.780181 Loss1: 0.362763 Loss2: 1.417418
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422152 Loss1: 0.103995 Loss2: 1.318157
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.753923 Loss1: 0.279385 Loss2: 1.474538
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.359641 Loss1: 0.050076 Loss2: 1.309566
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599359 Loss1: 0.182020 Loss2: 1.417339
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.339130 Loss1: 0.038090 Loss2: 1.301040
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.532379 Loss1: 0.128138 Loss2: 1.404240
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.329652 Loss1: 0.037633 Loss2: 1.292019
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.550438 Loss1: 0.136062 Loss2: 1.414375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485913 Loss1: 0.087421 Loss2: 1.398491 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487888 Loss1: 0.092546 Loss2: 1.395342
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.571861 Loss1: 0.711223 Loss2: 1.860637
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.797046 Loss1: 0.411067 Loss2: 1.385979
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.643780 Loss1: 0.227996 Loss2: 1.415784
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533010 Loss1: 0.151253 Loss2: 1.381758
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551118 Loss1: 0.163173 Loss2: 1.387945
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.499406 Loss1: 0.619722 Loss2: 1.879683
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448079 Loss1: 0.075497 Loss2: 1.372581
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.861533 Loss1: 0.460855 Loss2: 1.400678
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441800 Loss1: 0.077185 Loss2: 1.364616
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.776337 Loss1: 0.324890 Loss2: 1.451447
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467678 Loss1: 0.107006 Loss2: 1.360672
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.715316 Loss1: 0.291403 Loss2: 1.423913
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435212 Loss1: 0.077259 Loss2: 1.357953
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.647727 Loss1: 0.212992 Loss2: 1.434735
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438495 Loss1: 0.084166 Loss2: 1.354329
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.535812 Loss1: 0.134752 Loss2: 1.401060 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441796 Loss1: 0.054874 Loss2: 1.386922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434821 Loss1: 0.058481 Loss2: 1.376341
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.437939 Loss1: 0.602746 Loss2: 1.835193
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.912506 Loss1: 0.499787 Loss2: 1.412719
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.742343 Loss1: 0.302298 Loss2: 1.440044
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.696064 Loss1: 0.291597 Loss2: 1.404467
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.641701 Loss1: 0.249255 Loss2: 1.392446
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.759124 Loss1: 0.810670 Loss2: 1.948454
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.540283 Loss1: 0.160002 Loss2: 1.380282
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.845031 Loss1: 0.494877 Loss2: 1.350154
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.731721 Loss1: 0.333843 Loss2: 1.397878
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547584 Loss1: 0.163461 Loss2: 1.384123
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.509548 Loss1: 0.135054 Loss2: 1.374495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448080 Loss1: 0.082080 Loss2: 1.366000 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412021 Loss1: 0.072845 Loss2: 1.339176 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369990 Loss1: 0.039661 Loss2: 1.330329 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.464367 Loss1: 0.701322 Loss2: 1.763044 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.583777 Loss1: 0.254232 Loss2: 1.329545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.460976 Loss1: 0.161067 Loss2: 1.299910
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.394435 Loss1: 0.546042 Loss2: 1.848392
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.717004 Loss1: 0.314738 Loss2: 1.402266 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633047 Loss1: 0.207203 Loss2: 1.425843 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.601826 Loss1: 0.212928 Loss2: 1.388898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520932 Loss1: 0.127168 Loss2: 1.393764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.497584 Loss1: 0.115628 Loss2: 1.381956 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451923 Loss1: 0.074975 Loss2: 1.376948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421288 Loss1: 0.059485 Loss2: 1.361803 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.958984
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.494216 Loss1: 0.682822 Loss2: 1.811394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648054 Loss1: 0.225615 Loss2: 1.422439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524303 Loss1: 0.153806 Loss2: 1.370497
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.560204 Loss1: 0.700604 Loss2: 1.859600
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.758173 Loss1: 0.396869 Loss2: 1.361304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455249 Loss1: 0.097558 Loss2: 1.357691
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.645457 Loss1: 0.253322 Loss2: 1.392135
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440676 Loss1: 0.085823 Loss2: 1.354853
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.590224 Loss1: 0.233342 Loss2: 1.356882
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483127 Loss1: 0.119027 Loss2: 1.364100
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.561857 Loss1: 0.194276 Loss2: 1.367580
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450459 Loss1: 0.096761 Loss2: 1.353698
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516334 Loss1: 0.166515 Loss2: 1.349819
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.449803 Loss1: 0.095812 Loss2: 1.353991
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416591 Loss1: 0.070297 Loss2: 1.346294
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403583 Loss1: 0.069668 Loss2: 1.333915 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.452073 Loss1: 0.641983 Loss2: 1.810090 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634189 Loss1: 0.260829 Loss2: 1.373360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.493269 Loss1: 0.159453 Loss2: 1.333816
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.508062 Loss1: 0.635578 Loss2: 1.872485
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471981 Loss1: 0.147540 Loss2: 1.324442
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.846331 Loss1: 0.455048 Loss2: 1.391283
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445224 Loss1: 0.118437 Loss2: 1.326788
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.812011 Loss1: 0.361413 Loss2: 1.450598
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.430239 Loss1: 0.116904 Loss2: 1.313335
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.683160 Loss1: 0.290137 Loss2: 1.393024
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.407706 Loss1: 0.087701 Loss2: 1.320006
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.630392 Loss1: 0.219946 Loss2: 1.410445
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386491 Loss1: 0.071980 Loss2: 1.314510
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530304 Loss1: 0.146631 Loss2: 1.383673
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365426 Loss1: 0.056861 Loss2: 1.308565
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470112 Loss1: 0.095123 Loss2: 1.374989
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442848 Loss1: 0.078245 Loss2: 1.364603
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421820 Loss1: 0.063637 Loss2: 1.358183
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406214 Loss1: 0.052389 Loss2: 1.353825
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.433855 Loss1: 0.601012 Loss2: 1.832844
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.728567 Loss1: 0.388859 Loss2: 1.339707
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.723077 Loss1: 0.332512 Loss2: 1.390565
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.635953 Loss1: 0.274313 Loss2: 1.361640
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.633339 Loss1: 0.744144 Loss2: 1.889195
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593548 Loss1: 0.229016 Loss2: 1.364532
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.802419 Loss1: 0.432273 Loss2: 1.370146
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.512334 Loss1: 0.157788 Loss2: 1.354547
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.687133 Loss1: 0.281808 Loss2: 1.405324
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.632841 Loss1: 0.262856 Loss2: 1.369985
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487097 Loss1: 0.141089 Loss2: 1.346008
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.551116 Loss1: 0.183449 Loss2: 1.367667
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.442445 Loss1: 0.102392 Loss2: 1.340053
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481913 Loss1: 0.125290 Loss2: 1.356624
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424767 Loss1: 0.095810 Loss2: 1.328957
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412984 Loss1: 0.077506 Loss2: 1.335478 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.380104 Loss1: 0.043669 Loss2: 1.336435 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.540190 Loss1: 0.681528 Loss2: 1.858662 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.819139 Loss1: 0.349214 Loss2: 1.469925 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.677228 Loss1: 0.276533 Loss2: 1.400695
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.380629 Loss1: 0.553116 Loss2: 1.827513
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.616959 Loss1: 0.217385 Loss2: 1.399575
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.782425 Loss1: 0.390634 Loss2: 1.391791
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.695232 Loss1: 0.275368 Loss2: 1.419864 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.587272 Loss1: 0.195857 Loss2: 1.391415 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.552487 Loss1: 0.166201 Loss2: 1.386286 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.525736 Loss1: 0.142639 Loss2: 1.383098 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.503392 Loss1: 0.132918 Loss2: 1.370474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444542 Loss1: 0.081777 Loss2: 1.362765 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.483983 Loss1: 0.688296 Loss2: 1.795687 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.653883 Loss1: 0.294316 Loss2: 1.359567 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574193 Loss1: 0.233241 Loss2: 1.340952
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.461264 Loss1: 0.628084 Loss2: 1.833180
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.722450 Loss1: 0.350121 Loss2: 1.372329 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.625865 Loss1: 0.217636 Loss2: 1.408228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556531 Loss1: 0.191413 Loss2: 1.365118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545153 Loss1: 0.188469 Loss2: 1.356684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421206 Loss1: 0.108021 Loss2: 1.313185
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477383 Loss1: 0.117023 Loss2: 1.360361
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406526 Loss1: 0.093207 Loss2: 1.313319
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458119 Loss1: 0.104744 Loss2: 1.353375
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446101 Loss1: 0.097559 Loss2: 1.348543
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405227 Loss1: 0.058439 Loss2: 1.346788
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385027 Loss1: 0.046635 Loss2: 1.338392
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.371819 Loss1: 0.490943 Loss2: 1.880876
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.759246 Loss1: 0.389617 Loss2: 1.369629
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731301 Loss1: 0.287228 Loss2: 1.444074
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.661700 Loss1: 0.275434 Loss2: 1.386266
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.520922 Loss1: 0.642170 Loss2: 1.878752
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.634642 Loss1: 0.236571 Loss2: 1.398070
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.880800 Loss1: 0.494145 Loss2: 1.386655
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.568202 Loss1: 0.179351 Loss2: 1.388851
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727201 Loss1: 0.264975 Loss2: 1.462227
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514677 Loss1: 0.137179 Loss2: 1.377497
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.624679 Loss1: 0.235564 Loss2: 1.389115
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479186 Loss1: 0.106973 Loss2: 1.372213
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.632857 Loss1: 0.218676 Loss2: 1.414181
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443093 Loss1: 0.076631 Loss2: 1.366462
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556146 Loss1: 0.162579 Loss2: 1.393567
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437477 Loss1: 0.073459 Loss2: 1.364018
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545365 Loss1: 0.159980 Loss2: 1.385384
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485023 Loss1: 0.098445 Loss2: 1.386578
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460886 Loss1: 0.088661 Loss2: 1.372224
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.506092 Loss1: 0.133094 Loss2: 1.372998
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.416558 Loss1: 0.570255 Loss2: 1.846303
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.659683 Loss1: 0.299583 Loss2: 1.360100
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594306 Loss1: 0.210094 Loss2: 1.384212
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.531760 Loss1: 0.168922 Loss2: 1.362838
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.348304 Loss1: 0.527264 Loss2: 1.821040
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.700345 Loss1: 0.347962 Loss2: 1.352383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629236 Loss1: 0.237639 Loss2: 1.391596 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555923 Loss1: 0.197628 Loss2: 1.358295 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378872 Loss1: 0.042968 Loss2: 1.335904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381758 Loss1: 0.048587 Loss2: 1.333171 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470877 Loss1: 0.121235 Loss2: 1.349642 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400309 Loss1: 0.059859 Loss2: 1.340450 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994485
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.835302 Loss1: 0.463799 Loss2: 1.371503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547274 Loss1: 0.183646 Loss2: 1.363629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.653557 Loss1: 0.759149 Loss2: 1.894408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.773402 Loss1: 0.364097 Loss2: 1.409304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.693356 Loss1: 0.244958 Loss2: 1.448398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584579 Loss1: 0.190415 Loss2: 1.394164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542265 Loss1: 0.149137 Loss2: 1.393128 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498249 Loss1: 0.106811 Loss2: 1.391438 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446669 Loss1: 0.063421 Loss2: 1.383248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417444 Loss1: 0.050992 Loss2: 1.366452 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.829824 Loss1: 0.449169 Loss2: 1.380655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606769 Loss1: 0.231989 Loss2: 1.374779 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.533271 Loss1: 0.155865 Loss2: 1.377405
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.404065 Loss1: 0.599859 Loss2: 1.804206
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.697523 Loss1: 0.370646 Loss2: 1.326876 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451950 Loss1: 0.091638 Loss2: 1.360312
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.625267 Loss1: 0.276527 Loss2: 1.348740
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448802 Loss1: 0.089892 Loss2: 1.358910
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.542968 Loss1: 0.216425 Loss2: 1.326544
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435097 Loss1: 0.081629 Loss2: 1.353469
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556262 Loss1: 0.229021 Loss2: 1.327241
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419490 Loss1: 0.065414 Loss2: 1.354076
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471545 Loss1: 0.152600 Loss2: 1.318944
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408824 Loss1: 0.098423 Loss2: 1.310401
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381910 Loss1: 0.081847 Loss2: 1.300062
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371190 Loss1: 0.073073 Loss2: 1.298118
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348426 Loss1: 0.055464 Loss2: 1.292962
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.574823 Loss1: 0.714956 Loss2: 1.859867
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.868911 Loss1: 0.481449 Loss2: 1.387462
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.726480 Loss1: 0.286537 Loss2: 1.439943
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610707 Loss1: 0.220205 Loss2: 1.390502
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.496169 Loss1: 0.624908 Loss2: 1.871261
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.721719 Loss1: 0.357580 Loss2: 1.364139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.640168 Loss1: 0.241885 Loss2: 1.398283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.568372 Loss1: 0.204013 Loss2: 1.364359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.481487 Loss1: 0.121531 Loss2: 1.359956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449765 Loss1: 0.099016 Loss2: 1.350749 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.387377 Loss1: 0.058142 Loss2: 1.329235 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377900 Loss1: 0.050128 Loss2: 1.327772 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.829283 Loss1: 0.476480 Loss2: 1.352803 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526627 Loss1: 0.196412 Loss2: 1.330216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.377587 Loss1: 0.536912 Loss2: 1.840675
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479141 Loss1: 0.136928 Loss2: 1.342212
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.723791 Loss1: 0.359053 Loss2: 1.364738
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433364 Loss1: 0.106833 Loss2: 1.326531
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.639689 Loss1: 0.230397 Loss2: 1.409293
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.405329 Loss1: 0.082995 Loss2: 1.322334
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538393 Loss1: 0.170453 Loss2: 1.367941
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384162 Loss1: 0.068393 Loss2: 1.315769
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.486189 Loss1: 0.120858 Loss2: 1.365331
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.366867 Loss1: 0.061498 Loss2: 1.305369
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504144 Loss1: 0.137254 Loss2: 1.366891
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.348644 Loss1: 0.050471 Loss2: 1.298174
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470673 Loss1: 0.105068 Loss2: 1.365606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463106 Loss1: 0.106032 Loss2: 1.357074 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.882768 Loss1: 0.460539 Loss2: 1.422230 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642157 Loss1: 0.232597 Loss2: 1.409559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.530524 Loss1: 0.679836 Loss2: 1.850688
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611374 Loss1: 0.190012 Loss2: 1.421362
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.800117 Loss1: 0.413221 Loss2: 1.386895
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.570266 Loss1: 0.155562 Loss2: 1.414704
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.672966 Loss1: 0.250909 Loss2: 1.422057
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.540410 Loss1: 0.133798 Loss2: 1.406612
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562531 Loss1: 0.187673 Loss2: 1.374859
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.528979 Loss1: 0.125173 Loss2: 1.403806
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508145 Loss1: 0.133311 Loss2: 1.374834
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.486273 Loss1: 0.084253 Loss2: 1.402020
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492869 Loss1: 0.123133 Loss2: 1.369736
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466352 Loss1: 0.074007 Loss2: 1.392345
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484338 Loss1: 0.123803 Loss2: 1.360535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427237 Loss1: 0.071215 Loss2: 1.356022 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.912992 Loss1: 0.529756 Loss2: 1.383236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589901 Loss1: 0.230659 Loss2: 1.359242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.544410 Loss1: 0.630530 Loss2: 1.913880
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.569791 Loss1: 0.197332 Loss2: 1.372459
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.823718 Loss1: 0.417171 Loss2: 1.406547
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504088 Loss1: 0.143506 Loss2: 1.360582
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.773895 Loss1: 0.307290 Loss2: 1.466605
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470838 Loss1: 0.118511 Loss2: 1.352327
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.614649 Loss1: 0.208696 Loss2: 1.405953
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450032 Loss1: 0.102202 Loss2: 1.347830
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568403 Loss1: 0.163589 Loss2: 1.404815
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414362 Loss1: 0.072803 Loss2: 1.341559
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527429 Loss1: 0.126969 Loss2: 1.400460
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380874 Loss1: 0.045574 Loss2: 1.335300
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515642 Loss1: 0.125731 Loss2: 1.389912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.511279 Loss1: 0.124834 Loss2: 1.386444 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.759091 Loss1: 0.394766 Loss2: 1.364324 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569342 Loss1: 0.220753 Loss2: 1.348589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.647013 Loss1: 0.774336 Loss2: 1.872677
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473337 Loss1: 0.120868 Loss2: 1.352469
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.782819 Loss1: 0.418198 Loss2: 1.364621
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437894 Loss1: 0.098114 Loss2: 1.339780
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402405 Loss1: 0.068169 Loss2: 1.334236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.360377 Loss1: 0.036399 Loss2: 1.323978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.364332 Loss1: 0.045902 Loss2: 1.318430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369264 Loss1: 0.055970 Loss2: 1.313294 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424518 Loss1: 0.083150 Loss2: 1.341368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409097 Loss1: 0.077492 Loss2: 1.331605 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.586812 Loss1: 0.773679 Loss2: 1.813133
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784938 Loss1: 0.450210 Loss2: 1.334728
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.590417 Loss1: 0.240440 Loss2: 1.349977
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.525439 Loss1: 0.203239 Loss2: 1.322200
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.319651 Loss1: 0.531743 Loss2: 1.787908
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.726706 Loss1: 0.397660 Loss2: 1.329047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.581877 Loss1: 0.199785 Loss2: 1.382092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.569908 Loss1: 0.235115 Loss2: 1.334793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.525144 Loss1: 0.183623 Loss2: 1.341521 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494736 Loss1: 0.154659 Loss2: 1.340077 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450677 Loss1: 0.118942 Loss2: 1.331735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501970 Loss1: 0.175271 Loss2: 1.326699 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.959961
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.558359 Loss1: 0.701341 Loss2: 1.857018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611208 Loss1: 0.212386 Loss2: 1.398822 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.741852 Loss1: 0.842235 Loss2: 1.899617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.804076 Loss1: 0.431654 Loss2: 1.372421 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.683296 Loss1: 0.269593 Loss2: 1.413703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.580674 Loss1: 0.189633 Loss2: 1.391040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.559764 Loss1: 0.190180 Loss2: 1.369584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519276 Loss1: 0.137456 Loss2: 1.381820 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474055 Loss1: 0.111929 Loss2: 1.362126
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380788 Loss1: 0.045471 Loss2: 1.335317
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410369 Loss1: 0.061545 Loss2: 1.348825 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.404926 Loss1: 0.597772 Loss2: 1.807153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.719013 Loss1: 0.306304 Loss2: 1.412708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.641258 Loss1: 0.269003 Loss2: 1.372256
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.466950 Loss1: 0.641965 Loss2: 1.824985
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536873 Loss1: 0.154098 Loss2: 1.382776
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.829450 Loss1: 0.429655 Loss2: 1.399795
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461642 Loss1: 0.094447 Loss2: 1.367194
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604337 Loss1: 0.205811 Loss2: 1.398527
-DEBUG flwr 2023-10-12 01:04:22,989 | server.py:236 | fit_round 133 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469065 Loss1: 0.112543 Loss2: 1.356522
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.525910 Loss1: 0.169835 Loss2: 1.356074
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434053 Loss1: 0.084798 Loss2: 1.349256
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493676 Loss1: 0.137188 Loss2: 1.356488
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419739 Loss1: 0.068299 Loss2: 1.351440
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464240 Loss1: 0.112043 Loss2: 1.352197
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415150 Loss1: 0.073735 Loss2: 1.341414
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422672 Loss1: 0.078435 Loss2: 1.344236
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414824 Loss1: 0.074211 Loss2: 1.340613
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389797 Loss1: 0.058430 Loss2: 1.331368
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405707 Loss1: 0.074268 Loss2: 1.331439
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.546006 Loss1: 0.658702 Loss2: 1.887304
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.771561 Loss1: 0.367416 Loss2: 1.404145
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.717797 Loss1: 0.277855 Loss2: 1.439942
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.626842 Loss1: 0.240530 Loss2: 1.386312
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.317210 Loss1: 0.542938 Loss2: 1.774273
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.672852 Loss1: 0.370353 Loss2: 1.302499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.648397 Loss1: 0.276377 Loss2: 1.372020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523505 Loss1: 0.226089 Loss2: 1.297416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.511472 Loss1: 0.198507 Loss2: 1.312965 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486785 Loss1: 0.192993 Loss2: 1.293792 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429174 Loss1: 0.068927 Loss2: 1.360248
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425933 Loss1: 0.126758 Loss2: 1.299175
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386788 Loss1: 0.098236 Loss2: 1.288552
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366015 Loss1: 0.087691 Loss2: 1.278324
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366151 Loss1: 0.088076 Loss2: 1.278076
-(DefaultActor pid=3764) >> Training accuracy: 0.951042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.542829 Loss1: 0.699767 Loss2: 1.843062
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.754112 Loss1: 0.373162 Loss2: 1.380949
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.640568 Loss1: 0.240311 Loss2: 1.400257
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551568 Loss1: 0.185107 Loss2: 1.366461
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.611098 Loss1: 0.703034 Loss2: 1.908064
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531069 Loss1: 0.160249 Loss2: 1.370820
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.853755 Loss1: 0.500310 Loss2: 1.353445
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.685076 Loss1: 0.282767 Loss2: 1.402310
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505710 Loss1: 0.140277 Loss2: 1.365433
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538278 Loss1: 0.185702 Loss2: 1.352575
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475396 Loss1: 0.113658 Loss2: 1.361739
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487713 Loss1: 0.144203 Loss2: 1.343510
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470905 Loss1: 0.114215 Loss2: 1.356690
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477604 Loss1: 0.124109 Loss2: 1.353495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.455525 Loss1: 0.101432 Loss2: 1.354092 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401439 Loss1: 0.070059 Loss2: 1.331380 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997768
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 01:04:22,989][flwr][DEBUG] - fit_round 133 received 50 results and 0 failures
-INFO flwr 2023-10-12 01:05:05,544 | server.py:125 | fit progress: (133, 2.2147254682958315, {'accuracy': 0.5918}, 306813.32244291797)
->> Test accuracy: 0.591800
-[2023-10-12 01:05:05,544][flwr][INFO] - fit progress: (133, 2.2147254682958315, {'accuracy': 0.5918}, 306813.32244291797)
-DEBUG flwr 2023-10-12 01:05:05,544 | server.py:173 | evaluate_round 133: strategy sampled 50 clients (out of 50)
-[2023-10-12 01:05:05,544][flwr][DEBUG] - evaluate_round 133: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 01:14:14,076 | server.py:187 | evaluate_round 133 received 50 results and 0 failures
-[2023-10-12 01:14:14,076][flwr][DEBUG] - evaluate_round 133 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 01:14:14,076 | server.py:222 | fit_round 134: strategy sampled 50 clients (out of 50)
-[2023-10-12 01:14:14,076][flwr][DEBUG] - fit_round 134: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.417288 Loss1: 0.620175 Loss2: 1.797113 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.685492 Loss1: 0.289476 Loss2: 1.396016 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554907 Loss1: 0.222733 Loss2: 1.332174
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.738351 Loss1: 0.801204 Loss2: 1.937147
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.752648 Loss1: 0.431566 Loss2: 1.321082
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547834 Loss1: 0.211554 Loss2: 1.336280
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445506 Loss1: 0.119099 Loss2: 1.326406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440420 Loss1: 0.120279 Loss2: 1.320141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.408607 Loss1: 0.096604 Loss2: 1.312003 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441762 Loss1: 0.127484 Loss2: 1.314278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400030 Loss1: 0.087220 Loss2: 1.312809 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397847 Loss1: 0.094928 Loss2: 1.302919 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.509235 Loss1: 0.659343 Loss2: 1.849892
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.807895 Loss1: 0.405477 Loss2: 1.402418
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.681457 Loss1: 0.262061 Loss2: 1.419396
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.619441 Loss1: 0.224721 Loss2: 1.394720
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.541080 Loss1: 0.633200 Loss2: 1.907880
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.573508 Loss1: 0.169080 Loss2: 1.404428
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.914840 Loss1: 0.433164 Loss2: 1.481675
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526829 Loss1: 0.140776 Loss2: 1.386053
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.821959 Loss1: 0.311125 Loss2: 1.510834
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.489651 Loss1: 0.105580 Loss2: 1.384071
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.669620 Loss1: 0.204829 Loss2: 1.464792
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452331 Loss1: 0.080853 Loss2: 1.371479
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.685593 Loss1: 0.205250 Loss2: 1.480343
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.471493 Loss1: 0.103556 Loss2: 1.367937
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.588088 Loss1: 0.136912 Loss2: 1.451176
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425810 Loss1: 0.060451 Loss2: 1.365360
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.578252 Loss1: 0.123991 Loss2: 1.454261
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511815 Loss1: 0.071884 Loss2: 1.439930
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.522621 Loss1: 0.090919 Loss2: 1.431703
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466292 Loss1: 0.038049 Loss2: 1.428242
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.630299 Loss1: 0.733650 Loss2: 1.896649
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.825412 Loss1: 0.443766 Loss2: 1.381646
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635996 Loss1: 0.253090 Loss2: 1.382906
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580506 Loss1: 0.228612 Loss2: 1.351894
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.492262 Loss1: 0.638842 Loss2: 1.853420
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.795120 Loss1: 0.434906 Loss2: 1.360214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.694333 Loss1: 0.293993 Loss2: 1.400340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.578178 Loss1: 0.230834 Loss2: 1.347344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502988 Loss1: 0.152385 Loss2: 1.350603 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461391 Loss1: 0.112213 Loss2: 1.349179 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.343056 Loss1: 0.027069 Loss2: 1.315987
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450916 Loss1: 0.111607 Loss2: 1.339309
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448954 Loss1: 0.103336 Loss2: 1.345618
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410270 Loss1: 0.081524 Loss2: 1.328746
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422267 Loss1: 0.089374 Loss2: 1.332894
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.524616 Loss1: 0.566165 Loss2: 1.958451
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.816138 Loss1: 0.349756 Loss2: 1.466382
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.844111 Loss1: 0.330189 Loss2: 1.513922
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.713209 Loss1: 0.250934 Loss2: 1.462274
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.610876 Loss1: 0.742823 Loss2: 1.868053
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.653644 Loss1: 0.177341 Loss2: 1.476303
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.843329 Loss1: 0.466826 Loss2: 1.376503
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.609129 Loss1: 0.151998 Loss2: 1.457131
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.705856 Loss1: 0.278619 Loss2: 1.427237
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.593343 Loss1: 0.127614 Loss2: 1.465729
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543341 Loss1: 0.173906 Loss2: 1.369436
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.536822 Loss1: 0.086834 Loss2: 1.449988
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520365 Loss1: 0.149312 Loss2: 1.371054
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446649 Loss1: 0.090321 Loss2: 1.356328
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.523165 Loss1: 0.079202 Loss2: 1.443963
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436872 Loss1: 0.088871 Loss2: 1.348001
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.491973 Loss1: 0.055789 Loss2: 1.436184
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431514 Loss1: 0.088581 Loss2: 1.342933 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.624679 Loss1: 0.758426 Loss2: 1.866254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.677498 Loss1: 0.250127 Loss2: 1.427370 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.607709 Loss1: 0.231372 Loss2: 1.376337
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.567776 Loss1: 0.724262 Loss2: 1.843513
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517281 Loss1: 0.123835 Loss2: 1.393446
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.810279 Loss1: 0.438216 Loss2: 1.372063
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511807 Loss1: 0.139019 Loss2: 1.372788
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671328 Loss1: 0.269768 Loss2: 1.401560
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470873 Loss1: 0.102144 Loss2: 1.368729
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.624832 Loss1: 0.260188 Loss2: 1.364644
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456570 Loss1: 0.088111 Loss2: 1.368459
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508837 Loss1: 0.140706 Loss2: 1.368131
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396924 Loss1: 0.044616 Loss2: 1.352309
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.483398 Loss1: 0.129624 Loss2: 1.353774
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398879 Loss1: 0.049732 Loss2: 1.349147
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463471 Loss1: 0.110340 Loss2: 1.353131
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435956 Loss1: 0.090109 Loss2: 1.345847
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429037 Loss1: 0.087779 Loss2: 1.341258
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396688 Loss1: 0.064140 Loss2: 1.332548
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.355004 Loss1: 0.596227 Loss2: 1.758776
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.698765 Loss1: 0.363463 Loss2: 1.335301
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.571036 Loss1: 0.217588 Loss2: 1.353448
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.363535 Loss1: 0.566564 Loss2: 1.796971
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527993 Loss1: 0.196563 Loss2: 1.331430
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.771096 Loss1: 0.415645 Loss2: 1.355451
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480795 Loss1: 0.151845 Loss2: 1.328950
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677117 Loss1: 0.271982 Loss2: 1.405135
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.453778 Loss1: 0.126282 Loss2: 1.327496
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595053 Loss1: 0.243690 Loss2: 1.351363
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466840 Loss1: 0.140120 Loss2: 1.326720
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492863 Loss1: 0.131869 Loss2: 1.360994
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456510 Loss1: 0.129990 Loss2: 1.326520
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447399 Loss1: 0.104032 Loss2: 1.343367
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409276 Loss1: 0.080285 Loss2: 1.328991
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453858 Loss1: 0.111864 Loss2: 1.341994
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419035 Loss1: 0.099802 Loss2: 1.319234
-(DefaultActor pid=3765) >> Training accuracy: 0.973633
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363125 Loss1: 0.043202 Loss2: 1.319923 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.382802 Loss1: 0.557191 Loss2: 1.825610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.681165 Loss1: 0.281659 Loss2: 1.399506 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.612073 Loss1: 0.233638 Loss2: 1.378435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514855 Loss1: 0.139662 Loss2: 1.375193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.490309 Loss1: 0.126337 Loss2: 1.363972 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444382 Loss1: 0.080505 Loss2: 1.363877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435613 Loss1: 0.082599 Loss2: 1.353014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.493344 Loss1: 0.124100 Loss2: 1.369244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501777 Loss1: 0.140959 Loss2: 1.360818 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994485
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425713 Loss1: 0.073544 Loss2: 1.352169 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.485964 Loss1: 0.627088 Loss2: 1.858876
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.769218 Loss1: 0.396460 Loss2: 1.372758
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.689889 Loss1: 0.271166 Loss2: 1.418724
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654552 Loss1: 0.268108 Loss2: 1.386444
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.630948 Loss1: 0.724573 Loss2: 1.906375
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.920704 Loss1: 0.553002 Loss2: 1.367702
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.622727 Loss1: 0.243533 Loss2: 1.379195
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.579877 Loss1: 0.195882 Loss2: 1.383996 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.508515 Loss1: 0.134137 Loss2: 1.374378 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.492135 Loss1: 0.126740 Loss2: 1.365395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.487582 Loss1: 0.123761 Loss2: 1.363821 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429844 Loss1: 0.073843 Loss2: 1.356000 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390277 Loss1: 0.055928 Loss2: 1.334350 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.427226 Loss1: 0.602446 Loss2: 1.824780
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.682936 Loss1: 0.355261 Loss2: 1.327676
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635251 Loss1: 0.263838 Loss2: 1.371413
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.565322 Loss1: 0.237840 Loss2: 1.327482
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.511814 Loss1: 0.551820 Loss2: 1.959993
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526080 Loss1: 0.196392 Loss2: 1.329688
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.821891 Loss1: 0.378363 Loss2: 1.443528
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492577 Loss1: 0.154732 Loss2: 1.337845
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.717738 Loss1: 0.224916 Loss2: 1.492822
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409895 Loss1: 0.093719 Loss2: 1.316176
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.633354 Loss1: 0.186027 Loss2: 1.447327
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.412644 Loss1: 0.102813 Loss2: 1.309831
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.624098 Loss1: 0.173280 Loss2: 1.450818
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458756 Loss1: 0.141132 Loss2: 1.317624
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.587654 Loss1: 0.144705 Loss2: 1.442948
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419833 Loss1: 0.109221 Loss2: 1.310613
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.556594 Loss1: 0.116803 Loss2: 1.439792
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.532621 Loss1: 0.099411 Loss2: 1.433209
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.484832 Loss1: 0.059630 Loss2: 1.425201
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.469414 Loss1: 0.055151 Loss2: 1.414263
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.533823 Loss1: 0.712935 Loss2: 1.820888
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.713313 Loss1: 0.373495 Loss2: 1.339818
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.601159 Loss1: 0.216908 Loss2: 1.384251
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569893 Loss1: 0.235961 Loss2: 1.333932
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.471313 Loss1: 0.648702 Loss2: 1.822611
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.730420 Loss1: 0.372906 Loss2: 1.357514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.672919 Loss1: 0.262738 Loss2: 1.410180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537868 Loss1: 0.180768 Loss2: 1.357100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.489991 Loss1: 0.127472 Loss2: 1.362518 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463261 Loss1: 0.104381 Loss2: 1.358880 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.405067 Loss1: 0.060152 Loss2: 1.344916 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401539 Loss1: 0.060377 Loss2: 1.341163 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.474502 Loss1: 0.600225 Loss2: 1.874277 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.689260 Loss1: 0.253203 Loss2: 1.436057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573401 Loss1: 0.189651 Loss2: 1.383750
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.513933 Loss1: 0.633698 Loss2: 1.880235
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.739835 Loss1: 0.366105 Loss2: 1.373730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604276 Loss1: 0.201511 Loss2: 1.402765 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.512208 Loss1: 0.151857 Loss2: 1.360352 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.559374 Loss1: 0.193807 Loss2: 1.365567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485088 Loss1: 0.116791 Loss2: 1.368298 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452587 Loss1: 0.100456 Loss2: 1.352130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384870 Loss1: 0.043502 Loss2: 1.341368 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.509758 Loss1: 0.619515 Loss2: 1.890243 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.677577 Loss1: 0.220937 Loss2: 1.456640 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.585635 Loss1: 0.177382 Loss2: 1.408253
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.676392 Loss1: 0.750272 Loss2: 1.926120
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.533287 Loss1: 0.130967 Loss2: 1.402319
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.800145 Loss1: 0.451073 Loss2: 1.349073
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.657077 Loss1: 0.275179 Loss2: 1.381898
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.528445 Loss1: 0.134502 Loss2: 1.393943
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500884 Loss1: 0.098956 Loss2: 1.401928 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.509228 Loss1: 0.114106 Loss2: 1.395122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.483045 Loss1: 0.088468 Loss2: 1.394578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372130 Loss1: 0.059270 Loss2: 1.312860 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.371267 Loss1: 0.064739 Loss2: 1.306528 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.508885 Loss1: 0.672139 Loss2: 1.836746
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.668256 Loss1: 0.315320 Loss2: 1.352936
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.631898 Loss1: 0.258362 Loss2: 1.373536
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599523 Loss1: 0.255923 Loss2: 1.343600
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.458564 Loss1: 0.655957 Loss2: 1.802607
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492024 Loss1: 0.148225 Loss2: 1.343799
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.784019 Loss1: 0.452802 Loss2: 1.331217
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452742 Loss1: 0.112921 Loss2: 1.339821
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568495 Loss1: 0.195981 Loss2: 1.372514
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408339 Loss1: 0.079102 Loss2: 1.329237
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.451624 Loss1: 0.125110 Loss2: 1.326514
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435998 Loss1: 0.103016 Loss2: 1.332982
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.445108 Loss1: 0.127470 Loss2: 1.317639
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414307 Loss1: 0.083153 Loss2: 1.331154
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439436 Loss1: 0.117607 Loss2: 1.321829
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411964 Loss1: 0.087690 Loss2: 1.324274
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440295 Loss1: 0.117299 Loss2: 1.322996
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413204 Loss1: 0.094229 Loss2: 1.318975
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404038 Loss1: 0.092404 Loss2: 1.311633
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.360810 Loss1: 0.048788 Loss2: 1.312022
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.582341 Loss1: 0.698531 Loss2: 1.883810
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.837750 Loss1: 0.439026 Loss2: 1.398724
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.673806 Loss1: 0.230725 Loss2: 1.443081
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.571308 Loss1: 0.185395 Loss2: 1.385913
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.655407 Loss1: 0.796090 Loss2: 1.859317
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.723988 Loss1: 0.365161 Loss2: 1.358826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.606305 Loss1: 0.214374 Loss2: 1.391930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559914 Loss1: 0.206832 Loss2: 1.353081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518358 Loss1: 0.169458 Loss2: 1.348900 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459334 Loss1: 0.115809 Loss2: 1.343525 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432370 Loss1: 0.068613 Loss2: 1.363758
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408800 Loss1: 0.070235 Loss2: 1.338565
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.391680 Loss1: 0.062117 Loss2: 1.329563
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374078 Loss1: 0.050311 Loss2: 1.323767
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366759 Loss1: 0.049824 Loss2: 1.316935
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.454284 Loss1: 0.648860 Loss2: 1.805425
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.725323 Loss1: 0.352029 Loss2: 1.373294
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.592296 Loss1: 0.204343 Loss2: 1.387953
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.512837 Loss1: 0.155638 Loss2: 1.357199
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.488711 Loss1: 0.606321 Loss2: 1.882390
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.483551 Loss1: 0.124701 Loss2: 1.358849
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.907238 Loss1: 0.496389 Loss2: 1.410849
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.737316 Loss1: 0.322730 Loss2: 1.414585
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487522 Loss1: 0.133687 Loss2: 1.353835
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631703 Loss1: 0.252369 Loss2: 1.379334
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424376 Loss1: 0.080230 Loss2: 1.344146
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.525183 Loss1: 0.145730 Loss2: 1.379454
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413471 Loss1: 0.074613 Loss2: 1.338858
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485731 Loss1: 0.132133 Loss2: 1.353597
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404455 Loss1: 0.074456 Loss2: 1.329999
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462237 Loss1: 0.128676 Loss2: 1.333561 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388764 Loss1: 0.053570 Loss2: 1.335194 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.414680 Loss1: 0.604681 Loss2: 1.809999 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.699455 Loss1: 0.280476 Loss2: 1.418980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.501743 Loss1: 0.670356 Loss2: 1.831386
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.605213 Loss1: 0.230623 Loss2: 1.374590
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.796505 Loss1: 0.419809 Loss2: 1.376697
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546862 Loss1: 0.162982 Loss2: 1.383880
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.624706 Loss1: 0.214146 Loss2: 1.410560
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489816 Loss1: 0.121694 Loss2: 1.368122
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.544083 Loss1: 0.173577 Loss2: 1.370506
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464262 Loss1: 0.097672 Loss2: 1.366590
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439295 Loss1: 0.087823 Loss2: 1.351471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440684 Loss1: 0.087000 Loss2: 1.353684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414235 Loss1: 0.064969 Loss2: 1.349266 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415051 Loss1: 0.071997 Loss2: 1.343054 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.561569 Loss1: 0.670409 Loss2: 1.891160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.757829 Loss1: 0.308062 Loss2: 1.449767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638537 Loss1: 0.225234 Loss2: 1.413302
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.383431 Loss1: 0.545810 Loss2: 1.837622
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558549 Loss1: 0.151936 Loss2: 1.406613
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.788639 Loss1: 0.435554 Loss2: 1.353084
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488321 Loss1: 0.096511 Loss2: 1.391810
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592630 Loss1: 0.211896 Loss2: 1.380735
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463664 Loss1: 0.076283 Loss2: 1.387381
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.503199 Loss1: 0.151549 Loss2: 1.351650
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.476391 Loss1: 0.095323 Loss2: 1.381068
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.470070 Loss1: 0.126492 Loss2: 1.343579
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499001 Loss1: 0.120102 Loss2: 1.378898
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499205 Loss1: 0.152426 Loss2: 1.346779
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472958 Loss1: 0.085183 Loss2: 1.387774
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440437 Loss1: 0.091278 Loss2: 1.349160
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414517 Loss1: 0.076692 Loss2: 1.337825
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398440 Loss1: 0.063853 Loss2: 1.334588
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367932 Loss1: 0.043831 Loss2: 1.324101
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.527832 Loss1: 0.653866 Loss2: 1.873965
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.763444 Loss1: 0.402303 Loss2: 1.361141
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573964 Loss1: 0.179831 Loss2: 1.394133
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.532077 Loss1: 0.181001 Loss2: 1.351077
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.666736 Loss1: 0.745150 Loss2: 1.921586
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.898107 Loss1: 0.510493 Loss2: 1.387613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468299 Loss1: 0.118636 Loss2: 1.349662
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.721742 Loss1: 0.284193 Loss2: 1.437549
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480753 Loss1: 0.131536 Loss2: 1.349216
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581468 Loss1: 0.191685 Loss2: 1.389782
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439662 Loss1: 0.090321 Loss2: 1.349341
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.534431 Loss1: 0.139762 Loss2: 1.394669
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486457 Loss1: 0.104587 Loss2: 1.381870
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436102 Loss1: 0.092785 Loss2: 1.343317
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472422 Loss1: 0.098444 Loss2: 1.373978
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430682 Loss1: 0.092043 Loss2: 1.338639
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417677 Loss1: 0.053287 Loss2: 1.364390 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998884
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.641033 Loss1: 0.753437 Loss2: 1.887596 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.801129 Loss1: 0.380666 Loss2: 1.420463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.707412 Loss1: 0.275594 Loss2: 1.431818
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.599012 Loss1: 0.741107 Loss2: 1.857905
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.872142 Loss1: 0.460008 Loss2: 1.412134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.674221 Loss1: 0.266316 Loss2: 1.407906 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.603133 Loss1: 0.216550 Loss2: 1.386583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.538520 Loss1: 0.141929 Loss2: 1.396591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492951 Loss1: 0.112577 Loss2: 1.380374 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409329 Loss1: 0.045866 Loss2: 1.363464
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459253 Loss1: 0.091976 Loss2: 1.367276
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439384 Loss1: 0.079740 Loss2: 1.359644
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435210 Loss1: 0.077631 Loss2: 1.357579
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442345 Loss1: 0.088579 Loss2: 1.353766
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.537923 Loss1: 0.722900 Loss2: 1.815023
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.806789 Loss1: 0.468676 Loss2: 1.338114
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684221 Loss1: 0.307364 Loss2: 1.376857
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.585223 Loss1: 0.242809 Loss2: 1.342414
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.567155 Loss1: 0.717268 Loss2: 1.849886
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534057 Loss1: 0.200310 Loss2: 1.333747
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.829748 Loss1: 0.449368 Loss2: 1.380381
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.659562 Loss1: 0.261204 Loss2: 1.398358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.632779 Loss1: 0.267065 Loss2: 1.365715 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.562323 Loss1: 0.196535 Loss2: 1.365788 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502319 Loss1: 0.141526 Loss2: 1.360793 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381101 Loss1: 0.069118 Loss2: 1.311983
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.454679 Loss1: 0.106032 Loss2: 1.348648
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420124 Loss1: 0.079032 Loss2: 1.341092
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391792 Loss1: 0.056738 Loss2: 1.335054
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369658 Loss1: 0.042143 Loss2: 1.327515
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.472632 Loss1: 0.661150 Loss2: 1.811482
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.715620 Loss1: 0.384445 Loss2: 1.331175
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.621061 Loss1: 0.251397 Loss2: 1.369664
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.560221 Loss1: 0.223227 Loss2: 1.336994
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.420467 Loss1: 0.564821 Loss2: 1.855646
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527813 Loss1: 0.190545 Loss2: 1.337268
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.722910 Loss1: 0.312674 Loss2: 1.410236
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.646850 Loss1: 0.229238 Loss2: 1.417612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564043 Loss1: 0.172154 Loss2: 1.391890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555664 Loss1: 0.165731 Loss2: 1.389933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513504 Loss1: 0.123347 Loss2: 1.390156 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440271 Loss1: 0.059838 Loss2: 1.380433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423843 Loss1: 0.054846 Loss2: 1.368997 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.636277 Loss1: 0.773439 Loss2: 1.862838 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594367 Loss1: 0.202543 Loss2: 1.391824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.469043 Loss1: 0.615380 Loss2: 1.853663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.780779 Loss1: 0.399126 Loss2: 1.381653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.624821 Loss1: 0.212360 Loss2: 1.412461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537006 Loss1: 0.180744 Loss2: 1.356263 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.468764 Loss1: 0.113741 Loss2: 1.355024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444052 Loss1: 0.093831 Loss2: 1.350221 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423028 Loss1: 0.086347 Loss2: 1.336680 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394099 Loss1: 0.060780 Loss2: 1.333319 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-DEBUG flwr 2023-10-12 01:43:58,990 | server.py:236 | fit_round 134 received 50 results and 0 failures
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.506670 Loss1: 0.685971 Loss2: 1.820699
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.780868 Loss1: 0.431188 Loss2: 1.349680
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.676143 Loss1: 0.281499 Loss2: 1.394644
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575182 Loss1: 0.219106 Loss2: 1.356076
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.502941 Loss1: 0.677573 Loss2: 1.825367
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.788744 Loss1: 0.426647 Loss2: 1.362097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.708046 Loss1: 0.273344 Loss2: 1.434702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.572431 Loss1: 0.219153 Loss2: 1.353278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.583164 Loss1: 0.214259 Loss2: 1.368906 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485381 Loss1: 0.120254 Loss2: 1.365127 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468863 Loss1: 0.114498 Loss2: 1.354365 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.473163 Loss1: 0.127526 Loss2: 1.345637 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.734394 Loss1: 0.360200 Loss2: 1.374194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497468 Loss1: 0.137666 Loss2: 1.359802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517174 Loss1: 0.153713 Loss2: 1.363461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484981 Loss1: 0.120187 Loss2: 1.364793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463266 Loss1: 0.108331 Loss2: 1.354935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426749 Loss1: 0.072103 Loss2: 1.354646 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394906 Loss1: 0.046850 Loss2: 1.348056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401420 Loss1: 0.062010 Loss2: 1.339410 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453638 Loss1: 0.112436 Loss2: 1.341202 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967634
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.727180 Loss1: 0.759023 Loss2: 1.968157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.762680 Loss1: 0.279037 Loss2: 1.483643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.663898 Loss1: 0.757890 Loss2: 1.906008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.782274 Loss1: 0.407289 Loss2: 1.374984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601530 Loss1: 0.203185 Loss2: 1.398345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543674 Loss1: 0.169476 Loss2: 1.374198 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.470730 Loss1: 0.104770 Loss2: 1.365961 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461744 Loss1: 0.078908 Loss2: 1.382835 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428445 Loss1: 0.082405 Loss2: 1.346039 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411759 Loss1: 0.065436 Loss2: 1.346322 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 01:43:58,990][flwr][DEBUG] - fit_round 134 received 50 results and 0 failures
-INFO flwr 2023-10-12 01:44:40,461 | server.py:125 | fit progress: (134, 2.212735759754912, {'accuracy': 0.5886}, 309188.239732421)
->> Test accuracy: 0.588600
-[2023-10-12 01:44:40,461][flwr][INFO] - fit progress: (134, 2.212735759754912, {'accuracy': 0.5886}, 309188.239732421)
-DEBUG flwr 2023-10-12 01:44:40,461 | server.py:173 | evaluate_round 134: strategy sampled 50 clients (out of 50)
-[2023-10-12 01:44:40,461][flwr][DEBUG] - evaluate_round 134: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 01:53:44,878 | server.py:187 | evaluate_round 134 received 50 results and 0 failures
-[2023-10-12 01:53:44,878][flwr][DEBUG] - evaluate_round 134 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 01:53:44,879 | server.py:222 | fit_round 135: strategy sampled 50 clients (out of 50)
-[2023-10-12 01:53:44,879][flwr][DEBUG] - fit_round 135: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.785606 Loss1: 0.815206 Loss2: 1.970400
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.839257 Loss1: 0.411414 Loss2: 1.427843
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.745391 Loss1: 0.282733 Loss2: 1.462658
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586909 Loss1: 0.167369 Loss2: 1.419540
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.590139 Loss1: 0.642976 Loss2: 1.947163
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.969752 Loss1: 0.521016 Loss2: 1.448735 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.861999 Loss1: 0.329120 Loss2: 1.532879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.675765 Loss1: 0.233892 Loss2: 1.441874 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.676048 Loss1: 0.213154 Loss2: 1.462894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.583509 Loss1: 0.140841 Loss2: 1.442669 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515314 Loss1: 0.079518 Loss2: 1.435796 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.484540 Loss1: 0.073649 Loss2: 1.410891 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.867561 Loss1: 0.481590 Loss2: 1.385971 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.626852 Loss1: 0.194759 Loss2: 1.432093 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.557838 Loss1: 0.165200 Loss2: 1.392639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.809220 Loss1: 0.436537 Loss2: 1.372683 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.746370 Loss1: 0.349990 Loss2: 1.396379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539056 Loss1: 0.182211 Loss2: 1.356846 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456146 Loss1: 0.120466 Loss2: 1.335680 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399591 Loss1: 0.073910 Loss2: 1.325681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408359 Loss1: 0.088458 Loss2: 1.319901
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.548087 Loss1: 0.697130 Loss2: 1.850957
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369263 Loss1: 0.048946 Loss2: 1.320318
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.836885 Loss1: 0.483069 Loss2: 1.353815
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.612710 Loss1: 0.211694 Loss2: 1.401016
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.595755 Loss1: 0.250870 Loss2: 1.344885
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566266 Loss1: 0.202524 Loss2: 1.363742
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.482531 Loss1: 0.138589 Loss2: 1.343942
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478625 Loss1: 0.136834 Loss2: 1.341791
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.408449 Loss1: 0.542171 Loss2: 1.866278
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444509 Loss1: 0.102208 Loss2: 1.342301
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.787427 Loss1: 0.370354 Loss2: 1.417072
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395396 Loss1: 0.065697 Loss2: 1.329699
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727759 Loss1: 0.291557 Loss2: 1.436202
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361995 Loss1: 0.039278 Loss2: 1.322717
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.614667 Loss1: 0.216448 Loss2: 1.398219
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.579812 Loss1: 0.174106 Loss2: 1.405706
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537883 Loss1: 0.137449 Loss2: 1.400434
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545109 Loss1: 0.149111 Loss2: 1.395998
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.508935 Loss1: 0.116903 Loss2: 1.392031
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.560333 Loss1: 0.695234 Loss2: 1.865099
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.775555 Loss1: 0.376696 Loss2: 1.398859 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.660833 Loss1: 0.241600 Loss2: 1.419234 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.493085 Loss1: 0.111748 Loss2: 1.381337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441438 Loss1: 0.075116 Loss2: 1.366322 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413558 Loss1: 0.053008 Loss2: 1.360549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406969 Loss1: 0.049401 Loss2: 1.357568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393016 Loss1: 0.038638 Loss2: 1.354378 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.615198 Loss1: 0.197846 Loss2: 1.417352 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.589057 Loss1: 0.185627 Loss2: 1.403430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.540712 Loss1: 0.114383 Loss2: 1.426329 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472769 Loss1: 0.078684 Loss2: 1.394085 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558980 Loss1: 0.162515 Loss2: 1.396465 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.496324 Loss1: 0.102310 Loss2: 1.394014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488360 Loss1: 0.101985 Loss2: 1.386374
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.448538 Loss1: 0.599180 Loss2: 1.849359
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.694894 Loss1: 0.346796 Loss2: 1.348099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.602880 Loss1: 0.215885 Loss2: 1.386995 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536411 Loss1: 0.186628 Loss2: 1.349783 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.557256 Loss1: 0.196059 Loss2: 1.361197 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398612 Loss1: 0.059275 Loss2: 1.339337 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388748 Loss1: 0.057883 Loss2: 1.330866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372150 Loss1: 0.044891 Loss2: 1.327259 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.548533 Loss1: 0.185190 Loss2: 1.363343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.474712 Loss1: 0.107727 Loss2: 1.366984 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.438891 Loss1: 0.088617 Loss2: 1.350274
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.531678 Loss1: 0.690838 Loss2: 1.840840
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.779187 Loss1: 0.411643 Loss2: 1.367545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.720238 Loss1: 0.310542 Loss2: 1.409695 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604907 Loss1: 0.245863 Loss2: 1.359043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523653 Loss1: 0.169606 Loss2: 1.354047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440804 Loss1: 0.096223 Loss2: 1.344581 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385382 Loss1: 0.049386 Loss2: 1.335996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391015 Loss1: 0.064467 Loss2: 1.326547 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.575060 Loss1: 0.177171 Loss2: 1.397889 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484296 Loss1: 0.089633 Loss2: 1.394663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.452012 Loss1: 0.589486 Loss2: 1.862526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.827059 Loss1: 0.458835 Loss2: 1.368225 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521907 Loss1: 0.168863 Loss2: 1.353044 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486370 Loss1: 0.131749 Loss2: 1.354621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460324 Loss1: 0.110728 Loss2: 1.349596
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.593670 Loss1: 0.680942 Loss2: 1.912728
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411271 Loss1: 0.075411 Loss2: 1.335860
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.910646 Loss1: 0.491703 Loss2: 1.418943
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386548 Loss1: 0.051843 Loss2: 1.334705
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.730337 Loss1: 0.262954 Loss2: 1.467384
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384335 Loss1: 0.050684 Loss2: 1.333651
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606415 Loss1: 0.189413 Loss2: 1.417003
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.588939 Loss1: 0.169646 Loss2: 1.419294
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513962 Loss1: 0.107124 Loss2: 1.406838
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486048 Loss1: 0.087283 Loss2: 1.398765
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461609 Loss1: 0.069471 Loss2: 1.392139
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462691 Loss1: 0.071049 Loss2: 1.391642
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.507823 Loss1: 0.641272 Loss2: 1.866551
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442471 Loss1: 0.057762 Loss2: 1.384709
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.745073 Loss1: 0.372629 Loss2: 1.372444
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.612528 Loss1: 0.214173 Loss2: 1.398355
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.522975 Loss1: 0.161923 Loss2: 1.361051
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.479040 Loss1: 0.121945 Loss2: 1.357095
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470459 Loss1: 0.114256 Loss2: 1.356203
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431364 Loss1: 0.086613 Loss2: 1.344751
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.442719 Loss1: 0.616492 Loss2: 1.826227
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430176 Loss1: 0.091384 Loss2: 1.338793
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.869871 Loss1: 0.519744 Loss2: 1.350127
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431198 Loss1: 0.087977 Loss2: 1.343221
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.734983 Loss1: 0.342133 Loss2: 1.392850
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438492 Loss1: 0.090856 Loss2: 1.347636
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610421 Loss1: 0.269529 Loss2: 1.340892
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.511923 Loss1: 0.162262 Loss2: 1.349662
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468428 Loss1: 0.139813 Loss2: 1.328616
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436363 Loss1: 0.115383 Loss2: 1.320980
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405344 Loss1: 0.084981 Loss2: 1.320362
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.601484 Loss1: 0.743253 Loss2: 1.858231
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371511 Loss1: 0.060684 Loss2: 1.310826
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.815889 Loss1: 0.417994 Loss2: 1.397895
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358777 Loss1: 0.057260 Loss2: 1.301517
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.593626 Loss1: 0.203906 Loss2: 1.389720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.544588 Loss1: 0.163991 Loss2: 1.380596 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.512315 Loss1: 0.130815 Loss2: 1.381500
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.585452 Loss1: 0.657019 Loss2: 1.928433
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.891439 Loss1: 0.446173 Loss2: 1.445266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.750480 Loss1: 0.258872 Loss2: 1.491608 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.627481 Loss1: 0.201223 Loss2: 1.426257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514578 Loss1: 0.087409 Loss2: 1.427170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.472700 Loss1: 0.068575 Loss2: 1.404125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.353183 Loss1: 0.557193 Loss2: 1.795990
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461401 Loss1: 0.058468 Loss2: 1.402933
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.705484 Loss1: 0.350247 Loss2: 1.355237
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448654 Loss1: 0.050144 Loss2: 1.398510
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.518385 Loss1: 0.176677 Loss2: 1.341708 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.430889 Loss1: 0.099074 Loss2: 1.331815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.490143 Loss1: 0.658070 Loss2: 1.832072
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.397734 Loss1: 0.071001 Loss2: 1.326733
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.809164 Loss1: 0.444306 Loss2: 1.364858
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390448 Loss1: 0.069554 Loss2: 1.320894
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.764339 Loss1: 0.306412 Loss2: 1.457927
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.361476 Loss1: 0.047351 Loss2: 1.314124
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.638026 Loss1: 0.270452 Loss2: 1.367574
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356704 Loss1: 0.050217 Loss2: 1.306487
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505865 Loss1: 0.143739 Loss2: 1.362126 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440663 Loss1: 0.087539 Loss2: 1.353124 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422864 Loss1: 0.077455 Loss2: 1.345410
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.327929 Loss1: 0.477253 Loss2: 1.850676
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.735109 Loss1: 0.349451 Loss2: 1.385658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.607771 Loss1: 0.225201 Loss2: 1.382571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.547887 Loss1: 0.164907 Loss2: 1.382981 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.757776 Loss1: 0.374836 Loss2: 1.382940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.671174 Loss1: 0.233414 Loss2: 1.437760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.617290 Loss1: 0.251577 Loss2: 1.365713 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.585686 Loss1: 0.192560 Loss2: 1.393126 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997243
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478721 Loss1: 0.101022 Loss2: 1.377699 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446569 Loss1: 0.091032 Loss2: 1.355537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.478136 Loss1: 0.118083 Loss2: 1.360053 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.725479 Loss1: 0.369499 Loss2: 1.355980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539707 Loss1: 0.185137 Loss2: 1.354570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.477973 Loss1: 0.127851 Loss2: 1.350122
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.589185 Loss1: 0.702399 Loss2: 1.886786
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.782124 Loss1: 0.418563 Loss2: 1.363561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.697080 Loss1: 0.280181 Loss2: 1.416898 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551722 Loss1: 0.186638 Loss2: 1.365084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.508155 Loss1: 0.143261 Loss2: 1.364894 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.464581 Loss1: 0.108834 Loss2: 1.355746 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409709 Loss1: 0.069799 Loss2: 1.339910 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371382 Loss1: 0.044662 Loss2: 1.326720 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.880520 Loss1: 0.476473 Loss2: 1.404046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.617851 Loss1: 0.215718 Loss2: 1.402133 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571973 Loss1: 0.163723 Loss2: 1.408250
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.498595 Loss1: 0.610225 Loss2: 1.888370
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.736256 Loss1: 0.355777 Loss2: 1.380479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.751072 Loss1: 0.294763 Loss2: 1.456309 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594544 Loss1: 0.208510 Loss2: 1.386034 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.573937 Loss1: 0.186053 Loss2: 1.387884 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454970 Loss1: 0.078025 Loss2: 1.376945
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518853 Loss1: 0.130730 Loss2: 1.388123
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461100 Loss1: 0.088452 Loss2: 1.372648
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439700 Loss1: 0.066392 Loss2: 1.373308
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447286 Loss1: 0.081985 Loss2: 1.365301
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429541 Loss1: 0.063811 Loss2: 1.365730
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.415592 Loss1: 0.584351 Loss2: 1.831241
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.746833 Loss1: 0.405076 Loss2: 1.341757
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.655681 Loss1: 0.264942 Loss2: 1.390739
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584023 Loss1: 0.237379 Loss2: 1.346644
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545409 Loss1: 0.199767 Loss2: 1.345642
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.384019 Loss1: 0.554016 Loss2: 1.830003
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.769567 Loss1: 0.423141 Loss2: 1.346426 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615150 Loss1: 0.235215 Loss2: 1.379935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.585944 Loss1: 0.241494 Loss2: 1.344451 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490690 Loss1: 0.141128 Loss2: 1.349562 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415471 Loss1: 0.087360 Loss2: 1.328111
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.464085 Loss1: 0.124469 Loss2: 1.339616
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.454059 Loss1: 0.114116 Loss2: 1.339943
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462019 Loss1: 0.134701 Loss2: 1.327318
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405173 Loss1: 0.074607 Loss2: 1.330566
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399877 Loss1: 0.075696 Loss2: 1.324181
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.524071 Loss1: 0.685373 Loss2: 1.838698
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.773681 Loss1: 0.415230 Loss2: 1.358451
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.646056 Loss1: 0.255122 Loss2: 1.390934
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576907 Loss1: 0.220695 Loss2: 1.356213
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508072 Loss1: 0.162390 Loss2: 1.345682
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.345439 Loss1: 0.560824 Loss2: 1.784615
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456606 Loss1: 0.118853 Loss2: 1.337753
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445033 Loss1: 0.109503 Loss2: 1.335530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435193 Loss1: 0.099283 Loss2: 1.335910 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520197 Loss1: 0.169603 Loss2: 1.350594
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486832 Loss1: 0.152195 Loss2: 1.334637
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527910 Loss1: 0.178472 Loss2: 1.349438
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.432964 Loss1: 0.102741 Loss2: 1.330223
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419360 Loss1: 0.081061 Loss2: 1.338299 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385038 Loss1: 0.057384 Loss2: 1.327654 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.444708 Loss1: 0.612375 Loss2: 1.832333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358815 Loss1: 0.042510 Loss2: 1.316305
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.646675 Loss1: 0.256659 Loss2: 1.390016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548337 Loss1: 0.194267 Loss2: 1.354070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.489966 Loss1: 0.141513 Loss2: 1.348453
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.544620 Loss1: 0.697498 Loss2: 1.847122
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.701521 Loss1: 0.337127 Loss2: 1.364394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.605826 Loss1: 0.213006 Loss2: 1.392820 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527835 Loss1: 0.169192 Loss2: 1.358643 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439233 Loss1: 0.110389 Loss2: 1.328843
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503061 Loss1: 0.141996 Loss2: 1.361065
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476919 Loss1: 0.123086 Loss2: 1.353833
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435502 Loss1: 0.088950 Loss2: 1.346553
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447296 Loss1: 0.105263 Loss2: 1.342034
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423195 Loss1: 0.079045 Loss2: 1.344150
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.394786 Loss1: 0.584277 Loss2: 1.810509
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404624 Loss1: 0.063527 Loss2: 1.341097
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.621493 Loss1: 0.247066 Loss2: 1.374427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508897 Loss1: 0.173213 Loss2: 1.335684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.528335 Loss1: 0.642888 Loss2: 1.885447
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473121 Loss1: 0.141841 Loss2: 1.331280
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.893530 Loss1: 0.458669 Loss2: 1.434861
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.414755 Loss1: 0.085292 Loss2: 1.329463
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.825644 Loss1: 0.349829 Loss2: 1.475815
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.391316 Loss1: 0.070972 Loss2: 1.320345
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.616686 Loss1: 0.203497 Loss2: 1.413189
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379397 Loss1: 0.060754 Loss2: 1.318643
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.623299 Loss1: 0.198911 Loss2: 1.424388
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.343926 Loss1: 0.031560 Loss2: 1.312366
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529582 Loss1: 0.117275 Loss2: 1.412307 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.498703 Loss1: 0.101688 Loss2: 1.397016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.454833 Loss1: 0.669801 Loss2: 1.785032
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.502433 Loss1: 0.101397 Loss2: 1.401036
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642752 Loss1: 0.287422 Loss2: 1.355330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.499598 Loss1: 0.158163 Loss2: 1.341435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.419720 Loss1: 0.107094 Loss2: 1.312626
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.424244 Loss1: 0.593742 Loss2: 1.830502
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.697014 Loss1: 0.377486 Loss2: 1.319529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.670439 Loss1: 0.308661 Loss2: 1.361777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599570 Loss1: 0.275489 Loss2: 1.324081 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476163 Loss1: 0.155885 Loss2: 1.320279 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.368413 Loss1: 0.077539 Loss2: 1.290874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.366819 Loss1: 0.083026 Loss2: 1.283794 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.480923 Loss1: 0.677171 Loss2: 1.803752
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.332211 Loss1: 0.049608 Loss2: 1.282602
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.603681 Loss1: 0.231513 Loss2: 1.372168 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.429362 Loss1: 0.102119 Loss2: 1.327243 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473194 Loss1: 0.147264 Loss2: 1.325930
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.506882 Loss1: 0.662073 Loss2: 1.844809
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447167 Loss1: 0.123645 Loss2: 1.323522
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.696898 Loss1: 0.328324 Loss2: 1.368574
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466027 Loss1: 0.144469 Loss2: 1.321558
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615262 Loss1: 0.227437 Loss2: 1.387825
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435570 Loss1: 0.117565 Loss2: 1.318006
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533227 Loss1: 0.171552 Loss2: 1.361675
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424850 Loss1: 0.105095 Loss2: 1.319755
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498072 Loss1: 0.132784 Loss2: 1.365288
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468890 Loss1: 0.115754 Loss2: 1.353136
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443485 Loss1: 0.092977 Loss2: 1.350508
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413292 Loss1: 0.061831 Loss2: 1.351461
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453338 Loss1: 0.108126 Loss2: 1.345212
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.461632 Loss1: 0.596893 Loss2: 1.864740
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433129 Loss1: 0.083796 Loss2: 1.349333
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.703338 Loss1: 0.283840 Loss2: 1.419498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509187 Loss1: 0.133641 Loss2: 1.375546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455376 Loss1: 0.086267 Loss2: 1.369109
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.454632 Loss1: 0.633333 Loss2: 1.821298
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.797725 Loss1: 0.436618 Loss2: 1.361107 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656733 Loss1: 0.264798 Loss2: 1.391936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581718 Loss1: 0.233741 Loss2: 1.347978 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406368 Loss1: 0.061930 Loss2: 1.344438
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525188 Loss1: 0.173188 Loss2: 1.351999
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503927 Loss1: 0.158611 Loss2: 1.345316
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462738 Loss1: 0.125534 Loss2: 1.337205
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447879 Loss1: 0.107280 Loss2: 1.340599
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405515 Loss1: 0.077438 Loss2: 1.328077
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.474734 Loss1: 0.644375 Loss2: 1.830359
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361936 Loss1: 0.041889 Loss2: 1.320047
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671206 Loss1: 0.242391 Loss2: 1.428815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.552068 Loss1: 0.162740 Loss2: 1.389328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.580987 Loss1: 0.648876 Loss2: 1.932110
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499815 Loss1: 0.123620 Loss2: 1.376195
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.992958 Loss1: 0.534962 Loss2: 1.457996
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455327 Loss1: 0.078370 Loss2: 1.376956
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.797921 Loss1: 0.275626 Loss2: 1.522296
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.451002 Loss1: 0.087235 Loss2: 1.363767
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.753108 Loss1: 0.302623 Loss2: 1.450485
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424952 Loss1: 0.065336 Loss2: 1.359616
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425846 Loss1: 0.066195 Loss2: 1.359651 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.546558 Loss1: 0.101064 Loss2: 1.445494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.518052 Loss1: 0.084661 Loss2: 1.433391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.512510 Loss1: 0.085654 Loss2: 1.426856
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.457300 Loss1: 0.611248 Loss2: 1.846053
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.804795 Loss1: 0.436244 Loss2: 1.368551
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578089 Loss1: 0.174194 Loss2: 1.403895
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559466 Loss1: 0.210803 Loss2: 1.348662
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516806 Loss1: 0.157301 Loss2: 1.359505
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.607656 Loss1: 0.761381 Loss2: 1.846276
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476562 Loss1: 0.140493 Loss2: 1.336069
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470978 Loss1: 0.131650 Loss2: 1.339328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.474078 Loss1: 0.139677 Loss2: 1.334400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434423 Loss1: 0.100772 Loss2: 1.333651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416222 Loss1: 0.084516 Loss2: 1.331705 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394147 Loss1: 0.097453 Loss2: 1.296694 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.351369 Loss1: 0.067156 Loss2: 1.284213 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994420
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.782806 Loss1: 0.363453 Loss2: 1.419353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581901 Loss1: 0.161739 Loss2: 1.420163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.537341 Loss1: 0.124577 Loss2: 1.412764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.547685 Loss1: 0.134801 Loss2: 1.412884 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533598 Loss1: 0.118944 Loss2: 1.414653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.504704 Loss1: 0.097724 Loss2: 1.406980 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 02:22:08,427 | server.py:236 | fit_round 135 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490282 Loss1: 0.091401 Loss2: 1.398881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452161 Loss1: 0.051941 Loss2: 1.400221 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461444 Loss1: 0.088547 Loss2: 1.372897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419790 Loss1: 0.057640 Loss2: 1.362150 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.432886 Loss1: 0.634798 Loss2: 1.798088
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.741773 Loss1: 0.392899 Loss2: 1.348874
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588582 Loss1: 0.200794 Loss2: 1.387789
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.587299 Loss1: 0.241191 Loss2: 1.346109
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.680434 Loss1: 0.786407 Loss2: 1.894027
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492941 Loss1: 0.152864 Loss2: 1.340078
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.836635 Loss1: 0.472474 Loss2: 1.364162
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.650818 Loss1: 0.262450 Loss2: 1.388368
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467186 Loss1: 0.129273 Loss2: 1.337913
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434401 Loss1: 0.103972 Loss2: 1.330429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390732 Loss1: 0.069816 Loss2: 1.320916 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372014 Loss1: 0.051755 Loss2: 1.320259 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394140 Loss1: 0.062787 Loss2: 1.331354 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365969 Loss1: 0.043001 Loss2: 1.322969 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.677208 Loss1: 0.771412 Loss2: 1.905796
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.914726 Loss1: 0.533063 Loss2: 1.381663
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.876558 Loss1: 0.403539 Loss2: 1.473019
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604775 Loss1: 0.229602 Loss2: 1.375173
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568623 Loss1: 0.180345 Loss2: 1.388278
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469430 Loss1: 0.098930 Loss2: 1.370500
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472817 Loss1: 0.117339 Loss2: 1.355479
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473975 Loss1: 0.120443 Loss2: 1.353532
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424305 Loss1: 0.072546 Loss2: 1.351759
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394959 Loss1: 0.053692 Loss2: 1.341267
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 02:22:08,427][flwr][DEBUG] - fit_round 135 received 50 results and 0 failures
-INFO flwr 2023-10-12 02:22:50,554 | server.py:125 | fit progress: (135, 2.2166092506231974, {'accuracy': 0.5909}, 311478.33283696795)
->> Test accuracy: 0.590900
-[2023-10-12 02:22:50,554][flwr][INFO] - fit progress: (135, 2.2166092506231974, {'accuracy': 0.5909}, 311478.33283696795)
-DEBUG flwr 2023-10-12 02:22:50,555 | server.py:173 | evaluate_round 135: strategy sampled 50 clients (out of 50)
-[2023-10-12 02:22:50,555][flwr][DEBUG] - evaluate_round 135: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 02:31:56,688 | server.py:187 | evaluate_round 135 received 50 results and 0 failures
-[2023-10-12 02:31:56,688][flwr][DEBUG] - evaluate_round 135 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 02:31:56,688 | server.py:222 | fit_round 136: strategy sampled 50 clients (out of 50)
-[2023-10-12 02:31:56,688][flwr][DEBUG] - fit_round 136: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.431467 Loss1: 0.532830 Loss2: 1.898638
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.818669 Loss1: 0.400566 Loss2: 1.418103
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.720135 Loss1: 0.257127 Loss2: 1.463008
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.605790 Loss1: 0.177286 Loss2: 1.428505
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551012 Loss1: 0.132345 Loss2: 1.418667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.531163 Loss1: 0.120581 Loss2: 1.410583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495566 Loss1: 0.089336 Loss2: 1.406229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489298 Loss1: 0.088324 Loss2: 1.400974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.480232 Loss1: 0.078598 Loss2: 1.401634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462063 Loss1: 0.068526 Loss2: 1.393537 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432568 Loss1: 0.100844 Loss2: 1.331724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414058 Loss1: 0.081773 Loss2: 1.332285 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.763717 Loss1: 0.384591 Loss2: 1.379126 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542644 Loss1: 0.180773 Loss2: 1.361871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.425544 Loss1: 0.639530 Loss2: 1.786014
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.500070 Loss1: 0.133611 Loss2: 1.366459
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.731695 Loss1: 0.393677 Loss2: 1.338017
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.479541 Loss1: 0.116738 Loss2: 1.362803
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.595055 Loss1: 0.234102 Loss2: 1.360954
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491874 Loss1: 0.138965 Loss2: 1.352909
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498246 Loss1: 0.158213 Loss2: 1.340033
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489830 Loss1: 0.134779 Loss2: 1.355050
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.463264 Loss1: 0.135496 Loss2: 1.327768
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452218 Loss1: 0.100151 Loss2: 1.352067
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516763 Loss1: 0.188463 Loss2: 1.328300
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430447 Loss1: 0.083181 Loss2: 1.347266
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460753 Loss1: 0.137253 Loss2: 1.323499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396789 Loss1: 0.076495 Loss2: 1.320294 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.675290 Loss1: 0.342098 Loss2: 1.333192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.538831 Loss1: 0.212538 Loss2: 1.326293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.477926 Loss1: 0.144698 Loss2: 1.333228
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.680321 Loss1: 0.715921 Loss2: 1.964400
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433538 Loss1: 0.115191 Loss2: 1.318347
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.940398 Loss1: 0.519884 Loss2: 1.420514
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804251 Loss1: 0.317086 Loss2: 1.487166
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419837 Loss1: 0.103199 Loss2: 1.316638
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.645393 Loss1: 0.235371 Loss2: 1.410022
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389687 Loss1: 0.076096 Loss2: 1.313590
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.649434 Loss1: 0.218330 Loss2: 1.431104
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381256 Loss1: 0.077997 Loss2: 1.303259
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354189 Loss1: 0.057246 Loss2: 1.296943 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485825 Loss1: 0.077621 Loss2: 1.408204 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478879 Loss1: 0.085613 Loss2: 1.393266 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975446
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.726459 Loss1: 0.781648 Loss2: 1.944811
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.799527 Loss1: 0.440268 Loss2: 1.359259
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.687774 Loss1: 0.279394 Loss2: 1.408380
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535523 Loss1: 0.170485 Loss2: 1.365038
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484232 Loss1: 0.136177 Loss2: 1.348055
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460573 Loss1: 0.108885 Loss2: 1.351688 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422644 Loss1: 0.076895 Loss2: 1.345748 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411186 Loss1: 0.074705 Loss2: 1.336481 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.380908 Loss1: 0.050489 Loss2: 1.330418 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377322 Loss1: 0.052764 Loss2: 1.324558 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986779
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500243 Loss1: 0.110941 Loss2: 1.389302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.489061 Loss1: 0.102754 Loss2: 1.386306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456204 Loss1: 0.077184 Loss2: 1.379020
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.535793 Loss1: 0.641821 Loss2: 1.893972
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.818745 Loss1: 0.401794 Loss2: 1.416951
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.703537 Loss1: 0.256647 Loss2: 1.446890
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.669102 Loss1: 0.255871 Loss2: 1.413231
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.584655 Loss1: 0.165294 Loss2: 1.419361
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.286662 Loss1: 0.510935 Loss2: 1.775727
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523753 Loss1: 0.122194 Loss2: 1.401559
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653754 Loss1: 0.332938 Loss2: 1.320816
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496927 Loss1: 0.099457 Loss2: 1.397470
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.550189 Loss1: 0.196377 Loss2: 1.353811
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.494125 Loss1: 0.101344 Loss2: 1.392781
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444419 Loss1: 0.056077 Loss2: 1.388342
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.473899 Loss1: 0.160790 Loss2: 1.313109
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432075 Loss1: 0.049030 Loss2: 1.383045
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469053 Loss1: 0.157435 Loss2: 1.311619
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449062 Loss1: 0.135479 Loss2: 1.313583
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.396465 Loss1: 0.095771 Loss2: 1.300694
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397844 Loss1: 0.101466 Loss2: 1.296378
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379608 Loss1: 0.085475 Loss2: 1.294133
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.643427 Loss1: 0.744103 Loss2: 1.899324
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384712 Loss1: 0.088774 Loss2: 1.295938
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.823885 Loss1: 0.352112 Loss2: 1.471772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.608492 Loss1: 0.194080 Loss2: 1.414412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511648 Loss1: 0.115613 Loss2: 1.396035
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.495991 Loss1: 0.638858 Loss2: 1.857133
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.728153 Loss1: 0.370403 Loss2: 1.357750 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.626801 Loss1: 0.247957 Loss2: 1.378844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.541942 Loss1: 0.198242 Loss2: 1.343700 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.482851 Loss1: 0.095633 Loss2: 1.387218
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497014 Loss1: 0.141661 Loss2: 1.355353
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487183 Loss1: 0.146182 Loss2: 1.341002
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425793 Loss1: 0.088095 Loss2: 1.337698
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415487 Loss1: 0.083778 Loss2: 1.331709
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400450 Loss1: 0.072778 Loss2: 1.327672
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.445030 Loss1: 0.649384 Loss2: 1.795646
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388471 Loss1: 0.061032 Loss2: 1.327439
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.755361 Loss1: 0.350405 Loss2: 1.404956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571633 Loss1: 0.218239 Loss2: 1.353394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.435522 Loss1: 0.633812 Loss2: 1.801710
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487315 Loss1: 0.139650 Loss2: 1.347665
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.713657 Loss1: 0.340389 Loss2: 1.373268
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408364 Loss1: 0.083307 Loss2: 1.325057
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.647637 Loss1: 0.264793 Loss2: 1.382843
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418934 Loss1: 0.095709 Loss2: 1.323225
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.632393 Loss1: 0.263533 Loss2: 1.368860
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419242 Loss1: 0.098702 Loss2: 1.320540
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.540733 Loss1: 0.176564 Loss2: 1.364169
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403891 Loss1: 0.080728 Loss2: 1.323163
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470232 Loss1: 0.113640 Loss2: 1.356592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423767 Loss1: 0.083044 Loss2: 1.340723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.482909 Loss1: 0.666513 Loss2: 1.816395
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389198 Loss1: 0.054144 Loss2: 1.335053
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599374 Loss1: 0.224856 Loss2: 1.374519 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.459591 Loss1: 0.118647 Loss2: 1.340944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458632 Loss1: 0.117409 Loss2: 1.341223
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.553340 Loss1: 0.701962 Loss2: 1.851378
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.771185 Loss1: 0.399230 Loss2: 1.371956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.569987 Loss1: 0.180803 Loss2: 1.389184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481237 Loss1: 0.123728 Loss2: 1.357509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422963 Loss1: 0.085357 Loss2: 1.337606
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.453035 Loss1: 0.101750 Loss2: 1.351285
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452585 Loss1: 0.097485 Loss2: 1.355100
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450218 Loss1: 0.107865 Loss2: 1.342353
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424263 Loss1: 0.079362 Loss2: 1.344901
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391217 Loss1: 0.058368 Loss2: 1.332849
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.447644 Loss1: 0.586172 Loss2: 1.861472
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398820 Loss1: 0.068822 Loss2: 1.329997
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667651 Loss1: 0.275530 Loss2: 1.392121 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506226 Loss1: 0.145548 Loss2: 1.360678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485468 Loss1: 0.120807 Loss2: 1.364660
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.564720 Loss1: 0.705443 Loss2: 1.859277
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440682 Loss1: 0.085829 Loss2: 1.354853
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.816954 Loss1: 0.489947 Loss2: 1.327007
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.751570 Loss1: 0.346786 Loss2: 1.404784
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451376 Loss1: 0.105519 Loss2: 1.345857
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.602995 Loss1: 0.248866 Loss2: 1.354129
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455053 Loss1: 0.101444 Loss2: 1.353609
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.564687 Loss1: 0.197162 Loss2: 1.367525
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462791 Loss1: 0.103761 Loss2: 1.359031
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468130 Loss1: 0.115384 Loss2: 1.352746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401555 Loss1: 0.074807 Loss2: 1.326747 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376470 Loss1: 0.053469 Loss2: 1.323001
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.718864 Loss1: 0.714181 Loss2: 2.004684
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.857432 Loss1: 0.496690 Loss2: 1.360742
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.711676 Loss1: 0.299317 Loss2: 1.412359
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.663867 Loss1: 0.256357 Loss2: 1.407510
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.559268 Loss1: 0.194627 Loss2: 1.364641
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547570 Loss1: 0.175590 Loss2: 1.371980
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.505334 Loss1: 0.138318 Loss2: 1.367016
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.770477 Loss1: 0.382733 Loss2: 1.387744 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604166 Loss1: 0.197985 Loss2: 1.406181 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503094 Loss1: 0.134185 Loss2: 1.368909 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476748 Loss1: 0.110056 Loss2: 1.366692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470027 Loss1: 0.112163 Loss2: 1.357864
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.529065 Loss1: 0.625844 Loss2: 1.903221
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784348 Loss1: 0.375167 Loss2: 1.409181 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452819 Loss1: 0.100191 Loss2: 1.352628
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.713440 Loss1: 0.260306 Loss2: 1.453134
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654329 Loss1: 0.244475 Loss2: 1.409854
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.623765 Loss1: 0.220692 Loss2: 1.403073
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.556619 Loss1: 0.143619 Loss2: 1.413000
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488535 Loss1: 0.087693 Loss2: 1.400842
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.377205 Loss1: 0.558678 Loss2: 1.818527
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462234 Loss1: 0.076014 Loss2: 1.386220
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455231 Loss1: 0.069263 Loss2: 1.385968 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450783 Loss1: 0.066892 Loss2: 1.383890 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571675 Loss1: 0.221350 Loss2: 1.350325 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464442 Loss1: 0.124892 Loss2: 1.339550 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389031 Loss1: 0.060006 Loss2: 1.329025
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.401766 Loss1: 0.589319 Loss2: 1.812448
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.843900 Loss1: 0.419314 Loss2: 1.424585 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645610 Loss1: 0.264561 Loss2: 1.381049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499813 Loss1: 0.127591 Loss2: 1.372221 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.429896 Loss1: 0.074671 Loss2: 1.355226 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.455489 Loss1: 0.107941 Loss2: 1.347548 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399229 Loss1: 0.048999 Loss2: 1.350230 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.560521 Loss1: 0.211713 Loss2: 1.348808 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.418415 Loss1: 0.083814 Loss2: 1.334601 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376547 Loss1: 0.054842 Loss2: 1.321705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389876 Loss1: 0.075081 Loss2: 1.314794
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.555635 Loss1: 0.698694 Loss2: 1.856941
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383710 Loss1: 0.069590 Loss2: 1.314120
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.832389 Loss1: 0.453812 Loss2: 1.378577
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.707646 Loss1: 0.270727 Loss2: 1.436920
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556920 Loss1: 0.180917 Loss2: 1.376003
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593184 Loss1: 0.214361 Loss2: 1.378824
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489304 Loss1: 0.116516 Loss2: 1.372788
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.436197 Loss1: 0.652785 Loss2: 1.783411
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426963 Loss1: 0.071408 Loss2: 1.355555
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.724267 Loss1: 0.399830 Loss2: 1.324437
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.397102 Loss1: 0.050081 Loss2: 1.347021
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.615249 Loss1: 0.252222 Loss2: 1.363027
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378030 Loss1: 0.037320 Loss2: 1.340711
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523381 Loss1: 0.202274 Loss2: 1.321107
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375128 Loss1: 0.042164 Loss2: 1.332964
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.432386 Loss1: 0.113396 Loss2: 1.318990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.353366 Loss1: 0.058695 Loss2: 1.294671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.333875 Loss1: 0.037733 Loss2: 1.296143
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.288042 Loss1: 0.482744 Loss2: 1.805298
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.340780 Loss1: 0.052101 Loss2: 1.288679
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.640479 Loss1: 0.280488 Loss2: 1.359992
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647240 Loss1: 0.253517 Loss2: 1.393724
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564896 Loss1: 0.203666 Loss2: 1.361230
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524808 Loss1: 0.160903 Loss2: 1.363905
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465542 Loss1: 0.099777 Loss2: 1.365765
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.257270 Loss1: 0.519655 Loss2: 1.737615
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.716884 Loss1: 0.411839 Loss2: 1.305045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636964 Loss1: 0.287638 Loss2: 1.349326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.493487 Loss1: 0.185772 Loss2: 1.307715 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.490303 Loss1: 0.173389 Loss2: 1.316914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.353744 Loss1: 0.063981 Loss2: 1.289764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.343176 Loss1: 0.061639 Loss2: 1.281537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.350588 Loss1: 0.071130 Loss2: 1.279458 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583273 Loss1: 0.189202 Loss2: 1.394070 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547074 Loss1: 0.134182 Loss2: 1.412892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.315236 Loss1: 0.571436 Loss2: 1.743800
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525991 Loss1: 0.130368 Loss2: 1.395623
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.681471 Loss1: 0.354762 Loss2: 1.326709
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.509784 Loss1: 0.111638 Loss2: 1.398145
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592817 Loss1: 0.231252 Loss2: 1.361565
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.475968 Loss1: 0.088358 Loss2: 1.387610
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461820 Loss1: 0.083066 Loss2: 1.378754
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.489325 Loss1: 0.169890 Loss2: 1.319436
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500740 Loss1: 0.181233 Loss2: 1.319508
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.454865 Loss1: 0.133709 Loss2: 1.321156
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441991 Loss1: 0.130413 Loss2: 1.311578
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.401561 Loss1: 0.095375 Loss2: 1.306186
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.634384 Loss1: 0.806838 Loss2: 1.827545
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373719 Loss1: 0.063759 Loss2: 1.309960
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.729647 Loss1: 0.376243 Loss2: 1.353405
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352884 Loss1: 0.055334 Loss2: 1.297550
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.494539 Loss1: 0.172320 Loss2: 1.322219 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.399240 Loss1: 0.090079 Loss2: 1.309160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395647 Loss1: 0.090404 Loss2: 1.305243
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.535082 Loss1: 0.641893 Loss2: 1.893189
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.818883 Loss1: 0.422890 Loss2: 1.395993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682522 Loss1: 0.248424 Loss2: 1.434098 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.348951 Loss1: 0.061250 Loss2: 1.287702
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573223 Loss1: 0.195239 Loss2: 1.377984
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495360 Loss1: 0.114944 Loss2: 1.380417
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466953 Loss1: 0.101278 Loss2: 1.365675
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416960 Loss1: 0.059831 Loss2: 1.357130
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398055 Loss1: 0.044408 Loss2: 1.353647
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.549879 Loss1: 0.727150 Loss2: 1.822729
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392343 Loss1: 0.047922 Loss2: 1.344421
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400202 Loss1: 0.056137 Loss2: 1.344065 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559405 Loss1: 0.222763 Loss2: 1.336642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.490743 Loss1: 0.153318 Loss2: 1.337425 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436850 Loss1: 0.100407 Loss2: 1.336443
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.504766 Loss1: 0.670912 Loss2: 1.833854
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.885282 Loss1: 0.505841 Loss2: 1.379442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.650993 Loss1: 0.242953 Loss2: 1.408040 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.548680 Loss1: 0.183337 Loss2: 1.365342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443699 Loss1: 0.086621 Loss2: 1.357078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426306 Loss1: 0.079060 Loss2: 1.347246 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422090 Loss1: 0.087177 Loss2: 1.334913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395654 Loss1: 0.063298 Loss2: 1.332356 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549453 Loss1: 0.202326 Loss2: 1.347127 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.421287 Loss1: 0.085703 Loss2: 1.335584 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.391173 Loss1: 0.064180 Loss2: 1.326993
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.528793 Loss1: 0.704202 Loss2: 1.824591
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.850307 Loss1: 0.502806 Loss2: 1.347500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.687887 Loss1: 0.279110 Loss2: 1.408776 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.351216 Loss1: 0.038280 Loss2: 1.312936
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523781 Loss1: 0.181127 Loss2: 1.342654
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515793 Loss1: 0.162721 Loss2: 1.353073
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519049 Loss1: 0.167867 Loss2: 1.351182
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458432 Loss1: 0.117166 Loss2: 1.341266
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406994 Loss1: 0.073353 Loss2: 1.333641
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.434694 Loss1: 0.621257 Loss2: 1.813438
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407830 Loss1: 0.079481 Loss2: 1.328349
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388384 Loss1: 0.064629 Loss2: 1.323754 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599169 Loss1: 0.245736 Loss2: 1.353433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525826 Loss1: 0.153811 Loss2: 1.372015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500509 Loss1: 0.147595 Loss2: 1.352914
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.299890 Loss1: 0.509364 Loss2: 1.790526
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.715623 Loss1: 0.353135 Loss2: 1.362488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614331 Loss1: 0.230577 Loss2: 1.383754 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520288 Loss1: 0.160821 Loss2: 1.359467 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447952 Loss1: 0.111611 Loss2: 1.336341 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.444046 Loss1: 0.109202 Loss2: 1.334844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408898 Loss1: 0.084092 Loss2: 1.324805 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369505 Loss1: 0.046948 Loss2: 1.322557 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994485
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503492 Loss1: 0.141426 Loss2: 1.362066 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.487803 Loss1: 0.127212 Loss2: 1.360591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.677015 Loss1: 0.742502 Loss2: 1.934512
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418957 Loss1: 0.072940 Loss2: 1.346017
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397477 Loss1: 0.061076 Loss2: 1.336401 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.630273 Loss1: 0.230760 Loss2: 1.399513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529223 Loss1: 0.133804 Loss2: 1.395420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487477 Loss1: 0.107729 Loss2: 1.379747 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436550 Loss1: 0.069445 Loss2: 1.367105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403102 Loss1: 0.038939 Loss2: 1.364163 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.655659 Loss1: 0.216151 Loss2: 1.439508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.627540 Loss1: 0.175867 Loss2: 1.451673 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.387369 Loss1: 0.589570 Loss2: 1.797799 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.643670 Loss1: 0.312536 Loss2: 1.331134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.616463 Loss1: 0.247270 Loss2: 1.369193 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.527942 Loss1: 0.191430 Loss2: 1.336512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431299 Loss1: 0.107764 Loss2: 1.323535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.383989 Loss1: 0.073539 Loss2: 1.310451 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.364845 Loss1: 0.062920 Loss2: 1.301925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352917 Loss1: 0.051807 Loss2: 1.301110 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.548499 Loss1: 0.161296 Loss2: 1.387203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513819 Loss1: 0.124691 Loss2: 1.389128 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.560647 Loss1: 0.654809 Loss2: 1.905838 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.859027 Loss1: 0.454158 Loss2: 1.404869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.707866 Loss1: 0.263618 Loss2: 1.444248 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591986 Loss1: 0.202616 Loss2: 1.389370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490265 Loss1: 0.121382 Loss2: 1.368883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.506612 Loss1: 0.134070 Loss2: 1.372542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467879 Loss1: 0.095091 Loss2: 1.372789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458208 Loss1: 0.097801 Loss2: 1.360407 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.501244 Loss1: 0.149785 Loss2: 1.351459 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458831 Loss1: 0.115636 Loss2: 1.343196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.457396 Loss1: 0.118113 Loss2: 1.339283
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.411543 Loss1: 0.639287 Loss2: 1.772256
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.682610 Loss1: 0.363783 Loss2: 1.318827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.566406 Loss1: 0.207431 Loss2: 1.358974 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.515589 Loss1: 0.196411 Loss2: 1.319178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.392772 Loss1: 0.091163 Loss2: 1.301609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.332554 Loss1: 0.039561 Loss2: 1.292993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.327485 Loss1: 0.042763 Loss2: 1.284722 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 03:00:13,617 | server.py:236 | fit_round 136 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.329531 Loss1: 0.045747 Loss2: 1.283784 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.661025 Loss1: 0.262798 Loss2: 1.398227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499356 Loss1: 0.105961 Loss2: 1.393395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.367305 Loss1: 0.531189 Loss2: 1.836117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.789716 Loss1: 0.402568 Loss2: 1.387148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.699059 Loss1: 0.257475 Loss2: 1.441584 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.564271 Loss1: 0.171659 Loss2: 1.392612 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.513273 Loss1: 0.137709 Loss2: 1.375564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.381044 Loss1: 0.472970 Loss2: 1.908074
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.495814 Loss1: 0.115904 Loss2: 1.379910
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.773993 Loss1: 0.370381 Loss2: 1.403612
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453683 Loss1: 0.089757 Loss2: 1.363926
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647631 Loss1: 0.223049 Loss2: 1.424582
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440308 Loss1: 0.075316 Loss2: 1.364992
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517570 Loss1: 0.123088 Loss2: 1.394482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484299 Loss1: 0.097846 Loss2: 1.386453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471881 Loss1: 0.084976 Loss2: 1.386905
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.635856 Loss1: 0.756034 Loss2: 1.879822
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.832813 Loss1: 0.464031 Loss2: 1.368782 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416521 Loss1: 0.038991 Loss2: 1.377530
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.722592 Loss1: 0.290671 Loss2: 1.431922
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528052 Loss1: 0.168461 Loss2: 1.359591
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506924 Loss1: 0.146828 Loss2: 1.360096
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449271 Loss1: 0.093519 Loss2: 1.355752
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439914 Loss1: 0.093723 Loss2: 1.346191
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416270 Loss1: 0.077191 Loss2: 1.339079
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379225 Loss1: 0.046532 Loss2: 1.332693
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374936 Loss1: 0.045060 Loss2: 1.329875
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 03:00:13,617][flwr][DEBUG] - fit_round 136 received 50 results and 0 failures
-INFO flwr 2023-10-12 03:00:54,580 | server.py:125 | fit progress: (136, 2.2225120815987025, {'accuracy': 0.5923}, 313762.358835965)
->> Test accuracy: 0.592300
-[2023-10-12 03:00:54,580][flwr][INFO] - fit progress: (136, 2.2225120815987025, {'accuracy': 0.5923}, 313762.358835965)
-DEBUG flwr 2023-10-12 03:00:54,581 | server.py:173 | evaluate_round 136: strategy sampled 50 clients (out of 50)
-[2023-10-12 03:00:54,581][flwr][DEBUG] - evaluate_round 136: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 03:09:58,664 | server.py:187 | evaluate_round 136 received 50 results and 0 failures
-[2023-10-12 03:09:58,664][flwr][DEBUG] - evaluate_round 136 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 03:09:58,664 | server.py:222 | fit_round 137: strategy sampled 50 clients (out of 50)
-[2023-10-12 03:09:58,664][flwr][DEBUG] - fit_round 137: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.485478 Loss1: 0.622519 Loss2: 1.862959
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.857813 Loss1: 0.449963 Loss2: 1.407850
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.709538 Loss1: 0.251030 Loss2: 1.458508
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.491607 Loss1: 0.598024 Loss2: 1.893582
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601883 Loss1: 0.200457 Loss2: 1.401426
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.816381 Loss1: 0.419041 Loss2: 1.397341
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.602217 Loss1: 0.190620 Loss2: 1.411598
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.728677 Loss1: 0.263409 Loss2: 1.465268
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.559724 Loss1: 0.154980 Loss2: 1.404744
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.595585 Loss1: 0.190959 Loss2: 1.404625 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.581717 Loss1: 0.171735 Loss2: 1.409982 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.501870 Loss1: 0.107323 Loss2: 1.394548 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.522512 Loss1: 0.132019 Loss2: 1.390493 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.508699 Loss1: 0.131013 Loss2: 1.377686 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.543137 Loss1: 0.659005 Loss2: 1.884132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.711468 Loss1: 0.281006 Loss2: 1.430462 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606764 Loss1: 0.222341 Loss2: 1.384423
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.605643 Loss1: 0.691514 Loss2: 1.914129
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525909 Loss1: 0.146978 Loss2: 1.378931
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.846389 Loss1: 0.415281 Loss2: 1.431108
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454386 Loss1: 0.085534 Loss2: 1.368852
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.681330 Loss1: 0.251800 Loss2: 1.429530
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434984 Loss1: 0.075012 Loss2: 1.359972
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591532 Loss1: 0.178433 Loss2: 1.413099
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414225 Loss1: 0.058739 Loss2: 1.355486
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.567941 Loss1: 0.161507 Loss2: 1.406434
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390464 Loss1: 0.041217 Loss2: 1.349246
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.551164 Loss1: 0.144212 Loss2: 1.406952
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386795 Loss1: 0.045136 Loss2: 1.341659
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508886 Loss1: 0.111720 Loss2: 1.397166
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482517 Loss1: 0.093336 Loss2: 1.389181
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490061 Loss1: 0.099817 Loss2: 1.390244
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.481336 Loss1: 0.094778 Loss2: 1.386558
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.593595 Loss1: 0.717868 Loss2: 1.875727
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.740742 Loss1: 0.407779 Loss2: 1.332963
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.619969 Loss1: 0.255573 Loss2: 1.364396
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541344 Loss1: 0.180511 Loss2: 1.360834
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519010 Loss1: 0.180528 Loss2: 1.338482
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448296 Loss1: 0.111648 Loss2: 1.336648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424362 Loss1: 0.096679 Loss2: 1.327683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391851 Loss1: 0.068974 Loss2: 1.322877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390032 Loss1: 0.066654 Loss2: 1.323378 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373115 Loss1: 0.060167 Loss2: 1.312948 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986779
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433541 Loss1: 0.073325 Loss2: 1.360216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379347 Loss1: 0.036622 Loss2: 1.342726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385486 Loss1: 0.051296 Loss2: 1.334190
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.738395 Loss1: 0.784175 Loss2: 1.954221
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.856870 Loss1: 0.402117 Loss2: 1.454753
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.728249 Loss1: 0.251708 Loss2: 1.476542
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.644286 Loss1: 0.205804 Loss2: 1.438482
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.596763 Loss1: 0.158360 Loss2: 1.438403
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.788576 Loss1: 0.778307 Loss2: 2.010270
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.910861 Loss1: 0.527907 Loss2: 1.382953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.905953 Loss1: 0.398999 Loss2: 1.506954
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.504059 Loss1: 0.080981 Loss2: 1.423078
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497807 Loss1: 0.080544 Loss2: 1.417263 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482682 Loss1: 0.069432 Loss2: 1.413249 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466896 Loss1: 0.058785 Loss2: 1.408111 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433771 Loss1: 0.067349 Loss2: 1.366422 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990885
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.364864 Loss1: 0.599558 Loss2: 1.765306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561812 Loss1: 0.200883 Loss2: 1.360929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.414300 Loss1: 0.556360 Loss2: 1.857940
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.509002 Loss1: 0.193622 Loss2: 1.315380
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.686686 Loss1: 0.320362 Loss2: 1.366324
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.421215 Loss1: 0.101533 Loss2: 1.319682
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.645210 Loss1: 0.255126 Loss2: 1.390084
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.374669 Loss1: 0.071350 Loss2: 1.303319
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567127 Loss1: 0.202126 Loss2: 1.365000
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.355021 Loss1: 0.063365 Loss2: 1.291655
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389990 Loss1: 0.093721 Loss2: 1.296269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390716 Loss1: 0.094520 Loss2: 1.296196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377001 Loss1: 0.083764 Loss2: 1.293237 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.483213 Loss1: 0.121184 Loss2: 1.362029 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.402185 Loss1: 0.561148 Loss2: 1.841038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.664733 Loss1: 0.245299 Loss2: 1.419434 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.455432 Loss1: 0.600920 Loss2: 1.854512
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.585322 Loss1: 0.206930 Loss2: 1.378392
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.815019 Loss1: 0.445360 Loss2: 1.369659
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.563044 Loss1: 0.174352 Loss2: 1.388692
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.632638 Loss1: 0.218007 Loss2: 1.414631
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471999 Loss1: 0.098033 Loss2: 1.373966
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.430322 Loss1: 0.066264 Loss2: 1.364057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414036 Loss1: 0.056033 Loss2: 1.358003 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422811 Loss1: 0.072202 Loss2: 1.350609 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409720 Loss1: 0.057779 Loss2: 1.351941 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396142 Loss1: 0.058717 Loss2: 1.337425 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.281973 Loss1: 0.494861 Loss2: 1.787112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.597450 Loss1: 0.236867 Loss2: 1.360583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488300 Loss1: 0.153958 Loss2: 1.334343
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.452554 Loss1: 0.614457 Loss2: 1.838097
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.847190 Loss1: 0.459048 Loss2: 1.388142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.743536 Loss1: 0.327272 Loss2: 1.416264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557423 Loss1: 0.190404 Loss2: 1.367019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476188 Loss1: 0.112564 Loss2: 1.363624 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444590 Loss1: 0.089547 Loss2: 1.355043 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423885 Loss1: 0.074621 Loss2: 1.349264 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390558 Loss1: 0.054939 Loss2: 1.335619 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.827919 Loss1: 0.405831 Loss2: 1.422088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.682141 Loss1: 0.284239 Loss2: 1.397903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.599405 Loss1: 0.192932 Loss2: 1.406472
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.603547 Loss1: 0.718801 Loss2: 1.884745
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.554329 Loss1: 0.162947 Loss2: 1.391382
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.823492 Loss1: 0.458416 Loss2: 1.365076
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544671 Loss1: 0.152671 Loss2: 1.392000
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.644802 Loss1: 0.232611 Loss2: 1.412192
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526209 Loss1: 0.163349 Loss2: 1.362860
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500494 Loss1: 0.119383 Loss2: 1.381111
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514253 Loss1: 0.155876 Loss2: 1.358377
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477677 Loss1: 0.097674 Loss2: 1.380003
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477081 Loss1: 0.120575 Loss2: 1.356506
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458759 Loss1: 0.078261 Loss2: 1.380499
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423922 Loss1: 0.080242 Loss2: 1.343680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417854 Loss1: 0.082145 Loss2: 1.335709 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987723
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.523688 Loss1: 0.659828 Loss2: 1.863860
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.889290 Loss1: 0.481971 Loss2: 1.407319
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.711506 Loss1: 0.268625 Loss2: 1.442881
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.622225 Loss1: 0.223115 Loss2: 1.399110
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.474279 Loss1: 0.661173 Loss2: 1.813106
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.694341 Loss1: 0.356914 Loss2: 1.337427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.567798 Loss1: 0.200742 Loss2: 1.367056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519483 Loss1: 0.185317 Loss2: 1.334166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.440437 Loss1: 0.099905 Loss2: 1.340532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.435717 Loss1: 0.108092 Loss2: 1.327625 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.388445 Loss1: 0.066708 Loss2: 1.321738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.351584 Loss1: 0.044618 Loss2: 1.306966 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.414739 Loss1: 0.554819 Loss2: 1.859920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684405 Loss1: 0.262020 Loss2: 1.422384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.360866 Loss1: 0.557322 Loss2: 1.803544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.832002 Loss1: 0.496437 Loss2: 1.335565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684408 Loss1: 0.273713 Loss2: 1.410695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.675277 Loss1: 0.335741 Loss2: 1.339536 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.603430 Loss1: 0.235615 Loss2: 1.367815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539947 Loss1: 0.193828 Loss2: 1.346119 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.489011 Loss1: 0.158814 Loss2: 1.330198 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420694 Loss1: 0.092033 Loss2: 1.328661 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.750186 Loss1: 0.434711 Loss2: 1.315475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508185 Loss1: 0.204543 Loss2: 1.303642 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.509996 Loss1: 0.658399 Loss2: 1.851597
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.496278 Loss1: 0.180839 Loss2: 1.315439
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.921633 Loss1: 0.517581 Loss2: 1.404052
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.395717 Loss1: 0.097755 Loss2: 1.297962
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.787259 Loss1: 0.337856 Loss2: 1.449404
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.393317 Loss1: 0.101886 Loss2: 1.291431
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.658746 Loss1: 0.263609 Loss2: 1.395137
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382939 Loss1: 0.097246 Loss2: 1.285693
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.572850 Loss1: 0.170818 Loss2: 1.402031
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395758 Loss1: 0.105748 Loss2: 1.290009
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542461 Loss1: 0.168073 Loss2: 1.374389
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.341512 Loss1: 0.055849 Loss2: 1.285663
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464019 Loss1: 0.097505 Loss2: 1.366514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400018 Loss1: 0.052624 Loss2: 1.347395 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784418 Loss1: 0.393634 Loss2: 1.390784 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.543784 Loss1: 0.150324 Loss2: 1.393460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.586825 Loss1: 0.703239 Loss2: 1.883586
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.533770 Loss1: 0.146427 Loss2: 1.387344
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.743525 Loss1: 0.346917 Loss2: 1.396608
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471950 Loss1: 0.087440 Loss2: 1.384511
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.610836 Loss1: 0.193053 Loss2: 1.417783
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446159 Loss1: 0.068742 Loss2: 1.377417
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538930 Loss1: 0.155967 Loss2: 1.382964
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429045 Loss1: 0.058876 Loss2: 1.370169
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.533038 Loss1: 0.150390 Loss2: 1.382648
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452024 Loss1: 0.086509 Loss2: 1.365515
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481967 Loss1: 0.103551 Loss2: 1.378417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426594 Loss1: 0.059409 Loss2: 1.367185
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420730 Loss1: 0.059968 Loss2: 1.360762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425208 Loss1: 0.071659 Loss2: 1.353549 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.292760 Loss1: 0.519031 Loss2: 1.773729
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.728690 Loss1: 0.399698 Loss2: 1.328992
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.585931 Loss1: 0.201601 Loss2: 1.384329
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484253 Loss1: 0.151216 Loss2: 1.333038
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.546986 Loss1: 0.640234 Loss2: 1.906752
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.764091 Loss1: 0.345852 Loss2: 1.418239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.712068 Loss1: 0.263928 Loss2: 1.448140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.647329 Loss1: 0.231924 Loss2: 1.415405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448630 Loss1: 0.113532 Loss2: 1.335097
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.599203 Loss1: 0.185044 Loss2: 1.414159
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482840 Loss1: 0.151498 Loss2: 1.331342
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529276 Loss1: 0.126017 Loss2: 1.403259
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438909 Loss1: 0.097887 Loss2: 1.341021
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.506248 Loss1: 0.105918 Loss2: 1.400330
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453010 Loss1: 0.059256 Loss2: 1.393754
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438129 Loss1: 0.055172 Loss2: 1.382957
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426952 Loss1: 0.049369 Loss2: 1.377583
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.360135 Loss1: 0.551340 Loss2: 1.808795
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784911 Loss1: 0.450284 Loss2: 1.334627
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655106 Loss1: 0.287819 Loss2: 1.367288
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581336 Loss1: 0.235611 Loss2: 1.345725
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.505923 Loss1: 0.663940 Loss2: 1.841983
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.793842 Loss1: 0.424846 Loss2: 1.368996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669844 Loss1: 0.280283 Loss2: 1.389562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.494082 Loss1: 0.154091 Loss2: 1.339992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500722 Loss1: 0.154227 Loss2: 1.346495 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443664 Loss1: 0.110293 Loss2: 1.333371 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368267 Loss1: 0.071896 Loss2: 1.296371
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444165 Loss1: 0.115444 Loss2: 1.328721
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418582 Loss1: 0.090284 Loss2: 1.328298
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395093 Loss1: 0.073170 Loss2: 1.321923
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392872 Loss1: 0.067198 Loss2: 1.325674
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.487299 Loss1: 0.624883 Loss2: 1.862416
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.744701 Loss1: 0.396056 Loss2: 1.348644
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736871 Loss1: 0.325112 Loss2: 1.411759
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.543960 Loss1: 0.192854 Loss2: 1.351106
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.474728 Loss1: 0.676453 Loss2: 1.798275
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653858 Loss1: 0.329459 Loss2: 1.324400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572390 Loss1: 0.217265 Loss2: 1.355125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.486104 Loss1: 0.161260 Loss2: 1.324843 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450707 Loss1: 0.130176 Loss2: 1.320531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439911 Loss1: 0.121811 Loss2: 1.318100 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419442 Loss1: 0.114177 Loss2: 1.305265 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392480 Loss1: 0.084537 Loss2: 1.307943 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.789597 Loss1: 0.377590 Loss2: 1.412007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596156 Loss1: 0.186108 Loss2: 1.410048 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571104 Loss1: 0.157786 Loss2: 1.413318
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.455940 Loss1: 0.573962 Loss2: 1.881978
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.510764 Loss1: 0.109831 Loss2: 1.400933
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.782846 Loss1: 0.390570 Loss2: 1.392276
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469942 Loss1: 0.075708 Loss2: 1.394234
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701595 Loss1: 0.241106 Loss2: 1.460489
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427227 Loss1: 0.048792 Loss2: 1.378436
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604742 Loss1: 0.211493 Loss2: 1.393249
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428967 Loss1: 0.052984 Loss2: 1.375983
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.561048 Loss1: 0.161773 Loss2: 1.399275
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412106 Loss1: 0.041530 Loss2: 1.370576
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537872 Loss1: 0.151480 Loss2: 1.386392
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.514444 Loss1: 0.125221 Loss2: 1.389223
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.514608 Loss1: 0.133145 Loss2: 1.381462
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501770 Loss1: 0.119386 Loss2: 1.382384
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449126 Loss1: 0.064641 Loss2: 1.384485
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.309808 Loss1: 0.512789 Loss2: 1.797019
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.738193 Loss1: 0.379900 Loss2: 1.358292
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.711461 Loss1: 0.307765 Loss2: 1.403695
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.612364 Loss1: 0.247208 Loss2: 1.365155
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.560833 Loss1: 0.689095 Loss2: 1.871738
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.787614 Loss1: 0.411677 Loss2: 1.375937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.652770 Loss1: 0.236705 Loss2: 1.416065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.600550 Loss1: 0.230687 Loss2: 1.369863 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543191 Loss1: 0.168047 Loss2: 1.375144 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556846 Loss1: 0.196663 Loss2: 1.360183 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418934 Loss1: 0.083231 Loss2: 1.335702
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.535653 Loss1: 0.168138 Loss2: 1.367515
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488287 Loss1: 0.130036 Loss2: 1.358252
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481257 Loss1: 0.116541 Loss2: 1.364716
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482488 Loss1: 0.123959 Loss2: 1.358529
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.550030 Loss1: 0.692732 Loss2: 1.857298
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784477 Loss1: 0.397162 Loss2: 1.387315
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.743821 Loss1: 0.318684 Loss2: 1.425137
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586953 Loss1: 0.209494 Loss2: 1.377459
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.548852 Loss1: 0.615719 Loss2: 1.933132
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.792101 Loss1: 0.365736 Loss2: 1.426364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.689533 Loss1: 0.247742 Loss2: 1.441792 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.615754 Loss1: 0.204285 Loss2: 1.411469 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.577214 Loss1: 0.168701 Loss2: 1.408513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512632 Loss1: 0.107857 Loss2: 1.404775 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388993 Loss1: 0.049535 Loss2: 1.339458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472646 Loss1: 0.084124 Loss2: 1.388522
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501748 Loss1: 0.118461 Loss2: 1.383286
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.470665 Loss1: 0.084935 Loss2: 1.385730
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452415 Loss1: 0.071681 Loss2: 1.380733
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.609728 Loss1: 0.692529 Loss2: 1.917199
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.843118 Loss1: 0.424650 Loss2: 1.418467
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.803578 Loss1: 0.337424 Loss2: 1.466154
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586451 Loss1: 0.175714 Loss2: 1.410737
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.679830 Loss1: 0.749912 Loss2: 1.929919
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.706949 Loss1: 0.365430 Loss2: 1.341519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.626963 Loss1: 0.253719 Loss2: 1.373243
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533876 Loss1: 0.130793 Loss2: 1.403083
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.517442 Loss1: 0.174943 Loss2: 1.342499
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.493138 Loss1: 0.096712 Loss2: 1.396426
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456549 Loss1: 0.063627 Loss2: 1.392922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429547 Loss1: 0.044616 Loss2: 1.384931 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418987 Loss1: 0.040967 Loss2: 1.378020 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.336223 Loss1: 0.035308 Loss2: 1.300914 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.468766 Loss1: 0.642240 Loss2: 1.826527
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.717290 Loss1: 0.376696 Loss2: 1.340594
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624837 Loss1: 0.251482 Loss2: 1.373355
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523682 Loss1: 0.185596 Loss2: 1.338085
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.603416 Loss1: 0.694217 Loss2: 1.909199
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.721521 Loss1: 0.318837 Loss2: 1.402684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.623482 Loss1: 0.206190 Loss2: 1.417292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565673 Loss1: 0.184308 Loss2: 1.381366 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543173 Loss1: 0.158228 Loss2: 1.384944 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.558039 Loss1: 0.166597 Loss2: 1.391442 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.330348 Loss1: 0.032171 Loss2: 1.298177
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.543705 Loss1: 0.157748 Loss2: 1.385957
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477102 Loss1: 0.098271 Loss2: 1.378831
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432719 Loss1: 0.067222 Loss2: 1.365497
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424044 Loss1: 0.066986 Loss2: 1.357058
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.524342 Loss1: 0.683059 Loss2: 1.841283
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.735975 Loss1: 0.371479 Loss2: 1.364495
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.657713 Loss1: 0.266356 Loss2: 1.391357
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562300 Loss1: 0.194712 Loss2: 1.367587
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.548207 Loss1: 0.643270 Loss2: 1.904937
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507666 Loss1: 0.149513 Loss2: 1.358153
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.863991 Loss1: 0.475660 Loss2: 1.388332
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475307 Loss1: 0.121910 Loss2: 1.353397
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.697256 Loss1: 0.280968 Loss2: 1.416288
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447287 Loss1: 0.098498 Loss2: 1.348789
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581960 Loss1: 0.192185 Loss2: 1.389775
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419906 Loss1: 0.078034 Loss2: 1.341873
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522315 Loss1: 0.151823 Loss2: 1.370492
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394575 Loss1: 0.061820 Loss2: 1.332755
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499813 Loss1: 0.127110 Loss2: 1.372704
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426696 Loss1: 0.095236 Loss2: 1.331460
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436930 Loss1: 0.078397 Loss2: 1.358532
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442912 Loss1: 0.088213 Loss2: 1.354700
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441037 Loss1: 0.087103 Loss2: 1.353934
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406241 Loss1: 0.058150 Loss2: 1.348092
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.515564 Loss1: 0.595262 Loss2: 1.920302
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.797184 Loss1: 0.357029 Loss2: 1.440155
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.671982 Loss1: 0.212136 Loss2: 1.459847
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.608225 Loss1: 0.732155 Loss2: 1.876069
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.608927 Loss1: 0.194860 Loss2: 1.414066
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.867519 Loss1: 0.501575 Loss2: 1.365944
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.591514 Loss1: 0.165573 Loss2: 1.425941
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576046 Loss1: 0.159391 Loss2: 1.416655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518829 Loss1: 0.108780 Loss2: 1.410050 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470652 Loss1: 0.076036 Loss2: 1.394616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469477 Loss1: 0.079263 Loss2: 1.390214 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.486023 Loss1: 0.096904 Loss2: 1.389119 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370274 Loss1: 0.038822 Loss2: 1.331452 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997768
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.587482 Loss1: 0.637756 Loss2: 1.949726
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.858460 Loss1: 0.416929 Loss2: 1.441531
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.754219 Loss1: 0.274060 Loss2: 1.480158
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.668722 Loss1: 0.246299 Loss2: 1.422423
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.616638 Loss1: 0.722100 Loss2: 1.894538
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.845149 Loss1: 0.441649 Loss2: 1.403501 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.777217 Loss1: 0.327761 Loss2: 1.449456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.653474 Loss1: 0.251412 Loss2: 1.402063 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 03:39:08,208 | server.py:236 | fit_round 137 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.623179 Loss1: 0.219223 Loss2: 1.403955 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.604489 Loss1: 0.202523 Loss2: 1.401967 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.515446 Loss1: 0.113042 Loss2: 1.402403
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.558539 Loss1: 0.154341 Loss2: 1.404198
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.533356 Loss1: 0.142197 Loss2: 1.391159
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.478738 Loss1: 0.095750 Loss2: 1.382987
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450109 Loss1: 0.070358 Loss2: 1.379750
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.503532 Loss1: 0.613654 Loss2: 1.889878
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.942731 Loss1: 0.459814 Loss2: 1.482916
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.695455 Loss1: 0.233978 Loss2: 1.461477
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623067 Loss1: 0.194788 Loss2: 1.428279
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.469885 Loss1: 0.634412 Loss2: 1.835473
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617774 Loss1: 0.177245 Loss2: 1.440529
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.797458 Loss1: 0.442497 Loss2: 1.354960
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.551621 Loss1: 0.122002 Loss2: 1.429619
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.708372 Loss1: 0.302825 Loss2: 1.405546
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518030 Loss1: 0.098728 Loss2: 1.419302
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528423 Loss1: 0.174274 Loss2: 1.354149
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497977 Loss1: 0.159484 Loss2: 1.338493
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.543148 Loss1: 0.126381 Loss2: 1.416767
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465833 Loss1: 0.128694 Loss2: 1.337139
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.522229 Loss1: 0.101790 Loss2: 1.420439
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480003 Loss1: 0.147158 Loss2: 1.332844
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.493419 Loss1: 0.082523 Loss2: 1.410896
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432725 Loss1: 0.092932 Loss2: 1.339793 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.301758 Loss1: 0.455408 Loss2: 1.846350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.623355 Loss1: 0.202673 Loss2: 1.420682 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586792 Loss1: 0.207662 Loss2: 1.379131 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.595110 Loss1: 0.204865 Loss2: 1.390245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576983 Loss1: 0.189403 Loss2: 1.387580 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488689 Loss1: 0.100009 Loss2: 1.388681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454500 Loss1: 0.078966 Loss2: 1.375534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429073 Loss1: 0.061402 Loss2: 1.367671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416472 Loss1: 0.053632 Loss2: 1.362840 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995404
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.411833 Loss1: 0.073575 Loss2: 1.338258 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 03:39:08,208][flwr][DEBUG] - fit_round 137 received 50 results and 0 failures
-INFO flwr 2023-10-12 03:39:50,476 | server.py:125 | fit progress: (137, 2.2192010569115417, {'accuracy': 0.5942}, 316098.254376675)
->> Test accuracy: 0.594200
-[2023-10-12 03:39:50,476][flwr][INFO] - fit progress: (137, 2.2192010569115417, {'accuracy': 0.5942}, 316098.254376675)
-DEBUG flwr 2023-10-12 03:39:50,476 | server.py:173 | evaluate_round 137: strategy sampled 50 clients (out of 50)
-[2023-10-12 03:39:50,476][flwr][DEBUG] - evaluate_round 137: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 03:48:54,994 | server.py:187 | evaluate_round 137 received 50 results and 0 failures
-[2023-10-12 03:48:54,994][flwr][DEBUG] - evaluate_round 137 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 03:48:54,995 | server.py:222 | fit_round 138: strategy sampled 50 clients (out of 50)
-[2023-10-12 03:48:54,995][flwr][DEBUG] - fit_round 138: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.601519 Loss1: 0.637777 Loss2: 1.963742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.696500 Loss1: 0.219139 Loss2: 1.477361 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594755 Loss1: 0.163003 Loss2: 1.431753
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.427397 Loss1: 0.570787 Loss2: 1.856610
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.800804 Loss1: 0.372318 Loss2: 1.428486 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.662058 Loss1: 0.234226 Loss2: 1.427832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592049 Loss1: 0.202280 Loss2: 1.389769 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515050 Loss1: 0.120748 Loss2: 1.394301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512932 Loss1: 0.129853 Loss2: 1.383079 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443080 Loss1: 0.074651 Loss2: 1.368429 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433674 Loss1: 0.064017 Loss2: 1.369658 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.780473 Loss1: 0.339718 Loss2: 1.440755 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.625981 Loss1: 0.173675 Loss2: 1.452306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.446745 Loss1: 0.601353 Loss2: 1.845392
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.630615 Loss1: 0.175511 Loss2: 1.455105
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.764470 Loss1: 0.409035 Loss2: 1.355435
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.571610 Loss1: 0.126653 Loss2: 1.444958
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682581 Loss1: 0.273608 Loss2: 1.408973
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.558975 Loss1: 0.121475 Loss2: 1.437500
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.516773 Loss1: 0.083200 Loss2: 1.433573 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.497708 Loss1: 0.074581 Loss2: 1.423127 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481254 Loss1: 0.059238 Loss2: 1.422017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439104 Loss1: 0.097570 Loss2: 1.341534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411227 Loss1: 0.083291 Loss2: 1.327936 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.786201 Loss1: 0.434530 Loss2: 1.351670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630294 Loss1: 0.241859 Loss2: 1.388435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.307707 Loss1: 0.512679 Loss2: 1.795028 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.543734 Loss1: 0.164696 Loss2: 1.379038 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.632223 Loss1: 0.261894 Loss2: 1.370328 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396451 Loss1: 0.059043 Loss2: 1.337407 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.451606 Loss1: 0.110935 Loss2: 1.340670 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435342 Loss1: 0.106330 Loss2: 1.329012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394551 Loss1: 0.065445 Loss2: 1.329106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402388 Loss1: 0.079427 Loss2: 1.322961 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990809
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.550932 Loss1: 0.151322 Loss2: 1.399610 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.521249 Loss1: 0.123148 Loss2: 1.398102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.652270 Loss1: 0.696082 Loss2: 1.956188
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.496344 Loss1: 0.094747 Loss2: 1.401597
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.890742 Loss1: 0.486649 Loss2: 1.404093
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.498096 Loss1: 0.097750 Loss2: 1.400346
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462991 Loss1: 0.065998 Loss2: 1.396992 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571270 Loss1: 0.164612 Loss2: 1.406658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484088 Loss1: 0.086478 Loss2: 1.397610 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.389169 Loss1: 0.580445 Loss2: 1.808724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.636882 Loss1: 0.310881 Loss2: 1.326001 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.464574 Loss1: 0.133993 Loss2: 1.330581 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463387 Loss1: 0.136489 Loss2: 1.326899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.429620 Loss1: 0.104909 Loss2: 1.324710
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.444581 Loss1: 0.625034 Loss2: 1.819546
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.820012 Loss1: 0.449651 Loss2: 1.370361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.616376 Loss1: 0.220464 Loss2: 1.395912 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543099 Loss1: 0.181382 Loss2: 1.361716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447042 Loss1: 0.102031 Loss2: 1.345012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469979 Loss1: 0.118241 Loss2: 1.351738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449389 Loss1: 0.106740 Loss2: 1.342648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.704924 Loss1: 0.260736 Loss2: 1.444188 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572506 Loss1: 0.182650 Loss2: 1.389856 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.508317 Loss1: 0.141489 Loss2: 1.366827 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.522050 Loss1: 0.147788 Loss2: 1.374262
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.742213 Loss1: 0.721607 Loss2: 2.020606
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.777062 Loss1: 0.375556 Loss2: 1.401506 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977679
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.528190 Loss1: 0.153215 Loss2: 1.374975
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.678006 Loss1: 0.239981 Loss2: 1.438025
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.614026 Loss1: 0.199717 Loss2: 1.414309
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.531553 Loss1: 0.132277 Loss2: 1.399276
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.520570 Loss1: 0.120810 Loss2: 1.399759
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483659 Loss1: 0.098215 Loss2: 1.385444
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465372 Loss1: 0.084271 Loss2: 1.381101
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.525872 Loss1: 0.676869 Loss2: 1.849003
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.727637 Loss1: 0.372695 Loss2: 1.354943 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.624732 Loss1: 0.258836 Loss2: 1.365896 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473518 Loss1: 0.116457 Loss2: 1.357061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433875 Loss1: 0.083977 Loss2: 1.349898
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.459497 Loss1: 0.630630 Loss2: 1.828867
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.720798 Loss1: 0.377001 Loss2: 1.343798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.630509 Loss1: 0.227349 Loss2: 1.403160 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536151 Loss1: 0.195035 Loss2: 1.341116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.520234 Loss1: 0.172299 Loss2: 1.347935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435976 Loss1: 0.105107 Loss2: 1.330869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423156 Loss1: 0.092829 Loss2: 1.330327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397421 Loss1: 0.072671 Loss2: 1.324750 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522733 Loss1: 0.120730 Loss2: 1.402002 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468548 Loss1: 0.101340 Loss2: 1.367207 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.452238 Loss1: 0.088841 Loss2: 1.363397
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.417944 Loss1: 0.637927 Loss2: 1.780018
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630798 Loss1: 0.317993 Loss2: 1.312804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.550048 Loss1: 0.214130 Loss2: 1.335918 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.474395 Loss1: 0.160102 Loss2: 1.314293 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423574 Loss1: 0.116062 Loss2: 1.307512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417394 Loss1: 0.122408 Loss2: 1.294986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381963 Loss1: 0.086141 Loss2: 1.295821 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367276 Loss1: 0.075286 Loss2: 1.291991 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581390 Loss1: 0.240472 Loss2: 1.340919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493752 Loss1: 0.153378 Loss2: 1.340374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.560251 Loss1: 0.722949 Loss2: 1.837302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.754147 Loss1: 0.381893 Loss2: 1.372254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604393 Loss1: 0.212768 Loss2: 1.391624 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.590398 Loss1: 0.227049 Loss2: 1.363349 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.481790 Loss1: 0.127573 Loss2: 1.354217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440452 Loss1: 0.089190 Loss2: 1.351262
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.610874 Loss1: 0.743229 Loss2: 1.867645
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.685763 Loss1: 0.347566 Loss2: 1.338196 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384350 Loss1: 0.049740 Loss2: 1.334610
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551455 Loss1: 0.207375 Loss2: 1.344080
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517149 Loss1: 0.181373 Loss2: 1.335776
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.439546 Loss1: 0.115035 Loss2: 1.324511
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.425691 Loss1: 0.109360 Loss2: 1.316331
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406667 Loss1: 0.093475 Loss2: 1.313192
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.424559 Loss1: 0.623736 Loss2: 1.800823
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403179 Loss1: 0.095561 Loss2: 1.307619
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391524 Loss1: 0.086695 Loss2: 1.304829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379251 Loss1: 0.077670 Loss2: 1.301581 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496117 Loss1: 0.142793 Loss2: 1.353323 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415903 Loss1: 0.079375 Loss2: 1.336527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.383466 Loss1: 0.058154 Loss2: 1.325312
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.514304 Loss1: 0.663756 Loss2: 1.850547
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.816161 Loss1: 0.449916 Loss2: 1.366244 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.661038 Loss1: 0.246377 Loss2: 1.414661 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462674 Loss1: 0.095628 Loss2: 1.367046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478750 Loss1: 0.124945 Loss2: 1.353805 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489743 Loss1: 0.134935 Loss2: 1.354809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441576 Loss1: 0.093725 Loss2: 1.347851 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413928 Loss1: 0.067804 Loss2: 1.346124 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497651 Loss1: 0.139901 Loss2: 1.357750 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.429031 Loss1: 0.083506 Loss2: 1.345525 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400672 Loss1: 0.056969 Loss2: 1.343703
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.465346 Loss1: 0.613303 Loss2: 1.852043
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.810876 Loss1: 0.405843 Loss2: 1.405033 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.679084 Loss1: 0.231130 Loss2: 1.447954 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.495830 Loss1: 0.113149 Loss2: 1.382680 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458886 Loss1: 0.087048 Loss2: 1.371838 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461500 Loss1: 0.092103 Loss2: 1.369397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438399 Loss1: 0.071656 Loss2: 1.366743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416637 Loss1: 0.053332 Loss2: 1.363305 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495876 Loss1: 0.133447 Loss2: 1.362429 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399264 Loss1: 0.055595 Loss2: 1.343670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406801 Loss1: 0.070257 Loss2: 1.336544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388919 Loss1: 0.053204 Loss2: 1.335715 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.720272 Loss1: 0.337066 Loss2: 1.383206 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502428 Loss1: 0.144380 Loss2: 1.358049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419038 Loss1: 0.086252 Loss2: 1.332785 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421073 Loss1: 0.096149 Loss2: 1.324924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.382579 Loss1: 0.058755 Loss2: 1.323824 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995192
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559833 Loss1: 0.238274 Loss2: 1.321560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476856 Loss1: 0.150635 Loss2: 1.326222 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.541434 Loss1: 0.619428 Loss2: 1.922006 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.796449 Loss1: 0.363361 Loss2: 1.433088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.745905 Loss1: 0.295689 Loss2: 1.450215 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.639497 Loss1: 0.218106 Loss2: 1.421391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.539259 Loss1: 0.121790 Loss2: 1.417469 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.519646 Loss1: 0.108027 Loss2: 1.411618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489741 Loss1: 0.086049 Loss2: 1.403692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447042 Loss1: 0.050766 Loss2: 1.396276 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.483251 Loss1: 0.172883 Loss2: 1.310368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.412267 Loss1: 0.112350 Loss2: 1.299917 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.450389 Loss1: 0.652177 Loss2: 1.798212 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.786002 Loss1: 0.447002 Loss2: 1.339000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.646629 Loss1: 0.261829 Loss2: 1.384801 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.532904 Loss1: 0.196602 Loss2: 1.336302 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443868 Loss1: 0.118409 Loss2: 1.325459 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.402952 Loss1: 0.079288 Loss2: 1.323664
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.462311 Loss1: 0.611705 Loss2: 1.850606
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.741634 Loss1: 0.372409 Loss2: 1.369225 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375015 Loss1: 0.066520 Loss2: 1.308495
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636579 Loss1: 0.239086 Loss2: 1.397493
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540104 Loss1: 0.176978 Loss2: 1.363125
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505648 Loss1: 0.151113 Loss2: 1.354536
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511136 Loss1: 0.151175 Loss2: 1.359960
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.511394 Loss1: 0.156327 Loss2: 1.355067
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.359743 Loss1: 0.527218 Loss2: 1.832525
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.526614 Loss1: 0.168031 Loss2: 1.358583
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.487636 Loss1: 0.139744 Loss2: 1.347892
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.633651 Loss1: 0.250204 Loss2: 1.383447
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439622 Loss1: 0.092235 Loss2: 1.347387
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574168 Loss1: 0.175253 Loss2: 1.398915
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535103 Loss1: 0.158710 Loss2: 1.376393
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472648 Loss1: 0.095168 Loss2: 1.377480
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444958 Loss1: 0.085911 Loss2: 1.359047
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451473 Loss1: 0.086903 Loss2: 1.364570
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.454875 Loss1: 0.616816 Loss2: 1.838058
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414489 Loss1: 0.053130 Loss2: 1.361359
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427290 Loss1: 0.078339 Loss2: 1.348951 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400340 Loss1: 0.048952 Loss2: 1.351388 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519782 Loss1: 0.154141 Loss2: 1.365642 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.517543 Loss1: 0.155396 Loss2: 1.362147 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.502377 Loss1: 0.147334 Loss2: 1.355043
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.457296 Loss1: 0.513960 Loss2: 1.943337
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.785763 Loss1: 0.337122 Loss2: 1.448641 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.720344 Loss1: 0.230883 Loss2: 1.489461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.651684 Loss1: 0.194607 Loss2: 1.457078 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.519278 Loss1: 0.080054 Loss2: 1.439223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.513814 Loss1: 0.085447 Loss2: 1.428367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.492346 Loss1: 0.061706 Loss2: 1.430640 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458346 Loss1: 0.040933 Loss2: 1.417413 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569024 Loss1: 0.175780 Loss2: 1.393244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451150 Loss1: 0.082191 Loss2: 1.368959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423640 Loss1: 0.059022 Loss2: 1.364618
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.384650 Loss1: 0.541594 Loss2: 1.843056
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.776794 Loss1: 0.421994 Loss2: 1.354801 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656524 Loss1: 0.251212 Loss2: 1.405312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471634 Loss1: 0.130776 Loss2: 1.340858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433464 Loss1: 0.100173 Loss2: 1.333291 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.304882 Loss1: 0.507145 Loss2: 1.797738
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438366 Loss1: 0.109211 Loss2: 1.329155
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.805854 Loss1: 0.428362 Loss2: 1.377493
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395898 Loss1: 0.065567 Loss2: 1.330331
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.683449 Loss1: 0.269988 Loss2: 1.413461
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386059 Loss1: 0.065333 Loss2: 1.320726
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.504003 Loss1: 0.138054 Loss2: 1.365949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450297 Loss1: 0.106480 Loss2: 1.343817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415553 Loss1: 0.078790 Loss2: 1.336763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396176 Loss1: 0.063532 Loss2: 1.332644 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411222 Loss1: 0.080874 Loss2: 1.330348 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494967 Loss1: 0.117842 Loss2: 1.377124 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484410 Loss1: 0.118817 Loss2: 1.365593 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455672 Loss1: 0.094913 Loss2: 1.360759
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.351185 Loss1: 0.542763 Loss2: 1.808422
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466700 Loss1: 0.097417 Loss2: 1.369283
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.714156 Loss1: 0.374467 Loss2: 1.339689
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653023 Loss1: 0.278644 Loss2: 1.374379
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530402 Loss1: 0.185164 Loss2: 1.345237
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.488359 Loss1: 0.141224 Loss2: 1.347135
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443620 Loss1: 0.097208 Loss2: 1.346411
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.533517 Loss1: 0.710975 Loss2: 1.822542
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.396850 Loss1: 0.074647 Loss2: 1.322202
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.739523 Loss1: 0.376701 Loss2: 1.362822
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412927 Loss1: 0.089558 Loss2: 1.323370
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.584282 Loss1: 0.207376 Loss2: 1.376905
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374263 Loss1: 0.054835 Loss2: 1.319428
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.532850 Loss1: 0.190441 Loss2: 1.342409
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.342756 Loss1: 0.035117 Loss2: 1.307639
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452016 Loss1: 0.107003 Loss2: 1.345013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.392555 Loss1: 0.069830 Loss2: 1.322725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391486 Loss1: 0.069340 Loss2: 1.322146
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.522140 Loss1: 0.625212 Loss2: 1.896928
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367175 Loss1: 0.054938 Loss2: 1.312237
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.761256 Loss1: 0.350558 Loss2: 1.410698
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.707614 Loss1: 0.259994 Loss2: 1.447620
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.632195 Loss1: 0.221391 Loss2: 1.410804
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.577202 Loss1: 0.164467 Loss2: 1.412735
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531984 Loss1: 0.130396 Loss2: 1.401588
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.430394 Loss1: 0.598093 Loss2: 1.832302
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.723598 Loss1: 0.376859 Loss2: 1.346739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611416 Loss1: 0.214936 Loss2: 1.396479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508156 Loss1: 0.164185 Loss2: 1.343971 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441916 Loss1: 0.055111 Loss2: 1.386805
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492051 Loss1: 0.151647 Loss2: 1.340404
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463297 Loss1: 0.111747 Loss2: 1.351551
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.417927 Loss1: 0.083526 Loss2: 1.334401
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400918 Loss1: 0.074604 Loss2: 1.326314
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388943 Loss1: 0.071556 Loss2: 1.317387
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.379656 Loss1: 0.544768 Loss2: 1.834888
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368219 Loss1: 0.057253 Loss2: 1.310966
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622881 Loss1: 0.236696 Loss2: 1.386185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484976 Loss1: 0.141304 Loss2: 1.343672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491726 Loss1: 0.157974 Loss2: 1.333752
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.538139 Loss1: 0.695501 Loss2: 1.842638
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.768854 Loss1: 0.350615 Loss2: 1.418239 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594536 Loss1: 0.190903 Loss2: 1.403632 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.584691 Loss1: 0.200509 Loss2: 1.384182 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.530566 Loss1: 0.145143 Loss2: 1.385423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.560049 Loss1: 0.174923 Loss2: 1.385126 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484777 Loss1: 0.105047 Loss2: 1.379730 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475134 Loss1: 0.096038 Loss2: 1.379096 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559148 Loss1: 0.198138 Loss2: 1.361010 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.489629 Loss1: 0.127211 Loss2: 1.362418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.424543 Loss1: 0.071185 Loss2: 1.353358
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.570741 Loss1: 0.628174 Loss2: 1.942567
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399938 Loss1: 0.058490 Loss2: 1.341448
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.810092 Loss1: 0.380223 Loss2: 1.429870
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373225 Loss1: 0.045103 Loss2: 1.328122
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736777 Loss1: 0.273990 Loss2: 1.462787
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361877 Loss1: 0.034480 Loss2: 1.327398
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642057 Loss1: 0.206788 Loss2: 1.435269
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.598565 Loss1: 0.167401 Loss2: 1.431164
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542183 Loss1: 0.117229 Loss2: 1.424954
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512910 Loss1: 0.099402 Loss2: 1.413508
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.509675 Loss1: 0.095813 Loss2: 1.413862
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.464161 Loss1: 0.056674 Loss2: 1.407487
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.432171 Loss1: 0.592608 Loss2: 1.839563
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466354 Loss1: 0.063335 Loss2: 1.403018
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.686975 Loss1: 0.323058 Loss2: 1.363917
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.616989 Loss1: 0.223224 Loss2: 1.393765
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563901 Loss1: 0.186574 Loss2: 1.377327
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524258 Loss1: 0.159698 Loss2: 1.364560
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503604 Loss1: 0.136366 Loss2: 1.367238
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.462862 Loss1: 0.100407 Loss2: 1.362455
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.462954 Loss1: 0.621484 Loss2: 1.841469
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450639 Loss1: 0.097932 Loss2: 1.352707
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.777661 Loss1: 0.416930 Loss2: 1.360732
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.471793 Loss1: 0.116900 Loss2: 1.354893
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634475 Loss1: 0.239414 Loss2: 1.395062
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437480 Loss1: 0.081425 Loss2: 1.356055
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490401 Loss1: 0.143352 Loss2: 1.347048
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.459261 Loss1: 0.120637 Loss2: 1.338624
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.430695 Loss1: 0.098086 Loss2: 1.332609
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.421081 Loss1: 0.091296 Loss2: 1.329785
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.412766 Loss1: 0.083589 Loss2: 1.329177
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430032 Loss1: 0.107140 Loss2: 1.322892
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.586308 Loss1: 0.764085 Loss2: 1.822223
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384957 Loss1: 0.063820 Loss2: 1.321137
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.733328 Loss1: 0.399251 Loss2: 1.334077
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.613403 Loss1: 0.254963 Loss2: 1.358440
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584024 Loss1: 0.251119 Loss2: 1.332905
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485438 Loss1: 0.156979 Loss2: 1.328459
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431186 Loss1: 0.114674 Loss2: 1.316512
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.379259 Loss1: 0.071193 Loss2: 1.308066
-DEBUG flwr 2023-10-12 04:17:10,386 | server.py:236 | fit_round 138 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.370420 Loss1: 0.566634 Loss2: 1.803785
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.401131 Loss1: 0.101099 Loss2: 1.300033
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.670508 Loss1: 0.321707 Loss2: 1.348802
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434963 Loss1: 0.127287 Loss2: 1.307676
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.607684 Loss1: 0.234542 Loss2: 1.373142
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421996 Loss1: 0.115314 Loss2: 1.306682
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.538440 Loss1: 0.192688 Loss2: 1.345752
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.552127 Loss1: 0.193730 Loss2: 1.358397
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.486538 Loss1: 0.146108 Loss2: 1.340429
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444635 Loss1: 0.106859 Loss2: 1.337777
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446866 Loss1: 0.104385 Loss2: 1.342481
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.502665 Loss1: 0.682362 Loss2: 1.820302
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.785194 Loss1: 0.434382 Loss2: 1.350811 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.708432 Loss1: 0.313664 Loss2: 1.394768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554122 Loss1: 0.191449 Loss2: 1.362673 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485195 Loss1: 0.148334 Loss2: 1.336861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.382459 Loss1: 0.056201 Loss2: 1.326258 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 04:17:10,386][flwr][DEBUG] - fit_round 138 received 50 results and 0 failures
-INFO flwr 2023-10-12 04:17:50,641 | server.py:125 | fit progress: (138, 2.2246322159569103, {'accuracy': 0.5925}, 318378.419111409)
->> Test accuracy: 0.592500
-[2023-10-12 04:17:50,641][flwr][INFO] - fit progress: (138, 2.2246322159569103, {'accuracy': 0.5925}, 318378.419111409)
-DEBUG flwr 2023-10-12 04:17:50,641 | server.py:173 | evaluate_round 138: strategy sampled 50 clients (out of 50)
-[2023-10-12 04:17:50,641][flwr][DEBUG] - evaluate_round 138: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 04:26:57,586 | server.py:187 | evaluate_round 138 received 50 results and 0 failures
-[2023-10-12 04:26:57,586][flwr][DEBUG] - evaluate_round 138 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 04:26:57,587 | server.py:222 | fit_round 139: strategy sampled 50 clients (out of 50)
-[2023-10-12 04:26:57,587][flwr][DEBUG] - fit_round 139: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.522356 Loss1: 0.647756 Loss2: 1.874600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.776681 Loss1: 0.330577 Loss2: 1.446104 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.613739 Loss1: 0.226457 Loss2: 1.387281
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.469907 Loss1: 0.610251 Loss2: 1.859656
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.828833 Loss1: 0.440938 Loss2: 1.387895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.724543 Loss1: 0.284154 Loss2: 1.440390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.662874 Loss1: 0.271504 Loss2: 1.391371 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568984 Loss1: 0.173934 Loss2: 1.395050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511147 Loss1: 0.133345 Loss2: 1.377802 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502200 Loss1: 0.125205 Loss2: 1.376995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.475381 Loss1: 0.087070 Loss2: 1.388311 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.364029 Loss1: 0.499986 Loss2: 1.864043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.702309 Loss1: 0.279594 Loss2: 1.422715 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.497475 Loss1: 0.584479 Loss2: 1.912995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.878076 Loss1: 0.460171 Loss2: 1.417904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.762776 Loss1: 0.282802 Loss2: 1.479973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591552 Loss1: 0.179945 Loss2: 1.411607 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.566551 Loss1: 0.158477 Loss2: 1.408074 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493550 Loss1: 0.094586 Loss2: 1.398964 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424506 Loss1: 0.045029 Loss2: 1.379477 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415348 Loss1: 0.050391 Loss2: 1.364958 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.868505 Loss1: 0.486933 Loss2: 1.381571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570370 Loss1: 0.183233 Loss2: 1.387137 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518908 Loss1: 0.138130 Loss2: 1.380778
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.498089 Loss1: 0.603560 Loss2: 1.894530
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.762372 Loss1: 0.374005 Loss2: 1.388366 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.659519 Loss1: 0.214991 Loss2: 1.444527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579130 Loss1: 0.194042 Loss2: 1.385088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542961 Loss1: 0.158262 Loss2: 1.384699 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986607
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549162 Loss1: 0.156803 Loss2: 1.392359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433529 Loss1: 0.062071 Loss2: 1.371458 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419369 Loss1: 0.051634 Loss2: 1.367735 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.711962 Loss1: 0.337605 Loss2: 1.374357 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491454 Loss1: 0.133818 Loss2: 1.357636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472499 Loss1: 0.118854 Loss2: 1.353645
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.434508 Loss1: 0.555348 Loss2: 1.879161
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.453904 Loss1: 0.096719 Loss2: 1.357185
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.769182 Loss1: 0.389406 Loss2: 1.379777
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439625 Loss1: 0.085968 Loss2: 1.353658
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.658663 Loss1: 0.234793 Loss2: 1.423870
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538917 Loss1: 0.167863 Loss2: 1.371054
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473946 Loss1: 0.120116 Loss2: 1.353831
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502620 Loss1: 0.131255 Loss2: 1.371365
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.497693 Loss1: 0.140268 Loss2: 1.357425
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513416 Loss1: 0.140569 Loss2: 1.372847
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429556 Loss1: 0.076797 Loss2: 1.352758
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417808 Loss1: 0.059984 Loss2: 1.357824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408834 Loss1: 0.061174 Loss2: 1.347660 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.772763 Loss1: 0.378013 Loss2: 1.394749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606929 Loss1: 0.222382 Loss2: 1.384546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566064 Loss1: 0.174753 Loss2: 1.391311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488075 Loss1: 0.113943 Loss2: 1.374133 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468705 Loss1: 0.104839 Loss2: 1.363866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440839 Loss1: 0.073467 Loss2: 1.367372 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433097 Loss1: 0.077189 Loss2: 1.355908 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426581 Loss1: 0.069394 Loss2: 1.357186 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389948 Loss1: 0.059383 Loss2: 1.330564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395871 Loss1: 0.074658 Loss2: 1.321213 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.811680 Loss1: 0.481343 Loss2: 1.330337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572929 Loss1: 0.248422 Loss2: 1.324507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.569482 Loss1: 0.243218 Loss2: 1.326265
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.289978 Loss1: 0.449874 Loss2: 1.840104
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.675209 Loss1: 0.303955 Loss2: 1.371254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.381863 Loss1: 0.079132 Loss2: 1.302732 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.357823 Loss1: 0.053479 Loss2: 1.304344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350923 Loss1: 0.056046 Loss2: 1.294877 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986779
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490717 Loss1: 0.126148 Loss2: 1.364569 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414438 Loss1: 0.057223 Loss2: 1.357215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407840 Loss1: 0.059739 Loss2: 1.348101
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.567103 Loss1: 0.669583 Loss2: 1.897520
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.882402 Loss1: 0.449846 Loss2: 1.432557
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414308 Loss1: 0.064974 Loss2: 1.349334
-(DefaultActor pid=3764) >> Training accuracy: 0.990809
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.658414 Loss1: 0.276770 Loss2: 1.381645 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.479679 Loss1: 0.110724 Loss2: 1.368954 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450071 Loss1: 0.086374 Loss2: 1.363697
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.454644 Loss1: 0.610225 Loss2: 1.844419
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.767582 Loss1: 0.410137 Loss2: 1.357445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.686392 Loss1: 0.253896 Loss2: 1.432496 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356977 Loss1: 0.020175 Loss2: 1.336801
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579081 Loss1: 0.218239 Loss2: 1.360842
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550724 Loss1: 0.184512 Loss2: 1.366212
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521861 Loss1: 0.144534 Loss2: 1.377326
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455029 Loss1: 0.106268 Loss2: 1.348761
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447569 Loss1: 0.106965 Loss2: 1.340605
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.364397 Loss1: 0.561980 Loss2: 1.802417
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425581 Loss1: 0.082249 Loss2: 1.343331
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.639413 Loss1: 0.307514 Loss2: 1.331899
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405450 Loss1: 0.068863 Loss2: 1.336588
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576399 Loss1: 0.241183 Loss2: 1.335216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481245 Loss1: 0.149691 Loss2: 1.331554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.477324 Loss1: 0.147343 Loss2: 1.329981
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.473538 Loss1: 0.618785 Loss2: 1.854753
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409648 Loss1: 0.087269 Loss2: 1.322378
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.721390 Loss1: 0.359096 Loss2: 1.362294
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388320 Loss1: 0.074785 Loss2: 1.313535
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.706809 Loss1: 0.312046 Loss2: 1.394763
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400950 Loss1: 0.089134 Loss2: 1.311816
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550984 Loss1: 0.191611 Loss2: 1.359373
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530715 Loss1: 0.172716 Loss2: 1.357999
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510793 Loss1: 0.155519 Loss2: 1.355274
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465266 Loss1: 0.121006 Loss2: 1.344260
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422133 Loss1: 0.080036 Loss2: 1.342097
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399270 Loss1: 0.069201 Loss2: 1.330069
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.326448 Loss1: 0.496268 Loss2: 1.830180
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385748 Loss1: 0.057931 Loss2: 1.327817
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.686063 Loss1: 0.304320 Loss2: 1.381743
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.579835 Loss1: 0.176908 Loss2: 1.402926
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517727 Loss1: 0.150978 Loss2: 1.366749
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.482677 Loss1: 0.121526 Loss2: 1.361151
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454878 Loss1: 0.098356 Loss2: 1.356521
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.345976 Loss1: 0.565025 Loss2: 1.780951
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444345 Loss1: 0.094537 Loss2: 1.349809
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.685290 Loss1: 0.352162 Loss2: 1.333127
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447400 Loss1: 0.095484 Loss2: 1.351916
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608697 Loss1: 0.238381 Loss2: 1.370316
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445722 Loss1: 0.092204 Loss2: 1.353518
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.465648 Loss1: 0.151508 Loss2: 1.314140
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417514 Loss1: 0.068169 Loss2: 1.349344
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.448826 Loss1: 0.128703 Loss2: 1.320122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434553 Loss1: 0.120369 Loss2: 1.314184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.474085 Loss1: 0.645329 Loss2: 1.828757
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408468 Loss1: 0.100232 Loss2: 1.308236
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751292 Loss1: 0.385877 Loss2: 1.365415
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393690 Loss1: 0.088214 Loss2: 1.305476
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.539492 Loss1: 0.179577 Loss2: 1.359915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.427552 Loss1: 0.072714 Loss2: 1.354839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.396926 Loss1: 0.059212 Loss2: 1.337714
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.596215 Loss1: 0.643662 Loss2: 1.952553
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.823167 Loss1: 0.500294 Loss2: 1.322873
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394567 Loss1: 0.061980 Loss2: 1.332587
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386527 Loss1: 0.054592 Loss2: 1.331935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370024 Loss1: 0.047338 Loss2: 1.322686 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.413901 Loss1: 0.075548 Loss2: 1.338354 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362695 Loss1: 0.046502 Loss2: 1.316193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.339419 Loss1: 0.028247 Loss2: 1.311172
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.506325 Loss1: 0.656381 Loss2: 1.849944
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.681448 Loss1: 0.319377 Loss2: 1.362071
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.643210 Loss1: 0.247223 Loss2: 1.395987
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551159 Loss1: 0.195390 Loss2: 1.355769
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544429 Loss1: 0.178430 Loss2: 1.366000
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.569208 Loss1: 0.701083 Loss2: 1.868125
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.846219 Loss1: 0.457433 Loss2: 1.388786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.705828 Loss1: 0.270736 Loss2: 1.435092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.552187 Loss1: 0.183521 Loss2: 1.368666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528753 Loss1: 0.145083 Loss2: 1.383671 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405591 Loss1: 0.080823 Loss2: 1.324768
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496546 Loss1: 0.125192 Loss2: 1.371354
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485541 Loss1: 0.120305 Loss2: 1.365236
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491066 Loss1: 0.121072 Loss2: 1.369994
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.494946 Loss1: 0.129574 Loss2: 1.365373
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442610 Loss1: 0.074577 Loss2: 1.368032
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.375608 Loss1: 0.585662 Loss2: 1.789947
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.669700 Loss1: 0.324701 Loss2: 1.344999
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.570193 Loss1: 0.207857 Loss2: 1.362336
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.538624 Loss1: 0.196799 Loss2: 1.341825
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.501004 Loss1: 0.605762 Loss2: 1.895241
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490299 Loss1: 0.149129 Loss2: 1.341170
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.735560 Loss1: 0.350972 Loss2: 1.384589
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433084 Loss1: 0.100646 Loss2: 1.332438
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.722997 Loss1: 0.300261 Loss2: 1.422736
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428195 Loss1: 0.097223 Loss2: 1.330972
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612905 Loss1: 0.237092 Loss2: 1.375812
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.390735 Loss1: 0.068597 Loss2: 1.322138
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379659 Loss1: 0.062645 Loss2: 1.317014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387571 Loss1: 0.075176 Loss2: 1.312394 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483044 Loss1: 0.102410 Loss2: 1.380634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460609 Loss1: 0.095438 Loss2: 1.365172 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.449777 Loss1: 0.618427 Loss2: 1.831350
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.737162 Loss1: 0.380761 Loss2: 1.356401
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.641805 Loss1: 0.247081 Loss2: 1.394724
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.507560 Loss1: 0.152439 Loss2: 1.355121
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.444278 Loss1: 0.626243 Loss2: 1.818035
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.839711 Loss1: 0.501387 Loss2: 1.338324 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.649728 Loss1: 0.245961 Loss2: 1.403767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523785 Loss1: 0.180865 Loss2: 1.342921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487213 Loss1: 0.150981 Loss2: 1.336232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443117 Loss1: 0.105010 Loss2: 1.338107 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423091 Loss1: 0.097811 Loss2: 1.325279 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376561 Loss1: 0.058001 Loss2: 1.318561 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.378686 Loss1: 0.523706 Loss2: 1.854980
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.753223 Loss1: 0.348108 Loss2: 1.405115
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.706568 Loss1: 0.270413 Loss2: 1.436155
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.655874 Loss1: 0.240315 Loss2: 1.415559
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.612329 Loss1: 0.736394 Loss2: 1.875936
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.607766 Loss1: 0.199072 Loss2: 1.408694
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.768327 Loss1: 0.413808 Loss2: 1.354519
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.599472 Loss1: 0.225827 Loss2: 1.373645
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.566476 Loss1: 0.150356 Loss2: 1.416120
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509364 Loss1: 0.112935 Loss2: 1.396429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471717 Loss1: 0.079136 Loss2: 1.392581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.468574 Loss1: 0.082430 Loss2: 1.386144 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400566 Loss1: 0.069522 Loss2: 1.331044 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394654 Loss1: 0.073867 Loss2: 1.320788 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.452504 Loss1: 0.602809 Loss2: 1.849695
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.755973 Loss1: 0.407486 Loss2: 1.348488
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.623997 Loss1: 0.224295 Loss2: 1.399703
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575128 Loss1: 0.225187 Loss2: 1.349942
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.477727 Loss1: 0.586601 Loss2: 1.891126
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.816706 Loss1: 0.417554 Loss2: 1.399152 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.695964 Loss1: 0.247902 Loss2: 1.448062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574305 Loss1: 0.184330 Loss2: 1.389975 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520590 Loss1: 0.131997 Loss2: 1.388592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.518311 Loss1: 0.132968 Loss2: 1.385344 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364382 Loss1: 0.042873 Loss2: 1.321509
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.489423 Loss1: 0.111265 Loss2: 1.378158
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472141 Loss1: 0.092811 Loss2: 1.379329
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.459408 Loss1: 0.088813 Loss2: 1.370595
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.458198 Loss1: 0.088131 Loss2: 1.370067
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.451484 Loss1: 0.634862 Loss2: 1.816623
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.793753 Loss1: 0.449590 Loss2: 1.344163
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.641062 Loss1: 0.235390 Loss2: 1.405672
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.525015 Loss1: 0.178959 Loss2: 1.346056
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.566239 Loss1: 0.679204 Loss2: 1.887036
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.838459 Loss1: 0.439818 Loss2: 1.398641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.740124 Loss1: 0.297373 Loss2: 1.442751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.610297 Loss1: 0.219107 Loss2: 1.391190 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.578363 Loss1: 0.182586 Loss2: 1.395776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.545523 Loss1: 0.156394 Loss2: 1.389129 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420421 Loss1: 0.090242 Loss2: 1.330179
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.532050 Loss1: 0.146822 Loss2: 1.385227
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.489571 Loss1: 0.107421 Loss2: 1.382150
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466004 Loss1: 0.089208 Loss2: 1.376795
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440177 Loss1: 0.070998 Loss2: 1.369178
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.310235 Loss1: 0.503048 Loss2: 1.807187
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.707260 Loss1: 0.380840 Loss2: 1.326420
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.587639 Loss1: 0.221833 Loss2: 1.365806
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.479100 Loss1: 0.152562 Loss2: 1.326539
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.475187 Loss1: 0.619774 Loss2: 1.855413
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.766144 Loss1: 0.390914 Loss2: 1.375230 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.644731 Loss1: 0.232470 Loss2: 1.412261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595658 Loss1: 0.219272 Loss2: 1.376385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.565252 Loss1: 0.187676 Loss2: 1.377576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.557567 Loss1: 0.178137 Loss2: 1.379430 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363600 Loss1: 0.060462 Loss2: 1.303138
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.515544 Loss1: 0.144791 Loss2: 1.370753
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475864 Loss1: 0.103730 Loss2: 1.372134
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431791 Loss1: 0.066083 Loss2: 1.365709
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420781 Loss1: 0.062035 Loss2: 1.358746
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.521036 Loss1: 0.578500 Loss2: 1.942536
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.846690 Loss1: 0.445212 Loss2: 1.401478
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.698111 Loss1: 0.246115 Loss2: 1.451996
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551548 Loss1: 0.156546 Loss2: 1.395002
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.584980 Loss1: 0.666147 Loss2: 1.918832
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.847633 Loss1: 0.422939 Loss2: 1.424694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.760053 Loss1: 0.293552 Loss2: 1.466500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604992 Loss1: 0.176059 Loss2: 1.428933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558452 Loss1: 0.141766 Loss2: 1.416687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.518627 Loss1: 0.104413 Loss2: 1.414214 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483390 Loss1: 0.078537 Loss2: 1.404854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454109 Loss1: 0.062749 Loss2: 1.391360 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.816082 Loss1: 0.792934 Loss2: 2.023148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.932394 Loss1: 0.396716 Loss2: 1.535678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.635732 Loss1: 0.164643 Loss2: 1.471088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587236 Loss1: 0.135694 Loss2: 1.451542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.558042 Loss1: 0.102722 Loss2: 1.455320 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.549474 Loss1: 0.102731 Loss2: 1.446743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.514913 Loss1: 0.077491 Loss2: 1.437422 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508890 Loss1: 0.075106 Loss2: 1.433784 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478523 Loss1: 0.104442 Loss2: 1.374081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449889 Loss1: 0.091320 Loss2: 1.358569 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445875 Loss1: 0.083312 Loss2: 1.362563
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.390618 Loss1: 0.583186 Loss2: 1.807432
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.790851 Loss1: 0.398938 Loss2: 1.391913
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.652819 Loss1: 0.242904 Loss2: 1.409914
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.597449 Loss1: 0.231093 Loss2: 1.366356
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.545146 Loss1: 0.168061 Loss2: 1.377085
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.617830 Loss1: 0.753770 Loss2: 1.864060
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.735851 Loss1: 0.401611 Loss2: 1.334240
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481818 Loss1: 0.121964 Loss2: 1.359854
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.534345 Loss1: 0.174966 Loss2: 1.359379
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436089 Loss1: 0.085616 Loss2: 1.350472
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.412137 Loss1: 0.066005 Loss2: 1.346131 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397088 Loss1: 0.055222 Loss2: 1.341866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400494 Loss1: 0.068980 Loss2: 1.331514 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.358350 Loss1: 0.045950 Loss2: 1.312400 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.471715 Loss1: 0.612320 Loss2: 1.859395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591334 Loss1: 0.180628 Loss2: 1.410705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517647 Loss1: 0.158030 Loss2: 1.359617
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.380853 Loss1: 0.541931 Loss2: 1.838922
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612603 Loss1: 0.266599 Loss2: 1.346004 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.582858 Loss1: 0.227056 Loss2: 1.355803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478637 Loss1: 0.136910 Loss2: 1.341727 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.489919 Loss1: 0.158874 Loss2: 1.331045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420842 Loss1: 0.084913 Loss2: 1.335929 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403760 Loss1: 0.055031 Loss2: 1.348729
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406545 Loss1: 0.078216 Loss2: 1.328329
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.377948 Loss1: 0.058008 Loss2: 1.319940
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370485 Loss1: 0.055407 Loss2: 1.315078
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362398 Loss1: 0.047816 Loss2: 1.314582
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.353161 Loss1: 0.540504 Loss2: 1.812657
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.731595 Loss1: 0.397285 Loss2: 1.334310
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.650869 Loss1: 0.278192 Loss2: 1.372677
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500483 Loss1: 0.168058 Loss2: 1.332424
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.522015 Loss1: 0.659145 Loss2: 1.862870
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.849845 Loss1: 0.419470 Loss2: 1.430375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.799898 Loss1: 0.352105 Loss2: 1.447793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.656185 Loss1: 0.234739 Loss2: 1.421447 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576196 Loss1: 0.166897 Loss2: 1.409299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501106 Loss1: 0.109361 Loss2: 1.391744 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427070 Loss1: 0.054578 Loss2: 1.372492 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393176 Loss1: 0.027652 Loss2: 1.365524 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.688185 Loss1: 0.358544 Loss2: 1.329641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467356 Loss1: 0.150413 Loss2: 1.316944 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.528563 Loss1: 0.694022 Loss2: 1.834541
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448720 Loss1: 0.124290 Loss2: 1.324430
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.810976 Loss1: 0.417089 Loss2: 1.393887
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.425655 Loss1: 0.109727 Loss2: 1.315929
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.660691 Loss1: 0.255908 Loss2: 1.404783
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404575 Loss1: 0.089732 Loss2: 1.314843
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557070 Loss1: 0.203103 Loss2: 1.353968
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.368011 Loss1: 0.054760 Loss2: 1.313251
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391726 Loss1: 0.087334 Loss2: 1.304391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398258 Loss1: 0.089041 Loss2: 1.309217 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973633
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457577 Loss1: 0.111104 Loss2: 1.346473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378090 Loss1: 0.042436 Loss2: 1.335654 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.838215 Loss1: 0.447059 Loss2: 1.391156 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 04:55:41,879 | server.py:236 | fit_round 139 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.613794 Loss1: 0.230299 Loss2: 1.383495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.556313 Loss1: 0.166244 Loss2: 1.390070 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518568 Loss1: 0.145219 Loss2: 1.373349 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531144 Loss1: 0.157422 Loss2: 1.373722 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.496665 Loss1: 0.123514 Loss2: 1.373151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.480577 Loss1: 0.123328 Loss2: 1.357249 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425106 Loss1: 0.062144 Loss2: 1.362963 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445229 Loss1: 0.069492 Loss2: 1.375737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397081 Loss1: 0.042032 Loss2: 1.355048 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.957777 Loss1: 0.569596 Loss2: 1.388181 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.758868 Loss1: 0.355412 Loss2: 1.403456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.576883 Loss1: 0.193800 Loss2: 1.383082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485672 Loss1: 0.117859 Loss2: 1.367814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483533 Loss1: 0.125387 Loss2: 1.358146 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460904 Loss1: 0.106927 Loss2: 1.353977 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459629 Loss1: 0.110940 Loss2: 1.348689 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434623 Loss1: 0.091702 Loss2: 1.342921 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486095 Loss1: 0.094329 Loss2: 1.391767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429561 Loss1: 0.050406 Loss2: 1.379155 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 04:55:41,879][flwr][DEBUG] - fit_round 139 received 50 results and 0 failures
-INFO flwr 2023-10-12 04:56:24,401 | server.py:125 | fit progress: (139, 2.2211843315785686, {'accuracy': 0.5932}, 320692.179992624)
->> Test accuracy: 0.593200
-[2023-10-12 04:56:24,401][flwr][INFO] - fit progress: (139, 2.2211843315785686, {'accuracy': 0.5932}, 320692.179992624)
-DEBUG flwr 2023-10-12 04:56:24,402 | server.py:173 | evaluate_round 139: strategy sampled 50 clients (out of 50)
-[2023-10-12 04:56:24,402][flwr][DEBUG] - evaluate_round 139: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 05:05:31,838 | server.py:187 | evaluate_round 139 received 50 results and 0 failures
-[2023-10-12 05:05:31,838][flwr][DEBUG] - evaluate_round 139 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 05:05:31,838 | server.py:222 | fit_round 140: strategy sampled 50 clients (out of 50)
-[2023-10-12 05:05:31,838][flwr][DEBUG] - fit_round 140: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.605426 Loss1: 0.689924 Loss2: 1.915502
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.806395 Loss1: 0.452169 Loss2: 1.354226
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.661636 Loss1: 0.276274 Loss2: 1.385362
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572756 Loss1: 0.196644 Loss2: 1.376112
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.545873 Loss1: 0.199825 Loss2: 1.346047
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525945 Loss1: 0.169249 Loss2: 1.356695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.506048 Loss1: 0.145983 Loss2: 1.360066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459630 Loss1: 0.106401 Loss2: 1.353229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.442361 Loss1: 0.096171 Loss2: 1.346190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421560 Loss1: 0.082097 Loss2: 1.339463 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985577
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500528 Loss1: 0.134104 Loss2: 1.366424 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431227 Loss1: 0.079842 Loss2: 1.351384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397237 Loss1: 0.048975 Loss2: 1.348262
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.490742 Loss1: 0.632355 Loss2: 1.858388
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.777977 Loss1: 0.397341 Loss2: 1.380636
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700156 Loss1: 0.281753 Loss2: 1.418403
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.568380 Loss1: 0.182309 Loss2: 1.386072
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513593 Loss1: 0.134288 Loss2: 1.379305
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.389057 Loss1: 0.568894 Loss2: 1.820163
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475274 Loss1: 0.095243 Loss2: 1.380031
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449344 Loss1: 0.081873 Loss2: 1.367470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430792 Loss1: 0.067491 Loss2: 1.363301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428941 Loss1: 0.073122 Loss2: 1.355819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388694 Loss1: 0.032624 Loss2: 1.356070 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440407 Loss1: 0.099816 Loss2: 1.340591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390294 Loss1: 0.059583 Loss2: 1.330711 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372183 Loss1: 0.048100 Loss2: 1.324083
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.485745 Loss1: 0.613445 Loss2: 1.872300
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.726511 Loss1: 0.359416 Loss2: 1.367095
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.617705 Loss1: 0.221725 Loss2: 1.395981
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535088 Loss1: 0.177806 Loss2: 1.357282
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491260 Loss1: 0.130422 Loss2: 1.360838
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.373487 Loss1: 0.485397 Loss2: 1.888090
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461708 Loss1: 0.112261 Loss2: 1.349447
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447980 Loss1: 0.112093 Loss2: 1.335888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.702830 Loss1: 0.264932 Loss2: 1.437898
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.406364 Loss1: 0.066689 Loss2: 1.339675
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.652461 Loss1: 0.255305 Loss2: 1.397157
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388533 Loss1: 0.048906 Loss2: 1.339627
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548029 Loss1: 0.134195 Loss2: 1.413834
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385087 Loss1: 0.056744 Loss2: 1.328343
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483532 Loss1: 0.090241 Loss2: 1.393292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443631 Loss1: 0.064978 Loss2: 1.378653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.560487 Loss1: 0.664703 Loss2: 1.895784
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435579 Loss1: 0.058118 Loss2: 1.377462
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.604135 Loss1: 0.204361 Loss2: 1.399773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.493358 Loss1: 0.135898 Loss2: 1.357460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437964 Loss1: 0.082377 Loss2: 1.355587
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.588439 Loss1: 0.710597 Loss2: 1.877843
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.810603 Loss1: 0.415103 Loss2: 1.395500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.717229 Loss1: 0.277477 Loss2: 1.439752 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.616427 Loss1: 0.232754 Loss2: 1.383673 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356229 Loss1: 0.031433 Loss2: 1.324796
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.546361 Loss1: 0.153642 Loss2: 1.392718
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486654 Loss1: 0.112189 Loss2: 1.374465
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450680 Loss1: 0.080224 Loss2: 1.370456
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432191 Loss1: 0.067031 Loss2: 1.365160
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428914 Loss1: 0.070386 Loss2: 1.358527
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.417632 Loss1: 0.603208 Loss2: 1.814425
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415572 Loss1: 0.059481 Loss2: 1.356091
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.621259 Loss1: 0.237530 Loss2: 1.383729 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527304 Loss1: 0.181180 Loss2: 1.346125 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476136 Loss1: 0.126435 Loss2: 1.349701
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.519748 Loss1: 0.609860 Loss2: 1.909888
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.740097 Loss1: 0.379797 Loss2: 1.360300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.638493 Loss1: 0.250617 Loss2: 1.387877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557487 Loss1: 0.171318 Loss2: 1.386170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406509 Loss1: 0.079169 Loss2: 1.327340
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505830 Loss1: 0.143956 Loss2: 1.361874
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446268 Loss1: 0.082514 Loss2: 1.363753
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.401305 Loss1: 0.048720 Loss2: 1.352585
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390144 Loss1: 0.045735 Loss2: 1.344409
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396349 Loss1: 0.054756 Loss2: 1.341594
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394889 Loss1: 0.054535 Loss2: 1.340354
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.468492 Loss1: 0.580896 Loss2: 1.887596
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.804631 Loss1: 0.408879 Loss2: 1.395752
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.701159 Loss1: 0.257531 Loss2: 1.443627
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602967 Loss1: 0.195790 Loss2: 1.407177
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.579080 Loss1: 0.173671 Loss2: 1.405408
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.483837 Loss1: 0.633845 Loss2: 1.849993
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547212 Loss1: 0.142326 Loss2: 1.404886
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.773977 Loss1: 0.420942 Loss2: 1.353035
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486090 Loss1: 0.091504 Loss2: 1.394586
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.632175 Loss1: 0.215687 Loss2: 1.416489
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.468278 Loss1: 0.084860 Loss2: 1.383418
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.466442 Loss1: 0.129685 Loss2: 1.336757
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445867 Loss1: 0.063569 Loss2: 1.382299
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.478895 Loss1: 0.137474 Loss2: 1.341421
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436580 Loss1: 0.062009 Loss2: 1.374571
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.418270 Loss1: 0.085728 Loss2: 1.332542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371901 Loss1: 0.049674 Loss2: 1.322227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383986 Loss1: 0.067475 Loss2: 1.316511
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.688965 Loss1: 0.787972 Loss2: 1.900993
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.791276 Loss1: 0.426969 Loss2: 1.364307
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.677318 Loss1: 0.264521 Loss2: 1.412797
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.545990 Loss1: 0.191482 Loss2: 1.354508
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507709 Loss1: 0.153246 Loss2: 1.354463
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445902 Loss1: 0.088844 Loss2: 1.357059
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.674738 Loss1: 0.710194 Loss2: 1.964544
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.772865 Loss1: 0.435754 Loss2: 1.337111
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426601 Loss1: 0.085416 Loss2: 1.341185
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448780 Loss1: 0.109087 Loss2: 1.339694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404941 Loss1: 0.062777 Loss2: 1.342163 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385280 Loss1: 0.055088 Loss2: 1.330192 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430726 Loss1: 0.083053 Loss2: 1.347673 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405616 Loss1: 0.072039 Loss2: 1.333577 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.708568 Loss1: 0.339137 Loss2: 1.369431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524096 Loss1: 0.162307 Loss2: 1.361789 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.457230 Loss1: 0.102874 Loss2: 1.354356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445312 Loss1: 0.093536 Loss2: 1.351776 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443208 Loss1: 0.094592 Loss2: 1.348616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421850 Loss1: 0.077776 Loss2: 1.344075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438942 Loss1: 0.102816 Loss2: 1.336127 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408786 Loss1: 0.074482 Loss2: 1.334304 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464733 Loss1: 0.109313 Loss2: 1.355420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406753 Loss1: 0.070892 Loss2: 1.335861 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.854649 Loss1: 0.473591 Loss2: 1.381058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580049 Loss1: 0.203759 Loss2: 1.376290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499820 Loss1: 0.120524 Loss2: 1.379296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.771889 Loss1: 0.365414 Loss2: 1.406475
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491741 Loss1: 0.119381 Loss2: 1.372359
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642784 Loss1: 0.205946 Loss2: 1.436838
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449197 Loss1: 0.078020 Loss2: 1.371177
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.598390 Loss1: 0.187318 Loss2: 1.411072
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439808 Loss1: 0.078699 Loss2: 1.361109
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.410255 Loss1: 0.054011 Loss2: 1.356244
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550248 Loss1: 0.140444 Loss2: 1.409805
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386957 Loss1: 0.040497 Loss2: 1.346460
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496465 Loss1: 0.096086 Loss2: 1.400379
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.498927 Loss1: 0.111016 Loss2: 1.387911
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466984 Loss1: 0.075887 Loss2: 1.391096
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448843 Loss1: 0.066157 Loss2: 1.382686
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437992 Loss1: 0.059588 Loss2: 1.378404
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.461246 Loss1: 0.669529 Loss2: 1.791717
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.687211 Loss1: 0.361289 Loss2: 1.325922
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.641857 Loss1: 0.282644 Loss2: 1.359213
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.525372 Loss1: 0.202267 Loss2: 1.323105
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.458681 Loss1: 0.130715 Loss2: 1.327966
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.403970 Loss1: 0.606924 Loss2: 1.797046
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.660667 Loss1: 0.308658 Loss2: 1.352009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.625601 Loss1: 0.239523 Loss2: 1.386078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.570942 Loss1: 0.222046 Loss2: 1.348896 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487364 Loss1: 0.141824 Loss2: 1.345540 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.497997 Loss1: 0.152829 Loss2: 1.345168 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442476 Loss1: 0.101914 Loss2: 1.340562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410042 Loss1: 0.082091 Loss2: 1.327951 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.735884 Loss1: 0.318884 Loss2: 1.417000 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521677 Loss1: 0.165844 Loss2: 1.355833 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.610410 Loss1: 0.674813 Loss2: 1.935598
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462915 Loss1: 0.114407 Loss2: 1.348508
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436552 Loss1: 0.100427 Loss2: 1.336125 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419033 Loss1: 0.079322 Loss2: 1.339711 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.393646 Loss1: 0.061505 Loss2: 1.332141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378148 Loss1: 0.054907 Loss2: 1.323241 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390618 Loss1: 0.051047 Loss2: 1.339571 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361247 Loss1: 0.032535 Loss2: 1.328712 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.394819 Loss1: 0.561954 Loss2: 1.832865
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.617398 Loss1: 0.238392 Loss2: 1.379007
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.572315 Loss1: 0.183711 Loss2: 1.388603
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508106 Loss1: 0.137835 Loss2: 1.370272
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.611306 Loss1: 0.769122 Loss2: 1.842183
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.690536 Loss1: 0.352798 Loss2: 1.337738 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524214 Loss1: 0.159062 Loss2: 1.365152
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.582585 Loss1: 0.215852 Loss2: 1.366733
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516157 Loss1: 0.155640 Loss2: 1.360516
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.518253 Loss1: 0.187141 Loss2: 1.331113
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.455677 Loss1: 0.089516 Loss2: 1.366161
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.454115 Loss1: 0.124542 Loss2: 1.329573
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.403520 Loss1: 0.085497 Loss2: 1.318023
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.415912 Loss1: 0.063012 Loss2: 1.352901
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395840 Loss1: 0.087796 Loss2: 1.308044
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392148 Loss1: 0.043246 Loss2: 1.348902
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391931 Loss1: 0.086397 Loss2: 1.305533 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.482250 Loss1: 0.620479 Loss2: 1.861771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.720420 Loss1: 0.276723 Loss2: 1.443697 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.686337 Loss1: 0.301383 Loss2: 1.384955
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.278327 Loss1: 0.452319 Loss2: 1.826008
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.751257 Loss1: 0.378688 Loss2: 1.372569 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671686 Loss1: 0.262082 Loss2: 1.409604 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.560740 Loss1: 0.201999 Loss2: 1.358741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.560869 Loss1: 0.188403 Loss2: 1.372465 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416114 Loss1: 0.056599 Loss2: 1.359515 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.474170 Loss1: 0.117518 Loss2: 1.356652 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393690 Loss1: 0.049722 Loss2: 1.343968 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992647
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.653285 Loss1: 0.317030 Loss2: 1.336255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476380 Loss1: 0.151578 Loss2: 1.324801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462407 Loss1: 0.137199 Loss2: 1.325208
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.461105 Loss1: 0.621303 Loss2: 1.839801
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450790 Loss1: 0.129585 Loss2: 1.321205
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.705075 Loss1: 0.344041 Loss2: 1.361035
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481099 Loss1: 0.153560 Loss2: 1.327539
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.681520 Loss1: 0.288497 Loss2: 1.393023
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456968 Loss1: 0.133108 Loss2: 1.323860
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.533614 Loss1: 0.177284 Loss2: 1.356331
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518563 Loss1: 0.161529 Loss2: 1.357034
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397900 Loss1: 0.084010 Loss2: 1.313890
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506424 Loss1: 0.157864 Loss2: 1.348561
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384986 Loss1: 0.072174 Loss2: 1.312812
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471428 Loss1: 0.117882 Loss2: 1.353546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400234 Loss1: 0.058912 Loss2: 1.341321 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.787160 Loss1: 0.378474 Loss2: 1.408685 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.619936 Loss1: 0.206598 Loss2: 1.413337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.530976 Loss1: 0.124211 Loss2: 1.406765
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.410114 Loss1: 0.564814 Loss2: 1.845299
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514499 Loss1: 0.117434 Loss2: 1.397065
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.741973 Loss1: 0.381044 Loss2: 1.360929
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487240 Loss1: 0.102572 Loss2: 1.384668
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.641026 Loss1: 0.233776 Loss2: 1.407251
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471867 Loss1: 0.088068 Loss2: 1.383799
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521468 Loss1: 0.169394 Loss2: 1.352074
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.473423 Loss1: 0.087580 Loss2: 1.385843
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512700 Loss1: 0.161868 Loss2: 1.350832
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472194 Loss1: 0.094648 Loss2: 1.377545
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512668 Loss1: 0.166384 Loss2: 1.346284
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.515993 Loss1: 0.165547 Loss2: 1.350447
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.474062 Loss1: 0.124766 Loss2: 1.349297
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445498 Loss1: 0.103696 Loss2: 1.341802
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449867 Loss1: 0.102538 Loss2: 1.347329
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.579560 Loss1: 0.637351 Loss2: 1.942210
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.781972 Loss1: 0.354361 Loss2: 1.427611
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.660186 Loss1: 0.220212 Loss2: 1.439974
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.664239 Loss1: 0.261252 Loss2: 1.402987
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571570 Loss1: 0.153510 Loss2: 1.418060
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519147 Loss1: 0.122479 Loss2: 1.396669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.569404 Loss1: 0.169796 Loss2: 1.399607 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.560336 Loss1: 0.155039 Loss2: 1.405297 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.536569 Loss1: 0.122866 Loss2: 1.413703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.488763 Loss1: 0.098033 Loss2: 1.390730 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425344 Loss1: 0.061669 Loss2: 1.363675 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398464 Loss1: 0.050307 Loss2: 1.348157 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.744418 Loss1: 0.365817 Loss2: 1.378601 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554982 Loss1: 0.191693 Loss2: 1.363289 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507605 Loss1: 0.134366 Loss2: 1.373239
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.475483 Loss1: 0.653785 Loss2: 1.821698
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.819622 Loss1: 0.456559 Loss2: 1.363062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.648893 Loss1: 0.225997 Loss2: 1.422896 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.547241 Loss1: 0.202023 Loss2: 1.345219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501274 Loss1: 0.150292 Loss2: 1.350982 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.478806 Loss1: 0.122193 Loss2: 1.356613
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469036 Loss1: 0.124459 Loss2: 1.344576
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444316 Loss1: 0.100461 Loss2: 1.343855
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.444470 Loss1: 0.106556 Loss2: 1.337914
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421655 Loss1: 0.080916 Loss2: 1.340739
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397492 Loss1: 0.066588 Loss2: 1.330905
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.258566 Loss1: 0.416188 Loss2: 1.842379
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.692196 Loss1: 0.348194 Loss2: 1.344001
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.577227 Loss1: 0.217037 Loss2: 1.360189
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.595165 Loss1: 0.242423 Loss2: 1.352741
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.530069 Loss1: 0.178411 Loss2: 1.351658
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.635565 Loss1: 0.725369 Loss2: 1.910196
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.873403 Loss1: 0.452137 Loss2: 1.421266 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.751645 Loss1: 0.288920 Loss2: 1.462724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.582178 Loss1: 0.167143 Loss2: 1.415035 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529998 Loss1: 0.115113 Loss2: 1.414885 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539103 Loss1: 0.132509 Loss2: 1.406593 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.527398 Loss1: 0.126169 Loss2: 1.401229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.505109 Loss1: 0.106749 Loss2: 1.398360 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.703503 Loss1: 0.372263 Loss2: 1.331240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523709 Loss1: 0.183374 Loss2: 1.340336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.435415 Loss1: 0.103405 Loss2: 1.332010
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.466755 Loss1: 0.600540 Loss2: 1.866216
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.411224 Loss1: 0.080910 Loss2: 1.330314
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.755508 Loss1: 0.370847 Loss2: 1.384661
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.656272 Loss1: 0.243908 Loss2: 1.412364
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426367 Loss1: 0.100908 Loss2: 1.325458
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.582333 Loss1: 0.211737 Loss2: 1.370596
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405458 Loss1: 0.082315 Loss2: 1.323143
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.551155 Loss1: 0.162523 Loss2: 1.388632
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381413 Loss1: 0.066274 Loss2: 1.315140
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538252 Loss1: 0.162455 Loss2: 1.375797
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354828 Loss1: 0.043048 Loss2: 1.311780
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470958 Loss1: 0.094583 Loss2: 1.376375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413104 Loss1: 0.058621 Loss2: 1.354483 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.761896 Loss1: 0.371989 Loss2: 1.389906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.550784 Loss1: 0.166810 Loss2: 1.383974 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.476816 Loss1: 0.576942 Loss2: 1.899874
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.504625 Loss1: 0.125983 Loss2: 1.378642
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.788834 Loss1: 0.403654 Loss2: 1.385180
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489535 Loss1: 0.120892 Loss2: 1.368643
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.694034 Loss1: 0.260528 Loss2: 1.433506
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474370 Loss1: 0.105631 Loss2: 1.368740
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.644738 Loss1: 0.257704 Loss2: 1.387034
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467092 Loss1: 0.104374 Loss2: 1.362718
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529252 Loss1: 0.142555 Loss2: 1.386697
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455513 Loss1: 0.081714 Loss2: 1.373800
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491088 Loss1: 0.119496 Loss2: 1.371592
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449583 Loss1: 0.084744 Loss2: 1.364839
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415499 Loss1: 0.059604 Loss2: 1.355894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386612 Loss1: 0.041290 Loss2: 1.345322 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.817842 Loss1: 0.458282 Loss2: 1.359560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559114 Loss1: 0.195780 Loss2: 1.363334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568866 Loss1: 0.187704 Loss2: 1.381162
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.467412 Loss1: 0.623619 Loss2: 1.843793
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491744 Loss1: 0.133651 Loss2: 1.358094
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.772076 Loss1: 0.388658 Loss2: 1.383418
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.676751 Loss1: 0.248307 Loss2: 1.428443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.542220 Loss1: 0.166449 Loss2: 1.375771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507877 Loss1: 0.133242 Loss2: 1.374635 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490607 Loss1: 0.123618 Loss2: 1.366988 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459461 Loss1: 0.088017 Loss2: 1.371444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.455838 Loss1: 0.096398 Loss2: 1.359440 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.741851 Loss1: 0.390157 Loss2: 1.351694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.510010 Loss1: 0.163207 Loss2: 1.346804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.357784 Loss1: 0.496765 Loss2: 1.861019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.738021 Loss1: 0.351909 Loss2: 1.386113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.620589 Loss1: 0.195914 Loss2: 1.424676 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.609916 Loss1: 0.216105 Loss2: 1.393811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512808 Loss1: 0.119574 Loss2: 1.393234 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498968 Loss1: 0.117096 Loss2: 1.381872 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472108 Loss1: 0.096650 Loss2: 1.375459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417891 Loss1: 0.051785 Loss2: 1.366105 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.829927 Loss1: 0.441927 Loss2: 1.388000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647608 Loss1: 0.244630 Loss2: 1.402978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.647586 Loss1: 0.240970 Loss2: 1.406616
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.370324 Loss1: 0.528804 Loss2: 1.841520
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.735494 Loss1: 0.377660 Loss2: 1.357833 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682878 Loss1: 0.286611 Loss2: 1.396267 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.596557 Loss1: 0.230488 Loss2: 1.366069 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519836 Loss1: 0.161047 Loss2: 1.358789 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419105 Loss1: 0.057099 Loss2: 1.362006
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.457574 Loss1: 0.103014 Loss2: 1.354559
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426982 Loss1: 0.079702 Loss2: 1.347280
-DEBUG flwr 2023-10-12 05:33:54,001 | server.py:236 | fit_round 140 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415590 Loss1: 0.079765 Loss2: 1.335825
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406097 Loss1: 0.066120 Loss2: 1.339977
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390716 Loss1: 0.056680 Loss2: 1.334036
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.417383 Loss1: 0.569954 Loss2: 1.847429
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.708783 Loss1: 0.348099 Loss2: 1.360685
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568049 Loss1: 0.175942 Loss2: 1.392108
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491270 Loss1: 0.133750 Loss2: 1.357521
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506261 Loss1: 0.152602 Loss2: 1.353659
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.297677 Loss1: 0.527423 Loss2: 1.770254
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.688972 Loss1: 0.361644 Loss2: 1.327328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.596305 Loss1: 0.228970 Loss2: 1.367335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.494311 Loss1: 0.166345 Loss2: 1.327965 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505404 Loss1: 0.175507 Loss2: 1.329897 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452105 Loss1: 0.133176 Loss2: 1.318929 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370107 Loss1: 0.064543 Loss2: 1.305564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375884 Loss1: 0.070879 Loss2: 1.305005
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.334592 Loss1: 0.548561 Loss2: 1.786032
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.707262 Loss1: 0.362101 Loss2: 1.345161
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.554377 Loss1: 0.183820 Loss2: 1.370557
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.531933 Loss1: 0.191716 Loss2: 1.340217
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490760 Loss1: 0.151898 Loss2: 1.338861
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.407403 Loss1: 0.566509 Loss2: 1.840894
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519151 Loss1: 0.186100 Loss2: 1.333052
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.734945 Loss1: 0.354702 Loss2: 1.380243
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460674 Loss1: 0.126171 Loss2: 1.334504
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.639412 Loss1: 0.209188 Loss2: 1.430223
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461003 Loss1: 0.130593 Loss2: 1.330410
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.629796 Loss1: 0.243227 Loss2: 1.386569
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422393 Loss1: 0.098315 Loss2: 1.324078
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.532499 Loss1: 0.136135 Loss2: 1.396364
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392291 Loss1: 0.072760 Loss2: 1.319532
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491505 Loss1: 0.109846 Loss2: 1.381659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417648 Loss1: 0.049552 Loss2: 1.368096 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 05:33:54,001][flwr][DEBUG] - fit_round 140 received 50 results and 0 failures
-INFO flwr 2023-10-12 05:34:37,053 | server.py:125 | fit progress: (140, 2.210895585747192, {'accuracy': 0.5928}, 322984.83112155297)
->> Test accuracy: 0.592800
-[2023-10-12 05:34:37,053][flwr][INFO] - fit progress: (140, 2.210895585747192, {'accuracy': 0.5928}, 322984.83112155297)
-DEBUG flwr 2023-10-12 05:34:37,053 | server.py:173 | evaluate_round 140: strategy sampled 50 clients (out of 50)
-[2023-10-12 05:34:37,053][flwr][DEBUG] - evaluate_round 140: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 05:43:37,505 | server.py:187 | evaluate_round 140 received 50 results and 0 failures
-[2023-10-12 05:43:37,505][flwr][DEBUG] - evaluate_round 140 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 05:43:37,506 | server.py:222 | fit_round 141: strategy sampled 50 clients (out of 50)
-[2023-10-12 05:43:37,506][flwr][DEBUG] - fit_round 141: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.639876 Loss1: 0.646545 Loss2: 1.993331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.705954 Loss1: 0.300646 Loss2: 1.405308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.556240 Loss1: 0.177169 Loss2: 1.379071 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.521738 Loss1: 0.142967 Loss2: 1.378771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474054 Loss1: 0.096240 Loss2: 1.377814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451480 Loss1: 0.087686 Loss2: 1.363794
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.522813 Loss1: 0.160524 Loss2: 1.362289
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436098 Loss1: 0.070709 Loss2: 1.365389
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.472181 Loss1: 0.128893 Loss2: 1.343288
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.426551 Loss1: 0.087612 Loss2: 1.338939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.401178 Loss1: 0.074972 Loss2: 1.326206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397387 Loss1: 0.068108 Loss2: 1.329279 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387318 Loss1: 0.060846 Loss2: 1.326472 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587442 Loss1: 0.188866 Loss2: 1.398576 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515767 Loss1: 0.132635 Loss2: 1.383132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.489762 Loss1: 0.111827 Loss2: 1.377935
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.335909 Loss1: 0.528954 Loss2: 1.806956
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471497 Loss1: 0.090475 Loss2: 1.381022
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.661227 Loss1: 0.298691 Loss2: 1.362536
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447805 Loss1: 0.074267 Loss2: 1.373538
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.584636 Loss1: 0.212748 Loss2: 1.371888
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436185 Loss1: 0.065956 Loss2: 1.370228
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530344 Loss1: 0.178334 Loss2: 1.352010
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.461597 Loss1: 0.114721 Loss2: 1.346876
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.434846 Loss1: 0.094679 Loss2: 1.340166
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395328 Loss1: 0.069249 Loss2: 1.326079
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382291 Loss1: 0.058359 Loss2: 1.323931
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.555931 Loss1: 0.634863 Loss2: 1.921068
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.870145 Loss1: 0.430260 Loss2: 1.439885
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366623 Loss1: 0.047015 Loss2: 1.319609
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.705009 Loss1: 0.231157 Loss2: 1.473853
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381662 Loss1: 0.067429 Loss2: 1.314233
-(DefaultActor pid=3764) >> Training accuracy: 0.991728
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536429 Loss1: 0.106768 Loss2: 1.429661 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518520 Loss1: 0.101911 Loss2: 1.416609 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483925 Loss1: 0.070942 Loss2: 1.412984
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.630963 Loss1: 0.701831 Loss2: 1.929132
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.784116 Loss1: 0.391955 Loss2: 1.392160
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.512849 Loss1: 0.109302 Loss2: 1.403547
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.672891 Loss1: 0.249334 Loss2: 1.423557
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.493681 Loss1: 0.090103 Loss2: 1.403578
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528543 Loss1: 0.137889 Loss2: 1.390655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461498 Loss1: 0.081785 Loss2: 1.379713 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491516 Loss1: 0.121911 Loss2: 1.369606
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.386760 Loss1: 0.561860 Loss2: 1.824900
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.752528 Loss1: 0.386084 Loss2: 1.366445 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.504885 Loss1: 0.146852 Loss2: 1.358032 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437883 Loss1: 0.091869 Loss2: 1.346013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.454537 Loss1: 0.553390 Loss2: 1.901147
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439705 Loss1: 0.096066 Loss2: 1.343639
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.728936 Loss1: 0.338432 Loss2: 1.390504
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.431907 Loss1: 0.090215 Loss2: 1.341692
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633955 Loss1: 0.222472 Loss2: 1.411483
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404971 Loss1: 0.059602 Loss2: 1.345370
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.593829 Loss1: 0.193710 Loss2: 1.400119
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447735 Loss1: 0.113478 Loss2: 1.334257
-(DefaultActor pid=3765) >> Training accuracy: 0.978516
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.526011 Loss1: 0.140982 Loss2: 1.385029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455642 Loss1: 0.074781 Loss2: 1.380861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426871 Loss1: 0.058889 Loss2: 1.367982
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.495883 Loss1: 0.663786 Loss2: 1.832097
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406525 Loss1: 0.045518 Loss2: 1.361006
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.756044 Loss1: 0.406005 Loss2: 1.350038
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638040 Loss1: 0.241519 Loss2: 1.396521
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541082 Loss1: 0.186781 Loss2: 1.354301
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.489196 Loss1: 0.143762 Loss2: 1.345434
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449228 Loss1: 0.109471 Loss2: 1.339757
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.418561 Loss1: 0.568086 Loss2: 1.850475
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.427114 Loss1: 0.096495 Loss2: 1.330619
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.753317 Loss1: 0.356001 Loss2: 1.397316
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425295 Loss1: 0.094829 Loss2: 1.330466
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.631014 Loss1: 0.197359 Loss2: 1.433655
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405080 Loss1: 0.077100 Loss2: 1.327979
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.547672 Loss1: 0.149613 Loss2: 1.398059
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384630 Loss1: 0.064127 Loss2: 1.320503
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480520 Loss1: 0.095703 Loss2: 1.384817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421144 Loss1: 0.043507 Loss2: 1.377637 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.522753 Loss1: 0.655934 Loss2: 1.866819
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433178 Loss1: 0.061101 Loss2: 1.372077
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.872504 Loss1: 0.479271 Loss2: 1.393233
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417997 Loss1: 0.044921 Loss2: 1.373076
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.636363 Loss1: 0.253299 Loss2: 1.383064 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502221 Loss1: 0.129756 Loss2: 1.372465 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.498573 Loss1: 0.133426 Loss2: 1.365147
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.403294 Loss1: 0.578394 Loss2: 1.824900
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.690036 Loss1: 0.326345 Loss2: 1.363690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.637375 Loss1: 0.228246 Loss2: 1.409130 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573997 Loss1: 0.202850 Loss2: 1.371147 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.533745 Loss1: 0.158648 Loss2: 1.375097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.520869 Loss1: 0.152626 Loss2: 1.368243 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.656554 Loss1: 0.317256 Loss2: 1.339298 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551219 Loss1: 0.182319 Loss2: 1.368900 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.460175 Loss1: 0.131334 Loss2: 1.328841 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409349 Loss1: 0.080369 Loss2: 1.328980 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.390025 Loss1: 0.069984 Loss2: 1.320041
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.551790 Loss1: 0.700285 Loss2: 1.851505
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.364554 Loss1: 0.052466 Loss2: 1.312088
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.819462 Loss1: 0.437239 Loss2: 1.382222
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.339994 Loss1: 0.030726 Loss2: 1.309267
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.681873 Loss1: 0.252436 Loss2: 1.429437
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.549085 Loss1: 0.177245 Loss2: 1.371840
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528303 Loss1: 0.169732 Loss2: 1.358571
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472758 Loss1: 0.113090 Loss2: 1.359668
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428752 Loss1: 0.076391 Loss2: 1.352362
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440609 Loss1: 0.095169 Loss2: 1.345439
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.315187 Loss1: 0.504877 Loss2: 1.810310
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420674 Loss1: 0.081453 Loss2: 1.339222
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.727075 Loss1: 0.364516 Loss2: 1.362559
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382972 Loss1: 0.046944 Loss2: 1.336027
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.622475 Loss1: 0.212592 Loss2: 1.409883
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562777 Loss1: 0.210614 Loss2: 1.352163
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.573696 Loss1: 0.214731 Loss2: 1.358965
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533985 Loss1: 0.159090 Loss2: 1.374896
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513010 Loss1: 0.156700 Loss2: 1.356310
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.540424 Loss1: 0.706451 Loss2: 1.833973
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.813194 Loss1: 0.430571 Loss2: 1.382622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.708018 Loss1: 0.303435 Loss2: 1.404583 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422531 Loss1: 0.078205 Loss2: 1.344326
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532017 Loss1: 0.165564 Loss2: 1.366454
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503417 Loss1: 0.144321 Loss2: 1.359096
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492665 Loss1: 0.141171 Loss2: 1.351494
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416668 Loss1: 0.069902 Loss2: 1.346767
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411012 Loss1: 0.070876 Loss2: 1.340136
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.470332 Loss1: 0.576042 Loss2: 1.894290
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385163 Loss1: 0.052788 Loss2: 1.332375
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.749572 Loss1: 0.360313 Loss2: 1.389259
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.371543 Loss1: 0.043271 Loss2: 1.328271
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.667967 Loss1: 0.272730 Loss2: 1.395236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.550498 Loss1: 0.157594 Loss2: 1.392904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524829 Loss1: 0.136543 Loss2: 1.388285
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.424879 Loss1: 0.614511 Loss2: 1.810367
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.525854 Loss1: 0.141717 Loss2: 1.384136
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.670428 Loss1: 0.332094 Loss2: 1.338334
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.483824 Loss1: 0.103356 Loss2: 1.380468
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.645355 Loss1: 0.278009 Loss2: 1.367346
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.464974 Loss1: 0.091263 Loss2: 1.373711
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555983 Loss1: 0.220687 Loss2: 1.335297
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.461740 Loss1: 0.132273 Loss2: 1.329468
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.417362 Loss1: 0.091749 Loss2: 1.325614
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.424905 Loss1: 0.105087 Loss2: 1.319819
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397639 Loss1: 0.081526 Loss2: 1.316113
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.512749 Loss1: 0.654066 Loss2: 1.858683
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368716 Loss1: 0.058483 Loss2: 1.310234
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.824535 Loss1: 0.428940 Loss2: 1.395595
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.355791 Loss1: 0.052686 Loss2: 1.303105
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.653238 Loss1: 0.254846 Loss2: 1.398392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503607 Loss1: 0.105584 Loss2: 1.398023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463922 Loss1: 0.087976 Loss2: 1.375946
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.551729 Loss1: 0.662062 Loss2: 1.889667
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445695 Loss1: 0.075951 Loss2: 1.369744
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.701771 Loss1: 0.337689 Loss2: 1.364082
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411374 Loss1: 0.044046 Loss2: 1.367328
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.567995 Loss1: 0.193426 Loss2: 1.374569
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408903 Loss1: 0.048514 Loss2: 1.360389
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.484000 Loss1: 0.142184 Loss2: 1.341816
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.435870 Loss1: 0.093114 Loss2: 1.342756
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444693 Loss1: 0.106822 Loss2: 1.337871
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467412 Loss1: 0.122577 Loss2: 1.344835
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427238 Loss1: 0.089437 Loss2: 1.337801
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.453472 Loss1: 0.608280 Loss2: 1.845193
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398672 Loss1: 0.063081 Loss2: 1.335591
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.783403 Loss1: 0.404823 Loss2: 1.378580
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393123 Loss1: 0.059455 Loss2: 1.333668
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.699756 Loss1: 0.306545 Loss2: 1.393211 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562809 Loss1: 0.183766 Loss2: 1.379043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503632 Loss1: 0.123863 Loss2: 1.379769
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.465728 Loss1: 0.653184 Loss2: 1.812544
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479510 Loss1: 0.116533 Loss2: 1.362977
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.748271 Loss1: 0.406980 Loss2: 1.341291
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458482 Loss1: 0.096604 Loss2: 1.361879
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.638004 Loss1: 0.246653 Loss2: 1.391351
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417822 Loss1: 0.059063 Loss2: 1.358759
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520070 Loss1: 0.173742 Loss2: 1.346328
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485349 Loss1: 0.147606 Loss2: 1.337743
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455660 Loss1: 0.112886 Loss2: 1.342774
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480531 Loss1: 0.145933 Loss2: 1.334598
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.410963 Loss1: 0.086416 Loss2: 1.324547
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.525868 Loss1: 0.668211 Loss2: 1.857657
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412419 Loss1: 0.093607 Loss2: 1.318812
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.696869 Loss1: 0.358951 Loss2: 1.337918
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415991 Loss1: 0.087760 Loss2: 1.328231
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.521412 Loss1: 0.183552 Loss2: 1.337860 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449607 Loss1: 0.127219 Loss2: 1.322389 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.365729 Loss1: 0.540800 Loss2: 1.824929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.651842 Loss1: 0.312536 Loss2: 1.339306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.612277 Loss1: 0.233547 Loss2: 1.378731 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.511257 Loss1: 0.168500 Loss2: 1.342756 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456579 Loss1: 0.115462 Loss2: 1.341117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398825 Loss1: 0.069400 Loss2: 1.329425
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.393940 Loss1: 0.611779 Loss2: 1.782161
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.671038 Loss1: 0.333441 Loss2: 1.337597 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.570464 Loss1: 0.215704 Loss2: 1.354760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.438571 Loss1: 0.110709 Loss2: 1.327862 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.386088 Loss1: 0.078083 Loss2: 1.308005 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.373510 Loss1: 0.070490 Loss2: 1.303020 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.358719 Loss1: 0.064144 Loss2: 1.294575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.346754 Loss1: 0.055024 Loss2: 1.291730 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524469 Loss1: 0.156230 Loss2: 1.368239 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427367 Loss1: 0.082033 Loss2: 1.345334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.381693 Loss1: 0.550513 Loss2: 1.831180
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392302 Loss1: 0.051998 Loss2: 1.340304
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406821 Loss1: 0.070830 Loss2: 1.335991
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.720165 Loss1: 0.349313 Loss2: 1.370853
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.621079 Loss1: 0.203960 Loss2: 1.417119
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570666 Loss1: 0.207567 Loss2: 1.363099
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527790 Loss1: 0.146373 Loss2: 1.381417
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471079 Loss1: 0.107953 Loss2: 1.363127
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.599618 Loss1: 0.700848 Loss2: 1.898770
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420575 Loss1: 0.062561 Loss2: 1.358014
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.397678 Loss1: 0.053254 Loss2: 1.344423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.365024 Loss1: 0.024097 Loss2: 1.340927 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.525592 Loss1: 0.159697 Loss2: 1.365895 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468301 Loss1: 0.109807 Loss2: 1.358494 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.459092 Loss1: 0.099913 Loss2: 1.359178 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.390189 Loss1: 0.546614 Loss2: 1.843574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.613061 Loss1: 0.222170 Loss2: 1.390891 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510338 Loss1: 0.152824 Loss2: 1.357515 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459420 Loss1: 0.100601 Loss2: 1.358819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431233 Loss1: 0.084484 Loss2: 1.346750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417843 Loss1: 0.075118 Loss2: 1.342726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412213 Loss1: 0.066308 Loss2: 1.345906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416281 Loss1: 0.074819 Loss2: 1.341461 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.395390 Loss1: 0.061103 Loss2: 1.334287 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408354 Loss1: 0.089834 Loss2: 1.318519 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.755147 Loss1: 0.309489 Loss2: 1.445658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.662999 Loss1: 0.220265 Loss2: 1.442734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.650035 Loss1: 0.198052 Loss2: 1.451983
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.493778 Loss1: 0.657250 Loss2: 1.836527
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.742622 Loss1: 0.369482 Loss2: 1.373140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.595333 Loss1: 0.183522 Loss2: 1.411811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526598 Loss1: 0.171198 Loss2: 1.355400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509311 Loss1: 0.153848 Loss2: 1.355463 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485324 Loss1: 0.061951 Loss2: 1.423373
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.488579 Loss1: 0.127670 Loss2: 1.360909
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445073 Loss1: 0.091851 Loss2: 1.353222
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423713 Loss1: 0.078544 Loss2: 1.345169
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427455 Loss1: 0.085758 Loss2: 1.341697
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409549 Loss1: 0.069353 Loss2: 1.340195
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.513412 Loss1: 0.614096 Loss2: 1.899316
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.782593 Loss1: 0.379651 Loss2: 1.402942
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700321 Loss1: 0.252740 Loss2: 1.447580
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.652964 Loss1: 0.264435 Loss2: 1.388528
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546003 Loss1: 0.145863 Loss2: 1.400139
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.500001 Loss1: 0.676077 Loss2: 1.823923
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.745859 Loss1: 0.399126 Loss2: 1.346732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.668230 Loss1: 0.276886 Loss2: 1.391344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595448 Loss1: 0.241846 Loss2: 1.353601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544516 Loss1: 0.197021 Loss2: 1.347495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419436 Loss1: 0.050353 Loss2: 1.369082
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.534899 Loss1: 0.180179 Loss2: 1.354720
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465440 Loss1: 0.117629 Loss2: 1.347811
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416952 Loss1: 0.080836 Loss2: 1.336115
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445190 Loss1: 0.115946 Loss2: 1.329243
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400711 Loss1: 0.073243 Loss2: 1.327468
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.434786 Loss1: 0.577585 Loss2: 1.857201
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.814693 Loss1: 0.413724 Loss2: 1.400969
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.744261 Loss1: 0.299629 Loss2: 1.444632
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.661170 Loss1: 0.259606 Loss2: 1.401564
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546320 Loss1: 0.150518 Loss2: 1.395802
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.385880 Loss1: 0.511541 Loss2: 1.874340
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499339 Loss1: 0.112386 Loss2: 1.386953
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.712887 Loss1: 0.351255 Loss2: 1.361631
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451035 Loss1: 0.075023 Loss2: 1.376012
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684416 Loss1: 0.269067 Loss2: 1.415350
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424378 Loss1: 0.062679 Loss2: 1.361700
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.545974 Loss1: 0.188098 Loss2: 1.357875
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406764 Loss1: 0.046490 Loss2: 1.360274
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555569 Loss1: 0.195541 Loss2: 1.360028
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406431 Loss1: 0.053798 Loss2: 1.352633
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.592125 Loss1: 0.229304 Loss2: 1.362821
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.554602 Loss1: 0.180950 Loss2: 1.373652
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464423 Loss1: 0.099286 Loss2: 1.365137
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415632 Loss1: 0.067937 Loss2: 1.347695
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389381 Loss1: 0.049045 Loss2: 1.340336
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.493154 Loss1: 0.656989 Loss2: 1.836165
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.777525 Loss1: 0.426570 Loss2: 1.350954
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.689168 Loss1: 0.285303 Loss2: 1.403865
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573649 Loss1: 0.211888 Loss2: 1.361761
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.360259 Loss1: 0.537350 Loss2: 1.822909
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.678458 Loss1: 0.344780 Loss2: 1.333678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.576229 Loss1: 0.199470 Loss2: 1.376759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.504430 Loss1: 0.180522 Loss2: 1.323909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.480907 Loss1: 0.151510 Loss2: 1.329398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.432171 Loss1: 0.101808 Loss2: 1.330363 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.356285 Loss1: 0.046561 Loss2: 1.309725 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.323798 Loss1: 0.026158 Loss2: 1.297639 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.774467 Loss1: 0.404425 Loss2: 1.370042 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561386 Loss1: 0.202173 Loss2: 1.359213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.496420 Loss1: 0.134191 Loss2: 1.362229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.446433 Loss1: 0.094613 Loss2: 1.351820 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431575 Loss1: 0.089919 Loss2: 1.341656 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.406701 Loss1: 0.066204 Loss2: 1.340498 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397876 Loss1: 0.067551 Loss2: 1.330325 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400739 Loss1: 0.071159 Loss2: 1.329580 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387831 Loss1: 0.074104 Loss2: 1.313727 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.491048 Loss1: 0.600294 Loss2: 1.890754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.730257 Loss1: 0.287672 Loss2: 1.442585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.680071 Loss1: 0.283040 Loss2: 1.397032
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.594341 Loss1: 0.689924 Loss2: 1.904416
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574760 Loss1: 0.178388 Loss2: 1.396372
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.798775 Loss1: 0.433985 Loss2: 1.364791
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494491 Loss1: 0.110541 Loss2: 1.383950
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.703038 Loss1: 0.275221 Loss2: 1.427816
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564911 Loss1: 0.183798 Loss2: 1.381113
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491088 Loss1: 0.124399 Loss2: 1.366689
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.504852 Loss1: 0.137775 Loss2: 1.367077
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464083 Loss1: 0.086485 Loss2: 1.377598
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508905 Loss1: 0.135728 Loss2: 1.373177
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449472 Loss1: 0.083923 Loss2: 1.365549
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438979 Loss1: 0.077212 Loss2: 1.361767 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388502 Loss1: 0.043060 Loss2: 1.345442 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.327399 Loss1: 0.493011 Loss2: 1.834388 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599050 Loss1: 0.234001 Loss2: 1.365049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476115 Loss1: 0.131662 Loss2: 1.344453
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.441354 Loss1: 0.559184 Loss2: 1.882171
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.804305 Loss1: 0.417635 Loss2: 1.386670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.666761 Loss1: 0.225025 Loss2: 1.441736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.542005 Loss1: 0.157511 Loss2: 1.384493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505129 Loss1: 0.121821 Loss2: 1.383309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521290 Loss1: 0.131429 Loss2: 1.389861 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366614 Loss1: 0.056602 Loss2: 1.310012
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.481831 Loss1: 0.102723 Loss2: 1.379108
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470926 Loss1: 0.096009 Loss2: 1.374917
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447974 Loss1: 0.074474 Loss2: 1.373500
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456885 Loss1: 0.083316 Loss2: 1.373569
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.641737 Loss1: 0.706116 Loss2: 1.935620
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.738648 Loss1: 0.387560 Loss2: 1.351089
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.581905 Loss1: 0.214466 Loss2: 1.367439
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.485391 Loss1: 0.136758 Loss2: 1.348633
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.432016 Loss1: 0.626570 Loss2: 1.805445
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.441647 Loss1: 0.107446 Loss2: 1.334201 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420784 Loss1: 0.096107 Loss2: 1.324677 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.377063 Loss1: 0.056367 Loss2: 1.320696 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 06:11:59,436 | server.py:236 | fit_round 141 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371777 Loss1: 0.055790 Loss2: 1.315988 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359382 Loss1: 0.052803 Loss2: 1.306579 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431904 Loss1: 0.121155 Loss2: 1.310749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381122 Loss1: 0.081679 Loss2: 1.299442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.340376 Loss1: 0.045871 Loss2: 1.294505
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.459018 Loss1: 0.631565 Loss2: 1.827453
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.768871 Loss1: 0.417110 Loss2: 1.351762
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582284 Loss1: 0.180701 Loss2: 1.401583
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513061 Loss1: 0.164400 Loss2: 1.348661
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.465862 Loss1: 0.111730 Loss2: 1.354132
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.413760 Loss1: 0.623322 Loss2: 1.790438
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478641 Loss1: 0.135692 Loss2: 1.342948
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464979 Loss1: 0.117720 Loss2: 1.347259 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438188 Loss1: 0.091110 Loss2: 1.347077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398691 Loss1: 0.066446 Loss2: 1.332245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377371 Loss1: 0.046260 Loss2: 1.331111 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.420087 Loss1: 0.139395 Loss2: 1.280691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.361990 Loss1: 0.086698 Loss2: 1.275292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.331253 Loss1: 0.067182 Loss2: 1.264071
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.460980 Loss1: 0.584377 Loss2: 1.876603
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.726049 Loss1: 0.353855 Loss2: 1.372194
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.626435 Loss1: 0.219519 Loss2: 1.406916
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513187 Loss1: 0.154314 Loss2: 1.358873
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.454671 Loss1: 0.098355 Loss2: 1.356315
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.309757 Loss1: 0.492370 Loss2: 1.817387
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455714 Loss1: 0.106863 Loss2: 1.348851
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.457135 Loss1: 0.110483 Loss2: 1.346652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.712405 Loss1: 0.280242 Loss2: 1.432162
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439889 Loss1: 0.096689 Loss2: 1.343200
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.622767 Loss1: 0.242691 Loss2: 1.380076
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397285 Loss1: 0.060667 Loss2: 1.336618
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507059 Loss1: 0.123058 Loss2: 1.384001
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396620 Loss1: 0.068468 Loss2: 1.328152
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427490 Loss1: 0.064801 Loss2: 1.362689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401668 Loss1: 0.052665 Loss2: 1.349004 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 06:11:59,436][flwr][DEBUG] - fit_round 141 received 50 results and 0 failures
-INFO flwr 2023-10-12 06:12:40,690 | server.py:125 | fit progress: (141, 2.2251683026076123, {'accuracy': 0.5956}, 325268.468418965)
->> Test accuracy: 0.595600
-[2023-10-12 06:12:40,690][flwr][INFO] - fit progress: (141, 2.2251683026076123, {'accuracy': 0.5956}, 325268.468418965)
-DEBUG flwr 2023-10-12 06:12:40,690 | server.py:173 | evaluate_round 141: strategy sampled 50 clients (out of 50)
-[2023-10-12 06:12:40,690][flwr][DEBUG] - evaluate_round 141: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 06:21:43,473 | server.py:187 | evaluate_round 141 received 50 results and 0 failures
-[2023-10-12 06:21:43,473][flwr][DEBUG] - evaluate_round 141 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 06:21:43,474 | server.py:222 | fit_round 142: strategy sampled 50 clients (out of 50)
-[2023-10-12 06:21:43,474][flwr][DEBUG] - fit_round 142: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.373143 Loss1: 0.550470 Loss2: 1.822673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.636669 Loss1: 0.232665 Loss2: 1.404004 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522729 Loss1: 0.152452 Loss2: 1.370277
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.357071 Loss1: 0.559454 Loss2: 1.797617
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.485852 Loss1: 0.114785 Loss2: 1.371067
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.687208 Loss1: 0.329198 Loss2: 1.358010
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470025 Loss1: 0.108317 Loss2: 1.361709
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.637647 Loss1: 0.250825 Loss2: 1.386822
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488679 Loss1: 0.126142 Loss2: 1.362538
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521148 Loss1: 0.166737 Loss2: 1.354411
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454769 Loss1: 0.097775 Loss2: 1.356994
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.486828 Loss1: 0.131757 Loss2: 1.355071
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429673 Loss1: 0.073448 Loss2: 1.356225
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446356 Loss1: 0.098468 Loss2: 1.347888
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431025 Loss1: 0.083157 Loss2: 1.347868
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473912 Loss1: 0.133524 Loss2: 1.340388
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432952 Loss1: 0.082637 Loss2: 1.350314
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.411826 Loss1: 0.072325 Loss2: 1.339501
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.371383 Loss1: 0.034296 Loss2: 1.337086
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.627747 Loss1: 0.715366 Loss2: 1.912381
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.961996 Loss1: 0.509981 Loss2: 1.452014
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.823368 Loss1: 0.318749 Loss2: 1.504620
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.634780 Loss1: 0.196559 Loss2: 1.438221
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.500149 Loss1: 0.622540 Loss2: 1.877609
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.719919 Loss1: 0.363545 Loss2: 1.356375
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.607794 Loss1: 0.173225 Loss2: 1.434569
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.649128 Loss1: 0.251429 Loss2: 1.397700
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.601269 Loss1: 0.174397 Loss2: 1.426871
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.544959 Loss1: 0.178611 Loss2: 1.366347
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531463 Loss1: 0.109389 Loss2: 1.422074
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.492416 Loss1: 0.081288 Loss2: 1.411128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456601 Loss1: 0.051456 Loss2: 1.405145 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453804 Loss1: 0.061864 Loss2: 1.391940 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381164 Loss1: 0.048511 Loss2: 1.332653 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.457006 Loss1: 0.640789 Loss2: 1.816217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.632329 Loss1: 0.249748 Loss2: 1.382581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467464 Loss1: 0.140231 Loss2: 1.327233
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.576761 Loss1: 0.645360 Loss2: 1.931401
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.467832 Loss1: 0.136615 Loss2: 1.331217
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.844583 Loss1: 0.430863 Loss2: 1.413720
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458199 Loss1: 0.122178 Loss2: 1.336020
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.731178 Loss1: 0.285670 Loss2: 1.445508
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433845 Loss1: 0.106464 Loss2: 1.327381
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.643908 Loss1: 0.241966 Loss2: 1.401942
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436449 Loss1: 0.114289 Loss2: 1.322160
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.583600 Loss1: 0.172396 Loss2: 1.411204
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.382714 Loss1: 0.064615 Loss2: 1.318100
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504222 Loss1: 0.108915 Loss2: 1.395307
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372766 Loss1: 0.062970 Loss2: 1.309795
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484147 Loss1: 0.092351 Loss2: 1.391796
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460591 Loss1: 0.075236 Loss2: 1.385356
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419215 Loss1: 0.050385 Loss2: 1.368830
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421398 Loss1: 0.059301 Loss2: 1.362097
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.452315 Loss1: 0.621245 Loss2: 1.831069
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.674677 Loss1: 0.331620 Loss2: 1.343056
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.663561 Loss1: 0.279365 Loss2: 1.384196
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558291 Loss1: 0.210900 Loss2: 1.347391
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.322793 Loss1: 0.499531 Loss2: 1.823262
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.709553 Loss1: 0.340211 Loss2: 1.369342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590967 Loss1: 0.187200 Loss2: 1.403766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559045 Loss1: 0.205501 Loss2: 1.353544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.533647 Loss1: 0.159076 Loss2: 1.374571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446112 Loss1: 0.093696 Loss2: 1.352417 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412684 Loss1: 0.077407 Loss2: 1.335277 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399811 Loss1: 0.066892 Loss2: 1.332920 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.735766 Loss1: 0.350790 Loss2: 1.384977 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563365 Loss1: 0.182834 Loss2: 1.380532 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472869 Loss1: 0.106481 Loss2: 1.366387
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.369992 Loss1: 0.493024 Loss2: 1.876968
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.464198 Loss1: 0.095476 Loss2: 1.368722
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.738604 Loss1: 0.343447 Loss2: 1.395157
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450612 Loss1: 0.090316 Loss2: 1.360296
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.704796 Loss1: 0.266634 Loss2: 1.438162
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.585385 Loss1: 0.189067 Loss2: 1.396317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527075 Loss1: 0.136421 Loss2: 1.390654 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498096 Loss1: 0.112481 Loss2: 1.385616 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458327 Loss1: 0.079307 Loss2: 1.379021 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431254 Loss1: 0.063523 Loss2: 1.367730 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.792591 Loss1: 0.402788 Loss2: 1.389802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.618013 Loss1: 0.222467 Loss2: 1.395545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.534752 Loss1: 0.640734 Loss2: 1.894018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.746919 Loss1: 0.366039 Loss2: 1.380880 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.675276 Loss1: 0.247285 Loss2: 1.427991 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537703 Loss1: 0.160329 Loss2: 1.377375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506903 Loss1: 0.134364 Loss2: 1.372539 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438817 Loss1: 0.090508 Loss2: 1.348308 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434281 Loss1: 0.082155 Loss2: 1.352126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440125 Loss1: 0.087401 Loss2: 1.352724
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.238077 Loss1: 0.464031 Loss2: 1.774046
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.640705 Loss1: 0.315957 Loss2: 1.324748
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.569368 Loss1: 0.211813 Loss2: 1.357555
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.475228 Loss1: 0.147928 Loss2: 1.327300
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.426810 Loss1: 0.096958 Loss2: 1.329851
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.623647 Loss1: 0.714912 Loss2: 1.908736
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.752890 Loss1: 0.377939 Loss2: 1.374951
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.435219 Loss1: 0.112745 Loss2: 1.322475
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435399 Loss1: 0.113206 Loss2: 1.322193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425863 Loss1: 0.104750 Loss2: 1.321113 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381540 Loss1: 0.062064 Loss2: 1.319476 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.511321 Loss1: 0.145398 Loss2: 1.365923 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427944 Loss1: 0.078659 Loss2: 1.349286 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.403246 Loss1: 0.601926 Loss2: 1.801321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.602069 Loss1: 0.236208 Loss2: 1.365861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.487079 Loss1: 0.153356 Loss2: 1.333723
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.387701 Loss1: 0.518796 Loss2: 1.868905
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470948 Loss1: 0.143997 Loss2: 1.326951
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.735691 Loss1: 0.361423 Loss2: 1.374267
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.420704 Loss1: 0.102351 Loss2: 1.318352
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.607561 Loss1: 0.202177 Loss2: 1.405384
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402782 Loss1: 0.087719 Loss2: 1.315063
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563086 Loss1: 0.184811 Loss2: 1.378275
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389203 Loss1: 0.078240 Loss2: 1.310963
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505055 Loss1: 0.138675 Loss2: 1.366380
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.368627 Loss1: 0.060971 Loss2: 1.307656
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.544854 Loss1: 0.171069 Loss2: 1.373785
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359368 Loss1: 0.058668 Loss2: 1.300700
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488296 Loss1: 0.114943 Loss2: 1.373352
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486094 Loss1: 0.111131 Loss2: 1.374964
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463384 Loss1: 0.103556 Loss2: 1.359828
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422548 Loss1: 0.066845 Loss2: 1.355703
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.425697 Loss1: 0.582809 Loss2: 1.842888
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.783693 Loss1: 0.428111 Loss2: 1.355582
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.709078 Loss1: 0.281513 Loss2: 1.427565
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581629 Loss1: 0.218056 Loss2: 1.363574
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.442773 Loss1: 0.576171 Loss2: 1.866602
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.766868 Loss1: 0.402736 Loss2: 1.364132 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.703800 Loss1: 0.283831 Loss2: 1.419969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.623563 Loss1: 0.253458 Loss2: 1.370105 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.504352 Loss1: 0.127571 Loss2: 1.376780 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479692 Loss1: 0.119279 Loss2: 1.360413 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417689 Loss1: 0.083187 Loss2: 1.334503
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.442128 Loss1: 0.080851 Loss2: 1.361277
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417746 Loss1: 0.068050 Loss2: 1.349695
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396606 Loss1: 0.056756 Loss2: 1.339850
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368262 Loss1: 0.031640 Loss2: 1.336623
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.401750 Loss1: 0.557688 Loss2: 1.844062
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.796362 Loss1: 0.438888 Loss2: 1.357474
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655014 Loss1: 0.251258 Loss2: 1.403756
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586291 Loss1: 0.236372 Loss2: 1.349919
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.495083 Loss1: 0.717440 Loss2: 1.777643
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.511344 Loss1: 0.165743 Loss2: 1.345601
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653173 Loss1: 0.334481 Loss2: 1.318693
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451057 Loss1: 0.120456 Loss2: 1.330601
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636166 Loss1: 0.300785 Loss2: 1.335381
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.392404 Loss1: 0.067629 Loss2: 1.324774
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530678 Loss1: 0.212364 Loss2: 1.318314
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.368903 Loss1: 0.046498 Loss2: 1.322405
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475442 Loss1: 0.153673 Loss2: 1.321769
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.346524 Loss1: 0.037269 Loss2: 1.309255
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.415069 Loss1: 0.115930 Loss2: 1.299139
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.324625 Loss1: 0.026630 Loss2: 1.297994
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.366010 Loss1: 0.071089 Loss2: 1.294920
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.343197 Loss1: 0.052469 Loss2: 1.290728
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.333741 Loss1: 0.054625 Loss2: 1.279117
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.316824 Loss1: 0.040641 Loss2: 1.276183
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.410453 Loss1: 0.633082 Loss2: 1.777371
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.771858 Loss1: 0.433830 Loss2: 1.338028
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.626134 Loss1: 0.251470 Loss2: 1.374665
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513962 Loss1: 0.180838 Loss2: 1.333124
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.416090 Loss1: 0.590258 Loss2: 1.825832
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.723856 Loss1: 0.383760 Loss2: 1.340097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.619202 Loss1: 0.241531 Loss2: 1.377671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563549 Loss1: 0.210083 Loss2: 1.353467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515034 Loss1: 0.168358 Loss2: 1.346676 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455313 Loss1: 0.114627 Loss2: 1.340686 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.405846 Loss1: 0.076747 Loss2: 1.329099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371563 Loss1: 0.045476 Loss2: 1.326087 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.594151 Loss1: 0.698872 Loss2: 1.895279 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.680169 Loss1: 0.242525 Loss2: 1.437644 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.382400 Loss1: 0.567259 Loss2: 1.815141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.613364 Loss1: 0.279334 Loss2: 1.334029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.623449 Loss1: 0.257774 Loss2: 1.365676 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534109 Loss1: 0.193719 Loss2: 1.340390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.458218 Loss1: 0.130795 Loss2: 1.327423 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421716 Loss1: 0.087216 Loss2: 1.334500 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395440 Loss1: 0.073318 Loss2: 1.322122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374366 Loss1: 0.056546 Loss2: 1.317820 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.436497 Loss1: 0.584022 Loss2: 1.852474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.649162 Loss1: 0.246724 Loss2: 1.402437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523129 Loss1: 0.167151 Loss2: 1.355978
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.553959 Loss1: 0.712624 Loss2: 1.841335
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.840763 Loss1: 0.470870 Loss2: 1.369894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.766621 Loss1: 0.334418 Loss2: 1.432203 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.625823 Loss1: 0.250107 Loss2: 1.375716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569060 Loss1: 0.193729 Loss2: 1.375332 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.544972 Loss1: 0.170265 Loss2: 1.374707 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486384 Loss1: 0.128268 Loss2: 1.358116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454939 Loss1: 0.101686 Loss2: 1.353253 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.319894 Loss1: 0.443533 Loss2: 1.876361 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.676125 Loss1: 0.263027 Loss2: 1.413098 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562722 Loss1: 0.173913 Loss2: 1.388810
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.442189 Loss1: 0.593118 Loss2: 1.849071
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.777338 Loss1: 0.403865 Loss2: 1.373473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601799 Loss1: 0.193351 Loss2: 1.408448 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535471 Loss1: 0.171470 Loss2: 1.364002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.439529 Loss1: 0.082054 Loss2: 1.357475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431817 Loss1: 0.085725 Loss2: 1.346092 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413940 Loss1: 0.057697 Loss2: 1.356243
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423205 Loss1: 0.077944 Loss2: 1.345261
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.387627 Loss1: 0.045880 Loss2: 1.341747
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371337 Loss1: 0.040558 Loss2: 1.330779
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.363452 Loss1: 0.036738 Loss2: 1.326713
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.652062 Loss1: 0.711245 Loss2: 1.940817
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.876201 Loss1: 0.522014 Loss2: 1.354187
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656625 Loss1: 0.233783 Loss2: 1.422843
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536278 Loss1: 0.176790 Loss2: 1.359488
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513859 Loss1: 0.158765 Loss2: 1.355094
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471081 Loss1: 0.110735 Loss2: 1.360346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436404 Loss1: 0.087218 Loss2: 1.349186 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.399374 Loss1: 0.055755 Loss2: 1.343619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374620 Loss1: 0.041808 Loss2: 1.332811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389219 Loss1: 0.061808 Loss2: 1.327411 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986779
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447972 Loss1: 0.097680 Loss2: 1.350292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418682 Loss1: 0.076447 Loss2: 1.342235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411254 Loss1: 0.072220 Loss2: 1.339034
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.576999 Loss1: 0.696795 Loss2: 1.880205
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.735835 Loss1: 0.379359 Loss2: 1.356476
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.575088 Loss1: 0.200956 Loss2: 1.374132
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526223 Loss1: 0.179618 Loss2: 1.346605
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490074 Loss1: 0.140592 Loss2: 1.349482
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.384686 Loss1: 0.536133 Loss2: 1.848553
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459855 Loss1: 0.122336 Loss2: 1.337519
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442315 Loss1: 0.108565 Loss2: 1.333750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449250 Loss1: 0.124820 Loss2: 1.324429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408656 Loss1: 0.078929 Loss2: 1.329728 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385444 Loss1: 0.061605 Loss2: 1.323839 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435684 Loss1: 0.102996 Loss2: 1.332689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426622 Loss1: 0.099195 Loss2: 1.327427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424935 Loss1: 0.090390 Loss2: 1.334545
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.521059 Loss1: 0.611818 Loss2: 1.909241
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.855646 Loss1: 0.438112 Loss2: 1.417534
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.808367 Loss1: 0.318706 Loss2: 1.489661
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.672713 Loss1: 0.246021 Loss2: 1.426693
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.622681 Loss1: 0.185666 Loss2: 1.437015
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.467338 Loss1: 0.622232 Loss2: 1.845106
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.770216 Loss1: 0.399089 Loss2: 1.371127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.662883 Loss1: 0.257915 Loss2: 1.404968 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567474 Loss1: 0.197769 Loss2: 1.369705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493486 Loss1: 0.129469 Loss2: 1.364017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470326 Loss1: 0.114311 Loss2: 1.356015 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416218 Loss1: 0.070947 Loss2: 1.345271 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394442 Loss1: 0.059801 Loss2: 1.334640 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.795088 Loss1: 0.436614 Loss2: 1.358474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556239 Loss1: 0.194150 Loss2: 1.362089 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.652157 Loss1: 0.811779 Loss2: 1.840378 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.812025 Loss1: 0.467042 Loss2: 1.344983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.704911 Loss1: 0.331330 Loss2: 1.373581 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557595 Loss1: 0.229566 Loss2: 1.328029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.416436 Loss1: 0.088760 Loss2: 1.327676 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397002 Loss1: 0.068884 Loss2: 1.328118
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.417866 Loss1: 0.111775 Loss2: 1.306090
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.391548 Loss1: 0.089575 Loss2: 1.301972
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372359 Loss1: 0.070297 Loss2: 1.302062
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.339960 Loss1: 0.047017 Loss2: 1.292943
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365877 Loss1: 0.079266 Loss2: 1.286611
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.430476 Loss1: 0.580373 Loss2: 1.850103
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.777209 Loss1: 0.389221 Loss2: 1.387987
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.738286 Loss1: 0.323492 Loss2: 1.414793
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.600821 Loss1: 0.213715 Loss2: 1.387106
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.564707 Loss1: 0.650629 Loss2: 1.914078
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.828854 Loss1: 0.388355 Loss2: 1.440499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.735089 Loss1: 0.263135 Loss2: 1.471954 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.695735 Loss1: 0.274966 Loss2: 1.420769 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.599510 Loss1: 0.163971 Loss2: 1.435539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.541067 Loss1: 0.120332 Loss2: 1.420735 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419135 Loss1: 0.063307 Loss2: 1.355828
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509923 Loss1: 0.102919 Loss2: 1.407004
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467617 Loss1: 0.062855 Loss2: 1.404762
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466860 Loss1: 0.068589 Loss2: 1.398271
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435206 Loss1: 0.047843 Loss2: 1.387364
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.436034 Loss1: 0.569947 Loss2: 1.866087
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.767772 Loss1: 0.378008 Loss2: 1.389764
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.691940 Loss1: 0.257826 Loss2: 1.434115
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601359 Loss1: 0.211620 Loss2: 1.389740
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.360855 Loss1: 0.564177 Loss2: 1.796678
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.717398 Loss1: 0.353581 Loss2: 1.363817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.609932 Loss1: 0.225322 Loss2: 1.384610 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526672 Loss1: 0.173007 Loss2: 1.353665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522168 Loss1: 0.165251 Loss2: 1.356918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494390 Loss1: 0.136663 Loss2: 1.357726 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459469 Loss1: 0.109627 Loss2: 1.349841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.393230 Loss1: 0.059405 Loss2: 1.333825 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.596759 Loss1: 0.740306 Loss2: 1.856452 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.639046 Loss1: 0.220126 Loss2: 1.418921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.578167 Loss1: 0.716456 Loss2: 1.861711 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.725366 Loss1: 0.339377 Loss2: 1.385989 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670468 Loss1: 0.251643 Loss2: 1.418826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599444 Loss1: 0.219049 Loss2: 1.380396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519782 Loss1: 0.138870 Loss2: 1.380911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471901 Loss1: 0.099154 Loss2: 1.372747 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423982 Loss1: 0.068830 Loss2: 1.355151 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410577 Loss1: 0.063499 Loss2: 1.347078 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.726847 Loss1: 0.356327 Loss2: 1.370520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.567893 Loss1: 0.189293 Loss2: 1.378600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499161 Loss1: 0.129577 Loss2: 1.369584 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462799 Loss1: 0.102144 Loss2: 1.360656 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455267 Loss1: 0.096211 Loss2: 1.359055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429743 Loss1: 0.085823 Loss2: 1.343920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407608 Loss1: 0.067223 Loss2: 1.340385 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434079 Loss1: 0.091317 Loss2: 1.342762 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.475142 Loss1: 0.089210 Loss2: 1.385932 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.263007 Loss1: 0.412765 Loss2: 1.850242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.607251 Loss1: 0.197581 Loss2: 1.409670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.609744 Loss1: 0.228964 Loss2: 1.380780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.622780 Loss1: 0.206288 Loss2: 1.416492 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.577345 Loss1: 0.183908 Loss2: 1.393436 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.521527 Loss1: 0.116822 Loss2: 1.404705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488975 Loss1: 0.110178 Loss2: 1.378796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443322 Loss1: 0.066999 Loss2: 1.376323 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451843 Loss1: 0.083075 Loss2: 1.368768 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.393616 Loss1: 0.051405 Loss2: 1.342210 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.367902 Loss1: 0.525899 Loss2: 1.842003 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.628662 Loss1: 0.231544 Loss2: 1.397118 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 06:50:21,139 | server.py:236 | fit_round 142 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.718213 Loss1: 0.747387 Loss2: 1.970826
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573489 Loss1: 0.214800 Loss2: 1.358689
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.816808 Loss1: 0.457280 Loss2: 1.359528
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.545135 Loss1: 0.184136 Loss2: 1.361000
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465295 Loss1: 0.110961 Loss2: 1.354334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440321 Loss1: 0.094342 Loss2: 1.345978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.483457 Loss1: 0.118714 Loss2: 1.364743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427414 Loss1: 0.072836 Loss2: 1.354578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.403901 Loss1: 0.057962 Loss2: 1.345939 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385577 Loss1: 0.047793 Loss2: 1.337784 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.578686 Loss1: 0.691917 Loss2: 1.886769
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.836442 Loss1: 0.468989 Loss2: 1.367453
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.705561 Loss1: 0.296374 Loss2: 1.409187
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591489 Loss1: 0.222045 Loss2: 1.369444
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.588640 Loss1: 0.674603 Loss2: 1.914037
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.748963 Loss1: 0.352593 Loss2: 1.396369 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.716944 Loss1: 0.286597 Loss2: 1.430347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605245 Loss1: 0.201982 Loss2: 1.403263 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506517 Loss1: 0.118606 Loss2: 1.387911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467284 Loss1: 0.095294 Loss2: 1.371989 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407660 Loss1: 0.050406 Loss2: 1.357254 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411853 Loss1: 0.062786 Loss2: 1.349066 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 06:50:21,139][flwr][DEBUG] - fit_round 142 received 50 results and 0 failures
-INFO flwr 2023-10-12 06:51:03,693 | server.py:125 | fit progress: (142, 2.223556954068498, {'accuracy': 0.5928}, 327571.471800928)
->> Test accuracy: 0.592800
-[2023-10-12 06:51:03,693][flwr][INFO] - fit progress: (142, 2.223556954068498, {'accuracy': 0.5928}, 327571.471800928)
-DEBUG flwr 2023-10-12 06:51:03,694 | server.py:173 | evaluate_round 142: strategy sampled 50 clients (out of 50)
-[2023-10-12 06:51:03,694][flwr][DEBUG] - evaluate_round 142: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 07:00:09,959 | server.py:187 | evaluate_round 142 received 50 results and 0 failures
-[2023-10-12 07:00:09,959][flwr][DEBUG] - evaluate_round 142 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 07:00:09,959 | server.py:222 | fit_round 143: strategy sampled 50 clients (out of 50)
-[2023-10-12 07:00:09,959][flwr][DEBUG] - fit_round 143: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.482705 Loss1: 0.590210 Loss2: 1.892495
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.810926 Loss1: 0.396204 Loss2: 1.414723
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.686284 Loss1: 0.258978 Loss2: 1.427306
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.612892 Loss1: 0.214761 Loss2: 1.398131
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.469528 Loss1: 0.640543 Loss2: 1.828986
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.539264 Loss1: 0.136501 Loss2: 1.402763
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.719088 Loss1: 0.365228 Loss2: 1.353861
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494324 Loss1: 0.104669 Loss2: 1.389655
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.681203 Loss1: 0.285294 Loss2: 1.395909
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500489 Loss1: 0.120524 Loss2: 1.379964
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.560093 Loss1: 0.210953 Loss2: 1.349140
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462390 Loss1: 0.079498 Loss2: 1.382892
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.532445 Loss1: 0.178001 Loss2: 1.354443
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441155 Loss1: 0.060136 Loss2: 1.381019
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490856 Loss1: 0.138379 Loss2: 1.352478
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454794 Loss1: 0.079920 Loss2: 1.374874
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465365 Loss1: 0.123170 Loss2: 1.342194
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437796 Loss1: 0.093544 Loss2: 1.344252
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425424 Loss1: 0.090527 Loss2: 1.334898
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406543 Loss1: 0.079419 Loss2: 1.327124
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.450474 Loss1: 0.599791 Loss2: 1.850682
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.715450 Loss1: 0.347228 Loss2: 1.368222
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634918 Loss1: 0.239160 Loss2: 1.395758
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.530863 Loss1: 0.166608 Loss2: 1.364255
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.442645 Loss1: 0.557353 Loss2: 1.885292
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.520388 Loss1: 0.154171 Loss2: 1.366218
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659083 Loss1: 0.281116 Loss2: 1.377967
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491426 Loss1: 0.125922 Loss2: 1.365504
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.643954 Loss1: 0.230698 Loss2: 1.413255
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478846 Loss1: 0.125315 Loss2: 1.353531
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523197 Loss1: 0.145311 Loss2: 1.377886
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.526652 Loss1: 0.161640 Loss2: 1.365011
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557591 Loss1: 0.182226 Loss2: 1.375365
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.479524 Loss1: 0.117401 Loss2: 1.362123
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556788 Loss1: 0.177843 Loss2: 1.378945
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450767 Loss1: 0.095132 Loss2: 1.355635
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.498524 Loss1: 0.124727 Loss2: 1.373797
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470343 Loss1: 0.105386 Loss2: 1.364957
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458968 Loss1: 0.091527 Loss2: 1.367442
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417044 Loss1: 0.056497 Loss2: 1.360547
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.354854 Loss1: 0.569593 Loss2: 1.785261
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.613883 Loss1: 0.279038 Loss2: 1.334845
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.566235 Loss1: 0.206389 Loss2: 1.359846
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.637675 Loss1: 0.724037 Loss2: 1.913638
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484089 Loss1: 0.157350 Loss2: 1.326739
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472424 Loss1: 0.142504 Loss2: 1.329920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433188 Loss1: 0.104282 Loss2: 1.328906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.369306 Loss1: 0.055501 Loss2: 1.313805 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484279 Loss1: 0.115653 Loss2: 1.368626 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.421799 Loss1: 0.057980 Loss2: 1.363818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430260 Loss1: 0.074353 Loss2: 1.355906 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401776 Loss1: 0.063164 Loss2: 1.338613 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987981
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.629956 Loss1: 0.752873 Loss2: 1.877083
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784021 Loss1: 0.440435 Loss2: 1.343586
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.652821 Loss1: 0.273091 Loss2: 1.379731
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490985 Loss1: 0.140006 Loss2: 1.350979
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.464990 Loss1: 0.671516 Loss2: 1.793474
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.765434 Loss1: 0.435058 Loss2: 1.330376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.567311 Loss1: 0.220178 Loss2: 1.347133 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523474 Loss1: 0.213689 Loss2: 1.309785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.462947 Loss1: 0.140568 Loss2: 1.322379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450820 Loss1: 0.139489 Loss2: 1.311331 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995536
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427863 Loss1: 0.111212 Loss2: 1.316651 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370014 Loss1: 0.077187 Loss2: 1.292826 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.927666 Loss1: 0.505497 Loss2: 1.422169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.614265 Loss1: 0.226310 Loss2: 1.387955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561536 Loss1: 0.174630 Loss2: 1.386906
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.589615 Loss1: 0.638859 Loss2: 1.950755
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.877340 Loss1: 0.414810 Loss2: 1.462530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.784528 Loss1: 0.290673 Loss2: 1.493855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.680503 Loss1: 0.234841 Loss2: 1.445662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.618268 Loss1: 0.173174 Loss2: 1.445094 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979911
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.551577 Loss1: 0.115841 Loss2: 1.435736 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.472953 Loss1: 0.056097 Loss2: 1.416855 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470419 Loss1: 0.055516 Loss2: 1.414903
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.467796 Loss1: 0.656276 Loss2: 1.811520
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.790769 Loss1: 0.448514 Loss2: 1.342255
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.665526 Loss1: 0.267636 Loss2: 1.397890
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.515338 Loss1: 0.179037 Loss2: 1.336301
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535229 Loss1: 0.193800 Loss2: 1.341429
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.345598 Loss1: 0.510140 Loss2: 1.835458
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.720873 Loss1: 0.383766 Loss2: 1.337108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.716956 Loss1: 0.325334 Loss2: 1.391622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550605 Loss1: 0.200012 Loss2: 1.350593 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.459421 Loss1: 0.118509 Loss2: 1.340912 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.438108 Loss1: 0.101020 Loss2: 1.337088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441750 Loss1: 0.117048 Loss2: 1.324702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397056 Loss1: 0.075253 Loss2: 1.321803 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.855626 Loss1: 0.447825 Loss2: 1.407801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.514846 Loss1: 0.125514 Loss2: 1.389333 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490317 Loss1: 0.114274 Loss2: 1.376044
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.478160 Loss1: 0.621413 Loss2: 1.856747
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.814712 Loss1: 0.434442 Loss2: 1.380270 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.678011 Loss1: 0.242417 Loss2: 1.435595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561142 Loss1: 0.190104 Loss2: 1.371038 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556324 Loss1: 0.170410 Loss2: 1.385913 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435629 Loss1: 0.070155 Loss2: 1.365474
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493698 Loss1: 0.117016 Loss2: 1.376681
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455180 Loss1: 0.085078 Loss2: 1.370102
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443554 Loss1: 0.088237 Loss2: 1.355317
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410971 Loss1: 0.055090 Loss2: 1.355881
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414208 Loss1: 0.065362 Loss2: 1.348846
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.330902 Loss1: 0.486177 Loss2: 1.844725
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.733776 Loss1: 0.380347 Loss2: 1.353429
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.651127 Loss1: 0.263582 Loss2: 1.387545
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511464 Loss1: 0.156496 Loss2: 1.354968
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.447798 Loss1: 0.106178 Loss2: 1.341619
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465011 Loss1: 0.125569 Loss2: 1.339442 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484549 Loss1: 0.142062 Loss2: 1.342486 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426906 Loss1: 0.087196 Loss2: 1.339709 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408804 Loss1: 0.078514 Loss2: 1.330291 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380744 Loss1: 0.051319 Loss2: 1.329425 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428956 Loss1: 0.098976 Loss2: 1.329980 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395453 Loss1: 0.078701 Loss2: 1.316752 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.716811 Loss1: 0.376955 Loss2: 1.339856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497834 Loss1: 0.169523 Loss2: 1.328311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507577 Loss1: 0.175847 Loss2: 1.331730
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.290524 Loss1: 0.430347 Loss2: 1.860177
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.679285 Loss1: 0.283796 Loss2: 1.395490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.603269 Loss1: 0.195576 Loss2: 1.407693 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.558617 Loss1: 0.169075 Loss2: 1.389542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522862 Loss1: 0.130807 Loss2: 1.392054 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484635 Loss1: 0.101884 Loss2: 1.382751 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447322 Loss1: 0.070850 Loss2: 1.376472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408821 Loss1: 0.041788 Loss2: 1.367033 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997243
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.641077 Loss1: 0.239438 Loss2: 1.401639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.509356 Loss1: 0.152273 Loss2: 1.357083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.481585 Loss1: 0.639075 Loss2: 1.842510 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.746439 Loss1: 0.386327 Loss2: 1.360112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.651466 Loss1: 0.257560 Loss2: 1.393906 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565765 Loss1: 0.209457 Loss2: 1.356308 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472901 Loss1: 0.131483 Loss2: 1.341418 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.395115 Loss1: 0.069070 Loss2: 1.326045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408454 Loss1: 0.081607 Loss2: 1.326847
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.540601 Loss1: 0.657792 Loss2: 1.882809
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.447783 Loss1: 0.113092 Loss2: 1.334691
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.816880 Loss1: 0.378238 Loss2: 1.438642
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.712474 Loss1: 0.262667 Loss2: 1.449807
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575357 Loss1: 0.151119 Loss2: 1.424238
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.552655 Loss1: 0.135807 Loss2: 1.416849
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481684 Loss1: 0.080903 Loss2: 1.400781
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.574880 Loss1: 0.618225 Loss2: 1.956655
-(DefaultActor pid=3764) Epoch: 1 Loss: 2.022330 Loss1: 0.555320 Loss2: 1.467009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484214 Loss1: 0.086963 Loss2: 1.397251
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.875459 Loss1: 0.350867 Loss2: 1.524592
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438085 Loss1: 0.045550 Loss2: 1.392535
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.788514 Loss1: 0.318247 Loss2: 1.470267
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425123 Loss1: 0.043201 Loss2: 1.381922
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.680161 Loss1: 0.186716 Loss2: 1.493445
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.636553 Loss1: 0.162703 Loss2: 1.473850
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.613017 Loss1: 0.156235 Loss2: 1.456782
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.619123 Loss1: 0.159695 Loss2: 1.459427
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.563392 Loss1: 0.107947 Loss2: 1.455445
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.450959 Loss1: 0.585550 Loss2: 1.865408
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.549007 Loss1: 0.098334 Loss2: 1.450673
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.587793 Loss1: 0.194698 Loss2: 1.393095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.481296 Loss1: 0.130087 Loss2: 1.351209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.495424 Loss1: 0.146805 Loss2: 1.348618
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.412457 Loss1: 0.588636 Loss2: 1.823821
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486376 Loss1: 0.122477 Loss2: 1.363899
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.739325 Loss1: 0.359301 Loss2: 1.380024
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477438 Loss1: 0.128700 Loss2: 1.348739
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.758054 Loss1: 0.337935 Loss2: 1.420119
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430399 Loss1: 0.085102 Loss2: 1.345297
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.630512 Loss1: 0.246866 Loss2: 1.383646
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411552 Loss1: 0.076479 Loss2: 1.335073
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.517838 Loss1: 0.143511 Loss2: 1.374328
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461894 Loss1: 0.108017 Loss2: 1.353877
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434444 Loss1: 0.079638 Loss2: 1.354806
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397359 Loss1: 0.053731 Loss2: 1.343628
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399598 Loss1: 0.059959 Loss2: 1.339639
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.334866 Loss1: 0.490305 Loss2: 1.844562
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.711146 Loss1: 0.362371 Loss2: 1.348775 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.662119 Loss1: 0.295811 Loss2: 1.366308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511117 Loss1: 0.153444 Loss2: 1.357673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470957 Loss1: 0.118276 Loss2: 1.352681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446422 Loss1: 0.097618 Loss2: 1.348804 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411251 Loss1: 0.069593 Loss2: 1.341659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406531 Loss1: 0.070822 Loss2: 1.335709 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.598871 Loss1: 0.193543 Loss2: 1.405328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.474263 Loss1: 0.082920 Loss2: 1.391344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.600256 Loss1: 0.714870 Loss2: 1.885386
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.471577 Loss1: 0.086600 Loss2: 1.384977
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751717 Loss1: 0.399890 Loss2: 1.351827
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.479978 Loss1: 0.094236 Loss2: 1.385743
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534937 Loss1: 0.184919 Loss2: 1.350018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.486738 Loss1: 0.133260 Loss2: 1.353478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.413111 Loss1: 0.596483 Loss2: 1.816628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.697519 Loss1: 0.340953 Loss2: 1.356566 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601975 Loss1: 0.227019 Loss2: 1.374955 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505129 Loss1: 0.155391 Loss2: 1.349738 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453024 Loss1: 0.115279 Loss2: 1.337745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413637 Loss1: 0.080652 Loss2: 1.332984
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.588479 Loss1: 0.674276 Loss2: 1.914203
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.821231 Loss1: 0.394415 Loss2: 1.426817 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.713486 Loss1: 0.266393 Loss2: 1.447094 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551603 Loss1: 0.146771 Loss2: 1.404832 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481967 Loss1: 0.095911 Loss2: 1.386056 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456015 Loss1: 0.078808 Loss2: 1.377207 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437602 Loss1: 0.066115 Loss2: 1.371486 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405534 Loss1: 0.038849 Loss2: 1.366685 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502565 Loss1: 0.136492 Loss2: 1.366073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426883 Loss1: 0.069671 Loss2: 1.357212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418364 Loss1: 0.069942 Loss2: 1.348422
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.636045 Loss1: 0.639568 Loss2: 1.996476
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.795236 Loss1: 0.428620 Loss2: 1.366616
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392629 Loss1: 0.047987 Loss2: 1.344642
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.646546 Loss1: 0.263013 Loss2: 1.383534
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376816 Loss1: 0.041010 Loss2: 1.335806
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534060 Loss1: 0.166063 Loss2: 1.367997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476633 Loss1: 0.115290 Loss2: 1.361343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448952 Loss1: 0.092396 Loss2: 1.356556 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.494427 Loss1: 0.139252 Loss2: 1.355174 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985677
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598132 Loss1: 0.216930 Loss2: 1.381202 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.451459 Loss1: 0.100006 Loss2: 1.351452 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.526723 Loss1: 0.705093 Loss2: 1.821630
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467724 Loss1: 0.117926 Loss2: 1.349799
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.765681 Loss1: 0.380063 Loss2: 1.385618
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470380 Loss1: 0.121018 Loss2: 1.349361
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644721 Loss1: 0.246661 Loss2: 1.398060
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496135 Loss1: 0.142606 Loss2: 1.353528
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642825 Loss1: 0.252435 Loss2: 1.390390
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467995 Loss1: 0.120231 Loss2: 1.347764
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.582299 Loss1: 0.195426 Loss2: 1.386873
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452933 Loss1: 0.110698 Loss2: 1.342234
-(DefaultActor pid=3764) >> Training accuracy: 0.974609
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.453698 Loss1: 0.099678 Loss2: 1.354020 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450510 Loss1: 0.104375 Loss2: 1.346135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.435246 Loss1: 0.590467 Loss2: 1.844779
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427789 Loss1: 0.078555 Loss2: 1.349234
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642637 Loss1: 0.245045 Loss2: 1.397592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.532720 Loss1: 0.154113 Loss2: 1.378607 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460208 Loss1: 0.102358 Loss2: 1.357851
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.435190 Loss1: 0.540298 Loss2: 1.894892
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.775619 Loss1: 0.346138 Loss2: 1.429481 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.681484 Loss1: 0.214456 Loss2: 1.467028 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.653761 Loss1: 0.219044 Loss2: 1.434718 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.612934 Loss1: 0.169012 Loss2: 1.443922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.530021 Loss1: 0.105813 Loss2: 1.424208 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.482589 Loss1: 0.067123 Loss2: 1.415466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.766104 Loss1: 0.392557 Loss2: 1.373547 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556826 Loss1: 0.185060 Loss2: 1.371766 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.438232 Loss1: 0.079444 Loss2: 1.358787 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416740 Loss1: 0.071014 Loss2: 1.345726
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.362959 Loss1: 0.522407 Loss2: 1.840552
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.683637 Loss1: 0.306780 Loss2: 1.376857 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.592185 Loss1: 0.196818 Loss2: 1.395366 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.609169 Loss1: 0.237340 Loss2: 1.371829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443155 Loss1: 0.081356 Loss2: 1.361799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423961 Loss1: 0.084687 Loss2: 1.339274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428243 Loss1: 0.085465 Loss2: 1.342778 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377105 Loss1: 0.041077 Loss2: 1.336028 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.999023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.476965 Loss1: 0.127271 Loss2: 1.349694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.407726 Loss1: 0.075808 Loss2: 1.331918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.518492 Loss1: 0.676358 Loss2: 1.842134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.766420 Loss1: 0.369246 Loss2: 1.397174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.663136 Loss1: 0.241393 Loss2: 1.421744 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517901 Loss1: 0.139098 Loss2: 1.378803 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434724 Loss1: 0.077007 Loss2: 1.357717 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.402343 Loss1: 0.049925 Loss2: 1.352418
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.430816 Loss1: 0.550917 Loss2: 1.879899
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391857 Loss1: 0.045456 Loss2: 1.346401
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.658552 Loss1: 0.278704 Loss2: 1.379848
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366337 Loss1: 0.028423 Loss2: 1.337914
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.630839 Loss1: 0.220983 Loss2: 1.409857
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.508410 Loss1: 0.135486 Loss2: 1.372924
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528040 Loss1: 0.160972 Loss2: 1.367068
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496955 Loss1: 0.126060 Loss2: 1.370895
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.501272 Loss1: 0.140977 Loss2: 1.360295
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.497112 Loss1: 0.619026 Loss2: 1.878086
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458294 Loss1: 0.097880 Loss2: 1.360414
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.725306 Loss1: 0.341862 Loss2: 1.383445
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425850 Loss1: 0.070431 Loss2: 1.355419
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.689828 Loss1: 0.273344 Loss2: 1.416484
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422409 Loss1: 0.067213 Loss2: 1.355196
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.601548 Loss1: 0.191461 Loss2: 1.410087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517343 Loss1: 0.145447 Loss2: 1.371897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486207 Loss1: 0.119931 Loss2: 1.366276
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.271771 Loss1: 0.487352 Loss2: 1.784420
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.723920 Loss1: 0.373215 Loss2: 1.350705 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434333 Loss1: 0.077617 Loss2: 1.356716
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.647153 Loss1: 0.232281 Loss2: 1.414872
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535582 Loss1: 0.182504 Loss2: 1.353078
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.477374 Loss1: 0.131726 Loss2: 1.345647
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.436085 Loss1: 0.094316 Loss2: 1.341770
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435990 Loss1: 0.098570 Loss2: 1.337420
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.438036 Loss1: 0.585315 Loss2: 1.852721
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.746280 Loss1: 0.397202 Loss2: 1.349078 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.660543 Loss1: 0.261534 Loss2: 1.399009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427033 Loss1: 0.089844 Loss2: 1.337189
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589514 Loss1: 0.233273 Loss2: 1.356241
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574496 Loss1: 0.204669 Loss2: 1.369827
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499639 Loss1: 0.148825 Loss2: 1.350814
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.489837 Loss1: 0.134405 Loss2: 1.355431
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425585 Loss1: 0.083877 Loss2: 1.341707
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.508850 Loss1: 0.670532 Loss2: 1.838318
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434596 Loss1: 0.097660 Loss2: 1.336936
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831352 Loss1: 0.455326 Loss2: 1.376025
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445937 Loss1: 0.102588 Loss2: 1.343349
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.603049 Loss1: 0.225319 Loss2: 1.377730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487530 Loss1: 0.127711 Loss2: 1.359819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427224 Loss1: 0.072002 Loss2: 1.355222
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.474912 Loss1: 0.655109 Loss2: 1.819804
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421111 Loss1: 0.074077 Loss2: 1.347034
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.877831 Loss1: 0.506322 Loss2: 1.371509
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395521 Loss1: 0.051728 Loss2: 1.343793
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.747227 Loss1: 0.305406 Loss2: 1.441821
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407105 Loss1: 0.069289 Loss2: 1.337815
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.624252 Loss1: 0.253075 Loss2: 1.371177
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.629019 Loss1: 0.246398 Loss2: 1.382622
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515116 Loss1: 0.148835 Loss2: 1.366281
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491450 Loss1: 0.131624 Loss2: 1.359825
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425041 Loss1: 0.070690 Loss2: 1.354351
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.476932 Loss1: 0.631349 Loss2: 1.845583
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452035 Loss1: 0.102851 Loss2: 1.349185
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.951491 Loss1: 0.551356 Loss2: 1.400135
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435722 Loss1: 0.091378 Loss2: 1.344344
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.648902 Loss1: 0.280597 Loss2: 1.368305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446042 Loss1: 0.092028 Loss2: 1.354014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.413912 Loss1: 0.074487 Loss2: 1.339425
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.543727 Loss1: 0.698478 Loss2: 1.845249
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.792478 Loss1: 0.420409 Loss2: 1.372069 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 07:28:41,524 | server.py:236 | fit_round 143 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.657136 Loss1: 0.260672 Loss2: 1.396464 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390845 Loss1: 0.056751 Loss2: 1.334093
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569460 Loss1: 0.212876 Loss2: 1.356584
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471867 Loss1: 0.118461 Loss2: 1.353406
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.429210 Loss1: 0.089648 Loss2: 1.339562
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419669 Loss1: 0.083406 Loss2: 1.336263
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.392386 Loss1: 0.062570 Loss2: 1.329816
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.374247 Loss1: 0.518257 Loss2: 1.855990
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378859 Loss1: 0.055157 Loss2: 1.323702
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.783334 Loss1: 0.423291 Loss2: 1.360043
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377207 Loss1: 0.061514 Loss2: 1.315693
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567781 Loss1: 0.200969 Loss2: 1.366812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529868 Loss1: 0.167897 Loss2: 1.361971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423191 Loss1: 0.071657 Loss2: 1.351534
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.398865 Loss1: 0.573690 Loss2: 1.825175
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.642428 Loss1: 0.315069 Loss2: 1.327359 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.660555 Loss1: 0.308986 Loss2: 1.351569 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375729 Loss1: 0.045617 Loss2: 1.330112
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559480 Loss1: 0.227982 Loss2: 1.331498
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521842 Loss1: 0.183194 Loss2: 1.338648
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.436179 Loss1: 0.112446 Loss2: 1.323732
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451638 Loss1: 0.135936 Loss2: 1.315702
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410932 Loss1: 0.097299 Loss2: 1.313633
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.666727 Loss1: 0.731026 Loss2: 1.935700
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.762793 Loss1: 0.423199 Loss2: 1.339595 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399152 Loss1: 0.087255 Loss2: 1.311897
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590694 Loss1: 0.220520 Loss2: 1.370174
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529592 Loss1: 0.184544 Loss2: 1.345049
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508965 Loss1: 0.178036 Loss2: 1.330929
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443390 Loss1: 0.112214 Loss2: 1.331176
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.400293 Loss1: 0.078733 Loss2: 1.321560
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412218 Loss1: 0.092243 Loss2: 1.319976
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385439 Loss1: 0.071825 Loss2: 1.313614
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.363027 Loss1: 0.053108 Loss2: 1.309919
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 07:28:41,524][flwr][DEBUG] - fit_round 143 received 50 results and 0 failures
-INFO flwr 2023-10-12 07:29:23,708 | server.py:125 | fit progress: (143, 2.2164862047369107, {'accuracy': 0.5926}, 329871.486398289)
->> Test accuracy: 0.592600
-[2023-10-12 07:29:23,708][flwr][INFO] - fit progress: (143, 2.2164862047369107, {'accuracy': 0.5926}, 329871.486398289)
-DEBUG flwr 2023-10-12 07:29:23,708 | server.py:173 | evaluate_round 143: strategy sampled 50 clients (out of 50)
-[2023-10-12 07:29:23,708][flwr][DEBUG] - evaluate_round 143: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 07:38:29,300 | server.py:187 | evaluate_round 143 received 50 results and 0 failures
-[2023-10-12 07:38:29,300][flwr][DEBUG] - evaluate_round 143 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 07:38:29,301 | server.py:222 | fit_round 144: strategy sampled 50 clients (out of 50)
-[2023-10-12 07:38:29,301][flwr][DEBUG] - fit_round 144: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.540999 Loss1: 0.616980 Loss2: 1.924019
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.814660 Loss1: 0.379194 Loss2: 1.435466
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690154 Loss1: 0.220678 Loss2: 1.469476
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.664771 Loss1: 0.218725 Loss2: 1.446046
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.386895 Loss1: 0.536240 Loss2: 1.850655
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.620090 Loss1: 0.229017 Loss2: 1.391073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633204 Loss1: 0.239157 Loss2: 1.394047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.490555 Loss1: 0.112634 Loss2: 1.377922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.474315 Loss1: 0.112333 Loss2: 1.361982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463111 Loss1: 0.094704 Loss2: 1.368407 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.449880 Loss1: 0.081687 Loss2: 1.368193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429234 Loss1: 0.071562 Loss2: 1.357672 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.370708 Loss1: 0.545440 Loss2: 1.825268 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634206 Loss1: 0.236139 Loss2: 1.398066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.387528 Loss1: 0.623146 Loss2: 1.764382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.661972 Loss1: 0.352057 Loss2: 1.309915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.562395 Loss1: 0.213245 Loss2: 1.349150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.463071 Loss1: 0.156745 Loss2: 1.306326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.416294 Loss1: 0.111857 Loss2: 1.304437 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.408207 Loss1: 0.109925 Loss2: 1.298282 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386377 Loss1: 0.048848 Loss2: 1.337529
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404648 Loss1: 0.108427 Loss2: 1.296221
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.354433 Loss1: 0.059073 Loss2: 1.295359
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.357159 Loss1: 0.069834 Loss2: 1.287325
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.355659 Loss1: 0.071139 Loss2: 1.284519
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.707109 Loss1: 0.686543 Loss2: 2.020566
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.841569 Loss1: 0.463733 Loss2: 1.377836
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.671747 Loss1: 0.247217 Loss2: 1.424530
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.733593 Loss1: 0.316018 Loss2: 1.417574
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.610258 Loss1: 0.211022 Loss2: 1.399237
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.558162 Loss1: 0.164774 Loss2: 1.393387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438042 Loss1: 0.062299 Loss2: 1.375743 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.568461 Loss1: 0.191639 Loss2: 1.376822 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527408 Loss1: 0.153492 Loss2: 1.373917 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467601 Loss1: 0.100379 Loss2: 1.367222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441585 Loss1: 0.087732 Loss2: 1.353853
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.450607 Loss1: 0.637135 Loss2: 1.813472
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.739148 Loss1: 0.400849 Loss2: 1.338299
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416369 Loss1: 0.059690 Loss2: 1.356680
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575261 Loss1: 0.236578 Loss2: 1.338683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506625 Loss1: 0.165963 Loss2: 1.340663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439462 Loss1: 0.103656 Loss2: 1.335806
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.465616 Loss1: 0.620640 Loss2: 1.844976
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423604 Loss1: 0.093372 Loss2: 1.330233
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.797435 Loss1: 0.432455 Loss2: 1.364980
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399910 Loss1: 0.082268 Loss2: 1.317642
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677976 Loss1: 0.267423 Loss2: 1.410553
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383615 Loss1: 0.072067 Loss2: 1.311549
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.617346 Loss1: 0.245104 Loss2: 1.372242
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497323 Loss1: 0.130843 Loss2: 1.366480
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499012 Loss1: 0.138927 Loss2: 1.360085
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459504 Loss1: 0.104593 Loss2: 1.354911
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412433 Loss1: 0.064056 Loss2: 1.348377
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.588847 Loss1: 0.690706 Loss2: 1.898141
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392403 Loss1: 0.054960 Loss2: 1.337443
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751902 Loss1: 0.397028 Loss2: 1.354875
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405765 Loss1: 0.070399 Loss2: 1.335366
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587294 Loss1: 0.207961 Loss2: 1.379334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.453493 Loss1: 0.105739 Loss2: 1.347754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.407246 Loss1: 0.071277 Loss2: 1.335969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373824 Loss1: 0.041209 Loss2: 1.332615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.347838 Loss1: 0.031631 Loss2: 1.316207 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.494917 Loss1: 0.156747 Loss2: 1.338170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423063 Loss1: 0.096845 Loss2: 1.326218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406584 Loss1: 0.084766 Loss2: 1.321818
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.294680 Loss1: 0.471028 Loss2: 1.823652
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.619387 Loss1: 0.283132 Loss2: 1.336255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.550813 Loss1: 0.189334 Loss2: 1.361479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395822 Loss1: 0.085923 Loss2: 1.309898
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489706 Loss1: 0.153731 Loss2: 1.335974
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.441652 Loss1: 0.110045 Loss2: 1.331608
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437291 Loss1: 0.102710 Loss2: 1.334580
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424411 Loss1: 0.096391 Loss2: 1.328020
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417365 Loss1: 0.082736 Loss2: 1.334628
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422147 Loss1: 0.093865 Loss2: 1.328282
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.335606 Loss1: 0.528293 Loss2: 1.807312
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387422 Loss1: 0.066162 Loss2: 1.321260
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.681241 Loss1: 0.351148 Loss2: 1.330092
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.596970 Loss1: 0.233299 Loss2: 1.363670
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.491533 Loss1: 0.159179 Loss2: 1.332354
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.467089 Loss1: 0.146067 Loss2: 1.321022
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456950 Loss1: 0.123834 Loss2: 1.333115
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416950 Loss1: 0.094195 Loss2: 1.322755
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.362545 Loss1: 0.548366 Loss2: 1.814178
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388681 Loss1: 0.077251 Loss2: 1.311429
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.692946 Loss1: 0.382589 Loss2: 1.310357
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404363 Loss1: 0.096664 Loss2: 1.307698
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.621743 Loss1: 0.235247 Loss2: 1.386496
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.359343 Loss1: 0.049344 Loss2: 1.309999
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516213 Loss1: 0.200991 Loss2: 1.315222
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443598 Loss1: 0.128388 Loss2: 1.315211
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.415947 Loss1: 0.107509 Loss2: 1.308438
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.381802 Loss1: 0.087774 Loss2: 1.294029
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.371398 Loss1: 0.074428 Loss2: 1.296970
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.430662 Loss1: 0.556648 Loss2: 1.874013
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.370255 Loss1: 0.081484 Loss2: 1.288770
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.732934 Loss1: 0.353035 Loss2: 1.379899
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.333964 Loss1: 0.048335 Loss2: 1.285629
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.616071 Loss1: 0.236152 Loss2: 1.379918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.514079 Loss1: 0.147878 Loss2: 1.366201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476454 Loss1: 0.121123 Loss2: 1.355332
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.492014 Loss1: 0.637428 Loss2: 1.854587
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459551 Loss1: 0.100965 Loss2: 1.358586
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.860984 Loss1: 0.488263 Loss2: 1.372720
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445480 Loss1: 0.093341 Loss2: 1.352139
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.671356 Loss1: 0.239701 Loss2: 1.431655
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437427 Loss1: 0.096534 Loss2: 1.340893
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499878 Loss1: 0.126855 Loss2: 1.373023
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462308 Loss1: 0.100083 Loss2: 1.362225
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468704 Loss1: 0.110221 Loss2: 1.358483
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432020 Loss1: 0.075328 Loss2: 1.356692
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400528 Loss1: 0.046911 Loss2: 1.353617
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.426495 Loss1: 0.583043 Loss2: 1.843453
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.370059 Loss1: 0.027222 Loss2: 1.342837
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.674630 Loss1: 0.310062 Loss2: 1.364569
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367981 Loss1: 0.033009 Loss2: 1.334972
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567066 Loss1: 0.206175 Loss2: 1.360891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.428308 Loss1: 0.081379 Loss2: 1.346929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.402567 Loss1: 0.064872 Loss2: 1.337695
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.335503 Loss1: 0.521802 Loss2: 1.813700
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421434 Loss1: 0.091220 Loss2: 1.330214
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.689922 Loss1: 0.345918 Loss2: 1.344004
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402528 Loss1: 0.073913 Loss2: 1.328615
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.602778 Loss1: 0.214488 Loss2: 1.388290
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391107 Loss1: 0.064886 Loss2: 1.326221
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520902 Loss1: 0.170595 Loss2: 1.350308
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492914 Loss1: 0.143054 Loss2: 1.349860
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476413 Loss1: 0.129724 Loss2: 1.346688
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467005 Loss1: 0.123561 Loss2: 1.343444
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426197 Loss1: 0.084462 Loss2: 1.341735
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401891 Loss1: 0.063308 Loss2: 1.338583
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.325177 Loss1: 0.523637 Loss2: 1.801540
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364611 Loss1: 0.037219 Loss2: 1.327392
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.715113 Loss1: 0.357960 Loss2: 1.357153
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600047 Loss1: 0.203833 Loss2: 1.396213
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540227 Loss1: 0.175312 Loss2: 1.364916
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501272 Loss1: 0.138094 Loss2: 1.363178
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466786 Loss1: 0.112219 Loss2: 1.354567
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.521317 Loss1: 0.646601 Loss2: 1.874716
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471221 Loss1: 0.115707 Loss2: 1.355515
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462392 Loss1: 0.109946 Loss2: 1.352447 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424875 Loss1: 0.081187 Loss2: 1.343688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398115 Loss1: 0.059376 Loss2: 1.338738 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488343 Loss1: 0.115009 Loss2: 1.373334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419113 Loss1: 0.055439 Loss2: 1.363673 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.446387 Loss1: 0.596994 Loss2: 1.849393 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629058 Loss1: 0.237721 Loss2: 1.391338 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460297 Loss1: 0.118142 Loss2: 1.342155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.411342 Loss1: 0.084549 Loss2: 1.326793
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.463864 Loss1: 0.626728 Loss2: 1.837136
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.721485 Loss1: 0.400711 Loss2: 1.320773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667322 Loss1: 0.287101 Loss2: 1.380221 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549576 Loss1: 0.236615 Loss2: 1.312961 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367770 Loss1: 0.060112 Loss2: 1.307658
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.459858 Loss1: 0.127908 Loss2: 1.331951
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416184 Loss1: 0.105293 Loss2: 1.310891
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395277 Loss1: 0.091556 Loss2: 1.303720
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.362027 Loss1: 0.063146 Loss2: 1.298881
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.353984 Loss1: 0.058305 Loss2: 1.295680
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.653863 Loss1: 0.679410 Loss2: 1.974453
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.324039 Loss1: 0.036320 Loss2: 1.287719
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.743463 Loss1: 0.275255 Loss2: 1.468207 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569732 Loss1: 0.156069 Loss2: 1.413663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.348311 Loss1: 0.487638 Loss2: 1.860673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.677770 Loss1: 0.320459 Loss2: 1.357311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.627882 Loss1: 0.223004 Loss2: 1.404878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460523 Loss1: 0.066714 Loss2: 1.393809 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499362 Loss1: 0.139649 Loss2: 1.359713 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467656 Loss1: 0.118185 Loss2: 1.349471 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.641014 Loss1: 0.739025 Loss2: 1.901988
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493192 Loss1: 0.136576 Loss2: 1.356616
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428047 Loss1: 0.079511 Loss2: 1.348536 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.661035 Loss1: 0.301058 Loss2: 1.359977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.534981 Loss1: 0.163037 Loss2: 1.371944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.474923 Loss1: 0.561563 Loss2: 1.913360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.858597 Loss1: 0.452956 Loss2: 1.405641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406711 Loss1: 0.062567 Loss2: 1.344144 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.548443 Loss1: 0.146625 Loss2: 1.401818 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500140 Loss1: 0.109880 Loss2: 1.390260 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471192 Loss1: 0.081608 Loss2: 1.389584
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.741817 Loss1: 0.672240 Loss2: 2.069577
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436718 Loss1: 0.051350 Loss2: 1.385368
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.957891 Loss1: 0.417831 Loss2: 1.540060
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457437 Loss1: 0.081998 Loss2: 1.375440
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.826725 Loss1: 0.222645 Loss2: 1.604080
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.752408 Loss1: 0.226093 Loss2: 1.526314
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.709374 Loss1: 0.173926 Loss2: 1.535448
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.699383 Loss1: 0.169360 Loss2: 1.530023
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.642102 Loss1: 0.118478 Loss2: 1.523624
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.377178 Loss1: 0.551509 Loss2: 1.825669
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.634477 Loss1: 0.119250 Loss2: 1.515227
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.692514 Loss1: 0.362068 Loss2: 1.330446
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.604830 Loss1: 0.095886 Loss2: 1.508944
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615138 Loss1: 0.230828 Loss2: 1.384310
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.568149 Loss1: 0.069876 Loss2: 1.498273
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.442085 Loss1: 0.110026 Loss2: 1.332058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443692 Loss1: 0.123156 Loss2: 1.320536 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473716 Loss1: 0.144934 Loss2: 1.328782
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.222002 Loss1: 0.477196 Loss2: 1.744806
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.676514 Loss1: 0.369657 Loss2: 1.306857 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416630 Loss1: 0.106239 Loss2: 1.310391
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.644322 Loss1: 0.287551 Loss2: 1.356772
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.504213 Loss1: 0.176610 Loss2: 1.327603
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.464236 Loss1: 0.137404 Loss2: 1.326832
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420261 Loss1: 0.106878 Loss2: 1.313382
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448589 Loss1: 0.141570 Loss2: 1.307019
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.509738 Loss1: 0.630686 Loss2: 1.879052
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399132 Loss1: 0.087949 Loss2: 1.311183
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.710081 Loss1: 0.327589 Loss2: 1.382492
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394854 Loss1: 0.090825 Loss2: 1.304028
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.658908 Loss1: 0.240120 Loss2: 1.418788
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405931 Loss1: 0.104787 Loss2: 1.301144
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.565223 Loss1: 0.184342 Loss2: 1.380881
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.532132 Loss1: 0.145138 Loss2: 1.386994
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466426 Loss1: 0.096637 Loss2: 1.369789
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446440 Loss1: 0.085513 Loss2: 1.360927
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433807 Loss1: 0.069698 Loss2: 1.364109
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.571436 Loss1: 0.689304 Loss2: 1.882132
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413752 Loss1: 0.059919 Loss2: 1.353833
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.848313 Loss1: 0.438563 Loss2: 1.409751
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383285 Loss1: 0.037447 Loss2: 1.345838
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.578195 Loss1: 0.181010 Loss2: 1.397184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537923 Loss1: 0.153388 Loss2: 1.384535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474312 Loss1: 0.092918 Loss2: 1.381394
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.485369 Loss1: 0.553064 Loss2: 1.932305
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454826 Loss1: 0.081234 Loss2: 1.373592
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.706800 Loss1: 0.307778 Loss2: 1.399022
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433222 Loss1: 0.065090 Loss2: 1.368132
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647947 Loss1: 0.222857 Loss2: 1.425090
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431888 Loss1: 0.071655 Loss2: 1.360233
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541558 Loss1: 0.132276 Loss2: 1.409282
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547030 Loss1: 0.164770 Loss2: 1.382259
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497641 Loss1: 0.113810 Loss2: 1.383832
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467913 Loss1: 0.089188 Loss2: 1.378725
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459940 Loss1: 0.085348 Loss2: 1.374592
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456176 Loss1: 0.086228 Loss2: 1.369948
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.343830 Loss1: 0.484796 Loss2: 1.859034
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466307 Loss1: 0.095921 Loss2: 1.370386
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.728435 Loss1: 0.369322 Loss2: 1.359113
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642692 Loss1: 0.244228 Loss2: 1.398464
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561256 Loss1: 0.201443 Loss2: 1.359814
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507556 Loss1: 0.148971 Loss2: 1.358585
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469706 Loss1: 0.118480 Loss2: 1.351226
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.471542 Loss1: 0.603323 Loss2: 1.868219
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451537 Loss1: 0.102288 Loss2: 1.349249
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.835741 Loss1: 0.475949 Loss2: 1.359791
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435668 Loss1: 0.092213 Loss2: 1.343455
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645342 Loss1: 0.224042 Loss2: 1.421301
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396665 Loss1: 0.056549 Loss2: 1.340116
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586962 Loss1: 0.223486 Loss2: 1.363476
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367667 Loss1: 0.034459 Loss2: 1.333208
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473193 Loss1: 0.101197 Loss2: 1.371996 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447518 Loss1: 0.106234 Loss2: 1.341285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401539 Loss1: 0.064517 Loss2: 1.337021
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.464058 Loss1: 0.604545 Loss2: 1.859512
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.899710 Loss1: 0.530839 Loss2: 1.368871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640923 Loss1: 0.267776 Loss2: 1.373147 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.541512 Loss1: 0.177364 Loss2: 1.364148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.507106 Loss1: 0.142277 Loss2: 1.364829 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459500 Loss1: 0.097794 Loss2: 1.361706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443723 Loss1: 0.089869 Loss2: 1.353853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464964 Loss1: 0.120058 Loss2: 1.344906 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456813 Loss1: 0.133896 Loss2: 1.322917 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456098 Loss1: 0.124684 Loss2: 1.331414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421792 Loss1: 0.104521 Loss2: 1.317271
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.487764 Loss1: 0.604794 Loss2: 1.882970
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.831791 Loss1: 0.433815 Loss2: 1.397977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576533 Loss1: 0.176020 Loss2: 1.400512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546794 Loss1: 0.149255 Loss2: 1.397539 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.541437 Loss1: 0.150284 Loss2: 1.391153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.544741 Loss1: 0.148270 Loss2: 1.396471 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.476488 Loss1: 0.087816 Loss2: 1.388672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.469090 Loss1: 0.150383 Loss2: 1.318707
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491015 Loss1: 0.109805 Loss2: 1.381210
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.374655 Loss1: 0.071651 Loss2: 1.303004 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.356033 Loss1: 0.061021 Loss2: 1.295012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.440170 Loss1: 0.547853 Loss2: 1.892317
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.342149 Loss1: 0.052328 Loss2: 1.289821
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.732237 Loss1: 0.332503 Loss2: 1.399734
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.317729 Loss1: 0.035110 Loss2: 1.282619
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.548484 Loss1: 0.155564 Loss2: 1.392920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500221 Loss1: 0.114653 Loss2: 1.385568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.525997 Loss1: 0.139718 Loss2: 1.386279
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.471063 Loss1: 0.623868 Loss2: 1.847195
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511939 Loss1: 0.124970 Loss2: 1.386969
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.733277 Loss1: 0.363083 Loss2: 1.370194
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463419 Loss1: 0.078107 Loss2: 1.385313
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.607443 Loss1: 0.215254 Loss2: 1.392189
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454012 Loss1: 0.071214 Loss2: 1.382799
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.560373 Loss1: 0.187195 Loss2: 1.373178
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476179 Loss1: 0.114850 Loss2: 1.361328
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461947 Loss1: 0.099033 Loss2: 1.362914
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431865 Loss1: 0.080129 Loss2: 1.351736
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435264 Loss1: 0.083242 Loss2: 1.352022
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.381566 Loss1: 0.528412 Loss2: 1.853154
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433788 Loss1: 0.086314 Loss2: 1.347474
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401296 Loss1: 0.058952 Loss2: 1.342344 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.674467 Loss1: 0.304048 Loss2: 1.370419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486412 Loss1: 0.137374 Loss2: 1.349038 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427071 Loss1: 0.081196 Loss2: 1.345875
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.386106 Loss1: 0.541766 Loss2: 1.844339
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.775922 Loss1: 0.396458 Loss2: 1.379464 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.665704 Loss1: 0.245572 Loss2: 1.420132 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557987 Loss1: 0.179420 Loss2: 1.378568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481796 Loss1: 0.113107 Loss2: 1.368689 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448608 Loss1: 0.087626 Loss2: 1.360982 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432162 Loss1: 0.076688 Loss2: 1.355474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.534887 Loss1: 0.164161 Loss2: 1.370726 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.399715 Loss1: 0.065422 Loss2: 1.334293 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.388104 Loss1: 0.058194 Loss2: 1.329911 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.510778 Loss1: 0.603352 Loss2: 1.907425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372429 Loss1: 0.046565 Loss2: 1.325863 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604734 Loss1: 0.191272 Loss2: 1.413462 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.590985 Loss1: 0.173982 Loss2: 1.417002 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.535883 Loss1: 0.128476 Loss2: 1.407407
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.184061 Loss1: 0.404640 Loss2: 1.779422
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.490933 Loss1: 0.090672 Loss2: 1.400261
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653371 Loss1: 0.330216 Loss2: 1.323156
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.548280 Loss1: 0.190712 Loss2: 1.357567 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448677 Loss1: 0.056156 Loss2: 1.392521
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497367 Loss1: 0.178914 Loss2: 1.318453
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524355 Loss1: 0.197633 Loss2: 1.326722
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468546 Loss1: 0.146405 Loss2: 1.322141
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.418650 Loss1: 0.103914 Loss2: 1.314736
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400099 Loss1: 0.094462 Loss2: 1.305638
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.403006 Loss1: 0.489770 Loss2: 1.913236
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.747218 Loss1: 0.354547 Loss2: 1.392671
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370734 Loss1: 0.068998 Loss2: 1.301736
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.670104 Loss1: 0.246334 Loss2: 1.423770
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.344561 Loss1: 0.045018 Loss2: 1.299544
-(DefaultActor pid=3764) >> Training accuracy: 0.995404
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.632594 Loss1: 0.226077 Loss2: 1.406517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.532826 Loss1: 0.137880 Loss2: 1.394945 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 08:06:56,576 | server.py:236 | fit_round 144 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458552 Loss1: 0.076339 Loss2: 1.382213
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.389856 Loss1: 0.545673 Loss2: 1.844183
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.650262 Loss1: 0.295399 Loss2: 1.354863 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453007 Loss1: 0.080583 Loss2: 1.372424
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565637 Loss1: 0.187599 Loss2: 1.378038
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.493579 Loss1: 0.140457 Loss2: 1.353122
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485081 Loss1: 0.139594 Loss2: 1.345487
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447231 Loss1: 0.102114 Loss2: 1.345117
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436328 Loss1: 0.092163 Loss2: 1.344164
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.533794 Loss1: 0.664908 Loss2: 1.868887
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430466 Loss1: 0.092370 Loss2: 1.338097
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376685 Loss1: 0.043750 Loss2: 1.332935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392926 Loss1: 0.066281 Loss2: 1.326645 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.537242 Loss1: 0.157501 Loss2: 1.379741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488422 Loss1: 0.116244 Loss2: 1.372179 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441990 Loss1: 0.085833 Loss2: 1.356158
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.549750 Loss1: 0.694729 Loss2: 1.855021
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.799057 Loss1: 0.430483 Loss2: 1.368574 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.963542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.464172 Loss1: 0.102839 Loss2: 1.361334
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726227 Loss1: 0.286381 Loss2: 1.439845
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563163 Loss1: 0.196533 Loss2: 1.366630
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515271 Loss1: 0.155465 Loss2: 1.359806
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.440218 Loss1: 0.082395 Loss2: 1.357823
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425237 Loss1: 0.079351 Loss2: 1.345886
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.349944 Loss1: 0.528801 Loss2: 1.821143
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419215 Loss1: 0.071382 Loss2: 1.347833
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.757181 Loss1: 0.383254 Loss2: 1.373927
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402445 Loss1: 0.058486 Loss2: 1.343959
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655336 Loss1: 0.258475 Loss2: 1.396861
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424152 Loss1: 0.082642 Loss2: 1.341510
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484273 Loss1: 0.116940 Loss2: 1.367332 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.393473 Loss1: 0.049100 Loss2: 1.344373 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.359165 Loss1: 0.547683 Loss2: 1.811482
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.375704 Loss1: 0.039505 Loss2: 1.336199
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.361796 Loss1: 0.036545 Loss2: 1.325251 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373286 Loss1: 0.053224 Loss2: 1.320062 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506788 Loss1: 0.159769 Loss2: 1.347018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433333 Loss1: 0.103310 Loss2: 1.330023 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408054 Loss1: 0.080316 Loss2: 1.327738 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 08:06:56,576][flwr][DEBUG] - fit_round 144 received 50 results and 0 failures
-INFO flwr 2023-10-12 08:07:37,517 | server.py:125 | fit progress: (144, 2.2120984548958726, {'accuracy': 0.5939}, 332165.295221584)
->> Test accuracy: 0.593900
-[2023-10-12 08:07:37,517][flwr][INFO] - fit progress: (144, 2.2120984548958726, {'accuracy': 0.5939}, 332165.295221584)
-DEBUG flwr 2023-10-12 08:07:37,517 | server.py:173 | evaluate_round 144: strategy sampled 50 clients (out of 50)
-[2023-10-12 08:07:37,517][flwr][DEBUG] - evaluate_round 144: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 08:16:38,589 | server.py:187 | evaluate_round 144 received 50 results and 0 failures
-[2023-10-12 08:16:38,589][flwr][DEBUG] - evaluate_round 144 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 08:16:38,590 | server.py:222 | fit_round 145: strategy sampled 50 clients (out of 50)
-[2023-10-12 08:16:38,590][flwr][DEBUG] - fit_round 145: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.467809 Loss1: 0.619768 Loss2: 1.848041 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.616755 Loss1: 0.233312 Loss2: 1.383444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.530572 Loss1: 0.186984 Loss2: 1.343588
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.445451 Loss1: 0.522399 Loss2: 1.923052
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468728 Loss1: 0.124673 Loss2: 1.344055
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.757074 Loss1: 0.344255 Loss2: 1.412819
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456089 Loss1: 0.120998 Loss2: 1.335091
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.666770 Loss1: 0.216127 Loss2: 1.450643
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418713 Loss1: 0.086018 Loss2: 1.332695
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.606179 Loss1: 0.188111 Loss2: 1.418068
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403630 Loss1: 0.079509 Loss2: 1.324121
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541346 Loss1: 0.128288 Loss2: 1.413059
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.380817 Loss1: 0.058156 Loss2: 1.322661
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.507058 Loss1: 0.102751 Loss2: 1.404307
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368043 Loss1: 0.046581 Loss2: 1.321462
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.489596 Loss1: 0.090050 Loss2: 1.399546
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.474487 Loss1: 0.082895 Loss2: 1.391592
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.455659 Loss1: 0.064249 Loss2: 1.391410
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424743 Loss1: 0.042090 Loss2: 1.382653
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.447451 Loss1: 0.573547 Loss2: 1.873904
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.782068 Loss1: 0.402659 Loss2: 1.379409
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.743979 Loss1: 0.297458 Loss2: 1.446520
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.652312 Loss1: 0.273156 Loss2: 1.379156
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.420618 Loss1: 0.558677 Loss2: 1.861941
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.601424 Loss1: 0.267646 Loss2: 1.333777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588396 Loss1: 0.245767 Loss2: 1.342629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564533 Loss1: 0.228128 Loss2: 1.336405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.488791 Loss1: 0.150302 Loss2: 1.338489 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491241 Loss1: 0.155980 Loss2: 1.335261 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413563 Loss1: 0.054767 Loss2: 1.358796
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444140 Loss1: 0.115517 Loss2: 1.328622
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388346 Loss1: 0.070835 Loss2: 1.317511
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388536 Loss1: 0.075006 Loss2: 1.313529
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395648 Loss1: 0.085146 Loss2: 1.310503
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.324889 Loss1: 0.501141 Loss2: 1.823748
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.679608 Loss1: 0.309425 Loss2: 1.370183
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.590460 Loss1: 0.199961 Loss2: 1.390500
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547046 Loss1: 0.180951 Loss2: 1.366095
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.505518 Loss1: 0.646303 Loss2: 1.859216
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.541784 Loss1: 0.167940 Loss2: 1.373844
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.885516 Loss1: 0.478193 Loss2: 1.407323
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533570 Loss1: 0.167413 Loss2: 1.366157
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.676195 Loss1: 0.225235 Loss2: 1.450961
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484781 Loss1: 0.120747 Loss2: 1.364034
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581439 Loss1: 0.185874 Loss2: 1.395565
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.553049 Loss1: 0.162319 Loss2: 1.390729
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.524364 Loss1: 0.158000 Loss2: 1.366364
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.535397 Loss1: 0.145880 Loss2: 1.389517
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.446973 Loss1: 0.090763 Loss2: 1.356210
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.489860 Loss1: 0.110758 Loss2: 1.379102
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403949 Loss1: 0.053693 Loss2: 1.350257
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490828 Loss1: 0.121717 Loss2: 1.369111 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.513393 Loss1: 0.621958 Loss2: 1.891435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.664022 Loss1: 0.242118 Loss2: 1.421904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578508 Loss1: 0.189111 Loss2: 1.389397
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.476005 Loss1: 0.581582 Loss2: 1.894423
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.906707 Loss1: 0.481864 Loss2: 1.424842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.852759 Loss1: 0.379031 Loss2: 1.473728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599593 Loss1: 0.189683 Loss2: 1.409910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520239 Loss1: 0.111908 Loss2: 1.408332 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508625 Loss1: 0.114121 Loss2: 1.394504 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494307 Loss1: 0.101143 Loss2: 1.393164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452550 Loss1: 0.067464 Loss2: 1.385085 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454162 Loss1: 0.072359 Loss2: 1.381803
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.440326 Loss1: 0.625812 Loss2: 1.814513
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.802705 Loss1: 0.415511 Loss2: 1.387194
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.623463 Loss1: 0.218320 Loss2: 1.405143
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.487141 Loss1: 0.120323 Loss2: 1.366819
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.369209 Loss1: 0.504695 Loss2: 1.864513
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506613 Loss1: 0.144299 Loss2: 1.362314
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471123 Loss1: 0.109115 Loss2: 1.362008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448635 Loss1: 0.093459 Loss2: 1.355176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421841 Loss1: 0.071667 Loss2: 1.350174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417474 Loss1: 0.069593 Loss2: 1.347881 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404090 Loss1: 0.059571 Loss2: 1.344518 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449508 Loss1: 0.090521 Loss2: 1.358987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408665 Loss1: 0.064331 Loss2: 1.344334 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.416503 Loss1: 0.602059 Loss2: 1.814444
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.760484 Loss1: 0.410688 Loss2: 1.349795
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.621821 Loss1: 0.221392 Loss2: 1.400429
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.636645 Loss1: 0.274141 Loss2: 1.362504
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.376155 Loss1: 0.495788 Loss2: 1.880367
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.689259 Loss1: 0.306674 Loss2: 1.382585 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.613853 Loss1: 0.225439 Loss2: 1.388414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559444 Loss1: 0.174657 Loss2: 1.384786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512141 Loss1: 0.134412 Loss2: 1.377729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.457787 Loss1: 0.088505 Loss2: 1.369282 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.462848 Loss1: 0.094243 Loss2: 1.368606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440992 Loss1: 0.077016 Loss2: 1.363976 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.430679 Loss1: 0.594068 Loss2: 1.836611 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.666404 Loss1: 0.245862 Loss2: 1.420543 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524716 Loss1: 0.150105 Loss2: 1.374611
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.448155 Loss1: 0.600638 Loss2: 1.847517
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.788843 Loss1: 0.436067 Loss2: 1.352776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.717003 Loss1: 0.290877 Loss2: 1.426126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520466 Loss1: 0.173849 Loss2: 1.346617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.479935 Loss1: 0.129689 Loss2: 1.350246 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446873 Loss1: 0.098814 Loss2: 1.348058 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.442611 Loss1: 0.106173 Loss2: 1.336438 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428715 Loss1: 0.096642 Loss2: 1.332073 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.426952 Loss1: 0.550925 Loss2: 1.876027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667100 Loss1: 0.259037 Loss2: 1.408063 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.570058 Loss1: 0.672161 Loss2: 1.897897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.804369 Loss1: 0.417340 Loss2: 1.387028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600849 Loss1: 0.208107 Loss2: 1.392742 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536926 Loss1: 0.168861 Loss2: 1.368065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455250 Loss1: 0.093115 Loss2: 1.362135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444660 Loss1: 0.092324 Loss2: 1.352336 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.413371 Loss1: 0.068781 Loss2: 1.344589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390196 Loss1: 0.056960 Loss2: 1.333236 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.199565 Loss1: 0.432819 Loss2: 1.766746 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582851 Loss1: 0.227182 Loss2: 1.355669 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.385985 Loss1: 0.532534 Loss2: 1.853450
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.503246 Loss1: 0.187880 Loss2: 1.315366
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.767370 Loss1: 0.396354 Loss2: 1.371017
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.460206 Loss1: 0.133934 Loss2: 1.326272
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.390579 Loss1: 0.088464 Loss2: 1.302115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383331 Loss1: 0.083941 Loss2: 1.299390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.357466 Loss1: 0.060030 Loss2: 1.297437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.349357 Loss1: 0.055215 Loss2: 1.294142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.324000 Loss1: 0.033749 Loss2: 1.290251 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997243
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431217 Loss1: 0.071945 Loss2: 1.359271 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.730133 Loss1: 0.695613 Loss2: 2.034520
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.785487 Loss1: 0.399978 Loss2: 1.385508
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.757798 Loss1: 0.332383 Loss2: 1.425415
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.707216 Loss1: 0.258462 Loss2: 1.448754
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.560004 Loss1: 0.163241 Loss2: 1.396762
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.561774 Loss1: 0.166464 Loss2: 1.395310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.550965 Loss1: 0.138923 Loss2: 1.412041 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445277 Loss1: 0.055706 Loss2: 1.389572 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445017 Loss1: 0.063318 Loss2: 1.381699 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438240 Loss1: 0.061176 Loss2: 1.377064 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.424709 Loss1: 0.087660 Loss2: 1.337049 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386402 Loss1: 0.049805 Loss2: 1.336598 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.276846 Loss1: 0.426528 Loss2: 1.850318 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599232 Loss1: 0.197479 Loss2: 1.401753 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.322592 Loss1: 0.470663 Loss2: 1.851929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.721265 Loss1: 0.363996 Loss2: 1.357268 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.688617 Loss1: 0.279469 Loss2: 1.409148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.607654 Loss1: 0.254849 Loss2: 1.352805 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568791 Loss1: 0.202237 Loss2: 1.366554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512474 Loss1: 0.151143 Loss2: 1.361331 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.489334 Loss1: 0.145008 Loss2: 1.344326 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428230 Loss1: 0.085688 Loss2: 1.342542 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.760036 Loss1: 0.370327 Loss2: 1.389710 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599781 Loss1: 0.209682 Loss2: 1.390099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517962 Loss1: 0.128469 Loss2: 1.389493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481664 Loss1: 0.108061 Loss2: 1.373602 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491548 Loss1: 0.119397 Loss2: 1.372152 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470396 Loss1: 0.106504 Loss2: 1.363893 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451038 Loss1: 0.092481 Loss2: 1.358557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429135 Loss1: 0.070057 Loss2: 1.359078 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.503932 Loss1: 0.104230 Loss2: 1.399702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463157 Loss1: 0.075372 Loss2: 1.387785 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.777342 Loss1: 0.389089 Loss2: 1.388253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580202 Loss1: 0.198154 Loss2: 1.382048 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.504484 Loss1: 0.122456 Loss2: 1.382028
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.440642 Loss1: 0.534984 Loss2: 1.905658
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489889 Loss1: 0.121718 Loss2: 1.368171
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.815537 Loss1: 0.402027 Loss2: 1.413510
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441446 Loss1: 0.075349 Loss2: 1.366097
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727942 Loss1: 0.262922 Loss2: 1.465020
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.590983 Loss1: 0.179175 Loss2: 1.411808 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.595239 Loss1: 0.180241 Loss2: 1.414998 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466544 Loss1: 0.108189 Loss2: 1.358355
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559320 Loss1: 0.148751 Loss2: 1.410569
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502137 Loss1: 0.097187 Loss2: 1.404950
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.489131 Loss1: 0.092503 Loss2: 1.396628
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442454 Loss1: 0.054698 Loss2: 1.387756
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.480986 Loss1: 0.096994 Loss2: 1.383992
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.426640 Loss1: 0.583902 Loss2: 1.842737
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.700196 Loss1: 0.349657 Loss2: 1.350539
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.781752 Loss1: 0.368009 Loss2: 1.413743
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599950 Loss1: 0.242524 Loss2: 1.357426
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536614 Loss1: 0.179552 Loss2: 1.357062
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.367064 Loss1: 0.551829 Loss2: 1.815235
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505666 Loss1: 0.153006 Loss2: 1.352660
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.710058 Loss1: 0.365680 Loss2: 1.344377
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.454821 Loss1: 0.108277 Loss2: 1.346543
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563517 Loss1: 0.189300 Loss2: 1.374218
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411974 Loss1: 0.072578 Loss2: 1.339396
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468502 Loss1: 0.135437 Loss2: 1.333065
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396512 Loss1: 0.063026 Loss2: 1.333487
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.447275 Loss1: 0.117297 Loss2: 1.329978
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374871 Loss1: 0.046810 Loss2: 1.328061
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.417336 Loss1: 0.091901 Loss2: 1.325435
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.384186 Loss1: 0.055532 Loss2: 1.328654
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368075 Loss1: 0.048082 Loss2: 1.319993
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.367055 Loss1: 0.049470 Loss2: 1.317585
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374856 Loss1: 0.058771 Loss2: 1.316085
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.339107 Loss1: 0.526559 Loss2: 1.812548
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.733514 Loss1: 0.358498 Loss2: 1.375017
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741565 Loss1: 0.314806 Loss2: 1.426759
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.646194 Loss1: 0.266664 Loss2: 1.379530
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.553880 Loss1: 0.669251 Loss2: 1.884629
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.596197 Loss1: 0.208896 Loss2: 1.387300
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493654 Loss1: 0.118923 Loss2: 1.374731 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424838 Loss1: 0.067463 Loss2: 1.357375 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422203 Loss1: 0.070084 Loss2: 1.352119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404722 Loss1: 0.053276 Loss2: 1.351446 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428509 Loss1: 0.081488 Loss2: 1.347021 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401016 Loss1: 0.068435 Loss2: 1.332581 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978795
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.333985 Loss1: 0.517225 Loss2: 1.816760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656760 Loss1: 0.235019 Loss2: 1.421740 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.533923 Loss1: 0.664729 Loss2: 1.869194
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575647 Loss1: 0.200249 Loss2: 1.375398
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.794310 Loss1: 0.405187 Loss2: 1.389123
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.545704 Loss1: 0.164881 Loss2: 1.380823
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635288 Loss1: 0.221515 Loss2: 1.413773
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485588 Loss1: 0.103927 Loss2: 1.381661
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530577 Loss1: 0.160228 Loss2: 1.370349
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478042 Loss1: 0.107084 Loss2: 1.370958
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454728 Loss1: 0.083645 Loss2: 1.371083 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414799 Loss1: 0.056317 Loss2: 1.358482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387004 Loss1: 0.036124 Loss2: 1.350880 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414617 Loss1: 0.068530 Loss2: 1.346087 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.579060 Loss1: 0.738884 Loss2: 1.840176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.666277 Loss1: 0.258685 Loss2: 1.407592 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490212 Loss1: 0.153497 Loss2: 1.336715
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.497243 Loss1: 0.567660 Loss2: 1.929583
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451630 Loss1: 0.115196 Loss2: 1.336433
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.726389 Loss1: 0.320463 Loss2: 1.405926
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.441489 Loss1: 0.114164 Loss2: 1.327325
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.736342 Loss1: 0.287295 Loss2: 1.449047
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416566 Loss1: 0.091133 Loss2: 1.325433
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559738 Loss1: 0.162633 Loss2: 1.397105
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401560 Loss1: 0.084735 Loss2: 1.316825
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524022 Loss1: 0.129505 Loss2: 1.394517
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377321 Loss1: 0.068977 Loss2: 1.308344
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528746 Loss1: 0.138907 Loss2: 1.389838
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369652 Loss1: 0.062715 Loss2: 1.306937
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.482499 Loss1: 0.100116 Loss2: 1.382383
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.444706 Loss1: 0.061500 Loss2: 1.383206
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447006 Loss1: 0.069656 Loss2: 1.377349
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422675 Loss1: 0.049501 Loss2: 1.373174
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.484974 Loss1: 0.609583 Loss2: 1.875390
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.732496 Loss1: 0.336460 Loss2: 1.396036
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.672680 Loss1: 0.228376 Loss2: 1.444304
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557441 Loss1: 0.165531 Loss2: 1.391910
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.396948 Loss1: 0.541253 Loss2: 1.855695
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.582285 Loss1: 0.182838 Loss2: 1.399447
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.701350 Loss1: 0.341992 Loss2: 1.359358
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505025 Loss1: 0.114322 Loss2: 1.390703
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.607193 Loss1: 0.203953 Loss2: 1.403239
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443317 Loss1: 0.067800 Loss2: 1.375517
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564673 Loss1: 0.199520 Loss2: 1.365153
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.407613 Loss1: 0.034780 Loss2: 1.372833
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.546296 Loss1: 0.185844 Loss2: 1.360452
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.389807 Loss1: 0.031187 Loss2: 1.358621
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504410 Loss1: 0.128768 Loss2: 1.375642
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390133 Loss1: 0.043573 Loss2: 1.346560
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484781 Loss1: 0.124370 Loss2: 1.360411
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.456525 Loss1: 0.106548 Loss2: 1.349977
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446529 Loss1: 0.095218 Loss2: 1.351311
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427613 Loss1: 0.076473 Loss2: 1.351140
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.512941 Loss1: 0.658101 Loss2: 1.854841
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.694009 Loss1: 0.343870 Loss2: 1.350139
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.637651 Loss1: 0.245198 Loss2: 1.392452
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559895 Loss1: 0.200639 Loss2: 1.359256
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.345597 Loss1: 0.508631 Loss2: 1.836966
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498132 Loss1: 0.138020 Loss2: 1.360113
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.651261 Loss1: 0.306020 Loss2: 1.345241
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.422930 Loss1: 0.074723 Loss2: 1.348208
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.581786 Loss1: 0.204135 Loss2: 1.377652
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446420 Loss1: 0.106628 Loss2: 1.339792
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492485 Loss1: 0.139529 Loss2: 1.352956
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413658 Loss1: 0.074956 Loss2: 1.338702
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.479906 Loss1: 0.138001 Loss2: 1.341905
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423255 Loss1: 0.088321 Loss2: 1.334933
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467653 Loss1: 0.109956 Loss2: 1.357697
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404580 Loss1: 0.065567 Loss2: 1.339013
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439080 Loss1: 0.096606 Loss2: 1.342474
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420953 Loss1: 0.092684 Loss2: 1.328269
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401127 Loss1: 0.067516 Loss2: 1.333611
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399143 Loss1: 0.075258 Loss2: 1.323884
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.432636 Loss1: 0.589360 Loss2: 1.843276
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.748010 Loss1: 0.382062 Loss2: 1.365948
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648510 Loss1: 0.237558 Loss2: 1.410952
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.546720 Loss1: 0.178965 Loss2: 1.367755
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.230854 Loss1: 0.476219 Loss2: 1.754635
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.645220 Loss1: 0.330484 Loss2: 1.314736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.580651 Loss1: 0.247288 Loss2: 1.333363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.495721 Loss1: 0.174442 Loss2: 1.321279 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.433565 Loss1: 0.126548 Loss2: 1.307017 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.438520 Loss1: 0.129967 Loss2: 1.308553 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.945833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.424654 Loss1: 0.120736 Loss2: 1.303918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.340952 Loss1: 0.050489 Loss2: 1.290463 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.488485 Loss1: 0.603499 Loss2: 1.884986 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.625695 Loss1: 0.255259 Loss2: 1.370436 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.474103 Loss1: 0.134296 Loss2: 1.339807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477164 Loss1: 0.133924 Loss2: 1.343239 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464794 Loss1: 0.134171 Loss2: 1.330622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.406372 Loss1: 0.076563 Loss2: 1.329810 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373417 Loss1: 0.052034 Loss2: 1.321383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.353849 Loss1: 0.039937 Loss2: 1.313913 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.520622 Loss1: 0.152743 Loss2: 1.367879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443303 Loss1: 0.085113 Loss2: 1.358189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417534 Loss1: 0.070200 Loss2: 1.347333
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.429323 Loss1: 0.585370 Loss2: 1.843954
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.710138 Loss1: 0.358030 Loss2: 1.352108
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563168 Loss1: 0.181703 Loss2: 1.381465
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518575 Loss1: 0.171145 Loss2: 1.347430
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471463 Loss1: 0.129101 Loss2: 1.342362
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.654519 Loss1: 0.709188 Loss2: 1.945331
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449303 Loss1: 0.111108 Loss2: 1.338195
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.392427 Loss1: 0.067270 Loss2: 1.325157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380909 Loss1: 0.058675 Loss2: 1.322234 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372412 Loss1: 0.052600 Loss2: 1.319812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356446 Loss1: 0.040095 Loss2: 1.316351 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.613777 Loss1: 0.166965 Loss2: 1.446812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.551721 Loss1: 0.108331 Loss2: 1.443391 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.538607 Loss1: 0.095062 Loss2: 1.443545
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.298768 Loss1: 0.499736 Loss2: 1.799032
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.627367 Loss1: 0.279420 Loss2: 1.347946
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.531462 Loss1: 0.170580 Loss2: 1.360882
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.457616 Loss1: 0.130631 Loss2: 1.326985
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.485199 Loss1: 0.159323 Loss2: 1.325876
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.362453 Loss1: 0.490133 Loss2: 1.872321
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442701 Loss1: 0.112682 Loss2: 1.330019
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.748600 Loss1: 0.366789 Loss2: 1.381811
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.412595 Loss1: 0.089747 Loss2: 1.322848
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.681329 Loss1: 0.243597 Loss2: 1.437732
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409789 Loss1: 0.088783 Loss2: 1.321006
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.552443 Loss1: 0.166608 Loss2: 1.385835
-DEBUG flwr 2023-10-12 08:45:28,048 | server.py:236 | fit_round 145 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519160 Loss1: 0.143195 Loss2: 1.375964
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409132 Loss1: 0.096107 Loss2: 1.313025
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513986 Loss1: 0.143648 Loss2: 1.370337
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428891 Loss1: 0.115930 Loss2: 1.312961
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459488 Loss1: 0.096304 Loss2: 1.363184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.479345 Loss1: 0.108602 Loss2: 1.370743 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.748598 Loss1: 0.383358 Loss2: 1.365240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511969 Loss1: 0.129280 Loss2: 1.382689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.512377 Loss1: 0.582075 Loss2: 1.930302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.726564 Loss1: 0.300875 Loss2: 1.425688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.738378 Loss1: 0.283574 Loss2: 1.454804 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394619 Loss1: 0.055406 Loss2: 1.339213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395678 Loss1: 0.062612 Loss2: 1.333066 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985577
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533842 Loss1: 0.125044 Loss2: 1.408799 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485699 Loss1: 0.077956 Loss2: 1.407743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.419544 Loss1: 0.614643 Loss2: 1.804901
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478262 Loss1: 0.078767 Loss2: 1.399494
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.665538 Loss1: 0.279382 Loss2: 1.386156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.482905 Loss1: 0.152675 Loss2: 1.330230 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433405 Loss1: 0.113065 Loss2: 1.320340
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.405436 Loss1: 0.538982 Loss2: 1.866454
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.398625 Loss1: 0.085563 Loss2: 1.313063
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.658164 Loss1: 0.286997 Loss2: 1.371167
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410197 Loss1: 0.109270 Loss2: 1.300927
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.548796 Loss1: 0.148934 Loss2: 1.399861
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456994 Loss1: 0.142122 Loss2: 1.314873
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.480792 Loss1: 0.115604 Loss2: 1.365188
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425511 Loss1: 0.106229 Loss2: 1.319282
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.473733 Loss1: 0.114490 Loss2: 1.359243
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455604 Loss1: 0.098945 Loss2: 1.356659
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440105 Loss1: 0.085521 Loss2: 1.354585
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406421 Loss1: 0.055304 Loss2: 1.351117
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404559 Loss1: 0.058392 Loss2: 1.346167
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401556 Loss1: 0.058121 Loss2: 1.343435
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 08:45:28,048][flwr][DEBUG] - fit_round 145 received 50 results and 0 failures
-INFO flwr 2023-10-12 08:46:09,613 | server.py:125 | fit progress: (145, 2.2184009710059, {'accuracy': 0.596}, 334477.391319445)
->> Test accuracy: 0.596000
-[2023-10-12 08:46:09,613][flwr][INFO] - fit progress: (145, 2.2184009710059, {'accuracy': 0.596}, 334477.391319445)
-DEBUG flwr 2023-10-12 08:46:09,613 | server.py:173 | evaluate_round 145: strategy sampled 50 clients (out of 50)
-[2023-10-12 08:46:09,613][flwr][DEBUG] - evaluate_round 145: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 08:55:18,111 | server.py:187 | evaluate_round 145 received 50 results and 0 failures
-[2023-10-12 08:55:18,111][flwr][DEBUG] - evaluate_round 145 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 08:55:18,112 | server.py:222 | fit_round 146: strategy sampled 50 clients (out of 50)
-[2023-10-12 08:55:18,112][flwr][DEBUG] - fit_round 146: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.391963 Loss1: 0.575907 Loss2: 1.816057
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.672821 Loss1: 0.338091 Loss2: 1.334729
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599669 Loss1: 0.224509 Loss2: 1.375160
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.454731 Loss1: 0.115464 Loss2: 1.339267
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.447475 Loss1: 0.112062 Loss2: 1.335413 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.411699 Loss1: 0.082746 Loss2: 1.328953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395633 Loss1: 0.071546 Loss2: 1.324087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.366995 Loss1: 0.053389 Loss2: 1.313607 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.363362 Loss1: 0.054224 Loss2: 1.309137 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.345377 Loss1: 0.039908 Loss2: 1.305469 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.367105 Loss1: 0.050857 Loss2: 1.316248 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.446362 Loss1: 0.482934 Loss2: 1.963428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741104 Loss1: 0.233739 Loss2: 1.507365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.359480 Loss1: 0.521954 Loss2: 1.837526
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.626133 Loss1: 0.147949 Loss2: 1.478184
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.622859 Loss1: 0.290222 Loss2: 1.332636
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577309 Loss1: 0.111864 Loss2: 1.465445
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.544829 Loss1: 0.190744 Loss2: 1.354085
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.549534 Loss1: 0.083228 Loss2: 1.466306
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.467292 Loss1: 0.139089 Loss2: 1.328203
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.552232 Loss1: 0.096055 Loss2: 1.456177
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.553767 Loss1: 0.097678 Loss2: 1.456089 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.516792 Loss1: 0.063667 Loss2: 1.453125 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.481736 Loss1: 0.036335 Loss2: 1.445401 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371589 Loss1: 0.062241 Loss2: 1.309348 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.398775 Loss1: 0.572471 Loss2: 1.826304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.545019 Loss1: 0.182316 Loss2: 1.362703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563743 Loss1: 0.224028 Loss2: 1.339715
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.515204 Loss1: 0.627086 Loss2: 1.888118
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.496863 Loss1: 0.142447 Loss2: 1.354415
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.734082 Loss1: 0.405027 Loss2: 1.329055
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.645268 Loss1: 0.274028 Loss2: 1.371239
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463973 Loss1: 0.124269 Loss2: 1.339705
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.511389 Loss1: 0.177005 Loss2: 1.334384
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419702 Loss1: 0.090846 Loss2: 1.328856
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409418 Loss1: 0.081009 Loss2: 1.328409 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394437 Loss1: 0.073599 Loss2: 1.320838 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361228 Loss1: 0.045727 Loss2: 1.315502 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376081 Loss1: 0.074286 Loss2: 1.301796 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967548
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.269877 Loss1: 0.454762 Loss2: 1.815114
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.598257 Loss1: 0.272921 Loss2: 1.325335
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.515016 Loss1: 0.172604 Loss2: 1.342412
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511546 Loss1: 0.173995 Loss2: 1.337550
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.521461 Loss1: 0.645927 Loss2: 1.875535
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461599 Loss1: 0.132570 Loss2: 1.329029
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.711970 Loss1: 0.314883 Loss2: 1.397086
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.403615 Loss1: 0.074632 Loss2: 1.328984
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598221 Loss1: 0.183072 Loss2: 1.415149
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.382918 Loss1: 0.066290 Loss2: 1.316628
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.531279 Loss1: 0.140681 Loss2: 1.390599
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.371299 Loss1: 0.059425 Loss2: 1.311873
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.553872 Loss1: 0.163883 Loss2: 1.389989
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.369539 Loss1: 0.062350 Loss2: 1.307189
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494121 Loss1: 0.105414 Loss2: 1.388707
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.357190 Loss1: 0.050302 Loss2: 1.306887
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500104 Loss1: 0.120957 Loss2: 1.379147
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461013 Loss1: 0.086318 Loss2: 1.374695
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454837 Loss1: 0.081464 Loss2: 1.373373
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435478 Loss1: 0.067562 Loss2: 1.367916
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.405781 Loss1: 0.547062 Loss2: 1.858719
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.631542 Loss1: 0.272077 Loss2: 1.359465
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.559633 Loss1: 0.190323 Loss2: 1.369310
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.473436 Loss1: 0.120000 Loss2: 1.353436
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.432016 Loss1: 0.576545 Loss2: 1.855472
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.459927 Loss1: 0.114526 Loss2: 1.345400
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.688805 Loss1: 0.335824 Loss2: 1.352980
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.439260 Loss1: 0.084891 Loss2: 1.354369
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.555474 Loss1: 0.182718 Loss2: 1.372756
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.417840 Loss1: 0.070574 Loss2: 1.347266
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.546697 Loss1: 0.205247 Loss2: 1.341450
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.390971 Loss1: 0.056258 Loss2: 1.334713
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496585 Loss1: 0.151410 Loss2: 1.345175
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403435 Loss1: 0.068304 Loss2: 1.335132
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443199 Loss1: 0.111498 Loss2: 1.331700
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395546 Loss1: 0.061533 Loss2: 1.334013
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392509 Loss1: 0.070765 Loss2: 1.321744
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.377827 Loss1: 0.056290 Loss2: 1.321537
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.353197 Loss1: 0.039719 Loss2: 1.313477
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370881 Loss1: 0.061233 Loss2: 1.309649
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.345570 Loss1: 0.557833 Loss2: 1.787737
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.632102 Loss1: 0.312524 Loss2: 1.319578
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.534075 Loss1: 0.179457 Loss2: 1.354619
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.453111 Loss1: 0.133528 Loss2: 1.319583
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.380063 Loss1: 0.567520 Loss2: 1.812543
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.614452 Loss1: 0.277102 Loss2: 1.337350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.522538 Loss1: 0.155369 Loss2: 1.367169 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.479322 Loss1: 0.143281 Loss2: 1.336041 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.468887 Loss1: 0.139522 Loss2: 1.329365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465369 Loss1: 0.123202 Loss2: 1.342167 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.327133 Loss1: 0.039843 Loss2: 1.287290
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469807 Loss1: 0.132800 Loss2: 1.337007
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473516 Loss1: 0.137630 Loss2: 1.335886
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485215 Loss1: 0.143601 Loss2: 1.341613
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431323 Loss1: 0.091689 Loss2: 1.339634
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.538141 Loss1: 0.666968 Loss2: 1.871173
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.841444 Loss1: 0.431171 Loss2: 1.410274
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.728007 Loss1: 0.275863 Loss2: 1.452144
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572096 Loss1: 0.171395 Loss2: 1.400701
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.359005 Loss1: 0.529912 Loss2: 1.829093
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.724898 Loss1: 0.340932 Loss2: 1.383966 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.558910 Loss1: 0.145576 Loss2: 1.413334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526376 Loss1: 0.148261 Loss2: 1.378115 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555149 Loss1: 0.168046 Loss2: 1.387104 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501658 Loss1: 0.120820 Loss2: 1.380837 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450294 Loss1: 0.072022 Loss2: 1.378271 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419403 Loss1: 0.055732 Loss2: 1.363671 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.807612 Loss1: 0.423045 Loss2: 1.384567 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.627644 Loss1: 0.241155 Loss2: 1.386489 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.586569 Loss1: 0.667698 Loss2: 1.918871
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.602429 Loss1: 0.211427 Loss2: 1.391002
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.534587 Loss1: 0.149290 Loss2: 1.385297 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.547293 Loss1: 0.171104 Loss2: 1.376189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.558065 Loss1: 0.168612 Loss2: 1.389454 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469388 Loss1: 0.101005 Loss2: 1.368383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448477 Loss1: 0.089760 Loss2: 1.358717 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.378448 Loss1: 0.043875 Loss2: 1.334573 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.446684 Loss1: 0.644699 Loss2: 1.801984 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.557157 Loss1: 0.181756 Loss2: 1.375401 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500327 Loss1: 0.156954 Loss2: 1.343373
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.514464 Loss1: 0.582193 Loss2: 1.932271
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484148 Loss1: 0.146728 Loss2: 1.337419
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.757267 Loss1: 0.343073 Loss2: 1.414193
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.539500 Loss1: 0.203236 Loss2: 1.336263
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.760257 Loss1: 0.296832 Loss2: 1.463425
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451786 Loss1: 0.106311 Loss2: 1.345475
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.656757 Loss1: 0.233271 Loss2: 1.423486
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420593 Loss1: 0.091265 Loss2: 1.329328
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.565842 Loss1: 0.147110 Loss2: 1.418732
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403180 Loss1: 0.078110 Loss2: 1.325070
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.517785 Loss1: 0.112373 Loss2: 1.405412
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377146 Loss1: 0.059731 Loss2: 1.317415
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488748 Loss1: 0.089200 Loss2: 1.399548
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472031 Loss1: 0.076600 Loss2: 1.395431
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461125 Loss1: 0.071966 Loss2: 1.389159
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442680 Loss1: 0.057270 Loss2: 1.385410
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.349419 Loss1: 0.563646 Loss2: 1.785773
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.735494 Loss1: 0.411940 Loss2: 1.323554
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.675043 Loss1: 0.300913 Loss2: 1.374131
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.341355 Loss1: 0.514437 Loss2: 1.826918
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549005 Loss1: 0.228452 Loss2: 1.320553
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527634 Loss1: 0.185485 Loss2: 1.342149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460317 Loss1: 0.141128 Loss2: 1.319189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433758 Loss1: 0.119911 Loss2: 1.313846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.407594 Loss1: 0.105982 Loss2: 1.301612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418852 Loss1: 0.114309 Loss2: 1.304543 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411232 Loss1: 0.102933 Loss2: 1.308299 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407706 Loss1: 0.069427 Loss2: 1.338279 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.235759 Loss1: 0.479117 Loss2: 1.756641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568758 Loss1: 0.212073 Loss2: 1.356685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.503348 Loss1: 0.667339 Loss2: 1.836008
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488468 Loss1: 0.156008 Loss2: 1.332461
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.743624 Loss1: 0.401287 Loss2: 1.342338
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.445385 Loss1: 0.121450 Loss2: 1.323935
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556525 Loss1: 0.206139 Loss2: 1.350386
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.446935 Loss1: 0.127564 Loss2: 1.319372
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.483738 Loss1: 0.160163 Loss2: 1.323576
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473196 Loss1: 0.155413 Loss2: 1.317783
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405330 Loss1: 0.084210 Loss2: 1.321120 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373116 Loss1: 0.070982 Loss2: 1.302133 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361787 Loss1: 0.056508 Loss2: 1.305279 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.327793 Loss1: 0.033215 Loss2: 1.294578 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.386754 Loss1: 0.574189 Loss2: 1.812565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.659466 Loss1: 0.272783 Loss2: 1.386683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.660134 Loss1: 0.304824 Loss2: 1.355310
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.353123 Loss1: 0.532803 Loss2: 1.820319
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.628964 Loss1: 0.266221 Loss2: 1.362744 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556406 Loss1: 0.175552 Loss2: 1.380854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.504649 Loss1: 0.152006 Loss2: 1.352643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.478292 Loss1: 0.126874 Loss2: 1.351418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463014 Loss1: 0.114698 Loss2: 1.348316 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436129 Loss1: 0.090147 Loss2: 1.345982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402587 Loss1: 0.067534 Loss2: 1.335053 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.660479 Loss1: 0.663629 Loss2: 1.996850 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634787 Loss1: 0.194293 Loss2: 1.440494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551189 Loss1: 0.129604 Loss2: 1.421585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.565025 Loss1: 0.134554 Loss2: 1.430471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.521231 Loss1: 0.105817 Loss2: 1.415415 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497028 Loss1: 0.082448 Loss2: 1.414579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.483309 Loss1: 0.077505 Loss2: 1.405803 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.473938 Loss1: 0.069883 Loss2: 1.404055 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475225 Loss1: 0.133170 Loss2: 1.342055 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409641 Loss1: 0.076690 Loss2: 1.332951 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440417 Loss1: 0.113194 Loss2: 1.327224
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.416422 Loss1: 0.551876 Loss2: 1.864546
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.634355 Loss1: 0.290644 Loss2: 1.343711
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.529030 Loss1: 0.173942 Loss2: 1.355088
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508692 Loss1: 0.165471 Loss2: 1.343221
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492517 Loss1: 0.156281 Loss2: 1.336235
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.415851 Loss1: 0.579707 Loss2: 1.836144
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444931 Loss1: 0.101088 Loss2: 1.343842
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.716095 Loss1: 0.357619 Loss2: 1.358476
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467031 Loss1: 0.139807 Loss2: 1.327225
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.666432 Loss1: 0.284478 Loss2: 1.381955
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460909 Loss1: 0.122393 Loss2: 1.338516
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553656 Loss1: 0.200627 Loss2: 1.353028
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417611 Loss1: 0.086436 Loss2: 1.331175
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.537132 Loss1: 0.175961 Loss2: 1.361171
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371221 Loss1: 0.048360 Loss2: 1.322861
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483598 Loss1: 0.130284 Loss2: 1.353314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436359 Loss1: 0.094771 Loss2: 1.341589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410238 Loss1: 0.073624 Loss2: 1.336614
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.375591 Loss1: 0.560352 Loss2: 1.815239
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622294 Loss1: 0.293436 Loss2: 1.328858
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.507460 Loss1: 0.151723 Loss2: 1.355736
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.470361 Loss1: 0.156714 Loss2: 1.313647
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.494672 Loss1: 0.176054 Loss2: 1.318618
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.600459 Loss1: 0.706289 Loss2: 1.894170
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442944 Loss1: 0.114900 Loss2: 1.328044
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.799373 Loss1: 0.384220 Loss2: 1.415152
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469449 Loss1: 0.150228 Loss2: 1.319222
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.630219 Loss1: 0.188043 Loss2: 1.442176
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425080 Loss1: 0.105237 Loss2: 1.319843
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592401 Loss1: 0.201251 Loss2: 1.391151
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441873 Loss1: 0.125465 Loss2: 1.316408
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.619582 Loss1: 0.209286 Loss2: 1.410295
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421686 Loss1: 0.102115 Loss2: 1.319571
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.553117 Loss1: 0.163932 Loss2: 1.389185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480980 Loss1: 0.095519 Loss2: 1.385461 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435838 Loss1: 0.052743 Loss2: 1.383095
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.302283 Loss1: 0.429508 Loss2: 1.872776
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.667885 Loss1: 0.296070 Loss2: 1.371815
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.701417 Loss1: 0.294691 Loss2: 1.406726
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.566700 Loss1: 0.186830 Loss2: 1.379870
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561963 Loss1: 0.195301 Loss2: 1.366662
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530214 Loss1: 0.151333 Loss2: 1.378881
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.271884 Loss1: 0.437579 Loss2: 1.834306
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.510101 Loss1: 0.138505 Loss2: 1.371596
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.753081 Loss1: 0.374407 Loss2: 1.378674
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.699817 Loss1: 0.287251 Loss2: 1.412566 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631641 Loss1: 0.258349 Loss2: 1.373292 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539261 Loss1: 0.166510 Loss2: 1.372751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.479060 Loss1: 0.121444 Loss2: 1.357616 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.469959 Loss1: 0.121935 Loss2: 1.348023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.733472 Loss1: 0.387444 Loss2: 1.346028 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994485
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528114 Loss1: 0.175931 Loss2: 1.352183 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499804 Loss1: 0.141880 Loss2: 1.357924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.457185 Loss1: 0.115811 Loss2: 1.341374 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445630 Loss1: 0.102502 Loss2: 1.343128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401962 Loss1: 0.060212 Loss2: 1.341750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379219 Loss1: 0.045076 Loss2: 1.334143 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466872 Loss1: 0.090950 Loss2: 1.375923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446249 Loss1: 0.084820 Loss2: 1.361429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.345154 Loss1: 0.535820 Loss2: 1.809334
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422543 Loss1: 0.064119 Loss2: 1.358425
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.743979 Loss1: 0.395393 Loss2: 1.348586
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431647 Loss1: 0.074201 Loss2: 1.357446
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556156 Loss1: 0.210674 Loss2: 1.345481 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.574172 Loss1: 0.213361 Loss2: 1.360812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.493855 Loss1: 0.147141 Loss2: 1.346715
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.387350 Loss1: 0.565337 Loss2: 1.822013
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.716015 Loss1: 0.380261 Loss2: 1.335754 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.655697 Loss1: 0.261181 Loss2: 1.394516 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381629 Loss1: 0.057573 Loss2: 1.324055
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554476 Loss1: 0.219527 Loss2: 1.334950
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487860 Loss1: 0.157346 Loss2: 1.330513
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421224 Loss1: 0.098740 Loss2: 1.322483
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.383338 Loss1: 0.074462 Loss2: 1.308877
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.367653 Loss1: 0.061069 Loss2: 1.306584
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.474039 Loss1: 0.567456 Loss2: 1.906583
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381472 Loss1: 0.080408 Loss2: 1.301064
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348188 Loss1: 0.048684 Loss2: 1.299504 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.565386 Loss1: 0.155858 Loss2: 1.409529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.546001 Loss1: 0.146986 Loss2: 1.399015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.501395 Loss1: 0.108503 Loss2: 1.392892
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.566637 Loss1: 0.696733 Loss2: 1.869904
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446664 Loss1: 0.064144 Loss2: 1.382521
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.768147 Loss1: 0.424918 Loss2: 1.343229
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425239 Loss1: 0.046829 Loss2: 1.378411
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.626265 Loss1: 0.235734 Loss2: 1.390531
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.542654 Loss1: 0.193286 Loss2: 1.349368
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410795 Loss1: 0.037611 Loss2: 1.373184
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.435624 Loss1: 0.093622 Loss2: 1.342002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418386 Loss1: 0.093927 Loss2: 1.324459 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.374637 Loss1: 0.593610 Loss2: 1.781027 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.610428 Loss1: 0.298632 Loss2: 1.311796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.469696 Loss1: 0.146976 Loss2: 1.322720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463638 Loss1: 0.150796 Loss2: 1.312842 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432374 Loss1: 0.121284 Loss2: 1.311090 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440628 Loss1: 0.121644 Loss2: 1.318984 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417040 Loss1: 0.115099 Loss2: 1.301941 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364267 Loss1: 0.062407 Loss2: 1.301861 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.571668 Loss1: 0.180678 Loss2: 1.390990 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.488805 Loss1: 0.115092 Loss2: 1.373714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441868 Loss1: 0.069444 Loss2: 1.372424
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.367880 Loss1: 0.503889 Loss2: 1.863991
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.704233 Loss1: 0.321533 Loss2: 1.382700
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591171 Loss1: 0.178872 Loss2: 1.412300
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527495 Loss1: 0.150829 Loss2: 1.376666
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506294 Loss1: 0.130691 Loss2: 1.375603
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.536932 Loss1: 0.648417 Loss2: 1.888516
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.495817 Loss1: 0.123703 Loss2: 1.372113
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.806461 Loss1: 0.440388 Loss2: 1.366074
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466538 Loss1: 0.106433 Loss2: 1.360105
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.431611 Loss1: 0.068690 Loss2: 1.362921 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414917 Loss1: 0.064582 Loss2: 1.350336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384259 Loss1: 0.032365 Loss2: 1.351894 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480973 Loss1: 0.133838 Loss2: 1.347135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418026 Loss1: 0.076666 Loss2: 1.341360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408745 Loss1: 0.075182 Loss2: 1.333563
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.537182 Loss1: 0.628504 Loss2: 1.908678
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.878605 Loss1: 0.471948 Loss2: 1.406657
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.767831 Loss1: 0.287384 Loss2: 1.480446
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.634864 Loss1: 0.204451 Loss2: 1.430413
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572243 Loss1: 0.159149 Loss2: 1.413094
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.315188 Loss1: 0.526733 Loss2: 1.788455
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.522162 Loss1: 0.110324 Loss2: 1.411838
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.682146 Loss1: 0.342962 Loss2: 1.339183
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.508026 Loss1: 0.112438 Loss2: 1.395587
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.631028 Loss1: 0.260054 Loss2: 1.370973
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457917 Loss1: 0.066808 Loss2: 1.391109
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.575929 Loss1: 0.223701 Loss2: 1.352228
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463078 Loss1: 0.074224 Loss2: 1.388854
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424641 Loss1: 0.042643 Loss2: 1.381998
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.538028 Loss1: 0.187665 Loss2: 1.350363
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.515977 Loss1: 0.165942 Loss2: 1.350035
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464317 Loss1: 0.125750 Loss2: 1.338567
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438837 Loss1: 0.106260 Loss2: 1.332577
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409586 Loss1: 0.076013 Loss2: 1.333573
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.473004 Loss1: 0.616583 Loss2: 1.856421
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386903 Loss1: 0.065528 Loss2: 1.321375
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.643720 Loss1: 0.223335 Loss2: 1.420386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.493001 Loss1: 0.158876 Loss2: 1.334125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.617796 Loss1: 0.707527 Loss2: 1.910269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417244 Loss1: 0.078058 Loss2: 1.339186 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394176 Loss1: 0.068397 Loss2: 1.325779 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404462 Loss1: 0.082816 Loss2: 1.321646 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-12 09:24:18,695 | server.py:236 | fit_round 146 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.399204 Loss1: 0.103188 Loss2: 1.296016 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373922 Loss1: 0.077079 Loss2: 1.296843 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983073
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.369745 Loss1: 0.503802 Loss2: 1.865943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.642817 Loss1: 0.236721 Loss2: 1.406096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.447164 Loss1: 0.597375 Loss2: 1.849788 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.705031 Loss1: 0.328495 Loss2: 1.376537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.658676 Loss1: 0.244763 Loss2: 1.413913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.545685 Loss1: 0.174955 Loss2: 1.370730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523803 Loss1: 0.156986 Loss2: 1.366817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486162 Loss1: 0.113233 Loss2: 1.372929 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465819 Loss1: 0.110801 Loss2: 1.355018 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406521 Loss1: 0.058811 Loss2: 1.347710 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.617855 Loss1: 0.303835 Loss2: 1.314020 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524254 Loss1: 0.213542 Loss2: 1.310712 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.517277 Loss1: 0.642919 Loss2: 1.874358
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472610 Loss1: 0.164341 Loss2: 1.308269
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.771449 Loss1: 0.391551 Loss2: 1.379898
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.407289 Loss1: 0.108892 Loss2: 1.298396
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.640619 Loss1: 0.228678 Loss2: 1.411941
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.385572 Loss1: 0.093974 Loss2: 1.291598
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.548631 Loss1: 0.172005 Loss2: 1.376625
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.357780 Loss1: 0.072872 Loss2: 1.284908
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491730 Loss1: 0.121443 Loss2: 1.370286
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.347961 Loss1: 0.064097 Loss2: 1.283864
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474927 Loss1: 0.113339 Loss2: 1.361589
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.329549 Loss1: 0.051092 Loss2: 1.278456
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422622 Loss1: 0.075490 Loss2: 1.347132 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410270 Loss1: 0.071095 Loss2: 1.339174 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 09:24:18,695][flwr][DEBUG] - fit_round 146 received 50 results and 0 failures
-INFO flwr 2023-10-12 09:24:59,817 | server.py:125 | fit progress: (146, 2.2189620870370836, {'accuracy': 0.5958}, 336807.595103479)
->> Test accuracy: 0.595800
-[2023-10-12 09:24:59,817][flwr][INFO] - fit progress: (146, 2.2189620870370836, {'accuracy': 0.5958}, 336807.595103479)
-DEBUG flwr 2023-10-12 09:24:59,817 | server.py:173 | evaluate_round 146: strategy sampled 50 clients (out of 50)
-[2023-10-12 09:24:59,817][flwr][DEBUG] - evaluate_round 146: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 09:34:05,961 | server.py:187 | evaluate_round 146 received 50 results and 0 failures
-[2023-10-12 09:34:05,961][flwr][DEBUG] - evaluate_round 146 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 09:34:05,962 | server.py:222 | fit_round 147: strategy sampled 50 clients (out of 50)
-[2023-10-12 09:34:05,962][flwr][DEBUG] - fit_round 147: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.439478 Loss1: 0.647178 Loss2: 1.792300
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.739556 Loss1: 0.416941 Loss2: 1.322615
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618374 Loss1: 0.258735 Loss2: 1.359639
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516649 Loss1: 0.194620 Loss2: 1.322029
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.390286 Loss1: 0.525887 Loss2: 1.864400
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484017 Loss1: 0.156576 Loss2: 1.327442
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.642569 Loss1: 0.303594 Loss2: 1.338975
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515143 Loss1: 0.198389 Loss2: 1.316754
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608381 Loss1: 0.234781 Loss2: 1.373600
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.471092 Loss1: 0.153617 Loss2: 1.317475
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.512014 Loss1: 0.170759 Loss2: 1.341255
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458683 Loss1: 0.150714 Loss2: 1.307969
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.451767 Loss1: 0.113924 Loss2: 1.337844
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373054 Loss1: 0.064655 Loss2: 1.308399
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.414986 Loss1: 0.072866 Loss2: 1.342121
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.352746 Loss1: 0.054816 Loss2: 1.297931
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.414580 Loss1: 0.090591 Loss2: 1.323989
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397300 Loss1: 0.074712 Loss2: 1.322588
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373888 Loss1: 0.054144 Loss2: 1.319743
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.349167 Loss1: 0.034225 Loss2: 1.314942
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.396828 Loss1: 0.562697 Loss2: 1.834132
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.754116 Loss1: 0.403048 Loss2: 1.351068
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.577223 Loss1: 0.185835 Loss2: 1.391388
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.472034 Loss1: 0.124207 Loss2: 1.347827
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.306248 Loss1: 0.496178 Loss2: 1.810070
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.717176 Loss1: 0.390792 Loss2: 1.326384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677595 Loss1: 0.296692 Loss2: 1.380903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519623 Loss1: 0.187559 Loss2: 1.332064 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493724 Loss1: 0.161692 Loss2: 1.332031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459494 Loss1: 0.125851 Loss2: 1.333643 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385229 Loss1: 0.055134 Loss2: 1.330095
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.397016 Loss1: 0.079459 Loss2: 1.317557
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.370179 Loss1: 0.063924 Loss2: 1.306255
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381833 Loss1: 0.081424 Loss2: 1.300409
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367295 Loss1: 0.060096 Loss2: 1.307199
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.366598 Loss1: 0.560462 Loss2: 1.806136
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.693067 Loss1: 0.343614 Loss2: 1.349453
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.585633 Loss1: 0.197084 Loss2: 1.388549
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511064 Loss1: 0.168955 Loss2: 1.342109
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.388262 Loss1: 0.598216 Loss2: 1.790046
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.696851 Loss1: 0.365893 Loss2: 1.330958 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.624492 Loss1: 0.263308 Loss2: 1.361184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.515659 Loss1: 0.185407 Loss2: 1.330252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476541 Loss1: 0.141541 Loss2: 1.334999 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423046 Loss1: 0.105005 Loss2: 1.318041 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381734 Loss1: 0.052799 Loss2: 1.328935
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.407492 Loss1: 0.091372 Loss2: 1.316121
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372656 Loss1: 0.061726 Loss2: 1.310930
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371552 Loss1: 0.070684 Loss2: 1.300868
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.329891 Loss1: 0.036859 Loss2: 1.293032
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.202803 Loss1: 0.435325 Loss2: 1.767478
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.700650 Loss1: 0.363853 Loss2: 1.336797
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.583243 Loss1: 0.219169 Loss2: 1.364074
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.570411 Loss1: 0.630636 Loss2: 1.939774
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489983 Loss1: 0.163898 Loss2: 1.326085
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.773862 Loss1: 0.350635 Loss2: 1.423227
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.458412 Loss1: 0.124794 Loss2: 1.333617
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.766008 Loss1: 0.295842 Loss2: 1.470165
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442921 Loss1: 0.117462 Loss2: 1.325459
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.706137 Loss1: 0.270814 Loss2: 1.435323
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466167 Loss1: 0.139992 Loss2: 1.326175
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464551 Loss1: 0.135683 Loss2: 1.328868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433262 Loss1: 0.112741 Loss2: 1.320521 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424506 Loss1: 0.111946 Loss2: 1.312559 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.503817 Loss1: 0.102369 Loss2: 1.401448 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.226434 Loss1: 0.456432 Loss2: 1.770002 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.606523 Loss1: 0.220463 Loss2: 1.386060 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.533094 Loss1: 0.626955 Loss2: 1.906139
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520098 Loss1: 0.168427 Loss2: 1.351671
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.711004 Loss1: 0.310652 Loss2: 1.400352
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.505622 Loss1: 0.144328 Loss2: 1.361295
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588239 Loss1: 0.179428 Loss2: 1.408811
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444304 Loss1: 0.098917 Loss2: 1.345387
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586218 Loss1: 0.191666 Loss2: 1.394552
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418483 Loss1: 0.085033 Loss2: 1.333450
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393951 Loss1: 0.065652 Loss2: 1.328299 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404429 Loss1: 0.076526 Loss2: 1.327903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380573 Loss1: 0.056699 Loss2: 1.323874 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467728 Loss1: 0.092421 Loss2: 1.375307 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.394360 Loss1: 0.508376 Loss2: 1.885984 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.675670 Loss1: 0.235872 Loss2: 1.439798 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.566104 Loss1: 0.162956 Loss2: 1.403147
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.475559 Loss1: 0.566125 Loss2: 1.909434
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.684439 Loss1: 0.271980 Loss2: 1.412460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.618994 Loss1: 0.203496 Loss2: 1.415499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.603480 Loss1: 0.190415 Loss2: 1.413065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.599633 Loss1: 0.183439 Loss2: 1.416194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.590221 Loss1: 0.194053 Loss2: 1.396169 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412236 Loss1: 0.036061 Loss2: 1.376175
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.544342 Loss1: 0.141971 Loss2: 1.402371
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.524816 Loss1: 0.126725 Loss2: 1.398090
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.515409 Loss1: 0.123160 Loss2: 1.392248
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487280 Loss1: 0.099145 Loss2: 1.388136
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.694927 Loss1: 0.785432 Loss2: 1.909495
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.811983 Loss1: 0.431721 Loss2: 1.380262
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.664738 Loss1: 0.256024 Loss2: 1.408714
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.538378 Loss1: 0.179850 Loss2: 1.358527
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.439816 Loss1: 0.589027 Loss2: 1.850789
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.751907 Loss1: 0.364444 Loss2: 1.387463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.643844 Loss1: 0.230136 Loss2: 1.413707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436815 Loss1: 0.093119 Loss2: 1.343696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432521 Loss1: 0.091646 Loss2: 1.340875 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404290 Loss1: 0.058510 Loss2: 1.345780 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468142 Loss1: 0.101498 Loss2: 1.366644 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449390 Loss1: 0.080418 Loss2: 1.368972 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434371 Loss1: 0.075819 Loss2: 1.358552
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.281976 Loss1: 0.473434 Loss2: 1.808542
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.660212 Loss1: 0.297082 Loss2: 1.363130
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.584377 Loss1: 0.215242 Loss2: 1.369135
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.543992 Loss1: 0.194774 Loss2: 1.349218
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462034 Loss1: 0.114075 Loss2: 1.347959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465493 Loss1: 0.128904 Loss2: 1.336589 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414977 Loss1: 0.082700 Loss2: 1.332278 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403296 Loss1: 0.074935 Loss2: 1.328360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406285 Loss1: 0.080744 Loss2: 1.325540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.521441 Loss1: 0.201098 Loss2: 1.320343 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983456
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404834 Loss1: 0.090938 Loss2: 1.313896 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.374654 Loss1: 0.544741 Loss2: 1.829913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.602582 Loss1: 0.212280 Loss2: 1.390302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523340 Loss1: 0.176094 Loss2: 1.347246
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.300048 Loss1: 0.538552 Loss2: 1.761497
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461969 Loss1: 0.113967 Loss2: 1.348002
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.662438 Loss1: 0.334152 Loss2: 1.328286
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.663673 Loss1: 0.292332 Loss2: 1.371341 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.513218 Loss1: 0.189192 Loss2: 1.324026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507106 Loss1: 0.177234 Loss2: 1.329872 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.415553 Loss1: 0.108872 Loss2: 1.306681 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395764 Loss1: 0.095362 Loss2: 1.300403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.350294 Loss1: 0.059675 Loss2: 1.290619 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.412117 Loss1: 0.541216 Loss2: 1.870901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741785 Loss1: 0.258807 Loss2: 1.482977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.399310 Loss1: 0.609472 Loss2: 1.789838 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.639611 Loss1: 0.320215 Loss2: 1.319396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.519765 Loss1: 0.152509 Loss2: 1.367255 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.443516 Loss1: 0.128605 Loss2: 1.314911 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.430521 Loss1: 0.111477 Loss2: 1.319044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.394008 Loss1: 0.076311 Loss2: 1.317696 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.482241 Loss1: 0.086291 Loss2: 1.395950
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.356726 Loss1: 0.052053 Loss2: 1.304674
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386932 Loss1: 0.092467 Loss2: 1.294465
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.375597 Loss1: 0.078776 Loss2: 1.296821
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365398 Loss1: 0.059449 Loss2: 1.305949
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.434551 Loss1: 0.605132 Loss2: 1.829419
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.729483 Loss1: 0.365922 Loss2: 1.363561
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.602335 Loss1: 0.218334 Loss2: 1.384002
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.501935 Loss1: 0.144330 Loss2: 1.357605
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.482792 Loss1: 0.570902 Loss2: 1.911889
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.467495 Loss1: 0.114693 Loss2: 1.352802
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.771892 Loss1: 0.342301 Loss2: 1.429591
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416675 Loss1: 0.074266 Loss2: 1.342409
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670846 Loss1: 0.205835 Loss2: 1.465011
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.405791 Loss1: 0.068966 Loss2: 1.336825
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540602 Loss1: 0.121009 Loss2: 1.419593
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.386507 Loss1: 0.054221 Loss2: 1.332287
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.584445 Loss1: 0.168287 Loss2: 1.416158
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374076 Loss1: 0.048700 Loss2: 1.325376
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567091 Loss1: 0.145559 Loss2: 1.421532
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354693 Loss1: 0.031735 Loss2: 1.322958
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487839 Loss1: 0.076180 Loss2: 1.411659
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471245 Loss1: 0.073577 Loss2: 1.397668
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454438 Loss1: 0.060646 Loss2: 1.393791
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438939 Loss1: 0.046844 Loss2: 1.392095
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.270034 Loss1: 0.442183 Loss2: 1.827851
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.688816 Loss1: 0.345878 Loss2: 1.342938
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.584357 Loss1: 0.203807 Loss2: 1.380550
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.546648 Loss1: 0.188404 Loss2: 1.358245
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.432038 Loss1: 0.600901 Loss2: 1.831136
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.736853 Loss1: 0.386545 Loss2: 1.350308 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590436 Loss1: 0.209199 Loss2: 1.381236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.513464 Loss1: 0.165750 Loss2: 1.347714 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394814 Loss1: 0.069657 Loss2: 1.325157
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483416 Loss1: 0.138477 Loss2: 1.344939
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479081 Loss1: 0.138685 Loss2: 1.340396 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385012 Loss1: 0.071342 Loss2: 1.313670
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426092 Loss1: 0.086486 Loss2: 1.339606
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425131 Loss1: 0.101170 Loss2: 1.323961
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434241 Loss1: 0.103210 Loss2: 1.331031
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438992 Loss1: 0.100534 Loss2: 1.338458
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.449643 Loss1: 0.568490 Loss2: 1.881152
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.731711 Loss1: 0.345235 Loss2: 1.386476
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.677737 Loss1: 0.250222 Loss2: 1.427514
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.633469 Loss1: 0.242827 Loss2: 1.390642
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.580040 Loss1: 0.668171 Loss2: 1.911869
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.808819 Loss1: 0.405234 Loss2: 1.403586 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.664235 Loss1: 0.236832 Loss2: 1.427403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595937 Loss1: 0.196917 Loss2: 1.399020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501356 Loss1: 0.115619 Loss2: 1.385737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.489488 Loss1: 0.112487 Loss2: 1.377001 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389283 Loss1: 0.037666 Loss2: 1.351618
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.466267 Loss1: 0.091438 Loss2: 1.374829
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418245 Loss1: 0.054565 Loss2: 1.363680
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407037 Loss1: 0.051825 Loss2: 1.355213
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.432930 Loss1: 0.075995 Loss2: 1.356935
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.540996 Loss1: 0.587422 Loss2: 1.953574
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.837180 Loss1: 0.383053 Loss2: 1.454127
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.718096 Loss1: 0.222190 Loss2: 1.495906
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.629410 Loss1: 0.190895 Loss2: 1.438514
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.579330 Loss1: 0.649821 Loss2: 1.929509
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.867367 Loss1: 0.472360 Loss2: 1.395007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.548997 Loss1: 0.120539 Loss2: 1.428457
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.703385 Loss1: 0.244595 Loss2: 1.458791
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.541849 Loss1: 0.119253 Loss2: 1.422596
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.560189 Loss1: 0.164089 Loss2: 1.396100
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.516840 Loss1: 0.097926 Loss2: 1.418914
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514299 Loss1: 0.125219 Loss2: 1.389080
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470628 Loss1: 0.087465 Loss2: 1.383163
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.490330 Loss1: 0.072321 Loss2: 1.418009
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472095 Loss1: 0.097289 Loss2: 1.374807
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457265 Loss1: 0.043306 Loss2: 1.413958
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423969 Loss1: 0.052649 Loss2: 1.371321 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.423124 Loss1: 0.528722 Loss2: 1.894403 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.732768 Loss1: 0.295360 Loss2: 1.437408 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.592532 Loss1: 0.210789 Loss2: 1.381743
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.419044 Loss1: 0.519541 Loss2: 1.899503
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.563638 Loss1: 0.164902 Loss2: 1.398736
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.738800 Loss1: 0.341209 Loss2: 1.397591
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.544862 Loss1: 0.163429 Loss2: 1.381433
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.634243 Loss1: 0.213237 Loss2: 1.421006
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462610 Loss1: 0.082283 Loss2: 1.380327
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.544655 Loss1: 0.151962 Loss2: 1.392693
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473018 Loss1: 0.094289 Loss2: 1.378729
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.531096 Loss1: 0.145645 Loss2: 1.385451
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459970 Loss1: 0.087042 Loss2: 1.372928
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492242 Loss1: 0.108639 Loss2: 1.383603
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436857 Loss1: 0.067134 Loss2: 1.369722
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474418 Loss1: 0.096626 Loss2: 1.377792
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.463284 Loss1: 0.086267 Loss2: 1.377017
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453548 Loss1: 0.080741 Loss2: 1.372807
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428579 Loss1: 0.060839 Loss2: 1.367740
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.441080 Loss1: 0.560447 Loss2: 1.880633
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751926 Loss1: 0.370748 Loss2: 1.381178
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.640389 Loss1: 0.221428 Loss2: 1.418961
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529679 Loss1: 0.154002 Loss2: 1.375677
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.467268 Loss1: 0.645639 Loss2: 1.821629
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.712233 Loss1: 0.365850 Loss2: 1.346383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.626366 Loss1: 0.239583 Loss2: 1.386784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577426 Loss1: 0.222453 Loss2: 1.354973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491458 Loss1: 0.147870 Loss2: 1.343588 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.458700 Loss1: 0.117914 Loss2: 1.340786 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405557 Loss1: 0.058905 Loss2: 1.346652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426256 Loss1: 0.096387 Loss2: 1.329869
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408393 Loss1: 0.077443 Loss2: 1.330950
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363629 Loss1: 0.050865 Loss2: 1.312764
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351622 Loss1: 0.044349 Loss2: 1.307274
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.379907 Loss1: 0.494109 Loss2: 1.885799
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.700899 Loss1: 0.306560 Loss2: 1.394339
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.674436 Loss1: 0.230234 Loss2: 1.444202
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602516 Loss1: 0.198981 Loss2: 1.403535
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.615672 Loss1: 0.636758 Loss2: 1.978914
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.794492 Loss1: 0.408184 Loss2: 1.386308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.563378 Loss1: 0.161495 Loss2: 1.401883
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.747284 Loss1: 0.328877 Loss2: 1.418407
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.721464 Loss1: 0.293396 Loss2: 1.428068
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523308 Loss1: 0.127348 Loss2: 1.395959
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.615067 Loss1: 0.217976 Loss2: 1.397091
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.500207 Loss1: 0.108400 Loss2: 1.391807
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.479215 Loss1: 0.099172 Loss2: 1.380043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474102 Loss1: 0.100547 Loss2: 1.373554 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438035 Loss1: 0.071718 Loss2: 1.366316 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.413618 Loss1: 0.591206 Loss2: 1.822412
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.685985 Loss1: 0.353895 Loss2: 1.332090
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635439 Loss1: 0.262483 Loss2: 1.372956
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489960 Loss1: 0.159636 Loss2: 1.330324
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.488662 Loss1: 0.625078 Loss2: 1.863583
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.722749 Loss1: 0.398817 Loss2: 1.323932
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.450181 Loss1: 0.125917 Loss2: 1.324265
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564708 Loss1: 0.213822 Loss2: 1.350886
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443392 Loss1: 0.121692 Loss2: 1.321700
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395825 Loss1: 0.072814 Loss2: 1.323011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.359054 Loss1: 0.050479 Loss2: 1.308575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.359205 Loss1: 0.056940 Loss2: 1.302265 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354661 Loss1: 0.057925 Loss2: 1.296737 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368818 Loss1: 0.062437 Loss2: 1.306381 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.397043 Loss1: 0.573185 Loss2: 1.823858
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751137 Loss1: 0.396905 Loss2: 1.354233
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.753769 Loss1: 0.345274 Loss2: 1.408495
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.622626 Loss1: 0.262958 Loss2: 1.359668
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.467540 Loss1: 0.611770 Loss2: 1.855770
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.560295 Loss1: 0.183421 Loss2: 1.376874
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.731035 Loss1: 0.365230 Loss2: 1.365805
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518781 Loss1: 0.161655 Loss2: 1.357126
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.606925 Loss1: 0.200369 Loss2: 1.406556
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.526193 Loss1: 0.168442 Loss2: 1.357751
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588886 Loss1: 0.228706 Loss2: 1.360181
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.478367 Loss1: 0.125605 Loss2: 1.352762
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.551572 Loss1: 0.181120 Loss2: 1.370451
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419850 Loss1: 0.070607 Loss2: 1.349242
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471636 Loss1: 0.124094 Loss2: 1.347542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409200 Loss1: 0.066278 Loss2: 1.342922
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463142 Loss1: 0.121960 Loss2: 1.341181
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414369 Loss1: 0.074104 Loss2: 1.340265
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394107 Loss1: 0.061097 Loss2: 1.333010
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375721 Loss1: 0.047758 Loss2: 1.327963
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.742326 Loss1: 0.742260 Loss2: 2.000066
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.746486 Loss1: 0.381564 Loss2: 1.364922
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.617143 Loss1: 0.239683 Loss2: 1.377460
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.519000 Loss1: 0.142094 Loss2: 1.376906
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448837 Loss1: 0.091487 Loss2: 1.357350
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.441297 Loss1: 0.090913 Loss2: 1.350384 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.412862 Loss1: 0.064605 Loss2: 1.348256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380785 Loss1: 0.041429 Loss2: 1.339356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381394 Loss1: 0.053550 Loss2: 1.327844
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553914 Loss1: 0.187148 Loss2: 1.366766
-(DefaultActor pid=3765) >> Training accuracy: 0.976562
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389040 Loss1: 0.063010 Loss2: 1.326030
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530191 Loss1: 0.163623 Loss2: 1.366568
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477852 Loss1: 0.113720 Loss2: 1.364133
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.432773 Loss1: 0.083370 Loss2: 1.349403
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425656 Loss1: 0.084368 Loss2: 1.341288
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406189 Loss1: 0.066407 Loss2: 1.339782
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.379882 Loss1: 0.521097 Loss2: 1.858785
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385522 Loss1: 0.048033 Loss2: 1.337489
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.683322 Loss1: 0.241815 Loss2: 1.441507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517915 Loss1: 0.120910 Loss2: 1.397005 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473239 Loss1: 0.091370 Loss2: 1.381870
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.412889 Loss1: 0.516899 Loss2: 1.895990
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449370 Loss1: 0.072352 Loss2: 1.377018
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.619485 Loss1: 0.233891 Loss2: 1.385593
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.683984 Loss1: 0.267241 Loss2: 1.416743
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427277 Loss1: 0.050720 Loss2: 1.376557
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.643246 Loss1: 0.253078 Loss2: 1.390168
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406331 Loss1: 0.039659 Loss2: 1.366672
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.609121 Loss1: 0.206323 Loss2: 1.402798
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393046 Loss1: 0.036836 Loss2: 1.356210
-(DefaultActor pid=3765) >> Training accuracy: 0.998047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.496537 Loss1: 0.113837 Loss2: 1.382699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497278 Loss1: 0.115933 Loss2: 1.381346 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438804 Loss1: 0.070228 Loss2: 1.368577
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.403127 Loss1: 0.500058 Loss2: 1.903069
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.724658 Loss1: 0.332140 Loss2: 1.392517
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.626083 Loss1: 0.209885 Loss2: 1.416198
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564800 Loss1: 0.176079 Loss2: 1.388721
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524273 Loss1: 0.130546 Loss2: 1.393727
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.370823 Loss1: 0.596311 Loss2: 1.774513
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515673 Loss1: 0.125695 Loss2: 1.389978
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.702789 Loss1: 0.368935 Loss2: 1.333855
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495178 Loss1: 0.111485 Loss2: 1.383693
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.685914 Loss1: 0.290854 Loss2: 1.395060
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454770 Loss1: 0.081398 Loss2: 1.373372
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581164 Loss1: 0.241217 Loss2: 1.339948
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463991 Loss1: 0.086167 Loss2: 1.377824
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521836 Loss1: 0.190986 Loss2: 1.330850
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434499 Loss1: 0.064464 Loss2: 1.370035
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-12 10:03:15,266 | server.py:236 | fit_round 147 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.394007 Loss1: 0.076047 Loss2: 1.317960 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.411035 Loss1: 0.106922 Loss2: 1.304113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379304 Loss1: 0.071304 Loss2: 1.308001
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.320208 Loss1: 0.461667 Loss2: 1.858542
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.720447 Loss1: 0.339605 Loss2: 1.380842
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.673410 Loss1: 0.245440 Loss2: 1.427970
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.607835 Loss1: 0.229618 Loss2: 1.378217
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.591256 Loss1: 0.207616 Loss2: 1.383639
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.554277 Loss1: 0.619649 Loss2: 1.934627
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.923271 Loss1: 0.516448 Loss2: 1.406823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.776896 Loss1: 0.271572 Loss2: 1.505325 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477424 Loss1: 0.098789 Loss2: 1.378635
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.632182 Loss1: 0.221948 Loss2: 1.410233
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450259 Loss1: 0.078378 Loss2: 1.371881
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.559534 Loss1: 0.140855 Loss2: 1.418680
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501853 Loss1: 0.100419 Loss2: 1.401433
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427257 Loss1: 0.062559 Loss2: 1.364698
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464000 Loss1: 0.079207 Loss2: 1.384793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430866 Loss1: 0.056534 Loss2: 1.374333 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.636653 Loss1: 0.314225 Loss2: 1.322428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.493181 Loss1: 0.172299 Loss2: 1.320881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.442427 Loss1: 0.593463 Loss2: 1.848964
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468472 Loss1: 0.142465 Loss2: 1.326007
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.725733 Loss1: 0.364279 Loss2: 1.361454
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470819 Loss1: 0.147603 Loss2: 1.323216
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642356 Loss1: 0.237163 Loss2: 1.405193
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.411734 Loss1: 0.089862 Loss2: 1.321872
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586145 Loss1: 0.218988 Loss2: 1.367157
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409837 Loss1: 0.093591 Loss2: 1.316246
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.564303 Loss1: 0.196478 Loss2: 1.367825
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396757 Loss1: 0.085440 Loss2: 1.311317
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.489106 Loss1: 0.117821 Loss2: 1.371284
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397663 Loss1: 0.083573 Loss2: 1.314090
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488557 Loss1: 0.123736 Loss2: 1.364821 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464017 Loss1: 0.106249 Loss2: 1.357768 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.669644 Loss1: 0.310079 Loss2: 1.359565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535626 Loss1: 0.174825 Loss2: 1.360801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503270 Loss1: 0.142742 Loss2: 1.360527 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449057 Loss1: 0.097735 Loss2: 1.351323 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456055 Loss1: 0.106720 Loss2: 1.349335 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.431523 Loss1: 0.084883 Loss2: 1.346640 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444506 Loss1: 0.097403 Loss2: 1.347103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401907 Loss1: 0.056781 Loss2: 1.345126 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459210 Loss1: 0.099907 Loss2: 1.359303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403070 Loss1: 0.053092 Loss2: 1.349978 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 10:03:15,266][flwr][DEBUG] - fit_round 147 received 50 results and 0 failures
-INFO flwr 2023-10-12 10:03:56,846 | server.py:125 | fit progress: (147, 2.237486937365974, {'accuracy': 0.5946}, 339144.62492308597)
->> Test accuracy: 0.594600
-[2023-10-12 10:03:56,846][flwr][INFO] - fit progress: (147, 2.237486937365974, {'accuracy': 0.5946}, 339144.62492308597)
-DEBUG flwr 2023-10-12 10:03:56,847 | server.py:173 | evaluate_round 147: strategy sampled 50 clients (out of 50)
-[2023-10-12 10:03:56,847][flwr][DEBUG] - evaluate_round 147: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 10:13:05,012 | server.py:187 | evaluate_round 147 received 50 results and 0 failures
-[2023-10-12 10:13:05,012][flwr][DEBUG] - evaluate_round 147 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 10:13:05,012 | server.py:222 | fit_round 148: strategy sampled 50 clients (out of 50)
-[2023-10-12 10:13:05,012][flwr][DEBUG] - fit_round 148: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.546072 Loss1: 0.662736 Loss2: 1.883335
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.818955 Loss1: 0.417863 Loss2: 1.401092
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.711597 Loss1: 0.278420 Loss2: 1.433177
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.568763 Loss1: 0.177440 Loss2: 1.391323
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.520358 Loss1: 0.626275 Loss2: 1.894083
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.676621 Loss1: 0.293454 Loss2: 1.383167 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.663379 Loss1: 0.244206 Loss2: 1.419173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540413 Loss1: 0.156125 Loss2: 1.384287 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500917 Loss1: 0.122234 Loss2: 1.378682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470557 Loss1: 0.092678 Loss2: 1.377879 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453959 Loss1: 0.084879 Loss2: 1.369079 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445567 Loss1: 0.080784 Loss2: 1.364783 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.355027 Loss1: 0.533068 Loss2: 1.821959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.543277 Loss1: 0.169793 Loss2: 1.373484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.538225 Loss1: 0.201163 Loss2: 1.337062
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.436426 Loss1: 0.566692 Loss2: 1.869734
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.769554 Loss1: 0.381465 Loss2: 1.388088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450091 Loss1: 0.113531 Loss2: 1.336560
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671843 Loss1: 0.262508 Loss2: 1.409335
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443929 Loss1: 0.109536 Loss2: 1.334394
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.544374 Loss1: 0.167669 Loss2: 1.376705
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403677 Loss1: 0.075713 Loss2: 1.327963
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529613 Loss1: 0.142967 Loss2: 1.386647
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492958 Loss1: 0.120778 Loss2: 1.372180
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399820 Loss1: 0.080831 Loss2: 1.318988
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.482561 Loss1: 0.111814 Loss2: 1.370747
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423718 Loss1: 0.099842 Loss2: 1.323876
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.471525 Loss1: 0.107045 Loss2: 1.364480 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.265436 Loss1: 0.437991 Loss2: 1.827444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.578533 Loss1: 0.191272 Loss2: 1.387260 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.627089 Loss1: 0.665890 Loss2: 1.961199
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.571430 Loss1: 0.210419 Loss2: 1.361011
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.528395 Loss1: 0.153925 Loss2: 1.374470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526658 Loss1: 0.156819 Loss2: 1.369839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486468 Loss1: 0.121375 Loss2: 1.365094 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522523 Loss1: 0.115669 Loss2: 1.406854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469345 Loss1: 0.066248 Loss2: 1.403097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453764 Loss1: 0.067209 Loss2: 1.386556 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447314 Loss1: 0.062609 Loss2: 1.384704 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.478357 Loss1: 0.631486 Loss2: 1.846871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.650854 Loss1: 0.242952 Loss2: 1.407901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606564 Loss1: 0.238413 Loss2: 1.368151
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.519102 Loss1: 0.646046 Loss2: 1.873056
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547651 Loss1: 0.181409 Loss2: 1.366242
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.738124 Loss1: 0.329440 Loss2: 1.408684
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465565 Loss1: 0.103758 Loss2: 1.361808
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.637611 Loss1: 0.209732 Loss2: 1.427880
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.454210 Loss1: 0.103694 Loss2: 1.350516
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611229 Loss1: 0.214630 Loss2: 1.396599
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451677 Loss1: 0.097466 Loss2: 1.354211
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523032 Loss1: 0.125252 Loss2: 1.397780
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459529 Loss1: 0.110419 Loss2: 1.349110
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.534067 Loss1: 0.149247 Loss2: 1.384820
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433013 Loss1: 0.086695 Loss2: 1.346318
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508978 Loss1: 0.130046 Loss2: 1.378932
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.509661 Loss1: 0.127266 Loss2: 1.382395
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.492731 Loss1: 0.116679 Loss2: 1.376052
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464798 Loss1: 0.087147 Loss2: 1.377651
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.411979 Loss1: 0.580514 Loss2: 1.831464
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.723916 Loss1: 0.361498 Loss2: 1.362418
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647659 Loss1: 0.262727 Loss2: 1.384932
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.641397 Loss1: 0.269411 Loss2: 1.371986
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.596798 Loss1: 0.676110 Loss2: 1.920688
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.826159 Loss1: 0.432314 Loss2: 1.393846 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684749 Loss1: 0.257746 Loss2: 1.427003 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472292 Loss1: 0.121683 Loss2: 1.350609
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.578953 Loss1: 0.205037 Loss2: 1.373916
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411471 Loss1: 0.071150 Loss2: 1.340321
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535054 Loss1: 0.160247 Loss2: 1.374807
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373685 Loss1: 0.043494 Loss2: 1.330191
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519440 Loss1: 0.141358 Loss2: 1.378082
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480946 Loss1: 0.108887 Loss2: 1.372059
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367804 Loss1: 0.043160 Loss2: 1.324644
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454592 Loss1: 0.093400 Loss2: 1.361192 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.311372 Loss1: 0.510300 Loss2: 1.801072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.657311 Loss1: 0.278292 Loss2: 1.379019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563169 Loss1: 0.216240 Loss2: 1.346929
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.351283 Loss1: 0.528860 Loss2: 1.822423
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.684438 Loss1: 0.352108 Loss2: 1.332329 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.579232 Loss1: 0.218734 Loss2: 1.360499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509271 Loss1: 0.192088 Loss2: 1.317183 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495349 Loss1: 0.176408 Loss2: 1.318941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478162 Loss1: 0.159971 Loss2: 1.318191 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.348423 Loss1: 0.044808 Loss2: 1.303615
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422571 Loss1: 0.117187 Loss2: 1.305384
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.379943 Loss1: 0.076125 Loss2: 1.303818
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389637 Loss1: 0.090972 Loss2: 1.298665
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.346586 Loss1: 0.053597 Loss2: 1.292988
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.292697 Loss1: 0.474654 Loss2: 1.818043
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.657759 Loss1: 0.340989 Loss2: 1.316770
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.577568 Loss1: 0.217221 Loss2: 1.360346
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496862 Loss1: 0.166622 Loss2: 1.330240
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.441140 Loss1: 0.563106 Loss2: 1.878034
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.839652 Loss1: 0.454659 Loss2: 1.384993 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454464 Loss1: 0.130288 Loss2: 1.324176
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670938 Loss1: 0.215486 Loss2: 1.455452
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619550 Loss1: 0.235493 Loss2: 1.384057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563671 Loss1: 0.164331 Loss2: 1.399340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495182 Loss1: 0.110976 Loss2: 1.384205 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358094 Loss1: 0.056415 Loss2: 1.301679
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445149 Loss1: 0.072751 Loss2: 1.372399
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407710 Loss1: 0.047143 Loss2: 1.360567
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396682 Loss1: 0.039053 Loss2: 1.357629
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381206 Loss1: 0.030990 Loss2: 1.350216
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.416658 Loss1: 0.504117 Loss2: 1.912541
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.758195 Loss1: 0.345794 Loss2: 1.412402
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716579 Loss1: 0.276697 Loss2: 1.439882
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596493 Loss1: 0.186483 Loss2: 1.410010
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.347093 Loss1: 0.525118 Loss2: 1.821975
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.701640 Loss1: 0.345512 Loss2: 1.356129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.553966 Loss1: 0.176972 Loss2: 1.376994 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526367 Loss1: 0.175155 Loss2: 1.351212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475435 Loss1: 0.124699 Loss2: 1.350736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455662 Loss1: 0.112499 Loss2: 1.343164 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.379244 Loss1: 0.050291 Loss2: 1.328954 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378066 Loss1: 0.053272 Loss2: 1.324794 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.748245 Loss1: 0.405649 Loss2: 1.342596 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498424 Loss1: 0.158844 Loss2: 1.339580 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.444859 Loss1: 0.561286 Loss2: 1.883573
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484967 Loss1: 0.148468 Loss2: 1.336499
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.742464 Loss1: 0.355731 Loss2: 1.386733
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454783 Loss1: 0.120418 Loss2: 1.334365
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.700106 Loss1: 0.283086 Loss2: 1.417021
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414427 Loss1: 0.090463 Loss2: 1.323964
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.547234 Loss1: 0.168631 Loss2: 1.378603
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384318 Loss1: 0.064504 Loss2: 1.319814
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.477514 Loss1: 0.115260 Loss2: 1.362255
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385957 Loss1: 0.071056 Loss2: 1.314902
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.414110 Loss1: 0.058444 Loss2: 1.355667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.353719 Loss1: 0.040749 Loss2: 1.312970
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.401083 Loss1: 0.058577 Loss2: 1.342506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378174 Loss1: 0.038929 Loss2: 1.339244 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.714393 Loss1: 0.323580 Loss2: 1.390813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578326 Loss1: 0.177383 Loss2: 1.400942 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.506852 Loss1: 0.667944 Loss2: 1.838907
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.596376 Loss1: 0.196340 Loss2: 1.400036
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.825726 Loss1: 0.466168 Loss2: 1.359558
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.583018 Loss1: 0.179075 Loss2: 1.403943
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671390 Loss1: 0.265438 Loss2: 1.405952
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531147 Loss1: 0.127358 Loss2: 1.403789
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563020 Loss1: 0.202845 Loss2: 1.360174
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.510730 Loss1: 0.111452 Loss2: 1.399278
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.546468 Loss1: 0.191342 Loss2: 1.355126
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.487841 Loss1: 0.097592 Loss2: 1.390249
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508910 Loss1: 0.157007 Loss2: 1.351903
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457367 Loss1: 0.075025 Loss2: 1.382341
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.456845 Loss1: 0.120779 Loss2: 1.336066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435621 Loss1: 0.105392 Loss2: 1.330229 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.582041 Loss1: 0.234868 Loss2: 1.347174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.437453 Loss1: 0.105089 Loss2: 1.332363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.442547 Loss1: 0.616625 Loss2: 1.825922
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.434680 Loss1: 0.111759 Loss2: 1.322921
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.417257 Loss1: 0.089395 Loss2: 1.327862 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.387440 Loss1: 0.065199 Loss2: 1.322241 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.363511 Loss1: 0.054407 Loss2: 1.309103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.380612 Loss1: 0.077434 Loss2: 1.303178 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356438 Loss1: 0.053599 Loss2: 1.302839 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.357791 Loss1: 0.064991 Loss2: 1.292801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362693 Loss1: 0.075513 Loss2: 1.287180 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.338710 Loss1: 0.570151 Loss2: 1.768559
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.719149 Loss1: 0.403304 Loss2: 1.315844
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656720 Loss1: 0.281524 Loss2: 1.375196
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529149 Loss1: 0.205738 Loss2: 1.323411
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.457449 Loss1: 0.597590 Loss2: 1.859859
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.758316 Loss1: 0.366494 Loss2: 1.391822 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669592 Loss1: 0.240081 Loss2: 1.429511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591377 Loss1: 0.210614 Loss2: 1.380764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.582068 Loss1: 0.187192 Loss2: 1.394876 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484838 Loss1: 0.108359 Loss2: 1.376478 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491417 Loss1: 0.120300 Loss2: 1.371118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402236 Loss1: 0.046367 Loss2: 1.355868 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.380467 Loss1: 0.514404 Loss2: 1.866063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644417 Loss1: 0.234500 Loss2: 1.409917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.403169 Loss1: 0.527275 Loss2: 1.875893 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.728653 Loss1: 0.344340 Loss2: 1.384312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.668831 Loss1: 0.246299 Loss2: 1.422533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.589624 Loss1: 0.198337 Loss2: 1.391287 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.629386 Loss1: 0.231947 Loss2: 1.397439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.573195 Loss1: 0.179775 Loss2: 1.393420 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.504587 Loss1: 0.113734 Loss2: 1.390854 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442216 Loss1: 0.071960 Loss2: 1.370256 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.847996 Loss1: 0.424346 Loss2: 1.423651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.636840 Loss1: 0.235503 Loss2: 1.401336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.359753 Loss1: 0.528972 Loss2: 1.830781
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.636481 Loss1: 0.222202 Loss2: 1.414279
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.573766 Loss1: 0.256676 Loss2: 1.317090
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.544941 Loss1: 0.149201 Loss2: 1.395740
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.512413 Loss1: 0.189152 Loss2: 1.323261
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528944 Loss1: 0.139826 Loss2: 1.389118
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523988 Loss1: 0.210487 Loss2: 1.313501
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481130 Loss1: 0.099412 Loss2: 1.381718
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.490268 Loss1: 0.177208 Loss2: 1.313060
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450725 Loss1: 0.076555 Loss2: 1.374170
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.398572 Loss1: 0.093618 Loss2: 1.304954
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421261 Loss1: 0.053928 Loss2: 1.367333
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.353129 Loss1: 0.060960 Loss2: 1.292169 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.328231 Loss1: 0.046300 Loss2: 1.281930 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.742185 Loss1: 0.344626 Loss2: 1.397558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583875 Loss1: 0.176829 Loss2: 1.407046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555399 Loss1: 0.149806 Loss2: 1.405593
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.356504 Loss1: 0.460148 Loss2: 1.896356
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488616 Loss1: 0.089725 Loss2: 1.398892
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.805551 Loss1: 0.374122 Loss2: 1.431429
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.501949 Loss1: 0.110067 Loss2: 1.391882
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727297 Loss1: 0.254416 Loss2: 1.472881
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.493055 Loss1: 0.107401 Loss2: 1.385654
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640990 Loss1: 0.212154 Loss2: 1.428836
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.667435 Loss1: 0.237706 Loss2: 1.429729 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.534263 Loss1: 0.131472 Loss2: 1.402791
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.563747 Loss1: 0.124354 Loss2: 1.439394
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533784 Loss1: 0.109474 Loss2: 1.424311
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511155 Loss1: 0.096848 Loss2: 1.414307
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.495798 Loss1: 0.075914 Loss2: 1.419883
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.474521 Loss1: 0.063927 Loss2: 1.410594
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.502129 Loss1: 0.628483 Loss2: 1.873646
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.762855 Loss1: 0.367090 Loss2: 1.395764
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.685335 Loss1: 0.255446 Loss2: 1.429889
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587110 Loss1: 0.199259 Loss2: 1.387850
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.548659 Loss1: 0.154939 Loss2: 1.393720
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.504680 Loss1: 0.651076 Loss2: 1.853604
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.730949 Loss1: 0.374229 Loss2: 1.356720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605663 Loss1: 0.226338 Loss2: 1.379324 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487161 Loss1: 0.146846 Loss2: 1.340315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487995 Loss1: 0.147055 Loss2: 1.340940 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.458915 Loss1: 0.114514 Loss2: 1.344401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400842 Loss1: 0.073127 Loss2: 1.327715 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354321 Loss1: 0.040948 Loss2: 1.313372 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645726 Loss1: 0.213171 Loss2: 1.432555 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.431057 Loss1: 0.088891 Loss2: 1.342166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.637309 Loss1: 0.641907 Loss2: 1.995403 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.776652 Loss1: 0.399958 Loss2: 1.376695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.755589 Loss1: 0.356238 Loss2: 1.399350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595437 Loss1: 0.180229 Loss2: 1.415208 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377059 Loss1: 0.041242 Loss2: 1.335817
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.526873 Loss1: 0.152949 Loss2: 1.373924
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.518248 Loss1: 0.138635 Loss2: 1.379613
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.539799 Loss1: 0.159633 Loss2: 1.380167
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.474895 Loss1: 0.097268 Loss2: 1.377627
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421735 Loss1: 0.056981 Loss2: 1.364754
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407774 Loss1: 0.047103 Loss2: 1.360671
-(DefaultActor pid=3764) >> Training accuracy: 0.998698
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.384930 Loss1: 0.529922 Loss2: 1.855007
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.785662 Loss1: 0.429121 Loss2: 1.356541
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624222 Loss1: 0.222414 Loss2: 1.401808
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.579820 Loss1: 0.222110 Loss2: 1.357710
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.482629 Loss1: 0.131788 Loss2: 1.350841 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459397 Loss1: 0.123094 Loss2: 1.336304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433811 Loss1: 0.100022 Loss2: 1.333789 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403353 Loss1: 0.070887 Loss2: 1.332467 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381151 Loss1: 0.057136 Loss2: 1.324014 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.376774 Loss1: 0.057129 Loss2: 1.319644 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409963 Loss1: 0.088240 Loss2: 1.321722 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.344745 Loss1: 0.044158 Loss2: 1.300587 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.438854 Loss1: 0.626698 Loss2: 1.812155
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.697336 Loss1: 0.362336 Loss2: 1.334999
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624889 Loss1: 0.256062 Loss2: 1.368827
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513223 Loss1: 0.180674 Loss2: 1.332549
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.360114 Loss1: 0.570660 Loss2: 1.789454
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.715768 Loss1: 0.362343 Loss2: 1.353425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.619846 Loss1: 0.234189 Loss2: 1.385657 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509432 Loss1: 0.163110 Loss2: 1.346321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506462 Loss1: 0.160424 Loss2: 1.346038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390243 Loss1: 0.080726 Loss2: 1.309517 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431349 Loss1: 0.095504 Loss2: 1.335845 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366351 Loss1: 0.049260 Loss2: 1.317092 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.639849 Loss1: 0.293419 Loss2: 1.346430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.512110 Loss1: 0.161619 Loss2: 1.350490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.483307 Loss1: 0.580734 Loss2: 1.902574
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.505558 Loss1: 0.150093 Loss2: 1.355465
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.791483 Loss1: 0.408903 Loss2: 1.382580
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450077 Loss1: 0.104216 Loss2: 1.345860
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416518 Loss1: 0.076580 Loss2: 1.339938 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403929 Loss1: 0.065659 Loss2: 1.338270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405519 Loss1: 0.072029 Loss2: 1.333490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410275 Loss1: 0.079040 Loss2: 1.331234 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.393086 Loss1: 0.039394 Loss2: 1.353692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380078 Loss1: 0.040637 Loss2: 1.339441 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.457212 Loss1: 0.567919 Loss2: 1.889292
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.799031 Loss1: 0.396068 Loss2: 1.402963
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.640846 Loss1: 0.184694 Loss2: 1.456152
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533711 Loss1: 0.132967 Loss2: 1.400744
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.361636 Loss1: 0.540770 Loss2: 1.820866
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.760903 Loss1: 0.403920 Loss2: 1.356983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.652377 Loss1: 0.251947 Loss2: 1.400430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507926 Loss1: 0.158848 Loss2: 1.349077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507804 Loss1: 0.158654 Loss2: 1.349150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.483427 Loss1: 0.131850 Loss2: 1.351577 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.457615 Loss1: 0.111202 Loss2: 1.346413 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379398 Loss1: 0.050054 Loss2: 1.329344 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.497772 Loss1: 0.650539 Loss2: 1.847233 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.640770 Loss1: 0.221748 Loss2: 1.419022 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.510668 Loss1: 0.154548 Loss2: 1.356121
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.462028 Loss1: 0.611742 Loss2: 1.850286
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.740638 Loss1: 0.411365 Loss2: 1.329273 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.611458 Loss1: 0.229684 Loss2: 1.381774 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484096 Loss1: 0.136119 Loss2: 1.347978
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.505814 Loss1: 0.171011 Loss2: 1.334804
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.455790 Loss1: 0.108573 Loss2: 1.347216
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441781 Loss1: 0.120034 Loss2: 1.321747
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.416716 Loss1: 0.070282 Loss2: 1.346434
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479621 Loss1: 0.153119 Loss2: 1.326502
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441648 Loss1: 0.120265 Loss2: 1.321383
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435048 Loss1: 0.102131 Loss2: 1.332917
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383620 Loss1: 0.072498 Loss2: 1.311122 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.300506 Loss1: 0.464023 Loss2: 1.836483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.612420 Loss1: 0.183913 Loss2: 1.428507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.461945 Loss1: 0.611995 Loss2: 1.849950
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.635502 Loss1: 0.240334 Loss2: 1.395168
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.732680 Loss1: 0.376168 Loss2: 1.356512
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.563774 Loss1: 0.162893 Loss2: 1.400881
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.544170 Loss1: 0.150579 Loss2: 1.393591 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511851 Loss1: 0.119144 Loss2: 1.392707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474223 Loss1: 0.089350 Loss2: 1.384874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440709 Loss1: 0.062353 Loss2: 1.378356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418286 Loss1: 0.047507 Loss2: 1.370780 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.378037 Loss1: 0.039775 Loss2: 1.338263 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.397477 Loss1: 0.505844 Loss2: 1.891633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700054 Loss1: 0.251372 Loss2: 1.448682 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 10:42:12,207 | server.py:236 | fit_round 148 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.620781 Loss1: 0.223527 Loss2: 1.397254
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.347067 Loss1: 0.572688 Loss2: 1.774380
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.643800 Loss1: 0.310641 Loss2: 1.333159 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.580764 Loss1: 0.224123 Loss2: 1.356640 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.485067 Loss1: 0.154740 Loss2: 1.330326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.426544 Loss1: 0.095311 Loss2: 1.331234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.389108 Loss1: 0.070352 Loss2: 1.318756 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.376856 Loss1: 0.062177 Loss2: 1.314679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363791 Loss1: 0.058582 Loss2: 1.305209 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.226983 Loss1: 0.438136 Loss2: 1.788846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.632237 Loss1: 0.266766 Loss2: 1.365471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502352 Loss1: 0.169360 Loss2: 1.332992
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.390174 Loss1: 0.536622 Loss2: 1.853552
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.635246 Loss1: 0.304754 Loss2: 1.330493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437776 Loss1: 0.112972 Loss2: 1.324804
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.490584 Loss1: 0.137956 Loss2: 1.352629
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390859 Loss1: 0.076139 Loss2: 1.314720
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.436961 Loss1: 0.101785 Loss2: 1.335176
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.421471 Loss1: 0.099157 Loss2: 1.322314
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.371686 Loss1: 0.058878 Loss2: 1.312808
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.433210 Loss1: 0.110949 Loss2: 1.322261
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.344299 Loss1: 0.040642 Loss2: 1.303657
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.347243 Loss1: 0.048005 Loss2: 1.299238 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995404
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.360848 Loss1: 0.053614 Loss2: 1.307233 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 10:42:12,207][flwr][DEBUG] - fit_round 148 received 50 results and 0 failures
-INFO flwr 2023-10-12 10:42:53,108 | server.py:125 | fit progress: (148, 2.2302481027456897, {'accuracy': 0.5964}, 341480.886121305)
->> Test accuracy: 0.596400
-[2023-10-12 10:42:53,108][flwr][INFO] - fit progress: (148, 2.2302481027456897, {'accuracy': 0.5964}, 341480.886121305)
-DEBUG flwr 2023-10-12 10:42:53,108 | server.py:173 | evaluate_round 148: strategy sampled 50 clients (out of 50)
-[2023-10-12 10:42:53,108][flwr][DEBUG] - evaluate_round 148: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 10:51:56,443 | server.py:187 | evaluate_round 148 received 50 results and 0 failures
-[2023-10-12 10:51:56,443][flwr][DEBUG] - evaluate_round 148 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 10:51:56,443 | server.py:222 | fit_round 149: strategy sampled 50 clients (out of 50)
-[2023-10-12 10:51:56,443][flwr][DEBUG] - fit_round 149: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.220751 Loss1: 0.449531 Loss2: 1.771220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.536025 Loss1: 0.182320 Loss2: 1.353705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.533065 Loss1: 0.625213 Loss2: 1.907853
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.492035 Loss1: 0.169268 Loss2: 1.322767
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516236 Loss1: 0.175567 Loss2: 1.340669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506649 Loss1: 0.176465 Loss2: 1.330184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443458 Loss1: 0.115429 Loss2: 1.328029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420283 Loss1: 0.103006 Loss2: 1.317277 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402028 Loss1: 0.082984 Loss2: 1.319044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424660 Loss1: 0.083665 Loss2: 1.340995 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418492 Loss1: 0.079077 Loss2: 1.339415 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.435300 Loss1: 0.534976 Loss2: 1.900324
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.810591 Loss1: 0.412572 Loss2: 1.398019
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716156 Loss1: 0.269841 Loss2: 1.446315
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578787 Loss1: 0.186385 Loss2: 1.392402
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.326373 Loss1: 0.567152 Loss2: 1.759220
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.665553 Loss1: 0.367370 Loss2: 1.298183 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.577603 Loss1: 0.235743 Loss2: 1.341860 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.448046 Loss1: 0.166482 Loss2: 1.281563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.398858 Loss1: 0.117013 Loss2: 1.281845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.394034 Loss1: 0.114441 Loss2: 1.279593 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450348 Loss1: 0.070045 Loss2: 1.380304
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.333447 Loss1: 0.058170 Loss2: 1.275277
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.329303 Loss1: 0.064035 Loss2: 1.265268
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.311739 Loss1: 0.050327 Loss2: 1.261412
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.293055 Loss1: 0.042156 Loss2: 1.250899
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.495402 Loss1: 0.599931 Loss2: 1.895471
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.738603 Loss1: 0.352067 Loss2: 1.386536
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.637325 Loss1: 0.208978 Loss2: 1.428348
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.595586 Loss1: 0.211333 Loss2: 1.384253
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.430724 Loss1: 0.518711 Loss2: 1.912013
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.684575 Loss1: 0.327412 Loss2: 1.357163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669011 Loss1: 0.291826 Loss2: 1.377186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496870 Loss1: 0.141273 Loss2: 1.355597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484997 Loss1: 0.136371 Loss2: 1.348626 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.451143 Loss1: 0.096471 Loss2: 1.354671 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433168 Loss1: 0.064293 Loss2: 1.368875
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.442413 Loss1: 0.101245 Loss2: 1.341168
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424582 Loss1: 0.084417 Loss2: 1.340165
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433891 Loss1: 0.088589 Loss2: 1.345302
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388478 Loss1: 0.053599 Loss2: 1.334880
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.441836 Loss1: 0.544531 Loss2: 1.897304
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.755583 Loss1: 0.371991 Loss2: 1.383592
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.641753 Loss1: 0.220601 Loss2: 1.421152
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549894 Loss1: 0.164915 Loss2: 1.384979
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.568501 Loss1: 0.560345 Loss2: 2.008157
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.930603 Loss1: 0.423927 Loss2: 1.506676 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.822559 Loss1: 0.258577 Loss2: 1.563982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.703246 Loss1: 0.199835 Loss2: 1.503411 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.667974 Loss1: 0.161718 Loss2: 1.506256 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.650772 Loss1: 0.152563 Loss2: 1.498209 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410973 Loss1: 0.058012 Loss2: 1.352961
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.606518 Loss1: 0.117064 Loss2: 1.489454
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.588539 Loss1: 0.096774 Loss2: 1.491765
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.572093 Loss1: 0.086295 Loss2: 1.485797
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.558091 Loss1: 0.076585 Loss2: 1.481506
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.298732 Loss1: 0.437744 Loss2: 1.860988
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.671943 Loss1: 0.270722 Loss2: 1.401222
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690159 Loss1: 0.253729 Loss2: 1.436429
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.525206 Loss1: 0.635941 Loss2: 1.889264
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602653 Loss1: 0.198522 Loss2: 1.404131
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.928385 Loss1: 0.508791 Loss2: 1.419594
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589096 Loss1: 0.185146 Loss2: 1.403951
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.728037 Loss1: 0.272184 Loss2: 1.455853
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.583732 Loss1: 0.182975 Loss2: 1.400757
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.534812 Loss1: 0.132271 Loss2: 1.402540 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.526864 Loss1: 0.126993 Loss2: 1.399871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.478142 Loss1: 0.081100 Loss2: 1.397042 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446291 Loss1: 0.064665 Loss2: 1.381626 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.437834 Loss1: 0.060253 Loss2: 1.377581 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.676436 Loss1: 0.716643 Loss2: 1.959793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615946 Loss1: 0.204817 Loss2: 1.411129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.609792 Loss1: 0.670055 Loss2: 1.939737 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444510 Loss1: 0.090146 Loss2: 1.354364 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422974 Loss1: 0.077303 Loss2: 1.345671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393888 Loss1: 0.063216 Loss2: 1.330672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373955 Loss1: 0.042160 Loss2: 1.331795 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.382978 Loss1: 0.057775 Loss2: 1.325204 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396145 Loss1: 0.085506 Loss2: 1.310639 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980469
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.412563 Loss1: 0.548451 Loss2: 1.864112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.575828 Loss1: 0.172903 Loss2: 1.402925 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497805 Loss1: 0.145030 Loss2: 1.352775
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.314790 Loss1: 0.506201 Loss2: 1.808588
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.713530 Loss1: 0.351227 Loss2: 1.362303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.724648 Loss1: 0.299796 Loss2: 1.424852 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553162 Loss1: 0.195628 Loss2: 1.357534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520488 Loss1: 0.157784 Loss2: 1.362704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506891 Loss1: 0.147644 Loss2: 1.359247 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.489387 Loss1: 0.136917 Loss2: 1.352470 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452399 Loss1: 0.105698 Loss2: 1.346700 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.441714 Loss1: 0.538474 Loss2: 1.903240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.570240 Loss1: 0.163006 Loss2: 1.407234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.362499 Loss1: 0.528359 Loss2: 1.834140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.721881 Loss1: 0.386724 Loss2: 1.335157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.606266 Loss1: 0.223372 Loss2: 1.382894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.475279 Loss1: 0.135450 Loss2: 1.339828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.454388 Loss1: 0.118467 Loss2: 1.335921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.437381 Loss1: 0.105929 Loss2: 1.331452 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452438 Loss1: 0.125459 Loss2: 1.326979 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407482 Loss1: 0.080959 Loss2: 1.326522 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.795051 Loss1: 0.417324 Loss2: 1.377726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554017 Loss1: 0.182762 Loss2: 1.371255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484655 Loss1: 0.114918 Loss2: 1.369737
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.446386 Loss1: 0.601120 Loss2: 1.845265
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448840 Loss1: 0.091413 Loss2: 1.357427
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.726867 Loss1: 0.365779 Loss2: 1.361087
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462068 Loss1: 0.108859 Loss2: 1.353209
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.613902 Loss1: 0.204683 Loss2: 1.409219
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421323 Loss1: 0.066053 Loss2: 1.355270
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521490 Loss1: 0.163358 Loss2: 1.358132
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405730 Loss1: 0.053746 Loss2: 1.351984
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.472346 Loss1: 0.109849 Loss2: 1.362496
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403643 Loss1: 0.059379 Loss2: 1.344263
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429475 Loss1: 0.077945 Loss2: 1.351530
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422644 Loss1: 0.076024 Loss2: 1.346621
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397525 Loss1: 0.052280 Loss2: 1.345245
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395750 Loss1: 0.062236 Loss2: 1.333515
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397235 Loss1: 0.061363 Loss2: 1.335872
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.298004 Loss1: 0.500675 Loss2: 1.797329
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.578483 Loss1: 0.257448 Loss2: 1.321034
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.510478 Loss1: 0.169775 Loss2: 1.340702
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.441768 Loss1: 0.120276 Loss2: 1.321492
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.240414 Loss1: 0.474558 Loss2: 1.765856
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449687 Loss1: 0.138316 Loss2: 1.311371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.411885 Loss1: 0.100377 Loss2: 1.311508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380635 Loss1: 0.073889 Loss2: 1.306746 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.347596 Loss1: 0.052620 Loss2: 1.294975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.335095 Loss1: 0.044439 Loss2: 1.290656 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433234 Loss1: 0.101438 Loss2: 1.331796 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438947 Loss1: 0.114162 Loss2: 1.324785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445379 Loss1: 0.119938 Loss2: 1.325441
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.482635 Loss1: 0.632085 Loss2: 1.850550
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.777510 Loss1: 0.420151 Loss2: 1.357359
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638802 Loss1: 0.249462 Loss2: 1.389340
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527226 Loss1: 0.179920 Loss2: 1.347307
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514403 Loss1: 0.167597 Loss2: 1.346806
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.330554 Loss1: 0.494116 Loss2: 1.836438
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476498 Loss1: 0.127681 Loss2: 1.348817
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434515 Loss1: 0.100445 Loss2: 1.334070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.618694 Loss1: 0.210911 Loss2: 1.407783
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434172 Loss1: 0.099829 Loss2: 1.334344
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535128 Loss1: 0.161658 Loss2: 1.373471
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407104 Loss1: 0.071795 Loss2: 1.335310
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.462717 Loss1: 0.098950 Loss2: 1.363767
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383264 Loss1: 0.063794 Loss2: 1.319470
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404740 Loss1: 0.055892 Loss2: 1.348849 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373723 Loss1: 0.032775 Loss2: 1.340948 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.396693 Loss1: 0.559379 Loss2: 1.837314
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364303 Loss1: 0.031547 Loss2: 1.332756
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.613545 Loss1: 0.226306 Loss2: 1.387240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.441703 Loss1: 0.105291 Loss2: 1.336412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.426146 Loss1: 0.089961 Loss2: 1.336185
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.617152 Loss1: 0.691169 Loss2: 1.925982
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.413156 Loss1: 0.084178 Loss2: 1.328977
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.676414 Loss1: 0.295229 Loss2: 1.381185
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601204 Loss1: 0.207550 Loss2: 1.393654
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.386031 Loss1: 0.064699 Loss2: 1.321332
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536917 Loss1: 0.154642 Loss2: 1.382275
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.368967 Loss1: 0.052304 Loss2: 1.316663
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528128 Loss1: 0.155442 Loss2: 1.372686
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393184 Loss1: 0.075446 Loss2: 1.317738
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453844 Loss1: 0.089589 Loss2: 1.364255 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.474239 Loss1: 0.114933 Loss2: 1.359306 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972098
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441504 Loss1: 0.083325 Loss2: 1.358179
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.334150 Loss1: 0.549235 Loss2: 1.784914
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.645555 Loss1: 0.304850 Loss2: 1.340705
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.548720 Loss1: 0.192382 Loss2: 1.356338
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.460597 Loss1: 0.144621 Loss2: 1.315976
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.436803 Loss1: 0.122747 Loss2: 1.314056
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.362534 Loss1: 0.544179 Loss2: 1.818355
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443962 Loss1: 0.134562 Loss2: 1.309400
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659091 Loss1: 0.323431 Loss2: 1.335660
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431937 Loss1: 0.114411 Loss2: 1.317526
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.513220 Loss1: 0.151220 Loss2: 1.362000
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.475477 Loss1: 0.145833 Loss2: 1.329644
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.365495 Loss1: 0.061609 Loss2: 1.303886
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529766 Loss1: 0.188993 Loss2: 1.340773
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.365806 Loss1: 0.063346 Loss2: 1.302460
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494937 Loss1: 0.154564 Loss2: 1.340373
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355943 Loss1: 0.057560 Loss2: 1.298383
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429341 Loss1: 0.092439 Loss2: 1.336902 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384747 Loss1: 0.064770 Loss2: 1.319977 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.695834 Loss1: 0.357564 Loss2: 1.338270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.492578 Loss1: 0.165778 Loss2: 1.326800 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.452563 Loss1: 0.134573 Loss2: 1.317990
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.448185 Loss1: 0.567922 Loss2: 1.880263
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445821 Loss1: 0.128304 Loss2: 1.317517
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.682134 Loss1: 0.304654 Loss2: 1.377481
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420436 Loss1: 0.109238 Loss2: 1.311199
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.671277 Loss1: 0.251929 Loss2: 1.419348
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.388785 Loss1: 0.079643 Loss2: 1.309142
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573780 Loss1: 0.194346 Loss2: 1.379434
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.382502 Loss1: 0.070701 Loss2: 1.311802
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502968 Loss1: 0.124825 Loss2: 1.378142
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354580 Loss1: 0.052639 Loss2: 1.301941
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.453186 Loss1: 0.090950 Loss2: 1.362236
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.492371 Loss1: 0.130076 Loss2: 1.362295
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470707 Loss1: 0.096262 Loss2: 1.374444
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428434 Loss1: 0.073625 Loss2: 1.354809
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406734 Loss1: 0.058162 Loss2: 1.348571
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.471235 Loss1: 0.620877 Loss2: 1.850358
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.732246 Loss1: 0.357833 Loss2: 1.374414
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.654899 Loss1: 0.243045 Loss2: 1.411854
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604000 Loss1: 0.225939 Loss2: 1.378061
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.360526 Loss1: 0.555183 Loss2: 1.805343
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.709841 Loss1: 0.365257 Loss2: 1.344585 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.599575 Loss1: 0.200943 Loss2: 1.398632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540777 Loss1: 0.190068 Loss2: 1.350710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.511678 Loss1: 0.166561 Loss2: 1.345117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.535738 Loss1: 0.173859 Loss2: 1.361879 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429525 Loss1: 0.087455 Loss2: 1.342070 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424206 Loss1: 0.090562 Loss2: 1.333644 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.687012 Loss1: 0.330758 Loss2: 1.356254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491726 Loss1: 0.133581 Loss2: 1.358145 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.404249 Loss1: 0.571682 Loss2: 1.832566
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499244 Loss1: 0.153467 Loss2: 1.345777
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.735941 Loss1: 0.381445 Loss2: 1.354496
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.441978 Loss1: 0.094586 Loss2: 1.347392
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.617050 Loss1: 0.219705 Loss2: 1.397345
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418058 Loss1: 0.085136 Loss2: 1.332923
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519690 Loss1: 0.171988 Loss2: 1.347702
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419118 Loss1: 0.089728 Loss2: 1.329391
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476602 Loss1: 0.127019 Loss2: 1.349584
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396086 Loss1: 0.064005 Loss2: 1.332081
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466096 Loss1: 0.119622 Loss2: 1.346474
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387267 Loss1: 0.056246 Loss2: 1.331022
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413900 Loss1: 0.082715 Loss2: 1.331185 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382876 Loss1: 0.056718 Loss2: 1.326159 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.712392 Loss1: 0.344289 Loss2: 1.368103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551925 Loss1: 0.189114 Loss2: 1.362812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.509948 Loss1: 0.147561 Loss2: 1.362387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458941 Loss1: 0.108459 Loss2: 1.350482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410934 Loss1: 0.069639 Loss2: 1.341294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.381531 Loss1: 0.043003 Loss2: 1.338529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377432 Loss1: 0.047348 Loss2: 1.330084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378264 Loss1: 0.047286 Loss2: 1.330978 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.339105 Loss1: 0.035727 Loss2: 1.303378 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.410802 Loss1: 0.532317 Loss2: 1.878485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609962 Loss1: 0.207354 Loss2: 1.402608 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540658 Loss1: 0.172546 Loss2: 1.368112
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.334641 Loss1: 0.458150 Loss2: 1.876490
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653882 Loss1: 0.253380 Loss2: 1.400502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.668321 Loss1: 0.242394 Loss2: 1.425926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599416 Loss1: 0.188578 Loss2: 1.410838 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.605473 Loss1: 0.203233 Loss2: 1.402240 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.545469 Loss1: 0.138799 Loss2: 1.406670 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.578099 Loss1: 0.181593 Loss2: 1.396506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.504236 Loss1: 0.118362 Loss2: 1.385874 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979492
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.460055 Loss1: 0.659023 Loss2: 1.801032 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.643565 Loss1: 0.270877 Loss2: 1.372688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.294860 Loss1: 0.455486 Loss2: 1.839375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.698959 Loss1: 0.349368 Loss2: 1.349590 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629731 Loss1: 0.236113 Loss2: 1.393618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574921 Loss1: 0.207604 Loss2: 1.367317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509334 Loss1: 0.139128 Loss2: 1.370206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504939 Loss1: 0.136274 Loss2: 1.368665 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420911 Loss1: 0.066887 Loss2: 1.354023 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398905 Loss1: 0.058136 Loss2: 1.340769 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.779447 Loss1: 0.437385 Loss2: 1.342061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529168 Loss1: 0.206466 Loss2: 1.322702 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.458273 Loss1: 0.134992 Loss2: 1.323281
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.307286 Loss1: 0.453081 Loss2: 1.854205
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.725239 Loss1: 0.330706 Loss2: 1.394533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.674875 Loss1: 0.224838 Loss2: 1.450036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550151 Loss1: 0.142675 Loss2: 1.407476 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.504300 Loss1: 0.109081 Loss2: 1.395219 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997768
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.534551 Loss1: 0.143349 Loss2: 1.391202 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.468119 Loss1: 0.082138 Loss2: 1.385981 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.356836 Loss1: 0.505814 Loss2: 1.851022
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.475552 Loss1: 0.092394 Loss2: 1.383158
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.580292 Loss1: 0.203361 Loss2: 1.376931 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.486781 Loss1: 0.148112 Loss2: 1.338670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.439471 Loss1: 0.096149 Loss2: 1.343321
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.437164 Loss1: 0.626377 Loss2: 1.810787
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416706 Loss1: 0.083844 Loss2: 1.332862
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.767780 Loss1: 0.420927 Loss2: 1.346853
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410718 Loss1: 0.077386 Loss2: 1.333332
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.733661 Loss1: 0.327862 Loss2: 1.405800
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414356 Loss1: 0.087944 Loss2: 1.326412
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.583959 Loss1: 0.226042 Loss2: 1.357917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387628 Loss1: 0.060539 Loss2: 1.327089
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541083 Loss1: 0.186639 Loss2: 1.354444
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.505208 Loss1: 0.152377 Loss2: 1.352831
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.492102 Loss1: 0.148997 Loss2: 1.343105
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465628 Loss1: 0.128654 Loss2: 1.336975
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427469 Loss1: 0.095592 Loss2: 1.331878
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.412346 Loss1: 0.535861 Loss2: 1.876485
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383715 Loss1: 0.058509 Loss2: 1.325206
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.670623 Loss1: 0.228182 Loss2: 1.442441 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.590776 Loss1: 0.187135 Loss2: 1.403641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.389422 Loss1: 0.473895 Loss2: 1.915526
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519604 Loss1: 0.120967 Loss2: 1.398637
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491963 Loss1: 0.105861 Loss2: 1.386101
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.688972 Loss1: 0.292140 Loss2: 1.396832
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491208 Loss1: 0.103578 Loss2: 1.387631
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.693725 Loss1: 0.250447 Loss2: 1.443279
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.532582 Loss1: 0.148070 Loss2: 1.384512
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551838 Loss1: 0.142106 Loss2: 1.409732
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.510070 Loss1: 0.120343 Loss2: 1.389727
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524373 Loss1: 0.131549 Loss2: 1.392825
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503592 Loss1: 0.105094 Loss2: 1.398498
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474679 Loss1: 0.083229 Loss2: 1.391451
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450113 Loss1: 0.066685 Loss2: 1.383428
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426557 Loss1: 0.053956 Loss2: 1.372601
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.532077 Loss1: 0.653547 Loss2: 1.878530
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423052 Loss1: 0.051563 Loss2: 1.371489
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.728542 Loss1: 0.291334 Loss2: 1.437209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.530622 Loss1: 0.133119 Loss2: 1.397503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485315 Loss1: 0.083987 Loss2: 1.401328
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.286844 Loss1: 0.446802 Loss2: 1.840042
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.637434 Loss1: 0.265096 Loss2: 1.372338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.545651 Loss1: 0.154070 Loss2: 1.391581 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496211 Loss1: 0.131964 Loss2: 1.364247 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493208 Loss1: 0.121980 Loss2: 1.371228 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426350 Loss1: 0.072824 Loss2: 1.353526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404108 Loss1: 0.052021 Loss2: 1.352088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402643 Loss1: 0.059704 Loss2: 1.342939 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989890
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-12 11:20:37,912 | server.py:236 | fit_round 149 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547272 Loss1: 0.203213 Loss2: 1.344059 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440453 Loss1: 0.098789 Loss2: 1.341664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.523177 Loss1: 0.647112 Loss2: 1.876065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.706311 Loss1: 0.353388 Loss2: 1.352923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.647130 Loss1: 0.264437 Loss2: 1.382693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400107 Loss1: 0.081366 Loss2: 1.318741
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573360 Loss1: 0.185941 Loss2: 1.387419
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.534182 Loss1: 0.182098 Loss2: 1.352084
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511358 Loss1: 0.145123 Loss2: 1.366235
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484355 Loss1: 0.124757 Loss2: 1.359598
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423796 Loss1: 0.077134 Loss2: 1.346662
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408526 Loss1: 0.069302 Loss2: 1.339225
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.376987 Loss1: 0.578705 Loss2: 1.798282
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.590602 Loss1: 0.197905 Loss2: 1.392697 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.459087 Loss1: 0.118316 Loss2: 1.340771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443075 Loss1: 0.116664 Loss2: 1.326410
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.403003 Loss1: 0.530634 Loss2: 1.872369
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.417675 Loss1: 0.090673 Loss2: 1.327002
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.753977 Loss1: 0.379190 Loss2: 1.374787
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414413 Loss1: 0.090343 Loss2: 1.324070
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.593975 Loss1: 0.184971 Loss2: 1.409003
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427780 Loss1: 0.109612 Loss2: 1.318168
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.545980 Loss1: 0.177689 Loss2: 1.368291
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428596 Loss1: 0.101907 Loss2: 1.326689
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.472693 Loss1: 0.111010 Loss2: 1.361683
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465289 Loss1: 0.104144 Loss2: 1.361144
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423863 Loss1: 0.066686 Loss2: 1.357178
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428142 Loss1: 0.080935 Loss2: 1.347207
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409058 Loss1: 0.062639 Loss2: 1.346419
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389810 Loss1: 0.049370 Loss2: 1.340440
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 11:20:37,912][flwr][DEBUG] - fit_round 149 received 50 results and 0 failures
-INFO flwr 2023-10-12 11:21:20,012 | server.py:125 | fit progress: (149, 2.23768986547336, {'accuracy': 0.5952}, 343787.790518002)
->> Test accuracy: 0.595200
-[2023-10-12 11:21:20,012][flwr][INFO] - fit progress: (149, 2.23768986547336, {'accuracy': 0.5952}, 343787.790518002)
-DEBUG flwr 2023-10-12 11:21:20,012 | server.py:173 | evaluate_round 149: strategy sampled 50 clients (out of 50)
-[2023-10-12 11:21:20,012][flwr][DEBUG] - evaluate_round 149: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 11:30:25,431 | server.py:187 | evaluate_round 149 received 50 results and 0 failures
-[2023-10-12 11:30:25,431][flwr][DEBUG] - evaluate_round 149 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 11:30:25,431 | server.py:222 | fit_round 150: strategy sampled 50 clients (out of 50)
-[2023-10-12 11:30:25,431][flwr][DEBUG] - fit_round 150: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.403502 Loss1: 0.502800 Loss2: 1.900702
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.693667 Loss1: 0.291619 Loss2: 1.402048
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.666317 Loss1: 0.244674 Loss2: 1.421643
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.584682 Loss1: 0.185758 Loss2: 1.398924
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.522502 Loss1: 0.131177 Loss2: 1.391325 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499598 Loss1: 0.113156 Loss2: 1.386442 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464070 Loss1: 0.079125 Loss2: 1.384945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454768 Loss1: 0.073178 Loss2: 1.381590 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.442592 Loss1: 0.073487 Loss2: 1.369105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429613 Loss1: 0.058839 Loss2: 1.370774 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401015 Loss1: 0.071136 Loss2: 1.329880 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.390162 Loss1: 0.499812 Loss2: 1.890350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.675948 Loss1: 0.251715 Loss2: 1.424232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.507304 Loss1: 0.140236 Loss2: 1.367068
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.397544 Loss1: 0.587945 Loss2: 1.809599
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491038 Loss1: 0.129349 Loss2: 1.361689
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.773985 Loss1: 0.427968 Loss2: 1.346017
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.482529 Loss1: 0.126492 Loss2: 1.356037
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.665253 Loss1: 0.280174 Loss2: 1.385079
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481356 Loss1: 0.121238 Loss2: 1.360117
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526596 Loss1: 0.191274 Loss2: 1.335323
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447930 Loss1: 0.093739 Loss2: 1.354191
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554542 Loss1: 0.205092 Loss2: 1.349451
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.389587 Loss1: 0.047570 Loss2: 1.342017
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471125 Loss1: 0.137774 Loss2: 1.333351
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374259 Loss1: 0.031562 Loss2: 1.342697
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443138 Loss1: 0.109505 Loss2: 1.333633
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407215 Loss1: 0.088268 Loss2: 1.318947
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390383 Loss1: 0.077967 Loss2: 1.312416
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377848 Loss1: 0.072230 Loss2: 1.305618
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.384415 Loss1: 0.530760 Loss2: 1.853655
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.721673 Loss1: 0.361094 Loss2: 1.360579
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.669309 Loss1: 0.275871 Loss2: 1.393438
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591278 Loss1: 0.228231 Loss2: 1.363046
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.653741 Loss1: 0.722061 Loss2: 1.931680
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.512883 Loss1: 0.145073 Loss2: 1.367810
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.942917 Loss1: 0.554605 Loss2: 1.388312
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.804167 Loss1: 0.346922 Loss2: 1.457245
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526183 Loss1: 0.163982 Loss2: 1.362202
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.658963 Loss1: 0.279769 Loss2: 1.379194
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480325 Loss1: 0.123561 Loss2: 1.356764
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.551117 Loss1: 0.162738 Loss2: 1.388379
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429547 Loss1: 0.080640 Loss2: 1.348908
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499235 Loss1: 0.127717 Loss2: 1.371518
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396195 Loss1: 0.058030 Loss2: 1.338166
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390308 Loss1: 0.056856 Loss2: 1.333452 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410486 Loss1: 0.046157 Loss2: 1.364328 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994420
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.438238 Loss1: 0.636322 Loss2: 1.801916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.580592 Loss1: 0.233444 Loss2: 1.347148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467528 Loss1: 0.139256 Loss2: 1.328272
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.435037 Loss1: 0.553221 Loss2: 1.881815
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.779647 Loss1: 0.406948 Loss2: 1.372699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.697511 Loss1: 0.277671 Loss2: 1.419841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.552024 Loss1: 0.189262 Loss2: 1.362762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.498960 Loss1: 0.125898 Loss2: 1.373062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476739 Loss1: 0.116655 Loss2: 1.360083 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.342527 Loss1: 0.055773 Loss2: 1.286754
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461412 Loss1: 0.112496 Loss2: 1.348916
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425301 Loss1: 0.078941 Loss2: 1.346360
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415664 Loss1: 0.072691 Loss2: 1.342973
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380249 Loss1: 0.047062 Loss2: 1.333187
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.730549 Loss1: 0.730222 Loss2: 2.000327
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.763932 Loss1: 0.392402 Loss2: 1.371530
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.665857 Loss1: 0.266292 Loss2: 1.399564
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.597603 Loss1: 0.190344 Loss2: 1.407259
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.545221 Loss1: 0.169762 Loss2: 1.375459
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524568 Loss1: 0.148284 Loss2: 1.376283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487686 Loss1: 0.111525 Loss2: 1.376161 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444547 Loss1: 0.079690 Loss2: 1.364857 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.387147 Loss1: 0.036567 Loss2: 1.350580
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537540 Loss1: 0.175870 Loss2: 1.361670
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381982 Loss1: 0.039147 Loss2: 1.342835
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.490346 Loss1: 0.132369 Loss2: 1.357977
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491259 Loss1: 0.131674 Loss2: 1.359585
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426508 Loss1: 0.079095 Loss2: 1.347413
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.401546 Loss1: 0.059971 Loss2: 1.341575
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403618 Loss1: 0.071865 Loss2: 1.331753
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.572318 Loss1: 0.652281 Loss2: 1.920037
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368756 Loss1: 0.037819 Loss2: 1.330937
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.640632 Loss1: 0.245502 Loss2: 1.395130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.532614 Loss1: 0.168513 Loss2: 1.364100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.365867 Loss1: 0.568310 Loss2: 1.797557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.750510 Loss1: 0.384583 Loss2: 1.365928 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445463 Loss1: 0.092698 Loss2: 1.352764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453679 Loss1: 0.100770 Loss2: 1.352909 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.433364 Loss1: 0.101916 Loss2: 1.331448 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427151 Loss1: 0.100043 Loss2: 1.327108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397750 Loss1: 0.072823 Loss2: 1.324927 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390776 Loss1: 0.070387 Loss2: 1.320389 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.505228 Loss1: 0.132828 Loss2: 1.372400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.521783 Loss1: 0.138172 Loss2: 1.383611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.258982 Loss1: 0.468528 Loss2: 1.790454 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.689505 Loss1: 0.344955 Loss2: 1.344550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427468 Loss1: 0.071882 Loss2: 1.355585 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.470807 Loss1: 0.138257 Loss2: 1.332550 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.420215 Loss1: 0.099408 Loss2: 1.320807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.225711 Loss1: 0.443198 Loss2: 1.782513
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374696 Loss1: 0.055636 Loss2: 1.319060
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.335204 Loss1: 0.023223 Loss2: 1.311981
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.660776 Loss1: 0.322195 Loss2: 1.338581
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.340167 Loss1: 0.034131 Loss2: 1.306036
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568934 Loss1: 0.210257 Loss2: 1.358677
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517910 Loss1: 0.188153 Loss2: 1.329757
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471074 Loss1: 0.145284 Loss2: 1.325791
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433433 Loss1: 0.109941 Loss2: 1.323492
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445579 Loss1: 0.130300 Loss2: 1.315280
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.265581 Loss1: 0.438342 Loss2: 1.827239
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.618816 Loss1: 0.289256 Loss2: 1.329560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379715 Loss1: 0.071476 Loss2: 1.308239
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.550259 Loss1: 0.198869 Loss2: 1.351390
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375532 Loss1: 0.070218 Loss2: 1.305314
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.427805 Loss1: 0.096105 Loss2: 1.331700
-(DefaultActor pid=3765) >> Training accuracy: 0.983456
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460821 Loss1: 0.141925 Loss2: 1.318896
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.432856 Loss1: 0.110971 Loss2: 1.321885
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395155 Loss1: 0.078277 Loss2: 1.316878
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381727 Loss1: 0.062835 Loss2: 1.318892
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399274 Loss1: 0.090087 Loss2: 1.309187
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.370606 Loss1: 0.506950 Loss2: 1.863656
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348253 Loss1: 0.039888 Loss2: 1.308365
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.752905 Loss1: 0.373974 Loss2: 1.378931
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.659573 Loss1: 0.242092 Loss2: 1.417481
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540031 Loss1: 0.160609 Loss2: 1.379422
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.486497 Loss1: 0.107460 Loss2: 1.379038
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454209 Loss1: 0.092412 Loss2: 1.361797
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.367166 Loss1: 0.504801 Loss2: 1.862365
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467064 Loss1: 0.102161 Loss2: 1.364903
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.792888 Loss1: 0.425264 Loss2: 1.367624
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456804 Loss1: 0.100826 Loss2: 1.355978
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614657 Loss1: 0.197762 Loss2: 1.416895
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451630 Loss1: 0.098166 Loss2: 1.353465
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.569771 Loss1: 0.213829 Loss2: 1.355943
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.455673 Loss1: 0.102631 Loss2: 1.353041
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.536854 Loss1: 0.167814 Loss2: 1.369040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496147 Loss1: 0.139520 Loss2: 1.356627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452046 Loss1: 0.095368 Loss2: 1.356678
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.323916 Loss1: 0.510330 Loss2: 1.813586
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448487 Loss1: 0.092126 Loss2: 1.356361
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.635967 Loss1: 0.295058 Loss2: 1.340910
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.562207 Loss1: 0.189659 Loss2: 1.372547
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.434689 Loss1: 0.098631 Loss2: 1.336058
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.445415 Loss1: 0.112860 Loss2: 1.332554
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.387768 Loss1: 0.060435 Loss2: 1.327333
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.400943 Loss1: 0.513432 Loss2: 1.887512
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.371057 Loss1: 0.057079 Loss2: 1.313978
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.692847 Loss1: 0.312870 Loss2: 1.379976
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.363023 Loss1: 0.049012 Loss2: 1.314012
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.620398 Loss1: 0.210100 Loss2: 1.410299
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.346568 Loss1: 0.031930 Loss2: 1.314639
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554875 Loss1: 0.163132 Loss2: 1.391742
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368683 Loss1: 0.065329 Loss2: 1.303354
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510797 Loss1: 0.128902 Loss2: 1.381895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.497379 Loss1: 0.116552 Loss2: 1.380827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422724 Loss1: 0.053713 Loss2: 1.369011
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.460822 Loss1: 0.615423 Loss2: 1.845399
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.705351 Loss1: 0.309898 Loss2: 1.395453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574816 Loss1: 0.185071 Loss2: 1.389745 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525208 Loss1: 0.132937 Loss2: 1.392271 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513792 Loss1: 0.129380 Loss2: 1.384412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.466921 Loss1: 0.095228 Loss2: 1.371694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.472160 Loss1: 0.096220 Loss2: 1.375940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452737 Loss1: 0.078463 Loss2: 1.374273 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461729 Loss1: 0.119570 Loss2: 1.342159 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369557 Loss1: 0.050897 Loss2: 1.318660 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352628 Loss1: 0.043014 Loss2: 1.309614
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.525916 Loss1: 0.636061 Loss2: 1.889856
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.740406 Loss1: 0.347919 Loss2: 1.392487
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.646373 Loss1: 0.226498 Loss2: 1.419875
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563434 Loss1: 0.172896 Loss2: 1.390538
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.530301 Loss1: 0.145163 Loss2: 1.385138
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497672 Loss1: 0.118036 Loss2: 1.379636
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.406348 Loss1: 0.539388 Loss2: 1.866959
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473404 Loss1: 0.097089 Loss2: 1.376315
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.786486 Loss1: 0.416153 Loss2: 1.370333
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.482726 Loss1: 0.102243 Loss2: 1.380483
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.645104 Loss1: 0.219312 Loss2: 1.425792
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430453 Loss1: 0.063598 Loss2: 1.366855
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562288 Loss1: 0.194653 Loss2: 1.367635
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435713 Loss1: 0.075639 Loss2: 1.360074
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491976 Loss1: 0.118549 Loss2: 1.373427
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455446 Loss1: 0.093359 Loss2: 1.362087
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453277 Loss1: 0.093484 Loss2: 1.359793
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417470 Loss1: 0.067688 Loss2: 1.349782
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412888 Loss1: 0.065703 Loss2: 1.347185
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383383 Loss1: 0.037323 Loss2: 1.346060
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.263452 Loss1: 0.450101 Loss2: 1.813351
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572968 Loss1: 0.220091 Loss2: 1.352877
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.593899 Loss1: 0.223970 Loss2: 1.369929
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591632 Loss1: 0.230581 Loss2: 1.361051
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.493617 Loss1: 0.126369 Loss2: 1.367248
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.393380 Loss1: 0.551775 Loss2: 1.841605
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.435784 Loss1: 0.095661 Loss2: 1.340123
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406766 Loss1: 0.072094 Loss2: 1.334672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378489 Loss1: 0.047465 Loss2: 1.331024 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.355986 Loss1: 0.034259 Loss2: 1.321727 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.339907 Loss1: 0.020748 Loss2: 1.319159 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.552171 Loss1: 0.193674 Loss2: 1.358497 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442466 Loss1: 0.089264 Loss2: 1.353202 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392422 Loss1: 0.052499 Loss2: 1.339923
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.380179 Loss1: 0.543032 Loss2: 1.837146
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.739478 Loss1: 0.364287 Loss2: 1.375191
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.601936 Loss1: 0.207034 Loss2: 1.394902
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557637 Loss1: 0.193642 Loss2: 1.363995
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.559156 Loss1: 0.186750 Loss2: 1.372406
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.423379 Loss1: 0.517919 Loss2: 1.905461
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.710617 Loss1: 0.306628 Loss2: 1.403989 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.686193 Loss1: 0.247197 Loss2: 1.438996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636390 Loss1: 0.217909 Loss2: 1.418481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570026 Loss1: 0.158524 Loss2: 1.411502 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.543149 Loss1: 0.130233 Loss2: 1.412917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.489769 Loss1: 0.093491 Loss2: 1.396277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427931 Loss1: 0.036299 Loss2: 1.391631 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.729387 Loss1: 0.368981 Loss2: 1.360405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542547 Loss1: 0.168790 Loss2: 1.373757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472767 Loss1: 0.120429 Loss2: 1.352338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467465 Loss1: 0.123956 Loss2: 1.343508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421350 Loss1: 0.081857 Loss2: 1.339493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397335 Loss1: 0.067266 Loss2: 1.330068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375865 Loss1: 0.045890 Loss2: 1.329975 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484993 Loss1: 0.138425 Loss2: 1.346567 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387455 Loss1: 0.053033 Loss2: 1.334422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.363253 Loss1: 0.037578 Loss2: 1.325675 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741264 Loss1: 0.284766 Loss2: 1.456498 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547621 Loss1: 0.153826 Loss2: 1.393795 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504519 Loss1: 0.110756 Loss2: 1.393763
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.363257 Loss1: 0.513661 Loss2: 1.849596
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.730117 Loss1: 0.344774 Loss2: 1.385344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604320 Loss1: 0.194045 Loss2: 1.410275 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.546160 Loss1: 0.168552 Loss2: 1.377609 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485094 Loss1: 0.111505 Loss2: 1.373589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.413178 Loss1: 0.059783 Loss2: 1.353395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402136 Loss1: 0.063058 Loss2: 1.339078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394923 Loss1: 0.057857 Loss2: 1.337066 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.509646 Loss1: 0.145462 Loss2: 1.364184 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452196 Loss1: 0.101047 Loss2: 1.351149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460827 Loss1: 0.113151 Loss2: 1.347676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415890 Loss1: 0.067768 Loss2: 1.348122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417185 Loss1: 0.070361 Loss2: 1.346824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.394313 Loss1: 0.054230 Loss2: 1.340082 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447324 Loss1: 0.121499 Loss2: 1.325825 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403763 Loss1: 0.079822 Loss2: 1.323941 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401233 Loss1: 0.083656 Loss2: 1.317577
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.425292 Loss1: 0.571285 Loss2: 1.854007
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.781478 Loss1: 0.417222 Loss2: 1.364257
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.662187 Loss1: 0.241027 Loss2: 1.421159
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.514489 Loss1: 0.151141 Loss2: 1.363348
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462897 Loss1: 0.114327 Loss2: 1.348571
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.418382 Loss1: 0.536528 Loss2: 1.881853
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.745604 Loss1: 0.355679 Loss2: 1.389925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.599492 Loss1: 0.176073 Loss2: 1.423419 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551251 Loss1: 0.162940 Loss2: 1.388311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.488845 Loss1: 0.105912 Loss2: 1.382933 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384588 Loss1: 0.051801 Loss2: 1.332786
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487056 Loss1: 0.113316 Loss2: 1.373740
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.492216 Loss1: 0.111083 Loss2: 1.381133
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482192 Loss1: 0.101404 Loss2: 1.380788
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.478928 Loss1: 0.109669 Loss2: 1.369259
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453993 Loss1: 0.087862 Loss2: 1.366131
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.293408 Loss1: 0.505873 Loss2: 1.787535
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.620522 Loss1: 0.288837 Loss2: 1.331685
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595643 Loss1: 0.232138 Loss2: 1.363505
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.507976 Loss1: 0.175496 Loss2: 1.332479
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513307 Loss1: 0.180400 Loss2: 1.332906
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.533301 Loss1: 0.668129 Loss2: 1.865172
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.818465 Loss1: 0.439030 Loss2: 1.379435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682410 Loss1: 0.249910 Loss2: 1.432500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586009 Loss1: 0.217467 Loss2: 1.368541 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.571152 Loss1: 0.188384 Loss2: 1.382768 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484274 Loss1: 0.121762 Loss2: 1.362512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411925 Loss1: 0.063195 Loss2: 1.348730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373221 Loss1: 0.035604 Loss2: 1.337617 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.670024 Loss1: 0.291531 Loss2: 1.378493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.531247 Loss1: 0.155849 Loss2: 1.375398 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503812 Loss1: 0.136528 Loss2: 1.367285
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.247503 Loss1: 0.437598 Loss2: 1.809905
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.583879 Loss1: 0.237061 Loss2: 1.346818 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.528664 Loss1: 0.157630 Loss2: 1.371034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.512087 Loss1: 0.148510 Loss2: 1.363578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530276 Loss1: 0.168314 Loss2: 1.361962 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.498445 Loss1: 0.138618 Loss2: 1.359827 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418710 Loss1: 0.072733 Loss2: 1.345977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420716 Loss1: 0.075605 Loss2: 1.345111
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.375641 Loss1: 0.535205 Loss2: 1.840437
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.768719 Loss1: 0.412891 Loss2: 1.355828
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690207 Loss1: 0.274631 Loss2: 1.415576
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564462 Loss1: 0.199971 Loss2: 1.364491
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.563377 Loss1: 0.191129 Loss2: 1.372248
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509976 Loss1: 0.148448 Loss2: 1.361528
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.266764 Loss1: 0.439951 Loss2: 1.826814
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464921 Loss1: 0.108125 Loss2: 1.356796
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.679741 Loss1: 0.322055 Loss2: 1.357686
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461962 Loss1: 0.101384 Loss2: 1.360578
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.687537 Loss1: 0.280077 Loss2: 1.407460
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577730 Loss1: 0.198571 Loss2: 1.379159 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383317 Loss1: 0.044830 Loss2: 1.338487
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530384 Loss1: 0.159341 Loss2: 1.371043
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496726 Loss1: 0.122114 Loss2: 1.374612
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460326 Loss1: 0.096327 Loss2: 1.363999
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432852 Loss1: 0.071986 Loss2: 1.360867
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414255 Loss1: 0.061815 Loss2: 1.352440
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.409962 Loss1: 0.479198 Loss2: 1.930763
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388869 Loss1: 0.040881 Loss2: 1.347987
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.807304 Loss1: 0.342016 Loss2: 1.465289
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.822488 Loss1: 0.307293 Loss2: 1.515195
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.643990 Loss1: 0.182422 Loss2: 1.461569
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.582348 Loss1: 0.122787 Loss2: 1.459561
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.545454 Loss1: 0.094426 Loss2: 1.451028
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.694193 Loss1: 0.684553 Loss2: 2.009640
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.511031 Loss1: 0.068095 Loss2: 1.442936
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.835064 Loss1: 0.447720 Loss2: 1.387344
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.702590 Loss1: 0.268922 Loss2: 1.433667
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.510597 Loss1: 0.068475 Loss2: 1.442121
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509927 Loss1: 0.074492 Loss2: 1.435434 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.484170 Loss1: 0.051688 Loss2: 1.432481 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433522 Loss1: 0.057816 Loss2: 1.375706 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394705 Loss1: 0.034940 Loss2: 1.359765 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.769338 Loss1: 0.376371 Loss2: 1.392967 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.652584 Loss1: 0.233184 Loss2: 1.419400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568044 Loss1: 0.172577 Loss2: 1.395467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.716577 Loss1: 0.343593 Loss2: 1.372984
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.528449 Loss1: 0.131112 Loss2: 1.397337
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.603907 Loss1: 0.202030 Loss2: 1.401877
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478275 Loss1: 0.092095 Loss2: 1.386180
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539981 Loss1: 0.177013 Loss2: 1.362968
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459782 Loss1: 0.080810 Loss2: 1.378972
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430485 Loss1: 0.058579 Loss2: 1.371906
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484864 Loss1: 0.121580 Loss2: 1.363285
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404330 Loss1: 0.034128 Loss2: 1.370202
-DEBUG flwr 2023-10-12 11:58:53,801 | server.py:236 | fit_round 150 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493686 Loss1: 0.142215 Loss2: 1.351471
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471275 Loss1: 0.120118 Loss2: 1.351157
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462167 Loss1: 0.113351 Loss2: 1.348816
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451682 Loss1: 0.099760 Loss2: 1.351922
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418110 Loss1: 0.071551 Loss2: 1.346559
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.487037 Loss1: 0.611709 Loss2: 1.875327
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.841296 Loss1: 0.440099 Loss2: 1.401198
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.627741 Loss1: 0.204263 Loss2: 1.423478
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540253 Loss1: 0.170409 Loss2: 1.369843
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513449 Loss1: 0.139365 Loss2: 1.374084
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.407007 Loss1: 0.495838 Loss2: 1.911169
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.697548 Loss1: 0.300780 Loss2: 1.396767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600956 Loss1: 0.185975 Loss2: 1.414982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.558122 Loss1: 0.163015 Loss2: 1.395108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484296 Loss1: 0.107978 Loss2: 1.376318 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.458320 Loss1: 0.078883 Loss2: 1.379437 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436012 Loss1: 0.075847 Loss2: 1.360166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395522 Loss1: 0.042846 Loss2: 1.352676 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.786443 Loss1: 0.403569 Loss2: 1.382874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.580483 Loss1: 0.194154 Loss2: 1.386329 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536246 Loss1: 0.156913 Loss2: 1.379332
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.387294 Loss1: 0.501522 Loss2: 1.885772
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.678077 Loss1: 0.290765 Loss2: 1.387312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598397 Loss1: 0.178964 Loss2: 1.419433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536226 Loss1: 0.154038 Loss2: 1.382188 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485055 Loss1: 0.112152 Loss2: 1.372903 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414685 Loss1: 0.064067 Loss2: 1.350618
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516848 Loss1: 0.145283 Loss2: 1.371565
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530638 Loss1: 0.146256 Loss2: 1.384382
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.480997 Loss1: 0.105406 Loss2: 1.375592
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.459397 Loss1: 0.089632 Loss2: 1.369764
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449641 Loss1: 0.076760 Loss2: 1.372881
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 11:58:53,801][flwr][DEBUG] - fit_round 150 received 50 results and 0 failures
-INFO flwr 2023-10-12 11:59:35,575 | server.py:125 | fit progress: (150, 2.2385582904846144, {'accuracy': 0.5956}, 346083.35339983396)
->> Test accuracy: 0.595600
-[2023-10-12 11:59:35,575][flwr][INFO] - fit progress: (150, 2.2385582904846144, {'accuracy': 0.5956}, 346083.35339983396)
-DEBUG flwr 2023-10-12 11:59:35,575 | server.py:173 | evaluate_round 150: strategy sampled 50 clients (out of 50)
-[2023-10-12 11:59:35,575][flwr][DEBUG] - evaluate_round 150: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 12:08:36,595 | server.py:187 | evaluate_round 150 received 50 results and 0 failures
-[2023-10-12 12:08:36,595][flwr][DEBUG] - evaluate_round 150 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 12:08:36,596 | server.py:222 | fit_round 151: strategy sampled 50 clients (out of 50)
-[2023-10-12 12:08:36,596][flwr][DEBUG] - fit_round 151: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.408101 Loss1: 0.560553 Loss2: 1.847549
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.761150 Loss1: 0.393327 Loss2: 1.367824
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609584 Loss1: 0.203242 Loss2: 1.406342
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557224 Loss1: 0.200772 Loss2: 1.356452
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.386249 Loss1: 0.536195 Loss2: 1.850054
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.783634 Loss1: 0.418226 Loss2: 1.365408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.613723 Loss1: 0.224429 Loss2: 1.389294 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.545549 Loss1: 0.188671 Loss2: 1.356878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.582037 Loss1: 0.222015 Loss2: 1.360022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.517809 Loss1: 0.150417 Loss2: 1.367392 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367554 Loss1: 0.040001 Loss2: 1.327553
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453609 Loss1: 0.102277 Loss2: 1.351332
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417412 Loss1: 0.071927 Loss2: 1.345485
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413426 Loss1: 0.073559 Loss2: 1.339867
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399338 Loss1: 0.068250 Loss2: 1.331088
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.584236 Loss1: 0.646018 Loss2: 1.938219
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.795836 Loss1: 0.408608 Loss2: 1.387228
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716531 Loss1: 0.278786 Loss2: 1.437744
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.647755 Loss1: 0.222178 Loss2: 1.425577
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.310204 Loss1: 0.527361 Loss2: 1.782844
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587026 Loss1: 0.184049 Loss2: 1.402976 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481220 Loss1: 0.092494 Loss2: 1.388726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464182 Loss1: 0.089192 Loss2: 1.374990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452153 Loss1: 0.073550 Loss2: 1.378603 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425459 Loss1: 0.053851 Loss2: 1.371608 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.380259 Loss1: 0.077915 Loss2: 1.302344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354642 Loss1: 0.063133 Loss2: 1.291509 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.324261 Loss1: 0.038743 Loss2: 1.285519
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.311934 Loss1: 0.531696 Loss2: 1.780238
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.719784 Loss1: 0.394999 Loss2: 1.324785
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.676259 Loss1: 0.302180 Loss2: 1.374079
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.600609 Loss1: 0.258758 Loss2: 1.341851
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525992 Loss1: 0.178749 Loss2: 1.347243
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.300680 Loss1: 0.487695 Loss2: 1.812985
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473690 Loss1: 0.144933 Loss2: 1.328757
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442991 Loss1: 0.117814 Loss2: 1.325178 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415942 Loss1: 0.096521 Loss2: 1.319421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391840 Loss1: 0.073610 Loss2: 1.318231 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354091 Loss1: 0.049066 Loss2: 1.305025 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425554 Loss1: 0.082640 Loss2: 1.342914 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.377182 Loss1: 0.048192 Loss2: 1.328990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364994 Loss1: 0.039580 Loss2: 1.325414
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.324598 Loss1: 0.488713 Loss2: 1.835886
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.756887 Loss1: 0.372260 Loss2: 1.384628
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.630417 Loss1: 0.213692 Loss2: 1.416726
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547166 Loss1: 0.176420 Loss2: 1.370745
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.467117 Loss1: 0.101492 Loss2: 1.365625
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.430651 Loss1: 0.632035 Loss2: 1.798615
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.839256 Loss1: 0.515255 Loss2: 1.324002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.771071 Loss1: 0.358126 Loss2: 1.412945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592073 Loss1: 0.266409 Loss2: 1.325664 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.389508 Loss1: 0.050373 Loss2: 1.339135
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527532 Loss1: 0.170962 Loss2: 1.356571
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399857 Loss1: 0.062169 Loss2: 1.337688
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461517 Loss1: 0.142728 Loss2: 1.318789
-(DefaultActor pid=3765) >> Training accuracy: 0.974609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423694 Loss1: 0.104988 Loss2: 1.318706
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422509 Loss1: 0.101120 Loss2: 1.321389
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405460 Loss1: 0.094513 Loss2: 1.310947
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372540 Loss1: 0.066995 Loss2: 1.305546
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.376117 Loss1: 0.522390 Loss2: 1.853727
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.647915 Loss1: 0.283515 Loss2: 1.364401
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591753 Loss1: 0.192341 Loss2: 1.399412
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518644 Loss1: 0.150877 Loss2: 1.367767
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471640 Loss1: 0.112600 Loss2: 1.359040
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442011 Loss1: 0.092710 Loss2: 1.349302 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408144 Loss1: 0.066237 Loss2: 1.341907 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.396526 Loss1: 0.056576 Loss2: 1.339950 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384115 Loss1: 0.049065 Loss2: 1.335049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368661 Loss1: 0.036666 Loss2: 1.331995 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.375175 Loss1: 0.076744 Loss2: 1.298431 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.339997 Loss1: 0.053341 Loss2: 1.286655 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.618821 Loss1: 0.243619 Loss2: 1.375202 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.538060 Loss1: 0.166227 Loss2: 1.371833 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491208 Loss1: 0.119580 Loss2: 1.371629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455923 Loss1: 0.089174 Loss2: 1.366750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467732 Loss1: 0.109695 Loss2: 1.358037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491261 Loss1: 0.124585 Loss2: 1.366676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419881 Loss1: 0.062863 Loss2: 1.357019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405214 Loss1: 0.053328 Loss2: 1.351886 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996324
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.515718 Loss1: 0.177353 Loss2: 1.338365 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.968750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.296023 Loss1: 0.498663 Loss2: 1.797360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551843 Loss1: 0.205601 Loss2: 1.346242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482068 Loss1: 0.150732 Loss2: 1.331337
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.502423 Loss1: 0.606807 Loss2: 1.895616
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.777581 Loss1: 0.398733 Loss2: 1.378848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.721665 Loss1: 0.292332 Loss2: 1.429333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579394 Loss1: 0.187790 Loss2: 1.391604 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.350589 Loss1: 0.058102 Loss2: 1.292488
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.517441 Loss1: 0.136383 Loss2: 1.381058
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.356526 Loss1: 0.066145 Loss2: 1.290381
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470802 Loss1: 0.099228 Loss2: 1.371573
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.331398 Loss1: 0.044101 Loss2: 1.287297
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447732 Loss1: 0.077021 Loss2: 1.370711
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406794 Loss1: 0.044699 Loss2: 1.362095
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389991 Loss1: 0.040642 Loss2: 1.349349
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399122 Loss1: 0.048409 Loss2: 1.350714
-(DefaultActor pid=3764) >> Training accuracy: 0.979911
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.339456 Loss1: 0.551691 Loss2: 1.787765
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.753744 Loss1: 0.418704 Loss2: 1.335040
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.584077 Loss1: 0.215964 Loss2: 1.368113
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582056 Loss1: 0.240343 Loss2: 1.341714
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.291198 Loss1: 0.525052 Loss2: 1.766146
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.663931 Loss1: 0.341119 Loss2: 1.322811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.611648 Loss1: 0.245354 Loss2: 1.366295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.504903 Loss1: 0.181479 Loss2: 1.323424 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.470519 Loss1: 0.154159 Loss2: 1.316359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.395544 Loss1: 0.082334 Loss2: 1.313209 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.367329 Loss1: 0.061262 Loss2: 1.306067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376696 Loss1: 0.079425 Loss2: 1.297271 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.566034 Loss1: 0.585476 Loss2: 1.980558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.736185 Loss1: 0.252631 Loss2: 1.483554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.631892 Loss1: 0.175586 Loss2: 1.456306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.582300 Loss1: 0.141827 Loss2: 1.440473 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.542322 Loss1: 0.100597 Loss2: 1.441725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.551222 Loss1: 0.120366 Loss2: 1.430856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.523901 Loss1: 0.096118 Loss2: 1.427783 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.514764 Loss1: 0.091420 Loss2: 1.423344 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455982 Loss1: 0.082302 Loss2: 1.373680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461621 Loss1: 0.095391 Loss2: 1.366230 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461171 Loss1: 0.085966 Loss2: 1.375205
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.343509 Loss1: 0.559759 Loss2: 1.783750
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.607807 Loss1: 0.297993 Loss2: 1.309814
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.499857 Loss1: 0.170631 Loss2: 1.329225
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.413149 Loss1: 0.113836 Loss2: 1.299314
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.409678 Loss1: 0.115709 Loss2: 1.293969
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.364388 Loss1: 0.565054 Loss2: 1.799334
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.760474 Loss1: 0.406283 Loss2: 1.354191 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670180 Loss1: 0.278289 Loss2: 1.391890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528116 Loss1: 0.189353 Loss2: 1.338763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.465654 Loss1: 0.126527 Loss2: 1.339127 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449831 Loss1: 0.123433 Loss2: 1.326398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372578 Loss1: 0.054971 Loss2: 1.317607 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362392 Loss1: 0.048539 Loss2: 1.313854 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.703538 Loss1: 0.370319 Loss2: 1.333220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511580 Loss1: 0.172069 Loss2: 1.339511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.528678 Loss1: 0.662529 Loss2: 1.866149 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.742324 Loss1: 0.378961 Loss2: 1.363363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.597817 Loss1: 0.221449 Loss2: 1.376368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.578542 Loss1: 0.230781 Loss2: 1.347761 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475446 Loss1: 0.127262 Loss2: 1.348184 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.398632 Loss1: 0.072296 Loss2: 1.326336 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403789 Loss1: 0.083796 Loss2: 1.319993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373610 Loss1: 0.058856 Loss2: 1.314754
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.378270 Loss1: 0.531038 Loss2: 1.847232
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.816624 Loss1: 0.435869 Loss2: 1.380755
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.749127 Loss1: 0.311944 Loss2: 1.437183
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573547 Loss1: 0.196290 Loss2: 1.377257
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.511116 Loss1: 0.138251 Loss2: 1.372865
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.434280 Loss1: 0.592639 Loss2: 1.841640
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448793 Loss1: 0.086713 Loss2: 1.362080
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469180 Loss1: 0.109800 Loss2: 1.359380 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495943 Loss1: 0.133048 Loss2: 1.362895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447264 Loss1: 0.090008 Loss2: 1.357256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434509 Loss1: 0.081491 Loss2: 1.353018 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416727 Loss1: 0.077690 Loss2: 1.339037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401416 Loss1: 0.068920 Loss2: 1.332496 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416587 Loss1: 0.089984 Loss2: 1.326603
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.246264 Loss1: 0.441519 Loss2: 1.804745
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.618193 Loss1: 0.296231 Loss2: 1.321962
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.598749 Loss1: 0.237325 Loss2: 1.361423
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506558 Loss1: 0.172428 Loss2: 1.334130
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.438679 Loss1: 0.116097 Loss2: 1.322582
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.414213 Loss1: 0.536519 Loss2: 1.877694
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.390665 Loss1: 0.080497 Loss2: 1.310168
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406800 Loss1: 0.106224 Loss2: 1.300576 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.375674 Loss1: 0.070769 Loss2: 1.304905 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.360694 Loss1: 0.061861 Loss2: 1.298833 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.338231 Loss1: 0.040303 Loss2: 1.297928 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485678 Loss1: 0.109612 Loss2: 1.376066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441345 Loss1: 0.074894 Loss2: 1.366451 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482359 Loss1: 0.124684 Loss2: 1.357675
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.399650 Loss1: 0.556960 Loss2: 1.842691
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.757853 Loss1: 0.358833 Loss2: 1.399019
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.707946 Loss1: 0.258321 Loss2: 1.449625
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569187 Loss1: 0.183942 Loss2: 1.385244
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568321 Loss1: 0.176015 Loss2: 1.392306
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.438891 Loss1: 0.525856 Loss2: 1.913036
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525529 Loss1: 0.131037 Loss2: 1.394492
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.766089 Loss1: 0.353949 Loss2: 1.412140
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.556707 Loss1: 0.164797 Loss2: 1.391910
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.767124 Loss1: 0.304248 Loss2: 1.462876
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594560 Loss1: 0.184185 Loss2: 1.410375
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.519788 Loss1: 0.119263 Loss2: 1.400525
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555915 Loss1: 0.147561 Loss2: 1.408354
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493407 Loss1: 0.111203 Loss2: 1.382204
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.548517 Loss1: 0.147311 Loss2: 1.401206
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462081 Loss1: 0.079304 Loss2: 1.382777
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500135 Loss1: 0.111143 Loss2: 1.388992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423565 Loss1: 0.043468 Loss2: 1.380097 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.739698 Loss1: 0.372553 Loss2: 1.367145 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535295 Loss1: 0.162495 Loss2: 1.372800 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498261 Loss1: 0.136431 Loss2: 1.361830 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504320 Loss1: 0.140083 Loss2: 1.364237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474448 Loss1: 0.106688 Loss2: 1.367760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441596 Loss1: 0.080129 Loss2: 1.361467 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429194 Loss1: 0.074604 Loss2: 1.354590 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.585707 Loss1: 0.183089 Loss2: 1.402618
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405716 Loss1: 0.057008 Loss2: 1.348709
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.504786 Loss1: 0.131206 Loss2: 1.373581 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462029 Loss1: 0.082154 Loss2: 1.379875 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.837033 Loss1: 0.397010 Loss2: 1.440023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.593146 Loss1: 0.187788 Loss2: 1.405358 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544575 Loss1: 0.132525 Loss2: 1.412050 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533876 Loss1: 0.125747 Loss2: 1.408130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.498419 Loss1: 0.099047 Loss2: 1.399371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.463509 Loss1: 0.071136 Loss2: 1.392373 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.451470 Loss1: 0.060498 Loss2: 1.390972 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.463498 Loss1: 0.074020 Loss2: 1.389478 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426132 Loss1: 0.070930 Loss2: 1.355202 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.308835 Loss1: 0.523175 Loss2: 1.785660 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609843 Loss1: 0.236319 Loss2: 1.373524 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534165 Loss1: 0.210796 Loss2: 1.323370
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.328779 Loss1: 0.521749 Loss2: 1.807030
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526169 Loss1: 0.194642 Loss2: 1.331528
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.590866 Loss1: 0.255965 Loss2: 1.334901
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448025 Loss1: 0.125902 Loss2: 1.322123
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.478405 Loss1: 0.139735 Loss2: 1.338670
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402366 Loss1: 0.088114 Loss2: 1.314253
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.443794 Loss1: 0.122527 Loss2: 1.321267
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437382 Loss1: 0.125213 Loss2: 1.312169
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.430452 Loss1: 0.119737 Loss2: 1.310715
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386999 Loss1: 0.077396 Loss2: 1.309603
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427500 Loss1: 0.108678 Loss2: 1.318822
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.376634 Loss1: 0.072720 Loss2: 1.303914
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.388727 Loss1: 0.075367 Loss2: 1.313360
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.370179 Loss1: 0.064322 Loss2: 1.305857
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.356373 Loss1: 0.055524 Loss2: 1.300849
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365197 Loss1: 0.067133 Loss2: 1.298064
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.617015 Loss1: 0.731381 Loss2: 1.885634
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.606358 Loss1: 0.294954 Loss2: 1.311404
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.544699 Loss1: 0.219839 Loss2: 1.324860
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.442142 Loss1: 0.125279 Loss2: 1.316863
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.459267 Loss1: 0.589216 Loss2: 1.870051
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465938 Loss1: 0.166774 Loss2: 1.299164 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415808 Loss1: 0.115170 Loss2: 1.300638 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.372745 Loss1: 0.080588 Loss2: 1.292157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.356354 Loss1: 0.067337 Loss2: 1.289016 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.328878 Loss1: 0.046960 Loss2: 1.281918 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439925 Loss1: 0.086197 Loss2: 1.353728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412273 Loss1: 0.069272 Loss2: 1.343000 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391953 Loss1: 0.054180 Loss2: 1.337773
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.193232 Loss1: 0.417272 Loss2: 1.775960
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.601845 Loss1: 0.271488 Loss2: 1.330357
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.570086 Loss1: 0.207300 Loss2: 1.362786
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536246 Loss1: 0.196521 Loss2: 1.339725
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480360 Loss1: 0.145857 Loss2: 1.334502
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.505027 Loss1: 0.609114 Loss2: 1.895913
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.436042 Loss1: 0.104440 Loss2: 1.331602
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.868633 Loss1: 0.452406 Loss2: 1.416227
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432674 Loss1: 0.106780 Loss2: 1.325894
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.688596 Loss1: 0.232543 Loss2: 1.456053
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404080 Loss1: 0.081926 Loss2: 1.322154
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.591422 Loss1: 0.192825 Loss2: 1.398597
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385318 Loss1: 0.067670 Loss2: 1.317648
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527055 Loss1: 0.122761 Loss2: 1.404294
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498550 Loss1: 0.105988 Loss2: 1.392562
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393573 Loss1: 0.079278 Loss2: 1.314295
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467014 Loss1: 0.080376 Loss2: 1.386638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435915 Loss1: 0.055979 Loss2: 1.379935 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.626876 Loss1: 0.290351 Loss2: 1.336525 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.469847 Loss1: 0.131864 Loss2: 1.337983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.451037 Loss1: 0.615133 Loss2: 1.835904
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.427539 Loss1: 0.098682 Loss2: 1.328858
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.752948 Loss1: 0.399321 Loss2: 1.353627
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.392712 Loss1: 0.070403 Loss2: 1.322310
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614635 Loss1: 0.212306 Loss2: 1.402329
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.377676 Loss1: 0.060288 Loss2: 1.317388
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540532 Loss1: 0.191227 Loss2: 1.349305
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.356611 Loss1: 0.047965 Loss2: 1.308647
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513630 Loss1: 0.170931 Loss2: 1.342699
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.344145 Loss1: 0.038999 Loss2: 1.305146
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480153 Loss1: 0.137108 Loss2: 1.343045
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.328003 Loss1: 0.022792 Loss2: 1.305211
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440854 Loss1: 0.104797 Loss2: 1.336057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382785 Loss1: 0.061196 Loss2: 1.321589 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.679450 Loss1: 0.328043 Loss2: 1.351408 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542750 Loss1: 0.198374 Loss2: 1.344376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.225260 Loss1: 0.453310 Loss2: 1.771950
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503074 Loss1: 0.164281 Loss2: 1.338793
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.590326 Loss1: 0.253099 Loss2: 1.337227
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445420 Loss1: 0.117164 Loss2: 1.328255
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.513297 Loss1: 0.149468 Loss2: 1.363829
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420356 Loss1: 0.089857 Loss2: 1.330499
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419591 Loss1: 0.094926 Loss2: 1.324665
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529103 Loss1: 0.192595 Loss2: 1.336508
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.389494 Loss1: 0.068239 Loss2: 1.321255
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.480287 Loss1: 0.137746 Loss2: 1.342541
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354091 Loss1: 0.042673 Loss2: 1.311418
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465380 Loss1: 0.136624 Loss2: 1.328757
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448617 Loss1: 0.120434 Loss2: 1.328183
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426704 Loss1: 0.099739 Loss2: 1.326966
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.355718 Loss1: 0.038615 Loss2: 1.317104
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351257 Loss1: 0.041665 Loss2: 1.309592
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.349935 Loss1: 0.503451 Loss2: 1.846484
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.739060 Loss1: 0.340713 Loss2: 1.398347
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.679123 Loss1: 0.236069 Loss2: 1.443054
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.620851 Loss1: 0.210882 Loss2: 1.409969
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.509995 Loss1: 0.576119 Loss2: 1.933877
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.554542 Loss1: 0.134187 Loss2: 1.420355
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.543225 Loss1: 0.143766 Loss2: 1.399459 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.497452 Loss1: 0.092559 Loss2: 1.404893 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529042 Loss1: 0.196624 Loss2: 1.332418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482108 Loss1: 0.144827 Loss2: 1.337282 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467795 Loss1: 0.133775 Loss2: 1.334019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427584 Loss1: 0.100437 Loss2: 1.327148 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373772 Loss1: 0.058013 Loss2: 1.315759 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.264007 Loss1: 0.489111 Loss2: 1.774897
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.678175 Loss1: 0.342826 Loss2: 1.335350
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.549931 Loss1: 0.187646 Loss2: 1.362285
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.438346 Loss1: 0.545641 Loss2: 1.892705
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.538875 Loss1: 0.203457 Loss2: 1.335419
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.693990 Loss1: 0.315053 Loss2: 1.378938
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478343 Loss1: 0.128672 Loss2: 1.349671
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.408545 Loss1: 0.072671 Loss2: 1.335874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415710 Loss1: 0.087954 Loss2: 1.327756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420271 Loss1: 0.088552 Loss2: 1.331719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394699 Loss1: 0.067707 Loss2: 1.326993 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410660 Loss1: 0.087617 Loss2: 1.323043 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.999023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451336 Loss1: 0.086913 Loss2: 1.364423 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-12 12:37:31,319 | server.py:236 | fit_round 151 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.362476 Loss1: 0.544815 Loss2: 1.817661 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.614615 Loss1: 0.234973 Loss2: 1.379642 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.530323 Loss1: 0.192762 Loss2: 1.337561
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.442149 Loss1: 0.525407 Loss2: 1.916742
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.757993 Loss1: 0.350920 Loss2: 1.407073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.621264 Loss1: 0.179401 Loss2: 1.441863 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528074 Loss1: 0.124404 Loss2: 1.403670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.474030 Loss1: 0.077703 Loss2: 1.396327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472631 Loss1: 0.085948 Loss2: 1.386684 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367270 Loss1: 0.043158 Loss2: 1.324112
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441681 Loss1: 0.054940 Loss2: 1.386741
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425730 Loss1: 0.050198 Loss2: 1.375532
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440950 Loss1: 0.062210 Loss2: 1.378739
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431242 Loss1: 0.050128 Loss2: 1.381114
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.471228 Loss1: 0.644307 Loss2: 1.826921
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.638149 Loss1: 0.320299 Loss2: 1.317850
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.589087 Loss1: 0.248334 Loss2: 1.340753
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517248 Loss1: 0.195565 Loss2: 1.321683
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.517345 Loss1: 0.605322 Loss2: 1.912023
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.795819 Loss1: 0.379158 Loss2: 1.416661 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.750549 Loss1: 0.262503 Loss2: 1.488045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.632195 Loss1: 0.223334 Loss2: 1.408861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.534398 Loss1: 0.125605 Loss2: 1.408793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396331 Loss1: 0.102177 Loss2: 1.294154 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969866
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458787 Loss1: 0.070523 Loss2: 1.388264 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.483792 Loss1: 0.100572 Loss2: 1.383221 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 12:37:31,319][flwr][DEBUG] - fit_round 151 received 50 results and 0 failures
-INFO flwr 2023-10-12 12:38:13,715 | server.py:125 | fit progress: (151, 2.2425448860223303, {'accuracy': 0.5966}, 348401.49327949)
->> Test accuracy: 0.596600
-[2023-10-12 12:38:13,715][flwr][INFO] - fit progress: (151, 2.2425448860223303, {'accuracy': 0.5966}, 348401.49327949)
-DEBUG flwr 2023-10-12 12:38:13,715 | server.py:173 | evaluate_round 151: strategy sampled 50 clients (out of 50)
-[2023-10-12 12:38:13,715][flwr][DEBUG] - evaluate_round 151: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 12:47:21,319 | server.py:187 | evaluate_round 151 received 50 results and 0 failures
-[2023-10-12 12:47:21,319][flwr][DEBUG] - evaluate_round 151 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 12:47:21,319 | server.py:222 | fit_round 152: strategy sampled 50 clients (out of 50)
-[2023-10-12 12:47:21,319][flwr][DEBUG] - fit_round 152: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.651230 Loss1: 0.659511 Loss2: 1.991718
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.793533 Loss1: 0.426721 Loss2: 1.366811
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.660700 Loss1: 0.259889 Loss2: 1.400811
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.606571 Loss1: 0.214880 Loss2: 1.391690
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525602 Loss1: 0.155602 Loss2: 1.370000
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.747036 Loss1: 0.376578 Loss2: 1.370458 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437388 Loss1: 0.085084 Loss2: 1.352304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.514405 Loss1: 0.143035 Loss2: 1.371370 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495690 Loss1: 0.125455 Loss2: 1.370235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441042 Loss1: 0.082006 Loss2: 1.359037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402859 Loss1: 0.047229 Loss2: 1.355630 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384658 Loss1: 0.037358 Loss2: 1.347299 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.468521 Loss1: 0.164643 Loss2: 1.303878 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457025 Loss1: 0.160458 Loss2: 1.296567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.385430 Loss1: 0.501956 Loss2: 1.883473
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402357 Loss1: 0.103555 Loss2: 1.298802
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.597400 Loss1: 0.228137 Loss2: 1.369263
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380962 Loss1: 0.090655 Loss2: 1.290307
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.523039 Loss1: 0.156260 Loss2: 1.366778
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385016 Loss1: 0.097509 Loss2: 1.287507
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.451518 Loss1: 0.088924 Loss2: 1.362594
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407555 Loss1: 0.118378 Loss2: 1.289177
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.430909 Loss1: 0.089423 Loss2: 1.341486 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418389 Loss1: 0.087030 Loss2: 1.331359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404044 Loss1: 0.072726 Loss2: 1.331318
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.359818 Loss1: 0.550214 Loss2: 1.809604
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430930 Loss1: 0.092070 Loss2: 1.338861
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.610927 Loss1: 0.286319 Loss2: 1.324607
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526780 Loss1: 0.182367 Loss2: 1.344413
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.455214 Loss1: 0.141606 Loss2: 1.313609
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.403488 Loss1: 0.094730 Loss2: 1.308759
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473363 Loss1: 0.162310 Loss2: 1.311053
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.527772 Loss1: 0.646228 Loss2: 1.881543
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464639 Loss1: 0.126492 Loss2: 1.338147
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.752937 Loss1: 0.354182 Loss2: 1.398754
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439422 Loss1: 0.128723 Loss2: 1.310700
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.683285 Loss1: 0.260394 Loss2: 1.422891
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418857 Loss1: 0.114763 Loss2: 1.304095
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.549316 Loss1: 0.152252 Loss2: 1.397063
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402200 Loss1: 0.094909 Loss2: 1.307291
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502185 Loss1: 0.122950 Loss2: 1.379235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450204 Loss1: 0.088061 Loss2: 1.362143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427475 Loss1: 0.064862 Loss2: 1.362613
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.319790 Loss1: 0.465669 Loss2: 1.854121
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.656975 Loss1: 0.288957 Loss2: 1.368018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573143 Loss1: 0.200394 Loss2: 1.372749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497039 Loss1: 0.120731 Loss2: 1.376309 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539837 Loss1: 0.179402 Loss2: 1.360435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.494727 Loss1: 0.130169 Loss2: 1.364558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432051 Loss1: 0.078111 Loss2: 1.353939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413934 Loss1: 0.067158 Loss2: 1.346776 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546910 Loss1: 0.124924 Loss2: 1.421986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.513087 Loss1: 0.095251 Loss2: 1.417836 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.506836 Loss1: 0.095649 Loss2: 1.411187
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.390103 Loss1: 0.551926 Loss2: 1.838177
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464303 Loss1: 0.059803 Loss2: 1.404500
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.699423 Loss1: 0.349772 Loss2: 1.349652
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.700258 Loss1: 0.295527 Loss2: 1.404731
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570460 Loss1: 0.208312 Loss2: 1.362148
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.587926 Loss1: 0.222434 Loss2: 1.365492
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.534191 Loss1: 0.167874 Loss2: 1.366317
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451216 Loss1: 0.105812 Loss2: 1.345405
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.344622 Loss1: 0.474481 Loss2: 1.870141
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401446 Loss1: 0.058923 Loss2: 1.342523
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.663636 Loss1: 0.315974 Loss2: 1.347662
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403513 Loss1: 0.067727 Loss2: 1.335786
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.634989 Loss1: 0.234079 Loss2: 1.400910
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411970 Loss1: 0.079259 Loss2: 1.332711
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567735 Loss1: 0.215878 Loss2: 1.351857
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513842 Loss1: 0.159125 Loss2: 1.354717
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479657 Loss1: 0.126406 Loss2: 1.353251
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434492 Loss1: 0.089484 Loss2: 1.345008
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406462 Loss1: 0.066609 Loss2: 1.339853
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396839 Loss1: 0.065157 Loss2: 1.331681
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.304292 Loss1: 0.491942 Loss2: 1.812350
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367565 Loss1: 0.040889 Loss2: 1.326675
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.657267 Loss1: 0.326371 Loss2: 1.330896
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.653366 Loss1: 0.273913 Loss2: 1.379454
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561737 Loss1: 0.201727 Loss2: 1.360011
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468296 Loss1: 0.122325 Loss2: 1.345971
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.419253 Loss1: 0.083570 Loss2: 1.335683
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.533485 Loss1: 0.639335 Loss2: 1.894150
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.413178 Loss1: 0.083500 Loss2: 1.329678
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.815400 Loss1: 0.423900 Loss2: 1.391500
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.379558 Loss1: 0.051154 Loss2: 1.328404
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.744657 Loss1: 0.295951 Loss2: 1.448706
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.375866 Loss1: 0.052086 Loss2: 1.323781
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579122 Loss1: 0.188464 Loss2: 1.390658
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390703 Loss1: 0.072417 Loss2: 1.318286
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500728 Loss1: 0.119175 Loss2: 1.381553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458507 Loss1: 0.085583 Loss2: 1.372924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479200 Loss1: 0.110172 Loss2: 1.369028
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.453560 Loss1: 0.560460 Loss2: 1.893100
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460527 Loss1: 0.086583 Loss2: 1.373944
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.701059 Loss1: 0.314290 Loss2: 1.386770
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618155 Loss1: 0.210735 Loss2: 1.407420
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.571398 Loss1: 0.202049 Loss2: 1.369349
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.542440 Loss1: 0.167226 Loss2: 1.375214
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506272 Loss1: 0.137756 Loss2: 1.368516
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444404 Loss1: 0.086946 Loss2: 1.357458
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.476679 Loss1: 0.581962 Loss2: 1.894717
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427877 Loss1: 0.078754 Loss2: 1.349123
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.694317 Loss1: 0.318456 Loss2: 1.375861
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.420185 Loss1: 0.071976 Loss2: 1.348209
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670342 Loss1: 0.255645 Loss2: 1.414697
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402186 Loss1: 0.060330 Loss2: 1.341856
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576776 Loss1: 0.186489 Loss2: 1.390287
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.553607 Loss1: 0.173094 Loss2: 1.380513
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.568431 Loss1: 0.180619 Loss2: 1.387813
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.514314 Loss1: 0.133954 Loss2: 1.380360
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483615 Loss1: 0.108307 Loss2: 1.375307
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.469802 Loss1: 0.097393 Loss2: 1.372410
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.470287 Loss1: 0.589942 Loss2: 1.880346
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.469415 Loss1: 0.090929 Loss2: 1.378486
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.721354 Loss1: 0.342767 Loss2: 1.378587
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610830 Loss1: 0.192967 Loss2: 1.417863
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564458 Loss1: 0.186957 Loss2: 1.377501
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534990 Loss1: 0.150661 Loss2: 1.384329
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.453965 Loss1: 0.081532 Loss2: 1.372433
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.406232 Loss1: 0.548709 Loss2: 1.857523
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440817 Loss1: 0.079150 Loss2: 1.361667
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.780594 Loss1: 0.410481 Loss2: 1.370112
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417578 Loss1: 0.061361 Loss2: 1.356217
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614121 Loss1: 0.208233 Loss2: 1.405888
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424129 Loss1: 0.065574 Loss2: 1.358556
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498676 Loss1: 0.133765 Loss2: 1.364911
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391699 Loss1: 0.046127 Loss2: 1.345572
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478952 Loss1: 0.119924 Loss2: 1.359028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.456644 Loss1: 0.106973 Loss2: 1.349670 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422985 Loss1: 0.068644 Loss2: 1.354341
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.373639 Loss1: 0.567691 Loss2: 1.805949
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401069 Loss1: 0.052491 Loss2: 1.348578
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.661826 Loss1: 0.327323 Loss2: 1.334503
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.585712 Loss1: 0.238692 Loss2: 1.347020
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467790 Loss1: 0.140471 Loss2: 1.327319
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.391318 Loss1: 0.069094 Loss2: 1.322224
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.381296 Loss1: 0.073108 Loss2: 1.308189
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.369162 Loss1: 0.067424 Loss2: 1.301738
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.417609 Loss1: 0.520862 Loss2: 1.896747
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.373361 Loss1: 0.072135 Loss2: 1.301227
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.793586 Loss1: 0.360619 Loss2: 1.432967
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.376236 Loss1: 0.077229 Loss2: 1.299006
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.657579 Loss1: 0.194684 Loss2: 1.462895
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.336560 Loss1: 0.044619 Loss2: 1.291941
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540815 Loss1: 0.114423 Loss2: 1.426392
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.540006 Loss1: 0.120967 Loss2: 1.419039
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.514562 Loss1: 0.095436 Loss2: 1.419126
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504633 Loss1: 0.095918 Loss2: 1.408715
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452557 Loss1: 0.042803 Loss2: 1.409753
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453762 Loss1: 0.054738 Loss2: 1.399024
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.493958 Loss1: 0.681950 Loss2: 1.812009
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441050 Loss1: 0.048629 Loss2: 1.392421
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.716522 Loss1: 0.355976 Loss2: 1.360546
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595674 Loss1: 0.215030 Loss2: 1.380644
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.519154 Loss1: 0.175971 Loss2: 1.343183
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.464479 Loss1: 0.119928 Loss2: 1.344551
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.432781 Loss1: 0.098589 Loss2: 1.334193
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.379827 Loss1: 0.459506 Loss2: 1.920321
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422042 Loss1: 0.091124 Loss2: 1.330918
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.740546 Loss1: 0.323006 Loss2: 1.417540
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.408658 Loss1: 0.075949 Loss2: 1.332709
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.704954 Loss1: 0.253728 Loss2: 1.451226
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397879 Loss1: 0.075989 Loss2: 1.321890
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.612591 Loss1: 0.188021 Loss2: 1.424570
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426645 Loss1: 0.103229 Loss2: 1.323416
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527792 Loss1: 0.111840 Loss2: 1.415952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481959 Loss1: 0.078424 Loss2: 1.403535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462396 Loss1: 0.062608 Loss2: 1.399788
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.418470 Loss1: 0.539489 Loss2: 1.878980
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444588 Loss1: 0.055995 Loss2: 1.388593
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.811562 Loss1: 0.428022 Loss2: 1.383540
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.703289 Loss1: 0.254957 Loss2: 1.448332
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572124 Loss1: 0.176344 Loss2: 1.395779
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.610307 Loss1: 0.203892 Loss2: 1.406414
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.541901 Loss1: 0.148732 Loss2: 1.393169
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.478564 Loss1: 0.559418 Loss2: 1.919146
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.554183 Loss1: 0.167963 Loss2: 1.386220
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486637 Loss1: 0.092961 Loss2: 1.393676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424019 Loss1: 0.050537 Loss2: 1.373482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404522 Loss1: 0.036994 Loss2: 1.367529 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499700 Loss1: 0.124124 Loss2: 1.375577 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.494180 Loss1: 0.127536 Loss2: 1.366643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430795 Loss1: 0.066396 Loss2: 1.364399 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.636477 Loss1: 0.201238 Loss2: 1.435239 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.594659 Loss1: 0.183268 Loss2: 1.411391 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.463992 Loss1: 0.581314 Loss2: 1.882678
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.624620 Loss1: 0.207441 Loss2: 1.417179
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.707237 Loss1: 0.343250 Loss2: 1.363987
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.536693 Loss1: 0.135123 Loss2: 1.401570
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.466355 Loss1: 0.066291 Loss2: 1.400065 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448670 Loss1: 0.061385 Loss2: 1.387285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453424 Loss1: 0.073317 Loss2: 1.380107 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447223 Loss1: 0.105964 Loss2: 1.341259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446508 Loss1: 0.096643 Loss2: 1.349865 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429851 Loss1: 0.092345 Loss2: 1.337506
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.456508 Loss1: 0.604789 Loss2: 1.851719
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.741320 Loss1: 0.405660 Loss2: 1.335660
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656496 Loss1: 0.265617 Loss2: 1.390879
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517813 Loss1: 0.178333 Loss2: 1.339480
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.456297 Loss1: 0.127483 Loss2: 1.328814
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.486041 Loss1: 0.598241 Loss2: 1.887800
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.402068 Loss1: 0.077793 Loss2: 1.324275
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.798591 Loss1: 0.388662 Loss2: 1.409929
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.394043 Loss1: 0.077660 Loss2: 1.316383
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.374960 Loss1: 0.066372 Loss2: 1.308587
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.686647 Loss1: 0.232086 Loss2: 1.454561
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.387556 Loss1: 0.079714 Loss2: 1.307843
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.664153 Loss1: 0.269186 Loss2: 1.394967
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387906 Loss1: 0.083979 Loss2: 1.303927
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.641687 Loss1: 0.219136 Loss2: 1.422551
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560648 Loss1: 0.167053 Loss2: 1.393594
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.496294 Loss1: 0.107170 Loss2: 1.389125
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.451631 Loss1: 0.066457 Loss2: 1.385174
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439987 Loss1: 0.070954 Loss2: 1.369033
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415503 Loss1: 0.048223 Loss2: 1.367280
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.272902 Loss1: 0.458255 Loss2: 1.814647
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.646203 Loss1: 0.291825 Loss2: 1.354378
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.585006 Loss1: 0.201528 Loss2: 1.383478
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.480142 Loss1: 0.128996 Loss2: 1.351146
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443677 Loss1: 0.097035 Loss2: 1.346642
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.412019 Loss1: 0.524465 Loss2: 1.887554
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.726745 Loss1: 0.345133 Loss2: 1.381611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.702842 Loss1: 0.282591 Loss2: 1.420251 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595368 Loss1: 0.211839 Loss2: 1.383530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.564451 Loss1: 0.176491 Loss2: 1.387960 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410144 Loss1: 0.074594 Loss2: 1.335550
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502985 Loss1: 0.126406 Loss2: 1.376579
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.507774 Loss1: 0.129392 Loss2: 1.378382
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438332 Loss1: 0.067820 Loss2: 1.370513
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422735 Loss1: 0.055225 Loss2: 1.367510
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387683 Loss1: 0.030839 Loss2: 1.356844
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.449997 Loss1: 0.563119 Loss2: 1.886878
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.744936 Loss1: 0.358837 Loss2: 1.386099
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615728 Loss1: 0.200379 Loss2: 1.415349
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.568966 Loss1: 0.192364 Loss2: 1.376602
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.508082 Loss1: 0.121984 Loss2: 1.386098
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.490745 Loss1: 0.116237 Loss2: 1.374508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448830 Loss1: 0.075733 Loss2: 1.373098 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453775 Loss1: 0.084677 Loss2: 1.369097 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465024 Loss1: 0.098401 Loss2: 1.366622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529601 Loss1: 0.138106 Loss2: 1.391495
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405788 Loss1: 0.043645 Loss2: 1.362143
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.512170 Loss1: 0.121170 Loss2: 1.390999 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454715 Loss1: 0.072109 Loss2: 1.382605 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448760 Loss1: 0.074857 Loss2: 1.373904 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989890
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.674863 Loss1: 0.266367 Loss2: 1.408496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506043 Loss1: 0.149125 Loss2: 1.356918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484784 Loss1: 0.138837 Loss2: 1.345948
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.330621 Loss1: 0.484250 Loss2: 1.846371
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.740988 Loss1: 0.385982 Loss2: 1.355005 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.647384 Loss1: 0.233866 Loss2: 1.413518 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536577 Loss1: 0.176384 Loss2: 1.360193 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.481982 Loss1: 0.129184 Loss2: 1.352798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436346 Loss1: 0.090534 Loss2: 1.345813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388755 Loss1: 0.054275 Loss2: 1.334480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400145 Loss1: 0.068144 Loss2: 1.332001 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609008 Loss1: 0.263269 Loss2: 1.345739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475276 Loss1: 0.157642 Loss2: 1.317634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.411680 Loss1: 0.505726 Loss2: 1.905954 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.727576 Loss1: 0.345288 Loss2: 1.382288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.623729 Loss1: 0.197007 Loss2: 1.426723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555420 Loss1: 0.182686 Loss2: 1.372733 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477108 Loss1: 0.096171 Loss2: 1.380937 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449046 Loss1: 0.094580 Loss2: 1.354466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415104 Loss1: 0.062920 Loss2: 1.352184
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.457615 Loss1: 0.614212 Loss2: 1.843403
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.723902 Loss1: 0.363532 Loss2: 1.360370 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533184 Loss1: 0.176610 Loss2: 1.356575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462161 Loss1: 0.115760 Loss2: 1.346401 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463850 Loss1: 0.125386 Loss2: 1.338464 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426889 Loss1: 0.086933 Loss2: 1.339956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.462890 Loss1: 0.165086 Loss2: 1.297804
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417915 Loss1: 0.086866 Loss2: 1.331049
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.429232 Loss1: 0.151330 Loss2: 1.277902
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381644 Loss1: 0.049675 Loss2: 1.331969
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.326440 Loss1: 0.058183 Loss2: 1.268257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.302015 Loss1: 0.048578 Loss2: 1.253438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.319672 Loss1: 0.473006 Loss2: 1.846666
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.306396 Loss1: 0.058028 Loss2: 1.248368
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.640708 Loss1: 0.256962 Loss2: 1.383746
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.298350 Loss1: 0.053711 Loss2: 1.244639
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506229 Loss1: 0.135181 Loss2: 1.371048 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480610 Loss1: 0.114094 Loss2: 1.366516 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.419702 Loss1: 0.549147 Loss2: 1.870555
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.494302 Loss1: 0.123523 Loss2: 1.370779
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.694678 Loss1: 0.327393 Loss2: 1.367284
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432802 Loss1: 0.062236 Loss2: 1.370566
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.591242 Loss1: 0.207487 Loss2: 1.383756
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403673 Loss1: 0.042681 Loss2: 1.360993
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507000 Loss1: 0.148452 Loss2: 1.358549
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381768 Loss1: 0.028295 Loss2: 1.353473
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477741 Loss1: 0.118912 Loss2: 1.358829 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.395182 Loss1: 0.053158 Loss2: 1.342024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389677 Loss1: 0.049566 Loss2: 1.340112
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.376305 Loss1: 0.548765 Loss2: 1.827539
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.674719 Loss1: 0.320606 Loss2: 1.354113 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.548850 Loss1: 0.196634 Loss2: 1.352216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443835 Loss1: 0.099161 Loss2: 1.344675 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422493 Loss1: 0.089491 Loss2: 1.333002 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389131 Loss1: 0.064844 Loss2: 1.324287 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444531 Loss1: 0.119054 Loss2: 1.325477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437684 Loss1: 0.096421 Loss2: 1.341263 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530618 Loss1: 0.109236 Loss2: 1.421382 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.505089 Loss1: 0.095471 Loss2: 1.409617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.603898 Loss1: 0.646845 Loss2: 1.957053
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470530 Loss1: 0.068840 Loss2: 1.401690
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.838767 Loss1: 0.350748 Loss2: 1.488019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.615927 Loss1: 0.191931 Loss2: 1.423996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.368802 Loss1: 0.535011 Loss2: 1.833790 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.622183 Loss1: 0.282881 Loss2: 1.339302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588561 Loss1: 0.221213 Loss2: 1.367348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.501179 Loss1: 0.153380 Loss2: 1.347799 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.497593 Loss1: 0.159110 Loss2: 1.338483 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412725 Loss1: 0.086474 Loss2: 1.326251 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427161 Loss1: 0.098301 Loss2: 1.328861
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.364659 Loss1: 0.467256 Loss2: 1.897402
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.657282 Loss1: 0.249415 Loss2: 1.407867 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563240 Loss1: 0.159918 Loss2: 1.403322 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.560806 Loss1: 0.155068 Loss2: 1.405739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.506332 Loss1: 0.115085 Loss2: 1.391247 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.543332 Loss1: 0.146854 Loss2: 1.396478 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.509928 Loss1: 0.120101 Loss2: 1.389828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.486239 Loss1: 0.104122 Loss2: 1.382117 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440344 Loss1: 0.106790 Loss2: 1.333554 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408354 Loss1: 0.073203 Loss2: 1.335151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391633 Loss1: 0.064477 Loss2: 1.327156
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.416583 Loss1: 0.574679 Loss2: 1.841904
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.738457 Loss1: 0.357952 Loss2: 1.380504
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.642267 Loss1: 0.251428 Loss2: 1.390839
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526241 Loss1: 0.162367 Loss2: 1.363874
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.539546 Loss1: 0.178194 Loss2: 1.361352
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.579991 Loss1: 0.642716 Loss2: 1.937275
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.474915 Loss1: 0.111743 Loss2: 1.363173
-DEBUG flwr 2023-10-12 13:15:40,621 | server.py:236 | fit_round 152 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474375 Loss1: 0.122431 Loss2: 1.351944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419886 Loss1: 0.073741 Loss2: 1.346145 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407237 Loss1: 0.067081 Loss2: 1.340156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409438 Loss1: 0.074780 Loss2: 1.334658 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382840 Loss1: 0.065156 Loss2: 1.317684 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383309 Loss1: 0.075520 Loss2: 1.307789 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.769057 Loss1: 0.417960 Loss2: 1.351097 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586932 Loss1: 0.210885 Loss2: 1.376047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.430049 Loss1: 0.535284 Loss2: 1.894765 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.697514 Loss1: 0.303084 Loss2: 1.394429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.645078 Loss1: 0.226230 Loss2: 1.418847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385408 Loss1: 0.059717 Loss2: 1.325691 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387862 Loss1: 0.060998 Loss2: 1.326864 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530588 Loss1: 0.137044 Loss2: 1.393544 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467074 Loss1: 0.084550 Loss2: 1.382524 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421295 Loss1: 0.054719 Loss2: 1.366576
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.372429 Loss1: 0.483905 Loss2: 1.888524
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.732988 Loss1: 0.318101 Loss2: 1.414886
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.661865 Loss1: 0.204067 Loss2: 1.457798
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535675 Loss1: 0.122723 Loss2: 1.412952
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531488 Loss1: 0.127704 Loss2: 1.403784
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.343088 Loss1: 0.519204 Loss2: 1.823884
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516611 Loss1: 0.106580 Loss2: 1.410030
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.494135 Loss1: 0.101334 Loss2: 1.392801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481894 Loss1: 0.080775 Loss2: 1.401119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.468719 Loss1: 0.074308 Loss2: 1.394412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459868 Loss1: 0.071926 Loss2: 1.387942 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.383040 Loss1: 0.049608 Loss2: 1.333432 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.342222 Loss1: 0.024778 Loss2: 1.317444 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 13:15:40,621][flwr][DEBUG] - fit_round 152 received 50 results and 0 failures
-INFO flwr 2023-10-12 13:16:22,588 | server.py:125 | fit progress: (152, 2.2384562981776157, {'accuracy': 0.5963}, 350690.366838993)
->> Test accuracy: 0.596300
-[2023-10-12 13:16:22,588][flwr][INFO] - fit progress: (152, 2.2384562981776157, {'accuracy': 0.5963}, 350690.366838993)
-DEBUG flwr 2023-10-12 13:16:22,589 | server.py:173 | evaluate_round 152: strategy sampled 50 clients (out of 50)
-[2023-10-12 13:16:22,589][flwr][DEBUG] - evaluate_round 152: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 13:25:24,835 | server.py:187 | evaluate_round 152 received 50 results and 0 failures
-[2023-10-12 13:25:24,835][flwr][DEBUG] - evaluate_round 152 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 13:25:24,836 | server.py:222 | fit_round 153: strategy sampled 50 clients (out of 50)
-[2023-10-12 13:25:24,836][flwr][DEBUG] - fit_round 153: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.632526 Loss1: 0.652354 Loss2: 1.980173 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.675564 Loss1: 0.288148 Loss2: 1.387416 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.522478 Loss1: 0.160526 Loss2: 1.361953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488491 Loss1: 0.131740 Loss2: 1.356751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440138 Loss1: 0.087116 Loss2: 1.353022 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414429 Loss1: 0.061312 Loss2: 1.353117
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.602628 Loss1: 0.201584 Loss2: 1.401044
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.485761 Loss1: 0.116055 Loss2: 1.369705 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998698
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.465046 Loss1: 0.104751 Loss2: 1.360295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.388065 Loss1: 0.044318 Loss2: 1.343747 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404054 Loss1: 0.061018 Loss2: 1.343036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398721 Loss1: 0.056708 Loss2: 1.342013 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582122 Loss1: 0.208548 Loss2: 1.373574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.482684 Loss1: 0.145507 Loss2: 1.337178 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472786 Loss1: 0.133974 Loss2: 1.338812
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.406466 Loss1: 0.485417 Loss2: 1.921049
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.751206 Loss1: 0.327682 Loss2: 1.423523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.673503 Loss1: 0.215372 Loss2: 1.458130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.668779 Loss1: 0.232995 Loss2: 1.435784 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.598179 Loss1: 0.171390 Loss2: 1.426788 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.524360 Loss1: 0.104731 Loss2: 1.419629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.501155 Loss1: 0.094598 Loss2: 1.406557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478637 Loss1: 0.071075 Loss2: 1.407562 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.598975 Loss1: 0.177906 Loss2: 1.421069 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480144 Loss1: 0.072375 Loss2: 1.407768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.374971 Loss1: 0.584633 Loss2: 1.790338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.685967 Loss1: 0.356747 Loss2: 1.329220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.545879 Loss1: 0.187026 Loss2: 1.358853 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995192
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.430415 Loss1: 0.116105 Loss2: 1.314311 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.380238 Loss1: 0.069866 Loss2: 1.310373 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.360593 Loss1: 0.060211 Loss2: 1.300382
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.281173 Loss1: 0.473851 Loss2: 1.807322
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.587097 Loss1: 0.254756 Loss2: 1.332341 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.553175 Loss1: 0.191831 Loss2: 1.361343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503591 Loss1: 0.159370 Loss2: 1.344221 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431169 Loss1: 0.103614 Loss2: 1.327555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.363869 Loss1: 0.039176 Loss2: 1.324693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.338425 Loss1: 0.029735 Loss2: 1.308690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.335061 Loss1: 0.030707 Loss2: 1.304353 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482871 Loss1: 0.142901 Loss2: 1.339969 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436049 Loss1: 0.097131 Loss2: 1.338918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.424436 Loss1: 0.512659 Loss2: 1.911777
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405793 Loss1: 0.078527 Loss2: 1.327265
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784198 Loss1: 0.374755 Loss2: 1.409443
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388244 Loss1: 0.064328 Loss2: 1.323917
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541118 Loss1: 0.133689 Loss2: 1.407429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.549143 Loss1: 0.152889 Loss2: 1.396255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.548787 Loss1: 0.139470 Loss2: 1.409317
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.249482 Loss1: 0.419398 Loss2: 1.830084
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.628587 Loss1: 0.265669 Loss2: 1.362917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572416 Loss1: 0.182911 Loss2: 1.389506 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.482453 Loss1: 0.121518 Loss2: 1.360935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.442362 Loss1: 0.082858 Loss2: 1.359504 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.289658 Loss1: 0.501515 Loss2: 1.788143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.845610 Loss1: 0.531992 Loss2: 1.313618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.695789 Loss1: 0.298967 Loss2: 1.396823 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995404
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.552041 Loss1: 0.241321 Loss2: 1.310720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463734 Loss1: 0.164214 Loss2: 1.299520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.406231 Loss1: 0.110548 Loss2: 1.295684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.418240 Loss1: 0.532345 Loss2: 1.885895
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.383573 Loss1: 0.089333 Loss2: 1.294240
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.672031 Loss1: 0.293788 Loss2: 1.378243
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369054 Loss1: 0.077958 Loss2: 1.291095
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.512261 Loss1: 0.128602 Loss2: 1.383659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482208 Loss1: 0.119449 Loss2: 1.362759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461166 Loss1: 0.096542 Loss2: 1.364624
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.392917 Loss1: 0.562802 Loss2: 1.830115
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.694148 Loss1: 0.337150 Loss2: 1.356997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.689714 Loss1: 0.267526 Loss2: 1.422187 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402628 Loss1: 0.045805 Loss2: 1.356823
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536969 Loss1: 0.179166 Loss2: 1.357803
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546230 Loss1: 0.187215 Loss2: 1.359015
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.532667 Loss1: 0.166347 Loss2: 1.366319
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.499790 Loss1: 0.145068 Loss2: 1.354722
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.477555 Loss1: 0.118225 Loss2: 1.359330
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.479117 Loss1: 0.601114 Loss2: 1.878003
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417780 Loss1: 0.067548 Loss2: 1.350232
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404566 Loss1: 0.065238 Loss2: 1.339327 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500905 Loss1: 0.158985 Loss2: 1.341920 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459069 Loss1: 0.122575 Loss2: 1.336494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437977 Loss1: 0.099539 Loss2: 1.338438
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.579927 Loss1: 0.636148 Loss2: 1.943779
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.820323 Loss1: 0.422620 Loss2: 1.397703 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410252 Loss1: 0.073081 Loss2: 1.337171
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.678735 Loss1: 0.231925 Loss2: 1.446810
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.614289 Loss1: 0.210303 Loss2: 1.403986
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.570887 Loss1: 0.177122 Loss2: 1.393765
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516554 Loss1: 0.116243 Loss2: 1.400311
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.465221 Loss1: 0.079117 Loss2: 1.386105
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.466907 Loss1: 0.085434 Loss2: 1.381474
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.282405 Loss1: 0.476938 Loss2: 1.805467
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469030 Loss1: 0.087486 Loss2: 1.381544
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.634963 Loss1: 0.324458 Loss2: 1.310505
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442707 Loss1: 0.067647 Loss2: 1.375059
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.524562 Loss1: 0.181527 Loss2: 1.343034
-(DefaultActor pid=3765) >> Training accuracy: 0.994420
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.473860 Loss1: 0.160369 Loss2: 1.313490
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.429238 Loss1: 0.114211 Loss2: 1.315027
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420966 Loss1: 0.107904 Loss2: 1.313062
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.374200 Loss1: 0.074182 Loss2: 1.300018
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.352120 Loss1: 0.055476 Loss2: 1.296643
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.297878 Loss1: 0.504452 Loss2: 1.793426
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354517 Loss1: 0.063038 Loss2: 1.291478
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572784 Loss1: 0.260231 Loss2: 1.312553
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.333735 Loss1: 0.046869 Loss2: 1.286866
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.577557 Loss1: 0.228931 Loss2: 1.348626
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.519151 Loss1: 0.190834 Loss2: 1.328317
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.455769 Loss1: 0.138347 Loss2: 1.317422
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444765 Loss1: 0.126617 Loss2: 1.318148
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475058 Loss1: 0.166561 Loss2: 1.308497
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.494835 Loss1: 0.166238 Loss2: 1.328597
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.370588 Loss1: 0.557456 Loss2: 1.813132
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435959 Loss1: 0.122693 Loss2: 1.313266
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.664163 Loss1: 0.328105 Loss2: 1.336058
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388332 Loss1: 0.079066 Loss2: 1.309266
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.575972 Loss1: 0.209951 Loss2: 1.366021
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.464217 Loss1: 0.141180 Loss2: 1.323037
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.438073 Loss1: 0.121129 Loss2: 1.316944
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459978 Loss1: 0.143458 Loss2: 1.316520
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415626 Loss1: 0.099550 Loss2: 1.316076
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374440 Loss1: 0.066480 Loss2: 1.307961
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.424776 Loss1: 0.570110 Loss2: 1.854666
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.701389 Loss1: 0.342901 Loss2: 1.358488
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.375580 Loss1: 0.076991 Loss2: 1.298589
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.562985 Loss1: 0.180750 Loss2: 1.382235
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354317 Loss1: 0.050471 Loss2: 1.303846
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536207 Loss1: 0.166913 Loss2: 1.369294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.437576 Loss1: 0.086958 Loss2: 1.350618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.428967 Loss1: 0.078073 Loss2: 1.350894
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.496491 Loss1: 0.591380 Loss2: 1.905111
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396377 Loss1: 0.052611 Loss2: 1.343766
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.691030 Loss1: 0.328382 Loss2: 1.362649
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.741148 Loss1: 0.344948 Loss2: 1.396201
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389404 Loss1: 0.054096 Loss2: 1.335308
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515065 Loss1: 0.155765 Loss2: 1.359300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478975 Loss1: 0.123888 Loss2: 1.355088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.286539 Loss1: 0.448565 Loss2: 1.837973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.723750 Loss1: 0.344511 Loss2: 1.379239 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.646631 Loss1: 0.256701 Loss2: 1.389930 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505542 Loss1: 0.120415 Loss2: 1.385127 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466514 Loss1: 0.083211 Loss2: 1.383303
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.282894 Loss1: 0.532576 Loss2: 1.750318
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444811 Loss1: 0.073672 Loss2: 1.371139
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.669101 Loss1: 0.353098 Loss2: 1.316003
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449835 Loss1: 0.080876 Loss2: 1.368959
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.609604 Loss1: 0.250000 Loss2: 1.359604
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461625 Loss1: 0.092571 Loss2: 1.369054
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.466663 Loss1: 0.154660 Loss2: 1.312003
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.437988 Loss1: 0.122998 Loss2: 1.314990
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.406687 Loss1: 0.092382 Loss2: 1.314306
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426393 Loss1: 0.121890 Loss2: 1.304503
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411182 Loss1: 0.100603 Loss2: 1.310579
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.399417 Loss1: 0.506174 Loss2: 1.893242
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445527 Loss1: 0.139862 Loss2: 1.305666
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405660 Loss1: 0.100150 Loss2: 1.305511 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522386 Loss1: 0.143789 Loss2: 1.378597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444899 Loss1: 0.086740 Loss2: 1.358159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409279 Loss1: 0.054503 Loss2: 1.354776
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.475350 Loss1: 0.637212 Loss2: 1.838138
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.749461 Loss1: 0.385808 Loss2: 1.363653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604038 Loss1: 0.196565 Loss2: 1.407473 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561885 Loss1: 0.204012 Loss2: 1.357874 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496578 Loss1: 0.148765 Loss2: 1.347813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488222 Loss1: 0.140889 Loss2: 1.347333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440040 Loss1: 0.097823 Loss2: 1.342217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389708 Loss1: 0.054648 Loss2: 1.335060 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.553415 Loss1: 0.203181 Loss2: 1.350234 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477843 Loss1: 0.126290 Loss2: 1.351553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.249226 Loss1: 0.468477 Loss2: 1.780749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.632800 Loss1: 0.303028 Loss2: 1.329772 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.518338 Loss1: 0.165877 Loss2: 1.352461 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.419118 Loss1: 0.096425 Loss2: 1.322693 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.398384 Loss1: 0.085236 Loss2: 1.313148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.343908 Loss1: 0.523544 Loss2: 1.820364
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381530 Loss1: 0.067546 Loss2: 1.313983
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.648475 Loss1: 0.303301 Loss2: 1.345174
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388406 Loss1: 0.078497 Loss2: 1.309909
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551730 Loss1: 0.190950 Loss2: 1.360780
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362262 Loss1: 0.053961 Loss2: 1.308301
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.438281 Loss1: 0.120000 Loss2: 1.318281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.396156 Loss1: 0.081911 Loss2: 1.314245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.368120 Loss1: 0.060308 Loss2: 1.307811
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.393856 Loss1: 0.549978 Loss2: 1.843878
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.645953 Loss1: 0.292747 Loss2: 1.353206 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636758 Loss1: 0.244724 Loss2: 1.392034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.499572 Loss1: 0.149989 Loss2: 1.349583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445335 Loss1: 0.101295 Loss2: 1.344040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420011 Loss1: 0.081715 Loss2: 1.338296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428792 Loss1: 0.100062 Loss2: 1.328730 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400810 Loss1: 0.068990 Loss2: 1.331819 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465462 Loss1: 0.117127 Loss2: 1.348335 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.386972 Loss1: 0.061877 Loss2: 1.325096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371533 Loss1: 0.052396 Loss2: 1.319137
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.404246 Loss1: 0.549075 Loss2: 1.855170
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366854 Loss1: 0.053755 Loss2: 1.313099
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.589509 Loss1: 0.259250 Loss2: 1.330259
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.491344 Loss1: 0.160105 Loss2: 1.331239
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.427328 Loss1: 0.115852 Loss2: 1.311476
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.424153 Loss1: 0.117396 Loss2: 1.306756
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.387091 Loss1: 0.075770 Loss2: 1.311321
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.368391 Loss1: 0.441862 Loss2: 1.926528
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.393021 Loss1: 0.086360 Loss2: 1.306661
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.375802 Loss1: 0.071808 Loss2: 1.303994 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376218 Loss1: 0.078596 Loss2: 1.297622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.358067 Loss1: 0.064979 Loss2: 1.293088 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524590 Loss1: 0.113407 Loss2: 1.411183 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474980 Loss1: 0.077356 Loss2: 1.397624 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425802 Loss1: 0.046403 Loss2: 1.379399
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.286224 Loss1: 0.483382 Loss2: 1.802841
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612020 Loss1: 0.289275 Loss2: 1.322745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.504242 Loss1: 0.180444 Loss2: 1.323798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502550 Loss1: 0.164253 Loss2: 1.338297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416230 Loss1: 0.095312 Loss2: 1.320919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.384349 Loss1: 0.070601 Loss2: 1.313748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372896 Loss1: 0.063870 Loss2: 1.309026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.345205 Loss1: 0.042740 Loss2: 1.302465 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.510183 Loss1: 0.148054 Loss2: 1.362129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497384 Loss1: 0.145405 Loss2: 1.351979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.260447 Loss1: 0.481030 Loss2: 1.779417 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.689799 Loss1: 0.371521 Loss2: 1.318278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.510797 Loss1: 0.182523 Loss2: 1.328274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.408075 Loss1: 0.087529 Loss2: 1.320547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.403678 Loss1: 0.090981 Loss2: 1.312697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389330 Loss1: 0.079340 Loss2: 1.309991 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369069 Loss1: 0.056560 Loss2: 1.312509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381786 Loss1: 0.082959 Loss2: 1.298826 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480458 Loss1: 0.099298 Loss2: 1.381160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499840 Loss1: 0.115840 Loss2: 1.384001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.587350 Loss1: 0.600941 Loss2: 1.986409 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.820374 Loss1: 0.305365 Loss2: 1.515009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.661860 Loss1: 0.168808 Loss2: 1.493052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.620189 Loss1: 0.134254 Loss2: 1.485935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.582512 Loss1: 0.093168 Loss2: 1.489344 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.548296 Loss1: 0.059437 Loss2: 1.488858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.544113 Loss1: 0.070023 Loss2: 1.474089 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509454 Loss1: 0.046785 Loss2: 1.462669 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.589595 Loss1: 0.181424 Loss2: 1.408172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489239 Loss1: 0.098120 Loss2: 1.391119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477103 Loss1: 0.079418 Loss2: 1.397685
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.426641 Loss1: 0.550840 Loss2: 1.875801
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.731016 Loss1: 0.364572 Loss2: 1.366444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.546180 Loss1: 0.182902 Loss2: 1.363278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.458599 Loss1: 0.102170 Loss2: 1.356429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439680 Loss1: 0.097493 Loss2: 1.342187 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.737882 Loss1: 0.387977 Loss2: 1.349905
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423708 Loss1: 0.080058 Loss2: 1.343650
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.550040 Loss1: 0.179759 Loss2: 1.370281
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409748 Loss1: 0.067572 Loss2: 1.342176
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.471437 Loss1: 0.144200 Loss2: 1.327236
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380722 Loss1: 0.045885 Loss2: 1.334838
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.415477 Loss1: 0.092692 Loss2: 1.322786 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.353446 Loss1: 0.042472 Loss2: 1.310974 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.302165 Loss1: 0.463229 Loss2: 1.838936
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.367617 Loss1: 0.068280 Loss2: 1.299337
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.743497 Loss1: 0.376893 Loss2: 1.366604
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367458 Loss1: 0.065837 Loss2: 1.301620
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.636949 Loss1: 0.260738 Loss2: 1.376211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.518501 Loss1: 0.159095 Loss2: 1.359406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497114 Loss1: 0.127173 Loss2: 1.369941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450131 Loss1: 0.095170 Loss2: 1.354961 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427139 Loss1: 0.078134 Loss2: 1.349005 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417454 Loss1: 0.078406 Loss2: 1.339048 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514575 Loss1: 0.074939 Loss2: 1.439636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.485685 Loss1: 0.062676 Loss2: 1.423009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.448419 Loss1: 0.569699 Loss2: 1.878719 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.655119 Loss1: 0.201069 Loss2: 1.454050 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.553127 Loss1: 0.151177 Loss2: 1.401950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.535149 Loss1: 0.137807 Loss2: 1.397343
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.423228 Loss1: 0.582082 Loss2: 1.841146
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.670796 Loss1: 0.308702 Loss2: 1.362095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.651396 Loss1: 0.267124 Loss2: 1.384272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554201 Loss1: 0.199097 Loss2: 1.355104 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.485790 Loss1: 0.133667 Loss2: 1.352123 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460092 Loss1: 0.117219 Loss2: 1.342874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398937 Loss1: 0.070517 Loss2: 1.328420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384106 Loss1: 0.056873 Loss2: 1.327233 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.619726 Loss1: 0.233733 Loss2: 1.385993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492274 Loss1: 0.137359 Loss2: 1.354915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494047 Loss1: 0.143165 Loss2: 1.350881
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.291530 Loss1: 0.461757 Loss2: 1.829773
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.726669 Loss1: 0.338259 Loss2: 1.388410 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611427 Loss1: 0.192485 Loss2: 1.418942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557770 Loss1: 0.183221 Loss2: 1.374548 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503949 Loss1: 0.115189 Loss2: 1.388760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.430787 Loss1: 0.067335 Loss2: 1.363453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379921 Loss1: 0.030205 Loss2: 1.349716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374651 Loss1: 0.025453 Loss2: 1.349197 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478167 Loss1: 0.175488 Loss2: 1.302680 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.386009 Loss1: 0.097109 Loss2: 1.288900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.566369 Loss1: 0.566974 Loss2: 1.999396
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.369111 Loss1: 0.085216 Loss2: 1.283894
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.869082 Loss1: 0.381867 Loss2: 1.487215
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.350876 Loss1: 0.074290 Loss2: 1.276587
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.831428 Loss1: 0.293388 Loss2: 1.538040
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.309661 Loss1: 0.038453 Loss2: 1.271208
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.654769 Loss1: 0.162195 Loss2: 1.492574
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.312681 Loss1: 0.041430 Loss2: 1.271250
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.578056 Loss1: 0.097313 Loss2: 1.480743 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.594702 Loss1: 0.121672 Loss2: 1.473030 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.558322 Loss1: 0.086078 Loss2: 1.472245
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.366465 Loss1: 0.525944 Loss2: 1.840521
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.515602 Loss1: 0.051840 Loss2: 1.463762
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.668597 Loss1: 0.318339 Loss2: 1.350257
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.638626 Loss1: 0.228599 Loss2: 1.410027
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540751 Loss1: 0.179633 Loss2: 1.361118
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513647 Loss1: 0.162934 Loss2: 1.350714
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500221 Loss1: 0.139975 Loss2: 1.360246
-DEBUG flwr 2023-10-12 13:53:48,276 | server.py:236 | fit_round 153 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.405337 Loss1: 0.575100 Loss2: 1.830238
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465313 Loss1: 0.117616 Loss2: 1.347698
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.669386 Loss1: 0.322429 Loss2: 1.346956
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.444524 Loss1: 0.104494 Loss2: 1.340030
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.633418 Loss1: 0.250072 Loss2: 1.383347
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436857 Loss1: 0.093153 Loss2: 1.343704
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554691 Loss1: 0.187766 Loss2: 1.366926
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.504823 Loss1: 0.163625 Loss2: 1.341198
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530037 Loss1: 0.162751 Loss2: 1.367287 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450752 Loss1: 0.102884 Loss2: 1.347868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400781 Loss1: 0.060335 Loss2: 1.340445
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.395409 Loss1: 0.501850 Loss2: 1.893559
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389088 Loss1: 0.058565 Loss2: 1.330523
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.744131 Loss1: 0.364509 Loss2: 1.379622
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.717520 Loss1: 0.298435 Loss2: 1.419085
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.602615 Loss1: 0.220628 Loss2: 1.381987
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569018 Loss1: 0.181902 Loss2: 1.387116
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.507542 Loss1: 0.127697 Loss2: 1.379844
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491835 Loss1: 0.121137 Loss2: 1.370698
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439155 Loss1: 0.071004 Loss2: 1.368151
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419147 Loss1: 0.056251 Loss2: 1.362897
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394651 Loss1: 0.042356 Loss2: 1.352295
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 13:53:48,276][flwr][DEBUG] - fit_round 153 received 50 results and 0 failures
-INFO flwr 2023-10-12 13:54:29,172 | server.py:125 | fit progress: (153, 2.248646311485729, {'accuracy': 0.595}, 352976.950533177)
->> Test accuracy: 0.595000
-[2023-10-12 13:54:29,172][flwr][INFO] - fit progress: (153, 2.248646311485729, {'accuracy': 0.595}, 352976.950533177)
-DEBUG flwr 2023-10-12 13:54:29,172 | server.py:173 | evaluate_round 153: strategy sampled 50 clients (out of 50)
-[2023-10-12 13:54:29,172][flwr][DEBUG] - evaluate_round 153: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 14:03:33,955 | server.py:187 | evaluate_round 153 received 50 results and 0 failures
-[2023-10-12 14:03:33,955][flwr][DEBUG] - evaluate_round 153 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 14:03:33,956 | server.py:222 | fit_round 154: strategy sampled 50 clients (out of 50)
-[2023-10-12 14:03:33,956][flwr][DEBUG] - fit_round 154: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.138247 Loss1: 0.390402 Loss2: 1.747845
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.555261 Loss1: 0.244661 Loss2: 1.310601
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.488559 Loss1: 0.151811 Loss2: 1.336748
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.391332 Loss1: 0.494566 Loss2: 1.896766
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.744568 Loss1: 0.355877 Loss2: 1.388691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.710768 Loss1: 0.252853 Loss2: 1.457916 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592606 Loss1: 0.189863 Loss2: 1.402743 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.613281 Loss1: 0.214674 Loss2: 1.398608 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560132 Loss1: 0.158481 Loss2: 1.401651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.560568 Loss1: 0.162446 Loss2: 1.398121 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518926 Loss1: 0.118207 Loss2: 1.400719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.463939 Loss1: 0.082409 Loss2: 1.381530 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.448733 Loss1: 0.549822 Loss2: 1.898911
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.637962 Loss1: 0.271918 Loss2: 1.366045
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.564738 Loss1: 0.181621 Loss2: 1.383117
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.495996 Loss1: 0.119217 Loss2: 1.376779
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.251483 Loss1: 0.456494 Loss2: 1.794989
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.736313 Loss1: 0.376420 Loss2: 1.359893 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669897 Loss1: 0.254480 Loss2: 1.415417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535726 Loss1: 0.175158 Loss2: 1.360568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518821 Loss1: 0.156289 Loss2: 1.362532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512607 Loss1: 0.152438 Loss2: 1.360169 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448662 Loss1: 0.093359 Loss2: 1.355304 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426886 Loss1: 0.087753 Loss2: 1.339133 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.675357 Loss1: 0.299821 Loss2: 1.375536 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520927 Loss1: 0.153695 Loss2: 1.367232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534239 Loss1: 0.158798 Loss2: 1.375441
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.280256 Loss1: 0.444260 Loss2: 1.835996
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.483829 Loss1: 0.113607 Loss2: 1.370222
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.667961 Loss1: 0.285601 Loss2: 1.382360
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461597 Loss1: 0.099429 Loss2: 1.362168
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.665921 Loss1: 0.241830 Loss2: 1.424091
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556721 Loss1: 0.170149 Loss2: 1.386573 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.510009 Loss1: 0.126442 Loss2: 1.383567 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424136 Loss1: 0.069153 Loss2: 1.354984
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516707 Loss1: 0.130263 Loss2: 1.386444
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485652 Loss1: 0.104249 Loss2: 1.381403
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453336 Loss1: 0.080482 Loss2: 1.372854
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440943 Loss1: 0.068890 Loss2: 1.372054
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411617 Loss1: 0.043286 Loss2: 1.368330
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.349322 Loss1: 0.520791 Loss2: 1.828531
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.656344 Loss1: 0.317877 Loss2: 1.338467
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.590702 Loss1: 0.207714 Loss2: 1.382988
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.545661 Loss1: 0.196996 Loss2: 1.348666
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510213 Loss1: 0.160589 Loss2: 1.349624
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.538072 Loss1: 0.607914 Loss2: 1.930158
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444774 Loss1: 0.105571 Loss2: 1.339203
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.427280 Loss1: 0.096533 Loss2: 1.330746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574842 Loss1: 0.189435 Loss2: 1.385407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.536653 Loss1: 0.201458 Loss2: 1.335195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494899 Loss1: 0.160435 Loss2: 1.334464 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412612 Loss1: 0.085475 Loss2: 1.327138 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377549 Loss1: 0.057801 Loss2: 1.319748 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.399443 Loss1: 0.539537 Loss2: 1.859906
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.706901 Loss1: 0.341438 Loss2: 1.365464
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667029 Loss1: 0.270541 Loss2: 1.396487
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.548432 Loss1: 0.177886 Loss2: 1.370546
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.346149 Loss1: 0.469624 Loss2: 1.876525
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515012 Loss1: 0.153243 Loss2: 1.361770
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.656367 Loss1: 0.278426 Loss2: 1.377941
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.467803 Loss1: 0.114789 Loss2: 1.353014
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590435 Loss1: 0.187843 Loss2: 1.402592
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.500098 Loss1: 0.127237 Loss2: 1.372861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.466956 Loss1: 0.101076 Loss2: 1.365880 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450689 Loss1: 0.080430 Loss2: 1.370259 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408721 Loss1: 0.078572 Loss2: 1.330149
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419095 Loss1: 0.063051 Loss2: 1.356045
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426759 Loss1: 0.071244 Loss2: 1.355514
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401043 Loss1: 0.051778 Loss2: 1.349266
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388625 Loss1: 0.040043 Loss2: 1.348582
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.232303 Loss1: 0.452973 Loss2: 1.779330
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.601779 Loss1: 0.279661 Loss2: 1.322117
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.567198 Loss1: 0.209192 Loss2: 1.358006
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.557690 Loss1: 0.622495 Loss2: 1.935195
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497701 Loss1: 0.161130 Loss2: 1.336571
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.857872 Loss1: 0.461966 Loss2: 1.395906
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.457406 Loss1: 0.125677 Loss2: 1.331729
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.428108 Loss1: 0.105117 Loss2: 1.322991 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.427747 Loss1: 0.107836 Loss2: 1.319912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410026 Loss1: 0.089996 Loss2: 1.320030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.524331 Loss1: 0.132539 Loss2: 1.391792 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465150 Loss1: 0.076529 Loss2: 1.388621 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422780 Loss1: 0.051324 Loss2: 1.371456 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.540703 Loss1: 0.627024 Loss2: 1.913679
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.686943 Loss1: 0.356889 Loss2: 1.330054
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563568 Loss1: 0.212875 Loss2: 1.350694
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506701 Loss1: 0.174156 Loss2: 1.332545
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.227473 Loss1: 0.389165 Loss2: 1.838308
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437202 Loss1: 0.117666 Loss2: 1.319536 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.372863 Loss1: 0.062847 Loss2: 1.310017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.366815 Loss1: 0.061675 Loss2: 1.305140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.360525 Loss1: 0.059437 Loss2: 1.301087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.337432 Loss1: 0.038695 Loss2: 1.298737
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507237 Loss1: 0.127359 Loss2: 1.379878
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.488007 Loss1: 0.113895 Loss2: 1.374112
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458709 Loss1: 0.085514 Loss2: 1.373195
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468212 Loss1: 0.092156 Loss2: 1.376057
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453865 Loss1: 0.082804 Loss2: 1.371061
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.378868 Loss1: 0.541432 Loss2: 1.837437
-(DefaultActor pid=3764) >> Training accuracy: 0.995404
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446469 Loss1: 0.083243 Loss2: 1.363226
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.651245 Loss1: 0.300653 Loss2: 1.350592
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.600357 Loss1: 0.220751 Loss2: 1.379606
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.567884 Loss1: 0.195385 Loss2: 1.372499
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.495531 Loss1: 0.140844 Loss2: 1.354687
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473187 Loss1: 0.117664 Loss2: 1.355522
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.382468 Loss1: 0.497428 Loss2: 1.885040
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450464 Loss1: 0.107506 Loss2: 1.342959
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414390 Loss1: 0.075279 Loss2: 1.339111 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396967 Loss1: 0.067640 Loss2: 1.329328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377040 Loss1: 0.052260 Loss2: 1.324780 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485498 Loss1: 0.113485 Loss2: 1.372013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.456178 Loss1: 0.107788 Loss2: 1.348390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.470213 Loss1: 0.117330 Loss2: 1.352882
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.417538 Loss1: 0.546215 Loss2: 1.871323
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427866 Loss1: 0.074990 Loss2: 1.352876
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.792454 Loss1: 0.389186 Loss2: 1.403268
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.710670 Loss1: 0.263473 Loss2: 1.447197
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594601 Loss1: 0.200465 Loss2: 1.394136
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.592445 Loss1: 0.198956 Loss2: 1.393489
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.539827 Loss1: 0.147518 Loss2: 1.392308
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.503956 Loss1: 0.601910 Loss2: 1.902046
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.510189 Loss1: 0.115886 Loss2: 1.394304
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453759 Loss1: 0.076406 Loss2: 1.377353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445539 Loss1: 0.072386 Loss2: 1.373153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422271 Loss1: 0.062123 Loss2: 1.360148 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469296 Loss1: 0.103579 Loss2: 1.365716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418728 Loss1: 0.058706 Loss2: 1.360022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370224 Loss1: 0.027997 Loss2: 1.342227 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.669112 Loss1: 0.248423 Loss2: 1.420689 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558143 Loss1: 0.164350 Loss2: 1.393793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509163 Loss1: 0.131795 Loss2: 1.377369
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.389204 Loss1: 0.563536 Loss2: 1.825669
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468159 Loss1: 0.098254 Loss2: 1.369904
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.743435 Loss1: 0.419032 Loss2: 1.324403
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435858 Loss1: 0.073048 Loss2: 1.362810
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.665233 Loss1: 0.282129 Loss2: 1.383104
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441974 Loss1: 0.083274 Loss2: 1.358700
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.527678 Loss1: 0.199212 Loss2: 1.328466
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422646 Loss1: 0.067039 Loss2: 1.355607
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.494484 Loss1: 0.158545 Loss2: 1.335939
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.457937 Loss1: 0.132638 Loss2: 1.325299
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.399930 Loss1: 0.085570 Loss2: 1.314360
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386236 Loss1: 0.076374 Loss2: 1.309862
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.357252 Loss1: 0.048913 Loss2: 1.308339
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.570834 Loss1: 0.726476 Loss2: 1.844358
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357445 Loss1: 0.057314 Loss2: 1.300131
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656873 Loss1: 0.258014 Loss2: 1.398858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.548247 Loss1: 0.181659 Loss2: 1.366588 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461214 Loss1: 0.110491 Loss2: 1.350723
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.342109 Loss1: 0.462356 Loss2: 1.879753
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.705472 Loss1: 0.338855 Loss2: 1.366617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572653 Loss1: 0.170403 Loss2: 1.402250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478202 Loss1: 0.127588 Loss2: 1.350614 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435536 Loss1: 0.105711 Loss2: 1.329824
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.540355 Loss1: 0.189207 Loss2: 1.351148
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546738 Loss1: 0.162812 Loss2: 1.383926
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.457930 Loss1: 0.107976 Loss2: 1.349954
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417837 Loss1: 0.069423 Loss2: 1.348414
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449235 Loss1: 0.104468 Loss2: 1.344767
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.384636 Loss1: 0.532976 Loss2: 1.851659
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439662 Loss1: 0.092062 Loss2: 1.347600
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667075 Loss1: 0.243797 Loss2: 1.423278 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.469488 Loss1: 0.118346 Loss2: 1.351143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.483308 Loss1: 0.130705 Loss2: 1.352602
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.331145 Loss1: 0.560019 Loss2: 1.771126
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.727630 Loss1: 0.422136 Loss2: 1.305494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.576218 Loss1: 0.218165 Loss2: 1.358052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.495034 Loss1: 0.192038 Loss2: 1.302996 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412448 Loss1: 0.072675 Loss2: 1.339773
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.453530 Loss1: 0.151275 Loss2: 1.302255
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485063 Loss1: 0.174887 Loss2: 1.310176
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416057 Loss1: 0.110991 Loss2: 1.305066
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376470 Loss1: 0.079852 Loss2: 1.296617
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.352984 Loss1: 0.065630 Loss2: 1.287354
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.353868 Loss1: 0.478968 Loss2: 1.874899
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.353998 Loss1: 0.068566 Loss2: 1.285432
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624716 Loss1: 0.183769 Loss2: 1.440947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.496753 Loss1: 0.104545 Loss2: 1.392208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.544184 Loss1: 0.634968 Loss2: 1.909216
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480622 Loss1: 0.097640 Loss2: 1.382982
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.753129 Loss1: 0.392377 Loss2: 1.360752
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539682 Loss1: 0.149082 Loss2: 1.390600
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.654181 Loss1: 0.252203 Loss2: 1.401978
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488729 Loss1: 0.091545 Loss2: 1.397184
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462086 Loss1: 0.078957 Loss2: 1.383130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453007 Loss1: 0.070834 Loss2: 1.382173 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472177 Loss1: 0.126204 Loss2: 1.345973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410183 Loss1: 0.074795 Loss2: 1.335388 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395206 Loss1: 0.064042 Loss2: 1.331164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.455432 Loss1: 0.609639 Loss2: 1.845794
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.734582 Loss1: 0.365951 Loss2: 1.368632
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.612267 Loss1: 0.203599 Loss2: 1.408668
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.483390 Loss1: 0.129184 Loss2: 1.354206
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.456430 Loss1: 0.101484 Loss2: 1.354946
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.321321 Loss1: 0.473994 Loss2: 1.847328
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.701278 Loss1: 0.359615 Loss2: 1.341663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.682580 Loss1: 0.281582 Loss2: 1.400998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.604420 Loss1: 0.244438 Loss2: 1.359982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.553069 Loss1: 0.192324 Loss2: 1.360745 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.540399 Loss1: 0.182717 Loss2: 1.357682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408530 Loss1: 0.071101 Loss2: 1.337429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374586 Loss1: 0.050063 Loss2: 1.324523 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.717585 Loss1: 0.316353 Loss2: 1.401232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.603403 Loss1: 0.195190 Loss2: 1.408213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.592932 Loss1: 0.182522 Loss2: 1.410410
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.333215 Loss1: 0.521558 Loss2: 1.811657
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.677827 Loss1: 0.337069 Loss2: 1.340758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.552158 Loss1: 0.191895 Loss2: 1.360263 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520518 Loss1: 0.190693 Loss2: 1.329825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.490573 Loss1: 0.152130 Loss2: 1.338443 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.397240 Loss1: 0.082707 Loss2: 1.314533 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402314 Loss1: 0.090013 Loss2: 1.312301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369606 Loss1: 0.059211 Loss2: 1.310395
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.447653 Loss1: 0.518607 Loss2: 1.929046
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.866312 Loss1: 0.438495 Loss2: 1.427817
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.795615 Loss1: 0.301659 Loss2: 1.493956
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.728004 Loss1: 0.280692 Loss2: 1.447312
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.632564 Loss1: 0.184883 Loss2: 1.447680
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.581944 Loss1: 0.154120 Loss2: 1.427824
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.287352 Loss1: 0.470841 Loss2: 1.816512
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.564622 Loss1: 0.134000 Loss2: 1.430623
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.668875 Loss1: 0.332787 Loss2: 1.336089
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.536521 Loss1: 0.120943 Loss2: 1.415579
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.626429 Loss1: 0.251142 Loss2: 1.375287
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.497970 Loss1: 0.081938 Loss2: 1.416033
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562634 Loss1: 0.221210 Loss2: 1.341425
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.470239 Loss1: 0.059026 Loss2: 1.411213
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502715 Loss1: 0.154307 Loss2: 1.348408
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482161 Loss1: 0.140463 Loss2: 1.341697
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419948 Loss1: 0.084023 Loss2: 1.335925
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.401749 Loss1: 0.076238 Loss2: 1.325511
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392908 Loss1: 0.077314 Loss2: 1.315594
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369172 Loss1: 0.052179 Loss2: 1.316994
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.252896 Loss1: 0.461971 Loss2: 1.790926
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.613718 Loss1: 0.288811 Loss2: 1.324907
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.572873 Loss1: 0.211039 Loss2: 1.361834
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.532275 Loss1: 0.203079 Loss2: 1.329196
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551683 Loss1: 0.218910 Loss2: 1.332773
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.513584 Loss1: 0.599215 Loss2: 1.914369
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.482918 Loss1: 0.146162 Loss2: 1.336756
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432957 Loss1: 0.111879 Loss2: 1.321078 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420077 Loss1: 0.103415 Loss2: 1.316662 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396721 Loss1: 0.083049 Loss2: 1.313672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466253 Loss1: 0.106956 Loss2: 1.359296 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398490 Loss1: 0.051661 Loss2: 1.346830 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436963 Loss1: 0.099693 Loss2: 1.337270 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.452148 Loss1: 0.578404 Loss2: 1.873744
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.785702 Loss1: 0.350554 Loss2: 1.435148
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.639761 Loss1: 0.205288 Loss2: 1.434473
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.565322 Loss1: 0.163921 Loss2: 1.401401
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.521130 Loss1: 0.574216 Loss2: 1.946914
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.890616 Loss1: 0.459243 Loss2: 1.431372 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.756378 Loss1: 0.261604 Loss2: 1.494774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.617677 Loss1: 0.180723 Loss2: 1.436954 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.619905 Loss1: 0.178754 Loss2: 1.441151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567916 Loss1: 0.137091 Loss2: 1.430825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388416 Loss1: 0.023400 Loss2: 1.365016
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.539562 Loss1: 0.118048 Loss2: 1.421514
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.517137 Loss1: 0.098099 Loss2: 1.419037
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481018 Loss1: 0.062920 Loss2: 1.418098
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.480747 Loss1: 0.079247 Loss2: 1.401500
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.305967 Loss1: 0.527410 Loss2: 1.778557
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.717443 Loss1: 0.362328 Loss2: 1.355115
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.587895 Loss1: 0.215143 Loss2: 1.372751
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.357163 Loss1: 0.496500 Loss2: 1.860663
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.543943 Loss1: 0.198105 Loss2: 1.345838
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.638732 Loss1: 0.280045 Loss2: 1.358687
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473134 Loss1: 0.126087 Loss2: 1.347047
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.587898 Loss1: 0.208626 Loss2: 1.379272
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443652 Loss1: 0.103362 Loss2: 1.340291
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.549211 Loss1: 0.194212 Loss2: 1.354999
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.393599 Loss1: 0.066853 Loss2: 1.326747
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.369193 Loss1: 0.043957 Loss2: 1.325236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.360134 Loss1: 0.046626 Loss2: 1.313509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.341256 Loss1: 0.033224 Loss2: 1.308032 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399727 Loss1: 0.070524 Loss2: 1.329204 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.359668 Loss1: 0.462866 Loss2: 1.896802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.747361 Loss1: 0.252190 Loss2: 1.495171 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.453043 Loss1: 0.667189 Loss2: 1.785854
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.689788 Loss1: 0.234230 Loss2: 1.455559
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.723932 Loss1: 0.388552 Loss2: 1.335380
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.678406 Loss1: 0.217643 Loss2: 1.460763
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633748 Loss1: 0.260053 Loss2: 1.373695
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.628701 Loss1: 0.174255 Loss2: 1.454447
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509563 Loss1: 0.188029 Loss2: 1.321535
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.620520 Loss1: 0.168486 Loss2: 1.452034
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.568168 Loss1: 0.121997 Loss2: 1.446172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.508749 Loss1: 0.070495 Loss2: 1.438254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.502858 Loss1: 0.070256 Loss2: 1.432602 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366954 Loss1: 0.072136 Loss2: 1.294818 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.272306 Loss1: 0.472024 Loss2: 1.800282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.628392 Loss1: 0.233570 Loss2: 1.394822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520097 Loss1: 0.194557 Loss2: 1.325540
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.385548 Loss1: 0.524308 Loss2: 1.861241
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.699382 Loss1: 0.336436 Loss2: 1.362946 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.618679 Loss1: 0.227159 Loss2: 1.391520 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562929 Loss1: 0.176207 Loss2: 1.386722 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519689 Loss1: 0.156377 Loss2: 1.363312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499049 Loss1: 0.140881 Loss2: 1.358168 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359816 Loss1: 0.052653 Loss2: 1.307163
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436336 Loss1: 0.078734 Loss2: 1.357602
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422850 Loss1: 0.080286 Loss2: 1.342565
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399426 Loss1: 0.055984 Loss2: 1.343442
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385375 Loss1: 0.047601 Loss2: 1.337775
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.314888 Loss1: 0.423050 Loss2: 1.891838
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.672482 Loss1: 0.285057 Loss2: 1.387425
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.650640 Loss1: 0.244870 Loss2: 1.405770
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.548629 Loss1: 0.150387 Loss2: 1.398242
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.377551 Loss1: 0.513996 Loss2: 1.863555
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.721380 Loss1: 0.352726 Loss2: 1.368655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.615389 Loss1: 0.213587 Loss2: 1.401802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521038 Loss1: 0.152940 Loss2: 1.368098 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487743 Loss1: 0.125375 Loss2: 1.362368 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479272 Loss1: 0.124219 Loss2: 1.355053 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415090 Loss1: 0.049754 Loss2: 1.365336
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473490 Loss1: 0.120099 Loss2: 1.353392
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458268 Loss1: 0.108375 Loss2: 1.349893
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435047 Loss1: 0.087598 Loss2: 1.347450
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422920 Loss1: 0.077300 Loss2: 1.345620
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.436348 Loss1: 0.532730 Loss2: 1.903618
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.976560 Loss1: 0.543206 Loss2: 1.433353
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.757370 Loss1: 0.302603 Loss2: 1.454767
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.630738 Loss1: 0.230503 Loss2: 1.400235
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.298630 Loss1: 0.461986 Loss2: 1.836645
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.662743 Loss1: 0.326765 Loss2: 1.335978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622822 Loss1: 0.234610 Loss2: 1.388212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.508637 Loss1: 0.158316 Loss2: 1.350321 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 14:32:21,168 | server.py:236 | fit_round 154 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509184 Loss1: 0.157548 Loss2: 1.351635 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450770 Loss1: 0.105368 Loss2: 1.345402 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408045 Loss1: 0.051281 Loss2: 1.356763
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.414344 Loss1: 0.075790 Loss2: 1.338554
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460775 Loss1: 0.129440 Loss2: 1.331335
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.474760 Loss1: 0.142172 Loss2: 1.332588
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437355 Loss1: 0.087363 Loss2: 1.349992
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.375111 Loss1: 0.510639 Loss2: 1.864472
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.715088 Loss1: 0.345400 Loss2: 1.369688
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611322 Loss1: 0.201447 Loss2: 1.409875
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555331 Loss1: 0.175867 Loss2: 1.379464
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.367232 Loss1: 0.499558 Loss2: 1.867673
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.691294 Loss1: 0.312315 Loss2: 1.378979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.645099 Loss1: 0.225816 Loss2: 1.419284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.516237 Loss1: 0.136591 Loss2: 1.379646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.517620 Loss1: 0.144113 Loss2: 1.373507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476703 Loss1: 0.094065 Loss2: 1.382638 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407183 Loss1: 0.052759 Loss2: 1.354424
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484021 Loss1: 0.117005 Loss2: 1.367015
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473619 Loss1: 0.101613 Loss2: 1.372006
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452454 Loss1: 0.082972 Loss2: 1.369482
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.509118 Loss1: 0.143340 Loss2: 1.365779
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.293757 Loss1: 0.447858 Loss2: 1.845899
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.654759 Loss1: 0.311437 Loss2: 1.343322
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595099 Loss1: 0.216240 Loss2: 1.378859
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.495614 Loss1: 0.134173 Loss2: 1.361441
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.401776 Loss1: 0.512053 Loss2: 1.889723
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.705830 Loss1: 0.310297 Loss2: 1.395532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.673183 Loss1: 0.237302 Loss2: 1.435881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534587 Loss1: 0.140208 Loss2: 1.394379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527597 Loss1: 0.144407 Loss2: 1.383190 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.509663 Loss1: 0.118254 Loss2: 1.391409 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.479195 Loss1: 0.098949 Loss2: 1.380246 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448721 Loss1: 0.076909 Loss2: 1.371813 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 14:32:21,168][flwr][DEBUG] - fit_round 154 received 50 results and 0 failures
-INFO flwr 2023-10-12 14:33:01,217 | server.py:125 | fit progress: (154, 2.24084857896494, {'accuracy': 0.5961}, 355288.995210152)
->> Test accuracy: 0.596100
-[2023-10-12 14:33:01,217][flwr][INFO] - fit progress: (154, 2.24084857896494, {'accuracy': 0.5961}, 355288.995210152)
-DEBUG flwr 2023-10-12 14:33:01,217 | server.py:173 | evaluate_round 154: strategy sampled 50 clients (out of 50)
-[2023-10-12 14:33:01,217][flwr][DEBUG] - evaluate_round 154: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 14:42:06,296 | server.py:187 | evaluate_round 154 received 50 results and 0 failures
-[2023-10-12 14:42:06,296][flwr][DEBUG] - evaluate_round 154 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 14:42:06,297 | server.py:222 | fit_round 155: strategy sampled 50 clients (out of 50)
-[2023-10-12 14:42:06,297][flwr][DEBUG] - fit_round 155: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.378869 Loss1: 0.476945 Loss2: 1.901923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.665786 Loss1: 0.215034 Loss2: 1.450752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.567361 Loss1: 0.170312 Loss2: 1.397049
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.436190 Loss1: 0.567335 Loss2: 1.868854
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510138 Loss1: 0.119317 Loss2: 1.390822
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.718313 Loss1: 0.392533 Loss2: 1.325781
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.549249 Loss1: 0.194686 Loss2: 1.354563
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477696 Loss1: 0.095289 Loss2: 1.382408
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520620 Loss1: 0.172060 Loss2: 1.348559
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479729 Loss1: 0.105316 Loss2: 1.374413
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439243 Loss1: 0.064631 Loss2: 1.374612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430648 Loss1: 0.062367 Loss2: 1.368281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418771 Loss1: 0.052420 Loss2: 1.366351 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387713 Loss1: 0.075474 Loss2: 1.312239 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.422281 Loss1: 0.515482 Loss2: 1.906800
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.703307 Loss1: 0.293894 Loss2: 1.409413
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618879 Loss1: 0.202179 Loss2: 1.416700
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559273 Loss1: 0.155500 Loss2: 1.403774
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.229832 Loss1: 0.418984 Loss2: 1.810847
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503893 Loss1: 0.103996 Loss2: 1.399898
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.608073 Loss1: 0.283518 Loss2: 1.324555
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466181 Loss1: 0.077946 Loss2: 1.388235
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.513520 Loss1: 0.173492 Loss2: 1.340028
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464640 Loss1: 0.080927 Loss2: 1.383712
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.447525 Loss1: 0.123350 Loss2: 1.324176
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471046 Loss1: 0.090229 Loss2: 1.380818
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.427343 Loss1: 0.115769 Loss2: 1.311574
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.442877 Loss1: 0.060326 Loss2: 1.382551
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.385775 Loss1: 0.074129 Loss2: 1.311646
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437598 Loss1: 0.064941 Loss2: 1.372657
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406185 Loss1: 0.100496 Loss2: 1.305689
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.373025 Loss1: 0.065321 Loss2: 1.307704
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362384 Loss1: 0.056717 Loss2: 1.305667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367929 Loss1: 0.066475 Loss2: 1.301454
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.326608 Loss1: 0.510310 Loss2: 1.816298
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.788049 Loss1: 0.455399 Loss2: 1.332651
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638590 Loss1: 0.239885 Loss2: 1.398705
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491928 Loss1: 0.161666 Loss2: 1.330261
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.325670 Loss1: 0.473140 Loss2: 1.852530
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.670950 Loss1: 0.323111 Loss2: 1.347839 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.539823 Loss1: 0.170654 Loss2: 1.369170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539180 Loss1: 0.193149 Loss2: 1.346031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496088 Loss1: 0.152847 Loss2: 1.343241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.552119 Loss1: 0.195009 Loss2: 1.357109 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389065 Loss1: 0.073595 Loss2: 1.315470
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.466742 Loss1: 0.112156 Loss2: 1.354586
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457252 Loss1: 0.117252 Loss2: 1.340000
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458012 Loss1: 0.116232 Loss2: 1.341780
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418867 Loss1: 0.081938 Loss2: 1.336929
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.327252 Loss1: 0.524753 Loss2: 1.802500
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.616962 Loss1: 0.282026 Loss2: 1.334937
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.538729 Loss1: 0.178692 Loss2: 1.360037
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.495601 Loss1: 0.159143 Loss2: 1.336459
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.368966 Loss1: 0.502275 Loss2: 1.866692
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443277 Loss1: 0.113571 Loss2: 1.329706
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.757678 Loss1: 0.397680 Loss2: 1.359998
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438053 Loss1: 0.105685 Loss2: 1.332369
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605649 Loss1: 0.209970 Loss2: 1.395680
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441412 Loss1: 0.113823 Loss2: 1.327589
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.541421 Loss1: 0.170273 Loss2: 1.371147
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491667 Loss1: 0.128279 Loss2: 1.363388
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409142 Loss1: 0.077772 Loss2: 1.331371
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.458406 Loss1: 0.094807 Loss2: 1.363599
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.359037 Loss1: 0.035916 Loss2: 1.323121
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426799 Loss1: 0.081863 Loss2: 1.344936
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370627 Loss1: 0.060054 Loss2: 1.310574
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383097 Loss1: 0.044173 Loss2: 1.338924 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.482827 Loss1: 0.604305 Loss2: 1.878522 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.639311 Loss1: 0.237358 Loss2: 1.401953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.509585 Loss1: 0.148783 Loss2: 1.360803
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.449503 Loss1: 0.523504 Loss2: 1.926000
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.467047 Loss1: 0.109118 Loss2: 1.357930
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.837227 Loss1: 0.415910 Loss2: 1.421317
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.439541 Loss1: 0.084254 Loss2: 1.355287
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.726951 Loss1: 0.262770 Loss2: 1.464181
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.417444 Loss1: 0.073307 Loss2: 1.344137
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586938 Loss1: 0.173151 Loss2: 1.413788
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401975 Loss1: 0.058839 Loss2: 1.343136
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527350 Loss1: 0.110144 Loss2: 1.417207
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394434 Loss1: 0.058104 Loss2: 1.336331
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495467 Loss1: 0.081254 Loss2: 1.414214
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386601 Loss1: 0.052584 Loss2: 1.334017
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472376 Loss1: 0.074745 Loss2: 1.397631
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439322 Loss1: 0.046475 Loss2: 1.392846
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430081 Loss1: 0.042104 Loss2: 1.387977
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422746 Loss1: 0.042672 Loss2: 1.380074
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.293595 Loss1: 0.472801 Loss2: 1.820795
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.678430 Loss1: 0.345946 Loss2: 1.332484
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624274 Loss1: 0.235369 Loss2: 1.388904
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528087 Loss1: 0.183441 Loss2: 1.344647
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.362308 Loss1: 0.521873 Loss2: 1.840435
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.456206 Loss1: 0.110279 Loss2: 1.345927
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.744623 Loss1: 0.378563 Loss2: 1.366060
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492243 Loss1: 0.152575 Loss2: 1.339668
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.666345 Loss1: 0.261130 Loss2: 1.405215
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.498122 Loss1: 0.154845 Loss2: 1.343278
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.566621 Loss1: 0.187967 Loss2: 1.378654
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451047 Loss1: 0.096984 Loss2: 1.354063
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.561799 Loss1: 0.190633 Loss2: 1.371165
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.436641 Loss1: 0.104488 Loss2: 1.332153
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506155 Loss1: 0.133394 Loss2: 1.372761
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.456253 Loss1: 0.124284 Loss2: 1.331969
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.482611 Loss1: 0.122491 Loss2: 1.360120
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460374 Loss1: 0.096608 Loss2: 1.363765
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446148 Loss1: 0.088425 Loss2: 1.357722
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409402 Loss1: 0.066019 Loss2: 1.343383
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.602303 Loss1: 0.600118 Loss2: 2.002185
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.759582 Loss1: 0.392362 Loss2: 1.367220
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.651862 Loss1: 0.260064 Loss2: 1.391798
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.632715 Loss1: 0.216386 Loss2: 1.416329
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561524 Loss1: 0.187377 Loss2: 1.374147
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.763721 Loss1: 0.412799 Loss2: 1.350922 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635610 Loss1: 0.225501 Loss2: 1.410109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437987 Loss1: 0.075958 Loss2: 1.362029 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404910 Loss1: 0.051563 Loss2: 1.353347 [repeated 2x across cluster]
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434741 Loss1: 0.102031 Loss2: 1.332710 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414413 Loss1: 0.077563 Loss2: 1.336850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397714 Loss1: 0.064717 Loss2: 1.332997 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645341 Loss1: 0.245747 Loss2: 1.399594 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565978 Loss1: 0.207675 Loss2: 1.358303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.464808 Loss1: 0.575141 Loss2: 1.889667
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484708 Loss1: 0.130798 Loss2: 1.353910
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.731824 Loss1: 0.340853 Loss2: 1.390972
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432425 Loss1: 0.098334 Loss2: 1.334091
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669687 Loss1: 0.247814 Loss2: 1.421872
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.407016 Loss1: 0.073596 Loss2: 1.333420
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.382262 Loss1: 0.056870 Loss2: 1.325392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385581 Loss1: 0.068126 Loss2: 1.317455 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.442941 Loss1: 0.067014 Loss2: 1.375927 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423362 Loss1: 0.067738 Loss2: 1.355624 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408888 Loss1: 0.057343 Loss2: 1.351545
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.300895 Loss1: 0.481076 Loss2: 1.819819
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.628377 Loss1: 0.268149 Loss2: 1.360228
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.668126 Loss1: 0.280411 Loss2: 1.387715
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551465 Loss1: 0.181902 Loss2: 1.369563
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517603 Loss1: 0.157183 Loss2: 1.360420
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.360011 Loss1: 0.495426 Loss2: 1.864585
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.624695 Loss1: 0.258673 Loss2: 1.366022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608445 Loss1: 0.222692 Loss2: 1.385753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413919 Loss1: 0.063199 Loss2: 1.350720
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498122 Loss1: 0.139049 Loss2: 1.359072
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394662 Loss1: 0.054813 Loss2: 1.339849
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520869 Loss1: 0.161781 Loss2: 1.359087
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395090 Loss1: 0.058198 Loss2: 1.336892
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.532420 Loss1: 0.166643 Loss2: 1.365778
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469110 Loss1: 0.108096 Loss2: 1.361014
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466819 Loss1: 0.110669 Loss2: 1.356151
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428617 Loss1: 0.077525 Loss2: 1.351092
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402759 Loss1: 0.052238 Loss2: 1.350521
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.614000 Loss1: 0.638550 Loss2: 1.975450
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.714698 Loss1: 0.342848 Loss2: 1.371850
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638896 Loss1: 0.229838 Loss2: 1.409058
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557305 Loss1: 0.171921 Loss2: 1.385384
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.467599 Loss1: 0.107385 Loss2: 1.360214
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.428718 Loss1: 0.072186 Loss2: 1.356531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426146 Loss1: 0.073948 Loss2: 1.352198
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.680109 Loss1: 0.296650 Loss2: 1.383460
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421193 Loss1: 0.069907 Loss2: 1.351286
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604142 Loss1: 0.208622 Loss2: 1.395520
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.506075 Loss1: 0.124660 Loss2: 1.381415 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.478553 Loss1: 0.116195 Loss2: 1.362358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.493286 Loss1: 0.129149 Loss2: 1.364136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429023 Loss1: 0.073451 Loss2: 1.355572 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421894 Loss1: 0.065776 Loss2: 1.356118 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.708804 Loss1: 0.321875 Loss2: 1.386930 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478586 Loss1: 0.138979 Loss2: 1.339607 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439716 Loss1: 0.106040 Loss2: 1.333676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418303 Loss1: 0.098411 Loss2: 1.319892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418512 Loss1: 0.092329 Loss2: 1.326184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387746 Loss1: 0.065896 Loss2: 1.321850 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.607661 Loss1: 0.205341 Loss2: 1.402320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.536983 Loss1: 0.139847 Loss2: 1.397136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496889 Loss1: 0.115486 Loss2: 1.381403
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.410571 Loss1: 0.536082 Loss2: 1.874489
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450114 Loss1: 0.067834 Loss2: 1.382279
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.813157 Loss1: 0.425652 Loss2: 1.387505
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412396 Loss1: 0.043152 Loss2: 1.369244
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.716431 Loss1: 0.257132 Loss2: 1.459299
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.645059 Loss1: 0.244860 Loss2: 1.400199
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.619130 Loss1: 0.211799 Loss2: 1.407331
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.579428 Loss1: 0.172313 Loss2: 1.407115
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.533811 Loss1: 0.141265 Loss2: 1.392546
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.391678 Loss1: 0.524923 Loss2: 1.866754
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.543969 Loss1: 0.150279 Loss2: 1.393690
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.752610 Loss1: 0.384627 Loss2: 1.367982
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.496580 Loss1: 0.101966 Loss2: 1.394614
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601062 Loss1: 0.186059 Loss2: 1.415003
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458701 Loss1: 0.075884 Loss2: 1.382818
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556843 Loss1: 0.178490 Loss2: 1.378353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.482832 Loss1: 0.111463 Loss2: 1.371369 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477955 Loss1: 0.122492 Loss2: 1.355464
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.443623 Loss1: 0.564093 Loss2: 1.879530
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445195 Loss1: 0.087164 Loss2: 1.358031
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784954 Loss1: 0.397523 Loss2: 1.387432
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.455160 Loss1: 0.102870 Loss2: 1.352290
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.721368 Loss1: 0.282865 Loss2: 1.438503
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.619929 Loss1: 0.208714 Loss2: 1.411215
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.621261 Loss1: 0.216729 Loss2: 1.404532
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569418 Loss1: 0.158546 Loss2: 1.410872
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513210 Loss1: 0.117649 Loss2: 1.395562
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.278393 Loss1: 0.420466 Loss2: 1.857927
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483145 Loss1: 0.096279 Loss2: 1.386867
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.466575 Loss1: 0.083868 Loss2: 1.382707
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.672542 Loss1: 0.278331 Loss2: 1.394211
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423625 Loss1: 0.049632 Loss2: 1.373993
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.551470 Loss1: 0.135892 Loss2: 1.415579
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.500102 Loss1: 0.119347 Loss2: 1.380755
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556173 Loss1: 0.168610 Loss2: 1.387563
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460994 Loss1: 0.075276 Loss2: 1.385718
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412885 Loss1: 0.046724 Loss2: 1.366161
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.339328 Loss1: 0.495141 Loss2: 1.844187
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415987 Loss1: 0.053120 Loss2: 1.362867
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408822 Loss1: 0.050886 Loss2: 1.357936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431337 Loss1: 0.076544 Loss2: 1.354793 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516475 Loss1: 0.146832 Loss2: 1.369643 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476490 Loss1: 0.115538 Loss2: 1.360951 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446039 Loss1: 0.075746 Loss2: 1.370292
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.447886 Loss1: 0.563475 Loss2: 1.884410
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.712983 Loss1: 0.351841 Loss2: 1.361142 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.621370 Loss1: 0.218060 Loss2: 1.403311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516406 Loss1: 0.158359 Loss2: 1.358047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434998 Loss1: 0.085319 Loss2: 1.349679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421725 Loss1: 0.080849 Loss2: 1.340876 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387402 Loss1: 0.052370 Loss2: 1.335032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379091 Loss1: 0.048284 Loss2: 1.330807 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492251 Loss1: 0.138007 Loss2: 1.354244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.400996 Loss1: 0.059084 Loss2: 1.341912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404866 Loss1: 0.065555 Loss2: 1.339310
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.381022 Loss1: 0.537889 Loss2: 1.843133
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.699742 Loss1: 0.349207 Loss2: 1.350535 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608331 Loss1: 0.222676 Loss2: 1.385655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539597 Loss1: 0.173646 Loss2: 1.365950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508212 Loss1: 0.145633 Loss2: 1.362579 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493190 Loss1: 0.142066 Loss2: 1.351124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404578 Loss1: 0.065291 Loss2: 1.339288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378432 Loss1: 0.049817 Loss2: 1.328616 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521345 Loss1: 0.159002 Loss2: 1.362343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.486205 Loss1: 0.122378 Loss2: 1.363826 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484087 Loss1: 0.127856 Loss2: 1.356232
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.397837 Loss1: 0.528184 Loss2: 1.869653
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.711882 Loss1: 0.321838 Loss2: 1.390044 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390754 Loss1: 0.043201 Loss2: 1.347553
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614543 Loss1: 0.204027 Loss2: 1.410516
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555288 Loss1: 0.162047 Loss2: 1.393241
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542124 Loss1: 0.150622 Loss2: 1.391502
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.567567 Loss1: 0.177995 Loss2: 1.389571
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.555829 Loss1: 0.156701 Loss2: 1.399128
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.316415 Loss1: 0.439184 Loss2: 1.877232
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.522710 Loss1: 0.132093 Loss2: 1.390617
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499142 Loss1: 0.109273 Loss2: 1.389869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.470247 Loss1: 0.092370 Loss2: 1.377877 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527255 Loss1: 0.156437 Loss2: 1.370818 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467267 Loss1: 0.106676 Loss2: 1.360591 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450794 Loss1: 0.091117 Loss2: 1.359677
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.611270 Loss1: 0.684962 Loss2: 1.926308
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.799597 Loss1: 0.405307 Loss2: 1.394290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395293 Loss1: 0.049088 Loss2: 1.346205
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.686688 Loss1: 0.254913 Loss2: 1.431774
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536805 Loss1: 0.155710 Loss2: 1.381095
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522989 Loss1: 0.142058 Loss2: 1.380931
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.509386 Loss1: 0.120666 Loss2: 1.388720
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471667 Loss1: 0.094084 Loss2: 1.377584
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414210 Loss1: 0.044179 Loss2: 1.370031
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.369112 Loss1: 0.508060 Loss2: 1.861052
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.755136 Loss1: 0.390931 Loss2: 1.364206 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.649914 Loss1: 0.229136 Loss2: 1.420778 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.494194 Loss1: 0.137186 Loss2: 1.357008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431695 Loss1: 0.090108 Loss2: 1.341587 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436100 Loss1: 0.097845 Loss2: 1.338255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445938 Loss1: 0.101320 Loss2: 1.344618 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430471 Loss1: 0.081490 Loss2: 1.348981 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550062 Loss1: 0.145954 Loss2: 1.404108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476749 Loss1: 0.088806 Loss2: 1.387943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.322435 Loss1: 0.433999 Loss2: 1.888436 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.890798 Loss1: 0.478770 Loss2: 1.412028 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.809269 Loss1: 0.318902 Loss2: 1.490367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.595239 Loss1: 0.182655 Loss2: 1.412585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.494735 Loss1: 0.087826 Loss2: 1.406910 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459166 Loss1: 0.072284 Loss2: 1.386883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435302 Loss1: 0.053506 Loss2: 1.381795 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449789 Loss1: 0.067497 Loss2: 1.382292 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456790 Loss1: 0.113411 Loss2: 1.343379 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447613 Loss1: 0.105820 Loss2: 1.341793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409631 Loss1: 0.079403 Loss2: 1.330228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433417 Loss1: 0.099666 Loss2: 1.333751 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.431785 Loss1: 0.125215 Loss2: 1.306569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.381304 Loss1: 0.072426 Loss2: 1.308878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.316546 Loss1: 0.447878 Loss2: 1.868668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.718238 Loss1: 0.319453 Loss2: 1.398785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.603098 Loss1: 0.168511 Loss2: 1.434587 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535695 Loss1: 0.133963 Loss2: 1.401731 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530828 Loss1: 0.141016 Loss2: 1.389812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.263877 Loss1: 0.514569 Loss2: 1.749308
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.498078 Loss1: 0.101445 Loss2: 1.396633
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.616028 Loss1: 0.326456 Loss2: 1.289572
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461737 Loss1: 0.073353 Loss2: 1.388384
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.485292 Loss1: 0.168531 Loss2: 1.316761
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453484 Loss1: 0.073785 Loss2: 1.379698
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.406090 Loss1: 0.120092 Loss2: 1.285998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402980 Loss1: 0.129432 Loss2: 1.273548 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.334615 Loss1: 0.063380 Loss2: 1.271236
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.403559 Loss1: 0.535403 Loss2: 1.868155
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.307411 Loss1: 0.043235 Loss2: 1.264176
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.706246 Loss1: 0.338023 Loss2: 1.368223
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.289819 Loss1: 0.037343 Loss2: 1.252476
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605733 Loss1: 0.213931 Loss2: 1.391802
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538748 Loss1: 0.171142 Loss2: 1.367605
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.482648 Loss1: 0.127013 Loss2: 1.355635
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465512 Loss1: 0.108911 Loss2: 1.356601
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431207 Loss1: 0.080641 Loss2: 1.350566
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.296978 Loss1: 0.431287 Loss2: 1.865691
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441655 Loss1: 0.093179 Loss2: 1.348477
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.790284 Loss1: 0.428075 Loss2: 1.362209
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431345 Loss1: 0.083024 Loss2: 1.348321
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.654897 Loss1: 0.244189 Loss2: 1.410709
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392208 Loss1: 0.052060 Loss2: 1.340148
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544778 Loss1: 0.171846 Loss2: 1.372932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.452300 Loss1: 0.096494 Loss2: 1.355806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439808 Loss1: 0.088503 Loss2: 1.351305
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.176130 Loss1: 0.425300 Loss2: 1.750831
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.618978 Loss1: 0.307508 Loss2: 1.311470 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.498484 Loss1: 0.155142 Loss2: 1.343342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.420385 Loss1: 0.101103 Loss2: 1.319282 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.389078 Loss1: 0.085204 Loss2: 1.303874 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.393016 Loss1: 0.094549 Loss2: 1.298468 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394212 Loss1: 0.088080 Loss2: 1.306132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.566590 Loss1: 0.162699 Loss2: 1.403891 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516222 Loss1: 0.109705 Loss2: 1.406517 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452074 Loss1: 0.061461 Loss2: 1.390613 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 15:10:40,756 | server.py:236 | fit_round 155 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.478911 Loss1: 0.090064 Loss2: 1.388847
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.482957 Loss1: 0.552619 Loss2: 1.930338
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.810308 Loss1: 0.397540 Loss2: 1.412768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519832 Loss1: 0.113461 Loss2: 1.406371 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.525369 Loss1: 0.133427 Loss2: 1.391941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.499778 Loss1: 0.107766 Loss2: 1.392011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.494894 Loss1: 0.106041 Loss2: 1.388853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481113 Loss1: 0.090906 Loss2: 1.390208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487390 Loss1: 0.091878 Loss2: 1.395511 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497008 Loss1: 0.101141 Loss2: 1.395866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462135 Loss1: 0.082780 Loss2: 1.379355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.169090 Loss1: 0.389058 Loss2: 1.780032 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.570570 Loss1: 0.216682 Loss2: 1.353888 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.420162 Loss1: 0.103316 Loss2: 1.316845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.399735 Loss1: 0.084558 Loss2: 1.315177 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.376472 Loss1: 0.074652 Loss2: 1.301819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396437 Loss1: 0.097223 Loss2: 1.299214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425955 Loss1: 0.113248 Loss2: 1.312707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.564002 Loss1: 0.176612 Loss2: 1.387390 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988051
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.523073 Loss1: 0.137531 Loss2: 1.385542 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429265 Loss1: 0.061348 Loss2: 1.367918 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.664195 Loss1: 0.330800 Loss2: 1.333395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.506487 Loss1: 0.164298 Loss2: 1.342189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.422162 Loss1: 0.093950 Loss2: 1.328213 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374951 Loss1: 0.058988 Loss2: 1.315962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.339269 Loss1: 0.035167 Loss2: 1.304102 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 15:10:40,756][flwr][DEBUG] - fit_round 155 received 50 results and 0 failures
-INFO flwr 2023-10-12 15:11:22,006 | server.py:125 | fit progress: (155, 2.2388751522039834, {'accuracy': 0.5988}, 357589.78495004296)
->> Test accuracy: 0.598800
-[2023-10-12 15:11:22,006][flwr][INFO] - fit progress: (155, 2.2388751522039834, {'accuracy': 0.5988}, 357589.78495004296)
-DEBUG flwr 2023-10-12 15:11:22,007 | server.py:173 | evaluate_round 155: strategy sampled 50 clients (out of 50)
-[2023-10-12 15:11:22,007][flwr][DEBUG] - evaluate_round 155: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 15:20:26,416 | server.py:187 | evaluate_round 155 received 50 results and 0 failures
-[2023-10-12 15:20:26,416][flwr][DEBUG] - evaluate_round 155 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 15:20:26,417 | server.py:222 | fit_round 156: strategy sampled 50 clients (out of 50)
-[2023-10-12 15:20:26,417][flwr][DEBUG] - fit_round 156: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.429997 Loss1: 0.551551 Loss2: 1.878447
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.639817 Loss1: 0.261043 Loss2: 1.378774
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.604604 Loss1: 0.225012 Loss2: 1.379592
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.539861 Loss1: 0.170469 Loss2: 1.369392
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.347775 Loss1: 0.511610 Loss2: 1.836165
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535146 Loss1: 0.172136 Loss2: 1.363010
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.750215 Loss1: 0.379830 Loss2: 1.370385
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461290 Loss1: 0.099753 Loss2: 1.361537
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635557 Loss1: 0.222464 Loss2: 1.413094
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420600 Loss1: 0.074618 Loss2: 1.345982
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532191 Loss1: 0.162762 Loss2: 1.369429
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382827 Loss1: 0.040830 Loss2: 1.341997
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.482289 Loss1: 0.120930 Loss2: 1.361359
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.363839 Loss1: 0.030331 Loss2: 1.333508
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481697 Loss1: 0.130356 Loss2: 1.351341
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366065 Loss1: 0.039795 Loss2: 1.326270
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.477722 Loss1: 0.124960 Loss2: 1.352761
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422393 Loss1: 0.073212 Loss2: 1.349182
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424677 Loss1: 0.081927 Loss2: 1.342750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385322 Loss1: 0.042798 Loss2: 1.342524
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.548437 Loss1: 0.633609 Loss2: 1.914828
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.771984 Loss1: 0.400300 Loss2: 1.371684
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.612039 Loss1: 0.223414 Loss2: 1.388625
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569944 Loss1: 0.181158 Loss2: 1.388786
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.403738 Loss1: 0.550645 Loss2: 1.853093
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.507555 Loss1: 0.142086 Loss2: 1.365469 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458086 Loss1: 0.093780 Loss2: 1.364306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421088 Loss1: 0.074945 Loss2: 1.346143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407844 Loss1: 0.067324 Loss2: 1.340520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393974 Loss1: 0.060753 Loss2: 1.333221 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.432549 Loss1: 0.110570 Loss2: 1.321979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384906 Loss1: 0.071475 Loss2: 1.313431 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364807 Loss1: 0.056074 Loss2: 1.308733
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.392448 Loss1: 0.522562 Loss2: 1.869887
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.731652 Loss1: 0.362497 Loss2: 1.369155
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.629434 Loss1: 0.222229 Loss2: 1.407204
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.602565 Loss1: 0.231999 Loss2: 1.370567
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.537903 Loss1: 0.155461 Loss2: 1.382442
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.303592 Loss1: 0.485413 Loss2: 1.818179
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.705463 Loss1: 0.367557 Loss2: 1.337906 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.609958 Loss1: 0.227796 Loss2: 1.382163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523177 Loss1: 0.193026 Loss2: 1.330151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.454150 Loss1: 0.124039 Loss2: 1.330111 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.426724 Loss1: 0.104528 Loss2: 1.322197 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.353178 Loss1: 0.046361 Loss2: 1.306817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.313341 Loss1: 0.027566 Loss2: 1.285775 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.764687 Loss1: 0.402290 Loss2: 1.362397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551039 Loss1: 0.183044 Loss2: 1.367995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.337396 Loss1: 0.485000 Loss2: 1.852396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.741993 Loss1: 0.366272 Loss2: 1.375721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.765059 Loss1: 0.311696 Loss2: 1.453363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.668717 Loss1: 0.276560 Loss2: 1.392156 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570457 Loss1: 0.171993 Loss2: 1.398464 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531067 Loss1: 0.140358 Loss2: 1.390709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458598 Loss1: 0.088628 Loss2: 1.369970 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419722 Loss1: 0.059272 Loss2: 1.360450 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.650223 Loss1: 0.305244 Loss2: 1.344978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502904 Loss1: 0.150359 Loss2: 1.352545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.213526 Loss1: 0.452178 Loss2: 1.761347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.713436 Loss1: 0.377589 Loss2: 1.335847 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590894 Loss1: 0.212719 Loss2: 1.378175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573031 Loss1: 0.226698 Loss2: 1.346333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.534453 Loss1: 0.177151 Loss2: 1.357302 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395763 Loss1: 0.065732 Loss2: 1.330032 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.353900 Loss1: 0.040171 Loss2: 1.313729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.342358 Loss1: 0.030781 Loss2: 1.311577 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594849 Loss1: 0.152202 Loss2: 1.442647 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.545466 Loss1: 0.145664 Loss2: 1.399802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502873 Loss1: 0.103599 Loss2: 1.399274
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.418679 Loss1: 0.470016 Loss2: 1.948663
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.813955 Loss1: 0.376935 Loss2: 1.437021 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.733601 Loss1: 0.246473 Loss2: 1.487128 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.633496 Loss1: 0.195289 Loss2: 1.438207 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424818 Loss1: 0.043989 Loss2: 1.380829
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.600937 Loss1: 0.163252 Loss2: 1.437685
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.535701 Loss1: 0.098198 Loss2: 1.437503
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.519151 Loss1: 0.091053 Loss2: 1.428099
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510329 Loss1: 0.091056 Loss2: 1.419274
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473186 Loss1: 0.059303 Loss2: 1.413883
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.477709 Loss1: 0.591239 Loss2: 1.886470
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.474920 Loss1: 0.067511 Loss2: 1.407409
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.654395 Loss1: 0.213572 Loss2: 1.440822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527515 Loss1: 0.141694 Loss2: 1.385821 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537756 Loss1: 0.144005 Loss2: 1.393750
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.376222 Loss1: 0.522103 Loss2: 1.854118
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.693313 Loss1: 0.329801 Loss2: 1.363511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563137 Loss1: 0.165758 Loss2: 1.397379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555023 Loss1: 0.185778 Loss2: 1.369245 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443263 Loss1: 0.070258 Loss2: 1.373004
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495447 Loss1: 0.123297 Loss2: 1.372150
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461827 Loss1: 0.096890 Loss2: 1.364937
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455425 Loss1: 0.099007 Loss2: 1.356418
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454995 Loss1: 0.101554 Loss2: 1.353441
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429689 Loss1: 0.076331 Loss2: 1.353358
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.316180 Loss1: 0.435880 Loss2: 1.880300
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390892 Loss1: 0.043235 Loss2: 1.347657
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.583285 Loss1: 0.167882 Loss2: 1.415403 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507866 Loss1: 0.132870 Loss2: 1.374996 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473815 Loss1: 0.108726 Loss2: 1.365089
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.411696 Loss1: 0.543035 Loss2: 1.868661
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.729776 Loss1: 0.362135 Loss2: 1.367641 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636120 Loss1: 0.236958 Loss2: 1.399163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573930 Loss1: 0.195763 Loss2: 1.378167 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.498847 Loss1: 0.121720 Loss2: 1.377128
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.494944 Loss1: 0.133613 Loss2: 1.361330
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.438555 Loss1: 0.071534 Loss2: 1.367021
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.403279 Loss1: 0.055974 Loss2: 1.347305
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389720 Loss1: 0.043663 Loss2: 1.346056
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401579 Loss1: 0.060335 Loss2: 1.341245
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.617955 Loss1: 0.652061 Loss2: 1.965894
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398918 Loss1: 0.061199 Loss2: 1.337719
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.673628 Loss1: 0.256639 Loss2: 1.416989 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.449290 Loss1: 0.098133 Loss2: 1.351157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415560 Loss1: 0.066427 Loss2: 1.349134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.392045 Loss1: 0.050835 Loss2: 1.341211 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.383028 Loss1: 0.045812 Loss2: 1.337216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381027 Loss1: 0.047386 Loss2: 1.333641 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997596
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512818 Loss1: 0.131340 Loss2: 1.381479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460856 Loss1: 0.079793 Loss2: 1.381063 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453173 Loss1: 0.085144 Loss2: 1.368029
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.439562 Loss1: 0.582677 Loss2: 1.856885
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422366 Loss1: 0.060829 Loss2: 1.361538
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.746198 Loss1: 0.358517 Loss2: 1.387681
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417048 Loss1: 0.062248 Loss2: 1.354800
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.625520 Loss1: 0.203752 Loss2: 1.421768
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524011 Loss1: 0.163072 Loss2: 1.360939
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498758 Loss1: 0.127171 Loss2: 1.371587
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.486521 Loss1: 0.125785 Loss2: 1.360736
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416460 Loss1: 0.059493 Loss2: 1.356967
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.400299 Loss1: 0.582203 Loss2: 1.818096
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436207 Loss1: 0.081648 Loss2: 1.354559
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.743422 Loss1: 0.406626 Loss2: 1.336797
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.416049 Loss1: 0.061600 Loss2: 1.354450
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.557249 Loss1: 0.178499 Loss2: 1.378750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403439 Loss1: 0.060690 Loss2: 1.342749
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.432132 Loss1: 0.103161 Loss2: 1.328971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.489714 Loss1: 0.178135 Loss2: 1.311579 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440294 Loss1: 0.115107 Loss2: 1.325188
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.356818 Loss1: 0.516239 Loss2: 1.840579
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422635 Loss1: 0.111000 Loss2: 1.311635
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.679973 Loss1: 0.335700 Loss2: 1.344273
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406635 Loss1: 0.095924 Loss2: 1.310711
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.651129 Loss1: 0.255688 Loss2: 1.395441
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.608763 Loss1: 0.256143 Loss2: 1.352620
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.530520 Loss1: 0.181312 Loss2: 1.349208
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460660 Loss1: 0.104254 Loss2: 1.356406
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434477 Loss1: 0.101221 Loss2: 1.333255
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461905 Loss1: 0.126654 Loss2: 1.335251
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.334172 Loss1: 0.500348 Loss2: 1.833824
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434429 Loss1: 0.098270 Loss2: 1.336159
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.611078 Loss1: 0.275504 Loss2: 1.335574
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423321 Loss1: 0.089362 Loss2: 1.333959
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.575698 Loss1: 0.211768 Loss2: 1.363929
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496059 Loss1: 0.154652 Loss2: 1.341407
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460509 Loss1: 0.121752 Loss2: 1.338757
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421046 Loss1: 0.090858 Loss2: 1.330187
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.391691 Loss1: 0.071436 Loss2: 1.320255
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388567 Loss1: 0.070506 Loss2: 1.318061
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.371728 Loss1: 0.528483 Loss2: 1.843245
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370151 Loss1: 0.064484 Loss2: 1.305667
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.746875 Loss1: 0.383841 Loss2: 1.363034
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.344867 Loss1: 0.039535 Loss2: 1.305332
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.628887 Loss1: 0.208608 Loss2: 1.420279
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520248 Loss1: 0.160507 Loss2: 1.359742
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507596 Loss1: 0.140977 Loss2: 1.366619
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.501421 Loss1: 0.137423 Loss2: 1.363998
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475481 Loss1: 0.116208 Loss2: 1.359273
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.402505 Loss1: 0.545256 Loss2: 1.857249
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430929 Loss1: 0.083514 Loss2: 1.347415
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.682085 Loss1: 0.331936 Loss2: 1.350148
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414509 Loss1: 0.068460 Loss2: 1.346049
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.621623 Loss1: 0.226326 Loss2: 1.395297
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443137 Loss1: 0.093830 Loss2: 1.349307
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549306 Loss1: 0.180499 Loss2: 1.368807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464617 Loss1: 0.103781 Loss2: 1.360836 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453651 Loss1: 0.102470 Loss2: 1.351181
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.428554 Loss1: 0.579203 Loss2: 1.849351
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439434 Loss1: 0.094668 Loss2: 1.344766
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.772688 Loss1: 0.389742 Loss2: 1.382947
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454034 Loss1: 0.102512 Loss2: 1.351521
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.631232 Loss1: 0.215248 Loss2: 1.415984
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549069 Loss1: 0.180760 Loss2: 1.368309
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565579 Loss1: 0.182924 Loss2: 1.382655
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502187 Loss1: 0.132405 Loss2: 1.369783
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.494029 Loss1: 0.134080 Loss2: 1.359949
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447459 Loss1: 0.094638 Loss2: 1.352821
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.242686 Loss1: 0.434820 Loss2: 1.807866
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418404 Loss1: 0.068064 Loss2: 1.350341
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.622712 Loss1: 0.270192 Loss2: 1.352519
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374298 Loss1: 0.035972 Loss2: 1.338326
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.509165 Loss1: 0.143893 Loss2: 1.365272
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.477878 Loss1: 0.142662 Loss2: 1.335216
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.430335 Loss1: 0.091785 Loss2: 1.338551
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.419296 Loss1: 0.087438 Loss2: 1.331858
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392829 Loss1: 0.070383 Loss2: 1.322446
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.246943 Loss1: 0.428827 Loss2: 1.818116
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.599943 Loss1: 0.284065 Loss2: 1.315877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561907 Loss1: 0.215267 Loss2: 1.346640 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423506 Loss1: 0.100441 Loss2: 1.323065
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496352 Loss1: 0.161988 Loss2: 1.334364
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451224 Loss1: 0.136259 Loss2: 1.314965
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.417430 Loss1: 0.100383 Loss2: 1.317047
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.381843 Loss1: 0.076320 Loss2: 1.305523
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.374556 Loss1: 0.068399 Loss2: 1.306157
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.248154 Loss1: 0.425489 Loss2: 1.822664
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447233 Loss1: 0.137990 Loss2: 1.309243
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412348 Loss1: 0.108762 Loss2: 1.303587
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.598178 Loss1: 0.247079 Loss2: 1.351099
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563382 Loss1: 0.203501 Loss2: 1.359881
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539355 Loss1: 0.187957 Loss2: 1.351397
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455455 Loss1: 0.113014 Loss2: 1.342441
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427992 Loss1: 0.093695 Loss2: 1.334297
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.282163 Loss1: 0.493352 Loss2: 1.788812
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.681781 Loss1: 0.372926 Loss2: 1.308855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424865 Loss1: 0.095339 Loss2: 1.329526
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.565190 Loss1: 0.215491 Loss2: 1.349699
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401338 Loss1: 0.073612 Loss2: 1.327726
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.481996 Loss1: 0.161701 Loss2: 1.320295
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463102 Loss1: 0.146386 Loss2: 1.316716
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376995 Loss1: 0.059990 Loss2: 1.317005
-(DefaultActor pid=3764) >> Training accuracy: 0.994485
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.417334 Loss1: 0.112168 Loss2: 1.305166 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402335 Loss1: 0.095660 Loss2: 1.306674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354435 Loss1: 0.048536 Loss2: 1.305899
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.453674 Loss1: 0.562275 Loss2: 1.891399
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.718583 Loss1: 0.358709 Loss2: 1.359875
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.596761 Loss1: 0.194193 Loss2: 1.402567
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.548824 Loss1: 0.176875 Loss2: 1.371949
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524235 Loss1: 0.164645 Loss2: 1.359590
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476879 Loss1: 0.120532 Loss2: 1.356347
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.332680 Loss1: 0.497754 Loss2: 1.834926
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.612131 Loss1: 0.269444 Loss2: 1.342687 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561273 Loss1: 0.200467 Loss2: 1.360806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498042 Loss1: 0.141316 Loss2: 1.356725 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.444102 Loss1: 0.099491 Loss2: 1.344612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.372760 Loss1: 0.041392 Loss2: 1.331369 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.345728 Loss1: 0.027414 Loss2: 1.318314 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358830 Loss1: 0.045213 Loss2: 1.313617 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.687996 Loss1: 0.239983 Loss2: 1.448013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.531058 Loss1: 0.116281 Loss2: 1.414777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.496458 Loss1: 0.095357 Loss2: 1.401101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.472072 Loss1: 0.085719 Loss2: 1.386353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457964 Loss1: 0.073909 Loss2: 1.384055 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442961 Loss1: 0.058772 Loss2: 1.384189 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.552713 Loss1: 0.144083 Loss2: 1.408630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512977 Loss1: 0.112975 Loss2: 1.400001 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473536 Loss1: 0.067828 Loss2: 1.405708
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.335363 Loss1: 0.489328 Loss2: 1.846035
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.442656 Loss1: 0.049330 Loss2: 1.393326
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.715209 Loss1: 0.365990 Loss2: 1.349219
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428400 Loss1: 0.038497 Loss2: 1.389903
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.613053 Loss1: 0.218189 Loss2: 1.394865
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539056 Loss1: 0.182851 Loss2: 1.356205
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.532553 Loss1: 0.172304 Loss2: 1.360250
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.497481 Loss1: 0.143229 Loss2: 1.354252
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443816 Loss1: 0.096216 Loss2: 1.347600
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414308 Loss1: 0.068909 Loss2: 1.345399
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.449888 Loss1: 0.497472 Loss2: 1.952416
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440640 Loss1: 0.103124 Loss2: 1.337516
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751053 Loss1: 0.305090 Loss2: 1.445963
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392722 Loss1: 0.060610 Loss2: 1.332112
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.685986 Loss1: 0.218224 Loss2: 1.467762
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.651883 Loss1: 0.204236 Loss2: 1.447647
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.659997 Loss1: 0.207033 Loss2: 1.452964
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.569443 Loss1: 0.123720 Loss2: 1.445724
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523872 Loss1: 0.091221 Loss2: 1.432650
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.483814 Loss1: 0.581317 Loss2: 1.902497
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.485224 Loss1: 0.065520 Loss2: 1.419704
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474388 Loss1: 0.053586 Loss2: 1.420803
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.803833 Loss1: 0.367084 Loss2: 1.436749
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.456681 Loss1: 0.043049 Loss2: 1.413631
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.644634 Loss1: 0.198855 Loss2: 1.445779
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573256 Loss1: 0.158305 Loss2: 1.414950
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.553067 Loss1: 0.143520 Loss2: 1.409547
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493831 Loss1: 0.092539 Loss2: 1.401291
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.505592 Loss1: 0.110726 Loss2: 1.394865
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.302112 Loss1: 0.495466 Loss2: 1.806646
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.691244 Loss1: 0.330270 Loss2: 1.360974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610327 Loss1: 0.220032 Loss2: 1.390295 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506182 Loss1: 0.160913 Loss2: 1.345270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476046 Loss1: 0.128222 Loss2: 1.347824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439201 Loss1: 0.102615 Loss2: 1.336586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405700 Loss1: 0.069562 Loss2: 1.336138 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398777 Loss1: 0.066376 Loss2: 1.332401 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.431229 Loss1: 0.103666 Loss2: 1.327563 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415621 Loss1: 0.096814 Loss2: 1.318807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.394957 Loss1: 0.079683 Loss2: 1.315274
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.447678 Loss1: 0.565561 Loss2: 1.882117
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.356977 Loss1: 0.048182 Loss2: 1.308795
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.825776 Loss1: 0.421010 Loss2: 1.404767
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.321396 Loss1: 0.022527 Loss2: 1.298869
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.658656 Loss1: 0.219886 Loss2: 1.438770
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.525972 Loss1: 0.129170 Loss2: 1.396802
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.500265 Loss1: 0.115867 Loss2: 1.384398
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491475 Loss1: 0.112137 Loss2: 1.379338
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.525699 Loss1: 0.146261 Loss2: 1.379439
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.600294 Loss1: 0.648656 Loss2: 1.951639
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483598 Loss1: 0.109514 Loss2: 1.374084
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.410864 Loss1: 0.046551 Loss2: 1.364314 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405532 Loss1: 0.048003 Loss2: 1.357529 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.426899 Loss1: 0.106899 Loss2: 1.319999 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418096 Loss1: 0.093104 Loss2: 1.324992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402483 Loss1: 0.081128 Loss2: 1.321355 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.514684 Loss1: 0.146421 Loss2: 1.368263 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473072 Loss1: 0.139278 Loss2: 1.333795 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416811 Loss1: 0.087973 Loss2: 1.328838
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.292009 Loss1: 0.471116 Loss2: 1.820894
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.421615 Loss1: 0.094280 Loss2: 1.327335
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.683645 Loss1: 0.316371 Loss2: 1.367275
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.385711 Loss1: 0.059857 Loss2: 1.325854
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564413 Loss1: 0.170042 Loss2: 1.394372
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537519 Loss1: 0.177934 Loss2: 1.359585 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370176 Loss1: 0.057325 Loss2: 1.312851
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518776 Loss1: 0.144836 Loss2: 1.373940
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486699 Loss1: 0.128345 Loss2: 1.358354
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458742 Loss1: 0.096032 Loss2: 1.362711
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415505 Loss1: 0.062356 Loss2: 1.353148
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409324 Loss1: 0.061805 Loss2: 1.347518
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.188558 Loss1: 0.437672 Loss2: 1.750886
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.588081 Loss1: 0.280382 Loss2: 1.307698 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.456655 Loss1: 0.144702 Loss2: 1.311953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.446002 Loss1: 0.140637 Loss2: 1.305365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.403597 Loss1: 0.092331 Loss2: 1.311266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398405 Loss1: 0.092671 Loss2: 1.305734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.347401 Loss1: 0.049887 Loss2: 1.297514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.349651 Loss1: 0.057774 Loss2: 1.291878 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.534953 Loss1: 0.159291 Loss2: 1.375663 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.459313 Loss1: 0.090472 Loss2: 1.368841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448154 Loss1: 0.086895 Loss2: 1.361259
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.299732 Loss1: 0.486124 Loss2: 1.813608
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.713228 Loss1: 0.350861 Loss2: 1.362367
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.617667 Loss1: 0.214770 Loss2: 1.402897
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.538606 Loss1: 0.177259 Loss2: 1.361347
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461635 Loss1: 0.109974 Loss2: 1.351661
-DEBUG flwr 2023-10-12 15:48:54,176 | server.py:236 | fit_round 156 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.240408 Loss1: 0.403579 Loss2: 1.836828
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471239 Loss1: 0.120067 Loss2: 1.351172
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.666602 Loss1: 0.304054 Loss2: 1.362548
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418179 Loss1: 0.078156 Loss2: 1.340023
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.583403 Loss1: 0.190657 Loss2: 1.392746
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424983 Loss1: 0.087555 Loss2: 1.337428
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.508406 Loss1: 0.153811 Loss2: 1.354595
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407382 Loss1: 0.073926 Loss2: 1.333457
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492158 Loss1: 0.132574 Loss2: 1.359584
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397371 Loss1: 0.070065 Loss2: 1.327306
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.449193 Loss1: 0.102549 Loss2: 1.346644 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391466 Loss1: 0.053283 Loss2: 1.338184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.287712 Loss1: 0.442550 Loss2: 1.845162
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401783 Loss1: 0.062166 Loss2: 1.339617
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.584545 Loss1: 0.202179 Loss2: 1.382367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490493 Loss1: 0.128910 Loss2: 1.361583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481533 Loss1: 0.119144 Loss2: 1.362389
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.586007 Loss1: 0.652547 Loss2: 1.933460
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.841324 Loss1: 0.451972 Loss2: 1.389352
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441047 Loss1: 0.084605 Loss2: 1.356441
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653715 Loss1: 0.207667 Loss2: 1.446048
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432194 Loss1: 0.076668 Loss2: 1.355526
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555450 Loss1: 0.174223 Loss2: 1.381227
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408864 Loss1: 0.061109 Loss2: 1.347755
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.382004 Loss1: 0.041573 Loss2: 1.340431 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.523290 Loss1: 0.153507 Loss2: 1.369783 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.493373 Loss1: 0.118812 Loss2: 1.374562 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982143
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465397 Loss1: 0.092183 Loss2: 1.373214
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.351488 Loss1: 0.504318 Loss2: 1.847170
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.793470 Loss1: 0.433672 Loss2: 1.359798
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.695121 Loss1: 0.264338 Loss2: 1.430783
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561397 Loss1: 0.218642 Loss2: 1.342755
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526241 Loss1: 0.178945 Loss2: 1.347296
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.422544 Loss1: 0.600720 Loss2: 1.821824
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.737646 Loss1: 0.394747 Loss2: 1.342899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.712807 Loss1: 0.307408 Loss2: 1.405399 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497238 Loss1: 0.158900 Loss2: 1.338338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.498825 Loss1: 0.160244 Loss2: 1.338581 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354019 Loss1: 0.045054 Loss2: 1.308965
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429560 Loss1: 0.094812 Loss2: 1.334748
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.396335 Loss1: 0.069163 Loss2: 1.327172
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.379389 Loss1: 0.055376 Loss2: 1.324013
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386379 Loss1: 0.067839 Loss2: 1.318539
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357815 Loss1: 0.045969 Loss2: 1.311846
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 15:48:54,176][flwr][DEBUG] - fit_round 156 received 50 results and 0 failures
-INFO flwr 2023-10-12 15:49:37,514 | server.py:125 | fit progress: (156, 2.2486510133971804, {'accuracy': 0.5976}, 359885.292311408)
->> Test accuracy: 0.597600
-[2023-10-12 15:49:37,514][flwr][INFO] - fit progress: (156, 2.2486510133971804, {'accuracy': 0.5976}, 359885.292311408)
-DEBUG flwr 2023-10-12 15:49:37,514 | server.py:173 | evaluate_round 156: strategy sampled 50 clients (out of 50)
-[2023-10-12 15:49:37,514][flwr][DEBUG] - evaluate_round 156: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 15:58:40,820 | server.py:187 | evaluate_round 156 received 50 results and 0 failures
-[2023-10-12 15:58:40,820][flwr][DEBUG] - evaluate_round 156 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 15:58:40,821 | server.py:222 | fit_round 157: strategy sampled 50 clients (out of 50)
-[2023-10-12 15:58:40,821][flwr][DEBUG] - fit_round 157: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.445224 Loss1: 0.556040 Loss2: 1.889184
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.723500 Loss1: 0.376111 Loss2: 1.347388
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.576604 Loss1: 0.191790 Loss2: 1.384815
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547592 Loss1: 0.190320 Loss2: 1.357272
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.305553 Loss1: 0.469798 Loss2: 1.835755
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.603719 Loss1: 0.251906 Loss2: 1.351813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.536656 Loss1: 0.167608 Loss2: 1.369049 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.473824 Loss1: 0.120757 Loss2: 1.353067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.451532 Loss1: 0.106039 Loss2: 1.345493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.433346 Loss1: 0.088783 Loss2: 1.344563 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382698 Loss1: 0.049602 Loss2: 1.333096 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377355 Loss1: 0.051118 Loss2: 1.326237 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.565445 Loss1: 0.239004 Loss2: 1.326440 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.495459 Loss1: 0.161813 Loss2: 1.333646 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470210 Loss1: 0.132884 Loss2: 1.337326 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470885 Loss1: 0.138750 Loss2: 1.332135 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449768 Loss1: 0.115314 Loss2: 1.334453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418130 Loss1: 0.089807 Loss2: 1.328323 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408603 Loss1: 0.081630 Loss2: 1.326973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391789 Loss1: 0.070257 Loss2: 1.321532 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436917 Loss1: 0.072585 Loss2: 1.364332 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.254869 Loss1: 0.428783 Loss2: 1.826086 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.552366 Loss1: 0.174418 Loss2: 1.377948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.305311 Loss1: 0.494458 Loss2: 1.810854
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524555 Loss1: 0.160205 Loss2: 1.364350
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.600709 Loss1: 0.281143 Loss2: 1.319566
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.501612 Loss1: 0.143609 Loss2: 1.358003
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.555066 Loss1: 0.202752 Loss2: 1.352313
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470250 Loss1: 0.107156 Loss2: 1.363094
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507183 Loss1: 0.178131 Loss2: 1.329052
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458705 Loss1: 0.100531 Loss2: 1.358173
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.407394 Loss1: 0.060589 Loss2: 1.346805 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405361 Loss1: 0.062191 Loss2: 1.343171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385836 Loss1: 0.045659 Loss2: 1.340177 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374203 Loss1: 0.074126 Loss2: 1.300077 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.183526 Loss1: 0.388169 Loss2: 1.795356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.513151 Loss1: 0.190504 Loss2: 1.322646 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474814 Loss1: 0.136996 Loss2: 1.337818
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.379449 Loss1: 0.518876 Loss2: 1.860574
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.415050 Loss1: 0.106491 Loss2: 1.308559
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.702310 Loss1: 0.336085 Loss2: 1.366225
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.391060 Loss1: 0.084841 Loss2: 1.306219
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.569882 Loss1: 0.170691 Loss2: 1.399190
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.375792 Loss1: 0.064647 Loss2: 1.311145
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.514420 Loss1: 0.142991 Loss2: 1.371429
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.349130 Loss1: 0.053475 Loss2: 1.295654
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530931 Loss1: 0.164962 Loss2: 1.365969
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.352423 Loss1: 0.063296 Loss2: 1.289127
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500525 Loss1: 0.128075 Loss2: 1.372450
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.319358 Loss1: 0.029180 Loss2: 1.290178
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472224 Loss1: 0.100597 Loss2: 1.371627
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.522445 Loss1: 0.156486 Loss2: 1.365959
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461069 Loss1: 0.096509 Loss2: 1.364560
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419004 Loss1: 0.060790 Loss2: 1.358214
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.509955 Loss1: 0.618871 Loss2: 1.891084
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.760437 Loss1: 0.361556 Loss2: 1.398881
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.661861 Loss1: 0.229029 Loss2: 1.432833
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549990 Loss1: 0.155545 Loss2: 1.394446
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.500722 Loss1: 0.594182 Loss2: 1.906540
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.688787 Loss1: 0.300539 Loss2: 1.388247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590570 Loss1: 0.193499 Loss2: 1.397071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537648 Loss1: 0.149996 Loss2: 1.387652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520669 Loss1: 0.138076 Loss2: 1.382592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.428625 Loss1: 0.057236 Loss2: 1.371389 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389717 Loss1: 0.034780 Loss2: 1.354936
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406602 Loss1: 0.047170 Loss2: 1.359432
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409009 Loss1: 0.056023 Loss2: 1.352986
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387060 Loss1: 0.041335 Loss2: 1.345725
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393135 Loss1: 0.044780 Loss2: 1.348355
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.399865 Loss1: 0.463776 Loss2: 1.936089
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.717293 Loss1: 0.296608 Loss2: 1.420684
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.650374 Loss1: 0.199961 Loss2: 1.450412
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.635734 Loss1: 0.204773 Loss2: 1.430962
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.346035 Loss1: 0.526927 Loss2: 1.819108
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.589550 Loss1: 0.260388 Loss2: 1.329162 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.519234 Loss1: 0.180023 Loss2: 1.339211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.459447 Loss1: 0.130229 Loss2: 1.329219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.430100 Loss1: 0.114715 Loss2: 1.315385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.396277 Loss1: 0.079160 Loss2: 1.317117 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.377260 Loss1: 0.069543 Loss2: 1.307716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.365226 Loss1: 0.061534 Loss2: 1.303692 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.479348 Loss1: 0.540385 Loss2: 1.938963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.680474 Loss1: 0.223751 Loss2: 1.456723 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594535 Loss1: 0.155002 Loss2: 1.439533
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.395101 Loss1: 0.529064 Loss2: 1.866037
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.660966 Loss1: 0.298320 Loss2: 1.362646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572372 Loss1: 0.188850 Loss2: 1.383522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.493024 Loss1: 0.126629 Loss2: 1.366395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.464619 Loss1: 0.109984 Loss2: 1.354635 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.410649 Loss1: 0.061261 Loss2: 1.349388 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.967708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.501508 Loss1: 0.095014 Loss2: 1.406494
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.411922 Loss1: 0.074366 Loss2: 1.337557
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390176 Loss1: 0.061991 Loss2: 1.328185
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388582 Loss1: 0.058444 Loss2: 1.330139
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373700 Loss1: 0.046269 Loss2: 1.327432
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.315720 Loss1: 0.443408 Loss2: 1.872312
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.680969 Loss1: 0.280208 Loss2: 1.400761
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.625991 Loss1: 0.209826 Loss2: 1.416165
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.551872 Loss1: 0.631909 Loss2: 1.919963
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.600249 Loss1: 0.197747 Loss2: 1.402502
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611688 Loss1: 0.197570 Loss2: 1.414117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.556145 Loss1: 0.164509 Loss2: 1.391636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.527648 Loss1: 0.133250 Loss2: 1.394398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476241 Loss1: 0.138427 Loss2: 1.337814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436191 Loss1: 0.101495 Loss2: 1.334696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420923 Loss1: 0.100010 Loss2: 1.320913 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443415 Loss1: 0.078716 Loss2: 1.364699
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363221 Loss1: 0.045113 Loss2: 1.318109
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394937 Loss1: 0.089353 Loss2: 1.305584
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.290831 Loss1: 0.480339 Loss2: 1.810492
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.714783 Loss1: 0.370317 Loss2: 1.344465
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.583553 Loss1: 0.208350 Loss2: 1.375202
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498251 Loss1: 0.172759 Loss2: 1.325493
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.243224 Loss1: 0.413554 Loss2: 1.829671
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463742 Loss1: 0.118591 Loss2: 1.345151
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.629379 Loss1: 0.264579 Loss2: 1.364800
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.400420 Loss1: 0.082229 Loss2: 1.318190
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.569436 Loss1: 0.173745 Loss2: 1.395691
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422344 Loss1: 0.105549 Loss2: 1.316796
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481669 Loss1: 0.125710 Loss2: 1.355959
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.386066 Loss1: 0.076712 Loss2: 1.309353
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.490627 Loss1: 0.125065 Loss2: 1.365562
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.350931 Loss1: 0.045910 Loss2: 1.305021
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466219 Loss1: 0.110815 Loss2: 1.355404
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356351 Loss1: 0.051938 Loss2: 1.304414
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440927 Loss1: 0.089042 Loss2: 1.351885
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482700 Loss1: 0.129059 Loss2: 1.353641
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457021 Loss1: 0.100665 Loss2: 1.356356
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.432213 Loss1: 0.079771 Loss2: 1.352442
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.315962 Loss1: 0.477849 Loss2: 1.838113
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.654494 Loss1: 0.265205 Loss2: 1.389289
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.608450 Loss1: 0.191140 Loss2: 1.417310
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555317 Loss1: 0.168635 Loss2: 1.386681
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.357881 Loss1: 0.507314 Loss2: 1.850566
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.511178 Loss1: 0.126182 Loss2: 1.384996
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.708051 Loss1: 0.341738 Loss2: 1.366313
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598400 Loss1: 0.209606 Loss2: 1.388794
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480734 Loss1: 0.103184 Loss2: 1.377550
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.504192 Loss1: 0.139752 Loss2: 1.364440
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472970 Loss1: 0.104688 Loss2: 1.368282
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.477160 Loss1: 0.123703 Loss2: 1.353457
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449260 Loss1: 0.080364 Loss2: 1.368896
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450227 Loss1: 0.095820 Loss2: 1.354407
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465967 Loss1: 0.098392 Loss2: 1.367575
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432129 Loss1: 0.065727 Loss2: 1.366401 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420493 Loss1: 0.071247 Loss2: 1.349246 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.206958 Loss1: 0.402766 Loss2: 1.804191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574195 Loss1: 0.205123 Loss2: 1.369073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.265530 Loss1: 0.454541 Loss2: 1.810989
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517823 Loss1: 0.169412 Loss2: 1.348410
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.694368 Loss1: 0.376581 Loss2: 1.317787
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461196 Loss1: 0.128234 Loss2: 1.332962
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455649 Loss1: 0.116794 Loss2: 1.338855 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416639 Loss1: 0.085239 Loss2: 1.331400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422323 Loss1: 0.096984 Loss2: 1.325339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377846 Loss1: 0.054651 Loss2: 1.323194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385940 Loss1: 0.063743 Loss2: 1.322197 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370967 Loss1: 0.071660 Loss2: 1.299307 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.424759 Loss1: 0.590785 Loss2: 1.833974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.670082 Loss1: 0.268719 Loss2: 1.401363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558480 Loss1: 0.196146 Loss2: 1.362334
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.277574 Loss1: 0.421384 Loss2: 1.856190
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.580996 Loss1: 0.215353 Loss2: 1.365643
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.660678 Loss1: 0.310596 Loss2: 1.350082
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.510610 Loss1: 0.138327 Loss2: 1.372283
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.612478 Loss1: 0.212576 Loss2: 1.399902
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496012 Loss1: 0.131860 Loss2: 1.364153
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554481 Loss1: 0.197994 Loss2: 1.356486
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.489430 Loss1: 0.134289 Loss2: 1.355142
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.538602 Loss1: 0.181714 Loss2: 1.356888
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437732 Loss1: 0.080822 Loss2: 1.356910
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477505 Loss1: 0.124907 Loss2: 1.352598
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421794 Loss1: 0.083883 Loss2: 1.337912
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455726 Loss1: 0.108100 Loss2: 1.347625
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449125 Loss1: 0.102644 Loss2: 1.346481
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413046 Loss1: 0.072071 Loss2: 1.340975
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402593 Loss1: 0.066362 Loss2: 1.336231
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.313654 Loss1: 0.384566 Loss2: 1.929088
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.718063 Loss1: 0.303185 Loss2: 1.414879
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635434 Loss1: 0.203035 Loss2: 1.432399
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.588694 Loss1: 0.162110 Loss2: 1.426584
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.285162 Loss1: 0.446475 Loss2: 1.838687
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589240 Loss1: 0.176441 Loss2: 1.412799
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.631251 Loss1: 0.298411 Loss2: 1.332840
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.575015 Loss1: 0.151148 Loss2: 1.423867
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600175 Loss1: 0.229925 Loss2: 1.370250
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.527263 Loss1: 0.107702 Loss2: 1.419561
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.495715 Loss1: 0.155280 Loss2: 1.340434
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.522634 Loss1: 0.103201 Loss2: 1.419433
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.445800 Loss1: 0.122046 Loss2: 1.323754
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.458814 Loss1: 0.051029 Loss2: 1.407785
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423136 Loss1: 0.096763 Loss2: 1.326372
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466378 Loss1: 0.066949 Loss2: 1.399428
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.378787 Loss1: 0.064359 Loss2: 1.314429
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.364545 Loss1: 0.053138 Loss2: 1.311407
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.356790 Loss1: 0.051799 Loss2: 1.304991
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.345297 Loss1: 0.042103 Loss2: 1.303194
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.507113 Loss1: 0.585427 Loss2: 1.921686
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.739231 Loss1: 0.355660 Loss2: 1.383571
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.631595 Loss1: 0.220581 Loss2: 1.411015
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596142 Loss1: 0.192002 Loss2: 1.404140
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.307335 Loss1: 0.512219 Loss2: 1.795116
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.690401 Loss1: 0.365851 Loss2: 1.324550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.491707 Loss1: 0.109203 Loss2: 1.382504 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462453 Loss1: 0.091814 Loss2: 1.370639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453393 Loss1: 0.085940 Loss2: 1.367453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431750 Loss1: 0.061413 Loss2: 1.370337 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436885 Loss1: 0.119527 Loss2: 1.317358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371660 Loss1: 0.062705 Loss2: 1.308955 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.355316 Loss1: 0.050334 Loss2: 1.304981
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.314778 Loss1: 0.519703 Loss2: 1.795074
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.676582 Loss1: 0.363751 Loss2: 1.312831
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.584080 Loss1: 0.220749 Loss2: 1.363331
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482908 Loss1: 0.165617 Loss2: 1.317290
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.429472 Loss1: 0.109606 Loss2: 1.319866
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.532911 Loss1: 0.614673 Loss2: 1.918238
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.729269 Loss1: 0.354919 Loss2: 1.374350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635245 Loss1: 0.227844 Loss2: 1.407401 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.383200 Loss1: 0.084817 Loss2: 1.298384
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.582920 Loss1: 0.193132 Loss2: 1.389788
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.368902 Loss1: 0.064148 Loss2: 1.304754
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530492 Loss1: 0.158020 Loss2: 1.372472
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471424 Loss1: 0.102289 Loss2: 1.369135
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.340393 Loss1: 0.045532 Loss2: 1.294860
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427272 Loss1: 0.065328 Loss2: 1.361944 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382584 Loss1: 0.038441 Loss2: 1.344143 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.608133 Loss1: 0.309212 Loss2: 1.298922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.462169 Loss1: 0.150140 Loss2: 1.312029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.432241 Loss1: 0.137694 Loss2: 1.294547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.811023 Loss1: 0.404310 Loss2: 1.406713
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.414558 Loss1: 0.112607 Loss2: 1.301951
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.665806 Loss1: 0.234393 Loss2: 1.431413
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.396400 Loss1: 0.095982 Loss2: 1.300418
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640270 Loss1: 0.218878 Loss2: 1.421392
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.356454 Loss1: 0.063958 Loss2: 1.292497
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371055 Loss1: 0.082354 Loss2: 1.288701
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.559358 Loss1: 0.155064 Loss2: 1.404294
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.333396 Loss1: 0.045989 Loss2: 1.287406
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531308 Loss1: 0.132756 Loss2: 1.398553
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470204 Loss1: 0.077983 Loss2: 1.392222
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442389 Loss1: 0.058125 Loss2: 1.384264
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436272 Loss1: 0.058875 Loss2: 1.377398
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.462242 Loss1: 0.089777 Loss2: 1.372465
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.328598 Loss1: 0.489723 Loss2: 1.838875
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.664697 Loss1: 0.301361 Loss2: 1.363335
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599018 Loss1: 0.200706 Loss2: 1.398312
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499401 Loss1: 0.150091 Loss2: 1.349311
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.536762 Loss1: 0.174888 Loss2: 1.361874
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.669630 Loss1: 0.593938 Loss2: 2.075692
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.836734 Loss1: 0.413466 Loss2: 1.423267
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518311 Loss1: 0.153475 Loss2: 1.364836
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473545 Loss1: 0.113896 Loss2: 1.359649 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427847 Loss1: 0.080128 Loss2: 1.347719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.466188 Loss1: 0.123781 Loss2: 1.342407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.527950 Loss1: 0.104926 Loss2: 1.423024 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.519987 Loss1: 0.112443 Loss2: 1.407544 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.328688 Loss1: 0.482737 Loss2: 1.845951 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.531841 Loss1: 0.169602 Loss2: 1.362240 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491140 Loss1: 0.137805 Loss2: 1.353334
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.360830 Loss1: 0.501756 Loss2: 1.859074
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.656010 Loss1: 0.310477 Loss2: 1.345533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.640593 Loss1: 0.242663 Loss2: 1.397930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564558 Loss1: 0.205907 Loss2: 1.358651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505341 Loss1: 0.151478 Loss2: 1.353863 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467604 Loss1: 0.109474 Loss2: 1.358130 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370120 Loss1: 0.035866 Loss2: 1.334254
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446184 Loss1: 0.100146 Loss2: 1.346039
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457889 Loss1: 0.115315 Loss2: 1.342574
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462275 Loss1: 0.117268 Loss2: 1.345007
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409370 Loss1: 0.065878 Loss2: 1.343492
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.569735 Loss1: 0.636132 Loss2: 1.933602
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.784523 Loss1: 0.370247 Loss2: 1.414275
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.633759 Loss1: 0.200786 Loss2: 1.432973
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591396 Loss1: 0.188304 Loss2: 1.403092
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.401794 Loss1: 0.580027 Loss2: 1.821766
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.486905 Loss1: 0.066651 Loss2: 1.420254
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.647681 Loss1: 0.322922 Loss2: 1.324759
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497138 Loss1: 0.115269 Loss2: 1.381869
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.532100 Loss1: 0.188970 Loss2: 1.343131
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.474366 Loss1: 0.154095 Loss2: 1.320271
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483132 Loss1: 0.096161 Loss2: 1.386971
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.432288 Loss1: 0.126205 Loss2: 1.306083
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433171 Loss1: 0.052020 Loss2: 1.381151
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.379307 Loss1: 0.082214 Loss2: 1.297093
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419036 Loss1: 0.044382 Loss2: 1.374654
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414035 Loss1: 0.043413 Loss2: 1.370622 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.382226 Loss1: 0.095670 Loss2: 1.286557 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.331967 Loss1: 0.480332 Loss2: 1.851636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624347 Loss1: 0.238840 Loss2: 1.385507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.624695 Loss1: 0.249629 Loss2: 1.375066
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.231919 Loss1: 0.411521 Loss2: 1.820398
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.661229 Loss1: 0.306313 Loss2: 1.354915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.594611 Loss1: 0.209411 Loss2: 1.385200 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605436 Loss1: 0.252114 Loss2: 1.353322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.488113 Loss1: 0.130669 Loss2: 1.357444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366457 Loss1: 0.048994 Loss2: 1.317463 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420419 Loss1: 0.079633 Loss2: 1.340786 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416814 Loss1: 0.084267 Loss2: 1.332548 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982537
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.715627 Loss1: 0.344141 Loss2: 1.371486 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524059 Loss1: 0.159377 Loss2: 1.364682 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507321 Loss1: 0.151230 Loss2: 1.356091
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.446260 Loss1: 0.589455 Loss2: 1.856805
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.828979 Loss1: 0.452285 Loss2: 1.376694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636956 Loss1: 0.217321 Loss2: 1.419635 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530316 Loss1: 0.168354 Loss2: 1.361962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.470052 Loss1: 0.102670 Loss2: 1.367381 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446580 Loss1: 0.088095 Loss2: 1.358485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415128 Loss1: 0.071875 Loss2: 1.343253 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394355 Loss1: 0.056402 Loss2: 1.337952 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.600355 Loss1: 0.250565 Loss2: 1.349790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535670 Loss1: 0.166750 Loss2: 1.368920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525373 Loss1: 0.182714 Loss2: 1.342659
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.316597 Loss1: 0.474053 Loss2: 1.842544
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630057 Loss1: 0.282923 Loss2: 1.347133 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.555268 Loss1: 0.188050 Loss2: 1.367218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.515501 Loss1: 0.161301 Loss2: 1.354200 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484603 Loss1: 0.135483 Loss2: 1.349119 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406836 Loss1: 0.066148 Loss2: 1.340689
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427502 Loss1: 0.082156 Loss2: 1.345345
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436869 Loss1: 0.098469 Loss2: 1.338400
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457833 Loss1: 0.114412 Loss2: 1.343421
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408429 Loss1: 0.067402 Loss2: 1.341027
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406146 Loss1: 0.069788 Loss2: 1.336359
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.364329 Loss1: 0.515988 Loss2: 1.848341
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.734394 Loss1: 0.368053 Loss2: 1.366341
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.597546 Loss1: 0.189897 Loss2: 1.407649
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551292 Loss1: 0.184843 Loss2: 1.366449
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492074 Loss1: 0.114655 Loss2: 1.377419
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.341031 Loss1: 0.519299 Loss2: 1.821732
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.687991 Loss1: 0.316829 Loss2: 1.371162 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 16:27:53,474 | server.py:236 | fit_round 157 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.638557 Loss1: 0.245750 Loss2: 1.392807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.584880 Loss1: 0.211398 Loss2: 1.373482 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563597 Loss1: 0.191226 Loss2: 1.372372 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.534073 Loss1: 0.164899 Loss2: 1.369174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443697 Loss1: 0.083772 Loss2: 1.359925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381638 Loss1: 0.038966 Loss2: 1.342672 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.783935 Loss1: 0.263197 Loss2: 1.520738 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611955 Loss1: 0.156255 Loss2: 1.455700 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.338731 Loss1: 0.539727 Loss2: 1.799003
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.664311 Loss1: 0.209214 Loss2: 1.455097
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.699918 Loss1: 0.353788 Loss2: 1.346131
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.580206 Loss1: 0.131913 Loss2: 1.448293
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.664187 Loss1: 0.271636 Loss2: 1.392551
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.570019 Loss1: 0.125303 Loss2: 1.444716
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.504489 Loss1: 0.158295 Loss2: 1.346194
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.569382 Loss1: 0.119453 Loss2: 1.449929
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.465602 Loss1: 0.132070 Loss2: 1.333532
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.539700 Loss1: 0.099238 Loss2: 1.440462
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458611 Loss1: 0.123728 Loss2: 1.334883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383348 Loss1: 0.070045 Loss2: 1.313302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390992 Loss1: 0.075686 Loss2: 1.315306
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.377231 Loss1: 0.581477 Loss2: 1.795755
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.650971 Loss1: 0.315219 Loss2: 1.335752
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.560998 Loss1: 0.189251 Loss2: 1.371747
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498248 Loss1: 0.167011 Loss2: 1.331237
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.413102 Loss1: 0.081260 Loss2: 1.331842
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.449037 Loss1: 0.594100 Loss2: 1.854937
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.409544 Loss1: 0.092964 Loss2: 1.316580
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.758824 Loss1: 0.394158 Loss2: 1.364666
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.393185 Loss1: 0.083281 Loss2: 1.309904
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642672 Loss1: 0.238530 Loss2: 1.404142
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.388370 Loss1: 0.077080 Loss2: 1.311290
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556090 Loss1: 0.198631 Loss2: 1.357459
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409878 Loss1: 0.094336 Loss2: 1.315543
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495658 Loss1: 0.139682 Loss2: 1.355975
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396671 Loss1: 0.081632 Loss2: 1.315039
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.497956 Loss1: 0.149863 Loss2: 1.348092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419399 Loss1: 0.071788 Loss2: 1.347610 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 16:27:53,474][flwr][DEBUG] - fit_round 157 received 50 results and 0 failures
-INFO flwr 2023-10-12 16:28:36,026 | server.py:125 | fit progress: (157, 2.243841856051558, {'accuracy': 0.6005}, 362223.804914811)
->> Test accuracy: 0.600500
-[2023-10-12 16:28:36,026][flwr][INFO] - fit progress: (157, 2.243841856051558, {'accuracy': 0.6005}, 362223.804914811)
-DEBUG flwr 2023-10-12 16:28:36,027 | server.py:173 | evaluate_round 157: strategy sampled 50 clients (out of 50)
-[2023-10-12 16:28:36,027][flwr][DEBUG] - evaluate_round 157: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 16:37:42,171 | server.py:187 | evaluate_round 157 received 50 results and 0 failures
-[2023-10-12 16:37:42,171][flwr][DEBUG] - evaluate_round 157 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 16:37:42,172 | server.py:222 | fit_round 158: strategy sampled 50 clients (out of 50)
-[2023-10-12 16:37:42,172][flwr][DEBUG] - fit_round 158: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.289313 Loss1: 0.473308 Loss2: 1.816005 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.625995 Loss1: 0.219490 Loss2: 1.406505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.401960 Loss1: 0.512164 Loss2: 1.889796
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547245 Loss1: 0.163419 Loss2: 1.383826
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.617099 Loss1: 0.242554 Loss2: 1.374545
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.504250 Loss1: 0.128757 Loss2: 1.375493
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492259 Loss1: 0.117500 Loss2: 1.374759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473413 Loss1: 0.094441 Loss2: 1.378972 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458129 Loss1: 0.095264 Loss2: 1.362866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440250 Loss1: 0.078182 Loss2: 1.362068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.444040 Loss1: 0.083285 Loss2: 1.360755 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.467974 Loss1: 0.110739 Loss2: 1.357235 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.484167 Loss1: 0.545297 Loss2: 1.938870 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.602910 Loss1: 0.186874 Loss2: 1.416035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.634739 Loss1: 0.226521 Loss2: 1.408217
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.240782 Loss1: 0.414895 Loss2: 1.825888
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.636088 Loss1: 0.268572 Loss2: 1.367516 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.586725 Loss1: 0.190504 Loss2: 1.396221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.502655 Loss1: 0.145568 Loss2: 1.357087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.515067 Loss1: 0.137399 Loss2: 1.377668 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.478743 Loss1: 0.100394 Loss2: 1.378350 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368920 Loss1: 0.035926 Loss2: 1.332994 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365448 Loss1: 0.040076 Loss2: 1.325372 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.612780 Loss1: 0.299136 Loss2: 1.313644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.495326 Loss1: 0.179354 Loss2: 1.315973 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498304 Loss1: 0.181616 Loss2: 1.316688
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.438979 Loss1: 0.579218 Loss2: 1.859761
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.697770 Loss1: 0.380467 Loss2: 1.317303 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473305 Loss1: 0.154997 Loss2: 1.318307
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556365 Loss1: 0.200059 Loss2: 1.356306
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.392329 Loss1: 0.081446 Loss2: 1.310883
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.473424 Loss1: 0.142163 Loss2: 1.331261
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.392200 Loss1: 0.087461 Loss2: 1.304739
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.443949 Loss1: 0.129437 Loss2: 1.314512
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.387001 Loss1: 0.071354 Loss2: 1.315647
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393486 Loss1: 0.095707 Loss2: 1.297779
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.357260 Loss1: 0.057628 Loss2: 1.299632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.329277 Loss1: 0.034875 Loss2: 1.294402 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.342800 Loss1: 0.472461 Loss2: 1.870339
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.722260 Loss1: 0.337635 Loss2: 1.384625
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.663201 Loss1: 0.230706 Loss2: 1.432495
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599119 Loss1: 0.198501 Loss2: 1.400619
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.335383 Loss1: 0.490056 Loss2: 1.845327
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.677617 Loss1: 0.307043 Loss2: 1.370574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.685972 Loss1: 0.272489 Loss2: 1.413483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565701 Loss1: 0.186052 Loss2: 1.379649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505914 Loss1: 0.141679 Loss2: 1.364235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494817 Loss1: 0.121929 Loss2: 1.372889 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406693 Loss1: 0.059016 Loss2: 1.347677 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410287 Loss1: 0.065523 Loss2: 1.344764 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.296906 Loss1: 0.473852 Loss2: 1.823053
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.665410 Loss1: 0.300652 Loss2: 1.364758
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.557895 Loss1: 0.163285 Loss2: 1.394610
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536837 Loss1: 0.174594 Loss2: 1.362243
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.367057 Loss1: 0.498115 Loss2: 1.868942
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.698252 Loss1: 0.327383 Loss2: 1.370869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.615717 Loss1: 0.192758 Loss2: 1.422959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.610583 Loss1: 0.230955 Loss2: 1.379628 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405854 Loss1: 0.062619 Loss2: 1.343235
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550924 Loss1: 0.170910 Loss2: 1.380014
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408150 Loss1: 0.065909 Loss2: 1.342241
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504007 Loss1: 0.125054 Loss2: 1.378953
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398912 Loss1: 0.060504 Loss2: 1.338408
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.454687 Loss1: 0.094001 Loss2: 1.360686
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435024 Loss1: 0.077852 Loss2: 1.357172
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399182 Loss1: 0.043539 Loss2: 1.355643
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383832 Loss1: 0.037449 Loss2: 1.346383
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.206078 Loss1: 0.437592 Loss2: 1.768485
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.654132 Loss1: 0.321657 Loss2: 1.332475
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.581880 Loss1: 0.215313 Loss2: 1.366567
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.284839 Loss1: 0.446901 Loss2: 1.837938
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484222 Loss1: 0.154917 Loss2: 1.329305
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630589 Loss1: 0.289703 Loss2: 1.340887
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.408789 Loss1: 0.087587 Loss2: 1.321203
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605366 Loss1: 0.231964 Loss2: 1.373402
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450870 Loss1: 0.127950 Loss2: 1.322920
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431331 Loss1: 0.107648 Loss2: 1.323683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404134 Loss1: 0.084444 Loss2: 1.319690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391845 Loss1: 0.079413 Loss2: 1.312431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377492 Loss1: 0.062753 Loss2: 1.314739 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421464 Loss1: 0.095396 Loss2: 1.326068 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.322684 Loss1: 0.535228 Loss2: 1.787456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.600695 Loss1: 0.248783 Loss2: 1.351912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.464393 Loss1: 0.153666 Loss2: 1.310727
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.432257 Loss1: 0.522172 Loss2: 1.910085
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.425322 Loss1: 0.114118 Loss2: 1.311205
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.643770 Loss1: 0.281365 Loss2: 1.362404
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.387699 Loss1: 0.081158 Loss2: 1.306541
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.585398 Loss1: 0.203509 Loss2: 1.381888
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.379054 Loss1: 0.080578 Loss2: 1.298476
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.573254 Loss1: 0.201368 Loss2: 1.371886
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378627 Loss1: 0.080948 Loss2: 1.297679
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518505 Loss1: 0.153760 Loss2: 1.364746
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.332482 Loss1: 0.043060 Loss2: 1.289422
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470313 Loss1: 0.110065 Loss2: 1.360249
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.338832 Loss1: 0.055346 Loss2: 1.283486
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428282 Loss1: 0.077227 Loss2: 1.351055
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434076 Loss1: 0.091385 Loss2: 1.342691
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421246 Loss1: 0.080937 Loss2: 1.340310
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.432849 Loss1: 0.089768 Loss2: 1.343081
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.238357 Loss1: 0.402862 Loss2: 1.835495
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.591244 Loss1: 0.221428 Loss2: 1.369816
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.593524 Loss1: 0.193477 Loss2: 1.400047
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.355356 Loss1: 0.461774 Loss2: 1.893582
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517235 Loss1: 0.154555 Loss2: 1.362681
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516314 Loss1: 0.138815 Loss2: 1.377499 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511488 Loss1: 0.142037 Loss2: 1.369451 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459347 Loss1: 0.093723 Loss2: 1.365625 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436179 Loss1: 0.077413 Loss2: 1.358767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400019 Loss1: 0.052199 Loss2: 1.347820 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452209 Loss1: 0.073915 Loss2: 1.378294 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992647
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429346 Loss1: 0.062879 Loss2: 1.366467 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.472138 Loss1: 0.560654 Loss2: 1.911485
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.797428 Loss1: 0.414718 Loss2: 1.382710
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.679210 Loss1: 0.257884 Loss2: 1.421325
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.613300 Loss1: 0.227759 Loss2: 1.385541
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.276717 Loss1: 0.406362 Loss2: 1.870355
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.679282 Loss1: 0.269300 Loss2: 1.409983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.658359 Loss1: 0.210279 Loss2: 1.448080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619294 Loss1: 0.203888 Loss2: 1.415406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.593938 Loss1: 0.165826 Loss2: 1.428113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.589524 Loss1: 0.167711 Loss2: 1.421813 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518013 Loss1: 0.102304 Loss2: 1.415709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.472987 Loss1: 0.069010 Loss2: 1.403978 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.448532 Loss1: 0.574411 Loss2: 1.874121 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.588841 Loss1: 0.189439 Loss2: 1.399402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.329426 Loss1: 0.480141 Loss2: 1.849286 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.652284 Loss1: 0.298040 Loss2: 1.354244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.589783 Loss1: 0.210253 Loss2: 1.379530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.542244 Loss1: 0.175933 Loss2: 1.366310 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.533401 Loss1: 0.185287 Loss2: 1.348114 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464084 Loss1: 0.115232 Loss2: 1.348852 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412069 Loss1: 0.077377 Loss2: 1.334693 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384609 Loss1: 0.058238 Loss2: 1.326371 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.690692 Loss1: 0.347231 Loss2: 1.343460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491266 Loss1: 0.162793 Loss2: 1.328473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.393876 Loss1: 0.549444 Loss2: 1.844432
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.423141 Loss1: 0.092424 Loss2: 1.330717
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.648415 Loss1: 0.291521 Loss2: 1.356894
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442711 Loss1: 0.119577 Loss2: 1.323134
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.551349 Loss1: 0.169912 Loss2: 1.381437
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.388164 Loss1: 0.072225 Loss2: 1.315939
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.451253 Loss1: 0.105200 Loss2: 1.346053
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.370448 Loss1: 0.056396 Loss2: 1.314052
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.433682 Loss1: 0.088485 Loss2: 1.345197
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.346389 Loss1: 0.041740 Loss2: 1.304649
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.428430 Loss1: 0.085906 Loss2: 1.342524
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.324457 Loss1: 0.029687 Loss2: 1.294770
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414517 Loss1: 0.080712 Loss2: 1.333805 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408545 Loss1: 0.077862 Loss2: 1.330682 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.698460 Loss1: 0.309822 Loss2: 1.388639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.515049 Loss1: 0.140551 Loss2: 1.374498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.293404 Loss1: 0.469770 Loss2: 1.823635
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499536 Loss1: 0.129075 Loss2: 1.370460
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.660324 Loss1: 0.334573 Loss2: 1.325751
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.492972 Loss1: 0.122758 Loss2: 1.370214
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.584622 Loss1: 0.225060 Loss2: 1.359562
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490310 Loss1: 0.121021 Loss2: 1.369289
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.517482 Loss1: 0.181988 Loss2: 1.335494
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.487960 Loss1: 0.119775 Loss2: 1.368185
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.471269 Loss1: 0.149791 Loss2: 1.321477
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437181 Loss1: 0.068923 Loss2: 1.368258
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.418410 Loss1: 0.097977 Loss2: 1.320433
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417300 Loss1: 0.057057 Loss2: 1.360243
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396754 Loss1: 0.088085 Loss2: 1.308669 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.340444 Loss1: 0.042995 Loss2: 1.297449 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.696154 Loss1: 0.339986 Loss2: 1.356168 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513271 Loss1: 0.158547 Loss2: 1.354724 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506964 Loss1: 0.147206 Loss2: 1.359759
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.382545 Loss1: 0.565014 Loss2: 1.817532
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.753842 Loss1: 0.416575 Loss2: 1.337268 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564640 Loss1: 0.176456 Loss2: 1.388184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.494843 Loss1: 0.164249 Loss2: 1.330594 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.430548 Loss1: 0.101063 Loss2: 1.329484 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.346991 Loss1: 0.021043 Loss2: 1.325948
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421676 Loss1: 0.106684 Loss2: 1.314992
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419704 Loss1: 0.105632 Loss2: 1.314072
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429188 Loss1: 0.107646 Loss2: 1.321542
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449137 Loss1: 0.136350 Loss2: 1.312787
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414146 Loss1: 0.102649 Loss2: 1.311497
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.325990 Loss1: 0.537202 Loss2: 1.788788
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.667683 Loss1: 0.345809 Loss2: 1.321874
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.579771 Loss1: 0.218304 Loss2: 1.361467
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528727 Loss1: 0.208289 Loss2: 1.320437
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.457808 Loss1: 0.137696 Loss2: 1.320112
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.290714 Loss1: 0.444293 Loss2: 1.846421
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.716620 Loss1: 0.346683 Loss2: 1.369937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.646871 Loss1: 0.252399 Loss2: 1.394472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.582768 Loss1: 0.209408 Loss2: 1.373360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.538722 Loss1: 0.158071 Loss2: 1.380652 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423288 Loss1: 0.071871 Loss2: 1.351416 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379713 Loss1: 0.045489 Loss2: 1.334224 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369748 Loss1: 0.044347 Loss2: 1.325401
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.594153 Loss1: 0.710890 Loss2: 1.883263
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.805323 Loss1: 0.406082 Loss2: 1.399241
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.606754 Loss1: 0.214047 Loss2: 1.392707
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562550 Loss1: 0.200180 Loss2: 1.362370
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526792 Loss1: 0.163133 Loss2: 1.363659
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.577988 Loss1: 0.595644 Loss2: 1.982344
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454419 Loss1: 0.096566 Loss2: 1.357853
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.806282 Loss1: 0.384887 Loss2: 1.421395
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460657 Loss1: 0.115640 Loss2: 1.345017
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.733197 Loss1: 0.264018 Loss2: 1.469179
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423841 Loss1: 0.074467 Loss2: 1.349373
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406133 Loss1: 0.069102 Loss2: 1.337031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371985 Loss1: 0.040202 Loss2: 1.331784 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.543053 Loss1: 0.134800 Loss2: 1.408253 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454243 Loss1: 0.057859 Loss2: 1.396384 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440600 Loss1: 0.050921 Loss2: 1.389679
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.408853 Loss1: 0.540038 Loss2: 1.868815
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.712993 Loss1: 0.322889 Loss2: 1.390104
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.640444 Loss1: 0.208781 Loss2: 1.431663
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.637425 Loss1: 0.242213 Loss2: 1.395212
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519977 Loss1: 0.119841 Loss2: 1.400136
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.604223 Loss1: 0.655570 Loss2: 1.948653
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.763719 Loss1: 0.375009 Loss2: 1.388710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622627 Loss1: 0.224732 Loss2: 1.397894
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490572 Loss1: 0.117036 Loss2: 1.373535
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555304 Loss1: 0.143626 Loss2: 1.411678
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.494467 Loss1: 0.116189 Loss2: 1.378278
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433274 Loss1: 0.058080 Loss2: 1.375194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436014 Loss1: 0.068868 Loss2: 1.367146 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419673 Loss1: 0.069564 Loss2: 1.350110 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.319089 Loss1: 0.417770 Loss2: 1.901319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.708252 Loss1: 0.227674 Loss2: 1.480578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.205312 Loss1: 0.413250 Loss2: 1.792063
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.649715 Loss1: 0.228260 Loss2: 1.421455
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.694257 Loss1: 0.356313 Loss2: 1.337944
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.538166 Loss1: 0.113693 Loss2: 1.424473
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.632924 Loss1: 0.238030 Loss2: 1.394894
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497070 Loss1: 0.078890 Loss2: 1.418180
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492731 Loss1: 0.153672 Loss2: 1.339059
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517968 Loss1: 0.109919 Loss2: 1.408049
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.448025 Loss1: 0.106614 Loss2: 1.341411
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495528 Loss1: 0.088209 Loss2: 1.407318
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450901 Loss1: 0.122629 Loss2: 1.328272
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.479450 Loss1: 0.068352 Loss2: 1.411098
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461678 Loss1: 0.130016 Loss2: 1.331662
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469822 Loss1: 0.064049 Loss2: 1.405774
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412230 Loss1: 0.078624 Loss2: 1.333606 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.400398 Loss1: 0.525478 Loss2: 1.874920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.653620 Loss1: 0.230666 Loss2: 1.422954 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549838 Loss1: 0.150138 Loss2: 1.399700
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.229828 Loss1: 0.452161 Loss2: 1.777667
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547150 Loss1: 0.150544 Loss2: 1.396606
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.529645 Loss1: 0.228402 Loss2: 1.301244
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.544747 Loss1: 0.149315 Loss2: 1.395431
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.472845 Loss1: 0.166265 Loss2: 1.306580
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.518271 Loss1: 0.128031 Loss2: 1.390240
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.423095 Loss1: 0.132116 Loss2: 1.290979
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484443 Loss1: 0.098026 Loss2: 1.386417
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.472901 Loss1: 0.173421 Loss2: 1.299480
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456007 Loss1: 0.075307 Loss2: 1.380700
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421608 Loss1: 0.122213 Loss2: 1.299396
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437511 Loss1: 0.065276 Loss2: 1.372235
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392495 Loss1: 0.110772 Loss2: 1.281723
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.358855 Loss1: 0.074952 Loss2: 1.283903
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371708 Loss1: 0.092212 Loss2: 1.279496
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375519 Loss1: 0.095797 Loss2: 1.279722
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.305426 Loss1: 0.454398 Loss2: 1.851028
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.692037 Loss1: 0.336189 Loss2: 1.355848
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.584912 Loss1: 0.186131 Loss2: 1.398781
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547000 Loss1: 0.178536 Loss2: 1.368464
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.642354 Loss1: 0.598826 Loss2: 2.043528
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.792618 Loss1: 0.398041 Loss2: 1.394577
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.489014 Loss1: 0.131507 Loss2: 1.357507
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.796098 Loss1: 0.329906 Loss2: 1.466192
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410733 Loss1: 0.066563 Loss2: 1.344169 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401634 Loss1: 0.058779 Loss2: 1.342856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.568199 Loss1: 0.148566 Loss2: 1.419633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373815 Loss1: 0.043241 Loss2: 1.330575 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444297 Loss1: 0.057041 Loss2: 1.387256 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997396
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.353667 Loss1: 0.522991 Loss2: 1.830677
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.700031 Loss1: 0.362808 Loss2: 1.337223
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.691859 Loss1: 0.307290 Loss2: 1.384569
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.595329 Loss1: 0.245331 Loss2: 1.349998
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.424833 Loss1: 0.541776 Loss2: 1.883057
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.776960 Loss1: 0.391434 Loss2: 1.385526 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670437 Loss1: 0.222660 Loss2: 1.447777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565749 Loss1: 0.169623 Loss2: 1.396125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.568480 Loss1: 0.170544 Loss2: 1.397936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510122 Loss1: 0.114586 Loss2: 1.395536 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403167 Loss1: 0.087576 Loss2: 1.315590
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455823 Loss1: 0.075132 Loss2: 1.380691
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438035 Loss1: 0.065312 Loss2: 1.372723
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425102 Loss1: 0.059787 Loss2: 1.365315
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417781 Loss1: 0.051119 Loss2: 1.366662
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.303588 Loss1: 0.465375 Loss2: 1.838213
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.635988 Loss1: 0.272685 Loss2: 1.363303
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.562533 Loss1: 0.166048 Loss2: 1.396485
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559492 Loss1: 0.196385 Loss2: 1.363108
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.333375 Loss1: 0.498986 Loss2: 1.834389
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574891 Loss1: 0.204329 Loss2: 1.370563
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.660299 Loss1: 0.313226 Loss2: 1.347073
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.545083 Loss1: 0.174708 Loss2: 1.370376
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.583930 Loss1: 0.203784 Loss2: 1.380145
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509974 Loss1: 0.158957 Loss2: 1.351017
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469844 Loss1: 0.104213 Loss2: 1.365631
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483262 Loss1: 0.130192 Loss2: 1.353070
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458634 Loss1: 0.101635 Loss2: 1.356999
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492468 Loss1: 0.139829 Loss2: 1.352639
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448882 Loss1: 0.092701 Loss2: 1.356181
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423447 Loss1: 0.081121 Loss2: 1.342326
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422916 Loss1: 0.070761 Loss2: 1.352155
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366385 Loss1: 0.034919 Loss2: 1.331466 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.265704 Loss1: 0.464823 Loss2: 1.800882 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.520908 Loss1: 0.191717 Loss2: 1.329192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.503493 Loss1: 0.193359 Loss2: 1.310134
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.281962 Loss1: 0.433201 Loss2: 1.848761
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.578746 Loss1: 0.243392 Loss2: 1.335354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.483765 Loss1: 0.139809 Loss2: 1.343956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.453261 Loss1: 0.105624 Loss2: 1.347637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.413435 Loss1: 0.074276 Loss2: 1.339160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.388539 Loss1: 0.057670 Loss2: 1.330869 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.367027 Loss1: 0.043048 Loss2: 1.323979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.343592 Loss1: 0.029272 Loss2: 1.314320 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.543091 Loss1: 0.623194 Loss2: 1.919898 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647853 Loss1: 0.277146 Loss2: 1.370707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.465820 Loss1: 0.129455 Loss2: 1.336365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438632 Loss1: 0.099517 Loss2: 1.339116 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.400764 Loss1: 0.068410 Loss2: 1.332354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391223 Loss1: 0.075364 Loss2: 1.315859 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385136 Loss1: 0.066515 Loss2: 1.318621 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364703 Loss1: 0.051202 Loss2: 1.313502
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.464845 Loss1: 0.114020 Loss2: 1.350825
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-DEBUG flwr 2023-10-12 17:06:27,327 | server.py:236 | fit_round 158 received 50 results and 0 failures
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464382 Loss1: 0.128160 Loss2: 1.336221
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467214 Loss1: 0.129765 Loss2: 1.337449
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420980 Loss1: 0.088516 Loss2: 1.332464
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385587 Loss1: 0.059527 Loss2: 1.326059
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380547 Loss1: 0.064769 Loss2: 1.315777
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.447858 Loss1: 0.545547 Loss2: 1.902310
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.872249 Loss1: 0.454459 Loss2: 1.417790
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.714792 Loss1: 0.261015 Loss2: 1.453777
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610680 Loss1: 0.192396 Loss2: 1.418284
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.537130 Loss1: 0.132799 Loss2: 1.404331
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.383612 Loss1: 0.498149 Loss2: 1.885463
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516899 Loss1: 0.110623 Loss2: 1.406276
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.693379 Loss1: 0.286672 Loss2: 1.406707
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495624 Loss1: 0.101242 Loss2: 1.394382
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.620217 Loss1: 0.179168 Loss2: 1.441049
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.463185 Loss1: 0.074730 Loss2: 1.388455
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538181 Loss1: 0.136297 Loss2: 1.401884
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.446815 Loss1: 0.065080 Loss2: 1.381735
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469747 Loss1: 0.064657 Loss2: 1.405090
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.475277 Loss1: 0.093902 Loss2: 1.381375
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) 
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437185 Loss1: 0.048010 Loss2: 1.389175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428514 Loss1: 0.054767 Loss2: 1.373748 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415649 Loss1: 0.039492 Loss2: 1.376157
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.391259 Loss1: 0.470147 Loss2: 1.921112
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.688863 Loss1: 0.270819 Loss2: 1.418043
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568983 Loss1: 0.134965 Loss2: 1.434018
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572788 Loss1: 0.159071 Loss2: 1.413717
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.580992 Loss1: 0.168090 Loss2: 1.412902
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.374695 Loss1: 0.490235 Loss2: 1.884460
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.550299 Loss1: 0.132993 Loss2: 1.417306
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.672157 Loss1: 0.290251 Loss2: 1.381906
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.537309 Loss1: 0.119561 Loss2: 1.417747
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.641220 Loss1: 0.216047 Loss2: 1.425173
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.507608 Loss1: 0.094788 Loss2: 1.412819
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.632010 Loss1: 0.236440 Loss2: 1.395570
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.531300 Loss1: 0.125340 Loss2: 1.405960
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.634619 Loss1: 0.224207 Loss2: 1.410412
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.508198 Loss1: 0.098173 Loss2: 1.410025
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.499942 Loss1: 0.110377 Loss2: 1.389566 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.437039 Loss1: 0.061561 Loss2: 1.375478 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 17:06:27,327][flwr][DEBUG] - fit_round 158 received 50 results and 0 failures
-INFO flwr 2023-10-12 17:07:09,409 | server.py:125 | fit progress: (158, 2.2473408779778037, {'accuracy': 0.6015}, 364537.187364186)
->> Test accuracy: 0.601500
-[2023-10-12 17:07:09,409][flwr][INFO] - fit progress: (158, 2.2473408779778037, {'accuracy': 0.6015}, 364537.187364186)
-DEBUG flwr 2023-10-12 17:07:09,409 | server.py:173 | evaluate_round 158: strategy sampled 50 clients (out of 50)
-[2023-10-12 17:07:09,409][flwr][DEBUG] - evaluate_round 158: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 17:16:14,041 | server.py:187 | evaluate_round 158 received 50 results and 0 failures
-[2023-10-12 17:16:14,041][flwr][DEBUG] - evaluate_round 158 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 17:16:14,042 | server.py:222 | fit_round 159: strategy sampled 50 clients (out of 50)
-[2023-10-12 17:16:14,042][flwr][DEBUG] - fit_round 159: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.338345 Loss1: 0.431808 Loss2: 1.906537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615430 Loss1: 0.208778 Loss2: 1.406652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.566468 Loss1: 0.170348 Loss2: 1.396120
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.422885 Loss1: 0.613199 Loss2: 1.809686
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.495597 Loss1: 0.108133 Loss2: 1.387464
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.694463 Loss1: 0.366233 Loss2: 1.328230
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459333 Loss1: 0.083712 Loss2: 1.375621
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565836 Loss1: 0.204107 Loss2: 1.361729
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445166 Loss1: 0.076852 Loss2: 1.368314
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.582493 Loss1: 0.242866 Loss2: 1.339628
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420403 Loss1: 0.054329 Loss2: 1.366075
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500630 Loss1: 0.156159 Loss2: 1.344472
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.392766 Loss1: 0.037548 Loss2: 1.355218
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.424821 Loss1: 0.100371 Loss2: 1.324450
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393327 Loss1: 0.038600 Loss2: 1.354728
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.402159 Loss1: 0.080396 Loss2: 1.321763
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.395900 Loss1: 0.080461 Loss2: 1.315440
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387709 Loss1: 0.077347 Loss2: 1.310362
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379332 Loss1: 0.069409 Loss2: 1.309923
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.332687 Loss1: 0.502521 Loss2: 1.830166
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.669016 Loss1: 0.326892 Loss2: 1.342124
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.576848 Loss1: 0.185929 Loss2: 1.390919
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601732 Loss1: 0.258625 Loss2: 1.343107
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.380224 Loss1: 0.516601 Loss2: 1.863623
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.753170 Loss1: 0.369216 Loss2: 1.383953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578797 Loss1: 0.158436 Loss2: 1.420361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551533 Loss1: 0.180168 Loss2: 1.371365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505588 Loss1: 0.132605 Loss2: 1.372983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481688 Loss1: 0.112920 Loss2: 1.368768 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404106 Loss1: 0.068808 Loss2: 1.335298
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484347 Loss1: 0.116738 Loss2: 1.367609
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434749 Loss1: 0.078593 Loss2: 1.356156
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431562 Loss1: 0.074959 Loss2: 1.356603
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430426 Loss1: 0.081785 Loss2: 1.348641
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.187999 Loss1: 0.438124 Loss2: 1.749875
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.586493 Loss1: 0.284831 Loss2: 1.301662
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.522662 Loss1: 0.178625 Loss2: 1.344038
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.411552 Loss1: 0.108803 Loss2: 1.302749
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.376155 Loss1: 0.518389 Loss2: 1.857767
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.682319 Loss1: 0.319529 Loss2: 1.362790
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.428622 Loss1: 0.128730 Loss2: 1.299892
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.610146 Loss1: 0.223114 Loss2: 1.387032
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.386909 Loss1: 0.083478 Loss2: 1.303431
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.516868 Loss1: 0.152963 Loss2: 1.363905
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.356142 Loss1: 0.064078 Loss2: 1.292064
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.468671 Loss1: 0.108360 Loss2: 1.360311
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.358277 Loss1: 0.070119 Loss2: 1.288158
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.334167 Loss1: 0.052812 Loss2: 1.281355 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.344567 Loss1: 0.065110 Loss2: 1.279458 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396945 Loss1: 0.062224 Loss2: 1.334721 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.329177 Loss1: 0.455096 Loss2: 1.874080 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624186 Loss1: 0.229603 Loss2: 1.394582 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551739 Loss1: 0.171230 Loss2: 1.380509
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.391312 Loss1: 0.550710 Loss2: 1.840603
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.740785 Loss1: 0.376574 Loss2: 1.364211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605221 Loss1: 0.213878 Loss2: 1.391343 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535667 Loss1: 0.179565 Loss2: 1.356102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508986 Loss1: 0.154079 Loss2: 1.354907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477140 Loss1: 0.123356 Loss2: 1.353784 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368279 Loss1: 0.030473 Loss2: 1.337806
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425120 Loss1: 0.082968 Loss2: 1.342152
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.373576 Loss1: 0.039078 Loss2: 1.334498
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.353637 Loss1: 0.031410 Loss2: 1.322227
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.339158 Loss1: 0.023115 Loss2: 1.316043
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.506381 Loss1: 0.561230 Loss2: 1.945151
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.776967 Loss1: 0.338273 Loss2: 1.438694
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.683765 Loss1: 0.206679 Loss2: 1.477086
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582737 Loss1: 0.148624 Loss2: 1.434113
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.303763 Loss1: 0.529036 Loss2: 1.774727
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.628209 Loss1: 0.296911 Loss2: 1.331299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.493194 Loss1: 0.147260 Loss2: 1.345933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.437702 Loss1: 0.115465 Loss2: 1.322237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.412648 Loss1: 0.101896 Loss2: 1.310751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.385442 Loss1: 0.074145 Loss2: 1.311297 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.375333 Loss1: 0.072098 Loss2: 1.303236 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.350777 Loss1: 0.050825 Loss2: 1.299952 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.749629 Loss1: 0.363711 Loss2: 1.385919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.592796 Loss1: 0.202941 Loss2: 1.389855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.325188 Loss1: 0.496132 Loss2: 1.829056
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.548224 Loss1: 0.161939 Loss2: 1.386285
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.538649 Loss1: 0.154680 Loss2: 1.383969 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456172 Loss1: 0.085786 Loss2: 1.370386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420872 Loss1: 0.051902 Loss2: 1.368970 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414640 Loss1: 0.056380 Loss2: 1.358260 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404036 Loss1: 0.044957 Loss2: 1.359079 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399315 Loss1: 0.060664 Loss2: 1.338651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378695 Loss1: 0.048922 Loss2: 1.329774 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751971 Loss1: 0.366882 Loss2: 1.385089 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557568 Loss1: 0.174887 Loss2: 1.382681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.567416 Loss1: 0.577685 Loss2: 1.989730
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.477287 Loss1: 0.100727 Loss2: 1.376560
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481799 Loss1: 0.106433 Loss2: 1.375367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466660 Loss1: 0.100200 Loss2: 1.366460 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.408309 Loss1: 0.054615 Loss2: 1.353693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424585 Loss1: 0.074666 Loss2: 1.349919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412968 Loss1: 0.071560 Loss2: 1.341408 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400939 Loss1: 0.038793 Loss2: 1.362146 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.376251 Loss1: 0.527913 Loss2: 1.848337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563041 Loss1: 0.159932 Loss2: 1.403110 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516151 Loss1: 0.160843 Loss2: 1.355308
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.414660 Loss1: 0.475947 Loss2: 1.938713
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.679534 Loss1: 0.274503 Loss2: 1.405031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.678634 Loss1: 0.249092 Loss2: 1.429542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.614498 Loss1: 0.205820 Loss2: 1.408678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558938 Loss1: 0.154218 Loss2: 1.404720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523840 Loss1: 0.126456 Loss2: 1.397384 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372134 Loss1: 0.039477 Loss2: 1.332657
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533288 Loss1: 0.138290 Loss2: 1.394999
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.530627 Loss1: 0.133508 Loss2: 1.397119
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.538923 Loss1: 0.143949 Loss2: 1.394974
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.507872 Loss1: 0.112605 Loss2: 1.395267
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.448918 Loss1: 0.557641 Loss2: 1.891277
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.746628 Loss1: 0.343068 Loss2: 1.403561
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.659692 Loss1: 0.213552 Loss2: 1.446140
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572468 Loss1: 0.164561 Loss2: 1.407906
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.216752 Loss1: 0.455358 Loss2: 1.761394
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.622730 Loss1: 0.325332 Loss2: 1.297397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.527513 Loss1: 0.201057 Loss2: 1.326456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.409532 Loss1: 0.115712 Loss2: 1.293820 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.383372 Loss1: 0.093316 Loss2: 1.290056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.386994 Loss1: 0.097455 Loss2: 1.289539 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427382 Loss1: 0.055129 Loss2: 1.372253
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.379394 Loss1: 0.092662 Loss2: 1.286732
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.354831 Loss1: 0.071598 Loss2: 1.283232
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.331767 Loss1: 0.057936 Loss2: 1.273831
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.317538 Loss1: 0.048176 Loss2: 1.269363
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.293906 Loss1: 0.469776 Loss2: 1.824131
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.679346 Loss1: 0.335011 Loss2: 1.344335
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.576712 Loss1: 0.201950 Loss2: 1.374762
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484057 Loss1: 0.141254 Loss2: 1.342802
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.177043 Loss1: 0.398496 Loss2: 1.778547
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.701070 Loss1: 0.351502 Loss2: 1.349568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.648052 Loss1: 0.260420 Loss2: 1.387632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534837 Loss1: 0.185394 Loss2: 1.349443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.466195 Loss1: 0.125510 Loss2: 1.340684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446860 Loss1: 0.108947 Loss2: 1.337913 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400124 Loss1: 0.067341 Loss2: 1.332783 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383075 Loss1: 0.056277 Loss2: 1.326798 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.642279 Loss1: 0.240223 Loss2: 1.402055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533745 Loss1: 0.133810 Loss2: 1.399934 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.504998 Loss1: 0.108961 Loss2: 1.396037
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.318210 Loss1: 0.508480 Loss2: 1.809730
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.495239 Loss1: 0.102664 Loss2: 1.392575
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.672346 Loss1: 0.325301 Loss2: 1.347044
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441283 Loss1: 0.060015 Loss2: 1.381268
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.582981 Loss1: 0.204689 Loss2: 1.378292
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427177 Loss1: 0.053982 Loss2: 1.373195
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487301 Loss1: 0.149501 Loss2: 1.337800
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.458781 Loss1: 0.122640 Loss2: 1.336140 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415089 Loss1: 0.043018 Loss2: 1.372072
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.462029 Loss1: 0.122677 Loss2: 1.339352
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467758 Loss1: 0.128851 Loss2: 1.338907
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445422 Loss1: 0.115618 Loss2: 1.329804
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400769 Loss1: 0.073567 Loss2: 1.327202
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379027 Loss1: 0.059642 Loss2: 1.319384
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.317686 Loss1: 0.463774 Loss2: 1.853912
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.715240 Loss1: 0.364641 Loss2: 1.350599
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618549 Loss1: 0.208678 Loss2: 1.409870
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516815 Loss1: 0.160793 Loss2: 1.356022
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.459115 Loss1: 0.107562 Loss2: 1.351553
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.405501 Loss1: 0.577430 Loss2: 1.828071
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.731001 Loss1: 0.367471 Loss2: 1.363531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.618378 Loss1: 0.220890 Loss2: 1.397487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.541795 Loss1: 0.182767 Loss2: 1.359028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.552551 Loss1: 0.196727 Loss2: 1.355825 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494956 Loss1: 0.138998 Loss2: 1.355958 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.478996 Loss1: 0.135554 Loss2: 1.343442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446258 Loss1: 0.100606 Loss2: 1.345653 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.628667 Loss1: 0.265997 Loss2: 1.362670 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581656 Loss1: 0.229117 Loss2: 1.352539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.590714 Loss1: 0.231546 Loss2: 1.359167
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.392702 Loss1: 0.585629 Loss2: 1.807073
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.652474 Loss1: 0.324837 Loss2: 1.327637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.561609 Loss1: 0.200111 Loss2: 1.361498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.488025 Loss1: 0.163003 Loss2: 1.325022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.461508 Loss1: 0.138408 Loss2: 1.323101 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.434215 Loss1: 0.113005 Loss2: 1.321209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.378004 Loss1: 0.062782 Loss2: 1.315222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376075 Loss1: 0.074410 Loss2: 1.301665 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.663423 Loss1: 0.331075 Loss2: 1.332348 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556082 Loss1: 0.222166 Loss2: 1.333915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480287 Loss1: 0.144456 Loss2: 1.335831
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.251893 Loss1: 0.414576 Loss2: 1.837317
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.629165 Loss1: 0.290376 Loss2: 1.338789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636422 Loss1: 0.249556 Loss2: 1.386866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.527484 Loss1: 0.181195 Loss2: 1.346288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501555 Loss1: 0.160421 Loss2: 1.341134 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371757 Loss1: 0.048672 Loss2: 1.323085
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461757 Loss1: 0.119996 Loss2: 1.341761
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455783 Loss1: 0.125444 Loss2: 1.330339
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408221 Loss1: 0.077267 Loss2: 1.330954
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384735 Loss1: 0.063676 Loss2: 1.321059
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385359 Loss1: 0.064016 Loss2: 1.321343
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.497352 Loss1: 0.650302 Loss2: 1.847050
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.700658 Loss1: 0.371181 Loss2: 1.329478
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.548815 Loss1: 0.193189 Loss2: 1.355627
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.468865 Loss1: 0.148115 Loss2: 1.320749
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.421123 Loss1: 0.110590 Loss2: 1.310533
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.319991 Loss1: 0.494727 Loss2: 1.825264
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.419132 Loss1: 0.111324 Loss2: 1.307808
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.401178 Loss1: 0.085580 Loss2: 1.315598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629650 Loss1: 0.221192 Loss2: 1.408458
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.397220 Loss1: 0.087396 Loss2: 1.309824
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398918 Loss1: 0.095404 Loss2: 1.303515
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611768 Loss1: 0.238201 Loss2: 1.373567
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.352465 Loss1: 0.050813 Loss2: 1.301652
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.534314 Loss1: 0.158669 Loss2: 1.375645
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478623 Loss1: 0.113586 Loss2: 1.365037
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446205 Loss1: 0.090106 Loss2: 1.356099
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419276 Loss1: 0.069635 Loss2: 1.349641
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418572 Loss1: 0.075009 Loss2: 1.343563
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.530367 Loss1: 0.557933 Loss2: 1.972435
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430829 Loss1: 0.085362 Loss2: 1.345467
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.686635 Loss1: 0.261183 Loss2: 1.425452
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.731713 Loss1: 0.291417 Loss2: 1.440296
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623849 Loss1: 0.179758 Loss2: 1.444091
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531661 Loss1: 0.114709 Loss2: 1.416951
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516833 Loss1: 0.102831 Loss2: 1.414003
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524882 Loss1: 0.113954 Loss2: 1.410928
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.483845 Loss1: 0.559390 Loss2: 1.924455
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.482080 Loss1: 0.083112 Loss2: 1.398968
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.693073 Loss1: 0.313456 Loss2: 1.379617
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653823 Loss1: 0.266230 Loss2: 1.387593
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470628 Loss1: 0.067535 Loss2: 1.403093
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.618094 Loss1: 0.205815 Loss2: 1.412279
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440665 Loss1: 0.041716 Loss2: 1.398949
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495007 Loss1: 0.116506 Loss2: 1.378501 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433830 Loss1: 0.073113 Loss2: 1.360717 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.494628 Loss1: 0.567442 Loss2: 1.927186 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975962
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.649211 Loss1: 0.294180 Loss2: 1.355031 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555141 Loss1: 0.204109 Loss2: 1.351032 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.551356 Loss1: 0.204269 Loss2: 1.347086 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.491586 Loss1: 0.145838 Loss2: 1.345748
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.189580 Loss1: 0.391967 Loss2: 1.797612
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.446474 Loss1: 0.119723 Loss2: 1.326750
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.692248 Loss1: 0.331160 Loss2: 1.361087
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.581124 Loss1: 0.198555 Loss2: 1.382568 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.471358 Loss1: 0.127988 Loss2: 1.343370 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.384347 Loss1: 0.478347 Loss2: 1.906000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.716112 Loss1: 0.319792 Loss2: 1.396321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.629828 Loss1: 0.205024 Loss2: 1.424803 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.579669 Loss1: 0.176691 Loss2: 1.402978 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991728
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517089 Loss1: 0.129645 Loss2: 1.387443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456046 Loss1: 0.072797 Loss2: 1.383249 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449241 Loss1: 0.072387 Loss2: 1.376854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427332 Loss1: 0.059734 Loss2: 1.367598 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.525929 Loss1: 0.175165 Loss2: 1.350764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.434635 Loss1: 0.115369 Loss2: 1.319265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427306 Loss1: 0.108064 Loss2: 1.319241
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.286098 Loss1: 0.449780 Loss2: 1.836319
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.609428 Loss1: 0.265092 Loss2: 1.344335 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.571375 Loss1: 0.203690 Loss2: 1.367685 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.461405 Loss1: 0.112870 Loss2: 1.348535 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354787 Loss1: 0.059403 Loss2: 1.295385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448558 Loss1: 0.112798 Loss2: 1.335760
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444773 Loss1: 0.103698 Loss2: 1.341076
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416824 Loss1: 0.081439 Loss2: 1.335385
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.407881 Loss1: 0.076486 Loss2: 1.331395
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378431 Loss1: 0.056935 Loss2: 1.321496
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.378564 Loss1: 0.517587 Loss2: 1.860977
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355884 Loss1: 0.038799 Loss2: 1.317085
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.579043 Loss1: 0.209311 Loss2: 1.369731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469435 Loss1: 0.134969 Loss2: 1.334466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423324 Loss1: 0.089838 Loss2: 1.333487
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.242535 Loss1: 0.402432 Loss2: 1.840103
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622855 Loss1: 0.245803 Loss2: 1.377052 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563301 Loss1: 0.166824 Loss2: 1.396477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.493646 Loss1: 0.123995 Loss2: 1.369651 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.481811 Loss1: 0.123388 Loss2: 1.358423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490852 Loss1: 0.114230 Loss2: 1.376622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419757 Loss1: 0.055156 Loss2: 1.364601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.666199 Loss1: 0.315963 Loss2: 1.350235 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594069 Loss1: 0.237005 Loss2: 1.357064 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511355 Loss1: 0.159762 Loss2: 1.351593 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464062 Loss1: 0.117180 Loss2: 1.346882
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.469856 Loss1: 0.549379 Loss2: 1.920478
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.444282 Loss1: 0.100396 Loss2: 1.343886
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.685283 Loss1: 0.274592 Loss2: 1.410691
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422769 Loss1: 0.085728 Loss2: 1.337041
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609334 Loss1: 0.191587 Loss2: 1.417747
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421236 Loss1: 0.085317 Loss2: 1.335919
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.568097 Loss1: 0.168268 Loss2: 1.399828
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.530610 Loss1: 0.131704 Loss2: 1.398906
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.531182 Loss1: 0.137247 Loss2: 1.393935
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456293 Loss1: 0.065374 Loss2: 1.390919
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425774 Loss1: 0.042316 Loss2: 1.383458
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.442321 Loss1: 0.574322 Loss2: 1.867999
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422499 Loss1: 0.046721 Loss2: 1.375778
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.699251 Loss1: 0.322317 Loss2: 1.376934
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413207 Loss1: 0.044991 Loss2: 1.368216
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530218 Loss1: 0.163862 Loss2: 1.366355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479568 Loss1: 0.116548 Loss2: 1.363020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455068 Loss1: 0.096689 Loss2: 1.358378
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.147074 Loss1: 0.395776 Loss2: 1.751298
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.558658 Loss1: 0.251606 Loss2: 1.307052 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.543410 Loss1: 0.196273 Loss2: 1.347137 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.452232 Loss1: 0.134568 Loss2: 1.317664 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462858 Loss1: 0.144459 Loss2: 1.318400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467440 Loss1: 0.156432 Loss2: 1.311007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438965 Loss1: 0.116042 Loss2: 1.322922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426613 Loss1: 0.112665 Loss2: 1.313948 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970703
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516827 Loss1: 0.151785 Loss2: 1.365042 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459272 Loss1: 0.099340 Loss2: 1.359932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432728 Loss1: 0.078481 Loss2: 1.354248
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.372664 Loss1: 0.516868 Loss2: 1.855796
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.706056 Loss1: 0.336008 Loss2: 1.370049 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.601282 Loss1: 0.198272 Loss2: 1.403010 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.485913 Loss1: 0.123025 Loss2: 1.362888 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440289 Loss1: 0.090099 Loss2: 1.350189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423269 Loss1: 0.073881 Loss2: 1.349388 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386281 Loss1: 0.043143 Loss2: 1.343138 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380520 Loss1: 0.044562 Loss2: 1.335958 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.435595 Loss1: 0.101736 Loss2: 1.333859 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.381713 Loss1: 0.065079 Loss2: 1.316634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.378931 Loss1: 0.528532 Loss2: 1.850399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.624038 Loss1: 0.276469 Loss2: 1.347570 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-12 17:44:58,334 | server.py:236 | fit_round 159 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.519548 Loss1: 0.167513 Loss2: 1.352035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506862 Loss1: 0.169565 Loss2: 1.337297 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445479 Loss1: 0.099166 Loss2: 1.346313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404108 Loss1: 0.070062 Loss2: 1.334045 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397966 Loss1: 0.070064 Loss2: 1.327901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392897 Loss1: 0.066137 Loss2: 1.326759 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460217 Loss1: 0.149211 Loss2: 1.311006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.396605 Loss1: 0.078404 Loss2: 1.318201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405627 Loss1: 0.093274 Loss2: 1.312353
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.355811 Loss1: 0.499488 Loss2: 1.856323
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.662534 Loss1: 0.286739 Loss2: 1.375795
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409838 Loss1: 0.103539 Loss2: 1.306299
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591997 Loss1: 0.195270 Loss2: 1.396727
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378700 Loss1: 0.064733 Loss2: 1.313966
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524228 Loss1: 0.153753 Loss2: 1.370475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.453088 Loss1: 0.092921 Loss2: 1.360167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401272 Loss1: 0.046075 Loss2: 1.355197
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.290333 Loss1: 0.469694 Loss2: 1.820639
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.678731 Loss1: 0.319770 Loss2: 1.358961 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.655037 Loss1: 0.237610 Loss2: 1.417427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.452365 Loss1: 0.115736 Loss2: 1.336629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431091 Loss1: 0.101927 Loss2: 1.329164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424182 Loss1: 0.096219 Loss2: 1.327963 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 17:44:58,334][flwr][DEBUG] - fit_round 159 received 50 results and 0 failures
-INFO flwr 2023-10-12 17:45:39,229 | server.py:125 | fit progress: (159, 2.244518861222191, {'accuracy': 0.6021}, 366847.008015419)
->> Test accuracy: 0.602100
-[2023-10-12 17:45:39,229][flwr][INFO] - fit progress: (159, 2.244518861222191, {'accuracy': 0.6021}, 366847.008015419)
-DEBUG flwr 2023-10-12 17:45:39,230 | server.py:173 | evaluate_round 159: strategy sampled 50 clients (out of 50)
-[2023-10-12 17:45:39,230][flwr][DEBUG] - evaluate_round 159: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 17:54:44,009 | server.py:187 | evaluate_round 159 received 50 results and 0 failures
-[2023-10-12 17:54:44,009][flwr][DEBUG] - evaluate_round 159 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 17:54:44,010 | server.py:222 | fit_round 160: strategy sampled 50 clients (out of 50)
-[2023-10-12 17:54:44,010][flwr][DEBUG] - fit_round 160: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.370840 Loss1: 0.523599 Loss2: 1.847242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.708797 Loss1: 0.283447 Loss2: 1.425350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.615136 Loss1: 0.237217 Loss2: 1.377918
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.324577 Loss1: 0.507573 Loss2: 1.817005
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.655248 Loss1: 0.328962 Loss2: 1.326285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604775 Loss1: 0.240796 Loss2: 1.363979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509495 Loss1: 0.178279 Loss2: 1.331216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518528 Loss1: 0.175033 Loss2: 1.343495 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484162 Loss1: 0.140547 Loss2: 1.343614 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.394934 Loss1: 0.048519 Loss2: 1.346415
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406731 Loss1: 0.078185 Loss2: 1.328546
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389557 Loss1: 0.070436 Loss2: 1.319121
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.352441 Loss1: 0.043087 Loss2: 1.309354
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374441 Loss1: 0.070356 Loss2: 1.304085
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.430091 Loss1: 0.570859 Loss2: 1.859232
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.697094 Loss1: 0.330621 Loss2: 1.366472
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.676216 Loss1: 0.266857 Loss2: 1.409359
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.532337 Loss1: 0.166093 Loss2: 1.366243
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.290266 Loss1: 0.509298 Loss2: 1.780968
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.554798 Loss1: 0.183964 Loss2: 1.370835
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659849 Loss1: 0.350366 Loss2: 1.309483
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526461 Loss1: 0.158544 Loss2: 1.367917
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.566741 Loss1: 0.218117 Loss2: 1.348624
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446136 Loss1: 0.088555 Loss2: 1.357581
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.488071 Loss1: 0.182468 Loss2: 1.305603
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.416412 Loss1: 0.062623 Loss2: 1.353789
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.473136 Loss1: 0.164053 Loss2: 1.309083
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.382625 Loss1: 0.045633 Loss2: 1.336993
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.425970 Loss1: 0.114641 Loss2: 1.311329
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359101 Loss1: 0.030974 Loss2: 1.328127
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433671 Loss1: 0.139570 Loss2: 1.294101
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428584 Loss1: 0.126419 Loss2: 1.302165
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370453 Loss1: 0.072563 Loss2: 1.297889
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348177 Loss1: 0.059218 Loss2: 1.288958
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.363092 Loss1: 0.495452 Loss2: 1.867640
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.633475 Loss1: 0.289768 Loss2: 1.343707
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624071 Loss1: 0.248493 Loss2: 1.375578
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528440 Loss1: 0.179844 Loss2: 1.348596
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.403457 Loss1: 0.541267 Loss2: 1.862190
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.509220 Loss1: 0.165646 Loss2: 1.343574
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.750725 Loss1: 0.377376 Loss2: 1.373349
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455065 Loss1: 0.115347 Loss2: 1.339718
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.654976 Loss1: 0.239710 Loss2: 1.415266
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435334 Loss1: 0.104393 Loss2: 1.330941
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.533238 Loss1: 0.163046 Loss2: 1.370192
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384273 Loss1: 0.054877 Loss2: 1.329397
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520413 Loss1: 0.150865 Loss2: 1.369548
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.366403 Loss1: 0.050097 Loss2: 1.316307
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452290 Loss1: 0.092981 Loss2: 1.359309
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365428 Loss1: 0.055009 Loss2: 1.310419
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.389018 Loss1: 0.041609 Loss2: 1.347410
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382253 Loss1: 0.044578 Loss2: 1.337676
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.382717 Loss1: 0.051371 Loss2: 1.331346
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351640 Loss1: 0.021150 Loss2: 1.330490
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.346285 Loss1: 0.512432 Loss2: 1.833853
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.635782 Loss1: 0.295954 Loss2: 1.339828
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595200 Loss1: 0.230142 Loss2: 1.365058
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.577047 Loss1: 0.222516 Loss2: 1.354532
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.224477 Loss1: 0.435202 Loss2: 1.789275
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.669318 Loss1: 0.321219 Loss2: 1.348099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.638661 Loss1: 0.248459 Loss2: 1.390203 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534977 Loss1: 0.192044 Loss2: 1.342933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.473743 Loss1: 0.123840 Loss2: 1.349903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449094 Loss1: 0.109415 Loss2: 1.339679 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.395075 Loss1: 0.068112 Loss2: 1.326963 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.337292 Loss1: 0.021690 Loss2: 1.315602 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.999023
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.612629 Loss1: 0.249855 Loss2: 1.362773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524003 Loss1: 0.160291 Loss2: 1.363712 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.393338 Loss1: 0.514377 Loss2: 1.878961
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527923 Loss1: 0.155691 Loss2: 1.372232
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.727889 Loss1: 0.338649 Loss2: 1.389240
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473271 Loss1: 0.108803 Loss2: 1.364467
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446924 Loss1: 0.097267 Loss2: 1.349657 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461304 Loss1: 0.115283 Loss2: 1.346021 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450033 Loss1: 0.097938 Loss2: 1.352095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434608 Loss1: 0.085229 Loss2: 1.349380 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971680
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461369 Loss1: 0.084201 Loss2: 1.377168 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427631 Loss1: 0.051219 Loss2: 1.376412 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.326908 Loss1: 0.479521 Loss2: 1.847387
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.676163 Loss1: 0.312814 Loss2: 1.363349
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.641678 Loss1: 0.241267 Loss2: 1.400410
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564416 Loss1: 0.201830 Loss2: 1.362586
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.337674 Loss1: 0.422138 Loss2: 1.915536
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.683660 Loss1: 0.291794 Loss2: 1.391865 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.646058 Loss1: 0.221955 Loss2: 1.424103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.631437 Loss1: 0.229121 Loss2: 1.402316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545641 Loss1: 0.143154 Loss2: 1.402487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521632 Loss1: 0.126267 Loss2: 1.395365 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451429 Loss1: 0.073017 Loss2: 1.378412 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443613 Loss1: 0.077676 Loss2: 1.365936 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.390905 Loss1: 0.523846 Loss2: 1.867060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645236 Loss1: 0.219086 Loss2: 1.426150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564094 Loss1: 0.195974 Loss2: 1.368120
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.259045 Loss1: 0.490663 Loss2: 1.768382
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.668727 Loss1: 0.366514 Loss2: 1.302213 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.524667 Loss1: 0.181987 Loss2: 1.342680 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.533165 Loss1: 0.218655 Loss2: 1.314510 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455720 Loss1: 0.141573 Loss2: 1.314147 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431226 Loss1: 0.116773 Loss2: 1.314452 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384802 Loss1: 0.039560 Loss2: 1.345242
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440802 Loss1: 0.136726 Loss2: 1.304076
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399362 Loss1: 0.088331 Loss2: 1.311031
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.353134 Loss1: 0.057856 Loss2: 1.295278
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.347168 Loss1: 0.049741 Loss2: 1.297427
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.533785 Loss1: 0.599722 Loss2: 1.934063
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.737676 Loss1: 0.354365 Loss2: 1.383310
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644631 Loss1: 0.257215 Loss2: 1.387416
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.567032 Loss1: 0.153278 Loss2: 1.413754
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.363889 Loss1: 0.522743 Loss2: 1.841146
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466777 Loss1: 0.095563 Loss2: 1.371215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431276 Loss1: 0.065292 Loss2: 1.365984 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421747 Loss1: 0.065363 Loss2: 1.356384 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411792 Loss1: 0.062931 Loss2: 1.348861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399007 Loss1: 0.055002 Loss2: 1.344005 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426726 Loss1: 0.088945 Loss2: 1.337782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.360798 Loss1: 0.042862 Loss2: 1.317936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.349316 Loss1: 0.032853 Loss2: 1.316463
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.331084 Loss1: 0.505948 Loss2: 1.825136
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.612568 Loss1: 0.289539 Loss2: 1.323029
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561722 Loss1: 0.185695 Loss2: 1.376026
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.479075 Loss1: 0.151670 Loss2: 1.327404
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475589 Loss1: 0.147770 Loss2: 1.327819
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.420743 Loss1: 0.563153 Loss2: 1.857590
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.824393 Loss1: 0.436037 Loss2: 1.388356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.683363 Loss1: 0.234018 Loss2: 1.449345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551167 Loss1: 0.168609 Loss2: 1.382558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.543789 Loss1: 0.166582 Loss2: 1.377207 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463581 Loss1: 0.087351 Loss2: 1.376230 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424921 Loss1: 0.063175 Loss2: 1.361746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406707 Loss1: 0.059195 Loss2: 1.347512 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.665692 Loss1: 0.297410 Loss2: 1.368282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558852 Loss1: 0.189229 Loss2: 1.369623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.505344 Loss1: 0.139935 Loss2: 1.365409
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.464576 Loss1: 0.597003 Loss2: 1.867573
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473446 Loss1: 0.113800 Loss2: 1.359646
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.714377 Loss1: 0.380712 Loss2: 1.333666
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445684 Loss1: 0.090234 Loss2: 1.355450
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.595047 Loss1: 0.221520 Loss2: 1.373527
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.525355 Loss1: 0.183382 Loss2: 1.341973
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435769 Loss1: 0.090749 Loss2: 1.345020
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.499558 Loss1: 0.168710 Loss2: 1.330848
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409034 Loss1: 0.067019 Loss2: 1.342015
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443223 Loss1: 0.108686 Loss2: 1.334537
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386978 Loss1: 0.049653 Loss2: 1.337326
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.395089 Loss1: 0.075275 Loss2: 1.319815 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357360 Loss1: 0.051504 Loss2: 1.305856 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.326104 Loss1: 0.486158 Loss2: 1.839946
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.676368 Loss1: 0.313181 Loss2: 1.363186
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609684 Loss1: 0.205758 Loss2: 1.403926
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524750 Loss1: 0.170587 Loss2: 1.354163
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.595715 Loss1: 0.691684 Loss2: 1.904032
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.677944 Loss1: 0.342766 Loss2: 1.335177
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.505964 Loss1: 0.142640 Loss2: 1.363324
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445874 Loss1: 0.098631 Loss2: 1.347243 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446950 Loss1: 0.105395 Loss2: 1.341555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425111 Loss1: 0.081736 Loss2: 1.343375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.378538 Loss1: 0.079163 Loss2: 1.299375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.383240 Loss1: 0.075223 Loss2: 1.308017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.344152 Loss1: 0.051713 Loss2: 1.292439 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986779
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.311482 Loss1: 0.457515 Loss2: 1.853967
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.713230 Loss1: 0.336819 Loss2: 1.376411
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635885 Loss1: 0.206375 Loss2: 1.429510
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.359493 Loss1: 0.467527 Loss2: 1.891966
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508560 Loss1: 0.129808 Loss2: 1.378752
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.728603 Loss1: 0.344376 Loss2: 1.384227
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503328 Loss1: 0.132101 Loss2: 1.371227
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.646047 Loss1: 0.213771 Loss2: 1.432276
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519408 Loss1: 0.141813 Loss2: 1.377595
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537501 Loss1: 0.151503 Loss2: 1.385998
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.499777 Loss1: 0.133113 Loss2: 1.366664
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473251 Loss1: 0.104779 Loss2: 1.368471 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418022 Loss1: 0.054981 Loss2: 1.363041 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414129 Loss1: 0.055758 Loss2: 1.358371 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405474 Loss1: 0.051289 Loss2: 1.354185 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.235678 Loss1: 0.382598 Loss2: 1.853080 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.629200 Loss1: 0.196823 Loss2: 1.432377 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508670 Loss1: 0.125357 Loss2: 1.383313
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.245456 Loss1: 0.353625 Loss2: 1.891831
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.520615 Loss1: 0.143271 Loss2: 1.377344
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.647920 Loss1: 0.238570 Loss2: 1.409350
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.506002 Loss1: 0.121402 Loss2: 1.384600
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614951 Loss1: 0.180114 Loss2: 1.434837
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.493541 Loss1: 0.119782 Loss2: 1.373760
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.566767 Loss1: 0.162331 Loss2: 1.404435
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545929 Loss1: 0.132030 Loss2: 1.413899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491023 Loss1: 0.092846 Loss2: 1.398177 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407252 Loss1: 0.042079 Loss2: 1.365174
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.521524 Loss1: 0.125171 Loss2: 1.396352
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484620 Loss1: 0.085539 Loss2: 1.399081
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485451 Loss1: 0.093930 Loss2: 1.391521
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.464361 Loss1: 0.070062 Loss2: 1.394299
-(DefaultActor pid=3764) >> Training accuracy: 0.992647
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.667079 Loss1: 0.319563 Loss2: 1.347516 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524464 Loss1: 0.177103 Loss2: 1.347361 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478729 Loss1: 0.135660 Loss2: 1.343069
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.372477 Loss1: 0.522009 Loss2: 1.850468
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468787 Loss1: 0.122864 Loss2: 1.345922
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.684799 Loss1: 0.317855 Loss2: 1.366943
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415938 Loss1: 0.073794 Loss2: 1.342144
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.666209 Loss1: 0.265563 Loss2: 1.400646
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400743 Loss1: 0.073693 Loss2: 1.327050
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577641 Loss1: 0.199824 Loss2: 1.377817
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401297 Loss1: 0.071837 Loss2: 1.329461
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501929 Loss1: 0.134016 Loss2: 1.367913
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.362751 Loss1: 0.036597 Loss2: 1.326154
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.483806 Loss1: 0.123607 Loss2: 1.360198
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460855 Loss1: 0.110857 Loss2: 1.349997
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431264 Loss1: 0.079979 Loss2: 1.351285
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389642 Loss1: 0.049855 Loss2: 1.339788
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392199 Loss1: 0.061009 Loss2: 1.331191
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.123228 Loss1: 0.356559 Loss2: 1.766669
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.565835 Loss1: 0.246067 Loss2: 1.319767
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.464035 Loss1: 0.134924 Loss2: 1.329111
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.468810 Loss1: 0.161319 Loss2: 1.307492
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.463540 Loss1: 0.615145 Loss2: 1.848395
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.794037 Loss1: 0.460240 Loss2: 1.333797 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.652592 Loss1: 0.242682 Loss2: 1.409910 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.399288 Loss1: 0.087988 Loss2: 1.311300
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565172 Loss1: 0.237621 Loss2: 1.327551
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391784 Loss1: 0.082687 Loss2: 1.309098
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515558 Loss1: 0.163907 Loss2: 1.351652
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.435486 Loss1: 0.104427 Loss2: 1.331059
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378384 Loss1: 0.074187 Loss2: 1.304197
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433102 Loss1: 0.109701 Loss2: 1.323401
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384241 Loss1: 0.079776 Loss2: 1.304465
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400647 Loss1: 0.090953 Loss2: 1.309694 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982143
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.591286 Loss1: 0.609050 Loss2: 1.982236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.699990 Loss1: 0.284961 Loss2: 1.415030 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510982 Loss1: 0.146799 Loss2: 1.364183 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.706470 Loss1: 0.349977 Loss2: 1.356493 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.507101 Loss1: 0.124183 Loss2: 1.382917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551765 Loss1: 0.191690 Loss2: 1.360076 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990885
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550151 Loss1: 0.188228 Loss2: 1.361923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428400 Loss1: 0.076576 Loss2: 1.351823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373728 Loss1: 0.044065 Loss2: 1.329663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.358927 Loss1: 0.035011 Loss2: 1.323916 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.999023
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.493679 Loss1: 0.159363 Loss2: 1.334316 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.420535 Loss1: 0.092058 Loss2: 1.328477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.485690 Loss1: 0.599058 Loss2: 1.886632
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.385632 Loss1: 0.061484 Loss2: 1.324148
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.732956 Loss1: 0.386434 Loss2: 1.346522
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.344654 Loss1: 0.035476 Loss2: 1.309178
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.656425 Loss1: 0.257751 Loss2: 1.398673
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.337769 Loss1: 0.028623 Loss2: 1.309146
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.321375 Loss1: 0.022949 Loss2: 1.298426 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447674 Loss1: 0.112688 Loss2: 1.334986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442305 Loss1: 0.108292 Loss2: 1.334013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.378981 Loss1: 0.541169 Loss2: 1.837812 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.529273 Loss1: 0.160938 Loss2: 1.368335 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.446957 Loss1: 0.110521 Loss2: 1.336436 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466049 Loss1: 0.126556 Loss2: 1.339493
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.386517 Loss1: 0.466241 Loss2: 1.920276
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415331 Loss1: 0.078300 Loss2: 1.337031
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.762009 Loss1: 0.355217 Loss2: 1.406792
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.388293 Loss1: 0.060673 Loss2: 1.327619
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653641 Loss1: 0.191078 Loss2: 1.462563
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374901 Loss1: 0.051661 Loss2: 1.323240
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579640 Loss1: 0.174315 Loss2: 1.405326
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.344531 Loss1: 0.027408 Loss2: 1.317123
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569492 Loss1: 0.155358 Loss2: 1.414134
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.515424 Loss1: 0.097392 Loss2: 1.418032
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.503367 Loss1: 0.099083 Loss2: 1.404283
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477963 Loss1: 0.073938 Loss2: 1.404025
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450409 Loss1: 0.055915 Loss2: 1.394494
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.283547 Loss1: 0.450179 Loss2: 1.833368
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.459741 Loss1: 0.070011 Loss2: 1.389730
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.546765 Loss1: 0.194556 Loss2: 1.352209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.431003 Loss1: 0.100946 Loss2: 1.330057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.411147 Loss1: 0.089656 Loss2: 1.321491
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.356998 Loss1: 0.470513 Loss2: 1.886485
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.842365 Loss1: 0.409950 Loss2: 1.432416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.717368 Loss1: 0.249058 Loss2: 1.468310 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.667753 Loss1: 0.229218 Loss2: 1.438534 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.595172 Loss1: 0.152143 Loss2: 1.443029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.540806 Loss1: 0.114630 Loss2: 1.426176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499053 Loss1: 0.083913 Loss2: 1.415140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.473142 Loss1: 0.068879 Loss2: 1.404264 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.567586 Loss1: 0.158543 Loss2: 1.409043 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491370 Loss1: 0.095574 Loss2: 1.395797 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.366354 Loss1: 0.484484 Loss2: 1.881870
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.501597 Loss1: 0.116495 Loss2: 1.385101
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.680755 Loss1: 0.298454 Loss2: 1.382301
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.496149 Loss1: 0.107661 Loss2: 1.388488
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543513 Loss1: 0.131462 Loss2: 1.412052
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454677 Loss1: 0.074902 Loss2: 1.379775
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492567 Loss1: 0.123190 Loss2: 1.369377
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434978 Loss1: 0.055207 Loss2: 1.379771
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450485 Loss1: 0.084201 Loss2: 1.366284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443397 Loss1: 0.084153 Loss2: 1.359244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431665 Loss1: 0.071895 Loss2: 1.359771
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.279264 Loss1: 0.467429 Loss2: 1.811835
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401360 Loss1: 0.047221 Loss2: 1.354139
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.685564 Loss1: 0.359331 Loss2: 1.326234
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.556921 Loss1: 0.173691 Loss2: 1.383230
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516815 Loss1: 0.181678 Loss2: 1.335137
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479743 Loss1: 0.137724 Loss2: 1.342018
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.427868 Loss1: 0.098342 Loss2: 1.329526
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.319089 Loss1: 0.454881 Loss2: 1.864208
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.391703 Loss1: 0.070610 Loss2: 1.321094
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.670167 Loss1: 0.298258 Loss2: 1.371908
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384044 Loss1: 0.072639 Loss2: 1.311406
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.531005 Loss1: 0.141818 Loss2: 1.389187
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.366326 Loss1: 0.056125 Loss2: 1.310201
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.456484 Loss1: 0.081051 Loss2: 1.375433
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350060 Loss1: 0.046338 Loss2: 1.303723
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429528 Loss1: 0.075123 Loss2: 1.354406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416452 Loss1: 0.064986 Loss2: 1.351466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415165 Loss1: 0.067229 Loss2: 1.347936
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.214081 Loss1: 0.388084 Loss2: 1.825997
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394970 Loss1: 0.046824 Loss2: 1.348146
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.656214 Loss1: 0.283487 Loss2: 1.372727
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563278 Loss1: 0.155878 Loss2: 1.407400
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488522 Loss1: 0.109425 Loss2: 1.379097
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.469749 Loss1: 0.101843 Loss2: 1.367906
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438838 Loss1: 0.072498 Loss2: 1.366340
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.298077 Loss1: 0.494006 Loss2: 1.804071
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.666514 Loss1: 0.353921 Loss2: 1.312593 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636625 Loss1: 0.259198 Loss2: 1.377427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.466160 Loss1: 0.148921 Loss2: 1.317239 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998047
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400966 Loss1: 0.049585 Loss2: 1.351382
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.458326 Loss1: 0.148408 Loss2: 1.309919
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.412268 Loss1: 0.100800 Loss2: 1.311468
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425965 Loss1: 0.116149 Loss2: 1.309816
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.384785 Loss1: 0.088449 Loss2: 1.296336
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.339586 Loss1: 0.043846 Loss2: 1.295740
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.351935 Loss1: 0.498483 Loss2: 1.853452
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.328160 Loss1: 0.038913 Loss2: 1.289246
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.567877 Loss1: 0.179987 Loss2: 1.387890 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448593 Loss1: 0.085500 Loss2: 1.363093 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480071 Loss1: 0.119014 Loss2: 1.361057
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.370137 Loss1: 0.507996 Loss2: 1.862140
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440745 Loss1: 0.081704 Loss2: 1.359041
-DEBUG flwr 2023-10-12 18:23:27,711 | server.py:236 | fit_round 160 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.662879 Loss1: 0.292297 Loss2: 1.370582
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448483 Loss1: 0.094148 Loss2: 1.354335
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.597539 Loss1: 0.194308 Loss2: 1.403231
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437305 Loss1: 0.087888 Loss2: 1.349417
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520528 Loss1: 0.138399 Loss2: 1.382129
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443111 Loss1: 0.095829 Loss2: 1.347282
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492159 Loss1: 0.122294 Loss2: 1.369865
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482745 Loss1: 0.114384 Loss2: 1.368361
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.510993 Loss1: 0.146436 Loss2: 1.364557
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446362 Loss1: 0.082445 Loss2: 1.363917
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449219 Loss1: 0.090219 Loss2: 1.359000
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.414348 Loss1: 0.568315 Loss2: 1.846033
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434354 Loss1: 0.080116 Loss2: 1.354238
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561064 Loss1: 0.179024 Loss2: 1.382040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.421263 Loss1: 0.085922 Loss2: 1.335341 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.414993 Loss1: 0.081780 Loss2: 1.333213
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.255477 Loss1: 0.442124 Loss2: 1.813353
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.401868 Loss1: 0.076956 Loss2: 1.324912
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.637817 Loss1: 0.291607 Loss2: 1.346210
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432341 Loss1: 0.110001 Loss2: 1.322341
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568809 Loss1: 0.191169 Loss2: 1.377640
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399050 Loss1: 0.076814 Loss2: 1.322236
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507191 Loss1: 0.162146 Loss2: 1.345045
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375554 Loss1: 0.061589 Loss2: 1.313965
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.468567 Loss1: 0.123339 Loss2: 1.345228
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446069 Loss1: 0.106780 Loss2: 1.339289
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.418975 Loss1: 0.081845 Loss2: 1.337129
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.377673 Loss1: 0.047623 Loss2: 1.330050
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392348 Loss1: 0.070880 Loss2: 1.321468
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.496490 Loss1: 0.569479 Loss2: 1.927011
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391280 Loss1: 0.067145 Loss2: 1.324135
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.631431 Loss1: 0.204076 Loss2: 1.427354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611396 Loss1: 0.199969 Loss2: 1.411427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.592772 Loss1: 0.165268 Loss2: 1.427505
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.321253 Loss1: 0.422460 Loss2: 1.898794
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.559806 Loss1: 0.138766 Loss2: 1.421040
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.676548 Loss1: 0.294138 Loss2: 1.382410
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.507665 Loss1: 0.100212 Loss2: 1.407453
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.631877 Loss1: 0.201833 Loss2: 1.430044
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453588 Loss1: 0.055561 Loss2: 1.398027
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509345 Loss1: 0.115784 Loss2: 1.393561
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.444337 Loss1: 0.053737 Loss2: 1.390600
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.474287 Loss1: 0.089848 Loss2: 1.384440
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502033 Loss1: 0.114380 Loss2: 1.387653
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488800 Loss1: 0.107532 Loss2: 1.381268
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454061 Loss1: 0.075380 Loss2: 1.378681
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418751 Loss1: 0.044681 Loss2: 1.374069
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410413 Loss1: 0.037945 Loss2: 1.372468
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 18:23:27,711][flwr][DEBUG] - fit_round 160 received 50 results and 0 failures
-INFO flwr 2023-10-12 18:24:09,395 | server.py:125 | fit progress: (160, 2.2541892732294224, {'accuracy': 0.6005}, 369157.174040303)
->> Test accuracy: 0.600500
-[2023-10-12 18:24:09,395][flwr][INFO] - fit progress: (160, 2.2541892732294224, {'accuracy': 0.6005}, 369157.174040303)
-DEBUG flwr 2023-10-12 18:24:09,396 | server.py:173 | evaluate_round 160: strategy sampled 50 clients (out of 50)
-[2023-10-12 18:24:09,396][flwr][DEBUG] - evaluate_round 160: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 18:33:16,953 | server.py:187 | evaluate_round 160 received 50 results and 0 failures
-[2023-10-12 18:33:16,953][flwr][DEBUG] - evaluate_round 160 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 18:33:16,954 | server.py:222 | fit_round 161: strategy sampled 50 clients (out of 50)
-[2023-10-12 18:33:16,954][flwr][DEBUG] - fit_round 161: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.526520 Loss1: 0.635193 Loss2: 1.891327
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.629250 Loss1: 0.332634 Loss2: 1.296616
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563797 Loss1: 0.247182 Loss2: 1.316615
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.478115 Loss1: 0.150651 Loss2: 1.327464
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443371 Loss1: 0.137395 Loss2: 1.305976
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.335278 Loss1: 0.505356 Loss2: 1.829922
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442514 Loss1: 0.135131 Loss2: 1.307383
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592744 Loss1: 0.203255 Loss2: 1.389488 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.355223 Loss1: 0.065000 Loss2: 1.290223 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.333232 Loss1: 0.049611 Loss2: 1.283620 [repeated 2x across cluster]
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426080 Loss1: 0.085287 Loss2: 1.340793 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373194 Loss1: 0.040082 Loss2: 1.333113 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.340720 Loss1: 0.466614 Loss2: 1.874105
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352639 Loss1: 0.026699 Loss2: 1.325940
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.562026 Loss1: 0.160036 Loss2: 1.401990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468812 Loss1: 0.105909 Loss2: 1.362904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.425277 Loss1: 0.067564 Loss2: 1.357713
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.367000 Loss1: 0.495057 Loss2: 1.871943
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.719379 Loss1: 0.302634 Loss2: 1.416745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.634156 Loss1: 0.216990 Loss2: 1.417166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.547416 Loss1: 0.155939 Loss2: 1.391477 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461563 Loss1: 0.073553 Loss2: 1.388010 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431719 Loss1: 0.053314 Loss2: 1.378404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416078 Loss1: 0.045949 Loss2: 1.370129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417178 Loss1: 0.054393 Loss2: 1.362786 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995404
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559494 Loss1: 0.174877 Loss2: 1.384617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476844 Loss1: 0.100779 Loss2: 1.376065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.383713 Loss1: 0.545793 Loss2: 1.837920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.677916 Loss1: 0.343014 Loss2: 1.334902 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588878 Loss1: 0.217096 Loss2: 1.371783 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.440996 Loss1: 0.106836 Loss2: 1.334160 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.413477 Loss1: 0.092627 Loss2: 1.320850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.387677 Loss1: 0.069351 Loss2: 1.318326
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.290706 Loss1: 0.477559 Loss2: 1.813147
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.629416 Loss1: 0.276231 Loss2: 1.353185 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526580 Loss1: 0.159758 Loss2: 1.366822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.456348 Loss1: 0.117653 Loss2: 1.338696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415524 Loss1: 0.077096 Loss2: 1.338428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380171 Loss1: 0.052609 Loss2: 1.327563 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.367290 Loss1: 0.041181 Loss2: 1.326109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.345506 Loss1: 0.030128 Loss2: 1.315378 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524807 Loss1: 0.128071 Loss2: 1.396736 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.479212 Loss1: 0.093225 Loss2: 1.385987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486000 Loss1: 0.103137 Loss2: 1.382863
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.372112 Loss1: 0.460622 Loss2: 1.911489
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.483343 Loss1: 0.097908 Loss2: 1.385435
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.666736 Loss1: 0.261225 Loss2: 1.405511
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.637231 Loss1: 0.203929 Loss2: 1.433302
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.577572 Loss1: 0.156778 Loss2: 1.420793
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.538067 Loss1: 0.119179 Loss2: 1.418888
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.535098 Loss1: 0.122705 Loss2: 1.412393
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.576184 Loss1: 0.162636 Loss2: 1.413549
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.139122 Loss1: 0.371673 Loss2: 1.767449
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.617938 Loss1: 0.298930 Loss2: 1.319008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.580509 Loss1: 0.228425 Loss2: 1.352084 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.492280 Loss1: 0.084785 Loss2: 1.407495
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.506925 Loss1: 0.191269 Loss2: 1.315655
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.420438 Loss1: 0.110808 Loss2: 1.309630
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423763 Loss1: 0.120657 Loss2: 1.303106
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.385666 Loss1: 0.084541 Loss2: 1.301125
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368436 Loss1: 0.069831 Loss2: 1.298605
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.199049 Loss1: 0.414439 Loss2: 1.784610
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.618615 Loss1: 0.270561 Loss2: 1.348053 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.999023
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.340254 Loss1: 0.049711 Loss2: 1.290543
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.544999 Loss1: 0.177428 Loss2: 1.367571
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.468420 Loss1: 0.123370 Loss2: 1.345049
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.442502 Loss1: 0.102715 Loss2: 1.339787
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.425940 Loss1: 0.096678 Loss2: 1.329263
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422896 Loss1: 0.099242 Loss2: 1.323654
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.653300 Loss1: 0.667072 Loss2: 1.986229
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.758257 Loss1: 0.322192 Loss2: 1.436065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.724383 Loss1: 0.248869 Loss2: 1.475514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.362529 Loss1: 0.040763 Loss2: 1.321766
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.600519 Loss1: 0.169954 Loss2: 1.430566
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554150 Loss1: 0.122425 Loss2: 1.431724
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513270 Loss1: 0.090566 Loss2: 1.422704
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.492639 Loss1: 0.078593 Loss2: 1.414046
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470318 Loss1: 0.067498 Loss2: 1.402820
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461587 Loss1: 0.059234 Loss2: 1.402353
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.169292 Loss1: 0.383602 Loss2: 1.785690
-(DefaultActor pid=3764) >> Training accuracy: 0.994420
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.544063 Loss1: 0.239300 Loss2: 1.304763 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.475642 Loss1: 0.159170 Loss2: 1.316472 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.388497 Loss1: 0.094824 Loss2: 1.293673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.377347 Loss1: 0.075418 Loss2: 1.301929 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.347724 Loss1: 0.058320 Loss2: 1.289404 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.350222 Loss1: 0.066137 Loss2: 1.284085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.338853 Loss1: 0.054781 Loss2: 1.284072 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.434331 Loss1: 0.116643 Loss2: 1.317688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390330 Loss1: 0.085231 Loss2: 1.305099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395756 Loss1: 0.091632 Loss2: 1.304125
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.476454 Loss1: 0.591924 Loss2: 1.884530
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387838 Loss1: 0.088769 Loss2: 1.299068
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.687415 Loss1: 0.342812 Loss2: 1.344603
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573584 Loss1: 0.190151 Loss2: 1.383433
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490791 Loss1: 0.141721 Loss2: 1.349070
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461968 Loss1: 0.119816 Loss2: 1.342152
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.424871 Loss1: 0.084578 Loss2: 1.340293
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428121 Loss1: 0.092163 Loss2: 1.335957
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.300798 Loss1: 0.432313 Loss2: 1.868485
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.765671 Loss1: 0.395806 Loss2: 1.369865 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.609020 Loss1: 0.175432 Loss2: 1.433588 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594704 Loss1: 0.219958 Loss2: 1.374746 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.483255 Loss1: 0.109778 Loss2: 1.373477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443333 Loss1: 0.077898 Loss2: 1.365434 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433481 Loss1: 0.074842 Loss2: 1.358639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405110 Loss1: 0.049140 Loss2: 1.355970 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496420 Loss1: 0.172025 Loss2: 1.324395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444015 Loss1: 0.118156 Loss2: 1.325860 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426685 Loss1: 0.101962 Loss2: 1.324723
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.329097 Loss1: 0.492765 Loss2: 1.836332
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.648373 Loss1: 0.303052 Loss2: 1.345321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.591572 Loss1: 0.205678 Loss2: 1.385894 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562132 Loss1: 0.206298 Loss2: 1.355834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479722 Loss1: 0.120057 Loss2: 1.359666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409173 Loss1: 0.068931 Loss2: 1.340242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370450 Loss1: 0.041032 Loss2: 1.329418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370323 Loss1: 0.046073 Loss2: 1.324250 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523326 Loss1: 0.149678 Loss2: 1.373648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.464792 Loss1: 0.101361 Loss2: 1.363431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462883 Loss1: 0.101173 Loss2: 1.361709
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.331795 Loss1: 0.444942 Loss2: 1.886854
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.688676 Loss1: 0.276294 Loss2: 1.412382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.693342 Loss1: 0.240656 Loss2: 1.452685 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.608788 Loss1: 0.189878 Loss2: 1.418910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510473 Loss1: 0.105606 Loss2: 1.404867 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.340195 Loss1: 0.480487 Loss2: 1.859708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.703948 Loss1: 0.333145 Loss2: 1.370804 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.671347 Loss1: 0.239681 Loss2: 1.431665 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.557546 Loss1: 0.182746 Loss2: 1.374800 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456430 Loss1: 0.087237 Loss2: 1.369193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497236 Loss1: 0.130581 Loss2: 1.366655
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.393321 Loss1: 0.554910 Loss2: 1.838411
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.724616 Loss1: 0.370962 Loss2: 1.353654 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458187 Loss1: 0.093690 Loss2: 1.364498
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.684866 Loss1: 0.268415 Loss2: 1.416451
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595903 Loss1: 0.241820 Loss2: 1.354083
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.628527 Loss1: 0.248960 Loss2: 1.379567
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.544893 Loss1: 0.174080 Loss2: 1.370813
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500737 Loss1: 0.143341 Loss2: 1.357396
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.167823 Loss1: 0.397805 Loss2: 1.770018
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449835 Loss1: 0.090013 Loss2: 1.359822
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451848 Loss1: 0.100178 Loss2: 1.351671
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.654579 Loss1: 0.331428 Loss2: 1.323151
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421693 Loss1: 0.074492 Loss2: 1.347201
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595498 Loss1: 0.225137 Loss2: 1.370361
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556340 Loss1: 0.224635 Loss2: 1.331705
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.458833 Loss1: 0.121231 Loss2: 1.337602
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.421559 Loss1: 0.095913 Loss2: 1.325647
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.437748 Loss1: 0.121697 Loss2: 1.316051
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.332410 Loss1: 0.520344 Loss2: 1.812066
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389204 Loss1: 0.073275 Loss2: 1.315929
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.354939 Loss1: 0.044589 Loss2: 1.310350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.342887 Loss1: 0.042355 Loss2: 1.300532 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502469 Loss1: 0.145810 Loss2: 1.356658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472437 Loss1: 0.130157 Loss2: 1.342280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.405010 Loss1: 0.501482 Loss2: 1.903528 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.696523 Loss1: 0.300823 Loss2: 1.395700 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541935 Loss1: 0.137744 Loss2: 1.404192 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.539613 Loss1: 0.143205 Loss2: 1.396408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.228980 Loss1: 0.390182 Loss2: 1.838798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.626837 Loss1: 0.296182 Loss2: 1.330656 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622023 Loss1: 0.262690 Loss2: 1.359333 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.969792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.566056 Loss1: 0.215041 Loss2: 1.351015 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.448801 Loss1: 0.106835 Loss2: 1.341967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409917 Loss1: 0.085985 Loss2: 1.323932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385539 Loss1: 0.070057 Loss2: 1.315481 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357933 Loss1: 0.045650 Loss2: 1.312283 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.532531 Loss1: 0.179123 Loss2: 1.353408 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440299 Loss1: 0.083773 Loss2: 1.356526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.391727 Loss1: 0.048890 Loss2: 1.342836
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.564614 Loss1: 0.592901 Loss2: 1.971713
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.764532 Loss1: 0.380012 Loss2: 1.384520 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.706676 Loss1: 0.304885 Loss2: 1.401791
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398901 Loss1: 0.062637 Loss2: 1.336264
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565217 Loss1: 0.165392 Loss2: 1.399824
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396238 Loss1: 0.061414 Loss2: 1.334824
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476176 Loss1: 0.108008 Loss2: 1.368168 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428309 Loss1: 0.070791 Loss2: 1.357519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370428 Loss1: 0.033448 Loss2: 1.336980 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.567289 Loss1: 0.213889 Loss2: 1.353400 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.446575 Loss1: 0.128290 Loss2: 1.318285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475392 Loss1: 0.146538 Loss2: 1.328854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433866 Loss1: 0.113689 Loss2: 1.320177 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378899 Loss1: 0.063872 Loss2: 1.315027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.365177 Loss1: 0.057821 Loss2: 1.307356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.345329 Loss1: 0.043322 Loss2: 1.302007 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404103 Loss1: 0.089737 Loss2: 1.314366 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402896 Loss1: 0.094110 Loss2: 1.308785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373952 Loss1: 0.068296 Loss2: 1.305656
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.460698 Loss1: 0.591109 Loss2: 1.869589
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.707153 Loss1: 0.330538 Loss2: 1.376616
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.642289 Loss1: 0.234635 Loss2: 1.407653
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522022 Loss1: 0.148288 Loss2: 1.373734
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487224 Loss1: 0.118335 Loss2: 1.368889
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.559650 Loss1: 0.563817 Loss2: 1.995833
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461520 Loss1: 0.101058 Loss2: 1.360463
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455987 Loss1: 0.098820 Loss2: 1.357167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423844 Loss1: 0.072369 Loss2: 1.351475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414734 Loss1: 0.066100 Loss2: 1.348634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414552 Loss1: 0.067013 Loss2: 1.347539 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516921 Loss1: 0.090871 Loss2: 1.426049 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480796 Loss1: 0.067083 Loss2: 1.413713 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472555 Loss1: 0.062560 Loss2: 1.409994
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.257933 Loss1: 0.441784 Loss2: 1.816149
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.716813 Loss1: 0.349102 Loss2: 1.367712
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.614160 Loss1: 0.190748 Loss2: 1.423411
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570978 Loss1: 0.217399 Loss2: 1.353580
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498860 Loss1: 0.130139 Loss2: 1.368720
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.325895 Loss1: 0.486426 Loss2: 1.839469
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523781 Loss1: 0.170439 Loss2: 1.353342
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.649946 Loss1: 0.319070 Loss2: 1.330876
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590880 Loss1: 0.215490 Loss2: 1.375390
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.504026 Loss1: 0.137141 Loss2: 1.366885
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538253 Loss1: 0.200339 Loss2: 1.337914
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462574 Loss1: 0.110966 Loss2: 1.351608
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.466047 Loss1: 0.127252 Loss2: 1.338795
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.497195 Loss1: 0.146195 Loss2: 1.350999
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.417696 Loss1: 0.094981 Loss2: 1.322715
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440641 Loss1: 0.082160 Loss2: 1.358481
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.392441 Loss1: 0.084204 Loss2: 1.308237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367072 Loss1: 0.057843 Loss2: 1.309229 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.959375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.668746 Loss1: 0.327207 Loss2: 1.341539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534507 Loss1: 0.190893 Loss2: 1.343614 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.559997 Loss1: 0.195774 Loss2: 1.364223
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.163946 Loss1: 0.384661 Loss2: 1.779286
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463530 Loss1: 0.118355 Loss2: 1.345175
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.618806 Loss1: 0.285969 Loss2: 1.332837
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435854 Loss1: 0.101811 Loss2: 1.334043
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635997 Loss1: 0.259032 Loss2: 1.376966
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419296 Loss1: 0.080490 Loss2: 1.338806
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519470 Loss1: 0.179763 Loss2: 1.339707
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406218 Loss1: 0.077214 Loss2: 1.329004
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521312 Loss1: 0.178596 Loss2: 1.342716
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385531 Loss1: 0.060514 Loss2: 1.325018
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494444 Loss1: 0.151459 Loss2: 1.342985
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428243 Loss1: 0.095174 Loss2: 1.333069
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406846 Loss1: 0.080927 Loss2: 1.325919
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.377575 Loss1: 0.059263 Loss2: 1.318311
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370570 Loss1: 0.050372 Loss2: 1.320198
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.285394 Loss1: 0.398972 Loss2: 1.886422
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.639465 Loss1: 0.269044 Loss2: 1.370420
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.587412 Loss1: 0.189375 Loss2: 1.398037
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527752 Loss1: 0.147369 Loss2: 1.380383
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510013 Loss1: 0.135938 Loss2: 1.374074
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.303824 Loss1: 0.522820 Loss2: 1.781004
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461646 Loss1: 0.095059 Loss2: 1.366587
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446917 Loss1: 0.082197 Loss2: 1.364720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427420 Loss1: 0.068270 Loss2: 1.359150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411735 Loss1: 0.055656 Loss2: 1.356079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391186 Loss1: 0.044442 Loss2: 1.346744 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415629 Loss1: 0.114276 Loss2: 1.301352 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420732 Loss1: 0.121592 Loss2: 1.299139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352264 Loss1: 0.063124 Loss2: 1.289140
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.220909 Loss1: 0.399952 Loss2: 1.820957
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.627754 Loss1: 0.306599 Loss2: 1.321155
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591540 Loss1: 0.240151 Loss2: 1.351388
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523246 Loss1: 0.188381 Loss2: 1.334865
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490711 Loss1: 0.159416 Loss2: 1.331294
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.373473 Loss1: 0.489265 Loss2: 1.884208
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.744531 Loss1: 0.360868 Loss2: 1.383662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.643257 Loss1: 0.214596 Loss2: 1.428662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574462 Loss1: 0.185635 Loss2: 1.388826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501101 Loss1: 0.122067 Loss2: 1.379034 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.458268 Loss1: 0.081509 Loss2: 1.376759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423828 Loss1: 0.063759 Loss2: 1.360069 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422095 Loss1: 0.066597 Loss2: 1.355498 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.615421 Loss1: 0.215805 Loss2: 1.399616 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502907 Loss1: 0.104834 Loss2: 1.398073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.528526 Loss1: 0.136205 Loss2: 1.392321
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.267793 Loss1: 0.427452 Loss2: 1.840340
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.660738 Loss1: 0.307847 Loss2: 1.352891 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462369 Loss1: 0.076088 Loss2: 1.386281
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598334 Loss1: 0.219858 Loss2: 1.378476
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462285 Loss1: 0.081603 Loss2: 1.380682
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530852 Loss1: 0.175751 Loss2: 1.355101
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445346 Loss1: 0.068960 Loss2: 1.376386
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495440 Loss1: 0.150583 Loss2: 1.344857
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432883 Loss1: 0.056875 Loss2: 1.376008
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.462076 Loss1: 0.122058 Loss2: 1.340018
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.449708 Loss1: 0.116803 Loss2: 1.332905
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453040 Loss1: 0.118127 Loss2: 1.334913
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419234 Loss1: 0.086796 Loss2: 1.332438
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425999 Loss1: 0.095820 Loss2: 1.330179
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.476332 Loss1: 0.512977 Loss2: 1.963355
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.796182 Loss1: 0.376687 Loss2: 1.419495
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.740995 Loss1: 0.281283 Loss2: 1.459713
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.708990 Loss1: 0.274712 Loss2: 1.434278
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.711561 Loss1: 0.254444 Loss2: 1.457117
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.587536 Loss1: 0.168633 Loss2: 1.418903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.535418 Loss1: 0.119736 Loss2: 1.415682 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.481955 Loss1: 0.072083 Loss2: 1.409872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443864 Loss1: 0.042279 Loss2: 1.401585 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429732 Loss1: 0.037363 Loss2: 1.392368 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415835 Loss1: 0.084445 Loss2: 1.331390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398013 Loss1: 0.073977 Loss2: 1.324036 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414523 Loss1: 0.084571 Loss2: 1.329952
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.385471 Loss1: 0.549795 Loss2: 1.835676
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.642145 Loss1: 0.284811 Loss2: 1.357335
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599439 Loss1: 0.206697 Loss2: 1.392743
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482160 Loss1: 0.116770 Loss2: 1.365390
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443672 Loss1: 0.088601 Loss2: 1.355071
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.226629 Loss1: 0.439161 Loss2: 1.787468
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.431270 Loss1: 0.081828 Loss2: 1.349442
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406405 Loss1: 0.065151 Loss2: 1.341255 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.612924 Loss1: 0.222881 Loss2: 1.390043
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418582 Loss1: 0.079784 Loss2: 1.338799
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.545818 Loss1: 0.195114 Loss2: 1.350704
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406960 Loss1: 0.065887 Loss2: 1.341072
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503735 Loss1: 0.156955 Loss2: 1.346780
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371297 Loss1: 0.038240 Loss2: 1.333057
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456021 Loss1: 0.119885 Loss2: 1.336135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388348 Loss1: 0.062358 Loss2: 1.325989 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.298810 Loss1: 0.460930 Loss2: 1.837879
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.353546 Loss1: 0.031764 Loss2: 1.321782
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582498 Loss1: 0.204270 Loss2: 1.378228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.572689 Loss1: 0.219775 Loss2: 1.352914 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509992 Loss1: 0.148252 Loss2: 1.361740
-DEBUG flwr 2023-10-12 19:01:42,900 | server.py:236 | fit_round 161 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.425790 Loss1: 0.518017 Loss2: 1.907773
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470497 Loss1: 0.120580 Loss2: 1.349917
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.681069 Loss1: 0.315198 Loss2: 1.365871
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614592 Loss1: 0.220725 Loss2: 1.393866
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470915 Loss1: 0.124622 Loss2: 1.346293
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.410567 Loss1: 0.064104 Loss2: 1.346463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392457 Loss1: 0.061433 Loss2: 1.331024 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473072 Loss1: 0.095367 Loss2: 1.377705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438439 Loss1: 0.074819 Loss2: 1.363621 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.385465 Loss1: 0.505550 Loss2: 1.879916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647465 Loss1: 0.238710 Loss2: 1.408755 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.497661 Loss1: 0.608403 Loss2: 1.889258 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.705841 Loss1: 0.340608 Loss2: 1.365232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.678780 Loss1: 0.273604 Loss2: 1.405176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563890 Loss1: 0.188733 Loss2: 1.375157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528956 Loss1: 0.166869 Loss2: 1.362087 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441592 Loss1: 0.083083 Loss2: 1.358509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420280 Loss1: 0.072088 Loss2: 1.348193 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404697 Loss1: 0.064167 Loss2: 1.340530 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 19:01:42,900][flwr][DEBUG] - fit_round 161 received 50 results and 0 failures
-INFO flwr 2023-10-12 19:02:24,438 | server.py:125 | fit progress: (161, 2.2532033196653423, {'accuracy': 0.6029}, 371452.21645676397)
->> Test accuracy: 0.602900
-[2023-10-12 19:02:24,438][flwr][INFO] - fit progress: (161, 2.2532033196653423, {'accuracy': 0.6029}, 371452.21645676397)
-DEBUG flwr 2023-10-12 19:02:24,438 | server.py:173 | evaluate_round 161: strategy sampled 50 clients (out of 50)
-[2023-10-12 19:02:24,438][flwr][DEBUG] - evaluate_round 161: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 19:11:28,807 | server.py:187 | evaluate_round 161 received 50 results and 0 failures
-[2023-10-12 19:11:28,807][flwr][DEBUG] - evaluate_round 161 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 19:11:28,807 | server.py:222 | fit_round 162: strategy sampled 50 clients (out of 50)
-[2023-10-12 19:11:28,807][flwr][DEBUG] - fit_round 162: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.602624 Loss1: 0.692091 Loss2: 1.910534
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.730924 Loss1: 0.367264 Loss2: 1.363660
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.605269 Loss1: 0.230488 Loss2: 1.374782
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.545348 Loss1: 0.170319 Loss2: 1.375029
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.226651 Loss1: 0.430661 Loss2: 1.795990
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.435824 Loss1: 0.098901 Loss2: 1.336922 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402681 Loss1: 0.071311 Loss2: 1.331370 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.399159 Loss1: 0.070502 Loss2: 1.328657 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388903 Loss1: 0.070634 Loss2: 1.318269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381961 Loss1: 0.060044 Loss2: 1.321916 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458058 Loss1: 0.100169 Loss2: 1.357889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398515 Loss1: 0.056946 Loss2: 1.341569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.319209 Loss1: 0.444584 Loss2: 1.874625
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390015 Loss1: 0.054382 Loss2: 1.335633
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573939 Loss1: 0.193184 Loss2: 1.380755 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.469309 Loss1: 0.103886 Loss2: 1.365423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.479659 Loss1: 0.121061 Loss2: 1.358598
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.283383 Loss1: 0.491117 Loss2: 1.792266
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451483 Loss1: 0.088366 Loss2: 1.363117
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.587558 Loss1: 0.302071 Loss2: 1.285488
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429432 Loss1: 0.072448 Loss2: 1.356984
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.502674 Loss1: 0.200535 Loss2: 1.302139
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405570 Loss1: 0.056645 Loss2: 1.348924
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468338 Loss1: 0.166786 Loss2: 1.301552
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.436055 Loss1: 0.150465 Loss2: 1.285590
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403835 Loss1: 0.061104 Loss2: 1.342731
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.401521 Loss1: 0.120591 Loss2: 1.280930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.348327 Loss1: 0.077877 Loss2: 1.270451 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994420
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.314156 Loss1: 0.046457 Loss2: 1.267699
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.277658 Loss1: 0.446857 Loss2: 1.830801
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.648198 Loss1: 0.292580 Loss2: 1.355619
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.556686 Loss1: 0.174631 Loss2: 1.382055
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499987 Loss1: 0.151329 Loss2: 1.348658
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471231 Loss1: 0.129279 Loss2: 1.341952
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.373366 Loss1: 0.514772 Loss2: 1.858594
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.631394 Loss1: 0.258415 Loss2: 1.372979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601876 Loss1: 0.211428 Loss2: 1.390447 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555984 Loss1: 0.174825 Loss2: 1.381158 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.540615 Loss1: 0.154721 Loss2: 1.385894 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559425 Loss1: 0.175902 Loss2: 1.383524 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440660 Loss1: 0.077802 Loss2: 1.362858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401512 Loss1: 0.041547 Loss2: 1.359964 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.603176 Loss1: 0.262140 Loss2: 1.341037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.452981 Loss1: 0.121961 Loss2: 1.331020 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.454998 Loss1: 0.125525 Loss2: 1.329473
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.220825 Loss1: 0.375020 Loss2: 1.845805
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.615202 Loss1: 0.260151 Loss2: 1.355050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.599841 Loss1: 0.222912 Loss2: 1.376928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.548769 Loss1: 0.187626 Loss2: 1.361143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539959 Loss1: 0.185512 Loss2: 1.354447 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355728 Loss1: 0.049952 Loss2: 1.305775
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493791 Loss1: 0.142835 Loss2: 1.350956
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465533 Loss1: 0.103412 Loss2: 1.362121
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439946 Loss1: 0.088147 Loss2: 1.351799
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430376 Loss1: 0.083642 Loss2: 1.346734
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414802 Loss1: 0.069473 Loss2: 1.345328
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.388301 Loss1: 0.494899 Loss2: 1.893401
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.647305 Loss1: 0.264009 Loss2: 1.383296
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.608026 Loss1: 0.205388 Loss2: 1.402639
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551318 Loss1: 0.155914 Loss2: 1.395404
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491617 Loss1: 0.117624 Loss2: 1.373993
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.505647 Loss1: 0.639589 Loss2: 1.866058
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.676499 Loss1: 0.336004 Loss2: 1.340495 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635358 Loss1: 0.272375 Loss2: 1.362982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519466 Loss1: 0.189416 Loss2: 1.330051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408612 Loss1: 0.053041 Loss2: 1.355571
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507515 Loss1: 0.181311 Loss2: 1.326204
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385217 Loss1: 0.032443 Loss2: 1.352773
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.438752 Loss1: 0.109033 Loss2: 1.329719
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406903 Loss1: 0.088874 Loss2: 1.318029
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.401841 Loss1: 0.086409 Loss2: 1.315432
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370361 Loss1: 0.063714 Loss2: 1.306647
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364765 Loss1: 0.062988 Loss2: 1.301777
-(DefaultActor pid=3764) >> Training accuracy: 0.994420
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.395135 Loss1: 0.523528 Loss2: 1.871606
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.678777 Loss1: 0.297326 Loss2: 1.381451
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644098 Loss1: 0.236231 Loss2: 1.407867
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542354 Loss1: 0.167275 Loss2: 1.375079
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.384221 Loss1: 0.525334 Loss2: 1.858887
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.778370 Loss1: 0.393772 Loss2: 1.384597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.720467 Loss1: 0.273369 Loss2: 1.447099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.589239 Loss1: 0.202161 Loss2: 1.387078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.565150 Loss1: 0.166866 Loss2: 1.398283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492932 Loss1: 0.114389 Loss2: 1.378543 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425378 Loss1: 0.060958 Loss2: 1.364420 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409962 Loss1: 0.058234 Loss2: 1.351728 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.632080 Loss1: 0.251903 Loss2: 1.380177 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542827 Loss1: 0.161361 Loss2: 1.381467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.376338 Loss1: 0.534419 Loss2: 1.841919
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.486550 Loss1: 0.103459 Loss2: 1.383090
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.733348 Loss1: 0.369615 Loss2: 1.363733
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458897 Loss1: 0.082138 Loss2: 1.376759
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.675150 Loss1: 0.259446 Loss2: 1.415704
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455586 Loss1: 0.086443 Loss2: 1.369143
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.558089 Loss1: 0.185647 Loss2: 1.372442
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436415 Loss1: 0.064789 Loss2: 1.371626
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.479496 Loss1: 0.118805 Loss2: 1.360691
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425237 Loss1: 0.060553 Loss2: 1.364685
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.434134 Loss1: 0.079111 Loss2: 1.355023
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452649 Loss1: 0.086599 Loss2: 1.366050
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408336 Loss1: 0.069306 Loss2: 1.339030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378788 Loss1: 0.044999 Loss2: 1.333789 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.612401 Loss1: 0.287393 Loss2: 1.325008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476922 Loss1: 0.150742 Loss2: 1.326180 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.485417 Loss1: 0.156050 Loss2: 1.329367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.429952 Loss1: 0.102973 Loss2: 1.326979 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420923 Loss1: 0.105324 Loss2: 1.315600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425180 Loss1: 0.108833 Loss2: 1.316347 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.369964 Loss1: 0.049605 Loss2: 1.320360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365841 Loss1: 0.056029 Loss2: 1.309812 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389872 Loss1: 0.037294 Loss2: 1.352577 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367221 Loss1: 0.030553 Loss2: 1.336667 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.152470 Loss1: 0.356741 Loss2: 1.795728
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.645351 Loss1: 0.291268 Loss2: 1.354083
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610660 Loss1: 0.222072 Loss2: 1.388588
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517792 Loss1: 0.148723 Loss2: 1.369069
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.357847 Loss1: 0.510135 Loss2: 1.847712
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.715135 Loss1: 0.348483 Loss2: 1.366652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.680281 Loss1: 0.247604 Loss2: 1.432677 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464578 Loss1: 0.102516 Loss2: 1.362062
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550945 Loss1: 0.186892 Loss2: 1.364053
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443836 Loss1: 0.087960 Loss2: 1.355876
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492248 Loss1: 0.139605 Loss2: 1.352642
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424377 Loss1: 0.073603 Loss2: 1.350774
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446110 Loss1: 0.091218 Loss2: 1.354892
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411238 Loss1: 0.061700 Loss2: 1.349538
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433842 Loss1: 0.086107 Loss2: 1.347735
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414019 Loss1: 0.074982 Loss2: 1.339036
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396992 Loss1: 0.058515 Loss2: 1.338477
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373071 Loss1: 0.040541 Loss2: 1.332530
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.231599 Loss1: 0.429923 Loss2: 1.801676
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.601793 Loss1: 0.273789 Loss2: 1.328004
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.546778 Loss1: 0.181319 Loss2: 1.365459
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.468476 Loss1: 0.134928 Loss2: 1.333547
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.355937 Loss1: 0.532910 Loss2: 1.823027
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.445584 Loss1: 0.116437 Loss2: 1.329147
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.685561 Loss1: 0.344193 Loss2: 1.341368
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454259 Loss1: 0.121844 Loss2: 1.332415
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.660173 Loss1: 0.269077 Loss2: 1.391096
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.403602 Loss1: 0.082138 Loss2: 1.321465
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536329 Loss1: 0.181748 Loss2: 1.354581
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.412071 Loss1: 0.090065 Loss2: 1.322006
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.471807 Loss1: 0.125032 Loss2: 1.346775
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.368932 Loss1: 0.050685 Loss2: 1.318247
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512889 Loss1: 0.161549 Loss2: 1.351340
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350365 Loss1: 0.038040 Loss2: 1.312325
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.495799 Loss1: 0.148388 Loss2: 1.347411
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419580 Loss1: 0.083306 Loss2: 1.336274
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432991 Loss1: 0.093141 Loss2: 1.339851
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438798 Loss1: 0.103465 Loss2: 1.335333
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.525970 Loss1: 0.573768 Loss2: 1.952201
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.646663 Loss1: 0.321712 Loss2: 1.324951
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.633092 Loss1: 0.290956 Loss2: 1.342136
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570207 Loss1: 0.183800 Loss2: 1.386407
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514050 Loss1: 0.178338 Loss2: 1.335712
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.510215 Loss1: 0.170951 Loss2: 1.339263 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.454066 Loss1: 0.103758 Loss2: 1.350307 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.397727 Loss1: 0.068709 Loss2: 1.329018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.370225 Loss1: 0.050109 Loss2: 1.320116
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574768 Loss1: 0.167574 Loss2: 1.407194
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350708 Loss1: 0.031548 Loss2: 1.319160
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.537372 Loss1: 0.131826 Loss2: 1.405545
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.556729 Loss1: 0.155644 Loss2: 1.401085
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.506214 Loss1: 0.118872 Loss2: 1.387341
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.489560 Loss1: 0.101712 Loss2: 1.387848
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.459226 Loss1: 0.084049 Loss2: 1.375177
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.412171 Loss1: 0.496817 Loss2: 1.915355
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460243 Loss1: 0.080902 Loss2: 1.379341
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.619367 Loss1: 0.173161 Loss2: 1.446206 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611910 Loss1: 0.194391 Loss2: 1.417520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.548416 Loss1: 0.134030 Loss2: 1.414386
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.457888 Loss1: 0.600061 Loss2: 1.857827
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612766 Loss1: 0.317861 Loss2: 1.294905
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490916 Loss1: 0.088637 Loss2: 1.402279
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.472992 Loss1: 0.074916 Loss2: 1.398076 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449812 Loss1: 0.055311 Loss2: 1.394501 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.456185 Loss1: 0.071220 Loss2: 1.384964 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.334924 Loss1: 0.079731 Loss2: 1.255193 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.336046 Loss1: 0.083511 Loss2: 1.252535 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.614126 Loss1: 0.313557 Loss2: 1.300569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.454173 Loss1: 0.152537 Loss2: 1.301636 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.555850 Loss1: 0.605800 Loss2: 1.950050
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.422264 Loss1: 0.132489 Loss2: 1.289775
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.793107 Loss1: 0.365766 Loss2: 1.427340
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.376469 Loss1: 0.086603 Loss2: 1.289866
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.719019 Loss1: 0.267246 Loss2: 1.451772
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.358277 Loss1: 0.075418 Loss2: 1.282858
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.623815 Loss1: 0.215017 Loss2: 1.408798
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.363757 Loss1: 0.084620 Loss2: 1.279137
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574520 Loss1: 0.144382 Loss2: 1.430138
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403186 Loss1: 0.121420 Loss2: 1.281766
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.548112 Loss1: 0.141922 Loss2: 1.406190
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.348248 Loss1: 0.067328 Loss2: 1.280920
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457908 Loss1: 0.066159 Loss2: 1.391749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427822 Loss1: 0.041092 Loss2: 1.386730 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.654234 Loss1: 0.320928 Loss2: 1.333306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.486607 Loss1: 0.166466 Loss2: 1.320141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.465549 Loss1: 0.133845 Loss2: 1.331704 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.405008 Loss1: 0.086321 Loss2: 1.318687 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.400332 Loss1: 0.085876 Loss2: 1.314456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404820 Loss1: 0.087509 Loss2: 1.317312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.351805 Loss1: 0.044259 Loss2: 1.307546 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359890 Loss1: 0.055660 Loss2: 1.304230 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390790 Loss1: 0.096945 Loss2: 1.293845 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.243804 Loss1: 0.411334 Loss2: 1.832470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.629437 Loss1: 0.224226 Loss2: 1.405211 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528876 Loss1: 0.161134 Loss2: 1.367743
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.235815 Loss1: 0.413141 Loss2: 1.822674
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.514698 Loss1: 0.154243 Loss2: 1.360455
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.593470 Loss1: 0.242781 Loss2: 1.350689
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491132 Loss1: 0.130298 Loss2: 1.360834
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.462607 Loss1: 0.104681 Loss2: 1.357926
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.417431 Loss1: 0.069615 Loss2: 1.347817
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.469440 Loss1: 0.136245 Loss2: 1.333195
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.436785 Loss1: 0.098837 Loss2: 1.337948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446745 Loss1: 0.115996 Loss2: 1.330749 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379721 Loss1: 0.043521 Loss2: 1.336200
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437449 Loss1: 0.106811 Loss2: 1.330638
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.378684 Loss1: 0.048833 Loss2: 1.329851
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370981 Loss1: 0.048593 Loss2: 1.322387
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375072 Loss1: 0.056737 Loss2: 1.318335
-(DefaultActor pid=3764) >> Training accuracy: 0.990809
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.594210 Loss1: 0.239859 Loss2: 1.354350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527100 Loss1: 0.166733 Loss2: 1.360367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475506 Loss1: 0.111754 Loss2: 1.363752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472172 Loss1: 0.110750 Loss2: 1.361421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466277 Loss1: 0.109212 Loss2: 1.357065 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435657 Loss1: 0.087083 Loss2: 1.348574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405838 Loss1: 0.057159 Loss2: 1.348679 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421625 Loss1: 0.078923 Loss2: 1.342702 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390594 Loss1: 0.044256 Loss2: 1.346338 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.253183 Loss1: 0.447480 Loss2: 1.805703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611199 Loss1: 0.257369 Loss2: 1.353830 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.458806 Loss1: 0.137513 Loss2: 1.321293
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.430207 Loss1: 0.513078 Loss2: 1.917129
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.402440 Loss1: 0.094906 Loss2: 1.307535
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.722510 Loss1: 0.306201 Loss2: 1.416309
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.408021 Loss1: 0.104605 Loss2: 1.303416
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.729916 Loss1: 0.269165 Loss2: 1.460751
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383411 Loss1: 0.083308 Loss2: 1.300102
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556943 Loss1: 0.145795 Loss2: 1.411148
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.365435 Loss1: 0.067802 Loss2: 1.297633
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539954 Loss1: 0.124719 Loss2: 1.415235
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372278 Loss1: 0.075644 Loss2: 1.296634
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516620 Loss1: 0.108234 Loss2: 1.408386
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358333 Loss1: 0.068470 Loss2: 1.289863
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486279 Loss1: 0.083095 Loss2: 1.403184
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490411 Loss1: 0.093505 Loss2: 1.396906
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463481 Loss1: 0.077016 Loss2: 1.386465
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.451921 Loss1: 0.064119 Loss2: 1.387802
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.290555 Loss1: 0.453072 Loss2: 1.837484
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.571825 Loss1: 0.234603 Loss2: 1.337222
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.565356 Loss1: 0.223809 Loss2: 1.341547
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.495392 Loss1: 0.147034 Loss2: 1.348358
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.457709 Loss1: 0.554670 Loss2: 1.903040
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.707155 Loss1: 0.304505 Loss2: 1.402650 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.570459 Loss1: 0.167092 Loss2: 1.403367 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.524668 Loss1: 0.144470 Loss2: 1.380198 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493012 Loss1: 0.105935 Loss2: 1.387077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.462226 Loss1: 0.089706 Loss2: 1.372520 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384506 Loss1: 0.067500 Loss2: 1.317006
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.466131 Loss1: 0.093398 Loss2: 1.372733
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428545 Loss1: 0.066968 Loss2: 1.361577
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403972 Loss1: 0.045776 Loss2: 1.358196
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391169 Loss1: 0.037894 Loss2: 1.353276
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.429379 Loss1: 0.539738 Loss2: 1.889641
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.774809 Loss1: 0.381934 Loss2: 1.392875
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.765194 Loss1: 0.315126 Loss2: 1.450068
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694107 Loss1: 0.293151 Loss2: 1.400956
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.282055 Loss1: 0.446619 Loss2: 1.835436
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.654733 Loss1: 0.276112 Loss2: 1.378621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.569361 Loss1: 0.167516 Loss2: 1.401845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.552107 Loss1: 0.180659 Loss2: 1.371448 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528554 Loss1: 0.151828 Loss2: 1.376726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522772 Loss1: 0.140004 Loss2: 1.382768 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.481282 Loss1: 0.102692 Loss2: 1.378591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416897 Loss1: 0.054468 Loss2: 1.362429 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.485284 Loss1: 0.515182 Loss2: 1.970101 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.653393 Loss1: 0.166200 Loss2: 1.487193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.300948 Loss1: 0.498084 Loss2: 1.802864 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.711676 Loss1: 0.387084 Loss2: 1.324592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.597587 Loss1: 0.219541 Loss2: 1.378046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.531811 Loss1: 0.199466 Loss2: 1.332346 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542642 Loss1: 0.218282 Loss2: 1.324360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459501 Loss1: 0.127614 Loss2: 1.331887 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388902 Loss1: 0.074061 Loss2: 1.314841 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361710 Loss1: 0.060874 Loss2: 1.300836 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.662846 Loss1: 0.333944 Loss2: 1.328902 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488651 Loss1: 0.152821 Loss2: 1.335830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.234151 Loss1: 0.411851 Loss2: 1.822300
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.434105 Loss1: 0.107764 Loss2: 1.326342
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.569284 Loss1: 0.261078 Loss2: 1.308207
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443807 Loss1: 0.119852 Loss2: 1.323955
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.461712 Loss1: 0.128165 Loss2: 1.333547
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.403614 Loss1: 0.085144 Loss2: 1.318470
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.403687 Loss1: 0.095692 Loss2: 1.307995
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404688 Loss1: 0.085717 Loss2: 1.318970
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.363311 Loss1: 0.079223 Loss2: 1.284089
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.389731 Loss1: 0.069979 Loss2: 1.319752
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.367717 Loss1: 0.079271 Loss2: 1.288446
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361692 Loss1: 0.050363 Loss2: 1.311329
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431336 Loss1: 0.127219 Loss2: 1.304117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364517 Loss1: 0.075588 Loss2: 1.288929 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.717618 Loss1: 0.300904 Loss2: 1.416714 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.577586 Loss1: 0.164742 Loss2: 1.412845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.280506 Loss1: 0.479441 Loss2: 1.801065
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.575887 Loss1: 0.160234 Loss2: 1.415653
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515576 Loss1: 0.103334 Loss2: 1.412242
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.678547 Loss1: 0.338041 Loss2: 1.340505
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466163 Loss1: 0.070614 Loss2: 1.395549
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.553563 Loss1: 0.186788 Loss2: 1.366775
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.472400 Loss1: 0.083193 Loss2: 1.389207
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.484486 Loss1: 0.152469 Loss2: 1.332017
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444270 Loss1: 0.054194 Loss2: 1.390076
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446996 Loss1: 0.120531 Loss2: 1.326465
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457570 Loss1: 0.077137 Loss2: 1.380433
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429415 Loss1: 0.105552 Loss2: 1.323863
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419812 Loss1: 0.098987 Loss2: 1.320825
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418119 Loss1: 0.095235 Loss2: 1.322885
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394309 Loss1: 0.072776 Loss2: 1.321534
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407024 Loss1: 0.094391 Loss2: 1.312633
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.318733 Loss1: 0.481399 Loss2: 1.837334
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.651438 Loss1: 0.308210 Loss2: 1.343228
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.608433 Loss1: 0.210766 Loss2: 1.397667
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.486220 Loss1: 0.128998 Loss2: 1.357222
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476193 Loss1: 0.129800 Loss2: 1.346393
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.557782 Loss1: 0.630130 Loss2: 1.927652
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.740568 Loss1: 0.357563 Loss2: 1.383004 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.649490 Loss1: 0.234506 Loss2: 1.414984 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.504787 Loss1: 0.156823 Loss2: 1.347965
-DEBUG flwr 2023-10-12 19:40:12,582 | server.py:236 | fit_round 162 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.615773 Loss1: 0.224382 Loss2: 1.391391
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433402 Loss1: 0.086352 Loss2: 1.347050
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.560360 Loss1: 0.167358 Loss2: 1.393001
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397280 Loss1: 0.056907 Loss2: 1.340373
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539928 Loss1: 0.138575 Loss2: 1.401353
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487588 Loss1: 0.107993 Loss2: 1.379596
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483015 Loss1: 0.096590 Loss2: 1.386425
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422853 Loss1: 0.050020 Loss2: 1.372833
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412449 Loss1: 0.050988 Loss2: 1.361461
-(DefaultActor pid=3764) >> Training accuracy: 0.994420
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.255636 Loss1: 0.488725 Loss2: 1.766911
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.637320 Loss1: 0.300505 Loss2: 1.336815
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610557 Loss1: 0.246184 Loss2: 1.364373
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.480258 Loss1: 0.146373 Loss2: 1.333885
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.263098 Loss1: 0.463680 Loss2: 1.799417
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.611901 Loss1: 0.261675 Loss2: 1.350226 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.497250 Loss1: 0.136873 Loss2: 1.360378 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.453547 Loss1: 0.112508 Loss2: 1.341039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.415101 Loss1: 0.080556 Loss2: 1.334545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.415190 Loss1: 0.078886 Loss2: 1.336303 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.393293 Loss1: 0.064970 Loss2: 1.328323 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.345385 Loss1: 0.031979 Loss2: 1.313406 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.324195 Loss1: 0.435935 Loss2: 1.888260 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563061 Loss1: 0.174354 Loss2: 1.388707 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.335592 Loss1: 0.454990 Loss2: 1.880602 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.679563 Loss1: 0.300056 Loss2: 1.379507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.607802 Loss1: 0.187558 Loss2: 1.420244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521697 Loss1: 0.138836 Loss2: 1.382861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.517249 Loss1: 0.142156 Loss2: 1.375093 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476685 Loss1: 0.093388 Loss2: 1.383296 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437687 Loss1: 0.065392 Loss2: 1.372296 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401246 Loss1: 0.038867 Loss2: 1.362379 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 19:40:12,582][flwr][DEBUG] - fit_round 162 received 50 results and 0 failures
-INFO flwr 2023-10-12 19:40:53,988 | server.py:125 | fit progress: (162, 2.2612200133716716, {'accuracy': 0.6036}, 373761.76673800097)
->> Test accuracy: 0.603600
-[2023-10-12 19:40:53,988][flwr][INFO] - fit progress: (162, 2.2612200133716716, {'accuracy': 0.6036}, 373761.76673800097)
-DEBUG flwr 2023-10-12 19:40:53,989 | server.py:173 | evaluate_round 162: strategy sampled 50 clients (out of 50)
-[2023-10-12 19:40:53,989][flwr][DEBUG] - evaluate_round 162: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 19:50:01,998 | server.py:187 | evaluate_round 162 received 50 results and 0 failures
-[2023-10-12 19:50:01,998][flwr][DEBUG] - evaluate_round 162 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 19:50:01,998 | server.py:222 | fit_round 163: strategy sampled 50 clients (out of 50)
-[2023-10-12 19:50:01,998][flwr][DEBUG] - fit_round 163: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.384640 Loss1: 0.531320 Loss2: 1.853320
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.638172 Loss1: 0.285489 Loss2: 1.352683
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.580421 Loss1: 0.191166 Loss2: 1.389254
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536816 Loss1: 0.183022 Loss2: 1.353794
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.311966 Loss1: 0.482309 Loss2: 1.829657
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.819920 Loss1: 0.432433 Loss2: 1.387487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.710991 Loss1: 0.275587 Loss2: 1.435404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.578772 Loss1: 0.202492 Loss2: 1.376280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.504798 Loss1: 0.124695 Loss2: 1.380102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449860 Loss1: 0.082263 Loss2: 1.367596 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388800 Loss1: 0.064522 Loss2: 1.324278
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463963 Loss1: 0.104708 Loss2: 1.359255
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432131 Loss1: 0.071992 Loss2: 1.360139
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421139 Loss1: 0.072807 Loss2: 1.348331
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444586 Loss1: 0.095162 Loss2: 1.349424
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.181567 Loss1: 0.403429 Loss2: 1.778138
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.666768 Loss1: 0.324960 Loss2: 1.341808
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.544823 Loss1: 0.166072 Loss2: 1.378751
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.338022 Loss1: 0.459095 Loss2: 1.878928
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506174 Loss1: 0.168350 Loss2: 1.337824
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.616843 Loss1: 0.234816 Loss2: 1.382027
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.465359 Loss1: 0.113785 Loss2: 1.351574
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.574817 Loss1: 0.178524 Loss2: 1.396293
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449979 Loss1: 0.112056 Loss2: 1.337923
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.430380 Loss1: 0.090319 Loss2: 1.340061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421445 Loss1: 0.095326 Loss2: 1.326120 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449272 Loss1: 0.113689 Loss2: 1.335583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404171 Loss1: 0.072477 Loss2: 1.331694 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409418 Loss1: 0.052721 Loss2: 1.356697 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.324678 Loss1: 0.495527 Loss2: 1.829151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.593287 Loss1: 0.201593 Loss2: 1.391694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.565427 Loss1: 0.587277 Loss2: 1.978150
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502202 Loss1: 0.165207 Loss2: 1.336995
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.761424 Loss1: 0.376859 Loss2: 1.384564
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488046 Loss1: 0.155200 Loss2: 1.332846
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470245 Loss1: 0.134125 Loss2: 1.336120 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.413563 Loss1: 0.085070 Loss2: 1.328493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.383585 Loss1: 0.065675 Loss2: 1.317911 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379063 Loss1: 0.068574 Loss2: 1.310489 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383128 Loss1: 0.072874 Loss2: 1.310254 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446203 Loss1: 0.092042 Loss2: 1.354161 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.433403 Loss1: 0.547784 Loss2: 1.885619
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.692903 Loss1: 0.341412 Loss2: 1.351491
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.571115 Loss1: 0.178713 Loss2: 1.392402
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527973 Loss1: 0.165042 Loss2: 1.362932
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.417777 Loss1: 0.524548 Loss2: 1.893229
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.727309 Loss1: 0.341953 Loss2: 1.385356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727956 Loss1: 0.282064 Loss2: 1.445892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.638002 Loss1: 0.237583 Loss2: 1.400420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.610739 Loss1: 0.192470 Loss2: 1.418269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383032 Loss1: 0.051924 Loss2: 1.331108 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468462 Loss1: 0.086558 Loss2: 1.381904 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429074 Loss1: 0.057817 Loss2: 1.371257 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.673496 Loss1: 0.292436 Loss2: 1.381060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513578 Loss1: 0.131241 Loss2: 1.382337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.520802 Loss1: 0.139625 Loss2: 1.381177
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.300505 Loss1: 0.472745 Loss2: 1.827760
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498682 Loss1: 0.119584 Loss2: 1.379098
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.672172 Loss1: 0.327356 Loss2: 1.344816
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481352 Loss1: 0.100000 Loss2: 1.381352
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.555141 Loss1: 0.182038 Loss2: 1.373103
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.475828 Loss1: 0.102713 Loss2: 1.373115
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468896 Loss1: 0.134494 Loss2: 1.334402
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441728 Loss1: 0.068391 Loss2: 1.373337
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.438713 Loss1: 0.100622 Loss2: 1.338091
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409601 Loss1: 0.045704 Loss2: 1.363897
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.388645 Loss1: 0.059005 Loss2: 1.329639
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392571 Loss1: 0.070215 Loss2: 1.322355
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424928 Loss1: 0.103734 Loss2: 1.321194
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407072 Loss1: 0.086590 Loss2: 1.320482
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386993 Loss1: 0.063731 Loss2: 1.323262
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.434464 Loss1: 0.555503 Loss2: 1.878960
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.673687 Loss1: 0.315333 Loss2: 1.358354
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611619 Loss1: 0.217914 Loss2: 1.393705
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476922 Loss1: 0.126548 Loss2: 1.350373
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.486184 Loss1: 0.143587 Loss2: 1.342597
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.378244 Loss1: 0.539677 Loss2: 1.838567
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.773968 Loss1: 0.409174 Loss2: 1.364795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.687556 Loss1: 0.248892 Loss2: 1.438664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535990 Loss1: 0.169288 Loss2: 1.366702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539313 Loss1: 0.175179 Loss2: 1.364133 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.510161 Loss1: 0.136757 Loss2: 1.373404 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.474044 Loss1: 0.120531 Loss2: 1.353513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461508 Loss1: 0.103945 Loss2: 1.357563
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.581972 Loss1: 0.581808 Loss2: 2.000165
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.840129 Loss1: 0.463825 Loss2: 1.376304
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.751331 Loss1: 0.286369 Loss2: 1.464962
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.610045 Loss1: 0.197186 Loss2: 1.412859
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.644075 Loss1: 0.256910 Loss2: 1.387166
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514840 Loss1: 0.114379 Loss2: 1.400461
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470866 Loss1: 0.092800 Loss2: 1.378066
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.716599 Loss1: 0.316353 Loss2: 1.400246 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.715798 Loss1: 0.247065 Loss2: 1.468733 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.599331 Loss1: 0.168310 Loss2: 1.431021 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.558139 Loss1: 0.148760 Loss2: 1.409379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.496065 Loss1: 0.089790 Loss2: 1.406275
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.115943 Loss1: 0.376095 Loss2: 1.739848
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.477718 Loss1: 0.180532 Loss2: 1.297186 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.495823 Loss1: 0.178059 Loss2: 1.317764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.399421 Loss1: 0.116615 Loss2: 1.282806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.357277 Loss1: 0.073806 Loss2: 1.283472 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380926 Loss1: 0.101368 Loss2: 1.279559 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.346917 Loss1: 0.063267 Loss2: 1.283650 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.334126 Loss1: 0.055047 Loss2: 1.279079 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500912 Loss1: 0.156878 Loss2: 1.344034 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452769 Loss1: 0.111969 Loss2: 1.340800 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443001 Loss1: 0.114812 Loss2: 1.328189
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.217764 Loss1: 0.416063 Loss2: 1.801701
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460107 Loss1: 0.125361 Loss2: 1.334746
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.602088 Loss1: 0.255150 Loss2: 1.346938
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.532276 Loss1: 0.180571 Loss2: 1.351705
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498230 Loss1: 0.151449 Loss2: 1.346781
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.495508 Loss1: 0.157671 Loss2: 1.337837
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450159 Loss1: 0.110607 Loss2: 1.339552
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.281050 Loss1: 0.443361 Loss2: 1.837689
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630712 Loss1: 0.281636 Loss2: 1.349076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.583726 Loss1: 0.206794 Loss2: 1.376932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523725 Loss1: 0.167468 Loss2: 1.356257 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364967 Loss1: 0.039987 Loss2: 1.324980
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.431548 Loss1: 0.079774 Loss2: 1.351774
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.422635 Loss1: 0.083926 Loss2: 1.338709
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395261 Loss1: 0.063445 Loss2: 1.331816
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405614 Loss1: 0.075990 Loss2: 1.329624
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403964 Loss1: 0.081247 Loss2: 1.322717
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.341549 Loss1: 0.407409 Loss2: 1.934140
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421666 Loss1: 0.092990 Loss2: 1.328675
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.597020 Loss1: 0.199974 Loss2: 1.397046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.687734 Loss1: 0.277561 Loss2: 1.410172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.535897 Loss1: 0.139462 Loss2: 1.396435
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.351431 Loss1: 0.451363 Loss2: 1.900068
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.736883 Loss1: 0.336993 Loss2: 1.399890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.639140 Loss1: 0.200900 Loss2: 1.438241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.560814 Loss1: 0.151246 Loss2: 1.409568 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.468431 Loss1: 0.085746 Loss2: 1.382684
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.494472 Loss1: 0.098349 Loss2: 1.396123
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510000 Loss1: 0.119990 Loss2: 1.390009
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500201 Loss1: 0.105253 Loss2: 1.394948
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.524157 Loss1: 0.135315 Loss2: 1.388842
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.517056 Loss1: 0.114798 Loss2: 1.402258
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.344344 Loss1: 0.496064 Loss2: 1.848280
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442881 Loss1: 0.056144 Loss2: 1.386737
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.622684 Loss1: 0.227260 Loss2: 1.395424 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.436289 Loss1: 0.098427 Loss2: 1.337863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.421697 Loss1: 0.088488 Loss2: 1.333209
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.275021 Loss1: 0.430252 Loss2: 1.844769
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.641990 Loss1: 0.261932 Loss2: 1.380058 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.616497 Loss1: 0.208220 Loss2: 1.408276 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.541945 Loss1: 0.163764 Loss2: 1.378181 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447682 Loss1: 0.084779 Loss2: 1.362903 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414850 Loss1: 0.054686 Loss2: 1.360164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404649 Loss1: 0.055210 Loss2: 1.349439
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.426095 Loss1: 0.554635 Loss2: 1.871460
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.720590 Loss1: 0.353672 Loss2: 1.366917
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417280 Loss1: 0.066733 Loss2: 1.350548
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488583 Loss1: 0.131589 Loss2: 1.356994 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.406095 Loss1: 0.063231 Loss2: 1.342864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.392588 Loss1: 0.053530 Loss2: 1.339058
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.274306 Loss1: 0.446801 Loss2: 1.827506
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382703 Loss1: 0.049784 Loss2: 1.332918
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.665980 Loss1: 0.324032 Loss2: 1.341948
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399832 Loss1: 0.068576 Loss2: 1.331256
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.593784 Loss1: 0.209646 Loss2: 1.384138
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386248 Loss1: 0.053347 Loss2: 1.332901
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519434 Loss1: 0.167378 Loss2: 1.352056
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475484 Loss1: 0.136680 Loss2: 1.338804
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444301 Loss1: 0.097900 Loss2: 1.346402
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.421324 Loss1: 0.090591 Loss2: 1.330733
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438224 Loss1: 0.100506 Loss2: 1.337719
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398403 Loss1: 0.063248 Loss2: 1.335155
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.300880 Loss1: 0.452203 Loss2: 1.848676
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381090 Loss1: 0.057425 Loss2: 1.323665
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.728154 Loss1: 0.337348 Loss2: 1.390806
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.649628 Loss1: 0.220479 Loss2: 1.429149
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573577 Loss1: 0.178880 Loss2: 1.394697
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561188 Loss1: 0.159199 Loss2: 1.401989
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520530 Loss1: 0.130786 Loss2: 1.389744
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.248938 Loss1: 0.373710 Loss2: 1.875228
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659698 Loss1: 0.255800 Loss2: 1.403898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.582013 Loss1: 0.161341 Loss2: 1.420672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556657 Loss1: 0.154162 Loss2: 1.402495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.590100 Loss1: 0.189378 Loss2: 1.400722 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472745 Loss1: 0.081177 Loss2: 1.391568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.320740 Loss1: 0.490603 Loss2: 1.830136 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.680429 Loss1: 0.344007 Loss2: 1.336423 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994485
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474425 Loss1: 0.128526 Loss2: 1.345899 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487212 Loss1: 0.145738 Loss2: 1.341474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439166 Loss1: 0.113105 Loss2: 1.326061
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.351928 Loss1: 0.555130 Loss2: 1.796798
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.682637 Loss1: 0.359430 Loss2: 1.323207 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.591889 Loss1: 0.237323 Loss2: 1.354566 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.959375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496540 Loss1: 0.166090 Loss2: 1.330450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494446 Loss1: 0.161240 Loss2: 1.333206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.387994 Loss1: 0.076253 Loss2: 1.311741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385415 Loss1: 0.078558 Loss2: 1.306857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366501 Loss1: 0.065431 Loss2: 1.301071 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518150 Loss1: 0.179627 Loss2: 1.338523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449105 Loss1: 0.117145 Loss2: 1.331960 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.229337 Loss1: 0.434497 Loss2: 1.794840 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.669426 Loss1: 0.346215 Loss2: 1.323211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.658304 Loss1: 0.266744 Loss2: 1.391560 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469477 Loss1: 0.133729 Loss2: 1.335748 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.381119 Loss1: 0.056107 Loss2: 1.325012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374034 Loss1: 0.063396 Loss2: 1.310638
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.412753 Loss1: 0.562473 Loss2: 1.850279
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.672812 Loss1: 0.343393 Loss2: 1.329419 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.564981 Loss1: 0.203222 Loss2: 1.361759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.411881 Loss1: 0.086741 Loss2: 1.325140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.377086 Loss1: 0.070112 Loss2: 1.306974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.364861 Loss1: 0.057076 Loss2: 1.307785 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.342034 Loss1: 0.041380 Loss2: 1.300654 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350067 Loss1: 0.052603 Loss2: 1.297464 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506962 Loss1: 0.117218 Loss2: 1.389744 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445677 Loss1: 0.071658 Loss2: 1.374019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441448 Loss1: 0.068143 Loss2: 1.373306
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.341082 Loss1: 0.504316 Loss2: 1.836766
-(DefaultActor pid=3764) >> Training accuracy: 0.997768
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411924 Loss1: 0.045686 Loss2: 1.366237
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.733160 Loss1: 0.380879 Loss2: 1.352282
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.636539 Loss1: 0.240386 Loss2: 1.396153
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.543353 Loss1: 0.191218 Loss2: 1.352135
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.504982 Loss1: 0.146372 Loss2: 1.358610
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465974 Loss1: 0.115463 Loss2: 1.350511
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.167215 Loss1: 0.372563 Loss2: 1.794652
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462280 Loss1: 0.121115 Loss2: 1.341165
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.626063 Loss1: 0.277253 Loss2: 1.348810
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398999 Loss1: 0.063675 Loss2: 1.335324
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.503717 Loss1: 0.131740 Loss2: 1.371977
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431026 Loss1: 0.099140 Loss2: 1.331886
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.465642 Loss1: 0.124964 Loss2: 1.340678
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372080 Loss1: 0.046332 Loss2: 1.325749
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.445149 Loss1: 0.105148 Loss2: 1.340001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442101 Loss1: 0.102798 Loss2: 1.339303 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.357866 Loss1: 0.497673 Loss2: 1.860193
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435159 Loss1: 0.095843 Loss2: 1.339317
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424012 Loss1: 0.079983 Loss2: 1.344029 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596649 Loss1: 0.219354 Loss2: 1.377295 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514035 Loss1: 0.126022 Loss2: 1.388013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474644 Loss1: 0.109726 Loss2: 1.364918
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.304833 Loss1: 0.519418 Loss2: 1.785415
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.647931 Loss1: 0.317105 Loss2: 1.330826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598759 Loss1: 0.228242 Loss2: 1.370518 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400835 Loss1: 0.046661 Loss2: 1.354173
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.462532 Loss1: 0.128737 Loss2: 1.333795
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450002 Loss1: 0.119220 Loss2: 1.330783
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.407245 Loss1: 0.078787 Loss2: 1.328458
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.371936 Loss1: 0.059553 Loss2: 1.312382
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.380630 Loss1: 0.067935 Loss2: 1.312695
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.369883 Loss1: 0.515037 Loss2: 1.854846
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.355984 Loss1: 0.047109 Loss2: 1.308875
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.336523 Loss1: 0.026780 Loss2: 1.309743 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526391 Loss1: 0.143118 Loss2: 1.383273 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440061 Loss1: 0.080953 Loss2: 1.359108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435836 Loss1: 0.087024 Loss2: 1.348813
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.220436 Loss1: 0.403249 Loss2: 1.817187
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.594582 Loss1: 0.247904 Loss2: 1.346677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.538597 Loss1: 0.175151 Loss2: 1.363446 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405789 Loss1: 0.076962 Loss2: 1.328827
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.466191 Loss1: 0.135916 Loss2: 1.330275
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.416254 Loss1: 0.087165 Loss2: 1.329090
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.415919 Loss1: 0.093651 Loss2: 1.322268
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426410 Loss1: 0.106282 Loss2: 1.320127
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372437 Loss1: 0.066127 Loss2: 1.306310
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.289739 Loss1: 0.483614 Loss2: 1.806126
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.346455 Loss1: 0.040558 Loss2: 1.305897
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.337789 Loss1: 0.034607 Loss2: 1.303182 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.446094 Loss1: 0.102604 Loss2: 1.343490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.428157 Loss1: 0.115699 Loss2: 1.312458 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460427 Loss1: 0.136810 Loss2: 1.323617
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.275661 Loss1: 0.470932 Loss2: 1.804729
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.638963 Loss1: 0.303610 Loss2: 1.335353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592374 Loss1: 0.219030 Loss2: 1.373345 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374698 Loss1: 0.072731 Loss2: 1.301967
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563759 Loss1: 0.206907 Loss2: 1.356852
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519411 Loss1: 0.170568 Loss2: 1.348844
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476975 Loss1: 0.115487 Loss2: 1.361489
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.421611 Loss1: 0.086995 Loss2: 1.334616
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412118 Loss1: 0.076852 Loss2: 1.335266
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.292183 Loss1: 0.447004 Loss2: 1.845179
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384533 Loss1: 0.050326 Loss2: 1.334207
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370904 Loss1: 0.045934 Loss2: 1.324970 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513520 Loss1: 0.161528 Loss2: 1.351991 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.435353 Loss1: 0.090956 Loss2: 1.344397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.398619 Loss1: 0.060406 Loss2: 1.338213
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.382206 Loss1: 0.553426 Loss2: 1.828780
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.731652 Loss1: 0.378760 Loss2: 1.352892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.728559 Loss1: 0.308822 Loss2: 1.419737 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.617268 Loss1: 0.242785 Loss2: 1.374483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472429 Loss1: 0.107632 Loss2: 1.364798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405946 Loss1: 0.061439 Loss2: 1.344506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408107 Loss1: 0.071779 Loss2: 1.336329 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.733108 Loss1: 0.334561 Loss2: 1.398547
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408340 Loss1: 0.074964 Loss2: 1.333376
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575389 Loss1: 0.198587 Loss2: 1.376802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456660 Loss1: 0.088932 Loss2: 1.367728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.348807 Loss1: 0.514839 Loss2: 1.833967
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406044 Loss1: 0.047282 Loss2: 1.358763
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612477 Loss1: 0.274088 Loss2: 1.338388
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401684 Loss1: 0.048501 Loss2: 1.353183
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572569 Loss1: 0.218118 Loss2: 1.354451
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396808 Loss1: 0.052915 Loss2: 1.343893
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379965 Loss1: 0.037189 Loss2: 1.342775 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.457832 Loss1: 0.125793 Loss2: 1.332038 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453106 Loss1: 0.123314 Loss2: 1.329792 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426942 Loss1: 0.097287 Loss2: 1.329655
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.469698 Loss1: 0.598421 Loss2: 1.871277
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379933 Loss1: 0.057588 Loss2: 1.322345
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.594463 Loss1: 0.251911 Loss2: 1.342551
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.605272 Loss1: 0.246288 Loss2: 1.358983
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506141 Loss1: 0.129433 Loss2: 1.376708
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468168 Loss1: 0.124776 Loss2: 1.343392
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459485 Loss1: 0.121619 Loss2: 1.337866
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436358 Loss1: 0.092424 Loss2: 1.343934
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378998 Loss1: 0.040388 Loss2: 1.338610
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.360562 Loss1: 0.034611 Loss2: 1.325951 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380332 Loss1: 0.061166 Loss2: 1.319166 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492864 Loss1: 0.138972 Loss2: 1.353892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.417282 Loss1: 0.075189 Loss2: 1.342092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.304338 Loss1: 0.472389 Loss2: 1.831949 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.753325 Loss1: 0.368091 Loss2: 1.385234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361495 Loss1: 0.034964 Loss2: 1.326531 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-12 20:18:38,185 | server.py:236 | fit_round 163 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507663 Loss1: 0.122408 Loss2: 1.385255 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478210 Loss1: 0.107949 Loss2: 1.370261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.348777 Loss1: 0.491512 Loss2: 1.857265
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434588 Loss1: 0.069794 Loss2: 1.364795
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.719448 Loss1: 0.348976 Loss2: 1.370471
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439946 Loss1: 0.075868 Loss2: 1.364078
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428014 Loss1: 0.073660 Loss2: 1.354354 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.561223 Loss1: 0.204092 Loss2: 1.357132 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.496462 Loss1: 0.137539 Loss2: 1.358924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449705 Loss1: 0.087472 Loss2: 1.362234
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.297432 Loss1: 0.457815 Loss2: 1.839617
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.676778 Loss1: 0.326569 Loss2: 1.350209 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.586243 Loss1: 0.188394 Loss2: 1.397850 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478582 Loss1: 0.129423 Loss2: 1.349159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410061 Loss1: 0.077674 Loss2: 1.332387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384144 Loss1: 0.055330 Loss2: 1.328814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390152 Loss1: 0.063769 Loss2: 1.326383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600900 Loss1: 0.243885 Loss2: 1.357015
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368878 Loss1: 0.049798 Loss2: 1.319080
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503874 Loss1: 0.159124 Loss2: 1.344750 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461168 Loss1: 0.121924 Loss2: 1.339244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362695 Loss1: 0.046961 Loss2: 1.315735 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 20:18:38,185][flwr][DEBUG] - fit_round 163 received 50 results and 0 failures
-INFO flwr 2023-10-12 20:19:18,934 | server.py:125 | fit progress: (163, 2.254364108125242, {'accuracy': 0.6001}, 376066.71229861496)
->> Test accuracy: 0.600100
-[2023-10-12 20:19:18,934][flwr][INFO] - fit progress: (163, 2.254364108125242, {'accuracy': 0.6001}, 376066.71229861496)
-DEBUG flwr 2023-10-12 20:19:18,934 | server.py:173 | evaluate_round 163: strategy sampled 50 clients (out of 50)
-[2023-10-12 20:19:18,934][flwr][DEBUG] - evaluate_round 163: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 20:28:25,350 | server.py:187 | evaluate_round 163 received 50 results and 0 failures
-[2023-10-12 20:28:25,350][flwr][DEBUG] - evaluate_round 163 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 20:28:25,351 | server.py:222 | fit_round 164: strategy sampled 50 clients (out of 50)
-[2023-10-12 20:28:25,351][flwr][DEBUG] - fit_round 164: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.335590 Loss1: 0.462985 Loss2: 1.872604 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.613316 Loss1: 0.191351 Loss2: 1.421965 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549644 Loss1: 0.176474 Loss2: 1.373170
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.382404 Loss1: 0.485102 Loss2: 1.897301
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.670794 Loss1: 0.254867 Loss2: 1.415927 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564065 Loss1: 0.134712 Loss2: 1.429353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.511896 Loss1: 0.108790 Loss2: 1.403106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495003 Loss1: 0.095137 Loss2: 1.399866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481351 Loss1: 0.086175 Loss2: 1.395176 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459199 Loss1: 0.065811 Loss2: 1.393387 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438935 Loss1: 0.050229 Loss2: 1.388706 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.629204 Loss1: 0.287804 Loss2: 1.341400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.515603 Loss1: 0.159153 Loss2: 1.356450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.485690 Loss1: 0.575208 Loss2: 1.910482
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.483103 Loss1: 0.148046 Loss2: 1.335057
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.650077 Loss1: 0.270719 Loss2: 1.379359
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443014 Loss1: 0.105547 Loss2: 1.337467
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487903 Loss1: 0.154564 Loss2: 1.333339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432011 Loss1: 0.093194 Loss2: 1.338817 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401349 Loss1: 0.069333 Loss2: 1.332016 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390763 Loss1: 0.065912 Loss2: 1.324851 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428072 Loss1: 0.071369 Loss2: 1.356703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409501 Loss1: 0.058569 Loss2: 1.350933 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.536221 Loss1: 0.590187 Loss2: 1.946034
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.688669 Loss1: 0.348079 Loss2: 1.340589
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.642948 Loss1: 0.275431 Loss2: 1.367516
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587103 Loss1: 0.196811 Loss2: 1.390292
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.582797 Loss1: 0.240329 Loss2: 1.342468
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513338 Loss1: 0.154268 Loss2: 1.359070 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440341 Loss1: 0.097338 Loss2: 1.343003 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413153 Loss1: 0.085057 Loss2: 1.328096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395169 Loss1: 0.067697 Loss2: 1.327473
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.489529 Loss1: 0.135335 Loss2: 1.354193
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405403 Loss1: 0.084028 Loss2: 1.321375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492413 Loss1: 0.135892 Loss2: 1.356521
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493334 Loss1: 0.137014 Loss2: 1.356320
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453790 Loss1: 0.111197 Loss2: 1.342592
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432600 Loss1: 0.094030 Loss2: 1.338571
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420772 Loss1: 0.079937 Loss2: 1.340835
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.331990 Loss1: 0.479890 Loss2: 1.852100
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426036 Loss1: 0.091378 Loss2: 1.334658
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.598261 Loss1: 0.186738 Loss2: 1.411523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.497074 Loss1: 0.129718 Loss2: 1.367356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.414006 Loss1: 0.563533 Loss2: 1.850473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.698940 Loss1: 0.342005 Loss2: 1.356935 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629943 Loss1: 0.238844 Loss2: 1.391099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.500845 Loss1: 0.146004 Loss2: 1.354841 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.498309 Loss1: 0.144068 Loss2: 1.354241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.400649 Loss1: 0.063597 Loss2: 1.337052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373731 Loss1: 0.051263 Loss2: 1.322468 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357716 Loss1: 0.038921 Loss2: 1.318795 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.497031 Loss1: 0.155535 Loss2: 1.341496 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.449438 Loss1: 0.130520 Loss2: 1.318918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.282419 Loss1: 0.492741 Loss2: 1.789678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630634 Loss1: 0.314304 Loss2: 1.316330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.575089 Loss1: 0.215049 Loss2: 1.360039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.493255 Loss1: 0.175538 Loss2: 1.317717 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.402350 Loss1: 0.090155 Loss2: 1.312196 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.365431 Loss1: 0.061387 Loss2: 1.304045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.348993 Loss1: 0.052402 Loss2: 1.296591
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.204653 Loss1: 0.438530 Loss2: 1.766123
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.327894 Loss1: 0.037825 Loss2: 1.290069
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.591149 Loss1: 0.269875 Loss2: 1.321274
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.535311 Loss1: 0.183405 Loss2: 1.351906
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496432 Loss1: 0.181665 Loss2: 1.314767
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490988 Loss1: 0.155774 Loss2: 1.335213
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452921 Loss1: 0.136328 Loss2: 1.316593
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.335551 Loss1: 0.464699 Loss2: 1.870852
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.625697 Loss1: 0.256338 Loss2: 1.369359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556858 Loss1: 0.164835 Loss2: 1.392023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374194 Loss1: 0.068935 Loss2: 1.305259
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520822 Loss1: 0.149833 Loss2: 1.370989
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360637 Loss1: 0.061911 Loss2: 1.298726
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512194 Loss1: 0.146768 Loss2: 1.365426
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.540437 Loss1: 0.172483 Loss2: 1.367954
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509687 Loss1: 0.128337 Loss2: 1.381349
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462078 Loss1: 0.093179 Loss2: 1.368899
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418131 Loss1: 0.049553 Loss2: 1.368578
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392483 Loss1: 0.041735 Loss2: 1.350748
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.313295 Loss1: 0.433077 Loss2: 1.880218
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.642509 Loss1: 0.266026 Loss2: 1.376483
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.524168 Loss1: 0.121510 Loss2: 1.402659
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484896 Loss1: 0.108632 Loss2: 1.376264
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492434 Loss1: 0.125734 Loss2: 1.366700
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476045 Loss1: 0.109383 Loss2: 1.366662
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.501601 Loss1: 0.633595 Loss2: 1.868005
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.720708 Loss1: 0.397841 Loss2: 1.322867
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455255 Loss1: 0.089158 Loss2: 1.366097
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653072 Loss1: 0.297909 Loss2: 1.355162
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426083 Loss1: 0.075363 Loss2: 1.350720
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540053 Loss1: 0.219749 Loss2: 1.320305
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439893 Loss1: 0.078971 Loss2: 1.360922
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419146 Loss1: 0.058109 Loss2: 1.361037 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441680 Loss1: 0.126631 Loss2: 1.315048 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.380653 Loss1: 0.078205 Loss2: 1.302448 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357458 Loss1: 0.058816 Loss2: 1.298642
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.170830 Loss1: 0.357465 Loss2: 1.813365
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.569034 Loss1: 0.220874 Loss2: 1.348160
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.543846 Loss1: 0.181908 Loss2: 1.361938
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.492346 Loss1: 0.136382 Loss2: 1.355963
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471756 Loss1: 0.132566 Loss2: 1.339191
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.339754 Loss1: 0.423603 Loss2: 1.916151
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.483760 Loss1: 0.127197 Loss2: 1.356563
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.708701 Loss1: 0.305491 Loss2: 1.403210
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445139 Loss1: 0.106392 Loss2: 1.338746
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.617692 Loss1: 0.188977 Loss2: 1.428715
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543081 Loss1: 0.142258 Loss2: 1.400822
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441356 Loss1: 0.091916 Loss2: 1.349440
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512640 Loss1: 0.124898 Loss2: 1.387742
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408004 Loss1: 0.065709 Loss2: 1.342296
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.545204 Loss1: 0.155712 Loss2: 1.389493
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399524 Loss1: 0.064229 Loss2: 1.335295
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461259 Loss1: 0.077212 Loss2: 1.384047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420232 Loss1: 0.044646 Loss2: 1.375586 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.606916 Loss1: 0.264879 Loss2: 1.342037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523285 Loss1: 0.169823 Loss2: 1.353461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.486573 Loss1: 0.145291 Loss2: 1.341282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450985 Loss1: 0.109357 Loss2: 1.341628 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.407586 Loss1: 0.076816 Loss2: 1.330770 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394193 Loss1: 0.066017 Loss2: 1.328176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.366544 Loss1: 0.046463 Loss2: 1.320082 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372770 Loss1: 0.058976 Loss2: 1.313793 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.505835 Loss1: 0.065079 Loss2: 1.440756 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.210382 Loss1: 0.365467 Loss2: 1.844916 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611856 Loss1: 0.179464 Loss2: 1.432392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527549 Loss1: 0.142213 Loss2: 1.385337
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.426220 Loss1: 0.548544 Loss2: 1.877677
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.597305 Loss1: 0.245617 Loss2: 1.351689
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.529021 Loss1: 0.141629 Loss2: 1.387392
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.571020 Loss1: 0.182557 Loss2: 1.388463
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487373 Loss1: 0.101519 Loss2: 1.385854
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.474117 Loss1: 0.118167 Loss2: 1.355950
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.489639 Loss1: 0.106960 Loss2: 1.382680
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.472222 Loss1: 0.124323 Loss2: 1.347900
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.466343 Loss1: 0.085020 Loss2: 1.381323
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448295 Loss1: 0.071784 Loss2: 1.376511 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415255 Loss1: 0.040621 Loss2: 1.374635 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381211 Loss1: 0.051580 Loss2: 1.329632 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.224557 Loss1: 0.384060 Loss2: 1.840497 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.638631 Loss1: 0.211803 Loss2: 1.426828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.340107 Loss1: 0.470528 Loss2: 1.869579
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.532766 Loss1: 0.158451 Loss2: 1.374315
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.687381 Loss1: 0.314186 Loss2: 1.373194
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547672 Loss1: 0.161680 Loss2: 1.385992
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608079 Loss1: 0.197858 Loss2: 1.410221
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498913 Loss1: 0.105793 Loss2: 1.393120
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576547 Loss1: 0.201760 Loss2: 1.374787
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475745 Loss1: 0.101627 Loss2: 1.374117
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471392 Loss1: 0.103507 Loss2: 1.367885 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423680 Loss1: 0.055128 Loss2: 1.368552 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414284 Loss1: 0.054807 Loss2: 1.359477 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.456703 Loss1: 0.098479 Loss2: 1.358224 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.361146 Loss1: 0.480749 Loss2: 1.880397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635157 Loss1: 0.227578 Loss2: 1.407579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.571323 Loss1: 0.184300 Loss2: 1.387024
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.456040 Loss1: 0.554745 Loss2: 1.901295
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526180 Loss1: 0.138294 Loss2: 1.387887
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.775162 Loss1: 0.378130 Loss2: 1.397032
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516071 Loss1: 0.136117 Loss2: 1.379954
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670128 Loss1: 0.230482 Loss2: 1.439645
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461143 Loss1: 0.088989 Loss2: 1.372154
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.607423 Loss1: 0.204679 Loss2: 1.402744
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440170 Loss1: 0.073394 Loss2: 1.366776
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.597496 Loss1: 0.184941 Loss2: 1.412555
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434031 Loss1: 0.071474 Loss2: 1.362557
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.543102 Loss1: 0.138204 Loss2: 1.404897
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410993 Loss1: 0.051279 Loss2: 1.359714
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.530171 Loss1: 0.133588 Loss2: 1.396582
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493957 Loss1: 0.092949 Loss2: 1.401009
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.466001 Loss1: 0.081520 Loss2: 1.384481
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.487686 Loss1: 0.110326 Loss2: 1.377359
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.358347 Loss1: 0.529556 Loss2: 1.828792
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.674688 Loss1: 0.328017 Loss2: 1.346671
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.592373 Loss1: 0.209101 Loss2: 1.383272
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523938 Loss1: 0.178879 Loss2: 1.345059
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.273429 Loss1: 0.526122 Loss2: 1.747307
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.591879 Loss1: 0.293368 Loss2: 1.298511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.506497 Loss1: 0.174146 Loss2: 1.332351 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.502055 Loss1: 0.208522 Loss2: 1.293534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.417762 Loss1: 0.127287 Loss2: 1.290475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.403349 Loss1: 0.113327 Loss2: 1.290022 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409898 Loss1: 0.071098 Loss2: 1.338800
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.332087 Loss1: 0.050908 Loss2: 1.281180
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.318358 Loss1: 0.050477 Loss2: 1.267880
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.309337 Loss1: 0.042963 Loss2: 1.266373
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.303400 Loss1: 0.041742 Loss2: 1.261658
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.495497 Loss1: 0.598727 Loss2: 1.896770
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.665648 Loss1: 0.319929 Loss2: 1.345719
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648995 Loss1: 0.257820 Loss2: 1.391174
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498396 Loss1: 0.147896 Loss2: 1.350499
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476711 Loss1: 0.150807 Loss2: 1.325904
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451044 Loss1: 0.118454 Loss2: 1.332590 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414845 Loss1: 0.092408 Loss2: 1.322437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423541 Loss1: 0.093892 Loss2: 1.329649 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411469 Loss1: 0.092247 Loss2: 1.319222 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373015 Loss1: 0.056819 Loss2: 1.316195 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.379538 Loss1: 0.076119 Loss2: 1.303418 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.350631 Loss1: 0.059125 Loss2: 1.291507 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622329 Loss1: 0.302212 Loss2: 1.320117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.449476 Loss1: 0.130282 Loss2: 1.319194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.433822 Loss1: 0.117843 Loss2: 1.315978
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.277460 Loss1: 0.467790 Loss2: 1.809670
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455544 Loss1: 0.129444 Loss2: 1.326100
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612687 Loss1: 0.298177 Loss2: 1.314510
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.421733 Loss1: 0.104938 Loss2: 1.316794
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.525783 Loss1: 0.187276 Loss2: 1.338507
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424822 Loss1: 0.109375 Loss2: 1.315447
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.474499 Loss1: 0.152813 Loss2: 1.321686
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373831 Loss1: 0.054661 Loss2: 1.319170
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.467304 Loss1: 0.150327 Loss2: 1.316976
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395640 Loss1: 0.088827 Loss2: 1.306813
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427394 Loss1: 0.106650 Loss2: 1.320743
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.403159 Loss1: 0.095157 Loss2: 1.308002
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.359220 Loss1: 0.052578 Loss2: 1.306642
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.336086 Loss1: 0.039390 Loss2: 1.296696
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.324872 Loss1: 0.033440 Loss2: 1.291432
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.286597 Loss1: 0.446890 Loss2: 1.839708
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.592992 Loss1: 0.251815 Loss2: 1.341177
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.559781 Loss1: 0.192601 Loss2: 1.367180
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.509755 Loss1: 0.153347 Loss2: 1.356408
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468105 Loss1: 0.130075 Loss2: 1.338030
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442401 Loss1: 0.096206 Loss2: 1.346196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.465165 Loss1: 0.135535 Loss2: 1.329631 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438275 Loss1: 0.102160 Loss2: 1.336115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412287 Loss1: 0.077487 Loss2: 1.334801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403935 Loss1: 0.070638 Loss2: 1.333297 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397246 Loss1: 0.069122 Loss2: 1.328124 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381659 Loss1: 0.063703 Loss2: 1.317956 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.576099 Loss1: 0.263182 Loss2: 1.312917 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500758 Loss1: 0.173343 Loss2: 1.327415 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516162 Loss1: 0.202230 Loss2: 1.313932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480879 Loss1: 0.161243 Loss2: 1.319636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441053 Loss1: 0.117838 Loss2: 1.323215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454042 Loss1: 0.146371 Loss2: 1.307671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406154 Loss1: 0.093101 Loss2: 1.313052 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373844 Loss1: 0.066322 Loss2: 1.307522 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428554 Loss1: 0.068873 Loss2: 1.359681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418887 Loss1: 0.062911 Loss2: 1.355976 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.714676 Loss1: 0.351115 Loss2: 1.363561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498388 Loss1: 0.139281 Loss2: 1.359107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.243484 Loss1: 0.408875 Loss2: 1.834608
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488901 Loss1: 0.131526 Loss2: 1.357375
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.686819 Loss1: 0.348951 Loss2: 1.337868
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459352 Loss1: 0.107032 Loss2: 1.352320
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.597386 Loss1: 0.201713 Loss2: 1.395672
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424484 Loss1: 0.083301 Loss2: 1.341184
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526021 Loss1: 0.186519 Loss2: 1.339502
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422387 Loss1: 0.078121 Loss2: 1.344266
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527543 Loss1: 0.175055 Loss2: 1.352488
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.375421 Loss1: 0.038493 Loss2: 1.336928
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.415460 Loss1: 0.082090 Loss2: 1.333370
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350979 Loss1: 0.023965 Loss2: 1.327013
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417552 Loss1: 0.104405 Loss2: 1.313147 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351572 Loss1: 0.045882 Loss2: 1.305691 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.755497 Loss1: 0.371845 Loss2: 1.383651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.642724 Loss1: 0.254765 Loss2: 1.387959 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.566517 Loss1: 0.170127 Loss2: 1.396390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.534889 Loss1: 0.160242 Loss2: 1.374647 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.507915 Loss1: 0.128934 Loss2: 1.378982 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451415 Loss1: 0.083549 Loss2: 1.367866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438863 Loss1: 0.068740 Loss2: 1.370122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412342 Loss1: 0.054834 Loss2: 1.357509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.520752 Loss1: 0.148689 Loss2: 1.372063 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.309839 Loss1: 0.439413 Loss2: 1.870426 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.498734 Loss1: 0.145249 Loss2: 1.353485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.480055 Loss1: 0.135093 Loss2: 1.344962
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.401039 Loss1: 0.538731 Loss2: 1.862308
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.716719 Loss1: 0.350842 Loss2: 1.365877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.745143 Loss1: 0.316379 Loss2: 1.428765 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.586921 Loss1: 0.206395 Loss2: 1.380525 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.575174 Loss1: 0.194423 Loss2: 1.380751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542539 Loss1: 0.159206 Loss2: 1.383333 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404631 Loss1: 0.080406 Loss2: 1.324225
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.519834 Loss1: 0.139955 Loss2: 1.379879
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488025 Loss1: 0.114347 Loss2: 1.373678
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448189 Loss1: 0.081210 Loss2: 1.366979
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420261 Loss1: 0.061103 Loss2: 1.359159
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.363236 Loss1: 0.523503 Loss2: 1.839733
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.609405 Loss1: 0.259882 Loss2: 1.349524
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.541751 Loss1: 0.178583 Loss2: 1.363168
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.448742 Loss1: 0.097657 Loss2: 1.351085
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.405694 Loss1: 0.495653 Loss2: 1.910041
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.435710 Loss1: 0.102910 Loss2: 1.332800
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.700669 Loss1: 0.311029 Loss2: 1.389640
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.424615 Loss1: 0.090274 Loss2: 1.334341
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.657407 Loss1: 0.218992 Loss2: 1.438415
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445184 Loss1: 0.115990 Loss2: 1.329194
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.568919 Loss1: 0.162648 Loss2: 1.406271
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400446 Loss1: 0.078619 Loss2: 1.321827
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549409 Loss1: 0.150575 Loss2: 1.398833
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421754 Loss1: 0.097941 Loss2: 1.323813
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.575400 Loss1: 0.174682 Loss2: 1.400717
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.444183 Loss1: 0.118898 Loss2: 1.325285
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.493242 Loss1: 0.101265 Loss2: 1.391976
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448936 Loss1: 0.059642 Loss2: 1.389293
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440509 Loss1: 0.060408 Loss2: 1.380101
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410266 Loss1: 0.033070 Loss2: 1.377197
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.178753 Loss1: 0.360833 Loss2: 1.817920
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.551914 Loss1: 0.184561 Loss2: 1.367354
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.566561 Loss1: 0.181065 Loss2: 1.385496
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.264377 Loss1: 0.415940 Loss2: 1.848437
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563630 Loss1: 0.197073 Loss2: 1.366558
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.621313 Loss1: 0.278022 Loss2: 1.343292
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503783 Loss1: 0.136704 Loss2: 1.367079
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489307 Loss1: 0.121090 Loss2: 1.368217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514314 Loss1: 0.150564 Loss2: 1.363750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467336 Loss1: 0.110448 Loss2: 1.356888 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413255 Loss1: 0.056045 Loss2: 1.357210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391105 Loss1: 0.046404 Loss2: 1.344701 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996324
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365319 Loss1: 0.051088 Loss2: 1.314231 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.475046 Loss1: 0.587667 Loss2: 1.887379
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.723652 Loss1: 0.330765 Loss2: 1.392887
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.660096 Loss1: 0.230000 Loss2: 1.430096
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555795 Loss1: 0.170215 Loss2: 1.385579
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.221226 Loss1: 0.383561 Loss2: 1.837665
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.697357 Loss1: 0.310773 Loss2: 1.386584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.567820 Loss1: 0.161305 Loss2: 1.406515 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.491601 Loss1: 0.127158 Loss2: 1.364443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491588 Loss1: 0.126172 Loss2: 1.365416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504212 Loss1: 0.135539 Loss2: 1.368674 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.541872 Loss1: 0.174067 Loss2: 1.367805 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 20:57:20,922 | server.py:236 | fit_round 164 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473286 Loss1: 0.117372 Loss2: 1.355915 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.344679 Loss1: 0.527689 Loss2: 1.816990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568972 Loss1: 0.200332 Loss2: 1.368640 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.355499 Loss1: 0.502122 Loss2: 1.853377 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.670525 Loss1: 0.309464 Loss2: 1.361061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.613425 Loss1: 0.203462 Loss2: 1.409963 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551007 Loss1: 0.182801 Loss2: 1.368206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519031 Loss1: 0.158201 Loss2: 1.360830 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486749 Loss1: 0.124430 Loss2: 1.362319 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397742 Loss1: 0.059482 Loss2: 1.338260 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379548 Loss1: 0.048579 Loss2: 1.330969 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.657176 Loss1: 0.290838 Loss2: 1.366339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.584677 Loss1: 0.198532 Loss2: 1.386145 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.303735 Loss1: 0.488726 Loss2: 1.815009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443592 Loss1: 0.099162 Loss2: 1.344430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447005 Loss1: 0.111922 Loss2: 1.335083 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404800 Loss1: 0.071193 Loss2: 1.333607 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380922 Loss1: 0.054701 Loss2: 1.326222 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986779
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450328 Loss1: 0.095046 Loss2: 1.355282 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420999 Loss1: 0.075759 Loss2: 1.345241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406143 Loss1: 0.068838 Loss2: 1.337305 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 20:57:20,922][flwr][DEBUG] - fit_round 164 received 50 results and 0 failures
-INFO flwr 2023-10-12 20:58:02,362 | server.py:125 | fit progress: (164, 2.246745443191772, {'accuracy': 0.6016}, 378390.14048832597)
->> Test accuracy: 0.601600
-[2023-10-12 20:58:02,362][flwr][INFO] - fit progress: (164, 2.246745443191772, {'accuracy': 0.6016}, 378390.14048832597)
-DEBUG flwr 2023-10-12 20:58:02,362 | server.py:173 | evaluate_round 164: strategy sampled 50 clients (out of 50)
-[2023-10-12 20:58:02,362][flwr][DEBUG] - evaluate_round 164: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 21:07:07,276 | server.py:187 | evaluate_round 164 received 50 results and 0 failures
-[2023-10-12 21:07:07,276][flwr][DEBUG] - evaluate_round 164 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 21:07:07,277 | server.py:222 | fit_round 165: strategy sampled 50 clients (out of 50)
-[2023-10-12 21:07:07,277][flwr][DEBUG] - fit_round 165: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.317514 Loss1: 0.477396 Loss2: 1.840118
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.656635 Loss1: 0.276828 Loss2: 1.379807
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.671522 Loss1: 0.243973 Loss2: 1.427549
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.598090 Loss1: 0.220685 Loss2: 1.377405
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.314536 Loss1: 0.436719 Loss2: 1.877817
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544031 Loss1: 0.160585 Loss2: 1.383446
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.565131 Loss1: 0.155949 Loss2: 1.409181
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565077 Loss1: 0.163635 Loss2: 1.401442 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532332 Loss1: 0.127053 Loss2: 1.405279 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.478095 Loss1: 0.085876 Loss2: 1.392220 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447375 Loss1: 0.064410 Loss2: 1.382965 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431607 Loss1: 0.052189 Loss2: 1.379418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435640 Loss1: 0.060213 Loss2: 1.375428 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.368531 Loss1: 0.467678 Loss2: 1.900853 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.632967 Loss1: 0.236865 Loss2: 1.396102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.485840 Loss1: 0.575478 Loss2: 1.910362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.713924 Loss1: 0.338003 Loss2: 1.375922 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564693 Loss1: 0.169338 Loss2: 1.395355 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.577387 Loss1: 0.208109 Loss2: 1.369279
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.495832 Loss1: 0.113888 Loss2: 1.381944
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.439165 Loss1: 0.080308 Loss2: 1.358858
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.498536 Loss1: 0.138154 Loss2: 1.360381
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443135 Loss1: 0.083985 Loss2: 1.359149
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463899 Loss1: 0.112314 Loss2: 1.351586
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447165 Loss1: 0.091783 Loss2: 1.355382 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376882 Loss1: 0.033376 Loss2: 1.343506 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985577
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.390023 Loss1: 0.440988 Loss2: 1.949035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.590919 Loss1: 0.155426 Loss2: 1.435493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511433 Loss1: 0.097263 Loss2: 1.414170
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.105785 Loss1: 0.344170 Loss2: 1.761615
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.576234 Loss1: 0.258212 Loss2: 1.318022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.545206 Loss1: 0.183497 Loss2: 1.361709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.447023 Loss1: 0.130533 Loss2: 1.316490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.445338 Loss1: 0.122283 Loss2: 1.323055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469685 Loss1: 0.082732 Loss2: 1.386954 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398069 Loss1: 0.090596 Loss2: 1.307473 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379977 Loss1: 0.078594 Loss2: 1.301382 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996324
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.668129 Loss1: 0.310712 Loss2: 1.357417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522085 Loss1: 0.171056 Loss2: 1.351029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473398 Loss1: 0.126166 Loss2: 1.347232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.427649 Loss1: 0.088740 Loss2: 1.338908 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437547 Loss1: 0.106874 Loss2: 1.330673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395740 Loss1: 0.065714 Loss2: 1.330026 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404881 Loss1: 0.083049 Loss2: 1.321833 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483003 Loss1: 0.113355 Loss2: 1.369648 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385501 Loss1: 0.036636 Loss2: 1.348865 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405509 Loss1: 0.062896 Loss2: 1.342614 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.571230 Loss1: 0.227076 Loss2: 1.344154 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475311 Loss1: 0.155924 Loss2: 1.319387 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.409785 Loss1: 0.106930 Loss2: 1.302855
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.273808 Loss1: 0.431619 Loss2: 1.842189
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.651009 Loss1: 0.278508 Loss2: 1.372501 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.593893 Loss1: 0.194754 Loss2: 1.399139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.524175 Loss1: 0.162548 Loss2: 1.361626 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.445805 Loss1: 0.079745 Loss2: 1.366060 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440822 Loss1: 0.086954 Loss2: 1.353868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.304925 Loss1: 0.398311 Loss2: 1.906614
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429592 Loss1: 0.076291 Loss2: 1.353301
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.663028 Loss1: 0.268631 Loss2: 1.394397
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424411 Loss1: 0.077868 Loss2: 1.346543
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583551 Loss1: 0.167392 Loss2: 1.416158 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516243 Loss1: 0.119478 Loss2: 1.396764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.507493 Loss1: 0.101957 Loss2: 1.405535
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.300133 Loss1: 0.462452 Loss2: 1.837681
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.523763 Loss1: 0.123764 Loss2: 1.399999
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.616945 Loss1: 0.267637 Loss2: 1.349308
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.502916 Loss1: 0.100257 Loss2: 1.402659
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.514387 Loss1: 0.148159 Loss2: 1.366228
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.495544 Loss1: 0.098005 Loss2: 1.397539
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.489765 Loss1: 0.144485 Loss2: 1.345280
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441396 Loss1: 0.104396 Loss2: 1.337000
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.422220 Loss1: 0.087899 Loss2: 1.334320
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.398618 Loss1: 0.069489 Loss2: 1.329129
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398339 Loss1: 0.078313 Loss2: 1.320026
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.355556 Loss1: 0.511183 Loss2: 1.844373
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.378280 Loss1: 0.057018 Loss2: 1.321262
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.721185 Loss1: 0.358961 Loss2: 1.362224
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381087 Loss1: 0.061099 Loss2: 1.319988
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576788 Loss1: 0.208985 Loss2: 1.367803 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470221 Loss1: 0.107126 Loss2: 1.363094 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442584 Loss1: 0.092464 Loss2: 1.350120
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.167399 Loss1: 0.361980 Loss2: 1.805418
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.580255 Loss1: 0.237395 Loss2: 1.342860 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.536310 Loss1: 0.170984 Loss2: 1.365327 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414994 Loss1: 0.075958 Loss2: 1.339036
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.471489 Loss1: 0.131932 Loss2: 1.339557
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.470441 Loss1: 0.128486 Loss2: 1.341955
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.435389 Loss1: 0.094224 Loss2: 1.341165
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412133 Loss1: 0.074735 Loss2: 1.337399
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382148 Loss1: 0.050742 Loss2: 1.331406
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.219717 Loss1: 0.389617 Loss2: 1.830100
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.599382 Loss1: 0.236064 Loss2: 1.363318 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.999023
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.579618 Loss1: 0.195140 Loss2: 1.384478 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463386 Loss1: 0.101711 Loss2: 1.361674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464506 Loss1: 0.111252 Loss2: 1.353254 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461804 Loss1: 0.107412 Loss2: 1.354392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427414 Loss1: 0.084196 Loss2: 1.343218 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420141 Loss1: 0.080180 Loss2: 1.339961 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.394570 Loss1: 0.066095 Loss2: 1.328475 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390356 Loss1: 0.063153 Loss2: 1.327203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.324402 Loss1: 0.479423 Loss2: 1.844978
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368531 Loss1: 0.048851 Loss2: 1.319680
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.677940 Loss1: 0.325243 Loss2: 1.352697
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.358477 Loss1: 0.044912 Loss2: 1.313565
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498938 Loss1: 0.144952 Loss2: 1.353985 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.469081 Loss1: 0.114401 Loss2: 1.354681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449069 Loss1: 0.104945 Loss2: 1.344123
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.207868 Loss1: 0.398336 Loss2: 1.809532
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.595620 Loss1: 0.277615 Loss2: 1.318006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.650108 Loss1: 0.265163 Loss2: 1.384944 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386272 Loss1: 0.056459 Loss2: 1.329813
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492982 Loss1: 0.155859 Loss2: 1.337123
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.477509 Loss1: 0.155814 Loss2: 1.321694
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459899 Loss1: 0.123816 Loss2: 1.336083
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404189 Loss1: 0.087845 Loss2: 1.316345
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411699 Loss1: 0.096815 Loss2: 1.314884
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.386295 Loss1: 0.471056 Loss2: 1.915239
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385825 Loss1: 0.073150 Loss2: 1.312675
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398765 Loss1: 0.090129 Loss2: 1.308636 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.565092 Loss1: 0.151276 Loss2: 1.413816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497025 Loss1: 0.089612 Loss2: 1.407412 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463291 Loss1: 0.066070 Loss2: 1.397220
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.233329 Loss1: 0.447500 Loss2: 1.785829
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.620733 Loss1: 0.289036 Loss2: 1.331697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608136 Loss1: 0.245932 Loss2: 1.362204 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.511889 Loss1: 0.171427 Loss2: 1.340462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443616 Loss1: 0.111635 Loss2: 1.331982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.395411 Loss1: 0.069479 Loss2: 1.325932 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379650 Loss1: 0.060459 Loss2: 1.319192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.503320 Loss1: 0.166555 Loss2: 1.336765 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.433113 Loss1: 0.110111 Loss2: 1.323002 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473479 Loss1: 0.150709 Loss2: 1.322770 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424598 Loss1: 0.110531 Loss2: 1.314067
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.303015 Loss1: 0.413339 Loss2: 1.889676
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424462 Loss1: 0.107447 Loss2: 1.317015
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.591012 Loss1: 0.204541 Loss2: 1.386471
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.435486 Loss1: 0.119733 Loss2: 1.315753
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.531812 Loss1: 0.150308 Loss2: 1.381504
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.494983 Loss1: 0.113393 Loss2: 1.381590
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505318 Loss1: 0.128636 Loss2: 1.376682
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.515983 Loss1: 0.140244 Loss2: 1.375739
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533236 Loss1: 0.150553 Loss2: 1.382683
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.562311 Loss1: 0.179753 Loss2: 1.382558
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.271197 Loss1: 0.442550 Loss2: 1.828647
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.507191 Loss1: 0.119630 Loss2: 1.387561
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.691042 Loss1: 0.328745 Loss2: 1.362296
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.505226 Loss1: 0.122445 Loss2: 1.382781
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574257 Loss1: 0.182654 Loss2: 1.391603
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529626 Loss1: 0.168742 Loss2: 1.360884
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.483147 Loss1: 0.117713 Loss2: 1.365434
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514305 Loss1: 0.152435 Loss2: 1.361871
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.430299 Loss1: 0.073062 Loss2: 1.357237
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418762 Loss1: 0.075844 Loss2: 1.342918
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.358655 Loss1: 0.498288 Loss2: 1.860367
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408719 Loss1: 0.062598 Loss2: 1.346121
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.648023 Loss1: 0.292389 Loss2: 1.355634
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407697 Loss1: 0.068159 Loss2: 1.339538
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.547371 Loss1: 0.164915 Loss2: 1.382456
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519780 Loss1: 0.165189 Loss2: 1.354590
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502462 Loss1: 0.139442 Loss2: 1.363020
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469957 Loss1: 0.118511 Loss2: 1.351446
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419218 Loss1: 0.072695 Loss2: 1.346523
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447115 Loss1: 0.109559 Loss2: 1.337556
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.177105 Loss1: 0.378784 Loss2: 1.798321
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416747 Loss1: 0.072095 Loss2: 1.344652
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.589349 Loss1: 0.233522 Loss2: 1.355827
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407918 Loss1: 0.071841 Loss2: 1.336077
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.558703 Loss1: 0.181274 Loss2: 1.377429
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498248 Loss1: 0.149916 Loss2: 1.348332
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.422978 Loss1: 0.079509 Loss2: 1.343469
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.432015 Loss1: 0.087605 Loss2: 1.344410
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410522 Loss1: 0.071061 Loss2: 1.339461
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.258570 Loss1: 0.417462 Loss2: 1.841108
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.399894 Loss1: 0.065585 Loss2: 1.334308
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397548 Loss1: 0.068118 Loss2: 1.329430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370021 Loss1: 0.045155 Loss2: 1.324866 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469921 Loss1: 0.148539 Loss2: 1.321383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435426 Loss1: 0.112033 Loss2: 1.323393 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.590767 Loss1: 0.531812 Loss2: 2.058955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.671392 Loss1: 0.247484 Loss2: 1.423908 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.632442 Loss1: 0.218759 Loss2: 1.413682 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.531240 Loss1: 0.115089 Loss2: 1.416152 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.554991 Loss1: 0.147947 Loss2: 1.407045 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990885
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485759 Loss1: 0.073363 Loss2: 1.412396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.546175 Loss1: 0.157815 Loss2: 1.388360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505331 Loss1: 0.154292 Loss2: 1.351040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464088 Loss1: 0.107685 Loss2: 1.356403
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.342042 Loss1: 0.519414 Loss2: 1.822627
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.414344 Loss1: 0.071700 Loss2: 1.342644
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.680775 Loss1: 0.336546 Loss2: 1.344230
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.363404 Loss1: 0.031089 Loss2: 1.332315
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.588705 Loss1: 0.212402 Loss2: 1.376302
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490910 Loss1: 0.154625 Loss2: 1.336285
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.352999 Loss1: 0.028902 Loss2: 1.324097
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451560 Loss1: 0.122770 Loss2: 1.328790
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357782 Loss1: 0.041505 Loss2: 1.316276
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.400538 Loss1: 0.071217 Loss2: 1.329321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.362667 Loss1: 0.046083 Loss2: 1.316584 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356033 Loss1: 0.044739 Loss2: 1.311294
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.542953 Loss1: 0.600063 Loss2: 1.942890
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.644787 Loss1: 0.287749 Loss2: 1.357038
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.548376 Loss1: 0.186963 Loss2: 1.361412
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.544059 Loss1: 0.176119 Loss2: 1.367941
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528500 Loss1: 0.175507 Loss2: 1.352993
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477282 Loss1: 0.113743 Loss2: 1.363538
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.280727 Loss1: 0.385637 Loss2: 1.895091
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.707917 Loss1: 0.314245 Loss2: 1.393672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426089 Loss1: 0.087749 Loss2: 1.338340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395952 Loss1: 0.060206 Loss2: 1.335746 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502255 Loss1: 0.105019 Loss2: 1.397236 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443098 Loss1: 0.062362 Loss2: 1.380736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.355499 Loss1: 0.458375 Loss2: 1.897125
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422802 Loss1: 0.048556 Loss2: 1.374246
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.720468 Loss1: 0.326299 Loss2: 1.394169
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413316 Loss1: 0.041738 Loss2: 1.371578
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535686 Loss1: 0.136278 Loss2: 1.399409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512078 Loss1: 0.115751 Loss2: 1.396327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.475964 Loss1: 0.084620 Loss2: 1.391344
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.383044 Loss1: 0.561617 Loss2: 1.821426
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.498663 Loss1: 0.107182 Loss2: 1.391482
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.693175 Loss1: 0.336266 Loss2: 1.356909
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454103 Loss1: 0.064705 Loss2: 1.389398
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615754 Loss1: 0.207320 Loss2: 1.408435
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441810 Loss1: 0.062083 Loss2: 1.379727
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574981 Loss1: 0.220184 Loss2: 1.354797
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517426 Loss1: 0.151706 Loss2: 1.365720
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503251 Loss1: 0.139898 Loss2: 1.363353
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415275 Loss1: 0.070868 Loss2: 1.344407
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.416435 Loss1: 0.074952 Loss2: 1.341483
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.399784 Loss1: 0.533383 Loss2: 1.866401
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378472 Loss1: 0.042041 Loss2: 1.336431
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.688489 Loss1: 0.317129 Loss2: 1.371360
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.376655 Loss1: 0.047854 Loss2: 1.328801
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.489124 Loss1: 0.121239 Loss2: 1.367885 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429842 Loss1: 0.078436 Loss2: 1.351406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408589 Loss1: 0.062279 Loss2: 1.346310
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.321378 Loss1: 0.467608 Loss2: 1.853770
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.595423 Loss1: 0.241860 Loss2: 1.353562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561378 Loss1: 0.176345 Loss2: 1.385033 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388650 Loss1: 0.055743 Loss2: 1.332907
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573464 Loss1: 0.214387 Loss2: 1.359077
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535713 Loss1: 0.174733 Loss2: 1.360980
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514847 Loss1: 0.143408 Loss2: 1.371439
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490353 Loss1: 0.131026 Loss2: 1.359327
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462759 Loss1: 0.111358 Loss2: 1.351402
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.199656 Loss1: 0.443047 Loss2: 1.756609
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.420216 Loss1: 0.069803 Loss2: 1.350414
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396654 Loss1: 0.060732 Loss2: 1.335922
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.607047 Loss1: 0.293860 Loss2: 1.313188
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588986 Loss1: 0.235149 Loss2: 1.353837
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.503772 Loss1: 0.186476 Loss2: 1.317296
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503818 Loss1: 0.182538 Loss2: 1.321280
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459883 Loss1: 0.133952 Loss2: 1.325931
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.290927 Loss1: 0.489422 Loss2: 1.801505
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416724 Loss1: 0.106613 Loss2: 1.310111
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.375125 Loss1: 0.070248 Loss2: 1.304877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.378683 Loss1: 0.083216 Loss2: 1.295467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.338005 Loss1: 0.044731 Loss2: 1.293274 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.375761 Loss1: 0.067367 Loss2: 1.308395 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.328868 Loss1: 0.033388 Loss2: 1.295480 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.397648 Loss1: 0.509900 Loss2: 1.887748 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.698685 Loss1: 0.312615 Loss2: 1.386070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.518749 Loss1: 0.146440 Loss2: 1.372310 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464552 Loss1: 0.093525 Loss2: 1.371026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441460 Loss1: 0.075579 Loss2: 1.365881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435015 Loss1: 0.071822 Loss2: 1.363193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.577434 Loss1: 0.198439 Loss2: 1.378995
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419672 Loss1: 0.058036 Loss2: 1.361637
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398796 Loss1: 0.047640 Loss2: 1.351156 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.397051 Loss1: 0.065316 Loss2: 1.331735 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.377977 Loss1: 0.054126 Loss2: 1.323850 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.352856 Loss1: 0.034627 Loss2: 1.318229
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.366050 Loss1: 0.482077 Loss2: 1.883972
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.739729 Loss1: 0.366548 Loss2: 1.373181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553754 Loss1: 0.178302 Loss2: 1.375452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.454716 Loss1: 0.091182 Loss2: 1.363534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.409179 Loss1: 0.053630 Loss2: 1.355548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386245 Loss1: 0.039577 Loss2: 1.346668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371441 Loss1: 0.029839 Loss2: 1.341601 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365264 Loss1: 0.030395 Loss2: 1.334869 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537689 Loss1: 0.162842 Loss2: 1.374847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449327 Loss1: 0.088546 Loss2: 1.360782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.308116 Loss1: 0.511758 Loss2: 1.796358 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568978 Loss1: 0.187512 Loss2: 1.381466 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485014 Loss1: 0.148477 Loss2: 1.336537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420852 Loss1: 0.084174 Loss2: 1.336678
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.273812 Loss1: 0.432227 Loss2: 1.841584
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.569785 Loss1: 0.228494 Loss2: 1.341290 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.524282 Loss1: 0.178510 Loss2: 1.345772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.461990 Loss1: 0.128519 Loss2: 1.333471 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.440473 Loss1: 0.115603 Loss2: 1.324871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.386134 Loss1: 0.063858 Loss2: 1.322276 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422403 Loss1: 0.100451 Loss2: 1.321952 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374763 Loss1: 0.055328 Loss2: 1.319436 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622613 Loss1: 0.201703 Loss2: 1.420910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503832 Loss1: 0.140154 Loss2: 1.363678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542656 Loss1: 0.171157 Loss2: 1.371498
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.396373 Loss1: 0.497752 Loss2: 1.898621
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.663795 Loss1: 0.277117 Loss2: 1.386678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.620162 Loss1: 0.200910 Loss2: 1.419252 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586024 Loss1: 0.196366 Loss2: 1.389658 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.967708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.542397 Loss1: 0.145426 Loss2: 1.396971 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.503861 Loss1: 0.126546 Loss2: 1.377315 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449171 Loss1: 0.076943 Loss2: 1.372227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441988 Loss1: 0.072963 Loss2: 1.369026 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.734419 Loss1: 0.288143 Loss2: 1.446277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.611087 Loss1: 0.213133 Loss2: 1.397954 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.540260 Loss1: 0.150093 Loss2: 1.390167
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.417231 Loss1: 0.513366 Loss2: 1.903865
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.731425 Loss1: 0.372730 Loss2: 1.358695 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.650162 Loss1: 0.240008 Loss2: 1.410155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576897 Loss1: 0.201575 Loss2: 1.375322 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.550865 Loss1: 0.174064 Loss2: 1.376801 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415000 Loss1: 0.061265 Loss2: 1.353735 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 21:35:37,455 | server.py:236 | fit_round 165 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411073 Loss1: 0.070355 Loss2: 1.340718 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375491 Loss1: 0.037159 Loss2: 1.338333
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.528817 Loss1: 0.635737 Loss2: 1.893080
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.714096 Loss1: 0.336341 Loss2: 1.377755
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600194 Loss1: 0.205808 Loss2: 1.394386
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487833 Loss1: 0.129751 Loss2: 1.358082
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455685 Loss1: 0.109541 Loss2: 1.346144
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.417136 Loss1: 0.070970 Loss2: 1.346166
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.404440 Loss1: 0.517767 Loss2: 1.886673
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.644723 Loss1: 0.270860 Loss2: 1.373863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.622043 Loss1: 0.212025 Loss2: 1.410018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.530423 Loss1: 0.162573 Loss2: 1.367850 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997768
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.497918 Loss1: 0.135826 Loss2: 1.362092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416251 Loss1: 0.065308 Loss2: 1.350942 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429977 Loss1: 0.082062 Loss2: 1.347915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402507 Loss1: 0.053570 Loss2: 1.348936 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543252 Loss1: 0.191755 Loss2: 1.351497 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491547 Loss1: 0.173420 Loss2: 1.318127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427780 Loss1: 0.112426 Loss2: 1.315354 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391662 Loss1: 0.080420 Loss2: 1.311242 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 21:35:37,455][flwr][DEBUG] - fit_round 165 received 50 results and 0 failures
-INFO flwr 2023-10-12 21:36:18,414 | server.py:125 | fit progress: (165, 2.264890566420631, {'accuracy': 0.6029}, 380686.19234982197)
->> Test accuracy: 0.602900
-[2023-10-12 21:36:18,414][flwr][INFO] - fit progress: (165, 2.264890566420631, {'accuracy': 0.6029}, 380686.19234982197)
-DEBUG flwr 2023-10-12 21:36:18,414 | server.py:173 | evaluate_round 165: strategy sampled 50 clients (out of 50)
-[2023-10-12 21:36:18,414][flwr][DEBUG] - evaluate_round 165: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 21:45:25,013 | server.py:187 | evaluate_round 165 received 50 results and 0 failures
-[2023-10-12 21:45:25,013][flwr][DEBUG] - evaluate_round 165 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 21:45:25,014 | server.py:222 | fit_round 166: strategy sampled 50 clients (out of 50)
-[2023-10-12 21:45:25,014][flwr][DEBUG] - fit_round 166: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.343892 Loss1: 0.455765 Loss2: 1.888128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.559987 Loss1: 0.154160 Loss2: 1.405828 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508862 Loss1: 0.118094 Loss2: 1.390768
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.312016 Loss1: 0.448645 Loss2: 1.863371
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.626595 Loss1: 0.259954 Loss2: 1.366640 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460878 Loss1: 0.077268 Loss2: 1.383610
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.528342 Loss1: 0.136463 Loss2: 1.391879
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.482826 Loss1: 0.101874 Loss2: 1.380953
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520564 Loss1: 0.146246 Loss2: 1.374317
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502261 Loss1: 0.128442 Loss2: 1.373819 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439563 Loss1: 0.071239 Loss2: 1.368324
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452645 Loss1: 0.083857 Loss2: 1.368787
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417233 Loss1: 0.052609 Loss2: 1.364624
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444106 Loss1: 0.082640 Loss2: 1.361466
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450813 Loss1: 0.091753 Loss2: 1.359061
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431328 Loss1: 0.077585 Loss2: 1.353743
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453493 Loss1: 0.100098 Loss2: 1.353395
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.228002 Loss1: 0.371911 Loss2: 1.856090
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.565114 Loss1: 0.213702 Loss2: 1.351412
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.519700 Loss1: 0.169895 Loss2: 1.349804
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502005 Loss1: 0.141543 Loss2: 1.360462
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.305657 Loss1: 0.434672 Loss2: 1.870986
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.640762 Loss1: 0.278162 Loss2: 1.362600 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.623312 Loss1: 0.216585 Loss2: 1.406727 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543418 Loss1: 0.175969 Loss2: 1.367449 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.526583 Loss1: 0.155987 Loss2: 1.370596 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493931 Loss1: 0.118828 Loss2: 1.375103 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408571 Loss1: 0.072526 Loss2: 1.336045
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463330 Loss1: 0.098835 Loss2: 1.364495
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442181 Loss1: 0.084869 Loss2: 1.357313
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429725 Loss1: 0.074529 Loss2: 1.355196
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427862 Loss1: 0.075847 Loss2: 1.352015
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.355259 Loss1: 0.485756 Loss2: 1.869502
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.712887 Loss1: 0.342365 Loss2: 1.370522
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.581729 Loss1: 0.166990 Loss2: 1.414739
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.521122 Loss1: 0.149679 Loss2: 1.371443
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.241971 Loss1: 0.453889 Loss2: 1.788081
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.682450 Loss1: 0.364322 Loss2: 1.318128 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.594679 Loss1: 0.213169 Loss2: 1.381510 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.476814 Loss1: 0.152402 Loss2: 1.324412 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.464002 Loss1: 0.146404 Loss2: 1.317598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.445111 Loss1: 0.120439 Loss2: 1.324672 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399068 Loss1: 0.048794 Loss2: 1.350274
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.405164 Loss1: 0.094963 Loss2: 1.310202
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.392921 Loss1: 0.081967 Loss2: 1.310954
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397025 Loss1: 0.096964 Loss2: 1.300061
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382242 Loss1: 0.081528 Loss2: 1.300714
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.323155 Loss1: 0.532248 Loss2: 1.790907
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.699871 Loss1: 0.380586 Loss2: 1.319285
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615640 Loss1: 0.249902 Loss2: 1.365738
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.458909 Loss1: 0.156545 Loss2: 1.302364
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.334553 Loss1: 0.499128 Loss2: 1.835424
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659632 Loss1: 0.278610 Loss2: 1.381022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.615898 Loss1: 0.208366 Loss2: 1.407532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535687 Loss1: 0.167187 Loss2: 1.368500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.498983 Loss1: 0.113624 Loss2: 1.385359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460586 Loss1: 0.094578 Loss2: 1.366009 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443129 Loss1: 0.085071 Loss2: 1.358057 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443217 Loss1: 0.089720 Loss2: 1.353497 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.604780 Loss1: 0.231206 Loss2: 1.373575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.481413 Loss1: 0.112204 Loss2: 1.369209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473042 Loss1: 0.119416 Loss2: 1.353626
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.279015 Loss1: 0.469876 Loss2: 1.809139
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.467848 Loss1: 0.107754 Loss2: 1.360094
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.654178 Loss1: 0.303520 Loss2: 1.350658
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451005 Loss1: 0.093171 Loss2: 1.357834
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.584810 Loss1: 0.206769 Loss2: 1.378040
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535320 Loss1: 0.189728 Loss2: 1.345592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530061 Loss1: 0.159801 Loss2: 1.370261 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408270 Loss1: 0.060336 Loss2: 1.347933
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.525701 Loss1: 0.166667 Loss2: 1.359034
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.449583 Loss1: 0.095051 Loss2: 1.354533
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.401280 Loss1: 0.065651 Loss2: 1.335630
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424460 Loss1: 0.088679 Loss2: 1.335781
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.451663 Loss1: 0.115699 Loss2: 1.335964
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.226943 Loss1: 0.339157 Loss2: 1.887785
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.629531 Loss1: 0.222990 Loss2: 1.406541
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.553200 Loss1: 0.140242 Loss2: 1.412958
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526155 Loss1: 0.128053 Loss2: 1.398102
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.386532 Loss1: 0.496227 Loss2: 1.890305
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.496461 Loss1: 0.102508 Loss2: 1.393952
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.694110 Loss1: 0.319435 Loss2: 1.374675
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.490420 Loss1: 0.101498 Loss2: 1.388922
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.638363 Loss1: 0.232736 Loss2: 1.405627
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.510704 Loss1: 0.111343 Loss2: 1.399361
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.532452 Loss1: 0.132897 Loss2: 1.399555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474572 Loss1: 0.073078 Loss2: 1.401494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.493794 Loss1: 0.100150 Loss2: 1.393643 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988971
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441124 Loss1: 0.094500 Loss2: 1.346624 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.259635 Loss1: 0.404448 Loss2: 1.855187 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634083 Loss1: 0.233044 Loss2: 1.401040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.487672 Loss1: 0.129594 Loss2: 1.358078
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.294938 Loss1: 0.405776 Loss2: 1.889162
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.669777 Loss1: 0.256664 Loss2: 1.413112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605190 Loss1: 0.160255 Loss2: 1.444936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.560194 Loss1: 0.140831 Loss2: 1.419363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520329 Loss1: 0.107563 Loss2: 1.412766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476409 Loss1: 0.072415 Loss2: 1.403994 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456428 Loss1: 0.061043 Loss2: 1.395385 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461732 Loss1: 0.068514 Loss2: 1.393218 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.198654 Loss1: 0.387216 Loss2: 1.811438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.555608 Loss1: 0.191474 Loss2: 1.364134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.270251 Loss1: 0.436174 Loss2: 1.834077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.617431 Loss1: 0.282952 Loss2: 1.334479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588407 Loss1: 0.221600 Loss2: 1.366807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.516142 Loss1: 0.167688 Loss2: 1.348454 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485376 Loss1: 0.143061 Loss2: 1.342314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.413331 Loss1: 0.074386 Loss2: 1.338944 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378153 Loss1: 0.043003 Loss2: 1.335151
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408754 Loss1: 0.083880 Loss2: 1.324874
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407070 Loss1: 0.085121 Loss2: 1.321949
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384363 Loss1: 0.066007 Loss2: 1.318356
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386689 Loss1: 0.073577 Loss2: 1.313112
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.417888 Loss1: 0.514163 Loss2: 1.903725
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.700683 Loss1: 0.346545 Loss2: 1.354138
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.592323 Loss1: 0.192199 Loss2: 1.400124
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.512101 Loss1: 0.149416 Loss2: 1.362684
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.245226 Loss1: 0.418269 Loss2: 1.826957
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.589796 Loss1: 0.254721 Loss2: 1.335075 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.575961 Loss1: 0.201749 Loss2: 1.374212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.472763 Loss1: 0.127324 Loss2: 1.345439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378359 Loss1: 0.041867 Loss2: 1.336492 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364734 Loss1: 0.034574 Loss2: 1.330160 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450326 Loss1: 0.123354 Loss2: 1.326972 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374885 Loss1: 0.050650 Loss2: 1.324235 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.594273 Loss1: 0.241517 Loss2: 1.352757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467406 Loss1: 0.116068 Loss2: 1.351338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.380120 Loss1: 0.494090 Loss2: 1.886030
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468746 Loss1: 0.120809 Loss2: 1.347937
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.490112 Loss1: 0.137733 Loss2: 1.352379 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496405 Loss1: 0.141407 Loss2: 1.354998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425682 Loss1: 0.074007 Loss2: 1.351676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448905 Loss1: 0.101876 Loss2: 1.347029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410032 Loss1: 0.067359 Loss2: 1.342673 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422677 Loss1: 0.052014 Loss2: 1.370663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404955 Loss1: 0.044242 Loss2: 1.360713 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.316897 Loss1: 0.503839 Loss2: 1.813059
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.596110 Loss1: 0.271382 Loss2: 1.324727
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574842 Loss1: 0.215596 Loss2: 1.359246
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484868 Loss1: 0.143846 Loss2: 1.341022
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.333085 Loss1: 0.511892 Loss2: 1.821193
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.669315 Loss1: 0.311775 Loss2: 1.357540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.596799 Loss1: 0.205301 Loss2: 1.391497 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561573 Loss1: 0.201742 Loss2: 1.359831 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.525611 Loss1: 0.170054 Loss2: 1.355557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484044 Loss1: 0.124230 Loss2: 1.359815 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415660 Loss1: 0.067818 Loss2: 1.347842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391204 Loss1: 0.057568 Loss2: 1.333636 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.244977 Loss1: 0.441095 Loss2: 1.803883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.490391 Loss1: 0.136511 Loss2: 1.353880 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.436629 Loss1: 0.124899 Loss2: 1.311729
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.457163 Loss1: 0.528907 Loss2: 1.928256
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.706498 Loss1: 0.341054 Loss2: 1.365444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.582030 Loss1: 0.192907 Loss2: 1.389123 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565621 Loss1: 0.188366 Loss2: 1.377255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393287 Loss1: 0.085134 Loss2: 1.308153
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558902 Loss1: 0.184753 Loss2: 1.374150
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381412 Loss1: 0.075615 Loss2: 1.305797
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.574127 Loss1: 0.174819 Loss2: 1.399308
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508809 Loss1: 0.147410 Loss2: 1.361399
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378327 Loss1: 0.075882 Loss2: 1.302445
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451378 Loss1: 0.092496 Loss2: 1.358882 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.337917 Loss1: 0.518468 Loss2: 1.819449 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.534968 Loss1: 0.190537 Loss2: 1.344431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.486155 Loss1: 0.146912 Loss2: 1.339242
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.269733 Loss1: 0.437568 Loss2: 1.832166
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443967 Loss1: 0.117897 Loss2: 1.326070
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.614613 Loss1: 0.283175 Loss2: 1.331438
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.405902 Loss1: 0.078026 Loss2: 1.327877
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.537718 Loss1: 0.185628 Loss2: 1.352089
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.385103 Loss1: 0.067541 Loss2: 1.317562
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567872 Loss1: 0.206288 Loss2: 1.361584
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391827 Loss1: 0.070472 Loss2: 1.321355
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.540138 Loss1: 0.194031 Loss2: 1.346107
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384568 Loss1: 0.065383 Loss2: 1.319185
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490107 Loss1: 0.130543 Loss2: 1.359564
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369204 Loss1: 0.055764 Loss2: 1.313440
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446904 Loss1: 0.106160 Loss2: 1.340744
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433578 Loss1: 0.103882 Loss2: 1.329696
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443827 Loss1: 0.109147 Loss2: 1.334680
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400245 Loss1: 0.069603 Loss2: 1.330641
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.204682 Loss1: 0.416959 Loss2: 1.787724
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.678237 Loss1: 0.331086 Loss2: 1.347152
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.604100 Loss1: 0.215382 Loss2: 1.388717
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.532860 Loss1: 0.603859 Loss2: 1.929002
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.469862 Loss1: 0.126345 Loss2: 1.343517
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.455915 Loss1: 0.121447 Loss2: 1.334468 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.524970 Loss1: 0.166028 Loss2: 1.358941 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.474458 Loss1: 0.159132 Loss2: 1.315326 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.366857 Loss1: 0.043007 Loss2: 1.323850 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400423 Loss1: 0.092005 Loss2: 1.308417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374623 Loss1: 0.068744 Loss2: 1.305879 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.360769 Loss1: 0.502849 Loss2: 1.857920 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.608242 Loss1: 0.197567 Loss2: 1.410675 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520249 Loss1: 0.152192 Loss2: 1.368056
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.298691 Loss1: 0.449855 Loss2: 1.848836
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.744874 Loss1: 0.381967 Loss2: 1.362907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.573809 Loss1: 0.171874 Loss2: 1.401934 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528884 Loss1: 0.175006 Loss2: 1.353877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.481538 Loss1: 0.117413 Loss2: 1.364125 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459584 Loss1: 0.106508 Loss2: 1.353076 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411408 Loss1: 0.066750 Loss2: 1.344658
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452074 Loss1: 0.098241 Loss2: 1.353832
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419169 Loss1: 0.074320 Loss2: 1.344850
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419846 Loss1: 0.074216 Loss2: 1.345630
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400267 Loss1: 0.062030 Loss2: 1.338237
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.291838 Loss1: 0.400803 Loss2: 1.891035
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.688282 Loss1: 0.294649 Loss2: 1.393632
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.605837 Loss1: 0.178845 Loss2: 1.426992
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561476 Loss1: 0.158754 Loss2: 1.402722
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.446125 Loss1: 0.541415 Loss2: 1.904710
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.712920 Loss1: 0.312224 Loss2: 1.400696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622623 Loss1: 0.202744 Loss2: 1.419879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.570758 Loss1: 0.170629 Loss2: 1.400129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.511878 Loss1: 0.120113 Loss2: 1.391766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491890 Loss1: 0.108454 Loss2: 1.383435 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.489821 Loss1: 0.109835 Loss2: 1.379987 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423302 Loss1: 0.055116 Loss2: 1.368186 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.497832 Loss1: 0.603851 Loss2: 1.893981
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.655887 Loss1: 0.284821 Loss2: 1.371067
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.501880 Loss1: 0.131100 Loss2: 1.370780
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.454473 Loss1: 0.100519 Loss2: 1.353955
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.431398 Loss1: 0.519482 Loss2: 1.911917
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.687923 Loss1: 0.337444 Loss2: 1.350479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.589946 Loss1: 0.225038 Loss2: 1.364907 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.401083 Loss1: 0.067911 Loss2: 1.333172
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551911 Loss1: 0.193924 Loss2: 1.357988
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496737 Loss1: 0.157255 Loss2: 1.339482
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.390327 Loss1: 0.057227 Loss2: 1.333100
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372808 Loss1: 0.046064 Loss2: 1.326744 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360157 Loss1: 0.031257 Loss2: 1.328900 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403135 Loss1: 0.073112 Loss2: 1.330023 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.448267 Loss1: 0.553071 Loss2: 1.895196 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.668528 Loss1: 0.241439 Loss2: 1.427090 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535794 Loss1: 0.165634 Loss2: 1.370160
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.334088 Loss1: 0.436761 Loss2: 1.897328
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.717224 Loss1: 0.330657 Loss2: 1.386566 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635625 Loss1: 0.214159 Loss2: 1.421466 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537022 Loss1: 0.141472 Loss2: 1.395550 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513206 Loss1: 0.136079 Loss2: 1.377127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508766 Loss1: 0.121447 Loss2: 1.387319 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.474457 Loss1: 0.097354 Loss2: 1.377102 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424326 Loss1: 0.063265 Loss2: 1.361061 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.571732 Loss1: 0.225276 Loss2: 1.346456 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.510920 Loss1: 0.154531 Loss2: 1.356389 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499204 Loss1: 0.146735 Loss2: 1.352469 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487353 Loss1: 0.126723 Loss2: 1.360630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451388 Loss1: 0.104882 Loss2: 1.346506 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.455437 Loss1: 0.111398 Loss2: 1.344039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425265 Loss1: 0.082527 Loss2: 1.342738 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402808 Loss1: 0.062129 Loss2: 1.340679 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391715 Loss1: 0.049446 Loss2: 1.342269 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.248856 Loss1: 0.420750 Loss2: 1.828106 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.683341 Loss1: 0.265301 Loss2: 1.418041 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.539205 Loss1: 0.198279 Loss2: 1.340925
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.287805 Loss1: 0.500057 Loss2: 1.787748
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.654566 Loss1: 0.335012 Loss2: 1.319555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.577032 Loss1: 0.227196 Loss2: 1.349836 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.479678 Loss1: 0.159705 Loss2: 1.319973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.417600 Loss1: 0.105238 Loss2: 1.312362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.419347 Loss1: 0.110829 Loss2: 1.308518 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.382259 Loss1: 0.061395 Loss2: 1.320864
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.407285 Loss1: 0.099301 Loss2: 1.307984
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412729 Loss1: 0.105594 Loss2: 1.307134
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381324 Loss1: 0.073779 Loss2: 1.307546
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370369 Loss1: 0.073969 Loss2: 1.296400
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.510109 Loss1: 0.571035 Loss2: 1.939074
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.800208 Loss1: 0.366912 Loss2: 1.433295
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690123 Loss1: 0.210377 Loss2: 1.479746
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.617210 Loss1: 0.194940 Loss2: 1.422270
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.307473 Loss1: 0.440574 Loss2: 1.866900
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.574704 Loss1: 0.231734 Loss2: 1.342969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.539073 Loss1: 0.179546 Loss2: 1.359527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.450917 Loss1: 0.100007 Loss2: 1.350910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.395296 Loss1: 0.069876 Loss2: 1.325420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.424579 Loss1: 0.099782 Loss2: 1.324797 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392734 Loss1: 0.066373 Loss2: 1.326362 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.361369 Loss1: 0.046217 Loss2: 1.315152 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.221769 Loss1: 0.381556 Loss2: 1.840213 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.501897 Loss1: 0.133391 Loss2: 1.368506 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.456902 Loss1: 0.098880 Loss2: 1.358022
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.401011 Loss1: 0.497854 Loss2: 1.903157
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.422119 Loss1: 0.075176 Loss2: 1.346942
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.670172 Loss1: 0.288862 Loss2: 1.381310
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.404495 Loss1: 0.059055 Loss2: 1.345440
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.662143 Loss1: 0.251449 Loss2: 1.410694
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409062 Loss1: 0.071035 Loss2: 1.338027
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520932 Loss1: 0.142068 Loss2: 1.378864
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418565 Loss1: 0.076781 Loss2: 1.341784
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506377 Loss1: 0.136065 Loss2: 1.370312
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474586 Loss1: 0.097572 Loss2: 1.377014
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396436 Loss1: 0.055743 Loss2: 1.340693
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.414002 Loss1: 0.052152 Loss2: 1.361851
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373394 Loss1: 0.035491 Loss2: 1.337904
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397181 Loss1: 0.049729 Loss2: 1.347452 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.518380 Loss1: 0.558503 Loss2: 1.959877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.608371 Loss1: 0.207187 Loss2: 1.401184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.215610 Loss1: 0.355643 Loss2: 1.859967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.579172 Loss1: 0.214853 Loss2: 1.364319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426179 Loss1: 0.073852 Loss2: 1.352326 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419528 Loss1: 0.078390 Loss2: 1.341138 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384273 Loss1: 0.044132 Loss2: 1.340141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377732 Loss1: 0.048542 Loss2: 1.329189 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995192
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430749 Loss1: 0.068392 Loss2: 1.362357 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.447092 Loss1: 0.088565 Loss2: 1.358528 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412586 Loss1: 0.057474 Loss2: 1.355112
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.246424 Loss1: 0.407774 Loss2: 1.838650
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.623475 Loss1: 0.260813 Loss2: 1.362661
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.565350 Loss1: 0.169997 Loss2: 1.395352
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511354 Loss1: 0.151655 Loss2: 1.359699
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515566 Loss1: 0.150746 Loss2: 1.364820
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.311076 Loss1: 0.467981 Loss2: 1.843095
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.648944 Loss1: 0.309992 Loss2: 1.338952
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456170 Loss1: 0.097090 Loss2: 1.359081
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.542788 Loss1: 0.171313 Loss2: 1.371475
-DEBUG flwr 2023-10-12 22:14:38,884 | server.py:236 | fit_round 166 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.438489 Loss1: 0.087636 Loss2: 1.350853
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492349 Loss1: 0.143595 Loss2: 1.348755
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.408792 Loss1: 0.060188 Loss2: 1.348604
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500816 Loss1: 0.158848 Loss2: 1.341968
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400775 Loss1: 0.059809 Loss2: 1.340966
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379371 Loss1: 0.042307 Loss2: 1.337064 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.999023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446943 Loss1: 0.107426 Loss2: 1.339516 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403013 Loss1: 0.071526 Loss2: 1.331487 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.681287 Loss1: 0.312339 Loss2: 1.368949 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.466718 Loss1: 0.110053 Loss2: 1.356665 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.444966 Loss1: 0.087603 Loss2: 1.357363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458095 Loss1: 0.104696 Loss2: 1.353399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449428 Loss1: 0.100277 Loss2: 1.349151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457696 Loss1: 0.099697 Loss2: 1.358000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407575 Loss1: 0.053685 Loss2: 1.353889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402422 Loss1: 0.064283 Loss2: 1.338139 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413303 Loss1: 0.057168 Loss2: 1.356135 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412841 Loss1: 0.065576 Loss2: 1.347265 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 22:14:38,884][flwr][DEBUG] - fit_round 166 received 50 results and 0 failures
-INFO flwr 2023-10-12 22:15:20,286 | server.py:125 | fit progress: (166, 2.2735640943621673, {'accuracy': 0.6028}, 383028.06479311496)
->> Test accuracy: 0.602800
-[2023-10-12 22:15:20,286][flwr][INFO] - fit progress: (166, 2.2735640943621673, {'accuracy': 0.6028}, 383028.06479311496)
-DEBUG flwr 2023-10-12 22:15:20,287 | server.py:173 | evaluate_round 166: strategy sampled 50 clients (out of 50)
-[2023-10-12 22:15:20,287][flwr][DEBUG] - evaluate_round 166: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 22:24:24,139 | server.py:187 | evaluate_round 166 received 50 results and 0 failures
-[2023-10-12 22:24:24,139][flwr][DEBUG] - evaluate_round 166 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 22:24:24,139 | server.py:222 | fit_round 167: strategy sampled 50 clients (out of 50)
-[2023-10-12 22:24:24,139][flwr][DEBUG] - fit_round 167: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.300072 Loss1: 0.456071 Loss2: 1.844001
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.620255 Loss1: 0.260268 Loss2: 1.359987
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.521334 Loss1: 0.139540 Loss2: 1.381794
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.514715 Loss1: 0.156000 Loss2: 1.358715
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.402719 Loss1: 0.507377 Loss2: 1.895342
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.543585 Loss1: 0.176657 Loss2: 1.366928
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.668880 Loss1: 0.309978 Loss2: 1.358902
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455187 Loss1: 0.093547 Loss2: 1.361640
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642615 Loss1: 0.244649 Loss2: 1.397966
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.525967 Loss1: 0.161239 Loss2: 1.364728
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.490230 Loss1: 0.135988 Loss2: 1.354242
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524020 Loss1: 0.164424 Loss2: 1.359596
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427595 Loss1: 0.081583 Loss2: 1.346012
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530371 Loss1: 0.168229 Loss2: 1.362142
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.416131 Loss1: 0.073220 Loss2: 1.342912
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422674 Loss1: 0.076463 Loss2: 1.346211 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458923 Loss1: 0.107927 Loss2: 1.350995 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.350761 Loss1: 0.481714 Loss2: 1.869047 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.623876 Loss1: 0.197633 Loss2: 1.426243 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542537 Loss1: 0.163409 Loss2: 1.379127
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.364156 Loss1: 0.493373 Loss2: 1.870782
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.613784 Loss1: 0.240628 Loss2: 1.373156 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.529830 Loss1: 0.148832 Loss2: 1.380998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497723 Loss1: 0.137330 Loss2: 1.360394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.488536 Loss1: 0.130896 Loss2: 1.357640 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460695 Loss1: 0.092401 Loss2: 1.368294 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450592 Loss1: 0.086937 Loss2: 1.363655
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437979 Loss1: 0.085522 Loss2: 1.352457
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408660 Loss1: 0.063964 Loss2: 1.344696
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408147 Loss1: 0.066882 Loss2: 1.341265
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410446 Loss1: 0.077930 Loss2: 1.332516
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.336565 Loss1: 0.475211 Loss2: 1.861354
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.797165 Loss1: 0.407082 Loss2: 1.390083
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.714823 Loss1: 0.257211 Loss2: 1.457612
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589125 Loss1: 0.202522 Loss2: 1.386603
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.344303 Loss1: 0.481394 Loss2: 1.862909
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.650491 Loss1: 0.277710 Loss2: 1.372782 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.582499 Loss1: 0.204411 Loss2: 1.378088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551771 Loss1: 0.176467 Loss2: 1.375304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.498199 Loss1: 0.133617 Loss2: 1.364582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493467 Loss1: 0.129393 Loss2: 1.364074 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379252 Loss1: 0.027669 Loss2: 1.351583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428180 Loss1: 0.076685 Loss2: 1.351495
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419867 Loss1: 0.073366 Loss2: 1.346500
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412075 Loss1: 0.067558 Loss2: 1.344517
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411785 Loss1: 0.067297 Loss2: 1.344488
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.250349 Loss1: 0.477909 Loss2: 1.772440
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.576346 Loss1: 0.266659 Loss2: 1.309686
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.478144 Loss1: 0.143345 Loss2: 1.334799
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.455560 Loss1: 0.146102 Loss2: 1.309459
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.249948 Loss1: 0.403978 Loss2: 1.845970
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.602848 Loss1: 0.248561 Loss2: 1.354288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.628635 Loss1: 0.254813 Loss2: 1.373823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.518149 Loss1: 0.154374 Loss2: 1.363776 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523705 Loss1: 0.159978 Loss2: 1.363727 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476519 Loss1: 0.109609 Loss2: 1.366910 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456711 Loss1: 0.108153 Loss2: 1.348557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420608 Loss1: 0.076443 Loss2: 1.344164 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.398672 Loss1: 0.518075 Loss2: 1.880598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.630261 Loss1: 0.236521 Loss2: 1.393740 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.507054 Loss1: 0.153603 Loss2: 1.353451
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.457666 Loss1: 0.590166 Loss2: 1.867499
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.777495 Loss1: 0.429159 Loss2: 1.348337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.418805 Loss1: 0.073280 Loss2: 1.345524
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.624253 Loss1: 0.217922 Loss2: 1.406332
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481979 Loss1: 0.125840 Loss2: 1.356138
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416180 Loss1: 0.083325 Loss2: 1.332855
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.395851 Loss1: 0.067778 Loss2: 1.328073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371461 Loss1: 0.047382 Loss2: 1.324079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377226 Loss1: 0.054386 Loss2: 1.322840 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.346189 Loss1: 0.036931 Loss2: 1.309258 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.260192 Loss1: 0.410865 Loss2: 1.849327
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.602632 Loss1: 0.248360 Loss2: 1.354272
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.577964 Loss1: 0.202519 Loss2: 1.375445
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524546 Loss1: 0.169094 Loss2: 1.355453
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.148540 Loss1: 0.370969 Loss2: 1.777571
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.614326 Loss1: 0.293986 Loss2: 1.320340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.561762 Loss1: 0.181940 Loss2: 1.379823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.483741 Loss1: 0.161366 Loss2: 1.322375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.432169 Loss1: 0.105771 Loss2: 1.326397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421498 Loss1: 0.101662 Loss2: 1.319836 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.410553 Loss1: 0.102249 Loss2: 1.308304 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370539 Loss1: 0.072182 Loss2: 1.298358 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.343245 Loss1: 0.480736 Loss2: 1.862509 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599020 Loss1: 0.212383 Loss2: 1.386637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.513497 Loss1: 0.539955 Loss2: 1.973542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.794516 Loss1: 0.424604 Loss2: 1.369913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.640149 Loss1: 0.224524 Loss2: 1.415625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576914 Loss1: 0.191240 Loss2: 1.385675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.562160 Loss1: 0.190130 Loss2: 1.372030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490910 Loss1: 0.111221 Loss2: 1.379689
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427572 Loss1: 0.078149 Loss2: 1.349423
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445285 Loss1: 0.086730 Loss2: 1.358555
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407326 Loss1: 0.061844 Loss2: 1.345482
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432308 Loss1: 0.079237 Loss2: 1.353071 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.352538 Loss1: 0.450809 Loss2: 1.901729 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648094 Loss1: 0.244369 Loss2: 1.403726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559283 Loss1: 0.163595 Loss2: 1.395688
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.295446 Loss1: 0.458644 Loss2: 1.836802
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.714470 Loss1: 0.329064 Loss2: 1.385406 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605210 Loss1: 0.196391 Loss2: 1.408819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519053 Loss1: 0.141849 Loss2: 1.377204 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450596 Loss1: 0.086787 Loss2: 1.363809 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.418348 Loss1: 0.060764 Loss2: 1.357584 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.401142 Loss1: 0.053670 Loss2: 1.347472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374837 Loss1: 0.034177 Loss2: 1.340660 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.292024 Loss1: 0.456797 Loss2: 1.835228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568088 Loss1: 0.170460 Loss2: 1.397629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.530705 Loss1: 0.168618 Loss2: 1.362086 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471873 Loss1: 0.109220 Loss2: 1.362652 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.452206 Loss1: 0.098607 Loss2: 1.353599 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405586 Loss1: 0.061825 Loss2: 1.343762 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398620 Loss1: 0.061468 Loss2: 1.337151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405608 Loss1: 0.073102 Loss2: 1.332506 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.375568 Loss1: 0.057596 Loss2: 1.317972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.367214 Loss1: 0.057791 Loss2: 1.309422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.355778 Loss1: 0.046266 Loss2: 1.309511
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.272625 Loss1: 0.461189 Loss2: 1.811436
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.651324 Loss1: 0.328524 Loss2: 1.322800
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.564958 Loss1: 0.181208 Loss2: 1.383750
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.458513 Loss1: 0.141887 Loss2: 1.316627
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.436858 Loss1: 0.115560 Loss2: 1.321298
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.322495 Loss1: 0.459481 Loss2: 1.863014
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.418868 Loss1: 0.099320 Loss2: 1.319548
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.401604 Loss1: 0.092854 Loss2: 1.308750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.365695 Loss1: 0.058023 Loss2: 1.307672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378550 Loss1: 0.071645 Loss2: 1.306905 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366258 Loss1: 0.060842 Loss2: 1.305416 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441532 Loss1: 0.091375 Loss2: 1.350156 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410628 Loss1: 0.070899 Loss2: 1.339729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401623 Loss1: 0.060093 Loss2: 1.341530
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.275941 Loss1: 0.441762 Loss2: 1.834179
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.657015 Loss1: 0.316820 Loss2: 1.340195
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574909 Loss1: 0.200880 Loss2: 1.374029
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.486016 Loss1: 0.133881 Loss2: 1.352135
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.434664 Loss1: 0.098820 Loss2: 1.335844
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.217351 Loss1: 0.422528 Loss2: 1.794823
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449625 Loss1: 0.116544 Loss2: 1.333081
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.417370 Loss1: 0.091465 Loss2: 1.325905 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378511 Loss1: 0.052945 Loss2: 1.325566 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.369636 Loss1: 0.049391 Loss2: 1.320245 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365833 Loss1: 0.053658 Loss2: 1.312175 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.410836 Loss1: 0.105399 Loss2: 1.305437 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.380334 Loss1: 0.078844 Loss2: 1.301490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.350309 Loss1: 0.054745 Loss2: 1.295564
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.435927 Loss1: 0.504749 Loss2: 1.931178
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.747347 Loss1: 0.325376 Loss2: 1.421971
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635698 Loss1: 0.184894 Loss2: 1.450804
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.611580 Loss1: 0.185281 Loss2: 1.426298
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.512081 Loss1: 0.092693 Loss2: 1.419387
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.239721 Loss1: 0.432182 Loss2: 1.807539
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511774 Loss1: 0.104300 Loss2: 1.407474
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.504908 Loss1: 0.095634 Loss2: 1.409274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.538283 Loss1: 0.181979 Loss2: 1.356305
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.472926 Loss1: 0.072018 Loss2: 1.400909
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450451 Loss1: 0.055233 Loss2: 1.395218 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446471 Loss1: 0.056778 Loss2: 1.389693 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.396912 Loss1: 0.084447 Loss2: 1.312465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374078 Loss1: 0.066399 Loss2: 1.307679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.334585 Loss1: 0.033081 Loss2: 1.301504
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.513157 Loss1: 0.497430 Loss2: 2.015727
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.735154 Loss1: 0.362624 Loss2: 1.372530
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.679291 Loss1: 0.267044 Loss2: 1.412246
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576601 Loss1: 0.138806 Loss2: 1.437795
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527089 Loss1: 0.128837 Loss2: 1.398253
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.545261 Loss1: 0.159754 Loss2: 1.385507
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.535184 Loss1: 0.135348 Loss2: 1.399836
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.626430 Loss1: 0.267052 Loss2: 1.359378 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.687337 Loss1: 0.284351 Loss2: 1.402986 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985677
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523495 Loss1: 0.154251 Loss2: 1.369244 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.449211 Loss1: 0.087995 Loss2: 1.361217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436366 Loss1: 0.080985 Loss2: 1.355381
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.277702 Loss1: 0.399435 Loss2: 1.878267
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.638635 Loss1: 0.267695 Loss2: 1.370940 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400655 Loss1: 0.059425 Loss2: 1.341230
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.584912 Loss1: 0.176513 Loss2: 1.408399
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.656126 Loss1: 0.257762 Loss2: 1.398364
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.553980 Loss1: 0.156170 Loss2: 1.397810
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508906 Loss1: 0.123789 Loss2: 1.385117
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487553 Loss1: 0.098813 Loss2: 1.388740
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.333113 Loss1: 0.476362 Loss2: 1.856751
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480272 Loss1: 0.098147 Loss2: 1.382125
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.728421 Loss1: 0.350739 Loss2: 1.377681
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.487733 Loss1: 0.111583 Loss2: 1.376150
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.728785 Loss1: 0.265847 Loss2: 1.462939
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457078 Loss1: 0.085102 Loss2: 1.371976
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.639908 Loss1: 0.224817 Loss2: 1.415090 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.551027 Loss1: 0.157617 Loss2: 1.393410 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501316 Loss1: 0.114883 Loss2: 1.386434
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.293076 Loss1: 0.421308 Loss2: 1.871768
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.646968 Loss1: 0.290131 Loss2: 1.356837 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445099 Loss1: 0.066846 Loss2: 1.378253
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.566911 Loss1: 0.184862 Loss2: 1.382049
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.590345 Loss1: 0.227495 Loss2: 1.362850
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.620753 Loss1: 0.218413 Loss2: 1.402341
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511055 Loss1: 0.154803 Loss2: 1.356252
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433599 Loss1: 0.079697 Loss2: 1.353901
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.233410 Loss1: 0.396018 Loss2: 1.837393
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410103 Loss1: 0.064938 Loss2: 1.345165
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.611347 Loss1: 0.276187 Loss2: 1.335159
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413250 Loss1: 0.072605 Loss2: 1.340646
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.547332 Loss1: 0.177389 Loss2: 1.369944
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387209 Loss1: 0.047965 Loss2: 1.339244
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.471961 Loss1: 0.131666 Loss2: 1.340295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.442456 Loss1: 0.106491 Loss2: 1.335965 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399965 Loss1: 0.068375 Loss2: 1.331590
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.427283 Loss1: 0.516988 Loss2: 1.910295
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.653107 Loss1: 0.280500 Loss2: 1.372607 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401706 Loss1: 0.078812 Loss2: 1.322893
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.651525 Loss1: 0.263821 Loss2: 1.387704
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589088 Loss1: 0.196739 Loss2: 1.392348
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.598381 Loss1: 0.234493 Loss2: 1.363888
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471575 Loss1: 0.100396 Loss2: 1.371180
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440299 Loss1: 0.084412 Loss2: 1.355887
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400825 Loss1: 0.052631 Loss2: 1.348194
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.364070 Loss1: 0.525605 Loss2: 1.838465
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.676176 Loss1: 0.334317 Loss2: 1.341859 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983259
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429398 Loss1: 0.089816 Loss2: 1.339583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.643865 Loss1: 0.240131 Loss2: 1.403734
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526362 Loss1: 0.166439 Loss2: 1.359923
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.471590 Loss1: 0.117232 Loss2: 1.354357
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503508 Loss1: 0.150124 Loss2: 1.353384
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.529143 Loss1: 0.176279 Loss2: 1.352863
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.292838 Loss1: 0.425735 Loss2: 1.867103
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511193 Loss1: 0.142946 Loss2: 1.368247
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499801 Loss1: 0.155845 Loss2: 1.343956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.459515 Loss1: 0.106136 Loss2: 1.353379 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480917 Loss1: 0.128967 Loss2: 1.351949 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409662 Loss1: 0.066878 Loss2: 1.342783 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.402886 Loss1: 0.064926 Loss2: 1.337961
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.334003 Loss1: 0.455136 Loss2: 1.878867
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.643180 Loss1: 0.273802 Loss2: 1.369378 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386714 Loss1: 0.051127 Loss2: 1.335587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.593880 Loss1: 0.186920 Loss2: 1.406959
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534862 Loss1: 0.154024 Loss2: 1.380838
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492340 Loss1: 0.112144 Loss2: 1.380196
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465643 Loss1: 0.088667 Loss2: 1.376977
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.500729 Loss1: 0.127345 Loss2: 1.373384
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.289630 Loss1: 0.446008 Loss2: 1.843622
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460371 Loss1: 0.087801 Loss2: 1.372570
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415334 Loss1: 0.050288 Loss2: 1.365046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397711 Loss1: 0.045767 Loss2: 1.351944 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.502531 Loss1: 0.157778 Loss2: 1.344753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408327 Loss1: 0.075345 Loss2: 1.332982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.305536 Loss1: 0.492645 Loss2: 1.812892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.620825 Loss1: 0.304862 Loss2: 1.315963 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565481 Loss1: 0.218851 Loss2: 1.346630 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455947 Loss1: 0.135239 Loss2: 1.320708 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.385130 Loss1: 0.071122 Loss2: 1.314008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388225 Loss1: 0.085616 Loss2: 1.302609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370475 Loss1: 0.067700 Loss2: 1.302775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362788 Loss1: 0.065375 Loss2: 1.297413 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.439878 Loss1: 0.121038 Loss2: 1.318840 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383013 Loss1: 0.082847 Loss2: 1.300167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.367522 Loss1: 0.072887 Loss2: 1.294635
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.209397 Loss1: 0.318813 Loss2: 1.890584
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.658586 Loss1: 0.237965 Loss2: 1.420622 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.549409 Loss1: 0.121714 Loss2: 1.427695 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542783 Loss1: 0.121891 Loss2: 1.420892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.521887 Loss1: 0.104200 Loss2: 1.417687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518255 Loss1: 0.098500 Loss2: 1.419755 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.515206 Loss1: 0.099920 Loss2: 1.415287 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.492017 Loss1: 0.077519 Loss2: 1.414498 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983456
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.376429 Loss1: 0.088703 Loss2: 1.287726 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.331826 Loss1: 0.063482 Loss2: 1.268343 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.320681 Loss1: 0.056563 Loss2: 1.264118
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.350985 Loss1: 0.486501 Loss2: 1.864484
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.674170 Loss1: 0.307928 Loss2: 1.366242
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614990 Loss1: 0.218648 Loss2: 1.396342
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540353 Loss1: 0.174628 Loss2: 1.365725
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497361 Loss1: 0.131105 Loss2: 1.366256
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.298333 Loss1: 0.452850 Loss2: 1.845482
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490515 Loss1: 0.128519 Loss2: 1.361996
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.642971 Loss1: 0.284088 Loss2: 1.358883
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485998 Loss1: 0.119621 Loss2: 1.366377
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610950 Loss1: 0.217480 Loss2: 1.393470
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475969 Loss1: 0.111140 Loss2: 1.364830
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549417 Loss1: 0.185241 Loss2: 1.364177
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441514 Loss1: 0.086269 Loss2: 1.355245
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499305 Loss1: 0.138865 Loss2: 1.360439
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442770 Loss1: 0.088888 Loss2: 1.353882
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462080 Loss1: 0.117235 Loss2: 1.344845 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413150 Loss1: 0.060467 Loss2: 1.352684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384978 Loss1: 0.043545 Loss2: 1.341433
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.179571 Loss1: 0.353898 Loss2: 1.825673
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.681866 Loss1: 0.320020 Loss2: 1.361846
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629848 Loss1: 0.230889 Loss2: 1.398960
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538275 Loss1: 0.160812 Loss2: 1.377464
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.536521 Loss1: 0.163066 Loss2: 1.373454
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.227940 Loss1: 0.417733 Loss2: 1.810206
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530102 Loss1: 0.158128 Loss2: 1.371974
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509393 Loss1: 0.132178 Loss2: 1.377215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486012 Loss1: 0.119680 Loss2: 1.366333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444792 Loss1: 0.082938 Loss2: 1.361854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416110 Loss1: 0.058165 Loss2: 1.357945 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418228 Loss1: 0.081943 Loss2: 1.336285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379453 Loss1: 0.055304 Loss2: 1.324149 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383376 Loss1: 0.056570 Loss2: 1.326806
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.238171 Loss1: 0.447286 Loss2: 1.790885
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.614329 Loss1: 0.287192 Loss2: 1.327137
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.559282 Loss1: 0.197678 Loss2: 1.361604
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.502849 Loss1: 0.176133 Loss2: 1.326715
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.436359 Loss1: 0.107325 Loss2: 1.329033
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.370976 Loss1: 0.523055 Loss2: 1.847921
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.633766 Loss1: 0.271196 Loss2: 1.362569
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439393 Loss1: 0.112315 Loss2: 1.327078
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.556547 Loss1: 0.177289 Loss2: 1.379258
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404349 Loss1: 0.083913 Loss2: 1.320436
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541931 Loss1: 0.184375 Loss2: 1.357556
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389682 Loss1: 0.077051 Loss2: 1.312631
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.505052 Loss1: 0.142764 Loss2: 1.362288
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.335388 Loss1: 0.029925 Loss2: 1.305463
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480668 Loss1: 0.121088 Loss2: 1.359580
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.322692 Loss1: 0.027953 Loss2: 1.294739
-(DefaultActor pid=3764) >> Training accuracy: 0.999023
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456878 Loss1: 0.095403 Loss2: 1.361475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398081 Loss1: 0.050991 Loss2: 1.347089 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.186506 Loss1: 0.393722 Loss2: 1.792784
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612013 Loss1: 0.264356 Loss2: 1.347657
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.534254 Loss1: 0.163623 Loss2: 1.370631
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520600 Loss1: 0.184663 Loss2: 1.335937
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.207850 Loss1: 0.377343 Loss2: 1.830506
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.588279 Loss1: 0.243143 Loss2: 1.345136 [repeated 2x across cluster]
-DEBUG flwr 2023-10-12 22:53:02,257 | server.py:236 | fit_round 167 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.535803 Loss1: 0.171338 Loss2: 1.364465 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518091 Loss1: 0.176407 Loss2: 1.341684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.508694 Loss1: 0.162995 Loss2: 1.345699 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477666 Loss1: 0.128731 Loss2: 1.348934 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367093 Loss1: 0.049910 Loss2: 1.317183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485065 Loss1: 0.134739 Loss2: 1.350326
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434074 Loss1: 0.096333 Loss2: 1.337741
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433689 Loss1: 0.102715 Loss2: 1.330974
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411048 Loss1: 0.080396 Loss2: 1.330651
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.797144 Loss1: 0.431768 Loss2: 1.365376 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536820 Loss1: 0.174933 Loss2: 1.361887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500625 Loss1: 0.140775 Loss2: 1.359850
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.277203 Loss1: 0.434634 Loss2: 1.842569
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490399 Loss1: 0.130483 Loss2: 1.359916
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.735197 Loss1: 0.350220 Loss2: 1.384978
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.623915 Loss1: 0.198916 Loss2: 1.424998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589982 Loss1: 0.199007 Loss2: 1.390975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.554889 Loss1: 0.154863 Loss2: 1.400026 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.492920 Loss1: 0.113612 Loss2: 1.379308 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444450 Loss1: 0.071350 Loss2: 1.373100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418328 Loss1: 0.055223 Loss2: 1.363105
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.287120 Loss1: 0.414894 Loss2: 1.872226
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.684173 Loss1: 0.267689 Loss2: 1.416484
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.637886 Loss1: 0.199437 Loss2: 1.438450
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.578303 Loss1: 0.170102 Loss2: 1.408200
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.587975 Loss1: 0.165808 Loss2: 1.422167
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.564704 Loss1: 0.152732 Loss2: 1.411972
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.543159 Loss1: 0.136865 Loss2: 1.406294
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.538228 Loss1: 0.126014 Loss2: 1.412214
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.524905 Loss1: 0.115087 Loss2: 1.409818
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.515328 Loss1: 0.108344 Loss2: 1.406983
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 22:53:02,257][flwr][DEBUG] - fit_round 167 received 50 results and 0 failures
-INFO flwr 2023-10-12 22:53:44,190 | server.py:125 | fit progress: (167, 2.2618117819959744, {'accuracy': 0.6034}, 385331.968352496)
->> Test accuracy: 0.603400
-[2023-10-12 22:53:44,190][flwr][INFO] - fit progress: (167, 2.2618117819959744, {'accuracy': 0.6034}, 385331.968352496)
-DEBUG flwr 2023-10-12 22:53:44,190 | server.py:173 | evaluate_round 167: strategy sampled 50 clients (out of 50)
-[2023-10-12 22:53:44,190][flwr][DEBUG] - evaluate_round 167: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 23:02:50,075 | server.py:187 | evaluate_round 167 received 50 results and 0 failures
-[2023-10-12 23:02:50,075][flwr][DEBUG] - evaluate_round 167 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 23:02:50,076 | server.py:222 | fit_round 168: strategy sampled 50 clients (out of 50)
-[2023-10-12 23:02:50,076][flwr][DEBUG] - fit_round 168: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.253372 Loss1: 0.477824 Loss2: 1.775548
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622301 Loss1: 0.317462 Loss2: 1.304840
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.585747 Loss1: 0.219457 Loss2: 1.366290
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534789 Loss1: 0.224923 Loss2: 1.309866
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.334661 Loss1: 0.495947 Loss2: 1.838713
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463326 Loss1: 0.146071 Loss2: 1.317255
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.625032 Loss1: 0.270060 Loss2: 1.354972
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448709 Loss1: 0.138972 Loss2: 1.309738
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.599609 Loss1: 0.212114 Loss2: 1.387496
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402143 Loss1: 0.091488 Loss2: 1.310654
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.527709 Loss1: 0.162226 Loss2: 1.365483
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380753 Loss1: 0.078437 Loss2: 1.302316
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509655 Loss1: 0.151396 Loss2: 1.358259
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.345440 Loss1: 0.051843 Loss2: 1.293596
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.462270 Loss1: 0.105001 Loss2: 1.357270
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.323503 Loss1: 0.034127 Loss2: 1.289376
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469302 Loss1: 0.124613 Loss2: 1.344689
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434188 Loss1: 0.081882 Loss2: 1.352307
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425335 Loss1: 0.078879 Loss2: 1.346456
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391628 Loss1: 0.053083 Loss2: 1.338546
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.368550 Loss1: 0.462423 Loss2: 1.906127
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.769289 Loss1: 0.362974 Loss2: 1.406315
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.664457 Loss1: 0.228951 Loss2: 1.435507
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582049 Loss1: 0.173530 Loss2: 1.408518
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.293547 Loss1: 0.476383 Loss2: 1.817165
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.679437 Loss1: 0.339047 Loss2: 1.340389 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.560064 Loss1: 0.192376 Loss2: 1.367688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.482660 Loss1: 0.146983 Loss2: 1.335677 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.447810 Loss1: 0.112907 Loss2: 1.334903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.418077 Loss1: 0.092337 Loss2: 1.325740 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441597 Loss1: 0.058729 Loss2: 1.382868
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416516 Loss1: 0.093780 Loss2: 1.322736
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.379749 Loss1: 0.055082 Loss2: 1.324668
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372260 Loss1: 0.055540 Loss2: 1.316720
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372410 Loss1: 0.060197 Loss2: 1.312213
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.348561 Loss1: 0.448280 Loss2: 1.900281
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.701040 Loss1: 0.294791 Loss2: 1.406249
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.678831 Loss1: 0.232296 Loss2: 1.446535
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536075 Loss1: 0.128606 Loss2: 1.407469
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.291889 Loss1: 0.481511 Loss2: 1.810377
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.742546 Loss1: 0.385826 Loss2: 1.356720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.663110 Loss1: 0.249793 Loss2: 1.413317 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.524023 Loss1: 0.163911 Loss2: 1.360112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535172 Loss1: 0.181936 Loss2: 1.353236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.475659 Loss1: 0.122574 Loss2: 1.353086 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428489 Loss1: 0.089027 Loss2: 1.339462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430763 Loss1: 0.093044 Loss2: 1.337720 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.472043 Loss1: 0.542805 Loss2: 1.929239 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.583054 Loss1: 0.162553 Loss2: 1.420500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.396480 Loss1: 0.531251 Loss2: 1.865229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.689950 Loss1: 0.343187 Loss2: 1.346763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.595306 Loss1: 0.225315 Loss2: 1.369991 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467092 Loss1: 0.103836 Loss2: 1.363256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.464571 Loss1: 0.100819 Loss2: 1.363752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.479019 Loss1: 0.120267 Loss2: 1.358752 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.377376 Loss1: 0.052856 Loss2: 1.324520 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.360219 Loss1: 0.042482 Loss2: 1.317737 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.696768 Loss1: 0.334689 Loss2: 1.362079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.577289 Loss1: 0.210243 Loss2: 1.367046 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.215631 Loss1: 0.398228 Loss2: 1.817402
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.512392 Loss1: 0.144295 Loss2: 1.368097
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.628682 Loss1: 0.279429 Loss2: 1.349253
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.496574 Loss1: 0.130554 Loss2: 1.366019
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.620816 Loss1: 0.250975 Loss2: 1.369841
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487089 Loss1: 0.127207 Loss2: 1.359882
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.533113 Loss1: 0.178215 Loss2: 1.354898
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.512424 Loss1: 0.153349 Loss2: 1.359074
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.494861 Loss1: 0.146883 Loss2: 1.347978
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449363 Loss1: 0.096263 Loss2: 1.353100
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.445099 Loss1: 0.101421 Loss2: 1.343678
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405743 Loss1: 0.060099 Loss2: 1.345644
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415726 Loss1: 0.069595 Loss2: 1.346131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391938 Loss1: 0.065221 Loss2: 1.326716 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.692094 Loss1: 0.366647 Loss2: 1.325447 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.565929 Loss1: 0.191484 Loss2: 1.374445 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.425764 Loss1: 0.101834 Loss2: 1.323930 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436094 Loss1: 0.112667 Loss2: 1.323427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.397221 Loss1: 0.080198 Loss2: 1.317023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373648 Loss1: 0.068088 Loss2: 1.305560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399841 Loss1: 0.094586 Loss2: 1.305255 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.458478 Loss1: 0.086816 Loss2: 1.371662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475628 Loss1: 0.104373 Loss2: 1.371255 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428442 Loss1: 0.063202 Loss2: 1.365240
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.210154 Loss1: 0.389821 Loss2: 1.820333
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412347 Loss1: 0.054579 Loss2: 1.357769
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.570482 Loss1: 0.216186 Loss2: 1.354296
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.525213 Loss1: 0.148710 Loss2: 1.376503
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527844 Loss1: 0.167475 Loss2: 1.360369
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492303 Loss1: 0.132252 Loss2: 1.360051
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457749 Loss1: 0.103117 Loss2: 1.354631
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.251585 Loss1: 0.367300 Loss2: 1.884286
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.683412 Loss1: 0.303138 Loss2: 1.380274 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.585005 Loss1: 0.166506 Loss2: 1.418500 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540428 Loss1: 0.152203 Loss2: 1.388224 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379868 Loss1: 0.042302 Loss2: 1.337566
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523823 Loss1: 0.135325 Loss2: 1.388498
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499682 Loss1: 0.116115 Loss2: 1.383567
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437044 Loss1: 0.062606 Loss2: 1.374438
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433409 Loss1: 0.060069 Loss2: 1.373340
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431642 Loss1: 0.062768 Loss2: 1.368873
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435297 Loss1: 0.065853 Loss2: 1.369444
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.303363 Loss1: 0.501753 Loss2: 1.801610
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.626622 Loss1: 0.304741 Loss2: 1.321881
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.522195 Loss1: 0.174857 Loss2: 1.347338
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.446334 Loss1: 0.124048 Loss2: 1.322285
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.412658 Loss1: 0.097585 Loss2: 1.315074
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463099 Loss1: 0.149832 Loss2: 1.313267
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.353810 Loss1: 0.472429 Loss2: 1.881381
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.427098 Loss1: 0.112725 Loss2: 1.314373
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.663205 Loss1: 0.282046 Loss2: 1.381158
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.395632 Loss1: 0.082801 Loss2: 1.312831
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.658198 Loss1: 0.250465 Loss2: 1.407733
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399665 Loss1: 0.091159 Loss2: 1.308506
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557026 Loss1: 0.171284 Loss2: 1.385742
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360581 Loss1: 0.049136 Loss2: 1.311446
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528612 Loss1: 0.145820 Loss2: 1.382792
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.530425 Loss1: 0.155524 Loss2: 1.374902
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.499129 Loss1: 0.114177 Loss2: 1.384952
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.535562 Loss1: 0.162072 Loss2: 1.373490
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462493 Loss1: 0.087501 Loss2: 1.374992
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.513614 Loss1: 0.573681 Loss2: 1.939932
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426140 Loss1: 0.059966 Loss2: 1.366173
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610914 Loss1: 0.239967 Loss2: 1.370948 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468674 Loss1: 0.107428 Loss2: 1.361246 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.413627 Loss1: 0.061413 Loss2: 1.352215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.392695 Loss1: 0.057253 Loss2: 1.335442 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.370104 Loss1: 0.038244 Loss2: 1.331860 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387659 Loss1: 0.058155 Loss2: 1.329504 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.480104 Loss1: 0.106385 Loss2: 1.373719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467335 Loss1: 0.101952 Loss2: 1.365383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464573 Loss1: 0.102026 Loss2: 1.362547
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.282434 Loss1: 0.441070 Loss2: 1.841364
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.617330 Loss1: 0.264052 Loss2: 1.353279 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.613916 Loss1: 0.224903 Loss2: 1.389013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.483157 Loss1: 0.137446 Loss2: 1.345710 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449360 Loss1: 0.103061 Loss2: 1.346298 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.416829 Loss1: 0.075677 Loss2: 1.341152 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396731 Loss1: 0.061578 Loss2: 1.335153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.562359 Loss1: 0.196528 Loss2: 1.365832
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390625 Loss1: 0.056477 Loss2: 1.334149
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487367 Loss1: 0.146133 Loss2: 1.341234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.405956 Loss1: 0.072951 Loss2: 1.333005 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.351615 Loss1: 0.525725 Loss2: 1.825890
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.367389 Loss1: 0.045697 Loss2: 1.321692
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.602109 Loss1: 0.262909 Loss2: 1.339201
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366247 Loss1: 0.049138 Loss2: 1.317109
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.511345 Loss1: 0.166140 Loss2: 1.345205
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367027 Loss1: 0.047894 Loss2: 1.319133
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.386829 Loss1: 0.070854 Loss2: 1.315976 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.407093 Loss1: 0.092564 Loss2: 1.314530 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.402069 Loss1: 0.089142 Loss2: 1.312927
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.486656 Loss1: 0.586817 Loss2: 1.899839
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408768 Loss1: 0.089396 Loss2: 1.319372
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.667165 Loss1: 0.344765 Loss2: 1.322399
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.603753 Loss1: 0.237370 Loss2: 1.366383
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399215 Loss1: 0.075731 Loss2: 1.323484
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483813 Loss1: 0.171554 Loss2: 1.312259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.384176 Loss1: 0.080654 Loss2: 1.303522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.355755 Loss1: 0.059109 Loss2: 1.296646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364050 Loss1: 0.074593 Loss2: 1.289457 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.531926 Loss1: 0.192121 Loss2: 1.339805 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468575 Loss1: 0.130875 Loss2: 1.337700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458288 Loss1: 0.116046 Loss2: 1.342242
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.342693 Loss1: 0.476552 Loss2: 1.866141
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418300 Loss1: 0.084557 Loss2: 1.333744
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.715670 Loss1: 0.338721 Loss2: 1.376949
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421302 Loss1: 0.095129 Loss2: 1.326173
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.727735 Loss1: 0.304726 Loss2: 1.423009
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390035 Loss1: 0.065456 Loss2: 1.324579
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.607699 Loss1: 0.228268 Loss2: 1.379431
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503321 Loss1: 0.130880 Loss2: 1.372441
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472246 Loss1: 0.101242 Loss2: 1.371004
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459863 Loss1: 0.098700 Loss2: 1.361163
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427265 Loss1: 0.073241 Loss2: 1.354023
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406336 Loss1: 0.050410 Loss2: 1.355927
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.205404 Loss1: 0.382054 Loss2: 1.823350
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384818 Loss1: 0.038465 Loss2: 1.346353
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.566246 Loss1: 0.236709 Loss2: 1.329536
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.490318 Loss1: 0.162027 Loss2: 1.328291
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.418741 Loss1: 0.094107 Loss2: 1.324634
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.403701 Loss1: 0.098688 Loss2: 1.305013
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.385707 Loss1: 0.080915 Loss2: 1.304792
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.374833 Loss1: 0.523841 Loss2: 1.850992
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.381711 Loss1: 0.079698 Loss2: 1.302013
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.361469 Loss1: 0.055578 Loss2: 1.305891 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374945 Loss1: 0.069452 Loss2: 1.305493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350456 Loss1: 0.047679 Loss2: 1.302777 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420072 Loss1: 0.069038 Loss2: 1.351034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407420 Loss1: 0.074474 Loss2: 1.332946 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399354 Loss1: 0.065730 Loss2: 1.333623
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.468014 Loss1: 0.527405 Loss2: 1.940609
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388759 Loss1: 0.056594 Loss2: 1.332165
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.734670 Loss1: 0.336202 Loss2: 1.398467
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.698292 Loss1: 0.257477 Loss2: 1.440815
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.588525 Loss1: 0.179652 Loss2: 1.408873
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555953 Loss1: 0.167589 Loss2: 1.388363
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525401 Loss1: 0.126396 Loss2: 1.399005
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495368 Loss1: 0.096104 Loss2: 1.399264
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.287196 Loss1: 0.406974 Loss2: 1.880222
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.478213 Loss1: 0.096923 Loss2: 1.381291
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.621948 Loss1: 0.242551 Loss2: 1.379397
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448576 Loss1: 0.067398 Loss2: 1.381178
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.659857 Loss1: 0.251229 Loss2: 1.408628
-(DefaultActor pid=3765) >> Training accuracy: 0.995536
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430102 Loss1: 0.054767 Loss2: 1.375335
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.563239 Loss1: 0.169259 Loss2: 1.393981
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.560197 Loss1: 0.178297 Loss2: 1.381899
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512213 Loss1: 0.125318 Loss2: 1.386895
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.510024 Loss1: 0.129723 Loss2: 1.380301
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.530651 Loss1: 0.150151 Loss2: 1.380501
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.228593 Loss1: 0.359645 Loss2: 1.868948
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.491509 Loss1: 0.114155 Loss2: 1.377355
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.475328 Loss1: 0.098465 Loss2: 1.376863 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484541 Loss1: 0.122601 Loss2: 1.361940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.435511 Loss1: 0.080390 Loss2: 1.355121 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443959 Loss1: 0.095157 Loss2: 1.348802
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.489529 Loss1: 0.565487 Loss2: 1.924042
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.856265 Loss1: 0.422506 Loss2: 1.433759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.776780 Loss1: 0.289825 Loss2: 1.486955 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.653680 Loss1: 0.223573 Loss2: 1.430107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.518258 Loss1: 0.101775 Loss2: 1.416483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457214 Loss1: 0.048019 Loss2: 1.409195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.250242 Loss1: 0.455932 Loss2: 1.794310
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457582 Loss1: 0.058155 Loss2: 1.399427
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572330 Loss1: 0.263444 Loss2: 1.308885
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482464 Loss1: 0.087832 Loss2: 1.394633
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.441081 Loss1: 0.135424 Loss2: 1.305657 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.397725 Loss1: 0.094832 Loss2: 1.302893 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.357889 Loss1: 0.060142 Loss2: 1.297747
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.169017 Loss1: 0.362853 Loss2: 1.806164
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.644043 Loss1: 0.298918 Loss2: 1.345126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.612450 Loss1: 0.222863 Loss2: 1.389587 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611181 Loss1: 0.244967 Loss2: 1.366215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524503 Loss1: 0.167309 Loss2: 1.357195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435368 Loss1: 0.095514 Loss2: 1.339854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415044 Loss1: 0.080436 Loss2: 1.334608 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368231 Loss1: 0.042282 Loss2: 1.325949 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507646 Loss1: 0.138315 Loss2: 1.369331 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435303 Loss1: 0.073089 Loss2: 1.362214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.184549 Loss1: 0.353977 Loss2: 1.830572
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415125 Loss1: 0.060566 Loss2: 1.354559
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.605106 Loss1: 0.240704 Loss2: 1.364402
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399987 Loss1: 0.054486 Loss2: 1.345501
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439518 Loss1: 0.090259 Loss2: 1.349258
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.584187 Loss1: 0.188261 Loss2: 1.395925
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.518941 Loss1: 0.160999 Loss2: 1.357942
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507753 Loss1: 0.141330 Loss2: 1.366423
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.505320 Loss1: 0.140105 Loss2: 1.365216
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473608 Loss1: 0.103990 Loss2: 1.369618
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.225838 Loss1: 0.414739 Loss2: 1.811099
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455871 Loss1: 0.094263 Loss2: 1.361608
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.595823 Loss1: 0.228167 Loss2: 1.367657
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.471818 Loss1: 0.109257 Loss2: 1.362561
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.530370 Loss1: 0.135345 Loss2: 1.395026
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435731 Loss1: 0.077114 Loss2: 1.358617
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.477192 Loss1: 0.114906 Loss2: 1.362286 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420398 Loss1: 0.062171 Loss2: 1.358227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.193503 Loss1: 0.451672 Loss2: 1.741831
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404184 Loss1: 0.056456 Loss2: 1.347728
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.582935 Loss1: 0.281853 Loss2: 1.301082
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409886 Loss1: 0.059230 Loss2: 1.350656
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.487840 Loss1: 0.166331 Loss2: 1.321509
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410245 Loss1: 0.068746 Loss2: 1.341499
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.406091 Loss1: 0.108242 Loss2: 1.297849 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.410617 Loss1: 0.116958 Loss2: 1.293659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.306883 Loss1: 0.499873 Loss2: 1.807010
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.353707 Loss1: 0.066725 Loss2: 1.286983
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.685703 Loss1: 0.309829 Loss2: 1.375874
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.344689 Loss1: 0.061734 Loss2: 1.282955
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.549472 Loss1: 0.185931 Loss2: 1.363540
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.342051 Loss1: 0.056837 Loss2: 1.285214
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.450945 Loss1: 0.109904 Loss2: 1.341041 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434817 Loss1: 0.098014 Loss2: 1.336803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.407542 Loss1: 0.491791 Loss2: 1.915751
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.407905 Loss1: 0.072719 Loss2: 1.335186
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377177 Loss1: 0.055332 Loss2: 1.321844 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363751 Loss1: 0.041892 Loss2: 1.321859 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.536512 Loss1: 0.157727 Loss2: 1.378785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480163 Loss1: 0.105554 Loss2: 1.374609 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.333582 Loss1: 0.421417 Loss2: 1.912165 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.681579 Loss1: 0.295176 Loss2: 1.386403 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987723
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586518 Loss1: 0.199540 Loss2: 1.386978 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.548279 Loss1: 0.160995 Loss2: 1.387284 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481334 Loss1: 0.105773 Loss2: 1.375561
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.276471 Loss1: 0.372147 Loss2: 1.904324
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443914 Loss1: 0.060796 Loss2: 1.383118
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.751533 Loss1: 0.341724 Loss2: 1.409809
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414004 Loss1: 0.051234 Loss2: 1.362770
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.599291 Loss1: 0.150775 Loss2: 1.448515
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400619 Loss1: 0.037237 Loss2: 1.363382
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538493 Loss1: 0.128329 Loss2: 1.410163
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516898 Loss1: 0.109412 Loss2: 1.407486
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524707 Loss1: 0.117092 Loss2: 1.407616
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.510631 Loss1: 0.099578 Loss2: 1.411054
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470711 Loss1: 0.065074 Loss2: 1.405637
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.472505 Loss1: 0.078585 Loss2: 1.393920
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.296513 Loss1: 0.460914 Loss2: 1.835599
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.465317 Loss1: 0.070703 Loss2: 1.394614
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.665625 Loss1: 0.312040 Loss2: 1.353585
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.637107 Loss1: 0.261412 Loss2: 1.375695
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.543799 Loss1: 0.187006 Loss2: 1.356793
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.442748 Loss1: 0.088857 Loss2: 1.353892
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433535 Loss1: 0.095690 Loss2: 1.337845
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.264417 Loss1: 0.433008 Loss2: 1.831409
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434454 Loss1: 0.099737 Loss2: 1.334717
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.407221 Loss1: 0.071804 Loss2: 1.335417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.376939 Loss1: 0.055245 Loss2: 1.321694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358302 Loss1: 0.041862 Loss2: 1.316440 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.411986 Loss1: 0.083737 Loss2: 1.328250 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381421 Loss1: 0.067796 Loss2: 1.313625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.355651 Loss1: 0.046613 Loss2: 1.309039
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.464600 Loss1: 0.588610 Loss2: 1.875990
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.710072 Loss1: 0.324079 Loss2: 1.385993 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497369 Loss1: 0.131405 Loss2: 1.365964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458119 Loss1: 0.093727 Loss2: 1.364391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428450 Loss1: 0.071793 Loss2: 1.356657 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447866 Loss1: 0.092950 Loss2: 1.354915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421518 Loss1: 0.070726 Loss2: 1.350793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402824 Loss1: 0.055570 Loss2: 1.347254 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.391941 Loss1: 0.078148 Loss2: 1.313793 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396224 Loss1: 0.084173 Loss2: 1.312051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.365436 Loss1: 0.057586 Loss2: 1.307850
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.329817 Loss1: 0.435505 Loss2: 1.894312
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.632604 Loss1: 0.276919 Loss2: 1.355686 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.603961 Loss1: 0.238432 Loss2: 1.365529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508608 Loss1: 0.143193 Loss2: 1.365414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459761 Loss1: 0.110256 Loss2: 1.349505 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419076 Loss1: 0.072836 Loss2: 1.346239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.573938 Loss1: 0.227418 Loss2: 1.346519
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390778 Loss1: 0.050190 Loss2: 1.340588
-DEBUG flwr 2023-10-12 23:31:25,177 | server.py:236 | fit_round 168 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.494400 Loss1: 0.179760 Loss2: 1.314640
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378990 Loss1: 0.040314 Loss2: 1.338676
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.417437 Loss1: 0.120392 Loss2: 1.297045 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.355477 Loss1: 0.064961 Loss2: 1.290516 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.346073 Loss1: 0.057927 Loss2: 1.288146
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.202280 Loss1: 0.360971 Loss2: 1.841309
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.347190 Loss1: 0.062812 Loss2: 1.284378
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.708406 Loss1: 0.330033 Loss2: 1.378373
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.596143 Loss1: 0.188097 Loss2: 1.408046
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540021 Loss1: 0.161154 Loss2: 1.378867
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.452851 Loss1: 0.085158 Loss2: 1.367693
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454511 Loss1: 0.088115 Loss2: 1.366396
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.098461 Loss1: 0.331567 Loss2: 1.766893
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478788 Loss1: 0.119668 Loss2: 1.359120
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433298 Loss1: 0.068325 Loss2: 1.364973
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.548370 Loss1: 0.231262 Loss2: 1.317109
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.420104 Loss1: 0.073430 Loss2: 1.346674
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.504235 Loss1: 0.171884 Loss2: 1.332351
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397491 Loss1: 0.052675 Loss2: 1.344816
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.448794 Loss1: 0.129181 Loss2: 1.319613
-(DefaultActor pid=3765) >> Training accuracy: 0.979492
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450163 Loss1: 0.139163 Loss2: 1.311000
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420310 Loss1: 0.108183 Loss2: 1.312127
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.373723 Loss1: 0.066003 Loss2: 1.307719
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.360432 Loss1: 0.064126 Loss2: 1.296306
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.244198 Loss1: 0.433307 Loss2: 1.810890
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.701087 Loss1: 0.364773 Loss2: 1.336314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.339692 Loss1: 0.051394 Loss2: 1.288298
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.651641 Loss1: 0.264370 Loss2: 1.387271
-(DefaultActor pid=3764) >> Training accuracy: 0.998162
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.572684 Loss1: 0.212149 Loss2: 1.360536
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544733 Loss1: 0.185557 Loss2: 1.359177
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.496250 Loss1: 0.146318 Loss2: 1.349931
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475916 Loss1: 0.136344 Loss2: 1.339571
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.319601 Loss1: 0.429461 Loss2: 1.890140
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.428899 Loss1: 0.093214 Loss2: 1.335686
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.674691 Loss1: 0.289299 Loss2: 1.385392
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413769 Loss1: 0.079391 Loss2: 1.334377
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.619827 Loss1: 0.205052 Loss2: 1.414774
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421055 Loss1: 0.090801 Loss2: 1.330254
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.556610 Loss1: 0.156920 Loss2: 1.399690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.503913 Loss1: 0.118122 Loss2: 1.385791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452608 Loss1: 0.079994 Loss2: 1.372614 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-12 23:31:25,177][flwr][DEBUG] - fit_round 168 received 50 results and 0 failures
-INFO flwr 2023-10-12 23:32:06,757 | server.py:125 | fit progress: (168, 2.2627456317694423, {'accuracy': 0.604}, 387634.535789812)
->> Test accuracy: 0.604000
-[2023-10-12 23:32:06,757][flwr][INFO] - fit progress: (168, 2.2627456317694423, {'accuracy': 0.604}, 387634.535789812)
-DEBUG flwr 2023-10-12 23:32:06,758 | server.py:173 | evaluate_round 168: strategy sampled 50 clients (out of 50)
-[2023-10-12 23:32:06,758][flwr][DEBUG] - evaluate_round 168: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-12 23:41:14,708 | server.py:187 | evaluate_round 168 received 50 results and 0 failures
-[2023-10-12 23:41:14,708][flwr][DEBUG] - evaluate_round 168 received 50 results and 0 failures
-DEBUG flwr 2023-10-12 23:41:14,708 | server.py:222 | fit_round 169: strategy sampled 50 clients (out of 50)
-[2023-10-12 23:41:14,708][flwr][DEBUG] - fit_round 169: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.322958 Loss1: 0.505384 Loss2: 1.817574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.538757 Loss1: 0.192529 Loss2: 1.346228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474863 Loss1: 0.153734 Loss2: 1.321129
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.271811 Loss1: 0.420814 Loss2: 1.850997
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.685874 Loss1: 0.331881 Loss2: 1.353993 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.583935 Loss1: 0.204931 Loss2: 1.379004 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.502646 Loss1: 0.130407 Loss2: 1.372238 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521195 Loss1: 0.167468 Loss2: 1.353728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512985 Loss1: 0.150277 Loss2: 1.362708 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.345136 Loss1: 0.043682 Loss2: 1.301455
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.457785 Loss1: 0.111218 Loss2: 1.346568
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415076 Loss1: 0.070710 Loss2: 1.344366
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413448 Loss1: 0.073793 Loss2: 1.339655
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422887 Loss1: 0.083734 Loss2: 1.339154
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.258517 Loss1: 0.416270 Loss2: 1.842248
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.642901 Loss1: 0.302909 Loss2: 1.339992
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591520 Loss1: 0.217333 Loss2: 1.374187
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500307 Loss1: 0.149914 Loss2: 1.350393
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.307564 Loss1: 0.425720 Loss2: 1.881844
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.749695 Loss1: 0.357250 Loss2: 1.392445 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653631 Loss1: 0.202922 Loss2: 1.450709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553981 Loss1: 0.156129 Loss2: 1.397853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507041 Loss1: 0.117706 Loss2: 1.389335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479437 Loss1: 0.085653 Loss2: 1.393783 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374266 Loss1: 0.054099 Loss2: 1.320167
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456991 Loss1: 0.076479 Loss2: 1.380512
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425442 Loss1: 0.054757 Loss2: 1.370685
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421664 Loss1: 0.057158 Loss2: 1.364506
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433997 Loss1: 0.071697 Loss2: 1.362300
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.110002 Loss1: 0.299314 Loss2: 1.810687
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.538096 Loss1: 0.190118 Loss2: 1.347979
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.494595 Loss1: 0.148795 Loss2: 1.345800
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.413303 Loss1: 0.536270 Loss2: 1.877033
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.437644 Loss1: 0.081908 Loss2: 1.355736
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.684677 Loss1: 0.306444 Loss2: 1.378232
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.418860 Loss1: 0.080414 Loss2: 1.338446
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.581159 Loss1: 0.187603 Loss2: 1.393555
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.419933 Loss1: 0.083331 Loss2: 1.336602
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414614 Loss1: 0.080669 Loss2: 1.333946 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401300 Loss1: 0.068756 Loss2: 1.332544 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394799 Loss1: 0.058739 Loss2: 1.336060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415356 Loss1: 0.079826 Loss2: 1.335530 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977941
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409985 Loss1: 0.068518 Loss2: 1.341466 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.224501 Loss1: 0.402984 Loss2: 1.821517
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622296 Loss1: 0.264642 Loss2: 1.357654
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.556375 Loss1: 0.175524 Loss2: 1.380851
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.114129 Loss1: 0.336655 Loss2: 1.777473
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499254 Loss1: 0.129141 Loss2: 1.370114
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.603306 Loss1: 0.268261 Loss2: 1.335045
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577224 Loss1: 0.212325 Loss2: 1.364899
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578107 Loss1: 0.200941 Loss2: 1.377166
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533541 Loss1: 0.155757 Loss2: 1.377783
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.463746 Loss1: 0.122603 Loss2: 1.341143
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.527041 Loss1: 0.167398 Loss2: 1.359643
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.433328 Loss1: 0.101878 Loss2: 1.331450
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483061 Loss1: 0.125820 Loss2: 1.357241
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.418357 Loss1: 0.085377 Loss2: 1.332980
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.499359 Loss1: 0.143635 Loss2: 1.355724
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423169 Loss1: 0.094450 Loss2: 1.328719
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450533 Loss1: 0.094387 Loss2: 1.356145
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391342 Loss1: 0.066609 Loss2: 1.324733 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.466919 Loss1: 0.515476 Loss2: 1.951443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.816677 Loss1: 0.276612 Loss2: 1.540065 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.679660 Loss1: 0.219878 Loss2: 1.459782
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.644857 Loss1: 0.620238 Loss2: 2.024618
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.745990 Loss1: 0.346738 Loss2: 1.399252
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.687866 Loss1: 0.232849 Loss2: 1.455017
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.706873 Loss1: 0.287305 Loss2: 1.419569
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.590283 Loss1: 0.140061 Loss2: 1.450222 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.592756 Loss1: 0.145452 Loss2: 1.447303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.505360 Loss1: 0.115919 Loss2: 1.389441 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.556886 Loss1: 0.109235 Loss2: 1.447651 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424227 Loss1: 0.040720 Loss2: 1.383507 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993490
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.324346 Loss1: 0.497651 Loss2: 1.826695
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.706635 Loss1: 0.365241 Loss2: 1.341395
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618785 Loss1: 0.228876 Loss2: 1.389910
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541690 Loss1: 0.194910 Loss2: 1.346780
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.333452 Loss1: 0.491318 Loss2: 1.842134
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.610366 Loss1: 0.304588 Loss2: 1.305779
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480689 Loss1: 0.139416 Loss2: 1.341273
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563105 Loss1: 0.240545 Loss2: 1.322560
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463316 Loss1: 0.118394 Loss2: 1.344922
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404874 Loss1: 0.072996 Loss2: 1.331879 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.392616 Loss1: 0.062336 Loss2: 1.330281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.365270 Loss1: 0.044263 Loss2: 1.321007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368269 Loss1: 0.051690 Loss2: 1.316579 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386347 Loss1: 0.083296 Loss2: 1.303051 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.278562 Loss1: 0.495442 Loss2: 1.783119
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622281 Loss1: 0.305194 Loss2: 1.317087
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.547330 Loss1: 0.184824 Loss2: 1.362506
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.424139 Loss1: 0.104815 Loss2: 1.319324
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.314230 Loss1: 0.403354 Loss2: 1.910877
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.698600 Loss1: 0.263310 Loss2: 1.435290 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.594398 Loss1: 0.137849 Loss2: 1.456549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574172 Loss1: 0.148817 Loss2: 1.425355 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513410 Loss1: 0.089102 Loss2: 1.424308 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.489081 Loss1: 0.070744 Loss2: 1.418337 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486804 Loss1: 0.079311 Loss2: 1.407493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479911 Loss1: 0.073532 Loss2: 1.406378 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.317581 Loss1: 0.407522 Loss2: 1.910059 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.607923 Loss1: 0.179812 Loss2: 1.428111 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.544537 Loss1: 0.619969 Loss2: 1.924567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.814046 Loss1: 0.404218 Loss2: 1.409829 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.721144 Loss1: 0.290953 Loss2: 1.430190 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.598160 Loss1: 0.201583 Loss2: 1.396577 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.494660 Loss1: 0.104327 Loss2: 1.390334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443131 Loss1: 0.072002 Loss2: 1.371129 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.411542 Loss1: 0.049385 Loss2: 1.362157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398822 Loss1: 0.048067 Loss2: 1.350756 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.342754 Loss1: 0.487021 Loss2: 1.855733 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.503305 Loss1: 0.126377 Loss2: 1.376927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474230 Loss1: 0.128689 Loss2: 1.345540
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.366086 Loss1: 0.520212 Loss2: 1.845874
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.447286 Loss1: 0.106722 Loss2: 1.340565
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.775891 Loss1: 0.403045 Loss2: 1.372846
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.424380 Loss1: 0.089089 Loss2: 1.335290
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.662880 Loss1: 0.241892 Loss2: 1.420987
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.388273 Loss1: 0.055194 Loss2: 1.333079
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.566105 Loss1: 0.199201 Loss2: 1.366904
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.367677 Loss1: 0.038507 Loss2: 1.329170
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.584059 Loss1: 0.203073 Loss2: 1.380986
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.362271 Loss1: 0.042017 Loss2: 1.320254
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452961 Loss1: 0.085730 Loss2: 1.367230
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359610 Loss1: 0.041015 Loss2: 1.318595
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.420583 Loss1: 0.074681 Loss2: 1.345902
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398884 Loss1: 0.056906 Loss2: 1.341977
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.380581 Loss1: 0.045315 Loss2: 1.335266
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385982 Loss1: 0.056778 Loss2: 1.329204
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.207854 Loss1: 0.394008 Loss2: 1.813846
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.593273 Loss1: 0.263748 Loss2: 1.329525
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.473810 Loss1: 0.117934 Loss2: 1.355877
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.454807 Loss1: 0.121320 Loss2: 1.333486
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.367960 Loss1: 0.434058 Loss2: 1.933902
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.739198 Loss1: 0.323072 Loss2: 1.416126 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.681672 Loss1: 0.225554 Loss2: 1.456118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579861 Loss1: 0.162989 Loss2: 1.416872 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.560029 Loss1: 0.154906 Loss2: 1.405123 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511102 Loss1: 0.103823 Loss2: 1.407279 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389205 Loss1: 0.072588 Loss2: 1.316617
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.493745 Loss1: 0.097847 Loss2: 1.395898
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460965 Loss1: 0.072595 Loss2: 1.388370
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460619 Loss1: 0.077440 Loss2: 1.383179
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439949 Loss1: 0.057305 Loss2: 1.382644
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.256271 Loss1: 0.420118 Loss2: 1.836153
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.595368 Loss1: 0.244068 Loss2: 1.351300
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.663916 Loss1: 0.259072 Loss2: 1.404844
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524683 Loss1: 0.167546 Loss2: 1.357138
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.253657 Loss1: 0.469436 Loss2: 1.784221
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478794 Loss1: 0.122918 Loss2: 1.355876
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.631818 Loss1: 0.332676 Loss2: 1.299142
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461088 Loss1: 0.102617 Loss2: 1.358471
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.546561 Loss1: 0.202516 Loss2: 1.344044
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418437 Loss1: 0.069357 Loss2: 1.349080
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.454981 Loss1: 0.145855 Loss2: 1.309127
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424902 Loss1: 0.079255 Loss2: 1.345647
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.407052 Loss1: 0.103522 Loss2: 1.303530
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423450 Loss1: 0.082478 Loss2: 1.340972
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.386726 Loss1: 0.092759 Loss2: 1.293966
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417097 Loss1: 0.075950 Loss2: 1.341147
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.364733 Loss1: 0.074552 Loss2: 1.290181
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.352019 Loss1: 0.066245 Loss2: 1.285774
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354806 Loss1: 0.073824 Loss2: 1.280982
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.336645 Loss1: 0.062288 Loss2: 1.274357
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.248187 Loss1: 0.358309 Loss2: 1.889878
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.637395 Loss1: 0.259918 Loss2: 1.377477
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618451 Loss1: 0.237072 Loss2: 1.381379
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604297 Loss1: 0.192248 Loss2: 1.412049
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.346765 Loss1: 0.480200 Loss2: 1.866566
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.532810 Loss1: 0.148770 Loss2: 1.384040
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.715560 Loss1: 0.341939 Loss2: 1.373622
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497485 Loss1: 0.119574 Loss2: 1.377911
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.678441 Loss1: 0.261117 Loss2: 1.417324
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487196 Loss1: 0.109297 Loss2: 1.377899
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.565918 Loss1: 0.185103 Loss2: 1.380814
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419832 Loss1: 0.052686 Loss2: 1.367146
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523218 Loss1: 0.147625 Loss2: 1.375593
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435519 Loss1: 0.077217 Loss2: 1.358303
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493812 Loss1: 0.127794 Loss2: 1.366018
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396896 Loss1: 0.033784 Loss2: 1.363112
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452423 Loss1: 0.093668 Loss2: 1.358755
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411947 Loss1: 0.064801 Loss2: 1.347146
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.380444 Loss1: 0.037756 Loss2: 1.342688
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365402 Loss1: 0.032888 Loss2: 1.332514
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.299549 Loss1: 0.426444 Loss2: 1.873105
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.689568 Loss1: 0.295297 Loss2: 1.394271
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645209 Loss1: 0.206060 Loss2: 1.439149
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.557158 Loss1: 0.594298 Loss2: 1.962860
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502242 Loss1: 0.115198 Loss2: 1.387044
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475780 Loss1: 0.095177 Loss2: 1.380602 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445260 Loss1: 0.065198 Loss2: 1.380061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443448 Loss1: 0.068774 Loss2: 1.374674 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.507155 Loss1: 0.128829 Loss2: 1.378326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.523153 Loss1: 0.151594 Loss2: 1.371559 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.510857 Loss1: 0.132082 Loss2: 1.378775 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407576 Loss1: 0.046307 Loss2: 1.361268 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.426456 Loss1: 0.517393 Loss2: 1.909063
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.757847 Loss1: 0.377792 Loss2: 1.380055
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.654551 Loss1: 0.235255 Loss2: 1.419296
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574022 Loss1: 0.202494 Loss2: 1.371528
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.336759 Loss1: 0.523190 Loss2: 1.813569
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.607353 Loss1: 0.262438 Loss2: 1.344915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.584322 Loss1: 0.214326 Loss2: 1.369997 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.456952 Loss1: 0.119815 Loss2: 1.337137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487506 Loss1: 0.154133 Loss2: 1.333374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472991 Loss1: 0.130050 Loss2: 1.342941 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391250 Loss1: 0.051562 Loss2: 1.339688
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426149 Loss1: 0.102658 Loss2: 1.323492
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413453 Loss1: 0.084598 Loss2: 1.328855
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363178 Loss1: 0.046496 Loss2: 1.316682
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369724 Loss1: 0.062942 Loss2: 1.306782
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.304547 Loss1: 0.438132 Loss2: 1.866416
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.696029 Loss1: 0.319139 Loss2: 1.376890
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.627194 Loss1: 0.205117 Loss2: 1.422076
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508763 Loss1: 0.132708 Loss2: 1.376055
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.241287 Loss1: 0.432172 Loss2: 1.809116
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.440796 Loss1: 0.080146 Loss2: 1.360650
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.577448 Loss1: 0.243332 Loss2: 1.334116
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.494953 Loss1: 0.130386 Loss2: 1.364567
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.514986 Loss1: 0.168284 Loss2: 1.346702
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436191 Loss1: 0.077928 Loss2: 1.358263
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.459554 Loss1: 0.116893 Loss2: 1.342662
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422774 Loss1: 0.068814 Loss2: 1.353960
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.422537 Loss1: 0.096132 Loss2: 1.326405
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440968 Loss1: 0.091994 Loss2: 1.348974
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455926 Loss1: 0.135741 Loss2: 1.320185
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416708 Loss1: 0.060752 Loss2: 1.355956
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.411342 Loss1: 0.083513 Loss2: 1.327829
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.377948 Loss1: 0.058920 Loss2: 1.319028
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.357741 Loss1: 0.046309 Loss2: 1.311432
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356944 Loss1: 0.051537 Loss2: 1.305407
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.215744 Loss1: 0.356698 Loss2: 1.859046
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.633943 Loss1: 0.246215 Loss2: 1.387728
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.558173 Loss1: 0.145570 Loss2: 1.412603
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.293857 Loss1: 0.420760 Loss2: 1.873097
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547784 Loss1: 0.170504 Loss2: 1.377281
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.651889 Loss1: 0.289115 Loss2: 1.362774
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503197 Loss1: 0.120755 Loss2: 1.382442
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.550337 Loss1: 0.165386 Loss2: 1.384951
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.469349 Loss1: 0.091614 Loss2: 1.377735
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478273 Loss1: 0.111440 Loss2: 1.366833 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.476298 Loss1: 0.098056 Loss2: 1.378242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413212 Loss1: 0.049153 Loss2: 1.364059 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409110 Loss1: 0.051093 Loss2: 1.358017 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397684 Loss1: 0.045765 Loss2: 1.351919 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.281688 Loss1: 0.441947 Loss2: 1.839741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.533008 Loss1: 0.142412 Loss2: 1.390597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.348565 Loss1: 0.485954 Loss2: 1.862611
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499590 Loss1: 0.126703 Loss2: 1.372887
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.739223 Loss1: 0.345455 Loss2: 1.393767
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480533 Loss1: 0.104975 Loss2: 1.375559
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.683752 Loss1: 0.257277 Loss2: 1.426475
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458938 Loss1: 0.092631 Loss2: 1.366307
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445874 Loss1: 0.085048 Loss2: 1.360825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436249 Loss1: 0.077843 Loss2: 1.358406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438626 Loss1: 0.076458 Loss2: 1.362168 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405702 Loss1: 0.053395 Loss2: 1.352307 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421733 Loss1: 0.059632 Loss2: 1.362101 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.114787 Loss1: 0.304334 Loss2: 1.810453 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.630389 Loss1: 0.224569 Loss2: 1.405820 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.197931 Loss1: 0.391165 Loss2: 1.806767
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.577755 Loss1: 0.218196 Loss2: 1.359559
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.646744 Loss1: 0.323653 Loss2: 1.323090
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.542646 Loss1: 0.178979 Loss2: 1.363667
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.535787 Loss1: 0.183915 Loss2: 1.351873
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513187 Loss1: 0.156800 Loss2: 1.356387
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.475350 Loss1: 0.148067 Loss2: 1.327283
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.540146 Loss1: 0.176277 Loss2: 1.363869
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.502914 Loss1: 0.136971 Loss2: 1.365943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449457 Loss1: 0.101493 Loss2: 1.347964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413827 Loss1: 0.072455 Loss2: 1.341372 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371052 Loss1: 0.071555 Loss2: 1.299497 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.266470 Loss1: 0.461437 Loss2: 1.805033 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.564113 Loss1: 0.198332 Loss2: 1.365782 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.509788 Loss1: 0.166596 Loss2: 1.343192
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.168752 Loss1: 0.343173 Loss2: 1.825579
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.549813 Loss1: 0.192086 Loss2: 1.357728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.479745 Loss1: 0.118987 Loss2: 1.360758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.457585 Loss1: 0.117505 Loss2: 1.340080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.432617 Loss1: 0.090906 Loss2: 1.341710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.405979 Loss1: 0.070091 Loss2: 1.335888 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.393241 Loss1: 0.067713 Loss2: 1.325528 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363080 Loss1: 0.037057 Loss2: 1.326023 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.315543 Loss1: 0.467479 Loss2: 1.848064 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644572 Loss1: 0.262421 Loss2: 1.382151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.304975 Loss1: 0.446655 Loss2: 1.858320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.677959 Loss1: 0.311278 Loss2: 1.366681 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.654504 Loss1: 0.242106 Loss2: 1.412398 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.660932 Loss1: 0.284776 Loss2: 1.376156 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570720 Loss1: 0.187266 Loss2: 1.383455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491900 Loss1: 0.131610 Loss2: 1.360291 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439667 Loss1: 0.087511 Loss2: 1.352156 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388286 Loss1: 0.045565 Loss2: 1.342722 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.643721 Loss1: 0.301829 Loss2: 1.341892 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499474 Loss1: 0.147159 Loss2: 1.352315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.350809 Loss1: 0.494594 Loss2: 1.856214
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.464982 Loss1: 0.123087 Loss2: 1.341896
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445368 Loss1: 0.107812 Loss2: 1.337556 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419263 Loss1: 0.083967 Loss2: 1.335296 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.397799 Loss1: 0.063415 Loss2: 1.334383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379010 Loss1: 0.051982 Loss2: 1.327028 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387737 Loss1: 0.061950 Loss2: 1.325788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411982 Loss1: 0.088569 Loss2: 1.323413 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364666 Loss1: 0.052340 Loss2: 1.312326 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994420
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.281369 Loss1: 0.470007 Loss2: 1.811362
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.632423 Loss1: 0.301897 Loss2: 1.330526
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.539896 Loss1: 0.177334 Loss2: 1.362562
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.470343 Loss1: 0.140144 Loss2: 1.330199
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.347824 Loss1: 0.474287 Loss2: 1.873538
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.665057 Loss1: 0.298912 Loss2: 1.366145 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592123 Loss1: 0.204721 Loss2: 1.387402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.506024 Loss1: 0.141141 Loss2: 1.364883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450380 Loss1: 0.092613 Loss2: 1.357767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431185 Loss1: 0.079139 Loss2: 1.352047 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381613 Loss1: 0.070568 Loss2: 1.311046
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.414502 Loss1: 0.070006 Loss2: 1.344496
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386930 Loss1: 0.041472 Loss2: 1.345458
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.378498 Loss1: 0.042109 Loss2: 1.336388
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375419 Loss1: 0.042476 Loss2: 1.332942
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.284850 Loss1: 0.452271 Loss2: 1.832578
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.595923 Loss1: 0.262904 Loss2: 1.333019
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594012 Loss1: 0.220103 Loss2: 1.373909
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.441553 Loss1: 0.109173 Loss2: 1.332380
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.339600 Loss1: 0.455079 Loss2: 1.884521
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659427 Loss1: 0.267529 Loss2: 1.391899 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.571352 Loss1: 0.157346 Loss2: 1.414006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.510824 Loss1: 0.124969 Loss2: 1.385854 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.473228 Loss1: 0.097651 Loss2: 1.375577 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 00:10:42,908 | server.py:236 | fit_round 169 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501321 Loss1: 0.125186 Loss2: 1.376135 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469119 Loss1: 0.097333 Loss2: 1.371785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413490 Loss1: 0.043765 Loss2: 1.369724 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.231011 Loss1: 0.380021 Loss2: 1.850990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.613162 Loss1: 0.206321 Loss2: 1.406841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.317530 Loss1: 0.461134 Loss2: 1.856396
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.579661 Loss1: 0.185629 Loss2: 1.394032
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.596767 Loss1: 0.266840 Loss2: 1.329927
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544746 Loss1: 0.146049 Loss2: 1.398697
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502681 Loss1: 0.106204 Loss2: 1.396477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468034 Loss1: 0.084026 Loss2: 1.384008 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454487 Loss1: 0.079755 Loss2: 1.374733 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453735 Loss1: 0.076323 Loss2: 1.377413 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453909 Loss1: 0.080840 Loss2: 1.373070 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372246 Loss1: 0.067563 Loss2: 1.304683 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.315980 Loss1: 0.437346 Loss2: 1.878634
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.708193 Loss1: 0.365667 Loss2: 1.342525
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.593417 Loss1: 0.214588 Loss2: 1.378829
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.543428 Loss1: 0.200214 Loss2: 1.343215
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.351698 Loss1: 0.474637 Loss2: 1.877061
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510268 Loss1: 0.152704 Loss2: 1.357564
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.650336 Loss1: 0.268669 Loss2: 1.381667
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514291 Loss1: 0.180301 Loss2: 1.333989
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.603831 Loss1: 0.204209 Loss2: 1.399622
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440134 Loss1: 0.087079 Loss2: 1.353055
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520995 Loss1: 0.136957 Loss2: 1.384039
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419770 Loss1: 0.088396 Loss2: 1.331374
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509316 Loss1: 0.131385 Loss2: 1.377931
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386525 Loss1: 0.059966 Loss2: 1.326559
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499113 Loss1: 0.121098 Loss2: 1.378015
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367671 Loss1: 0.047696 Loss2: 1.319975
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452925 Loss1: 0.083590 Loss2: 1.369334
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446358 Loss1: 0.081800 Loss2: 1.364558
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433540 Loss1: 0.072718 Loss2: 1.360822
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422726 Loss1: 0.061110 Loss2: 1.361616
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 00:10:42,908][flwr][DEBUG] - fit_round 169 received 50 results and 0 failures
-INFO flwr 2023-10-13 00:11:25,013 | server.py:125 | fit progress: (169, 2.2763475324399174, {'accuracy': 0.6043}, 389992.79194534)
->> Test accuracy: 0.604300
-[2023-10-13 00:11:25,013][flwr][INFO] - fit progress: (169, 2.2763475324399174, {'accuracy': 0.6043}, 389992.79194534)
-DEBUG flwr 2023-10-13 00:11:25,014 | server.py:173 | evaluate_round 169: strategy sampled 50 clients (out of 50)
-[2023-10-13 00:11:25,014][flwr][DEBUG] - evaluate_round 169: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 00:20:30,365 | server.py:187 | evaluate_round 169 received 50 results and 0 failures
-[2023-10-13 00:20:30,365][flwr][DEBUG] - evaluate_round 169 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 00:20:30,366 | server.py:222 | fit_round 170: strategy sampled 50 clients (out of 50)
-[2023-10-13 00:20:30,366][flwr][DEBUG] - fit_round 170: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.381073 Loss1: 0.528030 Loss2: 1.853044
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.664291 Loss1: 0.335554 Loss2: 1.328737
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.598451 Loss1: 0.222522 Loss2: 1.375930
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.501364 Loss1: 0.160115 Loss2: 1.341248
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.278654 Loss1: 0.418864 Loss2: 1.859789
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.614582 Loss1: 0.255176 Loss2: 1.359407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.539865 Loss1: 0.158529 Loss2: 1.381336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.505946 Loss1: 0.148590 Loss2: 1.357356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.442965 Loss1: 0.090575 Loss2: 1.352390 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.437715 Loss1: 0.095860 Loss2: 1.341855 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994420
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432237 Loss1: 0.090221 Loss2: 1.342016 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411395 Loss1: 0.074379 Loss2: 1.337016 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.721000 Loss1: 0.369604 Loss2: 1.351396 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517967 Loss1: 0.158311 Loss2: 1.359656 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.294053 Loss1: 0.445798 Loss2: 1.848255
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448394 Loss1: 0.092107 Loss2: 1.356287
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.650274 Loss1: 0.300934 Loss2: 1.349340
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456777 Loss1: 0.098049 Loss2: 1.358728
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.551378 Loss1: 0.162168 Loss2: 1.389210
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449593 Loss1: 0.099085 Loss2: 1.350508
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496974 Loss1: 0.132930 Loss2: 1.364044
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429251 Loss1: 0.083346 Loss2: 1.345905
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.512252 Loss1: 0.157141 Loss2: 1.355112
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432072 Loss1: 0.082961 Loss2: 1.349110
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.457923 Loss1: 0.113700 Loss2: 1.344223
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415067 Loss1: 0.072554 Loss2: 1.342514
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.466321 Loss1: 0.128134 Loss2: 1.338187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391147 Loss1: 0.055552 Loss2: 1.335596 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.659995 Loss1: 0.294964 Loss2: 1.365031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547264 Loss1: 0.193652 Loss2: 1.353612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525187 Loss1: 0.146996 Loss2: 1.378191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.536376 Loss1: 0.173646 Loss2: 1.362730 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.505012 Loss1: 0.138385 Loss2: 1.366628 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.498963 Loss1: 0.136806 Loss2: 1.362157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.496554 Loss1: 0.133543 Loss2: 1.363011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476577 Loss1: 0.115690 Loss2: 1.360888 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441601 Loss1: 0.081460 Loss2: 1.360141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416234 Loss1: 0.065929 Loss2: 1.350304 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.202423 Loss1: 0.369345 Loss2: 1.833078
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.597055 Loss1: 0.243060 Loss2: 1.353994
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.490136 Loss1: 0.126203 Loss2: 1.363933
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474956 Loss1: 0.124514 Loss2: 1.350442
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.304588 Loss1: 0.422961 Loss2: 1.881628
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443784 Loss1: 0.098990 Loss2: 1.344794
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.646542 Loss1: 0.285333 Loss2: 1.361208
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.399054 Loss1: 0.061074 Loss2: 1.337980
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.521089 Loss1: 0.142373 Loss2: 1.378716
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.475010 Loss1: 0.122227 Loss2: 1.352783
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.400583 Loss1: 0.067048 Loss2: 1.333534
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.445559 Loss1: 0.101340 Loss2: 1.344219
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378697 Loss1: 0.044860 Loss2: 1.333837
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431120 Loss1: 0.084489 Loss2: 1.346631
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396801 Loss1: 0.067029 Loss2: 1.329772
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.371849 Loss1: 0.042398 Loss2: 1.329451
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380599 Loss1: 0.051113 Loss2: 1.329485
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.375340 Loss1: 0.052891 Loss2: 1.322449 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.368804 Loss1: 0.496846 Loss2: 1.871958 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582793 Loss1: 0.196915 Loss2: 1.385878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540248 Loss1: 0.168711 Loss2: 1.371537
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.321785 Loss1: 0.526080 Loss2: 1.795706
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.494640 Loss1: 0.134438 Loss2: 1.360202
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.637253 Loss1: 0.310998 Loss2: 1.326255
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477787 Loss1: 0.109989 Loss2: 1.367797
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.538335 Loss1: 0.150864 Loss2: 1.387470
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474310 Loss1: 0.113972 Loss2: 1.360339
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.469561 Loss1: 0.143479 Loss2: 1.326082
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452798 Loss1: 0.093934 Loss2: 1.358864
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.456027 Loss1: 0.129379 Loss2: 1.326648
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453775 Loss1: 0.096255 Loss2: 1.357520
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455060 Loss1: 0.122682 Loss2: 1.332378
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413066 Loss1: 0.060605 Loss2: 1.352461
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408488 Loss1: 0.087453 Loss2: 1.321035
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408308 Loss1: 0.089898 Loss2: 1.318410
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374935 Loss1: 0.055079 Loss2: 1.319856
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395013 Loss1: 0.082517 Loss2: 1.312497
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.366431 Loss1: 0.519313 Loss2: 1.847118
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.658690 Loss1: 0.300656 Loss2: 1.358034
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.643186 Loss1: 0.220913 Loss2: 1.422273
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527191 Loss1: 0.170268 Loss2: 1.356923
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.348089 Loss1: 0.475151 Loss2: 1.872938
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.626633 Loss1: 0.269486 Loss2: 1.357147 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.557506 Loss1: 0.170294 Loss2: 1.387212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.499089 Loss1: 0.146601 Loss2: 1.352488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485891 Loss1: 0.137814 Loss2: 1.348077 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.424894 Loss1: 0.077934 Loss2: 1.346960 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396351 Loss1: 0.067861 Loss2: 1.328490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435505 Loss1: 0.101673 Loss2: 1.333832
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407059 Loss1: 0.067026 Loss2: 1.340033
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.380094 Loss1: 0.050248 Loss2: 1.329847
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366614 Loss1: 0.037970 Loss2: 1.328644
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.257672 Loss1: 0.389909 Loss2: 1.867763
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.681868 Loss1: 0.284913 Loss2: 1.396955
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.605591 Loss1: 0.183173 Loss2: 1.422418
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.343056 Loss1: 0.420314 Loss2: 1.922741
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.531082 Loss1: 0.141314 Loss2: 1.389768
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.735194 Loss1: 0.321140 Loss2: 1.414055
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479034 Loss1: 0.083786 Loss2: 1.395248
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701593 Loss1: 0.245831 Loss2: 1.455762
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451043 Loss1: 0.065275 Loss2: 1.385768
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.614088 Loss1: 0.191715 Loss2: 1.422373
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463196 Loss1: 0.083808 Loss2: 1.379389
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430257 Loss1: 0.056924 Loss2: 1.373333 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431732 Loss1: 0.063491 Loss2: 1.368241 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417612 Loss1: 0.049280 Loss2: 1.368332 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457708 Loss1: 0.048097 Loss2: 1.409612 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.316916 Loss1: 0.471312 Loss2: 1.845605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.616091 Loss1: 0.239531 Loss2: 1.376560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.582206 Loss1: 0.215326 Loss2: 1.366881
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.316868 Loss1: 0.461893 Loss2: 1.854975
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.577991 Loss1: 0.211332 Loss2: 1.366659
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653260 Loss1: 0.286867 Loss2: 1.366392
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466315 Loss1: 0.115785 Loss2: 1.350530
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563668 Loss1: 0.165767 Loss2: 1.397901
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461167 Loss1: 0.113386 Loss2: 1.347781
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.482761 Loss1: 0.113192 Loss2: 1.369569
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417681 Loss1: 0.072634 Loss2: 1.345047
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.449887 Loss1: 0.092522 Loss2: 1.357365
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417892 Loss1: 0.081014 Loss2: 1.336878
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444769 Loss1: 0.085023 Loss2: 1.359745
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389276 Loss1: 0.059966 Loss2: 1.329310
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412188 Loss1: 0.061686 Loss2: 1.350502
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399438 Loss1: 0.050650 Loss2: 1.348788
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.393743 Loss1: 0.047302 Loss2: 1.346441
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388514 Loss1: 0.046982 Loss2: 1.341532
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.336359 Loss1: 0.447751 Loss2: 1.888608
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.656615 Loss1: 0.279192 Loss2: 1.377423
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.602041 Loss1: 0.191690 Loss2: 1.410351
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.515562 Loss1: 0.128182 Loss2: 1.387379
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.234796 Loss1: 0.339611 Loss2: 1.895185
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473922 Loss1: 0.102198 Loss2: 1.371724
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.664545 Loss1: 0.276847 Loss2: 1.387698
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444372 Loss1: 0.072442 Loss2: 1.371930
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622261 Loss1: 0.200563 Loss2: 1.421699
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424137 Loss1: 0.062816 Loss2: 1.361320
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.552216 Loss1: 0.154095 Loss2: 1.398121
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432029 Loss1: 0.071965 Loss2: 1.360064
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516586 Loss1: 0.124349 Loss2: 1.392237
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432993 Loss1: 0.073447 Loss2: 1.359546
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499510 Loss1: 0.113736 Loss2: 1.385774
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418996 Loss1: 0.062561 Loss2: 1.356435
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468087 Loss1: 0.085196 Loss2: 1.382891
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431217 Loss1: 0.059668 Loss2: 1.371549
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424896 Loss1: 0.057559 Loss2: 1.367337
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.508639 Loss1: 0.135395 Loss2: 1.373245
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.373067 Loss1: 0.420059 Loss2: 1.953008
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.690909 Loss1: 0.252160 Loss2: 1.438749
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.639991 Loss1: 0.182225 Loss2: 1.457766
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586442 Loss1: 0.144268 Loss2: 1.442174
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.329475 Loss1: 0.453459 Loss2: 1.876016
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.706951 Loss1: 0.317828 Loss2: 1.389122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629332 Loss1: 0.199888 Loss2: 1.429443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555376 Loss1: 0.157069 Loss2: 1.398306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518854 Loss1: 0.140189 Loss2: 1.378665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.489215 Loss1: 0.100841 Loss2: 1.388374 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.501755 Loss1: 0.080714 Loss2: 1.421041
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.459963 Loss1: 0.081286 Loss2: 1.378677
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459845 Loss1: 0.089080 Loss2: 1.370765
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448250 Loss1: 0.079468 Loss2: 1.368782
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453335 Loss1: 0.085922 Loss2: 1.367412
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.324678 Loss1: 0.473998 Loss2: 1.850681
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.700605 Loss1: 0.338089 Loss2: 1.362516
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.718149 Loss1: 0.294499 Loss2: 1.423650
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542458 Loss1: 0.164820 Loss2: 1.377638
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.223182 Loss1: 0.382114 Loss2: 1.841069
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.604503 Loss1: 0.240310 Loss2: 1.364193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.547300 Loss1: 0.163130 Loss2: 1.384170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478659 Loss1: 0.114616 Loss2: 1.364043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.462472 Loss1: 0.103425 Loss2: 1.359047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469480 Loss1: 0.111189 Loss2: 1.358290 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433396 Loss1: 0.072338 Loss2: 1.361057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414277 Loss1: 0.069001 Loss2: 1.345276 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.455584 Loss1: 0.109069 Loss2: 1.346516
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.380965 Loss1: 0.482022 Loss2: 1.898944
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.721067 Loss1: 0.328758 Loss2: 1.392309
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.647649 Loss1: 0.227202 Loss2: 1.420447
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547742 Loss1: 0.155204 Loss2: 1.392538
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547638 Loss1: 0.160308 Loss2: 1.387330
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.312027 Loss1: 0.507939 Loss2: 1.804088
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477245 Loss1: 0.085516 Loss2: 1.391729
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612832 Loss1: 0.268581 Loss2: 1.344251
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446357 Loss1: 0.070043 Loss2: 1.376314
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.542273 Loss1: 0.197659 Loss2: 1.344614
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.490822 Loss1: 0.153036 Loss2: 1.337785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.453676 Loss1: 0.110145 Loss2: 1.343531 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445428 Loss1: 0.070330 Loss2: 1.375098
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463289 Loss1: 0.132486 Loss2: 1.330803
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416512 Loss1: 0.089816 Loss2: 1.326696
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409443 Loss1: 0.087972 Loss2: 1.321471
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415700 Loss1: 0.101043 Loss2: 1.314657
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.426706 Loss1: 0.107210 Loss2: 1.319496
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.192364 Loss1: 0.342191 Loss2: 1.850173
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.693216 Loss1: 0.346158 Loss2: 1.347058
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.674341 Loss1: 0.277123 Loss2: 1.397217
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556341 Loss1: 0.190958 Loss2: 1.365383
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461074 Loss1: 0.102016 Loss2: 1.359058
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.447159 Loss1: 0.538947 Loss2: 1.908212
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449517 Loss1: 0.098680 Loss2: 1.350837
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.410520 Loss1: 0.067959 Loss2: 1.342561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398081 Loss1: 0.058030 Loss2: 1.340051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.375910 Loss1: 0.040289 Loss2: 1.335622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369663 Loss1: 0.042688 Loss2: 1.326976 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429253 Loss1: 0.077023 Loss2: 1.352230 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374938 Loss1: 0.033249 Loss2: 1.341690 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.688462 Loss1: 0.374270 Loss2: 1.314192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511501 Loss1: 0.151239 Loss2: 1.360262 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445161 Loss1: 0.124763 Loss2: 1.320398 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414075 Loss1: 0.089901 Loss2: 1.324174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.383303 Loss1: 0.080501 Loss2: 1.302803 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.346176 Loss1: 0.044408 Loss2: 1.301768 [repeated 2x across cluster]
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459165 Loss1: 0.106721 Loss2: 1.352445 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454840 Loss1: 0.108983 Loss2: 1.345857 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.318705 Loss1: 0.495629 Loss2: 1.823076
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417684 Loss1: 0.068773 Loss2: 1.348911
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.615713 Loss1: 0.266948 Loss2: 1.348765
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418514 Loss1: 0.073890 Loss2: 1.344624
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482868 Loss1: 0.135199 Loss2: 1.347668 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416864 Loss1: 0.089053 Loss2: 1.327811 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424111 Loss1: 0.094405 Loss2: 1.329706
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.197223 Loss1: 0.350996 Loss2: 1.846226
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.595731 Loss1: 0.225516 Loss2: 1.370214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.546905 Loss1: 0.163022 Loss2: 1.383883 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.514191 Loss1: 0.149885 Loss2: 1.364306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470293 Loss1: 0.103566 Loss2: 1.366726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.087939 Loss1: 0.334447 Loss2: 1.753492 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.575654 Loss1: 0.271084 Loss2: 1.304571 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.513579 Loss1: 0.191248 Loss2: 1.322331 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995404
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.400177 Loss1: 0.108585 Loss2: 1.291592 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.366190 Loss1: 0.077443 Loss2: 1.288747 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384717 Loss1: 0.093341 Loss2: 1.291376
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.252345 Loss1: 0.385905 Loss2: 1.866440
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.591804 Loss1: 0.231284 Loss2: 1.360521
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402133 Loss1: 0.108300 Loss2: 1.293833
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.554093 Loss1: 0.186165 Loss2: 1.367927
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391514 Loss1: 0.099322 Loss2: 1.292192
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.480671 Loss1: 0.119012 Loss2: 1.361660 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433694 Loss1: 0.081939 Loss2: 1.351755 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389122 Loss1: 0.038751 Loss2: 1.350371
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.346515 Loss1: 0.482788 Loss2: 1.863727
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.411145 Loss1: 0.072399 Loss2: 1.338746
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.645983 Loss1: 0.304210 Loss2: 1.341774
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425717 Loss1: 0.082156 Loss2: 1.343561
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551946 Loss1: 0.194801 Loss2: 1.357146
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542073 Loss1: 0.194916 Loss2: 1.347156
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.442756 Loss1: 0.098877 Loss2: 1.343878
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.405398 Loss1: 0.077450 Loss2: 1.327948
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383978 Loss1: 0.059802 Loss2: 1.324176
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.200968 Loss1: 0.377126 Loss2: 1.823842
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.360553 Loss1: 0.044582 Loss2: 1.315971
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.564187 Loss1: 0.225624 Loss2: 1.338563
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.375271 Loss1: 0.061433 Loss2: 1.313838
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578500 Loss1: 0.220797 Loss2: 1.357703
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371512 Loss1: 0.057133 Loss2: 1.314379
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.427825 Loss1: 0.092093 Loss2: 1.335732 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.376328 Loss1: 0.049995 Loss2: 1.326333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.394361 Loss1: 0.073573 Loss2: 1.320788
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.165693 Loss1: 0.355977 Loss2: 1.809716
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.680185 Loss1: 0.322714 Loss2: 1.357471 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352644 Loss1: 0.039198 Loss2: 1.313446
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618933 Loss1: 0.223023 Loss2: 1.395910
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551930 Loss1: 0.180076 Loss2: 1.371854
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574960 Loss1: 0.204042 Loss2: 1.370918
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472352 Loss1: 0.091044 Loss2: 1.381307
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441995 Loss1: 0.086478 Loss2: 1.355517
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.639500 Loss1: 0.603836 Loss2: 2.035663
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413950 Loss1: 0.060967 Loss2: 1.352983
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.869558 Loss1: 0.422573 Loss2: 1.446985
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.752888 Loss1: 0.264870 Loss2: 1.488018
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404877 Loss1: 0.057796 Loss2: 1.347082
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404444 Loss1: 0.060987 Loss2: 1.343457 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.559244 Loss1: 0.117097 Loss2: 1.442147 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501399 Loss1: 0.077670 Loss2: 1.423729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.492016 Loss1: 0.070424 Loss2: 1.421592 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979567
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.542765 Loss1: 0.169863 Loss2: 1.372901 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.477966 Loss1: 0.117786 Loss2: 1.360180 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.428238 Loss1: 0.071737 Loss2: 1.356501 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402032 Loss1: 0.056694 Loss2: 1.345338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.381608 Loss1: 0.044047 Loss2: 1.337562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.364221 Loss1: 0.031156 Loss2: 1.333065 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379608 Loss1: 0.053581 Loss2: 1.326027 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458398 Loss1: 0.088177 Loss2: 1.370222 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.477657 Loss1: 0.113601 Loss2: 1.364056 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983259
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.627361 Loss1: 0.284168 Loss2: 1.343192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527276 Loss1: 0.171328 Loss2: 1.355948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.286630 Loss1: 0.462829 Loss2: 1.823801 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.718264 Loss1: 0.367415 Loss2: 1.350849 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393969 Loss1: 0.080814 Loss2: 1.313155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.349181 Loss1: 0.036591 Loss2: 1.312589 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350243 Loss1: 0.043637 Loss2: 1.306607 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.442473 Loss1: 0.087938 Loss2: 1.354534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.393626 Loss1: 0.064757 Loss2: 1.328869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.075781 Loss1: 0.353238 Loss2: 1.722543 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.536901 Loss1: 0.208302 Loss2: 1.328599 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.450454 Loss1: 0.143922 Loss2: 1.306532 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.418594 Loss1: 0.121190 Loss2: 1.297404 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.398536 Loss1: 0.108808 Loss2: 1.289728 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382641 Loss1: 0.087762 Loss2: 1.294879 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.357016 Loss1: 0.078697 Loss2: 1.278319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.340876 Loss1: 0.063468 Loss2: 1.277408 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456092 Loss1: 0.132425 Loss2: 1.323668 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399540 Loss1: 0.080079 Loss2: 1.319461 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354515 Loss1: 0.042110 Loss2: 1.312405
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.338177 Loss1: 0.426180 Loss2: 1.911997
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.746212 Loss1: 0.333320 Loss2: 1.412892
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618742 Loss1: 0.171364 Loss2: 1.447378
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.583944 Loss1: 0.160908 Loss2: 1.423036
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.543223 Loss1: 0.132908 Loss2: 1.410315
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.211151 Loss1: 0.400966 Loss2: 1.810185
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.565174 Loss1: 0.249243 Loss2: 1.315931 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.486797 Loss1: 0.154439 Loss2: 1.332358 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.428191 Loss1: 0.118576 Loss2: 1.309615 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.415759 Loss1: 0.108937 Loss2: 1.306822 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437622 Loss1: 0.049673 Loss2: 1.387949
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.391343 Loss1: 0.081851 Loss2: 1.309492
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406729 Loss1: 0.106364 Loss2: 1.300365
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.380545 Loss1: 0.080549 Loss2: 1.299997
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386422 Loss1: 0.088399 Loss2: 1.298023
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369373 Loss1: 0.074920 Loss2: 1.294453
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.223853 Loss1: 0.416035 Loss2: 1.807818
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.607380 Loss1: 0.247358 Loss2: 1.360023
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.577673 Loss1: 0.188656 Loss2: 1.389018
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.530063 Loss1: 0.176892 Loss2: 1.353172
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.254838 Loss1: 0.425397 Loss2: 1.829442
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.648055 Loss1: 0.312071 Loss2: 1.335984 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.566080 Loss1: 0.196300 Loss2: 1.369780 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.505359 Loss1: 0.161938 Loss2: 1.343422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.489965 Loss1: 0.152708 Loss2: 1.337257 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431555 Loss1: 0.097336 Loss2: 1.334219 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-DEBUG flwr 2023-10-13 00:49:13,185 | server.py:236 | fit_round 170 received 50 results and 0 failures
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392195 Loss1: 0.072230 Loss2: 1.319965 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.365933 Loss1: 0.043651 Loss2: 1.322282 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.340518 Loss1: 0.517232 Loss2: 1.823286 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.690640 Loss1: 0.287909 Loss2: 1.402731 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527441 Loss1: 0.195837 Loss2: 1.331603
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.386574 Loss1: 0.516071 Loss2: 1.870503
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.627176 Loss1: 0.276219 Loss2: 1.350957 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.541380 Loss1: 0.163729 Loss2: 1.377651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.460020 Loss1: 0.122698 Loss2: 1.337323 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.415933 Loss1: 0.086536 Loss2: 1.329396 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.396459 Loss1: 0.070450 Loss2: 1.326009 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.381116 Loss1: 0.061221 Loss2: 1.319894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354447 Loss1: 0.045292 Loss2: 1.309156 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.361114 Loss1: 0.466246 Loss2: 1.894868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.603116 Loss1: 0.174755 Loss2: 1.428361 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.276768 Loss1: 0.448648 Loss2: 1.828120 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.552485 Loss1: 0.224687 Loss2: 1.327798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.550766 Loss1: 0.203416 Loss2: 1.347350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534848 Loss1: 0.181725 Loss2: 1.353122 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.425586 Loss1: 0.106512 Loss2: 1.319073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.436635 Loss1: 0.119889 Loss2: 1.316746 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.389339 Loss1: 0.072487 Loss2: 1.316853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.367893 Loss1: 0.060693 Loss2: 1.307200 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 00:49:13,185][flwr][DEBUG] - fit_round 170 received 50 results and 0 failures
-INFO flwr 2023-10-13 00:49:55,800 | server.py:125 | fit progress: (170, 2.275574233966133, {'accuracy': 0.6037}, 392303.578070072)
->> Test accuracy: 0.603700
-[2023-10-13 00:49:55,800][flwr][INFO] - fit progress: (170, 2.275574233966133, {'accuracy': 0.6037}, 392303.578070072)
-DEBUG flwr 2023-10-13 00:49:55,800 | server.py:173 | evaluate_round 170: strategy sampled 50 clients (out of 50)
-[2023-10-13 00:49:55,800][flwr][DEBUG] - evaluate_round 170: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 00:59:02,057 | server.py:187 | evaluate_round 170 received 50 results and 0 failures
-[2023-10-13 00:59:02,057][flwr][DEBUG] - evaluate_round 170 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 00:59:02,058 | server.py:222 | fit_round 171: strategy sampled 50 clients (out of 50)
-[2023-10-13 00:59:02,058][flwr][DEBUG] - fit_round 171: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.335487 Loss1: 0.443906 Loss2: 1.891581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591003 Loss1: 0.146861 Loss2: 1.444141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.306609 Loss1: 0.454723 Loss2: 1.851886
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.544153 Loss1: 0.144535 Loss2: 1.399618
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.632591 Loss1: 0.279213 Loss2: 1.353378
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.557761 Loss1: 0.155248 Loss2: 1.402513
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.630599 Loss1: 0.244609 Loss2: 1.385990
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.545162 Loss1: 0.141579 Loss2: 1.403583
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538919 Loss1: 0.168613 Loss2: 1.370307
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483913 Loss1: 0.082996 Loss2: 1.400917
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.511787 Loss1: 0.153088 Loss2: 1.358700
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474636 Loss1: 0.084876 Loss2: 1.389761
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470250 Loss1: 0.085964 Loss2: 1.384286 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.457147 Loss1: 0.066988 Loss2: 1.390158 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422574 Loss1: 0.075727 Loss2: 1.346847 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.194453 Loss1: 0.335254 Loss2: 1.859199 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609959 Loss1: 0.177525 Loss2: 1.432434 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551078 Loss1: 0.162497 Loss2: 1.388581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525461 Loss1: 0.132250 Loss2: 1.393211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.698784 Loss1: 0.217701 Loss2: 1.481083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.626838 Loss1: 0.148173 Loss2: 1.478665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.579740 Loss1: 0.113278 Loss2: 1.466462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.572314 Loss1: 0.118361 Loss2: 1.453952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.567068 Loss1: 0.109710 Loss2: 1.457358 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453974 Loss1: 0.075520 Loss2: 1.378454
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.582519 Loss1: 0.123509 Loss2: 1.459010
-(DefaultActor pid=3765) >> Training accuracy: 0.986213
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.541123 Loss1: 0.083721 Loss2: 1.457402
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.249180 Loss1: 0.404578 Loss2: 1.844603
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.565253 Loss1: 0.239305 Loss2: 1.325948
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.482598 Loss1: 0.134390 Loss2: 1.348209
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.460849 Loss1: 0.131001 Loss2: 1.329847
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.277462 Loss1: 0.414900 Loss2: 1.862562
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.421290 Loss1: 0.104859 Loss2: 1.316431
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.682820 Loss1: 0.311421 Loss2: 1.371400
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.406331 Loss1: 0.092547 Loss2: 1.313784
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.610211 Loss1: 0.211445 Loss2: 1.398766
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414257 Loss1: 0.104411 Loss2: 1.309846
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.545238 Loss1: 0.183497 Loss2: 1.361741
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405421 Loss1: 0.093926 Loss2: 1.311495
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520751 Loss1: 0.155056 Loss2: 1.365695
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406632 Loss1: 0.099674 Loss2: 1.306958
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519115 Loss1: 0.138332 Loss2: 1.380783
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401502 Loss1: 0.093310 Loss2: 1.308192
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448106 Loss1: 0.090495 Loss2: 1.357611
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473493 Loss1: 0.117700 Loss2: 1.355794
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449933 Loss1: 0.092764 Loss2: 1.357169
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399609 Loss1: 0.052531 Loss2: 1.347078
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.238580 Loss1: 0.399904 Loss2: 1.838677
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.582073 Loss1: 0.239254 Loss2: 1.342819
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.495462 Loss1: 0.145067 Loss2: 1.350395
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.472970 Loss1: 0.124021 Loss2: 1.348949
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.242532 Loss1: 0.391830 Loss2: 1.850702
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.620239 Loss1: 0.265849 Loss2: 1.354389 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.591481 Loss1: 0.227473 Loss2: 1.364008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.520122 Loss1: 0.155048 Loss2: 1.365073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502935 Loss1: 0.145148 Loss2: 1.357787 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.453779 Loss1: 0.099251 Loss2: 1.354528 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406601 Loss1: 0.071421 Loss2: 1.335181
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433550 Loss1: 0.089339 Loss2: 1.344211
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396214 Loss1: 0.053442 Loss2: 1.342773
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408174 Loss1: 0.076936 Loss2: 1.331238
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409583 Loss1: 0.072351 Loss2: 1.337231
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.156689 Loss1: 0.418383 Loss2: 1.738306
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.632918 Loss1: 0.347436 Loss2: 1.285481
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618161 Loss1: 0.263426 Loss2: 1.354734
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490578 Loss1: 0.197504 Loss2: 1.293074
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.252432 Loss1: 0.417333 Loss2: 1.835099
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659581 Loss1: 0.303691 Loss2: 1.355890 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.597934 Loss1: 0.199084 Loss2: 1.398850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.515254 Loss1: 0.151404 Loss2: 1.363850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.489535 Loss1: 0.133530 Loss2: 1.356005 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498845 Loss1: 0.134499 Loss2: 1.364345 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441484 Loss1: 0.091596 Loss2: 1.349888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435370 Loss1: 0.086759 Loss2: 1.348612 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.253823 Loss1: 0.392681 Loss2: 1.861142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.575048 Loss1: 0.208119 Loss2: 1.366929 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520632 Loss1: 0.164690 Loss2: 1.355942
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.398096 Loss1: 0.535517 Loss2: 1.862578
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480507 Loss1: 0.140105 Loss2: 1.340402
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.588194 Loss1: 0.254276 Loss2: 1.333918
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460672 Loss1: 0.120958 Loss2: 1.339714
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592097 Loss1: 0.233722 Loss2: 1.358376
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.407309 Loss1: 0.072365 Loss2: 1.334944
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.479343 Loss1: 0.145640 Loss2: 1.333703
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.539537 Loss1: 0.214302 Loss2: 1.325235
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434532 Loss1: 0.102526 Loss2: 1.332006
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512595 Loss1: 0.178165 Loss2: 1.334430
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429209 Loss1: 0.094715 Loss2: 1.334494
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471894 Loss1: 0.138977 Loss2: 1.332916
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430105 Loss1: 0.095627 Loss2: 1.334478
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412902 Loss1: 0.094956 Loss2: 1.317946 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.557300 Loss1: 0.531302 Loss2: 2.025998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.691212 Loss1: 0.285708 Loss2: 1.405504 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.563956 Loss1: 0.166587 Loss2: 1.397369 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.618960 Loss1: 0.259292 Loss2: 1.359668 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.487585 Loss1: 0.095148 Loss2: 1.392438 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.542496 Loss1: 0.166942 Loss2: 1.375554 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997396
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.586670 Loss1: 0.182135 Loss2: 1.404535 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453255 Loss1: 0.085153 Loss2: 1.368102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444289 Loss1: 0.079146 Loss2: 1.365143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441843 Loss1: 0.085387 Loss2: 1.356456 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.469384 Loss1: 0.132438 Loss2: 1.336945 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.396735 Loss1: 0.074942 Loss2: 1.321792 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.378756 Loss1: 0.064143 Loss2: 1.314613
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.368145 Loss1: 0.413062 Loss2: 1.955083
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.333558 Loss1: 0.027948 Loss2: 1.305611
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.750899 Loss1: 0.316877 Loss2: 1.434022
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.333320 Loss1: 0.034846 Loss2: 1.298474
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.672692 Loss1: 0.173523 Loss2: 1.499169
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.329296 Loss1: 0.032009 Loss2: 1.297287
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.593736 Loss1: 0.153624 Loss2: 1.440112
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.588496 Loss1: 0.146482 Loss2: 1.442014
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539305 Loss1: 0.101342 Loss2: 1.437963
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508723 Loss1: 0.077600 Loss2: 1.431123
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485958 Loss1: 0.064327 Loss2: 1.421631
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.525638 Loss1: 0.104760 Loss2: 1.420877
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.159092 Loss1: 0.379539 Loss2: 1.779553
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.484119 Loss1: 0.067636 Loss2: 1.416483
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622113 Loss1: 0.294871 Loss2: 1.327242
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.542858 Loss1: 0.172207 Loss2: 1.370651
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.456376 Loss1: 0.135147 Loss2: 1.321229
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.432616 Loss1: 0.112011 Loss2: 1.320605
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.411677 Loss1: 0.092948 Loss2: 1.318728
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.276714 Loss1: 0.438402 Loss2: 1.838312
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.707953 Loss1: 0.326256 Loss2: 1.381697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.611421 Loss1: 0.185991 Loss2: 1.425430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526712 Loss1: 0.154255 Loss2: 1.372457 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.480958 Loss1: 0.101227 Loss2: 1.379731 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426775 Loss1: 0.065717 Loss2: 1.361059 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389001 Loss1: 0.045026 Loss2: 1.343975 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362840 Loss1: 0.027876 Loss2: 1.334964 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.619930 Loss1: 0.195239 Loss2: 1.424691 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493789 Loss1: 0.092789 Loss2: 1.401000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464210 Loss1: 0.068617 Loss2: 1.395593
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.334384 Loss1: 0.443690 Loss2: 1.890694
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.724138 Loss1: 0.337965 Loss2: 1.386173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.736212 Loss1: 0.285582 Loss2: 1.450629 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.632195 Loss1: 0.219747 Loss2: 1.412448 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464030 Loss1: 0.075519 Loss2: 1.388511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433010 Loss1: 0.055850 Loss2: 1.377160
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.139921 Loss1: 0.344732 Loss2: 1.795189
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.561004 Loss1: 0.224063 Loss2: 1.336941 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.501587 Loss1: 0.148054 Loss2: 1.353534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.512838 Loss1: 0.171657 Loss2: 1.341181 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404173 Loss1: 0.080329 Loss2: 1.323844 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384289 Loss1: 0.064103 Loss2: 1.320186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.673708 Loss1: 0.213696 Loss2: 1.460012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.615334 Loss1: 0.178478 Loss2: 1.436856 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527811 Loss1: 0.109781 Loss2: 1.418029 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.502457 Loss1: 0.091870 Loss2: 1.410587 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.477589 Loss1: 0.074523 Loss2: 1.403066
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.489860 Loss1: 0.537712 Loss2: 1.952148
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.718744 Loss1: 0.362816 Loss2: 1.355927
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457530 Loss1: 0.059793 Loss2: 1.397737
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.505943 Loss1: 0.150946 Loss2: 1.354997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.459438 Loss1: 0.106423 Loss2: 1.353016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.359222 Loss1: 0.436481 Loss2: 1.922741 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401349 Loss1: 0.067430 Loss2: 1.333919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405775 Loss1: 0.077445 Loss2: 1.328330 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.534684 Loss1: 0.111148 Loss2: 1.423536 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.517492 Loss1: 0.095328 Loss2: 1.422164 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.258611 Loss1: 0.466651 Loss2: 1.791960
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484065 Loss1: 0.066780 Loss2: 1.417285
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.591920 Loss1: 0.269525 Loss2: 1.322396
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479081 Loss1: 0.062043 Loss2: 1.417038
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.506759 Loss1: 0.166416 Loss2: 1.340342
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450976 Loss1: 0.040417 Loss2: 1.410559
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.407791 Loss1: 0.105093 Loss2: 1.302697 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.377887 Loss1: 0.084586 Loss2: 1.293301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.333313 Loss1: 0.050283 Loss2: 1.283030
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.304507 Loss1: 0.444517 Loss2: 1.859991
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.767426 Loss1: 0.371623 Loss2: 1.395803 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.665772 Loss1: 0.216555 Loss2: 1.449217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.596825 Loss1: 0.188398 Loss2: 1.408427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490727 Loss1: 0.106747 Loss2: 1.383981 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473361 Loss1: 0.088511 Loss2: 1.384850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479695 Loss1: 0.098749 Loss2: 1.380945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410219 Loss1: 0.036906 Loss2: 1.373313 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511918 Loss1: 0.107899 Loss2: 1.404019 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447965 Loss1: 0.065039 Loss2: 1.382926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.280077 Loss1: 0.424501 Loss2: 1.855576
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433542 Loss1: 0.057203 Loss2: 1.376339
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.696214 Loss1: 0.332590 Loss2: 1.363624
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437459 Loss1: 0.061148 Loss2: 1.376311
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.438795 Loss1: 0.079801 Loss2: 1.358995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456262 Loss1: 0.099892 Loss2: 1.356370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451666 Loss1: 0.100931 Loss2: 1.350735
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.273316 Loss1: 0.459875 Loss2: 1.813440
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460049 Loss1: 0.107339 Loss2: 1.352710
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.614145 Loss1: 0.286734 Loss2: 1.327412
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421477 Loss1: 0.076454 Loss2: 1.345024
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.589000 Loss1: 0.204315 Loss2: 1.384684
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398251 Loss1: 0.059379 Loss2: 1.338872
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489278 Loss1: 0.162759 Loss2: 1.326518
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479649 Loss1: 0.154427 Loss2: 1.325222
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.423833 Loss1: 0.096079 Loss2: 1.327754
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395189 Loss1: 0.077233 Loss2: 1.317956
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384976 Loss1: 0.072499 Loss2: 1.312477
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395852 Loss1: 0.081261 Loss2: 1.314591
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.244902 Loss1: 0.374163 Loss2: 1.870739
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378781 Loss1: 0.071992 Loss2: 1.306788
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.577093 Loss1: 0.226092 Loss2: 1.351001
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.535066 Loss1: 0.176817 Loss2: 1.358249
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523548 Loss1: 0.146935 Loss2: 1.376613
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483714 Loss1: 0.136843 Loss2: 1.346871
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.434046 Loss1: 0.088241 Loss2: 1.345805
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.398192 Loss1: 0.053077 Loss2: 1.345116
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.296232 Loss1: 0.500870 Loss2: 1.795362
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381410 Loss1: 0.045272 Loss2: 1.336138
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.628208 Loss1: 0.321501 Loss2: 1.306707
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362646 Loss1: 0.032011 Loss2: 1.330636
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551321 Loss1: 0.201344 Loss2: 1.349977
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354305 Loss1: 0.027556 Loss2: 1.326750
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.598318 Loss1: 0.260429 Loss2: 1.337889
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534245 Loss1: 0.197725 Loss2: 1.336520
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.483590 Loss1: 0.146911 Loss2: 1.336679
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.412908 Loss1: 0.097557 Loss2: 1.315350
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.406924 Loss1: 0.090490 Loss2: 1.316435
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.360012 Loss1: 0.050675 Loss2: 1.309337
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.193313 Loss1: 0.336000 Loss2: 1.857313
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.342664 Loss1: 0.039297 Loss2: 1.303367
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.582160 Loss1: 0.212276 Loss2: 1.369883
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556127 Loss1: 0.171592 Loss2: 1.384535
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.533166 Loss1: 0.155333 Loss2: 1.377833
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.517453 Loss1: 0.141789 Loss2: 1.375664
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478519 Loss1: 0.103725 Loss2: 1.374794
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.396868 Loss1: 0.443197 Loss2: 1.953671
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490698 Loss1: 0.127204 Loss2: 1.363494
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411389 Loss1: 0.049160 Loss2: 1.362229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410620 Loss1: 0.056739 Loss2: 1.353881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403519 Loss1: 0.057168 Loss2: 1.346351 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.514556 Loss1: 0.103363 Loss2: 1.411194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461508 Loss1: 0.057322 Loss2: 1.404185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441886 Loss1: 0.044690 Loss2: 1.397197
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.476313 Loss1: 0.578872 Loss2: 1.897441
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437937 Loss1: 0.047792 Loss2: 1.390145
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.723792 Loss1: 0.363292 Loss2: 1.360500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.630910 Loss1: 0.247542 Loss2: 1.383368
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553577 Loss1: 0.163166 Loss2: 1.390411
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.468679 Loss1: 0.109559 Loss2: 1.359120
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.417976 Loss1: 0.069584 Loss2: 1.348392
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.398099 Loss1: 0.053096 Loss2: 1.345003
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.408266 Loss1: 0.509519 Loss2: 1.898748
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.643266 Loss1: 0.289846 Loss2: 1.353420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.626403 Loss1: 0.239143 Loss2: 1.387260 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998798
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.477179 Loss1: 0.114845 Loss2: 1.362334 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439231 Loss1: 0.081040 Loss2: 1.358191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452088 Loss1: 0.096603 Loss2: 1.355485
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.347625 Loss1: 0.433982 Loss2: 1.913642
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.723407 Loss1: 0.291125 Loss2: 1.432282 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.697724 Loss1: 0.233427 Loss2: 1.464296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.560290 Loss1: 0.124024 Loss2: 1.436266 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.516163 Loss1: 0.096775 Loss2: 1.419388 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491922 Loss1: 0.074893 Loss2: 1.417029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.492125 Loss1: 0.077492 Loss2: 1.414633 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454956 Loss1: 0.044714 Loss2: 1.410243 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.432898 Loss1: 0.087768 Loss2: 1.345130 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.402612 Loss1: 0.075889 Loss2: 1.326723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.289584 Loss1: 0.414097 Loss2: 1.875487
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.365717 Loss1: 0.039054 Loss2: 1.326663
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.343689 Loss1: 0.026902 Loss2: 1.316787
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653403 Loss1: 0.258312 Loss2: 1.395091
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600871 Loss1: 0.194052 Loss2: 1.406819
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557723 Loss1: 0.164312 Loss2: 1.393411
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.533694 Loss1: 0.143367 Loss2: 1.390327
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537778 Loss1: 0.137708 Loss2: 1.400070
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.267433 Loss1: 0.414140 Loss2: 1.853293
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.479641 Loss1: 0.094805 Loss2: 1.384836
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450876 Loss1: 0.070242 Loss2: 1.380634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431709 Loss1: 0.057616 Loss2: 1.374093 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453158 Loss1: 0.081542 Loss2: 1.371616 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460136 Loss1: 0.087580 Loss2: 1.372555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424793 Loss1: 0.074935 Loss2: 1.349858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.306887 Loss1: 0.414212 Loss2: 1.892674 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.650873 Loss1: 0.210058 Loss2: 1.440815 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.538618 Loss1: 0.151621 Loss2: 1.386997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.359786 Loss1: 0.465698 Loss2: 1.894088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.841464 Loss1: 0.446371 Loss2: 1.395093 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.750547 Loss1: 0.284615 Loss2: 1.465932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.594946 Loss1: 0.201787 Loss2: 1.393159 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.556893 Loss1: 0.156343 Loss2: 1.400550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488117 Loss1: 0.105260 Loss2: 1.382858 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.416328 Loss1: 0.046228 Loss2: 1.370100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.394644 Loss1: 0.035930 Loss2: 1.358714 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.503757 Loss1: 0.170204 Loss2: 1.333553 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441982 Loss1: 0.128789 Loss2: 1.313193 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446343 Loss1: 0.124475 Loss2: 1.321868
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.298825 Loss1: 0.442321 Loss2: 1.856504
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.610354 Loss1: 0.253054 Loss2: 1.357300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.607791 Loss1: 0.223019 Loss2: 1.384772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524400 Loss1: 0.163447 Loss2: 1.360953 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.540363 Loss1: 0.180309 Loss2: 1.360054 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433294 Loss1: 0.087530 Loss2: 1.345765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385870 Loss1: 0.051241 Loss2: 1.334629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366011 Loss1: 0.037953 Loss2: 1.328058 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.567648 Loss1: 0.186212 Loss2: 1.381436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506893 Loss1: 0.143702 Loss2: 1.363192 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.497390 Loss1: 0.134779 Loss2: 1.362610
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.270949 Loss1: 0.432149 Loss2: 1.838800
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.637939 Loss1: 0.289336 Loss2: 1.348603 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.598201 Loss1: 0.216782 Loss2: 1.381419 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581221 Loss1: 0.212402 Loss2: 1.368819 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525462 Loss1: 0.178885 Loss2: 1.346577 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.423746 Loss1: 0.086883 Loss2: 1.336863 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.393777 Loss1: 0.064279 Loss2: 1.329498 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380539 Loss1: 0.054027 Loss2: 1.326513 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.573955 Loss1: 0.193789 Loss2: 1.380167 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.499715 Loss1: 0.137797 Loss2: 1.361918 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.551088 Loss1: 0.179053 Loss2: 1.372035
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.273974 Loss1: 0.421347 Loss2: 1.852627
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.568346 Loss1: 0.233320 Loss2: 1.335026 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.479840 Loss1: 0.137028 Loss2: 1.342812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.438891 Loss1: 0.103607 Loss2: 1.335284 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.421840 Loss1: 0.097113 Loss2: 1.324727 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.374168 Loss1: 0.058285 Loss2: 1.315883 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 01:27:47,557 | server.py:236 | fit_round 171 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381505 Loss1: 0.066024 Loss2: 1.315481 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.394796 Loss1: 0.079728 Loss2: 1.315068 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.546066 Loss1: 0.180338 Loss2: 1.365728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500225 Loss1: 0.147247 Loss2: 1.352979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421420 Loss1: 0.079875 Loss2: 1.341545
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.405007 Loss1: 0.506303 Loss2: 1.898704
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.682005 Loss1: 0.299315 Loss2: 1.382690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.666771 Loss1: 0.252943 Loss2: 1.413829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.567017 Loss1: 0.182065 Loss2: 1.384952 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362750 Loss1: 0.040617 Loss2: 1.322133
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524509 Loss1: 0.142931 Loss2: 1.381578
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476718 Loss1: 0.101587 Loss2: 1.375130
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418993 Loss1: 0.059316 Loss2: 1.359677
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417126 Loss1: 0.056277 Loss2: 1.360849
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390146 Loss1: 0.038534 Loss2: 1.351613
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.399865 Loss1: 0.541918 Loss2: 1.857947
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.376434 Loss1: 0.027323 Loss2: 1.349110
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629102 Loss1: 0.217312 Loss2: 1.411790 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.478235 Loss1: 0.113823 Loss2: 1.364412 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425243 Loss1: 0.067667 Loss2: 1.357576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433992 Loss1: 0.081873 Loss2: 1.352119 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 01:27:47,557][flwr][DEBUG] - fit_round 171 received 50 results and 0 failures
-INFO flwr 2023-10-13 01:28:28,887 | server.py:125 | fit progress: (171, 2.2737315053376146, {'accuracy': 0.6067}, 394616.66555445397)
->> Test accuracy: 0.606700
-[2023-10-13 01:28:28,887][flwr][INFO] - fit progress: (171, 2.2737315053376146, {'accuracy': 0.6067}, 394616.66555445397)
-DEBUG flwr 2023-10-13 01:28:28,887 | server.py:173 | evaluate_round 171: strategy sampled 50 clients (out of 50)
-[2023-10-13 01:28:28,887][flwr][DEBUG] - evaluate_round 171: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 01:37:33,269 | server.py:187 | evaluate_round 171 received 50 results and 0 failures
-[2023-10-13 01:37:33,269][flwr][DEBUG] - evaluate_round 171 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 01:37:33,269 | server.py:222 | fit_round 172: strategy sampled 50 clients (out of 50)
-[2023-10-13 01:37:33,269][flwr][DEBUG] - fit_round 172: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.214392 Loss1: 0.365235 Loss2: 1.849157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.558037 Loss1: 0.161128 Loss2: 1.396909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.225112 Loss1: 0.415776 Loss2: 1.809337
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.503342 Loss1: 0.112057 Loss2: 1.391286
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.530272 Loss1: 0.211535 Loss2: 1.318737
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479777 Loss1: 0.102052 Loss2: 1.377725
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.497699 Loss1: 0.168879 Loss2: 1.328820
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473943 Loss1: 0.096060 Loss2: 1.377883
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.426638 Loss1: 0.103860 Loss2: 1.322777
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458285 Loss1: 0.084523 Loss2: 1.373762
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454012 Loss1: 0.081925 Loss2: 1.372087 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438347 Loss1: 0.065193 Loss2: 1.373154 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426053 Loss1: 0.060673 Loss2: 1.365381 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.361415 Loss1: 0.072863 Loss2: 1.288552 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.144352 Loss1: 0.348044 Loss2: 1.796308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.481290 Loss1: 0.138632 Loss2: 1.342657 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.324666 Loss1: 0.459365 Loss2: 1.865300
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.446403 Loss1: 0.123394 Loss2: 1.323008
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.416166 Loss1: 0.103740 Loss2: 1.312426 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.403635 Loss1: 0.081417 Loss2: 1.322218 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.421497 Loss1: 0.107613 Loss2: 1.313883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415221 Loss1: 0.098264 Loss2: 1.316956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430498 Loss1: 0.110509 Loss2: 1.319989 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429191 Loss1: 0.062792 Loss2: 1.366399 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983456
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414876 Loss1: 0.055038 Loss2: 1.359838 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.241366 Loss1: 0.396845 Loss2: 1.844521
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.585283 Loss1: 0.239784 Loss2: 1.345499
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.552762 Loss1: 0.184091 Loss2: 1.368671
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.503242 Loss1: 0.146907 Loss2: 1.356335
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.368414 Loss1: 0.494875 Loss2: 1.873539
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.745458 Loss1: 0.368515 Loss2: 1.376943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605036 Loss1: 0.216680 Loss2: 1.388356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.544607 Loss1: 0.173361 Loss2: 1.371247 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.467775 Loss1: 0.102627 Loss2: 1.365148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.436447 Loss1: 0.079102 Loss2: 1.357345 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401330 Loss1: 0.071076 Loss2: 1.330254
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412987 Loss1: 0.063417 Loss2: 1.349570
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.402618 Loss1: 0.059087 Loss2: 1.343530
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383691 Loss1: 0.045222 Loss2: 1.338469
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393038 Loss1: 0.056815 Loss2: 1.336223
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.189939 Loss1: 0.407168 Loss2: 1.782772
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.585765 Loss1: 0.277965 Loss2: 1.307800
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.478669 Loss1: 0.156176 Loss2: 1.322493
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.442816 Loss1: 0.137120 Loss2: 1.305695
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.255634 Loss1: 0.461516 Loss2: 1.794119
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.607847 Loss1: 0.287652 Loss2: 1.320195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543116 Loss1: 0.180532 Loss2: 1.362584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.418489 Loss1: 0.101933 Loss2: 1.316555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.411576 Loss1: 0.100163 Loss2: 1.311413 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.392192 Loss1: 0.080129 Loss2: 1.312063 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.354899 Loss1: 0.050893 Loss2: 1.304006 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.332669 Loss1: 0.039811 Loss2: 1.292858 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.698617 Loss1: 0.328472 Loss2: 1.370144 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513013 Loss1: 0.154108 Loss2: 1.358905 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531937 Loss1: 0.173037 Loss2: 1.358900
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.386827 Loss1: 0.496394 Loss2: 1.890433
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477068 Loss1: 0.112206 Loss2: 1.364862
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.734576 Loss1: 0.347539 Loss2: 1.387038
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425321 Loss1: 0.072169 Loss2: 1.353152
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.611564 Loss1: 0.185834 Loss2: 1.425730
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.406692 Loss1: 0.059786 Loss2: 1.346906
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.575337 Loss1: 0.187918 Loss2: 1.387420
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.392033 Loss1: 0.054310 Loss2: 1.337723
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.582646 Loss1: 0.193874 Loss2: 1.388772
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387368 Loss1: 0.046940 Loss2: 1.340428
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.560102 Loss1: 0.158073 Loss2: 1.402029
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486708 Loss1: 0.106212 Loss2: 1.380496
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.494401 Loss1: 0.117315 Loss2: 1.377086
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473917 Loss1: 0.095687 Loss2: 1.378229
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439742 Loss1: 0.070604 Loss2: 1.369137
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.142247 Loss1: 0.362636 Loss2: 1.779610
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.602363 Loss1: 0.263674 Loss2: 1.338689
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.559702 Loss1: 0.179278 Loss2: 1.380424
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.486833 Loss1: 0.141241 Loss2: 1.345592
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.230158 Loss1: 0.407623 Loss2: 1.822535
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612243 Loss1: 0.294691 Loss2: 1.317552 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.540507 Loss1: 0.196857 Loss2: 1.343650 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.452039 Loss1: 0.129639 Loss2: 1.322400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.405781 Loss1: 0.094273 Loss2: 1.311509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.406864 Loss1: 0.091148 Loss2: 1.315716 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406216 Loss1: 0.073369 Loss2: 1.332846
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.376778 Loss1: 0.073149 Loss2: 1.303629
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372372 Loss1: 0.074123 Loss2: 1.298249
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376643 Loss1: 0.079054 Loss2: 1.297589
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.358316 Loss1: 0.065565 Loss2: 1.292751
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.595305 Loss1: 0.644383 Loss2: 1.950921
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.715519 Loss1: 0.360454 Loss2: 1.355065
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568959 Loss1: 0.183123 Loss2: 1.385835
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.479004 Loss1: 0.131933 Loss2: 1.347071
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.245875 Loss1: 0.414754 Loss2: 1.831121
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.633247 Loss1: 0.291469 Loss2: 1.341778 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418690 Loss1: 0.078379 Loss2: 1.340311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.383616 Loss1: 0.055731 Loss2: 1.327886 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390466 Loss1: 0.068404 Loss2: 1.322062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408249 Loss1: 0.091553 Loss2: 1.316696 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463002 Loss1: 0.108263 Loss2: 1.354739 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388464 Loss1: 0.053830 Loss2: 1.334634 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385913 Loss1: 0.054241 Loss2: 1.331672
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.229142 Loss1: 0.390850 Loss2: 1.838293
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.588174 Loss1: 0.244017 Loss2: 1.344156
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568669 Loss1: 0.207059 Loss2: 1.361610
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.477536 Loss1: 0.119426 Loss2: 1.358110
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.429691 Loss1: 0.085844 Loss2: 1.343847
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.375132 Loss1: 0.487994 Loss2: 1.887139
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.666060 Loss1: 0.276728 Loss2: 1.389332 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.559780 Loss1: 0.155315 Loss2: 1.404465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530583 Loss1: 0.150527 Loss2: 1.380056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469894 Loss1: 0.094584 Loss2: 1.375311 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.475960 Loss1: 0.104022 Loss2: 1.371937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437034 Loss1: 0.068715 Loss2: 1.368319 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446949 Loss1: 0.086261 Loss2: 1.360687 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.553707 Loss1: 0.219101 Loss2: 1.334606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562474 Loss1: 0.208221 Loss2: 1.354253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487154 Loss1: 0.140675 Loss2: 1.346479
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.247585 Loss1: 0.359460 Loss2: 1.888125
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.648502 Loss1: 0.270046 Loss2: 1.378456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564458 Loss1: 0.165926 Loss2: 1.398532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.512752 Loss1: 0.129290 Loss2: 1.383462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529089 Loss1: 0.158887 Loss2: 1.370202 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.518691 Loss1: 0.132124 Loss2: 1.386567 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.444667 Loss1: 0.074839 Loss2: 1.369828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434219 Loss1: 0.076510 Loss2: 1.357709 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.571067 Loss1: 0.267801 Loss2: 1.303266 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498777 Loss1: 0.182617 Loss2: 1.316160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.447997 Loss1: 0.135459 Loss2: 1.312538
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.192961 Loss1: 0.398559 Loss2: 1.794402
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.634890 Loss1: 0.290602 Loss2: 1.344288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.500077 Loss1: 0.124250 Loss2: 1.375827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478887 Loss1: 0.137790 Loss2: 1.341097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.451992 Loss1: 0.112898 Loss2: 1.339094 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.398695 Loss1: 0.065031 Loss2: 1.333664 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379342 Loss1: 0.060810 Loss2: 1.318532 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.294251 Loss1: 0.483014 Loss2: 1.811238
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374805 Loss1: 0.054892 Loss2: 1.319913
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.581025 Loss1: 0.203522 Loss2: 1.377503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.539277 Loss1: 0.211226 Loss2: 1.328051 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450394 Loss1: 0.112563 Loss2: 1.337831
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.267498 Loss1: 0.402659 Loss2: 1.864839
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.393292 Loss1: 0.077298 Loss2: 1.315995
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.582999 Loss1: 0.224880 Loss2: 1.358120
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.349084 Loss1: 0.038645 Loss2: 1.310439
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.553696 Loss1: 0.191595 Loss2: 1.362101
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.328902 Loss1: 0.032521 Loss2: 1.296380
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555503 Loss1: 0.193101 Loss2: 1.362402
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.322214 Loss1: 0.028036 Loss2: 1.294179
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.482386 Loss1: 0.121860 Loss2: 1.360526
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420148 Loss1: 0.072967 Loss2: 1.347182
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.396334 Loss1: 0.059191 Loss2: 1.337143
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398513 Loss1: 0.065003 Loss2: 1.333509
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398295 Loss1: 0.070029 Loss2: 1.328267
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389017 Loss1: 0.053341 Loss2: 1.335677
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.268968 Loss1: 0.422733 Loss2: 1.846235
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.683493 Loss1: 0.316501 Loss2: 1.366992
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.666431 Loss1: 0.236262 Loss2: 1.430169
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601093 Loss1: 0.229098 Loss2: 1.371995
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.565857 Loss1: 0.186821 Loss2: 1.379036
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.408724 Loss1: 0.497578 Loss2: 1.911146
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.500318 Loss1: 0.132504 Loss2: 1.367813
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.477812 Loss1: 0.117499 Loss2: 1.360314 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434453 Loss1: 0.078455 Loss2: 1.355998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460660 Loss1: 0.109443 Loss2: 1.351217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459864 Loss1: 0.113013 Loss2: 1.346851 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414159 Loss1: 0.067318 Loss2: 1.346841 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.381913 Loss1: 0.048363 Loss2: 1.333550 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.646992 Loss1: 0.308186 Loss2: 1.338806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.464384 Loss1: 0.116380 Loss2: 1.348004 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.444385 Loss1: 0.112411 Loss2: 1.331974 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.423039 Loss1: 0.087869 Loss2: 1.335170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408577 Loss1: 0.076254 Loss2: 1.332323 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398962 Loss1: 0.067433 Loss2: 1.331529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433251 Loss1: 0.100244 Loss2: 1.333007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381662 Loss1: 0.053394 Loss2: 1.328268 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392868 Loss1: 0.034348 Loss2: 1.358520 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.239329 Loss1: 0.406835 Loss2: 1.832494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.552756 Loss1: 0.193348 Loss2: 1.359409 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491356 Loss1: 0.131212 Loss2: 1.360145
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.275477 Loss1: 0.440100 Loss2: 1.835377
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.634074 Loss1: 0.277297 Loss2: 1.356777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.546849 Loss1: 0.167337 Loss2: 1.379512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478724 Loss1: 0.123787 Loss2: 1.354937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502878 Loss1: 0.150719 Loss2: 1.352158 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500667 Loss1: 0.140179 Loss2: 1.360488 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445497 Loss1: 0.109347 Loss2: 1.336150
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455398 Loss1: 0.114009 Loss2: 1.341389
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470948 Loss1: 0.123760 Loss2: 1.347188
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.444023 Loss1: 0.098493 Loss2: 1.345531
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410900 Loss1: 0.069249 Loss2: 1.341651
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.117408 Loss1: 0.345376 Loss2: 1.772033
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.554323 Loss1: 0.240339 Loss2: 1.313984
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.562709 Loss1: 0.210126 Loss2: 1.352583
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541221 Loss1: 0.224248 Loss2: 1.316973
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.294016 Loss1: 0.432879 Loss2: 1.861137
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.748515 Loss1: 0.358368 Loss2: 1.390147
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513364 Loss1: 0.179193 Loss2: 1.334172
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.685448 Loss1: 0.221598 Loss2: 1.463850
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489490 Loss1: 0.167175 Loss2: 1.322314
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561973 Loss1: 0.181782 Loss2: 1.380191
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461725 Loss1: 0.145805 Loss2: 1.315920
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542523 Loss1: 0.149165 Loss2: 1.393359
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418630 Loss1: 0.105876 Loss2: 1.312754
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390439 Loss1: 0.087737 Loss2: 1.302703 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356912 Loss1: 0.054717 Loss2: 1.302195 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439343 Loss1: 0.067960 Loss2: 1.371384 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.171549 Loss1: 0.360303 Loss2: 1.811246 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.473751 Loss1: 0.102552 Loss2: 1.371199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.214603 Loss1: 0.367580 Loss2: 1.847023
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.443024 Loss1: 0.097906 Loss2: 1.345118
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.626913 Loss1: 0.273665 Loss2: 1.353248
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.437719 Loss1: 0.091741 Loss2: 1.345978
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.420229 Loss1: 0.075632 Loss2: 1.344597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428100 Loss1: 0.084976 Loss2: 1.343124 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404420 Loss1: 0.065199 Loss2: 1.339221 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402710 Loss1: 0.063764 Loss2: 1.338946 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389593 Loss1: 0.055612 Loss2: 1.333981 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433354 Loss1: 0.094683 Loss2: 1.338672 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.395035 Loss1: 0.520689 Loss2: 1.874346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.636247 Loss1: 0.222434 Loss2: 1.413812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.521994 Loss1: 0.567668 Loss2: 1.954326
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.562431 Loss1: 0.197330 Loss2: 1.365101
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521480 Loss1: 0.151071 Loss2: 1.370409 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451435 Loss1: 0.087088 Loss2: 1.364346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435607 Loss1: 0.081286 Loss2: 1.354321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499165 Loss1: 0.151856 Loss2: 1.347309 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406126 Loss1: 0.064019 Loss2: 1.342107 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399373 Loss1: 0.058167 Loss2: 1.341206 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.402313 Loss1: 0.492291 Loss2: 1.910022 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990885
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573249 Loss1: 0.172085 Loss2: 1.401164 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523026 Loss1: 0.133066 Loss2: 1.389960
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.281928 Loss1: 0.483442 Loss2: 1.798486
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.682876 Loss1: 0.349475 Loss2: 1.333401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622935 Loss1: 0.239564 Loss2: 1.383371 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528865 Loss1: 0.187239 Loss2: 1.341627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502611 Loss1: 0.155136 Loss2: 1.347476 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500259 Loss1: 0.167355 Loss2: 1.332904 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381069 Loss1: 0.029855 Loss2: 1.351214
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433655 Loss1: 0.104531 Loss2: 1.329125
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.385122 Loss1: 0.059195 Loss2: 1.325926
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.382447 Loss1: 0.063891 Loss2: 1.318556
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394456 Loss1: 0.076070 Loss2: 1.318386
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.285267 Loss1: 0.404296 Loss2: 1.880970
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.566922 Loss1: 0.209427 Loss2: 1.357495
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.517634 Loss1: 0.159431 Loss2: 1.358203
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513607 Loss1: 0.140289 Loss2: 1.373318
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.069545 Loss1: 0.359323 Loss2: 1.710222
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.506944 Loss1: 0.245292 Loss2: 1.261652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.476938 Loss1: 0.189197 Loss2: 1.287741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.470265 Loss1: 0.191555 Loss2: 1.278710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.419371 Loss1: 0.150069 Loss2: 1.269303 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.374247 Loss1: 0.102598 Loss2: 1.271649 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.322685 Loss1: 0.062834 Loss2: 1.259851 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.307643 Loss1: 0.051028 Loss2: 1.256615 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.356486 Loss1: 0.463252 Loss2: 1.893235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655659 Loss1: 0.203672 Loss2: 1.451987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.313043 Loss1: 0.487412 Loss2: 1.825631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.698082 Loss1: 0.355719 Loss2: 1.342363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.549722 Loss1: 0.171274 Loss2: 1.378448 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.506208 Loss1: 0.165765 Loss2: 1.340443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501232 Loss1: 0.157596 Loss2: 1.343637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.504406 Loss1: 0.161247 Loss2: 1.343158 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458047 Loss1: 0.126709 Loss2: 1.331337 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367545 Loss1: 0.044504 Loss2: 1.323042 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.655885 Loss1: 0.313751 Loss2: 1.342134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529323 Loss1: 0.165345 Loss2: 1.363978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475012 Loss1: 0.138840 Loss2: 1.336171
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.171411 Loss1: 0.382395 Loss2: 1.789015
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.586508 Loss1: 0.239427 Loss2: 1.347081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.627906 Loss1: 0.247475 Loss2: 1.380431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.524796 Loss1: 0.168940 Loss2: 1.355856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429984 Loss1: 0.102695 Loss2: 1.327289 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451548 Loss1: 0.097384 Loss2: 1.354163 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460215 Loss1: 0.116113 Loss2: 1.344101 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.102106 Loss1: 0.388181 Loss2: 1.713925
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410296 Loss1: 0.065202 Loss2: 1.345093
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.410911 Loss1: 0.133416 Loss2: 1.277495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.332129 Loss1: 0.080673 Loss2: 1.251455 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.342793 Loss1: 0.099551 Loss2: 1.243242
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.362272 Loss1: 0.457499 Loss2: 1.904773
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.311209 Loss1: 0.066099 Loss2: 1.245110
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.742021 Loss1: 0.393928 Loss2: 1.348094
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.302877 Loss1: 0.064783 Loss2: 1.238094
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.644204 Loss1: 0.229539 Loss2: 1.414665
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.313028 Loss1: 0.072862 Loss2: 1.240165
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.533468 Loss1: 0.161379 Loss2: 1.372089
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475141 Loss1: 0.114553 Loss2: 1.360588
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.275541 Loss1: 0.037317 Loss2: 1.238224
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.421066 Loss1: 0.070745 Loss2: 1.350321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415117 Loss1: 0.076073 Loss2: 1.339043 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398384 Loss1: 0.055476 Loss2: 1.342909
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.209437 Loss1: 0.424168 Loss2: 1.785269
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.600950 Loss1: 0.282990 Loss2: 1.317959
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.506499 Loss1: 0.181192 Loss2: 1.325307
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.439191 Loss1: 0.120602 Loss2: 1.318589
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.436375 Loss1: 0.127540 Loss2: 1.308835
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.357813 Loss1: 0.429585 Loss2: 1.928229
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.691545 Loss1: 0.258787 Loss2: 1.432759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.649581 Loss1: 0.200830 Loss2: 1.448751 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.597166 Loss1: 0.153821 Loss2: 1.443345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.583121 Loss1: 0.165673 Loss2: 1.417449 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549437 Loss1: 0.122844 Loss2: 1.426593 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486144 Loss1: 0.075078 Loss2: 1.411066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.492443 Loss1: 0.086806 Loss2: 1.405637 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.698253 Loss1: 0.292387 Loss2: 1.405866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.550987 Loss1: 0.150735 Loss2: 1.400251 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.505856 Loss1: 0.107900 Loss2: 1.397956
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.408741 Loss1: 0.513066 Loss2: 1.895675
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.666470 Loss1: 0.279281 Loss2: 1.387189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565465 Loss1: 0.185662 Loss2: 1.379803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530782 Loss1: 0.153739 Loss2: 1.377043 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.549250 Loss1: 0.173197 Loss2: 1.376054 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 02:06:34,321 | server.py:236 | fit_round 172 received 50 results and 0 failures
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501997 Loss1: 0.127939 Loss2: 1.374058 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440581 Loss1: 0.079517 Loss2: 1.361064 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397360 Loss1: 0.048130 Loss2: 1.349231 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.683835 Loss1: 0.284302 Loss2: 1.399532 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525374 Loss1: 0.171526 Loss2: 1.353848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473430 Loss1: 0.129122 Loss2: 1.344308
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.315409 Loss1: 0.478316 Loss2: 1.837093
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449341 Loss1: 0.111976 Loss2: 1.337365
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653696 Loss1: 0.308933 Loss2: 1.344763
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568363 Loss1: 0.179475 Loss2: 1.388888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497829 Loss1: 0.146287 Loss2: 1.351542 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455174 Loss1: 0.105451 Loss2: 1.349723 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.417860 Loss1: 0.088296 Loss2: 1.329565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435214 Loss1: 0.100581 Loss2: 1.334633 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 02:06:34,321][flwr][DEBUG] - fit_round 172 received 50 results and 0 failures
-INFO flwr 2023-10-13 02:07:15,038 | server.py:125 | fit progress: (172, 2.27676078781914, {'accuracy': 0.6068}, 396942.816128839)
->> Test accuracy: 0.606800
-[2023-10-13 02:07:15,038][flwr][INFO] - fit progress: (172, 2.27676078781914, {'accuracy': 0.6068}, 396942.816128839)
-DEBUG flwr 2023-10-13 02:07:15,038 | server.py:173 | evaluate_round 172: strategy sampled 50 clients (out of 50)
-[2023-10-13 02:07:15,038][flwr][DEBUG] - evaluate_round 172: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 02:16:22,112 | server.py:187 | evaluate_round 172 received 50 results and 0 failures
-[2023-10-13 02:16:22,112][flwr][DEBUG] - evaluate_round 172 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 02:16:22,112 | server.py:222 | fit_round 173: strategy sampled 50 clients (out of 50)
-[2023-10-13 02:16:22,112][flwr][DEBUG] - fit_round 173: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.228823 Loss1: 0.370823 Loss2: 1.858000 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.515252 Loss1: 0.136114 Loss2: 1.379138 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.461290 Loss1: 0.108244 Loss2: 1.353046
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.249642 Loss1: 0.347229 Loss2: 1.902413
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.438328 Loss1: 0.097135 Loss2: 1.341193
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.662446 Loss1: 0.270695 Loss2: 1.391751
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.390846 Loss1: 0.058818 Loss2: 1.332028
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.642712 Loss1: 0.211270 Loss2: 1.431442
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.417333 Loss1: 0.084539 Loss2: 1.332794
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.702589 Loss1: 0.286829 Loss2: 1.415761
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441825 Loss1: 0.111535 Loss2: 1.330290
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.581531 Loss1: 0.166074 Loss2: 1.415457
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417539 Loss1: 0.077104 Loss2: 1.340436
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.549766 Loss1: 0.146242 Loss2: 1.403524
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420198 Loss1: 0.084556 Loss2: 1.335641
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545704 Loss1: 0.141429 Loss2: 1.404275
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469427 Loss1: 0.082597 Loss2: 1.386829
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464099 Loss1: 0.086257 Loss2: 1.377842
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415930 Loss1: 0.039682 Loss2: 1.376248
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.129898 Loss1: 0.308459 Loss2: 1.821439
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.560038 Loss1: 0.214606 Loss2: 1.345432
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.489450 Loss1: 0.123629 Loss2: 1.365821
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.327069 Loss1: 0.441885 Loss2: 1.885184
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.548630 Loss1: 0.206444 Loss2: 1.342186
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.589392 Loss1: 0.228328 Loss2: 1.361064
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473632 Loss1: 0.124733 Loss2: 1.348899
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565298 Loss1: 0.183930 Loss2: 1.381367
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.453952 Loss1: 0.114515 Loss2: 1.339437
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431871 Loss1: 0.105067 Loss2: 1.326804 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400514 Loss1: 0.065168 Loss2: 1.335346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378951 Loss1: 0.051530 Loss2: 1.327421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368268 Loss1: 0.047113 Loss2: 1.321155 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383116 Loss1: 0.049819 Loss2: 1.333297 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.305216 Loss1: 0.453060 Loss2: 1.852156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618655 Loss1: 0.235054 Loss2: 1.383601 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559917 Loss1: 0.192415 Loss2: 1.367502
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.251810 Loss1: 0.403787 Loss2: 1.848023
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507722 Loss1: 0.148519 Loss2: 1.359203
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.629902 Loss1: 0.265273 Loss2: 1.364629
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461741 Loss1: 0.108038 Loss2: 1.353703
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563383 Loss1: 0.187485 Loss2: 1.375898
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459328 Loss1: 0.109183 Loss2: 1.350145
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577328 Loss1: 0.193735 Loss2: 1.383593
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423683 Loss1: 0.077441 Loss2: 1.346242
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555123 Loss1: 0.183032 Loss2: 1.372091
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422528 Loss1: 0.081247 Loss2: 1.341281
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523705 Loss1: 0.157453 Loss2: 1.366253
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378290 Loss1: 0.046083 Loss2: 1.332208
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463708 Loss1: 0.095569 Loss2: 1.368139
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445414 Loss1: 0.083883 Loss2: 1.361531
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405444 Loss1: 0.052183 Loss2: 1.353261
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372383 Loss1: 0.028861 Loss2: 1.343522
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.199244 Loss1: 0.306830 Loss2: 1.892414
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.647145 Loss1: 0.231291 Loss2: 1.415854
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.640363 Loss1: 0.195292 Loss2: 1.445071
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.221136 Loss1: 0.354876 Loss2: 1.866260
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.604998 Loss1: 0.181712 Loss2: 1.423286
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.594509 Loss1: 0.208381 Loss2: 1.386128
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.604910 Loss1: 0.178946 Loss2: 1.425964
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520888 Loss1: 0.104205 Loss2: 1.416683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514698 Loss1: 0.104417 Loss2: 1.410281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.471993 Loss1: 0.062621 Loss2: 1.409372 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423768 Loss1: 0.023293 Loss2: 1.400475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420870 Loss1: 0.032373 Loss2: 1.388497 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994485
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480364 Loss1: 0.101400 Loss2: 1.378964 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.276789 Loss1: 0.362517 Loss2: 1.914272 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.632820 Loss1: 0.234504 Loss2: 1.398316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578577 Loss1: 0.184189 Loss2: 1.394388
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.326640 Loss1: 0.405361 Loss2: 1.921278
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.666851 Loss1: 0.262023 Loss2: 1.404828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633406 Loss1: 0.178394 Loss2: 1.455012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537380 Loss1: 0.124624 Loss2: 1.412757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522372 Loss1: 0.120013 Loss2: 1.402359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.525290 Loss1: 0.119782 Loss2: 1.405508 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484789 Loss1: 0.078310 Loss2: 1.406478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458431 Loss1: 0.065774 Loss2: 1.392657 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.251919 Loss1: 0.437154 Loss2: 1.814765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.545262 Loss1: 0.197194 Loss2: 1.348067 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.521281 Loss1: 0.186649 Loss2: 1.334632
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.412577 Loss1: 0.509978 Loss2: 1.902599
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.662709 Loss1: 0.310029 Loss2: 1.352680
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.502499 Loss1: 0.179468 Loss2: 1.323031
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.415677 Loss1: 0.091709 Loss2: 1.323968 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.401901 Loss1: 0.089468 Loss2: 1.312433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.399248 Loss1: 0.089990 Loss2: 1.309258 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384337 Loss1: 0.071603 Loss2: 1.312734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.362143 Loss1: 0.056008 Loss2: 1.306135 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402586 Loss1: 0.062048 Loss2: 1.340539 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.400763 Loss1: 0.448129 Loss2: 1.952634
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.706096 Loss1: 0.279938 Loss2: 1.426158
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.650325 Loss1: 0.196121 Loss2: 1.454204
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581474 Loss1: 0.150642 Loss2: 1.430833
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.255799 Loss1: 0.420267 Loss2: 1.835532
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.626316 Loss1: 0.309330 Loss2: 1.316986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.570792 Loss1: 0.188988 Loss2: 1.381804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.548298 Loss1: 0.216852 Loss2: 1.331446 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.592805 Loss1: 0.236050 Loss2: 1.356755 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539829 Loss1: 0.189228 Loss2: 1.350601 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458907 Loss1: 0.057801 Loss2: 1.401106
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502224 Loss1: 0.172613 Loss2: 1.329611
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428930 Loss1: 0.097910 Loss2: 1.331021
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389108 Loss1: 0.068794 Loss2: 1.320314
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370243 Loss1: 0.055737 Loss2: 1.314505
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.273957 Loss1: 0.413004 Loss2: 1.860953
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.658726 Loss1: 0.308321 Loss2: 1.350405
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561110 Loss1: 0.177767 Loss2: 1.383344
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.505690 Loss1: 0.149069 Loss2: 1.356621
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.306393 Loss1: 0.433763 Loss2: 1.872630
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659778 Loss1: 0.296369 Loss2: 1.363408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.652701 Loss1: 0.242205 Loss2: 1.410495 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559792 Loss1: 0.192475 Loss2: 1.367316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484765 Loss1: 0.121081 Loss2: 1.363684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496167 Loss1: 0.131009 Loss2: 1.365158 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446069 Loss1: 0.085970 Loss2: 1.360099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402273 Loss1: 0.057532 Loss2: 1.344741 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.303073 Loss1: 0.438572 Loss2: 1.864502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.686169 Loss1: 0.244061 Loss2: 1.442108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601814 Loss1: 0.212021 Loss2: 1.389793
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.230693 Loss1: 0.361658 Loss2: 1.869035
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.633936 Loss1: 0.247784 Loss2: 1.386152 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.587626 Loss1: 0.164877 Loss2: 1.422749 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.547011 Loss1: 0.153618 Loss2: 1.393393 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500449 Loss1: 0.111987 Loss2: 1.388462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463328 Loss1: 0.075091 Loss2: 1.388237 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447084 Loss1: 0.068748 Loss2: 1.378336 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428496 Loss1: 0.049231 Loss2: 1.379264 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.645507 Loss1: 0.268377 Loss2: 1.377129 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.471969 Loss1: 0.097210 Loss2: 1.374759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.438711 Loss1: 0.073832 Loss2: 1.364879
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.355209 Loss1: 0.427852 Loss2: 1.927357
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416847 Loss1: 0.051128 Loss2: 1.365719
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.665789 Loss1: 0.270905 Loss2: 1.394884
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.437277 Loss1: 0.084908 Loss2: 1.352369
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.623485 Loss1: 0.192580 Loss2: 1.430905
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415012 Loss1: 0.060693 Loss2: 1.354319
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.583733 Loss1: 0.167505 Loss2: 1.416228
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.383800 Loss1: 0.029809 Loss2: 1.353991
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.580422 Loss1: 0.170673 Loss2: 1.409748
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381701 Loss1: 0.033755 Loss2: 1.347945
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513056 Loss1: 0.097301 Loss2: 1.415755
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509330 Loss1: 0.114664 Loss2: 1.394665
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459678 Loss1: 0.064712 Loss2: 1.394966
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.477250 Loss1: 0.086391 Loss2: 1.390859
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442547 Loss1: 0.056592 Loss2: 1.385955
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.212115 Loss1: 0.359239 Loss2: 1.852876
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.682353 Loss1: 0.297057 Loss2: 1.385296
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.655872 Loss1: 0.225612 Loss2: 1.430259
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542871 Loss1: 0.146503 Loss2: 1.396369
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.140867 Loss1: 0.359984 Loss2: 1.780884
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.528087 Loss1: 0.218073 Loss2: 1.310014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.470329 Loss1: 0.152943 Loss2: 1.317387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.455174 Loss1: 0.141824 Loss2: 1.313350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.432061 Loss1: 0.122158 Loss2: 1.309903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.400845 Loss1: 0.091987 Loss2: 1.308857 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372496 Loss1: 0.065527 Loss2: 1.306969 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.319192 Loss1: 0.024022 Loss2: 1.295170 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.999023
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.672394 Loss1: 0.343930 Loss2: 1.328464 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.448857 Loss1: 0.126790 Loss2: 1.322067 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.246072 Loss1: 0.367143 Loss2: 1.878929
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.428458 Loss1: 0.098801 Loss2: 1.329657
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.600941 Loss1: 0.224541 Loss2: 1.376400
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.401696 Loss1: 0.083398 Loss2: 1.318298
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565969 Loss1: 0.169924 Loss2: 1.396046
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.378031 Loss1: 0.069061 Loss2: 1.308970
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.499249 Loss1: 0.120194 Loss2: 1.379056
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.360329 Loss1: 0.058012 Loss2: 1.302317
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446917 Loss1: 0.086554 Loss2: 1.360362
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.349209 Loss1: 0.048195 Loss2: 1.301014
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427457 Loss1: 0.068711 Loss2: 1.358746
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.338174 Loss1: 0.045623 Loss2: 1.292551
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412146 Loss1: 0.061824 Loss2: 1.350322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391731 Loss1: 0.045350 Loss2: 1.346381 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.582414 Loss1: 0.240243 Loss2: 1.342171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.538956 Loss1: 0.173724 Loss2: 1.365233 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521248 Loss1: 0.172051 Loss2: 1.349197
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.308543 Loss1: 0.456036 Loss2: 1.852507
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.467202 Loss1: 0.120946 Loss2: 1.346256
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.641427 Loss1: 0.290577 Loss2: 1.350850
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442556 Loss1: 0.101667 Loss2: 1.340889
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.587201 Loss1: 0.186272 Loss2: 1.400929
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425209 Loss1: 0.087242 Loss2: 1.337967
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.590694 Loss1: 0.223750 Loss2: 1.366943
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.392736 Loss1: 0.062226 Loss2: 1.330511
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574543 Loss1: 0.200791 Loss2: 1.373752
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380448 Loss1: 0.054796 Loss2: 1.325651
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501201 Loss1: 0.127408 Loss2: 1.373794
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.479818 Loss1: 0.115888 Loss2: 1.363930
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.463019 Loss1: 0.102830 Loss2: 1.360189
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431129 Loss1: 0.075144 Loss2: 1.355985
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388323 Loss1: 0.048071 Loss2: 1.340252
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.284684 Loss1: 0.458580 Loss2: 1.826104
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.679661 Loss1: 0.324346 Loss2: 1.355315
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.560760 Loss1: 0.175750 Loss2: 1.385010
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.483521 Loss1: 0.143035 Loss2: 1.340486
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.477801 Loss1: 0.140083 Loss2: 1.337718
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.426807 Loss1: 0.089711 Loss2: 1.337096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390641 Loss1: 0.061018 Loss2: 1.329623 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414643 Loss1: 0.094985 Loss2: 1.319658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.362659 Loss1: 0.040636 Loss2: 1.322023 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.362133 Loss1: 0.047535 Loss2: 1.314598 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483582 Loss1: 0.110441 Loss2: 1.373141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452556 Loss1: 0.080700 Loss2: 1.371856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448094 Loss1: 0.082607 Loss2: 1.365487
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.255635 Loss1: 0.420517 Loss2: 1.835118
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.623098 Loss1: 0.282845 Loss2: 1.340253
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609404 Loss1: 0.231012 Loss2: 1.378392
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528859 Loss1: 0.182212 Loss2: 1.346647
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.439265 Loss1: 0.093973 Loss2: 1.345291
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.435014 Loss1: 0.093846 Loss2: 1.341168
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.314669 Loss1: 0.433532 Loss2: 1.881138
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439581 Loss1: 0.106893 Loss2: 1.332688
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.700336 Loss1: 0.322394 Loss2: 1.377942
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400113 Loss1: 0.067543 Loss2: 1.332570
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.619387 Loss1: 0.210207 Loss2: 1.409181
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377281 Loss1: 0.048424 Loss2: 1.328856
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.566811 Loss1: 0.177227 Loss2: 1.389584
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368427 Loss1: 0.046251 Loss2: 1.322176
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507913 Loss1: 0.136393 Loss2: 1.371520
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478491 Loss1: 0.112210 Loss2: 1.366281
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447228 Loss1: 0.077856 Loss2: 1.369372
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428609 Loss1: 0.061453 Loss2: 1.367156
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402944 Loss1: 0.048859 Loss2: 1.354084
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406717 Loss1: 0.060099 Loss2: 1.346617
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.244397 Loss1: 0.386394 Loss2: 1.858003
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.648888 Loss1: 0.294210 Loss2: 1.354677
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.548510 Loss1: 0.165021 Loss2: 1.383489
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.485024 Loss1: 0.119929 Loss2: 1.365095
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468174 Loss1: 0.118413 Loss2: 1.349762
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.501881 Loss1: 0.145910 Loss2: 1.355971
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.268787 Loss1: 0.440644 Loss2: 1.828143
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653703 Loss1: 0.279014 Loss2: 1.374690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.615898 Loss1: 0.215199 Loss2: 1.400699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576725 Loss1: 0.202073 Loss2: 1.374652 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522238 Loss1: 0.142383 Loss2: 1.379855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450134 Loss1: 0.090417 Loss2: 1.359716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419437 Loss1: 0.064929 Loss2: 1.354508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402558 Loss1: 0.051713 Loss2: 1.350846 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.452796 Loss1: 0.138638 Loss2: 1.314158 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.427682 Loss1: 0.117539 Loss2: 1.310143 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438526 Loss1: 0.119507 Loss2: 1.319019
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.241275 Loss1: 0.451807 Loss2: 1.789468
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.617677 Loss1: 0.292132 Loss2: 1.325545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568769 Loss1: 0.230419 Loss2: 1.338350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.502775 Loss1: 0.174089 Loss2: 1.328686 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358541 Loss1: 0.058005 Loss2: 1.300536
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455038 Loss1: 0.119464 Loss2: 1.335574
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443194 Loss1: 0.123184 Loss2: 1.320010
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419219 Loss1: 0.095909 Loss2: 1.323309
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.370445 Loss1: 0.059335 Loss2: 1.311110
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394039 Loss1: 0.085162 Loss2: 1.308877
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.290400 Loss1: 0.426898 Loss2: 1.863502
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.339215 Loss1: 0.039943 Loss2: 1.299272
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594931 Loss1: 0.207799 Loss2: 1.387132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.504620 Loss1: 0.138106 Loss2: 1.366514 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.496363 Loss1: 0.130228 Loss2: 1.366136
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.221806 Loss1: 0.403163 Loss2: 1.818643
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.454563 Loss1: 0.091905 Loss2: 1.362658
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.636580 Loss1: 0.309179 Loss2: 1.327402
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462386 Loss1: 0.098849 Loss2: 1.363536
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.554334 Loss1: 0.194668 Loss2: 1.359667
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.415318 Loss1: 0.059999 Loss2: 1.355319
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468633 Loss1: 0.127883 Loss2: 1.340750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.444832 Loss1: 0.090280 Loss2: 1.354552
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.444854 Loss1: 0.112876 Loss2: 1.331978
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456562 Loss1: 0.131181 Loss2: 1.325381
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453040 Loss1: 0.118946 Loss2: 1.334095
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424119 Loss1: 0.098089 Loss2: 1.326029
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383055 Loss1: 0.059526 Loss2: 1.323530
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.298282 Loss1: 0.436128 Loss2: 1.862154
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372549 Loss1: 0.052094 Loss2: 1.320455
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.617456 Loss1: 0.197354 Loss2: 1.420102 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534849 Loss1: 0.142220 Loss2: 1.392629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533935 Loss1: 0.143039 Loss2: 1.390895
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.292544 Loss1: 0.445174 Loss2: 1.847370
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463351 Loss1: 0.087614 Loss2: 1.375737
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.640108 Loss1: 0.313918 Loss2: 1.326190
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601957 Loss1: 0.234915 Loss2: 1.367042
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449756 Loss1: 0.082450 Loss2: 1.367306
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.448851 Loss1: 0.115460 Loss2: 1.333391
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438721 Loss1: 0.068280 Loss2: 1.370441
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.407290 Loss1: 0.088988 Loss2: 1.318302
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439106 Loss1: 0.073802 Loss2: 1.365304
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.367041 Loss1: 0.060233 Loss2: 1.306807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.351539 Loss1: 0.052775 Loss2: 1.298764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376656 Loss1: 0.079294 Loss2: 1.297362
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.437290 Loss1: 0.536750 Loss2: 1.900540
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.729552 Loss1: 0.370770 Loss2: 1.358781
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595921 Loss1: 0.202178 Loss2: 1.393742
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.465437 Loss1: 0.109250 Loss2: 1.356188
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.444851 Loss1: 0.094071 Loss2: 1.350780
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.401011 Loss1: 0.056651 Loss2: 1.344360
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.512823 Loss1: 0.517739 Loss2: 1.995084
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.727269 Loss1: 0.328212 Loss2: 1.399057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.617157 Loss1: 0.208094 Loss2: 1.409064 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.511299 Loss1: 0.108380 Loss2: 1.402919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397001 Loss1: 0.069367 Loss2: 1.327633
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495638 Loss1: 0.124937 Loss2: 1.370701
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470982 Loss1: 0.102472 Loss2: 1.368510
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453834 Loss1: 0.083760 Loss2: 1.370074
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453775 Loss1: 0.089695 Loss2: 1.364081
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422557 Loss1: 0.056290 Loss2: 1.366266
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379106 Loss1: 0.023086 Loss2: 1.356021
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.304279 Loss1: 0.418539 Loss2: 1.885740
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.714787 Loss1: 0.343147 Loss2: 1.371640
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634820 Loss1: 0.206987 Loss2: 1.427833
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573570 Loss1: 0.187274 Loss2: 1.386296
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531808 Loss1: 0.147746 Loss2: 1.384061
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.291808 Loss1: 0.392441 Loss2: 1.899367
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.722425 Loss1: 0.343236 Loss2: 1.379189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.611100 Loss1: 0.187961 Loss2: 1.423139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.577033 Loss1: 0.194055 Loss2: 1.382978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550365 Loss1: 0.151412 Loss2: 1.398953 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516963 Loss1: 0.124951 Loss2: 1.392012 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493802 Loss1: 0.111964 Loss2: 1.381838 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.452086 Loss1: 0.076470 Loss2: 1.375616 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.632344 Loss1: 0.280034 Loss2: 1.352310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533797 Loss1: 0.180481 Loss2: 1.353316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.467260 Loss1: 0.117812 Loss2: 1.349447
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.200760 Loss1: 0.400475 Loss2: 1.800285
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.525074 Loss1: 0.215670 Loss2: 1.309404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.519922 Loss1: 0.208846 Loss2: 1.311076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.437742 Loss1: 0.116647 Loss2: 1.321095 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.393133 Loss1: 0.093689 Loss2: 1.299444 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.386492 Loss1: 0.090060 Loss2: 1.296432 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.392125 Loss1: 0.096446 Loss2: 1.295679 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.312473 Loss1: 0.026663 Loss2: 1.285810 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.581731 Loss1: 0.235714 Loss2: 1.346017 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517549 Loss1: 0.151735 Loss2: 1.365813 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 02:44:52,111 | server.py:236 | fit_round 173 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.493705 Loss1: 0.504816 Loss2: 1.988889 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.673455 Loss1: 0.306412 Loss2: 1.367043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.391330 Loss1: 0.058702 Loss2: 1.332628
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.602709 Loss1: 0.230591 Loss2: 1.372118
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539798 Loss1: 0.143326 Loss2: 1.396473
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.397543 Loss1: 0.067517 Loss2: 1.330026
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402383 Loss1: 0.072904 Loss2: 1.329479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426293 Loss1: 0.101873 Loss2: 1.324421 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428646 Loss1: 0.072723 Loss2: 1.355923 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985677
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.469408 Loss1: 0.553554 Loss2: 1.915854 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574862 Loss1: 0.158858 Loss2: 1.416004 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.512688 Loss1: 0.129496 Loss2: 1.383192
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.426078 Loss1: 0.522225 Loss2: 1.903853
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.714241 Loss1: 0.356256 Loss2: 1.357985 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633171 Loss1: 0.220985 Loss2: 1.412187 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523353 Loss1: 0.153686 Loss2: 1.369667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.454688 Loss1: 0.097419 Loss2: 1.357269 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.424448 Loss1: 0.067497 Loss2: 1.356951 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408108 Loss1: 0.059924 Loss2: 1.348184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397638 Loss1: 0.064048 Loss2: 1.333591 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.394851 Loss1: 0.470484 Loss2: 1.924366 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.771152 Loss1: 0.257649 Loss2: 1.513503 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.624144 Loss1: 0.183873 Loss2: 1.440271
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.207071 Loss1: 0.331836 Loss2: 1.875234
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.605240 Loss1: 0.247455 Loss2: 1.357785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.571575 Loss1: 0.191240 Loss2: 1.380335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554738 Loss1: 0.174167 Loss2: 1.380571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.564500 Loss1: 0.194463 Loss2: 1.370038 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478391 Loss1: 0.106274 Loss2: 1.372117 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.464444 Loss1: 0.057162 Loss2: 1.407282
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471425 Loss1: 0.100149 Loss2: 1.371276
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467947 Loss1: 0.103091 Loss2: 1.364856
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424975 Loss1: 0.057863 Loss2: 1.367112
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405422 Loss1: 0.050094 Loss2: 1.355328
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 02:44:52,111][flwr][DEBUG] - fit_round 173 received 50 results and 0 failures
-INFO flwr 2023-10-13 02:45:34,454 | server.py:125 | fit progress: (173, 2.277818728559695, {'accuracy': 0.6063}, 399242.232187888)
->> Test accuracy: 0.606300
-[2023-10-13 02:45:34,454][flwr][INFO] - fit progress: (173, 2.277818728559695, {'accuracy': 0.6063}, 399242.232187888)
-DEBUG flwr 2023-10-13 02:45:34,454 | server.py:173 | evaluate_round 173: strategy sampled 50 clients (out of 50)
-[2023-10-13 02:45:34,454][flwr][DEBUG] - evaluate_round 173: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 02:54:37,975 | server.py:187 | evaluate_round 173 received 50 results and 0 failures
-[2023-10-13 02:54:37,975][flwr][DEBUG] - evaluate_round 173 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 02:54:37,976 | server.py:222 | fit_round 174: strategy sampled 50 clients (out of 50)
-[2023-10-13 02:54:37,976][flwr][DEBUG] - fit_round 174: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.244869 Loss1: 0.404431 Loss2: 1.840437
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.705238 Loss1: 0.349820 Loss2: 1.355417
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.741721 Loss1: 0.298569 Loss2: 1.443152
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.593132 Loss1: 0.236513 Loss2: 1.356620
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.288203 Loss1: 0.458088 Loss2: 1.830114
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.545754 Loss1: 0.203502 Loss2: 1.342252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.515159 Loss1: 0.173228 Loss2: 1.341930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.465614 Loss1: 0.134399 Loss2: 1.331214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.457557 Loss1: 0.128246 Loss2: 1.329311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472387 Loss1: 0.144859 Loss2: 1.327527 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371622 Loss1: 0.041715 Loss2: 1.329907
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395954 Loss1: 0.076395 Loss2: 1.319559
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389862 Loss1: 0.070423 Loss2: 1.319439
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376657 Loss1: 0.061425 Loss2: 1.315232
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377852 Loss1: 0.068737 Loss2: 1.309115
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.176901 Loss1: 0.326808 Loss2: 1.850094
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.598411 Loss1: 0.225274 Loss2: 1.373137
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.510457 Loss1: 0.127077 Loss2: 1.383380
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491087 Loss1: 0.117763 Loss2: 1.373324
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.199855 Loss1: 0.377692 Loss2: 1.822163
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475668 Loss1: 0.116200 Loss2: 1.359468
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.672809 Loss1: 0.338289 Loss2: 1.334520
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443008 Loss1: 0.082226 Loss2: 1.360783
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.626279 Loss1: 0.244181 Loss2: 1.382098
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.412444 Loss1: 0.052686 Loss2: 1.359758
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521649 Loss1: 0.179681 Loss2: 1.341969
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423094 Loss1: 0.067172 Loss2: 1.355923
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555476 Loss1: 0.209424 Loss2: 1.346052
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405120 Loss1: 0.049288 Loss2: 1.355832
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490087 Loss1: 0.141507 Loss2: 1.348579
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.397796 Loss1: 0.050095 Loss2: 1.347701
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439381 Loss1: 0.106579 Loss2: 1.332802
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386910 Loss1: 0.062454 Loss2: 1.324456
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386064 Loss1: 0.055945 Loss2: 1.330119
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369774 Loss1: 0.048937 Loss2: 1.320837
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.327817 Loss1: 0.489806 Loss2: 1.838011
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.566847 Loss1: 0.227630 Loss2: 1.339217
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.482781 Loss1: 0.141607 Loss2: 1.341174
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.460128 Loss1: 0.132121 Loss2: 1.328007
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.394161 Loss1: 0.491483 Loss2: 1.902678
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.758295 Loss1: 0.399625 Loss2: 1.358670
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562455 Loss1: 0.230731 Loss2: 1.331724
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669498 Loss1: 0.236997 Loss2: 1.432501
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468579 Loss1: 0.124435 Loss2: 1.344144
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.596636 Loss1: 0.221800 Loss2: 1.374835
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.411711 Loss1: 0.088016 Loss2: 1.323696
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.364171 Loss1: 0.046277 Loss2: 1.317894 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.358125 Loss1: 0.048126 Loss2: 1.309998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.342671 Loss1: 0.037875 Loss2: 1.304796 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376534 Loss1: 0.041484 Loss2: 1.335049 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.232433 Loss1: 0.401525 Loss2: 1.830908 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563939 Loss1: 0.193708 Loss2: 1.370231 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541548 Loss1: 0.176584 Loss2: 1.364964
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.107666 Loss1: 0.345140 Loss2: 1.762526
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.513275 Loss1: 0.158378 Loss2: 1.354897
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.491828 Loss1: 0.211875 Loss2: 1.279953
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499820 Loss1: 0.145493 Loss2: 1.354327
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.487547 Loss1: 0.198661 Loss2: 1.288886
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.465576 Loss1: 0.113827 Loss2: 1.351749
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496111 Loss1: 0.186278 Loss2: 1.309833
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427055 Loss1: 0.078469 Loss2: 1.348586
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.389122 Loss1: 0.099163 Loss2: 1.289959
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.387760 Loss1: 0.048580 Loss2: 1.339180
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.344642 Loss1: 0.064672 Loss2: 1.279971
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.382222 Loss1: 0.049730 Loss2: 1.332493
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.390548 Loss1: 0.109482 Loss2: 1.281066
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.369987 Loss1: 0.089595 Loss2: 1.280392
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.351978 Loss1: 0.078060 Loss2: 1.273918
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351524 Loss1: 0.074300 Loss2: 1.277224
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.165618 Loss1: 0.420037 Loss2: 1.745581
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572647 Loss1: 0.276073 Loss2: 1.296575
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.528127 Loss1: 0.197186 Loss2: 1.330940
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.445219 Loss1: 0.137751 Loss2: 1.307468
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.147175 Loss1: 0.382967 Loss2: 1.764208
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.569925 Loss1: 0.261956 Loss2: 1.307969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.562374 Loss1: 0.221503 Loss2: 1.340872 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519941 Loss1: 0.168859 Loss2: 1.351082 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460954 Loss1: 0.142883 Loss2: 1.318072 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452465 Loss1: 0.132553 Loss2: 1.319912 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388797 Loss1: 0.070258 Loss2: 1.318538 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348619 Loss1: 0.051041 Loss2: 1.297579 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.602605 Loss1: 0.224728 Loss2: 1.377877 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489916 Loss1: 0.116846 Loss2: 1.373071 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.454955 Loss1: 0.092309 Loss2: 1.362647 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.421306 Loss1: 0.062182 Loss2: 1.359123 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404558 Loss1: 0.053382 Loss2: 1.351176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393748 Loss1: 0.053199 Loss2: 1.340549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.392162 Loss1: 0.050353 Loss2: 1.341809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396064 Loss1: 0.055641 Loss2: 1.340423 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389549 Loss1: 0.043823 Loss2: 1.345726 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361376 Loss1: 0.025365 Loss2: 1.336011 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.171084 Loss1: 0.373407 Loss2: 1.797677
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.561206 Loss1: 0.261495 Loss2: 1.299711
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.531766 Loss1: 0.204219 Loss2: 1.327547
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.431433 Loss1: 0.119902 Loss2: 1.311531
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.191744 Loss1: 0.375735 Loss2: 1.816009
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.558150 Loss1: 0.199328 Loss2: 1.358822 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.530148 Loss1: 0.147186 Loss2: 1.382962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.469516 Loss1: 0.107487 Loss2: 1.362029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.464770 Loss1: 0.112136 Loss2: 1.352635 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470283 Loss1: 0.114364 Loss2: 1.355919 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437989 Loss1: 0.086237 Loss2: 1.351752 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425362 Loss1: 0.077364 Loss2: 1.347999 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.559136 Loss1: 0.255153 Loss2: 1.303982 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.459590 Loss1: 0.138244 Loss2: 1.321347 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.421562 Loss1: 0.117518 Loss2: 1.304044
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.239605 Loss1: 0.454804 Loss2: 1.784801
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.372005 Loss1: 0.070384 Loss2: 1.301620
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.564492 Loss1: 0.252437 Loss2: 1.312055
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.371783 Loss1: 0.076828 Loss2: 1.294954
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.544882 Loss1: 0.207100 Loss2: 1.337782
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.355437 Loss1: 0.063058 Loss2: 1.292380
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.422033 Loss1: 0.105699 Loss2: 1.316334
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.362265 Loss1: 0.068849 Loss2: 1.293416
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.404354 Loss1: 0.095021 Loss2: 1.309333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.326263 Loss1: 0.040540 Loss2: 1.285723
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.359317 Loss1: 0.051364 Loss2: 1.307953
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.337026 Loss1: 0.041769 Loss2: 1.295256
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.315083 Loss1: 0.028588 Loss2: 1.286495
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.309491 Loss1: 0.031037 Loss2: 1.278454
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.299720 Loss1: 0.026854 Loss2: 1.272866
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.398246 Loss1: 0.513173 Loss2: 1.885073
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.540915 Loss1: 0.228040 Loss2: 1.312875
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.465622 Loss1: 0.159989 Loss2: 1.305633
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.440724 Loss1: 0.128418 Loss2: 1.312306
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.400337 Loss1: 0.109425 Loss2: 1.290912
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.360693 Loss1: 0.074392 Loss2: 1.286301
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.235768 Loss1: 0.408041 Loss2: 1.827727
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.343207 Loss1: 0.061673 Loss2: 1.281534
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.651257 Loss1: 0.301536 Loss2: 1.349721
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.607139 Loss1: 0.219012 Loss2: 1.388127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532899 Loss1: 0.179524 Loss2: 1.353375 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997596
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498743 Loss1: 0.146913 Loss2: 1.351830 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439025 Loss1: 0.102042 Loss2: 1.336982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442533 Loss1: 0.099373 Loss2: 1.343160
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.272140 Loss1: 0.447165 Loss2: 1.824975
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.670455 Loss1: 0.308315 Loss2: 1.362141 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576804 Loss1: 0.208915 Loss2: 1.367889 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480796 Loss1: 0.129883 Loss2: 1.350913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478675 Loss1: 0.117399 Loss2: 1.361276 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.474442 Loss1: 0.124923 Loss2: 1.349519 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399410 Loss1: 0.052164 Loss2: 1.347246 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396821 Loss1: 0.061158 Loss2: 1.335663 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452273 Loss1: 0.108358 Loss2: 1.343915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427931 Loss1: 0.091631 Loss2: 1.336300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.281706 Loss1: 0.377959 Loss2: 1.903746 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574059 Loss1: 0.175340 Loss2: 1.398719 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.447905 Loss1: 0.073287 Loss2: 1.374617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.405298 Loss1: 0.041215 Loss2: 1.364083
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.361425 Loss1: 0.469381 Loss2: 1.892044
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.778714 Loss1: 0.376958 Loss2: 1.401756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.691151 Loss1: 0.239373 Loss2: 1.451778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.619137 Loss1: 0.209078 Loss2: 1.410059 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.620962 Loss1: 0.207075 Loss2: 1.413887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.573571 Loss1: 0.160532 Loss2: 1.413039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.550605 Loss1: 0.143981 Loss2: 1.406624 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.490499 Loss1: 0.087892 Loss2: 1.402607 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.717233 Loss1: 0.254696 Loss2: 1.462537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.548815 Loss1: 0.153720 Loss2: 1.395095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504801 Loss1: 0.103826 Loss2: 1.400975
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.233438 Loss1: 0.400983 Loss2: 1.832454
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.609076 Loss1: 0.270172 Loss2: 1.338904 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.553090 Loss1: 0.190848 Loss2: 1.362242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.510327 Loss1: 0.166996 Loss2: 1.343331 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459740 Loss1: 0.073176 Loss2: 1.386564
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541897 Loss1: 0.198102 Loss2: 1.343795
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508415 Loss1: 0.146597 Loss2: 1.361818
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.517605 Loss1: 0.171709 Loss2: 1.345896
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424545 Loss1: 0.077416 Loss2: 1.347129
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431633 Loss1: 0.096071 Loss2: 1.335562
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.218946 Loss1: 0.390068 Loss2: 1.828878
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.384637 Loss1: 0.054822 Loss2: 1.329815
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594409 Loss1: 0.184676 Loss2: 1.409732 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.481463 Loss1: 0.105841 Loss2: 1.375622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471986 Loss1: 0.095343 Loss2: 1.376643
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.427605 Loss1: 0.530031 Loss2: 1.897574
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.658229 Loss1: 0.302662 Loss2: 1.355567
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455773 Loss1: 0.088641 Loss2: 1.367133
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.428709 Loss1: 0.061290 Loss2: 1.367419 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424341 Loss1: 0.060246 Loss2: 1.364095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405672 Loss1: 0.047832 Loss2: 1.357840 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442007 Loss1: 0.094840 Loss2: 1.347167 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413950 Loss1: 0.078807 Loss2: 1.335143 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.317527 Loss1: 0.477306 Loss2: 1.840221
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.680235 Loss1: 0.328661 Loss2: 1.351573
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.540979 Loss1: 0.160267 Loss2: 1.380712
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.493181 Loss1: 0.144199 Loss2: 1.348982
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.228922 Loss1: 0.403868 Loss2: 1.825054
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.646999 Loss1: 0.313571 Loss2: 1.333428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.602320 Loss1: 0.232471 Loss2: 1.369848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.603393 Loss1: 0.249083 Loss2: 1.354310 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483877 Loss1: 0.138992 Loss2: 1.344886 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468727 Loss1: 0.137017 Loss2: 1.331710 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430321 Loss1: 0.095344 Loss2: 1.334976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368268 Loss1: 0.048723 Loss2: 1.319545 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.312128 Loss1: 0.397285 Loss2: 1.914843 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573125 Loss1: 0.155933 Loss2: 1.417192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.504052 Loss1: 0.111702 Loss2: 1.392350
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.289953 Loss1: 0.426120 Loss2: 1.863833
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.452331 Loss1: 0.074778 Loss2: 1.377553
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.650094 Loss1: 0.327259 Loss2: 1.322835
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454210 Loss1: 0.079153 Loss2: 1.375057
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.599003 Loss1: 0.234487 Loss2: 1.364516
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444007 Loss1: 0.065960 Loss2: 1.378047
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.491694 Loss1: 0.137724 Loss2: 1.353970
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501198 Loss1: 0.170667 Loss2: 1.330531
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405654 Loss1: 0.044120 Loss2: 1.361534
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469918 Loss1: 0.128108 Loss2: 1.341810
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.410326 Loss1: 0.053349 Loss2: 1.356977
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404973 Loss1: 0.069108 Loss2: 1.335865
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404178 Loss1: 0.051568 Loss2: 1.352610
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381697 Loss1: 0.062977 Loss2: 1.318720 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.167570 Loss1: 0.345709 Loss2: 1.821861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.504578 Loss1: 0.136617 Loss2: 1.367961 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.319633 Loss1: 0.442738 Loss2: 1.876895
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.473641 Loss1: 0.113815 Loss2: 1.359825
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.687981 Loss1: 0.300903 Loss2: 1.387078
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478011 Loss1: 0.112902 Loss2: 1.365109
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592164 Loss1: 0.183139 Loss2: 1.409025
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.431780 Loss1: 0.078101 Loss2: 1.353679
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564304 Loss1: 0.185093 Loss2: 1.379211
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485804 Loss1: 0.136819 Loss2: 1.348985
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418762 Loss1: 0.074362 Loss2: 1.344400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431803 Loss1: 0.087377 Loss2: 1.344426 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403817 Loss1: 0.065842 Loss2: 1.337975 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421670 Loss1: 0.057956 Loss2: 1.363714 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.277872 Loss1: 0.424286 Loss2: 1.853586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635946 Loss1: 0.220290 Loss2: 1.415656 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.575114 Loss1: 0.202219 Loss2: 1.372895
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.310592 Loss1: 0.483244 Loss2: 1.827347
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.537653 Loss1: 0.160294 Loss2: 1.377359
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.669316 Loss1: 0.314444 Loss2: 1.354872
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.537352 Loss1: 0.163320 Loss2: 1.374033
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.615991 Loss1: 0.227074 Loss2: 1.388917
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.507366 Loss1: 0.139315 Loss2: 1.368051
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534965 Loss1: 0.181994 Loss2: 1.352970
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.455961 Loss1: 0.094481 Loss2: 1.361480
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.561569 Loss1: 0.211013 Loss2: 1.350556
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.426043 Loss1: 0.071801 Loss2: 1.354242
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510163 Loss1: 0.146443 Loss2: 1.363720
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424959 Loss1: 0.075149 Loss2: 1.349811
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456358 Loss1: 0.105626 Loss2: 1.350732
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414325 Loss1: 0.073870 Loss2: 1.340455
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379955 Loss1: 0.041939 Loss2: 1.338015
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370743 Loss1: 0.042112 Loss2: 1.328630
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.237708 Loss1: 0.434041 Loss2: 1.803667
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.644447 Loss1: 0.288754 Loss2: 1.355694
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.556734 Loss1: 0.175717 Loss2: 1.381017
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.450749 Loss1: 0.497837 Loss2: 1.952911
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497391 Loss1: 0.149825 Loss2: 1.347566
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479977 Loss1: 0.134594 Loss2: 1.345383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.414412 Loss1: 0.071375 Loss2: 1.343037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497468 Loss1: 0.155922 Loss2: 1.341546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.488215 Loss1: 0.147737 Loss2: 1.340477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.510339 Loss1: 0.152045 Loss2: 1.358295 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388922 Loss1: 0.058520 Loss2: 1.330402
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490674 Loss1: 0.141486 Loss2: 1.349188
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.494881 Loss1: 0.148890 Loss2: 1.345991
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369574 Loss1: 0.043335 Loss2: 1.326239
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.244109 Loss1: 0.396407 Loss2: 1.847702 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990885
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.580807 Loss1: 0.191106 Loss2: 1.389700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.519279 Loss1: 0.140841 Loss2: 1.378438
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.154918 Loss1: 0.363458 Loss2: 1.791459
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.525908 Loss1: 0.175217 Loss2: 1.350690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.486171 Loss1: 0.136165 Loss2: 1.350006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.462628 Loss1: 0.126231 Loss2: 1.336397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.525799 Loss1: 0.173792 Loss2: 1.352008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450796 Loss1: 0.111647 Loss2: 1.339149 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431610 Loss1: 0.101768 Loss2: 1.329842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370250 Loss1: 0.047802 Loss2: 1.322448 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.091369 Loss1: 0.321302 Loss2: 1.770068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.508471 Loss1: 0.157205 Loss2: 1.351266 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.177351 Loss1: 0.381640 Loss2: 1.795711 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.587321 Loss1: 0.270392 Loss2: 1.316929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.509439 Loss1: 0.163492 Loss2: 1.345947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.506544 Loss1: 0.192604 Loss2: 1.313940 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.453151 Loss1: 0.133413 Loss2: 1.319738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.383274 Loss1: 0.071378 Loss2: 1.311896 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.378113 Loss1: 0.074462 Loss2: 1.303651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.341078 Loss1: 0.043956 Loss2: 1.297122 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.275980 Loss1: 0.489956 Loss2: 1.786024 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.564088 Loss1: 0.220568 Loss2: 1.343520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517554 Loss1: 0.183269 Loss2: 1.334285
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.330228 Loss1: 0.488230 Loss2: 1.841999
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.598509 Loss1: 0.255906 Loss2: 1.342603 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.559701 Loss1: 0.188112 Loss2: 1.371589 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.500582 Loss1: 0.134767 Loss2: 1.365815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435894 Loss1: 0.119664 Loss2: 1.316230
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487568 Loss1: 0.137158 Loss2: 1.350410
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371068 Loss1: 0.067767 Loss2: 1.303301
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421451 Loss1: 0.072842 Loss2: 1.348609
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363936 Loss1: 0.058200 Loss2: 1.305736
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.385982 Loss1: 0.049529 Loss2: 1.336453
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374302 Loss1: 0.042898 Loss2: 1.331405
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362855 Loss1: 0.038738 Loss2: 1.324117
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.349904 Loss1: 0.029660 Loss2: 1.320244
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.006617 Loss1: 0.257205 Loss2: 1.749412
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.467673 Loss1: 0.158404 Loss2: 1.309269
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.443332 Loss1: 0.129944 Loss2: 1.313389
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.249291 Loss1: 0.448638 Loss2: 1.800652
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.580764 Loss1: 0.262908 Loss2: 1.317855 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.482925 Loss1: 0.149550 Loss2: 1.333375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.442841 Loss1: 0.125355 Loss2: 1.317486 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.379912 Loss1: 0.079223 Loss2: 1.300688
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.427260 Loss1: 0.110709 Loss2: 1.316551
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.395514 Loss1: 0.095487 Loss2: 1.300027
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.402424 Loss1: 0.086742 Loss2: 1.315682
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.386931 Loss1: 0.076800 Loss2: 1.310132
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.337660 Loss1: 0.040253 Loss2: 1.297407
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.384162 Loss1: 0.084798 Loss2: 1.299364
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.331710 Loss1: 0.047525 Loss2: 1.284185
-(DefaultActor pid=3765) >> Training accuracy: 0.993566
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.371888 Loss1: 0.074390 Loss2: 1.297498 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.329629 Loss1: 0.484873 Loss2: 1.844756
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.587842 Loss1: 0.244382 Loss2: 1.343460
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.529347 Loss1: 0.169148 Loss2: 1.360198
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.537897 Loss1: 0.187670 Loss2: 1.350227
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.310670 Loss1: 0.392004 Loss2: 1.918666
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470899 Loss1: 0.124148 Loss2: 1.346751
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.665757 Loss1: 0.277748 Loss2: 1.388009
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484762 Loss1: 0.138621 Loss2: 1.346140
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572670 Loss1: 0.162954 Loss2: 1.409716
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436407 Loss1: 0.102272 Loss2: 1.334135
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.527961 Loss1: 0.145892 Loss2: 1.382069
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425914 Loss1: 0.093438 Loss2: 1.332476
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514672 Loss1: 0.128965 Loss2: 1.385707
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437992 Loss1: 0.105561 Loss2: 1.332431
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.544480 Loss1: 0.161331 Loss2: 1.383149
-DEBUG flwr 2023-10-13 03:23:49,504 | server.py:236 | fit_round 174 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445371 Loss1: 0.110133 Loss2: 1.335238
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518147 Loss1: 0.130359 Loss2: 1.387788
-(DefaultActor pid=3765) >> Training accuracy: 0.966667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.501566 Loss1: 0.120025 Loss2: 1.381541
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454669 Loss1: 0.078609 Loss2: 1.376060
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420465 Loss1: 0.051933 Loss2: 1.368533
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.427831 Loss1: 0.510732 Loss2: 1.917100
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.745759 Loss1: 0.362008 Loss2: 1.383751
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644437 Loss1: 0.233648 Loss2: 1.410789
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540068 Loss1: 0.139918 Loss2: 1.400150
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.228368 Loss1: 0.407173 Loss2: 1.821194
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.571278 Loss1: 0.242957 Loss2: 1.328321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.502062 Loss1: 0.158830 Loss2: 1.343232 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.490340 Loss1: 0.156575 Loss2: 1.333765 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.456343 Loss1: 0.128698 Loss2: 1.327644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432390 Loss1: 0.074903 Loss2: 1.357487 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981027
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396557 Loss1: 0.075097 Loss2: 1.321461 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365136 Loss1: 0.050755 Loss2: 1.314381 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.660425 Loss1: 0.267937 Loss2: 1.392488 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.553468 Loss1: 0.160295 Loss2: 1.393173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.100277 Loss1: 0.332858 Loss2: 1.767419
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544612 Loss1: 0.159850 Loss2: 1.384762
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.517593 Loss1: 0.130181 Loss2: 1.387412
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.587022 Loss1: 0.265064 Loss2: 1.321958
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488976 Loss1: 0.102755 Loss2: 1.386221
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.524305 Loss1: 0.162085 Loss2: 1.362220
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460171 Loss1: 0.089138 Loss2: 1.371033
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481408 Loss1: 0.156903 Loss2: 1.324505
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.446349 Loss1: 0.076208 Loss2: 1.370140
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.459599 Loss1: 0.131466 Loss2: 1.328133
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411377 Loss1: 0.048979 Loss2: 1.362397
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471681 Loss1: 0.143841 Loss2: 1.327840
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450408 Loss1: 0.122257 Loss2: 1.328151
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437758 Loss1: 0.119764 Loss2: 1.317994
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435706 Loss1: 0.113787 Loss2: 1.321919
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408815 Loss1: 0.092296 Loss2: 1.316520
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 03:23:49,504][flwr][DEBUG] - fit_round 174 received 50 results and 0 failures
-INFO flwr 2023-10-13 03:24:30,497 | server.py:125 | fit progress: (174, 2.2803944634934203, {'accuracy': 0.6048}, 401578.275854018)
->> Test accuracy: 0.604800
-[2023-10-13 03:24:30,497][flwr][INFO] - fit progress: (174, 2.2803944634934203, {'accuracy': 0.6048}, 401578.275854018)
-DEBUG flwr 2023-10-13 03:24:30,498 | server.py:173 | evaluate_round 174: strategy sampled 50 clients (out of 50)
-[2023-10-13 03:24:30,498][flwr][DEBUG] - evaluate_round 174: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 03:33:34,337 | server.py:187 | evaluate_round 174 received 50 results and 0 failures
-[2023-10-13 03:33:34,337][flwr][DEBUG] - evaluate_round 174 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 03:33:34,337 | server.py:222 | fit_round 175: strategy sampled 50 clients (out of 50)
-[2023-10-13 03:33:34,337][flwr][DEBUG] - fit_round 175: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.270808 Loss1: 0.488092 Loss2: 1.782716
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.618834 Loss1: 0.315748 Loss2: 1.303087
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.592273 Loss1: 0.223723 Loss2: 1.368550
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467045 Loss1: 0.154687 Loss2: 1.312358
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.302559 Loss1: 0.361764 Loss2: 1.940795
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.497138 Loss1: 0.181862 Loss2: 1.315276
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.666372 Loss1: 0.250082 Loss2: 1.416290
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.391463 Loss1: 0.077703 Loss2: 1.313760
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.612905 Loss1: 0.166214 Loss2: 1.446691
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415527 Loss1: 0.113368 Loss2: 1.302160
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588099 Loss1: 0.152273 Loss2: 1.435826
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.355627 Loss1: 0.056812 Loss2: 1.298815
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522922 Loss1: 0.103320 Loss2: 1.419602
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.362249 Loss1: 0.066659 Loss2: 1.295590
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531556 Loss1: 0.115523 Loss2: 1.416033
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.338494 Loss1: 0.047746 Loss2: 1.290748
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483565 Loss1: 0.069252 Loss2: 1.414313
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.451330 Loss1: 0.051688 Loss2: 1.399642
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460271 Loss1: 0.059176 Loss2: 1.401095
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.436621 Loss1: 0.038746 Loss2: 1.397875
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.234462 Loss1: 0.400243 Loss2: 1.834219
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.624392 Loss1: 0.275098 Loss2: 1.349294
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.667701 Loss1: 0.272848 Loss2: 1.394853
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.505620 Loss1: 0.157427 Loss2: 1.348193
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.275586 Loss1: 0.345542 Loss2: 1.930044
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.644547 Loss1: 0.245122 Loss2: 1.399425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.643980 Loss1: 0.223130 Loss2: 1.420850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555298 Loss1: 0.138990 Loss2: 1.416308 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528738 Loss1: 0.127571 Loss2: 1.401166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.526195 Loss1: 0.126508 Loss2: 1.399687 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421362 Loss1: 0.088011 Loss2: 1.333351
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494353 Loss1: 0.097766 Loss2: 1.396587
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481831 Loss1: 0.089259 Loss2: 1.392573
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.491266 Loss1: 0.094382 Loss2: 1.396884
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.455258 Loss1: 0.064559 Loss2: 1.390699
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.354060 Loss1: 0.413585 Loss2: 1.940474
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.696064 Loss1: 0.280864 Loss2: 1.415200
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.600107 Loss1: 0.151440 Loss2: 1.448666
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555299 Loss1: 0.140054 Loss2: 1.415245
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.267806 Loss1: 0.483474 Loss2: 1.784332
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.607557 Loss1: 0.289599 Loss2: 1.317958 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.493707 Loss1: 0.150343 Loss2: 1.343364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478164 Loss1: 0.162067 Loss2: 1.316097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.419404 Loss1: 0.103666 Loss2: 1.315738 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461742 Loss1: 0.143954 Loss2: 1.317788 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442276 Loss1: 0.054650 Loss2: 1.387626
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455089 Loss1: 0.141665 Loss2: 1.313424
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419200 Loss1: 0.103783 Loss2: 1.315416
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417626 Loss1: 0.108093 Loss2: 1.309533
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410380 Loss1: 0.097706 Loss2: 1.312674
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.185255 Loss1: 0.340079 Loss2: 1.845176
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.608553 Loss1: 0.263195 Loss2: 1.345358
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.572604 Loss1: 0.205028 Loss2: 1.367576
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489411 Loss1: 0.133214 Loss2: 1.356198
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.281332 Loss1: 0.420443 Loss2: 1.860889
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.683150 Loss1: 0.294873 Loss2: 1.388277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635241 Loss1: 0.218051 Loss2: 1.417190 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561933 Loss1: 0.168436 Loss2: 1.393497 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518129 Loss1: 0.131732 Loss2: 1.386397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495080 Loss1: 0.101490 Loss2: 1.393590 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427705 Loss1: 0.056572 Loss2: 1.371133 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409690 Loss1: 0.044740 Loss2: 1.364951 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.670764 Loss1: 0.287320 Loss2: 1.383445 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474741 Loss1: 0.108586 Loss2: 1.366155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.414112 Loss1: 0.070454 Loss2: 1.343658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390498 Loss1: 0.051491 Loss2: 1.339007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378222 Loss1: 0.047459 Loss2: 1.330762 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399951 Loss1: 0.073434 Loss2: 1.326517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368306 Loss1: 0.041480 Loss2: 1.326825 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.445858 Loss1: 0.090224 Loss2: 1.355633 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449859 Loss1: 0.101204 Loss2: 1.348654 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423454 Loss1: 0.072127 Loss2: 1.351328
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.146752 Loss1: 0.340489 Loss2: 1.806263
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404454 Loss1: 0.057682 Loss2: 1.346772
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.596534 Loss1: 0.290855 Loss2: 1.305679
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526533 Loss1: 0.188364 Loss2: 1.338170
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518422 Loss1: 0.188812 Loss2: 1.329610
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.474212 Loss1: 0.149415 Loss2: 1.324797
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462298 Loss1: 0.144261 Loss2: 1.318037
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.521090 Loss1: 0.199556 Loss2: 1.321535
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.218477 Loss1: 0.354665 Loss2: 1.863812
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457010 Loss1: 0.118653 Loss2: 1.338356
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.652655 Loss1: 0.267332 Loss2: 1.385323
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398900 Loss1: 0.078742 Loss2: 1.320157
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.560928 Loss1: 0.144078 Loss2: 1.416850
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.394936 Loss1: 0.079449 Loss2: 1.315487
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519769 Loss1: 0.139253 Loss2: 1.380516
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506163 Loss1: 0.125293 Loss2: 1.380870
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.495727 Loss1: 0.120009 Loss2: 1.375718
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463482 Loss1: 0.081544 Loss2: 1.381938
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455266 Loss1: 0.085373 Loss2: 1.369893
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.209571 Loss1: 0.430118 Loss2: 1.779453
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.550460 Loss1: 0.238187 Loss2: 1.312273 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.418779 Loss1: 0.052079 Loss2: 1.366700
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.480601 Loss1: 0.158100 Loss2: 1.322500
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.441818 Loss1: 0.128700 Loss2: 1.313119
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.413948 Loss1: 0.110884 Loss2: 1.303064
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.385721 Loss1: 0.085674 Loss2: 1.300047
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416908 Loss1: 0.123463 Loss2: 1.293445
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.184147 Loss1: 0.322047 Loss2: 1.862100
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.373733 Loss1: 0.081672 Loss2: 1.292062
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.358179 Loss1: 0.070038 Loss2: 1.288141 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.575104 Loss1: 0.160703 Loss2: 1.414401
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.334737 Loss1: 0.046078 Loss2: 1.288659
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.489262 Loss1: 0.107448 Loss2: 1.381814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473951 Loss1: 0.096180 Loss2: 1.377771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.265409 Loss1: 0.414035 Loss2: 1.851373
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493545 Loss1: 0.116563 Loss2: 1.376981
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.584084 Loss1: 0.227747 Loss2: 1.356337
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.456766 Loss1: 0.078588 Loss2: 1.378178
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.533611 Loss1: 0.149602 Loss2: 1.384010
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445029 Loss1: 0.074937 Loss2: 1.370092
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.482041 Loss1: 0.117410 Loss2: 1.364631 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448383 Loss1: 0.091983 Loss2: 1.356400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409853 Loss1: 0.063262 Loss2: 1.346591
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.327235 Loss1: 0.484758 Loss2: 1.842477
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.647251 Loss1: 0.285993 Loss2: 1.361258 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418298 Loss1: 0.078535 Loss2: 1.339763
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608200 Loss1: 0.223344 Loss2: 1.384856
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497905 Loss1: 0.138244 Loss2: 1.359661
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.477996 Loss1: 0.126775 Loss2: 1.351221
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447345 Loss1: 0.093142 Loss2: 1.354203
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.410880 Loss1: 0.070534 Loss2: 1.340347
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.291219 Loss1: 0.433147 Loss2: 1.858072
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386992 Loss1: 0.058457 Loss2: 1.328535
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.671924 Loss1: 0.310229 Loss2: 1.361695
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.412711 Loss1: 0.078792 Loss2: 1.333918
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.605489 Loss1: 0.199433 Loss2: 1.406057
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450303 Loss1: 0.108551 Loss2: 1.341752
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.477391 Loss1: 0.109120 Loss2: 1.368271 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473364 Loss1: 0.111068 Loss2: 1.362296 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458268 Loss1: 0.097530 Loss2: 1.360738
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.255017 Loss1: 0.430303 Loss2: 1.824714
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.673047 Loss1: 0.346309 Loss2: 1.326739 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564068 Loss1: 0.197453 Loss2: 1.366615 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.433155 Loss1: 0.104425 Loss2: 1.328729 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.356745 Loss1: 0.049755 Loss2: 1.306990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.357956 Loss1: 0.056485 Loss2: 1.301471 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.336850 Loss1: 0.040183 Loss2: 1.296666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.318594 Loss1: 0.028145 Loss2: 1.290449 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.436728 Loss1: 0.112778 Loss2: 1.323949 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395499 Loss1: 0.068246 Loss2: 1.327252 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.367530 Loss1: 0.051176 Loss2: 1.316354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367655 Loss1: 0.046574 Loss2: 1.321082 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.549413 Loss1: 0.165528 Loss2: 1.383885 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.518262 Loss1: 0.122948 Loss2: 1.395315 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.445101 Loss1: 0.499500 Loss2: 1.945602
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463406 Loss1: 0.088699 Loss2: 1.374707
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.701264 Loss1: 0.320659 Loss2: 1.380605
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491225 Loss1: 0.119510 Loss2: 1.371715
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624749 Loss1: 0.215746 Loss2: 1.409003
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428744 Loss1: 0.059775 Loss2: 1.368970
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433718 Loss1: 0.072709 Loss2: 1.361008 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.579446 Loss1: 0.171298 Loss2: 1.408148 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.498754 Loss1: 0.121912 Loss2: 1.376842 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.303144 Loss1: 0.419987 Loss2: 1.883157 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578523 Loss1: 0.186394 Loss2: 1.392129 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530438 Loss1: 0.174320 Loss2: 1.356118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487245 Loss1: 0.133198 Loss2: 1.354047
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.210028 Loss1: 0.426516 Loss2: 1.783512
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.619704 Loss1: 0.306891 Loss2: 1.312813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.528787 Loss1: 0.179926 Loss2: 1.348861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.463764 Loss1: 0.143727 Loss2: 1.320037 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463376 Loss1: 0.142434 Loss2: 1.320943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.376809 Loss1: 0.070775 Loss2: 1.306034 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.324345 Loss1: 0.032002 Loss2: 1.292342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.327174 Loss1: 0.037809 Loss2: 1.289365 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.569887 Loss1: 0.166016 Loss2: 1.403871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.463671 Loss1: 0.091637 Loss2: 1.372034 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441522 Loss1: 0.071950 Loss2: 1.369573
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.367518 Loss1: 0.509197 Loss2: 1.858322
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.651047 Loss1: 0.309907 Loss2: 1.341139 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.527918 Loss1: 0.162044 Loss2: 1.365874 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498857 Loss1: 0.159503 Loss2: 1.339355 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482541 Loss1: 0.107148 Loss2: 1.375392
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.458250 Loss1: 0.123299 Loss2: 1.334951
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.413852 Loss1: 0.081570 Loss2: 1.332282
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.378947 Loss1: 0.057446 Loss2: 1.321501
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.383756 Loss1: 0.064596 Loss2: 1.319160
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.359920 Loss1: 0.044802 Loss2: 1.315117
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.289381 Loss1: 0.405011 Loss2: 1.884370
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388055 Loss1: 0.080930 Loss2: 1.307125
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.582377 Loss1: 0.178449 Loss2: 1.403928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529900 Loss1: 0.161072 Loss2: 1.368828 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.521564 Loss1: 0.143374 Loss2: 1.378190
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.412002 Loss1: 0.502752 Loss2: 1.909250
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.707046 Loss1: 0.340198 Loss2: 1.366848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573141 Loss1: 0.182603 Loss2: 1.390539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482497 Loss1: 0.129404 Loss2: 1.353094 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472640 Loss1: 0.118435 Loss2: 1.354205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395436 Loss1: 0.062532 Loss2: 1.332904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371279 Loss1: 0.047998 Loss2: 1.323282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379069 Loss1: 0.055579 Loss2: 1.323491 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.613825 Loss1: 0.188381 Loss2: 1.425445 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486912 Loss1: 0.099283 Loss2: 1.387629 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.191025 Loss1: 0.332255 Loss2: 1.858770 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.714075 Loss1: 0.323632 Loss2: 1.390443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419416 Loss1: 0.053477 Loss2: 1.365938 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992788
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518253 Loss1: 0.124475 Loss2: 1.393778 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.493078 Loss1: 0.105146 Loss2: 1.387931 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.126650 Loss1: 0.299124 Loss2: 1.827526
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.511120 Loss1: 0.114099 Loss2: 1.397021
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.554217 Loss1: 0.203248 Loss2: 1.350968
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484886 Loss1: 0.090254 Loss2: 1.394632
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.495275 Loss1: 0.129398 Loss2: 1.365877
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454383 Loss1: 0.067690 Loss2: 1.386694
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.426015 Loss1: 0.084587 Loss2: 1.341428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.418025 Loss1: 0.070588 Loss2: 1.347437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.362685 Loss1: 0.530009 Loss2: 1.832676
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412247 Loss1: 0.072927 Loss2: 1.339320
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.724818 Loss1: 0.387200 Loss2: 1.337618
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425102 Loss1: 0.095318 Loss2: 1.329784
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.656108 Loss1: 0.244006 Loss2: 1.412102
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411433 Loss1: 0.074085 Loss2: 1.337348
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526693 Loss1: 0.179523 Loss2: 1.347170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473638 Loss1: 0.130558 Loss2: 1.343080 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438390 Loss1: 0.103398 Loss2: 1.334992
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.188555 Loss1: 0.389291 Loss2: 1.799264
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.576981 Loss1: 0.243209 Loss2: 1.333772 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.549162 Loss1: 0.183757 Loss2: 1.365404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.425695 Loss1: 0.102519 Loss2: 1.323176 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419289 Loss1: 0.095021 Loss2: 1.324268 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421066 Loss1: 0.099232 Loss2: 1.321834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415371 Loss1: 0.093109 Loss2: 1.322262 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413450 Loss1: 0.092852 Loss2: 1.320598 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.482250 Loss1: 0.103546 Loss2: 1.378704 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.416439 Loss1: 0.059395 Loss2: 1.357044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.268110 Loss1: 0.414474 Loss2: 1.853636
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402223 Loss1: 0.044055 Loss2: 1.358167
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.652066 Loss1: 0.300495 Loss2: 1.351571
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396251 Loss1: 0.047136 Loss2: 1.349115
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.518425 Loss1: 0.161994 Loss2: 1.356431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480149 Loss1: 0.120296 Loss2: 1.359853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437123 Loss1: 0.075553 Loss2: 1.361570
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.260801 Loss1: 0.448486 Loss2: 1.812315
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.547951 Loss1: 0.239375 Loss2: 1.308575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.481014 Loss1: 0.149479 Loss2: 1.331536 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390281 Loss1: 0.051183 Loss2: 1.339098
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.439530 Loss1: 0.117201 Loss2: 1.322329
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.412950 Loss1: 0.111128 Loss2: 1.301821
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.377641 Loss1: 0.073124 Loss2: 1.304517
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.363306 Loss1: 0.062880 Loss2: 1.300426
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.370453 Loss1: 0.073022 Loss2: 1.297431
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.307478 Loss1: 0.456197 Loss2: 1.851281
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.357834 Loss1: 0.060139 Loss2: 1.297695
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.344007 Loss1: 0.055590 Loss2: 1.288417 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496330 Loss1: 0.133647 Loss2: 1.362683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498589 Loss1: 0.137091 Loss2: 1.361498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460113 Loss1: 0.106101 Loss2: 1.354012
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.205951 Loss1: 0.361150 Loss2: 1.844801
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.640310 Loss1: 0.267046 Loss2: 1.373264 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.603068 Loss1: 0.208629 Loss2: 1.394439 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.441582 Loss1: 0.077231 Loss2: 1.364351 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.438597 Loss1: 0.077498 Loss2: 1.361099 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420710 Loss1: 0.069463 Loss2: 1.351247
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.134323 Loss1: 0.311119 Loss2: 1.823204
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418164 Loss1: 0.066683 Loss2: 1.351482
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.563711 Loss1: 0.220418 Loss2: 1.343293
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403025 Loss1: 0.051712 Loss2: 1.351313
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.511288 Loss1: 0.149311 Loss2: 1.361977
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481725 Loss1: 0.132107 Loss2: 1.349618
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.462522 Loss1: 0.118708 Loss2: 1.343815
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420509 Loss1: 0.077725 Loss2: 1.342784
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.429746 Loss1: 0.092313 Loss2: 1.337432
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.304504 Loss1: 0.431638 Loss2: 1.872866
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.582397 Loss1: 0.238469 Loss2: 1.343928
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414879 Loss1: 0.079489 Loss2: 1.335390
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.511782 Loss1: 0.162822 Loss2: 1.348960
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394661 Loss1: 0.063225 Loss2: 1.331435
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407753 Loss1: 0.084905 Loss2: 1.322848 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990809
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484907 Loss1: 0.143273 Loss2: 1.341634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435570 Loss1: 0.099304 Loss2: 1.336267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372628 Loss1: 0.050132 Loss2: 1.322496 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653346 Loss1: 0.240881 Loss2: 1.412465 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544246 Loss1: 0.166694 Loss2: 1.377552 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.582429 Loss1: 0.179323 Loss2: 1.403106
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.222913 Loss1: 0.383169 Loss2: 1.839744
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.714348 Loss1: 0.337084 Loss2: 1.377264 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.584449 Loss1: 0.164760 Loss2: 1.419689 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529077 Loss1: 0.160867 Loss2: 1.368210 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568688 Loss1: 0.182415 Loss2: 1.386273 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516737 Loss1: 0.141985 Loss2: 1.374752 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447157 Loss1: 0.080770 Loss2: 1.366387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.763956 Loss1: 0.393592 Loss2: 1.370365 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551316 Loss1: 0.153576 Loss2: 1.397739 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.578445 Loss1: 0.185743 Loss2: 1.392702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.518008 Loss1: 0.139428 Loss2: 1.378580 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449141 Loss1: 0.076757 Loss2: 1.372384 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.428419 Loss1: 0.058156 Loss2: 1.370263 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993490
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520720 Loss1: 0.110703 Loss2: 1.410018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487186 Loss1: 0.079293 Loss2: 1.407894 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495631 Loss1: 0.085372 Loss2: 1.410259
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.206763 Loss1: 0.432237 Loss2: 1.774526
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460948 Loss1: 0.057419 Loss2: 1.403529
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.606005 Loss1: 0.307598 Loss2: 1.298407
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.466547 Loss1: 0.064210 Loss2: 1.402337
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.527863 Loss1: 0.205405 Loss2: 1.322458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474480 Loss1: 0.076858 Loss2: 1.397622
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.488840 Loss1: 0.185948 Loss2: 1.302892
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446885 Loss1: 0.138104 Loss2: 1.308781
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421689 Loss1: 0.123322 Loss2: 1.298367
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.378100 Loss1: 0.082787 Loss2: 1.295313
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.369787 Loss1: 0.081402 Loss2: 1.288385
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.360096 Loss1: 0.075333 Loss2: 1.284763
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.258230 Loss1: 0.381670 Loss2: 1.876560
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.316539 Loss1: 0.042255 Loss2: 1.274284
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.663793 Loss1: 0.280895 Loss2: 1.382899
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551429 Loss1: 0.149699 Loss2: 1.401730
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536131 Loss1: 0.155514 Loss2: 1.380617
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476414 Loss1: 0.106743 Loss2: 1.369671
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485853 Loss1: 0.114244 Loss2: 1.371609
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441838 Loss1: 0.074249 Loss2: 1.367590
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.253583 Loss1: 0.431242 Loss2: 1.822342
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449727 Loss1: 0.090622 Loss2: 1.359105
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653863 Loss1: 0.318949 Loss2: 1.334914
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409599 Loss1: 0.048286 Loss2: 1.361314
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601742 Loss1: 0.224694 Loss2: 1.377048
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425034 Loss1: 0.069304 Loss2: 1.355730
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.476164 Loss1: 0.128174 Loss2: 1.347990
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.438281 Loss1: 0.101686 Loss2: 1.336596
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.398522 Loss1: 0.068697 Loss2: 1.329825
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.384847 Loss1: 0.068548 Loss2: 1.316299
-DEBUG flwr 2023-10-13 04:01:47,123 | server.py:236 | fit_round 175 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.349065 Loss1: 0.033569 Loss2: 1.315496
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.327160 Loss1: 0.020947 Loss2: 1.306213
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.295048 Loss1: 0.430823 Loss2: 1.864225
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.324283 Loss1: 0.027021 Loss2: 1.297261
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.643439 Loss1: 0.274003 Loss2: 1.369436
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.565511 Loss1: 0.174337 Loss2: 1.391174
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.493046 Loss1: 0.117763 Loss2: 1.375283
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.469397 Loss1: 0.105468 Loss2: 1.363929
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.424859 Loss1: 0.062979 Loss2: 1.361880
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.388322 Loss1: 0.035464 Loss2: 1.352858
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.169132 Loss1: 0.380409 Loss2: 1.788724
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391922 Loss1: 0.046111 Loss2: 1.345811
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.588078 Loss1: 0.284923 Loss2: 1.303156
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379110 Loss1: 0.036275 Loss2: 1.342836
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.546165 Loss1: 0.207249 Loss2: 1.338916
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369471 Loss1: 0.033375 Loss2: 1.336095
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507732 Loss1: 0.193817 Loss2: 1.313915
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485451 Loss1: 0.167648 Loss2: 1.317803
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.435855 Loss1: 0.119782 Loss2: 1.316074
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.382345 Loss1: 0.078278 Loss2: 1.304068
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.369164 Loss1: 0.065222 Loss2: 1.303942
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.341526 Loss1: 0.047206 Loss2: 1.294320
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.236759 Loss1: 0.413544 Loss2: 1.823215
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.335130 Loss1: 0.044385 Loss2: 1.290746
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.584948 Loss1: 0.240991 Loss2: 1.343957
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.546413 Loss1: 0.184088 Loss2: 1.362325
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489527 Loss1: 0.138021 Loss2: 1.351506
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470609 Loss1: 0.123904 Loss2: 1.346705
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440107 Loss1: 0.104042 Loss2: 1.336065
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.232005 Loss1: 0.421683 Loss2: 1.810322
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447129 Loss1: 0.110118 Loss2: 1.337011
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.638535 Loss1: 0.314982 Loss2: 1.323554
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447115 Loss1: 0.109639 Loss2: 1.337476
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.610898 Loss1: 0.254377 Loss2: 1.356522
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438778 Loss1: 0.105544 Loss2: 1.333233
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509108 Loss1: 0.184935 Loss2: 1.324173
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405638 Loss1: 0.071934 Loss2: 1.333704
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.550925 Loss1: 0.216204 Loss2: 1.334722 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445890 Loss1: 0.126863 Loss2: 1.319028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383649 Loss1: 0.070411 Loss2: 1.313238 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 04:01:47,123][flwr][DEBUG] - fit_round 175 received 50 results and 0 failures
-INFO flwr 2023-10-13 04:02:28,339 | server.py:125 | fit progress: (175, 2.274364816304594, {'accuracy': 0.6093}, 403856.117812133)
->> Test accuracy: 0.609300
-[2023-10-13 04:02:28,339][flwr][INFO] - fit progress: (175, 2.274364816304594, {'accuracy': 0.6093}, 403856.117812133)
-DEBUG flwr 2023-10-13 04:02:28,340 | server.py:173 | evaluate_round 175: strategy sampled 50 clients (out of 50)
-[2023-10-13 04:02:28,340][flwr][DEBUG] - evaluate_round 175: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 04:11:32,737 | server.py:187 | evaluate_round 175 received 50 results and 0 failures
-[2023-10-13 04:11:32,737][flwr][DEBUG] - evaluate_round 175 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 04:11:32,738 | server.py:222 | fit_round 176: strategy sampled 50 clients (out of 50)
-[2023-10-13 04:11:32,738][flwr][DEBUG] - fit_round 176: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.170290 Loss1: 0.358709 Loss2: 1.811581
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.516624 Loss1: 0.183547 Loss2: 1.333077
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.440048 Loss1: 0.112371 Loss2: 1.327677
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.453652 Loss1: 0.126275 Loss2: 1.327376
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.256428 Loss1: 0.464357 Loss2: 1.792070
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.621105 Loss1: 0.286483 Loss2: 1.334622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556050 Loss1: 0.178967 Loss2: 1.377083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.467025 Loss1: 0.116683 Loss2: 1.350342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509585 Loss1: 0.165360 Loss2: 1.344225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503621 Loss1: 0.153117 Loss2: 1.350505 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.337311 Loss1: 0.038668 Loss2: 1.298643
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452368 Loss1: 0.111953 Loss2: 1.340416
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411402 Loss1: 0.073117 Loss2: 1.338285
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372953 Loss1: 0.039798 Loss2: 1.333155
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.355299 Loss1: 0.031457 Loss2: 1.323842
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.360517 Loss1: 0.476504 Loss2: 1.884013
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.659602 Loss1: 0.267756 Loss2: 1.391846
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.676188 Loss1: 0.240093 Loss2: 1.436095
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.553713 Loss1: 0.145179 Loss2: 1.408534
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.279482 Loss1: 0.417777 Loss2: 1.861705
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.768148 Loss1: 0.373109 Loss2: 1.395039 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.766637 Loss1: 0.304473 Loss2: 1.462164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.603101 Loss1: 0.195943 Loss2: 1.407159 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.555395 Loss1: 0.148469 Loss2: 1.406926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492973 Loss1: 0.094674 Loss2: 1.398299 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.441062 Loss1: 0.062967 Loss2: 1.378096
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490902 Loss1: 0.096226 Loss2: 1.394676
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468465 Loss1: 0.082252 Loss2: 1.386213
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.471899 Loss1: 0.092637 Loss2: 1.379262
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437760 Loss1: 0.059466 Loss2: 1.378293
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.258640 Loss1: 0.451851 Loss2: 1.806789
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.645302 Loss1: 0.330897 Loss2: 1.314405
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.567917 Loss1: 0.194716 Loss2: 1.373201
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.469875 Loss1: 0.143258 Loss2: 1.326616
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.348707 Loss1: 0.484238 Loss2: 1.864470
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.668805 Loss1: 0.315377 Loss2: 1.353428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.571609 Loss1: 0.192502 Loss2: 1.379107 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.512455 Loss1: 0.151073 Loss2: 1.361382 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476909 Loss1: 0.131858 Loss2: 1.345051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456826 Loss1: 0.107659 Loss2: 1.349167 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430580 Loss1: 0.100762 Loss2: 1.329819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363804 Loss1: 0.037147 Loss2: 1.326657 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.285933 Loss1: 0.449490 Loss2: 1.836443 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.531912 Loss1: 0.150835 Loss2: 1.381077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534870 Loss1: 0.188996 Loss2: 1.345874
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.184729 Loss1: 0.342665 Loss2: 1.842065
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.655092 Loss1: 0.262479 Loss2: 1.392612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.612879 Loss1: 0.179475 Loss2: 1.433404 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.558543 Loss1: 0.175624 Loss2: 1.382919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.547182 Loss1: 0.139723 Loss2: 1.407459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.533802 Loss1: 0.140111 Loss2: 1.393692 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.503880 Loss1: 0.112917 Loss2: 1.390963 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.483981 Loss1: 0.100019 Loss2: 1.383961 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.643745 Loss1: 0.318115 Loss2: 1.325630 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522452 Loss1: 0.195403 Loss2: 1.327049 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.489932 Loss1: 0.151907 Loss2: 1.338025
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.176268 Loss1: 0.359960 Loss2: 1.816307
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.648673 Loss1: 0.272360 Loss2: 1.376313 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588743 Loss1: 0.172535 Loss2: 1.416208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.633957 Loss1: 0.261212 Loss2: 1.372745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.586913 Loss1: 0.185263 Loss2: 1.401649 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.488959 Loss1: 0.110787 Loss2: 1.378172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439248 Loss1: 0.071155 Loss2: 1.368093 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391486 Loss1: 0.038934 Loss2: 1.352553 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.627301 Loss1: 0.221988 Loss2: 1.405313 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479738 Loss1: 0.091321 Loss2: 1.388417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.236790 Loss1: 0.381260 Loss2: 1.855530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.670510 Loss1: 0.302434 Loss2: 1.368076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.610258 Loss1: 0.211251 Loss2: 1.399007 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380113 Loss1: 0.023398 Loss2: 1.356715 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997596
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498571 Loss1: 0.128468 Loss2: 1.370102 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.404721 Loss1: 0.050417 Loss2: 1.354304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.285044 Loss1: 0.386022 Loss2: 1.899022
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373994 Loss1: 0.025984 Loss2: 1.348009
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.672385 Loss1: 0.286309 Loss2: 1.386076
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.350777 Loss1: 0.013221 Loss2: 1.337557
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.508585 Loss1: 0.122133 Loss2: 1.386452 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.483386 Loss1: 0.105258 Loss2: 1.378128 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447080 Loss1: 0.071446 Loss2: 1.375634
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.137759 Loss1: 0.341440 Loss2: 1.796319
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.582810 Loss1: 0.266609 Loss2: 1.316201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563483 Loss1: 0.225363 Loss2: 1.338119 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423553 Loss1: 0.063356 Loss2: 1.360197
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.452111 Loss1: 0.108205 Loss2: 1.343906
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.427800 Loss1: 0.113680 Loss2: 1.314121
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.381372 Loss1: 0.068422 Loss2: 1.312950
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395928 Loss1: 0.083088 Loss2: 1.312840
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374615 Loss1: 0.070118 Loss2: 1.304497
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.198431 Loss1: 0.343346 Loss2: 1.855086
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.353855 Loss1: 0.053413 Loss2: 1.300443
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.603511 Loss1: 0.242730 Loss2: 1.360781
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.332291 Loss1: 0.036710 Loss2: 1.295581
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.483873 Loss1: 0.120113 Loss2: 1.363760 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438685 Loss1: 0.089619 Loss2: 1.349066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450091 Loss1: 0.107946 Loss2: 1.342146
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.173190 Loss1: 0.356233 Loss2: 1.816958
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.535931 Loss1: 0.212349 Loss2: 1.323583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.450922 Loss1: 0.121453 Loss2: 1.329468 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389081 Loss1: 0.052896 Loss2: 1.336185
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.427158 Loss1: 0.108984 Loss2: 1.318174
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.414870 Loss1: 0.106151 Loss2: 1.308719
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.475249 Loss1: 0.164134 Loss2: 1.311115
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.397545 Loss1: 0.084692 Loss2: 1.312854
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418143 Loss1: 0.107333 Loss2: 1.310810
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405887 Loss1: 0.086836 Loss2: 1.319052
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.231468 Loss1: 0.383402 Loss2: 1.848065
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397226 Loss1: 0.084012 Loss2: 1.313214
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.623658 Loss1: 0.228863 Loss2: 1.394795
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561747 Loss1: 0.157701 Loss2: 1.404047
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502368 Loss1: 0.113469 Loss2: 1.388899
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.482874 Loss1: 0.106527 Loss2: 1.376346
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.464801 Loss1: 0.087893 Loss2: 1.376908
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.197296 Loss1: 0.411789 Loss2: 1.785507
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464088 Loss1: 0.093350 Loss2: 1.370738
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.596793 Loss1: 0.251521 Loss2: 1.345272
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426448 Loss1: 0.049871 Loss2: 1.376577
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.519054 Loss1: 0.157186 Loss2: 1.361868
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.416939 Loss1: 0.052684 Loss2: 1.364255
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.508152 Loss1: 0.163152 Loss2: 1.345000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411780 Loss1: 0.055044 Loss2: 1.356736
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463823 Loss1: 0.117000 Loss2: 1.346823 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427364 Loss1: 0.089444 Loss2: 1.337920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395346 Loss1: 0.067751 Loss2: 1.327596
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.253146 Loss1: 0.459800 Loss2: 1.793347
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.651340 Loss1: 0.329938 Loss2: 1.321402
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382277 Loss1: 0.056232 Loss2: 1.326045
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.469101 Loss1: 0.155308 Loss2: 1.313794 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416896 Loss1: 0.106755 Loss2: 1.310140 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442746 Loss1: 0.133836 Loss2: 1.308910
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.230937 Loss1: 0.428585 Loss2: 1.802352
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.395694 Loss1: 0.085560 Loss2: 1.310133
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.596470 Loss1: 0.287002 Loss2: 1.309469
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372731 Loss1: 0.072436 Loss2: 1.300295
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.467831 Loss1: 0.138882 Loss2: 1.328949
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.342099 Loss1: 0.051475 Loss2: 1.290624
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.426544 Loss1: 0.112165 Loss2: 1.314379
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.404683 Loss1: 0.096619 Loss2: 1.308064
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.388419 Loss1: 0.084003 Loss2: 1.304417
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.350512 Loss1: 0.047015 Loss2: 1.303497
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.340241 Loss1: 0.049905 Loss2: 1.290336
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.282526 Loss1: 0.434037 Loss2: 1.848489
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.343712 Loss1: 0.058345 Loss2: 1.285366
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.638069 Loss1: 0.277748 Loss2: 1.360320
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.313480 Loss1: 0.026366 Loss2: 1.287114
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524484 Loss1: 0.166067 Loss2: 1.358417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460247 Loss1: 0.098493 Loss2: 1.361754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.423205 Loss1: 0.071303 Loss2: 1.351902
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.228603 Loss1: 0.383801 Loss2: 1.844802
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.388156 Loss1: 0.053162 Loss2: 1.334995
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.547448 Loss1: 0.211315 Loss2: 1.336133
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388233 Loss1: 0.052654 Loss2: 1.335579
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590292 Loss1: 0.229686 Loss2: 1.360606
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377627 Loss1: 0.048176 Loss2: 1.329451
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.525405 Loss1: 0.177610 Loss2: 1.347795
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.489710 Loss1: 0.160801 Loss2: 1.328909
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444757 Loss1: 0.103926 Loss2: 1.340830
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445640 Loss1: 0.108804 Loss2: 1.336837
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421582 Loss1: 0.094361 Loss2: 1.327221
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.304375 Loss1: 0.470149 Loss2: 1.834227
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.380113 Loss1: 0.058532 Loss2: 1.321581
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.584669 Loss1: 0.249759 Loss2: 1.334910
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.363299 Loss1: 0.044937 Loss2: 1.318362
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490260 Loss1: 0.145025 Loss2: 1.345236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.479867 Loss1: 0.138333 Loss2: 1.341534 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.457548 Loss1: 0.123373 Loss2: 1.334174
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.267792 Loss1: 0.395298 Loss2: 1.872494
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.606118 Loss1: 0.251780 Loss2: 1.354338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.532871 Loss1: 0.163355 Loss2: 1.369517 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378017 Loss1: 0.058490 Loss2: 1.319528
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.572642 Loss1: 0.194938 Loss2: 1.377704
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.548008 Loss1: 0.190127 Loss2: 1.357881
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460274 Loss1: 0.111205 Loss2: 1.349069
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.402279 Loss1: 0.056422 Loss2: 1.345857
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388249 Loss1: 0.050236 Loss2: 1.338013
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.172564 Loss1: 0.424527 Loss2: 1.748038
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369264 Loss1: 0.036659 Loss2: 1.332605
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351415 Loss1: 0.025066 Loss2: 1.326349
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.561913 Loss1: 0.258535 Loss2: 1.303378
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.519247 Loss1: 0.190439 Loss2: 1.328808
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.431601 Loss1: 0.119279 Loss2: 1.312322
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.423496 Loss1: 0.119379 Loss2: 1.304117
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.368456 Loss1: 0.068367 Loss2: 1.300089
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.074522 Loss1: 0.337049 Loss2: 1.737473
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.378463 Loss1: 0.091732 Loss2: 1.286731
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.494725 Loss1: 0.203230 Loss2: 1.291495
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.346282 Loss1: 0.063838 Loss2: 1.282444
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.410453 Loss1: 0.110532 Loss2: 1.299921
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.333023 Loss1: 0.057230 Loss2: 1.275794
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.436905 Loss1: 0.145101 Loss2: 1.291803
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.318702 Loss1: 0.043600 Loss2: 1.275103
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.393576 Loss1: 0.104256 Loss2: 1.289320 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.343388 Loss1: 0.056610 Loss2: 1.286778 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.434032 Loss1: 0.550658 Loss2: 1.883374
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.346929 Loss1: 0.064982 Loss2: 1.281946
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.307371 Loss1: 0.029652 Loss2: 1.277719 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.469488 Loss1: 0.120305 Loss2: 1.349183 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456840 Loss1: 0.124859 Loss2: 1.331981 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.376525 Loss1: 0.515245 Loss2: 1.861280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.665023 Loss1: 0.351071 Loss2: 1.313952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572042 Loss1: 0.207667 Loss2: 1.364375 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986607
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.462883 Loss1: 0.136401 Loss2: 1.326482 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.386517 Loss1: 0.063322 Loss2: 1.323195 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.387851 Loss1: 0.073608 Loss2: 1.314243
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.414718 Loss1: 0.538321 Loss2: 1.876397
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.668070 Loss1: 0.333168 Loss2: 1.334902 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998884
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.347564 Loss1: 0.040334 Loss2: 1.307231
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.547898 Loss1: 0.207827 Loss2: 1.340071
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.519463 Loss1: 0.168182 Loss2: 1.351281
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.456050 Loss1: 0.127908 Loss2: 1.328142
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.420737 Loss1: 0.093017 Loss2: 1.327719
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.394717 Loss1: 0.075823 Loss2: 1.318893
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404789 Loss1: 0.085273 Loss2: 1.319516
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.216630 Loss1: 0.353518 Loss2: 1.863112
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.596950 Loss1: 0.233951 Loss2: 1.362999 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497290 Loss1: 0.117287 Loss2: 1.380003 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484209 Loss1: 0.122452 Loss2: 1.361757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453530 Loss1: 0.089369 Loss2: 1.364160
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.433711 Loss1: 0.525226 Loss2: 1.908485
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.583995 Loss1: 0.210323 Loss2: 1.373672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.515449 Loss1: 0.139445 Loss2: 1.376004 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394347 Loss1: 0.052057 Loss2: 1.342289
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490723 Loss1: 0.118723 Loss2: 1.372000
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451079 Loss1: 0.094827 Loss2: 1.356252
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.419875 Loss1: 0.071503 Loss2: 1.348372
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.438699 Loss1: 0.084013 Loss2: 1.354686
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.408584 Loss1: 0.061369 Loss2: 1.347216
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.357420 Loss1: 0.450502 Loss2: 1.906918
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.411279 Loss1: 0.061759 Loss2: 1.349520
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.691331 Loss1: 0.301839 Loss2: 1.389492
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395678 Loss1: 0.054008 Loss2: 1.341670
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481742 Loss1: 0.102445 Loss2: 1.379298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.400093 Loss1: 0.036333 Loss2: 1.363759 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406213 Loss1: 0.055729 Loss2: 1.350485
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.166756 Loss1: 0.384847 Loss2: 1.781908
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.568706 Loss1: 0.253537 Loss2: 1.315169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.468045 Loss1: 0.137317 Loss2: 1.330728 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.441623 Loss1: 0.131102 Loss2: 1.310521 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.392820 Loss1: 0.088546 Loss2: 1.304274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.360825 Loss1: 0.070289 Loss2: 1.290536 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.368992 Loss1: 0.077673 Loss2: 1.291319 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364760 Loss1: 0.074308 Loss2: 1.290453 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508398 Loss1: 0.116087 Loss2: 1.392311 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461314 Loss1: 0.071573 Loss2: 1.389741 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420374 Loss1: 0.046317 Loss2: 1.374057
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.308139 Loss1: 0.474798 Loss2: 1.833341
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402967 Loss1: 0.031292 Loss2: 1.371675
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.678309 Loss1: 0.333490 Loss2: 1.344819
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399069 Loss1: 0.032680 Loss2: 1.366389
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.583783 Loss1: 0.172875 Loss2: 1.410908
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498550 Loss1: 0.159258 Loss2: 1.339291
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.454417 Loss1: 0.109732 Loss2: 1.344685
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.381333 Loss1: 0.048260 Loss2: 1.333073
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.389156 Loss1: 0.065145 Loss2: 1.324012
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.379942 Loss1: 0.061729 Loss2: 1.318213
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.148604 Loss1: 0.291161 Loss2: 1.857443
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.358604 Loss1: 0.044672 Loss2: 1.313932
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.542922 Loss1: 0.157699 Loss2: 1.385223
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.353843 Loss1: 0.044123 Loss2: 1.309720
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.493227 Loss1: 0.102647 Loss2: 1.390579
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487424 Loss1: 0.116622 Loss2: 1.370802
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.444549 Loss1: 0.071368 Loss2: 1.373181
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474011 Loss1: 0.100664 Loss2: 1.373347
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443430 Loss1: 0.072441 Loss2: 1.370990
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.303321 Loss1: 0.418883 Loss2: 1.884437
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.629183 Loss1: 0.261733 Loss2: 1.367450
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438621 Loss1: 0.066818 Loss2: 1.371803
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.579103 Loss1: 0.181339 Loss2: 1.397764
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.428120 Loss1: 0.055406 Loss2: 1.372713
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511263 Loss1: 0.133037 Loss2: 1.378225
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412875 Loss1: 0.049255 Loss2: 1.363620
-(DefaultActor pid=3764) >> Training accuracy: 0.995404
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476100 Loss1: 0.108873 Loss2: 1.367227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404345 Loss1: 0.047028 Loss2: 1.357317 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.376714 Loss1: 0.026004 Loss2: 1.350710
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.188944 Loss1: 0.359174 Loss2: 1.829770
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.728086 Loss1: 0.362599 Loss2: 1.365487 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.551457 Loss1: 0.183403 Loss2: 1.368054 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485838 Loss1: 0.109186 Loss2: 1.376652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437649 Loss1: 0.074322 Loss2: 1.363327 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.691265 Loss1: 0.228956 Loss2: 1.462308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576545 Loss1: 0.190910 Loss2: 1.385635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.529110 Loss1: 0.137090 Loss2: 1.392021 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472862 Loss1: 0.087818 Loss2: 1.385044 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425644 Loss1: 0.051467 Loss2: 1.374176 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.270611 Loss1: 0.418885 Loss2: 1.851725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.593746 Loss1: 0.209362 Loss2: 1.384385 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.473199 Loss1: 0.516366 Loss2: 1.956833 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.663525 Loss1: 0.332131 Loss2: 1.331393 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.586287 Loss1: 0.238160 Loss2: 1.348127 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.479438 Loss1: 0.117204 Loss2: 1.362233 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.416943 Loss1: 0.088770 Loss2: 1.328174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442634 Loss1: 0.122289 Loss2: 1.320345
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.434631 Loss1: 0.080783 Loss2: 1.353849
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.429584 Loss1: 0.109335 Loss2: 1.320249
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424773 Loss1: 0.073768 Loss2: 1.351005
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408671 Loss1: 0.082833 Loss2: 1.325838 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.260898 Loss1: 0.409725 Loss2: 1.851173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.603242 Loss1: 0.198488 Loss2: 1.404754 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532311 Loss1: 0.164119 Loss2: 1.368192
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.378301 Loss1: 0.485893 Loss2: 1.892408
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.505156 Loss1: 0.141650 Loss2: 1.363505
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.726349 Loss1: 0.316423 Loss2: 1.409926
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476775 Loss1: 0.117962 Loss2: 1.358813
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610539 Loss1: 0.191211 Loss2: 1.419328
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508682 Loss1: 0.150224 Loss2: 1.358458
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.503430 Loss1: 0.114619 Loss2: 1.388811
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.458036 Loss1: 0.095217 Loss2: 1.362820
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480466 Loss1: 0.099305 Loss2: 1.381161
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396170 Loss1: 0.053569 Loss2: 1.342602
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451195 Loss1: 0.068297 Loss2: 1.382898
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402283 Loss1: 0.063168 Loss2: 1.339116
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416343 Loss1: 0.048913 Loss2: 1.367430
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.446558 Loss1: 0.077867 Loss2: 1.368691
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425327 Loss1: 0.059537 Loss2: 1.365790
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405121 Loss1: 0.048232 Loss2: 1.356889
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.311515 Loss1: 0.453042 Loss2: 1.858473
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.654213 Loss1: 0.289604 Loss2: 1.364609
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.535126 Loss1: 0.144589 Loss2: 1.390537
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.495655 Loss1: 0.134598 Loss2: 1.361056
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.313189 Loss1: 0.401829 Loss2: 1.911360
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.658398 Loss1: 0.259705 Loss2: 1.398693 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.625148 Loss1: 0.198948 Loss2: 1.426200 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563498 Loss1: 0.155420 Loss2: 1.408079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.522737 Loss1: 0.119372 Loss2: 1.403365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470769 Loss1: 0.075020 Loss2: 1.395749 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.479689 Loss1: 0.106825 Loss2: 1.372864
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.464370 Loss1: 0.072315 Loss2: 1.392055
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427913 Loss1: 0.042811 Loss2: 1.385102
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.420059 Loss1: 0.043617 Loss2: 1.376442
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411740 Loss1: 0.039516 Loss2: 1.372224
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-DEBUG flwr 2023-10-13 04:40:01,874 | server.py:236 | fit_round 176 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.455792 Loss1: 0.506271 Loss2: 1.949522
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.716343 Loss1: 0.304765 Loss2: 1.411578
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.620461 Loss1: 0.170978 Loss2: 1.449483
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543648 Loss1: 0.130066 Loss2: 1.413581
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.118188 Loss1: 0.283003 Loss2: 1.835185
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.561708 Loss1: 0.191629 Loss2: 1.370079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.569061 Loss1: 0.192577 Loss2: 1.376484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526930 Loss1: 0.139503 Loss2: 1.387427 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519462 Loss1: 0.148016 Loss2: 1.371445 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508437 Loss1: 0.117510 Loss2: 1.390927 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495077 Loss1: 0.114192 Loss2: 1.380885 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440338 Loss1: 0.080220 Loss2: 1.360117 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.210669 Loss1: 0.407608 Loss2: 1.803061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.485984 Loss1: 0.144710 Loss2: 1.341274 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.310801 Loss1: 0.473210 Loss2: 1.837590 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.665165 Loss1: 0.317091 Loss2: 1.348074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.597263 Loss1: 0.226021 Loss2: 1.371242 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490055 Loss1: 0.144660 Loss2: 1.345396 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478120 Loss1: 0.141539 Loss2: 1.336581 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.419456 Loss1: 0.080450 Loss2: 1.339005 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410959 Loss1: 0.084511 Loss2: 1.326448 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367434 Loss1: 0.045787 Loss2: 1.321647 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.650593 Loss1: 0.270795 Loss2: 1.379798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532414 Loss1: 0.152184 Loss2: 1.380230 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473996 Loss1: 0.098161 Loss2: 1.375835 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.444713 Loss1: 0.083774 Loss2: 1.360939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413132 Loss1: 0.054079 Loss2: 1.359053 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 04:40:01,874][flwr][DEBUG] - fit_round 176 received 50 results and 0 failures
-INFO flwr 2023-10-13 04:40:42,106 | server.py:125 | fit progress: (176, 2.2847310698832186, {'accuracy': 0.6073}, 406149.884959183)
->> Test accuracy: 0.607300
-[2023-10-13 04:40:42,106][flwr][INFO] - fit progress: (176, 2.2847310698832186, {'accuracy': 0.6073}, 406149.884959183)
-DEBUG flwr 2023-10-13 04:40:42,107 | server.py:173 | evaluate_round 176: strategy sampled 50 clients (out of 50)
-[2023-10-13 04:40:42,107][flwr][DEBUG] - evaluate_round 176: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 04:49:44,719 | server.py:187 | evaluate_round 176 received 50 results and 0 failures
-[2023-10-13 04:49:44,719][flwr][DEBUG] - evaluate_round 176 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 04:49:44,719 | server.py:222 | fit_round 177: strategy sampled 50 clients (out of 50)
-[2023-10-13 04:49:44,719][flwr][DEBUG] - fit_round 177: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.117091 Loss1: 0.338446 Loss2: 1.778645
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.556786 Loss1: 0.224746 Loss2: 1.332040
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.520462 Loss1: 0.164888 Loss2: 1.355573
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.329108 Loss1: 0.467118 Loss2: 1.861990
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.512839 Loss1: 0.161003 Loss2: 1.351836
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.672503 Loss1: 0.294029 Loss2: 1.378474
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.466050 Loss1: 0.119651 Loss2: 1.346399
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.580015 Loss1: 0.165610 Loss2: 1.414404
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462442 Loss1: 0.123611 Loss2: 1.338832
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.505920 Loss1: 0.139919 Loss2: 1.366001
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.501164 Loss1: 0.155051 Loss2: 1.346114
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.485031 Loss1: 0.140522 Loss2: 1.344508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462242 Loss1: 0.110402 Loss2: 1.351840 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438515 Loss1: 0.099683 Loss2: 1.338832 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372458 Loss1: 0.037574 Loss2: 1.334884 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.289880 Loss1: 0.516681 Loss2: 1.773199 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.581005 Loss1: 0.219771 Loss2: 1.361234 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534950 Loss1: 0.219131 Loss2: 1.315819
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.179853 Loss1: 0.393738 Loss2: 1.786115
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.575659 Loss1: 0.236826 Loss2: 1.338833 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.535185 Loss1: 0.172249 Loss2: 1.362936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.467213 Loss1: 0.136064 Loss2: 1.331149 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.426117 Loss1: 0.096398 Loss2: 1.329719 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.371604 Loss1: 0.050335 Loss2: 1.321269 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.345910 Loss1: 0.038528 Loss2: 1.307382 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.353521 Loss1: 0.050512 Loss2: 1.303008 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.591600 Loss1: 0.268255 Loss2: 1.323345 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.503199 Loss1: 0.166889 Loss2: 1.336310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.455234 Loss1: 0.127333 Loss2: 1.327901
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.270680 Loss1: 0.414861 Loss2: 1.855819
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457135 Loss1: 0.128698 Loss2: 1.328436
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.700104 Loss1: 0.327819 Loss2: 1.372286
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.394111 Loss1: 0.075020 Loss2: 1.319091
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.631477 Loss1: 0.201134 Loss2: 1.430344
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.377881 Loss1: 0.061913 Loss2: 1.315968
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.547700 Loss1: 0.174021 Loss2: 1.373679
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.369407 Loss1: 0.062535 Loss2: 1.306872
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509225 Loss1: 0.138038 Loss2: 1.371187
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.348374 Loss1: 0.047845 Loss2: 1.300530
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481513 Loss1: 0.112713 Loss2: 1.368800
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.457182 Loss1: 0.096232 Loss2: 1.360950
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412807 Loss1: 0.058996 Loss2: 1.353811
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415173 Loss1: 0.063758 Loss2: 1.351415
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394165 Loss1: 0.054574 Loss2: 1.339591
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.210480 Loss1: 0.381470 Loss2: 1.829010
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572045 Loss1: 0.249485 Loss2: 1.322560
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.495749 Loss1: 0.152176 Loss2: 1.343573
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.452456 Loss1: 0.122449 Loss2: 1.330007
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.403887 Loss1: 0.075104 Loss2: 1.328783
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.410242 Loss1: 0.440968 Loss2: 1.969274
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.648994 Loss1: 0.303094 Loss2: 1.345901
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.386354 Loss1: 0.062226 Loss2: 1.324128
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.377116 Loss1: 0.061617 Loss2: 1.315499 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.362061 Loss1: 0.050135 Loss2: 1.311926 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378948 Loss1: 0.068293 Loss2: 1.310655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463629 Loss1: 0.096589 Loss2: 1.367040 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403337 Loss1: 0.053701 Loss2: 1.349636 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.331720 Loss1: 0.385345 Loss2: 1.946375 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.749427 Loss1: 0.265651 Loss2: 1.483776 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.690998 Loss1: 0.256621 Loss2: 1.434376
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.222830 Loss1: 0.434928 Loss2: 1.787902
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.616562 Loss1: 0.179330 Loss2: 1.437232
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.575019 Loss1: 0.271107 Loss2: 1.303913
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.576274 Loss1: 0.138071 Loss2: 1.438203
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.547416 Loss1: 0.200814 Loss2: 1.346602
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.530872 Loss1: 0.108151 Loss2: 1.422721
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.452867 Loss1: 0.130535 Loss2: 1.322332
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.531635 Loss1: 0.107202 Loss2: 1.424433
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.419659 Loss1: 0.103910 Loss2: 1.315748
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.526198 Loss1: 0.112133 Loss2: 1.414065
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.406693 Loss1: 0.092893 Loss2: 1.313800
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.517351 Loss1: 0.107400 Loss2: 1.409950
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392177 Loss1: 0.082664 Loss2: 1.309513
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.380744 Loss1: 0.071164 Loss2: 1.309580
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374648 Loss1: 0.077823 Loss2: 1.296826
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.329316 Loss1: 0.040606 Loss2: 1.288710
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.245002 Loss1: 0.352579 Loss2: 1.892424
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.679267 Loss1: 0.276167 Loss2: 1.403100
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610408 Loss1: 0.168751 Loss2: 1.441657
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547776 Loss1: 0.149289 Loss2: 1.398487
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.234936 Loss1: 0.381982 Loss2: 1.852955
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525067 Loss1: 0.125831 Loss2: 1.399236
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.589723 Loss1: 0.237150 Loss2: 1.352574
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526820 Loss1: 0.124715 Loss2: 1.402105
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.488411 Loss1: 0.132283 Loss2: 1.356128
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.456122 Loss1: 0.098696 Loss2: 1.357426
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.540314 Loss1: 0.139612 Loss2: 1.400702
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.414322 Loss1: 0.076417 Loss2: 1.337905
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.541393 Loss1: 0.144472 Loss2: 1.396921
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420638 Loss1: 0.082587 Loss2: 1.338050
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.534719 Loss1: 0.137524 Loss2: 1.397195
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.467483 Loss1: 0.129869 Loss2: 1.337615
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.505399 Loss1: 0.111987 Loss2: 1.393412
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379541 Loss1: 0.041960 Loss2: 1.337581 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.434275 Loss1: 0.535662 Loss2: 1.898613 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573182 Loss1: 0.225413 Loss2: 1.347768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.304018 Loss1: 0.442244 Loss2: 1.861774 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416634 Loss1: 0.106718 Loss2: 1.309915 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.369929 Loss1: 0.065534 Loss2: 1.304394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.359173 Loss1: 0.059427 Loss2: 1.299746 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.341395 Loss1: 0.047685 Loss2: 1.293710 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.341999 Loss1: 0.052276 Loss2: 1.289723 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.382472 Loss1: 0.057493 Loss2: 1.324979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.336417 Loss1: 0.027485 Loss2: 1.308932 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.346207 Loss1: 0.042611 Loss2: 1.303596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.263516 Loss1: 0.424200 Loss2: 1.839316
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.660231 Loss1: 0.309451 Loss2: 1.350780
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.660810 Loss1: 0.261614 Loss2: 1.399197
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570157 Loss1: 0.199874 Loss2: 1.370283
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498980 Loss1: 0.144016 Loss2: 1.354964
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.204966 Loss1: 0.396120 Loss2: 1.808846
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.600414 Loss1: 0.272502 Loss2: 1.327912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.508328 Loss1: 0.163275 Loss2: 1.345053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.473538 Loss1: 0.151030 Loss2: 1.322509 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.449256 Loss1: 0.127300 Loss2: 1.321957 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.352109 Loss1: 0.026207 Loss2: 1.325902
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443568 Loss1: 0.117145 Loss2: 1.326423
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422634 Loss1: 0.101074 Loss2: 1.321560
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.383649 Loss1: 0.065216 Loss2: 1.318433
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354635 Loss1: 0.044810 Loss2: 1.309825
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364730 Loss1: 0.057250 Loss2: 1.307480
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.275443 Loss1: 0.454281 Loss2: 1.821162
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.603899 Loss1: 0.263388 Loss2: 1.340511
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.536920 Loss1: 0.172663 Loss2: 1.364257
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529584 Loss1: 0.183330 Loss2: 1.346254
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.450658 Loss1: 0.112240 Loss2: 1.338418
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.311392 Loss1: 0.438242 Loss2: 1.873149
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.657676 Loss1: 0.297376 Loss2: 1.360301 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598399 Loss1: 0.186233 Loss2: 1.412166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507444 Loss1: 0.142625 Loss2: 1.364819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.537497 Loss1: 0.178474 Loss2: 1.359023 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.520597 Loss1: 0.147606 Loss2: 1.372992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475968 Loss1: 0.119071 Loss2: 1.356897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453933 Loss1: 0.099969 Loss2: 1.353964 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.713469 Loss1: 0.326914 Loss2: 1.386555 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574375 Loss1: 0.188741 Loss2: 1.385634 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510041 Loss1: 0.133222 Loss2: 1.376819
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.370804 Loss1: 0.463575 Loss2: 1.907229
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.762771 Loss1: 0.397079 Loss2: 1.365692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633159 Loss1: 0.220349 Loss2: 1.412810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.545476 Loss1: 0.147424 Loss2: 1.398052
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451552 Loss1: 0.082879 Loss2: 1.368673
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.567348 Loss1: 0.199204 Loss2: 1.368144
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447704 Loss1: 0.080572 Loss2: 1.367132
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.446728 Loss1: 0.085219 Loss2: 1.361509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437782 Loss1: 0.077260 Loss2: 1.360522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393960 Loss1: 0.040107 Loss2: 1.353853 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.293368 Loss1: 0.423052 Loss2: 1.870316
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.656123 Loss1: 0.260804 Loss2: 1.395320
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.616568 Loss1: 0.199413 Loss2: 1.417156
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533525 Loss1: 0.134344 Loss2: 1.399181
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.254928 Loss1: 0.363513 Loss2: 1.891415
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.679749 Loss1: 0.280965 Loss2: 1.398784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.623437 Loss1: 0.172701 Loss2: 1.450736 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.608819 Loss1: 0.208843 Loss2: 1.399976 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.643399 Loss1: 0.228504 Loss2: 1.414895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.515377 Loss1: 0.108384 Loss2: 1.406993 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418426 Loss1: 0.052399 Loss2: 1.366028
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.545137 Loss1: 0.155749 Loss2: 1.389388
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.478014 Loss1: 0.087461 Loss2: 1.390552
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460088 Loss1: 0.071472 Loss2: 1.388616
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431381 Loss1: 0.054223 Loss2: 1.377157
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.381244 Loss1: 0.472063 Loss2: 1.909181
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.683662 Loss1: 0.322044 Loss2: 1.361618
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648936 Loss1: 0.246692 Loss2: 1.402244
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.530583 Loss1: 0.166539 Loss2: 1.364044
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.198301 Loss1: 0.365400 Loss2: 1.832901
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.606636 Loss1: 0.272456 Loss2: 1.334179 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.555001 Loss1: 0.193914 Loss2: 1.361088 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.476163 Loss1: 0.122462 Loss2: 1.353701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.444732 Loss1: 0.111445 Loss2: 1.333287 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450884 Loss1: 0.117982 Loss2: 1.332902 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382553 Loss1: 0.052982 Loss2: 1.329571 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361855 Loss1: 0.046548 Loss2: 1.315306 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572662 Loss1: 0.207723 Loss2: 1.364939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.477152 Loss1: 0.102284 Loss2: 1.374869 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.460540 Loss1: 0.100366 Loss2: 1.360174 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440794 Loss1: 0.073758 Loss2: 1.367036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419589 Loss1: 0.061941 Loss2: 1.357648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404762 Loss1: 0.051176 Loss2: 1.353586 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428838 Loss1: 0.068931 Loss2: 1.359907 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404974 Loss1: 0.048186 Loss2: 1.356789 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416807 Loss1: 0.077902 Loss2: 1.338905 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413536 Loss1: 0.072584 Loss2: 1.340952 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.629621 Loss1: 0.306496 Loss2: 1.323125 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518768 Loss1: 0.181616 Loss2: 1.337152 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.474048 Loss1: 0.137352 Loss2: 1.336696
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.259024 Loss1: 0.444705 Loss2: 1.814319
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.592178 Loss1: 0.261978 Loss2: 1.330200 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.532597 Loss1: 0.182583 Loss2: 1.350013 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.452537 Loss1: 0.127310 Loss2: 1.325227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.429831 Loss1: 0.112501 Loss2: 1.317330 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415280 Loss1: 0.095539 Loss2: 1.319741 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384012 Loss1: 0.074796 Loss2: 1.309216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354980 Loss1: 0.051763 Loss2: 1.303217
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.148386 Loss1: 0.332490 Loss2: 1.815896
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.563694 Loss1: 0.213113 Loss2: 1.350581
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.536852 Loss1: 0.175788 Loss2: 1.361064
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526646 Loss1: 0.165686 Loss2: 1.360960
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484915 Loss1: 0.141073 Loss2: 1.343842
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.216204 Loss1: 0.412099 Loss2: 1.804104
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.610213 Loss1: 0.267350 Loss2: 1.342863 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592683 Loss1: 0.207348 Loss2: 1.385336 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498266 Loss1: 0.145574 Loss2: 1.352692 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495240 Loss1: 0.141189 Loss2: 1.354052 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.350185 Loss1: 0.023531 Loss2: 1.326654
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.454685 Loss1: 0.104803 Loss2: 1.349882
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.418952 Loss1: 0.083391 Loss2: 1.335560
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.402055 Loss1: 0.073968 Loss2: 1.328087
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398442 Loss1: 0.070027 Loss2: 1.328416
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367465 Loss1: 0.044278 Loss2: 1.323187
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.162582 Loss1: 0.354537 Loss2: 1.808045
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.640718 Loss1: 0.282844 Loss2: 1.357873
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.622771 Loss1: 0.219378 Loss2: 1.403392
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.510992 Loss1: 0.146082 Loss2: 1.364910
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.554463 Loss1: 0.175642 Loss2: 1.378821
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.161002 Loss1: 0.303116 Loss2: 1.857886
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.637712 Loss1: 0.252739 Loss2: 1.384974 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588606 Loss1: 0.188801 Loss2: 1.399805 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.516644 Loss1: 0.124013 Loss2: 1.392631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530647 Loss1: 0.147023 Loss2: 1.383624 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388766 Loss1: 0.042952 Loss2: 1.345813
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487798 Loss1: 0.096580 Loss2: 1.391218
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450860 Loss1: 0.072234 Loss2: 1.378626
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455355 Loss1: 0.075163 Loss2: 1.380192
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442376 Loss1: 0.070588 Loss2: 1.371788
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.448630 Loss1: 0.074894 Loss2: 1.373736
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.275255 Loss1: 0.430387 Loss2: 1.844869
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.638900 Loss1: 0.295776 Loss2: 1.343124
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526514 Loss1: 0.152439 Loss2: 1.374075
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528723 Loss1: 0.184495 Loss2: 1.344229
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451179 Loss1: 0.105797 Loss2: 1.345382
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.169468 Loss1: 0.350726 Loss2: 1.818741
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.570045 Loss1: 0.249781 Loss2: 1.320264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.486190 Loss1: 0.147891 Loss2: 1.338299 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.443172 Loss1: 0.112480 Loss2: 1.330691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.438547 Loss1: 0.112236 Loss2: 1.326311 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.404833 Loss1: 0.087578 Loss2: 1.317255 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455973 Loss1: 0.130391 Loss2: 1.325582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435441 Loss1: 0.118285 Loss2: 1.317156 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.978125
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.738366 Loss1: 0.332014 Loss2: 1.406353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.648617 Loss1: 0.232853 Loss2: 1.415764 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.309624 Loss1: 0.453709 Loss2: 1.855915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.664802 Loss1: 0.306309 Loss2: 1.358493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.548068 Loss1: 0.159628 Loss2: 1.388440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.506688 Loss1: 0.149464 Loss2: 1.357225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469382 Loss1: 0.123689 Loss2: 1.345693 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422195 Loss1: 0.075868 Loss2: 1.346326 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.411091 Loss1: 0.083172 Loss2: 1.327919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376512 Loss1: 0.045177 Loss2: 1.331335
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.371567 Loss1: 0.490233 Loss2: 1.881334
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.659236 Loss1: 0.314332 Loss2: 1.344903
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.644735 Loss1: 0.239898 Loss2: 1.404837
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558721 Loss1: 0.207117 Loss2: 1.351604
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531790 Loss1: 0.179516 Loss2: 1.352274
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.211199 Loss1: 0.373794 Loss2: 1.837406
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.681224 Loss1: 0.335815 Loss2: 1.345408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568904 Loss1: 0.195643 Loss2: 1.373261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530508 Loss1: 0.171075 Loss2: 1.359433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.463516 Loss1: 0.123175 Loss2: 1.340341 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.438265 Loss1: 0.099318 Loss2: 1.338947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389105 Loss1: 0.061617 Loss2: 1.327488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388921 Loss1: 0.067783 Loss2: 1.321138 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.588835 Loss1: 0.251189 Loss2: 1.337647 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511466 Loss1: 0.165073 Loss2: 1.346392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472213 Loss1: 0.132540 Loss2: 1.339673
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.170335 Loss1: 0.367310 Loss2: 1.803024
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.577140 Loss1: 0.254680 Loss2: 1.322460 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543491 Loss1: 0.197376 Loss2: 1.346115 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.515691 Loss1: 0.169126 Loss2: 1.346565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.525733 Loss1: 0.187299 Loss2: 1.338434 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487050 Loss1: 0.146795 Loss2: 1.340254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.442275 Loss1: 0.107922 Loss2: 1.334353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434412 Loss1: 0.104611 Loss2: 1.329801 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.680529 Loss1: 0.327548 Loss2: 1.352981 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506229 Loss1: 0.131900 Loss2: 1.374330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.355253 Loss1: 0.469926 Loss2: 1.885327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.678311 Loss1: 0.322095 Loss2: 1.356216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.596677 Loss1: 0.209671 Loss2: 1.387006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.546018 Loss1: 0.169243 Loss2: 1.376775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.479178 Loss1: 0.125030 Loss2: 1.354148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.419101 Loss1: 0.064119 Loss2: 1.354982 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389309 Loss1: 0.055200 Loss2: 1.334109 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.350141 Loss1: 0.025480 Loss2: 1.324661 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.568548 Loss1: 0.242741 Loss2: 1.325807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.443745 Loss1: 0.114033 Loss2: 1.329712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.431460 Loss1: 0.110627 Loss2: 1.320833
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.263078 Loss1: 0.400846 Loss2: 1.862232
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.388965 Loss1: 0.072149 Loss2: 1.316817
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.712462 Loss1: 0.347832 Loss2: 1.364630
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383828 Loss1: 0.070043 Loss2: 1.313785
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.584405 Loss1: 0.183803 Loss2: 1.400602
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.357584 Loss1: 0.049845 Loss2: 1.307739
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496115 Loss1: 0.117214 Loss2: 1.378901
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425976 Loss1: 0.119505 Loss2: 1.306471
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.520783 Loss1: 0.159251 Loss2: 1.361532
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370364 Loss1: 0.059262 Loss2: 1.311102
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476194 Loss1: 0.114747 Loss2: 1.361448
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455299 Loss1: 0.098179 Loss2: 1.357121
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429948 Loss1: 0.071251 Loss2: 1.358698
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449259 Loss1: 0.092224 Loss2: 1.357035
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439653 Loss1: 0.087851 Loss2: 1.351803
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.143110 Loss1: 0.365647 Loss2: 1.777462
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.595407 Loss1: 0.268595 Loss2: 1.326812
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.509656 Loss1: 0.156283 Loss2: 1.353373
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.454789 Loss1: 0.126046 Loss2: 1.328743
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.317963 Loss1: 0.462110 Loss2: 1.855853
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.642637 Loss1: 0.275910 Loss2: 1.366727 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590208 Loss1: 0.191570 Loss2: 1.398637 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478994 Loss1: 0.109009 Loss2: 1.369985 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.362038 Loss1: 0.049151 Loss2: 1.312887
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476882 Loss1: 0.115969 Loss2: 1.360914
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.333736 Loss1: 0.033263 Loss2: 1.300473
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.422132 Loss1: 0.060697 Loss2: 1.361435
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.329326 Loss1: 0.032043 Loss2: 1.297284
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434309 Loss1: 0.086773 Loss2: 1.347537
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.393530 Loss1: 0.044391 Loss2: 1.349139
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.380401 Loss1: 0.040193 Loss2: 1.340208
-DEBUG flwr 2023-10-13 05:18:23,011 | server.py:236 | fit_round 177 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374209 Loss1: 0.033647 Loss2: 1.340562
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.320010 Loss1: 0.433429 Loss2: 1.886581
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.600746 Loss1: 0.228928 Loss2: 1.371818
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.503340 Loss1: 0.125099 Loss2: 1.378242
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.445964 Loss1: 0.081120 Loss2: 1.364845
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.314921 Loss1: 0.461162 Loss2: 1.853759
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.434981 Loss1: 0.079238 Loss2: 1.355743
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.579335 Loss1: 0.253357 Loss2: 1.325978
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.435692 Loss1: 0.077119 Loss2: 1.358573
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.552899 Loss1: 0.211806 Loss2: 1.341092
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390500 Loss1: 0.040686 Loss2: 1.349814
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.457945 Loss1: 0.133731 Loss2: 1.324214
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.374264 Loss1: 0.035918 Loss2: 1.338346
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.463333 Loss1: 0.141549 Loss2: 1.321784
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384145 Loss1: 0.048480 Loss2: 1.335664
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.391651 Loss1: 0.079930 Loss2: 1.311721
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380777 Loss1: 0.044510 Loss2: 1.336266
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.366319 Loss1: 0.056566 Loss2: 1.309753
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.341416 Loss1: 0.036837 Loss2: 1.304579
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.323067 Loss1: 0.025116 Loss2: 1.297950
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.327711 Loss1: 0.038995 Loss2: 1.288716
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.132054 Loss1: 0.298821 Loss2: 1.833233
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.616124 Loss1: 0.261461 Loss2: 1.354663
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.587948 Loss1: 0.200820 Loss2: 1.387128
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.110666 Loss1: 0.330279 Loss2: 1.780387
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.473798 Loss1: 0.115273 Loss2: 1.358525
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.445898 Loss1: 0.105418 Loss2: 1.340480 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.420161 Loss1: 0.076401 Loss2: 1.343761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.398396 Loss1: 0.062597 Loss2: 1.335799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.376151 Loss1: 0.043613 Loss2: 1.332538 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.375043 Loss1: 0.053559 Loss2: 1.321484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366524 Loss1: 0.040189 Loss2: 1.326335 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994485
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354621 Loss1: 0.056779 Loss2: 1.297842 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 05:18:23,011][flwr][DEBUG] - fit_round 177 received 50 results and 0 failures
-INFO flwr 2023-10-13 05:19:04,964 | server.py:125 | fit progress: (177, 2.2940372186727798, {'accuracy': 0.6069}, 408452.7422024)
->> Test accuracy: 0.606900
-[2023-10-13 05:19:04,964][flwr][INFO] - fit progress: (177, 2.2940372186727798, {'accuracy': 0.6069}, 408452.7422024)
-DEBUG flwr 2023-10-13 05:19:04,964 | server.py:173 | evaluate_round 177: strategy sampled 50 clients (out of 50)
-[2023-10-13 05:19:04,964][flwr][DEBUG] - evaluate_round 177: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 05:28:09,864 | server.py:187 | evaluate_round 177 received 50 results and 0 failures
-[2023-10-13 05:28:09,864][flwr][DEBUG] - evaluate_round 177 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 05:28:09,865 | server.py:222 | fit_round 178: strategy sampled 50 clients (out of 50)
-[2023-10-13 05:28:09,865][flwr][DEBUG] - fit_round 178: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.201988 Loss1: 0.363664 Loss2: 1.838324 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.500116 Loss1: 0.143046 Loss2: 1.357070 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.459070 Loss1: 0.116699 Loss2: 1.342371
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.110972 Loss1: 0.339066 Loss2: 1.771905
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.545231 Loss1: 0.230566 Loss2: 1.314665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.536833 Loss1: 0.197555 Loss2: 1.339278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.461655 Loss1: 0.132235 Loss2: 1.329420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.435044 Loss1: 0.117397 Loss2: 1.317647 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447854 Loss1: 0.120328 Loss2: 1.327526 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427153 Loss1: 0.105888 Loss2: 1.321265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414928 Loss1: 0.101618 Loss2: 1.313310 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.144887 Loss1: 0.356440 Loss2: 1.788448 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.570396 Loss1: 0.209706 Loss2: 1.360690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.445271 Loss1: 0.508752 Loss2: 1.936519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.589599 Loss1: 0.215785 Loss2: 1.373813 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.553235 Loss1: 0.184423 Loss2: 1.368812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.505921 Loss1: 0.126656 Loss2: 1.379264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501266 Loss1: 0.134708 Loss2: 1.366557 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397138 Loss1: 0.071544 Loss2: 1.325594
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490170 Loss1: 0.128044 Loss2: 1.362126
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480649 Loss1: 0.115499 Loss2: 1.365149
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366367 Loss1: 0.045161 Loss2: 1.321206
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432739 Loss1: 0.078720 Loss2: 1.354019 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.294385 Loss1: 0.403157 Loss2: 1.891228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595256 Loss1: 0.192223 Loss2: 1.403032 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.570159 Loss1: 0.161326 Loss2: 1.408833
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.241395 Loss1: 0.389660 Loss2: 1.851735
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.605536 Loss1: 0.269109 Loss2: 1.336427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.513426 Loss1: 0.142415 Loss2: 1.371011 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.471471 Loss1: 0.125764 Loss2: 1.345707 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524959 Loss1: 0.185369 Loss2: 1.339590 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446619 Loss1: 0.104490 Loss2: 1.342129 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416926 Loss1: 0.080187 Loss2: 1.336739 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387924 Loss1: 0.058183 Loss2: 1.329741 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.286542 Loss1: 0.415740 Loss2: 1.870802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.648285 Loss1: 0.234501 Loss2: 1.413784 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.550529 Loss1: 0.163395 Loss2: 1.387134
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.249140 Loss1: 0.375199 Loss2: 1.873940
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653402 Loss1: 0.307071 Loss2: 1.346331 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.537957 Loss1: 0.170526 Loss2: 1.367431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.474391 Loss1: 0.134483 Loss2: 1.339908 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.451847 Loss1: 0.106871 Loss2: 1.344975 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.400577 Loss1: 0.067441 Loss2: 1.333136 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414889 Loss1: 0.057432 Loss2: 1.357456
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.414426 Loss1: 0.081138 Loss2: 1.333287
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.395576 Loss1: 0.067452 Loss2: 1.328124
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381596 Loss1: 0.056710 Loss2: 1.324886
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408808 Loss1: 0.089401 Loss2: 1.319407
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.197888 Loss1: 0.401438 Loss2: 1.796450
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.548728 Loss1: 0.222776 Loss2: 1.325952
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.494965 Loss1: 0.153819 Loss2: 1.341145
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.437386 Loss1: 0.116978 Loss2: 1.320408
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.355628 Loss1: 0.495760 Loss2: 1.859868
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.676864 Loss1: 0.292333 Loss2: 1.384531 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.716876 Loss1: 0.286917 Loss2: 1.429959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559335 Loss1: 0.171028 Loss2: 1.388307 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563457 Loss1: 0.182128 Loss2: 1.381328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.520689 Loss1: 0.135486 Loss2: 1.385204 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378881 Loss1: 0.062588 Loss2: 1.316293
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464900 Loss1: 0.087444 Loss2: 1.377456
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419930 Loss1: 0.059670 Loss2: 1.360260
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402457 Loss1: 0.044572 Loss2: 1.357885
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401603 Loss1: 0.050219 Loss2: 1.351384
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.320722 Loss1: 0.386922 Loss2: 1.933800
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.668454 Loss1: 0.266293 Loss2: 1.402161
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.673967 Loss1: 0.233721 Loss2: 1.440246
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.597658 Loss1: 0.172770 Loss2: 1.424887
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.272585 Loss1: 0.433390 Loss2: 1.839194
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589209 Loss1: 0.175408 Loss2: 1.413801
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.564191 Loss1: 0.230112 Loss2: 1.334079
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.597477 Loss1: 0.157332 Loss2: 1.440145
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605640 Loss1: 0.256487 Loss2: 1.349153
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.530733 Loss1: 0.122928 Loss2: 1.407806
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521012 Loss1: 0.163094 Loss2: 1.357918
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479690 Loss1: 0.078100 Loss2: 1.401590
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.536079 Loss1: 0.200717 Loss2: 1.335362
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.464056 Loss1: 0.067672 Loss2: 1.396384
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480795 Loss1: 0.139120 Loss2: 1.341676
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438821 Loss1: 0.055991 Loss2: 1.382830
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.402922 Loss1: 0.081985 Loss2: 1.320937
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415201 Loss1: 0.100786 Loss2: 1.314414
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363800 Loss1: 0.048573 Loss2: 1.315227
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.355235 Loss1: 0.054409 Loss2: 1.300826
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.159340 Loss1: 0.372747 Loss2: 1.786593
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.599935 Loss1: 0.265504 Loss2: 1.334430
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563304 Loss1: 0.192938 Loss2: 1.370366
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527650 Loss1: 0.184563 Loss2: 1.343087
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.258409 Loss1: 0.418413 Loss2: 1.839996
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510430 Loss1: 0.173638 Loss2: 1.336792
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.618430 Loss1: 0.272889 Loss2: 1.345541
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556717 Loss1: 0.201942 Loss2: 1.354775
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476540 Loss1: 0.130340 Loss2: 1.346199
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521438 Loss1: 0.162516 Loss2: 1.358922
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420850 Loss1: 0.095978 Loss2: 1.324871
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.494574 Loss1: 0.145287 Loss2: 1.349287
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414645 Loss1: 0.089980 Loss2: 1.324665
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441512 Loss1: 0.094932 Loss2: 1.346580
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384062 Loss1: 0.062976 Loss2: 1.321085
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355882 Loss1: 0.037847 Loss2: 1.318035 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394377 Loss1: 0.060480 Loss2: 1.333897 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.088995 Loss1: 0.361100 Loss2: 1.727895 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.471467 Loss1: 0.165230 Loss2: 1.306237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.447758 Loss1: 0.157577 Loss2: 1.290181
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.199876 Loss1: 0.397063 Loss2: 1.802813
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.404700 Loss1: 0.110194 Loss2: 1.294506
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.629875 Loss1: 0.316800 Loss2: 1.313075
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.538530 Loss1: 0.193526 Loss2: 1.345004
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.404341 Loss1: 0.125213 Loss2: 1.279128
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496336 Loss1: 0.174992 Loss2: 1.321343
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383001 Loss1: 0.096575 Loss2: 1.286426
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.499854 Loss1: 0.174783 Loss2: 1.325071
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.350148 Loss1: 0.067465 Loss2: 1.282683
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443920 Loss1: 0.126326 Loss2: 1.317594
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379391 Loss1: 0.100554 Loss2: 1.278837
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.387570 Loss1: 0.082697 Loss2: 1.304873
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.330276 Loss1: 0.051810 Loss2: 1.278466
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.340748 Loss1: 0.050056 Loss2: 1.290692 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.242006 Loss1: 0.372891 Loss2: 1.869115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.498137 Loss1: 0.111366 Loss2: 1.386771 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467913 Loss1: 0.102921 Loss2: 1.364992
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.173261 Loss1: 0.316951 Loss2: 1.856310
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448373 Loss1: 0.096264 Loss2: 1.352108
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.576268 Loss1: 0.220198 Loss2: 1.356070
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456267 Loss1: 0.100925 Loss2: 1.355342
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.497283 Loss1: 0.142782 Loss2: 1.354501
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390170 Loss1: 0.046056 Loss2: 1.344113
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.461576 Loss1: 0.117327 Loss2: 1.344249
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.383628 Loss1: 0.041817 Loss2: 1.341811
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.420754 Loss1: 0.078557 Loss2: 1.342197
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408756 Loss1: 0.072725 Loss2: 1.336032
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427198 Loss1: 0.089474 Loss2: 1.337724
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399067 Loss1: 0.055589 Loss2: 1.343478
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.401406 Loss1: 0.065845 Loss2: 1.335561
-(DefaultActor pid=3765) >> Training accuracy: 0.978125
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386059 Loss1: 0.059285 Loss2: 1.326773
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396357 Loss1: 0.068792 Loss2: 1.327565
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366011 Loss1: 0.033321 Loss2: 1.332690
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.239542 Loss1: 0.403758 Loss2: 1.835784
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.637046 Loss1: 0.291062 Loss2: 1.345983
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.557309 Loss1: 0.190024 Loss2: 1.367285
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496004 Loss1: 0.135692 Loss2: 1.360311
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.306687 Loss1: 0.451982 Loss2: 1.854705
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.632016 Loss1: 0.272678 Loss2: 1.359337 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.549340 Loss1: 0.163810 Loss2: 1.385530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.499185 Loss1: 0.141863 Loss2: 1.357322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.482637 Loss1: 0.131495 Loss2: 1.351142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.458041 Loss1: 0.104877 Loss2: 1.353164 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.472190 Loss1: 0.127280 Loss2: 1.344910
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441465 Loss1: 0.093526 Loss2: 1.347939
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.404757 Loss1: 0.063846 Loss2: 1.340911
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398299 Loss1: 0.057652 Loss2: 1.340647
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366370 Loss1: 0.029227 Loss2: 1.337143
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.117108 Loss1: 0.343758 Loss2: 1.773351
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.548908 Loss1: 0.230843 Loss2: 1.318065
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.550313 Loss1: 0.205494 Loss2: 1.344818
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.459388 Loss1: 0.130715 Loss2: 1.328673
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.257104 Loss1: 0.371076 Loss2: 1.886028
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463070 Loss1: 0.140319 Loss2: 1.322751
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.557373 Loss1: 0.205870 Loss2: 1.351503
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.509676 Loss1: 0.150358 Loss2: 1.359318
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.403576 Loss1: 0.084018 Loss2: 1.319558
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.477807 Loss1: 0.130514 Loss2: 1.347293
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404191 Loss1: 0.095318 Loss2: 1.308873
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.433940 Loss1: 0.092377 Loss2: 1.341563
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.357117 Loss1: 0.048596 Loss2: 1.308521
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.402610 Loss1: 0.064961 Loss2: 1.337650
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.355869 Loss1: 0.051634 Loss2: 1.304235
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.351413 Loss1: 0.053013 Loss2: 1.298400 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414474 Loss1: 0.078610 Loss2: 1.335864 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.304476 Loss1: 0.408114 Loss2: 1.896362 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.706193 Loss1: 0.234034 Loss2: 1.472160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573378 Loss1: 0.164612 Loss2: 1.408767
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.550090 Loss1: 0.555656 Loss2: 1.994434
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.692412 Loss1: 0.307033 Loss2: 1.385379 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.574688 Loss1: 0.177447 Loss2: 1.397241
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.539201 Loss1: 0.126926 Loss2: 1.412275
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532333 Loss1: 0.136851 Loss2: 1.395482
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.549391 Loss1: 0.145554 Loss2: 1.403837
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.502816 Loss1: 0.101168 Loss2: 1.401648 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493921 Loss1: 0.099097 Loss2: 1.394825 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.471790 Loss1: 0.085211 Loss2: 1.386578 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388977 Loss1: 0.032552 Loss2: 1.356426 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995192
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.120452 Loss1: 0.299373 Loss2: 1.821079
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.625328 Loss1: 0.275036 Loss2: 1.350292
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582508 Loss1: 0.190508 Loss2: 1.392000
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502299 Loss1: 0.146390 Loss2: 1.355908
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.284268 Loss1: 0.423849 Loss2: 1.860419
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630154 Loss1: 0.281572 Loss2: 1.348583
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.501716 Loss1: 0.145736 Loss2: 1.355980
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.557542 Loss1: 0.179990 Loss2: 1.377552
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460578 Loss1: 0.109564 Loss2: 1.351014
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521021 Loss1: 0.160523 Loss2: 1.360498
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428405 Loss1: 0.082907 Loss2: 1.345498
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475764 Loss1: 0.127032 Loss2: 1.348732
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426955 Loss1: 0.083412 Loss2: 1.343543
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438146 Loss1: 0.097635 Loss2: 1.340511 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416272 Loss1: 0.074117 Loss2: 1.342155 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413086 Loss1: 0.074756 Loss2: 1.338330 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.353118 Loss1: 0.448210 Loss2: 1.904909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.539530 Loss1: 0.168778 Loss2: 1.370753 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540733 Loss1: 0.168943 Loss2: 1.371790
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.152173 Loss1: 0.345080 Loss2: 1.807093
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.553300 Loss1: 0.203343 Loss2: 1.349957 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.560932 Loss1: 0.198743 Loss2: 1.362189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507534 Loss1: 0.153062 Loss2: 1.354472 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541812 Loss1: 0.190512 Loss2: 1.351300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516764 Loss1: 0.162730 Loss2: 1.354033 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444254 Loss1: 0.100753 Loss2: 1.343501 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407109 Loss1: 0.076682 Loss2: 1.330427 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.360988 Loss1: 0.481590 Loss2: 1.879398 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.646052 Loss1: 0.276207 Loss2: 1.369844 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.611189 Loss1: 0.244005 Loss2: 1.367184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.580273 Loss1: 0.208070 Loss2: 1.372203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.530827 Loss1: 0.160632 Loss2: 1.370195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486517 Loss1: 0.130995 Loss2: 1.355521 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469942 Loss1: 0.120162 Loss2: 1.349780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405778 Loss1: 0.056132 Loss2: 1.349646 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994420
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408700 Loss1: 0.071576 Loss2: 1.337124 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.349975 Loss1: 0.027712 Loss2: 1.322262 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.341730 Loss1: 0.028039 Loss2: 1.313691
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.170509 Loss1: 0.356997 Loss2: 1.813512
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.576713 Loss1: 0.217836 Loss2: 1.358877
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.560601 Loss1: 0.169163 Loss2: 1.391438
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500280 Loss1: 0.136113 Loss2: 1.364167
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480895 Loss1: 0.118078 Loss2: 1.362817
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.182178 Loss1: 0.369936 Loss2: 1.812242
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.453968 Loss1: 0.087377 Loss2: 1.366591
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.522463 Loss1: 0.204555 Loss2: 1.317908
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461574 Loss1: 0.109118 Loss2: 1.352456
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.498608 Loss1: 0.170429 Loss2: 1.328179
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.504163 Loss1: 0.171658 Loss2: 1.332505
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.417321 Loss1: 0.056799 Loss2: 1.360522
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.471971 Loss1: 0.151786 Loss2: 1.320185
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414587 Loss1: 0.064619 Loss2: 1.349968
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460717 Loss1: 0.137736 Loss2: 1.322982
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378330 Loss1: 0.034303 Loss2: 1.344027
-(DefaultActor pid=3765) >> Training accuracy: 0.999023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418096 Loss1: 0.102301 Loss2: 1.315795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382310 Loss1: 0.073759 Loss2: 1.308551 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.591397 Loss1: 0.248131 Loss2: 1.343267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.519725 Loss1: 0.169089 Loss2: 1.350636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478872 Loss1: 0.133936 Loss2: 1.344936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473398 Loss1: 0.120622 Loss2: 1.352776 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474066 Loss1: 0.124415 Loss2: 1.349651 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439833 Loss1: 0.096442 Loss2: 1.343390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.442969 Loss1: 0.105932 Loss2: 1.337037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406287 Loss1: 0.075796 Loss2: 1.330491 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420517 Loss1: 0.074117 Loss2: 1.346400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375900 Loss1: 0.040595 Loss2: 1.335306 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.682952 Loss1: 0.320866 Loss2: 1.362086 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576833 Loss1: 0.202650 Loss2: 1.374183 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470615 Loss1: 0.103813 Loss2: 1.366802 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455214 Loss1: 0.098967 Loss2: 1.356247 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425286 Loss1: 0.070930 Loss2: 1.354356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441033 Loss1: 0.094426 Loss2: 1.346607 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454050 Loss1: 0.107395 Loss2: 1.346655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413179 Loss1: 0.066797 Loss2: 1.346383 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464062 Loss1: 0.083456 Loss2: 1.380606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402806 Loss1: 0.037027 Loss2: 1.365779 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.668066 Loss1: 0.291850 Loss2: 1.376217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557603 Loss1: 0.162006 Loss2: 1.395597 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524880 Loss1: 0.141452 Loss2: 1.383428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475639 Loss1: 0.096011 Loss2: 1.379627 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439137 Loss1: 0.068233 Loss2: 1.370904 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443579 Loss1: 0.074873 Loss2: 1.368706 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418090 Loss1: 0.058511 Loss2: 1.359579 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420250 Loss1: 0.064939 Loss2: 1.355310 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382254 Loss1: 0.056013 Loss2: 1.326240 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.350788 Loss1: 0.031645 Loss2: 1.319143 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.266986 Loss1: 0.416186 Loss2: 1.850800
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.630827 Loss1: 0.263440 Loss2: 1.367387
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610154 Loss1: 0.211708 Loss2: 1.398446
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534890 Loss1: 0.170467 Loss2: 1.364423
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.445103 Loss1: 0.512622 Loss2: 1.932481
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.703427 Loss1: 0.320961 Loss2: 1.382467 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460721 Loss1: 0.100243 Loss2: 1.360478
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.749611 Loss1: 0.327655 Loss2: 1.421956
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.601781 Loss1: 0.209661 Loss2: 1.392120
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458554 Loss1: 0.099560 Loss2: 1.358994
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557700 Loss1: 0.177656 Loss2: 1.380044
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440699 Loss1: 0.084666 Loss2: 1.356034
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419199 Loss1: 0.067738 Loss2: 1.351461 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391880 Loss1: 0.049325 Loss2: 1.342555 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.474944 Loss1: 0.102760 Loss2: 1.372184 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.191031 Loss1: 0.353800 Loss2: 1.837231 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.536738 Loss1: 0.148330 Loss2: 1.388409 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.268299 Loss1: 0.424946 Loss2: 1.843353
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.521246 Loss1: 0.150413 Loss2: 1.370834
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.643569 Loss1: 0.301439 Loss2: 1.342130
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.500649 Loss1: 0.137699 Loss2: 1.362950
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.528535 Loss1: 0.146632 Loss2: 1.381903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539847 Loss1: 0.157055 Loss2: 1.382792 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.513765 Loss1: 0.138282 Loss2: 1.375484 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488156 Loss1: 0.115437 Loss2: 1.372719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.469133 Loss1: 0.100010 Loss2: 1.369123 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.974609
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452538 Loss1: 0.104001 Loss2: 1.348537 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.181425 Loss1: 0.325208 Loss2: 1.856217 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.555296 Loss1: 0.157192 Loss2: 1.398104 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.487685 Loss1: 0.106791 Loss2: 1.380894 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.481088 Loss1: 0.107721 Loss2: 1.373367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437441 Loss1: 0.062120 Loss2: 1.375320 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422278 Loss1: 0.058763 Loss2: 1.363515 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437117 Loss1: 0.077372 Loss2: 1.359745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.360963 Loss1: 0.050182 Loss2: 1.310781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.343341 Loss1: 0.037702 Loss2: 1.305638 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995404
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.335751 Loss1: 0.037042 Loss2: 1.298709 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.270093 Loss1: 0.423481 Loss2: 1.846612
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.656929 Loss1: 0.304590 Loss2: 1.352339
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.543249 Loss1: 0.158071 Loss2: 1.385178
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.505823 Loss1: 0.144681 Loss2: 1.361142
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.327192 Loss1: 0.466997 Loss2: 1.860195
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.625207 Loss1: 0.311476 Loss2: 1.313731 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.436658 Loss1: 0.086335 Loss2: 1.350323
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.561362 Loss1: 0.218355 Loss2: 1.343007
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.386651 Loss1: 0.042708 Loss2: 1.343943
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496148 Loss1: 0.166814 Loss2: 1.329334
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441972 Loss1: 0.118968 Loss2: 1.323004
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391863 Loss1: 0.058828 Loss2: 1.333035
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.410186 Loss1: 0.092815 Loss2: 1.317371
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.363085 Loss1: 0.031620 Loss2: 1.331466
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.371443 Loss1: 0.063211 Loss2: 1.308232
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363595 Loss1: 0.039194 Loss2: 1.324401
-DEBUG flwr 2023-10-13 05:57:46,315 | server.py:236 | fit_round 178 received 50 results and 0 failures
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397845 Loss1: 0.089627 Loss2: 1.308219 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.974330
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.232180 Loss1: 0.397314 Loss2: 1.834866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561106 Loss1: 0.199287 Loss2: 1.361819 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.438812 Loss1: 0.471132 Loss2: 1.967680
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.504641 Loss1: 0.171908 Loss2: 1.332733
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.452201 Loss1: 0.119026 Loss2: 1.333175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.430821 Loss1: 0.097699 Loss2: 1.333122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409834 Loss1: 0.079700 Loss2: 1.330133 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470960 Loss1: 0.124625 Loss2: 1.346335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484852 Loss1: 0.134695 Loss2: 1.350157 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438332 Loss1: 0.093368 Loss2: 1.344965 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413163 Loss1: 0.088362 Loss2: 1.324801 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.377881 Loss1: 0.433037 Loss2: 1.944844
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.726980 Loss1: 0.288634 Loss2: 1.438346
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.664300 Loss1: 0.189053 Loss2: 1.475247
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.567078 Loss1: 0.134049 Loss2: 1.433029
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.203454 Loss1: 0.336128 Loss2: 1.867326
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.569502 Loss1: 0.218382 Loss2: 1.351120 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.499530 Loss1: 0.147465 Loss2: 1.352065 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.482398 Loss1: 0.123474 Loss2: 1.358923 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441365 Loss1: 0.092217 Loss2: 1.349148 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.422719 Loss1: 0.080340 Loss2: 1.342379 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.447904 Loss1: 0.043657 Loss2: 1.404246
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428878 Loss1: 0.086846 Loss2: 1.342032
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390074 Loss1: 0.052681 Loss2: 1.337393
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373936 Loss1: 0.042108 Loss2: 1.331829
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377308 Loss1: 0.045129 Loss2: 1.332179
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 05:57:46,315][flwr][DEBUG] - fit_round 178 received 50 results and 0 failures
-INFO flwr 2023-10-13 05:58:28,897 | server.py:125 | fit progress: (178, 2.2912714462310744, {'accuracy': 0.6079}, 410816.67539118696)
->> Test accuracy: 0.607900
-[2023-10-13 05:58:28,897][flwr][INFO] - fit progress: (178, 2.2912714462310744, {'accuracy': 0.6079}, 410816.67539118696)
-DEBUG flwr 2023-10-13 05:58:28,897 | server.py:173 | evaluate_round 178: strategy sampled 50 clients (out of 50)
-[2023-10-13 05:58:28,897][flwr][DEBUG] - evaluate_round 178: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 06:07:37,631 | server.py:187 | evaluate_round 178 received 50 results and 0 failures
-[2023-10-13 06:07:37,631][flwr][DEBUG] - evaluate_round 178 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 06:07:37,632 | server.py:222 | fit_round 179: strategy sampled 50 clients (out of 50)
-[2023-10-13 06:07:37,632][flwr][DEBUG] - fit_round 179: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.266425 Loss1: 0.414109 Loss2: 1.852315
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.584580 Loss1: 0.213875 Loss2: 1.370706
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.510690 Loss1: 0.138652 Loss2: 1.372038
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502929 Loss1: 0.131545 Loss2: 1.371385
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.253272 Loss1: 0.385019 Loss2: 1.868253
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.460963 Loss1: 0.104443 Loss2: 1.356519
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.590211 Loss1: 0.216362 Loss2: 1.373849
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451777 Loss1: 0.098207 Loss2: 1.353571
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565950 Loss1: 0.173800 Loss2: 1.392150
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.445193 Loss1: 0.094510 Loss2: 1.350683
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.508789 Loss1: 0.128353 Loss2: 1.380436
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.412168 Loss1: 0.061007 Loss2: 1.351162
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.443972 Loss1: 0.085363 Loss2: 1.358609
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408985 Loss1: 0.062754 Loss2: 1.346231
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450744 Loss1: 0.093902 Loss2: 1.356843
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381007 Loss1: 0.039073 Loss2: 1.341935
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469808 Loss1: 0.120764 Loss2: 1.349043
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.495844 Loss1: 0.121572 Loss2: 1.374273
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438933 Loss1: 0.085366 Loss2: 1.353567
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407650 Loss1: 0.057935 Loss2: 1.349715
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.304376 Loss1: 0.462434 Loss2: 1.841942
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.722911 Loss1: 0.357156 Loss2: 1.365756
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595798 Loss1: 0.182252 Loss2: 1.413546
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.471866 Loss1: 0.122123 Loss2: 1.349743
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.363618 Loss1: 0.494289 Loss2: 1.869329
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.662030 Loss1: 0.340118 Loss2: 1.321912
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.493566 Loss1: 0.137814 Loss2: 1.355752
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.535132 Loss1: 0.198292 Loss2: 1.336840
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.401045 Loss1: 0.054052 Loss2: 1.346993
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.396150 Loss1: 0.063202 Loss2: 1.332948 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.379193 Loss1: 0.050287 Loss2: 1.328906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374324 Loss1: 0.048364 Loss2: 1.325960 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360132 Loss1: 0.042606 Loss2: 1.317526 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368338 Loss1: 0.055195 Loss2: 1.313144 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985577
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.420359 Loss1: 0.450568 Loss2: 1.969791
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.721646 Loss1: 0.310848 Loss2: 1.410798
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.682034 Loss1: 0.221293 Loss2: 1.460740
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.599215 Loss1: 0.162853 Loss2: 1.436362
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.149985 Loss1: 0.347231 Loss2: 1.802754
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.591749 Loss1: 0.246574 Loss2: 1.345174 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.494933 Loss1: 0.127175 Loss2: 1.367758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.472851 Loss1: 0.121230 Loss2: 1.351622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459090 Loss1: 0.062464 Loss2: 1.396626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454606 Loss1: 0.064693 Loss2: 1.389913 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415888 Loss1: 0.075173 Loss2: 1.340715 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.457168 Loss1: 0.110714 Loss2: 1.346455 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.583737 Loss1: 0.240838 Loss2: 1.342899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.494714 Loss1: 0.170137 Loss2: 1.324577 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.258626 Loss1: 0.456003 Loss2: 1.802623
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463407 Loss1: 0.126557 Loss2: 1.336850
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.628739 Loss1: 0.312230 Loss2: 1.316509
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.386618 Loss1: 0.067281 Loss2: 1.319338
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.361018 Loss1: 0.053806 Loss2: 1.307212 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.379452 Loss1: 0.070521 Loss2: 1.308930 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.352863 Loss1: 0.051342 Loss2: 1.301521 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.346725 Loss1: 0.044441 Loss2: 1.302284 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991728
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372785 Loss1: 0.083511 Loss2: 1.289274 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.302173 Loss1: 0.433427 Loss2: 1.868747 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.590378 Loss1: 0.190434 Loss2: 1.399945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.493859 Loss1: 0.140581 Loss2: 1.353279
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.216075 Loss1: 0.371185 Loss2: 1.844890
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.580250 Loss1: 0.234328 Loss2: 1.345921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.506933 Loss1: 0.148500 Loss2: 1.358433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.488691 Loss1: 0.134393 Loss2: 1.354298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.443329 Loss1: 0.098559 Loss2: 1.344771 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.419418 Loss1: 0.081896 Loss2: 1.337522 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.383863 Loss1: 0.054343 Loss2: 1.329519 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370219 Loss1: 0.047219 Loss2: 1.323000 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.716706 Loss1: 0.311517 Loss2: 1.405189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529366 Loss1: 0.120562 Loss2: 1.408804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.318996 Loss1: 0.456466 Loss2: 1.862530
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.489777 Loss1: 0.089855 Loss2: 1.399922
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.603087 Loss1: 0.256713 Loss2: 1.346374
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470557 Loss1: 0.074515 Loss2: 1.396042
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.511969 Loss1: 0.130709 Loss2: 1.381260
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.454013 Loss1: 0.067028 Loss2: 1.386985
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.486361 Loss1: 0.142018 Loss2: 1.344343
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.451868 Loss1: 0.066938 Loss2: 1.384930
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.442192 Loss1: 0.103150 Loss2: 1.339042
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429948 Loss1: 0.049574 Loss2: 1.380374
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.416889 Loss1: 0.079986 Loss2: 1.336902
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413880 Loss1: 0.038861 Loss2: 1.375018
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419918 Loss1: 0.081026 Loss2: 1.338892 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414195 Loss1: 0.076792 Loss2: 1.337403 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.643349 Loss1: 0.266613 Loss2: 1.376736 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497474 Loss1: 0.125803 Loss2: 1.371671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.511881 Loss1: 0.142837 Loss2: 1.369044 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.476314 Loss1: 0.105498 Loss2: 1.370816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.452735 Loss1: 0.082922 Loss2: 1.369813 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438874 Loss1: 0.073474 Loss2: 1.365399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456970 Loss1: 0.091167 Loss2: 1.365803 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431762 Loss1: 0.074601 Loss2: 1.357161 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413624 Loss1: 0.047639 Loss2: 1.365986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416813 Loss1: 0.056738 Loss2: 1.360075 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.628899 Loss1: 0.278571 Loss2: 1.350328 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516251 Loss1: 0.155345 Loss2: 1.360906 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.498144 Loss1: 0.146341 Loss2: 1.351803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.629853 Loss1: 0.270993 Loss2: 1.358860
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475429 Loss1: 0.114707 Loss2: 1.360723
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.545899 Loss1: 0.147089 Loss2: 1.398810
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447294 Loss1: 0.093706 Loss2: 1.353588
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557346 Loss1: 0.197353 Loss2: 1.359994
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418235 Loss1: 0.068748 Loss2: 1.349486
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418469 Loss1: 0.072757 Loss2: 1.345712
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.466117 Loss1: 0.094603 Loss2: 1.371514
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411663 Loss1: 0.063250 Loss2: 1.348413
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.398378 Loss1: 0.049746 Loss2: 1.348632
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.375168 Loss1: 0.033274 Loss2: 1.341894
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.360928 Loss1: 0.030635 Loss2: 1.330293
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.350527 Loss1: 0.027800 Loss2: 1.322726
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352897 Loss1: 0.034585 Loss2: 1.318313
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.302960 Loss1: 0.431037 Loss2: 1.871923
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.611959 Loss1: 0.230442 Loss2: 1.381517
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.550316 Loss1: 0.164380 Loss2: 1.385935
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.514989 Loss1: 0.145663 Loss2: 1.369327
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470726 Loss1: 0.103045 Loss2: 1.367682
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.270607 Loss1: 0.375860 Loss2: 1.894747
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.624318 Loss1: 0.243368 Loss2: 1.380950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.610907 Loss1: 0.193768 Loss2: 1.417140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.534956 Loss1: 0.150381 Loss2: 1.384575 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502313 Loss1: 0.110216 Loss2: 1.392097 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392852 Loss1: 0.046331 Loss2: 1.346521
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480054 Loss1: 0.096066 Loss2: 1.383988
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464231 Loss1: 0.090202 Loss2: 1.374029
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437920 Loss1: 0.064929 Loss2: 1.372991
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422296 Loss1: 0.053883 Loss2: 1.368413
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413535 Loss1: 0.051865 Loss2: 1.361670
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.231257 Loss1: 0.349799 Loss2: 1.881458
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.611681 Loss1: 0.240267 Loss2: 1.371414
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.569372 Loss1: 0.175521 Loss2: 1.393851
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523071 Loss1: 0.133605 Loss2: 1.389466
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475252 Loss1: 0.098471 Loss2: 1.376781
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.178364 Loss1: 0.375244 Loss2: 1.803121
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.579742 Loss1: 0.238932 Loss2: 1.340810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.549395 Loss1: 0.181798 Loss2: 1.367597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507871 Loss1: 0.160586 Loss2: 1.347285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.504111 Loss1: 0.158341 Loss2: 1.345770 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480478 Loss1: 0.130987 Loss2: 1.349492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424826 Loss1: 0.090858 Loss2: 1.333967 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.333453 Loss1: 0.437112 Loss2: 1.896341 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.665286 Loss1: 0.231598 Loss2: 1.433688 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.530731 Loss1: 0.126563 Loss2: 1.404169 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487423 Loss1: 0.105727 Loss2: 1.381696
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.168510 Loss1: 0.362162 Loss2: 1.806348
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459685 Loss1: 0.083865 Loss2: 1.375820
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.610888 Loss1: 0.258249 Loss2: 1.352639
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.533710 Loss1: 0.152702 Loss2: 1.381008 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.442289 Loss1: 0.108690 Loss2: 1.333600 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408874 Loss1: 0.045233 Loss2: 1.363641
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.422989 Loss1: 0.085762 Loss2: 1.337227
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.434163 Loss1: 0.103629 Loss2: 1.330534
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.397557 Loss1: 0.075833 Loss2: 1.321724
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.387857 Loss1: 0.065563 Loss2: 1.322294
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.375679 Loss1: 0.060779 Loss2: 1.314899
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.239463 Loss1: 0.425111 Loss2: 1.814352
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379873 Loss1: 0.064803 Loss2: 1.315070
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.547977 Loss1: 0.224211 Loss2: 1.323765
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.522012 Loss1: 0.174168 Loss2: 1.347843
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.434815 Loss1: 0.102693 Loss2: 1.332122
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.416705 Loss1: 0.097074 Loss2: 1.319631
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.381441 Loss1: 0.065674 Loss2: 1.315767
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.348027 Loss1: 0.476156 Loss2: 1.871871
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.350262 Loss1: 0.039899 Loss2: 1.310363
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.760749 Loss1: 0.384850 Loss2: 1.375899
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.345500 Loss1: 0.042386 Loss2: 1.303114
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.757422 Loss1: 0.287024 Loss2: 1.470397
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.330054 Loss1: 0.035352 Loss2: 1.294702
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.602833 Loss1: 0.209093 Loss2: 1.393740
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.326169 Loss1: 0.030789 Loss2: 1.295380
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.507875 Loss1: 0.117987 Loss2: 1.389888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430044 Loss1: 0.061007 Loss2: 1.369037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464989 Loss1: 0.102928 Loss2: 1.362061
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.142319 Loss1: 0.361986 Loss2: 1.780334
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444889 Loss1: 0.083617 Loss2: 1.361273
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.557377 Loss1: 0.257031 Loss2: 1.300346
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.499364 Loss1: 0.182105 Loss2: 1.317258
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499794 Loss1: 0.173405 Loss2: 1.326389
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.456579 Loss1: 0.142282 Loss2: 1.314297
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.421191 Loss1: 0.104970 Loss2: 1.316221
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.188304 Loss1: 0.375168 Loss2: 1.813135
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.405438 Loss1: 0.095684 Loss2: 1.309754
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612191 Loss1: 0.282183 Loss2: 1.330008
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.365178 Loss1: 0.063028 Loss2: 1.302150
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.537018 Loss1: 0.166734 Loss2: 1.370284
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.351760 Loss1: 0.054011 Loss2: 1.297750
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.465842 Loss1: 0.128215 Loss2: 1.337627
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368549 Loss1: 0.071666 Loss2: 1.296883
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.414341 Loss1: 0.096027 Loss2: 1.318314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.380946 Loss1: 0.064123 Loss2: 1.316824 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.360272 Loss1: 0.056473 Loss2: 1.303799
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.295047 Loss1: 0.464051 Loss2: 1.830996
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.332574 Loss1: 0.033037 Loss2: 1.299536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.654228 Loss1: 0.323530 Loss2: 1.330699
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.637315 Loss1: 0.266813 Loss2: 1.370501
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511083 Loss1: 0.181578 Loss2: 1.329505
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.457397 Loss1: 0.127186 Loss2: 1.330210
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.392872 Loss1: 0.072849 Loss2: 1.320022
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.195571 Loss1: 0.365950 Loss2: 1.829621
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.398081 Loss1: 0.094631 Loss2: 1.303450
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.358333 Loss1: 0.059716 Loss2: 1.298618
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.577109 Loss1: 0.220126 Loss2: 1.356984
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374174 Loss1: 0.067812 Loss2: 1.306362
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.482268 Loss1: 0.126486 Loss2: 1.355783
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368057 Loss1: 0.067945 Loss2: 1.300112
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496823 Loss1: 0.147812 Loss2: 1.349011
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.488977 Loss1: 0.146723 Loss2: 1.342255
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.532204 Loss1: 0.177797 Loss2: 1.354408
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.421698 Loss1: 0.084581 Loss2: 1.337116
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412170 Loss1: 0.083543 Loss2: 1.328627
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.438053 Loss1: 0.527513 Loss2: 1.910540
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386785 Loss1: 0.061563 Loss2: 1.325221
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370140 Loss1: 0.047752 Loss2: 1.322388 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.456486 Loss1: 0.143778 Loss2: 1.312708 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.371980 Loss1: 0.063955 Loss2: 1.308024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.359357 Loss1: 0.470448 Loss2: 1.888908 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.703413 Loss1: 0.307147 Loss2: 1.396266 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.501383 Loss1: 0.110457 Loss2: 1.390926 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441535 Loss1: 0.056666 Loss2: 1.384868 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435319 Loss1: 0.063500 Loss2: 1.371820
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.317894 Loss1: 0.468273 Loss2: 1.849620
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420461 Loss1: 0.053686 Loss2: 1.366775
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.639828 Loss1: 0.287410 Loss2: 1.352418
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403042 Loss1: 0.035238 Loss2: 1.367804
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.543432 Loss1: 0.170897 Loss2: 1.372535
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394578 Loss1: 0.031982 Loss2: 1.362596
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516332 Loss1: 0.159843 Loss2: 1.356489
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491489 Loss1: 0.145748 Loss2: 1.345742
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465017 Loss1: 0.110781 Loss2: 1.354237
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425620 Loss1: 0.083200 Loss2: 1.342419
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393274 Loss1: 0.061824 Loss2: 1.331450
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373361 Loss1: 0.046849 Loss2: 1.326513
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.326592 Loss1: 0.456034 Loss2: 1.870558
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.353545 Loss1: 0.031802 Loss2: 1.321743
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.707634 Loss1: 0.357720 Loss2: 1.349915
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.630165 Loss1: 0.221624 Loss2: 1.408541
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.525392 Loss1: 0.176008 Loss2: 1.349384
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501499 Loss1: 0.154053 Loss2: 1.347446
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.418467 Loss1: 0.076437 Loss2: 1.342030
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.174134 Loss1: 0.388832 Loss2: 1.785303
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416427 Loss1: 0.080077 Loss2: 1.336350
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.379198 Loss1: 0.052500 Loss2: 1.326698 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387677 Loss1: 0.066139 Loss2: 1.321537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366399 Loss1: 0.047590 Loss2: 1.318809 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416737 Loss1: 0.093872 Loss2: 1.322864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.360330 Loss1: 0.050309 Loss2: 1.310022 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.351548 Loss1: 0.047608 Loss2: 1.303940
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.217896 Loss1: 0.365147 Loss2: 1.852749
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.722354 Loss1: 0.323757 Loss2: 1.398598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537530 Loss1: 0.144293 Loss2: 1.393237 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.489296 Loss1: 0.509045 Loss2: 1.980250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751666 Loss1: 0.380587 Loss2: 1.371079 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.748974 Loss1: 0.312407 Loss2: 1.436567 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549298 Loss1: 0.130432 Loss2: 1.418866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.529876 Loss1: 0.152249 Loss2: 1.377627
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.452703 Loss1: 0.070806 Loss2: 1.381896
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446982 Loss1: 0.069314 Loss2: 1.377668 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.442750 Loss1: 0.078396 Loss2: 1.364355 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.142221 Loss1: 0.369918 Loss2: 1.772302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.521607 Loss1: 0.156828 Loss2: 1.364778 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.295455 Loss1: 0.492155 Loss2: 1.803300
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.455260 Loss1: 0.131937 Loss2: 1.323323
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.613382 Loss1: 0.283634 Loss2: 1.329748
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.440879 Loss1: 0.110697 Loss2: 1.330182
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455083 Loss1: 0.124256 Loss2: 1.330827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415898 Loss1: 0.096335 Loss2: 1.319563 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421895 Loss1: 0.099780 Loss2: 1.322116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453414 Loss1: 0.127980 Loss2: 1.325434 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423717 Loss1: 0.094643 Loss2: 1.329075 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.363075 Loss1: 0.057182 Loss2: 1.305894 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.186679 Loss1: 0.386108 Loss2: 1.800571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.567892 Loss1: 0.209420 Loss2: 1.358473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.488848 Loss1: 0.154857 Loss2: 1.333992
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.333310 Loss1: 0.395831 Loss2: 1.937479
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.713566 Loss1: 0.304296 Loss2: 1.409270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.661891 Loss1: 0.203461 Loss2: 1.458430 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.601432 Loss1: 0.190410 Loss2: 1.411022 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.537116 Loss1: 0.123104 Loss2: 1.414012 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524825 Loss1: 0.107438 Loss2: 1.417387 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351822 Loss1: 0.044554 Loss2: 1.307268
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475926 Loss1: 0.077561 Loss2: 1.398365
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479022 Loss1: 0.080662 Loss2: 1.398361
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433800 Loss1: 0.045491 Loss2: 1.388309
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434948 Loss1: 0.047160 Loss2: 1.387787
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.184096 Loss1: 0.403574 Loss2: 1.780522
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.618827 Loss1: 0.314744 Loss2: 1.304082
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588938 Loss1: 0.249812 Loss2: 1.339126
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.471029 Loss1: 0.152312 Loss2: 1.318717
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.264411 Loss1: 0.393369 Loss2: 1.871042
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.636883 Loss1: 0.268484 Loss2: 1.368399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.587230 Loss1: 0.189630 Loss2: 1.397600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534436 Loss1: 0.150659 Loss2: 1.383777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.575006 Loss1: 0.204666 Loss2: 1.370339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.562404 Loss1: 0.176260 Loss2: 1.386144 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496971 Loss1: 0.128288 Loss2: 1.368683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455618 Loss1: 0.087454 Loss2: 1.368164 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.239169 Loss1: 0.389612 Loss2: 1.849556 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600780 Loss1: 0.213343 Loss2: 1.387437 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.228370 Loss1: 0.394492 Loss2: 1.833878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.561532 Loss1: 0.203235 Loss2: 1.358297 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.524466 Loss1: 0.147207 Loss2: 1.377259 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.419196 Loss1: 0.066262 Loss2: 1.352933 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.418205 Loss1: 0.071087 Loss2: 1.347118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409518 Loss1: 0.070738 Loss2: 1.338780 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382687 Loss1: 0.050004 Loss2: 1.332683 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371351 Loss1: 0.040057 Loss2: 1.331294 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.654144 Loss1: 0.267110 Loss2: 1.387035 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.657153 Loss1: 0.268327 Loss2: 1.388826 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558913 Loss1: 0.156304 Loss2: 1.402609
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.291896 Loss1: 0.447356 Loss2: 1.844540
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.647708 Loss1: 0.316622 Loss2: 1.331086 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.493886 Loss1: 0.107239 Loss2: 1.386646
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.588002 Loss1: 0.231766 Loss2: 1.356235
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.490077 Loss1: 0.106323 Loss2: 1.383754
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526834 Loss1: 0.178668 Loss2: 1.348166
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.438568 Loss1: 0.109575 Loss2: 1.328994
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.489854 Loss1: 0.116046 Loss2: 1.373808
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442580 Loss1: 0.114933 Loss2: 1.327647
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.481633 Loss1: 0.096334 Loss2: 1.385299
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.374602 Loss1: 0.061773 Loss2: 1.312829 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.346334 Loss1: 0.039929 Loss2: 1.306405 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.294406 Loss1: 0.459723 Loss2: 1.834683
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.639042 Loss1: 0.293608 Loss2: 1.345434
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564382 Loss1: 0.188965 Loss2: 1.375417
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497086 Loss1: 0.157060 Loss2: 1.340026
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.241368 Loss1: 0.393325 Loss2: 1.848042
-DEBUG flwr 2023-10-13 06:36:20,724 | server.py:236 | fit_round 179 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.559068 Loss1: 0.226717 Loss2: 1.332352 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.512712 Loss1: 0.168632 Loss2: 1.344081 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476429 Loss1: 0.137028 Loss2: 1.339401 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.432267 Loss1: 0.111421 Loss2: 1.320846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.401919 Loss1: 0.080267 Loss2: 1.321652 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.382399 Loss1: 0.066360 Loss2: 1.316038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391484 Loss1: 0.077395 Loss2: 1.314089 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.232715 Loss1: 0.442106 Loss2: 1.790609 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.520645 Loss1: 0.163160 Loss2: 1.357485 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.459861 Loss1: 0.132547 Loss2: 1.327314
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.308646 Loss1: 0.450917 Loss2: 1.857729
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.534632 Loss1: 0.188430 Loss2: 1.346203 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.509574 Loss1: 0.160604 Loss2: 1.348970 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540623 Loss1: 0.197872 Loss2: 1.342751 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.512721 Loss1: 0.156796 Loss2: 1.355924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.518872 Loss1: 0.170830 Loss2: 1.348042 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.508676 Loss1: 0.139288 Loss2: 1.369389 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414575 Loss1: 0.075046 Loss2: 1.339530 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.231771 Loss1: 0.348954 Loss2: 1.882817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608773 Loss1: 0.162619 Loss2: 1.446153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513155 Loss1: 0.102872 Loss2: 1.410283 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.473990 Loss1: 0.081206 Loss2: 1.392784 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450128 Loss1: 0.063661 Loss2: 1.386467 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 06:36:20,724][flwr][DEBUG] - fit_round 179 received 50 results and 0 failures
-INFO flwr 2023-10-13 06:37:01,303 | server.py:125 | fit progress: (179, 2.289638937662204, {'accuracy': 0.6098}, 413129.08199989)
->> Test accuracy: 0.609800
-[2023-10-13 06:37:01,303][flwr][INFO] - fit progress: (179, 2.289638937662204, {'accuracy': 0.6098}, 413129.08199989)
-DEBUG flwr 2023-10-13 06:37:01,304 | server.py:173 | evaluate_round 179: strategy sampled 50 clients (out of 50)
-[2023-10-13 06:37:01,304][flwr][DEBUG] - evaluate_round 179: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 06:46:06,569 | server.py:187 | evaluate_round 179 received 50 results and 0 failures
-[2023-10-13 06:46:06,569][flwr][DEBUG] - evaluate_round 179 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 06:46:06,569 | server.py:222 | fit_round 180: strategy sampled 50 clients (out of 50)
-[2023-10-13 06:46:06,569][flwr][DEBUG] - fit_round 180: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.257761 Loss1: 0.407385 Loss2: 1.850376 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.493978 Loss1: 0.176831 Loss2: 1.317147 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.426828 Loss1: 0.094389 Loss2: 1.332439
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.319501 Loss1: 0.462983 Loss2: 1.856518
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.540133 Loss1: 0.204123 Loss2: 1.336010 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.500260 Loss1: 0.141829 Loss2: 1.358431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.473979 Loss1: 0.129512 Loss2: 1.344467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.444219 Loss1: 0.114547 Loss2: 1.329672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.406547 Loss1: 0.076215 Loss2: 1.330332 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415291 Loss1: 0.092256 Loss2: 1.323035 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442737 Loss1: 0.115859 Loss2: 1.326878 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.961458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.650228 Loss1: 0.249012 Loss2: 1.401216 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.585927 Loss1: 0.172863 Loss2: 1.413063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527216 Loss1: 0.127212 Loss2: 1.400004
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.139031 Loss1: 0.309317 Loss2: 1.829714
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.554426 Loss1: 0.145735 Loss2: 1.408690
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.565986 Loss1: 0.209186 Loss2: 1.356800
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.522096 Loss1: 0.121870 Loss2: 1.400226
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.554489 Loss1: 0.173633 Loss2: 1.380856
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.515962 Loss1: 0.153648 Loss2: 1.362313 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.459084 Loss1: 0.097403 Loss2: 1.361680 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447578 Loss1: 0.091319 Loss2: 1.356259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424303 Loss1: 0.067479 Loss2: 1.356824 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.355660 Loss1: 0.454527 Loss2: 1.901133 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991728
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.601790 Loss1: 0.239700 Loss2: 1.362090 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517342 Loss1: 0.157040 Loss2: 1.360302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.256637 Loss1: 0.432955 Loss2: 1.823683 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.627694 Loss1: 0.302182 Loss2: 1.325512 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425796 Loss1: 0.062373 Loss2: 1.363423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440753 Loss1: 0.093100 Loss2: 1.347652 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983259
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.453678 Loss1: 0.121826 Loss2: 1.331852 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431696 Loss1: 0.101995 Loss2: 1.329702 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.346398 Loss1: 0.418297 Loss2: 1.928100
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433570 Loss1: 0.112524 Loss2: 1.321046
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.728746 Loss1: 0.313850 Loss2: 1.414895
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403304 Loss1: 0.083834 Loss2: 1.319470
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.552850 Loss1: 0.138415 Loss2: 1.414435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460889 Loss1: 0.062340 Loss2: 1.398549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496771 Loss1: 0.106443 Loss2: 1.390328
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.169387 Loss1: 0.332793 Loss2: 1.836594
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445311 Loss1: 0.047191 Loss2: 1.398120
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.602656 Loss1: 0.243044 Loss2: 1.359613
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.449296 Loss1: 0.064316 Loss2: 1.384980
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578622 Loss1: 0.182567 Loss2: 1.396055
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440240 Loss1: 0.057866 Loss2: 1.382374
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539253 Loss1: 0.173053 Loss2: 1.366200
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.457696 Loss1: 0.099685 Loss2: 1.358012
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439995 Loss1: 0.084088 Loss2: 1.355907
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456848 Loss1: 0.104192 Loss2: 1.352656
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453846 Loss1: 0.105730 Loss2: 1.348115
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.310241 Loss1: 0.383609 Loss2: 1.926631
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416071 Loss1: 0.062414 Loss2: 1.353657
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389887 Loss1: 0.042835 Loss2: 1.347052 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542457 Loss1: 0.129074 Loss2: 1.413383 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533569 Loss1: 0.138987 Loss2: 1.394582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.230867 Loss1: 0.358480 Loss2: 1.872386 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.567159 Loss1: 0.203481 Loss2: 1.363678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.551058 Loss1: 0.190086 Loss2: 1.360972 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.527864 Loss1: 0.153766 Loss2: 1.374098 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.547108 Loss1: 0.170240 Loss2: 1.376869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445428 Loss1: 0.087371 Loss2: 1.358057 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426710 Loss1: 0.072925 Loss2: 1.353785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433599 Loss1: 0.082770 Loss2: 1.350828 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549131 Loss1: 0.188657 Loss2: 1.360474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449551 Loss1: 0.085448 Loss2: 1.364103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.246174 Loss1: 0.410329 Loss2: 1.835846 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.596023 Loss1: 0.249743 Loss2: 1.346280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564849 Loss1: 0.191284 Loss2: 1.373565 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.480581 Loss1: 0.136489 Loss2: 1.344091 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438335 Loss1: 0.095794 Loss2: 1.342540 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419094 Loss1: 0.087661 Loss2: 1.331433
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.213867 Loss1: 0.351237 Loss2: 1.862630
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.684041 Loss1: 0.326861 Loss2: 1.357180 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.407217 Loss1: 0.074849 Loss2: 1.332367
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.624715 Loss1: 0.230479 Loss2: 1.394235
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517521 Loss1: 0.155459 Loss2: 1.362062
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.432823 Loss1: 0.085129 Loss2: 1.347693
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466154 Loss1: 0.117450 Loss2: 1.348704
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439197 Loss1: 0.098203 Loss2: 1.340995
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.327169 Loss1: 0.464212 Loss2: 1.862957
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424670 Loss1: 0.083423 Loss2: 1.341247
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399114 Loss1: 0.063905 Loss2: 1.335209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387350 Loss1: 0.062337 Loss2: 1.325013 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.529756 Loss1: 0.159674 Loss2: 1.370082 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.564820 Loss1: 0.202474 Loss2: 1.362346 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.516602 Loss1: 0.146033 Loss2: 1.370569
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.231271 Loss1: 0.370808 Loss2: 1.860463
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.610435 Loss1: 0.248551 Loss2: 1.361884 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582902 Loss1: 0.199496 Loss2: 1.383406 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517358 Loss1: 0.140637 Loss2: 1.376720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448460 Loss1: 0.088560 Loss2: 1.359900 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.168099 Loss1: 0.320791 Loss2: 1.847308
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483748 Loss1: 0.123179 Loss2: 1.360569
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.594438 Loss1: 0.231570 Loss2: 1.362868
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.467377 Loss1: 0.098749 Loss2: 1.368627
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.551071 Loss1: 0.169195 Loss2: 1.381876
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.429122 Loss1: 0.072882 Loss2: 1.356239
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455600 Loss1: 0.091731 Loss2: 1.363869 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448866 Loss1: 0.095565 Loss2: 1.353301 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.365383 Loss1: 0.464125 Loss2: 1.901257
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450650 Loss1: 0.092999 Loss2: 1.357651
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.704283 Loss1: 0.298174 Loss2: 1.406109
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443371 Loss1: 0.094671 Loss2: 1.348700
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635796 Loss1: 0.202767 Loss2: 1.433029
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.442894 Loss1: 0.093764 Loss2: 1.349130
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.569211 Loss1: 0.161748 Loss2: 1.407462 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.524808 Loss1: 0.133203 Loss2: 1.391605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.495798 Loss1: 0.104267 Loss2: 1.391531
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.268770 Loss1: 0.329005 Loss2: 1.939765
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630344 Loss1: 0.206313 Loss2: 1.424030 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432455 Loss1: 0.056628 Loss2: 1.375827
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.622894 Loss1: 0.192165 Loss2: 1.430728
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.639929 Loss1: 0.190863 Loss2: 1.449065
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.565274 Loss1: 0.126757 Loss2: 1.438517
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.545588 Loss1: 0.130720 Loss2: 1.414868
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490957 Loss1: 0.075436 Loss2: 1.415521
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.199586 Loss1: 0.404758 Loss2: 1.794828
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.519694 Loss1: 0.109840 Loss2: 1.409854
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.598958 Loss1: 0.264914 Loss2: 1.334045
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.470278 Loss1: 0.062903 Loss2: 1.407376
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.511462 Loss1: 0.161432 Loss2: 1.350030
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.451223 Loss1: 0.049904 Loss2: 1.401319
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448590 Loss1: 0.127335 Loss2: 1.321255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418457 Loss1: 0.086876 Loss2: 1.331581 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.296658 Loss1: 0.441915 Loss2: 1.854742
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.402047 Loss1: 0.070822 Loss2: 1.331225
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.582657 Loss1: 0.232427 Loss2: 1.350230
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.352757 Loss1: 0.033465 Loss2: 1.319292
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.353461 Loss1: 0.039585 Loss2: 1.313876 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.436326 Loss1: 0.098641 Loss2: 1.337685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406864 Loss1: 0.074433 Loss2: 1.332431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430059 Loss1: 0.098117 Loss2: 1.331942
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.199690 Loss1: 0.389231 Loss2: 1.810459
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.573764 Loss1: 0.254831 Loss2: 1.318933 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.564789 Loss1: 0.207413 Loss2: 1.357376 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.386342 Loss1: 0.072949 Loss2: 1.313393 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.396567 Loss1: 0.093030 Loss2: 1.303537 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.374625 Loss1: 0.070573 Loss2: 1.304053 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.354468 Loss1: 0.057037 Loss2: 1.297431 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369108 Loss1: 0.069749 Loss2: 1.299359 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495044 Loss1: 0.132570 Loss2: 1.362474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430866 Loss1: 0.074228 Loss2: 1.356638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.423314 Loss1: 0.068121 Loss2: 1.355193
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.233404 Loss1: 0.357952 Loss2: 1.875452
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.670465 Loss1: 0.270571 Loss2: 1.399894 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610599 Loss1: 0.166251 Loss2: 1.444348 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.485806 Loss1: 0.085672 Loss2: 1.400134 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459085 Loss1: 0.077024 Loss2: 1.382062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470746 Loss1: 0.086299 Loss2: 1.384447 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448400 Loss1: 0.067745 Loss2: 1.380655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439051 Loss1: 0.059141 Loss2: 1.379909 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421308 Loss1: 0.082242 Loss2: 1.339066 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.385591 Loss1: 0.056632 Loss2: 1.328959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400618 Loss1: 0.078625 Loss2: 1.321993
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.256324 Loss1: 0.424156 Loss2: 1.832168
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351272 Loss1: 0.030980 Loss2: 1.320292
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.617740 Loss1: 0.265968 Loss2: 1.351772
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.567213 Loss1: 0.186460 Loss2: 1.380753
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569187 Loss1: 0.203232 Loss2: 1.365955
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.538643 Loss1: 0.173774 Loss2: 1.364869
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516033 Loss1: 0.158556 Loss2: 1.357477
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.453142 Loss1: 0.090110 Loss2: 1.363032
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.366367 Loss1: 0.438877 Loss2: 1.927491
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.428812 Loss1: 0.082417 Loss2: 1.346395
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.667865 Loss1: 0.275346 Loss2: 1.392520
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429500 Loss1: 0.087392 Loss2: 1.342108
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.669465 Loss1: 0.247275 Loss2: 1.422190
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405592 Loss1: 0.071161 Loss2: 1.334431
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.603425 Loss1: 0.200921 Loss2: 1.402504
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.584395 Loss1: 0.173953 Loss2: 1.410442
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506935 Loss1: 0.109527 Loss2: 1.397409
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470503 Loss1: 0.088551 Loss2: 1.381952
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437440 Loss1: 0.049424 Loss2: 1.388016
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433903 Loss1: 0.060531 Loss2: 1.373372
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.201687 Loss1: 0.375613 Loss2: 1.826074
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434347 Loss1: 0.061882 Loss2: 1.372465
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.627177 Loss1: 0.277864 Loss2: 1.349313
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574601 Loss1: 0.199744 Loss2: 1.374857
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542183 Loss1: 0.182113 Loss2: 1.360069
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475612 Loss1: 0.121312 Loss2: 1.354300
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487942 Loss1: 0.135136 Loss2: 1.352806
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.157578 Loss1: 0.346679 Loss2: 1.810899
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.553194 Loss1: 0.194527 Loss2: 1.358667 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.510125 Loss1: 0.148175 Loss2: 1.361950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.471428 Loss1: 0.120214 Loss2: 1.351213 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521269 Loss1: 0.170613 Loss2: 1.350656 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.442646 Loss1: 0.097590 Loss2: 1.345056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398532 Loss1: 0.060666 Loss2: 1.337866 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373373 Loss1: 0.042042 Loss2: 1.331332 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.622568 Loss1: 0.184376 Loss2: 1.438192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461238 Loss1: 0.086027 Loss2: 1.375210 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.379815 Loss1: 0.449035 Loss2: 1.930780 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.683472 Loss1: 0.309889 Loss2: 1.373584 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.583725 Loss1: 0.189325 Loss2: 1.394400 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.518398 Loss1: 0.129459 Loss2: 1.388939 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430973 Loss1: 0.076705 Loss2: 1.354268
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.462840 Loss1: 0.098087 Loss2: 1.364752
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.457843 Loss1: 0.093751 Loss2: 1.364092
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461377 Loss1: 0.098676 Loss2: 1.362701
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446251 Loss1: 0.086390 Loss2: 1.359861
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453099 Loss1: 0.099080 Loss2: 1.354019
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441291 Loss1: 0.086127 Loss2: 1.355164
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.201818 Loss1: 0.366190 Loss2: 1.835628
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.552327 Loss1: 0.205912 Loss2: 1.346415
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.456752 Loss1: 0.111173 Loss2: 1.345579
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.435601 Loss1: 0.091340 Loss2: 1.344261
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.398631 Loss1: 0.069627 Loss2: 1.329004
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.397415 Loss1: 0.075550 Loss2: 1.321865
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.309634 Loss1: 0.401419 Loss2: 1.908215
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.379271 Loss1: 0.056163 Loss2: 1.323108
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.690976 Loss1: 0.302545 Loss2: 1.388431
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380313 Loss1: 0.059117 Loss2: 1.321196
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.613596 Loss1: 0.183782 Loss2: 1.429815
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372740 Loss1: 0.057397 Loss2: 1.315343
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.559069 Loss1: 0.148795 Loss2: 1.410274
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364794 Loss1: 0.050638 Loss2: 1.314156
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518306 Loss1: 0.123928 Loss2: 1.394378
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.510686 Loss1: 0.120507 Loss2: 1.390178
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460720 Loss1: 0.073720 Loss2: 1.387000
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.463200 Loss1: 0.077063 Loss2: 1.386137
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433128 Loss1: 0.049461 Loss2: 1.383666
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.238952 Loss1: 0.372116 Loss2: 1.866836
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427515 Loss1: 0.058519 Loss2: 1.368996
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.616212 Loss1: 0.243249 Loss2: 1.372963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503188 Loss1: 0.152176 Loss2: 1.351011 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449044 Loss1: 0.116832 Loss2: 1.332211
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.225950 Loss1: 0.364659 Loss2: 1.861291
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404517 Loss1: 0.072969 Loss2: 1.331548
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.639614 Loss1: 0.283173 Loss2: 1.356441
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.365053 Loss1: 0.043292 Loss2: 1.321761
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568156 Loss1: 0.189903 Loss2: 1.378253
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.357200 Loss1: 0.042231 Loss2: 1.314968
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529177 Loss1: 0.162120 Loss2: 1.367057
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.343790 Loss1: 0.033097 Loss2: 1.310693
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.480975 Loss1: 0.125720 Loss2: 1.355254
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463803 Loss1: 0.112024 Loss2: 1.351778
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.457074 Loss1: 0.103633 Loss2: 1.353441
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.459848 Loss1: 0.114085 Loss2: 1.345763
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424714 Loss1: 0.082498 Loss2: 1.342216
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403734 Loss1: 0.066109 Loss2: 1.337625
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.274068 Loss1: 0.329538 Loss2: 1.944530
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.801325 Loss1: 0.342848 Loss2: 1.458477
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.675607 Loss1: 0.173088 Loss2: 1.502519
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.581900 Loss1: 0.118031 Loss2: 1.463869
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.560403 Loss1: 0.108813 Loss2: 1.451590
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.186938 Loss1: 0.327400 Loss2: 1.859538
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.560571 Loss1: 0.103782 Loss2: 1.456789
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.544918 Loss1: 0.082998 Loss2: 1.461919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.522049 Loss1: 0.067820 Loss2: 1.454229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.488743 Loss1: 0.042897 Loss2: 1.445846 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.467690 Loss1: 0.033248 Loss2: 1.434442 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476411 Loss1: 0.135209 Loss2: 1.341202 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410429 Loss1: 0.072197 Loss2: 1.338232 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383924 Loss1: 0.055205 Loss2: 1.328719
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.201262 Loss1: 0.389431 Loss2: 1.811832
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.513183 Loss1: 0.205800 Loss2: 1.307382
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.517924 Loss1: 0.189755 Loss2: 1.328170
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.422159 Loss1: 0.114465 Loss2: 1.307693
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.398672 Loss1: 0.103895 Loss2: 1.294777
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.232971 Loss1: 0.380336 Loss2: 1.852635
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.677669 Loss1: 0.321267 Loss2: 1.356402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563474 Loss1: 0.159819 Loss2: 1.403655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507881 Loss1: 0.142793 Loss2: 1.365089 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.494480 Loss1: 0.130293 Loss2: 1.364186 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.330210 Loss1: 0.042132 Loss2: 1.288078
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.458161 Loss1: 0.099626 Loss2: 1.358535
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.407629 Loss1: 0.063503 Loss2: 1.344126
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389143 Loss1: 0.045255 Loss2: 1.343888
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405702 Loss1: 0.063757 Loss2: 1.341946
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377313 Loss1: 0.044035 Loss2: 1.333278
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.215260 Loss1: 0.416078 Loss2: 1.799182
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.578914 Loss1: 0.262046 Loss2: 1.316868
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.527268 Loss1: 0.178887 Loss2: 1.348382
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.448555 Loss1: 0.123388 Loss2: 1.325168
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487307 Loss1: 0.162337 Loss2: 1.324969
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.233450 Loss1: 0.402798 Loss2: 1.830651
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.487245 Loss1: 0.155700 Loss2: 1.331545
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.611776 Loss1: 0.274032 Loss2: 1.337744
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431093 Loss1: 0.108232 Loss2: 1.322861
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.558238 Loss1: 0.202171 Loss2: 1.356067
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394756 Loss1: 0.071743 Loss2: 1.323012
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498510 Loss1: 0.146134 Loss2: 1.352376
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427163 Loss1: 0.107937 Loss2: 1.319227
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.436988 Loss1: 0.110682 Loss2: 1.326306
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398606 Loss1: 0.080430 Loss2: 1.318176
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420410 Loss1: 0.092567 Loss2: 1.327843
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.378095 Loss1: 0.055569 Loss2: 1.322525
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390121 Loss1: 0.070702 Loss2: 1.319419
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369074 Loss1: 0.052108 Loss2: 1.316966
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356695 Loss1: 0.046486 Loss2: 1.310210
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.490329 Loss1: 0.528978 Loss2: 1.961351
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.631018 Loss1: 0.266613 Loss2: 1.364406
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.566702 Loss1: 0.202425 Loss2: 1.364277
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.487616 Loss1: 0.112461 Loss2: 1.375155
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.422817 Loss1: 0.076148 Loss2: 1.346670
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452764 Loss1: 0.111051 Loss2: 1.341713
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.076021 Loss1: 0.312125 Loss2: 1.763896
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451084 Loss1: 0.106046 Loss2: 1.345038
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.513303 Loss1: 0.201198 Loss2: 1.312105
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.503077 Loss1: 0.183141 Loss2: 1.319936 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.517646 Loss1: 0.195721 Loss2: 1.321925 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.397760 Loss1: 0.084221 Loss2: 1.313539 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.336368 Loss1: 0.045906 Loss2: 1.290462 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.292165 Loss1: 0.396737 Loss2: 1.895428
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.320427 Loss1: 0.032339 Loss2: 1.288088
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.603943 Loss1: 0.222784 Loss2: 1.381159
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.309646 Loss1: 0.025675 Loss2: 1.283970
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516011 Loss1: 0.125486 Loss2: 1.390525 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.500706 Loss1: 0.114156 Loss2: 1.386551 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513418 Loss1: 0.133778 Loss2: 1.379641
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.279782 Loss1: 0.406785 Loss2: 1.872996
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.610538 Loss1: 0.259074 Loss2: 1.351465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608728 Loss1: 0.222560 Loss2: 1.386168 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466580 Loss1: 0.099465 Loss2: 1.367115
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.456017 Loss1: 0.101507 Loss2: 1.354510
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.444393 Loss1: 0.101376 Loss2: 1.343017
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.399334 Loss1: 0.057478 Loss2: 1.341856
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.380925 Loss1: 0.047934 Loss2: 1.332992
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.355139 Loss1: 0.032793 Loss2: 1.322347
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.224236 Loss1: 0.379462 Loss2: 1.844774
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.352951 Loss1: 0.029019 Loss2: 1.323932
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.662402 Loss1: 0.326025 Loss2: 1.336378
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370846 Loss1: 0.050198 Loss2: 1.320648
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.515044 Loss1: 0.147595 Loss2: 1.367448 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462510 Loss1: 0.114446 Loss2: 1.348064 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439252 Loss1: 0.097966 Loss2: 1.341286
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.217613 Loss1: 0.379412 Loss2: 1.838201
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.625984 Loss1: 0.240517 Loss2: 1.385467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.559312 Loss1: 0.144362 Loss2: 1.414951 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 07:14:10,680 | server.py:236 | fit_round 180 received 50 results and 0 failures
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562056 Loss1: 0.171544 Loss2: 1.390512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.542522 Loss1: 0.148283 Loss2: 1.394239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.467818 Loss1: 0.080165 Loss2: 1.387652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460622 Loss1: 0.078322 Loss2: 1.382300 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472212 Loss1: 0.094087 Loss2: 1.378125 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471021 Loss1: 0.138571 Loss2: 1.332450 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.421836 Loss1: 0.089518 Loss2: 1.332318 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.227897 Loss1: 0.352854 Loss2: 1.875043
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.395484 Loss1: 0.067399 Loss2: 1.328086
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.539834 Loss1: 0.185556 Loss2: 1.354278
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378326 Loss1: 0.055099 Loss2: 1.323227
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.573873 Loss1: 0.216413 Loss2: 1.357461
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378927 Loss1: 0.060557 Loss2: 1.318370
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.437984 Loss1: 0.105207 Loss2: 1.332777 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392149 Loss1: 0.060819 Loss2: 1.331331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.415758 Loss1: 0.498065 Loss2: 1.917693
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390021 Loss1: 0.069430 Loss2: 1.320590
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.696357 Loss1: 0.333743 Loss2: 1.362614
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374791 Loss1: 0.051841 Loss2: 1.322950
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380613 Loss1: 0.063606 Loss2: 1.317007 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443343 Loss1: 0.092761 Loss2: 1.350582 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426932 Loss1: 0.085369 Loss2: 1.341562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.544354 Loss1: 0.497747 Loss2: 2.046607 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.700226 Loss1: 0.316157 Loss2: 1.384069 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.554352 Loss1: 0.136144 Loss2: 1.418208 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494216 Loss1: 0.111937 Loss2: 1.382278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438453 Loss1: 0.060613 Loss2: 1.377840 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417442 Loss1: 0.058596 Loss2: 1.358846 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 07:14:10,680][flwr][DEBUG] - fit_round 180 received 50 results and 0 failures
-INFO flwr 2023-10-13 07:14:52,168 | server.py:125 | fit progress: (180, 2.291451721145703, {'accuracy': 0.609}, 415399.94684953)
->> Test accuracy: 0.609000
-[2023-10-13 07:14:52,168][flwr][INFO] - fit progress: (180, 2.291451721145703, {'accuracy': 0.609}, 415399.94684953)
-DEBUG flwr 2023-10-13 07:14:52,169 | server.py:173 | evaluate_round 180: strategy sampled 50 clients (out of 50)
-[2023-10-13 07:14:52,169][flwr][DEBUG] - evaluate_round 180: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 07:24:01,804 | server.py:187 | evaluate_round 180 received 50 results and 0 failures
-[2023-10-13 07:24:01,804][flwr][DEBUG] - evaluate_round 180 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 07:24:01,804 | server.py:222 | fit_round 181: strategy sampled 50 clients (out of 50)
-[2023-10-13 07:24:01,804][flwr][DEBUG] - fit_round 181: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.055213 Loss1: 0.307115 Loss2: 1.748098
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.520971 Loss1: 0.214537 Loss2: 1.306434
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.481422 Loss1: 0.162592 Loss2: 1.318829
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.259465 Loss1: 0.387961 Loss2: 1.871504
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.411431 Loss1: 0.105868 Loss2: 1.305563
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.633491 Loss1: 0.256592 Loss2: 1.376899
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.414876 Loss1: 0.117526 Loss2: 1.297350
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.577436 Loss1: 0.176182 Loss2: 1.401254
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.381526 Loss1: 0.077754 Loss2: 1.303771
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.388259 Loss1: 0.091378 Loss2: 1.296881 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.388888 Loss1: 0.090095 Loss2: 1.298793 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.341965 Loss1: 0.050869 Loss2: 1.291095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.341183 Loss1: 0.050330 Loss2: 1.290853 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.420694 Loss1: 0.065567 Loss2: 1.355126 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.255461 Loss1: 0.390123 Loss2: 1.865338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.633043 Loss1: 0.232610 Loss2: 1.400433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540091 Loss1: 0.176298 Loss2: 1.363793
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.135402 Loss1: 0.387890 Loss2: 1.747512
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.483438 Loss1: 0.122527 Loss2: 1.360911
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.510789 Loss1: 0.242442 Loss2: 1.268347
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.471254 Loss1: 0.114765 Loss2: 1.356490
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.426938 Loss1: 0.136815 Loss2: 1.290123
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428240 Loss1: 0.075140 Loss2: 1.353100
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.405678 Loss1: 0.134984 Loss2: 1.270694
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389122 Loss1: 0.043168 Loss2: 1.345954
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.355823 Loss1: 0.085485 Loss2: 1.270338
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399318 Loss1: 0.057649 Loss2: 1.341669
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.375498 Loss1: 0.106216 Loss2: 1.269282
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385060 Loss1: 0.049134 Loss2: 1.335926
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.345318 Loss1: 0.079807 Loss2: 1.265511
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.324185 Loss1: 0.062044 Loss2: 1.262142
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.287651 Loss1: 0.033022 Loss2: 1.254629
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.321848 Loss1: 0.069471 Loss2: 1.252377
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.272396 Loss1: 0.383378 Loss2: 1.889018
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.684376 Loss1: 0.300417 Loss2: 1.383959
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.585888 Loss1: 0.181738 Loss2: 1.404150
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569388 Loss1: 0.180888 Loss2: 1.388500
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.272052 Loss1: 0.445837 Loss2: 1.826216
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.580198 Loss1: 0.186771 Loss2: 1.393428
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.581963 Loss1: 0.236540 Loss2: 1.345423
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519749 Loss1: 0.127163 Loss2: 1.392586
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.469642 Loss1: 0.113499 Loss2: 1.356143
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.493503 Loss1: 0.119445 Loss2: 1.374059
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.426813 Loss1: 0.084017 Loss2: 1.342796
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449636 Loss1: 0.070144 Loss2: 1.379493
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.399333 Loss1: 0.071778 Loss2: 1.327555
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452672 Loss1: 0.076092 Loss2: 1.376580
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.397226 Loss1: 0.069312 Loss2: 1.327914
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414406 Loss1: 0.041870 Loss2: 1.372536
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422201 Loss1: 0.092782 Loss2: 1.329419
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388437 Loss1: 0.057825 Loss2: 1.330612
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.378017 Loss1: 0.054423 Loss2: 1.323594
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376448 Loss1: 0.063989 Loss2: 1.312459
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.266059 Loss1: 0.369103 Loss2: 1.896957
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.686842 Loss1: 0.290277 Loss2: 1.396565
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.632301 Loss1: 0.203521 Loss2: 1.428780
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554647 Loss1: 0.138826 Loss2: 1.415821
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.214189 Loss1: 0.375045 Loss2: 1.839145
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503442 Loss1: 0.102554 Loss2: 1.400889
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.577622 Loss1: 0.234175 Loss2: 1.343447
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.499335 Loss1: 0.099238 Loss2: 1.400096
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.633498 Loss1: 0.270001 Loss2: 1.363497
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.494645 Loss1: 0.098954 Loss2: 1.395691
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576836 Loss1: 0.224461 Loss2: 1.352375
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470673 Loss1: 0.076097 Loss2: 1.394575
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.565503 Loss1: 0.219808 Loss2: 1.345695
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.490980 Loss1: 0.096852 Loss2: 1.394127
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.509589 Loss1: 0.159640 Loss2: 1.349949
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461791 Loss1: 0.069079 Loss2: 1.392712
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.501473 Loss1: 0.160696 Loss2: 1.340778
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428304 Loss1: 0.088325 Loss2: 1.339978
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421420 Loss1: 0.092863 Loss2: 1.328557
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368248 Loss1: 0.040101 Loss2: 1.328148
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.226789 Loss1: 0.368879 Loss2: 1.857910
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572261 Loss1: 0.252654 Loss2: 1.319607
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.535560 Loss1: 0.197695 Loss2: 1.337865
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496257 Loss1: 0.157124 Loss2: 1.339133
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.281966 Loss1: 0.429962 Loss2: 1.852004
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.629938 Loss1: 0.286588 Loss2: 1.343350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543604 Loss1: 0.166310 Loss2: 1.377295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.441885 Loss1: 0.096627 Loss2: 1.345258 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.437220 Loss1: 0.100585 Loss2: 1.336635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427861 Loss1: 0.104102 Loss2: 1.323759
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.426032 Loss1: 0.091093 Loss2: 1.334939
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384339 Loss1: 0.071835 Loss2: 1.312504
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.381590 Loss1: 0.048692 Loss2: 1.332898
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390470 Loss1: 0.062266 Loss2: 1.328204
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390503 Loss1: 0.063976 Loss2: 1.326528
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398491 Loss1: 0.073475 Loss2: 1.325016
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.226126 Loss1: 0.390991 Loss2: 1.835134
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.599196 Loss1: 0.254552 Loss2: 1.344644
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.570849 Loss1: 0.203646 Loss2: 1.367202
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.503375 Loss1: 0.158084 Loss2: 1.345291
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.419994 Loss1: 0.542567 Loss2: 1.877427
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.582715 Loss1: 0.255515 Loss2: 1.327200
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.459358 Loss1: 0.110599 Loss2: 1.348760
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437634 Loss1: 0.097895 Loss2: 1.339739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395523 Loss1: 0.060248 Loss2: 1.335275 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.368097 Loss1: 0.042936 Loss2: 1.325161 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.361309 Loss1: 0.039047 Loss2: 1.322262 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.376451 Loss1: 0.061875 Loss2: 1.314576 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354145 Loss1: 0.041037 Loss2: 1.313108 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.153582 Loss1: 0.363319 Loss2: 1.790263
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.624071 Loss1: 0.296090 Loss2: 1.327981
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.553508 Loss1: 0.199600 Loss2: 1.353908
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489215 Loss1: 0.145851 Loss2: 1.343364
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.154739 Loss1: 0.381302 Loss2: 1.773438
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443258 Loss1: 0.122319 Loss2: 1.320939
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.578984 Loss1: 0.247750 Loss2: 1.331234
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.529106 Loss1: 0.179177 Loss2: 1.349929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492287 Loss1: 0.163587 Loss2: 1.328701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.445057 Loss1: 0.121722 Loss2: 1.323335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.410788 Loss1: 0.087221 Loss2: 1.323567 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.398727 Loss1: 0.081673 Loss2: 1.317055 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379979 Loss1: 0.071393 Loss2: 1.308586 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.291876 Loss1: 0.398422 Loss2: 1.893454 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.520800 Loss1: 0.125528 Loss2: 1.395271 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.505714 Loss1: 0.116538 Loss2: 1.389176
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.355530 Loss1: 0.463662 Loss2: 1.891868
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.636188 Loss1: 0.280921 Loss2: 1.355268 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568549 Loss1: 0.189459 Loss2: 1.379090 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.486898 Loss1: 0.132000 Loss2: 1.354898 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446357 Loss1: 0.099892 Loss2: 1.346465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.409628 Loss1: 0.065237 Loss2: 1.344391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405685 Loss1: 0.052131 Loss2: 1.353555
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416275 Loss1: 0.080416 Loss2: 1.335860
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.392865 Loss1: 0.061312 Loss2: 1.331553
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385715 Loss1: 0.060999 Loss2: 1.324716
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373205 Loss1: 0.049918 Loss2: 1.323287
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.379927 Loss1: 0.439970 Loss2: 1.939957
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.657944 Loss1: 0.283453 Loss2: 1.374492
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.623799 Loss1: 0.215432 Loss2: 1.408367
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.525606 Loss1: 0.124083 Loss2: 1.401523
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.213216 Loss1: 0.345260 Loss2: 1.867957
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.636729 Loss1: 0.246009 Loss2: 1.390720 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.656684 Loss1: 0.222789 Loss2: 1.433894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.611800 Loss1: 0.199958 Loss2: 1.411841 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433824 Loss1: 0.070810 Loss2: 1.363015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410679 Loss1: 0.046203 Loss2: 1.364476 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449670 Loss1: 0.060635 Loss2: 1.389035 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440299 Loss1: 0.063028 Loss2: 1.377272 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.489513 Loss1: 0.193201 Loss2: 1.296313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.428050 Loss1: 0.123610 Loss2: 1.304440 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.373212 Loss1: 0.089442 Loss2: 1.283770
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.222044 Loss1: 0.337253 Loss2: 1.884791
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.587168 Loss1: 0.219408 Loss2: 1.367760 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.586284 Loss1: 0.201426 Loss2: 1.384858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.509360 Loss1: 0.134647 Loss2: 1.374713 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476629 Loss1: 0.116721 Loss2: 1.359909 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460415 Loss1: 0.097784 Loss2: 1.362631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426187 Loss1: 0.071380 Loss2: 1.354807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391949 Loss1: 0.040473 Loss2: 1.351476 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.533315 Loss1: 0.197442 Loss2: 1.335873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.452380 Loss1: 0.107230 Loss2: 1.345150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476830 Loss1: 0.146185 Loss2: 1.330645
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.350859 Loss1: 0.447454 Loss2: 1.903405
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.414929 Loss1: 0.076718 Loss2: 1.338212
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.619202 Loss1: 0.209328 Loss2: 1.409874
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404385 Loss1: 0.073878 Loss2: 1.330507
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.607733 Loss1: 0.172967 Loss2: 1.434766
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382160 Loss1: 0.056281 Loss2: 1.325879
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528485 Loss1: 0.123142 Loss2: 1.405344
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497692 Loss1: 0.104921 Loss2: 1.392771
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386301 Loss1: 0.064558 Loss2: 1.321743
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.503573 Loss1: 0.110362 Loss2: 1.393210
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367585 Loss1: 0.049954 Loss2: 1.317631
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453258 Loss1: 0.066755 Loss2: 1.386503 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424846 Loss1: 0.052378 Loss2: 1.372468 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.544378 Loss1: 0.232499 Loss2: 1.311879 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.450182 Loss1: 0.133974 Loss2: 1.316209 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.401367 Loss1: 0.092130 Loss2: 1.309237
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.364795 Loss1: 0.504478 Loss2: 1.860317
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.435749 Loss1: 0.135790 Loss2: 1.299958
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.586953 Loss1: 0.240035 Loss2: 1.346917
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.509583 Loss1: 0.142724 Loss2: 1.366859 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492622 Loss1: 0.140517 Loss2: 1.352104 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460357 Loss1: 0.121673 Loss2: 1.338684 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.442328 Loss1: 0.091185 Loss2: 1.351143 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439145 Loss1: 0.108252 Loss2: 1.330893 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366873 Loss1: 0.044735 Loss2: 1.322138 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.604130 Loss1: 0.287460 Loss2: 1.316669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.465276 Loss1: 0.140069 Loss2: 1.325208 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.438253 Loss1: 0.123104 Loss2: 1.315148
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.369836 Loss1: 0.502914 Loss2: 1.866922
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659973 Loss1: 0.302725 Loss2: 1.357248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.568897 Loss1: 0.185203 Loss2: 1.383694 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481607 Loss1: 0.139612 Loss2: 1.341995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450522 Loss1: 0.115689 Loss2: 1.334834 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420707 Loss1: 0.088496 Loss2: 1.332211 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386689 Loss1: 0.065026 Loss2: 1.321664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.338662 Loss1: 0.030477 Loss2: 1.308186 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.534161 Loss1: 0.222499 Loss2: 1.311662 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.412845 Loss1: 0.097414 Loss2: 1.315432 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.393622 Loss1: 0.094805 Loss2: 1.298816
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.290169 Loss1: 0.394168 Loss2: 1.896001
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.614519 Loss1: 0.226977 Loss2: 1.387542 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556723 Loss1: 0.160813 Loss2: 1.395910 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.477508 Loss1: 0.089072 Loss2: 1.388436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.468023 Loss1: 0.094319 Loss2: 1.373704 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461283 Loss1: 0.081831 Loss2: 1.379452 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.403414 Loss1: 0.040785 Loss2: 1.362629 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402227 Loss1: 0.047803 Loss2: 1.354424 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.564269 Loss1: 0.220164 Loss2: 1.344106 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500561 Loss1: 0.138592 Loss2: 1.361968 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463842 Loss1: 0.125647 Loss2: 1.338195
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.268189 Loss1: 0.398724 Loss2: 1.869465
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.674224 Loss1: 0.310039 Loss2: 1.364185 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404929 Loss1: 0.065564 Loss2: 1.339365
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.708259 Loss1: 0.291357 Loss2: 1.416902
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398480 Loss1: 0.065358 Loss2: 1.333121
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.640763 Loss1: 0.254332 Loss2: 1.386431
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396624 Loss1: 0.066252 Loss2: 1.330372
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557281 Loss1: 0.175064 Loss2: 1.382217
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371908 Loss1: 0.045720 Loss2: 1.326187
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.497160 Loss1: 0.111605 Loss2: 1.385555
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443971 Loss1: 0.075213 Loss2: 1.368758
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431163 Loss1: 0.065576 Loss2: 1.365588
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.437481 Loss1: 0.079956 Loss2: 1.357524
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450763 Loss1: 0.091913 Loss2: 1.358850
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.249240 Loss1: 0.407272 Loss2: 1.841968
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.552465 Loss1: 0.199222 Loss2: 1.353243
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.514308 Loss1: 0.147752 Loss2: 1.366556
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.468913 Loss1: 0.116501 Loss2: 1.352412
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476768 Loss1: 0.132781 Loss2: 1.343986
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.250247 Loss1: 0.384472 Loss2: 1.865774
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.633033 Loss1: 0.267245 Loss2: 1.365788 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.518064 Loss1: 0.131482 Loss2: 1.386582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.482443 Loss1: 0.102808 Loss2: 1.379636 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503254 Loss1: 0.142726 Loss2: 1.360528 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365446 Loss1: 0.040667 Loss2: 1.324779
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473144 Loss1: 0.110229 Loss2: 1.362915
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406364 Loss1: 0.051216 Loss2: 1.355147
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406696 Loss1: 0.055354 Loss2: 1.351341
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419672 Loss1: 0.070966 Loss2: 1.348706
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416118 Loss1: 0.066961 Loss2: 1.349158
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.263783 Loss1: 0.451287 Loss2: 1.812496
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.708972 Loss1: 0.366809 Loss2: 1.342163
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609651 Loss1: 0.207842 Loss2: 1.401810
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498808 Loss1: 0.156684 Loss2: 1.342124
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488704 Loss1: 0.145630 Loss2: 1.343074
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.328559 Loss1: 0.446551 Loss2: 1.882008
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.646770 Loss1: 0.267310 Loss2: 1.379459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598778 Loss1: 0.197259 Loss2: 1.401519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556312 Loss1: 0.173923 Loss2: 1.382389 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493627 Loss1: 0.129600 Loss2: 1.364027 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368133 Loss1: 0.046246 Loss2: 1.321887
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498751 Loss1: 0.135741 Loss2: 1.363011
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.487062 Loss1: 0.125171 Loss2: 1.361892
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435289 Loss1: 0.072847 Loss2: 1.362442
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.423612 Loss1: 0.068044 Loss2: 1.355568
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401199 Loss1: 0.053045 Loss2: 1.348154
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.272994 Loss1: 0.436773 Loss2: 1.836222
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.654619 Loss1: 0.307222 Loss2: 1.347398
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.652631 Loss1: 0.247835 Loss2: 1.404796
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518023 Loss1: 0.154366 Loss2: 1.363656
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.529359 Loss1: 0.175738 Loss2: 1.353621
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.266601 Loss1: 0.416002 Loss2: 1.850598
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.667126 Loss1: 0.306491 Loss2: 1.360635 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.641261 Loss1: 0.237106 Loss2: 1.404156 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556793 Loss1: 0.188613 Loss2: 1.368180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497840 Loss1: 0.137563 Loss2: 1.360278 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.950000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.457002 Loss1: 0.099659 Loss2: 1.357343 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.388479 Loss1: 0.049215 Loss2: 1.339264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372291 Loss1: 0.036817 Loss2: 1.335474 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.576070 Loss1: 0.217877 Loss2: 1.358193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.443863 Loss1: 0.092331 Loss2: 1.351532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.235829 Loss1: 0.423879 Loss2: 1.811950
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.412888 Loss1: 0.063378 Loss2: 1.349510
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.587649 Loss1: 0.243794 Loss2: 1.343855
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.383914 Loss1: 0.045098 Loss2: 1.338816
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572493 Loss1: 0.198969 Loss2: 1.373524
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419566 Loss1: 0.086470 Loss2: 1.333096
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.395262 Loss1: 0.055981 Loss2: 1.339282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412381 Loss1: 0.075931 Loss2: 1.336450 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415919 Loss1: 0.074789 Loss2: 1.341131 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992647
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434585 Loss1: 0.091933 Loss2: 1.342652 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375959 Loss1: 0.044429 Loss2: 1.331530 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.137204 Loss1: 0.348021 Loss2: 1.789183
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.551310 Loss1: 0.227106 Loss2: 1.324205
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.650849 Loss1: 0.277727 Loss2: 1.373123
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541775 Loss1: 0.209991 Loss2: 1.331784
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.292976 Loss1: 0.415031 Loss2: 1.877945
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.672344 Loss1: 0.304657 Loss2: 1.367687 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.553584 Loss1: 0.149274 Loss2: 1.404310 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487099 Loss1: 0.121606 Loss2: 1.365493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.385167 Loss1: 0.072311 Loss2: 1.312856
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493802 Loss1: 0.128705 Loss2: 1.365097
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.380495 Loss1: 0.070832 Loss2: 1.309663
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476450 Loss1: 0.103584 Loss2: 1.372866
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395008 Loss1: 0.088674 Loss2: 1.306334
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435298 Loss1: 0.078664 Loss2: 1.356633
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449976 Loss1: 0.091939 Loss2: 1.358036
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417907 Loss1: 0.068324 Loss2: 1.349583
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466469 Loss1: 0.112373 Loss2: 1.354095
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.343747 Loss1: 0.444275 Loss2: 1.899472
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.605900 Loss1: 0.232009 Loss2: 1.373891
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.564338 Loss1: 0.171613 Loss2: 1.392725
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517320 Loss1: 0.142615 Loss2: 1.374705
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.420829 Loss1: 0.483474 Loss2: 1.937355
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.757206 Loss1: 0.426713 Loss2: 1.330493
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.505952 Loss1: 0.141663 Loss2: 1.364288
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.574536 Loss1: 0.202084 Loss2: 1.372452
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466023 Loss1: 0.103057 Loss2: 1.362965 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445883 Loss1: 0.083592 Loss2: 1.362292 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.418796 Loss1: 0.082321 Loss2: 1.336476 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413525 Loss1: 0.053564 Loss2: 1.359961 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373710 Loss1: 0.061793 Loss2: 1.311917 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986979
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.513209 Loss1: 0.517727 Loss2: 1.995482
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.749562 Loss1: 0.351205 Loss2: 1.398357
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.651386 Loss1: 0.229599 Loss2: 1.421787
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587343 Loss1: 0.173832 Loss2: 1.413511
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.216630 Loss1: 0.391231 Loss2: 1.825399
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.472094 Loss1: 0.076064 Loss2: 1.396030 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476547 Loss1: 0.097611 Loss2: 1.378937 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435927 Loss1: 0.060031 Loss2: 1.375896 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455218 Loss1: 0.081028 Loss2: 1.374190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431708 Loss1: 0.066512 Loss2: 1.365196 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433333 Loss1: 0.094117 Loss2: 1.339215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417544 Loss1: 0.087953 Loss2: 1.329591 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382798 Loss1: 0.049166 Loss2: 1.333631
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.181192 Loss1: 0.353584 Loss2: 1.827609
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.651210 Loss1: 0.285982 Loss2: 1.365228
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635200 Loss1: 0.223530 Loss2: 1.411670
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529661 Loss1: 0.165481 Loss2: 1.364180
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492433 Loss1: 0.120289 Loss2: 1.372144
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.143293 Loss1: 0.328856 Loss2: 1.814437
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.490782 Loss1: 0.130515 Loss2: 1.360268
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.598853 Loss1: 0.253608 Loss2: 1.345245
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458728 Loss1: 0.104979 Loss2: 1.353749
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.541179 Loss1: 0.162192 Loss2: 1.378986
-DEBUG flwr 2023-10-13 07:53:02,607 | server.py:236 | fit_round 181 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404807 Loss1: 0.051548 Loss2: 1.353259
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.479873 Loss1: 0.143202 Loss2: 1.336671
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398133 Loss1: 0.052996 Loss2: 1.345137
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.457656 Loss1: 0.122538 Loss2: 1.335117
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374478 Loss1: 0.034536 Loss2: 1.339941
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.433325 Loss1: 0.093942 Loss2: 1.339383
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441534 Loss1: 0.107328 Loss2: 1.334206
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434697 Loss1: 0.101937 Loss2: 1.332760
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409266 Loss1: 0.079988 Loss2: 1.329277
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365676 Loss1: 0.041045 Loss2: 1.324631
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.382029 Loss1: 0.439561 Loss2: 1.942468
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.708606 Loss1: 0.279241 Loss2: 1.429366
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.603697 Loss1: 0.149854 Loss2: 1.453843
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561946 Loss1: 0.131560 Loss2: 1.430386
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.561729 Loss1: 0.131260 Loss2: 1.430469
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.191933 Loss1: 0.399365 Loss2: 1.792568
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.528143 Loss1: 0.210842 Loss2: 1.317302 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.472699 Loss1: 0.147130 Loss2: 1.325569 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.427075 Loss1: 0.114571 Loss2: 1.312505 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.379194 Loss1: 0.084665 Loss2: 1.294529 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.390276 Loss1: 0.096934 Loss2: 1.293342 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.336511 Loss1: 0.047982 Loss2: 1.288529 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.327160 Loss1: 0.045608 Loss2: 1.281553 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.593852 Loss1: 0.216978 Loss2: 1.376875 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522238 Loss1: 0.142958 Loss2: 1.379280 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.185983 Loss1: 0.363380 Loss2: 1.822603 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.598348 Loss1: 0.237371 Loss2: 1.360977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.523362 Loss1: 0.152015 Loss2: 1.371347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.466410 Loss1: 0.111076 Loss2: 1.355334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476968 Loss1: 0.132043 Loss2: 1.344925 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450530 Loss1: 0.094131 Loss2: 1.356399 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449837 Loss1: 0.108302 Loss2: 1.341534 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 07:53:02,607][flwr][DEBUG] - fit_round 181 received 50 results and 0 failures
-INFO flwr 2023-10-13 07:53:43,836 | server.py:125 | fit progress: (181, 2.3034834168589535, {'accuracy': 0.6087}, 417731.61495577596)
->> Test accuracy: 0.608700
-[2023-10-13 07:53:43,836][flwr][INFO] - fit progress: (181, 2.3034834168589535, {'accuracy': 0.6087}, 417731.61495577596)
-DEBUG flwr 2023-10-13 07:53:43,837 | server.py:173 | evaluate_round 181: strategy sampled 50 clients (out of 50)
-[2023-10-13 07:53:43,837][flwr][DEBUG] - evaluate_round 181: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 08:02:53,395 | server.py:187 | evaluate_round 181 received 50 results and 0 failures
-[2023-10-13 08:02:53,395][flwr][DEBUG] - evaluate_round 181 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 08:02:53,396 | server.py:222 | fit_round 182: strategy sampled 50 clients (out of 50)
-[2023-10-13 08:02:53,396][flwr][DEBUG] - fit_round 182: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.283660 Loss1: 0.429136 Loss2: 1.854523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611377 Loss1: 0.207606 Loss2: 1.403772 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534026 Loss1: 0.163386 Loss2: 1.370639
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.151537 Loss1: 0.333499 Loss2: 1.818038
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503794 Loss1: 0.143439 Loss2: 1.360355
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.570800 Loss1: 0.252676 Loss2: 1.318124
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.475693 Loss1: 0.114494 Loss2: 1.361200
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.491118 Loss1: 0.153248 Loss2: 1.337870
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424085 Loss1: 0.067734 Loss2: 1.356351
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.428501 Loss1: 0.095210 Loss2: 1.333292
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433835 Loss1: 0.077011 Loss2: 1.356823
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.370818 Loss1: 0.055734 Loss2: 1.315084
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414634 Loss1: 0.065017 Loss2: 1.349617
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.378533 Loss1: 0.070736 Loss2: 1.307797
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393640 Loss1: 0.046127 Loss2: 1.347513
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.360796 Loss1: 0.053626 Loss2: 1.307170
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.325921 Loss1: 0.026795 Loss2: 1.299126
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.318342 Loss1: 0.024269 Loss2: 1.294073
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.322574 Loss1: 0.032832 Loss2: 1.289741
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.273443 Loss1: 0.387672 Loss2: 1.885771
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.659437 Loss1: 0.297570 Loss2: 1.361867
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.560295 Loss1: 0.171407 Loss2: 1.388888
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474619 Loss1: 0.121622 Loss2: 1.352996
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.146025 Loss1: 0.360925 Loss2: 1.785100
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.606901 Loss1: 0.299351 Loss2: 1.307550 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.494098 Loss1: 0.169007 Loss2: 1.325091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.422011 Loss1: 0.101964 Loss2: 1.320047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.409479 Loss1: 0.103901 Loss2: 1.305578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.400132 Loss1: 0.093578 Loss2: 1.306554 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.366705 Loss1: 0.036366 Loss2: 1.330339
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.382900 Loss1: 0.079181 Loss2: 1.303719
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.350702 Loss1: 0.051491 Loss2: 1.299210
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.337859 Loss1: 0.045990 Loss2: 1.291869
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.335602 Loss1: 0.045066 Loss2: 1.290537
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.187932 Loss1: 0.338311 Loss2: 1.849621
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.577913 Loss1: 0.209715 Loss2: 1.368198
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.523645 Loss1: 0.134136 Loss2: 1.389509
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.463801 Loss1: 0.091805 Loss2: 1.371995
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.289108 Loss1: 0.387007 Loss2: 1.902101
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468776 Loss1: 0.110570 Loss2: 1.358206
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.592546 Loss1: 0.206507 Loss2: 1.386038
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481839 Loss1: 0.118295 Loss2: 1.363544
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.555343 Loss1: 0.159456 Loss2: 1.395886
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.474426 Loss1: 0.087674 Loss2: 1.386752
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.513268 Loss1: 0.141916 Loss2: 1.371352
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.448788 Loss1: 0.077382 Loss2: 1.371406
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.529601 Loss1: 0.154547 Loss2: 1.375055
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456997 Loss1: 0.087444 Loss2: 1.369552
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470608 Loss1: 0.100812 Loss2: 1.369796
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445320 Loss1: 0.075968 Loss2: 1.369352
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421444 Loss1: 0.060724 Loss2: 1.360720
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431938 Loss1: 0.066298 Loss2: 1.365640 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.197878 Loss1: 0.415883 Loss2: 1.781995 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.466637 Loss1: 0.144992 Loss2: 1.321645 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476534 Loss1: 0.165784 Loss2: 1.310750
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.123308 Loss1: 0.311325 Loss2: 1.811982
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.553957 Loss1: 0.241618 Loss2: 1.312339 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.464528 Loss1: 0.148457 Loss2: 1.316071 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.431653 Loss1: 0.115146 Loss2: 1.316507 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.413322 Loss1: 0.111611 Loss2: 1.301710 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.345247 Loss1: 0.053094 Loss2: 1.292152
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.377265 Loss1: 0.069110 Loss2: 1.308155
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.336871 Loss1: 0.048688 Loss2: 1.288183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.394740 Loss1: 0.086227 Loss2: 1.308512
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426395 Loss1: 0.123234 Loss2: 1.303161
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410128 Loss1: 0.107157 Loss2: 1.302971
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388724 Loss1: 0.083207 Loss2: 1.305517
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.365878 Loss1: 0.490785 Loss2: 1.875093
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.643019 Loss1: 0.284302 Loss2: 1.358717
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.566989 Loss1: 0.167504 Loss2: 1.399485
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.544250 Loss1: 0.181673 Loss2: 1.362576
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.285990 Loss1: 0.424241 Loss2: 1.861749
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.629219 Loss1: 0.253622 Loss2: 1.375597 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.550106 Loss1: 0.172768 Loss2: 1.377338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.514222 Loss1: 0.136215 Loss2: 1.378007 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513421 Loss1: 0.146292 Loss2: 1.367130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.451082 Loss1: 0.081601 Loss2: 1.369481 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403626 Loss1: 0.061041 Loss2: 1.342585
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460617 Loss1: 0.099609 Loss2: 1.361008
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421138 Loss1: 0.066772 Loss2: 1.354366
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426231 Loss1: 0.070338 Loss2: 1.355893
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.460504 Loss1: 0.106777 Loss2: 1.353727
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.218701 Loss1: 0.345031 Loss2: 1.873670
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.657515 Loss1: 0.260942 Loss2: 1.396573
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.564826 Loss1: 0.149887 Loss2: 1.414939
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.286219 Loss1: 0.436442 Loss2: 1.849777
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522070 Loss1: 0.130476 Loss2: 1.391594
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.613183 Loss1: 0.299062 Loss2: 1.314121
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.471106 Loss1: 0.084609 Loss2: 1.386497
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462271 Loss1: 0.077551 Loss2: 1.384720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443976 Loss1: 0.066658 Loss2: 1.377318 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.454323 Loss1: 0.075822 Loss2: 1.378501 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445512 Loss1: 0.067996 Loss2: 1.377516 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451515 Loss1: 0.074558 Loss2: 1.376958 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.358095 Loss1: 0.047146 Loss2: 1.310949 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.128542 Loss1: 0.310149 Loss2: 1.818393
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.599995 Loss1: 0.244409 Loss2: 1.355586
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551718 Loss1: 0.163745 Loss2: 1.387973
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.346506 Loss1: 0.465369 Loss2: 1.881137
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488008 Loss1: 0.133337 Loss2: 1.354671
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.620238 Loss1: 0.256510 Loss2: 1.363728
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443744 Loss1: 0.090990 Loss2: 1.352754
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451376 Loss1: 0.102037 Loss2: 1.349339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415964 Loss1: 0.067278 Loss2: 1.348687 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410165 Loss1: 0.069481 Loss2: 1.340684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400547 Loss1: 0.066255 Loss2: 1.334293 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363273 Loss1: 0.028085 Loss2: 1.335187 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382582 Loss1: 0.052752 Loss2: 1.329830 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.225577 Loss1: 0.375742 Loss2: 1.849835
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.684812 Loss1: 0.308542 Loss2: 1.376270
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618973 Loss1: 0.203682 Loss2: 1.415291
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.303259 Loss1: 0.407799 Loss2: 1.895460
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.552943 Loss1: 0.176884 Loss2: 1.376059
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659458 Loss1: 0.279547 Loss2: 1.379911
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518589 Loss1: 0.133033 Loss2: 1.385556
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.589778 Loss1: 0.184192 Loss2: 1.405586
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.526880 Loss1: 0.149116 Loss2: 1.377764
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458357 Loss1: 0.087837 Loss2: 1.370519 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425890 Loss1: 0.067576 Loss2: 1.358313 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422929 Loss1: 0.073737 Loss2: 1.349193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396619 Loss1: 0.053890 Loss2: 1.342729 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425725 Loss1: 0.060976 Loss2: 1.364749 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.169718 Loss1: 0.300396 Loss2: 1.869321 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.512337 Loss1: 0.118277 Loss2: 1.394060 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.473577 Loss1: 0.087346 Loss2: 1.386231
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.522439 Loss1: 0.522125 Loss2: 2.000314
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.751303 Loss1: 0.362971 Loss2: 1.388332
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.429584 Loss1: 0.067254 Loss2: 1.362330
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.647696 Loss1: 0.239193 Loss2: 1.408503
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438718 Loss1: 0.070242 Loss2: 1.368476
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426301 Loss1: 0.063191 Loss2: 1.363111 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403941 Loss1: 0.047298 Loss2: 1.356644 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417744 Loss1: 0.060337 Loss2: 1.357407 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392163 Loss1: 0.040461 Loss2: 1.351702 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416897 Loss1: 0.062012 Loss2: 1.354884 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.240385 Loss1: 0.382352 Loss2: 1.858033
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.612410 Loss1: 0.262261 Loss2: 1.350149
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.516934 Loss1: 0.148659 Loss2: 1.368275
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.466481 Loss1: 0.110820 Loss2: 1.355661
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.273163 Loss1: 0.393227 Loss2: 1.879936
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448569 Loss1: 0.106097 Loss2: 1.342472
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.619600 Loss1: 0.246925 Loss2: 1.372674
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.436403 Loss1: 0.094513 Loss2: 1.341890
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.520017 Loss1: 0.118331 Loss2: 1.401687
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.405165 Loss1: 0.066917 Loss2: 1.338248
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.480308 Loss1: 0.103895 Loss2: 1.376413
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378154 Loss1: 0.048840 Loss2: 1.329314
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.443408 Loss1: 0.079362 Loss2: 1.364047
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372186 Loss1: 0.042650 Loss2: 1.329536
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.478839 Loss1: 0.114624 Loss2: 1.364215
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367401 Loss1: 0.039300 Loss2: 1.328101
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.490152 Loss1: 0.122063 Loss2: 1.368089
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.562389 Loss1: 0.176990 Loss2: 1.385399
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.503710 Loss1: 0.117668 Loss2: 1.386042
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.466687 Loss1: 0.086369 Loss2: 1.380318
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.268703 Loss1: 0.402444 Loss2: 1.866259
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.641209 Loss1: 0.289904 Loss2: 1.351305
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.579049 Loss1: 0.198650 Loss2: 1.380399
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496955 Loss1: 0.131015 Loss2: 1.365939
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.169141 Loss1: 0.370122 Loss2: 1.799020
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.557560 Loss1: 0.242180 Loss2: 1.315380 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.507371 Loss1: 0.158248 Loss2: 1.349123 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.466125 Loss1: 0.136071 Loss2: 1.330054 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.423812 Loss1: 0.105727 Loss2: 1.318085 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.398305 Loss1: 0.088091 Loss2: 1.310215 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388331 Loss1: 0.050272 Loss2: 1.338060
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.408564 Loss1: 0.095445 Loss2: 1.313119
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421773 Loss1: 0.107074 Loss2: 1.314699
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371534 Loss1: 0.062158 Loss2: 1.309375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361591 Loss1: 0.053152 Loss2: 1.308439
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.424874 Loss1: 0.485264 Loss2: 1.939610
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.689763 Loss1: 0.274042 Loss2: 1.415721
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.695469 Loss1: 0.237602 Loss2: 1.457866
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.714836 Loss1: 0.283434 Loss2: 1.431401
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.241819 Loss1: 0.434462 Loss2: 1.807357
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.632439 Loss1: 0.285154 Loss2: 1.347286 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.516284 Loss1: 0.161921 Loss2: 1.354363 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.473718 Loss1: 0.151152 Loss2: 1.322566 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.477620 Loss1: 0.141584 Loss2: 1.336036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423989 Loss1: 0.096861 Loss2: 1.327128 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.402300 Loss1: 0.085226 Loss2: 1.317074 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362579 Loss1: 0.052493 Loss2: 1.310087 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.161485 Loss1: 0.349515 Loss2: 1.811971 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.637117 Loss1: 0.263429 Loss2: 1.373688 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.218896 Loss1: 0.392657 Loss2: 1.826238 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.690623 Loss1: 0.367902 Loss2: 1.322721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.670509 Loss1: 0.272611 Loss2: 1.397897 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.502422 Loss1: 0.174857 Loss2: 1.327565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469694 Loss1: 0.141594 Loss2: 1.328100 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444846 Loss1: 0.114072 Loss2: 1.330774 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372113 Loss1: 0.061455 Loss2: 1.310658 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.349903 Loss1: 0.040867 Loss2: 1.309036 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.168880 Loss1: 0.262719 Loss2: 1.906161
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.673814 Loss1: 0.248656 Loss2: 1.425158
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.663844 Loss1: 0.198182 Loss2: 1.465662
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.565607 Loss1: 0.123684 Loss2: 1.441922
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.160229 Loss1: 0.335237 Loss2: 1.824993
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.617517 Loss1: 0.178649 Loss2: 1.438868
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.581869 Loss1: 0.218023 Loss2: 1.363846
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.551077 Loss1: 0.162547 Loss2: 1.388529
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523471 Loss1: 0.089917 Loss2: 1.433554
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.517156 Loss1: 0.154961 Loss2: 1.362194
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.509816 Loss1: 0.084183 Loss2: 1.425633
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492058 Loss1: 0.122615 Loss2: 1.369443
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.516345 Loss1: 0.093322 Loss2: 1.423023
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.428517 Loss1: 0.063618 Loss2: 1.364900
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.480661 Loss1: 0.063824 Loss2: 1.416837
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.399341 Loss1: 0.050785 Loss2: 1.348555
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.494181 Loss1: 0.078826 Loss2: 1.415355
-(DefaultActor pid=3765) >> Training accuracy: 0.991728
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.375099 Loss1: 0.035944 Loss2: 1.339155 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.305729 Loss1: 0.375587 Loss2: 1.930142 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610698 Loss1: 0.180020 Loss2: 1.430678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.531638 Loss1: 0.126055 Loss2: 1.405584
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.164458 Loss1: 0.366430 Loss2: 1.798028
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.569522 Loss1: 0.221688 Loss2: 1.347834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.527797 Loss1: 0.167741 Loss2: 1.360056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481405 Loss1: 0.142565 Loss2: 1.338841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460689 Loss1: 0.127460 Loss2: 1.333228 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.399610 Loss1: 0.067708 Loss2: 1.331902 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.401007 Loss1: 0.077218 Loss2: 1.323789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.342079 Loss1: 0.031058 Loss2: 1.311021 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.291117 Loss1: 0.388837 Loss2: 1.902280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.543512 Loss1: 0.168664 Loss2: 1.374848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.185373 Loss1: 0.390345 Loss2: 1.795028 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.626255 Loss1: 0.311581 Loss2: 1.314674 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.531256 Loss1: 0.192899 Loss2: 1.338357 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.489215 Loss1: 0.180716 Loss2: 1.308499 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450500 Loss1: 0.130234 Loss2: 1.320266 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471176 Loss1: 0.157177 Loss2: 1.313999 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428388 Loss1: 0.101989 Loss2: 1.326399 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362050 Loss1: 0.061482 Loss2: 1.300568 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.687774 Loss1: 0.355150 Loss2: 1.332625 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.603041 Loss1: 0.260112 Loss2: 1.342929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.315911 Loss1: 0.439279 Loss2: 1.876631
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.539623 Loss1: 0.182439 Loss2: 1.357183
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.690233 Loss1: 0.304862 Loss2: 1.385370
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.501520 Loss1: 0.146977 Loss2: 1.354544
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.629222 Loss1: 0.208286 Loss2: 1.420936
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.452789 Loss1: 0.107803 Loss2: 1.344986
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540470 Loss1: 0.156146 Loss2: 1.384324
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411346 Loss1: 0.068074 Loss2: 1.343272
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.489144 Loss1: 0.106893 Loss2: 1.382251
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404582 Loss1: 0.075824 Loss2: 1.328759
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.499718 Loss1: 0.123342 Loss2: 1.376376
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.382094 Loss1: 0.056412 Loss2: 1.325683
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440321 Loss1: 0.074252 Loss2: 1.366069 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408717 Loss1: 0.052978 Loss2: 1.355739 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.552492 Loss1: 0.207175 Loss2: 1.345316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496637 Loss1: 0.131338 Loss2: 1.365300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463343 Loss1: 0.110462 Loss2: 1.352882 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.427918 Loss1: 0.085969 Loss2: 1.341948 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415851 Loss1: 0.075052 Loss2: 1.340799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426287 Loss1: 0.088505 Loss2: 1.337782 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.416390 Loss1: 0.076900 Loss2: 1.339490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413238 Loss1: 0.075187 Loss2: 1.338051 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977539
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.462978 Loss1: 0.115952 Loss2: 1.347026 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419631 Loss1: 0.077370 Loss2: 1.342261 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.260061 Loss1: 0.384057 Loss2: 1.876004
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.618709 Loss1: 0.261230 Loss2: 1.357479
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.546809 Loss1: 0.169944 Loss2: 1.376865
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.463781 Loss1: 0.094970 Loss2: 1.368811
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.275173 Loss1: 0.408295 Loss2: 1.866878
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.542814 Loss1: 0.169157 Loss2: 1.373657 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.520762 Loss1: 0.144200 Loss2: 1.376562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.488327 Loss1: 0.118872 Loss2: 1.369456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.457372 Loss1: 0.104427 Loss2: 1.352945 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.448878 Loss1: 0.093457 Loss2: 1.355420 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.346225 Loss1: 0.017708 Loss2: 1.328517
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.432260 Loss1: 0.079107 Loss2: 1.353153
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427171 Loss1: 0.081986 Loss2: 1.345185
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400697 Loss1: 0.062308 Loss2: 1.338390
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415073 Loss1: 0.079751 Loss2: 1.335322
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.220437 Loss1: 0.433329 Loss2: 1.787108
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.629344 Loss1: 0.300716 Loss2: 1.328628
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.572714 Loss1: 0.195929 Loss2: 1.376785
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518469 Loss1: 0.172242 Loss2: 1.346228
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.191362 Loss1: 0.391879 Loss2: 1.799482
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.568923 Loss1: 0.266483 Loss2: 1.302441 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.505973 Loss1: 0.184958 Loss2: 1.321016 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.434914 Loss1: 0.121315 Loss2: 1.313600 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.396207 Loss1: 0.091535 Loss2: 1.304672 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.392972 Loss1: 0.097249 Loss2: 1.295722 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.339678 Loss1: 0.036437 Loss2: 1.303241
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.381357 Loss1: 0.085074 Loss2: 1.296283
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368265 Loss1: 0.070837 Loss2: 1.297428
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370685 Loss1: 0.081219 Loss2: 1.289466
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348639 Loss1: 0.057482 Loss2: 1.291157
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.451492 Loss1: 0.518729 Loss2: 1.932763
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.652526 Loss1: 0.284177 Loss2: 1.368349
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599556 Loss1: 0.210231 Loss2: 1.389325
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589994 Loss1: 0.209865 Loss2: 1.380129
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.245124 Loss1: 0.383972 Loss2: 1.861152
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.634066 Loss1: 0.276717 Loss2: 1.357348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.579452 Loss1: 0.200547 Loss2: 1.378905 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.465541 Loss1: 0.109369 Loss2: 1.356172 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422875 Loss1: 0.065348 Loss2: 1.357527 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405737 Loss1: 0.060845 Loss2: 1.344892 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.383048 Loss1: 0.054845 Loss2: 1.328204 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.371516 Loss1: 0.056814 Loss2: 1.314702 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.540547 Loss1: 0.234686 Loss2: 1.305861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.531341 Loss1: 0.195046 Loss2: 1.336295 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.469567 Loss1: 0.150261 Loss2: 1.319306 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.401390 Loss1: 0.082688 Loss2: 1.318702 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391996 Loss1: 0.083277 Loss2: 1.308718 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.368920 Loss1: 0.068309 Loss2: 1.300611 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.335157 Loss1: 0.035876 Loss2: 1.299280
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492121 Loss1: 0.111213 Loss2: 1.380908
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487827 Loss1: 0.108698 Loss2: 1.379129
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468739 Loss1: 0.086971 Loss2: 1.381769
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469487 Loss1: 0.096960 Loss2: 1.372527
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.499403 Loss1: 0.127841 Loss2: 1.371562
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.248699 Loss1: 0.412937 Loss2: 1.835763
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.522714 Loss1: 0.225324 Loss2: 1.297389 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.503409 Loss1: 0.124605 Loss2: 1.378804
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.454174 Loss1: 0.143829 Loss2: 1.310345
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.445422 Loss1: 0.141802 Loss2: 1.303620
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.453682 Loss1: 0.150698 Loss2: 1.302985
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.424212 Loss1: 0.123617 Loss2: 1.300595
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395775 Loss1: 0.104479 Loss2: 1.291296
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.262075 Loss1: 0.389617 Loss2: 1.872458
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.371102 Loss1: 0.077813 Loss2: 1.293289
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379415 Loss1: 0.088938 Loss2: 1.290477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.339469 Loss1: 0.054699 Loss2: 1.284771 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.481804 Loss1: 0.126977 Loss2: 1.354826 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 08:32:01,406 | server.py:236 | fit_round 182 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.506657 Loss1: 0.153049 Loss2: 1.353608 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469027 Loss1: 0.112648 Loss2: 1.356379
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.394626 Loss1: 0.471854 Loss2: 1.922772
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.635928 Loss1: 0.268822 Loss2: 1.367106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409238 Loss1: 0.067892 Loss2: 1.341346
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.541202 Loss1: 0.173796 Loss2: 1.367406
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.487487 Loss1: 0.114174 Loss2: 1.373313
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.439585 Loss1: 0.084938 Loss2: 1.354647
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.405437 Loss1: 0.057333 Loss2: 1.348105
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.424712 Loss1: 0.076913 Loss2: 1.347799
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409328 Loss1: 0.061375 Loss2: 1.347953
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.341125 Loss1: 0.392863 Loss2: 1.948262
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.677398 Loss1: 0.247605 Loss2: 1.429793 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.634765 Loss1: 0.191169 Loss2: 1.443595 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.589437 Loss1: 0.136777 Loss2: 1.452661 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.511615 Loss1: 0.086113 Loss2: 1.425501
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.287622 Loss1: 0.464200 Loss2: 1.823423
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485542 Loss1: 0.066412 Loss2: 1.419130
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.588262 Loss1: 0.264053 Loss2: 1.324210
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480871 Loss1: 0.069668 Loss2: 1.411203
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.533515 Loss1: 0.174704 Loss2: 1.358811
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444023 Loss1: 0.037548 Loss2: 1.406475
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498096 Loss1: 0.163646 Loss2: 1.334449
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.417888 Loss1: 0.090660 Loss2: 1.327229
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.389062 Loss1: 0.067214 Loss2: 1.321849
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.381326 Loss1: 0.067866 Loss2: 1.313460
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.354600 Loss1: 0.050522 Loss2: 1.304078
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.348337 Loss1: 0.047721 Loss2: 1.300616
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.194022 Loss1: 0.292650 Loss2: 1.901373
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.328395 Loss1: 0.030242 Loss2: 1.298153
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.632356 Loss1: 0.235331 Loss2: 1.397025
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601168 Loss1: 0.193639 Loss2: 1.407529
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498905 Loss1: 0.100271 Loss2: 1.398635
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.504646 Loss1: 0.121139 Loss2: 1.383507
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500672 Loss1: 0.110005 Loss2: 1.390667
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480492 Loss1: 0.096236 Loss2: 1.384256
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445738 Loss1: 0.065243 Loss2: 1.380495
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450801 Loss1: 0.075575 Loss2: 1.375226
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429147 Loss1: 0.055922 Loss2: 1.373225
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 08:32:01,406][flwr][DEBUG] - fit_round 182 received 50 results and 0 failures
-INFO flwr 2023-10-13 08:32:42,255 | server.py:125 | fit progress: (182, 2.2981419727063406, {'accuracy': 0.6113}, 420070.033974992)
->> Test accuracy: 0.611300
-[2023-10-13 08:32:42,255][flwr][INFO] - fit progress: (182, 2.2981419727063406, {'accuracy': 0.6113}, 420070.033974992)
-DEBUG flwr 2023-10-13 08:32:42,256 | server.py:173 | evaluate_round 182: strategy sampled 50 clients (out of 50)
-[2023-10-13 08:32:42,256][flwr][DEBUG] - evaluate_round 182: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 08:41:43,569 | server.py:187 | evaluate_round 182 received 50 results and 0 failures
-[2023-10-13 08:41:43,569][flwr][DEBUG] - evaluate_round 182 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 08:41:43,570 | server.py:222 | fit_round 183: strategy sampled 50 clients (out of 50)
-[2023-10-13 08:41:43,570][flwr][DEBUG] - fit_round 183: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.185188 Loss1: 0.405870 Loss2: 1.779318
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.564587 Loss1: 0.249417 Loss2: 1.315170
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526629 Loss1: 0.192360 Loss2: 1.334269
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.460706 Loss1: 0.141355 Loss2: 1.319351
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451803 Loss1: 0.138140 Loss2: 1.313663 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.426083 Loss1: 0.116128 Loss2: 1.309954 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.378578 Loss1: 0.072062 Loss2: 1.306516 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.343833 Loss1: 0.047718 Loss2: 1.296115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.335858 Loss1: 0.045971 Loss2: 1.289887 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.352207 Loss1: 0.062038 Loss2: 1.290169 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434297 Loss1: 0.093683 Loss2: 1.340614 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401380 Loss1: 0.075394 Loss2: 1.325987 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.542967 Loss1: 0.219932 Loss2: 1.323035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.398819 Loss1: 0.073339 Loss2: 1.325480 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.365353 Loss1: 0.065992 Loss2: 1.299361
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.238560 Loss1: 0.347830 Loss2: 1.890730
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.360505 Loss1: 0.060845 Loss2: 1.299659
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.707802 Loss1: 0.319075 Loss2: 1.388727
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.327318 Loss1: 0.036191 Loss2: 1.291127
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.612224 Loss1: 0.180361 Loss2: 1.431863
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.328403 Loss1: 0.038061 Loss2: 1.290342
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550604 Loss1: 0.158643 Loss2: 1.391961
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509704 Loss1: 0.113037 Loss2: 1.396667
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.335864 Loss1: 0.051986 Loss2: 1.283878
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.351658 Loss1: 0.067407 Loss2: 1.284251
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.474364 Loss1: 0.080124 Loss2: 1.394240
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.470684 Loss1: 0.083617 Loss2: 1.387068
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468789 Loss1: 0.087090 Loss2: 1.381698
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.462058 Loss1: 0.083546 Loss2: 1.378512
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433060 Loss1: 0.054252 Loss2: 1.378808
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.286151 Loss1: 0.409026 Loss2: 1.877125
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.618022 Loss1: 0.236333 Loss2: 1.381689
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.630193 Loss1: 0.218249 Loss2: 1.411944
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559648 Loss1: 0.163829 Loss2: 1.395818
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.419133 Loss1: 0.508497 Loss2: 1.910636
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.693454 Loss1: 0.338795 Loss2: 1.354659 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.560691 Loss1: 0.186366 Loss2: 1.374324 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.500348 Loss1: 0.123974 Loss2: 1.376374 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446033 Loss1: 0.092469 Loss2: 1.353565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427674 Loss1: 0.081076 Loss2: 1.346598 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433106 Loss1: 0.084226 Loss2: 1.348880 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373082 Loss1: 0.035608 Loss2: 1.337473 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.303279 Loss1: 0.359415 Loss2: 1.943864 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610946 Loss1: 0.144565 Loss2: 1.466381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.411924 Loss1: 0.491082 Loss2: 1.920842
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574627 Loss1: 0.138417 Loss2: 1.436211
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.560117 Loss1: 0.227605 Loss2: 1.332511
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527307 Loss1: 0.106351 Loss2: 1.420956
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.533474 Loss1: 0.109503 Loss2: 1.423971 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.549657 Loss1: 0.126669 Loss2: 1.422988 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.485951 Loss1: 0.066346 Loss2: 1.419605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470598 Loss1: 0.056583 Loss2: 1.414015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.462787 Loss1: 0.055880 Loss2: 1.406908 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.340525 Loss1: 0.044783 Loss2: 1.295742 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.190574 Loss1: 0.350027 Loss2: 1.840547
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.547232 Loss1: 0.217070 Loss2: 1.330161
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568789 Loss1: 0.220723 Loss2: 1.348067
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556483 Loss1: 0.209082 Loss2: 1.347401
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.246552 Loss1: 0.411446 Loss2: 1.835106
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.618442 Loss1: 0.272204 Loss2: 1.346238 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.560286 Loss1: 0.187707 Loss2: 1.372578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576032 Loss1: 0.221396 Loss2: 1.354636 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491453 Loss1: 0.132938 Loss2: 1.358514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449342 Loss1: 0.099850 Loss2: 1.349492 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.376822 Loss1: 0.062617 Loss2: 1.314205
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443562 Loss1: 0.100328 Loss2: 1.343234
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455477 Loss1: 0.114836 Loss2: 1.340641
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422414 Loss1: 0.076866 Loss2: 1.345548
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.444605 Loss1: 0.101327 Loss2: 1.343278
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.259710 Loss1: 0.351843 Loss2: 1.907867
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.584012 Loss1: 0.174383 Loss2: 1.409629
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.524879 Loss1: 0.111791 Loss2: 1.413087
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496758 Loss1: 0.090987 Loss2: 1.405771
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.169072 Loss1: 0.366308 Loss2: 1.802764
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517735 Loss1: 0.120105 Loss2: 1.397630
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.507448 Loss1: 0.178331 Loss2: 1.329118
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503987 Loss1: 0.104489 Loss2: 1.399498
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.551632 Loss1: 0.211957 Loss2: 1.339675
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.516312 Loss1: 0.181451 Loss2: 1.334862
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.487651 Loss1: 0.088588 Loss2: 1.399063
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.430199 Loss1: 0.104854 Loss2: 1.325345
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.514490 Loss1: 0.107048 Loss2: 1.407442
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.389339 Loss1: 0.074109 Loss2: 1.315230
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.460141 Loss1: 0.063287 Loss2: 1.396853
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416976 Loss1: 0.106409 Loss2: 1.310566
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443438 Loss1: 0.053441 Loss2: 1.389997
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.336002 Loss1: 0.035086 Loss2: 1.300916 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.174893 Loss1: 0.327827 Loss2: 1.847066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.549924 Loss1: 0.171483 Loss2: 1.378441 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.178640 Loss1: 0.341839 Loss2: 1.836802
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.547038 Loss1: 0.169419 Loss2: 1.377619
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.623045 Loss1: 0.245444 Loss2: 1.377601
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.537817 Loss1: 0.176873 Loss2: 1.360945
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565668 Loss1: 0.162742 Loss2: 1.402926
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448558 Loss1: 0.081580 Loss2: 1.366978
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.472672 Loss1: 0.092328 Loss2: 1.380344
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443489 Loss1: 0.085147 Loss2: 1.358342
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.435191 Loss1: 0.062238 Loss2: 1.372953
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433078 Loss1: 0.080376 Loss2: 1.352702
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449489 Loss1: 0.078871 Loss2: 1.370617
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428675 Loss1: 0.078041 Loss2: 1.350635
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444919 Loss1: 0.075851 Loss2: 1.369069
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.425076 Loss1: 0.073596 Loss2: 1.351479
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.461152 Loss1: 0.098016 Loss2: 1.363136 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.202721 Loss1: 0.363295 Loss2: 1.839426 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.472673 Loss1: 0.115782 Loss2: 1.356891 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.423791 Loss1: 0.082577 Loss2: 1.341214
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.197344 Loss1: 0.357041 Loss2: 1.840303
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.432723 Loss1: 0.103585 Loss2: 1.329138
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.508775 Loss1: 0.173831 Loss2: 1.334944
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.411651 Loss1: 0.082900 Loss2: 1.328751
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.502930 Loss1: 0.154927 Loss2: 1.348003
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.389054 Loss1: 0.070138 Loss2: 1.318916
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.461855 Loss1: 0.114687 Loss2: 1.347168
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.386217 Loss1: 0.063195 Loss2: 1.323022
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.432794 Loss1: 0.097519 Loss2: 1.335274
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.355347 Loss1: 0.037453 Loss2: 1.317894
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.412330 Loss1: 0.078223 Loss2: 1.334107
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374788 Loss1: 0.058573 Loss2: 1.316215
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.399465 Loss1: 0.074687 Loss2: 1.324778
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.356037 Loss1: 0.036751 Loss2: 1.319286
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.352015 Loss1: 0.037177 Loss2: 1.314838
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.337479 Loss1: 0.024607 Loss2: 1.312872
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.194882 Loss1: 0.375512 Loss2: 1.819370
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.604962 Loss1: 0.292801 Loss2: 1.312160
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.511762 Loss1: 0.170361 Loss2: 1.341401
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489160 Loss1: 0.166677 Loss2: 1.322483
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.265789 Loss1: 0.391654 Loss2: 1.874136
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451358 Loss1: 0.135887 Loss2: 1.315471
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.601940 Loss1: 0.217650 Loss2: 1.384290
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.408660 Loss1: 0.088338 Loss2: 1.320321
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.619512 Loss1: 0.214478 Loss2: 1.405034
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383269 Loss1: 0.074012 Loss2: 1.309257
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528780 Loss1: 0.121229 Loss2: 1.407550
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.352505 Loss1: 0.055084 Loss2: 1.297421
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524850 Loss1: 0.139056 Loss2: 1.385794
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.325426 Loss1: 0.032392 Loss2: 1.293034
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464848 Loss1: 0.077601 Loss2: 1.387246
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.326527 Loss1: 0.032270 Loss2: 1.294257
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478969 Loss1: 0.104174 Loss2: 1.374795
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438521 Loss1: 0.066558 Loss2: 1.371964
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424563 Loss1: 0.053640 Loss2: 1.370923
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399471 Loss1: 0.035777 Loss2: 1.363694
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.285835 Loss1: 0.431181 Loss2: 1.854654
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622299 Loss1: 0.274712 Loss2: 1.347586
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.578329 Loss1: 0.205001 Loss2: 1.373328
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540631 Loss1: 0.181358 Loss2: 1.359273
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.335877 Loss1: 0.456487 Loss2: 1.879390
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.657538 Loss1: 0.310360 Loss2: 1.347178
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.528994 Loss1: 0.168307 Loss2: 1.360687
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.627187 Loss1: 0.243642 Loss2: 1.383545
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515358 Loss1: 0.155893 Loss2: 1.359465
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.531099 Loss1: 0.160567 Loss2: 1.370533
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517962 Loss1: 0.152971 Loss2: 1.364991
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437026 Loss1: 0.087873 Loss2: 1.349153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422931 Loss1: 0.081648 Loss2: 1.341283 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404358 Loss1: 0.064938 Loss2: 1.339420 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385154 Loss1: 0.058134 Loss2: 1.327020 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987723
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.250111 Loss1: 0.407779 Loss2: 1.842331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.471871 Loss1: 0.123594 Loss2: 1.348277 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.413240 Loss1: 0.070096 Loss2: 1.343144
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.236376 Loss1: 0.387691 Loss2: 1.848685
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.573139 Loss1: 0.216880 Loss2: 1.356259 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543128 Loss1: 0.173516 Loss2: 1.369611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526058 Loss1: 0.164420 Loss2: 1.361638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.447240 Loss1: 0.101622 Loss2: 1.345617 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.442470 Loss1: 0.098395 Loss2: 1.344075 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360852 Loss1: 0.038112 Loss2: 1.322740
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468589 Loss1: 0.120151 Loss2: 1.348439
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408642 Loss1: 0.066926 Loss2: 1.341716
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388866 Loss1: 0.046010 Loss2: 1.342856
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397753 Loss1: 0.062549 Loss2: 1.335205
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.248546 Loss1: 0.465818 Loss2: 1.782728
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.566691 Loss1: 0.268229 Loss2: 1.298462
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.499875 Loss1: 0.168938 Loss2: 1.330936
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.417828 Loss1: 0.099338 Loss2: 1.318489
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.227025 Loss1: 0.376377 Loss2: 1.850648
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.591521 Loss1: 0.241553 Loss2: 1.349968 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.514252 Loss1: 0.149197 Loss2: 1.365055 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.469906 Loss1: 0.116126 Loss2: 1.353780 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.436434 Loss1: 0.092790 Loss2: 1.343643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.408555 Loss1: 0.073317 Loss2: 1.335237 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374692 Loss1: 0.083131 Loss2: 1.291562
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415722 Loss1: 0.084204 Loss2: 1.331518
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420013 Loss1: 0.083806 Loss2: 1.336207
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385625 Loss1: 0.053440 Loss2: 1.332185
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367834 Loss1: 0.039087 Loss2: 1.328747
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.224909 Loss1: 0.436787 Loss2: 1.788122
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.575651 Loss1: 0.264509 Loss2: 1.311143
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.573754 Loss1: 0.230210 Loss2: 1.343544
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.480136 Loss1: 0.152266 Loss2: 1.327870
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.357949 Loss1: 0.486502 Loss2: 1.871447
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.474433 Loss1: 0.161478 Loss2: 1.312955
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.610692 Loss1: 0.267734 Loss2: 1.342958
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.393023 Loss1: 0.071412 Loss2: 1.321611
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.523306 Loss1: 0.158537 Loss2: 1.364768
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390557 Loss1: 0.083396 Loss2: 1.307162
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.490300 Loss1: 0.148372 Loss2: 1.341928
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.444109 Loss1: 0.103511 Loss2: 1.340599
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.368000 Loss1: 0.069235 Loss2: 1.298765
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.418331 Loss1: 0.080608 Loss2: 1.337724
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.346414 Loss1: 0.050767 Loss2: 1.295647
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437504 Loss1: 0.106124 Loss2: 1.331381
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.325439 Loss1: 0.034659 Loss2: 1.290780
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407285 Loss1: 0.073061 Loss2: 1.334224 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.294680 Loss1: 0.427498 Loss2: 1.867182 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.604707 Loss1: 0.210033 Loss2: 1.394673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.552011 Loss1: 0.185199 Loss2: 1.366813
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.270023 Loss1: 0.410752 Loss2: 1.859271
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.469385 Loss1: 0.110866 Loss2: 1.358519
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.643874 Loss1: 0.275933 Loss2: 1.367941
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452207 Loss1: 0.091291 Loss2: 1.360916
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604063 Loss1: 0.179862 Loss2: 1.424202
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415098 Loss1: 0.069825 Loss2: 1.345273
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.558339 Loss1: 0.187904 Loss2: 1.370435
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.443910 Loss1: 0.089601 Loss2: 1.354309
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550634 Loss1: 0.179601 Loss2: 1.371033
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439573 Loss1: 0.083181 Loss2: 1.356391
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496551 Loss1: 0.118000 Loss2: 1.378551
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424183 Loss1: 0.073375 Loss2: 1.350808
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452113 Loss1: 0.092640 Loss2: 1.359473
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437953 Loss1: 0.075222 Loss2: 1.362731
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.419063 Loss1: 0.066764 Loss2: 1.352299
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400826 Loss1: 0.047058 Loss2: 1.353768
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.259113 Loss1: 0.426974 Loss2: 1.832139
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.704989 Loss1: 0.344856 Loss2: 1.360133
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.641408 Loss1: 0.222446 Loss2: 1.418961
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.505893 Loss1: 0.130437 Loss2: 1.375456
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.197463 Loss1: 0.339081 Loss2: 1.858382
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.678079 Loss1: 0.288429 Loss2: 1.389651 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.593554 Loss1: 0.164383 Loss2: 1.429170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519442 Loss1: 0.146262 Loss2: 1.373180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.464701 Loss1: 0.093441 Loss2: 1.371260 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.449974 Loss1: 0.078867 Loss2: 1.371107 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413440 Loss1: 0.051581 Loss2: 1.361860 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388691 Loss1: 0.039064 Loss2: 1.349627 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.595880 Loss1: 0.247690 Loss2: 1.348190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.461559 Loss1: 0.118460 Loss2: 1.343099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.294296 Loss1: 0.418137 Loss2: 1.876158
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451665 Loss1: 0.106383 Loss2: 1.345282
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468735 Loss1: 0.120289 Loss2: 1.348446 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.418705 Loss1: 0.077857 Loss2: 1.340848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401547 Loss1: 0.063098 Loss2: 1.338449 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.382336 Loss1: 0.049537 Loss2: 1.332799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383323 Loss1: 0.056730 Loss2: 1.326593 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397275 Loss1: 0.059545 Loss2: 1.337730 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.236916 Loss1: 0.375478 Loss2: 1.861438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.560554 Loss1: 0.156754 Loss2: 1.403800 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.548030 Loss1: 0.174033 Loss2: 1.373997
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.233662 Loss1: 0.373250 Loss2: 1.860411
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.654301 Loss1: 0.293730 Loss2: 1.360571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.560625 Loss1: 0.170822 Loss2: 1.389803 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.508736 Loss1: 0.144171 Loss2: 1.364565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.452456 Loss1: 0.096741 Loss2: 1.355714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443711 Loss1: 0.087133 Loss2: 1.356578 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374412 Loss1: 0.028426 Loss2: 1.345986
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434214 Loss1: 0.090374 Loss2: 1.343840
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390203 Loss1: 0.050324 Loss2: 1.339880
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395013 Loss1: 0.061096 Loss2: 1.333918
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.377709 Loss1: 0.044003 Loss2: 1.333706
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.294399 Loss1: 0.475606 Loss2: 1.818793
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.617529 Loss1: 0.289634 Loss2: 1.327895
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.636732 Loss1: 0.264941 Loss2: 1.371792
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.552368 Loss1: 0.214583 Loss2: 1.337785
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.180810 Loss1: 0.367344 Loss2: 1.813466
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468881 Loss1: 0.129483 Loss2: 1.339398
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630911 Loss1: 0.292360 Loss2: 1.338551
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.421901 Loss1: 0.095425 Loss2: 1.326477
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.546038 Loss1: 0.188514 Loss2: 1.357524
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422291 Loss1: 0.105366 Loss2: 1.316925
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.438798 Loss1: 0.108154 Loss2: 1.330644
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.383604 Loss1: 0.067189 Loss2: 1.316415
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450276 Loss1: 0.123282 Loss2: 1.326993
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.349137 Loss1: 0.037683 Loss2: 1.311454
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.445196 Loss1: 0.118280 Loss2: 1.326916
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.329085 Loss1: 0.027331 Loss2: 1.301754
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.414934 Loss1: 0.101357 Loss2: 1.313578
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.402453 Loss1: 0.084543 Loss2: 1.317910
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381763 Loss1: 0.065671 Loss2: 1.316093
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354732 Loss1: 0.045514 Loss2: 1.309219
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.219416 Loss1: 0.391551 Loss2: 1.827865
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.614158 Loss1: 0.275502 Loss2: 1.338656
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.519540 Loss1: 0.158032 Loss2: 1.361508
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488570 Loss1: 0.149417 Loss2: 1.339154
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.040371 Loss1: 0.276451 Loss2: 1.763920
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.533088 Loss1: 0.215652 Loss2: 1.317436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.459272 Loss1: 0.135041 Loss2: 1.324231 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.410106 Loss1: 0.096538 Loss2: 1.313568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.365375 Loss1: 0.040836 Loss2: 1.324539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.343354 Loss1: 0.025006 Loss2: 1.318348 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.358980 Loss1: 0.065836 Loss2: 1.293144 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.322166 Loss1: 0.034956 Loss2: 1.287210 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994485
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.588238 Loss1: 0.218519 Loss2: 1.369719 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.494056 Loss1: 0.115841 Loss2: 1.378215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.460908 Loss1: 0.097068 Loss2: 1.363840
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.188849 Loss1: 0.367406 Loss2: 1.821443
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.624549 Loss1: 0.259857 Loss2: 1.364691 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564790 Loss1: 0.168727 Loss2: 1.396063 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468516 Loss1: 0.103072 Loss2: 1.365443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.417242 Loss1: 0.068534 Loss2: 1.348707 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400384 Loss1: 0.044900 Loss2: 1.355484
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.397542 Loss1: 0.054017 Loss2: 1.343525
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.399496 Loss1: 0.064975 Loss2: 1.334521
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.384710 Loss1: 0.051489 Loss2: 1.333220
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395621 Loss1: 0.064042 Loss2: 1.331579
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397443 Loss1: 0.064688 Loss2: 1.332755
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.259556 Loss1: 0.434265 Loss2: 1.825291
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.710894 Loss1: 0.362534 Loss2: 1.348360
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.662147 Loss1: 0.254611 Loss2: 1.407536
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.545751 Loss1: 0.186705 Loss2: 1.359046
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491390 Loss1: 0.129292 Loss2: 1.362098
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.179691 Loss1: 0.320685 Loss2: 1.859006
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452282 Loss1: 0.094206 Loss2: 1.358076
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435703 Loss1: 0.091646 Loss2: 1.344057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425737 Loss1: 0.078718 Loss2: 1.347019 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413614 Loss1: 0.077671 Loss2: 1.335943 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415232 Loss1: 0.075239 Loss2: 1.339993 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456409 Loss1: 0.103525 Loss2: 1.352884 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421014 Loss1: 0.069650 Loss2: 1.351364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390448 Loss1: 0.043953 Loss2: 1.346495
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.109225 Loss1: 0.318101 Loss2: 1.791124
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.559506 Loss1: 0.227138 Loss2: 1.332367
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.558094 Loss1: 0.198653 Loss2: 1.359441
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500083 Loss1: 0.160920 Loss2: 1.339163
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473062 Loss1: 0.138397 Loss2: 1.334665
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.154758 Loss1: 0.268761 Loss2: 1.885997
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.538152 Loss1: 0.159905 Loss2: 1.378248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.491578 Loss1: 0.120369 Loss2: 1.371209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530785 Loss1: 0.146816 Loss2: 1.383969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.467996 Loss1: 0.096705 Loss2: 1.371291 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358144 Loss1: 0.040492 Loss2: 1.317652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461141 Loss1: 0.094770 Loss2: 1.366371
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448305 Loss1: 0.073515 Loss2: 1.374789
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439240 Loss1: 0.072296 Loss2: 1.366943
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422563 Loss1: 0.056236 Loss2: 1.366327
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403689 Loss1: 0.041599 Loss2: 1.362090
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-DEBUG flwr 2023-10-13 09:10:49,204 | server.py:236 | fit_round 183 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.454402 Loss1: 0.535829 Loss2: 1.918573
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.635164 Loss1: 0.317837 Loss2: 1.317326
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.543393 Loss1: 0.207724 Loss2: 1.335669
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489287 Loss1: 0.142639 Loss2: 1.346648
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.438950 Loss1: 0.123696 Loss2: 1.315254
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.392388 Loss1: 0.082184 Loss2: 1.310204
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.309582 Loss1: 0.410495 Loss2: 1.899087
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.627856 Loss1: 0.231734 Loss2: 1.396122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.337731 Loss1: 0.045740 Loss2: 1.291992 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.328192 Loss1: 0.035236 Loss2: 1.292956 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490099 Loss1: 0.109422 Loss2: 1.380677 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481190 Loss1: 0.104115 Loss2: 1.377075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.302358 Loss1: 0.432303 Loss2: 1.870055
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453751 Loss1: 0.075419 Loss2: 1.378332
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.575784 Loss1: 0.222447 Loss2: 1.353337
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.447027 Loss1: 0.076215 Loss2: 1.370813
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.441405 Loss1: 0.101330 Loss2: 1.340074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.413445 Loss1: 0.084108 Loss2: 1.329337 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408892 Loss1: 0.073394 Loss2: 1.335498
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.185434 Loss1: 0.371476 Loss2: 1.813957
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.654016 Loss1: 0.326225 Loss2: 1.327790 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.614503 Loss1: 0.219831 Loss2: 1.394672 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.357209 Loss1: 0.039831 Loss2: 1.317378
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532448 Loss1: 0.184307 Loss2: 1.348141
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503292 Loss1: 0.155604 Loss2: 1.347688
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506188 Loss1: 0.152741 Loss2: 1.353447
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.448771 Loss1: 0.111417 Loss2: 1.337354
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.403835 Loss1: 0.068049 Loss2: 1.335786
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.225828 Loss1: 0.353117 Loss2: 1.872712
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.361394 Loss1: 0.034637 Loss2: 1.326757
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354686 Loss1: 0.038592 Loss2: 1.316094 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.436375 Loss1: 0.065713 Loss2: 1.370662 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.410316 Loss1: 0.062831 Loss2: 1.347485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.407013 Loss1: 0.065081 Loss2: 1.341931
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.160182 Loss1: 0.351584 Loss2: 1.808597
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.575367 Loss1: 0.242479 Loss2: 1.332888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.507917 Loss1: 0.155653 Loss2: 1.352264 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.464814 Loss1: 0.134012 Loss2: 1.330802 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.433842 Loss1: 0.106835 Loss2: 1.327007 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389631 Loss1: 0.075479 Loss2: 1.314152 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369081 Loss1: 0.064222 Loss2: 1.304860 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 09:10:49,204][flwr][DEBUG] - fit_round 183 received 50 results and 0 failures
-INFO flwr 2023-10-13 09:11:30,238 | server.py:125 | fit progress: (183, 2.3107704240293168, {'accuracy': 0.6095}, 422398.016980325)
->> Test accuracy: 0.609500
-[2023-10-13 09:11:30,238][flwr][INFO] - fit progress: (183, 2.3107704240293168, {'accuracy': 0.6095}, 422398.016980325)
-DEBUG flwr 2023-10-13 09:11:30,239 | server.py:173 | evaluate_round 183: strategy sampled 50 clients (out of 50)
-[2023-10-13 09:11:30,239][flwr][DEBUG] - evaluate_round 183: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 09:20:34,104 | server.py:187 | evaluate_round 183 received 50 results and 0 failures
-[2023-10-13 09:20:34,104][flwr][DEBUG] - evaluate_round 183 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 09:20:34,104 | server.py:222 | fit_round 184: strategy sampled 50 clients (out of 50)
-[2023-10-13 09:20:34,104][flwr][DEBUG] - fit_round 184: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.198657 Loss1: 0.372254 Loss2: 1.826403
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.625395 Loss1: 0.299363 Loss2: 1.326032
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.511443 Loss1: 0.147102 Loss2: 1.364342
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490037 Loss1: 0.155776 Loss2: 1.334260
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.192223 Loss1: 0.342465 Loss2: 1.849758
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.466104 Loss1: 0.137983 Loss2: 1.328120
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.644637 Loss1: 0.292961 Loss2: 1.351675
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457810 Loss1: 0.134597 Loss2: 1.323213
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.571596 Loss1: 0.178175 Loss2: 1.393422
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434995 Loss1: 0.112754 Loss2: 1.322241
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.515529 Loss1: 0.155985 Loss2: 1.359545
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427298 Loss1: 0.104613 Loss2: 1.322685
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492495 Loss1: 0.133495 Loss2: 1.359000
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.361611 Loss1: 0.045988 Loss2: 1.315623
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464832 Loss1: 0.114215 Loss2: 1.350617
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.342380 Loss1: 0.037096 Loss2: 1.305283
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438974 Loss1: 0.091141 Loss2: 1.347834
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412203 Loss1: 0.070978 Loss2: 1.341225
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366589 Loss1: 0.027497 Loss2: 1.339092
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369521 Loss1: 0.039922 Loss2: 1.329600
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.367545 Loss1: 0.454659 Loss2: 1.912886
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.697477 Loss1: 0.356980 Loss2: 1.340497
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634283 Loss1: 0.234644 Loss2: 1.399640
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516642 Loss1: 0.131617 Loss2: 1.385025
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.154864 Loss1: 0.334717 Loss2: 1.820147
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.429537 Loss1: 0.076126 Loss2: 1.353410 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458789 Loss1: 0.112134 Loss2: 1.346655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427134 Loss1: 0.079231 Loss2: 1.347903 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443814 Loss1: 0.095145 Loss2: 1.348669 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450308 Loss1: 0.103979 Loss2: 1.346328 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.419498 Loss1: 0.083666 Loss2: 1.335832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396501 Loss1: 0.056318 Loss2: 1.340183 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.170696 Loss1: 0.327369 Loss2: 1.843327
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403955 Loss1: 0.066849 Loss2: 1.337107
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.486678 Loss1: 0.129738 Loss2: 1.356940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.465220 Loss1: 0.120787 Loss2: 1.344432 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457026 Loss1: 0.114705 Loss2: 1.342321
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.214853 Loss1: 0.342261 Loss2: 1.872592
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.684860 Loss1: 0.309155 Loss2: 1.375705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.606837 Loss1: 0.179430 Loss2: 1.427407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556099 Loss1: 0.174751 Loss2: 1.381348 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368243 Loss1: 0.041069 Loss2: 1.327174
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523517 Loss1: 0.144080 Loss2: 1.379437
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469463 Loss1: 0.097213 Loss2: 1.372251
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.454999 Loss1: 0.087045 Loss2: 1.367954
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445213 Loss1: 0.079283 Loss2: 1.365930
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440910 Loss1: 0.078727 Loss2: 1.362184
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.111964 Loss1: 0.291134 Loss2: 1.820830
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415087 Loss1: 0.053240 Loss2: 1.361848
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.470584 Loss1: 0.120020 Loss2: 1.350565 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.429645 Loss1: 0.092909 Loss2: 1.336735 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.417977 Loss1: 0.081319 Loss2: 1.336659 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468675 Loss1: 0.123336 Loss2: 1.345339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430301 Loss1: 0.085742 Loss2: 1.344559 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390161 Loss1: 0.052994 Loss2: 1.337167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380653 Loss1: 0.044020 Loss2: 1.336633 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998162
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.380288 Loss1: 0.058257 Loss2: 1.322032 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364113 Loss1: 0.054172 Loss2: 1.309941 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.653980 Loss1: 0.286098 Loss2: 1.367882 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.623507 Loss1: 0.248685 Loss2: 1.374822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.594512 Loss1: 0.199622 Loss2: 1.394890 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491038 Loss1: 0.104316 Loss2: 1.386721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.493862 Loss1: 0.123336 Loss2: 1.370526 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486267 Loss1: 0.111086 Loss2: 1.375182 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424909 Loss1: 0.065945 Loss2: 1.358964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410266 Loss1: 0.052610 Loss2: 1.357656 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397332 Loss1: 0.074827 Loss2: 1.322504 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.215373 Loss1: 0.399290 Loss2: 1.816083 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.480696 Loss1: 0.145330 Loss2: 1.335366 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.458987 Loss1: 0.123726 Loss2: 1.335261
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.221988 Loss1: 0.419875 Loss2: 1.802113
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.603891 Loss1: 0.277158 Loss2: 1.326733 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.541715 Loss1: 0.167185 Loss2: 1.374530 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.422641 Loss1: 0.096619 Loss2: 1.326022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.422440 Loss1: 0.101554 Loss2: 1.320886 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.412354 Loss1: 0.091664 Loss2: 1.320690 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.330400 Loss1: 0.034967 Loss2: 1.295433
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.400130 Loss1: 0.080500 Loss2: 1.319630
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.383707 Loss1: 0.066480 Loss2: 1.317227
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366137 Loss1: 0.057261 Loss2: 1.308876
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.342116 Loss1: 0.040473 Loss2: 1.301643
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.293255 Loss1: 0.415015 Loss2: 1.878240
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.670386 Loss1: 0.283672 Loss2: 1.386714
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.585826 Loss1: 0.181858 Loss2: 1.403967
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.565994 Loss1: 0.172953 Loss2: 1.393041
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.301675 Loss1: 0.393465 Loss2: 1.908210
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.620766 Loss1: 0.269489 Loss2: 1.351277 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.581516 Loss1: 0.201565 Loss2: 1.379951 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595882 Loss1: 0.198929 Loss2: 1.396953 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.503191 Loss1: 0.128511 Loss2: 1.374680
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514162 Loss1: 0.140671 Loss2: 1.373490
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.452610 Loss1: 0.075371 Loss2: 1.377239
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480393 Loss1: 0.105721 Loss2: 1.374672
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409571 Loss1: 0.042056 Loss2: 1.367515
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440603 Loss1: 0.073011 Loss2: 1.367592
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407187 Loss1: 0.051798 Loss2: 1.355389
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388125 Loss1: 0.036753 Loss2: 1.351372
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.389659 Loss1: 0.044812 Loss2: 1.344846
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.277816 Loss1: 0.368671 Loss2: 1.909145
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.629450 Loss1: 0.239983 Loss2: 1.389467
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.646434 Loss1: 0.239947 Loss2: 1.406487
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.571992 Loss1: 0.161384 Loss2: 1.410608
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.151213 Loss1: 0.359776 Loss2: 1.791436
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.552634 Loss1: 0.243109 Loss2: 1.309525 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.486546 Loss1: 0.170451 Loss2: 1.316095 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.424423 Loss1: 0.105597 Loss2: 1.318825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.407092 Loss1: 0.100267 Loss2: 1.306825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.389234 Loss1: 0.086242 Loss2: 1.302993 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438535 Loss1: 0.066512 Loss2: 1.372023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.375878 Loss1: 0.070563 Loss2: 1.305315
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.394511 Loss1: 0.100804 Loss2: 1.293708
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362527 Loss1: 0.056485 Loss2: 1.306042
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376414 Loss1: 0.079190 Loss2: 1.297224
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.211427 Loss1: 0.339785 Loss2: 1.871642
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.590083 Loss1: 0.209474 Loss2: 1.380610
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563541 Loss1: 0.170906 Loss2: 1.392635
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482860 Loss1: 0.087601 Loss2: 1.395259
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.248567 Loss1: 0.413841 Loss2: 1.834726
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612240 Loss1: 0.274255 Loss2: 1.337986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.537270 Loss1: 0.164513 Loss2: 1.372757 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.427422 Loss1: 0.088241 Loss2: 1.339180 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.415330 Loss1: 0.089967 Loss2: 1.325364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.391515 Loss1: 0.064020 Loss2: 1.327496 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450746 Loss1: 0.073571 Loss2: 1.377175
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.381807 Loss1: 0.059898 Loss2: 1.321910
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.398010 Loss1: 0.076381 Loss2: 1.321630
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397880 Loss1: 0.073797 Loss2: 1.324083
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382913 Loss1: 0.062166 Loss2: 1.320747
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.264274 Loss1: 0.437171 Loss2: 1.827103
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.648556 Loss1: 0.301598 Loss2: 1.346959
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.569705 Loss1: 0.205513 Loss2: 1.364191
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.478635 Loss1: 0.130253 Loss2: 1.348382
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.278168 Loss1: 0.459901 Loss2: 1.818266
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.606694 Loss1: 0.262179 Loss2: 1.344514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601997 Loss1: 0.219685 Loss2: 1.382312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543970 Loss1: 0.186074 Loss2: 1.357896 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493182 Loss1: 0.150934 Loss2: 1.342248 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467121 Loss1: 0.116626 Loss2: 1.350495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450640 Loss1: 0.117103 Loss2: 1.333537 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403135 Loss1: 0.071890 Loss2: 1.331245 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989258
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.559930 Loss1: 0.195746 Loss2: 1.364184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.470416 Loss1: 0.124523 Loss2: 1.345893 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.056140 Loss1: 0.313560 Loss2: 1.742580
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.427307 Loss1: 0.087000 Loss2: 1.340307
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.483631 Loss1: 0.179170 Loss2: 1.304462
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.402327 Loss1: 0.066130 Loss2: 1.336197
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.500607 Loss1: 0.161710 Loss2: 1.338897
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.453296 Loss1: 0.119191 Loss2: 1.334104
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.414552 Loss1: 0.111193 Loss2: 1.303358
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398581 Loss1: 0.058339 Loss2: 1.340243
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.376934 Loss1: 0.084824 Loss2: 1.292110
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412383 Loss1: 0.079052 Loss2: 1.333331
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.349697 Loss1: 0.053607 Loss2: 1.296090
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421669 Loss1: 0.093532 Loss2: 1.328137
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.321731 Loss1: 0.043317 Loss2: 1.278415 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.296840 Loss1: 0.026807 Loss2: 1.270033 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.699080 Loss1: 0.352103 Loss2: 1.346977 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499922 Loss1: 0.154360 Loss2: 1.345561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499082 Loss1: 0.156598 Loss2: 1.342484
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.302085 Loss1: 0.465668 Loss2: 1.836417
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.507617 Loss1: 0.161671 Loss2: 1.345946
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.638753 Loss1: 0.290437 Loss2: 1.348316
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485046 Loss1: 0.138786 Loss2: 1.346260
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.620493 Loss1: 0.222697 Loss2: 1.397796
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438490 Loss1: 0.103082 Loss2: 1.335408
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574224 Loss1: 0.220259 Loss2: 1.353965
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407615 Loss1: 0.073946 Loss2: 1.333669
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.457230 Loss1: 0.105989 Loss2: 1.351241
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384188 Loss1: 0.061926 Loss2: 1.322262
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455093 Loss1: 0.116970 Loss2: 1.338123
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.394412 Loss1: 0.062323 Loss2: 1.332089
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405853 Loss1: 0.073935 Loss2: 1.331918
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379270 Loss1: 0.054966 Loss2: 1.324305
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380522 Loss1: 0.061378 Loss2: 1.319144
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.159904 Loss1: 0.353821 Loss2: 1.806083
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.579105 Loss1: 0.254221 Loss2: 1.324884
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.521395 Loss1: 0.163943 Loss2: 1.357452
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500001 Loss1: 0.157469 Loss2: 1.342532
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.337682 Loss1: 0.472051 Loss2: 1.865631
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.573733 Loss1: 0.220069 Loss2: 1.353664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.515769 Loss1: 0.150972 Loss2: 1.364798 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.476386 Loss1: 0.131030 Loss2: 1.345356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495663 Loss1: 0.141882 Loss2: 1.353781 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.473465 Loss1: 0.124498 Loss2: 1.348967 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.421374 Loss1: 0.075049 Loss2: 1.346324 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391178 Loss1: 0.050386 Loss2: 1.340792 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.575731 Loss1: 0.229919 Loss2: 1.345812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.487019 Loss1: 0.133742 Loss2: 1.353277 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492196 Loss1: 0.145520 Loss2: 1.346676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503653 Loss1: 0.144080 Loss2: 1.359572 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479682 Loss1: 0.119203 Loss2: 1.360479 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452834 Loss1: 0.104569 Loss2: 1.348264 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.463968 Loss1: 0.111750 Loss2: 1.352218 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422891 Loss1: 0.076589 Loss2: 1.346302 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.410912 Loss1: 0.074753 Loss2: 1.336159 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.347019 Loss1: 0.024049 Loss2: 1.322970 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.656084 Loss1: 0.301032 Loss2: 1.355052 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.510033 Loss1: 0.140070 Loss2: 1.369963 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.481841 Loss1: 0.133666 Loss2: 1.348175
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.163247 Loss1: 0.347255 Loss2: 1.815992
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.571405 Loss1: 0.240303 Loss2: 1.331101 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543753 Loss1: 0.190979 Loss2: 1.352774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.511764 Loss1: 0.165068 Loss2: 1.346696 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446094 Loss1: 0.109995 Loss2: 1.336100 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989955
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.403158 Loss1: 0.078520 Loss2: 1.324638 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362866 Loss1: 0.048333 Loss2: 1.314533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366108 Loss1: 0.053918 Loss2: 1.312189
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.146249 Loss1: 0.323643 Loss2: 1.822606
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.604782 Loss1: 0.263100 Loss2: 1.341681
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.459205 Loss1: 0.098654 Loss2: 1.360550
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.408911 Loss1: 0.069306 Loss2: 1.339605
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.392706 Loss1: 0.068678 Loss2: 1.324028
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.157177 Loss1: 0.325239 Loss2: 1.831938
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.408315 Loss1: 0.082118 Loss2: 1.326197
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.559432 Loss1: 0.218441 Loss2: 1.340990
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.376133 Loss1: 0.046580 Loss2: 1.329553
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.541614 Loss1: 0.187811 Loss2: 1.353804
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.373547 Loss1: 0.055837 Loss2: 1.317709
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481631 Loss1: 0.118750 Loss2: 1.362881
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384499 Loss1: 0.069161 Loss2: 1.315338
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446031 Loss1: 0.108112 Loss2: 1.337919
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412196 Loss1: 0.092509 Loss2: 1.319687
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.370952 Loss1: 0.043425 Loss2: 1.327528 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.360313 Loss1: 0.040810 Loss2: 1.319503 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.360431 Loss1: 0.043337 Loss2: 1.317094
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.217751 Loss1: 0.364082 Loss2: 1.853669
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.627155 Loss1: 0.266930 Loss2: 1.360224
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.570102 Loss1: 0.181289 Loss2: 1.388814
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.600390 Loss1: 0.227518 Loss2: 1.372872
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.662886 Loss1: 0.270394 Loss2: 1.392492
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.194082 Loss1: 0.367611 Loss2: 1.826471
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.535699 Loss1: 0.160428 Loss2: 1.375271
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.558127 Loss1: 0.216278 Loss2: 1.341849
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.532809 Loss1: 0.163539 Loss2: 1.369270
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.514034 Loss1: 0.165946 Loss2: 1.348089
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.475426 Loss1: 0.101690 Loss2: 1.373736
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.484514 Loss1: 0.126740 Loss2: 1.357774
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423250 Loss1: 0.064586 Loss2: 1.358664
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.442993 Loss1: 0.106965 Loss2: 1.336028
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402047 Loss1: 0.050190 Loss2: 1.351857
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.379124 Loss1: 0.056328 Loss2: 1.322796 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.367883 Loss1: 0.053545 Loss2: 1.314338 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362287 Loss1: 0.053054 Loss2: 1.309233
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.213594 Loss1: 0.367695 Loss2: 1.845899
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.560329 Loss1: 0.235241 Loss2: 1.325088
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.508529 Loss1: 0.160508 Loss2: 1.348022
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498474 Loss1: 0.162953 Loss2: 1.335520
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.416345 Loss1: 0.087491 Loss2: 1.328854
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.270130 Loss1: 0.422650 Loss2: 1.847480
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.369055 Loss1: 0.051298 Loss2: 1.317757
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.591913 Loss1: 0.232863 Loss2: 1.359050
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.370162 Loss1: 0.061153 Loss2: 1.309009
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.604031 Loss1: 0.226720 Loss2: 1.377311
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.379235 Loss1: 0.067937 Loss2: 1.311299
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588694 Loss1: 0.204677 Loss2: 1.384018
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.366239 Loss1: 0.058192 Loss2: 1.308047
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.507965 Loss1: 0.144955 Loss2: 1.363010
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.347777 Loss1: 0.044594 Loss2: 1.303183
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.484785 Loss1: 0.125529 Loss2: 1.359256 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410009 Loss1: 0.064591 Loss2: 1.345418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.371918 Loss1: 0.031654 Loss2: 1.340264
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.215855 Loss1: 0.346923 Loss2: 1.868933
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.598861 Loss1: 0.224138 Loss2: 1.374723
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.543601 Loss1: 0.162203 Loss2: 1.381398
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517341 Loss1: 0.134298 Loss2: 1.383044
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.486784 Loss1: 0.119308 Loss2: 1.367476
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.430468 Loss1: 0.070690 Loss2: 1.359777
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.165380 Loss1: 0.346458 Loss2: 1.818922
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.427900 Loss1: 0.069312 Loss2: 1.358587
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.673537 Loss1: 0.298537 Loss2: 1.375000
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401799 Loss1: 0.049686 Loss2: 1.352112
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.621221 Loss1: 0.206701 Loss2: 1.414521
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400300 Loss1: 0.047894 Loss2: 1.352407
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.549292 Loss1: 0.173243 Loss2: 1.376049
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409323 Loss1: 0.063795 Loss2: 1.345528
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.550425 Loss1: 0.167398 Loss2: 1.383027
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496476 Loss1: 0.120625 Loss2: 1.375851
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.471386 Loss1: 0.104987 Loss2: 1.366399
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452818 Loss1: 0.088580 Loss2: 1.364238
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.503650 Loss1: 0.144235 Loss2: 1.359416
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.372601 Loss1: 0.479045 Loss2: 1.893555
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.598812 Loss1: 0.229317 Loss2: 1.369494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491539 Loss1: 0.113316 Loss2: 1.378223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.426918 Loss1: 0.059373 Loss2: 1.367545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408683 Loss1: 0.054619 Loss2: 1.354064 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410686 Loss1: 0.059643 Loss2: 1.351043 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409008 Loss1: 0.056144 Loss2: 1.352864 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.488085 Loss1: 0.128075 Loss2: 1.360010 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453805 Loss1: 0.088359 Loss2: 1.365446 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417258 Loss1: 0.071646 Loss2: 1.345612 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.321642 Loss1: 0.399963 Loss2: 1.921678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.525678 Loss1: 0.146192 Loss2: 1.379485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472136 Loss1: 0.108469 Loss2: 1.363667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.497251 Loss1: 0.128212 Loss2: 1.369039 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476190 Loss1: 0.107157 Loss2: 1.369033 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433648 Loss1: 0.064106 Loss2: 1.369542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.454704 Loss1: 0.087778 Loss2: 1.366927 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.437069 Loss1: 0.070964 Loss2: 1.366105 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.365227 Loss1: 0.045223 Loss2: 1.320004 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368938 Loss1: 0.053421 Loss2: 1.315517 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.975000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.552241 Loss1: 0.225173 Loss2: 1.327068 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.473510 Loss1: 0.129034 Loss2: 1.344477 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519877 Loss1: 0.177729 Loss2: 1.342148
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.281198 Loss1: 0.396827 Loss2: 1.884371
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.643391 Loss1: 0.278279 Loss2: 1.365112
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.541633 Loss1: 0.187812 Loss2: 1.353821
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.550152 Loss1: 0.158025 Loss2: 1.392127
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.481828 Loss1: 0.130743 Loss2: 1.351085
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481814 Loss1: 0.132443 Loss2: 1.349372
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434791 Loss1: 0.089029 Loss2: 1.345762
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.440870 Loss1: 0.083318 Loss2: 1.357552
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465430 Loss1: 0.128311 Loss2: 1.337119
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399621 Loss1: 0.064050 Loss2: 1.335571 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433703 Loss1: 0.096820 Loss2: 1.336883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.419937 Loss1: 0.086707 Loss2: 1.333230 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.620068 Loss1: 0.241226 Loss2: 1.378842 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484348 Loss1: 0.110160 Loss2: 1.374188 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.481687 Loss1: 0.111867 Loss2: 1.369820
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.202444 Loss1: 0.343554 Loss2: 1.858890
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473396 Loss1: 0.100498 Loss2: 1.372897
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.579286 Loss1: 0.223011 Loss2: 1.356275
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420615 Loss1: 0.061520 Loss2: 1.359095
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543818 Loss1: 0.170721 Loss2: 1.373097
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.388431 Loss1: 0.031615 Loss2: 1.356816
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.456268 Loss1: 0.086751 Loss2: 1.369517
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386504 Loss1: 0.039094 Loss2: 1.347410
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.448376 Loss1: 0.101387 Loss2: 1.346990
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387750 Loss1: 0.046155 Loss2: 1.341594
-DEBUG flwr 2023-10-13 09:48:58,822 | server.py:236 | fit_round 184 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.436912 Loss1: 0.093670 Loss2: 1.343242
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436592 Loss1: 0.092184 Loss2: 1.344407
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422125 Loss1: 0.087448 Loss2: 1.334677
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451598 Loss1: 0.114252 Loss2: 1.337346
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454525 Loss1: 0.104347 Loss2: 1.350178
-(DefaultActor pid=3764) >> Training accuracy: 0.971875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.256007 Loss1: 0.368155 Loss2: 1.887852
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.670335 Loss1: 0.283852 Loss2: 1.386483
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.612025 Loss1: 0.184920 Loss2: 1.427105
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536776 Loss1: 0.135137 Loss2: 1.401639
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.463613 Loss1: 0.482522 Loss2: 1.981091
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.657578 Loss1: 0.298487 Loss2: 1.359092 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.576019 Loss1: 0.213256 Loss2: 1.362763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.518869 Loss1: 0.131395 Loss2: 1.387474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.462826 Loss1: 0.100965 Loss2: 1.361861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427722 Loss1: 0.073791 Loss2: 1.353931
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.443737 Loss1: 0.065887 Loss2: 1.377850
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453889 Loss1: 0.080006 Loss2: 1.373882 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.432328 Loss1: 0.081591 Loss2: 1.350737 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.401736 Loss1: 0.464630 Loss2: 1.937106
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.754249 Loss1: 0.355541 Loss2: 1.398707
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.636121 Loss1: 0.201301 Loss2: 1.434820
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.618382 Loss1: 0.227766 Loss2: 1.390616
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.227357 Loss1: 0.364058 Loss2: 1.863299
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.605430 Loss1: 0.239818 Loss2: 1.365612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.595585 Loss1: 0.220107 Loss2: 1.375478 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.531228 Loss1: 0.157428 Loss2: 1.373800 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506938 Loss1: 0.149631 Loss2: 1.357307 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441469 Loss1: 0.088645 Loss2: 1.352824 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.379470 Loss1: 0.038409 Loss2: 1.341060 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401220 Loss1: 0.072188 Loss2: 1.329032 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 09:48:58,822][flwr][DEBUG] - fit_round 184 received 50 results and 0 failures
-INFO flwr 2023-10-13 09:49:40,817 | server.py:125 | fit progress: (184, 2.313936873937186, {'accuracy': 0.6104}, 424688.596004301)
->> Test accuracy: 0.610400
-[2023-10-13 09:49:40,817][flwr][INFO] - fit progress: (184, 2.313936873937186, {'accuracy': 0.6104}, 424688.596004301)
-DEBUG flwr 2023-10-13 09:49:40,818 | server.py:173 | evaluate_round 184: strategy sampled 50 clients (out of 50)
-[2023-10-13 09:49:40,818][flwr][DEBUG] - evaluate_round 184: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 09:58:44,423 | server.py:187 | evaluate_round 184 received 50 results and 0 failures
-[2023-10-13 09:58:44,423][flwr][DEBUG] - evaluate_round 184 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 09:58:44,424 | server.py:222 | fit_round 185: strategy sampled 50 clients (out of 50)
-[2023-10-13 09:58:44,424][flwr][DEBUG] - fit_round 185: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.475192 Loss1: 0.529156 Loss2: 1.946036
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.647571 Loss1: 0.305100 Loss2: 1.342471
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.601481 Loss1: 0.250879 Loss2: 1.350602
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.481783 Loss1: 0.118756 Loss2: 1.363027
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.441785 Loss1: 0.108605 Loss2: 1.333180
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.426714 Loss1: 0.101053 Loss2: 1.325661 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.380807 Loss1: 0.055103 Loss2: 1.325704 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.352949 Loss1: 0.037204 Loss2: 1.315745 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.329984 Loss1: 0.020065 Loss2: 1.309918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.328445 Loss1: 0.025422 Loss2: 1.303023 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991587
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.409275 Loss1: 0.074114 Loss2: 1.335161 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.349618 Loss1: 0.022510 Loss2: 1.327107 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361234 Loss1: 0.040942 Loss2: 1.320292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.137348 Loss1: 0.363035 Loss2: 1.774313
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.564696 Loss1: 0.237756 Loss2: 1.326940
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.498207 Loss1: 0.154062 Loss2: 1.344144
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.480300 Loss1: 0.157459 Loss2: 1.322841
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443273 Loss1: 0.125107 Loss2: 1.318165
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.270821 Loss1: 0.325460 Loss2: 1.945360
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.398224 Loss1: 0.075882 Loss2: 1.322343
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.689315 Loss1: 0.275438 Loss2: 1.413877
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.647059 Loss1: 0.197400 Loss2: 1.449658
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.387972 Loss1: 0.076309 Loss2: 1.311663
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567542 Loss1: 0.124769 Loss2: 1.442773
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449553 Loss1: 0.128906 Loss2: 1.320648
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515495 Loss1: 0.099742 Loss2: 1.415754
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402468 Loss1: 0.081340 Loss2: 1.321129
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512875 Loss1: 0.098219 Loss2: 1.414656
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439649 Loss1: 0.122738 Loss2: 1.316911
-(DefaultActor pid=3765) >> Training accuracy: 0.975586
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.492585 Loss1: 0.079344 Loss2: 1.413241 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.430807 Loss1: 0.030766 Loss2: 1.400041 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.751989 Loss1: 0.343044 Loss2: 1.408945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586966 Loss1: 0.173423 Loss2: 1.413543 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.519612 Loss1: 0.121140 Loss2: 1.398473 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.496720 Loss1: 0.100780 Loss2: 1.395940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514998 Loss1: 0.108889 Loss2: 1.406109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480358 Loss1: 0.080866 Loss2: 1.399493 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444331 Loss1: 0.052727 Loss2: 1.391604 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421205 Loss1: 0.038411 Loss2: 1.382794 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427264 Loss1: 0.089293 Loss2: 1.337971 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396815 Loss1: 0.062101 Loss2: 1.334714 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.680974 Loss1: 0.315880 Loss2: 1.365094 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558332 Loss1: 0.184032 Loss2: 1.374300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535099 Loss1: 0.160277 Loss2: 1.374822
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.150325 Loss1: 0.389116 Loss2: 1.761209
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503904 Loss1: 0.129793 Loss2: 1.374110
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.508960 Loss1: 0.210694 Loss2: 1.298266
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425338 Loss1: 0.061793 Loss2: 1.363545
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.485265 Loss1: 0.162928 Loss2: 1.322337
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.420108 Loss1: 0.117240 Loss2: 1.302868 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.416181 Loss1: 0.117934 Loss2: 1.298247 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.403750 Loss1: 0.101345 Loss2: 1.302405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.362195 Loss1: 0.063054 Loss2: 1.299140 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.335257 Loss1: 0.048725 Loss2: 1.286532 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.571599 Loss1: 0.257164 Loss2: 1.314435 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.387628 Loss1: 0.072818 Loss2: 1.314811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.424642 Loss1: 0.475225 Loss2: 1.949416 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.739869 Loss1: 0.343163 Loss2: 1.396706 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.617828 Loss1: 0.185900 Loss2: 1.431928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.599543 Loss1: 0.197921 Loss2: 1.401622 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.585787 Loss1: 0.171771 Loss2: 1.414016 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537573 Loss1: 0.121798 Loss2: 1.415775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477024 Loss1: 0.083780 Loss2: 1.393244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411122 Loss1: 0.032006 Loss2: 1.379117 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.600914 Loss1: 0.218336 Loss2: 1.382577 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.523178 Loss1: 0.136036 Loss2: 1.387142 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.547838 Loss1: 0.503199 Loss2: 2.044639
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531619 Loss1: 0.158903 Loss2: 1.372716
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484312 Loss1: 0.108197 Loss2: 1.376115 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.539172 Loss1: 0.163645 Loss2: 1.375527 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.484499 Loss1: 0.107825 Loss2: 1.376674 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.540889 Loss1: 0.130184 Loss2: 1.410705 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.544868 Loss1: 0.136323 Loss2: 1.408545 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.457431 Loss1: 0.069273 Loss2: 1.388158 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.291461 Loss1: 0.397181 Loss2: 1.894280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.604039 Loss1: 0.178075 Loss2: 1.425964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535944 Loss1: 0.156159 Loss2: 1.379785
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.368905 Loss1: 0.448015 Loss2: 1.920891
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.533994 Loss1: 0.145569 Loss2: 1.388425
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.631659 Loss1: 0.240596 Loss2: 1.391062
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592500 Loss1: 0.204359 Loss2: 1.388141
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505990 Loss1: 0.116259 Loss2: 1.389731
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561870 Loss1: 0.155890 Loss2: 1.405979
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461431 Loss1: 0.087546 Loss2: 1.373885
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.495837 Loss1: 0.120832 Loss2: 1.375005
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429902 Loss1: 0.054705 Loss2: 1.375197
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397422 Loss1: 0.036387 Loss2: 1.361034 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426750 Loss1: 0.069330 Loss2: 1.357420 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.486262 Loss1: 0.104942 Loss2: 1.381320 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985491
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.168345 Loss1: 0.344067 Loss2: 1.824277 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.507973 Loss1: 0.170752 Loss2: 1.337222 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.437122 Loss1: 0.100961 Loss2: 1.336160
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.265688 Loss1: 0.427481 Loss2: 1.838207
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.418874 Loss1: 0.108831 Loss2: 1.310043
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.621348 Loss1: 0.280527 Loss2: 1.340820
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433502 Loss1: 0.125325 Loss2: 1.308177
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.566073 Loss1: 0.186860 Loss2: 1.379213
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414597 Loss1: 0.092821 Loss2: 1.321776
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498896 Loss1: 0.154541 Loss2: 1.344354
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413647 Loss1: 0.102534 Loss2: 1.311113
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.501296 Loss1: 0.161172 Loss2: 1.340124
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388618 Loss1: 0.075023 Loss2: 1.313595
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.438380 Loss1: 0.099074 Loss2: 1.339306
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355362 Loss1: 0.049998 Loss2: 1.305364
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.428880 Loss1: 0.098271 Loss2: 1.330609
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381234 Loss1: 0.048712 Loss2: 1.332522
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.403966 Loss1: 0.079276 Loss2: 1.324690
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379673 Loss1: 0.056156 Loss2: 1.323517
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.218713 Loss1: 0.355331 Loss2: 1.863382
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.589874 Loss1: 0.231841 Loss2: 1.358033
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.600589 Loss1: 0.201040 Loss2: 1.399549
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.614031 Loss1: 0.226331 Loss2: 1.387700
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.040742 Loss1: 0.256593 Loss2: 1.784149
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.478148 Loss1: 0.154004 Loss2: 1.324145 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.423716 Loss1: 0.104514 Loss2: 1.319202 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.406733 Loss1: 0.093169 Loss2: 1.313564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.412366 Loss1: 0.100922 Loss2: 1.311444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.368825 Loss1: 0.053361 Loss2: 1.315464 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.336387 Loss1: 0.036810 Loss2: 1.299577 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.320573 Loss1: 0.031056 Loss2: 1.289517 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995404
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.602710 Loss1: 0.255608 Loss2: 1.347102 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.543328 Loss1: 0.175761 Loss2: 1.367566 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.479120 Loss1: 0.117116 Loss2: 1.362003
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.212863 Loss1: 0.363157 Loss2: 1.849706
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.617879 Loss1: 0.260745 Loss2: 1.357134 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.515565 Loss1: 0.132628 Loss2: 1.382937 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.503036 Loss1: 0.142982 Loss2: 1.360053 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483146 Loss1: 0.126026 Loss2: 1.357120 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422137 Loss1: 0.073337 Loss2: 1.348799
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470126 Loss1: 0.111598 Loss2: 1.358529
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.384999 Loss1: 0.037311 Loss2: 1.347687
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376171 Loss1: 0.034929 Loss2: 1.341242
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.387724 Loss1: 0.053271 Loss2: 1.334453
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380592 Loss1: 0.049463 Loss2: 1.331128
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.264046 Loss1: 0.406036 Loss2: 1.858010
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.576071 Loss1: 0.216095 Loss2: 1.359975
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.540497 Loss1: 0.171189 Loss2: 1.369308
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.532895 Loss1: 0.160976 Loss2: 1.371920
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.495392 Loss1: 0.136921 Loss2: 1.358471
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.144762 Loss1: 0.323987 Loss2: 1.820774
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.511605 Loss1: 0.148929 Loss2: 1.362676
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.571810 Loss1: 0.257514 Loss2: 1.314297
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450822 Loss1: 0.093778 Loss2: 1.357044
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.463911 Loss1: 0.121815 Loss2: 1.342097
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460460 Loss1: 0.096463 Loss2: 1.363997
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.470702 Loss1: 0.153274 Loss2: 1.317428
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439598 Loss1: 0.092664 Loss2: 1.346934
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.481328 Loss1: 0.160133 Loss2: 1.321194
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.424541 Loss1: 0.076807 Loss2: 1.347734
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443781 Loss1: 0.115097 Loss2: 1.328684
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.451244 Loss1: 0.133202 Loss2: 1.318042
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.417372 Loss1: 0.100077 Loss2: 1.317295
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383131 Loss1: 0.066693 Loss2: 1.316437
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.345276 Loss1: 0.038106 Loss2: 1.307170
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.247462 Loss1: 0.411162 Loss2: 1.836300
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.642684 Loss1: 0.295549 Loss2: 1.347134
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.535086 Loss1: 0.153739 Loss2: 1.381347
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.475674 Loss1: 0.125855 Loss2: 1.349819
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.441940 Loss1: 0.092948 Loss2: 1.348992
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.122081 Loss1: 0.314298 Loss2: 1.807782
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.550213 Loss1: 0.201620 Loss2: 1.348593 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.510642 Loss1: 0.153130 Loss2: 1.357512 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.451548 Loss1: 0.110203 Loss2: 1.341345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.420209 Loss1: 0.081405 Loss2: 1.338804 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.412186 Loss1: 0.073041 Loss2: 1.339145 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416107 Loss1: 0.088212 Loss2: 1.327896 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378903 Loss1: 0.043399 Loss2: 1.335504 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.724629 Loss1: 0.358449 Loss2: 1.366180 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558665 Loss1: 0.180593 Loss2: 1.378072 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.266326 Loss1: 0.374572 Loss2: 1.891755 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.697152 Loss1: 0.293992 Loss2: 1.403160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.606420 Loss1: 0.158994 Loss2: 1.447427 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.650303 Loss1: 0.240878 Loss2: 1.409425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.553984 Loss1: 0.142347 Loss2: 1.411637 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386155 Loss1: 0.044037 Loss2: 1.342118
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.576680 Loss1: 0.167666 Loss2: 1.409014
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.493157 Loss1: 0.089860 Loss2: 1.403297
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493174 Loss1: 0.103657 Loss2: 1.389517
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.518741 Loss1: 0.124869 Loss2: 1.393872
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482505 Loss1: 0.090235 Loss2: 1.392270
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.229582 Loss1: 0.343291 Loss2: 1.886291
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.632288 Loss1: 0.227899 Loss2: 1.404389
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.581640 Loss1: 0.167701 Loss2: 1.413939
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542552 Loss1: 0.135872 Loss2: 1.406681
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.499877 Loss1: 0.108181 Loss2: 1.391696
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.211311 Loss1: 0.341320 Loss2: 1.869990
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.480536 Loss1: 0.085042 Loss2: 1.395494
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630019 Loss1: 0.254530 Loss2: 1.375489
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468474 Loss1: 0.079430 Loss2: 1.389045
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.586039 Loss1: 0.195279 Loss2: 1.390760
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.527817 Loss1: 0.141272 Loss2: 1.386546
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486781 Loss1: 0.094490 Loss2: 1.392291
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574715 Loss1: 0.202455 Loss2: 1.372260
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461284 Loss1: 0.070348 Loss2: 1.390936
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.577838 Loss1: 0.182964 Loss2: 1.394874
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448995 Loss1: 0.064551 Loss2: 1.384444
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.464322 Loss1: 0.088576 Loss2: 1.375745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.412615 Loss1: 0.053985 Loss2: 1.358630 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.673631 Loss1: 0.275266 Loss2: 1.398365 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522589 Loss1: 0.120669 Loss2: 1.401920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.463291 Loss1: 0.084423 Loss2: 1.378868 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.509625 Loss1: 0.126428 Loss2: 1.383197 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473551 Loss1: 0.091704 Loss2: 1.381847 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440117 Loss1: 0.060278 Loss2: 1.379839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413574 Loss1: 0.040718 Loss2: 1.372856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393896 Loss1: 0.031894 Loss2: 1.362002 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376637 Loss1: 0.043307 Loss2: 1.333331 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378719 Loss1: 0.054914 Loss2: 1.323805 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.146609 Loss1: 0.292395 Loss2: 1.854215
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.567525 Loss1: 0.193632 Loss2: 1.373893
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526785 Loss1: 0.146570 Loss2: 1.380215
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502158 Loss1: 0.137980 Loss2: 1.364178
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.333542 Loss1: 0.445286 Loss2: 1.888255
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.574350 Loss1: 0.240992 Loss2: 1.333358 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493991 Loss1: 0.128516 Loss2: 1.365475
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.538558 Loss1: 0.180487 Loss2: 1.358071
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467806 Loss1: 0.106720 Loss2: 1.361086
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.501376 Loss1: 0.143707 Loss2: 1.357669
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.510221 Loss1: 0.176125 Loss2: 1.334096
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429491 Loss1: 0.065089 Loss2: 1.364403
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450518 Loss1: 0.111579 Loss2: 1.338939
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.424821 Loss1: 0.074957 Loss2: 1.349865
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408172 Loss1: 0.056144 Loss2: 1.352028 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376437 Loss1: 0.060245 Loss2: 1.316193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988839
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.266404 Loss1: 0.387565 Loss2: 1.878839 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.587726 Loss1: 0.187806 Loss2: 1.399919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516303 Loss1: 0.160790 Loss2: 1.355513
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.206666 Loss1: 0.410616 Loss2: 1.796050
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.527313 Loss1: 0.226684 Loss2: 1.300628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.522292 Loss1: 0.193326 Loss2: 1.328966 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.465003 Loss1: 0.154535 Loss2: 1.310468 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.415644 Loss1: 0.106174 Loss2: 1.309470 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.434320 Loss1: 0.126910 Loss2: 1.307410 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432260 Loss1: 0.076610 Loss2: 1.355650
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404690 Loss1: 0.097167 Loss2: 1.307523
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416081 Loss1: 0.112260 Loss2: 1.303821
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.367440 Loss1: 0.067545 Loss2: 1.299896
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.353105 Loss1: 0.056209 Loss2: 1.296896
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.143297 Loss1: 0.378910 Loss2: 1.764387
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.576836 Loss1: 0.279192 Loss2: 1.297645
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.567093 Loss1: 0.226007 Loss2: 1.341086
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476339 Loss1: 0.175571 Loss2: 1.300768
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.248843 Loss1: 0.415782 Loss2: 1.833061
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.697767 Loss1: 0.345457 Loss2: 1.352309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.570125 Loss1: 0.175269 Loss2: 1.394856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468414 Loss1: 0.124025 Loss2: 1.344389 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.444628 Loss1: 0.109078 Loss2: 1.335550 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.399148 Loss1: 0.063584 Loss2: 1.335564 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.326109 Loss1: 0.043134 Loss2: 1.282975
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.390837 Loss1: 0.063012 Loss2: 1.327825
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.375171 Loss1: 0.049949 Loss2: 1.325222
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371324 Loss1: 0.048476 Loss2: 1.322847
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365164 Loss1: 0.049169 Loss2: 1.315995
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.185374 Loss1: 0.391204 Loss2: 1.794170
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.624376 Loss1: 0.310001 Loss2: 1.314375
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.478711 Loss1: 0.144748 Loss2: 1.333963
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482909 Loss1: 0.161464 Loss2: 1.321445
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.260296 Loss1: 0.392259 Loss2: 1.868037
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.649419 Loss1: 0.302619 Loss2: 1.346800 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.530451 Loss1: 0.168914 Loss2: 1.361537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.460779 Loss1: 0.111633 Loss2: 1.349146 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.428797 Loss1: 0.097841 Loss2: 1.330956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446003 Loss1: 0.114819 Loss2: 1.331184 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.393578 Loss1: 0.061558 Loss2: 1.332020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384276 Loss1: 0.062828 Loss2: 1.321449 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.131586 Loss1: 0.342363 Loss2: 1.789223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.520203 Loss1: 0.182809 Loss2: 1.337394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.492049 Loss1: 0.165518 Loss2: 1.326531
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.120214 Loss1: 0.299219 Loss2: 1.820995
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630732 Loss1: 0.263276 Loss2: 1.367456 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.574202 Loss1: 0.172412 Loss2: 1.401790 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.450911 Loss1: 0.085016 Loss2: 1.365895 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.449794 Loss1: 0.090039 Loss2: 1.359754 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.422262 Loss1: 0.066604 Loss2: 1.355658 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.999023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.335404 Loss1: 0.032261 Loss2: 1.303143
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.397431 Loss1: 0.043975 Loss2: 1.353456
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396983 Loss1: 0.052763 Loss2: 1.344219
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404326 Loss1: 0.065586 Loss2: 1.338739
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404130 Loss1: 0.061012 Loss2: 1.343118
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.674359 Loss1: 0.312328 Loss2: 1.362031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516511 Loss1: 0.139434 Loss2: 1.377077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488774 Loss1: 0.127017 Loss2: 1.361757 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.447604 Loss1: 0.075971 Loss2: 1.371633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426331 Loss1: 0.069138 Loss2: 1.357193 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441468 Loss1: 0.096293 Loss2: 1.345175 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450231 Loss1: 0.099901 Loss2: 1.350330 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409521 Loss1: 0.059574 Loss2: 1.349947 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399165 Loss1: 0.072397 Loss2: 1.326768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.359745 Loss1: 0.034886 Loss2: 1.324859 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.504279 Loss1: 0.164991 Loss2: 1.339288 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.439074 Loss1: 0.102640 Loss2: 1.336435 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.256535 Loss1: 0.390109 Loss2: 1.866426
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.412623 Loss1: 0.082577 Loss2: 1.330045
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.616275 Loss1: 0.277894 Loss2: 1.338381
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.377271 Loss1: 0.052874 Loss2: 1.324397
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.558984 Loss1: 0.192959 Loss2: 1.366025
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.370399 Loss1: 0.050188 Loss2: 1.320211
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492535 Loss1: 0.141096 Loss2: 1.351439
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401125 Loss1: 0.082215 Loss2: 1.318910
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475055 Loss1: 0.140843 Loss2: 1.334213
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409112 Loss1: 0.090181 Loss2: 1.318931
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447006 Loss1: 0.110090 Loss2: 1.336915
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379377 Loss1: 0.053853 Loss2: 1.325524
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446348 Loss1: 0.100581 Loss2: 1.345767 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396695 Loss1: 0.064788 Loss2: 1.331907 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.556266 Loss1: 0.202735 Loss2: 1.353531 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.458958 Loss1: 0.105632 Loss2: 1.353325 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.428832 Loss1: 0.098847 Loss2: 1.329985 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.406372 Loss1: 0.081310 Loss2: 1.325063 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.412649 Loss1: 0.081056 Loss2: 1.331592 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.370728 Loss1: 0.037140 Loss2: 1.333589 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405304 Loss1: 0.086356 Loss2: 1.318948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.464874 Loss1: 0.100938 Loss2: 1.363936 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404441 Loss1: 0.055408 Loss2: 1.349032 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.328013 Loss1: 0.414486 Loss2: 1.913527 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 10:27:38,759 | server.py:236 | fit_round 185 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.569659 Loss1: 0.142701 Loss2: 1.426957 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573438 Loss1: 0.162849 Loss2: 1.410590
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.159029 Loss1: 0.347487 Loss2: 1.811542
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534675 Loss1: 0.131256 Loss2: 1.403418
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.530086 Loss1: 0.207067 Loss2: 1.323019
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488535 Loss1: 0.086438 Loss2: 1.402097
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.490378 Loss1: 0.157421 Loss2: 1.332957
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484185 Loss1: 0.087403 Loss2: 1.396782
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.465309 Loss1: 0.142292 Loss2: 1.323017
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.488965 Loss1: 0.095125 Loss2: 1.393840
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.415760 Loss1: 0.100941 Loss2: 1.314819
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.502971 Loss1: 0.099754 Loss2: 1.403217
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463616 Loss1: 0.148759 Loss2: 1.314858
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.516864 Loss1: 0.119692 Loss2: 1.397172
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.379234 Loss1: 0.066702 Loss2: 1.312532
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.394866 Loss1: 0.086384 Loss2: 1.308482
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.344739 Loss1: 0.042134 Loss2: 1.302605
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397560 Loss1: 0.100030 Loss2: 1.297530
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.148682 Loss1: 0.294512 Loss2: 1.854170
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.562201 Loss1: 0.204286 Loss2: 1.357914
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.524706 Loss1: 0.160873 Loss2: 1.363834
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.179003 Loss1: 0.373542 Loss2: 1.805461
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.479947 Loss1: 0.115719 Loss2: 1.364228
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.628254 Loss1: 0.285936 Loss2: 1.342318
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.439580 Loss1: 0.085915 Loss2: 1.353666
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.498357 Loss1: 0.139929 Loss2: 1.358429
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451316 Loss1: 0.103436 Loss2: 1.347880
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404383 Loss1: 0.057008 Loss2: 1.347375 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.383033 Loss1: 0.043553 Loss2: 1.339480 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.387076 Loss1: 0.057481 Loss2: 1.329595 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420807 Loss1: 0.083451 Loss2: 1.337356 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384017 Loss1: 0.063815 Loss2: 1.320201 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 10:27:38,759][flwr][DEBUG] - fit_round 185 received 50 results and 0 failures
-INFO flwr 2023-10-13 10:28:19,874 | server.py:125 | fit progress: (185, 2.306797981262207, {'accuracy': 0.6091}, 427007.65254263097)
->> Test accuracy: 0.609100
-[2023-10-13 10:28:19,874][flwr][INFO] - fit progress: (185, 2.306797981262207, {'accuracy': 0.6091}, 427007.65254263097)
-DEBUG flwr 2023-10-13 10:28:19,874 | server.py:173 | evaluate_round 185: strategy sampled 50 clients (out of 50)
-[2023-10-13 10:28:19,874][flwr][DEBUG] - evaluate_round 185: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 10:37:29,077 | server.py:187 | evaluate_round 185 received 50 results and 0 failures
-[2023-10-13 10:37:29,077][flwr][DEBUG] - evaluate_round 185 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 10:37:29,077 | server.py:222 | fit_round 186: strategy sampled 50 clients (out of 50)
-[2023-10-13 10:37:29,077][flwr][DEBUG] - fit_round 186: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.227668 Loss1: 0.369802 Loss2: 1.857866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599522 Loss1: 0.194869 Loss2: 1.404653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502821 Loss1: 0.137835 Loss2: 1.364986
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.222112 Loss1: 0.363083 Loss2: 1.859029
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.508664 Loss1: 0.153647 Loss2: 1.355017
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.583407 Loss1: 0.244463 Loss2: 1.338944
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524923 Loss1: 0.164571 Loss2: 1.360352
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.557447 Loss1: 0.181739 Loss2: 1.375708
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514548 Loss1: 0.153847 Loss2: 1.360702
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.523585 Loss1: 0.163602 Loss2: 1.359983
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.458306 Loss1: 0.100475 Loss2: 1.357831
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.448091 Loss1: 0.099020 Loss2: 1.349071
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431501 Loss1: 0.090993 Loss2: 1.340508
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444531 Loss1: 0.098196 Loss2: 1.346335
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408781 Loss1: 0.065010 Loss2: 1.343772
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.399406 Loss1: 0.059125 Loss2: 1.340281
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.407207 Loss1: 0.069890 Loss2: 1.337318
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.417102 Loss1: 0.077241 Loss2: 1.339861
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391278 Loss1: 0.057244 Loss2: 1.334033
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.105437 Loss1: 0.365772 Loss2: 1.739665
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.502668 Loss1: 0.220688 Loss2: 1.281980
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.502788 Loss1: 0.202923 Loss2: 1.299864
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.459732 Loss1: 0.152248 Loss2: 1.307483
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.361101 Loss1: 0.430741 Loss2: 1.930360
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.353443 Loss1: 0.069959 Loss2: 1.283484
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.613029 Loss1: 0.246000 Loss2: 1.367029
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.569447 Loss1: 0.201014 Loss2: 1.368433
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.346125 Loss1: 0.074668 Loss2: 1.271457
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.522710 Loss1: 0.129254 Loss2: 1.393455
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.321292 Loss1: 0.051590 Loss2: 1.269702
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.322583 Loss1: 0.056348 Loss2: 1.266235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.314943 Loss1: 0.053627 Loss2: 1.261316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.280684 Loss1: 0.026299 Loss2: 1.254385 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.383901 Loss1: 0.039674 Loss2: 1.344227 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998798
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.312868 Loss1: 0.464131 Loss2: 1.848737
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.658659 Loss1: 0.343466 Loss2: 1.315193
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.522039 Loss1: 0.186356 Loss2: 1.335683
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.475242 Loss1: 0.152572 Loss2: 1.322670
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.215604 Loss1: 0.369308 Loss2: 1.846296
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.643551 Loss1: 0.293844 Loss2: 1.349707 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.608670 Loss1: 0.216623 Loss2: 1.392047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.581528 Loss1: 0.217440 Loss2: 1.364088 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.363305 Loss1: 0.063202 Loss2: 1.300104 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360941 Loss1: 0.066334 Loss2: 1.294606 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438749 Loss1: 0.089255 Loss2: 1.349494 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422168 Loss1: 0.084932 Loss2: 1.337236 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.593773 Loss1: 0.230622 Loss2: 1.363150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558417 Loss1: 0.177450 Loss2: 1.380967 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535376 Loss1: 0.170656 Loss2: 1.364720
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.244848 Loss1: 0.381636 Loss2: 1.863212
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.598606 Loss1: 0.243400 Loss2: 1.355207 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.554354 Loss1: 0.176822 Loss2: 1.377532 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.535622 Loss1: 0.171306 Loss2: 1.364316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460513 Loss1: 0.108436 Loss2: 1.352077 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423925 Loss1: 0.067270 Loss2: 1.356656
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439948 Loss1: 0.096323 Loss2: 1.343625
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406273 Loss1: 0.068675 Loss2: 1.337598
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400438 Loss1: 0.066813 Loss2: 1.333626
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370716 Loss1: 0.041054 Loss2: 1.329662
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376085 Loss1: 0.046803 Loss2: 1.329283
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.272981 Loss1: 0.406166 Loss2: 1.866815
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.607033 Loss1: 0.239130 Loss2: 1.367903
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.514825 Loss1: 0.131080 Loss2: 1.383745
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474067 Loss1: 0.107048 Loss2: 1.367020
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472573 Loss1: 0.122893 Loss2: 1.349680
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.248370 Loss1: 0.391731 Loss2: 1.856640
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.636408 Loss1: 0.264524 Loss2: 1.371885 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543775 Loss1: 0.143127 Loss2: 1.400648 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.508822 Loss1: 0.139726 Loss2: 1.369096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497004 Loss1: 0.123669 Loss2: 1.373335 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456993 Loss1: 0.085709 Loss2: 1.371284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449796 Loss1: 0.092325 Loss2: 1.357471 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.514988 Loss1: 0.145998 Loss2: 1.368990 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976562
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591022 Loss1: 0.223657 Loss2: 1.367365 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515594 Loss1: 0.177474 Loss2: 1.338119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456482 Loss1: 0.109113 Loss2: 1.347369
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.352890 Loss1: 0.477020 Loss2: 1.875870
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.681688 Loss1: 0.308118 Loss2: 1.373570 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.636874 Loss1: 0.222291 Loss2: 1.414583 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.595902 Loss1: 0.206106 Loss2: 1.389796 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994420
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461922 Loss1: 0.093110 Loss2: 1.368812 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443456 Loss1: 0.082206 Loss2: 1.361249 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425126 Loss1: 0.071058 Loss2: 1.354068
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.076427 Loss1: 0.301597 Loss2: 1.774830
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427317 Loss1: 0.079185 Loss2: 1.348132
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.496744 Loss1: 0.181086 Loss2: 1.315658
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.579166 Loss1: 0.234584 Loss2: 1.344582
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502623 Loss1: 0.155749 Loss2: 1.346874
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.436867 Loss1: 0.105874 Loss2: 1.330993
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.419478 Loss1: 0.091728 Loss2: 1.327750
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.402619 Loss1: 0.477481 Loss2: 1.925138
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.411815 Loss1: 0.087574 Loss2: 1.324241
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.685166 Loss1: 0.343795 Loss2: 1.341372
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598768 Loss1: 0.220632 Loss2: 1.378136
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.418240 Loss1: 0.092792 Loss2: 1.325449
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395370 Loss1: 0.076457 Loss2: 1.318913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373176 Loss1: 0.058217 Loss2: 1.314959 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.456011 Loss1: 0.125256 Loss2: 1.330755 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361920 Loss1: 0.043229 Loss2: 1.318690 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987981
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.606507 Loss1: 0.276682 Loss2: 1.329826 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.449839 Loss1: 0.113285 Loss2: 1.336554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.436000 Loss1: 0.112275 Loss2: 1.323725 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.419307 Loss1: 0.088526 Loss2: 1.330781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435687 Loss1: 0.110530 Loss2: 1.325158 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403824 Loss1: 0.073899 Loss2: 1.329925 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377264 Loss1: 0.055704 Loss2: 1.321560 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359007 Loss1: 0.041996 Loss2: 1.317011 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483227 Loss1: 0.100076 Loss2: 1.383151 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427079 Loss1: 0.050740 Loss2: 1.376339 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.675944 Loss1: 0.366942 Loss2: 1.309002 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528314 Loss1: 0.175875 Loss2: 1.352439 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.227926 Loss1: 0.380670 Loss2: 1.847256 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.364728 Loss1: 0.054485 Loss2: 1.310243 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.584902 Loss1: 0.182444 Loss2: 1.402459 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487169 Loss1: 0.127520 Loss2: 1.359650 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485490 Loss1: 0.129143 Loss2: 1.356346 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447718 Loss1: 0.101699 Loss2: 1.346019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421573 Loss1: 0.070183 Loss2: 1.351390
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.210053 Loss1: 0.337425 Loss2: 1.872628
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415458 Loss1: 0.074818 Loss2: 1.340640
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.552822 Loss1: 0.199678 Loss2: 1.353143
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.513202 Loss1: 0.151863 Loss2: 1.361339
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467234 Loss1: 0.108950 Loss2: 1.358285
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.494334 Loss1: 0.143004 Loss2: 1.351330
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477334 Loss1: 0.124182 Loss2: 1.353151
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.209016 Loss1: 0.321060 Loss2: 1.887956
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432802 Loss1: 0.084192 Loss2: 1.348610
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.642776 Loss1: 0.243917 Loss2: 1.398859
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422176 Loss1: 0.080844 Loss2: 1.341333
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563176 Loss1: 0.143777 Loss2: 1.419399
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412626 Loss1: 0.070501 Loss2: 1.342125
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421732 Loss1: 0.081377 Loss2: 1.340355
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.571528 Loss1: 0.164851 Loss2: 1.406677
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513096 Loss1: 0.119763 Loss2: 1.393333
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479108 Loss1: 0.085604 Loss2: 1.393504
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.489190 Loss1: 0.099622 Loss2: 1.389568
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449489 Loss1: 0.065894 Loss2: 1.383595
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.175001 Loss1: 0.303981 Loss2: 1.871020
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422296 Loss1: 0.037585 Loss2: 1.384710
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.664887 Loss1: 0.271545 Loss2: 1.393342
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403341 Loss1: 0.027479 Loss2: 1.375862
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.614238 Loss1: 0.203027 Loss2: 1.411211 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.577349 Loss1: 0.157493 Loss2: 1.419856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.122484 Loss1: 0.343387 Loss2: 1.779096
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.530377 Loss1: 0.117648 Loss2: 1.412729
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.502761 Loss1: 0.204031 Loss2: 1.298730
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.542479 Loss1: 0.139480 Loss2: 1.402999
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.568230 Loss1: 0.153994 Loss2: 1.414236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.474867 Loss1: 0.074629 Loss2: 1.400238 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.364914 Loss1: 0.074923 Loss2: 1.289991 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.369283 Loss1: 0.077476 Loss2: 1.291807 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368100 Loss1: 0.081216 Loss2: 1.286884
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.350564 Loss1: 0.421884 Loss2: 1.928680
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.685102 Loss1: 0.294353 Loss2: 1.390749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.545147 Loss1: 0.145492 Loss2: 1.399655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478279 Loss1: 0.095004 Loss2: 1.383276 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.455323 Loss1: 0.073214 Loss2: 1.382109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427008 Loss1: 0.054235 Loss2: 1.372773 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.420864 Loss1: 0.051165 Loss2: 1.369699 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415214 Loss1: 0.047976 Loss2: 1.367237 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.395873 Loss1: 0.092293 Loss2: 1.303580 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.364204 Loss1: 0.065216 Loss2: 1.298988 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.339823 Loss1: 0.047657 Loss2: 1.292166
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.306717 Loss1: 0.437392 Loss2: 1.869325
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.317933 Loss1: 0.028934 Loss2: 1.288999
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.580577 Loss1: 0.236905 Loss2: 1.343673
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.547710 Loss1: 0.188955 Loss2: 1.358755
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524389 Loss1: 0.171046 Loss2: 1.353342
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476717 Loss1: 0.127008 Loss2: 1.349710
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.431865 Loss1: 0.091944 Loss2: 1.339921
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.126500 Loss1: 0.329877 Loss2: 1.796623
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.439085 Loss1: 0.103946 Loss2: 1.335139
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420933 Loss1: 0.089294 Loss2: 1.331638 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.535007 Loss1: 0.145600 Loss2: 1.389407
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397595 Loss1: 0.066371 Loss2: 1.331224
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.466367 Loss1: 0.117988 Loss2: 1.348379
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381129 Loss1: 0.057751 Loss2: 1.323379
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450375 Loss1: 0.101748 Loss2: 1.348627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441864 Loss1: 0.096862 Loss2: 1.345002 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.108470 Loss1: 0.263118 Loss2: 1.845352
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.407467 Loss1: 0.062738 Loss2: 1.344729
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.559349 Loss1: 0.190009 Loss2: 1.369340
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409791 Loss1: 0.069559 Loss2: 1.340232
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488080 Loss1: 0.127149 Loss2: 1.360932 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.445528 Loss1: 0.082614 Loss2: 1.362914 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426365 Loss1: 0.072468 Loss2: 1.353897 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419188 Loss1: 0.067481 Loss2: 1.351707 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412584 Loss1: 0.063817 Loss2: 1.348767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399593 Loss1: 0.050446 Loss2: 1.349147 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993566
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.413826 Loss1: 0.067036 Loss2: 1.346790 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419997 Loss1: 0.074058 Loss2: 1.345939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369973 Loss1: 0.030866 Loss2: 1.339107
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.116699 Loss1: 0.323478 Loss2: 1.793221
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.536478 Loss1: 0.229222 Loss2: 1.307255 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.459823 Loss1: 0.145785 Loss2: 1.314038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.378354 Loss1: 0.069446 Loss2: 1.308908 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.384570 Loss1: 0.075674 Loss2: 1.308896 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.349618 Loss1: 0.043598 Loss2: 1.306020 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.347297 Loss1: 0.049460 Loss2: 1.297837 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.351750 Loss1: 0.055987 Loss2: 1.295763 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.448065 Loss1: 0.098063 Loss2: 1.350002 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449228 Loss1: 0.107670 Loss2: 1.341558 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422852 Loss1: 0.078793 Loss2: 1.344059
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.238460 Loss1: 0.392533 Loss2: 1.845927
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.626749 Loss1: 0.269856 Loss2: 1.356893 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497293 Loss1: 0.135242 Loss2: 1.362052 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449485 Loss1: 0.101427 Loss2: 1.348057 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440657 Loss1: 0.093404 Loss2: 1.347252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.632897 Loss1: 0.285079 Loss2: 1.347818
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.457407 Loss1: 0.108955 Loss2: 1.348453
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.559297 Loss1: 0.175258 Loss2: 1.384039
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.481178 Loss1: 0.126260 Loss2: 1.354917
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.488645 Loss1: 0.136879 Loss2: 1.351766
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458310 Loss1: 0.110642 Loss2: 1.347668
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.393587 Loss1: 0.055403 Loss2: 1.338184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396048 Loss1: 0.070044 Loss2: 1.326004 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.218785 Loss1: 0.358190 Loss2: 1.860595
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391567 Loss1: 0.063252 Loss2: 1.328315
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.633670 Loss1: 0.267545 Loss2: 1.366125
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.398880 Loss1: 0.076012 Loss2: 1.322868
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511992 Loss1: 0.139979 Loss2: 1.372013 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.529722 Loss1: 0.148476 Loss2: 1.381246 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.471790 Loss1: 0.094152 Loss2: 1.377639
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.202312 Loss1: 0.369298 Loss2: 1.833014
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.518793 Loss1: 0.182298 Loss2: 1.336495 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407544 Loss1: 0.045576 Loss2: 1.361968
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.502184 Loss1: 0.152860 Loss2: 1.349324
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406737 Loss1: 0.051549 Loss2: 1.355187
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.447573 Loss1: 0.109983 Loss2: 1.337590
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.461356 Loss1: 0.125904 Loss2: 1.335452
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431562 Loss1: 0.092438 Loss2: 1.339123
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453910 Loss1: 0.121957 Loss2: 1.331953
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422082 Loss1: 0.092763 Loss2: 1.329319
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399931 Loss1: 0.075186 Loss2: 1.324745
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.197214 Loss1: 0.362537 Loss2: 1.834677
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.403559 Loss1: 0.070476 Loss2: 1.333083
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622950 Loss1: 0.289248 Loss2: 1.333702
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.600182 Loss1: 0.232976 Loss2: 1.367205
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.521873 Loss1: 0.158049 Loss2: 1.363824
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.444327 Loss1: 0.103839 Loss2: 1.340488
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438455 Loss1: 0.103896 Loss2: 1.334560
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447446 Loss1: 0.110628 Loss2: 1.336818
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.233575 Loss1: 0.389013 Loss2: 1.844563
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415051 Loss1: 0.084399 Loss2: 1.330652
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.632264 Loss1: 0.285623 Loss2: 1.346641
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379048 Loss1: 0.047974 Loss2: 1.331074
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.513273 Loss1: 0.144536 Loss2: 1.368737
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386472 Loss1: 0.065556 Loss2: 1.320916
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481376 Loss1: 0.127035 Loss2: 1.354341
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.437389 Loss1: 0.097582 Loss2: 1.339807
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.414611 Loss1: 0.083973 Loss2: 1.330637
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.426068 Loss1: 0.095818 Loss2: 1.330250
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405640 Loss1: 0.073003 Loss2: 1.332638
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409845 Loss1: 0.081254 Loss2: 1.328591
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.229158 Loss1: 0.370011 Loss2: 1.859147
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423242 Loss1: 0.102796 Loss2: 1.320446
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.636905 Loss1: 0.274795 Loss2: 1.362111
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.600007 Loss1: 0.196214 Loss2: 1.403793
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.563265 Loss1: 0.186915 Loss2: 1.376350
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.505961 Loss1: 0.130299 Loss2: 1.375662
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.479999 Loss1: 0.107639 Loss2: 1.372359
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.126906 Loss1: 0.313888 Loss2: 1.813018
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462383 Loss1: 0.092504 Loss2: 1.369879
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.498346 Loss1: 0.192841 Loss2: 1.305504
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439899 Loss1: 0.079954 Loss2: 1.359946
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.471176 Loss1: 0.166059 Loss2: 1.305117
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425663 Loss1: 0.073987 Loss2: 1.351676
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.460518 Loss1: 0.147948 Loss2: 1.312570
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401410 Loss1: 0.048868 Loss2: 1.352542
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.479291 Loss1: 0.173247 Loss2: 1.306044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424397 Loss1: 0.115698 Loss2: 1.308699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.382466 Loss1: 0.078051 Loss2: 1.304415
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.190316 Loss1: 0.355427 Loss2: 1.834889
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.345420 Loss1: 0.049708 Loss2: 1.295712
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.532015 Loss1: 0.205809 Loss2: 1.326206
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.450141 Loss1: 0.119274 Loss2: 1.330867
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.462895 Loss1: 0.131950 Loss2: 1.330946
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.421276 Loss1: 0.100339 Loss2: 1.320937
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.404150 Loss1: 0.088092 Loss2: 1.316057
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.053140 Loss1: 0.279058 Loss2: 1.774082
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.368099 Loss1: 0.059832 Loss2: 1.308267
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.508356 Loss1: 0.210771 Loss2: 1.297585
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.359914 Loss1: 0.058757 Loss2: 1.301158
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.394621 Loss1: 0.096821 Loss2: 1.297800
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.369572 Loss1: 0.064606 Loss2: 1.304967
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.357800 Loss1: 0.065211 Loss2: 1.292588
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358610 Loss1: 0.057748 Loss2: 1.300862
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.341781 Loss1: 0.062708 Loss2: 1.279073 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.341638 Loss1: 0.060533 Loss2: 1.281106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.323891 Loss1: 0.049402 Loss2: 1.274489
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.283863 Loss1: 0.415070 Loss2: 1.868794
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.288058 Loss1: 0.017996 Loss2: 1.270063
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.607313 Loss1: 0.230598 Loss2: 1.376714
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.549366 Loss1: 0.160883 Loss2: 1.388483
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517463 Loss1: 0.149438 Loss2: 1.368025
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.465138 Loss1: 0.099904 Loss2: 1.365235
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.434821 Loss1: 0.068507 Loss2: 1.366314
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.345692 Loss1: 0.433309 Loss2: 1.912383
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468370 Loss1: 0.115543 Loss2: 1.352828
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456950 Loss1: 0.094155 Loss2: 1.362796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.502269 Loss1: 0.140823 Loss2: 1.361446 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439785 Loss1: 0.085235 Loss2: 1.354550 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484254 Loss1: 0.115945 Loss2: 1.368309 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427692 Loss1: 0.074271 Loss2: 1.353420 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408178 Loss1: 0.056317 Loss2: 1.351861 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.558339 Loss1: 0.160233 Loss2: 1.398106 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510925 Loss1: 0.140830 Loss2: 1.370095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.528347 Loss1: 0.149141 Loss2: 1.379206
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.335459 Loss1: 0.400282 Loss2: 1.935177
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480066 Loss1: 0.107623 Loss2: 1.372443
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.624903 Loss1: 0.238013 Loss2: 1.386890
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445900 Loss1: 0.076311 Loss2: 1.369590
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.648726 Loss1: 0.215459 Loss2: 1.433267
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.456967 Loss1: 0.089864 Loss2: 1.367104
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.641428 Loss1: 0.236781 Loss2: 1.404647
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401783 Loss1: 0.033355 Loss2: 1.368428
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.620454 Loss1: 0.202523 Loss2: 1.417931
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.527490 Loss1: 0.128809 Loss2: 1.398680
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.469815 Loss1: 0.080213 Loss2: 1.389602
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.491285 Loss1: 0.105192 Loss2: 1.386093
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.484467 Loss1: 0.092759 Loss2: 1.391708
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.147760 Loss1: 0.333091 Loss2: 1.814669
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.478320 Loss1: 0.092674 Loss2: 1.385646
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.537980 Loss1: 0.180372 Loss2: 1.357608 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.449328 Loss1: 0.131889 Loss2: 1.317439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.407598 Loss1: 0.082193 Loss2: 1.325405
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.131664 Loss1: 0.315781 Loss2: 1.815883
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.532602 Loss1: 0.178556 Loss2: 1.354047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.512094 Loss1: 0.151654 Loss2: 1.360439 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 11:06:03,529 | server.py:236 | fit_round 186 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.499011 Loss1: 0.148384 Loss2: 1.350627 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535951 Loss1: 0.174813 Loss2: 1.361138 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440175 Loss1: 0.084260 Loss2: 1.355915 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389569 Loss1: 0.044807 Loss2: 1.344762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.386921 Loss1: 0.052465 Loss2: 1.334456 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.534457 Loss1: 0.150710 Loss2: 1.383747 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461459 Loss1: 0.102758 Loss2: 1.358700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460685 Loss1: 0.101330 Loss2: 1.359355
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.276626 Loss1: 0.406259 Loss2: 1.870367
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.610913 Loss1: 0.245818 Loss2: 1.365095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441214 Loss1: 0.088813 Loss2: 1.352401
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.570735 Loss1: 0.185748 Loss2: 1.384987
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.418015 Loss1: 0.065826 Loss2: 1.352189
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.589178 Loss1: 0.205414 Loss2: 1.383764
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402113 Loss1: 0.055028 Loss2: 1.347085
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557359 Loss1: 0.179005 Loss2: 1.378354
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524931 Loss1: 0.148766 Loss2: 1.376165
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480162 Loss1: 0.107676 Loss2: 1.372486
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457288 Loss1: 0.093549 Loss2: 1.363739
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446506 Loss1: 0.083969 Loss2: 1.362537
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.127112 Loss1: 0.302930 Loss2: 1.824181
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435689 Loss1: 0.073794 Loss2: 1.361895
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.466222 Loss1: 0.143858 Loss2: 1.322364 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.384751 Loss1: 0.069966 Loss2: 1.314785 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.372293 Loss1: 0.063694 Loss2: 1.308599
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.299141 Loss1: 0.443143 Loss2: 1.855998
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441202 Loss1: 0.135669 Loss2: 1.305533
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.749066 Loss1: 0.371539 Loss2: 1.377527
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.428256 Loss1: 0.106894 Loss2: 1.321361
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.634806 Loss1: 0.214841 Loss2: 1.419966
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.375241 Loss1: 0.059141 Loss2: 1.316100
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.569091 Loss1: 0.196230 Loss2: 1.372862
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370112 Loss1: 0.057635 Loss2: 1.312477
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.557721 Loss1: 0.175737 Loss2: 1.381984
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.489466 Loss1: 0.119987 Loss2: 1.369479
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.411216 Loss1: 0.055567 Loss2: 1.355649
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.390338 Loss1: 0.042419 Loss2: 1.347919
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401267 Loss1: 0.062526 Loss2: 1.338741
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368225 Loss1: 0.031701 Loss2: 1.336525
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 11:06:03,529][flwr][DEBUG] - fit_round 186 received 50 results and 0 failures
-INFO flwr 2023-10-13 11:06:45,183 | server.py:125 | fit progress: (186, 2.3054927869345816, {'accuracy': 0.6097}, 429312.96133268997)
->> Test accuracy: 0.609700
-[2023-10-13 11:06:45,183][flwr][INFO] - fit progress: (186, 2.3054927869345816, {'accuracy': 0.6097}, 429312.96133268997)
-DEBUG flwr 2023-10-13 11:06:45,183 | server.py:173 | evaluate_round 186: strategy sampled 50 clients (out of 50)
-[2023-10-13 11:06:45,183][flwr][DEBUG] - evaluate_round 186: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 11:15:51,073 | server.py:187 | evaluate_round 186 received 50 results and 0 failures
-[2023-10-13 11:15:51,073][flwr][DEBUG] - evaluate_round 186 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 11:15:51,073 | server.py:222 | fit_round 187: strategy sampled 50 clients (out of 50)
-[2023-10-13 11:15:51,073][flwr][DEBUG] - fit_round 187: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.336178 Loss1: 0.427350 Loss2: 1.908828
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.738245 Loss1: 0.326493 Loss2: 1.411751
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.578871 Loss1: 0.156192 Loss2: 1.422679
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.473635 Loss1: 0.466272 Loss2: 2.007363
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.638479 Loss1: 0.271163 Loss2: 1.367316 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.617815 Loss1: 0.232816 Loss2: 1.384998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594230 Loss1: 0.186596 Loss2: 1.407634 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.540711 Loss1: 0.168177 Loss2: 1.372534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528211 Loss1: 0.144558 Loss2: 1.383653
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483004 Loss1: 0.088084 Loss2: 1.394920
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.484209 Loss1: 0.092511 Loss2: 1.391697 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.451298 Loss1: 0.064869 Loss2: 1.386429 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454975 Loss1: 0.080890 Loss2: 1.374085 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983073
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.166681 Loss1: 0.334901 Loss2: 1.831780
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.588103 Loss1: 0.257545 Loss2: 1.330558
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.577657 Loss1: 0.187894 Loss2: 1.389763
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498366 Loss1: 0.143788 Loss2: 1.354578
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.232063 Loss1: 0.407795 Loss2: 1.824269
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473838 Loss1: 0.129139 Loss2: 1.344699
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.550588 Loss1: 0.219442 Loss2: 1.331146
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.552332 Loss1: 0.203028 Loss2: 1.349304
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.484963 Loss1: 0.149414 Loss2: 1.335549
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.489116 Loss1: 0.133063 Loss2: 1.356053
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.507574 Loss1: 0.178357 Loss2: 1.329218
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445077 Loss1: 0.098779 Loss2: 1.346298
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.445406 Loss1: 0.112716 Loss2: 1.332689
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440010 Loss1: 0.093667 Loss2: 1.346343
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431813 Loss1: 0.104025 Loss2: 1.327787
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423073 Loss1: 0.077284 Loss2: 1.345789
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440575 Loss1: 0.113238 Loss2: 1.327337
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386892 Loss1: 0.066454 Loss2: 1.320438
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427947 Loss1: 0.105303 Loss2: 1.322644
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413138 Loss1: 0.094111 Loss2: 1.319028
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.173522 Loss1: 0.349033 Loss2: 1.824490
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.561903 Loss1: 0.226446 Loss2: 1.335457
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.514099 Loss1: 0.177668 Loss2: 1.336431
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.448683 Loss1: 0.097202 Loss2: 1.351481
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.246111 Loss1: 0.391043 Loss2: 1.855068
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.412357 Loss1: 0.086235 Loss2: 1.326122
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.645475 Loss1: 0.286010 Loss2: 1.359465
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.389712 Loss1: 0.069700 Loss2: 1.320012
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578884 Loss1: 0.181219 Loss2: 1.397665
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.516238 Loss1: 0.157888 Loss2: 1.358350 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.379436 Loss1: 0.061981 Loss2: 1.317454
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493451 Loss1: 0.135928 Loss2: 1.357524
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.369158 Loss1: 0.054709 Loss2: 1.314449
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446819 Loss1: 0.092629 Loss2: 1.354190
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.343864 Loss1: 0.033780 Loss2: 1.310084
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.390146 Loss1: 0.049297 Loss2: 1.340850
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.373099 Loss1: 0.039475 Loss2: 1.333624
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.361475 Loss1: 0.033642 Loss2: 1.327833
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.341356 Loss1: 0.018763 Loss2: 1.322593
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.170430 Loss1: 0.346081 Loss2: 1.824350
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.544513 Loss1: 0.214247 Loss2: 1.330266
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.521264 Loss1: 0.171880 Loss2: 1.349384
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484939 Loss1: 0.146506 Loss2: 1.338433
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.221868 Loss1: 0.358749 Loss2: 1.863119
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.573207 Loss1: 0.212835 Loss2: 1.360372 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.535562 Loss1: 0.157399 Loss2: 1.378163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.475143 Loss1: 0.107025 Loss2: 1.368118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.475508 Loss1: 0.115860 Loss2: 1.359648 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.418545 Loss1: 0.064723 Loss2: 1.353822 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388999 Loss1: 0.070601 Loss2: 1.318398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431378 Loss1: 0.080609 Loss2: 1.350769
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.410889 Loss1: 0.062041 Loss2: 1.348849
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.389799 Loss1: 0.045443 Loss2: 1.344356
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365629 Loss1: 0.028059 Loss2: 1.337570
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.352379 Loss1: 0.432728 Loss2: 1.919651
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.610379 Loss1: 0.236339 Loss2: 1.374040
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.481217 Loss1: 0.098658 Loss2: 1.382559
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.449479 Loss1: 0.086788 Loss2: 1.362692
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.230863 Loss1: 0.375085 Loss2: 1.855777
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.657735 Loss1: 0.307155 Loss2: 1.350580 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572702 Loss1: 0.184884 Loss2: 1.387818 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469412 Loss1: 0.112218 Loss2: 1.357195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.447079 Loss1: 0.079222 Loss2: 1.367857 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.470901 Loss1: 0.114908 Loss2: 1.355993 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986607
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399969 Loss1: 0.065249 Loss2: 1.334720 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.363909 Loss1: 0.030733 Loss2: 1.333176 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.571033 Loss1: 0.248926 Loss2: 1.322107 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.433968 Loss1: 0.099395 Loss2: 1.334574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.176118 Loss1: 0.313078 Loss2: 1.863040 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.635367 Loss1: 0.267530 Loss2: 1.367837 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.354263 Loss1: 0.051231 Loss2: 1.303032 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.346167 Loss1: 0.045129 Loss2: 1.301038 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.346699 Loss1: 0.052915 Loss2: 1.293785 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.462276 Loss1: 0.099867 Loss2: 1.362408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.475484 Loss1: 0.118172 Loss2: 1.357312 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.408681 Loss1: 0.405310 Loss2: 2.003372 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.743273 Loss1: 0.224003 Loss2: 1.519270 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.640293 Loss1: 0.150689 Loss2: 1.489604 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.633029 Loss1: 0.143726 Loss2: 1.489303
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.157821 Loss1: 0.374904 Loss2: 1.782918
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.615691 Loss1: 0.300791 Loss2: 1.314900 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.582273 Loss1: 0.223110 Loss2: 1.359163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521227 Loss1: 0.196037 Loss2: 1.325190 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.522871 Loss1: 0.061030 Loss2: 1.461841
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446962 Loss1: 0.130669 Loss2: 1.316293
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.407337 Loss1: 0.096261 Loss2: 1.311076
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.381634 Loss1: 0.074081 Loss2: 1.307554
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374581 Loss1: 0.071555 Loss2: 1.303026
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.371341 Loss1: 0.072084 Loss2: 1.299257
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.297222 Loss1: 0.455821 Loss2: 1.841400
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354575 Loss1: 0.061477 Loss2: 1.293099
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551304 Loss1: 0.220626 Loss2: 1.330678 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.423490 Loss1: 0.119278 Loss2: 1.304212 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.391169 Loss1: 0.088126 Loss2: 1.303043
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.315591 Loss1: 0.434488 Loss2: 1.881103
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.595602 Loss1: 0.255805 Loss2: 1.339797 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.522107 Loss1: 0.168998 Loss2: 1.353109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404065 Loss1: 0.101839 Loss2: 1.302226
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.448178 Loss1: 0.107839 Loss2: 1.340339
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.352094 Loss1: 0.062271 Loss2: 1.289823
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.423214 Loss1: 0.094527 Loss2: 1.328686
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.398920 Loss1: 0.069789 Loss2: 1.329132
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.394278 Loss1: 0.072552 Loss2: 1.321726
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.415706 Loss1: 0.093067 Loss2: 1.322639
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.393027 Loss1: 0.073322 Loss2: 1.319704
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393946 Loss1: 0.074740 Loss2: 1.319206
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.225323 Loss1: 0.411990 Loss2: 1.813333
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.557404 Loss1: 0.235121 Loss2: 1.322283
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.601954 Loss1: 0.238361 Loss2: 1.363594
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.507220 Loss1: 0.161585 Loss2: 1.345635
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472442 Loss1: 0.137265 Loss2: 1.335178
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.202421 Loss1: 0.400857 Loss2: 1.801563
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.410369 Loss1: 0.076117 Loss2: 1.334253
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.392556 Loss1: 0.076930 Loss2: 1.315626 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.371742 Loss1: 0.055317 Loss2: 1.316425 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374052 Loss1: 0.059345 Loss2: 1.314706 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.353926 Loss1: 0.046883 Loss2: 1.307043 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.369024 Loss1: 0.056334 Loss2: 1.312690 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.353463 Loss1: 0.045000 Loss2: 1.308463 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.337268 Loss1: 0.036495 Loss2: 1.300773
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.451874 Loss1: 0.473545 Loss2: 1.978329
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.720768 Loss1: 0.318573 Loss2: 1.402195
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.665144 Loss1: 0.230647 Loss2: 1.434497
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.615148 Loss1: 0.175485 Loss2: 1.439664
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531094 Loss1: 0.124200 Loss2: 1.406894
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.549238 Loss1: 0.143107 Loss2: 1.406131
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.229488 Loss1: 0.348479 Loss2: 1.881009
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.508229 Loss1: 0.101038 Loss2: 1.407192
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.604779 Loss1: 0.239904 Loss2: 1.364875
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.480483 Loss1: 0.084068 Loss2: 1.396416
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.621471 Loss1: 0.203452 Loss2: 1.418019
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.503991 Loss1: 0.122565 Loss2: 1.381426 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417541 Loss1: 0.036498 Loss2: 1.381043
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496293 Loss1: 0.123427 Loss2: 1.372866
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.529534 Loss1: 0.150322 Loss2: 1.379212
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.458416 Loss1: 0.091824 Loss2: 1.366592
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448535 Loss1: 0.083751 Loss2: 1.364783
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445832 Loss1: 0.085964 Loss2: 1.359868
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.167338 Loss1: 0.312898 Loss2: 1.854440
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434635 Loss1: 0.070848 Loss2: 1.363787
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551288 Loss1: 0.154788 Loss2: 1.396500 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.483177 Loss1: 0.110363 Loss2: 1.372814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.334591 Loss1: 0.453470 Loss2: 1.881120
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470046 Loss1: 0.102142 Loss2: 1.367905
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.637224 Loss1: 0.263561 Loss2: 1.373663
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472512 Loss1: 0.106767 Loss2: 1.365745
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601238 Loss1: 0.196078 Loss2: 1.405160
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.439127 Loss1: 0.074811 Loss2: 1.364316
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444605 Loss1: 0.087225 Loss2: 1.357381 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416523 Loss1: 0.061025 Loss2: 1.355498 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981445
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427356 Loss1: 0.057661 Loss2: 1.369695 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390157 Loss1: 0.037713 Loss2: 1.352444 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.410327 Loss1: 0.060926 Loss2: 1.349401
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.118546 Loss1: 0.305628 Loss2: 1.812917
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.528186 Loss1: 0.180702 Loss2: 1.347483
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.477567 Loss1: 0.123535 Loss2: 1.354032
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.453892 Loss1: 0.110150 Loss2: 1.343742
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.464014 Loss1: 0.118513 Loss2: 1.345501
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.075173 Loss1: 0.303538 Loss2: 1.771635
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.493517 Loss1: 0.179725 Loss2: 1.313792 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.478111 Loss1: 0.155194 Loss2: 1.322917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.495350 Loss1: 0.175831 Loss2: 1.319519 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497054 Loss1: 0.177515 Loss2: 1.319539 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.442184 Loss1: 0.130572 Loss2: 1.311612 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.365320 Loss1: 0.064997 Loss2: 1.300322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379867 Loss1: 0.083179 Loss2: 1.296688
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.255626 Loss1: 0.406638 Loss2: 1.848987
-(DefaultActor pid=3764) >> Training accuracy: 0.988971
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.613056 Loss1: 0.281701 Loss2: 1.331356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513230 Loss1: 0.169517 Loss2: 1.343713 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468689 Loss1: 0.122487 Loss2: 1.346202 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461541 Loss1: 0.126517 Loss2: 1.335024 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453853 Loss1: 0.124162 Loss2: 1.329692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.467107 Loss1: 0.135041 Loss2: 1.332066 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419344 Loss1: 0.084833 Loss2: 1.334512 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.566869 Loss1: 0.156367 Loss2: 1.410502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511982 Loss1: 0.103739 Loss2: 1.408243 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.482841 Loss1: 0.078060 Loss2: 1.404781
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.159419 Loss1: 0.359804 Loss2: 1.799615
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.543072 Loss1: 0.232509 Loss2: 1.310564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.432381 Loss1: 0.117105 Loss2: 1.315275 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.383406 Loss1: 0.076594 Loss2: 1.306812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.361345 Loss1: 0.066255 Loss2: 1.295091 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.340729 Loss1: 0.045368 Loss2: 1.295361 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.331698 Loss1: 0.041224 Loss2: 1.290474 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.305491 Loss1: 0.023459 Loss2: 1.282032 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.453473 Loss1: 0.085378 Loss2: 1.368095 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.395524 Loss1: 0.050917 Loss2: 1.344607 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.377788 Loss1: 0.036645 Loss2: 1.341143
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.250496 Loss1: 0.387985 Loss2: 1.862511
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.657228 Loss1: 0.295179 Loss2: 1.362050 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528455 Loss1: 0.161400 Loss2: 1.367055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448693 Loss1: 0.094216 Loss2: 1.354477 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.081353 Loss1: 0.309522 Loss2: 1.771831
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.421983 Loss1: 0.067484 Loss2: 1.354499
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.489257 Loss1: 0.195957 Loss2: 1.293300
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415183 Loss1: 0.065051 Loss2: 1.350132
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.445085 Loss1: 0.138652 Loss2: 1.306433
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390944 Loss1: 0.044800 Loss2: 1.346145
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.407538 Loss1: 0.068933 Loss2: 1.338605
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.428392 Loss1: 0.123278 Loss2: 1.305114
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.457266 Loss1: 0.159050 Loss2: 1.298216
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.490827 Loss1: 0.190619 Loss2: 1.300208
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.457789 Loss1: 0.140782 Loss2: 1.317008
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429869 Loss1: 0.130460 Loss2: 1.299409
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.093099 Loss1: 0.369227 Loss2: 1.723871
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391267 Loss1: 0.086029 Loss2: 1.305238
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.478520 Loss1: 0.187797 Loss2: 1.290723
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351102 Loss1: 0.059884 Loss2: 1.291218
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.384502 Loss1: 0.107298 Loss2: 1.277204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.354874 Loss1: 0.080390 Loss2: 1.274484 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.198384 Loss1: 0.362341 Loss2: 1.836044
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.337267 Loss1: 0.071664 Loss2: 1.265603
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.648307 Loss1: 0.294622 Loss2: 1.353685
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.345828 Loss1: 0.076714 Loss2: 1.269114
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.305337 Loss1: 0.038645 Loss2: 1.266692 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.310457 Loss1: 0.048597 Loss2: 1.261860 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465526 Loss1: 0.106160 Loss2: 1.359366 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424236 Loss1: 0.081910 Loss2: 1.342327 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397520 Loss1: 0.055984 Loss2: 1.341536
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.253481 Loss1: 0.349939 Loss2: 1.903542
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.703079 Loss1: 0.315218 Loss2: 1.387861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.510295 Loss1: 0.120283 Loss2: 1.390012 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.482410 Loss1: 0.094842 Loss2: 1.387567 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.475901 Loss1: 0.095515 Loss2: 1.380386 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.462438 Loss1: 0.083287 Loss2: 1.379151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444982 Loss1: 0.071032 Loss2: 1.373950 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418975 Loss1: 0.054889 Loss2: 1.364086 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508931 Loss1: 0.126537 Loss2: 1.382394 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.479136 Loss1: 0.101062 Loss2: 1.378074 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.201323 Loss1: 0.335943 Loss2: 1.865380 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.972917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.590759 Loss1: 0.233687 Loss2: 1.357072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.470830 Loss1: 0.110570 Loss2: 1.360259 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.447516 Loss1: 0.098455 Loss2: 1.349061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431922 Loss1: 0.086560 Loss2: 1.345362 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445265 Loss1: 0.100280 Loss2: 1.344985 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.450331 Loss1: 0.129947 Loss2: 1.320384
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427414 Loss1: 0.073325 Loss2: 1.354088
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.431406 Loss1: 0.113866 Loss2: 1.317540
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420274 Loss1: 0.072884 Loss2: 1.347390
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.355917 Loss1: 0.057530 Loss2: 1.298387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.317132 Loss1: 0.033428 Loss2: 1.283705 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.280808 Loss1: 0.375140 Loss2: 1.905669
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.315449 Loss1: 0.036107 Loss2: 1.279343
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.633932 Loss1: 0.250332 Loss2: 1.383601
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.315802 Loss1: 0.034637 Loss2: 1.281165
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511106 Loss1: 0.122176 Loss2: 1.388930 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465752 Loss1: 0.093319 Loss2: 1.372433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.420146 Loss1: 0.053075 Loss2: 1.367071
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.275876 Loss1: 0.387060 Loss2: 1.888816
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.588672 Loss1: 0.238098 Loss2: 1.350574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.609092 Loss1: 0.219925 Loss2: 1.389167 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417295 Loss1: 0.059354 Loss2: 1.357941
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.538423 Loss1: 0.163220 Loss2: 1.375203
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.479888 Loss1: 0.125233 Loss2: 1.354655
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.442018 Loss1: 0.089018 Loss2: 1.353000
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.417796 Loss1: 0.065692 Loss2: 1.352103
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406672 Loss1: 0.064081 Loss2: 1.342591
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.102478 Loss1: 0.299615 Loss2: 1.802863
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379057 Loss1: 0.040033 Loss2: 1.339024
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.567860 Loss1: 0.223239 Loss2: 1.344621
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365869 Loss1: 0.037719 Loss2: 1.328150
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.454243 Loss1: 0.104088 Loss2: 1.350155 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.484097 Loss1: 0.129869 Loss2: 1.354228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449865 Loss1: 0.103104 Loss2: 1.346762 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.432090 Loss1: 0.088369 Loss2: 1.343721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391343 Loss1: 0.050431 Loss2: 1.340912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.438289 Loss1: 0.109534 Loss2: 1.328755 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.432747 Loss1: 0.102646 Loss2: 1.330100 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400020 Loss1: 0.072816 Loss2: 1.327204 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990385
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.179263 Loss1: 0.334472 Loss2: 1.844791 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.473643 Loss1: 0.128944 Loss2: 1.344700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.420366 Loss1: 0.102271 Loss2: 1.318095 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443611 Loss1: 0.122127 Loss2: 1.321483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.403919 Loss1: 0.078800 Loss2: 1.325119 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400781 Loss1: 0.082685 Loss2: 1.318096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.390957 Loss1: 0.070556 Loss2: 1.320401 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.368033 Loss1: 0.053543 Loss2: 1.314491 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.568368 Loss1: 0.095926 Loss2: 1.472442 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.498213 Loss1: 0.042687 Loss2: 1.455526 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.660971 Loss1: 0.261110 Loss2: 1.399861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.560400 Loss1: 0.149787 Loss2: 1.410613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.126808 Loss1: 0.340767 Loss2: 1.786041
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568208 Loss1: 0.157731 Loss2: 1.410477
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.497864 Loss1: 0.208666 Loss2: 1.289198
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.540205 Loss1: 0.126557 Loss2: 1.413648
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.457502 Loss1: 0.143077 Loss2: 1.314425
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.489709 Loss1: 0.086913 Loss2: 1.402796
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.443292 Loss1: 0.148733 Loss2: 1.294559
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.497862 Loss1: 0.098726 Loss2: 1.399136
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.469924 Loss1: 0.072068 Loss2: 1.397856 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.478442 Loss1: 0.084490 Loss2: 1.393953 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374083 Loss1: 0.080227 Loss2: 1.293856 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.319630 Loss1: 0.039124 Loss2: 1.280506 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.330343 Loss1: 0.452016 Loss2: 1.878328
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.658823 Loss1: 0.276077 Loss2: 1.382746
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599689 Loss1: 0.184565 Loss2: 1.415123
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.544022 Loss1: 0.163106 Loss2: 1.380917
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.164938 Loss1: 0.318556 Loss2: 1.846381
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.570005 Loss1: 0.200377 Loss2: 1.369628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.487760 Loss1: 0.103926 Loss2: 1.383833 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.477363 Loss1: 0.110574 Loss2: 1.366790 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.478063 Loss1: 0.119378 Loss2: 1.358685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.453189 Loss1: 0.088594 Loss2: 1.364594 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426558 Loss1: 0.069686 Loss2: 1.356872 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396737 Loss1: 0.046746 Loss2: 1.349991 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 11:44:30,134 | server.py:236 | fit_round 187 received 50 results and 0 failures
-(DefaultActor pid=3764) >> Training accuracy: 0.990234
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.519019 Loss1: 0.187859 Loss2: 1.331160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.444159 Loss1: 0.112128 Loss2: 1.332030 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.220397 Loss1: 0.334188 Loss2: 1.886208
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.452721 Loss1: 0.127324 Loss2: 1.325397
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.698510 Loss1: 0.323550 Loss2: 1.374960
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.391338 Loss1: 0.063651 Loss2: 1.327687
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432984 Loss1: 0.108059 Loss2: 1.324924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450101 Loss1: 0.121992 Loss2: 1.328109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.450036 Loss1: 0.112505 Loss2: 1.337532 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399124 Loss1: 0.067166 Loss2: 1.331958 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.500493 Loss1: 0.114364 Loss2: 1.386129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.453066 Loss1: 0.072488 Loss2: 1.380578 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.244684 Loss1: 0.336050 Loss2: 1.908634
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.684115 Loss1: 0.305199 Loss2: 1.378917
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.645790 Loss1: 0.201639 Loss2: 1.444151
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549086 Loss1: 0.152247 Loss2: 1.396839
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.185681 Loss1: 0.371577 Loss2: 1.814104
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.597352 Loss1: 0.264805 Loss2: 1.332547 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.515622 Loss1: 0.159386 Loss2: 1.356235 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.546544 Loss1: 0.206372 Loss2: 1.340172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509889 Loss1: 0.155648 Loss2: 1.354242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.466386 Loss1: 0.128383 Loss2: 1.338002 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431873 Loss1: 0.089552 Loss2: 1.342322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385146 Loss1: 0.056405 Loss2: 1.328741 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 11:44:30,134][flwr][DEBUG] - fit_round 187 received 50 results and 0 failures
-INFO flwr 2023-10-13 11:45:10,982 | server.py:125 | fit progress: (187, 2.303106297890599, {'accuracy': 0.6109}, 431618.760375392)
->> Test accuracy: 0.610900
-[2023-10-13 11:45:10,982][flwr][INFO] - fit progress: (187, 2.303106297890599, {'accuracy': 0.6109}, 431618.760375392)
-DEBUG flwr 2023-10-13 11:45:10,982 | server.py:173 | evaluate_round 187: strategy sampled 50 clients (out of 50)
-[2023-10-13 11:45:10,982][flwr][DEBUG] - evaluate_round 187: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 11:54:10,259 | server.py:187 | evaluate_round 187 received 50 results and 0 failures
-[2023-10-13 11:54:10,259][flwr][DEBUG] - evaluate_round 187 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 11:54:10,259 | server.py:222 | fit_round 188: strategy sampled 50 clients (out of 50)
-[2023-10-13 11:54:10,259][flwr][DEBUG] - fit_round 188: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.161443 Loss1: 0.308743 Loss2: 1.852700 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.597154 Loss1: 0.205320 Loss2: 1.391834 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.521249 Loss1: 0.140936 Loss2: 1.380313
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.370034 Loss1: 0.415784 Loss2: 1.954250
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.589548 Loss1: 0.264448 Loss2: 1.325101
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515079 Loss1: 0.150135 Loss2: 1.364943
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.447257 Loss1: 0.081630 Loss2: 1.365627 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.469906 Loss1: 0.111920 Loss2: 1.357986 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456811 Loss1: 0.096693 Loss2: 1.360118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.477290 Loss1: 0.136414 Loss2: 1.340876 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.470306 Loss1: 0.134550 Loss2: 1.335757 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406719 Loss1: 0.064373 Loss2: 1.342346 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.091355 Loss1: 0.286589 Loss2: 1.804765
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.590734 Loss1: 0.268853 Loss2: 1.321881
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.458391 Loss1: 0.109574 Loss2: 1.348817
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.411684 Loss1: 0.083995 Loss2: 1.327688
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.147898 Loss1: 0.314290 Loss2: 1.833608
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.380086 Loss1: 0.060608 Loss2: 1.319478
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.582703 Loss1: 0.209874 Loss2: 1.372829
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.373415 Loss1: 0.059614 Loss2: 1.313800
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.545515 Loss1: 0.162950 Loss2: 1.382564
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395162 Loss1: 0.083138 Loss2: 1.312024
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.483969 Loss1: 0.111844 Loss2: 1.372125
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384417 Loss1: 0.074537 Loss2: 1.309880
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.486639 Loss1: 0.127243 Loss2: 1.359396
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.365882 Loss1: 0.057048 Loss2: 1.308834
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.496921 Loss1: 0.133603 Loss2: 1.363318
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356780 Loss1: 0.050853 Loss2: 1.305927
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433265 Loss1: 0.071432 Loss2: 1.361833
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431060 Loss1: 0.074046 Loss2: 1.357014
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441135 Loss1: 0.085921 Loss2: 1.355214
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425651 Loss1: 0.066310 Loss2: 1.359341
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.285809 Loss1: 0.409188 Loss2: 1.876621
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.589578 Loss1: 0.221360 Loss2: 1.368218
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.572809 Loss1: 0.175938 Loss2: 1.396871
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489267 Loss1: 0.116613 Loss2: 1.372654
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.149851 Loss1: 0.304900 Loss2: 1.844951
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.523354 Loss1: 0.189789 Loss2: 1.333565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.438935 Loss1: 0.101057 Loss2: 1.337878 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.391535 Loss1: 0.063685 Loss2: 1.327850 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.368660 Loss1: 0.057989 Loss2: 1.310671 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.346263 Loss1: 0.035292 Loss2: 1.310972 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.976042
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439158 Loss1: 0.083598 Loss2: 1.355561
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.364253 Loss1: 0.061267 Loss2: 1.302986
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.344802 Loss1: 0.043666 Loss2: 1.301136
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373961 Loss1: 0.077158 Loss2: 1.296804
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348072 Loss1: 0.046484 Loss2: 1.301588
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.413851 Loss1: 0.450707 Loss2: 1.963145
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.744878 Loss1: 0.311379 Loss2: 1.433500
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.724691 Loss1: 0.249611 Loss2: 1.475080
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.627863 Loss1: 0.190584 Loss2: 1.437279
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.490515 Loss1: 0.510736 Loss2: 1.979778
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.581500 Loss1: 0.141889 Loss2: 1.439611
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.657785 Loss1: 0.284015 Loss2: 1.373770
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.553867 Loss1: 0.167302 Loss2: 1.386565
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.578639 Loss1: 0.153761 Loss2: 1.424878
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.502884 Loss1: 0.065335 Loss2: 1.437549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483121 Loss1: 0.062248 Loss2: 1.420873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474400 Loss1: 0.063229 Loss2: 1.411170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460507 Loss1: 0.046234 Loss2: 1.414274 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395233 Loss1: 0.040626 Loss2: 1.354607 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989183
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.131675 Loss1: 0.303915 Loss2: 1.827760
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.561416 Loss1: 0.210129 Loss2: 1.351288
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.535126 Loss1: 0.168985 Loss2: 1.366141
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.485232 Loss1: 0.113458 Loss2: 1.371775
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.177746 Loss1: 0.394057 Loss2: 1.783689
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.496428 Loss1: 0.146945 Loss2: 1.349483
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.553524 Loss1: 0.258064 Loss2: 1.295460
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.501729 Loss1: 0.163316 Loss2: 1.338413
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.540379 Loss1: 0.176355 Loss2: 1.364024
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.436674 Loss1: 0.124178 Loss2: 1.312496
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488139 Loss1: 0.126779 Loss2: 1.361360
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.434352 Loss1: 0.125285 Loss2: 1.309067
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453501 Loss1: 0.099202 Loss2: 1.354299
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.424943 Loss1: 0.115637 Loss2: 1.309306
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435348 Loss1: 0.086121 Loss2: 1.349227
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.376990 Loss1: 0.070431 Loss2: 1.306558
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.432693 Loss1: 0.088836 Loss2: 1.343856
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.338344 Loss1: 0.047027 Loss2: 1.291317 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.231372 Loss1: 0.406321 Loss2: 1.825050 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.522626 Loss1: 0.156564 Loss2: 1.366062 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.438296 Loss1: 0.105788 Loss2: 1.332508
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.105879 Loss1: 0.287253 Loss2: 1.818626
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.594498 Loss1: 0.235684 Loss2: 1.358814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564260 Loss1: 0.171210 Loss2: 1.393050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.567993 Loss1: 0.194418 Loss2: 1.373574 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530252 Loss1: 0.166762 Loss2: 1.363490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.502390 Loss1: 0.121120 Loss2: 1.381270 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.457122 Loss1: 0.098878 Loss2: 1.358244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408920 Loss1: 0.062281 Loss2: 1.346639 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.327308 Loss1: 0.425634 Loss2: 1.901674 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594834 Loss1: 0.197448 Loss2: 1.397386 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.213159 Loss1: 0.357808 Loss2: 1.855350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.576698 Loss1: 0.219615 Loss2: 1.357083 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.511305 Loss1: 0.147448 Loss2: 1.363857 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.465773 Loss1: 0.105812 Loss2: 1.359962 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.425660 Loss1: 0.085220 Loss2: 1.340440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.401694 Loss1: 0.064243 Loss2: 1.337451 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382841 Loss1: 0.044523 Loss2: 1.338318 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.344441 Loss1: 0.023160 Loss2: 1.321281 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.593696 Loss1: 0.229231 Loss2: 1.364465 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.448141 Loss1: 0.086432 Loss2: 1.361709 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.211671 Loss1: 0.381426 Loss2: 1.830245
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.427811 Loss1: 0.081002 Loss2: 1.346809
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.541585 Loss1: 0.211727 Loss2: 1.329858
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462377 Loss1: 0.111575 Loss2: 1.350802
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.499321 Loss1: 0.152683 Loss2: 1.346638
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450342 Loss1: 0.097657 Loss2: 1.352685
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.423212 Loss1: 0.092172 Loss2: 1.331040
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435798 Loss1: 0.074256 Loss2: 1.361542
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.405013 Loss1: 0.083732 Loss2: 1.321281
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.414796 Loss1: 0.071925 Loss2: 1.342872
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.397028 Loss1: 0.078621 Loss2: 1.318407
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433444 Loss1: 0.086260 Loss2: 1.347184
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.344844 Loss1: 0.037148 Loss2: 1.307697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.347192 Loss1: 0.047476 Loss2: 1.299717 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.540038 Loss1: 0.203120 Loss2: 1.336918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490103 Loss1: 0.142304 Loss2: 1.347799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476369 Loss1: 0.132811 Loss2: 1.343558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.432140 Loss1: 0.091557 Loss2: 1.340583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409476 Loss1: 0.076678 Loss2: 1.332798 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400009 Loss1: 0.071599 Loss2: 1.328410 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384219 Loss1: 0.054909 Loss2: 1.329310 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.388851 Loss1: 0.065898 Loss2: 1.322954 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.448208 Loss1: 0.082223 Loss2: 1.365986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421266 Loss1: 0.066981 Loss2: 1.354285 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.615236 Loss1: 0.260712 Loss2: 1.354523 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551289 Loss1: 0.186093 Loss2: 1.365196 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.293168 Loss1: 0.431748 Loss2: 1.861420
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506393 Loss1: 0.157528 Loss2: 1.348865
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.621968 Loss1: 0.258815 Loss2: 1.363154
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477467 Loss1: 0.128002 Loss2: 1.349466
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.553743 Loss1: 0.178374 Loss2: 1.375370
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444327 Loss1: 0.092721 Loss2: 1.351605
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.518922 Loss1: 0.153670 Loss2: 1.365251
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.419866 Loss1: 0.076721 Loss2: 1.343145
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509135 Loss1: 0.150510 Loss2: 1.358625
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.389626 Loss1: 0.052676 Loss2: 1.336950
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.508106 Loss1: 0.138152 Loss2: 1.369954
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355270 Loss1: 0.031490 Loss2: 1.323780
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440654 Loss1: 0.088333 Loss2: 1.352321 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415816 Loss1: 0.072428 Loss2: 1.343387 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.170423 Loss1: 0.279565 Loss2: 1.890858
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.666196 Loss1: 0.246510 Loss2: 1.419686
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.608314 Loss1: 0.173481 Loss2: 1.434832
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.578757 Loss1: 0.161981 Loss2: 1.416776
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.258893 Loss1: 0.411349 Loss2: 1.847543
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.691960 Loss1: 0.345911 Loss2: 1.346050
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.562637 Loss1: 0.131705 Loss2: 1.430932
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.701391 Loss1: 0.293794 Loss2: 1.407596
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.560143 Loss1: 0.128431 Loss2: 1.431712
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.578195 Loss1: 0.212223 Loss2: 1.365972
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.505483 Loss1: 0.080716 Loss2: 1.424767
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.493334 Loss1: 0.083737 Loss2: 1.409598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.497361 Loss1: 0.085694 Loss2: 1.411667 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.496730 Loss1: 0.085486 Loss2: 1.411244 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985294
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424120 Loss1: 0.077512 Loss2: 1.346608 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.198112 Loss1: 0.388534 Loss2: 1.809578
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.628774 Loss1: 0.343902 Loss2: 1.284872
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.507575 Loss1: 0.187888 Loss2: 1.319687
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.443873 Loss1: 0.141253 Loss2: 1.302621
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.074983 Loss1: 0.304329 Loss2: 1.770654
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.512274 Loss1: 0.188245 Loss2: 1.324029 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.527780 Loss1: 0.175084 Loss2: 1.352696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.352892 Loss1: 0.067669 Loss2: 1.285223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.345667 Loss1: 0.060312 Loss2: 1.285356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.318787 Loss1: 0.039809 Loss2: 1.278977 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.393082 Loss1: 0.075839 Loss2: 1.317244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373876 Loss1: 0.059923 Loss2: 1.313953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.360088 Loss1: 0.050805 Loss2: 1.309284
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.190700 Loss1: 0.311484 Loss2: 1.879216
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.566785 Loss1: 0.216036 Loss2: 1.350749
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.492555 Loss1: 0.135832 Loss2: 1.356722
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.522028 Loss1: 0.168713 Loss2: 1.353315
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.532931 Loss1: 0.168411 Loss2: 1.364520
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.141863 Loss1: 0.366737 Loss2: 1.775126
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491724 Loss1: 0.137370 Loss2: 1.354355
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.540823 Loss1: 0.248996 Loss2: 1.291827
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.496354 Loss1: 0.145656 Loss2: 1.350698
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.496441 Loss1: 0.183367 Loss2: 1.313074
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438751 Loss1: 0.083310 Loss2: 1.355440
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.428271 Loss1: 0.133390 Loss2: 1.294882
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409989 Loss1: 0.064086 Loss2: 1.345903
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.405343 Loss1: 0.121477 Loss2: 1.283866
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417307 Loss1: 0.075504 Loss2: 1.341803
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.338835 Loss1: 0.067060 Loss2: 1.271775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.335904 Loss1: 0.065571 Loss2: 1.270333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.321529 Loss1: 0.057415 Loss2: 1.264114
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.121378 Loss1: 0.358646 Loss2: 1.762732
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.493620 Loss1: 0.192767 Loss2: 1.300854
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.452000 Loss1: 0.148224 Loss2: 1.303776
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.375414 Loss1: 0.077532 Loss2: 1.297882
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.363801 Loss1: 0.083314 Loss2: 1.280487
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.351586 Loss1: 0.070612 Loss2: 1.280975
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.243118 Loss1: 0.392439 Loss2: 1.850678
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.325145 Loss1: 0.051653 Loss2: 1.273492
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.710439 Loss1: 0.329593 Loss2: 1.380845
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.312600 Loss1: 0.039835 Loss2: 1.272765
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.588969 Loss1: 0.172181 Loss2: 1.416788
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.311755 Loss1: 0.046178 Loss2: 1.265576
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.500445 Loss1: 0.135581 Loss2: 1.364863
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.299444 Loss1: 0.034709 Loss2: 1.264736
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476390 Loss1: 0.114923 Loss2: 1.361467
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460927 Loss1: 0.104697 Loss2: 1.356230
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.440277 Loss1: 0.087398 Loss2: 1.352880
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446238 Loss1: 0.099280 Loss2: 1.346959
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425120 Loss1: 0.082456 Loss2: 1.342664
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.201283 Loss1: 0.351210 Loss2: 1.850073
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415895 Loss1: 0.074143 Loss2: 1.341752
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.569279 Loss1: 0.169831 Loss2: 1.399448 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.464861 Loss1: 0.112030 Loss2: 1.352831 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.422056 Loss1: 0.068507 Loss2: 1.353549
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.164822 Loss1: 0.318070 Loss2: 1.846752
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.587688 Loss1: 0.238480 Loss2: 1.349208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.539891 Loss1: 0.179810 Loss2: 1.360081 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.484432 Loss1: 0.126403 Loss2: 1.358029 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.347646 Loss1: 0.016147 Loss2: 1.331498
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.461327 Loss1: 0.113283 Loss2: 1.348044
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441504 Loss1: 0.096091 Loss2: 1.345413
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404990 Loss1: 0.064751 Loss2: 1.340239
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.392811 Loss1: 0.054945 Loss2: 1.337866
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.374244 Loss1: 0.046098 Loss2: 1.328146
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.206773 Loss1: 0.331481 Loss2: 1.875292
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352735 Loss1: 0.027252 Loss2: 1.325483
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.662186 Loss1: 0.241377 Loss2: 1.420808 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.500829 Loss1: 0.125430 Loss2: 1.375399 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.467261 Loss1: 0.093087 Loss2: 1.374174
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.232515 Loss1: 0.415155 Loss2: 1.817360
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.477004 Loss1: 0.108403 Loss2: 1.368600
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.578478 Loss1: 0.257342 Loss2: 1.321136
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460390 Loss1: 0.094371 Loss2: 1.366019
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.476089 Loss1: 0.132680 Loss2: 1.343409
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407263 Loss1: 0.040522 Loss2: 1.366742
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.446421 Loss1: 0.121765 Loss2: 1.324657
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.440417 Loss1: 0.081039 Loss2: 1.359378
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.395525 Loss1: 0.085416 Loss2: 1.310110
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.370558 Loss1: 0.060711 Loss2: 1.309847
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.356417 Loss1: 0.054935 Loss2: 1.301481
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.359102 Loss1: 0.056406 Loss2: 1.302696
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369840 Loss1: 0.065529 Loss2: 1.304311
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.383494 Loss1: 0.451340 Loss2: 1.932154
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.367618 Loss1: 0.066186 Loss2: 1.301432
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.589389 Loss1: 0.204926 Loss2: 1.384463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478862 Loss1: 0.121592 Loss2: 1.357270 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.250274 Loss1: 0.420487 Loss2: 1.829787 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.399377 Loss1: 0.049597 Loss2: 1.349780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405780 Loss1: 0.061477 Loss2: 1.344303 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392292 Loss1: 0.053763 Loss2: 1.338529 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.426255 Loss1: 0.095835 Loss2: 1.330420 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409810 Loss1: 0.094976 Loss2: 1.314834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410685 Loss1: 0.088497 Loss2: 1.322188 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414463 Loss1: 0.097199 Loss2: 1.317263 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.430907 Loss1: 0.091530 Loss2: 1.339377 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.374625 Loss1: 0.047834 Loss2: 1.326790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.360368 Loss1: 0.047592 Loss2: 1.312776
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.174351 Loss1: 0.310632 Loss2: 1.863719
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.511612 Loss1: 0.156972 Loss2: 1.354640 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.507023 Loss1: 0.148071 Loss2: 1.358952 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.311498 Loss1: 0.011577 Loss2: 1.299920
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.499916 Loss1: 0.138301 Loss2: 1.361615
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.466028 Loss1: 0.121569 Loss2: 1.344459
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.467674 Loss1: 0.113006 Loss2: 1.354668
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438405 Loss1: 0.085816 Loss2: 1.352588
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420498 Loss1: 0.077559 Loss2: 1.342939
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.284785 Loss1: 0.414306 Loss2: 1.870479
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394378 Loss1: 0.055286 Loss2: 1.339092
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356503 Loss1: 0.029487 Loss2: 1.327017 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.477497 Loss1: 0.114578 Loss2: 1.362920 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449428 Loss1: 0.090716 Loss2: 1.358712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416887 Loss1: 0.064343 Loss2: 1.352544
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.205203 Loss1: 0.351090 Loss2: 1.854113
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.601360 Loss1: 0.254795 Loss2: 1.346565 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.580387 Loss1: 0.195250 Loss2: 1.385137 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408040 Loss1: 0.062572 Loss2: 1.345468
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556052 Loss1: 0.176419 Loss2: 1.379633
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500759 Loss1: 0.145751 Loss2: 1.355009
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480169 Loss1: 0.121618 Loss2: 1.358551
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491941 Loss1: 0.135525 Loss2: 1.356416
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419315 Loss1: 0.069601 Loss2: 1.349714
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.189759 Loss1: 0.379762 Loss2: 1.809997
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.601865 Loss1: 0.244934 Loss2: 1.356931 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.546860 Loss1: 0.171961 Loss2: 1.374899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.452794 Loss1: 0.107885 Loss2: 1.344909 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.392388 Loss1: 0.056230 Loss2: 1.336158 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.375537 Loss1: 0.046329 Loss2: 1.329208 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.441186 Loss1: 0.115392 Loss2: 1.325794 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.438069 Loss1: 0.110434 Loss2: 1.327635 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.361754 Loss1: 0.054394 Loss2: 1.307360 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.342497 Loss1: 0.044990 Loss2: 1.297508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.324735 Loss1: 0.033453 Loss2: 1.291282
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.301313 Loss1: 0.405139 Loss2: 1.896174
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.311112 Loss1: 0.023564 Loss2: 1.287549
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.621688 Loss1: 0.223256 Loss2: 1.398432
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599284 Loss1: 0.191597 Loss2: 1.407687
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.548994 Loss1: 0.140466 Loss2: 1.408528
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503253 Loss1: 0.116871 Loss2: 1.386382
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460725 Loss1: 0.077041 Loss2: 1.383683
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.226108 Loss1: 0.358288 Loss2: 1.867820
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.476225 Loss1: 0.092302 Loss2: 1.383923
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.640709 Loss1: 0.261609 Loss2: 1.379100
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459823 Loss1: 0.070800 Loss2: 1.389023
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444216 Loss1: 0.068859 Loss2: 1.375357
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.591443 Loss1: 0.178779 Loss2: 1.412664
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438712 Loss1: 0.061395 Loss2: 1.377317
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521151 Loss1: 0.133400 Loss2: 1.387751
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.519798 Loss1: 0.138957 Loss2: 1.380841
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.498572 Loss1: 0.115103 Loss2: 1.383469
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.480786 Loss1: 0.099947 Loss2: 1.380839
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460737 Loss1: 0.083470 Loss2: 1.377267
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.053062 Loss1: 0.287968 Loss2: 1.765094
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.478175 Loss1: 0.101002 Loss2: 1.377173
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.494480 Loss1: 0.185004 Loss2: 1.309476
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.469931 Loss1: 0.101784 Loss2: 1.368147
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500427 Loss1: 0.174672 Loss2: 1.325756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470042 Loss1: 0.142757 Loss2: 1.327285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444674 Loss1: 0.129198 Loss2: 1.315476
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.089627 Loss1: 0.264812 Loss2: 1.824815
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424011 Loss1: 0.112619 Loss2: 1.311392
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.621944 Loss1: 0.261635 Loss2: 1.360309
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.410089 Loss1: 0.095962 Loss2: 1.314127
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.628977 Loss1: 0.225310 Loss2: 1.403667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.394872 Loss1: 0.080430 Loss2: 1.314443
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.576984 Loss1: 0.202453 Loss2: 1.374531
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518705 Loss1: 0.150326 Loss2: 1.368379
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.517738 Loss1: 0.141129 Loss2: 1.376609
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.495733 Loss1: 0.129140 Loss2: 1.366593
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469557 Loss1: 0.092044 Loss2: 1.377514
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.318073 Loss1: 0.400629 Loss2: 1.917444
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399692 Loss1: 0.043243 Loss2: 1.356449
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388039 Loss1: 0.037983 Loss2: 1.350056 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556773 Loss1: 0.156487 Loss2: 1.400286 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 12:23:10,789 | server.py:236 | fit_round 188 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.500725 Loss1: 0.129479 Loss2: 1.371246 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436017 Loss1: 0.063911 Loss2: 1.372107 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423246 Loss1: 0.063034 Loss2: 1.360212 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.443604 Loss1: 0.082866 Loss2: 1.360738 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995536
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.368636 Loss1: 0.074205 Loss2: 1.294431 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.351218 Loss1: 0.055937 Loss2: 1.295281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.378445 Loss1: 0.452513 Loss2: 1.925932
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.304333 Loss1: 0.022001 Loss2: 1.282332
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.636033 Loss1: 0.273104 Loss2: 1.362929
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.326193 Loss1: 0.050184 Loss2: 1.276009
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.634874 Loss1: 0.250121 Loss2: 1.384754
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.307658 Loss1: 0.036699 Loss2: 1.270959
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534880 Loss1: 0.179704 Loss2: 1.355176 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472838 Loss1: 0.113937 Loss2: 1.358901 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.209381 Loss1: 0.344820 Loss2: 1.864561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.586713 Loss1: 0.214645 Loss2: 1.372068 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.555376 Loss1: 0.173803 Loss2: 1.381573 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.525899 Loss1: 0.144125 Loss2: 1.381775 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455025 Loss1: 0.085081 Loss2: 1.369944
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.277716 Loss1: 0.350109 Loss2: 1.927608
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.657453 Loss1: 0.262812 Loss2: 1.394641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.631642 Loss1: 0.186403 Loss2: 1.445238 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.424272 Loss1: 0.073407 Loss2: 1.350865
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.529691 Loss1: 0.124457 Loss2: 1.405234
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480119 Loss1: 0.090525 Loss2: 1.389595
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519013 Loss1: 0.129116 Loss2: 1.389898
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485444 Loss1: 0.084630 Loss2: 1.400814
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469816 Loss1: 0.080670 Loss2: 1.389146
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.157760 Loss1: 0.324641 Loss2: 1.833119
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.465601 Loss1: 0.084966 Loss2: 1.380635
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.545872 Loss1: 0.196226 Loss2: 1.349647
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426167 Loss1: 0.049253 Loss2: 1.376914
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492829 Loss1: 0.136986 Loss2: 1.355843 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485325 Loss1: 0.124489 Loss2: 1.360837 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.461301 Loss1: 0.103052 Loss2: 1.358249 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.417720 Loss1: 0.071118 Loss2: 1.346602 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 12:23:10,789][flwr][DEBUG] - fit_round 188 received 50 results and 0 failures
-INFO flwr 2023-10-13 12:23:53,127 | server.py:125 | fit progress: (188, 2.3212159514046324, {'accuracy': 0.6113}, 433940.905263817)
->> Test accuracy: 0.611300
-[2023-10-13 12:23:53,127][flwr][INFO] - fit progress: (188, 2.3212159514046324, {'accuracy': 0.6113}, 433940.905263817)
-DEBUG flwr 2023-10-13 12:23:53,127 | server.py:173 | evaluate_round 188: strategy sampled 50 clients (out of 50)
-[2023-10-13 12:23:53,127][flwr][DEBUG] - evaluate_round 188: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 12:32:56,723 | server.py:187 | evaluate_round 188 received 50 results and 0 failures
-[2023-10-13 12:32:56,723][flwr][DEBUG] - evaluate_round 188 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 12:32:56,724 | server.py:222 | fit_round 189: strategy sampled 50 clients (out of 50)
-[2023-10-13 12:32:56,724][flwr][DEBUG] - fit_round 189: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.178606 Loss1: 0.347552 Loss2: 1.831054
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.534171 Loss1: 0.169032 Loss2: 1.365140
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.538087 Loss1: 0.168735 Loss2: 1.369352
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.458064 Loss1: 0.473793 Loss2: 1.984271
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.477282 Loss1: 0.111005 Loss2: 1.366277
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.450689 Loss1: 0.097786 Loss2: 1.352902 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529108 Loss1: 0.148855 Loss2: 1.380253 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408535 Loss1: 0.063777 Loss2: 1.344758 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391582 Loss1: 0.050516 Loss2: 1.341066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412976 Loss1: 0.065536 Loss2: 1.347440 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398934 Loss1: 0.063060 Loss2: 1.335873 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.242359 Loss1: 0.391972 Loss2: 1.850387 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526036 Loss1: 0.150548 Loss2: 1.375488 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499101 Loss1: 0.127208 Loss2: 1.371893
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.169040 Loss1: 0.293901 Loss2: 1.875139
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.565458 Loss1: 0.174546 Loss2: 1.390912 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.528092 Loss1: 0.118350 Loss2: 1.409742 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.491993 Loss1: 0.098291 Loss2: 1.393703 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.494759 Loss1: 0.105385 Loss2: 1.389375 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.418740 Loss1: 0.070120 Loss2: 1.348620 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445735 Loss1: 0.072539 Loss2: 1.373196 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.420515 Loss1: 0.045640 Loss2: 1.374875 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991728
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.723064 Loss1: 0.397192 Loss2: 1.325871 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.483285 Loss1: 0.143929 Loss2: 1.339356 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.422052 Loss1: 0.079106 Loss2: 1.342945
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.254154 Loss1: 0.365855 Loss2: 1.888300
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.607481 Loss1: 0.225602 Loss2: 1.381879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.557054 Loss1: 0.154548 Loss2: 1.402506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.515352 Loss1: 0.127223 Loss2: 1.388129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524382 Loss1: 0.138737 Loss2: 1.385645 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.519294 Loss1: 0.124296 Loss2: 1.394998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485002 Loss1: 0.103932 Loss2: 1.381070 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439304 Loss1: 0.070601 Loss2: 1.368703 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.589097 Loss1: 0.257847 Loss2: 1.331250 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518693 Loss1: 0.171334 Loss2: 1.347359 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491047 Loss1: 0.146689 Loss2: 1.344358
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.215679 Loss1: 0.364161 Loss2: 1.851518
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.595647 Loss1: 0.234920 Loss2: 1.360727 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.397246 Loss1: 0.061864 Loss2: 1.335382
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.542903 Loss1: 0.161154 Loss2: 1.381749
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393034 Loss1: 0.060803 Loss2: 1.332231
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478569 Loss1: 0.113489 Loss2: 1.365079
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.397672 Loss1: 0.070502 Loss2: 1.327170
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502891 Loss1: 0.155410 Loss2: 1.347481
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.493029 Loss1: 0.127169 Loss2: 1.365860
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369499 Loss1: 0.046039 Loss2: 1.323460
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.391366 Loss1: 0.050400 Loss2: 1.340966 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.360036 Loss1: 0.029748 Loss2: 1.330288 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.539289 Loss1: 0.220747 Loss2: 1.318542 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.421859 Loss1: 0.097741 Loss2: 1.324118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.216878 Loss1: 0.366231 Loss2: 1.850647
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.390908 Loss1: 0.078161 Loss2: 1.312747
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.615219 Loss1: 0.262499 Loss2: 1.352720
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.384839 Loss1: 0.077450 Loss2: 1.307390
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.516410 Loss1: 0.138625 Loss2: 1.377785
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.347306 Loss1: 0.043281 Loss2: 1.304025
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519464 Loss1: 0.165740 Loss2: 1.353724
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.321308 Loss1: 0.020848 Loss2: 1.300460
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.489929 Loss1: 0.133910 Loss2: 1.356019
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.317948 Loss1: 0.024618 Loss2: 1.293330
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.591674 Loss1: 0.217945 Loss2: 1.373729
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.314078 Loss1: 0.026344 Loss2: 1.287734
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454304 Loss1: 0.105043 Loss2: 1.349261 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.415154 Loss1: 0.067562 Loss2: 1.347593 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.519544 Loss1: 0.176869 Loss2: 1.342676 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.423911 Loss1: 0.082988 Loss2: 1.340924 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.395473 Loss1: 0.066705 Loss2: 1.328768
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.082600 Loss1: 0.303590 Loss2: 1.779010
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.405309 Loss1: 0.078953 Loss2: 1.326356
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.501566 Loss1: 0.176297 Loss2: 1.325270
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390989 Loss1: 0.064489 Loss2: 1.326500
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.477143 Loss1: 0.147561 Loss2: 1.329582
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.375223 Loss1: 0.054404 Loss2: 1.320819
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487732 Loss1: 0.162561 Loss2: 1.325171
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.353043 Loss1: 0.036960 Loss2: 1.316083
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.454523 Loss1: 0.132393 Loss2: 1.322129
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.343762 Loss1: 0.034088 Loss2: 1.309675
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423059 Loss1: 0.101682 Loss2: 1.321377
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406958 Loss1: 0.096998 Loss2: 1.309961
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.381285 Loss1: 0.069049 Loss2: 1.312236
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369986 Loss1: 0.062456 Loss2: 1.307530
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.345125 Loss1: 0.042408 Loss2: 1.302717
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.154043 Loss1: 0.310161 Loss2: 1.843882
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.513228 Loss1: 0.155937 Loss2: 1.357291
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.463094 Loss1: 0.115060 Loss2: 1.348034
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.404937 Loss1: 0.054774 Loss2: 1.350163
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.392019 Loss1: 0.058651 Loss2: 1.333368
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.155333 Loss1: 0.331711 Loss2: 1.823622
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.588366 Loss1: 0.241022 Loss2: 1.347345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556011 Loss1: 0.195802 Loss2: 1.360210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389853 Loss1: 0.062103 Loss2: 1.327750
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.493218 Loss1: 0.130718 Loss2: 1.362499
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372380 Loss1: 0.044818 Loss2: 1.327562
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.416419 Loss1: 0.076091 Loss2: 1.340328
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386961 Loss1: 0.062509 Loss2: 1.324452
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.428435 Loss1: 0.087908 Loss2: 1.340527
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.396382 Loss1: 0.061074 Loss2: 1.335308
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382917 Loss1: 0.053290 Loss2: 1.329627
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383037 Loss1: 0.057939 Loss2: 1.325097
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.360497 Loss1: 0.036964 Loss2: 1.323533
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.165979 Loss1: 0.361891 Loss2: 1.804088
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.543798 Loss1: 0.225863 Loss2: 1.317935
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.470163 Loss1: 0.135827 Loss2: 1.334337
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.441304 Loss1: 0.115885 Loss2: 1.325419
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.430222 Loss1: 0.114733 Loss2: 1.315489
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.349737 Loss1: 0.467786 Loss2: 1.881951
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.621608 Loss1: 0.270074 Loss2: 1.351533
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460383 Loss1: 0.139566 Loss2: 1.320816
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.536487 Loss1: 0.170354 Loss2: 1.366133
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448471 Loss1: 0.129920 Loss2: 1.318551
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.525710 Loss1: 0.169929 Loss2: 1.355781
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.402815 Loss1: 0.076275 Loss2: 1.326540
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398546 Loss1: 0.080934 Loss2: 1.317612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380784 Loss1: 0.070263 Loss2: 1.310522 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.385058 Loss1: 0.047795 Loss2: 1.337263 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388734 Loss1: 0.060535 Loss2: 1.328198 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.290393 Loss1: 0.366511 Loss2: 1.923882
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.675804 Loss1: 0.262350 Loss2: 1.413453
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.618587 Loss1: 0.171312 Loss2: 1.447275
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561913 Loss1: 0.139246 Loss2: 1.422667
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.327963 Loss1: 0.473587 Loss2: 1.854377
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.653950 Loss1: 0.300522 Loss2: 1.353428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.541403 Loss1: 0.157536 Loss2: 1.383867 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536718 Loss1: 0.177687 Loss2: 1.359031 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.511086 Loss1: 0.150473 Loss2: 1.360613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487869 Loss1: 0.132043 Loss2: 1.355826 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436306 Loss1: 0.039435 Loss2: 1.396871
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456106 Loss1: 0.106023 Loss2: 1.350083
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413510 Loss1: 0.071472 Loss2: 1.342039
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399929 Loss1: 0.064518 Loss2: 1.335411
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397750 Loss1: 0.064497 Loss2: 1.333253
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.186888 Loss1: 0.371402 Loss2: 1.815487
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.603909 Loss1: 0.271005 Loss2: 1.332905
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.501591 Loss1: 0.150774 Loss2: 1.350817
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.445982 Loss1: 0.113267 Loss2: 1.332715
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.241619 Loss1: 0.343867 Loss2: 1.897752
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.560945 Loss1: 0.179140 Loss2: 1.381804 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.523594 Loss1: 0.133607 Loss2: 1.389987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.470504 Loss1: 0.082694 Loss2: 1.387810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.436080 Loss1: 0.067716 Loss2: 1.368364 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465019 Loss1: 0.094684 Loss2: 1.370335 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355110 Loss1: 0.050828 Loss2: 1.304282
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460075 Loss1: 0.090193 Loss2: 1.369882
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436709 Loss1: 0.069106 Loss2: 1.367603
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.426324 Loss1: 0.064663 Loss2: 1.361661
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.447613 Loss1: 0.084346 Loss2: 1.363267
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.230521 Loss1: 0.358303 Loss2: 1.872219
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.640420 Loss1: 0.272794 Loss2: 1.367626
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.637102 Loss1: 0.232656 Loss2: 1.404445
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.631423 Loss1: 0.213225 Loss2: 1.418199
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.189610 Loss1: 0.337755 Loss2: 1.851855
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.528058 Loss1: 0.152061 Loss2: 1.375996
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.634377 Loss1: 0.275391 Loss2: 1.358986
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448996 Loss1: 0.068714 Loss2: 1.380282
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.631237 Loss1: 0.227740 Loss2: 1.403497
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.444184 Loss1: 0.073323 Loss2: 1.370861
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553948 Loss1: 0.177051 Loss2: 1.376897
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434309 Loss1: 0.063110 Loss2: 1.371199
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.574179 Loss1: 0.200752 Loss2: 1.373427
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413672 Loss1: 0.051588 Loss2: 1.362084
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.524298 Loss1: 0.142575 Loss2: 1.381723
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405963 Loss1: 0.047229 Loss2: 1.358734
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494016 Loss1: 0.121754 Loss2: 1.372261
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.465536 Loss1: 0.107456 Loss2: 1.358079
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451416 Loss1: 0.097064 Loss2: 1.354352
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.429166 Loss1: 0.077916 Loss2: 1.351249
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.270435 Loss1: 0.375731 Loss2: 1.894704
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.583162 Loss1: 0.207387 Loss2: 1.375775
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.575379 Loss1: 0.183954 Loss2: 1.391424
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536814 Loss1: 0.155841 Loss2: 1.380973
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.094557 Loss1: 0.279206 Loss2: 1.815350
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.604817 Loss1: 0.245827 Loss2: 1.358990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.563867 Loss1: 0.158206 Loss2: 1.405661 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528730 Loss1: 0.152175 Loss2: 1.376555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.506029 Loss1: 0.137615 Loss2: 1.368414 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.483897 Loss1: 0.105293 Loss2: 1.378604 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463013 Loss1: 0.095131 Loss2: 1.367883 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.451497 Loss1: 0.078653 Loss2: 1.372844 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.207589 Loss1: 0.342871 Loss2: 1.864718 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582249 Loss1: 0.174094 Loss2: 1.408155 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.138971 Loss1: 0.291446 Loss2: 1.847524 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.577718 Loss1: 0.234343 Loss2: 1.343375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.523117 Loss1: 0.168562 Loss2: 1.354555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487959 Loss1: 0.129908 Loss2: 1.358051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527026 Loss1: 0.186740 Loss2: 1.340285 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.513144 Loss1: 0.159306 Loss2: 1.353838 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.436345 Loss1: 0.065705 Loss2: 1.370639
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427436 Loss1: 0.079679 Loss2: 1.347757
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.438011 Loss1: 0.095349 Loss2: 1.342662
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402461 Loss1: 0.061656 Loss2: 1.340805
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434001 Loss1: 0.097697 Loss2: 1.336304
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.248101 Loss1: 0.360758 Loss2: 1.887343
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.604853 Loss1: 0.213076 Loss2: 1.391777
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.593276 Loss1: 0.197604 Loss2: 1.395671
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.559337 Loss1: 0.161722 Loss2: 1.397615
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.187925 Loss1: 0.384281 Loss2: 1.803643
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.650304 Loss1: 0.303379 Loss2: 1.346925 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.528877 Loss1: 0.147014 Loss2: 1.381864 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.476704 Loss1: 0.129913 Loss2: 1.346791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.464067 Loss1: 0.115735 Loss2: 1.348332 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.443540 Loss1: 0.100010 Loss2: 1.343530 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.386516 Loss1: 0.055294 Loss2: 1.331222 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.347783 Loss1: 0.029147 Loss2: 1.318637 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.252185 Loss1: 0.383849 Loss2: 1.868336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.609767 Loss1: 0.206189 Loss2: 1.403578 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.187339 Loss1: 0.331375 Loss2: 1.855964 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.620420 Loss1: 0.267559 Loss2: 1.352860 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.534386 Loss1: 0.156307 Loss2: 1.378078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.488793 Loss1: 0.135581 Loss2: 1.353212 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.481617 Loss1: 0.134371 Loss2: 1.347246 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.470760 Loss1: 0.122240 Loss2: 1.348520 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.426706 Loss1: 0.089924 Loss2: 1.336782 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372922 Loss1: 0.043963 Loss2: 1.328958 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.676828 Loss1: 0.306728 Loss2: 1.370100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.531644 Loss1: 0.143297 Loss2: 1.388346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.501370 Loss1: 0.136523 Loss2: 1.364847
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.300027 Loss1: 0.387239 Loss2: 1.912789
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.490378 Loss1: 0.118020 Loss2: 1.372358
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.592048 Loss1: 0.207749 Loss2: 1.384299
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.578539 Loss1: 0.181601 Loss2: 1.396938 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574801 Loss1: 0.165688 Loss2: 1.409112 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.563229 Loss1: 0.171196 Loss2: 1.392033 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987723
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516898 Loss1: 0.115025 Loss2: 1.401873 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488093 Loss1: 0.111779 Loss2: 1.376314 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445502 Loss1: 0.063804 Loss2: 1.381698 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.613742 Loss1: 0.277231 Loss2: 1.336511 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.466998 Loss1: 0.124228 Loss2: 1.342770 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.296869 Loss1: 0.412716 Loss2: 1.884153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.564769 Loss1: 0.190373 Loss2: 1.374395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.536008 Loss1: 0.161160 Loss2: 1.374848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540638 Loss1: 0.168420 Loss2: 1.372218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.530079 Loss1: 0.157014 Loss2: 1.373065 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.450044 Loss1: 0.086866 Loss2: 1.363178 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450856 Loss1: 0.098389 Loss2: 1.352467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439912 Loss1: 0.088073 Loss2: 1.351839
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.337447 Loss1: 0.469144 Loss2: 1.868303
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.640440 Loss1: 0.291481 Loss2: 1.348959
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.580216 Loss1: 0.213383 Loss2: 1.366832
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488459 Loss1: 0.146583 Loss2: 1.341876
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.419927 Loss1: 0.089784 Loss2: 1.330142
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.158184 Loss1: 0.373868 Loss2: 1.784316
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440105 Loss1: 0.112120 Loss2: 1.327985
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.579535 Loss1: 0.253878 Loss2: 1.325657
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.401273 Loss1: 0.080849 Loss2: 1.320424
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.584770 Loss1: 0.221923 Loss2: 1.362847
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.363919 Loss1: 0.047792 Loss2: 1.316127
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.359467 Loss1: 0.045064 Loss2: 1.314403
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.502061 Loss1: 0.165640 Loss2: 1.336421
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.332687 Loss1: 0.032538 Loss2: 1.300149
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485512 Loss1: 0.151461 Loss2: 1.334051
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.465027 Loss1: 0.133128 Loss2: 1.331898
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.399986 Loss1: 0.080053 Loss2: 1.319933
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372613 Loss1: 0.053928 Loss2: 1.318685
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.341282 Loss1: 0.030928 Loss2: 1.310354
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.233868 Loss1: 0.350069 Loss2: 1.883799
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.325491 Loss1: 0.024287 Loss2: 1.301204
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.578062 Loss1: 0.184871 Loss2: 1.393191 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.469381 Loss1: 0.090918 Loss2: 1.378463 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.162405 Loss1: 0.340739 Loss2: 1.821665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.584316 Loss1: 0.257210 Loss2: 1.327106 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.550734 Loss1: 0.199976 Loss2: 1.350758 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.482828 Loss1: 0.139177 Loss2: 1.343650 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.461933 Loss1: 0.139814 Loss2: 1.322119 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439944 Loss1: 0.101456 Loss2: 1.338488 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390009 Loss1: 0.064062 Loss2: 1.325947 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372164 Loss1: 0.051710 Loss2: 1.320453 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.480564 Loss1: 0.159594 Loss2: 1.320970 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.371283 Loss1: 0.082194 Loss2: 1.289089 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.237496 Loss1: 0.391233 Loss2: 1.846264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.675118 Loss1: 0.323311 Loss2: 1.351808 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.316063 Loss1: 0.039326 Loss2: 1.276738 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.492439 Loss1: 0.136169 Loss2: 1.356269 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.417510 Loss1: 0.074790 Loss2: 1.342721 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.218343 Loss1: 0.405982 Loss2: 1.812361
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.404022 Loss1: 0.059860 Loss2: 1.344162
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.377242 Loss1: 0.044619 Loss2: 1.332623
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.531927 Loss1: 0.186097 Loss2: 1.345830
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362259 Loss1: 0.036698 Loss2: 1.325561
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.483435 Loss1: 0.139621 Loss2: 1.343814
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.437816 Loss1: 0.098080 Loss2: 1.339736
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.452726 Loss1: 0.121082 Loss2: 1.331644
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.415583 Loss1: 0.086998 Loss2: 1.328584
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.396710 Loss1: 0.072910 Loss2: 1.323800
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.358855 Loss1: 0.454945 Loss2: 1.903910
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.642295 Loss1: 0.279527 Loss2: 1.362768 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.610639 Loss1: 0.221885 Loss2: 1.388754 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370159 Loss1: 0.050872 Loss2: 1.319287
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.553945 Loss1: 0.170303 Loss2: 1.383643
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.443514 Loss1: 0.080745 Loss2: 1.362770
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.445466 Loss1: 0.090832 Loss2: 1.354634
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.401212 Loss1: 0.047209 Loss2: 1.354003
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.377873 Loss1: 0.039065 Loss2: 1.338808
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370827 Loss1: 0.034823 Loss2: 1.336003
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.234909 Loss1: 0.362111 Loss2: 1.872799
-(DefaultActor pid=3764) >> Training accuracy: 0.997768
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364476 Loss1: 0.034606 Loss2: 1.329869
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.648489 Loss1: 0.290934 Loss2: 1.357555
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.595583 Loss1: 0.174150 Loss2: 1.421432
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506954 Loss1: 0.145192 Loss2: 1.361762
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551686 Loss1: 0.190414 Loss2: 1.361272
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451718 Loss1: 0.086216 Loss2: 1.365502
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.214447 Loss1: 0.359768 Loss2: 1.854680
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.462267 Loss1: 0.103331 Loss2: 1.358936
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426653 Loss1: 0.071599 Loss2: 1.355054 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.404221 Loss1: 0.057414 Loss2: 1.346807 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410273 Loss1: 0.070038 Loss2: 1.340235 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482566 Loss1: 0.108859 Loss2: 1.373707 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.477311 Loss1: 0.110528 Loss2: 1.366783 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433728 Loss1: 0.067883 Loss2: 1.365845
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.188465 Loss1: 0.331708 Loss2: 1.856757
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414672 Loss1: 0.053463 Loss2: 1.361209
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.579873 Loss1: 0.238210 Loss2: 1.341663
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.525602 Loss1: 0.167283 Loss2: 1.358319
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.499319 Loss1: 0.145034 Loss2: 1.354285
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478347 Loss1: 0.132952 Loss2: 1.345394
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.434219 Loss1: 0.088406 Loss2: 1.345812
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.467014 Loss1: 0.125252 Loss2: 1.341762
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.196146 Loss1: 0.363266 Loss2: 1.832880
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.615674 Loss1: 0.294876 Loss2: 1.320798
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435950 Loss1: 0.093722 Loss2: 1.342227
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425063 Loss1: 0.087965 Loss2: 1.337098
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.545698 Loss1: 0.181216 Loss2: 1.364482
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423781 Loss1: 0.084544 Loss2: 1.339237
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496857 Loss1: 0.154625 Loss2: 1.342232
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.544080 Loss1: 0.197480 Loss2: 1.346600
-DEBUG flwr 2023-10-13 13:01:34,022 | server.py:236 | fit_round 189 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.494828 Loss1: 0.134963 Loss2: 1.359865
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.519516 Loss1: 0.173629 Loss2: 1.345888
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.446971 Loss1: 0.096332 Loss2: 1.350640
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.421740 Loss1: 0.080130 Loss2: 1.341609
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.106522 Loss1: 0.282686 Loss2: 1.823835
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388833 Loss1: 0.056966 Loss2: 1.331868
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.565513 Loss1: 0.228922 Loss2: 1.336591
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.534065 Loss1: 0.179418 Loss2: 1.354647
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506555 Loss1: 0.148508 Loss2: 1.358047
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.429268 Loss1: 0.094449 Loss2: 1.334819
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.450673 Loss1: 0.115377 Loss2: 1.335296
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.156320 Loss1: 0.311793 Loss2: 1.844528
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.407561 Loss1: 0.075476 Loss2: 1.332085
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.516889 Loss1: 0.166177 Loss2: 1.350713
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.387537 Loss1: 0.060576 Loss2: 1.326960
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.529575 Loss1: 0.175870 Loss2: 1.353705
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.401392 Loss1: 0.073018 Loss2: 1.328374
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.477211 Loss1: 0.124297 Loss2: 1.352914
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390440 Loss1: 0.061480 Loss2: 1.328961
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472165 Loss1: 0.130349 Loss2: 1.341816 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413008 Loss1: 0.073350 Loss2: 1.339658 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449887 Loss1: 0.114145 Loss2: 1.335743
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.133641 Loss1: 0.301669 Loss2: 1.831972
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438380 Loss1: 0.091886 Loss2: 1.346494
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.541895 Loss1: 0.216788 Loss2: 1.325107
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.486816 Loss1: 0.143641 Loss2: 1.343175
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.456710 Loss1: 0.116433 Loss2: 1.340277
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.426793 Loss1: 0.101748 Loss2: 1.325045
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.403551 Loss1: 0.080154 Loss2: 1.323397
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.394867 Loss1: 0.496907 Loss2: 1.897960
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409414 Loss1: 0.089673 Loss2: 1.319741
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.671022 Loss1: 0.318706 Loss2: 1.352316
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.414618 Loss1: 0.089180 Loss2: 1.325438
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399043 Loss1: 0.072227 Loss2: 1.326815 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.381061 Loss1: 0.063924 Loss2: 1.317136 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.430061 Loss1: 0.079274 Loss2: 1.350787 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.375498 Loss1: 0.038912 Loss2: 1.336586 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 13:01:34,022][flwr][DEBUG] - fit_round 189 received 50 results and 0 failures
-INFO flwr 2023-10-13 13:02:16,048 | server.py:125 | fit progress: (189, 2.321961476779974, {'accuracy': 0.6123}, 436243.826841095)
->> Test accuracy: 0.612300
-[2023-10-13 13:02:16,048][flwr][INFO] - fit progress: (189, 2.321961476779974, {'accuracy': 0.6123}, 436243.826841095)
-DEBUG flwr 2023-10-13 13:02:16,049 | server.py:173 | evaluate_round 189: strategy sampled 50 clients (out of 50)
-[2023-10-13 13:02:16,049][flwr][DEBUG] - evaluate_round 189: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 13:11:21,709 | server.py:187 | evaluate_round 189 received 50 results and 0 failures
-[2023-10-13 13:11:21,709][flwr][DEBUG] - evaluate_round 189 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 13:11:21,709 | server.py:222 | fit_round 190: strategy sampled 50 clients (out of 50)
-[2023-10-13 13:11:21,709][flwr][DEBUG] - fit_round 190: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.212667 Loss1: 0.387677 Loss2: 1.824990 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.521473 Loss1: 0.144069 Loss2: 1.377405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.479129 Loss1: 0.138915 Loss2: 1.340214
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.073393 Loss1: 0.286060 Loss2: 1.787333
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461185 Loss1: 0.119730 Loss2: 1.341455
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.569911 Loss1: 0.238411 Loss2: 1.331501
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.415991 Loss1: 0.073517 Loss2: 1.342474
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.503800 Loss1: 0.149889 Loss2: 1.353911
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416113 Loss1: 0.077183 Loss2: 1.338929
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468801 Loss1: 0.135820 Loss2: 1.332981
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.386429 Loss1: 0.055826 Loss2: 1.330603
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.570090 Loss1: 0.231348 Loss2: 1.338742
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377909 Loss1: 0.052115 Loss2: 1.325794
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.677601 Loss1: 0.267384 Loss2: 1.410218
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.367177 Loss1: 0.043865 Loss2: 1.323312
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.503253 Loss1: 0.155426 Loss2: 1.347827
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.529739 Loss1: 0.163654 Loss2: 1.366085
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.475513 Loss1: 0.124548 Loss2: 1.350965
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482615 Loss1: 0.138277 Loss2: 1.344338
-(DefaultActor pid=3764) >> Training accuracy: 0.982422
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.387586 Loss1: 0.453570 Loss2: 1.934016
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.618232 Loss1: 0.249031 Loss2: 1.369201
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.608209 Loss1: 0.233553 Loss2: 1.374657
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511366 Loss1: 0.112964 Loss2: 1.398403
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.191022 Loss1: 0.320896 Loss2: 1.870127
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460887 Loss1: 0.110603 Loss2: 1.350285 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426543 Loss1: 0.069889 Loss2: 1.356655 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415799 Loss1: 0.067018 Loss2: 1.348781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386690 Loss1: 0.042887 Loss2: 1.343803 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374418 Loss1: 0.033596 Loss2: 1.340823 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996394
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.485888 Loss1: 0.113798 Loss2: 1.372090 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.446022 Loss1: 0.075990 Loss2: 1.370032 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423668 Loss1: 0.063875 Loss2: 1.359793
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.182664 Loss1: 0.329784 Loss2: 1.852880
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.599539 Loss1: 0.222718 Loss2: 1.376821
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591826 Loss1: 0.194748 Loss2: 1.397079
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573962 Loss1: 0.190985 Loss2: 1.382977
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487465 Loss1: 0.109762 Loss2: 1.377703
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.175379 Loss1: 0.329420 Loss2: 1.845958
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.462510 Loss1: 0.078653 Loss2: 1.383857
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.532568 Loss1: 0.202259 Loss2: 1.330309
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.471412 Loss1: 0.131633 Loss2: 1.339779
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441640 Loss1: 0.071719 Loss2: 1.369922
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.457998 Loss1: 0.117471 Loss2: 1.340527
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422139 Loss1: 0.062777 Loss2: 1.359363
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.424137 Loss1: 0.098642 Loss2: 1.325495
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409473 Loss1: 0.051616 Loss2: 1.357857
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.415626 Loss1: 0.089721 Loss2: 1.325905
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413273 Loss1: 0.065067 Loss2: 1.348206
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.347635 Loss1: 0.036657 Loss2: 1.310977 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.337136 Loss1: 0.032140 Loss2: 1.304996 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.120732 Loss1: 0.300954 Loss2: 1.819779
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.574981 Loss1: 0.222814 Loss2: 1.352167
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551979 Loss1: 0.157740 Loss2: 1.394240
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.460316 Loss1: 0.101108 Loss2: 1.359207
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.291531 Loss1: 0.459011 Loss2: 1.832520
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.575700 Loss1: 0.238603 Loss2: 1.337097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.500258 Loss1: 0.135068 Loss2: 1.365190 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426341 Loss1: 0.082281 Loss2: 1.344060
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.433445 Loss1: 0.103121 Loss2: 1.330324
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398150 Loss1: 0.055668 Loss2: 1.342482
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.400702 Loss1: 0.078868 Loss2: 1.321834
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408801 Loss1: 0.072856 Loss2: 1.335944
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.433623 Loss1: 0.105106 Loss2: 1.328517
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.402915 Loss1: 0.079040 Loss2: 1.323875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.394837 Loss1: 0.058969 Loss2: 1.335869
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.341478 Loss1: 0.031351 Loss2: 1.310128 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.242088 Loss1: 0.370043 Loss2: 1.872045 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.567085 Loss1: 0.178121 Loss2: 1.388964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527490 Loss1: 0.150840 Loss2: 1.376650
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.259067 Loss1: 0.373545 Loss2: 1.885522
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.481810 Loss1: 0.118237 Loss2: 1.363573
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.729068 Loss1: 0.360876 Loss2: 1.368192
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461662 Loss1: 0.098331 Loss2: 1.363331
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.617078 Loss1: 0.190196 Loss2: 1.426882
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.436456 Loss1: 0.074690 Loss2: 1.361765
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.592521 Loss1: 0.210249 Loss2: 1.382272
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.429124 Loss1: 0.072898 Loss2: 1.356226
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.576929 Loss1: 0.184648 Loss2: 1.392281
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.434934 Loss1: 0.081151 Loss2: 1.353783
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463590 Loss1: 0.086521 Loss2: 1.377068
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.416840 Loss1: 0.064529 Loss2: 1.352311
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.445681 Loss1: 0.081820 Loss2: 1.363861
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.443750 Loss1: 0.081196 Loss2: 1.362554
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443538 Loss1: 0.082517 Loss2: 1.361022
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400096 Loss1: 0.043921 Loss2: 1.356175
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.292083 Loss1: 0.387287 Loss2: 1.904796
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.569666 Loss1: 0.189012 Loss2: 1.380654
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551431 Loss1: 0.168485 Loss2: 1.382946
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.514873 Loss1: 0.129353 Loss2: 1.385520
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.228004 Loss1: 0.374360 Loss2: 1.853644
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.610924 Loss1: 0.264679 Loss2: 1.346245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.596012 Loss1: 0.214656 Loss2: 1.381356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540751 Loss1: 0.171330 Loss2: 1.369421 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.473225 Loss1: 0.114551 Loss2: 1.358674 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441051 Loss1: 0.087608 Loss2: 1.353442 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.376896 Loss1: 0.038017 Loss2: 1.338879
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.402104 Loss1: 0.049730 Loss2: 1.352374
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.393008 Loss1: 0.052274 Loss2: 1.340734
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363298 Loss1: 0.028775 Loss2: 1.334523
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357449 Loss1: 0.032605 Loss2: 1.324844
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.222949 Loss1: 0.345363 Loss2: 1.877586
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.642340 Loss1: 0.255114 Loss2: 1.387226
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582147 Loss1: 0.159239 Loss2: 1.422908
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.234090 Loss1: 0.353669 Loss2: 1.880421
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534562 Loss1: 0.132713 Loss2: 1.401848
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.599867 Loss1: 0.230221 Loss2: 1.369646
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.508463 Loss1: 0.120123 Loss2: 1.388341
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.574937 Loss1: 0.180563 Loss2: 1.394374
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478489 Loss1: 0.090648 Loss2: 1.387841
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.541084 Loss1: 0.157290 Loss2: 1.383794
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.522226 Loss1: 0.133798 Loss2: 1.388428
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483022 Loss1: 0.093592 Loss2: 1.389429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.476281 Loss1: 0.090155 Loss2: 1.386126 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.476520 Loss1: 0.093454 Loss2: 1.383066 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454771 Loss1: 0.081039 Loss2: 1.373731 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.108236 Loss1: 0.261370 Loss2: 1.846866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.494805 Loss1: 0.104781 Loss2: 1.390024 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.148379 Loss1: 0.361416 Loss2: 1.786963
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.501967 Loss1: 0.119347 Loss2: 1.382620
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.566123 Loss1: 0.241865 Loss2: 1.324258
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470782 Loss1: 0.093868 Loss2: 1.376913
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.596398 Loss1: 0.237052 Loss2: 1.359347
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.512665 Loss1: 0.140424 Loss2: 1.372241
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.445783 Loss1: 0.105213 Loss2: 1.340570
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.528044 Loss1: 0.138508 Loss2: 1.389536
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.434216 Loss1: 0.112292 Loss2: 1.321925
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.522228 Loss1: 0.134357 Loss2: 1.387871
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.430606 Loss1: 0.106470 Loss2: 1.324136
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.477581 Loss1: 0.093807 Loss2: 1.383774
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.442308 Loss1: 0.123087 Loss2: 1.319221
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.458183 Loss1: 0.079192 Loss2: 1.378991
-(DefaultActor pid=3765) >> Training accuracy: 0.989258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370900 Loss1: 0.054572 Loss2: 1.316328 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.242497 Loss1: 0.424980 Loss2: 1.817517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.582577 Loss1: 0.231149 Loss2: 1.351428 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.291313 Loss1: 0.338669 Loss2: 1.952644
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534864 Loss1: 0.191162 Loss2: 1.343702
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.447964 Loss1: 0.108629 Loss2: 1.339335
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.656487 Loss1: 0.244587 Loss2: 1.411900
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.401368 Loss1: 0.078468 Loss2: 1.322899
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.580783 Loss1: 0.144838 Loss2: 1.435945
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.380913 Loss1: 0.069092 Loss2: 1.311821
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574728 Loss1: 0.157261 Loss2: 1.417467
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384692 Loss1: 0.073150 Loss2: 1.311542
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.540134 Loss1: 0.129522 Loss2: 1.410612
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374708 Loss1: 0.067563 Loss2: 1.307146
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528418 Loss1: 0.114677 Loss2: 1.413741
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371083 Loss1: 0.065505 Loss2: 1.305578
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.494159 Loss1: 0.087942 Loss2: 1.406218
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.482541 Loss1: 0.080165 Loss2: 1.402376
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479053 Loss1: 0.079764 Loss2: 1.399289
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.528438 Loss1: 0.128025 Loss2: 1.400413
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.201201 Loss1: 0.365734 Loss2: 1.835468
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.570782 Loss1: 0.224085 Loss2: 1.346697
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.497273 Loss1: 0.144966 Loss2: 1.352306
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.463982 Loss1: 0.118174 Loss2: 1.345808
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.359937 Loss1: 0.458283 Loss2: 1.901654
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.705988 Loss1: 0.311178 Loss2: 1.394811 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.678797 Loss1: 0.242486 Loss2: 1.436311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416285 Loss1: 0.081586 Loss2: 1.334698
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.570853 Loss1: 0.179268 Loss2: 1.391585
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.558040 Loss1: 0.159067 Loss2: 1.398973 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.546854 Loss1: 0.156801 Loss2: 1.390053 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390003 Loss1: 0.059745 Loss2: 1.330258
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474285 Loss1: 0.095330 Loss2: 1.378955
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449114 Loss1: 0.072970 Loss2: 1.376144
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454641 Loss1: 0.079993 Loss2: 1.374648
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427798 Loss1: 0.060309 Loss2: 1.367488
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.150076 Loss1: 0.317711 Loss2: 1.832365
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.527741 Loss1: 0.195228 Loss2: 1.332513
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.517182 Loss1: 0.179623 Loss2: 1.337559
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.480017 Loss1: 0.131365 Loss2: 1.348652
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.194202 Loss1: 0.332285 Loss2: 1.861917
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.539077 Loss1: 0.192886 Loss2: 1.346191 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.485185 Loss1: 0.127640 Loss2: 1.357546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.448127 Loss1: 0.092066 Loss2: 1.356062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450999 Loss1: 0.107835 Loss2: 1.343165 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.428052 Loss1: 0.079048 Loss2: 1.349004 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.357747 Loss1: 0.053597 Loss2: 1.304150
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.415016 Loss1: 0.067148 Loss2: 1.347868
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411097 Loss1: 0.069050 Loss2: 1.342047
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391338 Loss1: 0.054286 Loss2: 1.337052
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380139 Loss1: 0.044133 Loss2: 1.336006
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.176311 Loss1: 0.327510 Loss2: 1.848801
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.613438 Loss1: 0.274458 Loss2: 1.338980
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.571552 Loss1: 0.198288 Loss2: 1.373264
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500962 Loss1: 0.158829 Loss2: 1.342133
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.233060 Loss1: 0.357603 Loss2: 1.875456
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.623257 Loss1: 0.247743 Loss2: 1.375514 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.567476 Loss1: 0.174694 Loss2: 1.392783 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.562054 Loss1: 0.178263 Loss2: 1.383791 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.467464 Loss1: 0.091046 Loss2: 1.376418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444481 Loss1: 0.076167 Loss2: 1.368314 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373311 Loss1: 0.047721 Loss2: 1.325590
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455082 Loss1: 0.091570 Loss2: 1.363512
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435020 Loss1: 0.070413 Loss2: 1.364607
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445607 Loss1: 0.078313 Loss2: 1.367294
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.490517 Loss1: 0.127672 Loss2: 1.362845
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.221403 Loss1: 0.349570 Loss2: 1.871833
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.634087 Loss1: 0.264009 Loss2: 1.370078
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599286 Loss1: 0.182302 Loss2: 1.416984
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.537795 Loss1: 0.155702 Loss2: 1.382093
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.365969 Loss1: 0.418958 Loss2: 1.947010
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.637447 Loss1: 0.290822 Loss2: 1.346625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.536049 Loss1: 0.181690 Loss2: 1.354359 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.500240 Loss1: 0.125809 Loss2: 1.374431
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456968 Loss1: 0.095239 Loss2: 1.361729
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.466005 Loss1: 0.123014 Loss2: 1.342990
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440496 Loss1: 0.075069 Loss2: 1.365427
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.425800 Loss1: 0.068572 Loss2: 1.357228 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405943 Loss1: 0.051023 Loss2: 1.354919 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380867 Loss1: 0.051254 Loss2: 1.329612 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.185895 Loss1: 0.296738 Loss2: 1.889157
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.633312 Loss1: 0.247806 Loss2: 1.385506
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.576009 Loss1: 0.163507 Loss2: 1.412502
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.485887 Loss1: 0.105664 Loss2: 1.380222
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.128633 Loss1: 0.317722 Loss2: 1.810911
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.525118 Loss1: 0.145131 Loss2: 1.379988
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.533585 Loss1: 0.216512 Loss2: 1.317073
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473750 Loss1: 0.085485 Loss2: 1.388264
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.472829 Loss1: 0.145035 Loss2: 1.327794
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.434285 Loss1: 0.058519 Loss2: 1.375766
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.445941 Loss1: 0.117150 Loss2: 1.328790
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403341 Loss1: 0.037028 Loss2: 1.366313
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.431949 Loss1: 0.127537 Loss2: 1.304412
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408125 Loss1: 0.046837 Loss2: 1.361288
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.402287 Loss1: 0.097121 Loss2: 1.305166
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403936 Loss1: 0.050238 Loss2: 1.353698
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.382067 Loss1: 0.076858 Loss2: 1.305209
-(DefaultActor pid=3765) >> Training accuracy: 0.998958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.356999 Loss1: 0.058925 Loss2: 1.298073
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.341344 Loss1: 0.046064 Loss2: 1.295280
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.343272 Loss1: 0.052334 Loss2: 1.290939
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.190645 Loss1: 0.354841 Loss2: 1.835804
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.590111 Loss1: 0.261146 Loss2: 1.328965
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.514268 Loss1: 0.169103 Loss2: 1.345165
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.456263 Loss1: 0.107528 Loss2: 1.348734
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.208273 Loss1: 0.348168 Loss2: 1.860105
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.604209 Loss1: 0.209334 Loss2: 1.394875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.602195 Loss1: 0.192771 Loss2: 1.409424 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498950 Loss1: 0.105008 Loss2: 1.393942 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483935 Loss1: 0.095939 Loss2: 1.387995 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480039 Loss1: 0.093634 Loss2: 1.386404 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.468252 Loss1: 0.088418 Loss2: 1.379834 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.429657 Loss1: 0.052132 Loss2: 1.377525 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.183439 Loss1: 0.379020 Loss2: 1.804419 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.507927 Loss1: 0.162161 Loss2: 1.345766 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.316535 Loss1: 0.316534 Loss2: 2.000001 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.725656 Loss1: 0.269789 Loss2: 1.455867 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.676918 Loss1: 0.176426 Loss2: 1.500493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.641716 Loss1: 0.157170 Loss2: 1.484546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.596415 Loss1: 0.124124 Loss2: 1.472291 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.600200 Loss1: 0.124085 Loss2: 1.476115 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.537924 Loss1: 0.069358 Loss2: 1.468566 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.541699 Loss1: 0.078605 Loss2: 1.463094 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.511510 Loss1: 0.207530 Loss2: 1.303980 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.424808 Loss1: 0.116875 Loss2: 1.307933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.302146 Loss1: 0.335729 Loss2: 1.966417
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.403055 Loss1: 0.111206 Loss2: 1.291849
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.686548 Loss1: 0.262740 Loss2: 1.423808
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.380276 Loss1: 0.078049 Loss2: 1.302227
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.609913 Loss1: 0.167044 Loss2: 1.442869
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.341262 Loss1: 0.054238 Loss2: 1.287024
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557557 Loss1: 0.128746 Loss2: 1.428811
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.310512 Loss1: 0.031677 Loss2: 1.278835
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514566 Loss1: 0.101157 Loss2: 1.413409
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.301274 Loss1: 0.028093 Loss2: 1.273181
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477850 Loss1: 0.066685 Loss2: 1.411166
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.294684 Loss1: 0.024017 Loss2: 1.270667
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449622 Loss1: 0.049139 Loss2: 1.400484 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.456623 Loss1: 0.060461 Loss2: 1.396162 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.670261 Loss1: 0.259899 Loss2: 1.410362 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.586524 Loss1: 0.151278 Loss2: 1.435246 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.560522 Loss1: 0.149639 Loss2: 1.410884
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.281983 Loss1: 0.413645 Loss2: 1.868338
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.603073 Loss1: 0.277288 Loss2: 1.325786 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556710 Loss1: 0.197404 Loss2: 1.359306 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.457353 Loss1: 0.108649 Loss2: 1.348704 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.420058 Loss1: 0.093178 Loss2: 1.326880 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986607
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.422114 Loss1: 0.088602 Loss2: 1.333513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382607 Loss1: 0.058109 Loss2: 1.324498 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354342 Loss1: 0.041238 Loss2: 1.313104 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.474468 Loss1: 0.152052 Loss2: 1.322415 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.447050 Loss1: 0.129555 Loss2: 1.317495 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.172453 Loss1: 0.341034 Loss2: 1.831418 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.475631 Loss1: 0.152910 Loss2: 1.322721 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.494650 Loss1: 0.170743 Loss2: 1.323907 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478263 Loss1: 0.137353 Loss2: 1.340909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441367 Loss1: 0.112362 Loss2: 1.329005 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.373119 Loss1: 0.055158 Loss2: 1.317961 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.360787 Loss1: 0.054708 Loss2: 1.306078 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.327451 Loss1: 0.029328 Loss2: 1.298122
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.202800 Loss1: 0.364042 Loss2: 1.838758
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.530994 Loss1: 0.190558 Loss2: 1.340435
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.555174 Loss1: 0.194223 Loss2: 1.360951
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.502817 Loss1: 0.139186 Loss2: 1.363631
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.484914 Loss1: 0.141130 Loss2: 1.343784
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.259331 Loss1: 0.419589 Loss2: 1.839742
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.546538 Loss1: 0.180526 Loss2: 1.366012
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.549768 Loss1: 0.192675 Loss2: 1.357093
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495841 Loss1: 0.138268 Loss2: 1.357573
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.510030 Loss1: 0.153391 Loss2: 1.356639
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470371 Loss1: 0.129641 Loss2: 1.340730
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.503352 Loss1: 0.144372 Loss2: 1.358980
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.427708 Loss1: 0.077325 Loss2: 1.350383
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.463271 Loss1: 0.115514 Loss2: 1.347757
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423130 Loss1: 0.085631 Loss2: 1.337499
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.436568 Loss1: 0.084609 Loss2: 1.351959 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443987 Loss1: 0.095367 Loss2: 1.348620 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.440665 Loss1: 0.092189 Loss2: 1.348476
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.243257 Loss1: 0.390905 Loss2: 1.852352
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.518624 Loss1: 0.189070 Loss2: 1.329554
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.486036 Loss1: 0.141590 Loss2: 1.344446
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.443055 Loss1: 0.099768 Loss2: 1.343286
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.384770 Loss1: 0.056044 Loss2: 1.328725
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.460322 Loss1: 0.511631 Loss2: 1.948691
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.397903 Loss1: 0.073893 Loss2: 1.324010
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.618966 Loss1: 0.204282 Loss2: 1.414684
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.400728 Loss1: 0.080635 Loss2: 1.320093
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.606461 Loss1: 0.172032 Loss2: 1.434429
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.372630 Loss1: 0.050067 Loss2: 1.322564
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.564741 Loss1: 0.150177 Loss2: 1.414564
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409099 Loss1: 0.087695 Loss2: 1.321404
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.499478 Loss1: 0.101155 Loss2: 1.398323
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386007 Loss1: 0.062661 Loss2: 1.323347
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472863 Loss1: 0.079700 Loss2: 1.393163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.439306 Loss1: 0.051676 Loss2: 1.387630 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406531 Loss1: 0.032374 Loss2: 1.374158
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.240300 Loss1: 0.364245 Loss2: 1.876055
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.646890 Loss1: 0.285466 Loss2: 1.361424
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.688651 Loss1: 0.243344 Loss2: 1.445307
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591157 Loss1: 0.219965 Loss2: 1.371192
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.555128 Loss1: 0.173010 Loss2: 1.382119
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.120563 Loss1: 0.269771 Loss2: 1.850792
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465772 Loss1: 0.085007 Loss2: 1.380765
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.558844 Loss1: 0.208860 Loss2: 1.349984
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456833 Loss1: 0.094917 Loss2: 1.361917
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.560841 Loss1: 0.204141 Loss2: 1.356701
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467786 Loss1: 0.107877 Loss2: 1.359909
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498064 Loss1: 0.146166 Loss2: 1.351898
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459021 Loss1: 0.105597 Loss2: 1.353424
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.446381 Loss1: 0.105431 Loss2: 1.340950
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423156 Loss1: 0.069672 Loss2: 1.353484
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-DEBUG flwr 2023-10-13 13:40:19,952 | server.py:236 | fit_round 190 received 50 results and 0 failures
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.413672 Loss1: 0.076138 Loss2: 1.337534 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372529 Loss1: 0.049119 Loss2: 1.323410 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361855 Loss1: 0.043573 Loss2: 1.318282
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.307544 Loss1: 0.394128 Loss2: 1.913417
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.641432 Loss1: 0.286740 Loss2: 1.354691
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.593140 Loss1: 0.204000 Loss2: 1.389141
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527237 Loss1: 0.139744 Loss2: 1.387492
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.454912 Loss1: 0.100863 Loss2: 1.354049
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.411264 Loss1: 0.418539 Loss2: 1.992725
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.419640 Loss1: 0.069479 Loss2: 1.350161
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.394417 Loss1: 0.050859 Loss2: 1.343558 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380377 Loss1: 0.041801 Loss2: 1.338576 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374892 Loss1: 0.044742 Loss2: 1.330150 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373046 Loss1: 0.041866 Loss2: 1.331180 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411937 Loss1: 0.056981 Loss2: 1.354955 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387138 Loss1: 0.045140 Loss2: 1.341998 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.109207 Loss1: 0.245202 Loss2: 1.864005
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.616298 Loss1: 0.229478 Loss2: 1.386820
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594330 Loss1: 0.175895 Loss2: 1.418435
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535777 Loss1: 0.132127 Loss2: 1.403650
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.254784 Loss1: 0.410734 Loss2: 1.844051
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.609204 Loss1: 0.265331 Loss2: 1.343873
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.508479 Loss1: 0.113209 Loss2: 1.395270
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.555117 Loss1: 0.182782 Loss2: 1.372335
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.470780 Loss1: 0.074194 Loss2: 1.396586
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497392 Loss1: 0.152741 Loss2: 1.344651
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449637 Loss1: 0.060931 Loss2: 1.388706
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.434742 Loss1: 0.089847 Loss2: 1.344895
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436242 Loss1: 0.049368 Loss2: 1.386874
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.416856 Loss1: 0.040841 Loss2: 1.376015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.427624 Loss1: 0.058592 Loss2: 1.369031 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397243 Loss1: 0.069398 Loss2: 1.327845 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.266470 Loss1: 0.389658 Loss2: 1.876812 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.504189 Loss1: 0.157765 Loss2: 1.346424 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476218 Loss1: 0.130666 Loss2: 1.345553
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.058747 Loss1: 0.249259 Loss2: 1.809488
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.509419 Loss1: 0.174084 Loss2: 1.335335 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422562 Loss1: 0.090851 Loss2: 1.331710 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.420213 Loss1: 0.091027 Loss2: 1.329186 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384786 Loss1: 0.063632 Loss2: 1.321153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371830 Loss1: 0.053885 Loss2: 1.317945 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412741 Loss1: 0.081363 Loss2: 1.331378 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392053 Loss1: 0.061666 Loss2: 1.330387 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992647
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 13:40:19,952][flwr][DEBUG] - fit_round 190 received 50 results and 0 failures
-INFO flwr 2023-10-13 13:41:00,479 | server.py:125 | fit progress: (190, 2.3261600407167746, {'accuracy': 0.6111}, 438568.257645679)
->> Test accuracy: 0.611100
-[2023-10-13 13:41:00,479][flwr][INFO] - fit progress: (190, 2.3261600407167746, {'accuracy': 0.6111}, 438568.257645679)
-DEBUG flwr 2023-10-13 13:41:00,479 | server.py:173 | evaluate_round 190: strategy sampled 50 clients (out of 50)
-[2023-10-13 13:41:00,479][flwr][DEBUG] - evaluate_round 190: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 13:50:04,393 | server.py:187 | evaluate_round 190 received 50 results and 0 failures
-[2023-10-13 13:50:04,393][flwr][DEBUG] - evaluate_round 190 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 13:50:04,394 | server.py:222 | fit_round 191: strategy sampled 50 clients (out of 50)
-[2023-10-13 13:50:04,394][flwr][DEBUG] - fit_round 191: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.185731 Loss1: 0.343656 Loss2: 1.842075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.506183 Loss1: 0.141428 Loss2: 1.364755 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526790 Loss1: 0.167687 Loss2: 1.359103
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.343354 Loss1: 0.420383 Loss2: 1.922971
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.496067 Loss1: 0.137933 Loss2: 1.358134
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.671872 Loss1: 0.290893 Loss2: 1.380979
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.481811 Loss1: 0.127176 Loss2: 1.354635
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.589247 Loss1: 0.177295 Loss2: 1.411952
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.588108 Loss1: 0.194631 Loss2: 1.393477
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456393 Loss1: 0.108888 Loss2: 1.347505
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545766 Loss1: 0.164876 Loss2: 1.380890
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.424139 Loss1: 0.082858 Loss2: 1.341280
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.523786 Loss1: 0.139607 Loss2: 1.384179
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398699 Loss1: 0.062403 Loss2: 1.336296
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369698 Loss1: 0.040243 Loss2: 1.329455 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427786 Loss1: 0.068946 Loss2: 1.358840 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.155278 Loss1: 0.358626 Loss2: 1.796653 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.483249 Loss1: 0.157867 Loss2: 1.325381 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496726 Loss1: 0.161334 Loss2: 1.335392
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.209604 Loss1: 0.394263 Loss2: 1.815341
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.447021 Loss1: 0.124325 Loss2: 1.322695
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.642866 Loss1: 0.317613 Loss2: 1.325253
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.439957 Loss1: 0.107320 Loss2: 1.332636
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.522256 Loss1: 0.167857 Loss2: 1.354399
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.431740 Loss1: 0.107205 Loss2: 1.324536
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.541759 Loss1: 0.205091 Loss2: 1.336668
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426173 Loss1: 0.107929 Loss2: 1.318244
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.491167 Loss1: 0.150950 Loss2: 1.340216
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400218 Loss1: 0.087359 Loss2: 1.312859
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.455020 Loss1: 0.117566 Loss2: 1.337454
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377493 Loss1: 0.065334 Loss2: 1.312159
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446647 Loss1: 0.115661 Loss2: 1.330985
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.400602 Loss1: 0.075063 Loss2: 1.325539
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395504 Loss1: 0.076972 Loss2: 1.318533
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373551 Loss1: 0.056936 Loss2: 1.316615
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.243449 Loss1: 0.367943 Loss2: 1.875505
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.618274 Loss1: 0.246875 Loss2: 1.371399
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.528773 Loss1: 0.138015 Loss2: 1.390759
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.471835 Loss1: 0.100627 Loss2: 1.371208
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.217408 Loss1: 0.375394 Loss2: 1.842014
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.568657 Loss1: 0.226433 Loss2: 1.342224 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.482912 Loss1: 0.116785 Loss2: 1.366128 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.511905 Loss1: 0.164953 Loss2: 1.346952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485660 Loss1: 0.129771 Loss2: 1.355888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.456403 Loss1: 0.105556 Loss2: 1.350847 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383159 Loss1: 0.036700 Loss2: 1.346459
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.453974 Loss1: 0.108986 Loss2: 1.344987
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.456727 Loss1: 0.104161 Loss2: 1.352565
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.449918 Loss1: 0.104602 Loss2: 1.345315
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.491639 Loss1: 0.141064 Loss2: 1.350576
-(DefaultActor pid=3764) >> Training accuracy: 0.973958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.164159 Loss1: 0.303922 Loss2: 1.860237
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.653510 Loss1: 0.283526 Loss2: 1.369985
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.586558 Loss1: 0.180866 Loss2: 1.405693
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496215 Loss1: 0.119512 Loss2: 1.376702
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.142777 Loss1: 0.290779 Loss2: 1.851998
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.571433 Loss1: 0.222435 Loss2: 1.348998 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.555614 Loss1: 0.195101 Loss2: 1.360513 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.542672 Loss1: 0.182654 Loss2: 1.360018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.473164 Loss1: 0.126420 Loss2: 1.346745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.437942 Loss1: 0.094127 Loss2: 1.343815 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370962 Loss1: 0.030666 Loss2: 1.340295
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439966 Loss1: 0.097654 Loss2: 1.342312
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397209 Loss1: 0.060900 Loss2: 1.336309
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.388050 Loss1: 0.054423 Loss2: 1.333627
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376267 Loss1: 0.052184 Loss2: 1.324084
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.232607 Loss1: 0.343528 Loss2: 1.889079
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.637475 Loss1: 0.253854 Loss2: 1.383621
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.552873 Loss1: 0.145094 Loss2: 1.407779
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.471965 Loss1: 0.080924 Loss2: 1.391040
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.329185 Loss1: 0.381328 Loss2: 1.947857
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.454505 Loss1: 0.076521 Loss2: 1.377984
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.671924 Loss1: 0.228066 Loss2: 1.443858
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.414652 Loss1: 0.038356 Loss2: 1.376296
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.625627 Loss1: 0.178403 Loss2: 1.447224
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.412444 Loss1: 0.044788 Loss2: 1.367656
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.598711 Loss1: 0.153274 Loss2: 1.445438
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434600 Loss1: 0.076826 Loss2: 1.357774
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.546720 Loss1: 0.115735 Loss2: 1.430985
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413684 Loss1: 0.050914 Loss2: 1.362770
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.537741 Loss1: 0.111007 Loss2: 1.426734
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.413698 Loss1: 0.048914 Loss2: 1.364784
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.504183 Loss1: 0.079911 Loss2: 1.424273
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.492774 Loss1: 0.081373 Loss2: 1.411402
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.481837 Loss1: 0.074449 Loss2: 1.407387
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.476820 Loss1: 0.068289 Loss2: 1.408532
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.353324 Loss1: 0.490901 Loss2: 1.862423
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.642022 Loss1: 0.301701 Loss2: 1.340321
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.564750 Loss1: 0.205458 Loss2: 1.359292
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.484206 Loss1: 0.141064 Loss2: 1.343142
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.345673 Loss1: 0.394332 Loss2: 1.951341
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.764197 Loss1: 0.325659 Loss2: 1.438538 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.728655 Loss1: 0.202790 Loss2: 1.525865 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.605805 Loss1: 0.166052 Loss2: 1.439753 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.600584 Loss1: 0.153637 Loss2: 1.446948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.572090 Loss1: 0.123211 Loss2: 1.448879 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.512508 Loss1: 0.083000 Loss2: 1.429508 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454747 Loss1: 0.037061 Loss2: 1.417686 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.600584 Loss1: 0.253481 Loss2: 1.347103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.473253 Loss1: 0.118409 Loss2: 1.354844 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.250370 Loss1: 0.380760 Loss2: 1.869610
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.423914 Loss1: 0.084629 Loss2: 1.339285
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.578313 Loss1: 0.222402 Loss2: 1.355911
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416067 Loss1: 0.072036 Loss2: 1.344031
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.519653 Loss1: 0.153591 Loss2: 1.366062
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.402298 Loss1: 0.066939 Loss2: 1.335359
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487372 Loss1: 0.122598 Loss2: 1.364774
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410653 Loss1: 0.075460 Loss2: 1.335193
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483019 Loss1: 0.132577 Loss2: 1.350442
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374218 Loss1: 0.041282 Loss2: 1.332936
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484371 Loss1: 0.127516 Loss2: 1.356855
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371606 Loss1: 0.046514 Loss2: 1.325091
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445144 Loss1: 0.097839 Loss2: 1.347305 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408916 Loss1: 0.067272 Loss2: 1.341643 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.672022 Loss1: 0.279366 Loss2: 1.392656 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.552790 Loss1: 0.150450 Loss2: 1.402340 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.226336 Loss1: 0.401327 Loss2: 1.825009
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.496783 Loss1: 0.097166 Loss2: 1.399617
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.522605 Loss1: 0.192546 Loss2: 1.330059
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.464559 Loss1: 0.076299 Loss2: 1.388260
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.523930 Loss1: 0.192403 Loss2: 1.331527
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.474941 Loss1: 0.095309 Loss2: 1.379632
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.475738 Loss1: 0.136553 Loss2: 1.339185
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.456554 Loss1: 0.072362 Loss2: 1.384192
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.443731 Loss1: 0.112120 Loss2: 1.331611
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431870 Loss1: 0.050802 Loss2: 1.381068
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.420753 Loss1: 0.093273 Loss2: 1.327480
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.448914 Loss1: 0.073172 Loss2: 1.375742
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.422197 Loss1: 0.097554 Loss2: 1.324643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.364049 Loss1: 0.047964 Loss2: 1.316084 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.565575 Loss1: 0.230179 Loss2: 1.335396 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.577121 Loss1: 0.227249 Loss2: 1.349872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.550382 Loss1: 0.180633 Loss2: 1.369749 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.519585 Loss1: 0.173174 Loss2: 1.346411 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.514528 Loss1: 0.150573 Loss2: 1.363955 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.472651 Loss1: 0.129280 Loss2: 1.343371 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440237 Loss1: 0.100366 Loss2: 1.339870 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421427 Loss1: 0.086151 Loss2: 1.335276 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473344 Loss1: 0.094948 Loss2: 1.378396 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.282992 Loss1: 0.444576 Loss2: 1.838417 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.598856 Loss1: 0.213983 Loss2: 1.384873 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.495941 Loss1: 0.174665 Loss2: 1.321276
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.328719 Loss1: 0.411148 Loss2: 1.917571
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.425116 Loss1: 0.103432 Loss2: 1.321684
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.540217 Loss1: 0.215753 Loss2: 1.324464
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.455299 Loss1: 0.138210 Loss2: 1.317089
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.418415 Loss1: 0.105208 Loss2: 1.313207
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.396986 Loss1: 0.083498 Loss2: 1.313488 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.392213 Loss1: 0.079276 Loss2: 1.312937 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.363380 Loss1: 0.056336 Loss2: 1.307045 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.334142 Loss1: 0.031660 Loss2: 1.302482 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352571 Loss1: 0.053811 Loss2: 1.298760 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.206917 Loss1: 0.407430 Loss2: 1.799488
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.616762 Loss1: 0.280900 Loss2: 1.335862
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.511298 Loss1: 0.144479 Loss2: 1.366819
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.210359 Loss1: 0.313716 Loss2: 1.896643
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498289 Loss1: 0.159830 Loss2: 1.338460
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.562803 Loss1: 0.190352 Loss2: 1.372451
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.477824 Loss1: 0.134024 Loss2: 1.343801
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.525595 Loss1: 0.151380 Loss2: 1.374214
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.446734 Loss1: 0.090844 Loss2: 1.355890
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.487890 Loss1: 0.104452 Loss2: 1.383438
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.399893 Loss1: 0.070013 Loss2: 1.329881
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389414 Loss1: 0.058206 Loss2: 1.331208 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372380 Loss1: 0.045709 Loss2: 1.326671 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355467 Loss1: 0.036379 Loss2: 1.319088 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.393084 Loss1: 0.035991 Loss2: 1.357093 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.348305 Loss1: 0.405924 Loss2: 1.942381 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.659050 Loss1: 0.221230 Loss2: 1.437820 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.553851 Loss1: 0.142293 Loss2: 1.411558
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.149847 Loss1: 0.315692 Loss2: 1.834155
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.639674 Loss1: 0.244337 Loss2: 1.395336
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.549320 Loss1: 0.207905 Loss2: 1.341415
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.545076 Loss1: 0.138137 Loss2: 1.406939
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.516093 Loss1: 0.170824 Loss2: 1.345269
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.512817 Loss1: 0.124822 Loss2: 1.387994
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.472606 Loss1: 0.130892 Loss2: 1.341714
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.486645 Loss1: 0.101989 Loss2: 1.384656
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.458835 Loss1: 0.123063 Loss2: 1.335772
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.483168 Loss1: 0.092861 Loss2: 1.390307
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.448542 Loss1: 0.117269 Loss2: 1.331273
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.479998 Loss1: 0.096487 Loss2: 1.383511
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.388035 Loss1: 0.061527 Loss2: 1.326508
-(DefaultActor pid=3765) >> Training accuracy: 0.968750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445677 Loss1: 0.117141 Loss2: 1.328536
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399740 Loss1: 0.073783 Loss2: 1.325957
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434411 Loss1: 0.112373 Loss2: 1.322038
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.392461 Loss1: 0.444632 Loss2: 1.947829
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.606282 Loss1: 0.281277 Loss2: 1.325005
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.580058 Loss1: 0.244003 Loss2: 1.336054
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497574 Loss1: 0.137212 Loss2: 1.360362
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.449197 Loss1: 0.104781 Loss2: 1.344416
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442713 Loss1: 0.109362 Loss2: 1.333351 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414894 Loss1: 0.078480 Loss2: 1.336414 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.478459 Loss1: 0.143232 Loss2: 1.335227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421041 Loss1: 0.083977 Loss2: 1.337064
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.423535 Loss1: 0.093145 Loss2: 1.330390
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384486 Loss1: 0.051922 Loss2: 1.332563
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.436271 Loss1: 0.117350 Loss2: 1.318921
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.405811 Loss1: 0.086296 Loss2: 1.319515
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.398422 Loss1: 0.085707 Loss2: 1.312715
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399905 Loss1: 0.084962 Loss2: 1.314944
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.372123 Loss1: 0.058475 Loss2: 1.313648
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.276064 Loss1: 0.362263 Loss2: 1.913801
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354505 Loss1: 0.046421 Loss2: 1.308085
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.603337 Loss1: 0.177384 Loss2: 1.425952 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488225 Loss1: 0.086307 Loss2: 1.401918 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489432 Loss1: 0.090242 Loss2: 1.399190
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.197751 Loss1: 0.421509 Loss2: 1.776241
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.561605 Loss1: 0.264784 Loss2: 1.296822 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.538397 Loss1: 0.190772 Loss2: 1.347625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.423429 Loss1: 0.115462 Loss2: 1.307968 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.452323 Loss1: 0.059389 Loss2: 1.392934
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.462936 Loss1: 0.165743 Loss2: 1.297194
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447207 Loss1: 0.137802 Loss2: 1.309405
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.417620 Loss1: 0.111050 Loss2: 1.306570
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368163 Loss1: 0.070542 Loss2: 1.297621
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386086 Loss1: 0.088832 Loss2: 1.297254
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.246510 Loss1: 0.370753 Loss2: 1.875757
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.359458 Loss1: 0.071591 Loss2: 1.287868
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.612247 Loss1: 0.191308 Loss2: 1.420938 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.476753 Loss1: 0.097767 Loss2: 1.378986 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502056 Loss1: 0.112902 Loss2: 1.389154
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.058582 Loss1: 0.259075 Loss2: 1.799507
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.561758 Loss1: 0.237938 Loss2: 1.323820 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.538216 Loss1: 0.178287 Loss2: 1.359929 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.467906 Loss1: 0.132655 Loss2: 1.335250 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.407172 Loss1: 0.077127 Loss2: 1.330046 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368133 Loss1: 0.053931 Loss2: 1.314202 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.359784 Loss1: 0.049511 Loss2: 1.310273
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.242909 Loss1: 0.408035 Loss2: 1.834874
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356100 Loss1: 0.041552 Loss2: 1.314548
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.647963 Loss1: 0.256960 Loss2: 1.391003
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.580678 Loss1: 0.177202 Loss2: 1.403476
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.494355 Loss1: 0.114472 Loss2: 1.379883
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.458143 Loss1: 0.080680 Loss2: 1.377463
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444875 Loss1: 0.078684 Loss2: 1.366191
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.346110 Loss1: 0.422434 Loss2: 1.923676
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.662583 Loss1: 0.289299 Loss2: 1.373284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.557479 Loss1: 0.153462 Loss2: 1.404016 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413139 Loss1: 0.059542 Loss2: 1.353598
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.483195 Loss1: 0.102529 Loss2: 1.380666
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441203 Loss1: 0.079393 Loss2: 1.361810
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423139 Loss1: 0.073521 Loss2: 1.349618
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.417233 Loss1: 0.052159 Loss2: 1.365074 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431437 Loss1: 0.084440 Loss2: 1.346997 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393205 Loss1: 0.038885 Loss2: 1.354320
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.126611 Loss1: 0.344129 Loss2: 1.782481
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.514445 Loss1: 0.209619 Loss2: 1.304826
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.467335 Loss1: 0.147317 Loss2: 1.320019
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.427007 Loss1: 0.122509 Loss2: 1.304498
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.458060 Loss1: 0.146534 Loss2: 1.311526
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.114636 Loss1: 0.321506 Loss2: 1.793130
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.601651 Loss1: 0.300803 Loss2: 1.300848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.445900 Loss1: 0.122912 Loss2: 1.322988 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.401395 Loss1: 0.097507 Loss2: 1.303888 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.370110 Loss1: 0.080172 Loss2: 1.289938 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401694 Loss1: 0.092808 Loss2: 1.308886
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.355218 Loss1: 0.062370 Loss2: 1.292847
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.351190 Loss1: 0.063207 Loss2: 1.287983
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.363315 Loss1: 0.074003 Loss2: 1.289312
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.333561 Loss1: 0.045317 Loss2: 1.288244
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.330497 Loss1: 0.051828 Loss2: 1.278669
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.285904 Loss1: 0.358251 Loss2: 1.927652
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.622191 Loss1: 0.217270 Loss2: 1.404921
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.546154 Loss1: 0.116795 Loss2: 1.429358
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.501474 Loss1: 0.089542 Loss2: 1.411932
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.467236 Loss1: 0.067902 Loss2: 1.399334
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.301769 Loss1: 0.409422 Loss2: 1.892347
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.442241 Loss1: 0.047338 Loss2: 1.394903
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.660416 Loss1: 0.276612 Loss2: 1.383804
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.437463 Loss1: 0.051205 Loss2: 1.386258
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.609270 Loss1: 0.205386 Loss2: 1.403884
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415425 Loss1: 0.038344 Loss2: 1.377081
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521301 Loss1: 0.136333 Loss2: 1.384968
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.419339 Loss1: 0.044941 Loss2: 1.374398
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.547915 Loss1: 0.159447 Loss2: 1.388467
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430042 Loss1: 0.051526 Loss2: 1.378515
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491341 Loss1: 0.111127 Loss2: 1.380214
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438628 Loss1: 0.067216 Loss2: 1.371412
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450523 Loss1: 0.081004 Loss2: 1.369519
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416250 Loss1: 0.051124 Loss2: 1.365125
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397270 Loss1: 0.041443 Loss2: 1.355827
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.206636 Loss1: 0.330213 Loss2: 1.876423
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.574341 Loss1: 0.180168 Loss2: 1.394173
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.506447 Loss1: 0.105553 Loss2: 1.400895
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.485563 Loss1: 0.101498 Loss2: 1.384065
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.029182 Loss1: 0.277849 Loss2: 1.751333
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.502230 Loss1: 0.200629 Loss2: 1.301602 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.527190 Loss1: 0.197505 Loss2: 1.329684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.452094 Loss1: 0.141241 Loss2: 1.310853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.432749 Loss1: 0.120669 Loss2: 1.312080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.462888 Loss1: 0.150471 Loss2: 1.312417 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.444993 Loss1: 0.129487 Loss2: 1.315506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.464224 Loss1: 0.139957 Loss2: 1.324267 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.201808 Loss1: 0.346115 Loss2: 1.855694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551252 Loss1: 0.178479 Loss2: 1.372773 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.215479 Loss1: 0.348402 Loss2: 1.867077 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452260 Loss1: 0.102548 Loss2: 1.349712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404240 Loss1: 0.064169 Loss2: 1.340072 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.399114 Loss1: 0.059153 Loss2: 1.339961 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405311 Loss1: 0.069972 Loss2: 1.335338 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403282 Loss1: 0.070717 Loss2: 1.332565 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413481 Loss1: 0.060369 Loss2: 1.353111 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.468921 Loss1: 0.111311 Loss2: 1.357610 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.570759 Loss1: 0.233152 Loss2: 1.337606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.439165 Loss1: 0.109231 Loss2: 1.329934 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.412929 Loss1: 0.091381 Loss2: 1.321549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440431 Loss1: 0.110226 Loss2: 1.330205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.388484 Loss1: 0.070118 Loss2: 1.318366 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.416042 Loss1: 0.098688 Loss2: 1.317354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.373923 Loss1: 0.055766 Loss2: 1.318156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.360131 Loss1: 0.046973 Loss2: 1.313158 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366645 Loss1: 0.052456 Loss2: 1.314189 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.101001 Loss1: 0.252089 Loss2: 1.848912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.489639 Loss1: 0.129902 Loss2: 1.359737 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.135269 Loss1: 0.293715 Loss2: 1.841554
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.501833 Loss1: 0.130311 Loss2: 1.371522
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.481446 Loss1: 0.157387 Loss2: 1.324059
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531081 Loss1: 0.171104 Loss2: 1.359977
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.482269 Loss1: 0.150039 Loss2: 1.332230
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.634851 Loss1: 0.235125 Loss2: 1.399726
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.458574 Loss1: 0.112712 Loss2: 1.345862
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.563951 Loss1: 0.189503 Loss2: 1.374447
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.501800 Loss1: 0.133188 Loss2: 1.368611 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.448854 Loss1: 0.085218 Loss2: 1.363636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434038 Loss1: 0.077429 Loss2: 1.356609 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.445831 Loss1: 0.102191 Loss2: 1.343640 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.135398 Loss1: 0.330123 Loss2: 1.805276 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.538032 Loss1: 0.176413 Loss2: 1.361619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.457484 Loss1: 0.116496 Loss2: 1.340988
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.329944 Loss1: 0.453767 Loss2: 1.876176
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.447553 Loss1: 0.122825 Loss2: 1.324728
-DEBUG flwr 2023-10-13 14:19:11,361 | server.py:236 | fit_round 191 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.583984 Loss1: 0.225862 Loss2: 1.358123
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.434985 Loss1: 0.102898 Loss2: 1.332088
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.549633 Loss1: 0.191636 Loss2: 1.357998
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404334 Loss1: 0.075359 Loss2: 1.328974
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.494982 Loss1: 0.141574 Loss2: 1.353408
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.396319 Loss1: 0.068201 Loss2: 1.328118
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.443239 Loss1: 0.107447 Loss2: 1.335792
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406977 Loss1: 0.083850 Loss2: 1.323128
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429403 Loss1: 0.097437 Loss2: 1.331966
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.395772 Loss1: 0.074484 Loss2: 1.321288
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.411165 Loss1: 0.080362 Loss2: 1.330803
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.397559 Loss1: 0.069108 Loss2: 1.328451
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.378156 Loss1: 0.045410 Loss2: 1.332746
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351762 Loss1: 0.031698 Loss2: 1.320064
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.037145 Loss1: 0.247587 Loss2: 1.789558
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.473547 Loss1: 0.162982 Loss2: 1.310565
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.448649 Loss1: 0.119235 Loss2: 1.329414
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.104873 Loss1: 0.263363 Loss2: 1.841509
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.409596 Loss1: 0.088989 Loss2: 1.320607
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.576749 Loss1: 0.198371 Loss2: 1.378378
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.373185 Loss1: 0.065416 Loss2: 1.307769
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.360747 Loss1: 0.061552 Loss2: 1.299195 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.349069 Loss1: 0.050800 Loss2: 1.298269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.358976 Loss1: 0.063309 Loss2: 1.295668 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.327407 Loss1: 0.035969 Loss2: 1.291438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.316557 Loss1: 0.030781 Loss2: 1.285777 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991728
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435154 Loss1: 0.069081 Loss2: 1.366073 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.253335 Loss1: 0.378090 Loss2: 1.875244 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.511269 Loss1: 0.181909 Loss2: 1.329360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.218575 Loss1: 0.396634 Loss2: 1.821941 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.439055 Loss1: 0.117796 Loss2: 1.321259 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406835 Loss1: 0.087951 Loss2: 1.318884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.390186 Loss1: 0.076004 Loss2: 1.314183 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.378266 Loss1: 0.065702 Loss2: 1.312564 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.385693 Loss1: 0.075184 Loss2: 1.310509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422131 Loss1: 0.098980 Loss2: 1.323150 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373170 Loss1: 0.055162 Loss2: 1.318009 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 14:19:11,361][flwr][DEBUG] - fit_round 191 received 50 results and 0 failures
-INFO flwr 2023-10-13 14:19:51,945 | server.py:125 | fit progress: (191, 2.3261689364719698, {'accuracy': 0.6113}, 440899.72395899397)
->> Test accuracy: 0.611300
-[2023-10-13 14:19:51,945][flwr][INFO] - fit progress: (191, 2.3261689364719698, {'accuracy': 0.6113}, 440899.72395899397)
-DEBUG flwr 2023-10-13 14:19:51,946 | server.py:173 | evaluate_round 191: strategy sampled 50 clients (out of 50)
-[2023-10-13 14:19:51,946][flwr][DEBUG] - evaluate_round 191: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 14:28:58,152 | server.py:187 | evaluate_round 191 received 50 results and 0 failures
-[2023-10-13 14:28:58,152][flwr][DEBUG] - evaluate_round 191 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 14:28:58,152 | server.py:222 | fit_round 192: strategy sampled 50 clients (out of 50)
-[2023-10-13 14:28:58,152][flwr][DEBUG] - fit_round 192: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.133827 Loss1: 0.283427 Loss2: 1.850400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.506198 Loss1: 0.160782 Loss2: 1.345416 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.556869 Loss1: 0.193128 Loss2: 1.363741
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.239938 Loss1: 0.334456 Loss2: 1.905482
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473991 Loss1: 0.131300 Loss2: 1.342691
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.636476 Loss1: 0.256199 Loss2: 1.380277
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444761 Loss1: 0.104344 Loss2: 1.340417
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.562786 Loss1: 0.173795 Loss2: 1.388992
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416907 Loss1: 0.081746 Loss2: 1.335161
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.561019 Loss1: 0.175868 Loss2: 1.385152
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398878 Loss1: 0.064512 Loss2: 1.334366
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524190 Loss1: 0.150504 Loss2: 1.373685
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.392693 Loss1: 0.065919 Loss2: 1.326774
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460558 Loss1: 0.088303 Loss2: 1.372255
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390155 Loss1: 0.069097 Loss2: 1.321058
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437996 Loss1: 0.073042 Loss2: 1.364954
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437098 Loss1: 0.075178 Loss2: 1.361921
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410529 Loss1: 0.048643 Loss2: 1.361886
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421499 Loss1: 0.062016 Loss2: 1.359484
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.103124 Loss1: 0.268575 Loss2: 1.834548
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.571464 Loss1: 0.214140 Loss2: 1.357324
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.594260 Loss1: 0.203700 Loss2: 1.390560
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.217348 Loss1: 0.316307 Loss2: 1.901040
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.505292 Loss1: 0.130018 Loss2: 1.375274
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.620074 Loss1: 0.195486 Loss2: 1.424588
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.456059 Loss1: 0.094831 Loss2: 1.361227
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.438388 Loss1: 0.082218 Loss2: 1.356170 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.433765 Loss1: 0.072441 Loss2: 1.361324 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.437044 Loss1: 0.077943 Loss2: 1.359102 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.408659 Loss1: 0.055238 Loss2: 1.353421 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398028 Loss1: 0.053336 Loss2: 1.344692 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991728
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.497301 Loss1: 0.090009 Loss2: 1.407292 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.171909 Loss1: 0.257753 Loss2: 1.914156 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.544681 Loss1: 0.143886 Loss2: 1.400795 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555140 Loss1: 0.147777 Loss2: 1.407363
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.113823 Loss1: 0.323004 Loss2: 1.790818
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.481857 Loss1: 0.179288 Loss2: 1.302569 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.423408 Loss1: 0.117190 Loss2: 1.306218 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.393280 Loss1: 0.087045 Loss2: 1.306234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.356356 Loss1: 0.064974 Loss2: 1.291383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.348160 Loss1: 0.059862 Loss2: 1.288297 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.389919 Loss1: 0.024077 Loss2: 1.365843
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.332281 Loss1: 0.046460 Loss2: 1.285821
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.318408 Loss1: 0.035868 Loss2: 1.282540
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.312133 Loss1: 0.033778 Loss2: 1.278355
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.290609 Loss1: 0.021580 Loss2: 1.269029
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.135900 Loss1: 0.312314 Loss2: 1.823586
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.517011 Loss1: 0.207449 Loss2: 1.309562
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.502989 Loss1: 0.177981 Loss2: 1.325008
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.460752 Loss1: 0.131732 Loss2: 1.329020
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.220248 Loss1: 0.386659 Loss2: 1.833589
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.426951 Loss1: 0.112086 Loss2: 1.314865
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.502783 Loss1: 0.190713 Loss2: 1.312070
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.378692 Loss1: 0.069742 Loss2: 1.308950
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.516689 Loss1: 0.208299 Loss2: 1.308391
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.423118 Loss1: 0.103458 Loss2: 1.319660
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.343130 Loss1: 0.046146 Loss2: 1.296984
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.397014 Loss1: 0.099840 Loss2: 1.297173
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.323089 Loss1: 0.029470 Loss2: 1.293619
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.380421 Loss1: 0.091623 Loss2: 1.288798
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.333730 Loss1: 0.044804 Loss2: 1.288926
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.342966 Loss1: 0.055114 Loss2: 1.287852 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.359002 Loss1: 0.070139 Loss2: 1.288863 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.215378 Loss1: 0.366954 Loss2: 1.848424 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.536505 Loss1: 0.163604 Loss2: 1.372901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.518452 Loss1: 0.162760 Loss2: 1.355692
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.307895 Loss1: 0.425829 Loss2: 1.882066
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.652111 Loss1: 0.274448 Loss2: 1.377663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.574540 Loss1: 0.181090 Loss2: 1.393450 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540381 Loss1: 0.149819 Loss2: 1.390562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.502077 Loss1: 0.130059 Loss2: 1.372017 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.491096 Loss1: 0.123118 Loss2: 1.367978 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370645 Loss1: 0.048013 Loss2: 1.322632
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434600 Loss1: 0.065250 Loss2: 1.369350
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.476911 Loss1: 0.114206 Loss2: 1.362705
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.438866 Loss1: 0.073688 Loss2: 1.365178
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.447667 Loss1: 0.087816 Loss2: 1.359851
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.218505 Loss1: 0.391529 Loss2: 1.826976
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.668555 Loss1: 0.310701 Loss2: 1.357854
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.557824 Loss1: 0.167720 Loss2: 1.390104
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520258 Loss1: 0.166003 Loss2: 1.354255
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.097353 Loss1: 0.307483 Loss2: 1.789870
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.516271 Loss1: 0.189172 Loss2: 1.327099 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.447275 Loss1: 0.119355 Loss2: 1.327920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.457534 Loss1: 0.134439 Loss2: 1.323096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.428696 Loss1: 0.101453 Loss2: 1.327243 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.415962 Loss1: 0.092257 Loss2: 1.323705 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419178 Loss1: 0.102713 Loss2: 1.316465 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392560 Loss1: 0.079223 Loss2: 1.313337 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.629324 Loss1: 0.277231 Loss2: 1.352094 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.545900 Loss1: 0.197282 Loss2: 1.348618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.160261 Loss1: 0.336717 Loss2: 1.823544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.540252 Loss1: 0.216027 Loss2: 1.324225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.493232 Loss1: 0.159670 Loss2: 1.333562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.411273 Loss1: 0.079844 Loss2: 1.331429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.415379 Loss1: 0.095477 Loss2: 1.319901 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988839
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.387976 Loss1: 0.075636 Loss2: 1.312340 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.344106 Loss1: 0.038163 Loss2: 1.305943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.330363 Loss1: 0.032338 Loss2: 1.298025
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.330669 Loss1: 0.395390 Loss2: 1.935279
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.662919 Loss1: 0.292349 Loss2: 1.370570
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.642157 Loss1: 0.244222 Loss2: 1.397935
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.565810 Loss1: 0.162543 Loss2: 1.403267
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503860 Loss1: 0.129959 Loss2: 1.373901
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.513220 Loss1: 0.129953 Loss2: 1.383267
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.532314 Loss1: 0.500122 Loss2: 2.032193
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.711146 Loss1: 0.337727 Loss2: 1.373419
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461122 Loss1: 0.079379 Loss2: 1.381742
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.479074 Loss1: 0.111681 Loss2: 1.367394 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444125 Loss1: 0.069187 Loss2: 1.374938 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417426 Loss1: 0.053396 Loss2: 1.364029 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998884
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.432158 Loss1: 0.059084 Loss2: 1.373074 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427597 Loss1: 0.072956 Loss2: 1.354641 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996094
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.226298 Loss1: 0.407646 Loss2: 1.818652
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.592529 Loss1: 0.244503 Loss2: 1.348026
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526101 Loss1: 0.160887 Loss2: 1.365214
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524092 Loss1: 0.171061 Loss2: 1.353031
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.171577 Loss1: 0.366083 Loss2: 1.805494
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.531328 Loss1: 0.211488 Loss2: 1.319840 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.521312 Loss1: 0.176986 Loss2: 1.344326 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.458816 Loss1: 0.130369 Loss2: 1.328447 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.481418 Loss1: 0.156128 Loss2: 1.325290 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463977 Loss1: 0.123750 Loss2: 1.340227 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.443451 Loss1: 0.114197 Loss2: 1.329254 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396941 Loss1: 0.071745 Loss2: 1.325196 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.605227 Loss1: 0.371872 Loss2: 2.233355 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.872480 Loss1: 0.163146 Loss2: 1.709334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.827906 Loss1: 0.165682 Loss2: 1.662224
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.275373 Loss1: 0.389273 Loss2: 1.886100
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.594441 Loss1: 0.220740 Loss2: 1.373701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.559673 Loss1: 0.156303 Loss2: 1.403370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.501211 Loss1: 0.113913 Loss2: 1.387298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493327 Loss1: 0.114406 Loss2: 1.378921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501327 Loss1: 0.117841 Loss2: 1.383486 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.672461 Loss1: 0.040152 Loss2: 1.632309
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.491963 Loss1: 0.114492 Loss2: 1.377471
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.498993 Loss1: 0.121806 Loss2: 1.377188
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.480357 Loss1: 0.098795 Loss2: 1.381562
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.494482 Loss1: 0.108938 Loss2: 1.385544
-(DefaultActor pid=3764) >> Training accuracy: 0.964583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.222292 Loss1: 0.380587 Loss2: 1.841705
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.620708 Loss1: 0.282126 Loss2: 1.338582
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.563309 Loss1: 0.196813 Loss2: 1.366496
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.537538 Loss1: 0.178066 Loss2: 1.359473
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.205876 Loss1: 0.340850 Loss2: 1.865026
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.590049 Loss1: 0.234260 Loss2: 1.355789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.552730 Loss1: 0.180077 Loss2: 1.372653 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.482318 Loss1: 0.134272 Loss2: 1.348047 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450409 Loss1: 0.106119 Loss2: 1.344290 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.447409 Loss1: 0.100124 Loss2: 1.347285 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.423055 Loss1: 0.081593 Loss2: 1.341462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433199 Loss1: 0.095753 Loss2: 1.337446 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.260166 Loss1: 0.339806 Loss2: 1.920360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.517107 Loss1: 0.130880 Loss2: 1.386227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.130621 Loss1: 0.304383 Loss2: 1.826238 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.564738 Loss1: 0.194556 Loss2: 1.370182 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.483327 Loss1: 0.105583 Loss2: 1.377745 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.480501 Loss1: 0.100826 Loss2: 1.379675 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.433638 Loss1: 0.071338 Loss2: 1.362300 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374907 Loss1: 0.028537 Loss2: 1.346370 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389942 Loss1: 0.042154 Loss2: 1.347788 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.371300 Loss1: 0.029330 Loss2: 1.341970 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.521144 Loss1: 0.215264 Loss2: 1.305879 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.437613 Loss1: 0.119244 Loss2: 1.318369 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.393594 Loss1: 0.096935 Loss2: 1.296659
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.288089 Loss1: 0.360351 Loss2: 1.927739
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.368093 Loss1: 0.073887 Loss2: 1.294206
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.638444 Loss1: 0.203551 Loss2: 1.434893
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.380547 Loss1: 0.095113 Loss2: 1.285434
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605990 Loss1: 0.167682 Loss2: 1.438307
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.574899 Loss1: 0.151260 Loss2: 1.423639 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522431 Loss1: 0.111717 Loss2: 1.410714 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.372379 Loss1: 0.086658 Loss2: 1.285721
-(DefaultActor pid=3765) >> Training accuracy: 0.965625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.517601 Loss1: 0.102630 Loss2: 1.414970
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.542683 Loss1: 0.135226 Loss2: 1.407457
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487706 Loss1: 0.074820 Loss2: 1.412887
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485682 Loss1: 0.087876 Loss2: 1.397807
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.512452 Loss1: 0.108690 Loss2: 1.403762
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.284428 Loss1: 0.346025 Loss2: 1.938404
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.734292 Loss1: 0.298462 Loss2: 1.435830
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.577350 Loss1: 0.115680 Loss2: 1.461670
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.554485 Loss1: 0.126101 Loss2: 1.428384
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531816 Loss1: 0.108764 Loss2: 1.423052
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.276458 Loss1: 0.412574 Loss2: 1.863884
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.602290 Loss1: 0.247798 Loss2: 1.354492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.538151 Loss1: 0.165548 Loss2: 1.372603 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.469244 Loss1: 0.101760 Loss2: 1.367484 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.430074 Loss1: 0.083207 Loss2: 1.346866 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.435873 Loss1: 0.085709 Loss2: 1.350164 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.453536 Loss1: 0.107870 Loss2: 1.345666 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406776 Loss1: 0.068034 Loss2: 1.338742 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.615551 Loss1: 0.257777 Loss2: 1.357774 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.514501 Loss1: 0.138272 Loss2: 1.376229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507824 Loss1: 0.152260 Loss2: 1.355563
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.224196 Loss1: 0.393037 Loss2: 1.831159
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.597655 Loss1: 0.256636 Loss2: 1.341020 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.500050 Loss1: 0.158616 Loss2: 1.341433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.452607 Loss1: 0.098883 Loss2: 1.353724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469829 Loss1: 0.135065 Loss2: 1.334764 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485448 Loss1: 0.128782 Loss2: 1.356666
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464421 Loss1: 0.123530 Loss2: 1.340891
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438211 Loss1: 0.104221 Loss2: 1.333989
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414599 Loss1: 0.081488 Loss2: 1.333111
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.422061 Loss1: 0.088228 Loss2: 1.333833
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378985 Loss1: 0.052260 Loss2: 1.326724
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.067386 Loss1: 0.266859 Loss2: 1.800527
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.499115 Loss1: 0.195689 Loss2: 1.303427
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.431316 Loss1: 0.120736 Loss2: 1.310580
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.405915 Loss1: 0.098764 Loss2: 1.307150
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.393228 Loss1: 0.095286 Loss2: 1.297941
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.438354 Loss1: 0.431068 Loss2: 2.007286
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.662284 Loss1: 0.267945 Loss2: 1.394340
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.379279 Loss1: 0.081360 Loss2: 1.297919
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.677881 Loss1: 0.278875 Loss2: 1.399006
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.338076 Loss1: 0.047018 Loss2: 1.291057
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.328029 Loss1: 0.041547 Loss2: 1.286482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.333508 Loss1: 0.049920 Loss2: 1.283588 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.304307 Loss1: 0.023450 Loss2: 1.280856 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.424269 Loss1: 0.056750 Loss2: 1.367519 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991587
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.215571 Loss1: 0.345908 Loss2: 1.869664 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.549875 Loss1: 0.169787 Loss2: 1.380089 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.486246 Loss1: 0.107924 Loss2: 1.378322
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.067085 Loss1: 0.311703 Loss2: 1.755382
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506474 Loss1: 0.148731 Loss2: 1.357742
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.513877 Loss1: 0.220576 Loss2: 1.293301
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.479940 Loss1: 0.164908 Loss2: 1.315032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.404404 Loss1: 0.106290 Loss2: 1.298114 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.417158 Loss1: 0.125000 Loss2: 1.292158 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.410324 Loss1: 0.114045 Loss2: 1.296280 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433705 Loss1: 0.133736 Loss2: 1.299969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.359809 Loss1: 0.068551 Loss2: 1.291258 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.141116 Loss1: 0.303006 Loss2: 1.838111 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.534078 Loss1: 0.172756 Loss2: 1.361322 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.102821 Loss1: 0.308450 Loss2: 1.794370 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.536149 Loss1: 0.204963 Loss2: 1.331186 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.484664 Loss1: 0.142356 Loss2: 1.342308 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430343 Loss1: 0.089007 Loss2: 1.341336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.430259 Loss1: 0.093897 Loss2: 1.336363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.411088 Loss1: 0.081717 Loss2: 1.329371 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368095 Loss1: 0.063326 Loss2: 1.304769 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354102 Loss1: 0.051549 Loss2: 1.302553 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986328
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.543833 Loss1: 0.198436 Loss2: 1.345397 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.514752 Loss1: 0.157783 Loss2: 1.356969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.163012 Loss1: 0.333202 Loss2: 1.829810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.558328 Loss1: 0.232680 Loss2: 1.325648 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.516143 Loss1: 0.162971 Loss2: 1.353172 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.475949 Loss1: 0.142982 Loss2: 1.332967 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.434117 Loss1: 0.110338 Loss2: 1.323779 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.364893 Loss1: 0.050768 Loss2: 1.314126 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.352800 Loss1: 0.050851 Loss2: 1.301949 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.355141 Loss1: 0.052710 Loss2: 1.302430
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.150481 Loss1: 0.359328 Loss2: 1.791153
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.497392 Loss1: 0.219386 Loss2: 1.278007
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.506879 Loss1: 0.212890 Loss2: 1.293989
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.428448 Loss1: 0.125924 Loss2: 1.302524
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.375050 Loss1: 0.099086 Loss2: 1.275965
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.174831 Loss1: 0.298104 Loss2: 1.876727
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.353117 Loss1: 0.082344 Loss2: 1.270773
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.338212 Loss1: 0.063041 Loss2: 1.275171 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.324849 Loss1: 0.060287 Loss2: 1.264562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.625918 Loss1: 0.202380 Loss2: 1.423537
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.314607 Loss1: 0.060027 Loss2: 1.254580
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.585946 Loss1: 0.167085 Loss2: 1.418861
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.308112 Loss1: 0.049845 Loss2: 1.258267
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.544490 Loss1: 0.123115 Loss2: 1.421375 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.485032 Loss1: 0.085805 Loss2: 1.399226 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.471547 Loss1: 0.072894 Loss2: 1.398653 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.646879 Loss1: 0.231229 Loss2: 1.415650 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.574218 Loss1: 0.156564 Loss2: 1.417654 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.494348 Loss1: 0.088265 Loss2: 1.406084 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.492912 Loss1: 0.088933 Loss2: 1.403979 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.455894 Loss1: 0.061129 Loss2: 1.394765 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.419384 Loss1: 0.029539 Loss2: 1.389845 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993990
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.313965 Loss1: 0.072543 Loss2: 1.241422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.294067 Loss1: 0.048965 Loss2: 1.245102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.302850 Loss1: 0.061770 Loss2: 1.241080
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.168470 Loss1: 0.333193 Loss2: 1.835278
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.293421 Loss1: 0.055047 Loss2: 1.238374
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.710618 Loss1: 0.335643 Loss2: 1.374975
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.282955 Loss1: 0.047317 Loss2: 1.235638
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.617701 Loss1: 0.181885 Loss2: 1.435816
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.544812 Loss1: 0.160070 Loss2: 1.384742
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551446 Loss1: 0.155386 Loss2: 1.396060
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.541380 Loss1: 0.147868 Loss2: 1.393512
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.485016 Loss1: 0.109323 Loss2: 1.375693
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.139259 Loss1: 0.335256 Loss2: 1.804003
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.499405 Loss1: 0.113267 Loss2: 1.386138
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.486275 Loss1: 0.112842 Loss2: 1.373433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449926 Loss1: 0.072007 Loss2: 1.377919 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.395650 Loss1: 0.084212 Loss2: 1.311438 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.367744 Loss1: 0.062764 Loss2: 1.304980 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382858 Loss1: 0.077553 Loss2: 1.305306
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.183648 Loss1: 0.361844 Loss2: 1.821804
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.550502 Loss1: 0.214420 Loss2: 1.336082 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.536871 Loss1: 0.169935 Loss2: 1.366935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.547574 Loss1: 0.193798 Loss2: 1.353776 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448861 Loss1: 0.112509 Loss2: 1.336352 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401602 Loss1: 0.065368 Loss2: 1.336234 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.590425 Loss1: 0.224167 Loss2: 1.366258 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.615401 Loss1: 0.241016 Loss2: 1.374384 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463612 Loss1: 0.105534 Loss2: 1.358078 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439399 Loss1: 0.095355 Loss2: 1.344044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398026 Loss1: 0.056483 Loss2: 1.341543
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.309244 Loss1: 0.322139 Loss2: 1.987106
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368207 Loss1: 0.037301 Loss2: 1.330906
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.733840 Loss1: 0.279184 Loss2: 1.454656
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.684120 Loss1: 0.185216 Loss2: 1.498904
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.617747 Loss1: 0.157628 Loss2: 1.460120
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589608 Loss1: 0.130239 Loss2: 1.459369
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.563750 Loss1: 0.106539 Loss2: 1.457211
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.553089 Loss1: 0.099953 Loss2: 1.453136
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.273396 Loss1: 0.402173 Loss2: 1.871223
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.542186 Loss1: 0.089465 Loss2: 1.452721
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.656999 Loss1: 0.283937 Loss2: 1.373062
-DEBUG flwr 2023-10-13 14:57:27,128 | server.py:236 | fit_round 192 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.543397 Loss1: 0.094631 Loss2: 1.448766
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.618536 Loss1: 0.214689 Loss2: 1.403847
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.507511 Loss1: 0.069976 Loss2: 1.437535
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.498381 Loss1: 0.124901 Loss2: 1.373481
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.509155 Loss1: 0.140298 Loss2: 1.368858
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.464132 Loss1: 0.101737 Loss2: 1.362395
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.463600 Loss1: 0.110994 Loss2: 1.352606
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418062 Loss1: 0.065894 Loss2: 1.352168
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405036 Loss1: 0.057475 Loss2: 1.347561
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.195053 Loss1: 0.377397 Loss2: 1.817656
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405764 Loss1: 0.059566 Loss2: 1.346198
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.497278 Loss1: 0.183475 Loss2: 1.313803
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.554222 Loss1: 0.222084 Loss2: 1.332138
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489309 Loss1: 0.156680 Loss2: 1.332629
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480806 Loss1: 0.152060 Loss2: 1.328746
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.520822 Loss1: 0.192098 Loss2: 1.328724
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.253954 Loss1: 0.408552 Loss2: 1.845402
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448157 Loss1: 0.123395 Loss2: 1.324762
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.378224 Loss1: 0.056891 Loss2: 1.321333 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.376381 Loss1: 0.066951 Loss2: 1.309429 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.337897 Loss1: 0.033445 Loss2: 1.304452 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.372024 Loss1: 0.051269 Loss2: 1.320756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.342955 Loss1: 0.042792 Loss2: 1.300163 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.328348 Loss1: 0.032594 Loss2: 1.295754 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 14:57:27,128][flwr][DEBUG] - fit_round 192 received 50 results and 0 failures
-INFO flwr 2023-10-13 14:58:08,595 | server.py:125 | fit progress: (192, 2.328017218615681, {'accuracy': 0.6118}, 443196.373934433)
->> Test accuracy: 0.611800
-[2023-10-13 14:58:08,595][flwr][INFO] - fit progress: (192, 2.328017218615681, {'accuracy': 0.6118}, 443196.373934433)
-DEBUG flwr 2023-10-13 14:58:08,596 | server.py:173 | evaluate_round 192: strategy sampled 50 clients (out of 50)
-[2023-10-13 14:58:08,596][flwr][DEBUG] - evaluate_round 192: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 15:07:17,455 | server.py:187 | evaluate_round 192 received 50 results and 0 failures
-[2023-10-13 15:07:17,455][flwr][DEBUG] - evaluate_round 192 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 15:07:17,456 | server.py:222 | fit_round 193: strategy sampled 50 clients (out of 50)
-[2023-10-13 15:07:17,456][flwr][DEBUG] - fit_round 193: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.168701 Loss1: 0.349481 Loss2: 1.819221
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.555442 Loss1: 0.227573 Loss2: 1.327869
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.534980 Loss1: 0.186579 Loss2: 1.348401
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476527 Loss1: 0.135787 Loss2: 1.340740
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.370740 Loss1: 0.427824 Loss2: 1.942916
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.422478 Loss1: 0.098110 Loss2: 1.324368
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.685590 Loss1: 0.288106 Loss2: 1.397484
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.439851 Loss1: 0.114445 Loss2: 1.325406
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.605180 Loss1: 0.173123 Loss2: 1.432056
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390735 Loss1: 0.068590 Loss2: 1.322145
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.579909 Loss1: 0.173256 Loss2: 1.406653
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542003 Loss1: 0.142393 Loss2: 1.399610
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.373255 Loss1: 0.061499 Loss2: 1.311756
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.569525 Loss1: 0.156488 Loss2: 1.413037
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.352521 Loss1: 0.044736 Loss2: 1.307785
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.502982 Loss1: 0.106180 Loss2: 1.396803
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.335976 Loss1: 0.033151 Loss2: 1.302824
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499911 Loss1: 0.111983 Loss2: 1.387928 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.188976 Loss1: 0.380185 Loss2: 1.808792 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.547799 Loss1: 0.175990 Loss2: 1.371809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.464519 Loss1: 0.125536 Loss2: 1.338982
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.283184 Loss1: 0.410044 Loss2: 1.873140
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.408266 Loss1: 0.074105 Loss2: 1.334161
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.615681 Loss1: 0.245717 Loss2: 1.369963
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.541058 Loss1: 0.148608 Loss2: 1.392450
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.394273 Loss1: 0.057563 Loss2: 1.336710
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.497051 Loss1: 0.124610 Loss2: 1.372441
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416247 Loss1: 0.087778 Loss2: 1.328469
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.461713 Loss1: 0.097323 Loss2: 1.364390
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.388757 Loss1: 0.057206 Loss2: 1.331551
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431474 Loss1: 0.068670 Loss2: 1.362804
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377487 Loss1: 0.052938 Loss2: 1.324548
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358947 Loss1: 0.036965 Loss2: 1.321983 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.399748 Loss1: 0.051418 Loss2: 1.348330 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.186099 Loss1: 0.329019 Loss2: 1.857080 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.531004 Loss1: 0.164951 Loss2: 1.366052 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.472885 Loss1: 0.110173 Loss2: 1.362712
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.295917 Loss1: 0.380478 Loss2: 1.915439
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.425065 Loss1: 0.080536 Loss2: 1.344529
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.603817 Loss1: 0.231704 Loss2: 1.372113
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.504762 Loss1: 0.138148 Loss2: 1.366614
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440813 Loss1: 0.099924 Loss2: 1.340890
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.587171 Loss1: 0.204069 Loss2: 1.383102
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460525 Loss1: 0.118789 Loss2: 1.341737
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.523957 Loss1: 0.146345 Loss2: 1.377612
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410077 Loss1: 0.067725 Loss2: 1.342352
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.386393 Loss1: 0.048062 Loss2: 1.338331 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364518 Loss1: 0.029818 Loss2: 1.334700 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.432314 Loss1: 0.080760 Loss2: 1.351555 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.195897 Loss1: 0.332593 Loss2: 1.863304 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.565792 Loss1: 0.206392 Loss2: 1.359400 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.540743 Loss1: 0.165772 Loss2: 1.374971
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.297984 Loss1: 0.397584 Loss2: 1.900400
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.534384 Loss1: 0.169594 Loss2: 1.364791
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.639819 Loss1: 0.242171 Loss2: 1.397648
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478454 Loss1: 0.110319 Loss2: 1.368136
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.567356 Loss1: 0.162042 Loss2: 1.405314
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.483934 Loss1: 0.128193 Loss2: 1.355741
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521893 Loss1: 0.115369 Loss2: 1.406525
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.467842 Loss1: 0.111001 Loss2: 1.356841
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514317 Loss1: 0.131898 Loss2: 1.382418
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440409 Loss1: 0.083080 Loss2: 1.357329
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.488502 Loss1: 0.102093 Loss2: 1.386409
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459624 Loss1: 0.109189 Loss2: 1.350436
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441721 Loss1: 0.056799 Loss2: 1.384921
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414011 Loss1: 0.046125 Loss2: 1.367886
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402439 Loss1: 0.042725 Loss2: 1.359714
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423445 Loss1: 0.060469 Loss2: 1.362976
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.159714 Loss1: 0.323132 Loss2: 1.836582
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.575769 Loss1: 0.202419 Loss2: 1.373350
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.549267 Loss1: 0.158417 Loss2: 1.390850
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.459434 Loss1: 0.095480 Loss2: 1.363954
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.046004 Loss1: 0.327161 Loss2: 1.718843
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.455614 Loss1: 0.092003 Loss2: 1.363611
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.477212 Loss1: 0.224549 Loss2: 1.252663
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433877 Loss1: 0.076631 Loss2: 1.357246
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.452195 Loss1: 0.192289 Loss2: 1.259906
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.433078 Loss1: 0.166337 Loss2: 1.266742
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.421651 Loss1: 0.067264 Loss2: 1.354387
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.330387 Loss1: 0.086941 Loss2: 1.243447
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434460 Loss1: 0.084485 Loss2: 1.349975
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.318335 Loss1: 0.079365 Loss2: 1.238970
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.433240 Loss1: 0.082065 Loss2: 1.351175
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.303652 Loss1: 0.068225 Loss2: 1.235427
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.450147 Loss1: 0.097684 Loss2: 1.352463
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.240009 Loss1: 0.018442 Loss2: 1.221567 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.138918 Loss1: 0.308786 Loss2: 1.830132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.431711 Loss1: 0.105255 Loss2: 1.326455 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.402108 Loss1: 0.086940 Loss2: 1.315168
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.161398 Loss1: 0.351477 Loss2: 1.809921
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.520032 Loss1: 0.202539 Loss2: 1.317494 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.503418 Loss1: 0.170987 Loss2: 1.332431 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.470799 Loss1: 0.134750 Loss2: 1.336048 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.452682 Loss1: 0.136650 Loss2: 1.316032 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471217 Loss1: 0.146952 Loss2: 1.324265 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392251 Loss1: 0.075189 Loss2: 1.317062
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.425555 Loss1: 0.104092 Loss2: 1.321463
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396052 Loss1: 0.076285 Loss2: 1.319768
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.358477 Loss1: 0.045294 Loss2: 1.313183
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369067 Loss1: 0.063979 Loss2: 1.305087
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.330387 Loss1: 0.379572 Loss2: 1.950815
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.707168 Loss1: 0.284439 Loss2: 1.422729
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610157 Loss1: 0.153145 Loss2: 1.457012
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.535393 Loss1: 0.112268 Loss2: 1.423125
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.213117 Loss1: 0.334228 Loss2: 1.878889
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.628946 Loss1: 0.263866 Loss2: 1.365080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.698390 Loss1: 0.281466 Loss2: 1.416924 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.516861 Loss1: 0.136632 Loss2: 1.380229 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.503371 Loss1: 0.130034 Loss2: 1.373337 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487973 Loss1: 0.112717 Loss2: 1.375256 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486344 Loss1: 0.119423 Loss2: 1.366921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.437901 Loss1: 0.071398 Loss2: 1.366503 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.198570 Loss1: 0.359935 Loss2: 1.838635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568944 Loss1: 0.184882 Loss2: 1.384062 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.269235 Loss1: 0.395614 Loss2: 1.873621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.724753 Loss1: 0.352092 Loss2: 1.372662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.639316 Loss1: 0.213309 Loss2: 1.426006 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.512136 Loss1: 0.125917 Loss2: 1.386219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.561918 Loss1: 0.189220 Loss2: 1.372698 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511473 Loss1: 0.128603 Loss2: 1.382870 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428306 Loss1: 0.062175 Loss2: 1.366131 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.373978 Loss1: 0.022229 Loss2: 1.351749 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.703999 Loss1: 0.304315 Loss2: 1.399684 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557402 Loss1: 0.155565 Loss2: 1.401837 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.494012 Loss1: 0.087797 Loss2: 1.406215 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.465889 Loss1: 0.075409 Loss2: 1.390481 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448652 Loss1: 0.068080 Loss2: 1.380572 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461084 Loss1: 0.075528 Loss2: 1.385556 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445782 Loss1: 0.068392 Loss2: 1.377390 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421038 Loss1: 0.045431 Loss2: 1.375607 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370482 Loss1: 0.054715 Loss2: 1.315768 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.175716 Loss1: 0.383022 Loss2: 1.792694 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.535337 Loss1: 0.190995 Loss2: 1.344342 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491951 Loss1: 0.158986 Loss2: 1.332965
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.143165 Loss1: 0.301797 Loss2: 1.841368
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.533780 Loss1: 0.195968 Loss2: 1.337812 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.526453 Loss1: 0.184475 Loss2: 1.341978 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.460845 Loss1: 0.115605 Loss2: 1.345240 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.476232 Loss1: 0.146096 Loss2: 1.330137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482287 Loss1: 0.149118 Loss2: 1.333170 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422143 Loss1: 0.105497 Loss2: 1.316646
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433238 Loss1: 0.090556 Loss2: 1.342682
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.401143 Loss1: 0.076918 Loss2: 1.324225
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400209 Loss1: 0.076223 Loss2: 1.323986
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390056 Loss1: 0.064002 Loss2: 1.326054
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.244400 Loss1: 0.399811 Loss2: 1.844588
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.585703 Loss1: 0.255155 Loss2: 1.330548
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.502935 Loss1: 0.160831 Loss2: 1.342105
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.431974 Loss1: 0.103932 Loss2: 1.328042
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.217231 Loss1: 0.381597 Loss2: 1.835634
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.580776 Loss1: 0.222858 Loss2: 1.357917 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.533698 Loss1: 0.161795 Loss2: 1.371903 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492306 Loss1: 0.127146 Loss2: 1.365160 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.439679 Loss1: 0.101292 Loss2: 1.338387 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.406828 Loss1: 0.068196 Loss2: 1.338632 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.410038 Loss1: 0.083598 Loss2: 1.326440 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.405398 Loss1: 0.075055 Loss2: 1.330343 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.276557 Loss1: 0.430427 Loss2: 1.846130 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551919 Loss1: 0.229730 Loss2: 1.322189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.474525 Loss1: 0.162040 Loss2: 1.312485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461092 Loss1: 0.151548 Loss2: 1.309545 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419824 Loss1: 0.103156 Loss2: 1.316668 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.381625 Loss1: 0.062952 Loss2: 1.318673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.334678 Loss1: 0.029643 Loss2: 1.305035 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365271 Loss1: 0.071447 Loss2: 1.293824 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989183
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.379213 Loss1: 0.076534 Loss2: 1.302678 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.357316 Loss1: 0.059324 Loss2: 1.297992 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.349680 Loss1: 0.053252 Loss2: 1.296428
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.256889 Loss1: 0.366532 Loss2: 1.890357
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.703115 Loss1: 0.320495 Loss2: 1.382620
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.633349 Loss1: 0.198855 Loss2: 1.434494
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574695 Loss1: 0.180318 Loss2: 1.394377
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506621 Loss1: 0.119854 Loss2: 1.386768
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.217705 Loss1: 0.351349 Loss2: 1.866356
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524290 Loss1: 0.133438 Loss2: 1.390852
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.619299 Loss1: 0.258255 Loss2: 1.361044
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459593 Loss1: 0.080055 Loss2: 1.379537
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.533035 Loss1: 0.146702 Loss2: 1.386333
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435243 Loss1: 0.059049 Loss2: 1.376194
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478101 Loss1: 0.108574 Loss2: 1.369526
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.432704 Loss1: 0.059258 Loss2: 1.373446
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.511213 Loss1: 0.148122 Loss2: 1.363092
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.430556 Loss1: 0.061065 Loss2: 1.369491
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435568 Loss1: 0.077507 Loss2: 1.358061 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397409 Loss1: 0.049296 Loss2: 1.348113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388841 Loss1: 0.046085 Loss2: 1.342756
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.140243 Loss1: 0.275843 Loss2: 1.864400
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.597448 Loss1: 0.217645 Loss2: 1.379803
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.541485 Loss1: 0.144166 Loss2: 1.397319
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.427192 Loss1: 0.056717 Loss2: 1.370475
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.404634 Loss1: 0.044658 Loss2: 1.359976
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.129741 Loss1: 0.336576 Loss2: 1.793165
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.408450 Loss1: 0.053413 Loss2: 1.355037
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.412063 Loss1: 0.057569 Loss2: 1.354494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404506 Loss1: 0.052355 Loss2: 1.352151 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435761 Loss1: 0.083797 Loss2: 1.351964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412910 Loss1: 0.051226 Loss2: 1.361684 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.373354 Loss1: 0.079592 Loss2: 1.293762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354481 Loss1: 0.059650 Loss2: 1.294831 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.355232 Loss1: 0.064224 Loss2: 1.291008
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.279744 Loss1: 0.412953 Loss2: 1.866791
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.591357 Loss1: 0.235902 Loss2: 1.355455
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.539415 Loss1: 0.150973 Loss2: 1.388442
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520964 Loss1: 0.153256 Loss2: 1.367708
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.497578 Loss1: 0.123603 Loss2: 1.373975
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.211916 Loss1: 0.360487 Loss2: 1.851429
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.556370 Loss1: 0.210654 Loss2: 1.345716 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.462136 Loss1: 0.114661 Loss2: 1.347475 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.444256 Loss1: 0.101924 Loss2: 1.342333 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.419639 Loss1: 0.085319 Loss2: 1.334320 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387944 Loss1: 0.046581 Loss2: 1.341364
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.431390 Loss1: 0.099284 Loss2: 1.332106
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412998 Loss1: 0.081365 Loss2: 1.331632
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.424965 Loss1: 0.092873 Loss2: 1.332092
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.379962 Loss1: 0.049153 Loss2: 1.330809
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369364 Loss1: 0.044640 Loss2: 1.324724
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.231429 Loss1: 0.358948 Loss2: 1.872481
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.650256 Loss1: 0.275802 Loss2: 1.374454
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.565841 Loss1: 0.149347 Loss2: 1.416493
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.549424 Loss1: 0.160685 Loss2: 1.388739
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518914 Loss1: 0.128818 Loss2: 1.390096
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.251068 Loss1: 0.357926 Loss2: 1.893142
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.593080 Loss1: 0.205211 Loss2: 1.387868 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.524710 Loss1: 0.129816 Loss2: 1.394894 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.470220 Loss1: 0.086913 Loss2: 1.383308 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.467302 Loss1: 0.093648 Loss2: 1.373655 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.454798 Loss1: 0.081567 Loss2: 1.373231 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.469068 Loss1: 0.099218 Loss2: 1.369851 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.473549 Loss1: 0.097950 Loss2: 1.375599 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.535012 Loss1: 0.166225 Loss2: 1.368787 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498466 Loss1: 0.124309 Loss2: 1.374157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462975 Loss1: 0.101435 Loss2: 1.361541
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.158273 Loss1: 0.274764 Loss2: 1.883510
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.651476 Loss1: 0.236916 Loss2: 1.414560 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.601503 Loss1: 0.143854 Loss2: 1.457649 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.522789 Loss1: 0.114478 Loss2: 1.408311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.542439 Loss1: 0.130648 Loss2: 1.411792 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501795 Loss1: 0.083665 Loss2: 1.418130 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.478897 Loss1: 0.062581 Loss2: 1.416316 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.193048 Loss1: 0.326040 Loss2: 1.867008 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.597514 Loss1: 0.220668 Loss2: 1.376847 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448086 Loss1: 0.092967 Loss2: 1.355119 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.379632 Loss1: 0.424251 Loss2: 1.955381
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.424808 Loss1: 0.075411 Loss2: 1.349396
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.401546 Loss1: 0.058548 Loss2: 1.342998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.376121 Loss1: 0.042554 Loss2: 1.333567 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.365782 Loss1: 0.032785 Loss2: 1.332997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363344 Loss1: 0.036144 Loss2: 1.327200 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441244 Loss1: 0.100797 Loss2: 1.340446 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380232 Loss1: 0.042582 Loss2: 1.337650 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.584167 Loss1: 0.232319 Loss2: 1.351848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.558159 Loss1: 0.173123 Loss2: 1.385036 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.478876 Loss1: 0.131242 Loss2: 1.347633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.418553 Loss1: 0.072641 Loss2: 1.345912 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.392424 Loss1: 0.052119 Loss2: 1.340305 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.373852 Loss1: 0.043418 Loss2: 1.330434 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.377962 Loss1: 0.049757 Loss2: 1.328204 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384111 Loss1: 0.062183 Loss2: 1.321929 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.493847 Loss1: 0.109825 Loss2: 1.384022 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.474732 Loss1: 0.080850 Loss2: 1.393882 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.508232 Loss1: 0.165993 Loss2: 1.342239 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.451416 Loss1: 0.099063 Loss2: 1.352353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.453862 Loss1: 0.126396 Loss2: 1.327466 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505568 Loss1: 0.148407 Loss2: 1.357160 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.450495 Loss1: 0.097209 Loss2: 1.353286 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.425081 Loss1: 0.087573 Loss2: 1.337508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403285 Loss1: 0.067936 Loss2: 1.335349 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392166 Loss1: 0.057024 Loss2: 1.335142 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.378274 Loss1: 0.087512 Loss2: 1.290762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374774 Loss1: 0.082774 Loss2: 1.292000 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.574472 Loss1: 0.266786 Loss2: 1.307686 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467804 Loss1: 0.131907 Loss2: 1.335896 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.446590 Loss1: 0.137646 Loss2: 1.308944
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.220066 Loss1: 0.384029 Loss2: 1.836037
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468706 Loss1: 0.159586 Loss2: 1.309120
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.568872 Loss1: 0.222107 Loss2: 1.346765
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.413361 Loss1: 0.094648 Loss2: 1.318713
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.500599 Loss1: 0.134300 Loss2: 1.366298
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.476161 Loss1: 0.126389 Loss2: 1.349772 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.437473 Loss1: 0.091761 Loss2: 1.345712 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429853 Loss1: 0.094724 Loss2: 1.335129 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.372604 Loss1: 0.043830 Loss2: 1.328774 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366159 Loss1: 0.045901 Loss2: 1.320258 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.623125 Loss1: 0.204542 Loss2: 1.418583 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.546132 Loss1: 0.113318 Loss2: 1.432814 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.531776 Loss1: 0.115290 Loss2: 1.416485
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.068692 Loss1: 0.238348 Loss2: 1.830345
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.495301 Loss1: 0.083685 Loss2: 1.411617
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.616925 Loss1: 0.255846 Loss2: 1.361079
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473721 Loss1: 0.065601 Loss2: 1.408120
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.569713 Loss1: 0.167165 Loss2: 1.402548
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459093 Loss1: 0.059040 Loss2: 1.400053
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521689 Loss1: 0.142682 Loss2: 1.379007
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445268 Loss1: 0.045338 Loss2: 1.399930
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.531373 Loss1: 0.154492 Loss2: 1.376881
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426564 Loss1: 0.028228 Loss2: 1.398336
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.543097 Loss1: 0.154924 Loss2: 1.388173
-(DefaultActor pid=3765) >> Training accuracy: 0.998047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.518293 Loss1: 0.137240 Loss2: 1.381052
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.516095 Loss1: 0.132168 Loss2: 1.383927
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435118 Loss1: 0.052686 Loss2: 1.382432
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.408608 Loss1: 0.040593 Loss2: 1.368016
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.439710 Loss1: 0.416035 Loss2: 2.023675
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.736830 Loss1: 0.266584 Loss2: 1.470246
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.625664 Loss1: 0.123439 Loss2: 1.502224
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574517 Loss1: 0.102008 Loss2: 1.472510
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.559729 Loss1: 0.097414 Loss2: 1.462315
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.089681 Loss1: 0.268256 Loss2: 1.821425
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.508524 Loss1: 0.160428 Loss2: 1.348096 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.453778 Loss1: 0.105676 Loss2: 1.348102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.411437 Loss1: 0.069042 Loss2: 1.342395 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.396845 Loss1: 0.065416 Loss2: 1.331430 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.393801 Loss1: 0.062536 Loss2: 1.331265 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.390331 Loss1: 0.057975 Loss2: 1.332356 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362117 Loss1: 0.031217 Loss2: 1.330900
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.063865 Loss1: 0.253210 Loss2: 1.810655
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.498678 Loss1: 0.151063 Loss2: 1.347615
-DEBUG flwr 2023-10-13 15:36:02,122 | server.py:236 | fit_round 193 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.505347 Loss1: 0.154257 Loss2: 1.351090
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467145 Loss1: 0.116497 Loss2: 1.350648
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.428050 Loss1: 0.087832 Loss2: 1.340218
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.173857 Loss1: 0.324898 Loss2: 1.848959
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.562091 Loss1: 0.193995 Loss2: 1.368096 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.411844 Loss1: 0.077290 Loss2: 1.334554
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.492848 Loss1: 0.128644 Loss2: 1.364204
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.379836 Loss1: 0.043629 Loss2: 1.336207
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.479274 Loss1: 0.113472 Loss2: 1.365802
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402068 Loss1: 0.066200 Loss2: 1.335868
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496720 Loss1: 0.145635 Loss2: 1.351085
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.400707 Loss1: 0.069522 Loss2: 1.331185
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531660 Loss1: 0.165573 Loss2: 1.366086
-(DefaultActor pid=3765) >> Training accuracy: 0.971507
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.517980 Loss1: 0.144434 Loss2: 1.373547
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.473754 Loss1: 0.102046 Loss2: 1.371708
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.450007 Loss1: 0.091853 Loss2: 1.358154
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.427094 Loss1: 0.068896 Loss2: 1.358198
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.447594 Loss1: 0.448816 Loss2: 1.998778
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.606925 Loss1: 0.244682 Loss2: 1.362243
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.560957 Loss1: 0.187554 Loss2: 1.373403
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.555148 Loss1: 0.160946 Loss2: 1.394202
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.503227 Loss1: 0.126463 Loss2: 1.376764
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.485387 Loss1: 0.110118 Loss2: 1.375269
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.254633 Loss1: 0.372678 Loss2: 1.881956
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.649577 Loss1: 0.283946 Loss2: 1.365631 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399843 Loss1: 0.043773 Loss2: 1.356070 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997396
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409700 Loss1: 0.054762 Loss2: 1.354938 [repeated 2x across cluster]
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.506213 Loss1: 0.124721 Loss2: 1.381492 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.429411 Loss1: 0.066778 Loss2: 1.362632 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435028 Loss1: 0.087674 Loss2: 1.347353 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 15:36:02,122][flwr][DEBUG] - fit_round 193 received 50 results and 0 failures
-INFO flwr 2023-10-13 15:36:43,700 | server.py:125 | fit progress: (193, 2.32618732669483, {'accuracy': 0.6109}, 445511.478674337)
->> Test accuracy: 0.610900
-[2023-10-13 15:36:43,700][flwr][INFO] - fit progress: (193, 2.32618732669483, {'accuracy': 0.6109}, 445511.478674337)
-DEBUG flwr 2023-10-13 15:36:43,700 | server.py:173 | evaluate_round 193: strategy sampled 50 clients (out of 50)
-[2023-10-13 15:36:43,700][flwr][DEBUG] - evaluate_round 193: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 15:45:49,652 | server.py:187 | evaluate_round 193 received 50 results and 0 failures
-[2023-10-13 15:45:49,652][flwr][DEBUG] - evaluate_round 193 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 15:45:49,652 | server.py:222 | fit_round 194: strategy sampled 50 clients (out of 50)
-[2023-10-13 15:45:49,652][flwr][DEBUG] - fit_round 194: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.244026 Loss1: 0.376958 Loss2: 1.867067
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.594611 Loss1: 0.247402 Loss2: 1.347210
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.562384 Loss1: 0.204155 Loss2: 1.358230
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.509130 Loss1: 0.158645 Loss2: 1.350485
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.158961 Loss1: 0.305088 Loss2: 1.853873
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487935 Loss1: 0.145378 Loss2: 1.342557
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.521548 Loss1: 0.154454 Loss2: 1.367094
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.421943 Loss1: 0.089673 Loss2: 1.332271
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.547685 Loss1: 0.184445 Loss2: 1.363240
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.426379 Loss1: 0.099295 Loss2: 1.327084
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.526693 Loss1: 0.153573 Loss2: 1.373120
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.399957 Loss1: 0.070327 Loss2: 1.329630
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.485477 Loss1: 0.134801 Loss2: 1.350676
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.384173 Loss1: 0.066791 Loss2: 1.317381
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.437275 Loss1: 0.086080 Loss2: 1.351195
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355775 Loss1: 0.040199 Loss2: 1.315576
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.424664 Loss1: 0.076557 Loss2: 1.348107
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.433217 Loss1: 0.089697 Loss2: 1.343520
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.431174 Loss1: 0.073113 Loss2: 1.358061
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405260 Loss1: 0.061962 Loss2: 1.343298
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.011167 Loss1: 0.266041 Loss2: 1.745125
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.518031 Loss1: 0.208448 Loss2: 1.309583
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.444534 Loss1: 0.118918 Loss2: 1.325616
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.139644 Loss1: 0.308667 Loss2: 1.830976
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.390465 Loss1: 0.081411 Loss2: 1.309054
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.554395 Loss1: 0.228992 Loss2: 1.325404
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.370702 Loss1: 0.073616 Loss2: 1.297086
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.543825 Loss1: 0.201838 Loss2: 1.341987
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452444 Loss1: 0.146624 Loss2: 1.305821
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.506449 Loss1: 0.164280 Loss2: 1.342170
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.415559 Loss1: 0.106350 Loss2: 1.309209
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.388322 Loss1: 0.082213 Loss2: 1.306109 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388311 Loss1: 0.078126 Loss2: 1.310184 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387631 Loss1: 0.082451 Loss2: 1.305180 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.385663 Loss1: 0.079617 Loss2: 1.306046 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.342749 Loss1: 0.456357 Loss2: 1.886392 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.507048 Loss1: 0.166813 Loss2: 1.340235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.453397 Loss1: 0.118513 Loss2: 1.334884 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.430357 Loss1: 0.099362 Loss2: 1.330995 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.400332 Loss1: 0.071762 Loss2: 1.328569 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.404643 Loss1: 0.082376 Loss2: 1.322267 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.389727 Loss1: 0.071178 Loss2: 1.318549 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.341173 Loss1: 0.027128 Loss2: 1.314045 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995192
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.353571 Loss1: 0.053533 Loss2: 1.300037 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.332986 Loss1: 0.035569 Loss2: 1.297417 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.331224 Loss1: 0.038570 Loss2: 1.292655
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.216791 Loss1: 0.351785 Loss2: 1.865006
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.606684 Loss1: 0.248259 Loss2: 1.358424
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.507091 Loss1: 0.118728 Loss2: 1.388363
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.432117 Loss1: 0.086137 Loss2: 1.345980
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.427064 Loss1: 0.087383 Loss2: 1.339681
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.134957 Loss1: 0.289717 Loss2: 1.845240
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.510235 Loss1: 0.173806 Loss2: 1.336429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.517719 Loss1: 0.188659 Loss2: 1.329060 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.473345 Loss1: 0.128839 Loss2: 1.344506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.445506 Loss1: 0.122595 Loss2: 1.322912 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.402510 Loss1: 0.080848 Loss2: 1.321663 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.369793 Loss1: 0.054757 Loss2: 1.315036 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.342565 Loss1: 0.030863 Loss2: 1.311702 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.545012 Loss1: 0.220796 Loss2: 1.324216 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.526797 Loss1: 0.173449 Loss2: 1.353347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.043288 Loss1: 0.258190 Loss2: 1.785098 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.521938 Loss1: 0.189318 Loss2: 1.332620 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474756 Loss1: 0.134090 Loss2: 1.340666 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414489 Loss1: 0.075775 Loss2: 1.338714 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.442291 Loss1: 0.103673 Loss2: 1.338618 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474845 Loss1: 0.141732 Loss2: 1.333113 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436511 Loss1: 0.091591 Loss2: 1.344920
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.144582 Loss1: 0.269333 Loss2: 1.875248
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.599057 Loss1: 0.191339 Loss2: 1.407718 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414442 Loss1: 0.082454 Loss2: 1.331988
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.541700 Loss1: 0.124602 Loss2: 1.417098
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.539553 Loss1: 0.130084 Loss2: 1.409468
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518239 Loss1: 0.116136 Loss2: 1.402103
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498042 Loss1: 0.091653 Loss2: 1.406389
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468848 Loss1: 0.072981 Loss2: 1.395866
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.285711 Loss1: 0.379269 Loss2: 1.906442
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.574020 Loss1: 0.207654 Loss2: 1.366366
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.440912 Loss1: 0.050339 Loss2: 1.390573
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.541899 Loss1: 0.156681 Loss2: 1.385218
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.444497 Loss1: 0.059171 Loss2: 1.385326
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434447 Loss1: 0.045492 Loss2: 1.388955 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991728
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471574 Loss1: 0.100164 Loss2: 1.371410 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.447734 Loss1: 0.084550 Loss2: 1.363184 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441199 Loss1: 0.078991 Loss2: 1.362208
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.229066 Loss1: 0.357839 Loss2: 1.871228
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.595023 Loss1: 0.251242 Loss2: 1.343781 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.470026 Loss1: 0.116540 Loss2: 1.353485 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.467329 Loss1: 0.111380 Loss2: 1.355950 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.442182 Loss1: 0.088955 Loss2: 1.353227 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448134 Loss1: 0.096800 Loss2: 1.351334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.473663 Loss1: 0.145107 Loss2: 1.328556
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400588 Loss1: 0.049102 Loss2: 1.351486
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.474782 Loss1: 0.144591 Loss2: 1.330191
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403302 Loss1: 0.060796 Loss2: 1.342506
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.427993 Loss1: 0.098010 Loss2: 1.329983 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.369749 Loss1: 0.059767 Loss2: 1.309982 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.085819 Loss1: 0.258905 Loss2: 1.826914
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.344496 Loss1: 0.041595 Loss2: 1.302901
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.550107 Loss1: 0.231727 Loss2: 1.318380
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.347123 Loss1: 0.049190 Loss2: 1.297934
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.519418 Loss1: 0.181659 Loss2: 1.337759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457370 Loss1: 0.120595 Loss2: 1.336775 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.495090 Loss1: 0.159591 Loss2: 1.335499
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.162654 Loss1: 0.305638 Loss2: 1.857016
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.409616 Loss1: 0.071358 Loss2: 1.338257
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.610179 Loss1: 0.246094 Loss2: 1.364085
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.426605 Loss1: 0.096559 Loss2: 1.330046
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.541266 Loss1: 0.162941 Loss2: 1.378325
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393149 Loss1: 0.058602 Loss2: 1.334548
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557490 Loss1: 0.187888 Loss2: 1.369602
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.518372 Loss1: 0.150483 Loss2: 1.367889
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.539966 Loss1: 0.173879 Loss2: 1.366087
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.477191 Loss1: 0.110179 Loss2: 1.367012
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.515894 Loss1: 0.151583 Loss2: 1.364311
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.234163 Loss1: 0.393616 Loss2: 1.840546
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.490033 Loss1: 0.123007 Loss2: 1.367026
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.454370 Loss1: 0.092844 Loss2: 1.361526
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.592604 Loss1: 0.243860 Loss2: 1.348744
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.643052 Loss1: 0.261342 Loss2: 1.381710
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.573922 Loss1: 0.211048 Loss2: 1.362875
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.568269 Loss1: 0.190925 Loss2: 1.377344
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.528602 Loss1: 0.168610 Loss2: 1.359993
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479240 Loss1: 0.118063 Loss2: 1.361177
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.163139 Loss1: 0.374134 Loss2: 1.789005
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.460502 Loss1: 0.110730 Loss2: 1.349772
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.531623 Loss1: 0.230202 Loss2: 1.301421
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441804 Loss1: 0.095033 Loss2: 1.346770
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.496320 Loss1: 0.176920 Loss2: 1.319400
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.403738 Loss1: 0.065178 Loss2: 1.338560
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.422312 Loss1: 0.110379 Loss2: 1.311933
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.426884 Loss1: 0.130292 Loss2: 1.296592
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.387732 Loss1: 0.086438 Loss2: 1.301294
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392407 Loss1: 0.097267 Loss2: 1.295140
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.401018 Loss1: 0.094691 Loss2: 1.306327
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.376707 Loss1: 0.072918 Loss2: 1.303789
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.051973 Loss1: 0.280261 Loss2: 1.771712
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379446 Loss1: 0.083946 Loss2: 1.295499
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.449478 Loss1: 0.140032 Loss2: 1.309446
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.376048 Loss1: 0.072915 Loss2: 1.303133
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.370486 Loss1: 0.077535 Loss2: 1.292952
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.356732 Loss1: 0.073447 Loss2: 1.283285
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.345889 Loss1: 0.061986 Loss2: 1.283904
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.209142 Loss1: 0.341002 Loss2: 1.868140
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.328283 Loss1: 0.045240 Loss2: 1.283043
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.302555 Loss1: 0.026812 Loss2: 1.275742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.301131 Loss1: 0.030212 Loss2: 1.270919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.280892 Loss1: 0.012628 Loss2: 1.268264 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.999023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468490 Loss1: 0.102997 Loss2: 1.365493 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.460103 Loss1: 0.096177 Loss2: 1.363926 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.204091 Loss1: 0.376940 Loss2: 1.827150 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.502009 Loss1: 0.165716 Loss2: 1.336293 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.413640 Loss1: 0.087414 Loss2: 1.326226 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.382653 Loss1: 0.060395 Loss2: 1.322258
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.223299 Loss1: 0.363808 Loss2: 1.859491
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.675753 Loss1: 0.300834 Loss2: 1.374920 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.654428 Loss1: 0.239955 Loss2: 1.414473 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594068 Loss1: 0.189891 Loss2: 1.404177 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364301 Loss1: 0.059892 Loss2: 1.304408
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508476 Loss1: 0.116313 Loss2: 1.392162
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.507249 Loss1: 0.120151 Loss2: 1.387098
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.454176 Loss1: 0.075474 Loss2: 1.378702
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.450370 Loss1: 0.071792 Loss2: 1.378578
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433351 Loss1: 0.060035 Loss2: 1.373316
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.260871 Loss1: 0.438907 Loss2: 1.821964
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.445898 Loss1: 0.074209 Loss2: 1.371689
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591006 Loss1: 0.216134 Loss2: 1.374872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.492507 Loss1: 0.144391 Loss2: 1.348116 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437149 Loss1: 0.098881 Loss2: 1.338268
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.403648 Loss1: 0.413947 Loss2: 1.989702
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395364 Loss1: 0.069148 Loss2: 1.326216
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.708418 Loss1: 0.271962 Loss2: 1.436456
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382154 Loss1: 0.058020 Loss2: 1.324135
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.635555 Loss1: 0.185230 Loss2: 1.450325
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.660324 Loss1: 0.200867 Loss2: 1.459457
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372372 Loss1: 0.055206 Loss2: 1.317165
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.578938 Loss1: 0.151413 Loss2: 1.427525
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359721 Loss1: 0.045739 Loss2: 1.313982
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.533040 Loss1: 0.101690 Loss2: 1.431350 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.484648 Loss1: 0.071193 Loss2: 1.413454 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995536
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.450371 Loss1: 0.038516 Loss2: 1.411855
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.202631 Loss1: 0.312097 Loss2: 1.890535
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.616221 Loss1: 0.226754 Loss2: 1.389467
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.620720 Loss1: 0.217176 Loss2: 1.403545
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.530486 Loss1: 0.116893 Loss2: 1.413593
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527054 Loss1: 0.130148 Loss2: 1.396906
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.146094 Loss1: 0.306598 Loss2: 1.839497
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.569918 Loss1: 0.229342 Loss2: 1.340576 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.548487 Loss1: 0.193287 Loss2: 1.355200 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.528784 Loss1: 0.166771 Loss2: 1.362014 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.483348 Loss1: 0.131961 Loss2: 1.351387 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439389 Loss1: 0.095174 Loss2: 1.344215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399288 Loss1: 0.062488 Loss2: 1.336800 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406191 Loss1: 0.070650 Loss2: 1.335541 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.593192 Loss1: 0.226730 Loss2: 1.366462 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491093 Loss1: 0.110611 Loss2: 1.380482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468345 Loss1: 0.104049 Loss2: 1.364295
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.261914 Loss1: 0.377911 Loss2: 1.884003
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.695821 Loss1: 0.303601 Loss2: 1.392221 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.643035 Loss1: 0.221480 Loss2: 1.421555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.610244 Loss1: 0.208091 Loss2: 1.402153 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515854 Loss1: 0.119794 Loss2: 1.396061 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.431234 Loss1: 0.084081 Loss2: 1.347153
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.463817 Loss1: 0.080826 Loss2: 1.382991
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437800 Loss1: 0.059127 Loss2: 1.378672
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.402904 Loss1: 0.033913 Loss2: 1.368992
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396831 Loss1: 0.032099 Loss2: 1.364733
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399267 Loss1: 0.039322 Loss2: 1.359945
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.243961 Loss1: 0.338129 Loss2: 1.905833
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.630924 Loss1: 0.277862 Loss2: 1.353061
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.549921 Loss1: 0.167735 Loss2: 1.382186
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.525134 Loss1: 0.142899 Loss2: 1.382235
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480689 Loss1: 0.119792 Loss2: 1.360896
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.171193 Loss1: 0.320518 Loss2: 1.850675
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451789 Loss1: 0.085988 Loss2: 1.365801
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463965 Loss1: 0.099776 Loss2: 1.364189 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.492118 Loss1: 0.111005 Loss2: 1.381113
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.461730 Loss1: 0.103311 Loss2: 1.358418
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.447861 Loss1: 0.079526 Loss2: 1.368335
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.453967 Loss1: 0.091879 Loss2: 1.362088
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410337 Loss1: 0.057563 Loss2: 1.352774
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.432733 Loss1: 0.080339 Loss2: 1.352394
-(DefaultActor pid=3765) >> Training accuracy: 0.995536
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472615 Loss1: 0.117259 Loss2: 1.355356
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.476815 Loss1: 0.119308 Loss2: 1.357506
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.483471 Loss1: 0.118949 Loss2: 1.364522
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.502699 Loss1: 0.132408 Loss2: 1.370291
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.459694 Loss1: 0.093557 Loss2: 1.366136
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.262512 Loss1: 0.386270 Loss2: 1.876242
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.590367 Loss1: 0.210497 Loss2: 1.379870
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.593470 Loss1: 0.173483 Loss2: 1.419987
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.598422 Loss1: 0.202825 Loss2: 1.395597
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.610202 Loss1: 0.217215 Loss2: 1.392987
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.099774 Loss1: 0.319147 Loss2: 1.780627
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.546388 Loss1: 0.141743 Loss2: 1.404645
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.473939 Loss1: 0.082799 Loss2: 1.391141
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.582206 Loss1: 0.261383 Loss2: 1.320823
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.455017 Loss1: 0.076108 Loss2: 1.378909
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.575373 Loss1: 0.194216 Loss2: 1.381157
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431946 Loss1: 0.058374 Loss2: 1.373572
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.583563 Loss1: 0.249641 Loss2: 1.333922
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.434614 Loss1: 0.061790 Loss2: 1.372824
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.569981 Loss1: 0.200107 Loss2: 1.369874
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.501889 Loss1: 0.160321 Loss2: 1.341568
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.478051 Loss1: 0.139461 Loss2: 1.338590
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484362 Loss1: 0.140066 Loss2: 1.344295
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430027 Loss1: 0.094861 Loss2: 1.335166
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.221979 Loss1: 0.361281 Loss2: 1.860699
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.551918 Loss1: 0.191857 Loss2: 1.360061 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.485359 Loss1: 0.128671 Loss2: 1.356688 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433707 Loss1: 0.094289 Loss2: 1.339419 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.413594 Loss1: 0.079184 Loss2: 1.334410 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.392096 Loss1: 0.057528 Loss2: 1.334568 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.413381 Loss1: 0.081807 Loss2: 1.331574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401840 Loss1: 0.073982 Loss2: 1.327859 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476776 Loss1: 0.131371 Loss2: 1.345405 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409269 Loss1: 0.077024 Loss2: 1.332245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.392260 Loss1: 0.064088 Loss2: 1.328172
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.175629 Loss1: 0.321598 Loss2: 1.854031
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.583689 Loss1: 0.197741 Loss2: 1.385948 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.473015 Loss1: 0.082779 Loss2: 1.390235 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.447997 Loss1: 0.071480 Loss2: 1.376517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.441617 Loss1: 0.067607 Loss2: 1.374009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444098 Loss1: 0.069042 Loss2: 1.375056 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.513004 Loss1: 0.121822 Loss2: 1.391181 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.472924 Loss1: 0.094975 Loss2: 1.377948 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.441431 Loss1: 0.076412 Loss2: 1.365020 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.413139 Loss1: 0.046041 Loss2: 1.367097 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.437050 Loss1: 0.075302 Loss2: 1.361748
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.231619 Loss1: 0.347237 Loss2: 1.884382
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.660598 Loss1: 0.239650 Loss2: 1.420948
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.583236 Loss1: 0.140011 Loss2: 1.443225
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551314 Loss1: 0.144875 Loss2: 1.406439
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.520931 Loss1: 0.118759 Loss2: 1.402172
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.200129 Loss1: 0.381742 Loss2: 1.818387
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.460526 Loss1: 0.061577 Loss2: 1.398949
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447081 Loss1: 0.058542 Loss2: 1.388540 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453136 Loss1: 0.070478 Loss2: 1.382658 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.439232 Loss1: 0.051730 Loss2: 1.387502 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.386881 Loss1: 0.081787 Loss2: 1.305094 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.373926 Loss1: 0.078354 Loss2: 1.295572 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356774 Loss1: 0.067459 Loss2: 1.289315 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991071
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.033755 Loss1: 0.286523 Loss2: 1.747233
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.522821 Loss1: 0.211781 Loss2: 1.311040
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.464248 Loss1: 0.140641 Loss2: 1.323608
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.443204 Loss1: 0.124944 Loss2: 1.318260
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.086520 Loss1: 0.288289 Loss2: 1.798231
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.584844 Loss1: 0.234879 Loss2: 1.349965 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.533818 Loss1: 0.172427 Loss2: 1.361392 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.494401 Loss1: 0.141086 Loss2: 1.353315 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.447509 Loss1: 0.106391 Loss2: 1.341118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.411398 Loss1: 0.074627 Loss2: 1.336772 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.445424 Loss1: 0.117969 Loss2: 1.327454
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.400231 Loss1: 0.071524 Loss2: 1.328707
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.394023 Loss1: 0.066577 Loss2: 1.327446
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.369194 Loss1: 0.038511 Loss2: 1.330683
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352956 Loss1: 0.038758 Loss2: 1.314198
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.602672 Loss1: 0.274709 Loss2: 1.327962 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.443853 Loss1: 0.121909 Loss2: 1.321944 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.428700 Loss1: 0.109830 Loss2: 1.318870 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.411033 Loss1: 0.086056 Loss2: 1.324977 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446441 Loss1: 0.132132 Loss2: 1.314309 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.415366 Loss1: 0.101640 Loss2: 1.313726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.382666 Loss1: 0.067461 Loss2: 1.315205 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426150 Loss1: 0.108770 Loss2: 1.317380 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.492844 Loss1: 0.101158 Loss2: 1.391686 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.459904 Loss1: 0.071895 Loss2: 1.388009 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.156988 Loss1: 0.331212 Loss2: 1.825776
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572002 Loss1: 0.242287 Loss2: 1.329715
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.586779 Loss1: 0.231338 Loss2: 1.355441
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.495771 Loss1: 0.152136 Loss2: 1.343635
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.219267 Loss1: 0.370963 Loss2: 1.848304
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.572798 Loss1: 0.230255 Loss2: 1.342543 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.503726 Loss1: 0.148199 Loss2: 1.355527 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.443319 Loss1: 0.107938 Loss2: 1.335381 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.424302 Loss1: 0.089244 Loss2: 1.335058 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421603 Loss1: 0.088231 Loss2: 1.333372 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412314 Loss1: 0.082659 Loss2: 1.329655 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.356951 Loss1: 0.034120 Loss2: 1.322832 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.255978 Loss1: 0.391031 Loss2: 1.864947 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.615329 Loss1: 0.201232 Loss2: 1.414097 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.531989 Loss1: 0.153824 Loss2: 1.378165
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.152780 Loss1: 0.315290 Loss2: 1.837490
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.496921 Loss1: 0.161478 Loss2: 1.335443 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.474000 Loss1: 0.142182 Loss2: 1.331817 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.458315 Loss1: 0.116358 Loss2: 1.341958 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 16:14:28,127 | server.py:236 | fit_round 194 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.412951 Loss1: 0.077401 Loss2: 1.335549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.384954 Loss1: 0.055722 Loss2: 1.329232 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.453911 Loss1: 0.091773 Loss2: 1.362138
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.363517 Loss1: 0.043285 Loss2: 1.320232
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.355388 Loss1: 0.042566 Loss2: 1.312822
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362771 Loss1: 0.048798 Loss2: 1.313973
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369475 Loss1: 0.052091 Loss2: 1.317383
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.157513 Loss1: 0.341613 Loss2: 1.815900
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.552231 Loss1: 0.234181 Loss2: 1.318050
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.527813 Loss1: 0.180537 Loss2: 1.347276
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.442560 Loss1: 0.116745 Loss2: 1.325815
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.408301 Loss1: 0.478415 Loss2: 1.929886
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.615394 Loss1: 0.270669 Loss2: 1.344725
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.441386 Loss1: 0.120552 Loss2: 1.320835
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.509958 Loss1: 0.161247 Loss2: 1.348711
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.407261 Loss1: 0.076651 Loss2: 1.330610
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.379586 Loss1: 0.061329 Loss2: 1.318257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.340668 Loss1: 0.030216 Loss2: 1.310451 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.353874 Loss1: 0.047580 Loss2: 1.306294 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356029 Loss1: 0.051429 Loss2: 1.304600 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.332073 Loss1: 0.022949 Loss2: 1.309124 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998798
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.194974 Loss1: 0.337032 Loss2: 1.857942
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.565225 Loss1: 0.207424 Loss2: 1.357801
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.519721 Loss1: 0.152062 Loss2: 1.367659
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536291 Loss1: 0.155777 Loss2: 1.380513
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.231703 Loss1: 0.376848 Loss2: 1.854855
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.676438 Loss1: 0.317096 Loss2: 1.359343 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.663291 Loss1: 0.256066 Loss2: 1.407225 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.547093 Loss1: 0.180206 Loss2: 1.366887 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.554386 Loss1: 0.187725 Loss2: 1.366661 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480433 Loss1: 0.114404 Loss2: 1.366029 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.428616 Loss1: 0.077695 Loss2: 1.350921
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.438994 Loss1: 0.089623 Loss2: 1.349371
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431743 Loss1: 0.086471 Loss2: 1.345272
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.400823 Loss1: 0.065577 Loss2: 1.335247
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395798 Loss1: 0.063393 Loss2: 1.332405
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 16:14:28,127][flwr][DEBUG] - fit_round 194 received 50 results and 0 failures
-INFO flwr 2023-10-13 16:15:08,656 | server.py:125 | fit progress: (194, 2.3175561528998063, {'accuracy': 0.6115}, 447816.43457298796)
->> Test accuracy: 0.611500
-[2023-10-13 16:15:08,656][flwr][INFO] - fit progress: (194, 2.3175561528998063, {'accuracy': 0.6115}, 447816.43457298796)
-DEBUG flwr 2023-10-13 16:15:08,656 | server.py:173 | evaluate_round 194: strategy sampled 50 clients (out of 50)
-[2023-10-13 16:15:08,656][flwr][DEBUG] - evaluate_round 194: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 16:24:14,592 | server.py:187 | evaluate_round 194 received 50 results and 0 failures
-[2023-10-13 16:24:14,592][flwr][DEBUG] - evaluate_round 194 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 16:24:14,592 | server.py:222 | fit_round 195: strategy sampled 50 clients (out of 50)
-[2023-10-13 16:24:14,592][flwr][DEBUG] - fit_round 195: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.106921 Loss1: 0.312252 Loss2: 1.794669
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.523154 Loss1: 0.183749 Loss2: 1.339405
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.482911 Loss1: 0.138998 Loss2: 1.343914
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.443475 Loss1: 0.111075 Loss2: 1.332400
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.060736 Loss1: 0.287928 Loss2: 1.772808
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.447916 Loss1: 0.122205 Loss2: 1.325711
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.473331 Loss1: 0.169611 Loss2: 1.303720
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.395530 Loss1: 0.065063 Loss2: 1.330467
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.418497 Loss1: 0.099905 Loss2: 1.318592
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.371130 Loss1: 0.077519 Loss2: 1.293611 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.351764 Loss1: 0.061755 Loss2: 1.290009 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.333555 Loss1: 0.046524 Loss2: 1.287031 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993164
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.326392 Loss1: 0.043779 Loss2: 1.282613 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.305166 Loss1: 0.032572 Loss2: 1.272593 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.999081
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.303241 Loss1: 0.030287 Loss2: 1.272954
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.324939 Loss1: 0.403273 Loss2: 1.921666
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.728208 Loss1: 0.311342 Loss2: 1.416866
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.678984 Loss1: 0.226379 Loss2: 1.452605
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.628726 Loss1: 0.192491 Loss2: 1.436235
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.521876 Loss1: 0.104197 Loss2: 1.417679
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.169496 Loss1: 0.379678 Loss2: 1.789818
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.512091 Loss1: 0.095865 Loss2: 1.416226
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.601158 Loss1: 0.285935 Loss2: 1.315223
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.516353 Loss1: 0.107812 Loss2: 1.408541
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.569893 Loss1: 0.212479 Loss2: 1.357414
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.469714 Loss1: 0.060148 Loss2: 1.409567
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.449565 Loss1: 0.121159 Loss2: 1.328406
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.461789 Loss1: 0.059374 Loss2: 1.402415
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.456230 Loss1: 0.133180 Loss2: 1.323050
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.438442 Loss1: 0.037960 Loss2: 1.400483
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.388110 Loss1: 0.073258 Loss2: 1.314852
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.367231 Loss1: 0.063965 Loss2: 1.303266
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374976 Loss1: 0.077148 Loss2: 1.297828
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362651 Loss1: 0.063531 Loss2: 1.299120
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348192 Loss1: 0.048864 Loss2: 1.299329
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.077616 Loss1: 0.260527 Loss2: 1.817089
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.522168 Loss1: 0.165367 Loss2: 1.356801
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.504817 Loss1: 0.136892 Loss2: 1.367925
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.466885 Loss1: 0.108292 Loss2: 1.358593
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.091103 Loss1: 0.307845 Loss2: 1.783258
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.483992 Loss1: 0.162268 Loss2: 1.321724 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.485073 Loss1: 0.159198 Loss2: 1.325875 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.492694 Loss1: 0.158051 Loss2: 1.334643 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.458030 Loss1: 0.133273 Loss2: 1.324756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452563 Loss1: 0.123358 Loss2: 1.329205 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.385258 Loss1: 0.065685 Loss2: 1.319573 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.332357 Loss1: 0.028616 Loss2: 1.303742 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.597970 Loss1: 0.237353 Loss2: 1.360617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.557690 Loss1: 0.178128 Loss2: 1.379561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.548018 Loss1: 0.181819 Loss2: 1.366199
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.222479 Loss1: 0.379318 Loss2: 1.843161
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630753 Loss1: 0.273190 Loss2: 1.357562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556089 Loss1: 0.159542 Loss2: 1.396546 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.541391 Loss1: 0.168829 Loss2: 1.372562 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450922 Loss1: 0.084559 Loss2: 1.366363 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402314 Loss1: 0.061299 Loss2: 1.341015
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429649 Loss1: 0.067281 Loss2: 1.362368
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406252 Loss1: 0.053020 Loss2: 1.353232
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406079 Loss1: 0.059012 Loss2: 1.347067
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391734 Loss1: 0.048367 Loss2: 1.343367
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.390150 Loss1: 0.045887 Loss2: 1.344263
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.126024 Loss1: 0.294319 Loss2: 1.831705
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.550503 Loss1: 0.191788 Loss2: 1.358715
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.530257 Loss1: 0.161988 Loss2: 1.368269
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506901 Loss1: 0.141035 Loss2: 1.365865
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.115982 Loss1: 0.332372 Loss2: 1.783611
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.546584 Loss1: 0.209675 Loss2: 1.336909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.434833 Loss1: 0.093301 Loss2: 1.341533 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.425653 Loss1: 0.097991 Loss2: 1.327662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.442844 Loss1: 0.126678 Loss2: 1.316167 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.425729 Loss1: 0.096376 Loss2: 1.329353 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408154 Loss1: 0.091549 Loss2: 1.316604 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399515 Loss1: 0.079305 Loss2: 1.320210 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.567657 Loss1: 0.255785 Loss2: 1.311872 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.460923 Loss1: 0.132277 Loss2: 1.328646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.102479 Loss1: 0.334522 Loss2: 1.767957 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.476426 Loss1: 0.186515 Loss2: 1.289910 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393607 Loss1: 0.094166 Loss2: 1.299442 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.361114 Loss1: 0.059116 Loss2: 1.301998 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359324 Loss1: 0.059510 Loss2: 1.299814 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981971
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.325831 Loss1: 0.052733 Loss2: 1.273098 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.292988 Loss1: 0.025092 Loss2: 1.267896 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.282687 Loss1: 0.023186 Loss2: 1.259500 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561519 Loss1: 0.155946 Loss2: 1.405574 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.505701 Loss1: 0.137916 Loss2: 1.367785 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.437123 Loss1: 0.080150 Loss2: 1.356973
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.255463 Loss1: 0.348754 Loss2: 1.906709
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.632002 Loss1: 0.242528 Loss2: 1.389474 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.575708 Loss1: 0.163850 Loss2: 1.411858 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.536546 Loss1: 0.137360 Loss2: 1.399187 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393159 Loss1: 0.053530 Loss2: 1.339628
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.545079 Loss1: 0.158304 Loss2: 1.386775
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.511005 Loss1: 0.115466 Loss2: 1.395539
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465611 Loss1: 0.083032 Loss2: 1.382579
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.487377 Loss1: 0.108959 Loss2: 1.378417
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.473205 Loss1: 0.091972 Loss2: 1.381233
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.242872 Loss1: 0.379592 Loss2: 1.863279
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.443690 Loss1: 0.069940 Loss2: 1.373751
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.561223 Loss1: 0.149319 Loss2: 1.411905 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546184 Loss1: 0.169361 Loss2: 1.376823 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.542142 Loss1: 0.154686 Loss2: 1.387456
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.155552 Loss1: 0.362991 Loss2: 1.792561
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.521846 Loss1: 0.218913 Loss2: 1.302933 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.508729 Loss1: 0.190816 Loss2: 1.317913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478048 Loss1: 0.150821 Loss2: 1.327228 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410900 Loss1: 0.040119 Loss2: 1.370781
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.426316 Loss1: 0.115562 Loss2: 1.310754
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.405046 Loss1: 0.098270 Loss2: 1.306776
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.405747 Loss1: 0.104623 Loss2: 1.301124
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409738 Loss1: 0.109721 Loss2: 1.300016
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401785 Loss1: 0.101949 Loss2: 1.299836
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.221905 Loss1: 0.362550 Loss2: 1.859355
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.401367 Loss1: 0.099741 Loss2: 1.301626
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.572391 Loss1: 0.155543 Loss2: 1.416848 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.544861 Loss1: 0.176867 Loss2: 1.367994 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498731 Loss1: 0.109666 Loss2: 1.389065
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.083905 Loss1: 0.269733 Loss2: 1.814172
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.539739 Loss1: 0.193720 Loss2: 1.346019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.530254 Loss1: 0.164051 Loss2: 1.366204 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.444196 Loss1: 0.092251 Loss2: 1.351945 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441681 Loss1: 0.101980 Loss2: 1.339701 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433269 Loss1: 0.090489 Loss2: 1.342780 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.130928 Loss1: 0.314330 Loss2: 1.816598 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.536929 Loss1: 0.212841 Loss2: 1.324089 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.447207 Loss1: 0.113450 Loss2: 1.333757 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.388560 Loss1: 0.071625 Loss2: 1.316935 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.372250 Loss1: 0.057558 Loss2: 1.314692
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.024981 Loss1: 0.278960 Loss2: 1.746022
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.350460 Loss1: 0.039902 Loss2: 1.310559
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.483865 Loss1: 0.197826 Loss2: 1.286039
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.349049 Loss1: 0.046134 Loss2: 1.302915
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.488645 Loss1: 0.187769 Loss2: 1.300876
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.337780 Loss1: 0.036664 Loss2: 1.301116
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.421100 Loss1: 0.111718 Loss2: 1.309382
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.365241 Loss1: 0.077338 Loss2: 1.287903
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.376616 Loss1: 0.093398 Loss2: 1.283218
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.362731 Loss1: 0.084495 Loss2: 1.278236
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.355740 Loss1: 0.078671 Loss2: 1.277069
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.152411 Loss1: 0.308652 Loss2: 1.843760
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.337357 Loss1: 0.069891 Loss2: 1.267466
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.530649 Loss1: 0.194625 Loss2: 1.336024
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.316692 Loss1: 0.048121 Loss2: 1.268571
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.459123 Loss1: 0.109625 Loss2: 1.349498 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.397597 Loss1: 0.073194 Loss2: 1.324403 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.377096 Loss1: 0.058084 Loss2: 1.319012
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.142232 Loss1: 0.306341 Loss2: 1.835892
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382674 Loss1: 0.066031 Loss2: 1.316643
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.550878 Loss1: 0.195591 Loss2: 1.355287
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.353018 Loss1: 0.034212 Loss2: 1.318806
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.522098 Loss1: 0.151867 Loss2: 1.370231
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.359169 Loss1: 0.044287 Loss2: 1.314882
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.493526 Loss1: 0.134624 Loss2: 1.358902
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.528559 Loss1: 0.165585 Loss2: 1.362974
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482594 Loss1: 0.128838 Loss2: 1.353756
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422266 Loss1: 0.072040 Loss2: 1.350226
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.391988 Loss1: 0.047789 Loss2: 1.344199
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.137380 Loss1: 0.327263 Loss2: 1.810117
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370751 Loss1: 0.034765 Loss2: 1.335986
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.609572 Loss1: 0.291963 Loss2: 1.317609
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.374105 Loss1: 0.044290 Loss2: 1.329815
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497535 Loss1: 0.173996 Loss2: 1.323539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.375993 Loss1: 0.067885 Loss2: 1.308108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.349629 Loss1: 0.046342 Loss2: 1.303287
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.074650 Loss1: 0.313002 Loss2: 1.761647
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.530547 Loss1: 0.220955 Loss2: 1.309592 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.490927 Loss1: 0.159866 Loss2: 1.331060 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.415778 Loss1: 0.107311 Loss2: 1.308467 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.434708 Loss1: 0.128572 Loss2: 1.306136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.373408 Loss1: 0.067985 Loss2: 1.305423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.632595 Loss1: 0.293210 Loss2: 1.339385 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.547562 Loss1: 0.181197 Loss2: 1.366365 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981445
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.425941 Loss1: 0.090232 Loss2: 1.335709 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.380581 Loss1: 0.059286 Loss2: 1.321295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.190171 Loss1: 0.327330 Loss2: 1.862841 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.583533 Loss1: 0.223092 Loss2: 1.360441 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997768
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.513016 Loss1: 0.144935 Loss2: 1.368082 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.406008 Loss1: 0.054889 Loss2: 1.351119 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.389913 Loss1: 0.044006 Loss2: 1.345906
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.205044 Loss1: 0.323647 Loss2: 1.881398
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.566349 Loss1: 0.218666 Loss2: 1.347683 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.465733 Loss1: 0.109115 Loss2: 1.356618 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.348311 Loss1: 0.016796 Loss2: 1.331514
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.473356 Loss1: 0.119125 Loss2: 1.354231
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.408371 Loss1: 0.070929 Loss2: 1.337442
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.399221 Loss1: 0.060656 Loss2: 1.338564
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406105 Loss1: 0.069407 Loss2: 1.336698
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389288 Loss1: 0.054333 Loss2: 1.334956
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.267165 Loss1: 0.387412 Loss2: 1.879753
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396823 Loss1: 0.069448 Loss2: 1.327375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392015 Loss1: 0.057914 Loss2: 1.334101 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.466784 Loss1: 0.100865 Loss2: 1.365919 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.469065 Loss1: 0.122352 Loss2: 1.346713 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.192421 Loss1: 0.371645 Loss2: 1.820777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.527094 Loss1: 0.209555 Loss2: 1.317539 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.475673 Loss1: 0.134232 Loss2: 1.341441 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.420412 Loss1: 0.103214 Loss2: 1.317198 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.443394 Loss1: 0.115246 Loss2: 1.328149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.433100 Loss1: 0.104537 Loss2: 1.328563
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.252363 Loss1: 0.342010 Loss2: 1.910353
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.610236 Loss1: 0.216228 Loss2: 1.394007 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398824 Loss1: 0.076810 Loss2: 1.322014
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.577330 Loss1: 0.168219 Loss2: 1.409111
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.543746 Loss1: 0.139137 Loss2: 1.404609
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.479588 Loss1: 0.092928 Loss2: 1.386660
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.484448 Loss1: 0.097542 Loss2: 1.386906
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.472626 Loss1: 0.086315 Loss2: 1.386311
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.492805 Loss1: 0.492829 Loss2: 1.999976
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.431590 Loss1: 0.052181 Loss2: 1.379409
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.418813 Loss1: 0.043885 Loss2: 1.374928 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422074 Loss1: 0.054524 Loss2: 1.367550 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.464537 Loss1: 0.081625 Loss2: 1.382912 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438962 Loss1: 0.069825 Loss2: 1.369137 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.103908 Loss1: 0.326755 Loss2: 1.777153 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995192
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.449824 Loss1: 0.142545 Loss2: 1.307279 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.398059 Loss1: 0.103716 Loss2: 1.294343 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.385861 Loss1: 0.087727 Loss2: 1.298134
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.279053 Loss1: 0.375908 Loss2: 1.903144
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.681914 Loss1: 0.290809 Loss2: 1.391105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.610956 Loss1: 0.189116 Loss2: 1.421840 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498410 Loss1: 0.100114 Loss2: 1.398296 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475023 Loss1: 0.088460 Loss2: 1.386563 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425858 Loss1: 0.047893 Loss2: 1.377965 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.406829 Loss1: 0.036485 Loss2: 1.370344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377873 Loss1: 0.018688 Loss2: 1.359184 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.598415 Loss1: 0.182562 Loss2: 1.415853 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.473364 Loss1: 0.079030 Loss2: 1.394334 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.487232 Loss1: 0.096450 Loss2: 1.390782
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.237080 Loss1: 0.342669 Loss2: 1.894411
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.539358 Loss1: 0.192130 Loss2: 1.347229 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.539912 Loss1: 0.181281 Loss2: 1.358631 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.561204 Loss1: 0.194303 Loss2: 1.366901 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.422701 Loss1: 0.047143 Loss2: 1.375558
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.522385 Loss1: 0.160291 Loss2: 1.362094
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457641 Loss1: 0.109186 Loss2: 1.348455
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.413076 Loss1: 0.068957 Loss2: 1.344119
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411434 Loss1: 0.072611 Loss2: 1.338823
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.363800 Loss1: 0.033200 Loss2: 1.330600
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.140871 Loss1: 0.323096 Loss2: 1.817775
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364321 Loss1: 0.040969 Loss2: 1.323352
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.515139 Loss1: 0.159189 Loss2: 1.355950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.487319 Loss1: 0.142669 Loss2: 1.344650 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.442628 Loss1: 0.097301 Loss2: 1.345326
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.199541 Loss1: 0.344769 Loss2: 1.854772
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.523368 Loss1: 0.170219 Loss2: 1.353149 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.483216 Loss1: 0.127807 Loss2: 1.355409 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.464440 Loss1: 0.108169 Loss2: 1.356271 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.358739 Loss1: 0.045065 Loss2: 1.313674
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.461780 Loss1: 0.118881 Loss2: 1.342899
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.451427 Loss1: 0.107134 Loss2: 1.344293
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.448629 Loss1: 0.100748 Loss2: 1.347881
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.444382 Loss1: 0.092987 Loss2: 1.351395
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.417572 Loss1: 0.074609 Loss2: 1.342964
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.191455 Loss1: 0.324192 Loss2: 1.867263
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393884 Loss1: 0.047792 Loss2: 1.346092
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.547857 Loss1: 0.175615 Loss2: 1.372242 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.479838 Loss1: 0.119361 Loss2: 1.360476 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.516323 Loss1: 0.143243 Loss2: 1.373079
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.449263 Loss1: 0.458454 Loss2: 1.990809
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.733330 Loss1: 0.369134 Loss2: 1.364197 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.659386 Loss1: 0.254191 Loss2: 1.405195
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449317 Loss1: 0.084107 Loss2: 1.365210
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.453701 Loss1: 0.087593 Loss2: 1.366108 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.449715 Loss1: 0.087297 Loss2: 1.362418 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.430155 Loss1: 0.061059 Loss2: 1.369096 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.405151 Loss1: 0.046393 Loss2: 1.358759 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.161716 Loss1: 0.254523 Loss2: 1.907193
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.649007 Loss1: 0.263032 Loss2: 1.385976
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.603846 Loss1: 0.183761 Loss2: 1.420086
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.550027 Loss1: 0.135417 Loss2: 1.414610
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.172768 Loss1: 0.290366 Loss2: 1.882401
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.590555 Loss1: 0.193216 Loss2: 1.397339 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.542668 Loss1: 0.131107 Loss2: 1.411561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.528989 Loss1: 0.112025 Loss2: 1.416964 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.529902 Loss1: 0.123786 Loss2: 1.406116 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515944 Loss1: 0.106765 Loss2: 1.409179 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.464701 Loss1: 0.067366 Loss2: 1.397335 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460079 Loss1: 0.066091 Loss2: 1.393988 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985352
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.596924 Loss1: 0.229274 Loss2: 1.367650 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529990 Loss1: 0.161593 Loss2: 1.368397 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.469644 Loss1: 0.125464 Loss2: 1.344180
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.160479 Loss1: 0.346244 Loss2: 1.814235
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.535387 Loss1: 0.210876 Loss2: 1.324511 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.501389 Loss1: 0.172822 Loss2: 1.328567 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482760 Loss1: 0.153950 Loss2: 1.328810 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490683 Loss1: 0.171757 Loss2: 1.318926 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380008 Loss1: 0.038326 Loss2: 1.341681
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468290 Loss1: 0.144833 Loss2: 1.323457
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432512 Loss1: 0.116758 Loss2: 1.315754
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.389548 Loss1: 0.075265 Loss2: 1.314283
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.369717 Loss1: 0.063952 Loss2: 1.305765
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364182 Loss1: 0.068132 Loss2: 1.296050
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.178522 Loss1: 0.355209 Loss2: 1.823313
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.544921 Loss1: 0.213149 Loss2: 1.331772
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.479515 Loss1: 0.146754 Loss2: 1.332761
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.485731 Loss1: 0.150324 Loss2: 1.335407
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.417559 Loss1: 0.093678 Loss2: 1.323881
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.341446 Loss1: 0.437371 Loss2: 1.904076
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.637201 Loss1: 0.291768 Loss2: 1.345433 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.410917 Loss1: 0.089136 Loss2: 1.321781
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.572403 Loss1: 0.185289 Loss2: 1.387114
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408117 Loss1: 0.094313 Loss2: 1.313803
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.520342 Loss1: 0.156106 Loss2: 1.364237
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.457945 Loss1: 0.113099 Loss2: 1.344846
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410700 Loss1: 0.094398 Loss2: 1.316301
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.411455 Loss1: 0.059453 Loss2: 1.352002
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392424 Loss1: 0.070489 Loss2: 1.321935
-(DefaultActor pid=3764) >> Training accuracy: 0.980208
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.361454 Loss1: 0.034362 Loss2: 1.327092 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.347992 Loss1: 0.033534 Loss2: 1.314458 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997768
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.104496 Loss1: 0.240410 Loss2: 1.864086
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.529713 Loss1: 0.146080 Loss2: 1.383633
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.476706 Loss1: 0.098792 Loss2: 1.377913
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.442305 Loss1: 0.067614 Loss2: 1.374691
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.236544 Loss1: 0.333803 Loss2: 1.902741
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.464902 Loss1: 0.094031 Loss2: 1.370871
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.606824 Loss1: 0.216826 Loss2: 1.389998
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.468258 Loss1: 0.089034 Loss2: 1.379224
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.576527 Loss1: 0.162976 Loss2: 1.413550
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.550384 Loss1: 0.144905 Loss2: 1.405479
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.509885 Loss1: 0.133127 Loss2: 1.376759
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.524983 Loss1: 0.132835 Loss2: 1.392147
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.437638 Loss1: 0.064987 Loss2: 1.372651
-DEBUG flwr 2023-10-13 16:52:41,829 | server.py:236 | fit_round 195 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.523726 Loss1: 0.125428 Loss2: 1.398297
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410773 Loss1: 0.047755 Loss2: 1.363019
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.479256 Loss1: 0.083451 Loss2: 1.395804
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.411466 Loss1: 0.049007 Loss2: 1.362459
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.506580 Loss1: 0.103071 Loss2: 1.403509 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.201993 Loss1: 0.385439 Loss2: 1.816554 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.553060 Loss1: 0.194993 Loss2: 1.358066 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.504666 Loss1: 0.174671 Loss2: 1.329995
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.166126 Loss1: 0.323416 Loss2: 1.842710
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.508936 Loss1: 0.170300 Loss2: 1.338635
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.491430 Loss1: 0.153142 Loss2: 1.338288
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.475450 Loss1: 0.140980 Loss2: 1.334470
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.477628 Loss1: 0.141956 Loss2: 1.335673
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427444 Loss1: 0.094848 Loss2: 1.332596
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.506551 Loss1: 0.146758 Loss2: 1.359793
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.396005 Loss1: 0.074248 Loss2: 1.321758
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.429570 Loss1: 0.086723 Loss2: 1.342847
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386042 Loss1: 0.067072 Loss2: 1.318970
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444115 Loss1: 0.112880 Loss2: 1.331235
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369461 Loss1: 0.060298 Loss2: 1.309163
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.412861 Loss1: 0.073601 Loss2: 1.339260
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.413104 Loss1: 0.081076 Loss2: 1.332028
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.380772 Loss1: 0.048309 Loss2: 1.332463
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.363838 Loss1: 0.037975 Loss2: 1.325862
-(DefaultActor pid=3765) >> Training accuracy: 1.000000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.168519 Loss1: 0.304701 Loss2: 1.863818
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.578880 Loss1: 0.228278 Loss2: 1.350602
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556041 Loss1: 0.192204 Loss2: 1.363837
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519810 Loss1: 0.148193 Loss2: 1.371617
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493636 Loss1: 0.139448 Loss2: 1.354188
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.547696 Loss1: 0.175449 Loss2: 1.372248
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.519523 Loss1: 0.152571 Loss2: 1.366952
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.484714 Loss1: 0.119798 Loss2: 1.364916
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.469227 Loss1: 0.106314 Loss2: 1.362912
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.441004 Loss1: 0.087354 Loss2: 1.353650
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 16:52:41,829][flwr][DEBUG] - fit_round 195 received 50 results and 0 failures
-INFO flwr 2023-10-13 16:53:22,720 | server.py:125 | fit progress: (195, 2.337302811420002, {'accuracy': 0.6132}, 450110.49889478396)
->> Test accuracy: 0.613200
-[2023-10-13 16:53:22,720][flwr][INFO] - fit progress: (195, 2.337302811420002, {'accuracy': 0.6132}, 450110.49889478396)
-DEBUG flwr 2023-10-13 16:53:22,721 | server.py:173 | evaluate_round 195: strategy sampled 50 clients (out of 50)
-[2023-10-13 16:53:22,721][flwr][DEBUG] - evaluate_round 195: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 17:02:27,987 | server.py:187 | evaluate_round 195 received 50 results and 0 failures
-[2023-10-13 17:02:27,987][flwr][DEBUG] - evaluate_round 195 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 17:02:27,987 | server.py:222 | fit_round 196: strategy sampled 50 clients (out of 50)
-[2023-10-13 17:02:27,987][flwr][DEBUG] - fit_round 196: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.231675 Loss1: 0.349445 Loss2: 1.882230
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.562381 Loss1: 0.204576 Loss2: 1.357805
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.522060 Loss1: 0.153419 Loss2: 1.368641
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.481238 Loss1: 0.119053 Loss2: 1.362185
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.462663 Loss1: 0.115907 Loss2: 1.346756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444797 Loss1: 0.099033 Loss2: 1.345764 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.423120 Loss1: 0.079320 Loss2: 1.343801 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380350 Loss1: 0.044232 Loss2: 1.336118 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.383058 Loss1: 0.054192 Loss2: 1.328866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390660 Loss1: 0.059971 Loss2: 1.330689 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.356766 Loss1: 0.065703 Loss2: 1.291063 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.419612 Loss1: 0.471051 Loss2: 1.948561 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.639798 Loss1: 0.226709 Loss2: 1.413089 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.540265 Loss1: 0.148509 Loss2: 1.391756 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.498661 Loss1: 0.099902 Loss2: 1.398759 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.456879 Loss1: 0.071488 Loss2: 1.385391 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.447887 Loss1: 0.073872 Loss2: 1.374015 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.423163 Loss1: 0.052063 Loss2: 1.371100 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.404042 Loss1: 0.036648 Loss2: 1.367394 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998798
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404091 Loss1: 0.086330 Loss2: 1.317762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404485 Loss1: 0.090381 Loss2: 1.314103 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.372681 Loss1: 0.058106 Loss2: 1.314575
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.292822 Loss1: 0.352716 Loss2: 1.940106
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.638719 Loss1: 0.235973 Loss2: 1.402745
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.635357 Loss1: 0.230291 Loss2: 1.405066
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.587649 Loss1: 0.153314 Loss2: 1.434335
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.520927 Loss1: 0.115776 Loss2: 1.405151
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493101 Loss1: 0.096557 Loss2: 1.396544
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.096714 Loss1: 0.276185 Loss2: 1.820529
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.552157 Loss1: 0.210077 Loss2: 1.342080 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.528017 Loss1: 0.152521 Loss2: 1.375496 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.450030 Loss1: 0.105643 Loss2: 1.344387 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.450775 Loss1: 0.107207 Loss2: 1.343568 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406912 Loss1: 0.070640 Loss2: 1.336272 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384187 Loss1: 0.050234 Loss2: 1.333952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.358835 Loss1: 0.033849 Loss2: 1.324986 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996324
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498218 Loss1: 0.164931 Loss2: 1.333287 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463800 Loss1: 0.132456 Loss2: 1.331344 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.460837 Loss1: 0.126982 Loss2: 1.333855
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.102782 Loss1: 0.285755 Loss2: 1.817027
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.532256 Loss1: 0.197617 Loss2: 1.334639 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374880 Loss1: 0.058978 Loss2: 1.315902
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.485133 Loss1: 0.146106 Loss2: 1.339028
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380370 Loss1: 0.063824 Loss2: 1.316546
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.472702 Loss1: 0.128748 Loss2: 1.343954
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.440332 Loss1: 0.104282 Loss2: 1.336050
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.440497 Loss1: 0.105176 Loss2: 1.335321
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.435193 Loss1: 0.106765 Loss2: 1.328428
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.418577 Loss1: 0.088894 Loss2: 1.329683
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.122738 Loss1: 0.325661 Loss2: 1.797078
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.425042 Loss1: 0.094738 Loss2: 1.330304
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.504772 Loss1: 0.187559 Loss2: 1.317213
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365905 Loss1: 0.037157 Loss2: 1.328748
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.413962 Loss1: 0.116222 Loss2: 1.297740 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.395852 Loss1: 0.086360 Loss2: 1.309492 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.290584 Loss1: 0.350770 Loss2: 1.939814
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390100 Loss1: 0.082947 Loss2: 1.307154
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.674261 Loss1: 0.305050 Loss2: 1.369211
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.357836 Loss1: 0.064011 Loss2: 1.293825
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572048 Loss1: 0.167470 Loss2: 1.404578
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.349837 Loss1: 0.056464 Loss2: 1.293373
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.348034 Loss1: 0.060538 Loss2: 1.287497 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476237 Loss1: 0.099219 Loss2: 1.377018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.419786 Loss1: 0.055619 Loss2: 1.364167 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.147666 Loss1: 0.285385 Loss2: 1.862281 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526925 Loss1: 0.159281 Loss2: 1.367643 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.511010 Loss1: 0.153035 Loss2: 1.357975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.516558 Loss1: 0.152054 Loss2: 1.364504
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.170251 Loss1: 0.301788 Loss2: 1.868462
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.576129 Loss1: 0.219179 Loss2: 1.356950 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.501476 Loss1: 0.127195 Loss2: 1.374281 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.426900 Loss1: 0.077894 Loss2: 1.349006 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387375 Loss1: 0.044042 Loss2: 1.343333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.400775 Loss1: 0.065608 Loss2: 1.335168
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.398753 Loss1: 0.060634 Loss2: 1.338119
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.386852 Loss1: 0.051297 Loss2: 1.335556
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.380384 Loss1: 0.049435 Loss2: 1.330949
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.362082 Loss1: 0.034380 Loss2: 1.327702
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.131584 Loss1: 0.305166 Loss2: 1.826418
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.388097 Loss1: 0.064187 Loss2: 1.323910
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.547986 Loss1: 0.182365 Loss2: 1.365621 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.546030 Loss1: 0.198318 Loss2: 1.347712 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.457083 Loss1: 0.116953 Loss2: 1.340131
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.168734 Loss1: 0.323150 Loss2: 1.845584
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.550291 Loss1: 0.201319 Loss2: 1.348972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.556035 Loss1: 0.191139 Loss2: 1.364896 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.529790 Loss1: 0.153470 Loss2: 1.376320 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.402720 Loss1: 0.075082 Loss2: 1.327638
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497233 Loss1: 0.139696 Loss2: 1.357536
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460977 Loss1: 0.103954 Loss2: 1.357023
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431072 Loss1: 0.082329 Loss2: 1.348743
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.445282 Loss1: 0.100790 Loss2: 1.344492
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406720 Loss1: 0.063157 Loss2: 1.343563
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.155564 Loss1: 0.334187 Loss2: 1.821376
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.409343 Loss1: 0.070070 Loss2: 1.339273
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574537 Loss1: 0.184625 Loss2: 1.389913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.482334 Loss1: 0.126929 Loss2: 1.355405 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.456131 Loss1: 0.106994 Loss2: 1.349137
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.218029 Loss1: 0.330712 Loss2: 1.887317
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.599045 Loss1: 0.224072 Loss2: 1.374972 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.623064 Loss1: 0.214973 Loss2: 1.408091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.568034 Loss1: 0.176325 Loss2: 1.391709 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.970833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.408459 Loss1: 0.073046 Loss2: 1.335413
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.534076 Loss1: 0.162819 Loss2: 1.371258
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.492855 Loss1: 0.114236 Loss2: 1.378619
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455028 Loss1: 0.087824 Loss2: 1.367205
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413655 Loss1: 0.051997 Loss2: 1.361658
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395812 Loss1: 0.040168 Loss2: 1.355644
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.320230 Loss1: 0.354314 Loss2: 1.965916
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.399463 Loss1: 0.054883 Loss2: 1.344580
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.659788 Loss1: 0.201120 Loss2: 1.458668 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517179 Loss1: 0.098227 Loss2: 1.418952 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.505500 Loss1: 0.099000 Loss2: 1.406500
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.154389 Loss1: 0.306805 Loss2: 1.847584
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.546207 Loss1: 0.197012 Loss2: 1.349194 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.505455 Loss1: 0.141107 Loss2: 1.364348 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.445529 Loss1: 0.099808 Loss2: 1.345721 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.437593 Loss1: 0.102714 Loss2: 1.334879 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.407030 Loss1: 0.077814 Loss2: 1.329215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.356446 Loss1: 0.029135 Loss2: 1.327311 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375514 Loss1: 0.051587 Loss2: 1.323927 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.472625 Loss1: 0.136186 Loss2: 1.336439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.413208 Loss1: 0.088859 Loss2: 1.324348 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.402028 Loss1: 0.076070 Loss2: 1.325958
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.200270 Loss1: 0.320826 Loss2: 1.879443
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416113 Loss1: 0.099436 Loss2: 1.316677
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.521756 Loss1: 0.165680 Loss2: 1.356076
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.438779 Loss1: 0.116942 Loss2: 1.321837
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.453647 Loss1: 0.102763 Loss2: 1.350884
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.425488 Loss1: 0.074988 Loss2: 1.350501
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.372925 Loss1: 0.053161 Loss2: 1.319765
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.382047 Loss1: 0.050792 Loss2: 1.331255
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.375586 Loss1: 0.060650 Loss2: 1.314937
-(DefaultActor pid=3765) >> Training accuracy: 0.988281
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.359969 Loss1: 0.041039 Loss2: 1.318930 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.343690 Loss1: 0.026153 Loss2: 1.317537 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.338776 Loss1: 0.029809 Loss2: 1.308967
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.078153 Loss1: 0.302772 Loss2: 1.775381
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.552800 Loss1: 0.214642 Loss2: 1.338158
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.474509 Loss1: 0.127896 Loss2: 1.346613
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.444067 Loss1: 0.117208 Loss2: 1.326859
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.417145 Loss1: 0.090939 Loss2: 1.326206
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.243478 Loss1: 0.355540 Loss2: 1.887939
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.444378 Loss1: 0.124865 Loss2: 1.319513
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.412746 Loss1: 0.091977 Loss2: 1.320768 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.372004 Loss1: 0.060966 Loss2: 1.311037 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.374547 Loss1: 0.063614 Loss2: 1.310934 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.349988 Loss1: 0.044080 Loss2: 1.305907 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461133 Loss1: 0.084078 Loss2: 1.377055 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441299 Loss1: 0.075676 Loss2: 1.365623 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.492936 Loss1: 0.124618 Loss2: 1.368318
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.184982 Loss1: 0.390724 Loss2: 1.794258
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.655738 Loss1: 0.313986 Loss2: 1.341753
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.585003 Loss1: 0.205883 Loss2: 1.379120
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488080 Loss1: 0.145223 Loss2: 1.342857
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.453810 Loss1: 0.115892 Loss2: 1.337918
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.115722 Loss1: 0.285783 Loss2: 1.829939
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.476216 Loss1: 0.158964 Loss2: 1.317252 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.435943 Loss1: 0.118038 Loss2: 1.317905 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.397923 Loss1: 0.072723 Loss2: 1.325199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.390454 Loss1: 0.073588 Loss2: 1.316866 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.372364 Loss1: 0.058034 Loss2: 1.314330 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.365274 Loss1: 0.060029 Loss2: 1.305245 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.327392 Loss1: 0.025865 Loss2: 1.301527 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.509203 Loss1: 0.158922 Loss2: 1.350281 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.470215 Loss1: 0.101533 Loss2: 1.368681 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480994 Loss1: 0.125169 Loss2: 1.355825
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.153702 Loss1: 0.316536 Loss2: 1.837166
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.449143 Loss1: 0.090647 Loss2: 1.358496
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.655360 Loss1: 0.284272 Loss2: 1.371088
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.429531 Loss1: 0.075721 Loss2: 1.353810
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.616662 Loss1: 0.205814 Loss2: 1.410848
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436766 Loss1: 0.083348 Loss2: 1.353417
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539621 Loss1: 0.159463 Loss2: 1.380158
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399238 Loss1: 0.054145 Loss2: 1.345093
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.521933 Loss1: 0.142419 Loss2: 1.379515
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410807 Loss1: 0.072301 Loss2: 1.338506
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.461346 Loss1: 0.090103 Loss2: 1.371243
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461283 Loss1: 0.101680 Loss2: 1.359602
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434319 Loss1: 0.077096 Loss2: 1.357223
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.479730 Loss1: 0.124175 Loss2: 1.355555
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.435554 Loss1: 0.073265 Loss2: 1.362289
-(DefaultActor pid=3764) >> Training accuracy: 0.987305
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.265293 Loss1: 0.390665 Loss2: 1.874628
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.580088 Loss1: 0.223012 Loss2: 1.357076
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.536523 Loss1: 0.157277 Loss2: 1.379246
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.464003 Loss1: 0.107332 Loss2: 1.356672
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.440372 Loss1: 0.093674 Loss2: 1.346698
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.209080 Loss1: 0.357911 Loss2: 1.851169
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.556353 Loss1: 0.197075 Loss2: 1.359278 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.548386 Loss1: 0.157265 Loss2: 1.391121 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.530446 Loss1: 0.152182 Loss2: 1.378265 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500816 Loss1: 0.129859 Loss2: 1.370957 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.471526 Loss1: 0.094182 Loss2: 1.377343 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405827 Loss1: 0.045256 Loss2: 1.360571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.365503 Loss1: 0.023868 Loss2: 1.341635 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.708624 Loss1: 0.292341 Loss2: 1.416284 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.571538 Loss1: 0.145910 Loss2: 1.425627 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.593116 Loss1: 0.179788 Loss2: 1.413328
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.244292 Loss1: 0.333195 Loss2: 1.911097
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.574523 Loss1: 0.175597 Loss2: 1.398926 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.555451 Loss1: 0.157454 Loss2: 1.397997 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.571380 Loss1: 0.175058 Loss2: 1.396323 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.516515 Loss1: 0.123132 Loss2: 1.393382 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.531598 Loss1: 0.139809 Loss2: 1.391789 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.511923 Loss1: 0.117208 Loss2: 1.394715 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.472723 Loss1: 0.092265 Loss2: 1.380458 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.555110 Loss1: 0.216666 Loss2: 1.338444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.454631 Loss1: 0.109161 Loss2: 1.345470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.425414 Loss1: 0.087256 Loss2: 1.338158
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.136559 Loss1: 0.318815 Loss2: 1.817744
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.601045 Loss1: 0.273563 Loss2: 1.327482
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440932 Loss1: 0.105967 Loss2: 1.334966
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.581472 Loss1: 0.224764 Loss2: 1.356708
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.463471 Loss1: 0.129594 Loss2: 1.333876
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496438 Loss1: 0.136850 Loss2: 1.359588
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.445983 Loss1: 0.106712 Loss2: 1.339271
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.537718 Loss1: 0.197171 Loss2: 1.340547
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.407341 Loss1: 0.072821 Loss2: 1.334519
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392597 Loss1: 0.065995 Loss2: 1.326602 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427164 Loss1: 0.091548 Loss2: 1.335616 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397932 Loss1: 0.065923 Loss2: 1.332008 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.578345 Loss1: 0.216470 Loss2: 1.361875 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.450958 Loss1: 0.095475 Loss2: 1.355483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.125043 Loss1: 0.294555 Loss2: 1.830489
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.426657 Loss1: 0.082372 Loss2: 1.344285
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.564428 Loss1: 0.227438 Loss2: 1.336990
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.423563 Loss1: 0.076808 Loss2: 1.346755
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.477583 Loss1: 0.122878 Loss2: 1.354705
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.386676 Loss1: 0.044527 Loss2: 1.342150
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.469934 Loss1: 0.119636 Loss2: 1.350298
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.410105 Loss1: 0.073599 Loss2: 1.336506
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450032 Loss1: 0.113718 Loss2: 1.336314
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385974 Loss1: 0.051396 Loss2: 1.334578
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.436894 Loss1: 0.093747 Loss2: 1.343147
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373966 Loss1: 0.043850 Loss2: 1.330116
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.411385 Loss1: 0.079040 Loss2: 1.332345 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378086 Loss1: 0.046648 Loss2: 1.331437 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.586501 Loss1: 0.228669 Loss2: 1.357832 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497297 Loss1: 0.121934 Loss2: 1.375363 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.457139 Loss1: 0.097340 Loss2: 1.359799 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.439598 Loss1: 0.080642 Loss2: 1.358956 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.438751 Loss1: 0.079270 Loss2: 1.359481 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.426055 Loss1: 0.077535 Loss2: 1.348520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431692 Loss1: 0.079100 Loss2: 1.352592 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.420581 Loss1: 0.071581 Loss2: 1.349001 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439106 Loss1: 0.069778 Loss2: 1.369328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.446542 Loss1: 0.085407 Loss2: 1.361135 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.129116 Loss1: 0.309932 Loss2: 1.819184
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.570563 Loss1: 0.210379 Loss2: 1.360184
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.503231 Loss1: 0.132595 Loss2: 1.370636
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482384 Loss1: 0.137516 Loss2: 1.344867
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.270199 Loss1: 0.346059 Loss2: 1.924140
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.673015 Loss1: 0.269964 Loss2: 1.403051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.592478 Loss1: 0.150241 Loss2: 1.442236 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390030 Loss1: 0.058112 Loss2: 1.331919
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.516899 Loss1: 0.095648 Loss2: 1.421251
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.387946 Loss1: 0.056462 Loss2: 1.331484
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.459011 Loss1: 0.061290 Loss2: 1.397721
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.379389 Loss1: 0.047237 Loss2: 1.332151
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460286 Loss1: 0.066936 Loss2: 1.393350
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488887 Loss1: 0.093233 Loss2: 1.395655
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.365107 Loss1: 0.037611 Loss2: 1.327496
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458587 Loss1: 0.074535 Loss2: 1.384051 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.203043 Loss1: 0.340341 Loss2: 1.862702 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.485216 Loss1: 0.138054 Loss2: 1.347162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.462427 Loss1: 0.119640 Loss2: 1.342787
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.238681 Loss1: 0.356572 Loss2: 1.882109
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.480137 Loss1: 0.147682 Loss2: 1.332454
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.614955 Loss1: 0.234602 Loss2: 1.380353
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.428410 Loss1: 0.097119 Loss2: 1.331291
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.552167 Loss1: 0.137942 Loss2: 1.414225
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.428578 Loss1: 0.093814 Loss2: 1.334763
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.524378 Loss1: 0.134699 Loss2: 1.389679
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.380484 Loss1: 0.059953 Loss2: 1.320531
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.515564 Loss1: 0.126997 Loss2: 1.388567
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.376541 Loss1: 0.060653 Loss2: 1.315887
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.515214 Loss1: 0.123784 Loss2: 1.391431
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.361148 Loss1: 0.051702 Loss2: 1.309446
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461766 Loss1: 0.076969 Loss2: 1.384797
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.449551 Loss1: 0.064695 Loss2: 1.384855
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.448673 Loss1: 0.070759 Loss2: 1.377914
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.416985 Loss1: 0.043287 Loss2: 1.373697
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.313609 Loss1: 0.416037 Loss2: 1.897573
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.644910 Loss1: 0.329618 Loss2: 1.315292
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611216 Loss1: 0.226454 Loss2: 1.384762
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.551626 Loss1: 0.213040 Loss2: 1.338586
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.162749 Loss1: 0.318780 Loss2: 1.843968
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455647 Loss1: 0.107852 Loss2: 1.347796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408387 Loss1: 0.082811 Loss2: 1.325575 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.370083 Loss1: 0.058320 Loss2: 1.311763 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.360285 Loss1: 0.051424 Loss2: 1.308861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.364634 Loss1: 0.058139 Loss2: 1.306495 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990385
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.437409 Loss1: 0.095624 Loss2: 1.341785 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.397050 Loss1: 0.055502 Loss2: 1.341548 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.387635 Loss1: 0.055002 Loss2: 1.332634
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.151081 Loss1: 0.355705 Loss2: 1.795375
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.488661 Loss1: 0.186728 Loss2: 1.301933
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.440882 Loss1: 0.128057 Loss2: 1.312825
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.387251 Loss1: 0.089296 Loss2: 1.297955
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.377134 Loss1: 0.086294 Loss2: 1.290840
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.373115 Loss1: 0.435608 Loss2: 1.937507
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659526 Loss1: 0.272410 Loss2: 1.387116 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653793 Loss1: 0.234825 Loss2: 1.418968 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.556735 Loss1: 0.152336 Loss2: 1.404398 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.340113 Loss1: 0.058368 Loss2: 1.281745
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513339 Loss1: 0.124141 Loss2: 1.389198
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.369392 Loss1: 0.084581 Loss2: 1.284812
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512242 Loss1: 0.130321 Loss2: 1.381921
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.460996 Loss1: 0.081083 Loss2: 1.379913
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.455593 Loss1: 0.084668 Loss2: 1.370925
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.430147 Loss1: 0.058530 Loss2: 1.371617
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.431440 Loss1: 0.064350 Loss2: 1.367090
-(DefaultActor pid=3764) >> Training accuracy: 0.986607
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.135393 Loss1: 0.349076 Loss2: 1.786317
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.541175 Loss1: 0.198616 Loss2: 1.342558
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.508681 Loss1: 0.160723 Loss2: 1.347958
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.490513 Loss1: 0.160524 Loss2: 1.329989
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.201674 Loss1: 0.382291 Loss2: 1.819383
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.491195 Loss1: 0.158712 Loss2: 1.332483 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.475438 Loss1: 0.151753 Loss2: 1.323685 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.440631 Loss1: 0.107933 Loss2: 1.332699 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.453275 Loss1: 0.136323 Loss2: 1.316952 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.430071 Loss1: 0.104262 Loss2: 1.325809 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387983 Loss1: 0.063545 Loss2: 1.324438
-DEBUG flwr 2023-10-13 17:31:00,648 | server.py:236 | fit_round 196 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.409086 Loss1: 0.093709 Loss2: 1.315377
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.374739 Loss1: 0.061383 Loss2: 1.313355
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.404979 Loss1: 0.091529 Loss2: 1.313449
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.368413 Loss1: 0.057755 Loss2: 1.310658
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.379473 Loss1: 0.386654 Loss2: 1.992819
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.739319 Loss1: 0.285090 Loss2: 1.454229
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.641215 Loss1: 0.165813 Loss2: 1.475402
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.591179 Loss1: 0.127106 Loss2: 1.464073
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.389006 Loss1: 0.428273 Loss2: 1.960733
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.565479 Loss1: 0.227414 Loss2: 1.338065
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.559584 Loss1: 0.109065 Loss2: 1.450519
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.539817 Loss1: 0.201398 Loss2: 1.338419
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.561936 Loss1: 0.116212 Loss2: 1.445724 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.553450 Loss1: 0.111242 Loss2: 1.442207 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.483123 Loss1: 0.141183 Loss2: 1.341940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.510448 Loss1: 0.068424 Loss2: 1.442025 [repeated 3x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413322 Loss1: 0.072979 Loss2: 1.340343 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988281
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.266912 Loss1: 0.410314 Loss2: 1.856598
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.685159 Loss1: 0.332913 Loss2: 1.352247
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.599429 Loss1: 0.173571 Loss2: 1.425857
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.476552 Loss1: 0.120507 Loss2: 1.356045
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.194118 Loss1: 0.340141 Loss2: 1.853976
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.642473 Loss1: 0.270565 Loss2: 1.371909 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.540903 Loss1: 0.146697 Loss2: 1.394206 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.477737 Loss1: 0.099247 Loss2: 1.378490 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.497840 Loss1: 0.131021 Loss2: 1.366820 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460747 Loss1: 0.093615 Loss2: 1.367133 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.452740 Loss1: 0.089017 Loss2: 1.363723 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.405047 Loss1: 0.046649 Loss2: 1.358398 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995117
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 17:31:00,648][flwr][DEBUG] - fit_round 196 received 50 results and 0 failures
-INFO flwr 2023-10-13 17:31:41,751 | server.py:125 | fit progress: (196, 2.348089092646163, {'accuracy': 0.6137}, 452409.529389542)
->> Test accuracy: 0.613700
-[2023-10-13 17:31:41,751][flwr][INFO] - fit progress: (196, 2.348089092646163, {'accuracy': 0.6137}, 452409.529389542)
-DEBUG flwr 2023-10-13 17:31:41,751 | server.py:173 | evaluate_round 196: strategy sampled 50 clients (out of 50)
-[2023-10-13 17:31:41,751][flwr][DEBUG] - evaluate_round 196: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 17:40:44,621 | server.py:187 | evaluate_round 196 received 50 results and 0 failures
-[2023-10-13 17:40:44,621][flwr][DEBUG] - evaluate_round 196 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 17:40:44,621 | server.py:222 | fit_round 197: strategy sampled 50 clients (out of 50)
-[2023-10-13 17:40:44,621][flwr][DEBUG] - fit_round 197: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.200727 Loss1: 0.355533 Loss2: 1.845194
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.558443 Loss1: 0.201419 Loss2: 1.357024
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.520045 Loss1: 0.141039 Loss2: 1.379006
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534632 Loss1: 0.158483 Loss2: 1.376149
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.277008 Loss1: 0.391141 Loss2: 1.885868
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.506503 Loss1: 0.140177 Loss2: 1.366326
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.626831 Loss1: 0.294666 Loss2: 1.332165
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.489078 Loss1: 0.116404 Loss2: 1.372674
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.540700 Loss1: 0.167486 Loss2: 1.373214
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468454 Loss1: 0.106549 Loss2: 1.361905
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.480958 Loss1: 0.145317 Loss2: 1.335641
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.471135 Loss1: 0.132841 Loss2: 1.338294
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.473847 Loss1: 0.113549 Loss2: 1.360298
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452873 Loss1: 0.110795 Loss2: 1.342078
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.459572 Loss1: 0.096733 Loss2: 1.362840
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.432388 Loss1: 0.098114 Loss2: 1.334274
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.417448 Loss1: 0.065257 Loss2: 1.352191
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384998 Loss1: 0.056402 Loss2: 1.328596 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996652
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.204791 Loss1: 0.325240 Loss2: 1.879551 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.560038 Loss1: 0.158767 Loss2: 1.401271 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.468355 Loss1: 0.096247 Loss2: 1.372108
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.119361 Loss1: 0.325085 Loss2: 1.794276
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.436088 Loss1: 0.076713 Loss2: 1.359376
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.518631 Loss1: 0.212527 Loss2: 1.306104
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.452343 Loss1: 0.089806 Loss2: 1.362537
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.434115 Loss1: 0.112091 Loss2: 1.322024
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435978 Loss1: 0.075825 Loss2: 1.360153
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.436750 Loss1: 0.121698 Loss2: 1.315051
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.403599 Loss1: 0.053595 Loss2: 1.350004
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.367711 Loss1: 0.069730 Loss2: 1.297982
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399225 Loss1: 0.051072 Loss2: 1.348154
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.356191 Loss1: 0.059393 Loss2: 1.296798
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.374803 Loss1: 0.034745 Loss2: 1.340058
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.314309 Loss1: 0.022564 Loss2: 1.291745
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.315380 Loss1: 0.030060 Loss2: 1.285320
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.314729 Loss1: 0.033432 Loss2: 1.281297
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.310243 Loss1: 0.034811 Loss2: 1.275432
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.273429 Loss1: 0.394868 Loss2: 1.878561
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.623005 Loss1: 0.265425 Loss2: 1.357580
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568058 Loss1: 0.165908 Loss2: 1.402149
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.477767 Loss1: 0.107453 Loss2: 1.370314
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.122034 Loss1: 0.305240 Loss2: 1.816793
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.460254 Loss1: 0.092603 Loss2: 1.367652
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.516021 Loss1: 0.185600 Loss2: 1.330421
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.434731 Loss1: 0.075877 Loss2: 1.358854
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.447599 Loss1: 0.117034 Loss2: 1.330565
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.406397 Loss1: 0.052547 Loss2: 1.353850
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.426264 Loss1: 0.096606 Loss2: 1.329659
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384861 Loss1: 0.037649 Loss2: 1.347212
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.452228 Loss1: 0.143051 Loss2: 1.309177
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.364611 Loss1: 0.023504 Loss2: 1.341106
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.426207 Loss1: 0.100602 Loss2: 1.325604
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.370308 Loss1: 0.037275 Loss2: 1.333033
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.409761 Loss1: 0.092625 Loss2: 1.317135
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406374 Loss1: 0.087843 Loss2: 1.318532
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.408045 Loss1: 0.092059 Loss2: 1.315986
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.378524 Loss1: 0.063203 Loss2: 1.315321
-(DefaultActor pid=3764) >> Training accuracy: 0.983333
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.208335 Loss1: 0.371927 Loss2: 1.836408
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.590392 Loss1: 0.248693 Loss2: 1.341699
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.444664 Loss1: 0.097009 Loss2: 1.347655
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.423846 Loss1: 0.084874 Loss2: 1.338972
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.031811 Loss1: 0.240133 Loss2: 1.791678
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.539632 Loss1: 0.216988 Loss2: 1.322644 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.439185 Loss1: 0.097887 Loss2: 1.341298 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.432555 Loss1: 0.114291 Loss2: 1.318264 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.398991 Loss1: 0.079039 Loss2: 1.319952 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426121 Loss1: 0.106091 Loss2: 1.320031 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.352069 Loss1: 0.045149 Loss2: 1.306920 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.352242 Loss1: 0.053831 Loss2: 1.298410 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993566
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.589631 Loss1: 0.290863 Loss2: 1.298768 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.463548 Loss1: 0.129971 Loss2: 1.333577 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.421911 Loss1: 0.110936 Loss2: 1.310975 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408961 Loss1: 0.096271 Loss2: 1.312690 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.364390 Loss1: 0.058884 Loss2: 1.305507 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.364309 Loss1: 0.059975 Loss2: 1.304334 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.347688 Loss1: 0.053178 Loss2: 1.294510 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996652
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.439396 Loss1: 0.079845 Loss2: 1.359551 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.457555 Loss1: 0.098007 Loss2: 1.359548 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.100811 Loss1: 0.241183 Loss2: 1.859628 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.533228 Loss1: 0.156886 Loss2: 1.376342 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.539485 Loss1: 0.163268 Loss2: 1.376217 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.244382 Loss1: 0.351236 Loss2: 1.893146
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.493575 Loss1: 0.114090 Loss2: 1.379485
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.683647 Loss1: 0.288894 Loss2: 1.394753
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.497819 Loss1: 0.115433 Loss2: 1.382386
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.647429 Loss1: 0.203171 Loss2: 1.444258
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.516716 Loss1: 0.138015 Loss2: 1.378701
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.571116 Loss1: 0.152511 Loss2: 1.418605
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489529 Loss1: 0.106671 Loss2: 1.382858
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.442178 Loss1: 0.067461 Loss2: 1.374717 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.523884 Loss1: 0.124718 Loss2: 1.399166 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.499704 Loss1: 0.097942 Loss2: 1.401762 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.493046 Loss1: 0.108512 Loss2: 1.384534
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.075367 Loss1: 0.253290 Loss2: 1.822078
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.529883 Loss1: 0.180246 Loss2: 1.349637
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.508187 Loss1: 0.154208 Loss2: 1.353979
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.527338 Loss1: 0.169578 Loss2: 1.357760
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.579001 Loss1: 0.219936 Loss2: 1.359066
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.160282 Loss1: 0.333246 Loss2: 1.827036
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.561991 Loss1: 0.211752 Loss2: 1.350239 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.511081 Loss1: 0.135753 Loss2: 1.375328 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.451676 Loss1: 0.094658 Loss2: 1.357018 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.477770 Loss1: 0.119764 Loss2: 1.358005 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.998047
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391043 Loss1: 0.054560 Loss2: 1.336482
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423793 Loss1: 0.073668 Loss2: 1.350124
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.429972 Loss1: 0.080566 Loss2: 1.349405
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.488634 Loss1: 0.136564 Loss2: 1.352070
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.433922 Loss1: 0.075382 Loss2: 1.358540
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397177 Loss1: 0.054799 Loss2: 1.342379
-(DefaultActor pid=3764) >> Training accuracy: 0.991211
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.053114 Loss1: 0.306650 Loss2: 1.746464
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.496762 Loss1: 0.194465 Loss2: 1.302297
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.420389 Loss1: 0.117698 Loss2: 1.302690
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.384506 Loss1: 0.087721 Loss2: 1.296786
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.363351 Loss1: 0.076197 Loss2: 1.287153
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.102640 Loss1: 0.276102 Loss2: 1.826538
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.521166 Loss1: 0.161477 Loss2: 1.359689 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.460281 Loss1: 0.101299 Loss2: 1.358982 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.449170 Loss1: 0.094845 Loss2: 1.354325 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.424182 Loss1: 0.078468 Loss2: 1.345715 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.322838 Loss1: 0.050450 Loss2: 1.272388
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.422384 Loss1: 0.077809 Loss2: 1.344575
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.416335 Loss1: 0.070637 Loss2: 1.345698
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.441930 Loss1: 0.088619 Loss2: 1.353311
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.460270 Loss1: 0.108897 Loss2: 1.351372
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.461356 Loss1: 0.106179 Loss2: 1.355177
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.382634 Loss1: 0.430922 Loss2: 1.951712
-(DefaultActor pid=3764) >> Training accuracy: 0.985352
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.626513 Loss1: 0.281569 Loss2: 1.344944
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.554406 Loss1: 0.201693 Loss2: 1.352714
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.596482 Loss1: 0.222045 Loss2: 1.374437
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.571379 Loss1: 0.212124 Loss2: 1.359254
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.491787 Loss1: 0.142764 Loss2: 1.349023
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.472396 Loss1: 0.123140 Loss2: 1.349256
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.573299 Loss1: 0.252732 Loss2: 1.320567 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.471863 Loss1: 0.139801 Loss2: 1.332062 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990885
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.417862 Loss1: 0.114500 Loss2: 1.303362 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.400596 Loss1: 0.085912 Loss2: 1.314684 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.393192 Loss1: 0.086953 Loss2: 1.306239
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.187871 Loss1: 0.299006 Loss2: 1.888865
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.365088 Loss1: 0.057351 Loss2: 1.307736
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.653479 Loss1: 0.253640 Loss2: 1.399839
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380676 Loss1: 0.078845 Loss2: 1.301831
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.601699 Loss1: 0.166218 Loss2: 1.435482
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.542117 Loss1: 0.135821 Loss2: 1.406296
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.551881 Loss1: 0.147739 Loss2: 1.404142
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.547987 Loss1: 0.133546 Loss2: 1.414441
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.562730 Loss1: 0.155695 Loss2: 1.407035
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.557727 Loss1: 0.143106 Loss2: 1.414620
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.265879 Loss1: 0.340168 Loss2: 1.925710
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.516160 Loss1: 0.099424 Loss2: 1.416736
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.569610 Loss1: 0.161425 Loss2: 1.408185
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.485961 Loss1: 0.082833 Loss2: 1.403128
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.517572 Loss1: 0.111508 Loss2: 1.406063
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.532652 Loss1: 0.114207 Loss2: 1.418445
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.514078 Loss1: 0.111059 Loss2: 1.403018
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485483 Loss1: 0.078287 Loss2: 1.407196
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.479820 Loss1: 0.081296 Loss2: 1.398525
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486991 Loss1: 0.090353 Loss2: 1.396639
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.284704 Loss1: 0.362046 Loss2: 1.922658
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463755 Loss1: 0.061252 Loss2: 1.402502
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.625874 Loss1: 0.220411 Loss2: 1.405463
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.451786 Loss1: 0.052630 Loss2: 1.399157
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.569743 Loss1: 0.148369 Loss2: 1.421373
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.517160 Loss1: 0.104508 Loss2: 1.412653
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.491608 Loss1: 0.092927 Loss2: 1.398682
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.508973 Loss1: 0.112209 Loss2: 1.396764
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461625 Loss1: 0.066405 Loss2: 1.395220
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.436365 Loss1: 0.045528 Loss2: 1.390838
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.070514 Loss1: 0.291420 Loss2: 1.779094
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.438964 Loss1: 0.056866 Loss2: 1.382097
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.507120 Loss1: 0.195522 Loss2: 1.311598
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.459496 Loss1: 0.077171 Loss2: 1.382325
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.462412 Loss1: 0.147868 Loss2: 1.314544
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.451050 Loss1: 0.129662 Loss2: 1.321389
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.419467 Loss1: 0.104136 Loss2: 1.315331
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.387970 Loss1: 0.076717 Loss2: 1.311253
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.373118 Loss1: 0.060501 Loss2: 1.312617
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.344239 Loss1: 0.043443 Loss2: 1.300795
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.330792 Loss1: 0.421274 Loss2: 1.909518
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.345803 Loss1: 0.049167 Loss2: 1.296636
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.684289 Loss1: 0.291562 Loss2: 1.392727
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.359746 Loss1: 0.068916 Loss2: 1.290830
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.574836 Loss1: 0.165101 Loss2: 1.409735
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.509910 Loss1: 0.138181 Loss2: 1.371729
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.465078 Loss1: 0.098912 Loss2: 1.366166
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.453103 Loss1: 0.087848 Loss2: 1.365254
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.435534 Loss1: 0.074699 Loss2: 1.360835
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.095379 Loss1: 0.317587 Loss2: 1.777792
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.408649 Loss1: 0.058818 Loss2: 1.349832
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.555572 Loss1: 0.256546 Loss2: 1.299026
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.398727 Loss1: 0.049503 Loss2: 1.349224
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.533116 Loss1: 0.188139 Loss2: 1.344977
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.396969 Loss1: 0.052053 Loss2: 1.344916
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.441853 Loss1: 0.128358 Loss2: 1.313495 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.433560 Loss1: 0.121289 Loss2: 1.312271 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.410575 Loss1: 0.104504 Loss2: 1.306071
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.410392 Loss1: 0.427917 Loss2: 1.982475
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.700819 Loss1: 0.325284 Loss2: 1.375535
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368318 Loss1: 0.063936 Loss2: 1.304381
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.611368 Loss1: 0.205034 Loss2: 1.406334
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.370146 Loss1: 0.075090 Loss2: 1.295056
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.487609 Loss1: 0.117504 Loss2: 1.370105 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440187 Loss1: 0.065764 Loss2: 1.374423 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.409637 Loss1: 0.053807 Loss2: 1.355830 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.391677 Loss1: 0.043057 Loss2: 1.348620 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995192
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.502744 Loss1: 0.127019 Loss2: 1.375724 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.444178 Loss1: 0.093087 Loss2: 1.351090 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.162744 Loss1: 0.323031 Loss2: 1.839713
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.434389 Loss1: 0.086265 Loss2: 1.348125
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.425497 Loss1: 0.083370 Loss2: 1.342128
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.598864 Loss1: 0.230469 Loss2: 1.368396
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.394284 Loss1: 0.054962 Loss2: 1.339321
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551012 Loss1: 0.159037 Loss2: 1.391976
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414000 Loss1: 0.077948 Loss2: 1.336052
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.545045 Loss1: 0.165728 Loss2: 1.379317
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.518614 Loss1: 0.143076 Loss2: 1.375538
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.515190 Loss1: 0.137626 Loss2: 1.377564
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.470703 Loss1: 0.099364 Loss2: 1.371338
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449639 Loss1: 0.076355 Loss2: 1.373284
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.109478 Loss1: 0.256618 Loss2: 1.852859
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.462802 Loss1: 0.102752 Loss2: 1.360050
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.421937 Loss1: 0.061664 Loss2: 1.360273 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.501028 Loss1: 0.129600 Loss2: 1.371429 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.482052 Loss1: 0.119766 Loss2: 1.362286 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.461227 Loss1: 0.101993 Loss2: 1.359234
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.216456 Loss1: 0.366193 Loss2: 1.850263
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.548597 Loss1: 0.205551 Loss2: 1.343046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.494169 Loss1: 0.133283 Loss2: 1.360886 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.461834 Loss1: 0.123364 Loss2: 1.338470 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.413393 Loss1: 0.080570 Loss2: 1.332823 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.360502 Loss1: 0.047412 Loss2: 1.313090 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388691 Loss1: 0.077832 Loss2: 1.310859 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.349934 Loss1: 0.040128 Loss2: 1.309806 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.467196 Loss1: 0.100241 Loss2: 1.366956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.434046 Loss1: 0.082582 Loss2: 1.351464 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.124767 Loss1: 0.302217 Loss2: 1.822550 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.499550 Loss1: 0.164106 Loss2: 1.335444 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.493515 Loss1: 0.162108 Loss2: 1.331407 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.437357 Loss1: 0.109958 Loss2: 1.327398 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.394531 Loss1: 0.073925 Loss2: 1.320606 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.381791 Loss1: 0.064139 Loss2: 1.317652
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.273499 Loss1: 0.405757 Loss2: 1.867742
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.550811 Loss1: 0.232484 Loss2: 1.318327
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.385963 Loss1: 0.071666 Loss2: 1.314297
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.540711 Loss1: 0.213405 Loss2: 1.327305
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371580 Loss1: 0.060193 Loss2: 1.311387
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.435147 Loss1: 0.110465 Loss2: 1.324682 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.418458 Loss1: 0.099022 Loss2: 1.319436 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381496 Loss1: 0.058713 Loss2: 1.322783 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.361047 Loss1: 0.048928 Loss2: 1.312119 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993990
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.463439 Loss1: 0.157643 Loss2: 1.305795 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.426883 Loss1: 0.114648 Loss2: 1.312234 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.437472 Loss1: 0.130354 Loss2: 1.307117 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400511 Loss1: 0.097734 Loss2: 1.302777 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.346310 Loss1: 0.046890 Loss2: 1.299420 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.337780 Loss1: 0.047796 Loss2: 1.289984 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.403227 Loss1: 0.064381 Loss2: 1.338847 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.385861 Loss1: 0.054834 Loss2: 1.331027 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.226328 Loss1: 0.344497 Loss2: 1.881831 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.600109 Loss1: 0.192528 Loss2: 1.407581 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.558618 Loss1: 0.169111 Loss2: 1.389508 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.502172 Loss1: 0.106568 Loss2: 1.395603
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.212761 Loss1: 0.335624 Loss2: 1.877137
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.565062 Loss1: 0.190115 Loss2: 1.374948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.514652 Loss1: 0.136739 Loss2: 1.377913 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.449953 Loss1: 0.072084 Loss2: 1.377869 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.977083
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.461638 Loss1: 0.112044 Loss2: 1.349594 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.417868 Loss1: 0.059515 Loss2: 1.358353 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.415060 Loss1: 0.060009 Loss2: 1.355051 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404033 Loss1: 0.052954 Loss2: 1.351079 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.510051 Loss1: 0.161128 Loss2: 1.348923 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.441324 Loss1: 0.095804 Loss2: 1.345520 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.421297 Loss1: 0.085896 Loss2: 1.335401
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.119842 Loss1: 0.296956 Loss2: 1.822887
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.625843 Loss1: 0.289767 Loss2: 1.336076 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.551240 Loss1: 0.152416 Loss2: 1.398825 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.471082 Loss1: 0.107611 Loss2: 1.363470 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.409994 Loss1: 0.079780 Loss2: 1.330214
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.461676 Loss1: 0.118933 Loss2: 1.342743
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.500107 Loss1: 0.145421 Loss2: 1.354686
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452083 Loss1: 0.097230 Loss2: 1.354853
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.435599 Loss1: 0.091889 Loss2: 1.343709
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.414523 Loss1: 0.072468 Loss2: 1.342056
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.112567 Loss1: 0.323199 Loss2: 1.789368
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379344 Loss1: 0.043195 Loss2: 1.336149
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.497968 Loss1: 0.146405 Loss2: 1.351563 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.431750 Loss1: 0.092935 Loss2: 1.338814 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.034764 Loss1: 0.284468 Loss2: 1.750296
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.448759 Loss1: 0.123619 Loss2: 1.325139
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.557530 Loss1: 0.247715 Loss2: 1.309815
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.394795 Loss1: 0.063080 Loss2: 1.331716
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.558041 Loss1: 0.211063 Loss2: 1.346978
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.386205 Loss1: 0.065157 Loss2: 1.321047
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.439641 Loss1: 0.127730 Loss2: 1.311911
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394355 Loss1: 0.075413 Loss2: 1.318942
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.419953 Loss1: 0.118721 Loss2: 1.301232
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378649 Loss1: 0.061012 Loss2: 1.317636
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.353586 Loss1: 0.047872 Loss2: 1.305714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.334235 Loss1: 0.044533 Loss2: 1.289702 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.247018 Loss1: 0.362063 Loss2: 1.884955
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.302908 Loss1: 0.022521 Loss2: 1.280387
-(DefaultActor pid=3764) >> Training accuracy: 0.999023
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.508852 Loss1: 0.165249 Loss2: 1.343602 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.428069 Loss1: 0.093623 Loss2: 1.334446 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.221298 Loss1: 0.340196 Loss2: 1.881102 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.590023 Loss1: 0.209728 Loss2: 1.380295 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.531549 Loss1: 0.148270 Loss2: 1.383279 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.475793 Loss1: 0.090666 Loss2: 1.385127 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.985491
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.428551 Loss1: 0.067586 Loss2: 1.360965 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.410156 Loss1: 0.055203 Loss2: 1.354953 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.386896 Loss1: 0.037767 Loss2: 1.349129
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.142013 Loss1: 0.334101 Loss2: 1.807912
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.392089 Loss1: 0.044925 Loss2: 1.347164
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.537289 Loss1: 0.223250 Loss2: 1.314039
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.575152 Loss1: 0.231350 Loss2: 1.343802
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.539038 Loss1: 0.195703 Loss2: 1.343335
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488148 Loss1: 0.159627 Loss2: 1.328521
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.478396 Loss1: 0.144906 Loss2: 1.333491
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.056370 Loss1: 0.281217 Loss2: 1.775153
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.419952 Loss1: 0.100048 Loss2: 1.319903
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.514291 Loss1: 0.188194 Loss2: 1.326097
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393829 Loss1: 0.076178 Loss2: 1.317651
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.402824 Loss1: 0.089658 Loss2: 1.313166
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.503350 Loss1: 0.158227 Loss2: 1.345122
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.406323 Loss1: 0.093686 Loss2: 1.312637
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.443942 Loss1: 0.118026 Loss2: 1.325917
-(DefaultActor pid=3765) >> Training accuracy: 0.985417
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.450753 Loss1: 0.129782 Loss2: 1.320971
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441828 Loss1: 0.107015 Loss2: 1.334814
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.420895 Loss1: 0.102877 Loss2: 1.318018
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.412386 Loss1: 0.095685 Loss2: 1.316701
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.217629 Loss1: 0.379116 Loss2: 1.838514
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410111 Loss1: 0.090401 Loss2: 1.319710
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.380768 Loss1: 0.065276 Loss2: 1.315493 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482330 Loss1: 0.121284 Loss2: 1.361046 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.425854 Loss1: 0.084719 Loss2: 1.341135 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 18:09:27,834 | server.py:236 | fit_round 197 received 50 results and 0 failures
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440292 Loss1: 0.095466 Loss2: 1.344826
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.211001 Loss1: 0.403126 Loss2: 1.807875
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.556557 Loss1: 0.233012 Loss2: 1.323545 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.468800 Loss1: 0.116460 Loss2: 1.352339 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.401384 Loss1: 0.090675 Loss2: 1.310710 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.404809 Loss1: 0.095093 Loss2: 1.309717 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.368908 Loss1: 0.068548 Loss2: 1.300360 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.365743 Loss1: 0.067096 Loss2: 1.298646 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.391991 Loss1: 0.092438 Loss2: 1.299553 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.979167
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533204 Loss1: 0.142928 Loss2: 1.390276 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.440689 Loss1: 0.068058 Loss2: 1.372631 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.327209 Loss1: 0.378918 Loss2: 1.948290 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.665774 Loss1: 0.244942 Loss2: 1.420832 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.612067 Loss1: 0.160597 Loss2: 1.451470 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.513047 Loss1: 0.096952 Loss2: 1.416095 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.508514 Loss1: 0.086623 Loss2: 1.421891 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.485593 Loss1: 0.064887 Loss2: 1.420706
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.192893 Loss1: 0.327492 Loss2: 1.865401
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.571717 Loss1: 0.211477 Loss2: 1.360240 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.482293 Loss1: 0.067719 Loss2: 1.414574
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.581171 Loss1: 0.203648 Loss2: 1.377523
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.564341 Loss1: 0.181085 Loss2: 1.383256
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.494921 Loss1: 0.131961 Loss2: 1.362960
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463279 Loss1: 0.091875 Loss2: 1.371403
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.416829 Loss1: 0.058270 Loss2: 1.358559
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.176213 Loss1: 0.269023 Loss2: 1.907190
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.387410 Loss1: 0.040637 Loss2: 1.346773
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.391104 Loss1: 0.050288 Loss2: 1.340816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383574 Loss1: 0.042904 Loss2: 1.340670 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.484457 Loss1: 0.105034 Loss2: 1.379422 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.446555 Loss1: 0.077634 Loss2: 1.368921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395683 Loss1: 0.037213 Loss2: 1.358470 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 18:09:27,834][flwr][DEBUG] - fit_round 197 received 50 results and 0 failures
-INFO flwr 2023-10-13 18:10:09,046 | server.py:125 | fit progress: (197, 2.338596988600283, {'accuracy': 0.6144}, 454716.824874288)
->> Test accuracy: 0.614400
-[2023-10-13 18:10:09,046][flwr][INFO] - fit progress: (197, 2.338596988600283, {'accuracy': 0.6144}, 454716.824874288)
-DEBUG flwr 2023-10-13 18:10:09,047 | server.py:173 | evaluate_round 197: strategy sampled 50 clients (out of 50)
-[2023-10-13 18:10:09,047][flwr][DEBUG] - evaluate_round 197: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 18:19:12,332 | server.py:187 | evaluate_round 197 received 50 results and 0 failures
-[2023-10-13 18:19:12,332][flwr][DEBUG] - evaluate_round 197 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 18:19:12,333 | server.py:222 | fit_round 198: strategy sampled 50 clients (out of 50)
-[2023-10-13 18:19:12,333][flwr][DEBUG] - fit_round 198: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.132724 Loss1: 0.298493 Loss2: 1.834231 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.579569 Loss1: 0.179005 Loss2: 1.400564 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.125319 Loss1: 0.327294 Loss2: 1.798026
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.620264 Loss1: 0.234674 Loss2: 1.385590
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.555748 Loss1: 0.242826 Loss2: 1.312922
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.573020 Loss1: 0.186895 Loss2: 1.386125
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.495569 Loss1: 0.113787 Loss2: 1.381782 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458402 Loss1: 0.079521 Loss2: 1.378882 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.441214 Loss1: 0.073755 Loss2: 1.367459 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422302 Loss1: 0.057013 Loss2: 1.365289 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.414926 Loss1: 0.055040 Loss2: 1.359886 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.361954 Loss1: 0.057215 Loss2: 1.304739 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.116007 Loss1: 0.303640 Loss2: 1.812367 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.474718 Loss1: 0.138053 Loss2: 1.336664 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.204945 Loss1: 0.290746 Loss2: 1.914200
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489387 Loss1: 0.150456 Loss2: 1.338931
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.571278 Loss1: 0.183407 Loss2: 1.387872
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.455841 Loss1: 0.116706 Loss2: 1.339135
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443570 Loss1: 0.101411 Loss2: 1.342159 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.422972 Loss1: 0.093357 Loss2: 1.329615 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.386143 Loss1: 0.057715 Loss2: 1.328428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.370488 Loss1: 0.047298 Loss2: 1.323189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384867 Loss1: 0.065365 Loss2: 1.319502 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995117
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.442912 Loss1: 0.072998 Loss2: 1.369914 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.218162 Loss1: 0.325329 Loss2: 1.892833 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.585916 Loss1: 0.155942 Loss2: 1.429974 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.174420 Loss1: 0.306647 Loss2: 1.867773
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.525792 Loss1: 0.117366 Loss2: 1.408426
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.545159 Loss1: 0.185749 Loss2: 1.359410
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488908 Loss1: 0.094694 Loss2: 1.394214
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.538685 Loss1: 0.169677 Loss2: 1.369008
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.468101 Loss1: 0.068509 Loss2: 1.399592
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458368 Loss1: 0.058892 Loss2: 1.399475 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.448671 Loss1: 0.059189 Loss2: 1.389482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.489723 Loss1: 0.101123 Loss2: 1.388600 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.466089 Loss1: 0.073238 Loss2: 1.392851 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.395952 Loss1: 0.044508 Loss2: 1.351444 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.207876 Loss1: 0.325414 Loss2: 1.882462 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551334 Loss1: 0.164512 Loss2: 1.386822 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.521892 Loss1: 0.141561 Loss2: 1.380331
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.188227 Loss1: 0.354114 Loss2: 1.834113
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.575932 Loss1: 0.233182 Loss2: 1.342750 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.514180 Loss1: 0.152892 Loss2: 1.361288 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.461549 Loss1: 0.117147 Loss2: 1.344402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455591 Loss1: 0.115602 Loss2: 1.339990 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.480944 Loss1: 0.125636 Loss2: 1.355308 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398615 Loss1: 0.044325 Loss2: 1.354290
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.424556 Loss1: 0.083583 Loss2: 1.340973
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.409960 Loss1: 0.075752 Loss2: 1.334208
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.370568 Loss1: 0.038324 Loss2: 1.332244
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396936 Loss1: 0.070572 Loss2: 1.326365
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.201885 Loss1: 0.389139 Loss2: 1.812746
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.534718 Loss1: 0.217886 Loss2: 1.316832
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.526659 Loss1: 0.180672 Loss2: 1.345987
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.458456 Loss1: 0.139035 Loss2: 1.319421
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.110666 Loss1: 0.262719 Loss2: 1.847947
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.646269 Loss1: 0.269956 Loss2: 1.376312 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.583284 Loss1: 0.169105 Loss2: 1.414179 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.401937 Loss1: 0.090705 Loss2: 1.311232 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.387084 Loss1: 0.079961 Loss2: 1.307123 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358351 Loss1: 0.054688 Loss2: 1.303662 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412712 Loss1: 0.053312 Loss2: 1.359399 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.416596 Loss1: 0.061801 Loss2: 1.354795 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402757 Loss1: 0.050153 Loss2: 1.352603
-(DefaultActor pid=3764) >> Training accuracy: 0.995404
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.185164 Loss1: 0.361746 Loss2: 1.823418
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.615268 Loss1: 0.271465 Loss2: 1.343803
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.517697 Loss1: 0.163217 Loss2: 1.354480
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.472446 Loss1: 0.126887 Loss2: 1.345559
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.422158 Loss1: 0.092516 Loss2: 1.329642
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.189274 Loss1: 0.305830 Loss2: 1.883444
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.416314 Loss1: 0.087312 Loss2: 1.329002
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.634472 Loss1: 0.262512 Loss2: 1.371961
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.404292 Loss1: 0.069640 Loss2: 1.334652
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564136 Loss1: 0.163955 Loss2: 1.400181
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.387264 Loss1: 0.063899 Loss2: 1.323366
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.549452 Loss1: 0.152344 Loss2: 1.397109
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.362768 Loss1: 0.044934 Loss2: 1.317834
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.500690 Loss1: 0.120465 Loss2: 1.380225
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.343095 Loss1: 0.027976 Loss2: 1.315119
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.449827 Loss1: 0.076212 Loss2: 1.373615 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.435825 Loss1: 0.069301 Loss2: 1.366523 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.438855 Loss1: 0.076374 Loss2: 1.362480
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.142130 Loss1: 0.265782 Loss2: 1.876348
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.573827 Loss1: 0.191820 Loss2: 1.382007
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.555093 Loss1: 0.176232 Loss2: 1.378861
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.501046 Loss1: 0.111866 Loss2: 1.389180
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.473115 Loss1: 0.108037 Loss2: 1.365078
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.130489 Loss1: 0.328796 Loss2: 1.801693
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.522020 Loss1: 0.140717 Loss2: 1.381303
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.566319 Loss1: 0.254662 Loss2: 1.311657
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.488801 Loss1: 0.111366 Loss2: 1.377436
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.554374 Loss1: 0.204424 Loss2: 1.349950
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.453343 Loss1: 0.086060 Loss2: 1.367283
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.440875 Loss1: 0.122989 Loss2: 1.317886
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.437172 Loss1: 0.068954 Loss2: 1.368218
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455314 Loss1: 0.141952 Loss2: 1.313362
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.422728 Loss1: 0.061093 Loss2: 1.361635
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.360812 Loss1: 0.052056 Loss2: 1.308756 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.366440 Loss1: 0.068346 Loss2: 1.298094 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.341220 Loss1: 0.040079 Loss2: 1.301141
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.049001 Loss1: 0.284415 Loss2: 1.764585
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.479447 Loss1: 0.206096 Loss2: 1.273352
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.458959 Loss1: 0.170052 Loss2: 1.288907
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.395962 Loss1: 0.094153 Loss2: 1.301809
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.406151 Loss1: 0.120937 Loss2: 1.285215
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.415328 Loss1: 0.124123 Loss2: 1.291205
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.264408 Loss1: 0.393231 Loss2: 1.871177
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.395933 Loss1: 0.098177 Loss2: 1.297756
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.623652 Loss1: 0.254411 Loss2: 1.369241
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.379699 Loss1: 0.093387 Loss2: 1.286312
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.653532 Loss1: 0.247164 Loss2: 1.406368
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.327496 Loss1: 0.046713 Loss2: 1.280783
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521255 Loss1: 0.138769 Loss2: 1.382486
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.333152 Loss1: 0.055978 Loss2: 1.277175
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.496277 Loss1: 0.132369 Loss2: 1.363908
-(DefaultActor pid=3765) >> Training accuracy: 0.986458
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.446574 Loss1: 0.072902 Loss2: 1.373671
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.412657 Loss1: 0.054340 Loss2: 1.358317
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.413308 Loss1: 0.063058 Loss2: 1.350250
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410049 Loss1: 0.063531 Loss2: 1.346517
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.107546 Loss1: 0.303386 Loss2: 1.804160
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.402278 Loss1: 0.056041 Loss2: 1.346237
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.480122 Loss1: 0.138819 Loss2: 1.341303 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.430157 Loss1: 0.099212 Loss2: 1.330945 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.422634 Loss1: 0.090202 Loss2: 1.332433 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.407749 Loss1: 0.078409 Loss2: 1.329340 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400711 Loss1: 0.076414 Loss2: 1.324297 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.394407 Loss1: 0.071173 Loss2: 1.323234 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.412751 Loss1: 0.090941 Loss2: 1.321810 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.990234
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.391866 Loss1: 0.088587 Loss2: 1.303279 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354552 Loss1: 0.051920 Loss2: 1.302632 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.333789 Loss1: 0.043933 Loss2: 1.289856
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.267549 Loss1: 0.348492 Loss2: 1.919057
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.654532 Loss1: 0.288597 Loss2: 1.365936
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.552290 Loss1: 0.159667 Loss2: 1.392623
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.504770 Loss1: 0.119850 Loss2: 1.384920
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.516963 Loss1: 0.147530 Loss2: 1.369433
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.360965 Loss1: 0.414632 Loss2: 1.946333
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.477951 Loss1: 0.099606 Loss2: 1.378345
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.504369 Loss1: 0.124147 Loss2: 1.380223 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.452723 Loss1: 0.081619 Loss2: 1.371103 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.446504 Loss1: 0.072603 Loss2: 1.373901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.449623 Loss1: 0.079515 Loss2: 1.370108 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420576 Loss1: 0.056306 Loss2: 1.364270 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.396631 Loss1: 0.045795 Loss2: 1.350836 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.985577
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.753293 Loss1: 0.267960 Loss2: 1.485333 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.694490 Loss1: 0.196550 Loss2: 1.497939 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.602410 Loss1: 0.105057 Loss2: 1.497353 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.597925 Loss1: 0.111442 Loss2: 1.486483 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.636714 Loss1: 0.139967 Loss2: 1.496746 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.618290 Loss1: 0.106371 Loss2: 1.511919 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.621625 Loss1: 0.138992 Loss2: 1.482633 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.610257 Loss1: 0.119217 Loss2: 1.491040 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.338795 Loss1: 0.047656 Loss2: 1.291139 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.316398 Loss1: 0.031602 Loss2: 1.284796 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.569203 Loss1: 0.187169 Loss2: 1.382034 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.524464 Loss1: 0.127824 Loss2: 1.396640 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.464731 Loss1: 0.099021 Loss2: 1.365710
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.188305 Loss1: 0.308065 Loss2: 1.880240
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.500670 Loss1: 0.137643 Loss2: 1.363027
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.550107 Loss1: 0.194136 Loss2: 1.355972
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.504059 Loss1: 0.148091 Loss2: 1.355969 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.442736 Loss1: 0.086244 Loss2: 1.356491 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.410352 Loss1: 0.074954 Loss2: 1.335398 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.440775 Loss1: 0.103478 Loss2: 1.337297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.420316 Loss1: 0.079321 Loss2: 1.340994 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.395517 Loss1: 0.067560 Loss2: 1.327957 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.546595 Loss1: 0.161877 Loss2: 1.384717 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.526023 Loss1: 0.129948 Loss2: 1.396075 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.490051 Loss1: 0.106429 Loss2: 1.383623
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.184951 Loss1: 0.374075 Loss2: 1.810876
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.630826 Loss1: 0.293656 Loss2: 1.337170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.600112 Loss1: 0.225722 Loss2: 1.374389 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.481879 Loss1: 0.139053 Loss2: 1.342827 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.440611 Loss1: 0.110534 Loss2: 1.330078 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.401367 Loss1: 0.037964 Loss2: 1.363402
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.425914 Loss1: 0.096975 Loss2: 1.328939
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.456747 Loss1: 0.129215 Loss2: 1.327532
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408927 Loss1: 0.079493 Loss2: 1.329433
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396070 Loss1: 0.072872 Loss2: 1.323198
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.369819 Loss1: 0.054436 Loss2: 1.315383
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.228310 Loss1: 0.350366 Loss2: 1.877944
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.605900 Loss1: 0.254984 Loss2: 1.350916
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.521847 Loss1: 0.134875 Loss2: 1.386971
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.435544 Loss1: 0.084264 Loss2: 1.351281
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.441941 Loss1: 0.095941 Loss2: 1.346000
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.096096 Loss1: 0.321094 Loss2: 1.775002
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.566373 Loss1: 0.257077 Loss2: 1.309296 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.550806 Loss1: 0.201347 Loss2: 1.349459 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.488487 Loss1: 0.168122 Loss2: 1.320365 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.427853 Loss1: 0.117808 Loss2: 1.310044 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.380444 Loss1: 0.049938 Loss2: 1.330506
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.386983 Loss1: 0.075563 Loss2: 1.311420
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.385714 Loss1: 0.081412 Loss2: 1.304303
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.353881 Loss1: 0.056176 Loss2: 1.297705
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.343031 Loss1: 0.056785 Loss2: 1.286246
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.357527 Loss1: 0.068027 Loss2: 1.289500
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.248376 Loss1: 0.393699 Loss2: 1.854677
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.575024 Loss1: 0.218870 Loss2: 1.356154
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.565441 Loss1: 0.191505 Loss2: 1.373935
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.464164 Loss1: 0.108715 Loss2: 1.355449
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.422329 Loss1: 0.076284 Loss2: 1.346045
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.233446 Loss1: 0.364618 Loss2: 1.868828
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.578308 Loss1: 0.208680 Loss2: 1.369628 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.572129 Loss1: 0.186025 Loss2: 1.386104 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.493552 Loss1: 0.121981 Loss2: 1.371571 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.463767 Loss1: 0.102177 Loss2: 1.361590 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.452633 Loss1: 0.096565 Loss2: 1.356068 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408855 Loss1: 0.064636 Loss2: 1.344219 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.423586 Loss1: 0.072782 Loss2: 1.350804 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.987500
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.627728 Loss1: 0.268008 Loss2: 1.359720 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.478694 Loss1: 0.119639 Loss2: 1.359055 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.406979 Loss1: 0.069307 Loss2: 1.337672 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.383001 Loss1: 0.047643 Loss2: 1.335358 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.368551 Loss1: 0.035532 Loss2: 1.333018 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.342886 Loss1: 0.015368 Loss2: 1.327517 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.356452 Loss1: 0.033537 Loss2: 1.322916 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995192
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423716 Loss1: 0.093502 Loss2: 1.330214 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.439776 Loss1: 0.115313 Loss2: 1.324463 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.058885 Loss1: 0.243304 Loss2: 1.815581 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.482427 Loss1: 0.147487 Loss2: 1.334940 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.422179 Loss1: 0.093909 Loss2: 1.328270 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.388667 Loss1: 0.064559 Loss2: 1.324108 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.380315 Loss1: 0.053204 Loss2: 1.327112 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.373605 Loss1: 0.055187 Loss2: 1.318418 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.371453 Loss1: 0.050714 Loss2: 1.320739 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355087 Loss1: 0.040207 Loss2: 1.314880 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.349087 Loss1: 0.041884 Loss2: 1.307203 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.318540 Loss1: 0.027092 Loss2: 1.291448 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.332624 Loss1: 0.037487 Loss2: 1.295136 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997070
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.547432 Loss1: 0.163945 Loss2: 1.383487 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.508741 Loss1: 0.131136 Loss2: 1.377605 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.507590 Loss1: 0.129033 Loss2: 1.378557
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.193166 Loss1: 0.361654 Loss2: 1.831512
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.624240 Loss1: 0.265244 Loss2: 1.358996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.560298 Loss1: 0.157427 Loss2: 1.402871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.527462 Loss1: 0.167064 Loss2: 1.360397 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.460673 Loss1: 0.098689 Loss2: 1.361984 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.454218 Loss1: 0.095934 Loss2: 1.358284 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.436887 Loss1: 0.080708 Loss2: 1.356180
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.123454 Loss1: 0.278517 Loss2: 1.844937
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.421925 Loss1: 0.067218 Loss2: 1.354707
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.457836 Loss1: 0.130038 Loss2: 1.327798
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.413867 Loss1: 0.092447 Loss2: 1.321420
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.448951 Loss1: 0.121348 Loss2: 1.327603
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.384905 Loss1: 0.064643 Loss2: 1.320262
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.390134 Loss1: 0.076377 Loss2: 1.313757
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.359283 Loss1: 0.046438 Loss2: 1.312845
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.011434 Loss1: 0.239077 Loss2: 1.772357
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.384829 Loss1: 0.073660 Loss2: 1.311169
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.508312 Loss1: 0.185510 Loss2: 1.322802
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.368089 Loss1: 0.056115 Loss2: 1.311975
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.492934 Loss1: 0.158769 Loss2: 1.334165
-(DefaultActor pid=3765) >> Training accuracy: 0.990625
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.353874 Loss1: 0.045191 Loss2: 1.308683
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.444685 Loss1: 0.110552 Loss2: 1.334133
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.470418 Loss1: 0.148856 Loss2: 1.321562
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.481204 Loss1: 0.154306 Loss2: 1.326898
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.452552 Loss1: 0.119511 Loss2: 1.333041
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430550 Loss1: 0.101498 Loss2: 1.329052
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.173713 Loss1: 0.331817 Loss2: 1.841896
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.616318 Loss1: 0.266795 Loss2: 1.349522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400683 Loss1: 0.078964 Loss2: 1.321719
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.524068 Loss1: 0.146157 Loss2: 1.377910
-(DefaultActor pid=3764) >> Training accuracy: 0.983398
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.493500 Loss1: 0.139216 Loss2: 1.354285
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.472787 Loss1: 0.120965 Loss2: 1.351823
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.433090 Loss1: 0.087732 Loss2: 1.345358
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.375287 Loss1: 0.042589 Loss2: 1.332697
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.193980 Loss1: 0.330954 Loss2: 1.863026
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.372856 Loss1: 0.040231 Loss2: 1.332625
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.567941 Loss1: 0.237416 Loss2: 1.330525
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.344194 Loss1: 0.024578 Loss2: 1.319616
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.497133 Loss1: 0.141985 Loss2: 1.355148
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.338158 Loss1: 0.025486 Loss2: 1.312671
-(DefaultActor pid=3765) >> Training accuracy: 0.997917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.426002 Loss1: 0.092275 Loss2: 1.333728 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.404096 Loss1: 0.070590 Loss2: 1.333506 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.364007 Loss1: 0.041816 Loss2: 1.322191
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.198520 Loss1: 0.333745 Loss2: 1.864775
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354336 Loss1: 0.036691 Loss2: 1.317645
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.610115 Loss1: 0.251237 Loss2: 1.358878
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.356851 Loss1: 0.045862 Loss2: 1.310989
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.551988 Loss1: 0.175288 Loss2: 1.376699
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.483981 Loss1: 0.108325 Loss2: 1.375656
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.432969 Loss1: 0.074287 Loss2: 1.358682
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.423147 Loss1: 0.068377 Loss2: 1.354769
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.432590 Loss1: 0.083890 Loss2: 1.348700
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459064 Loss1: 0.109127 Loss2: 1.349938
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.175841 Loss1: 0.316833 Loss2: 1.859009
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.410158 Loss1: 0.058729 Loss2: 1.351429
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.533661 Loss1: 0.196899 Loss2: 1.336762
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378414 Loss1: 0.035709 Loss2: 1.342705
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.458426 Loss1: 0.125646 Loss2: 1.332780
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.413909 Loss1: 0.074039 Loss2: 1.339870
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.384278 Loss1: 0.060256 Loss2: 1.324021
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.386760 Loss1: 0.069789 Loss2: 1.316971
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.389155 Loss1: 0.075070 Loss2: 1.314085
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.369423 Loss1: 0.056948 Loss2: 1.312474
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.138619 Loss1: 0.309630 Loss2: 1.828990
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.587035 Loss1: 0.249733 Loss2: 1.337302 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.335328 Loss1: 0.032635 Loss2: 1.302694
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.545751 Loss1: 0.177030 Loss2: 1.368721
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.536149 Loss1: 0.179856 Loss2: 1.356293
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.527010 Loss1: 0.174764 Loss2: 1.352246
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.453205 Loss1: 0.103073 Loss2: 1.350132
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.451439 Loss1: 0.105900 Loss2: 1.345539
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.192633 Loss1: 0.321412 Loss2: 1.871221
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411761 Loss1: 0.071333 Loss2: 1.340427
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.652023 Loss1: 0.284653 Loss2: 1.367371
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.400919 Loss1: 0.066684 Loss2: 1.334234
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.565114 Loss1: 0.150948 Loss2: 1.414166
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398160 Loss1: 0.066195 Loss2: 1.331965
-(DefaultActor pid=3765) >> Training accuracy: 0.980208
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.452816 Loss1: 0.091415 Loss2: 1.361401 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.486058 Loss1: 0.119941 Loss2: 1.366117 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440222 Loss1: 0.072322 Loss2: 1.367901
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.226568 Loss1: 0.278353 Loss2: 1.948215
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.402440 Loss1: 0.046338 Loss2: 1.356101
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.636310 Loss1: 0.194765 Loss2: 1.441545
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375811 Loss1: 0.032423 Loss2: 1.343388
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.552032 Loss1: 0.115321 Loss2: 1.436711
-(DefaultActor pid=3764) >> Training accuracy: 0.993750
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.569282 Loss1: 0.128287 Loss2: 1.440995
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.535650 Loss1: 0.104169 Loss2: 1.431482
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.504382 Loss1: 0.077238 Loss2: 1.427143
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.523645 Loss1: 0.096263 Loss2: 1.427382
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.485333 Loss1: 0.062817 Loss2: 1.422517
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.122823 Loss1: 0.293259 Loss2: 1.829563
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.493880 Loss1: 0.075839 Loss2: 1.418041
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.519874 Loss1: 0.196134 Loss2: 1.323741
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.483784 Loss1: 0.064526 Loss2: 1.419258
-DEBUG flwr 2023-10-13 18:47:43,456 | server.py:236 | fit_round 198 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.509677 Loss1: 0.177327 Loss2: 1.332350
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.433892 Loss1: 0.101870 Loss2: 1.332022
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.409440 Loss1: 0.098355 Loss2: 1.311085
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.378752 Loss1: 0.071220 Loss2: 1.307532
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.383956 Loss1: 0.080486 Loss2: 1.303470
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.377391 Loss1: 0.494388 Loss2: 1.883003
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.354209 Loss1: 0.052030 Loss2: 1.302179
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.659518 Loss1: 0.309316 Loss2: 1.350201
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.332850 Loss1: 0.039599 Loss2: 1.293250
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.583905 Loss1: 0.191341 Loss2: 1.392564
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.330876 Loss1: 0.038044 Loss2: 1.292831
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.475606 Loss1: 0.141412 Loss2: 1.334194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.446727 Loss1: 0.111779 Loss2: 1.334948 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.409778 Loss1: 0.452163 Loss2: 1.957615 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.732197 Loss1: 0.395713 Loss2: 1.336484 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991071
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.539527 Loss1: 0.158430 Loss2: 1.381097 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.415295 Loss1: 0.073811 Loss2: 1.341484 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406226 Loss1: 0.069599 Loss2: 1.336627 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.398611 Loss1: 0.065834 Loss2: 1.332777
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.063162 Loss1: 0.239570 Loss2: 1.823593
-(DefaultActor pid=3764) >> Training accuracy: 0.990885
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379110 Loss1: 0.045266 Loss2: 1.333844
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.495389 Loss1: 0.130401 Loss2: 1.364988
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.472941 Loss1: 0.114503 Loss2: 1.358438
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.491061 Loss1: 0.125693 Loss2: 1.365369
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.455952 Loss1: 0.086726 Loss2: 1.369226
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.469994 Loss1: 0.110594 Loss2: 1.359400
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.253494 Loss1: 0.357332 Loss2: 1.896162
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.659698 Loss1: 0.261580 Loss2: 1.398118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.626064 Loss1: 0.193854 Loss2: 1.432209 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.594420 Loss1: 0.173555 Loss2: 1.420865 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384109 Loss1: 0.030810 Loss2: 1.353299
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.573808 Loss1: 0.170205 Loss2: 1.403604
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.579904 Loss1: 0.171086 Loss2: 1.408818
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.565547 Loss1: 0.159754 Loss2: 1.405793
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.564121 Loss1: 0.154232 Loss2: 1.409889
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.507845 Loss1: 0.097171 Loss2: 1.410674
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.484317 Loss1: 0.088175 Loss2: 1.396142
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 18:47:43,456][flwr][DEBUG] - fit_round 198 received 50 results and 0 failures
-INFO flwr 2023-10-13 18:48:24,455 | server.py:125 | fit progress: (198, 2.3383814542057415, {'accuracy': 0.615}, 457012.23382741597)
->> Test accuracy: 0.615000
-[2023-10-13 18:48:24,455][flwr][INFO] - fit progress: (198, 2.3383814542057415, {'accuracy': 0.615}, 457012.23382741597)
-DEBUG flwr 2023-10-13 18:48:24,456 | server.py:173 | evaluate_round 198: strategy sampled 50 clients (out of 50)
-[2023-10-13 18:48:24,456][flwr][DEBUG] - evaluate_round 198: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 18:57:25,895 | server.py:187 | evaluate_round 198 received 50 results and 0 failures
-[2023-10-13 18:57:25,895][flwr][DEBUG] - evaluate_round 198 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 18:57:25,895 | server.py:222 | fit_round 199: strategy sampled 50 clients (out of 50)
-[2023-10-13 18:57:25,895][flwr][DEBUG] - fit_round 199: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.139244 Loss1: 0.324870 Loss2: 1.814373
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.570316 Loss1: 0.246156 Loss2: 1.324160
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.541401 Loss1: 0.183793 Loss2: 1.357608
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.533156 Loss1: 0.183479 Loss2: 1.349677
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.501558 Loss1: 0.169364 Loss2: 1.332194 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461532 Loss1: 0.112771 Loss2: 1.348761 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480818 Loss1: 0.152711 Loss2: 1.328107 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.470335 Loss1: 0.139111 Loss2: 1.331224 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.431129 Loss1: 0.100690 Loss2: 1.330439 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358882 Loss1: 0.038968 Loss2: 1.319914 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.339432 Loss1: 0.030515 Loss2: 1.308917 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.187560 Loss1: 0.372856 Loss2: 1.814704 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.517873 Loss1: 0.158680 Loss2: 1.359192 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.475323 Loss1: 0.145237 Loss2: 1.330086
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.150044 Loss1: 0.305579 Loss2: 1.844465
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.533968 Loss1: 0.176620 Loss2: 1.357347 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.485257 Loss1: 0.125336 Loss2: 1.359921 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.428137 Loss1: 0.074194 Loss2: 1.353943 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.428944 Loss1: 0.078728 Loss2: 1.350216 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.421257 Loss1: 0.077753 Loss2: 1.343503 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.410522 Loss1: 0.097392 Loss2: 1.313129
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.383280 Loss1: 0.042386 Loss2: 1.340894
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389960 Loss1: 0.055597 Loss2: 1.334362
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.401223 Loss1: 0.065796 Loss2: 1.335427
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.385172 Loss1: 0.053279 Loss2: 1.331893
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.601154 Loss1: 0.290915 Loss2: 1.310238 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576152 Loss1: 0.195925 Loss2: 1.380227 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.189324 Loss1: 0.339166 Loss2: 1.850157 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.500554 Loss1: 0.161126 Loss2: 1.339428 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.465939 Loss1: 0.128149 Loss2: 1.337790 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421783 Loss1: 0.094501 Loss2: 1.327282 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384271 Loss1: 0.060614 Loss2: 1.323658 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.405853 Loss1: 0.069914 Loss2: 1.335939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.394506 Loss1: 0.070719 Loss2: 1.323787 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.378772 Loss1: 0.054948 Loss2: 1.323824
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.299709 Loss1: 0.418843 Loss2: 1.880865
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.579356 Loss1: 0.232040 Loss2: 1.347316 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.982292
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.574562 Loss1: 0.196477 Loss2: 1.378085 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.486473 Loss1: 0.132204 Loss2: 1.354269 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.452219 Loss1: 0.103457 Loss2: 1.348762
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.114577 Loss1: 0.300302 Loss2: 1.814275
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.607746 Loss1: 0.300790 Loss2: 1.306956 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.571467 Loss1: 0.194008 Loss2: 1.377460 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995536
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.522877 Loss1: 0.165201 Loss2: 1.357676 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.431985 Loss1: 0.096523 Loss2: 1.335462 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.386613 Loss1: 0.062782 Loss2: 1.323831
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.205399 Loss1: 0.330888 Loss2: 1.874511
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572067 Loss1: 0.211954 Loss2: 1.360113 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351808 Loss1: 0.038187 Loss2: 1.313621
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.505969 Loss1: 0.146952 Loss2: 1.359017
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.494601 Loss1: 0.119278 Loss2: 1.375323
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.496234 Loss1: 0.138632 Loss2: 1.357602
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.488806 Loss1: 0.126669 Loss2: 1.362137
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.529422 Loss1: 0.161640 Loss2: 1.367782
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.090056 Loss1: 0.241085 Loss2: 1.848971
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.537857 Loss1: 0.166080 Loss2: 1.371777
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.468906 Loss1: 0.102294 Loss2: 1.366612 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.454475 Loss1: 0.089950 Loss2: 1.364525 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.534004 Loss1: 0.186194 Loss2: 1.347810 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.507211 Loss1: 0.148605 Loss2: 1.358606 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.542536 Loss1: 0.180548 Loss2: 1.361988
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.196866 Loss1: 0.338795 Loss2: 1.858070
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.568647 Loss1: 0.214666 Loss2: 1.353981 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.494232 Loss1: 0.129428 Loss2: 1.364804 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.448718 Loss1: 0.103096 Loss2: 1.345622 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.409590 Loss1: 0.064876 Loss2: 1.344714 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411022 Loss1: 0.069540 Loss2: 1.341482 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399687 Loss1: 0.062551 Loss2: 1.337136 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.499042 Loss1: 0.150322 Loss2: 1.348720
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398133 Loss1: 0.067069 Loss2: 1.331064
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460249 Loss1: 0.124547 Loss2: 1.335702 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447592 Loss1: 0.095461 Loss2: 1.352132 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.124296 Loss1: 0.279073 Loss2: 1.845223
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.463988 Loss1: 0.117260 Loss2: 1.346729
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.548545 Loss1: 0.209516 Loss2: 1.339030
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441973 Loss1: 0.102005 Loss2: 1.339967
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.516251 Loss1: 0.162801 Loss2: 1.353450
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.414922 Loss1: 0.073646 Loss2: 1.341276
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470110 Loss1: 0.128731 Loss2: 1.341379 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.429922 Loss1: 0.086562 Loss2: 1.343360 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.383656 Loss1: 0.044311 Loss2: 1.339345
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.151142 Loss1: 0.289089 Loss2: 1.862053
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.513073 Loss1: 0.153398 Loss2: 1.359675 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.345527 Loss1: 0.021449 Loss2: 1.324078
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.498974 Loss1: 0.133495 Loss2: 1.365479
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468292 Loss1: 0.101858 Loss2: 1.366433
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.458726 Loss1: 0.099782 Loss2: 1.358944
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441652 Loss1: 0.089501 Loss2: 1.352151
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.422570 Loss1: 0.071960 Loss2: 1.350609
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.216418 Loss1: 0.341717 Loss2: 1.874701
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389589 Loss1: 0.043790 Loss2: 1.345799
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.593632 Loss1: 0.221779 Loss2: 1.371853
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.396008 Loss1: 0.057057 Loss2: 1.338951
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.514556 Loss1: 0.129264 Loss2: 1.385293
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.397257 Loss1: 0.052762 Loss2: 1.344494
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.458507 Loss1: 0.095757 Loss2: 1.362750 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.459998 Loss1: 0.096420 Loss2: 1.363578 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435363 Loss1: 0.083246 Loss2: 1.352117
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.108375 Loss1: 0.291808 Loss2: 1.816567
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.627435 Loss1: 0.270852 Loss2: 1.356584 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.615661 Loss1: 0.208170 Loss2: 1.407491 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.474855 Loss1: 0.130632 Loss2: 1.344223 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.418189 Loss1: 0.072607 Loss2: 1.345582 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.404504 Loss1: 0.064764 Loss2: 1.339740 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384195 Loss1: 0.047815 Loss2: 1.336380 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.382234 Loss1: 0.055592 Loss2: 1.326642 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.461091 Loss1: 0.112307 Loss2: 1.348784 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.431116 Loss1: 0.107701 Loss2: 1.323415 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.486969 Loss1: 0.159052 Loss2: 1.327917 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423333 Loss1: 0.103789 Loss2: 1.319545 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.973958
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.448993 Loss1: 0.113964 Loss2: 1.335029 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.386091 Loss1: 0.060847 Loss2: 1.325244 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.376319 Loss1: 0.057936 Loss2: 1.318383
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.248977 Loss1: 0.386640 Loss2: 1.862337
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.609478 Loss1: 0.272871 Loss2: 1.336607 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.997596
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.550076 Loss1: 0.189504 Loss2: 1.360572 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.493430 Loss1: 0.156867 Loss2: 1.336562 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.398451 Loss1: 0.071141 Loss2: 1.327311 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391343 Loss1: 0.069459 Loss2: 1.321883 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.405832 Loss1: 0.085622 Loss2: 1.320210 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.373417 Loss1: 0.053070 Loss2: 1.320347 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982143
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.531767 Loss1: 0.190639 Loss2: 1.341128 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.474827 Loss1: 0.124811 Loss2: 1.350015 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.381843 Loss1: 0.048436 Loss2: 1.333407 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.354188 Loss1: 0.030285 Loss2: 1.323903 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996394
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.457989 Loss1: 0.138270 Loss2: 1.319719 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.411515 Loss1: 0.109022 Loss2: 1.302494 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.374907 Loss1: 0.072853 Loss2: 1.302054 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.423635 Loss1: 0.130829 Loss2: 1.292806 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.388220 Loss1: 0.097161 Loss2: 1.291058 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.349528 Loss1: 0.052713 Loss2: 1.296815 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.404052 Loss1: 0.089072 Loss2: 1.314979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.340397 Loss1: 0.034279 Loss2: 1.306118 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.325776 Loss1: 0.028149 Loss2: 1.297627
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.275590 Loss1: 0.333265 Loss2: 1.942326
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351919 Loss1: 0.057232 Loss2: 1.294688
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.608821 Loss1: 0.199658 Loss2: 1.409163
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.628081 Loss1: 0.220475 Loss2: 1.407606
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.589781 Loss1: 0.171020 Loss2: 1.418762
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.557198 Loss1: 0.151502 Loss2: 1.405696
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.474458 Loss1: 0.064347 Loss2: 1.410111
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.158863 Loss1: 0.321334 Loss2: 1.837529
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.458238 Loss1: 0.060005 Loss2: 1.398233
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.422015 Loss1: 0.032319 Loss2: 1.389696 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.403232 Loss1: 0.024615 Loss2: 1.378617 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.393471 Loss1: 0.019208 Loss2: 1.374263 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.538066 Loss1: 0.165509 Loss2: 1.372557 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.430382 Loss1: 0.073397 Loss2: 1.356986 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.458366 Loss1: 0.106299 Loss2: 1.352068
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.177025 Loss1: 0.350409 Loss2: 1.826616
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433257 Loss1: 0.075350 Loss2: 1.357907
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.500748 Loss1: 0.180488 Loss2: 1.320260
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.472928 Loss1: 0.139781 Loss2: 1.333147
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.432596 Loss1: 0.104750 Loss2: 1.327845
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.431099 Loss1: 0.108110 Loss2: 1.322989
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.399514 Loss1: 0.081765 Loss2: 1.317749
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.119449 Loss1: 0.315699 Loss2: 1.803750
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.373888 Loss1: 0.055404 Loss2: 1.318484
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.473793 Loss1: 0.151311 Loss2: 1.322482
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.362962 Loss1: 0.053062 Loss2: 1.309899
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.498888 Loss1: 0.178598 Loss2: 1.320290
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.380439 Loss1: 0.071087 Loss2: 1.309351
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.521085 Loss1: 0.191815 Loss2: 1.329271
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.379786 Loss1: 0.068912 Loss2: 1.310874
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.399497 Loss1: 0.092017 Loss2: 1.307479 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.399994 Loss1: 0.092611 Loss2: 1.307383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.391905 Loss1: 0.086764 Loss2: 1.305141
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.064719 Loss1: 0.291510 Loss2: 1.773209
-(DefaultActor pid=3764) >> Training accuracy: 0.988542
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.522369 Loss1: 0.195553 Loss2: 1.326816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.467860 Loss1: 0.132225 Loss2: 1.335636 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.375115 Loss1: 0.060038 Loss2: 1.315076 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.398154 Loss1: 0.077416 Loss2: 1.320738 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.405060 Loss1: 0.080706 Loss2: 1.324354 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.396235 Loss1: 0.075053 Loss2: 1.321182 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398009 Loss1: 0.078785 Loss2: 1.319224 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987305
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.392185 Loss1: 0.067161 Loss2: 1.325024 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.350944 Loss1: 0.028729 Loss2: 1.322215 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.341445 Loss1: 0.029442 Loss2: 1.312003
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.093620 Loss1: 0.287245 Loss2: 1.806374
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.511554 Loss1: 0.159415 Loss2: 1.352139
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.466685 Loss1: 0.116732 Loss2: 1.349953
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.479597 Loss1: 0.126667 Loss2: 1.352929
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488828 Loss1: 0.137678 Loss2: 1.351150
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.134167 Loss1: 0.330085 Loss2: 1.804082
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.567653 Loss1: 0.216603 Loss2: 1.351050 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.483835 Loss1: 0.134280 Loss2: 1.349555 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.441390 Loss1: 0.100960 Loss2: 1.340430 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.473968 Loss1: 0.139884 Loss2: 1.334084 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.999023
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.475865 Loss1: 0.129482 Loss2: 1.346383 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.406096 Loss1: 0.076641 Loss2: 1.329455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.346291 Loss1: 0.030827 Loss2: 1.315464 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.493155 Loss1: 0.129435 Loss2: 1.363720 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.416930 Loss1: 0.080163 Loss2: 1.336767 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.409080 Loss1: 0.075951 Loss2: 1.333129
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.071479 Loss1: 0.303433 Loss2: 1.768047
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.540498 Loss1: 0.236465 Loss2: 1.304033 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.524323 Loss1: 0.208545 Loss2: 1.315778 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468485 Loss1: 0.144384 Loss2: 1.324101 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993304
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.417505 Loss1: 0.105461 Loss2: 1.312044 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.406883 Loss1: 0.113022 Loss2: 1.293861 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.346392 Loss1: 0.055849 Loss2: 1.290544 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.332917 Loss1: 0.048973 Loss2: 1.283945 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.489355 Loss1: 0.120972 Loss2: 1.368383 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.463192 Loss1: 0.097335 Loss2: 1.365857 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.484812 Loss1: 0.117205 Loss2: 1.367608
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.061808 Loss1: 0.267095 Loss2: 1.794713
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.434643 Loss1: 0.070764 Loss2: 1.363880
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.508529 Loss1: 0.176662 Loss2: 1.331868
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.412715 Loss1: 0.053235 Loss2: 1.359480
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.473627 Loss1: 0.128578 Loss2: 1.345049
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398325 Loss1: 0.045335 Loss2: 1.352991
-(DefaultActor pid=3765) >> Training accuracy: 0.989583
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.435527 Loss1: 0.103030 Loss2: 1.332497
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.420469 Loss1: 0.100379 Loss2: 1.320090
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.417526 Loss1: 0.094306 Loss2: 1.323220
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.394421 Loss1: 0.067225 Loss2: 1.327196
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.378583 Loss1: 0.058024 Loss2: 1.320559
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.075368 Loss1: 0.315408 Loss2: 1.759960
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.377827 Loss1: 0.060302 Loss2: 1.317524
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.498545 Loss1: 0.186394 Loss2: 1.312151
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.376303 Loss1: 0.056337 Loss2: 1.319966
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.472746 Loss1: 0.137900 Loss2: 1.334846
-(DefaultActor pid=3764) >> Training accuracy: 0.995404
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.450143 Loss1: 0.137366 Loss2: 1.312777
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.404550 Loss1: 0.099792 Loss2: 1.304758
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.384628 Loss1: 0.073474 Loss2: 1.311154
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.390843 Loss1: 0.092082 Loss2: 1.298761
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.160863 Loss1: 0.302408 Loss2: 1.858455
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.394987 Loss1: 0.090051 Loss2: 1.304936
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.357074 Loss1: 0.053366 Loss2: 1.303708 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.358075 Loss1: 0.061177 Loss2: 1.296898 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.455644 Loss1: 0.094926 Loss2: 1.360718 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.455958 Loss1: 0.092242 Loss2: 1.363716 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.127064 Loss1: 0.279197 Loss2: 1.847866 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.538492 Loss1: 0.169087 Loss2: 1.369405 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.496943 Loss1: 0.115316 Loss2: 1.381627 [repeated 3x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.473638 Loss1: 0.103128 Loss2: 1.370511 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.224195 Loss1: 0.368194 Loss2: 1.856001
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.517149 Loss1: 0.149028 Loss2: 1.368121
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.569020 Loss1: 0.212585 Loss2: 1.356434
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.549806 Loss1: 0.159217 Loss2: 1.390589
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.545220 Loss1: 0.178299 Loss2: 1.366922
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.517210 Loss1: 0.128227 Loss2: 1.388982
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.496470 Loss1: 0.124620 Loss2: 1.371850
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.488301 Loss1: 0.106115 Loss2: 1.382187
-(DefaultActor pid=3765) >> Training accuracy: 0.986328
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.459228 Loss1: 0.099882 Loss2: 1.359346 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.486592 Loss1: 0.126867 Loss2: 1.359725 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.454492 Loss1: 0.093309 Loss2: 1.361183
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.184068 Loss1: 0.356401 Loss2: 1.827668
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.425686 Loss1: 0.069619 Loss2: 1.356067
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.546666 Loss1: 0.212368 Loss2: 1.334297
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.501889 Loss1: 0.156094 Loss2: 1.345795
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.469834 Loss1: 0.126466 Loss2: 1.343368
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.417088 Loss1: 0.084373 Loss2: 1.332715
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.392654 Loss1: 0.064110 Loss2: 1.328543
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.165320 Loss1: 0.353981 Loss2: 1.811339
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.394215 Loss1: 0.071645 Loss2: 1.322570
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.374772 Loss1: 0.059218 Loss2: 1.315554 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.380259 Loss1: 0.061398 Loss2: 1.318862 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.355136 Loss1: 0.044719 Loss2: 1.310417 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994792
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.441496 Loss1: 0.113550 Loss2: 1.327946 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.436676 Loss1: 0.113516 Loss2: 1.323161 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.443724 Loss1: 0.124787 Loss2: 1.318937
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.222804 Loss1: 0.359263 Loss2: 1.863540
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.592598 Loss1: 0.249042 Loss2: 1.343556 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.477440 Loss1: 0.133504 Loss2: 1.343936 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.429785 Loss1: 0.091378 Loss2: 1.338407 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.425851 Loss1: 0.085950 Loss2: 1.339901 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.411879 Loss1: 0.074337 Loss2: 1.337541 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.399898 Loss1: 0.063261 Loss2: 1.336637 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.383374 Loss1: 0.052984 Loss2: 1.330390 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.528870 Loss1: 0.108321 Loss2: 1.420549 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.468130 Loss1: 0.069509 Loss2: 1.398621 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.441980 Loss1: 0.047392 Loss2: 1.394588
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.236112 Loss1: 0.374126 Loss2: 1.861986
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.590163 Loss1: 0.207519 Loss2: 1.382643 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.503103 Loss1: 0.118950 Loss2: 1.384153 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.466158 Loss1: 0.092485 Loss2: 1.373673 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.429795 Loss1: 0.056561 Loss2: 1.373234 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.428768 Loss1: 0.058752 Loss2: 1.370016 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.410989 Loss1: 0.047059 Loss2: 1.363930 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.426080 Loss1: 0.058822 Loss2: 1.367259 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.387414 Loss1: 0.060565 Loss2: 1.326848 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.368509 Loss1: 0.053272 Loss2: 1.315237 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.351160 Loss1: 0.034732 Loss2: 1.316428 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994141
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.457264 Loss1: 0.114235 Loss2: 1.343029 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.411792 Loss1: 0.097337 Loss2: 1.314455 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.150085 Loss1: 0.299151 Loss2: 1.850934 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.572279 Loss1: 0.213078 Loss2: 1.359201 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.593749 Loss1: 0.222571 Loss2: 1.371178 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.552321 Loss1: 0.170991 Loss2: 1.381329 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982292
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.512335 Loss1: 0.144636 Loss2: 1.367699 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.428611 Loss1: 0.082375 Loss2: 1.346236 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.410821 Loss1: 0.072893 Loss2: 1.337928
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.144726 Loss1: 0.304642 Loss2: 1.840084
-(DefaultActor pid=3764) >> Training accuracy: 0.990625
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.394275 Loss1: 0.054148 Loss2: 1.340127
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.535824 Loss1: 0.197918 Loss2: 1.337907
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.509913 Loss1: 0.159993 Loss2: 1.349920
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.430232 Loss1: 0.080407 Loss2: 1.349825
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.427061 Loss1: 0.100434 Loss2: 1.326627
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.400802 Loss1: 0.076143 Loss2: 1.324658
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.150087 Loss1: 0.343396 Loss2: 1.806692
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.403710 Loss1: 0.077514 Loss2: 1.326196
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.398365 Loss1: 0.073105 Loss2: 1.325260 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.353303 Loss1: 0.033787 Loss2: 1.319516 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384011 Loss1: 0.064395 Loss2: 1.319616 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.424856 Loss1: 0.089865 Loss2: 1.334991 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.379223 Loss1: 0.068138 Loss2: 1.311085 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 19:25:50,588 | server.py:236 | fit_round 199 received 50 results and 0 failures
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.354996 Loss1: 0.052733 Loss2: 1.302263
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.108938 Loss1: 0.313539 Loss2: 1.795399
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.359161 Loss1: 0.063509 Loss2: 1.295652
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.568549 Loss1: 0.236933 Loss2: 1.331615
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.525294 Loss1: 0.166568 Loss2: 1.358726
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.498101 Loss1: 0.163786 Loss2: 1.334315
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.507869 Loss1: 0.180187 Loss2: 1.327682
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.524757 Loss1: 0.188137 Loss2: 1.336621
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.112436 Loss1: 0.333518 Loss2: 1.778918
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.480531 Loss1: 0.146707 Loss2: 1.333824
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.612281 Loss1: 0.288801 Loss2: 1.323481
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.449374 Loss1: 0.110918 Loss2: 1.338456
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.524249 Loss1: 0.158683 Loss2: 1.365566
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.435258 Loss1: 0.108203 Loss2: 1.327055
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.482310 Loss1: 0.151760 Loss2: 1.330551
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.398610 Loss1: 0.072282 Loss2: 1.326328
-(DefaultActor pid=3765) >> Training accuracy: 0.972917
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.485636 Loss1: 0.151505 Loss2: 1.334131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.389179 Loss1: 0.065465 Loss2: 1.323714 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.362313 Loss1: 0.054655 Loss2: 1.307658 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 19:25:50,588][flwr][DEBUG] - fit_round 199 received 50 results and 0 failures
-INFO flwr 2023-10-13 19:26:32,808 | server.py:125 | fit progress: (199, 2.329215543529096, {'accuracy': 0.6152}, 459300.586970173)
->> Test accuracy: 0.615200
-[2023-10-13 19:26:32,808][flwr][INFO] - fit progress: (199, 2.329215543529096, {'accuracy': 0.6152}, 459300.586970173)
-DEBUG flwr 2023-10-13 19:26:32,809 | server.py:173 | evaluate_round 199: strategy sampled 50 clients (out of 50)
-[2023-10-13 19:26:32,809][flwr][DEBUG] - evaluate_round 199: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 19:35:37,050 | server.py:187 | evaluate_round 199 received 50 results and 0 failures
-[2023-10-13 19:35:37,050][flwr][DEBUG] - evaluate_round 199 received 50 results and 0 failures
-DEBUG flwr 2023-10-13 19:35:37,050 | server.py:222 | fit_round 200: strategy sampled 50 clients (out of 50)
-[2023-10-13 19:35:37,050][flwr][DEBUG] - fit_round 200: strategy sampled 50 clients (out of 50)
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.123144 Loss1: 0.284656 Loss2: 1.838488
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.572678 Loss1: 0.215347 Loss2: 1.357332
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.504437 Loss1: 0.124196 Loss2: 1.380241
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.224622 Loss1: 0.343734 Loss2: 1.880888
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.516982 Loss1: 0.152079 Loss2: 1.364903
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.604039 Loss1: 0.237957 Loss2: 1.366082
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.451865 Loss1: 0.088126 Loss2: 1.363739
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.537273 Loss1: 0.156397 Loss2: 1.380877
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454112 Loss1: 0.092276 Loss2: 1.361836
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.566884 Loss1: 0.184783 Loss2: 1.382102
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.461226 Loss1: 0.105991 Loss2: 1.355236
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.435405 Loss1: 0.079957 Loss2: 1.355448 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.428601 Loss1: 0.082321 Loss2: 1.346280 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.387229 Loss1: 0.038978 Loss2: 1.348251 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992188
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.440413 Loss1: 0.075131 Loss2: 1.365282 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.435115 Loss1: 0.420383 Loss2: 2.014731 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.591167 Loss1: 0.206626 Loss2: 1.384541 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.512870 Loss1: 0.117930 Loss2: 1.394940 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.549883 Loss1: 0.188886 Loss2: 1.360997 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.495116 Loss1: 0.125985 Loss2: 1.369131 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.445999 Loss1: 0.073043 Loss2: 1.372956 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993490
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.423262 Loss1: 0.047600 Loss2: 1.375662 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.395923 Loss1: 0.049299 Loss2: 1.346624 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.373822 Loss1: 0.037701 Loss2: 1.336122 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.077007 Loss1: 0.270504 Loss2: 1.806502
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.358416 Loss1: 0.026924 Loss2: 1.331491
-(DefaultActor pid=3764) >> Training accuracy: 1.000000
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.593755 Loss1: 0.187777 Loss2: 1.405978 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.470830 Loss1: 0.094949 Loss2: 1.375881 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.269573 Loss1: 0.398797 Loss2: 1.870776
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.443907 Loss1: 0.080815 Loss2: 1.363091
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.602148 Loss1: 0.226838 Loss2: 1.375310
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.468821 Loss1: 0.109701 Loss2: 1.359120
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.459338 Loss1: 0.097719 Loss2: 1.361619 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.421245 Loss1: 0.068511 Loss2: 1.352734 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.390343 Loss1: 0.043576 Loss2: 1.346766 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991211
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.465632 Loss1: 0.106071 Loss2: 1.359561 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409513 Loss1: 0.060336 Loss2: 1.349177 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.413065 Loss1: 0.069909 Loss2: 1.343157
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.106074 Loss1: 0.265765 Loss2: 1.840309
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.546552 Loss1: 0.189284 Loss2: 1.357267
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.529113 Loss1: 0.154882 Loss2: 1.374231
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.482836 Loss1: 0.123089 Loss2: 1.359747
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.433006 Loss1: 0.071308 Loss2: 1.361699
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.161881 Loss1: 0.288269 Loss2: 1.873613
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.455647 Loss1: 0.101230 Loss2: 1.354417
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.529152 Loss1: 0.178144 Loss2: 1.351009
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.509016 Loss1: 0.165762 Loss2: 1.343254
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.447636 Loss1: 0.085794 Loss2: 1.361842
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.483353 Loss1: 0.120100 Loss2: 1.363253
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.427429 Loss1: 0.069607 Loss2: 1.357822
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.460886 Loss1: 0.121199 Loss2: 1.339688
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.422966 Loss1: 0.077877 Loss2: 1.345089
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.429441 Loss1: 0.095548 Loss2: 1.333892
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.386132 Loss1: 0.036536 Loss2: 1.349597
-(DefaultActor pid=3765) >> Training accuracy: 0.997070
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.427106 Loss1: 0.093127 Loss2: 1.333979 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.375188 Loss1: 0.047803 Loss2: 1.327385 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.646329 Loss1: 0.285429 Loss2: 1.360900 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.474164 Loss1: 0.117523 Loss2: 1.356641 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.450519 Loss1: 0.096454 Loss2: 1.354065 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.423332 Loss1: 0.076536 Loss2: 1.346796 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.396817 Loss1: 0.055170 Loss2: 1.341647 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.393947 Loss1: 0.057713 Loss2: 1.336234 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.395078 Loss1: 0.058576 Loss2: 1.336502 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.378720 Loss1: 0.047066 Loss2: 1.331654 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.994141
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384443 Loss1: 0.070676 Loss2: 1.313767 [repeated 3x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.991667
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.023492 Loss1: 0.269253 Loss2: 1.754239 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.552560 Loss1: 0.204969 Loss2: 1.347591 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.160586 Loss1: 0.319572 Loss2: 1.841015
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.475446 Loss1: 0.150592 Loss2: 1.324854
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.561615 Loss1: 0.217756 Loss2: 1.343859
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.468850 Loss1: 0.150952 Loss2: 1.317897
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.512008 Loss1: 0.143936 Loss2: 1.368072
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.454676 Loss1: 0.136000 Loss2: 1.318676
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.438003 Loss1: 0.083840 Loss2: 1.354163
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.440704 Loss1: 0.117707 Loss2: 1.322997
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.421098 Loss1: 0.104762 Loss2: 1.316336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.474483 Loss1: 0.153486 Loss2: 1.320997 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.460256 Loss1: 0.136704 Loss2: 1.323552 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.982422
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.384907 Loss1: 0.051050 Loss2: 1.333857 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.977083
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.171106 Loss1: 0.327726 Loss2: 1.843379 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.473249 Loss1: 0.116253 Loss2: 1.356996 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.434546 Loss1: 0.090138 Loss2: 1.344408
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.192423 Loss1: 0.349492 Loss2: 1.842932
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.528712 Loss1: 0.216524 Loss2: 1.312189 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.363014 Loss1: 0.040191 Loss2: 1.322823
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.482840 Loss1: 0.162555 Loss2: 1.320285
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.430644 Loss1: 0.098795 Loss2: 1.331849 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.397028 Loss1: 0.083330 Loss2: 1.313697 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.341602 Loss1: 0.039018 Loss2: 1.302584 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.354047 Loss1: 0.046612 Loss2: 1.307435
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.339052 Loss1: 0.041978 Loss2: 1.297074
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.326414 Loss1: 0.036401 Loss2: 1.290013
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.318835 Loss1: 0.034383 Loss2: 1.284452
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.309580 Loss1: 0.025342 Loss2: 1.284238
-(DefaultActor pid=3764) >> Training accuracy: 0.998958
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.150550 Loss1: 0.320579 Loss2: 1.829971
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.569744 Loss1: 0.239625 Loss2: 1.330119
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.531307 Loss1: 0.171389 Loss2: 1.359919
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.472989 Loss1: 0.117131 Loss2: 1.355858
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.164069 Loss1: 0.362166 Loss2: 1.801903
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.604635 Loss1: 0.262583 Loss2: 1.342052 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.616954 Loss1: 0.236106 Loss2: 1.380847 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.558370 Loss1: 0.203525 Loss2: 1.354845 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.527436 Loss1: 0.164971 Loss2: 1.362465 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.472376 Loss1: 0.119652 Loss2: 1.352724 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.427598 Loss1: 0.094475 Loss2: 1.333123 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.406922 Loss1: 0.073933 Loss2: 1.332988 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.998047
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.134443 Loss1: 0.308850 Loss2: 1.825593 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.527824 Loss1: 0.139981 Loss2: 1.387843 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.481234 Loss1: 0.111635 Loss2: 1.369599
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.176156 Loss1: 0.333794 Loss2: 1.842362
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.568739 Loss1: 0.221720 Loss2: 1.347019 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.574490 Loss1: 0.197088 Loss2: 1.377402 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.537546 Loss1: 0.173373 Loss2: 1.364173 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493750 Loss1: 0.143889 Loss2: 1.349861 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.429796 Loss1: 0.073226 Loss2: 1.356570
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.476068 Loss1: 0.123580 Loss2: 1.352488
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.433669 Loss1: 0.081220 Loss2: 1.352448
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447655 Loss1: 0.091373 Loss2: 1.356281
-(DefaultActor pid=3765) >> Training accuracy: 0.980469
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.405882 Loss1: 0.060201 Loss2: 1.345681
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.409609 Loss1: 0.065443 Loss2: 1.344166
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393842 Loss1: 0.055208 Loss2: 1.338634
-(DefaultActor pid=3764) >> Training accuracy: 0.997917
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.148429 Loss1: 0.350120 Loss2: 1.798310
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.584591 Loss1: 0.260970 Loss2: 1.323621
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.603205 Loss1: 0.254799 Loss2: 1.348406
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511039 Loss1: 0.163078 Loss2: 1.347960
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.074616 Loss1: 0.231195 Loss2: 1.843422
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.521694 Loss1: 0.146817 Loss2: 1.374877 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.473021 Loss1: 0.098854 Loss2: 1.374168 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.426909 Loss1: 0.063849 Loss2: 1.363059 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.407368 Loss1: 0.050466 Loss2: 1.356902 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.399820 Loss1: 0.079569 Loss2: 1.320251 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.995833
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.416123 Loss1: 0.056795 Loss2: 1.359328 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.393208 Loss1: 0.036413 Loss2: 1.356795 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992647
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.681266 Loss1: 0.264857 Loss2: 1.416408 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.541558 Loss1: 0.122742 Loss2: 1.418816 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.589714 Loss1: 0.171831 Loss2: 1.417883
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.259542 Loss1: 0.351611 Loss2: 1.907931
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.624044 Loss1: 0.243057 Loss2: 1.380987 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.623066 Loss1: 0.187867 Loss2: 1.435199 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.571444 Loss1: 0.172704 Loss2: 1.398740 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.524308 Loss1: 0.139574 Loss2: 1.384734 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.993750
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.522054 Loss1: 0.126646 Loss2: 1.395408 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.495168 Loss1: 0.109908 Loss2: 1.385260 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.447303 Loss1: 0.076183 Loss2: 1.371119 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.986458
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.566328 Loss1: 0.195838 Loss2: 1.370490 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.511423 Loss1: 0.139919 Loss2: 1.371503 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.176848 Loss1: 0.338552 Loss2: 1.838297 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.598946 Loss1: 0.254929 Loss2: 1.344017 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.534607 Loss1: 0.178228 Loss2: 1.356380 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.468871 Loss1: 0.122536 Loss2: 1.346335 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.430706 Loss1: 0.095631 Loss2: 1.335074 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.979167
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.396879 Loss1: 0.068089 Loss2: 1.328790 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.354967 Loss1: 0.035615 Loss2: 1.319352 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.343527 Loss1: 0.031278 Loss2: 1.312249 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.995833
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.485368 Loss1: 0.171206 Loss2: 1.314162 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.420702 Loss1: 0.104543 Loss2: 1.316159 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.114750 Loss1: 0.269494 Loss2: 1.845256 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.569279 Loss1: 0.229685 Loss2: 1.339595 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.527135 Loss1: 0.158125 Loss2: 1.369010 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.545598 Loss1: 0.182507 Loss2: 1.363091 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.535854 Loss1: 0.171573 Loss2: 1.364281 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.981250
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.439435 Loss1: 0.082793 Loss2: 1.356641 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.393182 Loss1: 0.049078 Loss2: 1.344104 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.379530 Loss1: 0.043168 Loss2: 1.336362
-(DefaultActor pid=3764) >> Training accuracy: 0.984375
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.081560 Loss1: 0.322490 Loss2: 1.759070
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.514314 Loss1: 0.200341 Loss2: 1.313974
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.478262 Loss1: 0.158056 Loss2: 1.320206
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.447548 Loss1: 0.140971 Loss2: 1.306578
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.394458 Loss1: 0.083225 Loss2: 1.311233
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.153551 Loss1: 0.323979 Loss2: 1.829572
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.380684 Loss1: 0.078833 Loss2: 1.301851
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.517104 Loss1: 0.180415 Loss2: 1.336689
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.374308 Loss1: 0.079231 Loss2: 1.295077
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.524202 Loss1: 0.160868 Loss2: 1.363333
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.341214 Loss1: 0.043685 Loss2: 1.297529
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.478585 Loss1: 0.136709 Loss2: 1.341876
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.445712 Loss1: 0.113751 Loss2: 1.331961
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.355536 Loss1: 0.062284 Loss2: 1.293252
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.423286 Loss1: 0.089924 Loss2: 1.333362
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.318117 Loss1: 0.031761 Loss2: 1.286356
-(DefaultActor pid=3765) >> Training accuracy: 0.996094
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.382109 Loss1: 0.046684 Loss2: 1.335425 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.358586 Loss1: 0.034391 Loss2: 1.324195 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.996875
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.490167 Loss1: 0.182158 Loss2: 1.308009 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.431504 Loss1: 0.108726 Loss2: 1.322779 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.315268 Loss1: 0.423049 Loss2: 1.892219
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.395762 Loss1: 0.091888 Loss2: 1.303874
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.395628 Loss1: 0.087370 Loss2: 1.308258 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.408721 Loss1: 0.100648 Loss2: 1.308073 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.455823 Loss1: 0.144345 Loss2: 1.311478 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.440587 Loss1: 0.111953 Loss2: 1.328635 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.371486 Loss1: 0.063540 Loss2: 1.307946 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992708
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.471212 Loss1: 0.114449 Loss2: 1.356763 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.434603 Loss1: 0.079644 Loss2: 1.354959 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992188
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.299353 Loss1: 0.432174 Loss2: 1.867179
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.550145 Loss1: 0.227714 Loss2: 1.322431
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.535525 Loss1: 0.207270 Loss2: 1.328256
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488865 Loss1: 0.141279 Loss2: 1.347587
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.454802 Loss1: 0.129229 Loss2: 1.325573
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.458748 Loss1: 0.136870 Loss2: 1.321878 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.449778 Loss1: 0.118812 Loss2: 1.330966 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.391309 Loss1: 0.073258 Loss2: 1.318050 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.364473 Loss1: 0.050239 Loss2: 1.314233 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.384911 Loss1: 0.073441 Loss2: 1.311470 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.984375
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.488094 Loss1: 0.127339 Loss2: 1.360755 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.463009 Loss1: 0.108361 Loss2: 1.354648 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.485775 Loss1: 0.131388 Loss2: 1.354387
-(DefaultActor pid=3764) >> Training accuracy: 0.976042
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.268009 Loss1: 0.312917 Loss2: 1.955092
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.691183 Loss1: 0.256694 Loss2: 1.434489
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.605652 Loss1: 0.159744 Loss2: 1.445908
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.566272 Loss1: 0.127561 Loss2: 1.438711
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.556246 Loss1: 0.130367 Loss2: 1.425879
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.097510 Loss1: 0.253215 Loss2: 1.844295
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.525216 Loss1: 0.095879 Loss2: 1.429337
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.535823 Loss1: 0.109097 Loss2: 1.426726 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.553455 Loss1: 0.122923 Loss2: 1.430533 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.533471 Loss1: 0.096729 Loss2: 1.436742 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.489325 Loss1: 0.069131 Loss2: 1.420194 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.975000
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.378104 Loss1: 0.061582 Loss2: 1.316522 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.383124 Loss1: 0.068128 Loss2: 1.314996 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.406259 Loss1: 0.090706 Loss2: 1.315553
-(DefaultActor pid=3764) >> Training accuracy: 0.985417
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.133181 Loss1: 0.289059 Loss2: 1.844122
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.551878 Loss1: 0.210366 Loss2: 1.341512
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.528315 Loss1: 0.180284 Loss2: 1.348032
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.497400 Loss1: 0.139046 Loss2: 1.358354
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.488360 Loss1: 0.151158 Loss2: 1.337203
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.185591 Loss1: 0.326111 Loss2: 1.859479
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.533020 Loss1: 0.186893 Loss2: 1.346127 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.564151 Loss1: 0.195526 Loss2: 1.368625 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.519068 Loss1: 0.141220 Loss2: 1.377848 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.541789 Loss1: 0.175554 Loss2: 1.366235 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.971875
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.514154 Loss1: 0.143978 Loss2: 1.370175 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.414519 Loss1: 0.054349 Loss2: 1.360170 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.404784 Loss1: 0.051591 Loss2: 1.353193 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.981250
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.523760 Loss1: 0.173062 Loss2: 1.350698 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.488123 Loss1: 0.134557 Loss2: 1.353566 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.137149 Loss1: 0.271144 Loss2: 1.866005 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.504070 Loss1: 0.164132 Loss2: 1.339939 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.463835 Loss1: 0.139170 Loss2: 1.324665 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.445728 Loss1: 0.106289 Loss2: 1.339438 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.398980 Loss1: 0.069953 Loss2: 1.329027 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.345577 Loss1: 0.031026 Loss2: 1.314552 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.355005 Loss1: 0.052752 Loss2: 1.302253 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.366985 Loss1: 0.062017 Loss2: 1.304969
-(DefaultActor pid=3764) >> Training accuracy: 0.994792
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.157280 Loss1: 0.262863 Loss2: 1.894417
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.587164 Loss1: 0.174567 Loss2: 1.412597
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.568772 Loss1: 0.146186 Loss2: 1.422587
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.513904 Loss1: 0.095385 Loss2: 1.418520
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.515084 Loss1: 0.110591 Loss2: 1.404494
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.275275 Loss1: 0.337884 Loss2: 1.937391
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.631075 Loss1: 0.238010 Loss2: 1.393065 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.466761 Loss1: 0.061238 Loss2: 1.405523
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.627205 Loss1: 0.215388 Loss2: 1.411817
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.483497 Loss1: 0.086007 Loss2: 1.397490
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.540762 Loss1: 0.121538 Loss2: 1.419224
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.493089 Loss1: 0.098482 Loss2: 1.394608
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.468683 Loss1: 0.065801 Loss2: 1.402882
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.477083 Loss1: 0.084469 Loss2: 1.392614
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.461170 Loss1: 0.061324 Loss2: 1.399846
-(DefaultActor pid=3765) >> Training accuracy: 0.983398
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.481788 Loss1: 0.090657 Loss2: 1.391131 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.475143 Loss1: 0.085195 Loss2: 1.389949 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.993304
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.366797 Loss1: 0.394522 Loss2: 1.972275
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.586099 Loss1: 0.225544 Loss2: 1.360554
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.559907 Loss1: 0.206935 Loss2: 1.352971
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.534865 Loss1: 0.144601 Loss2: 1.390264
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.510690 Loss1: 0.144274 Loss2: 1.366416
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.429943 Loss1: 0.073607 Loss2: 1.356336 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.414885 Loss1: 0.056355 Loss2: 1.358529 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.400704 Loss1: 0.052805 Loss2: 1.347899 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.381362 Loss1: 0.036104 Loss2: 1.345258 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.377273 Loss1: 0.039068 Loss2: 1.338205 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.992788
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.408313 Loss1: 0.073890 Loss2: 1.334423 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.400509 Loss1: 0.079510 Loss2: 1.320999 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989955
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.562840 Loss1: 0.175927 Loss2: 1.386913 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.537138 Loss1: 0.131791 Loss2: 1.405346 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.517017 Loss1: 0.131977 Loss2: 1.385040
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.291122 Loss1: 0.365612 Loss2: 1.925510
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.530259 Loss1: 0.131955 Loss2: 1.398305
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.590019 Loss1: 0.198865 Loss2: 1.391154
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.508645 Loss1: 0.105076 Loss2: 1.403569
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.580267 Loss1: 0.183138 Loss2: 1.397128
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.508080 Loss1: 0.113412 Loss2: 1.394668
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.557648 Loss1: 0.145087 Loss2: 1.412562
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.470980 Loss1: 0.082156 Loss2: 1.388824
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.510631 Loss1: 0.122943 Loss2: 1.387689
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.499815 Loss1: 0.110068 Loss2: 1.389747
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.486846 Loss1: 0.096548 Loss2: 1.390299
-(DefaultActor pid=3765) >> Training accuracy: 0.983333
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.447535 Loss1: 0.066396 Loss2: 1.381139
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.440880 Loss1: 0.070632 Loss2: 1.370249
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.427277 Loss1: 0.061404 Loss2: 1.365873
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.439117 Loss1: 0.074488 Loss2: 1.364629
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.260142 Loss1: 0.362961 Loss2: 1.897181
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.605452 Loss1: 0.210984 Loss2: 1.394468
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.578128 Loss1: 0.172213 Loss2: 1.405915
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.576418 Loss1: 0.172813 Loss2: 1.403605
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.543764 Loss1: 0.152936 Loss2: 1.390828
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.503190 Loss1: 0.106817 Loss2: 1.396373 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.478686 Loss1: 0.085466 Loss2: 1.393220 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.450978 Loss1: 0.060540 Loss2: 1.390438 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.441577 Loss1: 0.061546 Loss2: 1.380031 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.439871 Loss1: 0.065638 Loss2: 1.374233 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.991667
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.356436 Loss1: 0.061841 Loss2: 1.294595 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.345467 Loss1: 0.060588 Loss2: 1.284879 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.595259 Loss1: 0.220677 Loss2: 1.374582 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.480765 Loss1: 0.095508 Loss2: 1.385257 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.443533 Loss1: 0.086420 Loss2: 1.357114
-(DefaultActor pid=3764) Epoch: 0 Loss: 2.177342 Loss1: 0.331154 Loss2: 1.846188
-(DefaultActor pid=3764) Epoch: 1 Loss: 1.563972 Loss1: 0.233203 Loss2: 1.330769 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 2 Loss: 1.514236 Loss1: 0.168365 Loss2: 1.345871 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 3 Loss: 1.462002 Loss1: 0.118494 Loss2: 1.343508 [repeated 2x across cluster]
-(DefaultActor pid=3764) Epoch: 4 Loss: 1.400725 Loss1: 0.080220 Loss2: 1.320505 [repeated 2x across cluster]
-DEBUG flwr 2023-10-13 20:04:17,585 | server.py:236 | fit_round 200 received 50 results and 0 failures
-(DefaultActor pid=3765) >> Training accuracy: 0.987500
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.415338 Loss1: 0.058902 Loss2: 1.356436
-(DefaultActor pid=3764) Epoch: 5 Loss: 1.378013 Loss1: 0.058293 Loss2: 1.319720
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 6 Loss: 1.372858 Loss1: 0.056827 Loss2: 1.316030
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.363239 Loss1: 0.052274 Loss2: 1.310965
-(DefaultActor pid=3764) Epoch: 8 Loss: 1.363045 Loss1: 0.058254 Loss2: 1.304791
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.331503 Loss1: 0.027893 Loss2: 1.303610
-(DefaultActor pid=3764) >> Training accuracy: 0.992708
-(DefaultActor pid=3764)  ** Training complete **
-(DefaultActor pid=3765) Epoch: 0 Loss: 2.137559 Loss1: 0.297910 Loss2: 1.839650
-(DefaultActor pid=3765) Epoch: 1 Loss: 1.615377 Loss1: 0.268106 Loss2: 1.347271
-(DefaultActor pid=3765) Epoch: 2 Loss: 1.523468 Loss1: 0.159762 Loss2: 1.363706
-(DefaultActor pid=3765) Epoch: 3 Loss: 1.500506 Loss1: 0.141497 Loss2: 1.359010
-(DefaultActor pid=3765) Epoch: 4 Loss: 1.449131 Loss1: 0.102266 Loss2: 1.346864
-(DefaultActor pid=3765) Epoch: 5 Loss: 1.446550 Loss1: 0.103510 Loss2: 1.343040 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 6 Loss: 1.437277 Loss1: 0.095959 Loss2: 1.341317 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 7 Loss: 1.382470 Loss1: 0.045135 Loss2: 1.337335 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 8 Loss: 1.376087 Loss1: 0.049066 Loss2: 1.327021 [repeated 2x across cluster]
-(DefaultActor pid=3765) Epoch: 9 Loss: 1.392091 Loss1: 0.068598 Loss2: 1.323493 [repeated 2x across cluster]
-(DefaultActor pid=3765) >> Training accuracy: 0.988542
-(DefaultActor pid=3765)  ** Training complete **
-(DefaultActor pid=3764) Epoch: 7 Loss: 1.434338 Loss1: 0.060036 Loss2: 1.374302 [repeated 3x across cluster]
-(DefaultActor pid=3764) Epoch: 9 Loss: 1.433239 Loss1: 0.061122 Loss2: 1.372116 [repeated 2x across cluster]
-(DefaultActor pid=3764) >> Training accuracy: 0.989583
-(DefaultActor pid=3764)  ** Training complete **
-[2023-10-13 20:04:17,585][flwr][DEBUG] - fit_round 200 received 50 results and 0 failures
-INFO flwr 2023-10-13 20:04:59,370 | server.py:125 | fit progress: (200, 2.3313485875297277, {'accuracy': 0.6149}, 461607.14849393995)
->> Test accuracy: 0.614900
-[2023-10-13 20:04:59,370][flwr][INFO] - fit progress: (200, 2.3313485875297277, {'accuracy': 0.6149}, 461607.14849393995)
-DEBUG flwr 2023-10-13 20:04:59,370 | server.py:173 | evaluate_round 200: strategy sampled 50 clients (out of 50)
-[2023-10-13 20:04:59,370][flwr][DEBUG] - evaluate_round 200: strategy sampled 50 clients (out of 50)
-DEBUG flwr 2023-10-13 20:14:03,498 | server.py:187 | evaluate_round 200 received 50 results and 0 failures
-[2023-10-13 20:14:03,498][flwr][DEBUG] - evaluate_round 200 received 50 results and 0 failures
-INFO flwr 2023-10-13 20:14:03,498 | server.py:153 | FL finished in 462151.27705474297
-[2023-10-13 20:14:03,498][flwr][INFO] - FL finished in 462151.27705474297
-INFO flwr 2023-10-13 20:14:04,179 | app.py:225 | app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0), (101, 0.0), (102, 0.0), (103, 0.0), (104, 0.0), (105, 0.0), (106, 0.0), (107, 0.0), (108, 0.0), (109, 0.0), (110, 0.0), (111, 0.0), (112, 0.0), (113, 0.0), (114, 0.0), (115, 0.0), (116, 0.0), (117, 0.0), (118, 0.0), (119, 0.0), (120, 0.0), (121, 0.0), (122, 0.0), (123, 0.0), (124, 0.0), (125, 0.0), (126, 0.0), (127, 0.0), (128, 0.0), (129, 0.0), (130, 0.0), (131, 0.0), (132, 0.0), (133, 0.0), (134, 0.0), (135, 0.0), (136, 0.0), (137, 0.0), (138, 0.0), (139, 0.0), (140, 0.0), (141, 0.0), (142, 0.0), (143, 0.0), (144, 0.0), (145, 0.0), (146, 0.0), (147, 0.0), (148, 0.0), (149, 0.0), (150, 0.0), (151, 0.0), (152, 0.0), (153, 0.0), (154, 0.0), (155, 0.0), (156, 0.0), (157, 0.0), (158, 0.0), (159, 0.0), (160, 0.0), (161, 0.0), (162, 0.0), (163, 0.0), (164, 0.0), (165, 0.0), (166, 0.0), (167, 0.0), (168, 0.0), (169, 0.0), (170, 0.0), (171, 0.0), (172, 0.0), (173, 0.0), (174, 0.0), (175, 0.0), (176, 0.0), (177, 0.0), (178, 0.0), (179, 0.0), (180, 0.0), (181, 0.0), (182, 0.0), (183, 0.0), (184, 0.0), (185, 0.0), (186, 0.0), (187, 0.0), (188, 0.0), (189, 0.0), (190, 0.0), (191, 0.0), (192, 0.0), (193, 0.0), (194, 0.0), (195, 0.0), (196, 0.0), (197, 0.0), (198, 0.0), (199, 0.0), (200, 0.0)]
-[2023-10-13 20:14:04,179][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0), (101, 0.0), (102, 0.0), (103, 0.0), (104, 0.0), (105, 0.0), (106, 0.0), (107, 0.0), (108, 0.0), (109, 0.0), (110, 0.0), (111, 0.0), (112, 0.0), (113, 0.0), (114, 0.0), (115, 0.0), (116, 0.0), (117, 0.0), (118, 0.0), (119, 0.0), (120, 0.0), (121, 0.0), (122, 0.0), (123, 0.0), (124, 0.0), (125, 0.0), (126, 0.0), (127, 0.0), (128, 0.0), (129, 0.0), (130, 0.0), (131, 0.0), (132, 0.0), (133, 0.0), (134, 0.0), (135, 0.0), (136, 0.0), (137, 0.0), (138, 0.0), (139, 0.0), (140, 0.0), (141, 0.0), (142, 0.0), (143, 0.0), (144, 0.0), (145, 0.0), (146, 0.0), (147, 0.0), (148, 0.0), (149, 0.0), (150, 0.0), (151, 0.0), (152, 0.0), (153, 0.0), (154, 0.0), (155, 0.0), (156, 0.0), (157, 0.0), (158, 0.0), (159, 0.0), (160, 0.0), (161, 0.0), (162, 0.0), (163, 0.0), (164, 0.0), (165, 0.0), (166, 0.0), (167, 0.0), (168, 0.0), (169, 0.0), (170, 0.0), (171, 0.0), (172, 0.0), (173, 0.0), (174, 0.0), (175, 0.0), (176, 0.0), (177, 0.0), (178, 0.0), (179, 0.0), (180, 0.0), (181, 0.0), (182, 0.0), (183, 0.0), (184, 0.0), (185, 0.0), (186, 0.0), (187, 0.0), (188, 0.0), (189, 0.0), (190, 0.0), (191, 0.0), (192, 0.0), (193, 0.0), (194, 0.0), (195, 0.0), (196, 0.0), (197, 0.0), (198, 0.0), (199, 0.0), (200, 0.0)]
-INFO flwr 2023-10-13 20:14:04,179 | app.py:226 | app_fit: metrics_distributed_fit {}
-[2023-10-13 20:14:04,179][flwr][INFO] - app_fit: metrics_distributed_fit {}
-INFO flwr 2023-10-13 20:14:04,179 | app.py:227 | app_fit: metrics_distributed {}
-[2023-10-13 20:14:04,179][flwr][INFO] - app_fit: metrics_distributed {}
-INFO flwr 2023-10-13 20:14:04,180 | app.py:228 | app_fit: losses_centralized [(0, 8.480555293659052), (1, 4.678643322600343), (2, 4.821835554445895), (3, 4.760899214698864), (4, 4.635519420757842), (5, 4.483498603772051), (6, 4.310671744636073), (7, 4.30479558816733), (8, 4.350954430552717), (9, 4.2343795116717065), (10, 4.114291173581498), (11, 4.006379742972767), (12, 3.9032422269876013), (13, 3.7946639914101303), (14, 3.694523496749683), (15, 3.606255607483105), (16, 3.5286484503517515), (17, 3.453257521120504), (18, 3.4021275843294285), (19, 3.324841410969012), (20, 3.2592665303629427), (21, 3.1982289168019644), (22, 3.1335429638719408), (23, 3.1089451796711445), (24, 3.037447242691113), (25, 2.9935917195420676), (26, 2.974912224486232), (27, 2.8897676616431043), (28, 2.9036672896089644), (29, 2.8099579156016388), (30, 2.813382360881891), (31, 2.7770677180335928), (32, 2.7599665619694767), (33, 2.7204779908299064), (34, 2.68708546359699), (35, 2.6667960539412574), (36, 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-[2023-10-13 20:14:04,180][flwr][INFO] - app_fit: losses_centralized [(0, 8.480555293659052), (1, 4.678643322600343), (2, 4.821835554445895), (3, 4.760899214698864), (4, 4.635519420757842), (5, 4.483498603772051), (6, 4.310671744636073), (7, 4.30479558816733), (8, 4.350954430552717), (9, 4.2343795116717065), (10, 4.114291173581498), (11, 4.006379742972767), (12, 3.9032422269876013), (13, 3.7946639914101303), (14, 3.694523496749683), (15, 3.606255607483105), (16, 3.5286484503517515), (17, 3.453257521120504), (18, 3.4021275843294285), (19, 3.324841410969012), (20, 3.2592665303629427), (21, 3.1982289168019644), (22, 3.1335429638719408), (23, 3.1089451796711445), (24, 3.037447242691113), (25, 2.9935917195420676), (26, 2.974912224486232), (27, 2.8897676616431043), (28, 2.9036672896089644), (29, 2.8099579156016388), (30, 2.813382360881891), (31, 2.7770677180335928), (32, 2.7599665619694767), (33, 2.7204779908299064), (34, 2.68708546359699), (35, 2.6667960539412574), (36, 2.6492519972804254), 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-INFO flwr 2023-10-13 20:14:04,180 | app.py:229 | app_fit: metrics_centralized {'accuracy': [(0, 0.01), (1, 0.01), (2, 0.01), (3, 0.01), (4, 0.0117), (5, 0.0252), (6, 0.0415), (7, 0.0497), (8, 0.0537), (9, 0.0634), (10, 0.0769), (11, 0.0903), (12, 0.1017), (13, 0.119), (14, 0.1316), (15, 0.1469), (16, 0.1625), (17, 0.177), (18, 0.1934), (19, 0.208), (20, 0.2236), (21, 0.2382), (22, 0.2531), (23, 0.2637), (24, 0.2814), (25, 0.295), (26, 0.3054), (27, 0.3206), (28, 0.3281), (29, 0.3431), (30, 0.3485), (31, 0.3589), (32, 0.3679), (33, 0.3764), (34, 0.3834), (35, 0.392), (36, 0.3976), (37, 0.4045), (38, 0.412), (39, 0.4201), (40, 0.4248), (41, 0.4319), (42, 0.4394), (43, 0.4441), (44, 0.4467), (45, 0.4568), (46, 0.4587), (47, 0.4661), (48, 0.47), (49, 0.4738), (50, 0.4764), (51, 0.4815), (52, 0.4836), (53, 0.4882), (54, 0.4907), (55, 0.4939), (56, 0.4999), (57, 0.4983), (58, 0.5016), (59, 0.5068), (60, 0.5081), (61, 0.51), (62, 0.5111), (63, 0.517), (64, 0.5197), (65, 0.5214), (66, 0.5231), (67, 0.5234), (68, 0.5227), (69, 0.5269), (70, 0.5317), (71, 0.5305), (72, 0.5342), (73, 0.5364), (74, 0.541), (75, 0.5379), (76, 0.5413), (77, 0.5416), (78, 0.5425), (79, 0.546), (80, 0.5467), (81, 0.5476), (82, 0.5485), (83, 0.5508), (84, 0.55), (85, 0.552), (86, 0.5525), (87, 0.5543), (88, 0.5567), (89, 0.5568), (90, 0.5571), (91, 0.5571), (92, 0.561), (93, 0.5612), (94, 0.5643), (95, 0.5634), (96, 0.5652), (97, 0.5673), (98, 0.5667), (99, 0.5666), (100, 0.5667), (101, 0.5696), (102, 0.5702), (103, 0.5712), (104, 0.5699), (105, 0.5705), (106, 0.5705), (107, 0.5746), (108, 0.5734), (109, 0.5739), (110, 0.5761), (111, 0.5772), (112, 0.5783), (113, 0.579), (114, 0.5787), (115, 0.5787), (116, 0.5796), (117, 0.5816), (118, 0.5821), (119, 0.5794), (120, 0.5816), (121, 0.5797), (122, 0.5839), (123, 0.5825), (124, 0.5851), (125, 0.5871), (126, 0.5847), (127, 0.5875), (128, 0.5867), (129, 0.5893), (130, 0.59), (131, 0.5878), (132, 0.5901), (133, 0.5918), (134, 0.5886), (135, 0.5909), (136, 0.5923), (137, 0.5942), (138, 0.5925), (139, 0.5932), (140, 0.5928), (141, 0.5956), (142, 0.5928), (143, 0.5926), (144, 0.5939), (145, 0.596), (146, 0.5958), (147, 0.5946), (148, 0.5964), (149, 0.5952), (150, 0.5956), (151, 0.5966), (152, 0.5963), (153, 0.595), (154, 0.5961), (155, 0.5988), (156, 0.5976), (157, 0.6005), (158, 0.6015), (159, 0.6021), (160, 0.6005), (161, 0.6029), (162, 0.6036), (163, 0.6001), (164, 0.6016), (165, 0.6029), (166, 0.6028), (167, 0.6034), (168, 0.604), (169, 0.6043), (170, 0.6037), (171, 0.6067), (172, 0.6068), (173, 0.6063), (174, 0.6048), (175, 0.6093), (176, 0.6073), (177, 0.6069), (178, 0.6079), (179, 0.6098), (180, 0.609), (181, 0.6087), (182, 0.6113), (183, 0.6095), (184, 0.6104), (185, 0.6091), (186, 0.6097), (187, 0.6109), (188, 0.6113), (189, 0.6123), (190, 0.6111), (191, 0.6113), (192, 0.6118), (193, 0.6109), (194, 0.6115), (195, 0.6132), (196, 0.6137), (197, 0.6144), (198, 0.615), (199, 0.6152), (200, 0.6149)]}
-[2023-10-13 20:14:04,180][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.01), (1, 0.01), (2, 0.01), (3, 0.01), (4, 0.0117), (5, 0.0252), (6, 0.0415), (7, 0.0497), (8, 0.0537), (9, 0.0634), (10, 0.0769), (11, 0.0903), (12, 0.1017), (13, 0.119), (14, 0.1316), (15, 0.1469), (16, 0.1625), (17, 0.177), (18, 0.1934), (19, 0.208), (20, 0.2236), (21, 0.2382), (22, 0.2531), (23, 0.2637), (24, 0.2814), (25, 0.295), (26, 0.3054), (27, 0.3206), (28, 0.3281), (29, 0.3431), (30, 0.3485), (31, 0.3589), (32, 0.3679), (33, 0.3764), (34, 0.3834), (35, 0.392), (36, 0.3976), (37, 0.4045), (38, 0.412), (39, 0.4201), (40, 0.4248), (41, 0.4319), (42, 0.4394), (43, 0.4441), (44, 0.4467), (45, 0.4568), (46, 0.4587), (47, 0.4661), (48, 0.47), (49, 0.4738), (50, 0.4764), (51, 0.4815), (52, 0.4836), (53, 0.4882), (54, 0.4907), (55, 0.4939), (56, 0.4999), (57, 0.4983), (58, 0.5016), (59, 0.5068), (60, 0.5081), (61, 0.51), (62, 0.5111), (63, 0.517), (64, 0.5197), (65, 0.5214), (66, 0.5231), (67, 0.5234), (68, 0.5227), (69, 0.5269), (70, 0.5317), (71, 0.5305), (72, 0.5342), (73, 0.5364), (74, 0.541), (75, 0.5379), (76, 0.5413), (77, 0.5416), (78, 0.5425), (79, 0.546), (80, 0.5467), (81, 0.5476), (82, 0.5485), (83, 0.5508), (84, 0.55), (85, 0.552), (86, 0.5525), (87, 0.5543), (88, 0.5567), (89, 0.5568), (90, 0.5571), (91, 0.5571), (92, 0.561), (93, 0.5612), (94, 0.5643), (95, 0.5634), (96, 0.5652), (97, 0.5673), (98, 0.5667), (99, 0.5666), (100, 0.5667), (101, 0.5696), (102, 0.5702), (103, 0.5712), (104, 0.5699), (105, 0.5705), (106, 0.5705), (107, 0.5746), (108, 0.5734), (109, 0.5739), (110, 0.5761), (111, 0.5772), (112, 0.5783), (113, 0.579), (114, 0.5787), (115, 0.5787), (116, 0.5796), (117, 0.5816), (118, 0.5821), (119, 0.5794), (120, 0.5816), (121, 0.5797), (122, 0.5839), (123, 0.5825), (124, 0.5851), (125, 0.5871), (126, 0.5847), (127, 0.5875), (128, 0.5867), (129, 0.5893), (130, 0.59), (131, 0.5878), (132, 0.5901), (133, 0.5918), (134, 0.5886), (135, 0.5909), (136, 0.5923), (137, 0.5942), (138, 0.5925), (139, 0.5932), (140, 0.5928), (141, 0.5956), (142, 0.5928), (143, 0.5926), (144, 0.5939), (145, 0.596), (146, 0.5958), (147, 0.5946), (148, 0.5964), (149, 0.5952), (150, 0.5956), (151, 0.5966), (152, 0.5963), (153, 0.595), (154, 0.5961), (155, 0.5988), (156, 0.5976), (157, 0.6005), (158, 0.6015), (159, 0.6021), (160, 0.6005), (161, 0.6029), (162, 0.6036), (163, 0.6001), (164, 0.6016), (165, 0.6029), (166, 0.6028), (167, 0.6034), (168, 0.604), (169, 0.6043), (170, 0.6037), (171, 0.6067), (172, 0.6068), (173, 0.6063), (174, 0.6048), (175, 0.6093), (176, 0.6073), (177, 0.6069), (178, 0.6079), (179, 0.6098), (180, 0.609), (181, 0.6087), (182, 0.6113), (183, 0.6095), (184, 0.6104), (185, 0.6091), (186, 0.6097), (187, 0.6109), (188, 0.6113), (189, 0.6123), (190, 0.6111), (191, 0.6113), (192, 0.6118), (193, 0.6109), (194, 0.6115), (195, 0.6132), (196, 0.6137), (197, 0.6144), (198, 0.615), (199, 0.6152), (200, 0.6149)]}
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-History (loss, centralized):
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-	round 174: 2.2803944634934203
-	round 175: 2.274364816304594
-	round 176: 2.2847310698832186
-	round 177: 2.2940372186727798
-	round 178: 2.2912714462310744
-	round 179: 2.289638937662204
-	round 180: 2.291451721145703
-	round 181: 2.3034834168589535
-	round 182: 2.2981419727063406
-	round 183: 2.3107704240293168
-	round 184: 2.313936873937186
-	round 185: 2.306797981262207
-	round 186: 2.3054927869345816
-	round 187: 2.303106297890599
-	round 188: 2.3212159514046324
-	round 189: 2.321961476779974
-	round 190: 2.3261600407167746
-	round 191: 2.3261689364719698
-	round 192: 2.328017218615681
-	round 193: 2.32618732669483
-	round 194: 2.3175561528998063
-	round 195: 2.337302811420002
-	round 196: 2.348089092646163
-	round 197: 2.338596988600283
-	round 198: 2.3383814542057415
-	round 199: 2.329215543529096
-	round 200: 2.3313485875297277
-History (metrics, centralized):
-{'accuracy': [(0, 0.01), (1, 0.01), (2, 0.01), (3, 0.01), (4, 0.0117), (5, 0.0252), (6, 0.0415), (7, 0.0497), (8, 0.0537), (9, 0.0634), (10, 0.0769), (11, 0.0903), (12, 0.1017), (13, 0.119), (14, 0.1316), (15, 0.1469), (16, 0.1625), (17, 0.177), (18, 0.1934), (19, 0.208), (20, 0.2236), (21, 0.2382), (22, 0.2531), (23, 0.2637), (24, 0.2814), (25, 0.295), (26, 0.3054), (27, 0.3206), (28, 0.3281), (29, 0.3431), (30, 0.3485), (31, 0.3589), (32, 0.3679), (33, 0.3764), (34, 0.3834), (35, 0.392), (36, 0.3976), (37, 0.4045), (38, 0.412), (39, 0.4201), (40, 0.4248), (41, 0.4319), (42, 0.4394), (43, 0.4441), (44, 0.4467), (45, 0.4568), (46, 0.4587), (47, 0.4661), (48, 0.47), (49, 0.4738), (50, 0.4764), (51, 0.4815), (52, 0.4836), (53, 0.4882), (54, 0.4907), (55, 0.4939), (56, 0.4999), (57, 0.4983), (58, 0.5016), (59, 0.5068), (60, 0.5081), (61, 0.51), (62, 0.5111), (63, 0.517), (64, 0.5197), (65, 0.5214), (66, 0.5231), (67, 0.5234), (68, 0.5227), (69, 0.5269), (70, 0.5317), (71, 0.5305), (72, 0.5342), (73, 0.5364), (74, 0.541), (75, 0.5379), (76, 0.5413), (77, 0.5416), (78, 0.5425), (79, 0.546), (80, 0.5467), (81, 0.5476), (82, 0.5485), (83, 0.5508), (84, 0.55), (85, 0.552), (86, 0.5525), (87, 0.5543), (88, 0.5567), (89, 0.5568), (90, 0.5571), (91, 0.5571), (92, 0.561), (93, 0.5612), (94, 0.5643), (95, 0.5634), (96, 0.5652), (97, 0.5673), (98, 0.5667), (99, 0.5666), (100, 0.5667), (101, 0.5696), (102, 0.5702), (103, 0.5712), (104, 0.5699), (105, 0.5705), (106, 0.5705), (107, 0.5746), (108, 0.5734), (109, 0.5739), (110, 0.5761), (111, 0.5772), (112, 0.5783), (113, 0.579), (114, 0.5787), (115, 0.5787), (116, 0.5796), (117, 0.5816), (118, 0.5821), (119, 0.5794), (120, 0.5816), (121, 0.5797), (122, 0.5839), (123, 0.5825), (124, 0.5851), (125, 0.5871), (126, 0.5847), (127, 0.5875), (128, 0.5867), (129, 0.5893), (130, 0.59), (131, 0.5878), (132, 0.5901), (133, 0.5918), (134, 0.5886), (135, 0.5909), (136, 0.5923), (137, 0.5942), (138, 0.5925), (139, 0.5932), (140, 0.5928), (141, 0.5956), (142, 0.5928), (143, 0.5926), (144, 0.5939), (145, 0.596), (146, 0.5958), (147, 0.5946), (148, 0.5964), (149, 0.5952), (150, 0.5956), (151, 0.5966), (152, 0.5963), (153, 0.595), (154, 0.5961), (155, 0.5988), (156, 0.5976), (157, 0.6005), (158, 0.6015), (159, 0.6021), (160, 0.6005), (161, 0.6029), (162, 0.6036), (163, 0.6001), (164, 0.6016), (165, 0.6029), (166, 0.6028), (167, 0.6034), (168, 0.604), (169, 0.6043), (170, 0.6037), (171, 0.6067), (172, 0.6068), (173, 0.6063), (174, 0.6048), (175, 0.6093), (176, 0.6073), (177, 0.6069), (178, 0.6079), (179, 0.6098), (180, 0.609), (181, 0.6087), (182, 0.6113), (183, 0.6095), (184, 0.6104), (185, 0.6091), (186, 0.6097), (187, 0.6109), (188, 0.6113), (189, 0.6123), (190, 0.6111), (191, 0.6113), (192, 0.6118), (193, 0.6109), (194, 0.6115), (195, 0.6132), (196, 0.6137), (197, 0.6144), (198, 0.615), (199, 0.6152), (200, 0.6149)]}
-[2023-10-13 20:14:05,330][matplotlib.legend][WARNING] - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
-/home/ubuntu/flower/baselines/moon/moon/utils.py:124: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown
-  plt.show()
diff --git a/baselines/moon/_static/cifar100_fedprox_log.txt b/baselines/moon/_static/cifar100_fedprox_log.txt
deleted file mode 100644
index d820dc54ded1..000000000000
--- a/baselines/moon/_static/cifar100_fedprox_log.txt
+++ /dev/null
@@ -1,17647 +0,0 @@
-num_clients: 10
-num_epochs: 10
-fraction_fit: 1.0
-batch_size: 64
-learning_rate: 0.01
-mu: 0.001
-temperature: 0.5
-alg: moon
-seed: 0
-server_device: cpu
-num_rounds: 100
-client_resources:
-  num_cpus: 4
-  num_gpus: 1
-dataset:
-  name: cifar100
-  dir: ./data/moon/
-  partition: noniid
-  beta: 0.5
-model:
-  name: resnet50
-  output_dim: 256
-  dir: ./models/moon/cifar100_fedprox/
-
-Files already downloaded and verified
-Files already downloaded and verified
-[2023-09-21 03:10:49,616][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=100, round_timeout=None)
-[2023-09-21 03:10:52,718][flwr][INFO] - Flower VCE: Ray initialized with resources: {'object_store_memory': 97622652518.0, 'memory': 217786189210.0, 'node:137.132.92.49': 1.0, 'CPU': 64.0, 'node:__internal_head__': 1.0, 'accelerator_type:G': 1.0, 'GPU': 1.0}
-[2023-09-21 03:10:52,719][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 1}
-[2023-09-21 03:10:52,737][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 1 actors
-[2023-09-21 03:10:52,737][flwr][INFO] - Initializing global parameters
-[2023-09-21 03:10:52,737][flwr][INFO] - Requesting initial parameters from one random client
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 03:10:58,274][flwr][INFO] - Received initial parameters from one random client
-[2023-09-21 03:10:58,275][flwr][INFO] - Evaluating initial parameters
-test acc: 0.01
-[2023-09-21 03:11:58,338][flwr][INFO] - initial parameters (loss, other metrics): 6.156129693832641, {'accuracy': 0.01}
-[2023-09-21 03:11:58,338][flwr][INFO] - FL starting
-[2023-09-21 03:11:58,339][flwr][DEBUG] - fit_round 1: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.014042721518987342
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.049003 Loss1: 4.048447 Loss2: 0.000556
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.862238 Loss1: 3.861674 Loss2: 0.000564
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.686501 Loss1: 3.685928 Loss2: 0.000573
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.561694 Loss1: 3.561111 Loss2: 0.000584
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.456870 Loss1: 3.456278 Loss2: 0.000592
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.378081 Loss1: 3.377487 Loss2: 0.000595
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.315237 Loss1: 3.314636 Loss2: 0.000602
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.250166 Loss1: 3.249565 Loss2: 0.000601
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.181074 Loss1: 3.180473 Loss2: 0.000601
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.136153 Loss1: 3.135551 Loss2: 0.000602
-(DefaultActor pid=2839578) >> Training accuracy: 0.236155
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.0
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.108069 Loss1: 4.107460 Loss2: 0.000609
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.894048 Loss1: 3.893440 Loss2: 0.000608
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.746697 Loss1: 3.746086 Loss2: 0.000611
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.570724 Loss1: 3.570102 Loss2: 0.000622
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.453746 Loss1: 3.453118 Loss2: 0.000628
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.375604 Loss1: 3.374980 Loss2: 0.000625
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.310639 Loss1: 3.310011 Loss2: 0.000627
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.226221 Loss1: 3.225583 Loss2: 0.000638
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.165215 Loss1: 3.164583 Loss2: 0.000632
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.103058 Loss1: 3.102428 Loss2: 0.000631
-(DefaultActor pid=2839578) >> Training accuracy: 0.211234
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.0006009615384615385
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.120604 Loss1: 4.120026 Loss2: 0.000578
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.908697 Loss1: 3.908105 Loss2: 0.000592
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.678700 Loss1: 3.678103 Loss2: 0.000597
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.535451 Loss1: 3.534834 Loss2: 0.000617
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.388606 Loss1: 3.387983 Loss2: 0.000624
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.318526 Loss1: 3.317911 Loss2: 0.000616
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.227636 Loss1: 3.227019 Loss2: 0.000617
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.132973 Loss1: 3.132353 Loss2: 0.000620
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.054064 Loss1: 3.053441 Loss2: 0.000623
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.015753 Loss1: 3.015131 Loss2: 0.000622
-(DefaultActor pid=2839578) >> Training accuracy: 0.294471
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.004006410256410256
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.122952 Loss1: 4.122338 Loss2: 0.000614
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.857820 Loss1: 3.857197 Loss2: 0.000623
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.680495 Loss1: 3.679874 Loss2: 0.000622
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.578917 Loss1: 3.578284 Loss2: 0.000632
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.498336 Loss1: 3.497704 Loss2: 0.000632
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.409808 Loss1: 3.409181 Loss2: 0.000627
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.335593 Loss1: 3.334967 Loss2: 0.000626
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.290330 Loss1: 3.289705 Loss2: 0.000625
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.210379 Loss1: 3.209757 Loss2: 0.000623
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.134445 Loss1: 3.133819 Loss2: 0.000626
-(DefaultActor pid=2839578) >> Training accuracy: 0.229768
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.001714939024390244
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.041751 Loss1: 4.041103 Loss2: 0.000648
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.812356 Loss1: 3.811710 Loss2: 0.000647
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.645221 Loss1: 3.644571 Loss2: 0.000649
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.510078 Loss1: 3.509421 Loss2: 0.000657
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.424783 Loss1: 3.424130 Loss2: 0.000653
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.309564 Loss1: 3.308913 Loss2: 0.000651
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.276595 Loss1: 3.275951 Loss2: 0.000644
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.169555 Loss1: 3.168903 Loss2: 0.000652
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.126639 Loss1: 3.125992 Loss2: 0.000647
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.063766 Loss1: 3.063118 Loss2: 0.000648
-(DefaultActor pid=2839578) >> Training accuracy: 0.245998
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.0021114864864864866
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.019123 Loss1: 4.018554 Loss2: 0.000569
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.771005 Loss1: 3.770423 Loss2: 0.000582
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.665702 Loss1: 3.665108 Loss2: 0.000593
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.573484 Loss1: 3.572897 Loss2: 0.000586
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.480599 Loss1: 3.480004 Loss2: 0.000596
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.412528 Loss1: 3.411919 Loss2: 0.000609
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.313641 Loss1: 3.313028 Loss2: 0.000613
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.239101 Loss1: 3.238484 Loss2: 0.000617
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.171927 Loss1: 3.171297 Loss2: 0.000630
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.115530 Loss1: 3.114911 Loss2: 0.000619
-(DefaultActor pid=2839578) >> Training accuracy: 0.243877
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.011513157894736841
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.063392 Loss1: 4.062766 Loss2: 0.000626
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.853136 Loss1: 3.852511 Loss2: 0.000625
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.776167 Loss1: 3.775535 Loss2: 0.000632
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.668716 Loss1: 3.668090 Loss2: 0.000626
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.590686 Loss1: 3.590043 Loss2: 0.000643
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.478641 Loss1: 3.477994 Loss2: 0.000647
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.383206 Loss1: 3.382564 Loss2: 0.000643
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.353055 Loss1: 3.352404 Loss2: 0.000651
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.301246 Loss1: 3.300592 Loss2: 0.000654
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.247619 Loss1: 3.246963 Loss2: 0.000656
-(DefaultActor pid=2839578) >> Training accuracy: 0.230880
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.0
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.046561 Loss1: 4.045941 Loss2: 0.000620
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.788855 Loss1: 3.788236 Loss2: 0.000619
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.685726 Loss1: 3.685103 Loss2: 0.000623
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.576464 Loss1: 3.575836 Loss2: 0.000628
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.486076 Loss1: 3.485447 Loss2: 0.000629
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.375406 Loss1: 3.374763 Loss2: 0.000643
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.286805 Loss1: 3.286156 Loss2: 0.000650
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.231012 Loss1: 3.230359 Loss2: 0.000653
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.168501 Loss1: 3.167851 Loss2: 0.000650
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.087646 Loss1: 3.086993 Loss2: 0.000653
-(DefaultActor pid=2839578) >> Training accuracy: 0.253038
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.03164556962025317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.045481 Loss1: 4.044875 Loss2: 0.000606
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.792342 Loss1: 3.791750 Loss2: 0.000592
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.629276 Loss1: 3.628680 Loss2: 0.000595
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.531024 Loss1: 3.530416 Loss2: 0.000608
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.455813 Loss1: 3.455215 Loss2: 0.000597
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.354602 Loss1: 3.353992 Loss2: 0.000610
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.291131 Loss1: 3.290518 Loss2: 0.000613
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.231288 Loss1: 3.230674 Loss2: 0.000614
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.166940 Loss1: 3.166321 Loss2: 0.000619
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.118945 Loss1: 3.118329 Loss2: 0.000616
-(DefaultActor pid=2839578) >> Training accuracy: 0.217563
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.03303006329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.030432 Loss1: 4.029803 Loss2: 0.000629
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.752761 Loss1: 3.752137 Loss2: 0.000624
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.633537 Loss1: 3.632907 Loss2: 0.000630
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.504689 Loss1: 3.504047 Loss2: 0.000642
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.426732 Loss1: 3.426088 Loss2: 0.000643
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.343557 Loss1: 3.342910 Loss2: 0.000647
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.271297 Loss1: 3.270646 Loss2: 0.000651
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.217741 Loss1: 3.217095 Loss2: 0.000646
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.140096 Loss1: 3.139444 Loss2: 0.000652
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.075658 Loss1: 3.075012 Loss2: 0.000646
-(DefaultActor pid=2839578) >> Training accuracy: 0.235562
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 03:43:24,366][flwr][DEBUG] - fit_round 1 received 10 results and 0 failures
-[2023-09-21 03:43:27,270][flwr][WARNING] - No fit_metrics_aggregation_fn provided
-test acc: 0.01
-[2023-09-21 03:44:06,379][flwr][INFO] - fit progress: (1, 4.6852914030178665, {'accuracy': 0.01}, 1928.0405755066313)
-[2023-09-21 03:44:06,380][flwr][DEBUG] - evaluate_round 1: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 03:44:44,813][flwr][DEBUG] - evaluate_round 1 received 10 results and 0 failures
-[2023-09-21 03:44:44,814][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
-[2023-09-21 03:44:44,814][flwr][DEBUG] - fit_round 2: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.008900316455696203
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.034845 Loss1: 4.034231 Loss2: 0.000614
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.600918 Loss1: 3.600247 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.472775 Loss1: 3.472079 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.401183 Loss1: 3.400483 Loss2: 0.000700
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.341920 Loss1: 3.341219 Loss2: 0.000702
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.261761 Loss1: 3.261041 Loss2: 0.000720
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.192689 Loss1: 3.191953 Loss2: 0.000736
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.127874 Loss1: 3.127146 Loss2: 0.000728
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.063087 Loss1: 3.062348 Loss2: 0.000739
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.042643 Loss1: 3.041913 Loss2: 0.000729
-(DefaultActor pid=2839578) >> Training accuracy: 0.243671
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.011242378048780487
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.006845 Loss1: 4.006151 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.526045 Loss1: 3.525288 Loss2: 0.000757
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.418526 Loss1: 3.417792 Loss2: 0.000733
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.356660 Loss1: 3.355912 Loss2: 0.000748
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.240215 Loss1: 3.239479 Loss2: 0.000735
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.157518 Loss1: 3.156767 Loss2: 0.000751
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.099843 Loss1: 3.099087 Loss2: 0.000756
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.045006 Loss1: 3.044251 Loss2: 0.000755
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.981187 Loss1: 2.980426 Loss2: 0.000761
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.943801 Loss1: 2.943038 Loss2: 0.000763
-(DefaultActor pid=2839578) >> Training accuracy: 0.259337
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005008012820512821
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.090613 Loss1: 4.090037 Loss2: 0.000576
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.635666 Loss1: 3.634994 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.507819 Loss1: 3.507149 Loss2: 0.000669
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.410626 Loss1: 3.409946 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.340432 Loss1: 3.339741 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.257841 Loss1: 3.257146 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.202210 Loss1: 3.201497 Loss2: 0.000713
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.113738 Loss1: 3.113032 Loss2: 0.000705
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.084920 Loss1: 3.084206 Loss2: 0.000714
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.011787 Loss1: 3.011068 Loss2: 0.000718
-(DefaultActor pid=2839578) >> Training accuracy: 0.246194
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.002175632911392405
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.072911 Loss1: 4.072328 Loss2: 0.000582
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.630913 Loss1: 3.630240 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.476790 Loss1: 3.476120 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.350535 Loss1: 3.349832 Loss2: 0.000703
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.257716 Loss1: 3.257002 Loss2: 0.000714
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.200790 Loss1: 3.200078 Loss2: 0.000712
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.111061 Loss1: 3.110367 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.045489 Loss1: 3.044779 Loss2: 0.000710
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.990485 Loss1: 2.989764 Loss2: 0.000722
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.939264 Loss1: 2.938527 Loss2: 0.000736
-(DefaultActor pid=2839578) >> Training accuracy: 0.252176
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005859375
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.087791 Loss1: 4.087373 Loss2: 0.000419
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.669801 Loss1: 3.669258 Loss2: 0.000542
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.488613 Loss1: 3.488062 Loss2: 0.000552
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.375456 Loss1: 3.374908 Loss2: 0.000547
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.264577 Loss1: 3.264023 Loss2: 0.000554
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.193226 Loss1: 3.192652 Loss2: 0.000574
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.144794 Loss1: 3.144211 Loss2: 0.000583
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.054862 Loss1: 3.054282 Loss2: 0.000579
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.014779 Loss1: 3.014197 Loss2: 0.000582
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.984606 Loss1: 2.984012 Loss2: 0.000594
-(DefaultActor pid=2839578) >> Training accuracy: 0.294705
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.004944620253164557
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.101180 Loss1: 4.100464 Loss2: 0.000716
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.658290 Loss1: 3.657497 Loss2: 0.000793
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.486543 Loss1: 3.485726 Loss2: 0.000817
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.406322 Loss1: 3.405507 Loss2: 0.000816
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.327568 Loss1: 3.326766 Loss2: 0.000802
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.264791 Loss1: 3.263977 Loss2: 0.000814
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.197666 Loss1: 3.196857 Loss2: 0.000809
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.162852 Loss1: 3.162047 Loss2: 0.000805
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.068547 Loss1: 3.067753 Loss2: 0.000794
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.043007 Loss1: 3.042205 Loss2: 0.000802
-(DefaultActor pid=2839578) >> Training accuracy: 0.259494
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.006610576923076923
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.077130 Loss1: 4.076656 Loss2: 0.000473
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.593987 Loss1: 3.593401 Loss2: 0.000586
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.419757 Loss1: 3.419166 Loss2: 0.000591
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.303391 Loss1: 3.302782 Loss2: 0.000610
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.225019 Loss1: 3.224406 Loss2: 0.000613
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.147130 Loss1: 3.146521 Loss2: 0.000608
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.079573 Loss1: 3.078955 Loss2: 0.000618
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.000903 Loss1: 3.000272 Loss2: 0.000631
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.915597 Loss1: 2.914979 Loss2: 0.000617
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.854371 Loss1: 2.853744 Loss2: 0.000627
-(DefaultActor pid=2839578) >> Training accuracy: 0.322115
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.023015202702702704
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.015924 Loss1: 4.015347 Loss2: 0.000577
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.570788 Loss1: 3.570074 Loss2: 0.000714
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.454587 Loss1: 3.453886 Loss2: 0.000701
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.358572 Loss1: 3.357867 Loss2: 0.000705
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.275433 Loss1: 3.274705 Loss2: 0.000728
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.210522 Loss1: 3.209788 Loss2: 0.000734
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.132102 Loss1: 3.131375 Loss2: 0.000726
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.066365 Loss1: 3.065640 Loss2: 0.000726
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.014664 Loss1: 3.013925 Loss2: 0.000740
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.949913 Loss1: 2.949176 Loss2: 0.000737
-(DefaultActor pid=2839578) >> Training accuracy: 0.292019
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.009868421052631578
-(DefaultActor pid=2839578) Epoch: 0 Loss: 4.067316 Loss1: 4.066544 Loss2: 0.000772
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.652317 Loss1: 3.651432 Loss2: 0.000885
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.506712 Loss1: 3.505853 Loss2: 0.000859
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.450474 Loss1: 3.449623 Loss2: 0.000851
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.391708 Loss1: 3.390863 Loss2: 0.000844
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.350177 Loss1: 3.349332 Loss2: 0.000845
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.285717 Loss1: 3.284876 Loss2: 0.000841
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.247498 Loss1: 3.246660 Loss2: 0.000837
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.172712 Loss1: 3.171888 Loss2: 0.000824
-(DefaultActor pid=2839578) Epoch: 9 Loss: 3.138771 Loss1: 3.137930 Loss2: 0.000841
-(DefaultActor pid=2839578) >> Training accuracy: 0.248561
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.02333860759493671
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.978601 Loss1: 3.977865 Loss2: 0.000736
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.598737 Loss1: 3.597915 Loss2: 0.000822
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.443873 Loss1: 3.443047 Loss2: 0.000826
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.340507 Loss1: 3.339685 Loss2: 0.000823
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.271314 Loss1: 3.270473 Loss2: 0.000841
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.228984 Loss1: 3.228140 Loss2: 0.000843
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.132339 Loss1: 3.131514 Loss2: 0.000825
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.077642 Loss1: 3.076820 Loss2: 0.000822
-(DefaultActor pid=2839578) Epoch: 8 Loss: 3.028018 Loss1: 3.027206 Loss2: 0.000811
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.956295 Loss1: 2.955485 Loss2: 0.000810
-(DefaultActor pid=2839578) >> Training accuracy: 0.273141
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 04:15:53,813][flwr][DEBUG] - fit_round 2 received 10 results and 0 failures
-test acc: 0.01
-[2023-09-21 04:16:44,175][flwr][INFO] - fit progress: (2, 5.889099098242129, {'accuracy': 0.01}, 3885.8362666419707)
-[2023-09-21 04:16:44,176][flwr][DEBUG] - evaluate_round 2: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 04:17:23,530][flwr][DEBUG] - evaluate_round 2 received 10 results and 0 failures
-[2023-09-21 04:17:23,532][flwr][DEBUG] - fit_round 3: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005859375
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.497664 Loss1: 3.497270 Loss2: 0.000393
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.230846 Loss1: 3.230431 Loss2: 0.000415
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.118327 Loss1: 3.117908 Loss2: 0.000419
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.026843 Loss1: 3.026438 Loss2: 0.000405
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.965072 Loss1: 2.964659 Loss2: 0.000413
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.912766 Loss1: 2.912347 Loss2: 0.000419
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.837211 Loss1: 2.836798 Loss2: 0.000413
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.791349 Loss1: 2.790936 Loss2: 0.000413
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.733879 Loss1: 2.733456 Loss2: 0.000424
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.695446 Loss1: 2.695027 Loss2: 0.000419
-(DefaultActor pid=2839578) >> Training accuracy: 0.354167
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.022804054054054054
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.473370 Loss1: 3.472964 Loss2: 0.000406
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.240647 Loss1: 3.240219 Loss2: 0.000428
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.118030 Loss1: 3.117603 Loss2: 0.000427
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.040329 Loss1: 3.039910 Loss2: 0.000419
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.945130 Loss1: 2.944708 Loss2: 0.000422
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.891144 Loss1: 2.890710 Loss2: 0.000434
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.822074 Loss1: 2.821645 Loss2: 0.000430
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.752725 Loss1: 2.752277 Loss2: 0.000448
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.693787 Loss1: 2.693343 Loss2: 0.000443
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.634574 Loss1: 2.634124 Loss2: 0.000450
-(DefaultActor pid=2839578) >> Training accuracy: 0.352618
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.02333860759493671
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.502686 Loss1: 3.502227 Loss2: 0.000458
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.254540 Loss1: 3.254071 Loss2: 0.000468
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.155766 Loss1: 3.155294 Loss2: 0.000472
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.081745 Loss1: 3.081275 Loss2: 0.000471
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.997191 Loss1: 2.996715 Loss2: 0.000476
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.938236 Loss1: 2.937753 Loss2: 0.000483
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.887327 Loss1: 2.886846 Loss2: 0.000481
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.817731 Loss1: 2.817243 Loss2: 0.000488
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.738270 Loss1: 2.737774 Loss2: 0.000495
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.683992 Loss1: 2.683503 Loss2: 0.000489
-(DefaultActor pid=2839578) >> Training accuracy: 0.346123
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.002175632911392405
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.488878 Loss1: 3.488457 Loss2: 0.000422
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.267578 Loss1: 3.267151 Loss2: 0.000428
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.134343 Loss1: 3.133918 Loss2: 0.000425
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.040148 Loss1: 3.039714 Loss2: 0.000434
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.982089 Loss1: 2.981665 Loss2: 0.000424
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.914704 Loss1: 2.914274 Loss2: 0.000430
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.861988 Loss1: 2.861555 Loss2: 0.000433
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.787332 Loss1: 2.786902 Loss2: 0.000430
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.761444 Loss1: 2.761014 Loss2: 0.000430
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.683602 Loss1: 2.683158 Loss2: 0.000444
-(DefaultActor pid=2839578) >> Training accuracy: 0.324169
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.004944620253164557
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.565644 Loss1: 3.565142 Loss2: 0.000502
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.278970 Loss1: 3.278460 Loss2: 0.000510
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.187793 Loss1: 3.187279 Loss2: 0.000515
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.109151 Loss1: 3.108649 Loss2: 0.000502
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.007663 Loss1: 3.007154 Loss2: 0.000509
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.953384 Loss1: 2.952874 Loss2: 0.000511
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.884369 Loss1: 2.883844 Loss2: 0.000525
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.837466 Loss1: 2.836956 Loss2: 0.000510
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.798176 Loss1: 2.797651 Loss2: 0.000525
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.746838 Loss1: 2.746311 Loss2: 0.000527
-(DefaultActor pid=2839578) >> Training accuracy: 0.333663
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005008012820512821
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.526725 Loss1: 3.526301 Loss2: 0.000424
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.291798 Loss1: 3.291357 Loss2: 0.000441
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.199626 Loss1: 3.199174 Loss2: 0.000452
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.097104 Loss1: 3.096654 Loss2: 0.000449
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.036548 Loss1: 3.036093 Loss2: 0.000456
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.967198 Loss1: 2.966736 Loss2: 0.000462
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.937590 Loss1: 2.937125 Loss2: 0.000466
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.861535 Loss1: 2.861068 Loss2: 0.000467
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.815555 Loss1: 2.815086 Loss2: 0.000469
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.744658 Loss1: 2.744191 Loss2: 0.000468
-(DefaultActor pid=2839578) >> Training accuracy: 0.325120
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.006610576923076923
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.503517 Loss1: 3.503171 Loss2: 0.000346
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.231768 Loss1: 3.231403 Loss2: 0.000365
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.091991 Loss1: 3.091628 Loss2: 0.000363
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.003812 Loss1: 3.003439 Loss2: 0.000373
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.935798 Loss1: 2.935424 Loss2: 0.000375
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.849083 Loss1: 2.848704 Loss2: 0.000379
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.770331 Loss1: 2.769948 Loss2: 0.000383
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.716012 Loss1: 2.715627 Loss2: 0.000385
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.647046 Loss1: 2.646652 Loss2: 0.000393
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.580274 Loss1: 2.579886 Loss2: 0.000389
-(DefaultActor pid=2839578) >> Training accuracy: 0.374199
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.009868421052631578
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.597554 Loss1: 3.597098 Loss2: 0.000456
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.361120 Loss1: 3.360637 Loss2: 0.000483
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.284197 Loss1: 3.283719 Loss2: 0.000478
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.205098 Loss1: 3.204626 Loss2: 0.000472
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.177774 Loss1: 3.177307 Loss2: 0.000466
-(DefaultActor pid=2839578) Epoch: 5 Loss: 3.105030 Loss1: 3.104551 Loss2: 0.000480
-(DefaultActor pid=2839578) Epoch: 6 Loss: 3.082643 Loss1: 3.082167 Loss2: 0.000476
-(DefaultActor pid=2839578) Epoch: 7 Loss: 3.013746 Loss1: 3.013268 Loss2: 0.000478
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.966661 Loss1: 2.966167 Loss2: 0.000494
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.913738 Loss1: 2.913248 Loss2: 0.000490
-(DefaultActor pid=2839578) >> Training accuracy: 0.269120
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.011051829268292682
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.452149 Loss1: 3.451674 Loss2: 0.000475
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.229586 Loss1: 3.229084 Loss2: 0.000502
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.147423 Loss1: 3.146943 Loss2: 0.000481
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.043248 Loss1: 3.042759 Loss2: 0.000489
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.983966 Loss1: 2.983476 Loss2: 0.000490
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.945689 Loss1: 2.945196 Loss2: 0.000493
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.880893 Loss1: 2.880399 Loss2: 0.000494
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.841460 Loss1: 2.840964 Loss2: 0.000496
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.767905 Loss1: 2.767406 Loss2: 0.000499
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.706575 Loss1: 2.706067 Loss2: 0.000508
-(DefaultActor pid=2839578) >> Training accuracy: 0.325076
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.008900316455696203
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.528368 Loss1: 3.527968 Loss2: 0.000401
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.256940 Loss1: 3.256524 Loss2: 0.000417
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.136524 Loss1: 3.136101 Loss2: 0.000424
-(DefaultActor pid=2839578) Epoch: 3 Loss: 3.100428 Loss1: 3.100007 Loss2: 0.000421
-(DefaultActor pid=2839578) Epoch: 4 Loss: 3.048198 Loss1: 3.047770 Loss2: 0.000428
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.973330 Loss1: 2.972898 Loss2: 0.000432
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.946641 Loss1: 2.946207 Loss2: 0.000433
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.876902 Loss1: 2.876460 Loss2: 0.000442
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.798364 Loss1: 2.797919 Loss2: 0.000444
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.760363 Loss1: 2.759915 Loss2: 0.000448
-(DefaultActor pid=2839578) >> Training accuracy: 0.310720
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 04:48:19,865][flwr][DEBUG] - fit_round 3 received 10 results and 0 failures
-test acc: 0.0159
-[2023-09-21 04:48:59,026][flwr][INFO] - fit progress: (3, 5.795179260424532, {'accuracy': 0.0159}, 5820.688017355744)
-[2023-09-21 04:48:59,028][flwr][DEBUG] - evaluate_round 3: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 04:49:38,619][flwr][DEBUG] - evaluate_round 3 received 10 results and 0 failures
-[2023-09-21 04:49:38,620][flwr][DEBUG] - fit_round 4: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.035799050632911396
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.217798 Loss1: 3.217414 Loss2: 0.000384
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.013310 Loss1: 3.012904 Loss2: 0.000407
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.903406 Loss1: 2.903003 Loss2: 0.000403
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.817941 Loss1: 2.817533 Loss2: 0.000408
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.726926 Loss1: 2.726519 Loss2: 0.000406
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.672959 Loss1: 2.672544 Loss2: 0.000416
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.579776 Loss1: 2.579364 Loss2: 0.000412
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.531181 Loss1: 2.530760 Loss2: 0.000421
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.476366 Loss1: 2.475951 Loss2: 0.000416
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.394170 Loss1: 2.393747 Loss2: 0.000423
-(DefaultActor pid=2839578) >> Training accuracy: 0.383307
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.01069078947368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.307423 Loss1: 3.307018 Loss2: 0.000405
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.099389 Loss1: 3.098978 Loss2: 0.000411
-(DefaultActor pid=2839578) Epoch: 2 Loss: 3.022815 Loss1: 3.022402 Loss2: 0.000412
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.940196 Loss1: 2.939770 Loss2: 0.000426
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.886811 Loss1: 2.886400 Loss2: 0.000411
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.834288 Loss1: 2.833865 Loss2: 0.000423
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.757556 Loss1: 2.757136 Loss2: 0.000420
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.697843 Loss1: 2.697422 Loss2: 0.000421
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.645784 Loss1: 2.645369 Loss2: 0.000415
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.582188 Loss1: 2.581761 Loss2: 0.000427
-(DefaultActor pid=2839578) >> Training accuracy: 0.345806
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005537974683544304
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.220522 Loss1: 3.220117 Loss2: 0.000405
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.015141 Loss1: 3.014706 Loss2: 0.000435
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.926200 Loss1: 2.925774 Loss2: 0.000426
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.835716 Loss1: 2.835286 Loss2: 0.000430
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.739549 Loss1: 2.739104 Loss2: 0.000444
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.718363 Loss1: 2.717920 Loss2: 0.000443
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.627129 Loss1: 2.626680 Loss2: 0.000449
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.574281 Loss1: 2.573836 Loss2: 0.000445
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.508525 Loss1: 2.508081 Loss2: 0.000443
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.459976 Loss1: 2.459532 Loss2: 0.000444
-(DefaultActor pid=2839578) >> Training accuracy: 0.374604
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.015202702702702704
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.180689 Loss1: 3.180329 Loss2: 0.000360
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.944385 Loss1: 2.944005 Loss2: 0.000380
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.824908 Loss1: 2.824526 Loss2: 0.000382
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.734459 Loss1: 2.734081 Loss2: 0.000378
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.690073 Loss1: 2.689687 Loss2: 0.000387
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.603358 Loss1: 2.602973 Loss2: 0.000385
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.532423 Loss1: 2.532043 Loss2: 0.000379
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.484561 Loss1: 2.484169 Loss2: 0.000392
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.469905 Loss1: 2.469515 Loss2: 0.000390
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.366994 Loss1: 2.366593 Loss2: 0.000402
-(DefaultActor pid=2839578) >> Training accuracy: 0.426098
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.005809294871794872
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.119125 Loss1: 3.118820 Loss2: 0.000304
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.945342 Loss1: 2.945023 Loss2: 0.000320
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.805461 Loss1: 2.805140 Loss2: 0.000321
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.752142 Loss1: 2.751816 Loss2: 0.000326
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.680353 Loss1: 2.680030 Loss2: 0.000323
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.608047 Loss1: 2.607717 Loss2: 0.000329
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.536142 Loss1: 2.535819 Loss2: 0.000323
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.453551 Loss1: 2.453219 Loss2: 0.000332
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.401894 Loss1: 2.401563 Loss2: 0.000331
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.302079 Loss1: 2.301741 Loss2: 0.000337
-(DefaultActor pid=2839578) >> Training accuracy: 0.415665
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.006510416666666667
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.194325 Loss1: 3.193981 Loss2: 0.000344
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.952950 Loss1: 2.952584 Loss2: 0.000366
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.872465 Loss1: 2.872104 Loss2: 0.000361
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.786886 Loss1: 2.786528 Loss2: 0.000358
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.711102 Loss1: 2.710739 Loss2: 0.000364
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.627909 Loss1: 2.627545 Loss2: 0.000363
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.545390 Loss1: 2.545025 Loss2: 0.000365
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.490828 Loss1: 2.490452 Loss2: 0.000376
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.410544 Loss1: 2.410168 Loss2: 0.000376
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.359539 Loss1: 2.359161 Loss2: 0.000378
-(DefaultActor pid=2839578) >> Training accuracy: 0.420139
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.016811708860759493
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.122448 Loss1: 3.122101 Loss2: 0.000348
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.965570 Loss1: 2.965203 Loss2: 0.000367
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.874570 Loss1: 2.874209 Loss2: 0.000362
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.793978 Loss1: 2.793616 Loss2: 0.000362
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.715998 Loss1: 2.715635 Loss2: 0.000362
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.661922 Loss1: 2.661548 Loss2: 0.000374
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.584703 Loss1: 2.584331 Loss2: 0.000372
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.528646 Loss1: 2.528269 Loss2: 0.000377
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.446880 Loss1: 2.446509 Loss2: 0.000371
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.390937 Loss1: 2.390558 Loss2: 0.000379
-(DefaultActor pid=2839578) >> Training accuracy: 0.394383
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.006724683544303798
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.218198 Loss1: 3.217857 Loss2: 0.000341
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.013430 Loss1: 3.013078 Loss2: 0.000352
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.920192 Loss1: 2.919841 Loss2: 0.000351
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.857596 Loss1: 2.857241 Loss2: 0.000355
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.779833 Loss1: 2.779473 Loss2: 0.000360
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.690679 Loss1: 2.690311 Loss2: 0.000368
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.631938 Loss1: 2.631570 Loss2: 0.000368
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.592852 Loss1: 2.592473 Loss2: 0.000379
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.476063 Loss1: 2.475686 Loss2: 0.000377
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.457981 Loss1: 2.457600 Loss2: 0.000381
-(DefaultActor pid=2839578) >> Training accuracy: 0.380934
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.015815548780487805
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.152229 Loss1: 3.151814 Loss2: 0.000415
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.021255 Loss1: 3.020834 Loss2: 0.000421
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.930871 Loss1: 2.930456 Loss2: 0.000414
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.820823 Loss1: 2.820410 Loss2: 0.000413
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.748219 Loss1: 2.747806 Loss2: 0.000413
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.673662 Loss1: 2.673241 Loss2: 0.000420
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.638957 Loss1: 2.638533 Loss2: 0.000424
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.579395 Loss1: 2.578974 Loss2: 0.000421
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.489916 Loss1: 2.489484 Loss2: 0.000432
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.471207 Loss1: 2.470778 Loss2: 0.000429
-(DefaultActor pid=2839578) >> Training accuracy: 0.349466
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.007612179487179487
-(DefaultActor pid=2839578) Epoch: 0 Loss: 3.214502 Loss1: 3.214151 Loss2: 0.000350
-(DefaultActor pid=2839578) Epoch: 1 Loss: 3.055020 Loss1: 3.054647 Loss2: 0.000373
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.937553 Loss1: 2.937179 Loss2: 0.000374
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.853051 Loss1: 2.852672 Loss2: 0.000378
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.762206 Loss1: 2.761822 Loss2: 0.000384
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.716567 Loss1: 2.716181 Loss2: 0.000387
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.654350 Loss1: 2.653964 Loss2: 0.000386
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.598232 Loss1: 2.597843 Loss2: 0.000390
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.530895 Loss1: 2.530509 Loss2: 0.000386
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.498543 Loss1: 2.498145 Loss2: 0.000398
-(DefaultActor pid=2839578) >> Training accuracy: 0.345353
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 05:20:43,992][flwr][DEBUG] - fit_round 4 received 10 results and 0 failures
-test acc: 0.0942
-[2023-09-21 05:21:33,867][flwr][INFO] - fit progress: (4, 4.141716613556249, {'accuracy': 0.0942}, 7775.528302751016)
-[2023-09-21 05:21:33,867][flwr][DEBUG] - evaluate_round 4: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 05:22:11,622][flwr][DEBUG] - evaluate_round 4 received 10 results and 0 failures
-[2023-09-21 05:22:11,624][flwr][DEBUG] - fit_round 5: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.08897569444444445
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.842027 Loss1: 2.841548 Loss2: 0.000479
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.630804 Loss1: 2.630309 Loss2: 0.000495
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.530924 Loss1: 2.530427 Loss2: 0.000497
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.460785 Loss1: 2.460283 Loss2: 0.000502
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.341677 Loss1: 2.341182 Loss2: 0.000495
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.277195 Loss1: 2.276695 Loss2: 0.000500
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.203589 Loss1: 2.203092 Loss2: 0.000496
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.133839 Loss1: 2.133330 Loss2: 0.000509
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.102173 Loss1: 2.101666 Loss2: 0.000506
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.009186 Loss1: 2.008675 Loss2: 0.000511
-(DefaultActor pid=2839578) >> Training accuracy: 0.469618
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.11223323170731707
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.898178 Loss1: 2.897678 Loss2: 0.000500
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.694501 Loss1: 2.693977 Loss2: 0.000524
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.590732 Loss1: 2.590220 Loss2: 0.000512
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.472786 Loss1: 2.472277 Loss2: 0.000510
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.449536 Loss1: 2.449024 Loss2: 0.000512
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.344897 Loss1: 2.344381 Loss2: 0.000516
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.329203 Loss1: 2.328683 Loss2: 0.000520
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.231047 Loss1: 2.230533 Loss2: 0.000514
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.180540 Loss1: 2.180023 Loss2: 0.000517
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.111728 Loss1: 2.111203 Loss2: 0.000526
-(DefaultActor pid=2839578) >> Training accuracy: 0.447218
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.08018092105263158
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.989248 Loss1: 2.988752 Loss2: 0.000496
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.801355 Loss1: 2.800844 Loss2: 0.000510
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.714794 Loss1: 2.714287 Loss2: 0.000507
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.643452 Loss1: 2.642939 Loss2: 0.000513
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.567204 Loss1: 2.566701 Loss2: 0.000503
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.531174 Loss1: 2.530662 Loss2: 0.000512
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.428815 Loss1: 2.428309 Loss2: 0.000505
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.368745 Loss1: 2.368242 Loss2: 0.000503
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.309541 Loss1: 2.309036 Loss2: 0.000505
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.267966 Loss1: 2.267452 Loss2: 0.000514
-(DefaultActor pid=2839578) >> Training accuracy: 0.454770
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.09134615384615384
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.889201 Loss1: 2.888725 Loss2: 0.000476
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.718225 Loss1: 2.717739 Loss2: 0.000486
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.641575 Loss1: 2.641079 Loss2: 0.000496
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.550665 Loss1: 2.550175 Loss2: 0.000489
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.476468 Loss1: 2.475974 Loss2: 0.000494
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.420052 Loss1: 2.419552 Loss2: 0.000500
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.352334 Loss1: 2.351841 Loss2: 0.000494
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.291334 Loss1: 2.290833 Loss2: 0.000500
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.238610 Loss1: 2.238106 Loss2: 0.000503
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.198241 Loss1: 2.197743 Loss2: 0.000498
-(DefaultActor pid=2839578) >> Training accuracy: 0.417668
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.06823575949367089
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.843715 Loss1: 2.843263 Loss2: 0.000452
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.640449 Loss1: 2.639978 Loss2: 0.000471
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.547165 Loss1: 2.546692 Loss2: 0.000473
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.480480 Loss1: 2.480006 Loss2: 0.000474
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.425617 Loss1: 2.425137 Loss2: 0.000480
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.340638 Loss1: 2.340153 Loss2: 0.000485
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.280861 Loss1: 2.280383 Loss2: 0.000477
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.226317 Loss1: 2.225833 Loss2: 0.000484
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.165107 Loss1: 2.164619 Loss2: 0.000487
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.082227 Loss1: 2.081746 Loss2: 0.000482
-(DefaultActor pid=2839578) >> Training accuracy: 0.429193
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.09715544871794872
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.766770 Loss1: 2.766324 Loss2: 0.000445
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.567833 Loss1: 2.567372 Loss2: 0.000461
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.478758 Loss1: 2.478300 Loss2: 0.000458
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.450178 Loss1: 2.449716 Loss2: 0.000463
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.328200 Loss1: 2.327738 Loss2: 0.000462
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.242607 Loss1: 2.242138 Loss2: 0.000469
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.184539 Loss1: 2.184071 Loss2: 0.000469
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.141077 Loss1: 2.140611 Loss2: 0.000466
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.066708 Loss1: 2.066237 Loss2: 0.000471
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.986865 Loss1: 1.986391 Loss2: 0.000475
-(DefaultActor pid=2839578) >> Training accuracy: 0.492188
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.061708860759493674
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.900424 Loss1: 2.899983 Loss2: 0.000441
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.686310 Loss1: 2.685853 Loss2: 0.000458
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.603197 Loss1: 2.602741 Loss2: 0.000457
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.505852 Loss1: 2.505391 Loss2: 0.000460
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.445087 Loss1: 2.444620 Loss2: 0.000467
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.399947 Loss1: 2.399487 Loss2: 0.000461
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.308242 Loss1: 2.307779 Loss2: 0.000463
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.259994 Loss1: 2.259527 Loss2: 0.000468
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.203556 Loss1: 2.203080 Loss2: 0.000477
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.153657 Loss1: 2.153182 Loss2: 0.000475
-(DefaultActor pid=2839578) >> Training accuracy: 0.458070
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.10027689873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.877591 Loss1: 2.877095 Loss2: 0.000496
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.656893 Loss1: 2.656379 Loss2: 0.000514
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.587690 Loss1: 2.587176 Loss2: 0.000514
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.501088 Loss1: 2.500580 Loss2: 0.000508
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.426702 Loss1: 2.426187 Loss2: 0.000515
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.342188 Loss1: 2.341670 Loss2: 0.000518
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.255267 Loss1: 2.254756 Loss2: 0.000510
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.215915 Loss1: 2.215398 Loss2: 0.000517
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.124104 Loss1: 2.123581 Loss2: 0.000523
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.090785 Loss1: 2.090260 Loss2: 0.000525
-(DefaultActor pid=2839578) >> Training accuracy: 0.483979
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.08544303797468354
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.901068 Loss1: 2.900552 Loss2: 0.000516
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.699047 Loss1: 2.698518 Loss2: 0.000529
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.591029 Loss1: 2.590499 Loss2: 0.000530
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.523788 Loss1: 2.523258 Loss2: 0.000530
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.441983 Loss1: 2.441450 Loss2: 0.000533
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.379017 Loss1: 2.378487 Loss2: 0.000530
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.313858 Loss1: 2.313323 Loss2: 0.000535
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.271021 Loss1: 2.270478 Loss2: 0.000543
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.200958 Loss1: 2.200423 Loss2: 0.000536
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.116327 Loss1: 2.115790 Loss2: 0.000536
-(DefaultActor pid=2839578) >> Training accuracy: 0.446598
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.06482263513513513
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.845257 Loss1: 2.844808 Loss2: 0.000449
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.600918 Loss1: 2.600450 Loss2: 0.000468
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.530885 Loss1: 2.530420 Loss2: 0.000465
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.437704 Loss1: 2.437239 Loss2: 0.000465
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.359634 Loss1: 2.359164 Loss2: 0.000470
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.290623 Loss1: 2.290151 Loss2: 0.000472
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.238191 Loss1: 2.237717 Loss2: 0.000475
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.165052 Loss1: 2.164583 Loss2: 0.000469
-(DefaultActor pid=2839578) Epoch: 8 Loss: 2.122357 Loss1: 2.121881 Loss2: 0.000476
-(DefaultActor pid=2839578) Epoch: 9 Loss: 2.051197 Loss1: 2.050721 Loss2: 0.000475
-(DefaultActor pid=2839578) >> Training accuracy: 0.488809
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 05:53:05,748][flwr][DEBUG] - fit_round 5 received 10 results and 0 failures
-test acc: 0.1964
-[2023-09-21 05:53:44,442][flwr][INFO] - fit progress: (5, 3.311973663183828, {'accuracy': 0.1964}, 9706.103674005717)
-[2023-09-21 05:53:44,443][flwr][DEBUG] - evaluate_round 5: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 05:54:22,319][flwr][DEBUG] - evaluate_round 5 received 10 results and 0 failures
-[2023-09-21 05:54:22,324][flwr][DEBUG] - fit_round 6: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.17246835443037975
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.610884 Loss1: 2.610286 Loss2: 0.000599
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.362191 Loss1: 2.361584 Loss2: 0.000607
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.280921 Loss1: 2.280314 Loss2: 0.000607
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.207432 Loss1: 2.206824 Loss2: 0.000608
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.143255 Loss1: 2.142647 Loss2: 0.000608
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.055508 Loss1: 2.054899 Loss2: 0.000608
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.994889 Loss1: 1.994277 Loss2: 0.000612
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.941048 Loss1: 1.940436 Loss2: 0.000612
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.860695 Loss1: 1.860082 Loss2: 0.000613
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.787635 Loss1: 1.787020 Loss2: 0.000615
-(DefaultActor pid=2839578) >> Training accuracy: 0.490704
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.16910601265822786
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.526193 Loss1: 2.525595 Loss2: 0.000598
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.341296 Loss1: 2.340695 Loss2: 0.000601
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.233416 Loss1: 2.232811 Loss2: 0.000605
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.168674 Loss1: 2.168063 Loss2: 0.000611
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.103103 Loss1: 2.102493 Loss2: 0.000611
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.055619 Loss1: 2.055008 Loss2: 0.000610
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.956009 Loss1: 1.955397 Loss2: 0.000612
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.895043 Loss1: 1.894429 Loss2: 0.000613
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.821400 Loss1: 1.820790 Loss2: 0.000610
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.781555 Loss1: 1.780938 Loss2: 0.000617
-(DefaultActor pid=2839578) >> Training accuracy: 0.465981
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.22275641025641027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.441899 Loss1: 2.441297 Loss2: 0.000601
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.253544 Loss1: 2.252936 Loss2: 0.000609
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.195931 Loss1: 2.195321 Loss2: 0.000610
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.081599 Loss1: 2.080990 Loss2: 0.000609
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.018430 Loss1: 2.017820 Loss2: 0.000610
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.936686 Loss1: 1.936070 Loss2: 0.000616
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.903456 Loss1: 1.902840 Loss2: 0.000616
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.798772 Loss1: 1.798154 Loss2: 0.000618
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.821713 Loss1: 1.821093 Loss2: 0.000619
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.689851 Loss1: 1.689232 Loss2: 0.000620
-(DefaultActor pid=2839578) >> Training accuracy: 0.551082
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.22804588607594936
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.553433 Loss1: 2.552793 Loss2: 0.000640
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.320722 Loss1: 2.320077 Loss2: 0.000646
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.247907 Loss1: 2.247261 Loss2: 0.000646
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.150732 Loss1: 2.150086 Loss2: 0.000646
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.085651 Loss1: 2.085004 Loss2: 0.000648
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.997454 Loss1: 1.996812 Loss2: 0.000641
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.969085 Loss1: 1.968441 Loss2: 0.000644
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.884816 Loss1: 1.884175 Loss2: 0.000641
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.839633 Loss1: 1.838990 Loss2: 0.000644
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.764731 Loss1: 1.764085 Loss2: 0.000646
-(DefaultActor pid=2839578) >> Training accuracy: 0.532239
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.16858552631578946
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.703196 Loss1: 2.702568 Loss2: 0.000628
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.509497 Loss1: 2.508859 Loss2: 0.000638
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.401490 Loss1: 2.400857 Loss2: 0.000632
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.316214 Loss1: 2.315580 Loss2: 0.000634
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.269867 Loss1: 2.269238 Loss2: 0.000629
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.209784 Loss1: 2.209148 Loss2: 0.000637
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.093191 Loss1: 2.092555 Loss2: 0.000636
-(DefaultActor pid=2839578) Epoch: 7 Loss: 2.056638 Loss1: 2.056007 Loss2: 0.000631
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.972283 Loss1: 1.971647 Loss2: 0.000636
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.893097 Loss1: 1.892462 Loss2: 0.000634
-(DefaultActor pid=2839578) >> Training accuracy: 0.495477
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.1935096153846154
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.595779 Loss1: 2.595175 Loss2: 0.000604
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.429828 Loss1: 2.429217 Loss2: 0.000611
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.332352 Loss1: 2.331739 Loss2: 0.000613
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.267508 Loss1: 2.266893 Loss2: 0.000615
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.211937 Loss1: 2.211321 Loss2: 0.000616
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.120407 Loss1: 2.119791 Loss2: 0.000616
-(DefaultActor pid=2839578) Epoch: 6 Loss: 2.066087 Loss1: 2.065470 Loss2: 0.000617
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.999616 Loss1: 1.998996 Loss2: 0.000620
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.973400 Loss1: 1.972780 Loss2: 0.000620
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.887252 Loss1: 1.886633 Loss2: 0.000619
-(DefaultActor pid=2839578) >> Training accuracy: 0.482372
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.16089527027027026
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.531171 Loss1: 2.530572 Loss2: 0.000599
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.329418 Loss1: 2.328813 Loss2: 0.000604
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.195457 Loss1: 2.194850 Loss2: 0.000607
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.135030 Loss1: 2.134423 Loss2: 0.000607
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.068874 Loss1: 2.068268 Loss2: 0.000605
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.009602 Loss1: 2.008991 Loss2: 0.000610
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.962338 Loss1: 1.961727 Loss2: 0.000611
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.894721 Loss1: 1.894111 Loss2: 0.000610
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.838573 Loss1: 1.837964 Loss2: 0.000608
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.766256 Loss1: 1.765639 Loss2: 0.000617
-(DefaultActor pid=2839578) >> Training accuracy: 0.552365
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.18928006329113925
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.615363 Loss1: 2.614735 Loss2: 0.000628
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.385206 Loss1: 2.384564 Loss2: 0.000642
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.294886 Loss1: 2.294245 Loss2: 0.000641
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.202774 Loss1: 2.202133 Loss2: 0.000641
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.120620 Loss1: 2.119979 Loss2: 0.000641
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.048082 Loss1: 2.047440 Loss2: 0.000642
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.966649 Loss1: 1.966008 Loss2: 0.000642
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.972476 Loss1: 1.971836 Loss2: 0.000639
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.850451 Loss1: 1.849805 Loss2: 0.000647
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.837381 Loss1: 1.836738 Loss2: 0.000643
-(DefaultActor pid=2839578) >> Training accuracy: 0.534415
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.212890625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.558546 Loss1: 2.557935 Loss2: 0.000612
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.307139 Loss1: 2.306524 Loss2: 0.000615
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.185024 Loss1: 2.184403 Loss2: 0.000621
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.105847 Loss1: 2.105227 Loss2: 0.000620
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.050408 Loss1: 2.049786 Loss2: 0.000622
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.964686 Loss1: 1.964064 Loss2: 0.000622
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.905946 Loss1: 1.905326 Loss2: 0.000620
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.849628 Loss1: 1.849005 Loss2: 0.000623
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.779105 Loss1: 1.778482 Loss2: 0.000623
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.707864 Loss1: 1.707236 Loss2: 0.000628
-(DefaultActor pid=2839578) >> Training accuracy: 0.547526
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.21017530487804878
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.580613 Loss1: 2.579992 Loss2: 0.000621
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.360116 Loss1: 2.359486 Loss2: 0.000629
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.295912 Loss1: 2.295285 Loss2: 0.000627
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.208185 Loss1: 2.207558 Loss2: 0.000628
-(DefaultActor pid=2839578) Epoch: 4 Loss: 2.102368 Loss1: 2.101741 Loss2: 0.000627
-(DefaultActor pid=2839578) Epoch: 5 Loss: 2.032918 Loss1: 2.032291 Loss2: 0.000627
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.997955 Loss1: 1.997327 Loss2: 0.000628
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.969829 Loss1: 1.969201 Loss2: 0.000628
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.848787 Loss1: 1.848162 Loss2: 0.000625
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.787994 Loss1: 1.787365 Loss2: 0.000629
-(DefaultActor pid=2839578) >> Training accuracy: 0.557355
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 06:25:20,076][flwr][DEBUG] - fit_round 6 received 10 results and 0 failures
-test acc: 0.2787
-[2023-09-21 06:25:59,530][flwr][INFO] - fit progress: (6, 2.8995907626593835, {'accuracy': 0.2787}, 11641.19161189394)
-[2023-09-21 06:25:59,531][flwr][DEBUG] - evaluate_round 6: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 06:26:36,542][flwr][DEBUG] - evaluate_round 6 received 10 results and 0 failures
-[2023-09-21 06:26:36,542][flwr][DEBUG] - fit_round 7: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.24527138157894737
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.481484 Loss1: 2.480817 Loss2: 0.000667
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.257154 Loss1: 2.256485 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.143389 Loss1: 2.142715 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 3 Loss: 2.045223 Loss1: 2.044552 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.995319 Loss1: 1.994649 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.954306 Loss1: 1.953636 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.860659 Loss1: 1.859990 Loss2: 0.000669
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.767192 Loss1: 1.766524 Loss2: 0.000667
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.716309 Loss1: 1.715638 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.596312 Loss1: 1.595641 Loss2: 0.000671
-(DefaultActor pid=2839578) >> Training accuracy: 0.567845
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3057725694444444
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.264856 Loss1: 2.264210 Loss2: 0.000646
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.043617 Loss1: 2.042963 Loss2: 0.000653
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.956283 Loss1: 1.955626 Loss2: 0.000657
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.844295 Loss1: 1.843639 Loss2: 0.000656
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.767495 Loss1: 1.766835 Loss2: 0.000660
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.678686 Loss1: 1.678026 Loss2: 0.000661
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.606195 Loss1: 1.605537 Loss2: 0.000658
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.569569 Loss1: 1.568910 Loss2: 0.000659
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.537661 Loss1: 1.537003 Loss2: 0.000658
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.451480 Loss1: 1.450820 Loss2: 0.000661
-(DefaultActor pid=2839578) >> Training accuracy: 0.610026
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.2674050632911392
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.283801 Loss1: 2.283151 Loss2: 0.000650
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.107849 Loss1: 2.107195 Loss2: 0.000654
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.991241 Loss1: 1.990589 Loss2: 0.000653
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.915996 Loss1: 1.915342 Loss2: 0.000654
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.865527 Loss1: 1.864873 Loss2: 0.000654
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.745645 Loss1: 1.744990 Loss2: 0.000655
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.702113 Loss1: 1.701460 Loss2: 0.000653
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.641497 Loss1: 1.640841 Loss2: 0.000655
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.577109 Loss1: 1.576453 Loss2: 0.000656
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.524116 Loss1: 1.523461 Loss2: 0.000655
-(DefaultActor pid=2839578) >> Training accuracy: 0.551622
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.2644382911392405
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.350628 Loss1: 2.349962 Loss2: 0.000665
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.090583 Loss1: 2.089911 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.008386 Loss1: 2.007711 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.930967 Loss1: 1.930293 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.839796 Loss1: 1.839120 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.761925 Loss1: 1.761252 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.747650 Loss1: 1.746974 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.648780 Loss1: 1.648098 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.578672 Loss1: 1.577995 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.539751 Loss1: 1.539074 Loss2: 0.000677
-(DefaultActor pid=2839578) >> Training accuracy: 0.605419
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.24809966216216217
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.297309 Loss1: 2.296655 Loss2: 0.000653
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.092773 Loss1: 2.092114 Loss2: 0.000659
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.959073 Loss1: 1.958415 Loss2: 0.000658
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.864718 Loss1: 1.864062 Loss2: 0.000656
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.819575 Loss1: 1.818917 Loss2: 0.000658
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.754351 Loss1: 1.753691 Loss2: 0.000660
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.687187 Loss1: 1.686527 Loss2: 0.000659
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.616783 Loss1: 1.616129 Loss2: 0.000655
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.541919 Loss1: 1.541260 Loss2: 0.000658
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.498129 Loss1: 1.497470 Loss2: 0.000659
-(DefaultActor pid=2839578) >> Training accuracy: 0.551520
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.25861378205128205
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.403830 Loss1: 2.403180 Loss2: 0.000650
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.160081 Loss1: 2.159430 Loss2: 0.000651
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.097445 Loss1: 2.096793 Loss2: 0.000652
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.997780 Loss1: 1.997125 Loss2: 0.000655
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.933168 Loss1: 1.932514 Loss2: 0.000654
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.870620 Loss1: 1.869966 Loss2: 0.000654
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.778662 Loss1: 1.778009 Loss2: 0.000653
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.698167 Loss1: 1.697512 Loss2: 0.000655
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.627072 Loss1: 1.626415 Loss2: 0.000657
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.604133 Loss1: 1.603477 Loss2: 0.000656
-(DefaultActor pid=2839578) >> Training accuracy: 0.549679
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.32535601265822783
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.295589 Loss1: 2.294914 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.054226 Loss1: 2.053539 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.955566 Loss1: 1.954885 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.868946 Loss1: 1.868267 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.799395 Loss1: 1.798717 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.721622 Loss1: 1.720945 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.677850 Loss1: 1.677174 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.627688 Loss1: 1.627008 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.569686 Loss1: 1.569009 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.546089 Loss1: 1.545408 Loss2: 0.000680
-(DefaultActor pid=2839578) >> Training accuracy: 0.590388
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3311298076923077
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.209410 Loss1: 2.208758 Loss2: 0.000653
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.973377 Loss1: 1.972717 Loss2: 0.000659
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.896091 Loss1: 1.895431 Loss2: 0.000660
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.822798 Loss1: 1.822139 Loss2: 0.000659
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.732872 Loss1: 1.732213 Loss2: 0.000659
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.674470 Loss1: 1.673812 Loss2: 0.000658
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.582294 Loss1: 1.581637 Loss2: 0.000657
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.536232 Loss1: 1.535575 Loss2: 0.000657
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.487366 Loss1: 1.486710 Loss2: 0.000657
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.436601 Loss1: 1.435940 Loss2: 0.000661
-(DefaultActor pid=2839578) >> Training accuracy: 0.615785
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.24485759493670886
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.334608 Loss1: 2.333962 Loss2: 0.000646
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.130277 Loss1: 2.129622 Loss2: 0.000655
-(DefaultActor pid=2839578) Epoch: 2 Loss: 2.006359 Loss1: 2.005705 Loss2: 0.000654
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.930811 Loss1: 1.930157 Loss2: 0.000654
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.850361 Loss1: 1.849706 Loss2: 0.000655
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.773485 Loss1: 1.772830 Loss2: 0.000655
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.712855 Loss1: 1.712199 Loss2: 0.000656
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.657515 Loss1: 1.656858 Loss2: 0.000656
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.592939 Loss1: 1.592286 Loss2: 0.000653
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.522356 Loss1: 1.521697 Loss2: 0.000659
-(DefaultActor pid=2839578) >> Training accuracy: 0.578521
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.2966844512195122
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.326112 Loss1: 2.325454 Loss2: 0.000657
-(DefaultActor pid=2839578) Epoch: 1 Loss: 2.104414 Loss1: 2.103751 Loss2: 0.000663
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.994160 Loss1: 1.993500 Loss2: 0.000661
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.915289 Loss1: 1.914627 Loss2: 0.000662
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.827631 Loss1: 1.826966 Loss2: 0.000664
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.749136 Loss1: 1.748474 Loss2: 0.000662
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.696656 Loss1: 1.695995 Loss2: 0.000661
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.646930 Loss1: 1.646269 Loss2: 0.000661
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.548566 Loss1: 1.547902 Loss2: 0.000664
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.498987 Loss1: 1.498325 Loss2: 0.000662
-(DefaultActor pid=2839578) >> Training accuracy: 0.587462
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 06:57:33,156][flwr][DEBUG] - fit_round 7 received 10 results and 0 failures
-test acc: 0.3389
-[2023-09-21 06:58:11,275][flwr][INFO] - fit progress: (7, 2.6269793068639005, {'accuracy': 0.3389}, 13572.936574874911)
-[2023-09-21 06:58:11,276][flwr][DEBUG] - evaluate_round 7: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 06:58:50,318][flwr][DEBUG] - evaluate_round 7 received 10 results and 0 failures
-[2023-09-21 06:58:50,318][flwr][DEBUG] - fit_round 8: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.36032774390243905
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.090185 Loss1: 2.089509 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.896411 Loss1: 1.895732 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.766720 Loss1: 1.766040 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.649179 Loss1: 1.648500 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.575999 Loss1: 1.575318 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.515597 Loss1: 1.514918 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.456732 Loss1: 1.456053 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.418675 Loss1: 1.417995 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.345109 Loss1: 1.344431 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.303375 Loss1: 1.302696 Loss2: 0.000679
-(DefaultActor pid=2839578) >> Training accuracy: 0.667302
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3948317307692308
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.002683 Loss1: 2.002012 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.793488 Loss1: 1.792814 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.691215 Loss1: 1.690539 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.584951 Loss1: 1.584274 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.536152 Loss1: 1.535477 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.441502 Loss1: 1.440826 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.375427 Loss1: 1.374748 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.326773 Loss1: 1.326096 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.249348 Loss1: 1.248672 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.218190 Loss1: 1.217511 Loss2: 0.000680
-(DefaultActor pid=2839578) >> Training accuracy: 0.666466
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.325751582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.118851 Loss1: 2.118167 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.857130 Loss1: 1.856443 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.767257 Loss1: 1.766568 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.676372 Loss1: 1.675685 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.630380 Loss1: 1.629695 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.527384 Loss1: 1.526696 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.473233 Loss1: 1.472546 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.424833 Loss1: 1.424144 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.370489 Loss1: 1.369801 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.349584 Loss1: 1.348892 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.659612
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3346518987341772
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.085113 Loss1: 2.084443 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.861052 Loss1: 1.860380 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.784864 Loss1: 1.784192 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.656445 Loss1: 1.655776 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.622037 Loss1: 1.621365 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.537711 Loss1: 1.537038 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.479621 Loss1: 1.478947 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.405331 Loss1: 1.404657 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.366447 Loss1: 1.365772 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.285344 Loss1: 1.284670 Loss2: 0.000674
-(DefaultActor pid=2839578) >> Training accuracy: 0.648141
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3213141025641026
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.180733 Loss1: 2.180071 Loss2: 0.000661
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.951921 Loss1: 1.951259 Loss2: 0.000662
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.840673 Loss1: 1.840009 Loss2: 0.000664
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.734268 Loss1: 1.733603 Loss2: 0.000665
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.676612 Loss1: 1.675947 Loss2: 0.000665
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.608794 Loss1: 1.608128 Loss2: 0.000666
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.545406 Loss1: 1.544740 Loss2: 0.000667
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.476264 Loss1: 1.475598 Loss2: 0.000667
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.387220 Loss1: 1.386553 Loss2: 0.000667
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.355198 Loss1: 1.354532 Loss2: 0.000666
-(DefaultActor pid=2839578) >> Training accuracy: 0.637620
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.32052364864864863
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.104289 Loss1: 2.103620 Loss2: 0.000669
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.866211 Loss1: 1.865539 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.771266 Loss1: 1.770592 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.642819 Loss1: 1.642145 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.579517 Loss1: 1.578844 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.509703 Loss1: 1.509032 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.464143 Loss1: 1.463471 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.410755 Loss1: 1.410085 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.335067 Loss1: 1.334395 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.264476 Loss1: 1.263804 Loss2: 0.000672
-(DefaultActor pid=2839578) >> Training accuracy: 0.652238
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.30636867088607594
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.134058 Loss1: 2.133389 Loss2: 0.000669
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.880838 Loss1: 1.880171 Loss2: 0.000668
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.772152 Loss1: 1.771481 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.670275 Loss1: 1.669606 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.604567 Loss1: 1.603896 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.530902 Loss1: 1.530233 Loss2: 0.000668
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.473577 Loss1: 1.472907 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.431417 Loss1: 1.430748 Loss2: 0.000669
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.323989 Loss1: 1.323320 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.296235 Loss1: 1.295564 Loss2: 0.000671
-(DefaultActor pid=2839578) >> Training accuracy: 0.622231
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.30160361842105265
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.302307 Loss1: 2.301630 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.989963 Loss1: 1.989280 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.879378 Loss1: 1.878696 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.821825 Loss1: 1.821142 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.728622 Loss1: 1.727941 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.665040 Loss1: 1.664359 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.620767 Loss1: 1.620086 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.534739 Loss1: 1.534057 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.468179 Loss1: 1.467498 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.415825 Loss1: 1.415145 Loss2: 0.000680
-(DefaultActor pid=2839578) >> Training accuracy: 0.618010
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3864715189873418
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.065775 Loss1: 2.065089 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.829913 Loss1: 1.829225 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.759148 Loss1: 1.758459 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.659585 Loss1: 1.658896 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.563335 Loss1: 1.562646 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.505092 Loss1: 1.504406 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.441659 Loss1: 1.440970 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.399921 Loss1: 1.399231 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.341090 Loss1: 1.340403 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.275567 Loss1: 1.274877 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.658030
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3500434027777778
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.106233 Loss1: 2.105566 Loss2: 0.000667
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.821204 Loss1: 1.820531 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.683732 Loss1: 1.683061 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.579992 Loss1: 1.579323 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.563132 Loss1: 1.562461 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.443790 Loss1: 1.443119 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.424410 Loss1: 1.423737 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.346587 Loss1: 1.345915 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.266106 Loss1: 1.265434 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.229760 Loss1: 1.229086 Loss2: 0.000674
-(DefaultActor pid=2839578) >> Training accuracy: 0.655165
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 07:34:30,949][flwr][DEBUG] - fit_round 8 received 10 results and 0 failures
-test acc: 0.3862
-[2023-09-21 07:35:18,210][flwr][INFO] - fit progress: (8, 2.455170800510687, {'accuracy': 0.3862}, 15799.871576023754)
-[2023-09-21 07:35:18,211][flwr][DEBUG] - evaluate_round 8: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 07:35:56,692][flwr][DEBUG] - evaluate_round 8 received 10 results and 0 failures
-[2023-09-21 07:35:56,693][flwr][DEBUG] - fit_round 9: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3560126582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.927283 Loss1: 1.926610 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.702426 Loss1: 1.701751 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.602820 Loss1: 1.602145 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.499452 Loss1: 1.498780 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.396106 Loss1: 1.395433 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.306156 Loss1: 1.305480 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.251587 Loss1: 1.250913 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.220946 Loss1: 1.220270 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.160069 Loss1: 1.159394 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.102738 Loss1: 1.102060 Loss2: 0.000678
-(DefaultActor pid=2839578) >> Training accuracy: 0.692247
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4024390243902439
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.889640 Loss1: 1.888962 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.647255 Loss1: 1.646576 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.553797 Loss1: 1.553116 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.489612 Loss1: 1.488931 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.408811 Loss1: 1.408131 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.317566 Loss1: 1.316884 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.288551 Loss1: 1.287872 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.211311 Loss1: 1.210632 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.170493 Loss1: 1.169813 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.128615 Loss1: 1.127937 Loss2: 0.000678
-(DefaultActor pid=2839578) >> Training accuracy: 0.710938
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.388251582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.897985 Loss1: 1.897311 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.678349 Loss1: 1.677672 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.556290 Loss1: 1.555613 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.488502 Loss1: 1.487825 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.416247 Loss1: 1.415571 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.295097 Loss1: 1.294421 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.280184 Loss1: 1.279508 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.214096 Loss1: 1.213419 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.180546 Loss1: 1.179868 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.145428 Loss1: 1.144750 Loss2: 0.000679
-(DefaultActor pid=2839578) >> Training accuracy: 0.693829
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.379746835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.956635 Loss1: 1.955954 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.700888 Loss1: 1.700204 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.578661 Loss1: 1.577975 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.530441 Loss1: 1.529756 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.358867 Loss1: 1.358180 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.342234 Loss1: 1.341549 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.280089 Loss1: 1.279402 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.229647 Loss1: 1.228962 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.191856 Loss1: 1.191166 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.104861 Loss1: 1.104173 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.708465
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3977864583333333
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.921093 Loss1: 1.920423 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.623214 Loss1: 1.622542 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.514003 Loss1: 1.513328 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.429523 Loss1: 1.428848 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.324683 Loss1: 1.324010 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.270632 Loss1: 1.269958 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.227159 Loss1: 1.226485 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.144824 Loss1: 1.144148 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.109372 Loss1: 1.108699 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.085896 Loss1: 1.085222 Loss2: 0.000675
-(DefaultActor pid=2839578) >> Training accuracy: 0.655816
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.44125791139240506
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.902037 Loss1: 1.901349 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.647134 Loss1: 1.646443 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.536952 Loss1: 1.536260 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.452393 Loss1: 1.451703 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.385573 Loss1: 1.384884 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.324288 Loss1: 1.323595 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.239454 Loss1: 1.238764 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.238154 Loss1: 1.237463 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.171281 Loss1: 1.170593 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.092184 Loss1: 1.091494 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.721717
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.36163651315789475
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.080123 Loss1: 2.079439 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.831850 Loss1: 1.831162 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.685333 Loss1: 1.684645 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.592516 Loss1: 1.591832 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.494913 Loss1: 1.494224 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.477352 Loss1: 1.476665 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.388652 Loss1: 1.387967 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.283609 Loss1: 1.282920 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.253341 Loss1: 1.252656 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.249124 Loss1: 1.248439 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.648438
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.35853040540540543
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.947335 Loss1: 1.946660 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.666278 Loss1: 1.665601 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.574491 Loss1: 1.573813 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.477389 Loss1: 1.476714 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.396951 Loss1: 1.396275 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.320744 Loss1: 1.320068 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.220249 Loss1: 1.219575 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.171852 Loss1: 1.171177 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.104214 Loss1: 1.103538 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.077997 Loss1: 1.077321 Loss2: 0.000675
-(DefaultActor pid=2839578) >> Training accuracy: 0.720228
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4511217948717949
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.843729 Loss1: 1.843054 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.612756 Loss1: 1.612079 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.503837 Loss1: 1.503159 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.406166 Loss1: 1.405486 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.343994 Loss1: 1.343312 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.278669 Loss1: 1.277990 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.211853 Loss1: 1.211174 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.114719 Loss1: 1.114039 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.103981 Loss1: 1.103302 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.037801 Loss1: 1.037122 Loss2: 0.000680
-(DefaultActor pid=2839578) >> Training accuracy: 0.692308
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3671875
-(DefaultActor pid=2839578) Epoch: 0 Loss: 2.039653 Loss1: 2.038987 Loss2: 0.000666
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.754892 Loss1: 1.754221 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.619528 Loss1: 1.618855 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.525886 Loss1: 1.525214 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.470324 Loss1: 1.469649 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.401900 Loss1: 1.401225 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.366652 Loss1: 1.365978 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.268322 Loss1: 1.267647 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.201946 Loss1: 1.201274 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.172559 Loss1: 1.171885 Loss2: 0.000675
-(DefaultActor pid=2839578) >> Training accuracy: 0.654046
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 08:12:25,796][flwr][DEBUG] - fit_round 9 received 10 results and 0 failures
-test acc: 0.414
-[2023-09-21 08:13:11,115][flwr][INFO] - fit progress: (9, 2.338781193803294, {'accuracy': 0.414}, 18072.776909645647)
-[2023-09-21 08:13:11,116][flwr][DEBUG] - evaluate_round 9: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 08:13:48,453][flwr][DEBUG] - evaluate_round 9 received 10 results and 0 failures
-[2023-09-21 08:13:48,454][flwr][DEBUG] - fit_round 10: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4331597222222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.768822 Loss1: 1.768151 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.462659 Loss1: 1.461987 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.329996 Loss1: 1.329323 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.268251 Loss1: 1.267578 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.172739 Loss1: 1.172065 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.133824 Loss1: 1.133149 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.033244 Loss1: 1.032570 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.972348 Loss1: 0.971675 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.951715 Loss1: 0.951039 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.881031 Loss1: 0.880356 Loss2: 0.000674
-(DefaultActor pid=2839578) >> Training accuracy: 0.751953
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.3918918918918919
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.797261 Loss1: 1.796590 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.506892 Loss1: 1.506215 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.341902 Loss1: 1.341226 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.288335 Loss1: 1.287660 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.252071 Loss1: 1.251393 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.123978 Loss1: 1.123303 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.073999 Loss1: 1.073323 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.036718 Loss1: 1.036042 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.014163 Loss1: 1.013485 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.923587 Loss1: 0.922910 Loss2: 0.000677
-(DefaultActor pid=2839578) >> Training accuracy: 0.738598
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.48036858974358976
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.678107 Loss1: 1.677433 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.467067 Loss1: 1.466389 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.335469 Loss1: 1.334790 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.232564 Loss1: 1.231886 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.146392 Loss1: 1.145714 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.092340 Loss1: 1.091659 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.058374 Loss1: 1.057695 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.002816 Loss1: 1.002135 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.955005 Loss1: 0.954324 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.912029 Loss1: 0.911351 Loss2: 0.000679
-(DefaultActor pid=2839578) >> Training accuracy: 0.764223
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.44397865853658536
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.752825 Loss1: 1.752148 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.499736 Loss1: 1.499059 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.358722 Loss1: 1.358043 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.347500 Loss1: 1.346823 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.226710 Loss1: 1.226030 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.171939 Loss1: 1.171261 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.119876 Loss1: 1.119198 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.053659 Loss1: 1.052980 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.985147 Loss1: 0.984468 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.957507 Loss1: 0.956830 Loss2: 0.000677
-(DefaultActor pid=2839578) >> Training accuracy: 0.721037
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.40705128205128205
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.831035 Loss1: 1.830368 Loss2: 0.000667
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.582848 Loss1: 1.582176 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.457690 Loss1: 1.457018 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.345657 Loss1: 1.344984 Loss2: 0.000673
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.284072 Loss1: 1.283401 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.162648 Loss1: 1.161976 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.148320 Loss1: 1.147647 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.090899 Loss1: 1.090228 Loss2: 0.000671
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.029172 Loss1: 1.028500 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.014691 Loss1: 1.014018 Loss2: 0.000673
-(DefaultActor pid=2839578) >> Training accuracy: 0.729167
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4019325657894737
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.919099 Loss1: 1.918416 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.684562 Loss1: 1.683875 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.530755 Loss1: 1.530068 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.412141 Loss1: 1.411457 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.343197 Loss1: 1.342511 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.296885 Loss1: 1.296203 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.200738 Loss1: 1.200052 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.125912 Loss1: 1.125227 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.094058 Loss1: 1.093377 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 9 Loss: 1.055026 Loss1: 1.054342 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.693873
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4293908227848101
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.769907 Loss1: 1.769226 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.553301 Loss1: 1.552612 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.410357 Loss1: 1.409673 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.303660 Loss1: 1.302973 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.236919 Loss1: 1.236234 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.217920 Loss1: 1.217233 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.103311 Loss1: 1.102623 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.034625 Loss1: 1.033938 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.002365 Loss1: 1.001676 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.945531 Loss1: 0.944844 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.728639
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4252373417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.746512 Loss1: 1.745836 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.553847 Loss1: 1.553167 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.391240 Loss1: 1.390563 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.317656 Loss1: 1.316978 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.236076 Loss1: 1.235398 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.153511 Loss1: 1.152832 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.114862 Loss1: 1.114183 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.040310 Loss1: 1.039632 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.008935 Loss1: 1.008255 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.951032 Loss1: 0.950352 Loss2: 0.000680
-(DefaultActor pid=2839578) >> Training accuracy: 0.730815
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.403876582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.784272 Loss1: 1.783602 Loss2: 0.000670
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.531645 Loss1: 1.530969 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.376906 Loss1: 1.376231 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.317698 Loss1: 1.317022 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.209270 Loss1: 1.208594 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.151584 Loss1: 1.150908 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.112031 Loss1: 1.111355 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.066369 Loss1: 1.065691 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 8 Loss: 1.018915 Loss1: 1.018240 Loss2: 0.000675
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.953510 Loss1: 0.952834 Loss2: 0.000675
-(DefaultActor pid=2839578) >> Training accuracy: 0.726266
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4814082278481013
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.730989 Loss1: 1.730302 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.499374 Loss1: 1.498682 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.365379 Loss1: 1.364688 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.299249 Loss1: 1.298557 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.230900 Loss1: 1.230207 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.132769 Loss1: 1.132078 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.121166 Loss1: 1.120473 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 1.031246 Loss1: 1.030555 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.964667 Loss1: 0.963977 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.941125 Loss1: 0.940437 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.757911
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 08:50:38,874][flwr][DEBUG] - fit_round 10 received 10 results and 0 failures
-test acc: 0.4484
-[2023-09-21 08:51:24,827][flwr][INFO] - fit progress: (10, 2.2332213046832585, {'accuracy': 0.4484}, 20366.488789497875)
-[2023-09-21 08:51:24,828][flwr][DEBUG] - evaluate_round 10: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 08:52:01,833][flwr][DEBUG] - evaluate_round 10 received 10 results and 0 failures
-[2023-09-21 08:52:01,834][flwr][DEBUG] - fit_round 11: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4740901898734177
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.629546 Loss1: 1.628861 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.375977 Loss1: 1.375289 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.255404 Loss1: 1.254717 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.142108 Loss1: 1.141419 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.094845 Loss1: 1.094158 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.007558 Loss1: 1.006872 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.975380 Loss1: 0.974691 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.942220 Loss1: 0.941533 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.902019 Loss1: 0.901331 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.815933 Loss1: 0.815243 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.773536
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4557291666666667
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.731246 Loss1: 1.730568 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.412883 Loss1: 1.412200 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.314994 Loss1: 1.314311 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.224059 Loss1: 1.223376 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.165273 Loss1: 1.164590 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.061898 Loss1: 1.061218 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.993720 Loss1: 0.993037 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.918988 Loss1: 0.918302 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.911651 Loss1: 0.910971 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.879353 Loss1: 0.878671 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.761619
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5041920731707317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.627778 Loss1: 1.627094 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.361173 Loss1: 1.360488 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.231485 Loss1: 1.230799 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.140573 Loss1: 1.139886 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.088595 Loss1: 1.087910 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.004723 Loss1: 1.004035 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.951726 Loss1: 0.951039 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.912833 Loss1: 0.912148 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.865669 Loss1: 0.864983 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.830272 Loss1: 0.829585 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.766578
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4715711805555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.645126 Loss1: 1.644449 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.311527 Loss1: 1.310848 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.241229 Loss1: 1.240547 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.125019 Loss1: 1.124338 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.052496 Loss1: 1.051815 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.958869 Loss1: 0.958187 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.903812 Loss1: 0.903132 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.857576 Loss1: 0.856894 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.829263 Loss1: 0.828581 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.799555 Loss1: 0.798872 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.770616
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4341216216216216
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.651177 Loss1: 1.650494 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.397449 Loss1: 1.396764 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.227983 Loss1: 1.227297 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.173176 Loss1: 1.172492 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.059811 Loss1: 1.059128 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.998535 Loss1: 0.997853 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.962478 Loss1: 0.961796 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.903385 Loss1: 0.902702 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.867992 Loss1: 0.867308 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.799688 Loss1: 0.799004 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.766258
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5019778481012658
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.609899 Loss1: 1.609205 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.372284 Loss1: 1.371587 Loss2: 0.000697
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.257488 Loss1: 1.256790 Loss2: 0.000698
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.152002 Loss1: 1.151306 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.076884 Loss1: 1.076187 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.019827 Loss1: 1.019129 Loss2: 0.000698
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.937625 Loss1: 0.936930 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.930517 Loss1: 0.929822 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.864317 Loss1: 0.863621 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.799665 Loss1: 0.798967 Loss2: 0.000698
-(DefaultActor pid=2839578) >> Training accuracy: 0.776108
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5442708333333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.537496 Loss1: 1.536816 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.335668 Loss1: 1.334985 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.209524 Loss1: 1.208840 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.115014 Loss1: 1.114332 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.020731 Loss1: 1.020049 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.968488 Loss1: 0.967804 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.885202 Loss1: 0.884518 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.852736 Loss1: 0.852054 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.789003 Loss1: 0.788318 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.782578 Loss1: 0.781895 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.788462
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4653876582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.608857 Loss1: 1.608169 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.379345 Loss1: 1.378652 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.237433 Loss1: 1.236740 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.161681 Loss1: 1.160991 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.104506 Loss1: 1.103814 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.000347 Loss1: 0.999654 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.966659 Loss1: 0.965968 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.922085 Loss1: 0.921390 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.857371 Loss1: 0.856678 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.839263 Loss1: 0.838571 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.771954
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.44551809210526316
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.788287 Loss1: 1.787590 Loss2: 0.000697
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.503190 Loss1: 1.502494 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.354582 Loss1: 1.353888 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.295410 Loss1: 1.294717 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.213724 Loss1: 1.213030 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.121306 Loss1: 1.120613 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 1.056888 Loss1: 1.056196 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.982969 Loss1: 0.982277 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.922348 Loss1: 0.921655 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.944368 Loss1: 0.943677 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.746916
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4467958860759494
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.654432 Loss1: 1.653752 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.404548 Loss1: 1.403866 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.264324 Loss1: 1.263638 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.162715 Loss1: 1.162031 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.108653 Loss1: 1.107971 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 5 Loss: 1.022951 Loss1: 1.022266 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.971305 Loss1: 0.970622 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.938535 Loss1: 0.937851 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.864571 Loss1: 0.863887 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.851177 Loss1: 0.850495 Loss2: 0.000682
-(DefaultActor pid=2839578) >> Training accuracy: 0.761472
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 09:28:22,281][flwr][DEBUG] - fit_round 11 received 10 results and 0 failures
-test acc: 0.4735
-[2023-09-21 09:29:04,934][flwr][INFO] - fit progress: (11, 2.1865856590362402, {'accuracy': 0.4735}, 22626.5953413439)
-[2023-09-21 09:29:04,934][flwr][DEBUG] - evaluate_round 11: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 09:29:40,921][flwr][DEBUG] - evaluate_round 11 received 10 results and 0 failures
-[2023-09-21 09:29:40,922][flwr][DEBUG] - fit_round 12: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4630489864864865
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.528320 Loss1: 1.527644 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.278407 Loss1: 1.277728 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.126384 Loss1: 1.125704 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.055715 Loss1: 1.055037 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.987289 Loss1: 0.986608 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.871775 Loss1: 0.871094 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.790207 Loss1: 0.789526 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.815851 Loss1: 0.815171 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.747741 Loss1: 0.747060 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.727658 Loss1: 0.726976 Loss2: 0.000682
-(DefaultActor pid=2839578) >> Training accuracy: 0.808488
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.47468354430379744
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.549113 Loss1: 1.548435 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.255203 Loss1: 1.254521 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.133198 Loss1: 1.132519 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.046259 Loss1: 1.045582 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.978001 Loss1: 0.977321 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.893616 Loss1: 0.892936 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.837677 Loss1: 0.836996 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.842093 Loss1: 0.841412 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.740360 Loss1: 0.739679 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.704260 Loss1: 0.703578 Loss2: 0.000682
-(DefaultActor pid=2839578) >> Training accuracy: 0.807358
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5332278481012658
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.498947 Loss1: 1.498261 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.239504 Loss1: 1.238813 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.097843 Loss1: 1.097149 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.044317 Loss1: 1.043624 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.949046 Loss1: 0.948353 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.868510 Loss1: 0.867815 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.830942 Loss1: 0.830249 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.775295 Loss1: 0.774600 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.725674 Loss1: 0.724981 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.714763 Loss1: 0.714069 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.777294
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.47738486842105265
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.690661 Loss1: 1.689969 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.353340 Loss1: 1.352646 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.226899 Loss1: 1.226205 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.136943 Loss1: 1.136248 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.072870 Loss1: 1.072178 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.988486 Loss1: 0.987794 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.921914 Loss1: 0.921220 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.873217 Loss1: 0.872527 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.818827 Loss1: 0.818138 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.778337 Loss1: 0.777646 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.775905
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4906684027777778
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.518150 Loss1: 1.517471 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.223734 Loss1: 1.223055 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.045218 Loss1: 1.044536 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.999969 Loss1: 0.999286 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.885450 Loss1: 0.884768 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.842167 Loss1: 0.841484 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.761405 Loss1: 0.760721 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.740901 Loss1: 0.740218 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.717582 Loss1: 0.716899 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.683080 Loss1: 0.682396 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.842231
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5310594512195121
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.491985 Loss1: 1.491303 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.194311 Loss1: 1.193629 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.077274 Loss1: 1.076591 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.026376 Loss1: 1.025690 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.984187 Loss1: 0.983506 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.912049 Loss1: 0.911362 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.875900 Loss1: 0.875216 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.806244 Loss1: 0.805562 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.719870 Loss1: 0.719187 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.731806 Loss1: 0.731123 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.812309
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.569511217948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.467515 Loss1: 1.466833 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.195044 Loss1: 1.194361 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.082558 Loss1: 1.081876 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.975696 Loss1: 0.975013 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.886436 Loss1: 0.885752 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.844075 Loss1: 0.843391 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.773278 Loss1: 0.772594 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.715304 Loss1: 0.714619 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.713455 Loss1: 0.712771 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.672192 Loss1: 0.671507 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.822917
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5176028481012658
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.539171 Loss1: 1.538490 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.273570 Loss1: 1.272887 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.179981 Loss1: 1.179298 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.032636 Loss1: 1.031951 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.944138 Loss1: 0.943455 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.937102 Loss1: 0.936418 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.866591 Loss1: 0.865906 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.812533 Loss1: 0.811849 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.804162 Loss1: 0.803477 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.717535 Loss1: 0.716851 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.770372
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.48617788461538464
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.577239 Loss1: 1.576565 Loss2: 0.000674
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.335966 Loss1: 1.335289 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.162797 Loss1: 1.162118 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.039584 Loss1: 1.038907 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 4 Loss: 1.022853 Loss1: 1.022174 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.925070 Loss1: 0.924392 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.876713 Loss1: 0.876035 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.801927 Loss1: 0.801248 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.786687 Loss1: 0.786009 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.699872 Loss1: 0.699191 Loss2: 0.000680
-(DefaultActor pid=2839578) >> Training accuracy: 0.745593
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.495253164556962
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.509940 Loss1: 1.509256 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.278487 Loss1: 1.277800 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.129119 Loss1: 1.128428 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.058697 Loss1: 1.058008 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.956509 Loss1: 0.955820 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.909721 Loss1: 0.909031 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.896175 Loss1: 0.895485 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.845213 Loss1: 0.844521 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.777606 Loss1: 0.776917 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.707496 Loss1: 0.706805 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.780261
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 10:05:47,325][flwr][DEBUG] - fit_round 12 received 10 results and 0 failures
-test acc: 0.4908
-[2023-09-21 10:06:28,442][flwr][INFO] - fit progress: (12, 2.121484987651959, {'accuracy': 0.4908}, 24870.103177347686)
-[2023-09-21 10:06:28,442][flwr][DEBUG] - evaluate_round 12: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 10:07:04,702][flwr][DEBUG] - evaluate_round 12 received 10 results and 0 failures
-[2023-09-21 10:07:04,703][flwr][DEBUG] - fit_round 13: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5360243055555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.389737 Loss1: 1.389057 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.107157 Loss1: 1.106475 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.956591 Loss1: 0.955908 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.886601 Loss1: 0.885917 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.776553 Loss1: 0.775869 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.737383 Loss1: 0.736697 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.673512 Loss1: 0.672828 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.643751 Loss1: 0.643064 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.628153 Loss1: 0.627467 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.554398 Loss1: 0.553713 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.821181
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.533623417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.438557 Loss1: 1.437869 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.140570 Loss1: 1.139875 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.046182 Loss1: 1.045490 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.956593 Loss1: 0.955898 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.850085 Loss1: 0.849389 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.800892 Loss1: 0.800198 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.715208 Loss1: 0.714516 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.733518 Loss1: 0.732824 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.682591 Loss1: 0.681896 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.645804 Loss1: 0.645112 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.827136
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5518196202531646
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.425491 Loss1: 1.424805 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.171357 Loss1: 1.170667 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.035637 Loss1: 1.034948 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.928868 Loss1: 0.928177 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.878914 Loss1: 0.878224 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.786956 Loss1: 0.786266 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.757463 Loss1: 0.756777 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.726530 Loss1: 0.725843 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.656689 Loss1: 0.655999 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.610231 Loss1: 0.609541 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.793315
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5142227564102564
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.476086 Loss1: 1.475410 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.209909 Loss1: 1.209228 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.050208 Loss1: 1.049528 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.929968 Loss1: 0.929287 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.893766 Loss1: 0.893085 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.819729 Loss1: 0.819049 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.805120 Loss1: 0.804437 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.694165 Loss1: 0.693482 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.690133 Loss1: 0.689449 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.634745 Loss1: 0.634063 Loss2: 0.000682
-(DefaultActor pid=2839578) >> Training accuracy: 0.844151
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.4995888157894737
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.555603 Loss1: 1.554914 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.287022 Loss1: 1.286332 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.131055 Loss1: 1.130364 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 1.007335 Loss1: 1.006644 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.960751 Loss1: 0.960060 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.889228 Loss1: 0.888539 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.810288 Loss1: 0.809600 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.746550 Loss1: 0.745860 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.745816 Loss1: 0.745125 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.662212 Loss1: 0.661521 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.823396
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5144382911392406
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.468708 Loss1: 1.468027 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.121961 Loss1: 1.121279 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.035017 Loss1: 1.034332 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.909480 Loss1: 0.908797 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.848390 Loss1: 0.847705 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.813788 Loss1: 0.813103 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.773006 Loss1: 0.772324 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.698911 Loss1: 0.698227 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.675887 Loss1: 0.675202 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.644252 Loss1: 0.643567 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.786986
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5735759493670886
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.412579 Loss1: 1.411888 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.132433 Loss1: 1.131740 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.990019 Loss1: 0.989325 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.937732 Loss1: 0.937038 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.846446 Loss1: 0.845749 Loss2: 0.000697
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.861525 Loss1: 0.860831 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.738973 Loss1: 0.738278 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.735227 Loss1: 0.734533 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.648211 Loss1: 0.647514 Loss2: 0.000697
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.611929 Loss1: 0.611238 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.817445
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.48627533783783783
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.501301 Loss1: 1.500618 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.155668 Loss1: 1.154983 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.011567 Loss1: 1.010881 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.943288 Loss1: 0.942602 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.845835 Loss1: 0.845149 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.735159 Loss1: 0.734473 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.720567 Loss1: 0.719880 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.690761 Loss1: 0.690076 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.648303 Loss1: 0.647619 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.625450 Loss1: 0.624764 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.797297
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5975560897435898
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.345728 Loss1: 1.345044 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.084854 Loss1: 1.084167 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.960891 Loss1: 0.960203 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.884947 Loss1: 0.884260 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.771226 Loss1: 0.770538 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.738926 Loss1: 0.738236 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.648350 Loss1: 0.647660 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.623435 Loss1: 0.622746 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.623111 Loss1: 0.622422 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.557646 Loss1: 0.556956 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.851562
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5520198170731707
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.376203 Loss1: 1.375519 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.129904 Loss1: 1.129217 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.002876 Loss1: 1.002190 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.934550 Loss1: 0.933864 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.840156 Loss1: 0.839471 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.772622 Loss1: 0.771934 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.736764 Loss1: 0.736078 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.713265 Loss1: 0.712576 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.641215 Loss1: 0.640528 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.633197 Loss1: 0.632509 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.822980
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 10:37:26,019][flwr][DEBUG] - fit_round 13 received 10 results and 0 failures
-test acc: 0.5082
-[2023-09-21 10:38:05,864][flwr][INFO] - fit progress: (13, 2.1080573364949453, {'accuracy': 0.5082}, 26767.525622681715)
-[2023-09-21 10:38:05,864][flwr][DEBUG] - evaluate_round 13: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 10:38:43,046][flwr][DEBUG] - evaluate_round 13 received 10 results and 0 failures
-[2023-09-21 10:38:43,047][flwr][DEBUG] - fit_round 14: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5224095394736842
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.495148 Loss1: 1.494462 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.176908 Loss1: 1.176220 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 1.068731 Loss1: 1.068042 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.924736 Loss1: 0.924045 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.833490 Loss1: 0.832801 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.787893 Loss1: 0.787203 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.720280 Loss1: 0.719591 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.707480 Loss1: 0.706793 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.631270 Loss1: 0.630584 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.611263 Loss1: 0.610575 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.828125
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5611155063291139
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.278083 Loss1: 1.277397 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.069906 Loss1: 1.069219 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.926905 Loss1: 0.926215 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.815089 Loss1: 0.814401 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.735671 Loss1: 0.734982 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.674660 Loss1: 0.673968 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.691047 Loss1: 0.690360 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.618026 Loss1: 0.617338 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.581989 Loss1: 0.581301 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.543672 Loss1: 0.542985 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.861155
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.586629746835443
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.336793 Loss1: 1.336111 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.037993 Loss1: 1.037308 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.926571 Loss1: 0.925886 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.803778 Loss1: 0.803090 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.779462 Loss1: 0.778776 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.745153 Loss1: 0.744465 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.686424 Loss1: 0.685738 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.619777 Loss1: 0.619092 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.568122 Loss1: 0.567434 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.567563 Loss1: 0.566876 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.836036
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.53125
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.340304 Loss1: 1.339625 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.034304 Loss1: 1.033622 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.916658 Loss1: 0.915980 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.865983 Loss1: 0.865302 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.757430 Loss1: 0.756749 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.689479 Loss1: 0.688797 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.676961 Loss1: 0.676279 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.666006 Loss1: 0.665324 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.607041 Loss1: 0.606362 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.533916 Loss1: 0.533235 Loss2: 0.000681
-(DefaultActor pid=2839578) >> Training accuracy: 0.831883
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5915743670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.325193 Loss1: 1.324509 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.050996 Loss1: 1.050307 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.895319 Loss1: 0.894631 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.804089 Loss1: 0.803400 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.764930 Loss1: 0.764239 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.692729 Loss1: 0.692040 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.634687 Loss1: 0.633997 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.637053 Loss1: 0.636362 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.620606 Loss1: 0.619918 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.574051 Loss1: 0.573363 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.852255
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5347222222222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.361732 Loss1: 1.361056 Loss2: 0.000676
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.995057 Loss1: 0.994378 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.859999 Loss1: 0.859318 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.780809 Loss1: 0.780129 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.730415 Loss1: 0.729734 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.715196 Loss1: 0.714514 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.622201 Loss1: 0.621522 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.550676 Loss1: 0.549995 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.560903 Loss1: 0.560221 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.526883 Loss1: 0.526200 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.888238
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6169871794871795
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.305988 Loss1: 1.305306 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.982471 Loss1: 0.981784 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.870373 Loss1: 0.869688 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.766332 Loss1: 0.765646 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.683106 Loss1: 0.682418 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.645629 Loss1: 0.644943 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.592413 Loss1: 0.591726 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.607627 Loss1: 0.606940 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.553094 Loss1: 0.552408 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.489491 Loss1: 0.488804 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.856771
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5813643292682927
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.285275 Loss1: 1.284595 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.031407 Loss1: 1.030722 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.918699 Loss1: 0.918018 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.843592 Loss1: 0.842908 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.743427 Loss1: 0.742744 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.712606 Loss1: 0.711924 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.612100 Loss1: 0.611417 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.611763 Loss1: 0.611078 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.554286 Loss1: 0.553602 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.514363 Loss1: 0.513680 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.837271
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5137246621621622
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.373630 Loss1: 1.372951 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.066205 Loss1: 1.065523 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.939168 Loss1: 0.938485 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.820080 Loss1: 0.819396 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.717720 Loss1: 0.717036 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.716809 Loss1: 0.716125 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.670649 Loss1: 0.669967 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.603208 Loss1: 0.602526 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.605108 Loss1: 0.604426 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.537407 Loss1: 0.536724 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.830659
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5436698717948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.388350 Loss1: 1.387678 Loss2: 0.000672
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.069646 Loss1: 1.068968 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.944736 Loss1: 0.944057 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.845517 Loss1: 0.844838 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.810837 Loss1: 0.810160 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.716508 Loss1: 0.715831 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.676502 Loss1: 0.675824 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.635517 Loss1: 0.634837 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.603081 Loss1: 0.602402 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.524852 Loss1: 0.524172 Loss2: 0.000681
-(DefaultActor pid=2839578) >> Training accuracy: 0.850561
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 11:08:38,840][flwr][DEBUG] - fit_round 14 received 10 results and 0 failures
-test acc: 0.5224
-[2023-09-21 11:09:20,673][flwr][INFO] - fit progress: (14, 2.0822741444499346, {'accuracy': 0.5224}, 28642.33480043197)
-[2023-09-21 11:09:20,674][flwr][DEBUG] - evaluate_round 14: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 11:09:56,967][flwr][DEBUG] - evaluate_round 14 received 10 results and 0 failures
-[2023-09-21 11:09:56,973][flwr][DEBUG] - fit_round 15: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.585245253164557
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.248063 Loss1: 1.247380 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.955273 Loss1: 0.954585 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.806013 Loss1: 0.805324 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.733134 Loss1: 0.732445 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.686385 Loss1: 0.685695 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.626458 Loss1: 0.625767 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.602848 Loss1: 0.602161 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.539285 Loss1: 0.538596 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.517281 Loss1: 0.516591 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.497788 Loss1: 0.497098 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.887856
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5658623417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.284005 Loss1: 1.283322 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.932303 Loss1: 0.931617 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.857271 Loss1: 0.856585 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.775608 Loss1: 0.774925 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.669581 Loss1: 0.668895 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.649746 Loss1: 0.649060 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.559732 Loss1: 0.559047 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.532957 Loss1: 0.532273 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.514418 Loss1: 0.513733 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.480694 Loss1: 0.480009 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.834256
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5639022435897436
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.301764 Loss1: 1.301086 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.009381 Loss1: 1.008699 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.843909 Loss1: 0.843228 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.783347 Loss1: 0.782664 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.716620 Loss1: 0.715938 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.625069 Loss1: 0.624385 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.625520 Loss1: 0.624837 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.551339 Loss1: 0.550657 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.528151 Loss1: 0.527469 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.487723 Loss1: 0.487040 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.853365
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5988948170731707
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.227612 Loss1: 1.226928 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.958121 Loss1: 0.957437 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.814602 Loss1: 0.813918 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.740707 Loss1: 0.740023 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.687803 Loss1: 0.687119 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.619615 Loss1: 0.618930 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.567862 Loss1: 0.567175 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.516920 Loss1: 0.516233 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.501689 Loss1: 0.501002 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.513696 Loss1: 0.513010 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.857088
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6346153846153846
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.169894 Loss1: 1.169209 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.903648 Loss1: 0.902962 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.765077 Loss1: 0.764389 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.686323 Loss1: 0.685635 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.625313 Loss1: 0.624624 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.525678 Loss1: 0.524990 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.489435 Loss1: 0.488745 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.472688 Loss1: 0.472000 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.485837 Loss1: 0.485148 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.437851 Loss1: 0.437164 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.850561
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6010680379746836
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.247205 Loss1: 1.246518 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.974121 Loss1: 0.973433 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.842835 Loss1: 0.842146 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.763555 Loss1: 0.762865 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.679691 Loss1: 0.679001 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.656608 Loss1: 0.655919 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.596825 Loss1: 0.596134 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.548391 Loss1: 0.547701 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.556992 Loss1: 0.556302 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.481873 Loss1: 0.481182 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.881329
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5646701388888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.238156 Loss1: 1.237477 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.932862 Loss1: 0.932181 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.823901 Loss1: 0.823219 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.710020 Loss1: 0.709337 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.628132 Loss1: 0.627449 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.594688 Loss1: 0.594004 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.544145 Loss1: 0.543462 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.511652 Loss1: 0.510967 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.487295 Loss1: 0.486611 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.413326 Loss1: 0.412641 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.863281
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6176819620253164
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.223622 Loss1: 1.222935 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.988298 Loss1: 0.987607 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.804023 Loss1: 0.803330 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.760880 Loss1: 0.760187 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.687859 Loss1: 0.687170 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.611328 Loss1: 0.610636 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.573867 Loss1: 0.573176 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.577203 Loss1: 0.576510 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.545243 Loss1: 0.544552 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.462672 Loss1: 0.461980 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.885878
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.542652027027027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.307491 Loss1: 1.306808 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.953105 Loss1: 0.952418 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.818019 Loss1: 0.817333 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.783487 Loss1: 0.782801 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.675442 Loss1: 0.674755 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.586629 Loss1: 0.585944 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.584960 Loss1: 0.584273 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.536859 Loss1: 0.536173 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.510628 Loss1: 0.509942 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.496409 Loss1: 0.495723 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.878167
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.553453947368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.413682 Loss1: 1.412990 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 1 Loss: 1.065420 Loss1: 1.064725 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.926535 Loss1: 0.925842 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.793192 Loss1: 0.792501 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.756182 Loss1: 0.755491 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.705888 Loss1: 0.705196 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.600377 Loss1: 0.599684 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.625310 Loss1: 0.624618 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.590795 Loss1: 0.590102 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.551023 Loss1: 0.550332 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.881990
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 11:40:21,240][flwr][DEBUG] - fit_round 15 received 10 results and 0 failures
-test acc: 0.5411
-[2023-09-21 11:41:07,624][flwr][INFO] - fit progress: (15, 2.0444571261588758, {'accuracy': 0.5411}, 30549.28551045386)
-[2023-09-21 11:41:07,624][flwr][DEBUG] - evaluate_round 15: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 11:41:44,262][flwr][DEBUG] - evaluate_round 15 received 10 results and 0 failures
-[2023-09-21 11:41:44,264][flwr][DEBUG] - fit_round 16: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5965189873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.171972 Loss1: 1.171290 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.907076 Loss1: 0.906391 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.775614 Loss1: 0.774930 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.675655 Loss1: 0.674972 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.642840 Loss1: 0.642156 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.554407 Loss1: 0.553721 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.536685 Loss1: 0.536000 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.505371 Loss1: 0.504685 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.491189 Loss1: 0.490504 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.459399 Loss1: 0.458714 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.851661
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5951522435897436
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.207781 Loss1: 1.207101 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.902751 Loss1: 0.902068 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.790941 Loss1: 0.790257 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.689388 Loss1: 0.688703 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.600925 Loss1: 0.600240 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.555160 Loss1: 0.554477 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.550878 Loss1: 0.550194 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.500430 Loss1: 0.499747 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.495864 Loss1: 0.495179 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.471448 Loss1: 0.470762 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.876002
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.629746835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.168234 Loss1: 1.167543 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.840973 Loss1: 0.840279 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.770074 Loss1: 0.769380 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.660800 Loss1: 0.660105 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.613675 Loss1: 0.612978 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.562730 Loss1: 0.562035 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.501028 Loss1: 0.500331 Loss2: 0.000697
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.472789 Loss1: 0.472091 Loss2: 0.000698
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.442583 Loss1: 0.441888 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.433179 Loss1: 0.432482 Loss2: 0.000697
-(DefaultActor pid=2839578) >> Training accuracy: 0.879549
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6358612804878049
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.161597 Loss1: 1.160916 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.871993 Loss1: 0.871308 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.736943 Loss1: 0.736258 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.668092 Loss1: 0.667406 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.610641 Loss1: 0.609956 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.571901 Loss1: 0.571212 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.533509 Loss1: 0.532821 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.463236 Loss1: 0.462549 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.454813 Loss1: 0.454124 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.431337 Loss1: 0.430650 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.877858
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.558910472972973
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.217862 Loss1: 1.217179 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.906881 Loss1: 0.906195 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.767560 Loss1: 0.766873 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.679200 Loss1: 0.678514 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.610980 Loss1: 0.610294 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.558068 Loss1: 0.557381 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.496385 Loss1: 0.495698 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.505302 Loss1: 0.504616 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.458835 Loss1: 0.458149 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.427147 Loss1: 0.426461 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.898649
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5840871710526315
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.339763 Loss1: 1.339074 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.988641 Loss1: 0.987948 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.856883 Loss1: 0.856191 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.751173 Loss1: 0.750482 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.684880 Loss1: 0.684187 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.618559 Loss1: 0.617868 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.618446 Loss1: 0.617755 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.514446 Loss1: 0.513754 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.499645 Loss1: 0.498953 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.510687 Loss1: 0.509997 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.850535
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6162974683544303
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.190312 Loss1: 1.189626 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.896312 Loss1: 0.895624 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.758354 Loss1: 0.757665 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.669706 Loss1: 0.669017 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.635962 Loss1: 0.635271 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.560865 Loss1: 0.560175 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.512129 Loss1: 0.511438 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.488315 Loss1: 0.487623 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.466634 Loss1: 0.465945 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.441537 Loss1: 0.440845 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.895570
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.65625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.109734 Loss1: 1.109050 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.801456 Loss1: 0.800768 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.676199 Loss1: 0.675510 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.627229 Loss1: 0.626540 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.594434 Loss1: 0.593745 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.481132 Loss1: 0.480443 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.480768 Loss1: 0.480080 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.472192 Loss1: 0.471503 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.405903 Loss1: 0.405211 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.411521 Loss1: 0.410832 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.913862
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5863715277777778
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.188047 Loss1: 1.187364 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.849622 Loss1: 0.848937 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.713098 Loss1: 0.712413 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.636415 Loss1: 0.635728 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.546417 Loss1: 0.545731 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.532960 Loss1: 0.532273 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.483972 Loss1: 0.483286 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.463146 Loss1: 0.462459 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.400550 Loss1: 0.399862 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.378643 Loss1: 0.377956 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.891059
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6071993670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.126219 Loss1: 1.125534 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.868880 Loss1: 0.868190 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.778676 Loss1: 0.777985 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.727137 Loss1: 0.726448 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.643110 Loss1: 0.642421 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.564256 Loss1: 0.563566 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.510575 Loss1: 0.509884 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.466732 Loss1: 0.466043 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.477715 Loss1: 0.477024 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.437627 Loss1: 0.436936 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.870847
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 12:12:36,772][flwr][DEBUG] - fit_round 16 received 10 results and 0 failures
-test acc: 0.5477
-[2023-09-21 12:13:15,888][flwr][INFO] - fit progress: (16, 2.057832792163276, {'accuracy': 0.5477}, 32477.549180646893)
-[2023-09-21 12:13:15,888][flwr][DEBUG] - evaluate_round 16: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 12:13:52,326][flwr][DEBUG] - evaluate_round 16 received 10 results and 0 failures
-[2023-09-21 12:13:52,327][flwr][DEBUG] - fit_round 17: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6040348101265823
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.142720 Loss1: 1.142041 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.797202 Loss1: 0.796519 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.699464 Loss1: 0.698782 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.629968 Loss1: 0.629285 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.534819 Loss1: 0.534136 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.521913 Loss1: 0.521231 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.469277 Loss1: 0.468593 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.466053 Loss1: 0.465371 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.438188 Loss1: 0.437504 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.431174 Loss1: 0.430491 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.900712
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.5941611842105263
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.226311 Loss1: 1.225624 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.929810 Loss1: 0.929122 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.749900 Loss1: 0.749210 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.681570 Loss1: 0.680882 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.616897 Loss1: 0.616207 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.543236 Loss1: 0.542547 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.537009 Loss1: 0.536319 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.532387 Loss1: 0.531699 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.454295 Loss1: 0.453608 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.429386 Loss1: 0.428699 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.872944
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6277689873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.123544 Loss1: 1.122860 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.820124 Loss1: 0.819435 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.694871 Loss1: 0.694183 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.609780 Loss1: 0.609092 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.567276 Loss1: 0.566587 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.531333 Loss1: 0.530643 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.458245 Loss1: 0.457557 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.433619 Loss1: 0.432930 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.404882 Loss1: 0.404190 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.396286 Loss1: 0.395595 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.884494
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.574535472972973
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.176506 Loss1: 1.175822 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.833712 Loss1: 0.833025 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.715611 Loss1: 0.714924 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.587471 Loss1: 0.586783 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.557997 Loss1: 0.557310 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.519834 Loss1: 0.519148 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.447374 Loss1: 0.446686 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.432000 Loss1: 0.431312 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.408963 Loss1: 0.408274 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.377571 Loss1: 0.376885 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.906461
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6694711538461539
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.044528 Loss1: 1.043845 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.780962 Loss1: 0.780275 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.636487 Loss1: 0.635799 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.592804 Loss1: 0.592116 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.482607 Loss1: 0.481918 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.441010 Loss1: 0.440321 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.440975 Loss1: 0.440287 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.375375 Loss1: 0.374685 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.352002 Loss1: 0.351314 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.321192 Loss1: 0.320503 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.918069
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6019631410256411
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.180263 Loss1: 1.179585 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.853258 Loss1: 0.852576 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.682382 Loss1: 0.681698 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.607328 Loss1: 0.606648 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.550287 Loss1: 0.549604 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.510910 Loss1: 0.510228 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.510569 Loss1: 0.509885 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.445767 Loss1: 0.445084 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.413736 Loss1: 0.413052 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.404868 Loss1: 0.404185 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.898438
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6530854430379747
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.107662 Loss1: 1.106975 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.817546 Loss1: 0.816852 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.708375 Loss1: 0.707683 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.580685 Loss1: 0.579990 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.589865 Loss1: 0.589172 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.515806 Loss1: 0.515112 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.457559 Loss1: 0.456865 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.457055 Loss1: 0.456360 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.423172 Loss1: 0.422479 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.383532 Loss1: 0.382839 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.897745
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6490091463414634
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.091658 Loss1: 1.090976 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.802920 Loss1: 0.802236 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.672022 Loss1: 0.671340 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.576981 Loss1: 0.576297 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.542945 Loss1: 0.542259 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.495907 Loss1: 0.495221 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.490823 Loss1: 0.490139 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.467997 Loss1: 0.467312 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.389936 Loss1: 0.389251 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.365524 Loss1: 0.364836 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.893483
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6426028481012658
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.147721 Loss1: 1.147036 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.820505 Loss1: 0.819816 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.725501 Loss1: 0.724812 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.625272 Loss1: 0.624580 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.536355 Loss1: 0.535666 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.517209 Loss1: 0.516520 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.484307 Loss1: 0.483617 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.481095 Loss1: 0.480404 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.384797 Loss1: 0.384107 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.385662 Loss1: 0.384972 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.916930
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6037326388888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.111239 Loss1: 1.110557 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.757280 Loss1: 0.756596 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.645653 Loss1: 0.644968 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.600523 Loss1: 0.599839 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.551401 Loss1: 0.550715 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.448230 Loss1: 0.447545 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.418582 Loss1: 0.417896 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.425059 Loss1: 0.424371 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.387687 Loss1: 0.387001 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.380974 Loss1: 0.380287 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.919705
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 12:44:43,389][flwr][DEBUG] - fit_round 17 received 10 results and 0 failures
-test acc: 0.5608
-[2023-09-21 12:45:24,585][flwr][INFO] - fit progress: (17, 2.031206720362837, {'accuracy': 0.5608}, 34406.24665048672)
-[2023-09-21 12:45:24,586][flwr][DEBUG] - evaluate_round 17: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 12:46:00,623][flwr][DEBUG] - evaluate_round 17 received 10 results and 0 failures
-[2023-09-21 12:46:00,624][flwr][DEBUG] - fit_round 18: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.596706081081081
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.094302 Loss1: 1.093617 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.772268 Loss1: 0.771580 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.646902 Loss1: 0.646215 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.584628 Loss1: 0.583940 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.489215 Loss1: 0.488528 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.435832 Loss1: 0.435146 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.367942 Loss1: 0.367254 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.376459 Loss1: 0.375771 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.351050 Loss1: 0.350364 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.339491 Loss1: 0.338803 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.918708
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6293512658227848
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.066817 Loss1: 1.066133 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.747724 Loss1: 0.747036 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.638336 Loss1: 0.637650 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.576429 Loss1: 0.575743 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.514288 Loss1: 0.513604 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.469632 Loss1: 0.468944 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.406558 Loss1: 0.405872 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.379111 Loss1: 0.378424 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.403106 Loss1: 0.402419 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.370391 Loss1: 0.369703 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.897350
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6266025641025641
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.073012 Loss1: 1.072333 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.752451 Loss1: 0.751767 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.648601 Loss1: 0.647916 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.613215 Loss1: 0.612529 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.501580 Loss1: 0.500899 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.480923 Loss1: 0.480239 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.425263 Loss1: 0.424579 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.415489 Loss1: 0.414805 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.409410 Loss1: 0.408727 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.327927 Loss1: 0.327245 Loss2: 0.000682
-(DefaultActor pid=2839578) >> Training accuracy: 0.906250
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6582278481012658
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.051163 Loss1: 1.050479 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.760670 Loss1: 0.759977 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.624285 Loss1: 0.623592 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.558168 Loss1: 0.557477 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.478668 Loss1: 0.477976 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.437583 Loss1: 0.436890 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.399258 Loss1: 0.398566 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.417357 Loss1: 0.416666 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.383603 Loss1: 0.382911 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.323092 Loss1: 0.322400 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.917524
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6293402777777778
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.079548 Loss1: 1.078864 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.730031 Loss1: 0.729346 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.589437 Loss1: 0.588752 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.547886 Loss1: 0.547201 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.492516 Loss1: 0.491829 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.425506 Loss1: 0.424819 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.332409 Loss1: 0.331721 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.360234 Loss1: 0.359546 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.340151 Loss1: 0.339465 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.274994 Loss1: 0.274306 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.943359
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6923076923076923
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.999405 Loss1: 0.998721 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.742395 Loss1: 0.741708 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.546003 Loss1: 0.545316 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.510316 Loss1: 0.509628 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.444151 Loss1: 0.443463 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.391229 Loss1: 0.390540 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.387143 Loss1: 0.386453 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.376873 Loss1: 0.376184 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.301309 Loss1: 0.300619 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.305137 Loss1: 0.304448 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.908454
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6611946202531646
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.033610 Loss1: 1.032924 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.779320 Loss1: 0.778629 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.628951 Loss1: 0.628260 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.553686 Loss1: 0.552996 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.479745 Loss1: 0.479055 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.457277 Loss1: 0.456585 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.459642 Loss1: 0.458949 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.422731 Loss1: 0.422038 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.370492 Loss1: 0.369801 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.376730 Loss1: 0.376040 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.881329
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.610608552631579
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.168973 Loss1: 1.168284 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.842909 Loss1: 0.842216 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.705051 Loss1: 0.704359 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.632374 Loss1: 0.631680 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.611334 Loss1: 0.610643 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.534202 Loss1: 0.533514 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.461604 Loss1: 0.460914 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.395185 Loss1: 0.394496 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.385466 Loss1: 0.384776 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.401138 Loss1: 0.400449 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.875206
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6686356707317073
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.040869 Loss1: 1.040187 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.730876 Loss1: 0.730191 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.629918 Loss1: 0.629233 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.566846 Loss1: 0.566162 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.490006 Loss1: 0.489321 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.427255 Loss1: 0.426569 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.452917 Loss1: 0.452230 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.383111 Loss1: 0.382423 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.334918 Loss1: 0.334231 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.366045 Loss1: 0.365358 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.893102
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6418117088607594
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.053644 Loss1: 1.052961 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.777673 Loss1: 0.776983 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.647216 Loss1: 0.646525 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.532519 Loss1: 0.531830 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.515363 Loss1: 0.514673 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.435360 Loss1: 0.434670 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.462182 Loss1: 0.461493 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.374925 Loss1: 0.374234 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.367130 Loss1: 0.366439 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.366999 Loss1: 0.366308 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.927611
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 13:16:56,476][flwr][DEBUG] - fit_round 18 received 10 results and 0 failures
-test acc: 0.5626
-[2023-09-21 13:17:37,215][flwr][INFO] - fit progress: (18, 2.0359792596996784, {'accuracy': 0.5626}, 36338.876606587786)
-[2023-09-21 13:17:37,216][flwr][DEBUG] - evaluate_round 18: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 13:18:12,370][flwr][DEBUG] - evaluate_round 18 received 10 results and 0 failures
-[2023-09-21 13:18:12,371][flwr][DEBUG] - fit_round 19: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6821598101265823
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.959848 Loss1: 0.959162 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.662881 Loss1: 0.662191 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.565824 Loss1: 0.565132 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.485351 Loss1: 0.484658 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.463298 Loss1: 0.462606 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.416257 Loss1: 0.415564 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.374890 Loss1: 0.374198 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.349709 Loss1: 0.349017 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.353259 Loss1: 0.352567 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.339978 Loss1: 0.339285 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.923457
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6408305921052632
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.126174 Loss1: 1.125487 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.799491 Loss1: 0.798799 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.630295 Loss1: 0.629606 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.577452 Loss1: 0.576762 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.505104 Loss1: 0.504412 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.459261 Loss1: 0.458571 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.432184 Loss1: 0.431493 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.403629 Loss1: 0.402938 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.392544 Loss1: 0.391855 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.375821 Loss1: 0.375131 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.898643
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7063301282051282
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.905123 Loss1: 0.904439 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.632716 Loss1: 0.632027 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.532526 Loss1: 0.531838 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.498864 Loss1: 0.498175 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.397904 Loss1: 0.397216 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.335703 Loss1: 0.335014 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.308992 Loss1: 0.308303 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.294917 Loss1: 0.294228 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.284271 Loss1: 0.283580 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.297317 Loss1: 0.296628 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.928285
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6588212025316456
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.976973 Loss1: 0.976289 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.713869 Loss1: 0.713178 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.596170 Loss1: 0.595479 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.486015 Loss1: 0.485325 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.488097 Loss1: 0.487406 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.408293 Loss1: 0.407603 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.388820 Loss1: 0.388129 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.339421 Loss1: 0.338728 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.300090 Loss1: 0.299398 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.286503 Loss1: 0.285812 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.912381
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6034628378378378
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.072035 Loss1: 1.071350 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.724948 Loss1: 0.724261 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.581759 Loss1: 0.581070 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.474492 Loss1: 0.473804 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.466861 Loss1: 0.466173 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.394737 Loss1: 0.394050 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.383053 Loss1: 0.382363 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.345827 Loss1: 0.345138 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.341975 Loss1: 0.341287 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.343478 Loss1: 0.342790 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.913429
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6414930555555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.027264 Loss1: 1.026580 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.689439 Loss1: 0.688755 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.579712 Loss1: 0.579027 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.481138 Loss1: 0.480452 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.406247 Loss1: 0.405559 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.391223 Loss1: 0.390537 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.353412 Loss1: 0.352724 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.325630 Loss1: 0.324943 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.328879 Loss1: 0.328191 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.296515 Loss1: 0.295827 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.936849
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6796875
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.953475 Loss1: 0.952795 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.670071 Loss1: 0.669385 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.562248 Loss1: 0.561561 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.494680 Loss1: 0.493991 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.432776 Loss1: 0.432088 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.409462 Loss1: 0.408774 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.396936 Loss1: 0.396248 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.337209 Loss1: 0.336521 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.362919 Loss1: 0.362230 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.359433 Loss1: 0.358745 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.881669
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6536787974683544
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.994324 Loss1: 0.993642 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.736840 Loss1: 0.736156 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.611929 Loss1: 0.611243 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.485980 Loss1: 0.485295 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.493053 Loss1: 0.492370 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.418748 Loss1: 0.418064 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.379157 Loss1: 0.378473 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.354676 Loss1: 0.353991 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.342452 Loss1: 0.341766 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.305841 Loss1: 0.305156 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.938489
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6683148734177216
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.014479 Loss1: 1.013791 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.701590 Loss1: 0.700900 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.590088 Loss1: 0.589396 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.491388 Loss1: 0.490698 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.480777 Loss1: 0.480085 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.462162 Loss1: 0.461470 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.421123 Loss1: 0.420432 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.378390 Loss1: 0.377698 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.338295 Loss1: 0.337603 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.321099 Loss1: 0.320407 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.928204
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.647636217948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.006496 Loss1: 1.005815 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.690885 Loss1: 0.690203 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.590653 Loss1: 0.589968 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.482642 Loss1: 0.481958 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.487397 Loss1: 0.486711 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.433124 Loss1: 0.432440 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.383816 Loss1: 0.383131 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.390940 Loss1: 0.390254 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.366844 Loss1: 0.366158 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.301749 Loss1: 0.301064 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.910256
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 13:49:35,955][flwr][DEBUG] - fit_round 19 received 10 results and 0 failures
-test acc: 0.5718
-[2023-09-21 13:50:34,062][flwr][INFO] - fit progress: (19, 2.0221455788460023, {'accuracy': 0.5718}, 38315.72371325875)
-[2023-09-21 13:50:34,063][flwr][DEBUG] - evaluate_round 19: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 13:51:12,471][flwr][DEBUG] - evaluate_round 19 received 10 results and 0 failures
-[2023-09-21 13:51:12,471][flwr][DEBUG] - fit_round 20: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7062895569620253
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.954811 Loss1: 0.954123 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.665533 Loss1: 0.664838 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.534502 Loss1: 0.533807 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.448819 Loss1: 0.448124 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.393489 Loss1: 0.392792 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.450359 Loss1: 0.449663 Loss2: 0.000696
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.334775 Loss1: 0.334080 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.341575 Loss1: 0.340880 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.291867 Loss1: 0.291170 Loss2: 0.000697
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.280014 Loss1: 0.279317 Loss2: 0.000697
-(DefaultActor pid=2839578) >> Training accuracy: 0.912184
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6754351265822784
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.895974 Loss1: 0.895288 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.656032 Loss1: 0.655343 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.559093 Loss1: 0.558404 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.470448 Loss1: 0.469755 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.420889 Loss1: 0.420197 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.356547 Loss1: 0.355858 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.366510 Loss1: 0.365818 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.387680 Loss1: 0.386991 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.314847 Loss1: 0.314157 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.288951 Loss1: 0.288260 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.921875
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6666666666666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.959341 Loss1: 0.958663 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.665053 Loss1: 0.664371 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.554571 Loss1: 0.553888 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.482737 Loss1: 0.482054 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.451966 Loss1: 0.451281 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.411543 Loss1: 0.410859 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.362976 Loss1: 0.362292 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.332367 Loss1: 0.331683 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.270884 Loss1: 0.270202 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.300204 Loss1: 0.299520 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.923478
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6890822784810127
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.969077 Loss1: 0.968394 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.664713 Loss1: 0.664025 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.527326 Loss1: 0.526642 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.473413 Loss1: 0.472726 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.431975 Loss1: 0.431287 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.384006 Loss1: 0.383317 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.329720 Loss1: 0.329033 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.316652 Loss1: 0.315965 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.276848 Loss1: 0.276161 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.263223 Loss1: 0.262535 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.916139
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6492598684210527
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.048296 Loss1: 1.047608 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.730649 Loss1: 0.729958 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.618650 Loss1: 0.617960 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.526269 Loss1: 0.525574 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.474201 Loss1: 0.473509 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.432615 Loss1: 0.431924 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.385725 Loss1: 0.385034 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.355371 Loss1: 0.354681 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.365749 Loss1: 0.365057 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.325013 Loss1: 0.324323 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.928865
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7271634615384616
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.880925 Loss1: 0.880241 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.577044 Loss1: 0.576357 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.501507 Loss1: 0.500818 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.403781 Loss1: 0.403092 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.366669 Loss1: 0.365980 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.330967 Loss1: 0.330278 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.333528 Loss1: 0.332841 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.297542 Loss1: 0.296852 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.266772 Loss1: 0.266081 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.268955 Loss1: 0.268265 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.921875
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6882621951219512
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.937918 Loss1: 0.937238 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.648408 Loss1: 0.647722 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.508054 Loss1: 0.507369 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.476899 Loss1: 0.476213 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.399151 Loss1: 0.398463 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.411347 Loss1: 0.410660 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.369446 Loss1: 0.368759 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.374146 Loss1: 0.373460 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.292739 Loss1: 0.292051 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.273411 Loss1: 0.272723 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.927973
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6247888513513513
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.019912 Loss1: 1.019229 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.691106 Loss1: 0.690419 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.528049 Loss1: 0.527361 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.467166 Loss1: 0.466479 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.428732 Loss1: 0.428044 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.344272 Loss1: 0.343585 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.345131 Loss1: 0.344444 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.317066 Loss1: 0.316380 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.293325 Loss1: 0.292638 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.254468 Loss1: 0.253781 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.939823
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6571180555555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.988701 Loss1: 0.988020 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.649398 Loss1: 0.648714 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.514444 Loss1: 0.513760 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.468484 Loss1: 0.467798 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.385076 Loss1: 0.384390 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.361170 Loss1: 0.360484 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.318545 Loss1: 0.317858 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.318763 Loss1: 0.318078 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.294864 Loss1: 0.294178 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.249111 Loss1: 0.248423 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.946615
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6617879746835443
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.949268 Loss1: 0.948585 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.654062 Loss1: 0.653376 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.533020 Loss1: 0.532335 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.480708 Loss1: 0.480021 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.442745 Loss1: 0.442059 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.417431 Loss1: 0.416743 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.369522 Loss1: 0.368836 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.346798 Loss1: 0.346113 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.318132 Loss1: 0.317447 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.275973 Loss1: 0.275287 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.939478
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 14:22:43,743][flwr][DEBUG] - fit_round 20 received 10 results and 0 failures
-test acc: 0.5753
-[2023-09-21 14:23:40,201][flwr][INFO] - fit progress: (20, 2.034489405421784, {'accuracy': 0.5753}, 40301.8622248359)
-[2023-09-21 14:23:40,201][flwr][DEBUG] - evaluate_round 20: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 14:24:20,889][flwr][DEBUG] - evaluate_round 20 received 10 results and 0 failures
-[2023-09-21 14:24:20,890][flwr][DEBUG] - fit_round 21: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7397836538461539
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.824833 Loss1: 0.824149 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.543139 Loss1: 0.542451 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.473494 Loss1: 0.472805 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.374403 Loss1: 0.373714 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.323439 Loss1: 0.322750 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.328259 Loss1: 0.327569 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.265237 Loss1: 0.264547 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.245049 Loss1: 0.244359 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.262205 Loss1: 0.261516 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.221613 Loss1: 0.220923 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.945112
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.65625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 1.012585 Loss1: 1.011897 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.689855 Loss1: 0.689164 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.586339 Loss1: 0.585646 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.490496 Loss1: 0.489807 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.423021 Loss1: 0.422330 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.383382 Loss1: 0.382693 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.393623 Loss1: 0.392932 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.349961 Loss1: 0.349271 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.298128 Loss1: 0.297436 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.313965 Loss1: 0.313278 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.912418
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7027294303797469
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.912616 Loss1: 0.911929 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.626376 Loss1: 0.625689 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.518194 Loss1: 0.517506 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.420392 Loss1: 0.419704 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.371976 Loss1: 0.371287 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.340598 Loss1: 0.339910 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.338659 Loss1: 0.337971 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.327412 Loss1: 0.326725 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.299027 Loss1: 0.298338 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.253208 Loss1: 0.252520 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.927215
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6932357594936709
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.882542 Loss1: 0.881860 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.602563 Loss1: 0.601875 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.477237 Loss1: 0.476551 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.440930 Loss1: 0.440242 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.404910 Loss1: 0.404222 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.371534 Loss1: 0.370847 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.322536 Loss1: 0.321848 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.291291 Loss1: 0.290602 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.302394 Loss1: 0.301705 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.250491 Loss1: 0.249801 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.929786
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6734775641025641
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.936235 Loss1: 0.935556 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.629486 Loss1: 0.628804 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.495261 Loss1: 0.494578 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.448358 Loss1: 0.447676 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.415090 Loss1: 0.414405 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.353631 Loss1: 0.352948 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.331765 Loss1: 0.331083 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.294503 Loss1: 0.293821 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.297316 Loss1: 0.296636 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.267443 Loss1: 0.266760 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.928085
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7122231012658228
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.906181 Loss1: 0.905493 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.622214 Loss1: 0.621522 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.531727 Loss1: 0.531034 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.428849 Loss1: 0.428155 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.415786 Loss1: 0.415095 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.355280 Loss1: 0.354589 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.298220 Loss1: 0.297529 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.297549 Loss1: 0.296857 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.256684 Loss1: 0.255989 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.254162 Loss1: 0.253471 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.942049
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.703125
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.880603 Loss1: 0.879925 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.581155 Loss1: 0.580472 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.468184 Loss1: 0.467500 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.380484 Loss1: 0.379798 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.392686 Loss1: 0.392003 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.368948 Loss1: 0.368261 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.338958 Loss1: 0.338274 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.298871 Loss1: 0.298185 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.298956 Loss1: 0.298268 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.262431 Loss1: 0.261745 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.934261
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6720920138888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.885867 Loss1: 0.885187 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.580765 Loss1: 0.580081 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.475862 Loss1: 0.475178 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.406948 Loss1: 0.406263 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.363567 Loss1: 0.362882 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.309969 Loss1: 0.309282 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.277164 Loss1: 0.276478 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.227772 Loss1: 0.227087 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.245619 Loss1: 0.244931 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225764 Loss1: 0.225077 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.948351
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6746439873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.919673 Loss1: 0.918993 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.578220 Loss1: 0.577537 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.518907 Loss1: 0.518223 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.409289 Loss1: 0.408605 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.390071 Loss1: 0.389389 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.364969 Loss1: 0.364286 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.309263 Loss1: 0.308580 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.289962 Loss1: 0.289278 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.260004 Loss1: 0.259320 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.288667 Loss1: 0.287982 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.927017
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6328125
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.979426 Loss1: 0.978741 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.635645 Loss1: 0.634957 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.518889 Loss1: 0.518200 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.479992 Loss1: 0.479303 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.351588 Loss1: 0.350898 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.362277 Loss1: 0.361589 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.317835 Loss1: 0.317144 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.279659 Loss1: 0.278970 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.242820 Loss1: 0.242130 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.266235 Loss1: 0.265545 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.932855
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 14:54:48,349][flwr][DEBUG] - fit_round 21 received 10 results and 0 failures
-test acc: 0.58
-[2023-09-21 14:55:45,146][flwr][INFO] - fit progress: (21, 2.0309638719970047, {'accuracy': 0.58}, 42226.8077736008)
-[2023-09-21 14:55:45,147][flwr][DEBUG] - evaluate_round 21: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 14:56:32,892][flwr][DEBUG] - evaluate_round 21 received 10 results and 0 failures
-[2023-09-21 14:56:32,894][flwr][DEBUG] - fit_round 22: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6914556962025317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.803949 Loss1: 0.803262 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.565996 Loss1: 0.565307 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.449576 Loss1: 0.448886 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.371917 Loss1: 0.371227 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.353012 Loss1: 0.352323 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.335038 Loss1: 0.334348 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.299778 Loss1: 0.299088 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.262341 Loss1: 0.261650 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.289537 Loss1: 0.288847 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.236109 Loss1: 0.235420 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.944818
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7445913461538461
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.805326 Loss1: 0.804641 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.526743 Loss1: 0.526055 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.450884 Loss1: 0.450194 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.350697 Loss1: 0.350007 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.304397 Loss1: 0.303706 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.279762 Loss1: 0.279072 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.280171 Loss1: 0.279481 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.249646 Loss1: 0.248956 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.203021 Loss1: 0.202330 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.177469 Loss1: 0.176777 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.953926
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7221123417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.820623 Loss1: 0.819936 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.582384 Loss1: 0.581692 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.479339 Loss1: 0.478646 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.356456 Loss1: 0.355763 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.336598 Loss1: 0.335904 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.317648 Loss1: 0.316955 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.325154 Loss1: 0.324462 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.281410 Loss1: 0.280718 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.303766 Loss1: 0.303073 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.283561 Loss1: 0.282868 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.934533
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6746961805555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.891036 Loss1: 0.890351 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.544994 Loss1: 0.544308 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.449309 Loss1: 0.448621 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.425047 Loss1: 0.424359 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.361126 Loss1: 0.360439 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.313393 Loss1: 0.312705 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.250910 Loss1: 0.250220 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.269423 Loss1: 0.268736 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.245849 Loss1: 0.245160 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.205879 Loss1: 0.205190 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.937500
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.714003164556962
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.851204 Loss1: 0.850517 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.592070 Loss1: 0.591380 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.494705 Loss1: 0.494015 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.405275 Loss1: 0.404584 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.382699 Loss1: 0.382008 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.350577 Loss1: 0.349887 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.316734 Loss1: 0.316042 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.289837 Loss1: 0.289146 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.233492 Loss1: 0.232801 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.245263 Loss1: 0.244572 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.947983
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6480152027027027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.912692 Loss1: 0.912006 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.567772 Loss1: 0.567084 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.456716 Loss1: 0.456029 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.378274 Loss1: 0.377584 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.375633 Loss1: 0.374943 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.308279 Loss1: 0.307591 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.293974 Loss1: 0.293284 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.271330 Loss1: 0.270640 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.258329 Loss1: 0.257641 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.293659 Loss1: 0.292970 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.924409
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6780427631578947
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.941828 Loss1: 0.941141 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.640836 Loss1: 0.640144 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.471174 Loss1: 0.470480 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.436413 Loss1: 0.435721 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.411261 Loss1: 0.410569 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.351410 Loss1: 0.350720 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.330966 Loss1: 0.330275 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.325650 Loss1: 0.324959 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.331087 Loss1: 0.330396 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.296963 Loss1: 0.296273 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.936678
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6861155063291139
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.833602 Loss1: 0.832919 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.579613 Loss1: 0.578926 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.462821 Loss1: 0.462135 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.403105 Loss1: 0.402417 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.351034 Loss1: 0.350348 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.377130 Loss1: 0.376444 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.285004 Loss1: 0.284315 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.269557 Loss1: 0.268872 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.236383 Loss1: 0.235696 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.230013 Loss1: 0.229326 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.959256
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6909054487179487
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.848580 Loss1: 0.847897 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.579539 Loss1: 0.578852 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.451849 Loss1: 0.451162 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.381812 Loss1: 0.381126 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.350630 Loss1: 0.349944 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.329147 Loss1: 0.328461 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.312975 Loss1: 0.312289 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.265735 Loss1: 0.265050 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.327254 Loss1: 0.326568 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.272125 Loss1: 0.271440 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.918269
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7244664634146342
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.843598 Loss1: 0.842914 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.552585 Loss1: 0.551898 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.419045 Loss1: 0.418355 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.366650 Loss1: 0.365961 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.342061 Loss1: 0.341372 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.308390 Loss1: 0.307701 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.321223 Loss1: 0.320533 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.253640 Loss1: 0.252950 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.241274 Loss1: 0.240582 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.250582 Loss1: 0.249891 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.923780
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 15:28:17,500][flwr][DEBUG] - fit_round 22 received 10 results and 0 failures
-test acc: 0.5794
-[2023-09-21 15:43:12,517][flwr][INFO] - fit progress: (22, 2.0352664707948604, {'accuracy': 0.5794}, 45074.17800773773)
-[2023-09-21 15:43:12,517][flwr][DEBUG] - evaluate_round 22: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 15:44:05,792][flwr][DEBUG] - evaluate_round 22 received 10 results and 0 failures
-[2023-09-21 15:44:05,793][flwr][DEBUG] - fit_round 23: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7167721518987342
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.824136 Loss1: 0.823451 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.549475 Loss1: 0.548785 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.418791 Loss1: 0.418101 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.381816 Loss1: 0.381126 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.298096 Loss1: 0.297404 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.303099 Loss1: 0.302408 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.288763 Loss1: 0.288072 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.267822 Loss1: 0.267129 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.247003 Loss1: 0.246311 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225090 Loss1: 0.224397 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.939082
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6919070512820513
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.805127 Loss1: 0.804447 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.542462 Loss1: 0.541778 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.451714 Loss1: 0.451029 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.376672 Loss1: 0.375988 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.325679 Loss1: 0.324995 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.334058 Loss1: 0.333373 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.288432 Loss1: 0.287749 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.279191 Loss1: 0.278505 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.260787 Loss1: 0.260102 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.240564 Loss1: 0.239880 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.944912
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6990131578947368
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.913341 Loss1: 0.912651 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.577036 Loss1: 0.576343 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.447269 Loss1: 0.446577 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.423009 Loss1: 0.422316 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.397099 Loss1: 0.396406 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.368308 Loss1: 0.367618 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.304916 Loss1: 0.304224 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.290928 Loss1: 0.290237 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.237614 Loss1: 0.236921 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.227034 Loss1: 0.226340 Loss2: 0.000694
-(DefaultActor pid=2839578) >> Training accuracy: 0.940789
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7062895569620253
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.819787 Loss1: 0.819105 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.540358 Loss1: 0.539672 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.389115 Loss1: 0.388429 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.391066 Loss1: 0.390379 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.351819 Loss1: 0.351133 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.299734 Loss1: 0.299048 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.290117 Loss1: 0.289430 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.244954 Loss1: 0.244267 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.250688 Loss1: 0.250001 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.237327 Loss1: 0.236640 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.935522
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6589949324324325
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.871702 Loss1: 0.871014 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.557355 Loss1: 0.556664 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.452732 Loss1: 0.452040 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.388189 Loss1: 0.387499 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.345209 Loss1: 0.344519 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.303643 Loss1: 0.302953 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.239421 Loss1: 0.238731 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.229152 Loss1: 0.228461 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.246465 Loss1: 0.245774 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.215249 Loss1: 0.214557 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.957981
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7195411392405063
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.786715 Loss1: 0.786030 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.542083 Loss1: 0.541394 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.427712 Loss1: 0.427023 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.356983 Loss1: 0.356293 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.335741 Loss1: 0.335049 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.318976 Loss1: 0.318286 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.281052 Loss1: 0.280360 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.273622 Loss1: 0.272932 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.237452 Loss1: 0.236762 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.205321 Loss1: 0.204630 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.943038
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7389240506329114
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.789821 Loss1: 0.789133 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.543761 Loss1: 0.543067 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.379188 Loss1: 0.378494 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.365347 Loss1: 0.364653 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.320259 Loss1: 0.319565 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.314512 Loss1: 0.313818 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.299502 Loss1: 0.298810 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.265154 Loss1: 0.264460 Loss2: 0.000695
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.225665 Loss1: 0.224972 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.211969 Loss1: 0.211274 Loss2: 0.000695
-(DefaultActor pid=2839578) >> Training accuracy: 0.949367
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6866319444444444
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.853024 Loss1: 0.852341 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.508157 Loss1: 0.507470 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.397796 Loss1: 0.397110 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.342883 Loss1: 0.342195 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.284955 Loss1: 0.284267 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.224961 Loss1: 0.224273 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.270997 Loss1: 0.270311 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.253158 Loss1: 0.252470 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.225463 Loss1: 0.224776 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225452 Loss1: 0.224764 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.932509
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7336128048780488
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.827469 Loss1: 0.826787 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.498889 Loss1: 0.498204 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.416946 Loss1: 0.416260 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.353177 Loss1: 0.352492 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.308768 Loss1: 0.308082 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.306896 Loss1: 0.306210 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.276188 Loss1: 0.275501 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.267651 Loss1: 0.266965 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.228126 Loss1: 0.227438 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225639 Loss1: 0.224951 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.946456
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7546073717948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.761326 Loss1: 0.760640 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.499356 Loss1: 0.498668 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.407487 Loss1: 0.406797 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.335937 Loss1: 0.335247 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.288890 Loss1: 0.288200 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.267197 Loss1: 0.266506 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237314 Loss1: 0.236623 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.193691 Loss1: 0.192999 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.201012 Loss1: 0.200322 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.222137 Loss1: 0.221447 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.958534
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 16:15:20,819][flwr][DEBUG] - fit_round 23 received 10 results and 0 failures
-test acc: 0.5859
-[2023-09-21 16:16:32,911][flwr][INFO] - fit progress: (23, 2.0376226549712233, {'accuracy': 0.5859}, 47074.572088341694)
-[2023-09-21 16:16:32,912][flwr][DEBUG] - evaluate_round 23: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 16:17:14,742][flwr][DEBUG] - evaluate_round 23 received 10 results and 0 failures
-[2023-09-21 16:17:14,743][flwr][DEBUG] - fit_round 24: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7341772151898734
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.751340 Loss1: 0.750656 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.490719 Loss1: 0.490032 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.363891 Loss1: 0.363202 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.348134 Loss1: 0.347444 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.316647 Loss1: 0.315959 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.282471 Loss1: 0.281784 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.268883 Loss1: 0.268195 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.275336 Loss1: 0.274645 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.253431 Loss1: 0.252741 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.199564 Loss1: 0.198875 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.959256
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7005208333333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.825215 Loss1: 0.824532 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.505377 Loss1: 0.504691 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.401535 Loss1: 0.400848 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.310919 Loss1: 0.310232 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.306895 Loss1: 0.306210 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.282627 Loss1: 0.281940 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.241331 Loss1: 0.240642 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.214543 Loss1: 0.213854 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.195775 Loss1: 0.195086 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.222747 Loss1: 0.222061 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.943576
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7459984756097561
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.758384 Loss1: 0.757703 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.472129 Loss1: 0.471443 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.371137 Loss1: 0.370452 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.332945 Loss1: 0.332260 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.295966 Loss1: 0.295280 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.278833 Loss1: 0.278146 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.226507 Loss1: 0.225819 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.252484 Loss1: 0.251796 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.235723 Loss1: 0.235036 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.208849 Loss1: 0.208161 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.944169
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7509889240506329
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.738613 Loss1: 0.737926 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.470530 Loss1: 0.469838 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.410247 Loss1: 0.409557 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.350161 Loss1: 0.349470 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.331707 Loss1: 0.331017 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.291249 Loss1: 0.290559 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.249274 Loss1: 0.248583 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.241145 Loss1: 0.240454 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.218389 Loss1: 0.217696 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.206936 Loss1: 0.206242 Loss2: 0.000694
-(DefaultActor pid=2839578) >> Training accuracy: 0.945016
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7087339743589743
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.757359 Loss1: 0.756680 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.506201 Loss1: 0.505518 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.403108 Loss1: 0.402424 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.340485 Loss1: 0.339802 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.290285 Loss1: 0.289601 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.285131 Loss1: 0.284446 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.283624 Loss1: 0.282940 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.278499 Loss1: 0.277816 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.210515 Loss1: 0.209830 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.221317 Loss1: 0.220634 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.955529
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7213212025316456
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.778736 Loss1: 0.778050 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.487969 Loss1: 0.487281 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.382482 Loss1: 0.381792 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.383730 Loss1: 0.383039 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.284243 Loss1: 0.283553 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.279700 Loss1: 0.279011 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.244235 Loss1: 0.243542 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.235012 Loss1: 0.234321 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.246669 Loss1: 0.245980 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.225132 Loss1: 0.224442 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.961630
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7638221153846154
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.691307 Loss1: 0.690626 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.444809 Loss1: 0.444122 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.361598 Loss1: 0.360911 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.289424 Loss1: 0.288736 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.281628 Loss1: 0.280942 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.255080 Loss1: 0.254394 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.213031 Loss1: 0.212342 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.190368 Loss1: 0.189680 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.236814 Loss1: 0.236126 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.201299 Loss1: 0.200609 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.965144
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7012746710526315
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.863654 Loss1: 0.862968 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.535128 Loss1: 0.534437 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.427521 Loss1: 0.426830 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.377123 Loss1: 0.376431 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.345876 Loss1: 0.345185 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.281138 Loss1: 0.280446 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.277964 Loss1: 0.277273 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.279213 Loss1: 0.278523 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.273045 Loss1: 0.272358 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.261927 Loss1: 0.261238 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.944901
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7122231012658228
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.770449 Loss1: 0.769770 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.512532 Loss1: 0.511849 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.390693 Loss1: 0.390008 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.327412 Loss1: 0.326730 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.299071 Loss1: 0.298386 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.287903 Loss1: 0.287218 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.303481 Loss1: 0.302797 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.255980 Loss1: 0.255295 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.236285 Loss1: 0.235599 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.199653 Loss1: 0.198968 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.933742
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6587837837837838
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.832835 Loss1: 0.832151 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.523753 Loss1: 0.523064 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.399227 Loss1: 0.398538 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.354134 Loss1: 0.353443 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.335702 Loss1: 0.335012 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.290098 Loss1: 0.289409 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.249435 Loss1: 0.248746 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.234401 Loss1: 0.233712 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.224138 Loss1: 0.223449 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.222572 Loss1: 0.221883 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.947213
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 16:49:17,180][flwr][DEBUG] - fit_round 24 received 10 results and 0 failures
-test acc: 0.5911
-[2023-09-21 16:50:27,960][flwr][INFO] - fit progress: (24, 2.040369967111764, {'accuracy': 0.5911}, 49109.62099220976)
-[2023-09-21 16:50:27,960][flwr][DEBUG] - evaluate_round 24: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 16:51:08,204][flwr][DEBUG] - evaluate_round 24 received 10 results and 0 failures
-[2023-09-21 16:51:08,206][flwr][DEBUG] - fit_round 25: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.737047697368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.821320 Loss1: 0.820633 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.510944 Loss1: 0.510253 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.388813 Loss1: 0.388122 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.355849 Loss1: 0.355158 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.315487 Loss1: 0.314796 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.323875 Loss1: 0.323183 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.300329 Loss1: 0.299637 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.233861 Loss1: 0.233171 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.202202 Loss1: 0.201511 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.198254 Loss1: 0.197564 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.956003
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7219145569620253
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.753810 Loss1: 0.753123 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.432384 Loss1: 0.431692 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.351928 Loss1: 0.351237 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.306713 Loss1: 0.306022 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.250338 Loss1: 0.249648 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.269751 Loss1: 0.269059 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.250043 Loss1: 0.249352 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.235157 Loss1: 0.234464 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.213640 Loss1: 0.212950 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.186728 Loss1: 0.186039 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.967168
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7242879746835443
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.722312 Loss1: 0.721630 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.442140 Loss1: 0.441455 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.364463 Loss1: 0.363778 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.360419 Loss1: 0.359732 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.294501 Loss1: 0.293816 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.259119 Loss1: 0.258434 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.247918 Loss1: 0.247232 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.249166 Loss1: 0.248480 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.226538 Loss1: 0.225851 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.209486 Loss1: 0.208801 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.951938
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7762419871794872
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.674149 Loss1: 0.673464 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.440279 Loss1: 0.439589 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.339242 Loss1: 0.338550 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.257786 Loss1: 0.257095 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.240745 Loss1: 0.240056 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.207922 Loss1: 0.207230 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.194243 Loss1: 0.193551 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.216477 Loss1: 0.215785 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.220482 Loss1: 0.219790 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.178628 Loss1: 0.177935 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.954127
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7287660256410257
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.708085 Loss1: 0.707404 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.534795 Loss1: 0.534111 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.396736 Loss1: 0.396050 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.336620 Loss1: 0.335935 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.285264 Loss1: 0.284578 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.265288 Loss1: 0.264603 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237579 Loss1: 0.236894 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.235607 Loss1: 0.234921 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.209277 Loss1: 0.208590 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.194240 Loss1: 0.193554 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.922676
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7478243670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.699292 Loss1: 0.698604 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.438248 Loss1: 0.437557 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.367209 Loss1: 0.366517 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.295911 Loss1: 0.295220 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.257589 Loss1: 0.256898 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.244775 Loss1: 0.244083 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.276036 Loss1: 0.275345 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.237333 Loss1: 0.236641 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.229056 Loss1: 0.228364 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.246526 Loss1: 0.245835 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.930380
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6720861486486487
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.788696 Loss1: 0.788011 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.480613 Loss1: 0.479923 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.364356 Loss1: 0.363665 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.313429 Loss1: 0.312738 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.262075 Loss1: 0.261386 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.224454 Loss1: 0.223764 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.208808 Loss1: 0.208118 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.227897 Loss1: 0.227207 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.216236 Loss1: 0.215546 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.198813 Loss1: 0.198123 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.938556
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7572408536585366
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.688046 Loss1: 0.687363 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.395401 Loss1: 0.394715 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.353045 Loss1: 0.352359 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.297340 Loss1: 0.296652 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.253839 Loss1: 0.253151 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.279134 Loss1: 0.278447 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.245478 Loss1: 0.244791 Loss2: 0.000688
-(DefaultActor pid=2839578) 
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.231056 Loss1: 0.230367 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.210081 Loss1: 0.209393 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.182395 Loss1: 0.181707 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.959985
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7215711805555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.739555 Loss1: 0.738870 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.471681 Loss1: 0.470995 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.333966 Loss1: 0.333277 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.296879 Loss1: 0.296190 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.250522 Loss1: 0.249834 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.213141 Loss1: 0.212450 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.207436 Loss1: 0.206747 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.240102 Loss1: 0.239413 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.188613 Loss1: 0.187923 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185434 Loss1: 0.184746 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.951606
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.765625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.677464 Loss1: 0.676775 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.458220 Loss1: 0.457527 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.375452 Loss1: 0.374760 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.329835 Loss1: 0.329142 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.287296 Loss1: 0.286603 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.251549 Loss1: 0.250856 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.262151 Loss1: 0.261459 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.214764 Loss1: 0.214072 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.202524 Loss1: 0.201832 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.209838 Loss1: 0.209144 Loss2: 0.000694
-(DefaultActor pid=2839578) >> Training accuracy: 0.949169
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 17:21:30,256][flwr][DEBUG] - fit_round 25 received 10 results and 0 failures
-test acc: 0.5934
-[2023-09-21 17:22:32,311][flwr][INFO] - fit progress: (25, 2.0471822914604942, {'accuracy': 0.5934}, 51033.97241008561)
-[2023-09-21 17:22:32,312][flwr][DEBUG] - evaluate_round 25: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 17:23:11,711][flwr][DEBUG] - evaluate_round 25 received 10 results and 0 failures
-[2023-09-21 17:23:11,712][flwr][DEBUG] - fit_round 26: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7365506329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.669510 Loss1: 0.668828 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.449897 Loss1: 0.449211 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.346823 Loss1: 0.346136 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.294352 Loss1: 0.293665 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.277189 Loss1: 0.276500 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.235354 Loss1: 0.234667 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.243955 Loss1: 0.243268 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.230148 Loss1: 0.229461 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.211436 Loss1: 0.210747 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.182153 Loss1: 0.181464 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.964399
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7852564102564102
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.664804 Loss1: 0.664119 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.401415 Loss1: 0.400725 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.328104 Loss1: 0.327414 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.233373 Loss1: 0.232681 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.226942 Loss1: 0.226251 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.219406 Loss1: 0.218714 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.201414 Loss1: 0.200722 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.218468 Loss1: 0.217777 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.175572 Loss1: 0.174881 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.142234 Loss1: 0.141543 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.974559
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7319078947368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.770001 Loss1: 0.769314 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.490776 Loss1: 0.490086 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.371023 Loss1: 0.370333 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.302129 Loss1: 0.301436 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.351687 Loss1: 0.350996 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.286225 Loss1: 0.285534 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.281405 Loss1: 0.280714 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.236885 Loss1: 0.236194 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.211957 Loss1: 0.211267 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.195370 Loss1: 0.194679 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.957442
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7460443037974683
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.667043 Loss1: 0.666358 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.434914 Loss1: 0.434224 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.329615 Loss1: 0.328922 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.321081 Loss1: 0.320388 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.270411 Loss1: 0.269719 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.270488 Loss1: 0.269797 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237802 Loss1: 0.237111 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.203686 Loss1: 0.202994 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.184049 Loss1: 0.183356 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.162606 Loss1: 0.161913 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.959454
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7204861111111112
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.706309 Loss1: 0.705625 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.448462 Loss1: 0.447774 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.323875 Loss1: 0.323188 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.293657 Loss1: 0.292970 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.250805 Loss1: 0.250116 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.244410 Loss1: 0.243723 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237775 Loss1: 0.237087 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.201405 Loss1: 0.200718 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.178896 Loss1: 0.178207 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.186976 Loss1: 0.186288 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.970703
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7583069620253164
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.693622 Loss1: 0.692937 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.458549 Loss1: 0.457858 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.309923 Loss1: 0.309232 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.297117 Loss1: 0.296425 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.290262 Loss1: 0.289573 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.255293 Loss1: 0.254603 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.237801 Loss1: 0.237111 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.242432 Loss1: 0.241741 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.201297 Loss1: 0.200606 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.171604 Loss1: 0.170911 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.961630
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7723496835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.673661 Loss1: 0.672974 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.415174 Loss1: 0.414482 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.303973 Loss1: 0.303279 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.291243 Loss1: 0.290549 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.272274 Loss1: 0.271583 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.259890 Loss1: 0.259197 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.226039 Loss1: 0.225346 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.211092 Loss1: 0.210398 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.167100 Loss1: 0.166406 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.144354 Loss1: 0.143661 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.938093
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6855996621621622
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.728826 Loss1: 0.728140 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.427256 Loss1: 0.426567 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.401747 Loss1: 0.401057 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.264634 Loss1: 0.263946 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.231522 Loss1: 0.230834 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.239431 Loss1: 0.238742 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.271579 Loss1: 0.270890 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.248929 Loss1: 0.248240 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.172702 Loss1: 0.172012 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.181553 Loss1: 0.180864 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.950802
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7629573170731707
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.643811 Loss1: 0.643130 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.419067 Loss1: 0.418382 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.338007 Loss1: 0.337321 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.294281 Loss1: 0.293594 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.285035 Loss1: 0.284347 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.224348 Loss1: 0.223661 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.197199 Loss1: 0.196513 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.181105 Loss1: 0.180417 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.196444 Loss1: 0.195757 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.236693 Loss1: 0.236006 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.939977
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7341746794871795
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.684625 Loss1: 0.683944 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.439069 Loss1: 0.438385 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.369685 Loss1: 0.368999 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.340351 Loss1: 0.339666 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.245763 Loss1: 0.245077 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.258707 Loss1: 0.258023 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.256680 Loss1: 0.255995 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.217604 Loss1: 0.216920 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.216025 Loss1: 0.215339 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.202511 Loss1: 0.201826 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.955729
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 17:54:08,148][flwr][DEBUG] - fit_round 26 received 10 results and 0 failures
-test acc: 0.5954
-[2023-09-21 17:55:14,493][flwr][INFO] - fit progress: (26, 2.0418881324533458, {'accuracy': 0.5954}, 52996.154130037874)
-[2023-09-21 17:55:14,493][flwr][DEBUG] - evaluate_round 26: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 17:55:53,704][flwr][DEBUG] - evaluate_round 26 received 10 results and 0 failures
-[2023-09-21 17:55:53,705][flwr][DEBUG] - fit_round 27: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7587025316455697
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.681782 Loss1: 0.681097 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.431009 Loss1: 0.430319 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.353590 Loss1: 0.352900 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.287417 Loss1: 0.286727 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.234019 Loss1: 0.233328 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.248372 Loss1: 0.247681 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.208655 Loss1: 0.207963 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.201118 Loss1: 0.200427 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.167107 Loss1: 0.166414 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.196697 Loss1: 0.196006 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.955894
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7375801282051282
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.649185 Loss1: 0.648504 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.443243 Loss1: 0.442559 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.348735 Loss1: 0.348051 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.288909 Loss1: 0.288225 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.242709 Loss1: 0.242025 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.234090 Loss1: 0.233407 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.210741 Loss1: 0.210056 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.177210 Loss1: 0.176526 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.166071 Loss1: 0.165385 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185508 Loss1: 0.184823 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.944712
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7751524390243902
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.601555 Loss1: 0.600874 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.398645 Loss1: 0.397959 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.299622 Loss1: 0.298937 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.232550 Loss1: 0.231863 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.273164 Loss1: 0.272477 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201610 Loss1: 0.200921 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.185934 Loss1: 0.185245 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.191952 Loss1: 0.191264 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.180501 Loss1: 0.179813 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.198592 Loss1: 0.197903 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.948361
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7936698717948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.578123 Loss1: 0.577438 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.383055 Loss1: 0.382368 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.279032 Loss1: 0.278343 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.248131 Loss1: 0.247442 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.225122 Loss1: 0.224432 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.174657 Loss1: 0.173968 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.168909 Loss1: 0.168219 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.201064 Loss1: 0.200375 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.184009 Loss1: 0.183321 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158466 Loss1: 0.157775 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.969952
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7417534722222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.635987 Loss1: 0.635305 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.388616 Loss1: 0.387931 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.322111 Loss1: 0.321425 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.252705 Loss1: 0.252017 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.240852 Loss1: 0.240164 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.217482 Loss1: 0.216793 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.193052 Loss1: 0.192363 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.207604 Loss1: 0.206916 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.155396 Loss1: 0.154709 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.155065 Loss1: 0.154375 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.957031
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7784810126582279
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.653335 Loss1: 0.652648 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.388584 Loss1: 0.387892 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.279713 Loss1: 0.279021 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.266886 Loss1: 0.266195 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.261425 Loss1: 0.260733 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201454 Loss1: 0.200763 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.186048 Loss1: 0.185357 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.201287 Loss1: 0.200596 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.205924 Loss1: 0.205231 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.170673 Loss1: 0.169979 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.963608
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7518503289473685
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.692000 Loss1: 0.691313 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.467726 Loss1: 0.467037 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.354952 Loss1: 0.354265 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.304963 Loss1: 0.304274 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.273744 Loss1: 0.273055 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.257777 Loss1: 0.257088 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.253406 Loss1: 0.252718 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.258352 Loss1: 0.257663 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.223508 Loss1: 0.222819 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.194444 Loss1: 0.193755 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.946752
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7389240506329114
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.649446 Loss1: 0.648761 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.413377 Loss1: 0.412687 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.338940 Loss1: 0.338251 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.283006 Loss1: 0.282316 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.206884 Loss1: 0.206196 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.217038 Loss1: 0.216348 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.215413 Loss1: 0.214722 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.232612 Loss1: 0.231923 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158354 Loss1: 0.157664 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.154161 Loss1: 0.153473 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.966772
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.6965793918918919
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.734338 Loss1: 0.733653 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.449680 Loss1: 0.448989 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.323263 Loss1: 0.322572 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.294686 Loss1: 0.293996 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.247183 Loss1: 0.246493 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.194697 Loss1: 0.194005 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.223873 Loss1: 0.223182 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.185619 Loss1: 0.184927 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.201469 Loss1: 0.200777 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.154650 Loss1: 0.153959 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.967061
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7478243670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.663665 Loss1: 0.662982 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.440147 Loss1: 0.439461 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.335647 Loss1: 0.334960 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.244528 Loss1: 0.243842 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.189579 Loss1: 0.188893 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.225017 Loss1: 0.224330 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.236503 Loss1: 0.235818 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.214839 Loss1: 0.214151 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.200212 Loss1: 0.199525 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185843 Loss1: 0.185157 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.946005
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 18:25:30,930][flwr][DEBUG] - fit_round 27 received 10 results and 0 failures
-test acc: 0.5962
-[2023-09-21 18:26:34,274][flwr][INFO] - fit progress: (27, 2.0698537910327364, {'accuracy': 0.5962}, 54875.935479042586)
-[2023-09-21 18:26:34,275][flwr][DEBUG] - evaluate_round 27: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 18:27:12,296][flwr][DEBUG] - evaluate_round 27 received 10 results and 0 failures
-[2023-09-21 18:27:12,297][flwr][DEBUG] - fit_round 28: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7693829113924051
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.625487 Loss1: 0.624802 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.398492 Loss1: 0.397803 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.336314 Loss1: 0.335624 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.272848 Loss1: 0.272159 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.249219 Loss1: 0.248531 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.247414 Loss1: 0.246724 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.245726 Loss1: 0.245035 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.194081 Loss1: 0.193390 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.174235 Loss1: 0.173545 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.205647 Loss1: 0.204956 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.960641
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7010135135135135
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.692299 Loss1: 0.691613 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.415334 Loss1: 0.414645 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.306172 Loss1: 0.305482 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.267952 Loss1: 0.267262 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.259115 Loss1: 0.258425 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.213631 Loss1: 0.212941 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.184821 Loss1: 0.184132 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.199693 Loss1: 0.199003 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.182766 Loss1: 0.182077 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.173181 Loss1: 0.172491 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.971706
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7470332278481012
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.590567 Loss1: 0.589885 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.368775 Loss1: 0.368090 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.295755 Loss1: 0.295070 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.267856 Loss1: 0.267171 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.265669 Loss1: 0.264983 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.228933 Loss1: 0.228246 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.188371 Loss1: 0.187685 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.175727 Loss1: 0.175041 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.189948 Loss1: 0.189262 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158662 Loss1: 0.157976 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.963014
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7768673780487805
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.614544 Loss1: 0.613861 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.394620 Loss1: 0.393935 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.292628 Loss1: 0.291942 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.244826 Loss1: 0.244141 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.216590 Loss1: 0.215903 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.194729 Loss1: 0.194042 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.207465 Loss1: 0.206778 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.206340 Loss1: 0.205653 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.198740 Loss1: 0.198053 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.176457 Loss1: 0.175770 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.955030
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7852056962025317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.605125 Loss1: 0.604436 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.411302 Loss1: 0.410610 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.307075 Loss1: 0.306382 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.238172 Loss1: 0.237480 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.230838 Loss1: 0.230144 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.222069 Loss1: 0.221375 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.199689 Loss1: 0.198996 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.195274 Loss1: 0.194581 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.153970 Loss1: 0.153277 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.133404 Loss1: 0.132710 Loss2: 0.000694
-(DefaultActor pid=2839578) >> Training accuracy: 0.976068
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7495659722222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.651949 Loss1: 0.651265 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.407713 Loss1: 0.407027 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.285877 Loss1: 0.285189 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.253701 Loss1: 0.253013 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.245600 Loss1: 0.244913 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201028 Loss1: 0.200340 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.170814 Loss1: 0.170125 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.155567 Loss1: 0.154878 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161770 Loss1: 0.161081 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.173849 Loss1: 0.173160 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.955946
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7478243670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.612782 Loss1: 0.612097 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.358741 Loss1: 0.358052 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.286460 Loss1: 0.285770 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.244081 Loss1: 0.243393 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.254408 Loss1: 0.253716 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.209746 Loss1: 0.209056 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.212843 Loss1: 0.212155 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.180765 Loss1: 0.180077 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.180519 Loss1: 0.179830 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.166627 Loss1: 0.165937 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.971717
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7952724358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.555943 Loss1: 0.555258 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.349070 Loss1: 0.348381 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.299922 Loss1: 0.299234 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.218985 Loss1: 0.218295 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.205310 Loss1: 0.204620 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.211645 Loss1: 0.210957 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.161473 Loss1: 0.160782 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.187475 Loss1: 0.186783 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.180604 Loss1: 0.179911 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.134798 Loss1: 0.134107 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.977163
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7530838815789473
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.729191 Loss1: 0.728503 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.437274 Loss1: 0.436582 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.346213 Loss1: 0.345522 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.308080 Loss1: 0.307390 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.236192 Loss1: 0.235500 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.231173 Loss1: 0.230481 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.202347 Loss1: 0.201656 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.207113 Loss1: 0.206420 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.178825 Loss1: 0.178133 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.200228 Loss1: 0.199537 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.957442
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7552083333333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.644463 Loss1: 0.643783 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.419119 Loss1: 0.418435 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.312434 Loss1: 0.311750 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.245112 Loss1: 0.244426 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.225883 Loss1: 0.225197 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.203500 Loss1: 0.202815 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.210504 Loss1: 0.209820 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.234559 Loss1: 0.233874 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.182646 Loss1: 0.181961 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158714 Loss1: 0.158029 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.963942
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 18:56:41,705][flwr][DEBUG] - fit_round 28 received 10 results and 0 failures
-test acc: 0.6041
-[2023-09-21 18:57:50,305][flwr][INFO] - fit progress: (28, 2.0548813068828644, {'accuracy': 0.6041}, 56751.96644514892)
-[2023-09-21 18:57:50,306][flwr][DEBUG] - evaluate_round 28: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 18:58:30,426][flwr][DEBUG] - evaluate_round 28 received 10 results and 0 failures
-[2023-09-21 18:58:30,427][flwr][DEBUG] - fit_round 29: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7974466463414634
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.499517 Loss1: 0.498836 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.408917 Loss1: 0.408232 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.303713 Loss1: 0.303027 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.240544 Loss1: 0.239856 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.200556 Loss1: 0.199869 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.198138 Loss1: 0.197451 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.186923 Loss1: 0.186237 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.169388 Loss1: 0.168701 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.178554 Loss1: 0.177867 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185125 Loss1: 0.184439 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.963224
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.796875
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.552599 Loss1: 0.551914 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.335998 Loss1: 0.335308 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.289382 Loss1: 0.288688 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.251998 Loss1: 0.251305 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.230438 Loss1: 0.229747 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.234804 Loss1: 0.234111 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.175976 Loss1: 0.175284 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.167702 Loss1: 0.167012 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.145913 Loss1: 0.145221 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.152713 Loss1: 0.152020 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.964992
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7620192307692307
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.611225 Loss1: 0.610542 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.381223 Loss1: 0.380538 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.305218 Loss1: 0.304533 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.254719 Loss1: 0.254033 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.228027 Loss1: 0.227342 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.184217 Loss1: 0.183531 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.191470 Loss1: 0.190785 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.193570 Loss1: 0.192885 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.152795 Loss1: 0.152108 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158597 Loss1: 0.157911 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.963141
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7452256944444444
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.603446 Loss1: 0.602762 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.417684 Loss1: 0.416998 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.249738 Loss1: 0.249052 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.237182 Loss1: 0.236494 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.167919 Loss1: 0.167231 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.202112 Loss1: 0.201424 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.165302 Loss1: 0.164614 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.161969 Loss1: 0.161282 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.148964 Loss1: 0.148277 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.141120 Loss1: 0.140433 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.967231
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.764391447368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.663510 Loss1: 0.662823 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.407834 Loss1: 0.407143 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.301532 Loss1: 0.300841 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.276905 Loss1: 0.276215 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.274562 Loss1: 0.273870 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.268013 Loss1: 0.267321 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.211488 Loss1: 0.210797 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.151737 Loss1: 0.151046 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.163795 Loss1: 0.163101 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.156869 Loss1: 0.156176 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.966900
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7644382911392406
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.598541 Loss1: 0.597855 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.374814 Loss1: 0.374126 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.278221 Loss1: 0.277530 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.263356 Loss1: 0.262666 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.187177 Loss1: 0.186486 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.160904 Loss1: 0.160213 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.159049 Loss1: 0.158359 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.146854 Loss1: 0.146164 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.177581 Loss1: 0.176890 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.185451 Loss1: 0.184762 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.954509
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8062900641025641
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.584494 Loss1: 0.583809 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.346588 Loss1: 0.345897 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.245786 Loss1: 0.245095 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.242348 Loss1: 0.241657 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.178784 Loss1: 0.178092 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.212423 Loss1: 0.211731 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.205808 Loss1: 0.205118 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.170764 Loss1: 0.170074 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.155943 Loss1: 0.155251 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115192 Loss1: 0.114500 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.981971
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7848101265822784
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.619163 Loss1: 0.618478 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.381133 Loss1: 0.380445 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.334213 Loss1: 0.333523 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.238292 Loss1: 0.237600 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.225693 Loss1: 0.225002 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.215145 Loss1: 0.214455 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.174892 Loss1: 0.174201 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.161453 Loss1: 0.160761 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.175724 Loss1: 0.175035 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.158866 Loss1: 0.158176 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.973892
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7058699324324325
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.611132 Loss1: 0.610447 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.381443 Loss1: 0.380754 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.289101 Loss1: 0.288411 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.273836 Loss1: 0.273147 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.242553 Loss1: 0.241861 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.194221 Loss1: 0.193531 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.198482 Loss1: 0.197790 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.177771 Loss1: 0.177079 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.154609 Loss1: 0.153918 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.153905 Loss1: 0.153214 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.964316
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7630537974683544
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.600840 Loss1: 0.600159 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.379274 Loss1: 0.378589 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.254920 Loss1: 0.254236 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.229998 Loss1: 0.229312 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.212197 Loss1: 0.211512 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.182872 Loss1: 0.182186 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.196518 Loss1: 0.195832 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.144197 Loss1: 0.143510 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.141239 Loss1: 0.140552 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.186518 Loss1: 0.185832 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.963410
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 19:28:49,825][flwr][DEBUG] - fit_round 29 received 10 results and 0 failures
-test acc: 0.6056
-[2023-09-21 19:29:58,360][flwr][INFO] - fit progress: (29, 2.0496039061119764, {'accuracy': 0.6056}, 58680.02112694876)
-[2023-09-21 19:29:58,360][flwr][DEBUG] - evaluate_round 29: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 19:30:37,618][flwr][DEBUG] - evaluate_round 29 received 10 results and 0 failures
-[2023-09-21 19:30:37,622][flwr][DEBUG] - fit_round 30: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7567274305555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.619163 Loss1: 0.618483 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.354422 Loss1: 0.353737 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.250100 Loss1: 0.249415 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.210168 Loss1: 0.209484 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.237348 Loss1: 0.236661 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.183717 Loss1: 0.183033 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.134518 Loss1: 0.133831 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.130511 Loss1: 0.129825 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.141262 Loss1: 0.140574 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.171775 Loss1: 0.171088 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.963108
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.772745253164557
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.537170 Loss1: 0.536486 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.320372 Loss1: 0.319684 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.264764 Loss1: 0.264077 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.268226 Loss1: 0.267537 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.203814 Loss1: 0.203124 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.203523 Loss1: 0.202833 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.186579 Loss1: 0.185890 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.168121 Loss1: 0.167431 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.177773 Loss1: 0.177082 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.168943 Loss1: 0.168255 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.964597
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7923018292682927
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.536380 Loss1: 0.535699 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.303742 Loss1: 0.303056 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.260898 Loss1: 0.260212 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.254468 Loss1: 0.253781 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.211443 Loss1: 0.210757 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.157854 Loss1: 0.157167 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.167154 Loss1: 0.166466 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.160240 Loss1: 0.159551 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161027 Loss1: 0.160339 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.142955 Loss1: 0.142268 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.969703
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7225506756756757
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.634419 Loss1: 0.633736 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.409264 Loss1: 0.408576 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.266493 Loss1: 0.265806 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.205440 Loss1: 0.204752 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.199161 Loss1: 0.198473 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.171140 Loss1: 0.170452 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164213 Loss1: 0.163524 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.155763 Loss1: 0.155073 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.139570 Loss1: 0.138878 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.116279 Loss1: 0.115590 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.983319
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7654246794871795
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.576078 Loss1: 0.575401 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.349580 Loss1: 0.348899 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.248985 Loss1: 0.248303 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.246659 Loss1: 0.245976 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.186492 Loss1: 0.185807 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.183709 Loss1: 0.183024 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.195294 Loss1: 0.194611 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.194443 Loss1: 0.193760 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.163554 Loss1: 0.162871 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.151310 Loss1: 0.150626 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.952724
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.790743670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.588646 Loss1: 0.587962 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.362225 Loss1: 0.361536 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.271961 Loss1: 0.271273 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.202422 Loss1: 0.201731 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182037 Loss1: 0.181346 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.225709 Loss1: 0.225018 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.193616 Loss1: 0.192924 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.172368 Loss1: 0.171677 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.142571 Loss1: 0.141879 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.151974 Loss1: 0.151284 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.955301
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.758504746835443
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.598116 Loss1: 0.597434 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.358994 Loss1: 0.358310 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.258907 Loss1: 0.258221 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.227296 Loss1: 0.226611 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.179692 Loss1: 0.179006 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.208747 Loss1: 0.208061 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.204248 Loss1: 0.203562 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.212948 Loss1: 0.212263 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.184698 Loss1: 0.184011 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.161763 Loss1: 0.161076 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.961234
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.799248417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.537271 Loss1: 0.536587 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.340708 Loss1: 0.340019 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.272131 Loss1: 0.271441 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.237960 Loss1: 0.237268 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.219939 Loss1: 0.219247 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201063 Loss1: 0.200371 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.182279 Loss1: 0.181587 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.151051 Loss1: 0.150360 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.162107 Loss1: 0.161415 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.129474 Loss1: 0.128784 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.957081
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8145032051282052
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.515882 Loss1: 0.515197 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.317556 Loss1: 0.316867 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.210685 Loss1: 0.209995 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.215758 Loss1: 0.215066 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.198075 Loss1: 0.197387 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.179987 Loss1: 0.179295 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.151450 Loss1: 0.150758 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.137068 Loss1: 0.136377 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.132534 Loss1: 0.131843 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.125621 Loss1: 0.124929 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.970152
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7598684210526315
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.604328 Loss1: 0.603642 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.402449 Loss1: 0.401757 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.306376 Loss1: 0.305684 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.233510 Loss1: 0.232818 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.222988 Loss1: 0.222296 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.200421 Loss1: 0.199730 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.227939 Loss1: 0.227249 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.216987 Loss1: 0.216297 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.181167 Loss1: 0.180478 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.170629 Loss1: 0.169940 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.975329
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 20:01:11,087][flwr][DEBUG] - fit_round 30 received 10 results and 0 failures
-test acc: 0.6064
-[2023-09-21 20:02:21,205][flwr][INFO] - fit progress: (30, 2.0852254760531954, {'accuracy': 0.6064}, 60622.86622950295)
-[2023-09-21 20:02:21,206][flwr][DEBUG] - evaluate_round 30: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 20:03:00,864][flwr][DEBUG] - evaluate_round 30 received 10 results and 0 failures
-[2023-09-21 20:03:00,866][flwr][DEBUG] - fit_round 31: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7846123417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.523108 Loss1: 0.522425 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.325361 Loss1: 0.324673 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.235289 Loss1: 0.234599 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.253728 Loss1: 0.253039 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.214257 Loss1: 0.213566 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.186834 Loss1: 0.186144 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.200372 Loss1: 0.199681 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.166633 Loss1: 0.165942 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.127651 Loss1: 0.126962 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.135003 Loss1: 0.134312 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.969739
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8048780487804879
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.525691 Loss1: 0.525013 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.336854 Loss1: 0.336170 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.248647 Loss1: 0.247964 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.214737 Loss1: 0.214053 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.174918 Loss1: 0.174232 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.180904 Loss1: 0.180218 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164346 Loss1: 0.163662 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.167090 Loss1: 0.166404 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.154995 Loss1: 0.154309 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.163444 Loss1: 0.162757 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.979040
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.770371835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.517359 Loss1: 0.516679 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.313782 Loss1: 0.313098 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.244133 Loss1: 0.243448 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.265762 Loss1: 0.265076 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.186522 Loss1: 0.185835 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.195407 Loss1: 0.194722 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.160318 Loss1: 0.159631 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.146082 Loss1: 0.145395 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.148072 Loss1: 0.147385 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.126008 Loss1: 0.125319 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.980222
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7706330128205128
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.533603 Loss1: 0.532924 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.337454 Loss1: 0.336771 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.258172 Loss1: 0.257488 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.236208 Loss1: 0.235524 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.207066 Loss1: 0.206382 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.191026 Loss1: 0.190339 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.174095 Loss1: 0.173410 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.154561 Loss1: 0.153875 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.179839 Loss1: 0.179152 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.155330 Loss1: 0.154644 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.964343
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7231841216216216
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.588376 Loss1: 0.587689 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.338421 Loss1: 0.337732 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.267117 Loss1: 0.266427 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.223808 Loss1: 0.223118 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.218573 Loss1: 0.217883 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.175265 Loss1: 0.174574 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.154397 Loss1: 0.153707 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.125324 Loss1: 0.124632 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.141807 Loss1: 0.141116 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.109875 Loss1: 0.109185 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.971284
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.759765625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.574755 Loss1: 0.574074 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.301825 Loss1: 0.301140 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.240789 Loss1: 0.240102 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.226338 Loss1: 0.225651 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.168455 Loss1: 0.167767 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.168387 Loss1: 0.167700 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.145961 Loss1: 0.145274 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.153771 Loss1: 0.153084 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.153999 Loss1: 0.153313 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.123707 Loss1: 0.123019 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.968967
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7984572784810127
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.503111 Loss1: 0.502426 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.340985 Loss1: 0.340298 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.231043 Loss1: 0.230354 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.225408 Loss1: 0.224717 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.187803 Loss1: 0.187114 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.152364 Loss1: 0.151673 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.165281 Loss1: 0.164590 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.173246 Loss1: 0.172554 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161406 Loss1: 0.160715 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.148087 Loss1: 0.147397 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.967168
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7845394736842105
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.606801 Loss1: 0.606115 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.361901 Loss1: 0.361209 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.283786 Loss1: 0.283094 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.245166 Loss1: 0.244475 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.229275 Loss1: 0.228583 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.201752 Loss1: 0.201061 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.200653 Loss1: 0.199962 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.193363 Loss1: 0.192671 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.156758 Loss1: 0.156066 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.147954 Loss1: 0.147265 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.961965
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8079509493670886
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.493360 Loss1: 0.492674 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.336259 Loss1: 0.335568 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.283828 Loss1: 0.283136 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.199340 Loss1: 0.198648 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.179063 Loss1: 0.178372 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.194192 Loss1: 0.193500 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.162154 Loss1: 0.161463 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.154563 Loss1: 0.153873 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.148478 Loss1: 0.147787 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.124265 Loss1: 0.123573 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.980024
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.819511217948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.479434 Loss1: 0.478748 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.354710 Loss1: 0.354019 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.239130 Loss1: 0.238439 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.200914 Loss1: 0.200222 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.160507 Loss1: 0.159816 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.171148 Loss1: 0.170455 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164997 Loss1: 0.164306 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.160128 Loss1: 0.159437 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.124878 Loss1: 0.124187 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.101679 Loss1: 0.100988 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.982772
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 20:33:46,396][flwr][DEBUG] - fit_round 31 received 10 results and 0 failures
-test acc: 0.6099
-[2023-09-21 20:34:57,564][flwr][INFO] - fit progress: (31, 2.0679767246063525, {'accuracy': 0.6099}, 62579.225837967824)
-[2023-09-21 20:34:57,565][flwr][DEBUG] - evaluate_round 31: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 20:35:37,789][flwr][DEBUG] - evaluate_round 31 received 10 results and 0 failures
-[2023-09-21 20:35:37,789][flwr][DEBUG] - fit_round 32: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7863924050632911
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.456769 Loss1: 0.456086 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.304253 Loss1: 0.303566 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.260574 Loss1: 0.259888 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.253337 Loss1: 0.252649 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.218889 Loss1: 0.218201 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.190845 Loss1: 0.190154 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.153912 Loss1: 0.153221 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.148447 Loss1: 0.147756 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158999 Loss1: 0.158309 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.150296 Loss1: 0.149606 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.963212
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7856012658227848
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.539172 Loss1: 0.538489 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.333032 Loss1: 0.332347 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.242573 Loss1: 0.241885 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.179616 Loss1: 0.178930 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.195439 Loss1: 0.194753 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.145121 Loss1: 0.144434 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.141976 Loss1: 0.141290 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.143698 Loss1: 0.143011 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.159499 Loss1: 0.158811 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.155614 Loss1: 0.154927 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.977057
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7654079861111112
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.539950 Loss1: 0.539266 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.355104 Loss1: 0.354417 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.238524 Loss1: 0.237836 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.204626 Loss1: 0.203938 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169661 Loss1: 0.168972 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.173286 Loss1: 0.172598 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.135333 Loss1: 0.134644 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.131767 Loss1: 0.131080 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.105979 Loss1: 0.105290 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.114558 Loss1: 0.113870 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.978299
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7931743421052632
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.577771 Loss1: 0.577085 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.360149 Loss1: 0.359458 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.273298 Loss1: 0.272606 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.225071 Loss1: 0.224379 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.174889 Loss1: 0.174197 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.167708 Loss1: 0.167018 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.174263 Loss1: 0.173571 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.160056 Loss1: 0.159364 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.139197 Loss1: 0.138505 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.171915 Loss1: 0.171223 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.967516
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8092606707317073
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.457395 Loss1: 0.456715 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.295522 Loss1: 0.294837 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.249686 Loss1: 0.249001 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.201236 Loss1: 0.200550 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.217139 Loss1: 0.216451 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.182533 Loss1: 0.181846 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.192310 Loss1: 0.191623 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.122003 Loss1: 0.121316 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.141275 Loss1: 0.140589 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128147 Loss1: 0.127460 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.979421
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7426097972972973
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.612818 Loss1: 0.612131 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.305374 Loss1: 0.304683 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.282880 Loss1: 0.282189 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.260701 Loss1: 0.260010 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.196549 Loss1: 0.195859 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.186041 Loss1: 0.185348 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.142509 Loss1: 0.141818 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.123152 Loss1: 0.122461 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.143999 Loss1: 0.143308 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.135691 Loss1: 0.135001 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.972551
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8214003164556962
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.464050 Loss1: 0.463367 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.264900 Loss1: 0.264212 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.207211 Loss1: 0.206524 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.212861 Loss1: 0.212171 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.193280 Loss1: 0.192591 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.175488 Loss1: 0.174797 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.153896 Loss1: 0.153204 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.172732 Loss1: 0.172041 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161750 Loss1: 0.161059 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.118550 Loss1: 0.117858 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.967761
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8117088607594937
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.478749 Loss1: 0.478063 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.343766 Loss1: 0.343077 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.251952 Loss1: 0.251262 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.217741 Loss1: 0.217051 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.206281 Loss1: 0.205591 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.189547 Loss1: 0.188857 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.152778 Loss1: 0.152088 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.146986 Loss1: 0.146295 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.139796 Loss1: 0.139104 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.157888 Loss1: 0.157197 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.964201
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8271233974358975
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.471295 Loss1: 0.470610 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.279778 Loss1: 0.279090 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.217228 Loss1: 0.216539 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.216527 Loss1: 0.215838 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169727 Loss1: 0.169037 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.136011 Loss1: 0.135322 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.148998 Loss1: 0.148308 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.140232 Loss1: 0.139542 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.129966 Loss1: 0.129275 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108679 Loss1: 0.107987 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.981971
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7938701923076923
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.490256 Loss1: 0.489577 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.320746 Loss1: 0.320063 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.260920 Loss1: 0.260236 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.219715 Loss1: 0.219029 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.195064 Loss1: 0.194379 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.196975 Loss1: 0.196290 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.159815 Loss1: 0.159131 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.163701 Loss1: 0.163017 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.167907 Loss1: 0.167223 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.143156 Loss1: 0.142472 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.966346
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 21:05:53,664][flwr][DEBUG] - fit_round 32 received 10 results and 0 failures
-test acc: 0.613
-[2023-09-21 21:06:58,207][flwr][INFO] - fit progress: (32, 2.0636839933288744, {'accuracy': 0.613}, 64499.868195127696)
-[2023-09-21 21:06:58,207][flwr][DEBUG] - evaluate_round 32: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 21:07:36,400][flwr][DEBUG] - evaluate_round 32 received 10 results and 0 failures
-[2023-09-21 21:07:36,401][flwr][DEBUG] - fit_round 33: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7402871621621622
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.542243 Loss1: 0.541556 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.328989 Loss1: 0.328299 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.242510 Loss1: 0.241820 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.178748 Loss1: 0.178057 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169804 Loss1: 0.169111 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.169070 Loss1: 0.168378 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.137944 Loss1: 0.137252 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.139542 Loss1: 0.138851 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.132137 Loss1: 0.131445 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.156518 Loss1: 0.155827 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.969595
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7923259493670886
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.504836 Loss1: 0.504151 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.266541 Loss1: 0.265852 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.231565 Loss1: 0.230877 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.241108 Loss1: 0.240420 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.223859 Loss1: 0.223170 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.166931 Loss1: 0.166242 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.139561 Loss1: 0.138872 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114380 Loss1: 0.113690 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110902 Loss1: 0.110212 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.119918 Loss1: 0.119228 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.972903
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8125
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.474601 Loss1: 0.473918 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.344254 Loss1: 0.343566 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.217006 Loss1: 0.216318 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.198807 Loss1: 0.198118 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.174061 Loss1: 0.173373 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.142375 Loss1: 0.141685 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.153248 Loss1: 0.152560 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.173363 Loss1: 0.172674 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.138275 Loss1: 0.137586 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.117487 Loss1: 0.116798 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.972310
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7979029605263158
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.544588 Loss1: 0.543899 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.323947 Loss1: 0.323254 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.279583 Loss1: 0.278891 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.229237 Loss1: 0.228546 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.198557 Loss1: 0.197867 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.181877 Loss1: 0.181185 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.206158 Loss1: 0.205467 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.189943 Loss1: 0.189251 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.178450 Loss1: 0.177756 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.130658 Loss1: 0.129965 Loss2: 0.000694
-(DefaultActor pid=2839578) >> Training accuracy: 0.982319
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8225990853658537
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.433366 Loss1: 0.432686 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.307178 Loss1: 0.306493 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.223623 Loss1: 0.222937 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.196286 Loss1: 0.195600 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169593 Loss1: 0.168906 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.153869 Loss1: 0.153182 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.158135 Loss1: 0.157448 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.141926 Loss1: 0.141240 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.137936 Loss1: 0.137249 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.136687 Loss1: 0.135999 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.975800
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8245648734177216
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.431125 Loss1: 0.430440 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.294704 Loss1: 0.294014 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.224906 Loss1: 0.224216 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.184543 Loss1: 0.183851 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.128496 Loss1: 0.127804 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.156242 Loss1: 0.155549 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.165521 Loss1: 0.164829 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114816 Loss1: 0.114124 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.140430 Loss1: 0.139737 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.141702 Loss1: 0.141010 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.973101
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8371394230769231
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.454715 Loss1: 0.454032 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.255957 Loss1: 0.255268 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.180039 Loss1: 0.179352 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.165767 Loss1: 0.165077 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.165454 Loss1: 0.164765 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.153813 Loss1: 0.153124 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164836 Loss1: 0.164147 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.164970 Loss1: 0.164280 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.135895 Loss1: 0.135206 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.105258 Loss1: 0.104568 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.983373
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8012820512820513
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.477950 Loss1: 0.477271 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.270843 Loss1: 0.270161 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.259757 Loss1: 0.259073 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.228074 Loss1: 0.227388 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.197695 Loss1: 0.197010 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.184945 Loss1: 0.184261 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.165779 Loss1: 0.165091 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.157265 Loss1: 0.156577 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.140863 Loss1: 0.140178 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.134003 Loss1: 0.133319 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.977564
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.779296875
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.495948 Loss1: 0.495266 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.280192 Loss1: 0.279507 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.223413 Loss1: 0.222727 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.224361 Loss1: 0.223675 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.177909 Loss1: 0.177222 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.176326 Loss1: 0.175639 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.148369 Loss1: 0.147682 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.143496 Loss1: 0.142809 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123530 Loss1: 0.122844 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.122483 Loss1: 0.121795 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.962891
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7946993670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.459389 Loss1: 0.458708 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.307003 Loss1: 0.306317 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.221549 Loss1: 0.220864 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.165780 Loss1: 0.165092 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.176616 Loss1: 0.175929 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.164944 Loss1: 0.164256 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.163481 Loss1: 0.162793 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.150074 Loss1: 0.149385 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.129963 Loss1: 0.129273 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.125092 Loss1: 0.124404 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.964399
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 21:38:08,490][flwr][DEBUG] - fit_round 33 received 10 results and 0 failures
-test acc: 0.6145
-[2023-09-21 21:39:18,183][flwr][INFO] - fit progress: (33, 2.0933550024946657, {'accuracy': 0.6145}, 66439.84413567372)
-[2023-09-21 21:39:18,183][flwr][DEBUG] - evaluate_round 33: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 21:39:56,884][flwr][DEBUG] - evaluate_round 33 received 10 results and 0 failures
-[2023-09-21 21:39:56,885][flwr][DEBUG] - fit_round 34: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8377403846153846
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.409916 Loss1: 0.409233 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.244411 Loss1: 0.243722 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.197967 Loss1: 0.197277 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.179189 Loss1: 0.178499 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.150673 Loss1: 0.149983 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.165807 Loss1: 0.165115 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.138972 Loss1: 0.138282 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.105856 Loss1: 0.105164 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.095269 Loss1: 0.094578 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.092845 Loss1: 0.092153 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.988782
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8067434210526315
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.530683 Loss1: 0.529994 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.297934 Loss1: 0.297245 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.258433 Loss1: 0.257740 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.246386 Loss1: 0.245693 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.190398 Loss1: 0.189706 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.165247 Loss1: 0.164556 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.150552 Loss1: 0.149859 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133404 Loss1: 0.132714 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158042 Loss1: 0.157349 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115816 Loss1: 0.115123 Loss2: 0.000694
-(DefaultActor pid=2839578) >> Training accuracy: 0.974095
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7986550632911392
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.452223 Loss1: 0.451541 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.273462 Loss1: 0.272774 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.220063 Loss1: 0.219374 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.211701 Loss1: 0.211014 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.187651 Loss1: 0.186961 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.166072 Loss1: 0.165381 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.138216 Loss1: 0.137527 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.162450 Loss1: 0.161759 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.161553 Loss1: 0.160863 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108909 Loss1: 0.108219 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.977650
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8325076219512195
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.418688 Loss1: 0.418004 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.267669 Loss1: 0.266984 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.204962 Loss1: 0.204277 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177176 Loss1: 0.176492 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.175536 Loss1: 0.174849 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.156842 Loss1: 0.156154 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.158999 Loss1: 0.158312 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.122170 Loss1: 0.121482 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109638 Loss1: 0.108951 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.107303 Loss1: 0.106616 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.977706
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8138844936708861
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.457006 Loss1: 0.456323 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.276227 Loss1: 0.275539 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.202918 Loss1: 0.202228 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.217902 Loss1: 0.217212 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182491 Loss1: 0.181800 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.188659 Loss1: 0.187968 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.155490 Loss1: 0.154799 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133102 Loss1: 0.132410 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.124510 Loss1: 0.123819 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.135701 Loss1: 0.135009 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.962421
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8465189873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.445745 Loss1: 0.445060 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.251985 Loss1: 0.251293 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.187483 Loss1: 0.186790 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.155108 Loss1: 0.154414 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.166183 Loss1: 0.165490 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.161270 Loss1: 0.160577 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.169390 Loss1: 0.168698 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.141415 Loss1: 0.140723 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.154108 Loss1: 0.153417 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.124881 Loss1: 0.124187 Loss2: 0.000694
-(DefaultActor pid=2839578) >> Training accuracy: 0.977057
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7836371527777778
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.480151 Loss1: 0.479468 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.304508 Loss1: 0.303822 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.224774 Loss1: 0.224088 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.170221 Loss1: 0.169533 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.155973 Loss1: 0.155285 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.161961 Loss1: 0.161273 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.141921 Loss1: 0.141234 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121965 Loss1: 0.121278 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.155334 Loss1: 0.154646 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.133664 Loss1: 0.132976 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.977214
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7459881756756757
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.519034 Loss1: 0.518347 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.294941 Loss1: 0.294252 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.185373 Loss1: 0.184683 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.215691 Loss1: 0.215001 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.180338 Loss1: 0.179647 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.160067 Loss1: 0.159376 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.150670 Loss1: 0.149979 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.115107 Loss1: 0.114418 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.150096 Loss1: 0.149405 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.127203 Loss1: 0.126511 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.961782
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8006810897435898
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.466680 Loss1: 0.465999 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.291337 Loss1: 0.290651 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.212643 Loss1: 0.211958 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.200803 Loss1: 0.200117 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.175401 Loss1: 0.174716 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.143558 Loss1: 0.142872 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.159303 Loss1: 0.158618 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.149873 Loss1: 0.149189 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158068 Loss1: 0.157382 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.127541 Loss1: 0.126856 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.974359
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7994462025316456
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.476988 Loss1: 0.476306 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.267038 Loss1: 0.266352 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.201137 Loss1: 0.200451 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.182875 Loss1: 0.182191 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.178174 Loss1: 0.177487 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.162445 Loss1: 0.161758 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164695 Loss1: 0.164008 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.164206 Loss1: 0.163519 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.158898 Loss1: 0.158212 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.140211 Loss1: 0.139525 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.972310
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 22:11:13,109][flwr][DEBUG] - fit_round 34 received 10 results and 0 failures
-test acc: 0.6151
-[2023-09-21 22:31:44,139][flwr][INFO] - fit progress: (34, 2.083816295043348, {'accuracy': 0.6151}, 69585.80032487493)
-[2023-09-21 22:31:44,139][flwr][DEBUG] - evaluate_round 34: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 22:32:23,132][flwr][DEBUG] - evaluate_round 34 received 10 results and 0 failures
-[2023-09-21 22:32:23,133][flwr][DEBUG] - fit_round 35: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8275316455696202
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.438230 Loss1: 0.437544 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.241562 Loss1: 0.240875 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.260274 Loss1: 0.259584 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.185402 Loss1: 0.184713 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169147 Loss1: 0.168457 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.164841 Loss1: 0.164150 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.144593 Loss1: 0.143903 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.115677 Loss1: 0.114986 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.102390 Loss1: 0.101698 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.130016 Loss1: 0.129325 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.966772
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8085443037974683
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.404777 Loss1: 0.404094 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.261682 Loss1: 0.260997 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.214479 Loss1: 0.213793 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.176964 Loss1: 0.176277 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182937 Loss1: 0.182250 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.157195 Loss1: 0.156507 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.138018 Loss1: 0.137330 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133634 Loss1: 0.132946 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.138450 Loss1: 0.137761 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.150715 Loss1: 0.150027 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.963212
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8047863924050633
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.450847 Loss1: 0.450164 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.264767 Loss1: 0.264080 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.217181 Loss1: 0.216493 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.176641 Loss1: 0.175954 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.173077 Loss1: 0.172387 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.177835 Loss1: 0.177146 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.116246 Loss1: 0.115556 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.128948 Loss1: 0.128258 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.134194 Loss1: 0.133506 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.141055 Loss1: 0.140365 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.967168
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7951388888888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.471486 Loss1: 0.470803 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.327426 Loss1: 0.326740 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.230257 Loss1: 0.229570 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.173076 Loss1: 0.172388 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.138595 Loss1: 0.137908 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.122002 Loss1: 0.121315 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108659 Loss1: 0.107970 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.111391 Loss1: 0.110703 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133210 Loss1: 0.132521 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115533 Loss1: 0.114844 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.972222
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8072916666666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.470692 Loss1: 0.470011 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.274626 Loss1: 0.273941 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.220483 Loss1: 0.219799 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.180690 Loss1: 0.180007 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.161003 Loss1: 0.160319 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.167979 Loss1: 0.167294 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.142323 Loss1: 0.141638 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.134915 Loss1: 0.134228 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.160491 Loss1: 0.159806 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.166348 Loss1: 0.165662 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.952524
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8057154605263158
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.495921 Loss1: 0.495235 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.299973 Loss1: 0.299282 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.235595 Loss1: 0.234904 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.204465 Loss1: 0.203775 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.167258 Loss1: 0.166566 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.150121 Loss1: 0.149429 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.147471 Loss1: 0.146780 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.111420 Loss1: 0.110728 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.127612 Loss1: 0.126922 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.114024 Loss1: 0.113333 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.976562
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8455300632911392
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.398409 Loss1: 0.397723 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.266191 Loss1: 0.265500 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190118 Loss1: 0.189427 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.181569 Loss1: 0.180877 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.160593 Loss1: 0.159901 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123160 Loss1: 0.122469 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115638 Loss1: 0.114945 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.154207 Loss1: 0.153516 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122585 Loss1: 0.121894 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.133263 Loss1: 0.132571 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.979826
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8267911585365854
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.387204 Loss1: 0.386522 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.264668 Loss1: 0.263983 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.233994 Loss1: 0.233310 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.195024 Loss1: 0.194339 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182445 Loss1: 0.181758 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.148046 Loss1: 0.147359 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.158863 Loss1: 0.158177 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.140713 Loss1: 0.140026 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.140433 Loss1: 0.139747 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.120064 Loss1: 0.119377 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.969322
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7447212837837838
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.443909 Loss1: 0.443224 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.298731 Loss1: 0.298044 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.240602 Loss1: 0.239914 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.196052 Loss1: 0.195362 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.181139 Loss1: 0.180450 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.172744 Loss1: 0.172055 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.171533 Loss1: 0.170843 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121226 Loss1: 0.120537 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.130068 Loss1: 0.129379 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.134423 Loss1: 0.133732 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.971495
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8461538461538461
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.380708 Loss1: 0.380025 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.235765 Loss1: 0.235076 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190585 Loss1: 0.189893 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.185309 Loss1: 0.184620 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.143665 Loss1: 0.142976 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.132314 Loss1: 0.131623 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115259 Loss1: 0.114568 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114924 Loss1: 0.114232 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.112534 Loss1: 0.111843 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090379 Loss1: 0.089688 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.983974
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 23:02:40,674][flwr][DEBUG] - fit_round 35 received 10 results and 0 failures
-test acc: 0.6162
-[2023-09-21 23:03:28,980][flwr][INFO] - fit progress: (35, 2.1125483806140886, {'accuracy': 0.6162}, 71490.64193736762)
-[2023-09-21 23:03:28,981][flwr][DEBUG] - evaluate_round 35: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 23:04:05,841][flwr][DEBUG] - evaluate_round 35 received 10 results and 0 failures
-[2023-09-21 23:04:05,842][flwr][DEBUG] - fit_round 36: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8539663461538461
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.363793 Loss1: 0.363109 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.258992 Loss1: 0.258304 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.182451 Loss1: 0.181760 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.141931 Loss1: 0.141241 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.161380 Loss1: 0.160689 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.121359 Loss1: 0.120670 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.109682 Loss1: 0.108992 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.112061 Loss1: 0.111369 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097658 Loss1: 0.096968 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104829 Loss1: 0.104138 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.983774
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8139391447368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.475478 Loss1: 0.474791 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.270133 Loss1: 0.269442 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.246163 Loss1: 0.245473 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.197857 Loss1: 0.197166 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.173443 Loss1: 0.172752 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.134408 Loss1: 0.133717 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.119257 Loss1: 0.118568 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.120187 Loss1: 0.119498 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122910 Loss1: 0.122219 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.131650 Loss1: 0.130960 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.977796
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8061708860759493
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.410781 Loss1: 0.410098 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.257303 Loss1: 0.256614 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.227816 Loss1: 0.227129 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.182781 Loss1: 0.182091 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.182073 Loss1: 0.181385 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.120316 Loss1: 0.119628 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.144927 Loss1: 0.144237 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.123623 Loss1: 0.122933 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.116142 Loss1: 0.115453 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.122907 Loss1: 0.122216 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.979430
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7964409722222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.492339 Loss1: 0.491656 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.285524 Loss1: 0.284836 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.170453 Loss1: 0.169766 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.148610 Loss1: 0.147923 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.132779 Loss1: 0.132092 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.127587 Loss1: 0.126899 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.120834 Loss1: 0.120146 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.106523 Loss1: 0.105834 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114751 Loss1: 0.114063 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.126275 Loss1: 0.125587 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.971571
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7599239864864865
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.481852 Loss1: 0.481166 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.263919 Loss1: 0.263229 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.226016 Loss1: 0.225326 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.144747 Loss1: 0.144057 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.157827 Loss1: 0.157136 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133248 Loss1: 0.132558 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.134697 Loss1: 0.134005 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.127542 Loss1: 0.126852 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133827 Loss1: 0.133136 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108603 Loss1: 0.107912 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.983742
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8482990506329114
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.385457 Loss1: 0.384773 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.255474 Loss1: 0.254785 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.207758 Loss1: 0.207068 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.188585 Loss1: 0.187896 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.172670 Loss1: 0.171980 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.163380 Loss1: 0.162689 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.133908 Loss1: 0.133216 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.130312 Loss1: 0.129620 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.149307 Loss1: 0.148614 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.146209 Loss1: 0.145517 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.972903
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8045886075949367
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.439354 Loss1: 0.438673 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.238819 Loss1: 0.238135 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.223125 Loss1: 0.222439 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177852 Loss1: 0.177166 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.164776 Loss1: 0.164090 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.126689 Loss1: 0.126001 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.150987 Loss1: 0.150298 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.124300 Loss1: 0.123612 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099013 Loss1: 0.098325 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.122536 Loss1: 0.121848 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.980617
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8332674050632911
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.408021 Loss1: 0.407338 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.242281 Loss1: 0.241593 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.232424 Loss1: 0.231737 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.170228 Loss1: 0.169540 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.138988 Loss1: 0.138300 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133228 Loss1: 0.132540 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.151076 Loss1: 0.150388 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.138498 Loss1: 0.137810 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.126525 Loss1: 0.125836 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.132889 Loss1: 0.132202 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.968354
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8094951923076923
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.425804 Loss1: 0.425125 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.298145 Loss1: 0.297461 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.195134 Loss1: 0.194448 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177887 Loss1: 0.177204 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.160974 Loss1: 0.160290 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.177750 Loss1: 0.177066 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.127688 Loss1: 0.127004 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.153289 Loss1: 0.152604 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109428 Loss1: 0.108743 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099926 Loss1: 0.099240 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.970353
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8376524390243902
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.360360 Loss1: 0.359680 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.249043 Loss1: 0.248360 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.179275 Loss1: 0.178591 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.154955 Loss1: 0.154269 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.187095 Loss1: 0.186409 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.189220 Loss1: 0.188532 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.171427 Loss1: 0.170740 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.127075 Loss1: 0.126387 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109851 Loss1: 0.109164 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108754 Loss1: 0.108067 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.973514
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-21 23:34:36,389][flwr][DEBUG] - fit_round 36 received 10 results and 0 failures
-test acc: 0.6189
-[2023-09-21 23:35:20,838][flwr][INFO] - fit progress: (36, 2.1186307085969576, {'accuracy': 0.6189}, 73402.4992567827)
-[2023-09-21 23:35:20,838][flwr][DEBUG] - evaluate_round 36: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-21 23:35:57,777][flwr][DEBUG] - evaluate_round 36 received 10 results and 0 failures
-[2023-09-21 23:35:57,778][flwr][DEBUG] - fit_round 37: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.762668918918919
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.403460 Loss1: 0.402774 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.250829 Loss1: 0.250139 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.196067 Loss1: 0.195375 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.161435 Loss1: 0.160744 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.149829 Loss1: 0.149137 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.142420 Loss1: 0.141729 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.125025 Loss1: 0.124331 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.116738 Loss1: 0.116046 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.137752 Loss1: 0.137060 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.152379 Loss1: 0.151686 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.977196
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8161057692307693
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.405371 Loss1: 0.404691 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.252109 Loss1: 0.251422 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.200008 Loss1: 0.199322 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.164363 Loss1: 0.163676 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.190054 Loss1: 0.189367 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.138788 Loss1: 0.138102 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.119922 Loss1: 0.119235 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.146485 Loss1: 0.145797 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.125748 Loss1: 0.125060 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.119112 Loss1: 0.118424 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.964744
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8533653846153846
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.333666 Loss1: 0.332982 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.194403 Loss1: 0.193717 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.166740 Loss1: 0.166053 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.145244 Loss1: 0.144555 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.139643 Loss1: 0.138954 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.161044 Loss1: 0.160354 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.125194 Loss1: 0.124504 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.110038 Loss1: 0.109348 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.104743 Loss1: 0.104055 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094713 Loss1: 0.094024 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.985777
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8481326219512195
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.330011 Loss1: 0.329330 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.235689 Loss1: 0.235004 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.185915 Loss1: 0.185229 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.152132 Loss1: 0.151444 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.171781 Loss1: 0.171095 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.168643 Loss1: 0.167956 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.132239 Loss1: 0.131551 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121394 Loss1: 0.120706 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.134610 Loss1: 0.133923 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.148253 Loss1: 0.147564 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.979230
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8073575949367089
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.383990 Loss1: 0.383307 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.236370 Loss1: 0.235681 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.204050 Loss1: 0.203362 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.158472 Loss1: 0.157782 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.140159 Loss1: 0.139469 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.145808 Loss1: 0.145120 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.119775 Loss1: 0.119085 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.157684 Loss1: 0.156994 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.130696 Loss1: 0.130005 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108793 Loss1: 0.108106 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.978244
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8107638888888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.403733 Loss1: 0.403050 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.250743 Loss1: 0.250056 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.221052 Loss1: 0.220364 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.190868 Loss1: 0.190179 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.148011 Loss1: 0.147322 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116631 Loss1: 0.115943 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.118644 Loss1: 0.117955 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.141826 Loss1: 0.141136 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111098 Loss1: 0.110409 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.108005 Loss1: 0.107316 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.983290
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8447389240506329
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.371958 Loss1: 0.371272 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.226956 Loss1: 0.226265 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.200847 Loss1: 0.200156 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.155808 Loss1: 0.155117 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.130278 Loss1: 0.129586 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116596 Loss1: 0.115904 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115152 Loss1: 0.114461 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133276 Loss1: 0.132583 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.120384 Loss1: 0.119691 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128082 Loss1: 0.127389 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.962816
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8147615131578947
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.420701 Loss1: 0.420014 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.271798 Loss1: 0.271106 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.230548 Loss1: 0.229857 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.184567 Loss1: 0.183874 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.164862 Loss1: 0.164170 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.145320 Loss1: 0.144629 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.135432 Loss1: 0.134742 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.143895 Loss1: 0.143204 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123137 Loss1: 0.122449 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128581 Loss1: 0.127890 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.971423
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8330696202531646
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.371860 Loss1: 0.371177 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.239364 Loss1: 0.238675 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.169857 Loss1: 0.169166 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.176834 Loss1: 0.176145 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133221 Loss1: 0.132532 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.146995 Loss1: 0.146304 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.157775 Loss1: 0.157086 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.141194 Loss1: 0.140502 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108031 Loss1: 0.107341 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.117334 Loss1: 0.116643 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.971915
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8140822784810127
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.380007 Loss1: 0.379325 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.220687 Loss1: 0.220001 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.159514 Loss1: 0.158827 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.138871 Loss1: 0.138183 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.152676 Loss1: 0.151989 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.183558 Loss1: 0.182872 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.207799 Loss1: 0.207112 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.129485 Loss1: 0.128796 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.121919 Loss1: 0.121232 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.130000 Loss1: 0.129313 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.979035
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 00:06:16,282][flwr][DEBUG] - fit_round 37 received 10 results and 0 failures
-test acc: 0.6191
-[2023-09-22 00:07:00,980][flwr][INFO] - fit progress: (37, 2.1180881943565586, {'accuracy': 0.6191}, 75302.64137112582)
-[2023-09-22 00:07:00,980][flwr][DEBUG] - evaluate_round 37: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 00:07:38,756][flwr][DEBUG] - evaluate_round 37 received 10 results and 0 failures
-[2023-09-22 00:07:38,757][flwr][DEBUG] - fit_round 38: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8469145569620253
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.362624 Loss1: 0.361940 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.266418 Loss1: 0.265730 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190526 Loss1: 0.189838 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.145726 Loss1: 0.145038 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.149673 Loss1: 0.148983 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.156444 Loss1: 0.155754 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.133522 Loss1: 0.132831 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099696 Loss1: 0.099007 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111632 Loss1: 0.110944 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.105704 Loss1: 0.105015 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.981804
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8515625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.298216 Loss1: 0.297535 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.226105 Loss1: 0.225417 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.160495 Loss1: 0.159805 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.141657 Loss1: 0.140968 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.119260 Loss1: 0.118570 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.128988 Loss1: 0.128298 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.135827 Loss1: 0.135137 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119492 Loss1: 0.118802 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080819 Loss1: 0.080130 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076583 Loss1: 0.075893 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.980569
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8182357594936709
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.364088 Loss1: 0.363407 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.218110 Loss1: 0.217424 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.186206 Loss1: 0.185521 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.155733 Loss1: 0.155046 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.150376 Loss1: 0.149690 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123245 Loss1: 0.122559 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.164252 Loss1: 0.163566 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.131330 Loss1: 0.130643 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123399 Loss1: 0.122713 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.113498 Loss1: 0.112811 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.966772
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8559451219512195
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.327417 Loss1: 0.326738 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.198433 Loss1: 0.197748 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.196700 Loss1: 0.196014 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177455 Loss1: 0.176769 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.152362 Loss1: 0.151677 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.132870 Loss1: 0.132183 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.093618 Loss1: 0.092933 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.111564 Loss1: 0.110879 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.121112 Loss1: 0.120426 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.136026 Loss1: 0.135339 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.975038
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8577927215189873
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.358146 Loss1: 0.357462 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.233330 Loss1: 0.232641 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.154986 Loss1: 0.154294 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.141412 Loss1: 0.140722 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.137155 Loss1: 0.136466 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.151246 Loss1: 0.150556 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.123730 Loss1: 0.123039 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117918 Loss1: 0.117227 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.115058 Loss1: 0.114364 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098941 Loss1: 0.098251 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.982199
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7652027027027027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.438918 Loss1: 0.438234 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.253588 Loss1: 0.252899 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190771 Loss1: 0.190081 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.167987 Loss1: 0.167297 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.171058 Loss1: 0.170368 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133245 Loss1: 0.132556 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.111441 Loss1: 0.110752 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.107569 Loss1: 0.106877 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.130338 Loss1: 0.129647 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.147769 Loss1: 0.147079 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.976351
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8182565789473685
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.406436 Loss1: 0.405748 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.252045 Loss1: 0.251356 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.234769 Loss1: 0.234079 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.168702 Loss1: 0.168010 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.139259 Loss1: 0.138568 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.119545 Loss1: 0.118855 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.122181 Loss1: 0.121492 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.143281 Loss1: 0.142592 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.134818 Loss1: 0.134129 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128071 Loss1: 0.127380 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.980058
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8209134615384616
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.359445 Loss1: 0.358768 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.241969 Loss1: 0.241285 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.225640 Loss1: 0.224957 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.206722 Loss1: 0.206039 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.172990 Loss1: 0.172305 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.159975 Loss1: 0.159290 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.132815 Loss1: 0.132129 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133588 Loss1: 0.132902 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.153433 Loss1: 0.152748 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.130310 Loss1: 0.129624 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.973758
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8253560126582279
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.354857 Loss1: 0.354175 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.218862 Loss1: 0.218176 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.200554 Loss1: 0.199867 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.153718 Loss1: 0.153029 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.148504 Loss1: 0.147815 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.104214 Loss1: 0.103527 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.158462 Loss1: 0.157772 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.110049 Loss1: 0.109361 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122048 Loss1: 0.121360 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.124115 Loss1: 0.123426 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.981606
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8159722222222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.402409 Loss1: 0.401727 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.191485 Loss1: 0.190799 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.185433 Loss1: 0.184746 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.158884 Loss1: 0.158197 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.145018 Loss1: 0.144332 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.179814 Loss1: 0.179128 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.163537 Loss1: 0.162850 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.124577 Loss1: 0.123889 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.104503 Loss1: 0.103812 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088566 Loss1: 0.087877 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.980903
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 00:38:05,680][flwr][DEBUG] - fit_round 38 received 10 results and 0 failures
-test acc: 0.6215
-[2023-09-22 00:38:51,169][flwr][INFO] - fit progress: (38, 2.109429546248037, {'accuracy': 0.6215}, 77212.83034385275)
-[2023-09-22 00:38:51,169][flwr][DEBUG] - evaluate_round 38: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 00:39:28,795][flwr][DEBUG] - evaluate_round 38 received 10 results and 0 failures
-[2023-09-22 00:39:28,795][flwr][DEBUG] - fit_round 39: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8306962025316456
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.347828 Loss1: 0.347145 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.222045 Loss1: 0.221360 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.169032 Loss1: 0.168344 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.150711 Loss1: 0.150024 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.126677 Loss1: 0.125991 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.136074 Loss1: 0.135386 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108946 Loss1: 0.108259 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119593 Loss1: 0.118905 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122454 Loss1: 0.121765 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.128670 Loss1: 0.127982 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.972508
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8326480263157895
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.395808 Loss1: 0.395119 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.267043 Loss1: 0.266352 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.208137 Loss1: 0.207445 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.161189 Loss1: 0.160497 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.161999 Loss1: 0.161308 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.157778 Loss1: 0.157085 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.125671 Loss1: 0.124980 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114642 Loss1: 0.113950 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.103106 Loss1: 0.102413 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079816 Loss1: 0.079123 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.988692
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8347355769230769
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.326374 Loss1: 0.325692 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.254130 Loss1: 0.253447 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.180193 Loss1: 0.179507 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.174561 Loss1: 0.173877 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.152548 Loss1: 0.151862 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.125128 Loss1: 0.124443 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101555 Loss1: 0.100869 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117962 Loss1: 0.117277 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114885 Loss1: 0.114200 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.111541 Loss1: 0.110855 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.973157
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8605182926829268
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.290069 Loss1: 0.289388 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.187512 Loss1: 0.186826 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.208819 Loss1: 0.208133 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.194543 Loss1: 0.193857 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.126479 Loss1: 0.125793 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.128573 Loss1: 0.127886 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106875 Loss1: 0.106188 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096335 Loss1: 0.095646 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.118879 Loss1: 0.118190 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.124774 Loss1: 0.124087 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.981326
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8094618055555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.378187 Loss1: 0.377503 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.242625 Loss1: 0.241939 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.190121 Loss1: 0.189433 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.181312 Loss1: 0.180625 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.142387 Loss1: 0.141698 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098805 Loss1: 0.098116 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076446 Loss1: 0.075757 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096569 Loss1: 0.095880 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063519 Loss1: 0.062831 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075763 Loss1: 0.075075 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.985026
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7865287162162162
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.409456 Loss1: 0.408769 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.206219 Loss1: 0.205528 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.181636 Loss1: 0.180945 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.160805 Loss1: 0.160115 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.154144 Loss1: 0.153454 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.151079 Loss1: 0.150388 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.140377 Loss1: 0.139686 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.108190 Loss1: 0.107501 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098733 Loss1: 0.098040 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098732 Loss1: 0.098040 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.979941
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8619462025316456
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.310426 Loss1: 0.309741 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.194811 Loss1: 0.194122 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.180196 Loss1: 0.179504 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.122899 Loss1: 0.122206 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.129303 Loss1: 0.128609 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.127038 Loss1: 0.126346 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.134284 Loss1: 0.133593 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.134981 Loss1: 0.134289 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.118997 Loss1: 0.118304 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.103712 Loss1: 0.103019 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.981013
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8330696202531646
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.347718 Loss1: 0.347034 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.203829 Loss1: 0.203141 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.150213 Loss1: 0.149524 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.132651 Loss1: 0.131962 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.132331 Loss1: 0.131643 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.167940 Loss1: 0.167252 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.132777 Loss1: 0.132089 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.145921 Loss1: 0.145231 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133956 Loss1: 0.133266 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.127463 Loss1: 0.126773 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.971915
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8633814102564102
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.313384 Loss1: 0.312700 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.196181 Loss1: 0.195493 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.158538 Loss1: 0.157850 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.155091 Loss1: 0.154401 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.145634 Loss1: 0.144945 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116975 Loss1: 0.116285 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087770 Loss1: 0.087079 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109252 Loss1: 0.108561 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108943 Loss1: 0.108252 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.093019 Loss1: 0.092327 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.976562
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8488924050632911
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.378133 Loss1: 0.377448 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.230632 Loss1: 0.229943 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.162450 Loss1: 0.161759 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135203 Loss1: 0.134512 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.130370 Loss1: 0.129678 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.117022 Loss1: 0.116331 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.122636 Loss1: 0.121946 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.126659 Loss1: 0.125968 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123258 Loss1: 0.122568 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.103043 Loss1: 0.102350 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.979628
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 01:08:44,661][flwr][DEBUG] - fit_round 39 received 10 results and 0 failures
-test acc: 0.6223
-[2023-09-22 01:17:01,546][flwr][INFO] - fit progress: (39, 2.0989707978769614, {'accuracy': 0.6223}, 79503.20757643087)
-[2023-09-22 01:17:01,547][flwr][DEBUG] - evaluate_round 39: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 01:17:39,896][flwr][DEBUG] - evaluate_round 39 received 10 results and 0 failures
-[2023-09-22 01:17:39,897][flwr][DEBUG] - fit_round 40: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8456003289473685
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.399010 Loss1: 0.398322 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.247761 Loss1: 0.247071 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.171125 Loss1: 0.170434 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.140004 Loss1: 0.139313 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.140056 Loss1: 0.139365 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.132114 Loss1: 0.131423 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.122413 Loss1: 0.121721 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.136674 Loss1: 0.135983 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133878 Loss1: 0.133188 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.116938 Loss1: 0.116246 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.979030
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8682753164556962
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.320604 Loss1: 0.319918 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.208731 Loss1: 0.208041 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.163293 Loss1: 0.162601 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.117979 Loss1: 0.117287 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.128975 Loss1: 0.128283 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108485 Loss1: 0.107795 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.112787 Loss1: 0.112094 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.133056 Loss1: 0.132363 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.107962 Loss1: 0.107270 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.126117 Loss1: 0.125424 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.975277
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8415464743589743
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.325218 Loss1: 0.324538 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.232557 Loss1: 0.231871 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.156836 Loss1: 0.156151 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135486 Loss1: 0.134802 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.128785 Loss1: 0.128097 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.115482 Loss1: 0.114796 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.124039 Loss1: 0.123352 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119637 Loss1: 0.118950 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.123604 Loss1: 0.122919 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.089314 Loss1: 0.088627 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.982772
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7871621621621622
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.409984 Loss1: 0.409300 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.222190 Loss1: 0.221499 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.177997 Loss1: 0.177306 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.141233 Loss1: 0.140543 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.093546 Loss1: 0.092855 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133099 Loss1: 0.132408 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.099419 Loss1: 0.098729 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.087211 Loss1: 0.086519 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083263 Loss1: 0.082571 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.100976 Loss1: 0.100285 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.979096
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8669969512195121
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.319334 Loss1: 0.318653 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.210538 Loss1: 0.209853 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.220966 Loss1: 0.220280 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.177039 Loss1: 0.176354 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.140752 Loss1: 0.140065 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.147806 Loss1: 0.147118 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.120123 Loss1: 0.119435 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117166 Loss1: 0.116477 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.133381 Loss1: 0.132692 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085089 Loss1: 0.084401 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.979802
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8356408227848101
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.341316 Loss1: 0.340634 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.207714 Loss1: 0.207027 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.152932 Loss1: 0.152246 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.158487 Loss1: 0.157799 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.104407 Loss1: 0.103718 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.114275 Loss1: 0.113588 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.148719 Loss1: 0.148030 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.162282 Loss1: 0.161593 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.151693 Loss1: 0.151003 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115004 Loss1: 0.114315 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.979035
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8320806962025317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.352418 Loss1: 0.351735 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.198213 Loss1: 0.197524 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.150360 Loss1: 0.149669 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.137073 Loss1: 0.136382 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.118619 Loss1: 0.117929 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133129 Loss1: 0.132437 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.150227 Loss1: 0.149537 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119557 Loss1: 0.118867 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.101903 Loss1: 0.101212 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096753 Loss1: 0.096064 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.980815
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.867988782051282
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.283432 Loss1: 0.282749 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.183959 Loss1: 0.183270 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.151058 Loss1: 0.150368 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.119741 Loss1: 0.119052 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.120959 Loss1: 0.120266 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.104469 Loss1: 0.103777 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101928 Loss1: 0.101238 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092920 Loss1: 0.092228 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.101631 Loss1: 0.100938 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098179 Loss1: 0.097488 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.979567
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.850870253164557
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.363746 Loss1: 0.363061 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.235824 Loss1: 0.235136 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.165863 Loss1: 0.165174 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.133063 Loss1: 0.132372 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.155452 Loss1: 0.154763 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.167013 Loss1: 0.166322 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.162273 Loss1: 0.161582 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121282 Loss1: 0.120590 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.130261 Loss1: 0.129570 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.120334 Loss1: 0.119642 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.971321
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8289930555555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.362343 Loss1: 0.361660 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.210924 Loss1: 0.210238 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.178588 Loss1: 0.177900 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.157844 Loss1: 0.157156 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.145273 Loss1: 0.144583 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.133708 Loss1: 0.133020 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.144706 Loss1: 0.144019 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.108456 Loss1: 0.107767 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099956 Loss1: 0.099268 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.100313 Loss1: 0.099624 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.975477
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 01:46:38,408][flwr][DEBUG] - fit_round 40 received 10 results and 0 failures
-test acc: 0.62
-[2023-09-22 01:47:24,380][flwr][INFO] - fit progress: (40, 2.123094680019842, {'accuracy': 0.62}, 81326.04166387487)
-[2023-09-22 01:47:24,381][flwr][DEBUG] - evaluate_round 40: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 01:48:01,636][flwr][DEBUG] - evaluate_round 40 received 10 results and 0 failures
-[2023-09-22 01:48:01,637][flwr][DEBUG] - fit_round 41: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8716376582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.273720 Loss1: 0.273036 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.201964 Loss1: 0.201275 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.172309 Loss1: 0.171620 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.145083 Loss1: 0.144393 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.159214 Loss1: 0.158522 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.112471 Loss1: 0.111781 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.092836 Loss1: 0.092146 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.107123 Loss1: 0.106433 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085659 Loss1: 0.084968 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104065 Loss1: 0.103372 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.978046
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8310917721518988
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.314985 Loss1: 0.314302 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.169887 Loss1: 0.169200 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.125992 Loss1: 0.125302 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.120499 Loss1: 0.119809 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.122544 Loss1: 0.121856 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.119697 Loss1: 0.119008 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.135058 Loss1: 0.134368 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095484 Loss1: 0.094795 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108805 Loss1: 0.108113 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.117026 Loss1: 0.116336 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.967366
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8477056962025317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.280621 Loss1: 0.279940 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.184776 Loss1: 0.184090 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.167141 Loss1: 0.166455 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.143440 Loss1: 0.142753 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.131867 Loss1: 0.131180 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.115832 Loss1: 0.115145 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.124777 Loss1: 0.124089 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.134444 Loss1: 0.133756 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.126647 Loss1: 0.125959 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104762 Loss1: 0.104075 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.974881
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8591844512195121
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.305974 Loss1: 0.305293 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.166080 Loss1: 0.165396 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.148628 Loss1: 0.147941 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.142095 Loss1: 0.141407 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.106123 Loss1: 0.105436 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.121671 Loss1: 0.120985 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.100568 Loss1: 0.099880 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.123846 Loss1: 0.123159 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114175 Loss1: 0.113486 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097383 Loss1: 0.096695 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.979230
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8727964743589743
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.273971 Loss1: 0.273289 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.177342 Loss1: 0.176654 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.136085 Loss1: 0.135397 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.128562 Loss1: 0.127873 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.123969 Loss1: 0.123280 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123582 Loss1: 0.122891 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.121249 Loss1: 0.120560 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.087608 Loss1: 0.086918 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098736 Loss1: 0.098046 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071833 Loss1: 0.071144 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987981
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8279079861111112
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.341015 Loss1: 0.340333 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.204182 Loss1: 0.203497 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.173621 Loss1: 0.172935 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.132385 Loss1: 0.131699 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.094044 Loss1: 0.093356 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.125651 Loss1: 0.124964 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108937 Loss1: 0.108248 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.111231 Loss1: 0.110541 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.107188 Loss1: 0.106499 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096245 Loss1: 0.095556 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987630
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8536184210526315
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.356853 Loss1: 0.356167 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.203536 Loss1: 0.202847 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.177217 Loss1: 0.176527 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.190684 Loss1: 0.189992 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.170102 Loss1: 0.169411 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.127867 Loss1: 0.127176 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108507 Loss1: 0.107817 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.170698 Loss1: 0.170007 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.154738 Loss1: 0.154046 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.114053 Loss1: 0.113362 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.979235
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8379407051282052
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.310502 Loss1: 0.309821 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.204976 Loss1: 0.204290 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.147792 Loss1: 0.147106 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.134726 Loss1: 0.134042 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.169385 Loss1: 0.168699 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.127871 Loss1: 0.127185 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.147197 Loss1: 0.146511 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.135605 Loss1: 0.134921 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.117247 Loss1: 0.116561 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104083 Loss1: 0.103397 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.980369
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7827280405405406
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.367662 Loss1: 0.366977 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.188179 Loss1: 0.187489 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.147913 Loss1: 0.147224 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.140608 Loss1: 0.139918 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133212 Loss1: 0.132522 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.113231 Loss1: 0.112541 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.114269 Loss1: 0.113580 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.116705 Loss1: 0.116015 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099381 Loss1: 0.098690 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.109546 Loss1: 0.108855 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.978252
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8409810126582279
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.329060 Loss1: 0.328376 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.205773 Loss1: 0.205083 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.164063 Loss1: 0.163374 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135085 Loss1: 0.134398 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.093243 Loss1: 0.092555 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106278 Loss1: 0.105590 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.137962 Loss1: 0.137273 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117555 Loss1: 0.116867 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.112357 Loss1: 0.111669 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.126612 Loss1: 0.125924 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.970728
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 02:17:45,792][flwr][DEBUG] - fit_round 41 received 10 results and 0 failures
-test acc: 0.6206
-[2023-09-22 02:18:31,491][flwr][INFO] - fit progress: (41, 2.148773836632506, {'accuracy': 0.6206}, 83193.15214766795)
-[2023-09-22 02:18:31,491][flwr][DEBUG] - evaluate_round 41: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 02:19:09,241][flwr][DEBUG] - evaluate_round 41 received 10 results and 0 failures
-[2023-09-22 02:19:09,242][flwr][DEBUG] - fit_round 42: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8732850609756098
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.290928 Loss1: 0.290247 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.178461 Loss1: 0.177777 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.148465 Loss1: 0.147779 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.117972 Loss1: 0.117286 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.129841 Loss1: 0.129154 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.112315 Loss1: 0.111630 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.088305 Loss1: 0.087617 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097594 Loss1: 0.096908 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.094075 Loss1: 0.093390 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074705 Loss1: 0.074019 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.979992
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8395965189873418
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.298833 Loss1: 0.298148 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.179965 Loss1: 0.179276 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.144727 Loss1: 0.144039 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135559 Loss1: 0.134868 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.107883 Loss1: 0.107194 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.114070 Loss1: 0.113382 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.117473 Loss1: 0.116784 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.125687 Loss1: 0.124998 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.128200 Loss1: 0.127511 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097221 Loss1: 0.096531 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.982595
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8619462025316456
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.316569 Loss1: 0.315886 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.215535 Loss1: 0.214847 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.170026 Loss1: 0.169337 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.121976 Loss1: 0.121285 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.120415 Loss1: 0.119726 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.115703 Loss1: 0.115012 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107390 Loss1: 0.106700 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.115117 Loss1: 0.114426 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110225 Loss1: 0.109535 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.101860 Loss1: 0.101168 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.983782
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8451522435897436
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.333463 Loss1: 0.332782 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.201181 Loss1: 0.200498 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.133278 Loss1: 0.132592 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.116656 Loss1: 0.115971 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.093124 Loss1: 0.092437 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.125780 Loss1: 0.125093 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.130203 Loss1: 0.129516 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.122431 Loss1: 0.121744 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.122946 Loss1: 0.122259 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098828 Loss1: 0.098141 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.983774
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8515625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.351770 Loss1: 0.351083 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.222209 Loss1: 0.221519 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.163038 Loss1: 0.162348 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.159413 Loss1: 0.158724 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.156401 Loss1: 0.155713 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.129650 Loss1: 0.128959 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.130890 Loss1: 0.130200 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.147547 Loss1: 0.146856 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093783 Loss1: 0.093092 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078412 Loss1: 0.077720 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.987048
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8415743670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.292624 Loss1: 0.291942 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.206498 Loss1: 0.205812 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.146300 Loss1: 0.145614 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.136430 Loss1: 0.135744 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.114114 Loss1: 0.113427 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098408 Loss1: 0.097722 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085565 Loss1: 0.084878 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121193 Loss1: 0.120507 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.115149 Loss1: 0.114461 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104778 Loss1: 0.104090 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.980222
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8287760416666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.314256 Loss1: 0.313574 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.195628 Loss1: 0.194943 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.178623 Loss1: 0.177936 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.164009 Loss1: 0.163323 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.150620 Loss1: 0.149931 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.158570 Loss1: 0.157882 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.123969 Loss1: 0.123280 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102670 Loss1: 0.101982 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.115638 Loss1: 0.114950 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104159 Loss1: 0.103471 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.974826
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8795490506329114
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.292456 Loss1: 0.291771 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.179950 Loss1: 0.179261 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.160349 Loss1: 0.159659 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.125019 Loss1: 0.124328 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.126012 Loss1: 0.125322 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.139559 Loss1: 0.138868 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.139431 Loss1: 0.138741 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091045 Loss1: 0.090353 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079593 Loss1: 0.078902 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099371 Loss1: 0.098680 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.979628
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8733974358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.275901 Loss1: 0.275217 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.189426 Loss1: 0.188739 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.153338 Loss1: 0.152651 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.108349 Loss1: 0.107660 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.100714 Loss1: 0.100025 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105299 Loss1: 0.104610 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091521 Loss1: 0.090832 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078779 Loss1: 0.078090 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055006 Loss1: 0.054317 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059620 Loss1: 0.058931 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987780
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7915962837837838
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.363612 Loss1: 0.362926 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.206669 Loss1: 0.205979 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.159505 Loss1: 0.158815 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.138590 Loss1: 0.137900 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.138480 Loss1: 0.137790 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098713 Loss1: 0.098021 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.088855 Loss1: 0.088164 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081933 Loss1: 0.081242 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110766 Loss1: 0.110078 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.129854 Loss1: 0.129162 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.978674
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 02:49:00,763][flwr][DEBUG] - fit_round 42 received 10 results and 0 failures
-test acc: 0.6283
-[2023-09-22 02:49:46,951][flwr][INFO] - fit progress: (42, 2.1465409287629416, {'accuracy': 0.6283}, 85068.61267694458)
-[2023-09-22 02:49:46,952][flwr][DEBUG] - evaluate_round 42: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 02:50:24,279][flwr][DEBUG] - evaluate_round 42 received 10 results and 0 failures
-[2023-09-22 02:50:24,280][flwr][DEBUG] - fit_round 43: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.7934966216216216
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.307708 Loss1: 0.307024 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.211459 Loss1: 0.210772 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.155377 Loss1: 0.154689 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.150986 Loss1: 0.150297 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.118145 Loss1: 0.117455 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106234 Loss1: 0.105544 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.111250 Loss1: 0.110561 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121360 Loss1: 0.120671 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.131742 Loss1: 0.131053 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097262 Loss1: 0.096574 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.978041
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8673780487804879
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246780 Loss1: 0.246099 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.194122 Loss1: 0.193436 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.127123 Loss1: 0.126439 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.115952 Loss1: 0.115266 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133161 Loss1: 0.132475 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.132210 Loss1: 0.131524 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106554 Loss1: 0.105868 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114851 Loss1: 0.114164 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109461 Loss1: 0.108775 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.119139 Loss1: 0.118452 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.972942
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8447389240506329
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.248744 Loss1: 0.248061 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.192221 Loss1: 0.191534 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.131185 Loss1: 0.130497 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.121172 Loss1: 0.120485 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098885 Loss1: 0.098197 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.128615 Loss1: 0.127927 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.118438 Loss1: 0.117752 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102022 Loss1: 0.101334 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.127128 Loss1: 0.126438 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.129064 Loss1: 0.128376 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.973695
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8479034810126582
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.272079 Loss1: 0.271397 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.166966 Loss1: 0.166279 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.124503 Loss1: 0.123816 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.113712 Loss1: 0.113023 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.130933 Loss1: 0.130244 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.124891 Loss1: 0.124200 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.104998 Loss1: 0.104308 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.129101 Loss1: 0.128411 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083351 Loss1: 0.082661 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090861 Loss1: 0.090170 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.982002
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8670886075949367
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.290447 Loss1: 0.289763 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.169287 Loss1: 0.168599 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.138890 Loss1: 0.138201 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.127354 Loss1: 0.126665 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.116426 Loss1: 0.115738 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.109800 Loss1: 0.109111 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.117779 Loss1: 0.117090 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.101999 Loss1: 0.101308 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.104420 Loss1: 0.103731 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094504 Loss1: 0.093815 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.976859
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8465711805555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.333998 Loss1: 0.333315 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.192263 Loss1: 0.191578 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.141086 Loss1: 0.140399 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.116961 Loss1: 0.116275 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101221 Loss1: 0.100534 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.094544 Loss1: 0.093856 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.105042 Loss1: 0.104354 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081525 Loss1: 0.080836 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087497 Loss1: 0.086807 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099929 Loss1: 0.099238 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.979601
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8653371710526315
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.326613 Loss1: 0.325927 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.209373 Loss1: 0.208684 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.151291 Loss1: 0.150601 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.128430 Loss1: 0.127740 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.154902 Loss1: 0.154211 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123907 Loss1: 0.123217 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107225 Loss1: 0.106536 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.104744 Loss1: 0.104056 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080689 Loss1: 0.080000 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066669 Loss1: 0.065980 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.984375
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8850870253164557
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.262435 Loss1: 0.261751 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.165235 Loss1: 0.164545 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.151944 Loss1: 0.151255 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.121181 Loss1: 0.120492 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.127727 Loss1: 0.127036 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108562 Loss1: 0.107872 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106547 Loss1: 0.105857 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.090073 Loss1: 0.089384 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093254 Loss1: 0.092563 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075362 Loss1: 0.074671 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.984771
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8477564102564102
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.320336 Loss1: 0.319656 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.223211 Loss1: 0.222526 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.151212 Loss1: 0.150526 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.118571 Loss1: 0.117886 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.113046 Loss1: 0.112361 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098061 Loss1: 0.097376 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.093257 Loss1: 0.092571 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.131110 Loss1: 0.130425 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.136311 Loss1: 0.135625 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.110391 Loss1: 0.109705 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.978966
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8832131410256411
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.251567 Loss1: 0.250886 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.152338 Loss1: 0.151650 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.131012 Loss1: 0.130325 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.106451 Loss1: 0.105763 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101182 Loss1: 0.100492 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092959 Loss1: 0.092269 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.080235 Loss1: 0.079544 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.090851 Loss1: 0.090160 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.086626 Loss1: 0.085935 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091078 Loss1: 0.090386 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.986579
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 03:20:27,636][flwr][DEBUG] - fit_round 43 received 10 results and 0 failures
-test acc: 0.6278
-[2023-09-22 03:21:14,774][flwr][INFO] - fit progress: (43, 2.1362349244352346, {'accuracy': 0.6278}, 86956.4356766548)
-[2023-09-22 03:21:14,775][flwr][DEBUG] - evaluate_round 43: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 03:21:52,345][flwr][DEBUG] - evaluate_round 43 received 10 results and 0 failures
-[2023-09-22 03:21:52,347][flwr][DEBUG] - fit_round 44: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8065878378378378
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.314972 Loss1: 0.314287 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.161408 Loss1: 0.160719 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.128631 Loss1: 0.127940 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.122502 Loss1: 0.121810 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.132435 Loss1: 0.131745 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092712 Loss1: 0.092022 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110657 Loss1: 0.109966 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091227 Loss1: 0.090536 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.102753 Loss1: 0.102063 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.102187 Loss1: 0.101496 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.983953
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.850870253164557
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.268531 Loss1: 0.267847 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.169299 Loss1: 0.168610 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.121889 Loss1: 0.121201 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.129088 Loss1: 0.128398 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.095953 Loss1: 0.095263 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078463 Loss1: 0.077772 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082060 Loss1: 0.081370 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.087643 Loss1: 0.086953 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.104421 Loss1: 0.103731 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.115935 Loss1: 0.115244 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.977255
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8848157051282052
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.266045 Loss1: 0.265360 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.165537 Loss1: 0.164846 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.153238 Loss1: 0.152547 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.113048 Loss1: 0.112359 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.090429 Loss1: 0.089740 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.103019 Loss1: 0.102329 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.093243 Loss1: 0.092553 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085122 Loss1: 0.084432 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072233 Loss1: 0.071542 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087200 Loss1: 0.086509 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.987780
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8504774305555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.311970 Loss1: 0.311286 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.193086 Loss1: 0.192399 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.128858 Loss1: 0.128171 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.111413 Loss1: 0.110725 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133823 Loss1: 0.133135 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108475 Loss1: 0.107786 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.096371 Loss1: 0.095680 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.093904 Loss1: 0.093215 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075273 Loss1: 0.074584 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082224 Loss1: 0.081533 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.990451
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8623798076923077
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.274120 Loss1: 0.273441 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.194158 Loss1: 0.193474 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.161035 Loss1: 0.160350 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.136695 Loss1: 0.136011 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.117297 Loss1: 0.116611 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.110523 Loss1: 0.109838 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110388 Loss1: 0.109700 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114042 Loss1: 0.113356 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.101043 Loss1: 0.100356 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104629 Loss1: 0.103943 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.978365
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8878560126582279
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.275048 Loss1: 0.274364 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.153385 Loss1: 0.152696 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.121303 Loss1: 0.120613 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.100138 Loss1: 0.099447 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.130260 Loss1: 0.129569 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.139997 Loss1: 0.139306 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081925 Loss1: 0.081235 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109304 Loss1: 0.108614 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098014 Loss1: 0.097324 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096674 Loss1: 0.095982 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.978441
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8661595394736842
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.332924 Loss1: 0.332239 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.207779 Loss1: 0.207090 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.149519 Loss1: 0.148829 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.131346 Loss1: 0.130657 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.118133 Loss1: 0.117441 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.118411 Loss1: 0.117719 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.138547 Loss1: 0.137856 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114165 Loss1: 0.113473 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078798 Loss1: 0.078106 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091990 Loss1: 0.091299 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.988076
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.853243670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.251018 Loss1: 0.250335 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.163357 Loss1: 0.162671 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.160832 Loss1: 0.160146 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.126222 Loss1: 0.125534 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.096539 Loss1: 0.095851 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116692 Loss1: 0.116003 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115302 Loss1: 0.114614 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.105741 Loss1: 0.105053 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.120625 Loss1: 0.119937 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.118341 Loss1: 0.117653 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.972310
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8835746951219512
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.231131 Loss1: 0.230449 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.140251 Loss1: 0.139566 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.127058 Loss1: 0.126371 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.111156 Loss1: 0.110469 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.104939 Loss1: 0.104253 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106585 Loss1: 0.105897 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.121351 Loss1: 0.120662 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.127896 Loss1: 0.127208 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084910 Loss1: 0.084221 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090213 Loss1: 0.089526 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.988948
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8738132911392406
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.279277 Loss1: 0.278593 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.166143 Loss1: 0.165455 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.134821 Loss1: 0.134132 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.119027 Loss1: 0.118338 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.146626 Loss1: 0.145936 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108401 Loss1: 0.107712 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107695 Loss1: 0.107006 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.100921 Loss1: 0.100231 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.095555 Loss1: 0.094864 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097260 Loss1: 0.096569 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.985364
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 03:51:54,485][flwr][DEBUG] - fit_round 44 received 10 results and 0 failures
-test acc: 0.6251
-[2023-09-22 03:52:41,459][flwr][INFO] - fit progress: (44, 2.1981266999777893, {'accuracy': 0.6251}, 88843.12023476185)
-[2023-09-22 03:52:41,459][flwr][DEBUG] - evaluate_round 44: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 03:53:18,692][flwr][DEBUG] - evaluate_round 44 received 10 results and 0 failures
-[2023-09-22 03:53:18,693][flwr][DEBUG] - fit_round 45: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8589743589743589
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.268189 Loss1: 0.267510 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.183437 Loss1: 0.182753 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.140724 Loss1: 0.140039 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.128834 Loss1: 0.128150 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.139195 Loss1: 0.138511 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.129692 Loss1: 0.129008 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.115057 Loss1: 0.114373 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.118817 Loss1: 0.118131 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081475 Loss1: 0.080790 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.114770 Loss1: 0.114085 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.978365
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8914161392405063
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.226891 Loss1: 0.226207 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.143172 Loss1: 0.142483 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.101741 Loss1: 0.101051 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.129039 Loss1: 0.128349 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.116743 Loss1: 0.116053 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092180 Loss1: 0.091492 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103384 Loss1: 0.102696 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.118370 Loss1: 0.117681 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110943 Loss1: 0.110255 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.097507 Loss1: 0.096817 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.984771
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8524525316455697
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.259259 Loss1: 0.258576 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.155619 Loss1: 0.154934 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.123316 Loss1: 0.122631 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.135842 Loss1: 0.135154 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.127597 Loss1: 0.126910 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.137728 Loss1: 0.137041 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.114426 Loss1: 0.113738 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095208 Loss1: 0.094520 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082123 Loss1: 0.081435 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087277 Loss1: 0.086589 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.980024
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8676819620253164
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.252600 Loss1: 0.251917 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.181649 Loss1: 0.180960 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.157661 Loss1: 0.156974 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.113181 Loss1: 0.112492 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105802 Loss1: 0.105114 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.113608 Loss1: 0.112919 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.123513 Loss1: 0.122824 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.100560 Loss1: 0.099871 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.094366 Loss1: 0.093676 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.084507 Loss1: 0.083818 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.981606
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8651315789473685
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.305363 Loss1: 0.304677 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.181645 Loss1: 0.180955 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.177677 Loss1: 0.176986 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.137949 Loss1: 0.137259 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.111460 Loss1: 0.110770 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.110104 Loss1: 0.109413 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086824 Loss1: 0.086132 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.098339 Loss1: 0.097648 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111963 Loss1: 0.111272 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.113313 Loss1: 0.112621 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.975946
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8890224358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.249083 Loss1: 0.248398 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.176157 Loss1: 0.175469 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.139125 Loss1: 0.138436 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.107282 Loss1: 0.106591 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088648 Loss1: 0.087959 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.094320 Loss1: 0.093629 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.092926 Loss1: 0.092237 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095457 Loss1: 0.094768 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075847 Loss1: 0.075157 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091157 Loss1: 0.090465 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.979567
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8892911585365854
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.216138 Loss1: 0.215456 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.162791 Loss1: 0.162106 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.157623 Loss1: 0.156938 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.115973 Loss1: 0.115286 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.153165 Loss1: 0.152479 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.117425 Loss1: 0.116741 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110800 Loss1: 0.110115 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102185 Loss1: 0.101500 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099101 Loss1: 0.098414 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081530 Loss1: 0.080845 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.981707
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8013091216216216
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.323981 Loss1: 0.323297 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.199447 Loss1: 0.198760 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.161122 Loss1: 0.160433 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.122968 Loss1: 0.122278 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.103262 Loss1: 0.102573 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.099341 Loss1: 0.098651 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.102385 Loss1: 0.101695 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080968 Loss1: 0.080278 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.103135 Loss1: 0.102446 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087628 Loss1: 0.086940 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.982475
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8372231012658228
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.272800 Loss1: 0.272118 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.164336 Loss1: 0.163649 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.123102 Loss1: 0.122415 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.109843 Loss1: 0.109155 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.120004 Loss1: 0.119314 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.095401 Loss1: 0.094713 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.089793 Loss1: 0.089105 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074298 Loss1: 0.073610 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072582 Loss1: 0.071894 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088548 Loss1: 0.087858 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.977255
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8474392361111112
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.325543 Loss1: 0.324860 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.191559 Loss1: 0.190871 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.145475 Loss1: 0.144788 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.136371 Loss1: 0.135684 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.122543 Loss1: 0.121855 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098366 Loss1: 0.097678 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076931 Loss1: 0.076244 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095266 Loss1: 0.094578 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068231 Loss1: 0.067541 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063820 Loss1: 0.063131 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.985460
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 04:23:26,237][flwr][DEBUG] - fit_round 45 received 10 results and 0 failures
-test acc: 0.623
-[2023-09-22 04:24:19,382][flwr][INFO] - fit progress: (45, 2.1977186043041583, {'accuracy': 0.623}, 90741.043649517)
-[2023-09-22 04:24:19,383][flwr][DEBUG] - evaluate_round 45: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 04:24:57,422][flwr][DEBUG] - evaluate_round 45 received 10 results and 0 failures
-[2023-09-22 04:24:57,422][flwr][DEBUG] - fit_round 46: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8502604166666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.269647 Loss1: 0.268966 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.167797 Loss1: 0.167112 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.145172 Loss1: 0.144486 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.109053 Loss1: 0.108367 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.089019 Loss1: 0.088331 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.088520 Loss1: 0.087832 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077364 Loss1: 0.076675 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059709 Loss1: 0.059021 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061514 Loss1: 0.060826 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072940 Loss1: 0.072252 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.984592
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8540348101265823
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.269054 Loss1: 0.268372 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.136163 Loss1: 0.135478 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.140149 Loss1: 0.139463 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.134322 Loss1: 0.133634 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.113855 Loss1: 0.113166 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.102109 Loss1: 0.101420 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087256 Loss1: 0.086567 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077095 Loss1: 0.076404 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079661 Loss1: 0.078970 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091628 Loss1: 0.090940 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.985562
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8881478658536586
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.226038 Loss1: 0.225357 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.155769 Loss1: 0.155086 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088314 Loss1: 0.087630 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.099979 Loss1: 0.099295 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.109694 Loss1: 0.109011 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.088112 Loss1: 0.087426 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091861 Loss1: 0.091175 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.107300 Loss1: 0.106614 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.105041 Loss1: 0.104355 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099137 Loss1: 0.098451 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.980373
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8963607594936709
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.236624 Loss1: 0.235941 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.168346 Loss1: 0.167658 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.139331 Loss1: 0.138641 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.130788 Loss1: 0.130098 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.110875 Loss1: 0.110184 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.100843 Loss1: 0.100151 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076928 Loss1: 0.076237 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081124 Loss1: 0.080432 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063981 Loss1: 0.063289 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060857 Loss1: 0.060166 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.989122
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8105996621621622
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.319853 Loss1: 0.319168 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.209550 Loss1: 0.208862 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.139736 Loss1: 0.139047 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.126647 Loss1: 0.125959 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105605 Loss1: 0.104915 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.097296 Loss1: 0.096606 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106436 Loss1: 0.105746 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085256 Loss1: 0.084565 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108894 Loss1: 0.108203 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082022 Loss1: 0.081332 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.985642
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8860759493670886
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.231835 Loss1: 0.231153 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.179649 Loss1: 0.178962 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.144172 Loss1: 0.143484 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.130270 Loss1: 0.129583 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.140911 Loss1: 0.140222 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.124227 Loss1: 0.123538 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.126464 Loss1: 0.125775 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109113 Loss1: 0.108425 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097949 Loss1: 0.097260 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091610 Loss1: 0.090921 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.982793
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8708881578947368
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.255938 Loss1: 0.255252 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.149564 Loss1: 0.148876 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.143960 Loss1: 0.143271 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.148248 Loss1: 0.147558 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.153689 Loss1: 0.152998 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.130884 Loss1: 0.130194 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.111390 Loss1: 0.110699 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.116714 Loss1: 0.116024 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.094475 Loss1: 0.093784 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074815 Loss1: 0.074124 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.982936
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8629807692307693
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.236459 Loss1: 0.235778 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.188766 Loss1: 0.188084 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.171077 Loss1: 0.170393 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.132693 Loss1: 0.132008 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.119916 Loss1: 0.119230 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.143976 Loss1: 0.143292 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.112901 Loss1: 0.112215 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.114324 Loss1: 0.113639 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087055 Loss1: 0.086370 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071887 Loss1: 0.071201 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.984575
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8894230769230769
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246482 Loss1: 0.245800 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.140240 Loss1: 0.139552 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.086661 Loss1: 0.085974 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.091771 Loss1: 0.091080 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.110297 Loss1: 0.109607 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089111 Loss1: 0.088420 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083344 Loss1: 0.082655 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.083493 Loss1: 0.082802 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082966 Loss1: 0.082277 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064031 Loss1: 0.063342 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989784
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8579905063291139
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246647 Loss1: 0.245965 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.175744 Loss1: 0.175058 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.116249 Loss1: 0.115561 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.120706 Loss1: 0.120019 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.114175 Loss1: 0.113485 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.104957 Loss1: 0.104271 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087153 Loss1: 0.086465 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.089581 Loss1: 0.088892 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091862 Loss1: 0.091172 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.105055 Loss1: 0.104367 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.984375
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 04:55:09,793][flwr][DEBUG] - fit_round 46 received 10 results and 0 failures
-test acc: 0.6263
-[2023-09-22 04:56:20,900][flwr][INFO] - fit progress: (46, 2.2119887084625782, {'accuracy': 0.6263}, 92662.56100891856)
-[2023-09-22 04:56:20,900][flwr][DEBUG] - evaluate_round 46: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 04:57:01,385][flwr][DEBUG] - evaluate_round 46 received 10 results and 0 failures
-[2023-09-22 04:57:01,386][flwr][DEBUG] - fit_round 47: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8876582278481012
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.198378 Loss1: 0.197697 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.149738 Loss1: 0.149051 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.100720 Loss1: 0.100030 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.080089 Loss1: 0.079398 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.102557 Loss1: 0.101867 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.115378 Loss1: 0.114690 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.084736 Loss1: 0.084046 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081986 Loss1: 0.081296 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083153 Loss1: 0.082462 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082725 Loss1: 0.082034 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.981210
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8635284810126582
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.221909 Loss1: 0.221228 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.148447 Loss1: 0.147761 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.113970 Loss1: 0.113285 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.098914 Loss1: 0.098228 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073522 Loss1: 0.072834 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.088341 Loss1: 0.087654 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101975 Loss1: 0.101287 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.089757 Loss1: 0.089070 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079816 Loss1: 0.079127 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.110526 Loss1: 0.109837 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.979233
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8561197916666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246656 Loss1: 0.245974 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.152264 Loss1: 0.151579 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.137290 Loss1: 0.136603 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.092037 Loss1: 0.091351 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079072 Loss1: 0.078384 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.073779 Loss1: 0.073092 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.089185 Loss1: 0.088495 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088245 Loss1: 0.087557 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078499 Loss1: 0.077811 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094061 Loss1: 0.093372 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.982422
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.893483231707317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.240052 Loss1: 0.239371 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.143438 Loss1: 0.142753 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.130261 Loss1: 0.129576 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.109011 Loss1: 0.108326 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105876 Loss1: 0.105191 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106283 Loss1: 0.105595 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.119213 Loss1: 0.118526 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.119749 Loss1: 0.119061 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098207 Loss1: 0.097520 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081569 Loss1: 0.080883 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.983994
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8152449324324325
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.289389 Loss1: 0.288706 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.134702 Loss1: 0.134015 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.108773 Loss1: 0.108084 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.081753 Loss1: 0.081063 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.115465 Loss1: 0.114774 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106842 Loss1: 0.106152 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077845 Loss1: 0.077153 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.076880 Loss1: 0.076192 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078002 Loss1: 0.077314 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068498 Loss1: 0.067808 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.980997
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8980368589743589
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.207357 Loss1: 0.206677 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.147210 Loss1: 0.146524 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.122153 Loss1: 0.121466 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.092150 Loss1: 0.091461 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091075 Loss1: 0.090386 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072978 Loss1: 0.072289 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072048 Loss1: 0.071357 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060669 Loss1: 0.059979 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084547 Loss1: 0.083857 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081586 Loss1: 0.080895 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.985777
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8601661392405063
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.224724 Loss1: 0.224042 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.158248 Loss1: 0.157560 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.129869 Loss1: 0.129181 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.116100 Loss1: 0.115413 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101502 Loss1: 0.100813 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.109807 Loss1: 0.109118 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.121932 Loss1: 0.121242 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.113831 Loss1: 0.113141 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108601 Loss1: 0.107912 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086840 Loss1: 0.086151 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.984771
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.875
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.262480 Loss1: 0.261794 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.154628 Loss1: 0.153938 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.110667 Loss1: 0.109976 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.082094 Loss1: 0.081403 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.087161 Loss1: 0.086470 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.101338 Loss1: 0.100646 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.121603 Loss1: 0.120912 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.108116 Loss1: 0.107425 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097594 Loss1: 0.096904 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094093 Loss1: 0.093403 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.984375
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8854825949367089
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.221609 Loss1: 0.220926 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.158984 Loss1: 0.158297 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.112692 Loss1: 0.112004 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.089782 Loss1: 0.089094 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105944 Loss1: 0.105255 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.110839 Loss1: 0.110151 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094122 Loss1: 0.093433 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097022 Loss1: 0.096331 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071451 Loss1: 0.070760 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075629 Loss1: 0.074938 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.984177
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8754006410256411
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.250336 Loss1: 0.249657 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.157938 Loss1: 0.157256 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.140942 Loss1: 0.140258 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.131687 Loss1: 0.131002 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088160 Loss1: 0.087475 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105111 Loss1: 0.104427 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081988 Loss1: 0.081302 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097192 Loss1: 0.096506 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.105377 Loss1: 0.104693 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096202 Loss1: 0.095517 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.981370
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 05:27:07,636][flwr][DEBUG] - fit_round 47 received 10 results and 0 failures
-test acc: 0.6254
-[2023-09-22 05:28:10,853][flwr][INFO] - fit progress: (47, 2.1827334691160405, {'accuracy': 0.6254}, 94572.51411859691)
-[2023-09-22 05:28:10,853][flwr][DEBUG] - evaluate_round 47: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 05:28:49,002][flwr][DEBUG] - evaluate_round 47 received 10 results and 0 failures
-[2023-09-22 05:28:49,010][flwr][DEBUG] - fit_round 48: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8268581081081081
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.290976 Loss1: 0.290292 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.124191 Loss1: 0.123501 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.118522 Loss1: 0.117832 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.102910 Loss1: 0.102221 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098887 Loss1: 0.098197 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078760 Loss1: 0.078069 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082651 Loss1: 0.081960 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094598 Loss1: 0.093909 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079493 Loss1: 0.078803 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074979 Loss1: 0.074290 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.986486
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8740110759493671
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.214294 Loss1: 0.213612 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.118901 Loss1: 0.118214 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.120177 Loss1: 0.119489 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.102646 Loss1: 0.101959 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.100646 Loss1: 0.099958 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.136593 Loss1: 0.135906 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.102817 Loss1: 0.102129 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.067336 Loss1: 0.066646 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087047 Loss1: 0.086358 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085333 Loss1: 0.084645 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.973892
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8657041139240507
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.234327 Loss1: 0.233645 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.123857 Loss1: 0.123172 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.121470 Loss1: 0.120781 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.119810 Loss1: 0.119121 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.118193 Loss1: 0.117504 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.095954 Loss1: 0.095266 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.112043 Loss1: 0.111354 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.082286 Loss1: 0.081597 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097922 Loss1: 0.097234 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086968 Loss1: 0.086279 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.981804
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.893483231707317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.205278 Loss1: 0.204599 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.145162 Loss1: 0.144477 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.107618 Loss1: 0.106933 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.100386 Loss1: 0.099700 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.115975 Loss1: 0.115290 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116794 Loss1: 0.116106 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087363 Loss1: 0.086676 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070789 Loss1: 0.070100 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076251 Loss1: 0.075562 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082603 Loss1: 0.081916 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.984375
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8606770833333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.244024 Loss1: 0.243341 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.136704 Loss1: 0.136018 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.131053 Loss1: 0.130365 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.129716 Loss1: 0.129030 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.112870 Loss1: 0.112182 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.099328 Loss1: 0.098640 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106952 Loss1: 0.106264 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088842 Loss1: 0.088152 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081425 Loss1: 0.080735 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069974 Loss1: 0.069286 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.985677
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8982371794871795
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.207581 Loss1: 0.206899 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.111369 Loss1: 0.110682 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.138776 Loss1: 0.138087 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.107972 Loss1: 0.107283 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088725 Loss1: 0.088036 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.080382 Loss1: 0.079692 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068338 Loss1: 0.067648 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.064417 Loss1: 0.063728 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053806 Loss1: 0.053117 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058201 Loss1: 0.057511 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.993590
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.875
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.236587 Loss1: 0.235902 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.158143 Loss1: 0.157453 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.139564 Loss1: 0.138873 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.124571 Loss1: 0.123880 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.106794 Loss1: 0.106103 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.122009 Loss1: 0.121319 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.125357 Loss1: 0.124666 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.093150 Loss1: 0.092459 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.090435 Loss1: 0.089745 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087395 Loss1: 0.086703 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.987253
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9054588607594937
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.215713 Loss1: 0.215029 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.123963 Loss1: 0.123276 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083106 Loss1: 0.082418 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.082331 Loss1: 0.081642 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079123 Loss1: 0.078433 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.096134 Loss1: 0.095445 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.097818 Loss1: 0.097129 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.113458 Loss1: 0.112768 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.100474 Loss1: 0.099785 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094762 Loss1: 0.094071 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.979430
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8705929487179487
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.211255 Loss1: 0.210575 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.152850 Loss1: 0.152168 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.101688 Loss1: 0.101004 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.104093 Loss1: 0.103408 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088363 Loss1: 0.087679 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098475 Loss1: 0.097788 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.084181 Loss1: 0.083495 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.101612 Loss1: 0.100926 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111337 Loss1: 0.110651 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.106991 Loss1: 0.106304 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.975761
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8920094936708861
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.217703 Loss1: 0.217019 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.121235 Loss1: 0.120547 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088958 Loss1: 0.088269 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.111087 Loss1: 0.110396 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091981 Loss1: 0.091292 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.100155 Loss1: 0.099466 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087088 Loss1: 0.086398 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109504 Loss1: 0.108814 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084049 Loss1: 0.083357 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098049 Loss1: 0.097359 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.983584
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 05:59:12,479][flwr][DEBUG] - fit_round 48 received 10 results and 0 failures
-test acc: 0.6296
-[2023-09-22 06:00:19,674][flwr][INFO] - fit progress: (48, 2.2119309980267534, {'accuracy': 0.6296}, 96501.33515052684)
-[2023-09-22 06:00:19,675][flwr][DEBUG] - evaluate_round 48: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 06:00:59,408][flwr][DEBUG] - evaluate_round 48 received 10 results and 0 failures
-[2023-09-22 06:00:59,409][flwr][DEBUG] - fit_round 49: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8821957236842105
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.261955 Loss1: 0.261268 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.175224 Loss1: 0.174534 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.137613 Loss1: 0.136922 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.104800 Loss1: 0.104110 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.096358 Loss1: 0.095667 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.099405 Loss1: 0.098715 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103829 Loss1: 0.103139 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.110146 Loss1: 0.109454 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082258 Loss1: 0.081565 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074628 Loss1: 0.073937 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.990954
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8641493055555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.260793 Loss1: 0.260110 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.127200 Loss1: 0.126514 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.119800 Loss1: 0.119113 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.100492 Loss1: 0.099804 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.117610 Loss1: 0.116921 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092187 Loss1: 0.091498 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087552 Loss1: 0.086863 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096358 Loss1: 0.095669 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.096524 Loss1: 0.095834 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075713 Loss1: 0.075023 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.987630
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8988381410256411
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.215812 Loss1: 0.215128 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.095514 Loss1: 0.094827 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.080295 Loss1: 0.079606 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.101805 Loss1: 0.101117 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079646 Loss1: 0.078957 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.083170 Loss1: 0.082481 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081221 Loss1: 0.080531 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091620 Loss1: 0.090930 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076989 Loss1: 0.076300 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078062 Loss1: 0.077373 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.985377
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8686708860759493
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.197535 Loss1: 0.196851 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.151177 Loss1: 0.150489 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.122762 Loss1: 0.122073 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.107385 Loss1: 0.106695 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.111596 Loss1: 0.110906 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.099931 Loss1: 0.099242 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.075917 Loss1: 0.075226 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072137 Loss1: 0.071448 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065463 Loss1: 0.064772 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071896 Loss1: 0.071205 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.985957
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8975474683544303
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.209747 Loss1: 0.209064 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.161961 Loss1: 0.161273 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.135291 Loss1: 0.134602 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.098260 Loss1: 0.097570 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.083404 Loss1: 0.082716 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.095267 Loss1: 0.094578 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077792 Loss1: 0.077102 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075028 Loss1: 0.074339 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089400 Loss1: 0.088709 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083845 Loss1: 0.083155 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.982199
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8763844936708861
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.220394 Loss1: 0.219710 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.118030 Loss1: 0.117342 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.111905 Loss1: 0.111217 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.129385 Loss1: 0.128698 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.119028 Loss1: 0.118340 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.116718 Loss1: 0.116032 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110028 Loss1: 0.109340 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077122 Loss1: 0.076433 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.096651 Loss1: 0.095960 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079948 Loss1: 0.079257 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.986748
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8973496835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.213781 Loss1: 0.213098 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.108607 Loss1: 0.107918 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.094685 Loss1: 0.093994 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.075335 Loss1: 0.074645 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098891 Loss1: 0.098201 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.125268 Loss1: 0.124577 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101141 Loss1: 0.100452 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.086433 Loss1: 0.085742 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080689 Loss1: 0.079996 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074779 Loss1: 0.074089 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.984375
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.816722972972973
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.264240 Loss1: 0.263556 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.179452 Loss1: 0.178761 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.136325 Loss1: 0.135634 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.099294 Loss1: 0.098605 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.080389 Loss1: 0.079698 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068731 Loss1: 0.068040 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.096018 Loss1: 0.095327 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061416 Loss1: 0.060725 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066445 Loss1: 0.065755 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050312 Loss1: 0.049623 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.987120
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8990091463414634
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.182394 Loss1: 0.181713 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.120763 Loss1: 0.120078 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.094246 Loss1: 0.093561 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.104693 Loss1: 0.104007 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.089464 Loss1: 0.088778 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.123547 Loss1: 0.122861 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.093693 Loss1: 0.093007 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078489 Loss1: 0.077802 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074246 Loss1: 0.073560 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.107473 Loss1: 0.106787 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.978468
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8812099358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.227968 Loss1: 0.227289 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.135014 Loss1: 0.134330 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.107319 Loss1: 0.106634 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087508 Loss1: 0.086823 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.089860 Loss1: 0.089174 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.070965 Loss1: 0.070280 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077450 Loss1: 0.076764 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095166 Loss1: 0.094481 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.108830 Loss1: 0.108143 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099631 Loss1: 0.098944 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.981370
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 06:32:04,195][flwr][DEBUG] - fit_round 49 received 10 results and 0 failures
-test acc: 0.6282
-[2023-09-22 06:33:08,740][flwr][INFO] - fit progress: (49, 2.2019196455471053, {'accuracy': 0.6282}, 98470.40143894171)
-[2023-09-22 06:33:08,741][flwr][DEBUG] - evaluate_round 49: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 06:33:46,636][flwr][DEBUG] - evaluate_round 49 received 10 results and 0 failures
-[2023-09-22 06:33:46,641][flwr][DEBUG] - fit_round 50: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9028876582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.190870 Loss1: 0.190187 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.104922 Loss1: 0.104234 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.091389 Loss1: 0.090700 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.119542 Loss1: 0.118854 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.133667 Loss1: 0.132977 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.097067 Loss1: 0.096377 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.110143 Loss1: 0.109451 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.121940 Loss1: 0.121249 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.088854 Loss1: 0.088163 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075811 Loss1: 0.075118 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.982002
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8955696202531646
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.200101 Loss1: 0.199417 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.139387 Loss1: 0.138702 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.133929 Loss1: 0.133242 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.130086 Loss1: 0.129399 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.106531 Loss1: 0.105843 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.087350 Loss1: 0.086661 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086242 Loss1: 0.085553 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075463 Loss1: 0.074775 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085205 Loss1: 0.084516 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098720 Loss1: 0.098031 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.979233
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9024390243902439
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.175464 Loss1: 0.174783 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.124557 Loss1: 0.123872 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.118693 Loss1: 0.118008 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086300 Loss1: 0.085614 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.090180 Loss1: 0.089495 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.076443 Loss1: 0.075755 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078956 Loss1: 0.078270 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.084594 Loss1: 0.083907 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060816 Loss1: 0.060129 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076886 Loss1: 0.076198 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.981707
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8243243243243243
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.246419 Loss1: 0.245734 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.144227 Loss1: 0.143538 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.102189 Loss1: 0.101502 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.103283 Loss1: 0.102593 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.111024 Loss1: 0.110334 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.112834 Loss1: 0.112143 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.130589 Loss1: 0.129899 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.112000 Loss1: 0.111308 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.070970 Loss1: 0.070278 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047936 Loss1: 0.047245 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.994088
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8908305921052632
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.201953 Loss1: 0.201266 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.133344 Loss1: 0.132654 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.113302 Loss1: 0.112612 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088094 Loss1: 0.087404 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.110447 Loss1: 0.109755 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.119621 Loss1: 0.118929 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.090458 Loss1: 0.089767 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102750 Loss1: 0.102059 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.095600 Loss1: 0.094909 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096042 Loss1: 0.095349 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.981908
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8669704861111112
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.251657 Loss1: 0.250973 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.132485 Loss1: 0.131799 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.095299 Loss1: 0.094614 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071282 Loss1: 0.070595 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060193 Loss1: 0.059506 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058463 Loss1: 0.057776 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082129 Loss1: 0.081440 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.125792 Loss1: 0.125104 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110278 Loss1: 0.109592 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.095256 Loss1: 0.094569 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.989366
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9018429487179487
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.198664 Loss1: 0.197981 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.134820 Loss1: 0.134133 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.125656 Loss1: 0.124968 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.097046 Loss1: 0.096357 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.093462 Loss1: 0.092771 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066594 Loss1: 0.065902 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051512 Loss1: 0.050822 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.065698 Loss1: 0.065007 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054362 Loss1: 0.053671 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060767 Loss1: 0.060076 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.990585
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8774038461538461
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.193253 Loss1: 0.192574 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.133331 Loss1: 0.132647 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.123458 Loss1: 0.122773 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.114672 Loss1: 0.113987 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086013 Loss1: 0.085327 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.087977 Loss1: 0.087293 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091283 Loss1: 0.090598 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096868 Loss1: 0.096180 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.101622 Loss1: 0.100936 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.089661 Loss1: 0.088975 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.982973
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8757911392405063
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.199801 Loss1: 0.199119 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.117425 Loss1: 0.116739 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.099686 Loss1: 0.098998 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.095811 Loss1: 0.095123 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101921 Loss1: 0.101233 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082496 Loss1: 0.081807 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.100109 Loss1: 0.099420 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095137 Loss1: 0.094447 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083228 Loss1: 0.082540 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085172 Loss1: 0.084481 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.983386
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.885878164556962
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.185992 Loss1: 0.185310 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.095690 Loss1: 0.095003 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.110820 Loss1: 0.110133 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.127984 Loss1: 0.127298 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.103429 Loss1: 0.102741 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.121589 Loss1: 0.120901 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094162 Loss1: 0.093474 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094342 Loss1: 0.093653 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063662 Loss1: 0.062974 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064557 Loss1: 0.063869 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.989517
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 07:05:07,821][flwr][DEBUG] - fit_round 50 received 10 results and 0 failures
-test acc: 0.6331
-[2023-09-22 07:06:37,577][flwr][INFO] - fit progress: (50, 2.2049550935864066, {'accuracy': 0.6331}, 100479.23796597496)
-[2023-09-22 07:06:37,577][flwr][DEBUG] - evaluate_round 50: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 07:07:16,665][flwr][DEBUG] - evaluate_round 50 received 10 results and 0 failures
-[2023-09-22 07:07:16,666][flwr][DEBUG] - fit_round 51: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9035823170731707
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.175415 Loss1: 0.174735 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.119686 Loss1: 0.119001 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.117820 Loss1: 0.117135 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.121226 Loss1: 0.120542 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101486 Loss1: 0.100801 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.107914 Loss1: 0.107228 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103980 Loss1: 0.103292 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095675 Loss1: 0.094988 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091955 Loss1: 0.091269 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083865 Loss1: 0.083179 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.985709
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8789556962025317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.184885 Loss1: 0.184202 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.115576 Loss1: 0.114889 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.092202 Loss1: 0.091514 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085379 Loss1: 0.084691 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.082839 Loss1: 0.082150 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.107595 Loss1: 0.106905 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091100 Loss1: 0.090413 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.102462 Loss1: 0.101774 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080680 Loss1: 0.079990 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096893 Loss1: 0.096204 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.979430
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8935032894736842
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.234641 Loss1: 0.233956 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.135527 Loss1: 0.134838 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.113436 Loss1: 0.112746 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.108958 Loss1: 0.108269 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.094245 Loss1: 0.093554 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106341 Loss1: 0.105650 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094114 Loss1: 0.093424 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078392 Loss1: 0.077701 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082706 Loss1: 0.082014 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091618 Loss1: 0.090928 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.982730
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9013053797468354
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.193550 Loss1: 0.192866 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.093386 Loss1: 0.092699 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083388 Loss1: 0.082700 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086124 Loss1: 0.085435 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073697 Loss1: 0.073007 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072441 Loss1: 0.071751 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065445 Loss1: 0.064755 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091881 Loss1: 0.091191 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.109561 Loss1: 0.108871 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.112923 Loss1: 0.112233 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.980222
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.877373417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.193234 Loss1: 0.192551 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.100142 Loss1: 0.099456 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.099782 Loss1: 0.099095 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.093616 Loss1: 0.092929 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.095586 Loss1: 0.094898 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.094676 Loss1: 0.093988 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094827 Loss1: 0.094139 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.090787 Loss1: 0.090098 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089252 Loss1: 0.088563 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071248 Loss1: 0.070559 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.984177
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8892227564102564
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.188684 Loss1: 0.188003 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.109382 Loss1: 0.108699 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.110488 Loss1: 0.109804 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.104757 Loss1: 0.104072 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.072381 Loss1: 0.071694 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062159 Loss1: 0.061475 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.116713 Loss1: 0.116027 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099851 Loss1: 0.099165 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.100004 Loss1: 0.099317 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078320 Loss1: 0.077633 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.989583
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8665364583333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.223648 Loss1: 0.222965 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.119915 Loss1: 0.119227 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.090970 Loss1: 0.090282 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.105773 Loss1: 0.105085 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.100287 Loss1: 0.099597 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.075048 Loss1: 0.074359 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.084898 Loss1: 0.084209 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.086505 Loss1: 0.085817 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089672 Loss1: 0.088984 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098767 Loss1: 0.098077 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.980035
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8344594594594594
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.237782 Loss1: 0.237098 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.157147 Loss1: 0.156458 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.107584 Loss1: 0.106896 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.128742 Loss1: 0.128054 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.109256 Loss1: 0.108567 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091561 Loss1: 0.090870 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.095527 Loss1: 0.094836 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077924 Loss1: 0.077232 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.110906 Loss1: 0.110215 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080897 Loss1: 0.080206 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.982897
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9060496794871795
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.194710 Loss1: 0.194028 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.120753 Loss1: 0.120066 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.123114 Loss1: 0.122427 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.114967 Loss1: 0.114278 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.099848 Loss1: 0.099158 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060538 Loss1: 0.059848 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065943 Loss1: 0.065252 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071650 Loss1: 0.070960 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081947 Loss1: 0.081257 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.096895 Loss1: 0.096204 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.980970
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9117879746835443
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.170802 Loss1: 0.170117 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.107753 Loss1: 0.107066 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.098381 Loss1: 0.097691 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.083459 Loss1: 0.082771 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079114 Loss1: 0.078424 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.096153 Loss1: 0.095462 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.109443 Loss1: 0.108753 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079013 Loss1: 0.078323 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078067 Loss1: 0.077376 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.095538 Loss1: 0.094848 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.981804
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 07:37:36,260][flwr][DEBUG] - fit_round 51 received 10 results and 0 failures
-test acc: 0.6347
-[2023-09-22 07:38:35,603][flwr][INFO] - fit progress: (51, 2.181678266951832, {'accuracy': 0.6347}, 102397.2644809708)
-[2023-09-22 07:38:35,603][flwr][DEBUG] - evaluate_round 51: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 07:39:14,109][flwr][DEBUG] - evaluate_round 51 received 10 results and 0 failures
-[2023-09-22 07:39:14,110][flwr][DEBUG] - fit_round 52: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8791232638888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.205996 Loss1: 0.205314 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.122995 Loss1: 0.122310 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069256 Loss1: 0.068570 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068894 Loss1: 0.068207 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079593 Loss1: 0.078907 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091908 Loss1: 0.091222 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.109386 Loss1: 0.108699 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074210 Loss1: 0.073521 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.097360 Loss1: 0.096672 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.118041 Loss1: 0.117352 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.983073
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.903391768292683
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.153114 Loss1: 0.152433 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.120135 Loss1: 0.119449 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.084659 Loss1: 0.083973 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069464 Loss1: 0.068776 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.070471 Loss1: 0.069785 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078616 Loss1: 0.077928 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072062 Loss1: 0.071375 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060992 Loss1: 0.060304 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081202 Loss1: 0.080515 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078622 Loss1: 0.077934 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.986662
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9005142405063291
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.164493 Loss1: 0.163811 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.106794 Loss1: 0.106108 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.098368 Loss1: 0.097679 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.078046 Loss1: 0.077358 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084622 Loss1: 0.083935 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.079029 Loss1: 0.078339 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.088639 Loss1: 0.087950 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075594 Loss1: 0.074906 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084665 Loss1: 0.083975 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.082094 Loss1: 0.081402 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.985759
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8338260135135135
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.219879 Loss1: 0.219196 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.142812 Loss1: 0.142125 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.108483 Loss1: 0.107794 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.108859 Loss1: 0.108171 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.090618 Loss1: 0.089930 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.069271 Loss1: 0.068582 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058724 Loss1: 0.058035 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061755 Loss1: 0.061066 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081414 Loss1: 0.080726 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070336 Loss1: 0.069647 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.978252
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.907051282051282
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.167577 Loss1: 0.166895 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.091378 Loss1: 0.090691 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083648 Loss1: 0.082959 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085781 Loss1: 0.085092 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.104549 Loss1: 0.103860 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.100150 Loss1: 0.099461 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078714 Loss1: 0.078025 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068670 Loss1: 0.067981 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078230 Loss1: 0.077539 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098125 Loss1: 0.097435 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.981370
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8834134615384616
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.163807 Loss1: 0.163128 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.121847 Loss1: 0.121163 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.108014 Loss1: 0.107330 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088145 Loss1: 0.087461 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101616 Loss1: 0.100931 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089094 Loss1: 0.088407 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086311 Loss1: 0.085624 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092480 Loss1: 0.091794 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111921 Loss1: 0.111236 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.100727 Loss1: 0.100041 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.982372
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8848892405063291
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.191586 Loss1: 0.190904 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.127238 Loss1: 0.126551 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.089309 Loss1: 0.088618 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.083157 Loss1: 0.082469 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084899 Loss1: 0.084209 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.087176 Loss1: 0.086488 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.079905 Loss1: 0.079215 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.117316 Loss1: 0.116628 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.096848 Loss1: 0.096158 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090283 Loss1: 0.089591 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.978244
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8900082236842105
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.194407 Loss1: 0.193722 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.112877 Loss1: 0.112189 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.087067 Loss1: 0.086379 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.070891 Loss1: 0.070201 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064048 Loss1: 0.063359 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.096083 Loss1: 0.095394 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107091 Loss1: 0.106401 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094389 Loss1: 0.093699 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077923 Loss1: 0.077232 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070956 Loss1: 0.070267 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.981908
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9064477848101266
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.164921 Loss1: 0.164237 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.109088 Loss1: 0.108398 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.099824 Loss1: 0.099133 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.098408 Loss1: 0.097718 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.082364 Loss1: 0.081673 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061554 Loss1: 0.060863 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067949 Loss1: 0.067257 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.067607 Loss1: 0.066917 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075263 Loss1: 0.074571 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072354 Loss1: 0.071662 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.986155
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8886471518987342
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.168941 Loss1: 0.168258 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.105610 Loss1: 0.104923 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.086014 Loss1: 0.085328 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.091712 Loss1: 0.091025 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.077969 Loss1: 0.077280 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105455 Loss1: 0.104768 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103936 Loss1: 0.103248 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.122103 Loss1: 0.121416 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.095164 Loss1: 0.094476 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.102305 Loss1: 0.101615 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.979628
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 08:09:16,446][flwr][DEBUG] - fit_round 52 received 10 results and 0 failures
-test acc: 0.6291
-[2023-09-22 08:10:14,663][flwr][INFO] - fit progress: (52, 2.2030137499300437, {'accuracy': 0.6291}, 104296.32475985959)
-[2023-09-22 08:10:14,664][flwr][DEBUG] - evaluate_round 52: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 08:10:54,678][flwr][DEBUG] - evaluate_round 52 received 10 results and 0 failures
-[2023-09-22 08:10:54,679][flwr][DEBUG] - fit_round 53: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.908922697368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.192943 Loss1: 0.192259 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.149768 Loss1: 0.149079 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.136060 Loss1: 0.135370 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.114696 Loss1: 0.114005 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.117991 Loss1: 0.117300 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.085299 Loss1: 0.084608 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.096773 Loss1: 0.096082 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.103478 Loss1: 0.102787 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067785 Loss1: 0.067093 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087254 Loss1: 0.086565 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.984992
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8760850694444444
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.198226 Loss1: 0.197543 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.111743 Loss1: 0.111057 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.105603 Loss1: 0.104916 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.100840 Loss1: 0.100153 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088670 Loss1: 0.087981 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.106346 Loss1: 0.105658 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.094824 Loss1: 0.094135 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095853 Loss1: 0.095165 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.096365 Loss1: 0.095676 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.118489 Loss1: 0.117802 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.980903
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9144435975609756
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.146122 Loss1: 0.145442 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.109500 Loss1: 0.108816 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088984 Loss1: 0.088299 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.089158 Loss1: 0.088471 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084375 Loss1: 0.083688 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.075072 Loss1: 0.074384 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071345 Loss1: 0.070657 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.093406 Loss1: 0.092718 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078378 Loss1: 0.077689 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081805 Loss1: 0.081118 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.985518
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9110576923076923
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.174968 Loss1: 0.174285 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.102167 Loss1: 0.101480 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088525 Loss1: 0.087836 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088671 Loss1: 0.087983 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.080049 Loss1: 0.079359 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063688 Loss1: 0.062996 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076587 Loss1: 0.075898 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075597 Loss1: 0.074905 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065457 Loss1: 0.064764 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053476 Loss1: 0.052784 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.991186
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8914161392405063
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.160003 Loss1: 0.159320 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.094407 Loss1: 0.093720 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.091105 Loss1: 0.090417 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.078758 Loss1: 0.078071 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098816 Loss1: 0.098128 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105619 Loss1: 0.104931 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.092667 Loss1: 0.091978 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092434 Loss1: 0.091745 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093179 Loss1: 0.092490 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.093256 Loss1: 0.092566 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.979628
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9185126582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.150637 Loss1: 0.149953 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.108621 Loss1: 0.107931 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.061349 Loss1: 0.060660 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.070652 Loss1: 0.069962 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.097630 Loss1: 0.096938 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.111295 Loss1: 0.110603 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085065 Loss1: 0.084374 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080486 Loss1: 0.079794 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053999 Loss1: 0.053307 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054765 Loss1: 0.054072 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.989913
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8340371621621622
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.215685 Loss1: 0.215001 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.119588 Loss1: 0.118900 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.103636 Loss1: 0.102948 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.090694 Loss1: 0.090004 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.092406 Loss1: 0.091717 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065990 Loss1: 0.065300 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091041 Loss1: 0.090352 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099183 Loss1: 0.098493 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.090248 Loss1: 0.089558 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071728 Loss1: 0.071038 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.991765
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.890625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.176255 Loss1: 0.175575 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.081758 Loss1: 0.081074 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.070453 Loss1: 0.069767 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058730 Loss1: 0.058046 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.053200 Loss1: 0.052516 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065084 Loss1: 0.064399 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071848 Loss1: 0.071162 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088584 Loss1: 0.087899 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080100 Loss1: 0.079413 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070343 Loss1: 0.069657 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.987380
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8981408227848101
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.164600 Loss1: 0.163916 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.119904 Loss1: 0.119216 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.092789 Loss1: 0.092099 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.096477 Loss1: 0.095788 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084054 Loss1: 0.083365 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082545 Loss1: 0.081855 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.095332 Loss1: 0.094644 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088635 Loss1: 0.087946 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076929 Loss1: 0.076240 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079832 Loss1: 0.079144 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.988924
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8839003164556962
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.171708 Loss1: 0.171023 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.104557 Loss1: 0.103868 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.085277 Loss1: 0.084587 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.089494 Loss1: 0.088805 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.123242 Loss1: 0.122552 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.118757 Loss1: 0.118067 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.122607 Loss1: 0.121918 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.096462 Loss1: 0.095771 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077973 Loss1: 0.077284 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057810 Loss1: 0.057120 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.990309
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 08:42:44,725][flwr][DEBUG] - fit_round 53 received 10 results and 0 failures
-test acc: 0.6303
-[2023-09-22 08:43:53,746][flwr][INFO] - fit progress: (53, 2.2342346537227447, {'accuracy': 0.6303}, 106315.40782714868)
-[2023-09-22 08:43:53,747][flwr][DEBUG] - evaluate_round 53: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 08:44:32,373][flwr][DEBUG] - evaluate_round 53 received 10 results and 0 failures
-[2023-09-22 08:44:32,373][flwr][DEBUG] - fit_round 54: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9056566455696202
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.167288 Loss1: 0.166604 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.097642 Loss1: 0.096955 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.113440 Loss1: 0.112752 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.095614 Loss1: 0.094925 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091308 Loss1: 0.090621 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.094624 Loss1: 0.093936 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.059245 Loss1: 0.058557 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085153 Loss1: 0.084463 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.098708 Loss1: 0.098019 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080631 Loss1: 0.079942 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.981408
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9124599358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.171042 Loss1: 0.170358 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078465 Loss1: 0.077778 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058451 Loss1: 0.057763 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.082387 Loss1: 0.081699 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.058704 Loss1: 0.058014 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074998 Loss1: 0.074309 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108664 Loss1: 0.107975 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097550 Loss1: 0.096861 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065083 Loss1: 0.064392 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054383 Loss1: 0.053692 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.993389
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8771701388888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.209629 Loss1: 0.208947 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.118121 Loss1: 0.117436 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.082247 Loss1: 0.081561 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.062497 Loss1: 0.061811 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073050 Loss1: 0.072364 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.071373 Loss1: 0.070686 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072974 Loss1: 0.072288 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.089055 Loss1: 0.088367 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074402 Loss1: 0.073714 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091367 Loss1: 0.090678 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.985460
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8795490506329114
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.170666 Loss1: 0.169984 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068806 Loss1: 0.068119 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060023 Loss1: 0.059334 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.075624 Loss1: 0.074937 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.122835 Loss1: 0.122146 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.119355 Loss1: 0.118668 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106346 Loss1: 0.105659 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075921 Loss1: 0.075233 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059692 Loss1: 0.059005 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066991 Loss1: 0.066301 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.983386
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9148246951219512
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144921 Loss1: 0.144241 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.103099 Loss1: 0.102415 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.079172 Loss1: 0.078487 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.095861 Loss1: 0.095174 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.092426 Loss1: 0.091739 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091826 Loss1: 0.091139 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.108913 Loss1: 0.108227 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088789 Loss1: 0.088101 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072294 Loss1: 0.071607 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066796 Loss1: 0.066109 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.990473
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9046052631578947
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.197543 Loss1: 0.196858 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.116165 Loss1: 0.115477 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.107419 Loss1: 0.106728 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.105602 Loss1: 0.104912 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.105169 Loss1: 0.104479 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.081557 Loss1: 0.080868 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.106111 Loss1: 0.105421 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.104374 Loss1: 0.103682 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080351 Loss1: 0.079660 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063836 Loss1: 0.063144 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.988076
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8924050632911392
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.160280 Loss1: 0.159597 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.097182 Loss1: 0.096497 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.081814 Loss1: 0.081127 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.081925 Loss1: 0.081239 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.065116 Loss1: 0.064429 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089481 Loss1: 0.088794 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.101293 Loss1: 0.100606 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092640 Loss1: 0.091953 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079051 Loss1: 0.078362 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074186 Loss1: 0.073499 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.983386
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9113924050632911
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.160154 Loss1: 0.159471 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.105171 Loss1: 0.104485 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083866 Loss1: 0.083179 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087309 Loss1: 0.086620 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079923 Loss1: 0.079233 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078734 Loss1: 0.078045 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078486 Loss1: 0.077799 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099406 Loss1: 0.098717 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091571 Loss1: 0.090882 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080001 Loss1: 0.079313 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.991297
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8410050675675675
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.213045 Loss1: 0.212361 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.103924 Loss1: 0.103235 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.094519 Loss1: 0.093830 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.062570 Loss1: 0.061880 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.062672 Loss1: 0.061982 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.081799 Loss1: 0.081109 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081252 Loss1: 0.080562 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061039 Loss1: 0.060348 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085062 Loss1: 0.084372 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078368 Loss1: 0.077679 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.985220
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8982371794871795
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.173393 Loss1: 0.172715 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.135783 Loss1: 0.135099 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.114653 Loss1: 0.113970 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.122311 Loss1: 0.121627 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.125289 Loss1: 0.124605 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.131248 Loss1: 0.130563 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091993 Loss1: 0.091309 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094328 Loss1: 0.093642 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071915 Loss1: 0.071229 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081809 Loss1: 0.081123 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.980970
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 09:15:06,540][flwr][DEBUG] - fit_round 54 received 10 results and 0 failures
-test acc: 0.634
-[2023-09-22 09:16:08,095][flwr][INFO] - fit progress: (54, 2.22973222568774, {'accuracy': 0.634}, 108249.7566356957)
-[2023-09-22 09:16:08,096][flwr][DEBUG] - evaluate_round 54: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 09:16:46,515][flwr][DEBUG] - evaluate_round 54 received 10 results and 0 failures
-[2023-09-22 09:16:46,516][flwr][DEBUG] - fit_round 55: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9125791139240507
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.163372 Loss1: 0.162688 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.111495 Loss1: 0.110807 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.090252 Loss1: 0.089562 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.082235 Loss1: 0.081547 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.077936 Loss1: 0.077247 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.086384 Loss1: 0.085694 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068065 Loss1: 0.067376 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044276 Loss1: 0.043584 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058871 Loss1: 0.058181 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075514 Loss1: 0.074822 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.984375
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.899129746835443
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144305 Loss1: 0.143623 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.086306 Loss1: 0.085622 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069513 Loss1: 0.068826 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067782 Loss1: 0.067096 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069383 Loss1: 0.068695 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061216 Loss1: 0.060528 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.075739 Loss1: 0.075051 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078943 Loss1: 0.078255 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.090825 Loss1: 0.090137 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075421 Loss1: 0.074733 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.981804
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9014423076923077
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.159107 Loss1: 0.158428 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.097084 Loss1: 0.096401 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.074567 Loss1: 0.073884 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.076810 Loss1: 0.076125 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073154 Loss1: 0.072471 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089229 Loss1: 0.088545 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.098183 Loss1: 0.097499 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.090698 Loss1: 0.090013 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078465 Loss1: 0.077781 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049228 Loss1: 0.048542 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.987780
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9197789634146342
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.147704 Loss1: 0.147023 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.095709 Loss1: 0.095026 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.077359 Loss1: 0.076674 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087118 Loss1: 0.086432 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.083503 Loss1: 0.082816 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.069149 Loss1: 0.068462 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062257 Loss1: 0.061571 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069879 Loss1: 0.069193 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.083850 Loss1: 0.083163 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.084040 Loss1: 0.083354 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.982279
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9150641025641025
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.143408 Loss1: 0.142726 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.089993 Loss1: 0.089305 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.098660 Loss1: 0.097972 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.092374 Loss1: 0.091685 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.080860 Loss1: 0.080171 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053030 Loss1: 0.052342 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073914 Loss1: 0.073225 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.065908 Loss1: 0.065219 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063004 Loss1: 0.062315 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081936 Loss1: 0.081246 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.980569
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8924050632911392
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.136082 Loss1: 0.135400 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.091117 Loss1: 0.090431 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088034 Loss1: 0.087345 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.075125 Loss1: 0.074437 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.087920 Loss1: 0.087232 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.070275 Loss1: 0.069587 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.089805 Loss1: 0.089117 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.100987 Loss1: 0.100300 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.092629 Loss1: 0.091940 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098159 Loss1: 0.097469 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.982199
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9137658227848101
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.165001 Loss1: 0.164319 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.101940 Loss1: 0.101253 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.068548 Loss1: 0.067860 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087349 Loss1: 0.086663 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.103791 Loss1: 0.103102 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105055 Loss1: 0.104368 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.118438 Loss1: 0.117749 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.107145 Loss1: 0.106456 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081288 Loss1: 0.080600 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069719 Loss1: 0.069029 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.989715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9052220394736842
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.180560 Loss1: 0.179876 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.107927 Loss1: 0.107239 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.103379 Loss1: 0.102687 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.083178 Loss1: 0.082488 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.077848 Loss1: 0.077157 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.087957 Loss1: 0.087267 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103090 Loss1: 0.102398 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095186 Loss1: 0.094496 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091498 Loss1: 0.090809 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065852 Loss1: 0.065162 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.986842
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.880859375
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.189659 Loss1: 0.188976 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.096331 Loss1: 0.095644 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.091621 Loss1: 0.090933 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071484 Loss1: 0.070795 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091237 Loss1: 0.090548 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.105195 Loss1: 0.104507 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.097614 Loss1: 0.096926 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080566 Loss1: 0.079878 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072465 Loss1: 0.071777 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056796 Loss1: 0.056107 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.988498
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.847339527027027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.205544 Loss1: 0.204860 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.104730 Loss1: 0.104042 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083760 Loss1: 0.083071 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067264 Loss1: 0.066574 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.070639 Loss1: 0.069949 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074859 Loss1: 0.074170 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.059811 Loss1: 0.059120 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075702 Loss1: 0.075011 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067956 Loss1: 0.067266 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052145 Loss1: 0.051454 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.986486
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 09:46:58,527][flwr][DEBUG] - fit_round 55 received 10 results and 0 failures
-test acc: 0.6324
-[2023-09-22 09:47:56,143][flwr][INFO] - fit progress: (55, 2.254546900526784, {'accuracy': 0.6324}, 110157.80404786859)
-[2023-09-22 09:47:56,144][flwr][DEBUG] - evaluate_round 55: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 09:48:34,892][flwr][DEBUG] - evaluate_round 55 received 10 results and 0 failures
-[2023-09-22 09:48:34,893][flwr][DEBUG] - fit_round 56: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8893229166666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.176493 Loss1: 0.175809 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.099395 Loss1: 0.098709 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.083752 Loss1: 0.083064 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.092986 Loss1: 0.092297 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.089814 Loss1: 0.089127 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.079300 Loss1: 0.078611 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078367 Loss1: 0.077678 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074888 Loss1: 0.074199 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.073758 Loss1: 0.073069 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076311 Loss1: 0.075622 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.982856
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9076891447368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.157224 Loss1: 0.156538 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.117247 Loss1: 0.116556 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.084953 Loss1: 0.084263 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.081797 Loss1: 0.081107 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.098019 Loss1: 0.097329 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078835 Loss1: 0.078142 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.080507 Loss1: 0.079816 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.097582 Loss1: 0.096890 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082997 Loss1: 0.082304 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078030 Loss1: 0.077337 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.989926
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9235899390243902
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.131041 Loss1: 0.130362 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.084688 Loss1: 0.084002 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.105627 Loss1: 0.104943 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085415 Loss1: 0.084730 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.068279 Loss1: 0.067593 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068026 Loss1: 0.067339 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071642 Loss1: 0.070954 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071487 Loss1: 0.070799 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.070820 Loss1: 0.070132 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055239 Loss1: 0.054551 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.992759
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9167325949367089
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.150346 Loss1: 0.149661 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.083677 Loss1: 0.082988 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.071807 Loss1: 0.071118 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047845 Loss1: 0.047155 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.071747 Loss1: 0.071057 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.071031 Loss1: 0.070340 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070082 Loss1: 0.069390 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048308 Loss1: 0.047619 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064545 Loss1: 0.063854 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071298 Loss1: 0.070607 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.990704
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8488175675675675
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.186883 Loss1: 0.186200 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.115384 Loss1: 0.114695 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.089395 Loss1: 0.088707 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088259 Loss1: 0.087570 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.101970 Loss1: 0.101281 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.095931 Loss1: 0.095241 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082551 Loss1: 0.081862 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.067215 Loss1: 0.066525 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058822 Loss1: 0.058131 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060070 Loss1: 0.059381 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989654
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9072516025641025
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.146384 Loss1: 0.145705 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.093807 Loss1: 0.093124 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.102518 Loss1: 0.101834 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086685 Loss1: 0.085999 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.062995 Loss1: 0.062311 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.097788 Loss1: 0.097102 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.107356 Loss1: 0.106670 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.091649 Loss1: 0.090963 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091814 Loss1: 0.091127 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.084596 Loss1: 0.083911 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.980970
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.915743670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.133483 Loss1: 0.132801 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.087133 Loss1: 0.086445 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.086427 Loss1: 0.085738 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086106 Loss1: 0.085416 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086350 Loss1: 0.085662 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.114939 Loss1: 0.114251 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.117489 Loss1: 0.116798 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.099871 Loss1: 0.099182 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064601 Loss1: 0.063910 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055310 Loss1: 0.054622 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9009098101265823
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.153825 Loss1: 0.153143 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.101965 Loss1: 0.101280 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.100819 Loss1: 0.100133 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066236 Loss1: 0.065551 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.075881 Loss1: 0.075196 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.081027 Loss1: 0.080340 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076344 Loss1: 0.075657 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075243 Loss1: 0.074555 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071710 Loss1: 0.071021 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085332 Loss1: 0.084643 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.977650
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9190705128205128
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.136276 Loss1: 0.135593 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.099828 Loss1: 0.099141 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.074669 Loss1: 0.073981 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.081215 Loss1: 0.080526 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069422 Loss1: 0.068733 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050076 Loss1: 0.049388 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036367 Loss1: 0.035678 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062592 Loss1: 0.061901 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076502 Loss1: 0.075813 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087546 Loss1: 0.086855 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.986579
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8912183544303798
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.136684 Loss1: 0.136002 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.085612 Loss1: 0.084924 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.076345 Loss1: 0.075657 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.087905 Loss1: 0.087216 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.071643 Loss1: 0.070953 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058402 Loss1: 0.057712 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072365 Loss1: 0.071676 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069774 Loss1: 0.069083 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076160 Loss1: 0.075470 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.103651 Loss1: 0.102962 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.986353
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 10:19:01,505][flwr][DEBUG] - fit_round 56 received 10 results and 0 failures
-test acc: 0.6339
-[2023-09-22 10:19:57,276][flwr][INFO] - fit progress: (56, 2.251486159170778, {'accuracy': 0.6339}, 112078.93746713502)
-[2023-09-22 10:19:57,277][flwr][DEBUG] - evaluate_round 56: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 10:20:37,401][flwr][DEBUG] - evaluate_round 56 received 10 results and 0 failures
-[2023-09-22 10:20:37,402][flwr][DEBUG] - fit_round 57: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9237804878048781
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.128587 Loss1: 0.127905 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065857 Loss1: 0.065173 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.062804 Loss1: 0.062119 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066879 Loss1: 0.066193 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.083335 Loss1: 0.082648 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.064871 Loss1: 0.064183 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073453 Loss1: 0.072767 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075907 Loss1: 0.075219 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059489 Loss1: 0.058802 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083056 Loss1: 0.082368 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.984947
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9004407051282052
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.147339 Loss1: 0.146660 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.074674 Loss1: 0.073992 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.050567 Loss1: 0.049884 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046682 Loss1: 0.045998 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.056050 Loss1: 0.055366 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.089697 Loss1: 0.089012 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083117 Loss1: 0.082431 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068742 Loss1: 0.068056 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067199 Loss1: 0.066513 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087766 Loss1: 0.087083 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.975962
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9153481012658228
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.143539 Loss1: 0.142857 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.099089 Loss1: 0.098403 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.092695 Loss1: 0.092008 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.079382 Loss1: 0.078694 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086745 Loss1: 0.086057 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092763 Loss1: 0.092073 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.090099 Loss1: 0.089410 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072921 Loss1: 0.072231 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066248 Loss1: 0.065559 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059581 Loss1: 0.058890 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.987342
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9230769230769231
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.131828 Loss1: 0.131147 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.075480 Loss1: 0.074793 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.063091 Loss1: 0.062404 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058533 Loss1: 0.057845 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.066521 Loss1: 0.065833 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.070447 Loss1: 0.069758 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068559 Loss1: 0.067868 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048766 Loss1: 0.048076 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063392 Loss1: 0.062701 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049320 Loss1: 0.048628 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.984375
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8823784722222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.183960 Loss1: 0.183278 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.098113 Loss1: 0.097428 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066742 Loss1: 0.066056 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066250 Loss1: 0.065563 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064434 Loss1: 0.063748 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063566 Loss1: 0.062879 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072523 Loss1: 0.071836 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.134785 Loss1: 0.134099 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.111926 Loss1: 0.111238 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091203 Loss1: 0.090516 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.985894
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9027549342105263
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.190850 Loss1: 0.190165 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.102582 Loss1: 0.101893 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.080788 Loss1: 0.080099 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.074897 Loss1: 0.074207 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.078899 Loss1: 0.078207 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078530 Loss1: 0.077838 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103668 Loss1: 0.102977 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.094816 Loss1: 0.094125 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081183 Loss1: 0.080490 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083965 Loss1: 0.083274 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.987048
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9268196202531646
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.147926 Loss1: 0.147242 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.080427 Loss1: 0.079737 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.070650 Loss1: 0.069960 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.074337 Loss1: 0.073645 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.083006 Loss1: 0.082312 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074695 Loss1: 0.074004 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085594 Loss1: 0.084901 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.092550 Loss1: 0.091859 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074418 Loss1: 0.073726 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058935 Loss1: 0.058243 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.989320
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8526182432432432
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.179405 Loss1: 0.178721 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.110108 Loss1: 0.109420 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.085313 Loss1: 0.084623 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069102 Loss1: 0.068412 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.065628 Loss1: 0.064939 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074693 Loss1: 0.074004 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053647 Loss1: 0.052956 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.083851 Loss1: 0.083159 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084047 Loss1: 0.083356 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057357 Loss1: 0.056666 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.988387
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9060522151898734
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144210 Loss1: 0.143529 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.090538 Loss1: 0.089851 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.080905 Loss1: 0.080218 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.080363 Loss1: 0.079676 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.091376 Loss1: 0.090687 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.092690 Loss1: 0.092002 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.063844 Loss1: 0.063155 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070747 Loss1: 0.070060 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047177 Loss1: 0.046489 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046374 Loss1: 0.045685 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992682
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.896756329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.149916 Loss1: 0.149233 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.090775 Loss1: 0.090088 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.068567 Loss1: 0.067879 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.073252 Loss1: 0.072564 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.081493 Loss1: 0.080804 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.073677 Loss1: 0.072987 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067829 Loss1: 0.067141 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074224 Loss1: 0.073533 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082365 Loss1: 0.081675 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078057 Loss1: 0.077366 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.986353
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 10:50:48,331][flwr][DEBUG] - fit_round 57 received 10 results and 0 failures
-test acc: 0.6369
-[2023-09-22 10:51:54,632][flwr][INFO] - fit progress: (57, 2.2792362104208705, {'accuracy': 0.6369}, 113996.29401065502)
-[2023-09-22 10:51:54,633][flwr][DEBUG] - evaluate_round 57: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 10:52:34,006][flwr][DEBUG] - evaluate_round 57 received 10 results and 0 failures
-[2023-09-22 10:52:34,007][flwr][DEBUG] - fit_round 58: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9145569620253164
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.134843 Loss1: 0.134158 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.096164 Loss1: 0.095476 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.085630 Loss1: 0.084941 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067671 Loss1: 0.066980 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.075326 Loss1: 0.074636 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.101878 Loss1: 0.101189 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091884 Loss1: 0.091194 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.104193 Loss1: 0.103502 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114539 Loss1: 0.113848 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083429 Loss1: 0.082736 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.993078
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9232772435897436
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144608 Loss1: 0.143927 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.102332 Loss1: 0.101647 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.079713 Loss1: 0.079025 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069301 Loss1: 0.068613 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.058416 Loss1: 0.057728 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036473 Loss1: 0.035784 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044998 Loss1: 0.044308 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049468 Loss1: 0.048778 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066418 Loss1: 0.065728 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064360 Loss1: 0.063672 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.984776
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9199695121951219
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.132937 Loss1: 0.132257 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.077936 Loss1: 0.077252 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064687 Loss1: 0.064002 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.074834 Loss1: 0.074149 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079693 Loss1: 0.079008 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.081262 Loss1: 0.080574 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073898 Loss1: 0.073212 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052274 Loss1: 0.051588 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044925 Loss1: 0.044237 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049436 Loss1: 0.048749 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.990091
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9090189873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.145638 Loss1: 0.144958 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078035 Loss1: 0.077351 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064996 Loss1: 0.064312 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071534 Loss1: 0.070850 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.058963 Loss1: 0.058278 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.069753 Loss1: 0.069067 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.082218 Loss1: 0.081531 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.083515 Loss1: 0.082829 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059950 Loss1: 0.059261 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072201 Loss1: 0.071515 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.985166
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.919202302631579
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.157386 Loss1: 0.156702 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.080713 Loss1: 0.080024 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.073363 Loss1: 0.072674 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.057504 Loss1: 0.056816 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.076195 Loss1: 0.075505 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.056178 Loss1: 0.055489 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057746 Loss1: 0.057057 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058340 Loss1: 0.057649 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077716 Loss1: 0.077025 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072807 Loss1: 0.072116 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.976151
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.896484375
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.179366 Loss1: 0.178684 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.084353 Loss1: 0.083665 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.081176 Loss1: 0.080491 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066666 Loss1: 0.065978 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063722 Loss1: 0.063035 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074520 Loss1: 0.073832 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065400 Loss1: 0.064711 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062468 Loss1: 0.061780 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052624 Loss1: 0.051935 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062044 Loss1: 0.061356 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.991536
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9145569620253164
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.130700 Loss1: 0.130017 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.088949 Loss1: 0.088263 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.087171 Loss1: 0.086483 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.089425 Loss1: 0.088737 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.099953 Loss1: 0.099264 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.096849 Loss1: 0.096160 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072393 Loss1: 0.071704 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.088184 Loss1: 0.087495 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089876 Loss1: 0.089188 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.093026 Loss1: 0.092337 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.981804
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8918117088607594
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144679 Loss1: 0.143998 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.102355 Loss1: 0.101668 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.080995 Loss1: 0.080307 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086094 Loss1: 0.085403 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084498 Loss1: 0.083809 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.085917 Loss1: 0.085228 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091265 Loss1: 0.090575 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080424 Loss1: 0.079732 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064075 Loss1: 0.063384 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073829 Loss1: 0.073137 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.981606
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8526182432432432
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.181932 Loss1: 0.181247 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.100775 Loss1: 0.100087 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.102997 Loss1: 0.102308 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069132 Loss1: 0.068442 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.074030 Loss1: 0.073340 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.075515 Loss1: 0.074825 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.087681 Loss1: 0.086991 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.098005 Loss1: 0.097315 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066655 Loss1: 0.065963 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078417 Loss1: 0.077725 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.990709
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9030448717948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.127790 Loss1: 0.127109 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.085841 Loss1: 0.085157 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.094675 Loss1: 0.093991 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.096008 Loss1: 0.095324 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.092625 Loss1: 0.091941 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091244 Loss1: 0.090559 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.090298 Loss1: 0.089611 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081182 Loss1: 0.080497 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085043 Loss1: 0.084357 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062991 Loss1: 0.062305 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.989583
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 11:23:41,408][flwr][DEBUG] - fit_round 58 received 10 results and 0 failures
-test acc: 0.6379
-[2023-09-22 11:24:44,869][flwr][INFO] - fit progress: (58, 2.2519221557215, {'accuracy': 0.6379}, 115966.53004605463)
-[2023-09-22 11:24:44,869][flwr][DEBUG] - evaluate_round 58: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 11:25:24,646][flwr][DEBUG] - evaluate_round 58 received 10 results and 0 failures
-[2023-09-22 11:25:24,648][flwr][DEBUG] - fit_round 59: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9193037974683544
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.130642 Loss1: 0.129959 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.086782 Loss1: 0.086094 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.102118 Loss1: 0.101429 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.086572 Loss1: 0.085883 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.068652 Loss1: 0.067964 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.073175 Loss1: 0.072486 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.064657 Loss1: 0.063968 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069834 Loss1: 0.069143 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.086151 Loss1: 0.085461 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073156 Loss1: 0.072467 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.988331
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8914930555555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.167947 Loss1: 0.167264 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.096267 Loss1: 0.095580 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065512 Loss1: 0.064825 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065051 Loss1: 0.064364 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063580 Loss1: 0.062892 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061127 Loss1: 0.060439 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077292 Loss1: 0.076602 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069326 Loss1: 0.068638 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080862 Loss1: 0.080173 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074526 Loss1: 0.073837 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.984592
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9264240506329114
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.104241 Loss1: 0.103559 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.076653 Loss1: 0.075965 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.063277 Loss1: 0.062588 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.070486 Loss1: 0.069796 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.078541 Loss1: 0.077851 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.057908 Loss1: 0.057218 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062614 Loss1: 0.061923 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061478 Loss1: 0.060786 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082326 Loss1: 0.081634 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050060 Loss1: 0.049368 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.991693
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8604307432432432
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.169320 Loss1: 0.168635 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.088719 Loss1: 0.088030 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045810 Loss1: 0.045121 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049124 Loss1: 0.048435 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060302 Loss1: 0.059613 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072245 Loss1: 0.071555 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.080025 Loss1: 0.079335 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071072 Loss1: 0.070383 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.062914 Loss1: 0.062225 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070019 Loss1: 0.069327 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.986909
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9040743670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.124525 Loss1: 0.123842 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.079348 Loss1: 0.078663 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059184 Loss1: 0.058497 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045832 Loss1: 0.045146 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.049875 Loss1: 0.049188 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061634 Loss1: 0.060947 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055440 Loss1: 0.054753 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061546 Loss1: 0.060857 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078290 Loss1: 0.077602 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.092324 Loss1: 0.091635 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.979826
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9236778846153846
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.134411 Loss1: 0.133726 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.073830 Loss1: 0.073143 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065536 Loss1: 0.064847 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.056755 Loss1: 0.056064 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051932 Loss1: 0.051241 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038628 Loss1: 0.037938 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048772 Loss1: 0.048082 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052714 Loss1: 0.052022 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049826 Loss1: 0.049135 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040987 Loss1: 0.040296 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.991787
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9176682692307693
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.144010 Loss1: 0.143331 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078683 Loss1: 0.078000 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.050241 Loss1: 0.049556 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068453 Loss1: 0.067768 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088272 Loss1: 0.087586 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.102390 Loss1: 0.101704 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.116279 Loss1: 0.115593 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068930 Loss1: 0.068242 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064885 Loss1: 0.064198 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053077 Loss1: 0.052390 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.980970
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8912183544303798
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.154235 Loss1: 0.153552 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.081173 Loss1: 0.080484 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.079785 Loss1: 0.079096 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071665 Loss1: 0.070976 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073199 Loss1: 0.072510 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074048 Loss1: 0.073358 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044421 Loss1: 0.043732 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068620 Loss1: 0.067928 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055651 Loss1: 0.054960 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054590 Loss1: 0.053898 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.988133
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9291158536585366
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103590 Loss1: 0.102909 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066192 Loss1: 0.065508 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045163 Loss1: 0.044479 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052207 Loss1: 0.051521 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043808 Loss1: 0.043121 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055219 Loss1: 0.054533 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.103266 Loss1: 0.102579 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.066970 Loss1: 0.066283 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066607 Loss1: 0.065919 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064232 Loss1: 0.063545 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.982088
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.91796875
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.163269 Loss1: 0.162585 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.095255 Loss1: 0.094566 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060455 Loss1: 0.059766 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.080770 Loss1: 0.080080 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.081536 Loss1: 0.080847 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078154 Loss1: 0.077463 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.097301 Loss1: 0.096611 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095880 Loss1: 0.095191 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.080082 Loss1: 0.079393 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075840 Loss1: 0.075149 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.980674
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 11:56:52,497][flwr][DEBUG] - fit_round 59 received 10 results and 0 failures
-test acc: 0.6354
-[2023-09-22 11:58:36,400][flwr][INFO] - fit progress: (59, 2.2759478625398093, {'accuracy': 0.6354}, 117998.06179395458)
-[2023-09-22 11:58:36,401][flwr][DEBUG] - evaluate_round 59: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 11:59:14,036][flwr][DEBUG] - evaluate_round 59 received 10 results and 0 failures
-[2023-09-22 11:59:14,037][flwr][DEBUG] - fit_round 60: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9010416666666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.140802 Loss1: 0.140119 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.092404 Loss1: 0.091718 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.078219 Loss1: 0.077532 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061041 Loss1: 0.060353 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.079201 Loss1: 0.078513 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063790 Loss1: 0.063102 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070037 Loss1: 0.069349 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.066704 Loss1: 0.066017 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046973 Loss1: 0.046284 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041775 Loss1: 0.041087 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994358
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9165348101265823
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.113589 Loss1: 0.112908 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072344 Loss1: 0.071657 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055700 Loss1: 0.055011 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050256 Loss1: 0.049570 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055228 Loss1: 0.054540 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.076516 Loss1: 0.075828 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073322 Loss1: 0.072636 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047131 Loss1: 0.046443 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072581 Loss1: 0.071892 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081622 Loss1: 0.080933 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.981013
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9155016447368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.131729 Loss1: 0.131043 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.083839 Loss1: 0.083150 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.071569 Loss1: 0.070879 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071684 Loss1: 0.070995 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086252 Loss1: 0.085562 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.093499 Loss1: 0.092808 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076033 Loss1: 0.075343 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068948 Loss1: 0.068258 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078216 Loss1: 0.077527 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068168 Loss1: 0.067476 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.988076
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9122596153846154
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.130060 Loss1: 0.129379 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.071540 Loss1: 0.070855 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059835 Loss1: 0.059151 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.063453 Loss1: 0.062769 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059722 Loss1: 0.059036 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068757 Loss1: 0.068070 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.090617 Loss1: 0.089933 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095835 Loss1: 0.095149 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093368 Loss1: 0.092683 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086888 Loss1: 0.086199 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987179
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.855785472972973
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.165608 Loss1: 0.164927 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.086624 Loss1: 0.085937 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.084446 Loss1: 0.083756 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061717 Loss1: 0.061026 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073367 Loss1: 0.072677 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091584 Loss1: 0.090895 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.064014 Loss1: 0.063323 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061050 Loss1: 0.060358 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059017 Loss1: 0.058328 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054216 Loss1: 0.053526 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.989231
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.928006329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105442 Loss1: 0.104760 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.079864 Loss1: 0.079176 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.081011 Loss1: 0.080321 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.083275 Loss1: 0.082584 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073762 Loss1: 0.073072 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082608 Loss1: 0.081918 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076863 Loss1: 0.076172 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075426 Loss1: 0.074736 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052991 Loss1: 0.052300 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060123 Loss1: 0.059432 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.989320
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8981408227848101
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.139829 Loss1: 0.139146 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067444 Loss1: 0.066756 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056657 Loss1: 0.055969 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.073841 Loss1: 0.073152 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.071606 Loss1: 0.070917 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061233 Loss1: 0.060543 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076223 Loss1: 0.075535 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080104 Loss1: 0.079415 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075340 Loss1: 0.074652 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069611 Loss1: 0.068920 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.987342
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9272836538461539
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.115755 Loss1: 0.115074 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068817 Loss1: 0.068130 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056486 Loss1: 0.055799 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053189 Loss1: 0.052501 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057391 Loss1: 0.056703 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058579 Loss1: 0.057890 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.077555 Loss1: 0.076864 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079177 Loss1: 0.078487 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058953 Loss1: 0.058264 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048434 Loss1: 0.047743 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.989383
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9309731012658228
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.129098 Loss1: 0.128416 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078072 Loss1: 0.077385 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.087011 Loss1: 0.086324 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085216 Loss1: 0.084527 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.071810 Loss1: 0.071122 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.057453 Loss1: 0.056764 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054078 Loss1: 0.053389 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063304 Loss1: 0.062616 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054829 Loss1: 0.054141 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067695 Loss1: 0.067006 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989320
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9233993902439024
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.131753 Loss1: 0.131073 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066099 Loss1: 0.065414 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065768 Loss1: 0.065081 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066390 Loss1: 0.065703 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073907 Loss1: 0.073220 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.075545 Loss1: 0.074856 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.066764 Loss1: 0.066077 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056409 Loss1: 0.055721 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076010 Loss1: 0.075321 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076068 Loss1: 0.075378 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987043
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 12:31:08,804][flwr][DEBUG] - fit_round 60 received 10 results and 0 failures
-test acc: 0.6394
-[2023-09-22 12:32:14,347][flwr][INFO] - fit progress: (60, 2.300492998700553, {'accuracy': 0.6394}, 120016.00814736774)
-[2023-09-22 12:32:14,347][flwr][DEBUG] - evaluate_round 60: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 12:32:52,792][flwr][DEBUG] - evaluate_round 60 received 10 results and 0 failures
-[2023-09-22 12:32:52,793][flwr][DEBUG] - fit_round 61: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9302591463414634
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103416 Loss1: 0.102736 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.069314 Loss1: 0.068630 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064702 Loss1: 0.064019 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048388 Loss1: 0.047702 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063229 Loss1: 0.062543 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058859 Loss1: 0.058171 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061518 Loss1: 0.060832 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060381 Loss1: 0.059694 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068819 Loss1: 0.068133 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088116 Loss1: 0.087429 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.979802
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.921073717948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.117948 Loss1: 0.117268 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.076936 Loss1: 0.076253 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.088159 Loss1: 0.087475 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067472 Loss1: 0.066786 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.099291 Loss1: 0.098605 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.098376 Loss1: 0.097692 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069047 Loss1: 0.068361 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.081835 Loss1: 0.081149 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.091241 Loss1: 0.090555 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.116217 Loss1: 0.115531 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.981571
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9256329113924051
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.114813 Loss1: 0.114131 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.063793 Loss1: 0.063107 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064886 Loss1: 0.064198 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053616 Loss1: 0.052927 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064398 Loss1: 0.063708 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062372 Loss1: 0.061684 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083259 Loss1: 0.082570 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069161 Loss1: 0.068472 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068197 Loss1: 0.067507 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050882 Loss1: 0.050192 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.990902
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8985363924050633
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.126361 Loss1: 0.125679 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066233 Loss1: 0.065549 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052060 Loss1: 0.051374 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049698 Loss1: 0.049011 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.073109 Loss1: 0.072421 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.108843 Loss1: 0.108154 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.078555 Loss1: 0.077866 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072515 Loss1: 0.071825 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065546 Loss1: 0.064855 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070832 Loss1: 0.070141 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.981804
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.862964527027027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.143786 Loss1: 0.143102 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.081736 Loss1: 0.081048 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.067516 Loss1: 0.066828 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046582 Loss1: 0.045893 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046841 Loss1: 0.046149 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050659 Loss1: 0.049968 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061968 Loss1: 0.061278 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057272 Loss1: 0.056583 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068659 Loss1: 0.067970 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066694 Loss1: 0.066002 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.991343
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9235197368421053
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.160666 Loss1: 0.159981 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.098809 Loss1: 0.098120 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.093732 Loss1: 0.093042 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.085002 Loss1: 0.084311 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.066655 Loss1: 0.065964 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062249 Loss1: 0.061559 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053609 Loss1: 0.052917 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059391 Loss1: 0.058701 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077120 Loss1: 0.076428 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080405 Loss1: 0.079713 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.986637
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.90625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.142670 Loss1: 0.141988 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067134 Loss1: 0.066448 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051590 Loss1: 0.050903 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.064188 Loss1: 0.063500 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054313 Loss1: 0.053626 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074083 Loss1: 0.073397 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071940 Loss1: 0.071252 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.073737 Loss1: 0.073050 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.068104 Loss1: 0.067416 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073249 Loss1: 0.072560 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.988331
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9286858974358975
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116392 Loss1: 0.115710 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.056861 Loss1: 0.056173 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.075214 Loss1: 0.074527 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.088098 Loss1: 0.087410 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.087003 Loss1: 0.086312 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072272 Loss1: 0.071582 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085433 Loss1: 0.084744 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077619 Loss1: 0.076928 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064574 Loss1: 0.063883 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071262 Loss1: 0.070568 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.985978
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9378955696202531
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095339 Loss1: 0.094657 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052799 Loss1: 0.052111 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.054848 Loss1: 0.054159 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059995 Loss1: 0.059304 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054415 Loss1: 0.053725 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052097 Loss1: 0.051406 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.075925 Loss1: 0.075234 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075368 Loss1: 0.074677 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084340 Loss1: 0.083647 Loss2: 0.000694
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086159 Loss1: 0.085467 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.984573
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8951822916666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.163926 Loss1: 0.163243 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.089450 Loss1: 0.088764 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066626 Loss1: 0.065939 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.080528 Loss1: 0.079841 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.076235 Loss1: 0.075548 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078904 Loss1: 0.078217 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.085788 Loss1: 0.085098 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077613 Loss1: 0.076923 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059832 Loss1: 0.059143 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070974 Loss1: 0.070283 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.987413
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 13:04:11,327][flwr][DEBUG] - fit_round 61 received 10 results and 0 failures
-test acc: 0.6379
-[2023-09-22 13:05:14,904][flwr][INFO] - fit progress: (61, 2.2899934441898577, {'accuracy': 0.6379}, 121996.56573300483)
-[2023-09-22 13:05:14,905][flwr][DEBUG] - evaluate_round 61: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 13:05:53,121][flwr][DEBUG] - evaluate_round 61 received 10 results and 0 failures
-[2023-09-22 13:05:53,123][flwr][DEBUG] - fit_round 62: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9173259493670886
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.137097 Loss1: 0.136415 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078660 Loss1: 0.077974 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.057480 Loss1: 0.056793 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.060165 Loss1: 0.059479 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052913 Loss1: 0.052228 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044016 Loss1: 0.043329 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062781 Loss1: 0.062093 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052611 Loss1: 0.051922 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.048484 Loss1: 0.047796 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042104 Loss1: 0.041416 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.987144
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9076522435897436
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.120681 Loss1: 0.120002 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.077306 Loss1: 0.076624 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.078202 Loss1: 0.077519 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.064580 Loss1: 0.063896 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038939 Loss1: 0.038255 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040172 Loss1: 0.039488 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048597 Loss1: 0.047912 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052879 Loss1: 0.052193 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041188 Loss1: 0.040502 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047691 Loss1: 0.047007 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.993389
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8600084459459459
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.153959 Loss1: 0.153275 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.079259 Loss1: 0.078570 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.073999 Loss1: 0.073310 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061111 Loss1: 0.060421 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057955 Loss1: 0.057265 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065237 Loss1: 0.064547 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.066316 Loss1: 0.065626 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079557 Loss1: 0.078867 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079170 Loss1: 0.078480 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061728 Loss1: 0.061038 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.988809
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9205411585365854
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.102850 Loss1: 0.102169 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.074770 Loss1: 0.074086 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.067762 Loss1: 0.067076 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066884 Loss1: 0.066198 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.074477 Loss1: 0.073792 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058961 Loss1: 0.058275 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055550 Loss1: 0.054863 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.109136 Loss1: 0.108450 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085779 Loss1: 0.085090 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074402 Loss1: 0.073714 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.987233
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9268092105263158
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.128960 Loss1: 0.128273 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078410 Loss1: 0.077721 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066368 Loss1: 0.065678 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061040 Loss1: 0.060350 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.068576 Loss1: 0.067884 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062459 Loss1: 0.061768 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067411 Loss1: 0.066719 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071813 Loss1: 0.071121 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054572 Loss1: 0.053881 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063293 Loss1: 0.062600 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.991365
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9336939102564102
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105073 Loss1: 0.104392 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.057546 Loss1: 0.056859 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060387 Loss1: 0.059699 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052464 Loss1: 0.051776 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045134 Loss1: 0.044446 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050807 Loss1: 0.050118 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055040 Loss1: 0.054350 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037978 Loss1: 0.037290 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034445 Loss1: 0.033756 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053801 Loss1: 0.053111 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.984776
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9325553797468354
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097668 Loss1: 0.096987 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.082032 Loss1: 0.081345 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049492 Loss1: 0.048805 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050125 Loss1: 0.049436 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054260 Loss1: 0.053572 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082202 Loss1: 0.081512 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.081063 Loss1: 0.080374 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078751 Loss1: 0.078063 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.078800 Loss1: 0.078113 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.094899 Loss1: 0.094209 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.983386
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9036787974683544
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.126656 Loss1: 0.125974 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.062474 Loss1: 0.061790 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065519 Loss1: 0.064835 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059851 Loss1: 0.059166 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.070283 Loss1: 0.069596 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.077933 Loss1: 0.077246 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072135 Loss1: 0.071447 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085405 Loss1: 0.084717 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057197 Loss1: 0.056507 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074552 Loss1: 0.073862 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.983188
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9032118055555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116264 Loss1: 0.115583 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068680 Loss1: 0.067993 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.057737 Loss1: 0.057051 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.054647 Loss1: 0.053960 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063063 Loss1: 0.062376 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065182 Loss1: 0.064495 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043215 Loss1: 0.042526 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050854 Loss1: 0.050166 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060158 Loss1: 0.059468 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062303 Loss1: 0.061613 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.988715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9396756329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.106302 Loss1: 0.105619 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.082909 Loss1: 0.082223 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051721 Loss1: 0.051033 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050189 Loss1: 0.049498 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057993 Loss1: 0.057304 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063575 Loss1: 0.062885 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039857 Loss1: 0.039167 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046375 Loss1: 0.045684 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045789 Loss1: 0.045098 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056552 Loss1: 0.055862 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.987540
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 13:36:46,510][flwr][DEBUG] - fit_round 62 received 10 results and 0 failures
-test acc: 0.6368
-[2023-09-22 13:37:51,307][flwr][INFO] - fit progress: (62, 2.312452397407434, {'accuracy': 0.6368}, 123952.96846021572)
-[2023-09-22 13:37:51,308][flwr][DEBUG] - evaluate_round 62: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 13:38:30,094][flwr][DEBUG] - evaluate_round 62 received 10 results and 0 failures
-[2023-09-22 13:38:30,095][flwr][DEBUG] - fit_round 63: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9064477848101266
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116315 Loss1: 0.115632 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065312 Loss1: 0.064625 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.057208 Loss1: 0.056520 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.079473 Loss1: 0.078784 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057359 Loss1: 0.056670 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063119 Loss1: 0.062431 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057897 Loss1: 0.057205 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.071158 Loss1: 0.070469 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066850 Loss1: 0.066159 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080424 Loss1: 0.079734 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.982397
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9187104430379747
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116141 Loss1: 0.115459 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065622 Loss1: 0.064934 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059657 Loss1: 0.058969 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065359 Loss1: 0.064673 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086870 Loss1: 0.086181 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.093759 Loss1: 0.093069 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086467 Loss1: 0.085777 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079177 Loss1: 0.078488 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069880 Loss1: 0.069191 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071663 Loss1: 0.070971 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.984968
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9168669871794872
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.116617 Loss1: 0.115940 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067009 Loss1: 0.066327 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.054372 Loss1: 0.053689 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044581 Loss1: 0.043898 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052085 Loss1: 0.051401 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.057673 Loss1: 0.056987 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039829 Loss1: 0.039143 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061014 Loss1: 0.060327 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067893 Loss1: 0.067206 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050373 Loss1: 0.049687 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.988582
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9340945512820513
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.098806 Loss1: 0.098125 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065376 Loss1: 0.064689 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058183 Loss1: 0.057496 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.056092 Loss1: 0.055404 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059936 Loss1: 0.059250 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047325 Loss1: 0.046637 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062337 Loss1: 0.061650 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080579 Loss1: 0.079891 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072818 Loss1: 0.072131 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063556 Loss1: 0.062866 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.986178
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9001736111111112
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.140283 Loss1: 0.139602 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.080284 Loss1: 0.079598 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.075714 Loss1: 0.075027 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066866 Loss1: 0.066179 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.074044 Loss1: 0.073358 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066494 Loss1: 0.065807 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071266 Loss1: 0.070579 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060667 Loss1: 0.059978 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079607 Loss1: 0.078919 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.087282 Loss1: 0.086593 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.986762
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9189082278481012
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.133779 Loss1: 0.133100 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072649 Loss1: 0.071964 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064563 Loss1: 0.063878 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048061 Loss1: 0.047375 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042426 Loss1: 0.041740 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034272 Loss1: 0.033586 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042225 Loss1: 0.041538 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.066910 Loss1: 0.066222 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076869 Loss1: 0.076181 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078459 Loss1: 0.077772 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.989517
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8684543918918919
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.126031 Loss1: 0.125347 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.079957 Loss1: 0.079271 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.075941 Loss1: 0.075253 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065499 Loss1: 0.064811 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.077014 Loss1: 0.076325 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059368 Loss1: 0.058678 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051689 Loss1: 0.050999 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059443 Loss1: 0.058751 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054721 Loss1: 0.054030 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060846 Loss1: 0.060156 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.988598
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9333465189873418
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.108949 Loss1: 0.108267 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065271 Loss1: 0.064581 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056596 Loss1: 0.055905 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043034 Loss1: 0.042346 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.062850 Loss1: 0.062162 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060924 Loss1: 0.060235 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.050015 Loss1: 0.049325 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.055382 Loss1: 0.054691 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.073124 Loss1: 0.072433 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081873 Loss1: 0.081183 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.985364
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9407393292682927
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.088696 Loss1: 0.088016 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050541 Loss1: 0.049857 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.053384 Loss1: 0.052699 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050471 Loss1: 0.049786 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.056220 Loss1: 0.055534 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060028 Loss1: 0.059343 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.066023 Loss1: 0.065337 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062357 Loss1: 0.061669 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.092716 Loss1: 0.092030 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066069 Loss1: 0.065382 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.986471
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9263980263157895
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.140000 Loss1: 0.139316 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.078907 Loss1: 0.078218 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.070443 Loss1: 0.069754 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068320 Loss1: 0.067631 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.070824 Loss1: 0.070136 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048573 Loss1: 0.047886 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.045849 Loss1: 0.045161 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052646 Loss1: 0.051956 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057146 Loss1: 0.056456 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044929 Loss1: 0.044240 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.992804
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 14:09:16,395][flwr][DEBUG] - fit_round 63 received 10 results and 0 failures
-test acc: 0.6388
-[2023-09-22 14:10:18,204][flwr][INFO] - fit progress: (63, 2.3152449610896, {'accuracy': 0.6388}, 125899.86559370672)
-[2023-09-22 14:10:18,205][flwr][DEBUG] - evaluate_round 63: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 14:10:57,634][flwr][DEBUG] - evaluate_round 63 received 10 results and 0 failures
-[2023-09-22 14:10:57,638][flwr][DEBUG] - fit_round 64: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9284855769230769
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.108665 Loss1: 0.107988 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.069719 Loss1: 0.069036 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055276 Loss1: 0.054594 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047811 Loss1: 0.047126 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069430 Loss1: 0.068744 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.077163 Loss1: 0.076479 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053870 Loss1: 0.053185 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059484 Loss1: 0.058798 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059459 Loss1: 0.058773 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088827 Loss1: 0.088142 Loss2: 0.000684
-(DefaultActor pid=2839578) >> Training accuracy: 0.976963
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9274839743589743
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091589 Loss1: 0.090907 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.056080 Loss1: 0.055392 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048647 Loss1: 0.047957 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.060056 Loss1: 0.059367 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.056729 Loss1: 0.056038 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.069593 Loss1: 0.068902 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058822 Loss1: 0.058133 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060393 Loss1: 0.059703 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043765 Loss1: 0.043075 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044035 Loss1: 0.043345 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.991587
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9021267361111112
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.122738 Loss1: 0.122057 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.080240 Loss1: 0.079555 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056126 Loss1: 0.055439 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066246 Loss1: 0.065560 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052182 Loss1: 0.051496 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.057143 Loss1: 0.056456 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069314 Loss1: 0.068628 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.064356 Loss1: 0.063670 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055299 Loss1: 0.054611 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045410 Loss1: 0.044722 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.993707
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9359177215189873
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.087224 Loss1: 0.086543 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059199 Loss1: 0.058514 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052537 Loss1: 0.051851 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048232 Loss1: 0.047546 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046178 Loss1: 0.045491 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046136 Loss1: 0.045449 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041571 Loss1: 0.040883 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.055516 Loss1: 0.054828 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052648 Loss1: 0.051960 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074587 Loss1: 0.073898 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.985562
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.92578125
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.126966 Loss1: 0.126281 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.082354 Loss1: 0.081666 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.072767 Loss1: 0.072078 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.075352 Loss1: 0.074664 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.085184 Loss1: 0.084494 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082187 Loss1: 0.081497 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062029 Loss1: 0.061340 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072294 Loss1: 0.071604 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061524 Loss1: 0.060835 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062636 Loss1: 0.061944 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.985814
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9143591772151899
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.107177 Loss1: 0.106495 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072620 Loss1: 0.071932 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059038 Loss1: 0.058351 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043800 Loss1: 0.043113 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.053651 Loss1: 0.052964 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066260 Loss1: 0.065571 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083459 Loss1: 0.082770 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075372 Loss1: 0.074682 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.073378 Loss1: 0.072689 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045999 Loss1: 0.045311 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.993869
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9380933544303798
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.085541 Loss1: 0.084860 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.051132 Loss1: 0.050446 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044484 Loss1: 0.043798 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044160 Loss1: 0.043473 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.049915 Loss1: 0.049228 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052544 Loss1: 0.051853 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062307 Loss1: 0.061616 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070805 Loss1: 0.070115 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060349 Loss1: 0.059660 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064593 Loss1: 0.063903 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.987737
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9228639240506329
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.115273 Loss1: 0.114590 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.061339 Loss1: 0.060652 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.063861 Loss1: 0.063173 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058042 Loss1: 0.057356 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052284 Loss1: 0.051596 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047551 Loss1: 0.046864 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062583 Loss1: 0.061897 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080922 Loss1: 0.080234 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.102532 Loss1: 0.101843 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.077674 Loss1: 0.076986 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.986748
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9336890243902439
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.104502 Loss1: 0.103821 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.073456 Loss1: 0.072774 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064591 Loss1: 0.063908 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069368 Loss1: 0.068684 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060986 Loss1: 0.060301 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.073538 Loss1: 0.072852 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061142 Loss1: 0.060455 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052871 Loss1: 0.052186 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066067 Loss1: 0.065380 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038269 Loss1: 0.037582 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.990282
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8743665540540541
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.139828 Loss1: 0.139144 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072055 Loss1: 0.071366 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069299 Loss1: 0.068612 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066229 Loss1: 0.065538 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063657 Loss1: 0.062968 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.084434 Loss1: 0.083745 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.095232 Loss1: 0.094544 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.087044 Loss1: 0.086354 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.086698 Loss1: 0.086007 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073186 Loss1: 0.072495 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.981208
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 14:42:14,178][flwr][DEBUG] - fit_round 64 received 10 results and 0 failures
-test acc: 0.6396
-[2023-09-22 14:43:50,672][flwr][INFO] - fit progress: (64, 2.304777619556878, {'accuracy': 0.6396}, 127912.33357553184)
-[2023-09-22 14:43:50,673][flwr][DEBUG] - evaluate_round 64: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 14:44:30,178][flwr][DEBUG] - evaluate_round 64 received 10 results and 0 failures
-[2023-09-22 14:44:30,179][flwr][DEBUG] - fit_round 65: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9222861842105263
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119930 Loss1: 0.119246 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.076098 Loss1: 0.075409 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069085 Loss1: 0.068394 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.066622 Loss1: 0.065932 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.065758 Loss1: 0.065066 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072351 Loss1: 0.071660 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048889 Loss1: 0.048200 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047314 Loss1: 0.046623 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039549 Loss1: 0.038856 Loss2: 0.000693
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056911 Loss1: 0.056218 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.987048
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9071180555555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119272 Loss1: 0.118592 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.073162 Loss1: 0.072476 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.067113 Loss1: 0.066427 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053314 Loss1: 0.052628 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043886 Loss1: 0.043198 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.082099 Loss1: 0.081412 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086428 Loss1: 0.085742 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070561 Loss1: 0.069873 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072613 Loss1: 0.071925 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066334 Loss1: 0.065647 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.988715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9328926282051282
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.114403 Loss1: 0.113720 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059083 Loss1: 0.058395 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043186 Loss1: 0.042498 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.071660 Loss1: 0.070970 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.076808 Loss1: 0.076118 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.054261 Loss1: 0.053571 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041057 Loss1: 0.040366 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057973 Loss1: 0.057281 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049759 Loss1: 0.049068 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046736 Loss1: 0.046044 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.990585
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8775337837837838
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.147328 Loss1: 0.146644 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.075351 Loss1: 0.074661 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.075138 Loss1: 0.074450 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.072519 Loss1: 0.071830 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057954 Loss1: 0.057264 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045732 Loss1: 0.045041 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046030 Loss1: 0.045338 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051496 Loss1: 0.050805 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056546 Loss1: 0.055855 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081158 Loss1: 0.080466 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.986064
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9228766025641025
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.120834 Loss1: 0.120152 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066408 Loss1: 0.065724 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.069538 Loss1: 0.068854 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.055573 Loss1: 0.054888 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059971 Loss1: 0.059285 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055162 Loss1: 0.054476 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070731 Loss1: 0.070044 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.065357 Loss1: 0.064670 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058015 Loss1: 0.057328 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051393 Loss1: 0.050706 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.989383
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9113924050632911
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.100109 Loss1: 0.099426 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048091 Loss1: 0.047404 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042625 Loss1: 0.041935 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048011 Loss1: 0.047322 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063529 Loss1: 0.062840 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060557 Loss1: 0.059869 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055546 Loss1: 0.054856 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036762 Loss1: 0.036074 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044547 Loss1: 0.043858 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040909 Loss1: 0.040220 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.993078
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9339398734177216
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.102362 Loss1: 0.101679 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066340 Loss1: 0.065654 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052239 Loss1: 0.051551 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048205 Loss1: 0.047517 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061995 Loss1: 0.061306 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055398 Loss1: 0.054710 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051673 Loss1: 0.050984 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068594 Loss1: 0.067905 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059346 Loss1: 0.058655 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058982 Loss1: 0.058293 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.988726
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9392800632911392
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097921 Loss1: 0.097238 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047394 Loss1: 0.046707 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039301 Loss1: 0.038614 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041251 Loss1: 0.040563 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039049 Loss1: 0.038359 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.065020 Loss1: 0.064331 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.059525 Loss1: 0.058835 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058931 Loss1: 0.058242 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.036356 Loss1: 0.035666 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040688 Loss1: 0.039995 Loss2: 0.000693
-(DefaultActor pid=2839578) >> Training accuracy: 0.992484
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9333079268292683
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.104834 Loss1: 0.104155 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066559 Loss1: 0.065874 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.053015 Loss1: 0.052331 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059711 Loss1: 0.059026 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.076500 Loss1: 0.075814 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058191 Loss1: 0.057505 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057188 Loss1: 0.056501 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056323 Loss1: 0.055635 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064675 Loss1: 0.063987 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090480 Loss1: 0.089791 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.984756
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.921875
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.127875 Loss1: 0.127192 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067132 Loss1: 0.066446 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043285 Loss1: 0.042598 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053121 Loss1: 0.052435 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.065095 Loss1: 0.064408 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.080672 Loss1: 0.079984 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056878 Loss1: 0.056188 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063679 Loss1: 0.062991 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057037 Loss1: 0.056349 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053413 Loss1: 0.052725 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992089
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 15:19:58,441][flwr][DEBUG] - fit_round 65 received 10 results and 0 failures
-test acc: 0.6413
-[2023-09-22 15:21:00,226][flwr][INFO] - fit progress: (65, 2.337633471138561, {'accuracy': 0.6413}, 130141.88727296283)
-[2023-09-22 15:21:00,226][flwr][DEBUG] - evaluate_round 65: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 15:21:40,210][flwr][DEBUG] - evaluate_round 65 received 10 results and 0 failures
-[2023-09-22 15:21:40,216][flwr][DEBUG] - fit_round 66: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9377003205128205
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091957 Loss1: 0.091277 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046404 Loss1: 0.045720 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048458 Loss1: 0.047771 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037004 Loss1: 0.036315 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052125 Loss1: 0.051437 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066221 Loss1: 0.065532 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.075248 Loss1: 0.074560 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074952 Loss1: 0.074261 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069883 Loss1: 0.069194 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061875 Loss1: 0.061185 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.989784
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9045138888888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.133654 Loss1: 0.132971 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.060481 Loss1: 0.059796 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048751 Loss1: 0.048065 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052949 Loss1: 0.052262 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047742 Loss1: 0.047056 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050045 Loss1: 0.049357 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.074323 Loss1: 0.073637 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069500 Loss1: 0.068812 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043930 Loss1: 0.043242 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048872 Loss1: 0.048186 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.991536
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9104034810126582
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.099667 Loss1: 0.098984 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050196 Loss1: 0.049509 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.065869 Loss1: 0.065183 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065066 Loss1: 0.064379 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064438 Loss1: 0.063751 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053160 Loss1: 0.052471 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053093 Loss1: 0.052401 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043266 Loss1: 0.042574 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049303 Loss1: 0.048614 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042232 Loss1: 0.041541 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.990704
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9240506329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.096170 Loss1: 0.095489 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.054546 Loss1: 0.053861 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040560 Loss1: 0.039874 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037603 Loss1: 0.036918 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038915 Loss1: 0.038228 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039124 Loss1: 0.038436 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065873 Loss1: 0.065184 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057891 Loss1: 0.057204 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.088987 Loss1: 0.088301 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062648 Loss1: 0.061960 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.987144
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9353243670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091047 Loss1: 0.090365 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046246 Loss1: 0.045559 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066122 Loss1: 0.065432 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.062641 Loss1: 0.061951 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045431 Loss1: 0.044740 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044207 Loss1: 0.043517 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.052440 Loss1: 0.051749 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069535 Loss1: 0.068845 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075899 Loss1: 0.075209 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071257 Loss1: 0.070565 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.992286
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9378810975609756
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.090510 Loss1: 0.089830 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050456 Loss1: 0.049773 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031151 Loss1: 0.030466 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035822 Loss1: 0.035138 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031114 Loss1: 0.030429 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029642 Loss1: 0.028957 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033672 Loss1: 0.032986 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033532 Loss1: 0.032847 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045175 Loss1: 0.044489 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080562 Loss1: 0.079876 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.989520
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9236778846153846
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.113763 Loss1: 0.113084 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065982 Loss1: 0.065300 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.064794 Loss1: 0.064111 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059702 Loss1: 0.059020 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.084141 Loss1: 0.083457 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.076019 Loss1: 0.075335 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068723 Loss1: 0.068038 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.076048 Loss1: 0.075364 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.114044 Loss1: 0.113359 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085503 Loss1: 0.084816 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.981170
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9298930921052632
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.122963 Loss1: 0.122277 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059455 Loss1: 0.058764 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.062485 Loss1: 0.061796 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036774 Loss1: 0.036083 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046447 Loss1: 0.045754 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052786 Loss1: 0.052095 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.052764 Loss1: 0.052073 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053250 Loss1: 0.052559 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061802 Loss1: 0.061112 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073289 Loss1: 0.072598 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.977590
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8737331081081081
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.136917 Loss1: 0.136233 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.072747 Loss1: 0.072060 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058265 Loss1: 0.057578 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.073851 Loss1: 0.073164 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069547 Loss1: 0.068860 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.070909 Loss1: 0.070220 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057462 Loss1: 0.056773 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069390 Loss1: 0.068701 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053073 Loss1: 0.052383 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059538 Loss1: 0.058848 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.985431
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9359177215189873
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105423 Loss1: 0.104741 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067160 Loss1: 0.066473 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052916 Loss1: 0.052229 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039278 Loss1: 0.038591 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047697 Loss1: 0.047010 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052566 Loss1: 0.051878 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.060839 Loss1: 0.060151 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069791 Loss1: 0.069102 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.070614 Loss1: 0.069925 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080551 Loss1: 0.079861 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.990309
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 15:51:50,335][flwr][DEBUG] - fit_round 66 received 10 results and 0 failures
-test acc: 0.6402
-[2023-09-22 15:52:58,592][flwr][INFO] - fit progress: (66, 2.3337900912799774, {'accuracy': 0.6402}, 132060.2530357819)
-[2023-09-22 15:52:58,592][flwr][DEBUG] - evaluate_round 66: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 15:53:37,554][flwr][DEBUG] - evaluate_round 66 received 10 results and 0 failures
-[2023-09-22 15:53:37,556][flwr][DEBUG] - fit_round 67: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9208860759493671
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.099872 Loss1: 0.099192 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052243 Loss1: 0.051557 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.050865 Loss1: 0.050177 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047261 Loss1: 0.046574 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032189 Loss1: 0.031503 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029637 Loss1: 0.028950 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039193 Loss1: 0.038505 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049583 Loss1: 0.048895 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058911 Loss1: 0.058223 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067973 Loss1: 0.067283 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.984968
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8794341216216216
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.151987 Loss1: 0.151303 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.055340 Loss1: 0.054652 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043924 Loss1: 0.043237 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038519 Loss1: 0.037831 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044541 Loss1: 0.043851 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052724 Loss1: 0.052035 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039920 Loss1: 0.039231 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045449 Loss1: 0.044760 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040038 Loss1: 0.039348 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033911 Loss1: 0.033221 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.994299
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9214743589743589
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103499 Loss1: 0.102821 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.062750 Loss1: 0.062065 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035691 Loss1: 0.035006 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049182 Loss1: 0.048496 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054230 Loss1: 0.053544 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052999 Loss1: 0.052313 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076360 Loss1: 0.075673 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.070593 Loss1: 0.069907 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074855 Loss1: 0.074168 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.088529 Loss1: 0.087844 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.979768
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9181170886075949
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.107547 Loss1: 0.106864 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.086421 Loss1: 0.085734 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.066897 Loss1: 0.066209 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067398 Loss1: 0.066710 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.086263 Loss1: 0.085573 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.074590 Loss1: 0.073900 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069333 Loss1: 0.068644 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047591 Loss1: 0.046902 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055519 Loss1: 0.054830 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046099 Loss1: 0.045409 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.993869
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9438291139240507
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097618 Loss1: 0.096936 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068328 Loss1: 0.067640 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.085150 Loss1: 0.084460 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052685 Loss1: 0.051997 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.063490 Loss1: 0.062800 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049897 Loss1: 0.049208 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040530 Loss1: 0.039839 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044219 Loss1: 0.043529 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049049 Loss1: 0.048359 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050510 Loss1: 0.049820 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.991297
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9397035256410257
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.088989 Loss1: 0.088306 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.060602 Loss1: 0.059916 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.062422 Loss1: 0.061735 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044143 Loss1: 0.043455 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052969 Loss1: 0.052282 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039587 Loss1: 0.038899 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040923 Loss1: 0.040234 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048163 Loss1: 0.047472 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049367 Loss1: 0.048676 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090389 Loss1: 0.089699 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.990585
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9373094512195121
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.098259 Loss1: 0.097579 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052508 Loss1: 0.051823 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058411 Loss1: 0.057726 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.051977 Loss1: 0.051291 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042982 Loss1: 0.042296 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025821 Loss1: 0.025134 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035642 Loss1: 0.034955 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051875 Loss1: 0.051187 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054846 Loss1: 0.054157 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058402 Loss1: 0.057715 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.988567
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9114583333333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.120255 Loss1: 0.119573 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044430 Loss1: 0.043746 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056121 Loss1: 0.055437 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049127 Loss1: 0.048441 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055048 Loss1: 0.054361 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.054684 Loss1: 0.053996 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054640 Loss1: 0.053952 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056614 Loss1: 0.055925 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061613 Loss1: 0.060925 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048991 Loss1: 0.048302 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987196
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9352384868421053
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.092188 Loss1: 0.091504 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050005 Loss1: 0.049317 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040656 Loss1: 0.039967 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038832 Loss1: 0.038143 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059594 Loss1: 0.058905 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060989 Loss1: 0.060300 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.083328 Loss1: 0.082637 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.083300 Loss1: 0.082612 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061904 Loss1: 0.061212 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065060 Loss1: 0.064370 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.983758
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9384889240506329
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.079959 Loss1: 0.079277 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052420 Loss1: 0.051734 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.053242 Loss1: 0.052556 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.040751 Loss1: 0.040064 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046999 Loss1: 0.046309 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049382 Loss1: 0.048694 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051650 Loss1: 0.050961 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056061 Loss1: 0.055372 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057129 Loss1: 0.056440 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069853 Loss1: 0.069165 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.989517
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 16:24:21,451][flwr][DEBUG] - fit_round 67 received 10 results and 0 failures
-test acc: 0.6406
-[2023-09-22 16:25:21,557][flwr][INFO] - fit progress: (67, 2.357942302767842, {'accuracy': 0.6406}, 134003.21802859986)
-[2023-09-22 16:25:21,557][flwr][DEBUG] - evaluate_round 67: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 16:25:59,262][flwr][DEBUG] - evaluate_round 67 received 10 results and 0 failures
-[2023-09-22 16:25:59,264][flwr][DEBUG] - fit_round 68: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9253700657894737
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.114290 Loss1: 0.113605 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.074070 Loss1: 0.073381 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.067374 Loss1: 0.066686 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049902 Loss1: 0.049213 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060133 Loss1: 0.059443 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.072144 Loss1: 0.071455 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048465 Loss1: 0.047776 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050756 Loss1: 0.050068 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042438 Loss1: 0.041750 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060536 Loss1: 0.059848 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.993010
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9407393292682927
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.083629 Loss1: 0.082946 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037256 Loss1: 0.036571 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036555 Loss1: 0.035869 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069039 Loss1: 0.068353 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046803 Loss1: 0.046117 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048884 Loss1: 0.048199 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046899 Loss1: 0.046214 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058408 Loss1: 0.057721 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077336 Loss1: 0.076649 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075827 Loss1: 0.075140 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.984947
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9256810897435898
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.101482 Loss1: 0.100803 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059443 Loss1: 0.058761 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045001 Loss1: 0.044317 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052080 Loss1: 0.051395 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.050132 Loss1: 0.049449 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.051264 Loss1: 0.050579 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056338 Loss1: 0.055654 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051177 Loss1: 0.050491 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069741 Loss1: 0.069055 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061864 Loss1: 0.061179 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.983774
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9155815972222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.113344 Loss1: 0.112662 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049462 Loss1: 0.048776 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048728 Loss1: 0.048041 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.056372 Loss1: 0.055685 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.050110 Loss1: 0.049422 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047123 Loss1: 0.046434 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070766 Loss1: 0.070077 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074782 Loss1: 0.074093 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056265 Loss1: 0.055575 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046994 Loss1: 0.046305 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989583
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8762668918918919
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.112566 Loss1: 0.111882 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.063387 Loss1: 0.062699 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056571 Loss1: 0.055882 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053260 Loss1: 0.052571 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046673 Loss1: 0.045983 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063334 Loss1: 0.062646 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055781 Loss1: 0.055092 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049033 Loss1: 0.048343 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046525 Loss1: 0.045835 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055001 Loss1: 0.054310 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.990287
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9149525316455697
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119418 Loss1: 0.118734 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050163 Loss1: 0.049476 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044949 Loss1: 0.044262 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061500 Loss1: 0.060813 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.069943 Loss1: 0.069256 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068072 Loss1: 0.067383 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076611 Loss1: 0.075922 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.066367 Loss1: 0.065677 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064945 Loss1: 0.064257 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047602 Loss1: 0.046913 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.990309
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9396756329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.085015 Loss1: 0.084334 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045597 Loss1: 0.044910 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035463 Loss1: 0.034776 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046436 Loss1: 0.045747 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061637 Loss1: 0.060949 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043355 Loss1: 0.042666 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038472 Loss1: 0.037782 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069360 Loss1: 0.068669 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064586 Loss1: 0.063896 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045434 Loss1: 0.044744 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.986946
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9377003205128205
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.092529 Loss1: 0.091847 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.053134 Loss1: 0.052447 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.046012 Loss1: 0.045324 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038053 Loss1: 0.037365 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040194 Loss1: 0.039505 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041622 Loss1: 0.040933 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042034 Loss1: 0.041345 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046921 Loss1: 0.046232 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033420 Loss1: 0.032729 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.039861 Loss1: 0.039172 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.993590
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9373022151898734
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.081316 Loss1: 0.080634 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.058704 Loss1: 0.058018 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.063016 Loss1: 0.062328 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049042 Loss1: 0.048354 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043528 Loss1: 0.042840 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039514 Loss1: 0.038825 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038454 Loss1: 0.037765 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052932 Loss1: 0.052243 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071655 Loss1: 0.070964 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071725 Loss1: 0.071036 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.982991
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.935126582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103578 Loss1: 0.102897 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.040463 Loss1: 0.039777 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035209 Loss1: 0.034522 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032558 Loss1: 0.031872 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034466 Loss1: 0.033779 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029250 Loss1: 0.028562 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041981 Loss1: 0.041292 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040400 Loss1: 0.039714 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039908 Loss1: 0.039220 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047516 Loss1: 0.046828 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992286
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 16:56:32,461][flwr][DEBUG] - fit_round 68 received 10 results and 0 failures
-test acc: 0.6389
-[2023-09-22 16:58:06,885][flwr][INFO] - fit progress: (68, 2.3550231862372866, {'accuracy': 0.6389}, 135968.54609194398)
-[2023-09-22 16:58:06,885][flwr][DEBUG] - evaluate_round 68: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 16:58:44,365][flwr][DEBUG] - evaluate_round 68 received 10 results and 0 failures
-[2023-09-22 16:58:44,375][flwr][DEBUG] - fit_round 69: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9340049342105263
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105940 Loss1: 0.105254 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.067407 Loss1: 0.066716 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051395 Loss1: 0.050706 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039983 Loss1: 0.039292 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039415 Loss1: 0.038725 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045496 Loss1: 0.044807 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042303 Loss1: 0.041611 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035340 Loss1: 0.034649 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041010 Loss1: 0.040318 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063622 Loss1: 0.062931 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.988487
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.939873417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.074400 Loss1: 0.073718 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045805 Loss1: 0.045118 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049652 Loss1: 0.048966 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.054523 Loss1: 0.053836 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054645 Loss1: 0.053957 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.064289 Loss1: 0.063599 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.074926 Loss1: 0.074237 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.080931 Loss1: 0.080243 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.129733 Loss1: 0.129043 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.099158 Loss1: 0.098470 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.981210
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9234572784810127
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105012 Loss1: 0.104329 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.061761 Loss1: 0.061075 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.046643 Loss1: 0.045956 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047684 Loss1: 0.046997 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.067646 Loss1: 0.066957 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.086588 Loss1: 0.085900 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076756 Loss1: 0.076065 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.068096 Loss1: 0.067406 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.093114 Loss1: 0.092425 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052971 Loss1: 0.052281 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.986748
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9432357594936709
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097226 Loss1: 0.096543 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046387 Loss1: 0.045700 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036664 Loss1: 0.035975 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033291 Loss1: 0.032602 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026493 Loss1: 0.025804 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049972 Loss1: 0.049281 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.052037 Loss1: 0.051346 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044972 Loss1: 0.044282 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063530 Loss1: 0.062840 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053903 Loss1: 0.053212 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.988528
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8743665540540541
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119457 Loss1: 0.118772 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.064397 Loss1: 0.063708 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055385 Loss1: 0.054697 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.062047 Loss1: 0.061357 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047514 Loss1: 0.046825 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053090 Loss1: 0.052401 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057535 Loss1: 0.056846 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.077657 Loss1: 0.076969 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.088162 Loss1: 0.087473 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054054 Loss1: 0.053363 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.994299
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9417067307692307
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.085592 Loss1: 0.084911 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043132 Loss1: 0.042444 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043686 Loss1: 0.042999 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.051617 Loss1: 0.050929 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060103 Loss1: 0.059415 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046484 Loss1: 0.045795 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057370 Loss1: 0.056680 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057078 Loss1: 0.056388 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069979 Loss1: 0.069289 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061758 Loss1: 0.061067 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.987981
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9284018987341772
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.079293 Loss1: 0.078612 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044032 Loss1: 0.043346 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.053291 Loss1: 0.052606 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050757 Loss1: 0.050072 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046938 Loss1: 0.046252 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048892 Loss1: 0.048205 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044945 Loss1: 0.044257 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053391 Loss1: 0.052704 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082963 Loss1: 0.082276 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081594 Loss1: 0.080906 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.981210
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9439786585365854
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.087808 Loss1: 0.087127 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.061243 Loss1: 0.060558 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058583 Loss1: 0.057898 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039139 Loss1: 0.038455 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045177 Loss1: 0.044493 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040498 Loss1: 0.039813 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057518 Loss1: 0.056831 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053963 Loss1: 0.053276 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049442 Loss1: 0.048754 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058714 Loss1: 0.058027 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.984566
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9395032051282052
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084573 Loss1: 0.083892 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.061404 Loss1: 0.060719 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056101 Loss1: 0.055416 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048052 Loss1: 0.047367 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043439 Loss1: 0.042754 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048118 Loss1: 0.047433 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039031 Loss1: 0.038344 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054209 Loss1: 0.053522 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.082408 Loss1: 0.081723 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.092369 Loss1: 0.091683 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.983774
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9175347222222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.093153 Loss1: 0.092472 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049692 Loss1: 0.049006 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045013 Loss1: 0.044327 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050501 Loss1: 0.049814 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051680 Loss1: 0.050993 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049985 Loss1: 0.049297 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058115 Loss1: 0.057426 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045817 Loss1: 0.045130 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038699 Loss1: 0.038011 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051970 Loss1: 0.051281 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.981120
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 17:31:38,944][flwr][DEBUG] - fit_round 69 received 10 results and 0 failures
-test acc: 0.6384
-[2023-09-22 17:32:32,855][flwr][INFO] - fit progress: (69, 2.3513612215892197, {'accuracy': 0.6384}, 138034.5161338998)
-[2023-09-22 17:32:32,855][flwr][DEBUG] - evaluate_round 69: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 17:33:10,642][flwr][DEBUG] - evaluate_round 69 received 10 results and 0 failures
-[2023-09-22 17:33:10,643][flwr][DEBUG] - fit_round 70: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9140625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.119463 Loss1: 0.118782 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.071342 Loss1: 0.070656 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045937 Loss1: 0.045251 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058029 Loss1: 0.057341 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.067718 Loss1: 0.067029 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039954 Loss1: 0.039266 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028651 Loss1: 0.027963 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054470 Loss1: 0.053783 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063414 Loss1: 0.062726 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061187 Loss1: 0.060500 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.991753
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9396756329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.092533 Loss1: 0.091850 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.065089 Loss1: 0.064402 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058525 Loss1: 0.057835 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.063476 Loss1: 0.062788 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.064584 Loss1: 0.063894 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.066715 Loss1: 0.066026 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073227 Loss1: 0.072540 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058114 Loss1: 0.057426 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044040 Loss1: 0.043351 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046474 Loss1: 0.045784 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.991100
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9342948717948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080228 Loss1: 0.079549 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042727 Loss1: 0.042045 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031514 Loss1: 0.030830 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036066 Loss1: 0.035382 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045213 Loss1: 0.044528 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059285 Loss1: 0.058602 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.072477 Loss1: 0.071792 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072364 Loss1: 0.071677 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.079568 Loss1: 0.078882 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062542 Loss1: 0.061856 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.988782
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9344161184210527
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.097717 Loss1: 0.097035 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.053892 Loss1: 0.053203 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051085 Loss1: 0.050397 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.065392 Loss1: 0.064703 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052913 Loss1: 0.052225 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052340 Loss1: 0.051651 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.079654 Loss1: 0.078965 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.095803 Loss1: 0.095114 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066962 Loss1: 0.066273 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062790 Loss1: 0.062101 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987459
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9501582278481012
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066120 Loss1: 0.065439 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045378 Loss1: 0.044691 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031553 Loss1: 0.030866 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.055279 Loss1: 0.054590 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039825 Loss1: 0.039136 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034229 Loss1: 0.033539 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029536 Loss1: 0.028846 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029118 Loss1: 0.028429 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034652 Loss1: 0.033964 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054739 Loss1: 0.054049 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.988133
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9285996835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.087119 Loss1: 0.086437 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041709 Loss1: 0.041024 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.054101 Loss1: 0.053415 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046652 Loss1: 0.045965 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051079 Loss1: 0.050392 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.067016 Loss1: 0.066329 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067258 Loss1: 0.066570 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063503 Loss1: 0.062815 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.112352 Loss1: 0.111666 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.168295 Loss1: 0.167607 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.980419
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8817567567567568
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.115889 Loss1: 0.115206 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.053325 Loss1: 0.052638 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.056085 Loss1: 0.055396 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049988 Loss1: 0.049300 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061727 Loss1: 0.061038 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039575 Loss1: 0.038887 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031204 Loss1: 0.030514 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036407 Loss1: 0.035718 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057572 Loss1: 0.056882 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063834 Loss1: 0.063144 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.991343
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9439102564102564
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.086145 Loss1: 0.085463 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035926 Loss1: 0.035240 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044957 Loss1: 0.044270 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048356 Loss1: 0.047669 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048071 Loss1: 0.047381 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050148 Loss1: 0.049458 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033177 Loss1: 0.032488 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040904 Loss1: 0.040214 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038573 Loss1: 0.037882 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056147 Loss1: 0.055456 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.980369
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9246439873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095269 Loss1: 0.094588 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066419 Loss1: 0.065732 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049280 Loss1: 0.048593 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068894 Loss1: 0.068206 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.078010 Loss1: 0.077323 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.079495 Loss1: 0.078807 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058481 Loss1: 0.057791 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.065968 Loss1: 0.065281 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076641 Loss1: 0.075953 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081012 Loss1: 0.080322 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.982991
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9455030487804879
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075611 Loss1: 0.074929 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043024 Loss1: 0.042340 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040302 Loss1: 0.039618 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034272 Loss1: 0.033586 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039260 Loss1: 0.038574 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052758 Loss1: 0.052073 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054299 Loss1: 0.053614 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047701 Loss1: 0.047015 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047465 Loss1: 0.046779 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043503 Loss1: 0.042816 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.991425
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 18:11:22,105][flwr][DEBUG] - fit_round 70 received 10 results and 0 failures
-test acc: 0.6375
-[2023-09-22 18:12:14,273][flwr][INFO] - fit progress: (70, 2.3543379908552566, {'accuracy': 0.6375}, 140415.93445685785)
-[2023-09-22 18:12:14,273][flwr][DEBUG] - evaluate_round 70: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 18:12:52,805][flwr][DEBUG] - evaluate_round 70 received 10 results and 0 failures
-[2023-09-22 18:12:52,806][flwr][DEBUG] - fit_round 71: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9439102564102564
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.100901 Loss1: 0.100221 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041426 Loss1: 0.040742 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044486 Loss1: 0.043802 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.056973 Loss1: 0.056288 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043711 Loss1: 0.043025 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037573 Loss1: 0.036888 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037881 Loss1: 0.037195 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031156 Loss1: 0.030470 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034636 Loss1: 0.033950 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053176 Loss1: 0.052490 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.990986
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9323575949367089
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.096905 Loss1: 0.096222 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066942 Loss1: 0.066256 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055176 Loss1: 0.054489 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.051938 Loss1: 0.051251 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.050546 Loss1: 0.049859 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036310 Loss1: 0.035624 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042218 Loss1: 0.041531 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063223 Loss1: 0.062537 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054918 Loss1: 0.054231 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058190 Loss1: 0.057503 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.991693
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9416534810126582
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068654 Loss1: 0.067972 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033145 Loss1: 0.032459 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.030833 Loss1: 0.030146 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029322 Loss1: 0.028636 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.049122 Loss1: 0.048435 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050815 Loss1: 0.050127 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056274 Loss1: 0.055585 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047814 Loss1: 0.047126 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044242 Loss1: 0.043553 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032952 Loss1: 0.032263 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.989913
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9165348101265823
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091832 Loss1: 0.091151 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038767 Loss1: 0.038079 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036376 Loss1: 0.035689 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053244 Loss1: 0.052556 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044880 Loss1: 0.044192 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046286 Loss1: 0.045596 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058321 Loss1: 0.057634 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054509 Loss1: 0.053820 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053186 Loss1: 0.052495 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044872 Loss1: 0.044180 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.989715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8832347972972973
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.107463 Loss1: 0.106779 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.077896 Loss1: 0.077208 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.070480 Loss1: 0.069792 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.057980 Loss1: 0.057290 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037195 Loss1: 0.036503 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053098 Loss1: 0.052407 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043671 Loss1: 0.042981 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050590 Loss1: 0.049899 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049177 Loss1: 0.048487 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044062 Loss1: 0.043372 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.991765
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9220920138888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091303 Loss1: 0.090621 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039632 Loss1: 0.038946 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.061003 Loss1: 0.060317 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043642 Loss1: 0.042953 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036386 Loss1: 0.035698 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037711 Loss1: 0.037022 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071228 Loss1: 0.070539 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047995 Loss1: 0.047306 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065243 Loss1: 0.064556 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057919 Loss1: 0.057231 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992839
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.946004746835443
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.079050 Loss1: 0.078366 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038568 Loss1: 0.037880 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036333 Loss1: 0.035645 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035055 Loss1: 0.034365 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046138 Loss1: 0.045449 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058657 Loss1: 0.057968 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042855 Loss1: 0.042165 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043108 Loss1: 0.042420 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049631 Loss1: 0.048940 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068528 Loss1: 0.067838 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.988924
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9428453947368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105061 Loss1: 0.104377 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066327 Loss1: 0.065638 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060015 Loss1: 0.059327 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.068707 Loss1: 0.068015 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.088987 Loss1: 0.088298 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.071662 Loss1: 0.070972 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.091969 Loss1: 0.091278 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.064646 Loss1: 0.063955 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066615 Loss1: 0.065924 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063055 Loss1: 0.062363 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.989309
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9493140243902439
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.086527 Loss1: 0.085848 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049830 Loss1: 0.049147 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051366 Loss1: 0.050682 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053292 Loss1: 0.052608 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039369 Loss1: 0.038684 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038308 Loss1: 0.037624 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044403 Loss1: 0.043717 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054274 Loss1: 0.053588 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055206 Loss1: 0.054519 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081809 Loss1: 0.081120 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.983994
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9421073717948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075651 Loss1: 0.074971 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033320 Loss1: 0.032634 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.037837 Loss1: 0.037149 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.040698 Loss1: 0.040011 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041841 Loss1: 0.041152 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.056517 Loss1: 0.055828 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065289 Loss1: 0.064601 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056909 Loss1: 0.056220 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.086667 Loss1: 0.085978 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073473 Loss1: 0.072782 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.987780
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 18:51:09,769][flwr][DEBUG] - fit_round 71 received 10 results and 0 failures
-test acc: 0.6414
-[2023-09-22 18:52:04,264][flwr][INFO] - fit progress: (71, 2.349217086935196, {'accuracy': 0.6414}, 142805.92535427166)
-[2023-09-22 18:52:04,264][flwr][DEBUG] - evaluate_round 71: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 18:52:41,300][flwr][DEBUG] - evaluate_round 71 received 10 results and 0 failures
-[2023-09-22 18:52:41,301][flwr][DEBUG] - fit_round 72: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9517911585365854
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070838 Loss1: 0.070158 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046178 Loss1: 0.045494 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042207 Loss1: 0.041522 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045027 Loss1: 0.044343 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036531 Loss1: 0.035846 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037566 Loss1: 0.036880 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034423 Loss1: 0.033737 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046455 Loss1: 0.045768 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052238 Loss1: 0.051552 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048480 Loss1: 0.047794 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.991044
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9192708333333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.099582 Loss1: 0.098900 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047940 Loss1: 0.047254 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.038968 Loss1: 0.038281 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039729 Loss1: 0.039042 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037813 Loss1: 0.037125 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041222 Loss1: 0.040533 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061761 Loss1: 0.061072 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.076198 Loss1: 0.075509 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.099594 Loss1: 0.098906 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.104604 Loss1: 0.103915 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.983724
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9463141025641025
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072431 Loss1: 0.071750 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.055566 Loss1: 0.054880 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.041131 Loss1: 0.040445 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036429 Loss1: 0.035742 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045075 Loss1: 0.044387 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047408 Loss1: 0.046720 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067615 Loss1: 0.066928 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053795 Loss1: 0.053106 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039316 Loss1: 0.038626 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.035358 Loss1: 0.034669 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.995393
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9394778481012658
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.077270 Loss1: 0.076591 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.052007 Loss1: 0.051322 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.054345 Loss1: 0.053660 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036923 Loss1: 0.036238 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051275 Loss1: 0.050587 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050843 Loss1: 0.050155 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076364 Loss1: 0.075676 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074320 Loss1: 0.073634 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044760 Loss1: 0.044073 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068516 Loss1: 0.067827 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.981804
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9409950657894737
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.088531 Loss1: 0.087848 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.070843 Loss1: 0.070156 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035356 Loss1: 0.034666 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039388 Loss1: 0.038699 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.056776 Loss1: 0.056086 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043787 Loss1: 0.043096 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048834 Loss1: 0.048144 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051243 Loss1: 0.050552 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054558 Loss1: 0.053868 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051210 Loss1: 0.050521 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.990543
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9475870253164557
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095270 Loss1: 0.094588 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049488 Loss1: 0.048800 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040795 Loss1: 0.040106 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048797 Loss1: 0.048108 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040141 Loss1: 0.039450 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046516 Loss1: 0.045826 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058355 Loss1: 0.057666 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052081 Loss1: 0.051391 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046534 Loss1: 0.045844 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048965 Loss1: 0.048274 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.992286
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9246439873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.089757 Loss1: 0.089076 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.056648 Loss1: 0.055963 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032668 Loss1: 0.031982 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041859 Loss1: 0.041172 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025865 Loss1: 0.025178 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029082 Loss1: 0.028394 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034982 Loss1: 0.034294 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029114 Loss1: 0.028425 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039996 Loss1: 0.039309 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043163 Loss1: 0.042475 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.991693
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9338942307692307
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075199 Loss1: 0.074520 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042381 Loss1: 0.041698 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044556 Loss1: 0.043872 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.058065 Loss1: 0.057380 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.066953 Loss1: 0.066267 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061578 Loss1: 0.060892 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073921 Loss1: 0.073235 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062678 Loss1: 0.061992 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.081578 Loss1: 0.080891 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052691 Loss1: 0.052004 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.990986
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.885768581081081
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.123308 Loss1: 0.122624 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.063256 Loss1: 0.062567 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.068793 Loss1: 0.068103 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.061266 Loss1: 0.060576 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051160 Loss1: 0.050471 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040698 Loss1: 0.040009 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039204 Loss1: 0.038515 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.055596 Loss1: 0.054907 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045399 Loss1: 0.044709 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056092 Loss1: 0.055402 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.991765
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9493670886075949
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.073836 Loss1: 0.073154 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041517 Loss1: 0.040831 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.041778 Loss1: 0.041093 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.053744 Loss1: 0.053057 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.053512 Loss1: 0.052826 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.078153 Loss1: 0.077465 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.086999 Loss1: 0.086310 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062783 Loss1: 0.062094 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051299 Loss1: 0.050610 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050124 Loss1: 0.049435 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.991495
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 19:30:49,992][flwr][DEBUG] - fit_round 72 received 10 results and 0 failures
-test acc: 0.6415
-[2023-09-22 19:31:44,214][flwr][INFO] - fit progress: (72, 2.383222926158113, {'accuracy': 0.6415}, 145185.87564541493)
-[2023-09-22 19:31:44,215][flwr][DEBUG] - evaluate_round 72: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 19:32:21,617][flwr][DEBUG] - evaluate_round 72 received 10 results and 0 failures
-[2023-09-22 19:32:21,617][flwr][DEBUG] - fit_round 73: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8895692567567568
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.124338 Loss1: 0.123656 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066147 Loss1: 0.065460 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.058437 Loss1: 0.057749 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.069973 Loss1: 0.069284 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054105 Loss1: 0.053416 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063834 Loss1: 0.063145 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069524 Loss1: 0.068836 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056442 Loss1: 0.055753 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057610 Loss1: 0.056920 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036138 Loss1: 0.035448 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.991765
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9251302083333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.093671 Loss1: 0.092988 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039408 Loss1: 0.038723 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039856 Loss1: 0.039170 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029875 Loss1: 0.029190 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034263 Loss1: 0.033577 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042257 Loss1: 0.041570 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040205 Loss1: 0.039518 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039799 Loss1: 0.039112 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043385 Loss1: 0.042697 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058456 Loss1: 0.057766 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.986762
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9449013157894737
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.076861 Loss1: 0.076178 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047412 Loss1: 0.046723 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052758 Loss1: 0.052070 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048278 Loss1: 0.047590 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051861 Loss1: 0.051172 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.067715 Loss1: 0.067026 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044268 Loss1: 0.043578 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051829 Loss1: 0.051140 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075585 Loss1: 0.074895 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054088 Loss1: 0.053398 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.990543
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9533227848101266
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063400 Loss1: 0.062718 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032488 Loss1: 0.031800 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043764 Loss1: 0.043076 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.042799 Loss1: 0.042110 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047284 Loss1: 0.046593 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049854 Loss1: 0.049164 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065176 Loss1: 0.064485 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051071 Loss1: 0.050379 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060140 Loss1: 0.059450 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049004 Loss1: 0.048313 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.989913
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9419070512820513
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067351 Loss1: 0.066671 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050160 Loss1: 0.049475 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.041262 Loss1: 0.040577 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032795 Loss1: 0.032107 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029896 Loss1: 0.029208 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028174 Loss1: 0.027487 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034348 Loss1: 0.033659 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034257 Loss1: 0.033568 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032243 Loss1: 0.031555 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.034759 Loss1: 0.034069 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.994792
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9248417721518988
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.078102 Loss1: 0.077420 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041534 Loss1: 0.040848 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040259 Loss1: 0.039573 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041840 Loss1: 0.041154 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051949 Loss1: 0.051261 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059108 Loss1: 0.058420 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.051577 Loss1: 0.050888 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035697 Loss1: 0.035008 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043662 Loss1: 0.042972 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061588 Loss1: 0.060898 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.988331
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9369066455696202
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.078848 Loss1: 0.078166 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048942 Loss1: 0.048257 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042299 Loss1: 0.041614 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047032 Loss1: 0.046346 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052395 Loss1: 0.051710 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047965 Loss1: 0.047278 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062340 Loss1: 0.061652 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.073844 Loss1: 0.073157 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069607 Loss1: 0.068918 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.090727 Loss1: 0.090040 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.976859
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9387019230769231
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084083 Loss1: 0.083406 Loss2: 0.000677
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048697 Loss1: 0.048014 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045057 Loss1: 0.044373 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047685 Loss1: 0.046999 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.058275 Loss1: 0.057590 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.054329 Loss1: 0.053644 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041892 Loss1: 0.041205 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043487 Loss1: 0.042800 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044469 Loss1: 0.043783 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059893 Loss1: 0.059207 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.987380
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9426424050632911
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.065678 Loss1: 0.064998 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.034237 Loss1: 0.033551 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049852 Loss1: 0.049166 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.054172 Loss1: 0.053486 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052542 Loss1: 0.051856 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059020 Loss1: 0.058334 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.061051 Loss1: 0.060364 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.072248 Loss1: 0.071561 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087174 Loss1: 0.086486 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075371 Loss1: 0.074682 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.983188
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9512195121951219
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.065546 Loss1: 0.064867 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048165 Loss1: 0.047483 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040205 Loss1: 0.039521 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047627 Loss1: 0.046943 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045993 Loss1: 0.045309 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061996 Loss1: 0.061310 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067645 Loss1: 0.066960 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069580 Loss1: 0.068893 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.087034 Loss1: 0.086349 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079627 Loss1: 0.078939 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.988948
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 20:10:37,308][flwr][DEBUG] - fit_round 73 received 10 results and 0 failures
-test acc: 0.6393
-[2023-09-22 20:11:31,937][flwr][INFO] - fit progress: (73, 2.378727243731197, {'accuracy': 0.6393}, 147573.59824875696)
-[2023-09-22 20:11:31,937][flwr][DEBUG] - evaluate_round 73: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 20:12:10,810][flwr][DEBUG] - evaluate_round 73 received 10 results and 0 failures
-[2023-09-22 20:12:10,814][flwr][DEBUG] - fit_round 74: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9452136075949367
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.076354 Loss1: 0.075671 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035143 Loss1: 0.034456 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032128 Loss1: 0.031440 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034362 Loss1: 0.033675 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028734 Loss1: 0.028047 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020491 Loss1: 0.019804 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025742 Loss1: 0.025056 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024693 Loss1: 0.024006 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031923 Loss1: 0.031235 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044936 Loss1: 0.044247 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.992880
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9503560126582279
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075064 Loss1: 0.074381 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044493 Loss1: 0.043805 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028837 Loss1: 0.028148 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033019 Loss1: 0.032329 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061686 Loss1: 0.060996 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055346 Loss1: 0.054655 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054877 Loss1: 0.054188 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054869 Loss1: 0.054179 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053093 Loss1: 0.052402 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065282 Loss1: 0.064592 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.989715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.931368670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.077098 Loss1: 0.076416 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.034495 Loss1: 0.033810 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025439 Loss1: 0.024754 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039686 Loss1: 0.039000 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051506 Loss1: 0.050819 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039541 Loss1: 0.038854 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031848 Loss1: 0.031162 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032786 Loss1: 0.032099 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.030776 Loss1: 0.030090 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030999 Loss1: 0.030311 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.995451
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9250395569620253
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.090135 Loss1: 0.089455 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041366 Loss1: 0.040681 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039461 Loss1: 0.038776 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029575 Loss1: 0.028889 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042187 Loss1: 0.041501 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038599 Loss1: 0.037913 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040054 Loss1: 0.039368 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045441 Loss1: 0.044754 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057125 Loss1: 0.056437 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062468 Loss1: 0.061781 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.985957
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9512746710526315
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.079084 Loss1: 0.078401 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.040292 Loss1: 0.039605 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035054 Loss1: 0.034365 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041172 Loss1: 0.040483 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047648 Loss1: 0.046960 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044251 Loss1: 0.043562 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.050943 Loss1: 0.050252 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061236 Loss1: 0.060547 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067332 Loss1: 0.066641 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054094 Loss1: 0.053402 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.987459
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9370993589743589
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070797 Loss1: 0.070118 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050305 Loss1: 0.049623 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033631 Loss1: 0.032947 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034342 Loss1: 0.033658 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.052746 Loss1: 0.052061 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.063708 Loss1: 0.063023 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054881 Loss1: 0.054195 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056632 Loss1: 0.055947 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075728 Loss1: 0.075043 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071936 Loss1: 0.071250 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.984175
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9455030487804879
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067252 Loss1: 0.066571 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046665 Loss1: 0.045980 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029187 Loss1: 0.028502 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031018 Loss1: 0.030332 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041244 Loss1: 0.040557 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.091765 Loss1: 0.091081 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068569 Loss1: 0.067882 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.067815 Loss1: 0.067130 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069817 Loss1: 0.069131 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041066 Loss1: 0.040380 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.994665
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9210069444444444
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095919 Loss1: 0.095237 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.059798 Loss1: 0.059112 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.051873 Loss1: 0.051185 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029744 Loss1: 0.029058 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032928 Loss1: 0.032240 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028852 Loss1: 0.028165 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034741 Loss1: 0.034053 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042778 Loss1: 0.042089 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041348 Loss1: 0.040659 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068371 Loss1: 0.067682 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.991536
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8975929054054054
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.093724 Loss1: 0.093040 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046813 Loss1: 0.046125 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.059631 Loss1: 0.058942 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048512 Loss1: 0.047821 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046128 Loss1: 0.045438 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042352 Loss1: 0.041660 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041560 Loss1: 0.040868 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051730 Loss1: 0.051039 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038683 Loss1: 0.037993 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030275 Loss1: 0.029585 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.995355
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9483173076923077
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059928 Loss1: 0.059246 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048492 Loss1: 0.047807 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.057384 Loss1: 0.056699 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.048050 Loss1: 0.047362 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039832 Loss1: 0.039143 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.060288 Loss1: 0.059599 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053442 Loss1: 0.052752 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052583 Loss1: 0.051894 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065430 Loss1: 0.064740 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053555 Loss1: 0.052865 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.990184
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 20:50:18,799][flwr][DEBUG] - fit_round 74 received 10 results and 0 failures
-test acc: 0.6429
-[2023-09-22 20:51:12,672][flwr][INFO] - fit progress: (74, 2.381040071336606, {'accuracy': 0.6429}, 149954.3338904637)
-[2023-09-22 20:51:12,673][flwr][DEBUG] - evaluate_round 74: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 20:52:07,176][flwr][DEBUG] - evaluate_round 74 received 10 results and 0 failures
-[2023-09-22 20:52:07,196][flwr][DEBUG] - fit_round 75: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9394778481012658
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080454 Loss1: 0.079772 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039940 Loss1: 0.039255 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.038687 Loss1: 0.038003 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.040625 Loss1: 0.039939 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039221 Loss1: 0.038535 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044109 Loss1: 0.043422 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.067420 Loss1: 0.066734 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063118 Loss1: 0.062432 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076863 Loss1: 0.076175 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076777 Loss1: 0.076088 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.985166
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9262152777777778
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.115416 Loss1: 0.114734 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.066430 Loss1: 0.065744 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055036 Loss1: 0.054349 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050305 Loss1: 0.049618 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059499 Loss1: 0.058812 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046963 Loss1: 0.046276 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041964 Loss1: 0.041276 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046374 Loss1: 0.045687 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063489 Loss1: 0.062801 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.077964 Loss1: 0.077277 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.986979
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9457236842105263
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.096508 Loss1: 0.095823 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.068707 Loss1: 0.068018 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.072841 Loss1: 0.072152 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.067501 Loss1: 0.066810 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055641 Loss1: 0.054951 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050812 Loss1: 0.050124 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054079 Loss1: 0.053388 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062504 Loss1: 0.061811 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060010 Loss1: 0.059320 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069441 Loss1: 0.068751 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.977385
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9285996835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.103728 Loss1: 0.103049 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042471 Loss1: 0.041784 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039042 Loss1: 0.038354 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045488 Loss1: 0.044799 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044209 Loss1: 0.043521 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041134 Loss1: 0.040446 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035714 Loss1: 0.035025 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057772 Loss1: 0.057083 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069399 Loss1: 0.068709 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072056 Loss1: 0.071365 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.980222
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9508384146341463
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.056260 Loss1: 0.055581 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039814 Loss1: 0.039131 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039695 Loss1: 0.039011 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037546 Loss1: 0.036861 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.045366 Loss1: 0.044681 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043884 Loss1: 0.043199 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046931 Loss1: 0.046246 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056702 Loss1: 0.056015 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042437 Loss1: 0.041751 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040475 Loss1: 0.039789 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.990282
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9433092948717948
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067522 Loss1: 0.066841 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044391 Loss1: 0.043706 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040834 Loss1: 0.040148 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031417 Loss1: 0.030730 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024772 Loss1: 0.024085 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028741 Loss1: 0.028054 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.049965 Loss1: 0.049278 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037178 Loss1: 0.036492 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038037 Loss1: 0.037350 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052282 Loss1: 0.051594 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.990385
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.944620253164557
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.073332 Loss1: 0.072651 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046922 Loss1: 0.046238 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045822 Loss1: 0.045136 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.052265 Loss1: 0.051578 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.050885 Loss1: 0.050198 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049818 Loss1: 0.049131 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046680 Loss1: 0.045992 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053758 Loss1: 0.053069 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076645 Loss1: 0.075956 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.075625 Loss1: 0.074935 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.983979
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9556962025316456
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050394 Loss1: 0.049712 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032923 Loss1: 0.032237 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022594 Loss1: 0.021905 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024395 Loss1: 0.023706 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025201 Loss1: 0.024513 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035310 Loss1: 0.034621 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048918 Loss1: 0.048231 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043153 Loss1: 0.042464 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031526 Loss1: 0.030837 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052025 Loss1: 0.051335 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.994066
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9429086538461539
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.071274 Loss1: 0.070595 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.051507 Loss1: 0.050825 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.050611 Loss1: 0.049925 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044747 Loss1: 0.044062 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048802 Loss1: 0.048116 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048283 Loss1: 0.047598 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036191 Loss1: 0.035505 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052569 Loss1: 0.051884 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057959 Loss1: 0.057273 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074203 Loss1: 0.073517 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.985978
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8944256756756757
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.095116 Loss1: 0.094434 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046390 Loss1: 0.045705 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.048384 Loss1: 0.047698 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.050931 Loss1: 0.050245 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044121 Loss1: 0.043435 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037633 Loss1: 0.036945 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040310 Loss1: 0.039621 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035117 Loss1: 0.034428 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035670 Loss1: 0.034983 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030725 Loss1: 0.030036 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994510
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 21:29:44,559][flwr][DEBUG] - fit_round 75 received 10 results and 0 failures
-test acc: 0.6427
-[2023-09-22 21:30:35,685][flwr][INFO] - fit progress: (75, 2.3815381104192035, {'accuracy': 0.6427}, 152317.34610947268)
-[2023-09-22 21:30:35,685][flwr][DEBUG] - evaluate_round 75: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 21:31:13,395][flwr][DEBUG] - evaluate_round 75 received 10 results and 0 failures
-[2023-09-22 21:31:13,395][flwr][DEBUG] - fit_round 76: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9246439873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058974 Loss1: 0.058293 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033159 Loss1: 0.032474 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031119 Loss1: 0.030432 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043028 Loss1: 0.042340 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033484 Loss1: 0.032797 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047904 Loss1: 0.047215 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.049797 Loss1: 0.049108 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.082351 Loss1: 0.081662 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.084541 Loss1: 0.083851 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.081905 Loss1: 0.081215 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.986155
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9481169871794872
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061283 Loss1: 0.060602 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027496 Loss1: 0.026810 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021668 Loss1: 0.020981 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016020 Loss1: 0.015333 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026790 Loss1: 0.026103 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030586 Loss1: 0.029899 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035344 Loss1: 0.034655 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.056884 Loss1: 0.056193 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052643 Loss1: 0.051952 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047885 Loss1: 0.047196 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989984
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.954077743902439
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061635 Loss1: 0.060955 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024944 Loss1: 0.024261 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019452 Loss1: 0.018767 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023099 Loss1: 0.022414 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022208 Loss1: 0.021524 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024915 Loss1: 0.024230 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027770 Loss1: 0.027084 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047466 Loss1: 0.046780 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075591 Loss1: 0.074905 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067997 Loss1: 0.067311 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.985137
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9513449367088608
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.071063 Loss1: 0.070381 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036146 Loss1: 0.035458 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034686 Loss1: 0.033998 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033582 Loss1: 0.032893 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046650 Loss1: 0.045960 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038124 Loss1: 0.037433 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036287 Loss1: 0.035596 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036087 Loss1: 0.035397 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047982 Loss1: 0.047292 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060277 Loss1: 0.059587 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.986946
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.955498417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069060 Loss1: 0.068379 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036593 Loss1: 0.035906 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.060269 Loss1: 0.059583 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045201 Loss1: 0.044515 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036853 Loss1: 0.036166 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043692 Loss1: 0.043006 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029116 Loss1: 0.028429 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035387 Loss1: 0.034700 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040602 Loss1: 0.039915 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.039045 Loss1: 0.038356 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.990506
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9442845394736842
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.073134 Loss1: 0.072449 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045873 Loss1: 0.045183 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049097 Loss1: 0.048408 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031886 Loss1: 0.031197 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031959 Loss1: 0.031268 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042152 Loss1: 0.041461 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048759 Loss1: 0.048067 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057782 Loss1: 0.057092 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056964 Loss1: 0.056272 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043916 Loss1: 0.043224 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.993832
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.8990709459459459
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.101032 Loss1: 0.100348 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042918 Loss1: 0.042232 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.047564 Loss1: 0.046876 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.045946 Loss1: 0.045258 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046633 Loss1: 0.045945 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053923 Loss1: 0.053234 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.058972 Loss1: 0.058282 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042097 Loss1: 0.041408 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049104 Loss1: 0.048414 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055330 Loss1: 0.054640 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.995566
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9387019230769231
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068325 Loss1: 0.067646 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044910 Loss1: 0.044228 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032926 Loss1: 0.032243 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044300 Loss1: 0.043617 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040122 Loss1: 0.039438 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035088 Loss1: 0.034403 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041439 Loss1: 0.040754 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044749 Loss1: 0.044065 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039984 Loss1: 0.039299 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059941 Loss1: 0.059256 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.989583
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9402689873417721
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055427 Loss1: 0.054747 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028479 Loss1: 0.027794 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026134 Loss1: 0.025449 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022107 Loss1: 0.021422 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017858 Loss1: 0.017173 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023655 Loss1: 0.022969 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035891 Loss1: 0.035206 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039067 Loss1: 0.038380 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035735 Loss1: 0.035047 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067840 Loss1: 0.067153 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.989122
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9292534722222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.099176 Loss1: 0.098494 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048754 Loss1: 0.048069 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039581 Loss1: 0.038895 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035030 Loss1: 0.034343 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.061472 Loss1: 0.060784 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062736 Loss1: 0.062049 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.062885 Loss1: 0.062196 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034912 Loss1: 0.034223 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031577 Loss1: 0.030889 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033412 Loss1: 0.032724 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.994358
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 22:07:43,019][flwr][DEBUG] - fit_round 76 received 10 results and 0 failures
-test acc: 0.6415
-[2023-09-22 22:08:31,152][flwr][INFO] - fit progress: (76, 2.4015440281968528, {'accuracy': 0.6415}, 154592.81349731563)
-[2023-09-22 22:08:31,153][flwr][DEBUG] - evaluate_round 76: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 22:09:08,895][flwr][DEBUG] - evaluate_round 76 received 10 results and 0 failures
-[2023-09-22 22:09:08,896][flwr][DEBUG] - fit_round 77: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9455180921052632
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.086566 Loss1: 0.085884 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039065 Loss1: 0.038377 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031894 Loss1: 0.031206 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032888 Loss1: 0.032200 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029340 Loss1: 0.028653 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030754 Loss1: 0.030067 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025573 Loss1: 0.024886 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033288 Loss1: 0.032600 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046868 Loss1: 0.046179 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047929 Loss1: 0.047241 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.990543
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9541139240506329
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072407 Loss1: 0.071725 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038887 Loss1: 0.038202 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042278 Loss1: 0.041591 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044217 Loss1: 0.043529 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032293 Loss1: 0.031605 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043977 Loss1: 0.043289 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042773 Loss1: 0.042084 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052204 Loss1: 0.051515 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069396 Loss1: 0.068705 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.068751 Loss1: 0.068062 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.987342
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9503205128205128
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059733 Loss1: 0.059051 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043322 Loss1: 0.042637 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031407 Loss1: 0.030720 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026724 Loss1: 0.026036 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022578 Loss1: 0.021891 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014106 Loss1: 0.013418 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.012059 Loss1: 0.011371 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023968 Loss1: 0.023279 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.021295 Loss1: 0.020606 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027056 Loss1: 0.026369 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.994992
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9505537974683544
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072603 Loss1: 0.071922 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.034108 Loss1: 0.033423 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028590 Loss1: 0.027905 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027031 Loss1: 0.026346 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041107 Loss1: 0.040420 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031095 Loss1: 0.030409 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.047251 Loss1: 0.046563 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046192 Loss1: 0.045504 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072032 Loss1: 0.071342 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.076024 Loss1: 0.075335 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.984771
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9472179878048781
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.056517 Loss1: 0.055834 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.045833 Loss1: 0.045149 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034324 Loss1: 0.033641 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023698 Loss1: 0.023014 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022245 Loss1: 0.021559 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021835 Loss1: 0.021150 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048898 Loss1: 0.048212 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052309 Loss1: 0.051622 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053169 Loss1: 0.052483 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043225 Loss1: 0.042538 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.996380
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9009712837837838
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091287 Loss1: 0.090604 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044155 Loss1: 0.043468 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.046258 Loss1: 0.045571 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035358 Loss1: 0.034669 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038968 Loss1: 0.038280 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053722 Loss1: 0.053034 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.049612 Loss1: 0.048922 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060352 Loss1: 0.059663 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055361 Loss1: 0.054672 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078397 Loss1: 0.077708 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.985008
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9333465189873418
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068998 Loss1: 0.068318 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031351 Loss1: 0.030667 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025849 Loss1: 0.025164 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025307 Loss1: 0.024622 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020390 Loss1: 0.019704 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024465 Loss1: 0.023778 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037962 Loss1: 0.037273 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035120 Loss1: 0.034431 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039694 Loss1: 0.039006 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062021 Loss1: 0.061331 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9481169871794872
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068448 Loss1: 0.067769 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035673 Loss1: 0.034992 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036377 Loss1: 0.035695 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029772 Loss1: 0.029090 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022315 Loss1: 0.021631 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024663 Loss1: 0.023980 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040535 Loss1: 0.039850 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038363 Loss1: 0.037678 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040793 Loss1: 0.040108 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051949 Loss1: 0.051263 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.992188
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.931640625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.088241 Loss1: 0.087558 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037624 Loss1: 0.036939 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032103 Loss1: 0.031416 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041020 Loss1: 0.040333 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030191 Loss1: 0.029503 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034160 Loss1: 0.033474 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035115 Loss1: 0.034427 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031822 Loss1: 0.031135 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046274 Loss1: 0.045586 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038974 Loss1: 0.038286 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.994358
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9392800632911392
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069153 Loss1: 0.068473 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.040057 Loss1: 0.039374 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049280 Loss1: 0.048595 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038128 Loss1: 0.037444 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051850 Loss1: 0.051166 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033419 Loss1: 0.032733 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039119 Loss1: 0.038432 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058929 Loss1: 0.058244 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052200 Loss1: 0.051513 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061883 Loss1: 0.061197 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.989122
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 22:45:49,474][flwr][DEBUG] - fit_round 77 received 10 results and 0 failures
-test acc: 0.6412
-[2023-09-22 22:46:37,766][flwr][INFO] - fit progress: (77, 2.441344348767314, {'accuracy': 0.6412}, 156879.42774670897)
-[2023-09-22 22:46:37,767][flwr][DEBUG] - evaluate_round 77: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 22:47:14,787][flwr][DEBUG] - evaluate_round 77 received 10 results and 0 failures
-[2023-09-22 22:47:14,788][flwr][DEBUG] - fit_round 78: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9582698170731707
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063682 Loss1: 0.063001 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029390 Loss1: 0.028706 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031792 Loss1: 0.031106 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035781 Loss1: 0.035095 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040689 Loss1: 0.040002 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043311 Loss1: 0.042624 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046652 Loss1: 0.045964 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063257 Loss1: 0.062570 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058838 Loss1: 0.058151 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057342 Loss1: 0.056655 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.988567
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9437099358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066517 Loss1: 0.065838 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032745 Loss1: 0.032061 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045058 Loss1: 0.044374 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046781 Loss1: 0.046096 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048889 Loss1: 0.048203 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049453 Loss1: 0.048768 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055895 Loss1: 0.055210 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057213 Loss1: 0.056527 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058528 Loss1: 0.057841 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.091227 Loss1: 0.090539 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.985176
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9491693037974683
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072688 Loss1: 0.072006 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043692 Loss1: 0.043007 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040359 Loss1: 0.039674 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033424 Loss1: 0.032735 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034763 Loss1: 0.034074 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040379 Loss1: 0.039690 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031801 Loss1: 0.031112 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.041630 Loss1: 0.040940 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.066985 Loss1: 0.066295 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.060518 Loss1: 0.059828 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.993078
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.953125
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069652 Loss1: 0.068969 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039790 Loss1: 0.039106 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039696 Loss1: 0.039010 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.030227 Loss1: 0.029540 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057810 Loss1: 0.057124 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045051 Loss1: 0.044366 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.073175 Loss1: 0.072487 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.060487 Loss1: 0.059798 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.071220 Loss1: 0.070533 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086426 Loss1: 0.085738 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.978837
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9288194444444444
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.091677 Loss1: 0.090997 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048854 Loss1: 0.048169 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043436 Loss1: 0.042751 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029394 Loss1: 0.028708 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028492 Loss1: 0.027805 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045459 Loss1: 0.044772 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031124 Loss1: 0.030438 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036027 Loss1: 0.035341 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028802 Loss1: 0.028115 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040216 Loss1: 0.039529 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.995009
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9490131578947368
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072877 Loss1: 0.072194 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033335 Loss1: 0.032646 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029172 Loss1: 0.028484 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028193 Loss1: 0.027504 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024653 Loss1: 0.023965 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020642 Loss1: 0.019955 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029284 Loss1: 0.028596 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045626 Loss1: 0.044936 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051617 Loss1: 0.050928 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052113 Loss1: 0.051423 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.984786
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.957871835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063260 Loss1: 0.062580 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044232 Loss1: 0.043544 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031965 Loss1: 0.031275 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026388 Loss1: 0.025698 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033071 Loss1: 0.032382 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028381 Loss1: 0.027690 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035516 Loss1: 0.034826 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038164 Loss1: 0.037476 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035169 Loss1: 0.034480 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043068 Loss1: 0.042379 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.993275
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9373022151898734
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.081311 Loss1: 0.080629 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032610 Loss1: 0.031924 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.042469 Loss1: 0.041782 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.041453 Loss1: 0.040764 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030493 Loss1: 0.029804 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036151 Loss1: 0.035465 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044168 Loss1: 0.043480 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050045 Loss1: 0.049356 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074700 Loss1: 0.074011 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078636 Loss1: 0.077949 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.982002
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9045608108108109
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080848 Loss1: 0.080166 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046563 Loss1: 0.045877 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.041552 Loss1: 0.040866 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027117 Loss1: 0.026429 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032386 Loss1: 0.031699 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035624 Loss1: 0.034935 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042785 Loss1: 0.042096 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058027 Loss1: 0.057338 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.050043 Loss1: 0.049353 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059359 Loss1: 0.058671 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989020
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9491185897435898
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060284 Loss1: 0.059604 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033944 Loss1: 0.033258 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039224 Loss1: 0.038540 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021283 Loss1: 0.020596 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020830 Loss1: 0.020143 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018047 Loss1: 0.017359 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.021207 Loss1: 0.020518 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026922 Loss1: 0.026232 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.022513 Loss1: 0.021823 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041694 Loss1: 0.041005 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.992588
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 23:23:33,045][flwr][DEBUG] - fit_round 78 received 10 results and 0 failures
-test acc: 0.6437
-[2023-09-22 23:24:22,885][flwr][INFO] - fit progress: (78, 2.4210258411904113, {'accuracy': 0.6437}, 159144.54672596185)
-[2023-09-22 23:24:22,886][flwr][DEBUG] - evaluate_round 78: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 23:25:00,105][flwr][DEBUG] - evaluate_round 78 received 10 results and 0 failures
-[2023-09-22 23:25:00,105][flwr][DEBUG] - fit_round 79: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9268663194444444
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084878 Loss1: 0.084197 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047403 Loss1: 0.046717 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031572 Loss1: 0.030887 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029794 Loss1: 0.029109 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041653 Loss1: 0.040966 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.061610 Loss1: 0.060924 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.071216 Loss1: 0.070529 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063028 Loss1: 0.062342 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.064156 Loss1: 0.063469 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058990 Loss1: 0.058303 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.990885
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9556962025316456
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052945 Loss1: 0.052265 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024562 Loss1: 0.023878 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020501 Loss1: 0.019815 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020880 Loss1: 0.020192 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020546 Loss1: 0.019859 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031588 Loss1: 0.030899 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035676 Loss1: 0.034988 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029385 Loss1: 0.028696 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055420 Loss1: 0.054732 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056505 Loss1: 0.055817 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.989715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9428401898734177
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.071983 Loss1: 0.071300 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043077 Loss1: 0.042391 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034445 Loss1: 0.033760 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027385 Loss1: 0.026698 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027350 Loss1: 0.026664 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038745 Loss1: 0.038059 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034285 Loss1: 0.033599 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024063 Loss1: 0.023376 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025266 Loss1: 0.024579 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030105 Loss1: 0.029418 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.986946
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9337420886075949
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064782 Loss1: 0.064101 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042236 Loss1: 0.041550 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.038623 Loss1: 0.037937 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028802 Loss1: 0.028116 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022088 Loss1: 0.021400 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026804 Loss1: 0.026116 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038280 Loss1: 0.037593 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038488 Loss1: 0.037800 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045043 Loss1: 0.044355 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050027 Loss1: 0.049339 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.993078
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9515224358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.062653 Loss1: 0.061972 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026049 Loss1: 0.025363 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026714 Loss1: 0.026028 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016547 Loss1: 0.015858 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036355 Loss1: 0.035669 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033425 Loss1: 0.032737 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029971 Loss1: 0.029285 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026315 Loss1: 0.025627 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028998 Loss1: 0.028310 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023626 Loss1: 0.022938 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.995393
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9515224358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.062748 Loss1: 0.062068 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027533 Loss1: 0.026850 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036826 Loss1: 0.036143 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032611 Loss1: 0.031927 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037788 Loss1: 0.037103 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036680 Loss1: 0.035996 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038898 Loss1: 0.038212 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045670 Loss1: 0.044985 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040588 Loss1: 0.039901 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.086744 Loss1: 0.086059 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.984575
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9505537974683544
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069812 Loss1: 0.069131 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044269 Loss1: 0.043584 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035682 Loss1: 0.034996 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033255 Loss1: 0.032568 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032098 Loss1: 0.031413 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039381 Loss1: 0.038694 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056275 Loss1: 0.055588 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061068 Loss1: 0.060382 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055921 Loss1: 0.055231 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055979 Loss1: 0.055291 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.985957
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9549753289473685
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072704 Loss1: 0.072020 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.040867 Loss1: 0.040178 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032756 Loss1: 0.032067 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034188 Loss1: 0.033499 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041236 Loss1: 0.040549 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040563 Loss1: 0.039874 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046290 Loss1: 0.045600 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061383 Loss1: 0.060692 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053127 Loss1: 0.052438 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053018 Loss1: 0.052327 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.991571
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9573170731707317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064043 Loss1: 0.063363 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029653 Loss1: 0.028970 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024504 Loss1: 0.023820 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027177 Loss1: 0.026491 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032432 Loss1: 0.031746 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042406 Loss1: 0.041721 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046137 Loss1: 0.045452 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037010 Loss1: 0.036325 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042207 Loss1: 0.041520 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062573 Loss1: 0.061887 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.992950
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9028716216216216
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.086226 Loss1: 0.085543 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037719 Loss1: 0.037034 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027953 Loss1: 0.027266 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028688 Loss1: 0.028000 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028605 Loss1: 0.027917 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037654 Loss1: 0.036965 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038340 Loss1: 0.037651 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038273 Loss1: 0.037584 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058956 Loss1: 0.058266 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057120 Loss1: 0.056431 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994721
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-22 23:56:39,095][flwr][DEBUG] - fit_round 79 received 10 results and 0 failures
-test acc: 0.6414
-[2023-09-22 23:57:27,164][flwr][INFO] - fit progress: (79, 2.445481828416879, {'accuracy': 0.6414}, 161128.82584654074)
-[2023-09-22 23:57:27,165][flwr][DEBUG] - evaluate_round 79: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-22 23:58:04,487][flwr][DEBUG] - evaluate_round 79 received 10 results and 0 failures
-[2023-09-22 23:58:04,488][flwr][DEBUG] - fit_round 80: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9338107638888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080463 Loss1: 0.079781 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.049802 Loss1: 0.049117 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.052514 Loss1: 0.051828 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043988 Loss1: 0.043300 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.060689 Loss1: 0.060001 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.059976 Loss1: 0.059288 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034054 Loss1: 0.033367 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040827 Loss1: 0.040139 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031308 Loss1: 0.030620 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048351 Loss1: 0.047663 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.994141
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9022381756756757
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.105723 Loss1: 0.105040 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039832 Loss1: 0.039143 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033744 Loss1: 0.033054 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027963 Loss1: 0.027274 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024330 Loss1: 0.023641 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023798 Loss1: 0.023108 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035635 Loss1: 0.034945 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030901 Loss1: 0.030210 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028339 Loss1: 0.027649 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.035737 Loss1: 0.035047 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.993454
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9539161392405063
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050153 Loss1: 0.049473 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028047 Loss1: 0.027362 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021453 Loss1: 0.020767 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020854 Loss1: 0.020166 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030052 Loss1: 0.029365 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030843 Loss1: 0.030155 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030394 Loss1: 0.029706 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027698 Loss1: 0.027009 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051713 Loss1: 0.051025 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071864 Loss1: 0.071175 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.982793
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9382911392405063
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066315 Loss1: 0.065633 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043817 Loss1: 0.043130 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029332 Loss1: 0.028646 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027096 Loss1: 0.026409 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027948 Loss1: 0.027260 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031544 Loss1: 0.030856 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033924 Loss1: 0.033237 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038279 Loss1: 0.037590 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042942 Loss1: 0.042254 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057971 Loss1: 0.057282 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987935
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9473892405063291
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059108 Loss1: 0.058427 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037260 Loss1: 0.036574 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.055409 Loss1: 0.054724 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044109 Loss1: 0.043423 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.066713 Loss1: 0.066026 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.049905 Loss1: 0.049217 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056472 Loss1: 0.055785 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048785 Loss1: 0.048097 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054737 Loss1: 0.054050 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083058 Loss1: 0.082369 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.981408
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9543269230769231
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057835 Loss1: 0.057154 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035212 Loss1: 0.034527 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027085 Loss1: 0.026398 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038832 Loss1: 0.038145 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030989 Loss1: 0.030300 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025774 Loss1: 0.025085 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033340 Loss1: 0.032653 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033272 Loss1: 0.032584 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.036703 Loss1: 0.036013 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.062499 Loss1: 0.061811 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989784
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9513449367088608
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061109 Loss1: 0.060428 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029687 Loss1: 0.029001 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029299 Loss1: 0.028612 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027110 Loss1: 0.026423 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030857 Loss1: 0.030169 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038227 Loss1: 0.037539 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039187 Loss1: 0.038499 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040575 Loss1: 0.039887 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034478 Loss1: 0.033789 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.031398 Loss1: 0.030709 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.995055
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9545641447368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063607 Loss1: 0.062924 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027447 Loss1: 0.026758 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036843 Loss1: 0.036154 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036272 Loss1: 0.035583 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038050 Loss1: 0.037360 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036724 Loss1: 0.036034 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036206 Loss1: 0.035515 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051816 Loss1: 0.051126 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060918 Loss1: 0.060227 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057215 Loss1: 0.056524 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.988898
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9607469512195121
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044306 Loss1: 0.043626 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019185 Loss1: 0.018502 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023460 Loss1: 0.022776 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022603 Loss1: 0.021918 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035080 Loss1: 0.034396 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.051815 Loss1: 0.051129 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038797 Loss1: 0.038112 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049787 Loss1: 0.049101 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046843 Loss1: 0.046156 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074826 Loss1: 0.074139 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.982088
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9527243589743589
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063854 Loss1: 0.063175 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.050307 Loss1: 0.049624 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044393 Loss1: 0.043709 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038509 Loss1: 0.037824 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039449 Loss1: 0.038764 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047499 Loss1: 0.046815 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029549 Loss1: 0.028864 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036795 Loss1: 0.036110 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049494 Loss1: 0.048807 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058887 Loss1: 0.058201 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.993590
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 00:26:55,551][flwr][DEBUG] - fit_round 80 received 10 results and 0 failures
-test acc: 0.64
-[2023-09-23 00:27:41,497][flwr][INFO] - fit progress: (80, 2.4491478545597185, {'accuracy': 0.64}, 162943.15885636583)
-[2023-09-23 00:27:41,498][flwr][DEBUG] - evaluate_round 80: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 00:28:18,541][flwr][DEBUG] - evaluate_round 80 received 10 results and 0 failures
-[2023-09-23 00:28:18,542][flwr][DEBUG] - fit_round 81: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.953125
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063023 Loss1: 0.062342 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027905 Loss1: 0.027219 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020624 Loss1: 0.019937 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031199 Loss1: 0.030512 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042991 Loss1: 0.042302 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035259 Loss1: 0.034570 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028469 Loss1: 0.027779 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030078 Loss1: 0.029390 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029312 Loss1: 0.028624 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029113 Loss1: 0.028424 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994191
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9299841772151899
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070361 Loss1: 0.069678 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048075 Loss1: 0.047388 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035377 Loss1: 0.034688 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.046578 Loss1: 0.045890 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032663 Loss1: 0.031976 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025846 Loss1: 0.025158 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.050461 Loss1: 0.049771 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.079188 Loss1: 0.078498 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056994 Loss1: 0.056304 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.057287 Loss1: 0.056598 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.990704
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9007601351351351
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.081927 Loss1: 0.081245 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.056894 Loss1: 0.056208 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045254 Loss1: 0.044568 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029624 Loss1: 0.028936 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035445 Loss1: 0.034757 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.032179 Loss1: 0.031491 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037361 Loss1: 0.036673 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.062488 Loss1: 0.061799 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.072382 Loss1: 0.071693 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.069150 Loss1: 0.068461 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.992399
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9525240384615384
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054524 Loss1: 0.053845 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031507 Loss1: 0.030825 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025512 Loss1: 0.024828 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032530 Loss1: 0.031846 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041360 Loss1: 0.040675 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033993 Loss1: 0.033309 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027515 Loss1: 0.026830 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040419 Loss1: 0.039734 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033386 Loss1: 0.032700 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036584 Loss1: 0.035898 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.994391
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9398871527777778
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.090336 Loss1: 0.089654 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.055137 Loss1: 0.054451 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.040947 Loss1: 0.040262 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038210 Loss1: 0.037523 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042268 Loss1: 0.041582 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048006 Loss1: 0.047317 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046690 Loss1: 0.046003 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040034 Loss1: 0.039345 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051701 Loss1: 0.051011 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040289 Loss1: 0.039601 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989800
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.955797697368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080935 Loss1: 0.080249 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038918 Loss1: 0.038230 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034336 Loss1: 0.033646 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024761 Loss1: 0.024072 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023399 Loss1: 0.022710 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036288 Loss1: 0.035597 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040299 Loss1: 0.039608 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.054552 Loss1: 0.053863 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056873 Loss1: 0.056182 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.078648 Loss1: 0.077957 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.987870
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9551028481012658
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068927 Loss1: 0.068244 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047667 Loss1: 0.046980 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035290 Loss1: 0.034603 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038431 Loss1: 0.037742 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.039673 Loss1: 0.038983 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042811 Loss1: 0.042122 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.049568 Loss1: 0.048878 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057617 Loss1: 0.056925 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049650 Loss1: 0.048960 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048233 Loss1: 0.047541 Loss2: 0.000692
-(DefaultActor pid=2839578) >> Training accuracy: 0.990704
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9569359756097561
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049108 Loss1: 0.048428 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020391 Loss1: 0.019708 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017105 Loss1: 0.016420 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018865 Loss1: 0.018181 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026731 Loss1: 0.026046 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027890 Loss1: 0.027204 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.024497 Loss1: 0.023812 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033313 Loss1: 0.032627 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052270 Loss1: 0.051584 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058175 Loss1: 0.057489 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.989520
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9454113924050633
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058487 Loss1: 0.057806 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038735 Loss1: 0.038051 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024455 Loss1: 0.023769 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034514 Loss1: 0.033828 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033159 Loss1: 0.032472 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028789 Loss1: 0.028102 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.046960 Loss1: 0.046273 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059139 Loss1: 0.058451 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.055025 Loss1: 0.054337 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067935 Loss1: 0.067247 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.983584
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9547072784810127
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057423 Loss1: 0.056741 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035337 Loss1: 0.034652 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035244 Loss1: 0.034558 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029535 Loss1: 0.028847 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026409 Loss1: 0.025722 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037457 Loss1: 0.036770 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042167 Loss1: 0.041479 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042640 Loss1: 0.041951 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044282 Loss1: 0.043593 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080399 Loss1: 0.079710 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.981408
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 00:57:14,238][flwr][DEBUG] - fit_round 81 received 10 results and 0 failures
-test acc: 0.6414
-[2023-09-23 00:58:00,414][flwr][INFO] - fit progress: (81, 2.4155959117526824, {'accuracy': 0.6414}, 164762.07565148594)
-[2023-09-23 00:58:00,415][flwr][DEBUG] - evaluate_round 81: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 00:58:38,637][flwr][DEBUG] - evaluate_round 81 received 10 results and 0 failures
-[2023-09-23 00:58:38,638][flwr][DEBUG] - fit_round 82: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9369066455696202
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064951 Loss1: 0.064269 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048171 Loss1: 0.047486 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033067 Loss1: 0.032382 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036741 Loss1: 0.036055 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022295 Loss1: 0.021607 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021661 Loss1: 0.020973 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.047969 Loss1: 0.047283 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.051828 Loss1: 0.051140 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.048797 Loss1: 0.048109 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032981 Loss1: 0.032292 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994066
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.935546875
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.080732 Loss1: 0.080049 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031089 Loss1: 0.030403 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029349 Loss1: 0.028661 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028728 Loss1: 0.028041 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032930 Loss1: 0.032243 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016456 Loss1: 0.015769 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026783 Loss1: 0.026098 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038241 Loss1: 0.037552 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.058475 Loss1: 0.057787 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063883 Loss1: 0.063193 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.990668
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9560032894736842
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066390 Loss1: 0.065708 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031376 Loss1: 0.030690 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025031 Loss1: 0.024343 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022391 Loss1: 0.021703 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031576 Loss1: 0.030888 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029732 Loss1: 0.029043 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029684 Loss1: 0.028995 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043483 Loss1: 0.042795 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041187 Loss1: 0.040497 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036952 Loss1: 0.036262 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.992804
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9521360759493671
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.065048 Loss1: 0.064368 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033248 Loss1: 0.032562 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035610 Loss1: 0.034925 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034444 Loss1: 0.033757 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025993 Loss1: 0.025308 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029503 Loss1: 0.028816 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048708 Loss1: 0.048021 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032681 Loss1: 0.031993 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056375 Loss1: 0.055688 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065276 Loss1: 0.064589 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.983979
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9497195512820513
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061700 Loss1: 0.061022 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031062 Loss1: 0.030380 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024575 Loss1: 0.023892 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.042257 Loss1: 0.041574 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.038400 Loss1: 0.037715 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.047764 Loss1: 0.047079 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.053061 Loss1: 0.052374 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046923 Loss1: 0.046237 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043008 Loss1: 0.042323 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056040 Loss1: 0.055354 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.991987
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9546493902439024
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053370 Loss1: 0.052691 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022844 Loss1: 0.022161 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023343 Loss1: 0.022660 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035893 Loss1: 0.035210 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026005 Loss1: 0.025322 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033457 Loss1: 0.032773 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029149 Loss1: 0.028465 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026362 Loss1: 0.025678 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031765 Loss1: 0.031080 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.037293 Loss1: 0.036607 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.993140
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9553006329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042494 Loss1: 0.041814 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031234 Loss1: 0.030548 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031502 Loss1: 0.030815 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.040892 Loss1: 0.040205 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042503 Loss1: 0.041816 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043463 Loss1: 0.042774 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.054546 Loss1: 0.053857 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039639 Loss1: 0.038949 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039921 Loss1: 0.039231 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049884 Loss1: 0.049195 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.990111
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.957871835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055141 Loss1: 0.054460 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026878 Loss1: 0.026193 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022625 Loss1: 0.021940 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015713 Loss1: 0.015028 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016303 Loss1: 0.015617 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026065 Loss1: 0.025379 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023873 Loss1: 0.023187 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034164 Loss1: 0.033477 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.053672 Loss1: 0.052985 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038563 Loss1: 0.037876 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.994462
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9565304487179487
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070620 Loss1: 0.069939 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026930 Loss1: 0.026244 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.030775 Loss1: 0.030090 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.047054 Loss1: 0.046367 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026659 Loss1: 0.025973 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036034 Loss1: 0.035347 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044753 Loss1: 0.044065 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.057596 Loss1: 0.056907 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059108 Loss1: 0.058419 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.067913 Loss1: 0.067224 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.990585
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.903293918918919
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.083241 Loss1: 0.082559 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.069119 Loss1: 0.068431 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.044358 Loss1: 0.043671 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.049590 Loss1: 0.048900 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048925 Loss1: 0.048236 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044944 Loss1: 0.044255 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036954 Loss1: 0.036264 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040766 Loss1: 0.040077 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043401 Loss1: 0.042713 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054339 Loss1: 0.053650 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987542
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 01:28:33,879][flwr][DEBUG] - fit_round 82 received 10 results and 0 failures
-test acc: 0.6446
-[2023-09-23 01:29:14,095][flwr][INFO] - fit progress: (82, 2.4423680827259635, {'accuracy': 0.6446}, 166635.7560589686)
-[2023-09-23 01:29:14,095][flwr][DEBUG] - evaluate_round 82: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 01:29:59,494][flwr][DEBUG] - evaluate_round 82 received 10 results and 0 failures
-[2023-09-23 01:29:59,496][flwr][DEBUG] - fit_round 83: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9535256410256411
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066798 Loss1: 0.066116 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020814 Loss1: 0.020127 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026035 Loss1: 0.025347 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029134 Loss1: 0.028446 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032174 Loss1: 0.031486 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033844 Loss1: 0.033155 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038761 Loss1: 0.038071 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027314 Loss1: 0.026625 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039561 Loss1: 0.038872 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054156 Loss1: 0.053465 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.992989
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9446614583333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060098 Loss1: 0.059416 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033443 Loss1: 0.032758 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.037392 Loss1: 0.036706 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.030248 Loss1: 0.029561 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029006 Loss1: 0.028319 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024126 Loss1: 0.023441 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017647 Loss1: 0.016959 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019338 Loss1: 0.018651 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032824 Loss1: 0.032136 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051854 Loss1: 0.051167 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.989583
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9598496835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.047663 Loss1: 0.046983 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018728 Loss1: 0.018044 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020545 Loss1: 0.019861 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019773 Loss1: 0.019087 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023955 Loss1: 0.023269 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024838 Loss1: 0.024152 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018967 Loss1: 0.018280 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028664 Loss1: 0.027977 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019669 Loss1: 0.018981 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.021382 Loss1: 0.020693 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.995253
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.959703947368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060880 Loss1: 0.060198 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036816 Loss1: 0.036130 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033738 Loss1: 0.033049 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023514 Loss1: 0.022826 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035901 Loss1: 0.035213 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040026 Loss1: 0.039337 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056898 Loss1: 0.056208 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.073229 Loss1: 0.072539 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.089446 Loss1: 0.088756 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085906 Loss1: 0.085217 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.985403
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9592225609756098
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068294 Loss1: 0.067615 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030240 Loss1: 0.029555 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029775 Loss1: 0.029091 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021421 Loss1: 0.020736 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027895 Loss1: 0.027209 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037170 Loss1: 0.036484 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043428 Loss1: 0.042742 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.078758 Loss1: 0.078072 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059359 Loss1: 0.058673 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053880 Loss1: 0.053193 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.991806
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.946993670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063069 Loss1: 0.062388 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029132 Loss1: 0.028447 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018054 Loss1: 0.017370 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022380 Loss1: 0.021694 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024626 Loss1: 0.023940 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021652 Loss1: 0.020965 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027487 Loss1: 0.026800 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028322 Loss1: 0.027635 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025064 Loss1: 0.024377 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.039242 Loss1: 0.038554 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992089
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9035050675675675
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.085831 Loss1: 0.085149 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.044517 Loss1: 0.043831 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026050 Loss1: 0.025363 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025358 Loss1: 0.024671 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023634 Loss1: 0.022946 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026613 Loss1: 0.025924 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029272 Loss1: 0.028584 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034768 Loss1: 0.034077 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039819 Loss1: 0.039129 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036749 Loss1: 0.036059 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.987120
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9535205696202531
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067496 Loss1: 0.066814 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029960 Loss1: 0.029273 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.030786 Loss1: 0.030099 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028098 Loss1: 0.027410 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040473 Loss1: 0.039784 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.062708 Loss1: 0.062021 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042223 Loss1: 0.041533 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027157 Loss1: 0.026468 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028714 Loss1: 0.028025 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.036605 Loss1: 0.035916 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.992682
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9515224358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057560 Loss1: 0.056881 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028758 Loss1: 0.028075 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022797 Loss1: 0.022112 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035423 Loss1: 0.034738 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.041735 Loss1: 0.041051 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.050212 Loss1: 0.049527 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031915 Loss1: 0.031230 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036227 Loss1: 0.035542 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043004 Loss1: 0.042319 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041706 Loss1: 0.041020 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.993590
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9416534810126582
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.067868 Loss1: 0.067186 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033472 Loss1: 0.032787 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026228 Loss1: 0.025542 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032547 Loss1: 0.031860 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040557 Loss1: 0.039870 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.038429 Loss1: 0.037741 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034226 Loss1: 0.033538 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045065 Loss1: 0.044378 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040011 Loss1: 0.039323 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.039544 Loss1: 0.038857 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.993275
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 02:00:05,773][flwr][DEBUG] - fit_round 83 received 10 results and 0 failures
-test acc: 0.6436
-[2023-09-23 02:00:54,928][flwr][INFO] - fit progress: (83, 2.4571505731667953, {'accuracy': 0.6436}, 168536.58993728273)
-[2023-09-23 02:00:54,929][flwr][DEBUG] - evaluate_round 83: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 02:01:32,139][flwr][DEBUG] - evaluate_round 83 received 10 results and 0 failures
-[2023-09-23 02:01:32,141][flwr][DEBUG] - fit_round 84: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9519382911392406
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.063177 Loss1: 0.062496 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033664 Loss1: 0.032978 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024772 Loss1: 0.024087 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022898 Loss1: 0.022212 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025247 Loss1: 0.024562 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035081 Loss1: 0.034397 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028536 Loss1: 0.027849 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029973 Loss1: 0.029286 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033405 Loss1: 0.032718 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048631 Loss1: 0.047944 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.991891
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9599095394736842
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.056444 Loss1: 0.055761 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035267 Loss1: 0.034580 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.039057 Loss1: 0.038369 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027981 Loss1: 0.027291 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026097 Loss1: 0.025408 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029261 Loss1: 0.028571 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036818 Loss1: 0.036129 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035374 Loss1: 0.034684 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.036234 Loss1: 0.035544 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073121 Loss1: 0.072431 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.985609
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9645579268292683
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072670 Loss1: 0.071991 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.046325 Loss1: 0.045641 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029569 Loss1: 0.028883 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036192 Loss1: 0.035505 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033666 Loss1: 0.032979 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.048533 Loss1: 0.047847 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055182 Loss1: 0.054494 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048938 Loss1: 0.048251 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077432 Loss1: 0.076744 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.080071 Loss1: 0.079383 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.987614
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.964003164556962
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058801 Loss1: 0.058119 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047359 Loss1: 0.046671 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035060 Loss1: 0.034374 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021430 Loss1: 0.020741 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024394 Loss1: 0.023706 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030818 Loss1: 0.030129 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041115 Loss1: 0.040426 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059294 Loss1: 0.058606 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.063558 Loss1: 0.062869 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.074486 Loss1: 0.073796 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.992286
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9361155063291139
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.072807 Loss1: 0.072125 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036010 Loss1: 0.035323 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028210 Loss1: 0.027524 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027761 Loss1: 0.027074 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032009 Loss1: 0.031320 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030693 Loss1: 0.030005 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042831 Loss1: 0.042142 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043566 Loss1: 0.042878 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039768 Loss1: 0.039077 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048099 Loss1: 0.047410 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994858
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9045608108108109
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084706 Loss1: 0.084024 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041193 Loss1: 0.040505 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.043768 Loss1: 0.043082 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044891 Loss1: 0.044203 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034902 Loss1: 0.034215 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042643 Loss1: 0.041956 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.032599 Loss1: 0.031911 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033652 Loss1: 0.032966 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037517 Loss1: 0.036828 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029023 Loss1: 0.028334 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.995144
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9582674050632911
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046678 Loss1: 0.045997 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022240 Loss1: 0.021556 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021389 Loss1: 0.020704 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031412 Loss1: 0.030725 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028966 Loss1: 0.028280 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027152 Loss1: 0.026466 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031127 Loss1: 0.030440 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034360 Loss1: 0.033673 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032008 Loss1: 0.031320 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.055018 Loss1: 0.054329 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9615384615384616
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061207 Loss1: 0.060524 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030605 Loss1: 0.029920 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024219 Loss1: 0.023532 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026068 Loss1: 0.025380 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027033 Loss1: 0.026345 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022097 Loss1: 0.021408 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.032278 Loss1: 0.031590 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040569 Loss1: 0.039881 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026930 Loss1: 0.026243 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027943 Loss1: 0.027252 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.998397
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9555288461538461
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064313 Loss1: 0.063635 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042620 Loss1: 0.041936 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031470 Loss1: 0.030786 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032394 Loss1: 0.031709 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025123 Loss1: 0.024438 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020477 Loss1: 0.019791 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.021108 Loss1: 0.020423 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022144 Loss1: 0.021459 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040398 Loss1: 0.039711 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.077825 Loss1: 0.077139 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.989984
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9351128472222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068401 Loss1: 0.067718 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035442 Loss1: 0.034756 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.049096 Loss1: 0.048410 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.054225 Loss1: 0.053539 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.059284 Loss1: 0.058597 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052341 Loss1: 0.051653 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043131 Loss1: 0.042442 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037859 Loss1: 0.037170 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.051217 Loss1: 0.050528 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059824 Loss1: 0.059135 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.986762
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 02:31:11,308][flwr][DEBUG] - fit_round 84 received 10 results and 0 failures
-test acc: 0.6438
-[2023-09-23 02:31:48,655][flwr][INFO] - fit progress: (84, 2.4649806056921473, {'accuracy': 0.6438}, 170390.31651637657)
-[2023-09-23 02:31:48,655][flwr][DEBUG] - evaluate_round 84: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 02:32:25,317][flwr][DEBUG] - evaluate_round 84 received 10 results and 0 failures
-[2023-09-23 02:32:25,318][flwr][DEBUG] - fit_round 85: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9426424050632911
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059814 Loss1: 0.059134 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023981 Loss1: 0.023295 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028435 Loss1: 0.027749 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025380 Loss1: 0.024693 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028160 Loss1: 0.027472 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039352 Loss1: 0.038662 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043783 Loss1: 0.043095 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039008 Loss1: 0.038318 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038866 Loss1: 0.038176 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043455 Loss1: 0.042766 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994660
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9440104166666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058635 Loss1: 0.057955 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042277 Loss1: 0.041591 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031394 Loss1: 0.030708 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032969 Loss1: 0.032282 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.043335 Loss1: 0.042647 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041710 Loss1: 0.041022 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029413 Loss1: 0.028725 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026659 Loss1: 0.025971 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031624 Loss1: 0.030934 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030673 Loss1: 0.029984 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.996745
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9489182692307693
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060784 Loss1: 0.060105 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037158 Loss1: 0.036474 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034215 Loss1: 0.033531 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026018 Loss1: 0.025334 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025145 Loss1: 0.024460 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022011 Loss1: 0.021326 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029210 Loss1: 0.028526 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030211 Loss1: 0.029525 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034638 Loss1: 0.033952 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042529 Loss1: 0.041844 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.994191
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9523338607594937
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059224 Loss1: 0.058543 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029797 Loss1: 0.029114 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026281 Loss1: 0.025596 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022521 Loss1: 0.021834 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.010690 Loss1: 0.010004 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.013125 Loss1: 0.012439 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.015117 Loss1: 0.014429 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.018739 Loss1: 0.018051 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029020 Loss1: 0.028332 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023393 Loss1: 0.022706 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.998616
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9539473684210527
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058691 Loss1: 0.058008 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037289 Loss1: 0.036602 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034826 Loss1: 0.034137 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035623 Loss1: 0.034935 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032072 Loss1: 0.031383 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037345 Loss1: 0.036657 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.033678 Loss1: 0.032988 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049200 Loss1: 0.048509 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032050 Loss1: 0.031359 Loss2: 0.000692
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046414 Loss1: 0.045723 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.989720
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9115287162162162
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.078671 Loss1: 0.077990 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028609 Loss1: 0.027923 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023841 Loss1: 0.023153 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023946 Loss1: 0.023258 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025250 Loss1: 0.024563 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018114 Loss1: 0.017427 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018857 Loss1: 0.018171 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022054 Loss1: 0.021365 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028414 Loss1: 0.027726 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033186 Loss1: 0.032497 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.995144
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9636075949367089
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048481 Loss1: 0.047800 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027239 Loss1: 0.026553 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018385 Loss1: 0.017697 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.012480 Loss1: 0.011791 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.013278 Loss1: 0.012588 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.008825 Loss1: 0.008135 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011110 Loss1: 0.010421 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.012547 Loss1: 0.011858 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.011822 Loss1: 0.011132 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.018274 Loss1: 0.017585 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.997429
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9541266025641025
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050596 Loss1: 0.049914 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025578 Loss1: 0.024893 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018997 Loss1: 0.018311 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038290 Loss1: 0.037603 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026535 Loss1: 0.025847 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040181 Loss1: 0.039494 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041881 Loss1: 0.041193 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039117 Loss1: 0.038428 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038702 Loss1: 0.038014 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054892 Loss1: 0.054203 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994992
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9523338607594937
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053866 Loss1: 0.053186 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022181 Loss1: 0.021495 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019480 Loss1: 0.018794 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011935 Loss1: 0.011248 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.010239 Loss1: 0.009552 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016143 Loss1: 0.015458 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017182 Loss1: 0.016496 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023343 Loss1: 0.022656 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024574 Loss1: 0.023888 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030370 Loss1: 0.029684 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.992682
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9649390243902439
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045624 Loss1: 0.044946 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028164 Loss1: 0.027480 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018708 Loss1: 0.018023 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023898 Loss1: 0.023213 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020443 Loss1: 0.019758 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021018 Loss1: 0.020333 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.014680 Loss1: 0.013995 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.011579 Loss1: 0.010895 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.011671 Loss1: 0.010986 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.014627 Loss1: 0.013941 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.997332
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 03:02:15,040][flwr][DEBUG] - fit_round 85 received 10 results and 0 failures
-test acc: 0.6483
-[2023-09-23 03:02:53,227][flwr][INFO] - fit progress: (85, 2.5026507737537544, {'accuracy': 0.6483}, 172254.88892117888)
-[2023-09-23 03:02:53,228][flwr][DEBUG] - evaluate_round 85: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 03:03:28,979][flwr][DEBUG] - evaluate_round 85 received 10 results and 0 failures
-[2023-09-23 03:03:28,980][flwr][DEBUG] - fit_round 86: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.964003164556962
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046439 Loss1: 0.045759 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024746 Loss1: 0.024061 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026984 Loss1: 0.026299 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032114 Loss1: 0.031428 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.054390 Loss1: 0.053704 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.058262 Loss1: 0.057576 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065606 Loss1: 0.064919 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063767 Loss1: 0.063079 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065166 Loss1: 0.064479 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064869 Loss1: 0.064181 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.980617
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9457465277777778
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061928 Loss1: 0.061247 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022826 Loss1: 0.022141 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018676 Loss1: 0.017990 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024888 Loss1: 0.024201 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035807 Loss1: 0.035121 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026729 Loss1: 0.026043 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025297 Loss1: 0.024611 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027936 Loss1: 0.027249 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025796 Loss1: 0.025108 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030787 Loss1: 0.030098 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.992405
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9136402027027027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.069521 Loss1: 0.068838 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029134 Loss1: 0.028448 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025818 Loss1: 0.025131 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022739 Loss1: 0.022051 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021053 Loss1: 0.020365 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024274 Loss1: 0.023586 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026769 Loss1: 0.026080 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031085 Loss1: 0.030396 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029567 Loss1: 0.028879 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044316 Loss1: 0.043626 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.992399
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.966376582278481
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054927 Loss1: 0.054246 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.047205 Loss1: 0.046519 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035048 Loss1: 0.034360 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038093 Loss1: 0.037405 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030938 Loss1: 0.030251 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035455 Loss1: 0.034766 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.044747 Loss1: 0.044059 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045644 Loss1: 0.044953 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025733 Loss1: 0.025042 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.031713 Loss1: 0.031022 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.994462
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9592927631578947
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055824 Loss1: 0.055140 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030474 Loss1: 0.029786 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020996 Loss1: 0.020306 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024315 Loss1: 0.023626 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017695 Loss1: 0.017007 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031434 Loss1: 0.030744 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026819 Loss1: 0.026129 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033426 Loss1: 0.032735 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046306 Loss1: 0.045615 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.083157 Loss1: 0.082467 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.986020
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9567307692307693
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054187 Loss1: 0.053506 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030987 Loss1: 0.030303 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036449 Loss1: 0.035763 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033534 Loss1: 0.032849 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022955 Loss1: 0.022271 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.013229 Loss1: 0.012543 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020143 Loss1: 0.019456 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026988 Loss1: 0.026302 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035765 Loss1: 0.035080 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043606 Loss1: 0.042920 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.993790
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9456091772151899
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052979 Loss1: 0.052297 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035237 Loss1: 0.034551 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027393 Loss1: 0.026706 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037879 Loss1: 0.037193 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051825 Loss1: 0.051138 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.056349 Loss1: 0.055662 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.052856 Loss1: 0.052167 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075094 Loss1: 0.074406 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.075334 Loss1: 0.074646 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.079569 Loss1: 0.078880 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.982793
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9605368589743589
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048970 Loss1: 0.048290 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028117 Loss1: 0.027433 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029937 Loss1: 0.029249 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023905 Loss1: 0.023219 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020584 Loss1: 0.019897 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025494 Loss1: 0.024807 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030295 Loss1: 0.029608 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026685 Loss1: 0.025998 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034957 Loss1: 0.034268 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049181 Loss1: 0.048492 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987179
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9655106707317073
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044310 Loss1: 0.043629 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035127 Loss1: 0.034443 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034443 Loss1: 0.033759 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.035780 Loss1: 0.035095 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033982 Loss1: 0.033296 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037948 Loss1: 0.037262 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055760 Loss1: 0.055074 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.055483 Loss1: 0.054796 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049904 Loss1: 0.049218 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.037986 Loss1: 0.037298 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.993331
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9566851265822784
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053904 Loss1: 0.053221 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033702 Loss1: 0.033016 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034790 Loss1: 0.034104 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043564 Loss1: 0.042877 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036648 Loss1: 0.035961 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.053389 Loss1: 0.052701 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048907 Loss1: 0.048220 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.052126 Loss1: 0.051438 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.074584 Loss1: 0.073896 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.093579 Loss1: 0.092891 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.980617
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 03:33:16,989][flwr][DEBUG] - fit_round 86 received 10 results and 0 failures
-test acc: 0.6444
-[2023-09-23 03:33:55,332][flwr][INFO] - fit progress: (86, 2.4607985724275485, {'accuracy': 0.6444}, 174116.99310424365)
-[2023-09-23 03:33:55,332][flwr][DEBUG] - evaluate_round 86: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 03:34:31,484][flwr][DEBUG] - evaluate_round 86 received 10 results and 0 failures
-[2023-09-23 03:34:31,485][flwr][DEBUG] - fit_round 87: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9442246835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046494 Loss1: 0.045816 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027297 Loss1: 0.026612 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013255 Loss1: 0.012569 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013866 Loss1: 0.013178 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.013514 Loss1: 0.012825 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021356 Loss1: 0.020669 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028683 Loss1: 0.027995 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026499 Loss1: 0.025812 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035720 Loss1: 0.035033 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041512 Loss1: 0.040823 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.993275
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9136402027027027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061266 Loss1: 0.060584 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035482 Loss1: 0.034796 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034681 Loss1: 0.033994 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037472 Loss1: 0.036784 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040434 Loss1: 0.039747 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.046945 Loss1: 0.046256 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056124 Loss1: 0.055435 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043577 Loss1: 0.042889 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044170 Loss1: 0.043481 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044813 Loss1: 0.044124 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.987965
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9645965189873418
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.047623 Loss1: 0.046941 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029169 Loss1: 0.028484 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022252 Loss1: 0.021566 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031589 Loss1: 0.030903 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.046650 Loss1: 0.045963 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035880 Loss1: 0.035193 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025783 Loss1: 0.025095 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022848 Loss1: 0.022159 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025575 Loss1: 0.024886 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025851 Loss1: 0.025162 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.994660
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9577323717948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059134 Loss1: 0.058453 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031375 Loss1: 0.030691 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031526 Loss1: 0.030840 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026395 Loss1: 0.025708 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027891 Loss1: 0.027203 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029580 Loss1: 0.028892 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030881 Loss1: 0.030194 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040182 Loss1: 0.039493 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044837 Loss1: 0.044149 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045301 Loss1: 0.044612 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.994391
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9442274305555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075459 Loss1: 0.074776 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022781 Loss1: 0.022097 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033725 Loss1: 0.033040 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025939 Loss1: 0.025253 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024830 Loss1: 0.024144 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037631 Loss1: 0.036945 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031063 Loss1: 0.030376 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042630 Loss1: 0.041942 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038259 Loss1: 0.037572 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047106 Loss1: 0.046418 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.987413
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9628164556962026
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037155 Loss1: 0.036476 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018302 Loss1: 0.017615 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021079 Loss1: 0.020391 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.014974 Loss1: 0.014285 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017089 Loss1: 0.016401 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017621 Loss1: 0.016932 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.024762 Loss1: 0.024072 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029308 Loss1: 0.028619 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027548 Loss1: 0.026858 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.034112 Loss1: 0.033422 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.995847
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9653201219512195
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049787 Loss1: 0.049108 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030026 Loss1: 0.029342 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026109 Loss1: 0.025424 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.030906 Loss1: 0.030220 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.042454 Loss1: 0.041768 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034458 Loss1: 0.033772 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026677 Loss1: 0.025990 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035393 Loss1: 0.034706 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.038607 Loss1: 0.037919 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040040 Loss1: 0.039352 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992759
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9603365384615384
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055119 Loss1: 0.054439 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037950 Loss1: 0.037266 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028835 Loss1: 0.028152 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022725 Loss1: 0.022041 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031438 Loss1: 0.030754 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025245 Loss1: 0.024560 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031665 Loss1: 0.030981 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040488 Loss1: 0.039804 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024695 Loss1: 0.024010 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.019548 Loss1: 0.018863 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.995593
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9613486842105263
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052370 Loss1: 0.051686 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.037862 Loss1: 0.037175 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.046298 Loss1: 0.045610 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.044229 Loss1: 0.043540 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048515 Loss1: 0.047826 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.056765 Loss1: 0.056077 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.068196 Loss1: 0.067506 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075327 Loss1: 0.074637 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.060666 Loss1: 0.059975 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.061980 Loss1: 0.061291 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.986431
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9535205696202531
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049677 Loss1: 0.048996 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021795 Loss1: 0.021111 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018668 Loss1: 0.017982 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018492 Loss1: 0.017807 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032235 Loss1: 0.031550 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026071 Loss1: 0.025383 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.032258 Loss1: 0.031571 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047149 Loss1: 0.046463 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069893 Loss1: 0.069206 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066496 Loss1: 0.065808 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.987935
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 04:04:22,961][flwr][DEBUG] - fit_round 87 received 10 results and 0 failures
-test acc: 0.6441
-[2023-09-23 04:05:00,726][flwr][INFO] - fit progress: (87, 2.49972779548968, {'accuracy': 0.6441}, 175982.38766917493)
-[2023-09-23 04:05:00,727][flwr][DEBUG] - evaluate_round 87: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 04:05:36,442][flwr][DEBUG] - evaluate_round 87 received 10 results and 0 failures
-[2023-09-23 04:05:36,443][flwr][DEBUG] - fit_round 88: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9541266025641025
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053543 Loss1: 0.052864 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039175 Loss1: 0.038493 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029693 Loss1: 0.029010 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018843 Loss1: 0.018157 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022774 Loss1: 0.022090 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022139 Loss1: 0.021454 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022160 Loss1: 0.021476 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.025665 Loss1: 0.024978 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052043 Loss1: 0.051357 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048278 Loss1: 0.047592 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.990184
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.963795731707317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045580 Loss1: 0.044900 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022739 Loss1: 0.022055 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018979 Loss1: 0.018295 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020053 Loss1: 0.019367 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.033292 Loss1: 0.032607 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021427 Loss1: 0.020743 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030224 Loss1: 0.029538 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.018308 Loss1: 0.017623 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026147 Loss1: 0.025462 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032590 Loss1: 0.031904 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.994474
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9375
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059923 Loss1: 0.059242 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038172 Loss1: 0.037486 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032415 Loss1: 0.031728 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036887 Loss1: 0.036199 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022523 Loss1: 0.021835 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026391 Loss1: 0.025703 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.024212 Loss1: 0.023524 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029453 Loss1: 0.028764 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040848 Loss1: 0.040160 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042590 Loss1: 0.041901 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.989517
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.903293918918919
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064029 Loss1: 0.063347 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032748 Loss1: 0.032061 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034030 Loss1: 0.033342 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024994 Loss1: 0.024307 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022479 Loss1: 0.021791 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024043 Loss1: 0.023355 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023317 Loss1: 0.022629 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023510 Loss1: 0.022821 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034321 Loss1: 0.033631 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.034892 Loss1: 0.034202 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994299
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9604430379746836
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043938 Loss1: 0.043260 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020095 Loss1: 0.019410 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.014814 Loss1: 0.014126 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.012570 Loss1: 0.011882 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020832 Loss1: 0.020145 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026942 Loss1: 0.026255 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029660 Loss1: 0.028972 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037039 Loss1: 0.036350 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035312 Loss1: 0.034624 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.034496 Loss1: 0.033808 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.994660
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9590585443037974
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.035319 Loss1: 0.034639 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023834 Loss1: 0.023150 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024426 Loss1: 0.023741 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017109 Loss1: 0.016423 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029574 Loss1: 0.028888 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.041724 Loss1: 0.041038 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029240 Loss1: 0.028554 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026587 Loss1: 0.025900 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028856 Loss1: 0.028169 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033860 Loss1: 0.033173 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.992880
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9566851265822784
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055748 Loss1: 0.055067 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030604 Loss1: 0.029920 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025746 Loss1: 0.025062 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032496 Loss1: 0.031811 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029543 Loss1: 0.028858 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028019 Loss1: 0.027332 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027091 Loss1: 0.026403 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035943 Loss1: 0.035255 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044464 Loss1: 0.043776 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.051306 Loss1: 0.050618 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.990309
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9627403846153846
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046058 Loss1: 0.045377 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028840 Loss1: 0.028153 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024814 Loss1: 0.024127 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018277 Loss1: 0.017590 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021757 Loss1: 0.021070 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042326 Loss1: 0.041638 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039562 Loss1: 0.038872 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050643 Loss1: 0.049954 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049337 Loss1: 0.048648 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.059600 Loss1: 0.058910 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.992388
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9518229166666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.060591 Loss1: 0.059908 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030769 Loss1: 0.030082 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.031979 Loss1: 0.031293 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.037555 Loss1: 0.036869 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035080 Loss1: 0.034393 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.042913 Loss1: 0.042225 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030845 Loss1: 0.030156 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037950 Loss1: 0.037263 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.050160 Loss1: 0.049473 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043260 Loss1: 0.042571 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.995009
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9607319078947368
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044966 Loss1: 0.044284 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021270 Loss1: 0.020585 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025405 Loss1: 0.024718 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026661 Loss1: 0.025974 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040798 Loss1: 0.040109 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036463 Loss1: 0.035775 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034923 Loss1: 0.034233 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048501 Loss1: 0.047810 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041662 Loss1: 0.040973 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047068 Loss1: 0.046378 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.985197
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 04:35:24,032][flwr][DEBUG] - fit_round 88 received 10 results and 0 failures
-test acc: 0.6475
-[2023-09-23 04:36:02,272][flwr][INFO] - fit progress: (88, 2.5036359633119725, {'accuracy': 0.6475}, 177843.93344982294)
-[2023-09-23 04:36:02,272][flwr][DEBUG] - evaluate_round 88: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 04:36:37,439][flwr][DEBUG] - evaluate_round 88 received 10 results and 0 failures
-[2023-09-23 04:36:37,440][flwr][DEBUG] - fit_round 89: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9573317307692307
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049367 Loss1: 0.048688 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021140 Loss1: 0.020458 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027071 Loss1: 0.026388 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039679 Loss1: 0.038996 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.036755 Loss1: 0.036071 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040840 Loss1: 0.040155 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.057719 Loss1: 0.057035 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.061348 Loss1: 0.060662 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045333 Loss1: 0.044648 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054706 Loss1: 0.054020 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.988181
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9503560126582279
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057917 Loss1: 0.057237 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.034338 Loss1: 0.033654 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020192 Loss1: 0.019505 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020266 Loss1: 0.019580 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020829 Loss1: 0.020142 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022925 Loss1: 0.022239 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027223 Loss1: 0.026535 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030776 Loss1: 0.030088 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033705 Loss1: 0.033016 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032704 Loss1: 0.032015 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.995055
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9604430379746836
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049887 Loss1: 0.049206 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022493 Loss1: 0.021808 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022624 Loss1: 0.021938 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025906 Loss1: 0.025221 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025139 Loss1: 0.024453 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017784 Loss1: 0.017097 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030989 Loss1: 0.030303 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023476 Loss1: 0.022788 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.021839 Loss1: 0.021149 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033314 Loss1: 0.032626 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.995649
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.911106418918919
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061845 Loss1: 0.061162 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030165 Loss1: 0.029478 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.029410 Loss1: 0.028723 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018700 Loss1: 0.018011 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021590 Loss1: 0.020902 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017399 Loss1: 0.016711 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.012870 Loss1: 0.012180 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022972 Loss1: 0.022283 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046827 Loss1: 0.046137 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050638 Loss1: 0.049949 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.990921
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.959256329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050680 Loss1: 0.049998 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022299 Loss1: 0.021612 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020728 Loss1: 0.020040 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027854 Loss1: 0.027166 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032719 Loss1: 0.032030 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039232 Loss1: 0.038543 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035053 Loss1: 0.034362 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.049502 Loss1: 0.048812 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.048734 Loss1: 0.048044 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046708 Loss1: 0.046017 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.991495
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9485677083333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052725 Loss1: 0.052043 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022559 Loss1: 0.021874 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019179 Loss1: 0.018495 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013342 Loss1: 0.012656 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026326 Loss1: 0.025640 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024188 Loss1: 0.023501 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020296 Loss1: 0.019609 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023508 Loss1: 0.022821 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027453 Loss1: 0.026767 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027958 Loss1: 0.027271 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992622
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9628429878048781
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.039530 Loss1: 0.038850 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020368 Loss1: 0.019685 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024035 Loss1: 0.023351 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016658 Loss1: 0.015975 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022881 Loss1: 0.022198 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.034818 Loss1: 0.034132 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.065099 Loss1: 0.064412 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045005 Loss1: 0.044318 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041120 Loss1: 0.040434 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046678 Loss1: 0.045991 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.991616
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9627403846153846
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054472 Loss1: 0.053791 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032977 Loss1: 0.032291 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021061 Loss1: 0.020376 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021378 Loss1: 0.020693 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019145 Loss1: 0.018459 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014852 Loss1: 0.014166 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.010774 Loss1: 0.010087 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.013405 Loss1: 0.012718 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.020769 Loss1: 0.020081 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.014531 Loss1: 0.013844 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.998397
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9600474683544303
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.041424 Loss1: 0.040744 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027899 Loss1: 0.027215 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025132 Loss1: 0.024447 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.043371 Loss1: 0.042686 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.048667 Loss1: 0.047980 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.040678 Loss1: 0.039990 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.076563 Loss1: 0.075875 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.074115 Loss1: 0.073427 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.054083 Loss1: 0.053395 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063584 Loss1: 0.062895 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.984573
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9650493421052632
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046344 Loss1: 0.045661 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019981 Loss1: 0.019293 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025461 Loss1: 0.024773 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028818 Loss1: 0.028130 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029690 Loss1: 0.029001 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028987 Loss1: 0.028299 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027103 Loss1: 0.026413 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038891 Loss1: 0.038202 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047295 Loss1: 0.046607 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.065990 Loss1: 0.065302 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.987253
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 05:06:18,288][flwr][DEBUG] - fit_round 89 received 10 results and 0 failures
-test acc: 0.6455
-[2023-09-23 05:07:31,433][flwr][INFO] - fit progress: (89, 2.477679441340815, {'accuracy': 0.6455}, 179733.09433418186)
-[2023-09-23 05:07:31,434][flwr][DEBUG] - evaluate_round 89: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 05:08:06,611][flwr][DEBUG] - evaluate_round 89 received 10 results and 0 failures
-[2023-09-23 05:08:06,613][flwr][DEBUG] - fit_round 90: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9703947368421053
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.039063 Loss1: 0.038380 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028870 Loss1: 0.028182 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024187 Loss1: 0.023499 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020736 Loss1: 0.020048 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020105 Loss1: 0.019415 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011630 Loss1: 0.010941 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016994 Loss1: 0.016305 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.021793 Loss1: 0.021104 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033752 Loss1: 0.033062 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.035227 Loss1: 0.034536 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.991365
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9625400641025641
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049572 Loss1: 0.048892 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027922 Loss1: 0.027238 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024240 Loss1: 0.023554 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018049 Loss1: 0.017365 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032493 Loss1: 0.031807 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022771 Loss1: 0.022085 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036589 Loss1: 0.035903 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026794 Loss1: 0.026107 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032393 Loss1: 0.031705 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040428 Loss1: 0.039740 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.995192
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9464003164556962
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.058719 Loss1: 0.058040 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025197 Loss1: 0.024512 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023906 Loss1: 0.023221 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019624 Loss1: 0.018938 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017256 Loss1: 0.016571 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021606 Loss1: 0.020919 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019454 Loss1: 0.018768 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026602 Loss1: 0.025916 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032324 Loss1: 0.031636 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.031850 Loss1: 0.031162 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994066
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9163851351351351
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.071346 Loss1: 0.070664 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.036677 Loss1: 0.035992 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026392 Loss1: 0.025707 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020627 Loss1: 0.019941 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024582 Loss1: 0.023895 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037030 Loss1: 0.036341 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026382 Loss1: 0.025694 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037871 Loss1: 0.037183 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027423 Loss1: 0.026734 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.026770 Loss1: 0.026081 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.996199
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9553006329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.056178 Loss1: 0.055497 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026399 Loss1: 0.025715 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025441 Loss1: 0.024756 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025590 Loss1: 0.024904 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.027205 Loss1: 0.026519 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035742 Loss1: 0.035056 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025878 Loss1: 0.025191 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031171 Loss1: 0.030484 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027534 Loss1: 0.026847 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033413 Loss1: 0.032726 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.993671
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.947265625
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.075781 Loss1: 0.075098 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042290 Loss1: 0.041605 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034510 Loss1: 0.033824 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026917 Loss1: 0.026230 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032695 Loss1: 0.032008 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027080 Loss1: 0.026393 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026420 Loss1: 0.025732 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024571 Loss1: 0.023882 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033672 Loss1: 0.032984 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.033817 Loss1: 0.033130 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.995226
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9679588607594937
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061982 Loss1: 0.061299 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028446 Loss1: 0.027759 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033572 Loss1: 0.032886 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023249 Loss1: 0.022561 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023763 Loss1: 0.023076 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031286 Loss1: 0.030600 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.064659 Loss1: 0.063972 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.050060 Loss1: 0.049372 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043162 Loss1: 0.042471 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054790 Loss1: 0.054101 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.989715
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9647943037974683
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048265 Loss1: 0.047585 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017741 Loss1: 0.017056 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013264 Loss1: 0.012578 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019929 Loss1: 0.019241 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022739 Loss1: 0.022050 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.044648 Loss1: 0.043960 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025053 Loss1: 0.024363 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023021 Loss1: 0.022333 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047861 Loss1: 0.047172 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049887 Loss1: 0.049200 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.990111
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9634146341463414
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043546 Loss1: 0.042866 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.016483 Loss1: 0.015801 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022010 Loss1: 0.021326 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023404 Loss1: 0.022722 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024780 Loss1: 0.024097 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021805 Loss1: 0.021121 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.012581 Loss1: 0.011895 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.017194 Loss1: 0.016509 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.020815 Loss1: 0.020131 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023017 Loss1: 0.022331 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.996951
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.960136217948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052493 Loss1: 0.051814 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030553 Loss1: 0.029871 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025001 Loss1: 0.024317 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026061 Loss1: 0.025377 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020875 Loss1: 0.020192 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025498 Loss1: 0.024813 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043803 Loss1: 0.043118 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043501 Loss1: 0.042814 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.061266 Loss1: 0.060581 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058320 Loss1: 0.057635 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.992588
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 05:37:51,453][flwr][DEBUG] - fit_round 90 received 10 results and 0 failures
-test acc: 0.6505
-[2023-09-23 05:38:27,895][flwr][INFO] - fit progress: (90, 2.4975950007621472, {'accuracy': 0.6505}, 181589.55689398665)
-[2023-09-23 05:38:27,896][flwr][DEBUG] - evaluate_round 90: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 05:39:03,049][flwr][DEBUG] - evaluate_round 90 received 10 results and 0 failures
-[2023-09-23 05:39:03,061][flwr][DEBUG] - fit_round 91: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.965891768292683
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042132 Loss1: 0.041453 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028339 Loss1: 0.027657 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032647 Loss1: 0.031962 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034987 Loss1: 0.034302 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.040123 Loss1: 0.039437 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.045042 Loss1: 0.044357 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043266 Loss1: 0.042580 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027638 Loss1: 0.026953 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028046 Loss1: 0.027358 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027103 Loss1: 0.026415 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.995808
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9537760416666666
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.047989 Loss1: 0.047307 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.042238 Loss1: 0.041554 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018954 Loss1: 0.018268 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028641 Loss1: 0.027954 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030655 Loss1: 0.029967 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039962 Loss1: 0.039276 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039488 Loss1: 0.038800 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.053689 Loss1: 0.053001 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.085681 Loss1: 0.084992 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.098153 Loss1: 0.097465 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.981988
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.959256329113924
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053307 Loss1: 0.052626 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023749 Loss1: 0.023064 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024534 Loss1: 0.023847 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022940 Loss1: 0.022252 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020000 Loss1: 0.019314 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016594 Loss1: 0.015907 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.013537 Loss1: 0.012850 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019692 Loss1: 0.019005 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.023664 Loss1: 0.022976 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029490 Loss1: 0.028802 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.996638
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9591346153846154
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043962 Loss1: 0.043284 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033109 Loss1: 0.032427 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023487 Loss1: 0.022804 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022641 Loss1: 0.021958 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034238 Loss1: 0.033553 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025820 Loss1: 0.025134 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028588 Loss1: 0.027903 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037037 Loss1: 0.036352 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.041717 Loss1: 0.041031 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.037751 Loss1: 0.037064 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.995192
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9622231012658228
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.039498 Loss1: 0.038819 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018178 Loss1: 0.017494 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017745 Loss1: 0.017058 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025331 Loss1: 0.024644 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020534 Loss1: 0.019848 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018018 Loss1: 0.017332 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019695 Loss1: 0.019007 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.021450 Loss1: 0.020762 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.023977 Loss1: 0.023290 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023060 Loss1: 0.022372 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.994462
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9625400641025641
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.062284 Loss1: 0.061605 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025276 Loss1: 0.024590 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022746 Loss1: 0.022060 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013193 Loss1: 0.012506 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019445 Loss1: 0.018759 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023698 Loss1: 0.023011 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018644 Loss1: 0.017956 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.040139 Loss1: 0.039451 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034832 Loss1: 0.034144 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027080 Loss1: 0.026392 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.996995
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9618275316455697
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048733 Loss1: 0.048052 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023778 Loss1: 0.023094 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019567 Loss1: 0.018881 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019123 Loss1: 0.018439 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021326 Loss1: 0.020640 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030149 Loss1: 0.029462 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020011 Loss1: 0.019323 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016392 Loss1: 0.015705 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.021148 Loss1: 0.020461 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.018601 Loss1: 0.017913 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.997627
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9089949324324325
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050654 Loss1: 0.049970 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018236 Loss1: 0.017549 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011357 Loss1: 0.010670 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013053 Loss1: 0.012365 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011441 Loss1: 0.010755 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.012374 Loss1: 0.011686 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.007921 Loss1: 0.007234 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.008461 Loss1: 0.007773 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.007162 Loss1: 0.006475 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.010242 Loss1: 0.009555 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.997889
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9519382911392406
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049110 Loss1: 0.048429 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022163 Loss1: 0.021479 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019519 Loss1: 0.018833 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013122 Loss1: 0.012435 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.013184 Loss1: 0.012496 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019928 Loss1: 0.019239 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.021041 Loss1: 0.020353 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022098 Loss1: 0.021410 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.025436 Loss1: 0.024747 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022685 Loss1: 0.021997 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.997824
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.96484375
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044294 Loss1: 0.043611 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017217 Loss1: 0.016531 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026505 Loss1: 0.025818 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015315 Loss1: 0.014627 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014695 Loss1: 0.014004 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021871 Loss1: 0.021182 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020353 Loss1: 0.019665 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030012 Loss1: 0.029324 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042255 Loss1: 0.041565 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.073268 Loss1: 0.072580 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.993010
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 06:08:37,792][flwr][DEBUG] - fit_round 91 received 10 results and 0 failures
-test acc: 0.6458
-[2023-09-23 06:09:14,883][flwr][INFO] - fit progress: (91, 2.5048911708612414, {'accuracy': 0.6458}, 183436.54472790193)
-[2023-09-23 06:09:14,884][flwr][DEBUG] - evaluate_round 91: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 06:09:50,340][flwr][DEBUG] - evaluate_round 91 received 10 results and 0 failures
-[2023-09-23 06:09:50,341][flwr][DEBUG] - fit_round 92: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9703322784810127
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037865 Loss1: 0.037185 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022882 Loss1: 0.022198 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015981 Loss1: 0.015295 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017076 Loss1: 0.016389 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016242 Loss1: 0.015556 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014905 Loss1: 0.014218 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.009774 Loss1: 0.009089 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.009552 Loss1: 0.008865 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019893 Loss1: 0.019206 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025180 Loss1: 0.024492 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.996440
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9637419871794872
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043150 Loss1: 0.042469 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023678 Loss1: 0.022992 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036134 Loss1: 0.035448 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038177 Loss1: 0.037489 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029206 Loss1: 0.028518 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028267 Loss1: 0.027578 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029948 Loss1: 0.029260 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026280 Loss1: 0.025591 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024707 Loss1: 0.024019 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022082 Loss1: 0.021392 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.996795
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9601151315789473
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054580 Loss1: 0.053896 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035662 Loss1: 0.034974 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021641 Loss1: 0.020951 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019804 Loss1: 0.019114 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026162 Loss1: 0.025472 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030307 Loss1: 0.029618 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.064865 Loss1: 0.064176 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.069432 Loss1: 0.068743 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052580 Loss1: 0.051890 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.047205 Loss1: 0.046514 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.987664
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9630142405063291
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.035359 Loss1: 0.034679 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021940 Loss1: 0.021255 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016822 Loss1: 0.016137 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.012462 Loss1: 0.011775 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017347 Loss1: 0.016660 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020249 Loss1: 0.019563 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018791 Loss1: 0.018103 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023877 Loss1: 0.023189 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019515 Loss1: 0.018828 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025060 Loss1: 0.024372 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.996835
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9452136075949367
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049317 Loss1: 0.048635 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022739 Loss1: 0.022054 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017643 Loss1: 0.016957 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025606 Loss1: 0.024919 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019053 Loss1: 0.018366 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022250 Loss1: 0.021562 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030696 Loss1: 0.030008 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028369 Loss1: 0.027682 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031995 Loss1: 0.031308 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025964 Loss1: 0.025275 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.996440
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9598496835443038
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046930 Loss1: 0.046249 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023680 Loss1: 0.022996 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027031 Loss1: 0.026346 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036462 Loss1: 0.035776 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028195 Loss1: 0.027508 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025636 Loss1: 0.024948 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030496 Loss1: 0.029809 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029735 Loss1: 0.029048 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029749 Loss1: 0.029062 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025588 Loss1: 0.024900 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.995055
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9714176829268293
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044528 Loss1: 0.043849 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032476 Loss1: 0.031793 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027292 Loss1: 0.026608 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024137 Loss1: 0.023453 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022779 Loss1: 0.022095 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019773 Loss1: 0.019088 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023781 Loss1: 0.023096 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.046481 Loss1: 0.045796 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056993 Loss1: 0.056308 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058107 Loss1: 0.057419 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992759
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9629407051282052
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045398 Loss1: 0.044719 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022989 Loss1: 0.022307 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.014525 Loss1: 0.013842 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013891 Loss1: 0.013208 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017482 Loss1: 0.016797 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.029608 Loss1: 0.028923 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023602 Loss1: 0.022917 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029267 Loss1: 0.028582 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035101 Loss1: 0.034416 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.040780 Loss1: 0.040095 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.992989
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9168074324324325
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.070268 Loss1: 0.069585 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.030677 Loss1: 0.029990 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.025184 Loss1: 0.024495 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016474 Loss1: 0.015786 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.012496 Loss1: 0.011807 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018575 Loss1: 0.017887 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017610 Loss1: 0.016923 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.017964 Loss1: 0.017276 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019865 Loss1: 0.019176 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032940 Loss1: 0.032251 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.994299
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9581163194444444
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053100 Loss1: 0.052419 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022431 Loss1: 0.021746 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035177 Loss1: 0.034492 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034730 Loss1: 0.034043 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.057455 Loss1: 0.056770 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.039091 Loss1: 0.038404 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036290 Loss1: 0.035602 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032477 Loss1: 0.031789 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029248 Loss1: 0.028558 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038034 Loss1: 0.037345 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992839
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 06:39:29,892][flwr][DEBUG] - fit_round 92 received 10 results and 0 failures
-test acc: 0.6463
-[2023-09-23 06:40:05,933][flwr][INFO] - fit progress: (92, 2.5276976867605705, {'accuracy': 0.6463}, 185287.59483586485)
-[2023-09-23 06:40:05,934][flwr][DEBUG] - evaluate_round 92: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 06:40:42,319][flwr][DEBUG] - evaluate_round 92 received 10 results and 0 failures
-[2023-09-23 06:40:42,320][flwr][DEBUG] - fit_round 93: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9712271341463414
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.036275 Loss1: 0.035596 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.035695 Loss1: 0.035013 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027542 Loss1: 0.026858 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034604 Loss1: 0.033920 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035680 Loss1: 0.034995 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.052913 Loss1: 0.052228 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.043832 Loss1: 0.043147 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.048488 Loss1: 0.047801 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047977 Loss1: 0.047290 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.071593 Loss1: 0.070906 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.990282
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9663461538461539
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037954 Loss1: 0.037276 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019291 Loss1: 0.018610 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021034 Loss1: 0.020352 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020067 Loss1: 0.019384 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022309 Loss1: 0.021626 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027965 Loss1: 0.027281 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027022 Loss1: 0.026336 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032616 Loss1: 0.031930 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037324 Loss1: 0.036639 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044185 Loss1: 0.043499 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.989183
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9539930555555556
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053676 Loss1: 0.052994 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023925 Loss1: 0.023241 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033304 Loss1: 0.032619 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.032385 Loss1: 0.031697 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026987 Loss1: 0.026302 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033667 Loss1: 0.032980 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.041581 Loss1: 0.040894 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028009 Loss1: 0.027321 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040878 Loss1: 0.040191 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050460 Loss1: 0.049771 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.987196
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.946004746835443
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.061121 Loss1: 0.060441 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032423 Loss1: 0.031737 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024899 Loss1: 0.024214 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028195 Loss1: 0.027508 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.035309 Loss1: 0.034623 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.027841 Loss1: 0.027154 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037767 Loss1: 0.037080 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.043503 Loss1: 0.042814 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037340 Loss1: 0.036651 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054083 Loss1: 0.053394 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.991891
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9693667763157895
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043551 Loss1: 0.042868 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.039708 Loss1: 0.039022 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032321 Loss1: 0.031633 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.027715 Loss1: 0.027028 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031750 Loss1: 0.031062 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021713 Loss1: 0.021024 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027630 Loss1: 0.026940 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031932 Loss1: 0.031244 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.056470 Loss1: 0.055780 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056400 Loss1: 0.055712 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992804
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9630142405063291
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049708 Loss1: 0.049028 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028382 Loss1: 0.027697 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027010 Loss1: 0.026323 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.036887 Loss1: 0.036202 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044751 Loss1: 0.044064 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.036088 Loss1: 0.035402 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.048379 Loss1: 0.047692 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.063291 Loss1: 0.062603 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.062761 Loss1: 0.062072 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063002 Loss1: 0.062315 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.993473
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9639423076923077
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.035412 Loss1: 0.034733 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017372 Loss1: 0.016689 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017448 Loss1: 0.016764 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.012312 Loss1: 0.011627 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014787 Loss1: 0.014102 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024803 Loss1: 0.024117 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011938 Loss1: 0.011251 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016946 Loss1: 0.016260 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.017644 Loss1: 0.016958 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023414 Loss1: 0.022727 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.998598
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9657832278481012
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046258 Loss1: 0.045577 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.032872 Loss1: 0.032185 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024003 Loss1: 0.023314 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017488 Loss1: 0.016800 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031174 Loss1: 0.030484 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.022327 Loss1: 0.021638 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034879 Loss1: 0.034189 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032771 Loss1: 0.032080 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037718 Loss1: 0.037028 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045095 Loss1: 0.044406 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.991693
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9206081081081081
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.084631 Loss1: 0.083949 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.048848 Loss1: 0.048162 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.035838 Loss1: 0.035152 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.038070 Loss1: 0.037383 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037008 Loss1: 0.036320 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035937 Loss1: 0.035249 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.034668 Loss1: 0.033979 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.038733 Loss1: 0.038044 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.062245 Loss1: 0.061555 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.089265 Loss1: 0.088575 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.979307
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9612341772151899
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052127 Loss1: 0.051447 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.038610 Loss1: 0.037927 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.033398 Loss1: 0.032712 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.059373 Loss1: 0.058686 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.062990 Loss1: 0.062303 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.055292 Loss1: 0.054606 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.056990 Loss1: 0.056302 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.075462 Loss1: 0.074775 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.067213 Loss1: 0.066526 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.070716 Loss1: 0.070028 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.990902
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 07:10:20,845][flwr][DEBUG] - fit_round 93 received 10 results and 0 failures
-test acc: 0.6485
-[2023-09-23 07:10:57,539][flwr][INFO] - fit progress: (93, 2.4764777248659833, {'accuracy': 0.6485}, 187139.200545819)
-[2023-09-23 07:10:57,539][flwr][DEBUG] - evaluate_round 93: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 07:11:33,217][flwr][DEBUG] - evaluate_round 93 received 10 results and 0 failures
-[2023-09-23 07:11:33,218][flwr][DEBUG] - fit_round 94: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9615384615384616
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048665 Loss1: 0.047986 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043339 Loss1: 0.042653 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.034747 Loss1: 0.034061 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019168 Loss1: 0.018480 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019800 Loss1: 0.019112 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017779 Loss1: 0.017091 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.012075 Loss1: 0.011388 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.010600 Loss1: 0.009912 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.009605 Loss1: 0.008917 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.016919 Loss1: 0.016230 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.997997
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9596518987341772
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042536 Loss1: 0.041855 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.013890 Loss1: 0.013208 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019819 Loss1: 0.019136 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019070 Loss1: 0.018386 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019815 Loss1: 0.019130 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019076 Loss1: 0.018391 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026609 Loss1: 0.025922 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023302 Loss1: 0.022616 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026585 Loss1: 0.025898 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.020283 Loss1: 0.019596 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.995253
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9593349358974359
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045659 Loss1: 0.044981 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024415 Loss1: 0.023733 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026583 Loss1: 0.025898 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015689 Loss1: 0.015004 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016112 Loss1: 0.015427 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017857 Loss1: 0.017174 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022093 Loss1: 0.021409 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029313 Loss1: 0.028629 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039290 Loss1: 0.038605 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.021702 Loss1: 0.021016 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.996194
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9671677215189873
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037293 Loss1: 0.036614 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021966 Loss1: 0.021282 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018188 Loss1: 0.017504 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016750 Loss1: 0.016066 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016732 Loss1: 0.016048 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015176 Loss1: 0.014490 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022625 Loss1: 0.021939 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019661 Loss1: 0.018974 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.012507 Loss1: 0.011820 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.013468 Loss1: 0.012780 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.999011
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9489715189873418
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044133 Loss1: 0.043454 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021258 Loss1: 0.020573 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015717 Loss1: 0.015032 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015532 Loss1: 0.014847 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.018824 Loss1: 0.018136 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019035 Loss1: 0.018347 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018971 Loss1: 0.018283 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.020482 Loss1: 0.019795 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.017057 Loss1: 0.016366 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.016098 Loss1: 0.015409 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.997429
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9208192567567568
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.062152 Loss1: 0.061472 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020449 Loss1: 0.019762 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019048 Loss1: 0.018361 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019411 Loss1: 0.018725 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023791 Loss1: 0.023104 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017660 Loss1: 0.016974 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031104 Loss1: 0.030416 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.030820 Loss1: 0.030131 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037049 Loss1: 0.036360 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.026228 Loss1: 0.025540 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.988176
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9666539634146342
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.031292 Loss1: 0.030612 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.009969 Loss1: 0.009287 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011106 Loss1: 0.010423 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.007964 Loss1: 0.007281 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.008328 Loss1: 0.007645 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.009542 Loss1: 0.008858 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016182 Loss1: 0.015497 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.015144 Loss1: 0.014459 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.014282 Loss1: 0.013597 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.009735 Loss1: 0.009049 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.998285
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9654605263157895
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059804 Loss1: 0.059121 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.029119 Loss1: 0.028430 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021419 Loss1: 0.020731 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029657 Loss1: 0.028968 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028318 Loss1: 0.027628 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024142 Loss1: 0.023451 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019595 Loss1: 0.018905 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031151 Loss1: 0.030461 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033985 Loss1: 0.033295 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041119 Loss1: 0.040429 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.992393
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9638053797468354
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040415 Loss1: 0.039734 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021616 Loss1: 0.020931 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016070 Loss1: 0.015383 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025176 Loss1: 0.024489 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025207 Loss1: 0.024519 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.035267 Loss1: 0.034578 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027087 Loss1: 0.026400 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.036258 Loss1: 0.035569 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040808 Loss1: 0.040120 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048852 Loss1: 0.048163 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989122
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9468315972222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055756 Loss1: 0.055075 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022388 Loss1: 0.021704 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022125 Loss1: 0.021440 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018037 Loss1: 0.017351 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011998 Loss1: 0.011313 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016525 Loss1: 0.015840 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.013969 Loss1: 0.013282 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.009472 Loss1: 0.008786 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.013453 Loss1: 0.012766 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.016239 Loss1: 0.015554 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.998481
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 07:41:13,480][flwr][DEBUG] - fit_round 94 received 10 results and 0 failures
-test acc: 0.65
-[2023-09-23 07:41:50,473][flwr][INFO] - fit progress: (94, 2.5375279696604696, {'accuracy': 0.65}, 188992.13494228758)
-[2023-09-23 07:41:50,474][flwr][DEBUG] - evaluate_round 94: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 07:42:25,775][flwr][DEBUG] - evaluate_round 94 received 10 results and 0 failures
-[2023-09-23 07:42:25,777][flwr][DEBUG] - fit_round 95: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9704649390243902
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037019 Loss1: 0.036339 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021132 Loss1: 0.020449 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022791 Loss1: 0.022106 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017327 Loss1: 0.016642 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024278 Loss1: 0.023593 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018836 Loss1: 0.018149 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028000 Loss1: 0.027313 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016292 Loss1: 0.015606 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026745 Loss1: 0.026059 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.020212 Loss1: 0.019525 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.998285
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9719551282051282
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.032404 Loss1: 0.031724 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.015465 Loss1: 0.014784 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017144 Loss1: 0.016462 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026868 Loss1: 0.026183 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055178 Loss1: 0.054494 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.043370 Loss1: 0.042685 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.037415 Loss1: 0.036730 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059919 Loss1: 0.059234 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.043597 Loss1: 0.042910 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.063085 Loss1: 0.062399 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.986178
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9681332236842105
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043869 Loss1: 0.043187 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026281 Loss1: 0.025594 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021101 Loss1: 0.020411 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018093 Loss1: 0.017405 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019563 Loss1: 0.018875 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015250 Loss1: 0.014561 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.032712 Loss1: 0.032022 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037144 Loss1: 0.036455 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045173 Loss1: 0.044483 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.053530 Loss1: 0.052840 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.980263
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9677610759493671
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044987 Loss1: 0.044307 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.022590 Loss1: 0.021905 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020620 Loss1: 0.019935 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026826 Loss1: 0.026139 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021663 Loss1: 0.020977 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030782 Loss1: 0.030096 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028912 Loss1: 0.028225 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037743 Loss1: 0.037056 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.050534 Loss1: 0.049846 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.045643 Loss1: 0.044955 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.993473
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.925464527027027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.068055 Loss1: 0.067373 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.041834 Loss1: 0.041147 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.030749 Loss1: 0.030061 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.039284 Loss1: 0.038595 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.055755 Loss1: 0.055066 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068559 Loss1: 0.067869 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.070822 Loss1: 0.070133 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.085798 Loss1: 0.085108 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.069460 Loss1: 0.068770 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.050048 Loss1: 0.049358 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.992188
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9641426282051282
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.031592 Loss1: 0.030912 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.011532 Loss1: 0.010847 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.010753 Loss1: 0.010069 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.010217 Loss1: 0.009532 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014239 Loss1: 0.013553 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017531 Loss1: 0.016844 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018630 Loss1: 0.017943 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024553 Loss1: 0.023866 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.018259 Loss1: 0.017571 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043181 Loss1: 0.042492 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989183
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9703322784810127
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.034704 Loss1: 0.034023 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020982 Loss1: 0.020298 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015163 Loss1: 0.014477 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017592 Loss1: 0.016905 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029520 Loss1: 0.028833 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016237 Loss1: 0.015548 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027273 Loss1: 0.026586 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.025509 Loss1: 0.024821 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.021940 Loss1: 0.021252 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054766 Loss1: 0.054078 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.990704
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9691455696202531
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045957 Loss1: 0.045275 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023198 Loss1: 0.022513 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019641 Loss1: 0.018957 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016155 Loss1: 0.015469 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025159 Loss1: 0.024473 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026356 Loss1: 0.025671 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028242 Loss1: 0.027555 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028739 Loss1: 0.028053 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042896 Loss1: 0.042212 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038589 Loss1: 0.037902 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.994660
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9503560126582279
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043213 Loss1: 0.042531 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018303 Loss1: 0.017617 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024400 Loss1: 0.023713 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025228 Loss1: 0.024541 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.029765 Loss1: 0.029076 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018362 Loss1: 0.017674 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017242 Loss1: 0.016554 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022706 Loss1: 0.022018 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.049075 Loss1: 0.048387 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.066372 Loss1: 0.065683 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.979628
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9505208333333334
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053350 Loss1: 0.052669 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028900 Loss1: 0.028215 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022214 Loss1: 0.021527 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033432 Loss1: 0.032744 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.051008 Loss1: 0.050321 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033752 Loss1: 0.033064 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.042195 Loss1: 0.041506 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.042784 Loss1: 0.042096 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.059162 Loss1: 0.058475 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048152 Loss1: 0.047464 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992405
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 08:12:09,963][flwr][DEBUG] - fit_round 95 received 10 results and 0 failures
-test acc: 0.6502
-[2023-09-23 08:12:50,860][flwr][INFO] - fit progress: (95, 2.5060083212943884, {'accuracy': 0.6502}, 190852.5212352858)
-[2023-09-23 08:12:50,860][flwr][DEBUG] - evaluate_round 95: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 08:13:25,919][flwr][DEBUG] - evaluate_round 95 received 10 results and 0 failures
-[2023-09-23 08:13:25,920][flwr][DEBUG] - fit_round 96: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9677610759493671
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.036361 Loss1: 0.035680 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.016516 Loss1: 0.015831 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013471 Loss1: 0.012784 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017758 Loss1: 0.017071 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.030353 Loss1: 0.029665 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026067 Loss1: 0.025378 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022157 Loss1: 0.021469 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.020356 Loss1: 0.019668 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024191 Loss1: 0.023503 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022188 Loss1: 0.021501 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.996835
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.95703125
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.066130 Loss1: 0.065448 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025653 Loss1: 0.024967 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032063 Loss1: 0.031376 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020108 Loss1: 0.019421 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.024899 Loss1: 0.024212 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025403 Loss1: 0.024715 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029626 Loss1: 0.028938 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024911 Loss1: 0.024223 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039909 Loss1: 0.039222 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.037799 Loss1: 0.037109 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.993707
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9661787974683544
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057462 Loss1: 0.056781 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023218 Loss1: 0.022534 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.036145 Loss1: 0.035459 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.028338 Loss1: 0.027653 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023363 Loss1: 0.022678 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019579 Loss1: 0.018894 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025198 Loss1: 0.024511 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033288 Loss1: 0.032601 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026939 Loss1: 0.026251 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.025183 Loss1: 0.024496 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.998418
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9651442307692307
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043898 Loss1: 0.043220 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020544 Loss1: 0.019862 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.019002 Loss1: 0.018319 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029119 Loss1: 0.028435 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020942 Loss1: 0.020258 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021220 Loss1: 0.020535 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.025828 Loss1: 0.025144 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.039115 Loss1: 0.038431 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.046947 Loss1: 0.046262 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.052073 Loss1: 0.051388 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.992388
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9619391025641025
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053508 Loss1: 0.052829 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025110 Loss1: 0.024427 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023808 Loss1: 0.023123 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.026615 Loss1: 0.025929 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020310 Loss1: 0.019625 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024501 Loss1: 0.023814 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.026973 Loss1: 0.026286 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035290 Loss1: 0.034602 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.042552 Loss1: 0.041864 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.048669 Loss1: 0.047981 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992588
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9560917721518988
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.047460 Loss1: 0.046780 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018430 Loss1: 0.017745 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016699 Loss1: 0.016014 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021413 Loss1: 0.020727 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.037473 Loss1: 0.036787 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026976 Loss1: 0.026290 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027201 Loss1: 0.026514 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034256 Loss1: 0.033568 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033794 Loss1: 0.033107 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029872 Loss1: 0.029184 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.994660
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9703322784810127
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040705 Loss1: 0.040025 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017206 Loss1: 0.016522 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016866 Loss1: 0.016182 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015633 Loss1: 0.014948 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022167 Loss1: 0.021481 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018314 Loss1: 0.017627 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030161 Loss1: 0.029475 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019505 Loss1: 0.018819 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.012911 Loss1: 0.012225 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022809 Loss1: 0.022121 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.995055
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9214527027027027
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.064208 Loss1: 0.063526 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.031647 Loss1: 0.030961 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023141 Loss1: 0.022456 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.031040 Loss1: 0.030353 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.018296 Loss1: 0.017608 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026694 Loss1: 0.026006 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.039199 Loss1: 0.038510 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.041766 Loss1: 0.041076 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.044895 Loss1: 0.044206 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.058453 Loss1: 0.057762 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.985642
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9693216463414634
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040457 Loss1: 0.039777 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019375 Loss1: 0.018692 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018020 Loss1: 0.017337 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016696 Loss1: 0.016011 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017491 Loss1: 0.016805 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028800 Loss1: 0.028114 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038127 Loss1: 0.037441 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059120 Loss1: 0.058435 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.077484 Loss1: 0.076798 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049455 Loss1: 0.048769 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.993712
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9677220394736842
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.054918 Loss1: 0.054237 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.033991 Loss1: 0.033303 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.026557 Loss1: 0.025869 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023316 Loss1: 0.022627 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.032120 Loss1: 0.031432 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021980 Loss1: 0.021289 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022169 Loss1: 0.021479 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.026901 Loss1: 0.026210 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.024411 Loss1: 0.023721 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029547 Loss1: 0.028859 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.995477
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 08:43:04,009][flwr][DEBUG] - fit_round 96 received 10 results and 0 failures
-test acc: 0.6487
-[2023-09-23 08:43:45,343][flwr][INFO] - fit progress: (96, 2.5280895025585406, {'accuracy': 0.6487}, 192707.00428564101)
-[2023-09-23 08:43:45,343][flwr][DEBUG] - evaluate_round 96: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 08:44:20,107][flwr][DEBUG] - evaluate_round 96 received 10 results and 0 failures
-[2023-09-23 08:44:20,109][flwr][DEBUG] - fit_round 97: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9533420138888888
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049795 Loss1: 0.049113 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026970 Loss1: 0.026285 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.032876 Loss1: 0.032192 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024772 Loss1: 0.024087 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.026438 Loss1: 0.025751 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.037465 Loss1: 0.036778 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.047362 Loss1: 0.046677 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031097 Loss1: 0.030411 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.033932 Loss1: 0.033245 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.028675 Loss1: 0.027988 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.996528
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9701891447368421
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044952 Loss1: 0.044270 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019958 Loss1: 0.019271 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017747 Loss1: 0.017061 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016175 Loss1: 0.015488 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025440 Loss1: 0.024753 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018867 Loss1: 0.018180 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016904 Loss1: 0.016217 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032947 Loss1: 0.032259 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047251 Loss1: 0.046562 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038341 Loss1: 0.037652 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989926
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9665743670886076
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040351 Loss1: 0.039673 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028802 Loss1: 0.028119 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021153 Loss1: 0.020469 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029458 Loss1: 0.028773 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.028080 Loss1: 0.027394 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.030290 Loss1: 0.029605 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027301 Loss1: 0.026615 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.031641 Loss1: 0.030955 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.032422 Loss1: 0.031735 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038663 Loss1: 0.037976 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.990704
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9645432692307693
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.030422 Loss1: 0.029744 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.011471 Loss1: 0.010787 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015973 Loss1: 0.015288 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011313 Loss1: 0.010627 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020292 Loss1: 0.019606 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015804 Loss1: 0.015117 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.017309 Loss1: 0.016622 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.029328 Loss1: 0.028641 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029111 Loss1: 0.028424 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.049342 Loss1: 0.048655 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.990385
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9659810126582279
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042424 Loss1: 0.041746 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026914 Loss1: 0.026229 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021859 Loss1: 0.021171 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018987 Loss1: 0.018298 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022736 Loss1: 0.022049 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033119 Loss1: 0.032433 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038359 Loss1: 0.037671 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.047378 Loss1: 0.046690 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040939 Loss1: 0.040248 Loss2: 0.000691
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032942 Loss1: 0.032251 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.995055
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.946004746835443
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045889 Loss1: 0.045209 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019516 Loss1: 0.018833 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021331 Loss1: 0.020646 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015146 Loss1: 0.014460 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016788 Loss1: 0.016101 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025270 Loss1: 0.024585 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.021657 Loss1: 0.020972 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023588 Loss1: 0.022903 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.040825 Loss1: 0.040138 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.054988 Loss1: 0.054301 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.991297
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9679588607594937
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.037676 Loss1: 0.036995 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.016664 Loss1: 0.015980 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.012839 Loss1: 0.012155 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.014704 Loss1: 0.014020 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014826 Loss1: 0.014142 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.017723 Loss1: 0.017038 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.027529 Loss1: 0.026843 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028311 Loss1: 0.027625 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026621 Loss1: 0.025936 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.027417 Loss1: 0.026730 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.995451
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9729420731707317
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.035275 Loss1: 0.034597 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023043 Loss1: 0.022360 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024052 Loss1: 0.023369 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.020789 Loss1: 0.020105 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.018176 Loss1: 0.017492 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025323 Loss1: 0.024640 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018830 Loss1: 0.018145 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.033568 Loss1: 0.032882 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026609 Loss1: 0.025924 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.024687 Loss1: 0.024002 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.998666
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9298986486486487
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.059536 Loss1: 0.058854 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.023688 Loss1: 0.023000 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013974 Loss1: 0.013287 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011462 Loss1: 0.010772 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020155 Loss1: 0.019466 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.028234 Loss1: 0.027546 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.023540 Loss1: 0.022852 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024256 Loss1: 0.023568 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029929 Loss1: 0.029241 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032576 Loss1: 0.031888 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.993666
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9625400641025641
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.049385 Loss1: 0.048707 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027258 Loss1: 0.026576 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.028254 Loss1: 0.027571 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033296 Loss1: 0.032612 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025567 Loss1: 0.024882 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.025437 Loss1: 0.024754 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020962 Loss1: 0.020278 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.034763 Loss1: 0.034078 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.050494 Loss1: 0.049808 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038905 Loss1: 0.038222 Loss2: 0.000683
-(DefaultActor pid=2839578) >> Training accuracy: 0.991587
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 09:13:54,272][flwr][DEBUG] - fit_round 97 received 10 results and 0 failures
-test acc: 0.6478
-[2023-09-23 09:15:54,899][flwr][INFO] - fit progress: (97, 2.522580636766391, {'accuracy': 0.6478}, 194636.56068836385)
-[2023-09-23 09:15:54,902][flwr][DEBUG] - evaluate_round 97: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 09:16:32,808][flwr][DEBUG] - evaluate_round 97 received 10 results and 0 failures
-[2023-09-23 09:16:32,809][flwr][DEBUG] - fit_round 98: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9689477848101266
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043246 Loss1: 0.042568 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.026034 Loss1: 0.025351 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022152 Loss1: 0.021468 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021924 Loss1: 0.021238 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016070 Loss1: 0.015385 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021802 Loss1: 0.021117 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018425 Loss1: 0.017739 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.020875 Loss1: 0.020189 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027116 Loss1: 0.026429 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.044780 Loss1: 0.044094 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.991891
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9208192567567568
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040364 Loss1: 0.039684 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020816 Loss1: 0.020130 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016369 Loss1: 0.015682 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015130 Loss1: 0.014443 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011197 Loss1: 0.010509 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014875 Loss1: 0.014187 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016851 Loss1: 0.016163 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016771 Loss1: 0.016082 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.028172 Loss1: 0.027483 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.019476 Loss1: 0.018786 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.997889
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9667467948717948
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.041250 Loss1: 0.040572 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024637 Loss1: 0.023954 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018166 Loss1: 0.017483 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021484 Loss1: 0.020800 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016231 Loss1: 0.015548 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.012452 Loss1: 0.011768 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011003 Loss1: 0.010319 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.019999 Loss1: 0.019315 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.027710 Loss1: 0.027025 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.024455 Loss1: 0.023770 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.993189
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9527294303797469
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.048303 Loss1: 0.047623 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019661 Loss1: 0.018977 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021468 Loss1: 0.020782 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023310 Loss1: 0.022622 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.034345 Loss1: 0.033660 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015572 Loss1: 0.014885 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.035943 Loss1: 0.035256 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.041307 Loss1: 0.040619 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.045036 Loss1: 0.044348 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.056624 Loss1: 0.055936 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.990309
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9535590277777778
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.045351 Loss1: 0.044670 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.024378 Loss1: 0.023694 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017211 Loss1: 0.016526 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.030460 Loss1: 0.029773 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.023297 Loss1: 0.022610 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.033375 Loss1: 0.032689 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.040584 Loss1: 0.039898 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.024490 Loss1: 0.023802 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.057119 Loss1: 0.056431 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.046756 Loss1: 0.046069 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.993924
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9722450657894737
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.046407 Loss1: 0.045727 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021358 Loss1: 0.020670 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016613 Loss1: 0.015927 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.016036 Loss1: 0.015349 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.008649 Loss1: 0.007962 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016581 Loss1: 0.015894 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016012 Loss1: 0.015324 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.015210 Loss1: 0.014521 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.022691 Loss1: 0.022003 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032173 Loss1: 0.031484 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.989309
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9669699367088608
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.030114 Loss1: 0.029434 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.012668 Loss1: 0.011984 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011995 Loss1: 0.011311 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013889 Loss1: 0.013206 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016393 Loss1: 0.015708 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021119 Loss1: 0.020434 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016007 Loss1: 0.015322 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027229 Loss1: 0.026543 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029445 Loss1: 0.028759 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.028365 Loss1: 0.027678 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.994858
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9744857594936709
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.038378 Loss1: 0.037698 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017256 Loss1: 0.016570 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.014967 Loss1: 0.014280 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.017898 Loss1: 0.017212 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.012548 Loss1: 0.011860 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.009958 Loss1: 0.009269 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.010409 Loss1: 0.009720 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.012332 Loss1: 0.011643 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.019730 Loss1: 0.019041 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.018117 Loss1: 0.017427 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.997231
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9708460365853658
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043103 Loss1: 0.042424 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.018204 Loss1: 0.017521 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.022927 Loss1: 0.022244 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.021335 Loss1: 0.020650 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.015270 Loss1: 0.014584 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.018255 Loss1: 0.017569 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.022159 Loss1: 0.021472 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023139 Loss1: 0.022453 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035110 Loss1: 0.034424 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.030563 Loss1: 0.029878 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.991616
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9645432692307693
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.033261 Loss1: 0.032581 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.014518 Loss1: 0.013835 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.009786 Loss1: 0.009100 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.019389 Loss1: 0.018703 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017234 Loss1: 0.016548 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011685 Loss1: 0.010999 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.010237 Loss1: 0.009550 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.017191 Loss1: 0.016504 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.030352 Loss1: 0.029665 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029698 Loss1: 0.029011 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.994992
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 09:46:05,996][flwr][DEBUG] - fit_round 98 received 10 results and 0 failures
-test acc: 0.6498
-[2023-09-23 09:46:47,389][flwr][INFO] - fit progress: (98, 2.5538510150802782, {'accuracy': 0.6498}, 196489.0504324357)
-[2023-09-23 09:46:47,390][flwr][DEBUG] - evaluate_round 98: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 09:47:22,495][flwr][DEBUG] - evaluate_round 98 received 10 results and 0 failures
-[2023-09-23 09:47:22,499][flwr][DEBUG] - fit_round 99: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9582674050632911
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.033692 Loss1: 0.033013 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.013624 Loss1: 0.012940 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.015558 Loss1: 0.014873 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.014197 Loss1: 0.013512 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022921 Loss1: 0.022235 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.012536 Loss1: 0.011850 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011964 Loss1: 0.011277 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.027978 Loss1: 0.027292 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.029356 Loss1: 0.028668 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.029887 Loss1: 0.029199 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.995055
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9719893292682927
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.026330 Loss1: 0.025651 Loss2: 0.000678
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.014551 Loss1: 0.013871 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013041 Loss1: 0.012360 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011547 Loss1: 0.010863 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.014278 Loss1: 0.013594 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.021600 Loss1: 0.020916 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.030148 Loss1: 0.029464 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016472 Loss1: 0.015787 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.014619 Loss1: 0.013934 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.021440 Loss1: 0.020755 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.997142
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9701522435897436
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.033573 Loss1: 0.032893 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.011700 Loss1: 0.011018 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011772 Loss1: 0.011090 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.022542 Loss1: 0.021859 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.012144 Loss1: 0.011461 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.014908 Loss1: 0.014225 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020496 Loss1: 0.019813 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.020957 Loss1: 0.020274 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.030572 Loss1: 0.029888 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.038901 Loss1: 0.038216 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.989583
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9633413461538461
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.043515 Loss1: 0.042834 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.027110 Loss1: 0.026424 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.045275 Loss1: 0.044589 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.033972 Loss1: 0.033284 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.044516 Loss1: 0.043828 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.064806 Loss1: 0.064117 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.069158 Loss1: 0.068468 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.059880 Loss1: 0.059190 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.076295 Loss1: 0.075604 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.072384 Loss1: 0.071694 Loss2: 0.000690
-(DefaultActor pid=2839578) >> Training accuracy: 0.993189
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9683544303797469
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.040033 Loss1: 0.039354 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019667 Loss1: 0.018984 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.013848 Loss1: 0.013164 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018064 Loss1: 0.017381 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011047 Loss1: 0.010363 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011111 Loss1: 0.010427 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019929 Loss1: 0.019244 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.035971 Loss1: 0.035286 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.035950 Loss1: 0.035264 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.026304 Loss1: 0.025617 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.996835
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9671052631578947
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.053684 Loss1: 0.053003 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028471 Loss1: 0.027783 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.027148 Loss1: 0.026459 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018084 Loss1: 0.017396 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017295 Loss1: 0.016606 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.015541 Loss1: 0.014853 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.014221 Loss1: 0.013533 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.014314 Loss1: 0.013624 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.015240 Loss1: 0.014552 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.010565 Loss1: 0.009876 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.998561
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9585503472222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.052564 Loss1: 0.051883 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025274 Loss1: 0.024590 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.024310 Loss1: 0.023625 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024950 Loss1: 0.024264 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.031186 Loss1: 0.030500 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.031246 Loss1: 0.030560 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.038404 Loss1: 0.037717 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.037991 Loss1: 0.037304 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.047933 Loss1: 0.047248 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.028462 Loss1: 0.027775 Loss2: 0.000687
-(DefaultActor pid=2839578) >> Training accuracy: 0.995009
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9258868243243243
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.055363 Loss1: 0.054680 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020148 Loss1: 0.019462 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016642 Loss1: 0.015956 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.029164 Loss1: 0.028476 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.016516 Loss1: 0.015829 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.016456 Loss1: 0.015768 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.019049 Loss1: 0.018361 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.023986 Loss1: 0.023297 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.020196 Loss1: 0.019507 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.032464 Loss1: 0.031776 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.993666
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9691455696202531
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.028547 Loss1: 0.027868 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.020967 Loss1: 0.020283 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.023397 Loss1: 0.022712 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.024016 Loss1: 0.023330 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.025892 Loss1: 0.025205 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.026240 Loss1: 0.025554 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.031003 Loss1: 0.030315 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.044505 Loss1: 0.043814 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.031198 Loss1: 0.030510 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.024042 Loss1: 0.023351 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.997824
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9721123417721519
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.039316 Loss1: 0.038635 Loss2: 0.000681
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017978 Loss1: 0.017293 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.021450 Loss1: 0.020764 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013030 Loss1: 0.012344 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017476 Loss1: 0.016787 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023552 Loss1: 0.022864 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.029610 Loss1: 0.028922 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022827 Loss1: 0.022139 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.026058 Loss1: 0.025370 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.035369 Loss1: 0.034680 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.997627
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 10:17:01,823][flwr][DEBUG] - fit_round 99 received 10 results and 0 failures
-test acc: 0.652
-[2023-09-23 10:17:43,808][flwr][INFO] - fit progress: (99, 2.5449396366128525, {'accuracy': 0.652}, 198345.46924422076)
-[2023-09-23 10:17:43,808][flwr][DEBUG] - evaluate_round 99: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 10:18:18,837][flwr][DEBUG] - evaluate_round 99 received 10 results and 0 failures
-[2023-09-23 10:18:18,842][flwr][DEBUG] - fit_round 100: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 76
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9734786184210527
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.044353 Loss1: 0.043670 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.043950 Loss1: 0.043263 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.038873 Loss1: 0.038188 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.034590 Loss1: 0.033902 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.047029 Loss1: 0.046339 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.068166 Loss1: 0.067479 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.055826 Loss1: 0.055136 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.058309 Loss1: 0.057619 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.052621 Loss1: 0.051931 Loss2: 0.000690
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.064928 Loss1: 0.064237 Loss2: 0.000691
-(DefaultActor pid=2839578) >> Training accuracy: 0.985609
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9655448717948718
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.034063 Loss1: 0.033382 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.019465 Loss1: 0.018783 Loss2: 0.000682
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.010984 Loss1: 0.010301 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.010069 Loss1: 0.009386 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.020609 Loss1: 0.019926 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023920 Loss1: 0.023238 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.028601 Loss1: 0.027916 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.028348 Loss1: 0.027662 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.034342 Loss1: 0.033656 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.041092 Loss1: 0.040407 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.994591
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 74
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9275760135135135
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.051620 Loss1: 0.050937 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.028096 Loss1: 0.027409 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.020132 Loss1: 0.019446 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.025032 Loss1: 0.024345 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.021723 Loss1: 0.021037 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011857 Loss1: 0.011170 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.016878 Loss1: 0.016191 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.013672 Loss1: 0.012985 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.017395 Loss1: 0.016708 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.023668 Loss1: 0.022979 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.998733
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 72
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9565972222222222
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.050698 Loss1: 0.050017 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025323 Loss1: 0.024639 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.018785 Loss1: 0.018100 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.018741 Loss1: 0.018055 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017641 Loss1: 0.016956 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.023082 Loss1: 0.022397 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.018558 Loss1: 0.017873 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.012182 Loss1: 0.011495 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.014745 Loss1: 0.014059 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.017660 Loss1: 0.016974 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.997179
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 82
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9702743902439024
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.042925 Loss1: 0.042247 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017910 Loss1: 0.017226 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.011749 Loss1: 0.011065 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.011142 Loss1: 0.010459 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.015154 Loss1: 0.014471 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.010980 Loss1: 0.010297 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.013642 Loss1: 0.012958 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.022394 Loss1: 0.021709 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.037644 Loss1: 0.036959 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.042038 Loss1: 0.041351 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.994855
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9719145569620253
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.038335 Loss1: 0.037655 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021065 Loss1: 0.020380 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.017498 Loss1: 0.016812 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.023759 Loss1: 0.023073 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.022440 Loss1: 0.021755 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.024551 Loss1: 0.023865 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.036570 Loss1: 0.035882 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.045798 Loss1: 0.045111 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.065955 Loss1: 0.065267 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.085343 Loss1: 0.084655 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.981408
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9596518987341772
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.057725 Loss1: 0.057045 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.025760 Loss1: 0.025075 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016294 Loss1: 0.015608 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015911 Loss1: 0.015224 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.017960 Loss1: 0.017273 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.020104 Loss1: 0.019419 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.020213 Loss1: 0.019526 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.032840 Loss1: 0.032152 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.039268 Loss1: 0.038579 Loss2: 0.000689
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.043937 Loss1: 0.043249 Loss2: 0.000688
-(DefaultActor pid=2839578) >> Training accuracy: 0.992682
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9677610759493671
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.036528 Loss1: 0.035848 Loss2: 0.000680
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.021069 Loss1: 0.020386 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.016254 Loss1: 0.015571 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.015606 Loss1: 0.014921 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.019928 Loss1: 0.019244 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.019712 Loss1: 0.019027 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.015299 Loss1: 0.014612 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.013719 Loss1: 0.013033 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.010660 Loss1: 0.009975 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.019541 Loss1: 0.018857 Loss2: 0.000685
-(DefaultActor pid=2839578) >> Training accuracy: 0.997231
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 78
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.969551282051282
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.030940 Loss1: 0.030260 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017179 Loss1: 0.016496 Loss2: 0.000683
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.012073 Loss1: 0.011389 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.013853 Loss1: 0.013169 Loss2: 0.000684
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.011877 Loss1: 0.011192 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.011186 Loss1: 0.010500 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.014932 Loss1: 0.014247 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.016097 Loss1: 0.015410 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.014862 Loss1: 0.014174 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.022251 Loss1: 0.021565 Loss2: 0.000686
-(DefaultActor pid=2839578) >> Training accuracy: 0.995593
-(DefaultActor pid=2839578)  ** Training complete **
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) n_training: 79
-(DefaultActor pid=2839578) >> Pre-Training Training accuracy: 0.9689477848101266
-(DefaultActor pid=2839578) Epoch: 0 Loss: 0.027259 Loss1: 0.026580 Loss2: 0.000679
-(DefaultActor pid=2839578) Epoch: 1 Loss: 0.017007 Loss1: 0.016321 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 2 Loss: 0.010313 Loss1: 0.009628 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 3 Loss: 0.014969 Loss1: 0.014284 Loss2: 0.000685
-(DefaultActor pid=2839578) Epoch: 4 Loss: 0.013018 Loss1: 0.012332 Loss2: 0.000686
-(DefaultActor pid=2839578) Epoch: 5 Loss: 0.013757 Loss1: 0.013070 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 6 Loss: 0.011565 Loss1: 0.010878 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 7 Loss: 0.010934 Loss1: 0.010246 Loss2: 0.000688
-(DefaultActor pid=2839578) Epoch: 8 Loss: 0.017679 Loss1: 0.016992 Loss2: 0.000687
-(DefaultActor pid=2839578) Epoch: 9 Loss: 0.028235 Loss1: 0.027546 Loss2: 0.000689
-(DefaultActor pid=2839578) >> Training accuracy: 0.991891
-(DefaultActor pid=2839578)  ** Training complete **
-[2023-09-23 10:47:47,269][flwr][DEBUG] - fit_round 100 received 10 results and 0 failures
-test acc: 0.6494
-[2023-09-23 10:48:28,827][flwr][INFO] - fit progress: (100, 2.5527703958197523, {'accuracy': 0.6494}, 200190.48873032397)
-[2023-09-23 10:48:28,828][flwr][DEBUG] - evaluate_round 100: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-(DefaultActor pid=2839578) device: cuda:0
-[2023-09-23 10:49:05,428][flwr][DEBUG] - evaluate_round 100 received 10 results and 0 failures
-[2023-09-23 10:49:05,429][flwr][INFO] - FL finished in 200227.09066704987
-[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0)]
-[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: metrics_distributed_fit {}
-[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: metrics_distributed {}
-[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: losses_centralized [(0, 6.156129693832641), (1, 4.6852914030178665), (2, 5.889099098242129), (3, 5.795179260424532), (4, 4.141716613556249), (5, 3.311973663183828), (6, 2.8995907626593835), (7, 2.6269793068639005), (8, 2.455170800510687), (9, 2.338781193803294), (10, 2.2332213046832585), (11, 2.1865856590362402), (12, 2.121484987651959), (13, 2.1080573364949453), (14, 2.0822741444499346), (15, 2.0444571261588758), (16, 2.057832792163276), (17, 2.031206720362837), (18, 2.0359792596996784), (19, 2.0221455788460023), (20, 2.034489405421784), (21, 2.0309638719970047), (22, 2.0352664707948604), (23, 2.0376226549712233), (24, 2.040369967111764), (25, 2.0471822914604942), (26, 2.0418881324533458), (27, 2.0698537910327364), (28, 2.0548813068828644), (29, 2.0496039061119764), (30, 2.0852254760531954), (31, 2.0679767246063525), (32, 2.0636839933288744), (33, 2.0933550024946657), (34, 2.083816295043348), (35, 2.1125483806140886), (36, 2.1186307085969576), (37, 2.1180881943565586), (38, 2.109429546248037), (39, 2.0989707978769614), (40, 2.123094680019842), (41, 2.148773836632506), (42, 2.1465409287629416), (43, 2.1362349244352346), (44, 2.1981266999777893), (45, 2.1977186043041583), (46, 2.2119887084625782), (47, 2.1827334691160405), (48, 2.2119309980267534), (49, 2.2019196455471053), (50, 2.2049550935864066), (51, 2.181678266951832), (52, 2.2030137499300437), (53, 2.2342346537227447), (54, 2.22973222568774), (55, 2.254546900526784), (56, 2.251486159170778), (57, 2.2792362104208705), (58, 2.2519221557215), (59, 2.2759478625398093), (60, 2.300492998700553), (61, 2.2899934441898577), (62, 2.312452397407434), (63, 2.3152449610896), (64, 2.304777619556878), (65, 2.337633471138561), (66, 2.3337900912799774), (67, 2.357942302767842), (68, 2.3550231862372866), (69, 2.3513612215892197), (70, 2.3543379908552566), (71, 2.349217086935196), (72, 2.383222926158113), (73, 2.378727243731197), (74, 2.381040071336606), (75, 2.3815381104192035), (76, 2.4015440281968528), (77, 2.441344348767314), (78, 2.4210258411904113), (79, 2.445481828416879), (80, 2.4491478545597185), (81, 2.4155959117526824), (82, 2.4423680827259635), (83, 2.4571505731667953), (84, 2.4649806056921473), (85, 2.5026507737537544), (86, 2.4607985724275485), (87, 2.49972779548968), (88, 2.5036359633119725), (89, 2.477679441340815), (90, 2.4975950007621472), (91, 2.5048911708612414), (92, 2.5276976867605705), (93, 2.4764777248659833), (94, 2.5375279696604696), (95, 2.5060083212943884), (96, 2.5280895025585406), (97, 2.522580636766391), (98, 2.5538510150802782), (99, 2.5449396366128525), (100, 2.5527703958197523)]
-[2023-09-23 10:49:05,466][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.01), (1, 0.01), (2, 0.01), (3, 0.0159), (4, 0.0942), (5, 0.1964), (6, 0.2787), (7, 0.3389), (8, 0.3862), (9, 0.414), (10, 0.4484), (11, 0.4735), (12, 0.4908), (13, 0.5082), (14, 0.5224), (15, 0.5411), (16, 0.5477), (17, 0.5608), (18, 0.5626), (19, 0.5718), (20, 0.5753), (21, 0.58), (22, 0.5794), (23, 0.5859), (24, 0.5911), (25, 0.5934), (26, 0.5954), (27, 0.5962), (28, 0.6041), (29, 0.6056), (30, 0.6064), (31, 0.6099), (32, 0.613), (33, 0.6145), (34, 0.6151), (35, 0.6162), (36, 0.6189), (37, 0.6191), (38, 0.6215), (39, 0.6223), (40, 0.62), (41, 0.6206), (42, 0.6283), (43, 0.6278), (44, 0.6251), (45, 0.623), (46, 0.6263), (47, 0.6254), (48, 0.6296), (49, 0.6282), (50, 0.6331), (51, 0.6347), (52, 0.6291), (53, 0.6303), (54, 0.634), (55, 0.6324), (56, 0.6339), (57, 0.6369), (58, 0.6379), (59, 0.6354), (60, 0.6394), (61, 0.6379), (62, 0.6368), (63, 0.6388), (64, 0.6396), (65, 0.6413), (66, 0.6402), (67, 0.6406), (68, 0.6389), (69, 0.6384), (70, 0.6375), (71, 0.6414), (72, 0.6415), (73, 0.6393), (74, 0.6429), (75, 0.6427), (76, 0.6415), (77, 0.6412), (78, 0.6437), (79, 0.6414), (80, 0.64), (81, 0.6414), (82, 0.6446), (83, 0.6436), (84, 0.6438), (85, 0.6483), (86, 0.6444), (87, 0.6441), (88, 0.6475), (89, 0.6455), (90, 0.6505), (91, 0.6458), (92, 0.6463), (93, 0.6485), (94, 0.65), (95, 0.6502), (96, 0.6487), (97, 0.6478), (98, 0.6498), (99, 0.652), (100, 0.6494)]}
diff --git a/baselines/moon/_static/cifar100_moon_log.txt b/baselines/moon/_static/cifar100_moon_log.txt
deleted file mode 100644
index 622d6da52391..000000000000
--- a/baselines/moon/_static/cifar100_moon_log.txt
+++ /dev/null
@@ -1,12852 +0,0 @@
-num_clients: 10
-num_epochs: 10
-fraction_fit: 1.0
-batch_size: 64
-learning_rate: 0.01
-mu: 1
-temperature: 0.5
-alg: moon
-seed: 0
-server_device: cpu
-num_rounds: 100
-client_resources:
-  num_cpus: 4
-  num_gpus: 1
-dataset:
-  name: cifar100
-  dir: ./data/moon/
-  partition: noniid
-  beta: 0.5
-model:
-  name: resnet50
-  output_dim: 256
-  dir: ./models/moon/cifar100/
-
-Files already downloaded and verified
-Files already downloaded and verified
-[2023-09-27 06:17:51,660][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=100, round_timeout=None)
-[2023-09-27 06:17:54,778][flwr][INFO] - Flower VCE: Ray initialized with resources: {'node:137.132.92.49': 1.0, 'object_store_memory': 49246977638.0, 'node:__internal_head__': 1.0, 'memory': 104909614490.0, 'accelerator_type:G': 1.0, 'GPU': 1.0, 'CPU': 64.0}
-[2023-09-27 06:17:54,779][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 1}
-[2023-09-27 06:17:54,794][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 1 actors
-[2023-09-27 06:17:54,795][flwr][INFO] - Initializing global parameters
-[2023-09-27 06:17:54,795][flwr][INFO] - Requesting initial parameters from one random client
-[2023-09-27 06:18:00,797][flwr][INFO] - Received initial parameters from one random client
-[2023-09-27 06:18:00,797][flwr][INFO] - Evaluating initial parameters
->> Test accuracy: 0.009000
-[2023-09-27 06:19:37,108][flwr][INFO] - initial parameters (loss, other metrics): 6.430294827531321, {'accuracy': 0.009}
-[2023-09-27 06:19:37,109][flwr][INFO] - FL starting
-[2023-09-27 06:19:37,110][flwr][DEBUG] - fit_round 1: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.369784 Loss1: 4.083765 Loss2: 0.286019
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.126744 Loss1: 3.863692 Loss2: 0.263052
-(DefaultActor pid=1838052) Epoch: 2 Loss: 4.037508 Loss1: 3.766124 Loss2: 0.271384
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.876198 Loss1: 3.598186 Loss2: 0.278012
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.764268 Loss1: 3.481796 Loss2: 0.282473
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.661503 Loss1: 3.374911 Loss2: 0.286592
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.559833 Loss1: 3.272496 Loss2: 0.287337
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.483088 Loss1: 3.196996 Loss2: 0.286092
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.444711 Loss1: 3.155687 Loss2: 0.289024
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.408479 Loss1: 3.119518 Loss2: 0.288961
-(DefaultActor pid=1838052) >> Training accuracy: 0.229628
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.346154 Loss1: 4.060577 Loss2: 0.285577
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.068530 Loss1: 3.804054 Loss2: 0.264476
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.919490 Loss1: 3.645709 Loss2: 0.273781
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.821928 Loss1: 3.545576 Loss2: 0.276352
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.758056 Loss1: 3.479816 Loss2: 0.278240
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.666878 Loss1: 3.387973 Loss2: 0.278905
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.616499 Loss1: 3.335261 Loss2: 0.281238
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.551534 Loss1: 3.269626 Loss2: 0.281908
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.505023 Loss1: 3.220934 Loss2: 0.284089
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.448401 Loss1: 3.164640 Loss2: 0.283761
-(DefaultActor pid=1838052) >> Training accuracy: 0.222508
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.362599 Loss1: 4.073072 Loss2: 0.289527
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.112317 Loss1: 3.844480 Loss2: 0.267837
-(DefaultActor pid=1838052) Epoch: 2 Loss: 4.049583 Loss1: 3.781704 Loss2: 0.267879
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.972073 Loss1: 3.702887 Loss2: 0.269187
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.885627 Loss1: 3.611692 Loss2: 0.273935
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.822126 Loss1: 3.546345 Loss2: 0.275780
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.754706 Loss1: 3.478197 Loss2: 0.276509
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.695414 Loss1: 3.417056 Loss2: 0.278358
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.641857 Loss1: 3.363491 Loss2: 0.278366
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.620797 Loss1: 3.340018 Loss2: 0.280779
-(DefaultActor pid=1838052) >> Training accuracy: 0.201686
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.339807 Loss1: 4.052010 Loss2: 0.287797
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.115066 Loss1: 3.853241 Loss2: 0.261826
-(DefaultActor pid=1838052) Epoch: 2 Loss: 4.038403 Loss1: 3.773888 Loss2: 0.264515
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.912889 Loss1: 3.643108 Loss2: 0.269782
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.793036 Loss1: 3.514153 Loss2: 0.278882
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.685227 Loss1: 3.403987 Loss2: 0.281240
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.606935 Loss1: 3.325554 Loss2: 0.281380
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.528747 Loss1: 3.246711 Loss2: 0.282036
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.488578 Loss1: 3.201779 Loss2: 0.286799
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.417189 Loss1: 3.132426 Loss2: 0.284763
-(DefaultActor pid=1838052) >> Training accuracy: 0.236090
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.427387 Loss1: 4.143911 Loss2: 0.283476
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.202346 Loss1: 3.942770 Loss2: 0.259576
-(DefaultActor pid=1838052) Epoch: 2 Loss: 4.089324 Loss1: 3.824064 Loss2: 0.265260
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.887307 Loss1: 3.607379 Loss2: 0.279927
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.790842 Loss1: 3.508758 Loss2: 0.282084
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.717089 Loss1: 3.435043 Loss2: 0.282046
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.634166 Loss1: 3.352003 Loss2: 0.282164
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.592898 Loss1: 3.310381 Loss2: 0.282517
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.522705 Loss1: 3.238769 Loss2: 0.283936
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.485529 Loss1: 3.201372 Loss2: 0.284157
-(DefaultActor pid=1838052) >> Training accuracy: 0.188101
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.329792 Loss1: 4.037371 Loss2: 0.292421
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.032470 Loss1: 3.766303 Loss2: 0.266167
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.935961 Loss1: 3.670046 Loss2: 0.265915
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.803394 Loss1: 3.528398 Loss2: 0.274996
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.736129 Loss1: 3.457929 Loss2: 0.278200
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.650748 Loss1: 3.370759 Loss2: 0.279989
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.577409 Loss1: 3.299005 Loss2: 0.278405
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.517058 Loss1: 3.233132 Loss2: 0.283926
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.459693 Loss1: 3.175883 Loss2: 0.283809
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.379744 Loss1: 3.096116 Loss2: 0.283628
-(DefaultActor pid=1838052) >> Training accuracy: 0.242089
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.301462 Loss1: 4.017734 Loss2: 0.283728
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.038520 Loss1: 3.773132 Loss2: 0.265387
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.943192 Loss1: 3.677212 Loss2: 0.265980
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.852988 Loss1: 3.579469 Loss2: 0.273519
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.755028 Loss1: 3.479172 Loss2: 0.275856
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.667556 Loss1: 3.387615 Loss2: 0.279941
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.582211 Loss1: 3.297829 Loss2: 0.284381
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.518371 Loss1: 3.229607 Loss2: 0.288764
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.431761 Loss1: 3.144861 Loss2: 0.286900
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.360612 Loss1: 3.073343 Loss2: 0.287269
-(DefaultActor pid=1838052) >> Training accuracy: 0.275549
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.422230 Loss1: 4.135496 Loss2: 0.286734
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.192760 Loss1: 3.930573 Loss2: 0.262187
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.963340 Loss1: 3.687979 Loss2: 0.275361
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.825529 Loss1: 3.543690 Loss2: 0.281840
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.717797 Loss1: 3.432226 Loss2: 0.285572
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.624084 Loss1: 3.339734 Loss2: 0.284350
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.551592 Loss1: 3.264009 Loss2: 0.287583
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.468409 Loss1: 3.182516 Loss2: 0.285894
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.405261 Loss1: 3.117090 Loss2: 0.288171
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.344626 Loss1: 3.057201 Loss2: 0.287425
-(DefaultActor pid=1838052) >> Training accuracy: 0.279848
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.337995 Loss1: 4.053333 Loss2: 0.284662
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.055274 Loss1: 3.792687 Loss2: 0.262587
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.971644 Loss1: 3.709966 Loss2: 0.261677
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.892300 Loss1: 3.626549 Loss2: 0.265751
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.776625 Loss1: 3.501122 Loss2: 0.275503
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.678157 Loss1: 3.398068 Loss2: 0.280089
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.631299 Loss1: 3.351895 Loss2: 0.279403
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.571316 Loss1: 3.290765 Loss2: 0.280551
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.509884 Loss1: 3.227711 Loss2: 0.282173
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.457262 Loss1: 3.177141 Loss2: 0.280121
-(DefaultActor pid=1838052) >> Training accuracy: 0.235562
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.333940 Loss1: 4.044128 Loss2: 0.289811
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.064459 Loss1: 3.800716 Loss2: 0.263743
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.971100 Loss1: 3.703330 Loss2: 0.267770
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.867576 Loss1: 3.596127 Loss2: 0.271449
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.799606 Loss1: 3.524901 Loss2: 0.274705
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.695966 Loss1: 3.417971 Loss2: 0.277995
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.632793 Loss1: 3.352437 Loss2: 0.280356
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.553112 Loss1: 3.271284 Loss2: 0.281829
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.489069 Loss1: 3.205962 Loss2: 0.283108
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.408333 Loss1: 3.120609 Loss2: 0.287724
-(DefaultActor pid=1838052) >> Training accuracy: 0.232205
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 06:49:48,032][flwr][DEBUG] - fit_round 1 received 10 results and 0 failures
-[2023-09-27 06:49:50,488][flwr][WARNING] - No fit_metrics_aggregation_fn provided
->> Test accuracy: 0.010000
-[2023-09-27 06:50:32,439][flwr][INFO] - fit progress: (1, 4.861440579350383, {'accuracy': 0.01}, 1855.3291893731803)
-[2023-09-27 06:50:32,440][flwr][DEBUG] - evaluate_round 1: strategy sampled 10 clients (out of 10)
-[2023-09-27 06:51:11,688][flwr][DEBUG] - evaluate_round 1 received 10 results and 0 failures
-[2023-09-27 06:51:11,689][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
-[2023-09-27 06:51:11,689][flwr][DEBUG] - fit_round 2: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.644373 Loss1: 4.184493 Loss2: 0.459881
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.198303 Loss1: 3.733462 Loss2: 0.464840
-(DefaultActor pid=1838052) Epoch: 2 Loss: 4.013687 Loss1: 3.536389 Loss2: 0.477298
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.932287 Loss1: 3.455386 Loss2: 0.476901
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.806476 Loss1: 3.340193 Loss2: 0.466283
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.746450 Loss1: 3.286199 Loss2: 0.460251
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.638469 Loss1: 3.183025 Loss2: 0.455444
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.604254 Loss1: 3.153255 Loss2: 0.450999
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.504562 Loss1: 3.064321 Loss2: 0.440241
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.448699 Loss1: 3.015176 Loss2: 0.433522
-(DefaultActor pid=1838052) >> Training accuracy: 0.283253
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.667550 Loss1: 4.214587 Loss2: 0.452963
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.220379 Loss1: 3.757211 Loss2: 0.463168
-(DefaultActor pid=1838052) Epoch: 2 Loss: 4.076105 Loss1: 3.599009 Loss2: 0.477095
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.985424 Loss1: 3.513090 Loss2: 0.472334
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.903181 Loss1: 3.436756 Loss2: 0.466425
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.843354 Loss1: 3.387080 Loss2: 0.456274
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.750510 Loss1: 3.302325 Loss2: 0.448185
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.706948 Loss1: 3.264065 Loss2: 0.442884
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.608896 Loss1: 3.177834 Loss2: 0.431062
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.583576 Loss1: 3.161815 Loss2: 0.421761
-(DefaultActor pid=1838052) >> Training accuracy: 0.209135
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.568606 Loss1: 4.104333 Loss2: 0.464273
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.077084 Loss1: 3.611639 Loss2: 0.465444
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.981827 Loss1: 3.515113 Loss2: 0.466714
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.905688 Loss1: 3.442643 Loss2: 0.463044
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.827841 Loss1: 3.372064 Loss2: 0.455777
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.795793 Loss1: 3.345474 Loss2: 0.450319
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.753882 Loss1: 3.308774 Loss2: 0.445108
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.679453 Loss1: 3.240107 Loss2: 0.439347
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.642235 Loss1: 3.206805 Loss2: 0.435430
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.559729 Loss1: 3.133299 Loss2: 0.426430
-(DefaultActor pid=1838052) >> Training accuracy: 0.218354
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.731526 Loss1: 4.305523 Loss2: 0.426003
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.210372 Loss1: 3.770380 Loss2: 0.439992
-(DefaultActor pid=1838052) Epoch: 2 Loss: 4.034736 Loss1: 3.588960 Loss2: 0.445775
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.953219 Loss1: 3.499736 Loss2: 0.453484
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.861044 Loss1: 3.415457 Loss2: 0.445586
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.774768 Loss1: 3.334986 Loss2: 0.439782
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.711004 Loss1: 3.280312 Loss2: 0.430692
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.650057 Loss1: 3.226051 Loss2: 0.424005
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.602845 Loss1: 3.186046 Loss2: 0.416799
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.520022 Loss1: 3.113187 Loss2: 0.406835
-(DefaultActor pid=1838052) >> Training accuracy: 0.253906
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.991454 Loss1: 3.935299 Loss2: 0.056156
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.588267 Loss1: 3.536850 Loss2: 0.051418
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.488563 Loss1: 3.436851 Loss2: 0.051712
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.424850 Loss1: 3.373044 Loss2: 0.051806
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.338276 Loss1: 3.285797 Loss2: 0.052479
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.266924 Loss1: 3.214751 Loss2: 0.052173
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.198642 Loss1: 3.144631 Loss2: 0.054011
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.145674 Loss1: 3.090927 Loss2: 0.054746
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.117068 Loss1: 3.060616 Loss2: 0.056452
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.049991 Loss1: 2.993472 Loss2: 0.056519
-(DefaultActor pid=1838052) >> Training accuracy: 0.273932
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.613039 Loss1: 4.153980 Loss2: 0.459058
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.198233 Loss1: 3.753234 Loss2: 0.444999
-(DefaultActor pid=1838052) Epoch: 2 Loss: 4.030522 Loss1: 3.574830 Loss2: 0.455692
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.948132 Loss1: 3.503039 Loss2: 0.445093
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.904944 Loss1: 3.469663 Loss2: 0.435281
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.841591 Loss1: 3.414879 Loss2: 0.426711
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.776404 Loss1: 3.358964 Loss2: 0.417439
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.756225 Loss1: 3.344306 Loss2: 0.411920
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.703344 Loss1: 3.302161 Loss2: 0.401183
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.673633 Loss1: 3.276277 Loss2: 0.397356
-(DefaultActor pid=1838052) >> Training accuracy: 0.230263
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.981784 Loss1: 3.929316 Loss2: 0.052467
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.588564 Loss1: 3.538386 Loss2: 0.050178
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.433607 Loss1: 3.382880 Loss2: 0.050727
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.367239 Loss1: 3.315783 Loss2: 0.051456
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.291270 Loss1: 3.238080 Loss2: 0.053190
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.207599 Loss1: 3.153870 Loss2: 0.053729
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.175349 Loss1: 3.120854 Loss2: 0.054495
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.106231 Loss1: 3.049030 Loss2: 0.057202
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.061699 Loss1: 3.004801 Loss2: 0.056898
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.002184 Loss1: 2.943285 Loss2: 0.058898
-(DefaultActor pid=1838052) >> Training accuracy: 0.233782
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.917315 Loss1: 3.863439 Loss2: 0.053876
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.527755 Loss1: 3.476411 Loss2: 0.051344
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.443776 Loss1: 3.392791 Loss2: 0.050985
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.370201 Loss1: 3.319509 Loss2: 0.050693
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.273357 Loss1: 3.222251 Loss2: 0.051106
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.221033 Loss1: 3.168118 Loss2: 0.052914
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.140141 Loss1: 3.086229 Loss2: 0.053911
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.109909 Loss1: 3.054987 Loss2: 0.054923
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.060353 Loss1: 3.003993 Loss2: 0.056361
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.992805 Loss1: 2.936097 Loss2: 0.056708
-(DefaultActor pid=1838052) >> Training accuracy: 0.285823
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 4.493748 Loss1: 3.995604 Loss2: 0.498144
-(DefaultActor pid=1838052) Epoch: 1 Loss: 4.122479 Loss1: 3.635837 Loss2: 0.486642
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.995465 Loss1: 3.511020 Loss2: 0.484445
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.901413 Loss1: 3.418500 Loss2: 0.482913
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.831103 Loss1: 3.356049 Loss2: 0.475053
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.765394 Loss1: 3.294033 Loss2: 0.471360
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.695697 Loss1: 3.234495 Loss2: 0.461201
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.642169 Loss1: 3.188603 Loss2: 0.453565
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.574997 Loss1: 3.126612 Loss2: 0.448385
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.513407 Loss1: 3.071809 Loss2: 0.441598
-(DefaultActor pid=1838052) >> Training accuracy: 0.244462
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.997624 Loss1: 3.939611 Loss2: 0.058013
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.596121 Loss1: 3.544284 Loss2: 0.051837
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.443146 Loss1: 3.392343 Loss2: 0.050802
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.356063 Loss1: 3.303394 Loss2: 0.052669
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.276302 Loss1: 3.222434 Loss2: 0.053868
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.230555 Loss1: 3.174897 Loss2: 0.055659
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.161892 Loss1: 3.105874 Loss2: 0.056018
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.117249 Loss1: 3.060366 Loss2: 0.056883
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.029329 Loss1: 2.971722 Loss2: 0.057607
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.963145 Loss1: 2.905953 Loss2: 0.057192
-(DefaultActor pid=1838052) >> Training accuracy: 0.280828
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 07:21:07,003][flwr][DEBUG] - fit_round 2 received 10 results and 0 failures
->> Test accuracy: 0.010000
-[2023-09-27 07:21:49,833][flwr][INFO] - fit progress: (2, 5.477163912008365, {'accuracy': 0.01}, 3732.7233330453746)
-[2023-09-27 07:21:49,835][flwr][DEBUG] - evaluate_round 2: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:22:29,717][flwr][DEBUG] - evaluate_round 2 received 10 results and 0 failures
-[2023-09-27 07:22:29,718][flwr][DEBUG] - fit_round 3: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.922555 Loss1: 3.557274 Loss2: 0.365281
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.606061 Loss1: 3.293600 Loss2: 0.312461
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.504368 Loss1: 3.205235 Loss2: 0.299133
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.428207 Loss1: 3.137463 Loss2: 0.290744
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.343418 Loss1: 3.053441 Loss2: 0.289977
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.261979 Loss1: 2.974511 Loss2: 0.287468
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.245885 Loss1: 2.958061 Loss2: 0.287823
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.149963 Loss1: 2.863760 Loss2: 0.286203
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.101458 Loss1: 2.813974 Loss2: 0.287485
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.039259 Loss1: 2.753741 Loss2: 0.285518
-(DefaultActor pid=1838052) >> Training accuracy: 0.305556
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.828347 Loss1: 3.534358 Loss2: 0.293988
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.554310 Loss1: 3.283775 Loss2: 0.270536
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.464295 Loss1: 3.198007 Loss2: 0.266288
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.406251 Loss1: 3.135592 Loss2: 0.270659
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.335067 Loss1: 3.064223 Loss2: 0.270844
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.284846 Loss1: 3.012912 Loss2: 0.271934
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.223960 Loss1: 2.954009 Loss2: 0.269951
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.172071 Loss1: 2.901174 Loss2: 0.270898
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.115965 Loss1: 2.841745 Loss2: 0.274220
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.019910 Loss1: 2.747705 Loss2: 0.272205
-(DefaultActor pid=1838052) >> Training accuracy: 0.294764
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.850639 Loss1: 3.529540 Loss2: 0.321099
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.566213 Loss1: 3.292225 Loss2: 0.273988
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.475061 Loss1: 3.208274 Loss2: 0.266787
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.390075 Loss1: 3.126561 Loss2: 0.263515
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.296098 Loss1: 3.034898 Loss2: 0.261200
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.265400 Loss1: 3.003450 Loss2: 0.261950
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.207430 Loss1: 2.944783 Loss2: 0.262647
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.139398 Loss1: 2.873270 Loss2: 0.266129
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.101593 Loss1: 2.837857 Loss2: 0.263736
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.051368 Loss1: 2.788499 Loss2: 0.262869
-(DefaultActor pid=1838052) >> Training accuracy: 0.291535
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.851295 Loss1: 3.547933 Loss2: 0.303362
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.576642 Loss1: 3.299925 Loss2: 0.276716
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.456705 Loss1: 3.181631 Loss2: 0.275075
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.424054 Loss1: 3.148893 Loss2: 0.275161
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.348423 Loss1: 3.070073 Loss2: 0.278351
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.269554 Loss1: 2.992072 Loss2: 0.277482
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.234234 Loss1: 2.952364 Loss2: 0.281870
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.166968 Loss1: 2.886840 Loss2: 0.280127
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.099678 Loss1: 2.821619 Loss2: 0.278058
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.040664 Loss1: 2.763697 Loss2: 0.276967
-(DefaultActor pid=1838052) >> Training accuracy: 0.298655
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.920883 Loss1: 3.602874 Loss2: 0.318009
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.616597 Loss1: 3.329797 Loss2: 0.286801
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.521105 Loss1: 3.239110 Loss2: 0.281994
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.448692 Loss1: 3.165734 Loss2: 0.282958
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.375298 Loss1: 3.097168 Loss2: 0.278130
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.332728 Loss1: 3.052265 Loss2: 0.280462
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.232993 Loss1: 2.955120 Loss2: 0.277873
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.249608 Loss1: 2.969839 Loss2: 0.279769
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.173826 Loss1: 2.898052 Loss2: 0.275773
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.128311 Loss1: 2.853575 Loss2: 0.274736
-(DefaultActor pid=1838052) >> Training accuracy: 0.309731
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.655502 Loss1: 3.590250 Loss2: 0.065252
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.382125 Loss1: 3.324907 Loss2: 0.057218
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.287139 Loss1: 3.230372 Loss2: 0.056767
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.214660 Loss1: 3.156809 Loss2: 0.057852
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.150348 Loss1: 3.091685 Loss2: 0.058662
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.088283 Loss1: 3.029709 Loss2: 0.058573
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.046257 Loss1: 2.986612 Loss2: 0.059645
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.989624 Loss1: 2.927651 Loss2: 0.061972
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.916961 Loss1: 2.855189 Loss2: 0.061772
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.882330 Loss1: 2.819250 Loss2: 0.063080
-(DefaultActor pid=1838052) >> Training accuracy: 0.290264
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.560737 Loss1: 3.498825 Loss2: 0.061912
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.303921 Loss1: 3.247327 Loss2: 0.056594
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.220241 Loss1: 3.163955 Loss2: 0.056286
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.143950 Loss1: 3.087456 Loss2: 0.056494
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.016872 Loss1: 2.959652 Loss2: 0.057220
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.943949 Loss1: 2.885173 Loss2: 0.058776
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.893974 Loss1: 2.834528 Loss2: 0.059446
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.821323 Loss1: 2.760880 Loss2: 0.060443
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.764922 Loss1: 2.703962 Loss2: 0.060959
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.714211 Loss1: 2.651170 Loss2: 0.063041
-(DefaultActor pid=1838052) >> Training accuracy: 0.354167
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.656666 Loss1: 3.592971 Loss2: 0.063694
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.445064 Loss1: 3.391160 Loss2: 0.053903
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.366218 Loss1: 3.313455 Loss2: 0.052763
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.322735 Loss1: 3.268185 Loss2: 0.054550
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.281451 Loss1: 3.227969 Loss2: 0.053482
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.210883 Loss1: 3.155668 Loss2: 0.055214
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.144232 Loss1: 3.087339 Loss2: 0.056893
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.122254 Loss1: 3.065861 Loss2: 0.056393
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.091077 Loss1: 3.032098 Loss2: 0.058979
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.018300 Loss1: 2.958169 Loss2: 0.060131
-(DefaultActor pid=1838052) >> Training accuracy: 0.278988
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.562108 Loss1: 3.510223 Loss2: 0.051884
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.283523 Loss1: 3.235999 Loss2: 0.047524
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.211609 Loss1: 3.164451 Loss2: 0.047158
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.125271 Loss1: 3.077918 Loss2: 0.047353
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.071585 Loss1: 3.023945 Loss2: 0.047639
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.004476 Loss1: 2.955765 Loss2: 0.048710
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.974421 Loss1: 2.924276 Loss2: 0.050145
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.940153 Loss1: 2.888216 Loss2: 0.051938
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.862987 Loss1: 2.810040 Loss2: 0.052947
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.843785 Loss1: 2.790512 Loss2: 0.053273
-(DefaultActor pid=1838052) >> Training accuracy: 0.315549
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.677349 Loss1: 3.559477 Loss2: 0.117872
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.399072 Loss1: 3.297309 Loss2: 0.101763
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.308856 Loss1: 3.209720 Loss2: 0.099136
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.226705 Loss1: 3.128742 Loss2: 0.097962
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.145654 Loss1: 3.047904 Loss2: 0.097750
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.146708 Loss1: 3.048723 Loss2: 0.097985
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.073893 Loss1: 2.975577 Loss2: 0.098316
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.008721 Loss1: 2.909682 Loss2: 0.099040
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.946285 Loss1: 2.846163 Loss2: 0.100123
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.894182 Loss1: 2.793671 Loss2: 0.100511
-(DefaultActor pid=1838052) >> Training accuracy: 0.314873
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 07:52:04,826][flwr][DEBUG] - fit_round 3 received 10 results and 0 failures
->> Test accuracy: 0.014100
-[2023-09-27 07:52:47,706][flwr][INFO] - fit progress: (3, 5.5055647475270035, {'accuracy': 0.0141}, 5590.596718014218)
-[2023-09-27 07:52:47,708][flwr][DEBUG] - evaluate_round 3: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:53:32,486][flwr][DEBUG] - evaluate_round 3 received 10 results and 0 failures
-[2023-09-27 07:53:32,488][flwr][DEBUG] - fit_round 4: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.463621 Loss1: 3.216236 Loss2: 0.247384
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.265612 Loss1: 3.055579 Loss2: 0.210033
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.145339 Loss1: 2.944292 Loss2: 0.201047
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.089212 Loss1: 2.891579 Loss2: 0.197632
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.050139 Loss1: 2.850335 Loss2: 0.199804
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.935722 Loss1: 2.736376 Loss2: 0.199345
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.871901 Loss1: 2.671933 Loss2: 0.199968
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.814916 Loss1: 2.618288 Loss2: 0.196628
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.778902 Loss1: 2.579310 Loss2: 0.199592
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.688708 Loss1: 2.490068 Loss2: 0.198640
-(DefaultActor pid=1838052) >> Training accuracy: 0.359573
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.827630 Loss1: 3.361022 Loss2: 0.466608
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.603237 Loss1: 3.193316 Loss2: 0.409921
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.511392 Loss1: 3.112292 Loss2: 0.399099
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.442427 Loss1: 3.044361 Loss2: 0.398066
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.385098 Loss1: 2.993434 Loss2: 0.391663
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.318931 Loss1: 2.924867 Loss2: 0.394064
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.294363 Loss1: 2.897327 Loss2: 0.397037
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.223198 Loss1: 2.830592 Loss2: 0.392606
-(DefaultActor pid=1838052) Epoch: 8 Loss: 3.175710 Loss1: 2.780731 Loss2: 0.394979
-(DefaultActor pid=1838052) Epoch: 9 Loss: 3.120332 Loss1: 2.727254 Loss2: 0.393078
-(DefaultActor pid=1838052) >> Training accuracy: 0.330181
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.282682 Loss1: 3.232115 Loss2: 0.050566
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.099791 Loss1: 3.053422 Loss2: 0.046369
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.987802 Loss1: 2.942296 Loss2: 0.045507
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.916563 Loss1: 2.870740 Loss2: 0.045823
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.851462 Loss1: 2.805344 Loss2: 0.046119
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.811548 Loss1: 2.763247 Loss2: 0.048301
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.725912 Loss1: 2.677642 Loss2: 0.048269
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.694947 Loss1: 2.645510 Loss2: 0.049438
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.591619 Loss1: 2.541165 Loss2: 0.050454
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.565490 Loss1: 2.512605 Loss2: 0.052885
-(DefaultActor pid=1838052) >> Training accuracy: 0.368473
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.259794 Loss1: 3.210508 Loss2: 0.049286
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.027122 Loss1: 2.982449 Loss2: 0.044673
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.952811 Loss1: 2.908267 Loss2: 0.044544
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.849582 Loss1: 2.804490 Loss2: 0.045092
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.753046 Loss1: 2.706741 Loss2: 0.046305
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.701504 Loss1: 2.654246 Loss2: 0.047258
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.697666 Loss1: 2.648308 Loss2: 0.049358
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.586713 Loss1: 2.536130 Loss2: 0.050583
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.558675 Loss1: 2.508268 Loss2: 0.050407
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.482649 Loss1: 2.430579 Loss2: 0.052070
-(DefaultActor pid=1838052) >> Training accuracy: 0.380490
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.208056 Loss1: 3.158335 Loss2: 0.049722
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.022045 Loss1: 2.975547 Loss2: 0.046498
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.924929 Loss1: 2.878575 Loss2: 0.046354
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.829727 Loss1: 2.783190 Loss2: 0.046537
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.759261 Loss1: 2.711374 Loss2: 0.047887
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.670441 Loss1: 2.620735 Loss2: 0.049707
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.614755 Loss1: 2.564584 Loss2: 0.050171
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.559418 Loss1: 2.507530 Loss2: 0.051888
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.472070 Loss1: 2.419986 Loss2: 0.052084
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.411061 Loss1: 2.358198 Loss2: 0.052862
-(DefaultActor pid=1838052) >> Training accuracy: 0.397035
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.293607 Loss1: 3.234862 Loss2: 0.058745
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.063403 Loss1: 3.010807 Loss2: 0.052596
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.975066 Loss1: 2.924591 Loss2: 0.050475
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.878276 Loss1: 2.826151 Loss2: 0.052124
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.810593 Loss1: 2.757815 Loss2: 0.052779
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.727358 Loss1: 2.673098 Loss2: 0.054259
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.681398 Loss1: 2.625040 Loss2: 0.056357
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.595525 Loss1: 2.538674 Loss2: 0.056851
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.558311 Loss1: 2.500617 Loss2: 0.057694
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.516464 Loss1: 2.457347 Loss2: 0.059117
-(DefaultActor pid=1838052) >> Training accuracy: 0.391710
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.222874 Loss1: 3.178139 Loss2: 0.044734
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.033196 Loss1: 2.989958 Loss2: 0.043239
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.946469 Loss1: 2.903984 Loss2: 0.042485
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.874091 Loss1: 2.831174 Loss2: 0.042918
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.834365 Loss1: 2.790535 Loss2: 0.043829
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.747757 Loss1: 2.703132 Loss2: 0.044625
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.691127 Loss1: 2.644962 Loss2: 0.046165
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.632341 Loss1: 2.584584 Loss2: 0.047757
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.556889 Loss1: 2.508931 Loss2: 0.047958
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.534964 Loss1: 2.485370 Loss2: 0.049593
-(DefaultActor pid=1838052) >> Training accuracy: 0.358188
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.557245 Loss1: 3.245894 Loss2: 0.311351
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.363731 Loss1: 3.095614 Loss2: 0.268118
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.268273 Loss1: 3.009341 Loss2: 0.258932
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.186438 Loss1: 2.929719 Loss2: 0.256719
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.142631 Loss1: 2.886133 Loss2: 0.256497
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.071703 Loss1: 2.816265 Loss2: 0.255438
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.036513 Loss1: 2.779075 Loss2: 0.257439
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.985743 Loss1: 2.727855 Loss2: 0.257887
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.887329 Loss1: 2.630912 Loss2: 0.256417
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.862139 Loss1: 2.602498 Loss2: 0.259641
-(DefaultActor pid=1838052) >> Training accuracy: 0.341179
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.671132 Loss1: 3.258866 Loss2: 0.412266
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.409693 Loss1: 3.050773 Loss2: 0.358921
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.336526 Loss1: 2.987356 Loss2: 0.349170
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.259327 Loss1: 2.916673 Loss2: 0.342654
-(DefaultActor pid=1838052) Epoch: 4 Loss: 3.202082 Loss1: 2.859971 Loss2: 0.342111
-(DefaultActor pid=1838052) Epoch: 5 Loss: 3.163279 Loss1: 2.821047 Loss2: 0.342233
-(DefaultActor pid=1838052) Epoch: 6 Loss: 3.097888 Loss1: 2.752977 Loss2: 0.344911
-(DefaultActor pid=1838052) Epoch: 7 Loss: 3.036831 Loss1: 2.694183 Loss2: 0.342647
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.970583 Loss1: 2.626212 Loss2: 0.344371
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.890262 Loss1: 2.547522 Loss2: 0.342740
-(DefaultActor pid=1838052) >> Training accuracy: 0.369665
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.392899 Loss1: 3.243730 Loss2: 0.149169
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.178866 Loss1: 3.058680 Loss2: 0.120186
-(DefaultActor pid=1838052) Epoch: 2 Loss: 3.082310 Loss1: 2.967496 Loss2: 0.114814
-(DefaultActor pid=1838052) Epoch: 3 Loss: 3.016231 Loss1: 2.901184 Loss2: 0.115047
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.970972 Loss1: 2.856173 Loss2: 0.114799
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.886622 Loss1: 2.770577 Loss2: 0.116045
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.806940 Loss1: 2.689884 Loss2: 0.117056
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.770023 Loss1: 2.653150 Loss2: 0.116873
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.701240 Loss1: 2.582509 Loss2: 0.118731
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.640212 Loss1: 2.519890 Loss2: 0.120322
-(DefaultActor pid=1838052) >> Training accuracy: 0.344551
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 08:23:25,915][flwr][DEBUG] - fit_round 4 received 10 results and 0 failures
->> Test accuracy: 0.088200
-[2023-09-27 08:24:10,741][flwr][INFO] - fit progress: (4, 4.169462466011413, {'accuracy': 0.0882}, 7473.631052463315)
-[2023-09-27 08:24:10,742][flwr][DEBUG] - evaluate_round 4: strategy sampled 10 clients (out of 10)
-[2023-09-27 08:24:50,446][flwr][DEBUG] - evaluate_round 4 received 10 results and 0 failures
-[2023-09-27 08:24:50,448][flwr][DEBUG] - fit_round 5: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.044559 Loss1: 2.934706 Loss2: 0.109853
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.769363 Loss1: 2.675678 Loss2: 0.093685
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.687641 Loss1: 2.598878 Loss2: 0.088763
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.599898 Loss1: 2.512106 Loss2: 0.087792
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.540585 Loss1: 2.453451 Loss2: 0.087134
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.468175 Loss1: 2.381435 Loss2: 0.086740
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.415930 Loss1: 2.328302 Loss2: 0.087628
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.352162 Loss1: 2.265323 Loss2: 0.086840
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.255332 Loss1: 2.167591 Loss2: 0.087741
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.220588 Loss1: 2.131302 Loss2: 0.089286
-(DefaultActor pid=1838052) >> Training accuracy: 0.450087
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.237433 Loss1: 2.956613 Loss2: 0.280820
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.010297 Loss1: 2.762902 Loss2: 0.247394
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.917174 Loss1: 2.676857 Loss2: 0.240317
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.818864 Loss1: 2.577074 Loss2: 0.241790
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.729454 Loss1: 2.488832 Loss2: 0.240622
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.697842 Loss1: 2.453622 Loss2: 0.244220
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.666894 Loss1: 2.421276 Loss2: 0.245618
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.568640 Loss1: 2.324768 Loss2: 0.243872
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.536173 Loss1: 2.290029 Loss2: 0.246144
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.454216 Loss1: 2.207339 Loss2: 0.246877
-(DefaultActor pid=1838052) >> Training accuracy: 0.425877
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.117160 Loss1: 3.058576 Loss2: 0.058585
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.923077 Loss1: 2.869900 Loss2: 0.053176
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.819140 Loss1: 2.768482 Loss2: 0.050658
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.749814 Loss1: 2.698656 Loss2: 0.051157
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.712673 Loss1: 2.660845 Loss2: 0.051828
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.648868 Loss1: 2.596063 Loss2: 0.052805
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.556787 Loss1: 2.503860 Loss2: 0.052927
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.532586 Loss1: 2.476940 Loss2: 0.055646
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.465118 Loss1: 2.408557 Loss2: 0.056561
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.425746 Loss1: 2.368631 Loss2: 0.057116
-(DefaultActor pid=1838052) >> Training accuracy: 0.403783
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.002072 Loss1: 2.947792 Loss2: 0.054280
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.826481 Loss1: 2.774957 Loss2: 0.051523
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.758476 Loss1: 2.708199 Loss2: 0.050277
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.700022 Loss1: 2.650502 Loss2: 0.049520
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.594647 Loss1: 2.545112 Loss2: 0.049535
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.561690 Loss1: 2.510262 Loss2: 0.051428
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.492958 Loss1: 2.441024 Loss2: 0.051934
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.460541 Loss1: 2.407330 Loss2: 0.053212
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.372188 Loss1: 2.318846 Loss2: 0.053341
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.323567 Loss1: 2.269347 Loss2: 0.054219
-(DefaultActor pid=1838052) >> Training accuracy: 0.411859
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.943132 Loss1: 2.896993 Loss2: 0.046140
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.743960 Loss1: 2.699422 Loss2: 0.044538
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.672550 Loss1: 2.627953 Loss2: 0.044596
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.581770 Loss1: 2.538061 Loss2: 0.043709
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.536205 Loss1: 2.490766 Loss2: 0.045438
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.472918 Loss1: 2.427032 Loss2: 0.045886
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.414953 Loss1: 2.367332 Loss2: 0.047621
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.341351 Loss1: 2.292401 Loss2: 0.048950
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.289473 Loss1: 2.239205 Loss2: 0.050268
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.229166 Loss1: 2.179152 Loss2: 0.050015
-(DefaultActor pid=1838052) >> Training accuracy: 0.428204
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.877014 Loss1: 2.828293 Loss2: 0.048721
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.673691 Loss1: 2.627127 Loss2: 0.046564
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.593290 Loss1: 2.547474 Loss2: 0.045815
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.528434 Loss1: 2.481120 Loss2: 0.047314
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.447478 Loss1: 2.400185 Loss2: 0.047293
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.360431 Loss1: 2.312344 Loss2: 0.048087
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.308135 Loss1: 2.258981 Loss2: 0.049154
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.246187 Loss1: 2.196381 Loss2: 0.049806
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.180565 Loss1: 2.129732 Loss2: 0.050833
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.146498 Loss1: 2.093765 Loss2: 0.052733
-(DefaultActor pid=1838052) >> Training accuracy: 0.489383
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.306062 Loss1: 3.003298 Loss2: 0.302764
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.057813 Loss1: 2.804032 Loss2: 0.253781
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.940257 Loss1: 2.693761 Loss2: 0.246495
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.891829 Loss1: 2.642646 Loss2: 0.249183
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.807374 Loss1: 2.561056 Loss2: 0.246318
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.773237 Loss1: 2.525510 Loss2: 0.247727
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.664517 Loss1: 2.414085 Loss2: 0.250432
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.626118 Loss1: 2.376161 Loss2: 0.249957
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.538084 Loss1: 2.288568 Loss2: 0.249516
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.490878 Loss1: 2.239237 Loss2: 0.251641
-(DefaultActor pid=1838052) >> Training accuracy: 0.409810
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.038147 Loss1: 2.911986 Loss2: 0.126161
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.805732 Loss1: 2.697179 Loss2: 0.108553
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.757872 Loss1: 2.653028 Loss2: 0.104844
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.660189 Loss1: 2.557299 Loss2: 0.102890
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.581510 Loss1: 2.478467 Loss2: 0.103042
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.501657 Loss1: 2.398222 Loss2: 0.103435
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.441542 Loss1: 2.338488 Loss2: 0.103054
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.363660 Loss1: 2.258737 Loss2: 0.104923
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.315893 Loss1: 2.209484 Loss2: 0.106408
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.222165 Loss1: 2.115981 Loss2: 0.106184
-(DefaultActor pid=1838052) >> Training accuracy: 0.459256
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.999583 Loss1: 2.949495 Loss2: 0.050088
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.776681 Loss1: 2.730141 Loss2: 0.046539
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.703977 Loss1: 2.656840 Loss2: 0.047137
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.601425 Loss1: 2.554533 Loss2: 0.046892
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.563780 Loss1: 2.517054 Loss2: 0.046725
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.488050 Loss1: 2.439933 Loss2: 0.048117
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.450278 Loss1: 2.401121 Loss2: 0.049156
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.399282 Loss1: 2.348249 Loss2: 0.051034
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.331828 Loss1: 2.281056 Loss2: 0.050772
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.283067 Loss1: 2.231295 Loss2: 0.051772
-(DefaultActor pid=1838052) >> Training accuracy: 0.430973
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.989596 Loss1: 2.938972 Loss2: 0.050624
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.717162 Loss1: 2.671275 Loss2: 0.045887
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.610293 Loss1: 2.565772 Loss2: 0.044521
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.548616 Loss1: 2.503526 Loss2: 0.045090
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.476767 Loss1: 2.431182 Loss2: 0.045585
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.435620 Loss1: 2.389084 Loss2: 0.046535
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.356208 Loss1: 2.308446 Loss2: 0.047762
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.303738 Loss1: 2.254503 Loss2: 0.049235
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.274659 Loss1: 2.225275 Loss2: 0.049384
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.187924 Loss1: 2.138006 Loss2: 0.049918
-(DefaultActor pid=1838052) >> Training accuracy: 0.471706
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 08:55:01,857][flwr][DEBUG] - fit_round 5 received 10 results and 0 failures
->> Test accuracy: 0.185100
-[2023-09-27 08:55:45,398][flwr][INFO] - fit progress: (5, 3.436301054665075, {'accuracy': 0.1851}, 9368.288240125403)
-[2023-09-27 08:55:45,399][flwr][DEBUG] - evaluate_round 5: strategy sampled 10 clients (out of 10)
-[2023-09-27 08:56:24,856][flwr][DEBUG] - evaluate_round 5 received 10 results and 0 failures
-[2023-09-27 08:56:24,864][flwr][DEBUG] - fit_round 6: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.751836 Loss1: 2.701415 Loss2: 0.050422
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.479313 Loss1: 2.430408 Loss2: 0.048905
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.431509 Loss1: 2.382269 Loss2: 0.049240
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.311491 Loss1: 2.262098 Loss2: 0.049393
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.262790 Loss1: 2.212745 Loss2: 0.050045
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.183130 Loss1: 2.132272 Loss2: 0.050859
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.146769 Loss1: 2.093164 Loss2: 0.053604
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.070664 Loss1: 2.017400 Loss2: 0.053264
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.071346 Loss1: 2.016372 Loss2: 0.054974
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.965831 Loss1: 1.910385 Loss2: 0.055447
-(DefaultActor pid=1838052) >> Training accuracy: 0.487935
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.155894 Loss1: 2.614803 Loss2: 0.541090
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.936520 Loss1: 2.429278 Loss2: 0.507242
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.854657 Loss1: 2.363557 Loss2: 0.491100
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.758776 Loss1: 2.275993 Loss2: 0.482783
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.711209 Loss1: 2.237653 Loss2: 0.473556
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.635475 Loss1: 2.163789 Loss2: 0.471686
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.563616 Loss1: 2.094371 Loss2: 0.469245
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.476910 Loss1: 2.011973 Loss2: 0.464937
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.424913 Loss1: 1.962074 Loss2: 0.462839
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.367295 Loss1: 1.905642 Loss2: 0.461652
-(DefaultActor pid=1838052) >> Training accuracy: 0.477453
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.089129 Loss1: 2.525116 Loss2: 0.564013
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.881016 Loss1: 2.343470 Loss2: 0.537546
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.773552 Loss1: 2.252192 Loss2: 0.521360
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.749501 Loss1: 2.236806 Loss2: 0.512695
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.625130 Loss1: 2.119113 Loss2: 0.506016
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.563063 Loss1: 2.062221 Loss2: 0.500842
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.494423 Loss1: 1.995556 Loss2: 0.498867
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.431896 Loss1: 1.933219 Loss2: 0.498677
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.374054 Loss1: 1.878336 Loss2: 0.495718
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.327299 Loss1: 1.832026 Loss2: 0.495273
-(DefaultActor pid=1838052) >> Training accuracy: 0.515024
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.688836 Loss1: 2.646750 Loss2: 0.042087
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.458496 Loss1: 2.416032 Loss2: 0.042463
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.338702 Loss1: 2.296307 Loss2: 0.042395
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.275615 Loss1: 2.232534 Loss2: 0.043082
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.201506 Loss1: 2.156234 Loss2: 0.045272
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.126003 Loss1: 2.080944 Loss2: 0.045059
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.078231 Loss1: 2.031971 Loss2: 0.046260
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.973676 Loss1: 1.926965 Loss2: 0.046711
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.944341 Loss1: 1.897105 Loss2: 0.047236
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.864110 Loss1: 1.815267 Loss2: 0.048844
-(DefaultActor pid=1838052) >> Training accuracy: 0.527097
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.915674 Loss1: 2.775380 Loss2: 0.140293
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.678610 Loss1: 2.552722 Loss2: 0.125888
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.587836 Loss1: 2.469362 Loss2: 0.118473
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.504990 Loss1: 2.390158 Loss2: 0.114832
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.436021 Loss1: 2.322543 Loss2: 0.113478
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.393003 Loss1: 2.281694 Loss2: 0.111309
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.308569 Loss1: 2.197315 Loss2: 0.111255
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.219614 Loss1: 2.107673 Loss2: 0.111940
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.161694 Loss1: 2.048745 Loss2: 0.112949
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.110654 Loss1: 1.997354 Loss2: 0.113301
-(DefaultActor pid=1838052) >> Training accuracy: 0.475329
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.271192 Loss1: 2.706923 Loss2: 0.564269
-(DefaultActor pid=1838052) Epoch: 1 Loss: 3.057707 Loss1: 2.520843 Loss2: 0.536865
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.949954 Loss1: 2.427119 Loss2: 0.522836
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.865103 Loss1: 2.354588 Loss2: 0.510515
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.785164 Loss1: 2.280804 Loss2: 0.504360
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.737701 Loss1: 2.238102 Loss2: 0.499598
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.649067 Loss1: 2.152446 Loss2: 0.496620
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.610668 Loss1: 2.115667 Loss2: 0.495001
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.544845 Loss1: 2.053600 Loss2: 0.491244
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.489181 Loss1: 2.000086 Loss2: 0.489094
-(DefaultActor pid=1838052) >> Training accuracy: 0.468750
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.655289 Loss1: 2.612728 Loss2: 0.042561
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.402221 Loss1: 2.360754 Loss2: 0.041467
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.352630 Loss1: 2.310891 Loss2: 0.041739
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.286953 Loss1: 2.245093 Loss2: 0.041860
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.187729 Loss1: 2.145578 Loss2: 0.042151
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.102283 Loss1: 2.059197 Loss2: 0.043086
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.056768 Loss1: 2.013463 Loss2: 0.043304
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.011705 Loss1: 1.966885 Loss2: 0.044819
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.928871 Loss1: 1.884365 Loss2: 0.044506
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.879843 Loss1: 1.834284 Loss2: 0.045559
-(DefaultActor pid=1838052) >> Training accuracy: 0.484164
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.716554 Loss1: 2.675632 Loss2: 0.040922
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.485425 Loss1: 2.444461 Loss2: 0.040964
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.417044 Loss1: 2.376554 Loss2: 0.040490
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.310046 Loss1: 2.269425 Loss2: 0.040620
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.257078 Loss1: 2.215514 Loss2: 0.041564
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.169135 Loss1: 2.126211 Loss2: 0.042925
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.112179 Loss1: 2.068664 Loss2: 0.043515
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.037349 Loss1: 1.991942 Loss2: 0.045408
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.022126 Loss1: 1.976244 Loss2: 0.045882
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.964382 Loss1: 1.917693 Loss2: 0.046688
-(DefaultActor pid=1838052) >> Training accuracy: 0.520174
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.640774 Loss1: 2.598306 Loss2: 0.042467
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.394114 Loss1: 2.351851 Loss2: 0.042263
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.327695 Loss1: 2.285256 Loss2: 0.042439
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.233984 Loss1: 2.191664 Loss2: 0.042320
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.182790 Loss1: 2.138782 Loss2: 0.044007
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.066764 Loss1: 2.022676 Loss2: 0.044088
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.030041 Loss1: 1.984802 Loss2: 0.045239
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.945564 Loss1: 1.899846 Loss2: 0.045718
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.914925 Loss1: 1.867621 Loss2: 0.047304
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.850702 Loss1: 1.802917 Loss2: 0.047786
-(DefaultActor pid=1838052) >> Training accuracy: 0.510200
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.702155 Loss1: 2.657159 Loss2: 0.044996
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.469334 Loss1: 2.425178 Loss2: 0.044156
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.374899 Loss1: 2.330835 Loss2: 0.044064
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.311232 Loss1: 2.265749 Loss2: 0.045483
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.233758 Loss1: 2.186783 Loss2: 0.046975
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.170333 Loss1: 2.122493 Loss2: 0.047840
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.129544 Loss1: 2.080849 Loss2: 0.048694
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.047190 Loss1: 1.997600 Loss2: 0.049590
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.965713 Loss1: 1.916071 Loss2: 0.049642
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.935802 Loss1: 1.884039 Loss2: 0.051763
-(DefaultActor pid=1838052) >> Training accuracy: 0.484756
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 09:26:44,172][flwr][DEBUG] - fit_round 6 received 10 results and 0 failures
->> Test accuracy: 0.265600
-[2023-09-27 09:27:27,904][flwr][INFO] - fit progress: (6, 2.9990923823639988, {'accuracy': 0.2656}, 11270.794376714155)
-[2023-09-27 09:27:27,905][flwr][DEBUG] - evaluate_round 6: strategy sampled 10 clients (out of 10)
-[2023-09-27 09:28:06,801][flwr][DEBUG] - evaluate_round 6 received 10 results and 0 failures
-[2023-09-27 09:28:06,802][flwr][DEBUG] - fit_round 7: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 3.038368 Loss1: 2.562169 Loss2: 0.476199
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.746011 Loss1: 2.317340 Loss2: 0.428671
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.675241 Loss1: 2.259863 Loss2: 0.415379
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.534589 Loss1: 2.128173 Loss2: 0.406416
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.485553 Loss1: 2.086853 Loss2: 0.398700
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.395840 Loss1: 1.996251 Loss2: 0.399589
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.302802 Loss1: 1.904951 Loss2: 0.397851
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.240049 Loss1: 1.845210 Loss2: 0.394839
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.199316 Loss1: 1.804891 Loss2: 0.394426
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.124580 Loss1: 1.730497 Loss2: 0.394083
-(DefaultActor pid=1838052) >> Training accuracy: 0.549753
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.888956 Loss1: 2.357722 Loss2: 0.531234
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.663728 Loss1: 2.152318 Loss2: 0.511410
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.542977 Loss1: 2.049075 Loss2: 0.493902
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.427323 Loss1: 1.940012 Loss2: 0.487311
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.337680 Loss1: 1.858943 Loss2: 0.478737
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.261514 Loss1: 1.784681 Loss2: 0.476833
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.190657 Loss1: 1.718342 Loss2: 0.472315
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.132809 Loss1: 1.664659 Loss2: 0.468150
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.052751 Loss1: 1.586620 Loss2: 0.466131
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.034692 Loss1: 1.569798 Loss2: 0.464894
-(DefaultActor pid=1838052) >> Training accuracy: 0.573134
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.437739 Loss1: 2.390755 Loss2: 0.046984
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.202863 Loss1: 2.155630 Loss2: 0.047233
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.101550 Loss1: 2.054831 Loss2: 0.046719
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.041015 Loss1: 1.993579 Loss2: 0.047435
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.975215 Loss1: 1.927540 Loss2: 0.047675
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.891276 Loss1: 1.843435 Loss2: 0.047841
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.818787 Loss1: 1.770216 Loss2: 0.048571
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.765475 Loss1: 1.715332 Loss2: 0.050143
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.701571 Loss1: 1.650092 Loss2: 0.051479
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.656374 Loss1: 1.605174 Loss2: 0.051200
-(DefaultActor pid=1838052) >> Training accuracy: 0.562896
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.945608 Loss1: 2.411046 Loss2: 0.534562
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.695073 Loss1: 2.191803 Loss2: 0.503270
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.587890 Loss1: 2.106691 Loss2: 0.481198
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.501471 Loss1: 2.031273 Loss2: 0.470198
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.414115 Loss1: 1.948835 Loss2: 0.465280
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.332738 Loss1: 1.874193 Loss2: 0.458545
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.271809 Loss1: 1.819450 Loss2: 0.452359
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.240149 Loss1: 1.788653 Loss2: 0.451496
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.149713 Loss1: 1.702144 Loss2: 0.447569
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.105232 Loss1: 1.659092 Loss2: 0.446140
-(DefaultActor pid=1838052) >> Training accuracy: 0.530854
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.928966 Loss1: 2.397249 Loss2: 0.531717
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.651359 Loss1: 2.145323 Loss2: 0.506036
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.541983 Loss1: 2.057517 Loss2: 0.484466
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.455260 Loss1: 1.982334 Loss2: 0.472926
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.377887 Loss1: 1.910885 Loss2: 0.467002
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.316031 Loss1: 1.853550 Loss2: 0.462481
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.277933 Loss1: 1.816566 Loss2: 0.461367
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.192708 Loss1: 1.737410 Loss2: 0.455299
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.125546 Loss1: 1.673634 Loss2: 0.451911
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.064328 Loss1: 1.610872 Loss2: 0.453456
-(DefaultActor pid=1838052) >> Training accuracy: 0.587416
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.507867 Loss1: 2.462027 Loss2: 0.045841
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.297519 Loss1: 2.250985 Loss2: 0.046534
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.211698 Loss1: 2.164862 Loss2: 0.046836
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.097358 Loss1: 2.050977 Loss2: 0.046381
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.084012 Loss1: 2.036266 Loss2: 0.047746
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.983402 Loss1: 1.934274 Loss2: 0.049128
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.905383 Loss1: 1.856372 Loss2: 0.049011
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.863379 Loss1: 1.813138 Loss2: 0.050241
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.780735 Loss1: 1.729572 Loss2: 0.051164
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.718726 Loss1: 1.666463 Loss2: 0.052263
-(DefaultActor pid=1838052) >> Training accuracy: 0.554688
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.901962 Loss1: 2.363081 Loss2: 0.538881
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.677309 Loss1: 2.163601 Loss2: 0.513708
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.572973 Loss1: 2.072346 Loss2: 0.500626
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.439842 Loss1: 1.951545 Loss2: 0.488297
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.378833 Loss1: 1.896198 Loss2: 0.482635
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.300539 Loss1: 1.821762 Loss2: 0.478778
-(DefaultActor pid=1838052) Epoch: 6 Loss: 2.233892 Loss1: 1.759312 Loss2: 0.474581
-(DefaultActor pid=1838052) Epoch: 7 Loss: 2.185474 Loss1: 1.710874 Loss2: 0.474600
-(DefaultActor pid=1838052) Epoch: 8 Loss: 2.128373 Loss1: 1.655531 Loss2: 0.472843
-(DefaultActor pid=1838052) Epoch: 9 Loss: 2.048312 Loss1: 1.583787 Loss2: 0.464524
-(DefaultActor pid=1838052) >> Training accuracy: 0.565665
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.320779 Loss1: 2.275763 Loss2: 0.045016
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.100775 Loss1: 2.055190 Loss2: 0.045585
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.014105 Loss1: 1.968281 Loss2: 0.045824
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.910302 Loss1: 1.864589 Loss2: 0.045712
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.851738 Loss1: 1.804991 Loss2: 0.046747
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.808684 Loss1: 1.762045 Loss2: 0.046639
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.773843 Loss1: 1.725070 Loss2: 0.048772
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.689813 Loss1: 1.640815 Loss2: 0.048997
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.668106 Loss1: 1.618305 Loss2: 0.049801
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.557219 Loss1: 1.507442 Loss2: 0.049777
-(DefaultActor pid=1838052) >> Training accuracy: 0.594151
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.496745 Loss1: 2.449957 Loss2: 0.046788
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.240896 Loss1: 2.193993 Loss2: 0.046902
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.139938 Loss1: 2.094332 Loss2: 0.045606
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.048838 Loss1: 2.002012 Loss2: 0.046826
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.011080 Loss1: 1.963032 Loss2: 0.048048
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.907839 Loss1: 1.860163 Loss2: 0.047676
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.823823 Loss1: 1.775656 Loss2: 0.048167
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.796489 Loss1: 1.746802 Loss2: 0.049687
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.736592 Loss1: 1.686067 Loss2: 0.050525
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.672962 Loss1: 1.621122 Loss2: 0.051840
-(DefaultActor pid=1838052) >> Training accuracy: 0.569027
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.426663 Loss1: 2.384081 Loss2: 0.042582
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.195642 Loss1: 2.152860 Loss2: 0.042782
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.124190 Loss1: 2.080782 Loss2: 0.043408
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.020221 Loss1: 1.976717 Loss2: 0.043504
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.967469 Loss1: 1.921816 Loss2: 0.045653
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.906630 Loss1: 1.860995 Loss2: 0.045635
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.816724 Loss1: 1.771206 Loss2: 0.045518
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.738499 Loss1: 1.691252 Loss2: 0.047246
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.703427 Loss1: 1.655645 Loss2: 0.047782
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.624083 Loss1: 1.576412 Loss2: 0.047671
-(DefaultActor pid=1838052) >> Training accuracy: 0.596037
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 10:06:13,486][flwr][DEBUG] - fit_round 7 received 10 results and 0 failures
->> Test accuracy: 0.327600
-[2023-09-27 10:13:56,312][flwr][INFO] - fit progress: (7, 2.694728255652772, {'accuracy': 0.3276}, 14059.20233049104)
-[2023-09-27 10:13:56,313][flwr][DEBUG] - evaluate_round 7: strategy sampled 10 clients (out of 10)
-[2023-09-27 10:14:41,267][flwr][DEBUG] - evaluate_round 7 received 10 results and 0 failures
-[2023-09-27 10:14:41,268][flwr][DEBUG] - fit_round 8: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.709258 Loss1: 2.193764 Loss2: 0.515494
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.450222 Loss1: 1.985959 Loss2: 0.464263
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.308524 Loss1: 1.860867 Loss2: 0.447658
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.224968 Loss1: 1.790743 Loss2: 0.434225
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.125124 Loss1: 1.697002 Loss2: 0.428122
-(DefaultActor pid=1838052) Epoch: 5 Loss: 2.040160 Loss1: 1.615971 Loss2: 0.424189
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.955509 Loss1: 1.532119 Loss2: 0.423391
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.929111 Loss1: 1.509179 Loss2: 0.419932
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.880534 Loss1: 1.460954 Loss2: 0.419580
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.817624 Loss1: 1.396569 Loss2: 0.421056
-(DefaultActor pid=1838052) >> Training accuracy: 0.621570
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.105405 Loss1: 2.058321 Loss2: 0.047084
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.905677 Loss1: 1.858115 Loss2: 0.047562
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.784470 Loss1: 1.736776 Loss2: 0.047694
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.713247 Loss1: 1.665482 Loss2: 0.047765
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.645971 Loss1: 1.597550 Loss2: 0.048421
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.559744 Loss1: 1.511669 Loss2: 0.048075
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.501264 Loss1: 1.451665 Loss2: 0.049599
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.445245 Loss1: 1.395123 Loss2: 0.050122
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.416307 Loss1: 1.365615 Loss2: 0.050692
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.376832 Loss1: 1.324568 Loss2: 0.052264
-(DefaultActor pid=1838052) >> Training accuracy: 0.654046
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.538070 Loss1: 2.184997 Loss2: 0.353073
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.293320 Loss1: 1.991480 Loss2: 0.301840
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.198845 Loss1: 1.904147 Loss2: 0.294698
-(DefaultActor pid=1838052) Epoch: 3 Loss: 2.088673 Loss1: 1.795117 Loss2: 0.293556
-(DefaultActor pid=1838052) Epoch: 4 Loss: 2.041745 Loss1: 1.752587 Loss2: 0.289158
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.933712 Loss1: 1.643607 Loss2: 0.290105
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.857243 Loss1: 1.566921 Loss2: 0.290322
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.790275 Loss1: 1.499669 Loss2: 0.290606
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.767002 Loss1: 1.477334 Loss2: 0.289667
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.670968 Loss1: 1.382848 Loss2: 0.288120
-(DefaultActor pid=1838052) >> Training accuracy: 0.609771
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.229676 Loss1: 2.180633 Loss2: 0.049043
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.999429 Loss1: 1.950458 Loss2: 0.048971
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.879991 Loss1: 1.832707 Loss2: 0.047284
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.788700 Loss1: 1.739867 Loss2: 0.048833
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.737861 Loss1: 1.688916 Loss2: 0.048946
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.653562 Loss1: 1.603552 Loss2: 0.050010
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.594129 Loss1: 1.543742 Loss2: 0.050388
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.529029 Loss1: 1.478005 Loss2: 0.051024
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.466471 Loss1: 1.415306 Loss2: 0.051165
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.427690 Loss1: 1.374947 Loss2: 0.052743
-(DefaultActor pid=1838052) >> Training accuracy: 0.622627
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.324779 Loss1: 2.273377 Loss2: 0.051402
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.085034 Loss1: 2.034113 Loss2: 0.050921
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.960676 Loss1: 1.911133 Loss2: 0.049543
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.878204 Loss1: 1.828605 Loss2: 0.049598
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.806229 Loss1: 1.755470 Loss2: 0.050759
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.752396 Loss1: 1.701374 Loss2: 0.051023
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.641017 Loss1: 1.589424 Loss2: 0.051593
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.616418 Loss1: 1.563836 Loss2: 0.052583
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.536063 Loss1: 1.482626 Loss2: 0.053438
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.484237 Loss1: 1.429649 Loss2: 0.054588
-(DefaultActor pid=1838052) >> Training accuracy: 0.627404
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.212654 Loss1: 2.161069 Loss2: 0.051585
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.970997 Loss1: 1.921090 Loss2: 0.049906
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.848390 Loss1: 1.798299 Loss2: 0.050091
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.776417 Loss1: 1.727323 Loss2: 0.049094
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.662752 Loss1: 1.614086 Loss2: 0.048666
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.643215 Loss1: 1.592441 Loss2: 0.050774
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.562824 Loss1: 1.512461 Loss2: 0.050364
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.519930 Loss1: 1.467670 Loss2: 0.052261
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.467383 Loss1: 1.415635 Loss2: 0.051748
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.416752 Loss1: 1.364691 Loss2: 0.052061
-(DefaultActor pid=1838052) >> Training accuracy: 0.605574
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.287485 Loss1: 2.192550 Loss2: 0.094935
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.080599 Loss1: 1.992231 Loss2: 0.088367
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.927901 Loss1: 1.844398 Loss2: 0.083503
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.848616 Loss1: 1.766995 Loss2: 0.081621
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.752736 Loss1: 1.673596 Loss2: 0.079140
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.666515 Loss1: 1.587857 Loss2: 0.078659
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.639753 Loss1: 1.560818 Loss2: 0.078935
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.596466 Loss1: 1.517104 Loss2: 0.079362
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.473657 Loss1: 1.394624 Loss2: 0.079033
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.442232 Loss1: 1.362043 Loss2: 0.080189
-(DefaultActor pid=1838052) >> Training accuracy: 0.628362
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.436166 Loss1: 2.382002 Loss2: 0.054165
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.124762 Loss1: 2.071675 Loss2: 0.053087
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.021695 Loss1: 1.968917 Loss2: 0.052779
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.889169 Loss1: 1.836666 Loss2: 0.052503
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.841533 Loss1: 1.788870 Loss2: 0.052664
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.781700 Loss1: 1.728068 Loss2: 0.053631
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.711755 Loss1: 1.657249 Loss2: 0.054506
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.617402 Loss1: 1.563040 Loss2: 0.054362
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.595740 Loss1: 1.539876 Loss2: 0.055864
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.565929 Loss1: 1.509126 Loss2: 0.056802
-(DefaultActor pid=1838052) >> Training accuracy: 0.578125
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.191149 Loss1: 2.140179 Loss2: 0.050971
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.984707 Loss1: 1.932545 Loss2: 0.052162
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.852725 Loss1: 1.801735 Loss2: 0.050989
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.796879 Loss1: 1.745036 Loss2: 0.051843
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.691202 Loss1: 1.639753 Loss2: 0.051449
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.613612 Loss1: 1.561049 Loss2: 0.052562
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.614165 Loss1: 1.561582 Loss2: 0.052583
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.494153 Loss1: 1.441415 Loss2: 0.052738
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.457285 Loss1: 1.402657 Loss2: 0.054628
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.398477 Loss1: 1.344362 Loss2: 0.054115
-(DefaultActor pid=1838052) >> Training accuracy: 0.618078
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.197662 Loss1: 2.145249 Loss2: 0.052413
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.952747 Loss1: 1.899454 Loss2: 0.053292
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.823356 Loss1: 1.771653 Loss2: 0.051703
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.720134 Loss1: 1.668449 Loss2: 0.051684
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.633180 Loss1: 1.580959 Loss2: 0.052222
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.594015 Loss1: 1.542053 Loss2: 0.051962
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.546247 Loss1: 1.492758 Loss2: 0.053489
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.460070 Loss1: 1.407149 Loss2: 0.052921
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.414514 Loss1: 1.360665 Loss2: 0.053849
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.344694 Loss1: 1.290703 Loss2: 0.053991
-(DefaultActor pid=1838052) >> Training accuracy: 0.652561
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 10:44:40,445][flwr][DEBUG] - fit_round 8 received 10 results and 0 failures
->> Test accuracy: 0.363100
-[2023-09-27 10:45:21,070][flwr][INFO] - fit progress: (8, 2.53471215883383, {'accuracy': 0.3631}, 15943.95996950008)
-[2023-09-27 10:45:21,070][flwr][DEBUG] - evaluate_round 8: strategy sampled 10 clients (out of 10)
-[2023-09-27 10:45:57,406][flwr][DEBUG] - evaluate_round 8 received 10 results and 0 failures
-[2023-09-27 10:45:57,408][flwr][DEBUG] - fit_round 9: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.483164 Loss1: 2.022059 Loss2: 0.461105
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.174246 Loss1: 1.761471 Loss2: 0.412776
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.076548 Loss1: 1.673937 Loss2: 0.402611
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.992947 Loss1: 1.595190 Loss2: 0.397757
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.920922 Loss1: 1.522376 Loss2: 0.398546
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.836413 Loss1: 1.444410 Loss2: 0.392002
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.757734 Loss1: 1.361894 Loss2: 0.395841
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.714487 Loss1: 1.321761 Loss2: 0.392726
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.692480 Loss1: 1.296318 Loss2: 0.396162
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.640328 Loss1: 1.243931 Loss2: 0.396396
-(DefaultActor pid=1838052) >> Training accuracy: 0.673457
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.291617 Loss1: 1.996569 Loss2: 0.295049
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.013461 Loss1: 1.759975 Loss2: 0.253486
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.893522 Loss1: 1.643657 Loss2: 0.249864
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.797450 Loss1: 1.547480 Loss2: 0.249970
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.730625 Loss1: 1.481469 Loss2: 0.249156
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.666812 Loss1: 1.416942 Loss2: 0.249870
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.581224 Loss1: 1.333046 Loss2: 0.248178
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.536576 Loss1: 1.286189 Loss2: 0.250387
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.498855 Loss1: 1.249098 Loss2: 0.249756
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.450144 Loss1: 1.199807 Loss2: 0.250336
-(DefaultActor pid=1838052) >> Training accuracy: 0.703125
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.032739 Loss1: 1.987099 Loss2: 0.045640
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.792446 Loss1: 1.745455 Loss2: 0.046992
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.694991 Loss1: 1.647607 Loss2: 0.047384
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.588681 Loss1: 1.541860 Loss2: 0.046821
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.514134 Loss1: 1.466740 Loss2: 0.047393
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.462381 Loss1: 1.414918 Loss2: 0.047463
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.409842 Loss1: 1.360360 Loss2: 0.049482
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.321396 Loss1: 1.272115 Loss2: 0.049281
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.265217 Loss1: 1.215398 Loss2: 0.049819
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.240747 Loss1: 1.190683 Loss2: 0.050064
-(DefaultActor pid=1838052) >> Training accuracy: 0.687302
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.073298 Loss1: 2.015616 Loss2: 0.057683
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.804739 Loss1: 1.747477 Loss2: 0.057262
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.725224 Loss1: 1.667854 Loss2: 0.057370
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.625716 Loss1: 1.568936 Loss2: 0.056780
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.579494 Loss1: 1.522323 Loss2: 0.057171
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.491105 Loss1: 1.434163 Loss2: 0.056941
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.426014 Loss1: 1.368936 Loss2: 0.057078
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.361619 Loss1: 1.304072 Loss2: 0.057547
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.303245 Loss1: 1.245014 Loss2: 0.058231
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.270276 Loss1: 1.210905 Loss2: 0.059371
-(DefaultActor pid=1838052) >> Training accuracy: 0.645174
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.505409 Loss1: 1.971813 Loss2: 0.533596
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.219221 Loss1: 1.716395 Loss2: 0.502826
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.062575 Loss1: 1.579922 Loss2: 0.482653
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.964055 Loss1: 1.492236 Loss2: 0.471818
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.905229 Loss1: 1.438894 Loss2: 0.466335
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.800004 Loss1: 1.339459 Loss2: 0.460545
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.779953 Loss1: 1.324905 Loss2: 0.455048
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.650619 Loss1: 1.198204 Loss2: 0.452415
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.674609 Loss1: 1.220895 Loss2: 0.453714
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.565232 Loss1: 1.110312 Loss2: 0.454920
-(DefaultActor pid=1838052) >> Training accuracy: 0.669705
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.044502 Loss1: 1.949320 Loss2: 0.095183
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.821164 Loss1: 1.732977 Loss2: 0.088187
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.713148 Loss1: 1.627787 Loss2: 0.085361
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.583881 Loss1: 1.501918 Loss2: 0.081963
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.563371 Loss1: 1.481926 Loss2: 0.081445
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.434184 Loss1: 1.353921 Loss2: 0.080263
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.435732 Loss1: 1.354814 Loss2: 0.080918
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.330903 Loss1: 1.250673 Loss2: 0.080230
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.273577 Loss1: 1.192867 Loss2: 0.080710
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.231068 Loss1: 1.150285 Loss2: 0.080784
-(DefaultActor pid=1838052) >> Training accuracy: 0.710047
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.207649 Loss1: 2.160165 Loss2: 0.047484
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.940629 Loss1: 1.893360 Loss2: 0.047270
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.800931 Loss1: 1.754174 Loss2: 0.046757
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.718512 Loss1: 1.671113 Loss2: 0.047400
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.630826 Loss1: 1.582458 Loss2: 0.048368
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.571100 Loss1: 1.523248 Loss2: 0.047852
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.491830 Loss1: 1.441916 Loss2: 0.049914
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.426030 Loss1: 1.376627 Loss2: 0.049403
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.380751 Loss1: 1.330678 Loss2: 0.050072
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.318328 Loss1: 1.267029 Loss2: 0.051299
-(DefaultActor pid=1838052) >> Training accuracy: 0.646587
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.017901 Loss1: 1.972448 Loss2: 0.045454
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.761279 Loss1: 1.715932 Loss2: 0.045347
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.638405 Loss1: 1.593540 Loss2: 0.044865
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.575547 Loss1: 1.529818 Loss2: 0.045729
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.501544 Loss1: 1.455872 Loss2: 0.045672
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.432503 Loss1: 1.385774 Loss2: 0.046729
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.367846 Loss1: 1.319872 Loss2: 0.047974
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.293503 Loss1: 1.245790 Loss2: 0.047713
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.260436 Loss1: 1.211115 Loss2: 0.049321
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.217975 Loss1: 1.168225 Loss2: 0.049750
-(DefaultActor pid=1838052) >> Training accuracy: 0.685600
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.935895 Loss1: 1.892041 Loss2: 0.043854
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.689612 Loss1: 1.644613 Loss2: 0.044999
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.585124 Loss1: 1.539809 Loss2: 0.045315
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.498261 Loss1: 1.453278 Loss2: 0.044983
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.438307 Loss1: 1.392096 Loss2: 0.046211
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.349123 Loss1: 1.301974 Loss2: 0.047149
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.300648 Loss1: 1.253797 Loss2: 0.046850
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.251814 Loss1: 1.203828 Loss2: 0.047986
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.180361 Loss1: 1.132411 Loss2: 0.047950
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.146891 Loss1: 1.097750 Loss2: 0.049141
-(DefaultActor pid=1838052) >> Training accuracy: 0.723558
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.112665 Loss1: 2.065152 Loss2: 0.047514
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.873670 Loss1: 1.826792 Loss2: 0.046877
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.739122 Loss1: 1.691739 Loss2: 0.047383
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.666848 Loss1: 1.618717 Loss2: 0.048130
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.558942 Loss1: 1.511299 Loss2: 0.047643
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.493940 Loss1: 1.445721 Loss2: 0.048218
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.425809 Loss1: 1.377186 Loss2: 0.048622
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.391005 Loss1: 1.341315 Loss2: 0.049690
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.319018 Loss1: 1.268520 Loss2: 0.050498
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.264777 Loss1: 1.213900 Loss2: 0.050876
-(DefaultActor pid=1838052) >> Training accuracy: 0.655048
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 11:15:44,745][flwr][DEBUG] - fit_round 9 received 10 results and 0 failures
->> Test accuracy: 0.402800
-[2023-09-27 11:16:27,246][flwr][INFO] - fit progress: (9, 2.3920893143541133, {'accuracy': 0.4028}, 17810.13637669524)
-[2023-09-27 11:16:27,247][flwr][DEBUG] - evaluate_round 9: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:17:06,115][flwr][DEBUG] - evaluate_round 9 received 10 results and 0 failures
-[2023-09-27 11:17:06,116][flwr][DEBUG] - fit_round 10: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.862358 Loss1: 1.811308 Loss2: 0.051051
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.528035 Loss1: 1.477194 Loss2: 0.050841
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.440354 Loss1: 1.389661 Loss2: 0.050694
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.341792 Loss1: 1.291691 Loss2: 0.050102
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.243090 Loss1: 1.193546 Loss2: 0.049543
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.248569 Loss1: 1.197097 Loss2: 0.051472
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.125267 Loss1: 1.074977 Loss2: 0.050290
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.122117 Loss1: 1.070431 Loss2: 0.051685
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.043366 Loss1: 0.991470 Loss2: 0.051896
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.992677 Loss1: 0.940407 Loss2: 0.052271
-(DefaultActor pid=1838052) >> Training accuracy: 0.747613
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.435301 Loss1: 1.877486 Loss2: 0.557815
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.133092 Loss1: 1.596880 Loss2: 0.536212
-(DefaultActor pid=1838052) Epoch: 2 Loss: 2.011163 Loss1: 1.492553 Loss2: 0.518610
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.893599 Loss1: 1.384484 Loss2: 0.509115
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.823359 Loss1: 1.321671 Loss2: 0.501688
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.721987 Loss1: 1.226574 Loss2: 0.495413
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.658681 Loss1: 1.162960 Loss2: 0.495721
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.612958 Loss1: 1.119015 Loss2: 0.493943
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.580621 Loss1: 1.087977 Loss2: 0.492644
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.490550 Loss1: 1.001240 Loss2: 0.489311
-(DefaultActor pid=1838052) >> Training accuracy: 0.728463
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.283596 Loss1: 1.721090 Loss2: 0.562506
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.059127 Loss1: 1.508027 Loss2: 0.551100
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.985261 Loss1: 1.446468 Loss2: 0.538793
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.875150 Loss1: 1.346869 Loss2: 0.528281
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.777953 Loss1: 1.254683 Loss2: 0.523271
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.708697 Loss1: 1.192789 Loss2: 0.515908
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.630432 Loss1: 1.117521 Loss2: 0.512911
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.608310 Loss1: 1.096894 Loss2: 0.511416
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.560256 Loss1: 1.052741 Loss2: 0.507514
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.485672 Loss1: 0.981775 Loss2: 0.503898
-(DefaultActor pid=1838052) >> Training accuracy: 0.761018
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.827917 Loss1: 1.782943 Loss2: 0.044974
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.598348 Loss1: 1.552477 Loss2: 0.045871
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.501987 Loss1: 1.456168 Loss2: 0.045819
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.386020 Loss1: 1.339919 Loss2: 0.046101
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.353128 Loss1: 1.305820 Loss2: 0.047308
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.260229 Loss1: 1.212748 Loss2: 0.047481
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.213464 Loss1: 1.165445 Loss2: 0.048019
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.130631 Loss1: 1.081682 Loss2: 0.048949
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.120580 Loss1: 1.070213 Loss2: 0.050366
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.031097 Loss1: 0.981006 Loss2: 0.050091
-(DefaultActor pid=1838052) >> Training accuracy: 0.729230
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.964355 Loss1: 1.918646 Loss2: 0.045709
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.680616 Loss1: 1.634708 Loss2: 0.045908
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.574703 Loss1: 1.528838 Loss2: 0.045865
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.476901 Loss1: 1.430997 Loss2: 0.045905
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.378372 Loss1: 1.331878 Loss2: 0.046494
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.320519 Loss1: 1.274074 Loss2: 0.046445
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.235180 Loss1: 1.187672 Loss2: 0.047509
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.199375 Loss1: 1.151468 Loss2: 0.047906
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.130082 Loss1: 1.081482 Loss2: 0.048600
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.051851 Loss1: 1.003203 Loss2: 0.048648
-(DefaultActor pid=1838052) >> Training accuracy: 0.691506
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.102657 Loss1: 1.986094 Loss2: 0.116563
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.802432 Loss1: 1.693205 Loss2: 0.109227
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.693528 Loss1: 1.589958 Loss2: 0.103570
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.586806 Loss1: 1.486075 Loss2: 0.100731
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.511076 Loss1: 1.411874 Loss2: 0.099202
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.435536 Loss1: 1.339157 Loss2: 0.096379
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.336721 Loss1: 1.240369 Loss2: 0.096352
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.322896 Loss1: 1.227519 Loss2: 0.095377
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.280009 Loss1: 1.184508 Loss2: 0.095501
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.224980 Loss1: 1.128843 Loss2: 0.096138
-(DefaultActor pid=1838052) >> Training accuracy: 0.689556
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.874148 Loss1: 1.827384 Loss2: 0.046764
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.653649 Loss1: 1.606344 Loss2: 0.047305
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.519549 Loss1: 1.473262 Loss2: 0.046288
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.439325 Loss1: 1.391900 Loss2: 0.047424
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.321988 Loss1: 1.274736 Loss2: 0.047252
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.295450 Loss1: 1.247660 Loss2: 0.047791
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.207968 Loss1: 1.160306 Loss2: 0.047662
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.182043 Loss1: 1.132560 Loss2: 0.049483
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.118976 Loss1: 1.068770 Loss2: 0.050206
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.064214 Loss1: 1.013434 Loss2: 0.050780
-(DefaultActor pid=1838052) >> Training accuracy: 0.723695
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.868656 Loss1: 1.826229 Loss2: 0.042426
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.609726 Loss1: 1.566389 Loss2: 0.043337
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.491913 Loss1: 1.448195 Loss2: 0.043718
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.437801 Loss1: 1.393220 Loss2: 0.044581
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.328415 Loss1: 1.284275 Loss2: 0.044139
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.257426 Loss1: 1.212318 Loss2: 0.045108
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.225654 Loss1: 1.180080 Loss2: 0.045574
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.154266 Loss1: 1.108592 Loss2: 0.045673
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.104742 Loss1: 1.058727 Loss2: 0.046016
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.065420 Loss1: 1.018403 Loss2: 0.047016
-(DefaultActor pid=1838052) >> Training accuracy: 0.692445
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.921002 Loss1: 1.874309 Loss2: 0.046693
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.617948 Loss1: 1.570759 Loss2: 0.047189
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.524450 Loss1: 1.477341 Loss2: 0.047108
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.436319 Loss1: 1.389338 Loss2: 0.046980
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.341560 Loss1: 1.294709 Loss2: 0.046851
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.262671 Loss1: 1.214963 Loss2: 0.047708
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.208369 Loss1: 1.159713 Loss2: 0.048656
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.147788 Loss1: 1.099275 Loss2: 0.048512
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.096264 Loss1: 1.047530 Loss2: 0.048734
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.112723 Loss1: 1.061931 Loss2: 0.050792
-(DefaultActor pid=1838052) >> Training accuracy: 0.721519
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.831443 Loss1: 1.785154 Loss2: 0.046289
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.618005 Loss1: 1.570813 Loss2: 0.047193
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.467992 Loss1: 1.420834 Loss2: 0.047157
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.412969 Loss1: 1.365451 Loss2: 0.047518
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.322851 Loss1: 1.275421 Loss2: 0.047430
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.244911 Loss1: 1.196595 Loss2: 0.048316
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.206775 Loss1: 1.157975 Loss2: 0.048800
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.148915 Loss1: 1.099588 Loss2: 0.049327
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.076589 Loss1: 1.027516 Loss2: 0.049073
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.013728 Loss1: 0.964659 Loss2: 0.049069
-(DefaultActor pid=1838052) >> Training accuracy: 0.703916
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 11:46:55,258][flwr][DEBUG] - fit_round 10 received 10 results and 0 failures
->> Test accuracy: 0.432500
-[2023-09-27 11:47:38,282][flwr][INFO] - fit progress: (10, 2.306128243287912, {'accuracy': 0.4325}, 19681.17216801131)
-[2023-09-27 11:47:38,282][flwr][DEBUG] - evaluate_round 10: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:48:15,010][flwr][DEBUG] - evaluate_round 10 received 10 results and 0 failures
-[2023-09-27 11:48:15,011][flwr][DEBUG] - fit_round 11: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.228839 Loss1: 1.690956 Loss2: 0.537883
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.972339 Loss1: 1.449610 Loss2: 0.522729
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.827159 Loss1: 1.322654 Loss2: 0.504505
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.732215 Loss1: 1.235593 Loss2: 0.496622
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.638652 Loss1: 1.147168 Loss2: 0.491484
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.610436 Loss1: 1.126918 Loss2: 0.483518
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.554755 Loss1: 1.071154 Loss2: 0.483601
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.469402 Loss1: 0.990560 Loss2: 0.478842
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.403971 Loss1: 0.927886 Loss2: 0.476085
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.435490 Loss1: 0.960198 Loss2: 0.475292
-(DefaultActor pid=1838052) >> Training accuracy: 0.773932
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.287210 Loss1: 1.733411 Loss2: 0.553799
-(DefaultActor pid=1838052) Epoch: 1 Loss: 2.069189 Loss1: 1.524927 Loss2: 0.544262
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.907554 Loss1: 1.375822 Loss2: 0.531732
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.830269 Loss1: 1.304631 Loss2: 0.525638
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.727497 Loss1: 1.212253 Loss2: 0.515244
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.635794 Loss1: 1.122507 Loss2: 0.513287
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.603246 Loss1: 1.093685 Loss2: 0.509561
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.507037 Loss1: 0.999390 Loss2: 0.507647
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.473524 Loss1: 0.970642 Loss2: 0.502882
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.449859 Loss1: 0.949968 Loss2: 0.499891
-(DefaultActor pid=1838052) >> Training accuracy: 0.729567
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.783118 Loss1: 1.675418 Loss2: 0.107700
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.536141 Loss1: 1.434422 Loss2: 0.101719
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.404730 Loss1: 1.309178 Loss2: 0.095552
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.290162 Loss1: 1.198426 Loss2: 0.091735
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.231024 Loss1: 1.140290 Loss2: 0.090734
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.151973 Loss1: 1.062849 Loss2: 0.089123
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.109323 Loss1: 1.020535 Loss2: 0.088789
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.065294 Loss1: 0.977294 Loss2: 0.088000
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.013230 Loss1: 0.925636 Loss2: 0.087595
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.893710 Loss1: 0.807978 Loss2: 0.085732
-(DefaultActor pid=1838052) >> Training accuracy: 0.783155
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.248558 Loss1: 1.686949 Loss2: 0.561609
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.968134 Loss1: 1.425322 Loss2: 0.542812
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.814263 Loss1: 1.290757 Loss2: 0.523507
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.677921 Loss1: 1.167483 Loss2: 0.510438
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.598105 Loss1: 1.097519 Loss2: 0.500586
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.539657 Loss1: 1.040890 Loss2: 0.498767
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.473407 Loss1: 0.981500 Loss2: 0.491907
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.425616 Loss1: 0.936394 Loss2: 0.489222
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.380020 Loss1: 0.893017 Loss2: 0.487003
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.333665 Loss1: 0.847062 Loss2: 0.486603
-(DefaultActor pid=1838052) >> Training accuracy: 0.766710
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.737535 Loss1: 1.687103 Loss2: 0.050432
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.457572 Loss1: 1.407846 Loss2: 0.049726
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.343201 Loss1: 1.294328 Loss2: 0.048873
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.279532 Loss1: 1.228860 Loss2: 0.050672
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.189242 Loss1: 1.138898 Loss2: 0.050345
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.110622 Loss1: 1.060680 Loss2: 0.049942
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.078308 Loss1: 1.027218 Loss2: 0.051090
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.977282 Loss1: 0.926006 Loss2: 0.051276
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.963781 Loss1: 0.912071 Loss2: 0.051710
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.890512 Loss1: 0.838414 Loss2: 0.052097
-(DefaultActor pid=1838052) >> Training accuracy: 0.762880
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.197270 Loss1: 1.659346 Loss2: 0.537924
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.966318 Loss1: 1.440425 Loss2: 0.525893
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.826330 Loss1: 1.311974 Loss2: 0.514356
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.717596 Loss1: 1.215235 Loss2: 0.502362
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.666870 Loss1: 1.168286 Loss2: 0.498584
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.602386 Loss1: 1.107464 Loss2: 0.494922
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.485532 Loss1: 0.994780 Loss2: 0.490752
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.496632 Loss1: 1.009584 Loss2: 0.487048
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.400894 Loss1: 0.915545 Loss2: 0.485349
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.346589 Loss1: 0.862341 Loss2: 0.484248
-(DefaultActor pid=1838052) >> Training accuracy: 0.746044
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.694693 Loss1: 1.609166 Loss2: 0.085527
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.410883 Loss1: 1.331592 Loss2: 0.079291
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.290418 Loss1: 1.215641 Loss2: 0.074777
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.233516 Loss1: 1.158229 Loss2: 0.075287
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.158469 Loss1: 1.084715 Loss2: 0.073754
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.070362 Loss1: 0.996491 Loss2: 0.073871
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.057596 Loss1: 0.983701 Loss2: 0.073895
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.968375 Loss1: 0.894020 Loss2: 0.074354
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.936037 Loss1: 0.861505 Loss2: 0.074532
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.882501 Loss1: 0.807091 Loss2: 0.075410
-(DefaultActor pid=1838052) >> Training accuracy: 0.783253
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.730202 Loss1: 1.683644 Loss2: 0.046559
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.459240 Loss1: 1.412478 Loss2: 0.046762
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.387769 Loss1: 1.340624 Loss2: 0.047145
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.257527 Loss1: 1.210388 Loss2: 0.047139
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.183747 Loss1: 1.136280 Loss2: 0.047467
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.104424 Loss1: 1.056289 Loss2: 0.048135
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.057739 Loss1: 1.010206 Loss2: 0.047533
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.007800 Loss1: 0.959688 Loss2: 0.048112
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.989570 Loss1: 0.941049 Loss2: 0.048521
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.956839 Loss1: 0.906850 Loss2: 0.049989
-(DefaultActor pid=1838052) >> Training accuracy: 0.722310
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.911975 Loss1: 1.865288 Loss2: 0.046687
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.601775 Loss1: 1.554788 Loss2: 0.046986
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.473948 Loss1: 1.427843 Loss2: 0.046105
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.375845 Loss1: 1.329082 Loss2: 0.046763
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.290734 Loss1: 1.243504 Loss2: 0.047230
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.229256 Loss1: 1.181716 Loss2: 0.047540
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.138686 Loss1: 1.090596 Loss2: 0.048090
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.117314 Loss1: 1.068909 Loss2: 0.048405
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.076808 Loss1: 1.027740 Loss2: 0.049068
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.039699 Loss1: 0.989777 Loss2: 0.049923
-(DefaultActor pid=1838052) >> Training accuracy: 0.709498
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.759646 Loss1: 1.716644 Loss2: 0.043003
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.466085 Loss1: 1.421826 Loss2: 0.044258
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.347210 Loss1: 1.303412 Loss2: 0.043798
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.301649 Loss1: 1.256793 Loss2: 0.044856
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.162983 Loss1: 1.118485 Loss2: 0.044498
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.138937 Loss1: 1.093364 Loss2: 0.045573
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.081846 Loss1: 1.035627 Loss2: 0.046219
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.994340 Loss1: 0.947921 Loss2: 0.046419
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.983943 Loss1: 0.936798 Loss2: 0.047145
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.884830 Loss1: 0.837533 Loss2: 0.047298
-(DefaultActor pid=1838052) >> Training accuracy: 0.734968
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 12:18:06,366][flwr][DEBUG] - fit_round 11 received 10 results and 0 failures
->> Test accuracy: 0.455400
-[2023-09-27 12:29:06,096][flwr][INFO] - fit progress: (11, 2.207922473883096, {'accuracy': 0.4554}, 22168.98662959831)
-[2023-09-27 12:29:06,097][flwr][DEBUG] - evaluate_round 11: strategy sampled 10 clients (out of 10)
-[2023-09-27 12:29:46,033][flwr][DEBUG] - evaluate_round 11 received 10 results and 0 failures
-[2023-09-27 12:29:46,034][flwr][DEBUG] - fit_round 12: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.696502 Loss1: 1.604173 Loss2: 0.092329
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.382078 Loss1: 1.296494 Loss2: 0.085584
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.268286 Loss1: 1.186455 Loss2: 0.081831
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.145832 Loss1: 1.067608 Loss2: 0.078224
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.084431 Loss1: 1.006865 Loss2: 0.077566
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.025428 Loss1: 0.947773 Loss2: 0.077656
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.936653 Loss1: 0.860779 Loss2: 0.075874
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.885741 Loss1: 0.810211 Loss2: 0.075530
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.873348 Loss1: 0.796690 Loss2: 0.076658
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.779120 Loss1: 0.704078 Loss2: 0.075042
-(DefaultActor pid=1838052) >> Training accuracy: 0.767103
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.139194 Loss1: 1.600817 Loss2: 0.538377
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.850734 Loss1: 1.323443 Loss2: 0.527291
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.730281 Loss1: 1.219105 Loss2: 0.511176
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.598443 Loss1: 1.095692 Loss2: 0.502751
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.543539 Loss1: 1.045003 Loss2: 0.498536
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.470764 Loss1: 0.975742 Loss2: 0.495022
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.452400 Loss1: 0.959250 Loss2: 0.493150
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.373787 Loss1: 0.886306 Loss2: 0.487480
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.330127 Loss1: 0.843642 Loss2: 0.486485
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.286330 Loss1: 0.801427 Loss2: 0.484903
-(DefaultActor pid=1838052) >> Training accuracy: 0.785799
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.604226 Loss1: 1.558196 Loss2: 0.046030
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.329578 Loss1: 1.282138 Loss2: 0.047440
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.228815 Loss1: 1.180986 Loss2: 0.047829
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.122086 Loss1: 1.074898 Loss2: 0.047188
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.046588 Loss1: 0.998788 Loss2: 0.047800
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.974297 Loss1: 0.926425 Loss2: 0.047872
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.915838 Loss1: 0.867125 Loss2: 0.048713
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.865167 Loss1: 0.816418 Loss2: 0.048749
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.873625 Loss1: 0.823902 Loss2: 0.049723
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.801558 Loss1: 0.751349 Loss2: 0.050209
-(DefaultActor pid=1838052) >> Training accuracy: 0.801028
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.269507 Loss1: 1.737487 Loss2: 0.532019
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.946939 Loss1: 1.427895 Loss2: 0.519044
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.818711 Loss1: 1.311530 Loss2: 0.507181
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.734302 Loss1: 1.236181 Loss2: 0.498121
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.613647 Loss1: 1.123944 Loss2: 0.489703
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.555663 Loss1: 1.070264 Loss2: 0.485399
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.476205 Loss1: 0.991962 Loss2: 0.484244
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.438511 Loss1: 0.955854 Loss2: 0.482657
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.370841 Loss1: 0.894179 Loss2: 0.476662
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.357362 Loss1: 0.882079 Loss2: 0.475284
-(DefaultActor pid=1838052) >> Training accuracy: 0.735609
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.619455 Loss1: 1.567618 Loss2: 0.051837
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.296341 Loss1: 1.244463 Loss2: 0.051877
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.163482 Loss1: 1.113277 Loss2: 0.050206
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.084031 Loss1: 1.033791 Loss2: 0.050240
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.981475 Loss1: 0.930854 Loss2: 0.050621
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.931489 Loss1: 0.880837 Loss2: 0.050652
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.887600 Loss1: 0.837607 Loss2: 0.049993
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.860959 Loss1: 0.810067 Loss2: 0.050892
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.797950 Loss1: 0.747796 Loss2: 0.050154
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.781181 Loss1: 0.730698 Loss2: 0.050483
-(DefaultActor pid=1838052) >> Training accuracy: 0.801649
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.580993 Loss1: 1.534829 Loss2: 0.046164
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.346125 Loss1: 1.298024 Loss2: 0.048101
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.170532 Loss1: 1.123020 Loss2: 0.047512
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.114665 Loss1: 1.066870 Loss2: 0.047794
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.017029 Loss1: 0.968992 Loss2: 0.048038
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.998879 Loss1: 0.950715 Loss2: 0.048164
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.935546 Loss1: 0.886967 Loss2: 0.048579
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.876100 Loss1: 0.827174 Loss2: 0.048925
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.852450 Loss1: 0.803249 Loss2: 0.049202
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.815558 Loss1: 0.765513 Loss2: 0.050045
-(DefaultActor pid=1838052) >> Training accuracy: 0.793636
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.983399 Loss1: 1.503712 Loss2: 0.479687
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.716486 Loss1: 1.286209 Loss2: 0.430277
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.565044 Loss1: 1.149048 Loss2: 0.415996
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.459187 Loss1: 1.050836 Loss2: 0.408351
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.374790 Loss1: 0.971740 Loss2: 0.403050
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.294766 Loss1: 0.895499 Loss2: 0.399267
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.271600 Loss1: 0.871334 Loss2: 0.400267
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.239451 Loss1: 0.839476 Loss2: 0.399976
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.170682 Loss1: 0.771547 Loss2: 0.399135
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.104936 Loss1: 0.713346 Loss2: 0.391590
-(DefaultActor pid=1838052) >> Training accuracy: 0.788662
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.648810 Loss1: 1.548754 Loss2: 0.100057
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.435098 Loss1: 1.342491 Loss2: 0.092606
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.263582 Loss1: 1.173651 Loss2: 0.089931
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.177296 Loss1: 1.090206 Loss2: 0.087089
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.095152 Loss1: 1.008439 Loss2: 0.086713
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.002073 Loss1: 0.916437 Loss2: 0.085637
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.950258 Loss1: 0.864610 Loss2: 0.085648
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.927875 Loss1: 0.842948 Loss2: 0.084927
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.900996 Loss1: 0.815524 Loss2: 0.085471
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.848823 Loss1: 0.763075 Loss2: 0.085747
-(DefaultActor pid=1838052) >> Training accuracy: 0.804589
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.685175 Loss1: 1.635260 Loss2: 0.049915
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.368898 Loss1: 1.320185 Loss2: 0.048713
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.247768 Loss1: 1.198963 Loss2: 0.048805
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.164373 Loss1: 1.115239 Loss2: 0.049135
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.080091 Loss1: 1.031298 Loss2: 0.048792
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.003993 Loss1: 0.955844 Loss2: 0.048149
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.989895 Loss1: 0.939535 Loss2: 0.050360
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.928191 Loss1: 0.878188 Loss2: 0.050004
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.915477 Loss1: 0.864241 Loss2: 0.051236
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.846737 Loss1: 0.795714 Loss2: 0.051023
-(DefaultActor pid=1838052) >> Training accuracy: 0.761218
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.613427 Loss1: 1.567732 Loss2: 0.045695
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.324408 Loss1: 1.277897 Loss2: 0.046511
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.214042 Loss1: 1.167681 Loss2: 0.046360
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.144975 Loss1: 1.098396 Loss2: 0.046579
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.060169 Loss1: 1.012972 Loss2: 0.047197
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.970030 Loss1: 0.922585 Loss2: 0.047444
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.960799 Loss1: 0.912538 Loss2: 0.048261
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.877419 Loss1: 0.828849 Loss2: 0.048570
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.827678 Loss1: 0.779264 Loss2: 0.048414
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.821562 Loss1: 0.772071 Loss2: 0.049490
-(DefaultActor pid=1838052) >> Training accuracy: 0.787184
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 12:59:34,357][flwr][DEBUG] - fit_round 12 received 10 results and 0 failures
->> Test accuracy: 0.476600
-[2023-09-27 13:00:16,314][flwr][INFO] - fit progress: (12, 2.172221914647867, {'accuracy': 0.4766}, 24039.204636499286)
-[2023-09-27 13:00:16,315][flwr][DEBUG] - evaluate_round 12: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:00:53,655][flwr][DEBUG] - evaluate_round 12 received 10 results and 0 failures
-[2023-09-27 13:00:53,656][flwr][DEBUG] - fit_round 13: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.554835 Loss1: 1.454155 Loss2: 0.100680
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.263591 Loss1: 1.171754 Loss2: 0.091837
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.091764 Loss1: 1.005758 Loss2: 0.086006
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.983549 Loss1: 0.899556 Loss2: 0.083993
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.906307 Loss1: 0.824280 Loss2: 0.082026
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.826566 Loss1: 0.744631 Loss2: 0.081935
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.795618 Loss1: 0.714581 Loss2: 0.081037
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.800221 Loss1: 0.717713 Loss2: 0.082508
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.749293 Loss1: 0.667136 Loss2: 0.082157
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.721261 Loss1: 0.639743 Loss2: 0.081518
-(DefaultActor pid=1838052) >> Training accuracy: 0.838108
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 2.004140 Loss1: 1.449903 Loss2: 0.554237
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.750747 Loss1: 1.209867 Loss2: 0.540881
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.638205 Loss1: 1.115335 Loss2: 0.522870
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.516286 Loss1: 1.000660 Loss2: 0.515626
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.425229 Loss1: 0.919964 Loss2: 0.505265
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.408799 Loss1: 0.906878 Loss2: 0.501921
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.320876 Loss1: 0.824557 Loss2: 0.496319
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.274672 Loss1: 0.781712 Loss2: 0.492960
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.262220 Loss1: 0.770368 Loss2: 0.491851
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.150150 Loss1: 0.665414 Loss2: 0.484735
-(DefaultActor pid=1838052) >> Training accuracy: 0.836630
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.920536 Loss1: 1.454712 Loss2: 0.465824
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.620226 Loss1: 1.199250 Loss2: 0.420976
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.498174 Loss1: 1.099452 Loss2: 0.398722
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.440593 Loss1: 1.042596 Loss2: 0.397997
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.312793 Loss1: 0.921517 Loss2: 0.391275
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.268877 Loss1: 0.881397 Loss2: 0.387480
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.186551 Loss1: 0.803247 Loss2: 0.383305
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.142036 Loss1: 0.756402 Loss2: 0.385634
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.101028 Loss1: 0.717452 Loss2: 0.383576
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.076308 Loss1: 0.694860 Loss2: 0.381448
-(DefaultActor pid=1838052) >> Training accuracy: 0.834059
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.567896 Loss1: 1.521016 Loss2: 0.046880
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.276825 Loss1: 1.228428 Loss2: 0.048397
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.142931 Loss1: 1.095957 Loss2: 0.046975
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.053065 Loss1: 1.005751 Loss2: 0.047314
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.982628 Loss1: 0.935532 Loss2: 0.047097
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.893231 Loss1: 0.845122 Loss2: 0.048109
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.842195 Loss1: 0.794813 Loss2: 0.047381
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.799314 Loss1: 0.750605 Loss2: 0.048709
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.763987 Loss1: 0.714904 Loss2: 0.049083
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.759205 Loss1: 0.709371 Loss2: 0.049834
-(DefaultActor pid=1838052) >> Training accuracy: 0.803085
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.665501 Loss1: 1.614046 Loss2: 0.051455
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.375214 Loss1: 1.322926 Loss2: 0.052288
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.246519 Loss1: 1.195564 Loss2: 0.050955
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.152787 Loss1: 1.101485 Loss2: 0.051301
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.069978 Loss1: 1.018989 Loss2: 0.050988
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.975712 Loss1: 0.924633 Loss2: 0.051080
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.924454 Loss1: 0.872859 Loss2: 0.051595
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.927438 Loss1: 0.875117 Loss2: 0.052321
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.873256 Loss1: 0.821236 Loss2: 0.052021
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.795077 Loss1: 0.742721 Loss2: 0.052357
-(DefaultActor pid=1838052) >> Training accuracy: 0.788240
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.895489 Loss1: 1.479517 Loss2: 0.415972
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.595703 Loss1: 1.230900 Loss2: 0.364803
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.474178 Loss1: 1.122575 Loss2: 0.351602
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.346187 Loss1: 1.003396 Loss2: 0.342791
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.256289 Loss1: 0.912047 Loss2: 0.344242
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.181732 Loss1: 0.843056 Loss2: 0.338676
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.150029 Loss1: 0.813942 Loss2: 0.336087
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.115359 Loss1: 0.777712 Loss2: 0.337646
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.084740 Loss1: 0.745850 Loss2: 0.338889
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.038159 Loss1: 0.702005 Loss2: 0.336154
-(DefaultActor pid=1838052) >> Training accuracy: 0.803402
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.470621 Loss1: 1.425996 Loss2: 0.044625
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.220362 Loss1: 1.174972 Loss2: 0.045389
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.073288 Loss1: 1.027647 Loss2: 0.045640
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.009783 Loss1: 0.963091 Loss2: 0.046692
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.935358 Loss1: 0.888386 Loss2: 0.046973
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.909304 Loss1: 0.861883 Loss2: 0.047421
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.841449 Loss1: 0.794086 Loss2: 0.047363
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.791900 Loss1: 0.743663 Loss2: 0.048238
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.744319 Loss1: 0.695903 Loss2: 0.048416
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.700492 Loss1: 0.652331 Loss2: 0.048161
-(DefaultActor pid=1838052) >> Training accuracy: 0.822389
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.571147 Loss1: 1.472625 Loss2: 0.098522
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.301049 Loss1: 1.208132 Loss2: 0.092917
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.142392 Loss1: 1.053642 Loss2: 0.088750
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.083360 Loss1: 0.995785 Loss2: 0.087575
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.004304 Loss1: 0.917974 Loss2: 0.086330
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.905985 Loss1: 0.821219 Loss2: 0.084765
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.866658 Loss1: 0.781908 Loss2: 0.084749
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.809350 Loss1: 0.724998 Loss2: 0.084352
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.779905 Loss1: 0.696061 Loss2: 0.083844
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.732342 Loss1: 0.648623 Loss2: 0.083719
-(DefaultActor pid=1838052) >> Training accuracy: 0.812078
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.498577 Loss1: 1.401504 Loss2: 0.097073
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.199662 Loss1: 1.113427 Loss2: 0.086235
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.063247 Loss1: 0.981113 Loss2: 0.082134
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.018538 Loss1: 0.938055 Loss2: 0.080483
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.967311 Loss1: 0.887273 Loss2: 0.080038
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.863235 Loss1: 0.783897 Loss2: 0.079338
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.811326 Loss1: 0.732853 Loss2: 0.078473
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.740894 Loss1: 0.663834 Loss2: 0.077060
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.717681 Loss1: 0.639783 Loss2: 0.077898
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.715556 Loss1: 0.635895 Loss2: 0.079661
-(DefaultActor pid=1838052) >> Training accuracy: 0.809495
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.482855 Loss1: 1.437660 Loss2: 0.045195
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.234490 Loss1: 1.187932 Loss2: 0.046558
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.082912 Loss1: 1.037670 Loss2: 0.045241
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.978647 Loss1: 0.933599 Loss2: 0.045048
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.913985 Loss1: 0.868135 Loss2: 0.045849
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.883662 Loss1: 0.837119 Loss2: 0.046543
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.818779 Loss1: 0.772541 Loss2: 0.046238
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.767398 Loss1: 0.720443 Loss2: 0.046955
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.718579 Loss1: 0.671454 Loss2: 0.047126
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.764798 Loss1: 0.716289 Loss2: 0.048509
-(DefaultActor pid=1838052) >> Training accuracy: 0.807355
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 13:30:49,534][flwr][DEBUG] - fit_round 13 received 10 results and 0 failures
->> Test accuracy: 0.503800
-[2023-09-27 13:31:30,256][flwr][INFO] - fit progress: (13, 2.099844414205216, {'accuracy': 0.5038}, 25913.14676936716)
-[2023-09-27 13:31:30,257][flwr][DEBUG] - evaluate_round 13: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:32:07,980][flwr][DEBUG] - evaluate_round 13 received 10 results and 0 failures
-[2023-09-27 13:32:07,990][flwr][DEBUG] - fit_round 14: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.557152 Loss1: 1.511130 Loss2: 0.046022
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.259024 Loss1: 1.212024 Loss2: 0.047000
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.122818 Loss1: 1.076116 Loss2: 0.046702
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.013356 Loss1: 0.966772 Loss2: 0.046584
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.937772 Loss1: 0.890585 Loss2: 0.047187
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.889109 Loss1: 0.842068 Loss2: 0.047041
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.818426 Loss1: 0.770466 Loss2: 0.047960
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.786141 Loss1: 0.738146 Loss2: 0.047995
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.726220 Loss1: 0.678479 Loss2: 0.047741
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.676681 Loss1: 0.628145 Loss2: 0.048536
-(DefaultActor pid=1838052) >> Training accuracy: 0.787829
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.453810 Loss1: 1.401582 Loss2: 0.052228
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.141189 Loss1: 1.089325 Loss2: 0.051863
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.984282 Loss1: 0.933786 Loss2: 0.050497
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.941054 Loss1: 0.889980 Loss2: 0.051074
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.860273 Loss1: 0.809583 Loss2: 0.050690
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.792863 Loss1: 0.742287 Loss2: 0.050576
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.759813 Loss1: 0.709023 Loss2: 0.050790
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.712590 Loss1: 0.660877 Loss2: 0.051714
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.642647 Loss1: 0.592295 Loss2: 0.050352
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.647938 Loss1: 0.596175 Loss2: 0.051763
-(DefaultActor pid=1838052) >> Training accuracy: 0.832476
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.416352 Loss1: 1.366951 Loss2: 0.049401
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.188801 Loss1: 1.139160 Loss2: 0.049640
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.990698 Loss1: 0.942223 Loss2: 0.048475
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.892987 Loss1: 0.844968 Loss2: 0.048019
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.848866 Loss1: 0.799971 Loss2: 0.048895
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.796335 Loss1: 0.747873 Loss2: 0.048462
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.781299 Loss1: 0.732501 Loss2: 0.048798
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.718373 Loss1: 0.669898 Loss2: 0.048476
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.691530 Loss1: 0.642592 Loss2: 0.048938
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.668425 Loss1: 0.619779 Loss2: 0.048646
-(DefaultActor pid=1838052) >> Training accuracy: 0.866297
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.403894 Loss1: 1.355331 Loss2: 0.048563
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.148994 Loss1: 1.099598 Loss2: 0.049396
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.988017 Loss1: 0.938865 Loss2: 0.049152
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.916696 Loss1: 0.867460 Loss2: 0.049236
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.864692 Loss1: 0.814977 Loss2: 0.049715
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.808746 Loss1: 0.759185 Loss2: 0.049560
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.714593 Loss1: 0.665119 Loss2: 0.049474
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.720373 Loss1: 0.670219 Loss2: 0.050154
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.685206 Loss1: 0.633750 Loss2: 0.051456
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.622513 Loss1: 0.571220 Loss2: 0.051293
-(DefaultActor pid=1838052) >> Training accuracy: 0.847112
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.375961 Loss1: 1.333037 Loss2: 0.042924
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.108578 Loss1: 1.063932 Loss2: 0.044646
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.009323 Loss1: 0.964388 Loss2: 0.044936
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.894059 Loss1: 0.849501 Loss2: 0.044557
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.838799 Loss1: 0.793374 Loss2: 0.045425
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.777399 Loss1: 0.732365 Loss2: 0.045034
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.707488 Loss1: 0.661873 Loss2: 0.045615
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.677475 Loss1: 0.631436 Loss2: 0.046040
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.671097 Loss1: 0.624670 Loss2: 0.046427
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.602229 Loss1: 0.555628 Loss2: 0.046601
-(DefaultActor pid=1838052) >> Training accuracy: 0.844937
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.396977 Loss1: 1.350401 Loss2: 0.046577
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.097820 Loss1: 1.049547 Loss2: 0.048273
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.985922 Loss1: 0.939401 Loss2: 0.046521
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.848148 Loss1: 0.801753 Loss2: 0.046395
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.777936 Loss1: 0.730802 Loss2: 0.047134
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.711903 Loss1: 0.664628 Loss2: 0.047276
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.717066 Loss1: 0.669050 Loss2: 0.048016
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627829 Loss1: 0.580129 Loss2: 0.047700
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.612711 Loss1: 0.564503 Loss2: 0.048208
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.563070 Loss1: 0.514047 Loss2: 0.049022
-(DefaultActor pid=1838052) >> Training accuracy: 0.839844
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.790708 Loss1: 1.318562 Loss2: 0.472146
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.481774 Loss1: 1.058937 Loss2: 0.422837
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.351668 Loss1: 0.944196 Loss2: 0.407472
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.243171 Loss1: 0.839179 Loss2: 0.403993
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.171931 Loss1: 0.776857 Loss2: 0.395074
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.120945 Loss1: 0.725885 Loss2: 0.395060
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.084169 Loss1: 0.692995 Loss2: 0.391173
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.047033 Loss1: 0.656470 Loss2: 0.390562
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.980545 Loss1: 0.592056 Loss2: 0.388490
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.945049 Loss1: 0.558628 Loss2: 0.386421
-(DefaultActor pid=1838052) >> Training accuracy: 0.844551
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.927190 Loss1: 1.374670 Loss2: 0.552521
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.634197 Loss1: 1.091279 Loss2: 0.542919
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.491793 Loss1: 0.960572 Loss2: 0.531220
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.443121 Loss1: 0.918524 Loss2: 0.524597
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.360523 Loss1: 0.848840 Loss2: 0.511683
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.292534 Loss1: 0.788095 Loss2: 0.504440
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.204907 Loss1: 0.706245 Loss2: 0.498662
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.167800 Loss1: 0.675282 Loss2: 0.492517
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.168541 Loss1: 0.676807 Loss2: 0.491734
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.073724 Loss1: 0.585894 Loss2: 0.487830
-(DefaultActor pid=1838052) >> Training accuracy: 0.796684
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.901270 Loss1: 1.409981 Loss2: 0.491289
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.561869 Loss1: 1.115767 Loss2: 0.446102
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.402262 Loss1: 0.974089 Loss2: 0.428173
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.325875 Loss1: 0.904090 Loss2: 0.421785
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.221278 Loss1: 0.805318 Loss2: 0.415960
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.162254 Loss1: 0.750492 Loss2: 0.411761
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.122975 Loss1: 0.712856 Loss2: 0.410119
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.083349 Loss1: 0.674138 Loss2: 0.409212
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.041990 Loss1: 0.633488 Loss2: 0.408502
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.987648 Loss1: 0.581224 Loss2: 0.406424
-(DefaultActor pid=1838052) >> Training accuracy: 0.828125
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.541563 Loss1: 1.442738 Loss2: 0.098825
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.245615 Loss1: 1.151689 Loss2: 0.093926
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.085352 Loss1: 0.998063 Loss2: 0.087290
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.965421 Loss1: 0.881662 Loss2: 0.083759
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.897522 Loss1: 0.813997 Loss2: 0.083525
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.893132 Loss1: 0.810288 Loss2: 0.082844
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.802376 Loss1: 0.721566 Loss2: 0.080810
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.751052 Loss1: 0.670483 Loss2: 0.080569
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.734165 Loss1: 0.653686 Loss2: 0.080479
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.698602 Loss1: 0.618290 Loss2: 0.080311
-(DefaultActor pid=1838052) >> Training accuracy: 0.846354
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 14:02:00,761][flwr][DEBUG] - fit_round 14 received 10 results and 0 failures
->> Test accuracy: 0.515100
-[2023-09-27 14:02:42,089][flwr][INFO] - fit progress: (14, 2.0913502991009065, {'accuracy': 0.5151}, 27784.97900109645)
-[2023-09-27 14:02:42,089][flwr][DEBUG] - evaluate_round 14: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:03:18,877][flwr][DEBUG] - evaluate_round 14 received 10 results and 0 failures
-[2023-09-27 14:03:18,878][flwr][DEBUG] - fit_round 15: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.869129 Loss1: 1.308595 Loss2: 0.560535
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.573622 Loss1: 1.021704 Loss2: 0.551918
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.447462 Loss1: 0.906873 Loss2: 0.540589
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.354771 Loss1: 0.822637 Loss2: 0.532134
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.265965 Loss1: 0.741893 Loss2: 0.524072
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.181272 Loss1: 0.666060 Loss2: 0.515212
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.199235 Loss1: 0.685760 Loss2: 0.513474
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.124943 Loss1: 0.615068 Loss2: 0.509875
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.109834 Loss1: 0.602950 Loss2: 0.506884
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.067237 Loss1: 0.565200 Loss2: 0.502038
-(DefaultActor pid=1838052) >> Training accuracy: 0.822191
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.828038 Loss1: 1.273057 Loss2: 0.554981
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.593089 Loss1: 1.052854 Loss2: 0.540234
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.429582 Loss1: 0.908420 Loss2: 0.521162
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.353168 Loss1: 0.838462 Loss2: 0.514706
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.301650 Loss1: 0.797783 Loss2: 0.503867
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.205751 Loss1: 0.706573 Loss2: 0.499178
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.176490 Loss1: 0.681927 Loss2: 0.494563
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.099609 Loss1: 0.609922 Loss2: 0.489687
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.028665 Loss1: 0.542186 Loss2: 0.486479
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.049175 Loss1: 0.562645 Loss2: 0.486530
-(DefaultActor pid=1838052) >> Training accuracy: 0.839597
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.870054 Loss1: 1.361167 Loss2: 0.508887
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.491865 Loss1: 1.009895 Loss2: 0.481970
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.401711 Loss1: 0.930827 Loss2: 0.470884
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.282198 Loss1: 0.817848 Loss2: 0.464349
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.243128 Loss1: 0.782798 Loss2: 0.460330
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.151812 Loss1: 0.693782 Loss2: 0.458029
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.140058 Loss1: 0.684226 Loss2: 0.455832
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.081519 Loss1: 0.628365 Loss2: 0.453154
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.038572 Loss1: 0.584713 Loss2: 0.453859
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.982956 Loss1: 0.532422 Loss2: 0.450534
-(DefaultActor pid=1838052) >> Training accuracy: 0.857171
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.623706 Loss1: 1.245031 Loss2: 0.378674
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.322409 Loss1: 0.993271 Loss2: 0.329138
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.165327 Loss1: 0.852323 Loss2: 0.313004
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.116715 Loss1: 0.807186 Loss2: 0.309529
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.038606 Loss1: 0.733570 Loss2: 0.305036
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.035618 Loss1: 0.732039 Loss2: 0.303578
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.936315 Loss1: 0.636771 Loss2: 0.299544
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.876060 Loss1: 0.576444 Loss2: 0.299616
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.840033 Loss1: 0.541692 Loss2: 0.298340
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.800036 Loss1: 0.502964 Loss2: 0.297072
-(DefaultActor pid=1838052) >> Training accuracy: 0.859947
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.256632 Loss1: 1.212641 Loss2: 0.043991
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.991994 Loss1: 0.945800 Loss2: 0.046195
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.868010 Loss1: 0.823083 Loss2: 0.044928
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.768437 Loss1: 0.724393 Loss2: 0.044044
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.747501 Loss1: 0.702641 Loss2: 0.044860
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.643532 Loss1: 0.598627 Loss2: 0.044905
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.604150 Loss1: 0.559140 Loss2: 0.045010
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.598801 Loss1: 0.553932 Loss2: 0.044869
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.527113 Loss1: 0.482246 Loss2: 0.044867
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.521045 Loss1: 0.475291 Loss2: 0.045754
-(DefaultActor pid=1838052) >> Training accuracy: 0.874199
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.336072 Loss1: 1.291875 Loss2: 0.044198
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.056133 Loss1: 1.010235 Loss2: 0.045897
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.910034 Loss1: 0.864552 Loss2: 0.045482
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.821806 Loss1: 0.776930 Loss2: 0.044876
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.756741 Loss1: 0.710725 Loss2: 0.046016
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.734409 Loss1: 0.688249 Loss2: 0.046161
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.646828 Loss1: 0.600766 Loss2: 0.046062
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.644152 Loss1: 0.597489 Loss2: 0.046663
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.577532 Loss1: 0.531558 Loss2: 0.045974
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.580927 Loss1: 0.533505 Loss2: 0.047422
-(DefaultActor pid=1838052) >> Training accuracy: 0.847508
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.320253 Loss1: 1.274614 Loss2: 0.045639
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.992112 Loss1: 0.945725 Loss2: 0.046387
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.864470 Loss1: 0.818449 Loss2: 0.046021
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.791462 Loss1: 0.745302 Loss2: 0.046160
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.728706 Loss1: 0.682335 Loss2: 0.046371
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.666581 Loss1: 0.620047 Loss2: 0.046534
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.586274 Loss1: 0.539887 Loss2: 0.046387
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.601000 Loss1: 0.553364 Loss2: 0.047636
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.518533 Loss1: 0.471377 Loss2: 0.047156
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.518376 Loss1: 0.470743 Loss2: 0.047633
-(DefaultActor pid=1838052) >> Training accuracy: 0.823351
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.300836 Loss1: 1.258486 Loss2: 0.042350
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.030871 Loss1: 0.986440 Loss2: 0.044431
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.908857 Loss1: 0.864670 Loss2: 0.044187
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.813293 Loss1: 0.769064 Loss2: 0.044229
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.733936 Loss1: 0.689634 Loss2: 0.044302
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.690252 Loss1: 0.645341 Loss2: 0.044911
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.692036 Loss1: 0.646373 Loss2: 0.045662
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.624007 Loss1: 0.578629 Loss2: 0.045378
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.586680 Loss1: 0.540861 Loss2: 0.045819
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.552512 Loss1: 0.505963 Loss2: 0.046549
-(DefaultActor pid=1838052) >> Training accuracy: 0.813884
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.440221 Loss1: 1.361511 Loss2: 0.078710
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.063102 Loss1: 0.987606 Loss2: 0.075497
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.975592 Loss1: 0.903563 Loss2: 0.072029
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.903377 Loss1: 0.832173 Loss2: 0.071204
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.775594 Loss1: 0.704977 Loss2: 0.070616
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.710341 Loss1: 0.641682 Loss2: 0.068659
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.689901 Loss1: 0.621679 Loss2: 0.068222
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627053 Loss1: 0.559046 Loss2: 0.068006
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.588798 Loss1: 0.519809 Loss2: 0.068988
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.555248 Loss1: 0.487502 Loss2: 0.067746
-(DefaultActor pid=1838052) >> Training accuracy: 0.869299
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.471926 Loss1: 1.427650 Loss2: 0.044276
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.154139 Loss1: 1.108520 Loss2: 0.045619
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.006635 Loss1: 0.961235 Loss2: 0.045400
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.952977 Loss1: 0.906991 Loss2: 0.045986
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.874394 Loss1: 0.828489 Loss2: 0.045905
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.810183 Loss1: 0.763152 Loss2: 0.047031
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.718065 Loss1: 0.671644 Loss2: 0.046421
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.670572 Loss1: 0.623228 Loss2: 0.047344
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.615017 Loss1: 0.567657 Loss2: 0.047360
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.627492 Loss1: 0.578667 Loss2: 0.048824
-(DefaultActor pid=1838052) >> Training accuracy: 0.847245
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 14:33:09,578][flwr][DEBUG] - fit_round 15 received 10 results and 0 failures
->> Test accuracy: 0.533200
-[2023-09-27 14:33:51,388][flwr][INFO] - fit progress: (15, 2.0594057168442603, {'accuracy': 0.5332}, 29654.278763433453)
-[2023-09-27 14:33:51,389][flwr][DEBUG] - evaluate_round 15: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:34:30,081][flwr][DEBUG] - evaluate_round 15 received 10 results and 0 failures
-[2023-09-27 14:34:30,082][flwr][DEBUG] - fit_round 16: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.319365 Loss1: 1.231179 Loss2: 0.088186
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.989450 Loss1: 0.908675 Loss2: 0.080775
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.888398 Loss1: 0.809992 Loss2: 0.078406
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.791136 Loss1: 0.715338 Loss2: 0.075797
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.725526 Loss1: 0.649637 Loss2: 0.075889
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.625476 Loss1: 0.551067 Loss2: 0.074410
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.634477 Loss1: 0.560165 Loss2: 0.074312
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.577461 Loss1: 0.503432 Loss2: 0.074029
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.519255 Loss1: 0.445199 Loss2: 0.074055
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.534348 Loss1: 0.459257 Loss2: 0.075092
-(DefaultActor pid=1838052) >> Training accuracy: 0.879747
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.665154 Loss1: 1.259666 Loss2: 0.405489
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.322873 Loss1: 0.989865 Loss2: 0.333008
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.167477 Loss1: 0.850643 Loss2: 0.316834
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.081441 Loss1: 0.769983 Loss2: 0.311458
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.976954 Loss1: 0.670694 Loss2: 0.306260
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.977265 Loss1: 0.668300 Loss2: 0.308965
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.868333 Loss1: 0.563778 Loss2: 0.304555
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.846894 Loss1: 0.542077 Loss2: 0.304816
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.837206 Loss1: 0.533814 Loss2: 0.303392
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.804672 Loss1: 0.499836 Loss2: 0.304836
-(DefaultActor pid=1838052) >> Training accuracy: 0.848157
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.222760 Loss1: 1.180292 Loss2: 0.042468
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.934930 Loss1: 0.890929 Loss2: 0.044001
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.821343 Loss1: 0.777787 Loss2: 0.043556
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.743289 Loss1: 0.699175 Loss2: 0.044114
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.704035 Loss1: 0.659915 Loss2: 0.044120
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.644134 Loss1: 0.599523 Loss2: 0.044610
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.596153 Loss1: 0.551381 Loss2: 0.044772
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.549509 Loss1: 0.504436 Loss2: 0.045073
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.523912 Loss1: 0.478274 Loss2: 0.045638
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.526950 Loss1: 0.481351 Loss2: 0.045600
-(DefaultActor pid=1838052) >> Training accuracy: 0.835839
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.240767 Loss1: 1.194333 Loss2: 0.046434
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.950228 Loss1: 0.902954 Loss2: 0.047274
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.853440 Loss1: 0.806351 Loss2: 0.047089
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.744117 Loss1: 0.697283 Loss2: 0.046834
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.669267 Loss1: 0.622003 Loss2: 0.047264
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.626842 Loss1: 0.579548 Loss2: 0.047294
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.566617 Loss1: 0.519363 Loss2: 0.047254
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.588784 Loss1: 0.540459 Loss2: 0.048324
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.518987 Loss1: 0.470823 Loss2: 0.048163
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.485265 Loss1: 0.436593 Loss2: 0.048672
-(DefaultActor pid=1838052) >> Training accuracy: 0.908537
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.821027 Loss1: 1.274033 Loss2: 0.546994
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.461127 Loss1: 0.936447 Loss2: 0.524680
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.297903 Loss1: 0.796373 Loss2: 0.501530
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.195251 Loss1: 0.701814 Loss2: 0.493438
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.170281 Loss1: 0.684465 Loss2: 0.485816
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.129800 Loss1: 0.650146 Loss2: 0.479654
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.023941 Loss1: 0.549766 Loss2: 0.474175
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.027316 Loss1: 0.555367 Loss2: 0.471949
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.009425 Loss1: 0.540247 Loss2: 0.469178
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.935867 Loss1: 0.467048 Loss2: 0.468819
-(DefaultActor pid=1838052) >> Training accuracy: 0.871833
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.424465 Loss1: 1.321884 Loss2: 0.102580
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.109187 Loss1: 1.011768 Loss2: 0.097419
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.968349 Loss1: 0.874718 Loss2: 0.093631
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.887493 Loss1: 0.797273 Loss2: 0.090220
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.798571 Loss1: 0.711186 Loss2: 0.087386
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.771472 Loss1: 0.683475 Loss2: 0.087997
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.710740 Loss1: 0.624208 Loss2: 0.086532
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.673604 Loss1: 0.587967 Loss2: 0.085637
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.614862 Loss1: 0.531050 Loss2: 0.083813
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.596927 Loss1: 0.512820 Loss2: 0.084108
-(DefaultActor pid=1838052) >> Training accuracy: 0.863487
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.243623 Loss1: 1.198888 Loss2: 0.044735
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.961667 Loss1: 0.915511 Loss2: 0.046155
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.850438 Loss1: 0.804972 Loss2: 0.045466
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.732224 Loss1: 0.686608 Loss2: 0.045616
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.674944 Loss1: 0.629683 Loss2: 0.045261
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.642545 Loss1: 0.596580 Loss2: 0.045964
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.610690 Loss1: 0.563999 Loss2: 0.046692
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.570927 Loss1: 0.524009 Loss2: 0.046919
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.551139 Loss1: 0.504300 Loss2: 0.046839
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.539553 Loss1: 0.491748 Loss2: 0.047805
-(DefaultActor pid=1838052) >> Training accuracy: 0.872429
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.182955 Loss1: 1.141674 Loss2: 0.041281
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.907693 Loss1: 0.864174 Loss2: 0.043519
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.815081 Loss1: 0.771494 Loss2: 0.043587
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.667460 Loss1: 0.624303 Loss2: 0.043158
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.618589 Loss1: 0.575520 Loss2: 0.043069
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.599503 Loss1: 0.555854 Loss2: 0.043650
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.563147 Loss1: 0.518972 Loss2: 0.044175
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.519071 Loss1: 0.473719 Loss2: 0.045352
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.479138 Loss1: 0.433944 Loss2: 0.045194
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.479000 Loss1: 0.433847 Loss2: 0.045153
-(DefaultActor pid=1838052) >> Training accuracy: 0.870393
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.265032 Loss1: 1.220601 Loss2: 0.044431
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.919550 Loss1: 0.873368 Loss2: 0.046182
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.793637 Loss1: 0.748834 Loss2: 0.044803
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.686991 Loss1: 0.642594 Loss2: 0.044397
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.642008 Loss1: 0.596989 Loss2: 0.045019
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.608282 Loss1: 0.562880 Loss2: 0.045402
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.554434 Loss1: 0.509358 Loss2: 0.045076
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.532932 Loss1: 0.486894 Loss2: 0.046039
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.493670 Loss1: 0.448155 Loss2: 0.045515
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.452236 Loss1: 0.406581 Loss2: 0.045654
-(DefaultActor pid=1838052) >> Training accuracy: 0.856337
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.279682 Loss1: 1.191143 Loss2: 0.088540
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.018267 Loss1: 0.935634 Loss2: 0.082633
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.885615 Loss1: 0.807029 Loss2: 0.078586
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.771969 Loss1: 0.696472 Loss2: 0.075497
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.695094 Loss1: 0.620698 Loss2: 0.074396
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.642214 Loss1: 0.569360 Loss2: 0.072854
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.619980 Loss1: 0.546553 Loss2: 0.073427
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.639584 Loss1: 0.566370 Loss2: 0.073214
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.585676 Loss1: 0.512339 Loss2: 0.073337
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.523151 Loss1: 0.451738 Loss2: 0.071413
-(DefaultActor pid=1838052) >> Training accuracy: 0.856606
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 15:04:25,755][flwr][DEBUG] - fit_round 16 received 10 results and 0 failures
->> Test accuracy: 0.541200
-[2023-09-27 15:05:07,094][flwr][INFO] - fit progress: (16, 2.043369851554164, {'accuracy': 0.5412}, 31529.984162893146)
-[2023-09-27 15:05:07,094][flwr][DEBUG] - evaluate_round 16: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:05:43,531][flwr][DEBUG] - evaluate_round 16 received 10 results and 0 failures
-[2023-09-27 15:05:43,535][flwr][DEBUG] - fit_round 17: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.724071 Loss1: 1.192031 Loss2: 0.532040
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.397202 Loss1: 0.905425 Loss2: 0.491777
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.248782 Loss1: 0.780901 Loss2: 0.467882
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.143744 Loss1: 0.689073 Loss2: 0.454671
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.061379 Loss1: 0.616862 Loss2: 0.444517
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.993349 Loss1: 0.550992 Loss2: 0.442358
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.975412 Loss1: 0.534316 Loss2: 0.441096
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.922566 Loss1: 0.488946 Loss2: 0.433620
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.881468 Loss1: 0.449421 Loss2: 0.432046
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.932681 Loss1: 0.497816 Loss2: 0.434866
-(DefaultActor pid=1838052) >> Training accuracy: 0.867880
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.814572 Loss1: 1.276490 Loss2: 0.538082
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.488853 Loss1: 0.968639 Loss2: 0.520214
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.320097 Loss1: 0.813617 Loss2: 0.506480
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.219865 Loss1: 0.721865 Loss2: 0.498000
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.190685 Loss1: 0.699883 Loss2: 0.490802
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.133862 Loss1: 0.647056 Loss2: 0.486806
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.042862 Loss1: 0.560342 Loss2: 0.482519
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.040098 Loss1: 0.557847 Loss2: 0.482251
-(DefaultActor pid=1838052) Epoch: 8 Loss: 1.004696 Loss1: 0.524774 Loss2: 0.479921
-(DefaultActor pid=1838052) Epoch: 9 Loss: 1.002923 Loss1: 0.525671 Loss2: 0.477252
-(DefaultActor pid=1838052) >> Training accuracy: 0.846423
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.203019 Loss1: 1.131675 Loss2: 0.071343
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.908636 Loss1: 0.841907 Loss2: 0.066729
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.792948 Loss1: 0.729141 Loss2: 0.063807
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.683123 Loss1: 0.621723 Loss2: 0.061400
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.647688 Loss1: 0.586367 Loss2: 0.061321
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.613439 Loss1: 0.553321 Loss2: 0.060118
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.555569 Loss1: 0.496033 Loss2: 0.059536
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.561862 Loss1: 0.502249 Loss2: 0.059613
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.533264 Loss1: 0.472994 Loss2: 0.060270
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.441068 Loss1: 0.381871 Loss2: 0.059197
-(DefaultActor pid=1838052) >> Training accuracy: 0.905261
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.593260 Loss1: 1.220183 Loss2: 0.373076
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.209884 Loss1: 0.899372 Loss2: 0.310512
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.078203 Loss1: 0.779001 Loss2: 0.299202
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.964305 Loss1: 0.673296 Loss2: 0.291010
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.900407 Loss1: 0.608347 Loss2: 0.292060
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.823449 Loss1: 0.538349 Loss2: 0.285099
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.813651 Loss1: 0.527237 Loss2: 0.286414
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.705440 Loss1: 0.423188 Loss2: 0.282252
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.764947 Loss1: 0.478261 Loss2: 0.286686
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.705319 Loss1: 0.422625 Loss2: 0.282694
-(DefaultActor pid=1838052) >> Training accuracy: 0.875845
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.619478 Loss1: 1.061922 Loss2: 0.557556
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.359962 Loss1: 0.803257 Loss2: 0.556705
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.225204 Loss1: 0.681625 Loss2: 0.543579
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.171590 Loss1: 0.635735 Loss2: 0.535854
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.076883 Loss1: 0.547675 Loss2: 0.529208
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.013207 Loss1: 0.491951 Loss2: 0.521256
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.970228 Loss1: 0.452400 Loss2: 0.517828
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.964139 Loss1: 0.453762 Loss2: 0.510377
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.937465 Loss1: 0.428026 Loss2: 0.509439
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.869677 Loss1: 0.364503 Loss2: 0.505174
-(DefaultActor pid=1838052) >> Training accuracy: 0.915665
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.281250 Loss1: 1.190858 Loss2: 0.090392
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.938171 Loss1: 0.854814 Loss2: 0.083357
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.850935 Loss1: 0.770949 Loss2: 0.079986
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.732652 Loss1: 0.655639 Loss2: 0.077013
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.654519 Loss1: 0.579889 Loss2: 0.074629
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.644013 Loss1: 0.570031 Loss2: 0.073982
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.579281 Loss1: 0.507002 Loss2: 0.072279
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.539600 Loss1: 0.467647 Loss2: 0.071953
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.494389 Loss1: 0.423889 Loss2: 0.070500
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.500732 Loss1: 0.429311 Loss2: 0.071421
-(DefaultActor pid=1838052) >> Training accuracy: 0.893429
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.178007 Loss1: 1.134182 Loss2: 0.043825
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.875088 Loss1: 0.829974 Loss2: 0.045114
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.741451 Loss1: 0.697497 Loss2: 0.043954
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.694167 Loss1: 0.649733 Loss2: 0.044435
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.644145 Loss1: 0.598985 Loss2: 0.045160
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.587464 Loss1: 0.542296 Loss2: 0.045169
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.542717 Loss1: 0.497174 Loss2: 0.045543
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.475920 Loss1: 0.430484 Loss2: 0.045436
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.470198 Loss1: 0.424211 Loss2: 0.045987
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.452873 Loss1: 0.406996 Loss2: 0.045877
-(DefaultActor pid=1838052) >> Training accuracy: 0.890427
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.681233 Loss1: 1.106129 Loss2: 0.575104
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.423035 Loss1: 0.851118 Loss2: 0.571917
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.306713 Loss1: 0.747175 Loss2: 0.559538
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.192403 Loss1: 0.643979 Loss2: 0.548424
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.126465 Loss1: 0.588898 Loss2: 0.537567
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.113644 Loss1: 0.583666 Loss2: 0.529978
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.041499 Loss1: 0.516176 Loss2: 0.525322
-(DefaultActor pid=1838052) Epoch: 7 Loss: 1.012730 Loss1: 0.492442 Loss2: 0.520288
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.993970 Loss1: 0.477058 Loss2: 0.516912
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.955380 Loss1: 0.442304 Loss2: 0.513076
-(DefaultActor pid=1838052) >> Training accuracy: 0.884337
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.174437 Loss1: 1.130578 Loss2: 0.043859
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.892509 Loss1: 0.846867 Loss2: 0.045642
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.749081 Loss1: 0.703812 Loss2: 0.045269
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.688468 Loss1: 0.643613 Loss2: 0.044855
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.637087 Loss1: 0.591454 Loss2: 0.045633
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.621744 Loss1: 0.575384 Loss2: 0.046360
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.513209 Loss1: 0.468082 Loss2: 0.045127
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.479677 Loss1: 0.434227 Loss2: 0.045450
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.521849 Loss1: 0.474908 Loss2: 0.046941
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.473112 Loss1: 0.426953 Loss2: 0.046159
-(DefaultActor pid=1838052) >> Training accuracy: 0.902888
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.220185 Loss1: 1.109585 Loss2: 0.110600
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.959343 Loss1: 0.855129 Loss2: 0.104213
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.794161 Loss1: 0.695801 Loss2: 0.098360
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.679846 Loss1: 0.585103 Loss2: 0.094743
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.637958 Loss1: 0.545511 Loss2: 0.092447
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.565120 Loss1: 0.475683 Loss2: 0.089437
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.574675 Loss1: 0.486980 Loss2: 0.087695
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.515102 Loss1: 0.428117 Loss2: 0.086985
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.520519 Loss1: 0.433546 Loss2: 0.086973
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.483020 Loss1: 0.397309 Loss2: 0.085711
-(DefaultActor pid=1838052) >> Training accuracy: 0.907769
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 15:35:38,006][flwr][DEBUG] - fit_round 17 received 10 results and 0 failures
->> Test accuracy: 0.555200
-[2023-09-27 15:36:19,112][flwr][INFO] - fit progress: (17, 2.0114177884385227, {'accuracy': 0.5552}, 33402.00278136041)
-[2023-09-27 15:36:19,113][flwr][DEBUG] - evaluate_round 17: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:36:55,813][flwr][DEBUG] - evaluate_round 17 received 10 results and 0 failures
-[2023-09-27 15:36:55,814][flwr][DEBUG] - fit_round 18: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.151850 Loss1: 1.102207 Loss2: 0.049643
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.882989 Loss1: 0.832441 Loss2: 0.050548
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.689155 Loss1: 0.639842 Loss2: 0.049314
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.631845 Loss1: 0.583206 Loss2: 0.048639
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.566820 Loss1: 0.517939 Loss2: 0.048881
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.551032 Loss1: 0.501674 Loss2: 0.049358
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.545742 Loss1: 0.495955 Loss2: 0.049787
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.455554 Loss1: 0.406643 Loss2: 0.048912
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.410234 Loss1: 0.361451 Loss2: 0.048784
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.360835 Loss1: 0.312199 Loss2: 0.048636
-(DefaultActor pid=1838052) >> Training accuracy: 0.899282
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.147591 Loss1: 1.095451 Loss2: 0.052140
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.905347 Loss1: 0.851917 Loss2: 0.053430
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.727907 Loss1: 0.676317 Loss2: 0.051590
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.639653 Loss1: 0.589274 Loss2: 0.050379
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.602735 Loss1: 0.552357 Loss2: 0.050378
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.525467 Loss1: 0.475621 Loss2: 0.049846
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.502656 Loss1: 0.452750 Loss2: 0.049905
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.486071 Loss1: 0.436450 Loss2: 0.049621
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.475068 Loss1: 0.424594 Loss2: 0.050474
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.484269 Loss1: 0.433953 Loss2: 0.050316
-(DefaultActor pid=1838052) >> Training accuracy: 0.906646
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.616979 Loss1: 1.129082 Loss2: 0.487896
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.294852 Loss1: 0.853851 Loss2: 0.441001
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.121358 Loss1: 0.693252 Loss2: 0.428106
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.088324 Loss1: 0.671312 Loss2: 0.417012
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.988800 Loss1: 0.571813 Loss2: 0.416988
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.916039 Loss1: 0.502736 Loss2: 0.413303
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.930943 Loss1: 0.520122 Loss2: 0.410821
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.890067 Loss1: 0.483481 Loss2: 0.406586
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.808451 Loss1: 0.404837 Loss2: 0.403613
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.821626 Loss1: 0.414988 Loss2: 0.406638
-(DefaultActor pid=1838052) >> Training accuracy: 0.891026
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.604111 Loss1: 1.026361 Loss2: 0.577750
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.379711 Loss1: 0.800240 Loss2: 0.579471
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.242236 Loss1: 0.671332 Loss2: 0.570903
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.134007 Loss1: 0.572132 Loss2: 0.561875
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.103937 Loss1: 0.550762 Loss2: 0.553175
-(DefaultActor pid=1838052) Epoch: 5 Loss: 1.058524 Loss1: 0.509558 Loss2: 0.548967
-(DefaultActor pid=1838052) Epoch: 6 Loss: 1.007588 Loss1: 0.464244 Loss2: 0.543344
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.978135 Loss1: 0.439950 Loss2: 0.538185
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.980892 Loss1: 0.446465 Loss2: 0.534427
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.942437 Loss1: 0.412905 Loss2: 0.529532
-(DefaultActor pid=1838052) >> Training accuracy: 0.889043
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.584543 Loss1: 1.062201 Loss2: 0.522342
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.264573 Loss1: 0.769338 Loss2: 0.495235
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.136152 Loss1: 0.663482 Loss2: 0.472670
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.050714 Loss1: 0.583485 Loss2: 0.467229
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.004701 Loss1: 0.547634 Loss2: 0.457067
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.951836 Loss1: 0.495918 Loss2: 0.455918
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.853237 Loss1: 0.406415 Loss2: 0.446822
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.835672 Loss1: 0.395297 Loss2: 0.440375
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.764212 Loss1: 0.326737 Loss2: 0.437475
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.786782 Loss1: 0.350071 Loss2: 0.436711
-(DefaultActor pid=1838052) >> Training accuracy: 0.903429
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.074072 Loss1: 1.027718 Loss2: 0.046354
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.784241 Loss1: 0.736678 Loss2: 0.047563
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.637449 Loss1: 0.591155 Loss2: 0.046295
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.554658 Loss1: 0.508476 Loss2: 0.046182
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.543558 Loss1: 0.496824 Loss2: 0.046734
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.506882 Loss1: 0.459398 Loss2: 0.047484
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.447064 Loss1: 0.400058 Loss2: 0.047006
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.409010 Loss1: 0.361930 Loss2: 0.047079
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.376404 Loss1: 0.329408 Loss2: 0.046997
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.414653 Loss1: 0.366102 Loss2: 0.048551
-(DefaultActor pid=1838052) >> Training accuracy: 0.919671
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.117711 Loss1: 1.073847 Loss2: 0.043865
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.845978 Loss1: 0.800484 Loss2: 0.045494
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.725233 Loss1: 0.679895 Loss2: 0.045338
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.626525 Loss1: 0.581438 Loss2: 0.045087
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.589257 Loss1: 0.543124 Loss2: 0.046133
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.511490 Loss1: 0.466347 Loss2: 0.045143
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.499907 Loss1: 0.453784 Loss2: 0.046123
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.462077 Loss1: 0.415951 Loss2: 0.046126
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.437042 Loss1: 0.390851 Loss2: 0.046190
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.412851 Loss1: 0.366421 Loss2: 0.046430
-(DefaultActor pid=1838052) >> Training accuracy: 0.885878
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.288395 Loss1: 1.185564 Loss2: 0.102831
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.993716 Loss1: 0.901586 Loss2: 0.092130
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.835738 Loss1: 0.749405 Loss2: 0.086333
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.762372 Loss1: 0.679679 Loss2: 0.082693
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.704111 Loss1: 0.622991 Loss2: 0.081119
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.637661 Loss1: 0.557796 Loss2: 0.079864
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.609971 Loss1: 0.531625 Loss2: 0.078347
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.564713 Loss1: 0.486830 Loss2: 0.077882
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.516841 Loss1: 0.439786 Loss2: 0.077055
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.499605 Loss1: 0.422977 Loss2: 0.076629
-(DefaultActor pid=1838052) >> Training accuracy: 0.883635
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.093677 Loss1: 1.045198 Loss2: 0.048479
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.799330 Loss1: 0.749327 Loss2: 0.050002
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.690984 Loss1: 0.641503 Loss2: 0.049482
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.617008 Loss1: 0.567725 Loss2: 0.049283
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.576823 Loss1: 0.528085 Loss2: 0.048738
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.551630 Loss1: 0.503076 Loss2: 0.048554
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.452142 Loss1: 0.403730 Loss2: 0.048412
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.435855 Loss1: 0.388393 Loss2: 0.047462
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.401437 Loss1: 0.352876 Loss2: 0.048561
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.444832 Loss1: 0.395207 Loss2: 0.049625
-(DefaultActor pid=1838052) >> Training accuracy: 0.850610
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.098568 Loss1: 1.051298 Loss2: 0.047270
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.846442 Loss1: 0.798179 Loss2: 0.048263
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.704741 Loss1: 0.656632 Loss2: 0.048108
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.633382 Loss1: 0.586161 Loss2: 0.047221
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.553920 Loss1: 0.506687 Loss2: 0.047233
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.520138 Loss1: 0.473042 Loss2: 0.047096
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.495444 Loss1: 0.448481 Loss2: 0.046963
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.477814 Loss1: 0.430215 Loss2: 0.047598
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.434395 Loss1: 0.386232 Loss2: 0.048162
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.431528 Loss1: 0.383876 Loss2: 0.047651
-(DefaultActor pid=1838052) >> Training accuracy: 0.902888
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 16:06:45,012][flwr][DEBUG] - fit_round 18 received 10 results and 0 failures
->> Test accuracy: 0.559500
-[2023-09-27 16:07:25,503][flwr][INFO] - fit progress: (18, 2.017150927846805, {'accuracy': 0.5595}, 35268.393114050385)
-[2023-09-27 16:07:25,503][flwr][DEBUG] - evaluate_round 18: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:08:03,143][flwr][DEBUG] - evaluate_round 18 received 10 results and 0 failures
-[2023-09-27 16:08:03,144][flwr][DEBUG] - fit_round 19: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.072423 Loss1: 1.025426 Loss2: 0.046998
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.724178 Loss1: 0.676316 Loss2: 0.047862
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.638816 Loss1: 0.590574 Loss2: 0.048242
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.541173 Loss1: 0.494070 Loss2: 0.047103
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.536851 Loss1: 0.488933 Loss2: 0.047917
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.471999 Loss1: 0.424226 Loss2: 0.047773
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.455944 Loss1: 0.407527 Loss2: 0.048417
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.437839 Loss1: 0.389738 Loss2: 0.048101
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.413090 Loss1: 0.364927 Loss2: 0.048163
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.384046 Loss1: 0.336231 Loss2: 0.047815
-(DefaultActor pid=1838052) >> Training accuracy: 0.913172
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.647601 Loss1: 1.170356 Loss2: 0.477245
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.276275 Loss1: 0.846552 Loss2: 0.429723
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.139066 Loss1: 0.726082 Loss2: 0.412985
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.072279 Loss1: 0.668411 Loss2: 0.403868
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.950794 Loss1: 0.552784 Loss2: 0.398009
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.899799 Loss1: 0.504604 Loss2: 0.395195
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.874083 Loss1: 0.479904 Loss2: 0.394179
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.858376 Loss1: 0.464795 Loss2: 0.393581
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.810812 Loss1: 0.420533 Loss2: 0.390279
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.784761 Loss1: 0.395265 Loss2: 0.389496
-(DefaultActor pid=1838052) >> Training accuracy: 0.914679
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.006817 Loss1: 0.963756 Loss2: 0.043061
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.740130 Loss1: 0.695481 Loss2: 0.044649
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.609965 Loss1: 0.566379 Loss2: 0.043586
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.513614 Loss1: 0.469943 Loss2: 0.043670
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.481199 Loss1: 0.436849 Loss2: 0.044349
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.431784 Loss1: 0.387609 Loss2: 0.044175
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.416802 Loss1: 0.371981 Loss2: 0.044821
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.383177 Loss1: 0.338348 Loss2: 0.044829
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.346540 Loss1: 0.301674 Loss2: 0.044866
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.342785 Loss1: 0.297885 Loss2: 0.044900
-(DefaultActor pid=1838052) >> Training accuracy: 0.910657
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.062043 Loss1: 1.018211 Loss2: 0.043832
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.768667 Loss1: 0.722160 Loss2: 0.046506
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.602303 Loss1: 0.557545 Loss2: 0.044758
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.573391 Loss1: 0.527849 Loss2: 0.045542
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.556267 Loss1: 0.510164 Loss2: 0.046103
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.509954 Loss1: 0.462856 Loss2: 0.047098
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.456527 Loss1: 0.410131 Loss2: 0.046396
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.404051 Loss1: 0.357903 Loss2: 0.046148
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.362776 Loss1: 0.317228 Loss2: 0.045548
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.369069 Loss1: 0.323157 Loss2: 0.045911
-(DefaultActor pid=1838052) >> Training accuracy: 0.917722
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.133527 Loss1: 1.086964 Loss2: 0.046563
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.771041 Loss1: 0.723159 Loss2: 0.047882
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.647489 Loss1: 0.601376 Loss2: 0.046113
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.552546 Loss1: 0.506683 Loss2: 0.045863
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.556132 Loss1: 0.508557 Loss2: 0.047575
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.504572 Loss1: 0.457466 Loss2: 0.047106
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.434810 Loss1: 0.388068 Loss2: 0.046742
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.448467 Loss1: 0.401154 Loss2: 0.047313
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.391151 Loss1: 0.344130 Loss2: 0.047021
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.368441 Loss1: 0.320417 Loss2: 0.048025
-(DefaultActor pid=1838052) >> Training accuracy: 0.926943
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.020442 Loss1: 0.974636 Loss2: 0.045807
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.732875 Loss1: 0.685751 Loss2: 0.047124
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.627098 Loss1: 0.579875 Loss2: 0.047223
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.540052 Loss1: 0.492797 Loss2: 0.047256
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.494960 Loss1: 0.448496 Loss2: 0.046464
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.493236 Loss1: 0.446072 Loss2: 0.047164
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.417185 Loss1: 0.370479 Loss2: 0.046706
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.404850 Loss1: 0.358624 Loss2: 0.046227
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.347062 Loss1: 0.301978 Loss2: 0.045085
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.316555 Loss1: 0.271186 Loss2: 0.045369
-(DefaultActor pid=1838052) >> Training accuracy: 0.924913
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.049166 Loss1: 1.006273 Loss2: 0.042892
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.760058 Loss1: 0.714711 Loss2: 0.045348
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.648274 Loss1: 0.603691 Loss2: 0.044583
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.579731 Loss1: 0.534787 Loss2: 0.044944
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.551903 Loss1: 0.505815 Loss2: 0.046087
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.467522 Loss1: 0.422879 Loss2: 0.044644
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.455439 Loss1: 0.410061 Loss2: 0.045378
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.418916 Loss1: 0.373362 Loss2: 0.045555
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.393119 Loss1: 0.348159 Loss2: 0.044960
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.415593 Loss1: 0.369383 Loss2: 0.046209
-(DefaultActor pid=1838052) >> Training accuracy: 0.909489
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.122089 Loss1: 1.016830 Loss2: 0.105260
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.828840 Loss1: 0.729226 Loss2: 0.099613
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.704360 Loss1: 0.609469 Loss2: 0.094891
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.625981 Loss1: 0.534246 Loss2: 0.091736
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.601096 Loss1: 0.510603 Loss2: 0.090494
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.545421 Loss1: 0.456452 Loss2: 0.088969
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.498218 Loss1: 0.411243 Loss2: 0.086975
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.469274 Loss1: 0.384182 Loss2: 0.085092
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.470382 Loss1: 0.384529 Loss2: 0.085853
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.401793 Loss1: 0.318421 Loss2: 0.083373
-(DefaultActor pid=1838052) >> Training accuracy: 0.879945
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.570058 Loss1: 1.010276 Loss2: 0.559782
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.311304 Loss1: 0.755484 Loss2: 0.555820
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.191218 Loss1: 0.653780 Loss2: 0.537438
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.092964 Loss1: 0.567382 Loss2: 0.525582
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.073860 Loss1: 0.557575 Loss2: 0.516285
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.973117 Loss1: 0.463550 Loss2: 0.509568
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.937814 Loss1: 0.434690 Loss2: 0.503123
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.902070 Loss1: 0.408125 Loss2: 0.493945
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.858425 Loss1: 0.365613 Loss2: 0.492812
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.861992 Loss1: 0.373332 Loss2: 0.488660
-(DefaultActor pid=1838052) >> Training accuracy: 0.911392
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.146627 Loss1: 1.054895 Loss2: 0.091732
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.830245 Loss1: 0.744791 Loss2: 0.085454
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.699353 Loss1: 0.620052 Loss2: 0.079301
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.642160 Loss1: 0.563889 Loss2: 0.078271
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.585266 Loss1: 0.509939 Loss2: 0.075327
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.548909 Loss1: 0.474029 Loss2: 0.074879
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.487629 Loss1: 0.414460 Loss2: 0.073169
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.455683 Loss1: 0.382777 Loss2: 0.072906
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.453079 Loss1: 0.380573 Loss2: 0.072506
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.384286 Loss1: 0.312532 Loss2: 0.071753
-(DefaultActor pid=1838052) >> Training accuracy: 0.917067
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 16:37:49,400][flwr][DEBUG] - fit_round 19 received 10 results and 0 failures
->> Test accuracy: 0.568500
-[2023-09-27 16:38:32,280][flwr][INFO] - fit progress: (19, 2.010581853100286, {'accuracy': 0.5685}, 37135.17036648141)
-[2023-09-27 16:38:32,280][flwr][DEBUG] - evaluate_round 19: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:39:09,510][flwr][DEBUG] - evaluate_round 19 received 10 results and 0 failures
-[2023-09-27 16:39:09,511][flwr][DEBUG] - fit_round 20: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.557567 Loss1: 0.981923 Loss2: 0.575644
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.276597 Loss1: 0.696957 Loss2: 0.579640
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.143573 Loss1: 0.575979 Loss2: 0.567594
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.078683 Loss1: 0.519164 Loss2: 0.559518
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.992975 Loss1: 0.440549 Loss2: 0.552426
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.963377 Loss1: 0.421037 Loss2: 0.542340
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.936405 Loss1: 0.399138 Loss2: 0.537267
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.927093 Loss1: 0.393949 Loss2: 0.533144
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.877520 Loss1: 0.351057 Loss2: 0.526463
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.851879 Loss1: 0.329258 Loss2: 0.522621
-(DefaultActor pid=1838052) >> Training accuracy: 0.922271
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.555893 Loss1: 0.969948 Loss2: 0.585945
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.299866 Loss1: 0.715770 Loss2: 0.584095
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.175296 Loss1: 0.603976 Loss2: 0.571320
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.065792 Loss1: 0.507682 Loss2: 0.558110
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.016069 Loss1: 0.469441 Loss2: 0.546628
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.995524 Loss1: 0.457796 Loss2: 0.537728
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.935638 Loss1: 0.405149 Loss2: 0.530488
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.883340 Loss1: 0.359196 Loss2: 0.524144
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.879462 Loss1: 0.359216 Loss2: 0.520247
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.870339 Loss1: 0.353070 Loss2: 0.517268
-(DefaultActor pid=1838052) >> Training accuracy: 0.894778
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.535467 Loss1: 1.039561 Loss2: 0.495906
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.170456 Loss1: 0.727478 Loss2: 0.442977
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.024480 Loss1: 0.603928 Loss2: 0.420552
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.953348 Loss1: 0.535009 Loss2: 0.418339
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.931060 Loss1: 0.520416 Loss2: 0.410644
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.834455 Loss1: 0.429233 Loss2: 0.405221
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.832354 Loss1: 0.429019 Loss2: 0.403335
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.787421 Loss1: 0.386765 Loss2: 0.400656
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.792282 Loss1: 0.391054 Loss2: 0.401229
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.749314 Loss1: 0.351064 Loss2: 0.398250
-(DefaultActor pid=1838052) >> Training accuracy: 0.926482
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.378333 Loss1: 0.982160 Loss2: 0.396173
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.022265 Loss1: 0.689239 Loss2: 0.333026
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.917970 Loss1: 0.596456 Loss2: 0.321513
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.823823 Loss1: 0.504976 Loss2: 0.318847
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.756528 Loss1: 0.444056 Loss2: 0.312472
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.745703 Loss1: 0.435626 Loss2: 0.310078
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.712403 Loss1: 0.405192 Loss2: 0.307212
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.694511 Loss1: 0.386857 Loss2: 0.307653
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.662818 Loss1: 0.358684 Loss2: 0.304134
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.674153 Loss1: 0.367485 Loss2: 0.306668
-(DefaultActor pid=1838052) >> Training accuracy: 0.875791
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.162589 Loss1: 1.112539 Loss2: 0.050049
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.814500 Loss1: 0.763900 Loss2: 0.050599
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.662577 Loss1: 0.613629 Loss2: 0.048948
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.611336 Loss1: 0.561951 Loss2: 0.049385
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.531084 Loss1: 0.481680 Loss2: 0.049404
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.519723 Loss1: 0.470308 Loss2: 0.049415
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.498210 Loss1: 0.449128 Loss2: 0.049082
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.429970 Loss1: 0.380877 Loss2: 0.049093
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.437739 Loss1: 0.388109 Loss2: 0.049629
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.386747 Loss1: 0.336892 Loss2: 0.049855
-(DefaultActor pid=1838052) >> Training accuracy: 0.919819
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.943452 Loss1: 0.901955 Loss2: 0.041497
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.692220 Loss1: 0.647867 Loss2: 0.044352
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.532901 Loss1: 0.489467 Loss2: 0.043434
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.481723 Loss1: 0.438085 Loss2: 0.043638
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.431016 Loss1: 0.387912 Loss2: 0.043104
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.401392 Loss1: 0.357521 Loss2: 0.043871
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.374015 Loss1: 0.330119 Loss2: 0.043896
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.344715 Loss1: 0.300534 Loss2: 0.044180
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.324748 Loss1: 0.280795 Loss2: 0.043954
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.285352 Loss1: 0.241212 Loss2: 0.044140
-(DefaultActor pid=1838052) >> Training accuracy: 0.951723
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.497004 Loss1: 0.923439 Loss2: 0.573564
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.242268 Loss1: 0.674792 Loss2: 0.567476
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.141855 Loss1: 0.587375 Loss2: 0.554480
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.062666 Loss1: 0.520272 Loss2: 0.542393
-(DefaultActor pid=1838052) Epoch: 4 Loss: 1.013499 Loss1: 0.483532 Loss2: 0.529968
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.960152 Loss1: 0.439046 Loss2: 0.521105
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.949741 Loss1: 0.435501 Loss2: 0.514240
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.867497 Loss1: 0.360525 Loss2: 0.506972
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.881732 Loss1: 0.378075 Loss2: 0.503657
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.808992 Loss1: 0.309981 Loss2: 0.499010
-(DefaultActor pid=1838052) >> Training accuracy: 0.918636
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.071353 Loss1: 1.026061 Loss2: 0.045292
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.740544 Loss1: 0.694702 Loss2: 0.045841
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.598450 Loss1: 0.553362 Loss2: 0.045088
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.543369 Loss1: 0.498021 Loss2: 0.045348
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.491014 Loss1: 0.445601 Loss2: 0.045413
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.432636 Loss1: 0.386774 Loss2: 0.045861
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.384475 Loss1: 0.339304 Loss2: 0.045171
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.372290 Loss1: 0.326946 Loss2: 0.045345
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.346189 Loss1: 0.300735 Loss2: 0.045454
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.341397 Loss1: 0.296020 Loss2: 0.045377
-(DefaultActor pid=1838052) >> Training accuracy: 0.947424
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.006887 Loss1: 0.963247 Loss2: 0.043640
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.703091 Loss1: 0.657852 Loss2: 0.045239
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.579959 Loss1: 0.535249 Loss2: 0.044710
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.500123 Loss1: 0.455449 Loss2: 0.044673
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.474804 Loss1: 0.429448 Loss2: 0.045356
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.383119 Loss1: 0.338756 Loss2: 0.044363
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.365224 Loss1: 0.320371 Loss2: 0.044853
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.395885 Loss1: 0.350060 Loss2: 0.045825
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.369360 Loss1: 0.323700 Loss2: 0.045660
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.334130 Loss1: 0.288798 Loss2: 0.045332
-(DefaultActor pid=1838052) >> Training accuracy: 0.931641
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.031388 Loss1: 0.985435 Loss2: 0.045953
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.702099 Loss1: 0.654730 Loss2: 0.047369
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.602965 Loss1: 0.556070 Loss2: 0.046895
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.555393 Loss1: 0.508033 Loss2: 0.047360
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.520120 Loss1: 0.472078 Loss2: 0.048042
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.445848 Loss1: 0.399014 Loss2: 0.046834
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.410376 Loss1: 0.363109 Loss2: 0.047267
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.379320 Loss1: 0.332344 Loss2: 0.046976
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.399758 Loss1: 0.351975 Loss2: 0.047783
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.346040 Loss1: 0.298618 Loss2: 0.047422
-(DefaultActor pid=1838052) >> Training accuracy: 0.929786
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 17:09:06,770][flwr][DEBUG] - fit_round 20 received 10 results and 0 failures
->> Test accuracy: 0.578400
-[2023-09-27 17:09:50,073][flwr][INFO] - fit progress: (20, 1.9967298349633384, {'accuracy': 0.5784}, 39012.96377548808)
-[2023-09-27 17:09:50,074][flwr][DEBUG] - evaluate_round 20: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:10:28,741][flwr][DEBUG] - evaluate_round 20 received 10 results and 0 failures
-[2023-09-27 17:10:28,742][flwr][DEBUG] - fit_round 21: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.441074 Loss1: 0.859878 Loss2: 0.581196
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.199901 Loss1: 0.618381 Loss2: 0.581520
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.056999 Loss1: 0.488822 Loss2: 0.568177
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.976549 Loss1: 0.420051 Loss2: 0.556498
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.960750 Loss1: 0.409162 Loss2: 0.551588
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.878549 Loss1: 0.336997 Loss2: 0.541552
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.887394 Loss1: 0.351022 Loss2: 0.536372
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.822501 Loss1: 0.291778 Loss2: 0.530723
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.848029 Loss1: 0.319236 Loss2: 0.528793
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.772479 Loss1: 0.250474 Loss2: 0.522005
-(DefaultActor pid=1838052) >> Training accuracy: 0.940505
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.045304 Loss1: 1.000116 Loss2: 0.045188
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.713351 Loss1: 0.667202 Loss2: 0.046149
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.604490 Loss1: 0.558919 Loss2: 0.045571
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.547714 Loss1: 0.501507 Loss2: 0.046207
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.494390 Loss1: 0.447834 Loss2: 0.046556
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.484695 Loss1: 0.437551 Loss2: 0.047144
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.403437 Loss1: 0.356885 Loss2: 0.046552
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.422633 Loss1: 0.375701 Loss2: 0.046932
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.415666 Loss1: 0.368436 Loss2: 0.047231
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.355755 Loss1: 0.308305 Loss2: 0.047451
-(DefaultActor pid=1838052) >> Training accuracy: 0.925781
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.952721 Loss1: 0.904454 Loss2: 0.048266
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.677717 Loss1: 0.628052 Loss2: 0.049665
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.570709 Loss1: 0.522028 Loss2: 0.048681
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.499929 Loss1: 0.450646 Loss2: 0.049283
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.481601 Loss1: 0.432837 Loss2: 0.048764
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.427915 Loss1: 0.378968 Loss2: 0.048947
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.352349 Loss1: 0.304148 Loss2: 0.048201
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.379245 Loss1: 0.330256 Loss2: 0.048990
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.332342 Loss1: 0.284393 Loss2: 0.047949
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.312073 Loss1: 0.264045 Loss2: 0.048028
-(DefaultActor pid=1838052) >> Training accuracy: 0.910601
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.327963 Loss1: 0.914075 Loss2: 0.413889
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.987661 Loss1: 0.628477 Loss2: 0.359184
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.887983 Loss1: 0.544178 Loss2: 0.343805
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.834299 Loss1: 0.497696 Loss2: 0.336603
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.737909 Loss1: 0.404606 Loss2: 0.333303
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.714861 Loss1: 0.384924 Loss2: 0.329937
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.686495 Loss1: 0.360132 Loss2: 0.326363
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.640504 Loss1: 0.315885 Loss2: 0.324618
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.651182 Loss1: 0.324467 Loss2: 0.326715
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.608174 Loss1: 0.287499 Loss2: 0.320675
-(DefaultActor pid=1838052) >> Training accuracy: 0.934335
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.967278 Loss1: 0.917821 Loss2: 0.049456
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.689100 Loss1: 0.637787 Loss2: 0.051313
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.575058 Loss1: 0.525446 Loss2: 0.049612
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.515126 Loss1: 0.465500 Loss2: 0.049627
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.453983 Loss1: 0.405027 Loss2: 0.048957
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.439727 Loss1: 0.391352 Loss2: 0.048376
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.392861 Loss1: 0.344768 Loss2: 0.048093
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.398073 Loss1: 0.349548 Loss2: 0.048525
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.357302 Loss1: 0.309088 Loss2: 0.048214
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.361836 Loss1: 0.313243 Loss2: 0.048593
-(DefaultActor pid=1838052) >> Training accuracy: 0.914663
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.959772 Loss1: 0.912866 Loss2: 0.046906
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.706844 Loss1: 0.657386 Loss2: 0.049459
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.547176 Loss1: 0.499532 Loss2: 0.047644
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.481747 Loss1: 0.434211 Loss2: 0.047536
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.471999 Loss1: 0.424831 Loss2: 0.047168
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.414049 Loss1: 0.365776 Loss2: 0.048273
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.386935 Loss1: 0.339662 Loss2: 0.047274
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.352964 Loss1: 0.305911 Loss2: 0.047053
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.361224 Loss1: 0.313369 Loss2: 0.047855
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.320947 Loss1: 0.273593 Loss2: 0.047354
-(DefaultActor pid=1838052) >> Training accuracy: 0.918908
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.913349 Loss1: 0.868458 Loss2: 0.044891
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.676273 Loss1: 0.629097 Loss2: 0.047176
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.566736 Loss1: 0.519984 Loss2: 0.046752
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.486017 Loss1: 0.439703 Loss2: 0.046314
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.438555 Loss1: 0.391941 Loss2: 0.046615
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.399655 Loss1: 0.352631 Loss2: 0.047023
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.354819 Loss1: 0.308622 Loss2: 0.046198
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.380022 Loss1: 0.333457 Loss2: 0.046565
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.320814 Loss1: 0.274735 Loss2: 0.046079
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.312016 Loss1: 0.265779 Loss2: 0.046237
-(DefaultActor pid=1838052) >> Training accuracy: 0.904345
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.983647 Loss1: 0.939547 Loss2: 0.044099
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.649935 Loss1: 0.604044 Loss2: 0.045891
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.540792 Loss1: 0.495479 Loss2: 0.045312
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.448605 Loss1: 0.403888 Loss2: 0.044717
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.432135 Loss1: 0.387196 Loss2: 0.044939
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.364650 Loss1: 0.320168 Loss2: 0.044481
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.369096 Loss1: 0.324701 Loss2: 0.044396
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.332601 Loss1: 0.287835 Loss2: 0.044766
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.309000 Loss1: 0.264531 Loss2: 0.044468
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.261675 Loss1: 0.217272 Loss2: 0.044402
-(DefaultActor pid=1838052) >> Training accuracy: 0.938585
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.965494 Loss1: 0.921074 Loss2: 0.044421
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.661705 Loss1: 0.615895 Loss2: 0.045810
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.524004 Loss1: 0.478808 Loss2: 0.045196
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.502442 Loss1: 0.456562 Loss2: 0.045880
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.469690 Loss1: 0.423445 Loss2: 0.046245
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.384042 Loss1: 0.338435 Loss2: 0.045608
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.399315 Loss1: 0.352755 Loss2: 0.046561
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.341316 Loss1: 0.295108 Loss2: 0.046208
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.306559 Loss1: 0.260701 Loss2: 0.045858
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.324713 Loss1: 0.278167 Loss2: 0.046546
-(DefaultActor pid=1838052) >> Training accuracy: 0.938093
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.056950 Loss1: 0.951987 Loss2: 0.104963
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.734951 Loss1: 0.635487 Loss2: 0.099464
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.618416 Loss1: 0.522625 Loss2: 0.095791
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.541144 Loss1: 0.448410 Loss2: 0.092734
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.462529 Loss1: 0.373575 Loss2: 0.088954
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.437146 Loss1: 0.350089 Loss2: 0.087057
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.435166 Loss1: 0.348518 Loss2: 0.086648
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.379911 Loss1: 0.296075 Loss2: 0.083836
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.368705 Loss1: 0.284690 Loss2: 0.084015
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.346089 Loss1: 0.262899 Loss2: 0.083189
-(DefaultActor pid=1838052) >> Training accuracy: 0.933488
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 17:40:22,056][flwr][DEBUG] - fit_round 21 received 10 results and 0 failures
->> Test accuracy: 0.582800
-[2023-09-27 17:41:04,561][flwr][INFO] - fit progress: (21, 2.0065166573174085, {'accuracy': 0.5828}, 40887.45143873524)
-[2023-09-27 17:41:04,561][flwr][DEBUG] - evaluate_round 21: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:41:42,490][flwr][DEBUG] - evaluate_round 21 received 10 results and 0 failures
-[2023-09-27 17:41:42,491][flwr][DEBUG] - fit_round 22: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.941718 Loss1: 0.865424 Loss2: 0.076293
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.696892 Loss1: 0.621861 Loss2: 0.075030
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.561636 Loss1: 0.489644 Loss2: 0.071992
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.484527 Loss1: 0.414949 Loss2: 0.069578
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.414811 Loss1: 0.347628 Loss2: 0.067184
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.406092 Loss1: 0.338522 Loss2: 0.067570
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.367119 Loss1: 0.301095 Loss2: 0.066024
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.326634 Loss1: 0.261858 Loss2: 0.064776
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.362481 Loss1: 0.295838 Loss2: 0.066643
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.348904 Loss1: 0.283527 Loss2: 0.065377
-(DefaultActor pid=1838052) >> Training accuracy: 0.928204
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.885240 Loss1: 0.803654 Loss2: 0.081586
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.634237 Loss1: 0.553165 Loss2: 0.081072
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.508780 Loss1: 0.431880 Loss2: 0.076900
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.464929 Loss1: 0.389477 Loss2: 0.075451
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.405151 Loss1: 0.331214 Loss2: 0.073937
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.357989 Loss1: 0.286904 Loss2: 0.071086
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.377463 Loss1: 0.305869 Loss2: 0.071594
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.342598 Loss1: 0.272293 Loss2: 0.070305
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.315901 Loss1: 0.246300 Loss2: 0.069601
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.298775 Loss1: 0.229496 Loss2: 0.069280
-(DefaultActor pid=1838052) >> Training accuracy: 0.955329
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.421984 Loss1: 0.844938 Loss2: 0.577047
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.171000 Loss1: 0.594710 Loss2: 0.576290
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.034807 Loss1: 0.469479 Loss2: 0.565329
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.009238 Loss1: 0.453666 Loss2: 0.555572
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.935657 Loss1: 0.388004 Loss2: 0.547654
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.888108 Loss1: 0.351023 Loss2: 0.537085
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.860570 Loss1: 0.328567 Loss2: 0.532003
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.844411 Loss1: 0.319333 Loss2: 0.525078
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.840301 Loss1: 0.319082 Loss2: 0.521219
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.774654 Loss1: 0.258259 Loss2: 0.516395
-(DefaultActor pid=1838052) >> Training accuracy: 0.931962
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.465225 Loss1: 0.874702 Loss2: 0.590523
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.151426 Loss1: 0.560967 Loss2: 0.590459
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.070212 Loss1: 0.492032 Loss2: 0.578180
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.975420 Loss1: 0.409807 Loss2: 0.565613
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.933141 Loss1: 0.377815 Loss2: 0.555326
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.905085 Loss1: 0.357038 Loss2: 0.548046
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.831857 Loss1: 0.294995 Loss2: 0.536862
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.848960 Loss1: 0.318074 Loss2: 0.530886
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.839222 Loss1: 0.313344 Loss2: 0.525878
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.780549 Loss1: 0.261909 Loss2: 0.518640
-(DefaultActor pid=1838052) >> Training accuracy: 0.950738
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.451825 Loss1: 0.885333 Loss2: 0.566492
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.145621 Loss1: 0.582361 Loss2: 0.563259
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.052582 Loss1: 0.510052 Loss2: 0.542530
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.934325 Loss1: 0.406857 Loss2: 0.527468
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.926565 Loss1: 0.408536 Loss2: 0.518028
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.880250 Loss1: 0.370399 Loss2: 0.509851
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.845695 Loss1: 0.342828 Loss2: 0.502866
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.810349 Loss1: 0.314635 Loss2: 0.495714
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.804705 Loss1: 0.312464 Loss2: 0.492241
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.776538 Loss1: 0.288647 Loss2: 0.487891
-(DefaultActor pid=1838052) >> Training accuracy: 0.924644
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.483488 Loss1: 0.953191 Loss2: 0.530296
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.114051 Loss1: 0.606851 Loss2: 0.507199
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.973157 Loss1: 0.488574 Loss2: 0.484584
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.846898 Loss1: 0.375816 Loss2: 0.471082
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.841865 Loss1: 0.379892 Loss2: 0.461973
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.822580 Loss1: 0.366168 Loss2: 0.456411
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.807519 Loss1: 0.350467 Loss2: 0.457052
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.773651 Loss1: 0.320141 Loss2: 0.453510
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.776132 Loss1: 0.327328 Loss2: 0.448803
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.738742 Loss1: 0.288053 Loss2: 0.450689
-(DefaultActor pid=1838052) >> Training accuracy: 0.940667
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.981012 Loss1: 0.938015 Loss2: 0.042997
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.752022 Loss1: 0.707032 Loss2: 0.044991
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.588469 Loss1: 0.543059 Loss2: 0.045410
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.520652 Loss1: 0.475320 Loss2: 0.045332
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.448172 Loss1: 0.403459 Loss2: 0.044714
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.438653 Loss1: 0.392293 Loss2: 0.046360
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.397857 Loss1: 0.352811 Loss2: 0.045046
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.357486 Loss1: 0.312429 Loss2: 0.045057
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.364831 Loss1: 0.318827 Loss2: 0.046003
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.338922 Loss1: 0.293680 Loss2: 0.045242
-(DefaultActor pid=1838052) >> Training accuracy: 0.936061
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.427771 Loss1: 0.844918 Loss2: 0.582853
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.163618 Loss1: 0.575676 Loss2: 0.587942
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.085144 Loss1: 0.506600 Loss2: 0.578544
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.040379 Loss1: 0.469790 Loss2: 0.570589
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.945710 Loss1: 0.381638 Loss2: 0.564072
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.907066 Loss1: 0.350437 Loss2: 0.556629
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.939953 Loss1: 0.388294 Loss2: 0.551659
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.874640 Loss1: 0.329594 Loss2: 0.545046
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.836881 Loss1: 0.297318 Loss2: 0.539563
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.843252 Loss1: 0.308878 Loss2: 0.534374
-(DefaultActor pid=1838052) >> Training accuracy: 0.922468
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.940165 Loss1: 0.897418 Loss2: 0.042747
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.635666 Loss1: 0.591524 Loss2: 0.044142
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.536437 Loss1: 0.492373 Loss2: 0.044064
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.477844 Loss1: 0.433476 Loss2: 0.044369
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.423757 Loss1: 0.380108 Loss2: 0.043649
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.391274 Loss1: 0.347670 Loss2: 0.043604
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.359430 Loss1: 0.315772 Loss2: 0.043658
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.351545 Loss1: 0.307039 Loss2: 0.044506
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.306453 Loss1: 0.262495 Loss2: 0.043958
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.291937 Loss1: 0.247541 Loss2: 0.044396
-(DefaultActor pid=1838052) >> Training accuracy: 0.933494
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.883845 Loss1: 0.841526 Loss2: 0.042319
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.608033 Loss1: 0.563382 Loss2: 0.044651
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.491709 Loss1: 0.447866 Loss2: 0.043843
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.410151 Loss1: 0.366840 Loss2: 0.043311
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.409852 Loss1: 0.365871 Loss2: 0.043981
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.387459 Loss1: 0.343453 Loss2: 0.044005
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.379443 Loss1: 0.334925 Loss2: 0.044518
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.347798 Loss1: 0.302837 Loss2: 0.044961
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.312934 Loss1: 0.268691 Loss2: 0.044242
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.300063 Loss1: 0.255966 Loss2: 0.044097
-(DefaultActor pid=1838052) >> Training accuracy: 0.917683
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 18:11:35,463][flwr][DEBUG] - fit_round 22 received 10 results and 0 failures
->> Test accuracy: 0.589800
-[2023-09-27 18:12:16,304][flwr][INFO] - fit progress: (22, 1.9742871688577694, {'accuracy': 0.5898}, 42759.194697763305)
-[2023-09-27 18:12:16,305][flwr][DEBUG] - evaluate_round 22: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:12:53,899][flwr][DEBUG] - evaluate_round 22 received 10 results and 0 failures
-[2023-09-27 18:12:53,901][flwr][DEBUG] - fit_round 23: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.897760 Loss1: 0.853816 Loss2: 0.043944
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.604854 Loss1: 0.559184 Loss2: 0.045670
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.472775 Loss1: 0.427964 Loss2: 0.044811
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.401707 Loss1: 0.357052 Loss2: 0.044655
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.378779 Loss1: 0.334109 Loss2: 0.044669
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.394755 Loss1: 0.349103 Loss2: 0.045652
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.348546 Loss1: 0.303126 Loss2: 0.045420
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.310794 Loss1: 0.266043 Loss2: 0.044751
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.297991 Loss1: 0.252554 Loss2: 0.045437
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.290053 Loss1: 0.244538 Loss2: 0.045515
-(DefaultActor pid=1838052) >> Training accuracy: 0.948576
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.832921 Loss1: 0.791847 Loss2: 0.041074
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.593942 Loss1: 0.550451 Loss2: 0.043491
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.508246 Loss1: 0.464336 Loss2: 0.043909
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.392121 Loss1: 0.349331 Loss2: 0.042789
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.385467 Loss1: 0.342090 Loss2: 0.043378
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.367000 Loss1: 0.323835 Loss2: 0.043165
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.344153 Loss1: 0.300110 Loss2: 0.044043
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.315541 Loss1: 0.271735 Loss2: 0.043805
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.319785 Loss1: 0.276011 Loss2: 0.043774
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.284982 Loss1: 0.241442 Loss2: 0.043539
-(DefaultActor pid=1838052) >> Training accuracy: 0.946314
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.482602 Loss1: 0.907027 Loss2: 0.575575
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.207923 Loss1: 0.630182 Loss2: 0.577740
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.067583 Loss1: 0.501542 Loss2: 0.566041
-(DefaultActor pid=1838052) Epoch: 3 Loss: 1.004722 Loss1: 0.451139 Loss2: 0.553583
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.927729 Loss1: 0.383389 Loss2: 0.544340
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.907322 Loss1: 0.371932 Loss2: 0.535390
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.898847 Loss1: 0.370326 Loss2: 0.528521
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.855222 Loss1: 0.330604 Loss2: 0.524617
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.832190 Loss1: 0.311103 Loss2: 0.521087
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.812630 Loss1: 0.299143 Loss2: 0.513487
-(DefaultActor pid=1838052) >> Training accuracy: 0.909334
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.848869 Loss1: 0.804794 Loss2: 0.044076
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.593681 Loss1: 0.546680 Loss2: 0.047001
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.471065 Loss1: 0.425092 Loss2: 0.045972
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.453508 Loss1: 0.406421 Loss2: 0.047087
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.388486 Loss1: 0.342365 Loss2: 0.046121
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.352411 Loss1: 0.307167 Loss2: 0.045245
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.309301 Loss1: 0.264170 Loss2: 0.045131
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.295137 Loss1: 0.249810 Loss2: 0.045327
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.309300 Loss1: 0.263729 Loss2: 0.045571
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.275973 Loss1: 0.230811 Loss2: 0.045162
-(DefaultActor pid=1838052) >> Training accuracy: 0.941851
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.937725 Loss1: 0.891583 Loss2: 0.046142
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.603266 Loss1: 0.555953 Loss2: 0.047313
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.518921 Loss1: 0.472649 Loss2: 0.046271
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.436992 Loss1: 0.391205 Loss2: 0.045786
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.416439 Loss1: 0.370635 Loss2: 0.045803
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.353077 Loss1: 0.307042 Loss2: 0.046035
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.305383 Loss1: 0.260458 Loss2: 0.044924
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.310008 Loss1: 0.264782 Loss2: 0.045226
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.311976 Loss1: 0.266445 Loss2: 0.045531
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.288780 Loss1: 0.242930 Loss2: 0.045849
-(DefaultActor pid=1838052) >> Training accuracy: 0.947213
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.354231 Loss1: 0.816539 Loss2: 0.537692
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.087861 Loss1: 0.594086 Loss2: 0.493775
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.968870 Loss1: 0.499127 Loss2: 0.469743
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.892358 Loss1: 0.431604 Loss2: 0.460753
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.855238 Loss1: 0.403312 Loss2: 0.451926
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.775091 Loss1: 0.334337 Loss2: 0.440754
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.776764 Loss1: 0.337149 Loss2: 0.439615
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.714903 Loss1: 0.275481 Loss2: 0.439422
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.696339 Loss1: 0.263274 Loss2: 0.433065
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.721158 Loss1: 0.287909 Loss2: 0.433249
-(DefaultActor pid=1838052) >> Training accuracy: 0.942840
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.879163 Loss1: 0.834244 Loss2: 0.044919
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.612132 Loss1: 0.565633 Loss2: 0.046498
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.513995 Loss1: 0.467981 Loss2: 0.046015
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.420509 Loss1: 0.375547 Loss2: 0.044962
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.415696 Loss1: 0.370325 Loss2: 0.045371
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.326305 Loss1: 0.282147 Loss2: 0.044159
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.296134 Loss1: 0.251415 Loss2: 0.044719
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.309509 Loss1: 0.264494 Loss2: 0.045015
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.297027 Loss1: 0.252582 Loss2: 0.044445
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.278421 Loss1: 0.233806 Loss2: 0.044614
-(DefaultActor pid=1838052) >> Training accuracy: 0.951147
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.903548 Loss1: 0.820889 Loss2: 0.082658
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.579367 Loss1: 0.500709 Loss2: 0.078658
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.508653 Loss1: 0.434270 Loss2: 0.074382
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.445606 Loss1: 0.373158 Loss2: 0.072448
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.423586 Loss1: 0.352290 Loss2: 0.071296
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.340060 Loss1: 0.271922 Loss2: 0.068137
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.322795 Loss1: 0.255081 Loss2: 0.067715
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.333363 Loss1: 0.264658 Loss2: 0.068705
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.287051 Loss1: 0.220343 Loss2: 0.066709
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.269206 Loss1: 0.203312 Loss2: 0.065894
-(DefaultActor pid=1838052) >> Training accuracy: 0.964627
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.379302 Loss1: 0.807354 Loss2: 0.571948
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.124667 Loss1: 0.558584 Loss2: 0.566083
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.028679 Loss1: 0.479081 Loss2: 0.549598
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.926977 Loss1: 0.390628 Loss2: 0.536349
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.878971 Loss1: 0.356331 Loss2: 0.522640
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.859091 Loss1: 0.345992 Loss2: 0.513099
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.814484 Loss1: 0.310347 Loss2: 0.504137
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.826766 Loss1: 0.325325 Loss2: 0.501441
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.773020 Loss1: 0.277156 Loss2: 0.495864
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.757246 Loss1: 0.267514 Loss2: 0.489732
-(DefaultActor pid=1838052) >> Training accuracy: 0.928163
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.830002 Loss1: 0.789474 Loss2: 0.040528
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.545267 Loss1: 0.502546 Loss2: 0.042721
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.460087 Loss1: 0.417907 Loss2: 0.042180
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.397142 Loss1: 0.354301 Loss2: 0.042840
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.359462 Loss1: 0.316694 Loss2: 0.042768
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.315923 Loss1: 0.272895 Loss2: 0.043027
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.312817 Loss1: 0.269844 Loss2: 0.042973
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.288614 Loss1: 0.245959 Loss2: 0.042655
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.285487 Loss1: 0.242437 Loss2: 0.043050
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.228473 Loss1: 0.185811 Loss2: 0.042661
-(DefaultActor pid=1838052) >> Training accuracy: 0.961138
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 18:42:30,195][flwr][DEBUG] - fit_round 23 received 10 results and 0 failures
->> Test accuracy: 0.593400
-[2023-09-27 18:43:10,608][flwr][INFO] - fit progress: (23, 1.9792633229932084, {'accuracy': 0.5934}, 44613.49830036238)
-[2023-09-27 18:43:10,608][flwr][DEBUG] - evaluate_round 23: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:43:47,608][flwr][DEBUG] - evaluate_round 23 received 10 results and 0 failures
-[2023-09-27 18:43:47,609][flwr][DEBUG] - fit_round 24: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.809671 Loss1: 0.767432 Loss2: 0.042239
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.554770 Loss1: 0.509987 Loss2: 0.044783
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.491414 Loss1: 0.446746 Loss2: 0.044668
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.391403 Loss1: 0.347328 Loss2: 0.044075
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.391900 Loss1: 0.347654 Loss2: 0.044246
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.365596 Loss1: 0.321002 Loss2: 0.044594
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.296963 Loss1: 0.252662 Loss2: 0.044301
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.281741 Loss1: 0.237689 Loss2: 0.044052
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.286024 Loss1: 0.241630 Loss2: 0.044394
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.229328 Loss1: 0.185938 Loss2: 0.043390
-(DefaultActor pid=1838052) >> Training accuracy: 0.957278
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.335275 Loss1: 0.803665 Loss2: 0.531610
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.031538 Loss1: 0.544039 Loss2: 0.487499
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.921955 Loss1: 0.470787 Loss2: 0.451167
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.820945 Loss1: 0.387759 Loss2: 0.433186
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.774924 Loss1: 0.350948 Loss2: 0.423976
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.714086 Loss1: 0.299265 Loss2: 0.414821
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.641442 Loss1: 0.235724 Loss2: 0.405718
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.641401 Loss1: 0.238626 Loss2: 0.402775
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.640612 Loss1: 0.235056 Loss2: 0.405556
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.607392 Loss1: 0.208483 Loss2: 0.398909
-(DefaultActor pid=1838052) >> Training accuracy: 0.955512
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.169631 Loss1: 0.757580 Loss2: 0.412051
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.893505 Loss1: 0.544042 Loss2: 0.349462
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.806313 Loss1: 0.473007 Loss2: 0.333307
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.674513 Loss1: 0.355356 Loss2: 0.319156
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.611235 Loss1: 0.300586 Loss2: 0.310649
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.581628 Loss1: 0.274044 Loss2: 0.307583
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.592211 Loss1: 0.282807 Loss2: 0.309403
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.598014 Loss1: 0.291208 Loss2: 0.306806
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.571527 Loss1: 0.267148 Loss2: 0.304380
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.552564 Loss1: 0.247574 Loss2: 0.304990
-(DefaultActor pid=1838052) >> Training accuracy: 0.947790
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.775766 Loss1: 0.735807 Loss2: 0.039959
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.550558 Loss1: 0.507518 Loss2: 0.043040
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.481144 Loss1: 0.438147 Loss2: 0.042997
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.378559 Loss1: 0.336400 Loss2: 0.042158
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.359676 Loss1: 0.317511 Loss2: 0.042165
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.330441 Loss1: 0.287966 Loss2: 0.042474
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.273435 Loss1: 0.231868 Loss2: 0.041566
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.281946 Loss1: 0.240010 Loss2: 0.041936
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.311953 Loss1: 0.269245 Loss2: 0.042708
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.276991 Loss1: 0.234055 Loss2: 0.042936
-(DefaultActor pid=1838052) >> Training accuracy: 0.946005
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.405272 Loss1: 0.817210 Loss2: 0.588063
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.145517 Loss1: 0.563906 Loss2: 0.581611
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.006487 Loss1: 0.436867 Loss2: 0.569619
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.958589 Loss1: 0.398926 Loss2: 0.559663
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.895410 Loss1: 0.344969 Loss2: 0.550440
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.875142 Loss1: 0.333187 Loss2: 0.541955
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.851563 Loss1: 0.315570 Loss2: 0.535993
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.880980 Loss1: 0.348838 Loss2: 0.532142
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.795961 Loss1: 0.269256 Loss2: 0.526705
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.757297 Loss1: 0.235669 Loss2: 0.521628
-(DefaultActor pid=1838052) >> Training accuracy: 0.958734
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.835096 Loss1: 0.756379 Loss2: 0.078716
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.575227 Loss1: 0.500019 Loss2: 0.075208
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.496603 Loss1: 0.425786 Loss2: 0.070816
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.430763 Loss1: 0.362188 Loss2: 0.068575
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.403293 Loss1: 0.335560 Loss2: 0.067733
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.353969 Loss1: 0.287625 Loss2: 0.066344
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.348013 Loss1: 0.282697 Loss2: 0.065317
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.286386 Loss1: 0.221949 Loss2: 0.064436
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.316084 Loss1: 0.250965 Loss2: 0.065119
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.289504 Loss1: 0.225389 Loss2: 0.064114
-(DefaultActor pid=1838052) >> Training accuracy: 0.931962
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.773026 Loss1: 0.734111 Loss2: 0.038915
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.488842 Loss1: 0.447579 Loss2: 0.041263
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.419913 Loss1: 0.378964 Loss2: 0.040949
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.339868 Loss1: 0.299392 Loss2: 0.040476
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.300203 Loss1: 0.259697 Loss2: 0.040506
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.307518 Loss1: 0.266035 Loss2: 0.041484
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.273466 Loss1: 0.231876 Loss2: 0.041590
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.252018 Loss1: 0.210434 Loss2: 0.041584
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.243432 Loss1: 0.201768 Loss2: 0.041664
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.234819 Loss1: 0.193425 Loss2: 0.041394
-(DefaultActor pid=1838052) >> Training accuracy: 0.958734
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.926591 Loss1: 0.844133 Loss2: 0.082458
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.622943 Loss1: 0.544533 Loss2: 0.078410
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.539840 Loss1: 0.465063 Loss2: 0.074777
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.532233 Loss1: 0.459447 Loss2: 0.072786
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.441220 Loss1: 0.370842 Loss2: 0.070377
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.361286 Loss1: 0.292582 Loss2: 0.068704
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.328175 Loss1: 0.259930 Loss2: 0.068245
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.347943 Loss1: 0.280649 Loss2: 0.067294
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.313023 Loss1: 0.246508 Loss2: 0.066515
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.303495 Loss1: 0.237389 Loss2: 0.066106
-(DefaultActor pid=1838052) >> Training accuracy: 0.935033
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.357395 Loss1: 0.784028 Loss2: 0.573367
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.069473 Loss1: 0.497718 Loss2: 0.571754
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.000071 Loss1: 0.439079 Loss2: 0.560992
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.947387 Loss1: 0.398598 Loss2: 0.548789
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.881775 Loss1: 0.341615 Loss2: 0.540161
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.857513 Loss1: 0.321647 Loss2: 0.535867
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.811181 Loss1: 0.284508 Loss2: 0.526673
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.811145 Loss1: 0.291165 Loss2: 0.519980
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.801091 Loss1: 0.286774 Loss2: 0.514317
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.737694 Loss1: 0.227459 Loss2: 0.510235
-(DefaultActor pid=1838052) >> Training accuracy: 0.955301
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.849381 Loss1: 0.808420 Loss2: 0.040961
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.570889 Loss1: 0.528026 Loss2: 0.042863
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.458986 Loss1: 0.415984 Loss2: 0.043002
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.358058 Loss1: 0.315957 Loss2: 0.042101
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.351924 Loss1: 0.310259 Loss2: 0.041665
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.332808 Loss1: 0.290141 Loss2: 0.042668
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.288577 Loss1: 0.246681 Loss2: 0.041896
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.285486 Loss1: 0.242406 Loss2: 0.043080
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.260894 Loss1: 0.217639 Loss2: 0.043255
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.247928 Loss1: 0.205018 Loss2: 0.042911
-(DefaultActor pid=1838052) >> Training accuracy: 0.948691
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 19:13:22,422][flwr][DEBUG] - fit_round 24 received 10 results and 0 failures
->> Test accuracy: 0.592300
-[2023-09-27 19:14:02,714][flwr][INFO] - fit progress: (24, 2.0303315805931823, {'accuracy': 0.5923}, 46465.60474496428)
-[2023-09-27 19:14:02,715][flwr][DEBUG] - evaluate_round 24: strategy sampled 10 clients (out of 10)
-[2023-09-27 19:14:40,522][flwr][DEBUG] - evaluate_round 24 received 10 results and 0 failures
-[2023-09-27 19:14:40,526][flwr][DEBUG] - fit_round 25: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.329997 Loss1: 0.809417 Loss2: 0.520580
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.038079 Loss1: 0.566591 Loss2: 0.471489
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.928189 Loss1: 0.483159 Loss2: 0.445030
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.860489 Loss1: 0.426273 Loss2: 0.434216
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.807056 Loss1: 0.381910 Loss2: 0.425146
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.743316 Loss1: 0.325851 Loss2: 0.417466
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.724209 Loss1: 0.310999 Loss2: 0.413210
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.691067 Loss1: 0.282567 Loss2: 0.408500
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.648827 Loss1: 0.246798 Loss2: 0.402028
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.706358 Loss1: 0.300824 Loss2: 0.405533
-(DefaultActor pid=1838052) >> Training accuracy: 0.899671
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.256178 Loss1: 0.764300 Loss2: 0.491878
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.952706 Loss1: 0.518371 Loss2: 0.434335
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.867101 Loss1: 0.452533 Loss2: 0.414569
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.779830 Loss1: 0.374151 Loss2: 0.405678
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.726078 Loss1: 0.328193 Loss2: 0.397885
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.705921 Loss1: 0.307748 Loss2: 0.398173
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.669683 Loss1: 0.279284 Loss2: 0.390398
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.651646 Loss1: 0.264214 Loss2: 0.387432
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.635348 Loss1: 0.250480 Loss2: 0.384868
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.618906 Loss1: 0.234758 Loss2: 0.384148
-(DefaultActor pid=1838052) >> Training accuracy: 0.934335
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.778346 Loss1: 0.735770 Loss2: 0.042576
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.515475 Loss1: 0.470531 Loss2: 0.044944
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.462843 Loss1: 0.417798 Loss2: 0.045045
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.365726 Loss1: 0.321141 Loss2: 0.044585
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.325845 Loss1: 0.281654 Loss2: 0.044190
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.347942 Loss1: 0.302778 Loss2: 0.045164
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.304242 Loss1: 0.259390 Loss2: 0.044852
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.258155 Loss1: 0.213793 Loss2: 0.044362
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.273992 Loss1: 0.229184 Loss2: 0.044808
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.267303 Loss1: 0.221804 Loss2: 0.045499
-(DefaultActor pid=1838052) >> Training accuracy: 0.951345
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.312456 Loss1: 0.712473 Loss2: 0.599983
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.057236 Loss1: 0.448906 Loss2: 0.608331
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.962449 Loss1: 0.364300 Loss2: 0.598149
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.918843 Loss1: 0.327828 Loss2: 0.591015
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.845136 Loss1: 0.264435 Loss2: 0.580702
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.814914 Loss1: 0.243952 Loss2: 0.570962
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.817702 Loss1: 0.250645 Loss2: 0.567058
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.809295 Loss1: 0.247987 Loss2: 0.561307
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.784566 Loss1: 0.228535 Loss2: 0.556031
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.712430 Loss1: 0.162854 Loss2: 0.549576
-(DefaultActor pid=1838052) >> Training accuracy: 0.968950
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.790465 Loss1: 0.745323 Loss2: 0.045142
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.520170 Loss1: 0.473148 Loss2: 0.047022
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.440760 Loss1: 0.394355 Loss2: 0.046406
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.408869 Loss1: 0.362846 Loss2: 0.046023
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.370658 Loss1: 0.324982 Loss2: 0.045676
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.318662 Loss1: 0.273805 Loss2: 0.044857
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.302257 Loss1: 0.256891 Loss2: 0.045366
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.298903 Loss1: 0.253518 Loss2: 0.045385
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.299818 Loss1: 0.254821 Loss2: 0.044997
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.240192 Loss1: 0.195716 Loss2: 0.044476
-(DefaultActor pid=1838052) >> Training accuracy: 0.948718
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.782021 Loss1: 0.740751 Loss2: 0.041270
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.534417 Loss1: 0.490867 Loss2: 0.043550
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.417567 Loss1: 0.374426 Loss2: 0.043141
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.375231 Loss1: 0.332057 Loss2: 0.043174
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.330891 Loss1: 0.287669 Loss2: 0.043222
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.319109 Loss1: 0.275819 Loss2: 0.043290
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.303572 Loss1: 0.260073 Loss2: 0.043499
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.287586 Loss1: 0.243874 Loss2: 0.043712
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.246327 Loss1: 0.203842 Loss2: 0.042485
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.279693 Loss1: 0.236235 Loss2: 0.043459
-(DefaultActor pid=1838052) >> Training accuracy: 0.932753
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.426305 Loss1: 0.842161 Loss2: 0.584143
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.088509 Loss1: 0.501216 Loss2: 0.587292
-(DefaultActor pid=1838052) Epoch: 2 Loss: 1.006018 Loss1: 0.435116 Loss2: 0.570902
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.941382 Loss1: 0.384386 Loss2: 0.556996
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.879953 Loss1: 0.329938 Loss2: 0.550015
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.846112 Loss1: 0.308679 Loss2: 0.537432
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.824629 Loss1: 0.293451 Loss2: 0.531178
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.793173 Loss1: 0.269141 Loss2: 0.524032
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.751007 Loss1: 0.234943 Loss2: 0.516064
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.711479 Loss1: 0.202238 Loss2: 0.509241
-(DefaultActor pid=1838052) >> Training accuracy: 0.948057
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.736281 Loss1: 0.691854 Loss2: 0.044426
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.480219 Loss1: 0.433988 Loss2: 0.046231
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.417790 Loss1: 0.372172 Loss2: 0.045618
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.388054 Loss1: 0.342259 Loss2: 0.045794
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.343320 Loss1: 0.298035 Loss2: 0.045285
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.276188 Loss1: 0.231426 Loss2: 0.044762
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.261829 Loss1: 0.216741 Loss2: 0.045088
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.302489 Loss1: 0.256886 Loss2: 0.045604
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.256206 Loss1: 0.210527 Loss2: 0.045679
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.242100 Loss1: 0.197070 Loss2: 0.045029
-(DefaultActor pid=1838052) >> Training accuracy: 0.956364
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.822499 Loss1: 0.774482 Loss2: 0.048017
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.535868 Loss1: 0.485756 Loss2: 0.050112
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.451289 Loss1: 0.402874 Loss2: 0.048415
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.374764 Loss1: 0.327667 Loss2: 0.047097
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.313717 Loss1: 0.266879 Loss2: 0.046838
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.267606 Loss1: 0.221725 Loss2: 0.045881
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.309855 Loss1: 0.263296 Loss2: 0.046559
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.269192 Loss1: 0.222515 Loss2: 0.046676
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.243778 Loss1: 0.198089 Loss2: 0.045689
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.229755 Loss1: 0.183835 Loss2: 0.045919
-(DefaultActor pid=1838052) >> Training accuracy: 0.951172
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.759659 Loss1: 0.719646 Loss2: 0.040012
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.474219 Loss1: 0.432075 Loss2: 0.042144
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.392891 Loss1: 0.351218 Loss2: 0.041673
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.352903 Loss1: 0.311112 Loss2: 0.041791
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.355364 Loss1: 0.313029 Loss2: 0.042335
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.282094 Loss1: 0.240547 Loss2: 0.041547
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.303291 Loss1: 0.260490 Loss2: 0.042801
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.308169 Loss1: 0.265280 Loss2: 0.042889
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.256916 Loss1: 0.214957 Loss2: 0.041959
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.238460 Loss1: 0.196458 Loss2: 0.042002
-(DefaultActor pid=1838052) >> Training accuracy: 0.967959
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 19:44:23,473][flwr][DEBUG] - fit_round 25 received 10 results and 0 failures
->> Test accuracy: 0.600600
-[2023-09-27 19:45:04,991][flwr][INFO] - fit progress: (25, 1.9995412224778732, {'accuracy': 0.6006}, 48327.880902301054)
-[2023-09-27 19:45:04,991][flwr][DEBUG] - evaluate_round 25: strategy sampled 10 clients (out of 10)
-[2023-09-27 19:45:42,323][flwr][DEBUG] - evaluate_round 25 received 10 results and 0 failures
-[2023-09-27 19:45:42,324][flwr][DEBUG] - fit_round 26: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.307609 Loss1: 0.736728 Loss2: 0.570881
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.063060 Loss1: 0.498692 Loss2: 0.564369
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.939616 Loss1: 0.390017 Loss2: 0.549599
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.850015 Loss1: 0.321918 Loss2: 0.528097
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.872049 Loss1: 0.349522 Loss2: 0.522527
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.825248 Loss1: 0.310281 Loss2: 0.514967
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.806860 Loss1: 0.296424 Loss2: 0.510435
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.734933 Loss1: 0.232399 Loss2: 0.502534
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.714305 Loss1: 0.216596 Loss2: 0.497709
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.720780 Loss1: 0.223876 Loss2: 0.496904
-(DefaultActor pid=1838052) >> Training accuracy: 0.948378
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.708218 Loss1: 0.666537 Loss2: 0.041680
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.443802 Loss1: 0.400048 Loss2: 0.043754
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.357236 Loss1: 0.314299 Loss2: 0.042937
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.310887 Loss1: 0.268357 Loss2: 0.042530
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.271997 Loss1: 0.229497 Loss2: 0.042501
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.244033 Loss1: 0.201567 Loss2: 0.042466
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.261948 Loss1: 0.219346 Loss2: 0.042602
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.199135 Loss1: 0.157392 Loss2: 0.041744
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.185488 Loss1: 0.143832 Loss2: 0.041655
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.199730 Loss1: 0.157406 Loss2: 0.042325
-(DefaultActor pid=1838052) >> Training accuracy: 0.953125
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.858796 Loss1: 0.810445 Loss2: 0.048351
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.573353 Loss1: 0.523456 Loss2: 0.049897
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.454274 Loss1: 0.406157 Loss2: 0.048117
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.372983 Loss1: 0.325553 Loss2: 0.047430
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.370592 Loss1: 0.322694 Loss2: 0.047898
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.348996 Loss1: 0.301763 Loss2: 0.047232
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.310236 Loss1: 0.263016 Loss2: 0.047220
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.280667 Loss1: 0.233432 Loss2: 0.047235
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.286905 Loss1: 0.240425 Loss2: 0.046480
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.302074 Loss1: 0.254421 Loss2: 0.047653
-(DefaultActor pid=1838052) >> Training accuracy: 0.933183
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.749445 Loss1: 0.702145 Loss2: 0.047300
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.513036 Loss1: 0.464738 Loss2: 0.048298
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.377657 Loss1: 0.331102 Loss2: 0.046555
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.327487 Loss1: 0.282172 Loss2: 0.045315
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.294247 Loss1: 0.248933 Loss2: 0.045314
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.309151 Loss1: 0.263982 Loss2: 0.045168
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.310645 Loss1: 0.264854 Loss2: 0.045791
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.269481 Loss1: 0.224453 Loss2: 0.045028
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.250297 Loss1: 0.206408 Loss2: 0.043889
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.242481 Loss1: 0.197844 Loss2: 0.044637
-(DefaultActor pid=1838052) >> Training accuracy: 0.964201
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.739217 Loss1: 0.697954 Loss2: 0.041262
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.460103 Loss1: 0.415727 Loss2: 0.044376
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.413208 Loss1: 0.369592 Loss2: 0.043616
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.345216 Loss1: 0.301352 Loss2: 0.043863
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.319468 Loss1: 0.276097 Loss2: 0.043372
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.269692 Loss1: 0.226086 Loss2: 0.043607
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.277958 Loss1: 0.234574 Loss2: 0.043384
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.240899 Loss1: 0.198079 Loss2: 0.042820
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.230003 Loss1: 0.186654 Loss2: 0.043348
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.215585 Loss1: 0.172640 Loss2: 0.042945
-(DefaultActor pid=1838052) >> Training accuracy: 0.965278
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.803408 Loss1: 0.711184 Loss2: 0.092224
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.539614 Loss1: 0.449625 Loss2: 0.089989
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.453037 Loss1: 0.367429 Loss2: 0.085608
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.364378 Loss1: 0.281564 Loss2: 0.082814
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.388432 Loss1: 0.307434 Loss2: 0.080998
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.388608 Loss1: 0.307669 Loss2: 0.080938
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.330087 Loss1: 0.251514 Loss2: 0.078573
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.282251 Loss1: 0.204598 Loss2: 0.077652
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.304125 Loss1: 0.227660 Loss2: 0.076466
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.280841 Loss1: 0.204271 Loss2: 0.076569
-(DefaultActor pid=1838052) >> Training accuracy: 0.946005
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.275490 Loss1: 0.684764 Loss2: 0.590726
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.015861 Loss1: 0.432333 Loss2: 0.583528
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.934169 Loss1: 0.363726 Loss2: 0.570443
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.911339 Loss1: 0.352716 Loss2: 0.558623
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.814563 Loss1: 0.267862 Loss2: 0.546701
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.850898 Loss1: 0.310980 Loss2: 0.539919
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.791972 Loss1: 0.256382 Loss2: 0.535590
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.789446 Loss1: 0.256866 Loss2: 0.532579
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.749567 Loss1: 0.224281 Loss2: 0.525286
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.706731 Loss1: 0.186564 Loss2: 0.520168
-(DefaultActor pid=1838052) >> Training accuracy: 0.957674
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.823103 Loss1: 0.777706 Loss2: 0.045397
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.531531 Loss1: 0.483836 Loss2: 0.047695
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.410695 Loss1: 0.363961 Loss2: 0.046734
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.358452 Loss1: 0.312262 Loss2: 0.046190
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.318534 Loss1: 0.272690 Loss2: 0.045844
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.289983 Loss1: 0.243936 Loss2: 0.046047
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.286393 Loss1: 0.240019 Loss2: 0.046373
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.226492 Loss1: 0.180647 Loss2: 0.045845
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.226848 Loss1: 0.182400 Loss2: 0.044448
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.231644 Loss1: 0.186726 Loss2: 0.044919
-(DefaultActor pid=1838052) >> Training accuracy: 0.942356
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.707280 Loss1: 0.666059 Loss2: 0.041222
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.460701 Loss1: 0.417215 Loss2: 0.043487
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.391393 Loss1: 0.348482 Loss2: 0.042912
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.342186 Loss1: 0.299330 Loss2: 0.042857
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.303638 Loss1: 0.261163 Loss2: 0.042475
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.267833 Loss1: 0.225248 Loss2: 0.042585
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.263346 Loss1: 0.220522 Loss2: 0.042824
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.260324 Loss1: 0.217184 Loss2: 0.043139
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.261633 Loss1: 0.218306 Loss2: 0.043327
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.242152 Loss1: 0.199277 Loss2: 0.042875
-(DefaultActor pid=1838052) >> Training accuracy: 0.955793
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.791956 Loss1: 0.704503 Loss2: 0.087453
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.546324 Loss1: 0.463850 Loss2: 0.082474
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.445188 Loss1: 0.367720 Loss2: 0.077468
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.381346 Loss1: 0.307673 Loss2: 0.073673
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.362376 Loss1: 0.290364 Loss2: 0.072013
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.329719 Loss1: 0.258371 Loss2: 0.071348
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.332203 Loss1: 0.261551 Loss2: 0.070653
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.332045 Loss1: 0.262244 Loss2: 0.069801
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.282110 Loss1: 0.212550 Loss2: 0.069560
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.252673 Loss1: 0.184938 Loss2: 0.067735
-(DefaultActor pid=1838052) >> Training accuracy: 0.962941
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 20:15:21,337][flwr][DEBUG] - fit_round 26 received 10 results and 0 failures
->> Test accuracy: 0.600900
-[2023-09-27 20:16:03,496][flwr][INFO] - fit progress: (26, 2.022627312535295, {'accuracy': 0.6009}, 50186.38644901337)
-[2023-09-27 20:16:03,496][flwr][DEBUG] - evaluate_round 26: strategy sampled 10 clients (out of 10)
-[2023-09-27 20:16:40,760][flwr][DEBUG] - evaluate_round 26 received 10 results and 0 failures
-[2023-09-27 20:16:40,768][flwr][DEBUG] - fit_round 27: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.716829 Loss1: 0.675524 Loss2: 0.041305
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.490902 Loss1: 0.446680 Loss2: 0.044222
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.368907 Loss1: 0.325645 Loss2: 0.043262
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.349402 Loss1: 0.305222 Loss2: 0.044180
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.298198 Loss1: 0.254888 Loss2: 0.043309
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.298342 Loss1: 0.254968 Loss2: 0.043374
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.249775 Loss1: 0.205971 Loss2: 0.043805
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.245906 Loss1: 0.203141 Loss2: 0.042766
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.268300 Loss1: 0.224518 Loss2: 0.043782
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.219230 Loss1: 0.176128 Loss2: 0.043102
-(DefaultActor pid=1838052) >> Training accuracy: 0.965783
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.717385 Loss1: 0.657095 Loss2: 0.060290
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.480629 Loss1: 0.421247 Loss2: 0.059381
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.395177 Loss1: 0.339883 Loss2: 0.055294
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.399366 Loss1: 0.344697 Loss2: 0.054669
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.312214 Loss1: 0.259410 Loss2: 0.052804
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.303250 Loss1: 0.251261 Loss2: 0.051989
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.289556 Loss1: 0.238906 Loss2: 0.050650
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.273054 Loss1: 0.222267 Loss2: 0.050787
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.259198 Loss1: 0.208929 Loss2: 0.050269
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.200483 Loss1: 0.151784 Loss2: 0.048699
-(DefaultActor pid=1838052) >> Training accuracy: 0.960938
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.236836 Loss1: 0.643126 Loss2: 0.593710
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.009365 Loss1: 0.413419 Loss2: 0.595946
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.927049 Loss1: 0.348469 Loss2: 0.578579
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.860755 Loss1: 0.293641 Loss2: 0.567114
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.835735 Loss1: 0.278047 Loss2: 0.557688
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.833953 Loss1: 0.285483 Loss2: 0.548471
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.779594 Loss1: 0.236050 Loss2: 0.543544
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.787101 Loss1: 0.249168 Loss2: 0.537933
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.751625 Loss1: 0.219536 Loss2: 0.532089
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.766342 Loss1: 0.237862 Loss2: 0.528479
-(DefaultActor pid=1838052) >> Training accuracy: 0.950648
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.679282 Loss1: 0.641185 Loss2: 0.038097
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.417522 Loss1: 0.377009 Loss2: 0.040513
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.333061 Loss1: 0.293008 Loss2: 0.040053
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.298908 Loss1: 0.258525 Loss2: 0.040383
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.286204 Loss1: 0.245257 Loss2: 0.040947
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.252820 Loss1: 0.211704 Loss2: 0.041116
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.230131 Loss1: 0.189316 Loss2: 0.040815
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.192185 Loss1: 0.152068 Loss2: 0.040116
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.213965 Loss1: 0.173337 Loss2: 0.040628
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.230187 Loss1: 0.189021 Loss2: 0.041166
-(DefaultActor pid=1838052) >> Training accuracy: 0.952324
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.298190 Loss1: 0.696432 Loss2: 0.601758
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.032305 Loss1: 0.433411 Loss2: 0.598894
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.912406 Loss1: 0.332215 Loss2: 0.580190
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.929515 Loss1: 0.359647 Loss2: 0.569869
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.841935 Loss1: 0.282316 Loss2: 0.559619
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.795458 Loss1: 0.245011 Loss2: 0.550446
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.773979 Loss1: 0.234177 Loss2: 0.539802
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.740368 Loss1: 0.206261 Loss2: 0.534107
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.727348 Loss1: 0.198459 Loss2: 0.528889
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.744922 Loss1: 0.218948 Loss2: 0.525974
-(DefaultActor pid=1838052) >> Training accuracy: 0.941623
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.725778 Loss1: 0.681765 Loss2: 0.044014
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.447611 Loss1: 0.402621 Loss2: 0.044990
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.363807 Loss1: 0.319400 Loss2: 0.044407
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.315280 Loss1: 0.271143 Loss2: 0.044137
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.319019 Loss1: 0.274760 Loss2: 0.044259
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.264092 Loss1: 0.220276 Loss2: 0.043816
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.252676 Loss1: 0.208778 Loss2: 0.043898
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.257009 Loss1: 0.213152 Loss2: 0.043856
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.228965 Loss1: 0.185850 Loss2: 0.043115
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.257806 Loss1: 0.213964 Loss2: 0.043842
-(DefaultActor pid=1838052) >> Training accuracy: 0.945016
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.757254 Loss1: 0.715112 Loss2: 0.042142
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.542875 Loss1: 0.497684 Loss2: 0.045191
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.430824 Loss1: 0.386217 Loss2: 0.044607
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.357960 Loss1: 0.313278 Loss2: 0.044682
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.328626 Loss1: 0.284560 Loss2: 0.044067
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.288506 Loss1: 0.243563 Loss2: 0.044942
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.264788 Loss1: 0.221060 Loss2: 0.043728
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.287106 Loss1: 0.242978 Loss2: 0.044128
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.286793 Loss1: 0.241560 Loss2: 0.045233
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.258187 Loss1: 0.213464 Loss2: 0.044723
-(DefaultActor pid=1838052) >> Training accuracy: 0.960732
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.691833 Loss1: 0.650921 Loss2: 0.040912
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.493886 Loss1: 0.449778 Loss2: 0.044109
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.340478 Loss1: 0.297366 Loss2: 0.043112
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.310208 Loss1: 0.267723 Loss2: 0.042485
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.314442 Loss1: 0.271369 Loss2: 0.043073
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.267343 Loss1: 0.224131 Loss2: 0.043212
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.278235 Loss1: 0.234406 Loss2: 0.043829
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.230124 Loss1: 0.187056 Loss2: 0.043068
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.234309 Loss1: 0.191550 Loss2: 0.042758
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.238355 Loss1: 0.195019 Loss2: 0.043336
-(DefaultActor pid=1838052) >> Training accuracy: 0.955301
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.769019 Loss1: 0.727786 Loss2: 0.041234
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.464093 Loss1: 0.420724 Loss2: 0.043368
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.363980 Loss1: 0.321986 Loss2: 0.041994
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.354912 Loss1: 0.312125 Loss2: 0.042787
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.303808 Loss1: 0.260812 Loss2: 0.042996
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.256324 Loss1: 0.214323 Loss2: 0.042001
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.245323 Loss1: 0.202433 Loss2: 0.042890
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.229945 Loss1: 0.187007 Loss2: 0.042938
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.235672 Loss1: 0.192590 Loss2: 0.043082
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.196757 Loss1: 0.154161 Loss2: 0.042595
-(DefaultActor pid=1838052) >> Training accuracy: 0.964738
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.781613 Loss1: 0.694398 Loss2: 0.087215
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.489808 Loss1: 0.408833 Loss2: 0.080975
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.399242 Loss1: 0.323572 Loss2: 0.075671
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.369989 Loss1: 0.295731 Loss2: 0.074258
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.334783 Loss1: 0.262468 Loss2: 0.072315
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.288946 Loss1: 0.219001 Loss2: 0.069945
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.273322 Loss1: 0.203766 Loss2: 0.069557
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.249249 Loss1: 0.181009 Loss2: 0.068240
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.244970 Loss1: 0.177835 Loss2: 0.067135
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.267737 Loss1: 0.200274 Loss2: 0.067463
-(DefaultActor pid=1838052) >> Training accuracy: 0.955301
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 20:46:23,403][flwr][DEBUG] - fit_round 27 received 10 results and 0 failures
->> Test accuracy: 0.607200
-[2023-09-27 20:47:17,348][flwr][INFO] - fit progress: (27, 1.9937346629060495, {'accuracy': 0.6072}, 52060.238440748304)
-[2023-09-27 20:47:17,349][flwr][DEBUG] - evaluate_round 27: strategy sampled 10 clients (out of 10)
-[2023-09-27 20:47:54,443][flwr][DEBUG] - evaluate_round 27 received 10 results and 0 failures
-[2023-09-27 20:47:54,445][flwr][DEBUG] - fit_round 28: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.177499 Loss1: 0.587720 Loss2: 0.589778
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.997574 Loss1: 0.407888 Loss2: 0.589686
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.914400 Loss1: 0.336873 Loss2: 0.577528
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.860932 Loss1: 0.292887 Loss2: 0.568044
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.882401 Loss1: 0.322464 Loss2: 0.559937
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.836113 Loss1: 0.281923 Loss2: 0.554190
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.780129 Loss1: 0.234181 Loss2: 0.545947
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.758080 Loss1: 0.220503 Loss2: 0.537577
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.755611 Loss1: 0.218884 Loss2: 0.536727
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.740003 Loss1: 0.208614 Loss2: 0.531389
-(DefaultActor pid=1838052) >> Training accuracy: 0.952927
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.280394 Loss1: 0.683823 Loss2: 0.596571
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.042676 Loss1: 0.436843 Loss2: 0.605832
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.958412 Loss1: 0.363102 Loss2: 0.595310
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.868183 Loss1: 0.289814 Loss2: 0.578369
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.833320 Loss1: 0.261704 Loss2: 0.571616
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.813932 Loss1: 0.249905 Loss2: 0.564027
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.814511 Loss1: 0.258781 Loss2: 0.555730
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.763751 Loss1: 0.214377 Loss2: 0.549374
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.754031 Loss1: 0.211335 Loss2: 0.542696
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.737770 Loss1: 0.197981 Loss2: 0.539790
-(DefaultActor pid=1838052) >> Training accuracy: 0.960093
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.121187 Loss1: 0.654946 Loss2: 0.466241
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.852466 Loss1: 0.436757 Loss2: 0.415709
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.754742 Loss1: 0.357456 Loss2: 0.397286
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.721636 Loss1: 0.329777 Loss2: 0.391860
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.675751 Loss1: 0.287343 Loss2: 0.388409
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.631540 Loss1: 0.249050 Loss2: 0.382490
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.603074 Loss1: 0.224895 Loss2: 0.378178
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.620134 Loss1: 0.240548 Loss2: 0.379586
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.560094 Loss1: 0.186140 Loss2: 0.373954
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.591902 Loss1: 0.219117 Loss2: 0.372785
-(DefaultActor pid=1838052) >> Training accuracy: 0.942445
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.641224 Loss1: 0.601713 Loss2: 0.039512
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.400158 Loss1: 0.358245 Loss2: 0.041913
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.355149 Loss1: 0.313395 Loss2: 0.041754
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.312157 Loss1: 0.270722 Loss2: 0.041435
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.307111 Loss1: 0.265116 Loss2: 0.041995
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.247563 Loss1: 0.205720 Loss2: 0.041844
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.232229 Loss1: 0.190890 Loss2: 0.041339
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.246193 Loss1: 0.204057 Loss2: 0.042136
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.226924 Loss1: 0.184765 Loss2: 0.042159
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.237727 Loss1: 0.195503 Loss2: 0.042224
-(DefaultActor pid=1838052) >> Training accuracy: 0.958270
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.645695 Loss1: 0.607548 Loss2: 0.038146
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.414433 Loss1: 0.374205 Loss2: 0.040228
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.365731 Loss1: 0.325263 Loss2: 0.040468
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.304798 Loss1: 0.264357 Loss2: 0.040441
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.288989 Loss1: 0.248294 Loss2: 0.040695
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.294973 Loss1: 0.252866 Loss2: 0.042107
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.269186 Loss1: 0.227791 Loss2: 0.041395
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.237118 Loss1: 0.196994 Loss2: 0.040124
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.257407 Loss1: 0.215507 Loss2: 0.041900
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.228797 Loss1: 0.187380 Loss2: 0.041417
-(DefaultActor pid=1838052) >> Training accuracy: 0.964003
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.702489 Loss1: 0.658110 Loss2: 0.044379
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.448254 Loss1: 0.402296 Loss2: 0.045958
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.344919 Loss1: 0.300397 Loss2: 0.044523
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.303985 Loss1: 0.259816 Loss2: 0.044169
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.309023 Loss1: 0.264674 Loss2: 0.044349
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.244956 Loss1: 0.201206 Loss2: 0.043750
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.216750 Loss1: 0.174343 Loss2: 0.042407
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.216073 Loss1: 0.173873 Loss2: 0.042200
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.216481 Loss1: 0.173423 Loss2: 0.043059
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.208570 Loss1: 0.165991 Loss2: 0.042579
-(DefaultActor pid=1838052) >> Training accuracy: 0.967665
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.727481 Loss1: 0.638008 Loss2: 0.089473
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.494464 Loss1: 0.408062 Loss2: 0.086402
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.406864 Loss1: 0.324648 Loss2: 0.082216
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.364106 Loss1: 0.285396 Loss2: 0.078710
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.312121 Loss1: 0.235078 Loss2: 0.077042
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.295697 Loss1: 0.221805 Loss2: 0.073892
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.293448 Loss1: 0.218643 Loss2: 0.074804
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.304306 Loss1: 0.230408 Loss2: 0.073898
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.252374 Loss1: 0.180240 Loss2: 0.072133
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.233606 Loss1: 0.163471 Loss2: 0.070135
-(DefaultActor pid=1838052) >> Training accuracy: 0.973299
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.613084 Loss1: 0.576290 Loss2: 0.036794
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.396140 Loss1: 0.356770 Loss2: 0.039370
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.305053 Loss1: 0.265649 Loss2: 0.039404
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.259517 Loss1: 0.220334 Loss2: 0.039183
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.247862 Loss1: 0.208343 Loss2: 0.039519
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.254341 Loss1: 0.214421 Loss2: 0.039920
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.246924 Loss1: 0.206685 Loss2: 0.040239
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.225944 Loss1: 0.185983 Loss2: 0.039961
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.179295 Loss1: 0.139808 Loss2: 0.039487
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166240 Loss1: 0.126612 Loss2: 0.039628
-(DefaultActor pid=1838052) >> Training accuracy: 0.968950
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.745247 Loss1: 0.704614 Loss2: 0.040633
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.477103 Loss1: 0.434564 Loss2: 0.042539
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.383070 Loss1: 0.340844 Loss2: 0.042225
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.328652 Loss1: 0.286451 Loss2: 0.042201
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.300784 Loss1: 0.258217 Loss2: 0.042567
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.288436 Loss1: 0.244919 Loss2: 0.043517
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.264212 Loss1: 0.221609 Loss2: 0.042603
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.248111 Loss1: 0.204903 Loss2: 0.043207
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.242711 Loss1: 0.199806 Loss2: 0.042906
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.266911 Loss1: 0.222853 Loss2: 0.044058
-(DefaultActor pid=1838052) >> Training accuracy: 0.943462
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.678786 Loss1: 0.637983 Loss2: 0.040803
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.471213 Loss1: 0.427199 Loss2: 0.044014
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.386896 Loss1: 0.343218 Loss2: 0.043678
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.352327 Loss1: 0.308400 Loss2: 0.043926
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.324248 Loss1: 0.280130 Loss2: 0.044118
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.291913 Loss1: 0.248312 Loss2: 0.043600
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.229252 Loss1: 0.186709 Loss2: 0.042543
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.236890 Loss1: 0.194043 Loss2: 0.042847
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.229988 Loss1: 0.187529 Loss2: 0.042459
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.229906 Loss1: 0.187175 Loss2: 0.042731
-(DefaultActor pid=1838052) >> Training accuracy: 0.958534
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 21:17:39,383][flwr][DEBUG] - fit_round 28 received 10 results and 0 failures
->> Test accuracy: 0.608100
-[2023-09-27 21:18:19,931][flwr][INFO] - fit progress: (28, 2.021510124206543, {'accuracy': 0.6081}, 53922.82100760145)
-[2023-09-27 21:18:19,931][flwr][DEBUG] - evaluate_round 28: strategy sampled 10 clients (out of 10)
-[2023-09-27 21:18:56,020][flwr][DEBUG] - evaluate_round 28 received 10 results and 0 failures
-[2023-09-27 21:18:56,023][flwr][DEBUG] - fit_round 29: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.609343 Loss1: 0.569689 Loss2: 0.039654
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.384192 Loss1: 0.342446 Loss2: 0.041746
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.308441 Loss1: 0.267434 Loss2: 0.041007
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.311209 Loss1: 0.268946 Loss2: 0.042263
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.273300 Loss1: 0.231416 Loss2: 0.041884
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.221078 Loss1: 0.179840 Loss2: 0.041238
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.219603 Loss1: 0.178329 Loss2: 0.041274
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.213994 Loss1: 0.172220 Loss2: 0.041774
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.183487 Loss1: 0.142291 Loss2: 0.041196
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.203540 Loss1: 0.162281 Loss2: 0.041260
-(DefaultActor pid=1838052) >> Training accuracy: 0.958460
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.167830 Loss1: 0.573188 Loss2: 0.594641
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.972766 Loss1: 0.374860 Loss2: 0.597906
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.872644 Loss1: 0.288810 Loss2: 0.583834
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.853867 Loss1: 0.281314 Loss2: 0.572553
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.831808 Loss1: 0.267523 Loss2: 0.564285
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.784296 Loss1: 0.228438 Loss2: 0.555858
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.785732 Loss1: 0.233632 Loss2: 0.552099
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.759950 Loss1: 0.213973 Loss2: 0.545976
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.730845 Loss1: 0.190183 Loss2: 0.540662
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.739384 Loss1: 0.201537 Loss2: 0.537848
-(DefaultActor pid=1838052) >> Training accuracy: 0.950554
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.635625 Loss1: 0.596136 Loss2: 0.039489
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.412654 Loss1: 0.371131 Loss2: 0.041523
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.353941 Loss1: 0.312029 Loss2: 0.041913
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.281941 Loss1: 0.241066 Loss2: 0.040875
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.285294 Loss1: 0.243241 Loss2: 0.042053
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.286111 Loss1: 0.243231 Loss2: 0.042880
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.277236 Loss1: 0.235021 Loss2: 0.042215
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.253191 Loss1: 0.210772 Loss2: 0.042420
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.217026 Loss1: 0.175335 Loss2: 0.041691
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.206248 Loss1: 0.165261 Loss2: 0.040988
-(DefaultActor pid=1838052) >> Training accuracy: 0.960136
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.226984 Loss1: 0.618265 Loss2: 0.608719
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.995025 Loss1: 0.379163 Loss2: 0.615863
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.897348 Loss1: 0.292678 Loss2: 0.604670
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.882489 Loss1: 0.285508 Loss2: 0.596981
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.860895 Loss1: 0.274788 Loss2: 0.586107
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.816783 Loss1: 0.234258 Loss2: 0.582525
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.782721 Loss1: 0.209984 Loss2: 0.572737
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.750611 Loss1: 0.183057 Loss2: 0.567554
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.757368 Loss1: 0.196490 Loss2: 0.560878
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.702494 Loss1: 0.145876 Loss2: 0.556618
-(DefaultActor pid=1838052) >> Training accuracy: 0.962023
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.721243 Loss1: 0.680995 Loss2: 0.040248
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.475314 Loss1: 0.432782 Loss2: 0.042533
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.384264 Loss1: 0.341708 Loss2: 0.042556
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.304846 Loss1: 0.262417 Loss2: 0.042429
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.279489 Loss1: 0.237825 Loss2: 0.041665
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.242468 Loss1: 0.201186 Loss2: 0.041282
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.239861 Loss1: 0.198775 Loss2: 0.041086
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.219473 Loss1: 0.178015 Loss2: 0.041458
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.235116 Loss1: 0.193465 Loss2: 0.041652
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.236709 Loss1: 0.194275 Loss2: 0.042434
-(DefaultActor pid=1838052) >> Training accuracy: 0.956414
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.147349 Loss1: 0.614236 Loss2: 0.533113
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.893210 Loss1: 0.399053 Loss2: 0.494157
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.837763 Loss1: 0.355695 Loss2: 0.482068
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.765410 Loss1: 0.294237 Loss2: 0.471173
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.734894 Loss1: 0.265958 Loss2: 0.468936
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.720206 Loss1: 0.255044 Loss2: 0.465162
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.671209 Loss1: 0.212200 Loss2: 0.459009
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.655429 Loss1: 0.197627 Loss2: 0.457802
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.650467 Loss1: 0.196233 Loss2: 0.454234
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.642643 Loss1: 0.190718 Loss2: 0.451925
-(DefaultActor pid=1838052) >> Training accuracy: 0.963212
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.149229 Loss1: 0.563162 Loss2: 0.586067
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.948661 Loss1: 0.357722 Loss2: 0.590939
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.823357 Loss1: 0.255749 Loss2: 0.567607
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.790932 Loss1: 0.231437 Loss2: 0.559494
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.789250 Loss1: 0.239456 Loss2: 0.549794
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.770460 Loss1: 0.227281 Loss2: 0.543179
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.758205 Loss1: 0.218504 Loss2: 0.539701
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.716766 Loss1: 0.188212 Loss2: 0.528554
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.696864 Loss1: 0.171569 Loss2: 0.525295
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.673486 Loss1: 0.156062 Loss2: 0.517424
-(DefaultActor pid=1838052) >> Training accuracy: 0.963341
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.678957 Loss1: 0.603156 Loss2: 0.075801
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.449492 Loss1: 0.375799 Loss2: 0.073693
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.379342 Loss1: 0.308845 Loss2: 0.070497
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.327709 Loss1: 0.257764 Loss2: 0.069944
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.303658 Loss1: 0.235983 Loss2: 0.067676
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.244616 Loss1: 0.178586 Loss2: 0.066030
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.212771 Loss1: 0.148628 Loss2: 0.064142
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.238458 Loss1: 0.174575 Loss2: 0.063883
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.218952 Loss1: 0.155028 Loss2: 0.063925
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.236997 Loss1: 0.172274 Loss2: 0.064723
-(DefaultActor pid=1838052) >> Training accuracy: 0.963608
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.748009 Loss1: 0.672297 Loss2: 0.075712
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.482392 Loss1: 0.407664 Loss2: 0.074728
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.375665 Loss1: 0.306668 Loss2: 0.068997
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.313917 Loss1: 0.246745 Loss2: 0.067172
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.262749 Loss1: 0.197428 Loss2: 0.065321
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.252916 Loss1: 0.189239 Loss2: 0.063677
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.227047 Loss1: 0.164002 Loss2: 0.063045
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.240649 Loss1: 0.177635 Loss2: 0.063014
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.240205 Loss1: 0.177488 Loss2: 0.062717
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.232059 Loss1: 0.169484 Loss2: 0.062576
-(DefaultActor pid=1838052) >> Training accuracy: 0.961149
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.657874 Loss1: 0.576924 Loss2: 0.080950
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.453362 Loss1: 0.376315 Loss2: 0.077048
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.354366 Loss1: 0.281917 Loss2: 0.072448
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.299531 Loss1: 0.229745 Loss2: 0.069786
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.284920 Loss1: 0.217802 Loss2: 0.067119
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.281298 Loss1: 0.214522 Loss2: 0.066776
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.236804 Loss1: 0.171236 Loss2: 0.065568
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.197984 Loss1: 0.134497 Loss2: 0.063487
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.225552 Loss1: 0.160845 Loss2: 0.064707
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.244948 Loss1: 0.180164 Loss2: 0.064784
-(DefaultActor pid=1838052) >> Training accuracy: 0.946994
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 21:48:32,756][flwr][DEBUG] - fit_round 29 received 10 results and 0 failures
->> Test accuracy: 0.613600
-[2023-09-27 21:49:13,084][flwr][INFO] - fit progress: (29, 2.0112326653620687, {'accuracy': 0.6136}, 55775.973916619085)
-[2023-09-27 21:49:13,084][flwr][DEBUG] - evaluate_round 29: strategy sampled 10 clients (out of 10)
-[2023-09-27 21:49:50,074][flwr][DEBUG] - evaluate_round 29 received 10 results and 0 failures
-[2023-09-27 21:49:50,084][flwr][DEBUG] - fit_round 30: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.624713 Loss1: 0.583612 Loss2: 0.041101
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.371815 Loss1: 0.327672 Loss2: 0.044144
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.326926 Loss1: 0.283500 Loss2: 0.043426
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.257823 Loss1: 0.215917 Loss2: 0.041906
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.229837 Loss1: 0.187765 Loss2: 0.042072
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.250343 Loss1: 0.207143 Loss2: 0.043200
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.252589 Loss1: 0.209742 Loss2: 0.042847
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.193767 Loss1: 0.151453 Loss2: 0.042314
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.221844 Loss1: 0.179983 Loss2: 0.041861
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.211218 Loss1: 0.168521 Loss2: 0.042697
-(DefaultActor pid=1838052) >> Training accuracy: 0.962891
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.611579 Loss1: 0.569620 Loss2: 0.041959
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.381968 Loss1: 0.338363 Loss2: 0.043605
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.334093 Loss1: 0.289867 Loss2: 0.044227
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.297800 Loss1: 0.254134 Loss2: 0.043666
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.257089 Loss1: 0.213895 Loss2: 0.043195
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.242959 Loss1: 0.199802 Loss2: 0.043157
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.222784 Loss1: 0.179572 Loss2: 0.043212
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.212993 Loss1: 0.170071 Loss2: 0.042922
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.219501 Loss1: 0.176038 Loss2: 0.043463
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.210239 Loss1: 0.167343 Loss2: 0.042896
-(DefaultActor pid=1838052) >> Training accuracy: 0.956290
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.539029 Loss1: 0.501446 Loss2: 0.037583
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.390278 Loss1: 0.350068 Loss2: 0.040210
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.295013 Loss1: 0.255239 Loss2: 0.039773
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.250562 Loss1: 0.210572 Loss2: 0.039990
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.237424 Loss1: 0.196656 Loss2: 0.040768
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.225065 Loss1: 0.184921 Loss2: 0.040145
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.222692 Loss1: 0.182174 Loss2: 0.040518
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.208484 Loss1: 0.168295 Loss2: 0.040189
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.192171 Loss1: 0.151839 Loss2: 0.040333
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.170491 Loss1: 0.130105 Loss2: 0.040386
-(DefaultActor pid=1838052) >> Training accuracy: 0.969703
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.151093 Loss1: 0.630034 Loss2: 0.521058
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.890118 Loss1: 0.415282 Loss2: 0.474836
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.736240 Loss1: 0.290739 Loss2: 0.445501
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.741215 Loss1: 0.302946 Loss2: 0.438269
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.707304 Loss1: 0.278793 Loss2: 0.428511
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.625389 Loss1: 0.203296 Loss2: 0.422093
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.663295 Loss1: 0.239767 Loss2: 0.423527
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.605965 Loss1: 0.192113 Loss2: 0.413852
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.592576 Loss1: 0.182260 Loss2: 0.410316
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.588795 Loss1: 0.174767 Loss2: 0.414027
-(DefaultActor pid=1838052) >> Training accuracy: 0.963894
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.181091 Loss1: 0.581691 Loss2: 0.599400
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.962498 Loss1: 0.355950 Loss2: 0.606548
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.895521 Loss1: 0.298311 Loss2: 0.597210
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.845417 Loss1: 0.258436 Loss2: 0.586981
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.798640 Loss1: 0.220894 Loss2: 0.577745
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.808439 Loss1: 0.236100 Loss2: 0.572339
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.823353 Loss1: 0.252429 Loss2: 0.570924
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.813892 Loss1: 0.248958 Loss2: 0.564933
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.757118 Loss1: 0.195711 Loss2: 0.561407
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.728902 Loss1: 0.175798 Loss2: 0.553103
-(DefaultActor pid=1838052) >> Training accuracy: 0.952123
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.583465 Loss1: 0.543655 Loss2: 0.039810
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.410598 Loss1: 0.367157 Loss2: 0.043441
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.328883 Loss1: 0.285642 Loss2: 0.043241
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.284070 Loss1: 0.241463 Loss2: 0.042607
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.270479 Loss1: 0.228150 Loss2: 0.042329
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.266857 Loss1: 0.223746 Loss2: 0.043111
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.220983 Loss1: 0.178536 Loss2: 0.042447
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.240032 Loss1: 0.197216 Loss2: 0.042816
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.197071 Loss1: 0.154885 Loss2: 0.042186
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.189636 Loss1: 0.147482 Loss2: 0.042154
-(DefaultActor pid=1838052) >> Training accuracy: 0.958070
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.061179 Loss1: 0.594748 Loss2: 0.466431
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.816578 Loss1: 0.402703 Loss2: 0.413875
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.727632 Loss1: 0.323853 Loss2: 0.403780
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.692014 Loss1: 0.300215 Loss2: 0.391799
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.640419 Loss1: 0.254008 Loss2: 0.386410
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.620816 Loss1: 0.236238 Loss2: 0.384577
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.594845 Loss1: 0.214773 Loss2: 0.380072
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.533945 Loss1: 0.160313 Loss2: 0.373632
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.565998 Loss1: 0.188468 Loss2: 0.377530
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.584388 Loss1: 0.207761 Loss2: 0.376627
-(DefaultActor pid=1838052) >> Training accuracy: 0.956883
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.642753 Loss1: 0.561934 Loss2: 0.080819
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.411801 Loss1: 0.335183 Loss2: 0.076619
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.322232 Loss1: 0.250782 Loss2: 0.071450
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.288031 Loss1: 0.219001 Loss2: 0.069030
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.267718 Loss1: 0.199920 Loss2: 0.067798
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.284620 Loss1: 0.217322 Loss2: 0.067297
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.271186 Loss1: 0.203976 Loss2: 0.067210
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.215731 Loss1: 0.150541 Loss2: 0.065190
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.238301 Loss1: 0.172974 Loss2: 0.065327
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.175744 Loss1: 0.112573 Loss2: 0.063171
-(DefaultActor pid=1838052) >> Training accuracy: 0.973892
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.561107 Loss1: 0.521005 Loss2: 0.040101
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.379943 Loss1: 0.337011 Loss2: 0.042933
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.302756 Loss1: 0.260599 Loss2: 0.042157
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.239388 Loss1: 0.198166 Loss2: 0.041222
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.207714 Loss1: 0.166879 Loss2: 0.040835
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.210695 Loss1: 0.168852 Loss2: 0.041843
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.194991 Loss1: 0.153470 Loss2: 0.041521
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.210504 Loss1: 0.168920 Loss2: 0.041585
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.192397 Loss1: 0.151314 Loss2: 0.041084
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.175819 Loss1: 0.134688 Loss2: 0.041130
-(DefaultActor pid=1838052) >> Training accuracy: 0.969952
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.236232 Loss1: 0.639819 Loss2: 0.596413
-(DefaultActor pid=1838052) Epoch: 1 Loss: 1.001039 Loss1: 0.396086 Loss2: 0.604953
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.903614 Loss1: 0.310821 Loss2: 0.592793
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.852472 Loss1: 0.268292 Loss2: 0.584181
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.815309 Loss1: 0.242300 Loss2: 0.573009
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.799533 Loss1: 0.236774 Loss2: 0.562760
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.774075 Loss1: 0.215839 Loss2: 0.558236
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.776684 Loss1: 0.224087 Loss2: 0.552597
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.770680 Loss1: 0.222491 Loss2: 0.548190
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.778668 Loss1: 0.235601 Loss2: 0.543067
-(DefaultActor pid=1838052) >> Training accuracy: 0.948602
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 22:19:27,048][flwr][DEBUG] - fit_round 30 received 10 results and 0 failures
->> Test accuracy: 0.613300
-[2023-09-27 22:20:07,654][flwr][INFO] - fit progress: (30, 2.0224276781082153, {'accuracy': 0.6133}, 57630.5447032582)
-[2023-09-27 22:20:07,655][flwr][DEBUG] - evaluate_round 30: strategy sampled 10 clients (out of 10)
-[2023-09-27 22:20:43,581][flwr][DEBUG] - evaluate_round 30 received 10 results and 0 failures
-[2023-09-27 22:20:43,587][flwr][DEBUG] - fit_round 31: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.542811 Loss1: 0.503471 Loss2: 0.039340
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.348682 Loss1: 0.306207 Loss2: 0.042476
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.317740 Loss1: 0.274736 Loss2: 0.043004
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.306664 Loss1: 0.263193 Loss2: 0.043471
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.230721 Loss1: 0.188776 Loss2: 0.041945
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.229566 Loss1: 0.187075 Loss2: 0.042491
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.214551 Loss1: 0.172369 Loss2: 0.042182
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.267446 Loss1: 0.223608 Loss2: 0.043838
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.258311 Loss1: 0.214109 Loss2: 0.044202
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.209247 Loss1: 0.166136 Loss2: 0.043111
-(DefaultActor pid=1838052) >> Training accuracy: 0.953916
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.526774 Loss1: 0.488778 Loss2: 0.037996
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.362124 Loss1: 0.321414 Loss2: 0.040709
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.306700 Loss1: 0.266653 Loss2: 0.040047
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.260542 Loss1: 0.220252 Loss2: 0.040289
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.236668 Loss1: 0.196618 Loss2: 0.040050
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.234770 Loss1: 0.193979 Loss2: 0.040790
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.203769 Loss1: 0.163228 Loss2: 0.040542
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.187119 Loss1: 0.146868 Loss2: 0.040251
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.166274 Loss1: 0.126210 Loss2: 0.040064
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.177694 Loss1: 0.137843 Loss2: 0.039851
-(DefaultActor pid=1838052) >> Training accuracy: 0.970465
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.872140 Loss1: 0.551539 Loss2: 0.320601
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.580989 Loss1: 0.327464 Loss2: 0.253525
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.519508 Loss1: 0.288421 Loss2: 0.231086
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.477374 Loss1: 0.252042 Loss2: 0.225332
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.440154 Loss1: 0.219887 Loss2: 0.220267
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.404187 Loss1: 0.185485 Loss2: 0.218701
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.397163 Loss1: 0.179884 Loss2: 0.217280
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.367259 Loss1: 0.152581 Loss2: 0.214678
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.379468 Loss1: 0.163574 Loss2: 0.215894
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.400454 Loss1: 0.184127 Loss2: 0.216326
-(DefaultActor pid=1838052) >> Training accuracy: 0.962421
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.583732 Loss1: 0.542629 Loss2: 0.041103
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.401465 Loss1: 0.356972 Loss2: 0.044493
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.312095 Loss1: 0.268307 Loss2: 0.043789
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.279607 Loss1: 0.236258 Loss2: 0.043349
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.239690 Loss1: 0.197323 Loss2: 0.042367
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.207612 Loss1: 0.164535 Loss2: 0.043077
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.194273 Loss1: 0.152346 Loss2: 0.041927
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.222839 Loss1: 0.180386 Loss2: 0.042453
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.200021 Loss1: 0.157479 Loss2: 0.042542
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.205539 Loss1: 0.163119 Loss2: 0.042420
-(DefaultActor pid=1838052) >> Training accuracy: 0.962139
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.646631 Loss1: 0.601819 Loss2: 0.044813
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.406513 Loss1: 0.359871 Loss2: 0.046642
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.322334 Loss1: 0.276869 Loss2: 0.045465
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.266368 Loss1: 0.221210 Loss2: 0.045158
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.295741 Loss1: 0.250208 Loss2: 0.045533
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.260588 Loss1: 0.215408 Loss2: 0.045180
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.266344 Loss1: 0.221237 Loss2: 0.045107
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.204396 Loss1: 0.159819 Loss2: 0.044578
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.154845 Loss1: 0.111555 Loss2: 0.043290
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.143092 Loss1: 0.101181 Loss2: 0.041911
-(DefaultActor pid=1838052) >> Training accuracy: 0.980997
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.609556 Loss1: 0.570044 Loss2: 0.039512
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.358535 Loss1: 0.316469 Loss2: 0.042066
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.287761 Loss1: 0.246567 Loss2: 0.041194
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.269137 Loss1: 0.228108 Loss2: 0.041028
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.253593 Loss1: 0.212118 Loss2: 0.041475
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.206246 Loss1: 0.165324 Loss2: 0.040922
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197727 Loss1: 0.157298 Loss2: 0.040429
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.198943 Loss1: 0.157862 Loss2: 0.041081
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.167709 Loss1: 0.126819 Loss2: 0.040890
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.164736 Loss1: 0.125097 Loss2: 0.039639
-(DefaultActor pid=1838052) >> Training accuracy: 0.975694
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.090813 Loss1: 0.507798 Loss2: 0.583015
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.913406 Loss1: 0.336587 Loss2: 0.576819
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.852258 Loss1: 0.294208 Loss2: 0.558050
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.824889 Loss1: 0.279507 Loss2: 0.545382
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.775578 Loss1: 0.239810 Loss2: 0.535768
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.724813 Loss1: 0.199476 Loss2: 0.525337
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.750865 Loss1: 0.227969 Loss2: 0.522897
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.697224 Loss1: 0.179899 Loss2: 0.517325
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.723800 Loss1: 0.210182 Loss2: 0.513618
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.688681 Loss1: 0.178816 Loss2: 0.509865
-(DefaultActor pid=1838052) >> Training accuracy: 0.956487
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.642603 Loss1: 0.564438 Loss2: 0.078165
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.449798 Loss1: 0.374076 Loss2: 0.075722
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.383380 Loss1: 0.312825 Loss2: 0.070555
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.325726 Loss1: 0.255855 Loss2: 0.069871
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.327188 Loss1: 0.259634 Loss2: 0.067554
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.260639 Loss1: 0.195199 Loss2: 0.065440
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.250584 Loss1: 0.186177 Loss2: 0.064407
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.274176 Loss1: 0.209443 Loss2: 0.064733
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.257072 Loss1: 0.192522 Loss2: 0.064551
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.208913 Loss1: 0.146138 Loss2: 0.062775
-(DefaultActor pid=1838052) >> Training accuracy: 0.967722
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.010193 Loss1: 0.551193 Loss2: 0.459000
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.772256 Loss1: 0.364258 Loss2: 0.407998
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.721445 Loss1: 0.328681 Loss2: 0.392765
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.652749 Loss1: 0.268531 Loss2: 0.384218
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613130 Loss1: 0.235376 Loss2: 0.377754
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.613734 Loss1: 0.237336 Loss2: 0.376397
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.601616 Loss1: 0.230316 Loss2: 0.371300
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.577861 Loss1: 0.205889 Loss2: 0.371971
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.530369 Loss1: 0.162171 Loss2: 0.368199
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.518571 Loss1: 0.154678 Loss2: 0.363892
-(DefaultActor pid=1838052) >> Training accuracy: 0.956290
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.548453 Loss1: 0.511459 Loss2: 0.036995
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.335441 Loss1: 0.295003 Loss2: 0.040438
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.273459 Loss1: 0.233219 Loss2: 0.040241
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.228162 Loss1: 0.189019 Loss2: 0.039143
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.219203 Loss1: 0.179503 Loss2: 0.039700
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.197192 Loss1: 0.157450 Loss2: 0.039742
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.174300 Loss1: 0.134998 Loss2: 0.039302
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.175769 Loss1: 0.136502 Loss2: 0.039267
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.180381 Loss1: 0.140610 Loss2: 0.039771
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.167129 Loss1: 0.127391 Loss2: 0.039738
-(DefaultActor pid=1838052) >> Training accuracy: 0.973157
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 22:50:01,733][flwr][DEBUG] - fit_round 31 received 10 results and 0 failures
->> Test accuracy: 0.618400
-[2023-09-27 22:50:42,237][flwr][INFO] - fit progress: (31, 2.050073419706509, {'accuracy': 0.6184}, 59465.12725453032)
-[2023-09-27 22:50:42,237][flwr][DEBUG] - evaluate_round 31: strategy sampled 10 clients (out of 10)
-[2023-09-27 22:51:18,717][flwr][DEBUG] - evaluate_round 31 received 10 results and 0 failures
-[2023-09-27 22:51:18,719][flwr][DEBUG] - fit_round 32: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.536587 Loss1: 0.497775 Loss2: 0.038812
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.360771 Loss1: 0.318940 Loss2: 0.041831
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.309850 Loss1: 0.268158 Loss2: 0.041692
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.253670 Loss1: 0.212143 Loss2: 0.041526
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.233662 Loss1: 0.192484 Loss2: 0.041178
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.190820 Loss1: 0.149959 Loss2: 0.040860
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.191075 Loss1: 0.150232 Loss2: 0.040843
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.177017 Loss1: 0.136279 Loss2: 0.040739
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.134248 Loss1: 0.094787 Loss2: 0.039461
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166040 Loss1: 0.126170 Loss2: 0.039869
-(DefaultActor pid=1838052) >> Training accuracy: 0.969739
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.550920 Loss1: 0.507856 Loss2: 0.043063
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.337583 Loss1: 0.292669 Loss2: 0.044914
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.303331 Loss1: 0.258569 Loss2: 0.044761
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.253884 Loss1: 0.209714 Loss2: 0.044170
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.228291 Loss1: 0.183977 Loss2: 0.044314
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.210484 Loss1: 0.166160 Loss2: 0.044324
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.191880 Loss1: 0.147996 Loss2: 0.043884
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.201165 Loss1: 0.157001 Loss2: 0.044164
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.190577 Loss1: 0.146577 Loss2: 0.044000
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.164904 Loss1: 0.121156 Loss2: 0.043749
-(DefaultActor pid=1838052) >> Training accuracy: 0.977650
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.166470 Loss1: 0.556362 Loss2: 0.610108
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.974741 Loss1: 0.353287 Loss2: 0.621454
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.854441 Loss1: 0.250079 Loss2: 0.604362
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.818817 Loss1: 0.226995 Loss2: 0.591821
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.802034 Loss1: 0.220669 Loss2: 0.581365
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.764549 Loss1: 0.191687 Loss2: 0.572863
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.764540 Loss1: 0.199930 Loss2: 0.564611
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.732172 Loss1: 0.177418 Loss2: 0.554754
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.752028 Loss1: 0.202027 Loss2: 0.550001
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.691193 Loss1: 0.146729 Loss2: 0.544464
-(DefaultActor pid=1838052) >> Training accuracy: 0.970920
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.112048 Loss1: 0.571827 Loss2: 0.540221
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.867117 Loss1: 0.360008 Loss2: 0.507109
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.783486 Loss1: 0.309916 Loss2: 0.473570
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.780830 Loss1: 0.322608 Loss2: 0.458222
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.728898 Loss1: 0.282441 Loss2: 0.446457
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.707764 Loss1: 0.271509 Loss2: 0.436255
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.649368 Loss1: 0.218577 Loss2: 0.430791
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.626353 Loss1: 0.204741 Loss2: 0.421612
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.596505 Loss1: 0.176256 Loss2: 0.420249
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.577360 Loss1: 0.161457 Loss2: 0.415903
-(DefaultActor pid=1838052) >> Training accuracy: 0.967516
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.094270 Loss1: 0.494531 Loss2: 0.599739
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.922611 Loss1: 0.317724 Loss2: 0.604887
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.867142 Loss1: 0.278349 Loss2: 0.588793
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.798692 Loss1: 0.223435 Loss2: 0.575257
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.789518 Loss1: 0.223103 Loss2: 0.566415
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.766258 Loss1: 0.210585 Loss2: 0.555674
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.732159 Loss1: 0.187299 Loss2: 0.544860
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.719440 Loss1: 0.177053 Loss2: 0.542388
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.700046 Loss1: 0.167097 Loss2: 0.532949
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.703518 Loss1: 0.174192 Loss2: 0.529325
-(DefaultActor pid=1838052) >> Training accuracy: 0.953887
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.609161 Loss1: 0.569645 Loss2: 0.039516
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.351188 Loss1: 0.309368 Loss2: 0.041820
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.282915 Loss1: 0.241856 Loss2: 0.041059
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.257201 Loss1: 0.215887 Loss2: 0.041314
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.231890 Loss1: 0.191220 Loss2: 0.040670
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.222289 Loss1: 0.181062 Loss2: 0.041228
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.206924 Loss1: 0.165580 Loss2: 0.041344
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.162623 Loss1: 0.122276 Loss2: 0.040346
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.170512 Loss1: 0.130312 Loss2: 0.040200
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.158871 Loss1: 0.118471 Loss2: 0.040400
-(DefaultActor pid=1838052) >> Training accuracy: 0.981208
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.809992 Loss1: 0.473596 Loss2: 0.336396
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.575195 Loss1: 0.317806 Loss2: 0.257390
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.520550 Loss1: 0.276588 Loss2: 0.243962
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.490347 Loss1: 0.253582 Loss2: 0.236764
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.424109 Loss1: 0.189517 Loss2: 0.234592
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.433677 Loss1: 0.200438 Loss2: 0.233238
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.403350 Loss1: 0.172916 Loss2: 0.230434
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.425388 Loss1: 0.193507 Loss2: 0.231881
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.421485 Loss1: 0.189359 Loss2: 0.232127
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.390347 Loss1: 0.160662 Loss2: 0.229685
-(DefaultActor pid=1838052) >> Training accuracy: 0.968157
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.542264 Loss1: 0.500784 Loss2: 0.041480
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.396876 Loss1: 0.351900 Loss2: 0.044976
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.319428 Loss1: 0.274870 Loss2: 0.044557
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.270113 Loss1: 0.226208 Loss2: 0.043906
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.230319 Loss1: 0.187018 Loss2: 0.043301
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.223836 Loss1: 0.180872 Loss2: 0.042965
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.219336 Loss1: 0.176068 Loss2: 0.043268
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.188449 Loss1: 0.145551 Loss2: 0.042897
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.166326 Loss1: 0.124630 Loss2: 0.041696
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163949 Loss1: 0.122289 Loss2: 0.041660
-(DefaultActor pid=1838052) >> Training accuracy: 0.970728
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.101020 Loss1: 0.500479 Loss2: 0.600541
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.892132 Loss1: 0.289279 Loss2: 0.602854
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.826895 Loss1: 0.236642 Loss2: 0.590252
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.826528 Loss1: 0.245984 Loss2: 0.580544
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.777244 Loss1: 0.208082 Loss2: 0.569162
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.764032 Loss1: 0.202602 Loss2: 0.561430
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.750622 Loss1: 0.198058 Loss2: 0.552564
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.734349 Loss1: 0.186303 Loss2: 0.548046
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.712161 Loss1: 0.172401 Loss2: 0.539759
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.673598 Loss1: 0.140213 Loss2: 0.533385
-(DefaultActor pid=1838052) >> Training accuracy: 0.968950
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.560746 Loss1: 0.520331 Loss2: 0.040415
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.386143 Loss1: 0.343115 Loss2: 0.043028
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.320650 Loss1: 0.277888 Loss2: 0.042763
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.269298 Loss1: 0.226781 Loss2: 0.042517
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.241543 Loss1: 0.199359 Loss2: 0.042184
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.232838 Loss1: 0.190580 Loss2: 0.042258
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197023 Loss1: 0.155540 Loss2: 0.041483
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.214894 Loss1: 0.172620 Loss2: 0.042274
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.186956 Loss1: 0.145389 Loss2: 0.041567
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.188300 Loss1: 0.146267 Loss2: 0.042034
-(DefaultActor pid=1838052) >> Training accuracy: 0.965144
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 23:20:44,388][flwr][DEBUG] - fit_round 32 received 10 results and 0 failures
->> Test accuracy: 0.619800
-[2023-09-27 23:21:24,660][flwr][INFO] - fit progress: (32, 2.048540641515019, {'accuracy': 0.6198}, 61307.55033142725)
-[2023-09-27 23:21:24,660][flwr][DEBUG] - evaluate_round 32: strategy sampled 10 clients (out of 10)
-[2023-09-27 23:22:02,969][flwr][DEBUG] - evaluate_round 32 received 10 results and 0 failures
-[2023-09-27 23:22:02,970][flwr][DEBUG] - fit_round 33: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.133193 Loss1: 0.536348 Loss2: 0.596845
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.941873 Loss1: 0.347818 Loss2: 0.594055
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.882994 Loss1: 0.310424 Loss2: 0.572571
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.809549 Loss1: 0.254939 Loss2: 0.554610
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.725978 Loss1: 0.188166 Loss2: 0.537812
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.733334 Loss1: 0.203863 Loss2: 0.529471
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.745452 Loss1: 0.215207 Loss2: 0.530245
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.721618 Loss1: 0.199076 Loss2: 0.522542
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.693973 Loss1: 0.174918 Loss2: 0.519055
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.704063 Loss1: 0.190802 Loss2: 0.513262
-(DefaultActor pid=1838052) >> Training accuracy: 0.962204
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.050017 Loss1: 0.457848 Loss2: 0.592169
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.894274 Loss1: 0.308711 Loss2: 0.585563
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.804050 Loss1: 0.240517 Loss2: 0.563533
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.790197 Loss1: 0.242794 Loss2: 0.547403
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.748458 Loss1: 0.210287 Loss2: 0.538171
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.771665 Loss1: 0.236549 Loss2: 0.535116
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.742235 Loss1: 0.215551 Loss2: 0.526683
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.680855 Loss1: 0.162937 Loss2: 0.517918
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.695080 Loss1: 0.180766 Loss2: 0.514314
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.675440 Loss1: 0.165756 Loss2: 0.509683
-(DefaultActor pid=1838052) >> Training accuracy: 0.966574
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.093594 Loss1: 0.503535 Loss2: 0.590059
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.903160 Loss1: 0.321787 Loss2: 0.581373
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.816497 Loss1: 0.257277 Loss2: 0.559220
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.806905 Loss1: 0.261082 Loss2: 0.545823
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.771880 Loss1: 0.235519 Loss2: 0.536361
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.735923 Loss1: 0.209668 Loss2: 0.526255
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.717031 Loss1: 0.195360 Loss2: 0.521671
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.720609 Loss1: 0.202913 Loss2: 0.517696
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.684922 Loss1: 0.168529 Loss2: 0.516393
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.647857 Loss1: 0.142263 Loss2: 0.505594
-(DefaultActor pid=1838052) >> Training accuracy: 0.972508
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.570801 Loss1: 0.528248 Loss2: 0.042553
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.349988 Loss1: 0.304826 Loss2: 0.045163
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.305277 Loss1: 0.261308 Loss2: 0.043969
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.283909 Loss1: 0.238669 Loss2: 0.045241
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.257624 Loss1: 0.212954 Loss2: 0.044670
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.207553 Loss1: 0.163951 Loss2: 0.043602
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.218424 Loss1: 0.174716 Loss2: 0.043708
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.210539 Loss1: 0.167077 Loss2: 0.043462
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.241151 Loss1: 0.196903 Loss2: 0.044249
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.204872 Loss1: 0.161557 Loss2: 0.043315
-(DefaultActor pid=1838052) >> Training accuracy: 0.968133
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.864245 Loss1: 0.447914 Loss2: 0.416332
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.664635 Loss1: 0.305953 Loss2: 0.358682
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.579162 Loss1: 0.249498 Loss2: 0.329664
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.547282 Loss1: 0.220724 Loss2: 0.326558
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.511747 Loss1: 0.192763 Loss2: 0.318984
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.485716 Loss1: 0.170435 Loss2: 0.315282
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.486268 Loss1: 0.171660 Loss2: 0.314608
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.496851 Loss1: 0.185283 Loss2: 0.311568
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.507988 Loss1: 0.193600 Loss2: 0.314388
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.450663 Loss1: 0.141109 Loss2: 0.309554
-(DefaultActor pid=1838052) >> Training accuracy: 0.968559
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.805456 Loss1: 0.514687 Loss2: 0.290769
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.578728 Loss1: 0.337664 Loss2: 0.241063
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.495694 Loss1: 0.266741 Loss2: 0.228953
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.445774 Loss1: 0.223967 Loss2: 0.221808
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.448817 Loss1: 0.226253 Loss2: 0.222564
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.409995 Loss1: 0.192296 Loss2: 0.217699
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.386264 Loss1: 0.170617 Loss2: 0.215646
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.364939 Loss1: 0.150403 Loss2: 0.214535
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.341609 Loss1: 0.132059 Loss2: 0.209549
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.368031 Loss1: 0.152627 Loss2: 0.215404
-(DefaultActor pid=1838052) >> Training accuracy: 0.972903
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.484217 Loss1: 0.446835 Loss2: 0.037382
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.329790 Loss1: 0.289171 Loss2: 0.040619
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.262454 Loss1: 0.222143 Loss2: 0.040311
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.228854 Loss1: 0.188928 Loss2: 0.039925
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.200577 Loss1: 0.159899 Loss2: 0.040678
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.165097 Loss1: 0.124809 Loss2: 0.040287
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.176630 Loss1: 0.136454 Loss2: 0.040176
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.194849 Loss1: 0.154486 Loss2: 0.040363
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.153122 Loss1: 0.113373 Loss2: 0.039749
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.145422 Loss1: 0.106092 Loss2: 0.039330
-(DefaultActor pid=1838052) >> Training accuracy: 0.979167
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.062219 Loss1: 0.474158 Loss2: 0.588061
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.920565 Loss1: 0.335937 Loss2: 0.584628
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.830214 Loss1: 0.269317 Loss2: 0.560897
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.809771 Loss1: 0.259475 Loss2: 0.550296
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.747882 Loss1: 0.208566 Loss2: 0.539317
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.762109 Loss1: 0.231108 Loss2: 0.531001
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.780106 Loss1: 0.252485 Loss2: 0.527622
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.755211 Loss1: 0.226376 Loss2: 0.528835
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.701976 Loss1: 0.184335 Loss2: 0.517641
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.706899 Loss1: 0.193698 Loss2: 0.513200
-(DefaultActor pid=1838052) >> Training accuracy: 0.944912
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.581569 Loss1: 0.500718 Loss2: 0.080852
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.391947 Loss1: 0.312857 Loss2: 0.079090
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.331267 Loss1: 0.256537 Loss2: 0.074729
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.259750 Loss1: 0.188306 Loss2: 0.071445
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.257864 Loss1: 0.187782 Loss2: 0.070082
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.260655 Loss1: 0.191931 Loss2: 0.068724
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.226730 Loss1: 0.159486 Loss2: 0.067244
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.207541 Loss1: 0.141379 Loss2: 0.066162
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.211372 Loss1: 0.144816 Loss2: 0.066555
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.210871 Loss1: 0.144156 Loss2: 0.066716
-(DefaultActor pid=1838052) >> Training accuracy: 0.976562
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.493139 Loss1: 0.455137 Loss2: 0.038001
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.327287 Loss1: 0.286079 Loss2: 0.041208
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.253732 Loss1: 0.213277 Loss2: 0.040454
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.244925 Loss1: 0.204048 Loss2: 0.040878
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.233245 Loss1: 0.192584 Loss2: 0.040661
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.204357 Loss1: 0.163164 Loss2: 0.041192
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.187279 Loss1: 0.146079 Loss2: 0.041200
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.158541 Loss1: 0.118384 Loss2: 0.040157
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.169959 Loss1: 0.129500 Loss2: 0.040459
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.152114 Loss1: 0.111393 Loss2: 0.040722
-(DefaultActor pid=1838052) >> Training accuracy: 0.967366
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-27 23:51:44,497][flwr][DEBUG] - fit_round 33 received 10 results and 0 failures
->> Test accuracy: 0.623000
-[2023-09-27 23:52:24,960][flwr][INFO] - fit progress: (33, 2.033169127881717, {'accuracy': 0.623}, 63167.85070159007)
-[2023-09-27 23:52:24,961][flwr][DEBUG] - evaluate_round 33: strategy sampled 10 clients (out of 10)
-[2023-09-27 23:53:01,869][flwr][DEBUG] - evaluate_round 33 received 10 results and 0 failures
-[2023-09-27 23:53:01,870][flwr][DEBUG] - fit_round 34: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.033783 Loss1: 0.450828 Loss2: 0.582955
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.852281 Loss1: 0.285015 Loss2: 0.567266
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.750209 Loss1: 0.206568 Loss2: 0.543641
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.724705 Loss1: 0.191985 Loss2: 0.532719
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.695212 Loss1: 0.170763 Loss2: 0.524449
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.718836 Loss1: 0.195270 Loss2: 0.523565
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.663196 Loss1: 0.147611 Loss2: 0.515585
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.653662 Loss1: 0.144184 Loss2: 0.509478
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.630743 Loss1: 0.124893 Loss2: 0.505850
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.644606 Loss1: 0.140279 Loss2: 0.504328
-(DefaultActor pid=1838052) >> Training accuracy: 0.982171
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.153076 Loss1: 0.557757 Loss2: 0.595318
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.957679 Loss1: 0.362115 Loss2: 0.595564
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.853342 Loss1: 0.269631 Loss2: 0.583711
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.802652 Loss1: 0.229733 Loss2: 0.572919
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.760242 Loss1: 0.201127 Loss2: 0.559115
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.734511 Loss1: 0.181603 Loss2: 0.552908
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.742028 Loss1: 0.192090 Loss2: 0.549938
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.747434 Loss1: 0.203012 Loss2: 0.544422
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.697952 Loss1: 0.156901 Loss2: 0.541050
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.710527 Loss1: 0.170070 Loss2: 0.540457
-(DefaultActor pid=1838052) >> Training accuracy: 0.967928
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.872657 Loss1: 0.460356 Loss2: 0.412301
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.670256 Loss1: 0.327720 Loss2: 0.342537
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.559326 Loss1: 0.244811 Loss2: 0.314515
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.523884 Loss1: 0.216160 Loss2: 0.307724
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.484698 Loss1: 0.182804 Loss2: 0.301895
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.466621 Loss1: 0.167603 Loss2: 0.299019
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.499292 Loss1: 0.195300 Loss2: 0.303992
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.454507 Loss1: 0.154558 Loss2: 0.299949
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.433313 Loss1: 0.139287 Loss2: 0.294026
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.423247 Loss1: 0.128395 Loss2: 0.294852
-(DefaultActor pid=1838052) >> Training accuracy: 0.967761
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.500527 Loss1: 0.457349 Loss2: 0.043178
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.314269 Loss1: 0.268559 Loss2: 0.045710
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.277822 Loss1: 0.233265 Loss2: 0.044557
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.275667 Loss1: 0.230932 Loss2: 0.044735
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.194530 Loss1: 0.150609 Loss2: 0.043921
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.193385 Loss1: 0.149493 Loss2: 0.043892
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.175828 Loss1: 0.132291 Loss2: 0.043537
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.170243 Loss1: 0.127300 Loss2: 0.042943
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.182269 Loss1: 0.138379 Loss2: 0.043890
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.165931 Loss1: 0.122174 Loss2: 0.043756
-(DefaultActor pid=1838052) >> Training accuracy: 0.981136
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.551112 Loss1: 0.476132 Loss2: 0.074980
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.385077 Loss1: 0.315383 Loss2: 0.069694
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.285819 Loss1: 0.219891 Loss2: 0.065928
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.275000 Loss1: 0.211621 Loss2: 0.063378
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.224239 Loss1: 0.162348 Loss2: 0.061891
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.239403 Loss1: 0.177772 Loss2: 0.061631
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.214142 Loss1: 0.152833 Loss2: 0.061309
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.210013 Loss1: 0.150119 Loss2: 0.059894
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.185056 Loss1: 0.125646 Loss2: 0.059410
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.177410 Loss1: 0.118415 Loss2: 0.058995
-(DefaultActor pid=1838052) >> Training accuracy: 0.975475
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.702658 Loss1: 0.441077 Loss2: 0.261581
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.477231 Loss1: 0.259880 Loss2: 0.217352
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.426483 Loss1: 0.214756 Loss2: 0.211727
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.429319 Loss1: 0.222379 Loss2: 0.206940
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.438939 Loss1: 0.227288 Loss2: 0.211651
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.378943 Loss1: 0.175132 Loss2: 0.203811
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.385334 Loss1: 0.178839 Loss2: 0.206494
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.389345 Loss1: 0.185156 Loss2: 0.204188
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.357754 Loss1: 0.153481 Loss2: 0.204273
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.337218 Loss1: 0.135774 Loss2: 0.201444
-(DefaultActor pid=1838052) >> Training accuracy: 0.970530
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.509580 Loss1: 0.469496 Loss2: 0.040084
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.313158 Loss1: 0.269755 Loss2: 0.043403
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.248491 Loss1: 0.206076 Loss2: 0.042415
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.221865 Loss1: 0.179359 Loss2: 0.042505
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.222380 Loss1: 0.179880 Loss2: 0.042500
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.191976 Loss1: 0.149401 Loss2: 0.042575
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.185717 Loss1: 0.143961 Loss2: 0.041757
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.177929 Loss1: 0.135902 Loss2: 0.042027
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.181871 Loss1: 0.139380 Loss2: 0.042490
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.142581 Loss1: 0.101281 Loss2: 0.041300
-(DefaultActor pid=1838052) >> Training accuracy: 0.982205
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.594359 Loss1: 0.552362 Loss2: 0.041998
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.348723 Loss1: 0.304296 Loss2: 0.044428
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.263012 Loss1: 0.220008 Loss2: 0.043004
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.273833 Loss1: 0.230466 Loss2: 0.043367
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.213875 Loss1: 0.170913 Loss2: 0.042962
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.193901 Loss1: 0.152038 Loss2: 0.041864
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.156248 Loss1: 0.115122 Loss2: 0.041127
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.186242 Loss1: 0.144995 Loss2: 0.041247
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.165645 Loss1: 0.124638 Loss2: 0.041006
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.174256 Loss1: 0.132878 Loss2: 0.041377
-(DefaultActor pid=1838052) >> Training accuracy: 0.977196
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.554435 Loss1: 0.471124 Loss2: 0.083311
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.348131 Loss1: 0.271784 Loss2: 0.076347
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.281211 Loss1: 0.208050 Loss2: 0.073161
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.289044 Loss1: 0.219507 Loss2: 0.069536
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.270999 Loss1: 0.201340 Loss2: 0.069659
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.253373 Loss1: 0.185583 Loss2: 0.067790
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.227021 Loss1: 0.160417 Loss2: 0.066604
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.227990 Loss1: 0.161239 Loss2: 0.066751
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.218643 Loss1: 0.152879 Loss2: 0.065765
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.188273 Loss1: 0.123198 Loss2: 0.065075
-(DefaultActor pid=1838052) >> Training accuracy: 0.976162
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.065766 Loss1: 0.477379 Loss2: 0.588387
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.866357 Loss1: 0.284431 Loss2: 0.581926
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.793329 Loss1: 0.234178 Loss2: 0.559151
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.787153 Loss1: 0.238629 Loss2: 0.548525
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.742902 Loss1: 0.204094 Loss2: 0.538808
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.717744 Loss1: 0.186071 Loss2: 0.531674
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.682854 Loss1: 0.159868 Loss2: 0.522986
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.690007 Loss1: 0.172432 Loss2: 0.517575
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.684791 Loss1: 0.166803 Loss2: 0.517988
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.656727 Loss1: 0.142949 Loss2: 0.513778
-(DefaultActor pid=1838052) >> Training accuracy: 0.976661
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 00:22:26,663][flwr][DEBUG] - fit_round 34 received 10 results and 0 failures
->> Test accuracy: 0.619400
-[2023-09-28 00:23:06,527][flwr][INFO] - fit progress: (34, 2.0350445369942882, {'accuracy': 0.6194}, 65009.41777968733)
-[2023-09-28 00:23:06,528][flwr][DEBUG] - evaluate_round 34: strategy sampled 10 clients (out of 10)
-[2023-09-28 00:23:43,163][flwr][DEBUG] - evaluate_round 34 received 10 results and 0 failures
-[2023-09-28 00:23:43,164][flwr][DEBUG] - fit_round 35: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.881965 Loss1: 0.456196 Loss2: 0.425769
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.732760 Loss1: 0.352555 Loss2: 0.380206
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.651459 Loss1: 0.282561 Loss2: 0.368898
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.577396 Loss1: 0.218995 Loss2: 0.358401
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.548025 Loss1: 0.197821 Loss2: 0.350204
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.543956 Loss1: 0.191210 Loss2: 0.352746
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.547633 Loss1: 0.192350 Loss2: 0.355284
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.512846 Loss1: 0.163037 Loss2: 0.349809
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.504983 Loss1: 0.156855 Loss2: 0.348128
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.489318 Loss1: 0.144545 Loss2: 0.344773
-(DefaultActor pid=1838052) >> Training accuracy: 0.955498
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.774092 Loss1: 0.412578 Loss2: 0.361514
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.591957 Loss1: 0.295418 Loss2: 0.296539
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.518265 Loss1: 0.241322 Loss2: 0.276943
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.495252 Loss1: 0.221559 Loss2: 0.273693
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.468616 Loss1: 0.198977 Loss2: 0.269640
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.445323 Loss1: 0.178724 Loss2: 0.266599
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.434252 Loss1: 0.171044 Loss2: 0.263208
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.435938 Loss1: 0.172418 Loss2: 0.263520
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.404837 Loss1: 0.143959 Loss2: 0.260878
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.423229 Loss1: 0.159392 Loss2: 0.263837
-(DefaultActor pid=1838052) >> Training accuracy: 0.974288
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.451612 Loss1: 0.408680 Loss2: 0.042932
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.293270 Loss1: 0.248079 Loss2: 0.045191
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.273564 Loss1: 0.228413 Loss2: 0.045150
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.250691 Loss1: 0.205134 Loss2: 0.045557
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.201395 Loss1: 0.156635 Loss2: 0.044760
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.166014 Loss1: 0.121913 Loss2: 0.044101
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.166397 Loss1: 0.122948 Loss2: 0.043449
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.162507 Loss1: 0.118848 Loss2: 0.043659
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160766 Loss1: 0.116754 Loss2: 0.044011
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.173217 Loss1: 0.129153 Loss2: 0.044064
-(DefaultActor pid=1838052) >> Training accuracy: 0.972706
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.051301 Loss1: 0.462961 Loss2: 0.588340
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.881437 Loss1: 0.297879 Loss2: 0.583557
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.787924 Loss1: 0.223775 Loss2: 0.564149
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.766272 Loss1: 0.216061 Loss2: 0.550211
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.726596 Loss1: 0.184598 Loss2: 0.541998
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.722047 Loss1: 0.184755 Loss2: 0.537292
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.697717 Loss1: 0.167785 Loss2: 0.529932
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.671787 Loss1: 0.147333 Loss2: 0.524453
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.640108 Loss1: 0.120682 Loss2: 0.519425
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.648877 Loss1: 0.134074 Loss2: 0.514803
-(DefaultActor pid=1838052) >> Training accuracy: 0.979818
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.008200 Loss1: 0.480630 Loss2: 0.527571
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.755938 Loss1: 0.287490 Loss2: 0.468448
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.733531 Loss1: 0.283376 Loss2: 0.450156
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.697026 Loss1: 0.253898 Loss2: 0.443128
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.641498 Loss1: 0.208883 Loss2: 0.432615
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.622536 Loss1: 0.196509 Loss2: 0.426028
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.615587 Loss1: 0.191656 Loss2: 0.423931
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.589394 Loss1: 0.169435 Loss2: 0.419960
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.580495 Loss1: 0.161769 Loss2: 0.418726
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.585534 Loss1: 0.167277 Loss2: 0.418257
-(DefaultActor pid=1838052) >> Training accuracy: 0.967548
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.561240 Loss1: 0.518389 Loss2: 0.042852
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.314516 Loss1: 0.269489 Loss2: 0.045027
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.241856 Loss1: 0.198992 Loss2: 0.042864
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.243758 Loss1: 0.200659 Loss2: 0.043099
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.206952 Loss1: 0.164689 Loss2: 0.042264
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.188019 Loss1: 0.145716 Loss2: 0.042303
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.241004 Loss1: 0.198330 Loss2: 0.042674
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.222825 Loss1: 0.179121 Loss2: 0.043704
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.198052 Loss1: 0.154933 Loss2: 0.043119
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.189439 Loss1: 0.146324 Loss2: 0.043115
-(DefaultActor pid=1838052) >> Training accuracy: 0.973684
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.500768 Loss1: 0.452001 Loss2: 0.048767
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.293858 Loss1: 0.246002 Loss2: 0.047856
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.223353 Loss1: 0.177282 Loss2: 0.046071
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.232571 Loss1: 0.184740 Loss2: 0.047832
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.214981 Loss1: 0.168349 Loss2: 0.046632
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.193339 Loss1: 0.147398 Loss2: 0.045941
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.181212 Loss1: 0.135680 Loss2: 0.045532
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.176537 Loss1: 0.130803 Loss2: 0.045734
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160348 Loss1: 0.115366 Loss2: 0.044981
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163699 Loss1: 0.118523 Loss2: 0.045176
-(DefaultActor pid=1838052) >> Training accuracy: 0.973892
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.441091 Loss1: 0.404010 Loss2: 0.037081
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.305314 Loss1: 0.264947 Loss2: 0.040367
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.261066 Loss1: 0.221188 Loss2: 0.039878
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.220977 Loss1: 0.180557 Loss2: 0.040419
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.199112 Loss1: 0.158952 Loss2: 0.040160
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.184796 Loss1: 0.144306 Loss2: 0.040489
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.177134 Loss1: 0.137761 Loss2: 0.039372
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.148755 Loss1: 0.109331 Loss2: 0.039424
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.155173 Loss1: 0.115386 Loss2: 0.039787
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.159042 Loss1: 0.119253 Loss2: 0.039790
-(DefaultActor pid=1838052) >> Training accuracy: 0.975991
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.604015 Loss1: 0.514748 Loss2: 0.089267
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.358291 Loss1: 0.270769 Loss2: 0.087523
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.310712 Loss1: 0.228035 Loss2: 0.082677
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.283604 Loss1: 0.203606 Loss2: 0.079998
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.253687 Loss1: 0.176773 Loss2: 0.076914
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.250676 Loss1: 0.174426 Loss2: 0.076250
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.242168 Loss1: 0.167325 Loss2: 0.074844
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.202716 Loss1: 0.130070 Loss2: 0.072645
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.207279 Loss1: 0.135272 Loss2: 0.072007
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.204517 Loss1: 0.133297 Loss2: 0.071220
-(DefaultActor pid=1838052) >> Training accuracy: 0.970439
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.440377 Loss1: 0.400558 Loss2: 0.039819
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.287125 Loss1: 0.244661 Loss2: 0.042464
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.252477 Loss1: 0.210558 Loss2: 0.041919
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.212548 Loss1: 0.171748 Loss2: 0.040800
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.184031 Loss1: 0.144167 Loss2: 0.039863
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.201869 Loss1: 0.161251 Loss2: 0.040618
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.202986 Loss1: 0.161117 Loss2: 0.041869
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.170125 Loss1: 0.129266 Loss2: 0.040859
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130420 Loss1: 0.090366 Loss2: 0.040053
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.129454 Loss1: 0.090075 Loss2: 0.039379
-(DefaultActor pid=1838052) >> Training accuracy: 0.969151
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 00:52:45,639][flwr][DEBUG] - fit_round 35 received 10 results and 0 failures
->> Test accuracy: 0.624700
-[2023-09-28 00:53:25,630][flwr][INFO] - fit progress: (35, 2.080257884039285, {'accuracy': 0.6247}, 66828.52071374701)
-[2023-09-28 00:53:25,631][flwr][DEBUG] - evaluate_round 35: strategy sampled 10 clients (out of 10)
-[2023-09-28 00:54:03,176][flwr][DEBUG] - evaluate_round 35 received 10 results and 0 failures
-[2023-09-28 00:54:03,177][flwr][DEBUG] - fit_round 36: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.004384 Loss1: 0.426333 Loss2: 0.578051
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.834268 Loss1: 0.261511 Loss2: 0.572756
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.744293 Loss1: 0.194714 Loss2: 0.549578
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.708946 Loss1: 0.170061 Loss2: 0.538885
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.715020 Loss1: 0.182942 Loss2: 0.532078
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.694159 Loss1: 0.166993 Loss2: 0.527165
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.668154 Loss1: 0.145636 Loss2: 0.522517
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.623612 Loss1: 0.108279 Loss2: 0.515333
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.612601 Loss1: 0.101562 Loss2: 0.511039
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.639298 Loss1: 0.130060 Loss2: 0.509238
-(DefaultActor pid=1838052) >> Training accuracy: 0.975561
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.035667 Loss1: 0.441950 Loss2: 0.593716
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.916822 Loss1: 0.321006 Loss2: 0.595816
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.827702 Loss1: 0.242358 Loss2: 0.585344
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.796618 Loss1: 0.223543 Loss2: 0.573076
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.768766 Loss1: 0.205838 Loss2: 0.562928
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.764374 Loss1: 0.210838 Loss2: 0.553536
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.766590 Loss1: 0.213938 Loss2: 0.552652
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.694140 Loss1: 0.150599 Loss2: 0.543541
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.699779 Loss1: 0.163463 Loss2: 0.536315
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.685114 Loss1: 0.155306 Loss2: 0.529808
-(DefaultActor pid=1838052) >> Training accuracy: 0.967928
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.448415 Loss1: 0.410281 Loss2: 0.038134
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.294767 Loss1: 0.253232 Loss2: 0.041535
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.246580 Loss1: 0.205361 Loss2: 0.041219
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.218614 Loss1: 0.177387 Loss2: 0.041227
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.195923 Loss1: 0.155260 Loss2: 0.040663
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.173704 Loss1: 0.133524 Loss2: 0.040180
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.177505 Loss1: 0.136655 Loss2: 0.040849
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.172077 Loss1: 0.131120 Loss2: 0.040957
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.150894 Loss1: 0.110661 Loss2: 0.040233
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.140986 Loss1: 0.100973 Loss2: 0.040013
-(DefaultActor pid=1838052) >> Training accuracy: 0.977255
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.475448 Loss1: 0.432289 Loss2: 0.043159
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.311390 Loss1: 0.264920 Loss2: 0.046470
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.307159 Loss1: 0.260762 Loss2: 0.046397
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.254904 Loss1: 0.209177 Loss2: 0.045727
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.198646 Loss1: 0.154617 Loss2: 0.044029
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.217051 Loss1: 0.173619 Loss2: 0.043432
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.196110 Loss1: 0.152361 Loss2: 0.043749
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.164707 Loss1: 0.121559 Loss2: 0.043148
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.149451 Loss1: 0.106931 Loss2: 0.042521
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.137956 Loss1: 0.096060 Loss2: 0.041896
-(DefaultActor pid=1838052) >> Training accuracy: 0.988064
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.004667 Loss1: 0.517676 Loss2: 0.486991
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.787042 Loss1: 0.342650 Loss2: 0.444391
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.682995 Loss1: 0.255840 Loss2: 0.427155
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.606668 Loss1: 0.196234 Loss2: 0.410434
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.593237 Loss1: 0.187490 Loss2: 0.405748
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.551658 Loss1: 0.152780 Loss2: 0.398878
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.559701 Loss1: 0.161798 Loss2: 0.397903
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.543919 Loss1: 0.150044 Loss2: 0.393875
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.512970 Loss1: 0.124253 Loss2: 0.388717
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.526106 Loss1: 0.132486 Loss2: 0.393620
-(DefaultActor pid=1838052) >> Training accuracy: 0.962838
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.467354 Loss1: 0.406619 Loss2: 0.060735
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.329361 Loss1: 0.269889 Loss2: 0.059473
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.249494 Loss1: 0.192400 Loss2: 0.057094
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.229654 Loss1: 0.174783 Loss2: 0.054871
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.213665 Loss1: 0.158439 Loss2: 0.055226
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.193187 Loss1: 0.139989 Loss2: 0.053198
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.210155 Loss1: 0.156308 Loss2: 0.053847
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.170748 Loss1: 0.119040 Loss2: 0.051708
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.191630 Loss1: 0.139132 Loss2: 0.052499
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.164328 Loss1: 0.112488 Loss2: 0.051841
-(DefaultActor pid=1838052) >> Training accuracy: 0.976464
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.445319 Loss1: 0.401135 Loss2: 0.044184
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.293550 Loss1: 0.247561 Loss2: 0.045989
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.213340 Loss1: 0.168804 Loss2: 0.044537
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.208307 Loss1: 0.163375 Loss2: 0.044932
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.201170 Loss1: 0.156149 Loss2: 0.045020
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.206998 Loss1: 0.162001 Loss2: 0.044997
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.165663 Loss1: 0.121473 Loss2: 0.044190
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.164476 Loss1: 0.120165 Loss2: 0.044311
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.165124 Loss1: 0.120775 Loss2: 0.044349
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.181228 Loss1: 0.136677 Loss2: 0.044551
-(DefaultActor pid=1838052) >> Training accuracy: 0.976464
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.450606 Loss1: 0.409279 Loss2: 0.041327
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.319035 Loss1: 0.274983 Loss2: 0.044052
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.255444 Loss1: 0.212576 Loss2: 0.042867
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.222652 Loss1: 0.180227 Loss2: 0.042425
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.209508 Loss1: 0.167220 Loss2: 0.042288
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.192106 Loss1: 0.151109 Loss2: 0.040997
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.184480 Loss1: 0.143231 Loss2: 0.041249
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.190162 Loss1: 0.148013 Loss2: 0.042149
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.182308 Loss1: 0.140774 Loss2: 0.041534
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.178621 Loss1: 0.136756 Loss2: 0.041865
-(DefaultActor pid=1838052) >> Training accuracy: 0.963410
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.435427 Loss1: 0.391637 Loss2: 0.043790
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.310643 Loss1: 0.264078 Loss2: 0.046565
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.239881 Loss1: 0.195119 Loss2: 0.044762
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.195046 Loss1: 0.151180 Loss2: 0.043866
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.222468 Loss1: 0.178569 Loss2: 0.043899
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.221185 Loss1: 0.176075 Loss2: 0.045109
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.171564 Loss1: 0.128735 Loss2: 0.042829
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.209668 Loss1: 0.166391 Loss2: 0.043277
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.197066 Loss1: 0.153165 Loss2: 0.043901
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.186030 Loss1: 0.142300 Loss2: 0.043731
-(DefaultActor pid=1838052) >> Training accuracy: 0.971955
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.431079 Loss1: 0.394451 Loss2: 0.036628
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.260713 Loss1: 0.221070 Loss2: 0.039643
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.236473 Loss1: 0.197392 Loss2: 0.039080
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.207589 Loss1: 0.168401 Loss2: 0.039188
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.187411 Loss1: 0.147889 Loss2: 0.039522
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.174918 Loss1: 0.136001 Loss2: 0.038916
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.168021 Loss1: 0.128809 Loss2: 0.039212
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.170653 Loss1: 0.130965 Loss2: 0.039689
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.156114 Loss1: 0.116347 Loss2: 0.039767
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.153759 Loss1: 0.114468 Loss2: 0.039291
-(DefaultActor pid=1838052) >> Training accuracy: 0.971799
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 01:23:00,304][flwr][DEBUG] - fit_round 36 received 10 results and 0 failures
->> Test accuracy: 0.627200
-[2023-09-28 01:23:40,441][flwr][INFO] - fit progress: (36, 2.0718342880852307, {'accuracy': 0.6272}, 68643.33109073108)
-[2023-09-28 01:23:40,441][flwr][DEBUG] - evaluate_round 36: strategy sampled 10 clients (out of 10)
-[2023-09-28 01:24:17,438][flwr][DEBUG] - evaluate_round 36 received 10 results and 0 failures
-[2023-09-28 01:24:17,438][flwr][DEBUG] - fit_round 37: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.810445 Loss1: 0.471249 Loss2: 0.339197
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.564282 Loss1: 0.292775 Loss2: 0.271507
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.460486 Loss1: 0.207309 Loss2: 0.253177
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.427260 Loss1: 0.181970 Loss2: 0.245290
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.419362 Loss1: 0.175408 Loss2: 0.243953
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.394846 Loss1: 0.151811 Loss2: 0.243035
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.370462 Loss1: 0.131531 Loss2: 0.238931
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.384271 Loss1: 0.146787 Loss2: 0.237484
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.380276 Loss1: 0.140025 Loss2: 0.240251
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.348957 Loss1: 0.113767 Loss2: 0.235190
-(DefaultActor pid=1838052) >> Training accuracy: 0.975507
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.965778 Loss1: 0.383375 Loss2: 0.582403
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.832350 Loss1: 0.255994 Loss2: 0.576356
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.799497 Loss1: 0.238697 Loss2: 0.560800
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.740749 Loss1: 0.192285 Loss2: 0.548464
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.718680 Loss1: 0.181131 Loss2: 0.537549
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.724454 Loss1: 0.192907 Loss2: 0.531548
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.690983 Loss1: 0.163403 Loss2: 0.527580
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.674683 Loss1: 0.153969 Loss2: 0.520714
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.645818 Loss1: 0.128254 Loss2: 0.517564
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.634768 Loss1: 0.123150 Loss2: 0.511618
-(DefaultActor pid=1838052) >> Training accuracy: 0.970553
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.401076 Loss1: 0.364839 Loss2: 0.036237
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.247880 Loss1: 0.208623 Loss2: 0.039257
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.223916 Loss1: 0.184252 Loss2: 0.039664
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.184548 Loss1: 0.145469 Loss2: 0.039079
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.162397 Loss1: 0.123427 Loss2: 0.038970
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.145824 Loss1: 0.106996 Loss2: 0.038828
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.136844 Loss1: 0.098610 Loss2: 0.038234
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.150703 Loss1: 0.111642 Loss2: 0.039060
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.145500 Loss1: 0.106543 Loss2: 0.038957
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.137395 Loss1: 0.098652 Loss2: 0.038743
-(DefaultActor pid=1838052) >> Training accuracy: 0.985176
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.969862 Loss1: 0.364683 Loss2: 0.605178
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.851277 Loss1: 0.242028 Loss2: 0.609249
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.797040 Loss1: 0.202748 Loss2: 0.594292
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.806942 Loss1: 0.221685 Loss2: 0.585258
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.760809 Loss1: 0.187012 Loss2: 0.573797
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.711695 Loss1: 0.151830 Loss2: 0.559864
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.703279 Loss1: 0.147686 Loss2: 0.555594
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.695986 Loss1: 0.146934 Loss2: 0.549053
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.670449 Loss1: 0.125563 Loss2: 0.544886
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.707857 Loss1: 0.168197 Loss2: 0.539660
-(DefaultActor pid=1838052) >> Training accuracy: 0.958079
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.377437 Loss1: 0.341308 Loss2: 0.036129
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.270031 Loss1: 0.230887 Loss2: 0.039143
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.250155 Loss1: 0.210417 Loss2: 0.039738
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.211890 Loss1: 0.172367 Loss2: 0.039523
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.178826 Loss1: 0.139163 Loss2: 0.039663
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.192750 Loss1: 0.153039 Loss2: 0.039711
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.176109 Loss1: 0.136231 Loss2: 0.039878
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.180502 Loss1: 0.140356 Loss2: 0.040145
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130092 Loss1: 0.090996 Loss2: 0.039096
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121654 Loss1: 0.083288 Loss2: 0.038366
-(DefaultActor pid=1838052) >> Training accuracy: 0.981013
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.452071 Loss1: 0.414766 Loss2: 0.037305
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.290111 Loss1: 0.249850 Loss2: 0.040261
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.230780 Loss1: 0.191009 Loss2: 0.039771
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.192630 Loss1: 0.152700 Loss2: 0.039930
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.199678 Loss1: 0.159419 Loss2: 0.040258
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.196609 Loss1: 0.156353 Loss2: 0.040256
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.170675 Loss1: 0.130459 Loss2: 0.040217
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.143848 Loss1: 0.103957 Loss2: 0.039891
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.159888 Loss1: 0.119834 Loss2: 0.040055
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.150512 Loss1: 0.110520 Loss2: 0.039992
-(DefaultActor pid=1838052) >> Training accuracy: 0.982205
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.435299 Loss1: 0.369485 Loss2: 0.065814
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.289655 Loss1: 0.229028 Loss2: 0.060627
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.267689 Loss1: 0.208688 Loss2: 0.059001
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.209366 Loss1: 0.153482 Loss2: 0.055884
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.203564 Loss1: 0.148883 Loss2: 0.054681
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.182769 Loss1: 0.128161 Loss2: 0.054608
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.174617 Loss1: 0.121091 Loss2: 0.053526
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.159125 Loss1: 0.106280 Loss2: 0.052845
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.172193 Loss1: 0.119267 Loss2: 0.052926
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.157035 Loss1: 0.104320 Loss2: 0.052715
-(DefaultActor pid=1838052) >> Training accuracy: 0.977848
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.508836 Loss1: 0.427532 Loss2: 0.081305
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.331576 Loss1: 0.249831 Loss2: 0.081744
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.287708 Loss1: 0.210121 Loss2: 0.077587
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.275275 Loss1: 0.199117 Loss2: 0.076158
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.263615 Loss1: 0.188778 Loss2: 0.074837
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.249887 Loss1: 0.175915 Loss2: 0.073972
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.247570 Loss1: 0.174491 Loss2: 0.073079
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.214315 Loss1: 0.143469 Loss2: 0.070846
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.191275 Loss1: 0.122367 Loss2: 0.068908
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.178610 Loss1: 0.109737 Loss2: 0.068873
-(DefaultActor pid=1838052) >> Training accuracy: 0.979030
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.422888 Loss1: 0.385750 Loss2: 0.037138
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.275621 Loss1: 0.235310 Loss2: 0.040311
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.232703 Loss1: 0.192602 Loss2: 0.040102
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.207591 Loss1: 0.167724 Loss2: 0.039867
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.181623 Loss1: 0.141836 Loss2: 0.039786
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.187898 Loss1: 0.147220 Loss2: 0.040678
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.198104 Loss1: 0.157263 Loss2: 0.040841
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.183048 Loss1: 0.142453 Loss2: 0.040595
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.153646 Loss1: 0.113569 Loss2: 0.040077
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163460 Loss1: 0.122976 Loss2: 0.040484
-(DefaultActor pid=1838052) >> Training accuracy: 0.967959
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.418127 Loss1: 0.381082 Loss2: 0.037045
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.294499 Loss1: 0.254179 Loss2: 0.040320
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.220567 Loss1: 0.180139 Loss2: 0.040428
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.196536 Loss1: 0.156745 Loss2: 0.039792
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.182663 Loss1: 0.142716 Loss2: 0.039948
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.173034 Loss1: 0.133222 Loss2: 0.039812
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.153829 Loss1: 0.113595 Loss2: 0.040235
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.156088 Loss1: 0.116533 Loss2: 0.039555
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.167497 Loss1: 0.126682 Loss2: 0.040815
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.175991 Loss1: 0.135116 Loss2: 0.040874
-(DefaultActor pid=1838052) >> Training accuracy: 0.969739
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 01:53:18,857][flwr][DEBUG] - fit_round 37 received 10 results and 0 failures
->> Test accuracy: 0.628500
-[2023-09-28 01:53:59,072][flwr][INFO] - fit progress: (37, 2.0764910361637323, {'accuracy': 0.6285}, 70461.9626628072)
-[2023-09-28 01:53:59,074][flwr][DEBUG] - evaluate_round 37: strategy sampled 10 clients (out of 10)
-[2023-09-28 01:54:36,391][flwr][DEBUG] - evaluate_round 37 received 10 results and 0 failures
-[2023-09-28 01:54:36,392][flwr][DEBUG] - fit_round 38: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.947822 Loss1: 0.374450 Loss2: 0.573373
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.848486 Loss1: 0.277471 Loss2: 0.571015
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.777773 Loss1: 0.224904 Loss2: 0.552869
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.752050 Loss1: 0.210657 Loss2: 0.541392
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.708309 Loss1: 0.175708 Loss2: 0.532601
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.662429 Loss1: 0.139916 Loss2: 0.522513
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.665203 Loss1: 0.146384 Loss2: 0.518819
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.663120 Loss1: 0.148298 Loss2: 0.514822
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.651463 Loss1: 0.141839 Loss2: 0.509624
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.658726 Loss1: 0.149726 Loss2: 0.509000
-(DefaultActor pid=1838052) >> Training accuracy: 0.972706
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.424511 Loss1: 0.350841 Loss2: 0.073670
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.289621 Loss1: 0.214440 Loss2: 0.075180
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.239398 Loss1: 0.168562 Loss2: 0.070836
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.191914 Loss1: 0.123757 Loss2: 0.068158
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.180414 Loss1: 0.114490 Loss2: 0.065924
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.171252 Loss1: 0.107022 Loss2: 0.064230
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.169429 Loss1: 0.105778 Loss2: 0.063651
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.184517 Loss1: 0.120550 Loss2: 0.063967
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.179749 Loss1: 0.116773 Loss2: 0.062977
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163132 Loss1: 0.100751 Loss2: 0.062381
-(DefaultActor pid=1838052) >> Training accuracy: 0.982372
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.986121 Loss1: 0.397978 Loss2: 0.588143
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.860657 Loss1: 0.275372 Loss2: 0.585286
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.777476 Loss1: 0.211531 Loss2: 0.565945
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.746671 Loss1: 0.188813 Loss2: 0.557858
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.699729 Loss1: 0.153610 Loss2: 0.546119
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.697623 Loss1: 0.158519 Loss2: 0.539104
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.661519 Loss1: 0.132408 Loss2: 0.529110
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.658148 Loss1: 0.133780 Loss2: 0.524368
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.664719 Loss1: 0.146350 Loss2: 0.518369
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.651168 Loss1: 0.136638 Loss2: 0.514530
-(DefaultActor pid=1838052) >> Training accuracy: 0.969343
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.361544 Loss1: 0.322763 Loss2: 0.038781
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.243783 Loss1: 0.201674 Loss2: 0.042109
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.204140 Loss1: 0.162170 Loss2: 0.041970
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.196888 Loss1: 0.155141 Loss2: 0.041747
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.191800 Loss1: 0.150225 Loss2: 0.041575
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.171654 Loss1: 0.130041 Loss2: 0.041612
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.186243 Loss1: 0.144255 Loss2: 0.041987
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.147774 Loss1: 0.107076 Loss2: 0.040697
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.167422 Loss1: 0.126134 Loss2: 0.041288
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.145339 Loss1: 0.104135 Loss2: 0.041204
-(DefaultActor pid=1838052) >> Training accuracy: 0.978468
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.831277 Loss1: 0.354209 Loss2: 0.477068
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.663230 Loss1: 0.248407 Loss2: 0.414823
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.618408 Loss1: 0.217446 Loss2: 0.400962
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.587431 Loss1: 0.190108 Loss2: 0.397323
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.619908 Loss1: 0.225034 Loss2: 0.394875
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.586103 Loss1: 0.193573 Loss2: 0.392530
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.526128 Loss1: 0.142016 Loss2: 0.384112
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.538322 Loss1: 0.155955 Loss2: 0.382367
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.550350 Loss1: 0.167412 Loss2: 0.382938
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.538203 Loss1: 0.155536 Loss2: 0.382667
-(DefaultActor pid=1838052) >> Training accuracy: 0.972903
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.495553 Loss1: 0.419142 Loss2: 0.076411
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.329229 Loss1: 0.255518 Loss2: 0.073711
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.255036 Loss1: 0.187344 Loss2: 0.067692
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.239961 Loss1: 0.174302 Loss2: 0.065659
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.231891 Loss1: 0.168089 Loss2: 0.063802
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.208998 Loss1: 0.146952 Loss2: 0.062045
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197066 Loss1: 0.136135 Loss2: 0.060931
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.173787 Loss1: 0.113702 Loss2: 0.060085
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.164605 Loss1: 0.105283 Loss2: 0.059322
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166683 Loss1: 0.108213 Loss2: 0.058471
-(DefaultActor pid=1838052) >> Training accuracy: 0.973606
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.012162 Loss1: 0.436682 Loss2: 0.575481
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.799008 Loss1: 0.235905 Loss2: 0.563103
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.756858 Loss1: 0.222804 Loss2: 0.534054
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.733322 Loss1: 0.207774 Loss2: 0.525548
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.702424 Loss1: 0.189256 Loss2: 0.513168
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.705594 Loss1: 0.197283 Loss2: 0.508311
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.719106 Loss1: 0.211684 Loss2: 0.507423
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.685717 Loss1: 0.182639 Loss2: 0.503077
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.644746 Loss1: 0.148841 Loss2: 0.495905
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.622592 Loss1: 0.128666 Loss2: 0.493926
-(DefaultActor pid=1838052) >> Training accuracy: 0.976562
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.431880 Loss1: 0.388548 Loss2: 0.043332
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.276171 Loss1: 0.230499 Loss2: 0.045672
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.218978 Loss1: 0.174298 Loss2: 0.044680
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.201014 Loss1: 0.156810 Loss2: 0.044205
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.187500 Loss1: 0.143486 Loss2: 0.044014
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.182115 Loss1: 0.138580 Loss2: 0.043535
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.175891 Loss1: 0.131818 Loss2: 0.044072
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.169518 Loss1: 0.126151 Loss2: 0.043367
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.161924 Loss1: 0.119234 Loss2: 0.042690
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.179014 Loss1: 0.135679 Loss2: 0.043335
-(DefaultActor pid=1838052) >> Training accuracy: 0.965745
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.429497 Loss1: 0.391638 Loss2: 0.037859
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.261756 Loss1: 0.221767 Loss2: 0.039989
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.185411 Loss1: 0.146013 Loss2: 0.039398
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.198675 Loss1: 0.158774 Loss2: 0.039901
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.200744 Loss1: 0.159850 Loss2: 0.040893
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.179659 Loss1: 0.139394 Loss2: 0.040265
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.169840 Loss1: 0.129769 Loss2: 0.040071
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.151742 Loss1: 0.111105 Loss2: 0.040637
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.138337 Loss1: 0.098767 Loss2: 0.039570
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.138291 Loss1: 0.098277 Loss2: 0.040014
-(DefaultActor pid=1838052) >> Training accuracy: 0.976661
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.440684 Loss1: 0.403122 Loss2: 0.037562
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.288016 Loss1: 0.246606 Loss2: 0.041410
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.252886 Loss1: 0.212527 Loss2: 0.040360
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.185070 Loss1: 0.145241 Loss2: 0.039830
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.176021 Loss1: 0.135887 Loss2: 0.040134
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.161470 Loss1: 0.121480 Loss2: 0.039990
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.157327 Loss1: 0.117544 Loss2: 0.039783
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.149039 Loss1: 0.109396 Loss2: 0.039643
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.142299 Loss1: 0.102509 Loss2: 0.039790
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.158509 Loss1: 0.118205 Loss2: 0.040304
-(DefaultActor pid=1838052) >> Training accuracy: 0.985243
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 02:23:30,222][flwr][DEBUG] - fit_round 38 received 10 results and 0 failures
->> Test accuracy: 0.628300
-[2023-09-28 02:24:10,207][flwr][INFO] - fit progress: (38, 2.065860210897062, {'accuracy': 0.6283}, 72273.0968576204)
-[2023-09-28 02:24:10,207][flwr][DEBUG] - evaluate_round 38: strategy sampled 10 clients (out of 10)
-[2023-09-28 02:24:47,513][flwr][DEBUG] - evaluate_round 38 received 10 results and 0 failures
-[2023-09-28 02:24:47,514][flwr][DEBUG] - fit_round 39: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.384961 Loss1: 0.344681 Loss2: 0.040280
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.260230 Loss1: 0.216422 Loss2: 0.043808
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.233905 Loss1: 0.190407 Loss2: 0.043497
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.206171 Loss1: 0.163037 Loss2: 0.043134
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.175923 Loss1: 0.133205 Loss2: 0.042717
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.172038 Loss1: 0.129571 Loss2: 0.042466
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197703 Loss1: 0.154523 Loss2: 0.043181
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.162750 Loss1: 0.120047 Loss2: 0.042702
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.167713 Loss1: 0.124752 Loss2: 0.042961
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.163586 Loss1: 0.121087 Loss2: 0.042499
-(DefaultActor pid=1838052) >> Training accuracy: 0.978244
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.419245 Loss1: 0.377613 Loss2: 0.041632
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.279324 Loss1: 0.233723 Loss2: 0.045601
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.247699 Loss1: 0.203414 Loss2: 0.044285
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.214926 Loss1: 0.170675 Loss2: 0.044251
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.191573 Loss1: 0.147695 Loss2: 0.043878
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.212024 Loss1: 0.168384 Loss2: 0.043639
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.154553 Loss1: 0.111660 Loss2: 0.042893
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.150527 Loss1: 0.107758 Loss2: 0.042770
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.155705 Loss1: 0.112207 Loss2: 0.043498
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166108 Loss1: 0.122595 Loss2: 0.043513
-(DefaultActor pid=1838052) >> Training accuracy: 0.975946
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.950546 Loss1: 0.364580 Loss2: 0.585967
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.790651 Loss1: 0.212926 Loss2: 0.577726
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.763584 Loss1: 0.202202 Loss2: 0.561381
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.735404 Loss1: 0.188785 Loss2: 0.546619
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.716583 Loss1: 0.176884 Loss2: 0.539699
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.735777 Loss1: 0.202108 Loss2: 0.533669
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.698596 Loss1: 0.171457 Loss2: 0.527138
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.701719 Loss1: 0.179092 Loss2: 0.522627
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.636034 Loss1: 0.120701 Loss2: 0.515333
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.626950 Loss1: 0.117925 Loss2: 0.509025
-(DefaultActor pid=1838052) >> Training accuracy: 0.969752
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.348304 Loss1: 0.312684 Loss2: 0.035619
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.226025 Loss1: 0.187666 Loss2: 0.038358
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.219118 Loss1: 0.180018 Loss2: 0.039100
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.180326 Loss1: 0.141487 Loss2: 0.038839
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.187125 Loss1: 0.147872 Loss2: 0.039253
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.190643 Loss1: 0.151182 Loss2: 0.039461
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.147020 Loss1: 0.107963 Loss2: 0.039056
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.135119 Loss1: 0.096145 Loss2: 0.038974
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.133582 Loss1: 0.095137 Loss2: 0.038445
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.145749 Loss1: 0.106441 Loss2: 0.039308
-(DefaultActor pid=1838052) >> Training accuracy: 0.970274
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 1.005744 Loss1: 0.411087 Loss2: 0.594657
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.825049 Loss1: 0.228833 Loss2: 0.596216
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.772448 Loss1: 0.192247 Loss2: 0.580201
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.776096 Loss1: 0.204248 Loss2: 0.571848
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.756268 Loss1: 0.193302 Loss2: 0.562966
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.699358 Loss1: 0.145462 Loss2: 0.553896
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.684253 Loss1: 0.136019 Loss2: 0.548234
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.691315 Loss1: 0.147532 Loss2: 0.543783
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.675440 Loss1: 0.136525 Loss2: 0.538914
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.639372 Loss1: 0.106977 Loss2: 0.532395
-(DefaultActor pid=1838052) >> Training accuracy: 0.975694
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.880657 Loss1: 0.420931 Loss2: 0.459726
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.699179 Loss1: 0.278237 Loss2: 0.420942
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.586774 Loss1: 0.188452 Loss2: 0.398322
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615108 Loss1: 0.223527 Loss2: 0.391581
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.573486 Loss1: 0.185209 Loss2: 0.388277
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.546234 Loss1: 0.162117 Loss2: 0.384117
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.502468 Loss1: 0.126442 Loss2: 0.376026
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.501679 Loss1: 0.124646 Loss2: 0.377034
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.523940 Loss1: 0.144373 Loss2: 0.379567
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.494666 Loss1: 0.119756 Loss2: 0.374909
-(DefaultActor pid=1838052) >> Training accuracy: 0.975084
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.357959 Loss1: 0.317613 Loss2: 0.040347
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.252431 Loss1: 0.208905 Loss2: 0.043526
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.182230 Loss1: 0.140668 Loss2: 0.041562
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.206335 Loss1: 0.163465 Loss2: 0.042870
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.191784 Loss1: 0.149030 Loss2: 0.042754
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.194444 Loss1: 0.151649 Loss2: 0.042795
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.169078 Loss1: 0.126995 Loss2: 0.042083
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.173534 Loss1: 0.131383 Loss2: 0.042150
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.163550 Loss1: 0.121244 Loss2: 0.042306
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.150125 Loss1: 0.108769 Loss2: 0.041356
-(DefaultActor pid=1838052) >> Training accuracy: 0.978837
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.387201 Loss1: 0.351239 Loss2: 0.035962
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.246278 Loss1: 0.207492 Loss2: 0.038786
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.208893 Loss1: 0.170108 Loss2: 0.038785
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.174972 Loss1: 0.136711 Loss2: 0.038260
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.161681 Loss1: 0.123439 Loss2: 0.038242
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.154717 Loss1: 0.116237 Loss2: 0.038479
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.166681 Loss1: 0.128114 Loss2: 0.038567
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.155058 Loss1: 0.116301 Loss2: 0.038757
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.148972 Loss1: 0.110280 Loss2: 0.038692
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.157001 Loss1: 0.117789 Loss2: 0.039212
-(DefaultActor pid=1838052) >> Training accuracy: 0.970134
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.348883 Loss1: 0.312887 Loss2: 0.035996
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.237904 Loss1: 0.198361 Loss2: 0.039542
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.193108 Loss1: 0.153567 Loss2: 0.039541
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.183219 Loss1: 0.143437 Loss2: 0.039783
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.161211 Loss1: 0.122015 Loss2: 0.039196
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.164748 Loss1: 0.125633 Loss2: 0.039116
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129833 Loss1: 0.090883 Loss2: 0.038949
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.131490 Loss1: 0.092257 Loss2: 0.039232
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.138833 Loss1: 0.099562 Loss2: 0.039271
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.135714 Loss1: 0.096327 Loss2: 0.039388
-(DefaultActor pid=1838052) >> Training accuracy: 0.978766
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.405173 Loss1: 0.341129 Loss2: 0.064044
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.305308 Loss1: 0.244066 Loss2: 0.061243
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.250796 Loss1: 0.191348 Loss2: 0.059448
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.227043 Loss1: 0.168130 Loss2: 0.058913
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.202156 Loss1: 0.145157 Loss2: 0.056999
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.190824 Loss1: 0.134405 Loss2: 0.056419
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.193735 Loss1: 0.136758 Loss2: 0.056977
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.181427 Loss1: 0.124822 Loss2: 0.056605
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160186 Loss1: 0.104372 Loss2: 0.055814
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.139883 Loss1: 0.085171 Loss2: 0.054713
-(DefaultActor pid=1838052) >> Training accuracy: 0.979233
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 02:53:44,476][flwr][DEBUG] - fit_round 39 received 10 results and 0 failures
->> Test accuracy: 0.632100
-[2023-09-28 02:54:24,943][flwr][INFO] - fit progress: (39, 2.087371099490327, {'accuracy': 0.6321}, 74087.83291663835)
-[2023-09-28 02:54:24,943][flwr][DEBUG] - evaluate_round 39: strategy sampled 10 clients (out of 10)
-[2023-09-28 02:55:01,197][flwr][DEBUG] - evaluate_round 39 received 10 results and 0 failures
-[2023-09-28 02:55:01,198][flwr][DEBUG] - fit_round 40: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.408748 Loss1: 0.371365 Loss2: 0.037383
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.256682 Loss1: 0.216206 Loss2: 0.040476
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.199833 Loss1: 0.159833 Loss2: 0.040000
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.186427 Loss1: 0.146193 Loss2: 0.040234
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.161897 Loss1: 0.122046 Loss2: 0.039851
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.153733 Loss1: 0.114602 Loss2: 0.039131
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.134971 Loss1: 0.095389 Loss2: 0.039582
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.142230 Loss1: 0.103251 Loss2: 0.038979
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160427 Loss1: 0.120315 Loss2: 0.040112
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.154296 Loss1: 0.113859 Loss2: 0.040437
-(DefaultActor pid=1838052) >> Training accuracy: 0.982319
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.330824 Loss1: 0.294385 Loss2: 0.036439
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.233003 Loss1: 0.193123 Loss2: 0.039880
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.201093 Loss1: 0.161182 Loss2: 0.039911
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.190235 Loss1: 0.150459 Loss2: 0.039776
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.192865 Loss1: 0.152882 Loss2: 0.039983
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.161443 Loss1: 0.121004 Loss2: 0.040439
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.153238 Loss1: 0.113122 Loss2: 0.040116
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.129261 Loss1: 0.089713 Loss2: 0.039548
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.127062 Loss1: 0.088358 Loss2: 0.038704
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.130641 Loss1: 0.091483 Loss2: 0.039158
-(DefaultActor pid=1838052) >> Training accuracy: 0.983386
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.407933 Loss1: 0.367325 Loss2: 0.040608
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.278527 Loss1: 0.233740 Loss2: 0.044788
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.214077 Loss1: 0.170411 Loss2: 0.043666
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.198942 Loss1: 0.156016 Loss2: 0.042925
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.188335 Loss1: 0.145823 Loss2: 0.042513
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.190338 Loss1: 0.147250 Loss2: 0.043088
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.168581 Loss1: 0.126148 Loss2: 0.042433
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.129846 Loss1: 0.087646 Loss2: 0.042200
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.133897 Loss1: 0.092052 Loss2: 0.041845
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.161388 Loss1: 0.119436 Loss2: 0.041952
-(DefaultActor pid=1838052) >> Training accuracy: 0.976362
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.682889 Loss1: 0.392449 Loss2: 0.290440
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.508769 Loss1: 0.271475 Loss2: 0.237294
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.423154 Loss1: 0.198237 Loss2: 0.224917
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.414200 Loss1: 0.194476 Loss2: 0.219724
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.399932 Loss1: 0.184020 Loss2: 0.215912
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.335244 Loss1: 0.123115 Loss2: 0.212129
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.353691 Loss1: 0.143417 Loss2: 0.210274
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.352336 Loss1: 0.140922 Loss2: 0.211414
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.335024 Loss1: 0.126958 Loss2: 0.208067
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.339112 Loss1: 0.130933 Loss2: 0.208179
-(DefaultActor pid=1838052) >> Training accuracy: 0.979941
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.873990 Loss1: 0.290741 Loss2: 0.583249
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.786760 Loss1: 0.209383 Loss2: 0.577378
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.753608 Loss1: 0.193459 Loss2: 0.560149
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.741402 Loss1: 0.192711 Loss2: 0.548691
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.726168 Loss1: 0.185820 Loss2: 0.540349
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.707480 Loss1: 0.170327 Loss2: 0.537153
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.700496 Loss1: 0.170853 Loss2: 0.529642
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.666595 Loss1: 0.142121 Loss2: 0.524474
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.636955 Loss1: 0.118994 Loss2: 0.517961
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.660299 Loss1: 0.142924 Loss2: 0.517375
-(DefaultActor pid=1838052) >> Training accuracy: 0.953125
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.416937 Loss1: 0.345542 Loss2: 0.071395
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.298435 Loss1: 0.224267 Loss2: 0.074168
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.254772 Loss1: 0.182867 Loss2: 0.071905
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.234351 Loss1: 0.164021 Loss2: 0.070330
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.208594 Loss1: 0.141584 Loss2: 0.067010
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.186025 Loss1: 0.121010 Loss2: 0.065015
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.186290 Loss1: 0.121658 Loss2: 0.064631
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.166847 Loss1: 0.102611 Loss2: 0.064236
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.193944 Loss1: 0.129933 Loss2: 0.064012
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.206284 Loss1: 0.141703 Loss2: 0.064581
-(DefaultActor pid=1838052) >> Training accuracy: 0.978244
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.387497 Loss1: 0.351502 Loss2: 0.035995
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.255647 Loss1: 0.216250 Loss2: 0.039397
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.194337 Loss1: 0.155517 Loss2: 0.038821
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.177022 Loss1: 0.138489 Loss2: 0.038533
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.173612 Loss1: 0.134330 Loss2: 0.039282
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.163418 Loss1: 0.123791 Loss2: 0.039627
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.159709 Loss1: 0.119983 Loss2: 0.039727
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.147168 Loss1: 0.108354 Loss2: 0.038814
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.184937 Loss1: 0.144985 Loss2: 0.039952
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.152178 Loss1: 0.112575 Loss2: 0.039603
-(DefaultActor pid=1838052) >> Training accuracy: 0.980419
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.326927 Loss1: 0.292648 Loss2: 0.034279
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.218566 Loss1: 0.181296 Loss2: 0.037270
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.173206 Loss1: 0.135855 Loss2: 0.037352
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.196992 Loss1: 0.158581 Loss2: 0.038411
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.184802 Loss1: 0.146134 Loss2: 0.038668
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.137278 Loss1: 0.099655 Loss2: 0.037624
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.140152 Loss1: 0.102617 Loss2: 0.037535
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.117930 Loss1: 0.080638 Loss2: 0.037291
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.103696 Loss1: 0.066361 Loss2: 0.037334
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095788 Loss1: 0.059174 Loss2: 0.036614
-(DefaultActor pid=1838052) >> Training accuracy: 0.990585
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.771447 Loss1: 0.365754 Loss2: 0.405693
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.630135 Loss1: 0.251205 Loss2: 0.378930
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.571623 Loss1: 0.209364 Loss2: 0.362259
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.596883 Loss1: 0.229713 Loss2: 0.367171
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.538459 Loss1: 0.180682 Loss2: 0.357778
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.516317 Loss1: 0.163355 Loss2: 0.352961
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.511856 Loss1: 0.159579 Loss2: 0.352277
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.516610 Loss1: 0.165799 Loss2: 0.350810
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.477944 Loss1: 0.131002 Loss2: 0.346942
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.485606 Loss1: 0.138995 Loss2: 0.346611
-(DefaultActor pid=1838052) >> Training accuracy: 0.964201
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.440309 Loss1: 0.366806 Loss2: 0.073503
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.272921 Loss1: 0.204990 Loss2: 0.067931
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.220161 Loss1: 0.155084 Loss2: 0.065078
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.218061 Loss1: 0.155425 Loss2: 0.062636
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.200922 Loss1: 0.138717 Loss2: 0.062204
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.184539 Loss1: 0.123208 Loss2: 0.061331
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.164389 Loss1: 0.104189 Loss2: 0.060200
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.174791 Loss1: 0.114803 Loss2: 0.059988
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.158840 Loss1: 0.099994 Loss2: 0.058845
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.165501 Loss1: 0.106472 Loss2: 0.059029
-(DefaultActor pid=1838052) >> Training accuracy: 0.977431
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 03:24:09,304][flwr][DEBUG] - fit_round 40 received 10 results and 0 failures
->> Test accuracy: 0.632600
-[2023-09-28 03:24:50,377][flwr][INFO] - fit progress: (40, 2.067515920335873, {'accuracy': 0.6326}, 75913.26707566809)
-[2023-09-28 03:24:50,377][flwr][DEBUG] - evaluate_round 40: strategy sampled 10 clients (out of 10)
-[2023-09-28 03:25:27,001][flwr][DEBUG] - evaluate_round 40 received 10 results and 0 failures
-[2023-09-28 03:25:27,002][flwr][DEBUG] - fit_round 41: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.365663 Loss1: 0.330417 Loss2: 0.035247
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.228918 Loss1: 0.190550 Loss2: 0.038369
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.179874 Loss1: 0.142129 Loss2: 0.037745
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.160471 Loss1: 0.122666 Loss2: 0.037806
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.145423 Loss1: 0.107171 Loss2: 0.038252
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.134378 Loss1: 0.096479 Loss2: 0.037899
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.141110 Loss1: 0.103334 Loss2: 0.037776
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.139664 Loss1: 0.101590 Loss2: 0.038074
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.145802 Loss1: 0.107697 Loss2: 0.038106
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.161847 Loss1: 0.122948 Loss2: 0.038899
-(DefaultActor pid=1838052) >> Training accuracy: 0.973695
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.931210 Loss1: 0.331647 Loss2: 0.599563
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.823036 Loss1: 0.225797 Loss2: 0.597239
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.744993 Loss1: 0.164794 Loss2: 0.580199
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.724070 Loss1: 0.160383 Loss2: 0.563687
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.718951 Loss1: 0.164273 Loss2: 0.554678
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.712391 Loss1: 0.164017 Loss2: 0.548374
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.700716 Loss1: 0.158603 Loss2: 0.542113
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.659692 Loss1: 0.126746 Loss2: 0.532946
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.648997 Loss1: 0.120491 Loss2: 0.528506
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.656761 Loss1: 0.134202 Loss2: 0.522559
-(DefaultActor pid=1838052) >> Training accuracy: 0.978244
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.853531 Loss1: 0.316967 Loss2: 0.536565
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.720408 Loss1: 0.204684 Loss2: 0.515724
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.682960 Loss1: 0.179036 Loss2: 0.503924
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.657902 Loss1: 0.163568 Loss2: 0.494334
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.649053 Loss1: 0.159953 Loss2: 0.489100
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.645362 Loss1: 0.157363 Loss2: 0.487999
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.589850 Loss1: 0.110590 Loss2: 0.479260
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.602257 Loss1: 0.121052 Loss2: 0.481205
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.591025 Loss1: 0.113572 Loss2: 0.477454
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.609282 Loss1: 0.132575 Loss2: 0.476707
-(DefaultActor pid=1838052) >> Training accuracy: 0.962619
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.336995 Loss1: 0.297144 Loss2: 0.039850
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.247467 Loss1: 0.204440 Loss2: 0.043028
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.171000 Loss1: 0.128535 Loss2: 0.042465
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136347 Loss1: 0.095475 Loss2: 0.040872
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.153248 Loss1: 0.112323 Loss2: 0.040925
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.165721 Loss1: 0.124008 Loss2: 0.041713
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.165849 Loss1: 0.122950 Loss2: 0.042899
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.156489 Loss1: 0.114811 Loss2: 0.041678
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.160582 Loss1: 0.118926 Loss2: 0.041655
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.159132 Loss1: 0.116929 Loss2: 0.042203
-(DefaultActor pid=1838052) >> Training accuracy: 0.977706
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.342911 Loss1: 0.307969 Loss2: 0.034942
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.234825 Loss1: 0.196638 Loss2: 0.038187
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.171980 Loss1: 0.133949 Loss2: 0.038031
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.155417 Loss1: 0.117580 Loss2: 0.037837
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.143503 Loss1: 0.105742 Loss2: 0.037760
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.153051 Loss1: 0.115299 Loss2: 0.037751
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.139322 Loss1: 0.101189 Loss2: 0.038133
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.133509 Loss1: 0.095521 Loss2: 0.037988
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112395 Loss1: 0.075142 Loss2: 0.037253
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118172 Loss1: 0.080728 Loss2: 0.037444
-(DefaultActor pid=1838052) >> Training accuracy: 0.987380
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.799106 Loss1: 0.348458 Loss2: 0.450648
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.674629 Loss1: 0.258734 Loss2: 0.415895
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.650436 Loss1: 0.246229 Loss2: 0.404207
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.559243 Loss1: 0.168224 Loss2: 0.391019
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.561473 Loss1: 0.175454 Loss2: 0.386019
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.512757 Loss1: 0.131254 Loss2: 0.381502
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.519132 Loss1: 0.141093 Loss2: 0.378039
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.542210 Loss1: 0.157917 Loss2: 0.384292
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.516640 Loss1: 0.140560 Loss2: 0.376080
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.485644 Loss1: 0.115314 Loss2: 0.370330
-(DefaultActor pid=1838052) >> Training accuracy: 0.974826
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.391346 Loss1: 0.354144 Loss2: 0.037202
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.270883 Loss1: 0.230388 Loss2: 0.040495
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.225941 Loss1: 0.185563 Loss2: 0.040378
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.192887 Loss1: 0.152032 Loss2: 0.040854
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.162173 Loss1: 0.121727 Loss2: 0.040446
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178743 Loss1: 0.137967 Loss2: 0.040776
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.179264 Loss1: 0.137918 Loss2: 0.041347
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.157555 Loss1: 0.117260 Loss2: 0.040296
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.164613 Loss1: 0.123855 Loss2: 0.040758
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.135625 Loss1: 0.095420 Loss2: 0.040205
-(DefaultActor pid=1838052) >> Training accuracy: 0.968339
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.354935 Loss1: 0.318966 Loss2: 0.035969
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.210173 Loss1: 0.171611 Loss2: 0.038562
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.186113 Loss1: 0.147160 Loss2: 0.038953
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.162910 Loss1: 0.124103 Loss2: 0.038807
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.199243 Loss1: 0.159341 Loss2: 0.039903
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.207098 Loss1: 0.166585 Loss2: 0.040513
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.177047 Loss1: 0.136647 Loss2: 0.040400
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.177057 Loss1: 0.136605 Loss2: 0.040452
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.150514 Loss1: 0.110885 Loss2: 0.039629
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.158607 Loss1: 0.118235 Loss2: 0.040371
-(DefaultActor pid=1838052) >> Training accuracy: 0.978165
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.435481 Loss1: 0.367341 Loss2: 0.068140
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.282816 Loss1: 0.216163 Loss2: 0.066653
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.230712 Loss1: 0.168692 Loss2: 0.062020
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.232974 Loss1: 0.171560 Loss2: 0.061413
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.215682 Loss1: 0.155480 Loss2: 0.060202
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.187192 Loss1: 0.128747 Loss2: 0.058445
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.193881 Loss1: 0.135563 Loss2: 0.058318
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.180464 Loss1: 0.122625 Loss2: 0.057839
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.154857 Loss1: 0.098097 Loss2: 0.056760
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.171681 Loss1: 0.114913 Loss2: 0.056768
-(DefaultActor pid=1838052) >> Training accuracy: 0.979519
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.374787 Loss1: 0.312822 Loss2: 0.061965
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.260902 Loss1: 0.202977 Loss2: 0.057926
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.223140 Loss1: 0.166527 Loss2: 0.056613
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.203431 Loss1: 0.147003 Loss2: 0.056428
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.200187 Loss1: 0.144343 Loss2: 0.055845
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.187361 Loss1: 0.131560 Loss2: 0.055801
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.191250 Loss1: 0.135888 Loss2: 0.055362
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.169950 Loss1: 0.114638 Loss2: 0.055312
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.184799 Loss1: 0.128627 Loss2: 0.056172
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.153772 Loss1: 0.099232 Loss2: 0.054541
-(DefaultActor pid=1838052) >> Training accuracy: 0.983979
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 03:54:28,045][flwr][DEBUG] - fit_round 41 received 10 results and 0 failures
->> Test accuracy: 0.634100
-[2023-09-28 03:55:08,509][flwr][INFO] - fit progress: (41, 2.094820894753209, {'accuracy': 0.6341}, 77731.39968940802)
-[2023-09-28 03:55:08,510][flwr][DEBUG] - evaluate_round 41: strategy sampled 10 clients (out of 10)
-[2023-09-28 03:55:45,284][flwr][DEBUG] - evaluate_round 41 received 10 results and 0 failures
-[2023-09-28 03:55:45,285][flwr][DEBUG] - fit_round 42: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.282961 Loss1: 0.247893 Loss2: 0.035068
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.198014 Loss1: 0.159779 Loss2: 0.038234
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.184060 Loss1: 0.145359 Loss2: 0.038701
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.156623 Loss1: 0.117933 Loss2: 0.038689
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.141080 Loss1: 0.103140 Loss2: 0.037940
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.162968 Loss1: 0.124399 Loss2: 0.038569
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.208433 Loss1: 0.168083 Loss2: 0.040350
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.151405 Loss1: 0.112030 Loss2: 0.039375
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.133598 Loss1: 0.094682 Loss2: 0.038916
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115680 Loss1: 0.077597 Loss2: 0.038083
-(DefaultActor pid=1838052) >> Training accuracy: 0.983994
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.343418 Loss1: 0.302792 Loss2: 0.040626
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.238469 Loss1: 0.195201 Loss2: 0.043268
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.190821 Loss1: 0.148473 Loss2: 0.042348
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.177271 Loss1: 0.135086 Loss2: 0.042185
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.163452 Loss1: 0.121658 Loss2: 0.041794
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.165660 Loss1: 0.124301 Loss2: 0.041359
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.141909 Loss1: 0.100319 Loss2: 0.041589
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.146433 Loss1: 0.105611 Loss2: 0.040822
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.163767 Loss1: 0.122136 Loss2: 0.041630
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.123815 Loss1: 0.082807 Loss2: 0.041008
-(DefaultActor pid=1838052) >> Training accuracy: 0.977057
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.667660 Loss1: 0.317764 Loss2: 0.349896
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.626047 Loss1: 0.286947 Loss2: 0.339101
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.556331 Loss1: 0.226211 Loss2: 0.330120
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.494485 Loss1: 0.177126 Loss2: 0.317359
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.499835 Loss1: 0.179844 Loss2: 0.319991
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.456447 Loss1: 0.145975 Loss2: 0.310473
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.475145 Loss1: 0.160171 Loss2: 0.314974
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.464884 Loss1: 0.153477 Loss2: 0.311406
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.446647 Loss1: 0.135771 Loss2: 0.310875
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.428262 Loss1: 0.119478 Loss2: 0.308784
-(DefaultActor pid=1838052) >> Training accuracy: 0.973101
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.921521 Loss1: 0.346193 Loss2: 0.575328
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.790455 Loss1: 0.217924 Loss2: 0.572531
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.736875 Loss1: 0.176813 Loss2: 0.560062
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.683661 Loss1: 0.136360 Loss2: 0.547300
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.679269 Loss1: 0.140169 Loss2: 0.539100
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.685928 Loss1: 0.150338 Loss2: 0.535590
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.689668 Loss1: 0.157337 Loss2: 0.532331
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.690349 Loss1: 0.163135 Loss2: 0.527214
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.687016 Loss1: 0.161105 Loss2: 0.525910
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.644956 Loss1: 0.121956 Loss2: 0.523001
-(DefaultActor pid=1838052) >> Training accuracy: 0.975160
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.349861 Loss1: 0.313053 Loss2: 0.036808
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.249057 Loss1: 0.208817 Loss2: 0.040239
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.207112 Loss1: 0.167218 Loss2: 0.039894
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.219699 Loss1: 0.178674 Loss2: 0.041025
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.190301 Loss1: 0.149370 Loss2: 0.040931
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178170 Loss1: 0.137478 Loss2: 0.040693
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.189778 Loss1: 0.148625 Loss2: 0.041153
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.163671 Loss1: 0.122896 Loss2: 0.040775
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140772 Loss1: 0.100118 Loss2: 0.040654
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.141555 Loss1: 0.101302 Loss2: 0.040253
-(DefaultActor pid=1838052) >> Training accuracy: 0.982525
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.705869 Loss1: 0.374161 Loss2: 0.331708
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.504914 Loss1: 0.225456 Loss2: 0.279458
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.432206 Loss1: 0.170160 Loss2: 0.262046
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.408075 Loss1: 0.151973 Loss2: 0.256102
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.418689 Loss1: 0.162836 Loss2: 0.255853
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.427930 Loss1: 0.170999 Loss2: 0.256931
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.387531 Loss1: 0.136020 Loss2: 0.251511
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.359555 Loss1: 0.111299 Loss2: 0.248256
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.377214 Loss1: 0.126171 Loss2: 0.251043
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.371156 Loss1: 0.123189 Loss2: 0.247968
-(DefaultActor pid=1838052) >> Training accuracy: 0.974090
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.391325 Loss1: 0.351154 Loss2: 0.040171
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.224268 Loss1: 0.181907 Loss2: 0.042361
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.223382 Loss1: 0.181233 Loss2: 0.042149
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.172724 Loss1: 0.131553 Loss2: 0.041171
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.141090 Loss1: 0.100974 Loss2: 0.040117
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.138989 Loss1: 0.098605 Loss2: 0.040383
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129905 Loss1: 0.090279 Loss2: 0.039626
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.147625 Loss1: 0.107419 Loss2: 0.040206
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.116048 Loss1: 0.077064 Loss2: 0.038984
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.110495 Loss1: 0.071319 Loss2: 0.039176
-(DefaultActor pid=1838052) >> Training accuracy: 0.981337
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.310546 Loss1: 0.274975 Loss2: 0.035570
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.227447 Loss1: 0.188508 Loss2: 0.038939
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.194796 Loss1: 0.155867 Loss2: 0.038929
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.177591 Loss1: 0.138462 Loss2: 0.039129
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.143985 Loss1: 0.105381 Loss2: 0.038604
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.135813 Loss1: 0.097171 Loss2: 0.038642
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.161289 Loss1: 0.122369 Loss2: 0.038920
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.163918 Loss1: 0.124671 Loss2: 0.039247
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.139108 Loss1: 0.099543 Loss2: 0.039565
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.151961 Loss1: 0.112641 Loss2: 0.039320
-(DefaultActor pid=1838052) >> Training accuracy: 0.973101
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.348037 Loss1: 0.279274 Loss2: 0.068764
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.245691 Loss1: 0.174621 Loss2: 0.071069
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.203232 Loss1: 0.135433 Loss2: 0.067799
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.184965 Loss1: 0.120127 Loss2: 0.064838
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.195637 Loss1: 0.130544 Loss2: 0.065093
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178709 Loss1: 0.115010 Loss2: 0.063699
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.165109 Loss1: 0.102430 Loss2: 0.062680
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.145173 Loss1: 0.084489 Loss2: 0.060684
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.142507 Loss1: 0.082294 Loss2: 0.060212
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.172323 Loss1: 0.111828 Loss2: 0.060495
-(DefaultActor pid=1838052) >> Training accuracy: 0.982171
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.840658 Loss1: 0.331950 Loss2: 0.508708
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.683527 Loss1: 0.223477 Loss2: 0.460050
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.647043 Loss1: 0.205072 Loss2: 0.441970
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.621233 Loss1: 0.190779 Loss2: 0.430454
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.596415 Loss1: 0.172472 Loss2: 0.423943
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.599424 Loss1: 0.176818 Loss2: 0.422607
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.564358 Loss1: 0.147170 Loss2: 0.417188
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.526255 Loss1: 0.114892 Loss2: 0.411363
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.514644 Loss1: 0.107009 Loss2: 0.407635
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.515364 Loss1: 0.111466 Loss2: 0.403898
-(DefaultActor pid=1838052) >> Training accuracy: 0.971284
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 04:25:23,403][flwr][DEBUG] - fit_round 42 received 10 results and 0 failures
->> Test accuracy: 0.636900
-[2023-09-28 04:26:05,111][flwr][INFO] - fit progress: (42, 2.0926969356049363, {'accuracy': 0.6369}, 79588.00169806927)
-[2023-09-28 04:26:05,112][flwr][DEBUG] - evaluate_round 42: strategy sampled 10 clients (out of 10)
-[2023-09-28 04:26:41,630][flwr][DEBUG] - evaluate_round 42 received 10 results and 0 failures
-[2023-09-28 04:26:41,632][flwr][DEBUG] - fit_round 43: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.622454 Loss1: 0.334983 Loss2: 0.287472
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.460811 Loss1: 0.224719 Loss2: 0.236092
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.404370 Loss1: 0.186312 Loss2: 0.218058
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.426915 Loss1: 0.210120 Loss2: 0.216795
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.390577 Loss1: 0.178378 Loss2: 0.212199
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.352321 Loss1: 0.143994 Loss2: 0.208328
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.332623 Loss1: 0.125436 Loss2: 0.207187
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.314753 Loss1: 0.110039 Loss2: 0.204715
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.292947 Loss1: 0.090591 Loss2: 0.202356
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.312968 Loss1: 0.108573 Loss2: 0.204396
-(DefaultActor pid=1838052) >> Training accuracy: 0.971073
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.277103 Loss1: 0.242914 Loss2: 0.034190
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.202641 Loss1: 0.165083 Loss2: 0.037558
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.165324 Loss1: 0.127421 Loss2: 0.037903
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.151122 Loss1: 0.113767 Loss2: 0.037355
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.163657 Loss1: 0.125336 Loss2: 0.038321
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.148759 Loss1: 0.110548 Loss2: 0.038211
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.138187 Loss1: 0.100298 Loss2: 0.037889
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.129537 Loss1: 0.091197 Loss2: 0.038340
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.127048 Loss1: 0.089250 Loss2: 0.037799
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.131357 Loss1: 0.093383 Loss2: 0.037974
-(DefaultActor pid=1838052) >> Training accuracy: 0.984756
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.551241 Loss1: 0.314662 Loss2: 0.236580
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.389170 Loss1: 0.192214 Loss2: 0.196956
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.364679 Loss1: 0.176759 Loss2: 0.187920
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.344949 Loss1: 0.160685 Loss2: 0.184263
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.323784 Loss1: 0.141348 Loss2: 0.182436
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.324854 Loss1: 0.144682 Loss2: 0.180172
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.354143 Loss1: 0.172713 Loss2: 0.181430
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.320019 Loss1: 0.141244 Loss2: 0.178775
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.291362 Loss1: 0.114669 Loss2: 0.176694
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.318065 Loss1: 0.136417 Loss2: 0.181648
-(DefaultActor pid=1838052) >> Training accuracy: 0.957081
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.345857 Loss1: 0.309490 Loss2: 0.036367
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.231980 Loss1: 0.193347 Loss2: 0.038633
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.195384 Loss1: 0.156432 Loss2: 0.038952
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.174435 Loss1: 0.135845 Loss2: 0.038590
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.190379 Loss1: 0.150994 Loss2: 0.039385
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.152666 Loss1: 0.113921 Loss2: 0.038745
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130272 Loss1: 0.092181 Loss2: 0.038091
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.133521 Loss1: 0.095190 Loss2: 0.038331
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120534 Loss1: 0.082588 Loss2: 0.037945
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.113473 Loss1: 0.076162 Loss2: 0.037311
-(DefaultActor pid=1838052) >> Training accuracy: 0.987540
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.312291 Loss1: 0.274862 Loss2: 0.037429
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.211997 Loss1: 0.171833 Loss2: 0.040164
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.172954 Loss1: 0.132881 Loss2: 0.040073
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.186943 Loss1: 0.146893 Loss2: 0.040049
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.174381 Loss1: 0.133920 Loss2: 0.040460
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.183817 Loss1: 0.143304 Loss2: 0.040513
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.155665 Loss1: 0.115521 Loss2: 0.040144
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.157943 Loss1: 0.118221 Loss2: 0.039722
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.136332 Loss1: 0.096591 Loss2: 0.039741
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116564 Loss1: 0.077551 Loss2: 0.039013
-(DefaultActor pid=1838052) >> Training accuracy: 0.984573
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.333276 Loss1: 0.298383 Loss2: 0.034893
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.217971 Loss1: 0.179511 Loss2: 0.038460
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.192641 Loss1: 0.153969 Loss2: 0.038673
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.182213 Loss1: 0.143692 Loss2: 0.038521
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.178010 Loss1: 0.139232 Loss2: 0.038778
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.179962 Loss1: 0.140685 Loss2: 0.039277
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.143453 Loss1: 0.104435 Loss2: 0.039018
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.138911 Loss1: 0.100512 Loss2: 0.038399
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.137207 Loss1: 0.098276 Loss2: 0.038931
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.105014 Loss1: 0.067202 Loss2: 0.037811
-(DefaultActor pid=1838052) >> Training accuracy: 0.989800
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.318975 Loss1: 0.282861 Loss2: 0.036114
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.226722 Loss1: 0.187669 Loss2: 0.039053
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.197444 Loss1: 0.158010 Loss2: 0.039434
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.154976 Loss1: 0.115749 Loss2: 0.039227
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.137773 Loss1: 0.098997 Loss2: 0.038776
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.170771 Loss1: 0.131585 Loss2: 0.039186
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.177031 Loss1: 0.136970 Loss2: 0.040061
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.169341 Loss1: 0.128799 Loss2: 0.040542
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.150091 Loss1: 0.109739 Loss2: 0.040352
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.131410 Loss1: 0.091581 Loss2: 0.039829
-(DefaultActor pid=1838052) >> Training accuracy: 0.985403
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.303265 Loss1: 0.268084 Loss2: 0.035180
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.195352 Loss1: 0.157304 Loss2: 0.038048
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.156414 Loss1: 0.118118 Loss2: 0.038296
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.170626 Loss1: 0.131955 Loss2: 0.038671
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.140194 Loss1: 0.101907 Loss2: 0.038287
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.153874 Loss1: 0.115353 Loss2: 0.038521
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.120149 Loss1: 0.081940 Loss2: 0.038209
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125134 Loss1: 0.087332 Loss2: 0.037802
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120537 Loss1: 0.082501 Loss2: 0.038036
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114448 Loss1: 0.076515 Loss2: 0.037933
-(DefaultActor pid=1838052) >> Training accuracy: 0.985957
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.318507 Loss1: 0.279583 Loss2: 0.038924
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.201696 Loss1: 0.160295 Loss2: 0.041401
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.165498 Loss1: 0.124511 Loss2: 0.040987
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.151609 Loss1: 0.110836 Loss2: 0.040773
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.151873 Loss1: 0.111333 Loss2: 0.040540
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.177589 Loss1: 0.136080 Loss2: 0.041510
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.178600 Loss1: 0.137028 Loss2: 0.041572
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.153412 Loss1: 0.112567 Loss2: 0.040845
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.142766 Loss1: 0.102032 Loss2: 0.040734
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.110102 Loss1: 0.070100 Loss2: 0.040002
-(DefaultActor pid=1838052) >> Training accuracy: 0.976963
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.827188 Loss1: 0.281634 Loss2: 0.545554
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.691270 Loss1: 0.165272 Loss2: 0.525998
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.623977 Loss1: 0.117932 Loss2: 0.506045
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.637940 Loss1: 0.134702 Loss2: 0.503238
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.679641 Loss1: 0.175277 Loss2: 0.504364
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.637436 Loss1: 0.140403 Loss2: 0.497033
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.598166 Loss1: 0.106050 Loss2: 0.492116
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.583214 Loss1: 0.095952 Loss2: 0.487262
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.594512 Loss1: 0.107120 Loss2: 0.487393
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.589025 Loss1: 0.103759 Loss2: 0.485266
-(DefaultActor pid=1838052) >> Training accuracy: 0.981370
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 04:56:23,506][flwr][DEBUG] - fit_round 43 received 10 results and 0 failures
->> Test accuracy: 0.635300
-[2023-09-28 04:57:04,439][flwr][INFO] - fit progress: (43, 2.1266209250821855, {'accuracy': 0.6353}, 81447.3296973533)
-[2023-09-28 04:57:04,440][flwr][DEBUG] - evaluate_round 43: strategy sampled 10 clients (out of 10)
-[2023-09-28 04:57:41,768][flwr][DEBUG] - evaluate_round 43 received 10 results and 0 failures
-[2023-09-28 04:57:41,769][flwr][DEBUG] - fit_round 44: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.387533 Loss1: 0.346942 Loss2: 0.040591
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.209495 Loss1: 0.167296 Loss2: 0.042198
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.184008 Loss1: 0.142733 Loss2: 0.041276
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.188618 Loss1: 0.147181 Loss2: 0.041437
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.146726 Loss1: 0.106148 Loss2: 0.040578
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.139971 Loss1: 0.099541 Loss2: 0.040430
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.154447 Loss1: 0.113913 Loss2: 0.040534
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.141856 Loss1: 0.100993 Loss2: 0.040862
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.114645 Loss1: 0.074961 Loss2: 0.039685
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127267 Loss1: 0.087459 Loss2: 0.039808
-(DefaultActor pid=1838052) >> Training accuracy: 0.979307
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.862240 Loss1: 0.286437 Loss2: 0.575803
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.755235 Loss1: 0.191662 Loss2: 0.563574
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.738925 Loss1: 0.183534 Loss2: 0.555392
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.716703 Loss1: 0.169719 Loss2: 0.546984
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.723830 Loss1: 0.177770 Loss2: 0.546060
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.693369 Loss1: 0.157645 Loss2: 0.535724
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.668064 Loss1: 0.139362 Loss2: 0.528702
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.648697 Loss1: 0.123244 Loss2: 0.525454
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.630081 Loss1: 0.107431 Loss2: 0.522651
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.634798 Loss1: 0.116332 Loss2: 0.518466
-(DefaultActor pid=1838052) >> Training accuracy: 0.982595
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.614427 Loss1: 0.319129 Loss2: 0.295299
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.450638 Loss1: 0.205423 Loss2: 0.245215
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.384596 Loss1: 0.158690 Loss2: 0.225906
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.387231 Loss1: 0.160728 Loss2: 0.226503
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.352342 Loss1: 0.132348 Loss2: 0.219994
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.361304 Loss1: 0.143110 Loss2: 0.218194
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.353245 Loss1: 0.133408 Loss2: 0.219837
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.343845 Loss1: 0.126226 Loss2: 0.217619
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.310026 Loss1: 0.097021 Loss2: 0.213005
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.285808 Loss1: 0.076902 Loss2: 0.208906
-(DefaultActor pid=1838052) >> Training accuracy: 0.987380
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.347964 Loss1: 0.313405 Loss2: 0.034559
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.213423 Loss1: 0.176397 Loss2: 0.037026
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.170107 Loss1: 0.132628 Loss2: 0.037479
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.138185 Loss1: 0.101779 Loss2: 0.036406
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.137981 Loss1: 0.101112 Loss2: 0.036869
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.136111 Loss1: 0.098948 Loss2: 0.037163
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.144079 Loss1: 0.107033 Loss2: 0.037046
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137191 Loss1: 0.099720 Loss2: 0.037472
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120727 Loss1: 0.083499 Loss2: 0.037228
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.131239 Loss1: 0.094089 Loss2: 0.037150
-(DefaultActor pid=1838052) >> Training accuracy: 0.981120
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.888154 Loss1: 0.282231 Loss2: 0.605923
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.778603 Loss1: 0.165931 Loss2: 0.612672
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.745413 Loss1: 0.145877 Loss2: 0.599537
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.783196 Loss1: 0.187291 Loss2: 0.595905
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.746624 Loss1: 0.153325 Loss2: 0.593299
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.736725 Loss1: 0.152470 Loss2: 0.584254
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.704185 Loss1: 0.127500 Loss2: 0.576685
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.742210 Loss1: 0.169364 Loss2: 0.572846
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.693950 Loss1: 0.126407 Loss2: 0.567543
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.661564 Loss1: 0.099625 Loss2: 0.561939
-(DefaultActor pid=1838052) >> Training accuracy: 0.977965
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.861854 Loss1: 0.271823 Loss2: 0.590032
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.778888 Loss1: 0.191630 Loss2: 0.587258
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.738646 Loss1: 0.165692 Loss2: 0.572953
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.692825 Loss1: 0.134218 Loss2: 0.558607
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.662459 Loss1: 0.110094 Loss2: 0.552365
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.667975 Loss1: 0.120855 Loss2: 0.547120
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.654712 Loss1: 0.110662 Loss2: 0.544050
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.669289 Loss1: 0.129668 Loss2: 0.539620
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.668415 Loss1: 0.129263 Loss2: 0.539152
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.651973 Loss1: 0.116077 Loss2: 0.535896
-(DefaultActor pid=1838052) >> Training accuracy: 0.983584
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.362656 Loss1: 0.326293 Loss2: 0.036363
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.273253 Loss1: 0.233908 Loss2: 0.039346
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.194030 Loss1: 0.155183 Loss2: 0.038847
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.178384 Loss1: 0.138811 Loss2: 0.039573
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.177862 Loss1: 0.138525 Loss2: 0.039336
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.149984 Loss1: 0.111535 Loss2: 0.038449
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.159844 Loss1: 0.121483 Loss2: 0.038361
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.159869 Loss1: 0.121057 Loss2: 0.038812
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.150881 Loss1: 0.111804 Loss2: 0.039077
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.136011 Loss1: 0.097154 Loss2: 0.038857
-(DefaultActor pid=1838052) >> Training accuracy: 0.984169
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.339182 Loss1: 0.262603 Loss2: 0.076580
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.213333 Loss1: 0.140377 Loss2: 0.072955
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.200058 Loss1: 0.132029 Loss2: 0.068030
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.200491 Loss1: 0.134855 Loss2: 0.065635
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.171949 Loss1: 0.108255 Loss2: 0.063693
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.208690 Loss1: 0.144811 Loss2: 0.063878
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.187412 Loss1: 0.124194 Loss2: 0.063219
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.177690 Loss1: 0.115653 Loss2: 0.062038
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.182263 Loss1: 0.120605 Loss2: 0.061658
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.156499 Loss1: 0.095973 Loss2: 0.060526
-(DefaultActor pid=1838052) >> Training accuracy: 0.981013
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.275111 Loss1: 0.242409 Loss2: 0.032702
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.181755 Loss1: 0.145795 Loss2: 0.035960
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.151643 Loss1: 0.115747 Loss2: 0.035897
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.157621 Loss1: 0.120639 Loss2: 0.036983
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.151281 Loss1: 0.114453 Loss2: 0.036828
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.133573 Loss1: 0.097270 Loss2: 0.036303
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.141143 Loss1: 0.104130 Loss2: 0.037013
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.131283 Loss1: 0.094072 Loss2: 0.037211
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.144935 Loss1: 0.107167 Loss2: 0.037768
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.136587 Loss1: 0.099254 Loss2: 0.037333
-(DefaultActor pid=1838052) >> Training accuracy: 0.974657
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.292809 Loss1: 0.258582 Loss2: 0.034227
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.210566 Loss1: 0.172722 Loss2: 0.037844
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.177470 Loss1: 0.140277 Loss2: 0.037193
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.163791 Loss1: 0.126341 Loss2: 0.037451
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.166139 Loss1: 0.127971 Loss2: 0.038167
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.195773 Loss1: 0.156794 Loss2: 0.038979
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.157543 Loss1: 0.118907 Loss2: 0.038636
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.136986 Loss1: 0.098942 Loss2: 0.038044
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.126202 Loss1: 0.088863 Loss2: 0.037339
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121048 Loss1: 0.083696 Loss2: 0.037352
-(DefaultActor pid=1838052) >> Training accuracy: 0.983979
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 05:27:13,441][flwr][DEBUG] - fit_round 44 received 10 results and 0 failures
->> Test accuracy: 0.639900
-[2023-09-28 05:27:54,176][flwr][INFO] - fit progress: (44, 2.132381713047576, {'accuracy': 0.6399}, 83297.06599513115)
-[2023-09-28 05:27:54,176][flwr][DEBUG] - evaluate_round 44: strategy sampled 10 clients (out of 10)
-[2023-09-28 05:28:31,533][flwr][DEBUG] - evaluate_round 44 received 10 results and 0 failures
-[2023-09-28 05:28:31,533][flwr][DEBUG] - fit_round 45: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.694145 Loss1: 0.282507 Loss2: 0.411639
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.614729 Loss1: 0.238218 Loss2: 0.376512
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.552899 Loss1: 0.195011 Loss2: 0.357888
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.508408 Loss1: 0.163700 Loss2: 0.344708
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.528889 Loss1: 0.186647 Loss2: 0.342242
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.538339 Loss1: 0.190846 Loss2: 0.347494
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.470620 Loss1: 0.133183 Loss2: 0.337438
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.439023 Loss1: 0.108614 Loss2: 0.330409
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.436418 Loss1: 0.107259 Loss2: 0.329158
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.424909 Loss1: 0.099916 Loss2: 0.324993
-(DefaultActor pid=1838052) >> Training accuracy: 0.976562
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.275192 Loss1: 0.241824 Loss2: 0.033368
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.202433 Loss1: 0.165061 Loss2: 0.037372
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.173176 Loss1: 0.135542 Loss2: 0.037635
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.164091 Loss1: 0.126809 Loss2: 0.037282
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.154477 Loss1: 0.117036 Loss2: 0.037441
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.136687 Loss1: 0.099535 Loss2: 0.037152
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.145230 Loss1: 0.108134 Loss2: 0.037096
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.129871 Loss1: 0.092498 Loss2: 0.037372
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.114875 Loss1: 0.078053 Loss2: 0.036822
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121990 Loss1: 0.084904 Loss2: 0.037086
-(DefaultActor pid=1838052) >> Training accuracy: 0.981804
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.667328 Loss1: 0.267837 Loss2: 0.399491
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.557072 Loss1: 0.202973 Loss2: 0.354099
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.553410 Loss1: 0.209670 Loss2: 0.343740
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.511822 Loss1: 0.174126 Loss2: 0.337696
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.463738 Loss1: 0.134718 Loss2: 0.329019
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.477067 Loss1: 0.148459 Loss2: 0.328608
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.444798 Loss1: 0.118039 Loss2: 0.326759
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.442297 Loss1: 0.116169 Loss2: 0.326127
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.445527 Loss1: 0.117500 Loss2: 0.328027
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.441979 Loss1: 0.117789 Loss2: 0.324190
-(DefaultActor pid=1838052) >> Training accuracy: 0.973101
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.876477 Loss1: 0.269410 Loss2: 0.607066
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.767157 Loss1: 0.163952 Loss2: 0.603205
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.761011 Loss1: 0.168608 Loss2: 0.592402
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.748511 Loss1: 0.163517 Loss2: 0.584994
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.719597 Loss1: 0.144902 Loss2: 0.574695
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.690769 Loss1: 0.121715 Loss2: 0.569053
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.684726 Loss1: 0.121797 Loss2: 0.562929
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.685218 Loss1: 0.128862 Loss2: 0.556355
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.675132 Loss1: 0.120600 Loss2: 0.554532
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.687241 Loss1: 0.137180 Loss2: 0.550061
-(DefaultActor pid=1838052) >> Training accuracy: 0.982397
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.307098 Loss1: 0.271716 Loss2: 0.035382
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.203708 Loss1: 0.166013 Loss2: 0.037695
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.176355 Loss1: 0.138474 Loss2: 0.037880
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.195096 Loss1: 0.156322 Loss2: 0.038774
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.173590 Loss1: 0.134492 Loss2: 0.039098
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.159439 Loss1: 0.120930 Loss2: 0.038509
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.162531 Loss1: 0.123505 Loss2: 0.039026
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.171742 Loss1: 0.132114 Loss2: 0.039628
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.142288 Loss1: 0.103034 Loss2: 0.039254
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.133059 Loss1: 0.094299 Loss2: 0.038759
-(DefaultActor pid=1838052) >> Training accuracy: 0.986020
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.300428 Loss1: 0.230797 Loss2: 0.069632
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.204254 Loss1: 0.138243 Loss2: 0.066011
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.193114 Loss1: 0.129060 Loss2: 0.064053
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.185847 Loss1: 0.122450 Loss2: 0.063396
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.161676 Loss1: 0.100239 Loss2: 0.061437
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.153279 Loss1: 0.091455 Loss2: 0.061824
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.163237 Loss1: 0.101550 Loss2: 0.061687
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123087 Loss1: 0.062324 Loss2: 0.060763
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.133392 Loss1: 0.073467 Loss2: 0.059925
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.154869 Loss1: 0.094029 Loss2: 0.060840
-(DefaultActor pid=1838052) >> Training accuracy: 0.979367
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.802437 Loss1: 0.261405 Loss2: 0.541033
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.703578 Loss1: 0.176709 Loss2: 0.526869
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.685384 Loss1: 0.174070 Loss2: 0.511314
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.684110 Loss1: 0.174921 Loss2: 0.509190
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.669401 Loss1: 0.165634 Loss2: 0.503768
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.623916 Loss1: 0.129022 Loss2: 0.494894
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.625147 Loss1: 0.128987 Loss2: 0.496160
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.623809 Loss1: 0.133765 Loss2: 0.490044
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.595621 Loss1: 0.109700 Loss2: 0.485920
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.608744 Loss1: 0.121270 Loss2: 0.487474
-(DefaultActor pid=1838052) >> Training accuracy: 0.983613
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.389837 Loss1: 0.302419 Loss2: 0.087418
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.252667 Loss1: 0.166550 Loss2: 0.086117
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.237164 Loss1: 0.155447 Loss2: 0.081718
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.220709 Loss1: 0.140645 Loss2: 0.080064
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.215519 Loss1: 0.136703 Loss2: 0.078816
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.175011 Loss1: 0.099589 Loss2: 0.075422
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.168935 Loss1: 0.095187 Loss2: 0.073748
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.171050 Loss1: 0.097288 Loss2: 0.073762
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.180124 Loss1: 0.106729 Loss2: 0.073395
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.190813 Loss1: 0.117432 Loss2: 0.073381
-(DefaultActor pid=1838052) >> Training accuracy: 0.980785
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.350054 Loss1: 0.310751 Loss2: 0.039303
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.208492 Loss1: 0.169185 Loss2: 0.039307
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.160491 Loss1: 0.122145 Loss2: 0.038346
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.151848 Loss1: 0.113563 Loss2: 0.038285
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.168821 Loss1: 0.130435 Loss2: 0.038386
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.151719 Loss1: 0.113197 Loss2: 0.038521
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.138338 Loss1: 0.100224 Loss2: 0.038114
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.127121 Loss1: 0.089321 Loss2: 0.037800
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.131555 Loss1: 0.094433 Loss2: 0.037122
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127171 Loss1: 0.089681 Loss2: 0.037490
-(DefaultActor pid=1838052) >> Training accuracy: 0.987342
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.323396 Loss1: 0.288707 Loss2: 0.034689
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.196987 Loss1: 0.160058 Loss2: 0.036929
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.174197 Loss1: 0.137207 Loss2: 0.036990
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.169050 Loss1: 0.131846 Loss2: 0.037205
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.139165 Loss1: 0.101712 Loss2: 0.037454
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.117286 Loss1: 0.079887 Loss2: 0.037399
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104113 Loss1: 0.067810 Loss2: 0.036303
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137924 Loss1: 0.100608 Loss2: 0.037316
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.143607 Loss1: 0.105937 Loss2: 0.037669
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.139370 Loss1: 0.101688 Loss2: 0.037682
-(DefaultActor pid=1838052) >> Training accuracy: 0.976128
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 05:58:07,652][flwr][DEBUG] - fit_round 45 received 10 results and 0 failures
->> Test accuracy: 0.641000
-[2023-09-28 06:00:22,375][flwr][INFO] - fit progress: (45, 2.1219718056364942, {'accuracy': 0.641}, 85245.26535388315)
-[2023-09-28 06:00:22,376][flwr][DEBUG] - evaluate_round 45: strategy sampled 10 clients (out of 10)
-[2023-09-28 06:00:59,471][flwr][DEBUG] - evaluate_round 45 received 10 results and 0 failures
-[2023-09-28 06:00:59,472][flwr][DEBUG] - fit_round 46: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.848461 Loss1: 0.264681 Loss2: 0.583780
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.788239 Loss1: 0.206051 Loss2: 0.582189
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.742917 Loss1: 0.175010 Loss2: 0.567907
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.694170 Loss1: 0.143787 Loss2: 0.550383
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.690724 Loss1: 0.143492 Loss2: 0.547232
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.659334 Loss1: 0.124818 Loss2: 0.534515
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.668685 Loss1: 0.135850 Loss2: 0.532835
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.678549 Loss1: 0.150061 Loss2: 0.528488
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.643480 Loss1: 0.117386 Loss2: 0.526094
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.602135 Loss1: 0.083892 Loss2: 0.518243
-(DefaultActor pid=1838052) >> Training accuracy: 0.984375
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.862052 Loss1: 0.243395 Loss2: 0.618657
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.776351 Loss1: 0.159908 Loss2: 0.616444
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.763344 Loss1: 0.155515 Loss2: 0.607828
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.757852 Loss1: 0.158222 Loss2: 0.599629
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.743615 Loss1: 0.150490 Loss2: 0.593125
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.732730 Loss1: 0.146038 Loss2: 0.586693
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.695341 Loss1: 0.117196 Loss2: 0.578145
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.673431 Loss1: 0.101184 Loss2: 0.572247
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.688424 Loss1: 0.118772 Loss2: 0.569652
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.654489 Loss1: 0.089141 Loss2: 0.565348
-(DefaultActor pid=1838052) >> Training accuracy: 0.980617
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.610135 Loss1: 0.239830 Loss2: 0.370305
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.513963 Loss1: 0.191855 Loss2: 0.322109
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.457236 Loss1: 0.146234 Loss2: 0.311002
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.464621 Loss1: 0.154738 Loss2: 0.309883
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.437215 Loss1: 0.130953 Loss2: 0.306262
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.428542 Loss1: 0.125567 Loss2: 0.302976
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.444156 Loss1: 0.137332 Loss2: 0.306825
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.420540 Loss1: 0.119004 Loss2: 0.301536
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.417245 Loss1: 0.116377 Loss2: 0.300869
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.393874 Loss1: 0.096342 Loss2: 0.297531
-(DefaultActor pid=1838052) >> Training accuracy: 0.981707
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.283858 Loss1: 0.249572 Loss2: 0.034286
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.208449 Loss1: 0.171456 Loss2: 0.036993
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.169710 Loss1: 0.132479 Loss2: 0.037231
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.145026 Loss1: 0.108198 Loss2: 0.036828
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.154962 Loss1: 0.117845 Loss2: 0.037117
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111089 Loss1: 0.074557 Loss2: 0.036532
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.117350 Loss1: 0.081058 Loss2: 0.036292
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110664 Loss1: 0.074594 Loss2: 0.036070
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.131536 Loss1: 0.094839 Loss2: 0.036697
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104676 Loss1: 0.068068 Loss2: 0.036608
-(DefaultActor pid=1838052) >> Training accuracy: 0.984375
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.342949 Loss1: 0.302269 Loss2: 0.040680
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.237851 Loss1: 0.194417 Loss2: 0.043434
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.205350 Loss1: 0.162466 Loss2: 0.042884
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.173088 Loss1: 0.130687 Loss2: 0.042402
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.139015 Loss1: 0.097887 Loss2: 0.041127
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.137494 Loss1: 0.096259 Loss2: 0.041235
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.128183 Loss1: 0.087332 Loss2: 0.040851
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.130188 Loss1: 0.088612 Loss2: 0.041576
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120243 Loss1: 0.079445 Loss2: 0.040798
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.106191 Loss1: 0.065861 Loss2: 0.040330
-(DefaultActor pid=1838052) >> Training accuracy: 0.985220
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.273775 Loss1: 0.237738 Loss2: 0.036037
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.195542 Loss1: 0.156508 Loss2: 0.039034
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.174747 Loss1: 0.136117 Loss2: 0.038630
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.146146 Loss1: 0.107817 Loss2: 0.038329
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.136748 Loss1: 0.099228 Loss2: 0.037520
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.180948 Loss1: 0.141743 Loss2: 0.039205
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.151197 Loss1: 0.112603 Loss2: 0.038594
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.114325 Loss1: 0.076983 Loss2: 0.037342
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.116101 Loss1: 0.078605 Loss2: 0.037495
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.122317 Loss1: 0.085320 Loss2: 0.036998
-(DefaultActor pid=1838052) >> Training accuracy: 0.978639
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.871202 Loss1: 0.266720 Loss2: 0.604482
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.790816 Loss1: 0.188830 Loss2: 0.601986
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.754011 Loss1: 0.165597 Loss2: 0.588414
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.767475 Loss1: 0.186716 Loss2: 0.580760
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.720211 Loss1: 0.146219 Loss2: 0.573992
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.710112 Loss1: 0.143476 Loss2: 0.566636
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.726080 Loss1: 0.162462 Loss2: 0.563618
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.730727 Loss1: 0.169599 Loss2: 0.561128
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.678655 Loss1: 0.123838 Loss2: 0.554817
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.671736 Loss1: 0.122570 Loss2: 0.549167
-(DefaultActor pid=1838052) >> Training accuracy: 0.982936
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.310331 Loss1: 0.230354 Loss2: 0.079978
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.216229 Loss1: 0.140083 Loss2: 0.076146
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.173376 Loss1: 0.101124 Loss2: 0.072252
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.186817 Loss1: 0.116595 Loss2: 0.070222
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.177515 Loss1: 0.108422 Loss2: 0.069093
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.166103 Loss1: 0.098147 Loss2: 0.067956
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.183675 Loss1: 0.116641 Loss2: 0.067033
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.225218 Loss1: 0.156767 Loss2: 0.068450
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.161302 Loss1: 0.092811 Loss2: 0.068491
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.135570 Loss1: 0.070487 Loss2: 0.065083
-(DefaultActor pid=1838052) >> Training accuracy: 0.983173
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.261168 Loss1: 0.225255 Loss2: 0.035914
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.163083 Loss1: 0.124911 Loss2: 0.038173
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.132043 Loss1: 0.094028 Loss2: 0.038015
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.130161 Loss1: 0.092217 Loss2: 0.037945
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.138869 Loss1: 0.100432 Loss2: 0.038437
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.133730 Loss1: 0.094818 Loss2: 0.038912
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.119449 Loss1: 0.080830 Loss2: 0.038618
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.105398 Loss1: 0.067491 Loss2: 0.037907
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.080825 Loss1: 0.043512 Loss2: 0.037313
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083325 Loss1: 0.046669 Loss2: 0.036656
-(DefaultActor pid=1838052) >> Training accuracy: 0.992788
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.257814 Loss1: 0.222057 Loss2: 0.035757
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.213831 Loss1: 0.174874 Loss2: 0.038957
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.204794 Loss1: 0.165859 Loss2: 0.038935
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.152337 Loss1: 0.114416 Loss2: 0.037920
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.127611 Loss1: 0.090751 Loss2: 0.036860
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.129386 Loss1: 0.092681 Loss2: 0.036706
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.111996 Loss1: 0.075578 Loss2: 0.036418
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.108422 Loss1: 0.072145 Loss2: 0.036277
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.126583 Loss1: 0.090568 Loss2: 0.036015
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.133889 Loss1: 0.096799 Loss2: 0.037090
-(DefaultActor pid=1838052) >> Training accuracy: 0.974684
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 06:30:30,150][flwr][DEBUG] - fit_round 46 received 10 results and 0 failures
->> Test accuracy: 0.642000
-[2023-09-28 06:31:10,847][flwr][INFO] - fit progress: (46, 2.146202865500039, {'accuracy': 0.642}, 87093.73735058215)
-[2023-09-28 06:31:10,848][flwr][DEBUG] - evaluate_round 46: strategy sampled 10 clients (out of 10)
-[2023-09-28 06:31:47,499][flwr][DEBUG] - evaluate_round 46 received 10 results and 0 failures
-[2023-09-28 06:31:47,503][flwr][DEBUG] - fit_round 47: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.826519 Loss1: 0.246986 Loss2: 0.579533
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.746512 Loss1: 0.175532 Loss2: 0.570980
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.682461 Loss1: 0.127524 Loss2: 0.554937
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.692002 Loss1: 0.146680 Loss2: 0.545322
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.681200 Loss1: 0.142896 Loss2: 0.538304
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.668204 Loss1: 0.135436 Loss2: 0.532768
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.632000 Loss1: 0.105026 Loss2: 0.526974
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.634739 Loss1: 0.111943 Loss2: 0.522797
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.623241 Loss1: 0.103227 Loss2: 0.520014
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.600112 Loss1: 0.084930 Loss2: 0.515182
-(DefaultActor pid=1838052) >> Training accuracy: 0.975672
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.754997 Loss1: 0.262774 Loss2: 0.492223
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.639769 Loss1: 0.176214 Loss2: 0.463555
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.578036 Loss1: 0.128873 Loss2: 0.449162
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.576761 Loss1: 0.134069 Loss2: 0.442692
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.560940 Loss1: 0.119660 Loss2: 0.441280
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.572139 Loss1: 0.135029 Loss2: 0.437109
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.566585 Loss1: 0.126113 Loss2: 0.440472
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.554006 Loss1: 0.119124 Loss2: 0.434882
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.540399 Loss1: 0.108085 Loss2: 0.432314
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.519639 Loss1: 0.089883 Loss2: 0.429755
-(DefaultActor pid=1838052) >> Training accuracy: 0.984573
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.293364 Loss1: 0.256640 Loss2: 0.036724
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.183397 Loss1: 0.143580 Loss2: 0.039817
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.142610 Loss1: 0.103847 Loss2: 0.038763
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.143210 Loss1: 0.104317 Loss2: 0.038893
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.126876 Loss1: 0.088171 Loss2: 0.038705
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.127572 Loss1: 0.088824 Loss2: 0.038748
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130539 Loss1: 0.091704 Loss2: 0.038835
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.118130 Loss1: 0.079573 Loss2: 0.038558
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.102604 Loss1: 0.064157 Loss2: 0.038447
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102383 Loss1: 0.064324 Loss2: 0.038059
-(DefaultActor pid=1838052) >> Training accuracy: 0.989583
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.234818 Loss1: 0.197355 Loss2: 0.037463
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.171210 Loss1: 0.130874 Loss2: 0.040336
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.163565 Loss1: 0.123192 Loss2: 0.040373
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.144254 Loss1: 0.104149 Loss2: 0.040105
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.127849 Loss1: 0.087980 Loss2: 0.039869
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.119997 Loss1: 0.079835 Loss2: 0.040163
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122741 Loss1: 0.082491 Loss2: 0.040249
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137312 Loss1: 0.096725 Loss2: 0.040586
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.164109 Loss1: 0.122871 Loss2: 0.041238
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.111260 Loss1: 0.070666 Loss2: 0.040594
-(DefaultActor pid=1838052) >> Training accuracy: 0.987233
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.884729 Loss1: 0.273550 Loss2: 0.611179
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.785469 Loss1: 0.176231 Loss2: 0.609238
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.732611 Loss1: 0.136143 Loss2: 0.596468
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.734576 Loss1: 0.147852 Loss2: 0.586724
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.686977 Loss1: 0.108979 Loss2: 0.577998
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.691269 Loss1: 0.121387 Loss2: 0.569882
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.686273 Loss1: 0.121189 Loss2: 0.565084
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.690263 Loss1: 0.129588 Loss2: 0.560675
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.697102 Loss1: 0.137766 Loss2: 0.559336
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.708139 Loss1: 0.152513 Loss2: 0.555626
-(DefaultActor pid=1838052) >> Training accuracy: 0.971495
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.818430 Loss1: 0.208513 Loss2: 0.609917
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.752321 Loss1: 0.143291 Loss2: 0.609030
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.755222 Loss1: 0.156614 Loss2: 0.598608
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.731815 Loss1: 0.143534 Loss2: 0.588282
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.717784 Loss1: 0.137515 Loss2: 0.580269
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.695648 Loss1: 0.123444 Loss2: 0.572205
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.676405 Loss1: 0.110015 Loss2: 0.566390
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.671684 Loss1: 0.110745 Loss2: 0.560938
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.647722 Loss1: 0.089282 Loss2: 0.558440
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.646258 Loss1: 0.093485 Loss2: 0.552773
-(DefaultActor pid=1838052) >> Training accuracy: 0.980168
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.270065 Loss1: 0.233474 Loss2: 0.036591
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.199851 Loss1: 0.160259 Loss2: 0.039592
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.174402 Loss1: 0.135148 Loss2: 0.039254
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.170691 Loss1: 0.130886 Loss2: 0.039805
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.152940 Loss1: 0.113856 Loss2: 0.039084
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.173180 Loss1: 0.133590 Loss2: 0.039590
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.134658 Loss1: 0.095189 Loss2: 0.039469
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110684 Loss1: 0.072387 Loss2: 0.038297
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.117008 Loss1: 0.078776 Loss2: 0.038232
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.111146 Loss1: 0.072311 Loss2: 0.038835
-(DefaultActor pid=1838052) >> Training accuracy: 0.986946
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.307561 Loss1: 0.270047 Loss2: 0.037514
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.181948 Loss1: 0.141734 Loss2: 0.040214
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.183103 Loss1: 0.142720 Loss2: 0.040383
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.191854 Loss1: 0.151215 Loss2: 0.040639
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.173827 Loss1: 0.132775 Loss2: 0.041052
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.158566 Loss1: 0.118015 Loss2: 0.040551
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.120985 Loss1: 0.081734 Loss2: 0.039251
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.127988 Loss1: 0.089236 Loss2: 0.038752
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.110851 Loss1: 0.071834 Loss2: 0.039017
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.139183 Loss1: 0.098842 Loss2: 0.040340
-(DefaultActor pid=1838052) >> Training accuracy: 0.981086
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.296703 Loss1: 0.226276 Loss2: 0.070427
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.197244 Loss1: 0.127861 Loss2: 0.069384
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.202944 Loss1: 0.135660 Loss2: 0.067284
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.198740 Loss1: 0.131345 Loss2: 0.067395
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.207827 Loss1: 0.140752 Loss2: 0.067075
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.184041 Loss1: 0.117666 Loss2: 0.066375
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.195945 Loss1: 0.129418 Loss2: 0.066527
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.164611 Loss1: 0.099591 Loss2: 0.065019
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.162838 Loss1: 0.098741 Loss2: 0.064097
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.166438 Loss1: 0.102473 Loss2: 0.063964
-(DefaultActor pid=1838052) >> Training accuracy: 0.984968
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.695491 Loss1: 0.242566 Loss2: 0.452924
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.630754 Loss1: 0.208238 Loss2: 0.422516
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.567405 Loss1: 0.162931 Loss2: 0.404475
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.539920 Loss1: 0.139801 Loss2: 0.400119
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.557226 Loss1: 0.160958 Loss2: 0.396268
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.525294 Loss1: 0.133701 Loss2: 0.391593
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.514169 Loss1: 0.125475 Loss2: 0.388694
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.502046 Loss1: 0.115714 Loss2: 0.386333
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.495029 Loss1: 0.111092 Loss2: 0.383938
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.496593 Loss1: 0.111865 Loss2: 0.384728
-(DefaultActor pid=1838052) >> Training accuracy: 0.972155
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 07:01:27,356][flwr][DEBUG] - fit_round 47 received 10 results and 0 failures
->> Test accuracy: 0.644900
-[2023-09-28 07:02:07,032][flwr][INFO] - fit progress: (47, 2.1143142310575174, {'accuracy': 0.6449}, 88949.92235814035)
-[2023-09-28 07:02:07,032][flwr][DEBUG] - evaluate_round 47: strategy sampled 10 clients (out of 10)
-[2023-09-28 07:02:43,465][flwr][DEBUG] - evaluate_round 47 received 10 results and 0 failures
-[2023-09-28 07:02:43,466][flwr][DEBUG] - fit_round 48: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.727833 Loss1: 0.278922 Loss2: 0.448911
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.547202 Loss1: 0.167729 Loss2: 0.379473
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.515864 Loss1: 0.158427 Loss2: 0.357438
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.487436 Loss1: 0.140862 Loss2: 0.346575
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.475624 Loss1: 0.129411 Loss2: 0.346213
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.448883 Loss1: 0.109095 Loss2: 0.339788
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.448099 Loss1: 0.109717 Loss2: 0.338382
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.439536 Loss1: 0.104155 Loss2: 0.335381
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.423729 Loss1: 0.089546 Loss2: 0.334183
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.423829 Loss1: 0.092041 Loss2: 0.331788
-(DefaultActor pid=1838052) >> Training accuracy: 0.983953
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.286425 Loss1: 0.229642 Loss2: 0.056784
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.211989 Loss1: 0.156159 Loss2: 0.055830
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.160044 Loss1: 0.106592 Loss2: 0.053452
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.162199 Loss1: 0.108761 Loss2: 0.053438
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.160813 Loss1: 0.108324 Loss2: 0.052489
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.148362 Loss1: 0.094858 Loss2: 0.053504
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131680 Loss1: 0.079353 Loss2: 0.052328
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.124515 Loss1: 0.072753 Loss2: 0.051763
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.169393 Loss1: 0.116404 Loss2: 0.052989
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.160758 Loss1: 0.107665 Loss2: 0.053093
-(DefaultActor pid=1838052) >> Training accuracy: 0.975277
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.746180 Loss1: 0.230774 Loss2: 0.515406
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.664873 Loss1: 0.160591 Loss2: 0.504282
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.644329 Loss1: 0.158118 Loss2: 0.486211
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.671457 Loss1: 0.183834 Loss2: 0.487622
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.614013 Loss1: 0.137242 Loss2: 0.476771
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.610826 Loss1: 0.137114 Loss2: 0.473712
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.602774 Loss1: 0.133873 Loss2: 0.468901
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.575182 Loss1: 0.111295 Loss2: 0.463887
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.596087 Loss1: 0.132990 Loss2: 0.463097
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.584741 Loss1: 0.122054 Loss2: 0.462687
-(DefaultActor pid=1838052) >> Training accuracy: 0.962025
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.217883 Loss1: 0.183429 Loss2: 0.034454
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.154075 Loss1: 0.117210 Loss2: 0.036865
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.144030 Loss1: 0.106734 Loss2: 0.037295
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.170348 Loss1: 0.132323 Loss2: 0.038024
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.138654 Loss1: 0.100226 Loss2: 0.038428
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.155939 Loss1: 0.116896 Loss2: 0.039044
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.123208 Loss1: 0.084723 Loss2: 0.038485
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.138917 Loss1: 0.100443 Loss2: 0.038473
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.129138 Loss1: 0.090389 Loss2: 0.038748
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.156871 Loss1: 0.117442 Loss2: 0.039429
-(DefaultActor pid=1838052) >> Training accuracy: 0.973704
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.789646 Loss1: 0.305331 Loss2: 0.484315
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.651313 Loss1: 0.184159 Loss2: 0.467154
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.629400 Loss1: 0.174960 Loss2: 0.454440
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.579172 Loss1: 0.134624 Loss2: 0.444548
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.549163 Loss1: 0.109020 Loss2: 0.440143
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.591700 Loss1: 0.150224 Loss2: 0.441476
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.559915 Loss1: 0.122428 Loss2: 0.437486
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.543989 Loss1: 0.107551 Loss2: 0.436438
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.536890 Loss1: 0.103134 Loss2: 0.433756
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.576015 Loss1: 0.139044 Loss2: 0.436970
-(DefaultActor pid=1838052) >> Training accuracy: 0.978299
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.250774 Loss1: 0.214780 Loss2: 0.035995
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.169270 Loss1: 0.129888 Loss2: 0.039382
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.144053 Loss1: 0.105509 Loss2: 0.038543
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.123957 Loss1: 0.085155 Loss2: 0.038802
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.121573 Loss1: 0.083010 Loss2: 0.038563
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.122617 Loss1: 0.084060 Loss2: 0.038557
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.111982 Loss1: 0.073865 Loss2: 0.038117
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.134627 Loss1: 0.096008 Loss2: 0.038619
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092891 Loss1: 0.054843 Loss2: 0.038049
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080483 Loss1: 0.043163 Loss2: 0.037321
-(DefaultActor pid=1838052) >> Training accuracy: 0.995393
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.259602 Loss1: 0.223912 Loss2: 0.035690
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.179866 Loss1: 0.141457 Loss2: 0.038409
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.161913 Loss1: 0.123177 Loss2: 0.038736
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.163654 Loss1: 0.124801 Loss2: 0.038853
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.157792 Loss1: 0.118816 Loss2: 0.038976
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.145221 Loss1: 0.106249 Loss2: 0.038973
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.137531 Loss1: 0.098146 Loss2: 0.039385
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.128791 Loss1: 0.090140 Loss2: 0.038651
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140113 Loss1: 0.100873 Loss2: 0.039239
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114801 Loss1: 0.075802 Loss2: 0.038999
-(DefaultActor pid=1838052) >> Training accuracy: 0.979441
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.249448 Loss1: 0.210898 Loss2: 0.038549
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.145104 Loss1: 0.104256 Loss2: 0.040848
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.137770 Loss1: 0.097518 Loss2: 0.040252
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.147649 Loss1: 0.107281 Loss2: 0.040369
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.131308 Loss1: 0.090839 Loss2: 0.040469
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.134800 Loss1: 0.094607 Loss2: 0.040194
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.150757 Loss1: 0.110370 Loss2: 0.040387
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.156451 Loss1: 0.115528 Loss2: 0.040923
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.132047 Loss1: 0.090969 Loss2: 0.041077
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114589 Loss1: 0.074398 Loss2: 0.040192
-(DefaultActor pid=1838052) >> Training accuracy: 0.975277
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.271715 Loss1: 0.232701 Loss2: 0.039014
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.188261 Loss1: 0.147135 Loss2: 0.041126
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.173655 Loss1: 0.132582 Loss2: 0.041073
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.127953 Loss1: 0.087420 Loss2: 0.040533
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.123004 Loss1: 0.082864 Loss2: 0.040140
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.127873 Loss1: 0.087787 Loss2: 0.040086
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131478 Loss1: 0.091193 Loss2: 0.040284
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.134727 Loss1: 0.094465 Loss2: 0.040261
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.128698 Loss1: 0.088009 Loss2: 0.040689
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.150162 Loss1: 0.108962 Loss2: 0.041199
-(DefaultActor pid=1838052) >> Training accuracy: 0.980168
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.693942 Loss1: 0.212595 Loss2: 0.481347
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.637007 Loss1: 0.163897 Loss2: 0.473110
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.616321 Loss1: 0.155035 Loss2: 0.461286
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.612769 Loss1: 0.151301 Loss2: 0.461468
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.552201 Loss1: 0.099944 Loss2: 0.452257
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.561007 Loss1: 0.112468 Loss2: 0.448540
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.588871 Loss1: 0.137574 Loss2: 0.451296
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.596754 Loss1: 0.145972 Loss2: 0.450783
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.559795 Loss1: 0.117142 Loss2: 0.442653
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.550189 Loss1: 0.108176 Loss2: 0.442012
-(DefaultActor pid=1838052) >> Training accuracy: 0.979628
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 07:32:20,414][flwr][DEBUG] - fit_round 48 received 10 results and 0 failures
->> Test accuracy: 0.644800
-[2023-09-28 07:33:00,998][flwr][INFO] - fit progress: (48, 2.1224685852139142, {'accuracy': 0.6448}, 90803.88817573013)
-[2023-09-28 07:33:00,998][flwr][DEBUG] - evaluate_round 48: strategy sampled 10 clients (out of 10)
-[2023-09-28 07:33:38,966][flwr][DEBUG] - evaluate_round 48 received 10 results and 0 failures
-[2023-09-28 07:33:38,967][flwr][DEBUG] - fit_round 49: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.800283 Loss1: 0.235867 Loss2: 0.564417
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.706499 Loss1: 0.153670 Loss2: 0.552829
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.670413 Loss1: 0.130323 Loss2: 0.540090
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.676188 Loss1: 0.144582 Loss2: 0.531606
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.668697 Loss1: 0.141998 Loss2: 0.526699
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.661675 Loss1: 0.135826 Loss2: 0.525849
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.649152 Loss1: 0.130247 Loss2: 0.518905
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.619118 Loss1: 0.104389 Loss2: 0.514729
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.607040 Loss1: 0.095288 Loss2: 0.511752
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.603873 Loss1: 0.095465 Loss2: 0.508408
-(DefaultActor pid=1838052) >> Training accuracy: 0.971217
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.260689 Loss1: 0.222543 Loss2: 0.038146
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.207535 Loss1: 0.165967 Loss2: 0.041568
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.203266 Loss1: 0.161251 Loss2: 0.042015
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.154076 Loss1: 0.112317 Loss2: 0.041758
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116052 Loss1: 0.075964 Loss2: 0.040089
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.110625 Loss1: 0.070975 Loss2: 0.039650
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.110625 Loss1: 0.071271 Loss2: 0.039354
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123693 Loss1: 0.083633 Loss2: 0.040060
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.119365 Loss1: 0.078453 Loss2: 0.040912
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112407 Loss1: 0.072468 Loss2: 0.039940
-(DefaultActor pid=1838052) >> Training accuracy: 0.991536
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.775402 Loss1: 0.225349 Loss2: 0.550054
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.688478 Loss1: 0.149645 Loss2: 0.538833
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.659594 Loss1: 0.131331 Loss2: 0.528263
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.623413 Loss1: 0.107100 Loss2: 0.516313
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.621473 Loss1: 0.107989 Loss2: 0.513485
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.647367 Loss1: 0.137248 Loss2: 0.510119
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.616397 Loss1: 0.105859 Loss2: 0.510538
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.607964 Loss1: 0.102598 Loss2: 0.505366
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.615918 Loss1: 0.111319 Loss2: 0.504599
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.612285 Loss1: 0.108382 Loss2: 0.503903
-(DefaultActor pid=1838052) >> Training accuracy: 0.986178
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.263045 Loss1: 0.222493 Loss2: 0.040552
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.178789 Loss1: 0.135662 Loss2: 0.043128
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.182321 Loss1: 0.139121 Loss2: 0.043200
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.168419 Loss1: 0.126118 Loss2: 0.042301
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.151001 Loss1: 0.108297 Loss2: 0.042705
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.165619 Loss1: 0.123631 Loss2: 0.041988
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130839 Loss1: 0.089206 Loss2: 0.041633
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.148221 Loss1: 0.106291 Loss2: 0.041930
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115141 Loss1: 0.073863 Loss2: 0.041278
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104098 Loss1: 0.063420 Loss2: 0.040678
-(DefaultActor pid=1838052) >> Training accuracy: 0.986946
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.461516 Loss1: 0.229614 Loss2: 0.231902
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.353949 Loss1: 0.150797 Loss2: 0.203152
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.351573 Loss1: 0.153141 Loss2: 0.198431
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.342210 Loss1: 0.143929 Loss2: 0.198281
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.360933 Loss1: 0.166062 Loss2: 0.194871
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.338493 Loss1: 0.143764 Loss2: 0.194729
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.307357 Loss1: 0.115126 Loss2: 0.192231
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.283312 Loss1: 0.093755 Loss2: 0.189556
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.267675 Loss1: 0.079356 Loss2: 0.188319
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.287598 Loss1: 0.099722 Loss2: 0.187876
-(DefaultActor pid=1838052) >> Training accuracy: 0.973892
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.249883 Loss1: 0.214250 Loss2: 0.035633
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.173586 Loss1: 0.134440 Loss2: 0.039146
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.119259 Loss1: 0.081119 Loss2: 0.038140
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.115610 Loss1: 0.077582 Loss2: 0.038028
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.122172 Loss1: 0.084039 Loss2: 0.038134
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108381 Loss1: 0.069624 Loss2: 0.038757
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131242 Loss1: 0.092572 Loss2: 0.038670
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137579 Loss1: 0.098005 Loss2: 0.039574
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.121424 Loss1: 0.082589 Loss2: 0.038835
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097894 Loss1: 0.059396 Loss2: 0.038498
-(DefaultActor pid=1838052) >> Training accuracy: 0.990704
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.801597 Loss1: 0.211495 Loss2: 0.590102
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.744493 Loss1: 0.158720 Loss2: 0.585773
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.726586 Loss1: 0.152879 Loss2: 0.573708
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.679242 Loss1: 0.119215 Loss2: 0.560027
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.684024 Loss1: 0.131668 Loss2: 0.552356
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.654164 Loss1: 0.107292 Loss2: 0.546872
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.660328 Loss1: 0.119914 Loss2: 0.540414
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.640434 Loss1: 0.101579 Loss2: 0.538855
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.645844 Loss1: 0.113561 Loss2: 0.532283
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.617444 Loss1: 0.086353 Loss2: 0.531091
-(DefaultActor pid=1838052) >> Training accuracy: 0.986946
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.285496 Loss1: 0.211652 Loss2: 0.073843
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.201770 Loss1: 0.131058 Loss2: 0.070712
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.184576 Loss1: 0.118144 Loss2: 0.066433
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.205506 Loss1: 0.141840 Loss2: 0.063666
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.174666 Loss1: 0.112929 Loss2: 0.061737
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.158089 Loss1: 0.098670 Loss2: 0.059419
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130196 Loss1: 0.072526 Loss2: 0.057671
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.136555 Loss1: 0.080045 Loss2: 0.056511
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.129871 Loss1: 0.073990 Loss2: 0.055882
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115067 Loss1: 0.059215 Loss2: 0.055852
-(DefaultActor pid=1838052) >> Training accuracy: 0.990076
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.206424 Loss1: 0.172822 Loss2: 0.033603
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.143090 Loss1: 0.106841 Loss2: 0.036249
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.135107 Loss1: 0.098263 Loss2: 0.036844
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.165496 Loss1: 0.127891 Loss2: 0.037605
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.121091 Loss1: 0.083405 Loss2: 0.037686
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123729 Loss1: 0.085839 Loss2: 0.037890
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.150633 Loss1: 0.112730 Loss2: 0.037903
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.127385 Loss1: 0.089024 Loss2: 0.038361
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096919 Loss1: 0.059232 Loss2: 0.037687
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121104 Loss1: 0.083005 Loss2: 0.038099
-(DefaultActor pid=1838052) >> Training accuracy: 0.985518
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.238868 Loss1: 0.204776 Loss2: 0.034092
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.192598 Loss1: 0.154961 Loss2: 0.037637
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.157438 Loss1: 0.119965 Loss2: 0.037473
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.175664 Loss1: 0.137015 Loss2: 0.038649
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.159847 Loss1: 0.120734 Loss2: 0.039113
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.135716 Loss1: 0.097406 Loss2: 0.038310
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.145672 Loss1: 0.106872 Loss2: 0.038800
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.152308 Loss1: 0.112920 Loss2: 0.039388
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.147572 Loss1: 0.108360 Loss2: 0.039212
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.137137 Loss1: 0.098422 Loss2: 0.038715
-(DefaultActor pid=1838052) >> Training accuracy: 0.977163
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 08:03:04,483][flwr][DEBUG] - fit_round 49 received 10 results and 0 failures
->> Test accuracy: 0.645500
-[2023-09-28 08:03:45,715][flwr][INFO] - fit progress: (49, 2.117806362458311, {'accuracy': 0.6455}, 92648.6054644552)
-[2023-09-28 08:03:45,716][flwr][DEBUG] - evaluate_round 49: strategy sampled 10 clients (out of 10)
-[2023-09-28 08:04:37,237][flwr][DEBUG] - evaluate_round 49 received 10 results and 0 failures
-[2023-09-28 08:04:37,237][flwr][DEBUG] - fit_round 50: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.457734 Loss1: 0.205913 Loss2: 0.251822
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.380481 Loss1: 0.156246 Loss2: 0.224236
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.355945 Loss1: 0.137161 Loss2: 0.218784
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.362627 Loss1: 0.144658 Loss2: 0.217969
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.331825 Loss1: 0.120099 Loss2: 0.211727
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.317311 Loss1: 0.105102 Loss2: 0.212208
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.324519 Loss1: 0.113263 Loss2: 0.211256
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.295451 Loss1: 0.088140 Loss2: 0.207311
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.310271 Loss1: 0.102181 Loss2: 0.208090
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.314135 Loss1: 0.105296 Loss2: 0.208839
-(DefaultActor pid=1838052) >> Training accuracy: 0.977453
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.221488 Loss1: 0.186279 Loss2: 0.035209
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.141165 Loss1: 0.103216 Loss2: 0.037949
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.130700 Loss1: 0.093343 Loss2: 0.037358
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.126159 Loss1: 0.088506 Loss2: 0.037653
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.119110 Loss1: 0.081530 Loss2: 0.037580
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.128196 Loss1: 0.090498 Loss2: 0.037698
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.137597 Loss1: 0.099508 Loss2: 0.038089
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.141456 Loss1: 0.102521 Loss2: 0.038934
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.123703 Loss1: 0.084977 Loss2: 0.038726
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118957 Loss1: 0.080489 Loss2: 0.038469
-(DefaultActor pid=1838052) >> Training accuracy: 0.984375
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.704669 Loss1: 0.192003 Loss2: 0.512666
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.657960 Loss1: 0.156845 Loss2: 0.501115
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.629961 Loss1: 0.139524 Loss2: 0.490437
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.643681 Loss1: 0.157373 Loss2: 0.486308
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.630713 Loss1: 0.148655 Loss2: 0.482058
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.599413 Loss1: 0.121895 Loss2: 0.477518
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.615635 Loss1: 0.139848 Loss2: 0.475787
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.611761 Loss1: 0.135872 Loss2: 0.475890
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.563042 Loss1: 0.095021 Loss2: 0.468021
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.582975 Loss1: 0.112476 Loss2: 0.470499
-(DefaultActor pid=1838052) >> Training accuracy: 0.969131
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.647117 Loss1: 0.274487 Loss2: 0.372630
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.483496 Loss1: 0.159219 Loss2: 0.324277
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.465954 Loss1: 0.150692 Loss2: 0.315262
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.462479 Loss1: 0.151867 Loss2: 0.310612
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.459287 Loss1: 0.146134 Loss2: 0.313153
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.437864 Loss1: 0.129334 Loss2: 0.308530
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.438143 Loss1: 0.131966 Loss2: 0.306176
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.402026 Loss1: 0.099147 Loss2: 0.302880
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.392727 Loss1: 0.090350 Loss2: 0.302378
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.419554 Loss1: 0.118235 Loss2: 0.301318
-(DefaultActor pid=1838052) >> Training accuracy: 0.972762
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.259399 Loss1: 0.222550 Loss2: 0.036849
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.182168 Loss1: 0.142731 Loss2: 0.039437
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.154777 Loss1: 0.115559 Loss2: 0.039218
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.141386 Loss1: 0.102205 Loss2: 0.039181
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.125012 Loss1: 0.086357 Loss2: 0.038655
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123779 Loss1: 0.085211 Loss2: 0.038568
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.136009 Loss1: 0.096874 Loss2: 0.039135
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.132676 Loss1: 0.094232 Loss2: 0.038444
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.171731 Loss1: 0.132074 Loss2: 0.039657
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.129421 Loss1: 0.090115 Loss2: 0.039306
-(DefaultActor pid=1838052) >> Training accuracy: 0.981908
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.246653 Loss1: 0.212370 Loss2: 0.034284
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.198191 Loss1: 0.160176 Loss2: 0.038015
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.155949 Loss1: 0.117837 Loss2: 0.038112
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.154647 Loss1: 0.115835 Loss2: 0.038813
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.138247 Loss1: 0.099470 Loss2: 0.038777
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.130950 Loss1: 0.092056 Loss2: 0.038895
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129848 Loss1: 0.091525 Loss2: 0.038323
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.118971 Loss1: 0.080526 Loss2: 0.038444
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.124823 Loss1: 0.085755 Loss2: 0.039068
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.111211 Loss1: 0.073216 Loss2: 0.037995
-(DefaultActor pid=1838052) >> Training accuracy: 0.990017
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.224596 Loss1: 0.190406 Loss2: 0.034190
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.144448 Loss1: 0.107461 Loss2: 0.036987
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.141178 Loss1: 0.104156 Loss2: 0.037023
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113409 Loss1: 0.076769 Loss2: 0.036639
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.114424 Loss1: 0.078083 Loss2: 0.036341
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.121248 Loss1: 0.084824 Loss2: 0.036425
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.123133 Loss1: 0.086295 Loss2: 0.036839
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.136495 Loss1: 0.099060 Loss2: 0.037435
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115815 Loss1: 0.078795 Loss2: 0.037020
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083860 Loss1: 0.047771 Loss2: 0.036090
-(DefaultActor pid=1838052) >> Training accuracy: 0.994191
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.272904 Loss1: 0.236451 Loss2: 0.036453
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.175755 Loss1: 0.136156 Loss2: 0.039599
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.140564 Loss1: 0.101775 Loss2: 0.038789
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.135949 Loss1: 0.096934 Loss2: 0.039014
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.135631 Loss1: 0.096733 Loss2: 0.038898
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.100073 Loss1: 0.062308 Loss2: 0.037765
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.102037 Loss1: 0.064019 Loss2: 0.038018
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110003 Loss1: 0.071402 Loss2: 0.038600
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112297 Loss1: 0.074184 Loss2: 0.038113
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.126564 Loss1: 0.087445 Loss2: 0.039119
-(DefaultActor pid=1838052) >> Training accuracy: 0.980769
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.225578 Loss1: 0.190930 Loss2: 0.034648
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.157522 Loss1: 0.119512 Loss2: 0.038010
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.143751 Loss1: 0.105687 Loss2: 0.038064
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.138982 Loss1: 0.101027 Loss2: 0.037956
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.124248 Loss1: 0.086214 Loss2: 0.038034
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.116804 Loss1: 0.078818 Loss2: 0.037985
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.113504 Loss1: 0.075649 Loss2: 0.037855
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.114159 Loss1: 0.076476 Loss2: 0.037683
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.111359 Loss1: 0.073182 Loss2: 0.038178
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116378 Loss1: 0.078189 Loss2: 0.038189
-(DefaultActor pid=1838052) >> Training accuracy: 0.984573
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.242084 Loss1: 0.206702 Loss2: 0.035382
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.166276 Loss1: 0.127862 Loss2: 0.038414
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.150098 Loss1: 0.111468 Loss2: 0.038630
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.122171 Loss1: 0.084220 Loss2: 0.037951
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110487 Loss1: 0.072252 Loss2: 0.038235
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.101929 Loss1: 0.063560 Loss2: 0.038369
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.121583 Loss1: 0.082590 Loss2: 0.038992
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.111044 Loss1: 0.071887 Loss2: 0.039157
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.103266 Loss1: 0.064945 Loss2: 0.038321
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.130210 Loss1: 0.090877 Loss2: 0.039333
-(DefaultActor pid=1838052) >> Training accuracy: 0.976266
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 08:33:56,504][flwr][DEBUG] - fit_round 50 received 10 results and 0 failures
->> Test accuracy: 0.643300
-[2023-09-28 08:34:36,467][flwr][INFO] - fit progress: (50, 2.142119585134732, {'accuracy': 0.6433}, 94499.35791312112)
-[2023-09-28 08:34:36,468][flwr][DEBUG] - evaluate_round 50: strategy sampled 10 clients (out of 10)
-[2023-09-28 08:35:13,180][flwr][DEBUG] - evaluate_round 50 received 10 results and 0 failures
-[2023-09-28 08:35:13,181][flwr][DEBUG] - fit_round 51: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.181487 Loss1: 0.148718 Loss2: 0.032768
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.107925 Loss1: 0.073830 Loss2: 0.034094
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.121426 Loss1: 0.086758 Loss2: 0.034669
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.158157 Loss1: 0.122031 Loss2: 0.036126
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.149733 Loss1: 0.113353 Loss2: 0.036380
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.164926 Loss1: 0.127911 Loss2: 0.037015
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.161250 Loss1: 0.124223 Loss2: 0.037027
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.138708 Loss1: 0.101653 Loss2: 0.037055
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.143373 Loss1: 0.106565 Loss2: 0.036808
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118444 Loss1: 0.082194 Loss2: 0.036249
-(DefaultActor pid=1838052) >> Training accuracy: 0.982660
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.270857 Loss1: 0.194077 Loss2: 0.076780
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.203219 Loss1: 0.124421 Loss2: 0.078798
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.164609 Loss1: 0.089741 Loss2: 0.074869
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.177767 Loss1: 0.103794 Loss2: 0.073973
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.190469 Loss1: 0.117454 Loss2: 0.073015
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.171507 Loss1: 0.099640 Loss2: 0.071868
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.154060 Loss1: 0.083339 Loss2: 0.070720
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.142557 Loss1: 0.073331 Loss2: 0.069225
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.149875 Loss1: 0.081347 Loss2: 0.068529
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.168748 Loss1: 0.100659 Loss2: 0.068090
-(DefaultActor pid=1838052) >> Training accuracy: 0.978837
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.813573 Loss1: 0.226774 Loss2: 0.586799
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.735238 Loss1: 0.153799 Loss2: 0.581439
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.697594 Loss1: 0.129181 Loss2: 0.568413
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.707821 Loss1: 0.144335 Loss2: 0.563486
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.704090 Loss1: 0.145462 Loss2: 0.558628
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.682774 Loss1: 0.127736 Loss2: 0.555039
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.674277 Loss1: 0.124096 Loss2: 0.550182
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.662181 Loss1: 0.118187 Loss2: 0.543993
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.649346 Loss1: 0.105790 Loss2: 0.543556
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.640203 Loss1: 0.103415 Loss2: 0.536788
-(DefaultActor pid=1838052) >> Training accuracy: 0.983553
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.215419 Loss1: 0.181200 Loss2: 0.034219
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.146605 Loss1: 0.109088 Loss2: 0.037517
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.166358 Loss1: 0.127716 Loss2: 0.038642
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.155105 Loss1: 0.116130 Loss2: 0.038976
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.152750 Loss1: 0.113407 Loss2: 0.039343
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.148395 Loss1: 0.108840 Loss2: 0.039555
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131333 Loss1: 0.092418 Loss2: 0.038916
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.113846 Loss1: 0.075342 Loss2: 0.038504
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.121290 Loss1: 0.082271 Loss2: 0.039019
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.125286 Loss1: 0.086368 Loss2: 0.038918
-(DefaultActor pid=1838052) >> Training accuracy: 0.978639
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.690874 Loss1: 0.212457 Loss2: 0.478417
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.635475 Loss1: 0.167072 Loss2: 0.468404
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.610756 Loss1: 0.155941 Loss2: 0.454815
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.582099 Loss1: 0.134551 Loss2: 0.447548
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.565325 Loss1: 0.124478 Loss2: 0.440847
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.557868 Loss1: 0.114745 Loss2: 0.443123
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.554997 Loss1: 0.113199 Loss2: 0.441798
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.585071 Loss1: 0.143322 Loss2: 0.441749
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.579197 Loss1: 0.135086 Loss2: 0.444111
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.537705 Loss1: 0.100189 Loss2: 0.437516
-(DefaultActor pid=1838052) >> Training accuracy: 0.980419
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.724328 Loss1: 0.205427 Loss2: 0.518900
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.645562 Loss1: 0.141635 Loss2: 0.503927
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.614026 Loss1: 0.120963 Loss2: 0.493064
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.607597 Loss1: 0.122487 Loss2: 0.485110
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.648116 Loss1: 0.163024 Loss2: 0.485092
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.656785 Loss1: 0.170533 Loss2: 0.486251
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.598964 Loss1: 0.122138 Loss2: 0.476826
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.625258 Loss1: 0.147797 Loss2: 0.477461
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.597226 Loss1: 0.124316 Loss2: 0.472910
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.583954 Loss1: 0.114254 Loss2: 0.469700
-(DefaultActor pid=1838052) >> Training accuracy: 0.972556
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.231887 Loss1: 0.197663 Loss2: 0.034224
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.192640 Loss1: 0.154701 Loss2: 0.037939
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.137638 Loss1: 0.100360 Loss2: 0.037277
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.122720 Loss1: 0.085216 Loss2: 0.037504
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.137258 Loss1: 0.099188 Loss2: 0.038070
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.130036 Loss1: 0.091834 Loss2: 0.038202
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.170992 Loss1: 0.131884 Loss2: 0.039107
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.130539 Loss1: 0.091626 Loss2: 0.038912
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.121011 Loss1: 0.082765 Loss2: 0.038246
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.126219 Loss1: 0.087645 Loss2: 0.038573
-(DefaultActor pid=1838052) >> Training accuracy: 0.985894
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.293581 Loss1: 0.256068 Loss2: 0.037513
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.175708 Loss1: 0.136829 Loss2: 0.038880
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.148066 Loss1: 0.110503 Loss2: 0.037563
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.111335 Loss1: 0.074055 Loss2: 0.037280
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.090379 Loss1: 0.053934 Loss2: 0.036446
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.104565 Loss1: 0.068076 Loss2: 0.036489
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.106649 Loss1: 0.070037 Loss2: 0.036612
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.095515 Loss1: 0.059022 Loss2: 0.036493
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.093688 Loss1: 0.057510 Loss2: 0.036178
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.103971 Loss1: 0.067940 Loss2: 0.036030
-(DefaultActor pid=1838052) >> Training accuracy: 0.986064
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.190078 Loss1: 0.157987 Loss2: 0.032091
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.127788 Loss1: 0.093067 Loss2: 0.034721
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.145164 Loss1: 0.109760 Loss2: 0.035405
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.133183 Loss1: 0.097519 Loss2: 0.035664
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.124934 Loss1: 0.088839 Loss2: 0.036095
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111510 Loss1: 0.075966 Loss2: 0.035544
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129809 Loss1: 0.093203 Loss2: 0.036606
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.108198 Loss1: 0.071841 Loss2: 0.036356
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101529 Loss1: 0.066063 Loss2: 0.035465
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.087608 Loss1: 0.052194 Loss2: 0.035414
-(DefaultActor pid=1838052) >> Training accuracy: 0.989583
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.256689 Loss1: 0.174100 Loss2: 0.082589
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.197408 Loss1: 0.117739 Loss2: 0.079669
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.184519 Loss1: 0.109261 Loss2: 0.075258
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.171440 Loss1: 0.098470 Loss2: 0.072969
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.182038 Loss1: 0.110677 Loss2: 0.071361
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178472 Loss1: 0.108123 Loss2: 0.070349
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.179728 Loss1: 0.108736 Loss2: 0.070992
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.168042 Loss1: 0.097720 Loss2: 0.070322
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.161427 Loss1: 0.091619 Loss2: 0.069807
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.165307 Loss1: 0.096843 Loss2: 0.068464
-(DefaultActor pid=1838052) >> Training accuracy: 0.982002
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 09:04:26,741][flwr][DEBUG] - fit_round 51 received 10 results and 0 failures
->> Test accuracy: 0.645700
-[2023-09-28 09:05:09,088][flwr][INFO] - fit progress: (51, 2.135459794404027, {'accuracy': 0.6457}, 96331.97838461725)
-[2023-09-28 09:05:09,088][flwr][DEBUG] - evaluate_round 51: strategy sampled 10 clients (out of 10)
-[2023-09-28 09:05:45,528][flwr][DEBUG] - evaluate_round 51 received 10 results and 0 failures
-[2023-09-28 09:05:45,529][flwr][DEBUG] - fit_round 52: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.756197 Loss1: 0.214852 Loss2: 0.541345
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.693283 Loss1: 0.154754 Loss2: 0.538529
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.692817 Loss1: 0.165807 Loss2: 0.527010
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.654689 Loss1: 0.143108 Loss2: 0.511581
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.635468 Loss1: 0.124345 Loss2: 0.511123
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.631203 Loss1: 0.125736 Loss2: 0.505466
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.623747 Loss1: 0.121335 Loss2: 0.502413
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.607221 Loss1: 0.108508 Loss2: 0.498713
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.601027 Loss1: 0.108429 Loss2: 0.492598
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.579998 Loss1: 0.091135 Loss2: 0.488863
-(DefaultActor pid=1838052) >> Training accuracy: 0.976780
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.189986 Loss1: 0.157608 Loss2: 0.032378
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.115845 Loss1: 0.081945 Loss2: 0.033900
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.110463 Loss1: 0.077251 Loss2: 0.033212
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.101246 Loss1: 0.067603 Loss2: 0.033643
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116610 Loss1: 0.082881 Loss2: 0.033729
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108667 Loss1: 0.074621 Loss2: 0.034046
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.088769 Loss1: 0.055169 Loss2: 0.033600
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.101782 Loss1: 0.067567 Loss2: 0.034215
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.099429 Loss1: 0.064942 Loss2: 0.034486
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.096631 Loss1: 0.062240 Loss2: 0.034391
-(DefaultActor pid=1838052) >> Training accuracy: 0.989901
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.776350 Loss1: 0.188007 Loss2: 0.588342
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.725609 Loss1: 0.139489 Loss2: 0.586120
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.725707 Loss1: 0.151253 Loss2: 0.574454
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.708753 Loss1: 0.144284 Loss2: 0.564469
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.673343 Loss1: 0.114586 Loss2: 0.558757
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.672116 Loss1: 0.118870 Loss2: 0.553246
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.646087 Loss1: 0.097794 Loss2: 0.548293
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.639955 Loss1: 0.095317 Loss2: 0.544637
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.628291 Loss1: 0.091072 Loss2: 0.537219
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.619683 Loss1: 0.086868 Loss2: 0.532815
-(DefaultActor pid=1838052) >> Training accuracy: 0.974881
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.823337 Loss1: 0.209947 Loss2: 0.613390
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.745215 Loss1: 0.136214 Loss2: 0.609000
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.696324 Loss1: 0.105661 Loss2: 0.590663
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.689577 Loss1: 0.111095 Loss2: 0.578482
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.699551 Loss1: 0.130541 Loss2: 0.569010
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.692823 Loss1: 0.131519 Loss2: 0.561304
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.662141 Loss1: 0.106808 Loss2: 0.555333
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.642739 Loss1: 0.096159 Loss2: 0.546581
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.647917 Loss1: 0.102385 Loss2: 0.545532
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.645947 Loss1: 0.104596 Loss2: 0.541351
-(DefaultActor pid=1838052) >> Training accuracy: 0.984164
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.242587 Loss1: 0.174474 Loss2: 0.068113
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.192377 Loss1: 0.122156 Loss2: 0.070221
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.166112 Loss1: 0.097289 Loss2: 0.068823
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.176376 Loss1: 0.108543 Loss2: 0.067833
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.179613 Loss1: 0.110915 Loss2: 0.068698
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.158915 Loss1: 0.091663 Loss2: 0.067252
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.125228 Loss1: 0.061470 Loss2: 0.063758
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.112320 Loss1: 0.049711 Loss2: 0.062609
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.123225 Loss1: 0.061059 Loss2: 0.062166
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112926 Loss1: 0.051877 Loss2: 0.061050
-(DefaultActor pid=1838052) >> Training accuracy: 0.991386
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.214881 Loss1: 0.179908 Loss2: 0.034973
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.144975 Loss1: 0.108824 Loss2: 0.036151
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.138126 Loss1: 0.102165 Loss2: 0.035961
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.131821 Loss1: 0.095644 Loss2: 0.036177
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.125141 Loss1: 0.089370 Loss2: 0.035770
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.119865 Loss1: 0.083848 Loss2: 0.036017
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.109315 Loss1: 0.073860 Loss2: 0.035456
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.111432 Loss1: 0.076283 Loss2: 0.035149
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.099649 Loss1: 0.064170 Loss2: 0.035479
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.103873 Loss1: 0.068948 Loss2: 0.034925
-(DefaultActor pid=1838052) >> Training accuracy: 0.982171
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.205148 Loss1: 0.170449 Loss2: 0.034699
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.137100 Loss1: 0.100053 Loss2: 0.037046
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.127334 Loss1: 0.089376 Loss2: 0.037958
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113812 Loss1: 0.076840 Loss2: 0.036972
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.118140 Loss1: 0.080745 Loss2: 0.037395
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.133459 Loss1: 0.094990 Loss2: 0.038469
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.137863 Loss1: 0.098934 Loss2: 0.038929
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123172 Loss1: 0.084290 Loss2: 0.038881
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.110611 Loss1: 0.072410 Loss2: 0.038201
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121041 Loss1: 0.082674 Loss2: 0.038367
-(DefaultActor pid=1838052) >> Training accuracy: 0.986353
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.235159 Loss1: 0.200091 Loss2: 0.035069
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.149182 Loss1: 0.112440 Loss2: 0.036742
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.139669 Loss1: 0.103264 Loss2: 0.036405
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.121448 Loss1: 0.085296 Loss2: 0.036152
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.135758 Loss1: 0.099053 Loss2: 0.036705
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.151579 Loss1: 0.114657 Loss2: 0.036922
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.160198 Loss1: 0.122279 Loss2: 0.037919
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125145 Loss1: 0.087564 Loss2: 0.037581
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.129268 Loss1: 0.092337 Loss2: 0.036930
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.108174 Loss1: 0.071576 Loss2: 0.036599
-(DefaultActor pid=1838052) >> Training accuracy: 0.981702
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.585557 Loss1: 0.194603 Loss2: 0.390955
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.514901 Loss1: 0.145923 Loss2: 0.368979
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.479095 Loss1: 0.124461 Loss2: 0.354634
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.476630 Loss1: 0.123072 Loss2: 0.353558
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.455177 Loss1: 0.107658 Loss2: 0.347519
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.473603 Loss1: 0.122775 Loss2: 0.350828
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.441905 Loss1: 0.098450 Loss2: 0.343455
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.474381 Loss1: 0.125397 Loss2: 0.348984
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.488132 Loss1: 0.135021 Loss2: 0.353111
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.505391 Loss1: 0.151465 Loss2: 0.353925
-(DefaultActor pid=1838052) >> Training accuracy: 0.973101
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.203402 Loss1: 0.169748 Loss2: 0.033654
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.166116 Loss1: 0.130925 Loss2: 0.035191
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.124180 Loss1: 0.089247 Loss2: 0.034933
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.109394 Loss1: 0.074631 Loss2: 0.034763
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116291 Loss1: 0.081436 Loss2: 0.034854
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.134818 Loss1: 0.099097 Loss2: 0.035721
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.097230 Loss1: 0.062531 Loss2: 0.034700
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100248 Loss1: 0.065854 Loss2: 0.034394
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.107072 Loss1: 0.072341 Loss2: 0.034731
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118100 Loss1: 0.082650 Loss2: 0.035450
-(DefaultActor pid=1838052) >> Training accuracy: 0.978046
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 09:34:48,206][flwr][DEBUG] - fit_round 52 received 10 results and 0 failures
->> Test accuracy: 0.646500
-[2023-09-28 09:35:30,596][flwr][INFO] - fit progress: (52, 2.1817148396382318, {'accuracy': 0.6465}, 98153.48656181712)
-[2023-09-28 09:35:30,597][flwr][DEBUG] - evaluate_round 52: strategy sampled 10 clients (out of 10)
-[2023-09-28 09:36:07,690][flwr][DEBUG] - evaluate_round 52 received 10 results and 0 failures
-[2023-09-28 09:36:07,691][flwr][DEBUG] - fit_round 53: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.785909 Loss1: 0.205183 Loss2: 0.580726
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.713460 Loss1: 0.143256 Loss2: 0.570204
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.691062 Loss1: 0.134219 Loss2: 0.556843
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.686015 Loss1: 0.132635 Loss2: 0.553380
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.667316 Loss1: 0.122301 Loss2: 0.545015
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646188 Loss1: 0.105339 Loss2: 0.540850
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.646962 Loss1: 0.109392 Loss2: 0.537569
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.643486 Loss1: 0.110881 Loss2: 0.532605
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.635133 Loss1: 0.102379 Loss2: 0.532754
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.624094 Loss1: 0.095864 Loss2: 0.528231
-(DefaultActor pid=1838052) >> Training accuracy: 0.978824
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.594565 Loss1: 0.193689 Loss2: 0.400876
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.488682 Loss1: 0.131459 Loss2: 0.357223
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.473077 Loss1: 0.138048 Loss2: 0.335030
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.410063 Loss1: 0.088469 Loss2: 0.321594
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.404997 Loss1: 0.090999 Loss2: 0.313998
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.380555 Loss1: 0.068825 Loss2: 0.311730
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.368171 Loss1: 0.059769 Loss2: 0.308403
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.394628 Loss1: 0.086258 Loss2: 0.308370
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.375409 Loss1: 0.067619 Loss2: 0.307791
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.396913 Loss1: 0.087849 Loss2: 0.309065
-(DefaultActor pid=1838052) >> Training accuracy: 0.978299
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.196169 Loss1: 0.163713 Loss2: 0.032457
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.123632 Loss1: 0.089180 Loss2: 0.034451
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.115925 Loss1: 0.081373 Loss2: 0.034553
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.132805 Loss1: 0.098240 Loss2: 0.034565
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.112175 Loss1: 0.076796 Loss2: 0.035379
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123331 Loss1: 0.088005 Loss2: 0.035325
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122996 Loss1: 0.087335 Loss2: 0.035661
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123696 Loss1: 0.087791 Loss2: 0.035905
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101946 Loss1: 0.066419 Loss2: 0.035527
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097290 Loss1: 0.061985 Loss2: 0.035306
-(DefaultActor pid=1838052) >> Training accuracy: 0.985518
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.687835 Loss1: 0.183848 Loss2: 0.503987
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.638064 Loss1: 0.148281 Loss2: 0.489783
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.619479 Loss1: 0.140005 Loss2: 0.479475
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.580644 Loss1: 0.110291 Loss2: 0.470353
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.586776 Loss1: 0.119592 Loss2: 0.467184
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.553178 Loss1: 0.091140 Loss2: 0.462038
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.577211 Loss1: 0.114262 Loss2: 0.462949
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.562757 Loss1: 0.102748 Loss2: 0.460009
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.539645 Loss1: 0.083061 Loss2: 0.456584
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.549722 Loss1: 0.093901 Loss2: 0.455821
-(DefaultActor pid=1838052) >> Training accuracy: 0.985377
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.188690 Loss1: 0.156643 Loss2: 0.032047
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.117524 Loss1: 0.083202 Loss2: 0.034322
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.104054 Loss1: 0.070364 Loss2: 0.033690
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085207 Loss1: 0.051681 Loss2: 0.033526
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092568 Loss1: 0.059100 Loss2: 0.033468
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.099905 Loss1: 0.065991 Loss2: 0.033914
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.130229 Loss1: 0.094555 Loss2: 0.035674
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.124643 Loss1: 0.089469 Loss2: 0.035173
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.145695 Loss1: 0.109769 Loss2: 0.035926
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127988 Loss1: 0.092199 Loss2: 0.035790
-(DefaultActor pid=1838052) >> Training accuracy: 0.985759
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.540069 Loss1: 0.157874 Loss2: 0.382194
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.438008 Loss1: 0.108731 Loss2: 0.329277
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.384962 Loss1: 0.089836 Loss2: 0.295126
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.445128 Loss1: 0.152949 Loss2: 0.292179
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.420591 Loss1: 0.131015 Loss2: 0.289576
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.412996 Loss1: 0.125577 Loss2: 0.287419
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.368398 Loss1: 0.084865 Loss2: 0.283532
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.365283 Loss1: 0.084307 Loss2: 0.280976
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.377866 Loss1: 0.096457 Loss2: 0.281409
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.382351 Loss1: 0.100940 Loss2: 0.281411
-(DefaultActor pid=1838052) >> Training accuracy: 0.981210
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.231614 Loss1: 0.196132 Loss2: 0.035482
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.139687 Loss1: 0.103584 Loss2: 0.036103
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.128435 Loss1: 0.092591 Loss2: 0.035844
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136101 Loss1: 0.099495 Loss2: 0.036606
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.115435 Loss1: 0.079209 Loss2: 0.036225
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096064 Loss1: 0.060614 Loss2: 0.035451
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.086670 Loss1: 0.051635 Loss2: 0.035035
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.081080 Loss1: 0.046136 Loss2: 0.034944
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101316 Loss1: 0.066131 Loss2: 0.035186
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104095 Loss1: 0.067969 Loss2: 0.036126
-(DefaultActor pid=1838052) >> Training accuracy: 0.989020
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.193302 Loss1: 0.159801 Loss2: 0.033501
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.131964 Loss1: 0.096918 Loss2: 0.035046
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.136597 Loss1: 0.101040 Loss2: 0.035557
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.123699 Loss1: 0.087964 Loss2: 0.035734
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.117441 Loss1: 0.081752 Loss2: 0.035690
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111029 Loss1: 0.075156 Loss2: 0.035873
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.110762 Loss1: 0.075227 Loss2: 0.035535
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.116619 Loss1: 0.080209 Loss2: 0.036409
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.135181 Loss1: 0.098116 Loss2: 0.037065
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.138863 Loss1: 0.101578 Loss2: 0.037285
-(DefaultActor pid=1838052) >> Training accuracy: 0.978365
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.241973 Loss1: 0.154011 Loss2: 0.087962
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.206534 Loss1: 0.120371 Loss2: 0.086163
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.204327 Loss1: 0.118790 Loss2: 0.085537
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.163651 Loss1: 0.080947 Loss2: 0.082704
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.157991 Loss1: 0.077559 Loss2: 0.080432
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.178191 Loss1: 0.097177 Loss2: 0.081015
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.173931 Loss1: 0.093324 Loss2: 0.080607
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.148825 Loss1: 0.069440 Loss2: 0.079385
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140472 Loss1: 0.062310 Loss2: 0.078162
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.132638 Loss1: 0.055639 Loss2: 0.076999
-(DefaultActor pid=1838052) >> Training accuracy: 0.990506
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.202804 Loss1: 0.167822 Loss2: 0.034982
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.142767 Loss1: 0.106446 Loss2: 0.036321
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.143885 Loss1: 0.107294 Loss2: 0.036591
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.112779 Loss1: 0.076322 Loss2: 0.036457
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110090 Loss1: 0.073669 Loss2: 0.036421
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.107590 Loss1: 0.071134 Loss2: 0.036456
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.115976 Loss1: 0.079162 Loss2: 0.036814
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.105005 Loss1: 0.068128 Loss2: 0.036878
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.105621 Loss1: 0.068754 Loss2: 0.036867
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114251 Loss1: 0.077168 Loss2: 0.037083
-(DefaultActor pid=1838052) >> Training accuracy: 0.990902
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 10:05:35,615][flwr][DEBUG] - fit_round 53 received 10 results and 0 failures
->> Test accuracy: 0.645700
-[2023-09-28 10:06:16,280][flwr][INFO] - fit progress: (53, 2.183458357382887, {'accuracy': 0.6457}, 99999.17066497216)
-[2023-09-28 10:06:16,281][flwr][DEBUG] - evaluate_round 53: strategy sampled 10 clients (out of 10)
-[2023-09-28 10:06:52,930][flwr][DEBUG] - evaluate_round 53 received 10 results and 0 failures
-[2023-09-28 10:06:52,932][flwr][DEBUG] - fit_round 54: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.622136 Loss1: 0.154174 Loss2: 0.467963
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.569218 Loss1: 0.137967 Loss2: 0.431251
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.575470 Loss1: 0.152849 Loss2: 0.422621
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.573431 Loss1: 0.158843 Loss2: 0.414587
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.562856 Loss1: 0.158140 Loss2: 0.404716
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.506398 Loss1: 0.108552 Loss2: 0.397847
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.497419 Loss1: 0.105152 Loss2: 0.392267
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.503289 Loss1: 0.107830 Loss2: 0.395459
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.502117 Loss1: 0.108815 Loss2: 0.393302
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.506279 Loss1: 0.112386 Loss2: 0.393893
-(DefaultActor pid=1838052) >> Training accuracy: 0.975870
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.180767 Loss1: 0.148063 Loss2: 0.032704
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.126421 Loss1: 0.091417 Loss2: 0.035004
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.141848 Loss1: 0.106017 Loss2: 0.035831
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136410 Loss1: 0.099675 Loss2: 0.036735
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.099563 Loss1: 0.063524 Loss2: 0.036039
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.118136 Loss1: 0.081980 Loss2: 0.036155
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.125191 Loss1: 0.088410 Loss2: 0.036781
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.092475 Loss1: 0.056595 Loss2: 0.035880
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.107760 Loss1: 0.071335 Loss2: 0.036425
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.105110 Loss1: 0.068568 Loss2: 0.036541
-(DefaultActor pid=1838052) >> Training accuracy: 0.985978
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.230630 Loss1: 0.194305 Loss2: 0.036325
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.161563 Loss1: 0.122558 Loss2: 0.039005
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.127220 Loss1: 0.087931 Loss2: 0.039290
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136200 Loss1: 0.096764 Loss2: 0.039437
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.118723 Loss1: 0.079306 Loss2: 0.039418
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.129795 Loss1: 0.090109 Loss2: 0.039686
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.125486 Loss1: 0.085560 Loss2: 0.039926
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123181 Loss1: 0.082874 Loss2: 0.040307
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081868 Loss1: 0.042757 Loss2: 0.039111
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083551 Loss1: 0.045462 Loss2: 0.038089
-(DefaultActor pid=1838052) >> Training accuracy: 0.991970
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.200038 Loss1: 0.167201 Loss2: 0.032838
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.152761 Loss1: 0.116529 Loss2: 0.036231
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.141273 Loss1: 0.104489 Loss2: 0.036784
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.138773 Loss1: 0.101916 Loss2: 0.036857
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.121501 Loss1: 0.084781 Loss2: 0.036719
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.110344 Loss1: 0.073392 Loss2: 0.036952
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.093005 Loss1: 0.056615 Loss2: 0.036391
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.104131 Loss1: 0.067697 Loss2: 0.036434
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.123408 Loss1: 0.086259 Loss2: 0.037149
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.145425 Loss1: 0.107747 Loss2: 0.037677
-(DefaultActor pid=1838052) >> Training accuracy: 0.983782
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.195537 Loss1: 0.163126 Loss2: 0.032411
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.138573 Loss1: 0.103339 Loss2: 0.035234
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.090075 Loss1: 0.055221 Loss2: 0.034854
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.091540 Loss1: 0.057504 Loss2: 0.034035
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.094832 Loss1: 0.060270 Loss2: 0.034562
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.112205 Loss1: 0.077027 Loss2: 0.035178
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122674 Loss1: 0.086297 Loss2: 0.036377
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.112849 Loss1: 0.076805 Loss2: 0.036044
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.126293 Loss1: 0.089979 Loss2: 0.036314
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127105 Loss1: 0.090114 Loss2: 0.036991
-(DefaultActor pid=1838052) >> Training accuracy: 0.981326
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.230388 Loss1: 0.196235 Loss2: 0.034153
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.164832 Loss1: 0.128574 Loss2: 0.036258
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.123411 Loss1: 0.087192 Loss2: 0.036219
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.115689 Loss1: 0.079673 Loss2: 0.036016
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.127602 Loss1: 0.090908 Loss2: 0.036695
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.141038 Loss1: 0.103870 Loss2: 0.037169
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.148759 Loss1: 0.110389 Loss2: 0.038370
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.114486 Loss1: 0.077101 Loss2: 0.037386
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.104428 Loss1: 0.067599 Loss2: 0.036829
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091997 Loss1: 0.055231 Loss2: 0.036767
-(DefaultActor pid=1838052) >> Training accuracy: 0.982730
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.205695 Loss1: 0.172571 Loss2: 0.033124
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.124160 Loss1: 0.088157 Loss2: 0.036004
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.124109 Loss1: 0.087829 Loss2: 0.036280
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.126048 Loss1: 0.088994 Loss2: 0.037054
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.108591 Loss1: 0.072476 Loss2: 0.036115
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.114148 Loss1: 0.077882 Loss2: 0.036266
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122066 Loss1: 0.085164 Loss2: 0.036902
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.123094 Loss1: 0.085698 Loss2: 0.037396
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120192 Loss1: 0.082755 Loss2: 0.037437
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.146598 Loss1: 0.108099 Loss2: 0.038499
-(DefaultActor pid=1838052) >> Training accuracy: 0.977255
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.409763 Loss1: 0.153484 Loss2: 0.256279
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.351551 Loss1: 0.129191 Loss2: 0.222360
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.313635 Loss1: 0.105180 Loss2: 0.208455
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.316109 Loss1: 0.111753 Loss2: 0.204356
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.314940 Loss1: 0.109583 Loss2: 0.205357
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.319605 Loss1: 0.114523 Loss2: 0.205082
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.335283 Loss1: 0.132100 Loss2: 0.203184
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.363245 Loss1: 0.156264 Loss2: 0.206981
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.291851 Loss1: 0.092542 Loss2: 0.199309
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.308711 Loss1: 0.108949 Loss2: 0.199762
-(DefaultActor pid=1838052) >> Training accuracy: 0.980024
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.279613 Loss1: 0.234386 Loss2: 0.045227
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.156388 Loss1: 0.112065 Loss2: 0.044323
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.143478 Loss1: 0.100560 Loss2: 0.042918
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.132019 Loss1: 0.088975 Loss2: 0.043044
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.136943 Loss1: 0.094208 Loss2: 0.042735
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.109744 Loss1: 0.068139 Loss2: 0.041605
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.100752 Loss1: 0.059691 Loss2: 0.041061
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.116631 Loss1: 0.074956 Loss2: 0.041675
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.105426 Loss1: 0.063764 Loss2: 0.041662
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.106936 Loss1: 0.066025 Loss2: 0.040910
-(DefaultActor pid=1838052) >> Training accuracy: 0.987753
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.192457 Loss1: 0.159655 Loss2: 0.032802
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.152515 Loss1: 0.116800 Loss2: 0.035715
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.143007 Loss1: 0.106848 Loss2: 0.036160
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.118045 Loss1: 0.081521 Loss2: 0.036524
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.096065 Loss1: 0.060511 Loss2: 0.035554
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077471 Loss1: 0.042620 Loss2: 0.034851
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104392 Loss1: 0.069349 Loss2: 0.035043
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.151025 Loss1: 0.113969 Loss2: 0.037056
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140034 Loss1: 0.102981 Loss2: 0.037052
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.147951 Loss1: 0.110101 Loss2: 0.037850
-(DefaultActor pid=1838052) >> Training accuracy: 0.971154
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 10:36:35,856][flwr][DEBUG] - fit_round 54 received 10 results and 0 failures
->> Test accuracy: 0.647400
-[2023-09-28 10:37:19,325][flwr][INFO] - fit progress: (54, 2.1794180731042125, {'accuracy': 0.6474}, 101862.21575574903)
-[2023-09-28 10:37:19,326][flwr][DEBUG] - evaluate_round 54: strategy sampled 10 clients (out of 10)
-[2023-09-28 10:37:56,797][flwr][DEBUG] - evaluate_round 54 received 10 results and 0 failures
-[2023-09-28 10:37:56,798][flwr][DEBUG] - fit_round 55: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.509107 Loss1: 0.139607 Loss2: 0.369500
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.391602 Loss1: 0.107168 Loss2: 0.284434
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.354173 Loss1: 0.087316 Loss2: 0.266857
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.374317 Loss1: 0.109795 Loss2: 0.264522
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.391970 Loss1: 0.124658 Loss2: 0.267312
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.431046 Loss1: 0.162478 Loss2: 0.268568
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.424645 Loss1: 0.156158 Loss2: 0.268487
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.367781 Loss1: 0.106355 Loss2: 0.261426
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.360489 Loss1: 0.099400 Loss2: 0.261089
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.357288 Loss1: 0.099691 Loss2: 0.257597
-(DefaultActor pid=1838052) >> Training accuracy: 0.981013
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.728560 Loss1: 0.181768 Loss2: 0.546792
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.665100 Loss1: 0.126072 Loss2: 0.539028
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.638633 Loss1: 0.107014 Loss2: 0.531619
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.634475 Loss1: 0.111062 Loss2: 0.523413
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.651574 Loss1: 0.130198 Loss2: 0.521376
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.620937 Loss1: 0.105771 Loss2: 0.515167
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.640100 Loss1: 0.126973 Loss2: 0.513127
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.642876 Loss1: 0.128065 Loss2: 0.514811
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.617850 Loss1: 0.108544 Loss2: 0.509306
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.624843 Loss1: 0.118169 Loss2: 0.506674
-(DefaultActor pid=1838052) >> Training accuracy: 0.983584
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.737243 Loss1: 0.177329 Loss2: 0.559914
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.669951 Loss1: 0.119622 Loss2: 0.550329
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.643896 Loss1: 0.106643 Loss2: 0.537252
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.655991 Loss1: 0.127084 Loss2: 0.528908
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.643710 Loss1: 0.117745 Loss2: 0.525965
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.679772 Loss1: 0.152641 Loss2: 0.527131
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.645790 Loss1: 0.126922 Loss2: 0.518867
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.639748 Loss1: 0.121880 Loss2: 0.517868
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.643863 Loss1: 0.130430 Loss2: 0.513433
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.603106 Loss1: 0.095421 Loss2: 0.507685
-(DefaultActor pid=1838052) >> Training accuracy: 0.985176
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.741672 Loss1: 0.149283 Loss2: 0.592389
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.737297 Loss1: 0.148724 Loss2: 0.588573
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.690914 Loss1: 0.116848 Loss2: 0.574066
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.644237 Loss1: 0.082112 Loss2: 0.562125
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.670881 Loss1: 0.112733 Loss2: 0.558148
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.657615 Loss1: 0.104420 Loss2: 0.553194
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.634579 Loss1: 0.087046 Loss2: 0.547533
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.639683 Loss1: 0.096185 Loss2: 0.543498
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.631597 Loss1: 0.091130 Loss2: 0.540467
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.641202 Loss1: 0.101451 Loss2: 0.539751
-(DefaultActor pid=1838052) >> Training accuracy: 0.982470
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.219434 Loss1: 0.147122 Loss2: 0.072313
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.155270 Loss1: 0.081840 Loss2: 0.073430
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.139659 Loss1: 0.069118 Loss2: 0.070541
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.173246 Loss1: 0.103464 Loss2: 0.069782
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.155793 Loss1: 0.085574 Loss2: 0.070219
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.149930 Loss1: 0.082565 Loss2: 0.067365
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.145283 Loss1: 0.077662 Loss2: 0.067621
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.132349 Loss1: 0.065489 Loss2: 0.066860
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.119049 Loss1: 0.052954 Loss2: 0.066096
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.136354 Loss1: 0.071514 Loss2: 0.064840
-(DefaultActor pid=1838052) >> Training accuracy: 0.987179
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.193966 Loss1: 0.160798 Loss2: 0.033168
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.125068 Loss1: 0.089561 Loss2: 0.035507
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.108657 Loss1: 0.073441 Loss2: 0.035215
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.117500 Loss1: 0.081755 Loss2: 0.035745
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110119 Loss1: 0.074182 Loss2: 0.035937
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.114053 Loss1: 0.077793 Loss2: 0.036259
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.081104 Loss1: 0.045143 Loss2: 0.035961
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.083663 Loss1: 0.048273 Loss2: 0.035390
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.083033 Loss1: 0.048109 Loss2: 0.034924
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089912 Loss1: 0.054380 Loss2: 0.035532
-(DefaultActor pid=1838052) >> Training accuracy: 0.987935
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.186867 Loss1: 0.151491 Loss2: 0.035375
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.133544 Loss1: 0.096096 Loss2: 0.037448
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.121823 Loss1: 0.084776 Loss2: 0.037047
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.111273 Loss1: 0.074171 Loss2: 0.037102
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.135255 Loss1: 0.098158 Loss2: 0.037097
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.127709 Loss1: 0.090205 Loss2: 0.037504
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104897 Loss1: 0.067449 Loss2: 0.037447
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110731 Loss1: 0.073745 Loss2: 0.036985
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112040 Loss1: 0.074573 Loss2: 0.037466
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.108008 Loss1: 0.071421 Loss2: 0.036587
-(DefaultActor pid=1838052) >> Training accuracy: 0.986155
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.805964 Loss1: 0.211358 Loss2: 0.594606
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.706638 Loss1: 0.123289 Loss2: 0.583350
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.700220 Loss1: 0.127327 Loss2: 0.572893
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.678621 Loss1: 0.114865 Loss2: 0.563757
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.693927 Loss1: 0.136865 Loss2: 0.557062
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.677190 Loss1: 0.123776 Loss2: 0.553414
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.634181 Loss1: 0.087688 Loss2: 0.546492
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.642666 Loss1: 0.102565 Loss2: 0.540102
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.643664 Loss1: 0.104839 Loss2: 0.538825
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.619228 Loss1: 0.083172 Loss2: 0.536057
-(DefaultActor pid=1838052) >> Training accuracy: 0.983758
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.216300 Loss1: 0.160449 Loss2: 0.055851
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.163901 Loss1: 0.107993 Loss2: 0.055908
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.163288 Loss1: 0.108135 Loss2: 0.055152
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.136151 Loss1: 0.082778 Loss2: 0.053374
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.152339 Loss1: 0.100592 Loss2: 0.051748
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.143100 Loss1: 0.090733 Loss2: 0.052367
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.146868 Loss1: 0.095544 Loss2: 0.051325
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.131349 Loss1: 0.080359 Loss2: 0.050990
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.132510 Loss1: 0.082124 Loss2: 0.050386
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.125518 Loss1: 0.075180 Loss2: 0.050338
-(DefaultActor pid=1838052) >> Training accuracy: 0.980903
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.236540 Loss1: 0.203229 Loss2: 0.033312
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.147496 Loss1: 0.112321 Loss2: 0.035175
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.123307 Loss1: 0.088721 Loss2: 0.034586
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.117232 Loss1: 0.082250 Loss2: 0.034982
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.134691 Loss1: 0.098697 Loss2: 0.035994
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108842 Loss1: 0.073523 Loss2: 0.035319
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.114903 Loss1: 0.079297 Loss2: 0.035606
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.093586 Loss1: 0.058267 Loss2: 0.035319
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101489 Loss1: 0.066643 Loss2: 0.034846
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.122730 Loss1: 0.087150 Loss2: 0.035580
-(DefaultActor pid=1838052) >> Training accuracy: 0.982052
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 11:07:26,968][flwr][DEBUG] - fit_round 55 received 10 results and 0 failures
->> Test accuracy: 0.648500
-[2023-09-28 11:08:07,543][flwr][INFO] - fit progress: (55, 2.1586610794829104, {'accuracy': 0.6485}, 103710.43312141532)
-[2023-09-28 11:08:07,543][flwr][DEBUG] - evaluate_round 55: strategy sampled 10 clients (out of 10)
-[2023-09-28 11:08:44,912][flwr][DEBUG] - evaluate_round 55 received 10 results and 0 failures
-[2023-09-28 11:08:44,913][flwr][DEBUG] - fit_round 56: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.727927 Loss1: 0.151545 Loss2: 0.576382
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.668980 Loss1: 0.106308 Loss2: 0.562671
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.654360 Loss1: 0.105175 Loss2: 0.549186
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.640883 Loss1: 0.104826 Loss2: 0.536057
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.628374 Loss1: 0.099566 Loss2: 0.528807
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.623337 Loss1: 0.102038 Loss2: 0.521299
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.619590 Loss1: 0.100232 Loss2: 0.519358
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.624106 Loss1: 0.106243 Loss2: 0.517863
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.610624 Loss1: 0.098335 Loss2: 0.512289
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.576537 Loss1: 0.067120 Loss2: 0.509416
-(DefaultActor pid=1838052) >> Training accuracy: 0.986111
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.537136 Loss1: 0.169005 Loss2: 0.368132
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.450082 Loss1: 0.137723 Loss2: 0.312360
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.400576 Loss1: 0.104170 Loss2: 0.296407
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.371847 Loss1: 0.081856 Loss2: 0.289991
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.379443 Loss1: 0.093832 Loss2: 0.285611
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.390227 Loss1: 0.102142 Loss2: 0.288085
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.383465 Loss1: 0.098755 Loss2: 0.284711
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.407772 Loss1: 0.121314 Loss2: 0.286458
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.382628 Loss1: 0.097965 Loss2: 0.284663
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.385287 Loss1: 0.101852 Loss2: 0.283435
-(DefaultActor pid=1838052) >> Training accuracy: 0.976357
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.159987 Loss1: 0.128010 Loss2: 0.031977
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.099831 Loss1: 0.065629 Loss2: 0.034202
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082502 Loss1: 0.049178 Loss2: 0.033324
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.086562 Loss1: 0.052876 Loss2: 0.033685
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092037 Loss1: 0.058212 Loss2: 0.033825
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.115565 Loss1: 0.081241 Loss2: 0.034324
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.131305 Loss1: 0.096259 Loss2: 0.035047
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125715 Loss1: 0.090216 Loss2: 0.035499
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.131941 Loss1: 0.096155 Loss2: 0.035786
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.117578 Loss1: 0.082577 Loss2: 0.035001
-(DefaultActor pid=1838052) >> Training accuracy: 0.986662
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.154054 Loss1: 0.115815 Loss2: 0.038238
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104346 Loss1: 0.064637 Loss2: 0.039709
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.118724 Loss1: 0.078461 Loss2: 0.040263
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.100779 Loss1: 0.060140 Loss2: 0.040639
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.123187 Loss1: 0.082709 Loss2: 0.040478
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.119476 Loss1: 0.077889 Loss2: 0.041586
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.110313 Loss1: 0.068974 Loss2: 0.041339
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110189 Loss1: 0.068995 Loss2: 0.041194
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.104789 Loss1: 0.064095 Loss2: 0.040694
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095657 Loss1: 0.055326 Loss2: 0.040332
-(DefaultActor pid=1838052) >> Training accuracy: 0.990309
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.780855 Loss1: 0.176837 Loss2: 0.604018
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.710486 Loss1: 0.111909 Loss2: 0.598577
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.718505 Loss1: 0.133631 Loss2: 0.584874
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.689565 Loss1: 0.112940 Loss2: 0.576625
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.665915 Loss1: 0.100332 Loss2: 0.565583
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.682013 Loss1: 0.122729 Loss2: 0.559284
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.662582 Loss1: 0.109458 Loss2: 0.553124
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.653371 Loss1: 0.105543 Loss2: 0.547828
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.623914 Loss1: 0.082041 Loss2: 0.541873
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.626898 Loss1: 0.088845 Loss2: 0.538053
-(DefaultActor pid=1838052) >> Training accuracy: 0.989443
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.223465 Loss1: 0.155426 Loss2: 0.068038
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.168936 Loss1: 0.101319 Loss2: 0.067618
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.162329 Loss1: 0.096148 Loss2: 0.066181
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.134258 Loss1: 0.068986 Loss2: 0.065271
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116840 Loss1: 0.053351 Loss2: 0.063488
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.144447 Loss1: 0.080872 Loss2: 0.063575
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.197688 Loss1: 0.131322 Loss2: 0.066366
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.188073 Loss1: 0.121150 Loss2: 0.066924
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.144763 Loss1: 0.078614 Loss2: 0.066148
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.138316 Loss1: 0.073393 Loss2: 0.064923
-(DefaultActor pid=1838052) >> Training accuracy: 0.985577
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.157841 Loss1: 0.126011 Loss2: 0.031830
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.127801 Loss1: 0.093433 Loss2: 0.034368
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.106549 Loss1: 0.071870 Loss2: 0.034679
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.096656 Loss1: 0.061872 Loss2: 0.034784
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.103501 Loss1: 0.068949 Loss2: 0.034552
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.110564 Loss1: 0.075314 Loss2: 0.035250
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.124421 Loss1: 0.088641 Loss2: 0.035780
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125161 Loss1: 0.089355 Loss2: 0.035806
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.117617 Loss1: 0.081615 Loss2: 0.036002
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116800 Loss1: 0.081025 Loss2: 0.035775
-(DefaultActor pid=1838052) >> Training accuracy: 0.976859
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.206014 Loss1: 0.135141 Loss2: 0.070874
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.157345 Loss1: 0.087309 Loss2: 0.070036
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.140422 Loss1: 0.072356 Loss2: 0.068067
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.125993 Loss1: 0.061009 Loss2: 0.064984
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.116148 Loss1: 0.051215 Loss2: 0.064933
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.120048 Loss1: 0.055735 Loss2: 0.064312
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129189 Loss1: 0.064914 Loss2: 0.064275
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125577 Loss1: 0.061014 Loss2: 0.064562
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.124191 Loss1: 0.059107 Loss2: 0.065084
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.119287 Loss1: 0.054317 Loss2: 0.064971
-(DefaultActor pid=1838052) >> Training accuracy: 0.984573
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.616891 Loss1: 0.166891 Loss2: 0.449999
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.548977 Loss1: 0.120219 Loss2: 0.428758
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.552758 Loss1: 0.125282 Loss2: 0.427476
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.558730 Loss1: 0.135606 Loss2: 0.423124
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.526450 Loss1: 0.110427 Loss2: 0.416023
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.511199 Loss1: 0.099162 Loss2: 0.412037
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.522820 Loss1: 0.110490 Loss2: 0.412330
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.491541 Loss1: 0.085275 Loss2: 0.406266
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.500004 Loss1: 0.092952 Loss2: 0.407051
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.493455 Loss1: 0.088163 Loss2: 0.405292
-(DefaultActor pid=1838052) >> Training accuracy: 0.988381
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.749918 Loss1: 0.176920 Loss2: 0.572999
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.669471 Loss1: 0.106581 Loss2: 0.562890
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.668420 Loss1: 0.119977 Loss2: 0.548443
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.671859 Loss1: 0.127550 Loss2: 0.544308
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.663311 Loss1: 0.125230 Loss2: 0.538081
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.629387 Loss1: 0.098818 Loss2: 0.530570
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.633850 Loss1: 0.107973 Loss2: 0.525877
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.628228 Loss1: 0.103050 Loss2: 0.525179
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.604798 Loss1: 0.085778 Loss2: 0.519020
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.612686 Loss1: 0.095127 Loss2: 0.517559
-(DefaultActor pid=1838052) >> Training accuracy: 0.976464
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 11:37:46,851][flwr][DEBUG] - fit_round 56 received 10 results and 0 failures
->> Test accuracy: 0.649400
-[2023-09-28 11:38:28,114][flwr][INFO] - fit progress: (56, 2.188702217115762, {'accuracy': 0.6494}, 105531.00491379201)
-[2023-09-28 11:38:28,115][flwr][DEBUG] - evaluate_round 56: strategy sampled 10 clients (out of 10)
-[2023-09-28 11:39:04,806][flwr][DEBUG] - evaluate_round 56 received 10 results and 0 failures
-[2023-09-28 11:39:04,807][flwr][DEBUG] - fit_round 57: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.159412 Loss1: 0.127576 Loss2: 0.031836
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.111505 Loss1: 0.077653 Loss2: 0.033852
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.096708 Loss1: 0.062394 Loss2: 0.034314
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.102927 Loss1: 0.068151 Loss2: 0.034776
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101493 Loss1: 0.066624 Loss2: 0.034870
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.095581 Loss1: 0.060473 Loss2: 0.035108
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.098483 Loss1: 0.062753 Loss2: 0.035729
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.112339 Loss1: 0.076345 Loss2: 0.035994
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.097793 Loss1: 0.061838 Loss2: 0.035955
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095055 Loss1: 0.059455 Loss2: 0.035601
-(DefaultActor pid=1838052) >> Training accuracy: 0.988377
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.654340 Loss1: 0.181008 Loss2: 0.473331
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.608649 Loss1: 0.159380 Loss2: 0.449269
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.566031 Loss1: 0.137019 Loss2: 0.429013
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.557758 Loss1: 0.136072 Loss2: 0.421685
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.515284 Loss1: 0.103467 Loss2: 0.411816
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.554279 Loss1: 0.140778 Loss2: 0.413501
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.515892 Loss1: 0.109788 Loss2: 0.406104
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.487456 Loss1: 0.086242 Loss2: 0.401213
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.480572 Loss1: 0.083434 Loss2: 0.397138
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.496667 Loss1: 0.099540 Loss2: 0.397128
-(DefaultActor pid=1838052) >> Training accuracy: 0.977163
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.715136 Loss1: 0.141886 Loss2: 0.573250
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.654387 Loss1: 0.100358 Loss2: 0.554030
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.632873 Loss1: 0.096072 Loss2: 0.536801
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.683262 Loss1: 0.147172 Loss2: 0.536090
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.647558 Loss1: 0.117567 Loss2: 0.529992
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.633394 Loss1: 0.111344 Loss2: 0.522050
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.661515 Loss1: 0.142375 Loss2: 0.519140
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.618256 Loss1: 0.102735 Loss2: 0.515521
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.617323 Loss1: 0.102929 Loss2: 0.514394
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.583372 Loss1: 0.074712 Loss2: 0.508660
-(DefaultActor pid=1838052) >> Training accuracy: 0.986353
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.385604 Loss1: 0.141929 Loss2: 0.243675
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.295075 Loss1: 0.104314 Loss2: 0.190761
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.273646 Loss1: 0.088892 Loss2: 0.184754
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.264531 Loss1: 0.082015 Loss2: 0.182517
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.262769 Loss1: 0.080669 Loss2: 0.182099
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.307022 Loss1: 0.122173 Loss2: 0.184848
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.296163 Loss1: 0.112681 Loss2: 0.183482
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.269282 Loss1: 0.087350 Loss2: 0.181932
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.253429 Loss1: 0.074806 Loss2: 0.178624
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.279642 Loss1: 0.098979 Loss2: 0.180663
-(DefaultActor pid=1838052) >> Training accuracy: 0.979367
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.185895 Loss1: 0.150937 Loss2: 0.034958
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.118102 Loss1: 0.081130 Loss2: 0.036972
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.095875 Loss1: 0.059081 Loss2: 0.036794
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.104535 Loss1: 0.067393 Loss2: 0.037142
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.108296 Loss1: 0.070291 Loss2: 0.038005
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.098406 Loss1: 0.061118 Loss2: 0.037287
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.102844 Loss1: 0.065647 Loss2: 0.037197
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.112077 Loss1: 0.074731 Loss2: 0.037347
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.116638 Loss1: 0.078688 Loss2: 0.037951
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112812 Loss1: 0.074695 Loss2: 0.038117
-(DefaultActor pid=1838052) >> Training accuracy: 0.986111
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.182736 Loss1: 0.146785 Loss2: 0.035951
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.131441 Loss1: 0.092782 Loss2: 0.038659
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.121543 Loss1: 0.082969 Loss2: 0.038574
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.100595 Loss1: 0.062073 Loss2: 0.038522
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.093788 Loss1: 0.055465 Loss2: 0.038324
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077691 Loss1: 0.040050 Loss2: 0.037641
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077059 Loss1: 0.040114 Loss2: 0.036945
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.111015 Loss1: 0.072657 Loss2: 0.038359
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112440 Loss1: 0.073292 Loss2: 0.039148
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114192 Loss1: 0.074667 Loss2: 0.039525
-(DefaultActor pid=1838052) >> Training accuracy: 0.988281
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.726076 Loss1: 0.126683 Loss2: 0.599393
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.680191 Loss1: 0.091661 Loss2: 0.588530
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.649898 Loss1: 0.083753 Loss2: 0.566144
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.641738 Loss1: 0.096627 Loss2: 0.545111
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.659660 Loss1: 0.117707 Loss2: 0.541953
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.616219 Loss1: 0.083791 Loss2: 0.532428
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.617898 Loss1: 0.090153 Loss2: 0.527745
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.616052 Loss1: 0.092467 Loss2: 0.523585
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.626489 Loss1: 0.102532 Loss2: 0.523957
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.614516 Loss1: 0.095230 Loss2: 0.519286
-(DefaultActor pid=1838052) >> Training accuracy: 0.986353
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.215743 Loss1: 0.179985 Loss2: 0.035759
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.138404 Loss1: 0.100404 Loss2: 0.038000
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.129299 Loss1: 0.090970 Loss2: 0.038329
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.122226 Loss1: 0.083535 Loss2: 0.038691
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.118416 Loss1: 0.080214 Loss2: 0.038202
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.113142 Loss1: 0.075164 Loss2: 0.037978
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.088402 Loss1: 0.051232 Loss2: 0.037170
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.088475 Loss1: 0.051600 Loss2: 0.036876
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092215 Loss1: 0.055680 Loss2: 0.036535
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095231 Loss1: 0.058321 Loss2: 0.036909
-(DefaultActor pid=1838052) >> Training accuracy: 0.992188
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.643341 Loss1: 0.200770 Loss2: 0.442571
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.561641 Loss1: 0.146401 Loss2: 0.415241
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.530616 Loss1: 0.131595 Loss2: 0.399021
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.517252 Loss1: 0.126751 Loss2: 0.390501
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.506054 Loss1: 0.119253 Loss2: 0.386801
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.477254 Loss1: 0.093293 Loss2: 0.383961
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.481116 Loss1: 0.102352 Loss2: 0.378764
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.512012 Loss1: 0.131244 Loss2: 0.380768
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.483105 Loss1: 0.103043 Loss2: 0.380063
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.462344 Loss1: 0.090528 Loss2: 0.371817
-(DefaultActor pid=1838052) >> Training accuracy: 0.980222
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.224140 Loss1: 0.155389 Loss2: 0.068751
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.172266 Loss1: 0.108930 Loss2: 0.063336
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.159034 Loss1: 0.096701 Loss2: 0.062333
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.144568 Loss1: 0.084533 Loss2: 0.060035
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.137390 Loss1: 0.077300 Loss2: 0.060090
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.128858 Loss1: 0.069550 Loss2: 0.059307
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.157239 Loss1: 0.096981 Loss2: 0.060259
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.135407 Loss1: 0.075841 Loss2: 0.059566
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115173 Loss1: 0.056191 Loss2: 0.058981
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114776 Loss1: 0.056364 Loss2: 0.058412
-(DefaultActor pid=1838052) >> Training accuracy: 0.990902
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 12:08:09,301][flwr][DEBUG] - fit_round 57 received 10 results and 0 failures
->> Test accuracy: 0.649000
-[2023-09-28 12:08:49,309][flwr][INFO] - fit progress: (57, 2.171707748224179, {'accuracy': 0.649}, 107352.19973800331)
-[2023-09-28 12:08:49,310][flwr][DEBUG] - evaluate_round 57: strategy sampled 10 clients (out of 10)
-[2023-09-28 12:09:26,119][flwr][DEBUG] - evaluate_round 57 received 10 results and 0 failures
-[2023-09-28 12:09:26,120][flwr][DEBUG] - fit_round 58: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.147945 Loss1: 0.111275 Loss2: 0.036670
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.112951 Loss1: 0.073892 Loss2: 0.039059
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.108352 Loss1: 0.069250 Loss2: 0.039102
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.097100 Loss1: 0.058187 Loss2: 0.038913
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.103745 Loss1: 0.064421 Loss2: 0.039324
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.099903 Loss1: 0.060657 Loss2: 0.039247
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.098456 Loss1: 0.058963 Loss2: 0.039494
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091128 Loss1: 0.052167 Loss2: 0.038961
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.091893 Loss1: 0.052927 Loss2: 0.038966
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098782 Loss1: 0.059483 Loss2: 0.039299
-(DefaultActor pid=1838052) >> Training accuracy: 0.991495
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.399444 Loss1: 0.153394 Loss2: 0.246050
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.342823 Loss1: 0.115139 Loss2: 0.227684
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.347885 Loss1: 0.122592 Loss2: 0.225294
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.321751 Loss1: 0.101174 Loss2: 0.220578
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.353968 Loss1: 0.129047 Loss2: 0.224921
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.330284 Loss1: 0.108709 Loss2: 0.221575
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.309639 Loss1: 0.092961 Loss2: 0.216678
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.282380 Loss1: 0.068092 Loss2: 0.214288
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.273853 Loss1: 0.061243 Loss2: 0.212610
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.286239 Loss1: 0.072773 Loss2: 0.213466
-(DefaultActor pid=1838052) >> Training accuracy: 0.983774
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.146454 Loss1: 0.114584 Loss2: 0.031870
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.135294 Loss1: 0.099943 Loss2: 0.035351
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.133512 Loss1: 0.096633 Loss2: 0.036879
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.102933 Loss1: 0.066629 Loss2: 0.036304
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.104468 Loss1: 0.068256 Loss2: 0.036212
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108812 Loss1: 0.072145 Loss2: 0.036667
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.099903 Loss1: 0.063179 Loss2: 0.036725
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.121946 Loss1: 0.084610 Loss2: 0.037337
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108276 Loss1: 0.070769 Loss2: 0.037507
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102496 Loss1: 0.064921 Loss2: 0.037575
-(DefaultActor pid=1838052) >> Training accuracy: 0.987805
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.372480 Loss1: 0.162994 Loss2: 0.209486
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.289361 Loss1: 0.108573 Loss2: 0.180788
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.266111 Loss1: 0.091865 Loss2: 0.174246
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.246521 Loss1: 0.075409 Loss2: 0.171112
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.248948 Loss1: 0.078991 Loss2: 0.169956
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.241948 Loss1: 0.072071 Loss2: 0.169877
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.255750 Loss1: 0.084756 Loss2: 0.170994
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.230603 Loss1: 0.060963 Loss2: 0.169640
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.228720 Loss1: 0.061114 Loss2: 0.167606
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.241194 Loss1: 0.071449 Loss2: 0.169745
-(DefaultActor pid=1838052) >> Training accuracy: 0.988528
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.187765 Loss1: 0.153198 Loss2: 0.034566
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.145260 Loss1: 0.107630 Loss2: 0.037630
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.117815 Loss1: 0.079658 Loss2: 0.038157
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.110414 Loss1: 0.072606 Loss2: 0.037808
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.127068 Loss1: 0.088935 Loss2: 0.038133
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.116233 Loss1: 0.077997 Loss2: 0.038236
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.103598 Loss1: 0.065384 Loss2: 0.038215
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.107716 Loss1: 0.069418 Loss2: 0.038298
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096825 Loss1: 0.058586 Loss2: 0.038240
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.090470 Loss1: 0.052614 Loss2: 0.037856
-(DefaultActor pid=1838052) >> Training accuracy: 0.988898
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.756585 Loss1: 0.162869 Loss2: 0.593716
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.726951 Loss1: 0.135710 Loss2: 0.591241
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.663453 Loss1: 0.092921 Loss2: 0.570531
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.657172 Loss1: 0.098783 Loss2: 0.558388
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.679877 Loss1: 0.127839 Loss2: 0.552038
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.658477 Loss1: 0.111548 Loss2: 0.546928
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.656905 Loss1: 0.117788 Loss2: 0.539117
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.643800 Loss1: 0.108751 Loss2: 0.535049
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.630536 Loss1: 0.098550 Loss2: 0.531986
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.643369 Loss1: 0.111169 Loss2: 0.532200
-(DefaultActor pid=1838052) >> Training accuracy: 0.978733
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.170237 Loss1: 0.131843 Loss2: 0.038394
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.107843 Loss1: 0.067098 Loss2: 0.040745
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.092017 Loss1: 0.051586 Loss2: 0.040430
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.097059 Loss1: 0.057047 Loss2: 0.040013
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.087190 Loss1: 0.047541 Loss2: 0.039649
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.097085 Loss1: 0.056877 Loss2: 0.040208
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.116938 Loss1: 0.076521 Loss2: 0.040417
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.141012 Loss1: 0.098385 Loss2: 0.042628
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.129578 Loss1: 0.086923 Loss2: 0.042655
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115640 Loss1: 0.072983 Loss2: 0.042657
-(DefaultActor pid=1838052) >> Training accuracy: 0.982002
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.570413 Loss1: 0.167244 Loss2: 0.403168
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.512570 Loss1: 0.130824 Loss2: 0.381746
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.528651 Loss1: 0.147983 Loss2: 0.380668
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.542733 Loss1: 0.163940 Loss2: 0.378792
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.493703 Loss1: 0.124773 Loss2: 0.368930
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.483122 Loss1: 0.118604 Loss2: 0.364518
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.464605 Loss1: 0.104856 Loss2: 0.359749
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.475765 Loss1: 0.113216 Loss2: 0.362549
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.459841 Loss1: 0.100882 Loss2: 0.358959
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.439759 Loss1: 0.084364 Loss2: 0.355394
-(DefaultActor pid=1838052) >> Training accuracy: 0.985759
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.185958 Loss1: 0.151364 Loss2: 0.034593
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.127864 Loss1: 0.090942 Loss2: 0.036922
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.097208 Loss1: 0.059825 Loss2: 0.037383
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.103684 Loss1: 0.066673 Loss2: 0.037011
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101618 Loss1: 0.063868 Loss2: 0.037750
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.116352 Loss1: 0.077908 Loss2: 0.038444
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.142148 Loss1: 0.102117 Loss2: 0.040031
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.132275 Loss1: 0.092133 Loss2: 0.040143
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.116635 Loss1: 0.077183 Loss2: 0.039452
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.111513 Loss1: 0.072632 Loss2: 0.038881
-(DefaultActor pid=1838052) >> Training accuracy: 0.984375
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.177659 Loss1: 0.139832 Loss2: 0.037827
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.123577 Loss1: 0.083665 Loss2: 0.039911
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.090999 Loss1: 0.052117 Loss2: 0.038882
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113299 Loss1: 0.074469 Loss2: 0.038830
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.113466 Loss1: 0.073697 Loss2: 0.039769
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.121984 Loss1: 0.082355 Loss2: 0.039630
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104431 Loss1: 0.065044 Loss2: 0.039387
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.094564 Loss1: 0.055599 Loss2: 0.038965
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092780 Loss1: 0.053955 Loss2: 0.038825
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.120653 Loss1: 0.081063 Loss2: 0.039590
-(DefaultActor pid=1838052) >> Training accuracy: 0.985377
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 12:38:19,010][flwr][DEBUG] - fit_round 58 received 10 results and 0 failures
->> Test accuracy: 0.649900
-[2023-09-28 12:39:00,197][flwr][INFO] - fit progress: (58, 2.195324074726897, {'accuracy': 0.6499}, 109163.08724016929)
-[2023-09-28 12:39:00,197][flwr][DEBUG] - evaluate_round 58: strategy sampled 10 clients (out of 10)
-[2023-09-28 12:39:36,655][flwr][DEBUG] - evaluate_round 58 received 10 results and 0 failures
-[2023-09-28 12:39:36,656][flwr][DEBUG] - fit_round 59: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.653095 Loss1: 0.140832 Loss2: 0.512263
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.606218 Loss1: 0.098673 Loss2: 0.507545
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.586061 Loss1: 0.087415 Loss2: 0.498646
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.592401 Loss1: 0.098309 Loss2: 0.494092
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.594689 Loss1: 0.102060 Loss2: 0.492628
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.576589 Loss1: 0.089144 Loss2: 0.487445
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.562617 Loss1: 0.080613 Loss2: 0.482004
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.568812 Loss1: 0.086782 Loss2: 0.482030
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.556661 Loss1: 0.078670 Loss2: 0.477991
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.553333 Loss1: 0.077364 Loss2: 0.475969
-(DefaultActor pid=1838052) >> Training accuracy: 0.981804
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.184157 Loss1: 0.145639 Loss2: 0.038518
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.124897 Loss1: 0.083406 Loss2: 0.041491
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.134244 Loss1: 0.093030 Loss2: 0.041214
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.114920 Loss1: 0.074005 Loss2: 0.040914
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.123072 Loss1: 0.082212 Loss2: 0.040860
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.125438 Loss1: 0.083750 Loss2: 0.041688
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.153332 Loss1: 0.110595 Loss2: 0.042737
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.127817 Loss1: 0.085845 Loss2: 0.041972
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140450 Loss1: 0.097830 Loss2: 0.042620
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.130074 Loss1: 0.087689 Loss2: 0.042385
-(DefaultActor pid=1838052) >> Training accuracy: 0.988281
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.149234 Loss1: 0.116420 Loss2: 0.032815
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104293 Loss1: 0.069407 Loss2: 0.034886
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.107612 Loss1: 0.072071 Loss2: 0.035541
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.108612 Loss1: 0.072672 Loss2: 0.035940
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.105247 Loss1: 0.069082 Loss2: 0.036164
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.101100 Loss1: 0.065256 Loss2: 0.035844
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101475 Loss1: 0.065106 Loss2: 0.036369
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.097806 Loss1: 0.061609 Loss2: 0.036197
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.107162 Loss1: 0.070622 Loss2: 0.036540
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098746 Loss1: 0.061433 Loss2: 0.037313
-(DefaultActor pid=1838052) >> Training accuracy: 0.989517
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.187738 Loss1: 0.154319 Loss2: 0.033419
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.129019 Loss1: 0.092595 Loss2: 0.036424
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.119049 Loss1: 0.082071 Loss2: 0.036978
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.115069 Loss1: 0.078422 Loss2: 0.036647
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.139890 Loss1: 0.102217 Loss2: 0.037673
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.124683 Loss1: 0.086434 Loss2: 0.038249
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101359 Loss1: 0.063691 Loss2: 0.037669
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.099210 Loss1: 0.061547 Loss2: 0.037663
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092146 Loss1: 0.054826 Loss2: 0.037320
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.069114 Loss1: 0.032843 Loss2: 0.036271
-(DefaultActor pid=1838052) >> Training accuracy: 0.993243
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.176875 Loss1: 0.119960 Loss2: 0.056915
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.118469 Loss1: 0.063024 Loss2: 0.055445
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.116921 Loss1: 0.063021 Loss2: 0.053900
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.103955 Loss1: 0.050088 Loss2: 0.053868
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.097100 Loss1: 0.044309 Loss2: 0.052792
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.105119 Loss1: 0.051736 Loss2: 0.053383
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.105521 Loss1: 0.052342 Loss2: 0.053179
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.125895 Loss1: 0.071644 Loss2: 0.054251
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108975 Loss1: 0.054526 Loss2: 0.054449
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121688 Loss1: 0.067216 Loss2: 0.054472
-(DefaultActor pid=1838052) >> Training accuracy: 0.985759
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.205910 Loss1: 0.140980 Loss2: 0.064930
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.140703 Loss1: 0.079438 Loss2: 0.061266
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.125620 Loss1: 0.065540 Loss2: 0.060081
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113243 Loss1: 0.055858 Loss2: 0.057386
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.132916 Loss1: 0.075341 Loss2: 0.057575
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.137875 Loss1: 0.079129 Loss2: 0.058746
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.144593 Loss1: 0.085284 Loss2: 0.059309
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.137234 Loss1: 0.078200 Loss2: 0.059035
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.125504 Loss1: 0.066772 Loss2: 0.058732
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115427 Loss1: 0.058178 Loss2: 0.057250
-(DefaultActor pid=1838052) >> Training accuracy: 0.988381
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.699866 Loss1: 0.136791 Loss2: 0.563075
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.652117 Loss1: 0.105541 Loss2: 0.546576
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.649402 Loss1: 0.112588 Loss2: 0.536814
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615120 Loss1: 0.085926 Loss2: 0.529194
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.618060 Loss1: 0.092448 Loss2: 0.525612
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646008 Loss1: 0.119965 Loss2: 0.526043
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.627227 Loss1: 0.107043 Loss2: 0.520184
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.646585 Loss1: 0.126096 Loss2: 0.520490
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.592714 Loss1: 0.078797 Loss2: 0.513917
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.579348 Loss1: 0.069590 Loss2: 0.509758
-(DefaultActor pid=1838052) >> Training accuracy: 0.988782
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.162598 Loss1: 0.125945 Loss2: 0.036653
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.113153 Loss1: 0.074789 Loss2: 0.038365
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.104121 Loss1: 0.065212 Loss2: 0.038909
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.101958 Loss1: 0.063653 Loss2: 0.038305
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.108612 Loss1: 0.070132 Loss2: 0.038480
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.113301 Loss1: 0.074857 Loss2: 0.038444
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.121263 Loss1: 0.081846 Loss2: 0.039417
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.107200 Loss1: 0.068371 Loss2: 0.038828
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115840 Loss1: 0.076288 Loss2: 0.039552
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102656 Loss1: 0.063284 Loss2: 0.039372
-(DefaultActor pid=1838052) >> Training accuracy: 0.987935
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.144898 Loss1: 0.112793 Loss2: 0.032105
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.096932 Loss1: 0.062308 Loss2: 0.034624
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.099578 Loss1: 0.064651 Loss2: 0.034927
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085400 Loss1: 0.050153 Loss2: 0.035247
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.080777 Loss1: 0.045469 Loss2: 0.035309
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096529 Loss1: 0.060788 Loss2: 0.035741
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104686 Loss1: 0.068223 Loss2: 0.036464
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.102462 Loss1: 0.065809 Loss2: 0.036653
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.119382 Loss1: 0.082198 Loss2: 0.037184
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.086069 Loss1: 0.049261 Loss2: 0.036807
-(DefaultActor pid=1838052) >> Training accuracy: 0.987995
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.158378 Loss1: 0.125507 Loss2: 0.032871
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.121435 Loss1: 0.084913 Loss2: 0.036522
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.105456 Loss1: 0.068902 Loss2: 0.036554
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.113813 Loss1: 0.076853 Loss2: 0.036960
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.138597 Loss1: 0.100987 Loss2: 0.037609
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.156129 Loss1: 0.117267 Loss2: 0.038862
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.114743 Loss1: 0.076456 Loss2: 0.038287
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.128988 Loss1: 0.090955 Loss2: 0.038033
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.132117 Loss1: 0.092912 Loss2: 0.039205
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112736 Loss1: 0.073646 Loss2: 0.039091
-(DefaultActor pid=1838052) >> Training accuracy: 0.989104
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 13:08:29,317][flwr][DEBUG] - fit_round 59 received 10 results and 0 failures
->> Test accuracy: 0.650500
-[2023-09-28 13:09:09,776][flwr][INFO] - fit progress: (59, 2.2019545649187253, {'accuracy': 0.6505}, 110972.66598086525)
-[2023-09-28 13:09:09,776][flwr][DEBUG] - evaluate_round 59: strategy sampled 10 clients (out of 10)
-[2023-09-28 13:09:46,393][flwr][DEBUG] - evaluate_round 59 received 10 results and 0 failures
-[2023-09-28 13:09:46,394][flwr][DEBUG] - fit_round 60: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.751069 Loss1: 0.151690 Loss2: 0.599378
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.701111 Loss1: 0.104400 Loss2: 0.596711
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.693096 Loss1: 0.106510 Loss2: 0.586586
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.681731 Loss1: 0.104970 Loss2: 0.576761
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.685732 Loss1: 0.117083 Loss2: 0.568650
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.671654 Loss1: 0.105583 Loss2: 0.566070
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.678010 Loss1: 0.117617 Loss2: 0.560394
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.636156 Loss1: 0.082512 Loss2: 0.553645
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.643292 Loss1: 0.095417 Loss2: 0.547876
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.639488 Loss1: 0.094310 Loss2: 0.545179
-(DefaultActor pid=1838052) >> Training accuracy: 0.980903
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.142712 Loss1: 0.109831 Loss2: 0.032881
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.084959 Loss1: 0.050008 Loss2: 0.034950
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.080724 Loss1: 0.045459 Loss2: 0.035266
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.095768 Loss1: 0.059836 Loss2: 0.035932
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.090865 Loss1: 0.054445 Loss2: 0.036420
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.088822 Loss1: 0.052165 Loss2: 0.036657
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101130 Loss1: 0.064114 Loss2: 0.037016
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100227 Loss1: 0.062874 Loss2: 0.037352
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.128180 Loss1: 0.090213 Loss2: 0.037967
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.142661 Loss1: 0.104231 Loss2: 0.038430
-(DefaultActor pid=1838052) >> Training accuracy: 0.978639
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.740027 Loss1: 0.167236 Loss2: 0.572791
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.652332 Loss1: 0.096570 Loss2: 0.555762
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.620631 Loss1: 0.081368 Loss2: 0.539263
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.627828 Loss1: 0.097872 Loss2: 0.529957
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.653539 Loss1: 0.125812 Loss2: 0.527727
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646119 Loss1: 0.121239 Loss2: 0.524881
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.623819 Loss1: 0.104872 Loss2: 0.518947
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.631050 Loss1: 0.113660 Loss2: 0.517390
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.607183 Loss1: 0.095232 Loss2: 0.511951
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.603305 Loss1: 0.095238 Loss2: 0.508067
-(DefaultActor pid=1838052) >> Training accuracy: 0.983347
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.171303 Loss1: 0.137473 Loss2: 0.033831
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.119225 Loss1: 0.083045 Loss2: 0.036179
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.090841 Loss1: 0.054224 Loss2: 0.036617
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.082015 Loss1: 0.045911 Loss2: 0.036104
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.099232 Loss1: 0.062279 Loss2: 0.036953
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.097228 Loss1: 0.059963 Loss2: 0.037265
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.115113 Loss1: 0.077174 Loss2: 0.037939
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.138358 Loss1: 0.099714 Loss2: 0.038644
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.124717 Loss1: 0.085977 Loss2: 0.038740
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114395 Loss1: 0.075898 Loss2: 0.038497
-(DefaultActor pid=1838052) >> Training accuracy: 0.982171
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.184530 Loss1: 0.151846 Loss2: 0.032684
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.126511 Loss1: 0.091017 Loss2: 0.035494
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.138256 Loss1: 0.101935 Loss2: 0.036321
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.118666 Loss1: 0.082137 Loss2: 0.036529
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.103594 Loss1: 0.066955 Loss2: 0.036639
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.074906 Loss1: 0.038983 Loss2: 0.035923
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.093259 Loss1: 0.057258 Loss2: 0.036001
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.084672 Loss1: 0.049298 Loss2: 0.035374
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.103158 Loss1: 0.067014 Loss2: 0.036144
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089134 Loss1: 0.052838 Loss2: 0.036295
-(DefaultActor pid=1838052) >> Training accuracy: 0.992188
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.694724 Loss1: 0.114901 Loss2: 0.579823
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.665133 Loss1: 0.095390 Loss2: 0.569743
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.675493 Loss1: 0.115889 Loss2: 0.559604
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.671360 Loss1: 0.115847 Loss2: 0.555513
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.643444 Loss1: 0.096497 Loss2: 0.546948
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.636226 Loss1: 0.094309 Loss2: 0.541917
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.670256 Loss1: 0.129916 Loss2: 0.540340
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.654326 Loss1: 0.116539 Loss2: 0.537786
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.658664 Loss1: 0.126082 Loss2: 0.532582
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.652680 Loss1: 0.122124 Loss2: 0.530557
-(DefaultActor pid=1838052) >> Training accuracy: 0.984375
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.162319 Loss1: 0.128887 Loss2: 0.033433
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.111079 Loss1: 0.075342 Loss2: 0.035737
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.106915 Loss1: 0.070976 Loss2: 0.035939
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107729 Loss1: 0.071400 Loss2: 0.036328
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086707 Loss1: 0.050236 Loss2: 0.036471
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.095095 Loss1: 0.058605 Loss2: 0.036490
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.089393 Loss1: 0.052873 Loss2: 0.036520
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.093167 Loss1: 0.056476 Loss2: 0.036690
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.109524 Loss1: 0.072323 Loss2: 0.037202
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.117482 Loss1: 0.079504 Loss2: 0.037978
-(DefaultActor pid=1838052) >> Training accuracy: 0.979826
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.154453 Loss1: 0.121755 Loss2: 0.032699
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104130 Loss1: 0.069552 Loss2: 0.034578
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.084324 Loss1: 0.049941 Loss2: 0.034382
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.073996 Loss1: 0.039681 Loss2: 0.034316
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.066741 Loss1: 0.032857 Loss2: 0.033884
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077275 Loss1: 0.043352 Loss2: 0.033923
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.069361 Loss1: 0.035100 Loss2: 0.034260
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100181 Loss1: 0.065035 Loss2: 0.035145
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.088846 Loss1: 0.053464 Loss2: 0.035383
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.092508 Loss1: 0.056627 Loss2: 0.035881
-(DefaultActor pid=1838052) >> Training accuracy: 0.988782
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.141494 Loss1: 0.104050 Loss2: 0.037444
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.106230 Loss1: 0.066460 Loss2: 0.039770
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.107260 Loss1: 0.067122 Loss2: 0.040138
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.108513 Loss1: 0.068242 Loss2: 0.040272
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.096926 Loss1: 0.056426 Loss2: 0.040500
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.098720 Loss1: 0.058246 Loss2: 0.040475
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.092002 Loss1: 0.052159 Loss2: 0.039843
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.111896 Loss1: 0.070867 Loss2: 0.041030
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.139531 Loss1: 0.097271 Loss2: 0.042260
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.132750 Loss1: 0.090053 Loss2: 0.042697
-(DefaultActor pid=1838052) >> Training accuracy: 0.981804
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.123642 Loss1: 0.091817 Loss2: 0.031825
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.092192 Loss1: 0.058508 Loss2: 0.033684
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.117623 Loss1: 0.083101 Loss2: 0.034521
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.128793 Loss1: 0.092451 Loss2: 0.036342
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110780 Loss1: 0.074024 Loss2: 0.036756
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.093124 Loss1: 0.056410 Loss2: 0.036714
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.095942 Loss1: 0.059540 Loss2: 0.036402
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.088939 Loss1: 0.052721 Loss2: 0.036218
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092372 Loss1: 0.055797 Loss2: 0.036575
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099736 Loss1: 0.063334 Loss2: 0.036402
-(DefaultActor pid=1838052) >> Training accuracy: 0.992188
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 13:38:41,244][flwr][DEBUG] - fit_round 60 received 10 results and 0 failures
->> Test accuracy: 0.650000
-[2023-09-28 13:39:21,703][flwr][INFO] - fit progress: (60, 2.1900063330373065, {'accuracy': 0.65}, 112784.59380016942)
-[2023-09-28 13:39:21,704][flwr][DEBUG] - evaluate_round 60: strategy sampled 10 clients (out of 10)
-[2023-09-28 13:39:58,629][flwr][DEBUG] - evaluate_round 60 received 10 results and 0 failures
-[2023-09-28 13:39:58,630][flwr][DEBUG] - fit_round 61: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.708451 Loss1: 0.111271 Loss2: 0.597179
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.680266 Loss1: 0.090237 Loss2: 0.590030
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.662436 Loss1: 0.087404 Loss2: 0.575033
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.647867 Loss1: 0.083162 Loss2: 0.564704
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.651275 Loss1: 0.090218 Loss2: 0.561058
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.657386 Loss1: 0.101817 Loss2: 0.555568
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.669883 Loss1: 0.117627 Loss2: 0.552256
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.660866 Loss1: 0.111149 Loss2: 0.549717
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.663261 Loss1: 0.116581 Loss2: 0.546680
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.640661 Loss1: 0.099569 Loss2: 0.541092
-(DefaultActor pid=1838052) >> Training accuracy: 0.977896
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.140845 Loss1: 0.108397 Loss2: 0.032448
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.100331 Loss1: 0.066292 Loss2: 0.034039
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.104372 Loss1: 0.069617 Loss2: 0.034755
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.097903 Loss1: 0.062426 Loss2: 0.035477
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.094600 Loss1: 0.058860 Loss2: 0.035740
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.072253 Loss1: 0.036842 Loss2: 0.035412
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.067760 Loss1: 0.032694 Loss2: 0.035067
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073460 Loss1: 0.038663 Loss2: 0.034797
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.078212 Loss1: 0.042819 Loss2: 0.035393
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.086291 Loss1: 0.050916 Loss2: 0.035375
-(DefaultActor pid=1838052) >> Training accuracy: 0.990585
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.150930 Loss1: 0.102451 Loss2: 0.048479
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.105339 Loss1: 0.058493 Loss2: 0.046846
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.100174 Loss1: 0.054461 Loss2: 0.045713
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.095474 Loss1: 0.050370 Loss2: 0.045104
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.126259 Loss1: 0.079790 Loss2: 0.046469
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.118289 Loss1: 0.071524 Loss2: 0.046765
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.113317 Loss1: 0.066866 Loss2: 0.046452
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.108587 Loss1: 0.062395 Loss2: 0.046193
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.094598 Loss1: 0.049046 Loss2: 0.045553
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115553 Loss1: 0.068956 Loss2: 0.046597
-(DefaultActor pid=1838052) >> Training accuracy: 0.987144
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.699011 Loss1: 0.124786 Loss2: 0.574225
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.644589 Loss1: 0.088705 Loss2: 0.555884
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.651779 Loss1: 0.108161 Loss2: 0.543618
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.651853 Loss1: 0.110912 Loss2: 0.540941
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.625602 Loss1: 0.092637 Loss2: 0.532965
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.596942 Loss1: 0.074117 Loss2: 0.522825
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.594180 Loss1: 0.074803 Loss2: 0.519377
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.589607 Loss1: 0.073445 Loss2: 0.516163
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.602258 Loss1: 0.088603 Loss2: 0.513655
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.610448 Loss1: 0.098796 Loss2: 0.511652
-(DefaultActor pid=1838052) >> Training accuracy: 0.981210
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.749375 Loss1: 0.153775 Loss2: 0.595600
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.702559 Loss1: 0.111876 Loss2: 0.590684
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.671072 Loss1: 0.094092 Loss2: 0.576981
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.663024 Loss1: 0.092936 Loss2: 0.570088
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.679122 Loss1: 0.114105 Loss2: 0.565017
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.662453 Loss1: 0.099268 Loss2: 0.563185
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.662273 Loss1: 0.101312 Loss2: 0.560961
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.641428 Loss1: 0.086006 Loss2: 0.555422
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.653304 Loss1: 0.101641 Loss2: 0.551663
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.649219 Loss1: 0.097954 Loss2: 0.551265
-(DefaultActor pid=1838052) >> Training accuracy: 0.980574
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.171309 Loss1: 0.115105 Loss2: 0.056204
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.133219 Loss1: 0.079876 Loss2: 0.053343
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.120097 Loss1: 0.066330 Loss2: 0.053767
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107116 Loss1: 0.054342 Loss2: 0.052774
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.109337 Loss1: 0.056835 Loss2: 0.052502
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.110910 Loss1: 0.058544 Loss2: 0.052365
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.113087 Loss1: 0.060175 Loss2: 0.052913
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.110739 Loss1: 0.058297 Loss2: 0.052442
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.102373 Loss1: 0.050141 Loss2: 0.052232
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.101863 Loss1: 0.050218 Loss2: 0.051645
-(DefaultActor pid=1838052) >> Training accuracy: 0.984169
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.707236 Loss1: 0.112525 Loss2: 0.594711
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.684146 Loss1: 0.098644 Loss2: 0.585503
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.677544 Loss1: 0.102870 Loss2: 0.574674
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.667324 Loss1: 0.098460 Loss2: 0.568864
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.638750 Loss1: 0.080610 Loss2: 0.558139
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.653583 Loss1: 0.102851 Loss2: 0.550732
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.640305 Loss1: 0.095133 Loss2: 0.545173
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.628427 Loss1: 0.085889 Loss2: 0.542538
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.629651 Loss1: 0.093232 Loss2: 0.536418
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.619996 Loss1: 0.088939 Loss2: 0.531057
-(DefaultActor pid=1838052) >> Training accuracy: 0.983584
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136121 Loss1: 0.105823 Loss2: 0.030298
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.108512 Loss1: 0.075440 Loss2: 0.033073
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.094577 Loss1: 0.060664 Loss2: 0.033913
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.105908 Loss1: 0.071405 Loss2: 0.034504
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.091261 Loss1: 0.057078 Loss2: 0.034182
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096809 Loss1: 0.062125 Loss2: 0.034684
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104387 Loss1: 0.068718 Loss2: 0.035669
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.103310 Loss1: 0.067804 Loss2: 0.035506
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085209 Loss1: 0.050299 Loss2: 0.034911
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.074145 Loss1: 0.039607 Loss2: 0.034538
-(DefaultActor pid=1838052) >> Training accuracy: 0.994792
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.163163 Loss1: 0.102603 Loss2: 0.060560
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.121270 Loss1: 0.063145 Loss2: 0.058126
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.126588 Loss1: 0.069740 Loss2: 0.056847
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.118671 Loss1: 0.063003 Loss2: 0.055668
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.131230 Loss1: 0.075859 Loss2: 0.055372
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111423 Loss1: 0.056915 Loss2: 0.054508
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.115012 Loss1: 0.060087 Loss2: 0.054924
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.102965 Loss1: 0.049019 Loss2: 0.053946
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.104747 Loss1: 0.050648 Loss2: 0.054099
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116404 Loss1: 0.062845 Loss2: 0.053559
-(DefaultActor pid=1838052) >> Training accuracy: 0.987935
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.208515 Loss1: 0.149132 Loss2: 0.059383
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.156770 Loss1: 0.100324 Loss2: 0.056446
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.148279 Loss1: 0.092262 Loss2: 0.056017
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.120779 Loss1: 0.066843 Loss2: 0.053935
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.109965 Loss1: 0.057769 Loss2: 0.052196
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.113252 Loss1: 0.061441 Loss2: 0.051811
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.129103 Loss1: 0.076954 Loss2: 0.052149
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.113142 Loss1: 0.061029 Loss2: 0.052113
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108926 Loss1: 0.057472 Loss2: 0.051454
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.115022 Loss1: 0.063616 Loss2: 0.051405
-(DefaultActor pid=1838052) >> Training accuracy: 0.988932
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 14:09:08,320][flwr][DEBUG] - fit_round 61 received 10 results and 0 failures
->> Test accuracy: 0.651200
-[2023-09-28 14:09:49,848][flwr][INFO] - fit progress: (61, 2.20785686230888, {'accuracy': 0.6512}, 114612.7381554232)
-[2023-09-28 14:09:49,848][flwr][DEBUG] - evaluate_round 61: strategy sampled 10 clients (out of 10)
-[2023-09-28 14:10:26,896][flwr][DEBUG] - evaluate_round 61 received 10 results and 0 failures
-[2023-09-28 14:10:26,898][flwr][DEBUG] - fit_round 62: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.449134 Loss1: 0.129271 Loss2: 0.319863
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.380067 Loss1: 0.093343 Loss2: 0.286724
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.375470 Loss1: 0.094202 Loss2: 0.281268
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.388596 Loss1: 0.110578 Loss2: 0.278018
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.381240 Loss1: 0.105335 Loss2: 0.275905
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.360677 Loss1: 0.087120 Loss2: 0.273557
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.365775 Loss1: 0.092334 Loss2: 0.273441
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.358685 Loss1: 0.085914 Loss2: 0.272771
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.346486 Loss1: 0.078835 Loss2: 0.267651
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.352327 Loss1: 0.082494 Loss2: 0.269833
-(DefaultActor pid=1838052) >> Training accuracy: 0.983188
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.675487 Loss1: 0.113259 Loss2: 0.562228
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.615851 Loss1: 0.072053 Loss2: 0.543798
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.617263 Loss1: 0.084157 Loss2: 0.533106
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.624560 Loss1: 0.089777 Loss2: 0.534783
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.614558 Loss1: 0.088169 Loss2: 0.526389
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646171 Loss1: 0.118979 Loss2: 0.527192
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.676914 Loss1: 0.146802 Loss2: 0.530111
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.628422 Loss1: 0.108848 Loss2: 0.519575
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.617618 Loss1: 0.100014 Loss2: 0.517604
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.599605 Loss1: 0.085358 Loss2: 0.514246
-(DefaultActor pid=1838052) >> Training accuracy: 0.982372
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.175652 Loss1: 0.141292 Loss2: 0.034360
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.120340 Loss1: 0.083944 Loss2: 0.036396
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.113403 Loss1: 0.075879 Loss2: 0.037524
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.095602 Loss1: 0.059090 Loss2: 0.036513
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.111543 Loss1: 0.074499 Loss2: 0.037044
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123502 Loss1: 0.086030 Loss2: 0.037472
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.112705 Loss1: 0.074997 Loss2: 0.037708
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.085782 Loss1: 0.049296 Loss2: 0.036486
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.082599 Loss1: 0.046666 Loss2: 0.035933
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.081234 Loss1: 0.045493 Loss2: 0.035740
-(DefaultActor pid=1838052) >> Training accuracy: 0.989865
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.135998 Loss1: 0.103107 Loss2: 0.032891
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.095150 Loss1: 0.059706 Loss2: 0.035444
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.086584 Loss1: 0.051304 Loss2: 0.035280
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.083266 Loss1: 0.047802 Loss2: 0.035464
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.084220 Loss1: 0.048457 Loss2: 0.035763
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083142 Loss1: 0.047630 Loss2: 0.035512
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.107685 Loss1: 0.072165 Loss2: 0.035520
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.136728 Loss1: 0.099369 Loss2: 0.037359
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130872 Loss1: 0.092659 Loss2: 0.038214
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114882 Loss1: 0.077643 Loss2: 0.037239
-(DefaultActor pid=1838052) >> Training accuracy: 0.980945
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.517744 Loss1: 0.166388 Loss2: 0.351356
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.448140 Loss1: 0.127522 Loss2: 0.320617
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.446152 Loss1: 0.135891 Loss2: 0.310261
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.436608 Loss1: 0.128695 Loss2: 0.307913
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.432190 Loss1: 0.127196 Loss2: 0.304994
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.436457 Loss1: 0.132963 Loss2: 0.303494
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.413621 Loss1: 0.117374 Loss2: 0.296246
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.409229 Loss1: 0.111688 Loss2: 0.297540
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.385048 Loss1: 0.092803 Loss2: 0.292244
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.386810 Loss1: 0.093718 Loss2: 0.293092
-(DefaultActor pid=1838052) >> Training accuracy: 0.978618
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.727502 Loss1: 0.123812 Loss2: 0.603690
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.671853 Loss1: 0.074913 Loss2: 0.596939
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.669790 Loss1: 0.081497 Loss2: 0.588293
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.664338 Loss1: 0.083886 Loss2: 0.580452
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.680255 Loss1: 0.103455 Loss2: 0.576801
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.691680 Loss1: 0.118532 Loss2: 0.573148
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.688788 Loss1: 0.118170 Loss2: 0.570618
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.671464 Loss1: 0.105196 Loss2: 0.566268
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.646562 Loss1: 0.084374 Loss2: 0.562188
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.621076 Loss1: 0.066417 Loss2: 0.554660
-(DefaultActor pid=1838052) >> Training accuracy: 0.990184
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.186288 Loss1: 0.118102 Loss2: 0.068187
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.136452 Loss1: 0.070246 Loss2: 0.066206
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.121155 Loss1: 0.055566 Loss2: 0.065590
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.127957 Loss1: 0.062433 Loss2: 0.065524
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.146322 Loss1: 0.080728 Loss2: 0.065594
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.142236 Loss1: 0.076006 Loss2: 0.066230
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.164359 Loss1: 0.097358 Loss2: 0.067001
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.133881 Loss1: 0.067991 Loss2: 0.065889
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130089 Loss1: 0.064500 Loss2: 0.065589
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.135148 Loss1: 0.069690 Loss2: 0.065458
-(DefaultActor pid=1838052) >> Training accuracy: 0.986946
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.150578 Loss1: 0.117391 Loss2: 0.033187
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.109658 Loss1: 0.074146 Loss2: 0.035512
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.105490 Loss1: 0.069562 Loss2: 0.035928
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.106721 Loss1: 0.070112 Loss2: 0.036609
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.117056 Loss1: 0.080213 Loss2: 0.036842
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.122173 Loss1: 0.084068 Loss2: 0.038105
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.141848 Loss1: 0.103130 Loss2: 0.038718
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.119964 Loss1: 0.081255 Loss2: 0.038709
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.123265 Loss1: 0.084745 Loss2: 0.038521
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.100937 Loss1: 0.063332 Loss2: 0.037605
-(DefaultActor pid=1838052) >> Training accuracy: 0.990704
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.644779 Loss1: 0.124909 Loss2: 0.519870
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.597199 Loss1: 0.115355 Loss2: 0.481845
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.563411 Loss1: 0.105547 Loss2: 0.457864
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.560848 Loss1: 0.115163 Loss2: 0.445685
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.546898 Loss1: 0.109724 Loss2: 0.437173
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.523513 Loss1: 0.095808 Loss2: 0.427705
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.536174 Loss1: 0.111649 Loss2: 0.424525
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.540602 Loss1: 0.112873 Loss2: 0.427729
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.524885 Loss1: 0.101478 Loss2: 0.423407
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.513735 Loss1: 0.094437 Loss2: 0.419298
-(DefaultActor pid=1838052) >> Training accuracy: 0.983507
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.118838 Loss1: 0.086092 Loss2: 0.032745
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.092394 Loss1: 0.057533 Loss2: 0.034862
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.086579 Loss1: 0.051473 Loss2: 0.035107
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081212 Loss1: 0.045701 Loss2: 0.035512
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.074770 Loss1: 0.039370 Loss2: 0.035401
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069609 Loss1: 0.034256 Loss2: 0.035352
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.087262 Loss1: 0.051911 Loss2: 0.035351
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068552 Loss1: 0.032864 Loss2: 0.035688
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077896 Loss1: 0.042149 Loss2: 0.035747
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.078640 Loss1: 0.042539 Loss2: 0.036100
-(DefaultActor pid=1838052) >> Training accuracy: 0.994660
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 14:39:29,182][flwr][DEBUG] - fit_round 62 received 10 results and 0 failures
->> Test accuracy: 0.653500
-[2023-09-28 14:40:10,049][flwr][INFO] - fit progress: (62, 2.1942758756323744, {'accuracy': 0.6535}, 116432.9392821202)
-[2023-09-28 14:40:10,049][flwr][DEBUG] - evaluate_round 62: strategy sampled 10 clients (out of 10)
-[2023-09-28 14:40:47,627][flwr][DEBUG] - evaluate_round 62 received 10 results and 0 failures
-[2023-09-28 14:40:47,629][flwr][DEBUG] - fit_round 63: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.657650 Loss1: 0.120610 Loss2: 0.537039
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.630092 Loss1: 0.108825 Loss2: 0.521267
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.588210 Loss1: 0.081034 Loss2: 0.507177
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.586595 Loss1: 0.087704 Loss2: 0.498891
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.602582 Loss1: 0.104608 Loss2: 0.497974
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.598731 Loss1: 0.102818 Loss2: 0.495913
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.562558 Loss1: 0.072516 Loss2: 0.490042
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.580376 Loss1: 0.091352 Loss2: 0.489024
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.587975 Loss1: 0.099302 Loss2: 0.488672
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.606956 Loss1: 0.119110 Loss2: 0.487846
-(DefaultActor pid=1838052) >> Training accuracy: 0.981013
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.415176 Loss1: 0.135584 Loss2: 0.279591
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.377940 Loss1: 0.110182 Loss2: 0.267758
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.376363 Loss1: 0.112532 Loss2: 0.263832
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.383371 Loss1: 0.117254 Loss2: 0.266117
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.371986 Loss1: 0.109499 Loss2: 0.262488
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.394755 Loss1: 0.132984 Loss2: 0.261771
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.408671 Loss1: 0.142601 Loss2: 0.266069
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.383928 Loss1: 0.121823 Loss2: 0.262105
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.359165 Loss1: 0.103691 Loss2: 0.255474
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.365025 Loss1: 0.106319 Loss2: 0.258705
-(DefaultActor pid=1838052) >> Training accuracy: 0.976661
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.148439 Loss1: 0.114579 Loss2: 0.033861
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.109939 Loss1: 0.074195 Loss2: 0.035745
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.098420 Loss1: 0.062911 Loss2: 0.035510
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.092353 Loss1: 0.056457 Loss2: 0.035896
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.095133 Loss1: 0.059938 Loss2: 0.035194
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.094704 Loss1: 0.059867 Loss2: 0.034836
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.071054 Loss1: 0.036293 Loss2: 0.034761
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.076405 Loss1: 0.041658 Loss2: 0.034747
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077542 Loss1: 0.043015 Loss2: 0.034527
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.078518 Loss1: 0.043992 Loss2: 0.034526
-(DefaultActor pid=1838052) >> Training accuracy: 0.989183
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.132120 Loss1: 0.100475 Loss2: 0.031645
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.094125 Loss1: 0.060037 Loss2: 0.034088
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.075500 Loss1: 0.041870 Loss2: 0.033630
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.082414 Loss1: 0.048595 Loss2: 0.033819
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.084483 Loss1: 0.050414 Loss2: 0.034069
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079457 Loss1: 0.045326 Loss2: 0.034131
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073725 Loss1: 0.039954 Loss2: 0.033772
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.062841 Loss1: 0.029498 Loss2: 0.033342
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.079904 Loss1: 0.045513 Loss2: 0.034391
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.086031 Loss1: 0.051954 Loss2: 0.034077
-(DefaultActor pid=1838052) >> Training accuracy: 0.987179
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.379791 Loss1: 0.155338 Loss2: 0.224454
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.297067 Loss1: 0.109315 Loss2: 0.187752
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.301045 Loss1: 0.118547 Loss2: 0.182498
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.281140 Loss1: 0.104895 Loss2: 0.176245
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.258016 Loss1: 0.085885 Loss2: 0.172131
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.242882 Loss1: 0.073445 Loss2: 0.169437
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.282904 Loss1: 0.109712 Loss2: 0.173192
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.272903 Loss1: 0.099443 Loss2: 0.173460
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.303985 Loss1: 0.128255 Loss2: 0.175730
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.277318 Loss1: 0.103778 Loss2: 0.173540
-(DefaultActor pid=1838052) >> Training accuracy: 0.980469
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.134136 Loss1: 0.102246 Loss2: 0.031890
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.076494 Loss1: 0.043251 Loss2: 0.033243
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066454 Loss1: 0.033756 Loss2: 0.032698
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.061142 Loss1: 0.028699 Loss2: 0.032443
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.071996 Loss1: 0.039062 Loss2: 0.032934
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.067057 Loss1: 0.033923 Loss2: 0.033133
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054223 Loss1: 0.021773 Loss2: 0.032450
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065887 Loss1: 0.033343 Loss2: 0.032544
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070118 Loss1: 0.037068 Loss2: 0.033050
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.071432 Loss1: 0.038453 Loss2: 0.032980
-(DefaultActor pid=1838052) >> Training accuracy: 0.994858
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.164402 Loss1: 0.132291 Loss2: 0.032112
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.113969 Loss1: 0.078976 Loss2: 0.034993
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.124206 Loss1: 0.088964 Loss2: 0.035241
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107451 Loss1: 0.071861 Loss2: 0.035590
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.098842 Loss1: 0.063456 Loss2: 0.035387
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.092259 Loss1: 0.057136 Loss2: 0.035124
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.089031 Loss1: 0.053505 Loss2: 0.035526
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082746 Loss1: 0.047009 Loss2: 0.035737
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101844 Loss1: 0.066477 Loss2: 0.035367
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098690 Loss1: 0.063002 Loss2: 0.035688
-(DefaultActor pid=1838052) >> Training accuracy: 0.989020
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.627562 Loss1: 0.120537 Loss2: 0.507025
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.602393 Loss1: 0.114329 Loss2: 0.488063
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.575494 Loss1: 0.097201 Loss2: 0.478293
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.590531 Loss1: 0.112548 Loss2: 0.477983
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.561345 Loss1: 0.094632 Loss2: 0.466713
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.557713 Loss1: 0.090582 Loss2: 0.467130
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.557728 Loss1: 0.093899 Loss2: 0.463829
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.571193 Loss1: 0.106586 Loss2: 0.464607
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.572074 Loss1: 0.107562 Loss2: 0.464511
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.534868 Loss1: 0.076133 Loss2: 0.458736
-(DefaultActor pid=1838052) >> Training accuracy: 0.981408
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.147709 Loss1: 0.082032 Loss2: 0.065677
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.111522 Loss1: 0.046316 Loss2: 0.065206
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.106664 Loss1: 0.043666 Loss2: 0.062998
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.102567 Loss1: 0.040967 Loss2: 0.061600
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.095852 Loss1: 0.036184 Loss2: 0.059668
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.111061 Loss1: 0.050907 Loss2: 0.060154
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.099343 Loss1: 0.041525 Loss2: 0.057818
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.118753 Loss1: 0.059314 Loss2: 0.059439
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.124178 Loss1: 0.064518 Loss2: 0.059660
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.114729 Loss1: 0.055522 Loss2: 0.059208
-(DefaultActor pid=1838052) >> Training accuracy: 0.986090
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.164241 Loss1: 0.096008 Loss2: 0.068233
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.114148 Loss1: 0.050413 Loss2: 0.063735
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.099730 Loss1: 0.036961 Loss2: 0.062769
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.104169 Loss1: 0.041946 Loss2: 0.062223
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.111008 Loss1: 0.048984 Loss2: 0.062024
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.121584 Loss1: 0.058231 Loss2: 0.063354
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.119534 Loss1: 0.055854 Loss2: 0.063680
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.130246 Loss1: 0.066042 Loss2: 0.064204
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120709 Loss1: 0.055979 Loss2: 0.064730
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.117142 Loss1: 0.053659 Loss2: 0.063482
-(DefaultActor pid=1838052) >> Training accuracy: 0.985403
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 15:09:53,062][flwr][DEBUG] - fit_round 63 received 10 results and 0 failures
->> Test accuracy: 0.653700
-[2023-09-28 15:10:32,662][flwr][INFO] - fit progress: (63, 2.2481949498859075, {'accuracy': 0.6537}, 118255.55269732606)
-[2023-09-28 15:10:32,663][flwr][DEBUG] - evaluate_round 63: strategy sampled 10 clients (out of 10)
-[2023-09-28 15:11:08,888][flwr][DEBUG] - evaluate_round 63 received 10 results and 0 failures
-[2023-09-28 15:11:08,889][flwr][DEBUG] - fit_round 64: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.709447 Loss1: 0.117464 Loss2: 0.591983
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.700546 Loss1: 0.113869 Loss2: 0.586677
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.669588 Loss1: 0.094780 Loss2: 0.574808
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.667779 Loss1: 0.102425 Loss2: 0.565354
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.678560 Loss1: 0.114023 Loss2: 0.564537
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.655814 Loss1: 0.095015 Loss2: 0.560800
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.635753 Loss1: 0.080159 Loss2: 0.555593
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.647856 Loss1: 0.093199 Loss2: 0.554657
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.638380 Loss1: 0.087793 Loss2: 0.550587
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.640686 Loss1: 0.094307 Loss2: 0.546379
-(DefaultActor pid=1838052) >> Training accuracy: 0.984976
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.667078 Loss1: 0.101403 Loss2: 0.565675
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.626178 Loss1: 0.073986 Loss2: 0.552192
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.634152 Loss1: 0.088128 Loss2: 0.546024
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.637685 Loss1: 0.092445 Loss2: 0.545240
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.631804 Loss1: 0.091105 Loss2: 0.540699
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.647677 Loss1: 0.106263 Loss2: 0.541413
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.609943 Loss1: 0.075696 Loss2: 0.534247
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.632207 Loss1: 0.098894 Loss2: 0.533314
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.633270 Loss1: 0.101398 Loss2: 0.531872
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.605310 Loss1: 0.076961 Loss2: 0.528349
-(DefaultActor pid=1838052) >> Training accuracy: 0.979167
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.529243 Loss1: 0.129694 Loss2: 0.399548
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.428318 Loss1: 0.098185 Loss2: 0.330133
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.413530 Loss1: 0.109906 Loss2: 0.303624
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.381099 Loss1: 0.087622 Loss2: 0.293477
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.378831 Loss1: 0.093522 Loss2: 0.285309
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.382816 Loss1: 0.097516 Loss2: 0.285299
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.379751 Loss1: 0.095819 Loss2: 0.283931
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.362102 Loss1: 0.081709 Loss2: 0.280394
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.378220 Loss1: 0.097966 Loss2: 0.280254
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.350933 Loss1: 0.072013 Loss2: 0.278921
-(DefaultActor pid=1838052) >> Training accuracy: 0.979167
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136386 Loss1: 0.106803 Loss2: 0.029583
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.094912 Loss1: 0.063571 Loss2: 0.031341
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.097730 Loss1: 0.065869 Loss2: 0.031861
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081732 Loss1: 0.050042 Loss2: 0.031689
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.079337 Loss1: 0.047981 Loss2: 0.031356
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079755 Loss1: 0.048417 Loss2: 0.031338
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.091367 Loss1: 0.059522 Loss2: 0.031845
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082365 Loss1: 0.050636 Loss2: 0.031729
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.095742 Loss1: 0.063782 Loss2: 0.031960
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099242 Loss1: 0.066931 Loss2: 0.032312
-(DefaultActor pid=1838052) >> Training accuracy: 0.981804
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.452683 Loss1: 0.157062 Loss2: 0.295622
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.397059 Loss1: 0.131810 Loss2: 0.265249
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.371570 Loss1: 0.116883 Loss2: 0.254687
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.391486 Loss1: 0.131299 Loss2: 0.260187
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.363112 Loss1: 0.113277 Loss2: 0.249835
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.386170 Loss1: 0.135112 Loss2: 0.251058
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.422380 Loss1: 0.166239 Loss2: 0.256142
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.377539 Loss1: 0.126742 Loss2: 0.250797
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.380907 Loss1: 0.130742 Loss2: 0.250165
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.400473 Loss1: 0.144700 Loss2: 0.255773
-(DefaultActor pid=1838052) >> Training accuracy: 0.980674
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.159482 Loss1: 0.105032 Loss2: 0.054450
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104208 Loss1: 0.052461 Loss2: 0.051747
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.110832 Loss1: 0.060238 Loss2: 0.050594
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107088 Loss1: 0.057550 Loss2: 0.049538
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.108731 Loss1: 0.060302 Loss2: 0.048430
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.107856 Loss1: 0.058842 Loss2: 0.049014
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.098192 Loss1: 0.050878 Loss2: 0.047314
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.113721 Loss1: 0.066585 Loss2: 0.047135
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.140807 Loss1: 0.092782 Loss2: 0.048025
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.152239 Loss1: 0.102381 Loss2: 0.049859
-(DefaultActor pid=1838052) >> Training accuracy: 0.982199
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.137212 Loss1: 0.106113 Loss2: 0.031100
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.102759 Loss1: 0.070171 Loss2: 0.032587
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.091058 Loss1: 0.058572 Loss2: 0.032486
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081038 Loss1: 0.048837 Loss2: 0.032202
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077755 Loss1: 0.045986 Loss2: 0.031769
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070167 Loss1: 0.038518 Loss2: 0.031649
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075124 Loss1: 0.044088 Loss2: 0.031036
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100403 Loss1: 0.068287 Loss2: 0.032116
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.084927 Loss1: 0.052594 Loss2: 0.032334
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.075823 Loss1: 0.043956 Loss2: 0.031867
-(DefaultActor pid=1838052) >> Training accuracy: 0.993275
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136263 Loss1: 0.106650 Loss2: 0.029612
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.081998 Loss1: 0.050344 Loss2: 0.031654
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067237 Loss1: 0.035793 Loss2: 0.031444
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.056976 Loss1: 0.026124 Loss2: 0.030852
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065236 Loss1: 0.033943 Loss2: 0.031292
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.062663 Loss1: 0.031222 Loss2: 0.031441
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.066351 Loss1: 0.034693 Loss2: 0.031658
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.093339 Loss1: 0.061047 Loss2: 0.032292
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096674 Loss1: 0.063522 Loss2: 0.033153
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.088109 Loss1: 0.054559 Loss2: 0.033550
-(DefaultActor pid=1838052) >> Training accuracy: 0.991495
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.619337 Loss1: 0.128296 Loss2: 0.491041
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.571407 Loss1: 0.102047 Loss2: 0.469360
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.594900 Loss1: 0.128071 Loss2: 0.466830
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.590155 Loss1: 0.127119 Loss2: 0.463036
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.579132 Loss1: 0.126071 Loss2: 0.453061
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.572402 Loss1: 0.117336 Loss2: 0.455066
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.573663 Loss1: 0.121514 Loss2: 0.452149
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.580977 Loss1: 0.127453 Loss2: 0.453524
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.528622 Loss1: 0.087810 Loss2: 0.440812
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.537969 Loss1: 0.096271 Loss2: 0.441698
-(DefaultActor pid=1838052) >> Training accuracy: 0.983041
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.153823 Loss1: 0.121816 Loss2: 0.032007
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.101274 Loss1: 0.067743 Loss2: 0.033530
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.103263 Loss1: 0.069307 Loss2: 0.033956
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.101900 Loss1: 0.066966 Loss2: 0.034934
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.109041 Loss1: 0.073829 Loss2: 0.035212
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.097528 Loss1: 0.062335 Loss2: 0.035193
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.091047 Loss1: 0.056116 Loss2: 0.034931
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.096329 Loss1: 0.061312 Loss2: 0.035017
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.105145 Loss1: 0.069420 Loss2: 0.035725
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.081426 Loss1: 0.046235 Loss2: 0.035191
-(DefaultActor pid=1838052) >> Training accuracy: 0.990921
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 15:40:08,743][flwr][DEBUG] - fit_round 64 received 10 results and 0 failures
->> Test accuracy: 0.654500
-[2023-09-28 15:40:48,740][flwr][INFO] - fit progress: (64, 2.2214675100085834, {'accuracy': 0.6545}, 120071.6302740383)
-[2023-09-28 15:40:48,740][flwr][DEBUG] - evaluate_round 64: strategy sampled 10 clients (out of 10)
-[2023-09-28 15:41:25,463][flwr][DEBUG] - evaluate_round 64 received 10 results and 0 failures
-[2023-09-28 15:41:25,464][flwr][DEBUG] - fit_round 65: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.491537 Loss1: 0.126625 Loss2: 0.364912
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.372112 Loss1: 0.054484 Loss2: 0.317628
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.372575 Loss1: 0.061709 Loss2: 0.310866
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.361990 Loss1: 0.053323 Loss2: 0.308667
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.350470 Loss1: 0.043432 Loss2: 0.307038
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.374422 Loss1: 0.067900 Loss2: 0.306522
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.362035 Loss1: 0.055712 Loss2: 0.306322
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.372731 Loss1: 0.065435 Loss2: 0.307296
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.400838 Loss1: 0.092472 Loss2: 0.308366
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.376981 Loss1: 0.068482 Loss2: 0.308499
-(DefaultActor pid=1838052) >> Training accuracy: 0.986842
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.151660 Loss1: 0.109606 Loss2: 0.042054
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.088826 Loss1: 0.047267 Loss2: 0.041559
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.079629 Loss1: 0.038916 Loss2: 0.040713
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.090216 Loss1: 0.049377 Loss2: 0.040839
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.083707 Loss1: 0.043184 Loss2: 0.040522
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.082246 Loss1: 0.041757 Loss2: 0.040489
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.074914 Loss1: 0.035647 Loss2: 0.039267
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.084049 Loss1: 0.044045 Loss2: 0.040004
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.100116 Loss1: 0.059804 Loss2: 0.040312
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.121081 Loss1: 0.079039 Loss2: 0.042042
-(DefaultActor pid=1838052) >> Training accuracy: 0.987847
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.187377 Loss1: 0.096938 Loss2: 0.090439
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.135771 Loss1: 0.052621 Loss2: 0.083150
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.119219 Loss1: 0.038587 Loss2: 0.080631
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.108355 Loss1: 0.030315 Loss2: 0.078041
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.117892 Loss1: 0.040161 Loss2: 0.077731
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.120905 Loss1: 0.043725 Loss2: 0.077180
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122229 Loss1: 0.044903 Loss2: 0.077326
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.119012 Loss1: 0.041008 Loss2: 0.078005
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.115447 Loss1: 0.037895 Loss2: 0.077552
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118607 Loss1: 0.041810 Loss2: 0.076797
-(DefaultActor pid=1838052) >> Training accuracy: 0.994591
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.212659 Loss1: 0.135288 Loss2: 0.077372
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.154261 Loss1: 0.079429 Loss2: 0.074832
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.148654 Loss1: 0.079654 Loss2: 0.069001
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.146168 Loss1: 0.079334 Loss2: 0.066834
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.132632 Loss1: 0.066900 Loss2: 0.065733
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.117874 Loss1: 0.054276 Loss2: 0.063598
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.122375 Loss1: 0.059881 Loss2: 0.062494
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.126805 Loss1: 0.063952 Loss2: 0.062852
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.107757 Loss1: 0.046632 Loss2: 0.061125
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098670 Loss1: 0.039258 Loss2: 0.059412
-(DefaultActor pid=1838052) >> Training accuracy: 0.993877
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.139913 Loss1: 0.108288 Loss2: 0.031625
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091350 Loss1: 0.058937 Loss2: 0.032413
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077688 Loss1: 0.045703 Loss2: 0.031985
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081254 Loss1: 0.049446 Loss2: 0.031808
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075019 Loss1: 0.043233 Loss2: 0.031787
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.095895 Loss1: 0.063940 Loss2: 0.031955
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.099040 Loss1: 0.066343 Loss2: 0.032697
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.096442 Loss1: 0.064011 Loss2: 0.032431
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.074916 Loss1: 0.042771 Loss2: 0.032144
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.088136 Loss1: 0.055745 Loss2: 0.032392
-(DefaultActor pid=1838052) >> Training accuracy: 0.985377
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.121626 Loss1: 0.090768 Loss2: 0.030858
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091318 Loss1: 0.058993 Loss2: 0.032325
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.086679 Loss1: 0.054610 Loss2: 0.032069
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.092199 Loss1: 0.059554 Loss2: 0.032645
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.081023 Loss1: 0.048253 Loss2: 0.032769
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108664 Loss1: 0.074908 Loss2: 0.033756
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.110886 Loss1: 0.076990 Loss2: 0.033896
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.089261 Loss1: 0.055479 Loss2: 0.033782
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.093229 Loss1: 0.060193 Loss2: 0.033036
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093428 Loss1: 0.060370 Loss2: 0.033058
-(DefaultActor pid=1838052) >> Training accuracy: 0.993078
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.103266 Loss1: 0.073756 Loss2: 0.029511
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.088320 Loss1: 0.057327 Loss2: 0.030994
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.097814 Loss1: 0.065752 Loss2: 0.032062
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.089636 Loss1: 0.057344 Loss2: 0.032293
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.098269 Loss1: 0.066040 Loss2: 0.032229
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.093992 Loss1: 0.061347 Loss2: 0.032645
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.082080 Loss1: 0.049326 Loss2: 0.032754
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075449 Loss1: 0.043535 Loss2: 0.031914
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081920 Loss1: 0.049976 Loss2: 0.031944
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.071128 Loss1: 0.039024 Loss2: 0.032103
-(DefaultActor pid=1838052) >> Training accuracy: 0.994066
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.133891 Loss1: 0.103975 Loss2: 0.029917
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.087301 Loss1: 0.056390 Loss2: 0.030911
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.072157 Loss1: 0.041367 Loss2: 0.030791
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.069328 Loss1: 0.038902 Loss2: 0.030426
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.087862 Loss1: 0.056112 Loss2: 0.031751
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.108097 Loss1: 0.075744 Loss2: 0.032352
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.109433 Loss1: 0.076456 Loss2: 0.032977
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.102952 Loss1: 0.070218 Loss2: 0.032735
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.099263 Loss1: 0.066228 Loss2: 0.033035
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.069053 Loss1: 0.036648 Loss2: 0.032405
-(DefaultActor pid=1838052) >> Training accuracy: 0.994066
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.479039 Loss1: 0.087113 Loss2: 0.391926
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.388362 Loss1: 0.056758 Loss2: 0.331604
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.412242 Loss1: 0.095255 Loss2: 0.316987
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.394112 Loss1: 0.084785 Loss2: 0.309327
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.379982 Loss1: 0.078258 Loss2: 0.301725
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.349858 Loss1: 0.053476 Loss2: 0.296383
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.357286 Loss1: 0.064589 Loss2: 0.292697
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.372418 Loss1: 0.077408 Loss2: 0.295009
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.410878 Loss1: 0.114548 Loss2: 0.296330
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.406804 Loss1: 0.109321 Loss2: 0.297483
-(DefaultActor pid=1838052) >> Training accuracy: 0.978659
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.122206 Loss1: 0.093049 Loss2: 0.029157
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.089760 Loss1: 0.058474 Loss2: 0.031286
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.076903 Loss1: 0.045289 Loss2: 0.031614
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068789 Loss1: 0.037541 Loss2: 0.031247
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077564 Loss1: 0.045935 Loss2: 0.031629
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075252 Loss1: 0.043512 Loss2: 0.031739
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.085769 Loss1: 0.053803 Loss2: 0.031966
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100278 Loss1: 0.067014 Loss2: 0.033264
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085922 Loss1: 0.052749 Loss2: 0.033173
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099525 Loss1: 0.066353 Loss2: 0.033173
-(DefaultActor pid=1838052) >> Training accuracy: 0.988726
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 16:10:15,517][flwr][DEBUG] - fit_round 65 received 10 results and 0 failures
->> Test accuracy: 0.654300
-[2023-09-28 16:10:57,965][flwr][INFO] - fit progress: (65, 2.2834309385226557, {'accuracy': 0.6543}, 121880.8550542253)
-[2023-09-28 16:10:57,965][flwr][DEBUG] - evaluate_round 65: strategy sampled 10 clients (out of 10)
-[2023-09-28 16:11:34,611][flwr][DEBUG] - evaluate_round 65 received 10 results and 0 failures
-[2023-09-28 16:11:34,612][flwr][DEBUG] - fit_round 66: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.609281 Loss1: 0.107658 Loss2: 0.501623
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.538693 Loss1: 0.072233 Loss2: 0.466460
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.545741 Loss1: 0.092775 Loss2: 0.452965
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.518550 Loss1: 0.078608 Loss2: 0.439942
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.526302 Loss1: 0.087554 Loss2: 0.438748
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.576302 Loss1: 0.136522 Loss2: 0.439780
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.542465 Loss1: 0.108631 Loss2: 0.433834
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.511823 Loss1: 0.083085 Loss2: 0.428737
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.489802 Loss1: 0.067782 Loss2: 0.422020
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.502745 Loss1: 0.079575 Loss2: 0.423170
-(DefaultActor pid=1838052) >> Training accuracy: 0.977965
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.152642 Loss1: 0.120920 Loss2: 0.031722
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.102516 Loss1: 0.068371 Loss2: 0.034146
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.089198 Loss1: 0.055013 Loss2: 0.034185
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.072435 Loss1: 0.038270 Loss2: 0.034165
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.071446 Loss1: 0.037823 Loss2: 0.033623
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070744 Loss1: 0.037002 Loss2: 0.033742
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079586 Loss1: 0.045640 Loss2: 0.033946
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.087464 Loss1: 0.052952 Loss2: 0.034512
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.100517 Loss1: 0.065746 Loss2: 0.034771
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091235 Loss1: 0.055771 Loss2: 0.035464
-(DefaultActor pid=1838052) >> Training accuracy: 0.990234
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.542297 Loss1: 0.124145 Loss2: 0.418152
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.504217 Loss1: 0.099105 Loss2: 0.405112
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.493792 Loss1: 0.095024 Loss2: 0.398768
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.480129 Loss1: 0.088449 Loss2: 0.391679
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.486150 Loss1: 0.092624 Loss2: 0.393526
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.500265 Loss1: 0.107743 Loss2: 0.392523
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.520217 Loss1: 0.124111 Loss2: 0.396106
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.503341 Loss1: 0.109925 Loss2: 0.393416
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.488204 Loss1: 0.097629 Loss2: 0.390575
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.492204 Loss1: 0.100468 Loss2: 0.391736
-(DefaultActor pid=1838052) >> Training accuracy: 0.982991
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.599444 Loss1: 0.115652 Loss2: 0.483792
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.549445 Loss1: 0.079867 Loss2: 0.469577
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.567592 Loss1: 0.101848 Loss2: 0.465744
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.554085 Loss1: 0.091254 Loss2: 0.462832
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.544290 Loss1: 0.086435 Loss2: 0.457854
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.526856 Loss1: 0.072888 Loss2: 0.453968
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.526981 Loss1: 0.075638 Loss2: 0.451343
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.516399 Loss1: 0.066282 Loss2: 0.450117
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.531068 Loss1: 0.082826 Loss2: 0.448243
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.545823 Loss1: 0.097306 Loss2: 0.448517
-(DefaultActor pid=1838052) >> Training accuracy: 0.978046
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.554038 Loss1: 0.111325 Loss2: 0.442714
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.528141 Loss1: 0.099493 Loss2: 0.428648
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.503303 Loss1: 0.080711 Loss2: 0.422592
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.507569 Loss1: 0.090223 Loss2: 0.417346
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.522225 Loss1: 0.101806 Loss2: 0.420419
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.516935 Loss1: 0.098392 Loss2: 0.418543
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.532025 Loss1: 0.112503 Loss2: 0.419522
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.529500 Loss1: 0.112486 Loss2: 0.417014
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.508774 Loss1: 0.092825 Loss2: 0.415949
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.485572 Loss1: 0.075617 Loss2: 0.409955
-(DefaultActor pid=1838052) >> Training accuracy: 0.985759
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.115332 Loss1: 0.079195 Loss2: 0.036137
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.098775 Loss1: 0.060893 Loss2: 0.037882
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.096833 Loss1: 0.058222 Loss2: 0.038611
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.091760 Loss1: 0.052874 Loss2: 0.038886
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075082 Loss1: 0.036783 Loss2: 0.038299
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081381 Loss1: 0.042966 Loss2: 0.038415
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.084531 Loss1: 0.045965 Loss2: 0.038566
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.097721 Loss1: 0.058317 Loss2: 0.039404
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092131 Loss1: 0.052885 Loss2: 0.039246
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097073 Loss1: 0.058028 Loss2: 0.039046
-(DefaultActor pid=1838052) >> Training accuracy: 0.990473
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.584882 Loss1: 0.137811 Loss2: 0.447072
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.520853 Loss1: 0.093218 Loss2: 0.427636
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.524077 Loss1: 0.100078 Loss2: 0.423999
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.526856 Loss1: 0.103819 Loss2: 0.423037
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.555784 Loss1: 0.131692 Loss2: 0.424092
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.535973 Loss1: 0.114513 Loss2: 0.421460
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.517775 Loss1: 0.101583 Loss2: 0.416192
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.487519 Loss1: 0.074169 Loss2: 0.413350
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.480131 Loss1: 0.067780 Loss2: 0.412351
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.494106 Loss1: 0.083957 Loss2: 0.410149
-(DefaultActor pid=1838052) >> Training accuracy: 0.980569
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.171131 Loss1: 0.105740 Loss2: 0.065391
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.131542 Loss1: 0.068779 Loss2: 0.062763
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.104967 Loss1: 0.045708 Loss2: 0.059260
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.116102 Loss1: 0.058936 Loss2: 0.057166
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.107920 Loss1: 0.052086 Loss2: 0.055834
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.134106 Loss1: 0.078386 Loss2: 0.055721
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.137103 Loss1: 0.081651 Loss2: 0.055451
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.115307 Loss1: 0.059881 Loss2: 0.055426
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101605 Loss1: 0.048334 Loss2: 0.053272
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104002 Loss1: 0.051168 Loss2: 0.052834
-(DefaultActor pid=1838052) >> Training accuracy: 0.985197
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.153199 Loss1: 0.119641 Loss2: 0.033558
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.093894 Loss1: 0.058032 Loss2: 0.035863
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.099825 Loss1: 0.064054 Loss2: 0.035770
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.082274 Loss1: 0.046183 Loss2: 0.036091
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086267 Loss1: 0.050589 Loss2: 0.035678
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079816 Loss1: 0.044382 Loss2: 0.035434
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.107897 Loss1: 0.071430 Loss2: 0.036467
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.144813 Loss1: 0.106495 Loss2: 0.038318
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.114678 Loss1: 0.076077 Loss2: 0.038601
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093660 Loss1: 0.055937 Loss2: 0.037724
-(DefaultActor pid=1838052) >> Training accuracy: 0.986698
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.566553 Loss1: 0.110588 Loss2: 0.455965
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.521766 Loss1: 0.079565 Loss2: 0.442200
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.502349 Loss1: 0.067125 Loss2: 0.435224
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.509342 Loss1: 0.075454 Loss2: 0.433889
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.501762 Loss1: 0.071327 Loss2: 0.430435
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.510315 Loss1: 0.078857 Loss2: 0.431457
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.551352 Loss1: 0.114932 Loss2: 0.436420
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.536451 Loss1: 0.103768 Loss2: 0.432683
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.555214 Loss1: 0.122310 Loss2: 0.432904
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.540710 Loss1: 0.108278 Loss2: 0.432432
-(DefaultActor pid=1838052) >> Training accuracy: 0.978046
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 16:40:25,647][flwr][DEBUG] - fit_round 66 received 10 results and 0 failures
->> Test accuracy: 0.656800
-[2023-09-28 16:41:04,576][flwr][INFO] - fit progress: (66, 2.208361754592615, {'accuracy': 0.6568}, 123687.46669059945)
-[2023-09-28 16:41:04,577][flwr][DEBUG] - evaluate_round 66: strategy sampled 10 clients (out of 10)
-[2023-09-28 16:41:41,097][flwr][DEBUG] - evaluate_round 66 received 10 results and 0 failures
-[2023-09-28 16:41:41,099][flwr][DEBUG] - fit_round 67: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.144103 Loss1: 0.106281 Loss2: 0.037822
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104395 Loss1: 0.064562 Loss2: 0.039834
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.102272 Loss1: 0.062049 Loss2: 0.040223
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081783 Loss1: 0.041702 Loss2: 0.040081
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.078866 Loss1: 0.038919 Loss2: 0.039947
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077160 Loss1: 0.037605 Loss2: 0.039555
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063891 Loss1: 0.024743 Loss2: 0.039147
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.074213 Loss1: 0.034938 Loss2: 0.039275
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085233 Loss1: 0.045624 Loss2: 0.039609
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099049 Loss1: 0.058675 Loss2: 0.040374
-(DefaultActor pid=1838052) >> Training accuracy: 0.990111
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.486114 Loss1: 0.149260 Loss2: 0.336853
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.429218 Loss1: 0.109031 Loss2: 0.320187
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.455352 Loss1: 0.131107 Loss2: 0.324245
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.432401 Loss1: 0.116793 Loss2: 0.315608
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.417203 Loss1: 0.108137 Loss2: 0.309066
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.432747 Loss1: 0.114039 Loss2: 0.318708
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.418835 Loss1: 0.106756 Loss2: 0.312079
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.424477 Loss1: 0.113405 Loss2: 0.311072
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.408894 Loss1: 0.098593 Loss2: 0.310301
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.390443 Loss1: 0.083911 Loss2: 0.306533
-(DefaultActor pid=1838052) >> Training accuracy: 0.979730
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.138999 Loss1: 0.099703 Loss2: 0.039296
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.112015 Loss1: 0.070446 Loss2: 0.041569
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082773 Loss1: 0.042034 Loss2: 0.040738
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085183 Loss1: 0.044321 Loss2: 0.040861
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101536 Loss1: 0.059687 Loss2: 0.041849
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.114248 Loss1: 0.071266 Loss2: 0.042982
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.103870 Loss1: 0.060731 Loss2: 0.043139
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.097441 Loss1: 0.054053 Loss2: 0.043388
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.078714 Loss1: 0.036539 Loss2: 0.042176
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.085655 Loss1: 0.043220 Loss2: 0.042435
-(DefaultActor pid=1838052) >> Training accuracy: 0.993389
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.126707 Loss1: 0.094732 Loss2: 0.031976
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.085050 Loss1: 0.051374 Loss2: 0.033676
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.071016 Loss1: 0.037843 Loss2: 0.033173
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.071298 Loss1: 0.038377 Loss2: 0.032921
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073372 Loss1: 0.040295 Loss2: 0.033077
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081242 Loss1: 0.047616 Loss2: 0.033626
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073700 Loss1: 0.040236 Loss2: 0.033464
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.070871 Loss1: 0.037866 Loss2: 0.033006
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064488 Loss1: 0.031406 Loss2: 0.033081
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073481 Loss1: 0.039989 Loss2: 0.033492
-(DefaultActor pid=1838052) >> Training accuracy: 0.991693
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.118071 Loss1: 0.080274 Loss2: 0.037796
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.085923 Loss1: 0.046431 Loss2: 0.039492
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.074427 Loss1: 0.034963 Loss2: 0.039464
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063173 Loss1: 0.023729 Loss2: 0.039444
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061693 Loss1: 0.022926 Loss2: 0.038766
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.071602 Loss1: 0.032415 Loss2: 0.039187
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.091506 Loss1: 0.051264 Loss2: 0.040242
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.102013 Loss1: 0.060526 Loss2: 0.041488
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112318 Loss1: 0.070267 Loss2: 0.042051
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.096936 Loss1: 0.054655 Loss2: 0.042281
-(DefaultActor pid=1838052) >> Training accuracy: 0.988528
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.135533 Loss1: 0.084684 Loss2: 0.050850
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.099246 Loss1: 0.050337 Loss2: 0.048909
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082653 Loss1: 0.035241 Loss2: 0.047411
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.073040 Loss1: 0.027464 Loss2: 0.045577
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.081682 Loss1: 0.037308 Loss2: 0.044374
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081369 Loss1: 0.037264 Loss2: 0.044104
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.076467 Loss1: 0.032184 Loss2: 0.044283
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073048 Loss1: 0.029390 Loss2: 0.043658
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081225 Loss1: 0.037840 Loss2: 0.043385
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.085049 Loss1: 0.041449 Loss2: 0.043600
-(DefaultActor pid=1838052) >> Training accuracy: 0.990585
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.521051 Loss1: 0.083417 Loss2: 0.437634
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.478469 Loss1: 0.063468 Loss2: 0.415000
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.495580 Loss1: 0.078826 Loss2: 0.416753
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.500096 Loss1: 0.084482 Loss2: 0.415613
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.508261 Loss1: 0.089120 Loss2: 0.419141
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.507315 Loss1: 0.091103 Loss2: 0.416213
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.528410 Loss1: 0.109096 Loss2: 0.419313
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.548101 Loss1: 0.124962 Loss2: 0.423139
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.528286 Loss1: 0.107963 Loss2: 0.420322
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.521246 Loss1: 0.108122 Loss2: 0.413125
-(DefaultActor pid=1838052) >> Training accuracy: 0.981707
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.127234 Loss1: 0.090148 Loss2: 0.037086
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079451 Loss1: 0.040716 Loss2: 0.038735
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.081414 Loss1: 0.042571 Loss2: 0.038844
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.077269 Loss1: 0.038176 Loss2: 0.039093
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060321 Loss1: 0.021716 Loss2: 0.038606
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060387 Loss1: 0.021888 Loss2: 0.038498
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.066474 Loss1: 0.027726 Loss2: 0.038748
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067434 Loss1: 0.028901 Loss2: 0.038533
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068671 Loss1: 0.030271 Loss2: 0.038401
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080309 Loss1: 0.040639 Loss2: 0.039671
-(DefaultActor pid=1838052) >> Training accuracy: 0.991536
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.368974 Loss1: 0.132962 Loss2: 0.236012
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.320482 Loss1: 0.093755 Loss2: 0.226727
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.339613 Loss1: 0.112773 Loss2: 0.226840
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.342240 Loss1: 0.116645 Loss2: 0.225594
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.370397 Loss1: 0.141207 Loss2: 0.229189
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.355442 Loss1: 0.129229 Loss2: 0.226213
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.328436 Loss1: 0.107103 Loss2: 0.221334
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.321069 Loss1: 0.101032 Loss2: 0.220037
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.303634 Loss1: 0.085497 Loss2: 0.218137
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.305157 Loss1: 0.087997 Loss2: 0.217160
-(DefaultActor pid=1838052) >> Training accuracy: 0.980469
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.109196 Loss1: 0.078425 Loss2: 0.030771
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.082973 Loss1: 0.050469 Loss2: 0.032504
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.080775 Loss1: 0.048016 Loss2: 0.032759
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074082 Loss1: 0.040551 Loss2: 0.033531
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.068635 Loss1: 0.035535 Loss2: 0.033100
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.071843 Loss1: 0.039049 Loss2: 0.032794
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072889 Loss1: 0.039905 Loss2: 0.032983
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.077716 Loss1: 0.044580 Loss2: 0.033136
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077618 Loss1: 0.044799 Loss2: 0.032819
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.099708 Loss1: 0.065559 Loss2: 0.034149
-(DefaultActor pid=1838052) >> Training accuracy: 0.989122
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 17:10:51,169][flwr][DEBUG] - fit_round 67 received 10 results and 0 failures
->> Test accuracy: 0.656200
-[2023-09-28 17:11:30,350][flwr][INFO] - fit progress: (67, 2.293970344736934, {'accuracy': 0.6562}, 125513.24056398915)
-[2023-09-28 17:11:30,351][flwr][DEBUG] - evaluate_round 67: strategy sampled 10 clients (out of 10)
-[2023-09-28 17:12:07,618][flwr][DEBUG] - evaluate_round 67 received 10 results and 0 failures
-[2023-09-28 17:12:07,620][flwr][DEBUG] - fit_round 68: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.457432 Loss1: 0.110428 Loss2: 0.347004
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.386061 Loss1: 0.071660 Loss2: 0.314401
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.351715 Loss1: 0.043828 Loss2: 0.307887
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.367665 Loss1: 0.063756 Loss2: 0.303909
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.364930 Loss1: 0.061683 Loss2: 0.303247
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.353188 Loss1: 0.052864 Loss2: 0.300324
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.366536 Loss1: 0.065584 Loss2: 0.300952
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.378014 Loss1: 0.076026 Loss2: 0.301988
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.376218 Loss1: 0.073473 Loss2: 0.302745
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.376597 Loss1: 0.075606 Loss2: 0.300991
-(DefaultActor pid=1838052) >> Training accuracy: 0.988076
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.107460 Loss1: 0.068304 Loss2: 0.039155
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.099088 Loss1: 0.057914 Loss2: 0.041174
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.084251 Loss1: 0.042893 Loss2: 0.041359
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080260 Loss1: 0.039042 Loss2: 0.041219
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077608 Loss1: 0.036372 Loss2: 0.041236
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075996 Loss1: 0.034347 Loss2: 0.041649
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.087761 Loss1: 0.045648 Loss2: 0.042113
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091441 Loss1: 0.048766 Loss2: 0.042675
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.089484 Loss1: 0.046708 Loss2: 0.042776
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116126 Loss1: 0.072445 Loss2: 0.043681
-(DefaultActor pid=1838052) >> Training accuracy: 0.987424
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.543858 Loss1: 0.127631 Loss2: 0.416227
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.532805 Loss1: 0.124001 Loss2: 0.408804
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.554013 Loss1: 0.143662 Loss2: 0.410351
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.540712 Loss1: 0.136215 Loss2: 0.404496
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.565044 Loss1: 0.159601 Loss2: 0.405443
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.540314 Loss1: 0.139713 Loss2: 0.400602
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.529025 Loss1: 0.128707 Loss2: 0.400318
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.498909 Loss1: 0.106733 Loss2: 0.392176
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.490284 Loss1: 0.099951 Loss2: 0.390333
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.522316 Loss1: 0.131414 Loss2: 0.390902
-(DefaultActor pid=1838052) >> Training accuracy: 0.973758
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.541812 Loss1: 0.140365 Loss2: 0.401448
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.518432 Loss1: 0.129041 Loss2: 0.389391
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.510413 Loss1: 0.123586 Loss2: 0.386827
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.526939 Loss1: 0.143590 Loss2: 0.383349
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.571913 Loss1: 0.187322 Loss2: 0.384591
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.517304 Loss1: 0.140078 Loss2: 0.377225
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.510538 Loss1: 0.136298 Loss2: 0.374240
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.502059 Loss1: 0.130077 Loss2: 0.371982
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.487606 Loss1: 0.119035 Loss2: 0.368571
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.437129 Loss1: 0.081657 Loss2: 0.355472
-(DefaultActor pid=1838052) >> Training accuracy: 0.986545
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.143564 Loss1: 0.112425 Loss2: 0.031139
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072578 Loss1: 0.041043 Loss2: 0.031535
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.078750 Loss1: 0.047127 Loss2: 0.031623
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.070137 Loss1: 0.038909 Loss2: 0.031228
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073456 Loss1: 0.041948 Loss2: 0.031508
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.067097 Loss1: 0.035592 Loss2: 0.031505
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.074530 Loss1: 0.043047 Loss2: 0.031484
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.090661 Loss1: 0.058220 Loss2: 0.032441
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.083866 Loss1: 0.051569 Loss2: 0.032297
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.122232 Loss1: 0.088880 Loss2: 0.033352
-(DefaultActor pid=1838052) >> Training accuracy: 0.985853
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.128777 Loss1: 0.094100 Loss2: 0.034677
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.084636 Loss1: 0.048400 Loss2: 0.036236
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.092204 Loss1: 0.055404 Loss2: 0.036799
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080585 Loss1: 0.043752 Loss2: 0.036833
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072141 Loss1: 0.035895 Loss2: 0.036246
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.074976 Loss1: 0.038772 Loss2: 0.036203
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.083089 Loss1: 0.046199 Loss2: 0.036890
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.103295 Loss1: 0.065526 Loss2: 0.037768
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.118002 Loss1: 0.079327 Loss2: 0.038675
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104051 Loss1: 0.065059 Loss2: 0.038993
-(DefaultActor pid=1838052) >> Training accuracy: 0.985957
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.127244 Loss1: 0.082039 Loss2: 0.045205
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091223 Loss1: 0.045755 Loss2: 0.045468
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.108544 Loss1: 0.063350 Loss2: 0.045194
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.099470 Loss1: 0.055049 Loss2: 0.044421
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101836 Loss1: 0.057714 Loss2: 0.044122
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.099944 Loss1: 0.055841 Loss2: 0.044103
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101323 Loss1: 0.058543 Loss2: 0.042780
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.135291 Loss1: 0.090915 Loss2: 0.044375
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092068 Loss1: 0.049571 Loss2: 0.042497
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.094039 Loss1: 0.052210 Loss2: 0.041829
-(DefaultActor pid=1838052) >> Training accuracy: 0.982002
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.658077 Loss1: 0.114001 Loss2: 0.544077
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.567152 Loss1: 0.077684 Loss2: 0.489468
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.535023 Loss1: 0.075271 Loss2: 0.459751
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.515785 Loss1: 0.072036 Loss2: 0.443749
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.547286 Loss1: 0.106127 Loss2: 0.441159
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.555352 Loss1: 0.114117 Loss2: 0.441235
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.541236 Loss1: 0.108045 Loss2: 0.433192
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.537121 Loss1: 0.102847 Loss2: 0.434274
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.519419 Loss1: 0.091093 Loss2: 0.428326
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.533166 Loss1: 0.107272 Loss2: 0.425894
-(DefaultActor pid=1838052) >> Training accuracy: 0.976562
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.665524 Loss1: 0.109146 Loss2: 0.556378
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.626066 Loss1: 0.078824 Loss2: 0.547242
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.626696 Loss1: 0.083973 Loss2: 0.542723
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.604763 Loss1: 0.066696 Loss2: 0.538067
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.597665 Loss1: 0.066208 Loss2: 0.531457
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.632417 Loss1: 0.096289 Loss2: 0.536128
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.619979 Loss1: 0.089515 Loss2: 0.530463
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.618692 Loss1: 0.086908 Loss2: 0.531784
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.600653 Loss1: 0.074261 Loss2: 0.526392
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.616513 Loss1: 0.088922 Loss2: 0.527590
-(DefaultActor pid=1838052) >> Training accuracy: 0.978639
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.130401 Loss1: 0.084558 Loss2: 0.045843
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.089246 Loss1: 0.043187 Loss2: 0.046059
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.083449 Loss1: 0.038977 Loss2: 0.044472
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.094212 Loss1: 0.049954 Loss2: 0.044257
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.088600 Loss1: 0.044362 Loss2: 0.044238
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.100342 Loss1: 0.056934 Loss2: 0.043408
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.105788 Loss1: 0.062292 Loss2: 0.043497
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.119396 Loss1: 0.075104 Loss2: 0.044292
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.104034 Loss1: 0.060377 Loss2: 0.043657
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.108598 Loss1: 0.065555 Loss2: 0.043043
-(DefaultActor pid=1838052) >> Training accuracy: 0.983188
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 17:41:11,575][flwr][DEBUG] - fit_round 68 received 10 results and 0 failures
->> Test accuracy: 0.656100
-[2023-09-28 17:44:23,129][flwr][INFO] - fit progress: (68, 2.207248735922975, {'accuracy': 0.6561}, 127486.01882739831)
-[2023-09-28 17:44:23,129][flwr][DEBUG] - evaluate_round 68: strategy sampled 10 clients (out of 10)
-[2023-09-28 17:45:00,558][flwr][DEBUG] - evaluate_round 68 received 10 results and 0 failures
-[2023-09-28 17:45:00,560][flwr][DEBUG] - fit_round 69: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.268587 Loss1: 0.121042 Loss2: 0.147545
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.219065 Loss1: 0.082442 Loss2: 0.136623
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.249787 Loss1: 0.108027 Loss2: 0.141760
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.247801 Loss1: 0.108066 Loss2: 0.139735
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.258516 Loss1: 0.118559 Loss2: 0.139956
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.238055 Loss1: 0.100527 Loss2: 0.137528
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.253793 Loss1: 0.116411 Loss2: 0.137382
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.268497 Loss1: 0.127226 Loss2: 0.141270
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.234380 Loss1: 0.097992 Loss2: 0.136388
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.225595 Loss1: 0.089466 Loss2: 0.136129
-(DefaultActor pid=1838052) >> Training accuracy: 0.975329
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.463904 Loss1: 0.082376 Loss2: 0.381528
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.411145 Loss1: 0.057931 Loss2: 0.353214
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.394757 Loss1: 0.048046 Loss2: 0.346711
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.405531 Loss1: 0.064897 Loss2: 0.340634
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.388763 Loss1: 0.050462 Loss2: 0.338301
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.374992 Loss1: 0.039926 Loss2: 0.335066
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.380532 Loss1: 0.047249 Loss2: 0.333283
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.380234 Loss1: 0.047932 Loss2: 0.332302
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.402401 Loss1: 0.068211 Loss2: 0.334190
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.400747 Loss1: 0.066767 Loss2: 0.333981
-(DefaultActor pid=1838052) >> Training accuracy: 0.981804
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.133629 Loss1: 0.100309 Loss2: 0.033320
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074403 Loss1: 0.040519 Loss2: 0.033884
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.076822 Loss1: 0.042507 Loss2: 0.034315
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080130 Loss1: 0.045457 Loss2: 0.034673
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069039 Loss1: 0.034484 Loss2: 0.034556
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.085547 Loss1: 0.050466 Loss2: 0.035081
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.094971 Loss1: 0.059310 Loss2: 0.035661
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.092185 Loss1: 0.056197 Loss2: 0.035988
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108536 Loss1: 0.072014 Loss2: 0.036522
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097218 Loss1: 0.060495 Loss2: 0.036723
-(DefaultActor pid=1838052) >> Training accuracy: 0.984968
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.627363 Loss1: 0.082523 Loss2: 0.544839
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.570130 Loss1: 0.054612 Loss2: 0.515518
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.541853 Loss1: 0.045596 Loss2: 0.496256
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.535243 Loss1: 0.047421 Loss2: 0.487822
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.543661 Loss1: 0.057734 Loss2: 0.485927
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.551119 Loss1: 0.067321 Loss2: 0.483798
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.540577 Loss1: 0.059918 Loss2: 0.480659
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.534073 Loss1: 0.055524 Loss2: 0.478550
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.533005 Loss1: 0.054591 Loss2: 0.478415
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.561753 Loss1: 0.082681 Loss2: 0.479072
-(DefaultActor pid=1838052) >> Training accuracy: 0.986946
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.718674 Loss1: 0.134736 Loss2: 0.583939
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.658627 Loss1: 0.088423 Loss2: 0.570205
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.635746 Loss1: 0.077747 Loss2: 0.558000
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.607413 Loss1: 0.060348 Loss2: 0.547065
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.605724 Loss1: 0.063200 Loss2: 0.542523
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.618902 Loss1: 0.079637 Loss2: 0.539266
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.625040 Loss1: 0.085806 Loss2: 0.539234
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627850 Loss1: 0.088755 Loss2: 0.539095
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.651810 Loss1: 0.112739 Loss2: 0.539071
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.613640 Loss1: 0.080258 Loss2: 0.533382
-(DefaultActor pid=1838052) >> Training accuracy: 0.984164
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.109721 Loss1: 0.077056 Loss2: 0.032665
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.081446 Loss1: 0.047094 Loss2: 0.034352
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082478 Loss1: 0.048274 Loss2: 0.034204
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068238 Loss1: 0.034323 Loss2: 0.033915
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.067689 Loss1: 0.034157 Loss2: 0.033531
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069160 Loss1: 0.035499 Loss2: 0.033661
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062251 Loss1: 0.028765 Loss2: 0.033486
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.062480 Loss1: 0.029419 Loss2: 0.033061
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064992 Loss1: 0.031639 Loss2: 0.033354
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073475 Loss1: 0.039927 Loss2: 0.033548
-(DefaultActor pid=1838052) >> Training accuracy: 0.980769
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.641556 Loss1: 0.091717 Loss2: 0.549839
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.592728 Loss1: 0.068669 Loss2: 0.524058
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.593242 Loss1: 0.085646 Loss2: 0.507596
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.560708 Loss1: 0.061365 Loss2: 0.499343
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.574020 Loss1: 0.079876 Loss2: 0.494144
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.573035 Loss1: 0.077043 Loss2: 0.495993
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.552019 Loss1: 0.062292 Loss2: 0.489727
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.536126 Loss1: 0.048702 Loss2: 0.487423
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.526953 Loss1: 0.043507 Loss2: 0.483446
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.513671 Loss1: 0.034382 Loss2: 0.479289
-(DefaultActor pid=1838052) >> Training accuracy: 0.995847
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.114460 Loss1: 0.081604 Loss2: 0.032856
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.093827 Loss1: 0.058701 Loss2: 0.035125
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.089313 Loss1: 0.053614 Loss2: 0.035699
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.071600 Loss1: 0.035977 Loss2: 0.035623
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.071175 Loss1: 0.036010 Loss2: 0.035165
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.068353 Loss1: 0.032871 Loss2: 0.035482
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072887 Loss1: 0.037550 Loss2: 0.035336
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061738 Loss1: 0.026748 Loss2: 0.034990
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.074849 Loss1: 0.039232 Loss2: 0.035616
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070607 Loss1: 0.034720 Loss2: 0.035887
-(DefaultActor pid=1838052) >> Training accuracy: 0.995236
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.133089 Loss1: 0.091298 Loss2: 0.041791
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.083437 Loss1: 0.041342 Loss2: 0.042095
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.087703 Loss1: 0.046627 Loss2: 0.041076
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.101200 Loss1: 0.060254 Loss2: 0.040947
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.097899 Loss1: 0.057231 Loss2: 0.040668
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.082610 Loss1: 0.042676 Loss2: 0.039934
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.086775 Loss1: 0.047452 Loss2: 0.039323
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.109761 Loss1: 0.069125 Loss2: 0.040637
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081347 Loss1: 0.041631 Loss2: 0.039716
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104671 Loss1: 0.065025 Loss2: 0.039646
-(DefaultActor pid=1838052) >> Training accuracy: 0.989383
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.159791 Loss1: 0.095969 Loss2: 0.063822
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.106470 Loss1: 0.044681 Loss2: 0.061789
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.099718 Loss1: 0.039773 Loss2: 0.059945
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.086197 Loss1: 0.026949 Loss2: 0.059247
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.082037 Loss1: 0.023696 Loss2: 0.058341
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081657 Loss1: 0.024254 Loss2: 0.057403
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075088 Loss1: 0.017820 Loss2: 0.057268
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.078734 Loss1: 0.022237 Loss2: 0.056498
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.095230 Loss1: 0.038036 Loss2: 0.057194
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.109066 Loss1: 0.050702 Loss2: 0.058364
-(DefaultActor pid=1838052) >> Training accuracy: 0.990668
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 18:14:31,543][flwr][DEBUG] - fit_round 69 received 10 results and 0 failures
->> Test accuracy: 0.660300
-[2023-09-28 18:15:11,639][flwr][INFO] - fit progress: (69, 2.268605324026114, {'accuracy': 0.6603}, 129334.52930339333)
-[2023-09-28 18:15:11,639][flwr][DEBUG] - evaluate_round 69: strategy sampled 10 clients (out of 10)
-[2023-09-28 18:15:49,178][flwr][DEBUG] - evaluate_round 69 received 10 results and 0 failures
-[2023-09-28 18:15:49,179][flwr][DEBUG] - fit_round 70: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.646806 Loss1: 0.099552 Loss2: 0.547254
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.577458 Loss1: 0.078660 Loss2: 0.498799
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.554041 Loss1: 0.084670 Loss2: 0.469371
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.545974 Loss1: 0.093966 Loss2: 0.452008
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.539311 Loss1: 0.092881 Loss2: 0.446430
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.533393 Loss1: 0.098560 Loss2: 0.434833
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.539089 Loss1: 0.103285 Loss2: 0.435804
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.556081 Loss1: 0.123898 Loss2: 0.432182
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.579610 Loss1: 0.148077 Loss2: 0.431533
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.545674 Loss1: 0.117004 Loss2: 0.428671
-(DefaultActor pid=1838052) >> Training accuracy: 0.979167
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.343501 Loss1: 0.112835 Loss2: 0.230666
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.307415 Loss1: 0.094452 Loss2: 0.212963
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.297129 Loss1: 0.094881 Loss2: 0.202248
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.303820 Loss1: 0.105282 Loss2: 0.198538
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.288536 Loss1: 0.094006 Loss2: 0.194530
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.312870 Loss1: 0.117492 Loss2: 0.195377
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.300906 Loss1: 0.105359 Loss2: 0.195547
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.274632 Loss1: 0.084936 Loss2: 0.189696
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.273972 Loss1: 0.085357 Loss2: 0.188614
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.265560 Loss1: 0.078532 Loss2: 0.187028
-(DefaultActor pid=1838052) >> Training accuracy: 0.983979
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.440135 Loss1: 0.081261 Loss2: 0.358874
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.411626 Loss1: 0.065571 Loss2: 0.346055
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.419928 Loss1: 0.074900 Loss2: 0.345028
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.414011 Loss1: 0.069928 Loss2: 0.344082
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.412520 Loss1: 0.071554 Loss2: 0.340966
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.415291 Loss1: 0.073139 Loss2: 0.342153
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.442672 Loss1: 0.097528 Loss2: 0.345144
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.405962 Loss1: 0.064831 Loss2: 0.341131
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.436424 Loss1: 0.090777 Loss2: 0.345647
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.421530 Loss1: 0.078928 Loss2: 0.342602
-(DefaultActor pid=1838052) >> Training accuracy: 0.987179
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136586 Loss1: 0.064532 Loss2: 0.072055
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.109094 Loss1: 0.039261 Loss2: 0.069833
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.108734 Loss1: 0.042626 Loss2: 0.066108
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.112493 Loss1: 0.047596 Loss2: 0.064897
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.101821 Loss1: 0.038261 Loss2: 0.063559
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.091834 Loss1: 0.030177 Loss2: 0.061657
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.092505 Loss1: 0.032026 Loss2: 0.060479
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091349 Loss1: 0.031462 Loss2: 0.059887
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101777 Loss1: 0.042011 Loss2: 0.059766
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098070 Loss1: 0.038295 Loss2: 0.059775
-(DefaultActor pid=1838052) >> Training accuracy: 0.990748
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.112007 Loss1: 0.079815 Loss2: 0.032192
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.090061 Loss1: 0.055321 Loss2: 0.034739
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.088951 Loss1: 0.053996 Loss2: 0.034954
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.065665 Loss1: 0.030883 Loss2: 0.034782
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063674 Loss1: 0.029638 Loss2: 0.034037
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070014 Loss1: 0.035530 Loss2: 0.034483
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.081776 Loss1: 0.046463 Loss2: 0.035313
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.080273 Loss1: 0.044882 Loss2: 0.035391
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.111226 Loss1: 0.075195 Loss2: 0.036032
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127576 Loss1: 0.090493 Loss2: 0.037083
-(DefaultActor pid=1838052) >> Training accuracy: 0.981013
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.127003 Loss1: 0.093643 Loss2: 0.033360
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079852 Loss1: 0.044640 Loss2: 0.035212
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.088045 Loss1: 0.052257 Loss2: 0.035788
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.083588 Loss1: 0.047489 Loss2: 0.036099
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.085742 Loss1: 0.049224 Loss2: 0.036518
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.067577 Loss1: 0.032034 Loss2: 0.035542
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077637 Loss1: 0.042006 Loss2: 0.035631
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075219 Loss1: 0.039093 Loss2: 0.036126
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092659 Loss1: 0.056261 Loss2: 0.036398
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.074339 Loss1: 0.038547 Loss2: 0.035791
-(DefaultActor pid=1838052) >> Training accuracy: 0.989913
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.147278 Loss1: 0.111368 Loss2: 0.035911
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.103074 Loss1: 0.065648 Loss2: 0.037426
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.097085 Loss1: 0.059190 Loss2: 0.037895
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.119185 Loss1: 0.080125 Loss2: 0.039061
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.102637 Loss1: 0.064203 Loss2: 0.038434
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077938 Loss1: 0.040406 Loss2: 0.037532
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.081058 Loss1: 0.043891 Loss2: 0.037167
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082165 Loss1: 0.044667 Loss2: 0.037498
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085876 Loss1: 0.047671 Loss2: 0.038205
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093055 Loss1: 0.055085 Loss2: 0.037970
-(DefaultActor pid=1838052) >> Training accuracy: 0.992188
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.099023 Loss1: 0.069583 Loss2: 0.029440
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.076317 Loss1: 0.045304 Loss2: 0.031014
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061240 Loss1: 0.029729 Loss2: 0.031510
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.054378 Loss1: 0.023093 Loss2: 0.031285
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050060 Loss1: 0.018968 Loss2: 0.031092
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047871 Loss1: 0.016947 Loss2: 0.030924
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.045186 Loss1: 0.014300 Loss2: 0.030886
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050256 Loss1: 0.019046 Loss2: 0.031210
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051590 Loss1: 0.020402 Loss2: 0.031189
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062751 Loss1: 0.030929 Loss2: 0.031822
-(DefaultActor pid=1838052) >> Training accuracy: 0.991386
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110367 Loss1: 0.078851 Loss2: 0.031516
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075344 Loss1: 0.042155 Loss2: 0.033189
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068468 Loss1: 0.035449 Loss2: 0.033019
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.072361 Loss1: 0.038738 Loss2: 0.033623
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069667 Loss1: 0.035820 Loss2: 0.033847
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083387 Loss1: 0.049237 Loss2: 0.034150
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079663 Loss1: 0.045275 Loss2: 0.034388
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068890 Loss1: 0.034541 Loss2: 0.034348
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076702 Loss1: 0.042193 Loss2: 0.034509
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.088159 Loss1: 0.053054 Loss2: 0.035105
-(DefaultActor pid=1838052) >> Training accuracy: 0.986946
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.140039 Loss1: 0.089912 Loss2: 0.050127
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.111543 Loss1: 0.062228 Loss2: 0.049314
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.093282 Loss1: 0.044038 Loss2: 0.049244
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074970 Loss1: 0.027372 Loss2: 0.047598
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.078369 Loss1: 0.031432 Loss2: 0.046937
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.099884 Loss1: 0.052206 Loss2: 0.047678
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.108809 Loss1: 0.059888 Loss2: 0.048921
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.109442 Loss1: 0.060518 Loss2: 0.048924
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.130704 Loss1: 0.080857 Loss2: 0.049848
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.119713 Loss1: 0.069845 Loss2: 0.049868
-(DefaultActor pid=1838052) >> Training accuracy: 0.985518
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 18:45:21,920][flwr][DEBUG] - fit_round 70 received 10 results and 0 failures
->> Test accuracy: 0.659100
-[2023-09-28 18:46:01,617][flwr][INFO] - fit progress: (70, 2.256014349171148, {'accuracy': 0.6591}, 131184.50726347417)
-[2023-09-28 18:46:01,618][flwr][DEBUG] - evaluate_round 70: strategy sampled 10 clients (out of 10)
-[2023-09-28 18:46:38,942][flwr][DEBUG] - evaluate_round 70 received 10 results and 0 failures
-[2023-09-28 18:46:38,944][flwr][DEBUG] - fit_round 71: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.115043 Loss1: 0.077548 Loss2: 0.037495
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080576 Loss1: 0.041318 Loss2: 0.039258
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067165 Loss1: 0.028006 Loss2: 0.039159
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068792 Loss1: 0.029834 Loss2: 0.038958
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086887 Loss1: 0.047157 Loss2: 0.039729
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.084277 Loss1: 0.044157 Loss2: 0.040119
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.080795 Loss1: 0.040485 Loss2: 0.040309
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.106616 Loss1: 0.065518 Loss2: 0.041098
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.118332 Loss1: 0.075751 Loss2: 0.042581
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.113944 Loss1: 0.071281 Loss2: 0.042662
-(DefaultActor pid=1838052) >> Training accuracy: 0.989984
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.685140 Loss1: 0.084760 Loss2: 0.600380
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.634056 Loss1: 0.051543 Loss2: 0.582513
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.621005 Loss1: 0.056900 Loss2: 0.564105
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.605215 Loss1: 0.054013 Loss2: 0.551203
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.606625 Loss1: 0.065987 Loss2: 0.540638
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.633599 Loss1: 0.096900 Loss2: 0.536699
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.613320 Loss1: 0.078812 Loss2: 0.534509
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.633197 Loss1: 0.101367 Loss2: 0.531830
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.623499 Loss1: 0.093301 Loss2: 0.530198
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.605620 Loss1: 0.081616 Loss2: 0.524004
-(DefaultActor pid=1838052) >> Training accuracy: 0.986155
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.137515 Loss1: 0.076528 Loss2: 0.060987
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.100703 Loss1: 0.039886 Loss2: 0.060817
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.094067 Loss1: 0.034358 Loss2: 0.059709
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.089674 Loss1: 0.030717 Loss2: 0.058957
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.100787 Loss1: 0.041265 Loss2: 0.059521
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.107454 Loss1: 0.046715 Loss2: 0.060739
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104928 Loss1: 0.044578 Loss2: 0.060350
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.095746 Loss1: 0.035357 Loss2: 0.060389
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.102357 Loss1: 0.041579 Loss2: 0.060778
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.119717 Loss1: 0.058181 Loss2: 0.061536
-(DefaultActor pid=1838052) >> Training accuracy: 0.991891
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.694692 Loss1: 0.100225 Loss2: 0.594467
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.627829 Loss1: 0.054463 Loss2: 0.573366
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.615180 Loss1: 0.055977 Loss2: 0.559204
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615965 Loss1: 0.065249 Loss2: 0.550717
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.609485 Loss1: 0.063140 Loss2: 0.546344
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.611348 Loss1: 0.068233 Loss2: 0.543115
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.632162 Loss1: 0.091486 Loss2: 0.540676
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.648222 Loss1: 0.107233 Loss2: 0.540989
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.645472 Loss1: 0.107046 Loss2: 0.538426
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.651161 Loss1: 0.112840 Loss2: 0.538321
-(DefaultActor pid=1838052) >> Training accuracy: 0.978441
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.648801 Loss1: 0.108837 Loss2: 0.539963
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.610026 Loss1: 0.078616 Loss2: 0.531410
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.615952 Loss1: 0.088405 Loss2: 0.527548
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615273 Loss1: 0.089574 Loss2: 0.525699
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.605574 Loss1: 0.085219 Loss2: 0.520355
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.582381 Loss1: 0.065590 Loss2: 0.516791
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.590598 Loss1: 0.078713 Loss2: 0.511885
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.619171 Loss1: 0.107734 Loss2: 0.511436
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.618853 Loss1: 0.108405 Loss2: 0.510448
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.622856 Loss1: 0.111080 Loss2: 0.511776
-(DefaultActor pid=1838052) >> Training accuracy: 0.986064
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110402 Loss1: 0.076295 Loss2: 0.034108
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.062757 Loss1: 0.027339 Loss2: 0.035419
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066469 Loss1: 0.031354 Loss2: 0.035116
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.072852 Loss1: 0.037586 Loss2: 0.035267
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075520 Loss1: 0.040157 Loss2: 0.035362
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.071826 Loss1: 0.036465 Loss2: 0.035361
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.066920 Loss1: 0.031999 Loss2: 0.034921
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.077239 Loss1: 0.041774 Loss2: 0.035465
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086388 Loss1: 0.050612 Loss2: 0.035776
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091087 Loss1: 0.054867 Loss2: 0.036221
-(DefaultActor pid=1838052) >> Training accuracy: 0.990885
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.672433 Loss1: 0.082608 Loss2: 0.589825
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.645533 Loss1: 0.065327 Loss2: 0.580206
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.637003 Loss1: 0.067948 Loss2: 0.569055
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.613918 Loss1: 0.054048 Loss2: 0.559869
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.620495 Loss1: 0.069221 Loss2: 0.551275
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.629582 Loss1: 0.081538 Loss2: 0.548044
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.603718 Loss1: 0.062210 Loss2: 0.541508
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.598205 Loss1: 0.061323 Loss2: 0.536882
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.604055 Loss1: 0.069106 Loss2: 0.534950
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.619580 Loss1: 0.087347 Loss2: 0.532233
-(DefaultActor pid=1838052) >> Training accuracy: 0.985562
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.109757 Loss1: 0.076193 Loss2: 0.033565
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079678 Loss1: 0.044107 Loss2: 0.035571
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067103 Loss1: 0.032056 Loss2: 0.035047
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074643 Loss1: 0.039004 Loss2: 0.035639
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.062913 Loss1: 0.027505 Loss2: 0.035408
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.061967 Loss1: 0.026893 Loss2: 0.035074
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065744 Loss1: 0.030531 Loss2: 0.035213
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067426 Loss1: 0.031929 Loss2: 0.035496
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086725 Loss1: 0.050158 Loss2: 0.036567
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.100391 Loss1: 0.063060 Loss2: 0.037331
-(DefaultActor pid=1838052) >> Training accuracy: 0.988692
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.473609 Loss1: 0.071246 Loss2: 0.402363
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.437430 Loss1: 0.053432 Loss2: 0.383998
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.446285 Loss1: 0.065308 Loss2: 0.380977
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.456000 Loss1: 0.075193 Loss2: 0.380807
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.450837 Loss1: 0.071238 Loss2: 0.379599
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.485873 Loss1: 0.103310 Loss2: 0.382563
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.484078 Loss1: 0.101832 Loss2: 0.382246
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.478338 Loss1: 0.094144 Loss2: 0.384195
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.444641 Loss1: 0.066606 Loss2: 0.378034
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.462776 Loss1: 0.085085 Loss2: 0.377692
-(DefaultActor pid=1838052) >> Training accuracy: 0.981517
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.097183 Loss1: 0.067574 Loss2: 0.029609
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.065240 Loss1: 0.034242 Loss2: 0.030998
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054308 Loss1: 0.023118 Loss2: 0.031190
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068394 Loss1: 0.036797 Loss2: 0.031596
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059101 Loss1: 0.027268 Loss2: 0.031833
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060467 Loss1: 0.028697 Loss2: 0.031769
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052685 Loss1: 0.021050 Loss2: 0.031635
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068028 Loss1: 0.035912 Loss2: 0.032116
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064845 Loss1: 0.032403 Loss2: 0.032442
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060485 Loss1: 0.028155 Loss2: 0.032330
-(DefaultActor pid=1838052) >> Training accuracy: 0.996194
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 19:16:04,756][flwr][DEBUG] - fit_round 71 received 10 results and 0 failures
->> Test accuracy: 0.658900
-[2023-09-28 19:19:03,949][flwr][INFO] - fit progress: (71, 2.247258449610049, {'accuracy': 0.6589}, 133166.83895978006)
-[2023-09-28 19:19:03,949][flwr][DEBUG] - evaluate_round 71: strategy sampled 10 clients (out of 10)
-[2023-09-28 19:19:40,418][flwr][DEBUG] - evaluate_round 71 received 10 results and 0 failures
-[2023-09-28 19:19:40,420][flwr][DEBUG] - fit_round 72: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.334006 Loss1: 0.071820 Loss2: 0.262185
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.293129 Loss1: 0.047685 Loss2: 0.245443
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.287226 Loss1: 0.046376 Loss2: 0.240851
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.279684 Loss1: 0.041344 Loss2: 0.238339
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.284995 Loss1: 0.047455 Loss2: 0.237540
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.278258 Loss1: 0.041550 Loss2: 0.236708
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.276271 Loss1: 0.040255 Loss2: 0.236016
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.272093 Loss1: 0.036380 Loss2: 0.235713
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.280662 Loss1: 0.043993 Loss2: 0.236669
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.293949 Loss1: 0.057071 Loss2: 0.236878
-(DefaultActor pid=1838052) >> Training accuracy: 0.988758
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.479851 Loss1: 0.106654 Loss2: 0.373197
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.412590 Loss1: 0.061989 Loss2: 0.350602
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.414077 Loss1: 0.068037 Loss2: 0.346039
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.418062 Loss1: 0.071172 Loss2: 0.346891
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.439831 Loss1: 0.090352 Loss2: 0.349479
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.458024 Loss1: 0.107813 Loss2: 0.350211
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.445916 Loss1: 0.097475 Loss2: 0.348442
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.416853 Loss1: 0.070546 Loss2: 0.346307
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.410498 Loss1: 0.066795 Loss2: 0.343704
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.417528 Loss1: 0.075143 Loss2: 0.342384
-(DefaultActor pid=1838052) >> Training accuracy: 0.989800
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.513457 Loss1: 0.107679 Loss2: 0.405778
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.477036 Loss1: 0.082281 Loss2: 0.394755
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.463054 Loss1: 0.073979 Loss2: 0.389075
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.452064 Loss1: 0.063262 Loss2: 0.388802
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.456399 Loss1: 0.069403 Loss2: 0.386996
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.481028 Loss1: 0.094961 Loss2: 0.386067
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.490946 Loss1: 0.102357 Loss2: 0.388589
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.473918 Loss1: 0.086257 Loss2: 0.387661
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.482137 Loss1: 0.094005 Loss2: 0.388132
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.482464 Loss1: 0.094539 Loss2: 0.387925
-(DefaultActor pid=1838052) >> Training accuracy: 0.973357
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.120752 Loss1: 0.079541 Loss2: 0.041211
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091950 Loss1: 0.049508 Loss2: 0.042443
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.083782 Loss1: 0.040954 Loss2: 0.042828
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074213 Loss1: 0.032148 Loss2: 0.042065
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.076725 Loss1: 0.035460 Loss2: 0.041264
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.080512 Loss1: 0.038619 Loss2: 0.041893
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.080688 Loss1: 0.038934 Loss2: 0.041755
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.096697 Loss1: 0.053971 Loss2: 0.042727
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081297 Loss1: 0.039327 Loss2: 0.041970
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080084 Loss1: 0.037570 Loss2: 0.042514
-(DefaultActor pid=1838052) >> Training accuracy: 0.993078
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.531238 Loss1: 0.118742 Loss2: 0.412496
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.512335 Loss1: 0.104622 Loss2: 0.407713
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.493497 Loss1: 0.092481 Loss2: 0.401016
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.522391 Loss1: 0.120563 Loss2: 0.401828
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.506143 Loss1: 0.106448 Loss2: 0.399695
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.524430 Loss1: 0.126602 Loss2: 0.397828
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.511061 Loss1: 0.114021 Loss2: 0.397040
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.461670 Loss1: 0.075242 Loss2: 0.386428
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.471400 Loss1: 0.081932 Loss2: 0.389468
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.452972 Loss1: 0.067832 Loss2: 0.385140
-(DefaultActor pid=1838052) >> Training accuracy: 0.985197
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092911 Loss1: 0.058672 Loss2: 0.034239
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069047 Loss1: 0.032958 Loss2: 0.036089
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066787 Loss1: 0.030402 Loss2: 0.036386
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.057206 Loss1: 0.021280 Loss2: 0.035926
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055699 Loss1: 0.020192 Loss2: 0.035507
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060013 Loss1: 0.024455 Loss2: 0.035558
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.061951 Loss1: 0.025597 Loss2: 0.036354
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.069018 Loss1: 0.032800 Loss2: 0.036218
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.071801 Loss1: 0.035471 Loss2: 0.036330
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070199 Loss1: 0.033657 Loss2: 0.036541
-(DefaultActor pid=1838052) >> Training accuracy: 0.992880
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110574 Loss1: 0.075103 Loss2: 0.035472
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079897 Loss1: 0.042734 Loss2: 0.037164
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077841 Loss1: 0.040029 Loss2: 0.037813
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062986 Loss1: 0.025772 Loss2: 0.037214
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070970 Loss1: 0.034054 Loss2: 0.036916
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.080646 Loss1: 0.043469 Loss2: 0.037177
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075353 Loss1: 0.037486 Loss2: 0.037868
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071902 Loss1: 0.034303 Loss2: 0.037600
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076319 Loss1: 0.038607 Loss2: 0.037712
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.097233 Loss1: 0.058246 Loss2: 0.038987
-(DefaultActor pid=1838052) >> Training accuracy: 0.987737
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.636792 Loss1: 0.082685 Loss2: 0.554107
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.599127 Loss1: 0.056549 Loss2: 0.542578
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.598873 Loss1: 0.064804 Loss2: 0.534070
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.577811 Loss1: 0.051737 Loss2: 0.526074
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.566156 Loss1: 0.046475 Loss2: 0.519681
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.558254 Loss1: 0.044284 Loss2: 0.513970
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.574858 Loss1: 0.060899 Loss2: 0.513958
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.597451 Loss1: 0.082501 Loss2: 0.514950
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.616385 Loss1: 0.097945 Loss2: 0.518440
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.607316 Loss1: 0.091676 Loss2: 0.515641
-(DefaultActor pid=1838052) >> Training accuracy: 0.984976
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.131483 Loss1: 0.098034 Loss2: 0.033449
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079606 Loss1: 0.044622 Loss2: 0.034984
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082109 Loss1: 0.047056 Loss2: 0.035052
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.075446 Loss1: 0.039955 Loss2: 0.035491
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.078943 Loss1: 0.043730 Loss2: 0.035213
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.068295 Loss1: 0.033194 Loss2: 0.035101
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.061638 Loss1: 0.026773 Loss2: 0.034866
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058294 Loss1: 0.023954 Loss2: 0.034339
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057168 Loss1: 0.023013 Loss2: 0.034155
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056410 Loss1: 0.022299 Loss2: 0.034112
-(DefaultActor pid=1838052) >> Training accuracy: 0.997466
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.097986 Loss1: 0.066894 Loss2: 0.031092
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074842 Loss1: 0.042094 Loss2: 0.032748
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066767 Loss1: 0.033720 Loss2: 0.033047
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.065897 Loss1: 0.032520 Loss2: 0.033378
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.056125 Loss1: 0.022931 Loss2: 0.033194
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.073999 Loss1: 0.040452 Loss2: 0.033548
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073208 Loss1: 0.038968 Loss2: 0.034239
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.080991 Loss1: 0.046097 Loss2: 0.034894
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.082998 Loss1: 0.048075 Loss2: 0.034923
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.088763 Loss1: 0.053191 Loss2: 0.035572
-(DefaultActor pid=1838052) >> Training accuracy: 0.989320
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 19:49:13,324][flwr][DEBUG] - fit_round 72 received 10 results and 0 failures
->> Test accuracy: 0.660600
-[2023-09-28 19:49:53,759][flwr][INFO] - fit progress: (72, 2.2466823984258855, {'accuracy': 0.6606}, 135016.6493149884)
-[2023-09-28 19:49:53,759][flwr][DEBUG] - evaluate_round 72: strategy sampled 10 clients (out of 10)
-[2023-09-28 19:50:29,577][flwr][DEBUG] - evaluate_round 72 received 10 results and 0 failures
-[2023-09-28 19:50:29,578][flwr][DEBUG] - fit_round 73: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.673638 Loss1: 0.108258 Loss2: 0.565380
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.633620 Loss1: 0.075549 Loss2: 0.558071
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.622391 Loss1: 0.074737 Loss2: 0.547653
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.625924 Loss1: 0.080615 Loss2: 0.545309
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.623605 Loss1: 0.078361 Loss2: 0.545244
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.623706 Loss1: 0.082353 Loss2: 0.541353
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.608523 Loss1: 0.070351 Loss2: 0.538172
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.594561 Loss1: 0.061707 Loss2: 0.532854
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.597341 Loss1: 0.064333 Loss2: 0.533009
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.633998 Loss1: 0.098491 Loss2: 0.535507
-(DefaultActor pid=1838052) >> Training accuracy: 0.981419
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.129152 Loss1: 0.098975 Loss2: 0.030177
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077955 Loss1: 0.046095 Loss2: 0.031859
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.084502 Loss1: 0.052430 Loss2: 0.032072
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.077832 Loss1: 0.045351 Loss2: 0.032481
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070781 Loss1: 0.038489 Loss2: 0.032292
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.059450 Loss1: 0.027727 Loss2: 0.031723
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.067981 Loss1: 0.036261 Loss2: 0.031721
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.070035 Loss1: 0.037557 Loss2: 0.032478
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068169 Loss1: 0.035598 Loss2: 0.032571
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055655 Loss1: 0.023594 Loss2: 0.032061
-(DefaultActor pid=1838052) >> Training accuracy: 0.996311
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.101370 Loss1: 0.071751 Loss2: 0.029619
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.065424 Loss1: 0.033880 Loss2: 0.031544
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060725 Loss1: 0.029431 Loss2: 0.031294
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.077161 Loss1: 0.045417 Loss2: 0.031744
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.090884 Loss1: 0.058174 Loss2: 0.032710
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.089796 Loss1: 0.056618 Loss2: 0.033178
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.082803 Loss1: 0.049882 Loss2: 0.032922
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.063326 Loss1: 0.030929 Loss2: 0.032396
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070572 Loss1: 0.037797 Loss2: 0.032775
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082457 Loss1: 0.049368 Loss2: 0.033089
-(DefaultActor pid=1838052) >> Training accuracy: 0.989720
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.538087 Loss1: 0.094301 Loss2: 0.443787
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.533680 Loss1: 0.091047 Loss2: 0.442632
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.500283 Loss1: 0.068120 Loss2: 0.432164
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.544778 Loss1: 0.112838 Loss2: 0.431940
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.569025 Loss1: 0.126417 Loss2: 0.442607
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.547477 Loss1: 0.113405 Loss2: 0.434072
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.534011 Loss1: 0.101425 Loss2: 0.432586
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.531248 Loss1: 0.099104 Loss2: 0.432143
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.549392 Loss1: 0.117979 Loss2: 0.431413
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.560925 Loss1: 0.126749 Loss2: 0.434175
-(DefaultActor pid=1838052) >> Training accuracy: 0.980419
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.336145 Loss1: 0.068925 Loss2: 0.267221
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.300541 Loss1: 0.051033 Loss2: 0.249508
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.279600 Loss1: 0.032579 Loss2: 0.247021
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.276894 Loss1: 0.031359 Loss2: 0.245535
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.291737 Loss1: 0.044615 Loss2: 0.247123
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.298416 Loss1: 0.051002 Loss2: 0.247415
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.283314 Loss1: 0.036133 Loss2: 0.247182
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.292676 Loss1: 0.044963 Loss2: 0.247713
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.301104 Loss1: 0.052937 Loss2: 0.248167
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.303304 Loss1: 0.054134 Loss2: 0.249170
-(DefaultActor pid=1838052) >> Training accuracy: 0.988982
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110477 Loss1: 0.078358 Loss2: 0.032119
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079401 Loss1: 0.045449 Loss2: 0.033952
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.092323 Loss1: 0.058018 Loss2: 0.034305
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.077091 Loss1: 0.042326 Loss2: 0.034765
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.074355 Loss1: 0.039666 Loss2: 0.034690
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.073698 Loss1: 0.038915 Loss2: 0.034783
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063901 Loss1: 0.029215 Loss2: 0.034685
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057051 Loss1: 0.023009 Loss2: 0.034043
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.060899 Loss1: 0.027083 Loss2: 0.033816
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062822 Loss1: 0.028278 Loss2: 0.034543
-(DefaultActor pid=1838052) >> Training accuracy: 0.995055
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092523 Loss1: 0.055047 Loss2: 0.037476
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072095 Loss1: 0.035770 Loss2: 0.036325
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068521 Loss1: 0.032609 Loss2: 0.035911
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.069830 Loss1: 0.033456 Loss2: 0.036373
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.068225 Loss1: 0.032033 Loss2: 0.036192
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.072579 Loss1: 0.036098 Loss2: 0.036481
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.104435 Loss1: 0.066799 Loss2: 0.037636
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.119332 Loss1: 0.080363 Loss2: 0.038969
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.120641 Loss1: 0.080805 Loss2: 0.039835
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.110755 Loss1: 0.071412 Loss2: 0.039343
-(DefaultActor pid=1838052) >> Training accuracy: 0.986155
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.096809 Loss1: 0.065676 Loss2: 0.031133
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074710 Loss1: 0.042281 Loss2: 0.032428
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067395 Loss1: 0.034999 Loss2: 0.032397
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060701 Loss1: 0.028483 Loss2: 0.032218
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060758 Loss1: 0.028451 Loss2: 0.032307
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058991 Loss1: 0.027065 Loss2: 0.031926
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.059636 Loss1: 0.027489 Loss2: 0.032147
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071412 Loss1: 0.038704 Loss2: 0.032708
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.071387 Loss1: 0.038098 Loss2: 0.033290
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070413 Loss1: 0.037115 Loss2: 0.033299
-(DefaultActor pid=1838052) >> Training accuracy: 0.991987
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.094807 Loss1: 0.065381 Loss2: 0.029426
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077399 Loss1: 0.045285 Loss2: 0.032114
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.089002 Loss1: 0.056321 Loss2: 0.032681
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.099601 Loss1: 0.066244 Loss2: 0.033358
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.106334 Loss1: 0.071603 Loss2: 0.034731
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.126074 Loss1: 0.090663 Loss2: 0.035411
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.118062 Loss1: 0.082748 Loss2: 0.035314
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.098797 Loss1: 0.063379 Loss2: 0.035418
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.101565 Loss1: 0.066549 Loss2: 0.035016
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.116716 Loss1: 0.081358 Loss2: 0.035358
-(DefaultActor pid=1838052) >> Training accuracy: 0.988133
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.114681 Loss1: 0.073248 Loss2: 0.041433
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.099172 Loss1: 0.055927 Loss2: 0.043246
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.106820 Loss1: 0.062141 Loss2: 0.044678
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.097524 Loss1: 0.052410 Loss2: 0.045114
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.083398 Loss1: 0.038435 Loss2: 0.044963
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.091689 Loss1: 0.046443 Loss2: 0.045246
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079409 Loss1: 0.034445 Loss2: 0.044964
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075943 Loss1: 0.031604 Loss2: 0.044339
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064228 Loss1: 0.020331 Loss2: 0.043897
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082329 Loss1: 0.038450 Loss2: 0.043879
-(DefaultActor pid=1838052) >> Training accuracy: 0.990854
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 20:19:53,958][flwr][DEBUG] - fit_round 73 received 10 results and 0 failures
->> Test accuracy: 0.661600
-[2023-09-28 20:20:34,203][flwr][INFO] - fit progress: (73, 2.2818810541789754, {'accuracy': 0.6616}, 136857.0931070121)
-[2023-09-28 20:20:34,203][flwr][DEBUG] - evaluate_round 73: strategy sampled 10 clients (out of 10)
-[2023-09-28 20:21:11,246][flwr][DEBUG] - evaluate_round 73 received 10 results and 0 failures
-[2023-09-28 20:21:11,252][flwr][DEBUG] - fit_round 74: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.662811 Loss1: 0.060218 Loss2: 0.602593
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.644562 Loss1: 0.047703 Loss2: 0.596859
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.662146 Loss1: 0.070087 Loss2: 0.592059
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.654467 Loss1: 0.068479 Loss2: 0.585988
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.666121 Loss1: 0.084771 Loss2: 0.581349
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.646969 Loss1: 0.069925 Loss2: 0.577044
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.627960 Loss1: 0.057820 Loss2: 0.570139
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.625864 Loss1: 0.060307 Loss2: 0.565557
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.640015 Loss1: 0.076381 Loss2: 0.563634
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.669658 Loss1: 0.103755 Loss2: 0.565904
-(DefaultActor pid=1838052) >> Training accuracy: 0.979233
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.091611 Loss1: 0.062414 Loss2: 0.029197
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069992 Loss1: 0.039330 Loss2: 0.030662
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056005 Loss1: 0.025673 Loss2: 0.030332
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062142 Loss1: 0.031355 Loss2: 0.030787
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059007 Loss1: 0.028390 Loss2: 0.030617
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.067739 Loss1: 0.036946 Loss2: 0.030793
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064407 Loss1: 0.033417 Loss2: 0.030991
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.074244 Loss1: 0.042876 Loss2: 0.031368
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067637 Loss1: 0.036559 Loss2: 0.031078
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082022 Loss1: 0.050128 Loss2: 0.031894
-(DefaultActor pid=1838052) >> Training accuracy: 0.989122
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.098536 Loss1: 0.069854 Loss2: 0.028681
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.076614 Loss1: 0.046335 Loss2: 0.030279
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068054 Loss1: 0.037250 Loss2: 0.030804
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060521 Loss1: 0.029808 Loss2: 0.030713
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.062661 Loss1: 0.031867 Loss2: 0.030794
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.062169 Loss1: 0.031186 Loss2: 0.030983
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.085040 Loss1: 0.053377 Loss2: 0.031664
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.076921 Loss1: 0.044467 Loss2: 0.032454
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.062378 Loss1: 0.030397 Loss2: 0.031982
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073518 Loss1: 0.041228 Loss2: 0.032289
-(DefaultActor pid=1838052) >> Training accuracy: 0.992682
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087522 Loss1: 0.057859 Loss2: 0.029663
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072561 Loss1: 0.040962 Loss2: 0.031599
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061290 Loss1: 0.029665 Loss2: 0.031626
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.073856 Loss1: 0.041715 Loss2: 0.032141
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060595 Loss1: 0.028602 Loss2: 0.031993
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070586 Loss1: 0.038157 Loss2: 0.032428
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064689 Loss1: 0.031982 Loss2: 0.032707
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.078051 Loss1: 0.045112 Loss2: 0.032938
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077190 Loss1: 0.044116 Loss2: 0.033073
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.095002 Loss1: 0.061274 Loss2: 0.033728
-(DefaultActor pid=1838052) >> Training accuracy: 0.986551
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.664619 Loss1: 0.074971 Loss2: 0.589648
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.627937 Loss1: 0.055767 Loss2: 0.572170
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.626250 Loss1: 0.061056 Loss2: 0.565194
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.624752 Loss1: 0.063988 Loss2: 0.560764
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.607934 Loss1: 0.052565 Loss2: 0.555369
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.613670 Loss1: 0.063656 Loss2: 0.550014
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.621288 Loss1: 0.074004 Loss2: 0.547284
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.633284 Loss1: 0.086433 Loss2: 0.546851
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.632014 Loss1: 0.085726 Loss2: 0.546287
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.629510 Loss1: 0.085179 Loss2: 0.544331
-(DefaultActor pid=1838052) >> Training accuracy: 0.980880
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.108521 Loss1: 0.078985 Loss2: 0.029536
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074599 Loss1: 0.043333 Loss2: 0.031266
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067773 Loss1: 0.036211 Loss2: 0.031562
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.066130 Loss1: 0.034709 Loss2: 0.031421
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.067987 Loss1: 0.036163 Loss2: 0.031824
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.091757 Loss1: 0.059033 Loss2: 0.032723
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.082074 Loss1: 0.049085 Loss2: 0.032989
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.081519 Loss1: 0.048207 Loss2: 0.033312
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.094912 Loss1: 0.061697 Loss2: 0.033215
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.094011 Loss1: 0.060625 Loss2: 0.033386
-(DefaultActor pid=1838052) >> Training accuracy: 0.991987
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102214 Loss1: 0.071542 Loss2: 0.030671
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.067833 Loss1: 0.035714 Loss2: 0.032119
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067619 Loss1: 0.035090 Loss2: 0.032529
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068597 Loss1: 0.035600 Loss2: 0.032997
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077607 Loss1: 0.043985 Loss2: 0.033621
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.074456 Loss1: 0.040703 Loss2: 0.033754
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072962 Loss1: 0.039494 Loss2: 0.033468
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073162 Loss1: 0.038953 Loss2: 0.034209
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.073964 Loss1: 0.039189 Loss2: 0.034775
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082256 Loss1: 0.047633 Loss2: 0.034623
-(DefaultActor pid=1838052) >> Training accuracy: 0.992569
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.136106 Loss1: 0.071801 Loss2: 0.064305
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.098531 Loss1: 0.034228 Loss2: 0.064303
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.089280 Loss1: 0.027232 Loss2: 0.062048
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.107391 Loss1: 0.045306 Loss2: 0.062085
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.110611 Loss1: 0.047980 Loss2: 0.062630
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.123781 Loss1: 0.060770 Loss2: 0.063011
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.113712 Loss1: 0.050657 Loss2: 0.063054
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.106327 Loss1: 0.044604 Loss2: 0.061724
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.112005 Loss1: 0.051273 Loss2: 0.060732
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.134000 Loss1: 0.071088 Loss2: 0.062912
-(DefaultActor pid=1838052) >> Training accuracy: 0.987847
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.116106 Loss1: 0.085451 Loss2: 0.030654
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.071673 Loss1: 0.039829 Loss2: 0.031844
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066850 Loss1: 0.035165 Loss2: 0.031685
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080442 Loss1: 0.047963 Loss2: 0.032479
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.067440 Loss1: 0.035327 Loss2: 0.032113
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.092456 Loss1: 0.059116 Loss2: 0.033340
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077594 Loss1: 0.044276 Loss2: 0.033319
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075301 Loss1: 0.042344 Loss2: 0.032957
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065022 Loss1: 0.032354 Loss2: 0.032667
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082146 Loss1: 0.049332 Loss2: 0.032814
-(DefaultActor pid=1838052) >> Training accuracy: 0.987331
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.105318 Loss1: 0.069342 Loss2: 0.035976
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.081928 Loss1: 0.044764 Loss2: 0.037164
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.081635 Loss1: 0.043635 Loss2: 0.038000
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.082892 Loss1: 0.043885 Loss2: 0.039007
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.080792 Loss1: 0.041621 Loss2: 0.039171
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075706 Loss1: 0.036601 Loss2: 0.039105
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065661 Loss1: 0.026649 Loss2: 0.039013
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055703 Loss1: 0.017051 Loss2: 0.038652
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.060982 Loss1: 0.022780 Loss2: 0.038202
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055909 Loss1: 0.017786 Loss2: 0.038124
-(DefaultActor pid=1838052) >> Training accuracy: 0.997196
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 20:50:41,720][flwr][DEBUG] - fit_round 74 received 10 results and 0 failures
->> Test accuracy: 0.659500
-[2023-09-28 20:51:20,535][flwr][INFO] - fit progress: (74, 2.2942246132003614, {'accuracy': 0.6595}, 138703.42533513112)
-[2023-09-28 20:51:20,535][flwr][DEBUG] - evaluate_round 74: strategy sampled 10 clients (out of 10)
-[2023-09-28 20:51:56,054][flwr][DEBUG] - evaluate_round 74 received 10 results and 0 failures
-[2023-09-28 20:51:56,055][flwr][DEBUG] - fit_round 75: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.100961 Loss1: 0.071570 Loss2: 0.029391
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.071864 Loss1: 0.040889 Loss2: 0.030975
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.063991 Loss1: 0.032526 Loss2: 0.031465
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.054525 Loss1: 0.023278 Loss2: 0.031247
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070200 Loss1: 0.038408 Loss2: 0.031793
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083295 Loss1: 0.051000 Loss2: 0.032295
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.091976 Loss1: 0.058426 Loss2: 0.033550
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.094828 Loss1: 0.060931 Loss2: 0.033897
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.085104 Loss1: 0.050948 Loss2: 0.034157
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.087167 Loss1: 0.053681 Loss2: 0.033486
-(DefaultActor pid=1838052) >> Training accuracy: 0.988726
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.585234 Loss1: 0.084618 Loss2: 0.500616
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.540224 Loss1: 0.058160 Loss2: 0.482064
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.526077 Loss1: 0.049158 Loss2: 0.476920
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.530687 Loss1: 0.059385 Loss2: 0.471302
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.539215 Loss1: 0.065975 Loss2: 0.473240
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.533129 Loss1: 0.062815 Loss2: 0.470314
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.526202 Loss1: 0.060700 Loss2: 0.465502
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.551335 Loss1: 0.079796 Loss2: 0.471539
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.531990 Loss1: 0.064049 Loss2: 0.467941
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.560655 Loss1: 0.091694 Loss2: 0.468961
-(DefaultActor pid=1838052) >> Training accuracy: 0.991319
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102854 Loss1: 0.068907 Loss2: 0.033947
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.081489 Loss1: 0.044799 Loss2: 0.036690
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069469 Loss1: 0.033243 Loss2: 0.036226
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.081516 Loss1: 0.045273 Loss2: 0.036242
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.082026 Loss1: 0.045216 Loss2: 0.036810
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.082577 Loss1: 0.045315 Loss2: 0.037262
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.076896 Loss1: 0.039447 Loss2: 0.037449
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.084641 Loss1: 0.047031 Loss2: 0.037610
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076642 Loss1: 0.039135 Loss2: 0.037507
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.067755 Loss1: 0.030196 Loss2: 0.037559
-(DefaultActor pid=1838052) >> Training accuracy: 0.998150
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.668555 Loss1: 0.081862 Loss2: 0.586694
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.639188 Loss1: 0.067752 Loss2: 0.571436
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.635683 Loss1: 0.071805 Loss2: 0.563878
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.622801 Loss1: 0.063000 Loss2: 0.559801
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.635413 Loss1: 0.080364 Loss2: 0.555049
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.631632 Loss1: 0.081016 Loss2: 0.550616
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.627095 Loss1: 0.079605 Loss2: 0.547490
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.618720 Loss1: 0.075704 Loss2: 0.543016
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.614972 Loss1: 0.075155 Loss2: 0.539817
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.627704 Loss1: 0.089986 Loss2: 0.537717
-(DefaultActor pid=1838052) >> Training accuracy: 0.985562
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.676030 Loss1: 0.063420 Loss2: 0.612610
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.664312 Loss1: 0.057112 Loss2: 0.607200
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.656573 Loss1: 0.053309 Loss2: 0.603264
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.644560 Loss1: 0.047814 Loss2: 0.596746
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.640101 Loss1: 0.053527 Loss2: 0.586574
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.656079 Loss1: 0.070769 Loss2: 0.585310
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.665353 Loss1: 0.079226 Loss2: 0.586127
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.668701 Loss1: 0.083833 Loss2: 0.584868
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.683217 Loss1: 0.100438 Loss2: 0.582779
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.682284 Loss1: 0.097867 Loss2: 0.584417
-(DefaultActor pid=1838052) >> Training accuracy: 0.986280
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.098711 Loss1: 0.069722 Loss2: 0.028989
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.061549 Loss1: 0.031013 Loss2: 0.030536
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.064640 Loss1: 0.033910 Loss2: 0.030730
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.056075 Loss1: 0.024962 Loss2: 0.031114
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054562 Loss1: 0.023331 Loss2: 0.031231
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051793 Loss1: 0.020928 Loss2: 0.030866
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.051821 Loss1: 0.020885 Loss2: 0.030936
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.053366 Loss1: 0.022093 Loss2: 0.031273
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.066980 Loss1: 0.035329 Loss2: 0.031650
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056260 Loss1: 0.024425 Loss2: 0.031835
-(DefaultActor pid=1838052) >> Training accuracy: 0.996795
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.514734 Loss1: 0.066974 Loss2: 0.447760
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.448740 Loss1: 0.048314 Loss2: 0.400426
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.432620 Loss1: 0.048649 Loss2: 0.383971
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.416950 Loss1: 0.040516 Loss2: 0.376434
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.420857 Loss1: 0.050784 Loss2: 0.370073
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.434074 Loss1: 0.066863 Loss2: 0.367211
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.461760 Loss1: 0.092839 Loss2: 0.368921
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.469688 Loss1: 0.098899 Loss2: 0.370789
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.458105 Loss1: 0.090900 Loss2: 0.367206
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.431319 Loss1: 0.065425 Loss2: 0.365893
-(DefaultActor pid=1838052) >> Training accuracy: 0.987935
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.079157 Loss1: 0.050941 Loss2: 0.028216
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.063251 Loss1: 0.033296 Loss2: 0.029955
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052138 Loss1: 0.021886 Loss2: 0.030252
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049388 Loss1: 0.019342 Loss2: 0.030046
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.052835 Loss1: 0.022726 Loss2: 0.030109
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055632 Loss1: 0.025359 Loss2: 0.030273
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050536 Loss1: 0.020187 Loss2: 0.030349
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058775 Loss1: 0.028010 Loss2: 0.030765
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.052309 Loss1: 0.021667 Loss2: 0.030642
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060332 Loss1: 0.029167 Loss2: 0.031165
-(DefaultActor pid=1838052) >> Training accuracy: 0.998418
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.095856 Loss1: 0.065338 Loss2: 0.030518
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058537 Loss1: 0.027021 Loss2: 0.031516
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057261 Loss1: 0.025834 Loss2: 0.031427
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058642 Loss1: 0.027144 Loss2: 0.031498
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.062246 Loss1: 0.030412 Loss2: 0.031834
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075241 Loss1: 0.042541 Loss2: 0.032699
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065909 Loss1: 0.032744 Loss2: 0.033165
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065675 Loss1: 0.032851 Loss2: 0.032824
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067400 Loss1: 0.034303 Loss2: 0.033096
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070451 Loss1: 0.036950 Loss2: 0.033500
-(DefaultActor pid=1838052) >> Training accuracy: 0.994191
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.133075 Loss1: 0.101967 Loss2: 0.031108
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075233 Loss1: 0.043227 Loss2: 0.032006
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.071783 Loss1: 0.039616 Loss2: 0.032167
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085448 Loss1: 0.052499 Loss2: 0.032949
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075417 Loss1: 0.042157 Loss2: 0.033261
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.084133 Loss1: 0.050532 Loss2: 0.033600
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.082056 Loss1: 0.048375 Loss2: 0.033682
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075076 Loss1: 0.041629 Loss2: 0.033448
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.083107 Loss1: 0.049111 Loss2: 0.033996
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080842 Loss1: 0.046809 Loss2: 0.034033
-(DefaultActor pid=1838052) >> Training accuracy: 0.994299
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 21:21:30,541][flwr][DEBUG] - fit_round 75 received 10 results and 0 failures
->> Test accuracy: 0.657800
-[2023-09-28 21:22:09,784][flwr][INFO] - fit progress: (75, 2.3269932089141383, {'accuracy': 0.6578}, 140552.6741445833)
-[2023-09-28 21:22:09,784][flwr][DEBUG] - evaluate_round 75: strategy sampled 10 clients (out of 10)
-[2023-09-28 21:22:45,506][flwr][DEBUG] - evaluate_round 75 received 10 results and 0 failures
-[2023-09-28 21:22:45,507][flwr][DEBUG] - fit_round 76: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.399900 Loss1: 0.102663 Loss2: 0.297236
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.374364 Loss1: 0.095677 Loss2: 0.278687
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.352855 Loss1: 0.080865 Loss2: 0.271990
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.354487 Loss1: 0.086840 Loss2: 0.267647
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.361324 Loss1: 0.091707 Loss2: 0.269617
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.345665 Loss1: 0.079899 Loss2: 0.265766
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.349906 Loss1: 0.085081 Loss2: 0.264824
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.347095 Loss1: 0.083651 Loss2: 0.263444
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.372121 Loss1: 0.102452 Loss2: 0.269670
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.349123 Loss1: 0.084244 Loss2: 0.264879
-(DefaultActor pid=1838052) >> Training accuracy: 0.982595
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.696535 Loss1: 0.071182 Loss2: 0.625353
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.678641 Loss1: 0.058537 Loss2: 0.620103
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.679190 Loss1: 0.066184 Loss2: 0.613007
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.658352 Loss1: 0.051042 Loss2: 0.607310
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.644844 Loss1: 0.048043 Loss2: 0.596802
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.634954 Loss1: 0.045486 Loss2: 0.589468
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.675581 Loss1: 0.085443 Loss2: 0.590138
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.672799 Loss1: 0.083243 Loss2: 0.589556
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.653171 Loss1: 0.066971 Loss2: 0.586200
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.671320 Loss1: 0.084675 Loss2: 0.586645
-(DefaultActor pid=1838052) >> Training accuracy: 0.981170
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.119381 Loss1: 0.064081 Loss2: 0.055300
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.091074 Loss1: 0.036028 Loss2: 0.055047
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.083848 Loss1: 0.030424 Loss2: 0.053424
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.075703 Loss1: 0.024196 Loss2: 0.051506
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065573 Loss1: 0.016157 Loss2: 0.049416
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065389 Loss1: 0.017040 Loss2: 0.048349
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.071336 Loss1: 0.022808 Loss2: 0.048527
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073862 Loss1: 0.024965 Loss2: 0.048896
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.095595 Loss1: 0.045775 Loss2: 0.049820
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.105788 Loss1: 0.055316 Loss2: 0.050472
-(DefaultActor pid=1838052) >> Training accuracy: 0.994093
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.625743 Loss1: 0.086412 Loss2: 0.539332
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.574517 Loss1: 0.049814 Loss2: 0.524703
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.595255 Loss1: 0.075762 Loss2: 0.519493
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.580232 Loss1: 0.063584 Loss2: 0.516648
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.566289 Loss1: 0.057292 Loss2: 0.508997
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.603420 Loss1: 0.094781 Loss2: 0.508639
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.617816 Loss1: 0.108345 Loss2: 0.509471
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.587952 Loss1: 0.082611 Loss2: 0.505341
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.579439 Loss1: 0.076812 Loss2: 0.502627
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.594713 Loss1: 0.088942 Loss2: 0.505771
-(DefaultActor pid=1838052) >> Training accuracy: 0.981606
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092705 Loss1: 0.058310 Loss2: 0.034396
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069664 Loss1: 0.034776 Loss2: 0.034888
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.074005 Loss1: 0.038663 Loss2: 0.035342
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067915 Loss1: 0.032066 Loss2: 0.035849
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072133 Loss1: 0.036108 Loss2: 0.036025
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083154 Loss1: 0.046855 Loss2: 0.036299
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.090030 Loss1: 0.053013 Loss2: 0.037018
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082173 Loss1: 0.044493 Loss2: 0.037679
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.066829 Loss1: 0.029950 Loss2: 0.036879
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.066364 Loss1: 0.029906 Loss2: 0.036458
-(DefaultActor pid=1838052) >> Training accuracy: 0.994264
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087835 Loss1: 0.057896 Loss2: 0.029939
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052184 Loss1: 0.021160 Loss2: 0.031023
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056343 Loss1: 0.025431 Loss2: 0.030912
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064756 Loss1: 0.033393 Loss2: 0.031363
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.068428 Loss1: 0.036074 Loss2: 0.032354
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.056363 Loss1: 0.023708 Loss2: 0.032655
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052947 Loss1: 0.021040 Loss2: 0.031907
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051079 Loss1: 0.019237 Loss2: 0.031842
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053274 Loss1: 0.021408 Loss2: 0.031866
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.050790 Loss1: 0.018793 Loss2: 0.031997
-(DefaultActor pid=1838052) >> Training accuracy: 0.997533
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.103851 Loss1: 0.074418 Loss2: 0.029433
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080445 Loss1: 0.048997 Loss2: 0.031448
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.071611 Loss1: 0.039823 Loss2: 0.031788
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068966 Loss1: 0.037027 Loss2: 0.031939
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.066391 Loss1: 0.034406 Loss2: 0.031985
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077420 Loss1: 0.044832 Loss2: 0.032588
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.068112 Loss1: 0.035650 Loss2: 0.032462
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.088299 Loss1: 0.055243 Loss2: 0.033057
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075243 Loss1: 0.041664 Loss2: 0.033579
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104118 Loss1: 0.070093 Loss2: 0.034026
-(DefaultActor pid=1838052) >> Training accuracy: 0.982897
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.099038 Loss1: 0.069523 Loss2: 0.029514
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077232 Loss1: 0.046077 Loss2: 0.031156
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069978 Loss1: 0.038319 Loss2: 0.031659
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.061146 Loss1: 0.029429 Loss2: 0.031716
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073974 Loss1: 0.042347 Loss2: 0.031627
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.080008 Loss1: 0.047633 Loss2: 0.032375
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.088683 Loss1: 0.055802 Loss2: 0.032882
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082435 Loss1: 0.049539 Loss2: 0.032896
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070851 Loss1: 0.037506 Loss2: 0.033345
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060619 Loss1: 0.027880 Loss2: 0.032739
-(DefaultActor pid=1838052) >> Training accuracy: 0.994191
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102080 Loss1: 0.072855 Loss2: 0.029226
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073629 Loss1: 0.042948 Loss2: 0.030681
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060107 Loss1: 0.029234 Loss2: 0.030873
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.059618 Loss1: 0.028541 Loss2: 0.031077
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.058735 Loss1: 0.027576 Loss2: 0.031158
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058436 Loss1: 0.027176 Loss2: 0.031260
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054376 Loss1: 0.023194 Loss2: 0.031182
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058051 Loss1: 0.026649 Loss2: 0.031402
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.061430 Loss1: 0.029453 Loss2: 0.031977
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070756 Loss1: 0.038589 Loss2: 0.032167
-(DefaultActor pid=1838052) >> Training accuracy: 0.992880
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.366481 Loss1: 0.091601 Loss2: 0.274881
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.299065 Loss1: 0.057926 Loss2: 0.241138
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.294784 Loss1: 0.061693 Loss2: 0.233091
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.298557 Loss1: 0.066685 Loss2: 0.231872
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.289508 Loss1: 0.061978 Loss2: 0.227530
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.316124 Loss1: 0.086633 Loss2: 0.229491
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.306495 Loss1: 0.077933 Loss2: 0.228562
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.293173 Loss1: 0.067702 Loss2: 0.225470
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.289981 Loss1: 0.065469 Loss2: 0.224512
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.281442 Loss1: 0.057991 Loss2: 0.223450
-(DefaultActor pid=1838052) >> Training accuracy: 0.991102
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 21:52:14,046][flwr][DEBUG] - fit_round 76 received 10 results and 0 failures
->> Test accuracy: 0.660600
-[2023-09-28 21:55:28,725][flwr][INFO] - fit progress: (76, 2.30433221423207, {'accuracy': 0.6606}, 142551.61542777205)
-[2023-09-28 21:55:28,726][flwr][DEBUG] - evaluate_round 76: strategy sampled 10 clients (out of 10)
-[2023-09-28 21:56:05,392][flwr][DEBUG] - evaluate_round 76 received 10 results and 0 failures
-[2023-09-28 21:56:05,393][flwr][DEBUG] - fit_round 77: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.597592 Loss1: 0.076634 Loss2: 0.520957
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.571603 Loss1: 0.059696 Loss2: 0.511907
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.557060 Loss1: 0.051033 Loss2: 0.506027
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.564381 Loss1: 0.062669 Loss2: 0.501712
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.556905 Loss1: 0.059306 Loss2: 0.497599
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.596124 Loss1: 0.097005 Loss2: 0.499119
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.584668 Loss1: 0.084570 Loss2: 0.500098
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.572677 Loss1: 0.078637 Loss2: 0.494041
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.588078 Loss1: 0.091992 Loss2: 0.496086
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.602399 Loss1: 0.106125 Loss2: 0.496274
-(DefaultActor pid=1838052) >> Training accuracy: 0.975946
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.094644 Loss1: 0.062216 Loss2: 0.032428
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053801 Loss1: 0.019953 Loss2: 0.033848
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.048810 Loss1: 0.015609 Loss2: 0.033201
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062887 Loss1: 0.029451 Loss2: 0.033436
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073223 Loss1: 0.038744 Loss2: 0.034480
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069574 Loss1: 0.034159 Loss2: 0.035415
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063609 Loss1: 0.028518 Loss2: 0.035091
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065102 Loss1: 0.030239 Loss2: 0.034863
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070694 Loss1: 0.035430 Loss2: 0.035264
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084454 Loss1: 0.048349 Loss2: 0.036106
-(DefaultActor pid=1838052) >> Training accuracy: 0.994462
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083777 Loss1: 0.053931 Loss2: 0.029845
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066622 Loss1: 0.035091 Loss2: 0.031532
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.059287 Loss1: 0.027415 Loss2: 0.031872
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067308 Loss1: 0.034965 Loss2: 0.032343
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054042 Loss1: 0.021809 Loss2: 0.032233
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075287 Loss1: 0.042612 Loss2: 0.032675
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072555 Loss1: 0.039173 Loss2: 0.033382
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058749 Loss1: 0.025416 Loss2: 0.033333
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.069571 Loss1: 0.035673 Loss2: 0.033898
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093707 Loss1: 0.059427 Loss2: 0.034280
-(DefaultActor pid=1838052) >> Training accuracy: 0.990785
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.676406 Loss1: 0.066580 Loss2: 0.609826
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.643161 Loss1: 0.044948 Loss2: 0.598213
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.632348 Loss1: 0.048714 Loss2: 0.583634
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.630539 Loss1: 0.055922 Loss2: 0.574617
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.642474 Loss1: 0.069688 Loss2: 0.572786
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.649234 Loss1: 0.081319 Loss2: 0.567915
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.643952 Loss1: 0.079815 Loss2: 0.564138
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.647614 Loss1: 0.083328 Loss2: 0.564286
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.647905 Loss1: 0.086661 Loss2: 0.561244
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.670576 Loss1: 0.113809 Loss2: 0.556768
-(DefaultActor pid=1838052) >> Training accuracy: 0.970926
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.613912 Loss1: 0.063271 Loss2: 0.550641
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.560748 Loss1: 0.047868 Loss2: 0.512880
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.562227 Loss1: 0.071101 Loss2: 0.491126
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.559659 Loss1: 0.082297 Loss2: 0.477362
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.550821 Loss1: 0.083435 Loss2: 0.467386
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.588974 Loss1: 0.123334 Loss2: 0.465640
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.558806 Loss1: 0.099727 Loss2: 0.459080
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.546088 Loss1: 0.092343 Loss2: 0.453745
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.561632 Loss1: 0.109580 Loss2: 0.452053
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.544499 Loss1: 0.095439 Loss2: 0.449060
-(DefaultActor pid=1838052) >> Training accuracy: 0.973323
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.112056 Loss1: 0.081646 Loss2: 0.030410
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072510 Loss1: 0.040337 Loss2: 0.032173
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069612 Loss1: 0.036840 Loss2: 0.032771
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.078847 Loss1: 0.045368 Loss2: 0.033479
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092569 Loss1: 0.058474 Loss2: 0.034095
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096612 Loss1: 0.061655 Loss2: 0.034957
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.106195 Loss1: 0.070434 Loss2: 0.035761
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.078158 Loss1: 0.042758 Loss2: 0.035401
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.079439 Loss1: 0.044316 Loss2: 0.035123
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.081057 Loss1: 0.046205 Loss2: 0.034853
-(DefaultActor pid=1838052) >> Training accuracy: 0.995355
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092459 Loss1: 0.059058 Loss2: 0.033401
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.068806 Loss1: 0.034275 Loss2: 0.034530
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067119 Loss1: 0.032246 Loss2: 0.034873
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.070883 Loss1: 0.035932 Loss2: 0.034952
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.066434 Loss1: 0.031064 Loss2: 0.035370
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.068068 Loss1: 0.032732 Loss2: 0.035335
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.081524 Loss1: 0.045140 Loss2: 0.036384
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091305 Loss1: 0.053969 Loss2: 0.037337
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.108100 Loss1: 0.070538 Loss2: 0.037562
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102360 Loss1: 0.064385 Loss2: 0.037975
-(DefaultActor pid=1838052) >> Training accuracy: 0.984771
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.097371 Loss1: 0.067350 Loss2: 0.030021
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.071794 Loss1: 0.039913 Loss2: 0.031881
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068408 Loss1: 0.036349 Loss2: 0.032059
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.078930 Loss1: 0.046362 Loss2: 0.032568
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072690 Loss1: 0.039680 Loss2: 0.033010
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065792 Loss1: 0.032553 Loss2: 0.033239
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062131 Loss1: 0.029457 Loss2: 0.032674
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067841 Loss1: 0.034644 Loss2: 0.033197
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.074115 Loss1: 0.040522 Loss2: 0.033593
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.079415 Loss1: 0.045891 Loss2: 0.033523
-(DefaultActor pid=1838052) >> Training accuracy: 0.983974
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.116270 Loss1: 0.082166 Loss2: 0.034104
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075795 Loss1: 0.040538 Loss2: 0.035258
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069994 Loss1: 0.034336 Loss2: 0.035659
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063215 Loss1: 0.027536 Loss2: 0.035680
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.056590 Loss1: 0.021256 Loss2: 0.035334
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.066524 Loss1: 0.031371 Loss2: 0.035154
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056687 Loss1: 0.021328 Loss2: 0.035359
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061177 Loss1: 0.026218 Loss2: 0.034959
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057940 Loss1: 0.022818 Loss2: 0.035122
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073775 Loss1: 0.038555 Loss2: 0.035220
-(DefaultActor pid=1838052) >> Training accuracy: 0.988281
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.104396 Loss1: 0.073863 Loss2: 0.030534
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058799 Loss1: 0.026884 Loss2: 0.031914
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052753 Loss1: 0.021073 Loss2: 0.031680
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.057159 Loss1: 0.025194 Loss2: 0.031965
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.079635 Loss1: 0.046387 Loss2: 0.033249
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.081566 Loss1: 0.048165 Loss2: 0.033401
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075514 Loss1: 0.041535 Loss2: 0.033978
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067435 Loss1: 0.033559 Loss2: 0.033876
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.071727 Loss1: 0.037944 Loss2: 0.033782
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083080 Loss1: 0.048374 Loss2: 0.034706
-(DefaultActor pid=1838052) >> Training accuracy: 0.991100
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 22:25:18,205][flwr][DEBUG] - fit_round 77 received 10 results and 0 failures
->> Test accuracy: 0.656300
-[2023-09-28 22:25:56,771][flwr][INFO] - fit progress: (77, 2.305668424303158, {'accuracy': 0.6563}, 144379.66140946606)
-[2023-09-28 22:25:56,771][flwr][DEBUG] - evaluate_round 77: strategy sampled 10 clients (out of 10)
-[2023-09-28 22:26:32,767][flwr][DEBUG] - evaluate_round 77 received 10 results and 0 failures
-[2023-09-28 22:26:32,768][flwr][DEBUG] - fit_round 78: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083075 Loss1: 0.051744 Loss2: 0.031331
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053950 Loss1: 0.021764 Loss2: 0.032187
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057015 Loss1: 0.024583 Loss2: 0.032432
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049274 Loss1: 0.016748 Loss2: 0.032527
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050862 Loss1: 0.018140 Loss2: 0.032722
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050724 Loss1: 0.017746 Loss2: 0.032977
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048264 Loss1: 0.015505 Loss2: 0.032759
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052376 Loss1: 0.019291 Loss2: 0.033085
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051750 Loss1: 0.018144 Loss2: 0.033606
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048041 Loss1: 0.015072 Loss2: 0.032968
-(DefaultActor pid=1838052) >> Training accuracy: 0.998095
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.595603 Loss1: 0.081339 Loss2: 0.514264
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.576154 Loss1: 0.071192 Loss2: 0.504962
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.575004 Loss1: 0.077685 Loss2: 0.497319
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.604136 Loss1: 0.106119 Loss2: 0.498018
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.580180 Loss1: 0.088119 Loss2: 0.492061
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.582607 Loss1: 0.094328 Loss2: 0.488278
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.580554 Loss1: 0.094833 Loss2: 0.485721
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.566255 Loss1: 0.085370 Loss2: 0.480885
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.559725 Loss1: 0.079679 Loss2: 0.480046
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.538916 Loss1: 0.062728 Loss2: 0.476188
-(DefaultActor pid=1838052) >> Training accuracy: 0.985777
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.076009 Loss1: 0.044700 Loss2: 0.031310
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052441 Loss1: 0.019989 Loss2: 0.032451
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.055676 Loss1: 0.023075 Loss2: 0.032601
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050056 Loss1: 0.017255 Loss2: 0.032801
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049178 Loss1: 0.016813 Loss2: 0.032366
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.049797 Loss1: 0.017547 Loss2: 0.032249
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043002 Loss1: 0.010840 Loss2: 0.032162
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046446 Loss1: 0.014020 Loss2: 0.032426
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.049287 Loss1: 0.016842 Loss2: 0.032444
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.057488 Loss1: 0.024833 Loss2: 0.032655
-(DefaultActor pid=1838052) >> Training accuracy: 0.997231
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.586939 Loss1: 0.072923 Loss2: 0.514016
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.546554 Loss1: 0.043326 Loss2: 0.503228
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.535822 Loss1: 0.042161 Loss2: 0.493661
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.540816 Loss1: 0.051927 Loss2: 0.488889
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.542749 Loss1: 0.056009 Loss2: 0.486740
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.556821 Loss1: 0.072961 Loss2: 0.483860
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.557422 Loss1: 0.073784 Loss2: 0.483638
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.571881 Loss1: 0.090374 Loss2: 0.481507
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.575873 Loss1: 0.094631 Loss2: 0.481242
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.554214 Loss1: 0.074231 Loss2: 0.479982
-(DefaultActor pid=1838052) >> Training accuracy: 0.988331
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.091189 Loss1: 0.060970 Loss2: 0.030219
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072022 Loss1: 0.040234 Loss2: 0.031787
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.062831 Loss1: 0.030206 Loss2: 0.032626
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058024 Loss1: 0.025500 Loss2: 0.032524
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061602 Loss1: 0.028833 Loss2: 0.032769
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.064033 Loss1: 0.031018 Loss2: 0.033015
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.069740 Loss1: 0.035846 Loss2: 0.033894
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073363 Loss1: 0.039215 Loss2: 0.034148
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.121869 Loss1: 0.086054 Loss2: 0.035816
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084808 Loss1: 0.049360 Loss2: 0.035448
-(DefaultActor pid=1838052) >> Training accuracy: 0.984592
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102884 Loss1: 0.068372 Loss2: 0.034513
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.078258 Loss1: 0.041540 Loss2: 0.036718
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068586 Loss1: 0.032327 Loss2: 0.036259
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068863 Loss1: 0.032495 Loss2: 0.036368
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.067506 Loss1: 0.031297 Loss2: 0.036209
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079790 Loss1: 0.043304 Loss2: 0.036486
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.078176 Loss1: 0.041161 Loss2: 0.037015
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.086292 Loss1: 0.049071 Loss2: 0.037221
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.095331 Loss1: 0.056669 Loss2: 0.038662
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.127224 Loss1: 0.087364 Loss2: 0.039860
-(DefaultActor pid=1838052) >> Training accuracy: 0.974095
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.084291 Loss1: 0.054354 Loss2: 0.029937
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.054611 Loss1: 0.023584 Loss2: 0.031026
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.051195 Loss1: 0.020267 Loss2: 0.030929
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055886 Loss1: 0.024655 Loss2: 0.031231
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054803 Loss1: 0.023010 Loss2: 0.031794
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.061706 Loss1: 0.029603 Loss2: 0.032102
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072652 Loss1: 0.039959 Loss2: 0.032693
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065282 Loss1: 0.031994 Loss2: 0.033288
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.079120 Loss1: 0.045150 Loss2: 0.033970
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080576 Loss1: 0.045907 Loss2: 0.034670
-(DefaultActor pid=1838052) >> Training accuracy: 0.995253
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.122064 Loss1: 0.063328 Loss2: 0.058736
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.087950 Loss1: 0.031135 Loss2: 0.056815
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.082478 Loss1: 0.028210 Loss2: 0.054268
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.084752 Loss1: 0.031523 Loss2: 0.053228
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.085940 Loss1: 0.032036 Loss2: 0.053904
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077569 Loss1: 0.024438 Loss2: 0.053131
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075111 Loss1: 0.023247 Loss2: 0.051864
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073674 Loss1: 0.022365 Loss2: 0.051309
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075663 Loss1: 0.024443 Loss2: 0.051221
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089700 Loss1: 0.038469 Loss2: 0.051231
-(DefaultActor pid=1838052) >> Training accuracy: 0.995055
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.618706 Loss1: 0.095667 Loss2: 0.523039
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.576135 Loss1: 0.067431 Loss2: 0.508704
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.562875 Loss1: 0.062791 Loss2: 0.500084
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.558842 Loss1: 0.063153 Loss2: 0.495688
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.541389 Loss1: 0.050092 Loss2: 0.491297
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.568880 Loss1: 0.079445 Loss2: 0.489435
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.621667 Loss1: 0.128281 Loss2: 0.493386
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.571306 Loss1: 0.084185 Loss2: 0.487121
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.548173 Loss1: 0.066027 Loss2: 0.482146
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.574584 Loss1: 0.090725 Loss2: 0.483859
-(DefaultActor pid=1838052) >> Training accuracy: 0.984586
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.132806 Loss1: 0.075939 Loss2: 0.056867
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.101232 Loss1: 0.042916 Loss2: 0.058315
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.096377 Loss1: 0.039147 Loss2: 0.057229
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.094908 Loss1: 0.038059 Loss2: 0.056849
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092162 Loss1: 0.036586 Loss2: 0.055577
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.087863 Loss1: 0.034053 Loss2: 0.053810
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.080659 Loss1: 0.026798 Loss2: 0.053860
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.093753 Loss1: 0.040667 Loss2: 0.053086
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.092050 Loss1: 0.038608 Loss2: 0.053442
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089870 Loss1: 0.036958 Loss2: 0.052912
-(DefaultActor pid=1838052) >> Training accuracy: 0.980769
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 22:55:51,267][flwr][DEBUG] - fit_round 78 received 10 results and 0 failures
->> Test accuracy: 0.657500
-[2023-09-28 22:56:28,163][flwr][INFO] - fit progress: (78, 2.337304946332694, {'accuracy': 0.6575}, 146211.0534162484)
-[2023-09-28 22:56:28,164][flwr][DEBUG] - evaluate_round 78: strategy sampled 10 clients (out of 10)
-[2023-09-28 22:57:04,445][flwr][DEBUG] - evaluate_round 78 received 10 results and 0 failures
-[2023-09-28 22:57:04,446][flwr][DEBUG] - fit_round 79: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.116836 Loss1: 0.082713 Loss2: 0.034123
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.078077 Loss1: 0.042284 Loss2: 0.035793
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.065937 Loss1: 0.030129 Loss2: 0.035808
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052446 Loss1: 0.017322 Loss2: 0.035125
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.051558 Loss1: 0.016586 Loss2: 0.034972
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.057345 Loss1: 0.022255 Loss2: 0.035090
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.051199 Loss1: 0.016086 Loss2: 0.035113
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051893 Loss1: 0.016741 Loss2: 0.035152
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053798 Loss1: 0.018748 Loss2: 0.035050
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055066 Loss1: 0.020098 Loss2: 0.034968
-(DefaultActor pid=1838052) >> Training accuracy: 0.995877
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.540314 Loss1: 0.081126 Loss2: 0.459188
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.523515 Loss1: 0.073024 Loss2: 0.450491
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.507723 Loss1: 0.064710 Loss2: 0.443014
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.530223 Loss1: 0.085689 Loss2: 0.444534
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.521980 Loss1: 0.080112 Loss2: 0.441868
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.569400 Loss1: 0.121992 Loss2: 0.447407
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.553960 Loss1: 0.113808 Loss2: 0.440151
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.570797 Loss1: 0.126655 Loss2: 0.444143
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.563982 Loss1: 0.120288 Loss2: 0.443694
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.523427 Loss1: 0.086116 Loss2: 0.437311
-(DefaultActor pid=1838052) >> Training accuracy: 0.977848
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092314 Loss1: 0.060404 Loss2: 0.031910
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058920 Loss1: 0.026211 Loss2: 0.032708
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060548 Loss1: 0.027785 Loss2: 0.032762
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050702 Loss1: 0.018107 Loss2: 0.032595
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055005 Loss1: 0.022464 Loss2: 0.032541
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051479 Loss1: 0.019155 Loss2: 0.032324
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.059182 Loss1: 0.026354 Loss2: 0.032828
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.063120 Loss1: 0.030043 Loss2: 0.033078
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.074833 Loss1: 0.041129 Loss2: 0.033704
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080170 Loss1: 0.045281 Loss2: 0.034889
-(DefaultActor pid=1838052) >> Training accuracy: 0.990704
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.663209 Loss1: 0.066803 Loss2: 0.596406
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.623014 Loss1: 0.046108 Loss2: 0.576906
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.607138 Loss1: 0.046419 Loss2: 0.560719
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.599095 Loss1: 0.049024 Loss2: 0.550071
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.591613 Loss1: 0.048053 Loss2: 0.543560
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.601400 Loss1: 0.062368 Loss2: 0.539032
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.606856 Loss1: 0.070412 Loss2: 0.536443
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.608452 Loss1: 0.070199 Loss2: 0.538253
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.583472 Loss1: 0.049461 Loss2: 0.534011
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.582071 Loss1: 0.052543 Loss2: 0.529528
-(DefaultActor pid=1838052) >> Training accuracy: 0.992089
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.510906 Loss1: 0.072879 Loss2: 0.438027
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.509500 Loss1: 0.080947 Loss2: 0.428554
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.499588 Loss1: 0.073597 Loss2: 0.425991
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.477514 Loss1: 0.057354 Loss2: 0.420160
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.472995 Loss1: 0.055014 Loss2: 0.417981
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.497627 Loss1: 0.078586 Loss2: 0.419042
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.525124 Loss1: 0.100674 Loss2: 0.424450
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.504942 Loss1: 0.085480 Loss2: 0.419461
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.503924 Loss1: 0.085821 Loss2: 0.418103
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.498209 Loss1: 0.079779 Loss2: 0.418430
-(DefaultActor pid=1838052) >> Training accuracy: 0.987981
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081562 Loss1: 0.049969 Loss2: 0.031593
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.060849 Loss1: 0.027900 Loss2: 0.032949
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058394 Loss1: 0.025172 Loss2: 0.033222
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052329 Loss1: 0.019887 Loss2: 0.032442
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055730 Loss1: 0.023287 Loss2: 0.032442
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.057857 Loss1: 0.025283 Loss2: 0.032574
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063084 Loss1: 0.030548 Loss2: 0.032537
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.070521 Loss1: 0.037230 Loss2: 0.033290
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065790 Loss1: 0.032429 Loss2: 0.033361
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.072347 Loss1: 0.038287 Loss2: 0.034060
-(DefaultActor pid=1838052) >> Training accuracy: 0.995593
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102724 Loss1: 0.069370 Loss2: 0.033354
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.063946 Loss1: 0.029121 Loss2: 0.034826
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.062589 Loss1: 0.027774 Loss2: 0.034815
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058169 Loss1: 0.022986 Loss2: 0.035183
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059620 Loss1: 0.024496 Loss2: 0.035124
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053685 Loss1: 0.018811 Loss2: 0.034874
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064694 Loss1: 0.029604 Loss2: 0.035090
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.079230 Loss1: 0.043069 Loss2: 0.036161
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076774 Loss1: 0.040107 Loss2: 0.036667
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084202 Loss1: 0.047287 Loss2: 0.036915
-(DefaultActor pid=1838052) >> Training accuracy: 0.991100
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.099033 Loss1: 0.067388 Loss2: 0.031644
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079275 Loss1: 0.045207 Loss2: 0.034068
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.075600 Loss1: 0.040843 Loss2: 0.034757
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.066058 Loss1: 0.031228 Loss2: 0.034830
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069085 Loss1: 0.034332 Loss2: 0.034754
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.077793 Loss1: 0.042794 Loss2: 0.034999
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.070580 Loss1: 0.035370 Loss2: 0.035210
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066324 Loss1: 0.031312 Loss2: 0.035012
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064416 Loss1: 0.029416 Loss2: 0.034999
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060377 Loss1: 0.025056 Loss2: 0.035320
-(DefaultActor pid=1838052) >> Training accuracy: 0.996505
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.090622 Loss1: 0.058285 Loss2: 0.032337
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075170 Loss1: 0.040964 Loss2: 0.034206
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077142 Loss1: 0.042102 Loss2: 0.035041
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.088198 Loss1: 0.052210 Loss2: 0.035988
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.088315 Loss1: 0.051627 Loss2: 0.036687
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.088429 Loss1: 0.051168 Loss2: 0.037262
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077179 Loss1: 0.039908 Loss2: 0.037271
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.108426 Loss1: 0.070101 Loss2: 0.038325
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.094707 Loss1: 0.055839 Loss2: 0.038868
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082099 Loss1: 0.043642 Loss2: 0.038457
-(DefaultActor pid=1838052) >> Training accuracy: 0.993521
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.117421 Loss1: 0.084595 Loss2: 0.032826
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075321 Loss1: 0.041531 Loss2: 0.033790
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.072326 Loss1: 0.038090 Loss2: 0.034236
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.073807 Loss1: 0.039784 Loss2: 0.034024
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073644 Loss1: 0.039408 Loss2: 0.034236
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.061155 Loss1: 0.026970 Loss2: 0.034185
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.055788 Loss1: 0.021959 Loss2: 0.033829
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055499 Loss1: 0.022001 Loss2: 0.033498
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.055984 Loss1: 0.022776 Loss2: 0.033208
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.071300 Loss1: 0.037738 Loss2: 0.033563
-(DefaultActor pid=1838052) >> Training accuracy: 0.996622
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 23:26:27,445][flwr][DEBUG] - fit_round 79 received 10 results and 0 failures
->> Test accuracy: 0.659500
-[2023-09-28 23:27:02,232][flwr][INFO] - fit progress: (79, 2.348709229844066, {'accuracy': 0.6595}, 148045.12243541144)
-[2023-09-28 23:27:02,233][flwr][DEBUG] - evaluate_round 79: strategy sampled 10 clients (out of 10)
-[2023-09-28 23:27:38,359][flwr][DEBUG] - evaluate_round 79 received 10 results and 0 failures
-[2023-09-28 23:27:38,360][flwr][DEBUG] - fit_round 80: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.100009 Loss1: 0.067586 Loss2: 0.032423
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074269 Loss1: 0.040333 Loss2: 0.033936
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.073600 Loss1: 0.039081 Loss2: 0.034519
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.066790 Loss1: 0.032285 Loss2: 0.034505
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054930 Loss1: 0.021025 Loss2: 0.033905
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053360 Loss1: 0.019617 Loss2: 0.033743
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.053232 Loss1: 0.019407 Loss2: 0.033825
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052460 Loss1: 0.018835 Loss2: 0.033625
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053508 Loss1: 0.020120 Loss2: 0.033388
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060306 Loss1: 0.026485 Loss2: 0.033821
-(DefaultActor pid=1838052) >> Training accuracy: 0.995877
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.709541 Loss1: 0.101496 Loss2: 0.608046
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.656735 Loss1: 0.059760 Loss2: 0.596975
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.657840 Loss1: 0.068090 Loss2: 0.589749
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.634617 Loss1: 0.052640 Loss2: 0.581977
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.619091 Loss1: 0.044384 Loss2: 0.574706
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.613078 Loss1: 0.043732 Loss2: 0.569346
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.612925 Loss1: 0.048325 Loss2: 0.564600
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.633442 Loss1: 0.067851 Loss2: 0.565592
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.636400 Loss1: 0.071667 Loss2: 0.564733
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.628630 Loss1: 0.066847 Loss2: 0.561783
-(DefaultActor pid=1838052) >> Training accuracy: 0.990921
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.085088 Loss1: 0.053504 Loss2: 0.031583
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073968 Loss1: 0.040870 Loss2: 0.033098
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.070461 Loss1: 0.036805 Loss2: 0.033656
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060082 Loss1: 0.026386 Loss2: 0.033695
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063947 Loss1: 0.030293 Loss2: 0.033654
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.076683 Loss1: 0.042451 Loss2: 0.034232
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065382 Loss1: 0.031190 Loss2: 0.034192
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057381 Loss1: 0.023287 Loss2: 0.034094
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.061563 Loss1: 0.027530 Loss2: 0.034033
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068177 Loss1: 0.033742 Loss2: 0.034435
-(DefaultActor pid=1838052) >> Training accuracy: 0.992880
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.089742 Loss1: 0.056923 Loss2: 0.032819
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073252 Loss1: 0.039029 Loss2: 0.034224
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061301 Loss1: 0.026587 Loss2: 0.034714
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055495 Loss1: 0.021744 Loss2: 0.033751
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054296 Loss1: 0.020511 Loss2: 0.033785
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053862 Loss1: 0.019937 Loss2: 0.033926
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049890 Loss1: 0.016415 Loss2: 0.033476
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046179 Loss1: 0.012690 Loss2: 0.033489
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.045865 Loss1: 0.012732 Loss2: 0.033133
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.061276 Loss1: 0.027622 Loss2: 0.033654
-(DefaultActor pid=1838052) >> Training accuracy: 0.997033
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.640713 Loss1: 0.063208 Loss2: 0.577506
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.621925 Loss1: 0.051060 Loss2: 0.570865
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.612004 Loss1: 0.047625 Loss2: 0.564378
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.592334 Loss1: 0.034832 Loss2: 0.557502
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.589422 Loss1: 0.038502 Loss2: 0.550920
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.593388 Loss1: 0.045729 Loss2: 0.547659
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.595747 Loss1: 0.049229 Loss2: 0.546518
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.597769 Loss1: 0.053477 Loss2: 0.544293
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.598820 Loss1: 0.053140 Loss2: 0.545680
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.617416 Loss1: 0.072013 Loss2: 0.545403
-(DefaultActor pid=1838052) >> Training accuracy: 0.985759
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078980 Loss1: 0.051571 Loss2: 0.027408
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053762 Loss1: 0.025146 Loss2: 0.028616
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.065714 Loss1: 0.036566 Loss2: 0.029148
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047236 Loss1: 0.018078 Loss2: 0.029158
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050175 Loss1: 0.021034 Loss2: 0.029141
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.043883 Loss1: 0.015112 Loss2: 0.028771
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050974 Loss1: 0.021949 Loss2: 0.029025
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050479 Loss1: 0.021136 Loss2: 0.029343
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.055393 Loss1: 0.025989 Loss2: 0.029404
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048298 Loss1: 0.019225 Loss2: 0.029072
-(DefaultActor pid=1838052) >> Training accuracy: 0.995192
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.088124 Loss1: 0.049328 Loss2: 0.038796
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.067073 Loss1: 0.027946 Loss2: 0.039127
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.059729 Loss1: 0.021508 Loss2: 0.038222
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067918 Loss1: 0.029730 Loss2: 0.038189
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063169 Loss1: 0.025277 Loss2: 0.037893
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065964 Loss1: 0.028538 Loss2: 0.037426
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056363 Loss1: 0.019451 Loss2: 0.036912
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057417 Loss1: 0.020753 Loss2: 0.036664
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.073220 Loss1: 0.036080 Loss2: 0.037140
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073025 Loss1: 0.035259 Loss2: 0.037766
-(DefaultActor pid=1838052) >> Training accuracy: 0.994462
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.101728 Loss1: 0.070010 Loss2: 0.031719
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069636 Loss1: 0.036139 Loss2: 0.033497
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058840 Loss1: 0.025508 Loss2: 0.033332
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058599 Loss1: 0.025459 Loss2: 0.033140
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063797 Loss1: 0.030687 Loss2: 0.033110
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070487 Loss1: 0.036536 Loss2: 0.033951
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.067109 Loss1: 0.032905 Loss2: 0.034204
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.080440 Loss1: 0.045560 Loss2: 0.034880
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.078726 Loss1: 0.043651 Loss2: 0.035074
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.074870 Loss1: 0.039825 Loss2: 0.035044
-(DefaultActor pid=1838052) >> Training accuracy: 0.992188
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.093041 Loss1: 0.062252 Loss2: 0.030789
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.065807 Loss1: 0.033181 Loss2: 0.032626
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057094 Loss1: 0.024727 Loss2: 0.032368
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.057320 Loss1: 0.024323 Loss2: 0.032998
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069547 Loss1: 0.036697 Loss2: 0.032850
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.063417 Loss1: 0.030144 Loss2: 0.033273
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065899 Loss1: 0.032478 Loss2: 0.033422
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066149 Loss1: 0.032261 Loss2: 0.033889
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068831 Loss1: 0.034530 Loss2: 0.034301
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.077950 Loss1: 0.043702 Loss2: 0.034247
-(DefaultActor pid=1838052) >> Training accuracy: 0.991425
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.140475 Loss1: 0.065257 Loss2: 0.075217
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.104977 Loss1: 0.032915 Loss2: 0.072062
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.107929 Loss1: 0.037584 Loss2: 0.070345
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.129382 Loss1: 0.057820 Loss2: 0.071562
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.103715 Loss1: 0.032730 Loss2: 0.070985
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.096207 Loss1: 0.027038 Loss2: 0.069169
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.101244 Loss1: 0.032322 Loss2: 0.068922
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.100334 Loss1: 0.031320 Loss2: 0.069015
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.128117 Loss1: 0.057827 Loss2: 0.070290
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.118358 Loss1: 0.047909 Loss2: 0.070449
-(DefaultActor pid=1838052) >> Training accuracy: 0.992188
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-28 23:57:04,005][flwr][DEBUG] - fit_round 80 received 10 results and 0 failures
->> Test accuracy: 0.660200
-[2023-09-28 23:57:40,852][flwr][INFO] - fit progress: (80, 2.37383095201212, {'accuracy': 0.6602}, 149883.74229009543)
-[2023-09-28 23:57:40,852][flwr][DEBUG] - evaluate_round 80: strategy sampled 10 clients (out of 10)
-[2023-09-28 23:58:17,165][flwr][DEBUG] - evaluate_round 80 received 10 results and 0 failures
-[2023-09-28 23:58:17,166][flwr][DEBUG] - fit_round 81: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.650115 Loss1: 0.059922 Loss2: 0.590192
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.609214 Loss1: 0.043426 Loss2: 0.565789
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.632361 Loss1: 0.070158 Loss2: 0.562203
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.606748 Loss1: 0.048576 Loss2: 0.558172
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.605377 Loss1: 0.053175 Loss2: 0.552201
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.605140 Loss1: 0.057401 Loss2: 0.547740
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.629063 Loss1: 0.081307 Loss2: 0.547755
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.599318 Loss1: 0.056089 Loss2: 0.543229
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.585232 Loss1: 0.045582 Loss2: 0.539650
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.589485 Loss1: 0.053723 Loss2: 0.535763
-(DefaultActor pid=1838052) >> Training accuracy: 0.987580
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.685330 Loss1: 0.075823 Loss2: 0.609506
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.642180 Loss1: 0.052032 Loss2: 0.590148
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.629133 Loss1: 0.054281 Loss2: 0.574852
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.647898 Loss1: 0.081706 Loss2: 0.566192
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.660330 Loss1: 0.097444 Loss2: 0.562886
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.650097 Loss1: 0.091888 Loss2: 0.558209
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.630429 Loss1: 0.079051 Loss2: 0.551378
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.656826 Loss1: 0.109643 Loss2: 0.547184
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.626867 Loss1: 0.084252 Loss2: 0.542615
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.632447 Loss1: 0.090888 Loss2: 0.541559
-(DefaultActor pid=1838052) >> Training accuracy: 0.985759
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.111174 Loss1: 0.080093 Loss2: 0.031082
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.068824 Loss1: 0.037101 Loss2: 0.031722
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.086077 Loss1: 0.053577 Loss2: 0.032500
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.084126 Loss1: 0.051107 Loss2: 0.033019
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065068 Loss1: 0.032158 Loss2: 0.032910
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.078637 Loss1: 0.045622 Loss2: 0.033015
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079222 Loss1: 0.046197 Loss2: 0.033026
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068654 Loss1: 0.035773 Loss2: 0.032881
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075832 Loss1: 0.042736 Loss2: 0.033096
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.074260 Loss1: 0.040688 Loss2: 0.033572
-(DefaultActor pid=1838052) >> Training accuracy: 0.991132
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.412938 Loss1: 0.088582 Loss2: 0.324356
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.393784 Loss1: 0.080933 Loss2: 0.312850
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.411551 Loss1: 0.104296 Loss2: 0.307255
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.421979 Loss1: 0.116710 Loss2: 0.305269
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.451120 Loss1: 0.140263 Loss2: 0.310856
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.461345 Loss1: 0.153417 Loss2: 0.307928
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.441644 Loss1: 0.141541 Loss2: 0.300103
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.409425 Loss1: 0.112465 Loss2: 0.296960
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.407464 Loss1: 0.110091 Loss2: 0.297374
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.415583 Loss1: 0.119029 Loss2: 0.296554
-(DefaultActor pid=1838052) >> Training accuracy: 0.962340
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083949 Loss1: 0.054536 Loss2: 0.029412
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073261 Loss1: 0.041843 Loss2: 0.031418
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.079477 Loss1: 0.047359 Loss2: 0.032118
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067868 Loss1: 0.035233 Loss2: 0.032634
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065301 Loss1: 0.032674 Loss2: 0.032628
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.083015 Loss1: 0.050076 Loss2: 0.032938
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.079738 Loss1: 0.046023 Loss2: 0.033714
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.098486 Loss1: 0.063853 Loss2: 0.034633
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086821 Loss1: 0.052440 Loss2: 0.034382
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070358 Loss1: 0.036102 Loss2: 0.034257
-(DefaultActor pid=1838052) >> Training accuracy: 0.994792
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.096208 Loss1: 0.066572 Loss2: 0.029636
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.072643 Loss1: 0.040762 Loss2: 0.031881
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.071773 Loss1: 0.039683 Loss2: 0.032090
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068587 Loss1: 0.035642 Loss2: 0.032946
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.074962 Loss1: 0.041888 Loss2: 0.033074
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.059242 Loss1: 0.026622 Loss2: 0.032620
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064571 Loss1: 0.031759 Loss2: 0.032812
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061340 Loss1: 0.028926 Loss2: 0.032414
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057428 Loss1: 0.024958 Loss2: 0.032470
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.075618 Loss1: 0.042404 Loss2: 0.033215
-(DefaultActor pid=1838052) >> Training accuracy: 0.991571
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.507350 Loss1: 0.047867 Loss2: 0.459482
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.469050 Loss1: 0.040804 Loss2: 0.428246
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.451553 Loss1: 0.027339 Loss2: 0.424213
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.447852 Loss1: 0.028515 Loss2: 0.419337
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.450468 Loss1: 0.031753 Loss2: 0.418715
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.461636 Loss1: 0.042675 Loss2: 0.418961
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.505051 Loss1: 0.081081 Loss2: 0.423970
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.498217 Loss1: 0.074067 Loss2: 0.424150
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.492316 Loss1: 0.070302 Loss2: 0.422013
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.526047 Loss1: 0.099369 Loss2: 0.426678
-(DefaultActor pid=1838052) >> Training accuracy: 0.971123
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.632165 Loss1: 0.063826 Loss2: 0.568340
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.625716 Loss1: 0.064756 Loss2: 0.560960
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.623001 Loss1: 0.068739 Loss2: 0.554261
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.634539 Loss1: 0.085599 Loss2: 0.548940
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.631543 Loss1: 0.085788 Loss2: 0.545755
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.635841 Loss1: 0.090986 Loss2: 0.544855
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.615024 Loss1: 0.076247 Loss2: 0.538778
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.615434 Loss1: 0.077993 Loss2: 0.537442
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.633434 Loss1: 0.096926 Loss2: 0.536508
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.648356 Loss1: 0.110089 Loss2: 0.538266
-(DefaultActor pid=1838052) >> Training accuracy: 0.984375
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.085361 Loss1: 0.055569 Loss2: 0.029792
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.059269 Loss1: 0.028249 Loss2: 0.031020
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058912 Loss1: 0.027800 Loss2: 0.031112
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060659 Loss1: 0.029189 Loss2: 0.031470
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055653 Loss1: 0.023984 Loss2: 0.031670
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.061281 Loss1: 0.029578 Loss2: 0.031702
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.068360 Loss1: 0.036137 Loss2: 0.032223
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065458 Loss1: 0.032711 Loss2: 0.032747
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.094737 Loss1: 0.060759 Loss2: 0.033977
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.112745 Loss1: 0.077679 Loss2: 0.035066
-(DefaultActor pid=1838052) >> Training accuracy: 0.980617
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.080547 Loss1: 0.051153 Loss2: 0.029394
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.056733 Loss1: 0.026428 Loss2: 0.030305
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057763 Loss1: 0.027085 Loss2: 0.030679
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050274 Loss1: 0.019315 Loss2: 0.030959
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064655 Loss1: 0.033214 Loss2: 0.031441
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055759 Loss1: 0.023941 Loss2: 0.031818
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065616 Loss1: 0.033739 Loss2: 0.031876
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057874 Loss1: 0.025861 Loss2: 0.032013
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.063671 Loss1: 0.031765 Loss2: 0.031905
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084382 Loss1: 0.051600 Loss2: 0.032782
-(DefaultActor pid=1838052) >> Training accuracy: 0.988726
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 00:26:46,927][flwr][DEBUG] - fit_round 81 received 10 results and 0 failures
->> Test accuracy: 0.661900
-[2023-09-29 00:27:22,528][flwr][INFO] - fit progress: (81, 2.300124180012237, {'accuracy': 0.6619}, 151665.4180758763)
-[2023-09-29 00:27:22,528][flwr][DEBUG] - evaluate_round 81: strategy sampled 10 clients (out of 10)
-[2023-09-29 00:27:57,875][flwr][DEBUG] - evaluate_round 81 received 10 results and 0 failures
-[2023-09-29 00:27:57,877][flwr][DEBUG] - fit_round 82: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087299 Loss1: 0.054185 Loss2: 0.033114
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.060703 Loss1: 0.026792 Loss2: 0.033911
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.053937 Loss1: 0.020360 Loss2: 0.033577
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051056 Loss1: 0.017983 Loss2: 0.033073
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055856 Loss1: 0.023043 Loss2: 0.032813
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.059866 Loss1: 0.026568 Loss2: 0.033297
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.060673 Loss1: 0.027191 Loss2: 0.033482
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.077418 Loss1: 0.042801 Loss2: 0.034617
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076519 Loss1: 0.042023 Loss2: 0.034496
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070885 Loss1: 0.036248 Loss2: 0.034637
-(DefaultActor pid=1838052) >> Training accuracy: 0.996044
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.682720 Loss1: 0.070605 Loss2: 0.612115
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.640273 Loss1: 0.037372 Loss2: 0.602902
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.623532 Loss1: 0.037785 Loss2: 0.585747
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.623010 Loss1: 0.047402 Loss2: 0.575608
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.623198 Loss1: 0.050493 Loss2: 0.572705
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.624922 Loss1: 0.059206 Loss2: 0.565717
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.609980 Loss1: 0.047577 Loss2: 0.562403
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.609400 Loss1: 0.053276 Loss2: 0.556124
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.618151 Loss1: 0.062195 Loss2: 0.555957
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.612446 Loss1: 0.061202 Loss2: 0.551244
-(DefaultActor pid=1838052) >> Training accuracy: 0.987196
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.672335 Loss1: 0.061776 Loss2: 0.610558
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.647458 Loss1: 0.049813 Loss2: 0.597645
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.635979 Loss1: 0.051667 Loss2: 0.584312
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.637866 Loss1: 0.055098 Loss2: 0.582768
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.633748 Loss1: 0.059202 Loss2: 0.574546
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.654132 Loss1: 0.079167 Loss2: 0.574965
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.656527 Loss1: 0.081148 Loss2: 0.575379
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.657765 Loss1: 0.089624 Loss2: 0.568141
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.644110 Loss1: 0.076497 Loss2: 0.567614
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.650452 Loss1: 0.086204 Loss2: 0.564249
-(DefaultActor pid=1838052) >> Training accuracy: 0.987253
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.644143 Loss1: 0.056603 Loss2: 0.587540
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.621297 Loss1: 0.049061 Loss2: 0.572236
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.609537 Loss1: 0.046330 Loss2: 0.563206
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.606262 Loss1: 0.054569 Loss2: 0.551692
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.589564 Loss1: 0.042820 Loss2: 0.546744
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.583139 Loss1: 0.044061 Loss2: 0.539078
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.588742 Loss1: 0.051846 Loss2: 0.536896
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.600833 Loss1: 0.065062 Loss2: 0.535771
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.602868 Loss1: 0.069150 Loss2: 0.533719
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.582602 Loss1: 0.051540 Loss2: 0.531062
-(DefaultActor pid=1838052) >> Training accuracy: 0.983782
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074816 Loss1: 0.043138 Loss2: 0.031678
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051042 Loss1: 0.018420 Loss2: 0.032622
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049053 Loss1: 0.016294 Loss2: 0.032759
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050653 Loss1: 0.018203 Loss2: 0.032450
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.045136 Loss1: 0.012512 Loss2: 0.032624
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046479 Loss1: 0.013944 Loss2: 0.032535
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.041666 Loss1: 0.009463 Loss2: 0.032203
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046929 Loss1: 0.014651 Loss2: 0.032278
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.041534 Loss1: 0.009216 Loss2: 0.032318
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040728 Loss1: 0.008806 Loss2: 0.031921
-(DefaultActor pid=1838052) >> Training accuracy: 0.999399
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.076821 Loss1: 0.046419 Loss2: 0.030402
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.046300 Loss1: 0.014956 Loss2: 0.031343
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.040427 Loss1: 0.009764 Loss2: 0.030663
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039016 Loss1: 0.008734 Loss2: 0.030282
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041677 Loss1: 0.011544 Loss2: 0.030133
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040800 Loss1: 0.010586 Loss2: 0.030215
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.036113 Loss1: 0.006274 Loss2: 0.029839
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.039009 Loss1: 0.009254 Loss2: 0.029755
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.037856 Loss1: 0.007726 Loss2: 0.030130
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.036718 Loss1: 0.006832 Loss2: 0.029886
-(DefaultActor pid=1838052) >> Training accuracy: 0.999428
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081558 Loss1: 0.052770 Loss2: 0.028788
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052226 Loss1: 0.022253 Loss2: 0.029973
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.048206 Loss1: 0.018430 Loss2: 0.029776
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052648 Loss1: 0.022695 Loss2: 0.029953
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061335 Loss1: 0.030679 Loss2: 0.030656
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055811 Loss1: 0.024712 Loss2: 0.031098
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.046948 Loss1: 0.016025 Loss2: 0.030922
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.054972 Loss1: 0.024167 Loss2: 0.030805
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.059571 Loss1: 0.028368 Loss2: 0.031204
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.059312 Loss1: 0.027551 Loss2: 0.031762
-(DefaultActor pid=1838052) >> Training accuracy: 0.995451
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.126804 Loss1: 0.060236 Loss2: 0.066567
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.089380 Loss1: 0.024109 Loss2: 0.065271
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.090925 Loss1: 0.026551 Loss2: 0.064374
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.090556 Loss1: 0.026293 Loss2: 0.064262
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.089017 Loss1: 0.025901 Loss2: 0.063116
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.082122 Loss1: 0.020443 Loss2: 0.061679
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.083408 Loss1: 0.022616 Loss2: 0.060792
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.089321 Loss1: 0.028593 Loss2: 0.060728
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096172 Loss1: 0.034752 Loss2: 0.061420
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.101354 Loss1: 0.038338 Loss2: 0.063016
-(DefaultActor pid=1838052) >> Training accuracy: 0.994660
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110154 Loss1: 0.046662 Loss2: 0.063491
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.079471 Loss1: 0.017277 Loss2: 0.062194
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.075121 Loss1: 0.013997 Loss2: 0.061124
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074263 Loss1: 0.013801 Loss2: 0.060462
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.078540 Loss1: 0.018050 Loss2: 0.060490
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.085424 Loss1: 0.024529 Loss2: 0.060895
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.103606 Loss1: 0.041049 Loss2: 0.062557
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.090467 Loss1: 0.028214 Loss2: 0.062253
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.082395 Loss1: 0.020390 Loss2: 0.062005
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093039 Loss1: 0.030804 Loss2: 0.062235
-(DefaultActor pid=1838052) >> Training accuracy: 0.995593
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.634412 Loss1: 0.080763 Loss2: 0.553649
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.606634 Loss1: 0.060216 Loss2: 0.546418
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.603575 Loss1: 0.061998 Loss2: 0.541576
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.584562 Loss1: 0.047953 Loss2: 0.536609
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.569311 Loss1: 0.041578 Loss2: 0.527732
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.581209 Loss1: 0.055066 Loss2: 0.526142
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.593361 Loss1: 0.066286 Loss2: 0.527075
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.580433 Loss1: 0.055778 Loss2: 0.524654
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.562449 Loss1: 0.039802 Loss2: 0.522647
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.565679 Loss1: 0.047562 Loss2: 0.518118
-(DefaultActor pid=1838052) >> Training accuracy: 0.979730
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 00:56:34,096][flwr][DEBUG] - fit_round 82 received 10 results and 0 failures
->> Test accuracy: 0.663200
-[2023-09-29 00:57:10,567][flwr][INFO] - fit progress: (82, 2.3621472944847692, {'accuracy': 0.6632}, 153453.45776463626)
-[2023-09-29 00:57:10,568][flwr][DEBUG] - evaluate_round 82: strategy sampled 10 clients (out of 10)
-[2023-09-29 00:57:46,371][flwr][DEBUG] - evaluate_round 82 received 10 results and 0 failures
-[2023-09-29 00:57:46,372][flwr][DEBUG] - fit_round 83: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.560626 Loss1: 0.065522 Loss2: 0.495105
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.534453 Loss1: 0.049963 Loss2: 0.484490
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.522993 Loss1: 0.044667 Loss2: 0.478325
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.522862 Loss1: 0.046264 Loss2: 0.476597
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.524931 Loss1: 0.052779 Loss2: 0.472152
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.520828 Loss1: 0.049326 Loss2: 0.471502
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.529461 Loss1: 0.056765 Loss2: 0.472696
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.534423 Loss1: 0.062418 Loss2: 0.472004
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.549236 Loss1: 0.076607 Loss2: 0.472630
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.532523 Loss1: 0.060981 Loss2: 0.471542
-(DefaultActor pid=1838052) >> Training accuracy: 0.986178
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.084626 Loss1: 0.051665 Loss2: 0.032961
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058278 Loss1: 0.023774 Loss2: 0.034504
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.065124 Loss1: 0.030724 Loss2: 0.034400
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.067144 Loss1: 0.031630 Loss2: 0.035513
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060622 Loss1: 0.025483 Loss2: 0.035139
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058093 Loss1: 0.023188 Loss2: 0.034905
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065562 Loss1: 0.030544 Loss2: 0.035017
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.060957 Loss1: 0.025299 Loss2: 0.035658
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.066910 Loss1: 0.031094 Loss2: 0.035816
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068515 Loss1: 0.032392 Loss2: 0.036123
-(DefaultActor pid=1838052) >> Training accuracy: 0.996745
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.422990 Loss1: 0.054669 Loss2: 0.368321
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.354482 Loss1: 0.029642 Loss2: 0.324840
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.339252 Loss1: 0.034769 Loss2: 0.304483
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.334324 Loss1: 0.036183 Loss2: 0.298141
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.338688 Loss1: 0.042619 Loss2: 0.296069
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.344737 Loss1: 0.049635 Loss2: 0.295103
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.345852 Loss1: 0.052054 Loss2: 0.293798
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.390916 Loss1: 0.094168 Loss2: 0.296748
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.413102 Loss1: 0.113011 Loss2: 0.300091
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.402107 Loss1: 0.099392 Loss2: 0.302715
-(DefaultActor pid=1838052) >> Training accuracy: 0.984177
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081212 Loss1: 0.049824 Loss2: 0.031389
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058839 Loss1: 0.025867 Loss2: 0.032972
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049905 Loss1: 0.017253 Loss2: 0.032652
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049802 Loss1: 0.016797 Loss2: 0.033005
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049694 Loss1: 0.016941 Loss2: 0.032753
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050261 Loss1: 0.017428 Loss2: 0.032832
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049292 Loss1: 0.016471 Loss2: 0.032821
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.048675 Loss1: 0.015813 Loss2: 0.032862
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.049463 Loss1: 0.016568 Loss2: 0.032895
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.044703 Loss1: 0.011808 Loss2: 0.032895
-(DefaultActor pid=1838052) >> Training accuracy: 0.996505
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071511 Loss1: 0.039791 Loss2: 0.031720
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052760 Loss1: 0.020461 Loss2: 0.032299
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052611 Loss1: 0.020156 Loss2: 0.032455
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.053571 Loss1: 0.020642 Loss2: 0.032929
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050849 Loss1: 0.017725 Loss2: 0.033123
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050736 Loss1: 0.017720 Loss2: 0.033016
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043657 Loss1: 0.010726 Loss2: 0.032931
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046082 Loss1: 0.013241 Loss2: 0.032840
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.050776 Loss1: 0.018104 Loss2: 0.032671
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.052767 Loss1: 0.019632 Loss2: 0.033136
-(DefaultActor pid=1838052) >> Training accuracy: 0.996761
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.095487 Loss1: 0.064731 Loss2: 0.030756
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057370 Loss1: 0.025305 Loss2: 0.032065
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052298 Loss1: 0.020369 Loss2: 0.031929
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051685 Loss1: 0.019858 Loss2: 0.031827
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061595 Loss1: 0.029078 Loss2: 0.032516
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.063783 Loss1: 0.030779 Loss2: 0.033004
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064145 Loss1: 0.030903 Loss2: 0.033243
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.069104 Loss1: 0.035647 Loss2: 0.033457
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.099534 Loss1: 0.064902 Loss2: 0.034632
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084546 Loss1: 0.049864 Loss2: 0.034681
-(DefaultActor pid=1838052) >> Training accuracy: 0.989913
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.125994 Loss1: 0.066143 Loss2: 0.059851
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.090098 Loss1: 0.032534 Loss2: 0.057564
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.085786 Loss1: 0.031042 Loss2: 0.054743
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.090646 Loss1: 0.036985 Loss2: 0.053661
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.083683 Loss1: 0.031376 Loss2: 0.052307
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.079680 Loss1: 0.027698 Loss2: 0.051982
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075054 Loss1: 0.023895 Loss2: 0.051159
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.070402 Loss1: 0.020323 Loss2: 0.050078
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077002 Loss1: 0.027669 Loss2: 0.049333
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.098980 Loss1: 0.048238 Loss2: 0.050741
-(DefaultActor pid=1838052) >> Training accuracy: 0.989654
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.476288 Loss1: 0.065004 Loss2: 0.411284
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.452704 Loss1: 0.053346 Loss2: 0.399358
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.483497 Loss1: 0.078498 Loss2: 0.404999
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.506350 Loss1: 0.101119 Loss2: 0.405231
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.491187 Loss1: 0.092530 Loss2: 0.398658
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.489430 Loss1: 0.090782 Loss2: 0.398648
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.467677 Loss1: 0.070543 Loss2: 0.397134
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.503094 Loss1: 0.099770 Loss2: 0.403323
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.502942 Loss1: 0.101409 Loss2: 0.401533
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.501221 Loss1: 0.097930 Loss2: 0.403290
-(DefaultActor pid=1838052) >> Training accuracy: 0.982199
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.084942 Loss1: 0.041165 Loss2: 0.043777
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.062798 Loss1: 0.019461 Loss2: 0.043336
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054233 Loss1: 0.011380 Loss2: 0.042852
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.053387 Loss1: 0.011304 Loss2: 0.042082
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.053790 Loss1: 0.011989 Loss2: 0.041801
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060872 Loss1: 0.018747 Loss2: 0.042125
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.061512 Loss1: 0.019222 Loss2: 0.042290
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.064517 Loss1: 0.021905 Loss2: 0.042612
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.093697 Loss1: 0.050073 Loss2: 0.043624
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.104607 Loss1: 0.058965 Loss2: 0.045642
-(DefaultActor pid=1838052) >> Training accuracy: 0.993790
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.108246 Loss1: 0.059243 Loss2: 0.049004
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080237 Loss1: 0.032553 Loss2: 0.047684
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.076748 Loss1: 0.029584 Loss2: 0.047164
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.084934 Loss1: 0.037710 Loss2: 0.047224
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086625 Loss1: 0.039144 Loss2: 0.047481
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.074429 Loss1: 0.027318 Loss2: 0.047111
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.077938 Loss1: 0.030690 Loss2: 0.047248
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071275 Loss1: 0.024334 Loss2: 0.046941
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075142 Loss1: 0.028241 Loss2: 0.046901
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080727 Loss1: 0.033198 Loss2: 0.047529
-(DefaultActor pid=1838052) >> Training accuracy: 0.995055
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 01:26:37,128][flwr][DEBUG] - fit_round 83 received 10 results and 0 failures
->> Test accuracy: 0.662700
-[2023-09-29 01:27:15,957][flwr][INFO] - fit progress: (83, 2.3891840624733094, {'accuracy': 0.6627}, 155258.84766899934)
-[2023-09-29 01:27:15,958][flwr][DEBUG] - evaluate_round 83: strategy sampled 10 clients (out of 10)
-[2023-09-29 01:27:51,609][flwr][DEBUG] - evaluate_round 83 received 10 results and 0 failures
-[2023-09-29 01:27:51,611][flwr][DEBUG] - fit_round 84: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.646308 Loss1: 0.057634 Loss2: 0.588674
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.617129 Loss1: 0.039464 Loss2: 0.577665
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.598269 Loss1: 0.033606 Loss2: 0.564663
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.606034 Loss1: 0.047600 Loss2: 0.558434
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.629266 Loss1: 0.071344 Loss2: 0.557922
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.641275 Loss1: 0.085096 Loss2: 0.556179
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.645134 Loss1: 0.087613 Loss2: 0.557521
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.643378 Loss1: 0.089636 Loss2: 0.553742
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.652173 Loss1: 0.100143 Loss2: 0.552030
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.665602 Loss1: 0.113378 Loss2: 0.552224
-(DefaultActor pid=1838052) >> Training accuracy: 0.976661
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.674546 Loss1: 0.064134 Loss2: 0.610411
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.646093 Loss1: 0.044742 Loss2: 0.601351
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.644085 Loss1: 0.048214 Loss2: 0.595870
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.642042 Loss1: 0.053671 Loss2: 0.588372
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.643980 Loss1: 0.059044 Loss2: 0.584935
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.645235 Loss1: 0.065713 Loss2: 0.579522
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.655888 Loss1: 0.079380 Loss2: 0.576508
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.632928 Loss1: 0.058495 Loss2: 0.574433
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.619829 Loss1: 0.051252 Loss2: 0.568577
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.641138 Loss1: 0.073325 Loss2: 0.567813
-(DefaultActor pid=1838052) >> Training accuracy: 0.985609
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071223 Loss1: 0.042425 Loss2: 0.028798
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.062819 Loss1: 0.032551 Loss2: 0.030267
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060055 Loss1: 0.029268 Loss2: 0.030787
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.069358 Loss1: 0.037881 Loss2: 0.031477
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060109 Loss1: 0.028229 Loss2: 0.031879
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053077 Loss1: 0.021484 Loss2: 0.031592
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.068203 Loss1: 0.036216 Loss2: 0.031987
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066700 Loss1: 0.034313 Loss2: 0.032386
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075012 Loss1: 0.042121 Loss2: 0.032891
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091448 Loss1: 0.057555 Loss2: 0.033893
-(DefaultActor pid=1838052) >> Training accuracy: 0.993331
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075941 Loss1: 0.046637 Loss2: 0.029304
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049421 Loss1: 0.019119 Loss2: 0.030302
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047607 Loss1: 0.017328 Loss2: 0.030279
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039889 Loss1: 0.009891 Loss2: 0.029998
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.040518 Loss1: 0.010363 Loss2: 0.030155
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038429 Loss1: 0.008680 Loss2: 0.029749
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.039956 Loss1: 0.010219 Loss2: 0.029738
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.037960 Loss1: 0.007993 Loss2: 0.029967
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.035958 Loss1: 0.006364 Loss2: 0.029594
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.042039 Loss1: 0.012103 Loss2: 0.029936
-(DefaultActor pid=1838052) >> Training accuracy: 0.999011
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.437679 Loss1: 0.076745 Loss2: 0.360934
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.415603 Loss1: 0.066189 Loss2: 0.349414
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.408454 Loss1: 0.058792 Loss2: 0.349662
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.406693 Loss1: 0.059172 Loss2: 0.347520
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.417608 Loss1: 0.070832 Loss2: 0.346776
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.435268 Loss1: 0.085835 Loss2: 0.349433
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.471314 Loss1: 0.116844 Loss2: 0.354470
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.445338 Loss1: 0.093422 Loss2: 0.351916
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.423798 Loss1: 0.075245 Loss2: 0.348553
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.410109 Loss1: 0.063976 Loss2: 0.346133
-(DefaultActor pid=1838052) >> Training accuracy: 0.989913
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.625479 Loss1: 0.081350 Loss2: 0.544129
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.559343 Loss1: 0.062836 Loss2: 0.496507
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.534733 Loss1: 0.068449 Loss2: 0.466284
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.511667 Loss1: 0.064322 Loss2: 0.447345
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.534439 Loss1: 0.097387 Loss2: 0.437052
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.544034 Loss1: 0.109997 Loss2: 0.434038
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.506247 Loss1: 0.078479 Loss2: 0.427768
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.531434 Loss1: 0.107913 Loss2: 0.423521
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.505557 Loss1: 0.085670 Loss2: 0.419887
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.515328 Loss1: 0.096650 Loss2: 0.418678
-(DefaultActor pid=1838052) >> Training accuracy: 0.976562
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074221 Loss1: 0.041831 Loss2: 0.032390
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.050897 Loss1: 0.018199 Loss2: 0.032698
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058037 Loss1: 0.025008 Loss2: 0.033029
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049048 Loss1: 0.016082 Loss2: 0.032966
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.045394 Loss1: 0.012697 Loss2: 0.032697
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042129 Loss1: 0.009837 Loss2: 0.032292
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052179 Loss1: 0.019538 Loss2: 0.032641
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052878 Loss1: 0.019957 Loss2: 0.032921
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.049492 Loss1: 0.016455 Loss2: 0.033038
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.050795 Loss1: 0.017681 Loss2: 0.033114
-(DefaultActor pid=1838052) >> Training accuracy: 0.997033
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075746 Loss1: 0.044934 Loss2: 0.030812
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058536 Loss1: 0.026738 Loss2: 0.031799
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.059402 Loss1: 0.026963 Loss2: 0.032440
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049213 Loss1: 0.016626 Loss2: 0.032587
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046463 Loss1: 0.014107 Loss2: 0.032356
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046871 Loss1: 0.014962 Loss2: 0.031909
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043159 Loss1: 0.011335 Loss2: 0.031825
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.047404 Loss1: 0.015375 Loss2: 0.032029
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.044449 Loss1: 0.012432 Loss2: 0.032017
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.045203 Loss1: 0.013259 Loss2: 0.031944
-(DefaultActor pid=1838052) >> Training accuracy: 0.997396
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.500466 Loss1: 0.065533 Loss2: 0.434933
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.461166 Loss1: 0.039483 Loss2: 0.421683
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.445385 Loss1: 0.026766 Loss2: 0.418618
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.457424 Loss1: 0.042901 Loss2: 0.414523
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.459675 Loss1: 0.046145 Loss2: 0.413530
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.458926 Loss1: 0.047195 Loss2: 0.411731
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.475668 Loss1: 0.061366 Loss2: 0.414302
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.465493 Loss1: 0.053474 Loss2: 0.412018
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.469351 Loss1: 0.058110 Loss2: 0.411240
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.478606 Loss1: 0.063698 Loss2: 0.414907
-(DefaultActor pid=1838052) >> Training accuracy: 0.992989
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083804 Loss1: 0.055040 Loss2: 0.028764
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066048 Loss1: 0.035930 Loss2: 0.030119
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.063106 Loss1: 0.032086 Loss2: 0.031020
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.065944 Loss1: 0.034708 Loss2: 0.031236
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064190 Loss1: 0.032887 Loss2: 0.031303
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.078181 Loss1: 0.045989 Loss2: 0.032193
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.076054 Loss1: 0.043241 Loss2: 0.032813
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.095161 Loss1: 0.061852 Loss2: 0.033310
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.081490 Loss1: 0.047510 Loss2: 0.033980
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.080811 Loss1: 0.047099 Loss2: 0.033712
-(DefaultActor pid=1838052) >> Training accuracy: 0.987196
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 01:56:32,781][flwr][DEBUG] - fit_round 84 received 10 results and 0 failures
->> Test accuracy: 0.662100
-[2023-09-29 01:57:15,538][flwr][INFO] - fit progress: (84, 2.332587601468205, {'accuracy': 0.6621}, 157058.4282416273)
-[2023-09-29 01:57:15,539][flwr][DEBUG] - evaluate_round 84: strategy sampled 10 clients (out of 10)
-[2023-09-29 01:57:52,996][flwr][DEBUG] - evaluate_round 84 received 10 results and 0 failures
-[2023-09-29 01:57:52,998][flwr][DEBUG] - fit_round 85: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.322791 Loss1: 0.055232 Loss2: 0.267559
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.285244 Loss1: 0.037820 Loss2: 0.247425
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.287282 Loss1: 0.040523 Loss2: 0.246759
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.287731 Loss1: 0.041694 Loss2: 0.246037
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.282650 Loss1: 0.037407 Loss2: 0.245243
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.284860 Loss1: 0.039288 Loss2: 0.245573
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.278924 Loss1: 0.033249 Loss2: 0.245675
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.292698 Loss1: 0.045641 Loss2: 0.247058
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.294988 Loss1: 0.047255 Loss2: 0.247733
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.302236 Loss1: 0.053264 Loss2: 0.248972
-(DefaultActor pid=1838052) >> Training accuracy: 0.994462
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.128180 Loss1: 0.054703 Loss2: 0.073477
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.103659 Loss1: 0.032380 Loss2: 0.071279
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.088555 Loss1: 0.021216 Loss2: 0.067339
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085394 Loss1: 0.019153 Loss2: 0.066242
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.086712 Loss1: 0.021835 Loss2: 0.064877
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.080585 Loss1: 0.017160 Loss2: 0.063426
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.085738 Loss1: 0.023051 Loss2: 0.062687
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.086766 Loss1: 0.024399 Loss2: 0.062366
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.083463 Loss1: 0.021919 Loss2: 0.061544
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.093506 Loss1: 0.031238 Loss2: 0.062268
-(DefaultActor pid=1838052) >> Training accuracy: 0.993056
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.089341 Loss1: 0.053175 Loss2: 0.036167
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066808 Loss1: 0.029781 Loss2: 0.037027
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.066669 Loss1: 0.029142 Loss2: 0.037527
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055482 Loss1: 0.018375 Loss2: 0.037107
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.057496 Loss1: 0.020404 Loss2: 0.037092
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051860 Loss1: 0.015067 Loss2: 0.036793
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049964 Loss1: 0.013343 Loss2: 0.036621
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051173 Loss1: 0.014531 Loss2: 0.036642
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.054691 Loss1: 0.017911 Loss2: 0.036780
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.059921 Loss1: 0.022524 Loss2: 0.037397
-(DefaultActor pid=1838052) >> Training accuracy: 0.997196
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.551919 Loss1: 0.064467 Loss2: 0.487453
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.540917 Loss1: 0.064491 Loss2: 0.476426
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.516836 Loss1: 0.051321 Loss2: 0.465515
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.527295 Loss1: 0.063401 Loss2: 0.463894
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.548783 Loss1: 0.082819 Loss2: 0.465963
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.554401 Loss1: 0.090741 Loss2: 0.463660
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.552947 Loss1: 0.091659 Loss2: 0.461288
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.539787 Loss1: 0.081648 Loss2: 0.458138
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.516523 Loss1: 0.063488 Loss2: 0.453035
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.551925 Loss1: 0.093691 Loss2: 0.458234
-(DefaultActor pid=1838052) >> Training accuracy: 0.980419
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.094666 Loss1: 0.052301 Loss2: 0.042365
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080976 Loss1: 0.039818 Loss2: 0.041158
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.067109 Loss1: 0.026241 Loss2: 0.040868
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064954 Loss1: 0.024471 Loss2: 0.040482
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.060815 Loss1: 0.020814 Loss2: 0.040001
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055470 Loss1: 0.015899 Loss2: 0.039571
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.064535 Loss1: 0.025242 Loss2: 0.039293
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.076058 Loss1: 0.035757 Loss2: 0.040301
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.069590 Loss1: 0.028983 Loss2: 0.040606
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068264 Loss1: 0.027805 Loss2: 0.040459
-(DefaultActor pid=1838052) >> Training accuracy: 0.994655
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.090421 Loss1: 0.054133 Loss2: 0.036288
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051305 Loss1: 0.014886 Loss2: 0.036418
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.050250 Loss1: 0.014828 Loss2: 0.035422
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052742 Loss1: 0.017108 Loss2: 0.035634
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.050410 Loss1: 0.014987 Loss2: 0.035423
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053429 Loss1: 0.017814 Loss2: 0.035615
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.058174 Loss1: 0.022278 Loss2: 0.035895
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051891 Loss1: 0.016225 Loss2: 0.035666
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057437 Loss1: 0.021543 Loss2: 0.035894
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.069653 Loss1: 0.033375 Loss2: 0.036278
-(DefaultActor pid=1838052) >> Training accuracy: 0.990076
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078763 Loss1: 0.046251 Loss2: 0.032512
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073281 Loss1: 0.039314 Loss2: 0.033967
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.060912 Loss1: 0.026061 Loss2: 0.034851
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055138 Loss1: 0.020250 Loss2: 0.034888
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.053296 Loss1: 0.018342 Loss2: 0.034954
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051810 Loss1: 0.016750 Loss2: 0.035060
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.055589 Loss1: 0.020464 Loss2: 0.035125
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.056311 Loss1: 0.020828 Loss2: 0.035483
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065645 Loss1: 0.029478 Loss2: 0.036167
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083232 Loss1: 0.045958 Loss2: 0.037274
-(DefaultActor pid=1838052) >> Training accuracy: 0.996638
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074140 Loss1: 0.043730 Loss2: 0.030410
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.061949 Loss1: 0.029508 Loss2: 0.032442
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.069943 Loss1: 0.036884 Loss2: 0.033059
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051314 Loss1: 0.017946 Loss2: 0.033368
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.055318 Loss1: 0.022054 Loss2: 0.033264
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065848 Loss1: 0.032504 Loss2: 0.033344
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.074375 Loss1: 0.039802 Loss2: 0.034573
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071447 Loss1: 0.036078 Loss2: 0.035369
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058252 Loss1: 0.023352 Loss2: 0.034900
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.065797 Loss1: 0.030838 Loss2: 0.034959
-(DefaultActor pid=1838052) >> Training accuracy: 0.994792
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.089037 Loss1: 0.056887 Loss2: 0.032150
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.065852 Loss1: 0.031480 Loss2: 0.034372
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.064095 Loss1: 0.029824 Loss2: 0.034271
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.055816 Loss1: 0.021945 Loss2: 0.033871
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.056452 Loss1: 0.022586 Loss2: 0.033866
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050701 Loss1: 0.017145 Loss2: 0.033556
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.053752 Loss1: 0.020458 Loss2: 0.033294
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.053546 Loss1: 0.020016 Loss2: 0.033530
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057578 Loss1: 0.023638 Loss2: 0.033940
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.045569 Loss1: 0.012137 Loss2: 0.033432
-(DefaultActor pid=1838052) >> Training accuracy: 0.996440
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071776 Loss1: 0.039710 Loss2: 0.032067
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049684 Loss1: 0.016913 Loss2: 0.032770
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047343 Loss1: 0.014601 Loss2: 0.032743
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.052429 Loss1: 0.019510 Loss2: 0.032919
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048307 Loss1: 0.014956 Loss2: 0.033352
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.044668 Loss1: 0.011529 Loss2: 0.033139
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047570 Loss1: 0.014352 Loss2: 0.033218
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055156 Loss1: 0.021346 Loss2: 0.033810
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.073598 Loss1: 0.039112 Loss2: 0.034486
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.067629 Loss1: 0.032357 Loss2: 0.035272
-(DefaultActor pid=1838052) >> Training accuracy: 0.995808
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 02:26:29,461][flwr][DEBUG] - fit_round 85 received 10 results and 0 failures
->> Test accuracy: 0.664700
-[2023-09-29 02:27:06,906][flwr][INFO] - fit progress: (85, 2.3793240404738403, {'accuracy': 0.6647}, 158849.7960057431)
-[2023-09-29 02:27:06,906][flwr][DEBUG] - evaluate_round 85: strategy sampled 10 clients (out of 10)
-[2023-09-29 02:27:43,055][flwr][DEBUG] - evaluate_round 85 received 10 results and 0 failures
-[2023-09-29 02:27:43,056][flwr][DEBUG] - fit_round 86: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.098312 Loss1: 0.051866 Loss2: 0.046446
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066325 Loss1: 0.021151 Loss2: 0.045174
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.063597 Loss1: 0.018819 Loss2: 0.044777
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058487 Loss1: 0.013831 Loss2: 0.044655
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054385 Loss1: 0.010293 Loss2: 0.044092
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058329 Loss1: 0.014156 Loss2: 0.044173
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.057388 Loss1: 0.013335 Loss2: 0.044053
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052976 Loss1: 0.009328 Loss2: 0.043648
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.062442 Loss1: 0.018632 Loss2: 0.043810
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062994 Loss1: 0.018489 Loss2: 0.044506
-(DefaultActor pid=1838052) >> Training accuracy: 0.997429
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.565815 Loss1: 0.066851 Loss2: 0.498964
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.511467 Loss1: 0.038389 Loss2: 0.473077
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.519245 Loss1: 0.057495 Loss2: 0.461750
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.521738 Loss1: 0.063405 Loss2: 0.458333
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.540733 Loss1: 0.084511 Loss2: 0.456222
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.546560 Loss1: 0.091270 Loss2: 0.455290
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.528773 Loss1: 0.077718 Loss2: 0.451055
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.510483 Loss1: 0.062616 Loss2: 0.447867
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.507306 Loss1: 0.060989 Loss2: 0.446317
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.493429 Loss1: 0.047965 Loss2: 0.445464
-(DefaultActor pid=1838052) >> Training accuracy: 0.993056
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.629554 Loss1: 0.076253 Loss2: 0.553301
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.569992 Loss1: 0.036956 Loss2: 0.533035
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.562682 Loss1: 0.041875 Loss2: 0.520807
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.567938 Loss1: 0.056825 Loss2: 0.511113
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.554777 Loss1: 0.047668 Loss2: 0.507109
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.566167 Loss1: 0.059678 Loss2: 0.506488
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.549748 Loss1: 0.045936 Loss2: 0.503811
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.544724 Loss1: 0.046451 Loss2: 0.498273
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.554891 Loss1: 0.059661 Loss2: 0.495230
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.583938 Loss1: 0.084249 Loss2: 0.499690
-(DefaultActor pid=1838052) >> Training accuracy: 0.985431
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.062920 Loss1: 0.035192 Loss2: 0.027728
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049286 Loss1: 0.020179 Loss2: 0.029107
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046895 Loss1: 0.017626 Loss2: 0.029269
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038727 Loss1: 0.009443 Loss2: 0.029285
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.042893 Loss1: 0.013678 Loss2: 0.029215
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048849 Loss1: 0.019102 Loss2: 0.029747
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.045990 Loss1: 0.016091 Loss2: 0.029899
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.040355 Loss1: 0.010603 Loss2: 0.029751
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.044719 Loss1: 0.014793 Loss2: 0.029926
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040910 Loss1: 0.010844 Loss2: 0.030065
-(DefaultActor pid=1838052) >> Training accuracy: 0.998022
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.102356 Loss1: 0.070962 Loss2: 0.031394
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077930 Loss1: 0.044071 Loss2: 0.033859
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068169 Loss1: 0.034233 Loss2: 0.033936
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.087022 Loss1: 0.052519 Loss2: 0.034503
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.091100 Loss1: 0.055530 Loss2: 0.035570
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.087556 Loss1: 0.051725 Loss2: 0.035832
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073193 Loss1: 0.037357 Loss2: 0.035835
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.069888 Loss1: 0.034641 Loss2: 0.035248
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.059791 Loss1: 0.024790 Loss2: 0.035002
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.059247 Loss1: 0.024804 Loss2: 0.034443
-(DefaultActor pid=1838052) >> Training accuracy: 0.995888
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.643846 Loss1: 0.051226 Loss2: 0.592619
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.635689 Loss1: 0.050054 Loss2: 0.585635
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.618762 Loss1: 0.041063 Loss2: 0.577699
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.612185 Loss1: 0.040198 Loss2: 0.571987
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.608022 Loss1: 0.041139 Loss2: 0.566883
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.619746 Loss1: 0.056459 Loss2: 0.563286
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.653737 Loss1: 0.089562 Loss2: 0.564175
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627689 Loss1: 0.066875 Loss2: 0.560814
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.623295 Loss1: 0.062652 Loss2: 0.560643
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.638409 Loss1: 0.079997 Loss2: 0.558412
-(DefaultActor pid=1838052) >> Training accuracy: 0.987981
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.114075 Loss1: 0.063345 Loss2: 0.050730
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.075356 Loss1: 0.025559 Loss2: 0.049797
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.064383 Loss1: 0.015967 Loss2: 0.048416
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068885 Loss1: 0.020872 Loss2: 0.048013
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073395 Loss1: 0.024735 Loss2: 0.048659
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.070446 Loss1: 0.022071 Loss2: 0.048375
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.067364 Loss1: 0.018918 Loss2: 0.048445
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066228 Loss1: 0.018160 Loss2: 0.048068
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.069515 Loss1: 0.021336 Loss2: 0.048179
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.066055 Loss1: 0.018213 Loss2: 0.047842
-(DefaultActor pid=1838052) >> Training accuracy: 0.998220
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075731 Loss1: 0.047413 Loss2: 0.028318
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.056773 Loss1: 0.027328 Loss2: 0.029445
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061285 Loss1: 0.031108 Loss2: 0.030177
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.058013 Loss1: 0.027323 Loss2: 0.030690
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054120 Loss1: 0.023175 Loss2: 0.030945
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.059778 Loss1: 0.028648 Loss2: 0.031130
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047247 Loss1: 0.016547 Loss2: 0.030700
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.047142 Loss1: 0.016357 Loss2: 0.030785
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057631 Loss1: 0.026043 Loss2: 0.031589
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070088 Loss1: 0.038027 Loss2: 0.032062
-(DefaultActor pid=1838052) >> Training accuracy: 0.995192
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071032 Loss1: 0.042002 Loss2: 0.029030
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.048883 Loss1: 0.018559 Loss2: 0.030324
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.050976 Loss1: 0.020605 Loss2: 0.030371
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.053120 Loss1: 0.022116 Loss2: 0.031004
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.051190 Loss1: 0.020081 Loss2: 0.031109
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.058446 Loss1: 0.026684 Loss2: 0.031762
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.069430 Loss1: 0.037346 Loss2: 0.032083
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.060756 Loss1: 0.028246 Loss2: 0.032510
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.059492 Loss1: 0.026565 Loss2: 0.032928
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.072500 Loss1: 0.039511 Loss2: 0.032989
-(DefaultActor pid=1838052) >> Training accuracy: 0.990854
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.068005 Loss1: 0.039228 Loss2: 0.028777
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.048408 Loss1: 0.018570 Loss2: 0.029838
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046657 Loss1: 0.016318 Loss2: 0.030339
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044512 Loss1: 0.014230 Loss2: 0.030283
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046111 Loss1: 0.015718 Loss2: 0.030393
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.052239 Loss1: 0.021514 Loss2: 0.030725
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048297 Loss1: 0.017267 Loss2: 0.031030
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.059264 Loss1: 0.028014 Loss2: 0.031251
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064141 Loss1: 0.032030 Loss2: 0.032112
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.057936 Loss1: 0.025680 Loss2: 0.032256
-(DefaultActor pid=1838052) >> Training accuracy: 0.991891
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 02:56:25,003][flwr][DEBUG] - fit_round 86 received 10 results and 0 failures
->> Test accuracy: 0.661700
-[2023-09-29 02:57:01,817][flwr][INFO] - fit progress: (86, 2.393653464393494, {'accuracy': 0.6617}, 160644.70745004108)
-[2023-09-29 02:57:01,818][flwr][DEBUG] - evaluate_round 86: strategy sampled 10 clients (out of 10)
-[2023-09-29 02:57:38,812][flwr][DEBUG] - evaluate_round 86 received 10 results and 0 failures
-[2023-09-29 02:57:38,813][flwr][DEBUG] - fit_round 87: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.441977 Loss1: 0.057166 Loss2: 0.384812
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.425802 Loss1: 0.054459 Loss2: 0.371344
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.412960 Loss1: 0.041865 Loss2: 0.371095
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.434099 Loss1: 0.064123 Loss2: 0.369977
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.437840 Loss1: 0.067717 Loss2: 0.370123
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.433845 Loss1: 0.062260 Loss2: 0.371585
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.463559 Loss1: 0.090712 Loss2: 0.372847
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.446612 Loss1: 0.075132 Loss2: 0.371480
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.466711 Loss1: 0.095313 Loss2: 0.371397
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.434169 Loss1: 0.063411 Loss2: 0.370757
-(DefaultActor pid=1838052) >> Training accuracy: 0.989122
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087774 Loss1: 0.056365 Loss2: 0.031409
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052053 Loss1: 0.019727 Loss2: 0.032326
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052429 Loss1: 0.020095 Loss2: 0.032334
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047850 Loss1: 0.016008 Loss2: 0.031842
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059816 Loss1: 0.027721 Loss2: 0.032095
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048522 Loss1: 0.016039 Loss2: 0.032483
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.045304 Loss1: 0.013364 Loss2: 0.031940
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.054348 Loss1: 0.021890 Loss2: 0.032458
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056932 Loss1: 0.024124 Loss2: 0.032809
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062324 Loss1: 0.028982 Loss2: 0.033342
-(DefaultActor pid=1838052) >> Training accuracy: 0.994721
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081804 Loss1: 0.051177 Loss2: 0.030627
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057129 Loss1: 0.025027 Loss2: 0.032102
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.050434 Loss1: 0.018483 Loss2: 0.031951
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.054252 Loss1: 0.022166 Loss2: 0.032086
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054452 Loss1: 0.022250 Loss2: 0.032202
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045141 Loss1: 0.012915 Loss2: 0.032226
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052367 Loss1: 0.020379 Loss2: 0.031988
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050487 Loss1: 0.017901 Loss2: 0.032587
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.048313 Loss1: 0.016015 Loss2: 0.032298
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056088 Loss1: 0.023270 Loss2: 0.032819
-(DefaultActor pid=1838052) >> Training accuracy: 0.999407
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.636684 Loss1: 0.056778 Loss2: 0.579906
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.611740 Loss1: 0.041284 Loss2: 0.570456
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.603837 Loss1: 0.042064 Loss2: 0.561772
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.615140 Loss1: 0.057017 Loss2: 0.558123
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.627169 Loss1: 0.074493 Loss2: 0.552676
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.603516 Loss1: 0.054001 Loss2: 0.549515
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.599270 Loss1: 0.055654 Loss2: 0.543616
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.584678 Loss1: 0.045363 Loss2: 0.539315
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.607078 Loss1: 0.069583 Loss2: 0.537495
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.616286 Loss1: 0.077487 Loss2: 0.538798
-(DefaultActor pid=1838052) >> Training accuracy: 0.984976
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.121922 Loss1: 0.060088 Loss2: 0.061834
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.094360 Loss1: 0.034200 Loss2: 0.060161
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077968 Loss1: 0.021446 Loss2: 0.056522
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.085332 Loss1: 0.030111 Loss2: 0.055221
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.075581 Loss1: 0.021793 Loss2: 0.053788
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069972 Loss1: 0.017692 Loss2: 0.052280
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.066736 Loss1: 0.015359 Loss2: 0.051377
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066577 Loss1: 0.015968 Loss2: 0.050609
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070718 Loss1: 0.020805 Loss2: 0.049913
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.089688 Loss1: 0.038623 Loss2: 0.051065
-(DefaultActor pid=1838052) >> Training accuracy: 0.994141
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.621822 Loss1: 0.055375 Loss2: 0.566447
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.595726 Loss1: 0.036942 Loss2: 0.558784
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.609728 Loss1: 0.055731 Loss2: 0.553997
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.589667 Loss1: 0.041511 Loss2: 0.548157
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.579453 Loss1: 0.038379 Loss2: 0.541075
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.586256 Loss1: 0.045967 Loss2: 0.540289
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.593709 Loss1: 0.055820 Loss2: 0.537889
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.575513 Loss1: 0.043548 Loss2: 0.531966
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.589252 Loss1: 0.057516 Loss2: 0.531735
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.598552 Loss1: 0.064957 Loss2: 0.533594
-(DefaultActor pid=1838052) >> Training accuracy: 0.988924
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.612545 Loss1: 0.047554 Loss2: 0.564990
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.583133 Loss1: 0.032256 Loss2: 0.550877
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.568848 Loss1: 0.026381 Loss2: 0.542467
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.573152 Loss1: 0.036581 Loss2: 0.536570
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.576809 Loss1: 0.041431 Loss2: 0.535378
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.585830 Loss1: 0.052412 Loss2: 0.533418
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.604535 Loss1: 0.072513 Loss2: 0.532022
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.613082 Loss1: 0.079389 Loss2: 0.533692
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.629492 Loss1: 0.096548 Loss2: 0.532944
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.614324 Loss1: 0.084391 Loss2: 0.529933
-(DefaultActor pid=1838052) >> Training accuracy: 0.987233
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.090831 Loss1: 0.038226 Loss2: 0.052606
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069818 Loss1: 0.019478 Loss2: 0.050340
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.075557 Loss1: 0.025874 Loss2: 0.049683
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062373 Loss1: 0.013056 Loss2: 0.049317
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064953 Loss1: 0.015992 Loss2: 0.048962
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.064154 Loss1: 0.014796 Loss2: 0.049358
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062411 Loss1: 0.013603 Loss2: 0.048808
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.069233 Loss1: 0.020252 Loss2: 0.048982
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086228 Loss1: 0.036396 Loss2: 0.049832
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.079028 Loss1: 0.029102 Loss2: 0.049926
-(DefaultActor pid=1838052) >> Training accuracy: 0.995393
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.080350 Loss1: 0.050719 Loss2: 0.029632
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058630 Loss1: 0.027738 Loss2: 0.030892
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.055597 Loss1: 0.024467 Loss2: 0.031130
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.062280 Loss1: 0.030733 Loss2: 0.031547
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.051160 Loss1: 0.019538 Loss2: 0.031622
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050861 Loss1: 0.019419 Loss2: 0.031442
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.044055 Loss1: 0.012528 Loss2: 0.031528
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.049248 Loss1: 0.017745 Loss2: 0.031503
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.076753 Loss1: 0.044457 Loss2: 0.032296
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.063160 Loss1: 0.030113 Loss2: 0.033048
-(DefaultActor pid=1838052) >> Training accuracy: 0.996916
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.079168 Loss1: 0.049752 Loss2: 0.029416
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053204 Loss1: 0.022319 Loss2: 0.030885
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045356 Loss1: 0.014763 Loss2: 0.030593
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044521 Loss1: 0.013821 Loss2: 0.030700
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048645 Loss1: 0.017830 Loss2: 0.030815
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048187 Loss1: 0.017324 Loss2: 0.030863
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063522 Loss1: 0.031869 Loss2: 0.031652
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075333 Loss1: 0.042817 Loss2: 0.032515
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070241 Loss1: 0.036981 Loss2: 0.033259
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.084268 Loss1: 0.050504 Loss2: 0.033764
-(DefaultActor pid=1838052) >> Training accuracy: 0.992880
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 03:26:11,144][flwr][DEBUG] - fit_round 87 received 10 results and 0 failures
->> Test accuracy: 0.663500
-[2023-09-29 03:26:46,229][flwr][INFO] - fit progress: (87, 2.3520108950785557, {'accuracy': 0.6635}, 162429.119870577)
-[2023-09-29 03:26:46,230][flwr][DEBUG] - evaluate_round 87: strategy sampled 10 clients (out of 10)
-[2023-09-29 03:27:21,550][flwr][DEBUG] - evaluate_round 87 received 10 results and 0 failures
-[2023-09-29 03:27:21,551][flwr][DEBUG] - fit_round 88: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.406120 Loss1: 0.055217 Loss2: 0.350903
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.394525 Loss1: 0.056053 Loss2: 0.338472
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.385928 Loss1: 0.052123 Loss2: 0.333805
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.404042 Loss1: 0.070612 Loss2: 0.333430
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.429045 Loss1: 0.096107 Loss2: 0.332938
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.410508 Loss1: 0.080114 Loss2: 0.330394
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.440353 Loss1: 0.109120 Loss2: 0.331233
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.428399 Loss1: 0.097356 Loss2: 0.331043
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.426665 Loss1: 0.095485 Loss2: 0.331180
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.419585 Loss1: 0.090529 Loss2: 0.329056
-(DefaultActor pid=1838052) >> Training accuracy: 0.983974
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.283785 Loss1: 0.043908 Loss2: 0.239877
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.261972 Loss1: 0.037606 Loss2: 0.224365
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.245820 Loss1: 0.026854 Loss2: 0.218966
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.249719 Loss1: 0.032139 Loss2: 0.217580
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.260903 Loss1: 0.041169 Loss2: 0.219734
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.249009 Loss1: 0.031811 Loss2: 0.217198
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.248649 Loss1: 0.032344 Loss2: 0.216305
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.264230 Loss1: 0.046876 Loss2: 0.217354
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.272846 Loss1: 0.050699 Loss2: 0.222147
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.276432 Loss1: 0.057447 Loss2: 0.218985
-(DefaultActor pid=1838052) >> Training accuracy: 0.991806
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.086839 Loss1: 0.052414 Loss2: 0.034424
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052031 Loss1: 0.016808 Loss2: 0.035223
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056386 Loss1: 0.021098 Loss2: 0.035288
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063880 Loss1: 0.028309 Loss2: 0.035571
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.054052 Loss1: 0.018579 Loss2: 0.035472
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.055271 Loss1: 0.019759 Loss2: 0.035512
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.051675 Loss1: 0.016443 Loss2: 0.035233
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.049606 Loss1: 0.014752 Loss2: 0.034854
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058029 Loss1: 0.023066 Loss2: 0.034963
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.075530 Loss1: 0.039412 Loss2: 0.036118
-(DefaultActor pid=1838052) >> Training accuracy: 0.993869
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.091475 Loss1: 0.060975 Loss2: 0.030500
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.059554 Loss1: 0.027771 Loss2: 0.031783
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057402 Loss1: 0.025458 Loss2: 0.031944
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.061707 Loss1: 0.029268 Loss2: 0.032439
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.063180 Loss1: 0.030305 Loss2: 0.032875
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.054745 Loss1: 0.021763 Loss2: 0.032982
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065746 Loss1: 0.032468 Loss2: 0.033278
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055243 Loss1: 0.021791 Loss2: 0.033452
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056812 Loss1: 0.023616 Loss2: 0.033196
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046542 Loss1: 0.014129 Loss2: 0.032413
-(DefaultActor pid=1838052) >> Training accuracy: 0.998944
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.251050 Loss1: 0.046935 Loss2: 0.204114
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.231294 Loss1: 0.039398 Loss2: 0.191896
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.229309 Loss1: 0.039683 Loss2: 0.189626
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.232342 Loss1: 0.043669 Loss2: 0.188672
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.239476 Loss1: 0.049881 Loss2: 0.189595
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.273283 Loss1: 0.081205 Loss2: 0.192079
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.264752 Loss1: 0.073799 Loss2: 0.190953
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.251732 Loss1: 0.060698 Loss2: 0.191035
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.247140 Loss1: 0.057498 Loss2: 0.189642
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.250640 Loss1: 0.061172 Loss2: 0.189468
-(DefaultActor pid=1838052) >> Training accuracy: 0.989913
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.062562 Loss1: 0.033727 Loss2: 0.028835
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.045783 Loss1: 0.015937 Loss2: 0.029846
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046619 Loss1: 0.016475 Loss2: 0.030144
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.041514 Loss1: 0.011360 Loss2: 0.030153
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.038226 Loss1: 0.008424 Loss2: 0.029802
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042710 Loss1: 0.012720 Loss2: 0.029990
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.040594 Loss1: 0.010387 Loss2: 0.030207
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.038089 Loss1: 0.007824 Loss2: 0.030265
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.041158 Loss1: 0.010990 Loss2: 0.030168
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.049498 Loss1: 0.018839 Loss2: 0.030659
-(DefaultActor pid=1838052) >> Training accuracy: 0.997231
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.069653 Loss1: 0.040023 Loss2: 0.029630
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049292 Loss1: 0.018712 Loss2: 0.030580
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047944 Loss1: 0.017048 Loss2: 0.030896
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047370 Loss1: 0.016371 Loss2: 0.030999
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.044873 Loss1: 0.013897 Loss2: 0.030976
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.044612 Loss1: 0.013685 Loss2: 0.030927
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043278 Loss1: 0.012095 Loss2: 0.031183
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046025 Loss1: 0.014387 Loss2: 0.031638
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.043346 Loss1: 0.012141 Loss2: 0.031205
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046710 Loss1: 0.015375 Loss2: 0.031335
-(DefaultActor pid=1838052) >> Training accuracy: 0.998220
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083065 Loss1: 0.050961 Loss2: 0.032104
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.062582 Loss1: 0.029127 Loss2: 0.033455
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.053545 Loss1: 0.020274 Loss2: 0.033271
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044876 Loss1: 0.012253 Loss2: 0.032624
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041910 Loss1: 0.009825 Loss2: 0.032084
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046827 Loss1: 0.014544 Loss2: 0.032282
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.044942 Loss1: 0.012618 Loss2: 0.032324
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051114 Loss1: 0.018452 Loss2: 0.032662
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053820 Loss1: 0.020822 Loss2: 0.032998
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060700 Loss1: 0.027631 Loss2: 0.033070
-(DefaultActor pid=1838052) >> Training accuracy: 0.995192
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.496139 Loss1: 0.060992 Loss2: 0.435147
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.446380 Loss1: 0.039522 Loss2: 0.406858
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.429557 Loss1: 0.035882 Loss2: 0.393675
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.432941 Loss1: 0.045940 Loss2: 0.387002
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.455643 Loss1: 0.069865 Loss2: 0.385778
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.453702 Loss1: 0.067119 Loss2: 0.386583
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.438078 Loss1: 0.056070 Loss2: 0.382008
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.446686 Loss1: 0.064971 Loss2: 0.381715
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.434574 Loss1: 0.053283 Loss2: 0.381292
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.448151 Loss1: 0.068566 Loss2: 0.379585
-(DefaultActor pid=1838052) >> Training accuracy: 0.983290
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.657218 Loss1: 0.050631 Loss2: 0.606587
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.627372 Loss1: 0.036014 Loss2: 0.591358
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.628207 Loss1: 0.046057 Loss2: 0.582151
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.619112 Loss1: 0.044577 Loss2: 0.574535
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.614398 Loss1: 0.045649 Loss2: 0.568748
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.606649 Loss1: 0.043863 Loss2: 0.562785
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.617395 Loss1: 0.056473 Loss2: 0.560923
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.656455 Loss1: 0.092393 Loss2: 0.564061
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.646316 Loss1: 0.083202 Loss2: 0.563114
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.669114 Loss1: 0.108516 Loss2: 0.560598
-(DefaultActor pid=1838052) >> Training accuracy: 0.976357
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 03:55:57,566][flwr][DEBUG] - fit_round 88 received 10 results and 0 failures
->> Test accuracy: 0.664800
-[2023-09-29 03:56:34,010][flwr][INFO] - fit progress: (88, 2.374164987867252, {'accuracy': 0.6648}, 164216.90060469508)
-[2023-09-29 03:56:34,011][flwr][DEBUG] - evaluate_round 88: strategy sampled 10 clients (out of 10)
-[2023-09-29 03:57:09,085][flwr][DEBUG] - evaluate_round 88 received 10 results and 0 failures
-[2023-09-29 03:57:09,086][flwr][DEBUG] - fit_round 89: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071644 Loss1: 0.037986 Loss2: 0.033658
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.054522 Loss1: 0.019637 Loss2: 0.034885
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054448 Loss1: 0.019171 Loss2: 0.035277
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051616 Loss1: 0.016458 Loss2: 0.035159
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049181 Loss1: 0.014406 Loss2: 0.034775
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.043774 Loss1: 0.009494 Loss2: 0.034280
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050579 Loss1: 0.015988 Loss2: 0.034591
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051464 Loss1: 0.016947 Loss2: 0.034518
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058776 Loss1: 0.024002 Loss2: 0.034774
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.067732 Loss1: 0.032148 Loss2: 0.035585
-(DefaultActor pid=1838052) >> Training accuracy: 0.996595
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.067589 Loss1: 0.038952 Loss2: 0.028637
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051780 Loss1: 0.021794 Loss2: 0.029985
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.038815 Loss1: 0.009042 Loss2: 0.029773
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038165 Loss1: 0.009023 Loss2: 0.029141
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.038011 Loss1: 0.008925 Loss2: 0.029086
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038225 Loss1: 0.009159 Loss2: 0.029066
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.038938 Loss1: 0.009714 Loss2: 0.029223
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.037943 Loss1: 0.008843 Loss2: 0.029100
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.039983 Loss1: 0.010648 Loss2: 0.029335
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.038570 Loss1: 0.009081 Loss2: 0.029489
-(DefaultActor pid=1838052) >> Training accuracy: 0.997231
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.686534 Loss1: 0.074371 Loss2: 0.612163
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.657312 Loss1: 0.054380 Loss2: 0.602932
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.673211 Loss1: 0.078687 Loss2: 0.594524
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.675816 Loss1: 0.084847 Loss2: 0.590969
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.675911 Loss1: 0.088687 Loss2: 0.587225
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.690383 Loss1: 0.104403 Loss2: 0.585980
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.641577 Loss1: 0.062766 Loss2: 0.578811
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.652875 Loss1: 0.078783 Loss2: 0.574093
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.649147 Loss1: 0.075334 Loss2: 0.573812
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.631461 Loss1: 0.059510 Loss2: 0.571951
-(DefaultActor pid=1838052) >> Training accuracy: 0.990902
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065218 Loss1: 0.036382 Loss2: 0.028837
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057600 Loss1: 0.027163 Loss2: 0.030438
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049763 Loss1: 0.018832 Loss2: 0.030931
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.043959 Loss1: 0.013536 Loss2: 0.030423
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048707 Loss1: 0.018073 Loss2: 0.030634
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045641 Loss1: 0.014881 Loss2: 0.030760
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.042749 Loss1: 0.012041 Loss2: 0.030708
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.053388 Loss1: 0.021917 Loss2: 0.031471
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.044603 Loss1: 0.012851 Loss2: 0.031752
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048225 Loss1: 0.016652 Loss2: 0.031573
-(DefaultActor pid=1838052) >> Training accuracy: 0.997255
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065614 Loss1: 0.035335 Loss2: 0.030279
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.043474 Loss1: 0.012917 Loss2: 0.030557
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046575 Loss1: 0.015530 Loss2: 0.031045
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046565 Loss1: 0.015338 Loss2: 0.031227
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.042827 Loss1: 0.011592 Loss2: 0.031235
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046022 Loss1: 0.014661 Loss2: 0.031361
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049548 Loss1: 0.017764 Loss2: 0.031783
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.049545 Loss1: 0.017461 Loss2: 0.032083
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053863 Loss1: 0.021655 Loss2: 0.032208
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048566 Loss1: 0.015900 Loss2: 0.032666
-(DefaultActor pid=1838052) >> Training accuracy: 0.997429
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.077659 Loss1: 0.048260 Loss2: 0.029400
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.050785 Loss1: 0.020109 Loss2: 0.030675
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049893 Loss1: 0.019366 Loss2: 0.030526
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048054 Loss1: 0.017444 Loss2: 0.030611
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043917 Loss1: 0.013451 Loss2: 0.030466
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047613 Loss1: 0.017170 Loss2: 0.030443
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.051153 Loss1: 0.020324 Loss2: 0.030829
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.059689 Loss1: 0.028226 Loss2: 0.031464
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.064105 Loss1: 0.032101 Loss2: 0.032004
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.061645 Loss1: 0.029003 Loss2: 0.032643
-(DefaultActor pid=1838052) >> Training accuracy: 0.991102
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092564 Loss1: 0.038759 Loss2: 0.053805
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.078068 Loss1: 0.024756 Loss2: 0.053312
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.079522 Loss1: 0.026320 Loss2: 0.053202
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.075898 Loss1: 0.023610 Loss2: 0.052288
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.073370 Loss1: 0.021671 Loss2: 0.051699
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.069883 Loss1: 0.018434 Loss2: 0.051449
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073775 Loss1: 0.022813 Loss2: 0.050962
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.074915 Loss1: 0.023716 Loss2: 0.051199
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070930 Loss1: 0.019377 Loss2: 0.051553
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091581 Loss1: 0.038770 Loss2: 0.052812
-(DefaultActor pid=1838052) >> Training accuracy: 0.991425
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.068098 Loss1: 0.039297 Loss2: 0.028800
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049934 Loss1: 0.019999 Loss2: 0.029935
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.044835 Loss1: 0.014822 Loss2: 0.030013
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.043770 Loss1: 0.013530 Loss2: 0.030240
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046480 Loss1: 0.016265 Loss2: 0.030215
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045519 Loss1: 0.015518 Loss2: 0.030001
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.063246 Loss1: 0.032089 Loss2: 0.031157
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.065597 Loss1: 0.033771 Loss2: 0.031826
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058311 Loss1: 0.026012 Loss2: 0.032300
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.063633 Loss1: 0.031042 Loss2: 0.032591
-(DefaultActor pid=1838052) >> Training accuracy: 0.994591
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.679083 Loss1: 0.056348 Loss2: 0.622735
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.662612 Loss1: 0.049964 Loss2: 0.612648
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.653118 Loss1: 0.051631 Loss2: 0.601487
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.633148 Loss1: 0.040020 Loss2: 0.593128
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.628647 Loss1: 0.042555 Loss2: 0.586092
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.645763 Loss1: 0.062115 Loss2: 0.583648
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.656558 Loss1: 0.072043 Loss2: 0.584515
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.644685 Loss1: 0.061009 Loss2: 0.583676
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.616666 Loss1: 0.043180 Loss2: 0.573486
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.628940 Loss1: 0.057480 Loss2: 0.571460
-(DefaultActor pid=1838052) >> Training accuracy: 0.987540
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066951 Loss1: 0.036722 Loss2: 0.030229
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049626 Loss1: 0.018328 Loss2: 0.031297
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045928 Loss1: 0.014585 Loss2: 0.031343
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.042695 Loss1: 0.011547 Loss2: 0.031148
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.038229 Loss1: 0.007239 Loss2: 0.030990
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.037012 Loss1: 0.006728 Loss2: 0.030283
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.037716 Loss1: 0.007597 Loss2: 0.030120
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.037339 Loss1: 0.006971 Loss2: 0.030367
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.037460 Loss1: 0.007272 Loss2: 0.030188
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046239 Loss1: 0.015746 Loss2: 0.030493
-(DefaultActor pid=1838052) >> Training accuracy: 0.997738
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 04:25:40,513][flwr][DEBUG] - fit_round 89 received 10 results and 0 failures
->> Test accuracy: 0.659700
-[2023-09-29 04:26:18,142][flwr][INFO] - fit progress: (89, 2.4286907109589624, {'accuracy': 0.6597}, 166001.0325497142)
-[2023-09-29 04:26:18,143][flwr][DEBUG] - evaluate_round 89: strategy sampled 10 clients (out of 10)
-[2023-09-29 04:26:54,281][flwr][DEBUG] - evaluate_round 89 received 10 results and 0 failures
-[2023-09-29 04:26:54,282][flwr][DEBUG] - fit_round 90: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.099711 Loss1: 0.040346 Loss2: 0.059365
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.082719 Loss1: 0.026215 Loss2: 0.056504
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.072163 Loss1: 0.017921 Loss2: 0.054242
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.074771 Loss1: 0.021458 Loss2: 0.053314
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.071455 Loss1: 0.018992 Loss2: 0.052464
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.072924 Loss1: 0.021364 Loss2: 0.051560
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073150 Loss1: 0.021409 Loss2: 0.051741
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.077892 Loss1: 0.026444 Loss2: 0.051447
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.070154 Loss1: 0.019211 Loss2: 0.050943
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.076582 Loss1: 0.026003 Loss2: 0.050579
-(DefaultActor pid=1838052) >> Training accuracy: 0.996299
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.655961 Loss1: 0.053607 Loss2: 0.602354
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.619723 Loss1: 0.037230 Loss2: 0.582492
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.614388 Loss1: 0.040233 Loss2: 0.574155
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.611986 Loss1: 0.043370 Loss2: 0.568617
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.594116 Loss1: 0.033595 Loss2: 0.560521
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.597528 Loss1: 0.041945 Loss2: 0.555583
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.619191 Loss1: 0.064829 Loss2: 0.554362
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.661083 Loss1: 0.100180 Loss2: 0.560903
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.639353 Loss1: 0.078275 Loss2: 0.561078
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.682824 Loss1: 0.120663 Loss2: 0.562161
-(DefaultActor pid=1838052) >> Training accuracy: 0.974760
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.659938 Loss1: 0.052746 Loss2: 0.607192
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.645730 Loss1: 0.056142 Loss2: 0.589588
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.640932 Loss1: 0.054897 Loss2: 0.586035
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.639587 Loss1: 0.057396 Loss2: 0.582191
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.636991 Loss1: 0.057324 Loss2: 0.579668
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.628252 Loss1: 0.057844 Loss2: 0.570408
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.648447 Loss1: 0.080644 Loss2: 0.567803
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.648938 Loss1: 0.079797 Loss2: 0.569141
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.667933 Loss1: 0.097857 Loss2: 0.570076
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.644379 Loss1: 0.078274 Loss2: 0.566104
-(DefaultActor pid=1838052) >> Training accuracy: 0.990506
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.711292 Loss1: 0.085927 Loss2: 0.625365
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.654889 Loss1: 0.045887 Loss2: 0.609002
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.656258 Loss1: 0.058611 Loss2: 0.597647
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.634479 Loss1: 0.044404 Loss2: 0.590076
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.653466 Loss1: 0.068631 Loss2: 0.584835
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.665276 Loss1: 0.079236 Loss2: 0.586040
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.652544 Loss1: 0.069815 Loss2: 0.582729
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.666323 Loss1: 0.085839 Loss2: 0.580484
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.667447 Loss1: 0.088625 Loss2: 0.578822
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.654704 Loss1: 0.079026 Loss2: 0.575678
-(DefaultActor pid=1838052) >> Training accuracy: 0.979096
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074800 Loss1: 0.044452 Loss2: 0.030348
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051950 Loss1: 0.020235 Loss2: 0.031714
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054670 Loss1: 0.022627 Loss2: 0.032044
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.051177 Loss1: 0.018928 Loss2: 0.032249
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.056524 Loss1: 0.023920 Loss2: 0.032604
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.054999 Loss1: 0.022551 Loss2: 0.032448
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054704 Loss1: 0.021533 Loss2: 0.033171
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066533 Loss1: 0.033235 Loss2: 0.033297
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065803 Loss1: 0.031931 Loss2: 0.033871
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068262 Loss1: 0.034207 Loss2: 0.034055
-(DefaultActor pid=1838052) >> Training accuracy: 0.995055
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110945 Loss1: 0.052890 Loss2: 0.058055
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.074827 Loss1: 0.018626 Loss2: 0.056201
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.074685 Loss1: 0.021105 Loss2: 0.053580
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.071167 Loss1: 0.018386 Loss2: 0.052781
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077753 Loss1: 0.025540 Loss2: 0.052213
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075235 Loss1: 0.023079 Loss2: 0.052156
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.083197 Loss1: 0.031209 Loss2: 0.051988
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.080750 Loss1: 0.029190 Loss2: 0.051561
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.072863 Loss1: 0.021286 Loss2: 0.051577
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.092515 Loss1: 0.040320 Loss2: 0.052195
-(DefaultActor pid=1838052) >> Training accuracy: 0.995009
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.459780 Loss1: 0.038036 Loss2: 0.421744
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.396971 Loss1: 0.035462 Loss2: 0.361510
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.388238 Loss1: 0.049946 Loss2: 0.338292
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.378118 Loss1: 0.051788 Loss2: 0.326329
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.365736 Loss1: 0.045025 Loss2: 0.320711
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.365241 Loss1: 0.048247 Loss2: 0.316994
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.387522 Loss1: 0.070606 Loss2: 0.316915
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.403280 Loss1: 0.083416 Loss2: 0.319864
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.376396 Loss1: 0.060637 Loss2: 0.315759
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.380419 Loss1: 0.065759 Loss2: 0.314659
-(DefaultActor pid=1838052) >> Training accuracy: 0.985364
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065606 Loss1: 0.037100 Loss2: 0.028506
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.043986 Loss1: 0.014832 Loss2: 0.029153
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046294 Loss1: 0.016839 Loss2: 0.029455
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.042382 Loss1: 0.013062 Loss2: 0.029320
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041054 Loss1: 0.011641 Loss2: 0.029413
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040659 Loss1: 0.011275 Loss2: 0.029385
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.041054 Loss1: 0.011601 Loss2: 0.029454
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041195 Loss1: 0.011699 Loss2: 0.029496
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.039134 Loss1: 0.009712 Loss2: 0.029422
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056027 Loss1: 0.026056 Loss2: 0.029971
-(DefaultActor pid=1838052) >> Training accuracy: 0.996044
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.398437 Loss1: 0.052001 Loss2: 0.346436
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.400188 Loss1: 0.062059 Loss2: 0.338128
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.385492 Loss1: 0.050303 Loss2: 0.335189
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.402294 Loss1: 0.069198 Loss2: 0.333096
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.434845 Loss1: 0.092613 Loss2: 0.342233
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.461109 Loss1: 0.113087 Loss2: 0.348022
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.431369 Loss1: 0.092947 Loss2: 0.338422
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.446240 Loss1: 0.104343 Loss2: 0.341897
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.441490 Loss1: 0.102412 Loss2: 0.339078
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.433401 Loss1: 0.094050 Loss2: 0.339351
-(DefaultActor pid=1838052) >> Training accuracy: 0.978277
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.070456 Loss1: 0.041161 Loss2: 0.029295
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053858 Loss1: 0.023229 Loss2: 0.030629
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056189 Loss1: 0.025228 Loss2: 0.030961
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.057119 Loss1: 0.025606 Loss2: 0.031514
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070508 Loss1: 0.038564 Loss2: 0.031944
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.063213 Loss1: 0.030894 Loss2: 0.032319
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.086509 Loss1: 0.053156 Loss2: 0.033353
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.067965 Loss1: 0.034153 Loss2: 0.033813
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.077385 Loss1: 0.043782 Loss2: 0.033603
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.070597 Loss1: 0.036688 Loss2: 0.033909
-(DefaultActor pid=1838052) >> Training accuracy: 0.992788
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 04:55:35,824][flwr][DEBUG] - fit_round 90 received 10 results and 0 failures
->> Test accuracy: 0.661100
-[2023-09-29 04:56:12,044][flwr][INFO] - fit progress: (90, 2.328940248908326, {'accuracy': 0.6611}, 167794.9346008231)
-[2023-09-29 04:56:12,045][flwr][DEBUG] - evaluate_round 90: strategy sampled 10 clients (out of 10)
-[2023-09-29 04:56:48,333][flwr][DEBUG] - evaluate_round 90 received 10 results and 0 failures
-[2023-09-29 04:56:48,334][flwr][DEBUG] - fit_round 91: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.278048 Loss1: 0.043487 Loss2: 0.234562
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.247817 Loss1: 0.028685 Loss2: 0.219131
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.248073 Loss1: 0.031345 Loss2: 0.216728
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.228627 Loss1: 0.013844 Loss2: 0.214783
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.237728 Loss1: 0.024063 Loss2: 0.213664
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.249886 Loss1: 0.035238 Loss2: 0.214647
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.253118 Loss1: 0.038162 Loss2: 0.214956
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.247070 Loss1: 0.031204 Loss2: 0.215865
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.247702 Loss1: 0.032687 Loss2: 0.215015
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.236886 Loss1: 0.022983 Loss2: 0.213903
-(DefaultActor pid=1838052) >> Training accuracy: 0.992950
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.085616 Loss1: 0.054907 Loss2: 0.030710
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058067 Loss1: 0.025834 Loss2: 0.032232
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045259 Loss1: 0.013395 Loss2: 0.031864
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048369 Loss1: 0.016932 Loss2: 0.031437
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049111 Loss1: 0.017230 Loss2: 0.031880
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.049908 Loss1: 0.018213 Loss2: 0.031695
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.042413 Loss1: 0.010766 Loss2: 0.031647
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.042048 Loss1: 0.010466 Loss2: 0.031582
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.045001 Loss1: 0.013453 Loss2: 0.031548
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.044398 Loss1: 0.012595 Loss2: 0.031803
-(DefaultActor pid=1838052) >> Training accuracy: 0.998047
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.645264 Loss1: 0.053895 Loss2: 0.591369
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.602606 Loss1: 0.021256 Loss2: 0.581350
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.598862 Loss1: 0.032383 Loss2: 0.566479
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.605079 Loss1: 0.041911 Loss2: 0.563168
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.609426 Loss1: 0.048423 Loss2: 0.561003
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.619658 Loss1: 0.058072 Loss2: 0.561587
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.618967 Loss1: 0.059063 Loss2: 0.559904
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.624178 Loss1: 0.066305 Loss2: 0.557873
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.603563 Loss1: 0.047533 Loss2: 0.556030
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.605525 Loss1: 0.051868 Loss2: 0.553657
-(DefaultActor pid=1838052) >> Training accuracy: 0.988726
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.626324 Loss1: 0.045146 Loss2: 0.581178
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.602448 Loss1: 0.037590 Loss2: 0.564859
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.582966 Loss1: 0.028281 Loss2: 0.554685
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.586348 Loss1: 0.038429 Loss2: 0.547919
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.587687 Loss1: 0.039263 Loss2: 0.548424
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.604981 Loss1: 0.057496 Loss2: 0.547486
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.604710 Loss1: 0.057606 Loss2: 0.547104
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.632001 Loss1: 0.085415 Loss2: 0.546586
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.646796 Loss1: 0.095731 Loss2: 0.551065
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.653394 Loss1: 0.097146 Loss2: 0.556248
-(DefaultActor pid=1838052) >> Training accuracy: 0.978165
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074778 Loss1: 0.046447 Loss2: 0.028332
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.055425 Loss1: 0.026101 Loss2: 0.029324
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052908 Loss1: 0.023377 Loss2: 0.029531
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044509 Loss1: 0.014790 Loss2: 0.029719
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046156 Loss1: 0.016593 Loss2: 0.029563
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046655 Loss1: 0.017003 Loss2: 0.029652
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.044128 Loss1: 0.014222 Loss2: 0.029905
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055137 Loss1: 0.024904 Loss2: 0.030233
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057377 Loss1: 0.026559 Loss2: 0.030818
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.058928 Loss1: 0.027724 Loss2: 0.031204
-(DefaultActor pid=1838052) >> Training accuracy: 0.993869
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066342 Loss1: 0.037334 Loss2: 0.029008
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.056524 Loss1: 0.026076 Loss2: 0.030448
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.048242 Loss1: 0.017536 Loss2: 0.030706
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.037796 Loss1: 0.007709 Loss2: 0.030087
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041330 Loss1: 0.011694 Loss2: 0.029636
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047287 Loss1: 0.017374 Loss2: 0.029913
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050999 Loss1: 0.020801 Loss2: 0.030198
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.053376 Loss1: 0.022682 Loss2: 0.030694
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056326 Loss1: 0.025188 Loss2: 0.031138
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.072278 Loss1: 0.039818 Loss2: 0.032460
-(DefaultActor pid=1838052) >> Training accuracy: 0.987580
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.304536 Loss1: 0.049048 Loss2: 0.255489
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.248568 Loss1: 0.043886 Loss2: 0.204683
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.241333 Loss1: 0.045335 Loss2: 0.195998
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.228504 Loss1: 0.036162 Loss2: 0.192342
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.235135 Loss1: 0.046558 Loss2: 0.188577
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.249628 Loss1: 0.060813 Loss2: 0.188815
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.270827 Loss1: 0.080172 Loss2: 0.190655
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.272455 Loss1: 0.080544 Loss2: 0.191911
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.303920 Loss1: 0.107821 Loss2: 0.196099
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.322621 Loss1: 0.122255 Loss2: 0.200366
-(DefaultActor pid=1838052) >> Training accuracy: 0.978837
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.119445 Loss1: 0.051789 Loss2: 0.067656
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.083153 Loss1: 0.018482 Loss2: 0.064671
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.077751 Loss1: 0.015082 Loss2: 0.062669
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.071829 Loss1: 0.011163 Loss2: 0.060666
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064264 Loss1: 0.005492 Loss2: 0.058772
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.063262 Loss1: 0.006034 Loss2: 0.057228
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062756 Loss1: 0.005667 Loss2: 0.057089
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.063206 Loss1: 0.006842 Loss2: 0.056363
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.065016 Loss1: 0.008635 Loss2: 0.056382
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.064530 Loss1: 0.008119 Loss2: 0.056411
-(DefaultActor pid=1838052) >> Training accuracy: 0.999155
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.082620 Loss1: 0.046441 Loss2: 0.036178
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058601 Loss1: 0.021345 Loss2: 0.037257
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.055978 Loss1: 0.018687 Loss2: 0.037291
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046008 Loss1: 0.009573 Loss2: 0.036435
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048691 Loss1: 0.012962 Loss2: 0.035729
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.052509 Loss1: 0.016164 Loss2: 0.036345
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054421 Loss1: 0.018097 Loss2: 0.036324
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.052984 Loss1: 0.016676 Loss2: 0.036308
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053165 Loss1: 0.016854 Loss2: 0.036311
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.048445 Loss1: 0.011942 Loss2: 0.036503
-(DefaultActor pid=1838052) >> Training accuracy: 0.998418
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.079656 Loss1: 0.049047 Loss2: 0.030609
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.063654 Loss1: 0.031437 Loss2: 0.032217
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061876 Loss1: 0.029031 Loss2: 0.032845
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064860 Loss1: 0.031735 Loss2: 0.033125
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.061875 Loss1: 0.028694 Loss2: 0.033180
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.051209 Loss1: 0.018172 Loss2: 0.033037
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.059833 Loss1: 0.026593 Loss2: 0.033240
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.055677 Loss1: 0.022103 Loss2: 0.033573
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.058216 Loss1: 0.024796 Loss2: 0.033420
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053941 Loss1: 0.020253 Loss2: 0.033688
-(DefaultActor pid=1838052) >> Training accuracy: 0.996711
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 05:25:31,916][flwr][DEBUG] - fit_round 91 received 10 results and 0 failures
->> Test accuracy: 0.661900
-[2023-09-29 05:26:08,211][flwr][INFO] - fit progress: (91, 2.403618623273441, {'accuracy': 0.6619}, 169591.10123086534)
-[2023-09-29 05:26:08,211][flwr][DEBUG] - evaluate_round 91: strategy sampled 10 clients (out of 10)
-[2023-09-29 05:26:43,417][flwr][DEBUG] - evaluate_round 91 received 10 results and 0 failures
-[2023-09-29 05:26:43,418][flwr][DEBUG] - fit_round 92: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066839 Loss1: 0.034493 Loss2: 0.032346
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.050711 Loss1: 0.018081 Loss2: 0.032630
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046551 Loss1: 0.014137 Loss2: 0.032414
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048531 Loss1: 0.016900 Loss2: 0.031631
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048574 Loss1: 0.016299 Loss2: 0.032274
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045525 Loss1: 0.013412 Loss2: 0.032112
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049398 Loss1: 0.017311 Loss2: 0.032087
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.058428 Loss1: 0.025977 Loss2: 0.032451
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.055433 Loss1: 0.022345 Loss2: 0.033089
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.077289 Loss1: 0.043251 Loss2: 0.034038
-(DefaultActor pid=1838052) >> Training accuracy: 0.988726
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.486372 Loss1: 0.047523 Loss2: 0.438849
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.471386 Loss1: 0.043961 Loss2: 0.427425
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.473173 Loss1: 0.049041 Loss2: 0.424132
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.495037 Loss1: 0.063946 Loss2: 0.431091
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.487301 Loss1: 0.060001 Loss2: 0.427300
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.534881 Loss1: 0.102083 Loss2: 0.432798
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.512794 Loss1: 0.082029 Loss2: 0.430766
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.485613 Loss1: 0.061011 Loss2: 0.424603
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.510489 Loss1: 0.085504 Loss2: 0.424984
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.525292 Loss1: 0.096709 Loss2: 0.428583
-(DefaultActor pid=1838052) >> Training accuracy: 0.983974
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.519259 Loss1: 0.051738 Loss2: 0.467521
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.497392 Loss1: 0.043794 Loss2: 0.453598
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.502559 Loss1: 0.053793 Loss2: 0.448767
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.498130 Loss1: 0.054180 Loss2: 0.443950
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.540610 Loss1: 0.091050 Loss2: 0.449560
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.536041 Loss1: 0.087576 Loss2: 0.448465
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.570638 Loss1: 0.118646 Loss2: 0.451992
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.539059 Loss1: 0.089925 Loss2: 0.449133
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.520386 Loss1: 0.075362 Loss2: 0.445025
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.506215 Loss1: 0.065885 Loss2: 0.440329
-(DefaultActor pid=1838052) >> Training accuracy: 0.982730
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071982 Loss1: 0.043314 Loss2: 0.028668
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.046466 Loss1: 0.016782 Loss2: 0.029684
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047904 Loss1: 0.018110 Loss2: 0.029794
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044018 Loss1: 0.014531 Loss2: 0.029487
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043433 Loss1: 0.014025 Loss2: 0.029408
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.056511 Loss1: 0.026424 Loss2: 0.030086
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049459 Loss1: 0.018996 Loss2: 0.030463
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041792 Loss1: 0.011327 Loss2: 0.030465
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.042709 Loss1: 0.012323 Loss2: 0.030386
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.043468 Loss1: 0.013291 Loss2: 0.030177
-(DefaultActor pid=1838052) >> Training accuracy: 0.998813
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074285 Loss1: 0.045570 Loss2: 0.028716
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.055638 Loss1: 0.025400 Loss2: 0.030238
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047103 Loss1: 0.016760 Loss2: 0.030343
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.045501 Loss1: 0.015671 Loss2: 0.029829
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.040759 Loss1: 0.011072 Loss2: 0.029687
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045841 Loss1: 0.016005 Loss2: 0.029836
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049399 Loss1: 0.019183 Loss2: 0.030216
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051655 Loss1: 0.021206 Loss2: 0.030448
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.048287 Loss1: 0.017566 Loss2: 0.030721
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.058442 Loss1: 0.027656 Loss2: 0.030786
-(DefaultActor pid=1838052) >> Training accuracy: 0.992286
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.093899 Loss1: 0.032514 Loss2: 0.061385
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.077668 Loss1: 0.019541 Loss2: 0.058127
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068053 Loss1: 0.012561 Loss2: 0.055492
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064018 Loss1: 0.011311 Loss2: 0.052707
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.069503 Loss1: 0.017885 Loss2: 0.051618
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.066001 Loss1: 0.014375 Loss2: 0.051626
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.070774 Loss1: 0.019384 Loss2: 0.051390
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.082145 Loss1: 0.030225 Loss2: 0.051919
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.093730 Loss1: 0.040668 Loss2: 0.053062
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082492 Loss1: 0.030072 Loss2: 0.052420
-(DefaultActor pid=1838052) >> Training accuracy: 0.996835
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075631 Loss1: 0.045635 Loss2: 0.029996
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047018 Loss1: 0.016308 Loss2: 0.030710
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046234 Loss1: 0.015733 Loss2: 0.030500
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.050188 Loss1: 0.019415 Loss2: 0.030772
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064549 Loss1: 0.033255 Loss2: 0.031294
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.056729 Loss1: 0.025137 Loss2: 0.031593
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048362 Loss1: 0.016581 Loss2: 0.031781
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050450 Loss1: 0.018814 Loss2: 0.031636
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.048231 Loss1: 0.016694 Loss2: 0.031537
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053126 Loss1: 0.021166 Loss2: 0.031960
-(DefaultActor pid=1838052) >> Training accuracy: 0.994855
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.087518 Loss1: 0.040373 Loss2: 0.047145
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066096 Loss1: 0.021539 Loss2: 0.044557
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.063151 Loss1: 0.019263 Loss2: 0.043888
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.060773 Loss1: 0.017433 Loss2: 0.043339
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.065943 Loss1: 0.022434 Loss2: 0.043509
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.053967 Loss1: 0.010789 Loss2: 0.043178
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052611 Loss1: 0.010274 Loss2: 0.042336
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.054604 Loss1: 0.012300 Loss2: 0.042303
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.050196 Loss1: 0.007973 Loss2: 0.042222
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055404 Loss1: 0.013385 Loss2: 0.042019
-(DefaultActor pid=1838052) >> Training accuracy: 0.998798
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.604502 Loss1: 0.068105 Loss2: 0.536397
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.568311 Loss1: 0.050922 Loss2: 0.517389
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.541038 Loss1: 0.034757 Loss2: 0.506282
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.531904 Loss1: 0.032554 Loss2: 0.499350
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.551042 Loss1: 0.052621 Loss2: 0.498421
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.563028 Loss1: 0.066790 Loss2: 0.496238
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.567526 Loss1: 0.070968 Loss2: 0.496559
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.564123 Loss1: 0.066032 Loss2: 0.498091
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.565659 Loss1: 0.070240 Loss2: 0.495419
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.589754 Loss1: 0.092261 Loss2: 0.497493
-(DefaultActor pid=1838052) >> Training accuracy: 0.968328
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078716 Loss1: 0.050033 Loss2: 0.028683
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049204 Loss1: 0.019304 Loss2: 0.029900
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.053939 Loss1: 0.023782 Loss2: 0.030157
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048861 Loss1: 0.018404 Loss2: 0.030458
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.047265 Loss1: 0.016910 Loss2: 0.030355
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.056918 Loss1: 0.026175 Loss2: 0.030743
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.054364 Loss1: 0.022993 Loss2: 0.031371
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.062183 Loss1: 0.030426 Loss2: 0.031757
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057481 Loss1: 0.025803 Loss2: 0.031678
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060523 Loss1: 0.028548 Loss2: 0.031975
-(DefaultActor pid=1838052) >> Training accuracy: 0.998264
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 05:55:12,283][flwr][DEBUG] - fit_round 92 received 10 results and 0 failures
->> Test accuracy: 0.663600
-[2023-09-29 05:55:50,087][flwr][INFO] - fit progress: (92, 2.4000819242609954, {'accuracy': 0.6636}, 171372.97783041233)
-[2023-09-29 05:55:50,088][flwr][DEBUG] - evaluate_round 92: strategy sampled 10 clients (out of 10)
-[2023-09-29 05:56:34,742][flwr][DEBUG] - evaluate_round 92 received 10 results and 0 failures
-[2023-09-29 05:56:34,743][flwr][DEBUG] - fit_round 93: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.653048 Loss1: 0.038956 Loss2: 0.614091
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.633762 Loss1: 0.033599 Loss2: 0.600162
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.637122 Loss1: 0.043031 Loss2: 0.594091
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.642095 Loss1: 0.051017 Loss2: 0.591078
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.638575 Loss1: 0.048681 Loss2: 0.589894
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.651951 Loss1: 0.062859 Loss2: 0.589092
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.656996 Loss1: 0.071069 Loss2: 0.585927
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.649762 Loss1: 0.066458 Loss2: 0.583304
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.655445 Loss1: 0.071662 Loss2: 0.583783
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.629852 Loss1: 0.051996 Loss2: 0.577856
-(DefaultActor pid=1838052) >> Training accuracy: 0.989139
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.522463 Loss1: 0.043584 Loss2: 0.478879
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.487804 Loss1: 0.037474 Loss2: 0.450330
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.492792 Loss1: 0.048766 Loss2: 0.444027
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.491697 Loss1: 0.052414 Loss2: 0.439283
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.495183 Loss1: 0.056223 Loss2: 0.438960
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.493880 Loss1: 0.054377 Loss2: 0.439503
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.533694 Loss1: 0.095665 Loss2: 0.438029
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.532861 Loss1: 0.090942 Loss2: 0.441920
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.501269 Loss1: 0.064613 Loss2: 0.436656
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.515259 Loss1: 0.078211 Loss2: 0.437048
-(DefaultActor pid=1838052) >> Training accuracy: 0.986178
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.578006 Loss1: 0.080094 Loss2: 0.497912
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.554748 Loss1: 0.062015 Loss2: 0.492734
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.545735 Loss1: 0.059965 Loss2: 0.485770
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.556583 Loss1: 0.072234 Loss2: 0.484349
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.572087 Loss1: 0.090286 Loss2: 0.481801
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.591907 Loss1: 0.108219 Loss2: 0.483687
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.617382 Loss1: 0.133684 Loss2: 0.483698
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.581354 Loss1: 0.102310 Loss2: 0.479044
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.554778 Loss1: 0.079653 Loss2: 0.475125
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.545187 Loss1: 0.073836 Loss2: 0.471351
-(DefaultActor pid=1838052) >> Training accuracy: 0.988064
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.612852 Loss1: 0.055534 Loss2: 0.557318
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.596783 Loss1: 0.048208 Loss2: 0.548575
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.585753 Loss1: 0.045534 Loss2: 0.540220
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.585347 Loss1: 0.048931 Loss2: 0.536416
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.595189 Loss1: 0.061432 Loss2: 0.533757
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.589526 Loss1: 0.059194 Loss2: 0.530332
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.598925 Loss1: 0.068320 Loss2: 0.530605
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.608990 Loss1: 0.079532 Loss2: 0.529458
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.590332 Loss1: 0.064468 Loss2: 0.525864
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.621915 Loss1: 0.091943 Loss2: 0.529972
-(DefaultActor pid=1838052) >> Training accuracy: 0.982991
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.121291 Loss1: 0.049527 Loss2: 0.071764
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.095413 Loss1: 0.026031 Loss2: 0.069383
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.094711 Loss1: 0.025777 Loss2: 0.068934
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.088054 Loss1: 0.019677 Loss2: 0.068377
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.079763 Loss1: 0.012245 Loss2: 0.067519
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.087945 Loss1: 0.020589 Loss2: 0.067355
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.100577 Loss1: 0.032301 Loss2: 0.068275
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.094817 Loss1: 0.026284 Loss2: 0.068534
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.102180 Loss1: 0.033523 Loss2: 0.068657
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.102909 Loss1: 0.033296 Loss2: 0.069614
-(DefaultActor pid=1838052) >> Training accuracy: 0.997122
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.514676 Loss1: 0.051195 Loss2: 0.463481
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.502860 Loss1: 0.055146 Loss2: 0.447714
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.495472 Loss1: 0.054906 Loss2: 0.440565
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.499863 Loss1: 0.065524 Loss2: 0.434339
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.495651 Loss1: 0.061609 Loss2: 0.434042
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.487733 Loss1: 0.058435 Loss2: 0.429298
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.505981 Loss1: 0.069998 Loss2: 0.435984
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.510141 Loss1: 0.075711 Loss2: 0.434430
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.525442 Loss1: 0.090563 Loss2: 0.434880
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.541394 Loss1: 0.103026 Loss2: 0.438368
-(DefaultActor pid=1838052) >> Training accuracy: 0.975475
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.061495 Loss1: 0.033823 Loss2: 0.027673
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.043595 Loss1: 0.015381 Loss2: 0.028214
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.035117 Loss1: 0.007124 Loss2: 0.027993
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.031894 Loss1: 0.004151 Loss2: 0.027743
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.032339 Loss1: 0.004826 Loss2: 0.027512
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.034908 Loss1: 0.007371 Loss2: 0.027537
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.036448 Loss1: 0.008722 Loss2: 0.027726
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.034844 Loss1: 0.007095 Loss2: 0.027749
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.034356 Loss1: 0.006567 Loss2: 0.027789
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.032581 Loss1: 0.005034 Loss2: 0.027547
-(DefaultActor pid=1838052) >> Training accuracy: 1.000000
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.580873 Loss1: 0.061184 Loss2: 0.519688
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.561393 Loss1: 0.050899 Loss2: 0.510494
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.595318 Loss1: 0.081433 Loss2: 0.513885
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.573135 Loss1: 0.069372 Loss2: 0.503763
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.589975 Loss1: 0.090190 Loss2: 0.499785
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.581884 Loss1: 0.081454 Loss2: 0.500429
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.600547 Loss1: 0.103347 Loss2: 0.497200
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.609911 Loss1: 0.111924 Loss2: 0.497987
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.576925 Loss1: 0.085424 Loss2: 0.491500
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.565529 Loss1: 0.072991 Loss2: 0.492537
-(DefaultActor pid=1838052) >> Training accuracy: 0.980024
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081193 Loss1: 0.051266 Loss2: 0.029927
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.058883 Loss1: 0.027832 Loss2: 0.031051
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047028 Loss1: 0.016487 Loss2: 0.030540
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046485 Loss1: 0.016016 Loss2: 0.030470
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046896 Loss1: 0.016253 Loss2: 0.030643
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045589 Loss1: 0.015356 Loss2: 0.030234
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048510 Loss1: 0.017702 Loss2: 0.030808
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.056103 Loss1: 0.025020 Loss2: 0.031083
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.050658 Loss1: 0.019106 Loss2: 0.031552
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.073398 Loss1: 0.041332 Loss2: 0.032066
-(DefaultActor pid=1838052) >> Training accuracy: 0.994721
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.074266 Loss1: 0.043600 Loss2: 0.030666
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.048159 Loss1: 0.016366 Loss2: 0.031793
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043844 Loss1: 0.012092 Loss2: 0.031752
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048190 Loss1: 0.016415 Loss2: 0.031774
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.052921 Loss1: 0.020521 Loss2: 0.032401
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047399 Loss1: 0.015030 Loss2: 0.032369
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052533 Loss1: 0.019771 Loss2: 0.032761
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.060908 Loss1: 0.027226 Loss2: 0.033682
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067250 Loss1: 0.033351 Loss2: 0.033899
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.081979 Loss1: 0.047634 Loss2: 0.034345
-(DefaultActor pid=1838052) >> Training accuracy: 0.991693
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 06:24:59,930][flwr][DEBUG] - fit_round 93 received 10 results and 0 failures
->> Test accuracy: 0.664800
-[2023-09-29 06:25:35,305][flwr][INFO] - fit progress: (93, 2.330398244598803, {'accuracy': 0.6648}, 173158.19589267345)
-[2023-09-29 06:25:35,306][flwr][DEBUG] - evaluate_round 93: strategy sampled 10 clients (out of 10)
-[2023-09-29 06:26:10,427][flwr][DEBUG] - evaluate_round 93 received 10 results and 0 failures
-[2023-09-29 06:26:10,428][flwr][DEBUG] - fit_round 94: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.110466 Loss1: 0.037187 Loss2: 0.073279
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.084070 Loss1: 0.014305 Loss2: 0.069765
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.080423 Loss1: 0.011326 Loss2: 0.069097
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.080557 Loss1: 0.011341 Loss2: 0.069215
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072644 Loss1: 0.004509 Loss2: 0.068135
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.075593 Loss1: 0.007769 Loss2: 0.067824
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.075085 Loss1: 0.007696 Loss2: 0.067389
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.075566 Loss1: 0.008201 Loss2: 0.067364
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.086404 Loss1: 0.018381 Loss2: 0.068023
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.077473 Loss1: 0.009193 Loss2: 0.068280
-(DefaultActor pid=1838052) >> Training accuracy: 0.999399
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.539367 Loss1: 0.044702 Loss2: 0.494665
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.518679 Loss1: 0.035253 Loss2: 0.483425
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.525908 Loss1: 0.049530 Loss2: 0.476378
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.534598 Loss1: 0.059113 Loss2: 0.475484
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.528559 Loss1: 0.059044 Loss2: 0.469515
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.527778 Loss1: 0.058273 Loss2: 0.469504
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.545428 Loss1: 0.075728 Loss2: 0.469700
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.559491 Loss1: 0.089147 Loss2: 0.470344
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.556594 Loss1: 0.084055 Loss2: 0.472539
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.545373 Loss1: 0.077072 Loss2: 0.468301
-(DefaultActor pid=1838052) >> Training accuracy: 0.983188
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.082356 Loss1: 0.040446 Loss2: 0.041910
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.059505 Loss1: 0.017612 Loss2: 0.041893
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056417 Loss1: 0.015382 Loss2: 0.041036
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.054102 Loss1: 0.013174 Loss2: 0.040929
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.058146 Loss1: 0.016976 Loss2: 0.041170
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.054487 Loss1: 0.013559 Loss2: 0.040929
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047388 Loss1: 0.007363 Loss2: 0.040026
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.047438 Loss1: 0.007583 Loss2: 0.039855
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.060456 Loss1: 0.020476 Loss2: 0.039980
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053489 Loss1: 0.012547 Loss2: 0.040942
-(DefaultActor pid=1838052) >> Training accuracy: 0.997596
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.057365 Loss1: 0.029190 Loss2: 0.028175
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.038585 Loss1: 0.010045 Loss2: 0.028540
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.038119 Loss1: 0.009607 Loss2: 0.028512
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.034691 Loss1: 0.006268 Loss2: 0.028423
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.037409 Loss1: 0.008847 Loss2: 0.028562
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.035451 Loss1: 0.006902 Loss2: 0.028549
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.031994 Loss1: 0.003631 Loss2: 0.028364
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.035473 Loss1: 0.007327 Loss2: 0.028146
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.032847 Loss1: 0.004526 Loss2: 0.028321
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.033471 Loss1: 0.005506 Loss2: 0.027965
-(DefaultActor pid=1838052) >> Training accuracy: 0.999604
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.069533 Loss1: 0.039347 Loss2: 0.030187
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.054217 Loss1: 0.023325 Loss2: 0.030892
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.051313 Loss1: 0.020047 Loss2: 0.031266
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046351 Loss1: 0.015106 Loss2: 0.031245
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043210 Loss1: 0.011960 Loss2: 0.031251
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048492 Loss1: 0.017376 Loss2: 0.031116
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050771 Loss1: 0.019146 Loss2: 0.031625
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.047249 Loss1: 0.015233 Loss2: 0.032015
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.042554 Loss1: 0.011245 Loss2: 0.031309
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.050523 Loss1: 0.018801 Loss2: 0.031723
-(DefaultActor pid=1838052) >> Training accuracy: 0.997033
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.080586 Loss1: 0.049815 Loss2: 0.030771
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049742 Loss1: 0.019389 Loss2: 0.030353
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.042376 Loss1: 0.012455 Loss2: 0.029921
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038378 Loss1: 0.008679 Loss2: 0.029699
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041654 Loss1: 0.012044 Loss2: 0.029610
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045943 Loss1: 0.015849 Loss2: 0.030094
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047259 Loss1: 0.016727 Loss2: 0.030531
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041804 Loss1: 0.011442 Loss2: 0.030363
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.039506 Loss1: 0.009459 Loss2: 0.030047
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.039729 Loss1: 0.009469 Loss2: 0.030260
-(DefaultActor pid=1838052) >> Training accuracy: 0.999155
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.058072 Loss1: 0.028313 Loss2: 0.029758
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047329 Loss1: 0.017114 Loss2: 0.030216
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045122 Loss1: 0.014904 Loss2: 0.030218
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.041075 Loss1: 0.011114 Loss2: 0.029961
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046530 Loss1: 0.016100 Loss2: 0.030430
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046487 Loss1: 0.015802 Loss2: 0.030685
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047831 Loss1: 0.017084 Loss2: 0.030747
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.048839 Loss1: 0.017357 Loss2: 0.031482
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.049066 Loss1: 0.017854 Loss2: 0.031212
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056523 Loss1: 0.024816 Loss2: 0.031707
-(DefaultActor pid=1838052) >> Training accuracy: 0.997904
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.293355 Loss1: 0.047727 Loss2: 0.245627
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.277894 Loss1: 0.041445 Loss2: 0.236449
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.293092 Loss1: 0.058348 Loss2: 0.234744
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.332158 Loss1: 0.091979 Loss2: 0.240179
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.341653 Loss1: 0.098441 Loss2: 0.243212
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.340289 Loss1: 0.101590 Loss2: 0.238699
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.365897 Loss1: 0.123295 Loss2: 0.242603
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.350486 Loss1: 0.107353 Loss2: 0.243134
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.359660 Loss1: 0.115577 Loss2: 0.244082
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.377701 Loss1: 0.133488 Loss2: 0.244213
-(DefaultActor pid=1838052) >> Training accuracy: 0.976562
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.122366 Loss1: 0.036535 Loss2: 0.085831
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.095511 Loss1: 0.014093 Loss2: 0.081418
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.088762 Loss1: 0.007852 Loss2: 0.080910
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.087355 Loss1: 0.006895 Loss2: 0.080460
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.092784 Loss1: 0.011999 Loss2: 0.080785
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.093917 Loss1: 0.012926 Loss2: 0.080991
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.098371 Loss1: 0.017149 Loss2: 0.081223
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.091655 Loss1: 0.010199 Loss2: 0.081456
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.091236 Loss1: 0.010059 Loss2: 0.081176
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.091141 Loss1: 0.009791 Loss2: 0.081350
-(DefaultActor pid=1838052) >> Training accuracy: 0.996242
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.311708 Loss1: 0.050380 Loss2: 0.261328
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.260853 Loss1: 0.019363 Loss2: 0.241489
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.257494 Loss1: 0.019530 Loss2: 0.237964
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.252537 Loss1: 0.017164 Loss2: 0.235373
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.259718 Loss1: 0.024401 Loss2: 0.235317
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.255187 Loss1: 0.020024 Loss2: 0.235164
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.251642 Loss1: 0.017524 Loss2: 0.234118
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.246466 Loss1: 0.013908 Loss2: 0.232558
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.258874 Loss1: 0.025401 Loss2: 0.233473
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.247594 Loss1: 0.013961 Loss2: 0.233633
-(DefaultActor pid=1838052) >> Training accuracy: 0.998915
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 06:54:46,133][flwr][DEBUG] - fit_round 94 received 10 results and 0 failures
->> Test accuracy: 0.666500
-[2023-09-29 06:55:23,773][flwr][INFO] - fit progress: (94, 2.432824038849852, {'accuracy': 0.6665}, 174946.66300495435)
-[2023-09-29 06:55:23,773][flwr][DEBUG] - evaluate_round 94: strategy sampled 10 clients (out of 10)
-[2023-09-29 06:55:58,588][flwr][DEBUG] - evaluate_round 94 received 10 results and 0 failures
-[2023-09-29 06:55:58,589][flwr][DEBUG] - fit_round 95: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.548781 Loss1: 0.046104 Loss2: 0.502677
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.510661 Loss1: 0.023152 Loss2: 0.487508
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.520165 Loss1: 0.035032 Loss2: 0.485133
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.528778 Loss1: 0.044354 Loss2: 0.484424
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.532262 Loss1: 0.049960 Loss2: 0.482302
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.538332 Loss1: 0.055550 Loss2: 0.482782
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.552523 Loss1: 0.068361 Loss2: 0.484161
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.557828 Loss1: 0.075605 Loss2: 0.482223
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.556086 Loss1: 0.073278 Loss2: 0.482809
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.540596 Loss1: 0.060924 Loss2: 0.479672
-(DefaultActor pid=1838052) >> Training accuracy: 0.988948
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.656795 Loss1: 0.043361 Loss2: 0.613434
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.635113 Loss1: 0.042885 Loss2: 0.592228
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.614583 Loss1: 0.043989 Loss2: 0.570594
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.603436 Loss1: 0.046760 Loss2: 0.556676
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.614414 Loss1: 0.060461 Loss2: 0.553953
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.607194 Loss1: 0.053821 Loss2: 0.553373
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.611967 Loss1: 0.062693 Loss2: 0.549274
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.603677 Loss1: 0.055121 Loss2: 0.548556
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.619788 Loss1: 0.071683 Loss2: 0.548104
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.610820 Loss1: 0.062930 Loss2: 0.547890
-(DefaultActor pid=1838052) >> Training accuracy: 0.988582
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.062750 Loss1: 0.032107 Loss2: 0.030643
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053580 Loss1: 0.021554 Loss2: 0.032026
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.049123 Loss1: 0.017326 Loss2: 0.031798
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044107 Loss1: 0.012237 Loss2: 0.031870
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.047437 Loss1: 0.015487 Loss2: 0.031950
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046549 Loss1: 0.014713 Loss2: 0.031835
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043280 Loss1: 0.011467 Loss2: 0.031814
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.062050 Loss1: 0.029514 Loss2: 0.032536
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.059366 Loss1: 0.026242 Loss2: 0.033124
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.058463 Loss1: 0.024762 Loss2: 0.033701
-(DefaultActor pid=1838052) >> Training accuracy: 0.995066
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.058094 Loss1: 0.029736 Loss2: 0.028358
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.038943 Loss1: 0.009556 Loss2: 0.029386
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.037454 Loss1: 0.008319 Loss2: 0.029135
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.040218 Loss1: 0.011036 Loss2: 0.029182
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.034829 Loss1: 0.005809 Loss2: 0.029020
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.036113 Loss1: 0.007183 Loss2: 0.028930
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.035847 Loss1: 0.006871 Loss2: 0.028976
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041231 Loss1: 0.012174 Loss2: 0.029057
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051941 Loss1: 0.021798 Loss2: 0.030143
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053211 Loss1: 0.022544 Loss2: 0.030667
-(DefaultActor pid=1838052) >> Training accuracy: 0.996638
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.084105 Loss1: 0.054194 Loss2: 0.029911
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051820 Loss1: 0.020611 Loss2: 0.031209
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.057332 Loss1: 0.025780 Loss2: 0.031552
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044786 Loss1: 0.013672 Loss2: 0.031114
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.045549 Loss1: 0.014553 Loss2: 0.030997
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042915 Loss1: 0.011967 Loss2: 0.030948
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.047287 Loss1: 0.016332 Loss2: 0.030955
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051627 Loss1: 0.020167 Loss2: 0.031460
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068786 Loss1: 0.036547 Loss2: 0.032239
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.067214 Loss1: 0.034116 Loss2: 0.033097
-(DefaultActor pid=1838052) >> Training accuracy: 0.988387
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065643 Loss1: 0.035393 Loss2: 0.030250
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.052707 Loss1: 0.021388 Loss2: 0.031319
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046568 Loss1: 0.015011 Loss2: 0.031557
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.046123 Loss1: 0.014323 Loss2: 0.031799
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.044752 Loss1: 0.013184 Loss2: 0.031568
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042386 Loss1: 0.010865 Loss2: 0.031522
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056096 Loss1: 0.024512 Loss2: 0.031584
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066697 Loss1: 0.034062 Loss2: 0.032635
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067985 Loss1: 0.034582 Loss2: 0.033403
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.068810 Loss1: 0.034585 Loss2: 0.034225
-(DefaultActor pid=1838052) >> Training accuracy: 0.996394
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.405841 Loss1: 0.049492 Loss2: 0.356349
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.388848 Loss1: 0.042845 Loss2: 0.346003
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.404767 Loss1: 0.061538 Loss2: 0.343228
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.428909 Loss1: 0.084414 Loss2: 0.344494
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.446586 Loss1: 0.096738 Loss2: 0.349849
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.448414 Loss1: 0.100036 Loss2: 0.348378
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.454726 Loss1: 0.105576 Loss2: 0.349151
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.438072 Loss1: 0.094007 Loss2: 0.344065
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.444214 Loss1: 0.098949 Loss2: 0.345265
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.439681 Loss1: 0.096403 Loss2: 0.343278
-(DefaultActor pid=1838052) >> Training accuracy: 0.984573
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.064985 Loss1: 0.033991 Loss2: 0.030994
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.050529 Loss1: 0.018462 Loss2: 0.032067
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047581 Loss1: 0.015370 Loss2: 0.032211
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047756 Loss1: 0.015387 Loss2: 0.032370
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.047028 Loss1: 0.014648 Loss2: 0.032380
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047866 Loss1: 0.015538 Loss2: 0.032328
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056711 Loss1: 0.023613 Loss2: 0.033098
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.045970 Loss1: 0.012980 Loss2: 0.032990
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051945 Loss1: 0.018882 Loss2: 0.033063
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.061848 Loss1: 0.027859 Loss2: 0.033989
-(DefaultActor pid=1838052) >> Training accuracy: 0.997033
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.593286 Loss1: 0.050753 Loss2: 0.542534
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.561686 Loss1: 0.033492 Loss2: 0.528193
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.570427 Loss1: 0.047984 Loss2: 0.522443
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.563443 Loss1: 0.043882 Loss2: 0.519561
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.568680 Loss1: 0.053131 Loss2: 0.515549
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.585074 Loss1: 0.069202 Loss2: 0.515872
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.616136 Loss1: 0.099058 Loss2: 0.517077
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.654474 Loss1: 0.133592 Loss2: 0.520881
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.620880 Loss1: 0.103116 Loss2: 0.517764
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.608952 Loss1: 0.094297 Loss2: 0.514655
-(DefaultActor pid=1838052) >> Training accuracy: 0.986155
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078255 Loss1: 0.044389 Loss2: 0.033866
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.045782 Loss1: 0.011853 Loss2: 0.033928
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047826 Loss1: 0.013691 Loss2: 0.034135
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.043581 Loss1: 0.009613 Loss2: 0.033968
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048812 Loss1: 0.014806 Loss2: 0.034007
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.043700 Loss1: 0.009758 Loss2: 0.033942
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043775 Loss1: 0.009868 Loss2: 0.033906
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.042608 Loss1: 0.008829 Loss2: 0.033779
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.042374 Loss1: 0.008419 Loss2: 0.033955
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.042471 Loss1: 0.008339 Loss2: 0.034132
-(DefaultActor pid=1838052) >> Training accuracy: 0.999349
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 07:24:52,482][flwr][DEBUG] - fit_round 95 received 10 results and 0 failures
->> Test accuracy: 0.667000
-[2023-09-29 07:25:30,114][flwr][INFO] - fit progress: (95, 2.362161700337078, {'accuracy': 0.667}, 176753.00478482526)
-[2023-09-29 07:25:30,115][flwr][DEBUG] - evaluate_round 95: strategy sampled 10 clients (out of 10)
-[2023-09-29 07:26:06,249][flwr][DEBUG] - evaluate_round 95 received 10 results and 0 failures
-[2023-09-29 07:26:06,250][flwr][DEBUG] - fit_round 96: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.060340 Loss1: 0.030559 Loss2: 0.029781
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049432 Loss1: 0.019117 Loss2: 0.030315
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.037841 Loss1: 0.007385 Loss2: 0.030456
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.036836 Loss1: 0.006613 Loss2: 0.030223
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.036979 Loss1: 0.006717 Loss2: 0.030263
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.035406 Loss1: 0.005311 Loss2: 0.030095
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.039030 Loss1: 0.008767 Loss2: 0.030263
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.040398 Loss1: 0.009674 Loss2: 0.030724
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.046717 Loss1: 0.015544 Loss2: 0.031173
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040527 Loss1: 0.009163 Loss2: 0.031364
-(DefaultActor pid=1838052) >> Training accuracy: 0.998616
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.623382 Loss1: 0.051037 Loss2: 0.572345
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.614379 Loss1: 0.051221 Loss2: 0.563157
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.603091 Loss1: 0.049289 Loss2: 0.553803
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.609932 Loss1: 0.061844 Loss2: 0.548088
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613025 Loss1: 0.068017 Loss2: 0.545007
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.600232 Loss1: 0.058569 Loss2: 0.541662
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.588822 Loss1: 0.049830 Loss2: 0.538992
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.574998 Loss1: 0.041718 Loss2: 0.533280
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.596328 Loss1: 0.065572 Loss2: 0.530755
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.622831 Loss1: 0.088661 Loss2: 0.534170
-(DefaultActor pid=1838052) >> Training accuracy: 0.978516
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.512524 Loss1: 0.046882 Loss2: 0.465641
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.501720 Loss1: 0.048880 Loss2: 0.452840
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.506365 Loss1: 0.056842 Loss2: 0.449524
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.490490 Loss1: 0.045535 Loss2: 0.444955
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.516481 Loss1: 0.071637 Loss2: 0.444844
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.519268 Loss1: 0.072506 Loss2: 0.446762
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.493083 Loss1: 0.050676 Loss2: 0.442406
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.492261 Loss1: 0.053369 Loss2: 0.438891
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.515195 Loss1: 0.074288 Loss2: 0.440907
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.538769 Loss1: 0.095002 Loss2: 0.443767
-(DefaultActor pid=1838052) >> Training accuracy: 0.968750
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.064732 Loss1: 0.035217 Loss2: 0.029515
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.040580 Loss1: 0.010175 Loss2: 0.030406
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.041445 Loss1: 0.011170 Loss2: 0.030276
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039524 Loss1: 0.009116 Loss2: 0.030409
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043641 Loss1: 0.013110 Loss2: 0.030531
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.049363 Loss1: 0.018658 Loss2: 0.030704
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.053109 Loss1: 0.022256 Loss2: 0.030852
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050274 Loss1: 0.018962 Loss2: 0.031312
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.047855 Loss1: 0.016522 Loss2: 0.031333
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.041157 Loss1: 0.009957 Loss2: 0.031200
-(DefaultActor pid=1838052) >> Training accuracy: 0.999199
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.075094 Loss1: 0.046130 Loss2: 0.028964
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.044847 Loss1: 0.015281 Loss2: 0.029565
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.045376 Loss1: 0.015603 Loss2: 0.029773
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.041503 Loss1: 0.011598 Loss2: 0.029905
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.040974 Loss1: 0.011429 Loss2: 0.029545
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.052354 Loss1: 0.022152 Loss2: 0.030202
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.057439 Loss1: 0.026540 Loss2: 0.030899
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.071672 Loss1: 0.039823 Loss2: 0.031848
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056706 Loss1: 0.025255 Loss2: 0.031451
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.083940 Loss1: 0.051395 Loss2: 0.032545
-(DefaultActor pid=1838052) >> Training accuracy: 0.992188
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.065331 Loss1: 0.031605 Loss2: 0.033726
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.048756 Loss1: 0.014647 Loss2: 0.034109
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047412 Loss1: 0.013197 Loss2: 0.034214
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047453 Loss1: 0.013241 Loss2: 0.034213
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.042436 Loss1: 0.008800 Loss2: 0.033635
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038424 Loss1: 0.004790 Loss2: 0.033634
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.039830 Loss1: 0.006700 Loss2: 0.033130
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.041534 Loss1: 0.008239 Loss2: 0.033295
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.041286 Loss1: 0.008091 Loss2: 0.033195
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.047830 Loss1: 0.014192 Loss2: 0.033638
-(DefaultActor pid=1838052) >> Training accuracy: 0.996440
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.056651 Loss1: 0.028744 Loss2: 0.027907
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.045854 Loss1: 0.016768 Loss2: 0.029087
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.037951 Loss1: 0.008564 Loss2: 0.029387
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.037614 Loss1: 0.008540 Loss2: 0.029073
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.040430 Loss1: 0.011339 Loss2: 0.029092
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.044693 Loss1: 0.014434 Loss2: 0.030259
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.050328 Loss1: 0.019740 Loss2: 0.030589
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.057468 Loss1: 0.026093 Loss2: 0.031376
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.057385 Loss1: 0.025755 Loss2: 0.031630
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055521 Loss1: 0.023553 Loss2: 0.031968
-(DefaultActor pid=1838052) >> Training accuracy: 0.996044
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.083450 Loss1: 0.054272 Loss2: 0.029177
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057452 Loss1: 0.026879 Loss2: 0.030573
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052028 Loss1: 0.021380 Loss2: 0.030647
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.043875 Loss1: 0.013398 Loss2: 0.030477
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043299 Loss1: 0.012940 Loss2: 0.030359
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042412 Loss1: 0.012065 Loss2: 0.030347
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.049777 Loss1: 0.019074 Loss2: 0.030703
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061648 Loss1: 0.030382 Loss2: 0.031267
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056023 Loss1: 0.024368 Loss2: 0.031655
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.049466 Loss1: 0.017674 Loss2: 0.031791
-(DefaultActor pid=1838052) >> Training accuracy: 0.998522
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.063258 Loss1: 0.029279 Loss2: 0.033979
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.043540 Loss1: 0.009752 Loss2: 0.033788
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046370 Loss1: 0.012488 Loss2: 0.033881
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.047796 Loss1: 0.013564 Loss2: 0.034233
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049889 Loss1: 0.015545 Loss2: 0.034344
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.047356 Loss1: 0.013055 Loss2: 0.034301
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.044150 Loss1: 0.009915 Loss2: 0.034235
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.046422 Loss1: 0.012095 Loss2: 0.034327
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.047747 Loss1: 0.012895 Loss2: 0.034852
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.053839 Loss1: 0.018898 Loss2: 0.034941
-(DefaultActor pid=1838052) >> Training accuracy: 0.995998
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.064461 Loss1: 0.035806 Loss2: 0.028655
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.049076 Loss1: 0.018939 Loss2: 0.030137
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043187 Loss1: 0.013175 Loss2: 0.030013
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.042490 Loss1: 0.012644 Loss2: 0.029846
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041436 Loss1: 0.011364 Loss2: 0.030071
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.039949 Loss1: 0.010021 Loss2: 0.029928
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.041656 Loss1: 0.011503 Loss2: 0.030154
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.043643 Loss1: 0.013289 Loss2: 0.030354
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.038146 Loss1: 0.007936 Loss2: 0.030210
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.038103 Loss1: 0.008037 Loss2: 0.030067
-(DefaultActor pid=1838052) >> Training accuracy: 0.999589
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 07:55:01,121][flwr][DEBUG] - fit_round 96 received 10 results and 0 failures
->> Test accuracy: 0.661600
-[2023-09-29 07:55:36,685][flwr][INFO] - fit progress: (96, 2.4384049723704404, {'accuracy': 0.6616}, 178559.57587395515)
-[2023-09-29 07:55:36,686][flwr][DEBUG] - evaluate_round 96: strategy sampled 10 clients (out of 10)
-[2023-09-29 07:56:11,644][flwr][DEBUG] - evaluate_round 96 received 10 results and 0 failures
-[2023-09-29 07:56:11,645][flwr][DEBUG] - fit_round 97: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.079201 Loss1: 0.044307 Loss2: 0.034894
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.051630 Loss1: 0.016083 Loss2: 0.035547
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058143 Loss1: 0.022472 Loss2: 0.035672
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048878 Loss1: 0.013551 Loss2: 0.035327
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.048773 Loss1: 0.013478 Loss2: 0.035295
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.045660 Loss1: 0.010540 Loss2: 0.035119
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.048224 Loss1: 0.013231 Loss2: 0.034993
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.059652 Loss1: 0.024064 Loss2: 0.035588
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.061979 Loss1: 0.025861 Loss2: 0.036118
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.062316 Loss1: 0.025888 Loss2: 0.036428
-(DefaultActor pid=1838052) >> Training accuracy: 0.997613
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.613362 Loss1: 0.041162 Loss2: 0.572199
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.602336 Loss1: 0.043445 Loss2: 0.558891
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.634721 Loss1: 0.074418 Loss2: 0.560303
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.646811 Loss1: 0.089407 Loss2: 0.557404
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613747 Loss1: 0.062880 Loss2: 0.550867
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.606836 Loss1: 0.059779 Loss2: 0.547057
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.629734 Loss1: 0.079527 Loss2: 0.550207
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.645443 Loss1: 0.094997 Loss2: 0.550446
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.660590 Loss1: 0.107696 Loss2: 0.552894
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.640517 Loss1: 0.092468 Loss2: 0.548049
-(DefaultActor pid=1838052) >> Training accuracy: 0.982936
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.052777 Loss1: 0.025819 Loss2: 0.026958
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.036519 Loss1: 0.008874 Loss2: 0.027644
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043347 Loss1: 0.015231 Loss2: 0.028116
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.048600 Loss1: 0.020040 Loss2: 0.028561
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.045275 Loss1: 0.016237 Loss2: 0.029037
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040131 Loss1: 0.011429 Loss2: 0.028702
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.036546 Loss1: 0.007969 Loss2: 0.028577
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.045030 Loss1: 0.016672 Loss2: 0.028358
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.056171 Loss1: 0.026744 Loss2: 0.029427
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.056962 Loss1: 0.026638 Loss2: 0.030324
-(DefaultActor pid=1838052) >> Training accuracy: 0.996638
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.628083 Loss1: 0.034002 Loss2: 0.594081
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.608314 Loss1: 0.027965 Loss2: 0.580349
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.615309 Loss1: 0.039854 Loss2: 0.575455
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.613916 Loss1: 0.041234 Loss2: 0.572682
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613677 Loss1: 0.044763 Loss2: 0.568913
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.603527 Loss1: 0.039002 Loss2: 0.564524
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.600781 Loss1: 0.040526 Loss2: 0.560255
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.617002 Loss1: 0.058709 Loss2: 0.558293
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.632651 Loss1: 0.068477 Loss2: 0.564175
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.628182 Loss1: 0.067241 Loss2: 0.560941
-(DefaultActor pid=1838052) >> Training accuracy: 0.989383
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.676872 Loss1: 0.044786 Loss2: 0.632087
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.656034 Loss1: 0.034128 Loss2: 0.621906
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.646525 Loss1: 0.031786 Loss2: 0.614739
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.646006 Loss1: 0.035973 Loss2: 0.610032
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.663049 Loss1: 0.055582 Loss2: 0.607467
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.659951 Loss1: 0.055409 Loss2: 0.604542
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.662044 Loss1: 0.060314 Loss2: 0.601730
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.673822 Loss1: 0.073299 Loss2: 0.600523
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.674618 Loss1: 0.075371 Loss2: 0.599248
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.666939 Loss1: 0.071995 Loss2: 0.594944
-(DefaultActor pid=1838052) >> Training accuracy: 0.977848
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.651725 Loss1: 0.051222 Loss2: 0.600503
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.630316 Loss1: 0.037295 Loss2: 0.593021
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.621509 Loss1: 0.038870 Loss2: 0.582639
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.619217 Loss1: 0.036743 Loss2: 0.582474
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.621586 Loss1: 0.044857 Loss2: 0.576729
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.637770 Loss1: 0.063554 Loss2: 0.574216
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.644034 Loss1: 0.069280 Loss2: 0.574754
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.640288 Loss1: 0.068926 Loss2: 0.571362
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.657318 Loss1: 0.085771 Loss2: 0.571547
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.651679 Loss1: 0.081227 Loss2: 0.570453
-(DefaultActor pid=1838052) >> Training accuracy: 0.978441
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.092739 Loss1: 0.037250 Loss2: 0.055489
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.070755 Loss1: 0.018018 Loss2: 0.052737
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068109 Loss1: 0.015535 Loss2: 0.052574
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063818 Loss1: 0.011243 Loss2: 0.052575
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.064558 Loss1: 0.012594 Loss2: 0.051964
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.060489 Loss1: 0.008800 Loss2: 0.051688
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.072672 Loss1: 0.019967 Loss2: 0.052705
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.073645 Loss1: 0.020675 Loss2: 0.052970
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068419 Loss1: 0.015763 Loss2: 0.052656
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.066765 Loss1: 0.014162 Loss2: 0.052603
-(DefaultActor pid=1838052) >> Training accuracy: 0.998220
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.581819 Loss1: 0.044835 Loss2: 0.536984
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.558548 Loss1: 0.032912 Loss2: 0.525636
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.553959 Loss1: 0.033114 Loss2: 0.520845
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.549609 Loss1: 0.034162 Loss2: 0.515447
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.549386 Loss1: 0.034103 Loss2: 0.515283
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.556947 Loss1: 0.043391 Loss2: 0.513556
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.566227 Loss1: 0.051572 Loss2: 0.514655
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.571161 Loss1: 0.057243 Loss2: 0.513918
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.569418 Loss1: 0.054606 Loss2: 0.514812
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.579376 Loss1: 0.063368 Loss2: 0.516008
-(DefaultActor pid=1838052) >> Training accuracy: 0.989329
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.067762 Loss1: 0.039682 Loss2: 0.028079
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047411 Loss1: 0.018534 Loss2: 0.028877
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.044362 Loss1: 0.015531 Loss2: 0.028831
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.042811 Loss1: 0.013756 Loss2: 0.029055
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.044947 Loss1: 0.015662 Loss2: 0.029284
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.043699 Loss1: 0.014184 Loss2: 0.029515
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.040352 Loss1: 0.011035 Loss2: 0.029318
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.039108 Loss1: 0.009739 Loss2: 0.029369
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.043160 Loss1: 0.013463 Loss2: 0.029697
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.037832 Loss1: 0.008469 Loss2: 0.029363
-(DefaultActor pid=1838052) >> Training accuracy: 0.997255
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078080 Loss1: 0.034478 Loss2: 0.043602
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.060356 Loss1: 0.018719 Loss2: 0.041637
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.054633 Loss1: 0.014571 Loss2: 0.040063
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.056015 Loss1: 0.016592 Loss2: 0.039423
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.046490 Loss1: 0.007593 Loss2: 0.038896
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.050656 Loss1: 0.012393 Loss2: 0.038263
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.053522 Loss1: 0.014756 Loss2: 0.038765
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.060399 Loss1: 0.021147 Loss2: 0.039252
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.060786 Loss1: 0.021442 Loss2: 0.039344
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.058868 Loss1: 0.019547 Loss2: 0.039320
-(DefaultActor pid=1838052) >> Training accuracy: 0.997596
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 08:24:41,483][flwr][DEBUG] - fit_round 97 received 10 results and 0 failures
->> Test accuracy: 0.665400
-[2023-09-29 08:25:17,679][flwr][INFO] - fit progress: (97, 2.410859399329359, {'accuracy': 0.6654}, 180340.56950572738)
-[2023-09-29 08:25:17,680][flwr][DEBUG] - evaluate_round 97: strategy sampled 10 clients (out of 10)
-[2023-09-29 08:25:53,375][flwr][DEBUG] - evaluate_round 97 received 10 results and 0 failures
-[2023-09-29 08:25:53,395][flwr][DEBUG] - fit_round 98: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.549777 Loss1: 0.043757 Loss2: 0.506020
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.533998 Loss1: 0.035112 Loss2: 0.498886
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.539338 Loss1: 0.042695 Loss2: 0.496644
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.533252 Loss1: 0.044032 Loss2: 0.489220
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.525870 Loss1: 0.039986 Loss2: 0.485883
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.537527 Loss1: 0.054712 Loss2: 0.482815
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.532382 Loss1: 0.049909 Loss2: 0.482473
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.557447 Loss1: 0.073527 Loss2: 0.483920
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.598190 Loss1: 0.108657 Loss2: 0.489533
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.604176 Loss1: 0.115428 Loss2: 0.488748
-(DefaultActor pid=1838052) >> Training accuracy: 0.984968
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.586126 Loss1: 0.057872 Loss2: 0.528254
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.560101 Loss1: 0.043648 Loss2: 0.516453
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.563934 Loss1: 0.047748 Loss2: 0.516186
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.555805 Loss1: 0.044376 Loss2: 0.511428
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.574949 Loss1: 0.065538 Loss2: 0.509411
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.572481 Loss1: 0.065818 Loss2: 0.506663
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.567877 Loss1: 0.061644 Loss2: 0.506233
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.546704 Loss1: 0.045630 Loss2: 0.501074
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.564503 Loss1: 0.062443 Loss2: 0.502060
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.555968 Loss1: 0.056911 Loss2: 0.499057
-(DefaultActor pid=1838052) >> Training accuracy: 0.986486
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.590116 Loss1: 0.043552 Loss2: 0.546564
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.516645 Loss1: 0.038507 Loss2: 0.478139
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.517280 Loss1: 0.055420 Loss2: 0.461861
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.515714 Loss1: 0.057608 Loss2: 0.458106
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.521289 Loss1: 0.064280 Loss2: 0.457009
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.566376 Loss1: 0.105529 Loss2: 0.460847
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.573215 Loss1: 0.111553 Loss2: 0.461663
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.550571 Loss1: 0.094488 Loss2: 0.456082
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.548599 Loss1: 0.095250 Loss2: 0.453349
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.547884 Loss1: 0.094037 Loss2: 0.453847
-(DefaultActor pid=1838052) >> Training accuracy: 0.977564
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.067766 Loss1: 0.037395 Loss2: 0.030371
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047285 Loss1: 0.016018 Loss2: 0.031267
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.044344 Loss1: 0.012981 Loss2: 0.031363
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038346 Loss1: 0.007416 Loss2: 0.030931
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.036410 Loss1: 0.005644 Loss2: 0.030766
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038429 Loss1: 0.008041 Loss2: 0.030389
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.037872 Loss1: 0.007634 Loss2: 0.030239
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.038358 Loss1: 0.008115 Loss2: 0.030243
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.039163 Loss1: 0.009079 Loss2: 0.030084
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.039657 Loss1: 0.009274 Loss2: 0.030383
-(DefaultActor pid=1838052) >> Training accuracy: 0.998616
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.090059 Loss1: 0.033480 Loss2: 0.056579
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.080192 Loss1: 0.026249 Loss2: 0.053943
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.079071 Loss1: 0.025447 Loss2: 0.053624
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.084066 Loss1: 0.030342 Loss2: 0.053724
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.077223 Loss1: 0.023740 Loss2: 0.053483
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.072529 Loss1: 0.019496 Loss2: 0.053034
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.078954 Loss1: 0.026482 Loss2: 0.052472
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.084011 Loss1: 0.030998 Loss2: 0.053013
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.096512 Loss1: 0.042889 Loss2: 0.053623
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.100532 Loss1: 0.045552 Loss2: 0.054980
-(DefaultActor pid=1838052) >> Training accuracy: 0.988715
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078757 Loss1: 0.041823 Loss2: 0.036934
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057158 Loss1: 0.019653 Loss2: 0.037505
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.056250 Loss1: 0.018631 Loss2: 0.037619
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.061552 Loss1: 0.023596 Loss2: 0.037957
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.059991 Loss1: 0.022248 Loss2: 0.037743
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.057174 Loss1: 0.019012 Loss2: 0.038162
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.052241 Loss1: 0.014950 Loss2: 0.037291
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.054873 Loss1: 0.017266 Loss2: 0.037607
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.055658 Loss1: 0.017738 Loss2: 0.037920
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.051158 Loss1: 0.013499 Loss2: 0.037658
-(DefaultActor pid=1838052) >> Training accuracy: 0.997533
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.060092 Loss1: 0.030627 Loss2: 0.029465
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.042577 Loss1: 0.012876 Loss2: 0.029700
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.046277 Loss1: 0.016380 Loss2: 0.029897
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.041854 Loss1: 0.011636 Loss2: 0.030218
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.039437 Loss1: 0.009516 Loss2: 0.029921
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040861 Loss1: 0.010740 Loss2: 0.030121
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.040881 Loss1: 0.010936 Loss2: 0.029945
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.039393 Loss1: 0.009473 Loss2: 0.029920
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.035240 Loss1: 0.005221 Loss2: 0.030020
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040414 Loss1: 0.010637 Loss2: 0.029777
-(DefaultActor pid=1838052) >> Training accuracy: 0.998220
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078735 Loss1: 0.027144 Loss2: 0.051590
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.064853 Loss1: 0.015020 Loss2: 0.049833
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.061920 Loss1: 0.012209 Loss2: 0.049711
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.059924 Loss1: 0.010502 Loss2: 0.049421
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.057479 Loss1: 0.008267 Loss2: 0.049212
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.057075 Loss1: 0.008080 Loss2: 0.048996
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.056646 Loss1: 0.008031 Loss2: 0.048615
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.051844 Loss1: 0.003231 Loss2: 0.048613
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.051384 Loss1: 0.003341 Loss2: 0.048043
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055235 Loss1: 0.007448 Loss2: 0.047787
-(DefaultActor pid=1838052) >> Training accuracy: 1.000000
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.250230 Loss1: 0.040699 Loss2: 0.209530
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.225524 Loss1: 0.026124 Loss2: 0.199401
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.220431 Loss1: 0.024229 Loss2: 0.196203
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.212984 Loss1: 0.017082 Loss2: 0.195902
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.206861 Loss1: 0.014108 Loss2: 0.192752
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.206119 Loss1: 0.014018 Loss2: 0.192101
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.226799 Loss1: 0.033210 Loss2: 0.193589
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.296702 Loss1: 0.093519 Loss2: 0.203182
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.252407 Loss1: 0.052149 Loss2: 0.200258
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.236775 Loss1: 0.038658 Loss2: 0.198117
-(DefaultActor pid=1838052) >> Training accuracy: 0.995808
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.085674 Loss1: 0.033271 Loss2: 0.052403
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.066818 Loss1: 0.016953 Loss2: 0.049864
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.058875 Loss1: 0.009403 Loss2: 0.049471
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068729 Loss1: 0.019642 Loss2: 0.049087
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070481 Loss1: 0.020923 Loss2: 0.049558
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.066848 Loss1: 0.016730 Loss2: 0.050119
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.062186 Loss1: 0.012442 Loss2: 0.049744
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061445 Loss1: 0.011959 Loss2: 0.049485
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.061224 Loss1: 0.011802 Loss2: 0.049422
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.060591 Loss1: 0.011275 Loss2: 0.049315
-(DefaultActor pid=1838052) >> Training accuracy: 0.997596
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 08:54:22,744][flwr][DEBUG] - fit_round 98 received 10 results and 0 failures
->> Test accuracy: 0.665100
-[2023-09-29 08:54:58,949][flwr][INFO] - fit progress: (98, 2.4004773120529737, {'accuracy': 0.6651}, 182121.83961292123)
-[2023-09-29 08:54:58,950][flwr][DEBUG] - evaluate_round 98: strategy sampled 10 clients (out of 10)
-[2023-09-29 08:55:34,687][flwr][DEBUG] - evaluate_round 98 received 10 results and 0 failures
-[2023-09-29 08:55:34,687][flwr][DEBUG] - fit_round 99: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.091405 Loss1: 0.048241 Loss2: 0.043164
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.069584 Loss1: 0.027362 Loss2: 0.042222
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.064826 Loss1: 0.022848 Loss2: 0.041977
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.064725 Loss1: 0.022653 Loss2: 0.042072
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.062853 Loss1: 0.021004 Loss2: 0.041849
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.073984 Loss1: 0.031241 Loss2: 0.042743
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.078138 Loss1: 0.035332 Loss2: 0.042805
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.068190 Loss1: 0.025580 Loss2: 0.042610
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.067457 Loss1: 0.024652 Loss2: 0.042805
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.069374 Loss1: 0.026923 Loss2: 0.042451
-(DefaultActor pid=1838052) >> Training accuracy: 0.995253
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.052547 Loss1: 0.024230 Loss2: 0.028318
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.037621 Loss1: 0.008835 Loss2: 0.028786
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.037036 Loss1: 0.008433 Loss2: 0.028603
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.037986 Loss1: 0.009429 Loss2: 0.028557
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.034258 Loss1: 0.006005 Loss2: 0.028253
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.035206 Loss1: 0.007043 Loss2: 0.028163
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.036100 Loss1: 0.007946 Loss2: 0.028155
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.032335 Loss1: 0.004322 Loss2: 0.028012
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.033711 Loss1: 0.005984 Loss2: 0.027726
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.031973 Loss1: 0.004002 Loss2: 0.027971
-(DefaultActor pid=1838052) >> Training accuracy: 1.000000
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066475 Loss1: 0.034365 Loss2: 0.032110
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053745 Loss1: 0.021121 Loss2: 0.032623
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.047927 Loss1: 0.014906 Loss2: 0.033020
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049496 Loss1: 0.016957 Loss2: 0.032538
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.049878 Loss1: 0.017380 Loss2: 0.032497
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.046233 Loss1: 0.014142 Loss2: 0.032090
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.045089 Loss1: 0.013204 Loss2: 0.031885
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.043781 Loss1: 0.011740 Loss2: 0.032041
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.048554 Loss1: 0.016643 Loss2: 0.031911
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.052603 Loss1: 0.019838 Loss2: 0.032765
-(DefaultActor pid=1838052) >> Training accuracy: 0.992588
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.445488 Loss1: 0.034367 Loss2: 0.411120
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.427664 Loss1: 0.037176 Loss2: 0.390488
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.439320 Loss1: 0.051871 Loss2: 0.387449
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.425915 Loss1: 0.041308 Loss2: 0.384607
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.414247 Loss1: 0.030455 Loss2: 0.383792
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.428394 Loss1: 0.046970 Loss2: 0.381424
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.460247 Loss1: 0.073402 Loss2: 0.386844
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.458096 Loss1: 0.072418 Loss2: 0.385677
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.450445 Loss1: 0.066240 Loss2: 0.384205
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.510458 Loss1: 0.121829 Loss2: 0.388629
-(DefaultActor pid=1838052) >> Training accuracy: 0.985777
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.633143 Loss1: 0.041697 Loss2: 0.591446
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.617976 Loss1: 0.037288 Loss2: 0.580688
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.617360 Loss1: 0.045875 Loss2: 0.571485
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.608476 Loss1: 0.039195 Loss2: 0.569281
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.613147 Loss1: 0.050556 Loss2: 0.562591
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.619712 Loss1: 0.057202 Loss2: 0.562510
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.611205 Loss1: 0.053241 Loss2: 0.557964
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.615260 Loss1: 0.059919 Loss2: 0.555341
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.631103 Loss1: 0.076967 Loss2: 0.554136
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.659488 Loss1: 0.098410 Loss2: 0.561078
-(DefaultActor pid=1838052) >> Training accuracy: 0.983386
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.062918 Loss1: 0.032763 Loss2: 0.030156
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.045068 Loss1: 0.014004 Loss2: 0.031063
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.050368 Loss1: 0.019171 Loss2: 0.031197
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039185 Loss1: 0.007998 Loss2: 0.031188
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.041897 Loss1: 0.010807 Loss2: 0.031090
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.040737 Loss1: 0.009418 Loss2: 0.031318
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.039845 Loss1: 0.008672 Loss2: 0.031174
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.039724 Loss1: 0.008396 Loss2: 0.031329
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.037339 Loss1: 0.006380 Loss2: 0.030960
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046528 Loss1: 0.015346 Loss2: 0.031182
-(DefaultActor pid=1838052) >> Training accuracy: 0.998972
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.068213 Loss1: 0.038528 Loss2: 0.029685
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.044380 Loss1: 0.014492 Loss2: 0.029888
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.038089 Loss1: 0.008528 Loss2: 0.029561
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.038544 Loss1: 0.009269 Loss2: 0.029275
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.037195 Loss1: 0.008015 Loss2: 0.029181
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.033440 Loss1: 0.004350 Loss2: 0.029090
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.032494 Loss1: 0.003690 Loss2: 0.028803
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.035732 Loss1: 0.006899 Loss2: 0.028833
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.036087 Loss1: 0.007021 Loss2: 0.029066
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.034972 Loss1: 0.005814 Loss2: 0.029157
-(DefaultActor pid=1838052) >> Training accuracy: 1.000000
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.109486 Loss1: 0.054376 Loss2: 0.055110
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.073680 Loss1: 0.022969 Loss2: 0.050711
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.068716 Loss1: 0.019366 Loss2: 0.049351
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.068770 Loss1: 0.020379 Loss2: 0.048391
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.072104 Loss1: 0.023777 Loss2: 0.048327
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065356 Loss1: 0.017112 Loss2: 0.048244
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.065114 Loss1: 0.017873 Loss2: 0.047241
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.061497 Loss1: 0.014405 Loss2: 0.047093
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.075357 Loss1: 0.028068 Loss2: 0.047289
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.082637 Loss1: 0.034325 Loss2: 0.048312
-(DefaultActor pid=1838052) >> Training accuracy: 0.995566
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.458671 Loss1: 0.031645 Loss2: 0.427026
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.439331 Loss1: 0.029116 Loss2: 0.410215
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.468083 Loss1: 0.056617 Loss2: 0.411466
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.462329 Loss1: 0.048776 Loss2: 0.413553
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.466376 Loss1: 0.055224 Loss2: 0.411153
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.457259 Loss1: 0.043735 Loss2: 0.413523
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.473618 Loss1: 0.061420 Loss2: 0.412197
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.455332 Loss1: 0.044716 Loss2: 0.410616
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.463711 Loss1: 0.051457 Loss2: 0.412255
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.505182 Loss1: 0.087670 Loss2: 0.417512
-(DefaultActor pid=1838052) >> Training accuracy: 0.985166
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.078853 Loss1: 0.027445 Loss2: 0.051408
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.057425 Loss1: 0.010058 Loss2: 0.047367
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.051580 Loss1: 0.005846 Loss2: 0.045734
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.049698 Loss1: 0.005078 Loss2: 0.044620
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.047146 Loss1: 0.003213 Loss2: 0.043933
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.048095 Loss1: 0.004686 Loss2: 0.043408
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.046344 Loss1: 0.003210 Loss2: 0.043134
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.050643 Loss1: 0.007985 Loss2: 0.042658
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.053782 Loss1: 0.010515 Loss2: 0.043267
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.055183 Loss1: 0.011663 Loss2: 0.043520
-(DefaultActor pid=1838052) >> Training accuracy: 0.998418
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 09:24:15,597][flwr][DEBUG] - fit_round 99 received 10 results and 0 failures
->> Test accuracy: 0.664000
-[2023-09-29 09:24:52,549][flwr][INFO] - fit progress: (99, 2.4279736249972457, {'accuracy': 0.664}, 183915.4392115213)
-[2023-09-29 09:24:52,549][flwr][DEBUG] - evaluate_round 99: strategy sampled 10 clients (out of 10)
-[2023-09-29 09:25:28,085][flwr][DEBUG] - evaluate_round 99 received 10 results and 0 failures
-[2023-09-29 09:25:28,086][flwr][DEBUG] - fit_round 100: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.633721 Loss1: 0.042953 Loss2: 0.590768
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.611071 Loss1: 0.029493 Loss2: 0.581578
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.628639 Loss1: 0.051905 Loss2: 0.576734
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.610027 Loss1: 0.036689 Loss2: 0.573338
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.615064 Loss1: 0.047613 Loss2: 0.567451
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.619173 Loss1: 0.051979 Loss2: 0.567193
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.614499 Loss1: 0.052522 Loss2: 0.561977
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.627429 Loss1: 0.062283 Loss2: 0.565146
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.637309 Loss1: 0.073219 Loss2: 0.564090
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.632829 Loss1: 0.072466 Loss2: 0.560363
-(DefaultActor pid=1838052) >> Training accuracy: 0.985197
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.619186 Loss1: 0.037708 Loss2: 0.581478
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.573218 Loss1: 0.031769 Loss2: 0.541448
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.566535 Loss1: 0.034625 Loss2: 0.531910
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.567666 Loss1: 0.038374 Loss2: 0.529291
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.563711 Loss1: 0.034891 Loss2: 0.528821
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.560821 Loss1: 0.035298 Loss2: 0.525523
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.559427 Loss1: 0.034536 Loss2: 0.524892
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.556643 Loss1: 0.035079 Loss2: 0.521564
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.575358 Loss1: 0.052009 Loss2: 0.523349
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.589059 Loss1: 0.063919 Loss2: 0.525140
-(DefaultActor pid=1838052) >> Training accuracy: 0.989183
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.421478 Loss1: 0.054516 Loss2: 0.366961
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.369016 Loss1: 0.036506 Loss2: 0.332509
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.365587 Loss1: 0.044795 Loss2: 0.320792
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.358666 Loss1: 0.042269 Loss2: 0.316397
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.384042 Loss1: 0.065753 Loss2: 0.318289
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.381686 Loss1: 0.062266 Loss2: 0.319421
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.412713 Loss1: 0.092310 Loss2: 0.320403
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.410705 Loss1: 0.087939 Loss2: 0.322766
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.431225 Loss1: 0.107201 Loss2: 0.324025
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.431145 Loss1: 0.107388 Loss2: 0.323757
-(DefaultActor pid=1838052) >> Training accuracy: 0.969172
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.506859 Loss1: 0.043866 Loss2: 0.462993
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.490824 Loss1: 0.043318 Loss2: 0.447506
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.476251 Loss1: 0.033295 Loss2: 0.442956
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.477720 Loss1: 0.037749 Loss2: 0.439971
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.505571 Loss1: 0.062868 Loss2: 0.442704
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.506398 Loss1: 0.064462 Loss2: 0.441936
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.509670 Loss1: 0.067286 Loss2: 0.442383
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.522342 Loss1: 0.079608 Loss2: 0.442733
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.520771 Loss1: 0.078097 Loss2: 0.442674
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.506931 Loss1: 0.069365 Loss2: 0.437566
-(DefaultActor pid=1838052) >> Training accuracy: 0.983724
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.056152 Loss1: 0.028145 Loss2: 0.028007
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.038266 Loss1: 0.009795 Loss2: 0.028472
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.034966 Loss1: 0.006531 Loss2: 0.028435
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.036343 Loss1: 0.007854 Loss2: 0.028489
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.035347 Loss1: 0.006803 Loss2: 0.028544
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.034385 Loss1: 0.005901 Loss2: 0.028484
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.033212 Loss1: 0.004810 Loss2: 0.028402
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.031462 Loss1: 0.003270 Loss2: 0.028192
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.030965 Loss1: 0.002861 Loss2: 0.028104
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.033267 Loss1: 0.005174 Loss2: 0.028093
-(DefaultActor pid=1838052) >> Training accuracy: 0.999047
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.414799 Loss1: 0.036181 Loss2: 0.378618
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.372005 Loss1: 0.026293 Loss2: 0.345712
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.375939 Loss1: 0.038718 Loss2: 0.337221
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.373756 Loss1: 0.040258 Loss2: 0.333498
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.383736 Loss1: 0.052527 Loss2: 0.331209
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.395758 Loss1: 0.064488 Loss2: 0.331270
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.442589 Loss1: 0.107571 Loss2: 0.335019
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.418919 Loss1: 0.082914 Loss2: 0.336005
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.420480 Loss1: 0.085369 Loss2: 0.335111
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.407298 Loss1: 0.074801 Loss2: 0.332498
-(DefaultActor pid=1838052) >> Training accuracy: 0.983386
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.073496 Loss1: 0.041620 Loss2: 0.031877
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.047048 Loss1: 0.015015 Loss2: 0.032033
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043382 Loss1: 0.011544 Loss2: 0.031838
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.045780 Loss1: 0.013962 Loss2: 0.031818
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.043644 Loss1: 0.011758 Loss2: 0.031886
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038814 Loss1: 0.007252 Loss2: 0.031562
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.042853 Loss1: 0.011127 Loss2: 0.031726
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.040428 Loss1: 0.008698 Loss2: 0.031731
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.038025 Loss1: 0.006288 Loss2: 0.031736
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.040730 Loss1: 0.008930 Loss2: 0.031800
-(DefaultActor pid=1838052) >> Training accuracy: 0.999209
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.071357 Loss1: 0.037867 Loss2: 0.033490
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.053780 Loss1: 0.019348 Loss2: 0.034432
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.052485 Loss1: 0.018421 Loss2: 0.034064
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.044673 Loss1: 0.010844 Loss2: 0.033829
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.042454 Loss1: 0.009210 Loss2: 0.033244
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.042526 Loss1: 0.009397 Loss2: 0.033129
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.043839 Loss1: 0.010529 Loss2: 0.033310
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.044904 Loss1: 0.011533 Loss2: 0.033371
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.047014 Loss1: 0.013463 Loss2: 0.033551
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.049394 Loss1: 0.015597 Loss2: 0.033797
-(DefaultActor pid=1838052) >> Training accuracy: 0.997627
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.081581 Loss1: 0.027750 Loss2: 0.053831
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.063456 Loss1: 0.013656 Loss2: 0.049800
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.062776 Loss1: 0.013436 Loss2: 0.049341
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.063601 Loss1: 0.014275 Loss2: 0.049326
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.070511 Loss1: 0.020976 Loss2: 0.049535
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.065924 Loss1: 0.016261 Loss2: 0.049664
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.073745 Loss1: 0.023804 Loss2: 0.049941
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.066668 Loss1: 0.016710 Loss2: 0.049958
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.068941 Loss1: 0.018549 Loss2: 0.050391
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.078619 Loss1: 0.028174 Loss2: 0.050445
-(DefaultActor pid=1838052) >> Training accuracy: 0.997796
-(DefaultActor pid=1838052)  ** Training complete **
-(DefaultActor pid=1838052) Epoch: 0 Loss: 0.066184 Loss1: 0.036712 Loss2: 0.029472
-(DefaultActor pid=1838052) Epoch: 1 Loss: 0.042205 Loss1: 0.011927 Loss2: 0.030277
-(DefaultActor pid=1838052) Epoch: 2 Loss: 0.043972 Loss1: 0.013629 Loss2: 0.030343
-(DefaultActor pid=1838052) Epoch: 3 Loss: 0.039843 Loss1: 0.009638 Loss2: 0.030206
-(DefaultActor pid=1838052) Epoch: 4 Loss: 0.037968 Loss1: 0.007669 Loss2: 0.030300
-(DefaultActor pid=1838052) Epoch: 5 Loss: 0.038546 Loss1: 0.008416 Loss2: 0.030130
-(DefaultActor pid=1838052) Epoch: 6 Loss: 0.046121 Loss1: 0.015874 Loss2: 0.030247
-(DefaultActor pid=1838052) Epoch: 7 Loss: 0.038334 Loss1: 0.008077 Loss2: 0.030257
-(DefaultActor pid=1838052) Epoch: 8 Loss: 0.036362 Loss1: 0.006121 Loss2: 0.030240
-(DefaultActor pid=1838052) Epoch: 9 Loss: 0.046879 Loss1: 0.016407 Loss2: 0.030472
-(DefaultActor pid=1838052) >> Training accuracy: 0.998616
-(DefaultActor pid=1838052)  ** Training complete **
-[2023-09-29 09:54:11,154][flwr][DEBUG] - fit_round 100 received 10 results and 0 failures
->> Test accuracy: 0.663500
-[2023-09-29 09:54:47,613][flwr][INFO] - fit progress: (100, 2.423790014970798, {'accuracy': 0.6635}, 185710.50300032226)
-[2023-09-29 09:54:47,613][flwr][DEBUG] - evaluate_round 100: strategy sampled 10 clients (out of 10)
-[2023-09-29 09:55:23,487][flwr][DEBUG] - evaluate_round 100 received 10 results and 0 failures
-[2023-09-29 09:55:23,488][flwr][INFO] - FL finished in 185746.37847093912
-[2023-09-29 09:55:23,512][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0)]
-[2023-09-29 09:55:23,512][flwr][INFO] - app_fit: metrics_distributed_fit {}
-[2023-09-29 09:55:23,513][flwr][INFO] - app_fit: metrics_distributed {}
-[2023-09-29 09:55:23,513][flwr][INFO] - app_fit: losses_centralized [(0, 6.430294827531321), (1, 4.861440579350383), (2, 5.477163912008365), (3, 5.5055647475270035), (4, 4.169462466011413), (5, 3.436301054665075), (6, 2.9990923823639988), (7, 2.694728255652772), (8, 2.53471215883383), (9, 2.3920893143541133), (10, 2.306128243287912), (11, 2.207922473883096), (12, 2.172221914647867), (13, 2.099844414205216), (14, 2.0913502991009065), (15, 2.0594057168442603), (16, 2.043369851554164), (17, 2.0114177884385227), (18, 2.017150927846805), (19, 2.010581853100286), (20, 1.9967298349633384), (21, 2.0065166573174085), (22, 1.9742871688577694), (23, 1.9792633229932084), (24, 2.0303315805931823), (25, 1.9995412224778732), (26, 2.022627312535295), (27, 1.9937346629060495), (28, 2.021510124206543), (29, 2.0112326653620687), (30, 2.0224276781082153), (31, 2.050073419706509), (32, 2.048540641515019), (33, 2.033169127881717), (34, 2.0350445369942882), (35, 2.080257884039285), (36, 2.0718342880852307), (37, 2.0764910361637323), (38, 2.065860210897062), (39, 2.087371099490327), (40, 2.067515920335873), (41, 2.094820894753209), (42, 2.0926969356049363), (43, 2.1266209250821855), (44, 2.132381713047576), (45, 2.1219718056364942), (46, 2.146202865500039), (47, 2.1143142310575174), (48, 2.1224685852139142), (49, 2.117806362458311), (50, 2.142119585134732), (51, 2.135459794404027), (52, 2.1817148396382318), (53, 2.183458357382887), (54, 2.1794180731042125), (55, 2.1586610794829104), (56, 2.188702217115762), (57, 2.171707748224179), (58, 2.195324074726897), (59, 2.2019545649187253), (60, 2.1900063330373065), (61, 2.20785686230888), (62, 2.1942758756323744), (63, 2.2481949498859075), (64, 2.2214675100085834), (65, 2.2834309385226557), (66, 2.208361754592615), (67, 2.293970344736934), (68, 2.207248735922975), (69, 2.268605324026114), (70, 2.256014349171148), (71, 2.247258449610049), (72, 2.2466823984258855), (73, 2.2818810541789754), (74, 2.2942246132003614), (75, 2.3269932089141383), (76, 2.30433221423207), (77, 2.305668424303158), (78, 2.337304946332694), (79, 2.348709229844066), (80, 2.37383095201212), (81, 2.300124180012237), (82, 2.3621472944847692), (83, 2.3891840624733094), (84, 2.332587601468205), (85, 2.3793240404738403), (86, 2.393653464393494), (87, 2.3520108950785557), (88, 2.374164987867252), (89, 2.4286907109589624), (90, 2.328940248908326), (91, 2.403618623273441), (92, 2.4000819242609954), (93, 2.330398244598803), (94, 2.432824038849852), (95, 2.362161700337078), (96, 2.4384049723704404), (97, 2.410859399329359), (98, 2.4004773120529737), (99, 2.4279736249972457), (100, 2.423790014970798)]
-[2023-09-29 09:55:23,513][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.009), (1, 0.01), (2, 0.01), (3, 0.0141), (4, 0.0882), (5, 0.1851), (6, 0.2656), (7, 0.3276), (8, 0.3631), (9, 0.4028), (10, 0.4325), (11, 0.4554), (12, 0.4766), (13, 0.5038), (14, 0.5151), (15, 0.5332), (16, 0.5412), (17, 0.5552), (18, 0.5595), (19, 0.5685), (20, 0.5784), (21, 0.5828), (22, 0.5898), (23, 0.5934), (24, 0.5923), (25, 0.6006), (26, 0.6009), (27, 0.6072), (28, 0.6081), (29, 0.6136), (30, 0.6133), (31, 0.6184), (32, 0.6198), (33, 0.623), (34, 0.6194), (35, 0.6247), (36, 0.6272), (37, 0.6285), (38, 0.6283), (39, 0.6321), (40, 0.6326), (41, 0.6341), (42, 0.6369), (43, 0.6353), (44, 0.6399), (45, 0.641), (46, 0.642), (47, 0.6449), (48, 0.6448), (49, 0.6455), (50, 0.6433), (51, 0.6457), (52, 0.6465), (53, 0.6457), (54, 0.6474), (55, 0.6485), (56, 0.6494), (57, 0.649), (58, 0.6499), (59, 0.6505), (60, 0.65), (61, 0.6512), (62, 0.6535), (63, 0.6537), (64, 0.6545), (65, 0.6543), (66, 0.6568), (67, 0.6562), (68, 0.6561), (69, 0.6603), (70, 0.6591), (71, 0.6589), (72, 0.6606), (73, 0.6616), (74, 0.6595), (75, 0.6578), (76, 0.6606), (77, 0.6563), (78, 0.6575), (79, 0.6595), (80, 0.6602), (81, 0.6619), (82, 0.6632), (83, 0.6627), (84, 0.6621), (85, 0.6647), (86, 0.6617), (87, 0.6635), (88, 0.6648), (89, 0.6597), (90, 0.6611), (91, 0.6619), (92, 0.6636), (93, 0.6648), (94, 0.6665), (95, 0.667), (96, 0.6616), (97, 0.6654), (98, 0.6651), (99, 0.664), (100, 0.6635)]}
-................
-History (loss, distributed):
-	round 1: 0.0
-	round 2: 0.0
-	round 3: 0.0
-	round 4: 0.0
-	round 5: 0.0
-	round 6: 0.0
-	round 7: 0.0
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-	round 9: 0.0
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-	round 11: 0.0
-	round 12: 0.0
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-	round 31: 0.0
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-	round 40: 0.0
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-	round 44: 0.0
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-	round 46: 0.0
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-	round 55: 0.0
-	round 56: 0.0
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-	round 60: 0.0
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-	round 63: 0.0
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-	round 65: 0.0
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-	round 68: 0.0
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-	round 97: 0.0
-	round 98: 0.0
-	round 99: 0.0
-	round 100: 0.0
-History (loss, centralized):
-	round 0: 6.430294827531321
-	round 1: 4.861440579350383
-	round 2: 5.477163912008365
-	round 3: 5.5055647475270035
-	round 4: 4.169462466011413
-	round 5: 3.436301054665075
-	round 6: 2.9990923823639988
-	round 7: 2.694728255652772
-	round 8: 2.53471215883383
-	round 9: 2.3920893143541133
-	round 10: 2.306128243287912
-	round 11: 2.207922473883096
-	round 12: 2.172221914647867
-	round 13: 2.099844414205216
-	round 14: 2.0913502991009065
-	round 15: 2.0594057168442603
-	round 16: 2.043369851554164
-	round 17: 2.0114177884385227
-	round 18: 2.017150927846805
-	round 19: 2.010581853100286
-	round 20: 1.9967298349633384
-	round 21: 2.0065166573174085
-	round 22: 1.9742871688577694
-	round 23: 1.9792633229932084
-	round 24: 2.0303315805931823
-	round 25: 1.9995412224778732
-	round 26: 2.022627312535295
-	round 27: 1.9937346629060495
-	round 28: 2.021510124206543
-	round 29: 2.0112326653620687
-	round 30: 2.0224276781082153
-	round 31: 2.050073419706509
-	round 32: 2.048540641515019
-	round 33: 2.033169127881717
-	round 34: 2.0350445369942882
-	round 35: 2.080257884039285
-	round 36: 2.0718342880852307
-	round 37: 2.0764910361637323
-	round 38: 2.065860210897062
-	round 39: 2.087371099490327
-	round 40: 2.067515920335873
-	round 41: 2.094820894753209
-	round 42: 2.0926969356049363
-	round 43: 2.1266209250821855
-	round 44: 2.132381713047576
-	round 45: 2.1219718056364942
-	round 46: 2.146202865500039
-	round 47: 2.1143142310575174
-	round 48: 2.1224685852139142
-	round 49: 2.117806362458311
-	round 50: 2.142119585134732
-	round 51: 2.135459794404027
-	round 52: 2.1817148396382318
-	round 53: 2.183458357382887
-	round 54: 2.1794180731042125
-	round 55: 2.1586610794829104
-	round 56: 2.188702217115762
-	round 57: 2.171707748224179
-	round 58: 2.195324074726897
-	round 59: 2.2019545649187253
-	round 60: 2.1900063330373065
-	round 61: 2.20785686230888
-	round 62: 2.1942758756323744
-	round 63: 2.2481949498859075
-	round 64: 2.2214675100085834
-	round 65: 2.2834309385226557
-	round 66: 2.208361754592615
-	round 67: 2.293970344736934
-	round 68: 2.207248735922975
-	round 69: 2.268605324026114
-	round 70: 2.256014349171148
-	round 71: 2.247258449610049
-	round 72: 2.2466823984258855
-	round 73: 2.2818810541789754
-	round 74: 2.2942246132003614
-	round 75: 2.3269932089141383
-	round 76: 2.30433221423207
-	round 77: 2.305668424303158
-	round 78: 2.337304946332694
-	round 79: 2.348709229844066
-	round 80: 2.37383095201212
-	round 81: 2.300124180012237
-	round 82: 2.3621472944847692
-	round 83: 2.3891840624733094
-	round 84: 2.332587601468205
-	round 85: 2.3793240404738403
-	round 86: 2.393653464393494
-	round 87: 2.3520108950785557
-	round 88: 2.374164987867252
-	round 89: 2.4286907109589624
-	round 90: 2.328940248908326
-	round 91: 2.403618623273441
-	round 92: 2.4000819242609954
-	round 93: 2.330398244598803
-	round 94: 2.432824038849852
-	round 95: 2.362161700337078
-	round 96: 2.4384049723704404
-	round 97: 2.410859399329359
-	round 98: 2.4004773120529737
-	round 99: 2.4279736249972457
-	round 100: 2.423790014970798
-History (metrics, centralized):
-{'accuracy': [(0, 0.009), (1, 0.01), (2, 0.01), (3, 0.0141), (4, 0.0882), (5, 0.1851), (6, 0.2656), (7, 0.3276), (8, 0.3631), (9, 0.4028), (10, 0.4325), (11, 0.4554), (12, 0.4766), (13, 0.5038), (14, 0.5151), (15, 0.5332), (16, 0.5412), (17, 0.5552), (18, 0.5595), (19, 0.5685), (20, 0.5784), (21, 0.5828), (22, 0.5898), (23, 0.5934), (24, 0.5923), (25, 0.6006), (26, 0.6009), (27, 0.6072), (28, 0.6081), (29, 0.6136), (30, 0.6133), (31, 0.6184), (32, 0.6198), (33, 0.623), (34, 0.6194), (35, 0.6247), (36, 0.6272), (37, 0.6285), (38, 0.6283), (39, 0.6321), (40, 0.6326), (41, 0.6341), (42, 0.6369), (43, 0.6353), (44, 0.6399), (45, 0.641), (46, 0.642), (47, 0.6449), (48, 0.6448), (49, 0.6455), (50, 0.6433), (51, 0.6457), (52, 0.6465), (53, 0.6457), (54, 0.6474), (55, 0.6485), (56, 0.6494), (57, 0.649), (58, 0.6499), (59, 0.6505), (60, 0.65), (61, 0.6512), (62, 0.6535), (63, 0.6537), (64, 0.6545), (65, 0.6543), (66, 0.6568), (67, 0.6562), (68, 0.6561), (69, 0.6603), (70, 0.6591), (71, 0.6589), (72, 0.6606), (73, 0.6616), (74, 0.6595), (75, 0.6578), (76, 0.6606), (77, 0.6563), (78, 0.6575), (79, 0.6595), (80, 0.6602), (81, 0.6619), (82, 0.6632), (83, 0.6627), (84, 0.6621), (85, 0.6647), (86, 0.6617), (87, 0.6635), (88, 0.6648), (89, 0.6597), (90, 0.6611), (91, 0.6619), (92, 0.6636), (93, 0.6648), (94, 0.6665), (95, 0.667), (96, 0.6616), (97, 0.6654), (98, 0.6651), (99, 0.664), (100, 0.6635)]}
-[2023-09-29 09:55:23,972][matplotlib.legend][WARNING] - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
diff --git a/baselines/moon/_static/cifar10_fedprox_log.txt b/baselines/moon/_static/cifar10_fedprox_log.txt
deleted file mode 100644
index 318a94f03fdd..000000000000
--- a/baselines/moon/_static/cifar10_fedprox_log.txt
+++ /dev/null
@@ -1,6852 +0,0 @@
-num_clients: 10
-num_epochs: 10
-fraction_fit: 1.0
-batch_size: 64
-learning_rate: 0.01
-mu: 0.01
-temperature: 0.5
-alg: fedprox
-seed: 0
-server_device: cpu
-num_rounds: 100
-client_resources:
-  num_cpus: 4
-  num_gpus: 1
-dataset:
-  name: cifar10
-  dir: ./data/moon/
-  partition: noniid
-  beta: 0.5
-model:
-  name: simple-cnn
-  output_dim: 256
-  dir: ./models/moon/cifar10_fedprox/
-
-Files already downloaded and verified
-Files already downloaded and verified
-[2023-09-21 03:09:22,298][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=100, round_timeout=None)
-[2023-09-21 03:09:25,352][flwr][INFO] - Flower VCE: Ray initialized with resources: {'node:137.132.92.49': 1.0, 'node:__internal_head__': 1.0, 'CPU': 64.0, 'memory': 222860751872.0, 'object_store_memory': 99797465088.0, 'GPU': 1.0, 'accelerator_type:G': 1.0}
-[2023-09-21 03:09:25,352][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 1}
-[2023-09-21 03:09:25,361][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 1 actors
-[2023-09-21 03:09:25,361][flwr][INFO] - Initializing global parameters
-[2023-09-21 03:09:25,361][flwr][INFO] - Requesting initial parameters from one random client
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 03:09:30,151][flwr][INFO] - Received initial parameters from one random client
-[2023-09-21 03:09:30,152][flwr][INFO] - Evaluating initial parameters
-test acc: 0.1
-[2023-09-21 03:09:31,539][flwr][INFO] - initial parameters (loss, other metrics): 2.304941604693477, {'accuracy': 0.1}
-[2023-09-21 03:09:31,540][flwr][INFO] - FL starting
-[2023-09-21 03:09:31,540][flwr][DEBUG] - fit_round 1: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.0
-(DefaultActor pid=2820544) >> Training accuracy: 0.712577
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.0
-(DefaultActor pid=2820544) >> Training accuracy: 0.637386
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.011408730158730158
-(DefaultActor pid=2820544) >> Training accuracy: 0.658854
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.14342350746268656
-(DefaultActor pid=2820544) >> Training accuracy: 0.553871
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.00026483050847457627
-(DefaultActor pid=2820544) >> Training accuracy: 0.650424
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.01782852564102564
-(DefaultActor pid=2820544) >> Training accuracy: 0.628806
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.36709104938271603
-(DefaultActor pid=2820544) >> Training accuracy: 0.694059
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.026721014492753624
-(DefaultActor pid=2820544) >> Training accuracy: 0.561141
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.0
-(DefaultActor pid=2820544) >> Training accuracy: 0.586075
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4395559210526316
-[2023-09-21 03:17:27,442][flwr][DEBUG] - fit_round 1 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.649671
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 03:17:27,482][flwr][WARNING] - No fit_metrics_aggregation_fn provided
-test acc: 0.1148
-[2023-09-21 03:17:29,253][flwr][INFO] - fit progress: (1, 2.2892096804353756, {'accuracy': 0.1148}, 477.7133622728288)
-[2023-09-21 03:17:29,253][flwr][DEBUG] - evaluate_round 1: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 03:18:01,534][flwr][DEBUG] - evaluate_round 1 received 10 results and 0 failures
-[2023-09-21 03:18:01,535][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
-[2023-09-21 03:18:01,535][flwr][DEBUG] - fit_round 2: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.09046052631578948
-(DefaultActor pid=2820544) >> Training accuracy: 0.652138
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3736758474576271
-(DefaultActor pid=2820544) >> Training accuracy: 0.655985
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.022154850746268658
-(DefaultActor pid=2820544) >> Training accuracy: 0.576259
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.1169969512195122
-(DefaultActor pid=2820544) >> Training accuracy: 0.675686
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.18410326086956522
-(DefaultActor pid=2820544) >> Training accuracy: 0.600091
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.05343364197530864
-(DefaultActor pid=2820544) >> Training accuracy: 0.735918
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.09623015873015874
-(DefaultActor pid=2820544) >> Training accuracy: 0.678943
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.15384615384615385
-(DefaultActor pid=2820544) >> Training accuracy: 0.664062
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.08449074074074074
-(DefaultActor pid=2820544) >> Training accuracy: 0.733218
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.01953125
-[2023-09-21 03:25:11,194][flwr][DEBUG] - fit_round 2 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.696957
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.2752
-[2023-09-21 03:25:12,670][flwr][INFO] - fit progress: (2, 1.9268630602108403, {'accuracy': 0.2752}, 941.1305831400678)
-[2023-09-21 03:25:12,671][flwr][DEBUG] - evaluate_round 2: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 03:25:43,833][flwr][DEBUG] - evaluate_round 2 received 10 results and 0 failures
-[2023-09-21 03:25:43,834][flwr][DEBUG] - fit_round 3: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.15692934782608695
-(DefaultActor pid=2820544) >> Training accuracy: 0.616621
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3135016025641026
-(DefaultActor pid=2820544) >> Training accuracy: 0.696715
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.10587993421052631
-(DefaultActor pid=2820544) >> Training accuracy: 0.724918
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.23342225609756098
-(DefaultActor pid=2820544) >> Training accuracy: 0.704459
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4245756172839506
-(DefaultActor pid=2820544) >> Training accuracy: 0.759452
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.26236007462686567
-(DefaultActor pid=2820544) >> Training accuracy: 0.593983
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.23387896825396826
-(DefaultActor pid=2820544) >> Training accuracy: 0.671875
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.29012345679012347
-(DefaultActor pid=2820544) >> Training accuracy: 0.766590
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3495762711864407
-(DefaultActor pid=2820544) >> Training accuracy: 0.707892
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.21957236842105263
-[2023-09-21 03:33:09,175][flwr][DEBUG] - fit_round 3 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.678454
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.3791
-[2023-09-21 03:33:10,642][flwr][INFO] - fit progress: (3, 1.6586600408767358, {'accuracy': 0.3791}, 1419.102149719838)
-[2023-09-21 03:33:10,642][flwr][DEBUG] - evaluate_round 3: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 03:33:42,112][flwr][DEBUG] - evaluate_round 3 received 10 results and 0 failures
-[2023-09-21 03:33:42,112][flwr][DEBUG] - fit_round 4: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.17393092105263158
-(DefaultActor pid=2820544) >> Training accuracy: 0.730469
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3125
-(DefaultActor pid=2820544) >> Training accuracy: 0.788194
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5727237654320988
-(DefaultActor pid=2820544) >> Training accuracy: 0.777199
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47896634615384615
-(DefaultActor pid=2820544) >> Training accuracy: 0.712139
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.27455357142857145
-(DefaultActor pid=2820544) >> Training accuracy: 0.715774
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3342391304347826
-(DefaultActor pid=2820544) >> Training accuracy: 0.644022
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3195503048780488
-(DefaultActor pid=2820544) >> Training accuracy: 0.717797
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3393640350877193
-(DefaultActor pid=2820544) >> Training accuracy: 0.680373
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.517478813559322
-(DefaultActor pid=2820544) >> Training accuracy: 0.732256
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.2943097014925373
-[2023-09-21 03:40:51,343][flwr][DEBUG] - fit_round 4 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.608675
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.4339
-[2023-09-21 03:40:52,829][flwr][INFO] - fit progress: (4, 1.5162620251171124, {'accuracy': 0.4339}, 1881.2891843491234)
-[2023-09-21 03:40:52,829][flwr][DEBUG] - evaluate_round 4: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 03:41:39,499][flwr][DEBUG] - evaluate_round 4 received 10 results and 0 failures
-[2023-09-21 03:41:39,500][flwr][DEBUG] - fit_round 5: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3761322463768116
-(DefaultActor pid=2820544) >> Training accuracy: 0.663270
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5558792372881356
-(DefaultActor pid=2820544) >> Training accuracy: 0.747881
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3636188271604938
-(DefaultActor pid=2820544) >> Training accuracy: 0.790123
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.35774253731343286
-(DefaultActor pid=2820544) >> Training accuracy: 0.656250
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.39634146341463417
-(DefaultActor pid=2820544) >> Training accuracy: 0.726753
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3304811507936508
-(DefaultActor pid=2820544) >> Training accuracy: 0.709821
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3432017543859649
-(DefaultActor pid=2820544) >> Training accuracy: 0.689693
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.2450657894736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.755345
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5923996913580247
-(DefaultActor pid=2820544) >> Training accuracy: 0.732253
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.539863782051282
-(DefaultActor pid=2820544) >> Training accuracy: 0.722957
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 03:48:56,490][flwr][DEBUG] - fit_round 5 received 10 results and 0 failures
-test acc: 0.4926
-[2023-09-21 03:48:58,100][flwr][INFO] - fit progress: (5, 1.3799412298126343, {'accuracy': 0.4926}, 2366.5606768671423)
-[2023-09-21 03:48:58,101][flwr][DEBUG] - evaluate_round 5: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 03:49:28,835][flwr][DEBUG] - evaluate_round 5 received 10 results and 0 failures
-[2023-09-21 03:49:28,836][flwr][DEBUG] - fit_round 6: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4095394736842105
-(DefaultActor pid=2820544) >> Training accuracy: 0.697094
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4529344512195122
-(DefaultActor pid=2820544) >> Training accuracy: 0.722942
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3699156746031746
-(DefaultActor pid=2820544) >> Training accuracy: 0.730407
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3548519736842105
-(DefaultActor pid=2820544) >> Training accuracy: 0.741776
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4369212962962963
-(DefaultActor pid=2820544) >> Training accuracy: 0.798418
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.40928171641791045
-(DefaultActor pid=2820544) >> Training accuracy: 0.662080
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5941506410256411
-(DefaultActor pid=2820544) >> Training accuracy: 0.738381
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6554783950617284
-(DefaultActor pid=2820544) >> Training accuracy: 0.788966
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4470108695652174
-(DefaultActor pid=2820544) >> Training accuracy: 0.663949
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5434322033898306
-[2023-09-21 03:56:35,432][flwr][DEBUG] - fit_round 6 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.740466
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.5244
-[2023-09-21 03:56:37,232][flwr][INFO] - fit progress: (6, 1.3085029963106394, {'accuracy': 0.5244}, 2825.6921509918757)
-[2023-09-21 03:56:37,233][flwr][DEBUG] - evaluate_round 6: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 03:57:08,634][flwr][DEBUG] - evaluate_round 6 received 10 results and 0 failures
-[2023-09-21 03:57:08,635][flwr][DEBUG] - fit_round 7: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4810956790123457
-(DefaultActor pid=2820544) >> Training accuracy: 0.804205
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4798460144927536
-(DefaultActor pid=2820544) >> Training accuracy: 0.674139
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.43940548780487804
-(DefaultActor pid=2820544) >> Training accuracy: 0.751905
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6456404320987654
-(DefaultActor pid=2820544) >> Training accuracy: 0.793596
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6338141025641025
-(DefaultActor pid=2820544) >> Training accuracy: 0.748998
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.42723880597014924
-(DefaultActor pid=2820544) >> Training accuracy: 0.670243
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.40316611842105265
-(DefaultActor pid=2820544) >> Training accuracy: 0.762747
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.3991815476190476
-(DefaultActor pid=2820544) >> Training accuracy: 0.745660
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.42077850877192985
-(DefaultActor pid=2820544) >> Training accuracy: 0.684211
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.602489406779661
-[2023-09-21 04:04:12,947][flwr][DEBUG] - fit_round 7 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.735434
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.5421
-[2023-09-21 04:04:14,292][flwr][INFO] - fit progress: (7, 1.270832797208914, {'accuracy': 0.5421}, 3282.7528554419987)
-[2023-09-21 04:04:14,293][flwr][DEBUG] - evaluate_round 7: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 04:04:44,141][flwr][DEBUG] - evaluate_round 7 received 10 results and 0 failures
-[2023-09-21 04:04:44,142][flwr][DEBUG] - fit_round 8: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6310911016949152
-(DefaultActor pid=2820544) >> Training accuracy: 0.760858
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4243551587301587
-(DefaultActor pid=2820544) >> Training accuracy: 0.743304
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.660108024691358
-(DefaultActor pid=2820544) >> Training accuracy: 0.798804
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4647484756097561
-(DefaultActor pid=2820544) >> Training accuracy: 0.743331
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.44029850746268656
-(DefaultActor pid=2820544) >> Training accuracy: 0.674674
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6386217948717948
-(DefaultActor pid=2820544) >> Training accuracy: 0.747997
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4237938596491228
-(DefaultActor pid=2820544) >> Training accuracy: 0.721765
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4903549382716049
-(DefaultActor pid=2820544) >> Training accuracy: 0.808449
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4120065789473684
-(DefaultActor pid=2820544) >> Training accuracy: 0.765008
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4941123188405797
-[2023-09-21 04:11:54,971][flwr][DEBUG] - fit_round 8 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.687274
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.5669
-[2023-09-21 04:11:56,421][flwr][INFO] - fit progress: (8, 1.2019853355785528, {'accuracy': 0.5669}, 3744.8811060180888)
-[2023-09-21 04:11:56,421][flwr][DEBUG] - evaluate_round 8: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 04:12:37,451][flwr][DEBUG] - evaluate_round 8 received 10 results and 0 failures
-[2023-09-21 04:12:37,452][flwr][DEBUG] - fit_round 9: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.46902412280701755
-(DefaultActor pid=2820544) >> Training accuracy: 0.706689
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6313559322033898
-(DefaultActor pid=2820544) >> Training accuracy: 0.783633
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47865853658536583
-(DefaultActor pid=2820544) >> Training accuracy: 0.747332
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6905864197530864
-(DefaultActor pid=2820544) >> Training accuracy: 0.792438
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5187952898550725
-(DefaultActor pid=2820544) >> Training accuracy: 0.698370
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.45785361842105265
-(DefaultActor pid=2820544) >> Training accuracy: 0.773643
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4967206790123457
-(DefaultActor pid=2820544) >> Training accuracy: 0.812500
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6368189102564102
-(DefaultActor pid=2820544) >> Training accuracy: 0.762220
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4820188492063492
-(DefaultActor pid=2820544) >> Training accuracy: 0.731895
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5013992537313433
-[2023-09-21 04:20:00,046][flwr][DEBUG] - fit_round 9 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.682603
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.5791
-[2023-09-21 04:20:01,448][flwr][INFO] - fit progress: (9, 1.1783232848865155, {'accuracy': 0.5791}, 4229.908353412058)
-[2023-09-21 04:20:01,449][flwr][DEBUG] - evaluate_round 9: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 04:20:37,215][flwr][DEBUG] - evaluate_round 9 received 10 results and 0 failures
-[2023-09-21 04:20:37,216][flwr][DEBUG] - fit_round 10: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5253623188405797
-(DefaultActor pid=2820544) >> Training accuracy: 0.692935
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6706730769230769
-(DefaultActor pid=2820544) >> Training accuracy: 0.771635
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47371031746031744
-(DefaultActor pid=2820544) >> Training accuracy: 0.752976
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6191737288135594
-(DefaultActor pid=2820544) >> Training accuracy: 0.788136
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5072294776119403
-(DefaultActor pid=2820544) >> Training accuracy: 0.690532
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5709876543209876
-(DefaultActor pid=2820544) >> Training accuracy: 0.824267
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.689429012345679
-(DefaultActor pid=2820544) >> Training accuracy: 0.818480
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4712271341463415
-(DefaultActor pid=2820544) >> Training accuracy: 0.759718
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47231359649122806
-(DefaultActor pid=2820544) >> Training accuracy: 0.722314
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4993832236842105
-[2023-09-21 04:27:37,375][flwr][DEBUG] - fit_round 10 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.778577
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.586
-[2023-09-21 04:27:38,723][flwr][INFO] - fit progress: (10, 1.1620713434280299, {'accuracy': 0.586}, 4687.183470572811)
-[2023-09-21 04:27:38,723][flwr][DEBUG] - evaluate_round 10: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 04:28:09,105][flwr][DEBUG] - evaluate_round 10 received 10 results and 0 failures
-[2023-09-21 04:28:09,106][flwr][DEBUG] - fit_round 11: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5274390243902439
-(DefaultActor pid=2820544) >> Training accuracy: 0.760480
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.503731343283582
-(DefaultActor pid=2820544) >> Training accuracy: 0.704991
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6559851694915254
-(DefaultActor pid=2820544) >> Training accuracy: 0.790254
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5400815217391305
-(DefaultActor pid=2820544) >> Training accuracy: 0.698596
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6760817307692307
-(DefaultActor pid=2820544) >> Training accuracy: 0.765425
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.47265625
-(DefaultActor pid=2820544) >> Training accuracy: 0.789679
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5001240079365079
-(DefaultActor pid=2820544) >> Training accuracy: 0.762773
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6844135802469136
-(DefaultActor pid=2820544) >> Training accuracy: 0.822338
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5229552469135802
-(DefaultActor pid=2820544) >> Training accuracy: 0.821566
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.46847587719298245
-[2023-09-21 04:35:06,696][flwr][DEBUG] - fit_round 11 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.723958
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.5979
-[2023-09-21 04:35:08,041][flwr][INFO] - fit progress: (11, 1.1295021063984392, {'accuracy': 0.5979}, 5136.501239712816)
-[2023-09-21 04:35:08,041][flwr][DEBUG] - evaluate_round 11: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 04:35:38,353][flwr][DEBUG] - evaluate_round 11 received 10 results and 0 failures
-[2023-09-21 04:35:38,354][flwr][DEBUG] - fit_round 12: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6792868589743589
-(DefaultActor pid=2820544) >> Training accuracy: 0.792268
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5021929824561403
-(DefaultActor pid=2820544) >> Training accuracy: 0.732182
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5318667763157895
-(DefaultActor pid=2820544) >> Training accuracy: 0.790090
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5883487654320988
-(DefaultActor pid=2820544) >> Training accuracy: 0.832176
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5593297101449275
-(DefaultActor pid=2820544) >> Training accuracy: 0.698822
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6096398305084746
-(DefaultActor pid=2820544) >> Training accuracy: 0.788400
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5159970238095238
-(DefaultActor pid=2820544) >> Training accuracy: 0.759177
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.49352134146341464
-(DefaultActor pid=2820544) >> Training accuracy: 0.773247
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5340485074626866
-(DefaultActor pid=2820544) >> Training accuracy: 0.697295
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7019675925925926
-[2023-09-21 04:42:32,819][flwr][DEBUG] - fit_round 12 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.819059
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6048
-[2023-09-21 04:42:34,278][flwr][INFO] - fit progress: (12, 1.1191708752141594, {'accuracy': 0.6048}, 5582.738002989907)
-[2023-09-21 04:42:34,278][flwr][DEBUG] - evaluate_round 12: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 04:43:04,165][flwr][DEBUG] - evaluate_round 12 received 10 results and 0 failures
-[2023-09-21 04:43:04,166][flwr][DEBUG] - fit_round 13: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5679347826086957
-(DefaultActor pid=2820544) >> Training accuracy: 0.713542
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7087191358024691
-(DefaultActor pid=2820544) >> Training accuracy: 0.819252
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5211509146341463
-(DefaultActor pid=2820544) >> Training accuracy: 0.771341
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5135261194029851
-(DefaultActor pid=2820544) >> Training accuracy: 0.712687
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5333719135802469
-(DefaultActor pid=2820544) >> Training accuracy: 0.827353
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5019188596491229
-(DefaultActor pid=2820544) >> Training accuracy: 0.725877
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.48725328947368424
-(DefaultActor pid=2820544) >> Training accuracy: 0.792969
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.694511217948718
-(DefaultActor pid=2820544) >> Training accuracy: 0.786258
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5372023809523809
-(DefaultActor pid=2820544) >> Training accuracy: 0.768601
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6607521186440678
-[2023-09-21 04:50:25,654][flwr][DEBUG] - fit_round 13 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.790254
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6034
-[2023-09-21 04:50:27,003][flwr][INFO] - fit progress: (13, 1.106805816054725, {'accuracy': 0.6034}, 6055.463005594909)
-[2023-09-21 04:50:27,003][flwr][DEBUG] - evaluate_round 13: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 04:50:57,831][flwr][DEBUG] - evaluate_round 13 received 10 results and 0 failures
-[2023-09-21 04:50:57,831][flwr][DEBUG] - fit_round 14: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5655864197530864
-(DefaultActor pid=2820544) >> Training accuracy: 0.831211
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7085262345679012
-(DefaultActor pid=2820544) >> Training accuracy: 0.825810
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5264862804878049
-(DefaultActor pid=2820544) >> Training accuracy: 0.775534
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5117872807017544
-(DefaultActor pid=2820544) >> Training accuracy: 0.739857
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5145970394736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.795436
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5711050724637681
-(DefaultActor pid=2820544) >> Training accuracy: 0.717391
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5369543650793651
-(DefaultActor pid=2820544) >> Training accuracy: 0.764013
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.653072033898305
-(DefaultActor pid=2820544) >> Training accuracy: 0.808792
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6794871794871795
-(DefaultActor pid=2820544) >> Training accuracy: 0.781050
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5412779850746269
-[2023-09-21 04:57:52,031][flwr][DEBUG] - fit_round 14 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.693330
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6153
-[2023-09-21 04:57:53,629][flwr][INFO] - fit progress: (14, 1.0845167545464853, {'accuracy': 0.6153}, 6502.089797993191)
-[2023-09-21 04:57:53,630][flwr][DEBUG] - evaluate_round 14: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 04:58:24,056][flwr][DEBUG] - evaluate_round 14 received 10 results and 0 failures
-[2023-09-21 04:58:24,057][flwr][DEBUG] - fit_round 15: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7139274691358025
-(DefaultActor pid=2820544) >> Training accuracy: 0.823302
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5082236842105263
-(DefaultActor pid=2820544) >> Training accuracy: 0.763432
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5536380597014925
-(DefaultActor pid=2820544) >> Training accuracy: 0.684701
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.649364406779661
-(DefaultActor pid=2820544) >> Training accuracy: 0.790784
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5596478174603174
-(DefaultActor pid=2820544) >> Training accuracy: 0.769717
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5449695121951219
-(DefaultActor pid=2820544) >> Training accuracy: 0.772675
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5729166666666666
-(DefaultActor pid=2820544) >> Training accuracy: 0.705163
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5244654605263158
-(DefaultActor pid=2820544) >> Training accuracy: 0.793174
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7009214743589743
-(DefaultActor pid=2820544) >> Training accuracy: 0.777043
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5723379629629629
-[2023-09-21 05:05:22,373][flwr][DEBUG] - fit_round 15 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.830826
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6155
-[2023-09-21 05:05:23,771][flwr][INFO] - fit progress: (15, 1.0962572912819468, {'accuracy': 0.6155}, 6952.231259225868)
-[2023-09-21 05:05:23,771][flwr][DEBUG] - evaluate_round 15: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 05:05:53,410][flwr][DEBUG] - evaluate_round 15 received 10 results and 0 failures
-[2023-09-21 05:05:53,411][flwr][DEBUG] - fit_round 16: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.4994517543859649
-(DefaultActor pid=2820544) >> Training accuracy: 0.740954
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5501399253731343
-(DefaultActor pid=2820544) >> Training accuracy: 0.722715
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5088404605263158
-(DefaultActor pid=2820544) >> Training accuracy: 0.801604
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6490995762711864
-(DefaultActor pid=2820544) >> Training accuracy: 0.806674
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6852964743589743
-(DefaultActor pid=2820544) >> Training accuracy: 0.783654
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5848765432098766
-(DefaultActor pid=2820544) >> Training accuracy: 0.839892
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5289634146341463
-(DefaultActor pid=2820544) >> Training accuracy: 0.762195
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5780009920634921
-(DefaultActor pid=2820544) >> Training accuracy: 0.778894
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.563858695652174
-(DefaultActor pid=2820544) >> Training accuracy: 0.715353
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6815200617283951
-[2023-09-21 05:13:05,054][flwr][DEBUG] - fit_round 16 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.829090
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6256
-[2023-09-21 05:13:06,472][flwr][INFO] - fit progress: (16, 1.0545658187363476, {'accuracy': 0.6256}, 7414.931961627211)
-[2023-09-21 05:13:06,472][flwr][DEBUG] - evaluate_round 16: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 05:13:37,043][flwr][DEBUG] - evaluate_round 16 received 10 results and 0 failures
-[2023-09-21 05:13:37,044][flwr][DEBUG] - fit_round 17: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5191885964912281
-(DefaultActor pid=2820544) >> Training accuracy: 0.748629
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.595679012345679
-(DefaultActor pid=2820544) >> Training accuracy: 0.845486
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7096836419753086
-(DefaultActor pid=2820544) >> Training accuracy: 0.828318
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7201522435897436
-(DefaultActor pid=2820544) >> Training accuracy: 0.798678
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5512152777777778
-(DefaultActor pid=2820544) >> Training accuracy: 0.770709
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.538945895522388
-(DefaultActor pid=2820544) >> Training accuracy: 0.709655
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5544819078947368
-(DefaultActor pid=2820544) >> Training accuracy: 0.795230
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6665783898305084
-(DefaultActor pid=2820544) >> Training accuracy: 0.785487
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5491615853658537
-(DefaultActor pid=2820544) >> Training accuracy: 0.779916
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6009963768115942
-[2023-09-21 05:21:20,281][flwr][DEBUG] - fit_round 17 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.716486
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6281
-[2023-09-21 05:21:29,598][flwr][INFO] - fit progress: (17, 1.061014118857277, {'accuracy': 0.6281}, 7918.058736578096)
-[2023-09-21 05:21:29,599][flwr][DEBUG] - evaluate_round 17: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 05:22:01,501][flwr][DEBUG] - evaluate_round 17 received 10 results and 0 failures
-[2023-09-21 05:22:01,502][flwr][DEBUG] - fit_round 18: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.694511217948718
-(DefaultActor pid=2820544) >> Training accuracy: 0.790465
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5331688596491229
-(DefaultActor pid=2820544) >> Training accuracy: 0.756031
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5461753731343284
-(DefaultActor pid=2820544) >> Training accuracy: 0.695896
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5341282894736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.800781
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5842391304347826
-(DefaultActor pid=2820544) >> Training accuracy: 0.705389
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5559275793650794
-(DefaultActor pid=2820544) >> Training accuracy: 0.758805
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5567835365853658
-(DefaultActor pid=2820544) >> Training accuracy: 0.771723
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5974151234567902
-(DefaultActor pid=2820544) >> Training accuracy: 0.843943
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.652542372881356
-(DefaultActor pid=2820544) >> Training accuracy: 0.800318
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7278163580246914
-[2023-09-21 05:29:01,699][flwr][DEBUG] - fit_round 18 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.832562
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6203
-[2023-09-21 05:29:03,103][flwr][INFO] - fit progress: (18, 1.083283578435453, {'accuracy': 0.6203}, 8371.562929124106)
-[2023-09-21 05:29:03,103][flwr][DEBUG] - evaluate_round 18: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 05:29:34,393][flwr][DEBUG] - evaluate_round 18 received 10 results and 0 failures
-[2023-09-21 05:29:34,394][flwr][DEBUG] - fit_round 19: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5164473684210527
-(DefaultActor pid=2820544) >> Training accuracy: 0.796053
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5565200617283951
-(DefaultActor pid=2820544) >> Training accuracy: 0.817515
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5530753968253969
-(DefaultActor pid=2820544) >> Training accuracy: 0.782490
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.714891975308642
-(DefaultActor pid=2820544) >> Training accuracy: 0.821373
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7111378205128205
-(DefaultActor pid=2820544) >> Training accuracy: 0.795873
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6046195652173914
-(DefaultActor pid=2820544) >> Training accuracy: 0.720788
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.668697033898305
-(DefaultActor pid=2820544) >> Training accuracy: 0.810117
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5345394736842105
-(DefaultActor pid=2820544) >> Training accuracy: 0.761239
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5623094512195121
-(DefaultActor pid=2820544) >> Training accuracy: 0.778963
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5408115671641791
-[2023-09-21 05:36:35,288][flwr][DEBUG] - fit_round 19 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.729011
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6361
-[2023-09-21 05:36:36,683][flwr][INFO] - fit progress: (19, 1.024500151411794, {'accuracy': 0.6361}, 8825.14358962886)
-[2023-09-21 05:36:36,684][flwr][DEBUG] - evaluate_round 19: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 05:37:07,686][flwr][DEBUG] - evaluate_round 19 received 10 results and 0 failures
-[2023-09-21 05:37:07,688][flwr][DEBUG] - fit_round 20: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5838815789473685
-(DefaultActor pid=2820544) >> Training accuracy: 0.797286
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7158564814814815
-(DefaultActor pid=2820544) >> Training accuracy: 0.816165
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5886194029850746
-(DefaultActor pid=2820544) >> Training accuracy: 0.720616
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5544969512195121
-(DefaultActor pid=2820544) >> Training accuracy: 0.790396
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.609375
-(DefaultActor pid=2820544) >> Training accuracy: 0.810764
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5997023809523809
-(DefaultActor pid=2820544) >> Training accuracy: 0.788938
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6694915254237288
-(DefaultActor pid=2820544) >> Training accuracy: 0.814883
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7081330128205128
-(DefaultActor pid=2820544) >> Training accuracy: 0.787460
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5921648550724637
-(DefaultActor pid=2820544) >> Training accuracy: 0.726676
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5139802631578947
-[2023-09-21 05:44:10,490][flwr][DEBUG] - fit_round 20 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.760143
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6331
-[2023-09-21 05:44:12,098][flwr][INFO] - fit progress: (20, 1.0367834657525863, {'accuracy': 0.6331}, 9280.558003693819)
-[2023-09-21 05:44:12,098][flwr][DEBUG] - evaluate_round 20: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 05:44:42,550][flwr][DEBUG] - evaluate_round 20 received 10 results and 0 failures
-[2023-09-21 05:44:42,551][flwr][DEBUG] - fit_round 21: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6028025793650794
-(DefaultActor pid=2820544) >> Training accuracy: 0.792163
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5877700617283951
-(DefaultActor pid=2820544) >> Training accuracy: 0.853588
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5697408536585366
-(DefaultActor pid=2820544) >> Training accuracy: 0.773438
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7010030864197531
-(DefaultActor pid=2820544) >> Training accuracy: 0.839313
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5837220149253731
-(DefaultActor pid=2820544) >> Training accuracy: 0.745336
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.557360197368421
-(DefaultActor pid=2820544) >> Training accuracy: 0.805510
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6591631355932204
-(DefaultActor pid=2820544) >> Training accuracy: 0.805614
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6105072463768116
-(DefaultActor pid=2820544) >> Training accuracy: 0.721920
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5328947368421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.767818
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7151442307692307
-[2023-09-21 05:51:58,643][flwr][DEBUG] - fit_round 21 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.805489
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6446
-[2023-09-21 05:52:00,414][flwr][INFO] - fit progress: (21, 1.0136565257566044, {'accuracy': 0.6446}, 9748.874145396054)
-[2023-09-21 05:52:00,414][flwr][DEBUG] - evaluate_round 21: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 05:52:30,601][flwr][DEBUG] - evaluate_round 21 received 10 results and 0 failures
-[2023-09-21 05:52:30,602][flwr][DEBUG] - fit_round 22: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7079475308641975
-(DefaultActor pid=2820544) >> Training accuracy: 0.833719
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6258680555555556
-(DefaultActor pid=2820544) >> Training accuracy: 0.794023
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5610608552631579
-(DefaultActor pid=2820544) >> Training accuracy: 0.806538
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6231884057971014
-(DefaultActor pid=2820544) >> Training accuracy: 0.739130
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5642149390243902
-(DefaultActor pid=2820544) >> Training accuracy: 0.790587
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7175480769230769
-(DefaultActor pid=2820544) >> Training accuracy: 0.806290
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6005015432098766
-(DefaultActor pid=2820544) >> Training accuracy: 0.837963
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5460526315789473
-(DefaultActor pid=2820544) >> Training accuracy: 0.738213
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5993470149253731
-(DefaultActor pid=2820544) >> Training accuracy: 0.749767
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.664989406779661
-[2023-09-21 05:59:42,140][flwr][DEBUG] - fit_round 22 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.808263
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6354
-[2023-09-21 05:59:43,525][flwr][INFO] - fit progress: (22, 1.0296277775170324, {'accuracy': 0.6354}, 10211.985237995163)
-[2023-09-21 05:59:43,525][flwr][DEBUG] - evaluate_round 22: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 06:00:14,922][flwr][DEBUG] - evaluate_round 22 received 10 results and 0 failures
-[2023-09-21 06:00:14,924][flwr][DEBUG] - fit_round 23: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5554496951219512
-(DefaultActor pid=2820544) >> Training accuracy: 0.788681
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5879197761194029
-(DefaultActor pid=2820544) >> Training accuracy: 0.723881
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6022376543209876
-(DefaultActor pid=2820544) >> Training accuracy: 0.836227
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5276864035087719
-(DefaultActor pid=2820544) >> Training accuracy: 0.758498
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7169471153846154
-(DefaultActor pid=2820544) >> Training accuracy: 0.798277
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7183641975308642
-(DefaultActor pid=2820544) >> Training accuracy: 0.826003
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.563733552631579
-(DefaultActor pid=2820544) >> Training accuracy: 0.791118
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5991847826086957
-(DefaultActor pid=2820544) >> Training accuracy: 0.734149
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6758474576271186
-(DefaultActor pid=2820544) >> Training accuracy: 0.819650
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5873015873015873
-[2023-09-21 06:07:23,946][flwr][DEBUG] - fit_round 23 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.776166
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6427
-[2023-09-21 06:07:25,352][flwr][INFO] - fit progress: (23, 1.0151812633196005, {'accuracy': 0.6427}, 10673.812659171876)
-[2023-09-21 06:07:25,353][flwr][DEBUG] - evaluate_round 23: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 06:07:56,172][flwr][DEBUG] - evaluate_round 23 received 10 results and 0 failures
-[2023-09-21 06:07:56,173][flwr][DEBUG] - fit_round 24: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5701219512195121
-(DefaultActor pid=2820544) >> Training accuracy: 0.787348
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6302083333333334
-(DefaultActor pid=2820544) >> Training accuracy: 0.721467
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6784957627118644
-(DefaultActor pid=2820544) >> Training accuracy: 0.804555
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.57421875
-(DefaultActor pid=2820544) >> Training accuracy: 0.803248
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.581856343283582
-(DefaultActor pid=2820544) >> Training accuracy: 0.738340
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7397762345679012
-(DefaultActor pid=2820544) >> Training accuracy: 0.839892
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5949900793650794
-(DefaultActor pid=2820544) >> Training accuracy: 0.779762
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6070601851851852
-(DefaultActor pid=2820544) >> Training accuracy: 0.848187
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5485197368421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.774671
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7309695512820513
-[2023-09-21 06:14:55,728][flwr][DEBUG] - fit_round 24 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.804087
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6476
-[2023-09-21 06:14:57,438][flwr][INFO] - fit progress: (24, 1.0049766376376532, {'accuracy': 0.6476}, 11125.898730413988)
-[2023-09-21 06:14:57,439][flwr][DEBUG] - evaluate_round 24: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 06:15:28,311][flwr][DEBUG] - evaluate_round 24 received 10 results and 0 failures
-[2023-09-21 06:15:28,312][flwr][DEBUG] - fit_round 25: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6182484567901234
-(DefaultActor pid=2820544) >> Training accuracy: 0.844715
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.71875
-(DefaultActor pid=2820544) >> Training accuracy: 0.832562
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5345394736842105
-(DefaultActor pid=2820544) >> Training accuracy: 0.753015
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6024305555555556
-(DefaultActor pid=2820544) >> Training accuracy: 0.779390
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5874533582089553
-(DefaultActor pid=2820544) >> Training accuracy: 0.720149
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5733612804878049
-(DefaultActor pid=2820544) >> Training accuracy: 0.798590
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7275641025641025
-(DefaultActor pid=2820544) >> Training accuracy: 0.805689
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6840572033898306
-(DefaultActor pid=2820544) >> Training accuracy: 0.824153
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.615036231884058
-(DefaultActor pid=2820544) >> Training accuracy: 0.733922
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5692845394736842
-[2023-09-21 06:22:38,068][flwr][DEBUG] - fit_round 25 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.810650
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6517
-[2023-09-21 06:22:39,477][flwr][INFO] - fit progress: (25, 1.000602862705438, {'accuracy': 0.6517}, 11587.93703436805)
-[2023-09-21 06:22:39,477][flwr][DEBUG] - evaluate_round 25: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 06:23:11,323][flwr][DEBUG] - evaluate_round 25 received 10 results and 0 failures
-[2023-09-21 06:23:11,324][flwr][DEBUG] - fit_round 26: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5608552631578947
-(DefaultActor pid=2820544) >> Training accuracy: 0.747259
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.591765873015873
-(DefaultActor pid=2820544) >> Training accuracy: 0.793775
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6248070987654321
-(DefaultActor pid=2820544) >> Training accuracy: 0.843557
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7195216049382716
-(DefaultActor pid=2820544) >> Training accuracy: 0.831790
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6340579710144928
-(DefaultActor pid=2820544) >> Training accuracy: 0.734601
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5625
-(DefaultActor pid=2820544) >> Training accuracy: 0.791921
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5982730263157895
-(DefaultActor pid=2820544) >> Training accuracy: 0.811472
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7183493589743589
-(DefaultActor pid=2820544) >> Training accuracy: 0.807692
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6618114406779662
-(DefaultActor pid=2820544) >> Training accuracy: 0.800053
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.590018656716418
-[2023-09-21 06:30:30,276][flwr][DEBUG] - fit_round 26 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.726679
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6437
-[2023-09-21 06:30:31,556][flwr][INFO] - fit progress: (26, 1.0190478839432469, {'accuracy': 0.6437}, 12060.016203787178)
-[2023-09-21 06:30:31,556][flwr][DEBUG] - evaluate_round 26: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 06:31:03,470][flwr][DEBUG] - evaluate_round 26 received 10 results and 0 failures
-[2023-09-21 06:31:03,471][flwr][DEBUG] - fit_round 27: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.578125
-(DefaultActor pid=2820544) >> Training accuracy: 0.805831
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5706623134328358
-(DefaultActor pid=2820544) >> Training accuracy: 0.729944
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6729343220338984
-(DefaultActor pid=2820544) >> Training accuracy: 0.819915
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5881696428571429
-(DefaultActor pid=2820544) >> Training accuracy: 0.788814
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6213768115942029
-(DefaultActor pid=2820544) >> Training accuracy: 0.736187
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5466694078947368
-(DefaultActor pid=2820544) >> Training accuracy: 0.828331
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5839120370370371
-(DefaultActor pid=2820544) >> Training accuracy: 0.842978
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7328317901234568
-(DefaultActor pid=2820544) >> Training accuracy: 0.836420
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7255608974358975
-(DefaultActor pid=2820544) >> Training accuracy: 0.815905
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5474232456140351
-[2023-09-21 06:38:02,695][flwr][DEBUG] - fit_round 27 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.771107
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6539
-[2023-09-21 06:38:04,097][flwr][INFO] - fit progress: (27, 0.9883538024684492, {'accuracy': 0.6539}, 12512.557022120804)
-[2023-09-21 06:38:04,097][flwr][DEBUG] - evaluate_round 27: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 06:38:35,068][flwr][DEBUG] - evaluate_round 27 received 10 results and 0 failures
-[2023-09-21 06:38:35,068][flwr][DEBUG] - fit_round 28: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5950838414634146
-(DefaultActor pid=2820544) >> Training accuracy: 0.794779
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7277644230769231
-(DefaultActor pid=2820544) >> Training accuracy: 0.807492
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5485197368421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.761787
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6811440677966102
-(DefaultActor pid=2820544) >> Training accuracy: 0.818326
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5758634868421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.799753
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6252264492753623
-(DefaultActor pid=2820544) >> Training accuracy: 0.736639
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7349537037037037
-(DefaultActor pid=2820544) >> Training accuracy: 0.853202
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6032986111111112
-(DefaultActor pid=2820544) >> Training accuracy: 0.780630
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6101466049382716
-(DefaultActor pid=2820544) >> Training accuracy: 0.851466
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5841884328358209
-[2023-09-21 06:45:30,072][flwr][DEBUG] - fit_round 28 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.754664
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6491
-[2023-09-21 06:45:31,486][flwr][INFO] - fit progress: (28, 0.9920400703867404, {'accuracy': 0.6491}, 12959.946306405123)
-[2023-09-21 06:45:31,486][flwr][DEBUG] - evaluate_round 28: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 06:46:03,421][flwr][DEBUG] - evaluate_round 28 received 10 results and 0 failures
-[2023-09-21 06:46:03,422][flwr][DEBUG] - fit_round 29: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6814088983050848
-(DefaultActor pid=2820544) >> Training accuracy: 0.821769
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5974506578947368
-(DefaultActor pid=2820544) >> Training accuracy: 0.820312
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5916511194029851
-(DefaultActor pid=2820544) >> Training accuracy: 0.741604
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6279438405797102
-(DefaultActor pid=2820544) >> Training accuracy: 0.735281
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6369598765432098
-(DefaultActor pid=2820544) >> Training accuracy: 0.849923
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7386188271604939
-(DefaultActor pid=2820544) >> Training accuracy: 0.849537
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6067708333333334
-(DefaultActor pid=2820544) >> Training accuracy: 0.789807
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5876524390243902
-(DefaultActor pid=2820544) >> Training accuracy: 0.798209
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7339743589743589
-(DefaultActor pid=2820544) >> Training accuracy: 0.801482
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5526315789473685
-[2023-09-21 06:53:15,962][flwr][DEBUG] - fit_round 29 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.770833
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6535
-[2023-09-21 06:53:17,393][flwr][INFO] - fit progress: (29, 0.9918740025153175, {'accuracy': 0.6535}, 13425.852999129798)
-[2023-09-21 06:53:17,393][flwr][DEBUG] - evaluate_round 29: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 06:53:55,270][flwr][DEBUG] - evaluate_round 29 received 10 results and 0 failures
-[2023-09-21 06:53:55,271][flwr][DEBUG] - fit_round 30: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6154891304347826
-(DefaultActor pid=2820544) >> Training accuracy: 0.752717
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7220293209876543
-(DefaultActor pid=2820544) >> Training accuracy: 0.842014
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6702860169491526
-(DefaultActor pid=2820544) >> Training accuracy: 0.802436
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7161458333333334
-(DefaultActor pid=2820544) >> Training accuracy: 0.805288
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6121735074626866
-(DefaultActor pid=2820544) >> Training accuracy: 0.738806
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.583079268292683
-(DefaultActor pid=2820544) >> Training accuracy: 0.794588
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5635964912280702
-(DefaultActor pid=2820544) >> Training accuracy: 0.752467
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5758634868421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.823191
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6294642857142857
-(DefaultActor pid=2820544) >> Training accuracy: 0.790427
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6213348765432098
-[2023-09-21 07:01:05,752][flwr][DEBUG] - fit_round 30 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.860147
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6504
-[2023-09-21 07:01:07,287][flwr][INFO] - fit progress: (30, 0.999864658418174, {'accuracy': 0.6504}, 13895.747061056085)
-[2023-09-21 07:01:07,287][flwr][DEBUG] - evaluate_round 30: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 07:01:38,500][flwr][DEBUG] - evaluate_round 30 received 10 results and 0 failures
-[2023-09-21 07:01:38,501][flwr][DEBUG] - fit_round 31: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7422839506172839
-(DefaultActor pid=2820544) >> Training accuracy: 0.838349
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6777012711864406
-(DefaultActor pid=2820544) >> Training accuracy: 0.824417
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5731907894736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.775493
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5848880597014925
-(DefaultActor pid=2820544) >> Training accuracy: 0.747435
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7307692307692307
-(DefaultActor pid=2820544) >> Training accuracy: 0.818710
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.620697463768116
-(DefaultActor pid=2820544) >> Training accuracy: 0.744565
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5838414634146342
-(DefaultActor pid=2820544) >> Training accuracy: 0.798209
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6180555555555556
-(DefaultActor pid=2820544) >> Training accuracy: 0.869020
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5629111842105263
-(DefaultActor pid=2820544) >> Training accuracy: 0.824836
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5952380952380952
-[2023-09-21 07:08:49,812][flwr][DEBUG] - fit_round 31 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.800595
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6604
-[2023-09-21 07:08:51,306][flwr][INFO] - fit progress: (31, 0.9666112412850316, {'accuracy': 0.6604}, 14359.766338087153)
-[2023-09-21 07:08:51,306][flwr][DEBUG] - evaluate_round 31: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 07:09:24,231][flwr][DEBUG] - evaluate_round 31 received 10 results and 0 failures
-[2023-09-21 07:09:24,233][flwr][DEBUG] - fit_round 32: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7417052469135802
-(DefaultActor pid=2820544) >> Training accuracy: 0.840471
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5679824561403509
-(DefaultActor pid=2820544) >> Training accuracy: 0.746711
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6274909420289855
-(DefaultActor pid=2820544) >> Training accuracy: 0.752944
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6209490740740741
-(DefaultActor pid=2820544) >> Training accuracy: 0.860532
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7070974576271186
-(DefaultActor pid=2820544) >> Training accuracy: 0.830244
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7323717948717948
-(DefaultActor pid=2820544) >> Training accuracy: 0.819311
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5836759868421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.823191
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5863185975609756
-(DefaultActor pid=2820544) >> Training accuracy: 0.804688
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6383928571428571
-(DefaultActor pid=2820544) >> Training accuracy: 0.792411
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6142723880597015
-[2023-09-21 07:16:55,930][flwr][DEBUG] - fit_round 32 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.725280
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6638
-[2023-09-21 07:17:04,344][flwr][INFO] - fit progress: (32, 0.9633961186622279, {'accuracy': 0.6638}, 14852.804559036158)
-[2023-09-21 07:17:04,345][flwr][DEBUG] - evaluate_round 32: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 07:17:51,379][flwr][DEBUG] - evaluate_round 32 received 10 results and 0 failures
-[2023-09-21 07:17:51,379][flwr][DEBUG] - fit_round 33: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7445913461538461
-(DefaultActor pid=2820544) >> Training accuracy: 0.810497
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7511574074074074
-(DefaultActor pid=2820544) >> Training accuracy: 0.851080
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.586890243902439
-(DefaultActor pid=2820544) >> Training accuracy: 0.799352
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6294367283950617
-(DefaultActor pid=2820544) >> Training accuracy: 0.861111
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7007415254237288
-(DefaultActor pid=2820544) >> Training accuracy: 0.828655
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6219161184210527
-(DefaultActor pid=2820544) >> Training accuracy: 0.823396
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6202876984126984
-(DefaultActor pid=2820544) >> Training accuracy: 0.785962
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6005130597014925
-(DefaultActor pid=2820544) >> Training accuracy: 0.758629
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6433423913043478
-(DefaultActor pid=2820544) >> Training accuracy: 0.755661
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5496162280701754
-[2023-09-21 07:25:20,205][flwr][DEBUG] - fit_round 33 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.767818
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6482
-[2023-09-21 07:25:22,088][flwr][INFO] - fit progress: (33, 1.003742164411484, {'accuracy': 0.6482}, 15350.548849062063)
-[2023-09-21 07:25:22,089][flwr][DEBUG] - evaluate_round 33: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 07:25:53,785][flwr][DEBUG] - evaluate_round 33 received 10 results and 0 failures
-[2023-09-21 07:25:53,786][flwr][DEBUG] - fit_round 34: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6200396825396826
-(DefaultActor pid=2820544) >> Training accuracy: 0.796751
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6035879629629629
-(DefaultActor pid=2820544) >> Training accuracy: 0.853974
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7415123456790124
-(DefaultActor pid=2820544) >> Training accuracy: 0.844329
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.675052966101695
-(DefaultActor pid=2820544) >> Training accuracy: 0.831568
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5859375
-(DefaultActor pid=2820544) >> Training accuracy: 0.810404
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5810032894736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.818462
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6272644927536232
-(DefaultActor pid=2820544) >> Training accuracy: 0.752264
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.742988782051282
-(DefaultActor pid=2820544) >> Training accuracy: 0.820112
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6000466417910447
-(DefaultActor pid=2820544) >> Training accuracy: 0.760261
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5482456140350878
-[2023-09-21 07:33:22,730][flwr][DEBUG] - fit_round 34 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.766996
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6594
-[2023-09-21 07:33:24,306][flwr][INFO] - fit progress: (34, 0.9889103397012899, {'accuracy': 0.6594}, 15832.766589079052)
-[2023-09-21 07:33:24,307][flwr][DEBUG] - evaluate_round 34: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 07:33:56,320][flwr][DEBUG] - evaluate_round 34 received 10 results and 0 failures
-[2023-09-21 07:33:56,321][flwr][DEBUG] - fit_round 35: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.588795731707317
-(DefaultActor pid=2820544) >> Training accuracy: 0.793064
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5627741228070176
-(DefaultActor pid=2820544) >> Training accuracy: 0.786732
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7438271604938271
-(DefaultActor pid=2820544) >> Training accuracy: 0.854745
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6324728260869565
-(DefaultActor pid=2820544) >> Training accuracy: 0.741395
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5965485074626866
-(DefaultActor pid=2820544) >> Training accuracy: 0.762360
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6255787037037037
-(DefaultActor pid=2820544) >> Training accuracy: 0.858410
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6005345394736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.816201
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6909427966101694
-(DefaultActor pid=2820544) >> Training accuracy: 0.824947
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7323717948717948
-(DefaultActor pid=2820544) >> Training accuracy: 0.802484
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6023065476190477
-(DefaultActor pid=2820544) >> Training accuracy: 0.794023
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 07:41:46,221][flwr][DEBUG] - fit_round 35 received 10 results and 0 failures
-test acc: 0.6561
-[2023-09-21 07:41:47,815][flwr][INFO] - fit progress: (35, 0.9822426000342201, {'accuracy': 0.6561}, 16336.27490788186)
-[2023-09-21 07:41:47,815][flwr][DEBUG] - evaluate_round 35: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 07:42:20,167][flwr][DEBUG] - evaluate_round 35 received 10 results and 0 failures
-[2023-09-21 07:42:20,169][flwr][DEBUG] - fit_round 36: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6333085317460317
-(DefaultActor pid=2820544) >> Training accuracy: 0.801835
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5906635802469136
-(DefaultActor pid=2820544) >> Training accuracy: 0.853009
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7322530864197531
-(DefaultActor pid=2820544) >> Training accuracy: 0.855903
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6374547101449275
-(DefaultActor pid=2820544) >> Training accuracy: 0.761775
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7439903846153846
-(DefaultActor pid=2820544) >> Training accuracy: 0.807091
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5746299342105263
-(DefaultActor pid=2820544) >> Training accuracy: 0.811061
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5597587719298246
-(DefaultActor pid=2820544) >> Training accuracy: 0.776316
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.609375
-(DefaultActor pid=2820544) >> Training accuracy: 0.804497
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6044776119402985
-(DefaultActor pid=2820544) >> Training accuracy: 0.735075
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6972987288135594
-[2023-09-21 07:50:16,332][flwr][DEBUG] - fit_round 36 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.824682
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6629
-[2023-09-21 07:50:18,132][flwr][INFO] - fit progress: (36, 0.962386382559237, {'accuracy': 0.6629}, 16846.592430986)
-[2023-09-21 07:50:18,132][flwr][DEBUG] - evaluate_round 36: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 07:50:49,770][flwr][DEBUG] - evaluate_round 36 received 10 results and 0 failures
-[2023-09-21 07:50:49,771][flwr][DEBUG] - fit_round 37: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.733573717948718
-(DefaultActor pid=2820544) >> Training accuracy: 0.821114
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6245335820895522
-(DefaultActor pid=2820544) >> Training accuracy: 0.741604
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6422371031746031
-(DefaultActor pid=2820544) >> Training accuracy: 0.793403
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6933262711864406
-(DefaultActor pid=2820544) >> Training accuracy: 0.813559
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7380401234567902
-(DefaultActor pid=2820544) >> Training accuracy: 0.860532
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6399456521739131
-(DefaultActor pid=2820544) >> Training accuracy: 0.752038
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6021792763157895
-(DefaultActor pid=2820544) >> Training accuracy: 0.817640
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6130401234567902
-(DefaultActor pid=2820544) >> Training accuracy: 0.849344
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6141387195121951
-(DefaultActor pid=2820544) >> Training accuracy: 0.803925
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.578125
-[2023-09-21 07:58:20,828][flwr][DEBUG] - fit_round 37 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.768914
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6614
-[2023-09-21 07:58:22,599][flwr][INFO] - fit progress: (37, 0.9726330711247441, {'accuracy': 0.6614}, 17331.05953423679)
-[2023-09-21 07:58:22,600][flwr][DEBUG] - evaluate_round 37: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 07:58:54,099][flwr][DEBUG] - evaluate_round 37 received 10 results and 0 failures
-[2023-09-21 07:58:54,100][flwr][DEBUG] - fit_round 38: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5981326219512195
-(DefaultActor pid=2820544) >> Training accuracy: 0.811928
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6390128968253969
-(DefaultActor pid=2820544) >> Training accuracy: 0.801835
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5529057017543859
-(DefaultActor pid=2820544) >> Training accuracy: 0.774945
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6694915254237288
-(DefaultActor pid=2820544) >> Training accuracy: 0.840042
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5999177631578947
-(DefaultActor pid=2820544) >> Training accuracy: 0.829975
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7453926282051282
-(DefaultActor pid=2820544) >> Training accuracy: 0.802885
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.626929012345679
-(DefaultActor pid=2820544) >> Training accuracy: 0.858218
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6229011194029851
-(DefaultActor pid=2820544) >> Training accuracy: 0.762593
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7370756172839507
-(DefaultActor pid=2820544) >> Training accuracy: 0.838542
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6283967391304348
-[2023-09-21 08:06:20,812][flwr][DEBUG] - fit_round 38 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.764719
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6656
-[2023-09-21 08:06:22,365][flwr][INFO] - fit progress: (38, 0.965197785498616, {'accuracy': 0.6656}, 17810.825569720007)
-[2023-09-21 08:06:22,366][flwr][DEBUG] - evaluate_round 38: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 08:06:54,159][flwr][DEBUG] - evaluate_round 38 received 10 results and 0 failures
-[2023-09-21 08:06:54,160][flwr][DEBUG] - fit_round 39: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5553728070175439
-(DefaultActor pid=2820544) >> Training accuracy: 0.773849
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6417824074074074
-(DefaultActor pid=2820544) >> Training accuracy: 0.866127
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6103078358208955
-(DefaultActor pid=2820544) >> Training accuracy: 0.762593
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6970338983050848
-(DefaultActor pid=2820544) >> Training accuracy: 0.826006
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6426630434782609
-(DefaultActor pid=2820544) >> Training accuracy: 0.755435
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7542067307692307
-(DefaultActor pid=2820544) >> Training accuracy: 0.820312
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6052631578947368
-(DefaultActor pid=2820544) >> Training accuracy: 0.822163
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7272376543209876
-(DefaultActor pid=2820544) >> Training accuracy: 0.852238
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6233878968253969
-(DefaultActor pid=2820544) >> Training accuracy: 0.801711
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6048018292682927
-[2023-09-21 08:14:38,416][flwr][DEBUG] - fit_round 39 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.814405
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.666
-[2023-09-21 08:14:39,999][flwr][INFO] - fit progress: (39, 0.9574256779286808, {'accuracy': 0.666}, 18308.459737964906)
-[2023-09-21 08:14:40,000][flwr][DEBUG] - evaluate_round 39: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 08:15:11,361][flwr][DEBUG] - evaluate_round 39 received 10 results and 0 failures
-[2023-09-21 08:15:11,361][flwr][DEBUG] - fit_round 40: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6251929012345679
-(DefaultActor pid=2820544) >> Training accuracy: 0.869213
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6101973684210527
-(DefaultActor pid=2820544) >> Training accuracy: 0.829975
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6128731343283582
-(DefaultActor pid=2820544) >> Training accuracy: 0.758629
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7467948717948718
-(DefaultActor pid=2820544) >> Training accuracy: 0.820312
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6885593220338984
-(DefaultActor pid=2820544) >> Training accuracy: 0.836600
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5641447368421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.777961
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7361111111111112
-(DefaultActor pid=2820544) >> Training accuracy: 0.838542
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6382688492063492
-(DefaultActor pid=2820544) >> Training accuracy: 0.809152
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6027057926829268
-(DefaultActor pid=2820544) >> Training accuracy: 0.767912
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.639266304347826
-[2023-09-21 08:23:00,325][flwr][DEBUG] - fit_round 40 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.751812
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6597
-[2023-09-21 08:23:01,939][flwr][INFO] - fit progress: (40, 0.9920141804522981, {'accuracy': 0.6597}, 18810.39952501189)
-[2023-09-21 08:23:01,940][flwr][DEBUG] - evaluate_round 40: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 08:23:33,599][flwr][DEBUG] - evaluate_round 40 received 10 results and 0 failures
-[2023-09-21 08:23:33,600][flwr][DEBUG] - fit_round 41: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6124588815789473
-(DefaultActor pid=2820544) >> Training accuracy: 0.824013
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7347608024691358
-(DefaultActor pid=2820544) >> Training accuracy: 0.856481
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5441337719298246
-(DefaultActor pid=2820544) >> Training accuracy: 0.788925
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7113347457627118
-(DefaultActor pid=2820544) >> Training accuracy: 0.845339
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6123511904761905
-(DefaultActor pid=2820544) >> Training accuracy: 0.795015
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6220561594202898
-(DefaultActor pid=2820544) >> Training accuracy: 0.747509
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6516203703703703
-(DefaultActor pid=2820544) >> Training accuracy: 0.878279
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6058768656716418
-(DefaultActor pid=2820544) >> Training accuracy: 0.749300
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7407852564102564
-(DefaultActor pid=2820544) >> Training accuracy: 0.823117
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5663109756097561
-[2023-09-21 08:31:07,100][flwr][DEBUG] - fit_round 41 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.813834
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6662
-[2023-09-21 08:31:09,115][flwr][INFO] - fit progress: (41, 0.9609055894251448, {'accuracy': 0.6662}, 19297.57527715387)
-[2023-09-21 08:31:09,116][flwr][DEBUG] - evaluate_round 41: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 08:31:40,554][flwr][DEBUG] - evaluate_round 41 received 10 results and 0 failures
-[2023-09-21 08:31:40,554][flwr][DEBUG] - fit_round 42: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6859110169491526
-(DefaultActor pid=2820544) >> Training accuracy: 0.844809
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7361111111111112
-(DefaultActor pid=2820544) >> Training accuracy: 0.858218
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6267149390243902
-(DefaultActor pid=2820544) >> Training accuracy: 0.816502
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6208022388059702
-(DefaultActor pid=2820544) >> Training accuracy: 0.749534
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6271219135802469
-(DefaultActor pid=2820544) >> Training accuracy: 0.871914
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5753837719298246
-(DefaultActor pid=2820544) >> Training accuracy: 0.763706
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6397192028985508
-(DefaultActor pid=2820544) >> Training accuracy: 0.762455
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6030016447368421
-(DefaultActor pid=2820544) >> Training accuracy: 0.830181
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6423611111111112
-(DefaultActor pid=2820544) >> Training accuracy: 0.800843
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7405849358974359
-[2023-09-21 08:39:32,059][flwr][DEBUG] - fit_round 42 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.815304
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6485
-[2023-09-21 08:40:04,596][flwr][INFO] - fit progress: (42, 0.9998491283613272, {'accuracy': 0.6485}, 19833.056645926088)
-[2023-09-21 08:40:04,597][flwr][DEBUG] - evaluate_round 42: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 08:40:40,242][flwr][DEBUG] - evaluate_round 42 received 10 results and 0 failures
-[2023-09-21 08:40:40,243][flwr][DEBUG] - fit_round 43: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7415865384615384
-(DefaultActor pid=2820544) >> Training accuracy: 0.824519
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6914724576271186
-(DefaultActor pid=2820544) >> Training accuracy: 0.840042
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5556469298245614
-(DefaultActor pid=2820544) >> Training accuracy: 0.771930
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7586805555555556
-(DefaultActor pid=2820544) >> Training accuracy: 0.856289
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6068978658536586
-(DefaultActor pid=2820544) >> Training accuracy: 0.815549
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6286231884057971
-(DefaultActor pid=2820544) >> Training accuracy: 0.757473
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6273148148148148
-(DefaultActor pid=2820544) >> Training accuracy: 0.867477
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5927220394736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.834704
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5907182835820896
-(DefaultActor pid=2820544) >> Training accuracy: 0.761660
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5901537698412699
-(DefaultActor pid=2820544) >> Training accuracy: 0.810764
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 08:48:02,839][flwr][DEBUG] - fit_round 43 received 10 results and 0 failures
-test acc: 0.6638
-[2023-09-21 08:48:04,306][flwr][INFO] - fit progress: (43, 0.970430683404112, {'accuracy': 0.6638}, 20312.766624896787)
-[2023-09-21 08:48:04,307][flwr][DEBUG] - evaluate_round 43: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 08:48:34,869][flwr][DEBUG] - evaluate_round 43 received 10 results and 0 failures
-[2023-09-21 08:48:34,870][flwr][DEBUG] - fit_round 44: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7457932692307693
-(DefaultActor pid=2820544) >> Training accuracy: 0.811298
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7567515432098766
-(DefaultActor pid=2820544) >> Training accuracy: 0.860532
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6312003968253969
-(DefaultActor pid=2820544) >> Training accuracy: 0.802207
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6526268115942029
-(DefaultActor pid=2820544) >> Training accuracy: 0.766078
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.609375
-(DefaultActor pid=2820544) >> Training accuracy: 0.755364
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5904605263157895
-(DefaultActor pid=2820544) >> Training accuracy: 0.834910
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6224922839506173
-(DefaultActor pid=2820544) >> Training accuracy: 0.855710
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5685307017543859
-(DefaultActor pid=2820544) >> Training accuracy: 0.782072
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6845868644067796
-(DefaultActor pid=2820544) >> Training accuracy: 0.829979
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6068978658536586
-[2023-09-21 08:56:26,493][flwr][DEBUG] - fit_round 44 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.810785
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6673
-[2023-09-21 08:56:27,857][flwr][INFO] - fit progress: (44, 0.9538876035342962, {'accuracy': 0.6673}, 20816.31762043899)
-[2023-09-21 08:56:27,858][flwr][DEBUG] - evaluate_round 44: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 08:56:58,951][flwr][DEBUG] - evaluate_round 44 received 10 results and 0 failures
-[2023-09-21 08:56:58,952][flwr][DEBUG] - fit_round 45: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6145055970149254
-(DefaultActor pid=2820544) >> Training accuracy: 0.749767
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6165707236842105
-(DefaultActor pid=2820544) >> Training accuracy: 0.828742
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.555921052631579
-(DefaultActor pid=2820544) >> Training accuracy: 0.788103
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6175685975609756
-(DefaultActor pid=2820544) >> Training accuracy: 0.815739
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6477623456790124
-(DefaultActor pid=2820544) >> Training accuracy: 0.864969
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6431051587301587
-(DefaultActor pid=2820544) >> Training accuracy: 0.791667
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6909427966101694
-(DefaultActor pid=2820544) >> Training accuracy: 0.835540
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7566105769230769
-(DefaultActor pid=2820544) >> Training accuracy: 0.811899
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7326388888888888
-(DefaultActor pid=2820544) >> Training accuracy: 0.842785
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644927536231884
-[2023-09-21 09:04:18,260][flwr][DEBUG] - fit_round 45 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.744339
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6649
-[2023-09-21 09:04:19,680][flwr][INFO] - fit progress: (45, 0.9652769756964601, {'accuracy': 0.6649}, 21288.140167111065)
-[2023-09-21 09:04:19,680][flwr][DEBUG] - evaluate_round 45: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 09:04:51,440][flwr][DEBUG] - evaluate_round 45 received 10 results and 0 failures
-[2023-09-21 09:04:51,441][flwr][DEBUG] - fit_round 46: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6297554347826086
-(DefaultActor pid=2820544) >> Training accuracy: 0.753397
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7457561728395061
-(DefaultActor pid=2820544) >> Training accuracy: 0.857639
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6957097457627118
-(DefaultActor pid=2820544) >> Training accuracy: 0.834746
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6023848684210527
-(DefaultActor pid=2820544) >> Training accuracy: 0.835526
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.749198717948718
-(DefaultActor pid=2820544) >> Training accuracy: 0.825721
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5931783536585366
-(DefaultActor pid=2820544) >> Training accuracy: 0.802973
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6392746913580247
-(DefaultActor pid=2820544) >> Training accuracy: 0.865741
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.613106343283582
-(DefaultActor pid=2820544) >> Training accuracy: 0.766091
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6470734126984127
-(DefaultActor pid=2820544) >> Training accuracy: 0.808904
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5570175438596491
-[2023-09-21 09:12:10,903][flwr][DEBUG] - fit_round 46 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.781250
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6647
-[2023-09-21 09:12:12,527][flwr][INFO] - fit progress: (46, 0.9712253968936567, {'accuracy': 0.6647}, 21760.987880504224)
-[2023-09-21 09:12:12,528][flwr][DEBUG] - evaluate_round 46: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 09:12:43,146][flwr][DEBUG] - evaluate_round 46 received 10 results and 0 failures
-[2023-09-21 09:12:43,147][flwr][DEBUG] - fit_round 47: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5902549342105263
-(DefaultActor pid=2820544) >> Training accuracy: 0.828947
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.553453947368421
-(DefaultActor pid=2820544) >> Training accuracy: 0.799616
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.636322463768116
-(DefaultActor pid=2820544) >> Training accuracy: 0.776268
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7097457627118644
-(DefaultActor pid=2820544) >> Training accuracy: 0.800847
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7455929487179487
-(DefaultActor pid=2820544) >> Training accuracy: 0.818510
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.638640873015873
-(DefaultActor pid=2820544) >> Training accuracy: 0.811880
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7449845679012346
-(DefaultActor pid=2820544) >> Training accuracy: 0.853395
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6203703703703703
-(DefaultActor pid=2820544) >> Training accuracy: 0.863619
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6133765243902439
-(DefaultActor pid=2820544) >> Training accuracy: 0.812691
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6177705223880597
-[2023-09-21 09:19:56,576][flwr][DEBUG] - fit_round 47 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.762826
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6737
-[2023-09-21 09:20:13,742][flwr][INFO] - fit progress: (47, 0.9433850042355327, {'accuracy': 0.6737}, 22242.20204451913)
-[2023-09-21 09:20:13,743][flwr][DEBUG] - evaluate_round 47: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 09:20:48,906][flwr][DEBUG] - evaluate_round 47 received 10 results and 0 failures
-[2023-09-21 09:20:48,907][flwr][DEBUG] - fit_round 48: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7544070512820513
-(DefaultActor pid=2820544) >> Training accuracy: 0.825321
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5709978070175439
-(DefaultActor pid=2820544) >> Training accuracy: 0.777686
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7440200617283951
-(DefaultActor pid=2820544) >> Training accuracy: 0.858218
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6951800847457628
-(DefaultActor pid=2820544) >> Training accuracy: 0.829979
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6517210144927537
-(DefaultActor pid=2820544) >> Training accuracy: 0.774457
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6556299603174603
-(DefaultActor pid=2820544) >> Training accuracy: 0.808036
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6429398148148148
-(DefaultActor pid=2820544) >> Training accuracy: 0.870177
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6221217105263158
-(DefaultActor pid=2820544) >> Training accuracy: 0.825041
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6140391791044776
-(DefaultActor pid=2820544) >> Training accuracy: 0.744403
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6251905487804879
-[2023-09-21 09:27:58,065][flwr][DEBUG] - fit_round 48 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.829078
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6741
-[2023-09-21 09:27:59,833][flwr][INFO] - fit progress: (48, 0.9481769482167764, {'accuracy': 0.6741}, 22708.29312482383)
-[2023-09-21 09:27:59,833][flwr][DEBUG] - evaluate_round 48: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 09:28:34,244][flwr][DEBUG] - evaluate_round 48 received 10 results and 0 failures
-[2023-09-21 09:28:34,245][flwr][DEBUG] - fit_round 49: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6412037037037037
-(DefaultActor pid=2820544) >> Training accuracy: 0.864776
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6641757246376812
-(DefaultActor pid=2820544) >> Training accuracy: 0.769475
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6439732142857143
-(DefaultActor pid=2820544) >> Training accuracy: 0.788070
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7467206790123457
-(DefaultActor pid=2820544) >> Training accuracy: 0.850116
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6168064024390244
-(DefaultActor pid=2820544) >> Training accuracy: 0.825648
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5581140350877193
-(DefaultActor pid=2820544) >> Training accuracy: 0.782346
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.622327302631579
-(DefaultActor pid=2820544) >> Training accuracy: 0.832648
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7658253205128205
-(DefaultActor pid=2820544) >> Training accuracy: 0.816506
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6885593220338984
-(DefaultActor pid=2820544) >> Training accuracy: 0.826006
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6187033582089553
-(DefaultActor pid=2820544) >> Training accuracy: 0.745336
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 09:36:02,552][flwr][DEBUG] - fit_round 49 received 10 results and 0 failures
-test acc: 0.6726
-[2023-09-21 09:36:04,282][flwr][INFO] - fit progress: (49, 0.9416967020057642, {'accuracy': 0.6726}, 23192.74199562706)
-[2023-09-21 09:36:04,282][flwr][DEBUG] - evaluate_round 49: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 09:36:34,398][flwr][DEBUG] - evaluate_round 49 received 10 results and 0 failures
-[2023-09-21 09:36:34,399][flwr][DEBUG] - fit_round 50: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5960115131578947
-(DefaultActor pid=2820544) >> Training accuracy: 0.842105
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6410060975609756
-(DefaultActor pid=2820544) >> Training accuracy: 0.818216
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7203389830508474
-(DefaultActor pid=2820544) >> Training accuracy: 0.837924
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7481971153846154
-(DefaultActor pid=2820544) >> Training accuracy: 0.829127
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6122685185185185
-(DefaultActor pid=2820544) >> Training accuracy: 0.861304
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6533061594202898
-(DefaultActor pid=2820544) >> Training accuracy: 0.774004
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6631944444444444
-(DefaultActor pid=2820544) >> Training accuracy: 0.816344
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6236007462686567
-(DefaultActor pid=2820544) >> Training accuracy: 0.723647
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7494212962962963
-(DefaultActor pid=2820544) >> Training accuracy: 0.852623
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5526315789473685
-(DefaultActor pid=2820544) >> Training accuracy: 0.778509
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 09:43:45,752][flwr][DEBUG] - fit_round 50 received 10 results and 0 failures
-test acc: 0.672
-[2023-09-21 09:43:47,117][flwr][INFO] - fit progress: (50, 0.9449833774338134, {'accuracy': 0.672}, 23655.57692045998)
-[2023-09-21 09:43:47,117][flwr][DEBUG] - evaluate_round 50: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 09:44:17,397][flwr][DEBUG] - evaluate_round 50 received 10 results and 0 failures
-[2023-09-21 09:44:17,398][flwr][DEBUG] - fit_round 51: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6986228813559322
-(DefaultActor pid=2820544) >> Training accuracy: 0.818591
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7472993827160493
-(DefaultActor pid=2820544) >> Training accuracy: 0.862076
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6348379629629629
-(DefaultActor pid=2820544) >> Training accuracy: 0.869599
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6284298780487805
-(DefaultActor pid=2820544) >> Training accuracy: 0.813453
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6011513157894737
-(DefaultActor pid=2820544) >> Training accuracy: 0.793311
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6296641791044776
-(DefaultActor pid=2820544) >> Training accuracy: 0.754198
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6639492753623188
-(DefaultActor pid=2820544) >> Training accuracy: 0.774230
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7371794871794872
-(DefaultActor pid=2820544) >> Training accuracy: 0.824119
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6485615079365079
-(DefaultActor pid=2820544) >> Training accuracy: 0.810888
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6079358552631579
-[2023-09-21 09:51:36,440][flwr][DEBUG] - fit_round 51 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.831209
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6596
-[2023-09-21 09:51:37,863][flwr][INFO] - fit progress: (51, 0.977153589931159, {'accuracy': 0.6596}, 24126.323618939146)
-[2023-09-21 09:51:37,864][flwr][DEBUG] - evaluate_round 51: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 09:52:08,503][flwr][DEBUG] - evaluate_round 51 received 10 results and 0 failures
-[2023-09-21 09:52:08,503][flwr][DEBUG] - fit_round 52: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644927536231884
-(DefaultActor pid=2820544) >> Training accuracy: 0.761322
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.698093220338983
-(DefaultActor pid=2820544) >> Training accuracy: 0.839513
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6166158536585366
-(DefaultActor pid=2820544) >> Training accuracy: 0.810213
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7407852564102564
-(DefaultActor pid=2820544) >> Training accuracy: 0.836338
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6158234126984127
-(DefaultActor pid=2820544) >> Training accuracy: 0.813864
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6077425373134329
-(DefaultActor pid=2820544) >> Training accuracy: 0.765159
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7488425925925926
-(DefaultActor pid=2820544) >> Training accuracy: 0.861497
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6350308641975309
-(DefaultActor pid=2820544) >> Training accuracy: 0.859568
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6089638157894737
-(DefaultActor pid=2820544) >> Training accuracy: 0.825863
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.584703947368421
-(DefaultActor pid=2820544) >> Training accuracy: 0.784539
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 09:59:28,650][flwr][DEBUG] - fit_round 52 received 10 results and 0 failures
-test acc: 0.6647
-[2023-09-21 09:59:30,545][flwr][INFO] - fit progress: (52, 0.9636962722284725, {'accuracy': 0.6647}, 24599.005290116183)
-[2023-09-21 09:59:30,546][flwr][DEBUG] - evaluate_round 52: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 10:00:01,755][flwr][DEBUG] - evaluate_round 52 received 10 results and 0 failures
-[2023-09-21 10:00:01,756][flwr][DEBUG] - fit_round 53: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.623070987654321
-(DefaultActor pid=2820544) >> Training accuracy: 0.864776
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6551177536231884
-(DefaultActor pid=2820544) >> Training accuracy: 0.758605
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7055084745762712
-(DefaultActor pid=2820544) >> Training accuracy: 0.838983
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644469246031746
-(DefaultActor pid=2820544) >> Training accuracy: 0.815476
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5849780701754386
-(DefaultActor pid=2820544) >> Training accuracy: 0.782072
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6089638157894737
-(DefaultActor pid=2820544) >> Training accuracy: 0.837788
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7405849358974359
-(DefaultActor pid=2820544) >> Training accuracy: 0.825521
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6312966417910447
-(DefaultActor pid=2820544) >> Training accuracy: 0.761894
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6219512195121951
-(DefaultActor pid=2820544) >> Training accuracy: 0.825648
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7393904320987654
-[2023-09-21 10:07:34,641][flwr][DEBUG] - fit_round 53 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.863040
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6687
-[2023-09-21 10:07:35,995][flwr][INFO] - fit progress: (53, 0.9485500901461409, {'accuracy': 0.6687}, 25084.455141921062)
-[2023-09-21 10:07:35,995][flwr][DEBUG] - evaluate_round 53: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 10:08:06,753][flwr][DEBUG] - evaluate_round 53 received 10 results and 0 failures
-[2023-09-21 10:08:06,753][flwr][DEBUG] - fit_round 54: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6366234756097561
-(DefaultActor pid=2820544) >> Training accuracy: 0.814787
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6407490079365079
-(DefaultActor pid=2820544) >> Training accuracy: 0.803323
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6512681159420289
-(DefaultActor pid=2820544) >> Training accuracy: 0.766304
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7588734567901234
-(DefaultActor pid=2820544) >> Training accuracy: 0.865934
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7020656779661016
-(DefaultActor pid=2820544) >> Training accuracy: 0.848517
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6398533950617284
-(DefaultActor pid=2820544) >> Training accuracy: 0.862847
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5797697368421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.772752
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.635485197368421
-(DefaultActor pid=2820544) >> Training accuracy: 0.840461
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7568108974358975
-(DefaultActor pid=2820544) >> Training accuracy: 0.800481
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6238339552238806
-[2023-09-21 10:15:20,795][flwr][DEBUG] - fit_round 54 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.756297
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6674
-[2023-09-21 10:15:22,179][flwr][INFO] - fit progress: (54, 0.9527737906756112, {'accuracy': 0.6674}, 25550.639077940024)
-[2023-09-21 10:15:22,179][flwr][DEBUG] - evaluate_round 54: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 10:15:53,033][flwr][DEBUG] - evaluate_round 54 received 10 results and 0 failures
-[2023-09-21 10:15:53,034][flwr][DEBUG] - fit_round 55: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.61328125
-(DefaultActor pid=2820544) >> Training accuracy: 0.822368
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5778508771929824
-(DefaultActor pid=2820544) >> Training accuracy: 0.792489
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6198694029850746
-(DefaultActor pid=2820544) >> Training accuracy: 0.752799
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6949152542372882
-(DefaultActor pid=2820544) >> Training accuracy: 0.847722
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6607142857142857
-(DefaultActor pid=2820544) >> Training accuracy: 0.816716
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7530864197530864
-(DefaultActor pid=2820544) >> Training accuracy: 0.856481
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6269054878048781
-(DefaultActor pid=2820544) >> Training accuracy: 0.815549
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6363811728395061
-(DefaultActor pid=2820544) >> Training accuracy: 0.876736
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6521739130434783
-(DefaultActor pid=2820544) >> Training accuracy: 0.770380
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7425881410256411
-[2023-09-21 10:23:26,976][flwr][DEBUG] - fit_round 55 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.821114
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6701
-[2023-09-21 10:23:58,108][flwr][INFO] - fit progress: (55, 0.9540609658335726, {'accuracy': 0.6701}, 26066.56836123299)
-[2023-09-21 10:23:58,109][flwr][DEBUG] - evaluate_round 55: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 10:24:37,793][flwr][DEBUG] - evaluate_round 55 received 10 results and 0 failures
-[2023-09-21 10:24:37,794][flwr][DEBUG] - fit_round 56: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6578351449275363
-(DefaultActor pid=2820544) >> Training accuracy: 0.768795
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6309799382716049
-(DefaultActor pid=2820544) >> Training accuracy: 0.859954
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.701271186440678
-(DefaultActor pid=2820544) >> Training accuracy: 0.839778
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6056743421052632
-(DefaultActor pid=2820544) >> Training accuracy: 0.845189
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7526041666666666
-(DefaultActor pid=2820544) >> Training accuracy: 0.828726
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6175373134328358
-(DefaultActor pid=2820544) >> Training accuracy: 0.773554
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6118521341463414
-(DefaultActor pid=2820544) >> Training accuracy: 0.819931
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6033442982456141
-(DefaultActor pid=2820544) >> Training accuracy: 0.789474
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6548859126984127
-(DefaultActor pid=2820544) >> Training accuracy: 0.820685
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7401620370370371
-[2023-09-21 10:31:44,945][flwr][DEBUG] - fit_round 56 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.869213
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6709
-[2023-09-21 10:31:46,745][flwr][INFO] - fit progress: (56, 0.9453530991420197, {'accuracy': 0.6709}, 26535.205561616924)
-[2023-09-21 10:31:46,746][flwr][DEBUG] - evaluate_round 56: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 10:32:17,198][flwr][DEBUG] - evaluate_round 56 received 10 results and 0 failures
-[2023-09-21 10:32:17,199][flwr][DEBUG] - fit_round 57: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6972987288135594
-(DefaultActor pid=2820544) >> Training accuracy: 0.819650
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6336287313432836
-(DefaultActor pid=2820544) >> Training accuracy: 0.778685
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.625
-(DefaultActor pid=2820544) >> Training accuracy: 0.799352
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6531498015873016
-(DefaultActor pid=2820544) >> Training accuracy: 0.819320
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6551177536231884
-(DefaultActor pid=2820544) >> Training accuracy: 0.768116
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.638695987654321
-(DefaultActor pid=2820544) >> Training accuracy: 0.873457
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6241776315789473
-(DefaultActor pid=2820544) >> Training accuracy: 0.817845
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7441907051282052
-(DefaultActor pid=2820544) >> Training accuracy: 0.835337
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5970394736842105
-(DefaultActor pid=2820544) >> Training accuracy: 0.781798
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7511574074074074
-[2023-09-21 10:39:36,000][flwr][DEBUG] - fit_round 57 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.853781
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6732
-[2023-09-21 10:39:37,745][flwr][INFO] - fit progress: (57, 0.9411518906061642, {'accuracy': 0.6732}, 27006.205031685065)
-[2023-09-21 10:39:37,745][flwr][DEBUG] - evaluate_round 57: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 10:40:08,681][flwr][DEBUG] - evaluate_round 57 received 10 results and 0 failures
-[2023-09-21 10:40:08,682][flwr][DEBUG] - fit_round 58: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6231496710526315
-(DefaultActor pid=2820544) >> Training accuracy: 0.834498
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6568700396825397
-(DefaultActor pid=2820544) >> Training accuracy: 0.817460
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.706302966101695
-(DefaultActor pid=2820544) >> Training accuracy: 0.846928
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5638706140350878
-(DefaultActor pid=2820544) >> Training accuracy: 0.797149
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6574074074074074
-(DefaultActor pid=2820544) >> Training accuracy: 0.867670
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6496829710144928
-(DefaultActor pid=2820544) >> Training accuracy: 0.774004
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6324314024390244
-(DefaultActor pid=2820544) >> Training accuracy: 0.810785
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7426697530864198
-(DefaultActor pid=2820544) >> Training accuracy: 0.866127
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7526041666666666
-(DefaultActor pid=2820544) >> Training accuracy: 0.832131
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.632695895522388
-[2023-09-21 10:47:16,783][flwr][DEBUG] - fit_round 58 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.763993
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6721
-[2023-09-21 10:47:18,303][flwr][INFO] - fit progress: (58, 0.9347897329079077, {'accuracy': 0.6721}, 27466.763446252793)
-[2023-09-21 10:47:18,303][flwr][DEBUG] - evaluate_round 58: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 10:47:49,450][flwr][DEBUG] - evaluate_round 58 received 10 results and 0 failures
-[2023-09-21 10:47:49,450][flwr][DEBUG] - fit_round 59: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6370045731707317
-(DefaultActor pid=2820544) >> Training accuracy: 0.818788
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.650588768115942
-(DefaultActor pid=2820544) >> Training accuracy: 0.767663
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6110197368421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.834087
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7520032051282052
-(DefaultActor pid=2820544) >> Training accuracy: 0.822716
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5770285087719298
-(DefaultActor pid=2820544) >> Training accuracy: 0.791118
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6770833333333334
-(DefaultActor pid=2820544) >> Training accuracy: 0.805928
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6424906716417911
-(DefaultActor pid=2820544) >> Training accuracy: 0.764459
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7386188271604939
-(DefaultActor pid=2820544) >> Training accuracy: 0.854552
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7166313559322034
-(DefaultActor pid=2820544) >> Training accuracy: 0.855932
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6410108024691358
-[2023-09-21 10:55:05,114][flwr][DEBUG] - fit_round 59 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.869985
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6748
-[2023-09-21 10:55:06,654][flwr][INFO] - fit progress: (59, 0.9279640743526788, {'accuracy': 0.6748}, 27935.114211172797)
-[2023-09-21 10:55:06,654][flwr][DEBUG] - evaluate_round 59: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 10:55:36,941][flwr][DEBUG] - evaluate_round 59 received 10 results and 0 failures
-[2023-09-21 10:55:36,942][flwr][DEBUG] - fit_round 60: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6490036231884058
-(DefaultActor pid=2820544) >> Training accuracy: 0.782156
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.581140350877193
-(DefaultActor pid=2820544) >> Training accuracy: 0.784265
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.642554012345679
-(DefaultActor pid=2820544) >> Training accuracy: 0.867670
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6506529850746269
-(DefaultActor pid=2820544) >> Training accuracy: 0.777985
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7449919871794872
-(DefaultActor pid=2820544) >> Training accuracy: 0.830729
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6206825657894737
-(DefaultActor pid=2820544) >> Training accuracy: 0.848684
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7523148148148148
-(DefaultActor pid=2820544) >> Training accuracy: 0.866705
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6794394841269841
-(DefaultActor pid=2820544) >> Training accuracy: 0.814732
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6196646341463414
-(DefaultActor pid=2820544) >> Training accuracy: 0.821646
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7097457627118644
-[2023-09-21 11:02:46,617][flwr][DEBUG] - fit_round 60 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.841631
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6675
-[2023-09-21 11:02:48,093][flwr][INFO] - fit progress: (60, 0.950613231990284, {'accuracy': 0.6675}, 28396.553373389877)
-[2023-09-21 11:02:48,093][flwr][DEBUG] - evaluate_round 60: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 11:03:18,883][flwr][DEBUG] - evaluate_round 60 received 10 results and 0 failures
-[2023-09-21 11:03:18,884][flwr][DEBUG] - fit_round 61: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6867055084745762
-(DefaultActor pid=2820544) >> Training accuracy: 0.815943
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6310634328358209
-(DefaultActor pid=2820544) >> Training accuracy: 0.774720
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6229039634146342
-(DefaultActor pid=2820544) >> Training accuracy: 0.826791
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7478780864197531
-(DefaultActor pid=2820544) >> Training accuracy: 0.857832
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7437900641025641
-(DefaultActor pid=2820544) >> Training accuracy: 0.821715
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6483410493827161
-(DefaultActor pid=2820544) >> Training accuracy: 0.875386
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5879934210526315
-(DefaultActor pid=2820544) >> Training accuracy: 0.792489
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6496775793650794
-(DefaultActor pid=2820544) >> Training accuracy: 0.811384
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6143092105263158
-(DefaultActor pid=2820544) >> Training accuracy: 0.833265
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6490036231884058
-[2023-09-21 11:10:36,104][flwr][DEBUG] - fit_round 61 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.778986
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6723
-[2023-09-21 11:10:37,335][flwr][INFO] - fit progress: (61, 0.9391038782489948, {'accuracy': 0.6723}, 28865.795383579098)
-[2023-09-21 11:10:37,335][flwr][DEBUG] - evaluate_round 61: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 11:11:07,493][flwr][DEBUG] - evaluate_round 61 received 10 results and 0 failures
-[2023-09-21 11:11:07,494][flwr][DEBUG] - fit_round 62: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5893640350877193
-(DefaultActor pid=2820544) >> Training accuracy: 0.781250
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6275652985074627
-(DefaultActor pid=2820544) >> Training accuracy: 0.779851
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7534054487179487
-(DefaultActor pid=2820544) >> Training accuracy: 0.802083
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6967690677966102
-(DefaultActor pid=2820544) >> Training accuracy: 0.846663
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6385030864197531
-(DefaultActor pid=2820544) >> Training accuracy: 0.871914
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6459573412698413
-(DefaultActor pid=2820544) >> Training accuracy: 0.817832
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6234756097560976
-(DefaultActor pid=2820544) >> Training accuracy: 0.812881
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7608024691358025
-(DefaultActor pid=2820544) >> Training accuracy: 0.861111
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6639492753623188
-(DefaultActor pid=2820544) >> Training accuracy: 0.780571
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6221217105263158
-[2023-09-21 11:18:15,242][flwr][DEBUG] - fit_round 62 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.819901
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6629
-[2023-09-21 11:18:16,600][flwr][INFO] - fit progress: (62, 0.9639180962460491, {'accuracy': 0.6629}, 29325.060337544885)
-[2023-09-21 11:18:16,600][flwr][DEBUG] - evaluate_round 62: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 11:18:46,804][flwr][DEBUG] - evaluate_round 62 received 10 results and 0 failures
-[2023-09-21 11:18:46,804][flwr][DEBUG] - fit_round 63: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7453703703703703
-(DefaultActor pid=2820544) >> Training accuracy: 0.869213
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6175685975609756
-(DefaultActor pid=2820544) >> Training accuracy: 0.809642
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6380597014925373
-(DefaultActor pid=2820544) >> Training accuracy: 0.767957
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6516617063492064
-(DefaultActor pid=2820544) >> Training accuracy: 0.820809
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644927536231884
-(DefaultActor pid=2820544) >> Training accuracy: 0.772871
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5866228070175439
-(DefaultActor pid=2820544) >> Training accuracy: 0.791941
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7347756410256411
-(DefaultActor pid=2820544) >> Training accuracy: 0.834335
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5972450657894737
-(DefaultActor pid=2820544) >> Training accuracy: 0.826891
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715572033898305
-(DefaultActor pid=2820544) >> Training accuracy: 0.845869
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6296296296296297
-[2023-09-21 11:26:00,102][flwr][DEBUG] - fit_round 63 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.867284
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6689
-[2023-09-21 11:26:05,098][flwr][INFO] - fit progress: (63, 0.9449828718416988, {'accuracy': 0.6689}, 29793.558222265914)
-[2023-09-21 11:26:05,098][flwr][DEBUG] - evaluate_round 63: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 11:26:35,007][flwr][DEBUG] - evaluate_round 63 received 10 results and 0 failures
-[2023-09-21 11:26:35,008][flwr][DEBUG] - fit_round 64: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6229011194029851
-(DefaultActor pid=2820544) >> Training accuracy: 0.773321
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6412450396825397
-(DefaultActor pid=2820544) >> Training accuracy: 0.822173
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6578351449275363
-(DefaultActor pid=2820544) >> Training accuracy: 0.762908
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6310975609756098
-(DefaultActor pid=2820544) >> Training accuracy: 0.822218
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6481481481481481
-(DefaultActor pid=2820544) >> Training accuracy: 0.865355
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7563657407407407
-(DefaultActor pid=2820544) >> Training accuracy: 0.867863
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6204769736842105
-(DefaultActor pid=2820544) >> Training accuracy: 0.836143
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5731907894736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.804550
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.710010593220339
-(DefaultActor pid=2820544) >> Training accuracy: 0.836335
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7646233974358975
-[2023-09-21 11:33:37,054][flwr][DEBUG] - fit_round 64 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.835136
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6708
-[2023-09-21 11:33:38,496][flwr][INFO] - fit progress: (64, 0.945188293537012, {'accuracy': 0.6708}, 30246.95655811485)
-[2023-09-21 11:33:38,497][flwr][DEBUG] - evaluate_round 64: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 11:34:09,158][flwr][DEBUG] - evaluate_round 64 received 10 results and 0 failures
-[2023-09-21 11:34:09,159][flwr][DEBUG] - fit_round 65: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6585648148148148
-(DefaultActor pid=2820544) >> Training accuracy: 0.880787
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6435688405797102
-(DefaultActor pid=2820544) >> Training accuracy: 0.777174
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6467013888888888
-(DefaultActor pid=2820544) >> Training accuracy: 0.807168
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7497996794871795
-(DefaultActor pid=2820544) >> Training accuracy: 0.818309
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.636660447761194
-(DefaultActor pid=2820544) >> Training accuracy: 0.780784
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7474922839506173
-(DefaultActor pid=2820544) >> Training accuracy: 0.863812
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.616234756097561
-(DefaultActor pid=2820544) >> Training accuracy: 0.822790
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6247944078947368
-(DefaultActor pid=2820544) >> Training accuracy: 0.835732
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7153072033898306
-(DefaultActor pid=2820544) >> Training accuracy: 0.850371
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5893640350877193
-[2023-09-21 11:41:31,948][flwr][DEBUG] - fit_round 65 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.787829
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.668
-[2023-09-21 11:41:33,356][flwr][INFO] - fit progress: (65, 0.9480573036038457, {'accuracy': 0.668}, 30721.81667397404)
-[2023-09-21 11:41:33,357][flwr][DEBUG] - evaluate_round 65: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 11:42:03,845][flwr][DEBUG] - evaluate_round 65 received 10 results and 0 failures
-[2023-09-21 11:42:03,846][flwr][DEBUG] - fit_round 66: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6392609126984127
-(DefaultActor pid=2820544) >> Training accuracy: 0.817956
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6567028985507246
-(DefaultActor pid=2820544) >> Training accuracy: 0.777174
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7598379629629629
-(DefaultActor pid=2820544) >> Training accuracy: 0.856674
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5836074561403509
-(DefaultActor pid=2820544) >> Training accuracy: 0.788925
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6101973684210527
-(DefaultActor pid=2820544) >> Training accuracy: 0.837171
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.706832627118644
-(DefaultActor pid=2820544) >> Training accuracy: 0.857786
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6291977611940298
-(DefaultActor pid=2820544) >> Training accuracy: 0.768657
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6485339506172839
-(DefaultActor pid=2820544) >> Training accuracy: 0.874614
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7483974358974359
-(DefaultActor pid=2820544) >> Training accuracy: 0.823518
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6213795731707317
-[2023-09-21 11:49:04,794][flwr][DEBUG] - fit_round 66 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.831745
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6687
-[2023-09-21 11:49:06,376][flwr][INFO] - fit progress: (66, 0.942290931178358, {'accuracy': 0.6687}, 31174.83631586982)
-[2023-09-21 11:49:06,376][flwr][DEBUG] - evaluate_round 66: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 11:49:37,075][flwr][DEBUG] - evaluate_round 66 received 10 results and 0 failures
-[2023-09-21 11:49:37,077][flwr][DEBUG] - fit_round 67: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5822368421052632
-(DefaultActor pid=2820544) >> Training accuracy: 0.779057
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7610176282051282
-(DefaultActor pid=2820544) >> Training accuracy: 0.829127
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6261660447761194
-(DefaultActor pid=2820544) >> Training accuracy: 0.756297
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7619598765432098
-(DefaultActor pid=2820544) >> Training accuracy: 0.862076
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6079358552631579
-(DefaultActor pid=2820544) >> Training accuracy: 0.838816
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6557539682539683
-(DefaultActor pid=2820544) >> Training accuracy: 0.818824
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.713718220338983
-(DefaultActor pid=2820544) >> Training accuracy: 0.847193
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6537590579710145
-(DefaultActor pid=2820544) >> Training accuracy: 0.771966
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6369598765432098
-(DefaultActor pid=2820544) >> Training accuracy: 0.868248
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6331935975609756
-[2023-09-21 11:56:48,318][flwr][DEBUG] - fit_round 67 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.815358
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6764
-[2023-09-21 11:56:49,655][flwr][INFO] - fit progress: (67, 0.92270217916836, {'accuracy': 0.6764}, 31638.11498604808)
-[2023-09-21 11:56:49,655][flwr][DEBUG] - evaluate_round 67: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 11:57:19,102][flwr][DEBUG] - evaluate_round 67 received 10 results and 0 failures
-[2023-09-21 11:57:19,103][flwr][DEBUG] - fit_round 68: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6400462962962963
-(DefaultActor pid=2820544) >> Training accuracy: 0.865548
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715042372881356
-(DefaultActor pid=2820544) >> Training accuracy: 0.833422
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6378264925373134
-(DefaultActor pid=2820544) >> Training accuracy: 0.774021
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6677989130434783
-(DefaultActor pid=2820544) >> Training accuracy: 0.772871
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7399839743589743
-(DefaultActor pid=2820544) >> Training accuracy: 0.840545
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7434413580246914
-(DefaultActor pid=2820544) >> Training accuracy: 0.868248
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6215049342105263
-(DefaultActor pid=2820544) >> Training accuracy: 0.835526
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6609623015873016
-(DefaultActor pid=2820544) >> Training accuracy: 0.825025
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6349085365853658
-(DefaultActor pid=2820544) >> Training accuracy: 0.822790
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5794956140350878
-[2023-09-21 12:04:20,577][flwr][DEBUG] - fit_round 68 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.789748
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6705
-[2023-09-21 12:04:21,982][flwr][INFO] - fit progress: (68, 0.9357810918325052, {'accuracy': 0.6705}, 32090.44285089709)
-[2023-09-21 12:04:21,983][flwr][DEBUG] - evaluate_round 68: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 12:04:51,756][flwr][DEBUG] - evaluate_round 68 received 10 results and 0 failures
-[2023-09-21 12:04:51,757][flwr][DEBUG] - fit_round 69: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6585648148148148
-(DefaultActor pid=2820544) >> Training accuracy: 0.869792
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6343368902439024
-(DefaultActor pid=2820544) >> Training accuracy: 0.827172
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7509645061728395
-(DefaultActor pid=2820544) >> Training accuracy: 0.867091
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6324013157894737
-(DefaultActor pid=2820544) >> Training accuracy: 0.842105
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7461939102564102
-(DefaultActor pid=2820544) >> Training accuracy: 0.826122
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6586061507936508
-(DefaultActor pid=2820544) >> Training accuracy: 0.815352
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5953947368421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.795504
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7052436440677966
-(DefaultActor pid=2820544) >> Training accuracy: 0.840572
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6466884328358209
-(DefaultActor pid=2820544) >> Training accuracy: 0.768424
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6664402173913043
-(DefaultActor pid=2820544) >> Training accuracy: 0.780797
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 12:12:02,139][flwr][DEBUG] - fit_round 69 received 10 results and 0 failures
-test acc: 0.6761
-[2023-09-21 12:12:04,106][flwr][INFO] - fit progress: (69, 0.9341149679578531, {'accuracy': 0.6761}, 32552.566110807005)
-[2023-09-21 12:12:04,106][flwr][DEBUG] - evaluate_round 69: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 12:12:34,752][flwr][DEBUG] - evaluate_round 69 received 10 results and 0 failures
-[2023-09-21 12:12:34,753][flwr][DEBUG] - fit_round 70: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.657608695652174
-(DefaultActor pid=2820544) >> Training accuracy: 0.773551
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6312881097560976
-(DefaultActor pid=2820544) >> Training accuracy: 0.815739
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6282649253731343
-(DefaultActor pid=2820544) >> Training accuracy: 0.776119
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6527777777777778
-(DefaultActor pid=2820544) >> Training accuracy: 0.878279
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6321957236842105
-(DefaultActor pid=2820544) >> Training accuracy: 0.836143
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6066337719298246
-(DefaultActor pid=2820544) >> Training accuracy: 0.802083
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7588141025641025
-(DefaultActor pid=2820544) >> Training accuracy: 0.842949
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6553819444444444
-(DefaultActor pid=2820544) >> Training accuracy: 0.814112
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7530864197530864
-(DefaultActor pid=2820544) >> Training accuracy: 0.870177
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7004766949152542
-[2023-09-21 12:19:48,750][flwr][DEBUG] - fit_round 70 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.843485
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6728
-[2023-09-21 12:19:50,541][flwr][INFO] - fit progress: (70, 0.9335093384924026, {'accuracy': 0.6728}, 33019.001658937894)
-[2023-09-21 12:19:50,542][flwr][DEBUG] - evaluate_round 70: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 12:20:20,736][flwr][DEBUG] - evaluate_round 70 received 10 results and 0 failures
-[2023-09-21 12:20:20,737][flwr][DEBUG] - fit_round 71: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6254664179104478
-(DefaultActor pid=2820544) >> Training accuracy: 0.756996
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5699013157894737
-(DefaultActor pid=2820544) >> Training accuracy: 0.807018
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.627858231707317
-(DefaultActor pid=2820544) >> Training accuracy: 0.817645
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7515432098765432
-(DefaultActor pid=2820544) >> Training accuracy: 0.866705
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7592147435897436
-(DefaultActor pid=2820544) >> Training accuracy: 0.794671
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6612318840579711
-(DefaultActor pid=2820544) >> Training accuracy: 0.795969
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6377467105263158
-(DefaultActor pid=2820544) >> Training accuracy: 0.839433
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.658179012345679
-(DefaultActor pid=2820544) >> Training accuracy: 0.885417
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7142478813559322
-(DefaultActor pid=2820544) >> Training accuracy: 0.853549
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6521577380952381
-(DefaultActor pid=2820544) >> Training accuracy: 0.821553
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 12:27:32,822][flwr][DEBUG] - fit_round 71 received 10 results and 0 failures
-test acc: 0.6729
-[2023-09-21 12:27:34,063][flwr][INFO] - fit progress: (71, 0.9386814056684415, {'accuracy': 0.6729}, 33482.52336926106)
-[2023-09-21 12:27:34,063][flwr][DEBUG] - evaluate_round 71: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 12:28:03,947][flwr][DEBUG] - evaluate_round 71 received 10 results and 0 failures
-[2023-09-21 12:28:03,948][flwr][DEBUG] - fit_round 72: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7213983050847458
-(DefaultActor pid=2820544) >> Training accuracy: 0.846663
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6657608695652174
-(DefaultActor pid=2820544) >> Training accuracy: 0.771966
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6672867063492064
-(DefaultActor pid=2820544) >> Training accuracy: 0.816096
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5803179824561403
-(DefaultActor pid=2820544) >> Training accuracy: 0.807566
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6379243827160493
-(DefaultActor pid=2820544) >> Training accuracy: 0.872685
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.602796052631579
-(DefaultActor pid=2820544) >> Training accuracy: 0.847039
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7638888888888888
-(DefaultActor pid=2820544) >> Training accuracy: 0.858603
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6317630597014925
-(DefaultActor pid=2820544) >> Training accuracy: 0.769823
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7566105769230769
-(DefaultActor pid=2820544) >> Training accuracy: 0.835537
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6230945121951219
-[2023-09-21 12:35:07,103][flwr][DEBUG] - fit_round 72 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.821265
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6683
-[2023-09-21 12:35:08,462][flwr][INFO] - fit progress: (72, 0.950544156300755, {'accuracy': 0.6683}, 33936.92198925698)
-[2023-09-21 12:35:08,462][flwr][DEBUG] - evaluate_round 72: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 12:35:38,635][flwr][DEBUG] - evaluate_round 72 received 10 results and 0 failures
-[2023-09-21 12:35:38,636][flwr][DEBUG] - fit_round 73: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7590144230769231
-(DefaultActor pid=2820544) >> Training accuracy: 0.830329
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6480978260869565
-(DefaultActor pid=2820544) >> Training accuracy: 0.787364
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6224922839506173
-(DefaultActor pid=2820544) >> Training accuracy: 0.864005
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5997121710526315
-(DefaultActor pid=2820544) >> Training accuracy: 0.842105
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6521577380952381
-(DefaultActor pid=2820544) >> Training accuracy: 0.828125
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7702546296296297
-(DefaultActor pid=2820544) >> Training accuracy: 0.855710
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5674342105263158
-(DefaultActor pid=2820544) >> Training accuracy: 0.792763
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6233675373134329
-(DefaultActor pid=2820544) >> Training accuracy: 0.772854
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6480564024390244
-(DefaultActor pid=2820544) >> Training accuracy: 0.826220
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.722457627118644
-[2023-09-21 12:42:41,330][flwr][DEBUG] - fit_round 73 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.849841
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6788
-[2023-09-21 12:42:42,688][flwr][INFO] - fit progress: (73, 0.9230360760094639, {'accuracy': 0.6788}, 34391.147913408)
-[2023-09-21 12:42:42,688][flwr][DEBUG] - evaluate_round 73: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 12:43:13,193][flwr][DEBUG] - evaluate_round 73 received 10 results and 0 failures
-[2023-09-21 12:43:13,195][flwr][DEBUG] - fit_round 74: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6490091463414634
-(DefaultActor pid=2820544) >> Training accuracy: 0.828316
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6414473684210527
-(DefaultActor pid=2820544) >> Training accuracy: 0.828536
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5890899122807017
-(DefaultActor pid=2820544) >> Training accuracy: 0.808388
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7594521604938271
-(DefaultActor pid=2820544) >> Training accuracy: 0.844136
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6587577160493827
-(DefaultActor pid=2820544) >> Training accuracy: 0.872299
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6308302238805971
-(DefaultActor pid=2820544) >> Training accuracy: 0.766091
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7129237288135594
-(DefaultActor pid=2820544) >> Training accuracy: 0.845074
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6655344202898551
-(DefaultActor pid=2820544) >> Training accuracy: 0.786685
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7568108974358975
-(DefaultActor pid=2820544) >> Training accuracy: 0.841346
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6526537698412699
-[2023-09-21 12:50:26,338][flwr][DEBUG] - fit_round 74 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.822049
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6734
-[2023-09-21 12:50:27,922][flwr][INFO] - fit progress: (74, 0.9372135468374807, {'accuracy': 0.6734}, 34856.38190944586)
-[2023-09-21 12:50:27,922][flwr][DEBUG] - evaluate_round 74: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 12:50:58,128][flwr][DEBUG] - evaluate_round 74 received 10 results and 0 failures
-[2023-09-21 12:50:58,129][flwr][DEBUG] - fit_round 75: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5923793859649122
-(DefaultActor pid=2820544) >> Training accuracy: 0.782346
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6598731884057971
-(DefaultActor pid=2820544) >> Training accuracy: 0.788043
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6552854938271605
-(DefaultActor pid=2820544) >> Training accuracy: 0.875386
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7521219135802469
-(DefaultActor pid=2820544) >> Training accuracy: 0.867670
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7057733050847458
-(DefaultActor pid=2820544) >> Training accuracy: 0.839778
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.654265873015873
-(DefaultActor pid=2820544) >> Training accuracy: 0.825521
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6217606707317073
-(DefaultActor pid=2820544) >> Training accuracy: 0.837462
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6173930921052632
-(DefaultActor pid=2820544) >> Training accuracy: 0.844367
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6354944029850746
-(DefaultActor pid=2820544) >> Training accuracy: 0.775886
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7485977564102564
-[2023-09-21 12:58:23,212][flwr][DEBUG] - fit_round 75 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.833133
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.671
-[2023-09-21 12:58:24,904][flwr][INFO] - fit progress: (75, 0.9488034000792823, {'accuracy': 0.671}, 35333.36447527679)
-[2023-09-21 12:58:24,904][flwr][DEBUG] - evaluate_round 75: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 12:58:56,235][flwr][DEBUG] - evaluate_round 75 received 10 results and 0 failures
-[2023-09-21 12:58:56,236][flwr][DEBUG] - fit_round 76: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7629243827160493
-(DefaultActor pid=2820544) >> Training accuracy: 0.867091
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6608382936507936
-(DefaultActor pid=2820544) >> Training accuracy: 0.812624
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7110699152542372
-(DefaultActor pid=2820544) >> Training accuracy: 0.853814
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6149259868421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.839227
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6097560975609756
-(DefaultActor pid=2820544) >> Training accuracy: 0.824314
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6512345679012346
-(DefaultActor pid=2820544) >> Training accuracy: 0.876543
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7600160256410257
-(DefaultActor pid=2820544) >> Training accuracy: 0.833133
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6308302238805971
-(DefaultActor pid=2820544) >> Training accuracy: 0.744170
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5819627192982456
-(DefaultActor pid=2820544) >> Training accuracy: 0.793037
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6621376811594203
-[2023-09-21 13:05:55,298][flwr][DEBUG] - fit_round 76 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.791440
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6748
-[2023-09-21 13:05:56,738][flwr][INFO] - fit progress: (76, 0.933324966758204, {'accuracy': 0.6748}, 35785.19834174914)
-[2023-09-21 13:05:56,738][flwr][DEBUG] - evaluate_round 76: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 13:06:27,449][flwr][DEBUG] - evaluate_round 76 received 10 results and 0 failures
-[2023-09-21 13:06:27,450][flwr][DEBUG] - fit_round 77: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6658950617283951
-(DefaultActor pid=2820544) >> Training accuracy: 0.870949
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6284950657894737
-(DefaultActor pid=2820544) >> Training accuracy: 0.832854
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6608382936507936
-(DefaultActor pid=2820544) >> Training accuracy: 0.818824
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6126143292682927
-(DefaultActor pid=2820544) >> Training accuracy: 0.814405
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7121292372881356
-(DefaultActor pid=2820544) >> Training accuracy: 0.835805
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7698317307692307
-(DefaultActor pid=2820544) >> Training accuracy: 0.827123
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7716049382716049
-(DefaultActor pid=2820544) >> Training accuracy: 0.866705
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6259328358208955
-(DefaultActor pid=2820544) >> Training accuracy: 0.770522
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6625905797101449
-(DefaultActor pid=2820544) >> Training accuracy: 0.778306
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.587171052631579
-[2023-09-21 13:13:30,708][flwr][DEBUG] - fit_round 77 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.790296
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6749
-[2023-09-21 13:13:32,087][flwr][INFO] - fit progress: (77, 0.9317882342841297, {'accuracy': 0.6749}, 36240.546968831215)
-[2023-09-21 13:13:32,087][flwr][DEBUG] - evaluate_round 77: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 13:14:02,493][flwr][DEBUG] - evaluate_round 77 received 10 results and 0 failures
-[2023-09-21 13:14:02,494][flwr][DEBUG] - fit_round 78: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7094809322033898
-(DefaultActor pid=2820544) >> Training accuracy: 0.846133
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6364272388059702
-(DefaultActor pid=2820544) >> Training accuracy: 0.778451
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6459603658536586
-(DefaultActor pid=2820544) >> Training accuracy: 0.829459
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5926535087719298
-(DefaultActor pid=2820544) >> Training accuracy: 0.802906
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6800271739130435
-(DefaultActor pid=2820544) >> Training accuracy: 0.771286
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6427469135802469
-(DefaultActor pid=2820544) >> Training accuracy: 0.870563
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6278782894736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.845806
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7640817901234568
-(DefaultActor pid=2820544) >> Training accuracy: 0.842785
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7700320512820513
-(DefaultActor pid=2820544) >> Training accuracy: 0.826522
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6634424603174603
-[2023-09-21 13:21:21,070][flwr][DEBUG] - fit_round 78 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.824777
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6794
-[2023-09-21 13:21:22,864][flwr][INFO] - fit progress: (78, 0.9255952789379766, {'accuracy': 0.6794}, 36711.32486301195)
-[2023-09-21 13:21:22,865][flwr][DEBUG] - evaluate_round 78: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 13:21:52,936][flwr][DEBUG] - evaluate_round 78 received 10 results and 0 failures
-[2023-09-21 13:21:52,937][flwr][DEBUG] - fit_round 79: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.671875
-(DefaultActor pid=2820544) >> Training accuracy: 0.782609
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6373355263157895
-(DefaultActor pid=2820544) >> Training accuracy: 0.842516
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5874451754385965
-(DefaultActor pid=2820544) >> Training accuracy: 0.799068
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7453703703703703
-(DefaultActor pid=2820544) >> Training accuracy: 0.854167
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6677827380952381
-(DefaultActor pid=2820544) >> Training accuracy: 0.831101
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6394589552238806
-(DefaultActor pid=2820544) >> Training accuracy: 0.774021
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.627858231707317
-(DefaultActor pid=2820544) >> Training accuracy: 0.829840
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6608796296296297
-(DefaultActor pid=2820544) >> Training accuracy: 0.877122
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715572033898305
-(DefaultActor pid=2820544) >> Training accuracy: 0.816737
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7604166666666666
-[2023-09-21 13:29:27,851][flwr][DEBUG] - fit_round 79 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.845954
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6773
-[2023-09-21 13:29:29,239][flwr][INFO] - fit progress: (79, 0.9406886980556451, {'accuracy': 0.6773}, 37197.69971890794)
-[2023-09-21 13:29:29,240][flwr][DEBUG] - evaluate_round 79: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 13:30:01,963][flwr][DEBUG] - evaluate_round 79 received 10 results and 0 failures
-[2023-09-21 13:30:01,963][flwr][DEBUG] - fit_round 80: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6582880434782609
-(DefaultActor pid=2820544) >> Training accuracy: 0.790987
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7554086538461539
-(DefaultActor pid=2820544) >> Training accuracy: 0.840545
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6272865853658537
-(DefaultActor pid=2820544) >> Training accuracy: 0.832127
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7463348765432098
-(DefaultActor pid=2820544) >> Training accuracy: 0.869792
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5918311403508771
-(DefaultActor pid=2820544) >> Training accuracy: 0.797149
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6716269841269841
-(DefaultActor pid=2820544) >> Training accuracy: 0.819320
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6178042763157895
-(DefaultActor pid=2820544) >> Training accuracy: 0.828536
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6520061728395061
-(DefaultActor pid=2820544) >> Training accuracy: 0.873843
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7094809322033898
-(DefaultActor pid=2820544) >> Training accuracy: 0.851165
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6315298507462687
-[2023-09-21 13:37:36,693][flwr][DEBUG] - fit_round 80 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.775886
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6798
-[2023-09-21 13:37:38,599][flwr][INFO] - fit progress: (80, 0.9281163449866322, {'accuracy': 0.6798}, 37687.05938832415)
-[2023-09-21 13:37:38,599][flwr][DEBUG] - evaluate_round 80: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 13:38:11,543][flwr][DEBUG] - evaluate_round 80 received 10 results and 0 failures
-[2023-09-21 13:38:11,545][flwr][DEBUG] - fit_round 81: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6639384920634921
-(DefaultActor pid=2820544) >> Training accuracy: 0.822669
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7741126543209876
-(DefaultActor pid=2820544) >> Training accuracy: 0.869599
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7538060897435898
-(DefaultActor pid=2820544) >> Training accuracy: 0.827524
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6408582089552238
-(DefaultActor pid=2820544) >> Training accuracy: 0.775653
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6677989130434783
-(DefaultActor pid=2820544) >> Training accuracy: 0.786458
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6398533950617284
-(DefaultActor pid=2820544) >> Training accuracy: 0.875193
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6091694078947368
-(DefaultActor pid=2820544) >> Training accuracy: 0.840461
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.725635593220339
-(DefaultActor pid=2820544) >> Training accuracy: 0.841367
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5896381578947368
-(DefaultActor pid=2820544) >> Training accuracy: 0.790844
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6257621951219512
-(DefaultActor pid=2820544) >> Training accuracy: 0.830602
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 13:45:59,871][flwr][DEBUG] - fit_round 81 received 10 results and 0 failures
-test acc: 0.6754
-[2023-09-21 13:46:01,500][flwr][INFO] - fit progress: (81, 0.9390814332916333, {'accuracy': 0.6754}, 38189.96000787895)
-[2023-09-21 13:46:01,500][flwr][DEBUG] - evaluate_round 81: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 13:46:34,062][flwr][DEBUG] - evaluate_round 81 received 10 results and 0 failures
-[2023-09-21 13:46:34,063][flwr][DEBUG] - fit_round 82: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7604166666666666
-(DefaultActor pid=2820544) >> Training accuracy: 0.873843
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6707427536231884
-(DefaultActor pid=2820544) >> Training accuracy: 0.784194
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6614583333333334
-(DefaultActor pid=2820544) >> Training accuracy: 0.875579
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6145198170731707
-(DefaultActor pid=2820544) >> Training accuracy: 0.828125
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6392257462686567
-(DefaultActor pid=2820544) >> Training accuracy: 0.777285
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7025953389830508
-(DefaultActor pid=2820544) >> Training accuracy: 0.838718
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6317845394736842
-(DefaultActor pid=2820544) >> Training accuracy: 0.831003
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6140350877192983
-(DefaultActor pid=2820544) >> Training accuracy: 0.782621
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6555059523809523
-(DefaultActor pid=2820544) >> Training accuracy: 0.832093
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7520032051282052
-[2023-09-21 13:55:28,943][flwr][DEBUG] - fit_round 82 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.827123
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6798
-[2023-09-21 13:55:31,512][flwr][INFO] - fit progress: (82, 0.928261538473562, {'accuracy': 0.6798}, 38759.97228321992)
-[2023-09-21 13:55:31,512][flwr][DEBUG] - evaluate_round 82: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 13:56:03,830][flwr][DEBUG] - evaluate_round 82 received 10 results and 0 failures
-[2023-09-21 13:56:03,831][flwr][DEBUG] - fit_round 83: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6878720238095238
-(DefaultActor pid=2820544) >> Training accuracy: 0.817832
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6630434782608695
-(DefaultActor pid=2820544) >> Training accuracy: 0.790534
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6411966463414634
-(DefaultActor pid=2820544) >> Training accuracy: 0.818026
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6531635802469136
-(DefaultActor pid=2820544) >> Training accuracy: 0.869406
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7163665254237288
-(DefaultActor pid=2820544) >> Training accuracy: 0.845604
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5693530701754386
-(DefaultActor pid=2820544) >> Training accuracy: 0.796875
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7780448717948718
-(DefaultActor pid=2820544) >> Training accuracy: 0.813902
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6350740131578947
-(DefaultActor pid=2820544) >> Training accuracy: 0.829564
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6494869402985075
-(DefaultActor pid=2820544) >> Training accuracy: 0.782183
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7413194444444444
-(DefaultActor pid=2820544) >> Training accuracy: 0.869599
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 14:03:58,129][flwr][DEBUG] - fit_round 83 received 10 results and 0 failures
-test acc: 0.6803
-[2023-09-21 14:03:59,983][flwr][INFO] - fit progress: (83, 0.9337312611528098, {'accuracy': 0.6803}, 39268.443283197936)
-[2023-09-21 14:03:59,984][flwr][DEBUG] - evaluate_round 83: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 14:04:31,937][flwr][DEBUG] - evaluate_round 83 received 10 results and 0 failures
-[2023-09-21 14:04:31,938][flwr][DEBUG] - fit_round 84: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5984100877192983
-(DefaultActor pid=2820544) >> Training accuracy: 0.796327
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.644483024691358
-(DefaultActor pid=2820544) >> Training accuracy: 0.866705
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7198093220338984
-(DefaultActor pid=2820544) >> Training accuracy: 0.845869
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6235608552631579
-(DefaultActor pid=2820544) >> Training accuracy: 0.843544
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7654320987654321
-(DefaultActor pid=2820544) >> Training accuracy: 0.874035
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7598157051282052
-(DefaultActor pid=2820544) >> Training accuracy: 0.833333
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6282393292682927
-(DefaultActor pid=2820544) >> Training accuracy: 0.826220
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6622023809523809
-(DefaultActor pid=2820544) >> Training accuracy: 0.824157
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6464552238805971
-(DefaultActor pid=2820544) >> Training accuracy: 0.780317
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6524003623188406
-[2023-09-21 14:12:23,958][flwr][DEBUG] - fit_round 84 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.787817
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6744
-[2023-09-21 14:12:25,817][flwr][INFO] - fit progress: (84, 0.9405634570807314, {'accuracy': 0.6744}, 39774.27761795977)
-[2023-09-21 14:12:25,818][flwr][DEBUG] - evaluate_round 84: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 14:12:57,696][flwr][DEBUG] - evaluate_round 84 received 10 results and 0 failures
-[2023-09-21 14:12:57,697][flwr][DEBUG] - fit_round 85: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7559799382716049
-(DefaultActor pid=2820544) >> Training accuracy: 0.869599
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6709692028985508
-(DefaultActor pid=2820544) >> Training accuracy: 0.764040
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6394589552238806
-(DefaultActor pid=2820544) >> Training accuracy: 0.773787
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6179496951219512
-(DefaultActor pid=2820544) >> Training accuracy: 0.837652
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6537422839506173
-(DefaultActor pid=2820544) >> Training accuracy: 0.872106
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7580128205128205
-(DefaultActor pid=2820544) >> Training accuracy: 0.833934
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6208881578947368
-(DefaultActor pid=2820544) >> Training accuracy: 0.837788
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6657986111111112
-(DefaultActor pid=2820544) >> Training accuracy: 0.826017
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5904605263157895
-(DefaultActor pid=2820544) >> Training accuracy: 0.808114
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7123940677966102
-(DefaultActor pid=2820544) >> Training accuracy: 0.856992
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 14:20:52,387][flwr][DEBUG] - fit_round 85 received 10 results and 0 failures
-test acc: 0.6786
-[2023-09-21 14:20:54,448][flwr][INFO] - fit progress: (85, 0.9334670937480256, {'accuracy': 0.6786}, 40282.90827135788)
-[2023-09-21 14:20:54,449][flwr][DEBUG] - evaluate_round 85: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 14:21:26,613][flwr][DEBUG] - evaluate_round 85 received 10 results and 0 failures
-[2023-09-21 14:21:26,614][flwr][DEBUG] - fit_round 86: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6194740853658537
-(DefaultActor pid=2820544) >> Training accuracy: 0.827363
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.666213768115942
-(DefaultActor pid=2820544) >> Training accuracy: 0.788496
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7566105769230769
-(DefaultActor pid=2820544) >> Training accuracy: 0.839343
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.622327302631579
-(DefaultActor pid=2820544) >> Training accuracy: 0.849095
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6473765432098766
-(DefaultActor pid=2820544) >> Training accuracy: 0.887539
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6455223880597015
-(DefaultActor pid=2820544) >> Training accuracy: 0.780317
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7494212962962963
-(DefaultActor pid=2820544) >> Training accuracy: 0.869792
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6809275793650794
-(DefaultActor pid=2820544) >> Training accuracy: 0.815972
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7251059322033898
-(DefaultActor pid=2820544) >> Training accuracy: 0.849841
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5825109649122807
-(DefaultActor pid=2820544) >> Training accuracy: 0.805921
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 14:29:56,854][flwr][DEBUG] - fit_round 86 received 10 results and 0 failures
-test acc: 0.6738
-[2023-09-21 14:29:58,704][flwr][INFO] - fit progress: (86, 0.9398519292045325, {'accuracy': 0.6738}, 40827.163921646774)
-[2023-09-21 14:29:58,704][flwr][DEBUG] - evaluate_round 86: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 14:30:32,641][flwr][DEBUG] - evaluate_round 86 received 10 results and 0 failures
-[2023-09-21 14:30:32,642][flwr][DEBUG] - fit_round 87: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7665895061728395
-(DefaultActor pid=2820544) >> Training accuracy: 0.869792
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7646233974358975
-(DefaultActor pid=2820544) >> Training accuracy: 0.825921
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6347179878048781
-(DefaultActor pid=2820544) >> Training accuracy: 0.833270
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6586061507936508
-(DefaultActor pid=2820544) >> Training accuracy: 0.813244
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6603260869565217
-(DefaultActor pid=2820544) >> Training accuracy: 0.785326
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6171875
-(DefaultActor pid=2820544) >> Training accuracy: 0.841077
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7134533898305084
-(DefaultActor pid=2820544) >> Training accuracy: 0.856197
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6343283582089553
-(DefaultActor pid=2820544) >> Training accuracy: 0.771922
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6466049382716049
-(DefaultActor pid=2820544) >> Training accuracy: 0.858603
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5679824561403509
-[2023-09-21 14:38:26,670][flwr][DEBUG] - fit_round 87 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.805921
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6793
-[2023-09-21 14:38:29,223][flwr][INFO] - fit progress: (87, 0.9277755803764819, {'accuracy': 0.6793}, 41337.683894667774)
-[2023-09-21 14:38:29,224][flwr][DEBUG] - evaluate_round 87: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 14:39:02,040][flwr][DEBUG] - evaluate_round 87 received 10 results and 0 failures
-[2023-09-21 14:39:02,041][flwr][DEBUG] - fit_round 88: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6424906716417911
-(DefaultActor pid=2820544) >> Training accuracy: 0.751866
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7192796610169492
-(DefaultActor pid=2820544) >> Training accuracy: 0.850371
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.75
-(DefaultActor pid=2820544) >> Training accuracy: 0.875965
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7528044871794872
-(DefaultActor pid=2820544) >> Training accuracy: 0.846554
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6291118421052632
-(DefaultActor pid=2820544) >> Training accuracy: 0.850740
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6006030701754386
-(DefaultActor pid=2820544) >> Training accuracy: 0.803728
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6368140243902439
-(DefaultActor pid=2820544) >> Training accuracy: 0.807736
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6608382936507936
-(DefaultActor pid=2820544) >> Training accuracy: 0.826017
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6730072463768116
-(DefaultActor pid=2820544) >> Training accuracy: 0.788270
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6616512345679012
-[2023-09-21 14:47:18,159][flwr][DEBUG] - fit_round 88 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.875579
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6815
-[2023-09-21 14:47:19,819][flwr][INFO] - fit progress: (88, 0.9132904114243322, {'accuracy': 0.6815}, 41868.27986165322)
-[2023-09-21 14:47:19,820][flwr][DEBUG] - evaluate_round 88: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 14:47:53,236][flwr][DEBUG] - evaluate_round 88 received 10 results and 0 failures
-[2023-09-21 14:47:53,237][flwr][DEBUG] - fit_round 89: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6466884328358209
-(DefaultActor pid=2820544) >> Training accuracy: 0.785215
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7700617283950617
-(DefaultActor pid=2820544) >> Training accuracy: 0.865741
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6049890350877193
-(DefaultActor pid=2820544) >> Training accuracy: 0.804276
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7610176282051282
-(DefaultActor pid=2820544) >> Training accuracy: 0.826522
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6408305921052632
-(DefaultActor pid=2820544) >> Training accuracy: 0.840255
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6770833333333334
-(DefaultActor pid=2820544) >> Training accuracy: 0.777174
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7182203389830508
-(DefaultActor pid=2820544) >> Training accuracy: 0.846398
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6759672619047619
-(DefaultActor pid=2820544) >> Training accuracy: 0.809028
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6387195121951219
-(DefaultActor pid=2820544) >> Training accuracy: 0.834604
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6585648148148148
-[2023-09-21 14:56:06,676][flwr][DEBUG] - fit_round 89 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.884645
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6773
-[2023-09-21 14:56:08,958][flwr][INFO] - fit progress: (89, 0.9310566076455405, {'accuracy': 0.6773}, 42397.418632068206)
-[2023-09-21 14:56:08,959][flwr][DEBUG] - evaluate_round 89: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 14:56:53,894][flwr][DEBUG] - evaluate_round 89 received 10 results and 0 failures
-[2023-09-21 14:56:53,894][flwr][DEBUG] - fit_round 90: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6550925925925926
-(DefaultActor pid=2820544) >> Training accuracy: 0.874807
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6688988095238095
-(DefaultActor pid=2820544) >> Training accuracy: 0.817088
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7648533950617284
-(DefaultActor pid=2820544) >> Training accuracy: 0.865934
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7437900641025641
-(DefaultActor pid=2820544) >> Training accuracy: 0.847356
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6107456140350878
-(DefaultActor pid=2820544) >> Training accuracy: 0.804825
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6621376811594203
-(DefaultActor pid=2820544) >> Training accuracy: 0.784873
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.637766768292683
-(DefaultActor pid=2820544) >> Training accuracy: 0.839367
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6206825657894737
-(DefaultActor pid=2820544) >> Training accuracy: 0.841900
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6988877118644068
-(DefaultActor pid=2820544) >> Training accuracy: 0.846663
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6450559701492538
-[2023-09-21 15:04:59,853][flwr][DEBUG] - fit_round 90 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.781250
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6795
-[2023-09-21 15:05:02,323][flwr][INFO] - fit progress: (90, 0.928617595769346, {'accuracy': 0.6795}, 42930.78378260601)
-[2023-09-21 15:05:02,324][flwr][DEBUG] - evaluate_round 90: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 15:05:36,104][flwr][DEBUG] - evaluate_round 90 received 10 results and 0 failures
-[2023-09-21 15:05:36,104][flwr][DEBUG] - fit_round 91: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7139830508474576
-(DefaultActor pid=2820544) >> Training accuracy: 0.842956
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6677989130434783
-(DefaultActor pid=2820544) >> Training accuracy: 0.790082
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5973135964912281
-(DefaultActor pid=2820544) >> Training accuracy: 0.805647
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6392257462686567
-(DefaultActor pid=2820544) >> Training accuracy: 0.773321
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6266447368421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.841283
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6588541666666666
-(DefaultActor pid=2820544) >> Training accuracy: 0.807044
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6402439024390244
-(DefaultActor pid=2820544) >> Training accuracy: 0.820122
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7514022435897436
-(DefaultActor pid=2820544) >> Training accuracy: 0.824920
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7667824074074074
-(DefaultActor pid=2820544) >> Training accuracy: 0.869792
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6489197530864198
-[2023-09-21 15:13:27,923][flwr][DEBUG] - fit_round 91 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.879051
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6796
-[2023-09-21 15:13:29,826][flwr][INFO] - fit progress: (91, 0.9291766289704905, {'accuracy': 0.6796}, 43438.28658555681)
-[2023-09-21 15:13:29,827][flwr][DEBUG] - evaluate_round 91: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 15:14:04,413][flwr][DEBUG] - evaluate_round 91 received 10 results and 0 failures
-[2023-09-21 15:14:04,414][flwr][DEBUG] - fit_round 92: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6423399390243902
-(DefaultActor pid=2820544) >> Training accuracy: 0.818026
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6765873015873016
-(DefaultActor pid=2820544) >> Training accuracy: 0.829489
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6361882716049383
-(DefaultActor pid=2820544) >> Training accuracy: 0.880787
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.615953947368421
-(DefaultActor pid=2820544) >> Training accuracy: 0.837171
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7542438271604939
-(DefaultActor pid=2820544) >> Training accuracy: 0.870370
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6134868421052632
-(DefaultActor pid=2820544) >> Training accuracy: 0.789474
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7134533898305084
-(DefaultActor pid=2820544) >> Training accuracy: 0.852754
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6441231343283582
-(DefaultActor pid=2820544) >> Training accuracy: 0.785215
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7411858974358975
-(DefaultActor pid=2820544) >> Training accuracy: 0.840144
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6487771739130435
-[2023-09-21 15:22:00,247][flwr][DEBUG] - fit_round 92 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.795516
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6799
-[2023-09-21 15:22:01,831][flwr][INFO] - fit progress: (92, 0.929426233894147, {'accuracy': 0.6799}, 43950.29138105223)
-[2023-09-21 15:22:01,832][flwr][DEBUG] - evaluate_round 92: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 15:22:37,183][flwr][DEBUG] - evaluate_round 92 received 10 results and 0 failures
-[2023-09-21 15:22:37,183][flwr][DEBUG] - fit_round 93: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715042372881356
-(DefaultActor pid=2820544) >> Training accuracy: 0.865731
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6527518656716418
-(DefaultActor pid=2820544) >> Training accuracy: 0.772854
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6723278985507246
-(DefaultActor pid=2820544) >> Training accuracy: 0.781476
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7594521604938271
-(DefaultActor pid=2820544) >> Training accuracy: 0.872106
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6496913580246914
-(DefaultActor pid=2820544) >> Training accuracy: 0.862269
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6463414634146342
-(DefaultActor pid=2820544) >> Training accuracy: 0.833270
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6588541666666666
-(DefaultActor pid=2820544) >> Training accuracy: 0.828125
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6346628289473685
-(DefaultActor pid=2820544) >> Training accuracy: 0.838816
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7614182692307693
-(DefaultActor pid=2820544) >> Training accuracy: 0.844151
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5978618421052632
-(DefaultActor pid=2820544) >> Training accuracy: 0.808662
-(DefaultActor pid=2820544)  ** Training complete **
-[2023-09-21 15:42:01,343][flwr][DEBUG] - fit_round 93 received 10 results and 0 failures
-test acc: 0.6791
-[2023-09-21 15:43:00,516][flwr][INFO] - fit progress: (93, 0.9336311500102948, {'accuracy': 0.6791}, 45208.97631138982)
-[2023-09-21 15:43:00,518][flwr][DEBUG] - evaluate_round 93: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 15:43:44,788][flwr][DEBUG] - evaluate_round 93 received 10 results and 0 failures
-[2023-09-21 15:43:44,789][flwr][DEBUG] - fit_round 94: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6697668650793651
-(DefaultActor pid=2820544) >> Training accuracy: 0.812252
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6137609649122807
-(DefaultActor pid=2820544) >> Training accuracy: 0.802632
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6413246268656716
-(DefaultActor pid=2820544) >> Training accuracy: 0.764459
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6331935975609756
-(DefaultActor pid=2820544) >> Training accuracy: 0.836890
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7754629629629629
-(DefaultActor pid=2820544) >> Training accuracy: 0.857832
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.749198717948718
-(DefaultActor pid=2820544) >> Training accuracy: 0.845353
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.713718220338983
-(DefaultActor pid=2820544) >> Training accuracy: 0.857256
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6377314814814815
-(DefaultActor pid=2820544) >> Training accuracy: 0.843557
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.609375
-(DefaultActor pid=2820544) >> Training accuracy: 0.841077
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6600996376811594
-[2023-09-21 15:51:49,403][flwr][DEBUG] - fit_round 94 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.780344
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6835
-[2023-09-21 15:51:51,810][flwr][INFO] - fit progress: (94, 0.9300604007495478, {'accuracy': 0.6835}, 45740.27047397988)
-[2023-09-21 15:51:51,811][flwr][DEBUG] - evaluate_round 94: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 15:52:24,781][flwr][DEBUG] - evaluate_round 94 received 10 results and 0 failures
-[2023-09-21 15:52:24,782][flwr][DEBUG] - fit_round 95: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7243114406779662
-(DefaultActor pid=2820544) >> Training accuracy: 0.846398
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6322294776119403
-(DefaultActor pid=2820544) >> Training accuracy: 0.766791
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6437114197530864
-(DefaultActor pid=2820544) >> Training accuracy: 0.875193
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.631859756097561
-(DefaultActor pid=2820544) >> Training accuracy: 0.832698
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7616185897435898
-(DefaultActor pid=2820544) >> Training accuracy: 0.816306
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6706349206349206
-(DefaultActor pid=2820544) >> Training accuracy: 0.825893
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6194490131578947
-(DefaultActor pid=2820544) >> Training accuracy: 0.831826
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5860745614035088
-(DefaultActor pid=2820544) >> Training accuracy: 0.796875
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7513503086419753
-(DefaultActor pid=2820544) >> Training accuracy: 0.859182
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6655344202898551
-[2023-09-21 16:00:16,329][flwr][DEBUG] - fit_round 95 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.792120
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6783
-[2023-09-21 16:00:18,806][flwr][INFO] - fit progress: (95, 0.9361367561756232, {'accuracy': 0.6783}, 46247.26676111622)
-[2023-09-21 16:00:18,807][flwr][DEBUG] - evaluate_round 95: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 16:00:51,717][flwr][DEBUG] - evaluate_round 95 received 10 results and 0 failures
-[2023-09-21 16:00:51,717][flwr][DEBUG] - fit_round 96: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6025904605263158
-(DefaultActor pid=2820544) >> Training accuracy: 0.853413
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.661911231884058
-(DefaultActor pid=2820544) >> Training accuracy: 0.779665
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6110197368421053
-(DefaultActor pid=2820544) >> Training accuracy: 0.792215
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6529850746268657
-(DefaultActor pid=2820544) >> Training accuracy: 0.776119
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6767113095238095
-(DefaultActor pid=2820544) >> Training accuracy: 0.838294
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7554012345679012
-(DefaultActor pid=2820544) >> Training accuracy: 0.860532
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6326219512195121
-(DefaultActor pid=2820544) >> Training accuracy: 0.821265
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7572115384615384
-(DefaultActor pid=2820544) >> Training accuracy: 0.840345
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7243114406779662
-(DefaultActor pid=2820544) >> Training accuracy: 0.843220
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6346450617283951
-[2023-09-21 16:09:05,277][flwr][DEBUG] - fit_round 96 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.867863
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6745
-[2023-09-21 16:09:08,397][flwr][INFO] - fit progress: (96, 0.9574828951503522, {'accuracy': 0.6745}, 46776.85781038599)
-[2023-09-21 16:09:08,398][flwr][DEBUG] - evaluate_round 96: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 16:09:43,234][flwr][DEBUG] - evaluate_round 96 received 10 results and 0 failures
-[2023-09-21 16:09:43,235][flwr][DEBUG] - fit_round 97: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6653079710144928
-(DefaultActor pid=2820544) >> Training accuracy: 0.781024
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6431327160493827
-(DefaultActor pid=2820544) >> Training accuracy: 0.865162
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6129954268292683
-(DefaultActor pid=2820544) >> Training accuracy: 0.827172
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6555059523809523
-(DefaultActor pid=2820544) >> Training accuracy: 0.828621
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.602796052631579
-(DefaultActor pid=2820544) >> Training accuracy: 0.844778
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7644675925925926
-(DefaultActor pid=2820544) >> Training accuracy: 0.870177
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6099232456140351
-(DefaultActor pid=2820544) >> Training accuracy: 0.814145
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7259004237288136
-(DefaultActor pid=2820544) >> Training accuracy: 0.859110
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.758613782051282
-(DefaultActor pid=2820544) >> Training accuracy: 0.850962
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6436567164179104
-[2023-09-21 16:18:28,589][flwr][DEBUG] - fit_round 97 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.777519
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6807
-[2023-09-21 16:18:31,190][flwr][INFO] - fit progress: (97, 0.9375480963780095, {'accuracy': 0.6807}, 47339.65043702116)
-[2023-09-21 16:18:31,191][flwr][DEBUG] - evaluate_round 97: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 16:19:04,799][flwr][DEBUG] - evaluate_round 97 received 10 results and 0 failures
-[2023-09-21 16:19:04,799][flwr][DEBUG] - fit_round 98: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6371951219512195
-(DefaultActor pid=2820544) >> Training accuracy: 0.843369
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7644230769230769
-(DefaultActor pid=2820544) >> Training accuracy: 0.813902
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6387593283582089
-(DefaultActor pid=2820544) >> Training accuracy: 0.771222
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7654320987654321
-(DefaultActor pid=2820544) >> Training accuracy: 0.879823
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6750452898550725
-(DefaultActor pid=2820544) >> Training accuracy: 0.782382
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6400462962962963
-(DefaultActor pid=2820544) >> Training accuracy: 0.874421
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6016995614035088
-(DefaultActor pid=2820544) >> Training accuracy: 0.799068
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6085526315789473
-(DefaultActor pid=2820544) >> Training accuracy: 0.835321
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.715572033898305
-(DefaultActor pid=2820544) >> Training accuracy: 0.862818
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6638144841269841
-[2023-09-21 16:26:57,735][flwr][DEBUG] - fit_round 98 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.828993
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6782
-[2023-09-21 16:27:00,900][flwr][INFO] - fit progress: (98, 0.9493495685795245, {'accuracy': 0.6782}, 47849.360782171134)
-[2023-09-21 16:27:00,901][flwr][DEBUG] - evaluate_round 98: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 16:27:35,216][flwr][DEBUG] - evaluate_round 98 received 10 results and 0 failures
-[2023-09-21 16:27:35,216][flwr][DEBUG] - fit_round 99: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7631172839506173
-(DefaultActor pid=2820544) >> Training accuracy: 0.876736
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7229872881355932
-(DefaultActor pid=2820544) >> Training accuracy: 0.858581
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6422574626865671
-(DefaultActor pid=2820544) >> Training accuracy: 0.776586
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6684027777777778
-(DefaultActor pid=2820544) >> Training accuracy: 0.832217
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5932017543859649
-(DefaultActor pid=2820544) >> Training accuracy: 0.816612
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6282793209876543
-(DefaultActor pid=2820544) >> Training accuracy: 0.885224
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6689311594202898
-(DefaultActor pid=2820544) >> Training accuracy: 0.781250
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7694310897435898
-(DefaultActor pid=2820544) >> Training accuracy: 0.846154
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6009457236842105
-(DefaultActor pid=2820544) >> Training accuracy: 0.840872
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6400533536585366
-[2023-09-21 16:35:31,701][flwr][DEBUG] - fit_round 99 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.824123
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6787
-[2023-09-21 16:35:34,030][flwr][INFO] - fit progress: (99, 0.9315846239606412, {'accuracy': 0.6787}, 48362.49043702893)
-[2023-09-21 16:35:34,031][flwr][DEBUG] - evaluate_round 99: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 16:36:07,745][flwr][DEBUG] - evaluate_round 99 received 10 results and 0 failures
-[2023-09-21 16:36:07,746][flwr][DEBUG] - fit_round 100: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6442901234567902
-(DefaultActor pid=2820544) >> Training accuracy: 0.874035
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 67
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6350279850746269
-(DefaultActor pid=2820544) >> Training accuracy: 0.766558
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 78
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7646233974358975
-(DefaultActor pid=2820544) >> Training accuracy: 0.838742
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 69
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6716485507246377
-(DefaultActor pid=2820544) >> Training accuracy: 0.788270
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 59
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7195444915254238
-(DefaultActor pid=2820544) >> Training accuracy: 0.862288
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 82
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6465320121951219
-(DefaultActor pid=2820544) >> Training accuracy: 0.831936
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 81
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.7644675925925926
-(DefaultActor pid=2820544) >> Training accuracy: 0.864583
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 57
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.5964912280701754
-(DefaultActor pid=2820544) >> Training accuracy: 0.810033
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 126
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.6626984126984127
-(DefaultActor pid=2820544) >> Training accuracy: 0.831225
-(DefaultActor pid=2820544)  ** Training complete **
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) n_training: 76
-(DefaultActor pid=2820544) >> Pre-Training Training accuracy: 0.614514802631579
-[2023-09-21 16:44:47,430][flwr][DEBUG] - fit_round 100 received 10 results and 0 failures
-(DefaultActor pid=2820544) >> Training accuracy: 0.851768
-(DefaultActor pid=2820544)  ** Training complete **
-test acc: 0.6852
-[2023-09-21 16:44:49,813][flwr][INFO] - fit progress: (100, 0.9221907237086433, {'accuracy': 0.6852}, 48918.27312586922)
-[2023-09-21 16:44:49,813][flwr][DEBUG] - evaluate_round 100: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 16:45:23,548][flwr][DEBUG] - evaluate_round 100 received 10 results and 0 failures
-(DefaultActor pid=2820544) device: cuda:0
-[2023-09-21 16:45:23,549][flwr][INFO] - FL finished in 48952.00935825519
-[2023-09-21 16:45:23,566][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0)]
-[2023-09-21 16:45:23,566][flwr][INFO] - app_fit: metrics_distributed_fit {}
-[2023-09-21 16:45:23,566][flwr][INFO] - app_fit: metrics_distributed {}
-[2023-09-21 16:45:23,567][flwr][INFO] - app_fit: losses_centralized [(0, 2.304941604693477), (1, 2.2892096804353756), (2, 1.9268630602108403), (3, 1.6586600408767358), (4, 1.5162620251171124), (5, 1.3799412298126343), (6, 1.3085029963106394), (7, 1.270832797208914), (8, 1.2019853355785528), (9, 1.1783232848865155), (10, 1.1620713434280299), (11, 1.1295021063984392), (12, 1.1191708752141594), (13, 1.106805816054725), (14, 1.0845167545464853), (15, 1.0962572912819468), (16, 1.0545658187363476), (17, 1.061014118857277), (18, 1.083283578435453), (19, 1.024500151411794), (20, 1.0367834657525863), (21, 1.0136565257566044), (22, 1.0296277775170324), (23, 1.0151812633196005), (24, 1.0049766376376532), (25, 1.000602862705438), (26, 1.0190478839432469), (27, 0.9883538024684492), (28, 0.9920400703867404), (29, 0.9918740025153175), (30, 0.999864658418174), (31, 0.9666112412850316), (32, 0.9633961186622279), (33, 1.003742164411484), (34, 0.9889103397012899), (35, 0.9822426000342201), (36, 0.962386382559237), (37, 0.9726330711247441), (38, 0.965197785498616), (39, 0.9574256779286808), (40, 0.9920141804522981), (41, 0.9609055894251448), (42, 0.9998491283613272), (43, 0.970430683404112), (44, 0.9538876035342962), (45, 0.9652769756964601), (46, 0.9712253968936567), (47, 0.9433850042355327), (48, 0.9481769482167764), (49, 0.9416967020057642), (50, 0.9449833774338134), (51, 0.977153589931159), (52, 0.9636962722284725), (53, 0.9485500901461409), (54, 0.9527737906756112), (55, 0.9540609658335726), (56, 0.9453530991420197), (57, 0.9411518906061642), (58, 0.9347897329079077), (59, 0.9279640743526788), (60, 0.950613231990284), (61, 0.9391038782489948), (62, 0.9639180962460491), (63, 0.9449828718416988), (64, 0.945188293537012), (65, 0.9480573036038457), (66, 0.942290931178358), (67, 0.92270217916836), (68, 0.9357810918325052), (69, 0.9341149679578531), (70, 0.9335093384924026), (71, 0.9386814056684415), (72, 0.950544156300755), (73, 0.9230360760094639), (74, 0.9372135468374807), (75, 0.9488034000792823), (76, 0.933324966758204), (77, 0.9317882342841297), (78, 0.9255952789379766), (79, 0.9406886980556451), (80, 0.9281163449866322), (81, 0.9390814332916333), (82, 0.928261538473562), (83, 0.9337312611528098), (84, 0.9405634570807314), (85, 0.9334670937480256), (86, 0.9398519292045325), (87, 0.9277755803764819), (88, 0.9132904114243322), (89, 0.9310566076455405), (90, 0.928617595769346), (91, 0.9291766289704905), (92, 0.929426233894147), (93, 0.9336311500102948), (94, 0.9300604007495478), (95, 0.9361367561756232), (96, 0.9574828951503522), (97, 0.9375480963780095), (98, 0.9493495685795245), (99, 0.9315846239606412), (100, 0.9221907237086433)]
-[2023-09-21 16:45:23,567][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.1), (1, 0.1148), (2, 0.2752), (3, 0.3791), (4, 0.4339), (5, 0.4926), (6, 0.5244), (7, 0.5421), (8, 0.5669), (9, 0.5791), (10, 0.586), (11, 0.5979), (12, 0.6048), (13, 0.6034), (14, 0.6153), (15, 0.6155), (16, 0.6256), (17, 0.6281), (18, 0.6203), (19, 0.6361), (20, 0.6331), (21, 0.6446), (22, 0.6354), (23, 0.6427), (24, 0.6476), (25, 0.6517), (26, 0.6437), (27, 0.6539), (28, 0.6491), (29, 0.6535), (30, 0.6504), (31, 0.6604), (32, 0.6638), (33, 0.6482), (34, 0.6594), (35, 0.6561), (36, 0.6629), (37, 0.6614), (38, 0.6656), (39, 0.666), (40, 0.6597), (41, 0.6662), (42, 0.6485), (43, 0.6638), (44, 0.6673), (45, 0.6649), (46, 0.6647), (47, 0.6737), (48, 0.6741), (49, 0.6726), (50, 0.672), (51, 0.6596), (52, 0.6647), (53, 0.6687), (54, 0.6674), (55, 0.6701), (56, 0.6709), (57, 0.6732), (58, 0.6721), (59, 0.6748), (60, 0.6675), (61, 0.6723), (62, 0.6629), (63, 0.6689), (64, 0.6708), (65, 0.668), (66, 0.6687), (67, 0.6764), (68, 0.6705), (69, 0.6761), (70, 0.6728), (71, 0.6729), (72, 0.6683), (73, 0.6788), (74, 0.6734), (75, 0.671), (76, 0.6748), (77, 0.6749), (78, 0.6794), (79, 0.6773), (80, 0.6798), (81, 0.6754), (82, 0.6798), (83, 0.6803), (84, 0.6744), (85, 0.6786), (86, 0.6738), (87, 0.6793), (88, 0.6815), (89, 0.6773), (90, 0.6795), (91, 0.6796), (92, 0.6799), (93, 0.6791), (94, 0.6835), (95, 0.6783), (96, 0.6745), (97, 0.6807), (98, 0.6782), (99, 0.6787), (100, 0.6852)]}
-................
-History (loss, distributed):
-	round 1: 0.0
-	round 2: 0.0
-	round 3: 0.0
-	round 4: 0.0
-	round 5: 0.0
-	round 6: 0.0
-	round 7: 0.0
-	round 8: 0.0
-	round 9: 0.0
-	round 10: 0.0
-	round 11: 0.0
-	round 12: 0.0
-	round 13: 0.0
-	round 14: 0.0
-	round 15: 0.0
-	round 16: 0.0
-	round 17: 0.0
-	round 18: 0.0
-	round 19: 0.0
-	round 20: 0.0
-	round 21: 0.0
-	round 22: 0.0
-	round 23: 0.0
-	round 24: 0.0
-	round 25: 0.0
-	round 26: 0.0
-	round 27: 0.0
-	round 28: 0.0
-	round 29: 0.0
-	round 30: 0.0
-	round 31: 0.0
-	round 32: 0.0
-	round 33: 0.0
-	round 34: 0.0
-	round 35: 0.0
-	round 36: 0.0
-	round 37: 0.0
-	round 38: 0.0
-	round 39: 0.0
-	round 40: 0.0
-	round 41: 0.0
-	round 42: 0.0
-	round 43: 0.0
-	round 44: 0.0
-	round 45: 0.0
-	round 46: 0.0
-	round 47: 0.0
-	round 48: 0.0
-	round 49: 0.0
-	round 50: 0.0
-	round 51: 0.0
-	round 52: 0.0
-	round 53: 0.0
-	round 54: 0.0
-	round 55: 0.0
-	round 56: 0.0
-	round 57: 0.0
-	round 58: 0.0
-	round 59: 0.0
-	round 60: 0.0
-	round 61: 0.0
-	round 62: 0.0
-	round 63: 0.0
-	round 64: 0.0
-	round 65: 0.0
-	round 66: 0.0
-	round 67: 0.0
-	round 68: 0.0
-	round 69: 0.0
-	round 70: 0.0
-	round 71: 0.0
-	round 72: 0.0
-	round 73: 0.0
-	round 74: 0.0
-	round 75: 0.0
-	round 76: 0.0
-	round 77: 0.0
-	round 78: 0.0
-	round 79: 0.0
-	round 80: 0.0
-	round 81: 0.0
-	round 82: 0.0
-	round 83: 0.0
-	round 84: 0.0
-	round 85: 0.0
-	round 86: 0.0
-	round 87: 0.0
-	round 88: 0.0
-	round 89: 0.0
-	round 90: 0.0
-	round 91: 0.0
-	round 92: 0.0
-	round 93: 0.0
-	round 94: 0.0
-	round 95: 0.0
-	round 96: 0.0
-	round 97: 0.0
-	round 98: 0.0
-	round 99: 0.0
-	round 100: 0.0
-History (loss, centralized):
-	round 0: 2.304941604693477
-	round 1: 2.2892096804353756
-	round 2: 1.9268630602108403
-	round 3: 1.6586600408767358
-	round 4: 1.5162620251171124
-	round 5: 1.3799412298126343
-	round 6: 1.3085029963106394
-	round 7: 1.270832797208914
-	round 8: 1.2019853355785528
-	round 9: 1.1783232848865155
-	round 10: 1.1620713434280299
-	round 11: 1.1295021063984392
-	round 12: 1.1191708752141594
-	round 13: 1.106805816054725
-	round 14: 1.0845167545464853
-	round 15: 1.0962572912819468
-	round 16: 1.0545658187363476
-	round 17: 1.061014118857277
-	round 18: 1.083283578435453
-	round 19: 1.024500151411794
-	round 20: 1.0367834657525863
-	round 21: 1.0136565257566044
-	round 22: 1.0296277775170324
-	round 23: 1.0151812633196005
-	round 24: 1.0049766376376532
-	round 25: 1.000602862705438
-	round 26: 1.0190478839432469
-	round 27: 0.9883538024684492
-	round 28: 0.9920400703867404
-	round 29: 0.9918740025153175
-	round 30: 0.999864658418174
-	round 31: 0.9666112412850316
-	round 32: 0.9633961186622279
-	round 33: 1.003742164411484
-	round 34: 0.9889103397012899
-	round 35: 0.9822426000342201
-	round 36: 0.962386382559237
-	round 37: 0.9726330711247441
-	round 38: 0.965197785498616
-	round 39: 0.9574256779286808
-	round 40: 0.9920141804522981
-	round 41: 0.9609055894251448
-	round 42: 0.9998491283613272
-	round 43: 0.970430683404112
-	round 44: 0.9538876035342962
-	round 45: 0.9652769756964601
-	round 46: 0.9712253968936567
-	round 47: 0.9433850042355327
-	round 48: 0.9481769482167764
-	round 49: 0.9416967020057642
-	round 50: 0.9449833774338134
-	round 51: 0.977153589931159
-	round 52: 0.9636962722284725
-	round 53: 0.9485500901461409
-	round 54: 0.9527737906756112
-	round 55: 0.9540609658335726
-	round 56: 0.9453530991420197
-	round 57: 0.9411518906061642
-	round 58: 0.9347897329079077
-	round 59: 0.9279640743526788
-	round 60: 0.950613231990284
-	round 61: 0.9391038782489948
-	round 62: 0.9639180962460491
-	round 63: 0.9449828718416988
-	round 64: 0.945188293537012
-	round 65: 0.9480573036038457
-	round 66: 0.942290931178358
-	round 67: 0.92270217916836
-	round 68: 0.9357810918325052
-	round 69: 0.9341149679578531
-	round 70: 0.9335093384924026
-	round 71: 0.9386814056684415
-	round 72: 0.950544156300755
-	round 73: 0.9230360760094639
-	round 74: 0.9372135468374807
-	round 75: 0.9488034000792823
-	round 76: 0.933324966758204
-	round 77: 0.9317882342841297
-	round 78: 0.9255952789379766
-	round 79: 0.9406886980556451
-	round 80: 0.9281163449866322
-	round 81: 0.9390814332916333
-	round 82: 0.928261538473562
-	round 83: 0.9337312611528098
-	round 84: 0.9405634570807314
-	round 85: 0.9334670937480256
-	round 86: 0.9398519292045325
-	round 87: 0.9277755803764819
-	round 88: 0.9132904114243322
-	round 89: 0.9310566076455405
-	round 90: 0.928617595769346
-	round 91: 0.9291766289704905
-	round 92: 0.929426233894147
-	round 93: 0.9336311500102948
-	round 94: 0.9300604007495478
-	round 95: 0.9361367561756232
-	round 96: 0.9574828951503522
-	round 97: 0.9375480963780095
-	round 98: 0.9493495685795245
-	round 99: 0.9315846239606412
-	round 100: 0.9221907237086433
-History (metrics, centralized):
-{'accuracy': [(0, 0.1), (1, 0.1148), (2, 0.2752), (3, 0.3791), (4, 0.4339), (5, 0.4926), (6, 0.5244), (7, 0.5421), (8, 0.5669), (9, 0.5791), (10, 0.586), (11, 0.5979), (12, 0.6048), (13, 0.6034), (14, 0.6153), (15, 0.6155), (16, 0.6256), (17, 0.6281), (18, 0.6203), (19, 0.6361), (20, 0.6331), (21, 0.6446), (22, 0.6354), (23, 0.6427), (24, 0.6476), (25, 0.6517), (26, 0.6437), (27, 0.6539), (28, 0.6491), (29, 0.6535), (30, 0.6504), (31, 0.6604), (32, 0.6638), (33, 0.6482), (34, 0.6594), (35, 0.6561), (36, 0.6629), (37, 0.6614), (38, 0.6656), (39, 0.666), (40, 0.6597), (41, 0.6662), (42, 0.6485), (43, 0.6638), (44, 0.6673), (45, 0.6649), (46, 0.6647), (47, 0.6737), (48, 0.6741), (49, 0.6726), (50, 0.672), (51, 0.6596), (52, 0.6647), (53, 0.6687), (54, 0.6674), (55, 0.6701), (56, 0.6709), (57, 0.6732), (58, 0.6721), (59, 0.6748), (60, 0.6675), (61, 0.6723), (62, 0.6629), (63, 0.6689), (64, 0.6708), (65, 0.668), (66, 0.6687), (67, 0.6764), (68, 0.6705), (69, 0.6761), (70, 0.6728), (71, 0.6729), (72, 0.6683), (73, 0.6788), (74, 0.6734), (75, 0.671), (76, 0.6748), (77, 0.6749), (78, 0.6794), (79, 0.6773), (80, 0.6798), (81, 0.6754), (82, 0.6798), (83, 0.6803), (84, 0.6744), (85, 0.6786), (86, 0.6738), (87, 0.6793), (88, 0.6815), (89, 0.6773), (90, 0.6795), (91, 0.6796), (92, 0.6799), (93, 0.6791), (94, 0.6835), (95, 0.6783), (96, 0.6745), (97, 0.6807), (98, 0.6782), (99, 0.6787), (100, 0.6852)]}
diff --git a/baselines/moon/_static/cifar10_moon_log.txt b/baselines/moon/_static/cifar10_moon_log.txt
deleted file mode 100644
index 9125bcf6bdc2..000000000000
--- a/baselines/moon/_static/cifar10_moon_log.txt
+++ /dev/null
@@ -1,12852 +0,0 @@
-num_clients: 10
-num_epochs: 10
-fraction_fit: 1.0
-batch_size: 64
-learning_rate: 0.01
-mu: 5
-temperature: 0.5
-alg: moon
-seed: 0
-server_device: cpu
-num_rounds: 100
-client_resources:
-  num_cpus: 4
-  num_gpus: 1
-dataset:
-  name: cifar10
-  dir: ./data/moon/
-  partition: noniid
-  beta: 0.5
-model:
-  name: simple-cnn
-  output_dim: 256
-  dir: ./models/moon/cifar10/
-
-Files already downloaded and verified
-Files already downloaded and verified
-[2023-09-27 06:17:37,469][flwr][INFO] - Starting Flower simulation, config: ServerConfig(num_rounds=100, round_timeout=None)
-[2023-09-27 06:17:40,589][flwr][INFO] - Flower VCE: Ray initialized with resources: {'node:137.132.92.49': 1.0, 'memory': 108447056896.0, 'node:__internal_head__': 1.0, 'CPU': 64.0, 'object_store_memory': 50763024384.0, 'GPU': 1.0, 'accelerator_type:G': 1.0}
-[2023-09-27 06:17:40,590][flwr][INFO] - Flower VCE: Resources for each Virtual Client: {'num_cpus': 4, 'num_gpus': 1}
-[2023-09-27 06:17:40,602][flwr][INFO] - Flower VCE: Creating VirtualClientEngineActorPool with 1 actors
-[2023-09-27 06:17:40,602][flwr][INFO] - Initializing global parameters
-[2023-09-27 06:17:40,602][flwr][INFO] - Requesting initial parameters from one random client
-[2023-09-27 06:17:45,852][flwr][INFO] - Received initial parameters from one random client
-[2023-09-27 06:17:45,852][flwr][INFO] - Evaluating initial parameters
->> Test accuracy: 0.100000
-[2023-09-27 06:17:47,163][flwr][INFO] - initial parameters (loss, other metrics): 2.3034089754183835, {'accuracy': 0.1}
-[2023-09-27 06:17:47,164][flwr][INFO] - FL starting
-[2023-09-27 06:17:47,164][flwr][DEBUG] - fit_round 1: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.158569 Loss1: 1.692832 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 4.896290 Loss1: 1.430553 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 4.739466 Loss1: 1.273728 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.667786 Loss1: 1.202048 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.633436 Loss1: 1.167699 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.567831 Loss1: 1.102093 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.526783 Loss1: 1.061046 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.494906 Loss1: 1.029169 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.480337 Loss1: 1.014599 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.474858 Loss1: 1.009121 Loss2: 3.465737
-(DefaultActor pid=1831567) >> Training accuracy: 0.651486
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.206951 Loss1: 1.741214 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 4.917848 Loss1: 1.452111 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 4.891381 Loss1: 1.425644 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.869079 Loss1: 1.403341 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.848765 Loss1: 1.383028 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.821104 Loss1: 1.355366 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.774738 Loss1: 1.309001 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.732943 Loss1: 1.267206 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.735277 Loss1: 1.269539 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.685486 Loss1: 1.219748 Loss2: 3.465737
-(DefaultActor pid=1831567) >> Training accuracy: 0.514529
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.113451 Loss1: 1.647714 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 4.689537 Loss1: 1.223800 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 4.516952 Loss1: 1.051215 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.468171 Loss1: 1.002433 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.399912 Loss1: 0.934175 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.363108 Loss1: 0.897370 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.359947 Loss1: 0.894210 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.317211 Loss1: 0.851474 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.317719 Loss1: 0.851982 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.280708 Loss1: 0.814970 Loss2: 3.465737
-(DefaultActor pid=1831567) >> Training accuracy: 0.725116
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.197811 Loss1: 1.732074 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 4.769177 Loss1: 1.303440 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 4.658409 Loss1: 1.192672 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.636492 Loss1: 1.170755 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.616583 Loss1: 1.150845 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.595197 Loss1: 1.129460 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.585674 Loss1: 1.119936 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.550918 Loss1: 1.085181 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.525555 Loss1: 1.059818 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.522698 Loss1: 1.056960 Loss2: 3.465737
-(DefaultActor pid=1831567) >> Training accuracy: 0.649364
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.481436 Loss1: 2.015699 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 5.241791 Loss1: 1.776053 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 5.046539 Loss1: 1.580802 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.911101 Loss1: 1.445364 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.850332 Loss1: 1.384595 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.794764 Loss1: 1.329027 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.796497 Loss1: 1.330759 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.714140 Loss1: 1.248403 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.667242 Loss1: 1.201504 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.698191 Loss1: 1.232454 Loss2: 3.465737
-(DefaultActor pid=1831567) >> Training accuracy: 0.569030
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.331566 Loss1: 1.865829 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 5.088427 Loss1: 1.622690 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 4.842888 Loss1: 1.377151 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.774306 Loss1: 1.308568 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.709764 Loss1: 1.244027 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.665516 Loss1: 1.199779 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.601637 Loss1: 1.135900 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.572209 Loss1: 1.106471 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.556724 Loss1: 1.090987 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.496975 Loss1: 1.031237 Loss2: 3.465737
-(DefaultActor pid=1831567) >> Training accuracy: 0.668174
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.450127 Loss1: 1.984389 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 5.171041 Loss1: 1.705303 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 4.996551 Loss1: 1.530813 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.886612 Loss1: 1.420875 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.790521 Loss1: 1.324784 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.699627 Loss1: 1.233889 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.659423 Loss1: 1.193686 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.625899 Loss1: 1.160162 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.587107 Loss1: 1.121369 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.591118 Loss1: 1.125381 Loss2: 3.465737
-(DefaultActor pid=1831567) >> Training accuracy: 0.653045
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.163323 Loss1: 1.697586 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 4.805612 Loss1: 1.339875 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 4.685387 Loss1: 1.219650 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.598157 Loss1: 1.132419 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.560990 Loss1: 1.095253 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.511063 Loss1: 1.045325 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.491972 Loss1: 1.026235 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.493953 Loss1: 1.028216 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.473142 Loss1: 1.007405 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.446181 Loss1: 0.980444 Loss2: 3.465737
-(DefaultActor pid=1831567) >> Training accuracy: 0.665799
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.032352 Loss1: 1.566614 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 4.630193 Loss1: 1.164456 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 4.512794 Loss1: 1.047056 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.449821 Loss1: 0.984083 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.378381 Loss1: 0.912643 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.333268 Loss1: 0.867531 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.301698 Loss1: 0.835961 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.266389 Loss1: 0.800651 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.268311 Loss1: 0.802573 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.253135 Loss1: 0.787397 Loss2: 3.465737
-(DefaultActor pid=1831567) >> Training accuracy: 0.720872
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 5.467179 Loss1: 2.001442 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 1 Loss: 5.238392 Loss1: 1.772655 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 5.084332 Loss1: 1.618595 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 4.978634 Loss1: 1.512896 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 4 Loss: 4.940774 Loss1: 1.475037 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 5 Loss: 4.867042 Loss1: 1.401304 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 6 Loss: 4.824176 Loss1: 1.358439 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 7 Loss: 4.797907 Loss1: 1.332169 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 4.768697 Loss1: 1.302959 Loss2: 3.465737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 4.729307 Loss1: 1.263569 Loss2: 3.465737
-[2023-09-27 06:25:59,433][flwr][DEBUG] - fit_round 1 received 10 results and 0 failures
-[2023-09-27 06:25:59,475][flwr][WARNING] - No fit_metrics_aggregation_fn provided
-(DefaultActor pid=1831567) >> Training accuracy: 0.571784
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.110800
-[2023-09-27 06:26:01,348][flwr][INFO] - fit progress: (1, 2.249783382629053, {'accuracy': 0.1108}, 494.1848308178596)
-[2023-09-27 06:26:01,349][flwr][DEBUG] - evaluate_round 1: strategy sampled 10 clients (out of 10)
-[2023-09-27 06:26:32,464][flwr][DEBUG] - evaluate_round 1 received 10 results and 0 failures
-[2023-09-27 06:26:32,464][flwr][WARNING] - No evaluate_metrics_aggregation_fn provided
-[2023-09-27 06:26:32,465][flwr][DEBUG] - fit_round 2: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.489323 Loss1: 1.316230 Loss2: 1.173093
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.128980 Loss1: 1.115258 Loss2: 1.013722
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.037592 Loss1: 1.055536 Loss2: 0.982056
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.972622 Loss1: 1.008581 Loss2: 0.964042
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.951247 Loss1: 0.994555 Loss2: 0.956692
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.932614 Loss1: 0.976732 Loss2: 0.955882
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.931079 Loss1: 0.982240 Loss2: 0.948839
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.899865 Loss1: 0.951152 Loss2: 0.948713
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.875930 Loss1: 0.934466 Loss2: 0.941465
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.861577 Loss1: 0.918292 Loss2: 0.943285
-(DefaultActor pid=1831567) >> Training accuracy: 0.687376
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 3.124021 Loss1: 1.691626 Loss2: 1.432395
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.548971 Loss1: 1.337176 Loss2: 1.211795
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.411907 Loss1: 1.243433 Loss2: 1.168474
-(DefaultActor pid=1831567) Epoch: 3 Loss: 2.354926 Loss1: 1.209341 Loss2: 1.145585
-(DefaultActor pid=1831567) Epoch: 4 Loss: 2.311437 Loss1: 1.182279 Loss2: 1.129158
-(DefaultActor pid=1831567) Epoch: 5 Loss: 2.264077 Loss1: 1.143350 Loss2: 1.120726
-(DefaultActor pid=1831567) Epoch: 6 Loss: 2.240020 Loss1: 1.131831 Loss2: 1.108189
-(DefaultActor pid=1831567) Epoch: 7 Loss: 2.213008 Loss1: 1.108307 Loss2: 1.104701
-(DefaultActor pid=1831567) Epoch: 8 Loss: 2.186627 Loss1: 1.084042 Loss2: 1.102585
-(DefaultActor pid=1831567) Epoch: 9 Loss: 2.222774 Loss1: 1.113324 Loss2: 1.109450
-(DefaultActor pid=1831567) >> Training accuracy: 0.604244
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.920703 Loss1: 1.506812 Loss2: 1.413891
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.458384 Loss1: 1.257722 Loss2: 1.200662
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.382522 Loss1: 1.212991 Loss2: 1.169531
-(DefaultActor pid=1831567) Epoch: 3 Loss: 2.301260 Loss1: 1.154995 Loss2: 1.146265
-(DefaultActor pid=1831567) Epoch: 4 Loss: 2.248873 Loss1: 1.115564 Loss2: 1.133309
-(DefaultActor pid=1831567) Epoch: 5 Loss: 2.219878 Loss1: 1.102111 Loss2: 1.117768
-(DefaultActor pid=1831567) Epoch: 6 Loss: 2.187055 Loss1: 1.080567 Loss2: 1.106488
-(DefaultActor pid=1831567) Epoch: 7 Loss: 2.163706 Loss1: 1.060792 Loss2: 1.102914
-(DefaultActor pid=1831567) Epoch: 8 Loss: 2.153145 Loss1: 1.057809 Loss2: 1.095336
-(DefaultActor pid=1831567) Epoch: 9 Loss: 2.114643 Loss1: 1.020472 Loss2: 1.094171
-(DefaultActor pid=1831567) >> Training accuracy: 0.655428
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.992441 Loss1: 1.714720 Loss2: 1.277721
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.537592 Loss1: 1.413801 Loss2: 1.123791
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.454944 Loss1: 1.357857 Loss2: 1.097088
-(DefaultActor pid=1831567) Epoch: 3 Loss: 2.383839 Loss1: 1.307906 Loss2: 1.075933
-(DefaultActor pid=1831567) Epoch: 4 Loss: 2.323788 Loss1: 1.267844 Loss2: 1.055944
-(DefaultActor pid=1831567) Epoch: 5 Loss: 2.250789 Loss1: 1.221746 Loss2: 1.029043
-(DefaultActor pid=1831567) Epoch: 6 Loss: 2.212965 Loss1: 1.199516 Loss2: 1.013450
-(DefaultActor pid=1831567) Epoch: 7 Loss: 2.180420 Loss1: 1.179093 Loss2: 1.001327
-(DefaultActor pid=1831567) Epoch: 8 Loss: 2.180193 Loss1: 1.182074 Loss2: 0.998118
-(DefaultActor pid=1831567) Epoch: 9 Loss: 2.149488 Loss1: 1.160679 Loss2: 0.988808
-(DefaultActor pid=1831567) >> Training accuracy: 0.593524
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.253620 Loss1: 1.033009 Loss2: 1.220610
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.840969 Loss1: 0.835251 Loss2: 1.005718
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.795802 Loss1: 0.813215 Loss2: 0.982587
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.749537 Loss1: 0.776824 Loss2: 0.972713
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.708988 Loss1: 0.745287 Loss2: 0.963701
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.741212 Loss1: 0.773054 Loss2: 0.968158
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.700433 Loss1: 0.739704 Loss2: 0.960730
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.680513 Loss1: 0.721711 Loss2: 0.958802
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.665503 Loss1: 0.709136 Loss2: 0.956367
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.651594 Loss1: 0.694869 Loss2: 0.956724
-(DefaultActor pid=1831567) >> Training accuracy: 0.759259
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.681585 Loss1: 1.210303 Loss2: 1.471283
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.142403 Loss1: 0.894329 Loss2: 1.248075
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.060230 Loss1: 0.854530 Loss2: 1.205700
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.999412 Loss1: 0.820088 Loss2: 1.179323
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.947119 Loss1: 0.785363 Loss2: 1.161756
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.932777 Loss1: 0.775603 Loss2: 1.157174
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.892730 Loss1: 0.743485 Loss2: 1.149246
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.848938 Loss1: 0.711241 Loss2: 1.137697
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.869207 Loss1: 0.726130 Loss2: 1.143077
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.842067 Loss1: 0.709037 Loss2: 1.133031
-(DefaultActor pid=1831567) >> Training accuracy: 0.756752
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.863051 Loss1: 1.466288 Loss2: 1.396763
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.332419 Loss1: 1.106958 Loss2: 1.225461
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.256490 Loss1: 1.052797 Loss2: 1.203693
-(DefaultActor pid=1831567) Epoch: 3 Loss: 2.223645 Loss1: 1.027398 Loss2: 1.196247
-(DefaultActor pid=1831567) Epoch: 4 Loss: 2.192343 Loss1: 1.006122 Loss2: 1.186221
-(DefaultActor pid=1831567) Epoch: 5 Loss: 2.140795 Loss1: 0.966229 Loss2: 1.174565
-(DefaultActor pid=1831567) Epoch: 6 Loss: 2.115790 Loss1: 0.945553 Loss2: 1.170237
-(DefaultActor pid=1831567) Epoch: 7 Loss: 2.091640 Loss1: 0.930235 Loss2: 1.161405
-(DefaultActor pid=1831567) Epoch: 8 Loss: 2.093366 Loss1: 0.929592 Loss2: 1.163774
-(DefaultActor pid=1831567) Epoch: 9 Loss: 2.077639 Loss1: 0.922619 Loss2: 1.155020
-(DefaultActor pid=1831567) >> Training accuracy: 0.688834
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.879141 Loss1: 1.274636 Loss2: 1.604504
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.380485 Loss1: 1.047008 Loss2: 1.333477
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.337278 Loss1: 1.031792 Loss2: 1.305486
-(DefaultActor pid=1831567) Epoch: 3 Loss: 2.306475 Loss1: 1.012002 Loss2: 1.294474
-(DefaultActor pid=1831567) Epoch: 4 Loss: 2.267435 Loss1: 0.980702 Loss2: 1.286733
-(DefaultActor pid=1831567) Epoch: 5 Loss: 2.262260 Loss1: 0.978342 Loss2: 1.283918
-(DefaultActor pid=1831567) Epoch: 6 Loss: 2.236680 Loss1: 0.955554 Loss2: 1.281127
-(DefaultActor pid=1831567) Epoch: 7 Loss: 2.223227 Loss1: 0.944070 Loss2: 1.279157
-(DefaultActor pid=1831567) Epoch: 8 Loss: 2.204390 Loss1: 0.926889 Loss2: 1.277502
-(DefaultActor pid=1831567) Epoch: 9 Loss: 2.225336 Loss1: 0.936644 Loss2: 1.288692
-(DefaultActor pid=1831567) >> Training accuracy: 0.682468
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.730791 Loss1: 1.555200 Loss2: 1.175591
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.226825 Loss1: 1.211303 Loss2: 1.015522
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.096587 Loss1: 1.125094 Loss2: 0.971493
-(DefaultActor pid=1831567) Epoch: 3 Loss: 2.053540 Loss1: 1.106777 Loss2: 0.946763
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.996769 Loss1: 1.067671 Loss2: 0.929098
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.927768 Loss1: 1.011452 Loss2: 0.916316
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.898969 Loss1: 0.992453 Loss2: 0.906516
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.859915 Loss1: 0.959470 Loss2: 0.900446
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.862439 Loss1: 0.962096 Loss2: 0.900344
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.839648 Loss1: 0.940929 Loss2: 0.898718
-(DefaultActor pid=1831567) >> Training accuracy: 0.694079
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 3.128362 Loss1: 1.555960 Loss2: 1.572402
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.599911 Loss1: 1.250455 Loss2: 1.349457
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.516453 Loss1: 1.222700 Loss2: 1.293753
-(DefaultActor pid=1831567) Epoch: 3 Loss: 2.375933 Loss1: 1.136756 Loss2: 1.239177
-(DefaultActor pid=1831567) Epoch: 4 Loss: 2.346102 Loss1: 1.125003 Loss2: 1.221099
-(DefaultActor pid=1831567) Epoch: 5 Loss: 2.324877 Loss1: 1.122106 Loss2: 1.202771
-(DefaultActor pid=1831567) Epoch: 6 Loss: 2.254508 Loss1: 1.070175 Loss2: 1.184333
-(DefaultActor pid=1831567) Epoch: 7 Loss: 2.206692 Loss1: 1.030726 Loss2: 1.175967
-(DefaultActor pid=1831567) Epoch: 8 Loss: 2.216142 Loss1: 1.047160 Loss2: 1.168981
-(DefaultActor pid=1831567) Epoch: 9 Loss: 2.211113 Loss1: 1.041155 Loss2: 1.169958
-[2023-09-27 06:33:26,467][flwr][DEBUG] - fit_round 2 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.681691
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.172300
-[2023-09-27 06:33:28,046][flwr][INFO] - fit progress: (2, 2.1481322312888245, {'accuracy': 0.1723}, 940.8822364257649)
-[2023-09-27 06:33:28,046][flwr][DEBUG] - evaluate_round 2: strategy sampled 10 clients (out of 10)
-[2023-09-27 06:33:59,059][flwr][DEBUG] - evaluate_round 2 received 10 results and 0 failures
-[2023-09-27 06:33:59,060][flwr][DEBUG] - fit_round 3: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.415017 Loss1: 1.340264 Loss2: 1.074752
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.172792 Loss1: 1.221425 Loss2: 0.951367
-(DefaultActor pid=1831567) Epoch: 2 Loss: 2.101085 Loss1: 1.166882 Loss2: 0.934204
-(DefaultActor pid=1831567) Epoch: 3 Loss: 2.080422 Loss1: 1.151924 Loss2: 0.928498
-(DefaultActor pid=1831567) Epoch: 4 Loss: 2.090270 Loss1: 1.156074 Loss2: 0.934196
-(DefaultActor pid=1831567) Epoch: 5 Loss: 2.042260 Loss1: 1.112338 Loss2: 0.929922
-(DefaultActor pid=1831567) Epoch: 6 Loss: 2.036564 Loss1: 1.105297 Loss2: 0.931267
-(DefaultActor pid=1831567) Epoch: 7 Loss: 2.032168 Loss1: 1.100159 Loss2: 0.932009
-(DefaultActor pid=1831567) Epoch: 8 Loss: 2.018011 Loss1: 1.089233 Loss2: 0.928778
-(DefaultActor pid=1831567) Epoch: 9 Loss: 2.010427 Loss1: 1.080171 Loss2: 0.930256
-(DefaultActor pid=1831567) >> Training accuracy: 0.633605
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.093357 Loss1: 1.172810 Loss2: 0.920547
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.843151 Loss1: 1.047722 Loss2: 0.795429
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.826291 Loss1: 1.034690 Loss2: 0.791601
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.781128 Loss1: 0.993119 Loss2: 0.788009
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.785663 Loss1: 0.994848 Loss2: 0.790815
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.754628 Loss1: 0.964852 Loss2: 0.789776
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.732483 Loss1: 0.937501 Loss2: 0.794982
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.717012 Loss1: 0.925001 Loss2: 0.792012
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.702077 Loss1: 0.912627 Loss2: 0.789450
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.705002 Loss1: 0.913456 Loss2: 0.791547
-(DefaultActor pid=1831567) >> Training accuracy: 0.710737
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.190994 Loss1: 1.182659 Loss2: 1.008334
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.880774 Loss1: 0.996359 Loss2: 0.884416
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.873274 Loss1: 0.992147 Loss2: 0.881127
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.802021 Loss1: 0.925518 Loss2: 0.876503
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.799720 Loss1: 0.920502 Loss2: 0.879218
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.769061 Loss1: 0.894752 Loss2: 0.874309
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.772411 Loss1: 0.893572 Loss2: 0.878839
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.779955 Loss1: 0.901409 Loss2: 0.878546
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.720463 Loss1: 0.848836 Loss2: 0.871627
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.743792 Loss1: 0.868272 Loss2: 0.875520
-(DefaultActor pid=1831567) >> Training accuracy: 0.724095
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.011623 Loss1: 1.122678 Loss2: 0.888945
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.759060 Loss1: 0.964328 Loss2: 0.794732
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.703840 Loss1: 0.915880 Loss2: 0.787960
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.698495 Loss1: 0.910532 Loss2: 0.787963
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.683558 Loss1: 0.898623 Loss2: 0.784935
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.678711 Loss1: 0.891963 Loss2: 0.786748
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.647680 Loss1: 0.865040 Loss2: 0.782640
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.650382 Loss1: 0.865970 Loss2: 0.784412
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.628939 Loss1: 0.850231 Loss2: 0.778708
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.611600 Loss1: 0.827350 Loss2: 0.784250
-(DefaultActor pid=1831567) >> Training accuracy: 0.696456
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.711629 Loss1: 0.887350 Loss2: 0.824279
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.483841 Loss1: 0.751461 Loss2: 0.732380
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.451170 Loss1: 0.723947 Loss2: 0.727223
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.423101 Loss1: 0.697866 Loss2: 0.725236
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.412495 Loss1: 0.687028 Loss2: 0.725467
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.409845 Loss1: 0.685068 Loss2: 0.724777
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.403730 Loss1: 0.677839 Loss2: 0.725891
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.378036 Loss1: 0.655308 Loss2: 0.722728
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.383578 Loss1: 0.660803 Loss2: 0.722774
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.367885 Loss1: 0.642426 Loss2: 0.725459
-(DefaultActor pid=1831567) >> Training accuracy: 0.773148
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.270632 Loss1: 1.308304 Loss2: 0.962328
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.964598 Loss1: 1.128164 Loss2: 0.836434
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.950813 Loss1: 1.116982 Loss2: 0.833831
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.906136 Loss1: 1.083079 Loss2: 0.823057
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.885191 Loss1: 1.061671 Loss2: 0.823519
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.879146 Loss1: 1.054564 Loss2: 0.824581
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.852256 Loss1: 1.028222 Loss2: 0.824035
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.869318 Loss1: 1.036677 Loss2: 0.832641
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.849916 Loss1: 1.021882 Loss2: 0.828034
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.825829 Loss1: 1.000298 Loss2: 0.825530
-(DefaultActor pid=1831567) >> Training accuracy: 0.641791
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.949525 Loss1: 1.101430 Loss2: 0.848096
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.777640 Loss1: 1.000434 Loss2: 0.777206
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.700591 Loss1: 0.945236 Loss2: 0.755355
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.670217 Loss1: 0.921027 Loss2: 0.749190
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.667481 Loss1: 0.915101 Loss2: 0.752380
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.643583 Loss1: 0.891951 Loss2: 0.751633
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.646424 Loss1: 0.891818 Loss2: 0.754606
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.639266 Loss1: 0.885003 Loss2: 0.754263
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.626275 Loss1: 0.874671 Loss2: 0.751604
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.609771 Loss1: 0.855592 Loss2: 0.754179
-(DefaultActor pid=1831567) >> Training accuracy: 0.697545
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.781840 Loss1: 0.901620 Loss2: 0.880220
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.544827 Loss1: 0.770414 Loss2: 0.774413
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.488349 Loss1: 0.726030 Loss2: 0.762319
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.487448 Loss1: 0.725119 Loss2: 0.762330
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.471013 Loss1: 0.710808 Loss2: 0.760205
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.458193 Loss1: 0.698750 Loss2: 0.759443
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.426932 Loss1: 0.670192 Loss2: 0.756740
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.433413 Loss1: 0.675099 Loss2: 0.758314
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.422528 Loss1: 0.662213 Loss2: 0.760315
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.391620 Loss1: 0.636294 Loss2: 0.755326
-(DefaultActor pid=1831567) >> Training accuracy: 0.780864
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.824429 Loss1: 1.094397 Loss2: 0.730032
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.619105 Loss1: 0.985364 Loss2: 0.633741
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.572357 Loss1: 0.946040 Loss2: 0.626316
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.573492 Loss1: 0.948926 Loss2: 0.624566
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.555508 Loss1: 0.931599 Loss2: 0.623910
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.505799 Loss1: 0.883942 Loss2: 0.621857
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.518284 Loss1: 0.893849 Loss2: 0.624435
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.501452 Loss1: 0.877521 Loss2: 0.623931
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.486402 Loss1: 0.862998 Loss2: 0.623404
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.495106 Loss1: 0.870252 Loss2: 0.624854
-(DefaultActor pid=1831567) >> Training accuracy: 0.690678
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.347707 Loss1: 1.278294 Loss2: 1.069413
-(DefaultActor pid=1831567) Epoch: 1 Loss: 2.039271 Loss1: 1.118373 Loss2: 0.920898
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.966311 Loss1: 1.069537 Loss2: 0.896774
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.906505 Loss1: 1.028925 Loss2: 0.877580
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.920017 Loss1: 1.043727 Loss2: 0.876290
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.863755 Loss1: 0.997565 Loss2: 0.866190
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.872020 Loss1: 1.003879 Loss2: 0.868140
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.842994 Loss1: 0.982384 Loss2: 0.860610
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.824816 Loss1: 0.964970 Loss2: 0.859846
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.827546 Loss1: 0.969686 Loss2: 0.857860
-[2023-09-27 06:41:23,136][flwr][DEBUG] - fit_round 3 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.669956
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.385000
-[2023-09-27 06:41:24,960][flwr][INFO] - fit progress: (3, 1.647436479029183, {'accuracy': 0.385}, 1417.7966564488597)
-[2023-09-27 06:41:24,961][flwr][DEBUG] - evaluate_round 3: strategy sampled 10 clients (out of 10)
-[2023-09-27 06:41:57,573][flwr][DEBUG] - evaluate_round 3 received 10 results and 0 failures
-[2023-09-27 06:41:57,574][flwr][DEBUG] - fit_round 4: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.959768 Loss1: 1.095686 Loss2: 0.864081
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.654144 Loss1: 0.923206 Loss2: 0.730938
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.616120 Loss1: 0.902758 Loss2: 0.713362
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.588044 Loss1: 0.878248 Loss2: 0.709796
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.574441 Loss1: 0.863692 Loss2: 0.710748
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.545140 Loss1: 0.837102 Loss2: 0.708038
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.543230 Loss1: 0.835479 Loss2: 0.707751
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.538823 Loss1: 0.831749 Loss2: 0.707074
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.559673 Loss1: 0.850185 Loss2: 0.709487
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.523678 Loss1: 0.817619 Loss2: 0.706059
-(DefaultActor pid=1831567) >> Training accuracy: 0.729030
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.761747 Loss1: 0.839047 Loss2: 0.922700
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.506742 Loss1: 0.721557 Loss2: 0.785185
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.469778 Loss1: 0.694414 Loss2: 0.775364
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.446657 Loss1: 0.674826 Loss2: 0.771831
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.431892 Loss1: 0.661955 Loss2: 0.769937
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.406065 Loss1: 0.634406 Loss2: 0.771659
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.419861 Loss1: 0.648368 Loss2: 0.771494
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.409758 Loss1: 0.643129 Loss2: 0.766629
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.365714 Loss1: 0.601492 Loss2: 0.764223
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.382863 Loss1: 0.614867 Loss2: 0.767996
-(DefaultActor pid=1831567) >> Training accuracy: 0.793596
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.642644 Loss1: 0.803485 Loss2: 0.839159
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.433953 Loss1: 0.708844 Loss2: 0.725109
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.391559 Loss1: 0.674852 Loss2: 0.716707
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.363835 Loss1: 0.649616 Loss2: 0.714219
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.370542 Loss1: 0.654846 Loss2: 0.715696
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.342658 Loss1: 0.629414 Loss2: 0.713244
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.334084 Loss1: 0.619713 Loss2: 0.714372
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.339267 Loss1: 0.624595 Loss2: 0.714672
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.338185 Loss1: 0.621479 Loss2: 0.716706
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.313594 Loss1: 0.601295 Loss2: 0.712299
-(DefaultActor pid=1831567) >> Training accuracy: 0.768326
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.190779 Loss1: 1.082939 Loss2: 1.107840
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.948287 Loss1: 1.002792 Loss2: 0.945496
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.918061 Loss1: 0.990976 Loss2: 0.927085
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.863494 Loss1: 0.945495 Loss2: 0.918000
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.854082 Loss1: 0.940077 Loss2: 0.914004
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.857931 Loss1: 0.940210 Loss2: 0.917721
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.813290 Loss1: 0.903588 Loss2: 0.909702
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.829213 Loss1: 0.913456 Loss2: 0.915757
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.807834 Loss1: 0.896602 Loss2: 0.911232
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.795018 Loss1: 0.883855 Loss2: 0.911163
-(DefaultActor pid=1831567) >> Training accuracy: 0.708534
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.849038 Loss1: 1.026859 Loss2: 0.822179
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.651148 Loss1: 0.916622 Loss2: 0.734526
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.626542 Loss1: 0.893981 Loss2: 0.732561
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.638858 Loss1: 0.901524 Loss2: 0.737334
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.601716 Loss1: 0.872871 Loss2: 0.728845
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.597795 Loss1: 0.868252 Loss2: 0.729543
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.583544 Loss1: 0.851136 Loss2: 0.732407
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.580667 Loss1: 0.847451 Loss2: 0.733217
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.565120 Loss1: 0.832593 Loss2: 0.732527
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.549264 Loss1: 0.819774 Loss2: 0.729489
-(DefaultActor pid=1831567) >> Training accuracy: 0.721354
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.177091 Loss1: 1.285170 Loss2: 0.891921
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.915349 Loss1: 1.153907 Loss2: 0.761442
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.865854 Loss1: 1.116816 Loss2: 0.749038
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.852904 Loss1: 1.110707 Loss2: 0.742197
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.829410 Loss1: 1.092330 Loss2: 0.737080
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.822381 Loss1: 1.086184 Loss2: 0.736196
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.809970 Loss1: 1.072961 Loss2: 0.737008
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.796816 Loss1: 1.059436 Loss2: 0.737380
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.759374 Loss1: 1.029298 Loss2: 0.730076
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.751741 Loss1: 1.020766 Loss2: 0.730975
-(DefaultActor pid=1831567) >> Training accuracy: 0.645154
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.883651 Loss1: 1.055108 Loss2: 0.828543
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.598716 Loss1: 0.899386 Loss2: 0.699330
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.578926 Loss1: 0.888044 Loss2: 0.690882
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.550933 Loss1: 0.865345 Loss2: 0.685588
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.557859 Loss1: 0.870223 Loss2: 0.687635
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.521566 Loss1: 0.836734 Loss2: 0.684832
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.524148 Loss1: 0.836299 Loss2: 0.687849
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.526126 Loss1: 0.834731 Loss2: 0.691395
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.507760 Loss1: 0.819961 Loss2: 0.687799
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.499967 Loss1: 0.814594 Loss2: 0.685373
-(DefaultActor pid=1831567) >> Training accuracy: 0.724466
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.079600 Loss1: 1.168584 Loss2: 0.911016
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.772841 Loss1: 1.030948 Loss2: 0.741894
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.706571 Loss1: 0.988061 Loss2: 0.718511
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.697365 Loss1: 0.984650 Loss2: 0.712715
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.663224 Loss1: 0.958731 Loss2: 0.704493
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.661957 Loss1: 0.956330 Loss2: 0.705627
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.661999 Loss1: 0.955479 Loss2: 0.706519
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.642397 Loss1: 0.937431 Loss2: 0.704966
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.634093 Loss1: 0.928832 Loss2: 0.705261
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.601517 Loss1: 0.899996 Loss2: 0.701522
-(DefaultActor pid=1831567) >> Training accuracy: 0.686952
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.980264 Loss1: 1.007347 Loss2: 0.972917
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.713912 Loss1: 0.898850 Loss2: 0.815062
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.681733 Loss1: 0.878808 Loss2: 0.802925
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.650499 Loss1: 0.844604 Loss2: 0.805895
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.640387 Loss1: 0.833196 Loss2: 0.807191
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.615824 Loss1: 0.818313 Loss2: 0.797511
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.630029 Loss1: 0.824238 Loss2: 0.805791
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.625690 Loss1: 0.819255 Loss2: 0.806435
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.607854 Loss1: 0.799895 Loss2: 0.807959
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.621111 Loss1: 0.811001 Loss2: 0.810111
-(DefaultActor pid=1831567) >> Training accuracy: 0.709216
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.144534 Loss1: 1.216463 Loss2: 0.928071
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.820308 Loss1: 1.055431 Loss2: 0.764877
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.788406 Loss1: 1.034002 Loss2: 0.754403
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.774354 Loss1: 1.018354 Loss2: 0.756000
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.766350 Loss1: 1.017112 Loss2: 0.749237
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.741835 Loss1: 0.987054 Loss2: 0.754781
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.750159 Loss1: 0.998012 Loss2: 0.752147
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.727953 Loss1: 0.979115 Loss2: 0.748839
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.713808 Loss1: 0.961341 Loss2: 0.752467
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.718466 Loss1: 0.970034 Loss2: 0.748432
-[2023-09-27 06:48:45,728][flwr][DEBUG] - fit_round 4 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.642724
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.444800
-[2023-09-27 06:48:47,367][flwr][INFO] - fit progress: (4, 1.4994674932461578, {'accuracy': 0.4448}, 1860.2031150087714)
-[2023-09-27 06:48:47,367][flwr][DEBUG] - evaluate_round 4: strategy sampled 10 clients (out of 10)
-[2023-09-27 06:49:20,039][flwr][DEBUG] - evaluate_round 4 received 10 results and 0 failures
-[2023-09-27 06:49:20,040][flwr][DEBUG] - fit_round 5: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 2.014243 Loss1: 1.188244 Loss2: 0.825999
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.818569 Loss1: 1.085272 Loss2: 0.733297
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.771165 Loss1: 1.046833 Loss2: 0.724332
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.782243 Loss1: 1.052951 Loss2: 0.729292
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.763231 Loss1: 1.035690 Loss2: 0.727541
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.764852 Loss1: 1.031938 Loss2: 0.732914
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.757314 Loss1: 1.028570 Loss2: 0.728744
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.732338 Loss1: 1.002636 Loss2: 0.729702
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.701840 Loss1: 0.977936 Loss2: 0.723904
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.706227 Loss1: 0.980068 Loss2: 0.726159
-(DefaultActor pid=1831567) >> Training accuracy: 0.653533
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.692891 Loss1: 0.959691 Loss2: 0.733200
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.506459 Loss1: 0.866903 Loss2: 0.639556
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.466208 Loss1: 0.831412 Loss2: 0.634795
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.441681 Loss1: 0.807731 Loss2: 0.633950
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.420730 Loss1: 0.786706 Loss2: 0.634024
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.434917 Loss1: 0.799899 Loss2: 0.635018
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.401121 Loss1: 0.766696 Loss2: 0.634425
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.391031 Loss1: 0.757915 Loss2: 0.633117
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.404584 Loss1: 0.766761 Loss2: 0.637823
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.406269 Loss1: 0.768637 Loss2: 0.637632
-(DefaultActor pid=1831567) >> Training accuracy: 0.732786
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.529666 Loss1: 0.795142 Loss2: 0.734524
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.325898 Loss1: 0.669581 Loss2: 0.656317
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.299674 Loss1: 0.645223 Loss2: 0.654450
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.263410 Loss1: 0.613045 Loss2: 0.650364
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.281851 Loss1: 0.629112 Loss2: 0.652738
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.272507 Loss1: 0.616743 Loss2: 0.655764
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258849 Loss1: 0.603788 Loss2: 0.655061
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.237609 Loss1: 0.582127 Loss2: 0.655482
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244634 Loss1: 0.588563 Loss2: 0.656070
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.252852 Loss1: 0.595281 Loss2: 0.657572
-(DefaultActor pid=1831567) >> Training accuracy: 0.795332
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.883753 Loss1: 1.104919 Loss2: 0.778834
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.712403 Loss1: 1.026433 Loss2: 0.685970
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.686781 Loss1: 1.006981 Loss2: 0.679801
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.672725 Loss1: 0.991034 Loss2: 0.681692
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.626696 Loss1: 0.951325 Loss2: 0.675371
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.636485 Loss1: 0.953849 Loss2: 0.682636
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.638114 Loss1: 0.956022 Loss2: 0.682092
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.602415 Loss1: 0.924855 Loss2: 0.677561
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.612828 Loss1: 0.931136 Loss2: 0.681692
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.595127 Loss1: 0.914920 Loss2: 0.680207
-(DefaultActor pid=1831567) >> Training accuracy: 0.646922
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.718595 Loss1: 0.959638 Loss2: 0.758958
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.544794 Loss1: 0.858285 Loss2: 0.686509
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.520121 Loss1: 0.838044 Loss2: 0.682077
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.519642 Loss1: 0.834129 Loss2: 0.685513
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.494727 Loss1: 0.815452 Loss2: 0.679276
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.495673 Loss1: 0.811537 Loss2: 0.684137
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.483132 Loss1: 0.799975 Loss2: 0.683157
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.467450 Loss1: 0.783536 Loss2: 0.683914
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.463590 Loss1: 0.779140 Loss2: 0.684450
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.439845 Loss1: 0.754819 Loss2: 0.685026
-(DefaultActor pid=1831567) >> Training accuracy: 0.740663
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.801618 Loss1: 0.957439 Loss2: 0.844179
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.656688 Loss1: 0.872454 Loss2: 0.784234
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.636137 Loss1: 0.855533 Loss2: 0.780604
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.630167 Loss1: 0.849935 Loss2: 0.780232
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.620729 Loss1: 0.838806 Loss2: 0.781924
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.610510 Loss1: 0.828549 Loss2: 0.781961
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.610964 Loss1: 0.825643 Loss2: 0.785321
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.585073 Loss1: 0.807889 Loss2: 0.777184
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.571801 Loss1: 0.790916 Loss2: 0.780884
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.579860 Loss1: 0.794278 Loss2: 0.785582
-(DefaultActor pid=1831567) >> Training accuracy: 0.732763
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.909832 Loss1: 1.091551 Loss2: 0.818282
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.681050 Loss1: 0.974363 Loss2: 0.706687
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.649299 Loss1: 0.950373 Loss2: 0.698926
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.655506 Loss1: 0.958529 Loss2: 0.696976
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.601037 Loss1: 0.911543 Loss2: 0.689494
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.568086 Loss1: 0.879798 Loss2: 0.688288
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.576533 Loss1: 0.888174 Loss2: 0.688359
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.585644 Loss1: 0.898440 Loss2: 0.687204
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.554587 Loss1: 0.863171 Loss2: 0.691416
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.552208 Loss1: 0.864154 Loss2: 0.688054
-(DefaultActor pid=1831567) >> Training accuracy: 0.702303
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.750890 Loss1: 0.952034 Loss2: 0.798856
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.566870 Loss1: 0.852061 Loss2: 0.714809
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.538249 Loss1: 0.829282 Loss2: 0.708967
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.554682 Loss1: 0.839347 Loss2: 0.715335
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.534906 Loss1: 0.824781 Loss2: 0.710126
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.521418 Loss1: 0.810705 Loss2: 0.710714
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.492046 Loss1: 0.783252 Loss2: 0.708795
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.494058 Loss1: 0.781443 Loss2: 0.712616
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.473160 Loss1: 0.762181 Loss2: 0.710979
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.485544 Loss1: 0.774253 Loss2: 0.711290
-(DefaultActor pid=1831567) >> Training accuracy: 0.743010
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.430940 Loss1: 0.728699 Loss2: 0.702241
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.289461 Loss1: 0.659202 Loss2: 0.630259
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260535 Loss1: 0.635518 Loss2: 0.625017
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.253021 Loss1: 0.626774 Loss2: 0.626247
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.250595 Loss1: 0.625559 Loss2: 0.625036
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.242292 Loss1: 0.613702 Loss2: 0.628589
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220224 Loss1: 0.594907 Loss2: 0.625318
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.219791 Loss1: 0.596135 Loss2: 0.623656
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.234372 Loss1: 0.607136 Loss2: 0.627236
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229269 Loss1: 0.600677 Loss2: 0.628592
-(DefaultActor pid=1831567) >> Training accuracy: 0.776042
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.738075 Loss1: 0.985871 Loss2: 0.752204
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.607921 Loss1: 0.935295 Loss2: 0.672627
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.595121 Loss1: 0.922947 Loss2: 0.672174
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.554395 Loss1: 0.887373 Loss2: 0.667022
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.550219 Loss1: 0.882243 Loss2: 0.667976
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.535800 Loss1: 0.862845 Loss2: 0.672955
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.529615 Loss1: 0.858552 Loss2: 0.671064
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.518144 Loss1: 0.846215 Loss2: 0.671929
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.505655 Loss1: 0.833487 Loss2: 0.672168
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.509816 Loss1: 0.837040 Loss2: 0.672776
-[2023-09-27 06:56:48,343][flwr][DEBUG] - fit_round 5 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.696514
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.489400
-[2023-09-27 06:56:50,049][flwr][INFO] - fit progress: (5, 1.3875796671111744, {'accuracy': 0.4894}, 2342.885829200037)
-[2023-09-27 06:56:50,050][flwr][DEBUG] - evaluate_round 5: strategy sampled 10 clients (out of 10)
-[2023-09-27 06:57:34,777][flwr][DEBUG] - evaluate_round 5 received 10 results and 0 failures
-[2023-09-27 06:57:34,778][flwr][DEBUG] - fit_round 6: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.949481 Loss1: 1.057654 Loss2: 0.891827
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.664165 Loss1: 0.932542 Loss2: 0.731623
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.634032 Loss1: 0.910815 Loss2: 0.723218
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.598717 Loss1: 0.882653 Loss2: 0.716064
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.600445 Loss1: 0.881961 Loss2: 0.718484
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.581901 Loss1: 0.863670 Loss2: 0.718231
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.592011 Loss1: 0.875328 Loss2: 0.716683
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.572243 Loss1: 0.853427 Loss2: 0.718816
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.553528 Loss1: 0.836697 Loss2: 0.716831
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.543531 Loss1: 0.825607 Loss2: 0.717925
-(DefaultActor pid=1831567) >> Training accuracy: 0.706414
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.781243 Loss1: 0.950038 Loss2: 0.831205
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.524293 Loss1: 0.830335 Loss2: 0.693957
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.488881 Loss1: 0.796452 Loss2: 0.692428
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.489036 Loss1: 0.797133 Loss2: 0.691903
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.497525 Loss1: 0.804665 Loss2: 0.692860
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.457374 Loss1: 0.769296 Loss2: 0.688078
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.456766 Loss1: 0.766715 Loss2: 0.690050
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.456312 Loss1: 0.766672 Loss2: 0.689641
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.437612 Loss1: 0.745680 Loss2: 0.691932
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.452189 Loss1: 0.757865 Loss2: 0.694325
-(DefaultActor pid=1831567) >> Training accuracy: 0.738377
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.685009 Loss1: 0.914753 Loss2: 0.770256
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.533744 Loss1: 0.841567 Loss2: 0.692177
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.522414 Loss1: 0.830460 Loss2: 0.691953
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.520060 Loss1: 0.828377 Loss2: 0.691682
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.519292 Loss1: 0.823110 Loss2: 0.696182
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.517640 Loss1: 0.823539 Loss2: 0.694101
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.477393 Loss1: 0.786311 Loss2: 0.691082
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.466187 Loss1: 0.775382 Loss2: 0.690806
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.469411 Loss1: 0.774163 Loss2: 0.695247
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.476252 Loss1: 0.779557 Loss2: 0.696695
-(DefaultActor pid=1831567) >> Training accuracy: 0.727307
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.714899 Loss1: 0.914384 Loss2: 0.800515
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.522630 Loss1: 0.837226 Loss2: 0.685403
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.481397 Loss1: 0.802115 Loss2: 0.679282
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.488365 Loss1: 0.808946 Loss2: 0.679420
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.471416 Loss1: 0.790751 Loss2: 0.680665
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.454509 Loss1: 0.775775 Loss2: 0.678734
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.449837 Loss1: 0.769987 Loss2: 0.679849
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.440428 Loss1: 0.758750 Loss2: 0.681678
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.437842 Loss1: 0.755136 Loss2: 0.682706
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.428721 Loss1: 0.747492 Loss2: 0.681229
-(DefaultActor pid=1831567) >> Training accuracy: 0.774054
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.570600 Loss1: 0.717890 Loss2: 0.852710
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.372785 Loss1: 0.644828 Loss2: 0.727957
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354526 Loss1: 0.631671 Loss2: 0.722855
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.326202 Loss1: 0.608120 Loss2: 0.718082
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333935 Loss1: 0.614410 Loss2: 0.719525
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.307776 Loss1: 0.587609 Loss2: 0.720168
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295713 Loss1: 0.575356 Loss2: 0.720356
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.304860 Loss1: 0.584288 Loss2: 0.720572
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.289421 Loss1: 0.567478 Loss2: 0.721943
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.282068 Loss1: 0.561936 Loss2: 0.720133
-(DefaultActor pid=1831567) >> Training accuracy: 0.801890
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.912906 Loss1: 1.078457 Loss2: 0.834449
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.662744 Loss1: 0.968059 Loss2: 0.694685
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.643141 Loss1: 0.952241 Loss2: 0.690900
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.635528 Loss1: 0.946002 Loss2: 0.689526
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.614003 Loss1: 0.926269 Loss2: 0.687734
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.610983 Loss1: 0.918155 Loss2: 0.692828
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.607673 Loss1: 0.923298 Loss2: 0.684375
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.602167 Loss1: 0.913330 Loss2: 0.688837
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.598345 Loss1: 0.902702 Loss2: 0.695643
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.561684 Loss1: 0.873343 Loss2: 0.688341
-(DefaultActor pid=1831567) >> Training accuracy: 0.676073
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.864469 Loss1: 0.983174 Loss2: 0.881295
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.646952 Loss1: 0.892650 Loss2: 0.754301
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.610051 Loss1: 0.865930 Loss2: 0.744121
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.614231 Loss1: 0.870160 Loss2: 0.744071
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.581443 Loss1: 0.843542 Loss2: 0.737901
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.587021 Loss1: 0.846743 Loss2: 0.740279
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.536245 Loss1: 0.801643 Loss2: 0.734602
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.531117 Loss1: 0.791209 Loss2: 0.739908
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.555219 Loss1: 0.814352 Loss2: 0.740866
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.510869 Loss1: 0.775725 Loss2: 0.735144
-(DefaultActor pid=1831567) >> Training accuracy: 0.733574
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.534970 Loss1: 0.699719 Loss2: 0.835251
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.354766 Loss1: 0.630411 Loss2: 0.724355
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.323006 Loss1: 0.604877 Loss2: 0.718129
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325520 Loss1: 0.607876 Loss2: 0.717644
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.319769 Loss1: 0.601734 Loss2: 0.718036
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.297679 Loss1: 0.580817 Loss2: 0.716862
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.284857 Loss1: 0.570394 Loss2: 0.714463
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.293728 Loss1: 0.577702 Loss2: 0.716026
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.278424 Loss1: 0.561386 Loss2: 0.717037
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.263424 Loss1: 0.546727 Loss2: 0.716698
-(DefaultActor pid=1831567) >> Training accuracy: 0.804012
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.959389 Loss1: 1.135186 Loss2: 0.824203
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.741100 Loss1: 1.045928 Loss2: 0.695172
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.742160 Loss1: 1.054101 Loss2: 0.688059
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.713174 Loss1: 1.028878 Loss2: 0.684295
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.690718 Loss1: 1.006516 Loss2: 0.684202
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.663452 Loss1: 0.982956 Loss2: 0.680496
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.669116 Loss1: 0.985029 Loss2: 0.684087
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.663717 Loss1: 0.978592 Loss2: 0.685125
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.641271 Loss1: 0.957595 Loss2: 0.683676
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.616372 Loss1: 0.931420 Loss2: 0.684952
-(DefaultActor pid=1831567) >> Training accuracy: 0.663043
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.863680 Loss1: 0.931315 Loss2: 0.932366
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.590819 Loss1: 0.806492 Loss2: 0.784327
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.572245 Loss1: 0.797498 Loss2: 0.774747
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.550774 Loss1: 0.775769 Loss2: 0.775006
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.537187 Loss1: 0.764298 Loss2: 0.772889
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.513759 Loss1: 0.740009 Loss2: 0.773750
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.502633 Loss1: 0.726434 Loss2: 0.776199
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.511660 Loss1: 0.736871 Loss2: 0.774790
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.518611 Loss1: 0.741715 Loss2: 0.776896
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.492375 Loss1: 0.719407 Loss2: 0.772968
-[2023-09-27 07:04:28,784][flwr][DEBUG] - fit_round 6 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.750000
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.541900
-[2023-09-27 07:04:30,382][flwr][INFO] - fit progress: (6, 1.274572859556911, {'accuracy': 0.5419}, 2803.2188604199328)
-[2023-09-27 07:04:30,383][flwr][DEBUG] - evaluate_round 6: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:05:02,430][flwr][DEBUG] - evaluate_round 6 received 10 results and 0 failures
-[2023-09-27 07:05:02,431][flwr][DEBUG] - fit_round 7: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.435357 Loss1: 0.701093 Loss2: 0.734264
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.271574 Loss1: 0.612838 Loss2: 0.658736
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.270210 Loss1: 0.614429 Loss2: 0.655781
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.245928 Loss1: 0.588767 Loss2: 0.657161
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.225781 Loss1: 0.574590 Loss2: 0.651190
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.213438 Loss1: 0.561002 Loss2: 0.652436
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220872 Loss1: 0.567949 Loss2: 0.652923
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.228524 Loss1: 0.574701 Loss2: 0.653823
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211820 Loss1: 0.559020 Loss2: 0.652799
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193645 Loss1: 0.540066 Loss2: 0.653579
-(DefaultActor pid=1831567) >> Training accuracy: 0.829090
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.901637 Loss1: 1.080371 Loss2: 0.821265
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.745633 Loss1: 1.010138 Loss2: 0.735495
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.719805 Loss1: 0.987385 Loss2: 0.732420
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.702559 Loss1: 0.971044 Loss2: 0.731515
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.698351 Loss1: 0.966140 Loss2: 0.732211
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.678714 Loss1: 0.946500 Loss2: 0.732214
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.671743 Loss1: 0.937814 Loss2: 0.733929
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.648845 Loss1: 0.912542 Loss2: 0.736303
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.662639 Loss1: 0.924705 Loss2: 0.737935
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.673463 Loss1: 0.935931 Loss2: 0.737532
-(DefaultActor pid=1831567) >> Training accuracy: 0.685688
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.661436 Loss1: 0.890856 Loss2: 0.770580
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.493395 Loss1: 0.799773 Loss2: 0.693622
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.453340 Loss1: 0.764514 Loss2: 0.688826
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.466542 Loss1: 0.777008 Loss2: 0.689534
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.454732 Loss1: 0.763514 Loss2: 0.691218
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.440306 Loss1: 0.749435 Loss2: 0.690871
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.429030 Loss1: 0.738533 Loss2: 0.690497
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.432540 Loss1: 0.739575 Loss2: 0.692965
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.448988 Loss1: 0.751452 Loss2: 0.697536
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.416622 Loss1: 0.721873 Loss2: 0.694748
-(DefaultActor pid=1831567) >> Training accuracy: 0.754954
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386384 Loss1: 0.652351 Loss2: 0.734032
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.276321 Loss1: 0.613222 Loss2: 0.663099
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262983 Loss1: 0.603429 Loss2: 0.659554
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.240873 Loss1: 0.581801 Loss2: 0.659073
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.243162 Loss1: 0.582909 Loss2: 0.660254
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211481 Loss1: 0.552956 Loss2: 0.658525
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.209546 Loss1: 0.550977 Loss2: 0.658569
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202302 Loss1: 0.544093 Loss2: 0.658208
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205841 Loss1: 0.547620 Loss2: 0.658221
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189089 Loss1: 0.530188 Loss2: 0.658900
-(DefaultActor pid=1831567) >> Training accuracy: 0.809221
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.650251 Loss1: 0.893308 Loss2: 0.756944
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.521360 Loss1: 0.845774 Loss2: 0.675586
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.517851 Loss1: 0.841608 Loss2: 0.676244
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.493973 Loss1: 0.816693 Loss2: 0.677281
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.485495 Loss1: 0.802130 Loss2: 0.683366
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.468271 Loss1: 0.787280 Loss2: 0.680991
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.478236 Loss1: 0.795216 Loss2: 0.683020
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.471842 Loss1: 0.788707 Loss2: 0.683135
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.462458 Loss1: 0.780196 Loss2: 0.682261
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.417471 Loss1: 0.740012 Loss2: 0.677459
-(DefaultActor pid=1831567) >> Training accuracy: 0.758413
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.814517 Loss1: 1.037505 Loss2: 0.777011
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.636276 Loss1: 0.953649 Loss2: 0.682627
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.627110 Loss1: 0.949248 Loss2: 0.677862
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.599820 Loss1: 0.923121 Loss2: 0.676699
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.579760 Loss1: 0.909241 Loss2: 0.670519
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.542626 Loss1: 0.871414 Loss2: 0.671211
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.561024 Loss1: 0.884639 Loss2: 0.676385
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.554627 Loss1: 0.875202 Loss2: 0.679424
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.554946 Loss1: 0.875982 Loss2: 0.678964
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.526332 Loss1: 0.850930 Loss2: 0.675402
-(DefaultActor pid=1831567) >> Training accuracy: 0.671409
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.635260 Loss1: 0.869212 Loss2: 0.766048
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.480182 Loss1: 0.799306 Loss2: 0.680876
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.452572 Loss1: 0.777021 Loss2: 0.675551
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.484670 Loss1: 0.802922 Loss2: 0.681747
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.438803 Loss1: 0.762365 Loss2: 0.676438
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.442346 Loss1: 0.764025 Loss2: 0.678321
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.432606 Loss1: 0.751242 Loss2: 0.681364
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.415513 Loss1: 0.736573 Loss2: 0.678941
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.418011 Loss1: 0.738729 Loss2: 0.679283
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.397196 Loss1: 0.717054 Loss2: 0.680142
-(DefaultActor pid=1831567) >> Training accuracy: 0.769531
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.711397 Loss1: 0.897639 Loss2: 0.813758
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.558160 Loss1: 0.814727 Loss2: 0.743432
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.561732 Loss1: 0.815823 Loss2: 0.745909
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.542268 Loss1: 0.796264 Loss2: 0.746004
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.530906 Loss1: 0.784081 Loss2: 0.746825
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.513561 Loss1: 0.769874 Loss2: 0.743687
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.515595 Loss1: 0.768147 Loss2: 0.747448
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.513422 Loss1: 0.769002 Loss2: 0.744420
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.518798 Loss1: 0.771551 Loss2: 0.747247
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.492441 Loss1: 0.745859 Loss2: 0.746582
-(DefaultActor pid=1831567) >> Training accuracy: 0.742808
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.778866 Loss1: 0.977694 Loss2: 0.801172
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.607632 Loss1: 0.916037 Loss2: 0.691595
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.565199 Loss1: 0.885379 Loss2: 0.679820
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.583809 Loss1: 0.901504 Loss2: 0.682305
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.547217 Loss1: 0.865505 Loss2: 0.681712
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.513597 Loss1: 0.833575 Loss2: 0.680022
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.508989 Loss1: 0.832459 Loss2: 0.676530
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.516859 Loss1: 0.838394 Loss2: 0.678465
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.501376 Loss1: 0.821828 Loss2: 0.679548
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.559230 Loss1: 0.874286 Loss2: 0.684944
-(DefaultActor pid=1831567) >> Training accuracy: 0.722039
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.571614 Loss1: 0.862365 Loss2: 0.709249
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.415244 Loss1: 0.795467 Loss2: 0.619778
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.364745 Loss1: 0.752305 Loss2: 0.612440
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.373379 Loss1: 0.762340 Loss2: 0.611038
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.344513 Loss1: 0.734419 Loss2: 0.610094
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.330328 Loss1: 0.716853 Loss2: 0.613475
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.348404 Loss1: 0.733027 Loss2: 0.615378
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.307706 Loss1: 0.694162 Loss2: 0.613544
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.280494 Loss1: 0.665255 Loss2: 0.615239
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.296710 Loss1: 0.682354 Loss2: 0.614356
-[2023-09-27 07:11:58,454][flwr][DEBUG] - fit_round 7 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.765890
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.552100
-[2023-09-27 07:11:59,883][flwr][INFO] - fit progress: (7, 1.238289627785119, {'accuracy': 0.5521}, 3252.718985403888)
-[2023-09-27 07:11:59,883][flwr][DEBUG] - evaluate_round 7: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:12:32,066][flwr][DEBUG] - evaluate_round 7 received 10 results and 0 failures
-[2023-09-27 07:12:32,067][flwr][DEBUG] - fit_round 8: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.682072 Loss1: 0.811707 Loss2: 0.870364
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.520389 Loss1: 0.770071 Loss2: 0.750318
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.462655 Loss1: 0.727571 Loss2: 0.735085
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.447234 Loss1: 0.712887 Loss2: 0.734347
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.436632 Loss1: 0.702306 Loss2: 0.734326
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.437953 Loss1: 0.699939 Loss2: 0.738013
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.430649 Loss1: 0.693252 Loss2: 0.737398
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.423962 Loss1: 0.685660 Loss2: 0.738302
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.407612 Loss1: 0.667534 Loss2: 0.740078
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.404828 Loss1: 0.659479 Loss2: 0.745349
-(DefaultActor pid=1831567) >> Training accuracy: 0.768803
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.636150 Loss1: 0.847176 Loss2: 0.788974
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.485820 Loss1: 0.773230 Loss2: 0.712590
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.497597 Loss1: 0.782785 Loss2: 0.714812
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.472706 Loss1: 0.756444 Loss2: 0.716262
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.475003 Loss1: 0.759464 Loss2: 0.715539
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.452171 Loss1: 0.738188 Loss2: 0.713983
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.444683 Loss1: 0.729882 Loss2: 0.714801
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.458533 Loss1: 0.742746 Loss2: 0.715787
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.465312 Loss1: 0.745772 Loss2: 0.719540
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.446197 Loss1: 0.731413 Loss2: 0.714784
-(DefaultActor pid=1831567) >> Training accuracy: 0.751488
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.500320 Loss1: 0.676306 Loss2: 0.824014
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.315050 Loss1: 0.601702 Loss2: 0.713348
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.275389 Loss1: 0.569898 Loss2: 0.705491
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261817 Loss1: 0.558701 Loss2: 0.703117
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.260959 Loss1: 0.556146 Loss2: 0.704812
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.257460 Loss1: 0.557293 Loss2: 0.700167
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.251971 Loss1: 0.547799 Loss2: 0.704172
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.257791 Loss1: 0.550827 Loss2: 0.706964
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.241905 Loss1: 0.536175 Loss2: 0.705730
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.222244 Loss1: 0.518498 Loss2: 0.703746
-(DefaultActor pid=1831567) >> Training accuracy: 0.816744
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.684813 Loss1: 0.872279 Loss2: 0.812534
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.476752 Loss1: 0.786270 Loss2: 0.690482
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.439966 Loss1: 0.756122 Loss2: 0.683844
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.441683 Loss1: 0.758542 Loss2: 0.683140
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.421568 Loss1: 0.742133 Loss2: 0.679435
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.420592 Loss1: 0.737779 Loss2: 0.682813
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.400930 Loss1: 0.718572 Loss2: 0.682358
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.411389 Loss1: 0.727822 Loss2: 0.683567
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.384770 Loss1: 0.699433 Loss2: 0.685337
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.388786 Loss1: 0.702527 Loss2: 0.686259
-(DefaultActor pid=1831567) >> Training accuracy: 0.738758
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.868116 Loss1: 1.010244 Loss2: 0.857873
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.632811 Loss1: 0.916836 Loss2: 0.715976
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.629890 Loss1: 0.918655 Loss2: 0.711235
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.602153 Loss1: 0.895759 Loss2: 0.706394
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.585578 Loss1: 0.877208 Loss2: 0.708370
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.568751 Loss1: 0.860097 Loss2: 0.708654
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.549774 Loss1: 0.842258 Loss2: 0.707516
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.580390 Loss1: 0.867635 Loss2: 0.712755
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.569928 Loss1: 0.859313 Loss2: 0.710615
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.549115 Loss1: 0.837251 Loss2: 0.711864
-(DefaultActor pid=1831567) >> Training accuracy: 0.693563
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.702762 Loss1: 0.876985 Loss2: 0.825777
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.532387 Loss1: 0.821003 Loss2: 0.711384
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.527182 Loss1: 0.818863 Loss2: 0.708319
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.496480 Loss1: 0.791985 Loss2: 0.704495
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.498896 Loss1: 0.791425 Loss2: 0.707470
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.494125 Loss1: 0.788244 Loss2: 0.705880
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.441542 Loss1: 0.736152 Loss2: 0.705390
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.457928 Loss1: 0.753082 Loss2: 0.704846
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.466015 Loss1: 0.759737 Loss2: 0.706278
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.435423 Loss1: 0.730287 Loss2: 0.705136
-(DefaultActor pid=1831567) >> Training accuracy: 0.760216
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.814310 Loss1: 0.961312 Loss2: 0.852998
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.592058 Loss1: 0.890154 Loss2: 0.701904
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.555251 Loss1: 0.859059 Loss2: 0.696192
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.559418 Loss1: 0.865639 Loss2: 0.693779
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.507137 Loss1: 0.818503 Loss2: 0.688634
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.525827 Loss1: 0.831868 Loss2: 0.693960
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.530041 Loss1: 0.835894 Loss2: 0.694147
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.498407 Loss1: 0.807515 Loss2: 0.690892
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.487322 Loss1: 0.796554 Loss2: 0.690768
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.482338 Loss1: 0.791043 Loss2: 0.691294
-(DefaultActor pid=1831567) >> Training accuracy: 0.712993
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.507601 Loss1: 0.697145 Loss2: 0.810456
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.271276 Loss1: 0.588720 Loss2: 0.682556
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.272344 Loss1: 0.591921 Loss2: 0.680423
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231021 Loss1: 0.554408 Loss2: 0.676613
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.271260 Loss1: 0.592121 Loss2: 0.679138
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.215031 Loss1: 0.535014 Loss2: 0.680018
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.211846 Loss1: 0.533333 Loss2: 0.678513
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207193 Loss1: 0.528694 Loss2: 0.678499
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211935 Loss1: 0.532695 Loss2: 0.679240
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.207777 Loss1: 0.530075 Loss2: 0.677702
-(DefaultActor pid=1831567) >> Training accuracy: 0.820795
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.634720 Loss1: 0.851593 Loss2: 0.783127
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.450865 Loss1: 0.777118 Loss2: 0.673747
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.424778 Loss1: 0.755693 Loss2: 0.669085
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.438706 Loss1: 0.766788 Loss2: 0.671918
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.392925 Loss1: 0.724328 Loss2: 0.668598
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.408994 Loss1: 0.738156 Loss2: 0.670838
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.411233 Loss1: 0.739364 Loss2: 0.671869
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.400411 Loss1: 0.725070 Loss2: 0.675341
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.367422 Loss1: 0.695461 Loss2: 0.671962
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.358954 Loss1: 0.688854 Loss2: 0.670101
-(DefaultActor pid=1831567) >> Training accuracy: 0.773849
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.838495 Loss1: 1.035475 Loss2: 0.803021
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.686672 Loss1: 1.000554 Loss2: 0.686119
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.642826 Loss1: 0.962633 Loss2: 0.680193
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.630633 Loss1: 0.950282 Loss2: 0.680351
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.636780 Loss1: 0.953487 Loss2: 0.683293
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.615341 Loss1: 0.933494 Loss2: 0.681847
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.592805 Loss1: 0.911235 Loss2: 0.681571
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.604849 Loss1: 0.919874 Loss2: 0.684974
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.584868 Loss1: 0.899313 Loss2: 0.685555
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.590336 Loss1: 0.906307 Loss2: 0.684029
-[2023-09-27 07:19:56,785][flwr][DEBUG] - fit_round 8 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.694067
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.587200
-[2023-09-27 07:19:58,773][flwr][INFO] - fit progress: (8, 1.1646041104587883, {'accuracy': 0.5872}, 3731.609143916052)
-[2023-09-27 07:19:58,773][flwr][DEBUG] - evaluate_round 8: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:20:32,157][flwr][DEBUG] - evaluate_round 8 received 10 results and 0 failures
-[2023-09-27 07:20:32,158][flwr][DEBUG] - fit_round 9: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.767282 Loss1: 0.963873 Loss2: 0.803410
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.556373 Loss1: 0.866648 Loss2: 0.689726
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.526153 Loss1: 0.838665 Loss2: 0.687488
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.515749 Loss1: 0.828779 Loss2: 0.686970
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.489376 Loss1: 0.800452 Loss2: 0.688924
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.515557 Loss1: 0.829296 Loss2: 0.686260
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.480050 Loss1: 0.795593 Loss2: 0.684457
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.453588 Loss1: 0.770481 Loss2: 0.683107
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.470970 Loss1: 0.781356 Loss2: 0.689614
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.433652 Loss1: 0.746402 Loss2: 0.687251
-(DefaultActor pid=1831567) >> Training accuracy: 0.712171
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.550982 Loss1: 0.819620 Loss2: 0.731362
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370935 Loss1: 0.735542 Loss2: 0.635392
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354556 Loss1: 0.724084 Loss2: 0.630471
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347261 Loss1: 0.714055 Loss2: 0.633206
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.342652 Loss1: 0.709036 Loss2: 0.633615
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.324781 Loss1: 0.687702 Loss2: 0.637079
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309977 Loss1: 0.675268 Loss2: 0.634709
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.316721 Loss1: 0.685143 Loss2: 0.631578
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.297810 Loss1: 0.664965 Loss2: 0.632845
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.283077 Loss1: 0.648773 Loss2: 0.634304
-(DefaultActor pid=1831567) >> Training accuracy: 0.767479
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.555717 Loss1: 0.836286 Loss2: 0.719432
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.407840 Loss1: 0.752557 Loss2: 0.655283
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.404346 Loss1: 0.746881 Loss2: 0.657465
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.384621 Loss1: 0.731089 Loss2: 0.653532
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.375291 Loss1: 0.718986 Loss2: 0.656305
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.391921 Loss1: 0.732660 Loss2: 0.659262
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.364259 Loss1: 0.704538 Loss2: 0.659721
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.357560 Loss1: 0.698481 Loss2: 0.659079
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.354468 Loss1: 0.694350 Loss2: 0.660118
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.338396 Loss1: 0.679380 Loss2: 0.659015
-(DefaultActor pid=1831567) >> Training accuracy: 0.758003
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.368403 Loss1: 0.620856 Loss2: 0.747547
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.246010 Loss1: 0.573449 Loss2: 0.672561
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.253389 Loss1: 0.582575 Loss2: 0.670814
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.244390 Loss1: 0.574049 Loss2: 0.670342
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.225271 Loss1: 0.555834 Loss2: 0.669436
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.215358 Loss1: 0.544428 Loss2: 0.670930
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197070 Loss1: 0.528094 Loss2: 0.668976
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185178 Loss1: 0.517247 Loss2: 0.667931
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.191626 Loss1: 0.520115 Loss2: 0.671511
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193648 Loss1: 0.522101 Loss2: 0.671547
-(DefaultActor pid=1831567) >> Training accuracy: 0.808063
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.777046 Loss1: 0.992993 Loss2: 0.784052
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.674895 Loss1: 0.971955 Loss2: 0.702941
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.647737 Loss1: 0.944167 Loss2: 0.703570
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.634876 Loss1: 0.927783 Loss2: 0.707093
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.623571 Loss1: 0.919684 Loss2: 0.703887
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.609314 Loss1: 0.905741 Loss2: 0.703573
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.601217 Loss1: 0.897688 Loss2: 0.703529
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.577935 Loss1: 0.871736 Loss2: 0.706199
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.590916 Loss1: 0.881814 Loss2: 0.709102
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.591979 Loss1: 0.880545 Loss2: 0.711434
-(DefaultActor pid=1831567) >> Training accuracy: 0.680480
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.562885 Loss1: 0.799018 Loss2: 0.763867
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.448291 Loss1: 0.765638 Loss2: 0.682653
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.410554 Loss1: 0.730680 Loss2: 0.679874
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.445275 Loss1: 0.761781 Loss2: 0.683494
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.398899 Loss1: 0.717315 Loss2: 0.681584
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.407695 Loss1: 0.724762 Loss2: 0.682933
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.414380 Loss1: 0.731791 Loss2: 0.682589
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.390353 Loss1: 0.705806 Loss2: 0.684546
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.369140 Loss1: 0.685658 Loss2: 0.683483
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.382612 Loss1: 0.695342 Loss2: 0.687270
-(DefaultActor pid=1831567) >> Training accuracy: 0.783100
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.387370 Loss1: 0.653918 Loss2: 0.733451
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243797 Loss1: 0.588296 Loss2: 0.655500
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218522 Loss1: 0.567125 Loss2: 0.651397
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210604 Loss1: 0.558838 Loss2: 0.651766
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.212335 Loss1: 0.560158 Loss2: 0.652177
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.198708 Loss1: 0.544851 Loss2: 0.653857
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.183338 Loss1: 0.530219 Loss2: 0.653119
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.172903 Loss1: 0.519848 Loss2: 0.653055
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165200 Loss1: 0.511362 Loss2: 0.653838
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172382 Loss1: 0.518499 Loss2: 0.653882
-(DefaultActor pid=1831567) >> Training accuracy: 0.823110
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.629405 Loss1: 0.851086 Loss2: 0.778319
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.489852 Loss1: 0.792948 Loss2: 0.696904
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.474484 Loss1: 0.779979 Loss2: 0.694504
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.450436 Loss1: 0.757155 Loss2: 0.693281
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.457664 Loss1: 0.759300 Loss2: 0.698365
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.444785 Loss1: 0.745912 Loss2: 0.698873
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.423247 Loss1: 0.724367 Loss2: 0.698879
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.442809 Loss1: 0.741324 Loss2: 0.701485
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.404128 Loss1: 0.703771 Loss2: 0.700356
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.445211 Loss1: 0.739935 Loss2: 0.705275
-(DefaultActor pid=1831567) >> Training accuracy: 0.739984
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.553776 Loss1: 0.835918 Loss2: 0.717858
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.433910 Loss1: 0.773012 Loss2: 0.660897
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.413715 Loss1: 0.751614 Loss2: 0.662101
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.414150 Loss1: 0.752723 Loss2: 0.661427
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.400037 Loss1: 0.737468 Loss2: 0.662569
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.395023 Loss1: 0.732583 Loss2: 0.662439
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.383715 Loss1: 0.722674 Loss2: 0.661041
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.389653 Loss1: 0.728063 Loss2: 0.661590
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.376249 Loss1: 0.713708 Loss2: 0.662540
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.383339 Loss1: 0.721227 Loss2: 0.662112
-(DefaultActor pid=1831567) >> Training accuracy: 0.756820
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.732463 Loss1: 0.964378 Loss2: 0.768086
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.574690 Loss1: 0.901147 Loss2: 0.673543
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.538939 Loss1: 0.870904 Loss2: 0.668036
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.540208 Loss1: 0.868575 Loss2: 0.671634
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.510269 Loss1: 0.843574 Loss2: 0.666696
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.513781 Loss1: 0.842776 Loss2: 0.671005
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.514410 Loss1: 0.844745 Loss2: 0.669665
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.510831 Loss1: 0.839799 Loss2: 0.671032
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.476011 Loss1: 0.808263 Loss2: 0.667749
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.490790 Loss1: 0.822013 Loss2: 0.668777
-[2023-09-27 07:27:40,836][flwr][DEBUG] - fit_round 9 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.670942
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.579100
-[2023-09-27 07:27:42,444][flwr][INFO] - fit progress: (9, 1.1750041659647665, {'accuracy': 0.5791}, 4195.280460507143)
-[2023-09-27 07:27:42,444][flwr][DEBUG] - evaluate_round 9: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:28:14,012][flwr][DEBUG] - evaluate_round 9 received 10 results and 0 failures
-[2023-09-27 07:28:14,012][flwr][DEBUG] - fit_round 10: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.815599 Loss1: 1.012174 Loss2: 0.803425
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.627114 Loss1: 0.937628 Loss2: 0.689486
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.628370 Loss1: 0.937943 Loss2: 0.690427
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.589757 Loss1: 0.902892 Loss2: 0.686865
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.598415 Loss1: 0.907724 Loss2: 0.690691
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.594773 Loss1: 0.902901 Loss2: 0.691873
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.573926 Loss1: 0.884511 Loss2: 0.689415
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.542708 Loss1: 0.853537 Loss2: 0.689171
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.558470 Loss1: 0.864553 Loss2: 0.693917
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.544092 Loss1: 0.851461 Loss2: 0.692632
-(DefaultActor pid=1831567) >> Training accuracy: 0.710598
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.612394 Loss1: 0.851570 Loss2: 0.760824
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.447102 Loss1: 0.790546 Loss2: 0.656556
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.440679 Loss1: 0.787215 Loss2: 0.653464
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.427512 Loss1: 0.774620 Loss2: 0.652892
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.394972 Loss1: 0.744030 Loss2: 0.650942
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.395143 Loss1: 0.745263 Loss2: 0.649880
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.358024 Loss1: 0.707940 Loss2: 0.650084
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.354622 Loss1: 0.706946 Loss2: 0.647676
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.361081 Loss1: 0.710356 Loss2: 0.650725
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.373208 Loss1: 0.718682 Loss2: 0.654525
-(DefaultActor pid=1831567) >> Training accuracy: 0.772636
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.577577 Loss1: 0.789356 Loss2: 0.788221
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.469519 Loss1: 0.752697 Loss2: 0.716822
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.459752 Loss1: 0.742093 Loss2: 0.717659
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.456028 Loss1: 0.740459 Loss2: 0.715569
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.447290 Loss1: 0.728944 Loss2: 0.718346
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.440171 Loss1: 0.722429 Loss2: 0.717742
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.434871 Loss1: 0.716208 Loss2: 0.718663
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.424500 Loss1: 0.704038 Loss2: 0.720462
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.424456 Loss1: 0.704341 Loss2: 0.720116
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.403840 Loss1: 0.685772 Loss2: 0.718067
-(DefaultActor pid=1831567) >> Training accuracy: 0.750372
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.603985 Loss1: 0.774449 Loss2: 0.829536
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.418736 Loss1: 0.707712 Loss2: 0.711024
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.382571 Loss1: 0.676302 Loss2: 0.706269
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.402931 Loss1: 0.696496 Loss2: 0.706434
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.394770 Loss1: 0.688370 Loss2: 0.706400
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.342543 Loss1: 0.637872 Loss2: 0.704671
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.346455 Loss1: 0.641350 Loss2: 0.705105
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.348748 Loss1: 0.641029 Loss2: 0.707720
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.375281 Loss1: 0.663269 Loss2: 0.712012
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.340315 Loss1: 0.630444 Loss2: 0.709872
-(DefaultActor pid=1831567) >> Training accuracy: 0.789725
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.893280 Loss1: 0.974201 Loss2: 0.919079
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.659243 Loss1: 0.880718 Loss2: 0.778525
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.657245 Loss1: 0.882756 Loss2: 0.774489
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.630225 Loss1: 0.860158 Loss2: 0.770067
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.590538 Loss1: 0.824885 Loss2: 0.765653
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.619691 Loss1: 0.848464 Loss2: 0.771227
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.624928 Loss1: 0.851318 Loss2: 0.773610
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.588176 Loss1: 0.818277 Loss2: 0.769898
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.580922 Loss1: 0.805782 Loss2: 0.775139
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.568940 Loss1: 0.799353 Loss2: 0.769587
-(DefaultActor pid=1831567) >> Training accuracy: 0.690765
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488231 Loss1: 0.669017 Loss2: 0.819214
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.278396 Loss1: 0.579735 Loss2: 0.698661
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.243164 Loss1: 0.547770 Loss2: 0.695394
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.227678 Loss1: 0.534646 Loss2: 0.693032
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.220596 Loss1: 0.526460 Loss2: 0.694136
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232642 Loss1: 0.536260 Loss2: 0.696382
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.208526 Loss1: 0.515715 Loss2: 0.692811
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229012 Loss1: 0.530883 Loss2: 0.698129
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.194903 Loss1: 0.501364 Loss2: 0.693538
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201546 Loss1: 0.506781 Loss2: 0.694765
-(DefaultActor pid=1831567) >> Training accuracy: 0.790509
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.458632 Loss1: 0.638149 Loss2: 0.820482
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.294972 Loss1: 0.580683 Loss2: 0.714289
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262323 Loss1: 0.558810 Loss2: 0.703513
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.248409 Loss1: 0.544596 Loss2: 0.703814
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.238214 Loss1: 0.536469 Loss2: 0.701746
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231400 Loss1: 0.530637 Loss2: 0.700764
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.214380 Loss1: 0.511745 Loss2: 0.702635
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222912 Loss1: 0.517069 Loss2: 0.705843
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.231532 Loss1: 0.528483 Loss2: 0.703049
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214245 Loss1: 0.507804 Loss2: 0.706442
-(DefaultActor pid=1831567) >> Training accuracy: 0.810571
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.630765 Loss1: 0.820427 Loss2: 0.810338
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.436797 Loss1: 0.747638 Loss2: 0.689159
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.409270 Loss1: 0.728725 Loss2: 0.680545
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.389757 Loss1: 0.707515 Loss2: 0.682241
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.404856 Loss1: 0.723527 Loss2: 0.681329
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.369030 Loss1: 0.686498 Loss2: 0.682532
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.364501 Loss1: 0.682721 Loss2: 0.681781
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.371983 Loss1: 0.691776 Loss2: 0.680207
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.356249 Loss1: 0.673448 Loss2: 0.682801
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.367241 Loss1: 0.682338 Loss2: 0.684902
-(DefaultActor pid=1831567) >> Training accuracy: 0.766387
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.772556 Loss1: 0.930209 Loss2: 0.842347
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.573863 Loss1: 0.862785 Loss2: 0.711078
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.523109 Loss1: 0.819599 Loss2: 0.703509
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.498672 Loss1: 0.797400 Loss2: 0.701271
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.536996 Loss1: 0.833562 Loss2: 0.703434
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.503271 Loss1: 0.799313 Loss2: 0.703959
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.510294 Loss1: 0.805138 Loss2: 0.705156
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.481032 Loss1: 0.775325 Loss2: 0.705707
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.485884 Loss1: 0.779005 Loss2: 0.706879
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.469104 Loss1: 0.763691 Loss2: 0.705413
-(DefaultActor pid=1831567) >> Training accuracy: 0.731086
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.614144 Loss1: 0.852872 Loss2: 0.761273
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.393520 Loss1: 0.734116 Loss2: 0.659404
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.392818 Loss1: 0.732513 Loss2: 0.660305
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.392237 Loss1: 0.731855 Loss2: 0.660382
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.391193 Loss1: 0.729649 Loss2: 0.661544
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.355547 Loss1: 0.694652 Loss2: 0.660894
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352225 Loss1: 0.693210 Loss2: 0.659015
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.369125 Loss1: 0.708619 Loss2: 0.660506
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.328146 Loss1: 0.668769 Loss2: 0.659377
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329398 Loss1: 0.668360 Loss2: 0.661037
-[2023-09-27 07:35:34,969][flwr][DEBUG] - fit_round 10 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.780428
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.600800
-[2023-09-27 07:35:36,460][flwr][INFO] - fit progress: (10, 1.128453626800269, {'accuracy': 0.6008}, 4669.296330975834)
-[2023-09-27 07:35:36,461][flwr][DEBUG] - evaluate_round 10: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:36:07,726][flwr][DEBUG] - evaluate_round 10 received 10 results and 0 failures
-[2023-09-27 07:36:07,727][flwr][DEBUG] - fit_round 11: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.533785 Loss1: 0.797140 Loss2: 0.736645
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.402241 Loss1: 0.729774 Loss2: 0.672468
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.378221 Loss1: 0.709039 Loss2: 0.669182
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.378378 Loss1: 0.708024 Loss2: 0.670354
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.364618 Loss1: 0.692593 Loss2: 0.672024
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.355564 Loss1: 0.682952 Loss2: 0.672612
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.361993 Loss1: 0.684666 Loss2: 0.677327
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347081 Loss1: 0.672100 Loss2: 0.674981
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.357602 Loss1: 0.681353 Loss2: 0.676250
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.337621 Loss1: 0.660798 Loss2: 0.676823
-(DefaultActor pid=1831567) >> Training accuracy: 0.783918
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.679884 Loss1: 0.926363 Loss2: 0.753521
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.528575 Loss1: 0.868938 Loss2: 0.659637
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.516750 Loss1: 0.859409 Loss2: 0.657340
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.528537 Loss1: 0.873242 Loss2: 0.655295
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.506278 Loss1: 0.850723 Loss2: 0.655555
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.462943 Loss1: 0.809983 Loss2: 0.652960
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.470518 Loss1: 0.813514 Loss2: 0.657004
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.476389 Loss1: 0.818866 Loss2: 0.657524
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.476091 Loss1: 0.816944 Loss2: 0.659146
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.478762 Loss1: 0.820048 Loss2: 0.658714
-(DefaultActor pid=1831567) >> Training accuracy: 0.701259
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.552934 Loss1: 0.793166 Loss2: 0.759769
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.410739 Loss1: 0.755692 Loss2: 0.655047
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363047 Loss1: 0.710858 Loss2: 0.652189
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.355318 Loss1: 0.703677 Loss2: 0.651641
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.309705 Loss1: 0.656545 Loss2: 0.653160
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.278812 Loss1: 0.629880 Loss2: 0.648932
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295662 Loss1: 0.642231 Loss2: 0.653431
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289132 Loss1: 0.635929 Loss2: 0.653203
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.276769 Loss1: 0.626963 Loss2: 0.649806
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.277223 Loss1: 0.623794 Loss2: 0.653429
-(DefaultActor pid=1831567) >> Training accuracy: 0.773040
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.780977 Loss1: 0.985803 Loss2: 0.795173
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.635304 Loss1: 0.922934 Loss2: 0.712370
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.626476 Loss1: 0.912382 Loss2: 0.714094
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.606571 Loss1: 0.891289 Loss2: 0.715282
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.588978 Loss1: 0.874679 Loss2: 0.714299
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.588099 Loss1: 0.872926 Loss2: 0.715173
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.567798 Loss1: 0.853443 Loss2: 0.714354
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.594739 Loss1: 0.873223 Loss2: 0.721517
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.578093 Loss1: 0.857344 Loss2: 0.720749
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.558957 Loss1: 0.840045 Loss2: 0.718913
-(DefaultActor pid=1831567) >> Training accuracy: 0.701766
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.617676 Loss1: 0.813111 Loss2: 0.804565
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.468541 Loss1: 0.749242 Loss2: 0.719298
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.468599 Loss1: 0.749154 Loss2: 0.719445
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.436253 Loss1: 0.720590 Loss2: 0.715663
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.429364 Loss1: 0.710381 Loss2: 0.718983
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.445850 Loss1: 0.721367 Loss2: 0.724483
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.417447 Loss1: 0.694709 Loss2: 0.722738
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.457138 Loss1: 0.732254 Loss2: 0.724884
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.413713 Loss1: 0.692159 Loss2: 0.721554
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.401992 Loss1: 0.677226 Loss2: 0.724765
-(DefaultActor pid=1831567) >> Training accuracy: 0.773037
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.531234 Loss1: 0.766907 Loss2: 0.764326
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.419242 Loss1: 0.736564 Loss2: 0.682677
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.377321 Loss1: 0.701734 Loss2: 0.675587
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.357220 Loss1: 0.680718 Loss2: 0.676502
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.364312 Loss1: 0.687887 Loss2: 0.676424
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341366 Loss1: 0.663260 Loss2: 0.678107
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.361421 Loss1: 0.680698 Loss2: 0.680723
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.357961 Loss1: 0.677994 Loss2: 0.679967
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.340704 Loss1: 0.658543 Loss2: 0.682160
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.343161 Loss1: 0.659754 Loss2: 0.683407
-(DefaultActor pid=1831567) >> Training accuracy: 0.784951
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.555648 Loss1: 0.781224 Loss2: 0.774424
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.457894 Loss1: 0.743054 Loss2: 0.714840
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.435514 Loss1: 0.721943 Loss2: 0.713571
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.433552 Loss1: 0.720266 Loss2: 0.713286
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.415315 Loss1: 0.703652 Loss2: 0.711664
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.436546 Loss1: 0.717354 Loss2: 0.719192
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.403619 Loss1: 0.688933 Loss2: 0.714686
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.417846 Loss1: 0.700225 Loss2: 0.717621
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.405390 Loss1: 0.688833 Loss2: 0.716557
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.396222 Loss1: 0.678921 Loss2: 0.717301
-(DefaultActor pid=1831567) >> Training accuracy: 0.768229
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348304 Loss1: 0.618043 Loss2: 0.730260
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.231708 Loss1: 0.573988 Loss2: 0.657720
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195189 Loss1: 0.543899 Loss2: 0.651290
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175077 Loss1: 0.525287 Loss2: 0.649790
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163480 Loss1: 0.513460 Loss2: 0.650020
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.166208 Loss1: 0.513266 Loss2: 0.652942
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161944 Loss1: 0.509201 Loss2: 0.652743
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162438 Loss1: 0.507985 Loss2: 0.654453
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161619 Loss1: 0.508648 Loss2: 0.652971
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136332 Loss1: 0.483603 Loss2: 0.652729
-(DefaultActor pid=1831567) >> Training accuracy: 0.816165
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.347200 Loss1: 0.630041 Loss2: 0.717159
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202751 Loss1: 0.558152 Loss2: 0.644599
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173849 Loss1: 0.531575 Loss2: 0.642274
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.182334 Loss1: 0.535654 Loss2: 0.646680
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.164860 Loss1: 0.518634 Loss2: 0.646226
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.155684 Loss1: 0.510339 Loss2: 0.645345
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.137568 Loss1: 0.489980 Loss2: 0.647589
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163274 Loss1: 0.514293 Loss2: 0.648982
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140543 Loss1: 0.490210 Loss2: 0.650333
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.138334 Loss1: 0.488841 Loss2: 0.649493
-(DefaultActor pid=1831567) >> Training accuracy: 0.834298
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.680971 Loss1: 0.903186 Loss2: 0.777785
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.499720 Loss1: 0.826270 Loss2: 0.673450
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.481330 Loss1: 0.811912 Loss2: 0.669417
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.498143 Loss1: 0.830680 Loss2: 0.667463
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.441093 Loss1: 0.776281 Loss2: 0.664813
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.460735 Loss1: 0.791239 Loss2: 0.669495
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.459340 Loss1: 0.788289 Loss2: 0.671051
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.469777 Loss1: 0.797680 Loss2: 0.672096
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.428577 Loss1: 0.752570 Loss2: 0.676008
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.416014 Loss1: 0.745213 Loss2: 0.670801
-[2023-09-27 07:43:04,575][flwr][DEBUG] - fit_round 11 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.746162
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.597800
-[2023-09-27 07:43:06,596][flwr][INFO] - fit progress: (11, 1.1231593939062126, {'accuracy': 0.5978}, 5119.431959697045)
-[2023-09-27 07:43:06,597][flwr][DEBUG] - evaluate_round 11: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:43:38,780][flwr][DEBUG] - evaluate_round 11 received 10 results and 0 failures
-[2023-09-27 07:43:38,781][flwr][DEBUG] - fit_round 12: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.560323 Loss1: 0.798809 Loss2: 0.761514
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.424971 Loss1: 0.754416 Loss2: 0.670555
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.422624 Loss1: 0.757047 Loss2: 0.665577
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.365493 Loss1: 0.703241 Loss2: 0.662252
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.373474 Loss1: 0.709200 Loss2: 0.664274
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362990 Loss1: 0.697882 Loss2: 0.665108
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.349536 Loss1: 0.683769 Loss2: 0.665767
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.364160 Loss1: 0.698090 Loss2: 0.666069
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.359105 Loss1: 0.688556 Loss2: 0.670549
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.358418 Loss1: 0.687509 Loss2: 0.670909
-(DefaultActor pid=1831567) >> Training accuracy: 0.769431
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.765948 Loss1: 0.916799 Loss2: 0.849150
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.536700 Loss1: 0.827548 Loss2: 0.709152
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.525938 Loss1: 0.819496 Loss2: 0.706442
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.489261 Loss1: 0.782236 Loss2: 0.707025
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.495004 Loss1: 0.789078 Loss2: 0.705926
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.460037 Loss1: 0.755036 Loss2: 0.705001
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.474926 Loss1: 0.768486 Loss2: 0.706440
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.452758 Loss1: 0.750684 Loss2: 0.702074
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.439569 Loss1: 0.731186 Loss2: 0.708383
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.452487 Loss1: 0.743884 Loss2: 0.708603
-(DefaultActor pid=1831567) >> Training accuracy: 0.748081
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.518506 Loss1: 0.754237 Loss2: 0.764270
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.374456 Loss1: 0.699560 Loss2: 0.674896
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.374428 Loss1: 0.701081 Loss2: 0.673347
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364044 Loss1: 0.690054 Loss2: 0.673991
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.334347 Loss1: 0.659737 Loss2: 0.674610
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.350395 Loss1: 0.676687 Loss2: 0.673708
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.332741 Loss1: 0.657140 Loss2: 0.675601
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.317960 Loss1: 0.642643 Loss2: 0.675317
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.338629 Loss1: 0.662017 Loss2: 0.676612
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.325917 Loss1: 0.648431 Loss2: 0.677487
-(DefaultActor pid=1831567) >> Training accuracy: 0.795641
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.409219 Loss1: 0.598002 Loss2: 0.811217
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236592 Loss1: 0.537354 Loss2: 0.699238
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226524 Loss1: 0.532725 Loss2: 0.693799
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202579 Loss1: 0.505976 Loss2: 0.696602
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201397 Loss1: 0.507463 Loss2: 0.693935
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191179 Loss1: 0.496941 Loss2: 0.694238
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.186290 Loss1: 0.487731 Loss2: 0.698558
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.183493 Loss1: 0.488124 Loss2: 0.695369
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.177810 Loss1: 0.483808 Loss2: 0.694003
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.182589 Loss1: 0.484876 Loss2: 0.697714
-(DefaultActor pid=1831567) >> Training accuracy: 0.837191
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.760507 Loss1: 0.970174 Loss2: 0.790333
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.595966 Loss1: 0.914477 Loss2: 0.681489
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.606599 Loss1: 0.923381 Loss2: 0.683218
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.571381 Loss1: 0.891533 Loss2: 0.679848
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.551802 Loss1: 0.870529 Loss2: 0.681273
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.559432 Loss1: 0.877836 Loss2: 0.681596
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.541575 Loss1: 0.861114 Loss2: 0.680462
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.521583 Loss1: 0.841282 Loss2: 0.680301
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.503648 Loss1: 0.823852 Loss2: 0.679796
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.535044 Loss1: 0.852418 Loss2: 0.682626
-(DefaultActor pid=1831567) >> Training accuracy: 0.711051
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.586741 Loss1: 0.762426 Loss2: 0.824315
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.403363 Loss1: 0.696723 Loss2: 0.706640
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.375464 Loss1: 0.672496 Loss2: 0.702968
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.362980 Loss1: 0.657039 Loss2: 0.705941
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.366239 Loss1: 0.662760 Loss2: 0.703480
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.336160 Loss1: 0.629010 Loss2: 0.707150
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.339857 Loss1: 0.634776 Loss2: 0.705081
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.328460 Loss1: 0.620929 Loss2: 0.707531
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306822 Loss1: 0.600272 Loss2: 0.706550
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.319197 Loss1: 0.611518 Loss2: 0.707679
-(DefaultActor pid=1831567) >> Training accuracy: 0.792903
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.575427 Loss1: 0.784921 Loss2: 0.790507
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.438122 Loss1: 0.714344 Loss2: 0.723779
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.421260 Loss1: 0.700394 Loss2: 0.720866
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.424372 Loss1: 0.701936 Loss2: 0.722436
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.436689 Loss1: 0.711501 Loss2: 0.725187
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.407652 Loss1: 0.683392 Loss2: 0.724260
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.408762 Loss1: 0.684407 Loss2: 0.724355
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.408258 Loss1: 0.683536 Loss2: 0.724722
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.409017 Loss1: 0.683464 Loss2: 0.725553
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.402606 Loss1: 0.676303 Loss2: 0.726303
-(DefaultActor pid=1831567) >> Training accuracy: 0.773562
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.599258 Loss1: 0.804549 Loss2: 0.794709
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.424250 Loss1: 0.743235 Loss2: 0.681014
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.375731 Loss1: 0.702497 Loss2: 0.673234
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.381837 Loss1: 0.707335 Loss2: 0.674502
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.351053 Loss1: 0.677990 Loss2: 0.673063
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.359974 Loss1: 0.686369 Loss2: 0.673604
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.335291 Loss1: 0.663661 Loss2: 0.671629
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.346949 Loss1: 0.673001 Loss2: 0.673948
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.321656 Loss1: 0.647823 Loss2: 0.673833
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.327283 Loss1: 0.652546 Loss2: 0.674737
-(DefaultActor pid=1831567) >> Training accuracy: 0.782965
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.784347 Loss1: 0.940359 Loss2: 0.843988
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.574727 Loss1: 0.859676 Loss2: 0.715051
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.555978 Loss1: 0.849322 Loss2: 0.706656
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.544357 Loss1: 0.836248 Loss2: 0.708109
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.570737 Loss1: 0.859714 Loss2: 0.711023
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.508001 Loss1: 0.799999 Loss2: 0.708002
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.518251 Loss1: 0.808246 Loss2: 0.710005
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.520710 Loss1: 0.808047 Loss2: 0.712663
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.503142 Loss1: 0.791628 Loss2: 0.711514
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.490204 Loss1: 0.778082 Loss2: 0.712121
-(DefaultActor pid=1831567) >> Training accuracy: 0.703358
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.441816 Loss1: 0.618548 Loss2: 0.823268
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.273062 Loss1: 0.554410 Loss2: 0.718652
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.246438 Loss1: 0.533174 Loss2: 0.713264
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237757 Loss1: 0.527019 Loss2: 0.710738
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.206606 Loss1: 0.496767 Loss2: 0.709839
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.209470 Loss1: 0.500079 Loss2: 0.709391
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.203409 Loss1: 0.493276 Loss2: 0.710133
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.188493 Loss1: 0.478090 Loss2: 0.710403
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183159 Loss1: 0.473446 Loss2: 0.709713
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.219679 Loss1: 0.505808 Loss2: 0.713870
-(DefaultActor pid=1831567) >> Training accuracy: 0.815972
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 07:50:36,714][flwr][DEBUG] - fit_round 12 received 10 results and 0 failures
->> Test accuracy: 0.612200
-[2023-09-27 07:50:38,307][flwr][INFO] - fit progress: (12, 1.0893270417143361, {'accuracy': 0.6122}, 5571.143073525745)
-[2023-09-27 07:50:38,307][flwr][DEBUG] - evaluate_round 12: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:51:10,006][flwr][DEBUG] - evaluate_round 12 received 10 results and 0 failures
-[2023-09-27 07:51:10,007][flwr][DEBUG] - fit_round 13: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.725892 Loss1: 0.934786 Loss2: 0.791106
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.587016 Loss1: 0.877035 Loss2: 0.709981
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.590304 Loss1: 0.878714 Loss2: 0.711590
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.574611 Loss1: 0.863718 Loss2: 0.710893
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.583946 Loss1: 0.872142 Loss2: 0.711803
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.580666 Loss1: 0.866145 Loss2: 0.714522
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.546354 Loss1: 0.836429 Loss2: 0.709925
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.555712 Loss1: 0.841188 Loss2: 0.714525
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.554583 Loss1: 0.836809 Loss2: 0.717774
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.545713 Loss1: 0.830436 Loss2: 0.715277
-(DefaultActor pid=1831567) >> Training accuracy: 0.686821
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.323828 Loss1: 0.610688 Loss2: 0.713139
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.160142 Loss1: 0.522676 Loss2: 0.637466
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.141188 Loss1: 0.508331 Loss2: 0.632857
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.142863 Loss1: 0.509955 Loss2: 0.632908
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.121871 Loss1: 0.487687 Loss2: 0.634184
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.141098 Loss1: 0.505057 Loss2: 0.636041
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.126344 Loss1: 0.492324 Loss2: 0.634020
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.118672 Loss1: 0.484499 Loss2: 0.634173
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.114078 Loss1: 0.480182 Loss2: 0.633896
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107437 Loss1: 0.472479 Loss2: 0.634958
-(DefaultActor pid=1831567) >> Training accuracy: 0.828897
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.512097 Loss1: 0.793155 Loss2: 0.718942
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.362093 Loss1: 0.706694 Loss2: 0.655398
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.343615 Loss1: 0.687345 Loss2: 0.656270
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339117 Loss1: 0.683525 Loss2: 0.655592
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.328873 Loss1: 0.673185 Loss2: 0.655688
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.314614 Loss1: 0.657937 Loss2: 0.656677
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.311882 Loss1: 0.654168 Loss2: 0.657714
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.317371 Loss1: 0.658308 Loss2: 0.659063
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.333581 Loss1: 0.673245 Loss2: 0.660337
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290425 Loss1: 0.628057 Loss2: 0.662367
-(DefaultActor pid=1831567) >> Training accuracy: 0.790968
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.717419 Loss1: 0.921159 Loss2: 0.796260
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.552100 Loss1: 0.855224 Loss2: 0.696876
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.529100 Loss1: 0.836191 Loss2: 0.692909
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.512662 Loss1: 0.820619 Loss2: 0.692043
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.481028 Loss1: 0.791659 Loss2: 0.689369
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.494022 Loss1: 0.801426 Loss2: 0.692597
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.502410 Loss1: 0.809678 Loss2: 0.692732
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.469498 Loss1: 0.779570 Loss2: 0.689928
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.470051 Loss1: 0.777379 Loss2: 0.692671
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.469253 Loss1: 0.774199 Loss2: 0.695054
-(DefaultActor pid=1831567) >> Training accuracy: 0.705924
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.334560 Loss1: 0.613507 Loss2: 0.721053
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.177061 Loss1: 0.532787 Loss2: 0.644274
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.168143 Loss1: 0.518661 Loss2: 0.649482
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.192708 Loss1: 0.543814 Loss2: 0.648894
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.173531 Loss1: 0.525472 Loss2: 0.648059
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.151228 Loss1: 0.503098 Loss2: 0.648130
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.160397 Loss1: 0.509334 Loss2: 0.651063
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133843 Loss1: 0.484947 Loss2: 0.648896
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.129902 Loss1: 0.479275 Loss2: 0.650627
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.135880 Loss1: 0.485394 Loss2: 0.650486
-(DefaultActor pid=1831567) >> Training accuracy: 0.833140
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.601572 Loss1: 0.868832 Loss2: 0.732740
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.460044 Loss1: 0.823405 Loss2: 0.636639
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.426078 Loss1: 0.794205 Loss2: 0.631873
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.408145 Loss1: 0.776295 Loss2: 0.631850
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.419276 Loss1: 0.787548 Loss2: 0.631727
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.409805 Loss1: 0.772503 Loss2: 0.637303
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386134 Loss1: 0.754279 Loss2: 0.631855
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.375408 Loss1: 0.741667 Loss2: 0.633741
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.381031 Loss1: 0.743636 Loss2: 0.637394
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.377067 Loss1: 0.740010 Loss2: 0.637057
-(DefaultActor pid=1831567) >> Training accuracy: 0.748904
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.473511 Loss1: 0.741311 Loss2: 0.732200
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.356619 Loss1: 0.706822 Loss2: 0.649797
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.332346 Loss1: 0.684819 Loss2: 0.647528
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.336388 Loss1: 0.685307 Loss2: 0.651080
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.339418 Loss1: 0.686912 Loss2: 0.652506
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.317759 Loss1: 0.667716 Loss2: 0.650043
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295051 Loss1: 0.643663 Loss2: 0.651388
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.302067 Loss1: 0.649211 Loss2: 0.652855
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.300369 Loss1: 0.644231 Loss2: 0.656138
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.293104 Loss1: 0.638017 Loss2: 0.655087
-(DefaultActor pid=1831567) >> Training accuracy: 0.799137
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.521691 Loss1: 0.761666 Loss2: 0.760024
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.427226 Loss1: 0.748119 Loss2: 0.679107
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.412431 Loss1: 0.734347 Loss2: 0.678084
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.391719 Loss1: 0.717154 Loss2: 0.674565
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.348796 Loss1: 0.669329 Loss2: 0.679467
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.360333 Loss1: 0.684079 Loss2: 0.676254
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.333519 Loss1: 0.657161 Loss2: 0.676357
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.345494 Loss1: 0.666681 Loss2: 0.678813
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.362947 Loss1: 0.681521 Loss2: 0.681426
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.331116 Loss1: 0.653252 Loss2: 0.677864
-(DefaultActor pid=1831567) >> Training accuracy: 0.791667
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493409 Loss1: 0.766468 Loss2: 0.726940
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382557 Loss1: 0.711428 Loss2: 0.671130
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.366924 Loss1: 0.699590 Loss2: 0.667334
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.351930 Loss1: 0.686857 Loss2: 0.665073
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.353773 Loss1: 0.687032 Loss2: 0.666740
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.332104 Loss1: 0.664379 Loss2: 0.667725
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350464 Loss1: 0.678807 Loss2: 0.671657
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347634 Loss1: 0.674540 Loss2: 0.673094
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.340173 Loss1: 0.667713 Loss2: 0.672461
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.348835 Loss1: 0.673174 Loss2: 0.675662
-(DefaultActor pid=1831567) >> Training accuracy: 0.773065
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.522577 Loss1: 0.781571 Loss2: 0.741006
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329258 Loss1: 0.693669 Loss2: 0.635589
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.289134 Loss1: 0.656745 Loss2: 0.632389
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.278786 Loss1: 0.644248 Loss2: 0.634538
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282477 Loss1: 0.648909 Loss2: 0.633569
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.254545 Loss1: 0.620421 Loss2: 0.634124
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.256020 Loss1: 0.621802 Loss2: 0.634217
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.252712 Loss1: 0.621521 Loss2: 0.631191
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232755 Loss1: 0.599330 Loss2: 0.633425
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.228804 Loss1: 0.593300 Loss2: 0.635504
-[2023-09-27 07:58:32,416][flwr][DEBUG] - fit_round 13 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.807203
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.616700
-[2023-09-27 07:58:34,101][flwr][INFO] - fit progress: (13, 1.0760116641894697, {'accuracy': 0.6167}, 6046.937410460785)
-[2023-09-27 07:58:34,102][flwr][DEBUG] - evaluate_round 13: strategy sampled 10 clients (out of 10)
-[2023-09-27 07:59:05,669][flwr][DEBUG] - evaluate_round 13 received 10 results and 0 failures
-[2023-09-27 07:59:05,670][flwr][DEBUG] - fit_round 14: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.404020 Loss1: 0.599873 Loss2: 0.804147
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.251751 Loss1: 0.553303 Loss2: 0.698448
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213655 Loss1: 0.518210 Loss2: 0.695444
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.201935 Loss1: 0.507946 Loss2: 0.693989
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203698 Loss1: 0.508318 Loss2: 0.695380
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183848 Loss1: 0.488824 Loss2: 0.695024
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.203135 Loss1: 0.506579 Loss2: 0.696556
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168492 Loss1: 0.472521 Loss2: 0.695971
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161366 Loss1: 0.463730 Loss2: 0.697636
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.143075 Loss1: 0.444768 Loss2: 0.698307
-(DefaultActor pid=1831567) >> Training accuracy: 0.826775
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.397180 Loss1: 0.604698 Loss2: 0.792482
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227775 Loss1: 0.531255 Loss2: 0.696520
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212031 Loss1: 0.520683 Loss2: 0.691348
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184616 Loss1: 0.495373 Loss2: 0.689244
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.174565 Loss1: 0.485503 Loss2: 0.689062
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184082 Loss1: 0.493562 Loss2: 0.690520
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165532 Loss1: 0.476401 Loss2: 0.689131
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.165962 Loss1: 0.476303 Loss2: 0.689659
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.167192 Loss1: 0.475846 Loss2: 0.691345
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.160406 Loss1: 0.468304 Loss2: 0.692102
-(DefaultActor pid=1831567) >> Training accuracy: 0.835841
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568945 Loss1: 0.767593 Loss2: 0.801352
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.420097 Loss1: 0.729891 Loss2: 0.690206
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.362511 Loss1: 0.681955 Loss2: 0.680556
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353467 Loss1: 0.672002 Loss2: 0.681465
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.342689 Loss1: 0.663275 Loss2: 0.679414
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328490 Loss1: 0.651243 Loss2: 0.677246
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.344880 Loss1: 0.662986 Loss2: 0.681894
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320875 Loss1: 0.638918 Loss2: 0.681957
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.307766 Loss1: 0.627048 Loss2: 0.680718
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.322690 Loss1: 0.637332 Loss2: 0.685358
-(DefaultActor pid=1831567) >> Training accuracy: 0.758575
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.739323 Loss1: 0.883367 Loss2: 0.855956
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.540058 Loss1: 0.813863 Loss2: 0.726195
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.503454 Loss1: 0.779681 Loss2: 0.723773
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.497687 Loss1: 0.778942 Loss2: 0.718745
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.494281 Loss1: 0.775995 Loss2: 0.718287
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.468234 Loss1: 0.743908 Loss2: 0.724326
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.482502 Loss1: 0.760430 Loss2: 0.722073
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.432490 Loss1: 0.709980 Loss2: 0.722510
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.454774 Loss1: 0.734012 Loss2: 0.720762
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.427800 Loss1: 0.703362 Loss2: 0.724438
-(DefaultActor pid=1831567) >> Training accuracy: 0.760143
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.537227 Loss1: 0.772702 Loss2: 0.764524
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.368339 Loss1: 0.694645 Loss2: 0.673694
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.339108 Loss1: 0.666835 Loss2: 0.672273
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.330296 Loss1: 0.657345 Loss2: 0.672952
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.334406 Loss1: 0.659497 Loss2: 0.674910
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316307 Loss1: 0.643574 Loss2: 0.672733
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.316153 Loss1: 0.640153 Loss2: 0.676000
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334452 Loss1: 0.655839 Loss2: 0.678613
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.298713 Loss1: 0.620638 Loss2: 0.678076
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.305099 Loss1: 0.626609 Loss2: 0.678490
-(DefaultActor pid=1831567) >> Training accuracy: 0.794819
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.741865 Loss1: 0.948532 Loss2: 0.793334
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.583736 Loss1: 0.900835 Loss2: 0.682901
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.542111 Loss1: 0.861600 Loss2: 0.680510
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.535744 Loss1: 0.854269 Loss2: 0.681476
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.513819 Loss1: 0.831268 Loss2: 0.682551
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.532953 Loss1: 0.849850 Loss2: 0.683103
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.513073 Loss1: 0.830398 Loss2: 0.682675
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.514231 Loss1: 0.827981 Loss2: 0.686250
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.516169 Loss1: 0.830897 Loss2: 0.685272
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.478276 Loss1: 0.791754 Loss2: 0.686522
-(DefaultActor pid=1831567) >> Training accuracy: 0.707428
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.507283 Loss1: 0.740491 Loss2: 0.766792
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.414350 Loss1: 0.708520 Loss2: 0.705830
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.400144 Loss1: 0.695155 Loss2: 0.704989
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.388718 Loss1: 0.684930 Loss2: 0.703788
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.382341 Loss1: 0.677957 Loss2: 0.704384
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.374709 Loss1: 0.668047 Loss2: 0.706662
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.359265 Loss1: 0.653751 Loss2: 0.705514
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.374998 Loss1: 0.667456 Loss2: 0.707542
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.372159 Loss1: 0.660479 Loss2: 0.711680
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.357991 Loss1: 0.650076 Loss2: 0.707916
-(DefaultActor pid=1831567) >> Training accuracy: 0.766865
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.598217 Loss1: 0.761112 Loss2: 0.837105
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394979 Loss1: 0.677304 Loss2: 0.717674
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.352117 Loss1: 0.637421 Loss2: 0.714695
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.359349 Loss1: 0.641678 Loss2: 0.717671
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317109 Loss1: 0.602337 Loss2: 0.714771
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.321617 Loss1: 0.607232 Loss2: 0.714385
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.341147 Loss1: 0.623181 Loss2: 0.717965
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329031 Loss1: 0.610898 Loss2: 0.718132
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.319175 Loss1: 0.600415 Loss2: 0.718760
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304420 Loss1: 0.585130 Loss2: 0.719291
-(DefaultActor pid=1831567) >> Training accuracy: 0.812235
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.529621 Loss1: 0.772814 Loss2: 0.756807
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382500 Loss1: 0.714579 Loss2: 0.667922
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.374563 Loss1: 0.708333 Loss2: 0.666230
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.378192 Loss1: 0.713334 Loss2: 0.664857
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.356143 Loss1: 0.690342 Loss2: 0.665800
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337139 Loss1: 0.673318 Loss2: 0.663821
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.337127 Loss1: 0.668923 Loss2: 0.668204
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.327199 Loss1: 0.660318 Loss2: 0.666881
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.329809 Loss1: 0.661817 Loss2: 0.667991
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.339422 Loss1: 0.669465 Loss2: 0.669957
-(DefaultActor pid=1831567) >> Training accuracy: 0.779647
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.717452 Loss1: 0.896624 Loss2: 0.820828
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.578020 Loss1: 0.876032 Loss2: 0.701988
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.515344 Loss1: 0.819546 Loss2: 0.695798
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.494783 Loss1: 0.799478 Loss2: 0.695305
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.488629 Loss1: 0.794073 Loss2: 0.694557
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.496212 Loss1: 0.798746 Loss2: 0.697466
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.488922 Loss1: 0.791145 Loss2: 0.697777
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.433228 Loss1: 0.735471 Loss2: 0.697757
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.451149 Loss1: 0.754588 Loss2: 0.696561
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.439627 Loss1: 0.742842 Loss2: 0.696785
-[2023-09-27 08:06:10,664][flwr][DEBUG] - fit_round 14 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.692164
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.632100
-[2023-09-27 08:06:12,553][flwr][INFO] - fit progress: (14, 1.0299229365758622, {'accuracy': 0.6321}, 6505.389118962921)
-[2023-09-27 08:06:12,554][flwr][DEBUG] - evaluate_round 14: strategy sampled 10 clients (out of 10)
-[2023-09-27 08:06:44,514][flwr][DEBUG] - evaluate_round 14 received 10 results and 0 failures
-[2023-09-27 08:06:44,515][flwr][DEBUG] - fit_round 15: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.318274 Loss1: 0.578763 Loss2: 0.739511
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.188297 Loss1: 0.524843 Loss2: 0.663454
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.174460 Loss1: 0.515031 Loss2: 0.659429
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.151525 Loss1: 0.493344 Loss2: 0.658181
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154321 Loss1: 0.496840 Loss2: 0.657480
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131162 Loss1: 0.474486 Loss2: 0.656676
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.151904 Loss1: 0.493647 Loss2: 0.658257
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.128384 Loss1: 0.470369 Loss2: 0.658015
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134306 Loss1: 0.472512 Loss2: 0.661794
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.112116 Loss1: 0.452291 Loss2: 0.659825
-(DefaultActor pid=1831567) >> Training accuracy: 0.842400
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.634437 Loss1: 0.866398 Loss2: 0.768039
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.473740 Loss1: 0.811159 Loss2: 0.662581
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.417314 Loss1: 0.761858 Loss2: 0.655456
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.424200 Loss1: 0.768739 Loss2: 0.655461
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.425411 Loss1: 0.765250 Loss2: 0.660161
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.413614 Loss1: 0.754931 Loss2: 0.658683
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.394052 Loss1: 0.733688 Loss2: 0.660364
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.379506 Loss1: 0.718253 Loss2: 0.661252
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.377109 Loss1: 0.717010 Loss2: 0.660099
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.383837 Loss1: 0.722488 Loss2: 0.661349
-(DefaultActor pid=1831567) >> Training accuracy: 0.746162
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.649027 Loss1: 0.851455 Loss2: 0.797572
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.519058 Loss1: 0.821664 Loss2: 0.697394
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.507567 Loss1: 0.810715 Loss2: 0.696852
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.520169 Loss1: 0.820612 Loss2: 0.699557
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.488859 Loss1: 0.790108 Loss2: 0.698752
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.497016 Loss1: 0.797458 Loss2: 0.699559
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.471984 Loss1: 0.775325 Loss2: 0.696659
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.459293 Loss1: 0.762600 Loss2: 0.696694
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.435714 Loss1: 0.739179 Loss2: 0.696535
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.444690 Loss1: 0.746173 Loss2: 0.698516
-(DefaultActor pid=1831567) >> Training accuracy: 0.739972
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485746 Loss1: 0.738720 Loss2: 0.747025
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304453 Loss1: 0.661628 Loss2: 0.642825
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.274540 Loss1: 0.636358 Loss2: 0.638182
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.298535 Loss1: 0.656770 Loss2: 0.641766
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.241287 Loss1: 0.601358 Loss2: 0.639928
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.246407 Loss1: 0.606669 Loss2: 0.639738
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243657 Loss1: 0.604333 Loss2: 0.639325
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.239341 Loss1: 0.597032 Loss2: 0.642309
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.203484 Loss1: 0.562275 Loss2: 0.641208
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.247621 Loss1: 0.599034 Loss2: 0.648586
-(DefaultActor pid=1831567) >> Training accuracy: 0.789989
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459273 Loss1: 0.730783 Loss2: 0.728490
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.360275 Loss1: 0.684454 Loss2: 0.675822
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.352184 Loss1: 0.674871 Loss2: 0.677313
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.342144 Loss1: 0.665283 Loss2: 0.676861
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.350099 Loss1: 0.672423 Loss2: 0.677676
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.346626 Loss1: 0.666596 Loss2: 0.680030
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.346859 Loss1: 0.665236 Loss2: 0.681623
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.351183 Loss1: 0.670503 Loss2: 0.680681
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.317445 Loss1: 0.638158 Loss2: 0.679288
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.324071 Loss1: 0.643905 Loss2: 0.680166
-(DefaultActor pid=1831567) >> Training accuracy: 0.782242
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.512058 Loss1: 0.758228 Loss2: 0.753829
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367199 Loss1: 0.682605 Loss2: 0.684594
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.333657 Loss1: 0.651108 Loss2: 0.682549
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.373510 Loss1: 0.686735 Loss2: 0.686775
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.324833 Loss1: 0.640880 Loss2: 0.683953
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.320383 Loss1: 0.633824 Loss2: 0.686560
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.324016 Loss1: 0.636108 Loss2: 0.687908
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.321285 Loss1: 0.633291 Loss2: 0.687994
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.304356 Loss1: 0.616832 Loss2: 0.687524
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307717 Loss1: 0.619022 Loss2: 0.688695
-(DefaultActor pid=1831567) >> Training accuracy: 0.772294
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.671069 Loss1: 0.927964 Loss2: 0.743106
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.538919 Loss1: 0.873850 Loss2: 0.665069
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.515793 Loss1: 0.848919 Loss2: 0.666874
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.501310 Loss1: 0.834527 Loss2: 0.666782
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.497779 Loss1: 0.832535 Loss2: 0.665243
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.502838 Loss1: 0.835603 Loss2: 0.667236
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.497056 Loss1: 0.826444 Loss2: 0.670612
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.500920 Loss1: 0.830690 Loss2: 0.670230
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.492077 Loss1: 0.823204 Loss2: 0.668874
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.468825 Loss1: 0.797736 Loss2: 0.671089
-(DefaultActor pid=1831567) >> Training accuracy: 0.694520
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481080 Loss1: 0.721209 Loss2: 0.759872
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367179 Loss1: 0.686680 Loss2: 0.680498
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.340255 Loss1: 0.662057 Loss2: 0.678198
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347622 Loss1: 0.671529 Loss2: 0.676093
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.291219 Loss1: 0.612249 Loss2: 0.678970
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.309928 Loss1: 0.630421 Loss2: 0.679507
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.307826 Loss1: 0.628640 Loss2: 0.679186
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.302722 Loss1: 0.621346 Loss2: 0.681376
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.308212 Loss1: 0.627367 Loss2: 0.680846
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.297546 Loss1: 0.616054 Loss2: 0.681492
-(DefaultActor pid=1831567) >> Training accuracy: 0.805921
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.506695 Loss1: 0.728798 Loss2: 0.777897
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.423015 Loss1: 0.723521 Loss2: 0.699494
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.379241 Loss1: 0.687310 Loss2: 0.691932
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.376259 Loss1: 0.679058 Loss2: 0.697201
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.357088 Loss1: 0.659761 Loss2: 0.697327
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.344303 Loss1: 0.647355 Loss2: 0.696948
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.351449 Loss1: 0.651099 Loss2: 0.700351
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.365707 Loss1: 0.668268 Loss2: 0.697439
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.313922 Loss1: 0.615592 Loss2: 0.698330
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329115 Loss1: 0.630841 Loss2: 0.698275
-(DefaultActor pid=1831567) >> Training accuracy: 0.797075
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.303284 Loss1: 0.578314 Loss2: 0.724970
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.178052 Loss1: 0.530455 Loss2: 0.647597
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.170699 Loss1: 0.522154 Loss2: 0.648545
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.143719 Loss1: 0.498636 Loss2: 0.645082
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.157930 Loss1: 0.509427 Loss2: 0.648502
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.125417 Loss1: 0.479466 Loss2: 0.645951
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122363 Loss1: 0.476899 Loss2: 0.645464
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.135759 Loss1: 0.487154 Loss2: 0.648605
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.131177 Loss1: 0.484722 Loss2: 0.646455
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107997 Loss1: 0.459113 Loss2: 0.648885
-[2023-09-27 08:14:07,039][flwr][DEBUG] - fit_round 15 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.843171
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.635800
-[2023-09-27 08:14:08,587][flwr][INFO] - fit progress: (15, 1.022295751796363, {'accuracy': 0.6358}, 6981.423728279769)
-[2023-09-27 08:14:08,588][flwr][DEBUG] - evaluate_round 15: strategy sampled 10 clients (out of 10)
-[2023-09-27 08:14:39,673][flwr][DEBUG] - evaluate_round 15 received 10 results and 0 failures
-[2023-09-27 08:14:39,673][flwr][DEBUG] - fit_round 16: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.679300 Loss1: 0.870646 Loss2: 0.808654
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.476782 Loss1: 0.789699 Loss2: 0.687083
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.460519 Loss1: 0.777455 Loss2: 0.683064
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.423578 Loss1: 0.742671 Loss2: 0.680907
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.436440 Loss1: 0.755765 Loss2: 0.680675
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.418655 Loss1: 0.735685 Loss2: 0.682970
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.417971 Loss1: 0.733385 Loss2: 0.684586
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.404048 Loss1: 0.719611 Loss2: 0.684436
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.383729 Loss1: 0.699428 Loss2: 0.684300
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.406373 Loss1: 0.719506 Loss2: 0.686867
-(DefaultActor pid=1831567) >> Training accuracy: 0.755208
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.675010 Loss1: 0.879926 Loss2: 0.795085
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.501250 Loss1: 0.821465 Loss2: 0.679785
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.490082 Loss1: 0.809959 Loss2: 0.680123
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.460390 Loss1: 0.783580 Loss2: 0.676810
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.485269 Loss1: 0.806837 Loss2: 0.678432
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.473281 Loss1: 0.792890 Loss2: 0.680391
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.452693 Loss1: 0.773374 Loss2: 0.679319
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.430596 Loss1: 0.751591 Loss2: 0.679005
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.455989 Loss1: 0.774810 Loss2: 0.681179
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.421739 Loss1: 0.742066 Loss2: 0.679674
-(DefaultActor pid=1831567) >> Training accuracy: 0.715019
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.474118 Loss1: 0.714576 Loss2: 0.759542
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.344134 Loss1: 0.678558 Loss2: 0.665576
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.320278 Loss1: 0.657115 Loss2: 0.663163
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.313361 Loss1: 0.649087 Loss2: 0.664275
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.301806 Loss1: 0.636089 Loss2: 0.665717
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303273 Loss1: 0.635803 Loss2: 0.667470
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.266526 Loss1: 0.600329 Loss2: 0.666197
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.286588 Loss1: 0.620047 Loss2: 0.666542
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.265914 Loss1: 0.596253 Loss2: 0.669661
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284088 Loss1: 0.614155 Loss2: 0.669933
-(DefaultActor pid=1831567) >> Training accuracy: 0.806127
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.503335 Loss1: 0.699654 Loss2: 0.803681
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347137 Loss1: 0.650245 Loss2: 0.696892
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.324682 Loss1: 0.633289 Loss2: 0.691393
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.295266 Loss1: 0.604412 Loss2: 0.690853
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.312995 Loss1: 0.620185 Loss2: 0.692810
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.305567 Loss1: 0.610004 Loss2: 0.695563
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298382 Loss1: 0.604399 Loss2: 0.693983
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.272986 Loss1: 0.578610 Loss2: 0.694376
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.277843 Loss1: 0.581392 Loss2: 0.696450
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.278657 Loss1: 0.581970 Loss2: 0.696687
-(DefaultActor pid=1831567) >> Training accuracy: 0.806939
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.506062 Loss1: 0.746045 Loss2: 0.760017
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.385900 Loss1: 0.708708 Loss2: 0.677192
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.372595 Loss1: 0.696498 Loss2: 0.676097
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.365435 Loss1: 0.689082 Loss2: 0.676352
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.347490 Loss1: 0.672396 Loss2: 0.675094
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.321883 Loss1: 0.644288 Loss2: 0.677595
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.304668 Loss1: 0.626217 Loss2: 0.678451
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334637 Loss1: 0.656455 Loss2: 0.678182
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.330959 Loss1: 0.648990 Loss2: 0.681969
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.306636 Loss1: 0.626346 Loss2: 0.680290
-(DefaultActor pid=1831567) >> Training accuracy: 0.798478
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.366328 Loss1: 0.582416 Loss2: 0.783912
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216539 Loss1: 0.532543 Loss2: 0.683997
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.177819 Loss1: 0.502110 Loss2: 0.675709
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.179255 Loss1: 0.501662 Loss2: 0.677594
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158795 Loss1: 0.484028 Loss2: 0.674767
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.155557 Loss1: 0.479851 Loss2: 0.675706
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.144943 Loss1: 0.467996 Loss2: 0.676947
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140414 Loss1: 0.461573 Loss2: 0.678841
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.146416 Loss1: 0.467862 Loss2: 0.678554
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.131136 Loss1: 0.452019 Loss2: 0.679117
-(DefaultActor pid=1831567) >> Training accuracy: 0.844136
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.482374 Loss1: 0.742112 Loss2: 0.740263
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.333709 Loss1: 0.699006 Loss2: 0.634703
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.289127 Loss1: 0.658140 Loss2: 0.630987
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.285301 Loss1: 0.653592 Loss2: 0.631709
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.277492 Loss1: 0.648477 Loss2: 0.629015
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.273321 Loss1: 0.641984 Loss2: 0.631336
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.265789 Loss1: 0.634307 Loss2: 0.631482
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273173 Loss1: 0.641437 Loss2: 0.631736
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239072 Loss1: 0.607578 Loss2: 0.631495
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.265827 Loss1: 0.632670 Loss2: 0.633156
-(DefaultActor pid=1831567) >> Training accuracy: 0.789444
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481886 Loss1: 0.728465 Loss2: 0.753420
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387081 Loss1: 0.690024 Loss2: 0.697058
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363992 Loss1: 0.668120 Loss2: 0.695872
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.359406 Loss1: 0.664785 Loss2: 0.694621
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.343113 Loss1: 0.649593 Loss2: 0.693520
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.351872 Loss1: 0.655729 Loss2: 0.696143
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.355378 Loss1: 0.657794 Loss2: 0.697584
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.336852 Loss1: 0.639654 Loss2: 0.697198
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.343467 Loss1: 0.645090 Loss2: 0.698376
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.335633 Loss1: 0.634385 Loss2: 0.701248
-(DefaultActor pid=1831567) >> Training accuracy: 0.780754
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.727581 Loss1: 0.910705 Loss2: 0.816876
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.591995 Loss1: 0.881009 Loss2: 0.710986
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.541183 Loss1: 0.833164 Loss2: 0.708019
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.543076 Loss1: 0.832549 Loss2: 0.710527
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.525178 Loss1: 0.813727 Loss2: 0.711450
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.548125 Loss1: 0.830855 Loss2: 0.717270
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.531694 Loss1: 0.818604 Loss2: 0.713091
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.525901 Loss1: 0.811439 Loss2: 0.714462
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.492925 Loss1: 0.781132 Loss2: 0.711794
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.514526 Loss1: 0.799138 Loss2: 0.715388
-(DefaultActor pid=1831567) >> Training accuracy: 0.717844
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.351156 Loss1: 0.575299 Loss2: 0.775857
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200833 Loss1: 0.518616 Loss2: 0.682218
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.164630 Loss1: 0.488147 Loss2: 0.676484
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165934 Loss1: 0.486553 Loss2: 0.679381
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167519 Loss1: 0.489993 Loss2: 0.677526
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.142958 Loss1: 0.464458 Loss2: 0.678500
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148333 Loss1: 0.471065 Loss2: 0.677268
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.141742 Loss1: 0.462617 Loss2: 0.679125
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.164072 Loss1: 0.483210 Loss2: 0.680862
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.117318 Loss1: 0.437872 Loss2: 0.679446
-(DefaultActor pid=1831567) >> Training accuracy: 0.838735
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 08:21:35,561][flwr][DEBUG] - fit_round 16 received 10 results and 0 failures
->> Test accuracy: 0.640900
-[2023-09-27 08:21:36,986][flwr][INFO] - fit progress: (16, 1.0062863030753577, {'accuracy': 0.6409}, 7429.822125886101)
-[2023-09-27 08:21:36,986][flwr][DEBUG] - evaluate_round 16: strategy sampled 10 clients (out of 10)
-[2023-09-27 08:22:09,039][flwr][DEBUG] - evaluate_round 16 received 10 results and 0 failures
-[2023-09-27 08:22:09,039][flwr][DEBUG] - fit_round 17: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.602871 Loss1: 0.846304 Loss2: 0.756567
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.473130 Loss1: 0.819852 Loss2: 0.653278
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.415443 Loss1: 0.763989 Loss2: 0.651454
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.405471 Loss1: 0.754470 Loss2: 0.651001
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.389351 Loss1: 0.739753 Loss2: 0.649598
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.382027 Loss1: 0.733686 Loss2: 0.648341
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.413846 Loss1: 0.761120 Loss2: 0.652726
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.355947 Loss1: 0.702086 Loss2: 0.653861
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.356272 Loss1: 0.704646 Loss2: 0.651626
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.371418 Loss1: 0.718447 Loss2: 0.652971
-(DefaultActor pid=1831567) >> Training accuracy: 0.763706
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.328250 Loss1: 0.570238 Loss2: 0.758012
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187072 Loss1: 0.510571 Loss2: 0.676501
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.180068 Loss1: 0.502769 Loss2: 0.677299
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165866 Loss1: 0.488759 Loss2: 0.677107
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.149205 Loss1: 0.471546 Loss2: 0.677659
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162787 Loss1: 0.485592 Loss2: 0.677195
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.143947 Loss1: 0.465983 Loss2: 0.677963
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.137620 Loss1: 0.460581 Loss2: 0.677039
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.144958 Loss1: 0.464258 Loss2: 0.680700
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.117051 Loss1: 0.437716 Loss2: 0.679335
-(DefaultActor pid=1831567) >> Training accuracy: 0.856096
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289048 Loss1: 0.557847 Loss2: 0.731201
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.160928 Loss1: 0.501013 Loss2: 0.659915
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.154063 Loss1: 0.496991 Loss2: 0.657072
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.164212 Loss1: 0.503326 Loss2: 0.660887
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.135842 Loss1: 0.482029 Loss2: 0.653813
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.118217 Loss1: 0.460396 Loss2: 0.657820
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.125671 Loss1: 0.468583 Loss2: 0.657088
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.129971 Loss1: 0.474291 Loss2: 0.655680
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.131991 Loss1: 0.472841 Loss2: 0.659149
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.118333 Loss1: 0.462938 Loss2: 0.655396
-(DefaultActor pid=1831567) >> Training accuracy: 0.813657
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495839 Loss1: 0.738307 Loss2: 0.757532
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347937 Loss1: 0.674049 Loss2: 0.673888
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318235 Loss1: 0.644805 Loss2: 0.673430
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.336807 Loss1: 0.661931 Loss2: 0.674876
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345347 Loss1: 0.669575 Loss2: 0.675771
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.315317 Loss1: 0.635160 Loss2: 0.680158
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.330035 Loss1: 0.650533 Loss2: 0.679502
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.308053 Loss1: 0.631286 Loss2: 0.676767
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.316624 Loss1: 0.640318 Loss2: 0.676306
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.295729 Loss1: 0.615132 Loss2: 0.680597
-(DefaultActor pid=1831567) >> Training accuracy: 0.802284
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.390078 Loss1: 0.708281 Loss2: 0.681797
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.311005 Loss1: 0.678565 Loss2: 0.632440
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.286772 Loss1: 0.654333 Loss2: 0.632439
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.292880 Loss1: 0.659090 Loss2: 0.633790
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.281745 Loss1: 0.646796 Loss2: 0.634949
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.286572 Loss1: 0.654517 Loss2: 0.632055
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.288557 Loss1: 0.654113 Loss2: 0.634444
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.285097 Loss1: 0.649628 Loss2: 0.635469
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.278100 Loss1: 0.640642 Loss2: 0.637459
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279626 Loss1: 0.639307 Loss2: 0.640318
-(DefaultActor pid=1831567) >> Training accuracy: 0.786582
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.630215 Loss1: 0.855710 Loss2: 0.774505
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.512468 Loss1: 0.832825 Loss2: 0.679643
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.470245 Loss1: 0.793739 Loss2: 0.676506
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.453196 Loss1: 0.778471 Loss2: 0.674725
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.466882 Loss1: 0.792012 Loss2: 0.674871
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.435821 Loss1: 0.757554 Loss2: 0.678268
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.418832 Loss1: 0.743141 Loss2: 0.675691
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.429565 Loss1: 0.751865 Loss2: 0.677700
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.419340 Loss1: 0.738714 Loss2: 0.680626
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.411394 Loss1: 0.732933 Loss2: 0.678461
-(DefaultActor pid=1831567) >> Training accuracy: 0.736940
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.463794 Loss1: 0.722100 Loss2: 0.741695
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332807 Loss1: 0.672097 Loss2: 0.660710
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.320879 Loss1: 0.659760 Loss2: 0.661119
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.288493 Loss1: 0.625961 Loss2: 0.662532
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.297089 Loss1: 0.631605 Loss2: 0.665484
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.280411 Loss1: 0.615663 Loss2: 0.664748
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.285947 Loss1: 0.620125 Loss2: 0.665822
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289646 Loss1: 0.622947 Loss2: 0.666699
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.287960 Loss1: 0.619192 Loss2: 0.668769
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.252946 Loss1: 0.586533 Loss2: 0.666413
-(DefaultActor pid=1831567) >> Training accuracy: 0.815378
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.474167 Loss1: 0.717688 Loss2: 0.756479
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.302376 Loss1: 0.647996 Loss2: 0.654380
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.274947 Loss1: 0.621852 Loss2: 0.653096
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.297508 Loss1: 0.644374 Loss2: 0.653133
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.269013 Loss1: 0.616888 Loss2: 0.652125
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.267916 Loss1: 0.611825 Loss2: 0.656091
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.246518 Loss1: 0.590489 Loss2: 0.656029
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212040 Loss1: 0.562038 Loss2: 0.650003
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.238960 Loss1: 0.583156 Loss2: 0.655803
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229736 Loss1: 0.571373 Loss2: 0.658364
-(DefaultActor pid=1831567) >> Training accuracy: 0.804025
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517329 Loss1: 0.741636 Loss2: 0.775693
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.392263 Loss1: 0.685922 Loss2: 0.706342
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.396239 Loss1: 0.688214 Loss2: 0.708025
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.359762 Loss1: 0.653764 Loss2: 0.705998
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.342426 Loss1: 0.634847 Loss2: 0.707578
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.338909 Loss1: 0.631570 Loss2: 0.707339
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.327554 Loss1: 0.618958 Loss2: 0.708596
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.315391 Loss1: 0.608085 Loss2: 0.707306
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.314083 Loss1: 0.603700 Loss2: 0.710382
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.311512 Loss1: 0.600576 Loss2: 0.710936
-(DefaultActor pid=1831567) >> Training accuracy: 0.788681
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.662025 Loss1: 0.903206 Loss2: 0.758820
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.534640 Loss1: 0.858949 Loss2: 0.675690
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.519827 Loss1: 0.841637 Loss2: 0.678190
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.522024 Loss1: 0.844181 Loss2: 0.677843
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.480295 Loss1: 0.803909 Loss2: 0.676385
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.486443 Loss1: 0.807269 Loss2: 0.679174
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.477285 Loss1: 0.799672 Loss2: 0.677613
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.460142 Loss1: 0.782700 Loss2: 0.677441
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.488527 Loss1: 0.806249 Loss2: 0.682278
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.458280 Loss1: 0.779159 Loss2: 0.679121
-[2023-09-27 08:29:27,233][flwr][DEBUG] - fit_round 17 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.697237
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.637900
-[2023-09-27 08:29:28,909][flwr][INFO] - fit progress: (17, 1.0163589238930053, {'accuracy': 0.6379}, 7901.745227077976)
-[2023-09-27 08:29:28,909][flwr][DEBUG] - evaluate_round 17: strategy sampled 10 clients (out of 10)
-[2023-09-27 08:30:01,490][flwr][DEBUG] - evaluate_round 17 received 10 results and 0 failures
-[2023-09-27 08:30:01,491][flwr][DEBUG] - fit_round 18: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.490662 Loss1: 0.730779 Loss2: 0.759882
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.348507 Loss1: 0.674206 Loss2: 0.674301
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.325600 Loss1: 0.654589 Loss2: 0.671011
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339310 Loss1: 0.661674 Loss2: 0.677637
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.305527 Loss1: 0.632610 Loss2: 0.672917
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.338161 Loss1: 0.659299 Loss2: 0.678862
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.313923 Loss1: 0.638229 Loss2: 0.675694
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.309916 Loss1: 0.633993 Loss2: 0.675923
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299823 Loss1: 0.623635 Loss2: 0.676188
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281868 Loss1: 0.605517 Loss2: 0.676351
-(DefaultActor pid=1831567) >> Training accuracy: 0.798678
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.660648 Loss1: 0.837632 Loss2: 0.823016
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.454964 Loss1: 0.756945 Loss2: 0.698019
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.432062 Loss1: 0.740191 Loss2: 0.691871
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.450487 Loss1: 0.753346 Loss2: 0.697141
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.442939 Loss1: 0.745275 Loss2: 0.697663
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.405409 Loss1: 0.713163 Loss2: 0.692246
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.415683 Loss1: 0.719498 Loss2: 0.696185
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.392359 Loss1: 0.693967 Loss2: 0.698392
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.397315 Loss1: 0.696532 Loss2: 0.700783
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.392206 Loss1: 0.689842 Loss2: 0.702364
-(DefaultActor pid=1831567) >> Training accuracy: 0.767544
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.640886 Loss1: 0.855296 Loss2: 0.785590
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.461817 Loss1: 0.787278 Loss2: 0.674539
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.454200 Loss1: 0.781606 Loss2: 0.672594
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.431244 Loss1: 0.760112 Loss2: 0.671131
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.438423 Loss1: 0.765857 Loss2: 0.672566
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.416566 Loss1: 0.744379 Loss2: 0.672187
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.423859 Loss1: 0.747573 Loss2: 0.676286
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.426227 Loss1: 0.748598 Loss2: 0.677630
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.415863 Loss1: 0.740014 Loss2: 0.675849
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.433754 Loss1: 0.757505 Loss2: 0.676248
-(DefaultActor pid=1831567) >> Training accuracy: 0.735541
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485117 Loss1: 0.725170 Loss2: 0.759948
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.313295 Loss1: 0.648392 Loss2: 0.664903
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.292939 Loss1: 0.630022 Loss2: 0.662916
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.288303 Loss1: 0.623664 Loss2: 0.664639
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.297463 Loss1: 0.632964 Loss2: 0.664498
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.308015 Loss1: 0.641040 Loss2: 0.666974
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.282158 Loss1: 0.613516 Loss2: 0.668642
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.295707 Loss1: 0.624751 Loss2: 0.670956
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257023 Loss1: 0.587492 Loss2: 0.669532
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.261797 Loss1: 0.590425 Loss2: 0.671372
-(DefaultActor pid=1831567) >> Training accuracy: 0.810855
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.700803 Loss1: 0.912897 Loss2: 0.787905
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.537293 Loss1: 0.854019 Loss2: 0.683273
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.505722 Loss1: 0.824960 Loss2: 0.680761
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.515926 Loss1: 0.835358 Loss2: 0.680568
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.485915 Loss1: 0.803965 Loss2: 0.681950
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.474077 Loss1: 0.792083 Loss2: 0.681994
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.460124 Loss1: 0.777241 Loss2: 0.682883
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.466878 Loss1: 0.783629 Loss2: 0.683249
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.468315 Loss1: 0.782952 Loss2: 0.685363
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.472552 Loss1: 0.785676 Loss2: 0.686877
-(DefaultActor pid=1831567) >> Training accuracy: 0.742074
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.465585 Loss1: 0.693322 Loss2: 0.772263
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.403285 Loss1: 0.684616 Loss2: 0.718669
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.373976 Loss1: 0.656280 Loss2: 0.717696
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.358104 Loss1: 0.641360 Loss2: 0.716744
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.363668 Loss1: 0.644388 Loss2: 0.719281
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362215 Loss1: 0.645202 Loss2: 0.717013
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.341331 Loss1: 0.623163 Loss2: 0.718169
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347990 Loss1: 0.627738 Loss2: 0.720253
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.353386 Loss1: 0.633358 Loss2: 0.720027
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.354316 Loss1: 0.633821 Loss2: 0.720495
-(DefaultActor pid=1831567) >> Training accuracy: 0.797495
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.478018 Loss1: 0.718571 Loss2: 0.759447
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.357720 Loss1: 0.694680 Loss2: 0.663040
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334312 Loss1: 0.676662 Loss2: 0.657650
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.286466 Loss1: 0.633591 Loss2: 0.652875
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.280485 Loss1: 0.626326 Loss2: 0.654159
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.283401 Loss1: 0.628311 Loss2: 0.655090
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.267164 Loss1: 0.613256 Loss2: 0.653908
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273329 Loss1: 0.616928 Loss2: 0.656401
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.256371 Loss1: 0.602023 Loss2: 0.654348
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.267762 Loss1: 0.609365 Loss2: 0.658398
-(DefaultActor pid=1831567) >> Training accuracy: 0.782393
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360928 Loss1: 0.571273 Loss2: 0.789655
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.193475 Loss1: 0.507807 Loss2: 0.685668
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183329 Loss1: 0.498893 Loss2: 0.684437
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.166732 Loss1: 0.480644 Loss2: 0.686088
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.157856 Loss1: 0.477021 Loss2: 0.680834
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.130804 Loss1: 0.447563 Loss2: 0.683241
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.127931 Loss1: 0.443124 Loss2: 0.684808
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154397 Loss1: 0.469563 Loss2: 0.684834
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.142583 Loss1: 0.454490 Loss2: 0.688092
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.127077 Loss1: 0.441939 Loss2: 0.685138
-(DefaultActor pid=1831567) >> Training accuracy: 0.856674
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.526807 Loss1: 0.696622 Loss2: 0.830186
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.321581 Loss1: 0.609247 Loss2: 0.712334
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.326288 Loss1: 0.613274 Loss2: 0.713014
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316966 Loss1: 0.606982 Loss2: 0.709984
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.301117 Loss1: 0.593547 Loss2: 0.707570
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.295480 Loss1: 0.584095 Loss2: 0.711385
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.291882 Loss1: 0.583105 Loss2: 0.708777
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.275009 Loss1: 0.563587 Loss2: 0.711422
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.271725 Loss1: 0.558853 Loss2: 0.712872
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.243688 Loss1: 0.532628 Loss2: 0.711060
-(DefaultActor pid=1831567) >> Training accuracy: 0.815413
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.287726 Loss1: 0.543158 Loss2: 0.744568
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.179410 Loss1: 0.517956 Loss2: 0.661454
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.134587 Loss1: 0.480033 Loss2: 0.654554
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.138706 Loss1: 0.486930 Loss2: 0.651775
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.122237 Loss1: 0.470926 Loss2: 0.651312
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.109997 Loss1: 0.458613 Loss2: 0.651384
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.099017 Loss1: 0.446339 Loss2: 0.652678
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.101098 Loss1: 0.448405 Loss2: 0.652693
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.094473 Loss1: 0.441671 Loss2: 0.652802
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.120724 Loss1: 0.463026 Loss2: 0.657698
-(DefaultActor pid=1831567) >> Training accuracy: 0.845100
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 08:37:08,247][flwr][DEBUG] - fit_round 18 received 10 results and 0 failures
->> Test accuracy: 0.645500
-[2023-09-27 08:37:09,722][flwr][INFO] - fit progress: (18, 0.9984818176149179, {'accuracy': 0.6455}, 8362.55843326496)
-[2023-09-27 08:37:09,722][flwr][DEBUG] - evaluate_round 18: strategy sampled 10 clients (out of 10)
-[2023-09-27 08:37:41,408][flwr][DEBUG] - evaluate_round 18 received 10 results and 0 failures
-[2023-09-27 08:37:41,409][flwr][DEBUG] - fit_round 19: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.447308 Loss1: 0.706907 Loss2: 0.740401
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.317943 Loss1: 0.657888 Loss2: 0.660055
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.310639 Loss1: 0.649833 Loss2: 0.660806
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.284068 Loss1: 0.625043 Loss2: 0.659025
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.294914 Loss1: 0.634652 Loss2: 0.660262
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.264590 Loss1: 0.606447 Loss2: 0.658143
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.262209 Loss1: 0.598638 Loss2: 0.663571
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.260502 Loss1: 0.598272 Loss2: 0.662229
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230508 Loss1: 0.567969 Loss2: 0.662539
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.258453 Loss1: 0.596465 Loss2: 0.661988
-(DefaultActor pid=1831567) >> Training accuracy: 0.813939
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326442 Loss1: 0.574461 Loss2: 0.751981
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.163242 Loss1: 0.497494 Loss2: 0.665748
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.124650 Loss1: 0.458949 Loss2: 0.665701
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.133593 Loss1: 0.468188 Loss2: 0.665405
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.122937 Loss1: 0.456914 Loss2: 0.666023
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.136470 Loss1: 0.467835 Loss2: 0.668635
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123320 Loss1: 0.456278 Loss2: 0.667041
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.123156 Loss1: 0.453604 Loss2: 0.669552
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.119037 Loss1: 0.451288 Loss2: 0.667749
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.086199 Loss1: 0.415051 Loss2: 0.671147
-(DefaultActor pid=1831567) >> Training accuracy: 0.861111
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.399242 Loss1: 0.691203 Loss2: 0.708039
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.318754 Loss1: 0.663555 Loss2: 0.655199
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.306266 Loss1: 0.650487 Loss2: 0.655780
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.278725 Loss1: 0.625370 Loss2: 0.653355
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293244 Loss1: 0.638976 Loss2: 0.654268
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.281363 Loss1: 0.624942 Loss2: 0.656421
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.287762 Loss1: 0.629804 Loss2: 0.657958
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.272129 Loss1: 0.615569 Loss2: 0.656560
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.286089 Loss1: 0.625098 Loss2: 0.660991
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.278309 Loss1: 0.619426 Loss2: 0.658883
-(DefaultActor pid=1831567) >> Training accuracy: 0.793899
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.294768 Loss1: 0.543600 Loss2: 0.751168
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.174185 Loss1: 0.499060 Loss2: 0.675125
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.144574 Loss1: 0.475951 Loss2: 0.668623
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144923 Loss1: 0.475189 Loss2: 0.669733
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.138160 Loss1: 0.467899 Loss2: 0.670261
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.114237 Loss1: 0.445738 Loss2: 0.668499
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122970 Loss1: 0.454542 Loss2: 0.668428
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.128622 Loss1: 0.456912 Loss2: 0.671710
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.118420 Loss1: 0.447918 Loss2: 0.670502
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.102246 Loss1: 0.430340 Loss2: 0.671906
-(DefaultActor pid=1831567) >> Training accuracy: 0.846836
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.453915 Loss1: 0.686066 Loss2: 0.767849
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346406 Loss1: 0.658568 Loss2: 0.687838
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356750 Loss1: 0.665224 Loss2: 0.691526
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339694 Loss1: 0.648894 Loss2: 0.690800
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.321220 Loss1: 0.631087 Loss2: 0.690133
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.327552 Loss1: 0.636686 Loss2: 0.690866
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.326879 Loss1: 0.632074 Loss2: 0.694804
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.299315 Loss1: 0.607090 Loss2: 0.692224
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.289947 Loss1: 0.596084 Loss2: 0.693863
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.300922 Loss1: 0.606967 Loss2: 0.693954
-(DefaultActor pid=1831567) >> Training accuracy: 0.808093
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.623506 Loss1: 0.862874 Loss2: 0.760632
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.535536 Loss1: 0.855057 Loss2: 0.680479
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.532056 Loss1: 0.852763 Loss2: 0.679293
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.492094 Loss1: 0.814142 Loss2: 0.677952
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.497565 Loss1: 0.818015 Loss2: 0.679550
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.487896 Loss1: 0.804792 Loss2: 0.683104
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.492773 Loss1: 0.809416 Loss2: 0.683357
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.444985 Loss1: 0.764181 Loss2: 0.680805
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.483467 Loss1: 0.797339 Loss2: 0.686128
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.463083 Loss1: 0.779404 Loss2: 0.683679
-(DefaultActor pid=1831567) >> Training accuracy: 0.732111
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.444237 Loss1: 0.699368 Loss2: 0.744869
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.269748 Loss1: 0.630093 Loss2: 0.639655
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251043 Loss1: 0.612364 Loss2: 0.638679
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.233567 Loss1: 0.595816 Loss2: 0.637751
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.217482 Loss1: 0.580007 Loss2: 0.637475
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226735 Loss1: 0.587145 Loss2: 0.639590
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.246018 Loss1: 0.602053 Loss2: 0.643964
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.217220 Loss1: 0.576671 Loss2: 0.640549
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196875 Loss1: 0.556891 Loss2: 0.639983
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.195424 Loss1: 0.552841 Loss2: 0.642583
-(DefaultActor pid=1831567) >> Training accuracy: 0.812500
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.636434 Loss1: 0.854367 Loss2: 0.782067
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.421876 Loss1: 0.750704 Loss2: 0.671172
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.423106 Loss1: 0.751620 Loss2: 0.671486
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.443349 Loss1: 0.768752 Loss2: 0.674597
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.380581 Loss1: 0.709079 Loss2: 0.671501
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.373890 Loss1: 0.698838 Loss2: 0.675052
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.373786 Loss1: 0.700208 Loss2: 0.673578
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.372446 Loss1: 0.698373 Loss2: 0.674072
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.376551 Loss1: 0.702150 Loss2: 0.674401
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.401844 Loss1: 0.723802 Loss2: 0.678042
-(DefaultActor pid=1831567) >> Training accuracy: 0.754112
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459279 Loss1: 0.716750 Loss2: 0.742529
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332403 Loss1: 0.659466 Loss2: 0.672937
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.315148 Loss1: 0.641674 Loss2: 0.673475
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291254 Loss1: 0.618323 Loss2: 0.672931
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.311324 Loss1: 0.635065 Loss2: 0.676259
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.294753 Loss1: 0.618616 Loss2: 0.676137
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.300353 Loss1: 0.621492 Loss2: 0.678861
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.291479 Loss1: 0.615327 Loss2: 0.676152
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.269529 Loss1: 0.591039 Loss2: 0.678489
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.271777 Loss1: 0.592158 Loss2: 0.679619
-(DefaultActor pid=1831567) >> Training accuracy: 0.789253
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.628972 Loss1: 0.869221 Loss2: 0.759751
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.477548 Loss1: 0.813932 Loss2: 0.663617
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.429877 Loss1: 0.768675 Loss2: 0.661203
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.419497 Loss1: 0.758834 Loss2: 0.660663
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.397369 Loss1: 0.736081 Loss2: 0.661288
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.435392 Loss1: 0.770308 Loss2: 0.665084
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.411979 Loss1: 0.747169 Loss2: 0.664810
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.378119 Loss1: 0.715275 Loss2: 0.662844
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.391234 Loss1: 0.726463 Loss2: 0.664771
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.402201 Loss1: 0.735888 Loss2: 0.666313
-[2023-09-27 08:44:36,522][flwr][DEBUG] - fit_round 19 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.732976
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.647400
-[2023-09-27 08:44:38,422][flwr][INFO] - fit progress: (19, 1.0004730579761651, {'accuracy': 0.6474}, 8811.258530317806)
-[2023-09-27 08:44:38,423][flwr][DEBUG] - evaluate_round 19: strategy sampled 10 clients (out of 10)
-[2023-09-27 08:45:15,962][flwr][DEBUG] - evaluate_round 19 received 10 results and 0 failures
-[2023-09-27 08:45:15,963][flwr][DEBUG] - fit_round 20: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475558 Loss1: 0.714390 Loss2: 0.761168
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.321916 Loss1: 0.646287 Loss2: 0.675630
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.300586 Loss1: 0.627348 Loss2: 0.673238
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.304026 Loss1: 0.626040 Loss2: 0.677986
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282089 Loss1: 0.607012 Loss2: 0.675076
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.273291 Loss1: 0.597064 Loss2: 0.676227
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.283280 Loss1: 0.605801 Loss2: 0.677478
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253943 Loss1: 0.578319 Loss2: 0.675624
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.274910 Loss1: 0.595535 Loss2: 0.679375
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259253 Loss1: 0.581962 Loss2: 0.677291
-(DefaultActor pid=1831567) >> Training accuracy: 0.820724
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.317436 Loss1: 0.552934 Loss2: 0.764502
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180813 Loss1: 0.500938 Loss2: 0.679875
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.145237 Loss1: 0.471003 Loss2: 0.674233
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.154307 Loss1: 0.482637 Loss2: 0.671670
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.133470 Loss1: 0.461388 Loss2: 0.672083
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.108877 Loss1: 0.436004 Loss2: 0.672873
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.117315 Loss1: 0.446132 Loss2: 0.671184
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.100614 Loss1: 0.427205 Loss2: 0.673409
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.111074 Loss1: 0.438244 Loss2: 0.672829
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.114337 Loss1: 0.437237 Loss2: 0.677099
-(DefaultActor pid=1831567) >> Training accuracy: 0.852623
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.617051 Loss1: 0.846284 Loss2: 0.770767
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.454355 Loss1: 0.790653 Loss2: 0.663702
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.437650 Loss1: 0.775915 Loss2: 0.661735
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.449419 Loss1: 0.788065 Loss2: 0.661354
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.427814 Loss1: 0.767218 Loss2: 0.660596
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.405306 Loss1: 0.744129 Loss2: 0.661177
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.410511 Loss1: 0.746641 Loss2: 0.663870
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.437831 Loss1: 0.773538 Loss2: 0.664293
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.368969 Loss1: 0.705038 Loss2: 0.663931
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.398739 Loss1: 0.733408 Loss2: 0.665331
-(DefaultActor pid=1831567) >> Training accuracy: 0.744869
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493420 Loss1: 0.749245 Loss2: 0.744174
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.323423 Loss1: 0.685102 Loss2: 0.638321
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.261660 Loss1: 0.629320 Loss2: 0.632340
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.263530 Loss1: 0.631639 Loss2: 0.631891
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.271083 Loss1: 0.640220 Loss2: 0.630863
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.253888 Loss1: 0.623802 Loss2: 0.630086
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.227512 Loss1: 0.595335 Loss2: 0.632177
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225991 Loss1: 0.592486 Loss2: 0.633505
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.234916 Loss1: 0.600736 Loss2: 0.634180
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.230125 Loss1: 0.594411 Loss2: 0.635714
-(DefaultActor pid=1831567) >> Training accuracy: 0.805640
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.322487 Loss1: 0.549265 Loss2: 0.773222
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.186087 Loss1: 0.509583 Loss2: 0.676503
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.146879 Loss1: 0.472542 Loss2: 0.674337
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.129235 Loss1: 0.455324 Loss2: 0.673912
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.146508 Loss1: 0.472264 Loss2: 0.674245
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.153488 Loss1: 0.479960 Loss2: 0.673528
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.128491 Loss1: 0.456385 Loss2: 0.672105
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.121813 Loss1: 0.449746 Loss2: 0.672067
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.102745 Loss1: 0.427490 Loss2: 0.675255
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.104846 Loss1: 0.430248 Loss2: 0.674598
-(DefaultActor pid=1831567) >> Training accuracy: 0.855131
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.455290 Loss1: 0.680659 Loss2: 0.774631
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.364396 Loss1: 0.644320 Loss2: 0.720076
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.360581 Loss1: 0.642460 Loss2: 0.718121
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364534 Loss1: 0.644964 Loss2: 0.719570
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.352131 Loss1: 0.631881 Loss2: 0.720249
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.339303 Loss1: 0.619717 Loss2: 0.719586
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338692 Loss1: 0.616257 Loss2: 0.722435
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.346869 Loss1: 0.624827 Loss2: 0.722042
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.335061 Loss1: 0.611372 Loss2: 0.723690
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.328024 Loss1: 0.605836 Loss2: 0.722187
-(DefaultActor pid=1831567) >> Training accuracy: 0.804688
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.523219 Loss1: 0.676625 Loss2: 0.846595
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.349708 Loss1: 0.617992 Loss2: 0.731716
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.344933 Loss1: 0.618839 Loss2: 0.726095
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.320620 Loss1: 0.592252 Loss2: 0.728367
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.311998 Loss1: 0.582785 Loss2: 0.729213
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289162 Loss1: 0.563281 Loss2: 0.725882
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295283 Loss1: 0.567283 Loss2: 0.728000
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.290304 Loss1: 0.561248 Loss2: 0.729056
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.319784 Loss1: 0.586281 Loss2: 0.733503
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.292894 Loss1: 0.561853 Loss2: 0.731041
-(DefaultActor pid=1831567) >> Training accuracy: 0.805085
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.483556 Loss1: 0.715243 Loss2: 0.768313
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.342139 Loss1: 0.660573 Loss2: 0.681565
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.333776 Loss1: 0.652385 Loss2: 0.681391
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.332876 Loss1: 0.652171 Loss2: 0.680705
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.336677 Loss1: 0.655185 Loss2: 0.681491
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316247 Loss1: 0.634036 Loss2: 0.682211
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.286934 Loss1: 0.604707 Loss2: 0.682226
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.296937 Loss1: 0.614655 Loss2: 0.682282
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.293419 Loss1: 0.608893 Loss2: 0.684526
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.263578 Loss1: 0.580923 Loss2: 0.682654
-(DefaultActor pid=1831567) >> Training accuracy: 0.809095
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.620449 Loss1: 0.854440 Loss2: 0.766009
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.501821 Loss1: 0.832867 Loss2: 0.668954
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.477826 Loss1: 0.807765 Loss2: 0.670061
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.474054 Loss1: 0.805338 Loss2: 0.668715
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.474957 Loss1: 0.804547 Loss2: 0.670410
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.426158 Loss1: 0.759032 Loss2: 0.667125
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.458155 Loss1: 0.783775 Loss2: 0.674380
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.415378 Loss1: 0.745348 Loss2: 0.670030
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.453044 Loss1: 0.779311 Loss2: 0.673733
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.467334 Loss1: 0.790254 Loss2: 0.677080
-(DefaultActor pid=1831567) >> Training accuracy: 0.728261
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.625509 Loss1: 0.840579 Loss2: 0.784930
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.441576 Loss1: 0.766230 Loss2: 0.675347
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.419406 Loss1: 0.746482 Loss2: 0.672924
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.393639 Loss1: 0.719990 Loss2: 0.673649
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.393570 Loss1: 0.717661 Loss2: 0.675909
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.391018 Loss1: 0.712867 Loss2: 0.678151
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.390787 Loss1: 0.714887 Loss2: 0.675901
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.369164 Loss1: 0.690275 Loss2: 0.678889
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.376331 Loss1: 0.698832 Loss2: 0.677500
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.361776 Loss1: 0.681073 Loss2: 0.680703
-[2023-09-27 08:54:24,219][flwr][DEBUG] - fit_round 20 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.744243
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.655300
-[2023-09-27 08:54:25,959][flwr][INFO] - fit progress: (20, 0.9751454913578095, {'accuracy': 0.6553}, 9398.79502367787)
-[2023-09-27 08:54:25,959][flwr][DEBUG] - evaluate_round 20: strategy sampled 10 clients (out of 10)
-[2023-09-27 08:54:57,962][flwr][DEBUG] - evaluate_round 20 received 10 results and 0 failures
-[2023-09-27 08:54:57,963][flwr][DEBUG] - fit_round 21: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382565 Loss1: 0.679574 Loss2: 0.702991
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.286234 Loss1: 0.633651 Loss2: 0.652582
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.305601 Loss1: 0.653372 Loss2: 0.652229
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.284846 Loss1: 0.632735 Loss2: 0.652111
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.279790 Loss1: 0.627712 Loss2: 0.652078
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.281296 Loss1: 0.627335 Loss2: 0.653961
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.269737 Loss1: 0.615599 Loss2: 0.654138
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.264872 Loss1: 0.607804 Loss2: 0.657069
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.279820 Loss1: 0.623710 Loss2: 0.656110
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.264892 Loss1: 0.609386 Loss2: 0.655506
-(DefaultActor pid=1831567) >> Training accuracy: 0.780506
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.273524 Loss1: 0.545656 Loss2: 0.727868
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.126820 Loss1: 0.478000 Loss2: 0.648820
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.145095 Loss1: 0.496738 Loss2: 0.648357
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.119783 Loss1: 0.467852 Loss2: 0.651931
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.125708 Loss1: 0.472034 Loss2: 0.653674
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.102569 Loss1: 0.450289 Loss2: 0.652281
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.089994 Loss1: 0.436586 Loss2: 0.653408
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.098476 Loss1: 0.443129 Loss2: 0.655348
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.085444 Loss1: 0.431174 Loss2: 0.654270
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.070908 Loss1: 0.418826 Loss2: 0.652082
-(DefaultActor pid=1831567) >> Training accuracy: 0.858025
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.467955 Loss1: 0.707741 Loss2: 0.760214
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.353785 Loss1: 0.655240 Loss2: 0.698546
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.316765 Loss1: 0.619747 Loss2: 0.697018
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.330263 Loss1: 0.634742 Loss2: 0.695521
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.310456 Loss1: 0.611869 Loss2: 0.698587
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.304883 Loss1: 0.606703 Loss2: 0.698180
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.310401 Loss1: 0.611272 Loss2: 0.699129
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.301266 Loss1: 0.599771 Loss2: 0.701495
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299247 Loss1: 0.596787 Loss2: 0.702460
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281133 Loss1: 0.579460 Loss2: 0.701674
-(DefaultActor pid=1831567) >> Training accuracy: 0.787538
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.269926 Loss1: 0.548169 Loss2: 0.721757
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.124904 Loss1: 0.475627 Loss2: 0.649276
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.122672 Loss1: 0.476590 Loss2: 0.646082
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.112199 Loss1: 0.465094 Loss2: 0.647105
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.103010 Loss1: 0.458611 Loss2: 0.644399
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.101328 Loss1: 0.453507 Loss2: 0.647821
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.089281 Loss1: 0.441319 Loss2: 0.647961
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.091210 Loss1: 0.444451 Loss2: 0.646759
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.083601 Loss1: 0.435663 Loss2: 0.647938
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.089940 Loss1: 0.441726 Loss2: 0.648214
-(DefaultActor pid=1831567) >> Training accuracy: 0.848765
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.620086 Loss1: 0.836674 Loss2: 0.783412
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.457771 Loss1: 0.773724 Loss2: 0.684046
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.458576 Loss1: 0.773777 Loss2: 0.684799
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.429625 Loss1: 0.745448 Loss2: 0.684177
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.441306 Loss1: 0.755522 Loss2: 0.685784
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.389357 Loss1: 0.704971 Loss2: 0.684386
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.429139 Loss1: 0.743399 Loss2: 0.685740
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.405216 Loss1: 0.719332 Loss2: 0.685884
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.387869 Loss1: 0.698407 Loss2: 0.689462
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.406337 Loss1: 0.717125 Loss2: 0.689212
-(DefaultActor pid=1831567) >> Training accuracy: 0.737873
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.398209 Loss1: 0.675966 Loss2: 0.722242
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.285977 Loss1: 0.642005 Loss2: 0.643972
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.276184 Loss1: 0.631793 Loss2: 0.644391
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.249570 Loss1: 0.604733 Loss2: 0.644837
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.261189 Loss1: 0.614636 Loss2: 0.646554
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232557 Loss1: 0.584799 Loss2: 0.647758
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224113 Loss1: 0.577650 Loss2: 0.646463
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222113 Loss1: 0.573275 Loss2: 0.648838
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.226921 Loss1: 0.575943 Loss2: 0.650978
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.215758 Loss1: 0.566216 Loss2: 0.649542
-(DefaultActor pid=1831567) >> Training accuracy: 0.822985
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.390686 Loss1: 0.676123 Loss2: 0.714562
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250812 Loss1: 0.634612 Loss2: 0.616201
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205009 Loss1: 0.592853 Loss2: 0.612156
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202391 Loss1: 0.587462 Loss2: 0.614929
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192140 Loss1: 0.575866 Loss2: 0.616274
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180336 Loss1: 0.564191 Loss2: 0.616144
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.189182 Loss1: 0.572895 Loss2: 0.616287
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.183448 Loss1: 0.567574 Loss2: 0.615874
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183818 Loss1: 0.564847 Loss2: 0.618971
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.170347 Loss1: 0.552946 Loss2: 0.617401
-(DefaultActor pid=1831567) >> Training accuracy: 0.812235
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.604783 Loss1: 0.853066 Loss2: 0.751717
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.502989 Loss1: 0.829274 Loss2: 0.673715
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.491108 Loss1: 0.815669 Loss2: 0.675439
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.470218 Loss1: 0.794664 Loss2: 0.675554
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.461099 Loss1: 0.782221 Loss2: 0.678878
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.428303 Loss1: 0.751887 Loss2: 0.676416
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.471648 Loss1: 0.792728 Loss2: 0.678920
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.453930 Loss1: 0.773895 Loss2: 0.680036
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.431963 Loss1: 0.753042 Loss2: 0.678921
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.423678 Loss1: 0.744500 Loss2: 0.679177
-(DefaultActor pid=1831567) >> Training accuracy: 0.726676
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.585205 Loss1: 0.812090 Loss2: 0.773115
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.410618 Loss1: 0.745442 Loss2: 0.665176
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.400270 Loss1: 0.739350 Loss2: 0.660920
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.391987 Loss1: 0.733144 Loss2: 0.658844
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.362120 Loss1: 0.702163 Loss2: 0.659957
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.363539 Loss1: 0.702174 Loss2: 0.661365
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.354037 Loss1: 0.690791 Loss2: 0.663246
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.375429 Loss1: 0.710451 Loss2: 0.664978
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.332803 Loss1: 0.667962 Loss2: 0.664842
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.344110 Loss1: 0.677908 Loss2: 0.666202
-(DefaultActor pid=1831567) >> Training accuracy: 0.771930
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.395932 Loss1: 0.669449 Loss2: 0.726483
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.327267 Loss1: 0.675388 Loss2: 0.651880
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.303336 Loss1: 0.650259 Loss2: 0.653077
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.299434 Loss1: 0.649157 Loss2: 0.650277
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.257500 Loss1: 0.607814 Loss2: 0.649685
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.294210 Loss1: 0.639640 Loss2: 0.654571
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.281178 Loss1: 0.623739 Loss2: 0.657439
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.286144 Loss1: 0.630450 Loss2: 0.655694
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.242663 Loss1: 0.588327 Loss2: 0.654336
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.270470 Loss1: 0.614638 Loss2: 0.655832
-(DefaultActor pid=1831567) >> Training accuracy: 0.819912
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 09:02:07,936][flwr][DEBUG] - fit_round 21 received 10 results and 0 failures
->> Test accuracy: 0.658000
-[2023-09-27 09:02:09,249][flwr][INFO] - fit progress: (21, 0.9615000673947623, {'accuracy': 0.658}, 9862.085126371123)
-[2023-09-27 09:02:09,249][flwr][DEBUG] - evaluate_round 21: strategy sampled 10 clients (out of 10)
-[2023-09-27 09:02:41,833][flwr][DEBUG] - evaluate_round 21 received 10 results and 0 failures
-[2023-09-27 09:02:41,834][flwr][DEBUG] - fit_round 22: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.304684 Loss1: 0.569267 Loss2: 0.735416
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.141615 Loss1: 0.484821 Loss2: 0.656795
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.127340 Loss1: 0.474538 Loss2: 0.652802
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.110002 Loss1: 0.458768 Loss2: 0.651235
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.102411 Loss1: 0.453178 Loss2: 0.649233
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.112564 Loss1: 0.463801 Loss2: 0.648763
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.108355 Loss1: 0.459117 Loss2: 0.649238
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.098207 Loss1: 0.445500 Loss2: 0.652707
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.058130 Loss1: 0.409228 Loss2: 0.648902
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.087851 Loss1: 0.434326 Loss2: 0.653525
-(DefaultActor pid=1831567) >> Training accuracy: 0.852045
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.444036 Loss1: 0.682885 Loss2: 0.761151
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.344976 Loss1: 0.636430 Loss2: 0.708546
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.328771 Loss1: 0.625235 Loss2: 0.703536
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331523 Loss1: 0.626431 Loss2: 0.705093
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317135 Loss1: 0.611704 Loss2: 0.705430
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.309719 Loss1: 0.600512 Loss2: 0.709208
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.324201 Loss1: 0.613959 Loss2: 0.710243
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.311558 Loss1: 0.600952 Loss2: 0.710606
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.318152 Loss1: 0.606733 Loss2: 0.711419
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.296720 Loss1: 0.589556 Loss2: 0.707164
-(DefaultActor pid=1831567) >> Training accuracy: 0.784970
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.433290 Loss1: 0.687870 Loss2: 0.745421
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.311491 Loss1: 0.650701 Loss2: 0.660790
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.257606 Loss1: 0.599079 Loss2: 0.658527
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.269667 Loss1: 0.610019 Loss2: 0.659648
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.259221 Loss1: 0.597956 Loss2: 0.661265
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.235624 Loss1: 0.573948 Loss2: 0.661675
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.265285 Loss1: 0.602286 Loss2: 0.662999
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.244493 Loss1: 0.580970 Loss2: 0.663523
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230959 Loss1: 0.568654 Loss2: 0.662304
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.224016 Loss1: 0.560166 Loss2: 0.663850
-(DefaultActor pid=1831567) >> Training accuracy: 0.820107
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.652388 Loss1: 0.871809 Loss2: 0.780579
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.490418 Loss1: 0.809320 Loss2: 0.681098
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.470041 Loss1: 0.788804 Loss2: 0.681238
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.473239 Loss1: 0.791023 Loss2: 0.682216
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.452847 Loss1: 0.771404 Loss2: 0.681443
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.471884 Loss1: 0.787782 Loss2: 0.684102
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.434727 Loss1: 0.753278 Loss2: 0.681449
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.436968 Loss1: 0.755003 Loss2: 0.681965
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.420875 Loss1: 0.738163 Loss2: 0.682711
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.442072 Loss1: 0.755011 Loss2: 0.687061
-(DefaultActor pid=1831567) >> Training accuracy: 0.746830
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.427636 Loss1: 0.712241 Loss2: 0.715394
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.284975 Loss1: 0.663817 Loss2: 0.621157
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.248494 Loss1: 0.634158 Loss2: 0.614336
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.250529 Loss1: 0.635477 Loss2: 0.615052
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.240366 Loss1: 0.626437 Loss2: 0.613928
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220273 Loss1: 0.606933 Loss2: 0.613340
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.217637 Loss1: 0.601080 Loss2: 0.616557
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.192991 Loss1: 0.578891 Loss2: 0.614101
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199235 Loss1: 0.584297 Loss2: 0.614938
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184007 Loss1: 0.568774 Loss2: 0.615233
-(DefaultActor pid=1831567) >> Training accuracy: 0.805259
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.455510 Loss1: 0.682026 Loss2: 0.773483
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.358602 Loss1: 0.667155 Loss2: 0.691447
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.342452 Loss1: 0.650669 Loss2: 0.691783
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316722 Loss1: 0.623029 Loss2: 0.693692
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.309369 Loss1: 0.618123 Loss2: 0.691246
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.298757 Loss1: 0.607152 Loss2: 0.691604
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.297630 Loss1: 0.604506 Loss2: 0.693124
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.282516 Loss1: 0.587788 Loss2: 0.694728
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305953 Loss1: 0.608668 Loss2: 0.697284
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308094 Loss1: 0.609656 Loss2: 0.698438
-(DefaultActor pid=1831567) >> Training accuracy: 0.799880
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327160 Loss1: 0.552122 Loss2: 0.775038
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172751 Loss1: 0.495045 Loss2: 0.677705
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.146569 Loss1: 0.469712 Loss2: 0.676857
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.118620 Loss1: 0.442940 Loss2: 0.675679
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.123851 Loss1: 0.444208 Loss2: 0.679643
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.134380 Loss1: 0.454934 Loss2: 0.679447
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.112995 Loss1: 0.433674 Loss2: 0.679321
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.113952 Loss1: 0.433295 Loss2: 0.680657
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.104336 Loss1: 0.425225 Loss2: 0.679111
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.100652 Loss1: 0.420907 Loss2: 0.679746
-(DefaultActor pid=1831567) >> Training accuracy: 0.853974
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.624781 Loss1: 0.830773 Loss2: 0.794008
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.448631 Loss1: 0.767829 Loss2: 0.680803
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.436013 Loss1: 0.752829 Loss2: 0.683183
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.386369 Loss1: 0.710556 Loss2: 0.675814
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.400776 Loss1: 0.719713 Loss2: 0.681062
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.379735 Loss1: 0.700918 Loss2: 0.678817
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.396632 Loss1: 0.711408 Loss2: 0.685224
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.360377 Loss1: 0.676275 Loss2: 0.684102
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.368206 Loss1: 0.680460 Loss2: 0.687746
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.319983 Loss1: 0.635765 Loss2: 0.684219
-(DefaultActor pid=1831567) >> Training accuracy: 0.768092
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.630742 Loss1: 0.837592 Loss2: 0.793150
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.491228 Loss1: 0.803105 Loss2: 0.688123
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.445726 Loss1: 0.762416 Loss2: 0.683310
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.420936 Loss1: 0.740006 Loss2: 0.680930
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.401144 Loss1: 0.721161 Loss2: 0.679983
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.431374 Loss1: 0.749100 Loss2: 0.682274
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.405549 Loss1: 0.723866 Loss2: 0.681683
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.390657 Loss1: 0.705794 Loss2: 0.684863
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.410603 Loss1: 0.724681 Loss2: 0.685923
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.393122 Loss1: 0.704450 Loss2: 0.688672
-(DefaultActor pid=1831567) >> Training accuracy: 0.744869
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.496330 Loss1: 0.661094 Loss2: 0.835236
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.334288 Loss1: 0.613990 Loss2: 0.720298
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.308284 Loss1: 0.587335 Loss2: 0.720949
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339974 Loss1: 0.615666 Loss2: 0.724308
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.276629 Loss1: 0.556373 Loss2: 0.720255
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.283874 Loss1: 0.560942 Loss2: 0.722932
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.262001 Loss1: 0.538904 Loss2: 0.723097
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268303 Loss1: 0.543864 Loss2: 0.724439
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.297200 Loss1: 0.567344 Loss2: 0.729856
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.270218 Loss1: 0.541812 Loss2: 0.728406
-[2023-09-27 09:09:36,223][flwr][DEBUG] - fit_round 22 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.831568
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.662200
-[2023-09-27 09:09:38,011][flwr][INFO] - fit progress: (22, 0.9505386705787037, {'accuracy': 0.6622}, 10310.847262899857)
-[2023-09-27 09:09:38,011][flwr][DEBUG] - evaluate_round 22: strategy sampled 10 clients (out of 10)
-[2023-09-27 09:10:13,522][flwr][DEBUG] - evaluate_round 22 received 10 results and 0 failures
-[2023-09-27 09:10:13,524][flwr][DEBUG] - fit_round 23: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.468812 Loss1: 0.698979 Loss2: 0.769833
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.344240 Loss1: 0.641695 Loss2: 0.702545
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.331760 Loss1: 0.629602 Loss2: 0.702158
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.318049 Loss1: 0.615234 Loss2: 0.702815
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293438 Loss1: 0.592562 Loss2: 0.700875
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289138 Loss1: 0.586097 Loss2: 0.703041
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.316909 Loss1: 0.612168 Loss2: 0.704741
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.283244 Loss1: 0.578192 Loss2: 0.705051
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.296414 Loss1: 0.591695 Loss2: 0.704719
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.285332 Loss1: 0.577772 Loss2: 0.707560
-(DefaultActor pid=1831567) >> Training accuracy: 0.802782
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.572051 Loss1: 0.821143 Loss2: 0.750908
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.440161 Loss1: 0.785657 Loss2: 0.654504
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.395702 Loss1: 0.744257 Loss2: 0.651446
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.406681 Loss1: 0.755970 Loss2: 0.650711
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.397699 Loss1: 0.744061 Loss2: 0.653639
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.385006 Loss1: 0.734114 Loss2: 0.650892
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.354033 Loss1: 0.701974 Loss2: 0.652058
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.336683 Loss1: 0.682097 Loss2: 0.654586
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.366586 Loss1: 0.711158 Loss2: 0.655428
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.366881 Loss1: 0.709015 Loss2: 0.657866
-(DefaultActor pid=1831567) >> Training accuracy: 0.739272
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.288392 Loss1: 0.537270 Loss2: 0.751121
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.170947 Loss1: 0.500160 Loss2: 0.670787
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.147124 Loss1: 0.478336 Loss2: 0.668788
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.121837 Loss1: 0.454834 Loss2: 0.667003
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.102029 Loss1: 0.433805 Loss2: 0.668224
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.112459 Loss1: 0.441948 Loss2: 0.670511
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.096986 Loss1: 0.426047 Loss2: 0.670939
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.109402 Loss1: 0.438522 Loss2: 0.670880
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.097111 Loss1: 0.423939 Loss2: 0.673172
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.097192 Loss1: 0.424373 Loss2: 0.672819
-(DefaultActor pid=1831567) >> Training accuracy: 0.867863
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.597257 Loss1: 0.808820 Loss2: 0.788437
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.423027 Loss1: 0.744493 Loss2: 0.678535
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.398212 Loss1: 0.721847 Loss2: 0.676365
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.401325 Loss1: 0.724146 Loss2: 0.677179
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.379283 Loss1: 0.701888 Loss2: 0.677395
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.384467 Loss1: 0.704419 Loss2: 0.680048
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.358902 Loss1: 0.674977 Loss2: 0.683925
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.367556 Loss1: 0.686232 Loss2: 0.681324
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.342858 Loss1: 0.661052 Loss2: 0.681806
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.360587 Loss1: 0.676688 Loss2: 0.683899
-(DefaultActor pid=1831567) >> Training accuracy: 0.768366
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.428432 Loss1: 0.687496 Loss2: 0.740935
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.313393 Loss1: 0.647671 Loss2: 0.665722
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.300294 Loss1: 0.638025 Loss2: 0.662269
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281637 Loss1: 0.618428 Loss2: 0.663210
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.279676 Loss1: 0.616077 Loss2: 0.663598
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289080 Loss1: 0.624154 Loss2: 0.664926
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.273831 Loss1: 0.606128 Loss2: 0.667703
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.256142 Loss1: 0.587432 Loss2: 0.668710
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.247503 Loss1: 0.581433 Loss2: 0.666070
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.246453 Loss1: 0.578119 Loss2: 0.668334
-(DefaultActor pid=1831567) >> Training accuracy: 0.823718
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.265150 Loss1: 0.529697 Loss2: 0.735453
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.167189 Loss1: 0.504967 Loss2: 0.662222
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.132634 Loss1: 0.477185 Loss2: 0.655449
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.115517 Loss1: 0.461392 Loss2: 0.654125
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.104731 Loss1: 0.450939 Loss2: 0.653792
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.090789 Loss1: 0.438238 Loss2: 0.652551
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.105477 Loss1: 0.449660 Loss2: 0.655818
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.081778 Loss1: 0.427637 Loss2: 0.654141
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.087530 Loss1: 0.431850 Loss2: 0.655680
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.075599 Loss1: 0.419914 Loss2: 0.655685
-(DefaultActor pid=1831567) >> Training accuracy: 0.840471
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.442020 Loss1: 0.684245 Loss2: 0.757774
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.290614 Loss1: 0.615379 Loss2: 0.675235
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.283440 Loss1: 0.607194 Loss2: 0.676246
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.279625 Loss1: 0.603613 Loss2: 0.676012
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.266080 Loss1: 0.587629 Loss2: 0.678451
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.263299 Loss1: 0.582931 Loss2: 0.680367
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.297882 Loss1: 0.613081 Loss2: 0.684801
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.260067 Loss1: 0.576776 Loss2: 0.683291
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257963 Loss1: 0.576382 Loss2: 0.681581
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.251181 Loss1: 0.567422 Loss2: 0.683759
-(DefaultActor pid=1831567) >> Training accuracy: 0.808388
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.608826 Loss1: 0.846855 Loss2: 0.761971
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.501592 Loss1: 0.821855 Loss2: 0.679737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.477842 Loss1: 0.796479 Loss2: 0.681363
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.449068 Loss1: 0.767603 Loss2: 0.681465
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.450676 Loss1: 0.768555 Loss2: 0.682121
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.431079 Loss1: 0.748785 Loss2: 0.682294
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.459467 Loss1: 0.777355 Loss2: 0.682111
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.451120 Loss1: 0.766002 Loss2: 0.685118
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.449702 Loss1: 0.765147 Loss2: 0.684555
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.435665 Loss1: 0.748333 Loss2: 0.687332
-(DefaultActor pid=1831567) >> Training accuracy: 0.743886
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.447104 Loss1: 0.697902 Loss2: 0.749202
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250192 Loss1: 0.608480 Loss2: 0.641712
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.239218 Loss1: 0.599073 Loss2: 0.640144
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.209416 Loss1: 0.568619 Loss2: 0.640796
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221406 Loss1: 0.580916 Loss2: 0.640490
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.223934 Loss1: 0.580617 Loss2: 0.643318
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200313 Loss1: 0.557508 Loss2: 0.642805
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207592 Loss1: 0.565727 Loss2: 0.641866
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180679 Loss1: 0.538091 Loss2: 0.642588
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.185295 Loss1: 0.542114 Loss2: 0.643180
-(DefaultActor pid=1831567) >> Training accuracy: 0.821504
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.380921 Loss1: 0.691242 Loss2: 0.689679
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.278230 Loss1: 0.635028 Loss2: 0.643203
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.279391 Loss1: 0.636691 Loss2: 0.642700
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.256569 Loss1: 0.616060 Loss2: 0.640509
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.265177 Loss1: 0.621017 Loss2: 0.644160
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.255761 Loss1: 0.609072 Loss2: 0.646690
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.247983 Loss1: 0.604386 Loss2: 0.643597
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.245796 Loss1: 0.601249 Loss2: 0.644547
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232108 Loss1: 0.589331 Loss2: 0.642777
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229978 Loss1: 0.583936 Loss2: 0.646042
-[2023-09-27 09:17:21,927][flwr][DEBUG] - fit_round 23 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.796131
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.666700
-[2023-09-27 09:17:23,375][flwr][INFO] - fit progress: (23, 0.9466991268407804, {'accuracy': 0.6667}, 10776.211382081732)
-[2023-09-27 09:17:23,376][flwr][DEBUG] - evaluate_round 23: strategy sampled 10 clients (out of 10)
-[2023-09-27 09:17:55,594][flwr][DEBUG] - evaluate_round 23 received 10 results and 0 failures
-[2023-09-27 09:17:55,595][flwr][DEBUG] - fit_round 24: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.442585 Loss1: 0.696537 Loss2: 0.746048
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.287944 Loss1: 0.632086 Loss2: 0.655858
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260650 Loss1: 0.607534 Loss2: 0.653116
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.265549 Loss1: 0.614910 Loss2: 0.650639
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.256463 Loss1: 0.604914 Loss2: 0.651549
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.264265 Loss1: 0.611488 Loss2: 0.652777
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.232195 Loss1: 0.580208 Loss2: 0.651988
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.234776 Loss1: 0.582677 Loss2: 0.652098
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.222000 Loss1: 0.568326 Loss2: 0.653674
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.223261 Loss1: 0.569499 Loss2: 0.653762
-(DefaultActor pid=1831567) >> Training accuracy: 0.786966
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.611949 Loss1: 0.836405 Loss2: 0.775544
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.516094 Loss1: 0.833549 Loss2: 0.682545
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.504144 Loss1: 0.824844 Loss2: 0.679300
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.458776 Loss1: 0.780132 Loss2: 0.678643
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.443208 Loss1: 0.765878 Loss2: 0.677330
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.442916 Loss1: 0.763962 Loss2: 0.678954
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.456964 Loss1: 0.775383 Loss2: 0.681581
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.430724 Loss1: 0.753145 Loss2: 0.677579
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.423485 Loss1: 0.742665 Loss2: 0.680820
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.406220 Loss1: 0.724844 Loss2: 0.681376
-(DefaultActor pid=1831567) >> Training accuracy: 0.743886
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.506055 Loss1: 0.657923 Loss2: 0.848132
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.337300 Loss1: 0.606138 Loss2: 0.731161
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.311947 Loss1: 0.586314 Loss2: 0.725633
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291171 Loss1: 0.565533 Loss2: 0.725638
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.305692 Loss1: 0.576049 Loss2: 0.729643
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.267456 Loss1: 0.539071 Loss2: 0.728386
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.282163 Loss1: 0.551173 Loss2: 0.730990
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289985 Loss1: 0.560338 Loss2: 0.729647
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.270409 Loss1: 0.537560 Loss2: 0.732849
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.248267 Loss1: 0.517354 Loss2: 0.730913
-(DefaultActor pid=1831567) >> Training accuracy: 0.833686
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.400963 Loss1: 0.670244 Loss2: 0.730719
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.294876 Loss1: 0.645512 Loss2: 0.649363
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.256280 Loss1: 0.605290 Loss2: 0.650990
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.243827 Loss1: 0.592801 Loss2: 0.651026
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.249524 Loss1: 0.596894 Loss2: 0.652630
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.247849 Loss1: 0.594655 Loss2: 0.653193
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.231193 Loss1: 0.578372 Loss2: 0.652821
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.221434 Loss1: 0.569845 Loss2: 0.651588
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.223261 Loss1: 0.566541 Loss2: 0.656719
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.205130 Loss1: 0.550873 Loss2: 0.654257
-(DefaultActor pid=1831567) >> Training accuracy: 0.807977
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.589442 Loss1: 0.824839 Loss2: 0.764603
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.433182 Loss1: 0.768295 Loss2: 0.664887
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.402562 Loss1: 0.741583 Loss2: 0.660979
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.387515 Loss1: 0.727527 Loss2: 0.659988
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.375725 Loss1: 0.715425 Loss2: 0.660300
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.388543 Loss1: 0.726197 Loss2: 0.662345
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.382612 Loss1: 0.722035 Loss2: 0.660577
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.344169 Loss1: 0.682310 Loss2: 0.661860
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.362639 Loss1: 0.699073 Loss2: 0.663566
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.340342 Loss1: 0.675105 Loss2: 0.665237
-(DefaultActor pid=1831567) >> Training accuracy: 0.753032
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.277194 Loss1: 0.540282 Loss2: 0.736912
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.129066 Loss1: 0.469851 Loss2: 0.659215
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.131327 Loss1: 0.477052 Loss2: 0.654275
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.101582 Loss1: 0.451179 Loss2: 0.650403
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.092248 Loss1: 0.441539 Loss2: 0.650709
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.117023 Loss1: 0.460657 Loss2: 0.656365
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.100613 Loss1: 0.446930 Loss2: 0.653683
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.087969 Loss1: 0.433744 Loss2: 0.654224
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.087819 Loss1: 0.432577 Loss2: 0.655242
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.072699 Loss1: 0.416770 Loss2: 0.655929
-(DefaultActor pid=1831567) >> Training accuracy: 0.854167
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.452005 Loss1: 0.665145 Loss2: 0.786861
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341335 Loss1: 0.614097 Loss2: 0.727238
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.339971 Loss1: 0.612550 Loss2: 0.727421
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.346923 Loss1: 0.614950 Loss2: 0.731972
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.326639 Loss1: 0.598125 Loss2: 0.728514
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.330303 Loss1: 0.602203 Loss2: 0.728100
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.331367 Loss1: 0.603890 Loss2: 0.727477
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.318702 Loss1: 0.589627 Loss2: 0.729075
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.329242 Loss1: 0.594719 Loss2: 0.734523
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.323982 Loss1: 0.589316 Loss2: 0.734666
-(DefaultActor pid=1831567) >> Training accuracy: 0.783854
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289518 Loss1: 0.528777 Loss2: 0.760741
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.150867 Loss1: 0.486177 Loss2: 0.664691
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.132503 Loss1: 0.465289 Loss2: 0.667214
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.114794 Loss1: 0.449958 Loss2: 0.664837
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.102086 Loss1: 0.438184 Loss2: 0.663902
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.133347 Loss1: 0.466684 Loss2: 0.666664
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.098338 Loss1: 0.433601 Loss2: 0.664738
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.116851 Loss1: 0.452951 Loss2: 0.663900
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.096390 Loss1: 0.429531 Loss2: 0.666859
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.085259 Loss1: 0.419530 Loss2: 0.665728
-(DefaultActor pid=1831567) >> Training accuracy: 0.862076
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.590187 Loss1: 0.816137 Loss2: 0.774051
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.407339 Loss1: 0.744876 Loss2: 0.662462
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.388933 Loss1: 0.728200 Loss2: 0.660733
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.379559 Loss1: 0.716746 Loss2: 0.662813
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.365034 Loss1: 0.703619 Loss2: 0.661414
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.358104 Loss1: 0.691825 Loss2: 0.666279
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.336700 Loss1: 0.670735 Loss2: 0.665965
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.349781 Loss1: 0.686691 Loss2: 0.663090
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.345652 Loss1: 0.675487 Loss2: 0.670165
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.350464 Loss1: 0.685416 Loss2: 0.665048
-(DefaultActor pid=1831567) >> Training accuracy: 0.770011
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462043 Loss1: 0.699976 Loss2: 0.762067
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346701 Loss1: 0.662361 Loss2: 0.684340
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.305083 Loss1: 0.623848 Loss2: 0.681236
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.310227 Loss1: 0.627174 Loss2: 0.683054
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.304995 Loss1: 0.619295 Loss2: 0.685700
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.311936 Loss1: 0.623459 Loss2: 0.688477
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.262375 Loss1: 0.577371 Loss2: 0.685004
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.285511 Loss1: 0.598022 Loss2: 0.687488
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.256919 Loss1: 0.572078 Loss2: 0.684841
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.262832 Loss1: 0.576323 Loss2: 0.686509
-[2023-09-27 09:24:50,901][flwr][DEBUG] - fit_round 24 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.821314
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.669900
-[2023-09-27 09:24:52,443][flwr][INFO] - fit progress: (24, 0.9378842182052783, {'accuracy': 0.6699}, 11225.279507511761)
-[2023-09-27 09:24:52,444][flwr][DEBUG] - evaluate_round 24: strategy sampled 10 clients (out of 10)
-[2023-09-27 09:25:24,111][flwr][DEBUG] - evaluate_round 24 received 10 results and 0 failures
-[2023-09-27 09:25:24,113][flwr][DEBUG] - fit_round 25: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291298 Loss1: 0.532985 Loss2: 0.758313
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.131819 Loss1: 0.461659 Loss2: 0.670160
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.141225 Loss1: 0.470075 Loss2: 0.671150
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.107678 Loss1: 0.436257 Loss2: 0.671421
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.106007 Loss1: 0.436567 Loss2: 0.669440
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.107363 Loss1: 0.434615 Loss2: 0.672748
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.102492 Loss1: 0.431918 Loss2: 0.670574
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.107574 Loss1: 0.434896 Loss2: 0.672678
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.084462 Loss1: 0.408173 Loss2: 0.676288
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.087364 Loss1: 0.413978 Loss2: 0.673386
-(DefaultActor pid=1831567) >> Training accuracy: 0.861883
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297911 Loss1: 0.513162 Loss2: 0.784750
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.163240 Loss1: 0.461377 Loss2: 0.701862
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.153529 Loss1: 0.458818 Loss2: 0.694711
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.124529 Loss1: 0.430932 Loss2: 0.693597
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.133000 Loss1: 0.439967 Loss2: 0.693032
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.119512 Loss1: 0.426214 Loss2: 0.693297
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.131649 Loss1: 0.437142 Loss2: 0.694507
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.108978 Loss1: 0.414884 Loss2: 0.694094
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.107696 Loss1: 0.413930 Loss2: 0.693766
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.105525 Loss1: 0.413505 Loss2: 0.692020
-(DefaultActor pid=1831567) >> Training accuracy: 0.853009
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.559663 Loss1: 0.786652 Loss2: 0.773011
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.396127 Loss1: 0.735033 Loss2: 0.661094
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.353533 Loss1: 0.696391 Loss2: 0.657142
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.357409 Loss1: 0.702681 Loss2: 0.654727
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.347556 Loss1: 0.692423 Loss2: 0.655133
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.343808 Loss1: 0.686236 Loss2: 0.657572
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.332582 Loss1: 0.672757 Loss2: 0.659825
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.323876 Loss1: 0.665043 Loss2: 0.658832
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.339127 Loss1: 0.678283 Loss2: 0.660844
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304752 Loss1: 0.645690 Loss2: 0.659062
-(DefaultActor pid=1831567) >> Training accuracy: 0.776042
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.347441 Loss1: 0.670608 Loss2: 0.676832
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.248378 Loss1: 0.617231 Loss2: 0.631147
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.256618 Loss1: 0.623606 Loss2: 0.633012
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.255551 Loss1: 0.623081 Loss2: 0.632470
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.225479 Loss1: 0.592018 Loss2: 0.633462
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.243790 Loss1: 0.610043 Loss2: 0.633747
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243294 Loss1: 0.609663 Loss2: 0.633631
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.216637 Loss1: 0.583240 Loss2: 0.633397
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.237761 Loss1: 0.600579 Loss2: 0.637182
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.233289 Loss1: 0.598867 Loss2: 0.634422
-(DefaultActor pid=1831567) >> Training accuracy: 0.805804
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.603617 Loss1: 0.830971 Loss2: 0.772646
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.442133 Loss1: 0.768705 Loss2: 0.673428
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.432666 Loss1: 0.763812 Loss2: 0.668854
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.423279 Loss1: 0.754392 Loss2: 0.668887
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.392107 Loss1: 0.721716 Loss2: 0.670391
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.424996 Loss1: 0.753468 Loss2: 0.671528
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.363118 Loss1: 0.692246 Loss2: 0.670872
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.392274 Loss1: 0.720560 Loss2: 0.671715
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.393743 Loss1: 0.717690 Loss2: 0.676053
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.393328 Loss1: 0.719999 Loss2: 0.673329
-(DefaultActor pid=1831567) >> Training accuracy: 0.736940
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.439437 Loss1: 0.687025 Loss2: 0.752413
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.313614 Loss1: 0.629478 Loss2: 0.684136
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.300109 Loss1: 0.618150 Loss2: 0.681959
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.280730 Loss1: 0.598353 Loss2: 0.682377
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302838 Loss1: 0.614916 Loss2: 0.687922
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.271411 Loss1: 0.586943 Loss2: 0.684468
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.271443 Loss1: 0.586872 Loss2: 0.684572
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.254209 Loss1: 0.567318 Loss2: 0.686891
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.242223 Loss1: 0.557742 Loss2: 0.684481
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.256396 Loss1: 0.569394 Loss2: 0.687002
-(DefaultActor pid=1831567) >> Training accuracy: 0.810976
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.380416 Loss1: 0.660370 Loss2: 0.720046
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.302112 Loss1: 0.654655 Loss2: 0.647457
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.272347 Loss1: 0.627405 Loss2: 0.644943
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.265572 Loss1: 0.616843 Loss2: 0.648729
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.263064 Loss1: 0.612045 Loss2: 0.651019
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232622 Loss1: 0.585685 Loss2: 0.646937
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.248488 Loss1: 0.597570 Loss2: 0.650918
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.231512 Loss1: 0.583667 Loss2: 0.647845
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.237681 Loss1: 0.583760 Loss2: 0.653920
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.258152 Loss1: 0.603976 Loss2: 0.654175
-(DefaultActor pid=1831567) >> Training accuracy: 0.793269
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.412691 Loss1: 0.677170 Loss2: 0.735521
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236814 Loss1: 0.600152 Loss2: 0.636662
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222843 Loss1: 0.589647 Loss2: 0.633196
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199805 Loss1: 0.566025 Loss2: 0.633780
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.202263 Loss1: 0.569327 Loss2: 0.632936
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.177151 Loss1: 0.543150 Loss2: 0.634001
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192694 Loss1: 0.557798 Loss2: 0.634896
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158053 Loss1: 0.524251 Loss2: 0.633802
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182180 Loss1: 0.547044 Loss2: 0.635136
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172206 Loss1: 0.537278 Loss2: 0.634928
-(DefaultActor pid=1831567) >> Training accuracy: 0.832097
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.596245 Loss1: 0.833172 Loss2: 0.763072
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.480003 Loss1: 0.795339 Loss2: 0.684665
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.466028 Loss1: 0.781772 Loss2: 0.684257
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.447583 Loss1: 0.759336 Loss2: 0.688247
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.424588 Loss1: 0.741306 Loss2: 0.683282
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.444529 Loss1: 0.756129 Loss2: 0.688400
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.427014 Loss1: 0.737842 Loss2: 0.689172
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.434003 Loss1: 0.745304 Loss2: 0.688699
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.442339 Loss1: 0.749924 Loss2: 0.692415
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.430417 Loss1: 0.737635 Loss2: 0.692782
-(DefaultActor pid=1831567) >> Training accuracy: 0.755888
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.364737 Loss1: 0.647355 Loss2: 0.717382
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.252550 Loss1: 0.610841 Loss2: 0.641709
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236448 Loss1: 0.594998 Loss2: 0.641450
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.240521 Loss1: 0.596934 Loss2: 0.643587
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.214728 Loss1: 0.569747 Loss2: 0.644981
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.205929 Loss1: 0.561922 Loss2: 0.644008
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.225666 Loss1: 0.578972 Loss2: 0.646695
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.216546 Loss1: 0.568983 Loss2: 0.647563
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.187083 Loss1: 0.540598 Loss2: 0.646485
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193139 Loss1: 0.543273 Loss2: 0.649866
-[2023-09-27 09:51:06,009][flwr][DEBUG] - fit_round 25 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.820518
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.661900
-[2023-09-27 09:51:07,723][flwr][INFO] - fit progress: (25, 0.9540704172640182, {'accuracy': 0.6619}, 12800.559811873827)
-[2023-09-27 09:51:07,724][flwr][DEBUG] - evaluate_round 25: strategy sampled 10 clients (out of 10)
-[2023-09-27 09:51:39,117][flwr][DEBUG] - evaluate_round 25 received 10 results and 0 failures
-[2023-09-27 09:51:39,118][flwr][DEBUG] - fit_round 26: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.579937 Loss1: 0.788341 Loss2: 0.791596
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.400771 Loss1: 0.725262 Loss2: 0.675509
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393012 Loss1: 0.715690 Loss2: 0.677322
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.358456 Loss1: 0.684957 Loss2: 0.673499
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.371962 Loss1: 0.693885 Loss2: 0.678077
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.363979 Loss1: 0.683321 Loss2: 0.680657
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.354206 Loss1: 0.672180 Loss2: 0.682025
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.357099 Loss1: 0.677719 Loss2: 0.679380
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.362558 Loss1: 0.680811 Loss2: 0.681747
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.335656 Loss1: 0.652158 Loss2: 0.683498
-(DefaultActor pid=1831567) >> Training accuracy: 0.757401
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.449867 Loss1: 0.652957 Loss2: 0.796910
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382986 Loss1: 0.638685 Loss2: 0.744300
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.352945 Loss1: 0.610763 Loss2: 0.742182
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.344095 Loss1: 0.602170 Loss2: 0.741925
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345198 Loss1: 0.599742 Loss2: 0.745456
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.358466 Loss1: 0.611384 Loss2: 0.747082
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322897 Loss1: 0.577306 Loss2: 0.745590
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.330038 Loss1: 0.583062 Loss2: 0.746975
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.323383 Loss1: 0.575720 Loss2: 0.747663
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.330938 Loss1: 0.580277 Loss2: 0.750660
-(DefaultActor pid=1831567) >> Training accuracy: 0.810640
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.278662 Loss1: 0.529956 Loss2: 0.748705
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.127076 Loss1: 0.472016 Loss2: 0.655060
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.099400 Loss1: 0.448599 Loss2: 0.650801
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.099539 Loss1: 0.447419 Loss2: 0.652121
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.089999 Loss1: 0.437392 Loss2: 0.652608
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.074432 Loss1: 0.420066 Loss2: 0.654366
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.073378 Loss1: 0.416858 Loss2: 0.656520
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.083157 Loss1: 0.427954 Loss2: 0.655203
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.081000 Loss1: 0.423217 Loss2: 0.657783
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.076021 Loss1: 0.419653 Loss2: 0.656368
-(DefaultActor pid=1831567) >> Training accuracy: 0.858410
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.256905 Loss1: 0.507756 Loss2: 0.749149
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.156353 Loss1: 0.482242 Loss2: 0.674110
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.129385 Loss1: 0.457267 Loss2: 0.672118
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.116327 Loss1: 0.447331 Loss2: 0.668996
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.116679 Loss1: 0.447464 Loss2: 0.669215
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.097869 Loss1: 0.427128 Loss2: 0.670741
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.094620 Loss1: 0.422649 Loss2: 0.671971
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.087470 Loss1: 0.416920 Loss2: 0.670550
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.088435 Loss1: 0.417269 Loss2: 0.671166
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.088999 Loss1: 0.417628 Loss2: 0.671370
-(DefaultActor pid=1831567) >> Training accuracy: 0.860532
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.575258 Loss1: 0.840257 Loss2: 0.735001
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.423118 Loss1: 0.774373 Loss2: 0.648745
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.439829 Loss1: 0.790934 Loss2: 0.648895
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.427558 Loss1: 0.778342 Loss2: 0.649215
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.422870 Loss1: 0.771653 Loss2: 0.651216
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.414211 Loss1: 0.764718 Loss2: 0.649493
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386701 Loss1: 0.736232 Loss2: 0.650469
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.384865 Loss1: 0.733927 Loss2: 0.650938
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.387554 Loss1: 0.735866 Loss2: 0.651687
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.367781 Loss1: 0.716147 Loss2: 0.651634
-(DefaultActor pid=1831567) >> Training accuracy: 0.759511
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.426826 Loss1: 0.689926 Loss2: 0.736900
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.273715 Loss1: 0.628046 Loss2: 0.645669
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.249497 Loss1: 0.608553 Loss2: 0.640944
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.247141 Loss1: 0.604232 Loss2: 0.642909
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.223931 Loss1: 0.579743 Loss2: 0.644187
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225793 Loss1: 0.585519 Loss2: 0.640275
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.231523 Loss1: 0.587130 Loss2: 0.644393
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225234 Loss1: 0.581247 Loss2: 0.643986
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.229293 Loss1: 0.582420 Loss2: 0.646873
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.206049 Loss1: 0.560457 Loss2: 0.645592
-(DefaultActor pid=1831567) >> Training accuracy: 0.809832
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.393214 Loss1: 0.658750 Loss2: 0.734464
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.275248 Loss1: 0.625547 Loss2: 0.649701
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238389 Loss1: 0.591130 Loss2: 0.647259
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.230892 Loss1: 0.580775 Loss2: 0.650117
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.247179 Loss1: 0.595497 Loss2: 0.651682
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218580 Loss1: 0.564239 Loss2: 0.654341
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.221888 Loss1: 0.568569 Loss2: 0.653319
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204439 Loss1: 0.550079 Loss2: 0.654360
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.219265 Loss1: 0.562102 Loss2: 0.657163
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.234365 Loss1: 0.577786 Loss2: 0.656579
-(DefaultActor pid=1831567) >> Training accuracy: 0.822780
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.424447 Loss1: 0.661867 Loss2: 0.762580
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.309849 Loss1: 0.626357 Loss2: 0.683492
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.322890 Loss1: 0.637699 Loss2: 0.685191
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.308170 Loss1: 0.623773 Loss2: 0.684397
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282029 Loss1: 0.595792 Loss2: 0.686237
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.284751 Loss1: 0.599392 Loss2: 0.685360
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.251167 Loss1: 0.563948 Loss2: 0.687219
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.299734 Loss1: 0.609962 Loss2: 0.689771
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.281578 Loss1: 0.589368 Loss2: 0.692210
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.242978 Loss1: 0.553357 Loss2: 0.689621
-(DefaultActor pid=1831567) >> Training accuracy: 0.806290
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517193 Loss1: 0.678666 Loss2: 0.838527
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.307189 Loss1: 0.585330 Loss2: 0.721859
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.310832 Loss1: 0.586600 Loss2: 0.724233
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.268351 Loss1: 0.548302 Loss2: 0.720050
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.275366 Loss1: 0.551415 Loss2: 0.723951
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.274670 Loss1: 0.549893 Loss2: 0.724776
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.260397 Loss1: 0.539858 Loss2: 0.720538
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.283776 Loss1: 0.553563 Loss2: 0.730213
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.270436 Loss1: 0.544095 Loss2: 0.726340
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.248022 Loss1: 0.518548 Loss2: 0.729474
-(DefaultActor pid=1831567) >> Training accuracy: 0.840042
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.581076 Loss1: 0.801565 Loss2: 0.779511
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.434514 Loss1: 0.753070 Loss2: 0.681444
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.430657 Loss1: 0.755300 Loss2: 0.675357
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.398434 Loss1: 0.721442 Loss2: 0.676992
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.385873 Loss1: 0.707093 Loss2: 0.678780
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.395713 Loss1: 0.717280 Loss2: 0.678433
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.372354 Loss1: 0.693839 Loss2: 0.678516
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.373755 Loss1: 0.693159 Loss2: 0.680596
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.378777 Loss1: 0.698205 Loss2: 0.680572
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.358462 Loss1: 0.679644 Loss2: 0.678818
-[2023-09-27 09:58:31,674][flwr][DEBUG] - fit_round 26 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.763993
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.666400
-[2023-09-27 09:58:33,024][flwr][INFO] - fit progress: (26, 0.9397244629578088, {'accuracy': 0.6664}, 13245.86027173698)
-[2023-09-27 09:58:33,024][flwr][DEBUG] - evaluate_round 26: strategy sampled 10 clients (out of 10)
-[2023-09-27 09:59:04,818][flwr][DEBUG] - evaluate_round 26 received 10 results and 0 failures
-[2023-09-27 09:59:04,819][flwr][DEBUG] - fit_round 27: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.449583 Loss1: 0.690530 Loss2: 0.759053
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.296770 Loss1: 0.609999 Loss2: 0.686771
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.301178 Loss1: 0.612058 Loss2: 0.689121
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.284295 Loss1: 0.596648 Loss2: 0.687648
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.266546 Loss1: 0.581180 Loss2: 0.685366
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.267941 Loss1: 0.579791 Loss2: 0.688151
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258347 Loss1: 0.568537 Loss2: 0.689810
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268895 Loss1: 0.580373 Loss2: 0.688522
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.264487 Loss1: 0.574231 Loss2: 0.690256
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.266871 Loss1: 0.574517 Loss2: 0.692353
-(DefaultActor pid=1831567) >> Training accuracy: 0.821456
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.553716 Loss1: 0.808962 Loss2: 0.744754
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.393486 Loss1: 0.742398 Loss2: 0.651088
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.394969 Loss1: 0.746892 Loss2: 0.648078
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.411690 Loss1: 0.760596 Loss2: 0.651094
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.378727 Loss1: 0.730115 Loss2: 0.648612
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341018 Loss1: 0.692348 Loss2: 0.648671
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.364923 Loss1: 0.714833 Loss2: 0.650090
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.381115 Loss1: 0.726566 Loss2: 0.654550
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.374915 Loss1: 0.720598 Loss2: 0.654317
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.325245 Loss1: 0.671102 Loss2: 0.654144
-(DefaultActor pid=1831567) >> Training accuracy: 0.773088
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.363789 Loss1: 0.644337 Loss2: 0.719452
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201952 Loss1: 0.578580 Loss2: 0.623372
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.206212 Loss1: 0.583278 Loss2: 0.622934
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210962 Loss1: 0.588102 Loss2: 0.622859
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192423 Loss1: 0.567312 Loss2: 0.625111
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.153705 Loss1: 0.528875 Loss2: 0.624830
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.185644 Loss1: 0.557967 Loss2: 0.627677
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.148644 Loss1: 0.523978 Loss2: 0.624666
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.170491 Loss1: 0.544268 Loss2: 0.626223
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.145878 Loss1: 0.517445 Loss2: 0.628433
-(DefaultActor pid=1831567) >> Training accuracy: 0.812235
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358632 Loss1: 0.651874 Loss2: 0.706758
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.267711 Loss1: 0.605041 Loss2: 0.662670
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236873 Loss1: 0.580189 Loss2: 0.656684
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261908 Loss1: 0.603533 Loss2: 0.658375
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.265325 Loss1: 0.604271 Loss2: 0.661053
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249532 Loss1: 0.588019 Loss2: 0.661513
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.251672 Loss1: 0.588851 Loss2: 0.662822
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.239603 Loss1: 0.577299 Loss2: 0.662304
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.237296 Loss1: 0.574499 Loss2: 0.662797
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.241677 Loss1: 0.576387 Loss2: 0.665290
-(DefaultActor pid=1831567) >> Training accuracy: 0.815228
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.616389 Loss1: 0.848396 Loss2: 0.767993
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.488945 Loss1: 0.805598 Loss2: 0.683347
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.465658 Loss1: 0.778338 Loss2: 0.687320
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.460593 Loss1: 0.774837 Loss2: 0.685755
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.462736 Loss1: 0.773943 Loss2: 0.688794
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.430867 Loss1: 0.742141 Loss2: 0.688726
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.423608 Loss1: 0.734616 Loss2: 0.688992
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.421408 Loss1: 0.731139 Loss2: 0.690269
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.427985 Loss1: 0.739306 Loss2: 0.688679
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.411158 Loss1: 0.723440 Loss2: 0.687718
-(DefaultActor pid=1831567) >> Training accuracy: 0.757246
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.444652 Loss1: 0.681700 Loss2: 0.762952
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.290312 Loss1: 0.610782 Loss2: 0.679530
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.270683 Loss1: 0.592385 Loss2: 0.678298
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281492 Loss1: 0.599640 Loss2: 0.681852
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.250848 Loss1: 0.571253 Loss2: 0.679594
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.258113 Loss1: 0.575518 Loss2: 0.682595
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.242819 Loss1: 0.561299 Loss2: 0.681520
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229820 Loss1: 0.544917 Loss2: 0.684904
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230244 Loss1: 0.547959 Loss2: 0.682285
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240552 Loss1: 0.555453 Loss2: 0.685099
-(DefaultActor pid=1831567) >> Training accuracy: 0.818462
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.295270 Loss1: 0.523966 Loss2: 0.771304
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.168281 Loss1: 0.478028 Loss2: 0.690253
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.141702 Loss1: 0.455448 Loss2: 0.686254
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.118096 Loss1: 0.430919 Loss2: 0.687177
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.111567 Loss1: 0.424975 Loss2: 0.686592
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.134685 Loss1: 0.443841 Loss2: 0.690844
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.127537 Loss1: 0.438621 Loss2: 0.688916
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.111485 Loss1: 0.419299 Loss2: 0.692186
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.100949 Loss1: 0.410271 Loss2: 0.690677
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.093137 Loss1: 0.399657 Loss2: 0.693480
-(DefaultActor pid=1831567) >> Training accuracy: 0.865162
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.252538 Loss1: 0.524924 Loss2: 0.727614
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.117815 Loss1: 0.464849 Loss2: 0.652966
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.101431 Loss1: 0.454336 Loss2: 0.647094
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.095650 Loss1: 0.446353 Loss2: 0.649297
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.067999 Loss1: 0.421154 Loss2: 0.646844
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.072220 Loss1: 0.426395 Loss2: 0.645825
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.053222 Loss1: 0.405447 Loss2: 0.647776
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.085172 Loss1: 0.436083 Loss2: 0.649089
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.075878 Loss1: 0.425026 Loss2: 0.650852
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.067523 Loss1: 0.417062 Loss2: 0.650461
-(DefaultActor pid=1831567) >> Training accuracy: 0.856674
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389390 Loss1: 0.665034 Loss2: 0.724355
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304669 Loss1: 0.653222 Loss2: 0.651447
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.270689 Loss1: 0.621811 Loss2: 0.648879
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.234148 Loss1: 0.587116 Loss2: 0.647032
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.251013 Loss1: 0.600719 Loss2: 0.650294
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.277636 Loss1: 0.622558 Loss2: 0.655079
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.217177 Loss1: 0.566579 Loss2: 0.650598
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202146 Loss1: 0.550539 Loss2: 0.651607
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.222114 Loss1: 0.567161 Loss2: 0.654952
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.212037 Loss1: 0.556720 Loss2: 0.655317
-(DefaultActor pid=1831567) >> Training accuracy: 0.810897
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.604415 Loss1: 0.812420 Loss2: 0.791995
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.427729 Loss1: 0.749371 Loss2: 0.678358
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.383589 Loss1: 0.712978 Loss2: 0.670611
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353658 Loss1: 0.679043 Loss2: 0.674615
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.364780 Loss1: 0.690445 Loss2: 0.674335
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362384 Loss1: 0.687861 Loss2: 0.674523
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352810 Loss1: 0.675408 Loss2: 0.677402
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.337719 Loss1: 0.662399 Loss2: 0.675320
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.333320 Loss1: 0.656117 Loss2: 0.677204
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.326474 Loss1: 0.653764 Loss2: 0.672710
-[2023-09-27 10:05:58,557][flwr][DEBUG] - fit_round 27 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.784265
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.664700
-[2023-09-27 10:06:00,240][flwr][INFO] - fit progress: (27, 0.9426276777118159, {'accuracy': 0.6647}, 13693.076540732756)
-[2023-09-27 10:06:00,241][flwr][DEBUG] - evaluate_round 27: strategy sampled 10 clients (out of 10)
-[2023-09-27 10:06:38,122][flwr][DEBUG] - evaluate_round 27 received 10 results and 0 failures
-[2023-09-27 10:06:38,123][flwr][DEBUG] - fit_round 28: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.418768 Loss1: 0.686215 Loss2: 0.732554
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.297841 Loss1: 0.654486 Loss2: 0.643356
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.256738 Loss1: 0.619040 Loss2: 0.637697
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.235252 Loss1: 0.600433 Loss2: 0.634819
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.228107 Loss1: 0.590599 Loss2: 0.637508
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232736 Loss1: 0.595422 Loss2: 0.637314
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212010 Loss1: 0.573910 Loss2: 0.638099
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.194359 Loss1: 0.558259 Loss2: 0.636099
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.209921 Loss1: 0.572485 Loss2: 0.637436
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198044 Loss1: 0.558994 Loss2: 0.639050
-(DefaultActor pid=1831567) >> Training accuracy: 0.799162
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.433249 Loss1: 0.668183 Loss2: 0.765066
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.315007 Loss1: 0.629281 Loss2: 0.685727
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.277019 Loss1: 0.594631 Loss2: 0.682388
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291903 Loss1: 0.610321 Loss2: 0.681582
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302241 Loss1: 0.616474 Loss2: 0.685767
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.273708 Loss1: 0.587307 Loss2: 0.686401
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.274596 Loss1: 0.584567 Loss2: 0.690030
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.263132 Loss1: 0.575076 Loss2: 0.688056
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.242392 Loss1: 0.553507 Loss2: 0.688885
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.223973 Loss1: 0.535816 Loss2: 0.688156
-(DefaultActor pid=1831567) >> Training accuracy: 0.818510
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.550636 Loss1: 0.770576 Loss2: 0.780060
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.404205 Loss1: 0.737243 Loss2: 0.666961
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.374638 Loss1: 0.708243 Loss2: 0.666395
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.357123 Loss1: 0.690724 Loss2: 0.666399
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.336926 Loss1: 0.670080 Loss2: 0.666846
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.344860 Loss1: 0.678120 Loss2: 0.666740
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338643 Loss1: 0.670485 Loss2: 0.668158
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.332172 Loss1: 0.661554 Loss2: 0.670618
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.323821 Loss1: 0.653988 Loss2: 0.669833
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308221 Loss1: 0.638238 Loss2: 0.669983
-(DefaultActor pid=1831567) >> Training accuracy: 0.766721
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.455305 Loss1: 0.636382 Loss2: 0.818923
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.291476 Loss1: 0.585002 Loss2: 0.706474
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.293225 Loss1: 0.588143 Loss2: 0.705082
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.266649 Loss1: 0.558072 Loss2: 0.708577
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.254135 Loss1: 0.547963 Loss2: 0.706172
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.247226 Loss1: 0.538264 Loss2: 0.708962
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.237383 Loss1: 0.529307 Loss2: 0.708076
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229799 Loss1: 0.521264 Loss2: 0.708535
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.209138 Loss1: 0.498008 Loss2: 0.711130
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.211328 Loss1: 0.502127 Loss2: 0.709200
-(DefaultActor pid=1831567) >> Training accuracy: 0.820180
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.401725 Loss1: 0.657920 Loss2: 0.743805
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.265344 Loss1: 0.606006 Loss2: 0.659338
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251176 Loss1: 0.589000 Loss2: 0.662176
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.234312 Loss1: 0.573394 Loss2: 0.660918
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221983 Loss1: 0.559297 Loss2: 0.662687
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249051 Loss1: 0.584100 Loss2: 0.664950
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.222472 Loss1: 0.557912 Loss2: 0.664560
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220432 Loss1: 0.560078 Loss2: 0.660354
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213997 Loss1: 0.549719 Loss2: 0.664279
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240863 Loss1: 0.574954 Loss2: 0.665910
-(DefaultActor pid=1831567) >> Training accuracy: 0.816406
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568702 Loss1: 0.824548 Loss2: 0.744154
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.441129 Loss1: 0.788478 Loss2: 0.652652
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.426707 Loss1: 0.774806 Loss2: 0.651901
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.405568 Loss1: 0.755009 Loss2: 0.650559
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.409905 Loss1: 0.759398 Loss2: 0.650507
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.402611 Loss1: 0.748745 Loss2: 0.653866
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.392475 Loss1: 0.737982 Loss2: 0.654492
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.378369 Loss1: 0.724729 Loss2: 0.653640
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.379661 Loss1: 0.722017 Loss2: 0.657645
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.372371 Loss1: 0.716533 Loss2: 0.655839
-(DefaultActor pid=1831567) >> Training accuracy: 0.763587
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.245802 Loss1: 0.524239 Loss2: 0.721563
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.106318 Loss1: 0.461317 Loss2: 0.645001
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.098987 Loss1: 0.454362 Loss2: 0.644625
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.077071 Loss1: 0.435293 Loss2: 0.641778
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.084853 Loss1: 0.442075 Loss2: 0.642778
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.074157 Loss1: 0.432492 Loss2: 0.641665
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.065995 Loss1: 0.424209 Loss2: 0.641786
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.059374 Loss1: 0.417833 Loss2: 0.641542
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.065397 Loss1: 0.419789 Loss2: 0.645609
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.047830 Loss1: 0.403059 Loss2: 0.644771
-(DefaultActor pid=1831567) >> Training accuracy: 0.850116
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.392082 Loss1: 0.645682 Loss2: 0.746399
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.311556 Loss1: 0.615339 Loss2: 0.696217
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.291585 Loss1: 0.593636 Loss2: 0.697949
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.302202 Loss1: 0.604944 Loss2: 0.697257
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.281444 Loss1: 0.582316 Loss2: 0.699128
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.290823 Loss1: 0.591659 Loss2: 0.699164
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.292169 Loss1: 0.592937 Loss2: 0.699232
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.294687 Loss1: 0.594369 Loss2: 0.700318
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.278720 Loss1: 0.580873 Loss2: 0.697847
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259913 Loss1: 0.559329 Loss2: 0.700584
-(DefaultActor pid=1831567) >> Training accuracy: 0.798611
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.259746 Loss1: 0.508897 Loss2: 0.750849
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.131362 Loss1: 0.467543 Loss2: 0.663820
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.123275 Loss1: 0.462183 Loss2: 0.661092
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.120915 Loss1: 0.461125 Loss2: 0.659791
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.086349 Loss1: 0.428965 Loss2: 0.657384
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.084815 Loss1: 0.425144 Loss2: 0.659670
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.076643 Loss1: 0.415860 Loss2: 0.660784
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.064891 Loss1: 0.405176 Loss2: 0.659715
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.064812 Loss1: 0.403675 Loss2: 0.661137
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.049509 Loss1: 0.387751 Loss2: 0.661758
-(DefaultActor pid=1831567) >> Training accuracy: 0.860725
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.573778 Loss1: 0.789138 Loss2: 0.784640
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.466705 Loss1: 0.778649 Loss2: 0.688056
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.447096 Loss1: 0.760558 Loss2: 0.686538
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.423077 Loss1: 0.736934 Loss2: 0.686144
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.405165 Loss1: 0.719516 Loss2: 0.685649
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.385724 Loss1: 0.702465 Loss2: 0.683259
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.397575 Loss1: 0.712839 Loss2: 0.684736
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.386951 Loss1: 0.699629 Loss2: 0.687322
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.369247 Loss1: 0.684339 Loss2: 0.684908
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.384623 Loss1: 0.697205 Loss2: 0.687418
-[2023-09-27 10:19:54,162][flwr][DEBUG] - fit_round 28 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.733442
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.678000
-[2023-09-27 10:19:55,522][flwr][INFO] - fit progress: (28, 0.9165207516080656, {'accuracy': 0.678}, 14528.358099716716)
-[2023-09-27 10:19:55,522][flwr][DEBUG] - evaluate_round 28: strategy sampled 10 clients (out of 10)
-[2023-09-27 10:20:27,050][flwr][DEBUG] - evaluate_round 28 received 10 results and 0 failures
-[2023-09-27 10:20:27,051][flwr][DEBUG] - fit_round 29: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370029 Loss1: 0.631503 Loss2: 0.738526
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.226544 Loss1: 0.596735 Loss2: 0.629808
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.206555 Loss1: 0.575237 Loss2: 0.631317
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199460 Loss1: 0.569987 Loss2: 0.629473
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169510 Loss1: 0.539560 Loss2: 0.629949
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.187580 Loss1: 0.557282 Loss2: 0.630298
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168789 Loss1: 0.536684 Loss2: 0.632105
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.150701 Loss1: 0.517597 Loss2: 0.633104
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.154675 Loss1: 0.521047 Loss2: 0.633628
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140726 Loss1: 0.505695 Loss2: 0.635031
-(DefaultActor pid=1831567) >> Training accuracy: 0.836600
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.390817 Loss1: 0.633910 Loss2: 0.756907
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.287583 Loss1: 0.611897 Loss2: 0.675686
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.259390 Loss1: 0.583481 Loss2: 0.675909
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.262353 Loss1: 0.586387 Loss2: 0.675967
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.243120 Loss1: 0.565592 Loss2: 0.677528
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.250752 Loss1: 0.573260 Loss2: 0.677493
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.264689 Loss1: 0.586816 Loss2: 0.677873
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229545 Loss1: 0.550821 Loss2: 0.678724
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.233407 Loss1: 0.554371 Loss2: 0.679036
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.232845 Loss1: 0.553213 Loss2: 0.679632
-(DefaultActor pid=1831567) >> Training accuracy: 0.806538
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.585579 Loss1: 0.810008 Loss2: 0.775571
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.429684 Loss1: 0.754251 Loss2: 0.675433
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.411040 Loss1: 0.735261 Loss2: 0.675779
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.416790 Loss1: 0.739172 Loss2: 0.677618
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.380690 Loss1: 0.704945 Loss2: 0.675745
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.377753 Loss1: 0.700152 Loss2: 0.677600
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.374199 Loss1: 0.697348 Loss2: 0.676850
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.367466 Loss1: 0.687880 Loss2: 0.679585
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.377289 Loss1: 0.698104 Loss2: 0.679185
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.339278 Loss1: 0.660231 Loss2: 0.679048
-(DefaultActor pid=1831567) >> Training accuracy: 0.751866
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.611101 Loss1: 0.830996 Loss2: 0.780105
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.468160 Loss1: 0.773560 Loss2: 0.694600
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.494242 Loss1: 0.798755 Loss2: 0.695487
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.446940 Loss1: 0.753391 Loss2: 0.693548
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.465876 Loss1: 0.769483 Loss2: 0.696393
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.421482 Loss1: 0.726651 Loss2: 0.694831
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.444376 Loss1: 0.745228 Loss2: 0.699148
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.421725 Loss1: 0.725207 Loss2: 0.696518
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.421947 Loss1: 0.724504 Loss2: 0.697443
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.418528 Loss1: 0.716543 Loss2: 0.701985
-(DefaultActor pid=1831567) >> Training accuracy: 0.725317
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.274756 Loss1: 0.530785 Loss2: 0.743971
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.144970 Loss1: 0.480711 Loss2: 0.664259
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.104926 Loss1: 0.439425 Loss2: 0.665501
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.095962 Loss1: 0.433343 Loss2: 0.662619
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.097205 Loss1: 0.434741 Loss2: 0.662464
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.093557 Loss1: 0.427506 Loss2: 0.666050
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.069530 Loss1: 0.406484 Loss2: 0.663046
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.059522 Loss1: 0.394130 Loss2: 0.665393
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.101034 Loss1: 0.433776 Loss2: 0.667258
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.073603 Loss1: 0.406521 Loss2: 0.667082
-(DefaultActor pid=1831567) >> Training accuracy: 0.867091
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.255015 Loss1: 0.483934 Loss2: 0.771080
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.154187 Loss1: 0.462894 Loss2: 0.691293
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155095 Loss1: 0.470317 Loss2: 0.684777
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.133934 Loss1: 0.446505 Loss2: 0.687429
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.100514 Loss1: 0.417344 Loss2: 0.683170
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.114542 Loss1: 0.431890 Loss2: 0.682652
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.092493 Loss1: 0.410354 Loss2: 0.682139
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.082487 Loss1: 0.400856 Loss2: 0.681631
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.112725 Loss1: 0.426506 Loss2: 0.686219
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.090143 Loss1: 0.404309 Loss2: 0.685834
-(DefaultActor pid=1831567) >> Training accuracy: 0.860918
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.371168 Loss1: 0.637341 Loss2: 0.733827
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.309142 Loss1: 0.622190 Loss2: 0.686952
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.281147 Loss1: 0.597276 Loss2: 0.683870
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.278825 Loss1: 0.596327 Loss2: 0.682498
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.268777 Loss1: 0.584700 Loss2: 0.684077
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249542 Loss1: 0.566618 Loss2: 0.682924
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.270567 Loss1: 0.584703 Loss2: 0.685864
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273444 Loss1: 0.586418 Loss2: 0.687025
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.265126 Loss1: 0.576587 Loss2: 0.688539
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.270341 Loss1: 0.582286 Loss2: 0.688055
-(DefaultActor pid=1831567) >> Training accuracy: 0.809028
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.432216 Loss1: 0.668746 Loss2: 0.763470
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.321014 Loss1: 0.627252 Loss2: 0.693762
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.274246 Loss1: 0.584233 Loss2: 0.690014
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.288295 Loss1: 0.594718 Loss2: 0.693578
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.276742 Loss1: 0.585401 Loss2: 0.691340
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.268025 Loss1: 0.573129 Loss2: 0.694896
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.270715 Loss1: 0.578046 Loss2: 0.692669
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273003 Loss1: 0.578904 Loss2: 0.694098
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239917 Loss1: 0.545208 Loss2: 0.694710
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.269890 Loss1: 0.571299 Loss2: 0.698591
-(DefaultActor pid=1831567) >> Training accuracy: 0.822790
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381699 Loss1: 0.649553 Loss2: 0.732146
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.262403 Loss1: 0.609453 Loss2: 0.652950
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.250059 Loss1: 0.599715 Loss2: 0.650344
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225126 Loss1: 0.573626 Loss2: 0.651499
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.249635 Loss1: 0.592861 Loss2: 0.656774
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222812 Loss1: 0.567778 Loss2: 0.655034
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.222320 Loss1: 0.566053 Loss2: 0.656266
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268306 Loss1: 0.607701 Loss2: 0.660605
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.233410 Loss1: 0.575375 Loss2: 0.658035
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201376 Loss1: 0.545252 Loss2: 0.656124
-(DefaultActor pid=1831567) >> Training accuracy: 0.825721
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.594404 Loss1: 0.792304 Loss2: 0.802101
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.407948 Loss1: 0.716385 Loss2: 0.691563
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.401503 Loss1: 0.710420 Loss2: 0.691083
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.360940 Loss1: 0.671525 Loss2: 0.689416
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.355550 Loss1: 0.661906 Loss2: 0.693644
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.378702 Loss1: 0.685507 Loss2: 0.693195
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.335081 Loss1: 0.643218 Loss2: 0.691863
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.343584 Loss1: 0.650090 Loss2: 0.693494
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.324952 Loss1: 0.630504 Loss2: 0.694448
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.337312 Loss1: 0.641988 Loss2: 0.695323
-[2023-09-27 10:27:26,438][flwr][DEBUG] - fit_round 29 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.780976
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.674000
-[2023-09-27 10:27:27,727][flwr][INFO] - fit progress: (29, 0.93534632089039, {'accuracy': 0.674}, 14980.56385146873)
-[2023-09-27 10:27:27,728][flwr][DEBUG] - evaluate_round 29: strategy sampled 10 clients (out of 10)
-[2023-09-27 10:28:02,214][flwr][DEBUG] - evaluate_round 29 received 10 results and 0 failures
-[2023-09-27 10:28:02,214][flwr][DEBUG] - fit_round 30: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.558626 Loss1: 0.817066 Loss2: 0.741560
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.413558 Loss1: 0.766425 Loss2: 0.647133
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.400337 Loss1: 0.754652 Loss2: 0.645686
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.394529 Loss1: 0.749216 Loss2: 0.645314
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.395406 Loss1: 0.745560 Loss2: 0.649846
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.426118 Loss1: 0.772786 Loss2: 0.653332
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.375800 Loss1: 0.724928 Loss2: 0.650872
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.378639 Loss1: 0.726266 Loss2: 0.652373
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.373143 Loss1: 0.718696 Loss2: 0.654447
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.368305 Loss1: 0.716517 Loss2: 0.651787
-(DefaultActor pid=1831567) >> Training accuracy: 0.749774
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.260257 Loss1: 0.501170 Loss2: 0.759087
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.140659 Loss1: 0.462086 Loss2: 0.678573
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.112292 Loss1: 0.438559 Loss2: 0.673733
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.114388 Loss1: 0.440584 Loss2: 0.673804
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.105189 Loss1: 0.432603 Loss2: 0.672586
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.099388 Loss1: 0.426862 Loss2: 0.672526
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.108283 Loss1: 0.432789 Loss2: 0.675495
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.087846 Loss1: 0.414836 Loss2: 0.673010
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.076672 Loss1: 0.402778 Loss2: 0.673893
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.083153 Loss1: 0.406479 Loss2: 0.676674
-(DefaultActor pid=1831567) >> Training accuracy: 0.843364
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.436802 Loss1: 0.626263 Loss2: 0.810539
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.269520 Loss1: 0.571260 Loss2: 0.698260
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.254141 Loss1: 0.557352 Loss2: 0.696789
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261991 Loss1: 0.565440 Loss2: 0.696551
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.278638 Loss1: 0.574527 Loss2: 0.704111
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.265103 Loss1: 0.564920 Loss2: 0.700183
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212292 Loss1: 0.513843 Loss2: 0.698450
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222560 Loss1: 0.521835 Loss2: 0.700725
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.208476 Loss1: 0.507235 Loss2: 0.701241
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.232460 Loss1: 0.528061 Loss2: 0.704399
-(DefaultActor pid=1831567) >> Training accuracy: 0.835275
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.404904 Loss1: 0.654320 Loss2: 0.750584
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.295220 Loss1: 0.621118 Loss2: 0.674102
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.289150 Loss1: 0.613698 Loss2: 0.675451
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.292535 Loss1: 0.616216 Loss2: 0.676319
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.248166 Loss1: 0.572596 Loss2: 0.675571
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.259981 Loss1: 0.581523 Loss2: 0.678458
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.235138 Loss1: 0.556705 Loss2: 0.678433
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.247438 Loss1: 0.567640 Loss2: 0.679799
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.224590 Loss1: 0.545108 Loss2: 0.679482
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.238383 Loss1: 0.560560 Loss2: 0.677823
-(DefaultActor pid=1831567) >> Training accuracy: 0.813101
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.549941 Loss1: 0.779077 Loss2: 0.770864
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.461824 Loss1: 0.783822 Loss2: 0.678002
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.424424 Loss1: 0.754189 Loss2: 0.670235
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.370290 Loss1: 0.698999 Loss2: 0.671291
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.425855 Loss1: 0.752385 Loss2: 0.673470
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.373312 Loss1: 0.701452 Loss2: 0.671860
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.368848 Loss1: 0.696468 Loss2: 0.672380
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.377855 Loss1: 0.701377 Loss2: 0.676478
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.371340 Loss1: 0.695974 Loss2: 0.675366
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.351777 Loss1: 0.679653 Loss2: 0.672124
-(DefaultActor pid=1831567) >> Training accuracy: 0.748601
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389986 Loss1: 0.668196 Loss2: 0.721790
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.264228 Loss1: 0.627347 Loss2: 0.636881
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.237283 Loss1: 0.605493 Loss2: 0.631791
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205321 Loss1: 0.572864 Loss2: 0.632457
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210440 Loss1: 0.578637 Loss2: 0.631803
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.198124 Loss1: 0.565521 Loss2: 0.632603
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.206264 Loss1: 0.572125 Loss2: 0.634138
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.193234 Loss1: 0.558451 Loss2: 0.634783
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.202801 Loss1: 0.565422 Loss2: 0.637379
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.191967 Loss1: 0.558019 Loss2: 0.633948
-(DefaultActor pid=1831567) >> Training accuracy: 0.814024
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.547663 Loss1: 0.783505 Loss2: 0.764158
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.357851 Loss1: 0.705407 Loss2: 0.652444
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.367947 Loss1: 0.712333 Loss2: 0.655614
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335306 Loss1: 0.684015 Loss2: 0.651290
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.307043 Loss1: 0.655365 Loss2: 0.651678
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.339054 Loss1: 0.684167 Loss2: 0.654887
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.319173 Loss1: 0.660908 Loss2: 0.658264
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300238 Loss1: 0.645922 Loss2: 0.654316
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.301182 Loss1: 0.645565 Loss2: 0.655617
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.305780 Loss1: 0.643004 Loss2: 0.662776
-(DefaultActor pid=1831567) >> Training accuracy: 0.795230
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.375261 Loss1: 0.640911 Loss2: 0.734350
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.246504 Loss1: 0.597998 Loss2: 0.648506
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.259804 Loss1: 0.610064 Loss2: 0.649740
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223819 Loss1: 0.575085 Loss2: 0.648734
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.232002 Loss1: 0.582823 Loss2: 0.649179
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218637 Loss1: 0.569355 Loss2: 0.649282
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.194277 Loss1: 0.543152 Loss2: 0.651125
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212205 Loss1: 0.559040 Loss2: 0.653165
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180128 Loss1: 0.528536 Loss2: 0.651592
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198392 Loss1: 0.545072 Loss2: 0.653319
-(DefaultActor pid=1831567) >> Training accuracy: 0.807566
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.400264 Loss1: 0.637098 Loss2: 0.763166
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.317031 Loss1: 0.603671 Loss2: 0.713360
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.311202 Loss1: 0.596910 Loss2: 0.714293
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.300785 Loss1: 0.585410 Loss2: 0.715375
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.288633 Loss1: 0.576285 Loss2: 0.712348
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.291267 Loss1: 0.573350 Loss2: 0.717917
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295193 Loss1: 0.575447 Loss2: 0.719746
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.285321 Loss1: 0.567441 Loss2: 0.717880
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.289892 Loss1: 0.571756 Loss2: 0.718136
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.264984 Loss1: 0.549373 Loss2: 0.715611
-(DefaultActor pid=1831567) >> Training accuracy: 0.812748
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.261714 Loss1: 0.518648 Loss2: 0.743065
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.110453 Loss1: 0.457037 Loss2: 0.653416
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.087576 Loss1: 0.439000 Loss2: 0.648575
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.114602 Loss1: 0.467021 Loss2: 0.647581
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.087260 Loss1: 0.436172 Loss2: 0.651088
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.070131 Loss1: 0.422431 Loss2: 0.647699
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.057946 Loss1: 0.407818 Loss2: 0.650129
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.050529 Loss1: 0.401546 Loss2: 0.648983
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.046195 Loss1: 0.395995 Loss2: 0.650200
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.051978 Loss1: 0.398700 Loss2: 0.653278
-[2023-09-27 10:35:04,289][flwr][DEBUG] - fit_round 30 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.857060
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.671100
-[2023-09-27 10:35:05,982][flwr][INFO] - fit progress: (30, 0.9313092758289923, {'accuracy': 0.6711}, 15438.818260560744)
-[2023-09-27 10:35:05,982][flwr][DEBUG] - evaluate_round 30: strategy sampled 10 clients (out of 10)
-[2023-09-27 10:35:36,618][flwr][DEBUG] - evaluate_round 30 received 10 results and 0 failures
-[2023-09-27 10:35:36,619][flwr][DEBUG] - fit_round 31: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.318481 Loss1: 0.524676 Loss2: 0.793805
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172348 Loss1: 0.467429 Loss2: 0.704919
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.148309 Loss1: 0.450472 Loss2: 0.697837
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.135866 Loss1: 0.439225 Loss2: 0.696641
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.117671 Loss1: 0.420694 Loss2: 0.696977
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.112772 Loss1: 0.415330 Loss2: 0.697442
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.117213 Loss1: 0.418585 Loss2: 0.698628
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.112376 Loss1: 0.413609 Loss2: 0.698767
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.099943 Loss1: 0.398749 Loss2: 0.701194
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107395 Loss1: 0.408351 Loss2: 0.699044
-(DefaultActor pid=1831567) >> Training accuracy: 0.868827
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381897 Loss1: 0.626073 Loss2: 0.755824
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.246133 Loss1: 0.594569 Loss2: 0.651564
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213192 Loss1: 0.559767 Loss2: 0.653424
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217011 Loss1: 0.563342 Loss2: 0.653669
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221570 Loss1: 0.563055 Loss2: 0.658514
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184986 Loss1: 0.527920 Loss2: 0.657066
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.184445 Loss1: 0.531058 Loss2: 0.653386
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.188682 Loss1: 0.528865 Loss2: 0.659817
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165120 Loss1: 0.506863 Loss2: 0.658257
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.151379 Loss1: 0.491876 Loss2: 0.659504
-(DefaultActor pid=1831567) >> Training accuracy: 0.834746
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.556718 Loss1: 0.759221 Loss2: 0.797498
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.414273 Loss1: 0.723142 Loss2: 0.691131
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.396355 Loss1: 0.709236 Loss2: 0.687119
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.372544 Loss1: 0.687609 Loss2: 0.684935
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.367964 Loss1: 0.681690 Loss2: 0.686274
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.381332 Loss1: 0.692552 Loss2: 0.688779
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352522 Loss1: 0.666001 Loss2: 0.686521
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.330141 Loss1: 0.641856 Loss2: 0.688285
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.322310 Loss1: 0.631865 Loss2: 0.690446
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.314573 Loss1: 0.620856 Loss2: 0.693717
-(DefaultActor pid=1831567) >> Training accuracy: 0.770833
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568077 Loss1: 0.802130 Loss2: 0.765947
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.440122 Loss1: 0.765017 Loss2: 0.675105
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.389755 Loss1: 0.719540 Loss2: 0.670214
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.394776 Loss1: 0.721660 Loss2: 0.673116
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.370134 Loss1: 0.699684 Loss2: 0.670449
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.390300 Loss1: 0.713919 Loss2: 0.676382
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.374032 Loss1: 0.699712 Loss2: 0.674320
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.359830 Loss1: 0.684656 Loss2: 0.675175
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.365339 Loss1: 0.689086 Loss2: 0.676253
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.346665 Loss1: 0.668452 Loss2: 0.678213
-(DefaultActor pid=1831567) >> Training accuracy: 0.744636
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389446 Loss1: 0.651919 Loss2: 0.737527
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.256708 Loss1: 0.596062 Loss2: 0.660646
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.248834 Loss1: 0.587579 Loss2: 0.661255
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.250901 Loss1: 0.590708 Loss2: 0.660193
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.244757 Loss1: 0.582184 Loss2: 0.662572
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.233933 Loss1: 0.570383 Loss2: 0.663550
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212883 Loss1: 0.550092 Loss2: 0.662791
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202128 Loss1: 0.539471 Loss2: 0.662657
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193710 Loss1: 0.529646 Loss2: 0.664064
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229367 Loss1: 0.565310 Loss2: 0.664057
-(DefaultActor pid=1831567) >> Training accuracy: 0.798878
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568768 Loss1: 0.810714 Loss2: 0.758054
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.453431 Loss1: 0.782540 Loss2: 0.670891
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.433000 Loss1: 0.761375 Loss2: 0.671625
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.412114 Loss1: 0.738572 Loss2: 0.673542
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.419463 Loss1: 0.743573 Loss2: 0.675890
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.407767 Loss1: 0.733066 Loss2: 0.674701
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.385884 Loss1: 0.709902 Loss2: 0.675982
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.388036 Loss1: 0.711226 Loss2: 0.676810
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.383085 Loss1: 0.700846 Loss2: 0.682239
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.378320 Loss1: 0.698095 Loss2: 0.680226
-(DefaultActor pid=1831567) >> Training accuracy: 0.757699
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.414641 Loss1: 0.659552 Loss2: 0.755089
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304909 Loss1: 0.615488 Loss2: 0.689421
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260009 Loss1: 0.572576 Loss2: 0.687433
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.274827 Loss1: 0.583199 Loss2: 0.691629
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.257452 Loss1: 0.567403 Loss2: 0.690049
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.262718 Loss1: 0.574698 Loss2: 0.688020
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.253865 Loss1: 0.564301 Loss2: 0.689564
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253584 Loss1: 0.559025 Loss2: 0.694559
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.260163 Loss1: 0.565931 Loss2: 0.694232
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.237485 Loss1: 0.546293 Loss2: 0.691191
-(DefaultActor pid=1831567) >> Training accuracy: 0.815739
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.265647 Loss1: 0.515405 Loss2: 0.750242
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.148273 Loss1: 0.477683 Loss2: 0.670590
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.102515 Loss1: 0.434877 Loss2: 0.667639
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.101623 Loss1: 0.431943 Loss2: 0.669680
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.080640 Loss1: 0.411121 Loss2: 0.669519
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.092136 Loss1: 0.419470 Loss2: 0.672666
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.086206 Loss1: 0.413277 Loss2: 0.672929
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.074010 Loss1: 0.401805 Loss2: 0.672205
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.093885 Loss1: 0.420124 Loss2: 0.673760
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.086745 Loss1: 0.410857 Loss2: 0.675888
-(DefaultActor pid=1831567) >> Training accuracy: 0.865355
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370387 Loss1: 0.642303 Loss2: 0.728084
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.252343 Loss1: 0.600848 Loss2: 0.651495
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.225648 Loss1: 0.572770 Loss2: 0.652878
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237439 Loss1: 0.584109 Loss2: 0.653330
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221949 Loss1: 0.566885 Loss2: 0.655064
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210578 Loss1: 0.555931 Loss2: 0.654647
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.203393 Loss1: 0.549504 Loss2: 0.653889
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.195181 Loss1: 0.541379 Loss2: 0.653802
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211408 Loss1: 0.554740 Loss2: 0.656669
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.202276 Loss1: 0.544649 Loss2: 0.657627
-(DefaultActor pid=1831567) >> Training accuracy: 0.819285
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339168 Loss1: 0.631968 Loss2: 0.707200
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.257929 Loss1: 0.599360 Loss2: 0.658570
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.244631 Loss1: 0.587868 Loss2: 0.656763
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.258766 Loss1: 0.599637 Loss2: 0.659129
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224585 Loss1: 0.566992 Loss2: 0.657592
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226515 Loss1: 0.565182 Loss2: 0.661333
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.236504 Loss1: 0.574970 Loss2: 0.661533
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.232089 Loss1: 0.573164 Loss2: 0.658924
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.214778 Loss1: 0.555741 Loss2: 0.659036
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.239289 Loss1: 0.576625 Loss2: 0.662664
-[2023-09-27 10:42:13,465][flwr][DEBUG] - fit_round 31 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.797991
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.667200
-[2023-09-27 10:42:14,811][flwr][INFO] - fit progress: (31, 0.950761117874243, {'accuracy': 0.6672}, 15867.64734768914)
-[2023-09-27 10:42:14,811][flwr][DEBUG] - evaluate_round 31: strategy sampled 10 clients (out of 10)
-[2023-09-27 10:42:45,511][flwr][DEBUG] - evaluate_round 31 received 10 results and 0 failures
-[2023-09-27 10:42:45,512][flwr][DEBUG] - fit_round 32: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.219102 Loss1: 0.496179 Loss2: 0.722924
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.116710 Loss1: 0.467179 Loss2: 0.649531
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.089487 Loss1: 0.441603 Loss2: 0.647885
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.064095 Loss1: 0.420517 Loss2: 0.643578
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.059225 Loss1: 0.413688 Loss2: 0.645536
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.067248 Loss1: 0.422453 Loss2: 0.644795
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.056614 Loss1: 0.410646 Loss2: 0.645967
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.044186 Loss1: 0.398396 Loss2: 0.645790
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.055185 Loss1: 0.406964 Loss2: 0.648221
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.046289 Loss1: 0.398926 Loss2: 0.647363
-(DefaultActor pid=1831567) >> Training accuracy: 0.842785
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.556820 Loss1: 0.782392 Loss2: 0.774428
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387926 Loss1: 0.717873 Loss2: 0.670053
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.372649 Loss1: 0.708212 Loss2: 0.664437
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335313 Loss1: 0.669520 Loss2: 0.665792
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.335238 Loss1: 0.669276 Loss2: 0.665962
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.335229 Loss1: 0.668545 Loss2: 0.666684
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.354810 Loss1: 0.684078 Loss2: 0.670731
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.340156 Loss1: 0.670673 Loss2: 0.669483
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.317825 Loss1: 0.647909 Loss2: 0.669916
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.288242 Loss1: 0.621178 Loss2: 0.667063
-(DefaultActor pid=1831567) >> Training accuracy: 0.783991
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.565338 Loss1: 0.817728 Loss2: 0.747610
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.464383 Loss1: 0.805782 Loss2: 0.658601
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.434822 Loss1: 0.776132 Loss2: 0.658689
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.405516 Loss1: 0.748178 Loss2: 0.657338
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.391029 Loss1: 0.735166 Loss2: 0.655863
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.390619 Loss1: 0.734760 Loss2: 0.655859
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386164 Loss1: 0.723952 Loss2: 0.662213
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.370069 Loss1: 0.709727 Loss2: 0.660341
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.357719 Loss1: 0.697410 Loss2: 0.660309
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.362144 Loss1: 0.697073 Loss2: 0.665070
-(DefaultActor pid=1831567) >> Training accuracy: 0.759737
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.254695 Loss1: 0.501699 Loss2: 0.752997
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.114124 Loss1: 0.450709 Loss2: 0.663414
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.101788 Loss1: 0.439228 Loss2: 0.662560
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.097778 Loss1: 0.436597 Loss2: 0.661180
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.083729 Loss1: 0.423001 Loss2: 0.660728
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.072088 Loss1: 0.408736 Loss2: 0.663352
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.067371 Loss1: 0.405534 Loss2: 0.661838
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.073227 Loss1: 0.408795 Loss2: 0.664432
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.072764 Loss1: 0.407384 Loss2: 0.665380
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.044675 Loss1: 0.380833 Loss2: 0.663842
-(DefaultActor pid=1831567) >> Training accuracy: 0.873071
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.411665 Loss1: 0.614747 Loss2: 0.796919
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.260302 Loss1: 0.565939 Loss2: 0.694363
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260779 Loss1: 0.565326 Loss2: 0.695453
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.246143 Loss1: 0.554117 Loss2: 0.692026
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211749 Loss1: 0.517765 Loss2: 0.693984
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.209946 Loss1: 0.515813 Loss2: 0.694133
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.213994 Loss1: 0.518900 Loss2: 0.695094
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212657 Loss1: 0.516355 Loss2: 0.696303
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.195912 Loss1: 0.499336 Loss2: 0.696576
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200235 Loss1: 0.506994 Loss2: 0.693241
-(DefaultActor pid=1831567) >> Training accuracy: 0.842426
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384404 Loss1: 0.643240 Loss2: 0.741164
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.267781 Loss1: 0.601291 Loss2: 0.666490
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.268303 Loss1: 0.600889 Loss2: 0.667414
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261032 Loss1: 0.590682 Loss2: 0.670350
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.243756 Loss1: 0.572748 Loss2: 0.671009
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.247519 Loss1: 0.578891 Loss2: 0.668628
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.230443 Loss1: 0.561862 Loss2: 0.668581
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.224945 Loss1: 0.554450 Loss2: 0.670494
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.226907 Loss1: 0.555753 Loss2: 0.671154
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229373 Loss1: 0.554106 Loss2: 0.675267
-(DefaultActor pid=1831567) >> Training accuracy: 0.819712
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.393278 Loss1: 0.635024 Loss2: 0.758254
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.284211 Loss1: 0.611999 Loss2: 0.672212
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.230400 Loss1: 0.559353 Loss2: 0.671047
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225985 Loss1: 0.554711 Loss2: 0.671273
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.217098 Loss1: 0.544388 Loss2: 0.672710
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226741 Loss1: 0.551213 Loss2: 0.675528
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.214185 Loss1: 0.543904 Loss2: 0.670281
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202325 Loss1: 0.528911 Loss2: 0.673414
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.216133 Loss1: 0.541267 Loss2: 0.674866
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193411 Loss1: 0.518714 Loss2: 0.674697
-(DefaultActor pid=1831567) >> Training accuracy: 0.823808
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370198 Loss1: 0.660590 Loss2: 0.709608
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.224096 Loss1: 0.599133 Loss2: 0.624963
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223966 Loss1: 0.603487 Loss2: 0.620479
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.201685 Loss1: 0.579327 Loss2: 0.622358
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.193420 Loss1: 0.571522 Loss2: 0.621898
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.188620 Loss1: 0.567968 Loss2: 0.620652
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.190054 Loss1: 0.568768 Loss2: 0.621286
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.184383 Loss1: 0.561887 Loss2: 0.622495
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175966 Loss1: 0.551829 Loss2: 0.624137
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.177887 Loss1: 0.553308 Loss2: 0.624579
-(DefaultActor pid=1831567) >> Training accuracy: 0.815168
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.411151 Loss1: 0.635296 Loss2: 0.775855
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.318345 Loss1: 0.591417 Loss2: 0.726928
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.315307 Loss1: 0.590673 Loss2: 0.724633
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.309690 Loss1: 0.583322 Loss2: 0.726369
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293438 Loss1: 0.567998 Loss2: 0.725440
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.288607 Loss1: 0.562863 Loss2: 0.725744
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309332 Loss1: 0.580953 Loss2: 0.728379
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.287193 Loss1: 0.561194 Loss2: 0.725999
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.280090 Loss1: 0.551444 Loss2: 0.728645
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.292674 Loss1: 0.564018 Loss2: 0.728656
-(DefaultActor pid=1831567) >> Training accuracy: 0.815972
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.554617 Loss1: 0.776580 Loss2: 0.778037
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.427736 Loss1: 0.745925 Loss2: 0.681811
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.429623 Loss1: 0.750332 Loss2: 0.679290
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.398889 Loss1: 0.718647 Loss2: 0.680242
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.376433 Loss1: 0.695796 Loss2: 0.680637
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.356132 Loss1: 0.676528 Loss2: 0.679604
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350100 Loss1: 0.667986 Loss2: 0.682114
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.354838 Loss1: 0.671459 Loss2: 0.683378
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.344060 Loss1: 0.660762 Loss2: 0.683298
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.353935 Loss1: 0.669574 Loss2: 0.684361
-[2023-09-27 10:49:36,782][flwr][DEBUG] - fit_round 32 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.758862
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.682900
-[2023-09-27 10:49:38,444][flwr][INFO] - fit progress: (32, 0.9076737036910681, {'accuracy': 0.6829}, 16311.280521166045)
-[2023-09-27 10:49:38,445][flwr][DEBUG] - evaluate_round 32: strategy sampled 10 clients (out of 10)
-[2023-09-27 10:50:09,857][flwr][DEBUG] - evaluate_round 32 received 10 results and 0 failures
-[2023-09-27 10:50:09,858][flwr][DEBUG] - fit_round 33: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384447 Loss1: 0.650191 Loss2: 0.734255
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258980 Loss1: 0.596251 Loss2: 0.662728
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251010 Loss1: 0.588198 Loss2: 0.662813
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.259525 Loss1: 0.594804 Loss2: 0.664721
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.272169 Loss1: 0.604377 Loss2: 0.667793
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.235765 Loss1: 0.569407 Loss2: 0.666358
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.210795 Loss1: 0.545710 Loss2: 0.665085
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.245027 Loss1: 0.575342 Loss2: 0.669685
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211726 Loss1: 0.542790 Loss2: 0.668936
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.220186 Loss1: 0.549887 Loss2: 0.670299
-(DefaultActor pid=1831567) >> Training accuracy: 0.825521
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.265495 Loss1: 0.511322 Loss2: 0.754173
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.127196 Loss1: 0.458161 Loss2: 0.669034
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.092651 Loss1: 0.430307 Loss2: 0.662343
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.093286 Loss1: 0.430985 Loss2: 0.662301
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.081962 Loss1: 0.418243 Loss2: 0.663719
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.076276 Loss1: 0.413053 Loss2: 0.663223
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.057289 Loss1: 0.393068 Loss2: 0.664222
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.076456 Loss1: 0.410825 Loss2: 0.665631
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.066189 Loss1: 0.403990 Loss2: 0.662199
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.066320 Loss1: 0.401038 Loss2: 0.665282
-(DefaultActor pid=1831567) >> Training accuracy: 0.850887
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.426434 Loss1: 0.648700 Loss2: 0.777734
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301711 Loss1: 0.593021 Loss2: 0.708690
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.282818 Loss1: 0.574997 Loss2: 0.707822
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.294671 Loss1: 0.584576 Loss2: 0.710095
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.283370 Loss1: 0.575162 Loss2: 0.708208
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.258721 Loss1: 0.550779 Loss2: 0.707942
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.255795 Loss1: 0.548102 Loss2: 0.707693
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.264079 Loss1: 0.555433 Loss2: 0.708646
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.246059 Loss1: 0.536434 Loss2: 0.709626
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.251750 Loss1: 0.537849 Loss2: 0.713901
-(DefaultActor pid=1831567) >> Training accuracy: 0.815168
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.255130 Loss1: 0.499268 Loss2: 0.755862
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.121540 Loss1: 0.447230 Loss2: 0.674310
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.110182 Loss1: 0.434118 Loss2: 0.676063
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.102790 Loss1: 0.426606 Loss2: 0.676183
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.089251 Loss1: 0.414063 Loss2: 0.675188
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073827 Loss1: 0.398519 Loss2: 0.675309
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.080708 Loss1: 0.404229 Loss2: 0.676480
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.088510 Loss1: 0.410947 Loss2: 0.677563
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.087178 Loss1: 0.408657 Loss2: 0.678521
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.074868 Loss1: 0.397316 Loss2: 0.677552
-(DefaultActor pid=1831567) >> Training accuracy: 0.861690
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.361457 Loss1: 0.623938 Loss2: 0.737519
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217346 Loss1: 0.578572 Loss2: 0.638775
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231183 Loss1: 0.592452 Loss2: 0.638731
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202983 Loss1: 0.566582 Loss2: 0.636401
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169496 Loss1: 0.529241 Loss2: 0.640256
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183071 Loss1: 0.544443 Loss2: 0.638628
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161426 Loss1: 0.525017 Loss2: 0.636409
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168391 Loss1: 0.529195 Loss2: 0.639196
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147507 Loss1: 0.508492 Loss2: 0.639015
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.138784 Loss1: 0.497418 Loss2: 0.641366
-(DefaultActor pid=1831567) >> Training accuracy: 0.823358
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.378798 Loss1: 0.648755 Loss2: 0.730043
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225786 Loss1: 0.573907 Loss2: 0.651879
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.220600 Loss1: 0.567683 Loss2: 0.652917
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.224488 Loss1: 0.570152 Loss2: 0.654335
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203382 Loss1: 0.550573 Loss2: 0.652809
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218113 Loss1: 0.562373 Loss2: 0.655740
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.207066 Loss1: 0.549129 Loss2: 0.657938
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190318 Loss1: 0.535404 Loss2: 0.654914
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.190155 Loss1: 0.531068 Loss2: 0.659087
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.211844 Loss1: 0.553469 Loss2: 0.658375
-(DefaultActor pid=1831567) >> Training accuracy: 0.830387
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341997 Loss1: 0.636561 Loss2: 0.705437
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.249810 Loss1: 0.586515 Loss2: 0.663295
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.254278 Loss1: 0.595750 Loss2: 0.658528
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231291 Loss1: 0.572974 Loss2: 0.658317
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.238060 Loss1: 0.575979 Loss2: 0.662080
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.237757 Loss1: 0.575831 Loss2: 0.661926
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.229946 Loss1: 0.568167 Loss2: 0.661779
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222174 Loss1: 0.562311 Loss2: 0.659863
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.227281 Loss1: 0.565225 Loss2: 0.662056
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.233201 Loss1: 0.568096 Loss2: 0.665105
-(DefaultActor pid=1831567) >> Training accuracy: 0.807664
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.594224 Loss1: 0.815967 Loss2: 0.778256
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.412860 Loss1: 0.735195 Loss2: 0.677665
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.403821 Loss1: 0.722719 Loss2: 0.681102
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.416456 Loss1: 0.736913 Loss2: 0.679543
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.380465 Loss1: 0.698373 Loss2: 0.682092
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.400679 Loss1: 0.718919 Loss2: 0.681760
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.356271 Loss1: 0.677291 Loss2: 0.678980
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.372839 Loss1: 0.690568 Loss2: 0.682272
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.340338 Loss1: 0.658452 Loss2: 0.681886
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.322825 Loss1: 0.639318 Loss2: 0.683507
-(DefaultActor pid=1831567) >> Training accuracy: 0.743004
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.564665 Loss1: 0.811934 Loss2: 0.752730
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.428956 Loss1: 0.759793 Loss2: 0.669163
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.400642 Loss1: 0.731659 Loss2: 0.668983
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.418535 Loss1: 0.750684 Loss2: 0.667852
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.413732 Loss1: 0.741934 Loss2: 0.671797
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.399114 Loss1: 0.724799 Loss2: 0.674315
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.400988 Loss1: 0.722601 Loss2: 0.678387
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.417294 Loss1: 0.740545 Loss2: 0.676748
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.368559 Loss1: 0.690758 Loss2: 0.677800
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.389428 Loss1: 0.713856 Loss2: 0.675573
-(DefaultActor pid=1831567) >> Training accuracy: 0.751359
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.531808 Loss1: 0.763448 Loss2: 0.768360
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.375101 Loss1: 0.709509 Loss2: 0.665593
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.348480 Loss1: 0.684443 Loss2: 0.664037
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.334976 Loss1: 0.670137 Loss2: 0.664839
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.337352 Loss1: 0.669817 Loss2: 0.667535
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306019 Loss1: 0.642383 Loss2: 0.663636
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.308911 Loss1: 0.641655 Loss2: 0.667256
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320185 Loss1: 0.653150 Loss2: 0.667035
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299893 Loss1: 0.633155 Loss2: 0.666739
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284668 Loss1: 0.615381 Loss2: 0.669287
-[2023-09-27 10:56:45,449][flwr][DEBUG] - fit_round 33 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.772752
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.682900
-[2023-09-27 10:56:47,281][flwr][INFO] - fit progress: (33, 0.908714735469879, {'accuracy': 0.6829}, 16740.117826285772)
-[2023-09-27 10:56:47,282][flwr][DEBUG] - evaluate_round 33: strategy sampled 10 clients (out of 10)
-[2023-09-27 10:57:18,579][flwr][DEBUG] - evaluate_round 33 received 10 results and 0 failures
-[2023-09-27 10:57:18,581][flwr][DEBUG] - fit_round 34: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475017 Loss1: 0.633700 Loss2: 0.841317
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.379035 Loss1: 0.594066 Loss2: 0.784969
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.379857 Loss1: 0.591406 Loss2: 0.788451
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.354553 Loss1: 0.568668 Loss2: 0.785885
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.353994 Loss1: 0.566431 Loss2: 0.787562
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.355304 Loss1: 0.568587 Loss2: 0.786717
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.339747 Loss1: 0.551052 Loss2: 0.788695
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.338278 Loss1: 0.549548 Loss2: 0.788729
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.363598 Loss1: 0.569157 Loss2: 0.794441
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.349777 Loss1: 0.556532 Loss2: 0.793246
-(DefaultActor pid=1831567) >> Training accuracy: 0.811136
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.239694 Loss1: 0.486988 Loss2: 0.752707
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.122264 Loss1: 0.457270 Loss2: 0.664993
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.095314 Loss1: 0.431496 Loss2: 0.663818
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.098679 Loss1: 0.435437 Loss2: 0.663243
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.075976 Loss1: 0.413044 Loss2: 0.662932
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.066267 Loss1: 0.403459 Loss2: 0.662808
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.090017 Loss1: 0.423201 Loss2: 0.666816
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.069447 Loss1: 0.404332 Loss2: 0.665116
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.062167 Loss1: 0.394915 Loss2: 0.667252
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.049129 Loss1: 0.382096 Loss2: 0.667034
-(DefaultActor pid=1831567) >> Training accuracy: 0.864583
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.252673 Loss1: 0.519799 Loss2: 0.732873
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.097341 Loss1: 0.442043 Loss2: 0.655298
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.093679 Loss1: 0.441285 Loss2: 0.652394
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.077316 Loss1: 0.429712 Loss2: 0.647604
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.068362 Loss1: 0.420616 Loss2: 0.647746
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.061788 Loss1: 0.412561 Loss2: 0.649228
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.063109 Loss1: 0.412738 Loss2: 0.650371
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.055122 Loss1: 0.404789 Loss2: 0.650332
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.056020 Loss1: 0.400592 Loss2: 0.655428
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.066587 Loss1: 0.412737 Loss2: 0.653849
-(DefaultActor pid=1831567) >> Training accuracy: 0.864198
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.424691 Loss1: 0.610894 Loss2: 0.813797
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.273467 Loss1: 0.574051 Loss2: 0.699416
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238297 Loss1: 0.540203 Loss2: 0.698095
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.245212 Loss1: 0.548649 Loss2: 0.696562
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234180 Loss1: 0.538169 Loss2: 0.696011
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225633 Loss1: 0.527005 Loss2: 0.698629
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196999 Loss1: 0.497917 Loss2: 0.699082
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.205749 Loss1: 0.509418 Loss2: 0.696331
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199982 Loss1: 0.498454 Loss2: 0.701527
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.166910 Loss1: 0.463805 Loss2: 0.703105
-(DefaultActor pid=1831567) >> Training accuracy: 0.841102
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386680 Loss1: 0.675369 Loss2: 0.711311
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.229557 Loss1: 0.598662 Loss2: 0.630895
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.217211 Loss1: 0.588027 Loss2: 0.629184
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.219590 Loss1: 0.592156 Loss2: 0.627434
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.205894 Loss1: 0.577543 Loss2: 0.628352
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191928 Loss1: 0.562599 Loss2: 0.629329
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179949 Loss1: 0.551027 Loss2: 0.628923
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169134 Loss1: 0.538692 Loss2: 0.630442
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.181324 Loss1: 0.549900 Loss2: 0.631424
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175284 Loss1: 0.543845 Loss2: 0.631439
-(DefaultActor pid=1831567) >> Training accuracy: 0.821456
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.398307 Loss1: 0.632853 Loss2: 0.765454
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.264394 Loss1: 0.585010 Loss2: 0.679384
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.254308 Loss1: 0.574451 Loss2: 0.679858
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.253563 Loss1: 0.575505 Loss2: 0.678058
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246062 Loss1: 0.563818 Loss2: 0.682244
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225206 Loss1: 0.547906 Loss2: 0.677300
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.222156 Loss1: 0.542002 Loss2: 0.680153
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207801 Loss1: 0.524881 Loss2: 0.682920
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.228696 Loss1: 0.544810 Loss2: 0.683885
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.213657 Loss1: 0.529395 Loss2: 0.684262
-(DefaultActor pid=1831567) >> Training accuracy: 0.841283
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.558379 Loss1: 0.805226 Loss2: 0.753153
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.431122 Loss1: 0.769990 Loss2: 0.661133
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.421131 Loss1: 0.763690 Loss2: 0.657441
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.389120 Loss1: 0.728422 Loss2: 0.660698
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.404049 Loss1: 0.740757 Loss2: 0.663292
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.381727 Loss1: 0.718302 Loss2: 0.663425
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.392166 Loss1: 0.726716 Loss2: 0.665450
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.368953 Loss1: 0.703824 Loss2: 0.665129
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.363911 Loss1: 0.697862 Loss2: 0.666048
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.332924 Loss1: 0.666688 Loss2: 0.666236
-(DefaultActor pid=1831567) >> Training accuracy: 0.752038
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.431423 Loss1: 0.627414 Loss2: 0.804009
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.328244 Loss1: 0.611440 Loss2: 0.716803
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.286193 Loss1: 0.570023 Loss2: 0.716170
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.297165 Loss1: 0.577086 Loss2: 0.720080
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.271972 Loss1: 0.551330 Loss2: 0.720642
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.276008 Loss1: 0.557192 Loss2: 0.718816
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.279315 Loss1: 0.559699 Loss2: 0.719615
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.269747 Loss1: 0.548033 Loss2: 0.721714
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.268322 Loss1: 0.547266 Loss2: 0.721056
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.235370 Loss1: 0.513323 Loss2: 0.722047
-(DefaultActor pid=1831567) >> Training accuracy: 0.824319
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.541979 Loss1: 0.786196 Loss2: 0.755783
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.391877 Loss1: 0.729492 Loss2: 0.662385
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.387285 Loss1: 0.723226 Loss2: 0.664059
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.362913 Loss1: 0.702732 Loss2: 0.660181
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.365882 Loss1: 0.701809 Loss2: 0.664073
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.339527 Loss1: 0.675377 Loss2: 0.664151
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.368029 Loss1: 0.703118 Loss2: 0.664911
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.374544 Loss1: 0.706783 Loss2: 0.667761
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331300 Loss1: 0.663424 Loss2: 0.667875
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.324130 Loss1: 0.656359 Loss2: 0.667770
-(DefaultActor pid=1831567) >> Training accuracy: 0.753032
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.562511 Loss1: 0.783677 Loss2: 0.778834
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.390501 Loss1: 0.717437 Loss2: 0.673064
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.369927 Loss1: 0.702149 Loss2: 0.667778
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335635 Loss1: 0.664647 Loss2: 0.670988
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.328444 Loss1: 0.656940 Loss2: 0.671504
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.347048 Loss1: 0.669645 Loss2: 0.677403
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.329111 Loss1: 0.654001 Loss2: 0.675110
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.312552 Loss1: 0.636825 Loss2: 0.675727
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.324556 Loss1: 0.649652 Loss2: 0.674904
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309247 Loss1: 0.630633 Loss2: 0.678614
-[2023-09-27 11:03:57,763][flwr][DEBUG] - fit_round 34 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.785910
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.684800
-[2023-09-27 11:03:59,362][flwr][INFO] - fit progress: (34, 0.8978847988878196, {'accuracy': 0.6848}, 17172.19873186201)
-[2023-09-27 11:03:59,363][flwr][DEBUG] - evaluate_round 34: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:04:31,584][flwr][DEBUG] - evaluate_round 34 received 10 results and 0 failures
-[2023-09-27 11:04:31,585][flwr][DEBUG] - fit_round 35: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.390210 Loss1: 0.642241 Loss2: 0.747969
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.278092 Loss1: 0.599312 Loss2: 0.678780
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262059 Loss1: 0.582444 Loss2: 0.679615
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.254097 Loss1: 0.573727 Loss2: 0.680370
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.244221 Loss1: 0.565445 Loss2: 0.678776
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.248959 Loss1: 0.569841 Loss2: 0.679118
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.230369 Loss1: 0.548705 Loss2: 0.681664
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220266 Loss1: 0.537827 Loss2: 0.682439
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213232 Loss1: 0.531522 Loss2: 0.681710
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.231077 Loss1: 0.546122 Loss2: 0.684956
-(DefaultActor pid=1831567) >> Training accuracy: 0.825648
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.532664 Loss1: 0.776279 Loss2: 0.756384
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.364373 Loss1: 0.715570 Loss2: 0.648803
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356376 Loss1: 0.702793 Loss2: 0.653583
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316278 Loss1: 0.665665 Loss2: 0.650613
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322679 Loss1: 0.672790 Loss2: 0.649889
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.300067 Loss1: 0.647063 Loss2: 0.653004
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.296383 Loss1: 0.641564 Loss2: 0.654819
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.283433 Loss1: 0.630643 Loss2: 0.652791
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.303772 Loss1: 0.647070 Loss2: 0.656702
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289017 Loss1: 0.633105 Loss2: 0.655912
-(DefaultActor pid=1831567) >> Training accuracy: 0.776316
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.299481 Loss1: 0.504577 Loss2: 0.794904
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.175022 Loss1: 0.468387 Loss2: 0.706635
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.135895 Loss1: 0.438528 Loss2: 0.697367
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.122444 Loss1: 0.427535 Loss2: 0.694909
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.114991 Loss1: 0.420576 Loss2: 0.694416
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.103506 Loss1: 0.407288 Loss2: 0.696218
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.106786 Loss1: 0.411241 Loss2: 0.695545
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.112447 Loss1: 0.414152 Loss2: 0.698295
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.106173 Loss1: 0.407903 Loss2: 0.698270
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.081215 Loss1: 0.382320 Loss2: 0.698895
-(DefaultActor pid=1831567) >> Training accuracy: 0.857253
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.544851 Loss1: 0.788498 Loss2: 0.756353
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.444979 Loss1: 0.768285 Loss2: 0.676694
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.440160 Loss1: 0.761097 Loss2: 0.679063
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.415233 Loss1: 0.737260 Loss2: 0.677973
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.383951 Loss1: 0.708409 Loss2: 0.675542
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.431077 Loss1: 0.749665 Loss2: 0.681411
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.388212 Loss1: 0.709792 Loss2: 0.678420
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.379347 Loss1: 0.698952 Loss2: 0.680395
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.378520 Loss1: 0.694073 Loss2: 0.684448
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.375880 Loss1: 0.694850 Loss2: 0.681031
-(DefaultActor pid=1831567) >> Training accuracy: 0.782382
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.555381 Loss1: 0.797564 Loss2: 0.757817
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.398338 Loss1: 0.731944 Loss2: 0.666394
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.395034 Loss1: 0.729616 Loss2: 0.665418
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.372497 Loss1: 0.703380 Loss2: 0.669118
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.364591 Loss1: 0.696700 Loss2: 0.667891
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362444 Loss1: 0.693190 Loss2: 0.669254
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.364459 Loss1: 0.694370 Loss2: 0.670089
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.351047 Loss1: 0.678397 Loss2: 0.672650
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.343448 Loss1: 0.669278 Loss2: 0.674170
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.365640 Loss1: 0.691362 Loss2: 0.674278
-(DefaultActor pid=1831567) >> Training accuracy: 0.760494
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.251233 Loss1: 0.502805 Loss2: 0.748428
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.129077 Loss1: 0.452560 Loss2: 0.676517
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.096307 Loss1: 0.427947 Loss2: 0.668360
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074935 Loss1: 0.406936 Loss2: 0.667998
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.092050 Loss1: 0.421427 Loss2: 0.670623
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.077240 Loss1: 0.404447 Loss2: 0.672794
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.066090 Loss1: 0.395255 Loss2: 0.670835
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.076338 Loss1: 0.404829 Loss2: 0.671509
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.088996 Loss1: 0.414027 Loss2: 0.674969
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.070604 Loss1: 0.397217 Loss2: 0.673387
-(DefaultActor pid=1831567) >> Training accuracy: 0.873071
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.366899 Loss1: 0.624854 Loss2: 0.742045
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.256077 Loss1: 0.595511 Loss2: 0.660566
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.228683 Loss1: 0.568906 Loss2: 0.659777
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225139 Loss1: 0.562270 Loss2: 0.662869
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211653 Loss1: 0.547844 Loss2: 0.663808
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206654 Loss1: 0.540480 Loss2: 0.666175
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204758 Loss1: 0.539974 Loss2: 0.664784
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.214991 Loss1: 0.545906 Loss2: 0.669085
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199832 Loss1: 0.531201 Loss2: 0.668631
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201108 Loss1: 0.531159 Loss2: 0.669949
-(DefaultActor pid=1831567) >> Training accuracy: 0.833470
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.401977 Loss1: 0.651244 Loss2: 0.750733
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206619 Loss1: 0.560637 Loss2: 0.645982
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198715 Loss1: 0.555527 Loss2: 0.643188
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205283 Loss1: 0.562423 Loss2: 0.642860
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196711 Loss1: 0.551850 Loss2: 0.644861
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.151224 Loss1: 0.507891 Loss2: 0.643333
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.162265 Loss1: 0.518712 Loss2: 0.643553
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161595 Loss1: 0.515314 Loss2: 0.646281
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.152900 Loss1: 0.504289 Loss2: 0.648610
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150007 Loss1: 0.501065 Loss2: 0.648942
-(DefaultActor pid=1831567) >> Training accuracy: 0.836070
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.351274 Loss1: 0.628353 Loss2: 0.722920
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243711 Loss1: 0.592945 Loss2: 0.650766
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.258755 Loss1: 0.606268 Loss2: 0.652487
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.221774 Loss1: 0.570163 Loss2: 0.651611
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.226613 Loss1: 0.573981 Loss2: 0.652631
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220626 Loss1: 0.568550 Loss2: 0.652076
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.216403 Loss1: 0.559945 Loss2: 0.656458
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.196424 Loss1: 0.541705 Loss2: 0.654719
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193325 Loss1: 0.538160 Loss2: 0.655165
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.219117 Loss1: 0.560545 Loss2: 0.658572
-(DefaultActor pid=1831567) >> Training accuracy: 0.831931
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.323305 Loss1: 0.604157 Loss2: 0.719147
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259338 Loss1: 0.591419 Loss2: 0.667919
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.233652 Loss1: 0.566311 Loss2: 0.667341
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.248800 Loss1: 0.580641 Loss2: 0.668159
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234628 Loss1: 0.566141 Loss2: 0.668487
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231585 Loss1: 0.564185 Loss2: 0.667400
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.239291 Loss1: 0.568216 Loss2: 0.671075
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.218905 Loss1: 0.548414 Loss2: 0.670491
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.210384 Loss1: 0.540575 Loss2: 0.669809
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.209167 Loss1: 0.538911 Loss2: 0.670256
-(DefaultActor pid=1831567) >> Training accuracy: 0.818948
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 11:11:44,114][flwr][DEBUG] - fit_round 35 received 10 results and 0 failures
->> Test accuracy: 0.682700
-[2023-09-27 11:11:45,679][flwr][INFO] - fit progress: (35, 0.9095158785486374, {'accuracy': 0.6827}, 17638.51521933591)
-[2023-09-27 11:11:45,679][flwr][DEBUG] - evaluate_round 35: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:12:17,059][flwr][DEBUG] - evaluate_round 35 received 10 results and 0 failures
-[2023-09-27 11:12:17,060][flwr][DEBUG] - fit_round 36: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.383397 Loss1: 0.606824 Loss2: 0.776574
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.314283 Loss1: 0.583446 Loss2: 0.730837
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.298306 Loss1: 0.568811 Loss2: 0.729496
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.286757 Loss1: 0.558560 Loss2: 0.728197
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.298195 Loss1: 0.566029 Loss2: 0.732166
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.276647 Loss1: 0.546496 Loss2: 0.730151
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.274698 Loss1: 0.543753 Loss2: 0.730945
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.273347 Loss1: 0.542291 Loss2: 0.731056
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.270192 Loss1: 0.536165 Loss2: 0.734027
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279044 Loss1: 0.546284 Loss2: 0.732760
-(DefaultActor pid=1831567) >> Training accuracy: 0.813616
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.233945 Loss1: 0.508357 Loss2: 0.725588
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092571 Loss1: 0.447691 Loss2: 0.644879
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.075411 Loss1: 0.433510 Loss2: 0.641902
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.079343 Loss1: 0.437253 Loss2: 0.642090
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.030520 Loss1: 0.390402 Loss2: 0.640117
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.050940 Loss1: 0.408725 Loss2: 0.642215
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051226 Loss1: 0.408277 Loss2: 0.642949
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.029197 Loss1: 0.386887 Loss2: 0.642310
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.025145 Loss1: 0.380993 Loss2: 0.644152
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.047941 Loss1: 0.399978 Loss2: 0.647963
-(DefaultActor pid=1831567) >> Training accuracy: 0.870563
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.184246 Loss1: 0.474458 Loss2: 0.709788
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.081247 Loss1: 0.447782 Loss2: 0.633465
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.064890 Loss1: 0.433040 Loss2: 0.631850
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.044086 Loss1: 0.414092 Loss2: 0.629994
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.058439 Loss1: 0.424406 Loss2: 0.634033
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.050779 Loss1: 0.417734 Loss2: 0.633045
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.045932 Loss1: 0.412805 Loss2: 0.633127
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030708 Loss1: 0.396648 Loss2: 0.634060
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.040893 Loss1: 0.405891 Loss2: 0.635002
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028094 Loss1: 0.393282 Loss2: 0.634812
-(DefaultActor pid=1831567) >> Training accuracy: 0.871914
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.568145 Loss1: 0.807623 Loss2: 0.760522
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.418200 Loss1: 0.749892 Loss2: 0.668308
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.429597 Loss1: 0.760372 Loss2: 0.669225
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.387558 Loss1: 0.719310 Loss2: 0.668248
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.396536 Loss1: 0.729373 Loss2: 0.667164
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.360599 Loss1: 0.689723 Loss2: 0.670875
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386071 Loss1: 0.717339 Loss2: 0.668732
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.382315 Loss1: 0.711005 Loss2: 0.671309
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.359620 Loss1: 0.687875 Loss2: 0.671745
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.341683 Loss1: 0.667787 Loss2: 0.673896
-(DefaultActor pid=1831567) >> Training accuracy: 0.770833
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.399399 Loss1: 0.642255 Loss2: 0.757145
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.274158 Loss1: 0.591437 Loss2: 0.682721
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.283996 Loss1: 0.601357 Loss2: 0.682639
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225926 Loss1: 0.545452 Loss2: 0.680474
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.239194 Loss1: 0.559014 Loss2: 0.680180
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.250772 Loss1: 0.566207 Loss2: 0.684565
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.241540 Loss1: 0.560914 Loss2: 0.680626
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.227764 Loss1: 0.543695 Loss2: 0.684069
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.223503 Loss1: 0.537202 Loss2: 0.686301
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200008 Loss1: 0.516313 Loss2: 0.683696
-(DefaultActor pid=1831567) >> Training accuracy: 0.817308
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.371809 Loss1: 0.607393 Loss2: 0.764416
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.249992 Loss1: 0.566656 Loss2: 0.683336
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.249456 Loss1: 0.569110 Loss2: 0.680345
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.243327 Loss1: 0.562596 Loss2: 0.680731
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221797 Loss1: 0.537224 Loss2: 0.684572
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.235401 Loss1: 0.553424 Loss2: 0.681977
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212449 Loss1: 0.528269 Loss2: 0.684180
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220939 Loss1: 0.536045 Loss2: 0.684893
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.204087 Loss1: 0.521217 Loss2: 0.682871
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.205834 Loss1: 0.519269 Loss2: 0.686565
-(DefaultActor pid=1831567) >> Training accuracy: 0.831003
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.536928 Loss1: 0.774324 Loss2: 0.762604
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394230 Loss1: 0.735886 Loss2: 0.658343
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356743 Loss1: 0.701514 Loss2: 0.655229
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.337404 Loss1: 0.678244 Loss2: 0.659160
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.307386 Loss1: 0.649024 Loss2: 0.658362
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.301628 Loss1: 0.642160 Loss2: 0.659468
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294270 Loss1: 0.636096 Loss2: 0.658174
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276010 Loss1: 0.616822 Loss2: 0.659189
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.276233 Loss1: 0.615452 Loss2: 0.660781
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.288924 Loss1: 0.629506 Loss2: 0.659418
-(DefaultActor pid=1831567) >> Training accuracy: 0.790022
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.367574 Loss1: 0.653970 Loss2: 0.713604
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236591 Loss1: 0.601235 Loss2: 0.635356
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.202091 Loss1: 0.570758 Loss2: 0.631333
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205355 Loss1: 0.573512 Loss2: 0.631842
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.199387 Loss1: 0.566891 Loss2: 0.632496
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.178302 Loss1: 0.546684 Loss2: 0.631618
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179693 Loss1: 0.546419 Loss2: 0.633273
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.186934 Loss1: 0.552938 Loss2: 0.633996
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.174617 Loss1: 0.536826 Loss2: 0.637791
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174913 Loss1: 0.538006 Loss2: 0.636907
-(DefaultActor pid=1831567) >> Training accuracy: 0.825457
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.539336 Loss1: 0.767164 Loss2: 0.772172
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.438389 Loss1: 0.756948 Loss2: 0.681441
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.402772 Loss1: 0.726928 Loss2: 0.675844
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.366110 Loss1: 0.690691 Loss2: 0.675419
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.348022 Loss1: 0.673214 Loss2: 0.674808
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.325518 Loss1: 0.649887 Loss2: 0.675631
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338548 Loss1: 0.662949 Loss2: 0.675599
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.337191 Loss1: 0.658117 Loss2: 0.679075
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.363622 Loss1: 0.681451 Loss2: 0.682171
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.316784 Loss1: 0.637116 Loss2: 0.679668
-(DefaultActor pid=1831567) >> Training accuracy: 0.771455
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.409619 Loss1: 0.636841 Loss2: 0.772777
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.254682 Loss1: 0.588228 Loss2: 0.666454
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.221690 Loss1: 0.561508 Loss2: 0.660182
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187933 Loss1: 0.527754 Loss2: 0.660180
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.177965 Loss1: 0.517053 Loss2: 0.660911
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206680 Loss1: 0.541280 Loss2: 0.665400
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.155394 Loss1: 0.493966 Loss2: 0.661427
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173381 Loss1: 0.507135 Loss2: 0.666246
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.168289 Loss1: 0.499681 Loss2: 0.668608
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176538 Loss1: 0.505568 Loss2: 0.670970
-(DefaultActor pid=1831567) >> Training accuracy: 0.828919
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 11:19:15,336][flwr][DEBUG] - fit_round 36 received 10 results and 0 failures
->> Test accuracy: 0.680400
-[2023-09-27 11:19:17,177][flwr][INFO] - fit progress: (36, 0.9122387365030404, {'accuracy': 0.6804}, 18090.013447194826)
-[2023-09-27 11:19:17,177][flwr][DEBUG] - evaluate_round 36: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:19:48,165][flwr][DEBUG] - evaluate_round 36 received 10 results and 0 failures
-[2023-09-27 11:19:48,166][flwr][DEBUG] - fit_round 37: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.319233 Loss1: 0.603299 Loss2: 0.715934
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227616 Loss1: 0.586480 Loss2: 0.641136
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215966 Loss1: 0.573170 Loss2: 0.642796
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200604 Loss1: 0.557570 Loss2: 0.643034
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191656 Loss1: 0.546564 Loss2: 0.645092
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225052 Loss1: 0.575145 Loss2: 0.649907
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192341 Loss1: 0.543111 Loss2: 0.649230
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190647 Loss1: 0.541712 Loss2: 0.648935
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.209962 Loss1: 0.561298 Loss2: 0.648665
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.190961 Loss1: 0.542186 Loss2: 0.648776
-(DefaultActor pid=1831567) >> Training accuracy: 0.818710
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.585184 Loss1: 0.793920 Loss2: 0.791264
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.454871 Loss1: 0.760101 Loss2: 0.694770
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.384791 Loss1: 0.694020 Loss2: 0.690770
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.378452 Loss1: 0.685596 Loss2: 0.692856
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.381580 Loss1: 0.689617 Loss2: 0.691963
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.372818 Loss1: 0.680421 Loss2: 0.692398
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.363875 Loss1: 0.666044 Loss2: 0.697830
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.362735 Loss1: 0.666137 Loss2: 0.696598
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.375477 Loss1: 0.676522 Loss2: 0.698954
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.371967 Loss1: 0.675847 Loss2: 0.696120
-(DefaultActor pid=1831567) >> Training accuracy: 0.750700
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.350585 Loss1: 0.628479 Loss2: 0.722106
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.255138 Loss1: 0.579474 Loss2: 0.675664
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.263238 Loss1: 0.588316 Loss2: 0.674922
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226910 Loss1: 0.556084 Loss2: 0.670826
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234403 Loss1: 0.559307 Loss2: 0.675096
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.229672 Loss1: 0.556098 Loss2: 0.673573
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224332 Loss1: 0.548598 Loss2: 0.675734
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.244946 Loss1: 0.567347 Loss2: 0.677600
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.227913 Loss1: 0.550891 Loss2: 0.677022
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.233896 Loss1: 0.555295 Loss2: 0.678601
-(DefaultActor pid=1831567) >> Training accuracy: 0.819320
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.377110 Loss1: 0.611828 Loss2: 0.765281
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.235703 Loss1: 0.569995 Loss2: 0.665708
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219834 Loss1: 0.558426 Loss2: 0.661408
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199773 Loss1: 0.539554 Loss2: 0.660219
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.181706 Loss1: 0.520879 Loss2: 0.660827
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167769 Loss1: 0.508537 Loss2: 0.659232
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187368 Loss1: 0.523161 Loss2: 0.664207
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173617 Loss1: 0.509193 Loss2: 0.664424
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134429 Loss1: 0.473774 Loss2: 0.660655
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.159387 Loss1: 0.493918 Loss2: 0.665469
-(DefaultActor pid=1831567) >> Training accuracy: 0.837129
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.296580 Loss1: 0.494273 Loss2: 0.802307
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.161230 Loss1: 0.442690 Loss2: 0.718540
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.128817 Loss1: 0.416341 Loss2: 0.712477
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.134163 Loss1: 0.425114 Loss2: 0.709050
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.120117 Loss1: 0.407146 Loss2: 0.712971
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.122403 Loss1: 0.408204 Loss2: 0.714199
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.115137 Loss1: 0.405347 Loss2: 0.709790
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.087925 Loss1: 0.377470 Loss2: 0.710455
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.108816 Loss1: 0.395335 Loss2: 0.713481
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.095654 Loss1: 0.381773 Loss2: 0.713882
-(DefaultActor pid=1831567) >> Training accuracy: 0.860147
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.557518 Loss1: 0.792415 Loss2: 0.765103
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.449832 Loss1: 0.766533 Loss2: 0.683298
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.437499 Loss1: 0.752264 Loss2: 0.685235
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.425691 Loss1: 0.741188 Loss2: 0.684504
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.395194 Loss1: 0.710438 Loss2: 0.684757
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.386820 Loss1: 0.699025 Loss2: 0.687795
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.384561 Loss1: 0.698069 Loss2: 0.686492
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.404385 Loss1: 0.712352 Loss2: 0.692032
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.396742 Loss1: 0.703567 Loss2: 0.693175
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.381311 Loss1: 0.691068 Loss2: 0.690244
-(DefaultActor pid=1831567) >> Training accuracy: 0.755888
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.388952 Loss1: 0.636919 Loss2: 0.752032
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236677 Loss1: 0.563239 Loss2: 0.673438
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.250906 Loss1: 0.577864 Loss2: 0.673041
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.221810 Loss1: 0.547710 Loss2: 0.674100
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.213258 Loss1: 0.540143 Loss2: 0.673114
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.229987 Loss1: 0.551632 Loss2: 0.678355
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.218580 Loss1: 0.541952 Loss2: 0.676627
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.193421 Loss1: 0.515120 Loss2: 0.678301
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.215550 Loss1: 0.538485 Loss2: 0.677065
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200092 Loss1: 0.522059 Loss2: 0.678033
-(DefaultActor pid=1831567) >> Training accuracy: 0.823602
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.276100 Loss1: 0.509299 Loss2: 0.766802
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.133558 Loss1: 0.440836 Loss2: 0.692722
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.125217 Loss1: 0.437541 Loss2: 0.687675
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.093261 Loss1: 0.406550 Loss2: 0.686711
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.095356 Loss1: 0.409606 Loss2: 0.685750
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.105725 Loss1: 0.416673 Loss2: 0.689052
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.088740 Loss1: 0.398834 Loss2: 0.689906
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.071193 Loss1: 0.381276 Loss2: 0.689917
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.073447 Loss1: 0.385350 Loss2: 0.688097
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.095836 Loss1: 0.403730 Loss2: 0.692106
-(DefaultActor pid=1831567) >> Training accuracy: 0.870563
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.411959 Loss1: 0.644734 Loss2: 0.767225
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.288458 Loss1: 0.590947 Loss2: 0.697511
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.284965 Loss1: 0.585766 Loss2: 0.699199
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.282864 Loss1: 0.582004 Loss2: 0.700860
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.254399 Loss1: 0.556521 Loss2: 0.697878
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249018 Loss1: 0.551157 Loss2: 0.697861
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.249782 Loss1: 0.550838 Loss2: 0.698945
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.242212 Loss1: 0.544648 Loss2: 0.697564
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232321 Loss1: 0.527975 Loss2: 0.704346
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.241878 Loss1: 0.538825 Loss2: 0.703053
-(DefaultActor pid=1831567) >> Training accuracy: 0.792492
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.559444 Loss1: 0.777973 Loss2: 0.781471
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.372298 Loss1: 0.698462 Loss2: 0.673837
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.357250 Loss1: 0.683380 Loss2: 0.673870
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335564 Loss1: 0.663920 Loss2: 0.671644
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345237 Loss1: 0.671214 Loss2: 0.674024
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.297774 Loss1: 0.626248 Loss2: 0.671526
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.318794 Loss1: 0.640793 Loss2: 0.678002
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.310843 Loss1: 0.634925 Loss2: 0.675918
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.288904 Loss1: 0.614189 Loss2: 0.674715
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279715 Loss1: 0.603732 Loss2: 0.675983
-[2023-09-27 11:26:32,759][flwr][DEBUG] - fit_round 37 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.765899
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.685100
-[2023-09-27 11:26:34,046][flwr][INFO] - fit progress: (37, 0.9061162161370055, {'accuracy': 0.6851}, 18526.882115995977)
-[2023-09-27 11:26:34,046][flwr][DEBUG] - evaluate_round 37: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:27:05,260][flwr][DEBUG] - evaluate_round 37 received 10 results and 0 failures
-[2023-09-27 11:27:05,261][flwr][DEBUG] - fit_round 38: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.357352 Loss1: 0.653402 Loss2: 0.703950
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.241971 Loss1: 0.607456 Loss2: 0.634514
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.220117 Loss1: 0.589572 Loss2: 0.630545
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190067 Loss1: 0.559410 Loss2: 0.630656
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211126 Loss1: 0.579375 Loss2: 0.631751
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193097 Loss1: 0.559101 Loss2: 0.633996
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175464 Loss1: 0.543360 Loss2: 0.632104
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.184492 Loss1: 0.552277 Loss2: 0.632216
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211846 Loss1: 0.576866 Loss2: 0.634980
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174794 Loss1: 0.539711 Loss2: 0.635082
-(DefaultActor pid=1831567) >> Training accuracy: 0.813453
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.398627 Loss1: 0.619241 Loss2: 0.779386
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.326149 Loss1: 0.593277 Loss2: 0.732872
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.296804 Loss1: 0.566718 Loss2: 0.730086
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.286250 Loss1: 0.558537 Loss2: 0.727713
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282109 Loss1: 0.550724 Loss2: 0.731385
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.280376 Loss1: 0.553000 Loss2: 0.727377
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.291293 Loss1: 0.562074 Loss2: 0.729219
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.274207 Loss1: 0.542975 Loss2: 0.731232
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.271548 Loss1: 0.537219 Loss2: 0.734329
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.282832 Loss1: 0.549288 Loss2: 0.733544
-(DefaultActor pid=1831567) >> Training accuracy: 0.819072
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.535791 Loss1: 0.772063 Loss2: 0.763728
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.363420 Loss1: 0.703860 Loss2: 0.659560
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.326551 Loss1: 0.667439 Loss2: 0.659112
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.312689 Loss1: 0.650701 Loss2: 0.661988
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.321756 Loss1: 0.657356 Loss2: 0.664400
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.313094 Loss1: 0.651256 Loss2: 0.661838
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.287719 Loss1: 0.626296 Loss2: 0.661423
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.267114 Loss1: 0.602875 Loss2: 0.664239
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306025 Loss1: 0.638073 Loss2: 0.667952
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.296240 Loss1: 0.631649 Loss2: 0.664591
-(DefaultActor pid=1831567) >> Training accuracy: 0.778509
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.415445 Loss1: 0.617497 Loss2: 0.797948
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.256768 Loss1: 0.567939 Loss2: 0.688829
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.230440 Loss1: 0.542943 Loss2: 0.687497
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223920 Loss1: 0.537274 Loss2: 0.686646
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196650 Loss1: 0.508357 Loss2: 0.688293
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.214170 Loss1: 0.521258 Loss2: 0.692912
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.190515 Loss1: 0.499033 Loss2: 0.691481
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.211131 Loss1: 0.516524 Loss2: 0.694607
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.171709 Loss1: 0.477834 Loss2: 0.693875
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175704 Loss1: 0.481156 Loss2: 0.694547
-(DefaultActor pid=1831567) >> Training accuracy: 0.843485
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.374802 Loss1: 0.639641 Loss2: 0.735161
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.239107 Loss1: 0.584567 Loss2: 0.654541
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208092 Loss1: 0.558764 Loss2: 0.649328
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187316 Loss1: 0.536272 Loss2: 0.651044
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191405 Loss1: 0.540348 Loss2: 0.651056
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.202489 Loss1: 0.549206 Loss2: 0.653283
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191418 Loss1: 0.534946 Loss2: 0.656472
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191512 Loss1: 0.536834 Loss2: 0.654678
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173654 Loss1: 0.519134 Loss2: 0.654520
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165176 Loss1: 0.509098 Loss2: 0.656078
-(DefaultActor pid=1831567) >> Training accuracy: 0.834087
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386690 Loss1: 0.623147 Loss2: 0.763543
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.289795 Loss1: 0.605893 Loss2: 0.683902
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251273 Loss1: 0.571983 Loss2: 0.679290
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.239560 Loss1: 0.557992 Loss2: 0.681568
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.245465 Loss1: 0.559299 Loss2: 0.686166
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.245344 Loss1: 0.557937 Loss2: 0.687407
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.247373 Loss1: 0.559873 Loss2: 0.687500
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229383 Loss1: 0.540214 Loss2: 0.689169
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.234133 Loss1: 0.545554 Loss2: 0.688579
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.217023 Loss1: 0.527354 Loss2: 0.689670
-(DefaultActor pid=1831567) >> Training accuracy: 0.811298
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.247659 Loss1: 0.492927 Loss2: 0.754731
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.118702 Loss1: 0.447733 Loss2: 0.670968
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.100463 Loss1: 0.430060 Loss2: 0.670403
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.087616 Loss1: 0.421657 Loss2: 0.665959
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.084302 Loss1: 0.414465 Loss2: 0.669837
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.079408 Loss1: 0.408517 Loss2: 0.670892
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.061586 Loss1: 0.391417 Loss2: 0.670169
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.077262 Loss1: 0.405389 Loss2: 0.671874
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.056351 Loss1: 0.384069 Loss2: 0.672282
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.062684 Loss1: 0.391201 Loss2: 0.671483
-(DefaultActor pid=1831567) >> Training accuracy: 0.872492
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.529249 Loss1: 0.761182 Loss2: 0.768068
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.437166 Loss1: 0.758191 Loss2: 0.678975
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.361330 Loss1: 0.690889 Loss2: 0.670441
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.360778 Loss1: 0.690226 Loss2: 0.670553
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.359911 Loss1: 0.683685 Loss2: 0.676226
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.376890 Loss1: 0.699768 Loss2: 0.677122
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.371297 Loss1: 0.692144 Loss2: 0.679153
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.336698 Loss1: 0.659196 Loss2: 0.677502
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.328461 Loss1: 0.649731 Loss2: 0.678730
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.341277 Loss1: 0.660849 Loss2: 0.680428
-(DefaultActor pid=1831567) >> Training accuracy: 0.757463
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.197499 Loss1: 0.488826 Loss2: 0.708672
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.098852 Loss1: 0.464329 Loss2: 0.634523
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.043535 Loss1: 0.409990 Loss2: 0.633546
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.053478 Loss1: 0.418166 Loss2: 0.635312
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.040013 Loss1: 0.406763 Loss2: 0.633250
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.050255 Loss1: 0.414422 Loss2: 0.635833
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.043880 Loss1: 0.409038 Loss2: 0.634842
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026629 Loss1: 0.389506 Loss2: 0.637122
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.029176 Loss1: 0.392816 Loss2: 0.636359
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.031169 Loss1: 0.390816 Loss2: 0.640353
-(DefaultActor pid=1831567) >> Training accuracy: 0.859182
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517237 Loss1: 0.795688 Loss2: 0.721549
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.393460 Loss1: 0.759157 Loss2: 0.634303
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.382975 Loss1: 0.749122 Loss2: 0.633852
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.361914 Loss1: 0.728013 Loss2: 0.633902
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.354625 Loss1: 0.718643 Loss2: 0.635982
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.364925 Loss1: 0.729546 Loss2: 0.635379
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.357019 Loss1: 0.719696 Loss2: 0.637323
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329931 Loss1: 0.691921 Loss2: 0.638010
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.324871 Loss1: 0.687474 Loss2: 0.637398
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.330100 Loss1: 0.692268 Loss2: 0.637832
-[2023-09-27 11:33:50,110][flwr][DEBUG] - fit_round 38 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.756114
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.688400
-[2023-09-27 11:33:51,801][flwr][INFO] - fit progress: (38, 0.8966945318368297, {'accuracy': 0.6884}, 18964.637040854897)
-[2023-09-27 11:33:51,801][flwr][DEBUG] - evaluate_round 38: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:34:22,935][flwr][DEBUG] - evaluate_round 38 received 10 results and 0 failures
-[2023-09-27 11:34:22,936][flwr][DEBUG] - fit_round 39: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.554307 Loss1: 0.787088 Loss2: 0.767219
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.371278 Loss1: 0.707448 Loss2: 0.663831
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336321 Loss1: 0.676177 Loss2: 0.660144
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.320814 Loss1: 0.660017 Loss2: 0.660797
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.326962 Loss1: 0.662436 Loss2: 0.664526
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.284836 Loss1: 0.621307 Loss2: 0.663529
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.290939 Loss1: 0.627115 Loss2: 0.663824
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.299901 Loss1: 0.635106 Loss2: 0.664795
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.261316 Loss1: 0.594872 Loss2: 0.666444
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290028 Loss1: 0.623444 Loss2: 0.666583
-(DefaultActor pid=1831567) >> Training accuracy: 0.773575
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.261773 Loss1: 0.493180 Loss2: 0.768594
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.114879 Loss1: 0.432242 Loss2: 0.682637
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.104399 Loss1: 0.426902 Loss2: 0.677497
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.112260 Loss1: 0.432534 Loss2: 0.679726
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.116061 Loss1: 0.434456 Loss2: 0.681605
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.082451 Loss1: 0.400317 Loss2: 0.682134
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.077627 Loss1: 0.399274 Loss2: 0.678354
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.075518 Loss1: 0.394913 Loss2: 0.680605
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.059709 Loss1: 0.376833 Loss2: 0.682876
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.063986 Loss1: 0.377555 Loss2: 0.686432
-(DefaultActor pid=1831567) >> Training accuracy: 0.861304
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.511542 Loss1: 0.780250 Loss2: 0.731292
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370132 Loss1: 0.726564 Loss2: 0.643568
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.360838 Loss1: 0.719568 Loss2: 0.641270
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331924 Loss1: 0.689597 Loss2: 0.642327
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.338457 Loss1: 0.696391 Loss2: 0.642066
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.338045 Loss1: 0.691858 Loss2: 0.646186
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.304153 Loss1: 0.655402 Loss2: 0.648751
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.306844 Loss1: 0.661725 Loss2: 0.645119
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.285599 Loss1: 0.641063 Loss2: 0.644535
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284982 Loss1: 0.638488 Loss2: 0.646494
-(DefaultActor pid=1831567) >> Training accuracy: 0.767491
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369842 Loss1: 0.605307 Loss2: 0.764534
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.230520 Loss1: 0.565070 Loss2: 0.665450
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208905 Loss1: 0.545113 Loss2: 0.663792
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208641 Loss1: 0.545673 Loss2: 0.662968
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.217596 Loss1: 0.550066 Loss2: 0.667530
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.196711 Loss1: 0.529934 Loss2: 0.666776
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.185355 Loss1: 0.518525 Loss2: 0.666829
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.153389 Loss1: 0.485839 Loss2: 0.667550
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161365 Loss1: 0.496106 Loss2: 0.665259
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154443 Loss1: 0.487983 Loss2: 0.666460
-(DefaultActor pid=1831567) >> Training accuracy: 0.823093
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.563585 Loss1: 0.789510 Loss2: 0.774075
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.439852 Loss1: 0.751222 Loss2: 0.688630
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.444716 Loss1: 0.753556 Loss2: 0.691159
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.440678 Loss1: 0.747265 Loss2: 0.693413
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.407841 Loss1: 0.718721 Loss2: 0.689121
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.396749 Loss1: 0.705322 Loss2: 0.691427
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.383521 Loss1: 0.688322 Loss2: 0.695199
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.400241 Loss1: 0.706824 Loss2: 0.693417
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.396418 Loss1: 0.700066 Loss2: 0.696351
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.395641 Loss1: 0.699185 Loss2: 0.696456
-(DefaultActor pid=1831567) >> Training accuracy: 0.763134
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.357377 Loss1: 0.616771 Loss2: 0.740606
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.247526 Loss1: 0.576776 Loss2: 0.670750
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.273280 Loss1: 0.603832 Loss2: 0.669449
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.242437 Loss1: 0.572325 Loss2: 0.670112
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.235109 Loss1: 0.563863 Loss2: 0.671245
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.242876 Loss1: 0.571229 Loss2: 0.671647
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224582 Loss1: 0.552258 Loss2: 0.672324
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.230843 Loss1: 0.559420 Loss2: 0.671423
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.221327 Loss1: 0.546756 Loss2: 0.674570
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.195018 Loss1: 0.523037 Loss2: 0.671980
-(DefaultActor pid=1831567) >> Training accuracy: 0.822316
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.396162 Loss1: 0.630285 Loss2: 0.765877
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.279431 Loss1: 0.594951 Loss2: 0.684480
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.243780 Loss1: 0.561163 Loss2: 0.682616
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.239644 Loss1: 0.556224 Loss2: 0.683421
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.240648 Loss1: 0.554456 Loss2: 0.686193
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.233874 Loss1: 0.550376 Loss2: 0.683498
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.207716 Loss1: 0.522440 Loss2: 0.685277
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220935 Loss1: 0.532408 Loss2: 0.688527
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193491 Loss1: 0.503809 Loss2: 0.689682
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.213768 Loss1: 0.522158 Loss2: 0.691610
-(DefaultActor pid=1831567) >> Training accuracy: 0.835732
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297886 Loss1: 0.495698 Loss2: 0.802187
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.173412 Loss1: 0.453982 Loss2: 0.719430
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.142445 Loss1: 0.432133 Loss2: 0.710312
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.119618 Loss1: 0.408475 Loss2: 0.711143
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.121476 Loss1: 0.410744 Loss2: 0.710732
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131538 Loss1: 0.418800 Loss2: 0.712738
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.111379 Loss1: 0.398430 Loss2: 0.712949
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.103688 Loss1: 0.391403 Loss2: 0.712285
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.098441 Loss1: 0.386367 Loss2: 0.712074
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107334 Loss1: 0.392965 Loss2: 0.714370
-(DefaultActor pid=1831567) >> Training accuracy: 0.864390
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326586 Loss1: 0.604602 Loss2: 0.721984
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.251506 Loss1: 0.572864 Loss2: 0.678642
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.242116 Loss1: 0.567527 Loss2: 0.674589
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.228294 Loss1: 0.554985 Loss2: 0.673309
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.240094 Loss1: 0.563449 Loss2: 0.676645
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.233397 Loss1: 0.558676 Loss2: 0.674721
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.239469 Loss1: 0.562656 Loss2: 0.676813
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.221566 Loss1: 0.545603 Loss2: 0.675964
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.206633 Loss1: 0.530340 Loss2: 0.676294
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229473 Loss1: 0.548812 Loss2: 0.680661
-(DefaultActor pid=1831567) >> Training accuracy: 0.812996
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.395186 Loss1: 0.644253 Loss2: 0.750933
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.270456 Loss1: 0.586397 Loss2: 0.684059
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.252313 Loss1: 0.568964 Loss2: 0.683348
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.260389 Loss1: 0.579862 Loss2: 0.680528
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.227028 Loss1: 0.543647 Loss2: 0.683380
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232485 Loss1: 0.546958 Loss2: 0.685527
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.234371 Loss1: 0.552103 Loss2: 0.682268
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225981 Loss1: 0.541659 Loss2: 0.684322
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.214592 Loss1: 0.527873 Loss2: 0.686720
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214070 Loss1: 0.527154 Loss2: 0.686916
-[2023-09-27 11:41:04,608][flwr][DEBUG] - fit_round 39 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.819169
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.683200
-[2023-09-27 11:41:05,990][flwr][INFO] - fit progress: (39, 0.9046378726966846, {'accuracy': 0.6832}, 19398.826610946096)
-[2023-09-27 11:41:05,991][flwr][DEBUG] - evaluate_round 39: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:41:43,281][flwr][DEBUG] - evaluate_round 39 received 10 results and 0 failures
-[2023-09-27 11:41:43,282][flwr][DEBUG] - fit_round 40: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.257980 Loss1: 0.506809 Loss2: 0.751170
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.088687 Loss1: 0.426436 Loss2: 0.662251
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.083518 Loss1: 0.423395 Loss2: 0.660123
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.071372 Loss1: 0.412241 Loss2: 0.659131
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.069970 Loss1: 0.407586 Loss2: 0.662384
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.064381 Loss1: 0.402067 Loss2: 0.662313
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.062979 Loss1: 0.402074 Loss2: 0.660905
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.049072 Loss1: 0.386241 Loss2: 0.662831
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.051168 Loss1: 0.385224 Loss2: 0.665944
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.032984 Loss1: 0.369535 Loss2: 0.663449
-(DefaultActor pid=1831567) >> Training accuracy: 0.861883
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353879 Loss1: 0.612086 Loss2: 0.741793
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.244073 Loss1: 0.585674 Loss2: 0.658399
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.197955 Loss1: 0.541323 Loss2: 0.656632
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210326 Loss1: 0.552278 Loss2: 0.658048
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218505 Loss1: 0.555821 Loss2: 0.662684
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180952 Loss1: 0.522929 Loss2: 0.658023
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193039 Loss1: 0.532778 Loss2: 0.660261
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.192272 Loss1: 0.530153 Loss2: 0.662118
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.186081 Loss1: 0.524101 Loss2: 0.661980
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.177725 Loss1: 0.518026 Loss2: 0.659699
-(DefaultActor pid=1831567) >> Training accuracy: 0.822574
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.557811 Loss1: 0.769171 Loss2: 0.788639
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.421844 Loss1: 0.730514 Loss2: 0.691330
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.389192 Loss1: 0.701069 Loss2: 0.688123
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.357735 Loss1: 0.675175 Loss2: 0.682561
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.372177 Loss1: 0.682764 Loss2: 0.689413
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.385484 Loss1: 0.694042 Loss2: 0.691442
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.359965 Loss1: 0.667791 Loss2: 0.692174
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.358296 Loss1: 0.664466 Loss2: 0.693830
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.344580 Loss1: 0.650471 Loss2: 0.694109
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.354642 Loss1: 0.659948 Loss2: 0.694694
-(DefaultActor pid=1831567) >> Training accuracy: 0.764226
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.344923 Loss1: 0.606622 Loss2: 0.738301
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.252972 Loss1: 0.591157 Loss2: 0.661815
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.252261 Loss1: 0.588604 Loss2: 0.663657
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199078 Loss1: 0.537905 Loss2: 0.661173
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.242167 Loss1: 0.575294 Loss2: 0.666873
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.219224 Loss1: 0.552092 Loss2: 0.667132
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224907 Loss1: 0.556150 Loss2: 0.668757
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.183507 Loss1: 0.516682 Loss2: 0.666825
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196920 Loss1: 0.529203 Loss2: 0.667716
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.206793 Loss1: 0.536728 Loss2: 0.670065
-(DefaultActor pid=1831567) >> Training accuracy: 0.813301
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381083 Loss1: 0.610133 Loss2: 0.770950
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206842 Loss1: 0.546694 Loss2: 0.660148
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194779 Loss1: 0.538453 Loss2: 0.656326
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.172872 Loss1: 0.518116 Loss2: 0.654756
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.173906 Loss1: 0.515545 Loss2: 0.658361
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.177190 Loss1: 0.519738 Loss2: 0.657452
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.178834 Loss1: 0.517738 Loss2: 0.661097
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.156450 Loss1: 0.497836 Loss2: 0.658614
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.160452 Loss1: 0.502042 Loss2: 0.658410
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148788 Loss1: 0.490884 Loss2: 0.657904
-(DefaultActor pid=1831567) >> Training accuracy: 0.845339
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.543509 Loss1: 0.781604 Loss2: 0.761905
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.373146 Loss1: 0.714763 Loss2: 0.658383
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.342847 Loss1: 0.684679 Loss2: 0.658168
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331479 Loss1: 0.670191 Loss2: 0.661288
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333387 Loss1: 0.669418 Loss2: 0.663968
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.326043 Loss1: 0.657670 Loss2: 0.668372
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294489 Loss1: 0.629890 Loss2: 0.664598
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.262679 Loss1: 0.597842 Loss2: 0.664837
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.290208 Loss1: 0.624528 Loss2: 0.665680
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279731 Loss1: 0.612740 Loss2: 0.666991
-(DefaultActor pid=1831567) >> Training accuracy: 0.787281
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.200364 Loss1: 0.492162 Loss2: 0.708202
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.066194 Loss1: 0.429940 Loss2: 0.636254
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.061912 Loss1: 0.426200 Loss2: 0.635712
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.052927 Loss1: 0.418876 Loss2: 0.634051
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.047297 Loss1: 0.413961 Loss2: 0.633336
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.043074 Loss1: 0.406560 Loss2: 0.636514
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.023041 Loss1: 0.389473 Loss2: 0.633568
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.065331 Loss1: 0.427782 Loss2: 0.637549
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.037835 Loss1: 0.399753 Loss2: 0.638082
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.031311 Loss1: 0.390748 Loss2: 0.640563
-(DefaultActor pid=1831567) >> Training accuracy: 0.861883
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382681 Loss1: 0.607292 Loss2: 0.775389
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.278428 Loss1: 0.557204 Loss2: 0.721224
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.281754 Loss1: 0.562880 Loss2: 0.718875
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.271283 Loss1: 0.551129 Loss2: 0.720154
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.257656 Loss1: 0.534294 Loss2: 0.723363
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.288304 Loss1: 0.561531 Loss2: 0.726772
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.257801 Loss1: 0.533116 Loss2: 0.724685
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268709 Loss1: 0.541925 Loss2: 0.726784
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.263918 Loss1: 0.536420 Loss2: 0.727498
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259120 Loss1: 0.531042 Loss2: 0.728078
-(DefaultActor pid=1831567) >> Training accuracy: 0.804812
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370434 Loss1: 0.650962 Loss2: 0.719472
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.238554 Loss1: 0.593317 Loss2: 0.645236
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.235767 Loss1: 0.591838 Loss2: 0.643929
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210106 Loss1: 0.568477 Loss2: 0.641629
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.188730 Loss1: 0.545544 Loss2: 0.643186
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.196510 Loss1: 0.551333 Loss2: 0.645178
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.188894 Loss1: 0.545286 Loss2: 0.643609
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.167962 Loss1: 0.522637 Loss2: 0.645325
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.198005 Loss1: 0.552705 Loss2: 0.645300
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.170997 Loss1: 0.524294 Loss2: 0.646703
-(DefaultActor pid=1831567) >> Training accuracy: 0.826601
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.526834 Loss1: 0.783310 Loss2: 0.743524
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.426421 Loss1: 0.769760 Loss2: 0.656661
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.398998 Loss1: 0.741329 Loss2: 0.657669
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.389652 Loss1: 0.733711 Loss2: 0.655941
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.361844 Loss1: 0.704729 Loss2: 0.657115
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.345729 Loss1: 0.687670 Loss2: 0.658059
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.369481 Loss1: 0.708065 Loss2: 0.661415
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.369034 Loss1: 0.709193 Loss2: 0.659842
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.352951 Loss1: 0.691476 Loss2: 0.661476
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.352285 Loss1: 0.689545 Loss2: 0.662740
-[2023-09-27 11:48:50,104][flwr][DEBUG] - fit_round 40 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.768569
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.679500
-[2023-09-27 11:48:51,532][flwr][INFO] - fit progress: (40, 0.9118956097017843, {'accuracy': 0.6795}, 19864.368800325785)
-[2023-09-27 11:48:51,533][flwr][DEBUG] - evaluate_round 40: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:49:23,488][flwr][DEBUG] - evaluate_round 40 received 10 results and 0 failures
-[2023-09-27 11:49:23,489][flwr][DEBUG] - fit_round 41: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.376407 Loss1: 0.619553 Loss2: 0.756854
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243427 Loss1: 0.566204 Loss2: 0.677223
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222532 Loss1: 0.549301 Loss2: 0.673231
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.213968 Loss1: 0.536578 Loss2: 0.677389
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.214146 Loss1: 0.536022 Loss2: 0.678124
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220728 Loss1: 0.541209 Loss2: 0.679518
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.206207 Loss1: 0.525074 Loss2: 0.681133
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204738 Loss1: 0.526478 Loss2: 0.678260
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178710 Loss1: 0.498880 Loss2: 0.679830
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.194124 Loss1: 0.510540 Loss2: 0.683584
-(DefaultActor pid=1831567) >> Training accuracy: 0.832237
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.287963 Loss1: 0.492734 Loss2: 0.795228
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.147870 Loss1: 0.436526 Loss2: 0.711344
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.140502 Loss1: 0.431421 Loss2: 0.709081
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144228 Loss1: 0.436314 Loss2: 0.707914
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.123869 Loss1: 0.415275 Loss2: 0.708595
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.103055 Loss1: 0.396813 Loss2: 0.706242
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.130262 Loss1: 0.418793 Loss2: 0.711469
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.100477 Loss1: 0.392754 Loss2: 0.707724
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.090206 Loss1: 0.383804 Loss2: 0.706402
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.091915 Loss1: 0.383129 Loss2: 0.708787
-(DefaultActor pid=1831567) >> Training accuracy: 0.861883
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.516518 Loss1: 0.744964 Loss2: 0.771554
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.372687 Loss1: 0.707443 Loss2: 0.665245
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.319154 Loss1: 0.657794 Loss2: 0.661360
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325080 Loss1: 0.661849 Loss2: 0.663231
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.329491 Loss1: 0.664612 Loss2: 0.664879
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.313604 Loss1: 0.649448 Loss2: 0.664156
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298542 Loss1: 0.632828 Loss2: 0.665715
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.298636 Loss1: 0.631276 Loss2: 0.667360
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.279631 Loss1: 0.612400 Loss2: 0.667232
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.287818 Loss1: 0.616759 Loss2: 0.671059
-(DefaultActor pid=1831567) >> Training accuracy: 0.791393
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.365785 Loss1: 0.611657 Loss2: 0.754128
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.221641 Loss1: 0.570172 Loss2: 0.651469
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208968 Loss1: 0.559349 Loss2: 0.649619
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.167859 Loss1: 0.517158 Loss2: 0.650701
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187681 Loss1: 0.537061 Loss2: 0.650620
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181050 Loss1: 0.533160 Loss2: 0.647891
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164370 Loss1: 0.513559 Loss2: 0.650810
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.129008 Loss1: 0.474661 Loss2: 0.654347
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.149378 Loss1: 0.497207 Loss2: 0.652171
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.127316 Loss1: 0.473884 Loss2: 0.653432
-(DefaultActor pid=1831567) >> Training accuracy: 0.847987
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340646 Loss1: 0.602314 Loss2: 0.738332
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.262707 Loss1: 0.571763 Loss2: 0.690944
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.254432 Loss1: 0.561460 Loss2: 0.692973
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.255567 Loss1: 0.561387 Loss2: 0.694181
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.248310 Loss1: 0.554881 Loss2: 0.693429
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.240960 Loss1: 0.547467 Loss2: 0.693494
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243251 Loss1: 0.548034 Loss2: 0.695217
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.252921 Loss1: 0.559345 Loss2: 0.693576
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.235438 Loss1: 0.538979 Loss2: 0.696459
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.213892 Loss1: 0.523196 Loss2: 0.690696
-(DefaultActor pid=1831567) >> Training accuracy: 0.812128
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.567676 Loss1: 0.793427 Loss2: 0.774248
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.455764 Loss1: 0.764081 Loss2: 0.691683
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.415178 Loss1: 0.729805 Loss2: 0.685373
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.413100 Loss1: 0.724345 Loss2: 0.688755
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.383355 Loss1: 0.693909 Loss2: 0.689446
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.387973 Loss1: 0.701037 Loss2: 0.686935
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.380574 Loss1: 0.687757 Loss2: 0.692818
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.368252 Loss1: 0.674560 Loss2: 0.693692
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.383209 Loss1: 0.690003 Loss2: 0.693206
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.365431 Loss1: 0.672769 Loss2: 0.692663
-(DefaultActor pid=1831567) >> Training accuracy: 0.776042
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.248701 Loss1: 0.496487 Loss2: 0.752214
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.110753 Loss1: 0.443500 Loss2: 0.667253
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.100764 Loss1: 0.433367 Loss2: 0.667396
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.090442 Loss1: 0.424998 Loss2: 0.665443
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.103193 Loss1: 0.431622 Loss2: 0.671570
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.065875 Loss1: 0.400600 Loss2: 0.665275
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.044218 Loss1: 0.375736 Loss2: 0.668482
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.059196 Loss1: 0.390542 Loss2: 0.668654
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.073570 Loss1: 0.403424 Loss2: 0.670146
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.045877 Loss1: 0.376693 Loss2: 0.669184
-(DefaultActor pid=1831567) >> Training accuracy: 0.867863
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.522217 Loss1: 0.770657 Loss2: 0.751560
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.399520 Loss1: 0.736843 Loss2: 0.662677
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.358212 Loss1: 0.695361 Loss2: 0.662851
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.350804 Loss1: 0.686513 Loss2: 0.664292
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.332878 Loss1: 0.666326 Loss2: 0.666553
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.339834 Loss1: 0.676487 Loss2: 0.663347
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.335552 Loss1: 0.667740 Loss2: 0.667812
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.332558 Loss1: 0.666783 Loss2: 0.665775
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.302575 Loss1: 0.636764 Loss2: 0.665811
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.299396 Loss1: 0.630077 Loss2: 0.669319
-(DefaultActor pid=1831567) >> Training accuracy: 0.756530
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.388003 Loss1: 0.642591 Loss2: 0.745412
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.256987 Loss1: 0.587417 Loss2: 0.669570
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.246513 Loss1: 0.579644 Loss2: 0.666868
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.247146 Loss1: 0.576699 Loss2: 0.670447
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.236310 Loss1: 0.566122 Loss2: 0.670188
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231619 Loss1: 0.559893 Loss2: 0.671727
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197853 Loss1: 0.527875 Loss2: 0.669978
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.217567 Loss1: 0.544998 Loss2: 0.672569
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.200916 Loss1: 0.529436 Loss2: 0.671479
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.197876 Loss1: 0.526946 Loss2: 0.670929
-(DefaultActor pid=1831567) >> Training accuracy: 0.825921
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.399071 Loss1: 0.652978 Loss2: 0.746093
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.257290 Loss1: 0.581346 Loss2: 0.675944
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223922 Loss1: 0.551169 Loss2: 0.672754
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.220860 Loss1: 0.546367 Loss2: 0.674493
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215777 Loss1: 0.541021 Loss2: 0.674757
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.217190 Loss1: 0.540204 Loss2: 0.676987
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.208949 Loss1: 0.533478 Loss2: 0.675472
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.221172 Loss1: 0.542989 Loss2: 0.678184
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199732 Loss1: 0.521592 Loss2: 0.678140
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.202723 Loss1: 0.525360 Loss2: 0.677363
-[2023-09-27 11:56:21,623][flwr][DEBUG] - fit_round 41 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.821646
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.691200
-[2023-09-27 11:56:23,203][flwr][INFO] - fit progress: (41, 0.8914938339600548, {'accuracy': 0.6912}, 20316.0390293058)
-[2023-09-27 11:56:23,204][flwr][DEBUG] - evaluate_round 41: strategy sampled 10 clients (out of 10)
-[2023-09-27 11:56:53,769][flwr][DEBUG] - evaluate_round 41 received 10 results and 0 failures
-[2023-09-27 11:56:53,770][flwr][DEBUG] - fit_round 42: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.424741 Loss1: 0.622516 Loss2: 0.802225
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.267781 Loss1: 0.581000 Loss2: 0.686780
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213061 Loss1: 0.532386 Loss2: 0.680675
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212231 Loss1: 0.528400 Loss2: 0.683831
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218670 Loss1: 0.535190 Loss2: 0.683480
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.214486 Loss1: 0.529014 Loss2: 0.685472
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164629 Loss1: 0.480160 Loss2: 0.684469
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154115 Loss1: 0.470578 Loss2: 0.683537
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.157672 Loss1: 0.473017 Loss2: 0.684654
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184818 Loss1: 0.496410 Loss2: 0.688409
-(DefaultActor pid=1831567) >> Training accuracy: 0.844280
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.215526 Loss1: 0.492215 Loss2: 0.723311
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.088936 Loss1: 0.440206 Loss2: 0.648730
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.056314 Loss1: 0.411450 Loss2: 0.644865
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074883 Loss1: 0.427299 Loss2: 0.647584
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066890 Loss1: 0.419796 Loss2: 0.647094
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.045756 Loss1: 0.398283 Loss2: 0.647473
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050583 Loss1: 0.405713 Loss2: 0.644869
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.034028 Loss1: 0.388249 Loss2: 0.645779
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028114 Loss1: 0.380549 Loss2: 0.647565
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.035081 Loss1: 0.386788 Loss2: 0.648292
-(DefaultActor pid=1831567) >> Training accuracy: 0.865741
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381300 Loss1: 0.650777 Loss2: 0.730522
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.244802 Loss1: 0.584880 Loss2: 0.659922
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238008 Loss1: 0.581718 Loss2: 0.656290
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.213890 Loss1: 0.561035 Loss2: 0.652855
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210178 Loss1: 0.554921 Loss2: 0.655257
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.204183 Loss1: 0.547830 Loss2: 0.656353
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196964 Loss1: 0.542264 Loss2: 0.654700
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.176982 Loss1: 0.521112 Loss2: 0.655870
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178596 Loss1: 0.520733 Loss2: 0.657864
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.182101 Loss1: 0.522437 Loss2: 0.659664
-(DefaultActor pid=1831567) >> Training accuracy: 0.822790
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.542440 Loss1: 0.767725 Loss2: 0.774715
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394420 Loss1: 0.719568 Loss2: 0.674852
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.372342 Loss1: 0.699737 Loss2: 0.672605
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.371302 Loss1: 0.696546 Loss2: 0.674756
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.379592 Loss1: 0.705050 Loss2: 0.674542
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.363315 Loss1: 0.689801 Loss2: 0.673514
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338685 Loss1: 0.659310 Loss2: 0.679375
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.330920 Loss1: 0.651391 Loss2: 0.679529
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.326348 Loss1: 0.647902 Loss2: 0.678445
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.332995 Loss1: 0.652562 Loss2: 0.680433
-(DefaultActor pid=1831567) >> Training accuracy: 0.767257
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.245516 Loss1: 0.492238 Loss2: 0.753277
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.086641 Loss1: 0.421109 Loss2: 0.665532
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.083036 Loss1: 0.420356 Loss2: 0.662680
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.080544 Loss1: 0.414313 Loss2: 0.666231
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.045255 Loss1: 0.381141 Loss2: 0.664114
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.047193 Loss1: 0.383292 Loss2: 0.663900
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.055083 Loss1: 0.387209 Loss2: 0.667874
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.046395 Loss1: 0.379363 Loss2: 0.667032
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.074728 Loss1: 0.404528 Loss2: 0.670200
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.030588 Loss1: 0.365412 Loss2: 0.665175
-(DefaultActor pid=1831567) >> Training accuracy: 0.857446
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.533228 Loss1: 0.756607 Loss2: 0.776621
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.377307 Loss1: 0.708298 Loss2: 0.669009
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337987 Loss1: 0.671443 Loss2: 0.666544
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.341406 Loss1: 0.675314 Loss2: 0.666092
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.291911 Loss1: 0.623348 Loss2: 0.668564
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.320487 Loss1: 0.651006 Loss2: 0.669481
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.296564 Loss1: 0.625391 Loss2: 0.671173
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300339 Loss1: 0.629325 Loss2: 0.671014
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299384 Loss1: 0.626350 Loss2: 0.673034
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.295474 Loss1: 0.621842 Loss2: 0.673632
-(DefaultActor pid=1831567) >> Training accuracy: 0.783991
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517114 Loss1: 0.772895 Loss2: 0.744219
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.398582 Loss1: 0.742560 Loss2: 0.656021
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.375209 Loss1: 0.721110 Loss2: 0.654099
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.365511 Loss1: 0.710825 Loss2: 0.654685
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.360379 Loss1: 0.705638 Loss2: 0.654741
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.343325 Loss1: 0.687790 Loss2: 0.655536
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338853 Loss1: 0.679266 Loss2: 0.659587
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.356552 Loss1: 0.694129 Loss2: 0.662423
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.339934 Loss1: 0.680213 Loss2: 0.659721
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.338509 Loss1: 0.676575 Loss2: 0.661934
-(DefaultActor pid=1831567) >> Training accuracy: 0.770833
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.364364 Loss1: 0.620791 Loss2: 0.743573
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.254249 Loss1: 0.586944 Loss2: 0.667305
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208033 Loss1: 0.544371 Loss2: 0.663662
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217304 Loss1: 0.551601 Loss2: 0.665703
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.186088 Loss1: 0.523291 Loss2: 0.662797
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210861 Loss1: 0.544158 Loss2: 0.666703
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.201819 Loss1: 0.535725 Loss2: 0.666094
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.209552 Loss1: 0.541786 Loss2: 0.667766
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182218 Loss1: 0.514510 Loss2: 0.667708
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.188624 Loss1: 0.518675 Loss2: 0.669949
-(DefaultActor pid=1831567) >> Training accuracy: 0.822574
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381148 Loss1: 0.605354 Loss2: 0.775793
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.285658 Loss1: 0.564166 Loss2: 0.721492
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.278621 Loss1: 0.558219 Loss2: 0.720402
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.268944 Loss1: 0.549011 Loss2: 0.719933
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.278635 Loss1: 0.553783 Loss2: 0.724852
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.271900 Loss1: 0.546238 Loss2: 0.725662
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.255037 Loss1: 0.531483 Loss2: 0.723554
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.281364 Loss1: 0.552786 Loss2: 0.728578
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267605 Loss1: 0.542787 Loss2: 0.724818
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.256727 Loss1: 0.531742 Loss2: 0.724985
-(DefaultActor pid=1831567) >> Training accuracy: 0.817584
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.373961 Loss1: 0.612551 Loss2: 0.761410
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.273541 Loss1: 0.587282 Loss2: 0.686259
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262731 Loss1: 0.576560 Loss2: 0.686171
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.220553 Loss1: 0.535741 Loss2: 0.684812
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224784 Loss1: 0.535752 Loss2: 0.689031
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226509 Loss1: 0.540937 Loss2: 0.685573
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220009 Loss1: 0.533183 Loss2: 0.686826
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.235305 Loss1: 0.545673 Loss2: 0.689633
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217751 Loss1: 0.524719 Loss2: 0.693032
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.237468 Loss1: 0.542930 Loss2: 0.694538
-[2023-09-27 12:03:37,273][flwr][DEBUG] - fit_round 42 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.828125
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.688800
-[2023-09-27 12:03:38,844][flwr][INFO] - fit progress: (42, 0.8886514548866894, {'accuracy': 0.6888}, 20751.680621833075)
-[2023-09-27 12:03:38,845][flwr][DEBUG] - evaluate_round 42: strategy sampled 10 clients (out of 10)
-[2023-09-27 12:04:09,944][flwr][DEBUG] - evaluate_round 42 received 10 results and 0 failures
-[2023-09-27 12:04:09,945][flwr][DEBUG] - fit_round 43: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.356694 Loss1: 0.618696 Loss2: 0.737998
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225755 Loss1: 0.565196 Loss2: 0.660558
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.242388 Loss1: 0.581801 Loss2: 0.660587
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.236985 Loss1: 0.573084 Loss2: 0.663901
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.213797 Loss1: 0.550970 Loss2: 0.662828
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222756 Loss1: 0.559375 Loss2: 0.663382
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.199399 Loss1: 0.532805 Loss2: 0.666594
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180752 Loss1: 0.515227 Loss2: 0.665525
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.190897 Loss1: 0.528043 Loss2: 0.662854
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.191332 Loss1: 0.525779 Loss2: 0.665553
-(DefaultActor pid=1831567) >> Training accuracy: 0.789864
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386452 Loss1: 0.618806 Loss2: 0.767646
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.213943 Loss1: 0.552617 Loss2: 0.661325
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188244 Loss1: 0.523276 Loss2: 0.664968
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189316 Loss1: 0.522900 Loss2: 0.666417
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.190635 Loss1: 0.522683 Loss2: 0.667952
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.182663 Loss1: 0.514885 Loss2: 0.667777
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.199213 Loss1: 0.528462 Loss2: 0.670750
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158958 Loss1: 0.491322 Loss2: 0.667636
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.154509 Loss1: 0.484391 Loss2: 0.670118
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136105 Loss1: 0.466507 Loss2: 0.669598
-(DefaultActor pid=1831567) >> Training accuracy: 0.841896
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.524434 Loss1: 0.749240 Loss2: 0.775195
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340306 Loss1: 0.670607 Loss2: 0.669699
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336495 Loss1: 0.667401 Loss2: 0.669094
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339967 Loss1: 0.671075 Loss2: 0.668892
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.323305 Loss1: 0.651728 Loss2: 0.671577
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.329731 Loss1: 0.656892 Loss2: 0.672839
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.332238 Loss1: 0.658299 Loss2: 0.673939
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.306453 Loss1: 0.631625 Loss2: 0.674828
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.255377 Loss1: 0.582824 Loss2: 0.672553
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309504 Loss1: 0.633321 Loss2: 0.676183
-(DefaultActor pid=1831567) >> Training accuracy: 0.793586
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327403 Loss1: 0.480040 Loss2: 0.847363
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.185415 Loss1: 0.434982 Loss2: 0.750432
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.146305 Loss1: 0.402456 Loss2: 0.743849
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165009 Loss1: 0.420266 Loss2: 0.744743
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.139546 Loss1: 0.396942 Loss2: 0.742605
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.129066 Loss1: 0.386271 Loss2: 0.742794
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.120700 Loss1: 0.378653 Loss2: 0.742047
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.135028 Loss1: 0.389427 Loss2: 0.745601
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.143643 Loss1: 0.392779 Loss2: 0.750864
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136093 Loss1: 0.386609 Loss2: 0.749484
-(DefaultActor pid=1831567) >> Training accuracy: 0.857060
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.379224 Loss1: 0.644364 Loss2: 0.734859
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243190 Loss1: 0.577596 Loss2: 0.665594
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222699 Loss1: 0.559696 Loss2: 0.663003
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.219089 Loss1: 0.559918 Loss2: 0.659171
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201418 Loss1: 0.537450 Loss2: 0.663968
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206730 Loss1: 0.544263 Loss2: 0.662467
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197535 Loss1: 0.534164 Loss2: 0.663371
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.211915 Loss1: 0.547377 Loss2: 0.664538
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193848 Loss1: 0.528589 Loss2: 0.665259
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.203817 Loss1: 0.532739 Loss2: 0.671078
-(DefaultActor pid=1831567) >> Training accuracy: 0.833079
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.548647 Loss1: 0.791457 Loss2: 0.757190
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.429926 Loss1: 0.755292 Loss2: 0.674635
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393001 Loss1: 0.718302 Loss2: 0.674699
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.393178 Loss1: 0.717512 Loss2: 0.675667
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.379242 Loss1: 0.704882 Loss2: 0.674360
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.378497 Loss1: 0.699814 Loss2: 0.678682
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.404002 Loss1: 0.724596 Loss2: 0.679406
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.361171 Loss1: 0.682339 Loss2: 0.678832
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.342484 Loss1: 0.665351 Loss2: 0.677132
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.357808 Loss1: 0.677323 Loss2: 0.680485
-(DefaultActor pid=1831567) >> Training accuracy: 0.774230
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.233074 Loss1: 0.477540 Loss2: 0.755534
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.144451 Loss1: 0.466630 Loss2: 0.677820
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.109160 Loss1: 0.434967 Loss2: 0.674193
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.089546 Loss1: 0.413710 Loss2: 0.675836
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.082447 Loss1: 0.408091 Loss2: 0.674356
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073672 Loss1: 0.400842 Loss2: 0.672830
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.067704 Loss1: 0.390693 Loss2: 0.677011
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.065486 Loss1: 0.390454 Loss2: 0.675032
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.035947 Loss1: 0.363258 Loss2: 0.672689
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.068228 Loss1: 0.392466 Loss2: 0.675762
-(DefaultActor pid=1831567) >> Training accuracy: 0.867477
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362477 Loss1: 0.599438 Loss2: 0.763039
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258638 Loss1: 0.572501 Loss2: 0.686137
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223688 Loss1: 0.539024 Loss2: 0.684665
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225656 Loss1: 0.541591 Loss2: 0.684065
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.229507 Loss1: 0.542402 Loss2: 0.687106
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.204123 Loss1: 0.516499 Loss2: 0.687624
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.177305 Loss1: 0.491109 Loss2: 0.686196
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.222192 Loss1: 0.533866 Loss2: 0.688327
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.207599 Loss1: 0.517822 Loss2: 0.689777
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200023 Loss1: 0.508088 Loss2: 0.691935
-(DefaultActor pid=1831567) >> Training accuracy: 0.830798
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.525663 Loss1: 0.756010 Loss2: 0.769653
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.417677 Loss1: 0.735012 Loss2: 0.682665
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.362736 Loss1: 0.683259 Loss2: 0.679477
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.368039 Loss1: 0.684197 Loss2: 0.683842
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.353899 Loss1: 0.672211 Loss2: 0.681688
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.347096 Loss1: 0.664986 Loss2: 0.682110
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.356586 Loss1: 0.671362 Loss2: 0.685224
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347425 Loss1: 0.662475 Loss2: 0.684950
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.348145 Loss1: 0.659709 Loss2: 0.688436
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.345772 Loss1: 0.658316 Loss2: 0.687456
-(DefaultActor pid=1831567) >> Training accuracy: 0.776353
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.331618 Loss1: 0.595966 Loss2: 0.735652
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.254519 Loss1: 0.564451 Loss2: 0.690068
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.233375 Loss1: 0.545459 Loss2: 0.687916
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.249050 Loss1: 0.558135 Loss2: 0.690916
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.249769 Loss1: 0.560000 Loss2: 0.689768
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.221412 Loss1: 0.535949 Loss2: 0.685463
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.237552 Loss1: 0.548687 Loss2: 0.688865
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.236073 Loss1: 0.544516 Loss2: 0.691557
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.246166 Loss1: 0.551697 Loss2: 0.694469
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.235750 Loss1: 0.543900 Loss2: 0.691850
-[2023-09-27 12:10:54,145][flwr][DEBUG] - fit_round 43 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.797247
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.693200
-[2023-09-27 12:10:55,971][flwr][INFO] - fit progress: (43, 0.8876500287756752, {'accuracy': 0.6932}, 21188.807384195738)
-[2023-09-27 12:10:55,972][flwr][DEBUG] - evaluate_round 43: strategy sampled 10 clients (out of 10)
-[2023-09-27 12:11:27,410][flwr][DEBUG] - evaluate_round 43 received 10 results and 0 failures
-[2023-09-27 12:11:27,411][flwr][DEBUG] - fit_round 44: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.379226 Loss1: 0.635263 Loss2: 0.743963
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.238906 Loss1: 0.571269 Loss2: 0.667638
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.250384 Loss1: 0.585894 Loss2: 0.664490
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.247543 Loss1: 0.581216 Loss2: 0.666328
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.230841 Loss1: 0.564382 Loss2: 0.666458
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193202 Loss1: 0.525371 Loss2: 0.667830
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204858 Loss1: 0.537621 Loss2: 0.667237
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191051 Loss1: 0.524164 Loss2: 0.666887
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.204976 Loss1: 0.532862 Loss2: 0.672114
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179300 Loss1: 0.508952 Loss2: 0.670348
-(DefaultActor pid=1831567) >> Training accuracy: 0.843750
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.204735 Loss1: 0.489710 Loss2: 0.715025
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.062048 Loss1: 0.422620 Loss2: 0.639428
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.060948 Loss1: 0.422593 Loss2: 0.638356
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.035464 Loss1: 0.401393 Loss2: 0.634070
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.055824 Loss1: 0.417440 Loss2: 0.638384
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.031942 Loss1: 0.393395 Loss2: 0.638547
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019894 Loss1: 0.378374 Loss2: 0.641520
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030693 Loss1: 0.391097 Loss2: 0.639596
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.034344 Loss1: 0.391649 Loss2: 0.642694
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.006119 Loss1: 0.365380 Loss2: 0.640739
-(DefaultActor pid=1831567) >> Training accuracy: 0.859761
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389665 Loss1: 0.581837 Loss2: 0.807828
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.323512 Loss1: 0.566961 Loss2: 0.756552
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.319457 Loss1: 0.563094 Loss2: 0.756363
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316199 Loss1: 0.559180 Loss2: 0.757019
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.313958 Loss1: 0.553855 Loss2: 0.760103
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.307890 Loss1: 0.548633 Loss2: 0.759257
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305430 Loss1: 0.545646 Loss2: 0.759784
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.292457 Loss1: 0.535388 Loss2: 0.757069
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.276833 Loss1: 0.521042 Loss2: 0.755791
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.287689 Loss1: 0.525843 Loss2: 0.761846
-(DefaultActor pid=1831567) >> Training accuracy: 0.811756
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.508866 Loss1: 0.764544 Loss2: 0.744323
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.371126 Loss1: 0.715040 Loss2: 0.656086
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.372522 Loss1: 0.716343 Loss2: 0.656179
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.388818 Loss1: 0.726505 Loss2: 0.662313
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.347988 Loss1: 0.688073 Loss2: 0.659915
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362443 Loss1: 0.703202 Loss2: 0.659242
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.377674 Loss1: 0.714576 Loss2: 0.663098
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.352069 Loss1: 0.689579 Loss2: 0.662490
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.353436 Loss1: 0.688892 Loss2: 0.664544
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.342448 Loss1: 0.676406 Loss2: 0.666042
-(DefaultActor pid=1831567) >> Training accuracy: 0.757473
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.525279 Loss1: 0.744128 Loss2: 0.781151
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.390036 Loss1: 0.705340 Loss2: 0.684696
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.397514 Loss1: 0.715276 Loss2: 0.682238
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.368793 Loss1: 0.685967 Loss2: 0.682826
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.386320 Loss1: 0.703586 Loss2: 0.682734
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.358623 Loss1: 0.671915 Loss2: 0.686708
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.331440 Loss1: 0.648093 Loss2: 0.683347
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.352731 Loss1: 0.669892 Loss2: 0.682839
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.337650 Loss1: 0.652281 Loss2: 0.685369
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.320569 Loss1: 0.635530 Loss2: 0.685038
-(DefaultActor pid=1831567) >> Training accuracy: 0.774254
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.349911 Loss1: 0.601862 Loss2: 0.748049
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.244792 Loss1: 0.575617 Loss2: 0.669175
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.228794 Loss1: 0.562093 Loss2: 0.666701
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.222719 Loss1: 0.551756 Loss2: 0.670963
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218550 Loss1: 0.545575 Loss2: 0.672974
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.217023 Loss1: 0.542746 Loss2: 0.674276
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.211359 Loss1: 0.539349 Loss2: 0.672011
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.211067 Loss1: 0.534872 Loss2: 0.676195
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196954 Loss1: 0.521673 Loss2: 0.675281
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193335 Loss1: 0.519793 Loss2: 0.673542
-(DefaultActor pid=1831567) >> Training accuracy: 0.831209
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.239219 Loss1: 0.478654 Loss2: 0.760565
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.105586 Loss1: 0.431928 Loss2: 0.673658
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.105641 Loss1: 0.430878 Loss2: 0.674762
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.089399 Loss1: 0.415559 Loss2: 0.673841
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085533 Loss1: 0.413036 Loss2: 0.672497
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.061133 Loss1: 0.388789 Loss2: 0.672344
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.064830 Loss1: 0.389143 Loss2: 0.675687
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.055497 Loss1: 0.379848 Loss2: 0.675649
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.059802 Loss1: 0.384676 Loss2: 0.675127
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.045076 Loss1: 0.367545 Loss2: 0.677532
-(DefaultActor pid=1831567) >> Training accuracy: 0.865355
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.490684 Loss1: 0.724729 Loss2: 0.765955
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.361970 Loss1: 0.694516 Loss2: 0.667454
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337429 Loss1: 0.672573 Loss2: 0.664856
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.322292 Loss1: 0.652702 Loss2: 0.669590
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.319199 Loss1: 0.649030 Loss2: 0.670169
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.305330 Loss1: 0.634609 Loss2: 0.670722
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295628 Loss1: 0.625312 Loss2: 0.670317
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.279888 Loss1: 0.606862 Loss2: 0.673026
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.273036 Loss1: 0.599284 Loss2: 0.673751
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.286792 Loss1: 0.613728 Loss2: 0.673064
-(DefaultActor pid=1831567) >> Training accuracy: 0.791667
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362190 Loss1: 0.603522 Loss2: 0.758669
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206981 Loss1: 0.557713 Loss2: 0.649268
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.179263 Loss1: 0.530491 Loss2: 0.648772
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.195216 Loss1: 0.545260 Loss2: 0.649956
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170458 Loss1: 0.520480 Loss2: 0.649978
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157299 Loss1: 0.505477 Loss2: 0.651822
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.150330 Loss1: 0.495449 Loss2: 0.654880
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.179526 Loss1: 0.524076 Loss2: 0.655450
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.151557 Loss1: 0.499479 Loss2: 0.652079
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.119880 Loss1: 0.464697 Loss2: 0.655184
-(DefaultActor pid=1831567) >> Training accuracy: 0.830508
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353582 Loss1: 0.619189 Loss2: 0.734392
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245153 Loss1: 0.580618 Loss2: 0.664535
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251420 Loss1: 0.588639 Loss2: 0.662780
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.241866 Loss1: 0.580499 Loss2: 0.661367
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.212292 Loss1: 0.551700 Loss2: 0.660592
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.208979 Loss1: 0.545254 Loss2: 0.663724
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.229008 Loss1: 0.563675 Loss2: 0.665333
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.199629 Loss1: 0.535291 Loss2: 0.664339
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175580 Loss1: 0.511959 Loss2: 0.663621
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.187356 Loss1: 0.521760 Loss2: 0.665596
-[2023-09-27 12:18:11,550][flwr][DEBUG] - fit_round 44 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.828887
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.688200
-[2023-09-27 12:19:08,031][flwr][INFO] - fit progress: (44, 0.8899790141910029, {'accuracy': 0.6882}, 21680.867300234735)
-[2023-09-27 12:19:08,032][flwr][DEBUG] - evaluate_round 44: strategy sampled 10 clients (out of 10)
-[2023-09-27 12:19:52,431][flwr][DEBUG] - evaluate_round 44 received 10 results and 0 failures
-[2023-09-27 12:19:52,432][flwr][DEBUG] - fit_round 45: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.540662 Loss1: 0.766665 Loss2: 0.773996
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.388599 Loss1: 0.708834 Loss2: 0.679764
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.389373 Loss1: 0.710460 Loss2: 0.678913
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.352199 Loss1: 0.674390 Loss2: 0.677809
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.378257 Loss1: 0.693156 Loss2: 0.685101
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.335052 Loss1: 0.654737 Loss2: 0.680315
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.345262 Loss1: 0.662370 Loss2: 0.682892
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.341220 Loss1: 0.656379 Loss2: 0.684840
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.335511 Loss1: 0.650784 Loss2: 0.684727
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.354078 Loss1: 0.667607 Loss2: 0.686471
-(DefaultActor pid=1831567) >> Training accuracy: 0.753731
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.396841 Loss1: 0.628455 Loss2: 0.768385
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.262142 Loss1: 0.581955 Loss2: 0.680187
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.225278 Loss1: 0.546578 Loss2: 0.678700
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223267 Loss1: 0.543521 Loss2: 0.679746
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.212318 Loss1: 0.533081 Loss2: 0.679237
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193569 Loss1: 0.514401 Loss2: 0.679167
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192670 Loss1: 0.512659 Loss2: 0.680011
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191915 Loss1: 0.511175 Loss2: 0.680739
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188025 Loss1: 0.506817 Loss2: 0.681207
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189407 Loss1: 0.505589 Loss2: 0.683818
-(DefaultActor pid=1831567) >> Training accuracy: 0.836965
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.530607 Loss1: 0.750426 Loss2: 0.780181
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347522 Loss1: 0.679951 Loss2: 0.667571
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334192 Loss1: 0.663128 Loss2: 0.671064
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.327108 Loss1: 0.655335 Loss2: 0.671773
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.288815 Loss1: 0.619792 Loss2: 0.669022
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.333040 Loss1: 0.657487 Loss2: 0.675553
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309377 Loss1: 0.634038 Loss2: 0.675339
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300262 Loss1: 0.629134 Loss2: 0.671127
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.278544 Loss1: 0.604739 Loss2: 0.673805
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.270571 Loss1: 0.594894 Loss2: 0.675677
-(DefaultActor pid=1831567) >> Training accuracy: 0.787007
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.371460 Loss1: 0.635608 Loss2: 0.735852
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.241619 Loss1: 0.576746 Loss2: 0.664873
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.233715 Loss1: 0.570902 Loss2: 0.662813
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199053 Loss1: 0.536031 Loss2: 0.663022
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.214607 Loss1: 0.550523 Loss2: 0.664084
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.190943 Loss1: 0.528228 Loss2: 0.662715
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182015 Loss1: 0.515897 Loss2: 0.666117
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202690 Loss1: 0.538567 Loss2: 0.664123
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185481 Loss1: 0.519197 Loss2: 0.666285
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180859 Loss1: 0.513444 Loss2: 0.667416
-(DefaultActor pid=1831567) >> Training accuracy: 0.827172
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.219198 Loss1: 0.485625 Loss2: 0.733573
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.089277 Loss1: 0.433301 Loss2: 0.655976
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.083246 Loss1: 0.429113 Loss2: 0.654133
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.071485 Loss1: 0.416817 Loss2: 0.654668
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.082756 Loss1: 0.426540 Loss2: 0.656216
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.033818 Loss1: 0.379865 Loss2: 0.653953
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.038560 Loss1: 0.383238 Loss2: 0.655322
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.038598 Loss1: 0.382349 Loss2: 0.656249
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.035807 Loss1: 0.378567 Loss2: 0.657240
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.038037 Loss1: 0.380601 Loss2: 0.657435
-(DefaultActor pid=1831567) >> Training accuracy: 0.882137
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.345236 Loss1: 0.595153 Loss2: 0.750082
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.269448 Loss1: 0.568871 Loss2: 0.700577
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.260760 Loss1: 0.561397 Loss2: 0.699363
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.245752 Loss1: 0.547979 Loss2: 0.697773
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.251896 Loss1: 0.552040 Loss2: 0.699856
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.242196 Loss1: 0.540327 Loss2: 0.701869
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.245368 Loss1: 0.545177 Loss2: 0.700192
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.236898 Loss1: 0.538537 Loss2: 0.698361
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239375 Loss1: 0.535016 Loss2: 0.704359
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.236435 Loss1: 0.533760 Loss2: 0.702676
-(DefaultActor pid=1831567) >> Training accuracy: 0.824033
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.415570 Loss1: 0.626142 Loss2: 0.789429
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225684 Loss1: 0.540442 Loss2: 0.685242
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.227072 Loss1: 0.541040 Loss2: 0.686032
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.195730 Loss1: 0.512288 Loss2: 0.683442
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210866 Loss1: 0.524881 Loss2: 0.685985
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.213600 Loss1: 0.525740 Loss2: 0.687860
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.185362 Loss1: 0.496698 Loss2: 0.688664
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.171893 Loss1: 0.486507 Loss2: 0.685386
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161885 Loss1: 0.473174 Loss2: 0.688711
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174778 Loss1: 0.486245 Loss2: 0.688533
-(DefaultActor pid=1831567) >> Training accuracy: 0.833951
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341027 Loss1: 0.580952 Loss2: 0.760075
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259609 Loss1: 0.578007 Loss2: 0.681602
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.271452 Loss1: 0.586594 Loss2: 0.684858
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.252278 Loss1: 0.570252 Loss2: 0.682026
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.231330 Loss1: 0.551461 Loss2: 0.679869
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222371 Loss1: 0.539899 Loss2: 0.682472
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.241424 Loss1: 0.557535 Loss2: 0.683888
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.209941 Loss1: 0.525951 Loss2: 0.683990
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.218129 Loss1: 0.533416 Loss2: 0.684713
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214609 Loss1: 0.527983 Loss2: 0.686626
-(DefaultActor pid=1831567) >> Training accuracy: 0.822115
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.323592 Loss1: 0.507285 Loss2: 0.816307
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.138096 Loss1: 0.417069 Loss2: 0.721027
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.132181 Loss1: 0.420797 Loss2: 0.711384
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.127307 Loss1: 0.414334 Loss2: 0.712972
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.118867 Loss1: 0.402282 Loss2: 0.716584
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.103603 Loss1: 0.392847 Loss2: 0.710756
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.095179 Loss1: 0.382803 Loss2: 0.712376
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.105662 Loss1: 0.390032 Loss2: 0.715629
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.090869 Loss1: 0.373630 Loss2: 0.717240
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.096051 Loss1: 0.378974 Loss2: 0.717077
-(DefaultActor pid=1831567) >> Training accuracy: 0.874614
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.539240 Loss1: 0.773452 Loss2: 0.765789
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.433056 Loss1: 0.750285 Loss2: 0.682771
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.397252 Loss1: 0.712060 Loss2: 0.685192
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.399261 Loss1: 0.716638 Loss2: 0.682623
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.387986 Loss1: 0.703749 Loss2: 0.684237
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.382628 Loss1: 0.696595 Loss2: 0.686033
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.383337 Loss1: 0.693777 Loss2: 0.689560
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.393373 Loss1: 0.701217 Loss2: 0.692157
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.366409 Loss1: 0.675414 Loss2: 0.690996
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.357304 Loss1: 0.665207 Loss2: 0.692097
-[2023-09-27 12:34:19,391][flwr][DEBUG] - fit_round 45 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.774004
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.690600
-[2023-09-27 12:34:21,047][flwr][INFO] - fit progress: (45, 0.8930932635697313, {'accuracy': 0.6906}, 22593.883164820727)
-[2023-09-27 12:34:21,047][flwr][DEBUG] - evaluate_round 45: strategy sampled 10 clients (out of 10)
-[2023-09-27 12:34:51,964][flwr][DEBUG] - evaluate_round 45 received 10 results and 0 failures
-[2023-09-27 12:34:51,965][flwr][DEBUG] - fit_round 46: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.519174 Loss1: 0.774902 Loss2: 0.744272
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.400126 Loss1: 0.742099 Loss2: 0.658027
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.431027 Loss1: 0.769044 Loss2: 0.661982
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.381556 Loss1: 0.722664 Loss2: 0.658892
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.365551 Loss1: 0.707898 Loss2: 0.657653
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.371698 Loss1: 0.708155 Loss2: 0.663543
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.347465 Loss1: 0.686076 Loss2: 0.661389
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.332567 Loss1: 0.669776 Loss2: 0.662792
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.330893 Loss1: 0.670076 Loss2: 0.660817
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309459 Loss1: 0.647001 Loss2: 0.662457
-(DefaultActor pid=1831567) >> Training accuracy: 0.783967
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.260663 Loss1: 0.512964 Loss2: 0.747700
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099822 Loss1: 0.437587 Loss2: 0.662236
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.076170 Loss1: 0.414168 Loss2: 0.662002
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.092802 Loss1: 0.430571 Loss2: 0.662231
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.060970 Loss1: 0.401718 Loss2: 0.659251
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.071738 Loss1: 0.409101 Loss2: 0.662637
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.065166 Loss1: 0.403180 Loss2: 0.661986
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.033784 Loss1: 0.371340 Loss2: 0.662444
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028840 Loss1: 0.367701 Loss2: 0.661139
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.072237 Loss1: 0.404997 Loss2: 0.667239
-(DefaultActor pid=1831567) >> Training accuracy: 0.866898
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.389219 Loss1: 0.612979 Loss2: 0.776240
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211821 Loss1: 0.543833 Loss2: 0.667988
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203491 Loss1: 0.539457 Loss2: 0.664034
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184746 Loss1: 0.521667 Loss2: 0.663079
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.155653 Loss1: 0.490296 Loss2: 0.665358
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162977 Loss1: 0.496150 Loss2: 0.666827
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.160500 Loss1: 0.490183 Loss2: 0.670317
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158081 Loss1: 0.488701 Loss2: 0.669380
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.145686 Loss1: 0.475495 Loss2: 0.670192
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.139441 Loss1: 0.469494 Loss2: 0.669947
-(DefaultActor pid=1831567) >> Training accuracy: 0.838453
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359411 Loss1: 0.615811 Loss2: 0.743600
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258452 Loss1: 0.589259 Loss2: 0.669194
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231762 Loss1: 0.565948 Loss2: 0.665814
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184424 Loss1: 0.518908 Loss2: 0.665515
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201148 Loss1: 0.534082 Loss2: 0.667066
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.197665 Loss1: 0.525448 Loss2: 0.672217
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171917 Loss1: 0.501082 Loss2: 0.670836
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169320 Loss1: 0.496711 Loss2: 0.672609
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193441 Loss1: 0.521840 Loss2: 0.671601
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.173736 Loss1: 0.501473 Loss2: 0.672263
-(DefaultActor pid=1831567) >> Training accuracy: 0.839227
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359361 Loss1: 0.609979 Loss2: 0.749381
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.233192 Loss1: 0.559281 Loss2: 0.673911
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231312 Loss1: 0.560300 Loss2: 0.671012
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.246056 Loss1: 0.573384 Loss2: 0.672671
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.212402 Loss1: 0.539147 Loss2: 0.673255
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191013 Loss1: 0.517436 Loss2: 0.673577
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.222504 Loss1: 0.543775 Loss2: 0.678728
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191320 Loss1: 0.516214 Loss2: 0.675106
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.200384 Loss1: 0.522521 Loss2: 0.677863
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189134 Loss1: 0.510812 Loss2: 0.678322
-(DefaultActor pid=1831567) >> Training accuracy: 0.840144
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.373018 Loss1: 0.627958 Loss2: 0.745060
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.246678 Loss1: 0.573146 Loss2: 0.673532
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231086 Loss1: 0.562501 Loss2: 0.668585
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223838 Loss1: 0.552527 Loss2: 0.671311
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204021 Loss1: 0.531714 Loss2: 0.672307
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.221638 Loss1: 0.549716 Loss2: 0.671923
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.201495 Loss1: 0.530530 Loss2: 0.670965
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200365 Loss1: 0.527735 Loss2: 0.672630
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.195594 Loss1: 0.522781 Loss2: 0.672813
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.187842 Loss1: 0.514576 Loss2: 0.673266
-(DefaultActor pid=1831567) >> Training accuracy: 0.818598
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.240206 Loss1: 0.479301 Loss2: 0.760905
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.100128 Loss1: 0.428951 Loss2: 0.671177
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.081063 Loss1: 0.411598 Loss2: 0.669465
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074939 Loss1: 0.404252 Loss2: 0.670687
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.061138 Loss1: 0.394088 Loss2: 0.667049
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.047271 Loss1: 0.377164 Loss2: 0.670107
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050661 Loss1: 0.379122 Loss2: 0.671539
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.047839 Loss1: 0.377821 Loss2: 0.670018
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.038265 Loss1: 0.367055 Loss2: 0.671209
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.040316 Loss1: 0.367383 Loss2: 0.672933
-(DefaultActor pid=1831567) >> Training accuracy: 0.874807
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.538479 Loss1: 0.761922 Loss2: 0.776557
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.386540 Loss1: 0.705572 Loss2: 0.680968
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.366569 Loss1: 0.687731 Loss2: 0.678838
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.376170 Loss1: 0.698747 Loss2: 0.677423
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.366040 Loss1: 0.682276 Loss2: 0.683764
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.352730 Loss1: 0.670123 Loss2: 0.682606
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.362248 Loss1: 0.679057 Loss2: 0.683191
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347620 Loss1: 0.664202 Loss2: 0.683418
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.342364 Loss1: 0.657592 Loss2: 0.684772
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.300903 Loss1: 0.620276 Loss2: 0.680627
-(DefaultActor pid=1831567) >> Training accuracy: 0.768190
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360037 Loss1: 0.584552 Loss2: 0.775485
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.291671 Loss1: 0.568758 Loss2: 0.722913
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.278329 Loss1: 0.556018 Loss2: 0.722312
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.265293 Loss1: 0.542868 Loss2: 0.722426
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246025 Loss1: 0.523246 Loss2: 0.722779
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.282761 Loss1: 0.555708 Loss2: 0.727054
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258317 Loss1: 0.530418 Loss2: 0.727898
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.259266 Loss1: 0.532805 Loss2: 0.726461
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.243714 Loss1: 0.517646 Loss2: 0.726068
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.232983 Loss1: 0.507193 Loss2: 0.725790
-(DefaultActor pid=1831567) >> Training accuracy: 0.821181
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.500969 Loss1: 0.735485 Loss2: 0.765484
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.365670 Loss1: 0.704062 Loss2: 0.661608
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.320685 Loss1: 0.656183 Loss2: 0.664502
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.305818 Loss1: 0.641446 Loss2: 0.664372
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.315027 Loss1: 0.649673 Loss2: 0.665354
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.309656 Loss1: 0.638381 Loss2: 0.671275
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298077 Loss1: 0.630287 Loss2: 0.667790
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276976 Loss1: 0.608901 Loss2: 0.668074
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.273642 Loss1: 0.606643 Loss2: 0.666998
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259826 Loss1: 0.588602 Loss2: 0.671224
-[2023-09-27 12:41:49,315][flwr][DEBUG] - fit_round 46 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.812500
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.688100
-[2023-09-27 12:41:50,634][flwr][INFO] - fit progress: (46, 0.896309151150548, {'accuracy': 0.6881}, 23043.470007136)
-[2023-09-27 12:41:50,634][flwr][DEBUG] - evaluate_round 46: strategy sampled 10 clients (out of 10)
-[2023-09-27 12:42:21,692][flwr][DEBUG] - evaluate_round 46 received 10 results and 0 failures
-[2023-09-27 12:42:21,693][flwr][DEBUG] - fit_round 47: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384295 Loss1: 0.607016 Loss2: 0.777279
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.276272 Loss1: 0.583376 Loss2: 0.692896
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236611 Loss1: 0.545478 Loss2: 0.691132
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.224907 Loss1: 0.535143 Loss2: 0.689764
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.226826 Loss1: 0.536168 Loss2: 0.690658
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.202867 Loss1: 0.513156 Loss2: 0.689711
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.227808 Loss1: 0.534605 Loss2: 0.693204
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.206042 Loss1: 0.511145 Loss2: 0.694897
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.201413 Loss1: 0.507216 Loss2: 0.694197
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.215941 Loss1: 0.518073 Loss2: 0.697867
-(DefaultActor pid=1831567) >> Training accuracy: 0.833676
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.487276 Loss1: 0.730478 Loss2: 0.756798
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.371065 Loss1: 0.711278 Loss2: 0.659787
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337291 Loss1: 0.678366 Loss2: 0.658925
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.294634 Loss1: 0.637075 Loss2: 0.657559
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.288455 Loss1: 0.629864 Loss2: 0.658591
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.285126 Loss1: 0.625333 Loss2: 0.659793
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.278102 Loss1: 0.619189 Loss2: 0.658913
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.296242 Loss1: 0.633690 Loss2: 0.662552
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.285275 Loss1: 0.623576 Loss2: 0.661699
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.257414 Loss1: 0.593378 Loss2: 0.664036
-(DefaultActor pid=1831567) >> Training accuracy: 0.772204
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.548712 Loss1: 0.776832 Loss2: 0.771880
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.432132 Loss1: 0.746481 Loss2: 0.685651
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.408598 Loss1: 0.723436 Loss2: 0.685163
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.398733 Loss1: 0.710109 Loss2: 0.688624
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.384542 Loss1: 0.696232 Loss2: 0.688310
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.402486 Loss1: 0.710397 Loss2: 0.692089
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.363751 Loss1: 0.675999 Loss2: 0.687752
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.396467 Loss1: 0.703564 Loss2: 0.692902
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.344741 Loss1: 0.655316 Loss2: 0.689425
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.342790 Loss1: 0.651785 Loss2: 0.691006
-(DefaultActor pid=1831567) >> Training accuracy: 0.783741
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360427 Loss1: 0.602068 Loss2: 0.758359
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.193507 Loss1: 0.538991 Loss2: 0.654516
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.227278 Loss1: 0.571734 Loss2: 0.655544
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177819 Loss1: 0.528541 Loss2: 0.649278
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170937 Loss1: 0.518509 Loss2: 0.652428
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157021 Loss1: 0.504325 Loss2: 0.652696
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.157342 Loss1: 0.502975 Loss2: 0.654366
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.147184 Loss1: 0.489976 Loss2: 0.657208
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.116643 Loss1: 0.462239 Loss2: 0.654404
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.132321 Loss1: 0.475815 Loss2: 0.656506
-(DefaultActor pid=1831567) >> Training accuracy: 0.827595
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339867 Loss1: 0.604926 Loss2: 0.734941
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225687 Loss1: 0.563915 Loss2: 0.661772
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212759 Loss1: 0.549012 Loss2: 0.663748
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212350 Loss1: 0.552455 Loss2: 0.659895
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187653 Loss1: 0.525770 Loss2: 0.661882
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.223646 Loss1: 0.558160 Loss2: 0.665486
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.201068 Loss1: 0.535620 Loss2: 0.665448
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.184951 Loss1: 0.517343 Loss2: 0.667607
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.215626 Loss1: 0.545267 Loss2: 0.670359
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.168145 Loss1: 0.498219 Loss2: 0.669926
-(DefaultActor pid=1831567) >> Training accuracy: 0.835737
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.312320 Loss1: 0.584557 Loss2: 0.727763
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245620 Loss1: 0.560183 Loss2: 0.685437
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.220902 Loss1: 0.540965 Loss2: 0.679936
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.247869 Loss1: 0.560169 Loss2: 0.687700
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218779 Loss1: 0.533453 Loss2: 0.685326
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.230759 Loss1: 0.543237 Loss2: 0.687522
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.236369 Loss1: 0.547459 Loss2: 0.688910
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.237779 Loss1: 0.548806 Loss2: 0.688973
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211841 Loss1: 0.526177 Loss2: 0.685664
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.228884 Loss1: 0.538582 Loss2: 0.690302
-(DefaultActor pid=1831567) >> Training accuracy: 0.829737
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.273799 Loss1: 0.501700 Loss2: 0.772099
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.108187 Loss1: 0.429030 Loss2: 0.679157
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086024 Loss1: 0.409148 Loss2: 0.676877
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.101473 Loss1: 0.424002 Loss2: 0.677471
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.082294 Loss1: 0.404859 Loss2: 0.677435
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.072927 Loss1: 0.397748 Loss2: 0.675179
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.065087 Loss1: 0.387785 Loss2: 0.677303
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.058873 Loss1: 0.377618 Loss2: 0.681254
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042787 Loss1: 0.364538 Loss2: 0.678250
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.063303 Loss1: 0.382388 Loss2: 0.680915
-(DefaultActor pid=1831567) >> Training accuracy: 0.871142
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.230503 Loss1: 0.477037 Loss2: 0.753466
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.097851 Loss1: 0.425837 Loss2: 0.672014
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.068180 Loss1: 0.398314 Loss2: 0.669866
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.090348 Loss1: 0.418064 Loss2: 0.672284
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.065906 Loss1: 0.397609 Loss2: 0.668297
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.072482 Loss1: 0.399875 Loss2: 0.672607
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.040333 Loss1: 0.368301 Loss2: 0.672031
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.072307 Loss1: 0.399421 Loss2: 0.672886
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.049362 Loss1: 0.377029 Loss2: 0.672333
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.043317 Loss1: 0.371580 Loss2: 0.671737
-(DefaultActor pid=1831567) >> Training accuracy: 0.868441
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339192 Loss1: 0.624902 Loss2: 0.714291
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212612 Loss1: 0.564364 Loss2: 0.648248
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.197193 Loss1: 0.554338 Loss2: 0.642855
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.201603 Loss1: 0.557223 Loss2: 0.644380
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.202087 Loss1: 0.552934 Loss2: 0.649153
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181033 Loss1: 0.534887 Loss2: 0.646146
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175362 Loss1: 0.526994 Loss2: 0.648368
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.165377 Loss1: 0.516926 Loss2: 0.648451
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.158411 Loss1: 0.513205 Loss2: 0.645206
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150663 Loss1: 0.504427 Loss2: 0.646236
-(DefaultActor pid=1831567) >> Training accuracy: 0.829078
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.509377 Loss1: 0.735399 Loss2: 0.773978
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.390224 Loss1: 0.713422 Loss2: 0.676802
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.365220 Loss1: 0.691335 Loss2: 0.673885
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.390660 Loss1: 0.713902 Loss2: 0.676758
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.352261 Loss1: 0.675085 Loss2: 0.677175
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.340058 Loss1: 0.663138 Loss2: 0.676919
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.328016 Loss1: 0.649673 Loss2: 0.678343
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.324667 Loss1: 0.644580 Loss2: 0.680087
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.316094 Loss1: 0.635028 Loss2: 0.681066
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.315193 Loss1: 0.631019 Loss2: 0.684174
-(DefaultActor pid=1831567) >> Training accuracy: 0.756996
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 12:49:03,919][flwr][DEBUG] - fit_round 47 received 10 results and 0 failures
->> Test accuracy: 0.695000
-[2023-09-27 12:49:05,426][flwr][INFO] - fit progress: (47, 0.8794247569938818, {'accuracy': 0.695}, 23478.262214047834)
-[2023-09-27 12:49:05,426][flwr][DEBUG] - evaluate_round 47: strategy sampled 10 clients (out of 10)
-[2023-09-27 12:49:36,775][flwr][DEBUG] - evaluate_round 47 received 10 results and 0 failures
-[2023-09-27 12:49:36,775][flwr][DEBUG] - fit_round 48: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.333916 Loss1: 0.587240 Loss2: 0.746676
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.247359 Loss1: 0.571602 Loss2: 0.675758
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238878 Loss1: 0.566366 Loss2: 0.672513
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.249689 Loss1: 0.571757 Loss2: 0.677931
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215672 Loss1: 0.543565 Loss2: 0.672107
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.216042 Loss1: 0.536988 Loss2: 0.679054
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.208428 Loss1: 0.528471 Loss2: 0.679957
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.216201 Loss1: 0.536138 Loss2: 0.680063
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178854 Loss1: 0.502305 Loss2: 0.676550
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.192472 Loss1: 0.512855 Loss2: 0.679617
-(DefaultActor pid=1831567) >> Training accuracy: 0.819912
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.528928 Loss1: 0.757035 Loss2: 0.771893
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.343505 Loss1: 0.670498 Loss2: 0.673007
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.350779 Loss1: 0.679158 Loss2: 0.671620
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.306773 Loss1: 0.637025 Loss2: 0.669748
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.309107 Loss1: 0.637871 Loss2: 0.671236
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.294317 Loss1: 0.618603 Loss2: 0.675714
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.302349 Loss1: 0.627284 Loss2: 0.675065
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.291708 Loss1: 0.614647 Loss2: 0.677061
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.319332 Loss1: 0.641353 Loss2: 0.677979
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.272179 Loss1: 0.594260 Loss2: 0.677919
-(DefaultActor pid=1831567) >> Training accuracy: 0.794956
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.247262 Loss1: 0.494544 Loss2: 0.752718
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.124499 Loss1: 0.444209 Loss2: 0.680290
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.114144 Loss1: 0.434558 Loss2: 0.679586
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.086074 Loss1: 0.407951 Loss2: 0.678123
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.082534 Loss1: 0.406061 Loss2: 0.676473
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.065492 Loss1: 0.388642 Loss2: 0.676849
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.074536 Loss1: 0.396481 Loss2: 0.678054
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.062223 Loss1: 0.384206 Loss2: 0.678018
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.072424 Loss1: 0.395530 Loss2: 0.676894
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.058028 Loss1: 0.381149 Loss2: 0.676879
-(DefaultActor pid=1831567) >> Training accuracy: 0.871721
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.404530 Loss1: 0.606017 Loss2: 0.798513
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.241847 Loss1: 0.548318 Loss2: 0.693529
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214050 Loss1: 0.523287 Loss2: 0.690763
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212708 Loss1: 0.522048 Loss2: 0.690661
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195004 Loss1: 0.502398 Loss2: 0.692606
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211168 Loss1: 0.516876 Loss2: 0.694292
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182983 Loss1: 0.484397 Loss2: 0.698586
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.178084 Loss1: 0.482187 Loss2: 0.695897
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178520 Loss1: 0.481317 Loss2: 0.697203
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200482 Loss1: 0.500929 Loss2: 0.699552
-(DefaultActor pid=1831567) >> Training accuracy: 0.843485
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.514834 Loss1: 0.772101 Loss2: 0.742733
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.389011 Loss1: 0.735454 Loss2: 0.653557
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.351930 Loss1: 0.697902 Loss2: 0.654027
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.344360 Loss1: 0.688990 Loss2: 0.655371
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.360047 Loss1: 0.703492 Loss2: 0.656555
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.342285 Loss1: 0.681891 Loss2: 0.660393
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.311529 Loss1: 0.653902 Loss2: 0.657628
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334732 Loss1: 0.677209 Loss2: 0.657523
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.332793 Loss1: 0.674072 Loss2: 0.658721
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.346838 Loss1: 0.684227 Loss2: 0.662611
-(DefaultActor pid=1831567) >> Training accuracy: 0.769248
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.361066 Loss1: 0.579990 Loss2: 0.781076
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.302655 Loss1: 0.566947 Loss2: 0.735707
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.282802 Loss1: 0.549858 Loss2: 0.732944
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.274998 Loss1: 0.543133 Loss2: 0.731865
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.258763 Loss1: 0.527591 Loss2: 0.731173
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.280331 Loss1: 0.546713 Loss2: 0.733619
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.277452 Loss1: 0.545069 Loss2: 0.732383
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.251023 Loss1: 0.518762 Loss2: 0.732261
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.266558 Loss1: 0.531170 Loss2: 0.735389
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.254253 Loss1: 0.519187 Loss2: 0.735066
-(DefaultActor pid=1831567) >> Training accuracy: 0.829241
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.205386 Loss1: 0.461198 Loss2: 0.744188
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.078932 Loss1: 0.420410 Loss2: 0.658522
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.067040 Loss1: 0.410279 Loss2: 0.656760
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.073094 Loss1: 0.413641 Loss2: 0.659452
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.053354 Loss1: 0.396938 Loss2: 0.656416
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.032002 Loss1: 0.376341 Loss2: 0.655660
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.044299 Loss1: 0.385338 Loss2: 0.658962
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.045986 Loss1: 0.386248 Loss2: 0.659737
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.047094 Loss1: 0.384948 Loss2: 0.662145
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.029627 Loss1: 0.369067 Loss2: 0.660561
-(DefaultActor pid=1831567) >> Training accuracy: 0.866898
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.308347 Loss1: 0.596310 Loss2: 0.712038
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.183224 Loss1: 0.542981 Loss2: 0.640243
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191324 Loss1: 0.551454 Loss2: 0.639870
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190049 Loss1: 0.549459 Loss2: 0.640590
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150015 Loss1: 0.506982 Loss2: 0.643033
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168075 Loss1: 0.522905 Loss2: 0.645169
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.143183 Loss1: 0.498250 Loss2: 0.644933
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.152376 Loss1: 0.505206 Loss2: 0.647170
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183341 Loss1: 0.534828 Loss2: 0.648513
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.122925 Loss1: 0.475684 Loss2: 0.647241
-(DefaultActor pid=1831567) >> Training accuracy: 0.834910
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.518945 Loss1: 0.768758 Loss2: 0.750188
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.392639 Loss1: 0.731993 Loss2: 0.660646
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.345411 Loss1: 0.687412 Loss2: 0.658000
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331650 Loss1: 0.674969 Loss2: 0.656681
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.319966 Loss1: 0.663243 Loss2: 0.656722
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.298611 Loss1: 0.641174 Loss2: 0.657437
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.303569 Loss1: 0.642780 Loss2: 0.660789
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.307967 Loss1: 0.646424 Loss2: 0.661543
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.311285 Loss1: 0.645507 Loss2: 0.665778
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304477 Loss1: 0.643216 Loss2: 0.661261
-(DefaultActor pid=1831567) >> Training accuracy: 0.769823
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382953 Loss1: 0.629597 Loss2: 0.753356
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245300 Loss1: 0.562753 Loss2: 0.682547
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262615 Loss1: 0.581906 Loss2: 0.680709
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226302 Loss1: 0.544830 Loss2: 0.681471
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.227932 Loss1: 0.546613 Loss2: 0.681319
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218532 Loss1: 0.537676 Loss2: 0.680856
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192419 Loss1: 0.509418 Loss2: 0.683001
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204432 Loss1: 0.521829 Loss2: 0.682603
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.222712 Loss1: 0.536010 Loss2: 0.686702
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.203882 Loss1: 0.518391 Loss2: 0.685490
-[2023-09-27 12:56:20,878][flwr][DEBUG] - fit_round 48 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.827934
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.694200
-[2023-09-27 12:56:22,569][flwr][INFO] - fit progress: (48, 0.8850831008566835, {'accuracy': 0.6942}, 23915.405494552106)
-[2023-09-27 12:56:22,570][flwr][DEBUG] - evaluate_round 48: strategy sampled 10 clients (out of 10)
-[2023-09-27 12:56:53,863][flwr][DEBUG] - evaluate_round 48 received 10 results and 0 failures
-[2023-09-27 12:56:53,864][flwr][DEBUG] - fit_round 49: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.205831 Loss1: 0.461481 Loss2: 0.744350
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.103200 Loss1: 0.438027 Loss2: 0.665172
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086822 Loss1: 0.420793 Loss2: 0.666029
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.075835 Loss1: 0.413296 Loss2: 0.662540
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.040837 Loss1: 0.377994 Loss2: 0.662843
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.040867 Loss1: 0.377129 Loss2: 0.663738
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.034103 Loss1: 0.368695 Loss2: 0.665408
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.034367 Loss1: 0.368031 Loss2: 0.666336
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.051984 Loss1: 0.383545 Loss2: 0.668439
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.044407 Loss1: 0.377266 Loss2: 0.667141
-(DefaultActor pid=1831567) >> Training accuracy: 0.859375
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.530797 Loss1: 0.770342 Loss2: 0.760454
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.402810 Loss1: 0.726040 Loss2: 0.676770
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.392216 Loss1: 0.716201 Loss2: 0.676015
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.376797 Loss1: 0.697660 Loss2: 0.679137
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.383840 Loss1: 0.702259 Loss2: 0.681581
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.378496 Loss1: 0.699156 Loss2: 0.679340
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.345480 Loss1: 0.663654 Loss2: 0.681826
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.369513 Loss1: 0.687914 Loss2: 0.681600
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.360079 Loss1: 0.675511 Loss2: 0.684569
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.357917 Loss1: 0.674021 Loss2: 0.683896
-(DefaultActor pid=1831567) >> Training accuracy: 0.774457
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.343907 Loss1: 0.586201 Loss2: 0.757705
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.262711 Loss1: 0.555317 Loss2: 0.707394
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.246324 Loss1: 0.541249 Loss2: 0.705075
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237878 Loss1: 0.535584 Loss2: 0.702294
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246817 Loss1: 0.539479 Loss2: 0.707339
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.236719 Loss1: 0.528661 Loss2: 0.708058
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.240025 Loss1: 0.532846 Loss2: 0.707179
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.233214 Loss1: 0.526048 Loss2: 0.707166
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232768 Loss1: 0.523901 Loss2: 0.708866
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.222679 Loss1: 0.512900 Loss2: 0.709779
-(DefaultActor pid=1831567) >> Training accuracy: 0.811508
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.257069 Loss1: 0.486958 Loss2: 0.770111
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.109189 Loss1: 0.427586 Loss2: 0.681603
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.085223 Loss1: 0.407812 Loss2: 0.677411
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.091179 Loss1: 0.409501 Loss2: 0.681678
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085487 Loss1: 0.403685 Loss2: 0.681802
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073977 Loss1: 0.391803 Loss2: 0.682173
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.078001 Loss1: 0.393495 Loss2: 0.684506
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.046892 Loss1: 0.364973 Loss2: 0.681919
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.048755 Loss1: 0.370508 Loss2: 0.678248
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.047053 Loss1: 0.366280 Loss2: 0.680773
-(DefaultActor pid=1831567) >> Training accuracy: 0.854552
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339006 Loss1: 0.630861 Loss2: 0.708146
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.214745 Loss1: 0.580586 Loss2: 0.634159
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193097 Loss1: 0.556118 Loss2: 0.636980
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.179641 Loss1: 0.546193 Loss2: 0.633448
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.179527 Loss1: 0.542860 Loss2: 0.636667
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180666 Loss1: 0.539376 Loss2: 0.641290
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161487 Loss1: 0.524529 Loss2: 0.636959
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151112 Loss1: 0.514684 Loss2: 0.636428
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.167279 Loss1: 0.527984 Loss2: 0.639295
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150279 Loss1: 0.510193 Loss2: 0.640085
-(DefaultActor pid=1831567) >> Training accuracy: 0.820884
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481290 Loss1: 0.739544 Loss2: 0.741747
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332603 Loss1: 0.686557 Loss2: 0.646047
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.317385 Loss1: 0.669253 Loss2: 0.648132
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.267769 Loss1: 0.620170 Loss2: 0.647599
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.274014 Loss1: 0.624070 Loss2: 0.649944
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.274661 Loss1: 0.626243 Loss2: 0.648418
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.270431 Loss1: 0.620648 Loss2: 0.649783
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.245305 Loss1: 0.593139 Loss2: 0.652166
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257319 Loss1: 0.607028 Loss2: 0.650290
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.269420 Loss1: 0.614278 Loss2: 0.655142
-(DefaultActor pid=1831567) >> Training accuracy: 0.793037
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.396318 Loss1: 0.619322 Loss2: 0.776996
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259682 Loss1: 0.569542 Loss2: 0.690140
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.232743 Loss1: 0.542639 Loss2: 0.690104
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202909 Loss1: 0.511152 Loss2: 0.691757
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224806 Loss1: 0.534991 Loss2: 0.689815
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.203079 Loss1: 0.511994 Loss2: 0.691085
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195233 Loss1: 0.505999 Loss2: 0.689234
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.201108 Loss1: 0.507711 Loss2: 0.693397
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.198189 Loss1: 0.503421 Loss2: 0.694768
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.208878 Loss1: 0.511988 Loss2: 0.696890
-(DefaultActor pid=1831567) >> Training accuracy: 0.835732
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.352101 Loss1: 0.612069 Loss2: 0.740032
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.220541 Loss1: 0.555826 Loss2: 0.664714
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212216 Loss1: 0.547771 Loss2: 0.664445
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231896 Loss1: 0.561024 Loss2: 0.670872
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.208795 Loss1: 0.538765 Loss2: 0.670030
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.197519 Loss1: 0.527229 Loss2: 0.670290
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.218589 Loss1: 0.545518 Loss2: 0.673071
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.203567 Loss1: 0.532452 Loss2: 0.671115
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.162831 Loss1: 0.491283 Loss2: 0.671548
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189999 Loss1: 0.513829 Loss2: 0.676171
-(DefaultActor pid=1831567) >> Training accuracy: 0.834535
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360562 Loss1: 0.611936 Loss2: 0.748626
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.209313 Loss1: 0.559421 Loss2: 0.649892
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173976 Loss1: 0.526970 Loss2: 0.647006
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.154367 Loss1: 0.506906 Loss2: 0.647460
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.147000 Loss1: 0.498657 Loss2: 0.648344
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.173448 Loss1: 0.520713 Loss2: 0.652735
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.150142 Loss1: 0.500380 Loss2: 0.649761
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.118740 Loss1: 0.469440 Loss2: 0.649301
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140291 Loss1: 0.489554 Loss2: 0.650737
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.119383 Loss1: 0.468674 Loss2: 0.650709
-(DefaultActor pid=1831567) >> Training accuracy: 0.850900
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.523930 Loss1: 0.737683 Loss2: 0.786247
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.391903 Loss1: 0.703971 Loss2: 0.687932
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.364666 Loss1: 0.680847 Loss2: 0.683819
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.363851 Loss1: 0.674647 Loss2: 0.689205
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.359019 Loss1: 0.669284 Loss2: 0.689736
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.367115 Loss1: 0.677336 Loss2: 0.689779
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352974 Loss1: 0.660336 Loss2: 0.692638
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.345760 Loss1: 0.649153 Loss2: 0.696607
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.333655 Loss1: 0.639827 Loss2: 0.693828
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.314111 Loss1: 0.621277 Loss2: 0.692834
-[2023-09-27 13:03:50,781][flwr][DEBUG] - fit_round 49 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.764925
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.697900
-[2023-09-27 13:03:52,107][flwr][INFO] - fit progress: (49, 0.8720772449200908, {'accuracy': 0.6979}, 24364.94322586106)
-[2023-09-27 13:03:52,107][flwr][DEBUG] - evaluate_round 49: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:04:23,238][flwr][DEBUG] - evaluate_round 49 received 10 results and 0 failures
-[2023-09-27 13:04:23,239][flwr][DEBUG] - fit_round 50: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329488 Loss1: 0.574049 Loss2: 0.755438
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234892 Loss1: 0.562977 Loss2: 0.671915
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215256 Loss1: 0.540728 Loss2: 0.674528
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212468 Loss1: 0.533005 Loss2: 0.679463
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.186633 Loss1: 0.513553 Loss2: 0.673080
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193858 Loss1: 0.518507 Loss2: 0.675351
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174424 Loss1: 0.497138 Loss2: 0.677286
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173264 Loss1: 0.495847 Loss2: 0.677416
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.174019 Loss1: 0.494337 Loss2: 0.679682
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184802 Loss1: 0.503815 Loss2: 0.680986
-(DefaultActor pid=1831567) >> Training accuracy: 0.841488
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.367244 Loss1: 0.617600 Loss2: 0.749643
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.257916 Loss1: 0.578054 Loss2: 0.679861
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.243926 Loss1: 0.568045 Loss2: 0.675880
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.206622 Loss1: 0.531440 Loss2: 0.675182
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.213104 Loss1: 0.537932 Loss2: 0.675172
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211871 Loss1: 0.533577 Loss2: 0.678294
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200658 Loss1: 0.521886 Loss2: 0.678773
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190284 Loss1: 0.511790 Loss2: 0.678494
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180457 Loss1: 0.500631 Loss2: 0.679826
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176787 Loss1: 0.493000 Loss2: 0.683787
-(DefaultActor pid=1831567) >> Training accuracy: 0.822980
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.375870 Loss1: 0.595551 Loss2: 0.780319
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225479 Loss1: 0.548515 Loss2: 0.676964
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191062 Loss1: 0.516010 Loss2: 0.675053
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.225912 Loss1: 0.544525 Loss2: 0.681386
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211210 Loss1: 0.525736 Loss2: 0.685475
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185369 Loss1: 0.503725 Loss2: 0.681644
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161132 Loss1: 0.480189 Loss2: 0.680943
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.170386 Loss1: 0.485740 Loss2: 0.684646
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.172963 Loss1: 0.490115 Loss2: 0.682848
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.156410 Loss1: 0.473070 Loss2: 0.683340
-(DefaultActor pid=1831567) >> Training accuracy: 0.850900
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326530 Loss1: 0.592627 Loss2: 0.733904
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.226738 Loss1: 0.564109 Loss2: 0.662629
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223401 Loss1: 0.557933 Loss2: 0.665467
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188539 Loss1: 0.522143 Loss2: 0.666396
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204558 Loss1: 0.536276 Loss2: 0.668282
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.201179 Loss1: 0.532333 Loss2: 0.668846
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195807 Loss1: 0.525340 Loss2: 0.670467
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.176122 Loss1: 0.508904 Loss2: 0.667218
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188129 Loss1: 0.516386 Loss2: 0.671742
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165711 Loss1: 0.495966 Loss2: 0.669745
-(DefaultActor pid=1831567) >> Training accuracy: 0.828926
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.219449 Loss1: 0.469354 Loss2: 0.750095
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.085495 Loss1: 0.423622 Loss2: 0.661873
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.070928 Loss1: 0.409425 Loss2: 0.661503
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.058661 Loss1: 0.395346 Loss2: 0.663315
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.074556 Loss1: 0.411237 Loss2: 0.663319
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.045669 Loss1: 0.384222 Loss2: 0.661447
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.040691 Loss1: 0.379531 Loss2: 0.661160
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.018287 Loss1: 0.355471 Loss2: 0.662816
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042613 Loss1: 0.378778 Loss2: 0.663835
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019421 Loss1: 0.353851 Loss2: 0.665570
-(DefaultActor pid=1831567) >> Training accuracy: 0.878472
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.508920 Loss1: 0.761210 Loss2: 0.747709
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387345 Loss1: 0.729720 Loss2: 0.657625
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.395457 Loss1: 0.734305 Loss2: 0.661152
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.367563 Loss1: 0.705018 Loss2: 0.662545
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.367144 Loss1: 0.708664 Loss2: 0.658480
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.373119 Loss1: 0.705528 Loss2: 0.667591
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.352669 Loss1: 0.684653 Loss2: 0.668017
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.316307 Loss1: 0.653289 Loss2: 0.663018
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331580 Loss1: 0.667685 Loss2: 0.663895
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.360940 Loss1: 0.690783 Loss2: 0.670157
-(DefaultActor pid=1831567) >> Training accuracy: 0.754982
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.368013 Loss1: 0.586852 Loss2: 0.781161
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.280686 Loss1: 0.551347 Loss2: 0.729339
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.271317 Loss1: 0.539871 Loss2: 0.731445
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.263253 Loss1: 0.533283 Loss2: 0.729970
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.260648 Loss1: 0.530967 Loss2: 0.729681
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.251908 Loss1: 0.520344 Loss2: 0.731565
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.263162 Loss1: 0.527629 Loss2: 0.735533
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.267522 Loss1: 0.531181 Loss2: 0.736341
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.253012 Loss1: 0.521863 Loss2: 0.731149
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250916 Loss1: 0.513227 Loss2: 0.737689
-(DefaultActor pid=1831567) >> Training accuracy: 0.822421
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.531702 Loss1: 0.767664 Loss2: 0.764038
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382971 Loss1: 0.714639 Loss2: 0.668332
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.352177 Loss1: 0.687447 Loss2: 0.664730
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331850 Loss1: 0.665801 Loss2: 0.666049
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.326441 Loss1: 0.659424 Loss2: 0.667017
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.329816 Loss1: 0.659326 Loss2: 0.670490
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.340003 Loss1: 0.670382 Loss2: 0.669620
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.322073 Loss1: 0.652807 Loss2: 0.669266
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305205 Loss1: 0.634911 Loss2: 0.670294
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307307 Loss1: 0.636915 Loss2: 0.670392
-(DefaultActor pid=1831567) >> Training accuracy: 0.777285
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.192522 Loss1: 0.463894 Loss2: 0.728627
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.085103 Loss1: 0.429725 Loss2: 0.655378
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.057078 Loss1: 0.402751 Loss2: 0.654327
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.070370 Loss1: 0.414405 Loss2: 0.655965
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.069916 Loss1: 0.411377 Loss2: 0.658539
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.062000 Loss1: 0.401953 Loss2: 0.660048
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.044360 Loss1: 0.388810 Loss2: 0.655550
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.046142 Loss1: 0.385850 Loss2: 0.660292
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.031329 Loss1: 0.374949 Loss2: 0.656380
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.033869 Loss1: 0.373285 Loss2: 0.660585
-(DefaultActor pid=1831567) >> Training accuracy: 0.871528
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.542379 Loss1: 0.762388 Loss2: 0.779991
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.357444 Loss1: 0.679413 Loss2: 0.678031
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.350013 Loss1: 0.675012 Loss2: 0.675001
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.318135 Loss1: 0.644297 Loss2: 0.673837
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.310695 Loss1: 0.633250 Loss2: 0.677445
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.300877 Loss1: 0.622548 Loss2: 0.678329
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299567 Loss1: 0.619326 Loss2: 0.680241
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.301052 Loss1: 0.617887 Loss2: 0.683165
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.272314 Loss1: 0.590687 Loss2: 0.681626
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.259797 Loss1: 0.580198 Loss2: 0.679599
-[2023-09-27 13:11:19,527][flwr][DEBUG] - fit_round 50 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.799068
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.691600
-[2023-09-27 13:11:20,922][flwr][INFO] - fit progress: (50, 0.8935196316851595, {'accuracy': 0.6916}, 24813.75863668602)
-[2023-09-27 13:11:20,923][flwr][DEBUG] - evaluate_round 50: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:11:51,765][flwr][DEBUG] - evaluate_round 50 received 10 results and 0 failures
-[2023-09-27 13:11:51,765][flwr][DEBUG] - fit_round 51: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369676 Loss1: 0.599857 Loss2: 0.769819
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.228444 Loss1: 0.561079 Loss2: 0.667365
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198833 Loss1: 0.533108 Loss2: 0.665725
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189212 Loss1: 0.523711 Loss2: 0.665502
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170465 Loss1: 0.501303 Loss2: 0.669162
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.155324 Loss1: 0.489232 Loss2: 0.666092
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148286 Loss1: 0.479311 Loss2: 0.668975
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.175643 Loss1: 0.507430 Loss2: 0.668212
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.159907 Loss1: 0.488308 Loss2: 0.671598
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.129929 Loss1: 0.462695 Loss2: 0.667234
-(DefaultActor pid=1831567) >> Training accuracy: 0.836864
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.252698 Loss1: 0.474150 Loss2: 0.778548
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.134749 Loss1: 0.444886 Loss2: 0.689863
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.089313 Loss1: 0.405423 Loss2: 0.683890
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.090509 Loss1: 0.406052 Loss2: 0.684457
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066572 Loss1: 0.385428 Loss2: 0.681144
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073644 Loss1: 0.392746 Loss2: 0.680899
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.061099 Loss1: 0.375473 Loss2: 0.685626
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.063514 Loss1: 0.379305 Loss2: 0.684209
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.065510 Loss1: 0.379986 Loss2: 0.685523
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.065989 Loss1: 0.376326 Loss2: 0.689663
-(DefaultActor pid=1831567) >> Training accuracy: 0.869599
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.209627 Loss1: 0.485330 Loss2: 0.724297
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.062609 Loss1: 0.417637 Loss2: 0.644972
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.061135 Loss1: 0.416571 Loss2: 0.644565
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.043968 Loss1: 0.399670 Loss2: 0.644298
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.013707 Loss1: 0.373562 Loss2: 0.640145
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.030321 Loss1: 0.387559 Loss2: 0.642762
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.006787 Loss1: 0.364807 Loss2: 0.641980
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.028197 Loss1: 0.385259 Loss2: 0.642938
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.010431 Loss1: 0.366140 Loss2: 0.644291
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.017947 Loss1: 0.373090 Loss2: 0.644857
-(DefaultActor pid=1831567) >> Training accuracy: 0.870563
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.311317 Loss1: 0.602673 Loss2: 0.708645
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200054 Loss1: 0.558530 Loss2: 0.641524
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205118 Loss1: 0.562051 Loss2: 0.643067
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191366 Loss1: 0.551155 Loss2: 0.640212
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185421 Loss1: 0.543436 Loss2: 0.641985
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.174266 Loss1: 0.533482 Loss2: 0.640783
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.167133 Loss1: 0.523831 Loss2: 0.643302
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.147575 Loss1: 0.504882 Loss2: 0.642693
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.152650 Loss1: 0.506080 Loss2: 0.646569
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.139968 Loss1: 0.493321 Loss2: 0.646647
-(DefaultActor pid=1831567) >> Training accuracy: 0.802591
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.502769 Loss1: 0.746020 Loss2: 0.756748
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332198 Loss1: 0.676619 Loss2: 0.655580
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.329595 Loss1: 0.672764 Loss2: 0.656832
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291170 Loss1: 0.632581 Loss2: 0.658588
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282455 Loss1: 0.624114 Loss2: 0.658341
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.285502 Loss1: 0.625739 Loss2: 0.659763
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.280680 Loss1: 0.620175 Loss2: 0.660505
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.277251 Loss1: 0.612468 Loss2: 0.664783
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267469 Loss1: 0.605335 Loss2: 0.662135
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.246542 Loss1: 0.584307 Loss2: 0.662235
-(DefaultActor pid=1831567) >> Training accuracy: 0.789200
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.534354 Loss1: 0.748475 Loss2: 0.785879
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.426062 Loss1: 0.733830 Loss2: 0.692232
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.386556 Loss1: 0.696161 Loss2: 0.690395
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.370162 Loss1: 0.679832 Loss2: 0.690330
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.357703 Loss1: 0.667251 Loss2: 0.690452
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341027 Loss1: 0.651162 Loss2: 0.689865
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.348202 Loss1: 0.652556 Loss2: 0.695646
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.339945 Loss1: 0.649336 Loss2: 0.690609
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.336431 Loss1: 0.643770 Loss2: 0.692661
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.337868 Loss1: 0.643739 Loss2: 0.694129
-(DefaultActor pid=1831567) >> Training accuracy: 0.773554
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.538965 Loss1: 0.784271 Loss2: 0.754695
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.384056 Loss1: 0.713201 Loss2: 0.670855
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.397616 Loss1: 0.723814 Loss2: 0.673802
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.361425 Loss1: 0.688131 Loss2: 0.673295
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.372172 Loss1: 0.698998 Loss2: 0.673174
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.365348 Loss1: 0.692467 Loss2: 0.672881
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.344183 Loss1: 0.670404 Loss2: 0.673779
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.309176 Loss1: 0.633927 Loss2: 0.675250
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.340131 Loss1: 0.660067 Loss2: 0.680065
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329782 Loss1: 0.650914 Loss2: 0.678868
-(DefaultActor pid=1831567) >> Training accuracy: 0.783514
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.318571 Loss1: 0.597921 Loss2: 0.720650
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212457 Loss1: 0.562649 Loss2: 0.649808
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.210490 Loss1: 0.558296 Loss2: 0.652194
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202500 Loss1: 0.552307 Loss2: 0.650192
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.179083 Loss1: 0.530578 Loss2: 0.648505
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.177744 Loss1: 0.529305 Loss2: 0.648440
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.177296 Loss1: 0.523454 Loss2: 0.653841
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158734 Loss1: 0.507762 Loss2: 0.650972
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163359 Loss1: 0.509076 Loss2: 0.654283
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165555 Loss1: 0.513595 Loss2: 0.651960
-(DefaultActor pid=1831567) >> Training accuracy: 0.823918
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.328914 Loss1: 0.580654 Loss2: 0.748260
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259594 Loss1: 0.554585 Loss2: 0.705010
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.255605 Loss1: 0.551772 Loss2: 0.703833
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237273 Loss1: 0.536272 Loss2: 0.701001
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.254118 Loss1: 0.548185 Loss2: 0.705933
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.232574 Loss1: 0.529367 Loss2: 0.703207
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.226360 Loss1: 0.519860 Loss2: 0.706500
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.219783 Loss1: 0.515663 Loss2: 0.704120
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217178 Loss1: 0.510942 Loss2: 0.706236
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.242955 Loss1: 0.536196 Loss2: 0.706759
-(DefaultActor pid=1831567) >> Training accuracy: 0.826141
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369767 Loss1: 0.611898 Loss2: 0.757869
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211972 Loss1: 0.540702 Loss2: 0.671271
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199474 Loss1: 0.527048 Loss2: 0.672426
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205365 Loss1: 0.531195 Loss2: 0.674171
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201150 Loss1: 0.523688 Loss2: 0.677462
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168433 Loss1: 0.497778 Loss2: 0.670656
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.188743 Loss1: 0.510713 Loss2: 0.678030
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.179115 Loss1: 0.503056 Loss2: 0.676059
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185269 Loss1: 0.508704 Loss2: 0.676565
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.167728 Loss1: 0.489583 Loss2: 0.678145
-[2023-09-27 13:18:50,366][flwr][DEBUG] - fit_round 51 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.838199
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.686400
-[2023-09-27 13:18:51,699][flwr][INFO] - fit progress: (51, 0.8929546851510057, {'accuracy': 0.6864}, 25264.535360032693)
-[2023-09-27 13:18:51,699][flwr][DEBUG] - evaluate_round 51: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:19:22,603][flwr][DEBUG] - evaluate_round 51 received 10 results and 0 failures
-[2023-09-27 13:19:22,604][flwr][DEBUG] - fit_round 52: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.524771 Loss1: 0.769963 Loss2: 0.754807
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.437541 Loss1: 0.766104 Loss2: 0.671437
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.396995 Loss1: 0.729561 Loss2: 0.667433
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.377354 Loss1: 0.708528 Loss2: 0.668826
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.360514 Loss1: 0.692201 Loss2: 0.668313
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341243 Loss1: 0.671018 Loss2: 0.670225
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.335678 Loss1: 0.666717 Loss2: 0.668961
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.337643 Loss1: 0.666662 Loss2: 0.670981
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.337028 Loss1: 0.662607 Loss2: 0.674420
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.348530 Loss1: 0.674542 Loss2: 0.673988
-(DefaultActor pid=1831567) >> Training accuracy: 0.772871
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.352897 Loss1: 0.580754 Loss2: 0.772143
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.196349 Loss1: 0.529161 Loss2: 0.667188
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194956 Loss1: 0.524900 Loss2: 0.670056
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165803 Loss1: 0.498155 Loss2: 0.667649
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.182541 Loss1: 0.511405 Loss2: 0.671136
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162586 Loss1: 0.492887 Loss2: 0.669698
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.152586 Loss1: 0.480544 Loss2: 0.672042
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.142528 Loss1: 0.472754 Loss2: 0.669774
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140630 Loss1: 0.471058 Loss2: 0.669572
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.151861 Loss1: 0.478917 Loss2: 0.672943
-(DefaultActor pid=1831567) >> Training accuracy: 0.849576
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.367350 Loss1: 0.631076 Loss2: 0.736274
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245103 Loss1: 0.574806 Loss2: 0.670297
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213947 Loss1: 0.550343 Loss2: 0.663604
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208758 Loss1: 0.541125 Loss2: 0.667633
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200215 Loss1: 0.532646 Loss2: 0.667568
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199071 Loss1: 0.529666 Loss2: 0.669405
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174407 Loss1: 0.508772 Loss2: 0.665635
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180312 Loss1: 0.509053 Loss2: 0.671259
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180074 Loss1: 0.513505 Loss2: 0.666569
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.168472 Loss1: 0.498498 Loss2: 0.669974
-(DefaultActor pid=1831567) >> Training accuracy: 0.822790
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.379579 Loss1: 0.620168 Loss2: 0.759412
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.255731 Loss1: 0.571207 Loss2: 0.684524
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.241625 Loss1: 0.555548 Loss2: 0.686078
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217380 Loss1: 0.534277 Loss2: 0.683104
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.189201 Loss1: 0.504386 Loss2: 0.684814
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.203011 Loss1: 0.517287 Loss2: 0.685724
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.215004 Loss1: 0.525645 Loss2: 0.689360
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.218173 Loss1: 0.529238 Loss2: 0.688934
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213548 Loss1: 0.523508 Loss2: 0.690040
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179349 Loss1: 0.489516 Loss2: 0.689833
-(DefaultActor pid=1831567) >> Training accuracy: 0.792268
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.365033 Loss1: 0.587731 Loss2: 0.777302
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.288043 Loss1: 0.562142 Loss2: 0.725902
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.252190 Loss1: 0.528784 Loss2: 0.723406
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.253414 Loss1: 0.531115 Loss2: 0.722299
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.253823 Loss1: 0.529783 Loss2: 0.724041
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.260111 Loss1: 0.534174 Loss2: 0.725938
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.248290 Loss1: 0.520506 Loss2: 0.727784
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.248324 Loss1: 0.520136 Loss2: 0.728188
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257834 Loss1: 0.526261 Loss2: 0.731574
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.227476 Loss1: 0.501095 Loss2: 0.726381
-(DefaultActor pid=1831567) >> Training accuracy: 0.805308
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.519840 Loss1: 0.763031 Loss2: 0.756810
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370047 Loss1: 0.706222 Loss2: 0.663825
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.361147 Loss1: 0.696479 Loss2: 0.664668
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.345445 Loss1: 0.682928 Loss2: 0.662517
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.305160 Loss1: 0.644769 Loss2: 0.660391
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.322354 Loss1: 0.659721 Loss2: 0.662633
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314781 Loss1: 0.648246 Loss2: 0.666535
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.314845 Loss1: 0.648197 Loss2: 0.666648
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.295039 Loss1: 0.626521 Loss2: 0.668519
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.301688 Loss1: 0.632699 Loss2: 0.668989
-(DefaultActor pid=1831567) >> Training accuracy: 0.768424
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.192172 Loss1: 0.464828 Loss2: 0.727345
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092068 Loss1: 0.438421 Loss2: 0.653647
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.052479 Loss1: 0.398153 Loss2: 0.654326
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.047981 Loss1: 0.395664 Loss2: 0.652318
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.052077 Loss1: 0.398168 Loss2: 0.653909
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.047259 Loss1: 0.390558 Loss2: 0.656701
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.030456 Loss1: 0.373678 Loss2: 0.656778
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.036238 Loss1: 0.379984 Loss2: 0.656254
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.024813 Loss1: 0.367482 Loss2: 0.657332
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.025695 Loss1: 0.368447 Loss2: 0.657248
-(DefaultActor pid=1831567) >> Training accuracy: 0.867670
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226332 Loss1: 0.476302 Loss2: 0.750031
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.093950 Loss1: 0.427426 Loss2: 0.666525
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.070362 Loss1: 0.403934 Loss2: 0.666428
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.061101 Loss1: 0.396861 Loss2: 0.664240
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.072756 Loss1: 0.408617 Loss2: 0.664139
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.053465 Loss1: 0.389004 Loss2: 0.664462
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.036440 Loss1: 0.370429 Loss2: 0.666011
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.025584 Loss1: 0.360722 Loss2: 0.664861
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.029650 Loss1: 0.362231 Loss2: 0.667419
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.027996 Loss1: 0.361199 Loss2: 0.666798
-(DefaultActor pid=1831567) >> Training accuracy: 0.869792
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.315011 Loss1: 0.596857 Loss2: 0.718154
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.189520 Loss1: 0.545140 Loss2: 0.644379
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.178131 Loss1: 0.532094 Loss2: 0.646037
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.173067 Loss1: 0.524525 Loss2: 0.648542
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.175328 Loss1: 0.526291 Loss2: 0.649036
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.158769 Loss1: 0.508524 Loss2: 0.650246
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.156795 Loss1: 0.505932 Loss2: 0.650863
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.164972 Loss1: 0.514317 Loss2: 0.650654
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.145418 Loss1: 0.493951 Loss2: 0.651467
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154207 Loss1: 0.500691 Loss2: 0.653516
-(DefaultActor pid=1831567) >> Training accuracy: 0.834498
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488409 Loss1: 0.707600 Loss2: 0.780809
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.354448 Loss1: 0.672980 Loss2: 0.681468
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334730 Loss1: 0.656665 Loss2: 0.678065
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.328182 Loss1: 0.646134 Loss2: 0.682048
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.306471 Loss1: 0.621321 Loss2: 0.685151
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.298457 Loss1: 0.613881 Loss2: 0.684575
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.307919 Loss1: 0.621249 Loss2: 0.686670
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.272565 Loss1: 0.586228 Loss2: 0.686337
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267132 Loss1: 0.582349 Loss2: 0.684783
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289894 Loss1: 0.599773 Loss2: 0.690121
-[2023-09-27 13:26:05,181][flwr][DEBUG] - fit_round 52 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.793860
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.695000
-[2023-09-27 13:26:06,692][flwr][INFO] - fit progress: (52, 0.8784053408490202, {'accuracy': 0.695}, 25699.52871695673)
-[2023-09-27 13:26:06,693][flwr][DEBUG] - evaluate_round 52: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:26:37,453][flwr][DEBUG] - evaluate_round 52 received 10 results and 0 failures
-[2023-09-27 13:26:37,454][flwr][DEBUG] - fit_round 53: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.247715 Loss1: 0.484390 Loss2: 0.763325
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.109400 Loss1: 0.427463 Loss2: 0.681936
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.081966 Loss1: 0.403707 Loss2: 0.678259
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.066915 Loss1: 0.386437 Loss2: 0.680477
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.071969 Loss1: 0.392096 Loss2: 0.679873
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.073082 Loss1: 0.395279 Loss2: 0.677803
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.046966 Loss1: 0.365563 Loss2: 0.681404
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.055015 Loss1: 0.374976 Loss2: 0.680039
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.043052 Loss1: 0.360242 Loss2: 0.682810
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.037408 Loss1: 0.353858 Loss2: 0.683550
-(DefaultActor pid=1831567) >> Training accuracy: 0.876543
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.483210 Loss1: 0.741269 Loss2: 0.741941
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382283 Loss1: 0.720816 Loss2: 0.661467
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363572 Loss1: 0.703434 Loss2: 0.660139
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.365129 Loss1: 0.704057 Loss2: 0.661073
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.351205 Loss1: 0.687190 Loss2: 0.664014
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.348922 Loss1: 0.684432 Loss2: 0.664491
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350524 Loss1: 0.683931 Loss2: 0.666593
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.324444 Loss1: 0.656307 Loss2: 0.668137
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.316868 Loss1: 0.648106 Loss2: 0.668763
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.341060 Loss1: 0.669861 Loss2: 0.671199
-(DefaultActor pid=1831567) >> Training accuracy: 0.759737
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382548 Loss1: 0.616507 Loss2: 0.766041
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.215006 Loss1: 0.556071 Loss2: 0.658935
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208225 Loss1: 0.548379 Loss2: 0.659846
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.185878 Loss1: 0.527084 Loss2: 0.658795
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.139408 Loss1: 0.481014 Loss2: 0.658394
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.140269 Loss1: 0.479020 Loss2: 0.661249
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.160450 Loss1: 0.497927 Loss2: 0.662523
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.131257 Loss1: 0.467868 Loss2: 0.663389
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120205 Loss1: 0.457591 Loss2: 0.662614
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.114676 Loss1: 0.452478 Loss2: 0.662198
-(DefaultActor pid=1831567) >> Training accuracy: 0.851430
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291978 Loss1: 0.562872 Loss2: 0.729106
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.233074 Loss1: 0.551895 Loss2: 0.681179
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218907 Loss1: 0.536106 Loss2: 0.682801
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.215831 Loss1: 0.530489 Loss2: 0.685342
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.221061 Loss1: 0.535114 Loss2: 0.685947
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210711 Loss1: 0.527490 Loss2: 0.683221
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197493 Loss1: 0.511869 Loss2: 0.685624
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.205659 Loss1: 0.519024 Loss2: 0.686635
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199233 Loss1: 0.511593 Loss2: 0.687640
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.204707 Loss1: 0.516687 Loss2: 0.688019
-(DefaultActor pid=1831567) >> Training accuracy: 0.831349
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.497412 Loss1: 0.744785 Loss2: 0.752628
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.351285 Loss1: 0.699289 Loss2: 0.651996
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318724 Loss1: 0.669402 Loss2: 0.649322
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.304114 Loss1: 0.652278 Loss2: 0.651837
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.294742 Loss1: 0.642394 Loss2: 0.652348
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.277135 Loss1: 0.625100 Loss2: 0.652034
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.260145 Loss1: 0.607375 Loss2: 0.652769
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.267181 Loss1: 0.609315 Loss2: 0.657866
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.245916 Loss1: 0.590298 Loss2: 0.655618
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.258656 Loss1: 0.602523 Loss2: 0.656133
-(DefaultActor pid=1831567) >> Training accuracy: 0.788103
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348422 Loss1: 0.586652 Loss2: 0.761770
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.222996 Loss1: 0.543363 Loss2: 0.679634
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201713 Loss1: 0.524850 Loss2: 0.676863
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191989 Loss1: 0.514936 Loss2: 0.677053
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.186240 Loss1: 0.508369 Loss2: 0.677871
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191848 Loss1: 0.513581 Loss2: 0.678268
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.173851 Loss1: 0.494466 Loss2: 0.679385
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.183878 Loss1: 0.501187 Loss2: 0.682691
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173026 Loss1: 0.488346 Loss2: 0.684680
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176497 Loss1: 0.493589 Loss2: 0.682907
-(DefaultActor pid=1831567) >> Training accuracy: 0.833470
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327788 Loss1: 0.595910 Loss2: 0.731877
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225735 Loss1: 0.563675 Loss2: 0.662060
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.210461 Loss1: 0.548068 Loss2: 0.662393
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194621 Loss1: 0.531331 Loss2: 0.663291
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191610 Loss1: 0.526936 Loss2: 0.664674
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.207348 Loss1: 0.537442 Loss2: 0.669906
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.166883 Loss1: 0.501415 Loss2: 0.665468
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182010 Loss1: 0.516137 Loss2: 0.665873
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178205 Loss1: 0.510931 Loss2: 0.667274
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175195 Loss1: 0.507675 Loss2: 0.667520
-(DefaultActor pid=1831567) >> Training accuracy: 0.845954
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493809 Loss1: 0.714529 Loss2: 0.779280
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.401196 Loss1: 0.719542 Loss2: 0.681654
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.370101 Loss1: 0.686492 Loss2: 0.683609
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.359652 Loss1: 0.678253 Loss2: 0.681399
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.369696 Loss1: 0.683836 Loss2: 0.685859
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.351792 Loss1: 0.663225 Loss2: 0.688567
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.367079 Loss1: 0.676127 Loss2: 0.690952
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.342085 Loss1: 0.654948 Loss2: 0.687137
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.310607 Loss1: 0.625908 Loss2: 0.684699
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.340250 Loss1: 0.651145 Loss2: 0.689105
-(DefaultActor pid=1831567) >> Training accuracy: 0.770289
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348740 Loss1: 0.615751 Loss2: 0.732989
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236878 Loss1: 0.575607 Loss2: 0.661271
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219551 Loss1: 0.556354 Loss2: 0.663197
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.213054 Loss1: 0.551391 Loss2: 0.661664
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201391 Loss1: 0.540645 Loss2: 0.660746
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184956 Loss1: 0.523852 Loss2: 0.661104
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.180188 Loss1: 0.517516 Loss2: 0.662672
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202408 Loss1: 0.536795 Loss2: 0.665613
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173551 Loss1: 0.509561 Loss2: 0.663990
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.153932 Loss1: 0.488912 Loss2: 0.665020
-(DefaultActor pid=1831567) >> Training accuracy: 0.829268
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.280389 Loss1: 0.476103 Loss2: 0.804286
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.141747 Loss1: 0.423798 Loss2: 0.717949
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.120216 Loss1: 0.408588 Loss2: 0.711628
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.114581 Loss1: 0.404514 Loss2: 0.710068
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.105167 Loss1: 0.394410 Loss2: 0.710757
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.092021 Loss1: 0.380307 Loss2: 0.711714
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.103213 Loss1: 0.390512 Loss2: 0.712701
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.065282 Loss1: 0.353987 Loss2: 0.711295
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.079711 Loss1: 0.368236 Loss2: 0.711475
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.082995 Loss1: 0.367358 Loss2: 0.715637
-[2023-09-27 13:33:37,964][flwr][DEBUG] - fit_round 53 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.871528
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.695700
-[2023-09-27 13:33:39,904][flwr][INFO] - fit progress: (53, 0.8694329051354441, {'accuracy': 0.6957}, 26152.74077124009)
-[2023-09-27 13:33:39,905][flwr][DEBUG] - evaluate_round 53: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:34:10,406][flwr][DEBUG] - evaluate_round 53 received 10 results and 0 failures
-[2023-09-27 13:34:10,407][flwr][DEBUG] - fit_round 54: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362001 Loss1: 0.633961 Loss2: 0.728041
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.249663 Loss1: 0.585434 Loss2: 0.664229
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215409 Loss1: 0.557034 Loss2: 0.658375
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183037 Loss1: 0.525314 Loss2: 0.657723
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.208118 Loss1: 0.546147 Loss2: 0.661971
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191514 Loss1: 0.531472 Loss2: 0.660042
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.181157 Loss1: 0.520613 Loss2: 0.660544
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.176699 Loss1: 0.512104 Loss2: 0.664595
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163123 Loss1: 0.498932 Loss2: 0.664191
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.163273 Loss1: 0.497887 Loss2: 0.665385
-(DefaultActor pid=1831567) >> Training accuracy: 0.833651
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.345068 Loss1: 0.583856 Loss2: 0.761212
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.252150 Loss1: 0.543384 Loss2: 0.708766
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.235417 Loss1: 0.528200 Loss2: 0.707217
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.246408 Loss1: 0.538127 Loss2: 0.708281
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234829 Loss1: 0.526151 Loss2: 0.708679
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222793 Loss1: 0.515385 Loss2: 0.707407
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.234438 Loss1: 0.521957 Loss2: 0.712481
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.220465 Loss1: 0.510807 Loss2: 0.709658
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239711 Loss1: 0.525875 Loss2: 0.713836
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.227176 Loss1: 0.515554 Loss2: 0.711622
-(DefaultActor pid=1831567) >> Training accuracy: 0.825645
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.497680 Loss1: 0.759641 Loss2: 0.738039
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.375377 Loss1: 0.717991 Loss2: 0.657386
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.362163 Loss1: 0.706649 Loss2: 0.655514
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.354674 Loss1: 0.698820 Loss2: 0.655854
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.338994 Loss1: 0.679684 Loss2: 0.659310
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337021 Loss1: 0.678427 Loss2: 0.658594
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.340257 Loss1: 0.681870 Loss2: 0.658388
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.347651 Loss1: 0.687226 Loss2: 0.660425
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.338822 Loss1: 0.673279 Loss2: 0.665542
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.343187 Loss1: 0.678104 Loss2: 0.665083
-(DefaultActor pid=1831567) >> Training accuracy: 0.776947
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.171316 Loss1: 0.486828 Loss2: 0.684488
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.042427 Loss1: 0.423750 Loss2: 0.618677
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.037536 Loss1: 0.421537 Loss2: 0.615999
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.014967 Loss1: 0.398717 Loss2: 0.616250
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.000424 Loss1: 0.384899 Loss2: 0.615525
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.001818 Loss1: 0.382649 Loss2: 0.619169
-(DefaultActor pid=1831567) Epoch: 6 Loss: 0.984814 Loss1: 0.367643 Loss2: 0.617171
-(DefaultActor pid=1831567) Epoch: 7 Loss: 0.994382 Loss1: 0.375418 Loss2: 0.618964
-(DefaultActor pid=1831567) Epoch: 8 Loss: 0.996229 Loss1: 0.375597 Loss2: 0.620632
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.985759 Loss1: 0.363194 Loss2: 0.622564
-(DefaultActor pid=1831567) >> Training accuracy: 0.874807
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.373517 Loss1: 0.593225 Loss2: 0.780292
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.233321 Loss1: 0.555269 Loss2: 0.678052
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.175395 Loss1: 0.497823 Loss2: 0.677572
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190448 Loss1: 0.513359 Loss2: 0.677089
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167698 Loss1: 0.492792 Loss2: 0.674906
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.171162 Loss1: 0.490436 Loss2: 0.680726
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.178391 Loss1: 0.495100 Loss2: 0.683291
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.159085 Loss1: 0.476173 Loss2: 0.682912
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134973 Loss1: 0.453883 Loss2: 0.681091
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154721 Loss1: 0.472110 Loss2: 0.682611
-(DefaultActor pid=1831567) >> Training accuracy: 0.847193
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.223557 Loss1: 0.478856 Loss2: 0.744702
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092645 Loss1: 0.434314 Loss2: 0.658331
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048860 Loss1: 0.393926 Loss2: 0.654934
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.031393 Loss1: 0.380164 Loss2: 0.651230
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.043801 Loss1: 0.386166 Loss2: 0.657634
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.042580 Loss1: 0.385221 Loss2: 0.657360
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.059586 Loss1: 0.401863 Loss2: 0.657723
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030000 Loss1: 0.372258 Loss2: 0.657742
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.018259 Loss1: 0.360409 Loss2: 0.657850
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.025003 Loss1: 0.366568 Loss2: 0.658435
-(DefaultActor pid=1831567) >> Training accuracy: 0.869020
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.538590 Loss1: 0.751949 Loss2: 0.786641
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.331578 Loss1: 0.641120 Loss2: 0.690458
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.342827 Loss1: 0.655768 Loss2: 0.687059
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.330478 Loss1: 0.638345 Loss2: 0.692133
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.343470 Loss1: 0.648667 Loss2: 0.694802
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.323379 Loss1: 0.628490 Loss2: 0.694889
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.310277 Loss1: 0.621976 Loss2: 0.688301
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276107 Loss1: 0.582599 Loss2: 0.693507
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.304031 Loss1: 0.608093 Loss2: 0.695939
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.302375 Loss1: 0.606582 Loss2: 0.695794
-(DefaultActor pid=1831567) >> Training accuracy: 0.800987
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.333758 Loss1: 0.592220 Loss2: 0.741538
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.213435 Loss1: 0.547510 Loss2: 0.665925
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199287 Loss1: 0.533527 Loss2: 0.665760
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177523 Loss1: 0.514790 Loss2: 0.662734
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184366 Loss1: 0.517247 Loss2: 0.667119
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180756 Loss1: 0.511514 Loss2: 0.669242
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.169854 Loss1: 0.500171 Loss2: 0.669683
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.164272 Loss1: 0.493142 Loss2: 0.671130
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147194 Loss1: 0.478406 Loss2: 0.668788
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.169697 Loss1: 0.496685 Loss2: 0.673013
-(DefaultActor pid=1831567) >> Training accuracy: 0.842722
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.352130 Loss1: 0.605413 Loss2: 0.746717
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.255865 Loss1: 0.580964 Loss2: 0.674902
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208314 Loss1: 0.534667 Loss2: 0.673647
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.209395 Loss1: 0.534881 Loss2: 0.674514
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.220647 Loss1: 0.543750 Loss2: 0.676897
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.201971 Loss1: 0.525439 Loss2: 0.676532
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193236 Loss1: 0.516627 Loss2: 0.676609
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200158 Loss1: 0.519986 Loss2: 0.680172
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.223173 Loss1: 0.539067 Loss2: 0.684106
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.194929 Loss1: 0.512975 Loss2: 0.681954
-(DefaultActor pid=1831567) >> Training accuracy: 0.838742
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.490632 Loss1: 0.725746 Loss2: 0.764886
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.386952 Loss1: 0.708861 Loss2: 0.678090
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.371194 Loss1: 0.694567 Loss2: 0.676627
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325565 Loss1: 0.650084 Loss2: 0.675481
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.373198 Loss1: 0.694656 Loss2: 0.678542
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.331869 Loss1: 0.654552 Loss2: 0.677316
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.330080 Loss1: 0.651699 Loss2: 0.678382
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.323758 Loss1: 0.643123 Loss2: 0.680636
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.324449 Loss1: 0.642821 Loss2: 0.681628
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290754 Loss1: 0.612609 Loss2: 0.678144
-[2023-09-27 13:40:58,659][flwr][DEBUG] - fit_round 54 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.764925
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.697900
-[2023-09-27 13:40:59,981][flwr][INFO] - fit progress: (54, 0.8700203883190887, {'accuracy': 0.6979}, 26592.817508330103)
-[2023-09-27 13:40:59,981][flwr][DEBUG] - evaluate_round 54: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:41:34,985][flwr][DEBUG] - evaluate_round 54 received 10 results and 0 failures
-[2023-09-27 13:41:34,986][flwr][DEBUG] - fit_round 55: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326257 Loss1: 0.576070 Loss2: 0.750187
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.242953 Loss1: 0.571023 Loss2: 0.671930
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213013 Loss1: 0.546022 Loss2: 0.666992
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.198382 Loss1: 0.526287 Loss2: 0.672095
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.164294 Loss1: 0.492841 Loss2: 0.671453
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.193778 Loss1: 0.523820 Loss2: 0.669959
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174500 Loss1: 0.502002 Loss2: 0.672498
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168380 Loss1: 0.496736 Loss2: 0.671644
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175149 Loss1: 0.500396 Loss2: 0.674753
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.164259 Loss1: 0.491449 Loss2: 0.672810
-(DefaultActor pid=1831567) >> Training accuracy: 0.826275
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.499274 Loss1: 0.758392 Loss2: 0.740882
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.286091 Loss1: 0.646272 Loss2: 0.639819
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.288737 Loss1: 0.648348 Loss2: 0.640389
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.282485 Loss1: 0.640600 Loss2: 0.641885
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.253065 Loss1: 0.611623 Loss2: 0.641441
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249175 Loss1: 0.602783 Loss2: 0.646392
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.275367 Loss1: 0.631054 Loss2: 0.644313
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.271504 Loss1: 0.624798 Loss2: 0.646706
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.218826 Loss1: 0.571794 Loss2: 0.647031
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.258956 Loss1: 0.608815 Loss2: 0.650141
-(DefaultActor pid=1831567) >> Training accuracy: 0.780428
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.494342 Loss1: 0.720317 Loss2: 0.774025
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.366267 Loss1: 0.685749 Loss2: 0.680518
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.351119 Loss1: 0.669544 Loss2: 0.681574
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.361675 Loss1: 0.679023 Loss2: 0.682652
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.348671 Loss1: 0.666813 Loss2: 0.681858
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.340961 Loss1: 0.658301 Loss2: 0.682660
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.342277 Loss1: 0.657332 Loss2: 0.684945
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320231 Loss1: 0.633604 Loss2: 0.686626
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305770 Loss1: 0.617971 Loss2: 0.687799
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.323931 Loss1: 0.633187 Loss2: 0.690744
-(DefaultActor pid=1831567) >> Training accuracy: 0.756996
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370088 Loss1: 0.593129 Loss2: 0.776959
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.238569 Loss1: 0.565367 Loss2: 0.673202
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208216 Loss1: 0.541472 Loss2: 0.666744
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.182914 Loss1: 0.514559 Loss2: 0.668355
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.165835 Loss1: 0.497329 Loss2: 0.668507
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.156847 Loss1: 0.487968 Loss2: 0.668880
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.149130 Loss1: 0.476695 Loss2: 0.672436
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.143711 Loss1: 0.472813 Loss2: 0.670899
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136332 Loss1: 0.466036 Loss2: 0.670296
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.120818 Loss1: 0.448642 Loss2: 0.672176
-(DefaultActor pid=1831567) >> Training accuracy: 0.833951
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.310844 Loss1: 0.578766 Loss2: 0.732078
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234164 Loss1: 0.546138 Loss2: 0.688026
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238034 Loss1: 0.548633 Loss2: 0.689401
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217668 Loss1: 0.527669 Loss2: 0.690000
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198614 Loss1: 0.513256 Loss2: 0.685358
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.236214 Loss1: 0.543804 Loss2: 0.692409
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.215277 Loss1: 0.523292 Loss2: 0.691985
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.206877 Loss1: 0.518243 Loss2: 0.688634
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.204212 Loss1: 0.514749 Loss2: 0.689463
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.202626 Loss1: 0.507728 Loss2: 0.694898
-(DefaultActor pid=1831567) >> Training accuracy: 0.834201
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.305924 Loss1: 0.487922 Loss2: 0.818001
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.145136 Loss1: 0.422717 Loss2: 0.722419
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.130176 Loss1: 0.406553 Loss2: 0.723623
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.112758 Loss1: 0.393912 Loss2: 0.718846
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.115495 Loss1: 0.390105 Loss2: 0.725390
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.086187 Loss1: 0.365375 Loss2: 0.720811
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.120973 Loss1: 0.391992 Loss2: 0.728981
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.085838 Loss1: 0.360989 Loss2: 0.724849
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.113746 Loss1: 0.391684 Loss2: 0.722062
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.109828 Loss1: 0.380502 Loss2: 0.729326
-(DefaultActor pid=1831567) >> Training accuracy: 0.863812
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340350 Loss1: 0.613029 Loss2: 0.727321
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227310 Loss1: 0.569291 Loss2: 0.658018
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.221539 Loss1: 0.564515 Loss2: 0.657023
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187517 Loss1: 0.528573 Loss2: 0.658945
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192426 Loss1: 0.534711 Loss2: 0.657715
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.153790 Loss1: 0.499285 Loss2: 0.654505
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.158838 Loss1: 0.503309 Loss2: 0.655529
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185985 Loss1: 0.525076 Loss2: 0.660909
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.159795 Loss1: 0.501990 Loss2: 0.657805
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146988 Loss1: 0.489223 Loss2: 0.657765
-(DefaultActor pid=1831567) >> Training accuracy: 0.833841
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.232255 Loss1: 0.486683 Loss2: 0.745572
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.104400 Loss1: 0.439791 Loss2: 0.664608
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.090004 Loss1: 0.423123 Loss2: 0.666880
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.041556 Loss1: 0.379733 Loss2: 0.661823
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.047803 Loss1: 0.384710 Loss2: 0.663093
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.053465 Loss1: 0.387593 Loss2: 0.665872
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.042090 Loss1: 0.373808 Loss2: 0.668283
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.062236 Loss1: 0.393246 Loss2: 0.668990
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.038912 Loss1: 0.368483 Loss2: 0.670429
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019888 Loss1: 0.352725 Loss2: 0.667162
-(DefaultActor pid=1831567) >> Training accuracy: 0.880594
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517841 Loss1: 0.773281 Loss2: 0.744560
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.386019 Loss1: 0.720090 Loss2: 0.665929
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.364515 Loss1: 0.698879 Loss2: 0.665636
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.363198 Loss1: 0.694144 Loss2: 0.669053
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.346480 Loss1: 0.677754 Loss2: 0.668726
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.349011 Loss1: 0.681118 Loss2: 0.667893
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.334240 Loss1: 0.664090 Loss2: 0.670150
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.348112 Loss1: 0.677295 Loss2: 0.670818
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.301946 Loss1: 0.628856 Loss2: 0.673090
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.323579 Loss1: 0.651323 Loss2: 0.672256
-(DefaultActor pid=1831567) >> Training accuracy: 0.771060
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.312927 Loss1: 0.596236 Loss2: 0.716691
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216526 Loss1: 0.570812 Loss2: 0.645714
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192467 Loss1: 0.546340 Loss2: 0.646127
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.214608 Loss1: 0.564432 Loss2: 0.650176
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169207 Loss1: 0.521643 Loss2: 0.647565
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184782 Loss1: 0.535076 Loss2: 0.649706
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163469 Loss1: 0.513725 Loss2: 0.649744
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180547 Loss1: 0.526875 Loss2: 0.653672
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.144114 Loss1: 0.491872 Loss2: 0.652242
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.168253 Loss1: 0.513529 Loss2: 0.654724
-[2023-09-27 13:48:34,336][flwr][DEBUG] - fit_round 55 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.832131
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.702800
-[2023-09-27 13:48:35,822][flwr][INFO] - fit progress: (55, 0.8612132831312977, {'accuracy': 0.7028}, 27048.658763975836)
-[2023-09-27 13:48:35,823][flwr][DEBUG] - evaluate_round 55: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:49:06,456][flwr][DEBUG] - evaluate_round 55 received 10 results and 0 failures
-[2023-09-27 13:49:06,457][flwr][DEBUG] - fit_round 56: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.521974 Loss1: 0.761758 Loss2: 0.760216
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.388698 Loss1: 0.715260 Loss2: 0.673438
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.389518 Loss1: 0.711472 Loss2: 0.678046
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.375567 Loss1: 0.699418 Loss2: 0.676149
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.356923 Loss1: 0.678205 Loss2: 0.678718
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.343104 Loss1: 0.666888 Loss2: 0.676217
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.351482 Loss1: 0.674153 Loss2: 0.677329
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.366400 Loss1: 0.685615 Loss2: 0.680785
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.350989 Loss1: 0.667406 Loss2: 0.683583
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.324810 Loss1: 0.643460 Loss2: 0.681350
-(DefaultActor pid=1831567) >> Training accuracy: 0.783741
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.202950 Loss1: 0.484341 Loss2: 0.718609
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.043935 Loss1: 0.407972 Loss2: 0.635964
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.066111 Loss1: 0.430409 Loss2: 0.635702
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019180 Loss1: 0.384869 Loss2: 0.634311
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.011656 Loss1: 0.376273 Loss2: 0.635383
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.028323 Loss1: 0.392982 Loss2: 0.635342
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.025466 Loss1: 0.388186 Loss2: 0.637280
-(DefaultActor pid=1831567) Epoch: 7 Loss: 0.992641 Loss1: 0.354810 Loss2: 0.637832
-(DefaultActor pid=1831567) Epoch: 8 Loss: 0.998410 Loss1: 0.360874 Loss2: 0.637537
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.009581 Loss1: 0.372235 Loss2: 0.637346
-(DefaultActor pid=1831567) >> Training accuracy: 0.878472
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.338790 Loss1: 0.585962 Loss2: 0.752828
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187752 Loss1: 0.534523 Loss2: 0.653230
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.161958 Loss1: 0.513010 Loss2: 0.648949
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.133166 Loss1: 0.487492 Loss2: 0.645674
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154501 Loss1: 0.502735 Loss2: 0.651766
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.141295 Loss1: 0.488429 Loss2: 0.652866
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.110485 Loss1: 0.456541 Loss2: 0.653943
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.150346 Loss1: 0.494253 Loss2: 0.656093
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.121474 Loss1: 0.467455 Loss2: 0.654019
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.103903 Loss1: 0.448955 Loss2: 0.654949
-(DefaultActor pid=1831567) >> Training accuracy: 0.843485
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.376918 Loss1: 0.629300 Loss2: 0.747618
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.239395 Loss1: 0.574486 Loss2: 0.664909
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192204 Loss1: 0.530750 Loss2: 0.661454
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205937 Loss1: 0.539814 Loss2: 0.666123
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185689 Loss1: 0.518198 Loss2: 0.667491
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186690 Loss1: 0.519477 Loss2: 0.667214
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163594 Loss1: 0.494390 Loss2: 0.669204
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161235 Loss1: 0.493287 Loss2: 0.667947
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184405 Loss1: 0.514316 Loss2: 0.670089
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172468 Loss1: 0.502057 Loss2: 0.670412
-(DefaultActor pid=1831567) >> Training accuracy: 0.838816
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369468 Loss1: 0.604311 Loss2: 0.765157
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.247643 Loss1: 0.558355 Loss2: 0.689288
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236079 Loss1: 0.548372 Loss2: 0.687707
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.224967 Loss1: 0.534924 Loss2: 0.690043
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203860 Loss1: 0.516592 Loss2: 0.687267
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211234 Loss1: 0.522089 Loss2: 0.689145
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.223435 Loss1: 0.532598 Loss2: 0.690837
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.226178 Loss1: 0.531318 Loss2: 0.694860
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.215550 Loss1: 0.518701 Loss2: 0.696848
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184872 Loss1: 0.491514 Loss2: 0.693358
-(DefaultActor pid=1831567) >> Training accuracy: 0.836939
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.476066 Loss1: 0.733313 Loss2: 0.742753
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.353571 Loss1: 0.697059 Loss2: 0.656511
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.329620 Loss1: 0.675190 Loss2: 0.654430
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.307466 Loss1: 0.649602 Loss2: 0.657865
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.339999 Loss1: 0.678897 Loss2: 0.661102
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328404 Loss1: 0.670066 Loss2: 0.658337
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.329058 Loss1: 0.668017 Loss2: 0.661042
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288036 Loss1: 0.628973 Loss2: 0.659063
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.290377 Loss1: 0.629554 Loss2: 0.660823
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308199 Loss1: 0.644858 Loss2: 0.663341
-(DefaultActor pid=1831567) >> Training accuracy: 0.765858
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384337 Loss1: 0.637792 Loss2: 0.746545
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234595 Loss1: 0.552350 Loss2: 0.682246
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218924 Loss1: 0.541215 Loss2: 0.677709
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.228275 Loss1: 0.546283 Loss2: 0.681992
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196895 Loss1: 0.520279 Loss2: 0.676615
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220729 Loss1: 0.538566 Loss2: 0.682163
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197170 Loss1: 0.513707 Loss2: 0.683462
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.177271 Loss1: 0.495701 Loss2: 0.681570
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183333 Loss1: 0.499320 Loss2: 0.684013
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.208038 Loss1: 0.520540 Loss2: 0.687497
-(DefaultActor pid=1831567) >> Training accuracy: 0.843369
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.564877 Loss1: 0.739735 Loss2: 0.825142
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.388682 Loss1: 0.672963 Loss2: 0.715719
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.380320 Loss1: 0.665891 Loss2: 0.714429
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.330005 Loss1: 0.613044 Loss2: 0.716961
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.321650 Loss1: 0.606975 Loss2: 0.714675
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328845 Loss1: 0.616297 Loss2: 0.712548
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.329981 Loss1: 0.613877 Loss2: 0.716104
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.332752 Loss1: 0.614391 Loss2: 0.718361
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.325792 Loss1: 0.607031 Loss2: 0.718762
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.316995 Loss1: 0.596541 Loss2: 0.720454
-(DefaultActor pid=1831567) >> Training accuracy: 0.808114
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329089 Loss1: 0.571206 Loss2: 0.757883
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245349 Loss1: 0.540071 Loss2: 0.705278
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.236246 Loss1: 0.534470 Loss2: 0.701776
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.237850 Loss1: 0.533258 Loss2: 0.704592
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.231049 Loss1: 0.525075 Loss2: 0.705974
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220566 Loss1: 0.513164 Loss2: 0.707403
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258197 Loss1: 0.546880 Loss2: 0.711317
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225660 Loss1: 0.519102 Loss2: 0.706557
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.225280 Loss1: 0.515115 Loss2: 0.710165
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.232372 Loss1: 0.521541 Loss2: 0.710831
-(DefaultActor pid=1831567) >> Training accuracy: 0.828621
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.154468 Loss1: 0.463140 Loss2: 0.691328
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.030206 Loss1: 0.404587 Loss2: 0.625619
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.038038 Loss1: 0.412716 Loss2: 0.625322
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019304 Loss1: 0.393652 Loss2: 0.625652
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.002812 Loss1: 0.379776 Loss2: 0.623036
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.012174 Loss1: 0.386604 Loss2: 0.625570
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.001269 Loss1: 0.376264 Loss2: 0.625005
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.007877 Loss1: 0.380856 Loss2: 0.627021
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.007006 Loss1: 0.377708 Loss2: 0.629297
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.983878 Loss1: 0.353319 Loss2: 0.630559
-[2023-09-27 13:55:52,024][flwr][DEBUG] - fit_round 56 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.876157
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.695100
-[2023-09-27 13:55:53,658][flwr][INFO] - fit progress: (56, 0.8703011946556286, {'accuracy': 0.6951}, 27486.493984027766)
-[2023-09-27 13:55:53,658][flwr][DEBUG] - evaluate_round 56: strategy sampled 10 clients (out of 10)
-[2023-09-27 13:56:25,431][flwr][DEBUG] - evaluate_round 56 received 10 results and 0 failures
-[2023-09-27 13:56:25,432][flwr][DEBUG] - fit_round 57: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.382444 Loss1: 0.591541 Loss2: 0.790903
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223973 Loss1: 0.542060 Loss2: 0.681913
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199853 Loss1: 0.517805 Loss2: 0.682048
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191658 Loss1: 0.510469 Loss2: 0.681189
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.168365 Loss1: 0.489598 Loss2: 0.678767
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.191864 Loss1: 0.508567 Loss2: 0.683297
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.142463 Loss1: 0.460846 Loss2: 0.681617
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140906 Loss1: 0.460653 Loss2: 0.680253
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.142776 Loss1: 0.460728 Loss2: 0.682048
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.157794 Loss1: 0.473836 Loss2: 0.683958
-(DefaultActor pid=1831567) >> Training accuracy: 0.849576
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495419 Loss1: 0.728565 Loss2: 0.766854
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.369880 Loss1: 0.689877 Loss2: 0.680003
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.368960 Loss1: 0.691816 Loss2: 0.677144
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.342911 Loss1: 0.666090 Loss2: 0.676822
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317326 Loss1: 0.641041 Loss2: 0.676285
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.324800 Loss1: 0.645743 Loss2: 0.679057
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305976 Loss1: 0.627756 Loss2: 0.678220
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.316854 Loss1: 0.636133 Loss2: 0.680721
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.327175 Loss1: 0.645421 Loss2: 0.681754
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309089 Loss1: 0.625557 Loss2: 0.683532
-(DefaultActor pid=1831567) >> Training accuracy: 0.787547
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.354494 Loss1: 0.616445 Loss2: 0.738049
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.224329 Loss1: 0.561508 Loss2: 0.662822
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.202689 Loss1: 0.540413 Loss2: 0.662276
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181075 Loss1: 0.520998 Loss2: 0.660077
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198205 Loss1: 0.537237 Loss2: 0.660968
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.172437 Loss1: 0.509686 Loss2: 0.662751
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179162 Loss1: 0.516092 Loss2: 0.663070
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191265 Loss1: 0.529911 Loss2: 0.661354
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182088 Loss1: 0.514279 Loss2: 0.667809
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.167100 Loss1: 0.502684 Loss2: 0.664416
-(DefaultActor pid=1831567) >> Training accuracy: 0.832508
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.279624 Loss1: 0.559270 Loss2: 0.720354
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.222820 Loss1: 0.547274 Loss2: 0.675546
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.224467 Loss1: 0.544533 Loss2: 0.679934
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.206739 Loss1: 0.529719 Loss2: 0.677020
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.207430 Loss1: 0.529020 Loss2: 0.678410
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.213733 Loss1: 0.533705 Loss2: 0.680028
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.199570 Loss1: 0.515452 Loss2: 0.684118
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.205572 Loss1: 0.524231 Loss2: 0.681341
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.190161 Loss1: 0.509743 Loss2: 0.680418
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184205 Loss1: 0.502656 Loss2: 0.681549
-(DefaultActor pid=1831567) >> Training accuracy: 0.836310
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.468304 Loss1: 0.731398 Loss2: 0.736906
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.368430 Loss1: 0.714158 Loss2: 0.654272
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.361851 Loss1: 0.707606 Loss2: 0.654245
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353223 Loss1: 0.697832 Loss2: 0.655391
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.328896 Loss1: 0.670570 Loss2: 0.658326
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.333734 Loss1: 0.675862 Loss2: 0.657871
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.343425 Loss1: 0.681623 Loss2: 0.661802
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.331006 Loss1: 0.669969 Loss2: 0.661038
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299843 Loss1: 0.640893 Loss2: 0.658950
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.336065 Loss1: 0.673097 Loss2: 0.662968
-(DefaultActor pid=1831567) >> Training accuracy: 0.772418
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.262517 Loss1: 0.477013 Loss2: 0.785504
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.132536 Loss1: 0.432188 Loss2: 0.700348
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.099735 Loss1: 0.404045 Loss2: 0.695690
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.097428 Loss1: 0.402497 Loss2: 0.694930
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085557 Loss1: 0.390099 Loss2: 0.695457
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.075295 Loss1: 0.377391 Loss2: 0.697904
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.074548 Loss1: 0.375151 Loss2: 0.699397
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.071116 Loss1: 0.372998 Loss2: 0.698118
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.071245 Loss1: 0.372997 Loss2: 0.698247
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.061594 Loss1: 0.364517 Loss2: 0.697077
-(DefaultActor pid=1831567) >> Training accuracy: 0.870563
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348240 Loss1: 0.588697 Loss2: 0.759544
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.214477 Loss1: 0.537738 Loss2: 0.676738
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226577 Loss1: 0.548871 Loss2: 0.677707
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.193584 Loss1: 0.517523 Loss2: 0.676061
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203965 Loss1: 0.525339 Loss2: 0.678626
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.166164 Loss1: 0.488595 Loss2: 0.677569
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182830 Loss1: 0.502652 Loss2: 0.680177
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212804 Loss1: 0.528285 Loss2: 0.684519
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205760 Loss1: 0.522182 Loss2: 0.683578
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.178495 Loss1: 0.496305 Loss2: 0.682190
-(DefaultActor pid=1831567) >> Training accuracy: 0.827919
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341696 Loss1: 0.613517 Loss2: 0.728179
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.204684 Loss1: 0.546570 Loss2: 0.658114
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.210961 Loss1: 0.556702 Loss2: 0.654260
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.196320 Loss1: 0.536861 Loss2: 0.659459
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196673 Loss1: 0.536421 Loss2: 0.660252
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.189845 Loss1: 0.528018 Loss2: 0.661826
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168119 Loss1: 0.508003 Loss2: 0.660116
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202122 Loss1: 0.538287 Loss2: 0.663835
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.168200 Loss1: 0.505684 Loss2: 0.662515
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.160455 Loss1: 0.497208 Loss2: 0.663248
-(DefaultActor pid=1831567) >> Training accuracy: 0.829728
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485326 Loss1: 0.731698 Loss2: 0.753628
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304922 Loss1: 0.647740 Loss2: 0.657182
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.319242 Loss1: 0.659464 Loss2: 0.659778
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281561 Loss1: 0.624653 Loss2: 0.656908
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.282377 Loss1: 0.623814 Loss2: 0.658562
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.287511 Loss1: 0.625527 Loss2: 0.661984
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.282633 Loss1: 0.621632 Loss2: 0.661001
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.270237 Loss1: 0.609373 Loss2: 0.660864
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.266173 Loss1: 0.599464 Loss2: 0.666710
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.244149 Loss1: 0.579529 Loss2: 0.664620
-(DefaultActor pid=1831567) >> Training accuracy: 0.804002
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.258780 Loss1: 0.466259 Loss2: 0.792520
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.122900 Loss1: 0.414808 Loss2: 0.708092
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.101581 Loss1: 0.400063 Loss2: 0.701518
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.102560 Loss1: 0.400069 Loss2: 0.702491
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.090204 Loss1: 0.388253 Loss2: 0.701951
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.085824 Loss1: 0.382052 Loss2: 0.703772
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.080462 Loss1: 0.376926 Loss2: 0.703536
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.069502 Loss1: 0.363005 Loss2: 0.706497
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.089892 Loss1: 0.385080 Loss2: 0.704812
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.061822 Loss1: 0.355121 Loss2: 0.706701
-[2023-09-27 14:03:38,970][flwr][DEBUG] - fit_round 57 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.873457
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.697300
-[2023-09-27 14:03:40,742][flwr][INFO] - fit progress: (57, 0.8694903847698967, {'accuracy': 0.6973}, 27953.578594708815)
-[2023-09-27 14:03:40,743][flwr][DEBUG] - evaluate_round 57: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:04:11,294][flwr][DEBUG] - evaluate_round 57 received 10 results and 0 failures
-[2023-09-27 14:04:11,295][flwr][DEBUG] - fit_round 58: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362219 Loss1: 0.592152 Loss2: 0.770066
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227314 Loss1: 0.540047 Loss2: 0.687268
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.220008 Loss1: 0.532216 Loss2: 0.687792
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.197700 Loss1: 0.510479 Loss2: 0.687221
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195502 Loss1: 0.505342 Loss2: 0.690159
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.209849 Loss1: 0.517852 Loss2: 0.691996
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.194334 Loss1: 0.502461 Loss2: 0.691873
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200955 Loss1: 0.506696 Loss2: 0.694259
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183803 Loss1: 0.488220 Loss2: 0.695583
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.173535 Loss1: 0.478593 Loss2: 0.694942
-(DefaultActor pid=1831567) >> Training accuracy: 0.841283
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.370857 Loss1: 0.568611 Loss2: 0.802246
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301047 Loss1: 0.549659 Loss2: 0.751387
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.271623 Loss1: 0.525241 Loss2: 0.746382
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.279020 Loss1: 0.531022 Loss2: 0.747998
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.280974 Loss1: 0.532872 Loss2: 0.748102
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.256962 Loss1: 0.508081 Loss2: 0.748882
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.255481 Loss1: 0.510923 Loss2: 0.744559
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.277813 Loss1: 0.525686 Loss2: 0.752127
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.264653 Loss1: 0.515438 Loss2: 0.749216
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250171 Loss1: 0.502302 Loss2: 0.747870
-(DefaultActor pid=1831567) >> Training accuracy: 0.833333
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.344302 Loss1: 0.604707 Loss2: 0.739594
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.177673 Loss1: 0.529423 Loss2: 0.648250
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.160180 Loss1: 0.514676 Loss2: 0.645504
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.130209 Loss1: 0.487684 Loss2: 0.642525
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.135252 Loss1: 0.487961 Loss2: 0.647290
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.120168 Loss1: 0.471969 Loss2: 0.648198
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.137457 Loss1: 0.492357 Loss2: 0.645100
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.113678 Loss1: 0.468015 Loss2: 0.645663
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.102611 Loss1: 0.456501 Loss2: 0.646111
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146184 Loss1: 0.494613 Loss2: 0.651571
-(DefaultActor pid=1831567) >> Training accuracy: 0.847987
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.542613 Loss1: 0.732746 Loss2: 0.809867
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.366507 Loss1: 0.669717 Loss2: 0.696790
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.343393 Loss1: 0.649346 Loss2: 0.694047
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325546 Loss1: 0.626613 Loss2: 0.698933
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322167 Loss1: 0.626347 Loss2: 0.695821
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.325615 Loss1: 0.628778 Loss2: 0.696838
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.304698 Loss1: 0.605716 Loss2: 0.698982
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.305734 Loss1: 0.604647 Loss2: 0.701088
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.294631 Loss1: 0.594564 Loss2: 0.700067
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.282434 Loss1: 0.583770 Loss2: 0.698664
-(DefaultActor pid=1831567) >> Training accuracy: 0.798520
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.217143 Loss1: 0.474505 Loss2: 0.742639
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.070647 Loss1: 0.413974 Loss2: 0.656673
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.062811 Loss1: 0.407301 Loss2: 0.655510
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.049701 Loss1: 0.393503 Loss2: 0.656198
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.055754 Loss1: 0.397994 Loss2: 0.657760
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.050714 Loss1: 0.393039 Loss2: 0.657675
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.022228 Loss1: 0.364792 Loss2: 0.657435
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.039350 Loss1: 0.379458 Loss2: 0.659892
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.033409 Loss1: 0.372424 Loss2: 0.660985
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.006485 Loss1: 0.346462 Loss2: 0.660022
-(DefaultActor pid=1831567) >> Training accuracy: 0.875386
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.512532 Loss1: 0.765472 Loss2: 0.747060
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.375124 Loss1: 0.716653 Loss2: 0.658471
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.345172 Loss1: 0.684963 Loss2: 0.660208
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.360707 Loss1: 0.696220 Loss2: 0.664487
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.350324 Loss1: 0.687561 Loss2: 0.662762
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.334220 Loss1: 0.668373 Loss2: 0.665847
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.340940 Loss1: 0.674097 Loss2: 0.666843
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.326970 Loss1: 0.658491 Loss2: 0.668479
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.301865 Loss1: 0.631266 Loss2: 0.670598
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.333858 Loss1: 0.660807 Loss2: 0.673052
-(DefaultActor pid=1831567) >> Training accuracy: 0.778306
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.385162 Loss1: 0.610276 Loss2: 0.774887
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.260794 Loss1: 0.556435 Loss2: 0.704359
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.262439 Loss1: 0.559153 Loss2: 0.703286
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.214198 Loss1: 0.514653 Loss2: 0.699545
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246846 Loss1: 0.541576 Loss2: 0.705270
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.237645 Loss1: 0.533538 Loss2: 0.704107
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.197714 Loss1: 0.497882 Loss2: 0.699832
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.217066 Loss1: 0.516042 Loss2: 0.701024
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.200700 Loss1: 0.496199 Loss2: 0.704500
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.202336 Loss1: 0.496335 Loss2: 0.706001
-(DefaultActor pid=1831567) >> Training accuracy: 0.839939
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.173668 Loss1: 0.474135 Loss2: 0.699534
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.070918 Loss1: 0.434506 Loss2: 0.636412
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.039834 Loss1: 0.408815 Loss2: 0.631019
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019584 Loss1: 0.387621 Loss2: 0.631963
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.033219 Loss1: 0.397759 Loss2: 0.635459
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.000468 Loss1: 0.367334 Loss2: 0.633134
-(DefaultActor pid=1831567) Epoch: 6 Loss: 0.998884 Loss1: 0.364387 Loss2: 0.634497
-(DefaultActor pid=1831567) Epoch: 7 Loss: 0.993859 Loss1: 0.359407 Loss2: 0.634452
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.018727 Loss1: 0.379847 Loss2: 0.638880
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.998591 Loss1: 0.364089 Loss2: 0.634502
-(DefaultActor pid=1831567) >> Training accuracy: 0.868827
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.386434 Loss1: 0.613558 Loss2: 0.772875
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.241774 Loss1: 0.550251 Loss2: 0.691523
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231956 Loss1: 0.540595 Loss2: 0.691360
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.259430 Loss1: 0.562282 Loss2: 0.697148
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.209930 Loss1: 0.516477 Loss2: 0.693453
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.217835 Loss1: 0.521350 Loss2: 0.696485
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.218236 Loss1: 0.522020 Loss2: 0.696216
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200203 Loss1: 0.502805 Loss2: 0.697398
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.201799 Loss1: 0.503028 Loss2: 0.698771
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189914 Loss1: 0.489281 Loss2: 0.700633
-(DefaultActor pid=1831567) >> Training accuracy: 0.822115
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475258 Loss1: 0.712274 Loss2: 0.762984
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370794 Loss1: 0.697793 Loss2: 0.673002
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356677 Loss1: 0.680536 Loss2: 0.676142
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353942 Loss1: 0.683370 Loss2: 0.670572
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.331249 Loss1: 0.658877 Loss2: 0.672372
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328212 Loss1: 0.656639 Loss2: 0.671573
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.288406 Loss1: 0.613534 Loss2: 0.674871
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.310470 Loss1: 0.633153 Loss2: 0.677317
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.309677 Loss1: 0.633542 Loss2: 0.676135
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.334650 Loss1: 0.655448 Loss2: 0.679202
-[2023-09-27 14:10:55,808][flwr][DEBUG] - fit_round 58 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.770289
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.696200
-[2023-09-27 14:10:57,489][flwr][INFO] - fit progress: (58, 0.8769776419328805, {'accuracy': 0.6962}, 28390.325752868783)
-[2023-09-27 14:10:57,490][flwr][DEBUG] - evaluate_round 58: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:11:28,672][flwr][DEBUG] - evaluate_round 58 received 10 results and 0 failures
-[2023-09-27 14:11:28,673][flwr][DEBUG] - fit_round 59: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329928 Loss1: 0.611223 Loss2: 0.718705
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225512 Loss1: 0.573324 Loss2: 0.652188
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.207174 Loss1: 0.559078 Loss2: 0.648096
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183047 Loss1: 0.538757 Loss2: 0.644290
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.176648 Loss1: 0.528854 Loss2: 0.647794
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.172469 Loss1: 0.527828 Loss2: 0.644641
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.162533 Loss1: 0.515471 Loss2: 0.647062
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161918 Loss1: 0.513835 Loss2: 0.648083
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.156319 Loss1: 0.506630 Loss2: 0.649689
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148608 Loss1: 0.499402 Loss2: 0.649206
-(DefaultActor pid=1831567) >> Training accuracy: 0.818979
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.503620 Loss1: 0.763119 Loss2: 0.740501
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.377187 Loss1: 0.719117 Loss2: 0.658070
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.370827 Loss1: 0.710818 Loss2: 0.660010
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.349174 Loss1: 0.687166 Loss2: 0.662008
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345450 Loss1: 0.683364 Loss2: 0.662086
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.333582 Loss1: 0.670027 Loss2: 0.663555
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.336145 Loss1: 0.673329 Loss2: 0.662816
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.326280 Loss1: 0.662171 Loss2: 0.664108
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.334467 Loss1: 0.667395 Loss2: 0.667073
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.326074 Loss1: 0.656445 Loss2: 0.669628
-(DefaultActor pid=1831567) >> Training accuracy: 0.775362
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348568 Loss1: 0.603917 Loss2: 0.744652
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.210051 Loss1: 0.548670 Loss2: 0.661381
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.175652 Loss1: 0.517893 Loss2: 0.657760
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184210 Loss1: 0.526507 Loss2: 0.657703
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.174719 Loss1: 0.513909 Loss2: 0.660810
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.160111 Loss1: 0.499032 Loss2: 0.661079
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163530 Loss1: 0.501290 Loss2: 0.662241
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.150454 Loss1: 0.486430 Loss2: 0.664024
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.150506 Loss1: 0.487282 Loss2: 0.663224
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.135696 Loss1: 0.471303 Loss2: 0.664393
-(DefaultActor pid=1831567) >> Training accuracy: 0.846834
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.306028 Loss1: 0.589684 Loss2: 0.716344
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201386 Loss1: 0.550162 Loss2: 0.651224
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.207560 Loss1: 0.553234 Loss2: 0.654327
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194195 Loss1: 0.539419 Loss2: 0.654776
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170663 Loss1: 0.518831 Loss2: 0.651832
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.170459 Loss1: 0.516849 Loss2: 0.653610
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168473 Loss1: 0.515425 Loss2: 0.653048
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163691 Loss1: 0.507790 Loss2: 0.655901
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.172597 Loss1: 0.514197 Loss2: 0.658399
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.159077 Loss1: 0.498496 Loss2: 0.660581
-(DefaultActor pid=1831567) >> Training accuracy: 0.837941
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462127 Loss1: 0.732617 Loss2: 0.729509
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.318311 Loss1: 0.683606 Loss2: 0.634705
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.281504 Loss1: 0.648028 Loss2: 0.633476
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.280935 Loss1: 0.646369 Loss2: 0.634566
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.247977 Loss1: 0.610659 Loss2: 0.637318
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.255042 Loss1: 0.615945 Loss2: 0.639097
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.238694 Loss1: 0.601909 Loss2: 0.636785
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.247621 Loss1: 0.611351 Loss2: 0.636270
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232252 Loss1: 0.594367 Loss2: 0.637885
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.209504 Loss1: 0.574327 Loss2: 0.635176
-(DefaultActor pid=1831567) >> Training accuracy: 0.802357
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.334009 Loss1: 0.573014 Loss2: 0.760995
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.253526 Loss1: 0.539548 Loss2: 0.713977
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.251058 Loss1: 0.537188 Loss2: 0.713870
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.230532 Loss1: 0.519392 Loss2: 0.711140
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.241025 Loss1: 0.526598 Loss2: 0.714427
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.226554 Loss1: 0.512176 Loss2: 0.714378
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.249007 Loss1: 0.533694 Loss2: 0.715312
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.230061 Loss1: 0.514220 Loss2: 0.715841
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.232714 Loss1: 0.517720 Loss2: 0.714994
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.212955 Loss1: 0.500796 Loss2: 0.712159
-(DefaultActor pid=1831567) >> Training accuracy: 0.835938
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.566436 Loss1: 0.754765 Loss2: 0.811671
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.400606 Loss1: 0.694367 Loss2: 0.706239
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393994 Loss1: 0.687373 Loss2: 0.706621
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.367226 Loss1: 0.661768 Loss2: 0.705458
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.391618 Loss1: 0.680575 Loss2: 0.711043
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.346028 Loss1: 0.638490 Loss2: 0.707538
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.386915 Loss1: 0.672781 Loss2: 0.714134
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.344558 Loss1: 0.633705 Loss2: 0.710853
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.335837 Loss1: 0.621841 Loss2: 0.713996
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.340631 Loss1: 0.629768 Loss2: 0.710863
-(DefaultActor pid=1831567) >> Training accuracy: 0.792211
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.249695 Loss1: 0.464351 Loss2: 0.785344
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.117896 Loss1: 0.421055 Loss2: 0.696841
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.103740 Loss1: 0.406743 Loss2: 0.696998
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.101055 Loss1: 0.405224 Loss2: 0.695831
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.062880 Loss1: 0.366163 Loss2: 0.696717
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.076957 Loss1: 0.379709 Loss2: 0.697249
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.067617 Loss1: 0.367956 Loss2: 0.699661
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.078176 Loss1: 0.377252 Loss2: 0.700924
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.062052 Loss1: 0.367049 Loss2: 0.695003
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.051767 Loss1: 0.353099 Loss2: 0.698668
-(DefaultActor pid=1831567) >> Training accuracy: 0.872299
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.383086 Loss1: 0.590812 Loss2: 0.792274
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227859 Loss1: 0.533276 Loss2: 0.694583
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222058 Loss1: 0.532107 Loss2: 0.689951
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181909 Loss1: 0.493580 Loss2: 0.688329
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.182112 Loss1: 0.491993 Loss2: 0.690119
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.161984 Loss1: 0.472330 Loss2: 0.689654
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.147265 Loss1: 0.459679 Loss2: 0.687586
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168300 Loss1: 0.474110 Loss2: 0.694190
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.154999 Loss1: 0.461267 Loss2: 0.693732
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154603 Loss1: 0.459350 Loss2: 0.695252
-(DefaultActor pid=1831567) >> Training accuracy: 0.841631
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.237189 Loss1: 0.472048 Loss2: 0.765141
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.113985 Loss1: 0.430118 Loss2: 0.683866
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.107151 Loss1: 0.422870 Loss2: 0.684281
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.061145 Loss1: 0.379852 Loss2: 0.681293
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.050102 Loss1: 0.369025 Loss2: 0.681077
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.063404 Loss1: 0.380347 Loss2: 0.683057
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.053786 Loss1: 0.368923 Loss2: 0.684863
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.053042 Loss1: 0.369200 Loss2: 0.683842
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.073313 Loss1: 0.387803 Loss2: 0.685510
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.060096 Loss1: 0.376460 Loss2: 0.683636
-[2023-09-27 14:18:12,279][flwr][DEBUG] - fit_round 59 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.877894
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.703200
-[2023-09-27 14:18:14,157][flwr][INFO] - fit progress: (59, 0.8658733374584978, {'accuracy': 0.7032}, 28826.99361290969)
-[2023-09-27 14:18:14,158][flwr][DEBUG] - evaluate_round 59: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:18:44,626][flwr][DEBUG] - evaluate_round 59 received 10 results and 0 failures
-[2023-09-27 14:18:44,627][flwr][DEBUG] - fit_round 60: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.522455 Loss1: 0.762286 Loss2: 0.760168
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.378740 Loss1: 0.707269 Loss2: 0.671470
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.373832 Loss1: 0.697831 Loss2: 0.676001
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.362338 Loss1: 0.689826 Loss2: 0.672512
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.352582 Loss1: 0.680443 Loss2: 0.672139
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.333344 Loss1: 0.659366 Loss2: 0.673978
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.333651 Loss1: 0.657007 Loss2: 0.676644
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.343738 Loss1: 0.664853 Loss2: 0.678885
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331197 Loss1: 0.650599 Loss2: 0.680598
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.318345 Loss1: 0.637836 Loss2: 0.680509
-(DefaultActor pid=1831567) >> Training accuracy: 0.781703
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.531023 Loss1: 0.740747 Loss2: 0.790276
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340137 Loss1: 0.653682 Loss2: 0.686454
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.349762 Loss1: 0.663680 Loss2: 0.686082
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.315836 Loss1: 0.633888 Loss2: 0.681948
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293101 Loss1: 0.610349 Loss2: 0.682752
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.290177 Loss1: 0.605478 Loss2: 0.684698
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294729 Loss1: 0.608208 Loss2: 0.686521
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.287072 Loss1: 0.601629 Loss2: 0.685443
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.280046 Loss1: 0.591969 Loss2: 0.688078
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.283918 Loss1: 0.596812 Loss2: 0.687105
-(DefaultActor pid=1831567) >> Training accuracy: 0.782621
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.203166 Loss1: 0.469278 Loss2: 0.733889
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.064799 Loss1: 0.413783 Loss2: 0.651015
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.042575 Loss1: 0.397410 Loss2: 0.645165
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.044282 Loss1: 0.396907 Loss2: 0.647375
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.046915 Loss1: 0.399676 Loss2: 0.647238
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.029162 Loss1: 0.382074 Loss2: 0.647088
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019351 Loss1: 0.369560 Loss2: 0.649791
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.003105 Loss1: 0.353489 Loss2: 0.649616
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.025436 Loss1: 0.373871 Loss2: 0.651565
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.027528 Loss1: 0.374986 Loss2: 0.652542
-(DefaultActor pid=1831567) >> Training accuracy: 0.883681
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.504058 Loss1: 0.751220 Loss2: 0.752838
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367567 Loss1: 0.702733 Loss2: 0.664834
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336442 Loss1: 0.675659 Loss2: 0.660783
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.331778 Loss1: 0.668138 Loss2: 0.663639
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.351899 Loss1: 0.683510 Loss2: 0.668389
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306023 Loss1: 0.642511 Loss2: 0.663512
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.302522 Loss1: 0.638786 Loss2: 0.663736
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.309908 Loss1: 0.640948 Loss2: 0.668960
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.295272 Loss1: 0.628078 Loss2: 0.667194
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.296369 Loss1: 0.628715 Loss2: 0.667653
-(DefaultActor pid=1831567) >> Training accuracy: 0.789179
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.350557 Loss1: 0.594104 Loss2: 0.756453
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.244441 Loss1: 0.565355 Loss2: 0.679086
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222966 Loss1: 0.538709 Loss2: 0.684257
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223316 Loss1: 0.541034 Loss2: 0.682282
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.190147 Loss1: 0.510285 Loss2: 0.679862
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210575 Loss1: 0.527337 Loss2: 0.683238
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196177 Loss1: 0.514366 Loss2: 0.681810
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.216329 Loss1: 0.533297 Loss2: 0.683032
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.207266 Loss1: 0.520951 Loss2: 0.686314
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.209946 Loss1: 0.523129 Loss2: 0.686818
-(DefaultActor pid=1831567) >> Training accuracy: 0.835136
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329516 Loss1: 0.590243 Loss2: 0.739272
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211511 Loss1: 0.554974 Loss2: 0.656537
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.180673 Loss1: 0.524338 Loss2: 0.656335
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.178839 Loss1: 0.521097 Loss2: 0.657742
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.171298 Loss1: 0.513656 Loss2: 0.657642
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.169363 Loss1: 0.508492 Loss2: 0.660871
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.158528 Loss1: 0.497873 Loss2: 0.660655
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163694 Loss1: 0.501604 Loss2: 0.662090
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.150523 Loss1: 0.491624 Loss2: 0.658899
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.139250 Loss1: 0.477130 Loss2: 0.662121
-(DefaultActor pid=1831567) >> Training accuracy: 0.831003
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.184126 Loss1: 0.465604 Loss2: 0.718522
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.079608 Loss1: 0.423463 Loss2: 0.656145
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.051986 Loss1: 0.404234 Loss2: 0.647752
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.034591 Loss1: 0.386901 Loss2: 0.647690
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.029269 Loss1: 0.379525 Loss2: 0.649744
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.028227 Loss1: 0.374845 Loss2: 0.653382
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019984 Loss1: 0.369423 Loss2: 0.650561
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.037100 Loss1: 0.385608 Loss2: 0.651491
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.009401 Loss1: 0.356406 Loss2: 0.652995
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.010419 Loss1: 0.358603 Loss2: 0.651816
-(DefaultActor pid=1831567) >> Training accuracy: 0.873843
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.332807 Loss1: 0.556011 Loss2: 0.776796
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.260772 Loss1: 0.532623 Loss2: 0.728149
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.244416 Loss1: 0.521077 Loss2: 0.723339
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.262450 Loss1: 0.536712 Loss2: 0.725738
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.247870 Loss1: 0.520294 Loss2: 0.727576
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.253872 Loss1: 0.524866 Loss2: 0.729006
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.239849 Loss1: 0.511634 Loss2: 0.728216
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.246569 Loss1: 0.515781 Loss2: 0.730788
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.254486 Loss1: 0.519398 Loss2: 0.735087
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.222016 Loss1: 0.491949 Loss2: 0.730068
-(DefaultActor pid=1831567) >> Training accuracy: 0.830605
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.384972 Loss1: 0.613068 Loss2: 0.771904
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250663 Loss1: 0.552971 Loss2: 0.697692
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.241323 Loss1: 0.544325 Loss2: 0.696999
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.238076 Loss1: 0.539942 Loss2: 0.698133
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.244866 Loss1: 0.546798 Loss2: 0.698068
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.251045 Loss1: 0.551098 Loss2: 0.699946
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204507 Loss1: 0.508467 Loss2: 0.696040
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207571 Loss1: 0.507129 Loss2: 0.700442
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.187385 Loss1: 0.491588 Loss2: 0.695797
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.219355 Loss1: 0.520172 Loss2: 0.699182
-(DefaultActor pid=1831567) >> Training accuracy: 0.821456
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.379876 Loss1: 0.602918 Loss2: 0.776958
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202026 Loss1: 0.528213 Loss2: 0.673812
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.207688 Loss1: 0.532522 Loss2: 0.675166
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183791 Loss1: 0.508914 Loss2: 0.674877
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.226159 Loss1: 0.547541 Loss2: 0.678618
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.161430 Loss1: 0.486873 Loss2: 0.674557
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.151547 Loss1: 0.475619 Loss2: 0.675928
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.152089 Loss1: 0.474746 Loss2: 0.677344
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.126261 Loss1: 0.446014 Loss2: 0.680246
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.145105 Loss1: 0.467740 Loss2: 0.677365
-[2023-09-27 14:25:42,560][flwr][DEBUG] - fit_round 60 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.822034
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.700000
-[2023-09-27 14:25:43,898][flwr][INFO] - fit progress: (60, 0.8708832763825742, {'accuracy': 0.7}, 29276.734741013963)
-[2023-09-27 14:25:43,899][flwr][DEBUG] - evaluate_round 60: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:26:14,460][flwr][DEBUG] - evaluate_round 60 received 10 results and 0 failures
-[2023-09-27 14:26:14,461][flwr][DEBUG] - fit_round 61: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326675 Loss1: 0.586113 Loss2: 0.740562
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171048 Loss1: 0.527742 Loss2: 0.643306
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155933 Loss1: 0.513194 Loss2: 0.642738
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.129527 Loss1: 0.487805 Loss2: 0.641722
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.103373 Loss1: 0.464390 Loss2: 0.638983
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.144919 Loss1: 0.500198 Loss2: 0.644721
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122246 Loss1: 0.477053 Loss2: 0.645193
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.127260 Loss1: 0.480967 Loss2: 0.646293
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.128916 Loss1: 0.478331 Loss2: 0.650585
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.119934 Loss1: 0.471438 Loss2: 0.648496
-(DefaultActor pid=1831567) >> Training accuracy: 0.840572
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.512263 Loss1: 0.720696 Loss2: 0.791568
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.373909 Loss1: 0.684901 Loss2: 0.689008
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.356014 Loss1: 0.667483 Loss2: 0.688531
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347293 Loss1: 0.656539 Loss2: 0.690754
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322818 Loss1: 0.631324 Loss2: 0.691493
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.310772 Loss1: 0.622201 Loss2: 0.688571
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350056 Loss1: 0.657094 Loss2: 0.692962
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320188 Loss1: 0.623923 Loss2: 0.696265
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.346804 Loss1: 0.648557 Loss2: 0.698247
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307713 Loss1: 0.612253 Loss2: 0.695460
-(DefaultActor pid=1831567) >> Training accuracy: 0.776819
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.304942 Loss1: 0.601054 Loss2: 0.703888
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.168042 Loss1: 0.536537 Loss2: 0.631505
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195444 Loss1: 0.563956 Loss2: 0.631488
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165582 Loss1: 0.532816 Loss2: 0.632765
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140631 Loss1: 0.510554 Loss2: 0.630077
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.143186 Loss1: 0.511005 Loss2: 0.632182
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.153428 Loss1: 0.518841 Loss2: 0.634587
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168327 Loss1: 0.534017 Loss2: 0.634310
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138791 Loss1: 0.504384 Loss2: 0.634407
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.130521 Loss1: 0.494919 Loss2: 0.635601
-(DefaultActor pid=1831567) >> Training accuracy: 0.838986
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.223580 Loss1: 0.463147 Loss2: 0.760433
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.116158 Loss1: 0.440342 Loss2: 0.675816
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.060734 Loss1: 0.393718 Loss2: 0.667016
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.065373 Loss1: 0.396064 Loss2: 0.669309
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.060688 Loss1: 0.391728 Loss2: 0.668960
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.049201 Loss1: 0.380413 Loss2: 0.668788
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.039199 Loss1: 0.369985 Loss2: 0.669214
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.024170 Loss1: 0.358392 Loss2: 0.665778
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.032074 Loss1: 0.358817 Loss2: 0.673257
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.043997 Loss1: 0.370889 Loss2: 0.673108
-(DefaultActor pid=1831567) >> Training accuracy: 0.864776
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320712 Loss1: 0.581110 Loss2: 0.739601
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.221973 Loss1: 0.550207 Loss2: 0.671766
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208937 Loss1: 0.537187 Loss2: 0.671750
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.209296 Loss1: 0.537663 Loss2: 0.671633
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210649 Loss1: 0.534881 Loss2: 0.675768
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185648 Loss1: 0.510586 Loss2: 0.675062
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.203287 Loss1: 0.527603 Loss2: 0.675683
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.178404 Loss1: 0.502720 Loss2: 0.675684
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185459 Loss1: 0.507376 Loss2: 0.678084
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180984 Loss1: 0.503115 Loss2: 0.677869
-(DefaultActor pid=1831567) >> Training accuracy: 0.836939
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226226 Loss1: 0.463359 Loss2: 0.762867
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.083575 Loss1: 0.402705 Loss2: 0.680870
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.098719 Loss1: 0.420740 Loss2: 0.677978
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.060769 Loss1: 0.382157 Loss2: 0.678612
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.057776 Loss1: 0.376141 Loss2: 0.681636
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.074879 Loss1: 0.395737 Loss2: 0.679142
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.057444 Loss1: 0.376823 Loss2: 0.680621
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030635 Loss1: 0.350577 Loss2: 0.680058
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.070820 Loss1: 0.388215 Loss2: 0.682605
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.038893 Loss1: 0.358097 Loss2: 0.680797
-(DefaultActor pid=1831567) >> Training accuracy: 0.881752
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.445912 Loss1: 0.721061 Loss2: 0.724852
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301384 Loss1: 0.672282 Loss2: 0.629102
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.300373 Loss1: 0.670024 Loss2: 0.630349
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.266794 Loss1: 0.634554 Loss2: 0.632239
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.252384 Loss1: 0.618778 Loss2: 0.633606
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.260924 Loss1: 0.631436 Loss2: 0.629488
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.228790 Loss1: 0.596196 Loss2: 0.632594
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.227653 Loss1: 0.594380 Loss2: 0.633274
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.211889 Loss1: 0.580158 Loss2: 0.631731
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.234693 Loss1: 0.599922 Loss2: 0.634771
-(DefaultActor pid=1831567) >> Training accuracy: 0.790844
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.343631 Loss1: 0.574220 Loss2: 0.769411
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.257289 Loss1: 0.537156 Loss2: 0.720133
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.252166 Loss1: 0.532201 Loss2: 0.719966
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226176 Loss1: 0.508227 Loss2: 0.717949
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.251014 Loss1: 0.529842 Loss2: 0.721173
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.242765 Loss1: 0.522259 Loss2: 0.720505
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.230411 Loss1: 0.508391 Loss2: 0.722020
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.224135 Loss1: 0.504523 Loss2: 0.719612
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.239271 Loss1: 0.513379 Loss2: 0.725892
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.230073 Loss1: 0.508547 Loss2: 0.721526
-(DefaultActor pid=1831567) >> Training accuracy: 0.829613
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.349797 Loss1: 0.594158 Loss2: 0.755639
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217188 Loss1: 0.545983 Loss2: 0.671205
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.206539 Loss1: 0.534886 Loss2: 0.671653
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200808 Loss1: 0.526806 Loss2: 0.674001
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.182749 Loss1: 0.508409 Loss2: 0.674340
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163592 Loss1: 0.488860 Loss2: 0.674731
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.167280 Loss1: 0.489923 Loss2: 0.677357
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151241 Loss1: 0.477195 Loss2: 0.674046
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.155215 Loss1: 0.478530 Loss2: 0.676685
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152720 Loss1: 0.477374 Loss2: 0.675346
-(DefaultActor pid=1831567) >> Training accuracy: 0.842722
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.492221 Loss1: 0.740646 Loss2: 0.751576
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.368763 Loss1: 0.701530 Loss2: 0.667233
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363037 Loss1: 0.694703 Loss2: 0.668335
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.341035 Loss1: 0.677538 Loss2: 0.663497
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.366742 Loss1: 0.696868 Loss2: 0.669874
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341182 Loss1: 0.672693 Loss2: 0.668489
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.330835 Loss1: 0.661225 Loss2: 0.669610
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334119 Loss1: 0.662203 Loss2: 0.671916
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305017 Loss1: 0.630681 Loss2: 0.674336
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.312275 Loss1: 0.639207 Loss2: 0.673068
-(DefaultActor pid=1831567) >> Training accuracy: 0.776042
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 14:32:57,547][flwr][DEBUG] - fit_round 61 received 10 results and 0 failures
->> Test accuracy: 0.695300
-[2023-09-27 14:32:59,190][flwr][INFO] - fit progress: (61, 0.8809174903855918, {'accuracy': 0.6953}, 29712.026695901062)
-[2023-09-27 14:32:59,191][flwr][DEBUG] - evaluate_round 61: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:33:38,989][flwr][DEBUG] - evaluate_round 61 received 10 results and 0 failures
-[2023-09-27 14:33:38,990][flwr][DEBUG] - fit_round 62: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.515077 Loss1: 0.705244 Loss2: 0.809833
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.382614 Loss1: 0.675866 Loss2: 0.706748
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.378682 Loss1: 0.670697 Loss2: 0.707985
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.321875 Loss1: 0.616494 Loss2: 0.705381
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.339706 Loss1: 0.636170 Loss2: 0.703537
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.315958 Loss1: 0.607961 Loss2: 0.707998
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.300139 Loss1: 0.592913 Loss2: 0.707226
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288297 Loss1: 0.579139 Loss2: 0.709158
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299504 Loss1: 0.586625 Loss2: 0.712879
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.273917 Loss1: 0.563871 Loss2: 0.710046
-(DefaultActor pid=1831567) >> Training accuracy: 0.802632
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.478822 Loss1: 0.730280 Loss2: 0.748543
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340293 Loss1: 0.686988 Loss2: 0.653305
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.332048 Loss1: 0.677169 Loss2: 0.654879
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.337180 Loss1: 0.678483 Loss2: 0.658696
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317538 Loss1: 0.657624 Loss2: 0.659914
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.297543 Loss1: 0.637583 Loss2: 0.659960
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309719 Loss1: 0.651748 Loss2: 0.657971
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288905 Loss1: 0.630578 Loss2: 0.658327
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306724 Loss1: 0.647154 Loss2: 0.659570
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.277348 Loss1: 0.616263 Loss2: 0.661085
-(DefaultActor pid=1831567) >> Training accuracy: 0.776353
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.349216 Loss1: 0.599338 Loss2: 0.749878
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212419 Loss1: 0.539766 Loss2: 0.672654
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195049 Loss1: 0.526058 Loss2: 0.668990
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.224972 Loss1: 0.551381 Loss2: 0.673592
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187843 Loss1: 0.510842 Loss2: 0.677001
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.218565 Loss1: 0.542344 Loss2: 0.676222
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.214663 Loss1: 0.536524 Loss2: 0.678139
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173666 Loss1: 0.498109 Loss2: 0.675557
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.168897 Loss1: 0.491096 Loss2: 0.677800
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175125 Loss1: 0.496972 Loss2: 0.678153
-(DefaultActor pid=1831567) >> Training accuracy: 0.838542
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.345392 Loss1: 0.557428 Loss2: 0.787963
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225435 Loss1: 0.537486 Loss2: 0.687949
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212264 Loss1: 0.523341 Loss2: 0.688924
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200309 Loss1: 0.509638 Loss2: 0.690671
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198414 Loss1: 0.509108 Loss2: 0.689306
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157888 Loss1: 0.469593 Loss2: 0.688295
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165263 Loss1: 0.477525 Loss2: 0.687738
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.149231 Loss1: 0.456983 Loss2: 0.692247
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.171233 Loss1: 0.478060 Loss2: 0.693173
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.155512 Loss1: 0.464876 Loss2: 0.690636
-(DefaultActor pid=1831567) >> Training accuracy: 0.845074
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.217826 Loss1: 0.481216 Loss2: 0.736610
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.060610 Loss1: 0.406504 Loss2: 0.654106
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.055238 Loss1: 0.401947 Loss2: 0.653292
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.057087 Loss1: 0.406948 Loss2: 0.650139
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.041089 Loss1: 0.390057 Loss2: 0.651032
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.030372 Loss1: 0.376248 Loss2: 0.654124
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.042924 Loss1: 0.388352 Loss2: 0.654572
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.033391 Loss1: 0.379357 Loss2: 0.654034
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.041063 Loss1: 0.385796 Loss2: 0.655267
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.005051 Loss1: 0.350481 Loss2: 0.654570
-(DefaultActor pid=1831567) >> Training accuracy: 0.877894
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.311420 Loss1: 0.569441 Loss2: 0.741980
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225054 Loss1: 0.528523 Loss2: 0.696531
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215983 Loss1: 0.518774 Loss2: 0.697209
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.220200 Loss1: 0.523234 Loss2: 0.696967
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215170 Loss1: 0.517475 Loss2: 0.697695
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.237090 Loss1: 0.533873 Loss2: 0.703216
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.205291 Loss1: 0.507447 Loss2: 0.697844
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204364 Loss1: 0.503466 Loss2: 0.700898
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213313 Loss1: 0.510639 Loss2: 0.702674
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176584 Loss1: 0.479810 Loss2: 0.696774
-(DefaultActor pid=1831567) >> Training accuracy: 0.830481
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.397408 Loss1: 0.624917 Loss2: 0.772491
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.251157 Loss1: 0.550621 Loss2: 0.700536
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.224139 Loss1: 0.528204 Loss2: 0.695935
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.239343 Loss1: 0.538702 Loss2: 0.700641
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216114 Loss1: 0.514627 Loss2: 0.701488
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.203075 Loss1: 0.498268 Loss2: 0.704807
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.225847 Loss1: 0.522072 Loss2: 0.703774
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.197847 Loss1: 0.494999 Loss2: 0.702848
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.218003 Loss1: 0.514596 Loss2: 0.703407
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.187671 Loss1: 0.484174 Loss2: 0.703497
-(DefaultActor pid=1831567) >> Training accuracy: 0.820122
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.258195 Loss1: 0.459880 Loss2: 0.798315
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.129754 Loss1: 0.408654 Loss2: 0.721100
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.102059 Loss1: 0.382905 Loss2: 0.719154
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.127615 Loss1: 0.409511 Loss2: 0.718104
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.115623 Loss1: 0.392995 Loss2: 0.722628
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.095292 Loss1: 0.373557 Loss2: 0.721734
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.094366 Loss1: 0.371930 Loss2: 0.722436
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.110347 Loss1: 0.386015 Loss2: 0.724332
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.088301 Loss1: 0.367535 Loss2: 0.720766
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.082422 Loss1: 0.359112 Loss2: 0.723310
-(DefaultActor pid=1831567) >> Training accuracy: 0.869020
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.520529 Loss1: 0.762255 Loss2: 0.758274
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.391834 Loss1: 0.719607 Loss2: 0.672228
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.387400 Loss1: 0.713868 Loss2: 0.673533
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.360994 Loss1: 0.688600 Loss2: 0.672394
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.348885 Loss1: 0.674626 Loss2: 0.674259
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.356135 Loss1: 0.682714 Loss2: 0.673421
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.343615 Loss1: 0.666146 Loss2: 0.677469
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.330153 Loss1: 0.654297 Loss2: 0.675855
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.337656 Loss1: 0.660978 Loss2: 0.676678
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.330285 Loss1: 0.653480 Loss2: 0.676805
-(DefaultActor pid=1831567) >> Training accuracy: 0.758379
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348198 Loss1: 0.600474 Loss2: 0.747723
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200599 Loss1: 0.535921 Loss2: 0.664678
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219120 Loss1: 0.553215 Loss2: 0.665905
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187394 Loss1: 0.524851 Loss2: 0.662543
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.188790 Loss1: 0.523456 Loss2: 0.665334
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.149214 Loss1: 0.486686 Loss2: 0.662528
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.162575 Loss1: 0.495464 Loss2: 0.667111
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163362 Loss1: 0.494790 Loss2: 0.668572
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.154197 Loss1: 0.485220 Loss2: 0.668977
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140106 Loss1: 0.470354 Loss2: 0.669753
-[2023-09-27 14:40:43,586][flwr][DEBUG] - fit_round 62 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.833265
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.690700
-[2023-09-27 14:40:44,860][flwr][INFO] - fit progress: (62, 0.8817045592461912, {'accuracy': 0.6907}, 30177.696814930066)
-[2023-09-27 14:40:44,861][flwr][DEBUG] - evaluate_round 62: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:41:15,632][flwr][DEBUG] - evaluate_round 62 received 10 results and 0 failures
-[2023-09-27 14:41:15,633][flwr][DEBUG] - fit_round 63: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226843 Loss1: 0.447229 Loss2: 0.779614
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.123498 Loss1: 0.424485 Loss2: 0.699013
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.097748 Loss1: 0.399982 Loss2: 0.697766
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.076791 Loss1: 0.383229 Loss2: 0.693562
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.088134 Loss1: 0.393642 Loss2: 0.694491
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.078291 Loss1: 0.386579 Loss2: 0.691712
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.056119 Loss1: 0.365015 Loss2: 0.691104
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.072717 Loss1: 0.378537 Loss2: 0.694180
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.061771 Loss1: 0.368584 Loss2: 0.693186
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.060516 Loss1: 0.364542 Loss2: 0.695974
-(DefaultActor pid=1831567) >> Training accuracy: 0.881559
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.300211 Loss1: 0.587473 Loss2: 0.712738
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.199432 Loss1: 0.557283 Loss2: 0.642148
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.171365 Loss1: 0.532251 Loss2: 0.639114
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175490 Loss1: 0.540192 Loss2: 0.635298
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.164516 Loss1: 0.529138 Loss2: 0.635378
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167524 Loss1: 0.528380 Loss2: 0.639144
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148919 Loss1: 0.510844 Loss2: 0.638075
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151114 Loss1: 0.512004 Loss2: 0.639110
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.143363 Loss1: 0.501638 Loss2: 0.641725
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134767 Loss1: 0.495479 Loss2: 0.639288
-(DefaultActor pid=1831567) >> Training accuracy: 0.833841
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.497253 Loss1: 0.727041 Loss2: 0.770213
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.355612 Loss1: 0.674700 Loss2: 0.680912
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.369486 Loss1: 0.689530 Loss2: 0.679956
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.362141 Loss1: 0.678712 Loss2: 0.683429
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322146 Loss1: 0.643444 Loss2: 0.678702
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337990 Loss1: 0.652923 Loss2: 0.685068
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322909 Loss1: 0.638807 Loss2: 0.684102
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.313395 Loss1: 0.627850 Loss2: 0.685545
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.308851 Loss1: 0.622100 Loss2: 0.686752
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.306366 Loss1: 0.620218 Loss2: 0.686148
-(DefaultActor pid=1831567) >> Training accuracy: 0.777752
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321727 Loss1: 0.563622 Loss2: 0.758105
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.235225 Loss1: 0.530906 Loss2: 0.704319
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.243605 Loss1: 0.539304 Loss2: 0.704302
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.238641 Loss1: 0.530940 Loss2: 0.707701
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.223415 Loss1: 0.516822 Loss2: 0.706593
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.234895 Loss1: 0.525528 Loss2: 0.709367
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200224 Loss1: 0.492601 Loss2: 0.707623
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.210157 Loss1: 0.501186 Loss2: 0.708972
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188507 Loss1: 0.481417 Loss2: 0.707091
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.220000 Loss1: 0.510315 Loss2: 0.709686
-(DefaultActor pid=1831567) >> Training accuracy: 0.828621
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.519055 Loss1: 0.758830 Loss2: 0.760226
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.404027 Loss1: 0.725143 Loss2: 0.678884
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.366808 Loss1: 0.694178 Loss2: 0.672630
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364398 Loss1: 0.686283 Loss2: 0.678115
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.369368 Loss1: 0.688934 Loss2: 0.680433
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.359227 Loss1: 0.680044 Loss2: 0.679184
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.323842 Loss1: 0.645099 Loss2: 0.678742
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.371408 Loss1: 0.686490 Loss2: 0.684918
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.320955 Loss1: 0.639516 Loss2: 0.681439
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.332290 Loss1: 0.651131 Loss2: 0.681160
-(DefaultActor pid=1831567) >> Training accuracy: 0.776947
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.483242 Loss1: 0.726110 Loss2: 0.757132
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.335080 Loss1: 0.677128 Loss2: 0.657952
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.308584 Loss1: 0.648877 Loss2: 0.659707
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.292785 Loss1: 0.632724 Loss2: 0.660061
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.285252 Loss1: 0.625042 Loss2: 0.660210
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.275011 Loss1: 0.614412 Loss2: 0.660599
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.265410 Loss1: 0.602963 Loss2: 0.662447
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268015 Loss1: 0.602994 Loss2: 0.665021
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.258789 Loss1: 0.594463 Loss2: 0.664326
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.255676 Loss1: 0.590317 Loss2: 0.665359
-(DefaultActor pid=1831567) >> Training accuracy: 0.797149
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.374430 Loss1: 0.597466 Loss2: 0.776964
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250120 Loss1: 0.546629 Loss2: 0.703492
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.248402 Loss1: 0.544627 Loss2: 0.703775
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.249140 Loss1: 0.542073 Loss2: 0.707067
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224527 Loss1: 0.516774 Loss2: 0.707752
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.234051 Loss1: 0.522616 Loss2: 0.711435
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.224892 Loss1: 0.514170 Loss2: 0.710722
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.203762 Loss1: 0.495708 Loss2: 0.708054
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.220095 Loss1: 0.507321 Loss2: 0.712774
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198902 Loss1: 0.487312 Loss2: 0.711590
-(DefaultActor pid=1831567) >> Training accuracy: 0.813301
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341269 Loss1: 0.575729 Loss2: 0.765540
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.229904 Loss1: 0.546410 Loss2: 0.683494
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.189457 Loss1: 0.508986 Loss2: 0.680470
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.203888 Loss1: 0.523398 Loss2: 0.680490
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.208298 Loss1: 0.524845 Loss2: 0.683453
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186039 Loss1: 0.505311 Loss2: 0.680728
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187630 Loss1: 0.498368 Loss2: 0.689263
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.178493 Loss1: 0.491584 Loss2: 0.686908
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175043 Loss1: 0.489657 Loss2: 0.685386
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165438 Loss1: 0.477487 Loss2: 0.687951
-(DefaultActor pid=1831567) >> Training accuracy: 0.844161
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.364723 Loss1: 0.579033 Loss2: 0.785690
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.250337 Loss1: 0.570505 Loss2: 0.679832
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201217 Loss1: 0.522536 Loss2: 0.678681
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191798 Loss1: 0.513035 Loss2: 0.678764
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154916 Loss1: 0.477440 Loss2: 0.677476
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.126674 Loss1: 0.451394 Loss2: 0.675280
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.154523 Loss1: 0.475207 Loss2: 0.679316
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155978 Loss1: 0.474343 Loss2: 0.681635
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.133315 Loss1: 0.454404 Loss2: 0.678911
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.153601 Loss1: 0.472868 Loss2: 0.680733
-(DefaultActor pid=1831567) >> Training accuracy: 0.845604
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.207631 Loss1: 0.442698 Loss2: 0.764933
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092862 Loss1: 0.408410 Loss2: 0.684452
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.116319 Loss1: 0.433298 Loss2: 0.683021
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.060464 Loss1: 0.378038 Loss2: 0.682426
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.069804 Loss1: 0.388845 Loss2: 0.680959
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.052355 Loss1: 0.370651 Loss2: 0.681703
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.054814 Loss1: 0.372502 Loss2: 0.682311
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.045106 Loss1: 0.360860 Loss2: 0.684246
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.051653 Loss1: 0.367633 Loss2: 0.684020
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.024956 Loss1: 0.340431 Loss2: 0.684525
-[2023-09-27 14:48:03,660][flwr][DEBUG] - fit_round 63 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.865741
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.700800
-[2023-09-27 14:48:05,273][flwr][INFO] - fit progress: (63, 0.8681536297828626, {'accuracy': 0.7008}, 30618.10951640876)
-[2023-09-27 14:48:05,274][flwr][DEBUG] - evaluate_round 63: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:48:37,051][flwr][DEBUG] - evaluate_round 63 received 10 results and 0 failures
-[2023-09-27 14:48:37,051][flwr][DEBUG] - fit_round 64: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462730 Loss1: 0.727581 Loss2: 0.735149
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340665 Loss1: 0.690840 Loss2: 0.649825
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334233 Loss1: 0.683406 Loss2: 0.650827
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.312743 Loss1: 0.660758 Loss2: 0.651985
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.292086 Loss1: 0.642122 Loss2: 0.649963
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.307211 Loss1: 0.655502 Loss2: 0.651709
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298864 Loss1: 0.639975 Loss2: 0.658888
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.284432 Loss1: 0.631827 Loss2: 0.652604
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.282537 Loss1: 0.630138 Loss2: 0.652398
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.267936 Loss1: 0.611715 Loss2: 0.656221
-(DefaultActor pid=1831567) >> Training accuracy: 0.783815
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340451 Loss1: 0.555263 Loss2: 0.785188
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258675 Loss1: 0.528608 Loss2: 0.730066
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.278325 Loss1: 0.544433 Loss2: 0.733892
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.263432 Loss1: 0.528968 Loss2: 0.734464
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.253636 Loss1: 0.520821 Loss2: 0.732814
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249463 Loss1: 0.515537 Loss2: 0.733926
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.240179 Loss1: 0.507139 Loss2: 0.733040
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.232492 Loss1: 0.500384 Loss2: 0.732108
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.235685 Loss1: 0.501710 Loss2: 0.733976
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240152 Loss1: 0.504016 Loss2: 0.736136
-(DefaultActor pid=1831567) >> Training accuracy: 0.838542
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.489630 Loss1: 0.745296 Loss2: 0.744334
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394515 Loss1: 0.735581 Loss2: 0.658933
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.350947 Loss1: 0.695256 Loss2: 0.655691
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.334373 Loss1: 0.673456 Loss2: 0.660917
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.335003 Loss1: 0.673931 Loss2: 0.661072
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.327984 Loss1: 0.666188 Loss2: 0.661795
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.315706 Loss1: 0.655885 Loss2: 0.659822
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.324045 Loss1: 0.662286 Loss2: 0.661760
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.313953 Loss1: 0.651240 Loss2: 0.662713
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.314386 Loss1: 0.647870 Loss2: 0.666516
-(DefaultActor pid=1831567) >> Training accuracy: 0.784194
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.399496 Loss1: 0.611060 Loss2: 0.788437
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258414 Loss1: 0.542933 Loss2: 0.715481
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.256036 Loss1: 0.542251 Loss2: 0.713786
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.245409 Loss1: 0.531328 Loss2: 0.714081
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.219432 Loss1: 0.506244 Loss2: 0.713188
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.221484 Loss1: 0.505643 Loss2: 0.715841
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.241084 Loss1: 0.521421 Loss2: 0.719663
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.235368 Loss1: 0.517858 Loss2: 0.717509
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217206 Loss1: 0.496045 Loss2: 0.721162
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.206466 Loss1: 0.487169 Loss2: 0.719297
-(DefaultActor pid=1831567) >> Training accuracy: 0.836128
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.175564 Loss1: 0.445561 Loss2: 0.730003
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.049593 Loss1: 0.400521 Loss2: 0.649072
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.070014 Loss1: 0.420614 Loss2: 0.649400
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.036553 Loss1: 0.387703 Loss2: 0.648850
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.027433 Loss1: 0.380765 Loss2: 0.646667
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.030092 Loss1: 0.379138 Loss2: 0.650954
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.008932 Loss1: 0.358183 Loss2: 0.650750
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.012894 Loss1: 0.360868 Loss2: 0.652026
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042608 Loss1: 0.388062 Loss2: 0.654546
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.001309 Loss1: 0.350433 Loss2: 0.650876
-(DefaultActor pid=1831567) >> Training accuracy: 0.883681
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.169928 Loss1: 0.463094 Loss2: 0.706834
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.041522 Loss1: 0.400164 Loss2: 0.641358
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.026008 Loss1: 0.384078 Loss2: 0.641931
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.034367 Loss1: 0.392500 Loss2: 0.641867
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.014188 Loss1: 0.372892 Loss2: 0.641296
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.018014 Loss1: 0.377105 Loss2: 0.640909
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.006873 Loss1: 0.367820 Loss2: 0.639053
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.010586 Loss1: 0.364711 Loss2: 0.645875
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.012302 Loss1: 0.365459 Loss2: 0.646843
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028604 Loss1: 0.381551 Loss2: 0.647053
-(DefaultActor pid=1831567) >> Training accuracy: 0.877315
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.339453 Loss1: 0.583892 Loss2: 0.755561
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212505 Loss1: 0.539010 Loss2: 0.673495
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198972 Loss1: 0.527749 Loss2: 0.671224
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.198444 Loss1: 0.522764 Loss2: 0.675681
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.161938 Loss1: 0.488278 Loss2: 0.673660
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.166027 Loss1: 0.489818 Loss2: 0.676208
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.181071 Loss1: 0.502525 Loss2: 0.678546
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.174202 Loss1: 0.494434 Loss2: 0.679768
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165939 Loss1: 0.486223 Loss2: 0.679716
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140544 Loss1: 0.462378 Loss2: 0.678165
-(DefaultActor pid=1831567) >> Training accuracy: 0.842722
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.505932 Loss1: 0.711266 Loss2: 0.794666
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346657 Loss1: 0.660898 Loss2: 0.685759
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.312651 Loss1: 0.629361 Loss2: 0.683290
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.306625 Loss1: 0.619631 Loss2: 0.686994
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.321894 Loss1: 0.631526 Loss2: 0.690368
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.302433 Loss1: 0.610537 Loss2: 0.691896
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.297075 Loss1: 0.605474 Loss2: 0.691601
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.293479 Loss1: 0.600675 Loss2: 0.692803
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.295090 Loss1: 0.595839 Loss2: 0.699251
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290331 Loss1: 0.592970 Loss2: 0.697361
-(DefaultActor pid=1831567) >> Training accuracy: 0.795504
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.354779 Loss1: 0.578571 Loss2: 0.776208
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.208587 Loss1: 0.532334 Loss2: 0.676253
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193248 Loss1: 0.517957 Loss2: 0.675291
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.165130 Loss1: 0.489251 Loss2: 0.675880
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.161601 Loss1: 0.485775 Loss2: 0.675826
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.156122 Loss1: 0.480572 Loss2: 0.675551
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.132965 Loss1: 0.457182 Loss2: 0.675783
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.132009 Loss1: 0.451864 Loss2: 0.680145
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138004 Loss1: 0.456428 Loss2: 0.681576
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.129263 Loss1: 0.447331 Loss2: 0.681932
-(DefaultActor pid=1831567) >> Training accuracy: 0.807998
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.299247 Loss1: 0.584624 Loss2: 0.714624
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.185035 Loss1: 0.543908 Loss2: 0.641127
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195437 Loss1: 0.554468 Loss2: 0.640969
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.161360 Loss1: 0.517894 Loss2: 0.643466
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.160834 Loss1: 0.517437 Loss2: 0.643397
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.152825 Loss1: 0.512109 Loss2: 0.640716
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170487 Loss1: 0.524337 Loss2: 0.646149
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.159103 Loss1: 0.513268 Loss2: 0.645835
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.146806 Loss1: 0.500530 Loss2: 0.646276
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146908 Loss1: 0.501169 Loss2: 0.645739
-[2023-09-27 14:55:23,352][flwr][DEBUG] - fit_round 64 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.838942
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.695000
-[2023-09-27 14:55:24,899][flwr][INFO] - fit progress: (64, 0.8740351014434339, {'accuracy': 0.695}, 31057.73582381988)
-[2023-09-27 14:55:24,900][flwr][DEBUG] - evaluate_round 64: strategy sampled 10 clients (out of 10)
-[2023-09-27 14:55:56,851][flwr][DEBUG] - evaluate_round 64 received 10 results and 0 failures
-[2023-09-27 14:55:56,852][flwr][DEBUG] - fit_round 65: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.207375 Loss1: 0.447012 Loss2: 0.760363
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099528 Loss1: 0.422736 Loss2: 0.676792
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.076674 Loss1: 0.400434 Loss2: 0.676241
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.043602 Loss1: 0.366687 Loss2: 0.676915
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.053771 Loss1: 0.377058 Loss2: 0.676713
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.049069 Loss1: 0.372029 Loss2: 0.677040
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.038666 Loss1: 0.361560 Loss2: 0.677106
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.033600 Loss1: 0.356060 Loss2: 0.677540
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042125 Loss1: 0.362494 Loss2: 0.679631
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.042600 Loss1: 0.361343 Loss2: 0.681257
-(DefaultActor pid=1831567) >> Training accuracy: 0.882330
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.515185 Loss1: 0.740192 Loss2: 0.774993
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.361249 Loss1: 0.678534 Loss2: 0.682715
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.376112 Loss1: 0.684847 Loss2: 0.691265
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.382798 Loss1: 0.692025 Loss2: 0.690773
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.379424 Loss1: 0.687565 Loss2: 0.691859
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.349952 Loss1: 0.661144 Loss2: 0.688808
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.375819 Loss1: 0.681146 Loss2: 0.694673
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.336607 Loss1: 0.641342 Loss2: 0.695265
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.336012 Loss1: 0.642657 Loss2: 0.693355
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.339486 Loss1: 0.643512 Loss2: 0.695974
-(DefaultActor pid=1831567) >> Training accuracy: 0.767437
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.312861 Loss1: 0.560726 Loss2: 0.752134
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.242303 Loss1: 0.531703 Loss2: 0.710600
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226373 Loss1: 0.519685 Loss2: 0.706688
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.232507 Loss1: 0.521377 Loss2: 0.711130
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.232685 Loss1: 0.522315 Loss2: 0.710370
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.215794 Loss1: 0.502958 Loss2: 0.712836
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.198916 Loss1: 0.489260 Loss2: 0.709656
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.221066 Loss1: 0.510762 Loss2: 0.710304
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.222944 Loss1: 0.507841 Loss2: 0.715103
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.207292 Loss1: 0.493992 Loss2: 0.713300
-(DefaultActor pid=1831567) >> Training accuracy: 0.832589
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.316522 Loss1: 0.571148 Loss2: 0.745374
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.260666 Loss1: 0.578910 Loss2: 0.681756
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.224280 Loss1: 0.544827 Loss2: 0.679453
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.207829 Loss1: 0.528263 Loss2: 0.679566
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191228 Loss1: 0.514200 Loss2: 0.677028
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.194057 Loss1: 0.514577 Loss2: 0.679480
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.194499 Loss1: 0.510625 Loss2: 0.683874
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.193228 Loss1: 0.510382 Loss2: 0.682845
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.177941 Loss1: 0.493139 Loss2: 0.684802
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180268 Loss1: 0.494923 Loss2: 0.685345
-(DefaultActor pid=1831567) >> Training accuracy: 0.838542
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.492752 Loss1: 0.720467 Loss2: 0.772285
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.349707 Loss1: 0.667209 Loss2: 0.682499
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.373813 Loss1: 0.690959 Loss2: 0.682854
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347384 Loss1: 0.661941 Loss2: 0.685442
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.354493 Loss1: 0.669655 Loss2: 0.684838
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337615 Loss1: 0.650772 Loss2: 0.686842
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.309858 Loss1: 0.622971 Loss2: 0.686887
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.303439 Loss1: 0.613670 Loss2: 0.689770
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.297019 Loss1: 0.610502 Loss2: 0.686517
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.311448 Loss1: 0.622483 Loss2: 0.688965
-(DefaultActor pid=1831567) >> Training accuracy: 0.753498
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.239908 Loss1: 0.455092 Loss2: 0.784816
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.102348 Loss1: 0.397705 Loss2: 0.704643
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.112180 Loss1: 0.409105 Loss2: 0.703075
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.085666 Loss1: 0.384653 Loss2: 0.701013
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.105391 Loss1: 0.398290 Loss2: 0.707101
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.087925 Loss1: 0.383089 Loss2: 0.704835
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.097966 Loss1: 0.390569 Loss2: 0.707397
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.070290 Loss1: 0.365952 Loss2: 0.704339
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.055476 Loss1: 0.354127 Loss2: 0.701349
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.076319 Loss1: 0.372626 Loss2: 0.703693
-(DefaultActor pid=1831567) >> Training accuracy: 0.877894
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.294918 Loss1: 0.604212 Loss2: 0.690706
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.177588 Loss1: 0.556040 Loss2: 0.621548
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.154169 Loss1: 0.536157 Loss2: 0.618012
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.145975 Loss1: 0.527108 Loss2: 0.618867
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.128596 Loss1: 0.510104 Loss2: 0.618492
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.147436 Loss1: 0.530094 Loss2: 0.617342
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.118716 Loss1: 0.500354 Loss2: 0.618362
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.124762 Loss1: 0.506673 Loss2: 0.618089
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.105961 Loss1: 0.486767 Loss2: 0.619194
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.108944 Loss1: 0.488826 Loss2: 0.620119
-(DefaultActor pid=1831567) >> Training accuracy: 0.835175
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340422 Loss1: 0.590586 Loss2: 0.749837
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.222969 Loss1: 0.553612 Loss2: 0.669356
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199148 Loss1: 0.532361 Loss2: 0.666787
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181992 Loss1: 0.514387 Loss2: 0.667605
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.182503 Loss1: 0.510388 Loss2: 0.672114
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.165641 Loss1: 0.494642 Loss2: 0.671000
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.151327 Loss1: 0.479230 Loss2: 0.672097
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151696 Loss1: 0.480783 Loss2: 0.670913
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.164138 Loss1: 0.492378 Loss2: 0.671760
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.159164 Loss1: 0.487265 Loss2: 0.671899
-(DefaultActor pid=1831567) >> Training accuracy: 0.839638
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358534 Loss1: 0.583398 Loss2: 0.775135
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.221122 Loss1: 0.553252 Loss2: 0.667870
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194882 Loss1: 0.528287 Loss2: 0.666595
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.168131 Loss1: 0.502021 Loss2: 0.666110
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184780 Loss1: 0.517065 Loss2: 0.667715
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.147048 Loss1: 0.477287 Loss2: 0.669761
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.154805 Loss1: 0.488070 Loss2: 0.666735
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140136 Loss1: 0.473094 Loss2: 0.667042
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.132122 Loss1: 0.461925 Loss2: 0.670197
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.156467 Loss1: 0.487555 Loss2: 0.668912
-(DefaultActor pid=1831567) >> Training accuracy: 0.845074
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477047 Loss1: 0.726062 Loss2: 0.750985
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.284871 Loss1: 0.637985 Loss2: 0.646886
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.279929 Loss1: 0.630424 Loss2: 0.649506
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281116 Loss1: 0.629427 Loss2: 0.651689
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.284758 Loss1: 0.631359 Loss2: 0.653398
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.258842 Loss1: 0.607752 Loss2: 0.651090
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.289525 Loss1: 0.633423 Loss2: 0.656101
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253601 Loss1: 0.593747 Loss2: 0.659854
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.253531 Loss1: 0.593779 Loss2: 0.659752
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.251962 Loss1: 0.592415 Loss2: 0.659548
-[2023-09-27 15:02:52,509][flwr][DEBUG] - fit_round 65 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.805921
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.697600
-[2023-09-27 15:02:53,877][flwr][INFO] - fit progress: (65, 0.8715874363248721, {'accuracy': 0.6976}, 31506.713589180727)
-[2023-09-27 15:02:53,878][flwr][DEBUG] - evaluate_round 65: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:03:25,153][flwr][DEBUG] - evaluate_round 65 received 10 results and 0 failures
-[2023-09-27 15:03:25,154][flwr][DEBUG] - fit_round 66: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324580 Loss1: 0.579214 Loss2: 0.745366
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.221395 Loss1: 0.527187 Loss2: 0.694209
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.234917 Loss1: 0.536418 Loss2: 0.698499
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.239570 Loss1: 0.541908 Loss2: 0.697662
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216113 Loss1: 0.520929 Loss2: 0.695184
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.208219 Loss1: 0.513202 Loss2: 0.695018
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.199438 Loss1: 0.502841 Loss2: 0.696597
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190995 Loss1: 0.494246 Loss2: 0.696749
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.193010 Loss1: 0.494696 Loss2: 0.698314
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201187 Loss1: 0.503612 Loss2: 0.697575
-(DefaultActor pid=1831567) >> Training accuracy: 0.826761
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.476845 Loss1: 0.740963 Loss2: 0.735882
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340792 Loss1: 0.690511 Loss2: 0.650281
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336483 Loss1: 0.685433 Loss2: 0.651051
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333696 Loss1: 0.679620 Loss2: 0.654076
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317663 Loss1: 0.665835 Loss2: 0.651829
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.360265 Loss1: 0.704195 Loss2: 0.656071
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.331417 Loss1: 0.678492 Loss2: 0.652925
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.294654 Loss1: 0.639948 Loss2: 0.654707
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.284581 Loss1: 0.628864 Loss2: 0.655716
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.272518 Loss1: 0.615498 Loss2: 0.657020
-(DefaultActor pid=1831567) >> Training accuracy: 0.775362
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.229757 Loss1: 0.466181 Loss2: 0.763576
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.095326 Loss1: 0.406474 Loss2: 0.688852
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086796 Loss1: 0.395250 Loss2: 0.691545
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.088028 Loss1: 0.396502 Loss2: 0.691526
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.083553 Loss1: 0.392880 Loss2: 0.690673
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.069885 Loss1: 0.377775 Loss2: 0.692110
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.064911 Loss1: 0.373674 Loss2: 0.691236
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.054366 Loss1: 0.361243 Loss2: 0.693122
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.064557 Loss1: 0.372941 Loss2: 0.691617
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.038392 Loss1: 0.349260 Loss2: 0.689133
-(DefaultActor pid=1831567) >> Training accuracy: 0.874421
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475308 Loss1: 0.711790 Loss2: 0.763517
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.307360 Loss1: 0.649707 Loss2: 0.657653
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307078 Loss1: 0.648617 Loss2: 0.658461
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291675 Loss1: 0.627265 Loss2: 0.664410
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.284796 Loss1: 0.624027 Loss2: 0.660769
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.276481 Loss1: 0.614622 Loss2: 0.661859
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.239224 Loss1: 0.577258 Loss2: 0.661966
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.246407 Loss1: 0.582331 Loss2: 0.664075
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.269585 Loss1: 0.604402 Loss2: 0.665183
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.237662 Loss1: 0.574316 Loss2: 0.663346
-(DefaultActor pid=1831567) >> Training accuracy: 0.800164
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321621 Loss1: 0.577607 Loss2: 0.744014
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201801 Loss1: 0.538461 Loss2: 0.663340
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199502 Loss1: 0.535523 Loss2: 0.663979
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189504 Loss1: 0.522562 Loss2: 0.666942
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.177123 Loss1: 0.509212 Loss2: 0.667911
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164551 Loss1: 0.497621 Loss2: 0.666930
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179468 Loss1: 0.510520 Loss2: 0.668948
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.148415 Loss1: 0.478923 Loss2: 0.669492
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.118326 Loss1: 0.451072 Loss2: 0.667254
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.141467 Loss1: 0.472273 Loss2: 0.669193
-(DefaultActor pid=1831567) >> Training accuracy: 0.841900
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353940 Loss1: 0.597815 Loss2: 0.756125
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180156 Loss1: 0.522212 Loss2: 0.657944
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.166096 Loss1: 0.508951 Loss2: 0.657145
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162591 Loss1: 0.504667 Loss2: 0.657923
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158976 Loss1: 0.499324 Loss2: 0.659652
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.128344 Loss1: 0.468236 Loss2: 0.660108
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123985 Loss1: 0.461659 Loss2: 0.662326
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.127268 Loss1: 0.465302 Loss2: 0.661966
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.101952 Loss1: 0.439711 Loss2: 0.662242
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.089118 Loss1: 0.430331 Loss2: 0.658788
-(DefaultActor pid=1831567) >> Training accuracy: 0.859640
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.498746 Loss1: 0.745817 Loss2: 0.752929
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.376295 Loss1: 0.711740 Loss2: 0.664554
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.339839 Loss1: 0.676623 Loss2: 0.663215
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.346557 Loss1: 0.683643 Loss2: 0.662913
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.314766 Loss1: 0.653285 Loss2: 0.661480
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.295718 Loss1: 0.632078 Loss2: 0.663640
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.302646 Loss1: 0.638429 Loss2: 0.664217
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.292246 Loss1: 0.629251 Loss2: 0.662995
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.292343 Loss1: 0.626339 Loss2: 0.666004
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304420 Loss1: 0.635741 Loss2: 0.668679
-(DefaultActor pid=1831567) >> Training accuracy: 0.791278
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.200191 Loss1: 0.454593 Loss2: 0.745599
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.077804 Loss1: 0.413368 Loss2: 0.664437
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.060196 Loss1: 0.397051 Loss2: 0.663145
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.060160 Loss1: 0.395172 Loss2: 0.664988
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.036566 Loss1: 0.376336 Loss2: 0.660230
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.033694 Loss1: 0.370182 Loss2: 0.663512
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.048713 Loss1: 0.384112 Loss2: 0.664601
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.014180 Loss1: 0.347999 Loss2: 0.666181
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.027484 Loss1: 0.359666 Loss2: 0.667817
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.044227 Loss1: 0.375419 Loss2: 0.668808
-(DefaultActor pid=1831567) >> Training accuracy: 0.878279
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.333283 Loss1: 0.600590 Loss2: 0.732693
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216747 Loss1: 0.556008 Loss2: 0.660739
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.200480 Loss1: 0.541320 Loss2: 0.659160
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184451 Loss1: 0.520701 Loss2: 0.663750
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.179479 Loss1: 0.516522 Loss2: 0.662957
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.178353 Loss1: 0.515782 Loss2: 0.662570
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163678 Loss1: 0.500867 Loss2: 0.662811
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.197176 Loss1: 0.529711 Loss2: 0.667465
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.167647 Loss1: 0.501929 Loss2: 0.665718
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.175172 Loss1: 0.507252 Loss2: 0.667920
-(DefaultActor pid=1831567) >> Training accuracy: 0.839944
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359117 Loss1: 0.585248 Loss2: 0.773869
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.266894 Loss1: 0.560551 Loss2: 0.706343
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.248093 Loss1: 0.544937 Loss2: 0.703156
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.234971 Loss1: 0.532741 Loss2: 0.702230
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.222008 Loss1: 0.518362 Loss2: 0.703645
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211284 Loss1: 0.504727 Loss2: 0.706557
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.190040 Loss1: 0.488265 Loss2: 0.701775
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.189740 Loss1: 0.485830 Loss2: 0.703910
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188283 Loss1: 0.480208 Loss2: 0.708075
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.193635 Loss1: 0.484527 Loss2: 0.709108
-(DefaultActor pid=1831567) >> Training accuracy: 0.832889
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 15:10:24,111][flwr][DEBUG] - fit_round 66 received 10 results and 0 failures
->> Test accuracy: 0.699200
-[2023-09-27 15:10:25,830][flwr][INFO] - fit progress: (66, 0.8717131253819876, {'accuracy': 0.6992}, 31958.666429997887)
-[2023-09-27 15:10:25,831][flwr][DEBUG] - evaluate_round 66: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:10:56,958][flwr][DEBUG] - evaluate_round 66 received 10 results and 0 failures
-[2023-09-27 15:10:56,959][flwr][DEBUG] - fit_round 67: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.469715 Loss1: 0.718741 Loss2: 0.750974
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.316742 Loss1: 0.661560 Loss2: 0.655182
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.305060 Loss1: 0.650921 Loss2: 0.654139
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.278406 Loss1: 0.623822 Loss2: 0.654584
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.278099 Loss1: 0.622094 Loss2: 0.656005
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.266994 Loss1: 0.606715 Loss2: 0.660279
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.257253 Loss1: 0.599745 Loss2: 0.657507
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.240874 Loss1: 0.583605 Loss2: 0.657269
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244358 Loss1: 0.583427 Loss2: 0.660931
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.235278 Loss1: 0.574967 Loss2: 0.660311
-(DefaultActor pid=1831567) >> Training accuracy: 0.790844
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.355516 Loss1: 0.583898 Loss2: 0.771618
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.258328 Loss1: 0.557525 Loss2: 0.700803
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.239538 Loss1: 0.539370 Loss2: 0.700168
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.223034 Loss1: 0.520454 Loss2: 0.702580
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.229398 Loss1: 0.529757 Loss2: 0.699641
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.207454 Loss1: 0.503135 Loss2: 0.704318
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.225024 Loss1: 0.519945 Loss2: 0.705079
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207399 Loss1: 0.502971 Loss2: 0.704428
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.202843 Loss1: 0.497609 Loss2: 0.705233
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184758 Loss1: 0.477511 Loss2: 0.707247
-(DefaultActor pid=1831567) >> Training accuracy: 0.831530
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.497941 Loss1: 0.727073 Loss2: 0.770869
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346958 Loss1: 0.669434 Loss2: 0.677525
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337405 Loss1: 0.660417 Loss2: 0.676988
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.337373 Loss1: 0.658180 Loss2: 0.679193
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.341524 Loss1: 0.662184 Loss2: 0.679340
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.315899 Loss1: 0.633725 Loss2: 0.682174
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.317345 Loss1: 0.633876 Loss2: 0.683469
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.304792 Loss1: 0.621203 Loss2: 0.683589
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.320871 Loss1: 0.633592 Loss2: 0.687278
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304663 Loss1: 0.620241 Loss2: 0.684423
-(DefaultActor pid=1831567) >> Training accuracy: 0.782416
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.217328 Loss1: 0.468127 Loss2: 0.749201
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.081478 Loss1: 0.414459 Loss2: 0.667019
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.062623 Loss1: 0.399000 Loss2: 0.663623
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.062132 Loss1: 0.397476 Loss2: 0.664656
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.043142 Loss1: 0.376358 Loss2: 0.666784
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.039785 Loss1: 0.374114 Loss2: 0.665671
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.038138 Loss1: 0.371857 Loss2: 0.666282
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.037605 Loss1: 0.371833 Loss2: 0.665772
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.038853 Loss1: 0.370018 Loss2: 0.668836
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.033169 Loss1: 0.363896 Loss2: 0.669273
-(DefaultActor pid=1831567) >> Training accuracy: 0.872878
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341986 Loss1: 0.582007 Loss2: 0.759979
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217837 Loss1: 0.546653 Loss2: 0.671184
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.196040 Loss1: 0.520998 Loss2: 0.675041
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194215 Loss1: 0.521486 Loss2: 0.672728
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185449 Loss1: 0.512024 Loss2: 0.673426
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.151455 Loss1: 0.476120 Loss2: 0.675335
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168672 Loss1: 0.490176 Loss2: 0.678496
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173523 Loss1: 0.492036 Loss2: 0.681487
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.148473 Loss1: 0.468324 Loss2: 0.680149
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.132146 Loss1: 0.454653 Loss2: 0.677493
-(DefaultActor pid=1831567) >> Training accuracy: 0.839021
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.319064 Loss1: 0.562713 Loss2: 0.756351
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234961 Loss1: 0.527147 Loss2: 0.707814
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218561 Loss1: 0.511103 Loss2: 0.707458
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.230382 Loss1: 0.520057 Loss2: 0.710326
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.239128 Loss1: 0.528194 Loss2: 0.710934
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.228948 Loss1: 0.517093 Loss2: 0.711855
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.215982 Loss1: 0.505369 Loss2: 0.710613
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.208194 Loss1: 0.496267 Loss2: 0.711927
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.216259 Loss1: 0.502190 Loss2: 0.714069
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.224912 Loss1: 0.513385 Loss2: 0.711527
-(DefaultActor pid=1831567) >> Training accuracy: 0.838666
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.354522 Loss1: 0.583538 Loss2: 0.770985
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.208124 Loss1: 0.539063 Loss2: 0.669061
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205317 Loss1: 0.537763 Loss2: 0.667554
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191349 Loss1: 0.519543 Loss2: 0.671806
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.155120 Loss1: 0.492247 Loss2: 0.662873
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.152435 Loss1: 0.483687 Loss2: 0.668747
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.134018 Loss1: 0.467246 Loss2: 0.666772
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140414 Loss1: 0.470106 Loss2: 0.670308
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.119548 Loss1: 0.447168 Loss2: 0.672380
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.141009 Loss1: 0.470674 Loss2: 0.670335
-(DefaultActor pid=1831567) >> Training accuracy: 0.849047
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.498620 Loss1: 0.732167 Loss2: 0.766453
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394749 Loss1: 0.711771 Loss2: 0.682978
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.361064 Loss1: 0.679327 Loss2: 0.681737
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.361856 Loss1: 0.676711 Loss2: 0.685145
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.366269 Loss1: 0.681607 Loss2: 0.684662
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.353257 Loss1: 0.666655 Loss2: 0.686602
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.362695 Loss1: 0.672572 Loss2: 0.690124
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.351697 Loss1: 0.659118 Loss2: 0.692579
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.360369 Loss1: 0.667452 Loss2: 0.692918
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.328717 Loss1: 0.638099 Loss2: 0.690618
-(DefaultActor pid=1831567) >> Training accuracy: 0.771286
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.222339 Loss1: 0.457808 Loss2: 0.764532
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.093748 Loss1: 0.411114 Loss2: 0.682635
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.071809 Loss1: 0.391829 Loss2: 0.679979
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.059070 Loss1: 0.379406 Loss2: 0.679664
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.056031 Loss1: 0.376254 Loss2: 0.679777
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.046910 Loss1: 0.367463 Loss2: 0.679448
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.053208 Loss1: 0.369660 Loss2: 0.683547
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.039738 Loss1: 0.357286 Loss2: 0.682452
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.037773 Loss1: 0.352633 Loss2: 0.685140
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028848 Loss1: 0.343795 Loss2: 0.685053
-(DefaultActor pid=1831567) >> Training accuracy: 0.878858
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.292541 Loss1: 0.602283 Loss2: 0.690258
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.190664 Loss1: 0.564594 Loss2: 0.626070
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.145342 Loss1: 0.522815 Loss2: 0.622527
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162237 Loss1: 0.536716 Loss2: 0.625522
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150775 Loss1: 0.524585 Loss2: 0.626190
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.135085 Loss1: 0.509731 Loss2: 0.625354
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.154984 Loss1: 0.530562 Loss2: 0.624422
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.122515 Loss1: 0.499949 Loss2: 0.622567
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.128759 Loss1: 0.499414 Loss2: 0.629346
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.109307 Loss1: 0.481010 Loss2: 0.628297
-(DefaultActor pid=1831567) >> Training accuracy: 0.832698
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 15:17:38,596][flwr][DEBUG] - fit_round 67 received 10 results and 0 failures
->> Test accuracy: 0.697100
-[2023-09-27 15:17:40,233][flwr][INFO] - fit progress: (67, 0.8726276551572659, {'accuracy': 0.6971}, 32393.0690530031)
-[2023-09-27 15:17:40,233][flwr][DEBUG] - evaluate_round 67: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:18:11,781][flwr][DEBUG] - evaluate_round 67 received 10 results and 0 failures
-[2023-09-27 15:18:11,782][flwr][DEBUG] - fit_round 68: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.205122 Loss1: 0.478130 Loss2: 0.726993
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.039065 Loss1: 0.391876 Loss2: 0.647190
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.068473 Loss1: 0.420981 Loss2: 0.647493
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.042930 Loss1: 0.397086 Loss2: 0.645845
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.029401 Loss1: 0.383887 Loss2: 0.645514
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.033554 Loss1: 0.385090 Loss2: 0.648464
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.011791 Loss1: 0.363737 Loss2: 0.648054
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.017600 Loss1: 0.371125 Loss2: 0.646475
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.006600 Loss1: 0.359718 Loss2: 0.646883
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.987520 Loss1: 0.340452 Loss2: 0.647068
-(DefaultActor pid=1831567) >> Training accuracy: 0.880594
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348801 Loss1: 0.576460 Loss2: 0.772340
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.204376 Loss1: 0.528374 Loss2: 0.676002
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194581 Loss1: 0.519450 Loss2: 0.675131
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177953 Loss1: 0.504771 Loss2: 0.673182
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.141922 Loss1: 0.468883 Loss2: 0.673039
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167613 Loss1: 0.492622 Loss2: 0.674991
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148280 Loss1: 0.475168 Loss2: 0.673113
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.124487 Loss1: 0.447681 Loss2: 0.676806
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.159613 Loss1: 0.479192 Loss2: 0.680421
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152348 Loss1: 0.473291 Loss2: 0.679057
-(DefaultActor pid=1831567) >> Training accuracy: 0.834481
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.491634 Loss1: 0.729809 Loss2: 0.761825
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367456 Loss1: 0.696983 Loss2: 0.670473
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363809 Loss1: 0.690428 Loss2: 0.673381
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353391 Loss1: 0.682042 Loss2: 0.671349
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.298181 Loss1: 0.629619 Loss2: 0.668562
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.313122 Loss1: 0.641716 Loss2: 0.671405
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.302440 Loss1: 0.633082 Loss2: 0.669358
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.314106 Loss1: 0.640751 Loss2: 0.673355
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.266658 Loss1: 0.597539 Loss2: 0.669118
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.274577 Loss1: 0.598971 Loss2: 0.675606
-(DefaultActor pid=1831567) >> Training accuracy: 0.778685
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.484356 Loss1: 0.722878 Loss2: 0.761477
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370471 Loss1: 0.692881 Loss2: 0.677590
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.387640 Loss1: 0.707470 Loss2: 0.680170
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.369453 Loss1: 0.691590 Loss2: 0.677863
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.361990 Loss1: 0.680278 Loss2: 0.681713
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.334908 Loss1: 0.655272 Loss2: 0.679636
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.341384 Loss1: 0.660433 Loss2: 0.680951
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.350182 Loss1: 0.667380 Loss2: 0.682802
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.346301 Loss1: 0.661757 Loss2: 0.684544
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308283 Loss1: 0.625141 Loss2: 0.683142
-(DefaultActor pid=1831567) >> Training accuracy: 0.777174
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321736 Loss1: 0.601547 Loss2: 0.720189
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.191921 Loss1: 0.546189 Loss2: 0.645732
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183512 Loss1: 0.540652 Loss2: 0.642859
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208899 Loss1: 0.559043 Loss2: 0.649856
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.172929 Loss1: 0.525632 Loss2: 0.647297
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.170729 Loss1: 0.519403 Loss2: 0.651326
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.185862 Loss1: 0.533258 Loss2: 0.652604
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.160960 Loss1: 0.509421 Loss2: 0.651539
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.150595 Loss1: 0.499289 Loss2: 0.651306
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.138745 Loss1: 0.486267 Loss2: 0.652478
-(DefaultActor pid=1831567) >> Training accuracy: 0.844551
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.246719 Loss1: 0.475308 Loss2: 0.771411
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.108469 Loss1: 0.416084 Loss2: 0.692386
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.077635 Loss1: 0.384667 Loss2: 0.692967
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.089646 Loss1: 0.396419 Loss2: 0.693227
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.062572 Loss1: 0.374239 Loss2: 0.688333
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.040659 Loss1: 0.354273 Loss2: 0.686386
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050832 Loss1: 0.358374 Loss2: 0.692458
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.060901 Loss1: 0.369867 Loss2: 0.691034
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.054416 Loss1: 0.361474 Loss2: 0.692942
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.053776 Loss1: 0.364695 Loss2: 0.689082
-(DefaultActor pid=1831567) >> Training accuracy: 0.876736
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.300825 Loss1: 0.570855 Loss2: 0.729970
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180070 Loss1: 0.529306 Loss2: 0.650763
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.186318 Loss1: 0.533423 Loss2: 0.652895
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199491 Loss1: 0.542784 Loss2: 0.656707
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.159562 Loss1: 0.507756 Loss2: 0.651806
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157771 Loss1: 0.502573 Loss2: 0.655199
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.173871 Loss1: 0.515303 Loss2: 0.658568
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.156083 Loss1: 0.495682 Loss2: 0.660401
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147053 Loss1: 0.488628 Loss2: 0.658425
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148954 Loss1: 0.490242 Loss2: 0.658712
-(DefaultActor pid=1831567) >> Training accuracy: 0.839638
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320182 Loss1: 0.556745 Loss2: 0.763437
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259852 Loss1: 0.541007 Loss2: 0.718845
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.237912 Loss1: 0.525682 Loss2: 0.712231
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.216524 Loss1: 0.503535 Loss2: 0.712988
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234390 Loss1: 0.516878 Loss2: 0.717511
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.219124 Loss1: 0.503461 Loss2: 0.715663
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212641 Loss1: 0.495887 Loss2: 0.716754
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.192911 Loss1: 0.477483 Loss2: 0.715428
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.243801 Loss1: 0.522549 Loss2: 0.721251
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.213379 Loss1: 0.493718 Loss2: 0.719661
-(DefaultActor pid=1831567) >> Training accuracy: 0.821677
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.418927 Loss1: 0.612525 Loss2: 0.806402
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.287809 Loss1: 0.555693 Loss2: 0.732116
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.242502 Loss1: 0.515733 Loss2: 0.726769
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.257708 Loss1: 0.530252 Loss2: 0.727456
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.245050 Loss1: 0.514275 Loss2: 0.730774
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.222742 Loss1: 0.495079 Loss2: 0.727663
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.242589 Loss1: 0.509807 Loss2: 0.732781
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229372 Loss1: 0.500981 Loss2: 0.728391
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.218140 Loss1: 0.484859 Loss2: 0.733281
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214098 Loss1: 0.481503 Loss2: 0.732595
-(DefaultActor pid=1831567) >> Training accuracy: 0.829459
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.527542 Loss1: 0.734032 Loss2: 0.793510
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.340239 Loss1: 0.655287 Loss2: 0.684952
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.319218 Loss1: 0.634707 Loss2: 0.684510
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.307086 Loss1: 0.619910 Loss2: 0.687176
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.324607 Loss1: 0.639066 Loss2: 0.685541
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303088 Loss1: 0.616547 Loss2: 0.686541
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.271768 Loss1: 0.581670 Loss2: 0.690098
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.260635 Loss1: 0.571758 Loss2: 0.688877
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.275516 Loss1: 0.585505 Loss2: 0.690011
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281424 Loss1: 0.592353 Loss2: 0.689071
-[2023-09-27 15:25:11,538][flwr][DEBUG] - fit_round 68 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.809211
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.696600
-[2023-09-27 15:25:12,869][flwr][INFO] - fit progress: (68, 0.8746880760398535, {'accuracy': 0.6966}, 32845.704982507974)
-[2023-09-27 15:25:12,869][flwr][DEBUG] - evaluate_round 68: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:25:44,072][flwr][DEBUG] - evaluate_round 68 received 10 results and 0 failures
-[2023-09-27 15:25:44,073][flwr][DEBUG] - fit_round 69: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187205 Loss1: 0.429962 Loss2: 0.757243
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.094303 Loss1: 0.419557 Loss2: 0.674746
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.059656 Loss1: 0.388051 Loss2: 0.671605
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.059840 Loss1: 0.386298 Loss2: 0.673542
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.053661 Loss1: 0.380752 Loss2: 0.672909
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.040436 Loss1: 0.367601 Loss2: 0.672835
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.039572 Loss1: 0.364289 Loss2: 0.675283
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.042065 Loss1: 0.364522 Loss2: 0.677543
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.023682 Loss1: 0.345504 Loss2: 0.678179
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.031063 Loss1: 0.353691 Loss2: 0.677372
-(DefaultActor pid=1831567) >> Training accuracy: 0.863040
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.314545 Loss1: 0.612154 Loss2: 0.702391
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.176407 Loss1: 0.543437 Loss2: 0.632971
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.180679 Loss1: 0.546668 Loss2: 0.634011
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.169818 Loss1: 0.537819 Loss2: 0.631999
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.171686 Loss1: 0.538444 Loss2: 0.633242
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.148360 Loss1: 0.514336 Loss2: 0.634023
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.143693 Loss1: 0.509247 Loss2: 0.634446
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.125408 Loss1: 0.491464 Loss2: 0.633943
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.121319 Loss1: 0.489445 Loss2: 0.631874
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.122004 Loss1: 0.488951 Loss2: 0.633054
-(DefaultActor pid=1831567) >> Training accuracy: 0.834604
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.250749 Loss1: 0.460149 Loss2: 0.790600
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.109730 Loss1: 0.405830 Loss2: 0.703900
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.105792 Loss1: 0.402948 Loss2: 0.702844
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.089547 Loss1: 0.385118 Loss2: 0.704429
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.095039 Loss1: 0.387393 Loss2: 0.707647
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.080770 Loss1: 0.375531 Loss2: 0.705239
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.075400 Loss1: 0.369809 Loss2: 0.705591
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.063039 Loss1: 0.358005 Loss2: 0.705033
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.058584 Loss1: 0.350707 Loss2: 0.707877
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.067648 Loss1: 0.362453 Loss2: 0.705194
-(DefaultActor pid=1831567) >> Training accuracy: 0.866127
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.331819 Loss1: 0.572834 Loss2: 0.758985
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.218971 Loss1: 0.545731 Loss2: 0.673240
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193338 Loss1: 0.518787 Loss2: 0.674551
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183960 Loss1: 0.511152 Loss2: 0.672809
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198451 Loss1: 0.521603 Loss2: 0.676848
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163399 Loss1: 0.487508 Loss2: 0.675890
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.172615 Loss1: 0.495565 Loss2: 0.677049
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162474 Loss1: 0.484685 Loss2: 0.677789
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.146818 Loss1: 0.467552 Loss2: 0.679267
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.163156 Loss1: 0.485871 Loss2: 0.677285
-(DefaultActor pid=1831567) >> Training accuracy: 0.842105
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.342918 Loss1: 0.587914 Loss2: 0.755004
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.231685 Loss1: 0.544100 Loss2: 0.687585
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226098 Loss1: 0.536823 Loss2: 0.689275
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.219406 Loss1: 0.528210 Loss2: 0.691196
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.222810 Loss1: 0.530115 Loss2: 0.692695
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.188125 Loss1: 0.494831 Loss2: 0.693294
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.178000 Loss1: 0.484027 Loss2: 0.693973
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.214960 Loss1: 0.518759 Loss2: 0.696201
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.208177 Loss1: 0.513483 Loss2: 0.694695
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172051 Loss1: 0.477291 Loss2: 0.694760
-(DefaultActor pid=1831567) >> Training accuracy: 0.849960
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.269498 Loss1: 0.545146 Loss2: 0.724353
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.224011 Loss1: 0.538585 Loss2: 0.685426
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203467 Loss1: 0.518405 Loss2: 0.685062
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205169 Loss1: 0.520085 Loss2: 0.685084
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191794 Loss1: 0.505920 Loss2: 0.685874
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.195002 Loss1: 0.508490 Loss2: 0.686513
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170980 Loss1: 0.488846 Loss2: 0.682134
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180228 Loss1: 0.492419 Loss2: 0.687809
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180023 Loss1: 0.491779 Loss2: 0.688244
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172682 Loss1: 0.489276 Loss2: 0.683406
-(DefaultActor pid=1831567) >> Training accuracy: 0.835565
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.458025 Loss1: 0.727180 Loss2: 0.730844
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301342 Loss1: 0.656307 Loss2: 0.645035
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.299237 Loss1: 0.658454 Loss2: 0.640783
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.268467 Loss1: 0.624621 Loss2: 0.643846
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246515 Loss1: 0.604150 Loss2: 0.642365
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.251522 Loss1: 0.606183 Loss2: 0.645338
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.221314 Loss1: 0.579819 Loss2: 0.641495
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.243572 Loss1: 0.597511 Loss2: 0.646061
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.226139 Loss1: 0.577612 Loss2: 0.648528
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.236435 Loss1: 0.586427 Loss2: 0.650007
-(DefaultActor pid=1831567) >> Training accuracy: 0.811404
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324342 Loss1: 0.582088 Loss2: 0.742254
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.182271 Loss1: 0.536726 Loss2: 0.645545
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.150145 Loss1: 0.504727 Loss2: 0.645418
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.148318 Loss1: 0.503157 Loss2: 0.645160
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.142549 Loss1: 0.500541 Loss2: 0.642008
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.141492 Loss1: 0.495429 Loss2: 0.646063
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.121696 Loss1: 0.473558 Loss2: 0.648138
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.099439 Loss1: 0.450822 Loss2: 0.648618
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.099553 Loss1: 0.449702 Loss2: 0.649850
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.118841 Loss1: 0.471313 Loss2: 0.647528
-(DefaultActor pid=1831567) >> Training accuracy: 0.839513
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.508980 Loss1: 0.737851 Loss2: 0.771129
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.373491 Loss1: 0.688614 Loss2: 0.684877
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.360817 Loss1: 0.676714 Loss2: 0.684103
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.313857 Loss1: 0.633253 Loss2: 0.680604
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.335389 Loss1: 0.653649 Loss2: 0.681740
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.318044 Loss1: 0.636219 Loss2: 0.681825
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.315374 Loss1: 0.630800 Loss2: 0.684574
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.327494 Loss1: 0.641732 Loss2: 0.685762
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.317271 Loss1: 0.632846 Loss2: 0.684425
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307618 Loss1: 0.620972 Loss2: 0.686646
-(DefaultActor pid=1831567) >> Training accuracy: 0.787080
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.466594 Loss1: 0.710285 Loss2: 0.756309
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.415244 Loss1: 0.740308 Loss2: 0.674936
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.392771 Loss1: 0.719691 Loss2: 0.673080
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.385194 Loss1: 0.712794 Loss2: 0.672400
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333331 Loss1: 0.660250 Loss2: 0.673081
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.317061 Loss1: 0.645104 Loss2: 0.671957
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314585 Loss1: 0.640212 Loss2: 0.674372
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.315870 Loss1: 0.639403 Loss2: 0.676467
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.302825 Loss1: 0.625707 Loss2: 0.677118
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.297976 Loss1: 0.619549 Loss2: 0.678427
-(DefaultActor pid=1831567) >> Training accuracy: 0.775589
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 15:32:26,256][flwr][DEBUG] - fit_round 69 received 10 results and 0 failures
->> Test accuracy: 0.699900
-[2023-09-27 15:32:27,985][flwr][INFO] - fit progress: (69, 0.8618371694232709, {'accuracy': 0.6999}, 33280.821781103965)
-[2023-09-27 15:32:27,986][flwr][DEBUG] - evaluate_round 69: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:32:58,258][flwr][DEBUG] - evaluate_round 69 received 10 results and 0 failures
-[2023-09-27 15:32:58,259][flwr][DEBUG] - fit_round 70: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493993 Loss1: 0.747787 Loss2: 0.746206
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.361313 Loss1: 0.703321 Loss2: 0.657992
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.376423 Loss1: 0.714148 Loss2: 0.662275
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353793 Loss1: 0.689079 Loss2: 0.664714
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.326336 Loss1: 0.662470 Loss2: 0.663867
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.312079 Loss1: 0.649174 Loss2: 0.662905
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.319986 Loss1: 0.654825 Loss2: 0.665161
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.315922 Loss1: 0.648441 Loss2: 0.667480
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.311368 Loss1: 0.647183 Loss2: 0.664185
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289370 Loss1: 0.623476 Loss2: 0.665894
-(DefaultActor pid=1831567) >> Training accuracy: 0.794158
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353382 Loss1: 0.586784 Loss2: 0.766598
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.255952 Loss1: 0.558028 Loss2: 0.697924
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.238305 Loss1: 0.540145 Loss2: 0.698160
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205374 Loss1: 0.507285 Loss2: 0.698089
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204686 Loss1: 0.503653 Loss2: 0.701033
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.195847 Loss1: 0.492221 Loss2: 0.703627
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.202945 Loss1: 0.499497 Loss2: 0.703447
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.200529 Loss1: 0.495921 Loss2: 0.704608
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.187148 Loss1: 0.482242 Loss2: 0.704906
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179840 Loss1: 0.474027 Loss2: 0.705813
-(DefaultActor pid=1831567) >> Training accuracy: 0.834223
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488112 Loss1: 0.721507 Loss2: 0.766605
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.366333 Loss1: 0.688324 Loss2: 0.678009
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.368786 Loss1: 0.690202 Loss2: 0.678585
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.353599 Loss1: 0.675228 Loss2: 0.678371
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.323849 Loss1: 0.647767 Loss2: 0.676082
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.329771 Loss1: 0.653160 Loss2: 0.676612
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.319857 Loss1: 0.642425 Loss2: 0.677432
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.319065 Loss1: 0.639572 Loss2: 0.679493
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306841 Loss1: 0.627086 Loss2: 0.679754
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.291320 Loss1: 0.610617 Loss2: 0.680703
-(DefaultActor pid=1831567) >> Training accuracy: 0.791045
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.209483 Loss1: 0.489088 Loss2: 0.720395
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.031709 Loss1: 0.392545 Loss2: 0.639165
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.047009 Loss1: 0.410442 Loss2: 0.636567
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.024305 Loss1: 0.386521 Loss2: 0.637783
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.003441 Loss1: 0.363370 Loss2: 0.640071
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.012551 Loss1: 0.375449 Loss2: 0.637103
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.008384 Loss1: 0.366513 Loss2: 0.641870
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.017504 Loss1: 0.375639 Loss2: 0.641865
-(DefaultActor pid=1831567) Epoch: 8 Loss: 0.989203 Loss1: 0.349271 Loss2: 0.639933
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.999849 Loss1: 0.359266 Loss2: 0.640583
-(DefaultActor pid=1831567) >> Training accuracy: 0.869985
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297138 Loss1: 0.556606 Loss2: 0.740531
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.188052 Loss1: 0.526560 Loss2: 0.661491
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.170896 Loss1: 0.509223 Loss2: 0.661673
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181371 Loss1: 0.517695 Loss2: 0.663676
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.193683 Loss1: 0.528860 Loss2: 0.664823
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.182521 Loss1: 0.517034 Loss2: 0.665487
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.145584 Loss1: 0.483669 Loss2: 0.661915
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.157764 Loss1: 0.490569 Loss2: 0.667194
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.122032 Loss1: 0.455154 Loss2: 0.666878
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152289 Loss1: 0.483917 Loss2: 0.668372
-(DefaultActor pid=1831567) >> Training accuracy: 0.836965
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.484259 Loss1: 0.700955 Loss2: 0.783304
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.334666 Loss1: 0.656738 Loss2: 0.677928
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.283993 Loss1: 0.608463 Loss2: 0.675530
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.303651 Loss1: 0.627326 Loss2: 0.676324
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317336 Loss1: 0.642303 Loss2: 0.675033
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.287606 Loss1: 0.609260 Loss2: 0.678346
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.283019 Loss1: 0.603970 Loss2: 0.679048
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.275782 Loss1: 0.596652 Loss2: 0.679130
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.284478 Loss1: 0.604860 Loss2: 0.679618
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.237155 Loss1: 0.557914 Loss2: 0.679241
-(DefaultActor pid=1831567) >> Training accuracy: 0.804825
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.361047 Loss1: 0.597405 Loss2: 0.763642
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.237672 Loss1: 0.552715 Loss2: 0.684957
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.235150 Loss1: 0.550877 Loss2: 0.684273
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.201025 Loss1: 0.511729 Loss2: 0.689297
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195534 Loss1: 0.506288 Loss2: 0.689246
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.179664 Loss1: 0.493456 Loss2: 0.686208
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191758 Loss1: 0.507592 Loss2: 0.684166
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190807 Loss1: 0.502147 Loss2: 0.688660
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.200824 Loss1: 0.512390 Loss2: 0.688434
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198943 Loss1: 0.507548 Loss2: 0.691395
-(DefaultActor pid=1831567) >> Training accuracy: 0.844952
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320884 Loss1: 0.566351 Loss2: 0.754532
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.247665 Loss1: 0.538607 Loss2: 0.709057
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.242032 Loss1: 0.534372 Loss2: 0.707659
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208429 Loss1: 0.503974 Loss2: 0.704455
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210692 Loss1: 0.503731 Loss2: 0.706961
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.219642 Loss1: 0.510296 Loss2: 0.709347
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.219648 Loss1: 0.510841 Loss2: 0.708807
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212378 Loss1: 0.501070 Loss2: 0.711308
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213210 Loss1: 0.501968 Loss2: 0.711241
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214207 Loss1: 0.503043 Loss2: 0.711164
-(DefaultActor pid=1831567) >> Training accuracy: 0.834697
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.231432 Loss1: 0.462417 Loss2: 0.769015
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.093953 Loss1: 0.402645 Loss2: 0.691308
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.081724 Loss1: 0.394148 Loss2: 0.687576
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074943 Loss1: 0.384736 Loss2: 0.690207
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.071562 Loss1: 0.380205 Loss2: 0.691357
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.071631 Loss1: 0.378005 Loss2: 0.693626
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.069039 Loss1: 0.374527 Loss2: 0.694512
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.069688 Loss1: 0.375430 Loss2: 0.694258
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.059300 Loss1: 0.363374 Loss2: 0.695926
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.057981 Loss1: 0.366186 Loss2: 0.691795
-(DefaultActor pid=1831567) >> Training accuracy: 0.871914
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360314 Loss1: 0.576333 Loss2: 0.783981
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206654 Loss1: 0.523216 Loss2: 0.683438
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183590 Loss1: 0.499376 Loss2: 0.684213
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.176158 Loss1: 0.490610 Loss2: 0.685549
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192296 Loss1: 0.501264 Loss2: 0.691031
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164320 Loss1: 0.475297 Loss2: 0.689023
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165409 Loss1: 0.476799 Loss2: 0.688610
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.149126 Loss1: 0.458571 Loss2: 0.690555
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.139301 Loss1: 0.448844 Loss2: 0.690457
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134761 Loss1: 0.442556 Loss2: 0.692205
-[2023-09-27 15:40:08,733][flwr][DEBUG] - fit_round 70 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.854608
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.701300
-[2023-09-27 15:40:10,370][flwr][INFO] - fit progress: (70, 0.8689377218389663, {'accuracy': 0.7013}, 33743.20618806314)
-[2023-09-27 15:40:10,370][flwr][DEBUG] - evaluate_round 70: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:40:40,686][flwr][DEBUG] - evaluate_round 70 received 10 results and 0 failures
-[2023-09-27 15:40:40,687][flwr][DEBUG] - fit_round 71: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.514160 Loss1: 0.736470 Loss2: 0.777690
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.367250 Loss1: 0.680143 Loss2: 0.687106
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.341041 Loss1: 0.660198 Loss2: 0.680842
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333397 Loss1: 0.649890 Loss2: 0.683507
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.334300 Loss1: 0.649606 Loss2: 0.684693
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.331900 Loss1: 0.649031 Loss2: 0.682869
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.306782 Loss1: 0.619799 Loss2: 0.686983
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.337787 Loss1: 0.647131 Loss2: 0.690656
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.319849 Loss1: 0.623004 Loss2: 0.696845
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.335501 Loss1: 0.641388 Loss2: 0.694113
-(DefaultActor pid=1831567) >> Training accuracy: 0.789646
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.467585 Loss1: 0.706581 Loss2: 0.761004
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329991 Loss1: 0.659959 Loss2: 0.670032
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.302627 Loss1: 0.632478 Loss2: 0.670149
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.292220 Loss1: 0.620221 Loss2: 0.671999
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.279501 Loss1: 0.606280 Loss2: 0.673221
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.293307 Loss1: 0.617080 Loss2: 0.676227
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.264369 Loss1: 0.590279 Loss2: 0.674090
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.303390 Loss1: 0.624261 Loss2: 0.679128
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.262773 Loss1: 0.584593 Loss2: 0.678181
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.246745 Loss1: 0.568907 Loss2: 0.677839
-(DefaultActor pid=1831567) >> Training accuracy: 0.814419
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.296418 Loss1: 0.589665 Loss2: 0.706752
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.174463 Loss1: 0.536784 Loss2: 0.637679
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.187433 Loss1: 0.552229 Loss2: 0.635204
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177672 Loss1: 0.539767 Loss2: 0.637906
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154052 Loss1: 0.520607 Loss2: 0.633445
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.142724 Loss1: 0.506555 Loss2: 0.636169
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.135451 Loss1: 0.499217 Loss2: 0.636234
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154435 Loss1: 0.514852 Loss2: 0.639584
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134915 Loss1: 0.494794 Loss2: 0.640120
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.119853 Loss1: 0.481597 Loss2: 0.638256
-(DefaultActor pid=1831567) >> Training accuracy: 0.826410
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.149072 Loss1: 0.443256 Loss2: 0.705816
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.046541 Loss1: 0.409861 Loss2: 0.636681
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.035375 Loss1: 0.404116 Loss2: 0.631260
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.015038 Loss1: 0.382235 Loss2: 0.632803
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.012296 Loss1: 0.380987 Loss2: 0.631310
-(DefaultActor pid=1831567) Epoch: 5 Loss: 0.989841 Loss1: 0.359149 Loss2: 0.630691
-(DefaultActor pid=1831567) Epoch: 6 Loss: 0.992291 Loss1: 0.362226 Loss2: 0.630065
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.004523 Loss1: 0.372567 Loss2: 0.631956
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.003859 Loss1: 0.372861 Loss2: 0.630998
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.991287 Loss1: 0.357151 Loss2: 0.634135
-(DefaultActor pid=1831567) >> Training accuracy: 0.867477
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.311559 Loss1: 0.575923 Loss2: 0.735636
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202813 Loss1: 0.532888 Loss2: 0.669925
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195561 Loss1: 0.526732 Loss2: 0.668829
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.220087 Loss1: 0.545253 Loss2: 0.674834
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.171340 Loss1: 0.500028 Loss2: 0.671312
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199236 Loss1: 0.522632 Loss2: 0.676604
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170589 Loss1: 0.495295 Loss2: 0.675294
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.160706 Loss1: 0.485036 Loss2: 0.675670
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.160512 Loss1: 0.484508 Loss2: 0.676004
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.185563 Loss1: 0.503722 Loss2: 0.681841
-(DefaultActor pid=1831567) >> Training accuracy: 0.845353
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488137 Loss1: 0.726327 Loss2: 0.761810
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.395277 Loss1: 0.712392 Loss2: 0.682885
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.385406 Loss1: 0.700575 Loss2: 0.684831
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.348354 Loss1: 0.664413 Loss2: 0.683941
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.344082 Loss1: 0.661644 Loss2: 0.682438
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.349710 Loss1: 0.667249 Loss2: 0.682461
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.324077 Loss1: 0.638248 Loss2: 0.685829
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.334923 Loss1: 0.650011 Loss2: 0.684912
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.361780 Loss1: 0.674118 Loss2: 0.687662
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.326573 Loss1: 0.634226 Loss2: 0.692347
-(DefaultActor pid=1831567) >> Training accuracy: 0.786458
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341206 Loss1: 0.584915 Loss2: 0.756291
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217007 Loss1: 0.541349 Loss2: 0.675658
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.179415 Loss1: 0.506065 Loss2: 0.673350
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.211554 Loss1: 0.533675 Loss2: 0.677879
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.154285 Loss1: 0.479635 Loss2: 0.674650
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164791 Loss1: 0.489573 Loss2: 0.675219
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165380 Loss1: 0.487857 Loss2: 0.677524
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.170546 Loss1: 0.491930 Loss2: 0.678616
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.135664 Loss1: 0.457553 Loss2: 0.678112
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.141720 Loss1: 0.462516 Loss2: 0.679204
-(DefaultActor pid=1831567) >> Training accuracy: 0.843956
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.203871 Loss1: 0.442046 Loss2: 0.761825
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.092592 Loss1: 0.412812 Loss2: 0.679780
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.070500 Loss1: 0.393814 Loss2: 0.676687
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.069893 Loss1: 0.393014 Loss2: 0.676879
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.039407 Loss1: 0.365411 Loss2: 0.673997
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.043348 Loss1: 0.362641 Loss2: 0.680708
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.042123 Loss1: 0.362811 Loss2: 0.679312
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.037503 Loss1: 0.358754 Loss2: 0.678749
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.035484 Loss1: 0.357277 Loss2: 0.678207
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.054302 Loss1: 0.372059 Loss2: 0.682244
-(DefaultActor pid=1831567) >> Training accuracy: 0.870177
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.338447 Loss1: 0.572901 Loss2: 0.765546
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.163775 Loss1: 0.503687 Loss2: 0.660088
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195008 Loss1: 0.533825 Loss2: 0.661182
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.125849 Loss1: 0.465757 Loss2: 0.660092
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.131975 Loss1: 0.474601 Loss2: 0.657374
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.152817 Loss1: 0.490825 Loss2: 0.661993
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.139700 Loss1: 0.477387 Loss2: 0.662313
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133332 Loss1: 0.469225 Loss2: 0.664106
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.148682 Loss1: 0.484180 Loss2: 0.664502
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.118784 Loss1: 0.452878 Loss2: 0.665905
-(DefaultActor pid=1831567) >> Training accuracy: 0.847987
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.277582 Loss1: 0.551481 Loss2: 0.726101
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200031 Loss1: 0.519371 Loss2: 0.680660
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.202424 Loss1: 0.519838 Loss2: 0.682586
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.197650 Loss1: 0.515810 Loss2: 0.681840
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.206942 Loss1: 0.516321 Loss2: 0.690621
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.184926 Loss1: 0.495966 Loss2: 0.688960
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.179255 Loss1: 0.493719 Loss2: 0.685536
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.194243 Loss1: 0.507175 Loss2: 0.687068
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182642 Loss1: 0.496603 Loss2: 0.686039
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.195516 Loss1: 0.507390 Loss2: 0.688126
-[2023-09-27 15:47:22,549][flwr][DEBUG] - fit_round 71 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.842262
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.704200
-[2023-09-27 15:47:24,011][flwr][INFO] - fit progress: (71, 0.8598026045785544, {'accuracy': 0.7042}, 34176.8469454227)
-[2023-09-27 15:47:24,012][flwr][DEBUG] - evaluate_round 71: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:47:54,613][flwr][DEBUG] - evaluate_round 71 received 10 results and 0 failures
-[2023-09-27 15:47:54,614][flwr][DEBUG] - fit_round 72: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.336425 Loss1: 0.564673 Loss2: 0.771752
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.186469 Loss1: 0.515297 Loss2: 0.671172
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.171589 Loss1: 0.499942 Loss2: 0.671646
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.156270 Loss1: 0.487576 Loss2: 0.668694
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163632 Loss1: 0.492814 Loss2: 0.670818
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164688 Loss1: 0.489886 Loss2: 0.674802
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.113033 Loss1: 0.438414 Loss2: 0.674620
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.128438 Loss1: 0.450376 Loss2: 0.678063
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147661 Loss1: 0.468983 Loss2: 0.678678
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146797 Loss1: 0.468915 Loss2: 0.677882
-(DefaultActor pid=1831567) >> Training accuracy: 0.843485
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.451305 Loss1: 0.707160 Loss2: 0.744145
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387557 Loss1: 0.725558 Loss2: 0.661999
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.330910 Loss1: 0.671036 Loss2: 0.659873
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.323897 Loss1: 0.664442 Loss2: 0.659454
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345581 Loss1: 0.682962 Loss2: 0.662620
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.347205 Loss1: 0.679733 Loss2: 0.667472
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314600 Loss1: 0.646708 Loss2: 0.667893
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.323786 Loss1: 0.657914 Loss2: 0.665871
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.313730 Loss1: 0.645689 Loss2: 0.668041
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.327825 Loss1: 0.656825 Loss2: 0.671001
-(DefaultActor pid=1831567) >> Training accuracy: 0.778759
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.330791 Loss1: 0.555146 Loss2: 0.775645
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.242805 Loss1: 0.515727 Loss2: 0.727078
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.237749 Loss1: 0.509697 Loss2: 0.728052
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.255584 Loss1: 0.526715 Loss2: 0.728868
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.234015 Loss1: 0.503489 Loss2: 0.730526
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.235609 Loss1: 0.508271 Loss2: 0.727338
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243311 Loss1: 0.511725 Loss2: 0.731587
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.244342 Loss1: 0.509396 Loss2: 0.734946
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.224621 Loss1: 0.493765 Loss2: 0.730857
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.215770 Loss1: 0.483266 Loss2: 0.732504
-(DefaultActor pid=1831567) >> Training accuracy: 0.819692
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485176 Loss1: 0.716361 Loss2: 0.768814
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.331040 Loss1: 0.665171 Loss2: 0.665869
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307138 Loss1: 0.639825 Loss2: 0.667313
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.305615 Loss1: 0.640229 Loss2: 0.665386
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.269716 Loss1: 0.605081 Loss2: 0.664635
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.261888 Loss1: 0.600493 Loss2: 0.661395
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.260611 Loss1: 0.593319 Loss2: 0.667292
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.243367 Loss1: 0.576435 Loss2: 0.666932
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.277572 Loss1: 0.607945 Loss2: 0.669628
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.249654 Loss1: 0.581862 Loss2: 0.667791
-(DefaultActor pid=1831567) >> Training accuracy: 0.813596
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.196431 Loss1: 0.474940 Loss2: 0.721491
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.050413 Loss1: 0.412705 Loss2: 0.637708
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.025201 Loss1: 0.387721 Loss2: 0.637480
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.014504 Loss1: 0.377039 Loss2: 0.637465
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.024563 Loss1: 0.385896 Loss2: 0.638667
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.006084 Loss1: 0.368532 Loss2: 0.637553
-(DefaultActor pid=1831567) Epoch: 6 Loss: 0.997144 Loss1: 0.357444 Loss2: 0.639699
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.012011 Loss1: 0.373440 Loss2: 0.638571
-(DefaultActor pid=1831567) Epoch: 8 Loss: 0.988331 Loss1: 0.350404 Loss2: 0.637927
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.016751 Loss1: 0.374149 Loss2: 0.642602
-(DefaultActor pid=1831567) >> Training accuracy: 0.871335
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.338885 Loss1: 0.595092 Loss2: 0.743793
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202131 Loss1: 0.541054 Loss2: 0.661076
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.189219 Loss1: 0.529123 Loss2: 0.660096
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162577 Loss1: 0.501753 Loss2: 0.660824
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.171755 Loss1: 0.509975 Loss2: 0.661780
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167798 Loss1: 0.502876 Loss2: 0.664922
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.147203 Loss1: 0.481893 Loss2: 0.665310
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151308 Loss1: 0.486670 Loss2: 0.664638
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.158843 Loss1: 0.492885 Loss2: 0.665958
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.139016 Loss1: 0.473522 Loss2: 0.665494
-(DefaultActor pid=1831567) >> Training accuracy: 0.844161
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.259160 Loss1: 0.476859 Loss2: 0.782301
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.123202 Loss1: 0.403774 Loss2: 0.719428
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.104798 Loss1: 0.393173 Loss2: 0.711625
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.096962 Loss1: 0.385925 Loss2: 0.711038
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.089291 Loss1: 0.381869 Loss2: 0.707422
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.065985 Loss1: 0.357285 Loss2: 0.708701
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.085268 Loss1: 0.369651 Loss2: 0.715617
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.066297 Loss1: 0.352917 Loss2: 0.713381
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.074104 Loss1: 0.361837 Loss2: 0.712267
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.066836 Loss1: 0.349119 Loss2: 0.717717
-(DefaultActor pid=1831567) >> Training accuracy: 0.880787
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462069 Loss1: 0.714208 Loss2: 0.747860
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.353502 Loss1: 0.691824 Loss2: 0.661679
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.351199 Loss1: 0.687643 Loss2: 0.663555
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.315941 Loss1: 0.650420 Loss2: 0.665521
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.294875 Loss1: 0.634399 Loss2: 0.660475
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.293148 Loss1: 0.629174 Loss2: 0.663974
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305874 Loss1: 0.637822 Loss2: 0.668052
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.292717 Loss1: 0.627034 Loss2: 0.665682
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.263877 Loss1: 0.598831 Loss2: 0.665046
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.294166 Loss1: 0.625478 Loss2: 0.668689
-(DefaultActor pid=1831567) >> Training accuracy: 0.770056
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289469 Loss1: 0.555893 Loss2: 0.733576
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223837 Loss1: 0.558659 Loss2: 0.665178
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194883 Loss1: 0.532647 Loss2: 0.662235
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.185817 Loss1: 0.521994 Loss2: 0.663823
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.178100 Loss1: 0.513529 Loss2: 0.664571
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181001 Loss1: 0.515735 Loss2: 0.665266
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191828 Loss1: 0.527267 Loss2: 0.664561
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162150 Loss1: 0.497268 Loss2: 0.664881
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.150779 Loss1: 0.487534 Loss2: 0.663245
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148445 Loss1: 0.484553 Loss2: 0.663892
-(DefaultActor pid=1831567) >> Training accuracy: 0.847756
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.342409 Loss1: 0.587398 Loss2: 0.755011
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216083 Loss1: 0.533672 Loss2: 0.682411
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215177 Loss1: 0.532418 Loss2: 0.682759
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188954 Loss1: 0.505386 Loss2: 0.683568
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.203603 Loss1: 0.516774 Loss2: 0.686829
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.200192 Loss1: 0.513877 Loss2: 0.686315
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175795 Loss1: 0.486762 Loss2: 0.689033
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.192052 Loss1: 0.504121 Loss2: 0.687931
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.176323 Loss1: 0.486184 Loss2: 0.690138
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174844 Loss1: 0.486031 Loss2: 0.688813
-[2023-09-27 15:54:36,757][flwr][DEBUG] - fit_round 72 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.838034
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.700200
-[2023-09-27 15:54:38,104][flwr][INFO] - fit progress: (72, 0.8614236178299108, {'accuracy': 0.7002}, 34610.94003685191)
-[2023-09-27 15:54:38,104][flwr][DEBUG] - evaluate_round 72: strategy sampled 10 clients (out of 10)
-[2023-09-27 15:55:08,304][flwr][DEBUG] - evaluate_round 72 received 10 results and 0 failures
-[2023-09-27 15:55:08,305][flwr][DEBUG] - fit_round 73: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340173 Loss1: 0.578090 Loss2: 0.762083
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.271073 Loss1: 0.571719 Loss2: 0.699355
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223428 Loss1: 0.531177 Loss2: 0.692251
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208997 Loss1: 0.512973 Loss2: 0.696023
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200849 Loss1: 0.507222 Loss2: 0.693627
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.215254 Loss1: 0.518443 Loss2: 0.696811
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191118 Loss1: 0.491652 Loss2: 0.699467
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.181624 Loss1: 0.482536 Loss2: 0.699088
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183441 Loss1: 0.484871 Loss2: 0.698570
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198953 Loss1: 0.497329 Loss2: 0.701624
-(DefaultActor pid=1831567) >> Training accuracy: 0.825721
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.511806 Loss1: 0.739789 Loss2: 0.772018
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.394516 Loss1: 0.704731 Loss2: 0.689785
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393777 Loss1: 0.704116 Loss2: 0.689661
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.368046 Loss1: 0.679846 Loss2: 0.688200
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.339370 Loss1: 0.648567 Loss2: 0.690803
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.356738 Loss1: 0.665197 Loss2: 0.691541
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338111 Loss1: 0.643082 Loss2: 0.695029
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.344463 Loss1: 0.650378 Loss2: 0.694085
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.360928 Loss1: 0.664982 Loss2: 0.695946
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308758 Loss1: 0.614728 Loss2: 0.694031
-(DefaultActor pid=1831567) >> Training accuracy: 0.783741
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.216823 Loss1: 0.448976 Loss2: 0.767847
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.091402 Loss1: 0.404878 Loss2: 0.686524
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.083542 Loss1: 0.397458 Loss2: 0.686084
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.069089 Loss1: 0.380520 Loss2: 0.688569
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066429 Loss1: 0.378131 Loss2: 0.688297
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.051365 Loss1: 0.363381 Loss2: 0.687984
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.040094 Loss1: 0.353934 Loss2: 0.686161
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.057268 Loss1: 0.367930 Loss2: 0.689338
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.047272 Loss1: 0.359976 Loss2: 0.687296
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.064678 Loss1: 0.372513 Loss2: 0.692166
-(DefaultActor pid=1831567) >> Training accuracy: 0.883488
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.345006 Loss1: 0.595545 Loss2: 0.749460
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.196359 Loss1: 0.525203 Loss2: 0.671156
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.187573 Loss1: 0.515817 Loss2: 0.671756
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.166838 Loss1: 0.496536 Loss2: 0.670301
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.173728 Loss1: 0.500614 Loss2: 0.673114
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163559 Loss1: 0.493465 Loss2: 0.670094
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.178657 Loss1: 0.497797 Loss2: 0.680860
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.153194 Loss1: 0.475030 Loss2: 0.678164
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134683 Loss1: 0.457412 Loss2: 0.677272
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.170479 Loss1: 0.486148 Loss2: 0.684331
-(DefaultActor pid=1831567) >> Training accuracy: 0.846834
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.293301 Loss1: 0.564006 Loss2: 0.729295
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194760 Loss1: 0.507894 Loss2: 0.686867
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198936 Loss1: 0.511192 Loss2: 0.687745
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190061 Loss1: 0.503966 Loss2: 0.686096
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.199843 Loss1: 0.512906 Loss2: 0.686936
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.182960 Loss1: 0.494111 Loss2: 0.688849
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187015 Loss1: 0.497133 Loss2: 0.689882
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.195108 Loss1: 0.502512 Loss2: 0.692596
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178795 Loss1: 0.484888 Loss2: 0.693907
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.177779 Loss1: 0.486692 Loss2: 0.691088
-(DefaultActor pid=1831567) >> Training accuracy: 0.836806
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.230230 Loss1: 0.456883 Loss2: 0.773348
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099805 Loss1: 0.409041 Loss2: 0.690764
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.077108 Loss1: 0.390925 Loss2: 0.686183
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.058917 Loss1: 0.378595 Loss2: 0.680322
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085336 Loss1: 0.395284 Loss2: 0.690052
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.061655 Loss1: 0.377346 Loss2: 0.684309
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050742 Loss1: 0.367633 Loss2: 0.683109
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.041725 Loss1: 0.357859 Loss2: 0.683867
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.039937 Loss1: 0.352290 Loss2: 0.687647
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.016720 Loss1: 0.330901 Loss2: 0.685820
-(DefaultActor pid=1831567) >> Training accuracy: 0.874228
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.508416 Loss1: 0.722858 Loss2: 0.785558
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.339187 Loss1: 0.645844 Loss2: 0.693343
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.346755 Loss1: 0.653854 Loss2: 0.692901
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.301726 Loss1: 0.610159 Loss2: 0.691567
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.296414 Loss1: 0.602584 Loss2: 0.693830
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303014 Loss1: 0.613220 Loss2: 0.689794
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.285535 Loss1: 0.591030 Loss2: 0.694505
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.295980 Loss1: 0.601693 Loss2: 0.694287
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.302282 Loss1: 0.604890 Loss2: 0.697391
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.274996 Loss1: 0.578029 Loss2: 0.696967
-(DefaultActor pid=1831567) >> Training accuracy: 0.810307
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.488969 Loss1: 0.718254 Loss2: 0.770715
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.356428 Loss1: 0.676600 Loss2: 0.679828
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.353246 Loss1: 0.675173 Loss2: 0.678073
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.336501 Loss1: 0.657201 Loss2: 0.679300
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.320631 Loss1: 0.640465 Loss2: 0.680167
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.327437 Loss1: 0.644609 Loss2: 0.682828
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.326048 Loss1: 0.639542 Loss2: 0.686506
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.309938 Loss1: 0.627604 Loss2: 0.682334
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.310836 Loss1: 0.626800 Loss2: 0.684037
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.293762 Loss1: 0.610060 Loss2: 0.683701
-(DefaultActor pid=1831567) >> Training accuracy: 0.776119
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291961 Loss1: 0.595821 Loss2: 0.696139
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171482 Loss1: 0.542376 Loss2: 0.629106
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.158523 Loss1: 0.534168 Loss2: 0.624355
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.158470 Loss1: 0.534761 Loss2: 0.623709
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150436 Loss1: 0.523863 Loss2: 0.626573
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.136320 Loss1: 0.510976 Loss2: 0.625344
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.124970 Loss1: 0.499009 Loss2: 0.625961
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.112571 Loss1: 0.486571 Loss2: 0.625999
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.130798 Loss1: 0.500780 Loss2: 0.630018
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.116941 Loss1: 0.489125 Loss2: 0.627816
-(DefaultActor pid=1831567) >> Training accuracy: 0.843369
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369154 Loss1: 0.590637 Loss2: 0.778516
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.192586 Loss1: 0.521045 Loss2: 0.671541
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.164102 Loss1: 0.495451 Loss2: 0.668651
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.197955 Loss1: 0.524360 Loss2: 0.673595
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.137361 Loss1: 0.469023 Loss2: 0.668339
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146145 Loss1: 0.476017 Loss2: 0.670128
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.149640 Loss1: 0.478328 Loss2: 0.671312
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.167322 Loss1: 0.492525 Loss2: 0.674797
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134768 Loss1: 0.459325 Loss2: 0.675443
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.100868 Loss1: 0.426402 Loss2: 0.674466
-[2023-09-27 16:01:54,172][flwr][DEBUG] - fit_round 73 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.858845
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.701900
-[2023-09-27 16:01:55,606][flwr][INFO] - fit progress: (73, 0.8532162613381212, {'accuracy': 0.7019}, 35048.44225243013)
-[2023-09-27 16:01:55,607][flwr][DEBUG] - evaluate_round 73: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:02:26,970][flwr][DEBUG] - evaluate_round 73 received 10 results and 0 failures
-[2023-09-27 16:02:26,971][flwr][DEBUG] - fit_round 74: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.344600 Loss1: 0.580651 Loss2: 0.763949
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.228773 Loss1: 0.535982 Loss2: 0.692791
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214914 Loss1: 0.523744 Loss2: 0.691170
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.222157 Loss1: 0.530014 Loss2: 0.692143
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200632 Loss1: 0.505013 Loss2: 0.695619
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.192252 Loss1: 0.494716 Loss2: 0.697536
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.201779 Loss1: 0.509577 Loss2: 0.692202
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.187945 Loss1: 0.491474 Loss2: 0.696471
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.192705 Loss1: 0.496778 Loss2: 0.695926
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.169177 Loss1: 0.473582 Loss2: 0.695596
-(DefaultActor pid=1831567) >> Training accuracy: 0.828316
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.331301 Loss1: 0.574748 Loss2: 0.756553
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187882 Loss1: 0.515766 Loss2: 0.672116
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.200985 Loss1: 0.526085 Loss2: 0.674900
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.193234 Loss1: 0.516450 Loss2: 0.676784
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167899 Loss1: 0.491051 Loss2: 0.676848
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.177020 Loss1: 0.499784 Loss2: 0.677236
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.166838 Loss1: 0.487859 Loss2: 0.678978
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.166071 Loss1: 0.486589 Loss2: 0.679482
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173112 Loss1: 0.493501 Loss2: 0.679612
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.142091 Loss1: 0.462295 Loss2: 0.679796
-(DefaultActor pid=1831567) >> Training accuracy: 0.822163
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.451986 Loss1: 0.684760 Loss2: 0.767227
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.327229 Loss1: 0.663180 Loss2: 0.664049
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.291629 Loss1: 0.632128 Loss2: 0.659501
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.282351 Loss1: 0.620990 Loss2: 0.661361
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.273754 Loss1: 0.612869 Loss2: 0.660885
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.274513 Loss1: 0.608154 Loss2: 0.666358
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.250923 Loss1: 0.585349 Loss2: 0.665574
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.232189 Loss1: 0.568194 Loss2: 0.663994
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.249269 Loss1: 0.584292 Loss2: 0.664978
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.264051 Loss1: 0.594918 Loss2: 0.669132
-(DefaultActor pid=1831567) >> Training accuracy: 0.810581
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.224970 Loss1: 0.459444 Loss2: 0.765527
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.127509 Loss1: 0.430640 Loss2: 0.696869
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.095937 Loss1: 0.400554 Loss2: 0.695383
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.065018 Loss1: 0.370024 Loss2: 0.694994
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.064013 Loss1: 0.368813 Loss2: 0.695199
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.055646 Loss1: 0.363493 Loss2: 0.692154
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.062347 Loss1: 0.366912 Loss2: 0.695435
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.076480 Loss1: 0.381414 Loss2: 0.695066
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.057384 Loss1: 0.358893 Loss2: 0.698491
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.060966 Loss1: 0.358973 Loss2: 0.701993
-(DefaultActor pid=1831567) >> Training accuracy: 0.879630
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.204543 Loss1: 0.464813 Loss2: 0.739729
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.055880 Loss1: 0.402034 Loss2: 0.653846
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.051711 Loss1: 0.399004 Loss2: 0.652707
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019424 Loss1: 0.369401 Loss2: 0.650023
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.029337 Loss1: 0.377938 Loss2: 0.651400
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.042409 Loss1: 0.387674 Loss2: 0.654735
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.023730 Loss1: 0.367335 Loss2: 0.656395
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.018659 Loss1: 0.364322 Loss2: 0.654337
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.031413 Loss1: 0.374578 Loss2: 0.656834
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.999847 Loss1: 0.347473 Loss2: 0.652375
-(DefaultActor pid=1831567) >> Training accuracy: 0.888310
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.465357 Loss1: 0.719948 Loss2: 0.745409
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.333890 Loss1: 0.669082 Loss2: 0.664808
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.327856 Loss1: 0.664692 Loss2: 0.663164
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.332504 Loss1: 0.670114 Loss2: 0.662390
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.291643 Loss1: 0.625761 Loss2: 0.665883
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.318113 Loss1: 0.649480 Loss2: 0.668633
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.313051 Loss1: 0.643736 Loss2: 0.669315
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.321064 Loss1: 0.651646 Loss2: 0.669418
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.288623 Loss1: 0.621203 Loss2: 0.667420
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.319290 Loss1: 0.643963 Loss2: 0.675327
-(DefaultActor pid=1831567) >> Training accuracy: 0.768890
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348266 Loss1: 0.571811 Loss2: 0.776456
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.218643 Loss1: 0.542929 Loss2: 0.675714
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.190151 Loss1: 0.518906 Loss2: 0.671245
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.145594 Loss1: 0.476543 Loss2: 0.669052
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.162825 Loss1: 0.489515 Loss2: 0.673309
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.117843 Loss1: 0.447640 Loss2: 0.670203
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.126025 Loss1: 0.452015 Loss2: 0.674010
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.125467 Loss1: 0.452140 Loss2: 0.673327
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.126849 Loss1: 0.454223 Loss2: 0.672626
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134821 Loss1: 0.455813 Loss2: 0.679009
-(DefaultActor pid=1831567) >> Training accuracy: 0.854343
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.478894 Loss1: 0.724788 Loss2: 0.754107
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.400770 Loss1: 0.725458 Loss2: 0.675313
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.374656 Loss1: 0.697536 Loss2: 0.677120
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.345210 Loss1: 0.670413 Loss2: 0.674797
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.344420 Loss1: 0.668691 Loss2: 0.675729
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.323637 Loss1: 0.648293 Loss2: 0.675344
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.332824 Loss1: 0.652130 Loss2: 0.680694
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.344103 Loss1: 0.663461 Loss2: 0.680643
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.321787 Loss1: 0.642124 Loss2: 0.679663
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.316958 Loss1: 0.635384 Loss2: 0.681574
-(DefaultActor pid=1831567) >> Training accuracy: 0.788496
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297056 Loss1: 0.573653 Loss2: 0.723403
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194730 Loss1: 0.541421 Loss2: 0.653309
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198437 Loss1: 0.544831 Loss2: 0.653605
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.154076 Loss1: 0.503434 Loss2: 0.650642
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.165408 Loss1: 0.507925 Loss2: 0.657483
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162534 Loss1: 0.506791 Loss2: 0.655742
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.177748 Loss1: 0.518207 Loss2: 0.659541
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.170181 Loss1: 0.509775 Loss2: 0.660406
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147395 Loss1: 0.488552 Loss2: 0.658843
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134027 Loss1: 0.475833 Loss2: 0.658194
-(DefaultActor pid=1831567) >> Training accuracy: 0.843750
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.284563 Loss1: 0.547660 Loss2: 0.736902
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212707 Loss1: 0.515293 Loss2: 0.697413
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219901 Loss1: 0.525904 Loss2: 0.693997
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.227688 Loss1: 0.529976 Loss2: 0.697712
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.205453 Loss1: 0.511279 Loss2: 0.694174
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.205583 Loss1: 0.507244 Loss2: 0.698339
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.205412 Loss1: 0.509162 Loss2: 0.696250
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.198814 Loss1: 0.501600 Loss2: 0.697214
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.192913 Loss1: 0.494993 Loss2: 0.697921
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.182734 Loss1: 0.483972 Loss2: 0.698762
-[2023-09-27 16:09:43,162][flwr][DEBUG] - fit_round 74 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.833829
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.707000
-[2023-09-27 16:09:44,477][flwr][INFO] - fit progress: (74, 0.8540887534618378, {'accuracy': 0.707}, 35517.31335649779)
-[2023-09-27 16:09:44,477][flwr][DEBUG] - evaluate_round 74: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:10:15,092][flwr][DEBUG] - evaluate_round 74 received 10 results and 0 failures
-[2023-09-27 16:10:15,093][flwr][DEBUG] - fit_round 75: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.502115 Loss1: 0.719985 Loss2: 0.782130
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.330057 Loss1: 0.651349 Loss2: 0.678708
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.316951 Loss1: 0.631628 Loss2: 0.685322
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.302792 Loss1: 0.617423 Loss2: 0.685369
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302635 Loss1: 0.615714 Loss2: 0.686921
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.273219 Loss1: 0.588987 Loss2: 0.684232
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.281725 Loss1: 0.594362 Loss2: 0.687363
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.317820 Loss1: 0.627679 Loss2: 0.690141
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.282871 Loss1: 0.590203 Loss2: 0.692669
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290932 Loss1: 0.596556 Loss2: 0.694377
-(DefaultActor pid=1831567) >> Training accuracy: 0.810855
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.479868 Loss1: 0.720336 Loss2: 0.759532
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.385353 Loss1: 0.703987 Loss2: 0.681367
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.369321 Loss1: 0.689484 Loss2: 0.679837
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.366558 Loss1: 0.688819 Loss2: 0.677740
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.345893 Loss1: 0.663756 Loss2: 0.682136
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341157 Loss1: 0.660201 Loss2: 0.680957
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.325764 Loss1: 0.644899 Loss2: 0.680865
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.314603 Loss1: 0.628574 Loss2: 0.686029
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.322057 Loss1: 0.638271 Loss2: 0.683787
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.307278 Loss1: 0.623536 Loss2: 0.683742
-(DefaultActor pid=1831567) >> Training accuracy: 0.785100
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.214522 Loss1: 0.455660 Loss2: 0.758862
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.094062 Loss1: 0.411210 Loss2: 0.682852
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.049965 Loss1: 0.373132 Loss2: 0.676833
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.044883 Loss1: 0.365853 Loss2: 0.679030
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.061798 Loss1: 0.381472 Loss2: 0.680326
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.042378 Loss1: 0.362997 Loss2: 0.679381
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.038579 Loss1: 0.358917 Loss2: 0.679662
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.028544 Loss1: 0.349153 Loss2: 0.679391
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.020073 Loss1: 0.341928 Loss2: 0.678145
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.017182 Loss1: 0.337323 Loss2: 0.679859
-(DefaultActor pid=1831567) >> Training accuracy: 0.880401
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.240953 Loss1: 0.468632 Loss2: 0.772320
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099270 Loss1: 0.408311 Loss2: 0.690959
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.078647 Loss1: 0.389884 Loss2: 0.688763
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.085908 Loss1: 0.396036 Loss2: 0.689872
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.065214 Loss1: 0.375442 Loss2: 0.689772
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.062346 Loss1: 0.371994 Loss2: 0.690352
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051676 Loss1: 0.358650 Loss2: 0.693026
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.047084 Loss1: 0.355982 Loss2: 0.691102
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.042395 Loss1: 0.351721 Loss2: 0.690674
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.057126 Loss1: 0.365192 Loss2: 0.691934
-(DefaultActor pid=1831567) >> Training accuracy: 0.875000
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321836 Loss1: 0.576552 Loss2: 0.745284
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171384 Loss1: 0.524889 Loss2: 0.646495
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155391 Loss1: 0.510283 Loss2: 0.645108
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.143672 Loss1: 0.497436 Loss2: 0.646236
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.127625 Loss1: 0.479523 Loss2: 0.648103
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.112800 Loss1: 0.466597 Loss2: 0.646203
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.109990 Loss1: 0.463317 Loss2: 0.646673
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.110118 Loss1: 0.457770 Loss2: 0.652348
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.070017 Loss1: 0.420652 Loss2: 0.649365
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.089201 Loss1: 0.436810 Loss2: 0.652391
-(DefaultActor pid=1831567) >> Training accuracy: 0.846398
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.299968 Loss1: 0.549596 Loss2: 0.750372
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.233675 Loss1: 0.524013 Loss2: 0.709662
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.221865 Loss1: 0.519126 Loss2: 0.702740
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199441 Loss1: 0.489653 Loss2: 0.709788
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216038 Loss1: 0.510401 Loss2: 0.705637
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199478 Loss1: 0.494784 Loss2: 0.704694
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204857 Loss1: 0.497543 Loss2: 0.707314
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.202234 Loss1: 0.490900 Loss2: 0.711334
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213125 Loss1: 0.501179 Loss2: 0.711946
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.214239 Loss1: 0.502535 Loss2: 0.711704
-(DefaultActor pid=1831567) >> Training accuracy: 0.823041
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.330214 Loss1: 0.621633 Loss2: 0.708581
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.179871 Loss1: 0.545539 Loss2: 0.634332
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.162210 Loss1: 0.531599 Loss2: 0.630611
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175085 Loss1: 0.543097 Loss2: 0.631989
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.151452 Loss1: 0.517180 Loss2: 0.634272
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.139405 Loss1: 0.506090 Loss2: 0.633315
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.133520 Loss1: 0.499955 Loss2: 0.633565
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.153985 Loss1: 0.516318 Loss2: 0.637667
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.135914 Loss1: 0.501375 Loss2: 0.634539
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.122434 Loss1: 0.484461 Loss2: 0.637973
-(DefaultActor pid=1831567) >> Training accuracy: 0.834223
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358781 Loss1: 0.589394 Loss2: 0.769387
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.205560 Loss1: 0.520599 Loss2: 0.684961
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199228 Loss1: 0.514248 Loss2: 0.684980
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.191348 Loss1: 0.500152 Loss2: 0.691196
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.180358 Loss1: 0.493717 Loss2: 0.686641
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186058 Loss1: 0.497422 Loss2: 0.688636
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.190092 Loss1: 0.500064 Loss2: 0.690029
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173598 Loss1: 0.485194 Loss2: 0.688405
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.161815 Loss1: 0.472023 Loss2: 0.689792
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.160992 Loss1: 0.468880 Loss2: 0.692111
-(DefaultActor pid=1831567) >> Training accuracy: 0.847656
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.483087 Loss1: 0.714751 Loss2: 0.768336
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.355205 Loss1: 0.678597 Loss2: 0.676608
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.366748 Loss1: 0.688472 Loss2: 0.678276
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.349401 Loss1: 0.669569 Loss2: 0.679831
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333295 Loss1: 0.651672 Loss2: 0.681623
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303148 Loss1: 0.622241 Loss2: 0.680907
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305182 Loss1: 0.623816 Loss2: 0.681365
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.328102 Loss1: 0.645764 Loss2: 0.682338
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.320889 Loss1: 0.638661 Loss2: 0.682228
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284087 Loss1: 0.604088 Loss2: 0.679999
-(DefaultActor pid=1831567) >> Training accuracy: 0.779384
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321583 Loss1: 0.573715 Loss2: 0.747869
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227081 Loss1: 0.541040 Loss2: 0.686042
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205794 Loss1: 0.520034 Loss2: 0.685760
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.182863 Loss1: 0.501928 Loss2: 0.680935
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.209174 Loss1: 0.523582 Loss2: 0.685592
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.194279 Loss1: 0.505867 Loss2: 0.688412
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193025 Loss1: 0.502345 Loss2: 0.690680
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185773 Loss1: 0.497874 Loss2: 0.687899
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.166123 Loss1: 0.482616 Loss2: 0.683507
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.164496 Loss1: 0.475870 Loss2: 0.688627
-[2023-09-27 16:17:04,733][flwr][DEBUG] - fit_round 75 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.832732
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.699300
-[2023-09-27 16:17:06,491][flwr][INFO] - fit progress: (75, 0.8667597494567164, {'accuracy': 0.6993}, 35959.32748264214)
-[2023-09-27 16:17:06,492][flwr][DEBUG] - evaluate_round 75: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:17:37,747][flwr][DEBUG] - evaluate_round 75 received 10 results and 0 failures
-[2023-09-27 16:17:37,748][flwr][DEBUG] - fit_round 76: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226004 Loss1: 0.458585 Loss2: 0.767419
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.107330 Loss1: 0.410310 Loss2: 0.697019
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.092540 Loss1: 0.400449 Loss2: 0.692091
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.081804 Loss1: 0.390856 Loss2: 0.690948
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.078567 Loss1: 0.382121 Loss2: 0.696445
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.052464 Loss1: 0.360447 Loss2: 0.692016
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.055926 Loss1: 0.362926 Loss2: 0.693000
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.045158 Loss1: 0.354051 Loss2: 0.691107
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.049630 Loss1: 0.357080 Loss2: 0.692550
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.059644 Loss1: 0.361937 Loss2: 0.697707
-(DefaultActor pid=1831567) >> Training accuracy: 0.868441
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.293394 Loss1: 0.544260 Loss2: 0.749134
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223622 Loss1: 0.519098 Loss2: 0.704525
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201087 Loss1: 0.500222 Loss2: 0.700865
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.206916 Loss1: 0.503527 Loss2: 0.703388
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.199996 Loss1: 0.496720 Loss2: 0.703276
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.209446 Loss1: 0.501394 Loss2: 0.708052
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.213602 Loss1: 0.505498 Loss2: 0.708104
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.196643 Loss1: 0.490548 Loss2: 0.706095
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217511 Loss1: 0.508410 Loss2: 0.709100
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200966 Loss1: 0.490279 Loss2: 0.710687
-(DefaultActor pid=1831567) >> Training accuracy: 0.835938
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353523 Loss1: 0.576142 Loss2: 0.777381
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171577 Loss1: 0.498993 Loss2: 0.672584
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.176833 Loss1: 0.505726 Loss2: 0.671107
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.152724 Loss1: 0.478161 Loss2: 0.674563
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.135177 Loss1: 0.461607 Loss2: 0.673570
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131690 Loss1: 0.455682 Loss2: 0.676008
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123595 Loss1: 0.447043 Loss2: 0.676552
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140369 Loss1: 0.460158 Loss2: 0.680211
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.119849 Loss1: 0.438177 Loss2: 0.681671
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.120149 Loss1: 0.442064 Loss2: 0.678085
-(DefaultActor pid=1831567) >> Training accuracy: 0.854343
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.311809 Loss1: 0.589801 Loss2: 0.722008
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202174 Loss1: 0.565027 Loss2: 0.637147
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.144537 Loss1: 0.510103 Loss2: 0.634434
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144244 Loss1: 0.506237 Loss2: 0.638006
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.143088 Loss1: 0.502120 Loss2: 0.640968
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.127538 Loss1: 0.487532 Loss2: 0.640006
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.138449 Loss1: 0.497536 Loss2: 0.640913
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.125271 Loss1: 0.484279 Loss2: 0.640992
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136590 Loss1: 0.491039 Loss2: 0.645551
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.105161 Loss1: 0.462974 Loss2: 0.642188
-(DefaultActor pid=1831567) >> Training accuracy: 0.838610
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.381557 Loss1: 0.578484 Loss2: 0.803073
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.259417 Loss1: 0.534147 Loss2: 0.725270
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.245684 Loss1: 0.521635 Loss2: 0.724048
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.250634 Loss1: 0.522868 Loss2: 0.727767
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.231255 Loss1: 0.502601 Loss2: 0.728654
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.225750 Loss1: 0.496970 Loss2: 0.728780
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.229749 Loss1: 0.500275 Loss2: 0.729474
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.223212 Loss1: 0.495545 Loss2: 0.727667
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.214489 Loss1: 0.481962 Loss2: 0.732527
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.212198 Loss1: 0.480003 Loss2: 0.732195
-(DefaultActor pid=1831567) >> Training accuracy: 0.835747
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.186526 Loss1: 0.467716 Loss2: 0.718810
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.056653 Loss1: 0.419503 Loss2: 0.637150
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.031771 Loss1: 0.395091 Loss2: 0.636681
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.015167 Loss1: 0.380631 Loss2: 0.634536
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.013900 Loss1: 0.376037 Loss2: 0.637862
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.028508 Loss1: 0.391881 Loss2: 0.636627
-(DefaultActor pid=1831567) Epoch: 6 Loss: 0.984662 Loss1: 0.350514 Loss2: 0.634148
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.005505 Loss1: 0.367835 Loss2: 0.637669
-(DefaultActor pid=1831567) Epoch: 8 Loss: 0.989317 Loss1: 0.351103 Loss2: 0.638214
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.989186 Loss1: 0.351299 Loss2: 0.637887
-(DefaultActor pid=1831567) >> Training accuracy: 0.875965
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.283676 Loss1: 0.570229 Loss2: 0.713447
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.190909 Loss1: 0.546263 Loss2: 0.644647
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183837 Loss1: 0.541959 Loss2: 0.641878
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.146117 Loss1: 0.503408 Loss2: 0.642708
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.144665 Loss1: 0.500822 Loss2: 0.643843
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.126606 Loss1: 0.481420 Loss2: 0.645186
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.155799 Loss1: 0.506598 Loss2: 0.649201
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.136956 Loss1: 0.491317 Loss2: 0.645640
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.145576 Loss1: 0.495821 Loss2: 0.649755
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.146839 Loss1: 0.499427 Loss2: 0.647412
-(DefaultActor pid=1831567) >> Training accuracy: 0.831931
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.485142 Loss1: 0.723236 Loss2: 0.761906
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.350851 Loss1: 0.681168 Loss2: 0.669683
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.323805 Loss1: 0.654937 Loss2: 0.668868
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.309694 Loss1: 0.639716 Loss2: 0.669978
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.287386 Loss1: 0.620650 Loss2: 0.666736
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.292739 Loss1: 0.620596 Loss2: 0.672143
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.337438 Loss1: 0.661098 Loss2: 0.676340
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.281530 Loss1: 0.610230 Loss2: 0.671300
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305291 Loss1: 0.629737 Loss2: 0.675554
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289251 Loss1: 0.610930 Loss2: 0.678321
-(DefaultActor pid=1831567) >> Training accuracy: 0.784049
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.482032 Loss1: 0.709749 Loss2: 0.772283
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.323624 Loss1: 0.656858 Loss2: 0.666766
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.282381 Loss1: 0.618812 Loss2: 0.663569
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.246366 Loss1: 0.581216 Loss2: 0.665150
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.278155 Loss1: 0.610673 Loss2: 0.667483
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.278304 Loss1: 0.610930 Loss2: 0.667374
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.269031 Loss1: 0.596919 Loss2: 0.672112
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.286952 Loss1: 0.617080 Loss2: 0.669872
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.270615 Loss1: 0.600170 Loss2: 0.670444
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.246471 Loss1: 0.572406 Loss2: 0.674065
-(DefaultActor pid=1831567) >> Training accuracy: 0.796327
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.470974 Loss1: 0.734741 Loss2: 0.736232
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.378506 Loss1: 0.721270 Loss2: 0.657236
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.341881 Loss1: 0.686922 Loss2: 0.654958
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.334215 Loss1: 0.679748 Loss2: 0.654467
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.292327 Loss1: 0.636119 Loss2: 0.656208
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316074 Loss1: 0.660589 Loss2: 0.655485
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322862 Loss1: 0.662745 Loss2: 0.660117
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.322756 Loss1: 0.662163 Loss2: 0.660593
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.313001 Loss1: 0.652161 Loss2: 0.660840
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.261875 Loss1: 0.604238 Loss2: 0.657637
-[2023-09-27 16:24:10,946][flwr][DEBUG] - fit_round 76 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.783967
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.696300
-[2023-09-27 16:24:12,446][flwr][INFO] - fit progress: (76, 0.8745543113150916, {'accuracy': 0.6963}, 36385.282472547144)
-[2023-09-27 16:24:12,447][flwr][DEBUG] - evaluate_round 76: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:24:43,195][flwr][DEBUG] - evaluate_round 76 received 10 results and 0 failures
-[2023-09-27 16:24:43,195][flwr][DEBUG] - fit_round 77: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.213556 Loss1: 0.448184 Loss2: 0.765371
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.095992 Loss1: 0.407451 Loss2: 0.688541
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086714 Loss1: 0.398183 Loss2: 0.688531
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.055346 Loss1: 0.370480 Loss2: 0.684866
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.049464 Loss1: 0.364235 Loss2: 0.685229
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.065399 Loss1: 0.377501 Loss2: 0.687898
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.065562 Loss1: 0.376526 Loss2: 0.689037
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.080210 Loss1: 0.385975 Loss2: 0.694234
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.043833 Loss1: 0.353873 Loss2: 0.689960
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.038701 Loss1: 0.349232 Loss2: 0.689470
-(DefaultActor pid=1831567) >> Training accuracy: 0.879630
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348746 Loss1: 0.577289 Loss2: 0.771457
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211800 Loss1: 0.520267 Loss2: 0.691533
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.195518 Loss1: 0.505790 Loss2: 0.689728
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194018 Loss1: 0.498965 Loss2: 0.695053
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.197448 Loss1: 0.501449 Loss2: 0.695999
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186966 Loss1: 0.490302 Loss2: 0.696664
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.189115 Loss1: 0.492359 Loss2: 0.696756
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162907 Loss1: 0.469329 Loss2: 0.693578
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.164616 Loss1: 0.470132 Loss2: 0.694484
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180351 Loss1: 0.479344 Loss2: 0.701007
-(DefaultActor pid=1831567) >> Training accuracy: 0.841900
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289243 Loss1: 0.541033 Loss2: 0.748210
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.215328 Loss1: 0.511809 Loss2: 0.703519
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.234231 Loss1: 0.524290 Loss2: 0.709941
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.212587 Loss1: 0.504456 Loss2: 0.708131
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216008 Loss1: 0.508149 Loss2: 0.707859
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.202222 Loss1: 0.495510 Loss2: 0.706711
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.189268 Loss1: 0.484225 Loss2: 0.705043
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.209346 Loss1: 0.500517 Loss2: 0.708829
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205078 Loss1: 0.495305 Loss2: 0.709774
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.205632 Loss1: 0.495131 Loss2: 0.710501
-(DefaultActor pid=1831567) >> Training accuracy: 0.839782
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.307384 Loss1: 0.587292 Loss2: 0.720092
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.178572 Loss1: 0.532379 Loss2: 0.646193
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.166027 Loss1: 0.523693 Loss2: 0.642334
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.147704 Loss1: 0.507555 Loss2: 0.640150
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.160195 Loss1: 0.518714 Loss2: 0.641481
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.158831 Loss1: 0.515034 Loss2: 0.643796
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.131630 Loss1: 0.489643 Loss2: 0.641988
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.142870 Loss1: 0.496376 Loss2: 0.646494
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.117543 Loss1: 0.472445 Loss2: 0.645098
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.121112 Loss1: 0.474011 Loss2: 0.647101
-(DefaultActor pid=1831567) >> Training accuracy: 0.838605
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.350635 Loss1: 0.595556 Loss2: 0.755079
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.162725 Loss1: 0.511560 Loss2: 0.651165
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.160459 Loss1: 0.506392 Loss2: 0.654068
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.137562 Loss1: 0.486040 Loss2: 0.651522
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.131131 Loss1: 0.481705 Loss2: 0.649426
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.130149 Loss1: 0.476259 Loss2: 0.653890
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.129501 Loss1: 0.473114 Loss2: 0.656388
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133140 Loss1: 0.478900 Loss2: 0.654240
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.103801 Loss1: 0.446286 Loss2: 0.657515
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.100772 Loss1: 0.445554 Loss2: 0.655218
-(DefaultActor pid=1831567) >> Training accuracy: 0.843485
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340539 Loss1: 0.581058 Loss2: 0.759481
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236578 Loss1: 0.545454 Loss2: 0.691124
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.226125 Loss1: 0.532627 Loss2: 0.693498
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226987 Loss1: 0.532337 Loss2: 0.694649
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216085 Loss1: 0.520224 Loss2: 0.695861
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185831 Loss1: 0.491562 Loss2: 0.694270
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187582 Loss1: 0.490793 Loss2: 0.696789
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.187859 Loss1: 0.491075 Loss2: 0.696784
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.178884 Loss1: 0.481775 Loss2: 0.697110
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.185457 Loss1: 0.484004 Loss2: 0.701453
-(DefaultActor pid=1831567) >> Training accuracy: 0.842147
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.217665 Loss1: 0.483030 Loss2: 0.734635
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.055532 Loss1: 0.401890 Loss2: 0.653642
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.038699 Loss1: 0.390071 Loss2: 0.648628
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.027711 Loss1: 0.382020 Loss2: 0.645691
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.037703 Loss1: 0.389364 Loss2: 0.648339
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.032627 Loss1: 0.379192 Loss2: 0.653435
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.023711 Loss1: 0.371270 Loss2: 0.652441
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.021331 Loss1: 0.369670 Loss2: 0.651661
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.009719 Loss1: 0.358401 Loss2: 0.651317
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.994343 Loss1: 0.341885 Loss2: 0.652458
-(DefaultActor pid=1831567) >> Training accuracy: 0.867863
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459797 Loss1: 0.704602 Loss2: 0.755194
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.327741 Loss1: 0.664998 Loss2: 0.662744
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318677 Loss1: 0.655601 Loss2: 0.663076
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.338414 Loss1: 0.671142 Loss2: 0.667272
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.311649 Loss1: 0.643325 Loss2: 0.668324
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.312683 Loss1: 0.645514 Loss2: 0.667169
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.306126 Loss1: 0.636684 Loss2: 0.669442
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.311093 Loss1: 0.639155 Loss2: 0.671939
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.272422 Loss1: 0.602821 Loss2: 0.669600
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290152 Loss1: 0.620200 Loss2: 0.669952
-(DefaultActor pid=1831567) >> Training accuracy: 0.775187
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493945 Loss1: 0.721836 Loss2: 0.772108
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387447 Loss1: 0.702515 Loss2: 0.684932
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.348697 Loss1: 0.664913 Loss2: 0.683784
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364860 Loss1: 0.679166 Loss2: 0.685693
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.354275 Loss1: 0.666162 Loss2: 0.688113
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.341691 Loss1: 0.651173 Loss2: 0.690518
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322065 Loss1: 0.634637 Loss2: 0.687429
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.360281 Loss1: 0.666021 Loss2: 0.694260
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331961 Loss1: 0.639373 Loss2: 0.692587
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.326884 Loss1: 0.630845 Loss2: 0.696039
-(DefaultActor pid=1831567) >> Training accuracy: 0.795516
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.439665 Loss1: 0.699077 Loss2: 0.740588
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.301818 Loss1: 0.648632 Loss2: 0.653185
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.277183 Loss1: 0.623547 Loss2: 0.653636
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.266859 Loss1: 0.617375 Loss2: 0.649484
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.277380 Loss1: 0.621713 Loss2: 0.655668
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.251029 Loss1: 0.595742 Loss2: 0.655287
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.243204 Loss1: 0.588311 Loss2: 0.654893
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.260481 Loss1: 0.602568 Loss2: 0.657913
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244246 Loss1: 0.583421 Loss2: 0.660825
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.255936 Loss1: 0.592188 Loss2: 0.663748
-[2023-09-27 16:31:33,440][flwr][DEBUG] - fit_round 77 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.797697
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.699700
-[2023-09-27 16:31:35,148][flwr][INFO] - fit progress: (77, 0.8666860393632334, {'accuracy': 0.6997}, 36827.984321075026)
-[2023-09-27 16:31:35,149][flwr][DEBUG] - evaluate_round 77: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:32:05,859][flwr][DEBUG] - evaluate_round 77 received 10 results and 0 failures
-[2023-09-27 16:32:05,860][flwr][DEBUG] - fit_round 78: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.374009 Loss1: 0.589121 Loss2: 0.784888
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.215234 Loss1: 0.532749 Loss2: 0.682485
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.196449 Loss1: 0.515066 Loss2: 0.681383
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.169540 Loss1: 0.488417 Loss2: 0.681122
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.175567 Loss1: 0.489796 Loss2: 0.685772
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.138523 Loss1: 0.454405 Loss2: 0.684118
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.143649 Loss1: 0.460875 Loss2: 0.682774
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.138348 Loss1: 0.453685 Loss2: 0.684663
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.121375 Loss1: 0.436629 Loss2: 0.684746
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.144044 Loss1: 0.457999 Loss2: 0.686045
-(DefaultActor pid=1831567) >> Training accuracy: 0.846663
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.479313 Loss1: 0.722783 Loss2: 0.756530
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.352197 Loss1: 0.684368 Loss2: 0.667829
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.333746 Loss1: 0.666683 Loss2: 0.667063
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.326882 Loss1: 0.656749 Loss2: 0.670132
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.296928 Loss1: 0.626361 Loss2: 0.670566
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.334401 Loss1: 0.663618 Loss2: 0.670784
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.300875 Loss1: 0.629992 Loss2: 0.670883
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289261 Loss1: 0.617023 Loss2: 0.672238
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.283579 Loss1: 0.609926 Loss2: 0.673653
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.294849 Loss1: 0.621759 Loss2: 0.673090
-(DefaultActor pid=1831567) >> Training accuracy: 0.784049
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.348658 Loss1: 0.592667 Loss2: 0.755991
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.222373 Loss1: 0.535930 Loss2: 0.686443
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.215028 Loss1: 0.526824 Loss2: 0.688204
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.186045 Loss1: 0.501838 Loss2: 0.684207
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185415 Loss1: 0.498893 Loss2: 0.686521
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.195085 Loss1: 0.507280 Loss2: 0.687804
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174554 Loss1: 0.486721 Loss2: 0.687833
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185074 Loss1: 0.496803 Loss2: 0.688271
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163518 Loss1: 0.474826 Loss2: 0.688692
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.161351 Loss1: 0.470563 Loss2: 0.690788
-(DefaultActor pid=1831567) >> Training accuracy: 0.840130
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495368 Loss1: 0.714571 Loss2: 0.780797
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.323360 Loss1: 0.651311 Loss2: 0.672048
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.304320 Loss1: 0.634349 Loss2: 0.669971
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.279751 Loss1: 0.608184 Loss2: 0.671566
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.275793 Loss1: 0.604925 Loss2: 0.670868
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.275197 Loss1: 0.603321 Loss2: 0.671876
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.264126 Loss1: 0.591395 Loss2: 0.672731
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253112 Loss1: 0.575144 Loss2: 0.677968
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.256098 Loss1: 0.581087 Loss2: 0.675010
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240149 Loss1: 0.565273 Loss2: 0.674877
-(DefaultActor pid=1831567) >> Training accuracy: 0.790570
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493426 Loss1: 0.749714 Loss2: 0.743712
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.362381 Loss1: 0.700417 Loss2: 0.661964
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354591 Loss1: 0.694560 Loss2: 0.660031
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333777 Loss1: 0.672008 Loss2: 0.661770
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.340689 Loss1: 0.677724 Loss2: 0.662965
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.317825 Loss1: 0.657645 Loss2: 0.660180
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299239 Loss1: 0.634651 Loss2: 0.664588
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.298907 Loss1: 0.634754 Loss2: 0.664153
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.286487 Loss1: 0.623998 Loss2: 0.662489
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.305315 Loss1: 0.637589 Loss2: 0.667726
-(DefaultActor pid=1831567) >> Training accuracy: 0.792799
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.206330 Loss1: 0.465463 Loss2: 0.740867
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.049569 Loss1: 0.391773 Loss2: 0.657796
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.026041 Loss1: 0.368387 Loss2: 0.657654
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.050688 Loss1: 0.392791 Loss2: 0.657897
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.032422 Loss1: 0.373041 Loss2: 0.659380
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.021058 Loss1: 0.364303 Loss2: 0.656755
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019856 Loss1: 0.360569 Loss2: 0.659287
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.013852 Loss1: 0.355680 Loss2: 0.658171
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.002921 Loss1: 0.342389 Loss2: 0.660532
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.011757 Loss1: 0.352119 Loss2: 0.659639
-(DefaultActor pid=1831567) >> Training accuracy: 0.887924
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291649 Loss1: 0.565359 Loss2: 0.726290
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172886 Loss1: 0.525365 Loss2: 0.647522
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.174225 Loss1: 0.522472 Loss2: 0.651753
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.152999 Loss1: 0.502351 Loss2: 0.650647
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150679 Loss1: 0.497896 Loss2: 0.652783
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.143681 Loss1: 0.490112 Loss2: 0.653569
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.145483 Loss1: 0.493054 Loss2: 0.652429
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.114156 Loss1: 0.458856 Loss2: 0.655300
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136134 Loss1: 0.480356 Loss2: 0.655778
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.128779 Loss1: 0.472561 Loss2: 0.656218
-(DefaultActor pid=1831567) >> Training accuracy: 0.846012
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.255727 Loss1: 0.452376 Loss2: 0.803351
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.122764 Loss1: 0.392769 Loss2: 0.729995
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.133290 Loss1: 0.405996 Loss2: 0.727294
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.131115 Loss1: 0.400548 Loss2: 0.730567
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.095392 Loss1: 0.368422 Loss2: 0.726970
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.103974 Loss1: 0.374435 Loss2: 0.729539
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.089656 Loss1: 0.362872 Loss2: 0.726784
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.079411 Loss1: 0.350004 Loss2: 0.729407
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.063497 Loss1: 0.337688 Loss2: 0.725808
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.096777 Loss1: 0.366574 Loss2: 0.730203
-(DefaultActor pid=1831567) >> Training accuracy: 0.877508
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.313227 Loss1: 0.582140 Loss2: 0.731087
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.189986 Loss1: 0.529135 Loss2: 0.660851
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192311 Loss1: 0.533174 Loss2: 0.659137
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210086 Loss1: 0.546054 Loss2: 0.664031
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204102 Loss1: 0.541048 Loss2: 0.663053
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.175997 Loss1: 0.513701 Loss2: 0.662295
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187869 Loss1: 0.524694 Loss2: 0.663175
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154544 Loss1: 0.490852 Loss2: 0.663691
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.141473 Loss1: 0.479175 Loss2: 0.662298
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140492 Loss1: 0.477531 Loss2: 0.662961
-(DefaultActor pid=1831567) >> Training accuracy: 0.851362
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.308545 Loss1: 0.546334 Loss2: 0.762211
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.235599 Loss1: 0.512505 Loss2: 0.723094
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222549 Loss1: 0.502690 Loss2: 0.719859
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.221329 Loss1: 0.498714 Loss2: 0.722615
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.218855 Loss1: 0.496288 Loss2: 0.722567
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231990 Loss1: 0.510736 Loss2: 0.721254
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220709 Loss1: 0.499798 Loss2: 0.720911
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.229528 Loss1: 0.505456 Loss2: 0.724072
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.212370 Loss1: 0.486261 Loss2: 0.726109
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229951 Loss1: 0.504289 Loss2: 0.725662
-[2023-09-27 16:39:01,891][flwr][DEBUG] - fit_round 78 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.835565
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.700200
-[2023-09-27 16:39:03,407][flwr][INFO] - fit progress: (78, 0.8683196400491574, {'accuracy': 0.7002}, 37276.24380477099)
-[2023-09-27 16:39:03,408][flwr][DEBUG] - evaluate_round 78: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:39:34,188][flwr][DEBUG] - evaluate_round 78 received 10 results and 0 failures
-[2023-09-27 16:39:34,189][flwr][DEBUG] - fit_round 79: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.489681 Loss1: 0.727224 Loss2: 0.762457
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.383131 Loss1: 0.705417 Loss2: 0.677715
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.348042 Loss1: 0.668252 Loss2: 0.679791
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.346453 Loss1: 0.666310 Loss2: 0.680144
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.336539 Loss1: 0.654414 Loss2: 0.682125
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.344074 Loss1: 0.659196 Loss2: 0.684878
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.324119 Loss1: 0.637318 Loss2: 0.686801
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.306630 Loss1: 0.618376 Loss2: 0.688254
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.352125 Loss1: 0.660356 Loss2: 0.691770
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.320029 Loss1: 0.628390 Loss2: 0.691639
-(DefaultActor pid=1831567) >> Training accuracy: 0.787591
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.360518 Loss1: 0.575321 Loss2: 0.785197
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.248956 Loss1: 0.546778 Loss2: 0.702178
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.210532 Loss1: 0.512002 Loss2: 0.698530
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.210935 Loss1: 0.505981 Loss2: 0.704954
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216805 Loss1: 0.511255 Loss2: 0.705550
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.190501 Loss1: 0.485374 Loss2: 0.705127
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200200 Loss1: 0.493245 Loss2: 0.706955
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.164120 Loss1: 0.458391 Loss2: 0.705730
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.166339 Loss1: 0.460693 Loss2: 0.705646
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189312 Loss1: 0.477605 Loss2: 0.711707
-(DefaultActor pid=1831567) >> Training accuracy: 0.854646
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495876 Loss1: 0.724985 Loss2: 0.770891
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.314112 Loss1: 0.643827 Loss2: 0.670284
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.326094 Loss1: 0.655655 Loss2: 0.670439
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.296382 Loss1: 0.622307 Loss2: 0.674075
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.275470 Loss1: 0.603335 Loss2: 0.672135
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.279871 Loss1: 0.604248 Loss2: 0.675623
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.256204 Loss1: 0.585886 Loss2: 0.670318
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.253652 Loss1: 0.578914 Loss2: 0.674738
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.281052 Loss1: 0.600979 Loss2: 0.680073
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250059 Loss1: 0.572523 Loss2: 0.677536
-(DefaultActor pid=1831567) >> Training accuracy: 0.805647
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.184380 Loss1: 0.438733 Loss2: 0.745647
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.083660 Loss1: 0.415062 Loss2: 0.668599
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.077668 Loss1: 0.419510 Loss2: 0.658157
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.028396 Loss1: 0.368085 Loss2: 0.660312
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.031409 Loss1: 0.370374 Loss2: 0.661035
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.026018 Loss1: 0.363775 Loss2: 0.662243
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.020482 Loss1: 0.357879 Loss2: 0.662604
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.025906 Loss1: 0.367783 Loss2: 0.658123
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.013335 Loss1: 0.353317 Loss2: 0.660018
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.013566 Loss1: 0.348777 Loss2: 0.664788
-(DefaultActor pid=1831567) >> Training accuracy: 0.850502
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.281430 Loss1: 0.542710 Loss2: 0.738720
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.208496 Loss1: 0.511369 Loss2: 0.697127
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208448 Loss1: 0.511693 Loss2: 0.696755
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.207889 Loss1: 0.511083 Loss2: 0.696806
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215982 Loss1: 0.517900 Loss2: 0.698082
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199179 Loss1: 0.501121 Loss2: 0.698058
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196513 Loss1: 0.498598 Loss2: 0.697915
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.188006 Loss1: 0.489388 Loss2: 0.698618
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.192434 Loss1: 0.491164 Loss2: 0.701270
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.196435 Loss1: 0.495597 Loss2: 0.700838
-(DefaultActor pid=1831567) >> Training accuracy: 0.840278
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481537 Loss1: 0.724912 Loss2: 0.756625
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347002 Loss1: 0.681248 Loss2: 0.665754
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.334949 Loss1: 0.666013 Loss2: 0.668935
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.320480 Loss1: 0.652806 Loss2: 0.667675
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.314398 Loss1: 0.646674 Loss2: 0.667724
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316226 Loss1: 0.646884 Loss2: 0.669342
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.311239 Loss1: 0.641263 Loss2: 0.669975
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.284108 Loss1: 0.611345 Loss2: 0.672763
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.314525 Loss1: 0.639999 Loss2: 0.674526
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.293639 Loss1: 0.621826 Loss2: 0.671813
-(DefaultActor pid=1831567) >> Training accuracy: 0.788713
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327462 Loss1: 0.606930 Loss2: 0.720531
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.195008 Loss1: 0.546712 Loss2: 0.648296
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.162201 Loss1: 0.516293 Loss2: 0.645907
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.158942 Loss1: 0.515698 Loss2: 0.643244
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170684 Loss1: 0.522754 Loss2: 0.647929
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.134766 Loss1: 0.485816 Loss2: 0.648949
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.132603 Loss1: 0.485482 Loss2: 0.647120
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.150677 Loss1: 0.502316 Loss2: 0.648360
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136352 Loss1: 0.490269 Loss2: 0.646084
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.102434 Loss1: 0.453732 Loss2: 0.648702
-(DefaultActor pid=1831567) >> Training accuracy: 0.834794
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.218258 Loss1: 0.459014 Loss2: 0.759244
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.089135 Loss1: 0.408481 Loss2: 0.680654
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.058281 Loss1: 0.381046 Loss2: 0.677235
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.058548 Loss1: 0.381754 Loss2: 0.676794
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.041523 Loss1: 0.364552 Loss2: 0.676971
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.049728 Loss1: 0.373045 Loss2: 0.676682
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.045667 Loss1: 0.365474 Loss2: 0.680193
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.021993 Loss1: 0.344049 Loss2: 0.677944
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.011483 Loss1: 0.333384 Loss2: 0.678100
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.029786 Loss1: 0.349982 Loss2: 0.679804
-(DefaultActor pid=1831567) >> Training accuracy: 0.889660
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324411 Loss1: 0.579175 Loss2: 0.745236
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.167526 Loss1: 0.521566 Loss2: 0.645960
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.153990 Loss1: 0.504877 Loss2: 0.649113
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.145543 Loss1: 0.497577 Loss2: 0.647966
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.119814 Loss1: 0.473530 Loss2: 0.646284
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.114120 Loss1: 0.464528 Loss2: 0.649592
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.102373 Loss1: 0.450470 Loss2: 0.651902
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140993 Loss1: 0.487162 Loss2: 0.653831
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.115831 Loss1: 0.463786 Loss2: 0.652045
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.091007 Loss1: 0.439457 Loss2: 0.651550
-(DefaultActor pid=1831567) >> Training accuracy: 0.857521
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.306864 Loss1: 0.573517 Loss2: 0.733347
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.219735 Loss1: 0.547251 Loss2: 0.672484
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203239 Loss1: 0.530915 Loss2: 0.672324
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181774 Loss1: 0.511264 Loss2: 0.670511
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195833 Loss1: 0.520701 Loss2: 0.675132
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.197259 Loss1: 0.521380 Loss2: 0.675879
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171717 Loss1: 0.497523 Loss2: 0.674195
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169910 Loss1: 0.492287 Loss2: 0.677623
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.128973 Loss1: 0.453897 Loss2: 0.675076
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.137801 Loss1: 0.462459 Loss2: 0.675343
-(DefaultActor pid=1831567) >> Training accuracy: 0.839343
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 16:46:36,918][flwr][DEBUG] - fit_round 79 received 10 results and 0 failures
->> Test accuracy: 0.699400
-[2023-09-27 16:46:51,400][flwr][INFO] - fit progress: (79, 0.8694912555118719, {'accuracy': 0.6994}, 37744.23628887907)
-[2023-09-27 16:46:51,400][flwr][DEBUG] - evaluate_round 79: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:47:30,322][flwr][DEBUG] - evaluate_round 79 received 10 results and 0 failures
-[2023-09-27 16:47:30,323][flwr][DEBUG] - fit_round 80: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.454493 Loss1: 0.723664 Loss2: 0.730829
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.339708 Loss1: 0.687802 Loss2: 0.651906
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.312909 Loss1: 0.663822 Loss2: 0.649087
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.326385 Loss1: 0.675338 Loss2: 0.651048
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.314774 Loss1: 0.662518 Loss2: 0.652256
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.310456 Loss1: 0.655531 Loss2: 0.654925
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298010 Loss1: 0.641606 Loss2: 0.656404
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.268482 Loss1: 0.615733 Loss2: 0.652748
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.320107 Loss1: 0.662105 Loss2: 0.658002
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.283731 Loss1: 0.625588 Loss2: 0.658144
-(DefaultActor pid=1831567) >> Training accuracy: 0.783967
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.328291 Loss1: 0.565311 Loss2: 0.762981
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.195891 Loss1: 0.509624 Loss2: 0.686268
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231968 Loss1: 0.543275 Loss2: 0.688693
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.226243 Loss1: 0.538262 Loss2: 0.687982
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185108 Loss1: 0.497793 Loss2: 0.687315
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206626 Loss1: 0.515994 Loss2: 0.690632
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.196014 Loss1: 0.501950 Loss2: 0.694065
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.195426 Loss1: 0.505985 Loss2: 0.689441
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180321 Loss1: 0.487959 Loss2: 0.692361
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.170034 Loss1: 0.476739 Loss2: 0.693295
-(DefaultActor pid=1831567) >> Training accuracy: 0.841146
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.365451 Loss1: 0.595498 Loss2: 0.769953
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236919 Loss1: 0.542041 Loss2: 0.694878
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191502 Loss1: 0.502629 Loss2: 0.688873
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.211844 Loss1: 0.513758 Loss2: 0.698085
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184251 Loss1: 0.493998 Loss2: 0.690253
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.202649 Loss1: 0.508044 Loss2: 0.694605
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.180795 Loss1: 0.485625 Loss2: 0.695170
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.177056 Loss1: 0.478426 Loss2: 0.698630
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.186964 Loss1: 0.490092 Loss2: 0.696872
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.162090 Loss1: 0.461919 Loss2: 0.700172
-(DefaultActor pid=1831567) >> Training accuracy: 0.840511
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.254640 Loss1: 0.463941 Loss2: 0.790699
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.121312 Loss1: 0.408946 Loss2: 0.712366
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.110466 Loss1: 0.401493 Loss2: 0.708973
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.084075 Loss1: 0.374209 Loss2: 0.709866
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.085935 Loss1: 0.376579 Loss2: 0.709356
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.097952 Loss1: 0.386425 Loss2: 0.711527
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.071768 Loss1: 0.358037 Loss2: 0.713731
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.072209 Loss1: 0.360187 Loss2: 0.712022
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.059443 Loss1: 0.346472 Loss2: 0.712971
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.065948 Loss1: 0.354313 Loss2: 0.711635
-(DefaultActor pid=1831567) >> Training accuracy: 0.866898
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459694 Loss1: 0.696204 Loss2: 0.763490
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.306950 Loss1: 0.652029 Loss2: 0.654921
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.310914 Loss1: 0.654534 Loss2: 0.656380
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.284436 Loss1: 0.625903 Loss2: 0.658533
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.227217 Loss1: 0.576183 Loss2: 0.651034
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.263319 Loss1: 0.611006 Loss2: 0.652313
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.233488 Loss1: 0.579877 Loss2: 0.653611
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.251796 Loss1: 0.593255 Loss2: 0.658541
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.250598 Loss1: 0.593881 Loss2: 0.656717
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289120 Loss1: 0.627479 Loss2: 0.661641
-(DefaultActor pid=1831567) >> Training accuracy: 0.799616
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.269004 Loss1: 0.540504 Loss2: 0.728500
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.234204 Loss1: 0.543390 Loss2: 0.690814
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.204314 Loss1: 0.515942 Loss2: 0.688372
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.196022 Loss1: 0.505734 Loss2: 0.690288
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184733 Loss1: 0.495071 Loss2: 0.689662
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.194362 Loss1: 0.503303 Loss2: 0.691059
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.184447 Loss1: 0.491237 Loss2: 0.693210
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.172962 Loss1: 0.482379 Loss2: 0.690583
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.203060 Loss1: 0.507610 Loss2: 0.695450
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179909 Loss1: 0.485599 Loss2: 0.694310
-(DefaultActor pid=1831567) >> Training accuracy: 0.830481
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297454 Loss1: 0.568408 Loss2: 0.729046
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180181 Loss1: 0.532017 Loss2: 0.648164
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.160266 Loss1: 0.512059 Loss2: 0.648207
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.170723 Loss1: 0.519121 Loss2: 0.651602
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.146723 Loss1: 0.493858 Loss2: 0.652865
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146239 Loss1: 0.492483 Loss2: 0.653757
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.139678 Loss1: 0.485026 Loss2: 0.654652
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133895 Loss1: 0.480571 Loss2: 0.653324
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.127217 Loss1: 0.473554 Loss2: 0.653663
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.106672 Loss1: 0.453622 Loss2: 0.653050
-(DefaultActor pid=1831567) >> Training accuracy: 0.840461
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.209776 Loss1: 0.459287 Loss2: 0.750490
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.111342 Loss1: 0.439064 Loss2: 0.672278
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.067302 Loss1: 0.395541 Loss2: 0.671761
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.040194 Loss1: 0.371852 Loss2: 0.668342
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.052917 Loss1: 0.384846 Loss2: 0.668071
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.033326 Loss1: 0.364983 Loss2: 0.668342
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.034612 Loss1: 0.364301 Loss2: 0.670311
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.034158 Loss1: 0.360371 Loss2: 0.673787
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.017835 Loss1: 0.347053 Loss2: 0.670782
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.016462 Loss1: 0.344951 Loss2: 0.671510
-(DefaultActor pid=1831567) >> Training accuracy: 0.863040
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.354327 Loss1: 0.571872 Loss2: 0.782454
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201609 Loss1: 0.519288 Loss2: 0.682321
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.178111 Loss1: 0.497327 Loss2: 0.680785
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.166653 Loss1: 0.483096 Loss2: 0.683556
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167567 Loss1: 0.481819 Loss2: 0.685748
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.148876 Loss1: 0.466829 Loss2: 0.682047
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.147886 Loss1: 0.460418 Loss2: 0.687469
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.133137 Loss1: 0.447643 Loss2: 0.685494
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.158036 Loss1: 0.468022 Loss2: 0.690014
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.142751 Loss1: 0.452918 Loss2: 0.689832
-(DefaultActor pid=1831567) >> Training accuracy: 0.838189
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.537864 Loss1: 0.741343 Loss2: 0.796521
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.404987 Loss1: 0.699644 Loss2: 0.705344
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.380391 Loss1: 0.676326 Loss2: 0.704065
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.369740 Loss1: 0.664977 Loss2: 0.704762
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.343714 Loss1: 0.639463 Loss2: 0.704251
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.348802 Loss1: 0.641958 Loss2: 0.706844
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.328083 Loss1: 0.623463 Loss2: 0.704620
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.328473 Loss1: 0.623006 Loss2: 0.705467
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.329175 Loss1: 0.620607 Loss2: 0.708568
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.328157 Loss1: 0.615535 Loss2: 0.712623
-[2023-09-27 16:54:12,710][flwr][DEBUG] - fit_round 80 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.779151
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.692700
-[2023-09-27 16:54:14,354][flwr][INFO] - fit progress: (80, 0.8883003936217616, {'accuracy': 0.6927}, 38187.19075930398)
-[2023-09-27 16:54:14,355][flwr][DEBUG] - evaluate_round 80: strategy sampled 10 clients (out of 10)
-[2023-09-27 16:54:45,994][flwr][DEBUG] - evaluate_round 80 received 10 results and 0 failures
-[2023-09-27 16:54:45,995][flwr][DEBUG] - fit_round 81: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.300540 Loss1: 0.546124 Loss2: 0.754416
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.215495 Loss1: 0.502057 Loss2: 0.713438
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203739 Loss1: 0.494000 Loss2: 0.709739
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.209664 Loss1: 0.500396 Loss2: 0.709267
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.219611 Loss1: 0.506395 Loss2: 0.713216
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.211257 Loss1: 0.496688 Loss2: 0.714569
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.212045 Loss1: 0.496708 Loss2: 0.715337
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.213101 Loss1: 0.497642 Loss2: 0.715459
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.207396 Loss1: 0.493332 Loss2: 0.714063
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.209689 Loss1: 0.491359 Loss2: 0.718330
-(DefaultActor pid=1831567) >> Training accuracy: 0.833457
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187680 Loss1: 0.447050 Loss2: 0.740630
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.061610 Loss1: 0.402402 Loss2: 0.659208
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.044217 Loss1: 0.384807 Loss2: 0.659409
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.039955 Loss1: 0.380685 Loss2: 0.659271
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.045495 Loss1: 0.385480 Loss2: 0.660015
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.017948 Loss1: 0.360314 Loss2: 0.657634
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.028419 Loss1: 0.366645 Loss2: 0.661774
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026755 Loss1: 0.363917 Loss2: 0.662838
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.033666 Loss1: 0.371787 Loss2: 0.661879
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.003370 Loss1: 0.343162 Loss2: 0.660209
-(DefaultActor pid=1831567) >> Training accuracy: 0.858410
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324305 Loss1: 0.584975 Loss2: 0.739330
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.191577 Loss1: 0.518751 Loss2: 0.672826
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.184092 Loss1: 0.515493 Loss2: 0.668600
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194349 Loss1: 0.518621 Loss2: 0.675727
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.168716 Loss1: 0.495332 Loss2: 0.673384
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183847 Loss1: 0.511194 Loss2: 0.672653
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.176124 Loss1: 0.502404 Loss2: 0.673720
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180820 Loss1: 0.503281 Loss2: 0.677539
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147135 Loss1: 0.470189 Loss2: 0.676945
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.178842 Loss1: 0.497939 Loss2: 0.680902
-(DefaultActor pid=1831567) >> Training accuracy: 0.845353
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.453269 Loss1: 0.707223 Loss2: 0.746047
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.335721 Loss1: 0.680495 Loss2: 0.655226
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.335820 Loss1: 0.678961 Loss2: 0.656860
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.302291 Loss1: 0.645425 Loss2: 0.656866
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.297271 Loss1: 0.640139 Loss2: 0.657132
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.288180 Loss1: 0.627032 Loss2: 0.661148
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.293450 Loss1: 0.632152 Loss2: 0.661298
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.294177 Loss1: 0.632655 Loss2: 0.661522
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.276674 Loss1: 0.613893 Loss2: 0.662781
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.295908 Loss1: 0.630861 Loss2: 0.665047
-(DefaultActor pid=1831567) >> Training accuracy: 0.778685
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.518419 Loss1: 0.722636 Loss2: 0.795784
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.406849 Loss1: 0.701113 Loss2: 0.705737
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.393866 Loss1: 0.685118 Loss2: 0.708749
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.391511 Loss1: 0.680939 Loss2: 0.710572
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.369378 Loss1: 0.661238 Loss2: 0.708140
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.362421 Loss1: 0.650714 Loss2: 0.711706
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.338002 Loss1: 0.627718 Loss2: 0.710284
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.331386 Loss1: 0.617772 Loss2: 0.713614
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.358380 Loss1: 0.644738 Loss2: 0.713643
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.352306 Loss1: 0.639416 Loss2: 0.712890
-(DefaultActor pid=1831567) >> Training accuracy: 0.778759
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.226462 Loss1: 0.461796 Loss2: 0.764665
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.085327 Loss1: 0.402304 Loss2: 0.683022
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.080871 Loss1: 0.398117 Loss2: 0.682754
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.080696 Loss1: 0.396185 Loss2: 0.684512
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066125 Loss1: 0.380581 Loss2: 0.685544
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.068261 Loss1: 0.383840 Loss2: 0.684421
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.041232 Loss1: 0.354763 Loss2: 0.686469
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.049936 Loss1: 0.364873 Loss2: 0.685063
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.032306 Loss1: 0.345683 Loss2: 0.686623
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.030793 Loss1: 0.343582 Loss2: 0.687212
-(DefaultActor pid=1831567) >> Training accuracy: 0.893711
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.355668 Loss1: 0.577239 Loss2: 0.778429
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217776 Loss1: 0.521577 Loss2: 0.696199
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214805 Loss1: 0.517901 Loss2: 0.696904
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200087 Loss1: 0.503026 Loss2: 0.697061
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187831 Loss1: 0.491580 Loss2: 0.696251
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.188989 Loss1: 0.492543 Loss2: 0.696446
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182921 Loss1: 0.482080 Loss2: 0.700841
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.181522 Loss1: 0.479544 Loss2: 0.701978
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.174566 Loss1: 0.469970 Loss2: 0.704595
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176864 Loss1: 0.474183 Loss2: 0.702682
-(DefaultActor pid=1831567) >> Training accuracy: 0.843133
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.327246 Loss1: 0.578864 Loss2: 0.748381
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.164927 Loss1: 0.517694 Loss2: 0.647233
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.142656 Loss1: 0.501796 Loss2: 0.640861
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.131374 Loss1: 0.487031 Loss2: 0.644343
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.121453 Loss1: 0.479665 Loss2: 0.641788
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.139153 Loss1: 0.492593 Loss2: 0.646560
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.104358 Loss1: 0.456990 Loss2: 0.647368
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.121340 Loss1: 0.474532 Loss2: 0.646808
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.092212 Loss1: 0.444599 Loss2: 0.647613
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.102187 Loss1: 0.454107 Loss2: 0.648080
-(DefaultActor pid=1831567) >> Training accuracy: 0.854078
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.469989 Loss1: 0.703244 Loss2: 0.766745
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.361671 Loss1: 0.686308 Loss2: 0.675363
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.310838 Loss1: 0.638611 Loss2: 0.672227
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.301971 Loss1: 0.626805 Loss2: 0.675166
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.290725 Loss1: 0.616052 Loss2: 0.674673
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.279431 Loss1: 0.603771 Loss2: 0.675660
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.276540 Loss1: 0.598715 Loss2: 0.677825
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.269534 Loss1: 0.589600 Loss2: 0.679934
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.261710 Loss1: 0.580629 Loss2: 0.681081
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.256590 Loss1: 0.573644 Loss2: 0.682947
-(DefaultActor pid=1831567) >> Training accuracy: 0.783991
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.323510 Loss1: 0.597050 Loss2: 0.726460
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.181453 Loss1: 0.535667 Loss2: 0.645786
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.164301 Loss1: 0.519993 Loss2: 0.644308
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.154907 Loss1: 0.511565 Loss2: 0.643342
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158417 Loss1: 0.513735 Loss2: 0.644681
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163088 Loss1: 0.515852 Loss2: 0.647236
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.144812 Loss1: 0.497199 Loss2: 0.647613
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155809 Loss1: 0.509354 Loss2: 0.646456
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120312 Loss1: 0.474323 Loss2: 0.645989
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.134396 Loss1: 0.486348 Loss2: 0.648048
-[2023-09-27 17:01:26,837][flwr][DEBUG] - fit_round 81 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.832127
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.701700
-[2023-09-27 17:01:28,342][flwr][INFO] - fit progress: (81, 0.8609136937144465, {'accuracy': 0.7017}, 38621.17888039211)
-[2023-09-27 17:01:28,343][flwr][DEBUG] - evaluate_round 81: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:01:59,473][flwr][DEBUG] - evaluate_round 81 received 10 results and 0 failures
-[2023-09-27 17:01:59,474][flwr][DEBUG] - fit_round 82: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.225876 Loss1: 0.459700 Loss2: 0.766177
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.102310 Loss1: 0.406331 Loss2: 0.695979
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.079561 Loss1: 0.389791 Loss2: 0.689770
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.074747 Loss1: 0.382154 Loss2: 0.692594
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.068374 Loss1: 0.373046 Loss2: 0.695328
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.057631 Loss1: 0.365959 Loss2: 0.691672
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051816 Loss1: 0.358996 Loss2: 0.692820
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.050105 Loss1: 0.356746 Loss2: 0.693359
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.055659 Loss1: 0.357930 Loss2: 0.697729
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.035115 Loss1: 0.337155 Loss2: 0.697960
-(DefaultActor pid=1831567) >> Training accuracy: 0.873650
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.478549 Loss1: 0.754050 Loss2: 0.724498
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341815 Loss1: 0.700035 Loss2: 0.641780
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.326330 Loss1: 0.686113 Loss2: 0.640217
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.319127 Loss1: 0.675639 Loss2: 0.643488
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.320766 Loss1: 0.678518 Loss2: 0.642247
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.316427 Loss1: 0.671002 Loss2: 0.645424
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.306544 Loss1: 0.660470 Loss2: 0.646073
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.278041 Loss1: 0.633493 Loss2: 0.644548
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299236 Loss1: 0.652151 Loss2: 0.647085
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281247 Loss1: 0.633193 Loss2: 0.648054
-(DefaultActor pid=1831567) >> Training accuracy: 0.793705
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187097 Loss1: 0.450672 Loss2: 0.736425
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.059204 Loss1: 0.400660 Loss2: 0.658544
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.066863 Loss1: 0.406648 Loss2: 0.660215
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.037349 Loss1: 0.379068 Loss2: 0.658281
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.036073 Loss1: 0.377496 Loss2: 0.658577
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.026326 Loss1: 0.365932 Loss2: 0.660393
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.013307 Loss1: 0.355435 Loss2: 0.657872
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.009402 Loss1: 0.351155 Loss2: 0.658248
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.006964 Loss1: 0.345660 Loss2: 0.661303
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.010783 Loss1: 0.349893 Loss2: 0.660891
-(DefaultActor pid=1831567) >> Training accuracy: 0.884259
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.362159 Loss1: 0.608009 Loss2: 0.754150
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217056 Loss1: 0.528950 Loss2: 0.688106
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198089 Loss1: 0.510476 Loss2: 0.687613
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189345 Loss1: 0.504348 Loss2: 0.684997
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.209595 Loss1: 0.519426 Loss2: 0.690169
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.173807 Loss1: 0.486358 Loss2: 0.687449
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195002 Loss1: 0.505296 Loss2: 0.689705
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.187773 Loss1: 0.497466 Loss2: 0.690307
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.174982 Loss1: 0.485423 Loss2: 0.689559
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.181688 Loss1: 0.489080 Loss2: 0.692607
-(DefaultActor pid=1831567) >> Training accuracy: 0.835938
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.461120 Loss1: 0.696088 Loss2: 0.765032
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341877 Loss1: 0.659731 Loss2: 0.682146
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.348368 Loss1: 0.662036 Loss2: 0.686332
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.364534 Loss1: 0.673649 Loss2: 0.690884
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.332826 Loss1: 0.647652 Loss2: 0.685174
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.305762 Loss1: 0.619896 Loss2: 0.685866
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299146 Loss1: 0.614193 Loss2: 0.684953
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320626 Loss1: 0.633404 Loss2: 0.687221
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.306998 Loss1: 0.616918 Loss2: 0.690080
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.260878 Loss1: 0.570644 Loss2: 0.690234
-(DefaultActor pid=1831567) >> Training accuracy: 0.787547
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.331205 Loss1: 0.559605 Loss2: 0.771600
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.182568 Loss1: 0.510097 Loss2: 0.672471
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.181649 Loss1: 0.509024 Loss2: 0.672625
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.173257 Loss1: 0.498579 Loss2: 0.674678
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.148088 Loss1: 0.471666 Loss2: 0.676422
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.150377 Loss1: 0.472769 Loss2: 0.677608
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.131895 Loss1: 0.454303 Loss2: 0.677592
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.136146 Loss1: 0.456524 Loss2: 0.679622
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.125933 Loss1: 0.443957 Loss2: 0.681977
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.102352 Loss1: 0.424265 Loss2: 0.678087
-(DefaultActor pid=1831567) >> Training accuracy: 0.850371
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.304025 Loss1: 0.581984 Loss2: 0.722041
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.175310 Loss1: 0.532924 Loss2: 0.642386
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.183051 Loss1: 0.536709 Loss2: 0.646342
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.140776 Loss1: 0.496954 Loss2: 0.643823
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.131045 Loss1: 0.488541 Loss2: 0.642504
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.123096 Loss1: 0.480013 Loss2: 0.643083
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.132190 Loss1: 0.483286 Loss2: 0.648904
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.123279 Loss1: 0.477473 Loss2: 0.645807
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.124815 Loss1: 0.473885 Loss2: 0.650930
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.117974 Loss1: 0.468031 Loss2: 0.649943
-(DefaultActor pid=1831567) >> Training accuracy: 0.848890
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.464372 Loss1: 0.728034 Loss2: 0.736338
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.274654 Loss1: 0.642626 Loss2: 0.632028
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.278112 Loss1: 0.643639 Loss2: 0.634472
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.242153 Loss1: 0.613317 Loss2: 0.628836
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.236202 Loss1: 0.601388 Loss2: 0.634814
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.236666 Loss1: 0.603316 Loss2: 0.633350
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.215005 Loss1: 0.579945 Loss2: 0.635060
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.236204 Loss1: 0.600520 Loss2: 0.635684
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.199749 Loss1: 0.564576 Loss2: 0.635172
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.189876 Loss1: 0.555664 Loss2: 0.634212
-(DefaultActor pid=1831567) >> Training accuracy: 0.807292
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.252011 Loss1: 0.533859 Loss2: 0.718152
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.196107 Loss1: 0.514239 Loss2: 0.681868
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193181 Loss1: 0.515873 Loss2: 0.677308
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190092 Loss1: 0.509964 Loss2: 0.680128
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.180031 Loss1: 0.499533 Loss2: 0.680498
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.179383 Loss1: 0.499448 Loss2: 0.679935
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163178 Loss1: 0.485575 Loss2: 0.677603
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.178003 Loss1: 0.495305 Loss2: 0.682698
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.180236 Loss1: 0.496272 Loss2: 0.683964
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.162133 Loss1: 0.484266 Loss2: 0.677866
-(DefaultActor pid=1831567) >> Training accuracy: 0.834945
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.347016 Loss1: 0.580076 Loss2: 0.766940
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.212751 Loss1: 0.525317 Loss2: 0.687434
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.237546 Loss1: 0.545027 Loss2: 0.692519
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231030 Loss1: 0.538090 Loss2: 0.692940
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.195393 Loss1: 0.504312 Loss2: 0.691081
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.187265 Loss1: 0.497527 Loss2: 0.689738
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195500 Loss1: 0.503040 Loss2: 0.692460
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185035 Loss1: 0.490835 Loss2: 0.694200
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.176327 Loss1: 0.480496 Loss2: 0.695832
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.188059 Loss1: 0.493385 Loss2: 0.694673
-[2023-09-27 17:08:42,475][flwr][DEBUG] - fit_round 82 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.833534
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.695700
-[2023-09-27 17:08:43,977][flwr][INFO] - fit progress: (82, 0.874075241743947, {'accuracy': 0.6957}, 39056.813858431764)
-[2023-09-27 17:08:43,978][flwr][DEBUG] - evaluate_round 82: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:09:19,184][flwr][DEBUG] - evaluate_round 82 received 10 results and 0 failures
-[2023-09-27 17:09:19,185][flwr][DEBUG] - fit_round 83: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.302863 Loss1: 0.558000 Loss2: 0.744863
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.229562 Loss1: 0.526489 Loss2: 0.703073
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.216321 Loss1: 0.513711 Loss2: 0.702610
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202443 Loss1: 0.502556 Loss2: 0.699887
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.196037 Loss1: 0.494868 Loss2: 0.701169
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199328 Loss1: 0.493659 Loss2: 0.705669
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191297 Loss1: 0.487447 Loss2: 0.703850
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.177889 Loss1: 0.475652 Loss2: 0.702237
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.181066 Loss1: 0.477087 Loss2: 0.703978
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.198783 Loss1: 0.494595 Loss2: 0.704189
-(DefaultActor pid=1831567) >> Training accuracy: 0.829613
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.521168 Loss1: 0.725138 Loss2: 0.796031
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.389501 Loss1: 0.683016 Loss2: 0.706485
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.375343 Loss1: 0.667924 Loss2: 0.707419
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.371667 Loss1: 0.663850 Loss2: 0.707817
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.356949 Loss1: 0.647938 Loss2: 0.709011
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.352264 Loss1: 0.644742 Loss2: 0.707522
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.374415 Loss1: 0.661857 Loss2: 0.712558
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.345334 Loss1: 0.632427 Loss2: 0.712907
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.342216 Loss1: 0.628971 Loss2: 0.713245
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329653 Loss1: 0.616644 Loss2: 0.713009
-(DefaultActor pid=1831567) >> Training accuracy: 0.791667
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.353717 Loss1: 0.597056 Loss2: 0.756661
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194164 Loss1: 0.521044 Loss2: 0.673120
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.204645 Loss1: 0.531414 Loss2: 0.673231
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188113 Loss1: 0.519557 Loss2: 0.668555
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.179617 Loss1: 0.506214 Loss2: 0.673403
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.172761 Loss1: 0.502201 Loss2: 0.670560
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164486 Loss1: 0.492547 Loss2: 0.671940
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.154586 Loss1: 0.478079 Loss2: 0.676508
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.157216 Loss1: 0.483414 Loss2: 0.673802
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.155398 Loss1: 0.481830 Loss2: 0.673568
-(DefaultActor pid=1831567) >> Training accuracy: 0.841082
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.199093 Loss1: 0.443312 Loss2: 0.755781
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.094829 Loss1: 0.419068 Loss2: 0.675761
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.050883 Loss1: 0.376262 Loss2: 0.674621
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.055012 Loss1: 0.377787 Loss2: 0.677225
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.051650 Loss1: 0.376031 Loss2: 0.675619
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.036899 Loss1: 0.362411 Loss2: 0.674488
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.027037 Loss1: 0.349722 Loss2: 0.677315
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.037622 Loss1: 0.362304 Loss2: 0.675318
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028121 Loss1: 0.352230 Loss2: 0.675891
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019191 Loss1: 0.340139 Loss2: 0.679052
-(DefaultActor pid=1831567) >> Training accuracy: 0.884645
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.301001 Loss1: 0.538386 Loss2: 0.762615
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.183999 Loss1: 0.522973 Loss2: 0.661026
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.160129 Loss1: 0.503322 Loss2: 0.656807
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175228 Loss1: 0.514732 Loss2: 0.660495
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.143664 Loss1: 0.481100 Loss2: 0.662564
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.117056 Loss1: 0.455274 Loss2: 0.661782
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171384 Loss1: 0.507385 Loss2: 0.663999
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.112225 Loss1: 0.451424 Loss2: 0.660801
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.115973 Loss1: 0.453613 Loss2: 0.662360
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.096143 Loss1: 0.432459 Loss2: 0.663684
-(DefaultActor pid=1831567) >> Training accuracy: 0.858581
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.501064 Loss1: 0.722270 Loss2: 0.778794
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.332527 Loss1: 0.648550 Loss2: 0.683977
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.321813 Loss1: 0.637834 Loss2: 0.683979
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.273402 Loss1: 0.591213 Loss2: 0.682189
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.301487 Loss1: 0.619598 Loss2: 0.681889
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.278418 Loss1: 0.590420 Loss2: 0.687999
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.297957 Loss1: 0.609346 Loss2: 0.688612
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.252867 Loss1: 0.568012 Loss2: 0.684854
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.266297 Loss1: 0.578498 Loss2: 0.687799
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.262122 Loss1: 0.573044 Loss2: 0.689079
-(DefaultActor pid=1831567) >> Training accuracy: 0.796053
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.302774 Loss1: 0.564176 Loss2: 0.738599
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.189899 Loss1: 0.518799 Loss2: 0.671100
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.182887 Loss1: 0.510823 Loss2: 0.672063
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.180236 Loss1: 0.508055 Loss2: 0.672181
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.191515 Loss1: 0.513948 Loss2: 0.677567
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.161628 Loss1: 0.485697 Loss2: 0.675931
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187716 Loss1: 0.510473 Loss2: 0.677243
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.163040 Loss1: 0.482543 Loss2: 0.680497
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185930 Loss1: 0.504539 Loss2: 0.681391
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.151684 Loss1: 0.471194 Loss2: 0.680491
-(DefaultActor pid=1831567) >> Training accuracy: 0.849359
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.321015 Loss1: 0.564107 Loss2: 0.756909
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.193873 Loss1: 0.517826 Loss2: 0.676047
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193025 Loss1: 0.518312 Loss2: 0.674713
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188700 Loss1: 0.511190 Loss2: 0.677510
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187875 Loss1: 0.504033 Loss2: 0.683843
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163188 Loss1: 0.482684 Loss2: 0.680504
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.180822 Loss1: 0.499981 Loss2: 0.680841
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.153541 Loss1: 0.473894 Loss2: 0.679647
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.155197 Loss1: 0.472857 Loss2: 0.682340
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.163529 Loss1: 0.483963 Loss2: 0.679566
-(DefaultActor pid=1831567) >> Training accuracy: 0.848067
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493233 Loss1: 0.736185 Loss2: 0.757048
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.355788 Loss1: 0.694075 Loss2: 0.661713
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.284866 Loss1: 0.627067 Loss2: 0.657799
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.318868 Loss1: 0.658848 Loss2: 0.660019
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.293667 Loss1: 0.633287 Loss2: 0.660379
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.291577 Loss1: 0.632259 Loss2: 0.659318
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.282500 Loss1: 0.621221 Loss2: 0.661279
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.285835 Loss1: 0.626831 Loss2: 0.659004
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.273515 Loss1: 0.606602 Loss2: 0.666913
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290968 Loss1: 0.626486 Loss2: 0.664482
-(DefaultActor pid=1831567) >> Training accuracy: 0.763526
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.183256 Loss1: 0.443283 Loss2: 0.739973
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.055212 Loss1: 0.394707 Loss2: 0.660505
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048904 Loss1: 0.389723 Loss2: 0.659181
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.035898 Loss1: 0.375126 Loss2: 0.660772
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.038818 Loss1: 0.381313 Loss2: 0.657505
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.039003 Loss1: 0.380314 Loss2: 0.658689
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.042613 Loss1: 0.380687 Loss2: 0.661926
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.028748 Loss1: 0.367317 Loss2: 0.661431
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.025529 Loss1: 0.364591 Loss2: 0.660938
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.025992 Loss1: 0.365217 Loss2: 0.660775
-[2023-09-27 17:16:15,659][flwr][DEBUG] - fit_round 83 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.874807
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.694500
-[2023-09-27 17:16:17,231][flwr][INFO] - fit progress: (83, 0.8777990603980165, {'accuracy': 0.6945}, 39510.06702556601)
-[2023-09-27 17:16:17,231][flwr][DEBUG] - evaluate_round 83: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:16:47,942][flwr][DEBUG] - evaluate_round 83 received 10 results and 0 failures
-[2023-09-27 17:16:47,943][flwr][DEBUG] - fit_round 84: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.511909 Loss1: 0.748393 Loss2: 0.763516
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.306131 Loss1: 0.648063 Loss2: 0.658068
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.276425 Loss1: 0.616415 Loss2: 0.660010
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.281843 Loss1: 0.622920 Loss2: 0.658923
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.262386 Loss1: 0.603416 Loss2: 0.658970
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.254404 Loss1: 0.592910 Loss2: 0.661494
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.251656 Loss1: 0.588401 Loss2: 0.663255
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.259139 Loss1: 0.594753 Loss2: 0.664386
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.247935 Loss1: 0.584515 Loss2: 0.663420
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240786 Loss1: 0.577216 Loss2: 0.663570
-(DefaultActor pid=1831567) >> Training accuracy: 0.787829
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.184028 Loss1: 0.455350 Loss2: 0.728678
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.067037 Loss1: 0.417673 Loss2: 0.649364
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.035302 Loss1: 0.389534 Loss2: 0.645768
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019697 Loss1: 0.371475 Loss2: 0.648222
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.024274 Loss1: 0.379043 Loss2: 0.645232
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.002072 Loss1: 0.354566 Loss2: 0.647506
-(DefaultActor pid=1831567) Epoch: 6 Loss: 0.999567 Loss1: 0.351994 Loss2: 0.647573
-(DefaultActor pid=1831567) Epoch: 7 Loss: 0.997191 Loss1: 0.349373 Loss2: 0.647818
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.015637 Loss1: 0.365776 Loss2: 0.649862
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.984840 Loss1: 0.334446 Loss2: 0.650394
-(DefaultActor pid=1831567) >> Training accuracy: 0.879437
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.304335 Loss1: 0.553626 Loss2: 0.750709
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.167857 Loss1: 0.516815 Loss2: 0.651042
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.167665 Loss1: 0.514051 Loss2: 0.653614
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.132824 Loss1: 0.477408 Loss2: 0.655415
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.138154 Loss1: 0.481919 Loss2: 0.656235
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.129181 Loss1: 0.471896 Loss2: 0.657285
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.111151 Loss1: 0.456446 Loss2: 0.654705
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.117285 Loss1: 0.457952 Loss2: 0.659333
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.125185 Loss1: 0.465218 Loss2: 0.659968
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.104483 Loss1: 0.446060 Loss2: 0.658423
-(DefaultActor pid=1831567) >> Training accuracy: 0.860699
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.279080 Loss1: 0.558179 Loss2: 0.720902
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.164000 Loss1: 0.525789 Loss2: 0.638212
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.140375 Loss1: 0.499769 Loss2: 0.640606
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.146081 Loss1: 0.505231 Loss2: 0.640850
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.126785 Loss1: 0.484901 Loss2: 0.641884
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.115231 Loss1: 0.471233 Loss2: 0.643997
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.109766 Loss1: 0.467207 Loss2: 0.642558
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.119686 Loss1: 0.474354 Loss2: 0.645332
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.111459 Loss1: 0.466691 Loss2: 0.644767
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.153473 Loss1: 0.504805 Loss2: 0.648668
-(DefaultActor pid=1831567) >> Training accuracy: 0.847039
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.242915 Loss1: 0.450491 Loss2: 0.792424
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.111014 Loss1: 0.396338 Loss2: 0.714676
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.093148 Loss1: 0.380185 Loss2: 0.712962
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.087021 Loss1: 0.375632 Loss2: 0.711389
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.105451 Loss1: 0.392375 Loss2: 0.713076
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.091174 Loss1: 0.377574 Loss2: 0.713599
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.073757 Loss1: 0.359771 Loss2: 0.713986
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.062586 Loss1: 0.349977 Loss2: 0.712609
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.090144 Loss1: 0.369366 Loss2: 0.720778
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.057589 Loss1: 0.343899 Loss2: 0.713690
-(DefaultActor pid=1831567) >> Training accuracy: 0.877894
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.290740 Loss1: 0.544058 Loss2: 0.746682
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.196518 Loss1: 0.525171 Loss2: 0.671347
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.196757 Loss1: 0.523999 Loss2: 0.672758
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.197675 Loss1: 0.523953 Loss2: 0.673721
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.175683 Loss1: 0.503722 Loss2: 0.671961
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168597 Loss1: 0.495248 Loss2: 0.673349
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193261 Loss1: 0.518184 Loss2: 0.675077
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.181404 Loss1: 0.503997 Loss2: 0.677406
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.167498 Loss1: 0.491333 Loss2: 0.676165
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.145591 Loss1: 0.470144 Loss2: 0.675448
-(DefaultActor pid=1831567) >> Training accuracy: 0.844952
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.336098 Loss1: 0.578863 Loss2: 0.757235
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243505 Loss1: 0.551497 Loss2: 0.692009
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.205652 Loss1: 0.511414 Loss2: 0.694238
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.208691 Loss1: 0.514798 Loss2: 0.693893
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200079 Loss1: 0.505226 Loss2: 0.694853
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186198 Loss1: 0.495519 Loss2: 0.690680
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.193322 Loss1: 0.496973 Loss2: 0.696349
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.175833 Loss1: 0.479102 Loss2: 0.696731
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.172246 Loss1: 0.477133 Loss2: 0.695113
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154825 Loss1: 0.458806 Loss2: 0.696020
-(DefaultActor pid=1831567) >> Training accuracy: 0.833460
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.286909 Loss1: 0.544129 Loss2: 0.742780
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.220157 Loss1: 0.522833 Loss2: 0.697324
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.182329 Loss1: 0.486484 Loss2: 0.695846
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199298 Loss1: 0.498317 Loss2: 0.700981
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201486 Loss1: 0.500701 Loss2: 0.700785
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.189287 Loss1: 0.486290 Loss2: 0.702997
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.198554 Loss1: 0.494571 Loss2: 0.703984
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.197461 Loss1: 0.490516 Loss2: 0.706946
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185523 Loss1: 0.481665 Loss2: 0.703858
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.187013 Loss1: 0.481986 Loss2: 0.705027
-(DefaultActor pid=1831567) >> Training accuracy: 0.842758
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.465796 Loss1: 0.702315 Loss2: 0.763481
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.373122 Loss1: 0.691994 Loss2: 0.681128
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336274 Loss1: 0.658786 Loss2: 0.677488
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333983 Loss1: 0.652930 Loss2: 0.681053
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.315414 Loss1: 0.633408 Loss2: 0.682006
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.299174 Loss1: 0.618318 Loss2: 0.680855
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.313772 Loss1: 0.633575 Loss2: 0.680197
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.320372 Loss1: 0.638994 Loss2: 0.681378
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.299203 Loss1: 0.615951 Loss2: 0.683253
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.291353 Loss1: 0.606160 Loss2: 0.685193
-(DefaultActor pid=1831567) >> Training accuracy: 0.775653
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.448403 Loss1: 0.715355 Loss2: 0.733048
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.342255 Loss1: 0.694559 Loss2: 0.647696
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.325408 Loss1: 0.673492 Loss2: 0.651916
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.333408 Loss1: 0.681928 Loss2: 0.651480
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.313137 Loss1: 0.662541 Loss2: 0.650596
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.302836 Loss1: 0.652306 Loss2: 0.650530
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294985 Loss1: 0.642527 Loss2: 0.652458
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300004 Loss1: 0.647365 Loss2: 0.652639
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.275509 Loss1: 0.618361 Loss2: 0.657148
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.291177 Loss1: 0.634584 Loss2: 0.656593
-[2023-09-27 17:23:40,172][flwr][DEBUG] - fit_round 84 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.786005
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.704200
-[2023-09-27 17:23:54,237][flwr][INFO] - fit progress: (84, 0.8604000634469163, {'accuracy': 0.7042}, 39967.073669589125)
-[2023-09-27 17:23:54,238][flwr][DEBUG] - evaluate_round 84: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:24:29,969][flwr][DEBUG] - evaluate_round 84 received 10 results and 0 failures
-[2023-09-27 17:24:29,970][flwr][DEBUG] - fit_round 85: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.160340 Loss1: 0.450565 Loss2: 0.709775
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.052789 Loss1: 0.416908 Loss2: 0.635881
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048348 Loss1: 0.411766 Loss2: 0.636583
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.012145 Loss1: 0.378601 Loss2: 0.633544
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.012006 Loss1: 0.375012 Loss2: 0.636994
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.012042 Loss1: 0.376060 Loss2: 0.635983
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.020037 Loss1: 0.384886 Loss2: 0.635151
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.004418 Loss1: 0.366581 Loss2: 0.637836
-(DefaultActor pid=1831567) Epoch: 8 Loss: 0.995722 Loss1: 0.358187 Loss2: 0.637534
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.006914 Loss1: 0.367057 Loss2: 0.639857
-(DefaultActor pid=1831567) >> Training accuracy: 0.886381
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477253 Loss1: 0.725951 Loss2: 0.751302
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.360460 Loss1: 0.686412 Loss2: 0.674048
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.365709 Loss1: 0.689773 Loss2: 0.675936
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.339781 Loss1: 0.663907 Loss2: 0.675874
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.353661 Loss1: 0.674501 Loss2: 0.679160
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.325687 Loss1: 0.646949 Loss2: 0.678738
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.295778 Loss1: 0.619518 Loss2: 0.676260
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.315358 Loss1: 0.637391 Loss2: 0.677967
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.297099 Loss1: 0.617509 Loss2: 0.679591
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.309207 Loss1: 0.626139 Loss2: 0.683068
-(DefaultActor pid=1831567) >> Training accuracy: 0.769475
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477415 Loss1: 0.733194 Loss2: 0.744221
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.321785 Loss1: 0.666469 Loss2: 0.655317
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307781 Loss1: 0.649267 Loss2: 0.658514
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.317912 Loss1: 0.661619 Loss2: 0.656293
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.300332 Loss1: 0.641158 Loss2: 0.659174
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.301529 Loss1: 0.640404 Loss2: 0.661125
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.275357 Loss1: 0.614089 Loss2: 0.661268
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.295697 Loss1: 0.632951 Loss2: 0.662746
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.256720 Loss1: 0.594717 Loss2: 0.662004
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.281671 Loss1: 0.618467 Loss2: 0.663203
-(DefaultActor pid=1831567) >> Training accuracy: 0.775653
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.293255 Loss1: 0.580838 Loss2: 0.712417
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.185930 Loss1: 0.548371 Loss2: 0.637559
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.148577 Loss1: 0.513739 Loss2: 0.634839
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.149550 Loss1: 0.512291 Loss2: 0.637259
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140327 Loss1: 0.503474 Loss2: 0.636853
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.145479 Loss1: 0.510679 Loss2: 0.634800
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.137767 Loss1: 0.499839 Loss2: 0.637928
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.125535 Loss1: 0.490953 Loss2: 0.634582
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.108608 Loss1: 0.474914 Loss2: 0.633694
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.112302 Loss1: 0.473605 Loss2: 0.638698
-(DefaultActor pid=1831567) >> Training accuracy: 0.840320
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187136 Loss1: 0.446767 Loss2: 0.740369
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.041990 Loss1: 0.377508 Loss2: 0.664482
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.052184 Loss1: 0.389076 Loss2: 0.663108
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.036773 Loss1: 0.372221 Loss2: 0.664552
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.036315 Loss1: 0.371610 Loss2: 0.664705
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.036649 Loss1: 0.370391 Loss2: 0.666258
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.020316 Loss1: 0.356734 Loss2: 0.663582
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.017098 Loss1: 0.350302 Loss2: 0.666796
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.021524 Loss1: 0.356243 Loss2: 0.665281
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.016540 Loss1: 0.349253 Loss2: 0.667287
-(DefaultActor pid=1831567) >> Training accuracy: 0.857446
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297794 Loss1: 0.561182 Loss2: 0.736612
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223167 Loss1: 0.549715 Loss2: 0.673452
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.171950 Loss1: 0.499806 Loss2: 0.672144
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217295 Loss1: 0.539805 Loss2: 0.677490
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.211295 Loss1: 0.534907 Loss2: 0.676388
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163929 Loss1: 0.487970 Loss2: 0.675959
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.172629 Loss1: 0.498440 Loss2: 0.674189
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.186112 Loss1: 0.504329 Loss2: 0.681783
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184383 Loss1: 0.501537 Loss2: 0.682846
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172241 Loss1: 0.491433 Loss2: 0.680808
-(DefaultActor pid=1831567) >> Training accuracy: 0.835537
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.373373 Loss1: 0.578050 Loss2: 0.795323
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.218361 Loss1: 0.508862 Loss2: 0.709499
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214792 Loss1: 0.505042 Loss2: 0.709750
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.228219 Loss1: 0.516499 Loss2: 0.711720
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198076 Loss1: 0.487207 Loss2: 0.710869
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.195631 Loss1: 0.482154 Loss2: 0.713477
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.204262 Loss1: 0.492218 Loss2: 0.712044
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.176083 Loss1: 0.464509 Loss2: 0.711574
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.189108 Loss1: 0.476887 Loss2: 0.712221
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180192 Loss1: 0.465650 Loss2: 0.714543
-(DefaultActor pid=1831567) >> Training accuracy: 0.847656
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.294847 Loss1: 0.547763 Loss2: 0.747084
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.214251 Loss1: 0.511814 Loss2: 0.702438
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219682 Loss1: 0.516983 Loss2: 0.702699
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189519 Loss1: 0.486346 Loss2: 0.703173
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.200952 Loss1: 0.500028 Loss2: 0.700924
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186632 Loss1: 0.483038 Loss2: 0.703593
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195770 Loss1: 0.492572 Loss2: 0.703198
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.201433 Loss1: 0.496100 Loss2: 0.705333
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196742 Loss1: 0.488844 Loss2: 0.707898
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.200205 Loss1: 0.493016 Loss2: 0.707189
-(DefaultActor pid=1831567) >> Training accuracy: 0.828001
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.481746 Loss1: 0.711044 Loss2: 0.770702
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.310266 Loss1: 0.646617 Loss2: 0.663648
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.292209 Loss1: 0.625788 Loss2: 0.666421
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.283623 Loss1: 0.617919 Loss2: 0.665704
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.283731 Loss1: 0.615132 Loss2: 0.668599
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249304 Loss1: 0.581357 Loss2: 0.667947
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.252732 Loss1: 0.580602 Loss2: 0.672130
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.250952 Loss1: 0.578051 Loss2: 0.672901
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.245483 Loss1: 0.571088 Loss2: 0.674395
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250520 Loss1: 0.572722 Loss2: 0.677798
-(DefaultActor pid=1831567) >> Training accuracy: 0.804276
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.356034 Loss1: 0.584095 Loss2: 0.771939
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.176992 Loss1: 0.508895 Loss2: 0.668097
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.193763 Loss1: 0.526787 Loss2: 0.666976
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.146080 Loss1: 0.475481 Loss2: 0.670598
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.147305 Loss1: 0.475649 Loss2: 0.671655
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.135773 Loss1: 0.463828 Loss2: 0.671945
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122502 Loss1: 0.451973 Loss2: 0.670529
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.122670 Loss1: 0.451619 Loss2: 0.671052
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.121276 Loss1: 0.443490 Loss2: 0.677786
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.103704 Loss1: 0.434117 Loss2: 0.669586
-(DefaultActor pid=1831567) >> Training accuracy: 0.863347
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 17:31:28,587][flwr][DEBUG] - fit_round 85 received 10 results and 0 failures
->> Test accuracy: 0.704300
-[2023-09-27 17:31:30,159][flwr][INFO] - fit progress: (85, 0.8605043698614017, {'accuracy': 0.7043}, 40422.99546373589)
-[2023-09-27 17:31:30,160][flwr][DEBUG] - evaluate_round 85: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:32:01,178][flwr][DEBUG] - evaluate_round 85 received 10 results and 0 failures
-[2023-09-27 17:32:01,179][flwr][DEBUG] - fit_round 86: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359345 Loss1: 0.606351 Loss2: 0.752994
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.203693 Loss1: 0.521575 Loss2: 0.682118
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201479 Loss1: 0.521484 Loss2: 0.679994
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.200524 Loss1: 0.519196 Loss2: 0.681328
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198766 Loss1: 0.512428 Loss2: 0.686339
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181249 Loss1: 0.495183 Loss2: 0.686066
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.152408 Loss1: 0.465567 Loss2: 0.686841
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161698 Loss1: 0.476988 Loss2: 0.684710
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173334 Loss1: 0.487385 Loss2: 0.685949
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.179672 Loss1: 0.493657 Loss2: 0.686015
-(DefaultActor pid=1831567) >> Training accuracy: 0.843750
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477050 Loss1: 0.734247 Loss2: 0.742803
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347213 Loss1: 0.687358 Loss2: 0.659855
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.333732 Loss1: 0.674332 Loss2: 0.659400
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.314433 Loss1: 0.653336 Loss2: 0.661097
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.318225 Loss1: 0.659153 Loss2: 0.659072
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.293807 Loss1: 0.633525 Loss2: 0.660282
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.298165 Loss1: 0.638663 Loss2: 0.659502
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.300376 Loss1: 0.636412 Loss2: 0.663964
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.309555 Loss1: 0.645504 Loss2: 0.664051
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.277064 Loss1: 0.612957 Loss2: 0.664107
-(DefaultActor pid=1831567) >> Training accuracy: 0.795516
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324563 Loss1: 0.562687 Loss2: 0.761876
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.208274 Loss1: 0.527189 Loss2: 0.681085
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173865 Loss1: 0.496547 Loss2: 0.677318
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.184841 Loss1: 0.503033 Loss2: 0.681809
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.198149 Loss1: 0.515095 Loss2: 0.683053
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183530 Loss1: 0.499757 Loss2: 0.683773
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.173924 Loss1: 0.488517 Loss2: 0.685407
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169248 Loss1: 0.482869 Loss2: 0.686378
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.173774 Loss1: 0.486286 Loss2: 0.687488
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.174884 Loss1: 0.485361 Loss2: 0.689523
-(DefaultActor pid=1831567) >> Training accuracy: 0.844752
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.283958 Loss1: 0.553808 Loss2: 0.730150
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.171366 Loss1: 0.523505 Loss2: 0.647861
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.166175 Loss1: 0.517619 Loss2: 0.648555
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.163863 Loss1: 0.513155 Loss2: 0.650708
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.151326 Loss1: 0.498446 Loss2: 0.652880
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.118918 Loss1: 0.467104 Loss2: 0.651814
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.153945 Loss1: 0.496503 Loss2: 0.657442
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.136785 Loss1: 0.480268 Loss2: 0.656517
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.129705 Loss1: 0.472436 Loss2: 0.657269
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.113162 Loss1: 0.461206 Loss2: 0.651956
-(DefaultActor pid=1831567) >> Training accuracy: 0.856497
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.211306 Loss1: 0.442984 Loss2: 0.768322
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.086067 Loss1: 0.401933 Loss2: 0.684133
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.056998 Loss1: 0.375972 Loss2: 0.681026
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.034764 Loss1: 0.356506 Loss2: 0.678258
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.061188 Loss1: 0.379131 Loss2: 0.682057
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.031814 Loss1: 0.351060 Loss2: 0.680755
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.056553 Loss1: 0.375073 Loss2: 0.681480
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.032874 Loss1: 0.347962 Loss2: 0.684913
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.054129 Loss1: 0.370511 Loss2: 0.683618
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028980 Loss1: 0.344518 Loss2: 0.684462
-(DefaultActor pid=1831567) >> Training accuracy: 0.890432
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.477639 Loss1: 0.683112 Loss2: 0.794527
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.368458 Loss1: 0.665862 Loss2: 0.702596
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.377903 Loss1: 0.674809 Loss2: 0.703094
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.379525 Loss1: 0.671944 Loss2: 0.707581
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333171 Loss1: 0.629963 Loss2: 0.703208
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.340103 Loss1: 0.635074 Loss2: 0.705029
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.343396 Loss1: 0.636296 Loss2: 0.707100
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.333581 Loss1: 0.624011 Loss2: 0.709569
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.303505 Loss1: 0.598638 Loss2: 0.704867
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.306341 Loss1: 0.597615 Loss2: 0.708726
-(DefaultActor pid=1831567) >> Training accuracy: 0.785215
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.247883 Loss1: 0.459232 Loss2: 0.788651
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.120874 Loss1: 0.406444 Loss2: 0.714430
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.106560 Loss1: 0.396665 Loss2: 0.709894
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.083283 Loss1: 0.367731 Loss2: 0.715551
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.104850 Loss1: 0.387797 Loss2: 0.717053
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.078205 Loss1: 0.366630 Loss2: 0.711575
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.070225 Loss1: 0.357942 Loss2: 0.712283
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.072836 Loss1: 0.360414 Loss2: 0.712423
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.064615 Loss1: 0.349562 Loss2: 0.715053
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.065910 Loss1: 0.348294 Loss2: 0.717616
-(DefaultActor pid=1831567) >> Training accuracy: 0.879630
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.242443 Loss1: 0.531965 Loss2: 0.710478
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172917 Loss1: 0.501454 Loss2: 0.671463
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.171926 Loss1: 0.500509 Loss2: 0.671417
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.175920 Loss1: 0.504292 Loss2: 0.671628
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.165067 Loss1: 0.489082 Loss2: 0.675985
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.160811 Loss1: 0.486977 Loss2: 0.673834
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.180050 Loss1: 0.505648 Loss2: 0.674402
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.169040 Loss1: 0.492025 Loss2: 0.677015
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165634 Loss1: 0.487204 Loss2: 0.678430
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201633 Loss1: 0.519976 Loss2: 0.681656
-(DefaultActor pid=1831567) >> Training accuracy: 0.821429
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.344840 Loss1: 0.577810 Loss2: 0.767030
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.172130 Loss1: 0.513238 Loss2: 0.658892
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155893 Loss1: 0.492616 Loss2: 0.663277
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144785 Loss1: 0.482231 Loss2: 0.662554
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.117232 Loss1: 0.451731 Loss2: 0.665500
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.125389 Loss1: 0.460046 Loss2: 0.665343
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123653 Loss1: 0.456409 Loss2: 0.667245
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.132489 Loss1: 0.465209 Loss2: 0.667280
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.099558 Loss1: 0.432235 Loss2: 0.667322
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.113593 Loss1: 0.442957 Loss2: 0.670636
-(DefaultActor pid=1831567) >> Training accuracy: 0.824947
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.471939 Loss1: 0.694220 Loss2: 0.777719
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.325193 Loss1: 0.658658 Loss2: 0.666535
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.290463 Loss1: 0.627366 Loss2: 0.663096
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.307049 Loss1: 0.641149 Loss2: 0.665900
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.271640 Loss1: 0.604207 Loss2: 0.667433
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.259730 Loss1: 0.591870 Loss2: 0.667859
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.263458 Loss1: 0.596534 Loss2: 0.666924
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.248134 Loss1: 0.576444 Loss2: 0.671690
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.254429 Loss1: 0.584432 Loss2: 0.669997
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.240560 Loss1: 0.568057 Loss2: 0.672503
-[2023-09-27 17:38:39,577][flwr][DEBUG] - fit_round 86 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.793311
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.705100
-[2023-09-27 17:38:41,245][flwr][INFO] - fit progress: (86, 0.8559781925175518, {'accuracy': 0.7051}, 40854.081239733845)
-[2023-09-27 17:38:41,245][flwr][DEBUG] - evaluate_round 86: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:39:12,925][flwr][DEBUG] - evaluate_round 86 received 10 results and 0 failures
-[2023-09-27 17:39:12,926][flwr][DEBUG] - fit_round 87: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.212844 Loss1: 0.459272 Loss2: 0.753572
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.099947 Loss1: 0.420493 Loss2: 0.679454
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.065534 Loss1: 0.391962 Loss2: 0.673572
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.065012 Loss1: 0.389745 Loss2: 0.675267
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.048358 Loss1: 0.374550 Loss2: 0.673808
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.037587 Loss1: 0.365669 Loss2: 0.671918
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.025979 Loss1: 0.350454 Loss2: 0.675525
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.032480 Loss1: 0.355991 Loss2: 0.676489
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028530 Loss1: 0.349800 Loss2: 0.678731
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.028513 Loss1: 0.351308 Loss2: 0.677205
-(DefaultActor pid=1831567) >> Training accuracy: 0.868441
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.326975 Loss1: 0.576469 Loss2: 0.750505
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.189623 Loss1: 0.510943 Loss2: 0.678680
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.202465 Loss1: 0.524097 Loss2: 0.678368
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.186211 Loss1: 0.505256 Loss2: 0.680955
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.201551 Loss1: 0.517600 Loss2: 0.683951
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.190209 Loss1: 0.504179 Loss2: 0.686030
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174712 Loss1: 0.489347 Loss2: 0.685365
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.180301 Loss1: 0.493938 Loss2: 0.686363
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.159619 Loss1: 0.473494 Loss2: 0.686125
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.156252 Loss1: 0.470681 Loss2: 0.685571
-(DefaultActor pid=1831567) >> Training accuracy: 0.834535
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.306147 Loss1: 0.566444 Loss2: 0.739703
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.199767 Loss1: 0.535454 Loss2: 0.664313
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.167367 Loss1: 0.505792 Loss2: 0.661575
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.169264 Loss1: 0.508166 Loss2: 0.661098
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.166125 Loss1: 0.506712 Loss2: 0.659413
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146998 Loss1: 0.483551 Loss2: 0.663446
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161635 Loss1: 0.498424 Loss2: 0.663210
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.167304 Loss1: 0.502853 Loss2: 0.664451
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.146333 Loss1: 0.479222 Loss2: 0.667111
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136104 Loss1: 0.474022 Loss2: 0.662082
-(DefaultActor pid=1831567) >> Training accuracy: 0.842226
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.315746 Loss1: 0.543785 Loss2: 0.771961
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.249234 Loss1: 0.523134 Loss2: 0.726100
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.218247 Loss1: 0.491393 Loss2: 0.726854
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.215265 Loss1: 0.489837 Loss2: 0.725429
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.224916 Loss1: 0.496089 Loss2: 0.728827
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.230328 Loss1: 0.500462 Loss2: 0.729867
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.230058 Loss1: 0.501214 Loss2: 0.728844
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.212903 Loss1: 0.483111 Loss2: 0.729792
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.217099 Loss1: 0.485469 Loss2: 0.731630
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.229469 Loss1: 0.498552 Loss2: 0.730918
-(DefaultActor pid=1831567) >> Training accuracy: 0.840030
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.467405 Loss1: 0.716095 Loss2: 0.751310
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341810 Loss1: 0.672168 Loss2: 0.669641
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.345875 Loss1: 0.677810 Loss2: 0.668065
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.340935 Loss1: 0.672768 Loss2: 0.668167
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.337178 Loss1: 0.668836 Loss2: 0.668342
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.330788 Loss1: 0.655794 Loss2: 0.674995
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314629 Loss1: 0.641568 Loss2: 0.673061
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288973 Loss1: 0.615714 Loss2: 0.673259
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.286328 Loss1: 0.614329 Loss2: 0.671998
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.289640 Loss1: 0.613719 Loss2: 0.675921
-(DefaultActor pid=1831567) >> Training accuracy: 0.788270
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.341352 Loss1: 0.572081 Loss2: 0.769271
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.214798 Loss1: 0.529993 Loss2: 0.684805
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.180327 Loss1: 0.495210 Loss2: 0.685116
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.173049 Loss1: 0.488257 Loss2: 0.684792
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.166772 Loss1: 0.481867 Loss2: 0.684905
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.155285 Loss1: 0.470068 Loss2: 0.685217
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.145191 Loss1: 0.459242 Loss2: 0.685950
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.174089 Loss1: 0.484652 Loss2: 0.689437
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183487 Loss1: 0.490627 Loss2: 0.692861
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.149560 Loss1: 0.456440 Loss2: 0.693119
-(DefaultActor pid=1831567) >> Training accuracy: 0.849095
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.336307 Loss1: 0.584579 Loss2: 0.751728
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.166299 Loss1: 0.518260 Loss2: 0.648039
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.140218 Loss1: 0.493721 Loss2: 0.646497
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.131254 Loss1: 0.486480 Loss2: 0.644774
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.101042 Loss1: 0.455461 Loss2: 0.645581
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.126969 Loss1: 0.480393 Loss2: 0.646577
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.128723 Loss1: 0.479223 Loss2: 0.649500
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.114548 Loss1: 0.462392 Loss2: 0.652155
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.096796 Loss1: 0.448230 Loss2: 0.648566
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.113309 Loss1: 0.462303 Loss2: 0.651006
-(DefaultActor pid=1831567) >> Training accuracy: 0.836335
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.452722 Loss1: 0.695038 Loss2: 0.757684
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.362993 Loss1: 0.693831 Loss2: 0.669162
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.321902 Loss1: 0.651141 Loss2: 0.670761
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.343692 Loss1: 0.670966 Loss2: 0.672726
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.318813 Loss1: 0.647413 Loss2: 0.671400
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.302612 Loss1: 0.633022 Loss2: 0.669589
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299352 Loss1: 0.626836 Loss2: 0.672517
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.282935 Loss1: 0.609785 Loss2: 0.673150
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.303050 Loss1: 0.625345 Loss2: 0.677705
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.265307 Loss1: 0.591289 Loss2: 0.674018
-(DefaultActor pid=1831567) >> Training accuracy: 0.772155
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.230634 Loss1: 0.472436 Loss2: 0.758198
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.058699 Loss1: 0.382249 Loss2: 0.676451
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.068595 Loss1: 0.391312 Loss2: 0.677283
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.042831 Loss1: 0.367588 Loss2: 0.675244
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.033409 Loss1: 0.358210 Loss2: 0.675199
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.036338 Loss1: 0.358679 Loss2: 0.677658
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.033882 Loss1: 0.356291 Loss2: 0.677591
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.029456 Loss1: 0.348450 Loss2: 0.681006
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.013775 Loss1: 0.334378 Loss2: 0.679397
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.039898 Loss1: 0.359406 Loss2: 0.680492
-(DefaultActor pid=1831567) >> Training accuracy: 0.870949
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.463191 Loss1: 0.709005 Loss2: 0.754185
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.298160 Loss1: 0.646077 Loss2: 0.652084
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.270958 Loss1: 0.617013 Loss2: 0.653945
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.262086 Loss1: 0.608870 Loss2: 0.653216
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.246622 Loss1: 0.591891 Loss2: 0.654731
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.271897 Loss1: 0.613514 Loss2: 0.658383
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.278423 Loss1: 0.616663 Loss2: 0.661761
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.245395 Loss1: 0.585188 Loss2: 0.660207
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.248369 Loss1: 0.587752 Loss2: 0.660617
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.250175 Loss1: 0.586231 Loss2: 0.663944
-[2023-09-27 17:46:11,513][flwr][DEBUG] - fit_round 87 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.815241
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.698700
-[2023-09-27 17:46:13,159][flwr][INFO] - fit progress: (87, 0.8728009527102827, {'accuracy': 0.6987}, 41305.99577134708)
-[2023-09-27 17:46:13,160][flwr][DEBUG] - evaluate_round 87: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:46:45,188][flwr][DEBUG] - evaluate_round 87 received 10 results and 0 failures
-[2023-09-27 17:46:45,189][flwr][DEBUG] - fit_round 88: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.507696 Loss1: 0.709089 Loss2: 0.798607
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.387023 Loss1: 0.676297 Loss2: 0.710726
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.357412 Loss1: 0.651522 Loss2: 0.705889
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.345929 Loss1: 0.634469 Loss2: 0.711460
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.362956 Loss1: 0.648897 Loss2: 0.714059
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.330503 Loss1: 0.621274 Loss2: 0.709229
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.310002 Loss1: 0.601561 Loss2: 0.708441
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.350400 Loss1: 0.637343 Loss2: 0.713056
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.334327 Loss1: 0.621262 Loss2: 0.713065
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.330045 Loss1: 0.615202 Loss2: 0.714843
-(DefaultActor pid=1831567) >> Training accuracy: 0.772388
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.359104 Loss1: 0.570685 Loss2: 0.788419
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.193210 Loss1: 0.507838 Loss2: 0.685371
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.177976 Loss1: 0.494145 Loss2: 0.683832
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.187695 Loss1: 0.503486 Loss2: 0.684210
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.185553 Loss1: 0.498892 Loss2: 0.686661
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.145772 Loss1: 0.458610 Loss2: 0.687162
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171713 Loss1: 0.479698 Loss2: 0.692015
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.172531 Loss1: 0.481374 Loss2: 0.691157
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.132534 Loss1: 0.438657 Loss2: 0.693877
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148068 Loss1: 0.454983 Loss2: 0.693085
-(DefaultActor pid=1831567) >> Training accuracy: 0.847193
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.208443 Loss1: 0.429822 Loss2: 0.778621
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.104958 Loss1: 0.401076 Loss2: 0.703883
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.099087 Loss1: 0.394571 Loss2: 0.704516
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.079020 Loss1: 0.376602 Loss2: 0.702418
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.060163 Loss1: 0.359267 Loss2: 0.700895
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.069035 Loss1: 0.362111 Loss2: 0.706924
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.078325 Loss1: 0.371693 Loss2: 0.706631
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.063704 Loss1: 0.358920 Loss2: 0.704784
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.044958 Loss1: 0.339818 Loss2: 0.705140
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.049494 Loss1: 0.342667 Loss2: 0.706827
-(DefaultActor pid=1831567) >> Training accuracy: 0.870563
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.332638 Loss1: 0.567895 Loss2: 0.764743
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200776 Loss1: 0.517807 Loss2: 0.682970
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198664 Loss1: 0.516387 Loss2: 0.682277
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.181389 Loss1: 0.497813 Loss2: 0.683576
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169682 Loss1: 0.486459 Loss2: 0.683223
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.172825 Loss1: 0.487354 Loss2: 0.685471
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175502 Loss1: 0.487868 Loss2: 0.687634
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.164361 Loss1: 0.477662 Loss2: 0.686699
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.185053 Loss1: 0.496850 Loss2: 0.688204
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.176714 Loss1: 0.487104 Loss2: 0.689610
-(DefaultActor pid=1831567) >> Training accuracy: 0.833534
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.276563 Loss1: 0.559325 Loss2: 0.717239
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.167086 Loss1: 0.528872 Loss2: 0.638214
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.136623 Loss1: 0.502317 Loss2: 0.634305
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.147576 Loss1: 0.506191 Loss2: 0.641386
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.126460 Loss1: 0.490300 Loss2: 0.636160
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.123122 Loss1: 0.486061 Loss2: 0.637061
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.123249 Loss1: 0.483670 Loss2: 0.639579
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.138241 Loss1: 0.494568 Loss2: 0.643673
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.116994 Loss1: 0.473059 Loss2: 0.643936
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.122993 Loss1: 0.481765 Loss2: 0.641229
-(DefaultActor pid=1831567) >> Training accuracy: 0.854441
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.492965 Loss1: 0.708319 Loss2: 0.784646
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.328627 Loss1: 0.649596 Loss2: 0.679031
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.323273 Loss1: 0.646267 Loss2: 0.677006
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.287120 Loss1: 0.610106 Loss2: 0.677014
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.284346 Loss1: 0.604022 Loss2: 0.680324
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289594 Loss1: 0.608433 Loss2: 0.681161
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.294931 Loss1: 0.616283 Loss2: 0.678648
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.250975 Loss1: 0.570843 Loss2: 0.680132
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.253681 Loss1: 0.571321 Loss2: 0.682360
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.279920 Loss1: 0.595082 Loss2: 0.684838
-(DefaultActor pid=1831567) >> Training accuracy: 0.789200
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.349843 Loss1: 0.596748 Loss2: 0.753096
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.203796 Loss1: 0.519480 Loss2: 0.684316
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.194858 Loss1: 0.512883 Loss2: 0.681974
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202151 Loss1: 0.519542 Loss2: 0.682609
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187764 Loss1: 0.503629 Loss2: 0.684135
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.163181 Loss1: 0.480975 Loss2: 0.682205
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.167219 Loss1: 0.484701 Loss2: 0.682517
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.159322 Loss1: 0.474632 Loss2: 0.684690
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.177151 Loss1: 0.488382 Loss2: 0.688769
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.169023 Loss1: 0.481480 Loss2: 0.687543
-(DefaultActor pid=1831567) >> Training accuracy: 0.846608
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289917 Loss1: 0.529333 Loss2: 0.760584
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.206155 Loss1: 0.491296 Loss2: 0.714859
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.219486 Loss1: 0.503103 Loss2: 0.716383
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.233054 Loss1: 0.513557 Loss2: 0.719497
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.227749 Loss1: 0.506306 Loss2: 0.721443
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.220545 Loss1: 0.503683 Loss2: 0.716861
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191197 Loss1: 0.471091 Loss2: 0.720106
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.207861 Loss1: 0.487059 Loss2: 0.720802
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205797 Loss1: 0.485649 Loss2: 0.720148
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.183696 Loss1: 0.466759 Loss2: 0.716938
-(DefaultActor pid=1831567) >> Training accuracy: 0.833705
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.496859 Loss1: 0.742783 Loss2: 0.754076
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370987 Loss1: 0.704410 Loss2: 0.666578
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.353889 Loss1: 0.687871 Loss2: 0.666017
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.349900 Loss1: 0.681503 Loss2: 0.668397
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.331821 Loss1: 0.663050 Loss2: 0.668771
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.321716 Loss1: 0.651898 Loss2: 0.669818
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299527 Loss1: 0.629927 Loss2: 0.669600
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.314935 Loss1: 0.642438 Loss2: 0.672497
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.294102 Loss1: 0.625447 Loss2: 0.668656
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.308984 Loss1: 0.635914 Loss2: 0.673070
-(DefaultActor pid=1831567) >> Training accuracy: 0.789629
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.220996 Loss1: 0.461679 Loss2: 0.759317
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.082360 Loss1: 0.409727 Loss2: 0.672633
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048222 Loss1: 0.377019 Loss2: 0.671203
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.038329 Loss1: 0.370163 Loss2: 0.668166
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.055247 Loss1: 0.385241 Loss2: 0.670005
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.030428 Loss1: 0.359434 Loss2: 0.670995
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.008357 Loss1: 0.337125 Loss2: 0.671231
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.049904 Loss1: 0.378715 Loss2: 0.671189
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.019051 Loss1: 0.348804 Loss2: 0.670247
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.021424 Loss1: 0.349997 Loss2: 0.671427
-[2023-09-27 17:53:25,458][flwr][DEBUG] - fit_round 88 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.882330
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.705700
-[2023-09-27 17:53:26,908][flwr][INFO] - fit progress: (88, 0.8516317514565807, {'accuracy': 0.7057}, 41739.744232431985)
-[2023-09-27 17:53:26,908][flwr][DEBUG] - evaluate_round 88: strategy sampled 10 clients (out of 10)
-[2023-09-27 17:53:58,203][flwr][DEBUG] - evaluate_round 88 received 10 results and 0 failures
-[2023-09-27 17:53:58,204][flwr][DEBUG] - fit_round 89: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.452386 Loss1: 0.693835 Loss2: 0.758551
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.357453 Loss1: 0.687537 Loss2: 0.669916
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.337236 Loss1: 0.671361 Loss2: 0.665876
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.314879 Loss1: 0.646546 Loss2: 0.668333
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.297221 Loss1: 0.628683 Loss2: 0.668538
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.310280 Loss1: 0.639767 Loss2: 0.670514
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.299484 Loss1: 0.624642 Loss2: 0.674842
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.274881 Loss1: 0.601612 Loss2: 0.673269
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.294358 Loss1: 0.619767 Loss2: 0.674592
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.272395 Loss1: 0.596455 Loss2: 0.675940
-(DefaultActor pid=1831567) >> Training accuracy: 0.774254
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.181574 Loss1: 0.445350 Loss2: 0.736225
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.058674 Loss1: 0.395081 Loss2: 0.663593
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.051431 Loss1: 0.389730 Loss2: 0.661701
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.031158 Loss1: 0.370897 Loss2: 0.660260
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.037268 Loss1: 0.374907 Loss2: 0.662361
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.038831 Loss1: 0.376114 Loss2: 0.662717
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.046306 Loss1: 0.382121 Loss2: 0.664185
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.021971 Loss1: 0.357502 Loss2: 0.664470
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.010978 Loss1: 0.347886 Loss2: 0.663092
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.023760 Loss1: 0.356510 Loss2: 0.667251
-(DefaultActor pid=1831567) >> Training accuracy: 0.873457
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.514023 Loss1: 0.718774 Loss2: 0.795249
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.331784 Loss1: 0.645518 Loss2: 0.686266
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.309540 Loss1: 0.624276 Loss2: 0.685264
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.303571 Loss1: 0.614226 Loss2: 0.689344
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.300127 Loss1: 0.608100 Loss2: 0.692027
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.288028 Loss1: 0.592691 Loss2: 0.695337
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.287911 Loss1: 0.594403 Loss2: 0.693508
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.271804 Loss1: 0.575280 Loss2: 0.696524
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.287946 Loss1: 0.587771 Loss2: 0.700175
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.247065 Loss1: 0.550962 Loss2: 0.696102
-(DefaultActor pid=1831567) >> Training accuracy: 0.805099
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.281318 Loss1: 0.559530 Loss2: 0.721788
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.192242 Loss1: 0.535556 Loss2: 0.656686
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173294 Loss1: 0.519226 Loss2: 0.654068
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.178973 Loss1: 0.521325 Loss2: 0.657648
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.180165 Loss1: 0.520875 Loss2: 0.659290
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.162578 Loss1: 0.503912 Loss2: 0.658666
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.145789 Loss1: 0.488991 Loss2: 0.656798
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.149394 Loss1: 0.489282 Loss2: 0.660112
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.118080 Loss1: 0.456507 Loss2: 0.661573
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.142555 Loss1: 0.479314 Loss2: 0.663241
-(DefaultActor pid=1831567) >> Training accuracy: 0.854968
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.333484 Loss1: 0.529336 Loss2: 0.804148
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.236371 Loss1: 0.516041 Loss2: 0.720330
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.223614 Loss1: 0.504772 Loss2: 0.718842
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.202688 Loss1: 0.482105 Loss2: 0.720583
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.207822 Loss1: 0.485379 Loss2: 0.722443
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.203013 Loss1: 0.479661 Loss2: 0.723352
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.186613 Loss1: 0.466090 Loss2: 0.720523
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204616 Loss1: 0.478132 Loss2: 0.726484
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.181779 Loss1: 0.460093 Loss2: 0.721686
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.172772 Loss1: 0.446961 Loss2: 0.725811
-(DefaultActor pid=1831567) >> Training accuracy: 0.853618
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475638 Loss1: 0.722122 Loss2: 0.753515
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341297 Loss1: 0.670470 Loss2: 0.670826
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.349809 Loss1: 0.676017 Loss2: 0.673793
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.345737 Loss1: 0.671914 Loss2: 0.673823
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.365876 Loss1: 0.686791 Loss2: 0.679086
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.326483 Loss1: 0.649642 Loss2: 0.676841
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.314135 Loss1: 0.638001 Loss2: 0.676134
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.299855 Loss1: 0.621455 Loss2: 0.678401
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.331602 Loss1: 0.649710 Loss2: 0.681893
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.334676 Loss1: 0.649689 Loss2: 0.684987
-(DefaultActor pid=1831567) >> Training accuracy: 0.770380
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.332744 Loss1: 0.584889 Loss2: 0.747855
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.159260 Loss1: 0.509702 Loss2: 0.649558
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.149457 Loss1: 0.498705 Loss2: 0.650752
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.142427 Loss1: 0.492229 Loss2: 0.650198
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.150700 Loss1: 0.498734 Loss2: 0.651967
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.123166 Loss1: 0.471818 Loss2: 0.651348
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.131629 Loss1: 0.480161 Loss2: 0.651468
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.085854 Loss1: 0.433084 Loss2: 0.652770
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.101304 Loss1: 0.450984 Loss2: 0.650320
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.098123 Loss1: 0.444352 Loss2: 0.653770
-(DefaultActor pid=1831567) >> Training accuracy: 0.848517
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.261070 Loss1: 0.527813 Loss2: 0.733257
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194124 Loss1: 0.503330 Loss2: 0.690794
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.204267 Loss1: 0.513225 Loss2: 0.691042
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199023 Loss1: 0.504795 Loss2: 0.694227
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.175123 Loss1: 0.482196 Loss2: 0.692927
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185242 Loss1: 0.491085 Loss2: 0.694157
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.169619 Loss1: 0.474313 Loss2: 0.695307
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.198778 Loss1: 0.501530 Loss2: 0.697247
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184473 Loss1: 0.492145 Loss2: 0.692328
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.185007 Loss1: 0.490712 Loss2: 0.694295
-(DefaultActor pid=1831567) >> Training accuracy: 0.849330
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.282585 Loss1: 0.550141 Loss2: 0.732444
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201534 Loss1: 0.543432 Loss2: 0.658102
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.179518 Loss1: 0.523651 Loss2: 0.655867
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.158126 Loss1: 0.503242 Loss2: 0.654884
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.188389 Loss1: 0.530328 Loss2: 0.658061
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.148145 Loss1: 0.493280 Loss2: 0.654865
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.138794 Loss1: 0.483080 Loss2: 0.655714
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.142856 Loss1: 0.482327 Loss2: 0.660528
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138812 Loss1: 0.480708 Loss2: 0.658104
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148346 Loss1: 0.489521 Loss2: 0.658825
-(DefaultActor pid=1831567) >> Training accuracy: 0.841463
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.210049 Loss1: 0.458839 Loss2: 0.751210
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.062024 Loss1: 0.394565 Loss2: 0.667459
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.034032 Loss1: 0.371846 Loss2: 0.662186
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.051450 Loss1: 0.385698 Loss2: 0.665752
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.035752 Loss1: 0.371081 Loss2: 0.664672
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.011511 Loss1: 0.347388 Loss2: 0.664123
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.005968 Loss1: 0.340251 Loss2: 0.665717
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.013078 Loss1: 0.345954 Loss2: 0.667124
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.010020 Loss1: 0.342597 Loss2: 0.667423
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.013268 Loss1: 0.345716 Loss2: 0.667552
-[2023-09-27 18:00:32,959][flwr][DEBUG] - fit_round 89 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.879630
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.703800
-[2023-09-27 18:00:34,342][flwr][INFO] - fit progress: (89, 0.8544797211790237, {'accuracy': 0.7038}, 42167.1785627068)
-[2023-09-27 18:00:34,343][flwr][DEBUG] - evaluate_round 89: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:01:04,805][flwr][DEBUG] - evaluate_round 89 received 10 results and 0 failures
-[2023-09-27 18:01:04,805][flwr][DEBUG] - fit_round 90: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.189360 Loss1: 0.456074 Loss2: 0.733286
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.042452 Loss1: 0.391218 Loss2: 0.651234
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.049769 Loss1: 0.396813 Loss2: 0.652956
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.016271 Loss1: 0.365395 Loss2: 0.650876
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.033044 Loss1: 0.381477 Loss2: 0.651567
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.018037 Loss1: 0.365510 Loss2: 0.652528
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.016064 Loss1: 0.363898 Loss2: 0.652166
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.012260 Loss1: 0.358922 Loss2: 0.653338
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.009925 Loss1: 0.356042 Loss2: 0.653883
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.991634 Loss1: 0.337703 Loss2: 0.653931
-(DefaultActor pid=1831567) >> Training accuracy: 0.873264
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.234589 Loss1: 0.526749 Loss2: 0.707840
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187658 Loss1: 0.517869 Loss2: 0.669789
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188182 Loss1: 0.517551 Loss2: 0.670631
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.164242 Loss1: 0.495755 Loss2: 0.668487
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.174373 Loss1: 0.498871 Loss2: 0.675503
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.154103 Loss1: 0.481411 Loss2: 0.672692
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170796 Loss1: 0.497946 Loss2: 0.672850
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158529 Loss1: 0.485932 Loss2: 0.672598
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.152844 Loss1: 0.478981 Loss2: 0.673863
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.157527 Loss1: 0.484101 Loss2: 0.673426
-(DefaultActor pid=1831567) >> Training accuracy: 0.837302
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.206051 Loss1: 0.448987 Loss2: 0.757063
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.065366 Loss1: 0.382917 Loss2: 0.682449
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.074031 Loss1: 0.393411 Loss2: 0.680621
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.045905 Loss1: 0.363903 Loss2: 0.682002
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.062472 Loss1: 0.377905 Loss2: 0.684567
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.045865 Loss1: 0.364285 Loss2: 0.681579
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051254 Loss1: 0.368724 Loss2: 0.682530
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.033756 Loss1: 0.351388 Loss2: 0.682367
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.038641 Loss1: 0.356027 Loss2: 0.682614
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.033596 Loss1: 0.350917 Loss2: 0.682679
-(DefaultActor pid=1831567) >> Training accuracy: 0.879051
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.343663 Loss1: 0.571863 Loss2: 0.771800
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.213291 Loss1: 0.517522 Loss2: 0.695769
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222679 Loss1: 0.528656 Loss2: 0.694024
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.211040 Loss1: 0.512906 Loss2: 0.698133
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.180337 Loss1: 0.482840 Loss2: 0.697497
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181217 Loss1: 0.486530 Loss2: 0.694687
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192197 Loss1: 0.493913 Loss2: 0.698284
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.181084 Loss1: 0.482580 Loss2: 0.698504
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184070 Loss1: 0.481412 Loss2: 0.702658
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165431 Loss1: 0.465150 Loss2: 0.700281
-(DefaultActor pid=1831567) >> Training accuracy: 0.833333
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.474439 Loss1: 0.719106 Loss2: 0.755333
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.302590 Loss1: 0.653387 Loss2: 0.649203
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.273491 Loss1: 0.623948 Loss2: 0.649543
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.269132 Loss1: 0.616871 Loss2: 0.652262
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.238496 Loss1: 0.584726 Loss2: 0.653769
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.259003 Loss1: 0.603300 Loss2: 0.655703
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.246966 Loss1: 0.592169 Loss2: 0.654797
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.233184 Loss1: 0.576782 Loss2: 0.656402
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.241995 Loss1: 0.586250 Loss2: 0.655745
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.217479 Loss1: 0.561856 Loss2: 0.655623
-(DefaultActor pid=1831567) >> Training accuracy: 0.804002
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462536 Loss1: 0.712483 Loss2: 0.750053
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.347465 Loss1: 0.683730 Loss2: 0.663734
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.344140 Loss1: 0.676052 Loss2: 0.668088
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.326454 Loss1: 0.659523 Loss2: 0.666931
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322738 Loss1: 0.652626 Loss2: 0.670112
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306891 Loss1: 0.636237 Loss2: 0.670653
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.312336 Loss1: 0.639618 Loss2: 0.672718
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329874 Loss1: 0.656171 Loss2: 0.673703
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.335400 Loss1: 0.657385 Loss2: 0.678015
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.287976 Loss1: 0.612411 Loss2: 0.675565
-(DefaultActor pid=1831567) >> Training accuracy: 0.788496
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.337981 Loss1: 0.580833 Loss2: 0.757149
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225143 Loss1: 0.539147 Loss2: 0.685996
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.209822 Loss1: 0.528529 Loss2: 0.681293
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.193855 Loss1: 0.508738 Loss2: 0.685116
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.186846 Loss1: 0.501477 Loss2: 0.685369
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.187799 Loss1: 0.499974 Loss2: 0.687826
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.181962 Loss1: 0.494842 Loss2: 0.687119
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.162221 Loss1: 0.473803 Loss2: 0.688418
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.162253 Loss1: 0.473352 Loss2: 0.688901
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.151189 Loss1: 0.464028 Loss2: 0.687161
-(DefaultActor pid=1831567) >> Training accuracy: 0.842607
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.301743 Loss1: 0.587541 Loss2: 0.714202
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.157302 Loss1: 0.518303 Loss2: 0.638998
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.146565 Loss1: 0.505322 Loss2: 0.641242
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.128767 Loss1: 0.489313 Loss2: 0.639454
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.146081 Loss1: 0.505722 Loss2: 0.640358
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.126216 Loss1: 0.484663 Loss2: 0.641553
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.113824 Loss1: 0.471306 Loss2: 0.642519
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.116703 Loss1: 0.474980 Loss2: 0.641723
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.115442 Loss1: 0.470788 Loss2: 0.644653
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.095804 Loss1: 0.453626 Loss2: 0.642178
-(DefaultActor pid=1831567) >> Training accuracy: 0.835526
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.377279 Loss1: 0.594101 Loss2: 0.783178
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.198322 Loss1: 0.511916 Loss2: 0.686406
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.173333 Loss1: 0.495554 Loss2: 0.677779
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.173129 Loss1: 0.488962 Loss2: 0.684167
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.170702 Loss1: 0.486122 Loss2: 0.684579
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146742 Loss1: 0.465695 Loss2: 0.681046
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.130422 Loss1: 0.445812 Loss2: 0.684610
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140535 Loss1: 0.455527 Loss2: 0.685008
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163487 Loss1: 0.474109 Loss2: 0.689377
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148919 Loss1: 0.461548 Loss2: 0.687370
-(DefaultActor pid=1831567) >> Training accuracy: 0.857256
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.492912 Loss1: 0.703841 Loss2: 0.789071
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.364643 Loss1: 0.666982 Loss2: 0.697661
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.357271 Loss1: 0.661572 Loss2: 0.695698
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.347292 Loss1: 0.651212 Loss2: 0.696080
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.355154 Loss1: 0.656572 Loss2: 0.698582
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.350352 Loss1: 0.646372 Loss2: 0.703979
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.322612 Loss1: 0.623740 Loss2: 0.698872
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.302210 Loss1: 0.604206 Loss2: 0.698004
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.310726 Loss1: 0.609371 Loss2: 0.701355
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.290251 Loss1: 0.589053 Loss2: 0.701198
-(DefaultActor pid=1831567) >> Training accuracy: 0.784515
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 18:08:01,063][flwr][DEBUG] - fit_round 90 received 10 results and 0 failures
->> Test accuracy: 0.707800
-[2023-09-27 18:08:02,593][flwr][INFO] - fit progress: (90, 0.8642163840345681, {'accuracy': 0.7078}, 42615.42914600996)
-[2023-09-27 18:08:02,593][flwr][DEBUG] - evaluate_round 90: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:08:33,394][flwr][DEBUG] - evaluate_round 90 received 10 results and 0 failures
-[2023-09-27 18:08:33,395][flwr][DEBUG] - fit_round 91: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.294979 Loss1: 0.556368 Loss2: 0.738611
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.162098 Loss1: 0.522108 Loss2: 0.639990
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.155986 Loss1: 0.518373 Loss2: 0.637613
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.128554 Loss1: 0.491833 Loss2: 0.636721
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.111824 Loss1: 0.474510 Loss2: 0.637314
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.110636 Loss1: 0.471091 Loss2: 0.639545
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.098417 Loss1: 0.457262 Loss2: 0.641155
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.066887 Loss1: 0.428834 Loss2: 0.638053
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.091870 Loss1: 0.448358 Loss2: 0.643512
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.062314 Loss1: 0.421832 Loss2: 0.640482
-(DefaultActor pid=1831567) >> Training accuracy: 0.845339
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493497 Loss1: 0.730413 Loss2: 0.763084
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370013 Loss1: 0.690305 Loss2: 0.679708
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.363701 Loss1: 0.684713 Loss2: 0.678989
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.363809 Loss1: 0.681712 Loss2: 0.682097
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.324046 Loss1: 0.645950 Loss2: 0.678097
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.349229 Loss1: 0.667585 Loss2: 0.681644
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.308214 Loss1: 0.624818 Loss2: 0.683396
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.317602 Loss1: 0.631997 Loss2: 0.685604
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.318911 Loss1: 0.634010 Loss2: 0.684901
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.310421 Loss1: 0.623896 Loss2: 0.686525
-(DefaultActor pid=1831567) >> Training accuracy: 0.785779
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517917 Loss1: 0.710012 Loss2: 0.807905
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329462 Loss1: 0.627631 Loss2: 0.701831
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318209 Loss1: 0.616289 Loss2: 0.701920
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.300614 Loss1: 0.598626 Loss2: 0.701988
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.322061 Loss1: 0.616791 Loss2: 0.705270
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.318034 Loss1: 0.610770 Loss2: 0.707264
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.303179 Loss1: 0.595885 Loss2: 0.707294
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.292641 Loss1: 0.585433 Loss2: 0.707208
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.269458 Loss1: 0.562348 Loss2: 0.707110
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.263640 Loss1: 0.552000 Loss2: 0.711639
-(DefaultActor pid=1831567) >> Training accuracy: 0.816338
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.432917 Loss1: 0.702612 Loss2: 0.730305
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.304803 Loss1: 0.658064 Loss2: 0.646739
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307836 Loss1: 0.662376 Loss2: 0.645460
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.319741 Loss1: 0.669472 Loss2: 0.650269
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.269302 Loss1: 0.622324 Loss2: 0.646979
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.271815 Loss1: 0.624838 Loss2: 0.646977
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.246114 Loss1: 0.597448 Loss2: 0.648666
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.265835 Loss1: 0.616166 Loss2: 0.649670
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.263890 Loss1: 0.616116 Loss2: 0.647774
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.227660 Loss1: 0.579879 Loss2: 0.647781
-(DefaultActor pid=1831567) >> Training accuracy: 0.801306
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358521 Loss1: 0.562808 Loss2: 0.795713
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.227293 Loss1: 0.518631 Loss2: 0.708662
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.212402 Loss1: 0.504053 Loss2: 0.708349
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.203046 Loss1: 0.491615 Loss2: 0.711431
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192293 Loss1: 0.480715 Loss2: 0.711578
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.180329 Loss1: 0.470618 Loss2: 0.709711
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174071 Loss1: 0.464474 Loss2: 0.709597
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.173945 Loss1: 0.460343 Loss2: 0.713602
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182034 Loss1: 0.465099 Loss2: 0.716935
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.147392 Loss1: 0.435798 Loss2: 0.711595
-(DefaultActor pid=1831567) >> Training accuracy: 0.856086
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.307380 Loss1: 0.536494 Loss2: 0.770885
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.216238 Loss1: 0.494038 Loss2: 0.722200
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.225971 Loss1: 0.502515 Loss2: 0.723456
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.207055 Loss1: 0.483219 Loss2: 0.723836
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.215577 Loss1: 0.492835 Loss2: 0.722742
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.230746 Loss1: 0.503986 Loss2: 0.726759
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.200397 Loss1: 0.473729 Loss2: 0.726667
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.227474 Loss1: 0.496640 Loss2: 0.730834
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230160 Loss1: 0.501758 Loss2: 0.728402
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.217404 Loss1: 0.487501 Loss2: 0.729903
-(DefaultActor pid=1831567) >> Training accuracy: 0.834449
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.337852 Loss1: 0.602597 Loss2: 0.735255
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200805 Loss1: 0.537873 Loss2: 0.662932
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199264 Loss1: 0.539885 Loss2: 0.659379
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.160408 Loss1: 0.504864 Loss2: 0.655544
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163692 Loss1: 0.507403 Loss2: 0.656289
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.157329 Loss1: 0.499938 Loss2: 0.657392
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.158848 Loss1: 0.503091 Loss2: 0.655756
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.145980 Loss1: 0.486383 Loss2: 0.659597
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.141462 Loss1: 0.483447 Loss2: 0.658015
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.161944 Loss1: 0.500553 Loss2: 0.661392
-(DefaultActor pid=1831567) >> Training accuracy: 0.842797
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.319831 Loss1: 0.573926 Loss2: 0.745905
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.197501 Loss1: 0.528371 Loss2: 0.669130
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191130 Loss1: 0.518606 Loss2: 0.672524
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.183073 Loss1: 0.512701 Loss2: 0.670371
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.151648 Loss1: 0.478066 Loss2: 0.673582
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.167461 Loss1: 0.492573 Loss2: 0.674888
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.163458 Loss1: 0.488279 Loss2: 0.675179
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168624 Loss1: 0.492040 Loss2: 0.676583
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.169069 Loss1: 0.490129 Loss2: 0.678940
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.130059 Loss1: 0.454723 Loss2: 0.675337
-(DefaultActor pid=1831567) >> Training accuracy: 0.857372
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.194107 Loss1: 0.451307 Loss2: 0.742799
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.059528 Loss1: 0.398308 Loss2: 0.661220
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.044508 Loss1: 0.384862 Loss2: 0.659645
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.022587 Loss1: 0.360838 Loss2: 0.661748
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.028337 Loss1: 0.368630 Loss2: 0.659707
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.041214 Loss1: 0.378977 Loss2: 0.662237
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.027167 Loss1: 0.366105 Loss2: 0.661062
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.027200 Loss1: 0.363810 Loss2: 0.663390
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.002653 Loss1: 0.339337 Loss2: 0.663316
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.023565 Loss1: 0.360593 Loss2: 0.662972
-(DefaultActor pid=1831567) >> Training accuracy: 0.885802
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.196679 Loss1: 0.446161 Loss2: 0.750518
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.045282 Loss1: 0.377927 Loss2: 0.667355
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048063 Loss1: 0.382531 Loss2: 0.665533
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.027698 Loss1: 0.361793 Loss2: 0.665905
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.032380 Loss1: 0.367531 Loss2: 0.664849
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.029629 Loss1: 0.363490 Loss2: 0.666139
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.000711 Loss1: 0.334718 Loss2: 0.665993
-(DefaultActor pid=1831567) Epoch: 7 Loss: 0.993695 Loss1: 0.327346 Loss2: 0.666349
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.028469 Loss1: 0.360459 Loss2: 0.668010
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.001857 Loss1: 0.335765 Loss2: 0.666092
-[2023-09-27 18:15:50,451][flwr][DEBUG] - fit_round 91 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.889853
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.706800
-[2023-09-27 18:15:51,867][flwr][INFO] - fit progress: (91, 0.8547620579076651, {'accuracy': 0.7068}, 43084.703277640045)
-[2023-09-27 18:15:51,867][flwr][DEBUG] - evaluate_round 91: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:16:23,352][flwr][DEBUG] - evaluate_round 91 received 10 results and 0 failures
-[2023-09-27 18:16:23,353][flwr][DEBUG] - fit_round 92: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.351543 Loss1: 0.578846 Loss2: 0.772697
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.243998 Loss1: 0.541146 Loss2: 0.702852
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.222491 Loss1: 0.522831 Loss2: 0.699659
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.199841 Loss1: 0.504622 Loss2: 0.695219
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.210201 Loss1: 0.505858 Loss2: 0.704343
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.204536 Loss1: 0.500909 Loss2: 0.703627
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.189801 Loss1: 0.485901 Loss2: 0.703900
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.197366 Loss1: 0.493599 Loss2: 0.703768
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.182151 Loss1: 0.478116 Loss2: 0.704036
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.180293 Loss1: 0.476894 Loss2: 0.703399
-(DefaultActor pid=1831567) >> Training accuracy: 0.844703
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.251869 Loss1: 0.534211 Loss2: 0.717659
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.175667 Loss1: 0.501590 Loss2: 0.674077
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188722 Loss1: 0.513730 Loss2: 0.674991
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.169337 Loss1: 0.493255 Loss2: 0.676081
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163984 Loss1: 0.487986 Loss2: 0.675998
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.174638 Loss1: 0.495261 Loss2: 0.679377
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164467 Loss1: 0.485972 Loss2: 0.678495
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168049 Loss1: 0.486853 Loss2: 0.681197
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163368 Loss1: 0.481280 Loss2: 0.682088
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.160771 Loss1: 0.482318 Loss2: 0.678453
-(DefaultActor pid=1831567) >> Training accuracy: 0.838790
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.172887 Loss1: 0.441809 Loss2: 0.731078
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.084074 Loss1: 0.426978 Loss2: 0.657096
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.029409 Loss1: 0.376360 Loss2: 0.653049
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.032970 Loss1: 0.377559 Loss2: 0.655411
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.015917 Loss1: 0.361354 Loss2: 0.654563
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.009810 Loss1: 0.354008 Loss2: 0.655802
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.015264 Loss1: 0.357263 Loss2: 0.658001
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026928 Loss1: 0.366495 Loss2: 0.660432
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.015967 Loss1: 0.356020 Loss2: 0.659947
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.997532 Loss1: 0.337055 Loss2: 0.660477
-(DefaultActor pid=1831567) >> Training accuracy: 0.887539
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.287935 Loss1: 0.570554 Loss2: 0.717381
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.144843 Loss1: 0.507838 Loss2: 0.637005
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.135667 Loss1: 0.499830 Loss2: 0.635838
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.143380 Loss1: 0.506462 Loss2: 0.636918
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.107132 Loss1: 0.469850 Loss2: 0.637283
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131515 Loss1: 0.490799 Loss2: 0.640716
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.106118 Loss1: 0.467513 Loss2: 0.638605
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.116198 Loss1: 0.476441 Loss2: 0.639757
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120011 Loss1: 0.477753 Loss2: 0.642258
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.118780 Loss1: 0.473423 Loss2: 0.645357
-(DefaultActor pid=1831567) >> Training accuracy: 0.860814
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.178923 Loss1: 0.436106 Loss2: 0.742817
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.078437 Loss1: 0.403982 Loss2: 0.674455
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.067005 Loss1: 0.394671 Loss2: 0.672334
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.051285 Loss1: 0.378344 Loss2: 0.672941
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.039626 Loss1: 0.369180 Loss2: 0.670446
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.021414 Loss1: 0.350442 Loss2: 0.670972
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.051188 Loss1: 0.377768 Loss2: 0.673420
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.029950 Loss1: 0.354403 Loss2: 0.675547
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.014912 Loss1: 0.342021 Loss2: 0.672892
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.018811 Loss1: 0.344981 Loss2: 0.673829
-(DefaultActor pid=1831567) >> Training accuracy: 0.878086
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.462531 Loss1: 0.702067 Loss2: 0.760464
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.294381 Loss1: 0.642764 Loss2: 0.651617
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.281311 Loss1: 0.632306 Loss2: 0.649005
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.277225 Loss1: 0.622776 Loss2: 0.654449
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.266947 Loss1: 0.614359 Loss2: 0.652589
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.231863 Loss1: 0.578445 Loss2: 0.653419
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.264977 Loss1: 0.610300 Loss2: 0.654677
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.244049 Loss1: 0.589301 Loss2: 0.654748
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230472 Loss1: 0.573826 Loss2: 0.656645
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.224607 Loss1: 0.569687 Loss2: 0.654920
-(DefaultActor pid=1831567) >> Training accuracy: 0.793860
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.358662 Loss1: 0.569663 Loss2: 0.789000
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.207384 Loss1: 0.526834 Loss2: 0.680551
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.156308 Loss1: 0.476574 Loss2: 0.679734
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.188480 Loss1: 0.505617 Loss2: 0.682863
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158037 Loss1: 0.474731 Loss2: 0.683306
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.121957 Loss1: 0.440553 Loss2: 0.681405
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.124763 Loss1: 0.440323 Loss2: 0.684440
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.140078 Loss1: 0.454957 Loss2: 0.685121
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136283 Loss1: 0.450308 Loss2: 0.685975
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.128613 Loss1: 0.437972 Loss2: 0.690642
-(DefaultActor pid=1831567) >> Training accuracy: 0.835805
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.526140 Loss1: 0.725320 Loss2: 0.800821
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.376417 Loss1: 0.670078 Loss2: 0.706339
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354365 Loss1: 0.647216 Loss2: 0.707149
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.358819 Loss1: 0.651866 Loss2: 0.706953
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.355480 Loss1: 0.644841 Loss2: 0.710639
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337490 Loss1: 0.630012 Loss2: 0.707477
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.323005 Loss1: 0.616342 Loss2: 0.706663
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.325215 Loss1: 0.615539 Loss2: 0.709677
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.327249 Loss1: 0.615864 Loss2: 0.711385
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.306910 Loss1: 0.595276 Loss2: 0.711634
-(DefaultActor pid=1831567) >> Training accuracy: 0.778685
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.337870 Loss1: 0.566669 Loss2: 0.771201
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.226108 Loss1: 0.534820 Loss2: 0.691289
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213716 Loss1: 0.523421 Loss2: 0.690295
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.213647 Loss1: 0.520545 Loss2: 0.693101
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.189085 Loss1: 0.498545 Loss2: 0.690539
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.179577 Loss1: 0.484876 Loss2: 0.694701
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.182556 Loss1: 0.489988 Loss2: 0.692567
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155918 Loss1: 0.461616 Loss2: 0.694303
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.184532 Loss1: 0.490362 Loss2: 0.694171
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.166559 Loss1: 0.471545 Loss2: 0.695014
-(DefaultActor pid=1831567) >> Training accuracy: 0.837340
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.460598 Loss1: 0.729050 Loss2: 0.731547
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.333445 Loss1: 0.688571 Loss2: 0.644873
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.338793 Loss1: 0.692664 Loss2: 0.646129
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.300545 Loss1: 0.652508 Loss2: 0.648037
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302119 Loss1: 0.654644 Loss2: 0.647475
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.291726 Loss1: 0.643242 Loss2: 0.648484
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.312348 Loss1: 0.660165 Loss2: 0.652183
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.283779 Loss1: 0.631234 Loss2: 0.652544
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267355 Loss1: 0.615903 Loss2: 0.651452
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.243184 Loss1: 0.590469 Loss2: 0.652715
-[2023-09-27 18:23:04,059][flwr][DEBUG] - fit_round 92 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.784873
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.704600
-[2023-09-27 18:23:05,693][flwr][INFO] - fit progress: (92, 0.8611169435536138, {'accuracy': 0.7046}, 43518.529675052036)
-[2023-09-27 18:23:05,694][flwr][DEBUG] - evaluate_round 92: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:23:38,189][flwr][DEBUG] - evaluate_round 92 received 10 results and 0 failures
-[2023-09-27 18:23:38,190][flwr][DEBUG] - fit_round 93: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.325426 Loss1: 0.575375 Loss2: 0.750051
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.156793 Loss1: 0.511973 Loss2: 0.644821
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.132132 Loss1: 0.487476 Loss2: 0.644656
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.111729 Loss1: 0.466460 Loss2: 0.645269
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.114760 Loss1: 0.466222 Loss2: 0.648538
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.113449 Loss1: 0.467182 Loss2: 0.646267
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.100095 Loss1: 0.447896 Loss2: 0.652199
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.096133 Loss1: 0.447784 Loss2: 0.648349
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.094667 Loss1: 0.446190 Loss2: 0.648477
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.085448 Loss1: 0.437037 Loss2: 0.648411
-(DefaultActor pid=1831567) >> Training accuracy: 0.855403
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.463395 Loss1: 0.724386 Loss2: 0.739009
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.325966 Loss1: 0.668234 Loss2: 0.657732
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.304147 Loss1: 0.647735 Loss2: 0.656411
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325142 Loss1: 0.667558 Loss2: 0.657584
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.292579 Loss1: 0.634925 Loss2: 0.657654
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289136 Loss1: 0.632459 Loss2: 0.656677
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.292192 Loss1: 0.635907 Loss2: 0.656285
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276686 Loss1: 0.614482 Loss2: 0.662205
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.269726 Loss1: 0.611497 Loss2: 0.658229
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.263570 Loss1: 0.603388 Loss2: 0.660183
-(DefaultActor pid=1831567) >> Training accuracy: 0.770289
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.464784 Loss1: 0.712405 Loss2: 0.752379
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.365528 Loss1: 0.694593 Loss2: 0.670935
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.354858 Loss1: 0.681156 Loss2: 0.673702
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.344296 Loss1: 0.669940 Loss2: 0.674356
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317381 Loss1: 0.644267 Loss2: 0.673115
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.328224 Loss1: 0.654782 Loss2: 0.673442
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.326740 Loss1: 0.649627 Loss2: 0.677113
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.321813 Loss1: 0.643943 Loss2: 0.677871
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.300997 Loss1: 0.623078 Loss2: 0.677919
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.327048 Loss1: 0.646776 Loss2: 0.680272
-(DefaultActor pid=1831567) >> Training accuracy: 0.791893
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.193993 Loss1: 0.449521 Loss2: 0.744472
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.074319 Loss1: 0.403644 Loss2: 0.670674
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.074835 Loss1: 0.405537 Loss2: 0.669298
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.042998 Loss1: 0.376236 Loss2: 0.666763
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.038369 Loss1: 0.372127 Loss2: 0.666243
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.051072 Loss1: 0.385972 Loss2: 0.665100
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019304 Loss1: 0.353373 Loss2: 0.665931
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.024217 Loss1: 0.357541 Loss2: 0.666676
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.025361 Loss1: 0.358038 Loss2: 0.667323
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.013142 Loss1: 0.343471 Loss2: 0.669671
-(DefaultActor pid=1831567) >> Training accuracy: 0.880015
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.231381 Loss1: 0.452354 Loss2: 0.779026
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.087691 Loss1: 0.391983 Loss2: 0.695708
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.096610 Loss1: 0.403890 Loss2: 0.692719
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.063825 Loss1: 0.369256 Loss2: 0.694569
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.070408 Loss1: 0.376095 Loss2: 0.694313
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.055987 Loss1: 0.360212 Loss2: 0.695775
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.030272 Loss1: 0.333391 Loss2: 0.696881
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.044749 Loss1: 0.344850 Loss2: 0.699899
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.044953 Loss1: 0.349695 Loss2: 0.695258
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.052648 Loss1: 0.355191 Loss2: 0.697458
-(DefaultActor pid=1831567) >> Training accuracy: 0.893133
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.301782 Loss1: 0.579410 Loss2: 0.722372
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.194094 Loss1: 0.545876 Loss2: 0.648218
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.151485 Loss1: 0.506404 Loss2: 0.645081
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.149181 Loss1: 0.506922 Loss2: 0.642259
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140851 Loss1: 0.499185 Loss2: 0.641667
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.121224 Loss1: 0.478471 Loss2: 0.642753
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.113021 Loss1: 0.470083 Loss2: 0.642938
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.139641 Loss1: 0.494492 Loss2: 0.645148
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120894 Loss1: 0.477873 Loss2: 0.643022
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.140672 Loss1: 0.494499 Loss2: 0.646174
-(DefaultActor pid=1831567) >> Training accuracy: 0.833079
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.298304 Loss1: 0.530855 Loss2: 0.767449
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.223839 Loss1: 0.502678 Loss2: 0.721161
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.224144 Loss1: 0.501080 Loss2: 0.723063
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.231501 Loss1: 0.508884 Loss2: 0.722617
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.216354 Loss1: 0.491568 Loss2: 0.724786
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.233464 Loss1: 0.509450 Loss2: 0.724014
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.225305 Loss1: 0.498979 Loss2: 0.726326
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.191735 Loss1: 0.465923 Loss2: 0.725811
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.225156 Loss1: 0.495800 Loss2: 0.729356
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.220930 Loss1: 0.492516 Loss2: 0.728414
-(DefaultActor pid=1831567) >> Training accuracy: 0.832093
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.351336 Loss1: 0.564150 Loss2: 0.787186
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.213609 Loss1: 0.513801 Loss2: 0.699808
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.199672 Loss1: 0.503045 Loss2: 0.696627
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.198488 Loss1: 0.500754 Loss2: 0.697734
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.184695 Loss1: 0.487244 Loss2: 0.697452
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.199644 Loss1: 0.499999 Loss2: 0.699645
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.174135 Loss1: 0.474337 Loss2: 0.699798
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.168871 Loss1: 0.467736 Loss2: 0.701135
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.164158 Loss1: 0.465639 Loss2: 0.698519
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.153929 Loss1: 0.448802 Loss2: 0.705127
-(DefaultActor pid=1831567) >> Training accuracy: 0.833470
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.322577 Loss1: 0.573174 Loss2: 0.749403
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.186984 Loss1: 0.511238 Loss2: 0.675746
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.204359 Loss1: 0.524852 Loss2: 0.679507
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.177219 Loss1: 0.500605 Loss2: 0.676614
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163514 Loss1: 0.488260 Loss2: 0.675255
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.179300 Loss1: 0.501152 Loss2: 0.678147
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.161264 Loss1: 0.480482 Loss2: 0.680783
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.146652 Loss1: 0.466107 Loss2: 0.680545
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.165313 Loss1: 0.481820 Loss2: 0.683493
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.162573 Loss1: 0.479772 Loss2: 0.682802
-(DefaultActor pid=1831567) >> Training accuracy: 0.849159
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.517826 Loss1: 0.721557 Loss2: 0.796269
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.318928 Loss1: 0.636076 Loss2: 0.682852
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.298937 Loss1: 0.617749 Loss2: 0.681187
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.283541 Loss1: 0.596455 Loss2: 0.687086
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.287802 Loss1: 0.601870 Loss2: 0.685932
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.249674 Loss1: 0.564548 Loss2: 0.685127
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.248086 Loss1: 0.560766 Loss2: 0.687320
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.276135 Loss1: 0.587420 Loss2: 0.688715
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.257769 Loss1: 0.567340 Loss2: 0.690428
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.260884 Loss1: 0.567150 Loss2: 0.693734
-[2023-09-27 18:30:16,379][flwr][DEBUG] - fit_round 93 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.802632
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.701000
-[2023-09-27 18:30:18,020][flwr][INFO] - fit progress: (93, 0.863727054847315, {'accuracy': 0.701}, 43950.856044563)
-[2023-09-27 18:30:18,020][flwr][DEBUG] - evaluate_round 93: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:30:49,439][flwr][DEBUG] - evaluate_round 93 received 10 results and 0 failures
-[2023-09-27 18:30:49,440][flwr][DEBUG] - fit_round 94: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.284334 Loss1: 0.541783 Loss2: 0.742551
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201367 Loss1: 0.503209 Loss2: 0.698158
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.213209 Loss1: 0.514012 Loss2: 0.699198
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.217906 Loss1: 0.517147 Loss2: 0.700759
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.202073 Loss1: 0.500899 Loss2: 0.701174
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.192157 Loss1: 0.493714 Loss2: 0.698443
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.195092 Loss1: 0.492737 Loss2: 0.702355
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.190636 Loss1: 0.491573 Loss2: 0.699063
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.175294 Loss1: 0.474175 Loss2: 0.701120
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.177087 Loss1: 0.477381 Loss2: 0.699706
-(DefaultActor pid=1831567) >> Training accuracy: 0.834697
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.441438 Loss1: 0.671319 Loss2: 0.770120
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.310803 Loss1: 0.649771 Loss2: 0.661031
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.307069 Loss1: 0.641457 Loss2: 0.665611
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.269858 Loss1: 0.606673 Loss2: 0.663184
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.257730 Loss1: 0.589791 Loss2: 0.667939
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.289883 Loss1: 0.621899 Loss2: 0.667984
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.262278 Loss1: 0.592255 Loss2: 0.670023
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.240381 Loss1: 0.569781 Loss2: 0.670601
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.230591 Loss1: 0.557554 Loss2: 0.673038
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.243533 Loss1: 0.569869 Loss2: 0.673664
-(DefaultActor pid=1831567) >> Training accuracy: 0.794682
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.493836 Loss1: 0.699601 Loss2: 0.794235
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.370966 Loss1: 0.670091 Loss2: 0.700875
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.349535 Loss1: 0.648649 Loss2: 0.700886
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.367288 Loss1: 0.663899 Loss2: 0.703389
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.337395 Loss1: 0.633779 Loss2: 0.703616
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.323327 Loss1: 0.621362 Loss2: 0.701965
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.329066 Loss1: 0.622700 Loss2: 0.706366
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.310036 Loss1: 0.606028 Loss2: 0.704008
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.305618 Loss1: 0.597332 Loss2: 0.708286
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.329407 Loss1: 0.619508 Loss2: 0.709900
-(DefaultActor pid=1831567) >> Training accuracy: 0.779618
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.352572 Loss1: 0.586365 Loss2: 0.766206
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.203686 Loss1: 0.510058 Loss2: 0.693628
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.214102 Loss1: 0.525922 Loss2: 0.688180
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.204074 Loss1: 0.515192 Loss2: 0.688882
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167185 Loss1: 0.478086 Loss2: 0.689099
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183637 Loss1: 0.491924 Loss2: 0.691713
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171360 Loss1: 0.477898 Loss2: 0.693461
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.161998 Loss1: 0.468296 Loss2: 0.693702
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.183975 Loss1: 0.488018 Loss2: 0.695957
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.162270 Loss1: 0.468762 Loss2: 0.693508
-(DefaultActor pid=1831567) >> Training accuracy: 0.837843
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.204173 Loss1: 0.444924 Loss2: 0.759249
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.082537 Loss1: 0.397211 Loss2: 0.685326
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.073158 Loss1: 0.389569 Loss2: 0.683589
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.076363 Loss1: 0.389245 Loss2: 0.687118
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.050854 Loss1: 0.367652 Loss2: 0.683202
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.029922 Loss1: 0.346297 Loss2: 0.683625
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.049366 Loss1: 0.366992 Loss2: 0.682374
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.040104 Loss1: 0.354667 Loss2: 0.685437
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.026651 Loss1: 0.340179 Loss2: 0.686472
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.036104 Loss1: 0.351641 Loss2: 0.684463
-(DefaultActor pid=1831567) >> Training accuracy: 0.882137
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.301704 Loss1: 0.558812 Loss2: 0.742892
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.237614 Loss1: 0.565717 Loss2: 0.671897
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.203275 Loss1: 0.532228 Loss2: 0.671047
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.232280 Loss1: 0.556995 Loss2: 0.675284
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.163986 Loss1: 0.490989 Loss2: 0.672997
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.175691 Loss1: 0.501185 Loss2: 0.674507
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.176839 Loss1: 0.500432 Loss2: 0.676407
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155433 Loss1: 0.479466 Loss2: 0.675967
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.123040 Loss1: 0.447939 Loss2: 0.675101
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.148414 Loss1: 0.471561 Loss2: 0.676853
-(DefaultActor pid=1831567) >> Training accuracy: 0.830128
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.369436 Loss1: 0.573036 Loss2: 0.796400
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.200074 Loss1: 0.506652 Loss2: 0.693423
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188791 Loss1: 0.492121 Loss2: 0.696671
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.179861 Loss1: 0.481935 Loss2: 0.697926
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.177674 Loss1: 0.480588 Loss2: 0.697086
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.166069 Loss1: 0.466342 Loss2: 0.699728
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.144000 Loss1: 0.442944 Loss2: 0.701055
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.147022 Loss1: 0.447897 Loss2: 0.699125
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138534 Loss1: 0.437387 Loss2: 0.701147
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.154773 Loss1: 0.452995 Loss2: 0.701778
-(DefaultActor pid=1831567) >> Training accuracy: 0.851430
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.213504 Loss1: 0.461886 Loss2: 0.751618
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.046841 Loss1: 0.377288 Loss2: 0.669553
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.047122 Loss1: 0.379367 Loss2: 0.667755
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.034462 Loss1: 0.367118 Loss2: 0.667345
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.031365 Loss1: 0.364404 Loss2: 0.666961
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.036852 Loss1: 0.368860 Loss2: 0.667993
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.033271 Loss1: 0.360068 Loss2: 0.673203
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.007271 Loss1: 0.338347 Loss2: 0.668924
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.008925 Loss1: 0.340505 Loss2: 0.668420
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.029345 Loss1: 0.356519 Loss2: 0.672826
-(DefaultActor pid=1831567) >> Training accuracy: 0.866898
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.264431 Loss1: 0.546285 Loss2: 0.718145
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.168259 Loss1: 0.522943 Loss2: 0.645316
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.147093 Loss1: 0.501937 Loss2: 0.645157
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.161478 Loss1: 0.515780 Loss2: 0.645698
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.131088 Loss1: 0.486227 Loss2: 0.644861
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.119966 Loss1: 0.476295 Loss2: 0.643671
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.136016 Loss1: 0.490464 Loss2: 0.645552
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.102041 Loss1: 0.455758 Loss2: 0.646283
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.127955 Loss1: 0.482095 Loss2: 0.645860
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.088252 Loss1: 0.441785 Loss2: 0.646467
-(DefaultActor pid=1831567) >> Training accuracy: 0.851974
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.448148 Loss1: 0.715612 Loss2: 0.732536
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.346284 Loss1: 0.701961 Loss2: 0.644323
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.331316 Loss1: 0.682599 Loss2: 0.648718
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316897 Loss1: 0.669054 Loss2: 0.647843
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.307082 Loss1: 0.658963 Loss2: 0.648119
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.290226 Loss1: 0.639495 Loss2: 0.650731
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.286541 Loss1: 0.635356 Loss2: 0.651184
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.290383 Loss1: 0.637239 Loss2: 0.653144
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.322632 Loss1: 0.667043 Loss2: 0.655589
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.282846 Loss1: 0.630161 Loss2: 0.652685
-[2023-09-27 18:37:21,786][flwr][DEBUG] - fit_round 94 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.798007
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.702800
-[2023-09-27 18:37:23,188][flwr][INFO] - fit progress: (94, 0.8653616505309035, {'accuracy': 0.7028}, 44376.02431618376)
-[2023-09-27 18:37:23,188][flwr][DEBUG] - evaluate_round 94: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:37:54,653][flwr][DEBUG] - evaluate_round 94 received 10 results and 0 failures
-[2023-09-27 18:37:54,654][flwr][DEBUG] - fit_round 95: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.316856 Loss1: 0.571226 Loss2: 0.745630
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.155570 Loss1: 0.508550 Loss2: 0.647020
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.139890 Loss1: 0.496123 Loss2: 0.643767
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.122866 Loss1: 0.480756 Loss2: 0.642110
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.117961 Loss1: 0.473134 Loss2: 0.644827
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.146275 Loss1: 0.500143 Loss2: 0.646132
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.111790 Loss1: 0.462332 Loss2: 0.649458
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.103812 Loss1: 0.458314 Loss2: 0.645498
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.090381 Loss1: 0.439838 Loss2: 0.650543
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.080745 Loss1: 0.434032 Loss2: 0.646713
-(DefaultActor pid=1831567) >> Training accuracy: 0.851165
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.487706 Loss1: 0.739298 Loss2: 0.748407
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.362299 Loss1: 0.699581 Loss2: 0.662718
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.302995 Loss1: 0.643947 Loss2: 0.659048
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.310271 Loss1: 0.646475 Loss2: 0.663796
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.289273 Loss1: 0.625730 Loss2: 0.663543
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.302476 Loss1: 0.638628 Loss2: 0.663848
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.280980 Loss1: 0.619076 Loss2: 0.661904
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.289295 Loss1: 0.623049 Loss2: 0.666246
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.301853 Loss1: 0.633611 Loss2: 0.668242
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.262533 Loss1: 0.598032 Loss2: 0.664500
-(DefaultActor pid=1831567) >> Training accuracy: 0.759562
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.237018 Loss1: 0.463876 Loss2: 0.773142
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.103305 Loss1: 0.412817 Loss2: 0.690488
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.086297 Loss1: 0.394972 Loss2: 0.691325
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.069363 Loss1: 0.381145 Loss2: 0.688217
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.034136 Loss1: 0.348533 Loss2: 0.685603
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.044830 Loss1: 0.358319 Loss2: 0.686512
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.040177 Loss1: 0.353230 Loss2: 0.686947
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030902 Loss1: 0.341689 Loss2: 0.689213
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.026209 Loss1: 0.336138 Loss2: 0.690071
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.020302 Loss1: 0.328255 Loss2: 0.692047
-(DefaultActor pid=1831567) >> Training accuracy: 0.887539
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.298527 Loss1: 0.563281 Loss2: 0.735246
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.199376 Loss1: 0.533650 Loss2: 0.665727
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.181749 Loss1: 0.518691 Loss2: 0.663058
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.176622 Loss1: 0.511005 Loss2: 0.665617
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.169133 Loss1: 0.504940 Loss2: 0.664193
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.138229 Loss1: 0.475606 Loss2: 0.662624
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.159360 Loss1: 0.496568 Loss2: 0.662792
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.141627 Loss1: 0.479510 Loss2: 0.662116
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140942 Loss1: 0.476722 Loss2: 0.664219
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.143873 Loss1: 0.479359 Loss2: 0.664513
-(DefaultActor pid=1831567) >> Training accuracy: 0.837081
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.324517 Loss1: 0.578401 Loss2: 0.746115
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.187351 Loss1: 0.512985 Loss2: 0.674365
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.191372 Loss1: 0.517379 Loss2: 0.673992
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194812 Loss1: 0.516612 Loss2: 0.678200
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158879 Loss1: 0.481923 Loss2: 0.676956
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.190141 Loss1: 0.513073 Loss2: 0.677069
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.171741 Loss1: 0.490934 Loss2: 0.680808
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.144107 Loss1: 0.464636 Loss2: 0.679471
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188015 Loss1: 0.507643 Loss2: 0.680372
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.136205 Loss1: 0.454416 Loss2: 0.681790
-(DefaultActor pid=1831567) >> Training accuracy: 0.833333
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.282082 Loss1: 0.540317 Loss2: 0.741765
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.205360 Loss1: 0.509383 Loss2: 0.695978
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.196343 Loss1: 0.497577 Loss2: 0.698766
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.178458 Loss1: 0.480210 Loss2: 0.698247
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.187210 Loss1: 0.489266 Loss2: 0.697945
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186918 Loss1: 0.484520 Loss2: 0.702398
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.186107 Loss1: 0.485508 Loss2: 0.700599
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182623 Loss1: 0.481712 Loss2: 0.700911
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.187831 Loss1: 0.483649 Loss2: 0.704182
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.173323 Loss1: 0.470130 Loss2: 0.703194
-(DefaultActor pid=1831567) >> Training accuracy: 0.837178
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320297 Loss1: 0.550211 Loss2: 0.770086
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.184722 Loss1: 0.503197 Loss2: 0.681525
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.178988 Loss1: 0.497446 Loss2: 0.681542
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.182458 Loss1: 0.499382 Loss2: 0.683077
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.168378 Loss1: 0.484381 Loss2: 0.683997
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.188880 Loss1: 0.500844 Loss2: 0.688036
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.154772 Loss1: 0.469608 Loss2: 0.685163
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155044 Loss1: 0.467918 Loss2: 0.687126
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.135461 Loss1: 0.448298 Loss2: 0.687162
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.145028 Loss1: 0.456880 Loss2: 0.688149
-(DefaultActor pid=1831567) >> Training accuracy: 0.852590
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.495990 Loss1: 0.710876 Loss2: 0.785114
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.341345 Loss1: 0.660309 Loss2: 0.681036
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.292733 Loss1: 0.609185 Loss2: 0.683548
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.297011 Loss1: 0.613572 Loss2: 0.683439
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.269372 Loss1: 0.583180 Loss2: 0.686192
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.274426 Loss1: 0.586240 Loss2: 0.688186
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.293868 Loss1: 0.602524 Loss2: 0.691344
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.287838 Loss1: 0.597690 Loss2: 0.690148
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.259306 Loss1: 0.568871 Loss2: 0.690435
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.251407 Loss1: 0.563095 Loss2: 0.688312
-(DefaultActor pid=1831567) >> Training accuracy: 0.810855
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187186 Loss1: 0.443795 Loss2: 0.743391
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.074813 Loss1: 0.401128 Loss2: 0.673685
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.048879 Loss1: 0.378320 Loss2: 0.670559
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.046702 Loss1: 0.377557 Loss2: 0.669144
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.026352 Loss1: 0.358174 Loss2: 0.668178
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.040384 Loss1: 0.369319 Loss2: 0.671065
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.024406 Loss1: 0.355106 Loss2: 0.669300
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.012009 Loss1: 0.342021 Loss2: 0.669988
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.022820 Loss1: 0.352900 Loss2: 0.669919
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.029622 Loss1: 0.359393 Loss2: 0.670228
-(DefaultActor pid=1831567) >> Training accuracy: 0.876543
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.503427 Loss1: 0.720445 Loss2: 0.782981
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.397137 Loss1: 0.698479 Loss2: 0.698657
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.359998 Loss1: 0.661072 Loss2: 0.698925
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.335523 Loss1: 0.638765 Loss2: 0.696758
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.338304 Loss1: 0.640188 Loss2: 0.698116
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.345705 Loss1: 0.643139 Loss2: 0.702566
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.359666 Loss1: 0.654302 Loss2: 0.705363
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.354000 Loss1: 0.646598 Loss2: 0.707403
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.326801 Loss1: 0.618195 Loss2: 0.708606
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.333106 Loss1: 0.624610 Loss2: 0.708496
-(DefaultActor pid=1831567) >> Training accuracy: 0.788496
-(DefaultActor pid=1831567)  ** Training complete **
-[2023-09-27 18:44:59,614][flwr][DEBUG] - fit_round 95 received 10 results and 0 failures
->> Test accuracy: 0.700100
-[2023-09-27 18:45:01,048][flwr][INFO] - fit progress: (95, 0.8646818039516291, {'accuracy': 0.7001}, 44833.88447048701)
-[2023-09-27 18:45:01,049][flwr][DEBUG] - evaluate_round 95: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:45:30,939][flwr][DEBUG] - evaluate_round 95 received 10 results and 0 failures
-[2023-09-27 18:45:30,940][flwr][DEBUG] - fit_round 96: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.288744 Loss1: 0.560342 Loss2: 0.728402
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.159504 Loss1: 0.509641 Loss2: 0.649862
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.140537 Loss1: 0.489457 Loss2: 0.651080
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.167064 Loss1: 0.511377 Loss2: 0.655688
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.138387 Loss1: 0.482603 Loss2: 0.655784
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.140277 Loss1: 0.484831 Loss2: 0.655446
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.116925 Loss1: 0.460514 Loss2: 0.656412
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.143003 Loss1: 0.481793 Loss2: 0.661210
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.139304 Loss1: 0.481123 Loss2: 0.658181
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.124868 Loss1: 0.465988 Loss2: 0.658880
-(DefaultActor pid=1831567) >> Training accuracy: 0.841488
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.428814 Loss1: 0.698598 Loss2: 0.730215
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.334131 Loss1: 0.685155 Loss2: 0.648976
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.325535 Loss1: 0.677307 Loss2: 0.648227
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.323937 Loss1: 0.671848 Loss2: 0.652089
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.302027 Loss1: 0.650828 Loss2: 0.651199
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.303485 Loss1: 0.650651 Loss2: 0.652835
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.283947 Loss1: 0.634075 Loss2: 0.649873
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.277392 Loss1: 0.623505 Loss2: 0.653887
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.274906 Loss1: 0.622631 Loss2: 0.652274
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.257234 Loss1: 0.601792 Loss2: 0.655442
-(DefaultActor pid=1831567) >> Training accuracy: 0.792572
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.473494 Loss1: 0.710747 Loss2: 0.762747
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.291565 Loss1: 0.638894 Loss2: 0.652670
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.282740 Loss1: 0.630835 Loss2: 0.651905
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.277852 Loss1: 0.622076 Loss2: 0.655776
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.274873 Loss1: 0.621905 Loss2: 0.652967
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.250611 Loss1: 0.593183 Loss2: 0.657428
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.261898 Loss1: 0.601413 Loss2: 0.660485
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.248181 Loss1: 0.591291 Loss2: 0.656890
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.238720 Loss1: 0.577922 Loss2: 0.660798
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.215073 Loss1: 0.556728 Loss2: 0.658345
-(DefaultActor pid=1831567) >> Training accuracy: 0.784265
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.467212 Loss1: 0.676933 Loss2: 0.790279
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.354591 Loss1: 0.657622 Loss2: 0.696969
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.351915 Loss1: 0.654534 Loss2: 0.697382
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.351898 Loss1: 0.650860 Loss2: 0.701038
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.330436 Loss1: 0.635493 Loss2: 0.694944
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.309470 Loss1: 0.613470 Loss2: 0.696000
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.334594 Loss1: 0.637471 Loss2: 0.697122
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.316240 Loss1: 0.619222 Loss2: 0.697019
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.314703 Loss1: 0.611402 Loss2: 0.703301
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.293362 Loss1: 0.590205 Loss2: 0.703157
-(DefaultActor pid=1831567) >> Training accuracy: 0.788479
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.291462 Loss1: 0.541927 Loss2: 0.749536
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.210765 Loss1: 0.502617 Loss2: 0.708148
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.198882 Loss1: 0.493469 Loss2: 0.705413
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.207849 Loss1: 0.497879 Loss2: 0.709969
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.204472 Loss1: 0.494490 Loss2: 0.709982
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.186488 Loss1: 0.478672 Loss2: 0.707816
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.192465 Loss1: 0.482060 Loss2: 0.710405
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.194145 Loss1: 0.482648 Loss2: 0.711497
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.188969 Loss1: 0.478142 Loss2: 0.710827
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.190271 Loss1: 0.478907 Loss2: 0.711364
-(DefaultActor pid=1831567) >> Training accuracy: 0.842014
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.194833 Loss1: 0.441395 Loss2: 0.753438
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.091309 Loss1: 0.407771 Loss2: 0.683538
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.069153 Loss1: 0.386394 Loss2: 0.682759
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.068251 Loss1: 0.385100 Loss2: 0.683151
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.046245 Loss1: 0.365392 Loss2: 0.680853
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.057654 Loss1: 0.376274 Loss2: 0.681380
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.050373 Loss1: 0.367011 Loss2: 0.683362
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.030496 Loss1: 0.344615 Loss2: 0.685881
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.048540 Loss1: 0.363626 Loss2: 0.684915
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.048037 Loss1: 0.360398 Loss2: 0.687639
-(DefaultActor pid=1831567) >> Training accuracy: 0.885995
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.314268 Loss1: 0.577496 Loss2: 0.736772
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211993 Loss1: 0.539921 Loss2: 0.672072
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.187048 Loss1: 0.517789 Loss2: 0.669259
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.160192 Loss1: 0.492722 Loss2: 0.667470
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.178088 Loss1: 0.506376 Loss2: 0.671712
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.159631 Loss1: 0.490426 Loss2: 0.669205
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.148493 Loss1: 0.478736 Loss2: 0.669757
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.156976 Loss1: 0.485330 Loss2: 0.671646
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.163938 Loss1: 0.488990 Loss2: 0.674948
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.132456 Loss1: 0.458752 Loss2: 0.673704
-(DefaultActor pid=1831567) >> Training accuracy: 0.842416
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340894 Loss1: 0.557626 Loss2: 0.783268
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.245302 Loss1: 0.543755 Loss2: 0.701547
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.206622 Loss1: 0.502862 Loss2: 0.703760
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.205806 Loss1: 0.501850 Loss2: 0.703956
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.193825 Loss1: 0.490193 Loss2: 0.703633
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.206163 Loss1: 0.497161 Loss2: 0.709002
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.211008 Loss1: 0.497886 Loss2: 0.713121
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.184585 Loss1: 0.472049 Loss2: 0.712536
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.203050 Loss1: 0.490636 Loss2: 0.712414
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.178666 Loss1: 0.468942 Loss2: 0.709724
-(DefaultActor pid=1831567) >> Training accuracy: 0.849359
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.329475 Loss1: 0.556760 Loss2: 0.772716
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.180745 Loss1: 0.508451 Loss2: 0.672293
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.217876 Loss1: 0.540870 Loss2: 0.677006
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162911 Loss1: 0.488819 Loss2: 0.674091
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140895 Loss1: 0.464988 Loss2: 0.675907
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.152158 Loss1: 0.475428 Loss2: 0.676731
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.122531 Loss1: 0.446423 Loss2: 0.676107
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.135906 Loss1: 0.456941 Loss2: 0.678965
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.110301 Loss1: 0.432059 Loss2: 0.678243
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152469 Loss1: 0.469805 Loss2: 0.682664
-(DefaultActor pid=1831567) >> Training accuracy: 0.860964
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.193263 Loss1: 0.444518 Loss2: 0.748745
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.069663 Loss1: 0.404769 Loss2: 0.664894
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.039570 Loss1: 0.378314 Loss2: 0.661255
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.019393 Loss1: 0.361638 Loss2: 0.657755
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.020042 Loss1: 0.355208 Loss2: 0.664834
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.027609 Loss1: 0.363943 Loss2: 0.663666
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.026107 Loss1: 0.361008 Loss2: 0.665099
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026726 Loss1: 0.360788 Loss2: 0.665938
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.033963 Loss1: 0.366608 Loss2: 0.667354
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.010579 Loss1: 0.345125 Loss2: 0.665454
-[2023-09-27 18:52:14,917][flwr][DEBUG] - fit_round 96 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.895255
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.706400
-[2023-09-27 18:52:24,051][flwr][INFO] - fit progress: (96, 0.8532181625929884, {'accuracy': 0.7064}, 45276.887487936765)
-[2023-09-27 18:52:24,052][flwr][DEBUG] - evaluate_round 96: strategy sampled 10 clients (out of 10)
-[2023-09-27 18:53:01,650][flwr][DEBUG] - evaluate_round 96 received 10 results and 0 failures
-[2023-09-27 18:53:01,651][flwr][DEBUG] - fit_round 97: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.457590 Loss1: 0.702180 Loss2: 0.755410
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.356703 Loss1: 0.680357 Loss2: 0.676346
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.341841 Loss1: 0.665529 Loss2: 0.676312
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.316971 Loss1: 0.640023 Loss2: 0.676949
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.312898 Loss1: 0.633377 Loss2: 0.679521
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.338020 Loss1: 0.657155 Loss2: 0.680865
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.327908 Loss1: 0.645670 Loss2: 0.682239
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329162 Loss1: 0.648483 Loss2: 0.680679
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.300097 Loss1: 0.617658 Loss2: 0.682439
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.283042 Loss1: 0.600664 Loss2: 0.682378
-(DefaultActor pid=1831567) >> Training accuracy: 0.795516
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.213683 Loss1: 0.452483 Loss2: 0.761200
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.068170 Loss1: 0.392939 Loss2: 0.675231
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.060927 Loss1: 0.386080 Loss2: 0.674847
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.018747 Loss1: 0.345161 Loss2: 0.673586
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.038639 Loss1: 0.363253 Loss2: 0.675385
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.022878 Loss1: 0.349847 Loss2: 0.673031
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.032260 Loss1: 0.354359 Loss2: 0.677901
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.026103 Loss1: 0.350443 Loss2: 0.675660
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.022278 Loss1: 0.347389 Loss2: 0.674889
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.022364 Loss1: 0.345581 Loss2: 0.676783
-(DefaultActor pid=1831567) >> Training accuracy: 0.861690
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.283780 Loss1: 0.572852 Loss2: 0.710927
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.174768 Loss1: 0.537456 Loss2: 0.637312
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.151877 Loss1: 0.519539 Loss2: 0.632337
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.144127 Loss1: 0.505746 Loss2: 0.638382
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.120727 Loss1: 0.483485 Loss2: 0.637243
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.127400 Loss1: 0.486454 Loss2: 0.640946
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.115845 Loss1: 0.474545 Loss2: 0.641299
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.123749 Loss1: 0.484609 Loss2: 0.639140
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.126372 Loss1: 0.487473 Loss2: 0.638898
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.089254 Loss1: 0.447748 Loss2: 0.641506
-(DefaultActor pid=1831567) >> Training accuracy: 0.841654
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.292639 Loss1: 0.534990 Loss2: 0.757649
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.225268 Loss1: 0.508129 Loss2: 0.717139
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.209869 Loss1: 0.495838 Loss2: 0.714030
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.218568 Loss1: 0.498577 Loss2: 0.719992
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.193902 Loss1: 0.475837 Loss2: 0.718065
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.210156 Loss1: 0.491381 Loss2: 0.718775
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.214581 Loss1: 0.492736 Loss2: 0.721845
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182756 Loss1: 0.467129 Loss2: 0.715627
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213842 Loss1: 0.490886 Loss2: 0.722955
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.201447 Loss1: 0.477891 Loss2: 0.723556
-(DefaultActor pid=1831567) >> Training accuracy: 0.839286
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.318824 Loss1: 0.545167 Loss2: 0.773657
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.203498 Loss1: 0.512905 Loss2: 0.690593
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.185574 Loss1: 0.496584 Loss2: 0.688990
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189562 Loss1: 0.502379 Loss2: 0.687183
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.208434 Loss1: 0.512820 Loss2: 0.695614
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168675 Loss1: 0.476093 Loss2: 0.692583
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.168864 Loss1: 0.473532 Loss2: 0.695331
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182015 Loss1: 0.488552 Loss2: 0.693463
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.144859 Loss1: 0.451672 Loss2: 0.693187
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.141699 Loss1: 0.451198 Loss2: 0.690502
-(DefaultActor pid=1831567) >> Training accuracy: 0.836143
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.171188 Loss1: 0.445680 Loss2: 0.725508
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.089351 Loss1: 0.436189 Loss2: 0.653162
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.041324 Loss1: 0.391976 Loss2: 0.649347
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.017775 Loss1: 0.369533 Loss2: 0.648242
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.021445 Loss1: 0.369220 Loss2: 0.652225
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.009838 Loss1: 0.361353 Loss2: 0.648485
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.019587 Loss1: 0.366519 Loss2: 0.653068
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.013729 Loss1: 0.360007 Loss2: 0.653721
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.019606 Loss1: 0.368731 Loss2: 0.650875
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019582 Loss1: 0.367152 Loss2: 0.652430
-(DefaultActor pid=1831567) >> Training accuracy: 0.880208
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.491278 Loss1: 0.702040 Loss2: 0.789239
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.345583 Loss1: 0.665182 Loss2: 0.680402
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.291087 Loss1: 0.608133 Loss2: 0.682954
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291043 Loss1: 0.607514 Loss2: 0.683529
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.290201 Loss1: 0.604547 Loss2: 0.685654
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.286333 Loss1: 0.597891 Loss2: 0.688443
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.260539 Loss1: 0.574716 Loss2: 0.685823
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.258497 Loss1: 0.568455 Loss2: 0.690042
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.275961 Loss1: 0.585499 Loss2: 0.690462
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.284669 Loss1: 0.594387 Loss2: 0.690282
-(DefaultActor pid=1831567) >> Training accuracy: 0.797149
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.357632 Loss1: 0.580104 Loss2: 0.777528
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.175590 Loss1: 0.507595 Loss2: 0.667995
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.147009 Loss1: 0.481688 Loss2: 0.665320
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.145014 Loss1: 0.475669 Loss2: 0.669344
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.130571 Loss1: 0.461142 Loss2: 0.669429
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.138414 Loss1: 0.466395 Loss2: 0.672019
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.155574 Loss1: 0.484294 Loss2: 0.671280
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.120315 Loss1: 0.449537 Loss2: 0.670778
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.104806 Loss1: 0.433246 Loss2: 0.671560
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.114966 Loss1: 0.440151 Loss2: 0.674814
-(DefaultActor pid=1831567) >> Training accuracy: 0.841102
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.297051 Loss1: 0.558942 Loss2: 0.738109
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.202843 Loss1: 0.533015 Loss2: 0.669828
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.186473 Loss1: 0.517995 Loss2: 0.668479
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189819 Loss1: 0.520481 Loss2: 0.669338
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.181632 Loss1: 0.512335 Loss2: 0.669297
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.164162 Loss1: 0.492424 Loss2: 0.671738
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.167453 Loss1: 0.493898 Loss2: 0.673555
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155807 Loss1: 0.481367 Loss2: 0.674439
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.147445 Loss1: 0.472076 Loss2: 0.675369
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.126730 Loss1: 0.452144 Loss2: 0.674586
-(DefaultActor pid=1831567) >> Training accuracy: 0.850761
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.438487 Loss1: 0.688212 Loss2: 0.750275
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.335217 Loss1: 0.672873 Loss2: 0.662345
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336285 Loss1: 0.674308 Loss2: 0.661976
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.303626 Loss1: 0.641813 Loss2: 0.661813
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317460 Loss1: 0.652235 Loss2: 0.665225
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306189 Loss1: 0.642377 Loss2: 0.663812
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.290045 Loss1: 0.624661 Loss2: 0.665383
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.282455 Loss1: 0.610597 Loss2: 0.671859
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.282146 Loss1: 0.611351 Loss2: 0.670796
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.286279 Loss1: 0.616739 Loss2: 0.669540
-[2023-09-27 18:59:42,034][flwr][DEBUG] - fit_round 97 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.751399
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.700800
-[2023-09-27 18:59:43,571][flwr][INFO] - fit progress: (97, 0.8687959163904951, {'accuracy': 0.7008}, 45716.40724924812)
-[2023-09-27 18:59:43,571][flwr][DEBUG] - evaluate_round 97: strategy sampled 10 clients (out of 10)
-[2023-09-27 19:00:13,690][flwr][DEBUG] - evaluate_round 97 received 10 results and 0 failures
-[2023-09-27 19:00:13,691][flwr][DEBUG] - fit_round 98: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.340778 Loss1: 0.578274 Loss2: 0.762504
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.217503 Loss1: 0.525593 Loss2: 0.691910
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.188921 Loss1: 0.498884 Loss2: 0.690037
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.192616 Loss1: 0.503187 Loss2: 0.689430
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.167441 Loss1: 0.480594 Loss2: 0.686847
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.181009 Loss1: 0.491707 Loss2: 0.689303
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.187761 Loss1: 0.493647 Loss2: 0.694114
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.156396 Loss1: 0.461697 Loss2: 0.694699
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.196454 Loss1: 0.501302 Loss2: 0.695152
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150453 Loss1: 0.456620 Loss2: 0.693833
-(DefaultActor pid=1831567) >> Training accuracy: 0.847561
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.334039 Loss1: 0.569615 Loss2: 0.764425
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211468 Loss1: 0.526037 Loss2: 0.685431
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.189189 Loss1: 0.506787 Loss2: 0.682402
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.190688 Loss1: 0.504082 Loss2: 0.686606
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.183153 Loss1: 0.497236 Loss2: 0.685917
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.159037 Loss1: 0.472996 Loss2: 0.686041
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.191776 Loss1: 0.502463 Loss2: 0.689313
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.182074 Loss1: 0.493778 Loss2: 0.688297
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.151472 Loss1: 0.459110 Loss2: 0.692362
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.161699 Loss1: 0.472848 Loss2: 0.688851
-(DefaultActor pid=1831567) >> Training accuracy: 0.840345
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.516448 Loss1: 0.726828 Loss2: 0.789620
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.359716 Loss1: 0.665007 Loss2: 0.694710
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.318312 Loss1: 0.622245 Loss2: 0.696067
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.366114 Loss1: 0.668739 Loss2: 0.697375
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.307320 Loss1: 0.608819 Loss2: 0.698501
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.326793 Loss1: 0.629980 Loss2: 0.696812
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.296732 Loss1: 0.599405 Loss2: 0.697326
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.329807 Loss1: 0.628644 Loss2: 0.701163
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.330613 Loss1: 0.626087 Loss2: 0.704526
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.304473 Loss1: 0.601777 Loss2: 0.702697
-(DefaultActor pid=1831567) >> Training accuracy: 0.766558
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.212875 Loss1: 0.433981 Loss2: 0.778894
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.121500 Loss1: 0.413957 Loss2: 0.707543
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.075628 Loss1: 0.372668 Loss2: 0.702960
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.080289 Loss1: 0.378796 Loss2: 0.701493
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.066194 Loss1: 0.363546 Loss2: 0.702648
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.068614 Loss1: 0.365850 Loss2: 0.702765
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.078752 Loss1: 0.372679 Loss2: 0.706073
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.066208 Loss1: 0.358167 Loss2: 0.708042
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.053666 Loss1: 0.346697 Loss2: 0.706969
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.049440 Loss1: 0.343968 Loss2: 0.705472
-(DefaultActor pid=1831567) >> Training accuracy: 0.874228
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.472642 Loss1: 0.732679 Loss2: 0.739963
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329749 Loss1: 0.676406 Loss2: 0.653343
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.330769 Loss1: 0.678224 Loss2: 0.652545
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.315542 Loss1: 0.662665 Loss2: 0.652877
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.313586 Loss1: 0.658395 Loss2: 0.655192
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.296021 Loss1: 0.638439 Loss2: 0.657582
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.301093 Loss1: 0.644666 Loss2: 0.656426
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.288354 Loss1: 0.630816 Loss2: 0.657538
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.286993 Loss1: 0.627537 Loss2: 0.659456
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.291139 Loss1: 0.630316 Loss2: 0.660822
-(DefaultActor pid=1831567) >> Training accuracy: 0.779212
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.220410 Loss1: 0.478165 Loss2: 0.742245
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.053255 Loss1: 0.392492 Loss2: 0.660763
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.024705 Loss1: 0.365900 Loss2: 0.658804
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.025169 Loss1: 0.370392 Loss2: 0.654777
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.009374 Loss1: 0.356163 Loss2: 0.653211
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.023851 Loss1: 0.368569 Loss2: 0.655282
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.000706 Loss1: 0.342766 Loss2: 0.657940
-(DefaultActor pid=1831567) Epoch: 7 Loss: 0.995205 Loss1: 0.340750 Loss2: 0.654455
-(DefaultActor pid=1831567) Epoch: 8 Loss: 0.996985 Loss1: 0.338515 Loss2: 0.658470
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.006919 Loss1: 0.347615 Loss2: 0.659304
-(DefaultActor pid=1831567) >> Training accuracy: 0.892554
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.471849 Loss1: 0.712952 Loss2: 0.758898
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.293735 Loss1: 0.637650 Loss2: 0.656085
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.304341 Loss1: 0.643622 Loss2: 0.660719
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.275161 Loss1: 0.619078 Loss2: 0.656083
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.265082 Loss1: 0.606998 Loss2: 0.658084
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.234615 Loss1: 0.577565 Loss2: 0.657050
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.248457 Loss1: 0.591460 Loss2: 0.656997
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.249269 Loss1: 0.587749 Loss2: 0.661520
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244943 Loss1: 0.584278 Loss2: 0.660665
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.233966 Loss1: 0.573224 Loss2: 0.660742
-(DefaultActor pid=1831567) >> Training accuracy: 0.811129
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.296514 Loss1: 0.569608 Loss2: 0.726906
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.166274 Loss1: 0.516972 Loss2: 0.649303
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.163934 Loss1: 0.510833 Loss2: 0.653101
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.170285 Loss1: 0.513186 Loss2: 0.657100
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.148328 Loss1: 0.493126 Loss2: 0.655201
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.125511 Loss1: 0.467494 Loss2: 0.658018
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.138427 Loss1: 0.481398 Loss2: 0.657029
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.126706 Loss1: 0.469617 Loss2: 0.657089
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.136797 Loss1: 0.477691 Loss2: 0.659106
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.110737 Loss1: 0.453488 Loss2: 0.657249
-(DefaultActor pid=1831567) >> Training accuracy: 0.847451
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.375727 Loss1: 0.594384 Loss2: 0.781343
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.169987 Loss1: 0.493643 Loss2: 0.676344
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.162873 Loss1: 0.486416 Loss2: 0.676457
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162291 Loss1: 0.485268 Loss2: 0.677022
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.148723 Loss1: 0.470465 Loss2: 0.678258
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.117542 Loss1: 0.439935 Loss2: 0.677607
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.159691 Loss1: 0.480352 Loss2: 0.679339
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.104509 Loss1: 0.426286 Loss2: 0.678223
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.140061 Loss1: 0.458261 Loss2: 0.681801
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.107715 Loss1: 0.428076 Loss2: 0.679639
-(DefaultActor pid=1831567) >> Training accuracy: 0.868114
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.278558 Loss1: 0.530530 Loss2: 0.748028
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.201024 Loss1: 0.496145 Loss2: 0.704879
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.207920 Loss1: 0.500060 Loss2: 0.707860
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.189060 Loss1: 0.483168 Loss2: 0.705892
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.192593 Loss1: 0.487171 Loss2: 0.705422
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.214368 Loss1: 0.506101 Loss2: 0.708267
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.184584 Loss1: 0.474435 Loss2: 0.710148
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.201394 Loss1: 0.490466 Loss2: 0.710928
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.209376 Loss1: 0.495708 Loss2: 0.713668
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.190802 Loss1: 0.479628 Loss2: 0.711174
-[2023-09-27 19:06:49,009][flwr][DEBUG] - fit_round 98 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.843006
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.706400
-[2023-09-27 19:06:50,363][flwr][INFO] - fit progress: (98, 0.8529771449276433, {'accuracy': 0.7064}, 46143.19974304596)
-[2023-09-27 19:06:50,364][flwr][DEBUG] - evaluate_round 98: strategy sampled 10 clients (out of 10)
-[2023-09-27 19:07:20,600][flwr][DEBUG] - evaluate_round 98 received 10 results and 0 failures
-[2023-09-27 19:07:20,601][flwr][DEBUG] - fit_round 99: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.164623 Loss1: 0.445211 Loss2: 0.719412
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.048527 Loss1: 0.402063 Loss2: 0.646464
-(DefaultActor pid=1831567) Epoch: 2 Loss: 0.998323 Loss1: 0.357478 Loss2: 0.640845
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.021874 Loss1: 0.376715 Loss2: 0.645159
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.014261 Loss1: 0.374058 Loss2: 0.640203
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.013677 Loss1: 0.369069 Loss2: 0.644609
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.007988 Loss1: 0.362983 Loss2: 0.645005
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.004943 Loss1: 0.359774 Loss2: 0.645169
-(DefaultActor pid=1831567) Epoch: 8 Loss: 0.993500 Loss1: 0.347044 Loss2: 0.646456
-(DefaultActor pid=1831567) Epoch: 9 Loss: 0.991017 Loss1: 0.343741 Loss2: 0.647276
-(DefaultActor pid=1831567) >> Training accuracy: 0.881366
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.299637 Loss1: 0.539278 Loss2: 0.760359
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.143325 Loss1: 0.488973 Loss2: 0.654352
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.174077 Loss1: 0.516414 Loss2: 0.657664
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.128350 Loss1: 0.473661 Loss2: 0.654689
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.123968 Loss1: 0.466989 Loss2: 0.656979
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.131668 Loss1: 0.472689 Loss2: 0.658980
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.137678 Loss1: 0.477352 Loss2: 0.660326
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.130493 Loss1: 0.473549 Loss2: 0.656944
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.100390 Loss1: 0.441545 Loss2: 0.658846
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.064859 Loss1: 0.405819 Loss2: 0.659040
-(DefaultActor pid=1831567) >> Training accuracy: 0.865466
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.450386 Loss1: 0.702282 Loss2: 0.748103
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.329910 Loss1: 0.667420 Loss2: 0.662489
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.336599 Loss1: 0.671811 Loss2: 0.664788
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.291464 Loss1: 0.628291 Loss2: 0.663173
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.285995 Loss1: 0.623004 Loss2: 0.662991
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.306405 Loss1: 0.639902 Loss2: 0.666503
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.278717 Loss1: 0.612445 Loss2: 0.666272
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.264067 Loss1: 0.597467 Loss2: 0.666601
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.267597 Loss1: 0.600834 Loss2: 0.666763
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.287706 Loss1: 0.620472 Loss2: 0.667235
-(DefaultActor pid=1831567) >> Training accuracy: 0.791744
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.250568 Loss1: 0.516503 Loss2: 0.734065
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.210341 Loss1: 0.513909 Loss2: 0.696432
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192100 Loss1: 0.494179 Loss2: 0.697921
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.192256 Loss1: 0.495781 Loss2: 0.696476
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.176562 Loss1: 0.479718 Loss2: 0.696844
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.185815 Loss1: 0.485001 Loss2: 0.700814
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.188467 Loss1: 0.489019 Loss2: 0.699448
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.185235 Loss1: 0.479053 Loss2: 0.706182
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.205322 Loss1: 0.503167 Loss2: 0.702155
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.165443 Loss1: 0.463038 Loss2: 0.702405
-(DefaultActor pid=1831567) >> Training accuracy: 0.840278
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.475794 Loss1: 0.698289 Loss2: 0.777505
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.331082 Loss1: 0.659091 Loss2: 0.671992
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.320618 Loss1: 0.641205 Loss2: 0.679413
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.261531 Loss1: 0.587621 Loss2: 0.673909
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.310595 Loss1: 0.629424 Loss2: 0.681171
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.284640 Loss1: 0.605688 Loss2: 0.678951
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.258328 Loss1: 0.579615 Loss2: 0.678712
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.249605 Loss1: 0.569061 Loss2: 0.680544
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.244968 Loss1: 0.561187 Loss2: 0.683781
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.265018 Loss1: 0.583168 Loss2: 0.681850
-(DefaultActor pid=1831567) >> Training accuracy: 0.791667
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.187514 Loss1: 0.438562 Loss2: 0.748952
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.052822 Loss1: 0.382883 Loss2: 0.669939
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.042007 Loss1: 0.376476 Loss2: 0.665532
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.013801 Loss1: 0.348379 Loss2: 0.665421
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.021087 Loss1: 0.354356 Loss2: 0.666731
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.016216 Loss1: 0.349176 Loss2: 0.667040
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.023239 Loss1: 0.353417 Loss2: 0.669823
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.003867 Loss1: 0.332973 Loss2: 0.670894
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.013841 Loss1: 0.342780 Loss2: 0.671061
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.008050 Loss1: 0.337970 Loss2: 0.670081
-(DefaultActor pid=1831567) >> Training accuracy: 0.893519
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.480497 Loss1: 0.732158 Loss2: 0.748338
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.342843 Loss1: 0.678047 Loss2: 0.664796
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.339697 Loss1: 0.674175 Loss2: 0.665522
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325708 Loss1: 0.659722 Loss2: 0.665986
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.317717 Loss1: 0.649137 Loss2: 0.668580
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.314103 Loss1: 0.645051 Loss2: 0.669053
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.350054 Loss1: 0.677634 Loss2: 0.672420
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.306497 Loss1: 0.635392 Loss2: 0.671104
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.302386 Loss1: 0.630899 Loss2: 0.671487
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.302250 Loss1: 0.628331 Loss2: 0.673919
-(DefaultActor pid=1831567) >> Training accuracy: 0.779891
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.320256 Loss1: 0.556884 Loss2: 0.763372
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.211613 Loss1: 0.516663 Loss2: 0.694950
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.208962 Loss1: 0.515800 Loss2: 0.693161
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.194747 Loss1: 0.497351 Loss2: 0.697396
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.199814 Loss1: 0.505053 Loss2: 0.694760
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.183912 Loss1: 0.488795 Loss2: 0.695118
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.165819 Loss1: 0.469369 Loss2: 0.696450
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.172995 Loss1: 0.473736 Loss2: 0.699258
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.203491 Loss1: 0.502811 Loss2: 0.700680
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.184346 Loss1: 0.485527 Loss2: 0.698819
-(DefaultActor pid=1831567) >> Training accuracy: 0.852764
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.314450 Loss1: 0.546293 Loss2: 0.768157
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.198277 Loss1: 0.515580 Loss2: 0.682696
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.201336 Loss1: 0.516567 Loss2: 0.684769
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.172246 Loss1: 0.487869 Loss2: 0.684377
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.144172 Loss1: 0.459224 Loss2: 0.684948
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.168924 Loss1: 0.481233 Loss2: 0.687691
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.175815 Loss1: 0.489488 Loss2: 0.686327
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.155720 Loss1: 0.468937 Loss2: 0.686783
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.170158 Loss1: 0.481686 Loss2: 0.688472
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.152306 Loss1: 0.464561 Loss2: 0.687745
-(DefaultActor pid=1831567) >> Training accuracy: 0.844161
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.325654 Loss1: 0.596603 Loss2: 0.729051
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.197408 Loss1: 0.541432 Loss2: 0.655976
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.192692 Loss1: 0.540041 Loss2: 0.652651
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.146838 Loss1: 0.493521 Loss2: 0.653317
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.140368 Loss1: 0.490018 Loss2: 0.650351
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.130523 Loss1: 0.479261 Loss2: 0.651262
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.147788 Loss1: 0.494155 Loss2: 0.653633
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.143442 Loss1: 0.490436 Loss2: 0.653006
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.138952 Loss1: 0.487763 Loss2: 0.651188
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.124620 Loss1: 0.471595 Loss2: 0.653025
-[2023-09-27 19:14:06,284][flwr][DEBUG] - fit_round 99 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.845846
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.705700
-[2023-09-27 19:14:07,675][flwr][INFO] - fit progress: (99, 0.8568882018613359, {'accuracy': 0.7057}, 46580.51172851771)
-[2023-09-27 19:14:07,676][flwr][DEBUG] - evaluate_round 99: strategy sampled 10 clients (out of 10)
-[2023-09-27 19:14:38,070][flwr][DEBUG] - evaluate_round 99 received 10 results and 0 failures
-[2023-09-27 19:14:38,071][flwr][DEBUG] - fit_round 100: strategy sampled 10 clients (out of 10)
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.186312 Loss1: 0.439108 Loss2: 0.747204
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.059389 Loss1: 0.394870 Loss2: 0.664519
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.034510 Loss1: 0.366840 Loss2: 0.667670
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.043886 Loss1: 0.378863 Loss2: 0.665023
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.048814 Loss1: 0.379796 Loss2: 0.669018
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.041255 Loss1: 0.374520 Loss2: 0.666735
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.014572 Loss1: 0.349016 Loss2: 0.665556
-(DefaultActor pid=1831567) Epoch: 7 Loss: 0.999291 Loss1: 0.331946 Loss2: 0.667345
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.027996 Loss1: 0.357560 Loss2: 0.670437
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.013616 Loss1: 0.345285 Loss2: 0.668331
-(DefaultActor pid=1831567) >> Training accuracy: 0.891975
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.489291 Loss1: 0.699721 Loss2: 0.789569
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.369014 Loss1: 0.670312 Loss2: 0.698703
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.345978 Loss1: 0.647902 Loss2: 0.698076
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.325854 Loss1: 0.626532 Loss2: 0.699322
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.355271 Loss1: 0.654259 Loss2: 0.701012
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.318410 Loss1: 0.618734 Loss2: 0.699677
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.305149 Loss1: 0.603749 Loss2: 0.701400
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.313890 Loss1: 0.611219 Loss2: 0.702671
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.314912 Loss1: 0.610003 Loss2: 0.704909
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.319659 Loss1: 0.612447 Loss2: 0.707211
-(DefaultActor pid=1831567) >> Training accuracy: 0.749534
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.310976 Loss1: 0.571327 Loss2: 0.739648
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.210961 Loss1: 0.546513 Loss2: 0.664447
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.165625 Loss1: 0.506521 Loss2: 0.659104
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.166968 Loss1: 0.502704 Loss2: 0.664265
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.158452 Loss1: 0.491865 Loss2: 0.666587
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.174907 Loss1: 0.504750 Loss2: 0.670157
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.164945 Loss1: 0.495783 Loss2: 0.669163
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.151184 Loss1: 0.483295 Loss2: 0.667889
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.133278 Loss1: 0.466447 Loss2: 0.666831
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.143732 Loss1: 0.473862 Loss2: 0.669870
-(DefaultActor pid=1831567) >> Training accuracy: 0.846955
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.464313 Loss1: 0.707903 Loss2: 0.756410
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.376465 Loss1: 0.699296 Loss2: 0.677168
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.346178 Loss1: 0.667975 Loss2: 0.678203
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.334925 Loss1: 0.658813 Loss2: 0.676112
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.333571 Loss1: 0.655151 Loss2: 0.678419
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.337280 Loss1: 0.657483 Loss2: 0.679797
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.325959 Loss1: 0.643266 Loss2: 0.682693
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.295503 Loss1: 0.614013 Loss2: 0.681490
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.334531 Loss1: 0.651543 Loss2: 0.682988
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.315943 Loss1: 0.632955 Loss2: 0.682988
-(DefaultActor pid=1831567) >> Training accuracy: 0.782382
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.303401 Loss1: 0.548972 Loss2: 0.754429
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.160303 Loss1: 0.501955 Loss2: 0.658348
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.156430 Loss1: 0.496788 Loss2: 0.659642
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.142267 Loss1: 0.482168 Loss2: 0.660099
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.123734 Loss1: 0.462111 Loss2: 0.661622
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.114698 Loss1: 0.454316 Loss2: 0.660382
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.129943 Loss1: 0.465322 Loss2: 0.664622
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.118602 Loss1: 0.452474 Loss2: 0.666128
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.120383 Loss1: 0.454934 Loss2: 0.665448
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.094397 Loss1: 0.429316 Loss2: 0.665081
-(DefaultActor pid=1831567) >> Training accuracy: 0.860434
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.319415 Loss1: 0.570902 Loss2: 0.748513
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.209982 Loss1: 0.531365 Loss2: 0.678617
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.175753 Loss1: 0.499539 Loss2: 0.676215
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.162175 Loss1: 0.489621 Loss2: 0.672553
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.165618 Loss1: 0.490827 Loss2: 0.674792
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.170681 Loss1: 0.493302 Loss2: 0.677379
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.170429 Loss1: 0.492096 Loss2: 0.678334
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.149901 Loss1: 0.473123 Loss2: 0.676779
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.134043 Loss1: 0.455546 Loss2: 0.678497
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.150426 Loss1: 0.470493 Loss2: 0.679933
-(DefaultActor pid=1831567) >> Training accuracy: 0.835175
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.198478 Loss1: 0.446652 Loss2: 0.751826
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.073016 Loss1: 0.393536 Loss2: 0.679479
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.076403 Loss1: 0.396213 Loss2: 0.680189
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.064346 Loss1: 0.382484 Loss2: 0.681862
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.037385 Loss1: 0.356044 Loss2: 0.681341
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.038743 Loss1: 0.357641 Loss2: 0.681101
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.024350 Loss1: 0.343749 Loss2: 0.680601
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.050445 Loss1: 0.368122 Loss2: 0.682323
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.032503 Loss1: 0.348422 Loss2: 0.684081
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.019709 Loss1: 0.337958 Loss2: 0.681752
-(DefaultActor pid=1831567) >> Training accuracy: 0.871335
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.459724 Loss1: 0.694398 Loss2: 0.765326
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.281041 Loss1: 0.622245 Loss2: 0.658796
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.293258 Loss1: 0.631570 Loss2: 0.661688
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.286642 Loss1: 0.621989 Loss2: 0.664654
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.273285 Loss1: 0.612967 Loss2: 0.660318
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.264030 Loss1: 0.601495 Loss2: 0.662535
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.272734 Loss1: 0.602941 Loss2: 0.669793
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.225071 Loss1: 0.560418 Loss2: 0.664653
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.243618 Loss1: 0.577967 Loss2: 0.665651
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.228452 Loss1: 0.561613 Loss2: 0.666839
-(DefaultActor pid=1831567) >> Training accuracy: 0.805099
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.309363 Loss1: 0.535557 Loss2: 0.773806
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.219919 Loss1: 0.492848 Loss2: 0.727071
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.231249 Loss1: 0.499739 Loss2: 0.731510
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.232671 Loss1: 0.503736 Loss2: 0.728935
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.238966 Loss1: 0.506470 Loss2: 0.732496
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.216264 Loss1: 0.489373 Loss2: 0.726891
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.220475 Loss1: 0.488938 Loss2: 0.731536
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.204671 Loss1: 0.473770 Loss2: 0.730901
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.213265 Loss1: 0.482392 Loss2: 0.730873
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.219283 Loss1: 0.483676 Loss2: 0.735607
-(DefaultActor pid=1831567) >> Training accuracy: 0.807168
-(DefaultActor pid=1831567)  ** Training complete **
-(DefaultActor pid=1831567) Epoch: 0 Loss: 1.289845 Loss1: 0.564746 Loss2: 0.725099
-(DefaultActor pid=1831567) Epoch: 1 Loss: 1.146777 Loss1: 0.498783 Loss2: 0.647994
-(DefaultActor pid=1831567) Epoch: 2 Loss: 1.145596 Loss1: 0.497244 Loss2: 0.648352
-(DefaultActor pid=1831567) Epoch: 3 Loss: 1.147653 Loss1: 0.494979 Loss2: 0.652675
-(DefaultActor pid=1831567) Epoch: 4 Loss: 1.147044 Loss1: 0.492462 Loss2: 0.654582
-(DefaultActor pid=1831567) Epoch: 5 Loss: 1.129521 Loss1: 0.474271 Loss2: 0.655250
-(DefaultActor pid=1831567) Epoch: 6 Loss: 1.120133 Loss1: 0.467975 Loss2: 0.652158
-(DefaultActor pid=1831567) Epoch: 7 Loss: 1.158980 Loss1: 0.503951 Loss2: 0.655029
-(DefaultActor pid=1831567) Epoch: 8 Loss: 1.126423 Loss1: 0.471174 Loss2: 0.655249
-(DefaultActor pid=1831567) Epoch: 9 Loss: 1.100029 Loss1: 0.442179 Loss2: 0.657851
-[2023-09-27 19:21:27,388][flwr][DEBUG] - fit_round 100 received 10 results and 0 failures
-(DefaultActor pid=1831567) >> Training accuracy: 0.855058
-(DefaultActor pid=1831567)  ** Training complete **
->> Test accuracy: 0.707100
-[2023-09-27 19:21:29,430][flwr][INFO] - fit progress: (100, 0.8504751595064474, {'accuracy': 0.7071}, 47022.266861764714)
-[2023-09-27 19:21:29,431][flwr][DEBUG] - evaluate_round 100: strategy sampled 10 clients (out of 10)
-[2023-09-27 19:22:00,469][flwr][DEBUG] - evaluate_round 100 received 10 results and 0 failures
-[2023-09-27 19:22:00,470][flwr][INFO] - FL finished in 47053.30649761995
-[2023-09-27 19:22:00,487][flwr][INFO] - app_fit: losses_distributed [(1, 0.0), (2, 0.0), (3, 0.0), (4, 0.0), (5, 0.0), (6, 0.0), (7, 0.0), (8, 0.0), (9, 0.0), (10, 0.0), (11, 0.0), (12, 0.0), (13, 0.0), (14, 0.0), (15, 0.0), (16, 0.0), (17, 0.0), (18, 0.0), (19, 0.0), (20, 0.0), (21, 0.0), (22, 0.0), (23, 0.0), (24, 0.0), (25, 0.0), (26, 0.0), (27, 0.0), (28, 0.0), (29, 0.0), (30, 0.0), (31, 0.0), (32, 0.0), (33, 0.0), (34, 0.0), (35, 0.0), (36, 0.0), (37, 0.0), (38, 0.0), (39, 0.0), (40, 0.0), (41, 0.0), (42, 0.0), (43, 0.0), (44, 0.0), (45, 0.0), (46, 0.0), (47, 0.0), (48, 0.0), (49, 0.0), (50, 0.0), (51, 0.0), (52, 0.0), (53, 0.0), (54, 0.0), (55, 0.0), (56, 0.0), (57, 0.0), (58, 0.0), (59, 0.0), (60, 0.0), (61, 0.0), (62, 0.0), (63, 0.0), (64, 0.0), (65, 0.0), (66, 0.0), (67, 0.0), (68, 0.0), (69, 0.0), (70, 0.0), (71, 0.0), (72, 0.0), (73, 0.0), (74, 0.0), (75, 0.0), (76, 0.0), (77, 0.0), (78, 0.0), (79, 0.0), (80, 0.0), (81, 0.0), (82, 0.0), (83, 0.0), (84, 0.0), (85, 0.0), (86, 0.0), (87, 0.0), (88, 0.0), (89, 0.0), (90, 0.0), (91, 0.0), (92, 0.0), (93, 0.0), (94, 0.0), (95, 0.0), (96, 0.0), (97, 0.0), (98, 0.0), (99, 0.0), (100, 0.0)]
-[2023-09-27 19:22:00,488][flwr][INFO] - app_fit: metrics_distributed_fit {}
-[2023-09-27 19:22:00,488][flwr][INFO] - app_fit: metrics_distributed {}
-[2023-09-27 19:22:00,488][flwr][INFO] - app_fit: losses_centralized [(0, 2.3034089754183835), (1, 2.249783382629053), (2, 2.1481322312888245), (3, 1.647436479029183), (4, 1.4994674932461578), (5, 1.3875796671111744), (6, 1.274572859556911), (7, 1.238289627785119), (8, 1.1646041104587883), (9, 1.1750041659647665), (10, 1.128453626800269), (11, 1.1231593939062126), (12, 1.0893270417143361), (13, 1.0760116641894697), (14, 1.0299229365758622), (15, 1.022295751796363), (16, 1.0062863030753577), (17, 1.0163589238930053), (18, 0.9984818176149179), (19, 1.0004730579761651), (20, 0.9751454913578095), (21, 0.9615000673947623), (22, 0.9505386705787037), (23, 0.9466991268407804), (24, 0.9378842182052783), (25, 0.9540704172640182), (26, 0.9397244629578088), (27, 0.9426276777118159), (28, 0.9165207516080656), (29, 0.93534632089039), (30, 0.9313092758289923), (31, 0.950761117874243), (32, 0.9076737036910681), (33, 0.908714735469879), (34, 0.8978847988878196), (35, 0.9095158785486374), (36, 0.9122387365030404), (37, 0.9061162161370055), (38, 0.8966945318368297), (39, 0.9046378726966846), (40, 0.9118956097017843), (41, 0.8914938339600548), (42, 0.8886514548866894), (43, 0.8876500287756752), (44, 0.8899790141910029), (45, 0.8930932635697313), (46, 0.896309151150548), (47, 0.8794247569938818), (48, 0.8850831008566835), (49, 0.8720772449200908), (50, 0.8935196316851595), (51, 0.8929546851510057), (52, 0.8784053408490202), (53, 0.8694329051354441), (54, 0.8700203883190887), (55, 0.8612132831312977), (56, 0.8703011946556286), (57, 0.8694903847698967), (58, 0.8769776419328805), (59, 0.8658733374584978), (60, 0.8708832763825742), (61, 0.8809174903855918), (62, 0.8817045592461912), (63, 0.8681536297828626), (64, 0.8740351014434339), (65, 0.8715874363248721), (66, 0.8717131253819876), (67, 0.8726276551572659), (68, 0.8746880760398535), (69, 0.8618371694232709), (70, 0.8689377218389663), (71, 0.8598026045785544), (72, 0.8614236178299108), (73, 0.8532162613381212), (74, 0.8540887534618378), (75, 0.8667597494567164), (76, 0.8745543113150916), (77, 0.8666860393632334), (78, 0.8683196400491574), (79, 0.8694912555118719), (80, 0.8883003936217616), (81, 0.8609136937144465), (82, 0.874075241743947), (83, 0.8777990603980165), (84, 0.8604000634469163), (85, 0.8605043698614017), (86, 0.8559781925175518), (87, 0.8728009527102827), (88, 0.8516317514565807), (89, 0.8544797211790237), (90, 0.8642163840345681), (91, 0.8547620579076651), (92, 0.8611169435536138), (93, 0.863727054847315), (94, 0.8653616505309035), (95, 0.8646818039516291), (96, 0.8532181625929884), (97, 0.8687959163904951), (98, 0.8529771449276433), (99, 0.8568882018613359), (100, 0.8504751595064474)]
-[2023-09-27 19:22:00,489][flwr][INFO] - app_fit: metrics_centralized {'accuracy': [(0, 0.1), (1, 0.1108), (2, 0.1723), (3, 0.385), (4, 0.4448), (5, 0.4894), (6, 0.5419), (7, 0.5521), (8, 0.5872), (9, 0.5791), (10, 0.6008), (11, 0.5978), (12, 0.6122), (13, 0.6167), (14, 0.6321), (15, 0.6358), (16, 0.6409), (17, 0.6379), (18, 0.6455), (19, 0.6474), (20, 0.6553), (21, 0.658), (22, 0.6622), (23, 0.6667), (24, 0.6699), (25, 0.6619), (26, 0.6664), (27, 0.6647), (28, 0.678), (29, 0.674), (30, 0.6711), (31, 0.6672), (32, 0.6829), (33, 0.6829), (34, 0.6848), (35, 0.6827), (36, 0.6804), (37, 0.6851), (38, 0.6884), (39, 0.6832), (40, 0.6795), (41, 0.6912), (42, 0.6888), (43, 0.6932), (44, 0.6882), (45, 0.6906), (46, 0.6881), (47, 0.695), (48, 0.6942), (49, 0.6979), (50, 0.6916), (51, 0.6864), (52, 0.695), (53, 0.6957), (54, 0.6979), (55, 0.7028), (56, 0.6951), (57, 0.6973), (58, 0.6962), (59, 0.7032), (60, 0.7), (61, 0.6953), (62, 0.6907), (63, 0.7008), (64, 0.695), (65, 0.6976), (66, 0.6992), (67, 0.6971), (68, 0.6966), (69, 0.6999), (70, 0.7013), (71, 0.7042), (72, 0.7002), (73, 0.7019), (74, 0.707), (75, 0.6993), (76, 0.6963), (77, 0.6997), (78, 0.7002), (79, 0.6994), (80, 0.6927), (81, 0.7017), (82, 0.6957), (83, 0.6945), (84, 0.7042), (85, 0.7043), (86, 0.7051), (87, 0.6987), (88, 0.7057), (89, 0.7038), (90, 0.7078), (91, 0.7068), (92, 0.7046), (93, 0.701), (94, 0.7028), (95, 0.7001), (96, 0.7064), (97, 0.7008), (98, 0.7064), (99, 0.7057), (100, 0.7071)]}
-................
-History (loss, distributed):
-	round 1: 0.0
-	round 2: 0.0
-	round 3: 0.0
-	round 4: 0.0
-	round 5: 0.0
-	round 6: 0.0
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-	round 65: 0.0
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-	round 68: 0.0
-	round 69: 0.0
-	round 70: 0.0
-	round 71: 0.0
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-	round 96: 0.0
-	round 97: 0.0
-	round 98: 0.0
-	round 99: 0.0
-	round 100: 0.0
-History (loss, centralized):
-	round 0: 2.3034089754183835
-	round 1: 2.249783382629053
-	round 2: 2.1481322312888245
-	round 3: 1.647436479029183
-	round 4: 1.4994674932461578
-	round 5: 1.3875796671111744
-	round 6: 1.274572859556911
-	round 7: 1.238289627785119
-	round 8: 1.1646041104587883
-	round 9: 1.1750041659647665
-	round 10: 1.128453626800269
-	round 11: 1.1231593939062126
-	round 12: 1.0893270417143361
-	round 13: 1.0760116641894697
-	round 14: 1.0299229365758622
-	round 15: 1.022295751796363
-	round 16: 1.0062863030753577
-	round 17: 1.0163589238930053
-	round 18: 0.9984818176149179
-	round 19: 1.0004730579761651
-	round 20: 0.9751454913578095
-	round 21: 0.9615000673947623
-	round 22: 0.9505386705787037
-	round 23: 0.9466991268407804
-	round 24: 0.9378842182052783
-	round 25: 0.9540704172640182
-	round 26: 0.9397244629578088
-	round 27: 0.9426276777118159
-	round 28: 0.9165207516080656
-	round 29: 0.93534632089039
-	round 30: 0.9313092758289923
-	round 31: 0.950761117874243
-	round 32: 0.9076737036910681
-	round 33: 0.908714735469879
-	round 34: 0.8978847988878196
-	round 35: 0.9095158785486374
-	round 36: 0.9122387365030404
-	round 37: 0.9061162161370055
-	round 38: 0.8966945318368297
-	round 39: 0.9046378726966846
-	round 40: 0.9118956097017843
-	round 41: 0.8914938339600548
-	round 42: 0.8886514548866894
-	round 43: 0.8876500287756752
-	round 44: 0.8899790141910029
-	round 45: 0.8930932635697313
-	round 46: 0.896309151150548
-	round 47: 0.8794247569938818
-	round 48: 0.8850831008566835
-	round 49: 0.8720772449200908
-	round 50: 0.8935196316851595
-	round 51: 0.8929546851510057
-	round 52: 0.8784053408490202
-	round 53: 0.8694329051354441
-	round 54: 0.8700203883190887
-	round 55: 0.8612132831312977
-	round 56: 0.8703011946556286
-	round 57: 0.8694903847698967
-	round 58: 0.8769776419328805
-	round 59: 0.8658733374584978
-	round 60: 0.8708832763825742
-	round 61: 0.8809174903855918
-	round 62: 0.8817045592461912
-	round 63: 0.8681536297828626
-	round 64: 0.8740351014434339
-	round 65: 0.8715874363248721
-	round 66: 0.8717131253819876
-	round 67: 0.8726276551572659
-	round 68: 0.8746880760398535
-	round 69: 0.8618371694232709
-	round 70: 0.8689377218389663
-	round 71: 0.8598026045785544
-	round 72: 0.8614236178299108
-	round 73: 0.8532162613381212
-	round 74: 0.8540887534618378
-	round 75: 0.8667597494567164
-	round 76: 0.8745543113150916
-	round 77: 0.8666860393632334
-	round 78: 0.8683196400491574
-	round 79: 0.8694912555118719
-	round 80: 0.8883003936217616
-	round 81: 0.8609136937144465
-	round 82: 0.874075241743947
-	round 83: 0.8777990603980165
-	round 84: 0.8604000634469163
-	round 85: 0.8605043698614017
-	round 86: 0.8559781925175518
-	round 87: 0.8728009527102827
-	round 88: 0.8516317514565807
-	round 89: 0.8544797211790237
-	round 90: 0.8642163840345681
-	round 91: 0.8547620579076651
-	round 92: 0.8611169435536138
-	round 93: 0.863727054847315
-	round 94: 0.8653616505309035
-	round 95: 0.8646818039516291
-	round 96: 0.8532181625929884
-	round 97: 0.8687959163904951
-	round 98: 0.8529771449276433
-	round 99: 0.8568882018613359
-	round 100: 0.8504751595064474
-History (metrics, centralized):
-{'accuracy': [(0, 0.1), (1, 0.1108), (2, 0.1723), (3, 0.385), (4, 0.4448), (5, 0.4894), (6, 0.5419), (7, 0.5521), (8, 0.5872), (9, 0.5791), (10, 0.6008), (11, 0.5978), (12, 0.6122), (13, 0.6167), (14, 0.6321), (15, 0.6358), (16, 0.6409), (17, 0.6379), (18, 0.6455), (19, 0.6474), (20, 0.6553), (21, 0.658), (22, 0.6622), (23, 0.6667), (24, 0.6699), (25, 0.6619), (26, 0.6664), (27, 0.6647), (28, 0.678), (29, 0.674), (30, 0.6711), (31, 0.6672), (32, 0.6829), (33, 0.6829), (34, 0.6848), (35, 0.6827), (36, 0.6804), (37, 0.6851), (38, 0.6884), (39, 0.6832), (40, 0.6795), (41, 0.6912), (42, 0.6888), (43, 0.6932), (44, 0.6882), (45, 0.6906), (46, 0.6881), (47, 0.695), (48, 0.6942), (49, 0.6979), (50, 0.6916), (51, 0.6864), (52, 0.695), (53, 0.6957), (54, 0.6979), (55, 0.7028), (56, 0.6951), (57, 0.6973), (58, 0.6962), (59, 0.7032), (60, 0.7), (61, 0.6953), (62, 0.6907), (63, 0.7008), (64, 0.695), (65, 0.6976), (66, 0.6992), (67, 0.6971), (68, 0.6966), (69, 0.6999), (70, 0.7013), (71, 0.7042), (72, 0.7002), (73, 0.7019), (74, 0.707), (75, 0.6993), (76, 0.6963), (77, 0.6997), (78, 0.7002), (79, 0.6994), (80, 0.6927), (81, 0.7017), (82, 0.6957), (83, 0.6945), (84, 0.7042), (85, 0.7043), (86, 0.7051), (87, 0.6987), (88, 0.7057), (89, 0.7038), (90, 0.7078), (91, 0.7068), (92, 0.7046), (93, 0.701), (94, 0.7028), (95, 0.7001), (96, 0.7064), (97, 0.7008), (98, 0.7064), (99, 0.7057), (100, 0.7071)]}
-[2023-09-27 19:22:00,636][matplotlib.legend][WARNING] - No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.

From 578d1a399b8767e58f61f5050cf6dd1bcf1ebe04 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 20 Oct 2023 09:38:03 +0800
Subject: [PATCH 47/51] update README

---
 baselines/moon/README.md                      |  20 ++++++++++++------
 .../moon/_static/cifar100_50clients_moon.png  | Bin 29532 -> 0 bytes
 .../moon/_static/cifar100_moon_fedprox.png    | Bin 34021 -> 41839 bytes
 .../moon/_static/cifar10_moon_fedprox.png     | Bin 37724 -> 44203 bytes
 4 files changed, 13 insertions(+), 7 deletions(-)
 delete mode 100644 baselines/moon/_static/cifar100_50clients_moon.png

diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index e775c76d1b93..063e714889ea 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -26,7 +26,7 @@ dataset: [CIFAR-10, CIFAR-100]
 
 ****Hardware Setup:**** The experiments are run on a server with 4x Intel Xeon Gold 6226R and 8x Nvidia GeForce RTX 3090. A machine with at least 1x 16GB GPU should be able to run the experiments in a reasonable time.
 
-****Contributors:**** Qinbin Li
+****Contributors:**** [Qinbin Li](https://qinbinli.com)
 
 ****Description:**** MOON requires to compute the model-contrastive loss in local training, which requires access to the local model of the previous round (Lines 14-17 of Algorithm 1 of the paper). Since currently `FlowerClient` does not preserve the states when starting a new round, we store the local models into the specified `model.dir` in local training indexed by the client ID, which will be loaded to the corresponding client in the next round.
 
@@ -105,9 +105,9 @@ python -m moon.main --config-name cifar100_fedprox
 
 ## Expected Results
 
-You can find the output log in `_static` directory. After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071 (see `_static/cifar10_moon_log.txt`). 
+You can find the output logs of a single run in this [link](https://drive.google.com/drive/folders/1YZEU2NcHWEHVyuJMlc1QvBSAvNMjH-aR?usp=share_link). After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071. 
 
-For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852 (see `_static/cifar10_fedprox_log.txt`). For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6636 (see`_static/cifar100_moon_log.txt`). For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
+For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852. For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6636. For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
 
 
 |  | CIFAR-10 | CIFAR-100 |
@@ -125,16 +125,22 @@ You can find the curve comparing MOON and FedProx on CIFAR-10 and CIFAR-100 belo
 You can tune the hyperparameter `mu` for both MOON and FedProx by changing the configuration file in `conf`.
 
 ### Figure 8(a)
-You can run the experiments in Figure 8 of the paper. To run MOON on CIFAR-100 with 50 clients (Figure 8(a) of the paper):
+You can run the experiments in Figure 8 of the paper. To run MOON (`mu=10`) on CIFAR-100 with 50 clients (Figure 8(a) of the paper):
 ```bash
 python -m moon.main --config-name cifar100_50clients
 ```
 
-You can find the curve presenting MOON (\mu=10) below.
+To run FedProx on CIFAR-100 with 50 clients (Figure 8(a) of the paper):
+```bash
+python -m moon.main --config-name cifar100_50clients_fedprox
+```
+
+
+You can find the curve presenting MOON and FedProx below.
 
-<img alt="CIFAR-100" src="_static/cifar100_50clients_moon.png" width="500"/>
+<img alt="CIFAR-100" src="_static/cifar100_50clients_moon_fedprox.png" width="500"/>
 
-To run MOON on CIFAR-100 with 100 clients (Figure 8(b) of the paper):
+You may also run MOON on CIFAR-100 with 100 clients (Figure 8(b) of the paper):
 ```bash
 python -m moon.main --config-name cifar100_100clients
 ```
\ No newline at end of file
diff --git a/baselines/moon/_static/cifar100_50clients_moon.png b/baselines/moon/_static/cifar100_50clients_moon.png
deleted file mode 100644
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From 0c421112b41ae74befb0402ca2959e87d45dfb19 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 20 Oct 2023 09:39:38 +0800
Subject: [PATCH 48/51] add comments

---
 baselines/moon/moon/client.py |  1 -
 baselines/moon/moon/models.py | 14 ++++++++------
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/baselines/moon/moon/client.py b/baselines/moon/moon/client.py
index e1edd123d539..7d075682eb4d 100644
--- a/baselines/moon/moon/client.py
+++ b/baselines/moon/moon/client.py
@@ -71,7 +71,6 @@ def fit(
     ) -> Tuple[NDArrays, int, Dict]:
         """Implement distributed fit function for a given client."""
         self.set_parameters(parameters)
-        # if self.prev_net is None:
         prev_net = init_net(self.dataset, self.model, self.output_dim)
         if not os.path.exists(os.path.join(self.model_dir, str(self.net_id))):
             prev_net = copy.deepcopy(self.net)
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
index 6b34a0b5cb27..ebcbf46b57ca 100644
--- a/baselines/moon/moon/models.py
+++ b/baselines/moon/moon/models.py
@@ -380,8 +380,6 @@ def train_moon(
     device="cpu",
 ):
     """Training function for MOON."""
-    # net = nn.DataParallel(net)
-    # net.cuda()
     net.to(device)
     global_net.to(device)
     previous_net.to(device)
@@ -415,25 +413,29 @@ def train_moon(
             target.requires_grad = False
             target = target.long()
 
+            # pro1 is the representation by the current model (Line 14 of Algorithm 1)
             _, pro1, out = net(x)
+            # pro2 is the representation by the global model (Line 15 of Algorithm 1)
             _, pro2, _ = global_net(x)
-
+            # posi is the positive pair
             posi = cos(pro1, pro2)
             logits = posi.reshape(-1, 1)
 
             previous_net.to(device)
+            # pro 3 is the representation by the previous model (Line 16 of Algorithm 1)
             _, pro3, _ = previous_net(x)
+            # nega is the negative pair
             nega = cos(pro1, pro3)
             logits = torch.cat((logits, nega.reshape(-1, 1)), dim=1)
 
             previous_net.to("cpu")
-
             logits /= temperature
             labels = torch.zeros(x.size(0)).cuda().long()
-
+            # compute the model-contrastive loss (Line 17 of Algorithm 1)
             loss2 = mu * criterion(logits, labels)
-
+            # compute the cross-entropy loss (Line 13 of Algorithm 1)
             loss1 = criterion(out, target)
+            # compute the loss (Line 18 of Algorithm 1)
             loss = loss1 + loss2
 
             loss.backward()

From 66a52cbcfbcaac76074546bee9f0803a4bd97928 Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 20 Oct 2023 09:39:55 +0800
Subject: [PATCH 49/51] update strategy

---
 baselines/moon/moon/main.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 221ab2bf0e7a..7b1761b78f40 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -94,7 +94,8 @@ def main(cfg: DictConfig) -> None:
     # if needed by your method.)
     # strategy = instantiate(cfg.strategy, <additional arguments if desired>)
     strategy = fl.server.strategy.FedAvg(
-        fraction_fit=cfg.fraction_fit, evaluate_fn=evaluate_fn
+        fraction_fit=cfg.fraction_fit,
+        evaluate_fn=evaluate_fn,
     )
     # 5. Start Simulation
     # history = fl.simulation.start_simulation(<arguments for simulation>)

From 9f5f93a2c03e00080ac24c50aa8cfa17b0d7367d Mon Sep 17 00:00:00 2001
From: Qinbin Li <liqinbin1998@gmail.com>
Date: Fri, 20 Oct 2023 09:51:03 +0800
Subject: [PATCH 50/51] erase model dir in the beginning

---
 baselines/moon/moon/main.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index a26bd77e6385..902ccfa8395c 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -3,6 +3,7 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
+import os
 import random
 import shutil
 from pathlib import Path
@@ -31,8 +32,13 @@ def main(cfg: DictConfig) -> None:
     cfg : DictConfig
         An omegaconf object that stores the hydra config.
     """
+    # Clean the model directory to save models for MOON
+    if cfg.alg == "moon":
+        if os.path.exists(cfg.model.dir):
+            shutil.rmtree(cfg.model.dir)
     # 1. Print parsed config
     print(OmegaConf.to_yaml(cfg))
+
     # 2. Prepare your dataset
     np.random.seed(cfg.seed)
     torch.manual_seed(cfg.seed)

From b1c3d2a586954ec43e804bc21fe01f3a65a62736 Mon Sep 17 00:00:00 2001
From: jafermarq <jafermarq@gmail.com>
Date: Sun, 22 Oct 2023 01:11:44 +0000
Subject: [PATCH 51/51] to changelog

---
 doc/source/ref-changelog.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 891632edaaf5..b8ac4d6bf867 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -28,6 +28,8 @@
 
   - FedMeta [#2438](https://github.com/adap/flower/pull/2438)
 
+  - MOON [#2421](https://github.com/adap/flower/pull/2421)
+
 - **Update Flower Examples** ([#2384](https://github.com/adap/flower/pull/2384)), ([#2425](https://github.com/adap/flower/pull/2425))
 
 - **General updates to baselines** ([#2301](https://github.com/adap/flower/pull/2301), [#2305](https://github.com/adap/flower/pull/2305), [#2307](https://github.com/adap/flower/pull/2307), [#2327](https://github.com/adap/flower/pull/2327), [#2435](https://github.com/adap/flower/pull/2435))